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b/.doctrees/nbsphinx/examples/advanced/functions-and-tuning-curves.ipynb @@ -0,0 +1,803 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Improving function approximation by adjusting tuning curves\n", + "\n", + "This tutorial shows how adjusting the tuning curves of neurons\n", + "can help implement specific functions with Nengo.\n", + "As an example we will try to to compute\n", + "the Heaviside step function,\n", + "which is 1 for all $x > 0$ and 0 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:02.119301Z", + "iopub.status.busy": "2020-11-25T16:46:02.118470Z", + "iopub.status.idle": "2020-11-25T16:46:03.027487Z", + "shell.execute_reply": "2020-11-25T16:46:03.027973Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The naive approach\n", + "\n", + "As a first pass, we can try to implement the Heaviside step function\n", + "using an ensemble with default parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.032018Z", + "iopub.status.busy": "2020-11-25T16:46:03.031456Z", + "iopub.status.idle": "2020-11-25T16:46:03.034622Z", + "shell.execute_reply": "2020-11-25T16:46:03.035023Z" + } + }, + "outputs": [], + "source": [ + "n_neurons = 150\n", + "duration = 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.040175Z", + "iopub.status.busy": "2020-11-25T16:46:03.038711Z", + "iopub.status.idle": "2020-11-25T16:46:03.040889Z", + "shell.execute_reply": "2020-11-25T16:46:03.041294Z" + } + }, + "outputs": [], + "source": [ + "def stimulus_fn(t):\n", + " return (2.0 * t / duration) - 1.0\n", + "\n", + "\n", + "def heaviside(x):\n", + " return x > 0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.049765Z", + "iopub.status.busy": "2020-11-25T16:46:03.048648Z", + "iopub.status.idle": "2020-11-25T16:46:03.052168Z", + "shell.execute_reply": "2020-11-25T16:46:03.051596Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network() as model_naive:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_naive = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.059630Z", + "iopub.status.busy": "2020-11-25T16:46:03.058877Z", + "iopub.status.idle": "2020-11-25T16:46:03.460874Z", + "shell.execute_reply": "2020-11-25T16:46:03.460415Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_naive) as sim_naive:\n", + " sim_naive.run(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.466625Z", + "iopub.status.busy": "2020-11-25T16:46:03.465327Z", + "iopub.status.idle": "2020-11-25T16:46:03.649214Z", + "shell.execute_reply": "2020-11-25T16:46:03.648725Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dGPL8SsD8ZZxbXN3Gr5Syi+fSfh34asdJ2oYFsuyh0by8IpWxfWLxN/jRu0PtY/pfvWUQ/TtF0ikqmBV7Mu3uaXj9V4OtQ0inDOrMlEHVZ/lVTX7Du7erdd+e4MlRQ7fWs14D93vq/b1txIgRbNiwwaF82rRpXHXVVdx4440N3uczzzxDeHg4jz76qDtCFB6UmprKFVdcwbfffsvkyZM9+l4Lt9Y/JUT7iCDOFFRPc7Bo5khCA/09dob5wKU9uX14N6JtzmpH9oyxjrF31rTz4GW9OJJd5DQx/XFSX9YdzOb9O6tHlG/+42VoDXGRwdyUYm5VHmb5wlzo5Ozflu38RlV3EFd930eFBrL4wVF0jzFfBXWLqU48Vw/sSKDBj8v7tcff4Mdfbky22+/Op8aj0Qx6znzvQ1Wzku2X+7UXdmZIt2hKjSaiwwKJqWN6joX/N5yb/7ORUfU0T7mb3FnsJs6SgBDutPtEHtmFZdb5euoyYUAc722sHpY4MD7Kg5GZz/5tkwCYv/zT5k7E4KecPk2vS9tQPrvXcUQUwIwxPZgxxn5YbQebO3Jd9bebkx3ufahqspo9OclaZtvn0SY4gP9NH0p6dhFKKSYMiKM2kaH201JcNdD5xH5d2ro2LffQxLbsfHo8kSGem2DOGUkEbhIeHk5hYSFaax544AG+++47unTpQmBg9R/Htm3beOSRRygsLCQmJoZ3332Xjh078uabbzJ//nzKy8vp2bMn77//PqGhMp+7qHYws8A6w6etF667gD9+6Th5mcGmWSY4wHddgf4G385ref3geIcyPz9F+ry6r9rG9o5lbB3t+zXtfHo8Z/JL6VVHc5GrvJ0EoIUmgnHjxjmU3Xzzzdx3330UFxczaZJjr/60adOYNm0a2dnZDs04ziaKq82XX35Jamoqe/fuJTMzk6SkJKZPn17ntNL1TVUtRH5phdPysCD76RJCAgzcPaY7JeXm+rMm9uWesY43rAn3igwJ8MkXuLu0yETgS2vXruXWW2/FYDDQqVMnLr3UPFui7bTSACaTiY4dzZeRu3fvZvbs2eTm5lJYWMiVV17ps/hF01TbIyGvGtiJ3GIjhWUVvLQ8lU1PXEZkaABpZwr5eMtxJl/gvKlCCFstMhHUdQYfGhpa5/qYmJgGXQG4Smtd67TSrkxVLZq23r17s3TpUgYPdn9nrKlSM3/tYbuywV2jePTKPhj8FHeMSEBrzfSRiYQEmq8QerYPZ/ezckIhXCMPpnGzMWPGsGDBAkwmE6dOnWL1avMUurbTSgMYjUbrQ2pqTlUtmp/IyEgmTJhA+/bt66/cQEt2neL7/Wfsyr64byQjelSPLFFKWZOAEA0licDNrrvuOnr16kVSUhK33367dUKwqmmlH3/8cZKTkxk0aJB1pFHVVNUjR46kb9++vgxfNNLZs2f58ssvOX268bN91kae8ys8zaNzDXmCzDVkrzX/7k3J5s2bufjii1myZAkTJ050676v+ed6fsnIsyurb9SLEDU11bmGhBD1qDBVOiSBXw2TGXiFe7XIzmIhWoJSo4mhc1dal28f3o3bhyfQ1cWbk4RwVYtJBFprp3cvtmTNrVlPNMz8tYet9w8kxoTx3BSH2dyFcIsW0TQUHBxMTk5Oq/pi1FqTk5NDcHDDb7sXTdvZonKOZBdRUGq0lgX5t4g/VdFEtYgrgvj4eDIyMsjKatysjM1VcHAw8fGOt9AL70tKSmLdunUkJSXVX7kOZRUmBlse3n794OqJywIlEQgPahGJICAggMTExPorCuEhERERjBo16rz3U/WgGcDuQTO29wwI4W5ymiGEG2RlZfHee+9x4sT5PWQvp7Dcafmj43s7LRfCHSQRCOEGhw4d4o477mDXLseZQBuiuNzktNzXs3iKlk0+XUI0IVWzhgrhTZIIhHADd41Yq+2KQAhPahGdxUI0Fed7L0uxsToRDE1oy/RRCfSNa3O+YQlRJ0kEQjQhH20+Zn0dEezPBCfP8xXC3SQRCOEGAwcOZMeOHW4dxmz7uEkhPEkSgRBuEBYWRnJyslv32Tfu/J9/K4QrJBEI4QanTp3iiy++4Oqrr6Zr14bPDppXbOTeD7cB8PiEviTHRzI0sa27wxTCKRk1JIQbHDlyhJkzZ7J///5Gbb/3VD4bDuUAEB0awIieMXLvgPAa+aQJ0QTYDj+NDgv0YSSiNZJEIEQTUGaqfhxldKgkAuFdHk0ESqkJSqlUpVSaUmqWk/VdlVKrlVI/K6V+UUpN8mQ8QjRVts8l7t0h3IeRiNbIY4lAKWUAXgcmAknArUqpmnP0zgYWaq0vBG4B/uWpeIRoyspsEkGUXBEIL/PkqKGhQJrW+jCAUuoTYAqw16aOBqpum4wETnowHiE8ZvDgwRw6dIi4uLhGbV9qlKklhO94MhF0Bo7bLGcAw2rUeQZYoZR6AAgDLne2I6XUDGAG0KiheUJ4WnBwMN27d2/09m+tOwLAdw+PcVdIQrjM153FtwLvaq3jgUnA+0oph5i01vO11ila65TY2FivBylEfTIyMpg3bx7p6emN2j41swCAnu2lf0B4nycTwQmgi81yvKXM1p3AQgCt9UYgGJBHMYlmJz09nSeeeIK0tLQGb1tUZp56+pI+sec9aZ0QjeHJRPAT0EsplaiUCsTcGbyoRp1jwGUASql+mBNB63rwsGj1hjxvfkbxBZ0jfRyJaK08lgi01hXATGA5sA/z6KA9SqnnlFLXWKr9HrhbKbUT+BiYpt01sbsQzcAPB7IoNZpHDMVFhvg4GtFaeXSuIa31EmBJjbKnbF7vBUZ6MgYhmiKtNSVGE9Pe2WItk1Yh4Su+7iwWolX64UAWSU8tp+r6199Pcf3gzr4NSrRaMvuoEG4wbNgwzpw5Q2Rk/e38lZWa+WsP25U9MakfQf4GT4UnRJ0kEQjhBgEBAbg6tPmDzUetM41W6RgZ7ImwhHCJNA0J4Qbp6enMnj2bQ4cO1Vt3/+kCh7IJ/Rt3R7IQ7iCJQAg3OHbsGHPnzuXo0aP11g3yt/+z6xwVgp88llL4kCQCIbwsoMYDZ4w2U1AL4QuSCITwspoTzFXKrTPCxyQRCOFlxeX2icBPbiAQPiajhoTwshJLIhiW2JbYiCDuv6SnjyMSrZ0kAiHcYNSoUZSUlBAQEFBv3eLyCi7oHMmC/xvuhciEqJ8kAiHcwM/Pj+Dguu8F2HMyj8mvrQdgaGJbb4QlhEukj0AIN0hLS+Ohhx7i4MGDtdbZaHMTWWig3EUsmg5JBEK4QUZGBq+99honTtR85EY128FBkghEUyKJQAgv0VRngrBAaZUVTYckAiG8xPaKIDxYEoFoOiQRCOEltreNRYbUP7pICG+RRCCEGyilMBgMdT5z2PYO4uQuUV6ISgjXyPWpEG4wduxYKioq6qxTZqyeU2hsL9emrBbCG+SKQAgv0Frz6qrqoaUy26hoSiQRCOEG+/fvZ/r06ezfv9/p+rfWH7G+vn14N2+FJYRLJBEI4QanTp3inXfeITMz0+n67/ZWlz83ZYC3whLCJZIIhPCCCBkuKpowSQRCuIGu55kCZ4vKCQ7wY/5tQ7wUkRCuk9MUIdyo5vDR03mlvLh0H0dzirmsbwfGy7OJRRMkiUAIN/D39ycqKgp/f/s/qf9tTOfrHScBaCM3kYkmShKBEG4wZswYzp0751Bu22IkdxOLpsqjfQRKqQlKqVSlVJpSalYtdW5WSu1VSu1RSn3kyXiE8DaD9MKJZsBjH1OllAF4HZgIJAG3KqWSatTpBTwBjNRa9wd+56l4hPCkXbt2cdNNN7F37167clP1zcT1digL4SuePF8ZCqRprQ9rrcuBT4ApNercDbyutT4HoLU+48F4hPCYM2fO8Nlnn5GTY374jNaac0XllBqrH1R//6XybGLRNHkyEXQGjtssZ1jKbPUGeiulflRKbVJKTXC2I6XUDKXUVqXU1qysLA+FK4T7vLYqjQvnfMeJ3BJrWZtg6SMQTZOvWzD9gV7AOOBW4E2lVFTNSlrr+VrrFK11SmysTNYlmr4PNx8F4Ex+qY8jEaJ+nkwEJ4AuNsvxljJbGcAirbVRa30EOIA5MQjRrPlZ7ic4V2wE4OHLe/syHCHq5MlE8BPQSymVqJQKBG4BFtWo8xXmqwGUUjGYm4oOezAmITwiKCiILl26EBQUBIDBryoRlDMwPpKHLpfzG9F0uXQfgVJqpNb6x/rKbGmtK5RSM4HlgAF4W2u9Ryn1HLBVa73Ism68UmovYAL+oLXOaewvI4SvjBo1imPHjlmX/Q3mRFBQWkGwvzyoXjRtrt5Q9g9gsAtldrTWS4AlNcqesnmtgUcsP0K0GAabqSaCAnzdFSdE3er8hCqlhiulfg/EKqUesfl5BvNZvhAC2L59OxMmTGDPnj1AddMQQHCA/KmIpq2+U5VAIBzzlUOEzU8+cKNnQxOi+cjJyWH58uXk5uYCkghE81Jn05DW+gfgB6XUu1rro16KSYhmzy4R+EvTkGjaXO0jeFcp5XB/vNb6UjfHI0SzV1ZhwlRZ/eciVwSiqXM1ETxq8zoYuAGocH84QjR/w1/8nrNF5dblYOksFk2cS4lAa72tRtGPSqktHohHiGYpLCyMpKQkQkJCOFuUa7fOX6YgFU2cq/cRtLVZ9AOGAJEeiUiIZmjEiBHWEUMsXGy3LqewzAcRCeE6V5uGtgEaUJibhI4Ad3oqKCGam/KKSvJKjMRGBFnLOkeFcCK3hG7twnwYmRD1c7VpKNHTgQjRnP3mxQ9Z9J95bF78sbVsXJ9Yruwfx4ge7XwYmRD1c7VpKBi4DxiF+cpgHfCG1lqmVhQC+GHXEcpO7OWTDQeAEAAqtWZMb5ktVzR9rvZivQf0xzytxD8tr9/3VFBCNDdVM0q8uyHdWmY7hFSIpszVPoIBWmvbx0yutkwUJ4TA/iH1VSQPiObC1SuC7Uqpi6sWlFLDgK2eCUmIliHAoOqvJEQT4OoVwRBgg1Kqap7drkCqUmoX5klEB3okOiGaCb+gMAI79kEFhljLOkaG1LGFEE2Hq4nA6bOEhRBmQZ370vH2v1qXbx3ahRljuvswIiFc52oieF5rfZttgVLq/ZplQgiz8f3jZI4h0Wy42kfQ33ZBKeWPublICAGUZuzl5NszKT9jftJqRJCr51hC+F59D6Z5QilVAAxUSuUrpQosy5nA116JUIgmzmiqRJeXYMxKRxvLGd0rhiHdon0dlhAuqzMRaK1f1FpHAC9prdtorSMsP+201k94KUYhmrRSo8lu/OiMMd1RSkYMiebD1evXpUqpMTULtdZr3RyPEM1OidFUvaAUQfKwetHMuJoI/mDzOhgYinkiOnkwjWj1lu46bbccJE8kE82Mq5POXW27rJTqArziiYCEaG6eXrQHv5AIgrsl4xcYSqAkAtHMNHZoQwbQz52BCNGcBXXqQ4db5ppfSyIQzYyrs4/+A/Oso2DuYL4Q2O6poIRozoLk/gHRzLh66rIXOGD52QQ8prX+jceiEqKZ0Frj76e4sm0OJ964k/LMwwTKoylFM1PnFYHlxrEXgOmA7TxDbyultmitjR6OT4gmrdxUSUWlxmAqpyIvE20yEhEsN5OJ5qW+U5eXgLZAotZ6sNZ6MNAdiAJe9nBsQjR5xWXmoaNBAdV/SjK1hGhu6ksEVwF3a60Lqgq01vnAvcCk+naulJqglEpVSqUppWbVUe8GpZRWSqW4GrgQTUFReQUAwZZ7B359cVdfhiNEo9SXCLTWjo/c0FqbqO48dkopZQBeByYCScCtSqkkJ/UigIeAza4GLURTUVxuf0Vwc4okAtH81JcI9iqlbq9ZqJT6DbC/nm2HAmla68Na63LgE2CKk3pzgD8D8vxj0ayknSng/g/Ng+di23fg2muvJTpa5hgSzU99vVr3A18opaZjvpMYIAXz07mvq2fbzsBxm+UMYJhtBaXUYKCL1nqxUsr27mVq1JsBzADo2lXOuETT8MGmYxw8UwhA/4GDuP+mL30ckRCNU2ci0FqfAIYppS6leirqJVrrVef7xkopP+BvwLT66mqt5wPzAVJSUuRJsKJJyMyvvoiVIaOiOXPp06u1/l5r/Q/Lj6tJ4ATQxWY53lJWJQIYAKxRSqUDFwOLpMNYNBcmm6fTb9vwA7Gxsfz8888+jEiIxvHkacxPQC+lVKJSKhC4BVhUtVJrnae1jtFaJ2itEzDfqHaN1nqrB2MSwm1sE0GlyUh2djYmk6mOLYRomjyWCLTWFcBMYDmwD1iotd6jlHpOKXWNp95XCG+psEkE/n7y/AHRfHn0Fkit9RJgSY2yp2qpO86TsQjhbgabL39JBKI5kx4uIRrJtoPYIJ3FohmTT68QjVRs82Syzp06cdttt9GuXTsfRiRE48jsWEI00K6MPJ5atBujqdJaNnjwYCaOe8+HUQnReJIIhGigZ77Zw8/Hcu3KAqRpSDRj8ukVooEqbK4EqqxcvoTg4GC5j0A0S5IIhGggo8nx5nY/oKysDCdzNArR5EkiEKKBbG8kqyLDR0VzJolAiAYyVlY3DfVqH86RFyfhJ4lANGOSCIRooAqbpqGLEtuilCQB0bxJIhCikQL9/ZgzZQAACQkJ3HfffcTExPg4KiEaToaPCtEIXdqG8N3DY63TTAwcOJDXX3/dx1EJ0ThyRSBEA+WXGrmkT3u7h9RXVlZSUVEho4ZEsySJQAgXfbE9g5eXp5JfYqRNcIDdum+++YaAgAB27Njhm+CEOA/SNCSEix5ZuNP6uk2I/OmIlkOuCISoQ36pkYRZi1n8yym78sgQ+ysCaRISzZkkAiHqkJ5dBMC/f0izKw8JdH5FIENJRXMkiUCIOlTdRbz7RL5deWFphS/CEcIjJBEIUYeScufPIL64e1u75V69evH444/Tvn17b4QlhFtJj5cQdSgx2ieC0b1i+OevBjv0EfTv35958+Z5MzQh3EauCISoQ6nRfsrpS/q0d0gCAOXl5Zw7d46KCmkyEs2PJAIh6lBa44ogwOC8M3jx4sW0bduWPXv2eCMsIdxKEoEQdSitsE8E/vIkMtECyadaiDrUbBoKlEQgWiD5VAtRh5pNQ+eKy30UiRCeI4lAiDqUGk0oBf83tjsAA+OjfBuQEB4gw0eFqEOp0URIgIEnJvbj/8b0oG1YoNN6/fr1Y86cOcTFxXk5QiHOn0evCJRSE5RSqUqpNKXULCfrH1FK7VVK/aKUWqWU6ubJeIRoqFJjpXW66dqSAEDfvn2ZPXs2HTp08FZoQriNxxKBUsoAvA5MBJKAW5VSSTWq/QykaK0HAp8Bf/FUPEI0RqnRRLB//X8mxcXFHDt2jPJy6UMQzY8nrwiGAmla68Na63LgE2CKbQWt9WqtdbFlcRMQ78F4hGiwEqPJ7gE0tVm6dCndunUjNTXVC1EJ4V6eTASdgeM2yxmWstrcCSx1tkIpNUMptVUptTUrK8uNIQpRt1JjJUEuJAIhmrMmMWpIKfUbIAV4ydl6rfV8rXWK1jolNjbWu8GJVqmswsTfVqRytqiM4IAm8WcihMd4ctTQCaCLzXK8pcyOUupy4ElgrNa6zIPxCOGy5Xsyee178zMIRvRo5+NohPAsT57q/AT0UkolKqUCgVuARbYVlFIXAv8BrtFan/FgLEI0SIhNc5ArfQRCNGceSwRa6wpgJrAc2Acs1FrvUUo9p5S6xlLtJSAc+FQptUMptaiW3QnhVbZ3FLvSNJScnMwrr7xCx44dPRmWEB7h0RvKtNZLgCU1yp6yeX25J99fiIbKKijj+n//iMHmkZOhtTyW0lbPnj156KGHPBmaEB4jdxYLYWPVvkyOny2xK6vrRrIqeXl5ZGRk0KNHD4KDgz0VnhAeIcMhhLAREujYH9C7Q0S9261YsYIBAwZw6NAhT4QlhEdJIhDCRoCTaabH95dpI0TLJolACBta2y9/ff9I2gQ7PppSiJZEEoEQNorL7Z85nNwlyjeBCOFFkghEq/bm2sPMeG+rdfmL7Q73PArR4smoIdGqzV2yz/p6TeoZNh7OadR+UlJSeOutt+jUqZO7QhPCayQRCAF8+8tJZn70s13Zj7MudXn7xMREEhMT3R2WEF4hTUNCgEMSePaa/nSOCnF5+5ycHDZt2kRxcXH9lYVoYiQRiFYlr8To8ED6mp6+Ook7RiQ0aL+rVq1i+PDhpKenNz44IXxEEoFoVZKfXcE1/1xfZ53fjpQmHtG6SCIQLd7RnCISZi1m+Z7TABzILHRa775xPTjy4qRGvYeueQOCEM2IdBaLFm9nRh4Ai3actJaVGk18vcN+qGj32HCUzWRzjXG+2wvhC5IIRItX9dV8Mq96Mrm+f1rmUC8iWP4cROskTUOi1fj5WG6d68f2bvxjUEeMGMHChQvp3Lmux3IL0TTJKZAQwIDObc7rSWRdunShS5cu9VcUogmSKwLR4tXVjRtmmXbacJ5t+6dPn2blypUUFRWd136E8AVJBKLFK6+odFres304829PAcDP7/wSwZo1a7jiiis4fvz4ee1HCF+QRCBavLfWH3Fa3rtDOKZK8/VC+4ggb4YkRJMiiUC0WOUVlRzOKmTfqXxrWXx0CJf3Mz9oJq5NCMN7tOOuUYnMve4CX4UphM9JIhAt0idbjtF79lKO5tjP/WPwU7RvYz77jw4NIMDgx+yrkogJlysC0XpJImjCjmQXcaag1NdhNEuzvtgFwPf7z9iVhwQY6BEbDoDc+yWEmSQCG1uOnK13QjJvuuTlNQydu6pJxdRUlRpN3PW/rQ7/h+9vOmpXLyTQwK+HdeXBy3oxzY1zCo0dO5alS5cSHx/vtn0K4S2tPhGs3JtJqdFEZn4pN/9nI49+urPWutuOniNh1mK2HzvntvcvqzCx/dg5vv3lJFkFZU7r7D6RV+c+TuaWODxi0RWfbDnG01/vbvB2TVHq6QJW7svkn6vTOFdcXmu90EADwQEGHrmiN+FB7ruNpmPHjkyYMIHw8HC37VMIb2k1iUBrzeA53/HfdYetZb9k5HLXe1t57tu91iGGm4+cJa/YyCnLdATnispZYZmsbO2BLAC+/tl+jprM/FK++rnuRxwu232KI9lFPPHFLhJmLUZrjdaaF5fs5/p/bWDmRz8z/d2fnG574xsbqbSMbkk7U8Azi/ZYYwEYMe/7WreteQyqJke78d8bmPXFLv638Wg9WzUPf1m+H4CcwjLOFRm9/v7Hjx/nyy+/pKCgwOvvLcT5ajWJoLCsgrNF5Ty/uPrRhJssjyVcsec0c77dC0BWQRk3/2cjw1/8Hq01Mz/ezoz3t7H5cA7Hzpo7Hmt+ed7zwTZ+t2AHN/x7A59uPU6ukzPSez7YziUvr+HjLccASM8pps+flvHuhnRrnV11nPnvP13A1ztOcPnf1vLuhnRuf3sLABsOZVt+l7N29f+77jAJsxazNb26fNSfVzPqz6sB2HrU9auabUfPWRORM+UVlVSYnI/V95Yf08z/l3tO5jPptXW11pt8gWceJblu3Tquv/56Tp065ZH9C+FJHp1iQik1AXgVMAD/1VrPq7E+CHgPGALkAFO11umeiCW3uPosccfxXAZ1ieKFJeazyOzCclbszbSuT800n9VlnCtha7r5C3Pq/E1O95tXbCSn0PzFv+3oObYdPUdEkD+7nr0SgMpK7dBhCfBT+lmnNzptTT9LdmE5vTvYNzGcKy7n8c9/caj/qzc3W1+nnSkgOjQQk9bWhPevNYfo1u4UJ86VcCLXfJWzbLf9l9WKPafxU4pT+aX8eDCby5M6sHTXKd6adhFrD2Rx+9tb+NNVSdw5ynmbeu/ZS+ndIZwVD491uv58lRpNZJwroWf76mNSYarE39Cw85gDz08kwCA9xELU5LFEoJQyAK8DVwAZwE9KqUVa67021e4EzmmteyqlbgH+DEz1RDx5JdWJ4NrXfyQ0sP55Zd5af4SyWu5KrazUvLMh3XolYaugrILPt2Vww5B4uv9xidPtn/56j9PyG9/Y6LT8TEEppUb7WH5Kt78KuPxvax22c5aE7vlgu93yjPe32S0vszSF5ZcarVdBqafz0VqjlOJMQSntI4Lttqma4z+/1MiB0wWkJLS1W6+1pqyikuAAA4VlFXy5PYNfD+vm0h29j366k29/OcWeZ68kLMif1NMFXPnKWv52czLj+8fVeudwTYH+reYCWIgGUZ56oIZSajjwjNb6SsvyEwBa6xdt6iy31NmolPIHTgOxuo6gUlJS9NatWxscz19XpPLCX1/DmH3MrtwvNJKokbcCkP/T11Tk2p8tG9rEEjnsBgDyNn2GqcDcFNOhTTCZ+aX4R3eiTco1AOT++DGVxdXNO7cN78YnaYqIQRMAOPfD/9DlJXb7D4zrRfgFlwFw9vv/gsm+0zeocz/CksaidSXnVs63WxcW5I8prj+hvYdTaSwjd807Dr93SPcUQnqkUFlaSO66DxzWh/YeQXC3gZiKzpG3YYHdugviIzkUMYjg+H5U5J0hf8sXBPr7UV5RyYVdowgJ8Gfu4zO54dPTGM+eYFTZT+zMyONkbgm/GtaV4nITbYdM4pk7JvDiB8v593/mc1NKPHtP5rPnZD7j+sTyt+f+SPfuPbjvlYUU711NZEiAXQyPPfYY17yzj1MHdnBVWDohgQYOZhaw4ZC5KShyxC0YwqIwHd9J3n77JDq4WzSHu12NX1Aoc4eUs2nt9w6//8svv0xQUBCff/45a9assVunlOK1114D4KOPPmLjRvv9BwcH89JLLwEwceJEli1bRmpqKr1793Z4HyF8TSm1TWud4mydJ5uGOgO2E69kAMNqq6O1rlBK5QHtgGzbSkqpGcAMgK5duzYqmOjQQEqP7aI0fYdduX90J2siKD26g7IT++3WB3bobk0EJYe3Yswy9w9UdTkHdelvTQQlaVuoyD1t3fbNVEVgt8HWRFB8YKNdogDQpgprIijevx5trDFyyOBPWJK5yaVo7w92q4qAiMEhhPYeDpUmh/VgTmQhPVKorCh3uj4gpivB3QZSWVbssH7TXoi+rD3E98NUkk/R3h+omlJtveVC6NfXTQTCMBWd47MvqhPJ/w6YE0ZMQWe+Omqg5NA2ivb+wAepCj+lKK+o5Ps0A2dm3oVfZByfr91B3ncfW58JoDE/R+Duu+/G4KeoOHuSz5cswE9BuamS4jLzENGIlCkYwqIoOnPMIf6taQaibx8PQaEcS9vPRx995PD7z5s3j6CgILZv3+6w3jYRbNmyxWF9RESENRF06NCB3r17ExcX5/AeQjR1nrwiuBGYoLW+y7J8GzBMaz3Tps5uS50My/IhS51sZ/uExl8RVEmYtbjR23rCc1P6c+2FnRn4zAqn6xNjwjiS3bJmtOwbF8H+0wU8f+0Axid1YP7aw/x3/RHio0NY//il3Dp/ExsP5/D4hL7cO64HF81dSVZBGY9P6MtvRybwzc6T/OEzx/6SmuZeN4Anv9xNgEFxcG7jHkEpREtR1xWBJxtNTwC2E7THW8qc1rE0DUVi7jT2iW8fGMVTVyXRMTK41jqb/3iZ9XVtT7T64r4RDmU7nrqCKYMcR6zcPjyBNsEBtU569u0Do5yWN+cpEfafNnfGG/wUQ19YxX8tk8KdyS/jQGYBGy2juf68bD+mSm29v+LPy/bT90/LWLXPsd8D4J6xPayvB8ZHWl/fMFhu8hKiLp5MBD8BvZRSiUqpQOAWYFGNOouAOyyvbwS+r6t/wBOGJrZl/eOXsPPp8QzoHMn0UYlsfOIyZk/uB0D3mDBr3f9NH0qHNsHWL5mC0grrfPYAf77hAj6/dziDu0Zz+/BuAPSIDePvU5OJCg3k1VsuZNXvx/LOby8C4K07qpPz+scvtdtXlbAgf1b9vno0zspHxjL3ugHcNbp6BM+imSO5vF8H2oUFWss+vWe43X4mXWDfZDGqZ4yLR8hznrBMA1Gl3FTJ+L/bd3i/uuqgw3ZVndk1BRoUo3uZf6+wQH8qTOaPUkADRxcJ0dp47C9Ea10BzASWA/uAhVrrPUqp55RS11iqvQW0U0qlAY8AszwVT5VfDetK56gQDr8wiTlT+vPe9KHER4c6dFLeNbo7Kx8Zw7cPVp+Rj7F8yXx898UA3DeuB29PM3+pJ3Vsw9SLujKkm3m0TFSo+Us5t9jIdRdWn5H2iA3nkj7tSZ83mcsss2CCeUTLst+NoWf7cJ6/dgDXX9jZejVQNTcOmOfQ//WwbtwztgcXJUQDMDA+iv/ekcK2P10BQJe2IVxkM2pnakoX/vXrIdbllY+M4Zlrkhp87G4d2oUBndtwnlP3N8hbNjcA1qesopLnrx0AwLSRCRgt9zb4y5BRIerksT4CTznfPoLGWJ16hrxiI9de6Pg8Wq01f195kJuGxNOlbai1/HReKZNeW8fn944g0eaqorE+2nyMC7tG0a9jG2tZZaVGY25iqXL8bDFtQgKIDAkg5fmVZBeWceTFSSilmPbOFtakZrF19uUUlFZwyctriI8OYfGDo7nh3xtIO1Po9L1//tMV/Hgom6sGmpu2HvtsJwu3ZtClbQjHz5Y43SalW3SDblprrN+OTCAqJJB/rUlj5SNj6dI2lMpKjZ+fYufxXG57azNv3p7CsO7tPB6LEE1ZXX0EkghasOLyCio11jl18kuN7Dyey+hesRzILGD839fSs304Kx8xNz0dyS5ix/FzPLxgJ1MGdeLrHSeJaxPMJpt+EQCjqZLcYiNHsou4+T+O9z1EhQbQNy7C4W7nhhjZs531buG6PHtNf+4YkdDo9xGitfDV8FHhY6GB9v+9bYIDGN0rFoA4S4f4vTYdrIkxYXRrG0pmfhm3Du3KExP7ERrk2G8RYPAjNiKI2IggPplxMZEhAWw8lMP0UYkUl1fgpxR3v2efrNPnTeZcUTk7M3IZ16c9d/3vJ1badPou+91oJryyjqjQAJ69pj9TBnUm5fnvyC6sfQI5ME8dIoQ4P5IIWqk2wQGkz5vsUO7np6yjb2r2mzhzsaXJparJqir5VDVXzb1ugLVOdFgg4/q0B+CvNw8i+dnqIbN9OkTw+b0j6Nk+3Pq+syb2q3M2WDA/X0AIcX5kOIXwiP6dzImhX8c2dp3dVSJDAlj32CXWZaUUQ7pF2yWfmPBAh+3A/LjJL+4bwQOX9uRXwxp3g6EQoppcEQiPePjy3ozuFcvgrtG11unSNpTvfz/WOp9RTbXdp3F1cicGd42uc99CCNdJIhAe4W/wszYJ1aV7bDjdnVwxAHYjpEIDDRSXm3j9V4O5IqmD0/pCiMaRRCCarNBAf76ZOYqNh7OZdEFHDmQWcGlfSQJCuJskAtGkXRAfyQWWO7njo0PrqS2EaAzpLBZCiFZOEoEQQrRykgiEEKKVk0QghBCtnCQCIYRo5SQRCCFEKyeJQAghWjlJBEII0co1u+cRKKWygKON3DwGyHZjOO4icTWMxNUwTTUuaLqxtcS4ummtY52taHaJ4HwopbbW9mAGX5K4GkbiapimGhc03dhaW1zSNCSEEK2cJAIhhGjlWlsimO/rAGohcTWMxNUwTTUuaLqxtaq4WlUfgRBCCEet7YpACCFEDZIIhBCilWsxiUApNUEplaqUSlNKzXKyPkgptcCyfrNSKsFm3ROW8lSl1JVejusRpdRepdQvSqlVSqluNutMSqkdlp9FXo5rmlIqy+b977JZd4dS6qDl5w4vx/V3m5gOKKVybdZ55Hgppd5WSp1RSu2uZb1SSr1mifkXpdRgm3WePFb1xfVrSzy7lFIblFLJNuvSLeU7lFJb3RmXi7GNU0rl2fx/PWWzrs7PgIfj+oNNTLstn6m2lnUeOWZKqS5KqdWW74E9SqmHnNTx7GdMa93sfwADcAjoDgQCO4GkGnXuA96wvL4FWGB5nWSpHwQkWvZj8GJclwChltf3VsVlWS704fGaBvzTybZtgcOWf6Mtr6O9FVeN+g8Ab3vheI0BBgO7a1k/CVgKKOBiYLOnj5WLcY2oej9gYlVcluV0IMYTx8vF2MYB357vZ8DdcdWoezXwvaePGdARGGx5HQEccPL36NHPWEu5IhgKpGmtD2uty4FPgCk16kwB/md5/RlwmVJKWco/0VqXaa2PAGmW/XklLq31aq11sWVxExDvpvc+r7jqcCXwndb6rNb6HPAdMMFHcd0KfOym966V1notcLaOKlOA97TZJiBKKdURzx6reuPSWm+wvC9477NV9d71HbPanM9n091xeevzdUprvd3yugDYB3SuUc2jn7GWkgg6A8dtljNwPJDWOlrrCiAPaOfitp6My9admLN+lWCl1Fal1Cal1LVuiqkhcd1guQz9TCnVpYHbejIuLE1oicD3NsWeOl71qS1uTx6rhqr52dLACqXUNqXUDB/FNFwptVMptVQp1d9S1iSOmVIqFPMX6uc2xR4/ZsrcZH0hsLnGKo9+xuTh9U2EUuo3QAow1qa4m9b6hFKqO/C9UmqX1vqQl0L6BvhYa12mlPo/zFdTl3rpvV1xC/CZ1tpkU+bL49VkKaUuwZwIRtkUj7Icq/bAd0qp/ZazZW/Zjvn/q1ApNQn4Cujlxfevz9XAj1pr26sHjx4zpVQ45sTzO611vrv264qWckVwAuhisxxvKXNaRynlD0QCOS5u68m4UEpdDjwJXKO1Lqsq11qfsPx7GFiD+UzBK3FprXNsYvkvMMTVbT0Zl41bqHHZ7sHjVZ/a4vbksXKJUmog5v+/KVrrnKpym2N1BvgS9zWHukRrna+1LrS8XgIEKKViaALHzKKuz5fbj5lSKgBzEvhQa/2Fkyqe/Yy5u+PDFz+Yr2wOY24qqOpg6l+jzv3YdxYvtLzuj31n8WHc11nsSlwXYu4c61WjPBoIsryOAQ7ipk4zF+PqaPP6OmCTru6cOmKJL9ryuq234rLU64u5405543hZ9plA7R2fk7HvyNvi6WPlYlxdMfd5jahRHgZE2LzeAExwZ1wuxBZX9f+H+Qv1mOX4ufQZ8FRclvWRmPsRwrxxzCy/93vAK3XU8ehnzK3/8b78wdyrfgDzl+qTlrLnMJ9lAwQDn1r+MLYA3W22fdKyXSow0ctxrQQygR2Wn0WW8hHALssfwi7gTi/H9SKwx/L+q4G+NttOtxzHNOC33ozLsvwMMK/Gdh47XpjPDE8BRsxtsHcC9wD3WNYr4HVLzLuAFC8dq/ri+i9wzuaztdVS3t1ynHZa/o+fdGdcLsY20+bztQmbZOXsM+CtuCx1pmEeQGK7nceOGeYmOw38YvN/NcmbnzGZYkIIIVq5ltJHIIQQopEkEQghRCsniUAIIVo5SQRCCNHKSSIQQohWThKBEG6glIpSSt3n6ziEaAxJBEK4RxTmGW6FaHYkEQjhHvOAHpa56l/ydTBCNITcUCaEG1hmjfxWaz3A17EI0VByRSCEEK2cJAIhhGjlJBEI4R4FmB8zKESzI4lACDfQ5rn+f7Q88Fw6i0WzIp3FQgjRyskVgRBCtHKSCIQQopWTRCCEEK2cJAIhhGjlJBEIIUQrJ4lACCFaOUkEQgjRyv0/UUhS6bTv/PQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_naive.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that this approach does work,\n", + "but there is room for improvement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating the tuning curves\n", + "\n", + "Let us take a look at\n", + "the tuning curves of the neurons in the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.688439Z", + "iopub.status.busy": "2020-11-25T16:46:03.654083Z", + "iopub.status.idle": "2020-11-25T16:46:04.024975Z", + "shell.execute_reply": "2020-11-25T16:46:04.025423Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Firing rate [Hz]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_naive))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "About half of these neurons are tuned to fire more for smaller values.\n", + "But these values are not relevant\n", + "for the Heaviside step function,\n", + "since the output is always 0\n", + "when input is less than 0.\n", + "We can change all neurons to be tuned\n", + "to fire more for larger values\n", + "by setting all the encoders to be positive." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.033810Z", + "iopub.status.busy": "2020-11-25T16:46:04.033310Z", + "iopub.status.idle": "2020-11-25T16:46:04.036526Z", + "shell.execute_reply": "2020-11-25T16:46:04.036926Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network() as model_pos_enc:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens = nengo.Ensemble(\n", + " n_neurons=n_neurons, dimensions=1, encoders=nengo.dists.Choice([[1.0]])\n", + " )\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_pos_enc = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.042363Z", + "iopub.status.busy": "2020-11-25T16:46:04.041583Z", + "iopub.status.idle": "2020-11-25T16:46:04.373382Z", + "shell.execute_reply": "2020-11-25T16:46:04.372905Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_pos_enc) as sim_pos_enc:\n", + " sim_pos_enc.run(duration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting tuning curves:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.379060Z", + "iopub.status.busy": "2020-11-25T16:46:04.378232Z", + "iopub.status.idle": "2020-11-25T16:46:04.689463Z", + "shell.execute_reply": "2020-11-25T16:46:04.689881Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Firing rate [Hz]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_pos_enc))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the resulting decoded Heaviside step function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.709140Z", + "iopub.status.busy": "2020-11-25T16:46:04.701129Z", + "iopub.status.idle": "2020-11-25T16:46:04.875204Z", + "shell.execute_reply": "2020-11-25T16:46:04.875623Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_pos_enc.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\")\n", + "plt.plot(t, sim_pos_enc.data[p_pos_enc], label=\"pos. enc.\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compared to the naive approach,\n", + "the representation of outputs lower than 1 is less noisy,\n", + "but otherwise there is little improvement.\n", + "Even though the tuning curves are all positive,\n", + "they are still covering a lot of irrelevant parts of the input space.\n", + "Because all outputs should be 0 when input is less than 0,\n", + "there is no need to have neurons tuned to inputs less than 0.\n", + "Let's shift all the intercepts to the range $(0.0, 1.0)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intercept distributions\n", + "\n", + "Not only can the range of intercepts be important,\n", + "but also the distribution of intercepts.\n", + "Let us take a look at the Heaviside step function:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.894226Z", + "iopub.status.busy": "2020-11-25T16:46:04.893311Z", + "iopub.status.idle": "2020-11-25T16:46:05.016106Z", + "shell.execute_reply": "2020-11-25T16:46:05.015630Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.1, 1.1)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.linspace(-1, 1, 100)\n", + "plt.figure()\n", + "plt.plot(xs, heaviside(xs))\n", + "plt.ylim(-0.1, 1.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function is mostly constant,\n", + "except for the large jump at 0.\n", + "The constant parts are easy to approximate\n", + "and do not need a lot of neural resources,\n", + "but the highly nonlinear jump\n", + "requires more neural resources\n", + "for an accurate representation.\n", + "\n", + "Let us compare the thresholding of a scalar in three ways:\n", + "\n", + "1. With a uniform distribution of intercepts (the default)\n", + "2. With all intercepts at 0 (where we have the nonlinearity)\n", + "3. With an exponential distribution\n", + "\n", + "The last approach is in between\n", + "the two extremes of a uniform distribution\n", + "and placing all intercepts at 0.\n", + "It will distribute most intercepts close to 0,\n", + "but some intercepts will still be at larger values." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:05.031799Z", + "iopub.status.busy": "2020-11-25T16:46:05.023763Z", + "iopub.status.idle": "2020-11-25T16:46:05.033616Z", + "shell.execute_reply": "2020-11-25T16:46:05.033999Z" + } + }, + "outputs": [], + "source": [ + "threshold = 0.0\n", + "\n", + "with nengo.Network() as model_dists:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens_uniform = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Uniform(threshold, 1.0),\n", + " )\n", + " ens_fixed = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Choice([threshold]),\n", + " )\n", + " ens_exp = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Exponential(0.15, threshold, 1.0),\n", + " )\n", + "\n", + " out_uniform = nengo.Node(size_in=1)\n", + " out_fixed = nengo.Node(size_in=1)\n", + " out_exp = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens_uniform)\n", + " nengo.Connection(stimulus, ens_fixed)\n", + " nengo.Connection(stimulus, ens_exp)\n", + " nengo.Connection(ens_uniform, out_uniform, function=heaviside)\n", + " nengo.Connection(ens_fixed, out_fixed, function=heaviside)\n", + " nengo.Connection(ens_exp, out_exp, function=heaviside)\n", + "\n", + " p_uniform = nengo.Probe(out_uniform, synapse=0.005)\n", + " p_fixed = nengo.Probe(out_fixed, synapse=0.005)\n", + " p_exp = nengo.Probe(out_exp, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:05.039143Z", + "iopub.status.busy": "2020-11-25T16:46:05.038364Z", + "iopub.status.idle": "2020-11-25T16:46:05.470922Z", + "shell.execute_reply": "2020-11-25T16:46:05.470457Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_dists) as sim_dists:\n", + " sim_dists.run(duration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us look at the tuning curves first." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:05.493289Z", + "iopub.status.busy": "2020-11-25T16:46:05.487102Z", + "iopub.status.idle": "2020-11-25T16:46:06.474469Z", + "shell.execute_reply": "2020-11-25T16:46:06.474022Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_uniform, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Uniform intercepts\")\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_fixed, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Fixed intercepts\")\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_exp, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Exponential intercept distribution\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us look at how these three ensembles\n", + "approximate the thresholding function." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:06.480901Z", + "iopub.status.busy": "2020-11-25T16:46:06.480042Z", + "iopub.status.idle": "2020-11-25T16:46:06.683018Z", + "shell.execute_reply": "2020-11-25T16:46:06.683432Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_dists.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\", c=\"gray\")\n", + "plt.plot(t, sim_dists.data[p_uniform], label=\"Uniform intercepts\")\n", + "plt.plot(t, sim_dists.data[p_fixed], label=\"Fixed intercepts\")\n", + "plt.plot(t, sim_dists.data[p_exp], label=\"Exp. intercept dist.\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the fixed intercepts\n", + "produce slightly higher decoded values close to the threshold,\n", + "but the slope is lower than for uniform intercepts.\n", + "The best approximation of the thresholding\n", + "is done with the exponential intercept distribution.\n", + "Here we get a quick rise to 1 at the threshold\n", + "and a fairly constant representation of 1\n", + "for inputs sufficiently above 0.\n", + "All three distributions perfectly represent values below 0.\n", + "\n", + "Nengo provides the `ThresholdingEnsemble` preset\n", + "to make it easier to assign intercepts\n", + "according to the exponential distribution,\n", + "and also adjusts the encoders and evaluation points accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:06.692487Z", + "iopub.status.busy": "2020-11-25T16:46:06.691367Z", + "iopub.status.idle": "2020-11-25T16:46:06.694038Z", + "shell.execute_reply": "2020-11-25T16:46:06.693603Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network() as model_final:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " with nengo.presets.ThresholdingEnsembles(0.0):\n", + " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_final = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:06.699268Z", + "iopub.status.busy": "2020-11-25T16:46:06.698394Z", + "iopub.status.idle": "2020-11-25T16:46:06.994952Z", + "shell.execute_reply": "2020-11-25T16:46:06.995598Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_final) as sim_final:\n", + " sim_final.run(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:07.001264Z", + "iopub.status.busy": "2020-11-25T16:46:07.000374Z", + "iopub.status.idle": "2020-11-25T16:46:07.166273Z", + "shell.execute_reply": "2020-11-25T16:46:07.166652Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_final.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_final.data[p_final], label=\"final\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## The takeaway\n", + "\n", + "Adjusting ensemble parameters in the right way\n", + "can sometimes help in implementing functions more acurately in neurons.\n", + "Think about how your function maps from\n", + "the input vector space to the output vector space,\n", + "and consider ways to modify ensemble parameters\n", + "to better cover important parts\n", + "of the input vector space." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/advanced/inhibitory-gating.ipynb b/.doctrees/nbsphinx/examples/advanced/inhibitory-gating.ipynb new file mode 100644 index 0000000000..ccfa560c27 --- /dev/null +++ b/.doctrees/nbsphinx/examples/advanced/inhibitory-gating.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inhibitory gating of ensembles" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.432474Z", + "iopub.status.busy": "2020-11-25T16:46:10.431599Z", + "iopub.status.idle": "2020-11-25T16:46:10.925564Z", + "shell.execute_reply": "2020-11-25T16:46:10.925052Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "Our model consists of two ensembles (called A and B)\n", + "that receive inputs from a common sine wave signal generator.\n", + "\n", + "Ensemble A is gated using the output of a node,\n", + "while Ensemble B is gated using the output of a third ensemble (C).\n", + "This is to demonstrate that ensembles can be gated\n", + "using either node outputs, or decoded outputs from ensembles." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.931988Z", + "iopub.status.busy": "2020-11-25T16:46:10.931419Z", + "iopub.status.idle": "2020-11-25T16:46:10.934990Z", + "shell.execute_reply": "2020-11-25T16:46:10.934540Z" + } + }, + "outputs": [], + "source": [ + "n_neurons = 30\n", + "\n", + "model = nengo.Network(label=\"Inhibitory Gating\")\n", + "with model:\n", + " A = nengo.Ensemble(n_neurons, dimensions=1)\n", + " B = nengo.Ensemble(n_neurons, dimensions=1)\n", + " C = nengo.Ensemble(n_neurons, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "As described in Step 1, this model requires two inputs.\n", + "\n", + "1. A sine wave signal that is used to drive ensembles A and B\n", + "2. An inhibitory control signal used to (directly) gate ensemble A,\n", + " and (indirectly through ensemble C) gate ensemble B." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.940994Z", + "iopub.status.busy": "2020-11-25T16:46:10.939493Z", + "iopub.status.idle": "2020-11-25T16:46:10.941616Z", + "shell.execute_reply": "2020-11-25T16:46:10.942034Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(np.sin)\n", + " inhib = nengo.Node(Piecewise({0: 0, 2.5: 1, 5: 0, 7.5: 1, 10: 0, 12.5: 1}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the different components of the model\n", + "\n", + "In this model, we need to make the following connections:\n", + "\n", + "1. From sine wave generator to Ensemble A\n", + "2. From sine wave generator to Ensemble B\n", + "3. From inhibitory control signal to the neurons of Ensemble A\n", + " (to directly drive the currents of the neurons)\n", + "4. From inhibitory control signal to Ensemble C\n", + "5. From Ensemble C to the neurons of Ensemble B\n", + " (this demonstrates that the decoded output of Ensemble C\n", + " can be used to gate Ensemble B)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.950930Z", + "iopub.status.busy": "2020-11-25T16:46:10.949450Z", + "iopub.status.idle": "2020-11-25T16:46:10.951548Z", + "shell.execute_reply": "2020-11-25T16:46:10.951987Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, A)\n", + " nengo.Connection(sin, B)\n", + " nengo.Connection(inhib, A.neurons, transform=[[-2.5]] * n_neurons)\n", + " nengo.Connection(inhib, C)\n", + " nengo.Connection(C, B.neurons, transform=[[-2.5]] * n_neurons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later.\n", + "Let's collect all the data produced." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.959360Z", + "iopub.status.busy": "2020-11-25T16:46:10.957898Z", + "iopub.status.idle": "2020-11-25T16:46:10.960002Z", + "shell.execute_reply": "2020-11-25T16:46:10.960400Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " inhib_probe = nengo.Probe(inhib)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " C_probe = nengo.Probe(C, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.965546Z", + "iopub.status.busy": "2020-11-25T16:46:10.964788Z", + "iopub.status.idle": "2020-11-25T16:46:14.587431Z", + "shell.execute_reply": "2020-11-25T16:46:14.586937Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:14.593579Z", + "iopub.status.busy": "2020-11-25T16:46:14.592823Z", + "iopub.status.idle": "2020-11-25T16:46:14.932696Z", + "shell.execute_reply": "2020-11-25T16:46:14.933120Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of Ensemble A\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], label=\"Sine input\")\n", + "plt.plot(sim.trange(), sim.data[inhib_probe], label=\"Inhibitory signal\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:14.939304Z", + "iopub.status.busy": "2020-11-25T16:46:14.938560Z", + "iopub.status.idle": "2020-11-25T16:46:15.277444Z", + "shell.execute_reply": "2020-11-25T16:46:15.277867Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of Ensemble B and C\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded output of B\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], label=\"Sine input\")\n", + "plt.plot(sim.trange(), sim.data[C_probe], label=\"Decoded output of C\")\n", + "plt.plot(sim.trange(), sim.data[inhib_probe], label=\"Inhibitory signal\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/advanced/izhikevich.ipynb b/.doctrees/nbsphinx/examples/advanced/izhikevich.ipynb new file mode 100644 index 0000000000..c903636e2d --- /dev/null +++ b/.doctrees/nbsphinx/examples/advanced/izhikevich.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using the Izhikevich neuron model\n", + "\n", + "The [Izhikevich neuron model](\n", + "http://www.izhikevich.org/publications/spikes.htm)\n", + "is a quadratic integrate-and-fire type model\n", + "with a recovery variable.\n", + "It is able to replicate several characteristics\n", + "of biological neurons while remaining\n", + "computationally efficient.\n", + "\n", + "The Izhikevich neuron model is implemented in Nengo.\n", + "To use it, use a `nengo.Izhikevich` instance\n", + "as the `neuron_type` of an ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:16.604912Z", + "iopub.status.busy": "2020-11-25T16:46:16.604037Z", + "iopub.status.idle": "2020-11-25T16:46:17.098505Z", + "shell.execute_reply": "2020-11-25T16:46:17.098936Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import tuning_curves\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:17.106886Z", + "iopub.status.busy": "2020-11-25T16:46:17.106372Z", + "iopub.status.idle": "2020-11-25T16:46:17.109391Z", + "shell.execute_reply": "2020-11-25T16:46:17.109782Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network(seed=0) as model:\n", + " u = nengo.Node(lambda t: np.sin(2 * np.pi * t))\n", + " ens = nengo.Ensemble(10, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(u, ens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the usual decoded output and neural activity\n", + "that can always be probed,\n", + "you can probe the voltage and recovery terms\n", + "of the Izhikevich model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:17.116456Z", + "iopub.status.busy": "2020-11-25T16:46:17.114914Z", + "iopub.status.idle": "2020-11-25T16:46:17.117067Z", + "shell.execute_reply": "2020-11-25T16:46:17.117485Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " out_p = nengo.Probe(ens, synapse=0.03)\n", + " spikes_p = nengo.Probe(ens.neurons)\n", + " voltage_p = nengo.Probe(ens.neurons, \"voltage\")\n", + " recovery_p = nengo.Probe(ens.neurons, \"recovery\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulating this model shows that we are able\n", + "to decode a time-varying scalar with\n", + "an ensemble of Izhikevich neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:17.124339Z", + "iopub.status.busy": "2020-11-25T16:46:17.123515Z", + "iopub.status.idle": "2020-11-25T16:46:18.200139Z", + "shell.execute_reply": "2020-11-25T16:46:18.199618Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Neuron')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, sim.data[out_p])\n", + "plt.ylabel(\"Decoded output\")\n", + "ax = plt.subplot(2, 1, 2)\n", + "rasterplot(t, sim.data[spikes_p], ax=ax)\n", + "plt.ylabel(\"Neuron\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing you might notice is a slight bump in the decoded value\n", + "at the start of the simulation.\n", + "This occurs because of the adaptive nature of the Izhikevic model;\n", + "it is easier to initiate the first spike than it is to ellicit\n", + "further spikes.\n", + "\n", + "Let's use a constant input and\n", + "look at the first 100 ms of the simulation in more detail\n", + "to see the difference between the first spike and subsequent spikes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:18.208888Z", + "iopub.status.busy": "2020-11-25T16:46:18.208010Z", + "iopub.status.idle": "2020-11-25T16:46:19.364513Z", + "shell.execute_reply": "2020-11-25T16:46:19.364915Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def izh_plot(sim):\n", + " t = sim.trange()\n", + " plt.figure(figsize=(12, 10))\n", + " plt.subplot(4, 1, 1)\n", + " plt.plot(t, sim.data[out_p])\n", + " plt.ylabel(\"Decoded output\")\n", + " plt.xlim(right=t[-1])\n", + " ax = plt.subplot(4, 1, 2)\n", + " rasterplot(t, sim.data[spikes_p], ax=ax)\n", + " plt.ylabel(\"Neuron\")\n", + " plt.xlim(right=t[-1])\n", + " plt.subplot(4, 1, 3)\n", + " plt.plot(t, sim.data[voltage_p])\n", + " plt.ylabel(\"Voltage\")\n", + " plt.xlim(right=t[-1])\n", + " plt.subplot(4, 1, 4)\n", + " plt.plot(t, sim.data[recovery_p])\n", + " plt.ylabel(\"Recovery\")\n", + " plt.xlim(right=t[-1])\n", + "\n", + "\n", + "u.output = 0.2\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.1)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Those neurons that have an encoder of -1\n", + "receive negative current, and therefore\n", + "remain at a low voltage.\n", + "\n", + "Those neurons that have an encoder of 1\n", + "receive positive current, and start spiking rapidly.\n", + "However, as they spike, the recovery variable grows,\n", + "until it reaches a balance with the voltage\n", + "such that the cells spike regularly.\n", + "\n", + "This occurs because, by default,\n", + "we use a set of parameters\n", + "that models a \"regular spiking\" neuron.\n", + "We can use parameters\n", + "that model several different\n", + "classes of neurons instead." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intrinsically bursting (IB)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:19.371147Z", + "iopub.status.busy": "2020-11-25T16:46:19.370371Z", + "iopub.status.idle": "2020-11-25T16:46:20.587713Z", + "shell.execute_reply": "2020-11-25T16:46:20.588147Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(reset_voltage=-55, reset_recovery=4)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chattering (CH)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:20.594352Z", + "iopub.status.busy": "2020-11-25T16:46:20.593577Z", + "iopub.status.idle": "2020-11-25T16:46:21.814639Z", + "shell.execute_reply": "2020-11-25T16:46:21.814126Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(reset_voltage=-50, reset_recovery=2)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fast spiking (FS)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:21.820727Z", + "iopub.status.busy": "2020-11-25T16:46:21.819929Z", + "iopub.status.idle": "2020-11-25T16:46:23.058824Z", + "shell.execute_reply": "2020-11-25T16:46:23.058365Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Low-threshold spiking (LTS)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:23.064772Z", + "iopub.status.busy": "2020-11-25T16:46:23.063979Z", + "iopub.status.idle": "2020-11-25T16:46:24.314362Z", + "shell.execute_reply": "2020-11-25T16:46:24.314756Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(coupling=0.25)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resonator (RZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:24.320902Z", + "iopub.status.busy": "2020-11-25T16:46:24.320104Z", + "iopub.status.idle": "2020-11-25T16:46:25.554105Z", + "shell.execute_reply": "2020-11-25T16:46:25.553384Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1, coupling=0.26)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caveats\n", + "\n", + "Unfortunately, Izhikevich neurons can't necessarily\n", + "be used in all of the situations that LIFs are used,\n", + "due to the more complex dynamics illustrated above.\n", + "\n", + "The way that Nengo encodes and decodes information\n", + "with neurons is informed by the tuning curves\n", + "of those neurons.\n", + "With Izhikevich neurons, the firing rate\n", + "with a certain input current `J` changes;\n", + "the spike rate is initially higher due\n", + "to the adaptation illustrated above.\n", + "\n", + "We try our best to generate tuning curves\n", + "for Izhikevich neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:25.562824Z", + "iopub.status.busy": "2020-11-25T16:46:25.561209Z", + "iopub.status.idle": "2020-11-25T16:46:25.563524Z", + "shell.execute_reply": "2020-11-25T16:46:25.563973Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network(seed=0) as model:\n", + " u = nengo.Node(lambda t: np.sin(2 * np.pi * t))\n", + " ens = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(u, ens)\n", + " out_p = nengo.Probe(ens, synapse=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:25.569407Z", + "iopub.status.busy": "2020-11-25T16:46:25.568630Z", + "iopub.status.idle": "2020-11-25T16:46:26.665408Z", + "shell.execute_reply": "2020-11-25T16:46:26.664964Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens, sim))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But these are not as accurate and clean\n", + "as LIF curves, which is detrimental\n", + "for function decoding." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:26.673320Z", + "iopub.status.busy": "2020-11-25T16:46:26.671844Z", + "iopub.status.idle": "2020-11-25T16:46:26.673970Z", + "shell.execute_reply": "2020-11-25T16:46:26.674392Z" + } + }, + "outputs": [], + "source": [ + "u.output = lambda t: np.sin(2 * np.pi * t)\n", + "with model:\n", + " square = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(ens, square, function=lambda x: x ** 2)\n", + " square_p = nengo.Probe(square, synapse=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:26.680590Z", + "iopub.status.busy": "2020-11-25T16:46:26.679809Z", + "iopub.status.idle": "2020-11-25T16:46:29.348655Z", + "shell.execute_reply": "2020-11-25T16:46:29.349074Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(t, sim.data[out_p], label=\"Ensemble 1 (sin wave)\")\n", + "plt.plot(t, sim.data[square_p], label=\"Ensemble 2 (sin^2)\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of these weird dynamics\n", + "can be overcome by using Izhikevich\n", + "neurons with different parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:29.356034Z", + "iopub.status.busy": "2020-11-25T16:46:29.355141Z", + "iopub.status.idle": "2020-11-25T16:46:31.950101Z", + "shell.execute_reply": "2020-11-25T16:46:31.950536Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "square.neuron_type = nengo.Izhikevich(tau_recovery=0.2)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(t, sim.data[out_p], label=\"Ensemble 1 (sin wave)\")\n", + "plt.plot(t, sim.data[square_p], label=\"Ensemble 2 (sin^2)\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generally, however, Izhikevich neurons are most useful\n", + "when trying to match known physiological properties\n", + "of the system being modelled." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/advanced/matrix-multiplication.ipynb b/.doctrees/nbsphinx/examples/advanced/matrix-multiplication.ipynb new file mode 100644 index 0000000000..f8cb0f10b4 --- /dev/null +++ b/.doctrees/nbsphinx/examples/advanced/matrix-multiplication.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matrix multiplication\n", + "\n", + "This example demonstrates how to perform\n", + "general matrix multiplication using Nengo.\n", + "The matrix can change during the computation,\n", + "which makes it distinct from doing static matrix multiplication\n", + "with neural connection weights (as done in all neural networks).\n", + "\n", + "Note that the order of operands in matrix multiplication matters.\n", + "We will be computing $A \\cdot B$ which is equivalent to $(B \\cdot A)^{\\top}$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:33.454016Z", + "iopub.status.busy": "2020-11-25T16:46:33.453163Z", + "iopub.status.idle": "2020-11-25T16:46:33.944879Z", + "shell.execute_reply": "2020-11-25T16:46:33.943974Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:33.968799Z", + "iopub.status.busy": "2020-11-25T16:46:33.968236Z", + "iopub.status.idle": "2020-11-25T16:46:33.971978Z", + "shell.execute_reply": "2020-11-25T16:46:33.971338Z" + } + }, + "outputs": [], + "source": [ + "N = 100\n", + "Amat = np.asarray([[0.5, -0.5], [-0.2, 0.3]])\n", + "Bmat = np.asarray([[0.58, -1.0], [0.7, 0.1]])\n", + "\n", + "# Values should stay within the range (-radius, radius)\n", + "radius = 1\n", + "\n", + "model = nengo.Network(label=\"Matrix Multiplication\", seed=123)\n", + "with model:\n", + " # Make 2 EnsembleArrays to store the input\n", + " A = nengo.networks.EnsembleArray(N, Amat.size, radius=radius)\n", + " B = nengo.networks.EnsembleArray(N, Bmat.size, radius=radius)\n", + "\n", + " # connect inputs to them so we can set their value\n", + " inputA = nengo.Node(Amat.ravel())\n", + " inputB = nengo.Node(Bmat.ravel())\n", + " nengo.Connection(inputA, A.input)\n", + " nengo.Connection(inputB, B.input)\n", + " A_probe = nengo.Probe(A.output, sample_every=0.01, synapse=0.01)\n", + " B_probe = nengo.Probe(B.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:33.979505Z", + "iopub.status.busy": "2020-11-25T16:46:33.978472Z", + "iopub.status.idle": "2020-11-25T16:46:34.693888Z", + "shell.execute_reply": "2020-11-25T16:46:34.694291Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "plt.figure()\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"A\")\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[A_probe])\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"B\")\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[B_probe])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:34.743106Z", + "iopub.status.busy": "2020-11-25T16:46:34.742582Z", + "iopub.status.idle": "2020-11-25T16:46:34.746964Z", + "shell.execute_reply": "2020-11-25T16:46:34.746522Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A->C\n", + "[[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]]\n", + "B->C\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 0. 1.]\n", + " [1. 0. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "with model:\n", + " # The C matrix is composed of populations that each contain\n", + " # one element of A and one element of B.\n", + " # These elements will be multiplied together in the next step.\n", + "\n", + " # The appropriate encoders make the multiplication more accurate\n", + " # Check the \"multiplication\" example to see how multiplication\n", + " # can be implemented in neurons.\n", + " c_size = Amat.size * Bmat.shape[1]\n", + " C = nengo.networks.Product(N, dimensions=c_size)\n", + "\n", + "# Determine the transformation matrices to get the correct pairwise\n", + "# products computed. This looks a bit like black magic but if\n", + "# you manually try multiplying two matrices together, you can see\n", + "# the underlying pattern. Basically, we need to build up D1*D2*D3\n", + "# pairs of numbers in C to compute the product of. If i,j,k are the\n", + "# indexes into the D1*D2*D3 products, we want to compute the product\n", + "# of element (i,j) in A with the element (j,k) in B. The index in\n", + "# A of (i,j) is j+i*D2 and the index in B of (j,k) is k+j*D3.\n", + "# The index in C is j+k*D2+i*D2*D3.\n", + "transformA = np.zeros((c_size, Amat.size))\n", + "transformB = np.zeros((c_size, Bmat.size))\n", + "\n", + "for i in range(Amat.shape[0]):\n", + " for j in range(Amat.shape[1]):\n", + " for k in range(Bmat.shape[1]):\n", + " c_index = j + k * Amat.shape[1] + i * Bmat.size\n", + " transformA[c_index][j + i * Amat.shape[1]] = 1\n", + " transformB[c_index][k + j * Bmat.shape[1]] = 1\n", + "\n", + "print(\"A->C\")\n", + "print(transformA)\n", + "print(\"B->C\")\n", + "print(transformB)\n", + "\n", + "with model:\n", + " nengo.Connection(A.output, C.input_a, transform=transformA)\n", + " nengo.Connection(B.output, C.input_b, transform=transformB)\n", + " C_probe = nengo.Probe(C.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:34.754128Z", + "iopub.status.busy": "2020-11-25T16:46:34.753354Z", + "iopub.status.idle": "2020-11-25T16:46:36.107713Z", + "shell.execute_reply": "2020-11-25T16:46:36.107182Z" + } + }, + "outputs": [], + "source": [ + "# Look at C\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:36.113240Z", + "iopub.status.busy": "2020-11-25T16:46:36.112412Z", + "iopub.status.idle": "2020-11-25T16:46:36.251075Z", + "shell.execute_reply": "2020-11-25T16:46:36.251483Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'C')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[C_probe])\n", + "plt.title(\"C\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:36.265512Z", + "iopub.status.busy": "2020-11-25T16:46:36.264981Z", + "iopub.status.idle": "2020-11-25T16:46:36.269083Z", + "shell.execute_reply": "2020-11-25T16:46:36.268597Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C->D\n", + "[[1. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 1.]]\n" + ] + } + ], + "source": [ + "with model:\n", + " # Now do the appropriate summing\n", + " D = nengo.networks.EnsembleArray(\n", + " N, n_ensembles=Amat.shape[0] * Bmat.shape[1], radius=radius\n", + " )\n", + "\n", + "# The mapping for this transformation is much easier, since we want to\n", + "# combine D2 pairs of elements (we sum D2 products together)\n", + "transformC = np.zeros((D.dimensions, c_size))\n", + "for i in range(c_size):\n", + " transformC[i // Bmat.shape[0]][i] = 1\n", + "print(\"C->D\")\n", + "print(transformC)\n", + "\n", + "with model:\n", + " nengo.Connection(C.output, D.input, transform=transformC)\n", + " D_probe = nengo.Probe(D.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:36.276268Z", + "iopub.status.busy": "2020-11-25T16:46:36.275485Z", + "iopub.status.idle": "2020-11-25T16:46:37.657663Z", + "shell.execute_reply": "2020-11-25T16:46:37.656788Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:37.668436Z", + "iopub.status.busy": "2020-11-25T16:46:37.662395Z", + "iopub.status.idle": "2020-11-25T16:46:37.842650Z", + "shell.execute_reply": "2020-11-25T16:46:37.841945Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'D')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[D_probe])\n", + "for d in np.dot(Amat, Bmat).flatten():\n", + " plt.axhline(d, color=\"k\")\n", + "plt.title(\"D\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/advanced/nef-algorithm.ipynb b/.doctrees/nbsphinx/examples/advanced/nef-algorithm.ipynb new file mode 100644 index 0000000000..416e089260 --- /dev/null +++ b/.doctrees/nbsphinx/examples/advanced/nef-algorithm.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The NEF algorithm\n", + "\n", + "While Nengo provides a flexible,\n", + "general-purpose approach to neural modelling,\n", + "some aspects rely on the Neural Engineering Framework (NEF)\n", + "to help specify network behavior.\n", + "The theory behind the Neural Engineering Framework\n", + "is developed at length in\n", + "[Eliasmith & Anderson, 2003: \"Neural Engineering\"](\n", + "https://mitpress.mit.edu/books/neural-engineering)\n", + "and a short summary is available in\n", + "[Stewart, 2012: \"A Technical Overview of the Neural Engineering Framework\"](\n", + "http://compneuro.uwaterloo.ca/publications/stewart2012d.html).\n", + "\n", + "However, for some people,\n", + "the best description of an algorithm\n", + "is the code itself.\n", + "With that in mind,\n", + "the following is a complete implementation of the NEF\n", + "for the special case of\n", + "two one-dimensional populations with a single connection between them.\n", + "You can adjust the function being computed,\n", + "the input to the system,\n", + "and various neural parameters.\n", + "\n", + "This example does not use Nengo at all.\n", + "It is Python code that only requires\n", + "Numpy (for the matrix inversion)\n", + "and Matplotlib (to produce graphs of the output)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The NEF is a method for building large-scale neural models\n", + "using realistic neurons.\n", + "It is a neural compiler:\n", + "you specify the high-level computations the model needs to compute,\n", + "and the properties of the neurons themselves,\n", + "and the NEF determines the neural connections\n", + "needed to perform those operations.\n", + "\n", + "This script shows how to build a\n", + "simple feed-forward network of leaky integrate-and-fire neurons\n", + "where each population encodes a one-dimensional value\n", + "and the connection weights between the populations\n", + "are optimized to compute some arbitrary function.\n", + "This same approach is used in Nengo,\n", + "extended to multi-dimensional representation,\n", + "multiple populations of neurons, and recurrent connections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To change the input to the system, change `input`.\n", + "To change the function computed by the weights, change `function`.\n", + "\n", + "The size of the populations and their neural properties\n", + "can also be adjusted by changing the parameters below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:39.143254Z", + "iopub.status.busy": "2020-11-25T16:46:39.142398Z", + "iopub.status.idle": "2020-11-25T16:46:39.469298Z", + "shell.execute_reply": "2020-11-25T16:46:39.469761Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import math\n", + "import random\n", + "\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "dt = 0.001 # simulation time step\n", + "t_rc = 0.02 # membrane RC time constant\n", + "t_ref = 0.002 # refractory period\n", + "t_pstc = 0.1 # post-synaptic time constant\n", + "N_A = 50 # number of neurons in first population\n", + "N_B = 40 # number of neurons in second population\n", + "N_samples = 100 # number of sample points to use when finding decoders\n", + "rate_A = 25, 75 # range of maximum firing rates for population A\n", + "rate_B = 50, 100 # range of maximum firing rates for population B\n", + "\n", + "\n", + "def input(t):\n", + " \"\"\"The input to the system over time\"\"\"\n", + " return math.sin(t)\n", + "\n", + "\n", + "def function(x):\n", + " \"\"\"The function to compute between A and B.\"\"\"\n", + " return x * x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:39.501235Z", + "iopub.status.busy": "2020-11-25T16:46:39.496079Z", + "iopub.status.idle": "2020-11-25T16:46:41.604526Z", + "shell.execute_reply": "2020-11-25T16:46:41.604026Z" + } + }, + "outputs": [], + "source": [ + "# create random encoders for the two populations\n", + "encoder_A = [random.choice([-1, 1]) for i in range(N_A)]\n", + "encoder_B = [random.choice([-1, 1]) for i in range(N_B)]\n", + "\n", + "\n", + "def generate_gain_and_bias(count, intercept_low, intercept_high, rate_low, rate_high):\n", + " gain = []\n", + " bias = []\n", + " for _ in range(count):\n", + " # desired intercept (x value for which the neuron starts firing\n", + " intercept = random.uniform(intercept_low, intercept_high)\n", + " # desired maximum rate (firing rate when x is maximum)\n", + " rate = random.uniform(rate_low, rate_high)\n", + "\n", + " # this algorithm is specific to LIF neurons, but should\n", + " # generate gain and bias values to produce the desired\n", + " # intercept and rate\n", + " z = 1.0 / (1 - math.exp((t_ref - (1.0 / rate)) / t_rc))\n", + " g = (1 - z) / (intercept - 1.0)\n", + " b = 1 - g * intercept\n", + " gain.append(g)\n", + " bias.append(b)\n", + " return gain, bias\n", + "\n", + "\n", + "# random gain and bias for the two populations\n", + "gain_A, bias_A = generate_gain_and_bias(N_A, -1, 1, rate_A[0], rate_A[1])\n", + "gain_B, bias_B = generate_gain_and_bias(N_B, -1, 1, rate_B[0], rate_B[1])\n", + "\n", + "\n", + "def run_neurons(input, v, ref):\n", + " \"\"\"Run the neuron model.\n", + "\n", + " A simple leaky integrate-and-fire model, scaled so that v=0 is resting\n", + " voltage and v=1 is the firing threshold.\n", + " \"\"\"\n", + " spikes = []\n", + " for i, _ in enumerate(v):\n", + " dV = dt * (input[i] - v[i]) / t_rc # the LIF voltage change equation\n", + " v[i] += dV\n", + " if v[i] < 0:\n", + " v[i] = 0 # don't allow voltage to go below 0\n", + "\n", + " if ref[i] > 0: # if we are in our refractory period\n", + " v[i] = 0 # keep voltage at zero and\n", + " ref[i] -= dt # decrease the refractory period\n", + "\n", + " if v[i] > 1: # if we have hit threshold\n", + " spikes.append(True) # spike\n", + " v[i] = 0 # reset the voltage\n", + " ref[i] = t_ref # and set the refractory period\n", + " else:\n", + " spikes.append(False)\n", + " return spikes\n", + "\n", + "\n", + "def compute_response(x, encoder, gain, bias, time_limit=0.5):\n", + " \"\"\"Measure the spike rate of a population for a given value x.\"\"\"\n", + " N = len(encoder) # number of neurons\n", + " v = [0] * N # voltage\n", + " ref = [0] * N # refractory period\n", + "\n", + " # compute input corresponding to x\n", + " input = []\n", + " for i in range(N):\n", + " input.append(x * encoder[i] * gain[i] + bias[i])\n", + " v[i] = random.uniform(0, 1) # randomize the initial voltage level\n", + "\n", + " count = [0] * N # spike count for each neuron\n", + "\n", + " # feed the input into the population for a given amount of time\n", + " t = 0\n", + " while t < time_limit:\n", + " spikes = run_neurons(input, v, ref)\n", + " for i, s in enumerate(spikes):\n", + " if s:\n", + " count[i] += 1\n", + " t += dt\n", + " return [c / time_limit for c in count] # return the spike rate (in Hz)\n", + "\n", + "\n", + "def compute_tuning_curves(encoder, gain, bias):\n", + " \"\"\"Compute the tuning curves for a population\"\"\"\n", + " # generate a set of x values to sample at\n", + " x_values = [i * 2.0 / N_samples - 1.0 for i in range(N_samples)]\n", + "\n", + " # build up a matrix of neural responses to each input (i.e. tuning curves)\n", + " A = []\n", + " for x in x_values:\n", + " response = compute_response(x, encoder, gain, bias)\n", + " A.append(response)\n", + " return x_values, A\n", + "\n", + "\n", + "def compute_decoder(encoder, gain, bias, function=lambda x: x):\n", + " # get the tuning curves\n", + " x_values, A = compute_tuning_curves(encoder, gain, bias)\n", + "\n", + " # get the desired decoded value for each sample point\n", + " value = numpy.array([[function(x)] for x in x_values])\n", + "\n", + " # find the optimal linear decoder\n", + " A = numpy.array(A).T\n", + " Gamma = numpy.dot(A, A.T)\n", + " Upsilon = numpy.dot(A, value)\n", + " Ginv = numpy.linalg.pinv(Gamma)\n", + " decoder = numpy.dot(Ginv, Upsilon) / dt\n", + " return decoder\n", + "\n", + "\n", + "# find the decoders for A and B\n", + "decoder_A = compute_decoder(encoder_A, gain_A, bias_A, function=function)\n", + "decoder_B = compute_decoder(encoder_B, gain_B, bias_B)\n", + "\n", + "# compute the weight matrix\n", + "weights = numpy.dot(decoder_A, [encoder_B])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Running the simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:41.633060Z", + "iopub.status.busy": "2020-11-25T16:46:41.626818Z", + "iopub.status.idle": "2020-11-25T16:46:43.244630Z", + "shell.execute_reply": "2020-11-25T16:46:43.245267Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.0\n", + "0.001 0.0\n", + "0.5000000000000003 0.10169046205375874\n", + "1.0000000000000007 0.5600060592004141\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5009999999999455 0.9016353842815601\n", + "2.0009999999998906 0.9053721164863286\n", + "2.5009999999998356 0.4807937869520167\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0009999999997805 0.13653211370079643\n", + "3.5009999999997254 0.03712836245916179\n", + "4.000999999999671 0.37309816783966915\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.500999999999838 0.8510836666141086\n", + "5.000000000000004 0.9670552038667225\n", + "5.500000000000171 0.6562015670959472\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.000000000000338 0.24694255703539536\n", + "6.500000000000505 0.03460342314695294\n", + "7.000000000000672 0.3003104797831265\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.500000000000839 0.704584764277374\n", + "8.000000000001005 0.9772856245237679\n", + "8.500000000000728 0.7757873426223485\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.000000000000451 0.36021073760393646\n", + "9.500000000000174 0.05867723255776759\n" + ] + } + ], + "source": [ + "v_A = [0.0] * N_A # voltage for population A\n", + "ref_A = [0.0] * N_A # refractory period for population A\n", + "input_A = [0.0] * N_A # input for population A\n", + "\n", + "v_B = [0.0] * N_B # voltage for population B\n", + "ref_B = [0.0] * N_B # refractory period for population B\n", + "input_B = [0.0] * N_B # input for population B\n", + "\n", + "# scaling factor for the post-synaptic filter\n", + "pstc_scale = 1.0 - math.exp(-dt / t_pstc)\n", + "\n", + "# for storing simulation data to plot afterward\n", + "inputs = []\n", + "times = []\n", + "outputs = []\n", + "ideal = []\n", + "\n", + "output = 0.0 # the decoded output value from population B\n", + "t = 0\n", + "while t < 10.0: # noqa: C901 (tell static checker to ignore complexity)\n", + " # call the input function to determine the input value\n", + " x = input(t)\n", + "\n", + " # convert the input value into an input for each neuron\n", + " for i in range(N_A):\n", + " input_A[i] = x * encoder_A[i] * gain_A[i] + bias_A[i]\n", + "\n", + " # run population A and determine which neurons spike\n", + " spikes_A = run_neurons(input_A, v_A, ref_A)\n", + "\n", + " # decay all of the inputs (implementing the post-synaptic filter)\n", + " for j in range(N_B):\n", + " input_B[j] *= 1.0 - pstc_scale\n", + " # for each neuron that spikes, increase the input current\n", + " # of all the neurons it is connected to by the synaptic\n", + " # connection weight\n", + " for i, s in enumerate(spikes_A):\n", + " if s:\n", + " for j in range(N_B):\n", + " input_B[j] += weights[i][j] * pstc_scale\n", + "\n", + " # compute the total input into each neuron in population B\n", + " # (taking into account gain and bias)\n", + " total_B = [0] * N_B\n", + " for j in range(N_B):\n", + " total_B[j] = gain_B[j] * input_B[j] + bias_B[j]\n", + "\n", + " # run population B and determine which neurons spike\n", + " spikes_B = run_neurons(total_B, v_B, ref_B)\n", + "\n", + " # for each neuron in B that spikes, update our decoded value\n", + " # (also applying the same post-synaptic filter)\n", + " output *= 1.0 - pstc_scale\n", + " for j, s in enumerate(spikes_B):\n", + " if s:\n", + " output += decoder_B[j][0] * pstc_scale\n", + "\n", + " if t % 0.5 <= dt:\n", + " print(t, output)\n", + " times.append(t)\n", + " inputs.append(x)\n", + " outputs.append(output)\n", + " ideal.append(function(x))\n", + " t += dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:43.269258Z", + "iopub.status.busy": "2020-11-25T16:46:43.252984Z", + "iopub.status.idle": "2020-11-25T16:46:45.885376Z", + "shell.execute_reply": "2020-11-25T16:46:45.885917Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x, A = compute_tuning_curves(encoder_A, gain_A, bias_A)\n", + "x, B = compute_tuning_curves(encoder_B, gain_B, bias_B)\n", + "\n", + "plt.figure()\n", + "plt.plot(x, A)\n", + "plt.title(\"Tuning curves for population A\")\n", + "\n", + "plt.figure()\n", + "plt.plot(x, B)\n", + "plt.title(\"Tuning curves for population B\")\n", + "\n", + "plt.figure()\n", + "plt.plot(times, inputs, label=\"input\")\n", + "plt.plot(times, ideal, label=\"ideal\")\n", + "plt.plot(times, outputs, label=\"output\")\n", + "plt.title(\"Simulation results\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/advanced/nef-summary.ipynb b/.doctrees/nbsphinx/examples/advanced/nef-summary.ipynb new file mode 100644 index 0000000000..4a87c90366 --- /dev/null +++ b/.doctrees/nbsphinx/examples/advanced/nef-summary.ipynb @@ -0,0 +1,2046 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NEF summary\n", + "\n", + "The Neural Engineering Framework (NEF)\n", + "is one set of theoretical methods that are used in\n", + "Nengo for constructing neural models.\n", + "The NEF is based on [Eliasmith & Anderson's (2003) book](\n", + "https://mitpress.mit.edu/books/neural-engineering) from MIT Press.\n", + "This notebook introduces the three main principles\n", + "discussed in that book and implemented in Nengo." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.154169Z", + "iopub.status.busy": "2020-11-25T16:46:47.153329Z", + "iopub.status.idle": "2020-11-25T16:46:47.654677Z", + "shell.execute_reply": "2020-11-25T16:46:47.654182Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Uniform\n", + "from nengo.processes import WhiteSignal\n", + "from nengo.utils.ensemble import tuning_curves\n", + "from nengo.utils.ipython import hide_input\n", + "from nengo.utils.matplotlib import rasterplot\n", + "\n", + "\n", + "def aligned(n_neurons, radius=0.9):\n", + " intercepts = np.linspace(-radius, radius, n_neurons)\n", + " encoders = np.tile([[1], [-1]], (n_neurons // 2, 1))\n", + " intercepts *= encoders[:, 0]\n", + " return intercepts, encoders\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 1: Representation\n", + "\n", + "### Encoding\n", + "\n", + "Neural populations represent time-varying signals\n", + "through their spiking responses.\n", + "A signal is a vector of real numbers of arbitrary length.\n", + "This example is a 1D signal going from -1 to 1 in 1 second." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.660281Z", + "iopub.status.busy": "2020-11-25T16:46:47.659424Z", + "iopub.status.idle": "2020-11-25T16:46:47.663042Z", + "shell.execute_reply": "2020-11-25T16:46:47.662595Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.732381Z", + "iopub.status.busy": "2020-11-25T16:46:47.718269Z", + "iopub.status.idle": "2020-11-25T16:46:47.868457Z", + "shell.execute_reply": "2020-11-25T16:46:47.868850Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], lw=2)\n", + "plt.title(\"Input signal\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These signals drive neural populations\n", + "based on each neuron's *tuning curve*\n", + "(which is similar to the current-frequency curve,\n", + "if you're familiar with that).\n", + "\n", + "The tuning curve describes how much\n", + "a particular neuron will fire as a function of the input signal." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.873763Z", + "iopub.status.busy": "2020-11-25T16:46:47.873257Z", + "iopub.status.idle": "2020-11-25T16:46:47.876501Z", + "shell.execute_reply": "2020-11-25T16:46:47.876888Z" + } + }, + "outputs": [], + "source": [ + "intercepts, encoders = aligned(8) # Makes evenly spaced intercepts\n", + "with model:\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " max_rates=Uniform(80, 100),\n", + " encoders=encoders,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.882684Z", + "iopub.status.busy": "2020-11-25T16:46:47.881661Z", + "iopub.status.idle": "2020-11-25T16:46:48.067499Z", + "shell.execute_reply": "2020-11-25T16:46:48.067965Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(A, sim)\n", + "\n", + "plt.figure()\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can drive these neurons with our input signal\n", + "and observe their spiking activity over time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.073896Z", + "iopub.status.busy": "2020-11-25T16:46:48.073403Z", + "iopub.status.idle": "2020-11-25T16:46:48.076478Z", + "shell.execute_reply": "2020-11-25T16:46:48.076861Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.083129Z", + "iopub.status.busy": "2020-11-25T16:46:48.082331Z", + "iopub.status.idle": "2020-11-25T16:46:48.444215Z", + "shell.execute_reply": "2020-11-25T16:46:48.444607Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure()\n", + "ax = plt.subplot(1, 1, 1)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "ax.set_xlim(0, 1)\n", + "ax.set_ylabel(\"Neuron\")\n", + "ax.set_xlabel(\"Time (s)\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decoding\n", + "\n", + "We can estimate the input signal\n", + "originally encoded by decoding the pattern of spikes.\n", + "To do this, we first filter the spike train\n", + "with a temporal filter that accounts for\n", + "postsynaptic current (PSC) activity." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.452444Z", + "iopub.status.busy": "2020-11-25T16:46:48.451936Z", + "iopub.status.idle": "2020-11-25T16:46:48.455422Z", + "shell.execute_reply": "2020-11-25T16:46:48.454971Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)\n", + " intercepts, encoders = aligned(8) # Makes evenly spaced intercepts\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " max_rates=Uniform(80, 100),\n", + " encoders=encoders,\n", + " )\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.462529Z", + "iopub.status.busy": "2020-11-25T16:46:48.461757Z", + "iopub.status.idle": "2020-11-25T16:46:48.740973Z", + "shell.execute_reply": "2020-11-25T16:46:48.741396Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "scale = 180\n", + "plt.figure()\n", + "for i in range(A.n_neurons):\n", + " plt.plot(sim.trange(), sim.data[A_spikes][:, i] - i * scale)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(scale * (-A.n_neurons + 1), scale)\n", + "plt.ylabel(\"Neuron\")\n", + "plt.yticks(\n", + " np.arange(scale / 1.8, (-A.n_neurons + 1) * scale, -scale), np.arange(A.n_neurons)\n", + ")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we mulitply those filtered spike trains\n", + "with decoding weights and sum them together\n", + "to give an estimate of the input based on the spikes.\n", + "\n", + "The decoding weights are determined\n", + "by minimizing the squared difference\n", + "between the decoded estimate and the actual input signal." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.745655Z", + "iopub.status.busy": "2020-11-25T16:46:48.745173Z", + "iopub.status.idle": "2020-11-25T16:46:48.748619Z", + "shell.execute_reply": "2020-11-25T16:46:48.748195Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms PSC filter" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.754420Z", + "iopub.status.busy": "2020-11-25T16:46:48.753658Z", + "iopub.status.idle": "2020-11-25T16:46:49.078797Z", + "shell.execute_reply": "2020-11-25T16:46:49.079215Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded estimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The accuracy of the decoded estimate increases\n", + "as the number of neurons increases." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.087828Z", + "iopub.status.busy": "2020-11-25T16:46:49.086482Z", + "iopub.status.idle": "2020-11-25T16:46:49.088675Z", + "shell.execute_reply": "2020-11-25T16:46:49.089083Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.097764Z", + "iopub.status.busy": "2020-11-25T16:46:49.096696Z", + "iopub.status.idle": "2020-11-25T16:46:49.787411Z", + "shell.execute_reply": "2020-11-25T16:46:49.787902Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(15, 3.5))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "eval_points, activities = tuning_curves(A, sim)\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "\n", + "ax = plt.subplot(1, 3, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded esimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any smooth signal can be encoded and decoded." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.795816Z", + "iopub.status.busy": "2020-11-25T16:46:49.795070Z", + "iopub.status.idle": "2020-11-25T16:46:49.797625Z", + "shell.execute_reply": "2020-11-25T16:46:49.797188Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(WhiteSignal(1, high=5), size_out=1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.805169Z", + "iopub.status.busy": "2020-11-25T16:46:49.804385Z", + "iopub.status.idle": "2020-11-25T16:46:50.346763Z", + "shell.execute_reply": "2020-11-25T16:46:50.346342Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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wcOFC3Wf37t2Lb7/9FgsXLsSvf/1rzJkzB1u2bEFycjIWL14MALjpppuwZs0abN26FR0dHVi0aBEuuugilJaW4o033sDGjRsRFxeHm2++Ge+99x7WrVuHq666Cvfcc4/f7R4rLN9dh9nZzcjb/jLQUOYaqV4/pmXI1NAqFgHWLK7QDevde5KFSh89WTBhX3GeZEbjPcUZLSfUbRsqjLaxr/4VLW1i1js2O09f7epPfCBE9TRlv4Q4tABYv78Ro3PTIq2O32ysbMSfP9iCc2v/gxviPnFGPD0duLPced/d6vyRaqsLr5JB8uWXX2Lz5s2967Wampqwe/dufP311/jNb36Dfv36AQAGDBiApqYmNDY2Yvbs2QCkY48uvvhiNDY2orGxEbNmzQIAXHbZZfjss8+85r9s2TLMmzcPVqsVgwcPxsknn+ymW2trK3788UdcfPHFvbKuri4A0mHjV155JX7xi1/gZz/7mW7dzjzzTMTHx2PixImw2+044wxp99/EiRN7j1lasmQJHn74YbS3t6OhoQFFRUU499xzXfLZuXMntm7ditNOOw0AYLfbMWjQID9bOjbotNmxpqIBK5LvAz6vBmbc5JrA22aVa78FrovqhfwE4EshBAH4DxE9H2mFGIbpG0S1MZYYZ4FIisOG/Y34Rekw3w9EEVurmvDL51cgKyUR81I3AFo3Sl/91RnubnNu7bd3ec70zH+YrmcglJWVwWq1Ijc3F0SEJ598EnPnznVJ88UXX5hSlqf8P/30U5/POhwOZGZm9q53U/Pcc89h1apVWLx4MaZOnYp169yP6ElMlN6JxWJBfHw8hLzjz2KxoKenB52dnfjd736HtWvXYtiwYZg/fz46O939ZRERioqKsGLFCiNVjmnW7z+CTpsDqcntkqC9HohLAk78I7DkAWDatZFVMDhmElGVECIXwFdCiJ+IaFmklWIYJvaJ6mlKAJg8vD827PcytRGFdPXYcfNbG5CVkoiPbjwBmZ0H3BP9pDqLr7sNsMrGWI8XYywKqK2txfXXX4+bbroJQgjMnTsXzz77LGw2yUHvrl270NbWhtNOOw0vv/wy2tulH+WGhgZkZGSgf//+WL58OQDg9ddfx+zZs5GZmYnMzEx8//33AIA33nijtzxP+c+aNQsLFiyA3W5HdXU1lixZ4qZreno6RowYgXfffReAZBQp06B79+7Fsccei/vvvx85OTmorKz0uy0Uwys7Oxutra0uuznT0tLQ0tICABg7dixqa2t7jTGbzYZt27b5XV4s8OOeelgtAtY4+VDw1hogPhmYfScwvwkYfmxkFQwCIqqSrzUAPgQwPbIaMQzTV4jqkTEAmDIsE//+djdau3qQmhj16gIAXlxejvK6Nrx21XTkpHlwZqleO9Pd6hwZi0JjrKOjA5MnT4bNZkNcXBwuu+wy/OEPfwAAXHPNNaioqEBJSQmICDk5Ofjoo49wxhlnYOPGjSgtLUVCQgLOOussPPjgg3j11Vdx/fXXo729HSNHjsTLL78MAHj55Zdx1VVXQQiB008/vbdsT/lfeOGF+PbbbzF+/HgMHz4cM2bM0NX9jTfewA033IC///3vsNls+OUvf4ni4mLccccd2L17N4gIp5xyCoqLi7F06VK/2iUzMxPXXnstJkyYgIEDB2LatGm9cVdeeSWuv/56JCcnY8WKFXjvvfdwyy23oKmpCT09PbjttttQVFTk76uIelZXNGDCkAxYOuMlQVttn3BjIYRIAWAhohY5fDqA+yOsFsMwfYVAVv2H6zN16lT6bmcN5d+1iL7fXeu2GyIaaeuy0aT5X9BVL68m6mgk+t/F+ufxqT/f/0v63JtO9PmfXfLrKzvsGN/E+rvutPXQmHs+pb99so3onxOk/vzoWKInphjOA1G6mxLASACb5M82APf4esaf3ZQ/vPM/jzIz78O9m1KrgxKvlSu887cn3WR6beNN7ikfPX086aKW6YWNygIJa2XB7qb0t51C0e+0evn7/vV0V+fhrV95Kk/7t8a7KX1QPDQDALClqinCmhjjvXUH0NRhww0njQI2vwPsdl07RSm57g91t0nragCgszH0SjJMCNhyoAndPQ5MGzEAsMqj2K01QHy/yCpmAkRURkTF8qeIiB4w8tz0c0fqhrX3K957y6PMzHutDt509DesF6fVQYnXyhX2b/ncTabXNt7knvLR08eTLmqZXtioLJCwVuarjfXC6nt/2ykU/U6rl7/vX093dR7e+pWn8rR/a77a1Z/4QIh6YyyzXwKGZCZjawwYY0SE11fsQ/GwTEzN7w9Y3KdVxYXPuT+49CFg/0op3B5b6+P6LHW7gZZDkdYiplhTIfXd0vz+gEWepiS75OiVYRiG8UjUG2MAUDQ4HZVVVcD8DGDTgkir45Ht1c3YXdOKi6cOlXbeWayuCYrnAUM9OO1W/It588PEhA6HHaj5CTi0RQp3twIt1ZHWKqZYU9GAUTkpyEpNBKzxzgh7d+SUYhiGiQFiwhibMCQDjiMV0s2KpyKqizcWbjyIOIvAWRNlH1JCY4xd+ByQmO49E5v7OZXSNDQTUhr3Az0dgKPHu3uREBHr79jhIKytaMC0ggGywO6MrN6k/xDDMAwDIEaMsaLB6bCTrOqhzcCbl0RWIR0cDsLCTQcxuzAHA1Lkbf2bVaN4p8tLTITTqWUb6ey07G53uU1KSkJ9fX3M/1hHPWpnpGHe0UpEqK+vR1JS7E7n7appQXNnj9MY62qJrEIMwzAxREz4iiganIEkqKY6drkv8os0Ww82obqpE3fMHSsJ7DagXHaVMPnXQOlVbs/Mtb6I7x2XuQo1P2JDhw7FgQMHUFtbGwq1GVuHtHmi5RDgkHyZocbmnC5u2hEWNZKSkjB06NCwlBUKlPVi0woGSG3aVhNhjaKLpq/2IeO0fI+yGRfNc3tGKzP73oie3u591clbmf7I/c3DjPzVMr2wUVkgYV91UDDS/ko+enKj5Yai34Xq/QfSV5R4o33f09+ApzY2TCBbMMP1mTp1KhFJh0Jff9+jru4g7D1uW1Ujyb++2kUFdy+iupZOSXBkv1PX7g7XxNWbiZoO0t3vb3J3c/G3vPArf5TSs+EtonvTqXX5c0RvXOJ8B1/f7wwzhrj5zfU0/YGvyOFwEO39zr1fGwRR6toikI/y/UVEVHnXMre66skijVYnb/exUqe+ij/tz+/FN0b7vqe/ASUc6HdYTExTCiFQOECz/qo7ug4P/3ZnDYqHZiJr3b+ljQbqnXja3WQDJwLpg5xry9T0dAA9vOA51OypaUHTx3cBAF74Yg2q6o4AqQOlSB7V8QsiwpqKBpQWDJA2rjRpTpxIyYmMYgzDMDFCTBhjADAqU3OAcFeL5H4gCqhr7cLmA404eVwusOTvknDP19L18o89PnfcyCy8hAvcI7qazVeS6aWxvRuXv7gKA6gRAJCTm4cDdY04YukvJVj/WuSUi0GqGjtQ3dSJ6cp6seYq6aq4t7j6y8goxjAMEyPEjDE2Lu6wq2D5Y8BTpUCV+wHP4eaHPXUgAk4aqxoBWCof6p3c3+Nz8VYLdk74A1bQRNeIzuj3qRbLPPzFTjS0OjdK/HLmBGQlEnY0xYOgMfrL+RxoX6ypaAAAlBbIfb25CuiXDRxzrnSfmhchzRiGYWKDmDHGBqLOVbD2v9K1rc49cZhZWVaPtKQ4FOXpnMEXn+L12ZPH5aHToXkNbIyFjMqGdryzphKXTnUaCNaedoy27UQataDboplSfvXcMGsYe6ypOIK0xDiMGyi7bWmqAjKGABc8C9y4Bkjw/jfAMAxztBMzxlhqvIeIbne/XOFmVVkDphcMgLVhr3tkgvejYE4YnYUeoancC3OAnZ+ZqCGj8NL35bAIgWtPGOIUbvsQADDRUo5ER4eHJxlPrK1oQEl+f1gt8qhiZyOQPEBaK5lTGFHdooW0U4YbkkUarU7K/ZIlS9zuY6VOfRV/2p/fi2+M9H2l32tlALB1TH1Q5ceGMXZ4OywtB/XjInyWY01zJ8rq2nDsyAGAno7xOqNlKtKS4pGWIqc59npnxLJHTdSSAYCuHjs+2liF04vyMDBZNR0pfPwZHNoSWsVimMb2buw63IppBarp+O42Hg3ToLflPaht8CFCq5Nyv3TpUrf7WKlTX8Wf9uf34hsjfV/p91oZAKys3BhU+aYYY0KIM4QQO4UQe4QQd+vEXymEqBVCbJQ/1/hVwLMzgIrlaLFmusd1Rnax+8pyab3McSOznM5Cj73BmcDHNCUAtI84AwBQP/AEp7BqLfD/hgHs7NU0vt1Rg8Z2Gy4uHebqZV9ljPXoud6LcB+LZtbvl/yLTc0fIE1P/mcWUL8HSEiNsGYMwzCxQ9DGmBDCCuBpAGcCGA9gnhBivE7SBUQ0Wf68GEhZ3UnZKOnUHLQdZm/pWlaV1SM1MQ7jB6U7dYlLcCZQhz0wbPYVmNj5Ir5pG+0a0dXMOytNZNGWamSnJmLm6GzXfqMOy+/r8IgLnbIIj75GM2sqjiDOIjB5WCaw7mXp6CN7N4+MMQzD+IEZI2PTAewhojIi6gbwNoDzTchXQnVMTXxCIlqhmfaLwDmCalaXN6C0oD/irBbnjzo5/MpjdG4q0jOz8PVenfVv7cHNQzMS3T0OLNtZi1OPyZXWNqkNMMXgvXwhrLJPuF0tKiO6vSGMmsYW6yqOoGhIBpITrEC8an0kG2MMwzCGMcMYGwKgUnV/QJZp+bkQYrMQ4j0hxDBPmQkhrhNCrBVCrK2trXUeSwMgKSEB3dAsdo/gyFhzpw17altxWcJSYNtHTsPQz6lFIQTmjMvB93t1DK82NsbMYHV5A1q6enDKMfIuSnW/aamWrlmjIWSDYnOD6k+DDWJdunsc2HSgEaX58nqxhjJnZLz3jSsMwzCMk3At4P8EQAERTQLwFYBXPSUkoueJqJSISnNyclyMsfiEBKQmatb09HSGRmMDbK5sAhFwyu6/A+9e4fScn6s3S+udWWNy0N5td49gQ8AUluysQWKcRZqiBIA21VmfShvHJ/duuKjrUvWzjgZg2SPAhv+FSdvYYOvBJnT1OJzG2IbXnZEGpueZ2GH27Nle7xmmr6Lu60rYlywQzDDGqgCoR7qGyrJeiKieiJShiBcBTDWcu8oYEw47CvM0C4MjODK2Yf8RV4FiGI4/D7hhBTDfuL+wGaOyEGcR2DngJOlg8UvkH342xkxhxd56TM3vj+TajdK6pmadna+JacDEiwEAyzAV1cmyW4b2BuDbvwMf3xg+hWOAtbKz16kFOo6NrYlh1ib6KSt7ItIqGEara37+Zpe4OXPmeE3PhBduf3NRt6e67ythX7JAMMMYWwNgjBBihBAiAcAvASxUJxBCqA9hPA/ADsO5q4wxdDVj7MA01/gNr0s/rhFgY2UjRueqjEPFGLMmAnn+jY6lJcWjJL8//oDbgQueBkaeJEW0R96pbazT1G7DjkPN0o7XF06Wdvw17gOsmtEbazww6w7gjjIMHjkel8U/CuQWAa18VqUe+3dtxryMrchNS3L/pyiOjTEt5RX/jrQKhtHqqr7Xq0cs1a0vwu1vLp76uxL2JQuEoI0xIuoBcBOALyAZWe8Q0TYhxP1CiPPkZLcIIbYJITYBuAXAlYYLUC+e7mjE2Lw09zRvzQtQ+8AhImysbJR2kSkseUC6Wj15qPXO7MIcbDvYjNqWLsk1gDXBOTJmY2ekgbK6ogFEwLEjBjiFK54C0gYBg6e4JrZYgJQszBqTgz01rehKyJBcNTAuEBHmH7gK/6/rQclY1Z4aEeDfAMMwzNGIKWvGiOhTIiokolFE9IAs+ysRLZTDfyKiIiIqJqI5RPST4czVI0OdTSjUjowBgKMnyBr4T2VDB+rbul2NMQUh3GUGmDVGOtvy+z21Uh79siRj7PA24IGBwDuXB6Hx0cvKsnokxllQrH1X6UOAS97QfebEQmltWU1PqjSKprDq+RBpGVtU1LcjDvKu4UfHAKtfcE3A05QMwzCGiX4P/OqzJ+1dzvPv1Dh0Fr6HmA2V0vTptOxu0/IsGpyOrJQELNsl1zl5gLRo/PB26X77xzxlFgCryxswZXgmkuKtrhHpg6UzFAFg+PEuUWPz0pCTloh9HYmuxv4394VY29hAORy8l2UPu97zAn6GYRjDxIYxlpjRezsgRedLPgIHa2+sbERSvAVjtv7LtDwtFoGZY7KxfHctHA4CarZJEVvfcyaKwChgLNNps2NHdTNKhvcH7DbXyHR5KePvtwG/ft8lSgiBE8dko0zbtezmGd+xzLqKI6iBzsL9ZFnm64gphmEYppfo/8ZsqwWyXT3TP515B35MUB0d5ND8yIaBbQebMX5QOiw9mrVcY88OKt9ZY3JQ19qN7dUqz/tHKpzhCLryiEV2VDejx0GYPCgJWPKga2RSpnTNGKp7oPuJY7LR317rKsw/3i3d0cjafQ3ojs9wj8geK10j8A+SGQgh/iuEqBFCbFXJBgghvhJC7JavOlaob0YU3GKeogAeKa82NT81Wl3V93r1MLtu0YC6fZWw2VezCGf7h7LfRQue+rsS9iULhOg3xtrrgBRpLRWSpC//2pEX4tpOTcXt4RsxcjgIOw42o2hwBlDxg2vkzwM66akXZa3Sst21TseZtaoldjY2xvxh8wHJKDiu5h3g+3+6RvrwEn/siCwcItWi/+yxQH0Z0NVitpoxRUNbN/bWtmEAmoGpVzqNWgAYNEm6drVGQjUzeAXAGRrZ3QC+IaIxAL6R7/1m5Mhbg9NMw2MVh03NT41WV/W9Xj3Mrls0oG5fJWz21SzC2f6h7HfRgqf+roR9yQIh+o2xtjqgXzZw01rpA2BMXiratA5Sw+gCovJIO1q6elCS1QO0HpKEky6RrjojLP6Qm5aEYwalY9muWuCqz90TaEfizGb960Brre90McKmykbkpCUiLUnnAHAfXuIHZybj7ZTLnILsMUDTfuCxY0zWMrZYXV4PCxxI7mmU/lGSHeUiIQ04+S9A6VXA1CsiqmOgENEyANrzr86H01H1qwAuCKdODMP0fWLDGEvJln4IU3MBAIV67i1awjd0uv2gNIU4Nk91TuaF/wH+as4ZhrPGZGPdviNoG1AEQLMzM5QjY/V7gYU3AR9cE7oywsymA40oHpoBIfcdFwz4wioeOdh5M0L2sNx9dI+Mrdhbj1HxDRDkkIwxi+zGorsFSEoHznlccqDbd8gjIuUL5hCAvEgqwzBM3yO6jTGHXVoPpkxTyhTm6nzRV60Lk1LSejGrRWDkgCSnUAjAYvX8kB/MKsyBzU5YsbcegOacy1CuGVPW+ZR95/f5mtFIS6cNZXVtmDQ0U9/VQmezu0zDdLVvsmHTzFMuhllZ1oDFcbdLNynZQFdsrg8LBCIiuP1RMgzDBEeUG2PyOrCUbBdxRr945KWrflyTMoAa467LgmV7dTNG56QiScj6nfJXU/MvLeiP5HirtG5MSyiNMbUX9V06U6QxxtaqZhABk4Zm6G/yiE9yl2mYNmIA6kh2p5I+1BnRB4zVQKhv7cLOwy1IIHlXaXyKcw3d6X+PnGKh5bByioh8Zf8yDMOYSkwaY4A0Vflg6p+AY84F+hcAR8rDpta2g00oGpzu1C8z39T8E+OsOG7kAGndmJZQeuK3tTvDiuf//SuB+RlhHXk0C2VH6oQhGU63Fr94HbjyU2DaNcDkS33mMTI7Bb+K+yceK3gBSM0BBhVLEZWrQ6V2VLO6XDMVn5YHkOz8ddix4VcoPCwEoCyCuwLAx0YffPyrXUHde5P/sSDPa7w/Zahl3tIHki5QPYKVBdO2gGv7KmGzr/7URyvzp/2DbQt1nFb3QPILtH8YfdZofoH0daN/D/4SG8ZYP31j7LWmYtgvfh3oP8LV/UMIqWvtwuHmLowfnO78gQ/B0S+zCnNQUd/uHhHKkbHuNmdYafudn0rXsqWhKzdE/FTdjOzUBGSnqhy3DpsOFJwAnP2YoWllIQRGjRiJDw7JfXDMXOn6snbD3dHBirJ69EuwwjHmdEmgPk6qD6wTE0K8BWAFgLFCiANCiKsB/APAaUKI3QBOle8N8cQ3u4O69ya/Y8Qgr/H+lKGWeUsfSLpA9QhWFkzbAq7tq4TNvvpTH63Mn/YPti3UcVrdA8kv0P5h9Fmj+QXS143+PfiLzhazKEKZWtKsGQOAwrxUdNocqGxoR0H/AuCnxdIaM5PWbXlCWbw/fnA64JDXymgPnDaBWYXudQYQ2pExtcuGNS9K52OG0OAMNTsPtzgPlleMMYv/9Zia3x+fbT2EmuZO5CqjQMr1KGNlWT1KCwbAYu8Ghk53jfThKiQWICJPB92eElZFGIY5qoiNkTGdacox8o7KXYdbgAEjJMOtuSrkKm2TjbGiQaqprwB+4H0xMjsFQzKT3SPU67rMpku1oP3QFuD9q0Nax1BidxB2HW7B2Dx5vVevUen//x9Thks+PjdUNkbEwXC0UNfahV2HW6UD122d7mvu4mPfGGMYhokE0W2M2e1AYrquC4IxuakAgN01rUDGMEn4v5+H3PP39upmDMlMRka/+KB+4H0hhMCswhy8Rme5Rih+xtrqgRVPm7uQXHvYM+A8/mfzAvPKCQP7G9rRaXNgnNvImP/vqmhwOuKtAuv3Hwmrc+FoY7m8oeTEMdlSP4zT/LPQB0bGGIZhIkF0G2OOHqDfAN2otKR4DMlMxs5DLZLBBgB1u4DKNSFVqXfxPuAcJQnRqNHswmzc2/UrbD53MXDqfEn4rbxj7ZNbgC/+DBxYayyztS9LC/G15zPu/hoH6xrx+wUbgYa97s817pOuB9cD7eb4UQsHOw/JvuB6jbHA31VSvBVFgzOwYX8jMFqerco5+hy/Lt1Zi6yUBEwYnCGN0Gr/STLgt41hGIZxJ7qNMbJ7XRRcmJcqTVOqvd6H0EN9e3cPyuvapPViANAq73APwZoxADh+dDYsFiu+qMsGZv7eNVIZAXzpVNeF9574XD7BRb0urG438MbPseHpy/DZVg9Oc9UbIx4e4W7MRSk/HWqBECoHwfbAR8YAYMrwTGw+0IieEXOk3bu548xRNFw4HNIn0Mert2L5rhrMKsyBxSKktYuK5/1Zd0pGrhDeMzkKufWUMUHd+5IbiTdShlrmLX0g6QLVI1iZWW0bDgKpoz/tb0ZbBBLnjzwYHc3qb0b6utG/B78hoqj9TC3IIHrxNPLEg4u305g/f0q2mj1E96ZLn00LPKYPlrUVDZR/1yL6ctshSaCUeWBtyMr8+TM/0LlPLnctr7OF6NXznfd7vvWdkZK26WCvqHr3eqJ706nt3lzafbiF6G95znTK5/8Nc72v3hyaiprMb19bS7MfVrXL1/cTzc8MOL+PN1ZR/l2LaMuBRqJnTyB64xITtAwjf8sl+t/FgT1btb73/f/w2ZuS7JExRB/fbJ5+MgDWUhR895jxmTp1qtnNo8+3D4annKMRddsqYfVVT+bPlelzBPodFuUjYw6v5weOyUtDt92BSvWZxCHcbbj9oDQa1TsyFgZmFeZgS1UTGtq6nVNji/8Q8AiPMnLY3dmBxxeuBAD0QydGZyXpjypq1+BVrQ+s3DDjspMSkKYpg5hOnjIsE4C8iD+5f1iP3zKFnk5g9xeBPdtY2RssPfS2M794nQ0mTPhZatjTBuMv6rZVwuqrnsyfK8PIxLQxNlaegtpzRHVoeCiNsepmZPaLx+AMeRfZqJOl65CpIStzVmEOiOTF08qanOpNno0xWwew8jnJzYce8m7Mw8+eg4ea73bKu1tdktmVcxgB0MiTnBHKujJbR9QuZu/otqOivg1jB6qM5p7uoNY0De2fjOzURGzYdwQYOg04tDm6pmyrN0lrAg/oOOdVT092BXKupnOTSILyjWHr5DViDMMwJhEDxpjn/75H56ZCCGBHvdoY03GUahLbDjajaHA6hLI2prvNeXh0iJg4JAOZ/eKxbFed08CyJrju4FR2PALA0oeAz+8Ctn2on6GtA7sPt2BYk2bhv+YEA+s5j/eG620qFwbdcvs+MBB4+1f+VicslNe1gci54xaAdIh1ELv9hBAoGZ4pjYxl5kt9M5pGx3Z+Jl31jrFSuyxR1jn6QVOHyugkee2Zvct9NyXDMAwTEDFtjCUnWDGsfz/srO0Ajr9ZEto6JKPFZq6n+h67Az8dasH4QfJoy3/PBCpXhdzruNUiMHN0NpbvrgUpRpewAKkDnYnUo1oth2SFPdS/pxN/X7zDXf7y2dL13CeAWze7tPuuRgdww49yWarNAoFOe4WYinpJxxHZsvHVWAnsWCQ5sQ2CKcP7o7yuDS1JgyVB/R6grQ546XSgNrijMILGm+sO9VRzAP+sbKw84rwhcvYtA2d7MgzDML6JcmOMgDjvX/iFeWnYdahFOqTYmiiNEv3vZ8AD7udnBcPe2jZ09zhQNDhDEuyXjRMf+pnBrMIc1LR0oTFrsiRISnf1AN+t+oHd9JZ09bDDc2vFISzVPfNSNrJSBwL9813qtbeRUJ00EsgeK6VTT4EGNO0VWsrrpLoUZKdIfejdK4DOxqCPkpoyPBMAsJ7kXTMH1gH7fpSM8kW3BZW3X1Sudn3ngNMY0/N5pzbGnpsJNB3wnLetA/h6PtDR2CvauF9tjDmc7RiGvs8wDHM0EP3GmI9jeArzUlFeJxlKsCZI63jKvpMiD201TZVt6sX7dXucESFya6HmpLE5sAjgtf43SYJ+WdI0kYKeawsPzmA3r/hC37O/QqI8eqRaq9dGSVi8uVqa5utucx1dUf1oRwv76tuQnZqIVIsNuD/Lech5U6X3B30waWgGLAJYe1CeoutqlkYpgfBtbGg5BLx0GrDwJld570kJKmPMYQcqvnce+q5Q85Pn/MuXA98/Dnx8IwCgvrULuw+rRl73/eB8/2yMGeaZjc/ohr3JDDP7bt9p/Cjbk67e6hBo/YzU299nTW1fddsqYfVVT+bP1QDe2lp77yutJ1m48FZ2oHGBPh9Iuxr9ewiE6DbGQD53DRbmpaHHQdLUlDVOGhlTdh1u/8g0TbYfbEZinAUjs1OAp1QL9kN8FiYA5KYl4fhR2fhgSz0od7z0w9vTDaTJB7badIwxrUyeoitqX407zxjruTBlejI+Ccg/AQCQmpaORb3GWLvrJgkjPs7CTEVdO0Zk95Oc1JJqFE+uT6D0S4jDuIHpkvPXhBRpelhZqxWusyq7ZMNIa/wpo5VH9gHLHpWmTe8fALxyNvDlX1zTenMHpqyH3CVNQX+x7bB73TbJOyp5N6Vhnt30rG7Ym8wwc/4UkB6eZJ509VaHQOtnpN7+Pmtq+6rbVgmrr3oyf64G8NbW2ntfaT3JwoW3sgONC/T5QNrV6N9DIES3MWZoZExas7XzUIs0SuWwOUd3Alis7IltB5sxbmAa4qyaJnOEZ0fheZMHY199OzrsQirT3iWdPCAsrlNWKbnStUl1TicRSDaasuM6ce6kwZ4L6j/CGZZPNhgxOBcbKxvRgSTJyFMbYFFojJXXtyE/K8V19DAhFbjso6DzLsnPxMbKRlBCCrDna2Dj/4LO0y/UmzXUKCcMrH0J+PZvwNPTnHGHt2jSethpC7itKXtvXSWGpmv+IVI2e/DIGMMwjClEtzEG8jkNODInBRYB7D7cIo2itRx2/mCZZCgQEbZXN2O8sl5MqEbDQnlwt4ozJgxEQpwFDR3kHBmLS5CMjLpdrjstAefUnKyjkN0T5CT2wKI3kgYAv3rH9fgp+Qd+3HBps8DBdiFPU6pHxqJrzVhbVw9qW7qkxfs9KsMlLlFqryCZMqw/Wrt64OhoBBr3OyPC5X2+t09rpqH9mS72ts5PebeWOOypacH6/Y34reUj/TQxYIwJIY4XQvxKCHG58om0TgzDMFpMMcaEEGcIIXYKIfYIIdwmw4UQiUKIBXL8KiFEgXENvU9TJsVbUZCVgl2HW4HmKmDXZ5LPJcDNd1agVDV2oKnDJp1JSSRN2/QvkCI9jVSYTHpSPE4Zl4vadgccdps06mNNlNYt7VgI/Pikqz6qujc2NQIAHLAgwd4BfHCda+bDjpWuQtMd5Lyy+g9AYV4qKluFPE2pGj3pMqeNzULZSVmgHRnT1i1ASvL7AwCsXapF8cIqGchmHtruCZ0+TUTortro+9nBJR7zcOYvG3uWOCxYU4l+Fhv6t7u6Pek9ozTKd1MKIV4H8CiAmQCmyZ/SiCrFMAyjQ9C/UEIIK4CnAZwJYDyAeUKI8ZpkVwM4QkSjATwO4CHDBfiYpgTkHZWHdf7bN2lkbPtByU/T+MHp8kgYOacDwzQyBgC/OnY4Ou0WNLS0ST/+cYmSN3gAqNkuufNok6dmVXV/Zel2AICjX470Q6xscACAa5cA5z0JjDrFfU2V4tQ1vh9mF+ZgX4sA2TQL+O3hq78RKuok3fKz+rmOjAlz1vYVZPXDgJQErM48yymcdrW0Ni3I3ZqG2PmpdJUNv0WbD+LKR95AwpE9Xh6SueR16erNgJZHvcgSh7fXVOKlrDfd03TIxlj0+xkrBXACEf2OiG6WP7dEWimGYRgtZgwXTAewh4jKiKgbwNsAztekOR/Aq3L4PQCnCGFwXsfAETaFeam9IyIumOR2YdvBZlgEMG5gmtMQ6ZclXcM0MgYAJ4zKRnxCAuqb2iQj0JoA3LhGiswaAyz5uzOxPPqxv74dn62XvObHpeUCINfF/UNKgJyxwGUfuB64DjjrlpCCWYU5aKUEUJdmmjKavNBDNTKWrRkZC/T4KA1CCEwZlollHSOdwlTZjUqoRwkdDmD18wCkSco/f7gFj7+1CK+23+j9OWVUUDHcDUxTdtqBls4elEDeeZk+RPI/l5QZMyNjALYCGOgzVRi4ofgG3bA3Waj18CTzpKu3OgRaPyP19vfZSLZvKPDW1tp7X2k9ycKFt7IDjQv0eX/btfbJp3qvahkAPLSrpDftwLg4L4uyPWOGMTYEgNpnwAFZppuGiHoANAHIMpS7gZGxMXlpcOjNEJk0MrbtYBNG5qSiX0Kc0xhLzpSuYTTGLBaBvMxUdHV3oa2jXRoZS82RDA1bO7D9Y2fixv0ghwP3L9qGIqv8egZP9q9A5bin9MGYVjAA3ZZkWKgHWP2CM00Y62+Eiro25KQlIjUxznXUMnOYaWWU5PfH3hbVSJtijJk0Le6RNueGlCPt3Xhz1X5cN8n3iJ9D+TOPS5Jclujp2dMtrTuT+3eXrQczRmYhUcijoyNmSf7nOhudLkKif2QsG8B2IcQXQoiFyicSivxu8u90w95kodbDk8yTrt7qEGj9jNTb32cj2b6hwFtba+99pfUkCxfeyg40LtDn/W3Xuqef7r2qZQAw4v3VvWmzrHGDfCqrgznDBSYihLgOwHUAMHWQxZAx5nIgtBqTfhy3HWzG9BHywnZl52JSpnQN4zQlAAxOIQyrLwfqAeTKozPxKdKPqGYqbsXyr7H3p/34OukZIC4FGHsWsOF1Z4JT/uq9sDl/BqZfB6TmIAlAzoABQCOAPV850yx9RBoRmhEdX3YV9W0oyJJH+BRDcdq1wOy7TCtjyrBM/AjVKGKSvLEj1MaYakSyrbMHN5w0CpcUCGCnTlprQm/9exyEeCEgLFapn6x4Cjh1vuQgeOHNzmf6ZQETL5Yepx7cduoY4D156lW7Hu6E24Cs0ebVLTTMj7QCDMMwRjBjZKwKgHrYYags000jhIgDkAHJnHCDiJ4nolIikhbaGpimLMhKQZxFZ9bThJGxhrZuVDd1Sov3AfdpSgPGoplYs5yuJw4rS7dsbcCBNdDusHtr2WZcnLkLVrIBV34C5KmW8h13I3DiH70XJoQ08iYzLDvdPU3TfuAL4z5zQk1Ffbu0eB9wGi+lv3GpR7AUD8vEGjrGKVBcqaz6j2ll6KIy/HOtzbjj9LFOlxZqsscCd+3rvY0TDnSTFf/4TOXstWod8Mmtrs+116O5sQ4AkCY6cOzwVOe6O9K4w5h5G2CJ7s3YRLQUwE8A0uTPDlnGMAwTVZjxbboGwBghxAghRAKAXwLQTgUsBHCFHL4IwLdEBreeGTB2EuIsGJGdgqdz73ON6GoGDm3Rf8ggiuf98/c9CPzwb+DTO6SIoVOl0ZafvxhU/n5z+t9B8nmYqw+0obWrR/I7pnZlIUPdbbhoXKK0ZmhwiaubkNKr/C46P0s1GhSFbg1aZbcWBcqZlNUbpTorO19NIiUxDqMGqlyAJMth9ahjCHB0O0fGEqkLlsYK/TV7cQku6/8skPz1Pbd0L54Y+KAkbDkkn/3qenj6zr2qnZNvX+o0uJSRYAXNc9GIEOIXAFYDuBjALwCsEkJcFFmtGIZh3AnaGJPXgN0E4AsAOwC8Q0TbhBD3CyHOk5O9BCBLCLEHwB8AGD8LQu2E1AuFA9OwoLUYOPMR14hPbjNclB7b5J2UeXvfA776C3BAnhtOSJWm8TKHB5W/3yT3h5BHC1d2FuCWtzaABhbLcZJR8GLBPwEAl07JQm6SQzKchJBcYSgMMNauaoZkqAywgZMC0z+EVChnUiojY00HgIxhkrd8kymRz6kEAAwqlhbHpwW0btMwK3ZrBpzfvhR4/2rnvbJJIcV9FDAhIQH3nVeEJfulEa49FfLI2bG/dUmX1N3gvNn7DTB0uhQ+db5rhib4bAsD9wCYRkRXENHlkDYb/cXHMwzDMGHHlHkGIvqUiAqJaBQRPSDL/kpEC+VwJxFdTESjiWg6EZUZyjhtEDBsmu90AApz01B5pB3ddvnoFsVNQ5DrWrYdbNY/yzGSR8HIP7qnHTsJ3/5Ug4UtY2C3JqKttRmrkk7Asz9Jus0YmihNbSmjWOpRxgCOcYq3qqaClam5KGJfvTRvW5Atjwq117s6sTWRkuH9nTdCAEUXhnQzg83uwPurJPcVjlPmS8Kaba6JlNMgZv7B7XlhiccVxxdg/i9mAABGr5HWC35Z4XqCxKhU1RpIcgB7vwWOvT4q37cBLESkPoajHj6+84QQ/xVC1Aghtqpk84UQVUKIjfLnLG95MAzT98i+8UaXqydZvb2nOpD8o3vRR5rxXeljB6aCCKhrkRcc5x4jjVoF6Rl928EmFA3S2SCgOkg77MhG1UlFw/HYxcU41OYAerpxpKkRhzutuOdn8mhGdyvQ0+E0xuISPWRokKFOw9hujWD9PeDi8BWQjTFjm3b9pSS/P+Z0PYZPTpRn5OP7Ae11kq+3EPD2mko0NksuKSwpHuo06RJpJGvEidL9dd854+Qp6smjXHeVfrnPgc0Yg82p0j8v/doPAgUnAtOucSaaoTmUPHb4XN5JeaUQ4koAiwF86uOZVwCcoSN/nIgmyx9feRhm9Sf6/5eq5do0gcZ5Ky8UGKlbqGTB5OEr7E3mb3o9ffytjxGMvgt/7v2pQzjeudF2DLQNc26+yeXqSXaop+dgIPlHtzHmB2PkMyprmlU/ho37pR1jnc0B5dnW1YPyujZMHKQzzRVJY0yZjopLxs+nDsVvThwLqyDkJXbjrJLR+Nm0UfKZlbI/MsUflIHNEF4ZUoIDo38FAGiwRd1GXJTLbi1SEuOAjiNAzQ7Jh1oIKMjqh6Z++VjakCkJtn4gXde84PGZQHE4CC8uL0NRtjya6cnAPOdx4BrVTtfBU1SjovL70hwv9ui8YzFp/lpMOle1qzK+n/O5sWe6ugW5eT1wpWm2SMiQ/Rj+G8B/AEySP88TkddttUS0DECDtzRmsmZxhU+5Nk2gcd7KCwVG6hYqWTB5+Ap7k/mbXk8ff+tjBKPvwp97f+oQjndutB3D+TfgD33GGMsf0A8JVgvWiiJJMF7ld7a9LqA8fzrUDCJgQp7OiJLWQWo4UaYb5anShETphzO+uxlxSSnSaGB8CrDsEWlhv/LDasLut6ysbABATZdmmtPeo5M6vOyrb8MIZVTsp8XStN14rf9hc1Ccv27Yf0QSjJeXR5rkXFbNst212Fffjov675bWKuZqD7iQ0TvHVdFHMcS109PKM2PmOmUJ/YBDm6Xw+Atc02eNAgo0JzVEIfIGoU+J6AMi+oP8+TCILG8SQmyWpzH7+07OMAxjnD5jjMVZLRiVm4ofWvKA+U2Sk0qFAEeElMX743N1no/oyJjyw6qMkKmMxQR5bY9ygHdDmak7H5PjpC5T1REP3LrJ6R4jCpy/lte1O9eL7V8pjSAp5zGGgJL8/thb24bG9m5g1p2S0KQzMNW8tmIfctISMbxlEzBqDpAxVD+hniGo+D5T1jhq0yiGvcUCJMr+0uL7AdN/Kxn0Y/Vm7GKG9UIIY4tOvfMsgFEAJgOoBvCYCXkyDMP00meMMUA6FmnXYR3Hm+QIKL+tVU0YkJKA3A8vcY8Ms38xF5TF1IrvJ7Uuekai2hibfTdw2UeBly27ClneNlRyGdFPGimLtDHW0mlDXavKrYWtXXLHEOSaQW9MkXdUrt9/xPlOugKbEvfE/vp2LNlZg3nTh0P0dEoOZq3xQKHGSLplo/e69o6Oaowx9b2yQzI+GTjmHOCeg0CiB4fKscGxAFYIIfbKo1pbhBCb/c2EiA4TkZ2IHABegLQrk2EYxjT6mDGWhqrGDsn3lhpHYFNo2w42Y0aeHaJmuwnamcjPX5IWVedNkO7VozEZ2pOo4HqG4Jw/SaMrgTJc2o23uHk0Gtq6nYZghI2x3p2UyjRld3vIp5KnDOuPBKsFq8oapNFJa4J07mP9Xl2/b4Hwv1X7YBUClx47XDqI3JNRlZmvn4HiGsbTc+r3prg+ieROYXOZC2lE62QA5wI4R776hRBCfbzJhZDOvGQYhjGNPmeMAcCuw5qDkB12ndTe6eqxY9fhFpycss934nDTPx+Y+4Bz/U+NyrN63kTpOvdBp8xMB60n/gGrfrEeDUjHlqom55qjCBtjLjspe7qAXZ8BdbtDWmZyghWTh2ViZZl8mER8suT1/8kS4IWTge3BHYPY0W3HgjWVmFs0EHnpSa5uSrRrvzytB5z8K9d47TSq+pQK5R1qHbzGLuTh4xEhxFsAVgAYK4Q4IIS4GsDDqlG1OQB+768iP777hm542tkFLvcKuUO3u6RRo75Xh3989w2vcZ5kvnQ0EtaL0+qt1UHbDkZ09bdO6nb0JlPr5KkNlbA3mb/ptWFP+nlrOyPvRO85Re7pPejp4qtt9N6JXjlmyLT18aWDr7YIpv8rYb2/ZaP0KWPsGNkFhbLWqxeHDVj3KrBvheG8dlS3wGYnFMdVuEee/3QQWoaAqVc6wynytOGMG51OSM00xixWHDNScnS7ubLRuV4tzGd0aul1+JrdT9pFCUgjSSHmuJEDsKWqCS2dNqmd1e3wzmVB5b1wUxWaOmy4ujgJ2PejtP4rTrMzdsZN7g5Z1SjnZirHGmmnMtXOf5WDyAdODErvKGIxgEXy9RsAZQA+8/YAEc0jokFEFE9EQ4noJSK6jIgmEtEkIjqPiPz2I7Tivbd0w9PPHelyr7B/y+cuadSo79XhFe+95TXOk8yXjkbCenFavbU6aNvBiK7+1kndjt5kap08taES9ibzN7027Ek/b21n5J3oPafIPb0HPV18tY3eO9ErxwyZtj6+dPDVFsH0fyWs97dslD5ljA3JTMaAlARsOdAoCZSpm54u4JNbgJeNL0beKO+SG2LTjIxNuQyY8msTtDUR9ZmT6lENZQ2QyUcXpSfFY2ROCjZXNTmnKavWAzsWmVqOP1TUtyM3LRH9EuL0dxWGiONGZsFBwNqKI9I0X7PKxYwyJehwAMv/6RrnAyLCqz/uw7iBaSh5bwbw8plShHa6Ma8ImOlloKb3QHsdw/SPO6XTA7ToyWIQlQE1kYjGQFrrZfw/MoZhmDDRp4wxIQQmDsnA5gPSeZI4Sz4aqX6v33ltOtCE3LREJB3ZBYw8CbhjL/CL14GzHjVPYTOZLBuI6ukq5fjPeHONMQCYNCQDmw80OtcZfXANsOBS08sxSkVdm3Pxfk+H98QmMmW4tG5sZVm9ZPxWb3JGKmdiVq0FvrkPWHy74XzX7z+C7dXNuLpU40VBGYlU/Ib5Wg/ZOzKmM3LpyamyH86WYwkiWg9pUT/DMExU0aeMMQCYNDQDu2ta0dFtd66rqZXXVCk7/wywqbIRU4f0g2jYCww7Vpr+G39eSAwbU7jgacmlh5pGeVQvBId6TxyaicPNXWjULhV77yrpQPUwU6H2MWaTjbELnw95uS7rxqyJzqk+wHkUV9MBWeB1uZILr/64D2lJcTi/W+NgVRkRU1x2eFq4r6DshjRy/NWUy4BjzvOdLkYQQvxB9bldCPEmgIC8YzMMw4SSPmeMTRySAbuDsL262fnDpRhjcUnO0SIvNLXbUFbXhlnZzZJbjOzCEGocBkLg+2r8oHQAwL4mzcjM1velA9XDiOTWohv5io8x5UgiZWQqxCjrxuzK9GhihnR0lDIapSySN3geaE1LJz7bWo3bxjYhYdmD+olKrwJ+uxwYOdt7Zso/D8MMeGM4/yngktcN6RgjpKk+iZDWjoXGCzDDMEwQ9DljbNLQTACSj7DeRc51u6Rr8wFg1XM+89gkrzkbny0/nxCThyQ7CYExpmyWKD9iMz1vf1HcWvSOjCnTlGFy0aCsG2u2ye2cOVwaJVOMMWW9Vo+xHadvr66EzU44N18nfUqOdBUCGDTJd2ZDpgKX/A844x+Gyu5LENF9RHQfgEeI6AEieoOIQr+rQ4cZF83TDWvvm77a51GmDWvvzzj+eo9xSn56Ml86Ggn7itPTQZvGm9xTftr28KSTp3prn1e3oV67e5P5uvqSBVIvb2Ff9776GuC9T+n1RW95eaqXt7qa0Vc8pTWz/ythvX5qlD5njOWlJyInLVFaN6aMjCnGGADs+cZnHpsqGwEAo7M163NijVPula4hcHya2S8BgzKSUN4QDZ73lZ2UKUBnE7BAXj8XJmNsakF/JMdb0dApnOXGJQB22RhTpk19re/a+y0cL5+Ft1eWYVZhDnITdNp23Fn+K3jMucEfEh+DCCFmCCG2A/hJvi8WQjwTCV2Ov/hS3bD2vuWb/R5l2rD2PqM6w2Ockp+ezJeORsK+4vR00KbxJveUn7Y9POnkqd7a59VtqNfu3mS+rr5kgdTLW9jXva++BnjvU3p90Vtenurlra5m9BVPac3s/0pYr58apc8ZY0IITBqSgS1VjfrHwyT6HuXaWNmIkTkpSLHKU5rBHrAdKZSTB0IwMgYAxwxKx656nZExa3h/+PfJPsbys/oBFT84IzwdqG0yiXFWnDA6G4fb5f4SlyiPjMnG1O4vjWX07pWw7PsBx7StwhXHDQO+ud81/oLngASdQ+sZT/wLkuPXegAgok0AZnl7gGEYJhL0OWMMACbKi/jb7TojQnHeR0scDsKaigZMyx8g+ScDwuoqwVyU9XGhORLomEFp+iNjaXkhKc8T5XXtyEuX3Vo0VzkjksN3nvOccTkotMtOZiuWSyNjh7dIo2IVyyW5p5Gxw9ulY6bkEbSXEh7DSdYtQMcR13SxfTRRRCCiSo3Ifw/QDMMwIaZPGmMlw/uDCNh+uN09Uu/swKYqoOUwAGDn4RbkdVXg+KEJgF0xxmJ1ZEw2xkI4Mtbi0DFUkweEpDxPVNS3OY9BOrDGGRHCcym1nDQ2F6lQudRQHM9++X9OmScntM/OAJ6b6XKKgbWr0RmvvL+kdHOUPXqoFEIcD4CEEPFCiNsB7Ii0UgzDMFr6pjGW3x9Wi8C6w6qdk9N/K53T197g/sDj44HHpB2Tq8vq8GHCX3FSwwLgzV9I8XrTnbFArzEWGqNk3MB01EBn9InCO/iwT22MqY/3CSNDMpPRYlGtr1CO4Nq/yinzcSJAp0U1BfnBtc6wMt1s1sjYLRuAP+7ynS72uR7AjQCGAKgCMFm+ZxiGiSr6pDGWmhiHosHp+K5KZYSk5kq7z9rrvT67fU85UkUn0rtU7ohidmQstGvGRmSnwBqvGhn7xevA+POdI4phQHFr4XT4Ki+an3VH2HRQiE9QtcXoU6Vrl8r3m03HGFPJWuw6Rv9FLzvDiSaNjA0YGfap5EhARHVEdCkR5RFRLhH9moi8fwFEmLRThnuVaeONxnmThRtPOvgr9xUXSN567blkyRKknTK896onM3IFgK1j6nXzDrRewWCk3GD6m9F6BfJ+/ZFHQ583Qp80xgBgWsEArD+gOjA8Pln6MetqloyF3V+7PUNE2Le/HAAgWlXOO2N1Ab+yZipE04ZWi8DYvDTcm/1P4NbNklNca4K01mnFM9IxQCGmok52a6H4GOvpBIYfD5z8f16eCg39EiRj6pupzwKn/10SKiNkOeP0TwbodQgLZEFnCn3Cz5xhXjNmCCHEX718wusEz08yTnN34quWaeONxnmThRtPOvgr9xUXSN567bl06VJknJbfe9WTGbkCwMrKjbp5B1qvYDBSbjD9zWi9Anm//sijoc8boU8bY109DtSPOFcSxCVKn54uYNmjwBs/B8qXuTxT+8OrSOiQjTB5DRmA2HVtMe0a4Ox/StcQccygdCxsGAbKlP/7sMQDrYeBL/4E7PzU+8MmUNG7k1IZGeuM2CkJ8bJP18/3W5zngiobCjLz9UfGmpzryy2CJC/4njBrZKzv06bzAYCrAdwVKaUYhmE80YeNMWlUqKFNXhQdlyy5G7B3Aw3yWZWNrhutcr++FSNFtXTTUu2MiNWRMWscMO3qkBqT4wam4Ui7DTUt8vSgekrXxxopM6hQfIz1HoXUGZLjn4ygTIqvOdCG6ibNKFjWKP3NI221rveFcz0f/H0U+goLBCJ6TPkAeB5AMoDfAHgbwMiIKscwDKNDnzXGslITMTYvDQ3N8j/F8UnSaEVPl9NVhc5i7/nxr0mBDtVC/1hdMxYGCgdKU2e7DstTwmo3IGEwxsrr2zAwPQnJCfKwVE9HxIwxBQcE3l6t8aiQkiO1R3c7ULa0d5elQz0dDkijX6fOl45TOvU+17gw7g6NdYQQA4QQfwewGUAcgBIiuouIanw8yjAME3b6rDEGACeNy0F3uzwakZgujYw5bEB3qySz6bi+YPyiME8yxnYe0jHGOnVGgkxmX3275OxVwdYZNs/7npg2Igtvr3H1Ko1+8rq9jgbgtfOAH58E7D3YW17umk4xJK/5Gph5mxQ+/hZpzRljCCHEIwDWAGgBMJGI5hPRER+PMQzDRIw+bYzNGZuLFMgGV2Kacx2P4t6ip9PQweGRHmmJZrJTE5GVkoDdh2UDVz2K6Aj9rsqKujaMyFa5hOhsBJIyQ16uLr98E5hyGc44YToON3dhQ+nDzrgk2e1Fo9NI62mpwd6KcjQL1akQelORp/8NuHGVu5zxxB8BDAbwfwAOCiGa5U+LECL0/yEwfYrZs2e7XPVk/l61+TBMUMaYPBXwlRBit3zVdXkuhLALITbKn4XBlOkPU/P741VxgXSTO955TI+yqNrW4eJoU5fznwaSM0OlYp9gTF4qdtXojIz5atsgae60ob5N5dbC1imNevYLr9PZXgZOBM5/CicfMxCjclJw5y55NGvodKdB3+x0mbLox01I6KwHZah2+7DhHzREZCGiZCJKI6J01SeNiCK+C6Ks7AlDcn/u/X3Wl9wbRp7xpzwjMqPPBVO2pzLnzJmDsrInMGfOnF65Es7P3+xy1ab1FK+gyAOpp9F4f/INpk/586zZsmDyMhIXDoIdGbsbwDdENAbAN/K9Hh1ENFn+nBdkmYaJt1rQU3g2SuPeR098qnPUoVlenG/r0F/XNPdBZ3gAr/f1RWFeGvYcbgURaYwxHwdjB8k+2a1FgTJNqazzC9OZlJ6wWgRuO7UQu2ta8cHcVcCVi5x97/2re9MtW7kSx8bvQXr2IOfDvEi/z1Ne8W9Dcn/u/X3Wl9wbRp7xpzwjMqPPBVO20fbVCwcbb1RHPQJ9H8G0h6/7QOsV6v7hra0C+Vswk2CNsfMBvCqHXwVwQZD5mc45kwahrrULP+6tdxoKir+n1f/pXdfURKp1R4MmO8P84+iTMXlpaOnqQXVTp+s05dJ/OJ2whoBy2a1F78jYF/dI1wgbYwBw9sRBmF4wAPd+sQ8HWx26h6dfLhYjxdEKUbnaKeSRMYZhmKOOYI2xPCJSfEAcAuDJrXeSEGKtEGKlEMq8YXiYMy4XaUlx+GhDlf7Zfts/AgDYoDIiMoc5wzo/oowrhbnSmqedh1vcvf1/fFPIylXcWuQPkI2xbR9I1ygwxiwWgYcvmgSHg3DFf1ejXscmnSzkg8XVLi/Y+GcYhjnq8GmMCSG+FkJs1fmcr05HRATA02r4fCIqBfArAP8SQozyUt51suG2tra21lMywyTFW3HOpMH4dGs1GjOL3OJbu6SpNEuiahG4+secRyp8ouyo3H24xd1dyJZ3QlZuRX0bBmXIbi3UDlUT+nl+KIwUZKfgxSumofJIO657c6tLXHtirvPml28CqQOlMBtjDMMwRx0+jTEiOpWIJuh8PgZwWAgxCADkq64PHyKqkq9lAL4DMMVLec8TUSkRlebk5ARQJXeunlmATpsDb29udItbu0+S9cy8HcibCJReBSSoDLO4BLdnGFf6pyQgJy0Ruw63Al0tvh8wiYq6Nqdbi6q1zois0WHTwRczRmVh8S0nYtrogS7yfjmqRfujTgau+Qo453HXvscwDMMcFQQ7TbkQwBVy+AoAH2sTCCH6CyES5XA2gBMAbA+yXL8YnZuGU4/JxUurDrvFbS2TzgbMHTgUuOF76QdRDU9TGqIwL1UeGQujMVbf7nRrUbNDuvYviLozHEflpOLuc4pdhcqpDnHJkl+0zOHSPwJMRBFCDBNCLBFCbBdCbBNC3CrLDe0cV3OoS9+1y4iCW3rDj5RXu8kVmT/3RtN6KlMvzhPaZ/SeM1pHozK9/DzpHmjZ2uc8ta82HGy8v/X0VAd/0ujV12h7+LrXxgHAN5mPeS0/WFmg+RuJCxtEFPAHQBakXZS7AXwNYIAsLwXwohw+HsAWAJvk69VG8586dSqZxe7DzTT6z4uI7k0nujedbMseJ7o3nV7+yyWSbO8S1wfkdNTeYJoOfZl7P95Kx/zlM3Isvt3ZdsonBDS2d1P+XYvoue/2yArIZVVvDkl5QVO3x6njI2OIXjlHCj86LtKaRRUA1lIQ30nBfgAMguStHwDSAOwCMB7AwwDuluV3A3jIV15xhcf4rG/etxt8yvy595XWk8xInDf8zdNsmdl5empTvXCw8f7oaSQukOfNvA+kT5stC1e/1yPQ77CgRsaIqJ6ITiGiMSRNZzbI8rVEdI0c/pGIJhJRsXx9KZgyA2V0bhr+co5zzdgDK6W1YrOGyyNf2rVhFzwHJA8AElLB+KYwLw3t3XZUldwBnPGQMyJEnuP3qQ8IX6PqUopz1WijfwFwjHxo/fDjnCNj2g0PTEQhomoiWi+HWwDsADAEMbBznGGY2OWo+iW4fEZBb7jBIs0yjEyRpxK0xtjkecBd5XwupUEK8+QdlUcIOO56Z0SI1kCVyzspR2SnAIv/4IyIVmPMYgUu+R9w9deSoU8OSd58ILJ6MR4RQhRAWt+6CsZ3jjMMw/jNUWWMqXnimrlSoE3ecxCtP+Ixwpg85cDwVtcIR2gcv+6rlxy+upxLCQAJ0bVezI1h06Tdnsq5qJMuiaw+jC5CiFQA7wO4jYhcjlCSpyIMnKPGMAxjjKPWGOs9v7Bul3zPxlgwZCTHIy89UVrEDwB3lgMFJwIOe0jKq6iT3FokxWm6sCVGurRNdjw8+tTI6sG4IYSIh2SIvUFEsvM6YzvHGYZhAiFGfrlM5LyngNKrgcRUIOcYoLNJkidG/Mi6mKcwL815RmW/AdKnqwVY/hhgN/fQ8PL6NhRkpZieb9hQjDHud1GFEEIAeAnADiL6pyrK585xLXkJvpc4/LHAfbZTK/Pn3ldaTzIjcd7wN0+zZWbnqW3Tx7+S/mk/sc75z+WJdXY8/tUu3Xi1XB2vDQdST19xgTxv5n0gfdpsWbj6vakEsuo/XB8zd1Pq8tyJId3xd7Rx/yfbaOz/fUp2u0MSvPsbZ/uufcXUsqbc/yXd/f5mos5mZxlf3WtqGSHlsfGSzuXfR1qTqAKR3005E9IU5GYAG+XPWfCwc9zbJ+TfX0zYyL9rkctVCWvlvq7aMNP3CPQ7LC785l8U0dMtXQeXRFaPPkJhXio6bQ5UHmmXdjlaVN3LxLVjTR02NLR1SweEK+8QACZebFoZIUdZM6Z3RBcTMYjoewDCQ/Qp4dSFYZijh6NvmlKNYiDMuDGyevQRlEX8Ow/JU5VqY8zEY36UMykLslOAfT84I+Kj4xgkQ/TIxzdFmYNahmEYJvwc3cYYyfP/vG7HFAp7d1QqxpjVGWniSQYVso+xkdkpwDuXScIRs4ABI0wrI+QoI2Pc9xiGYY56jm5jTJmN4NEJU0hNjMOQzGSnewv1yJiJ/trKatsgBDBc7dai5ArPD0QzbIwxDMMc9RzdxphyFmB2YWT16EMU5qU6R8Y2ve2MIPNcXJTXtWFo/2QkxqlH3mLsQPdznwCyxgDWo3vZJsPEAreeMsblqoS1cl9XbZhhFI5uY+z4m4D5TUBKVqQ16TMUDkxDWW0bbHaHcyoOAOzmLeAvr2vDiGzNMVUJMbReDACmXgncvDbSWjDhYMn/8x3Wu/ck81WGrzh/7pVwIHXwt1yjskDSGMnDS9zvTyt0uSphrdzXVRs2UnZA7W1WPzPS//x91qx3HGheRu/9aVujf6deOLqNMcZ0xualodvukM6OLL3aGbFjIUDBOy0nIpTXtUnrxdREu+d95uhl6T98h/XuPcl8leErzp97JRxIHfwt16gskDRG8jASF2p81c1oe5vVz4y0hb/PmvWOA83L6L0/bWtCn2FjjDGVwt4dla1A8TxnxE+LpE+Q1LZ2obWrRzqTUk2IzsBkGIZhmFDDxhhjKqNzUyGEvKMyBIesl9eqDgh3OJwR8cmml8UwDMMw4YCNMcZUkuKtKMhKkYwxrW8xE9xblNepjLH6Paq8Y2wBP8MwDMPIsDHGmE5hXip2Hm5xN5Ds3foP+EF5XRsS4iwYnJkMPD1NEuZNBDKHBZ03wzAMw0QCNsYY0ynMS0NFXRs6SeO2wQRjrKyuDQsSH4T1tXOdwhNuDTpfhgkFz2x8Bph9t/O++CzdMABg9t1Seo3MEB7SacvXLcfbvRKW8/BWH7d7nXx789DRWVdXI/n4yNtQG2jiIoZWb61M3X+8vQvNO/SYn4d3oaePXzp7eNaMd+wrL72/K5e8vPQNl77upT29/n0ESiAHWobrwwftxiYLN1ZR/l2L6Kfdu52HeN+bTrTx7aDzPuWx71zzvDedaM83JmjNRAOI8EHhZn6mTp1KE16Z4FI/9b02zpMsGDzl500v7b03nf2990enQPIxq7xowFddQvkuAsVXfma8Y19x/sj9+fs08jcx4ZUJAX+H8cgYYzpjB0o7KvfUd7lGlH0HlC8POF+7g7C/vt09YsRJAefJMAzDMJGGjTHGdAqyUhBvFfip3uYaselN4NVzAs73YGMHevScx1q4GzMMwzCxC/+KMaaTEGfBiOwUbK+xAXdXAn/cZUq+ZXVtSEaX74QMwzAME0OwMcaEhMK8NOyqaQGS0t0dsr59aUB5lte2IhnBbwJgGIZhmGiCjTEmJIzNS0NlQwfaunrcnb8G6Il/T20rcpLMO+OSYULNDcU3oPbJp3Tvbyi+QTe92eV7k+vpUvvkUy7x6riHdpXoptO7V/JV19+ITkbj1LropfPWzr7KiwZ81dlb31I/Hw39zUi8v/r5+1699UVtG2n/BrTPewsHTCCr/sP14d2UscvnW6sp/65FtGH/ESKHg+jF0113QC64zO88f/Hcj3Trv99w303J9BnQx3ZTEhFtHzvOpY7a+0iip4ta5k13o/UKVX195RtN7WwWvtrc1/s82jHaRz31cyPhQL/DeGSMCQnKGZW7DrUAQgCXvuOaYPvHfue5u6YVYzKFq/C3ge/OZBiGYZhoIChjTAhxsRBimxDCIYQo9ZLuDCHETiHEHiFEBD3qMeFi+IB+SIyzSMciAUBielD5Ne5dg+z2vSiOP+AakRRcvgzDMAwTaYIdGdsK4GcAlnlKIISwAngawJkAxgOYJ4QYH2S5TJRjtQiMyUvFT4dkY0wIYLJm4T6RscyIkPn6qfgy8S4Mt9a7xsWn6D/DMAzDMDFCUMYYEe0gop0+kk0HsIeIyoioG8DbAM4PplwmNhg/KB3bDjaBFKPLojkeydZhLKPu1t5gVqLDNS6hXxAaMgzDMEzkCceasSEAKlX3B2QZ08eZMCQDR9ptqG7q1E/gMLgz0uZ8vt+h1U55QioQz8YYYx5CiGFCiCVCiO3yEoxbZfl8IUSVEGKj/DnLV14K2Tfe6PU+kujpopZ5091ovUJVX1/5RlM7m4WvNvf1Po92jPZRT/3cSDhQfBpjQoivhRBbdT4hGd0SQlwnhFgrhFhbW1sbiiKYMFE0WFrPte1gsyTobHRNsOdrZ3jlc8DCW/Qz6nGOoImDG5zy9CHS9CfDmEcPgD8S0XgAxwG4UbWs4nEimix/PjWS2epPypBz8029YQBu9+q0oUQv//KCs9zSqPXT010v7CkvJZ2n8r3p5itOW542ra9yQ93eZqDVUVtn9b36famfjVR/86csf99RoPkZ7aOe+rn6eb1wMG3q0xgjolOJaILOx+h2uCoAw1T3Q2WZp/KeJ6JSIirNyckxWAQTjYwbmA4hgG0HmyRBe4Nrgvd+A7TWSOHP7wLWv6qfkc3DyFr6IHMUZRgZIqomovVyuAXADgQxkr9mcYVu2Mi92ejl702HYMJG7oONM1Iff5+PNvxpU3/bP5z191WWv+/IrPz87f/+xPtLOKYp1wAYI4QYIYRIAPBLAAvDUC4TYVIS4zAiO8U5MqYYY5nDnYnaNQvyO44A3/8LcDjXhjU2N+kX0C/bPGUZRoMQogDAFACrZNFNQojNQoj/CiH6R04zhmH6GsG6trhQCHEAwAwAi4UQX8jywUKITwGAiHoA3ATgC0j/Zb5DRNuCU5uJFYoGZ2C7Yox1yMbYpF86Eyx/zPWBRb8Hvr4X2PcD0NMFtDfgQI1mRE0hPtl8hRkGgBAiFcD7AG4jomYAzwIYBWAygGoAj3l+mmEYxj+C3U35IRENJaJEIsojormy/CARnaVK9ykRFRLRKCJ6IFilmdihaHA6qho7cKStG8jMl4RpA50JtrwrGV0KyrQlCHjzEuDhEe7GWLI8KBGXGDK9maMXIUQ8JEPsDSL6AACI6DAR2YnIAeAFSLvEGYZhTIE98DMhRVnEv726GfjlG8BlH7o7gK1c5Qzb5YPAhRUoWwIAGL3zP67pp/xaug4qDoXKzFGMEEIAeAnADiL6p0quXqB4ISQfiwzDMKbAxhgTUooGZwCQF/GnZAOjTgYsmm53ZJ8zrIyS9TgX7Y9u3+CafsJFwDXfAlMuC4XKzNHNCQAuA3Cyxo3Fw0KILUKIzQDmAPi9kcymnV2gG/7x3Tdc7pX4H999wy0PrUx97y1Oe6+UpyfTu88dut1nPTyl18bp1VetizatNh+9dtGW5ymtXr09pTXa1nphf9L6KkvBW5tq6+DtXRrtb6G69/Z+Af136a3PaOWB9hlvbaQX9ifeX9gYY0LKgJQEDMpIwtaqZqdQ6/y1epMzTPLCfW8OYRNSgaFT2a0FYzpE9D0RCSKapHZjQUSXEdFEWX4eEVUbyW/6uSN1wyvee8vlXolf8d5bbnloZep7b3Hae6U8PZne/f4tn/ush6f02ji9+qp10abV5qPXLtryPKXVq7entEbbWi/sT1pfZSl4a1NtHby9S6P9LVT33t4voP8uvfUZrTzQPuOtjfTC/sT7CxtjTMiZOCQDmw80OgVaY2zNC86w4q2/pxMYMVs/w8RUU/VjGIZhmEjCxhgTckry+6Oivh31rfIUpGKMpeS6J1ZGxrpaPGeYwOdRMgzDMH0HNsaYkFMyXNr9uLGyURJYrNI1KcM9sWJoLboNKF+qnyEfDs4wDMP0IdgYY0LOxCEZiLMIrN9/RBIoI2PWePfERg4P124AYBiGYZgYhn/VmJCTnGDFMYPSsWF/oyQQ8siYMkKmpsaDP2DBXZWJTZq+2ud2P+OiebrxilxPpnevjTvj+Os9plXy9FS2Xrnqe09hX3XS00vvGU/lepMFW5a39tLe+wr7k9ZbWb76g14ab/GA8TbR5uWtPfT086dOnp7zlJc3uZF360mmzddInzf6d+EP/AvHhIUpwzOxqbIRdgcBDpskTMo0ngE5fKdhmCik5Zv9bvfHX3ypbrwi15Pp3WvjMqozPKZV8vRUtl656ntPYV910tNL7xlP5XqTBVuWt/bS3vsK+5PWW1m++oNeGm/xgPE20eblrT309POnTp6e85SXN7mRd+tJps3XSJ83+nfhD2yMMWGhZHh/tHXb8dOhZucZlSl8tiTDMAzDsDHGhIVpIwYAAFaVNQB5RZKw+FfGMyi5IgRaMQzDMEzkifOdhGGCZ0hmMgqy+uHHvXW4auY04K9HPC/ETxsEtGh8ao46GRh3truPMoZhGIaJcfiXjQkbx4/OxicbD6LH7kCc1cugbHyyuywhFRhzauiUYxiGYZgIwdOUTNg4YVQ2Wrp6sLmqySk861H3hHFJ7jL2us/EKGmnDDd8v2TJEo8yI/dppwx3i1fSaMv1VzdPYX/utbp6q4dWrqe/Xr300m0dU+8m8yRX6+FP2J94b+n12iXQ9vYnT09yI/3PW38FPLe/Xn/V6yvaOvjqK1q5np6e0hnp80b/LvyBjTEmbMwYlQUA+H53nVM4/Vr3hEUXAgC+sxwHKr1akg0I/MwvhokkGaflG75funSpR5mR+4zT8t3ilTTacv3VzVPYn3utrt7qoZXr6a9XL710Kys3usk8ydV6+BP2J95ber12CbS9/cnTk9xI//PWXwHP7a/XX/X6irYOvvqKVq6np6d0Rvq80b8Lf2BjjAkbA1ISMGV4Jr7afthrup4T/ogL8Bg+G/8IxNwHgN9vB1J1jk5iGIZhmD4AG2NMWDlzwkBsqWpCZUO7xzRr9jViY+cgzB6bI60fyxgSRg0ZhmEYJrywMcaElblFAwEAX2w75BTesAK4YlHv7adbqpEUb8FJY3PCrR7DMAzDhB02xpiwkp+VgmMGpeOTzSrXFXnjgREn9t5+vu0Q5ozNRb8E3uzLMAzD9H3YGGPCzkVTh2JTZSO2qndVAkDBiSgvuhm1LV04t3hwZJRjmAgye/ZsnzJ/7z3JIoE3PTzFBfJMMHmrZf6E/Yn3lt6o7sEQaHsEcu+tPH91CcUz0YIgokjr4JHS0lJau3ZtpNVgTKapw4bjHvwG5xYPwsMXFbvEXfnyamw72Iwf7joZCXH8v8LRhhBiHRGVRloPM+hr319lZU9g5MhbI60Gw4QVdb9Xwt5kgX6H8a8dE3YykuNxcelQfLC+CmW1rb3ydfuO4LudtbhiRj4bYgwTZZRX/DvSKjBM2FH3eyXsSxYI/IvHRISbTx6DpHgr7nxvM7p7HGjv7sGfP9iC7NRE/OaEEZFWj2EYhmHCBq+QZiJCTloiHvzZRNzy1gac99T3cBBhd00rXvnNdKQkcrdkGIZhjh74V4+JGOcVD0acReCJr3cj3mrB85eVYnYhu7NgGIZhji6CMsaEEBcDmA/gGADTiUh3taoQogJACwA7gJ6+skCXCZ6zJg7CWRMHRVoNhmEYhokYwa4Z2wrgZwCWGUg7h4gmsyHGMEy0IoRIEkKsFkJsEkJsE0LcJ8tHCCFWCSH2CCEWCCESIq1ruBlRcEukVQg7j5RXu4WDvWrDTHSj7vdK2JcsEIIyxohoBxHtDEoDhmGY6KELwMlEVAxgMoAzhBDHAXgIwONENBrAEQBXR07FyHA0urV4rOKwWzjYqzbMRDfqfq+EfckCIVy7KQnAl0KIdUKI68JUJsMwjF+QhOJvJV7+EICTAbwny18FcEH4tWMYpq/ic82YEOJrAAN1ou4hoo8NljOTiKqEELkAvhJC/EREulObsrF2HQAMHz7cYPYMwzDmIISwAlgHYDSApwHsBdBIRD1ykgMA+PR6hmFMw6cxRkSnBlsIEVXJ1xohxIcApsPDOjMieh7A84DkwTrYshmGYfyBiOwAJgshMgF8CGBcZDViGKavE/JpSiFEihAiTQkDOB3Swn+GYZiohYgaASwBMANAphBC+ed1KICqSOnFMEzfIyhjTAhxoRDiAKQvq8VCiC9k+WAhxKdysjwA3wshNgFYDWAxEX0eTLkMwzChQAiRI4+IQQiRDOA0ADsgGWUXycmuAGB0iQYTw/yxIM8tHOxVG2YYIMoPChdCtADoC7s1swHURVoJk+C6RB99pR4AMJaI0iJVuBBiEqQF+lZI/6y+Q0T3CyFGAngbwAAAGwD8moi6fORVC2BfiFVmGCa6yCciv72XR7sxtrYv+CXrK/UAuC7RSF+pB9C36sIwDGMUPiicYRiGYRgmgrAxxjAMwzAME0Gi3Rh7PtIKmERfqQfAdYlG+ko9gL5VF4ZhGENE9ZoxhmEYhmGYvk60j4wxDMMwDMP0aSJujAkhzhBC7BRC7BFC3K0TnyiEWCDHrxJCFERATUMYqMsfhBDbhRCbhRDfCCHyI6GnEXzVRZXu50IIEkJE5Q44I/UQQvxCfi/bhBBvhltHoxjoX8OFEEuEEBvkPnZWJPT0hRDiv0KIGiGErvNnIfFvuZ6bhRAl4dbRH/rKdxh/f0Un/B0WfYTkO4yIIvaB5MtnL4CRABIAbAIwXpPmdwCek8O/BLAgkjoHWZc5APrJ4RtiuS5yujRIx1qtBFAaab0DfCdjIPmN6i/f50Za7yDq8jyAG+TweAAVkdbbQ11mASgBsNVD/FkAPgMgABwHYFWkdQ7yvUT9dxh/f0Xf95cf74W/w8JfF9O/wyI9MjYdwB4iKiOibkhOFc/XpDkfkhNGAHgPwClCCBFGHY3isy5EtISI2uXblZCOVYlGjLwXAPgbgIcAdIZTOT8wUo9rATxNREcA6fzUMOtoFCN1IQDpcjgDwMEw6mcYIloGoMFLkvMBvEYSKyEdRTQoPNr5TV/5DuPvr+iEv8OikFB8h0XaGBsCoFJ1f0CW6aYhoh4ATQCywqKdfxipi5qrIVnO0YjPusjDrsOIaHE4FfMTI++kEEChEOIHIcRKIcQZYdPOP4zUZT6AXwvpiLJPAdwcHtVMx9+/pUjSV77D+PsrOuHvsNjE7++wOG+RTGgQQvwaQCmA2ZHWJRCEEBYA/wRwZYRVMYM4SMP8J0H6T3+ZEGIiSYdExxrzALxCRI8JIWYAeF0IMYGIHJFWjOk78PdX1MHfYX2ASI+MVQEYprofKst00wgh4iANXdaHRTv/MFIXCCFOBXAPgPPIx9l2EcRXXdIATADwnRCiAtKc+MIoXARr5J0cALCQiGxEVA5gF6QvtmjDSF2uBvAOABDRCgBJkM6tjDUM/S1FCX3lO4y/v6Lv+wvg77Cj5zsswovg4gCUARgB54K+Ik2aG+G6+PWdSOocZF2mQFrAOCbS+gZbF0367xCFC2ANvpMzALwqh7MhDS1nRVr3AOvyGYAr5fAxkNZbiEjr7qE+BfC8+PVsuC5+XR1pfYN8L1H/HcbfX9H3/eXHe+HvsMjUx9TvsGio0FmQLPm9AO6RZfdD+s8LkCzjdwHsAbAawMhI6xxEXb4GcBjARvmzMNI6B1oXTdpo/jLz9U4EpCmL7QC2APhlpHUOoi7jAfwgf8ltBHB6pHX2UI+3AFQDsEH6r/5qANcDuF71Tp6W67klWvuWH+8lJr7D+Psr8noH+F74Oyz89TD9O4w98DMMwzAMw0SQSK8ZYxiGYRiGOaphY4xhGIZhGCaCsDHGMAzDMAwTQdgYYxiGYRiGiSBsjDEMwzAMw0QQNsYYhmEYhmEiCBtjTNAIIbKEEBvlzyEhRJUcbhVCPBOiMm8TQlzuJf4cIcT9oSibYZi+A39/MdEA+xljTEUIMR9AKxE9GsIy4gCsB1BC0sHLemmEnOYEImoPlS4Mw/Qd+PuLiRQ8MsaEDCHESUKIRXJ4vhDiVSHEciHEPiHEz4QQDwshtgghPhdCxMvppgohlgoh1gkhvhBCDNLJ+mQA65UvMiHELUKI7UKIzUKItwGApP8yvgNwTlgqyzBMn4K/v5hwwsYYE05GQfoiOg/A/wAsIaKJADoAnC1/oT0J4CIimgrgvwAe0MnnBADrVPd3A5hCRJMgHUmhsBbAiabXgmGYoxH+/mJCRlykFWCOKj4jIpsQYgsAK4DPZfkWSIeujgUwAcBX0ig9rJDO/9IyCMAO1f1mAG8IIT4C8JFKXgNgsHnqMwxzFMPfX0zIYGOMCSddAEBEDiGEjZwLFh2Q+qIAsI2IZvjIpwPS4csKZwOYBeBcAPcIISbKUwBJclqGYZhg4e8vJmTwNCUTTewEkCOEmAEAQoh4IUSRTrodAEbLaSwAhhHREgB3AcgAkCqnKwSwNeRaMwzD8PcXEwRsjDFRAxF1A7gIwENCiE0ANgI4XifpZ5D+kwSkqYD/yVMHGwD8m4ga5bg5ABaHUmeGYRiAv7+Y4GDXFkxMIoT4EMCdRLTbQ3wegDeJ6JTwasYwDOMd/v5itLAxxsQkQoixAPKIaJmH+GkAbES0MayKMQzD+IC/vxgtbIwxDMMwDMNEEF4zxjAMwzAME0HYGGMYhmEYhokgbIwxDMMwDMNEEDbGGIZhGIZhIggbYwzDMAzDMBHk/wOWW6EJ+oa6DAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded esimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 2: Transformation\n", + "\n", + "Encoding and decoding allow us to encode signals over time,\n", + "and decode transformations of those signals.\n", + "\n", + "In fact, we can decode arbitrary transformations of the input signal,\n", + "not just the signal itself (as in the previous example).\n", + "\n", + "Let's decode the square of our white noise input." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.355536Z", + "iopub.status.busy": "2020-11-25T16:46:50.354365Z", + "iopub.status.idle": "2020-11-25T16:46:50.358031Z", + "shell.execute_reply": "2020-11-25T16:46:50.358663Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(WhiteSignal(1, high=5), size_out=1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " Asquare = nengo.Node(size_in=1)\n", + " nengo.Connection(input, A)\n", + " nengo.Connection(A, Asquare, function=np.square)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " Asquare_probe = nengo.Probe(Asquare, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.366502Z", + "iopub.status.busy": "2020-11-25T16:46:50.365674Z", + "iopub.status.idle": "2020-11-25T16:46:50.956715Z", + "shell.execute_reply": "2020-11-25T16:46:50.957153Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[Asquare_probe], label=\"Decoded esimate\")\n", + "plt.plot(sim.trange(), np.square(sim.data[input_probe]), label=\"Input signal squared\")\n", + "plt.legend(loc=\"best\", fontsize=\"medium\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes])\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the spike trains are exactly the same.\n", + "The only difference is how we're interpreting those spikes.\n", + "We told Nengo to compute a new set of decoders\n", + "that estimate the function $x^2$.\n", + "\n", + "In general, the transformation principle\n", + "determines how we can decode spike trains\n", + "to compute linear and nonlinear transformations of signals\n", + "encoded in a population of neurons.\n", + "We can then project those transformed signals\n", + "into another population, and repeat the process.\n", + "Essentially, this provides a means of\n", + "computing the neural connection weights\n", + "to compute an arbitrary function between populations.\n", + "\n", + "Suppose we are representing a sine wave." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.965051Z", + "iopub.status.busy": "2020-11-25T16:46:50.964551Z", + "iopub.status.idle": "2020-11-25T16:46:50.967527Z", + "shell.execute_reply": "2020-11-25T16:46:50.968216Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: np.sin(np.pi * t))\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.975228Z", + "iopub.status.busy": "2020-11-25T16:46:50.974404Z", + "iopub.status.idle": "2020-11-25T16:46:51.671040Z", + "shell.execute_reply": "2020-11-25T16:46:51.670576Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.title(\"A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear transformations of that signal\n", + "involve solving for the usual decoders,\n", + "and scaling those decoding weights.\n", + "Let us flip this sine wave upside down\n", + "as it is transmitted between two populations\n", + "(i.e. population A and population -A)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:51.677512Z", + "iopub.status.busy": "2020-11-25T16:46:51.677019Z", + "iopub.status.idle": "2020-11-25T16:46:51.680129Z", + "shell.execute_reply": "2020-11-25T16:46:51.680539Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " minusA = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(A, minusA, function=lambda x: -x)\n", + " minusA_spikes = nengo.Probe(minusA.neurons)\n", + " minusA_probe = nengo.Probe(minusA, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:51.689640Z", + "iopub.status.busy": "2020-11-25T16:46:51.688871Z", + "iopub.status.idle": "2020-11-25T16:46:52.574321Z", + "shell.execute_reply": "2020-11-25T16:46:52.574974Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.plot(sim.trange(), sim.data[minusA_probe])\n", + "plt.title(\"-A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(2, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(2, 2, 4)\n", + "rasterplot(sim.trange(), sim.data[minusA_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"-A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nonlinear transformations involve\n", + "solving for a new set of decoding weights.\n", + "Let us add a third population connected\n", + "to the second one and use it to compute $(-A)^2$." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:52.581507Z", + "iopub.status.busy": "2020-11-25T16:46:52.581009Z", + "iopub.status.idle": "2020-11-25T16:46:52.584468Z", + "shell.execute_reply": "2020-11-25T16:46:52.584057Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " A_squared = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(minusA, A_squared, function=lambda x: x ** 2)\n", + " A_squared_spikes = nengo.Probe(A_squared.neurons)\n", + " A_squared_probe = nengo.Probe(A_squared, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:52.596134Z", + "iopub.status.busy": "2020-11-25T16:46:52.595308Z", + "iopub.status.idle": "2020-11-25T16:46:53.754257Z", + "shell.execute_reply": "2020-11-25T16:46:53.754682Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 6.5))\n", + "plt.subplot(3, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(3, 2, 3)\n", + "plt.plot(sim.trange(), sim.data[minusA_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"-A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(3, 2, 5)\n", + "plt.plot(sim.trange(), sim.data[A_squared_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"(-A)^2\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(3, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(3, 2, 4)\n", + "rasterplot(sim.trange(), sim.data[minusA_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"-A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(3, 2, 6)\n", + "rasterplot(sim.trange(), sim.data[A_squared_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"(-A)^2\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 3: Dynamics\n", + "\n", + "So far, we have been considering the values\n", + "represented by ensembles as generic \"signals.\"\n", + "However, if we think of them instead\n", + "as state variables in a dynamical system,\n", + "then we can apply the methods of control theory\n", + "or dynamic systems theory to brain models.\n", + "Nengo automatically translates from standard dynamical systems descriptions\n", + "to descriptions consistent with neural dynamics.\n", + "\n", + "In order to get interesting dynamics,\n", + "we can connect populations recurrently (i.e., to themselves).\n", + "\n", + "Below is a simple harmonic oscillator\n", + "implemented using this third principle.\n", + "It needs is a bit of input to get it started." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:53.762833Z", + "iopub.status.busy": "2020-11-25T16:46:53.762328Z", + "iopub.status.idle": "2020-11-25T16:46:53.765389Z", + "shell.execute_reply": "2020-11-25T16:46:53.766009Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: [1, 0] if t < 0.1 else [0, 0])\n", + " oscillator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(input, oscillator)\n", + " nengo.Connection(oscillator, oscillator, transform=[[1, 1], [-1, 1]], synapse=0.1)\n", + " oscillator_probe = nengo.Probe(oscillator, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:53.773456Z", + "iopub.status.busy": "2020-11-25T16:46:53.772439Z", + "iopub.status.idle": "2020-11-25T16:46:54.633754Z", + "shell.execute_reply": "2020-11-25T16:46:54.633303Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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kyj/93Hz4yiUYGOGHxe/twbbsGrSpTe78HuP8DtPjZb8Cay4lj+vzAL3WKtHfYGAx9bUd6MyEV7b1XpFCq/VFGZkrTbmANpjy6nbjRfiyj/ch+4UF8JL2om5bZRawiavOHXcbED2y9/ZFoM9TVNeGQB8p7v/hBHbm1hrnFXMGReCLG8bYXKekvh1eUhGeXX8Gf5+qwqubsu1uPzFEgYc5o8pcWsfc0Hpy8SCbuVg8E5NDsL+g3pXDMhphnQ2ty0fHGg0+lgX0Zjm/6k6aXa0qHSL85ahuUaOlw1ovMkQhg0wiQlWLCgYWmP56pvE1hUyMtbdNQHqkHzo0esQEeiMm0BvjkoKh0RnwxZ5CHCluxEUf7kViiAIHCurRrtFj6fh4vHzJUJeOVcAx/c7Yau7QWumPeDxnq/wYoFGSP1ss/Yl4J5TWN9Elw6Otnrt5SiL25Ndh67lq3DQ5yd17a5+DH5PlbdbGBwBg/4fA5PuM/36+qxAAMD4pGL6cUTU42uSSPlhYb+Gp6xFWX2x6nDIbeLbZFFYs3AmkzLEY/ugvWcbH3906Hku/OGj8v6ZVjYjemN29OdD6uZ32ja1Pdp43Glo8g57ehMJXejFv7tOppsc15wRjS8Am3x0swSd7O1CyKdP43KML0nD5qFgrz4pKq0dmTg3OVLRgQ1YlCuraLF6/aXIifjhUig6tHs8tGQyGIekaW8/V4JKP9gEgifTmIURzHBlaAKgNrWCFDA2cuPZPyyciPdIPKzdmw0cmxhOLM4zjnrkwA9/uL0ZxfRsmJIcgPtgHD/54AqUNHcYxcokIap0B1S1kcv7jHRNxsLABr/+TAwC4cVIifj1Whvo2DWICvVHeZFp3ZHwgjpc04ZavD6Oe258lw6NxwbAoTE8Lw5MXZGD5jAHIKmvCu1vzUFTXhowofxwpbkRKmC/WHS5BsEJuV95CwDX6lbGl1RvQptFbuZo9nrO17TmyFNtJOEyYRJZ5mwGQHCEeW56TIB+ynefWn+1ZY4snarjl/3NfALY8Rf7MjK11R4gb+7PrLWeev989GRd/uBf5NcqeN7Z4HjdrXRcxFKg+Bay5jBhfZvCzSwCYlBKKopWLjUUKn+4swNMXZqBHqThueuwbATyUDTzPhQTr8oDQVIvhLMti5UbrGT3LkiqrgRF+ntxb2xz42PL/EUt7fh8E+jQsy+LFDefw5Z5Ci+efv2gwrp+YaPzfYGCRV6PEsi8PorbV1F9QImLw6II0bDpdhayyZiuv1DN/nrH5vt4yscut2zKi/HG2sgUTkoPx8iVD8fvxchwvbUJGtD/un52K7dk1eHdrHh6Zn4Z5nLZWZmYmho2dhGAuCf0lG14iPy+pRQoJAOx+dBYMBhZrD5Vgakoo4oN9cPd3x7DxNJEJ4nO+ePhcLwAWhhZAQpSBPlKjoQUQeZ4/T1Yg0t8Ll42OQZtaj6cvyDBep/efr8cNqw7h+b+sCweyJlg7MgTo6VfGVj2XdBjqZ2n08Dlb9uLq3SZiCFCQCTxaaPt1uaUifC0XQrxjerLN4SPNdKoMBtaYPO9R2s36N3YOhU68mxhaZpgbjJ2N28HR/gCAl//Oxm1Tk3suFFeQaXosMwu/LvsZeDPNarh5i4ysZ60T0L/aW9jzxtaXZvtx71FAZJazt+UZqyIFvvoWAF68eAguGxWLQU+T6tB5b+/q+apQgx7YtML0fyfjVuC/jUqrx/qTFWhs1+DLPYVYOj4ew2W1CEzMwLyMCItrxfbsaqtiodW3jENquB+Uah1OljYhi0tod+aV4jE32Mzhr8Wf7izAgDAFzte2YV5GBMYlBePGSYnQ6lmjRuND8yyvJRcMi8YFw6wjFMFdrPYTiRgLXa2Pl40GABwqbMCVn5qMrTumJUOtM1gYXJ3p7PHmE+2rWlT4cAe5dqw9WIz3rxmFBUMiMXFACPatmIXfj5dbfabDnt2Md64agYtGRPeeBuS/mP5lbLWRH1JIp5PcW+rhBPn9JA+rs1FlE4Mee/PrAIBqlnCyrMnC+PIYDQVkOftp69fMjS+uqm+Ng273UrOk/rLGjp7LOysmoQJcudryeb9I67EgyasAMDQmwOK7+OXOibjs4/0WWm09hp6bhc56EpBzXqnrfgNWX0KKFDrlnfHhBABYxl2gH5mfZnze4y2qOmPesuq633vufQX6PB9nnrfIqxIxwP2zU3HuWL2F2jpAlOCf/O00ACA90g9TUkIxLC4Qt3x9BBq9dR/CGycloqpZhU12hKLN9a2CFTLMGRQOtc6AP05UYM6gCDy2cBD0BhZ3TU9BgI3fvaQPtK0dlxSMn5ZPxIAwX6Mh97+fs2yOvWZcHMQiBmsOWF+nZWIRHp43EIOi/HH9V4eg1bN48vdTOFfZggHhvmBZFnKpGC9cPAReEhEuHx2Lq9/9B+eVEjyw7gR+OVaGVTeOFeRxXKRfGVstHcQ97N/J0yISMaQ010X3sUfY8TL+KCGiodeMi3c6/PovD+HUc/Odjus2fCh04ALbr6fOI2HQmnNARAbquBni8ukDHG62R5P8d75KlolTrF+bvoLkPdXlA6HEdX/HamIYvHrZMIuhoxOCsXhoFDacqkRzh9ZmBZRH0KpMj83bOw2YZXp8bj0whCT9m3sXDz9hykW7eXKS0dj6+1QVFg/rQXHcXDPNtQEze+59BfocbWodzla2QKnSYUhMgIWhtf6eKVDr9Ijw94K5/ySnqhWP/pKFk2bVhL5yCYrq21BU32ZhaE0aEIJ950kelSPvTnqkHz5YOgqZOTXYeLoKz1yYgUAuVeOdq0YYJyNiEWPT0OpLjE0Mtvj/hYuH4J5ZKYgN8gbDMKhtVeP6rw7hjxMVdiM5Gr0Br5ilHgyO9odSrcN72/MstLkfmjsQ980maQt3jvDCtGnTcenH+7A7rw4pT2wEANwwMcGoci/gmH5lbPHlubY8Rj4ysWc8Wy0VdOMyLibimrvfQH0IuQkFOfhhD4zwRW610tgI1eM0cTOgkFTbr49YSoyt89uAiAx8sZt4wq6fmGBzOF9R+cAPx3tes8on2Pq5OE6GojDTaGzxZHBhT3NmpIVhw6lKrPgly+jG9zjntzsfozXlZfAhFAAWvTTNW1L9eqysZ42t3W+S5djbeu49BfokI1/YYhGq95aK8f41IzHHRsJ1SX07ln150KKLBs+R4kZkRPmjoI4UIPGaVryhZU56pB8+WTYacqkIta1qPPXHGbxx+TDIJCLMGxxpzKni+beHw2QSkcVkNsxPjo33k+IUtU6P1zblWOXFfbJsFBratKhpVWF7dg0Ka9vwwbWjUNeqRnZVCz7fXWjclt7AGtXmRSIG989OxU1fm+R+vtlfjKzyZsxMC8eyCQldDp3+F+hXfsAWFTG2bHkifGQSz0g/8BWGcusbtgUXvG18eKqc3CQd/dCfuqAHc4X0OqCxiCT4S+z8WKJHkWUlcVvzhqs9r8+rlxNvUUWzyubrbqfNSaWQiJtXbHgYgKlhuT2mDwwDAOzhQr49Ap9zZkvt/opvyFJtktu4/weSTH/T5ESr4fdzM9Jt2bZFZz3OzMd7530Fep06pRofZeYDrOXzP94x0Who/XCoBEOe+QfTXtuBNw6rMO31HUZD60IbFdpDYvyNhpuj3NvHFw1CYqgCUQHeGBYbiD/unozU3igS6QPIJWJcPCIGcwZF4MsbxuDja8k1/K61x/DFngJMSA7B0xdkoFWtww1fHcLDP500GloA8NivpzDqhS1YvvooGlTks5+ZHo6Tz8zD65ebogFKlQ5vbcnFtV8c9Kxw+L+c/uXZ4pL/bOXaeMyztf4Bslz8puNxzoyxTiSGKJwPchetnHcu4yL7Y4I4D9apH6Fa8onxaXs6WtE9LQi64UGy9Aq0/Xr8RIt/X/zLcUItX3beqtL1XJHCoU/JMmGy9Wv8d7Ppf8CE5QDIzLOovh1PLrY2zB+Yk4p3t+VZPe9ReINXJLXtXRTo1xwracR72/KMgqB+cgmmpoZidGIQbpiYCJ2BRXZVC+qVGny6qwBKtQ5TU0NxqshSEmf9yQoMivJHbavKqLTOi3byfLJsNOZlROClv88h3E+OJSOiEeAtNVaeCxCGxgYYdcq0egNeumQIqppV+CurEld/dsDuesNiAzAkJgAGA4sfDpdiE4APTmeiRaXD9IFhGJ9Mft9vXjEcl42OxY6cGtzy9WE8+dtpvHXViB44sn8f/erMbOrQQCxijJpP5ijkEs9Y3bynIWKw43Fi0z4xMODikXEOh/eomGkLJ5Mw7Gqq4V/tOe90TI+758/+QZbzX7L9usSsdZBBb9TM+XDpKKebblXpPJ/LoTKr2rPVg9L88+SS5A8XNYJhYLOprPnnX9bYjtigHjifPptOlkOv8Px7CfQpWlRaYw4kz/Gn5xqTqGtb1Zi0chu0epO7K9LfC0vHx+ON6lqUKS0nwucqW9AZL6kIaRF+ePKCDGPuUo9GAP7lSMUiXDueTJqXTx+AWW9mGvW7eO6dlYL3t+cjq6wZ+TVKrL11PKRiEX47WoyMaH/8lVWJX46VYcGQSPx5z2QM4XQVZ6aFY9rAMPx6vBybz1ZjfFIwLh0Vi4VDIntmovovoF+FEdvUeihkYps3eh+Z2DOipg2c4eHM2DIjFC1U/ajGcC1XVDYakrqVY1yIyt9Jbs/C1wAAkg7SZ+zFi+kSI21dOD2Goxv9wtfJsv48BkaQytFFQ21XKgIw5jqVNVnnkbgdc+kNZ5z6CVouUZhl7Q+7cVIiAOBIp75wHkNBQq8YJ+Rr/VdQafW4+MO9GPbsZtS2qvHuVSNw9vn5KHxlkdHQMhhYrDtcYmFoASQF4bovD+FkrePrG3+v3vbwDPxxzxSrJHEB11HIJVjA5a89sWgQtjw4DRlR/nh/e75xTLtGj0s+2odt56px8xA53rlqBEJ9SZrJbd8ewZIP9uIRs2rI55eQ+4G3TIzTFc24+7tjWLkp26KQ579MvzK2OjR6u25kH5kYHT3RG9ERXN5NBNOAi0ZY5yV0JiWcGAT7bSSCupUTa8nSP8bxOE5CYd+erQBs51bY4mhxD93sAUsPVmf4KsWS/ThQQIwbRx44/mL03HprgT+3k08+UyROtT/m9p1kqdfgNJf394IDg5c/xx5Yd8Ide+icimNkGePcWyjw70et02Psi1st+hBKxCL4yCTG35XewOL21UfxxuZcDImxTKXIqW6FM2amhaHglcU4//IixAR6u3X//+s8d9EQFK1cjNumJSM1wg+/3jUJr10+DE8sGoRwruDmvlkpkElEeO+4GoOe3oRWlQ6hvnI8NJd0ufjlWBme+v00WJZFfIgPTj83H7sfnYl9K2bjmnHx+GxXAea9vcvzDoN/Af3K2GrX6o0Cpp1RyCRoc3cYUW/dp8ohgUTqIYppsBnq7MwS7mb5/nYP596IOQPFO9DxuHDivftaRjxc/l6Oj4GXI8iluKh2C9rvIXwQIPMFW21bXbozi4cSz9ahQhe8Tl3l7/8jy5lP2B8TOQyQeAF1eThdQbyFM7hEflskhfZg3p/aTqsqgX4Jy7J48rfTxmrply4ZgpPPzMO4JOJ1Kqlvx8eZ5/HIzyex9Vw1nlg0CCsWDELeSwvtijmbs2/FLBStXIxVN40DYDtULuBevKRiXDkmDrdNS0aQD+m3+PuJCqMy/dJx8ZiRFoamdg1+P15uXG/1gWI8+TvRRPOVS+AlFUMsYvDyJWQimFejxOe7Cnr+gPoY/Spnq0Ojt9t810cudr+CPBdOwwBKaQPOcxTJNFA1Cea7vx8raerK3tGjt62qbEVArMW/zvKyeJfzt/uL8bwntVh2ceHBpOmOxzEMoFGCOfQpACdjgd7JNXAUjhaJiDRHXS6e2kEubo5m+z3aWmMHlyvnH+t4nMC/GpZlsTuvDh/uyMfBwgZcMy4eKq0eS4ZHw8/sfPv5WBne4wo05mVEIDbIG8u+PGhvswBIvtBnuwrwxOJBiBa8WL3KFzeMwTtb86AzGJCq8sW27Brk1yqx6sZx+GzXebyxOddi/NqDJVBpDciracXCIVG4c8YAMAyDT5aNxvI1R/Hmllw0d2jxxOJB/3q5ja7Sv4wtrc6uZ8tH5oEE+XYuvDfyWqrhBp9QiAC8IP0awLtOxw8Io1Ck7y68V2jkMudjpaYKw0tHOQk5ogeT5HmtMxcSsxkY8ONyG1V/dugxJXYvJ1Wrug4gbzMYXA8WIocGoflr1S0qzzbVPvkDWS5+w3PvIdDrPLDuBP44QX5vzy0ZjOsnJlj8LjZkVeKno6XGisSoAC+MiA/EnWuPOdwuwwAPz0vDw/Os22oJ9DxxwT5480pTj9z/rdqCdTn1mPLqdjx/keWEcNMDU/HShnP45VgZQhQyvLopG78fL0eIrwyfXDcaWx+ahjlv7cIXewqhM7CYlxGBt7bk4u2rRvRsIVgv06/CiB0avYWgozk+MjFUWgP0Bjcm61UR7wLkzpPdAeBwcZNLmw/0kcHPSwKJJz0snG4WHyKkJUrh2qljrgjtdo5z7XnSFjofu4CozAdBiRQKY5YXCz1T4cEkf1fC0b4kjywMrvUcLLUhFulWOrhQK813IPCv5J8zVUZDa3C0P26YlGg0tDaeqsTVn+3H3d8dQ161KaRc2azCa5tybG4PIEry78zwxomnrHuTCvQd5iZIcOmoGLSpdVi+xtJwXvDObtS2qnHxiGjM5XTUcqpbse98Pfbl1yMl3A8FLy/CLVOS8PW+Iiz94iCOFDd6Pj2mj9GvjK12jd7YB7EzCi5x3q3eLQ2XixQ+iGp4Zm6ty2/RqtJB504DsTNHvnJp+MHARQAAX9DdvNMjiaAgr+7vUbwpqpS4istIpoGq9+EL3CzOo0n+5/4kywDn7ZswYikAIIGpRmq4c2ORFx/ccKqyy7snIAAAVZxA8Q0TE6xaXL244Zyx6OTWqUkOtzMiLhAPziEJ1hXNHQj0EvX5Njn/dSQiBm9dOQInnplnvKYDpM/iQ3MHoqShHb+fqMAPh0sBAN/fNgEA8OORUuRxObtPLh6ExxamG1XmO7TWPS77M/3K2FJp7Xu2+Ofdmrf1FyekqbCfpGxOQrAPXtNeSf7ROlYw57lmHNHj4tXx3c6JNWQ59laq4ZtriRzFZH86ZfKnLyQ6OFobzWPdjojidOY8Q+NF56jCgnwTcLd6RDuz/yOyvORj52O5Sr87JX/i5UuHOh3OF1kU1bV1efecotcCjAiY9qjn3kOgV2BZFptOV2HRu7vxzJ9nkBrui6cvHIzc6lYkrtiAZ/88g6WfH0BFcwdGxQcCsF29KzNrWvzogjTcPycVRSsXY/5g+9IrAn0PqViEX+6cZDS4ghRSpIb7WtxXrx0fj9u/PQIA2J5dg7lv78LvJ8qh1hlw/cRENLQRodr1Jytw19qj+PlomfUb9UP6lbHVrnFQjSj3gLHFY6/FTSfq2zSoZjnvS3O548EcKs76X7WnqCt7Rg/lMfBK7EN3Lacazidw7+yCV48KDedhs9dAuxNqr1AAwDPS1VTj/biKy+f/8qD8QzvXEih+kvOxvsRNP0t8AlEUKv1yiRgZUf6ebTtUkAmwBsDQA95LgR7hZGkTLv5wLy79eB+WrzmKs5xW3nMXDYZYxCCI8058va8IhXVtuGFiIoIV9mVXhsUG4ME5A3Hu+QWYNCC0R45BwDMo5BJsemAavrpxDKpb1MZ8vMtGxWLSgBD8eaLCqqfvQz+eRPpTm/Dutjw8MCcVYxODMDMtDH+fqsL//XTSc/eHPkS/MrY6tA6qEbkwYpu7Gjt3QajtRGkTVP5c25vGQseDOXjPxNtbc52M7AZi+uah5WJSbcZo6TwlvMTFt/uLXd8vGpq5WdHgS6mGv72/yaXN2wtLuxWRBBi0hM4z52XKD4zwc6ApZoaBZbtyutJz+Auy5HXMBP7VGAws3ticgxOlTTjOVUI/e2EGsl8ghtLag8X47Vi5UeahTqnGptNV2Hqu2ub2YgK98dPyibh/TqrdyIPAv49Z6RG4b1YKJqeE4IJhUfj1eBn2na+3MLSkYsvowae7zmP1/mJ8uHQUVt00Dmlc38obvjqE1Qc8dI/oI/QvY8uRZ8uYs+Umz1ZTCVmGpVOvUlLfDt8ILp+hhs5TEuvJEmg+lGmg/0zOt4jQKA6hlrsI8SUGAa+G73aquAT/MLoqpjN1rhnbHq9A1HYA9fmAjFITy6yVj1RNl0d24fBo6Ays55rE5m4iy9hxntm+QI/y8c7z2J1HPKHJoQpsf3g6lo5PgJdUDJVWj5Ubs7HpTJVRf06rZ1HVYrvh/KqbxmLviln/2XL//s5D89Kw9tYJeOmSoZiVFm7xmr+XBKnhxJi6ZUoSzjw3Hy9fMhT1bRp8spPobm28f6oxvyszuwZHixt6JuWkF+g3xpZGZ4DOwNr1RPAzKrcJm2q4ipvp/6NepUWlhdyHK+3f8jTVOh7tWM97hS6myBUCyd/Iq1GiWRYJnN9G/TYKmRhHPJVgXn8eAENtbM1OD8e3urldeqvieg/kPfHVoHK675llWVTxoegWulB0bBAx2Es8XZHoTLZCoM+z/3w93tycgyXDo3Hq2Xn4/Z7JSA7zRZ1SjdPlzXhvWx5aVTp8dt1o3Dc71e52Fg2NxBWjY5EY0oPCugK9RoC3FF/eONZCSPmy0bFGQdRIfy8YWBaP/XoKAPDV3kJc+P4eNHVoMXFACKamhmJbdg0u+3g/dub0z5BivzG2Orh2AN522vXwOVtua9mT8zdZMnQfoVqnR2WzCg06+pCdx+G9c4GOm2LzrDlIxid2cArslJIFbdxn3tzugZye+jwitiql8wA+u/4s0kSkYsbYgNsJE5NDAMA4G3MrORvIMm0R1fCGNg3u1NxP/uH1xZzAt30qrPVgkrzAvx6WZXHN5wdgYEmjYj8vqVEY997vj+OC9/fgo0zSC/bGVYeNoqUAMDzWUv7GSyrG61cM79kuBgK9ztaHpmP/Y7Nw6agYfLu/2FiF/tLf53DFJ/vx0iVDkBhCtLVOlTcblejNI05/n65EdlUP9tPtIfqPscV9Wc6kH9yWs8UnuMeMphp+upycPDty6oGMi4Fg5y0rPA5vbAXQGVt8Ce/R0IvIEyrXtJ4OFnqgx+Opn4DmUpdW+VPPJaJTGit8ReX4JA80wN33AVk6a5XE8eaWXJSzXIJxM10VT2wQubiVNdJVwLqEhjPgYsa4f9sCPQLLsiisa8O5SlNbrcRQcs4YDCzuWnvUqfTJyTJyLQj1lSHnxQV468oRHttfgb6LWMQgKsAbb105An/fZ+rzyjBAdlUr9p+vx693Tcb8waTQJ6usCQYDi4+XjcIVo2MxMj4Qm05X4eIP96Ky2QPXq17ELcYWwzALGIbJYRgmn2GYFTZev5FhmFqGYU5wf3Q6Ay7Ae7bs5Wy5Xfrh5Pdk6RdFNZyXbnj7qhGAfzSgpJNOAIBZ6SQWrnN3LHvb82RJeQy83ld6GFcFV7iLar01t4wH4IH+ZnzWd1CiS6tlGThD9/urqMYH+RBv5BY7CcDdgs/5ix5JNdzfS4o6cF4Evp+iEwK8iXfipb/Pubx7TqnjCjciPdiOqQfoC9ew3uJYSSNmvpGJRe/thkwswk/LJ8JHJsEb/+Tg3u+P4+9TVZidHo6vbjQZ1OZaSzwfXzsKe1fMglwiJMELAGlm5wh/qf4rqxKjXtiCvBqShrMjpxbJj/+NcS9tw6JhUfjtrsn454FpUGkNmPjKdrAerezpWbptbDEMIwbwIYCFADIAXMMwTIaNoetYlh3B/X3R3fftDJ/8a68a0ejZclfOlo5LCBXTdTwq4EI4YxKDAZ8QkvNFqbXFazztynNzLJtX/aY8hu+4MKI4iOt/V3+ear2kMBJKqGml7MFIC2/sRY+iGs6yLMQiBosncoKMbXSfZ4S/3Li+26mha4rN4+clgaEvOaR5UdyUruXB9QX6yjWspymqa0NxfRt+40I5F42Ixq5HZ2JsIvHgfrAj3yiGuy27Bjd/fcS4bnaVyQu2eGgUCl9ZhIVDowRDS8CCN68YbvN5/n5oLnY9itM0jAv2MUoGveyJCWIv4Y6r9jgA+SzLFrAsqwHwA4CL3LBdl1A58Wx5SUVgGDfmbLnIb8dJyCfAW2oKGdXRyTncNpV4YjS63q3SmJlGxFu9xt9Mntj/PtV6YVxF4ld76OQuqNn8JFlSVvJ1aPXQG1gwXENwKsV2mCoS/z5V5fIuupvT5c2IC/YGJFyOmosGoNvP/1bO25c01fG4vk2fuIb1JCzL4rqvDmLuW7uw7nAprhgdi3evHolIM+02PiXDT25/MuYtFePlS4YK1YYCNrl0VAx+u2sS9j82CyIG5NoFYEJyMEIUMnxxPfGWjogLxPDnNhv1tvb8byauGB2Lz3cX4u7vjvULD5c7GlHHADBPmikDMN7GuMsYhpkGIBfAgyzLWiXaMAxzO4DbASA+nu5GyMOHB+3puDAMA4VMgja1G242lB4pc/icLbGIAfibfW0uEGXb8jcnNYIkONdzyrtuQasCwAAzrCImdtnBV4nwxg1lzpZMQmx6jbvDoN6cnEScrdPNmgquMiY8wAtIvwBo8EDCuysYuM9j/J3Uq2SVNWNYbACQz52D6hYL7S1n1LSqkODOCrG8f8jShX3og3jkGhYREYHMzEy37qhSqXTLNotb9ChtUCHCh0F1O4sEps643XYtiy9Pq42pGVcNFOGLU7a38+EsOY4f2tutfXHXMfUl+tsxdfd4mgFEKRiUNpDr1oGCBiT5i3D2zCnMiJMgk+udu3n/CbAVJO1htI8BP4E0N58T3IQgL/d69Hv6O3KHsUXDegDfsyyrZhjmDgDfAJjVeRDLsp8B+AwAxowZ45Ip6yxBHiCGmFu0hto4Ne4FK6lXGRoTABGfs8SpsNMmdvM5Q9/sK8K14xOo39MhjUUAWCB4gEurTUgONhk5LjavLq53s/RAWBpQuBMYuYxqeGEdef/EEAVQtAdQNQHtDYCP88T3+2en4r3teVA5EM51mbLDZNlKl6gPAOVNHVgwJBJY+Bqw8VGy/xSGzsuXDMXjv53CgYJ69xpb/x26dA2bMWOGW3ciMzMT7tjm7rxaYN8hvHrVGAwI80VcMEmI/+VoGd7akovKZtOk9ItT1pM8uUSEITEBmDx1fLdDh+46pr5EfzsmdxzPr6NUUGsNmPb6DgBAYYsBbx21TC35q4hFRGyMUVZEG1yEF/46hyf3abB8+gAsnzEAUrF7jK6e/o7csdflAMzL2WK554ywLFvPsiz/qX4BgK6EzwVM0g/2f/gKmdg9CfJ8exXKKj4AKGtsRzqvmcXfHLc9R7Uu7xnKrVZSv59TeEMvkM6DmMtVIo7mxUmHX+NyNSLg5rynQ5+RJWUI41RZEwCQcvQhl5EnKZX8M6L9wbLAmQo3liSruW2Nu51q+Elu9rcnrw4IJY18aQ12Xv7h+0OuVW5SETfB/dvsWfrENawnaNfo8ObmHFz35SEAJGeGN7RaVFo8/NNJlDd1ONXH+uOeyfjlzklCjpYANRH+XogP8cE3N5vEj9/olNPV3KG1UJK/bmIiNj84DcNiA/HmllyMeG5zj+2vu3GHsXUYQCrDMEkMw8gAXA3gT/MBDMOYl7stAeD2rDfes2UvZ4u8JnGPZ6udkzBQ0PX4Uuv0aGzXGsUlaY0Dc0YnBGHSgBCX17MLf5MOiKEa/vo/OQCAY8VN5InAeOKR0dGFNh9fRKruWlQeUjGn4L3t+QCAQB8ZMPQK8mRHE9W6cZx8Qm2rbaXsLlFxnCwpjXa+wOC2acmmKsbaHKp1M6KJ4OgJzmBzC7zO2r87hAj0kWtYT/Dutjy8vz0fFw6PxifLRmHRUHJYLMviRbP+nwUOGpePjA80yokICLjK9IFhWHXjWADAjpwaHHx8NiL9TbmCta1q/HSk1DgxTwxV4CtufJtGjys/3Q+luyScepBuG1ssy+oA3APgH5AL0I8sy55hGOZ5hmGWcMPuYxjmDMMwJwHcB+DG7r5vZ3iPlY/UfmRUIRe7J2erjTO2fOiMrZoWcpOM8HfeONgeTe0a7Dtfb6xM7DatVQAYwDeSavg4rkLpMc5oQmA8aT5MqWIeyIVCC2rd6J2TB1B7hazwIzovtFpVAT4kj2DNgZKuvZ8tDn9JllxzaWdkcZ65qamhgF8kOf7abKp1fTzR45E32DOWOB7Xx+kr17CeoF6pgUImxvvXjMSCIVHGkMzG01X48UgZ5mVE4K4ZA3DJSNuTsO9vm4Df7pps7HkqINAVxiQGYf7gCGzIqsT4l7dZtXt65OcsDHtuMxa8swvnKlvgLRPjnatGAAAOFTbghfV07e76Em4JfrIs+zfLsgNZlh3AsuxL3HNPsyz7J/f4MZZlB7MsO5xl2Zksy9LdIVyADyN6yewfkrdM4h7ph2ouW5Qi1wcgSckAEO5v1jh48CVkqafbn/NcqWyRu1rGKKuJZ45S9kHLJXMP5EOhfPixic74kHEX9fUn6VTbnaJTA+pmQBHufCxMlZyTUzjvYGAiIJaTvoQURHGGslv7C0ZxEhRSOiP8fc4zF+orJ97R8HRqz5bI3RpngKnAoC8I9HaTvnAN8yRbzlbjsV+z8PPRMgv9I4B4td7ekou0CD98vGw0Hpo7EK02PNDXjIsjOZsCAt3Ez0uKD5faluxZddNYvHDRYLSqdMiuajUK6ob5me6fbRqd+xwPPUQfEuzpHh0aPcQixnhTt0Wgt9RC16PLNHEzej5R3Al8TzoLzxbftFfTamMNa+ZlEO9Hizv2HwCOr3VpeGObBjKxCHIuf8xVY2sut//HS93UI5HXyPINoxrOVyJGBXChXJEICEqgztnijZVjJU0u7aZD8rqWf2AUh/WLJEZzb9HAfXb9wNjq7/xxotyYrzfTrGHw3vw6zHgjE3k1SnjJxGju0CLliY3Y2knA99zzC/DKpcMEiQcBtyERi7Dt4em4b3Yqxpl157hp1WHjxBIAwv3kYFkWk1NCkfXsPFw6KgZ/ZVXi2i8OoNpOA/S+SP8xtrR6eEvFDi8GwQoZGpRukE/QqYDIYdS5Vw+uOwmgk7HFN+1V0SVc3zMrBQDcd3KJJNT5VgDxrA0I9zV9vv4xpC8kZYI2n0t33F3GCm9sUXq26pQklHvh8GjTkyIpkLfFPfvTG5z9k2i1GVwLjefXuCmU21QCiGXUYVCB3oPPzZo+MAz3mjWQ3pVba6wSPlnahKmvbrda95H5aQ4LjwQEusqAMF88NHcg7uXubzzmAti3rz6KHK5Ay99LireuHIFnL8zAgYIGXPj+HqPGZl+n3xhb7Rq90wuCn5cESo2u+xVxymrAl+4mD5B+YQAQxOX9AAC8OUu+lU4oM1hBtrF8zTHq93WIRAYMv5p6eEVTB2ICzYxFsZR4tyjDWAzDID3SD7PT6T83hyh5Y4vOs8X/eHmBVfLkGWI4G+j0vxK4Bqpad+iFGfQAIwamPtz1bfhwIdGCHVTD+YbaJQ3uCkXXkJw/wdvRZ9HoDLhzzVHctfYYAn2kuGacZTFGfZsGDAM8uXgQANLh4te7JlmM+Td5DwT+naRH+tt8nm81tivX1O2jsU2DN7cQQfCkUIUp2tLH+XfsJQUqzrPlCIVcApYFVNpu3ixbKkh/Q0oSQhSYmBxi6XXjvQE/Xke1Dd7YcgvqViLbQBkGBYDKZpUpBMcTlEidYA6QFh/bsul7QjreoRNkSVkRagojmhmMw68hS1UT1TZ4D4CzprxUKGsAVk99Hql1ZPb2f/MGmp6cxSnoU34HL15C+hfaysfpElk/AM1uLBgQcDvTXtuBjafJhC7S3wsLhhAPV2lDO+a8tRM/Hy3DTZOSjIVDO3NrcelH+yy2wXewEBDwFGF+cuxdYSVbh1cuHYqoAC98u78Yp8uJ1JCflwSBnOPifK0Slc3/jslAvzG22jU6h7IPANHZArrZH1GvJTdKP3pjq7pFZdEGAwAQOZQsQ1KtV7CBj0wCsYghlWjdpS6PLOW+VMPbNTo0d2itj8E3kroa0Ry3NNTe8RK3D3SesqpmFbykIuOPFACQMocsKZuCv3Ul0YQxuEMrrIUTMvWnk97gK1r9vc32P5FrkXPeOvRjC76C7EBBA90+CvyraWzTWFR5PX8RMbZVWj1u+/YI8muUSA33xYIhkXh7q/3WYV/s7uVOCwL/CWICvfH65cMsnpNLRHj36pGoalbhgvf3IK+6FRKxCB9cMwpJoQrUKTX450zvt1Gjod/U73ZoDU6VvX34ZtRqHano6gqtVQBYao8Ey7KoaVFbViICJIwXNsjUJ5GCsYlB7olP67h4eMQQquH7zxOpC6vqD+8goLWStC+SettY05LrJybg2/3FaNfq4e8mFWDavohfcH0ZLb2LnKHWVEIq+5zAu7ob3NE2iVeNpzyP/jxJxvPNWgGYihRa6ZLk+ZxBt2rUjF/uvm0JuJW/uCbSf907BUNiTFpoz60/i+yqVrx79QhcNCIGBgOLMD85as3yZEbFB+Kn5ZNQ1aJCuF8Xr5UCAi4iFjHwMRMfv+WbI1g2IR467t4z9+1d2PrQdAyLDcDt05Lx2K+ncL5WCYOB9UzFtRvpN56tDo2OIozIeba6o7XV4tpNsk6pgUZvQISfjfJ+v0jqnC2AhBIrmtzgMq3g877oPDT1XFHB8LhAyxf8OI0uXuTVCXz46oQ7K/q6A59cf+wbquHRXM7aX+6Qr3DRs8WLysYEmhm1EhkJ5VJ2AQBIw9emdjcYi7wYbANdNadAz8KyLF7fRNQpBkeb8mGOFjfg+0Mk9Hv/Dyfw3cESJD/+t4WhBZCq2525NYgJ9HZbexQBAWdcOioWZ59fgOGxpsnBmgMlWHnpUFw9luQbihjgq71FeOzXU8bXd+S4KT3Fg/SbX5FaZ4CX1PHh8J6t8ibXG0kb4cNmlMYWnyvEt8SwwC/KJWMrIUSByuaO7ofh+EbakcMcj+OoayMXYvOLNgAgIJYs1XTyFXyrn+IGN/dI7Cq8N4vSWPH3IiG8Te5wW7eUk0o+H9e6AgSYhxEBwCsQ6KDPIQv1lRmN527RyhmcUXTnkEDPcbq8Gc/8ecbYrcE8p6XzpeP97XlW6984KRFzMyIQ6e/cWy0g4AmmDySFT7dNTQJAqtj5nOG8GiWSwxS4dnw8nltC+vPuza+HoY/rbvUbY0ujMxh7CNqDz9fpltYWf5OhNLb4WaRND6dfJKCsoq6Giw3yhoEFapVq54Md0VoFSH2ob/RljR0I9JHaD73m/E21nYs5VepWVTe1wrTczWPmE1TDG7mw31VjbLTF8Y+l7vHoVjc1X2RBWck3JiEIE5KDrfdBEco1FadDIZfgbGVL98PRdVyOD6XBLtAznKloxgXv78EPh0uxZHg0vrttPKLNvKF78uuMj8+/vAgi7vxbddNY4/PjkoLx+fVjjC2eBAR6Gl7A9PPdhbhgWBR+PlaG0xXkOn3H6qN47s8zeOmSobhhUiLGJAThq72FeObPM725y07pZ8aW4zAibxl3dCdBvqUCkHgTjwIFJ8vICWLTUPGLAgw66jAc3z+qqrvVF42FQEgK9Y2+pkVl0bvKyKALyVJPZzz5yiUIUchQ2l3PVg33oxLRaf8Ucqr78wbb0INiRED+Vuq3TgxxU0+4lgrqECJAQrBWXi2ANKRuLgUok/b/OEHCl1/t7Wb4rzKLLAWNrT4FfxpodAa8eMkQTBpgWVDz3jbiyfpw6SjUtKqMXv6bVh02jil00BdRQKAnuGCYyZnxV1Yl9AYWFU0dRvHTqABvKNU6PPLTSRzhqsNXHyjGY79m4ViJm4Sz3Uy/MbbUOoND9XjAVI3V2p0E4ZZylzwSvHL6ULMEVSN8zlMrXQ4Qn+Dcbd0bZY3pvSk4W9FiWcXHI5ETw7OBvlqpvk1jVLLuMrwQbNwEquFljQ5Cuc0lRDeN0liZN5h8bt3W2ire65JWW051q4XQn5GgRKIV1lZr/ZoNZqQR97yXk4mJU2TcZymEEfsUG06ZriU5VfbD+3KJCHevtdbse+vK4bh7ZoqNNQQEeo4ghQx5Ly3EpAGm6MuZihbUKdXwloqxv6AeT/x2CsG+JkmkxBAffH+o1D05tR6g3xhbGr0BMoljA8hLKoJYxKCtW8ZWpUsaW/xM0mYIyo9o3tDmbfEaUd3WearKMlUkUlDRrLIf6dR1AFnrurc/rnJuPVnq6XKPKm1pbPHwPSop856yuRvY9u7ohfFSE2d+oxrOn6821ff57/HgJ1TbemwhEa88UtxN+YeWCkDmS1WFKtBzTEs15bqMTTS1QFHr9Bj6zD/G/9/fnmez9dQLf5391/WcE+ifSMUi7OMq4W+clAgAKKhtg56bGP9xogKj4oOQ++JCPDAnFUWcDuKSEfT3556k/xhbFJ4thmGgkImh7I6oo4uCpg5x0bPFC5t+vrsbIaB27iZLaWzxYpoj4gO7/p426JZnSM15thImOR7H0diuhUwsMno2LUi/gCwptbbun01m/d3K3mrj8mamPUo1/LuDJO9vlK3vgA/lUgrUslwF6t+nupnkX3MOCEzo3jYE3Mb+8/UY/PQmXPP5AQBAUqilht6aAyVGj/7n14/B/CG2Pdss8K9pfyLQ/7l+IrnGpIT74rPrRgMAZpn19jxY0ICBT27EO1tNhR4DwujkgHqa/mVsUcj2+3lJoeyq9INWRcJOlC1ieBZzfcms4PNdKD1bbmkCyxsV42+nGs6H4FLC7AigjrnZpbfn24J0q6E2LzcgodP/OVfZAo3eYPvz478DyobOMYEkfFbXnYq+Ds7gTZzs0mrTB9oIO/J5X1q6Ctu0CD+X3tMuTSVAJJ1Om4DnOVrcgDaN6bpmfs0xGFhju5NHF6SBAfDaJus2WwwD/HrnJChsTUoEBHqBJ7j7xZO/n8btq48iOsALjy1KNzoeVJwzYFxiMD5YOhIA8O3+4t7ZWSf0H2NLT2ds+colXQ8jNnI3eUohTT4R3DyPwgKJDPAJpfZsAcAFw6Jsh8No4Y0KygbOfH5YbJCdcJHxZk+XR8YXCjR2R+vJoAPi6bxaAGlBYhfe2Kqwzl+xRQiXI/D7cdeV842UHiRL72DH4zgOFhLj7CJb7nEJl7OQt5lqW24x2PU64uENsFHdKdAr3D0zBbdOIWXyEhFj1BQEgD9OlmNnbi3mZURg+sAw3PrtEZvbCPaRWXeJEBDoRXbmWF67K5pVYMAYhaWfXDwIRSsX48flEzEsJhAyscg90jYeoF8YW3oDC72BhUzsPOnX10uClq5KD/AaW2HO1cYBGN/HYYsdf9e0tiL9vdDU3g2v0Im1ZEnpnePFDq0U8Hn47VB6hvhZc06Vkmq8TVRNQAB9JR8AC5E8C/jt1GRTbYcXeDxU1I2cpx2vkCVlJd/Wc+Sz9fNy4HEoO2z/NTt0OVzUVEz6OgYJYcS+QnZVK77cW4jEEB/sWzELErOUinOVJM/wTEULkkPtt+i6YVKiUYtQQKAv8P72fKvnpBIGj8xPA2CKvBTXt2Ha6zug0Rvw/aGS7uVle4h+YWxpdCT/h8azFeQj63q7FTVnIISlUQ3nlepvn+agkatflKl1CwXBvjJ0aPXo0HTxRskns1M2cOaNrTBfOzNeXhC0ma7CkA9jdXT1Rq9TE10p3jvkbLjeAIYBpqfZ8eTJFMTDJKWf0Q+J8TcKtHaJtAVk6UdnbPGSD25tRg50fdLBa2yFD3bfzgh0iyAfGVgWuHZ8AsLNZFqqW1T4bBepFi5v6sDTf5y2u42BEXS9UgUEeoqShnaIGMBbKkZ6JLl31LSoMS8jAgqZGLd9ewRqnd7YnQQg95a+6N36zxlbwQpp10NYvPilnE7sj89LsqmPxONiy54Q7obb0NVjyLiYLCmNreL6dvjJJfD3tjPj5Y2tphKq7QVwEhLZlS1U4613aJ9LwyubVWBZ2NYJ41GEmQoHKIgP9jEKpXYJVQsQN556OC/CazcEOOFuQEqfFPrQ3IEAupE3x+vCUZ5DAp7DYGDx1uYc3PDVIQDA8VLLqtpVe4uMj8UiBj8dLbPahogBFg2NxMRk4fsU6FvMy4iAgQUMLGtMn7lzzVHEBHmjTaNHcX07Gtu0GBITgBULScTpw6WjEO8uPUQ30i+MLT5Jzlm7HgAIVsjR0KYBS6mrZEF9HiCWmyQbnMDfJPk2LzbxiyJJ63o6t2ewgoTzuqy11dEIxI6jHr76AGkcbfdGHxAHiKRALV0Yzo8LI/KNoV2m4jhZUh4DL9qY4OjH5x9t8tZQEOor77qxCwCFO6lV66nOU0UIoG0DNHRilMO4kGpzRxdd7Xw1pYuthgTcz9qDxXhvez5yqltxxehYLBtvGdrl9fG+u228XUkHAwvcMiXZOBESEOgrvH7FcPxy50SodQbs4PK3KppVePjHk8Yx+TUk4sR3CLn7u2NY8M4uYyV9X6FfGFt8h3AfmfOcrWCFFFo9C2VXYrq1uUSxW0yX13CIS2wO8XUQ/vGLBMACbXTSA3wj4i6rsCtrXK6mdBi+ksiIOGcbnQp+t1ve+HBJ5bOfphrOq+07TPyNHEIqHCkN8IY2DZratV3rUWngLgAM3U9PzXltr5vgID+Kl2BopKvC4T2tXfZs1eWSc4iyUETAc8xMDzdK3iwaGoVJKSbvVE5Vq/E68XHmeZx+bj7m2+qiAHTteigg0AOMTgjGtIGW96wxnIact1RsTIfIKjdNYLOrWvFDd8Wz3Uw/MbbIhYImuZP3DDW2deFG09FoutlTUNNKbvR+zjxbAHVFIp+o3qX+jixLKiop1eP58Gzn2bIV3sHULYcAIClUQSvAbw0vXUF5DHwTXodhRL9oQK+mFjblqwOzHSh024VXeqeUzNh0moSYi+odeK2CSBUabY9Ef87YaurogneOZUmRRUcTdRcFAc8R4e+FEF8ZhsT4Y3KKZRjw2T/PYC2n0XbRiBjcsfoI/jlju5DFiyIFQ0Cgt7h5cqLxsVwiwjXj4nDNuDh0aPV4+MeTUOv0mJAcjFBfOWakheGTZaNw4fC+JW7aL35hfLK4t5TOswUA9W1daOasbgG86Juz7syttd2A2hyjsCld3hbvleiSsahRktYulFVkfMPrCHuViDw+rhlb8wZHQCoWdS2U21pFBDwpNbaqmjvg5yVxrB3k75rB+9LFRF9K0xXPVvYGsqQ8dt4Vfu34ePuDgnljiy40y+c+FNd3wTuq4YpEDN1sJi7gFjadrkJlswoPzhlolbP60iVDjPbw//10Envz7f9GxycLIWGBvsu01DBjgrxaZ8D72/ONl9D3rxkJuUQMuUSMZRPikZlTiyExAW4vKOou/cLYUmnJTc+bKoxIbtIuVyQaDCQvKX8b/Sos+XMIX/5fTqfzJBWLEOnvhfO1XZBO4LWYWuiqH/m8sAhHXiGA5CCVHaI2IMJ85dDoDGjpipK/stql5sdVLSrnumS8d7GFztjiPZXruuKmZjkDLXYM1XDeuzg6wYFH1TuIFG1QerZ4D/DZii4UKfBNx2c87vq6Am7n3u9JDmORDcM5NsjH6icZ6iuzmpSuu52ux6iAQG9xx5qjFpGEjzPPY+95kjt63w/HUd7UgZc2nDW2x7tj9VHk13Qh8uBB+oWxxc/+5TTViD7E2q131dhSNZGl1jVvQJCzpFNeXHT3G9TbTApVoLKZTjHcgpIDLg2v4YytMD86LxJt0je/vVpbjZWdoaxxqYFzVbPKubHIV1Se+4NqmwPCSa7SybIm6v0wcvJ7sqQssuDPU4fnEcOQhtQNrhUdbD5Lp41mAZ+EL1Qi9jp1StPvx9b5Yd6/892rRyDUV4Y6pcZCdmXR0EjBqyXQ55mVbn3NL20g98B2jR6XfrQXn+8uNDo3zlS0YM5bu4ypPH2BfmFs8Z4tL4owYhAXRmx2VRiUF+28mK7hr1ZvgETEYKmj8A9gSrYPSqTelWBFF7XCeMmKyQ9QDa9u4cOIToyVy74kS0ph024ZW2WHAAm9JlZNq9r5/vO9Lhnn5w8AhPt5IdxPjhFxgdT7YaT8KFnK6EqT65UaBPpILUQqbRKa6lJFpdPQsD02P0mWlScdjxPwOPwsHgAG2GindaCAhA1fu2wY0iP9bbaY+vtUFZq6U1krINADXDMuHsunDwBgWVn+wdKRkIgYzMuIRPYLC/DAnFQApFPM/xakG50rfYF+YWy54tnylUsgFjGuJwfXcr3EQgbQDW9VQ2dgjb30HJJxMZFPoKTLxhbvPaNsWlzTqoJYxBi1vezCe5oo887CeWNL6aKxpeOOmbKJtsHAcsYWhWEREE/tmQNISX23lPxldAKSvx8vp3sf/2hi7FKGcmelk1Csy3lzYu48TZzq2noCbmdIjKkrwtqD1pWofAXuo79kQewgeVRQjRf4N/DAnFRcOjLGmGs6OSUEj/6cBZ2BhUqrh5dUbEyKv3ZCPO6cMcD5JLUH6Tt70g348ni5xLlngmEYBHp34UbZzIkBBjtQgzfDlO9EcaP3i6T2CgFAkEKGpo4uSA8Ec4YipVp6dYsa4X5y53INvlySv5JOvoJXo3fZs1XNqV8PuZRqeH2bBnoD69yzBQDBidQ5TwAQ6C3rWjWffwww7GpAROdFa6UtyfeNIMUPlAajljt3qLfPw/dDHHSha+sJuB1+wjUlJRQrLx1m9XpRHQn5SkQMUsJ98dQFGTa306ViIQGBHqSiqQPpT23Cr2Y9affm1xujJLy3KzFEgRFxgfh0ZwE+3GHd6qc36RfGFt/jjUbUFCAq5k2uSic0FRNBU8rmwdQhOIDcKNUt1KKU4X5ysGwX8s4UoUDSNOrh1S0qoxfKIXzbmX3vUW3X31sCmVjkurHFq9THjKYafqyESDkoaGbuAXEuKfk3tGtwoMDF/ogGAzGq/V0rSV44hELmgs/94wVHncD366xxVRx3z1tk6UJ7IwHPwMuC7MmvszkhigsmN6B3rx6Jb/cX4YW/zlqNuW5CAqIC7DSZFxDoI5inCI2MD8SUlFCkRfjhw6WjAABvbM5FvVKNdo3OeE07X9ON/rseoF8YW654tgBwni0XDZXGYnKTFNF9ZHxint0Gzua4KP/Aa0bxgp3UtNUCPvSJzbWtaos+a3bhc8Gqsqi2yzAMwvzkrhtb/OfjR2es8P2xBnL9GB0i8XKpRyWvWuySSnF7PWDQUWuE8ZOI0kaKogw+YZ1SvoKfEda0CF6NfyM6vQFzM8gkp7PgI8uy+P14OVbtLYSIAXQGg90y+NUH6IRwBQR6k2CFzJj//PQFGThS3IDSxnYMjjZJMWWVN+Pz3YWoaFbhhokJePPK4b21uzbpF8YWf1OiydkCgEAfmethxKZil0Qcq1v4fCcKY4uXMqAMJfLeMpda9rAs0FBAna/Fb58qDMowQMJkIH4i9bZD/eSuV4q0VpLcNkphWV5ZOCmMQun8CJfkX0lnML58yVAAQEWTC8eg5IxFSumKCq7V0NVjnRRZAIBXIFlue55q2/w5VOOKwasTEqn7ClvP1eD1f0ge6diEIIvcu9PlLXhg3QnUKTW4a0YKlgyPtquxlRIuNJ8W+HfAT87f2pILldaAdo0et35zxOilFzMM5g+OwIAwBb7ZX4wjxXQi1T2FW4wthmEWMAyTwzBMPsMwK2y8LmcYZh33+kGGYRLd8b48ap0BMrGIuhVMl3K2DHrSqoeS6hY1wnzlDhNTjRhV5Ok8W7wBVO3KjbKGCyGUHqIartbp0diuRYQfZbjIP8aU10ZBuJ8cedUuunlbq8hnRWn01rWq4S0VQ0Ghv4Yl75Mlr/DuhOhA8rk0uJLvUpBJli6q3yfTGIuRRGiVVpKBDw+7ZPBquTD3uNvp13EjDMPMZRjmc4ZhRnD/u21Hevsa5ipzMyJw36wUMAzw5pZcC4Fa88nFBzvysflsNb4/VIIBYQrcPs0y53TlpUN7bJ8FBLrDQ3PJ/Xd3Xh1+vINM7Ldl12B3Hkmd2Jlbi8HRAbhvtqkisS/RbWOLYRgxgA8BLASQAeAahmE6Z2LeAqCRZdkUAG8DeLW772tOu1oHHzldCBEgOVsut7tpLHJJTJPaKwSYbr7NdCKZIZwRV+1KGFHFCVhOvItqOB9eogqDAkBALBFLNdAl7W85W42qFpVrPdlaK6kNFYBUO4b6yew30TYneQZZtpQ7HMYT6ks+F1vl9HbhZRNc9GxF0+TUSORASAq1sr6vXAJvqdiYW0hF1Smy1NGddyqtHrnVrcYOD27gZgCPAFjGMMwsACPcsdG+cA1zFbGI4bowAHfNGIDEUJOB5SuXGPP8bp2ShLmDIhAb5A0Rw+CzXQUW2/myqw3hBQR6mEFR/vj25nEAgKyyJoxPMkU4ZqWH44lFg1DTosK3+0lo3JYcSm/iDs/WOAD5LMsWsCyrAfADgIs6jbkIwDfc458BzGao7oB0tKq0CJbRV+YFesugVOvo823aG0h7EhfU42taKPSdePjQ3p53qIaLRQzCfOWuhRFP/0yWIjprv6qFzzmjPAbfCIDVU/cXnDSACClS584ZDESpnheXpaBOqTYaRU7hDaBWulAunwPTJQkOSmOrkKsmc9hE2xxFOHVFKMMwiPCXuxZG5JPvh11NNTy/Rol5b+/Czly6faKglWXZJpZl/w/APABj3bTdXr+GuUplcwfe3EJ01aZ3ytkCgKvGkqrRkoZ2/HC4FGWNHVZdJ+KCvbFiYbrnd/Y/hsFp2xCBrqKQiyEVM3hxwzkcLGzAnTNIhf327Bqsz6rAuJe34SgXPqSKKvUg7jC2YgCYu2TKuOdsjmFZVgegGYB7ZItZFtcXPortqquAc39RrRIZ4GJyMH+TGXU99W5Vt1Iol/Pw1+yAzh+bfSL85a6FEQNiyZKyGvGTzPMAKCv5AFNFImWC9rVcc+sc2mbOTVwirwvCnbWtLhhbEjnJe1LShXJ5Y4v3PlERP5HoeVEKmn7EfQc0Yr0AgJJ9QPFeau9iuJ+Xa9WImx4jy8A4quF8iJL6d+CcDfwDlmVXAPjWTdvt3WtYFwjwluL6ieQ3ZCs3ZQKnCr/5bDUe/414JDvbABvvn4aEEIoQtQA1zR1ajHlpK14/3IE3/smhvj7wXuB/zlThs13n8dvxMqMzoF6pxsZTldh8pgpZZU2oaVFB74JBV69UY9/5OqzeX4T3t+WhpCs9UfsApQ3tuOzj/dDqTcf+MXeNBMikY0YamXhMGhDS54ytPhXU5HIwbgeA+HiKpGCyEkaouDykddcCzzrXGfLnetu10vbm46vsokdSDVdp9Whq17qm0p08k9pQAcgNzKVGwsfXkKXCehZsi9QIP2zLrrGo9nCIP3dvaq005Q854HQF+Z7u+/44zjy/wPn21ZxRNuleuv0BUNbYgSkpdMcLwKW8My+pGNEBXih3xdhqbwCiPVghI5KQakdtGyB3XoEZ5i93rT+idxAxRgPpfpsuyZ84gGGYdwE8wLKsRT8llmXf79aGPYD5NSwiIgKZmZlu3b5SqTRuc4ovi28BHDh9HoMZ03mb3aDHW0dVUEgB8371/jIGLRrTjWrXrt3wkfb+Dcn8mP7tNKgMGBJkQFGTHh/uyMdHmfkYFioGwwBNahZNKhZtWhbDwsQYHCJGQbMBuY161LSz6Gw+Pft7FgJkDEparSdPIgYIkDFIDRLhwgEyxPkRv4lGz6JRxSKvSY/cRgNyGvSobrfc8ltbcjE2UoyFSVIkBdBN5PrCd6RzYGAGezE4cWgfGhrIBO90aYPT/e3pY3KHsVUOwHyqG8s9Z2tMGcMwEgABAKzKY1iW/QzAZwAwZswYetM9II463wkwNRIuaWhDBo0x8cst1NsGTFUT1CE4AAhKACpPUA+P8PfCoSIXdJ7qOYE3SjHNr/aSXA7aCk+jdhRlzhOvSk/9Jbdzp8vAhVTD29Q6tGv0rhm8wUlAXZ7zcRxhfnKjvIRTWJZ8NgNmUW8/JtAb45PpKi8BAPNeAjb9j+TnURhbAd5SFNa1Qa3T08mm1J6j3xcQaRKGcaG3pn1aAfzJMMxVLMu2MwwzH8DTLMtO7u6GOTx2DZsxY4abdpGQmZkJfpu3fXsEQDt2l+vw7T3zjLmJ/iWN0BzaBz5VbucjM3DHastGvgCQpYvCirm9H0Y0P6b+wKUgxzRg2DisOVCMDacq4SuXIC7CC2P85JBKRFh/sgJHqjUI8pFiTGI4hkQHIDHUB0mhCiQEK5BV3oSv9xahTaPDlRNDMSklFFKRCFUtKlS1qFDdrEJFUwc2n63Gob0diAn0RlO7Bm1m+ZEB3lKMTQzHLUlByIgKMFaertpXiO8OlOBQlQoTkoMxZ1AE0iL9EKyQoaS+HS0qLXzlUijkYmRE+SPc36vPfEfvh1Tg3u+P45H5acZqXABoULH4pyEE04bIkLUjHy0aFukjJzhMwejpY3KHsXUYQCrDMEkgF6SrASztNOZPADcA2A/gcgDbWZf7hDjg/izgeS7viaJRsUZPTshXNmZjwRCKhsDByUQ2ISyNandM6vEuGFu+ESTfyaCnMogi/OVoatca2xQ4JXoUdWIzAGg47TLqdge+EQAjIknyFCybkIAXN5zDjZMS6bZ/6iey9A6kGl7SQLx+gc4agZvjHw0U7aYeHuJK3pyqGdAoqQVNWZZFfZsLYVAAOPkdWe57H1i40unwv06S7+rTnQXGCh538i7Xu0/azZYZLMs+yTDMUgA7GYbRAFACsKoY7Aa9fw3rAvHBpnD0usOluHoc8TiOig/Cd7eNx9LPDyLMT46EEIXR0Hpy8SC8uIEYzX2pSW9/JC7YB48tGoTHFg2yeu2JRYNQ26pGQoiPzQKeqalhmJpq7ZUfigCL/5vaNfh6XxGK6toQrJAjWCFFmJ8cI+KCkBrua7NC/7GFg3DPzBT8cKgU3+wvMp4PtpCKGVw2KhYjvFzsVuIhjhY3wksqMkqdrLppLDo0ety19hi+P1SCv++bCj8vCV7ZmI071x7Fb3e5az7WfbptbLEsq2MY5h4A/wAQA/iKZdkzDMM8D+AIy7J/AvgSwGqGYfIBNIBczNyHSAQsfA3Y+Ci5qTkxtvjQ0kXDKZW80y8gN7CgBKrhpvCJCzdKL+5HpFGaHjvAXGuLKu+irZZoYVEyOiHIqF9GhVhKDC5KzxZvIG7PrsGjC2hm19xFI4xuJv77CbIfLt3ofULI+UNp8IYoZPRhOD48SZmX167RQ6W1L0Zpk8GXkgbRCrpUovQofxwqbEALbWWuPAAYfhX9/rgJhmFmA7gNQBuAKAA3syyb43gtevrENawLPHVBBq6bkIBrvziIlzacw6z0cKM3fQ0nVjoxOQTJjxlT3SxyJF0KIQu4FYVcAoUbpAkCfWR4YA69JBGPn5cUt01Lxm3TklGvVCO3WomGNg0SQnwQ6CNFm1qP5g4t1p+swLojpVinM2B30zFcOz4eIhEDnZ5FgLcUSWGKHpNY2Hq2GmsPFmP6wHAM5vqClja04+k/zgAgBmx6pB82nCKTyItH0OdA9wRu+ZRYlv0bwN+dnnva7LEKwBXueC+78FpVO14GrljlcKiMC41Rt+xpLjXlJFFg9GzRalQBpgbC9flU7WjCzUQpnRpbaiU5Bhl9MuzRrgjC+UdTe7Z4Ooc27FK0iywpw6Cf7iQl7rZmh3bhhUE7Gqn0qlQ6A6paVCisa0NSqJPPlg/jBiVS7cqJ0iYAcM3YGrEU2PqM6Tic8OLFQzDv7V3GylOHqJoBdbOpN6Kz4a4Y6s55AsBTLMvuYRhmKIB1DMM8xLLsdne9QZ+4hrnID4dKsOJXkvzuJ5egqUOLcH8vGAwsDhWSFIN95+ssEuPrzJq/mzeyFvjvEuIrx0Q7HvRxScG4b3Yqnvt+J3bm1mLDKcu8YhFDOnSM4ibndUoNlgyPxqKhkW5vcF7R3AGtnsWSEdEYHhsIANiVW4eYQG+kR/phRloYkh83/YSX0DpTeoh+oSAPAEiaSpbn6eUZeD0Op9ScA6JHUG/3m/1FAFwMYem5i2DZUarh4a60W+GV6SlDcK5UuljgorEV6iuHvxflD5Lvi+giLhkrfEVlI915sTefVKl+sbvAyUgA7VxFK6XRfoYrIKBqNcTDS4jw4qlO4M/Pv7IoCjOauJxIykpE3lvmjgsey7KzWJbdwz0+BaKH9WK3N/wvx9tMrPfA47ON54pIxOCve6dCKmZQp9Tg97tNHu0dOSbR3p+P0osQC/x3CfOT4/KBMux9bBa+vGEMvrt1PNbdPgGfXjca985KRZifHOtPVmDT6SoU1bXh/346ibEvbsUjP53EwYJ6uCvafuWYOIT7yXHf98cx6oUtAIALh0ehvKkD27JrMPftXRbjC+roeg33FH2qGrFbeAcBjBhIne/+bbdUutTAma8SdEmGJ+Ni0mrFQOdt48OIVF4JDaevEzmMatv87PfiES7eKP1jgPOZ1MMXDInABpobPUDChyEp1NuODfJGWWOHa+W/Ms6w2fEicN1vToe/dtkw3PrtEbqWJ7U5pNUQZSPzdi7RdQhtNShAQrkAkE0ngeLSzJMvQAmgq0TkWyXNyaAXAqaFZdlKLrT4n6a0wVSN/P2hEtw61aQOHxnghZlp4dh8ttqhvEdNqwrhrnjgBf6z+HtJMXuQ5e95/mAinmswsNAZWEjFDI4UN+LnI2X4K6sCPx0tw5AYf9wzMxWxQd6QS0RICfd17d7IIRWLMDohCBtPm+R57v/hhPGxXCLCd7eNxwPrTqC0oQOpEf1P1LTvwOqBUz9SDQ31ldN1fdF2kPCJC+rxSaEK13uO8V6JvC1Uw4N8pPCWilHeSCE9cOQrsvShy+XhG1xf6KpXwjsI0LQCejpJjQg/LzS2a+nEZWuzSS4VJV5SsVFFm5pQLkmcUph1IifMyjdCd4iyhlQ7Sug8bWXc90pdoMATO5baqOZzLYbFUoSTjDlnsVTbzq8hs8oAbxe8uy7AsqwLmhv9k7tnpuB/XL7jyTJLyZuGNg02nyUe7Vpu8mR+TXrjCiJBcv2XhyyMNgGBriASMZBJRGAYBmMTg/Hq5cNw+Mk5ePWyoVCqdFi+5igueH8P5r69C0s+2Itt56pd9njtyqu1MLTMeeeqETj17Hy8tOEcShs6kBLua5R46iv0H8+WORQJzrPTw5FJo2zd0USWLjRwlogY140tvrkyRck+QLxmoX4yut58/DYTJlFte/NZckK7nMAp5aqj6vOBcOeJ7MZmyC1qxAU7EPrkQ5O5G6l2o0WlRX6NEjJXDZWgBECqAOLpPicfLozzxj85WD59gOPBmjZARn9OdDnEE5YO5G2mHj5pQIix8tQhvLAvpcFez52X1GFiAZfp0Orx6qZsAMDjiyx/b1IxmUn6yiXGtib5NUqE+8kxLDYQl4+ORYC3FA+uO4FF7+7GjkdmuFb5KiDgBB+ZBFeNjcdlo2KxPbsGegOLOqUan+0uwC3fHMGw2ADcMzMFUokIX+4uRFSAFx6Zn2ZTMqm0oR03rTps970uHhmD9ScrcKykCQBwvweqq7tL/7wSttWZ8m/s4COn7AvHt4ehzHcCiGdobBJduMgC3wiX2tGUNnSgtKED71ztRGy1eB9ZUiaXnyonVUrJzpK+OxMziiybiqmMrTB/vhmyE2Mrm6umktCFO45w+mNnK7tQbeUdRP0d8K5wR2J7RvLpPJY8/l4SYyjRJQJiSY6eTk3VJ7GpXUv3OZ38nizFdJcMvsl4ch/rT9afaFObzo/1Jytw+zSTwc/3o1w8NAop4aYJXE2rGlvPVSNxhalCMcxP3uea9gr0HyRiEeYNNkUZrh4Xj9+OleP9HXm4fTXJUQ73k+NQYQM2nq7C7dOSccuUJIvJvq3zc2pqKHbn1RkjVHMGRUAiYqAzsDhT0eJ6ZMbD9K8w4qI3yLLdSmvQis1niIvdqQJ41jqXdkGl1aNVrUNMIEXz4M7EjjV5ENxJOV3SPU9dV0RZAVOlHaUSPl+t6bRlTMkBsrz0M6rtikXktI4N6sJ30FIGnFhLPXxicgjGJjrxevLhz6Akqm2yLIsWlQ5RgV3IpeHDfJSFCryh1a5xEvptoiwm4fh6XxEAUiUn4BnC/OT46sYxAICX/85Gs1l1NS95su5IKT7f5biA477ZqfQtoQQEuolULMKVY+Ow/eEZ+OrGMfjyhjHY9ehM/PPgNEwaEIK3tuRi6ms7cPVn+7H5DImyBClkOPT4bKSaRYx255F7pQ937nrLxLh6HCngUaoplQZ6kP5lbPGioxTG1u3TSDJpo7NGwipu1p8whWoXtnB5El0qffcNN1UOUnDpKFLZ5u7Gp13yCAFcXhtDCgoo4HXInAqDxpFO70icSrXdknqSL/TKpUOpxncHf2+JxU3OJm1cBdike6i2yW/Puys3QL7akVLvjM/5caqE7x1MdLxcxJaoooD72MlVFzIMoDCrTgxSyLB4KJHD+Xjnebx79QgAsBIRFjHAteMoW6MJCLgRqViEWekRmD0oAl5SMZJCFfjs+jH49a5JSIvww4GCBty++ihWbszGusMlGPfyNuTVKK22MzU1zHgPHJ1AJr4uyS71EP3L2OLzSdqde4f40M9PR5y0+ZF6k1wkJ2FJnmMlJLk60McFyQEeRTgxFPV0VvmQaJLY7PBmz7KAWOZST8EBYQrMSncsDGsTsRQAC2T9QDU8yEcGqZhBlbNwLm88Sx2EGs3I4pKFI7vSk2/0TWSpptP/ClbInBsqvOFDKfvAe1u7pOrO62D9Sfd9p0WSmaK5/pIVrVVARwP1RMDdxr+AfSYkh4BhSMi/vtPEcdkEIsI8ONrfqKn19b4iDIoyVbgaWOBAofPJqYBATzEqPgjf3z4BZ56bj6vGxOGTnefxv19OWYwRMcDI+EAAwKYzVdhyjlyb+CbVLsku9RD9zNjihCjP/O506HCuAkvuzHvQVmfaLgUDuByVRUNdrIQDTMKXlAnOoZzWVq2jG2VHI6DXAH708es2tR6hvl0wFnkai6iGiUQMtHoWn+w873hgzTnS/FhKZzzxVViprmhU8cRPIMtWOsMiNsgH9W0ax2G4fE57k7JVTw0Xxo3uSiiaf48GCu0vACEKcg45NBiL95IlZWiSr84c15W8RQGXWDg0Ct/ePA7na9uw/qTl9/MPF4Lx85LA30uKqankOnauk+faUeKxgEBvoZBL8Orlw/DjHRMtnn/h4iEwsMBxLhkeIJW2+/Lr8OjPWQCApNC+lyvaz4wt7uJ+9nenQ/kbcWaOk4rErB+AZnpBzcpmou0U1pXKnrjxZNlqu7y1M3xeWJEj8TYld3xOWhiZU9WicksrCbfRUEjdpgdw4ulzhosNtfm8MIcSHHyD8eBk+2PMUKqI4dalfCcZnfePJ4QzqusdVbXyBRbTH6Xa5rkqcjOf5wGNLQFrTnMFLYWdrgMXDCNhxPM1bfDzkuCja0dhQJh10YtaZ0Diig3Ir6Hs5iAg0IOMSwq28FQ99ftpqzFJIQpUc70+k0MVmJJK7yDpKfqXsSWmdx3y1Q251dYx4O6QX6OEmGFc10cCgIGcICvlccQFkxt9dauDG2XBDrKU0nlJTpeTENy2cxSyGLYYeR1Z6pyE1jj4vC2HmiuNhdSGSrcJ5PJXKBXrY4OIcVPa6ECrSCwl+08p68Er0vv1gE4M79nq7Ka34PAX3AO6/Kv7fzgOwFr7ScAz/HKMyIQ0tlv+5obFBiLS3ws51a244pP9qG5R4XwtMch8ZNYe/QDvbnizBQQ8SFO79QR6eFyg8fE72/KwmusIE9OVwqgeoH8ZWy4gFjFIj/TDmAQHlWRazlsx7RHq7f5zphoafRc7pPPCqZQhrBCFHCIGqHWUYK7jDDGKfouAKXfnzhlOdKPswQuC1mZTDeflN/4+Zcebp1URBXwXPHNhfnJcPZaurYwV/jEAI6I2tniDt7TBgWerpdKl3pp8GJFvyeRJvG3cdK3gE+P5yYATLhxGvINPX5DR1d0ScIGPrh0FP7kEm05XYYeZp14mEWHeYHJNyalqRUq4Hz5cOgohChl0ehbzB5s8j9eMi0dYD5xvAgKuYk8H8CTXPxYA3tuWZ9TYGpPQN9MX+p+x5UJ+lVKtwxFHDZf5EBxl8+Bu94CSepGqL8oQlljEwMAC723Ptz9IoyTGg4KuITPfa3FCMp14pRXDriJLynYMiSHEM/T7CTvHrOK8I150TXP1Bhb1SnXXBRrFUmIYURpbYb5yyCUilDnybCmrXepAEB/sg3GJwV2v5ON/A21uSnz2CSb6Yz50FzG+ijKoDyap9jeUah3e2ZoLtd4AA2vqSckzaQA5FzR6A1iWxeJhUbh5ShI0eoOF931Hdg3e3Zrntj52AgLuwGBgcfkn+yyemzMoHGkRfkgKVWDOIOtJ+N0zu+go8DD9z9gafwdZapy3oKhsdiI5YMx3ortRtnVFhLIz/jHUOlVUtNUSA45S0LSsqQMM00WNKgBQc8m3vDaWExYMIXklfDjRiuz1ZEnZQqehTQMDC4Tb2x4NgfHUxhbDMIgN8nbs2VLWuGRsVTarEBnQjdJlvmqz4hjV8FunJNkMKxlpKqVu0wMAFc0q+MolXQulC7hEWWM7/j5VZZz9/3Co1MJgmpcRgcFcf83kx//GG//k4PV/cgCQThcAyQ2salHh7a25QnNqgT5Fm0ZnrC7nOVHajBaVFoV1bYgO9MaTiwdZvH7dl4dQUt/3WlD1v6shrzJe78Dbw8FL+mvthf34UnfKEBYvBvrYQvpkbiv8o6g9W+bYnZG2VgN+9JWRta1qBPvIjKKILpM0nSwpjaP7ZpPm0nYr7w5yQqYiOi8JL5vQpQIFHheMLYB4AzedsRMGVSsBbRv1OcSyLKqaVV0TNOW5YhVZaukuOP7eUrRr9PZ/B81lJkkJCnKqWjDA1XZVAl0iPdIfp56dZ+x12DlJXiRi8NNyUs3FssAHO0zXxT9OkOrFVrWpkjYtsgsVvAICHqCxTYPHfrXOJa1Tqo2OkhsmJWLZhARMNIvEHCisxw2rDkHfxyRo+p+xlcQJX1IoXreqiMu9wZ6wqdHYovNK8PlO6WY6Ni7jH00tCgoAKzjDrsOeiGp9PnUYFADO1yoR0J3wT0AsIPGmln/wlorhLRWjssmOlzFtAVlOvp9qex9wIVWqXCR7BMYDrRXUSf78zcqmsVLKefgodbvq2zTQ6A2I6opGGA9vGCnpihyCFCQxusqep7e5zCXPVk2r2vVWTwJdxs9Larz23DVzgLGNlC0+vW40NtxnEmhOCLGsXi2odVDZLCDQg7yzNRd/Zdm/Fx5/aq5RakkqIabMxvun4vGFg1BY14YbVx3CKxvPWUmd9Bb9z9jiDYtG58YWL6Z5sLDB9gBlDQCGOg+slvNsdUujyi+aiLLqHFQYmnGwgOTl/HnChgYSy5IbpQvG1qHChu5dcBkGCEkB2u18plbDGXRo9Vh9wM73pWom+WaUOWB8OHJ8UhdzzgBibLEGag8j366n1lZVqJYzYJJnUG2LN3iiuqKxxeMTAoAxKdc7gS8SOWorf1HVDKibqY0tlmVR06rukeR+ARNxXFXsusOlVkazt1SMh+YOhK9cgjtWH8XO3FoMjQlAcpgCq28ej8tHm77bVzdlC3lbAn0CR+3inrkwA0EKGfQGFks/P4BdubWYMygCg6L8cdu0ZLx0yRAcKKjHpzsL7OcD9zD9z9jyDgLkAVSeFT6MqHMURvQJoW6+y88uu1XVw+s8UeZtXcO12rDZ20zVDOg6qMOIdsNIrqIIoWqZREX5MSLKSsnJsiYAgJe0G6e2i/IPd80goVCbOYC8R4tS0LSCC4NGdSdnSywhBirlOZQYQrxQvISABc3chYrS2KpsVkGjMxi9ZQI9w6KhkbhweDTOVLRYhRIZhsF9s1PxzIWkOnRHdg0uHhmDgto2/HyszEIjrrJZ5XY5HAEBVzlU2GDMLeQJVsiw9SGSpsJrS+oMBnRoyX2ruUODymZyLl87PgEnnp4HPy8JMrNr8dz6M/ill/MR+5+xBQBBdDk3TgUplTWu5TspNWAYILgrrXp4/EnCOG0ocVhsIACg3VZyvjEMSncM7WqyjcQQ14QxrfCLIh41Sm6YmAA/L4ntGXVVlqkikQJe4NFRKMUpLhpbfDK7zR6PNWcAsZza2KrithEV0E2tmIAYk6HkBD7kyjd2tYD/HilztvicuS71dRToMgzDoLyxHX5yCUYlBNocs2hoFEbGB+JocaNRcPa9bXnYX2A5MfpgR777Jl4CAi6i0urxxG/WuVrLxsfjgXVEw4/P8d2RXYOiujaEKGQ4XNSIf06bcmcVcgkuGxWLVpUWq/YW4eGfTvZqK7H+aWxVnQLy/nE6zEsqRpif3HiDsEJZ7ZK+U52SJJd3qwrLxUbCQQqSX/X9IRuGAe/ZoOzrqORaznRZY4vHJ4TkPBnoLthJoQq0qnS2w3AAED2ye/vjKi5qbfE9GAtqbXgEWiqJAU0pKruJu1iEdNczJPcDzm+jHu7vJbEt1cB3T6D0bPESGKMd6dcJeIxWtQ7/nLGt0+cjE2N4bCAMLMnxHMa1LDv21FzkvrgQUjGZoKw/WYHUJzai2YaQpICAp3n+r7PIq1HitcuHYcuD0wAA6ZF+uGdWKi4eQe6PvMmUXdWKDq0e9W0a/G9BOq6bmGixrWeXDMa+x2Ybi9bGvLQV//fTyV45t/unscVDkXsQE+iNMnuercYiao8EAJQ1dnSvigwgXiEAOPgp1XC5hHgQTpXb8P7w4qj8Np3A53p0uRKRR8SFXVvpeuklcGEsK8+KmjNeMi7q3v64iotaW3wriTc251q/2FZDGoxTsu888TJ0WWOLh/dqUSbmt6h0aGzXWs/86vJIhS9lkciD604C6JuNYPs7715NJiVP2KjgAoBfjpXj631F8JaKkVXWjKFcc+obvjqEssZ2YziZZ87bOz27wwICnahtVeO7g+S6++7WPIT7eUEuESG7qhV/nqzAixvOAQA6NHpc+P4evLM1z7ju8unJENu5bvp6kXtSQ5sGPx8t65Xm6/3T2OI9IRRJ2jFB3rY9Wzo1SVQPTKB+2125tfYrumjhxTvLDlGv4udlJ6dMyblUKW+UudXkxtztfJuEyWRZcYJqeCJXuaZUd2rm7GIYlA/jXTaKvnLOLgFxLmlt2aWlwhQa7kmaS8ny3HqXVqvr3NS89CDx8lHqtPEEdSeULtAlYoO8ER3gBbXegAMF1jeTySkhuHx0LGQSEf7vp5NYe7AEL1w8BKfKmzHrzZ3IqyGTm+eWDAZgamkmINBTfJx53vi4vKkDw5/fDK3egInJIWjkVAMenDMQegNr4WA4/MQch9fhK0bH4b5ZKdjMecp+P16OU7U6tHW+53iQ/mlspS8mSwpRxxCFDE3tNhKw+UouSuV13iNQp6RP5rYJX80XN4F6Fb7yTtVZ/qGlApD7A150UhSVzSqIGFholnQJ/sa87lqq4XwyOC/FYYQ3tijDoGcqyI/PLV4V3zCgZJ/zcRw2RUENeiK9IaH3dgb5SHHt+Hjq8Xa5+nuy9KYL571z1QgAQGlnL2/FcWq9LnMciqQKeASGYfDFDWMRHeCFqz87gA93WGoNRgV4440rhuP2aaTP6B3TkpFk5s1Ki/BDkI8Uz/x5BoC1ZpeAgKcZGR9oTMvg+eKGMdhfUI+X/iZerQVDIjEphdyjlk2IR+Eri5wWpckkIjw0Lw0DwnwxbWAYtpytxptH1Zj++o4eq77tn8ZWxiVkSVERV9msQmO71rr/Em9sUeZsNXNtMrqdawMAIamAhv5CNz2NGIRWCdot5S6FQUvq2xAV4G27stEVksjsAUFJVMP599uV2ymMePoXsvSmaxPDp4hdOJz+mO1y9g+yrLLuMG+LySlEHsSibU/uJrLMWke1jRaVFo3tWmNz624RNpAsKeUfhnL5O8X1Zucdpc6YOclhCiweFtW9AgWBLpMR7Y+/75+KuRkReG9bnk0NQZ2e3FyOFDdi2ZcHMZdLls+pbsW14y09+UeLG6DVG6wncgICbmZHdg3u/f64sUgIIP1VV+0tAkDyQL+9eRwGRvgi3I8YZGsOlLh0rRGLGFwyMho6zjkyJSXUfq6wm+mfxhafzNtU6nRoDffFZuZ0EoBs4278tBpbXPjlWc4F3y30aqD6FFXOGQBUceWuD647YflCS4VLxlZFswox3dF34hFznqXGQpdWO1TUKeyr4sTowtKo1n/4p5MuvR8VStvJxp1ZNJSEOm22iRh7K9U2+KpYvrl1t+DzxCiFTeOCfCAWMZbeDL7AYskH1G9br9R0rxpXoNv4yCSIDfKGzsDalLW5b3YKLh4RbdRVWzI8Gh9fOwoA8FGmpTfs6s8OIPWJjRj74lZBf0vAo8QFk0nmpAGmyEqon9yYy/vz8omYNjAM7Ro9frBVEEaJubTJ7ycqMPnV7T2iNt8/jS2pF7nZUKjI8735rAQdjZ4tujAibx13S2OLh/fItdppAdOJeO4kbVN3mn02u+bZqmhyQ4J/F5maasOobSggS8pKPt672C2NLZ6Fr5GlmC4kOTQmEIDJ6AZgKnIYdT3VNniNLbcYvFLue9z+AtVwmUSEuCBvFJgbW0bZhxiqbSjVOjR3aBHT1b6aAm7jUGEDJiaH2BSGZBgGFwwj1wWZRIQ2tQ7zB5PJgoEFvrxhDPy9JJg0IMTo6Qr1kwveSgGPwLIsbv/2CFb8kgXAVCQEABOSTFEN/vx7fv1ZfLGHTOS/unGMy+/3vwXpyHlxAUaGk4jKVWPj7CbWu5P+aWwBpArs+Gqnw64YQ7xgVkYS79mizNlyq7E14S6ypMyVuYgrh71wuFkidkcTSZDvaKLahsHAorpFZb9HoYcZl0h+VBbhCheq4ABg8TBy/OmR3WiXxBPP5cxRanzxeWebzHRejP05/ekS9os4rxg/w+tpiurbsSGr0uTB4OVHKDW2cqpIgUXnnAuBnkchl+BIcQOe/P2UzRDgnIwIvHHFcGh0Bqz49RQyc2tw46REAEBymC8mDgjBucoWPLF4EML95BiTECR4tgQ8wqbTVdh8thpHihut9PluX30USaEKY+Xs5jNVWHekFLdNTcLp5+ZjVjr9/cEcuUSMKwbKEOgjxZoDJXhu/ZluH4cz+q+xxeNE64mvuOFLSo00FZMefzK6hrpuNbb4hGrKEJCc6wv14Q5TJYexki5uPNU26pRqaPUsorujXG4LA12uBy8MalEZWryHOowHABsc9NFyGV7vjCIUDZCbGwC0mYvLps4DvAKJoj4FL/x1FgAQ2p0m2uYMuhAIG+Tyamo+f/HX28jSn86ztS+fTFCCBfX4XufJxYOwcEgU1hwowZRXd9gcMznFdF4+t/4skriq4Pnv7MI/Z6rR2K5F6hMbUdOqxk9HyzD2pW3osCWeLCDQDeq4vMK0CD/jOchzorQJhXVtuGJMLN7anIPbVx9FpL8XHpmf3u1q2WhfEdbeSu6Pq/YWeTwvsf8aWyOvI0uNY50hPjk7vXO3+6ZSIGQAdU++LWerIWIAP3eUS5dysg+/30k1nHevWjSj5kv/Y0ZTbYM3ctzm2Zr9DFlS9khMCSdGbXYl933xHjnK5Hi34xMCMGIi/0FJeqQfduWaJaQ3FpFzqLfwDnJp/y8dSYwqKykUGZ2nLZM79oxoN3gWBbrFsNhAPH0Bac9Tp1SjtMHaSx4V4I3rJybA30uC4vp2KNU6DIsNsKuyXadUQ0cpVCwgQAPLsmjh0j9yqltxlmsavXy66br51pXDMSQmAO9tJ5GCKludOrrI4OgA3DCRhMrf3JzjZHT36L/GFu/RUTqvxhqbGGQtF6CscinfqVaphkImcU9ewzguodqFBHOrUvsfuTwhyjAo39ev221iePjm12103rkBnLFVwt8UNj5KlsF0FY1qnZtnJQxDDK42emMlmwujGbVbCnZQC8oCJFeLN3jcQnAyyT2k+A0AwJIR5Hw3ViQmTgUih1G/HZ/3KOgz9Q20ZoaRzQ4TAJ6/aAheu5x8x6//k4OssmZ8yCXL2+LLPYW92vJEoP/QrtHh/e35Vj0QAcvz9aEfT+LLPaZ74SPz04zdDtzBBE7q6PPdhajvrDPoRvqvsaXmKtmOrnI6NMxPjoqmTtay0jXl71aVFhe4Q3IAoK6ANOeWKcQoMbpCI4aQZXAy1fpuTc4GiL4XAORtoRru7yVFuJ8c+ZywonH95BlU6ze2kdmRWyr5eIKTSesnSi7hDKXmDi1QuJs8mf0X1bosy6K8qcM9YWieaO6mWU0nXzGEy4soquMMXm07MThdhA9rC/Qu4X5eOPj4bADAR5nnodUbbBpKE5JDMG2gaVI2f3AkHplvuwL4na15WPTebiF/S6Db3LTqMN7aYqPrBsg19N5ZKfhk2SgkhvgYU0TeunI47p6Z4tZijbUHTYbd6Be3um27nenWVZFhmGCGYbYwDJPHLW0qKDIMo2cY5gT392d33pOaoVeSJUWC+KBIf5Q0tBur2dBYTMreK45TvRXLsmhq19ruLdcVeBHSsHTqVSQi8lXyKvCo5GQQxHRehoomFXxkYvh7u8krEcuFLzvowogAUNOqxi/HuAo4Pvw2+kaqdXkBxykprhuqdglLc6mhNm8obT5TBXRw1a2JU6nWPVNBJgfHS5pc2kWHBHKJ7ZRVrbxG3MbTXO5bQyF1JaI5/6aqtT59DXMDEf5eeHgu0Vwb9cIWJD/+NyqbLcPEgT4yfHvzODw4h4x74rdTuHVqEnY9MhOXj7Yu7siuasW4l7chu6rF8wcg0C85U9GMg4UNdidmob4y3DljANQ6g7FwaF5GBC51R3eQTnx141gkm+WKeSp3q7tT0BUAtrEsmwpgG/e/LTpYlh3B/S3p5nvS4cPl+pxY43RofAjJSalt5bxbBZlkWUNXodCi0kFnYI39l9xC2iLSJoWSKZx0Qr1Rwd61mWdlcweiA73dd6P0CiTLve92bf1N3KlE6V2M8CeGzuWj6SrnqPCLJGFQPV1LhyWcZzPQR2aqJJ3/EtW6vGQEXx3rFvjvgFJUlf/uDxc1AtVniaHM0Anc8qLAvEDmv4i+ew1zE3fOGIDrJiSgVUXO43Y7Se6XjSaG9dqDJUh7chN8vST4+ajtyUZtqxoL3tktGFwCLlFQq0Tiig1Y/N4eAMALFw2xeD0twg9FKxfjyJNzIRWLcP8PJ4yvqToLj7sJmUSEy7hJBcOg+6LeduiusXURgG+4x98AuLib23MfLvRy46un9vCNkHnV+Mu+pFr/eAnxYvi480sKTCAeNkp3fTjnValpVVGvY05FU4dRvsAtdMFou2lyIgCuRyIfSpXS7VObRg+pmMEwTgndLRj7VB6mGs4XF+zOqwM2P0WeDKRrvbN89VEAgFbvxvAMb2wV2K5GswUvN2PglfNT51Gt9+3+IgCArLtNzHuevnsNcxMSsQgvXDzEKO1w3/fHsTvPOo8vNsgHv941yfj/vLd34e/7HHtmN5+pRrum5/rLCfy76Vx88+gvWRb5VznVrbjv++M4U9GMb/ebdDIfX5SOb24a67H9WjaBJMmzLKmA9ATdvTJGsCzL19tXAbA3rfViGOYIwzAHGIa5uJvv6XZ4bY+v9xWRJ3jlbF5ryQl8+HGKLWHOrhKUAGjbiAo8BXwI69k/z5q0oab+H/Xb5VS3ui9fq4sMjw0EwLUd8gkG0hZTr1uvVCNEIYfUnTf7WO7HTZnkH+BNwsi/HCsDIrhOApS9CXm5Bd5D5xZErn8WfEpPUyMnLBhLJxrIh0F7SyOsG/SLaxgNz1yYgZsmJ+JMRQueX3/Wprr8qPggnHt+AS4fHYs6pRqbz1ahaOVibH2ItOCanBKCJxaZ5ETe2pKLN/6xnXcjIACQfGZeMmRsomV1eYS/3JjryvPnyQosfm8PXvjrLGICvXHh8GgsHOLZFmD+ZlGpKz/Z75H3cBr3YhhmK4BIGy89Yf4Py7IswzD2puUJLMuWMwyTDGA7wzCnWJY933kQwzC3A7gdAOLj3dCMd8ZjQOYrgLbDoQr5qHhyQ+SrEtBYBIhlgB9dwjvffNpt+kiAKVdo56vAkvecDuddnx1aPVBFlHhhoJtx1ivVUGkN1iX/3WXMLcCZ36iHh3OGRkVjOwY0lQAD51Ov++MR+twqavy40/7H64FnnYubikUM5gyKQH5NK+ltmTDF5beclU5flOESeh1V/t4j89Pw+j850NQXkScoE+R5r+j/zRvY1T30GL11DYuIiEBmZmb3dr4TSqWyW9uc7gcoRsjxwQkllr6/GYuSpEgMsPbIS5RkAvnO1jzImkuRFiTClBgJ9uTX40ihZc/Zr/YWYoxXNRgG8Ja4fkPs7jH1RfrbMXX1eNq0LO7eZl+cu7pFjZ+OlGFYmBhZtdbh7fKmDjw+msH5rEOw+rF1k87HNDFajP0Vetw1XOqR787p1Zdl2Tn2XmMYppphmCiWZSsZhokCYNMFwLJsObcsYBgmE8BIwPqzY1n2MwCfAcCYMWO6H0/hK/Eai4Fw+8nmIi528vPRMqy8bBjR2AqIpfYMfLqTHIq/l5sS5AFg5DJg/wdAFH3pfUaUP/JrlaaigHQ6z9ChQpLEPjPNzTf64r0k76e5nCrRmves1ZzYCOhU1OrtRqkFd+OCej3P1nNEhJUVlYIZMNvl9d0+e0ueQXIQldVU38GMtDC8/k8OIk99Qp6gbFdU1tiB+GAfSPpgGLE3r2EzZszo/gGYkZmZie5uczrLosnrNNYcKIFB7o+fL5pkNWbaNBb+m7Lx9b4ivHZYheFxgfjj3pnYdLoSy9ccsxp/F3dDPf7UXAS5KGrrjmPqa/S3Y+rq8RgMLG5VnzO217HFUxdk4LqJCbjs4304Xd6MxBCFsW3YhcOjMW/WcPdGLDg6H9Oa4sNARQ3GjhqBye4stOLo7hH8CeAG7vENAP7oPIBhmCCGYeTc41AAkwGc7eb70hHEaTRRtO1JDlUYhTXRWknt1QJIFR1gMtrcAn+jr6V30Z+tbCGJyj9ygq5yOnHJO9eSi6fWRlihW/AaTTV0Xzff43FA4VryRH0B1Xq8Nte7V49wafecIpEDcROotcoAYjBKoSMVgAF0ye4NnIKyR/pzjeeEcXlvpxPig33AwPXzoKyxHbH/zp6Iffsa5gEYhsGLFw/FyPhAHCluxLiXtuLXY5aeYZGIweOLBiHrGZKzd7K0Cc+tP4MtZy1t0Rlplr+Nyz7ZhztWH+mRxr4CfZvShnZ8vrvApJ0I4I7p1lJEbWodjhY3IqusGQYWRkPrgmFReP+akR4xtGyh0pLr3nPrz6C5Xev27Xf3KFYCmMswTB6AOdz/YBhmDMMwX3BjBgE4wjDMSQA7AKxkWbZnLlS8Z2v/B06Hjk0MNoYD0VLhkqCpR+CrKQ30XzqfAGuE0kjg1fNHJ9DlF1EzgyvsaqMT1eS9OiM6DpInFrxCtR6vEZYQonAysgto2qj3HwAenjcQqUwZGLCAH51njN9/j9ygePmHzJVUw/28pAiF6xVmZY0d/1Zjq29fwzzILVOSMGdQOGpa1Sgyb0Buhnll1ur9xdh8tgoPzhloLITo7FUuqG3DP2eqcaqczist0H+Z+toOvLIxG5vPmlqufbrTNIH285Lg0QVpuHd2qqk4DaQi8boJCVaVip4mq6wJAJBbrYRc6n4Dr1taBSzL1gOwipWwLHsEwK3c430AhnbnfbqMIoQkKFOEc2KDvFGnVEOl0cGrtRLwp1P+blURY2hEXGB39tQ+R74CLnibaqhVyyHKnnyjE4KQXdWKMZ2SF7tNQBwAhoRxKVkyPBrgBYUVdK5cXm040NuNYVyeuLFA9SkicuvrPMyqM7BYLD5A/vGiq4zkS/I/u46utZJL8In6LrQNCmc4jTDKatzaVjVqWtVE8uJfRp+/hnmQC4ZFY1xiMLae24b3tucjIUSBC4ZHQS6xzOHKeXEB2tV6lDd14IL39+Dtrbk48NhsXPbxPiITYoOtZ6s9d00U6LMYDCxqlWqb/VHfv2Yk7v2eaFe+e/UILBwSBb2BxQM/HMfvJ0gh2Nnn58NH1jsdKK4eF4/PdhXg0BOzPSL/0PcSLNxNRyNw+menw2K4WXlVVTmg11CHEfkZnKfKRQFQSzl01TGy9mCJUTrCrUhkJMmc79NIgYUwLGWrm63nSGjDqmWRO0iaTpZ8Y28nXDoyBmqWu9CkX0C1zk9HyOeTGuHnZGQ3OP0L9dCrAojTpt3bVk65NXwIylP6NAKew99binFJwQhRyPDwTyfxv5+tw81yiRhBChmGxAQggdMk/PtUJe6fnWq3cf1nuwqw8N3dmP1mJv44Ue7RYxDoG2h0Bsx6MxPjX96GIc/8Y/HaTZMTUVBLvKeDovxx0YgYGFgWN319yGho3TljQK8ZWoApjWbcS9s8sv3+b2zxOGmgyl9EKkvyyBOUni1euX1ckgcaJqdweb2UieIZ0f4IQxP5Z9CFVOvwnjk+78ztyP2BE2vph3M3bAMjdVmry63VoDxBRH+FVoVdIhbhCvl+aCElOV8U/Hqc3IwSQ/qGbMKUKGK118gTqcbzvc0u94C6s4Bn8ZKK8eMdE3HkyTlYOj4ev5+oQHWLym5Ie8uD05EY4oPn/zqLR3/JwoQBIbh0ZIxRfZ5HozegpkUFL6kYD647gfO1yp44HIFeZOXGbKPau9pMgPTSUTF4cnEG3t6ai8kpIdhw7xQU17ch/alNOFBAirN+Wj4R/1tA3zHF3bAsi505pnQRT6jI/3eMrYMfO3yZz/cZsude8gRlf0Je0O9RO73EusXQK8iy7AjV8BFxgQhmuHybdDpji/9RmHdZdyt1XEywtdrxOI4F6cRozYy8wclIa9xaoMDjy3l3jn3jeJwZMn07wLqeZO7xNjca23k5nQltOgk1K8HucroLjo67Mcf3EWNRwHUYhjFWU49/eRvu+8F2qzKZRISf75yEn5dPRKgv6WX60iVDcfu0ZMzuJFtS36aBRmeAgQVmv7nT1EpMoF+x/3w9Zry+A1/ttaw45HOIfz1WjgGP/w0A2Jtfj81nq/DihnMASD/ZopWLrfS3ehqN3mBMzF84JFIII3aJMTeT5T+POxwWxOWb+Kk4EVHKBHk+fBgb5IEbTTunZ7P2MupVZniRHoG0Cvq1nEcrla/EdDfDriZLyh6JkW0khHWwhM4wqG5ROR/UHfgig7zNdOMNBkQwjdhvyDD12qRgYISHPn8AGHU9WTbYL782R2zQQA0pnvqdroG1QP/gmnFxkHG96jZkVWLKq9ttKs2H+soxJjEYzy0ZjKyyZgx6ehPOVbXgg6WjjGN85SQclFdj8mjtyq31WN85gd7j6T9O2+x8YZVDzLF8zTFsOVuNOYPC8dIlPZsEbw/zPEW3601y9H9jK2481TC+7L7AwHkygpOo1ntnKwk7eiTnKXkGWYZnUK/yIPstAEAXTncS87PNoe5sc2POiKVkSVnRF91MGmgHxNJ5CssayQ/juSWDXd83GsQSIGoEeUyTO1ebDQAIYlqx/3y9k8HAWU55Pbfag2EWvhl2zkbnYw16+LSVYp1+pktvMTI+0PX9EuhTJIQokPviQnxx/RjcMzMFYhGD2789inOVLcaKWXMWD4syauNd+tE+DHp6E5YMj8acQeGYmxFhNNx4XtxwDulPbULiig0obbAvdCnQt8mpakXakxux4J1dSFyxAXk1SgyK8sedM0zRkWNPzcWKX08BAIbE+OOyUbHY/ajpmnLjpER8fv2YXs3RAkj4ML+mFetPmjq1BHii0Ar/BWOLD8WNWOZ0aEq4L5JFdLk5PHy5u0dCWHwlGaVOFQA0KMgJXyGhU+Dn3bkeK9vn29VUUx7D1mcAAEfb6GQrCrhcEKP6vyeoPEGWNN6t4r0AgHu092H5mqNOhz/226lu7BglvN7Xjhedjy3cBUanQjFLJ1vBh9GnpdJrkQn0beZkROD/5qdhZFwgOrR6LHx3N674ZL8xv9OcvStm4fhTc43GdoC3FFNSQnHZqFhjc3JbTH1tB25adQif7DyP3WVaweP1L6FDo8f8d3ZBrTMgu8oUFt56rhofZ5o0fq//isj3fH79GHy4dBR+OVaGqa+RHq2Lh0bh2SWDPZ824YTiFj3Gv7wNc97aZaySHBLjj/tnp3rk/XrXrOwJRGIgcijQXud06LBof6AFYNMWg/Y0qFdqrPWtepHoNmI85VS3UuXQ8GFEj80wePmGo6uACcupVzvQFACDgXVqxP7OVTp5VOMpOBloKAA2/s95C6HGIrBSHxSr6IyVfM6zuGKhB5ND4yeSJU2fxn2kNdRJAzHaG9s0DhXBj3Cl/5HubGIu0Cd47qIhmJ4WhgPnG7DuSCmeX38Wr18x3GpckEKG+2an4u61x7D6gEnmZWJyCPYXEO/uDRMTcKaiBUeKTVIRO3JqsYNLSh45rBoLh0R5RthXoNu0aFjsP1+Pz3aZDKq/75uKRe/ttjn+TEULvrh+DAJ9pLi8U6/BZz0VhXCBglolPs1SQ8dK8dplwzAsLgBRAd4e82oB/wVjCyDJ7m3Oja30QDJL7/CJBk0GlkqrR4dWb2wC3ZeoV3qoutBV/KMB72AgMMGl1dp0IhTUtZlU/Z2gkHvwVB51PbD1WaCRIudp/wecoU5302jjGrR61GBnGCBpGlC4y/lYkQQQSVDmnQa0a7FyYzZevdx+y6jrvzoEAJjqzibsAn2CAG8pLhkZi0tGxiJIIcMnO8/DwALJYQosG5+AADOZlplp4Tj7/AJodAYMfJKEq/cX1OOpCzLwyt/n8M1+x1p793x3HMBxPDI/DXfPTPHkYQm4yEeZ+Xhtezuw/YDF87YMrSkpoXhsUToi/b2wJ78Ot35Liru2PDjNs9I2FGj1Bmw5W41v9xcZqyCfWDQAV46N65H37/9hRIA006XIGZpfT/KdxOe3UG2W10c6XmJb2M8tJHNx7naKBPO8rcaHBXYUoc3hQ0BzBrneA9AlkqYB5RQVlSqSv9TiT9y4V3yyz+kqe/Od50V1G4oQNACn8iKO8LhGFW9o1WQ7Hpe3GUicgge4Uv7fKTWSPDkjFOh97po5AMEKGX45VobX/8nBtNd3YEe2dRtJmUSEn5ZPxOBo0irshb/OIi3SD2F+cgyKMrUPSwn3tZDL4Vt1vf5PDn47XobqFhUMBhYspcaggGd4a3MOXtuUY/X8tIG20wYeXZCGv09V4uKP9uL+H04AAL65eVyvGFrVLSp8uCMfd609ioXv7sbw5zbjrrXHUNbYgRUL0/HkBC/cPIUuN9sd/Dc8W0GJwJnfAL3WYWNdPxFp17Nv0pegSQ/m2xAMiw3s/j7aI+MioGAHEaUcd5vjsblkRrnW+1qcpmiXUcgZZJeMdN6guFtEjwTO/k70whypqv96OwDAv4UUHTQ66U/lsQbUnfExywdTtQBednpO7nvX+PDxRel4+e9sHC1utNsGyRP9t+ySOBUo2g1Un7bflP38drIsyMSyZQl45s8zFno5jlD0cqKrgGfx95Ji4/1TodYasOFUJX47Xoabvj6MaQPD8PG1oyw8y2MTg/G/BelGr2d1iwotKh1GxAVCImJwqrwZ+TWWBSHm/fMeXHfS+Pi+2al4aK6lhpeA5yAJ40rk1Shx11rrhuM8u3JNzovkMAU+v34MPt9VgGs+O2D01j97YQYuGRlr4QH1NCqtHlvPVePXY+XYmVsLvYFFUqgCiSE+mJAcjMkDQjEzPRxiEYPMzNIeDVv/N66Q/lEAqydSCn72VbEDmsmsP7vdl8rYSgjxwe484N5ZHnR7py0E/noAUFNo1HCh0jPx12DfSeceny2csZgc5oGegubwwqA154D4CfbHybj9uHED8IlzY/EAlw/y1pXWeSRuRWTmAK4+AyRMtD2ujpPdWPQGGBX5EV/28T4UrVxsc3hxA528hVu47EvgzYHAXw8CQy+3Peavh8hSLLe4CKl1eqsWLgAsqi09UiAi0KeI8Cd5eXfOGICbJifiko/2YVduLTafrcJFw2MszoFpA8Nw9vn50BtY+HlJ8dC6E/j1eDlkEhHeuWoE8mpa8eEOU/5PsEJmbMhuznvb8rBqbyGWjo/H1WPjkRTq4WvVfxCDgcXyNUeh1RugVOusWjBJRIxRS+/LG8bglm9MUYr0SD+8ccVwzH5zp/G5CH85Xrp4KOZkeDhiYobewOKL3QX4YEc+WlU6RAV44fZpybhqTBwS+8g58x8JI3L5JE7ytsTqZhQiBoUNdPlOaw6QFi4erarw5U7YPRT9Ec/+DgDIqiHeCGfl1bxsRaInGjibE8hVRn7lJLmcb6uUOAX8ddtRGOG9bWT/h/dkD7ajq+y/dmINWQ65DDPTnVfnXfsFqdh5/qIeSBjlm2KrHTSZ5s/jO0jIkTfC+c+5MxtPVwIAnlg0yD37KPCvwUsqxmfXjYZUzODBdSeR/tQmpD25Ec+vP2usLPSRSeDHCaW+fOlQXDAsChqdAQ+sO4EOjQH3zkrB/xakY1yk2KahxdOq0uHTnQW46IM9KG/qMLZVEeg6LMviqd9P49ZvjqBNo8Pms9XYkVNrs9eluaTDA+tOID3SD69eNhRXj41DSUM7Lnh/j8X4u2akeNzQOl+rxB2rj+DpP05j/ckKXPvFAbyyMRvjk4Kx9tbx2PO/WfjfgvQ+Y2gB/xVji6+Ic1SRqGkDGguRhHJjeM0RPZZLwN8AHd0kecIHA8kzcMVY4kk6UkwnJOrtiZ6C5tAkx3fKd/LmcphKHBiMJ8uI9yshuAeUy2c/TZYFO22/rjMz0L2DkBLuPEeBb0B9WU+3ubHXGDwwgSjmc2HGpy4g+m72TvVvuaTny0YLbXr+i8QF+2DTA9Pw3JLBGBkfCLXOgK/2FmLaazvww6ES6MyMIi+pGB8sHYUf75iICcnB+O5QMd7fno9XN2VjeqwU2x+ebhw7186NukWlw+SV25H6xEa8uilbyOfqBnoDi9UHirH1XDWGPmspaTOv0+f/xW5TYZBYxODz68fgVHkzfjhcatSXjAn0RtHKxShauRg3eLg6/1BhAy79aB/2na/HT0fKcO/3x3GqrBmvXz4Mn18/BpNTQvtkVet/w9ii8Wy1EFGzHMUYnK91bmy5og7ebXitMJ392R+UtUDNGSBuPPy8SHTYPPehM/zsU9ITJ6XCrFJNY8d44gsYBswCAPxwOwnVXfqR7ST5Do1Jl0ci7oHTePKDZKm0o8PWWGR6zBnIY7hcrToblaHmukIeraQ0J4TTj9nwsPVrJQdJbuCQS41PzUwj7Vc+MtPP4TG/0QX1YE6GQN9iQJgvbpiUiPeuGYkpKaFIDlMgOtAbK349hfnv7EJVs2WHh3FJwfjh9onIfmGhMST4+hEVZnFhqHA/OT6/fgxevWwoAMBPLoGX1Pr3/XHmeVz12QGsOVCMDo0e727Nw9qDjise/+vkVbfi2i8O4K+sCry1JdfitRAzeZeTZU0Wr01OCcWT472w6saxaGrX4spP92NHNrleP7k4A0UrF2Pvilke2+82tQ77ztfhwx35uOXrw1j2xUGE+Mqw4d6pOPnMPPxy50Rse3gGrhgT1+vaXY74b+RsGT1bDvKYCjIBACsbZ6LBoEFzu9ZhYh9fBfeYJ/WRePj9Pr+N5HDZophz5UYNx0UDY/DQj/YNLQDG5Mce86qMXAYcX0NynuLGWr++7Xmy5BT/ed2sejvhhT8oq+TchnneVlOJKTTK8+d9ZDn9f8anRicG4UhxIyav3I6cFy2/N17fzCOdB+xxxdfAJ5OBfBvVtl/NI8vOx8XR0KZBsNkFuU5p+l768gVOoGeI8PfCmlvJb5dlWWw8XYW7vzuGZV8ehETE4KsbxyI60FIL758HpqG8qQPrNu9HaFwyfjpShpzqVlz56X48Mj8NS8fH47uDJVbv5e8lQYtKh0OFDThU2IAnzdpKLRoS5VAX7r8Ay7LIzK1FVIAX2jV6PPfnGWMUALBdwW1+nQ1WyHHLlCSkhvth7cESPLskAy//uAffFxBh6spmFYbFBiDUV+ax5PctZ6uxM7cGx4qbkF3VAr4v+oAwBS4fE4tH56chkGuxNzqhd/sq0vLfMLa8gwCR1Oi9ssmJ7wAAKanp2JEDnKlsxqQB9rWDvthTAKCHxBxT55FKsd+WAyvszN6Ofk2W0SMtXKgsy9q8GRbXE+/dEE+16enMjMeJsbXnbeCa76xf5z1bE+4EAIsLpq1j+OME+S5X3WTDcPM07wwFnu2UwF/KadBkXGR86oHZA/HpzgKbFX2vbCTisx4truhMBEVuWNJ0i38np4Rgb349vtpTiP8za7Y+9iUiM5LWy9o5An0PhmGwaGgU5mdEYtMZ4gmetHI7RsUHYtHQKNw8OQkiEQOZRISkUAUmREswY2oywvzkuP+HEzhU2IArOCHMa8bFw1sqxtSBoXh1Yzayq1rRorJfhTzyhS1YNDQSUrEIAyP8/pOaXWsPllgYoPaQihmrnoZ89efhogZ8f6gEudWtmPf2LrRr9ABINGfFwnQsnz7Axha7B8uy2Jtfj6/3FWLruRooZGKMSgjCPTNTMDIhCCPjAo0G1r+R/4axJRKTKkRltf0xnCE2e/pMfJ5zwMr93ZmaFuKZWDKcrmF1txi/HNi0AlA12R/Deeb4BtqhvnLUKdWoVaoR7mdtEPKh0mXj6dr6dBu+sXfOBuvXDAYg7x/y2IY0xM7cWszgQloA0NSuMSpTezy535wZjwGZr1g/bx7eNTNozHPhVFq9UUuLZVn8fYq7CaX0oBgow5DG2m21wMp4YAXnNWgzm+lGWPbh/GjpaAx/fjM+2JFvNLbMQ7i3TUv2+G4L/Dt5f+lI6PQscqtbsT27Bj8dKcWLG84hJtAbNa1qXD8xwWISddGIGFw0IgaVzR3IzKnFY7+ewveHSjA42h+PLkjDMxcOxg2rDjlsAxTqKzf+tgCS35MUqsDcjAhM7snfWg+wen8RxiQGIzbI2yrvioa4IB+jHuPbVw2HWCTCmfJmXP7xPgul/+smJCCZqca1i2dif0E9xie515PUotLil6NlWH2gGAW1bQjmOhLcOysF0p5IEekh/hvGFgD4BNvP2TLojbk4vBjfmgPFuNRBiI3vDN4jIRSGIeGdphKSrdz5Pcute/DdPXMAnlt/Fvd+dxzr7rAjVYAeDAGZv49OA0jMZii1toU2rxwTix+PlOHmrw+j4BWTfMKNqw4bH/doKfi4220bW2/b9xhF+MtR3aLGmgPFuHUqMUxuNSudHhBGp5DvNoIHEGNLZeaZ2/io3eH+3qZLxK3fHMYXN4zFoKc3GZ+7XEiOF7CDVCyCVEyqhYfHBeJkWRMqmlW4k0theGtLLqakhiIWOkw3815HBXjj6rFxeHnDObSqdThT0YL0pzbhk2WjMCst3Ogts0Xn/MidubXYmVuLr/cV4cTTc5FV1ozhsYE9qv3kKZ7640yX1w3ykUKl1ePlS4ZiRloYJq3cbnxteGwAbpyUiEVDoxDqK0NymC8yM+sgk4gw3Y6YKS35Na346WgZpCIRAn2kKKhrw+/Hy9Gu0WNEXCDeunI4Fg2N8rzIcy/wHzK2Qu3nbJklN/OlysdKmpxuctFQ+5pdbid4ADG2GgqAkE4u3N+sew6OjCfJ2QcLrSsSW2w0lO0RQlKA+nzg11uBK781Pf8xZwwued9i+INzB+LHI2XGeD3PidImz+6nPXzMZnTF+4CESeRxG6ekPf9lq1U+XjYal360Dy9uOIdbOLXibTaUt3uMpT8AryaSx3X5QGiKSXLjvhNWwxmGgVwiglpnwNZzNUhcYfJMvnzJUM/vr0C/4bklg7HvfD3e25aHymYVZqWHY+vZarSqdVB6n8YLFw0xanUxDINTz81Hi0qLB384gaMljVi+xrbIJu/Fd8aI561zFVdeOhRXjyPefXt6cj1Nh0aPJ38/DX9vCZLDfPHXyQp4ScVgYSkm6iqjE4JwlPNYrb5lPIbEBKBFpcXYF02dR+YMCscXN7g/NaOkvh3vbMvF78fLIWIYGFgWBhaQS0RYMjwa109MxNCeSmnpJf47xlZjEdBw3rZn6OebyJKr+osO8EK7gy70Na0kxOhIG8btTPs/Ui12fA0w5xnL1+q4ypIb/jI+NdzsxO3c0Pku7qK1eFiU5/bXFkt/BN4fBZz9w/brI6+z+DcqwJRQW1LfjvgQHxwuMhmP39w8ziO76ZBLPgN+ux1YtdCUt+UTSmRFJtxlNXxojOl7+HJPIV7ccM74/1ouobhHMW9G/cFoy8/cTnL84SfnYJiNMMXSngpBC/QLEkIUSAhR4KoxcdCzLKRiEWa9kYlWtQ5rD5ZAozNgVEIQkkMVGJ9Mujb4e0nx5Y1jodbpcaCgAT8cKsG+8/UW1eB1SjXW3DIeOdWt+ONEObLKmu3tghUrfj2FkoZ27MmvQ1ZZMxYMjsTiYVHw95aipL4Nl4yKhS9XLazRGfDHiXIMivLH4Gh/MAwDlmWRW62EzmDA4GjTb51lWegNrEX+bFFdG3bm1kKrN2B0QhCGxgRAZ2AhYhgcKWrAO1vzcP2kBEhEDH45VmYhJko+i67frj+7bjTmDSbOgW/3F+GC9/dgVHwgTpY1Q8+9x1/3TsGQmO4bPAYDi525tfjpaCnign3QqtLhx8NErf3Wqcm4Y1oygnxkUGp0kIgY+PxHuk/8N44SIIYWAHQ0WnooAEDOtV+ZvgIAcOXYOLy7Lc8iz8acY8VNAICl411rrtwtQrmWFXvesjS2WipNj5OmGh+ahwezypsxwkz4c08+CafGBfWAPpU55h45vnWSec9HByHNaa/vQPYLC4yJswAwrTeaHw+/ihhbAPBKHBA/kRhaqfNt7r95zoG5oQWg93JI7tgNfMqdK8dXm54X2Z7V+3tJIRYxxosyALx2mf3m1AICjhCJGIi4Ru3vXD0CK389iH0VOvx0tAw/HS2DTCzC1zeNxY6cGtwzMxUBPlLIJWJMHxiG6QPDjDk+ob5yRPh74cpP92PZlwe7vD/m0iabzlRZhCn5UF1GlD/igr3xzxlT3m/n38TKS4fiijFxaGjT4KZ/2hGyZysmJIdAJGKw/qT94iw/L4lRc++Q2WTy9mnJOFnWZKwe9JVL0KbRW7ynPW6ZkoSoAC9MTgnFL0fLsHJjNt7emofUcF/8ye3LsZIm3DIlCdMHhrkltGowsPhmfxFW7S1CSUM7ghUybDxdBYmIwdLx8bh7ZoqxCwFAriv/Jf47xtaClSTJXN1ibWwVcd3LQ0nlSqS/F1gW+PtUpc28rdf/ITlGw3vS7elrShBHe4PpGM6tt7vKgDAFzte24eIP9xpbxpjrO93Rm8nNO14mRuNrXCPQiz60OezIk3MwhnNzpz+1yeK1XpccULeYEvuTZ9gdtvORGZj+eqbFc/Za+PQIUTYMpacdN1M///IiPLf+DFbtLULeSwv7VeKqQO8xLDYQtw+T47v75qNOqcaO7Bo88nMWlnLdFeqUGtw/OxUR/l7GghN/LylummxqINzZAwTAGPq+dGQM7pg+AD4yMXbl1eKJ35xX6dnibGULzlZaCkt3NnpW/HoKK349Zfy/vk2DDacq4YxWO9WVnfXtGIbB+KRg7DtvX8LI30uCS0bGIFghQ0lDO1Z9c8SYXxyskKFDo8PwuEAU1CpxycgYPLFoULdabam0ehTXt6OssR3/396dx1VV5w0c/3wBAQERAQEhARH3JRdc0BY1S8tKLXtyqrHmaX2aFmuayedpb5oWZ+lpmZ6mqWZstcUarTRfajpq5S6ihntuiIjiAoqg8Hv+OAe84L2Kwr33AN/363Vf3Hvuj3u/58A953vP+f1+3zW7D/PqvM30S23Fb0d0YkS3BHIPlRDaLKDaVYqmqukkW83sP/ae1VZh6kpuag5WTvmwfHuh22SrciRfii9HwgH0/ZVVLmZyu1OXsCo7l1/7+mnNP7pzIAOen1dtmWvC4pf5aO753prrafFfrFulpAy3zWMj3M9D9fk9njv9e92D2fBKjYSl980em6fEhDMhM4X3ftzBGzf34aoePr58687Th+Fp+8vCuHerzyPmwROjuvLoyM6aaCmviI0IYUT3BGav30twUAAz1+7ly9W5fLk6l8AAYdLIzmTnHmbngaM8f12Pqst2W56/quo1NuUX8f6PO3hweAcumTyfL1bn8sXqXJKimlclHWDVtd1xwP0Ey659m+pL/3bRZO08RFl5BSkxYUwc3sHjpNMi7qs27C8urUpebhvUjrTW4Tw5fR3HT1Twxs19eHL6evYXlzLFruwQFhxIRmo0D1yWzuheSfXW6XzLvmLe/3E7c3P2sedwSbVYeydH8endmVVfhLWW5SlNJ9lKucj6uXMJdBt7avmf7bpuIafOUiVGWac6P162ixeuc9DlkrCY05eteMf62W3MaU+5nrJ9ec4mjp88dVbrjZv71Hd0tRPnoY5enOfJYT+6cwA3/f3UZYLMtBgyUv04kV2rFLjpM/jIntn/3qVup6xw9ezo7jw7ursPgjsH/5NnnZ07Q3F2VwEBQqiHS41K1YfI0GZVHbTLTlbwyfKdPDF9PeUVhj/MPHUZftSrixnfry0d41vww9YDjOmdyNU9E+kY34Lfj7E+Z6/9oje3T1nBiG7xNG8WSG7WqWTLNdF67z/78/dF2zhRXsGuwpJqidawznEUFJWy51CJxwmWPalM2vrac0Qtswcr9UluxTfZp854dYiLYPO+YgBGdItn9vp8uidFIghrc0/1P0uKas4dF6dxRbd4IkKC2F9cytjeF/Dxsp1Vk1QHCFQYuDGjLc+N7V4vX4w25xfxwtISXli9EBHYsLeI4MAALusSx39ktCU1Nozk6DBKT1bQIS7C/1ccHKrpJFvR9mnnvWurLy+zz2z1v7Nq0ZnKv3y6Yld9R1Z7l/4OFv3Jur/9e/js1lPPhbifXPLiDrEs2ryfV2oUE/bb2RV3B+sbppzxVwa1j+XZ0d14cvp65j58KelxPp4uwZ2OV1hnuE6WQuuO/o7m/ASHWTelHCg4KIBfZqZyacc49hUdJzU2nKtfXczeI9YApanLrX1xgFgjCa/uWX3Ow8u6xFe7XC8ifLnaqjzxwLB0Xv1uCwC3T1lOhbG6j9x9aRrR4cHc99FqAH47ohNd2kRy/EQ5nZ/4ltBmATQLCKCo1PPEqpUqk7aVOw5WS+AqY7hvaDqJUc15YvqpS5uVfcL+PiGDNi2bc6zsJHe/v5JFm/ezbf9R/jJnEwVFpUxdvpO8w8erashWrtOwLvHk5B3hF/3PffCKMYYftx0ga9chWkeE0POCKLYWFPPIZ2sIooJBieGUlVdwdc82jO+f7PGqg3Kv6SRblQf5Hd+7fz7z19UeVs6P9E12XrVRe28ttGaO98tIuCCXf+5/njp1ziWe50n6x239SH9sVrVlk3xRYuhMHvoJXrYnz5y41uMoOFcTMlOZkJnq3bjOVSsfDpBQqolKjgkjOcb6UjDj/sHc8vZSNuUX07pFCNPuGcQr8zazaPPZp0R4+cZevHxjL8DqzD1tVS65h0q4ZWAKhUfL+Hn/UZ6sMXfVla8s4t4h7clItUbx9m8Xwzu3ZnCy3DBvQz7HyspZsu0AXRIi+cPMHB4Ylk5EaBDPz9xAWHAgw7vEk3/kOEt/LuTDOwbQN6UVk6Zl83V2Hpd2ak1YcCCf35NZVQLnopfmAzDlhx3MWpfHoWMnOFxyggCBhy/vyHcb9vHy3E10iIvgqWu6smVfMev3HMEYw8ThHQkIkGqDodypWZHj4NEypq3azUfLdrLNTV3g3slRTEgrZezIvmfdxsqzppNsuTpxHJqFWiPiKtU4M1Q5P9L8jfuqkq3yCsMW+3RvZZFhn7t7IfztkurLXOrx1RQUGMC6Z0bQ/SmrI/dDwzt6pdTCOWmZdHq5G6WUOou4FqE8P7YH4978kVdu7EVyTBidE1owbdVu7npvBQePlREREsSEzFQWbi6gX2o0I7slnNYJPCBATiuebIzhx60HePjTNcS2CGZdrtUh3rWj+sJNBVzz2mL2F5fVmNsrl+DAgKqzZQDHysrZuLeIjfnW1ZN/byrgxVkb2Li3iBev78lt7y7jqF2NITMthjCXihNv/tt6z7G9kzhScoKHLu9I96SW/HpoOqt2HqJ7UuQ5zQlWUFTKlB+2M2/DPrbsKyI6PJi02AhahAaxYFMBZSet6Sj+fEM6w7vEc+BoKcu3F1JcWs4tA5P5cfGiWr+Xcq9pJVtpQ625qgq3WmVVXGf+Dqw+DLXXBVEAfL5yN7faE665VkN3/WD4VEKNPmQjX4LAM/8ZI0KC2PjcSMorTJOZ00Qp1ThlpEaz6bkrCQ6yuntc3jWeBZv2sbWgmPCQIFbuKGD+RutM1z++3w7A+mdGEB4SxKFjZTwxfT292kYxrs8F1aY7EBEGpcfyw6RhlJVXVHUo37i3iDk/7WV413hW7TjE1OU76ZfaikHtY1ix4yDprSP4KnsPl3RoTUZqNPuLS6tqEwYECM9c243pWbm8tXAbESFBhIcE8shna4iPDOHJa7qyq7CE1+dvIbRZAI+P6sItA1P4bOVu8g8fr1aPtDLGvh6+6OcfOU6L0KCqffwXq3bz1Zo95B0+zrb9RzlRXsGg9jHcNiiVwqMn2La/mJ17jjG+X1tuGpBM54TIqtdqGdaMNF9Xt2jkmtaRd9D9VrL1/nXwyMZTtRKHPn5aU9dvQte8vpjJ1/esmp8K/DjtgAhc/w5Mux0mroOotrX6NSfMjKyUUvWhMtECSI0N58M7BlY9LjxaxgszcxjVs01Vaa+M5+YyOD2WuTnWPv+rNXv44+wN3DIghasvTOTCC1pW7dNrDgTplNCCTgnWlY/OCZHVJvP9pd214f7LOlSLT/Zv5fJLBhFnD1K6ZWAKs9fvpUubSHYfPMaMrD1MurIzMXa/p66JkXRtE0mqPXrvlwPPrYvCx8t28tSM9aTFhvPpPZmYCnh57iZ2FZYwvEscF6XHctOAZE2g/KhpJVuVlwqL98IWlykRLv6N2+avjO/Fg1OzAPjdtOyq5TPuG+ytCGunxzjrppRSqpro8GD+eMOFAGx8biSvzdvCws0F7D5ojUAc0yuROy9J4/XvtvD24p95e/HP1X7/5Rsv5KL01uQfOU63xMiqmeBPVhi+XbeXoZ3jqmaV9yQpIqAq0QJrAtTKQUntYsO5uEP1GoO1HbD04dIdfLxsJwPbxXDfsHRaNm/G5Nkb+b8FW+mb0ors3Ye48W9LyDtcwuGSE0y6srP/u40ooKklW9Euk3h+cJ31s2WyxzmGrujqfkh8T/sSo1JKKecKCQrkkRGdTrscB9b0N49Oy+bTFbvpkxxVVQ/Xdf6rgWnRLNlmTdlwedd45vxknRmbetdAQoICKDxaRvPgQBJbNq86K+VO3uESHvlsDTHhIXRKaMGlHVtXlcY5eLSMdxb/zMjuCaTGhrM5v4icvCJW7Chka8FRkqJCaRUWTPNmgcxat5fi0pPk5G3nX1l76NU2irk5+dw0IJnfj+7O19l7eOiTLAanx/LfV3aha2Kkx5iUb9Up2RKRG4CngS5Af2PMCg/tRgKvAIHA28aYF+vyvuct3E15lPuWe2zePDiQ7S+OqlZ8d9p/DfJGZEopP2hw+zBVb0SEyeMuZPK4C6uWHTxaRtauQ/zqn9ZxwbXo/XcuBeTHv7XE7Wv2ahtFp/gW5O8t5R/blhETEczCTQXsL3aZo2sN/HH2Rsb0SiQ4KIC5OfsoPFrG6/O3VHutmPBgOiW0YENeEYdLTnD8RDmlJyt46pqu9ElpxaPTspmbk88Dl3XgoeEdEBFG90piaOc4WoQE6XxXDlPXM1vrgOuAv3lqICKBwF+By4HdwHIRmWGM+amO731+Jq6D/7Unlxz6uDUq8Sy2vziK5dsLiW8RWjUEWSnVKDS8fZjymlbhwQztHEfOsyMpPFZGm8jQqv67h46VkZNXxKvzNtM1MZLB6TFEh4fw+cpdfLBkJyO7JXDk+AnmbcjnQPFJ2sUeI3v3ITLbx9AlIZL4lqFce2Ei+UeOc+u7y/h+6wECRejaJpIB7aLZWlBMelwEHeOtPmLJ0WFnTJj+de9gth84dtq8g02t5mBDUadkyxiTA2ftLN4f2GKM2Wa3nQqMBvyzo4pqa007YMwZCx/X1M+fM5YrpbyiQe7DlNc1Dw4kKbh6Pb+osGAy28eQ2b56JY9ebaN4bkyPasvmfjef4cOGuH3tlJhwFvx2aJ1jDAoMcMYEz6pWfNFnKwlwnXZ9NzDAXUMRuQu4CyA5+dxnwD0neopVKVU757UPi4+PZ8GCBfUaSHFxcb2/pr81xnU6fuxoo1qnxvg38vU6nTXZEpG5gLue4o8ZY6bXZzDGmLeAtwAyMjLclOJUSqlz48992JAhQ+rz5VmwYAH1/Zr+puvkfI1tfcD363TWZMsYM7yO75ELuE4GdYG9TCmlvE73YUopf6t7SfCzWw50EJF2IhIMjAdm+OB9lVKqPug+TClVJ3VKtkRkrIjsBjKBb0Rktr08UURmAhhjTgL3AbOBHOBTY8x6T6+plFK+ovswpZQv1HU04pfAl26W7wGucnk8E5hZl/dSSqn6pvswpZQv+OIyolJKKaVUk6XJllJKKaWUF2mypZRSSinlRZpsKaWUUkp5kSZbSimllFJepMmWUkoppZQXabKllFJKKeVFmmwppZRSSnmRJltKKaWUUl6kyZZSSimllBeJMcbfMbglIgXAjnP4lVhgv5fCqQunxgXOjc2pcYFzY3NqXHBusaUYY1p7MxhfOY99WG04+e98vnSdnK+xrQ94Z5087r8cm2ydKxFZYYzJ8HccNTk1LnBubE6NC5wbm1PjAmfH1tA0xm2p6+R8jW19wPfrpJcRlVJKKaW8SJMtpZRSSikvakzJ1lv+DsADp8YFzo3NqXGBc2Nzalzg7Ngamsa4LXWdnK+xrQ/4eJ0aTZ8tpZRSSiknakxntpRSSimlHKfBJVsiMlJENorIFhGZ5Ob5EBH5xH5+qYikOiSu20SkQESy7NsdPorrXRHZJyLrPDwvIvKqHXe2iPRxSFxDROSwy/Z60kdxtRWR+SLyk4isF5EH3bTx1zarTWw+324iEioiy0RkjR3XM27a+OVz2dCJyA32Nq0QEY8jp862/3ESEYkWkTkistn+2cpDu3KX/+MZvo7zbJx6LKoLpx7H6sIxx0BjTIO5AYHAViANCAbWAF1rtLkXeNO+Px74xCFx3Qa87odtdgnQB1jn4fmrgFmAAAOBpQ6JawjwtR+2Vxugj32/BbDJzd/SX9usNrH5fLvZ2yHCvt8MWAoMrNHG55/LxnADugCdgAVAhoc2Z93/OOkGTAYm2fcnAS95aFfs71jPsA6OPBb5YJ38chyr43o54hjY0M5s9Qe2GGO2GWPKgKnA6BptRgNT7PufA5eJiDggLr8wxiwECs/QZDTwnrEsAaJEpI0D4vILY0yeMWaVfb8IyAGSajTz1zarTWw+Z2+HYvthM/tWszOoPz6XDZ4xJscYs/EszRy7//HA9X9hCjDGf6GcN6cei+qiof0f1YpTjoENLdlKAna5PN7N6QebqjbGmJPAYSDGAXEBXG+fpvxcRNp6Oabaqm3s/pBpX5qaJSLdfP3m9mn/3lhnalz5fZudITbww3YTkUARyQL2AXOMMR63mQ8/l02F3/8fz1G8MSbPvr8XiPfQLlREVojIEhEZ45vQas2px6K6aMjHsbrwyecnqL5fUHn0FfCxMaZURO7G+sYzzM8xOdkqrNIHxSJyFfAvoIOv3lxEIoBpwERjzBFfvW9tnCU2v2w3Y0w50EtEooAvRaS7McZtHwlVnYjMBRLcPPWYMWa6r+OpD2daJ9cHxhgjIp6GxKcYY3JFJA34TkTWGmO21nes6pzocew8NbRkKxdwzaQvsJe5a7NbRIKAlsABf8dljHGN4W2sfgtOUJtt6nOuSYQxZqaIvCEiscYYr9fnEpFmWMnMh8aYL9w08ds2O1ts/txu9nseEpH5wEjANdnyx+eyQTDGDK/jSzjuM3ymdRKRfBFpY4zJsy/X7PPwGrn2z20isgDrTK5Tki2nHovqoiEfx+rCJ5+fhnYZcTnQQUTaiUgwVqfDmqNUZgC32vfHAd8ZuxecP+OqcQ34Wqz+Nk4wA5hgj8gYCBx2OcXvNyKSUNm/QUT6Y/2ven1HZb/nO0COMeYvHpr5ZZvVJjZ/bDcRaW2f0UJEmgOXAxtqNPPH57KpqM1+0Ulc/xduBU47eycirUQkxL4fCwwGfvJZhGfn1GNRXTTk41hd+GZ/7o1e9968YY0c2IT1Decxe9mzwLX2/VDgM2ALsAxIc0hcLwDrsUZ4zAc6+yiuj4E84ATWtejbgXuAe+znBfirHfdaPIx48kNc97lsryXAIB/FdRFW5+5sIMu+XeWQbVab2Hy+3YCewGo7rnXAk/Zyv38uG/oNGGt/PkqBfGC2vTwRmOnS7rT9j1NvWP2W5gGbgblAtL08A3jbvj/I/mytsX/e7u+43ayHI49FXl4nvxzH6rhOjjgG6gzySimllFJe1NAuIyqllFJKNSiabCmllFJKeZEmW0oppZRSXqTJllJKKaWUF2mypZRSSinlRZpsKaWUUkp5kSZbqs5EJEZEsuzbXhHJte8Xi8gbXnrPiSIy4QzPXy0iz3rjvZVSSqlzofNsqXolIk8DxcaYP3nxPYKwagD2MVaBV3dtxG4z2BhzzFuxKKWUXaLqeWPMHBF5DmhpjLnf33Ep59AzW8prRGSIiHxt339aRKaIyCIR2SEi14nIZBFZKyLf2jX/EJG+IvJvEVkpIrNrlIeoNAxYVZloicgDIvKTXYl+KlgFboEFwNU+WVmlVFP2FPCYiNyMVcNxon/DUU6jyZbypfZYidK1wAfAfGNMD6AEGGUnXK8B44wxfYF3gT+4eZ3BwEqXx5OA3saYnlhlGCqtAC6u97VQSikXxpiFWGVfHgbGG2PKRSRNRN4Rkc/9HJ5yAE22lC/NMsacwKo/FQh8ay9fC6QCnYDuwBwRyQIex6rAXlMboMDlcTbwoYjcArheVtyHVUNOKaW8RkR6YO2XyowxRQDGmG3GmNv9G5lyCk22lC+VAhhjKoAT5lSHwQogCOub4XpjTC/71sMYc4Wb1ynBKvJaaRRWIdE+wHK7Txd2mxIvrIdSSgFgd3X4EBgNFIvISD+HpBxIky3lJBuB1iKSCSAizUSkm5t2OUC63SYAaGuMmQ88CrQEIux2HYF1Xo9aKdUkiUgY8AXwG2NMDvB7rP5bSlWjyZZyDGNMGTAOeElE1gBZwCA3TWcBl9j3A4EPRGQtsBp41RhzyH5uKPCNN2NWSjVdxphjxphMY8wc+/FCY0zll8UYEXkT6C0i/+3XQJXf6dQPqkESkS+B3xljNnt4Ph74yBhzmW8jU0opparTZEs1SCLSCYi3RwG5e74fVr+wLJ8GppRSStWgyZZSSimllBdpny2llFJKKS/SZEsppZRSyos02VJKKaWU8iJNtpRSSimlvEiTLaWUUkopL9JkSymllFLKizTZUkoppZTyov8HxAcNvzVNHTUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(3)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[oscillator_probe])\n", + "plt.ylim(-1.2, 1.2)\n", + "plt.xlabel(\"Time (s)\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(sim.data[oscillator_probe][:, 0], sim.data[oscillator_probe][:, 1])\n", + "plt.grid()\n", + "plt.axis([-1.2, 1.2, -1.2, 1.2])\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$x_2$\")\n", + "hide_input()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/advanced/processes.ipynb b/.doctrees/nbsphinx/examples/advanced/processes.ipynb new file mode 100644 index 0000000000..bc03e5b286 --- /dev/null +++ b/.doctrees/nbsphinx/examples/advanced/processes.ipynb @@ -0,0 +1,908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Processes and how to use them\n", + "\n", + "Processes in Nengo can be used to describe\n", + "general functions or dynamical systems,\n", + "including those with randomness.\n", + "They can be useful if you want a `Node` output\n", + "that has a state (like a dynamical system),\n", + "and they're also used for things like\n", + "injecting noise into Ensembles\n", + "so that you can not only have \"white\" noise\n", + "that samples from a distribution,\n", + "but can also have \"colored\" noise\n", + "where subsequent samples are correlated with past samples.\n", + "\n", + "This notebook will first present the basic process interface,\n", + "then demonstrate some of the built-in Nengo processes\n", + "and how they can be used in your code.\n", + "It will also describe how to create your own custom process." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:56.358265Z", + "iopub.status.busy": "2020-11-25T16:46:56.357412Z", + "iopub.status.idle": "2020-11-25T16:46:56.850162Z", + "shell.execute_reply": "2020-11-25T16:46:56.849656Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interface\n", + "\n", + "We will begin by looking at how to run an existing process instance.\n", + "\n", + "The key functions for running processes\n", + "are `run`, `run_steps`, and `apply`.\n", + "The first two are for running without an input,\n", + "and the third is for applying the process to an input.\n", + "\n", + "There are also two helper functions,\n", + "`trange` and `ntrange`,\n", + "which return the time points corresponding to a process output,\n", + "given either a length of time or a number of steps, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `run`: running a process for a length of time\n", + "\n", + "The `run` function runs a process\n", + "for a given length of time, without any input.\n", + "Many of the random processes in `nengo.processes` will be run this way,\n", + "since they do not require an input signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:56.860486Z", + "iopub.status.busy": "2020-11-25T16:46:56.857504Z", + "iopub.status.idle": "2020-11-25T16:46:57.105882Z", + "shell.execute_reply": "2020-11-25T16:46:57.106294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'process output')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create a process (details on the FilteredNoise process below)\n", + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "\n", + "# run the process for two seconds\n", + "y = process.run(2.0)\n", + "\n", + "# get a corresponding two-second time range\n", + "t = process.trange(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(t, y)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"process output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `run_steps`: running a process for a number of steps\n", + "\n", + "To run the process for a number of steps, use the `run_steps` function.\n", + "The length of the generated signal will depend on the process's `default_dt`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.115005Z", + "iopub.status.busy": "2020-11-25T16:46:57.113583Z", + "iopub.status.idle": "2020-11-25T16:46:57.341441Z", + "shell.execute_reply": "2020-11-25T16:46:57.341840Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'process output')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "\n", + "# run the process for 1000 steps\n", + "y = process.run_steps(1000)\n", + "\n", + "# get a corresponding 1000-step time range\n", + "t = process.ntrange(1000)\n", + "\n", + "plt.figure()\n", + "plt.plot(t, y)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"process output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `apply`: running a process with an input\n", + "\n", + "To run a process with an input, use the `apply` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.350510Z", + "iopub.status.busy": "2020-11-25T16:46:57.347483Z", + "iopub.status.idle": "2020-11-25T16:46:57.656443Z", + "shell.execute_reply": "2020-11-25T16:46:57.656866Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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aM/j6xlZ3lwrPY9ONH5zpXpM5APb9SYv/THqgzSFfLz1z48PZmV6ExSrR6wQEhsMdf4O3VsGuX8Ntv+nEd6Bwh51pRVgl3ZY2akPtCHoZtY0WzFYrIe5WE1vMcOg1TQYYOsnuKba7kVZFR2EjYPGv4PwXWsxA0aWktnc4Sf4RKDgKM5/UpB87LE4yUFrdyIm88msPjpoH078JB/6qPYeiS0kxFjEkxI+xQ7u2ydz19LkdgbM7975AZb0JgSDQ3WrizM+gIk/LHXdAVNgAEocEk2os5olbR187MPVROL1OSykdewf4q0E2XUWKsah9uvGh18AnCCbe5/CUefHX4kFTR7T4LBc9B2lbYNvT8M2doFP3h11BvcnC3sxS1kyN6vImc9ejPvEuwl7b6Y6sqawzE+Crx8vdP1bjRvAfqKUMOiE5ycCRC2WU1bSYdyAELH1Bk5V2/7Zdtis6Trt1Y1MdpG+BcavBN8jhaSH+3swcNbBtuwnfIEj+BVw6pqWdKrqELzNtxYLdP0HOo45ACLFECHFOCJElhGiTSiOEGC6E2C2EOC6EOCWEuN2T9nQn9tpOu2LFihWsX7++OVjcYLLQ0J4mcxYTZGzXAsR65zuIxUkGrFLTLFsROV7bGRz+p9aiQOFx2q0bZ+2ExmoYu9rlqcmJBrJLasgpqW59YPw9EDUDUp+Dhmq7axWdS4qxiCBfL24aNai7TdGCj574QmtekA2MAnyAk0DSdee8CvxH0/dJQK6r5506daq8HqPR2OaxvkhxZZ08mXdVNpjMLs89c/aM3H3wT/LT30XK7GNvujzfarXKm/43VT7+78NtD9ZckfJ/o6T84KEOWK1oL4//+7C86X9TpdVqdXqe2WKW+wr2ydffWyrf+nOcPFN00uWavLIaOeKZLfIfn2e1PXjxkJQ/D5Zyz4s3Yr7CDSwWq5z6qx3yO+8edXluSW2JfPfs2/KX7yyQh4+/2eHXBI5IB9dVT8YIZgBZUsocACHEB8AqwNjSDwG2KEkIcAmFQyrrzC6bzJmtZopriymqKeJ7aa9BxGA49XsmF+7imRnPMHbQWLvrbEVH647mU2+y4NeyYnnAQLjp2/DF81rOuYOgs+LGcVc33lewj19/9Wvyq/O1B4J94dMHmWqYys9u/hmjQkbZXWeLB6UYi/jW3NGtD0ZP1yTEfX+CaY+Bf2gnvSvF9RzPK6e0utGpLGSymnjl5Cu8ceYNTFYTwRYLEypysDtQ4AbxpDQ0DGjZ1jK/6bGWPAc8JITIB7YB/2nviYQQTwghjgghjpSUlHjC1h6PrcmcM1mo3lxPTkUO5fXlDPAewL/KTazziedH039EXlUeD259kI8yPnK4vlXR0fXc/G3wC4Xd/9sJ70bhCFe6sZSSf5z8B0+mPomP3offxz7A/tw8ds/4Fc/OeJbs8mzu23Ifuy7ucvgayUkGjl68Sml1Q9uD838C9RXw1d866y0p7NBcLBhvv1iwxlTDt1K+xSunXiF5RDIbB83jy4ISVt30tEfs6e5g8f3Av6SUUcDtwNtCiDY2SSlflVJOk1JOCw+3P7lH9vHeOLYmcyEOsoXqzfXkVuQipWRE8AhC9H5MvXqZ+KS7+VrS19h4x0ZuGnoTvzzwS/599t92n+OmUYMIall01BK/EJj1PS0LKe9wJ74zRUtS05zrxn889kf+euKvrBy9krUr1rK46DxBfqEMHrOMBxMfZN2KdcSGxvJfn/8XKRdS7D7H4iQD0tFQoiETIWmVlk5aW9aZb03RghRjITNHDbSbBl5rquXxHY9zrOgYv5n9G16Y8zyjsr5AjJzrNBngRvCkIygAolv8HNX0WEseA9YCSCkPAH5A+6azAH5+fly5cqVPO4PKOq3JnJ+dJnMmi4ncylyEEMQEx1BXWYdfXSHovGDMYgCCfYL5y4K/kDwimRePvMjm7M1tnsfHS9dcdGS12vm/nPktLQPpyz90+vtTtG4y5+PV9k/zw/QPeePMG9wz5h5+NetX+KJrSga4XasUBgwBBl5b/BrjBo/j2T3PcqrkVJvnGTs0mKEhfo5nGc99Rgs+H369U9+fQiOnpJrskhqSE9vu+qzSyo/3/pizV87y+3m/Z+XolVB0BsovQOJyj9nkyRjBYSBOCDESzQHcB1xf8ngRWAj8SwiRiOYI2q39REVFkZ+fT1+VjaSUXK6oZ4CPnvRynzbHrtRfwWQ1Ee4fTk5RDn5+fkQde14rImuR+++l8+L5Oc9T0VDBLw78gjFhY4gf2HoGbnKSgS2nLnM8r7x1rjmATwDMeEKLFRSnQ0SCx95zf0TTjRvsykLHio7x20O/5daoW/nJzJ+gEzrI/UKTcRKWtTo3wDuAPy/4Mw9sfYDv7vou61auY7D/tfsrIQSLkgysPZJHXaMFf5/rbi4MYyE2GQ7+A255Crz9PfJ++ys2B7zIzuf8yqlX2JW3i2emP8PC4Qu1B9O2AEJz+J7CURS5M77Q5J4MtOyh/9f02C+BlfJaptA+tIyiE8BiV89pL2uor5NytlCOeGaL3JNR3ObY7w79To771zj52fnPrj1YlKZlfxx6ze7zldaWyvkfzpe3f3y7rG6sbnWsvLZRjv7xVvnbbWn2jakulfJXBinXf7vD70dhn99uS5Ojf7xVltc2tnq8sqFSLv5osVyybomsaqi6dmDLD6X8daSUjbV2ny+jLENOfXuq/Hbqt9tkE+3JKJYjntkid5wttG/M+b1Of4cUHWfN3/fJpX/c0+bxk8Un5cR/T5TP7Hmm9ef1t1lSvn7bDb8uTrKGPBojkFJuk1KOkVKOllL+pumxn0kpNzV9b5RSzpJSTpRSTpJS7vCkPb0VW77xzJGtdeMTxSd42/g298bfy+KYxdcOpDfJPvGt7xRtDPIfxO9u/R15VXn8+difWx0L8ffmplGDHM+4DRgEU74Gpz6EiuuVPsWNkGIs5KZRg9roxs8fep6i2iJ+O+e3BPoEag9arZC+DWIXOrxjjwuL4wdTf8Ce/D2sy2zd3XbmSFs8yMHnPGKWNs1s/8tamxJFp3CluoGjF6622fXVmev4yZc/IWJABP9v5v+7ljF2NReKTrfZ9XU23R0sVrjAYpXsTC9qoxs3Whr5+f6fExkQyQ+m/qD1orQtWvfQ4CEOn3da5DTuS7iP99Pf50TxiVbHFiVG2C86snHzUyCtKrOkE7HpxosSW2eRHLx8kE3Zm/jGuG8wKWLStQOXjkPVJUhwrhvfn3A/M4fM5KUjL1Fady0brDkelFaMxV48SAiY9X3tQpS2qeNvTNGKnenFWGXbHlKvnXqNC5UX+PWsXxPk0yIgnNbUxNLF53yjKEfQwzmRd9VuvvEbZ94gpyKHn938MwK8A64dKM+DyyfcuoP43pTvERkQyXP7n8NkNTU/vqi5CZ2DYGLYCK330LG3VRVqJ5Ga1lY3NllM/O/B/yUqMIonJjzRekH6ZhB6GHOb0+fVCR0/nflT6i31/OFI6yB/cpKBKzWNnMi7an9xwjIIG6nGWnYiKcYihl7XZO5C5QX+dfZfrBi1ghlDrpslnb4VDONg4EiP2qUcQQ8nxVjcJt+4uLaYN868QfKIZGYPm916wblt2r8JK1w+d4B3AM/MeIbsimw+zvi4+fGosAEkNRUdOWTGt6ChAk6vbdf7UdgnxVhE0nVN5t5Je4ecihx+PPPH+Hn5tV6QvhViZrvVCDAmJIZHxj7C5pzNHCm81mHU1oRuh6PPWafXOpPmfQWXT3bofSmuUddoYW9mCYuSDM3Sj5SS3x76Lb56X3447YetF1SXwMUDHpeFQDmCHo893fgvx/+CyWriB1N+0HZB2mYIT4DBsW49/4LoBUyPnM5fT/yVysbK5sdtRUdX7BUdAUTP0HLOD76q5hvfIPZ046v1V3nl1CvMi5rHrVG3tl5QkgGlGZDo2tnbeHzC40QGRPL7I79vTrO+Fg9y4vAnPwjeA+DQq+16T4q2fJlVSr3J2upz3ndpH/sK9vEfE/+jVWYX0HRTJz0uC4FyBD2a5nzjFr8458rOsSFrAw8kPEB0cHTrBbVlcGF/u35xhBA8Pe1pKhoqePXktT/25Kaio532io60hVoqaUka5H7ZrvelaI093fifp/9JnbmubfwHWiQDuJ9O6O/lz7cnfpszV86QejG1+fHkJAM5JTVkO4oH+YfBhHu0duSqwOyGSDEWtkr6sEorfz72Z4YFDuP+hPvbLkjfCiHDtcaPHkY5gh6MvXzjl4+/TJBPUFvNGODcpyAt7S48SRyUyMrRK3k//X2KarTXHDs0mGGh/s7vFsfdpRWYqbvFGyLFWMSwUP9m3biwppAP0j9g5eiVjAq10zMofSsMnQIh13dscc7K0SsZHTKaPx/7M2arlgnkMh4EmsM318Oxt9r1eoprWKySnWnFzEuIaE76SL2QSlpZGt+e9G289ddVGDdUQc5u7W+5C2YVKEfQg0kxFjVfkAHOXjnLF/lf8PWkrxPiG9J2QfoWCI6CIZPa/VpPTnwSq7Ty+hmtmlRrQhfB3swS6hot9hd5+8OUr2sXpor8dr+mooVunBjRrBv//eTfkUi+PfHbbRdUXtImkXWgylSv0/PdKd8ltzKXTdlaJpDNATl1BIaxMGK2VmlsdfC7oHDKibyrXKm5lvRhtpp5+fjLjA4ZzbKRdmIAmSlgaewSWQiUI+ixlFY3cPTiVRa1KEN/5eQrBPkE8UBi25m0NNZA9i4tsNSBO4iooChWxa5iXcY6Cmu03PJFSQbqTVa+zLLThM7G9McAqcZZdhCbbmy7M8+rzGNj1kbujb+XIYF20n/Tt2r/upEMYI/50fMZP3g8r5167dquINHAsYtXKalyEA8CmPE4VFyErFTH5ygcsqO5yZzWK23b+W3kVubyn5P/E7290aLpW2HAIBh+U5fYpxxBD2VXWjGyhW6cXpbO7rzdfC3pa63zjG1kpWrb9xvoR/L4hMeRUvLP0/8E3Cg6AggdDrGL4Pi7qvCoA6ReVyz4xtk30As93xj3DfsL0rfAoDgIH9Oh1xNC8Pj4x8mvzufT858C1+JBux3Fg0CLRwSEK3mog6QYi7hp1CCC/by1nffp1xkTNoYFwxe0PdncCJk7tIFSDuZPdzbKEfRQUtJa68avnHyFIO8gHkx80P6C9K2aXj/8lg6/5rDAYayOW80nmZ9QXFuMj5eOeQkRjouObEz5ulbclL2zw6/dH7EVC9p04+LaYjZmbWRV7CrCB9jpslt3VQvM32DzsbnRc4kLi+P1069jldZm+dFhGimAlw9MvF+LQ1U5uTFQtCG7pJqcFkkfu/N2k1ORw2PjHrM/cyJ3DzRUdnjX1xGUI+iBXK8b51bkknoxlfsT7yfYJ7jtgnaMpHTFo+MexSItvJv2LuBG0RFow0wCIuCo/fbWCvtcXyz4jvEdLNLCo2Mftb8g4zOwmm/4AqETOh4f/zjZFdnsurirOR70ZZaTeBDAlIe1ZIQT793Q6/c3WiZ9SCl5/fTrRAVGtW4L05K0LeAdAKPmdZmNyhH0QK7lG0cCWmGRt87bfooZQO7epi6UNx5Yig6KZtHwRXx07iNqTDXMiw/HW++k6Ai0FsiTHtCckbpbdJsdxiK89ZpuXNFQwYfnPuS2mNvapgXbSN8CQUNg6OQbfu3FIxYzPGg4r556FSklyUmR1Jus7M100sF3cKzWg+jYW6p2pB2ktkj6OFx4mNOlp3l03KN46ezctFmtWv1A3CLw9mt73EMoR9ADSTEWEuTnxcxRAymvL2dj1kaWj1retuDEhu0OYvT8Tnn9R8c9SpWpinUZ6wj2c6PoCDR5SN0ttouWuvEH6R9Qa67lsXGP2T/ZVKcNqU9YBrob/7PV6/Q8Mu4R0srSOFJ0hJmjBhLk52AoUUumPAxXz2s3HwqX2JI+bLu+f57+J4P8BrEqdpX9BQVHoLqoS2UhUI6gx9Gcbxwfgbdex9qMtdRb6vl60tftL7BatfiAky6U7WXc4HFMj5zO28a3MVlNLEp0UXQEMGi0lmJ47C3NJoVTbLrxokQDjZZG3k9/n9nDZreZD3FtwS4w1XZqOuGKUSsI9Q3lbePbeOt1zIuPYFe6i3hQ0krwDVFBYzdpmfRxruwcBy4f4KGkh/DV+9pfkLZZGygVl9yldipH0MM4fvFavrHtAjFr6Cxiwxy0jCg4CtWFnZ5v/MjYRyiqLWL7+e3uFR2Btiu4eh4uqEpjV7TUjT/L/Ywr9Vf4WuLXHC9I36qNC42Z7ficduLn5cfdY+7m87zPyavMa44HHbvoJB7k7a9VGhs3qUpjN9jRVCyYNCSY99Pfx0+v/Z/bRUpN/ht5K/iHdqmdyhH0MFJa6Mafnv+U0rpSvj7WwW4AtF+cFiMpO4s5w+YwOmQ076S909wt0aUjSFqpXayOvd2ptvRFbLrx0BA/3k17l5EhI7l56M32T7aYtWydMUuaR1J2Fvcl3Idep+e99Pea40Gp7jh8S4PWdkLhkLpGC19maUkfFQ0VbM3ZyrJRy+wXgwKUpENZTpcVkbVEOYIeRkpaUfMQ+XfT3iU2NJabhzi4QNjuIK4bSdkZCCG4P+F+jFeMnCo9RXKSG0VH3v5a24m0zVqJvMIuLXXjU6WnOHvlLA8kPGA/lRDg4n6oK/NIF8qIAREsiVnCJ5mfIHT17sWDhkwAw3g49UGn29OX2JtZ0pz08UnWJ9Rb6u0Xg9qwzR7w5EhKByhH0INomW98pvQMaWVp3Bt/r+MLRGkGXMny2FDrFaNXEOgdyPvp77tXdARarrm5TpMOFHZpqRu/m/YuQd5B2pByR6RvBS8/rXDPAzyU9BC15lrWZ67XmtCV1pBV7GLOxMT7NFmyJMMjNvUFUoxFBPl5MTUmmA/SP2BG5AzGhDkpBEzf7HKglKdQjqAH0awbJxr48NyH+Hv5s3yUk4t8Wvu7ULaHAd4DWBW7is9yP8MQanJddATaL/LAUXDyfY/Y1Bew6caDQupIyU1hddxqBngPsH+ylJojGL0AfALsn3ODjB00likRU3g//X0WJGiFbC53BePvBqFTn7MDLFbJrvRi5sdHsP/SXi7XXHa+GyjP02Y+dIMsBMoR9ChSjEWMGxZMgH8j23O3s3zU8mszau2RvhWGTYPgoR6z6d74ezFbzXyc+THJSQbXRUdCaLuC3L1QftFjdvVWbLpxcpKBjzI+wiIt3Jdwn+MFl09ARZ7Hh5PcG38v+dX5XKg9wbhhwc7bigAEGWD0Qm12tcoSa0PLpI93099laMBQ5kXNc7yguYeUcgT9mpKqBo41NZnblL2JBksD98Tf43hBRQFcOubxC8TIkJHcMvQW1masZX78INdFR6BllQCcUtPLrsemG8+LH8i6jHXMjZpLdJCDAjLQLhBCB2OWetSuRSMWMdBvIGvPrWVRooHjeeXO40GgyUOVBaqmwA62pI9oQwWHCw83B+Udkr6lXQOlOhvlCHoIu9KLkFIbHL/23FomhE8gYWCC4wW2kZTtmFLVUe5PuJ/i2mJqvU+6V3QUFqNVoJ78QFWgXodNN67zPkVZfZlzZw9aAHHELAgY5FG7fPQ+3BF7B1/kf8GUUUIbSpTm4nNOWAa+wdrnrGiFrVjw0wsb8NZ5szp2teOTa8vgwr5u2w2AcgQ9BttwkmqRTm5lLvfG3+t8QdpmGDwGBsd53LY5w+YwLHAYH2d+xHx3io5Au1u8kgkFxzxuX2+hpW68PutjhgQM4ZahTpoEXsnWJsB1wcxagDVj1mCVVk5X7GBYqD+prhyBtz8krQLjRq0NugKArOJqckprmJcQwuaczSSPSCbUL9TxgnOfgrR22edsD+UIegBak7lSkpMMrM1YS4hvCLfF3OZkQVMXyi66g9Dr9NwZdycHCw8yZbTFddERaBcILz8VTGyBTTeeMtrCgcsHuDPuTtdyAXTZBSI6KJpbht3CJ5mfsDBxMHszS6ltdNFafOL9YKq5lvqoaN4xewWdoaqxijVj1jhfkL5VGyjVCT2kOopyBD2AvZklNJit3BTny+6Lu1k1epXjEnTQulBKS5duJe+IvQO90FPMHveKjvxCNPvOrNP6qyuadeNi9qAXeudyAWgX1yETtZkPXcQ9Y+6huK4YQ2QODWYrezOdDCUCGH6zZp9y+M2kpmlJHzsLNhETHMM0wzTHJzfWaO3bOzhQqrNQjqAHkGIsItjPi8uWfZil2fUFohO7ULpLxIAI5kTNYdv5TcwcFeI6TgDa3WLdVW3IhoIUYxEzRoWw9fxGbo26FUOAwfHJVYWQf6jLdeNbo27FMMDAiYptBLsTD9LpYMJ9kPM5VF7uEht7Mrakj2mxjRwvPs6aMWsc1wGB1kjQXN+tshAoR9Dt2HTjefHhbMrewITBExz3FYJO70LZHtbEreFK/RVGRl9wr+ho1DwYMFjbFfRzbLrxyOhcyurLXMsFtmSALnYEXjov7hpzF19dPsBN8bgXDxp/NyDh7PousbEnY0v6qPXbh5fOixWjXSRzpG/VugKMmNU1BjpAOYJu5liTbpwYU0FWeZbj9rQ2snc3daHs+juIWcNmETEggnzL54AbRUd6Lxh7B5zb3u9bTtj+r/LMu4kMiGTWUBd/+GlbtMK8iMQusK41q2NXIxD4hR2jrKaRoxdcxIPCx0DkeOXw0T7noaF69hV+xqLhWkquQywmyPhUSw2+wYFSN4pyBN2MTTe+bNmLr96XpSNd5Iunb9XaAMfM6RoDW+Cl82J17GqOFn9FQpTJddERaHeL5jpI3+Z5A3swqWlFxEc1cqzkoOsgcV05nN/TbbpxZEAktwy7hbOVO/HWS9fZQ6B9zgVHtUynfkpto5m9maUkjM6lsrHS9a4v98umgVLdKwuBcgTdTqqxiBmjgki9uJ3kEcn2B9PbsJg1yWDMbZ3ehdJd7oy7E4CIoafcKzqKmgEh0f36btGmG4cPOYFO6FzHgDI+A6sJEl3sDj3InbF3UlxXRNLoIlKMRUhX9SBjtd8LznzieeN6KHszS2kwW7nqtZfhQcOZHjnd+YK0TeA9QJsl0s141BEIIZYIIc4JIbKEEM86OOceIYRRCHFWCNGvxlvZdOPhUdlUmapcXyDyvtK6UHqoyZw7DA0cyi3DbuFC4+dIaXFddKTTwbg7tcEq/bR/vaYbW7jY+AVzhs0hMiDS+YK0TVoywLCpXWOgHeZFzyPUNxTvkMOcL3UxlAggNFrLIDqzrt8WEaYYiwgKKiOr8hR3jbkLnXByebVaNPkvLrnTBkrdCB5zBEIIPfBXYCmQBNwvhEi67pw44MfALCnlWOD7nrKnJ2LTjQssexkWOIxpkU7SzED7xdH7aj1eupE1cWsoayghwpDrnmwwbo02dN24weO29URSjEVEGC5wtfGKa2ffWANZqVrFeBcnA7TER+/D8lHLya45iNDXuG42CFoL8pJ0KDrreQN7GLakjxEjjOiF3nk3WYC8Q1BTDIkuzusiPPmbNgPIklLmSCkbgQ+A6/e6jwN/lVJeBZBSuuhx3LdIMRaSEGXmRMlhVsWucn4H0dyFcj74OmlE1wXMjZ7LQL+BDDScdK/oKHK8VgXdDweZ2HTjgYZThPmGcWvUrc4XZKZo6YQ94AKxOm41Zmlm+PB099KFx64Goe+XMuCxi1cpq6nnqu4As4fNdjxf3EbaJtD7aDJvD8CTjmAYkNfi5/ymx1oyBhgjhNgnhPhKCLHE3hMJIZ4QQhwRQhwpKXHR8KyXUFLVwPG8cgxDTwOwarQLPbjwFFRc7NZ+JDa8dd7cPvJ2Cs3HaJBVrouOhNB2BRf2a83y+hF7M0tpsFZTZD7KslHL8HYV20nbDAMGaTJLNzMmbAxjB41FBh7iRN5ViqvqnS8IGKzdqJz5uN/JQynGInyDsqg0XXGd+Sel9jmPXgC+TmKCXUh3B4u9gDhgHnA/8JoQIvT6k6SUr0opp0kpp4WHh3ethR5iZ1oRUlrJM+1h5pCZDA100Ura1oUy3rNdKN3ljtg7sEgzQQNPu3e3OH4NWq55/womphiLCBp0GrM0ub5AmBu0QHHCsm5PJ7RxZ9ydXDVfQPgWsDPNjQ37uDVa+/H8w543rocgpdTkv6FnCPENYW7UXOcLLh3XWov3gF2fDU86ggKgZX/dqKbHWpIPbJJSmqSU54EMNMfQ50kxFmEIL6Sk/rLrCwRo8YHhN2t3XT2A+IHxJA5MJCj8hHtFR4NGw5BJ/UoesunGQeEniA+Ld95NFrTq3MaqHnWBWDJyCb56X0IjjrtuKwKaE/Pyg9Mfed64HkJ2STXny0qp0h/n9pG346P3cb4gbbMmofWQmzrwrCM4DMQJIUYKIXyA+4Dr5xduQNsNIIQYjCYV5XjQph5BbaOZL7NKMQw14u/lz4LoBc4XlOVA8dkekW/cklWxq6iSuZSbL7guOgIt1/zyCSjN8rhtPYFjF69Sbr5IlTzPHbF3uF5g3KTViIx0cUfZhQT7BJM8IhkCj/Nl9mXX8SC/YE33PrteS3fuB+wwFuEdfAqLO7s+KbX4wMg5MMBJsVkX4zFHIKU0A08BnwFpwFop5VkhxC+FELZbns+AK0III7AbeFpKecVTNvUUtHzjRoqth5gXPc/xmEIbtmKsHuYIbh95O146L3xDj7lXXDbuTkD0m2BiirEI39Cj6IUXt49yMU7UYoZzWyF+CXi5uKPsYlbHrsYka7H4n2JPhot4EGjyUE0J5O7xvHE9gFRjESHhJ4gNjSVpYJLzk0vSm+aM95xdH3g4RiCl3CalHCOlHC2l/E3TYz+TUm5q+l5KKX8opUySUo6XUvaLCRcpxiKCwnKoMVc6n0lsI30LGMZrA196EGF+YcyLmodf2El2GC+5LjoKHqr1VDnd93PNpZTsMF7CL+wkc6Nudd5qAODCl1qDvi4YNNRepkVOY0jAUPwHHncvHhS3WBtYc+ZjzxvXzZRUNXCiKIMGL23X57TBHGi7PkSPSPpoSXcHi/sdNt3YMNRIqG8oNw91kR1SXQIXv+rWIjJnrIpdhVlUkt9w3HXREcC41drAmmKj543rRrJLqsmrP45ZVLoXAzI2VZl2c42IPXRCx8rRK8A/k52ZGZgtLmYUe/tp+nf6Vq2fTh9mZ1oRXsHH0Ak9y0a5sWNP2wTDb9JmPvcglCPoYo5euEpZbRVl8ji3xdyGt85FOuG5bYDscbKQjVnDZhHmOxDvkKPuFR0lrtSyn85u8Lht3ckOYxHeIUcJ8QljTpSLvlBWq7bri10EPi5kwm5CK5CS1Pocci8elHSHtsM5/4WnTetWdhgv4xd2nNnDZrmuHbiSDUVneuSuTzmCLibFWIhviBGTbOD2kS50Y9DuqkKHg2Gc543rAN46b1aOXoF3UBrbjW4EgQMjNHnIuKFPy0Pbjdl4B6WxMna5a2effwiqi7Spbj2U4cHDmTB4Et4hx9hx1o140OgFmjzUh1tT1zaa2X/pAFJf4V4yQNpm7d/e5giEEFVCiEo7X1VCiMquMrKvYMs3HhxpZGjAUCZFTHK+oKEKcnZDwopunV7kilWxq0BYSa/e47roCLTW1KUZfVYeKq6qJ736CxAW14WCoF0g9D6att6DuSNuJTrfYrZnHXYdD7LJQ2lb+qw8tCejFAKPEOAV7Lp2ALTPeejkLp045y5OHYGUMkhKGWznK0hKGdxVRvYVskuquVBeTJU4y9KRS523lACt54ylscfKQjbiwuIYHZyAV8hR94qO+rg8tCutGK/gY4wMiid+YLzzk6XU4gOj5muplz2Y22JuQy+8KWW/66FEoMlD9eWQ0zfloW1nc/AOOsvy0W7UDlQUQMGRHrkbgHZKQ0KICCHEcNuXp4zqq+wwFuEVfBqJ1c3A0pamdgM3ed64G+TehDvR+11io9GNitI+Lg9tNB5B71/AvQkuGswBXD6ptQ5J6lnphPYI9glmztB5eAWf5NOz+a4X2OQhY9+ThyxWyRcFKaAzszruDtcL0rdo/3Zja3FnuOUIhBArhRCZwHngCyAX+NSDdvVJUoxFhISfJi4sjrgwFwXU5kZt1m/8UnA2xKSHcPuo29HhxcnyVNdFR9Bn5aHaRjMny1MR6F3XDoCWRSL0EO/GuT2AexJWo/OqZVPGTtcn92F56OiFq5j8D2Hwi3FdOwDari88EQY7GUPbjbi7I/gVcBOQIaUcCSwEvvKYVX2Q4qp6ThZm06DPYdlIN3YDuXuhoVKLD/QCQnxDmDRoFrqg4+w+50YwsY/KQ7vPFSKCjjFp0C2E+YU5P9kmC8XM7lFVps64eejN+OtCyTfvpbjSnXjQ6j4pD3186ij6ARe5J2G169qB6hK4uL9H7/rcdQSmpopfnRBCJ6XcDbhonq9oyc60YryCTgK4HkcJ2lbSOwBG9Zx2A654ePwadF41fHBmh+uT+6g89MGZHei8avj6eBdjCgFKzmk1FT34AnE9XjovFkYvxSswnU2nM10v6IPykJSSXQXbQOq4c4wbn925rSCtPa6auCXuOoJyIUQgsAd4VwjxJ6DGc2b1PXYYC/EfeJIpEVNcdxq1WrW2ErELe8T0Ine5NXo2PoRwsjzVddERaHeLfUgeslglJ6+m4k0Qc6PdmCmd1jOrTF3xyIS7EMLK+swtrk/28u1z8tC5ogpqfQ4xOnCq69oB0HZ9YSPBMNbzxnUQdx3BKqAO+AGwHcgGeodm0QOoaTCz/+JprF5F7gWJC45CdWGPzTBwhJfOi5kRyVj9jXyedd71gj4mD32RdQHrACMzwhe5rh0AzRFEz4QgF6MrexjxA+MJ1ceQ2/AFNQ3uxIP6ljz0r2Op6LwreSDpLtcn15VrRXVJK3t0CrhbjkBKWSOltEgpzVLKf0sp/9wfmsN1FnszSyFQK0NfPMKNXPH0LaDz0uaZ9jKemHIPQlh469QG1ycHhvcpeeitU+sRwsITU+51fXLZeSg83eucvY3k4cvQ+RXw0Sk3ssT6WHHZF5e2oZMDuCPejb/PjO3amNYeLAuB+1lDdwohMoUQFaqgrP3sOHsZn5CTzB46i1C/UOcnS6k5gpg54O8i2NgDmWRIxF+O4FRFquuiI+gz8pCUkpPlO/GTUUw2uJNFskH7txfFB1ry5NS7QOpYl7HB9clevlpWVPoWLRuuF6PNHThBQuAc17UDoO12g6Ng6BSP23YjuCsN/Q5YKaUMUQVl7cNssbIr9yvwqmD5aDe04OY2tb1LN27JLMMSLN757Mw+6frkPiIPfZ5zBrP3BW6OWOw6iwS09ztsao+sMnWHiIBwBusncqH+SxpMbmj/Y+/Q5KFe3nvolSMbEDoTD41zQxaqr4DsnVrrEF3P7ubjrnVFUso0j1rSRzl64Sp1vkfw0fm5X4beCwOILXly6hqk1POvU27MHQgM19Ine7k89Oapj5FSx5NT73Z9ctl5bUDPWDcKznowt41YDl6VvH0q1fXJzfLQBo/b5Un2XPoUnTmCZWNmuj753HatM0Av+JzddQRHhBAfCiHub5KJ7hRC3OlRy/oI28/m4x18mgXRC10PoIFeG0BsSXxEJAHm8Zyp2I3J6sbdYtIdvVoeslgtnCrfxQBLEkmGKNcLmmWhnlll6i5PTFuOtPizPmOj65P7gDyUeeUCVSKDxKD56Ny5wz+7XpOFonp+pr27jiAYqAUWo2ULrQB67y1rFyGlZHvO5wh9HStj3cgW6uUBxJbMjlyCRVfJtszPXZ9sk4eMblxQeiAp5/dh0V3llgg3m8b1clnIxsABAwgXM7nYcIjKBjdChjZ5qJdOLvv70Q+RUvDQODfugVvKQj04W8iGu1lDj9r5+oanjevtZBZXc1V3kAH6EG4a6ka/oOY2tb3fxz46ZSlWcwDvnHVjSlVgOAy/pWl6U+/j36c/Rlr8+MYUNz63PiIL2VgaswKEiXfOuFFTMGo++AT2ys9ZSsmXhZ8h6mNZEp/gesG5T3uNLATuZw392c7Xr4QQvXtv62G2njmPV2AaySMWu5lXvhmGTOxxIyk7wtghYfg3TONc5UHK68tdL0haCSVpUOpGtWoPosZUg7H8S3wbpjB+qDvFRRu0f3u5LGTj61PmYGkIZ33mBtcne/tprbbTt4LV4nHbOpMjhUepk8WMDV6Al94dWWhDr5GFwH1pyA+YBGQ2fU0AooDHhBB/9IhlfYDNWTsQOjNr4t1IEay8pA0o6QOyEIAQgluH3I4UZjZmbXW9wBYcT+tdd4tbsz/DKhqZZbjNzWyh9X1CFrIRGeJPOLMobEjjYuVF1wuSVkJtKVw84HnjOpE3T61DWn24N8mN5oC9TBYC9x3BBGC+lPJlKeXLwCIgAViNFjdQXEdxZT2XzfsJ0huYGD7R9YL0potlDy88aQ93T5iJpX4IH6a5UUgUMgyGTet1ssF7xk+wNg7ivgm3uj65LEdrO91L5AJ3uT1mGVIK3jd+4vrk2GTw8utVn3O9uZ6vinZhrRpPcqIbDryXyULgviMIAwJb/BwADJRSWoCGTreqD7DhdDr6gCySRyxx704xbRMMjodwF4NMehHTY8Lwqp1BXu05sq66McYyaaWmn5e7cWfZA7hUfYnsqpPoa6YzY6Qb3UNtqZN9RBaysWpCEpaaODZlb8IqXfSY8g2E0Qs1GdTqRj+qHsCui7swyToSgxYQ6OvlekEvyhay0Z6CshNCiDeFEP8CjgP/J4QIANxIIu5/bMjYhhCSr427w/XJNVcgd1+fkYVseOl1zBmSDFLHhiw3MoJs798WNO/hbMrS7LzFsNg93di4Qdv19BFZyEa8IYhg881Umks4XOhGy4mklVB1CS4d87xxncAHaeuxmkK5I8GNRoJ15ZC9S8uQ6iWyELifNfQ6cAuwAVgPzJZS/rOpB9HTHrSvV1LTYOZC45eE6mOIDXNjEMW5bSAtfc4RACwbOwZTdQIbMjdhtrpoUDZwFBjG9wpHIKVkXcYGzDUjWTlunOsFzbLQHR63rasRQrBk5CKkxY+PM9yQAcfcpvXS6gXpwsW1xZwoPYSpYjKLxw5xvcAmCyXd4XHbOhNXw+sTmv6dAgwB8pq+IpseU9hh/ekT6PzyWBi9xL0FaZu1u8QhbsQSehm3jgmHqmlUmMo4cMmNAGHiCrj4FVQVed64G+BkyUmK6vKRVdO09+iKPioL2VgyNhpT5URSL6RS3ehinrF/GIycq/3e9/Bq8q05W5FYGe03F0Own+sFxg29ThYC1zuCHzb9+/sWXy+2+FLY4aNzm0EKHp/sTuFJJeTsbiqq6j1bSXcJ9PViRuQshDWAje7IQ0krAXltxmsPZVP2JpDeTI+Y655u3EdlIRvTY8Lwq5+JSTaw44Ibg4kSV8DV81B0xvPGdRApJZ9kbMBSO5xliRNcL+ilshC4cARSyieavv07sEpKOR/YDVQA/+1h23olJrOFnLq9hOkTGBbsxlYyc4e2leyDspCN25KG0VA+kZ0Xd1HRUOH85PAEGBTbo9NIGywNbMvZjqlyLEuSYlwv6MOykA0vvY4FMdPAFM4Gd2oKEpZr1eQ9WAZMK0sjtyoHU8VUkpPcaPnSS2UhcD9Y/FMpZaUQYjawAPgnmnNQXMcnZw+CdwkLom5zb0HaJgg0QNQMzxrWjSxKNGCqmIpZmvgs9zPnJwuh7Y7O74Xasq4xsJ18nvc5NeYqTBVTWZRocL2gj8tCNhaPjaTh6lSOlxx3XVPQC6rJN2VvQkgvDPqZjDEEul7QS2UhcN8R2MoAlwGvSSm3Am404+5/rE3fhJR6npjmRg6xqQ4yU7S7ox7epvZGMAT7MW5wIj6WYe7LQ9Ki3WH1QDZnb0ZvDWVs2BT3dOOz6/u0LGRjzphwqJ4KCDZmu5kl1kOryU0WE1tztmKuTuK2xJGuU8DryiFrZ6+UhcB9R1AghHgFuBfYJoTwbcfafoPZYiazZi+hjGdokBt55dm7wFTbp2UhG4uTIqksncSp0lPkVOQ4P3nIJAgZ3iPloZLaEvYWfElt2SQWJ7kh/ZVmQuEpcKdRWS8n0NeLW2JG4dWQwKYsN2oKEntuNfnegr2UN5TTUD7FvV1f2mawmmBs7/yc3b2Y3wN8BtwmpSwHBgIqbfQ6NqTvReormT/MzWwh4ybwC9X68fdxkpMiMVdMQqBjU5aLP3whNOeYvQsaqrrGQDfRiqYsmMqnuacbn14HiF57gWgvyUkGqkomUVhbyKHCQ85PDonS2m30QHlofeZ6fAgl0JrE9Bg3JgWeWacNqB/WO5Mp3a0jqJVSfiKlzGz6+bKU0mVqgBBiiRDinBAiSwjxrJPz7hJCSCFE7xPXWvBh2kakxZfHp7nRctrcCBmfaj3a9W40pOvljDEEEh1iIFiOZ3P2Ziyumo4lrtACbxkuYgpdiJSSDVkbCJRxRAcNd60bS6ldIGJmgzuJA32ARYkGzNVJ+IgB7smAiT2vmlzb9e3FVDGFhfFDXBcLVhXB+T0w7q5eKQuBB+UdIYQe+CuwFEgC7hdCtBnmKoQIAr4HHPSULV1Bg6WBjOr9BFknMzws1PWC3L1ac6peOrO2vQghSE4yUHp5AsV1xXx1+SvnC6JnakH0HiQbHC8+Tm5lLleLJpOcZHCtG18+qY0dHb+mawzsARiC/ZgYFY5vw1T3agp6YDX5puxNWKSFqlLtc3aJcQNIK4x3YzpdD8WTOv8MIEtKmSOlbAQ+AOylTfwKeAGo96AtHmdzxk6soo65Q9uRLeQdoPVo7yckJxmor4zHXx/kOpio00HCMi2YbqrrGgNdsD5rPT46f+rLx7l3gTj9Eei8+1QjQXdYnGSgsGA89ZZ61zUFg0aDYVyPkYdsu77BXgl4Ww3uFQue/kh7DxFuzCnooXjSEQxDq0K2kd/0WDNN1cnRTVlIDhFCPCGEOCKEOFJSUtL5lnYCH6RtxGoO5OHJC12fbLVo3UbHLNZ6tPcTpo0II9Tfn8HMZNfFXVQ2uphqlbhSC6Zn7ewaA51QY6rhs9zPCBczCfUPYNoIF7qx1aplC8UuhAFuJA70IZKTDFjroxnoE+W+PJR3EKoKPW+cC2y7vtorU5k1ehABrooFr+ZC/mFNFurFdFvmjxBCB/wB+C9X50opX5VSTpNSTgsPd8NDdzFVjVVkVB7Cv3EqCZGhrhfkHYSakn53p+il17EgPoKC/HE0WBpc1xTEzNbaEfQA2eCz3M+oM9dxOX88C+IjXOvGFw9AZQGM6z+ykI24iECGDwzAr34mx4qPua4p6EHV5Ouz1uOnH0DR5XgWubPrO9M0gU85AocUANEtfo5qesxGEDAO+FwIkQvcBGzqjQHjrdk7kMLM7MjF7rWcNm4EvS/EJXveuB5GcpKBinIDQwbEuM4e0ntrwfRzn3b7wPP1mesZ4j+CivKh7slCZ9aB9wCIX+p543oYtnjQ+fMJ6ISO9VkuGtE1V5N3r8O37fpi/G4B6eNe2ujpj7V4VtgIzxvoQTzpCA4DcUKIkUIIH+A+oPkvX0pZIaUcLKWMkVLGAF8BK6WURzxok0f4MG0j1sZB3DvhFtcnW62aI4hdBL5Bnjeuh3HrmHB8vPSEy1mcKDlBbkWu8wWJK6GhQsvK6CZyKnI4UXKCcDEHHy+9a93YYtKqieOXav33+yHJSQYaG4OID57BhqwNmKwmxyf3kGpy266vsngyE6NDXRcLFqdB8dk+sevzmCOQUpqBp9DqD9KAtVLKs0KIXwoh+owmUlJbQlbVCfS1U5ge44YWnH8Yqi736b4zzgjw9WLW6EHkXoxHJ3RsyNrgfMGoedrA87Tua1m8IXMDeqHnwoUE93TjnM+hrqxPXCA6yrQRYYQO8Man9hZK60rZk+fCkSeuaKom39Y1BtphfeZ6hgeN5NzFgSx2KxlgndYvqQ/8LXs0RiCl3CalHCOlHC2l/E3TYz+TUrbRBKSU83rjbmBrzjZAcrNhkfvDSfS+MMbNorM+SHJSJAWlPkwZfAvrs9Zjsji5W7QNPD/3abcMPDdZTWzK3sSU8FvIL/Vyv4jML0QLFPdTvPQ6FiREcCozEsMAAx9lfuR8wdDJEBINad0TJ8gp13Z9o/3mA8K1/GerERk5FwIjusRGT6LaRNwgH6VvwlI3jNXjJrs+uVkWWgh+wZ43roeyKFH7wwmXcymrL2NX3i7nCxKXa8H1PBeVqh5gT94ertRfYaBVq/622e6Qxlot6Jm4Erx8u8DCnsviJAOVdVamD1rC/oL9FFQXOD5ZCC1dOHsXNLioPfAAn2R+gpfw4krROEYMGkBchAtJr+ColjHUR2pElCO4AXIqcrhYk4GsnsycODeymQqOaJkkvbBNbWcSEezHxOhQ0s8PZUjAED7KcHG3GJsMep9uySpZm7GWyIBIMs5HMTE6lAhXunHmZ9BY3WcuEDfCnLhwfLx0WCunI4Tgk0wXw+0TloGlAbK7Nl24wdLAxuyNzImax+EsM8mJbhQLnl6n/U4mLO8aIz2McgQ3wNbsrSAFUwbNd60bgxZA1PtAfP+VhWwsTjJwKr+KJSPu4ODlg1yovOD4ZL9gbQuevqVLJ1rlVeax/9J+lgxfxan8Kvd045MfQNBQiHFjvm0fxxYP2nfOwqyhs1ifud75uNLht4D/QK3GpgvZkbuD8oZyYn2TabRYXaeNWkxaEVn8UvAP7RIbPY1yBB1Eq0DcgrkmlmVJblQU2mSh0Qs1/bifY9NgBzTcjJfwYl3GOucLEpdrW/Gis543romPMj9CL/QENGrZYC514+oSrRJ6wj2g03eBhT2f5KRI8q/WcXPEMkrqStiT7yRorPfSLq4Z27WLbRfxUcZHxATHkJM3hNAB3q6LBbN2Qm0pTLy/awzsApQj6CAnS05SXHcJU+Uk17oxaJpiZX6fyDDoDOIiAhkxaAAHMkzMi57HxqyNNFqc1ArE3w6ILpOHGi2NbMjcwPzo+XyVaXZPNz6zTst8mXhfl9jYG7D9bZQVjybcP9y1w09YpvXgyt3bBdZBxtUMjhcf587Yu9h9roQFCW4UC558HwYM0lLA+wjKEXSQLTlbENKbpOBbXOvG0JQt5NMvC4zsIYQgOdHAgewrLB95J1cbrpJ6IdXxgsAIrXCnixxB6oVUrjZcZcWou9ifdcU93fjk+9oshYjELrGxNxAR7Mek6FB2ppeyOm41XxZ8yeXqy44XjF6gFeJ1kTy09txafHQ+RHnfSkWdybX8V3dVy2Abf3ef6hqsHEEHMFlNbD//GY2VidzmzsxaKZtkoQVKFmpBcpKBRouV2opRRAVGuQ4aJy6HwtOaRORh1masJToomtqKkTRarK5loSKj1m20D8kFnUVykoFT+RXMMWjt2Z1+zt7+2t9J+lZNTvUgtaZatuRsYcnIJRzIrMfHS+c66ePsBi2g3cd2fcoRdIADlw5Q0ViOqXKSewHEgqNQkdfvs4WuZ+qIMMIGeLMrvYQ1Y9ZwpOgIOeVOppclNM158PDdYnZ5NkeLjnL3mLvZmVZC2ABvprrSjU++DzqvXt9zxhPY/kZOX9QzN3ouH2d+TIOlwfGCxBVa0eWlYx61a9v5bdSYarh7zN2kpBUyO3aw66SPk+9rLTGGTPKobV2NcgQdYEv2FvQygGG+k4h1pRuD1oVS561koevw0uuYnxDBrvRiVoxahY/Oh/fS33O8YOAoiBjrcUfwQfoHeOu8WTZyBbvSi5nvSje2WuDUWi3NNbDnNUXsbmKb4kEpxiIeTHyQsvoytp/f7njBmNtA6D0qA0op+fDch8SHxeNrGUleWZ3rXd+VbK1h5MT7eu0AGkcoR9BOakw17MrbTWPFeBYnDnOtG0up9VofPb/PpJp1JouTDFTUmcgqhNtH3c6m7E1UNFQ4XpC4XOvsWVPqEXsqGyvZmL2R20feTlYh7unGOZ9DdWGfkws6i2vxoFKSQqcwOmQ076a9i3SUCuwfpnWe9WCV8dGio6SXpXNvwr2kGosBWJjgIunj1IeAgPH3eMyu7kI5gnay6+IuGiz1NJRPcq8LZcExqLioZCEH2IqOUoxFPJT4EHXmOueFRwnLtGlQHupJsz5zPXXmOh5KeohUY7F7uvHJD7TYj9rxOSQ5yYDJItmbeYUHEh8grSyNEyUnHC9IXAFXMqHknEfseSftHUJ9Q1kxagUpaUVMclUsaLVqstCoeRAyzPF5vRTlCNrJ1pyt+DGYIBHrWjcGMDbJQgm3e964XkiArxezYweTYixiTNgYZkTO4L309xwXHkVOgJDhHpGHzFYz76a9y/TI6cSHxbunG9dXau2Tx93V71tKOMMWD0oxFrJ81HKCfIJ4N+1dxwvim/5ePCAP5VXlseviLu4eczflNXAqv8L1Td3FA9pc5T6aDKAcQTsorSvlwOUD1JdPZGFCpOt8Yynh7EbtLsLfDafRT0lOMpB/tY70wioeTHyQwppCdl100H9ICE0eyt4NDVWdaseui7u4XHOZhxIf4lxRlXu68ZmPwVwHkx7sVFv6GloTOgO70ovx1vlxV9xdpF5IpbDGwVSykGEwdIpH5KH30t5DL/Tcl3AfqWlFAK7lv+Nvg0+Q9rvXB1GOoB1sP78dq7RSc2WCe7LQpSZZSBWROWVhYgRCQIqxiLlRc4kKjOKdtHccL7D1pMlyUnfQAd5Je4eowCjmRs0l5WwRQmi2OeXYv7UA9rCpnWpLXyQ5yUBlvZnD58u4L+E+JJIP0j9wvCBxedPfkJNmde2kurGa9VnruW3kbUQMiCDFWETMoAHOkz7qyrW00Ql3g09Ap9nSk1COoB1szdlKqD4GL+sQbh0z2PWCM580yULLPG9cLyYiSCs6Sk0rQq/T80DiAxwvPs6Z0jP2Fwy/Wavs7ER56EzpGY4XH+fBxAfR6/TXdOMgJ7rx5VNw6ThM+XqfyyLxBLeOGYyPl44dxiKGBQ5j4fCFrD23lupGB91GE1Zo/3ZiPGh91npqTDV8LelrVDeYOZB9heQkF8WCpz/Sdn1Tvt5pdvQ0lCNwk5zyHM5cOUP91UnMjh3MAB8X+cZWq+YIYhcpWcgNFiVqRUeFFfWsjl1NkE8Qr59+3f7JOn1TT5odnTbC8vXTrxPkE8QdsXdQWFHPqfwK16MKj72lzZaY0PeySDzBAJ9r8SApJY+Ne4wqUxVrM9baXxA+BgbFddoIS5PFxFvGt5gSMYWxg8byxbkSrcmcO59z5ARtZkIfRTkCN9mYvRGd0FNaONY9WejiAai6pNoRu4lNo01JKyLQJ5AHEh4g9WIq2eXZ9hckLNdGWObe+AjL7PJsUi+m8kDCAwT6BLqnG5vqtNqBpJUwwI3JdApAk4cKyrV40NjBY7lpyE28bXzbcYFZ4nLI/VJr7XCDbMnZQmFNId8c/00AUoyFrosFLx2HwlN9ejcAyhG4hcVqYUv2FqJ8J4M1yLVuDFoQsZ8OL+8IsRGBxDQVHQE8mPgg/l7+/PP0P+0vGDUfvAM6RR56/fTr+Hv582CiFvB1Szc2btQcUR+/QHQ2LeNBAN8c/01K60rZmOVgFGnCcq2RX8ZnN/S6FquF18+8TuLARGYPm43JYmVXejELEgzOkz6OvQVe/lpvoT6McgRu8NXlryiuK6b+6mTXujFoLXSNG7RxlH00uNTZCKGNBzyQXUpVvYkwvzDuHnM3n57/lLyqvLYLvP0gbhGkb7uhnjT5VflsO7+NNWPWEOYX5r5ufOwtrdJZzR1oF7Z4kM0RzIicwbhB43jzzJv2U4aHToGgITcsD+24sIMLlRd4fMLjCCE4nFtGZb3Z+e6+sUYbQDP2jj5fDKocgRtszNpIkHcw2RdGuCcL5XwBtVeULNROkpMiMVkkX2SUAPDw2IfRCR1vnnnT/oKE5VpFb0HHR12/ceYNdELHw0kPAzTrxk5nE5dmwoV9MPlrKkjcAZKTDJwuqOByRR1CCL45/pvkV+ezNcfO7k6n05ItsnZqY0A7gFVaee30a4wKGcXC4doc6RRjEb5eOudJH2c3QENlv9j1KUfggqrGKnbl7SI2YA5IL/eazJ35GHxD+lS/8q7gWtGRdrcYMSCCO+PuZH3Wevu7grjFWqO3DhYd5VXmsT5zPXfG3YkhoClG4Y5ufPifWjaYqh3oELa/odSmz3n+8PkkDkzk7yf/jsneQJqEZVrWTs7uDr3eZ7mfkXk1k8cnPI5O6JBSkmIscp70ISUcelVrMDf85g69bm9COQIXfJb7GQ2WBmquTGbk4ABGh7toMmeqbxpevkJVmrYTvU6wIMHA7vRiTBZN7nliwhN4CS/+cvwvbRf4h8LIW7Wiow6MsPzLib/gpfPiWxO+BdBKN9brHNzpN1TBifdg7GoIcuOmQNGG0eFaPGhHkyPQCR3fnfJdCqoLWJdpZ3BNzBythUcHistMVhMvH3+ZuLA4lsZo8br0wiryr7ooFsw/ApdPwIzH+8WuTzkCF2zM2khM8EhOZQeyKDHCdZO5rBRtOzletSPuCC2LjkDbFXwt6WtsO7+NtCtpbRckLIOy7Hb3pEkvS2fb+W08lPQQ4QO0XkKHz7uhG5/8QPt8ZzzRrtdTXMMWD/oq5wqV9doOYNbQWUw1TOWVk69Qa7pOAtJ7a/G2jE/B4mTmsR0+yfiEvKo8vj/l++ibxoemGLViwQXOkj4OvQq+wTChfzQSVI7ACRcqL3Ci5AQJgQswWXCuG9s4vQ4CwiHmVs8b2Ae5dcxgfJuKjmw8Ou5RQnxD+OOxP7ZdEG+bUeB+MFFKyR+P/pEgnyAeGftI8+MpaS50Yynh0GtaL/qoaW6/nqIttnjQnqZ4kBCC7035Hlfqr/CW8a22CxKWaSmkF/e7/Rq1plr+ceofTImYwpxh14L6KUYXxYJVRVrr+EkPgK8bbeb7AMoROGFj1kZ0QkdV6QQGBvi4bjLXUKWluSXdoQ3iVrSb64uOAIJ8gnh8/OPsv7Sfz/M+b70geAhETW+XbLA7bzf7Lu3jWxO+RYivNjHOLd34/B4oPQczv9Uv5AJPMnVEGAMDfJrjQQCTIyaTPCKZ10+/TkH1dW0lYheBl1+7Pud/nPoHpXWl/GDqD5p38pcr6jhd4KLJ3LF/g9UE0x9v13vqzShH4ACL1cLmnM3MjLyJ/edMLEiIcKwb2zj3qRbUUtlCN4St6Cjt8rWmcg8kPkBsaCy/PfjbttJBwjJNz63Id/nc9eZ6fnf4d8SGxvJA4gPNj7ulGx96VWttMfbO9r4lxXVo8aCIVvEggKenPY0Qgt8d+l3rBT4B10ZYuhEPyi7P5u2zb3NH7B1MipjU/HhqmjZ7wGHSh8UER96A0QthcGy731dvRTkCBxy4fIDCmkLGBS9yrRvbOPMxBEdB1AzPG9iHWZhoaFV0BOCt8+anN/2USzWXePXUq60X2HrSuFFc9uqpVymoLuAnM3+Ct+7a8HGbbrzQUbuBK9na8095WKthUNwwtnjQoaZ4EMCQwCE8MeEJduXt4ou8L1ovSFgGlfma03eCVVr5zcHfMMB7AD+Y+oNWx1KMRc6TPs58oo3JnPmtjrylXotyBA74OONjwnzDKCmKw9dLx5w4F03maq5o3TDH3anlPis6THiQL5OjQ0lJa92ieKphKnfE3sG/zv6LUyWnrh0YHKul+bkoOjpZcpLXz7zOytErmR45vdWxFGMRk6NDCQ9ykOm1/2XQ+8DMJzv0nhRtmROnxYNaOnyAh5MeJjY0lucOPMfV+hatJcYsBaFzKQ+9l/YehwsP84OpP2Cg37X2H1X1Jg5klzouFpQS9v0JwhO1saP9CHXFskNpXSmf533OitEr2JVW5l6TuTMfg9WsxhV2EouSDJwpqORyRV2rx5+e/jSGAQZ+tOdHrbtWJiyDC/uhtgx71Jhq+MnenxA5IJJnZzzb6phNN17kaNdXVaSljE66X6WMdiL24kEA3npvnp/zPBUNFfziwC+uHQsYBMNvcbrzy7yayUtHX2Ju1FzuimudufdFRgkmi3TcZC4zBYrPwqzv9bubuf71bt1kQ9YGzNLM5NAlFJS7MZwE4NQHEDkeDGM9b2A/4PqiIxvBPsG8cOsLFNYU8tN9P8VitWgHmnvStB2KbpVWfrz3xxRUF/Cb2b8hyCeo1XHbazjUjQ+9ApZGuPk/b/BdKa7HFg8yXq5s9Xj8wHi+O/m77Ly4kzfOvHHtQOJyKEnTpLrrqGio4Pu7v0+gTyDP3fJcm7v+FGOR86SPfX+C4GHatLl+hkcdgRBiiRDinBAiSwjxrJ3jPxRCGIUQp4QQO4UQIzxpjztYpZWPMz5mqmEqZy/4OdeNbZRmQsHRfpNz3BWMDg9k5OCAVmmkNiZFTOK/p/03Oy/u5MUjL2p3jEMna3/E18kGUkpePPIiu/N28/T0p5kW2TbtMyWt2LFu3FClVRInruhXwcOuwhYPsg2Qb8nDYx9macxS/njsj2zPbXLwttke11WT15nr+MHnP+ByzWX+NP9PDPZvLeWaLFZ2pxc7TvrIPwIXvoSbvwNePp3y3noTHnMEQgg98FdgKZAE3C+ESLrutOPANCnlBGAdcF2qQNdzqPAQ+dX53BV3Fylphc51YxsnP9C0S5Ut1GnYKzpqyUNJD/FQ4kO8k/YOvzn4GyzSql0ksndqzcLQZhC/cPgF3ja+zf0J9/NAwgNtnselbnzoVaivgNnf7+y3qMBxPAi034Ffzf4VkyMm8+yeZ9mQtQFCh2uzAVrEgyoaKvjOzu9wpPAIv5r1q1ZZQjZcFgt+8TvwC9WSAfohntwRzACypJQ5UspG4ANgVcsTpJS7pZS2XMCvgCgP2uMWH2d8TLBPMOPDZnOmoNJ1EZnVCqc+1FLbgtwoOFO4TXKSQWtCd67E7vGnpz/No2Mf5cNzH/Lw9oc5OWwc0lyPzNjBqZJTfOOzb/Bu2rt8Lelr/HjGj+1e6G26sd0LRF25JheMWaJGUXqQ5KRIzhRUcqm8rs0xX70v/1j0D6ZHTud/9v0PP9n7E/Li5kP+YcxXL5ByIYW7N9/N8eLj/HbOb1k2yv40wB1NTebsJn3kHYLMz7TYQD8pILseT1Y9DQNadgrLB2Y6Of8x4FN7B4QQTwBPAAwfPryz7GtDWX0ZqRdTuTf+XvaeqwBwHR+4uB8q8mDhzz1mV39lyvAwBjUVHa2YOLTNcZ3Q8cNpP2TMwDE8f+h5Hip5kYEjouHoLynDTKhvKP87+39ZMXqFw9dIMRYxKMCHKcPt6MZf/U3bDcz/SWe+LcV1JCcZeGF7OqlpRXz95pg2xwd4D+BvC//GK6de4fXTr7NZmomIHkrNltXUWE2MDBnJ20vfZtzgcXaf31YsOCfOQdLHrl9r3QD6WcpoS3pE+asQ4iFgGjDX3nEp5avAqwDTpk1rf3cxN9mQtQGz1cxdcXfxy0+KGDU4wPlwEtBkIZ9ANZfYA9iKjrafLcRkseLtYIDI8lHLmRc1j+252zl97DUoy2HcvOdYGruKQB/Hn59NN75tbGRb3bjmChz4GyStgiETO/NtKa4jNiKQUYMDSDHadwSgZRI9Nfkp7h5zN1vPbyX74F8YYPbmpgX/x9zouXjpHF/K0i5XUVBex3cX2onxnN8D57+A237br2eHeNIRFADRLX6OanqsFUKIRcD/A+ZKKR3Mq/M8FquFD9M/ZHrkdAz+MXyVk8mjs0Y6X2Sq0yZVJa0CnwFdY2g/Y1GSgY+O5nPofBmzYh3XcgT6BLJmzBrWeEfAv1eADNActBMONenGdtNGv3gBTDUw78c3+hYUbrAoycCb+85TWW8i2M/b4XmGAAPfGPcNuHIFdv8aQhO0VuROaG4yl3Dd52y1wo7/gaChMO3RzngbvRZPxggOA3FCiJFCCB/gPmBTyxOEEJOBV4CVUsq2aQNdyOf5n3Op5hIPJDzAF+ec6MYtObdN60Q54d6uMbIf4qjoyCEjZmnb/LPrXZ6a4kg3LjJqmUJTH4WIxA5YrWgvruJBbRh7h/Zv2ianpwGOkz5OvKNVKSf/Arz922VvX8NjjkBKaQaeAj4D0oC1UsqzQohfCiFWNp32f0Ag8JEQ4oQQwvWn6iHeT3ufyIBI5kXPc64bt+TkB1rKohpX6DEG+HgxJ65t0ZFDdHpth5ax3WFxGTjRjaWE7c+AbxAs+GknvAOFO7SMB7nF4DitbufEu057D12uqLOf9FFXDqm/gOiZfX4esTt4tI5ASrlNSjlGSjlaSvmbpsd+JqXc1PT9IimlQUo5qelrpfNn9AxZV7M4WHiQe+PvRUodu885yTe2UVGgtZSYeF+/q0LsahwVHTlk2jfAXK91kXSATTdus+s7tVbTjRf8FAYMtL9Y0ek0N6E717oJnVOmPQaFp+HiVw5PsRULtvmcd/5CGye79AXVSRZVWQzA++nv46Pz4a64uzh0vowqd5rMnXgPpFWbW6vwKAsS2jahc4phrLZLO/RPrZukHezqxpWX4NOnIfomzZkoupTkJANV9WYO5jjeybViwj3a5LKv/ubwlB1GO0kf2bu0DqM3f0crRFQoR1DRUMHmnM0sG7WMML8wUoxF+HnrmBMX7niR1QrH39bGJA50EVBW3DDhQb5MGR7mviMAuOU/tU6VR/9l93BKWiFThodd042tFtj4HTA3wh1/0yQmRZcyJy68KR7UtrjMLj4BMP2bWpzg8sk2hyvrTXyVc6X1TV3NFdj4FAweo6S/FvR7R7D23FrqzHU8mPhgi+Ek4fj7OLkQ5O6B8gsw+etdZ2g/Z1GigbOXKimwU3Rkl7jFWuD4ixe0WoAWXCrXdONWzcd2/0a7U1zyWxg0uhMtV7iLv4++ffEggFu+C/5hWvbPdWvaJH1YzLDuEagphTtf7fcB4pb0a0dQb67nnbR3mDVsFvED4zFermzSjZ3MMgU49rZWjp7ouFBJ0bnY/ph3prm5KxACFv9aCxhv+1GrQ6lp1+nGR96Evb/X2gv08zTC7iY5ycClinrOXnIzHuQfCvP/n1YLcPTNVodsSR+Th4dpu/jN39XiP8tfUpLQdfRrR7ApexNl9WU8Nu4xwEm+cUtqy7St6IR71YCSLqRl0ZHbDJsCtz6tdYbd/3Lzwyk23Tg8APb/BbZ8X9tB3P5/nW+4ol3Y4kGp7jp80ILGoxfAp89CjjbMxmSxXkv6sNTDhie1DKO5z8LkBz1kfe+l3zoCs9XMm2feZPzg8UwzaB0pU4xFrXVje5xaq7UknqKCxF2NsyZ0Dpn7Ixi7Gnb8FDb9J1WFWRzOKebh4SXw3j2w4/9pLazveRu8XDQXVHicDsWDdDq463UYOArevRu+fIlj6Tk01tfyQMhpeHW+1g9s/k9hXpsmyAp6SIuJ7iD1Qir51fn817T/QghBQXkdZy9V8uzSBMeLpNSCxEMnaznMii4lOcnAK3ty+PxcCSvt9B6yi04Pd76mda3c/zJBx94i3RswAr4hWmuBmU+qFOAeRHKSgec/TaegvI5hoW7q+AMGwiNbtYB/6nPM5DnO+QH7gbAYeOAjGLPYg1b3bvrlb7/FauGVU68wMmQk86PnA9e0Z6dpo3mHoOgMTFFB4u5gcnuLjmzovSH5l/DdE6yL+C6viLuxrn4VfnAabv62cgI9jGQHQ4lcEjAIHvgA+fhu/ur1ddaHPqo5gO8cVk7ABf3yL2B77nayyrP49qRvo29KE0wxFjEq3MlQa4DDr4FvMIy/p4ssVbRErxMsTIzg8/RiGs1uFh21wBQczS+KZ5M99j/RTbxXy0FX9DhGhwcyKryd8aAWGMVo/q96CaZZ/6U5gH44aKa99DtHYLaa+duJvzEmbAyLR2h3Cc35xs4mkVUXw9kNMOnBftuzvCewKNFAVYOZg+evtHvtwRytWNDhzFpFjyE5UYsHVdS1Ix7UhC3pY36Ci+w/RTP9zhFszt7MxaqLPDXpKXRCe/ufu9Nk7ui/wWrSClgU3cacuHD8vHXtlw2AFGOh62JBRY8gOcmA2Sr5/Fz7e1G6lfShaEW/cgS1plpePv4yEwZPYF70vObHW+Ub28Ni1nKUR81Xc2u7GX8fPbNjw9tXdITWZC41rdh1saCiR2CLB6Wmtc8RXGpK+nDZIkbRin7lCF47/RoldSX8aMaPmscWNpqtfJ5ezMJEJ03mzm2DygKY8XgXWqtwxOL2Fh1Bc7HgYnWB6BV0NB7UplhQ4Rb9xhHkVeXx77P/ZsWoFUwMvzZx6tD5MqoazM5nEx98BUKitdm1im5nQWJE+5rQ0aJYMFHpxr2F5KTIdseD3Er6ULSh3ziCrTlb8dJ58f2p32/1uE03nu1o+lXBUbjwpTbPVDUi6xEMDvRlajuLjlKMRUwdHsbgQKUb9xZmxw7Gz9v9oUR2m8wp3KLfOIJvTfgWH6/8mIgB1+4Irw0ncaIb7/uzljI65eEuslThDslJBoyXK8m/Wuvy3AKlG/dKtCZ04aS6GQ+yJX0o+a/99BtHIIQgOii61WNnL1VyqaLecdpo2Xmtr9C0b4BfcBdYqXCXRe0oOrKdY3c2saJHk5zofjzIlvQxKdrFZEFFG/qNI7CHS934wF9B6LUWBIoeha3oyJ2sktQ0pRv3VtyNB7mV9KFwSL93BA5145pSOP6O1mU0eEjXG6dwia0JnbOiI6Ub927cjQe5lfShcEi/dQT5V2sxXnaiG+/7I1gaYNb3utQuhfssdqPoSOnGvR934kEukz4UTum3jmBnk6Rg1xFUFWnzbsffA+FjutgyhbtMig5jcKDzJnQpxiIGByrduDfjqgmdW0kfCqf0W0eQYixidHgAo+zpxvv+pM0cmPujtscUPQa9TrAwwcAX50rsFh0168YJBqUb92JGhQcyOjyAFAfDapqTPtSur8P0S0dQUWfTje3oiZWX4MjrMPF+Nbu2F5CcpDWh+yqnbdHRwfNXmnRjdYHo7SQnRXIwp8xuPMiW9LFQNZnrMP3SEXx+rhizVdqfTbzzlyCtMPfprjdM0W5mOSk6SjEW4eetY5bSjXs9yUkRDuNBtqSPQapYsMP0S0fgUDcuOAon34ebv6NNNVL0eJqLjtJaFx1JKUlVunGfwVE8qKC8znnSh8It+p0jaDRb+eJcSVvd2GqF7T+GgAiY/cPuM1DRbpKTDFy+ruhI6cZ9C0fxIFsAWX3ON0a/cwRf5TjQjY++AXkHYdHPVRVxL2NhQgQ6ATta3C2mGIvQKd24T2EvHuQ06UPhNv3OEaSmFeHvrWd2XAvduPwipPwcRs3TJpApehWDAn2ZOqJ10VGKsYipI5Ru3JeYHTcYf2998+fsNOlD0S76lSO4phsPxs+7STe2mGH9f4CUsOLPIFSaYW8kOclA2uVK8spqXRcLKnolft565sQNbo4HXUv6UJ/zjdKvHIFNN27VfGzXL7U208v/AGEjus84xQ1hm0OcmlZ0rcmcmk3c51jUFA86U1DZIukjtLvN6vV4dbcBXcmO63XjI29qxWPTvgET7+te4xQ3RHPRUVNOudKN+ya2eNDW05f54lwJt48foooFOwGP7giEEEuEEOeEEFlCiGftHPcVQnzYdPygECLGk/a00o0PvQZbfgBxi2HJC558WUUXkZwUycHzZRzMKVO6cR/FFg/61/7zqliwE/GYIxBC6IG/AkuBJOB+IUTSdac9BlyVUsYCLwEeuyLnldWSdrmSO2MaYd03YNt/w5jb4J63wMvHUy+r6EKSkwxYrFLpxn2c5CQD9SZr26QPRYfx5I5gBpAlpcyRUjYCHwCrrjtnFfDvpu/XAQuF8Ey09kLK3/nS97vc/9UqSNsC8/8f3PceePt74uUU3cDk6FAGB/oyONCXyUo37rPYdnutkj4UN4QnYwTDgLwWP+cDMx2dI6U0CyEqgEFAacuThBBPAE8ADB8+vEPGREfHUF06HSbeBOPvhpBhHXoeRc9FpxP8fEUSsul7Rd9k5OAAnr4tnjlqN9Bp9IpgsZTyVeBVgGnTprkeXmqHEbfcBbfc1al2KXoeKyYO7W4TFF3Ad+bHdrcJfQpPSkMFQMshwVFNj9k9RwjhBYQAbdtIKhQKhcJjeNIRHAbihBAjhRA+wH3ApuvO2QQ83PT9GmCXbNk5TKFQKBQex2PSUJPm/xTwGaAH3pBSnhVC/BI4IqXcBLwOvC2EyALK0JyFQqFQKLoQj8YIpJTbgG3XPfazFt/XA3d70gaFQqFQOKdftZhQKBQKRVuUI1AoFIp+jnIECoVC0c9RjkChUCj6OaK3ZWsKIUqACx1cPpjrqpb7Aeo99w/Ue+4f3Mh7HiGlDLd3oNc5ghtBCHFESjmtu+3oStR77h+o99w/8NR7VtKQQqFQ9HOUI1AoFIp+Tn9zBK92twHdgHrP/QP1nvsHHnnP/SpGoFAoFIq29LcdgUKhUCiuQzkChUKh6Of0G0cghFgihDgnhMgSQjzb3fZ4GiHEG0KIYiHEme62pasQQkQLIXYLIYxCiLNCiO91t02eRAjhJ4Q4JIQ42fR+f9HdNnUVQgi9EOK4EGJLd9vSFQghcoUQp4UQJ4QQRzr9+ftDjEAIoQcygGS0kZmHgfullMZuNcyDCCFuBaqBt6SU47rbnq5ACDEEGCKlPCaECAKOAnf01c+5ab53gJSyWgjhDXwJfE9K+VU3m+ZxhBA/BKYBwVLK5d1tj6cRQuQC06SUHimg6y87ghlAlpQyR0rZCHwArOpmmzyKlHIP2oyHfoOU8rKU8ljT91VAGtpc7D6J1Khu+tG76avP39kJIaKAZcA/u9uWvkJ/cQTDgLwWP+fThy8QChBCxACTgYPdbIpHaZJITgDFQIqUsk+/3yb+CPwIsHazHV2JBHYIIY4KIZ7o7CfvL45A0Y8QQgQCHwPfl1JWdrc9nkRKaZFSTkKbCT5DCNGnZUAhxHKgWEp5tLtt6WJmSymnAEuB7zRJv51Gf3EEBUB0i5+jmh5T9DGatPKPgXellJ90tz1dhZSyHNgNLOlmUzzNLGBlk2b+AbBACPFO95rkeaSUBU3/FgPr0eTuTqO/OILDQJwQYqQQwgdtNvKmbrZJ0ck0BU9fB9KklH/obns8jRAiXAgR2vS9P1oyRHq3GuVhpJQ/llJGSSlj0P6Od0kpH+pmszyKECKgKfkBIUQAsBjo1GzAfuEIpJRm4CngM7QA4lop5dnutcqzCCHeBw4A8UKIfCHEY91tUxcwC/ga2l3iiaav27vbKA8yBNgthDiFdrOTIqXsF+mU/QwD8KUQ4iRwCNgqpdzemS/QL9JHFQqFQuGYfrEjUCgUCoVjlCNQKBSKfo5yBAqFQtHPUY5AoVAo+jnKESgUCkU/RzkChUKh6OcoR6Do1wghQoUQ327x81AhxDoPvM5zQogCIcQvnZwzuqn2odrROQqFJ1B1BIp+TVNzui2ebtUthHgOqJZSvujGudVSykBP2qNQtETtCBT9necB2534/wkhYmzDfIQQjwghNgghUpoGgzwlhPhh00CUr4QQA5vOGy2E2N7UGXKvECLB1YsKIea2qH4+bmshoFB0B17dbYBC0c08C4xr6uBp2yG0ZBxaO2s/IAt4Rko5WQjxEvB1tJbIrwJPSikzhRAzgb8BC1y87n8D35FS7mvqllrfOW9HoWg/yhEoFM7Z3TTkpkoIUQFsbnr8NDCh6SJ+C/CR1vMOAF83nncf8AchxLvAJ1LK/E62W6FwG+UIFArnNLT43triZyva348OKLftKNxFSvm8EGIrcDuwTwhxm5SyT3cOVfRcVIxA0d+pAjqszzcNvjkvhLgbtFbYQoiJrtYJIUZLKU9LKV9A6xzqMq6gUHgK5QgU/Rop5RW0O/IzQoj/6+DTPAg81tQm+CzuzcP+ftNrngJMwKcdfG2F4oZR6aMKRReg0kcVPRm1I1AouoZq4Al3CsqAoi6zSqFA7QgUCoWi36N2BAqFQtHPUY5AoVAo+jnKESgUCkU/RzkChUKh6Of8f7+tJKGOduFCAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.synapses.Lowpass(0.2)\n", + "\n", + "t = process.trange(5)\n", + "x = np.minimum(t % 2, 2 - (t % 2)) # sawtooth wave\n", + "y = process.apply(x) # general to all Processes\n", + "z = process.filtfilt(x) # specific to Synapses\n", + "\n", + "plt.figure()\n", + "plt.plot(t, x, label=\"input\")\n", + "plt.plot(t, y, label=\"output\")\n", + "plt.plot(t, z, label=\"filtfilt\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"signal\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that Synapses are a special kind of process,\n", + "and have the additional functions `filt` and `filtfilt`.\n", + "`filt` works mostly the same as `apply`,\n", + "but with some additional functionality\n", + "such as the ability to filter along any axis.\n", + "`filtfilt` provides zero-phase filtering." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing the time-step (`dt` and `default_dt`)\n", + "\n", + "To run a process with a different time-step,\n", + "you can either pass the new time step (`dt`) when calling the functions,\n", + "or change the `default_dt` property of the process." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.666527Z", + "iopub.status.busy": "2020-11-25T16:46:57.663882Z", + "iopub.status.idle": "2020-11-25T16:46:57.866899Z", + "shell.execute_reply": "2020-11-25T16:46:57.866478Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'output')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "y1 = process.run(2.0, dt=0.05)\n", + "t1 = process.trange(2.0, dt=0.05)\n", + "\n", + "process = nengo.processes.FilteredNoise(\n", + " synapse=nengo.synapses.Alpha(0.1), default_dt=0.1, seed=0\n", + ")\n", + "y2 = process.run(2.0)\n", + "t2 = process.trange(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(t1, y1, label=\"dt = %s\" % 0.05)\n", + "plt.plot(t2, y2, label=\"dt = %s\" % 0.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `WhiteSignal`\n", + "\n", + "The `WhiteSignal` process is used to generate band-limited white noise,\n", + "with only frequencies below a given cutoff frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.881169Z", + "iopub.status.busy": "2020-11-25T16:46:57.880313Z", + "iopub.status.idle": "2020-11-25T16:46:58.072700Z", + "shell.execute_reply": "2020-11-25T16:46:58.072286Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Network() as model:\n", + " a = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, seed=0))\n", + " b = nengo.Node(nengo.processes.WhiteSignal(1.0, high=10, seed=0))\n", + " c = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, rms=0.3, seed=0))\n", + " d = nengo.Node(nengo.processes.WhiteSignal(0.5, high=5, seed=0))\n", + " ap = nengo.Probe(a)\n", + " bp = nengo.Probe(b)\n", + " cp = nengo.Probe(c)\n", + " dp = nengo.Probe(d)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[ap], label=\"5 Hz cutoff\")\n", + "plt.plot(sim.trange(), sim.data[bp], label=\"10 Hz cutoff\")\n", + "plt.plot(sim.trange(), sim.data[cp], label=\"5 Hz cutoff, 0.3 RMS amplitude\")\n", + "plt.plot(sim.trange(), sim.data[dp], label=\"5 Hz cutoff, 0.5 s period\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.legend(loc=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the 10 Hz signal (green)\n", + "has similar low frequency characteristics\n", + "as the 5 Hz signal (blue),\n", + "but with additional higher-frequency components.\n", + "The 0.3 RMS amplitude 5 Hz signal (red)\n", + "is the same as the original 5 Hz signal (blue),\n", + "but scaled down (the default RMS amplitude is 0.5).\n", + "Finally, the signal with a 0.5 s period\n", + "(instead of a 1 s period like the others)\n", + "is completely different,\n", + "because changing the period changes\n", + "the spacing of the random frequency components\n", + "and thus creates a completely different signal.\n", + "Note how the signal with the 0.5 s period repeats itself;\n", + "for example, the value at $t = 0$\n", + "is the same as the value at $t = 0.5$,\n", + "and the value at $t = 0.4$\n", + "is the same as the value at $t = 0.9$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `WhiteNoise`\n", + "\n", + "The `WhiteNoise` process generates white noise,\n", + "with equal power across all frequencies.\n", + "By default, it is scaled so that the integral process (Brownian noise)\n", + "will have the same standard deviation regardless of `dt`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.082305Z", + "iopub.status.busy": "2020-11-25T16:46:58.080852Z", + "iopub.status.idle": "2020-11-25T16:46:58.220578Z", + "shell.execute_reply": "2020-11-25T16:46:58.220119Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 1))\n", + "\n", + "t = process.trange(0.5)\n", + "y = process.run(0.5)\n", + "plt.figure()\n", + "plt.plot(t, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One use of the `WhiteNoise` process\n", + "is to inject noise into neural populations.\n", + "Here, we create two identical ensembles,\n", + "but add a bit of noise to one and no noise to the other.\n", + "We plot the membrane voltages of both." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.231328Z", + "iopub.status.busy": "2020-11-25T16:46:58.230209Z", + "iopub.status.idle": "2020-11-25T16:46:58.403907Z", + "shell.execute_reply": "2020-11-25T16:46:58.403125Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 0.01), seed=1)\n", + "\n", + "with nengo.Network() as model:\n", + " ens_args = dict(encoders=[[1]], intercepts=[0.01], max_rates=[100])\n", + " a = nengo.Ensemble(1, 1, **ens_args)\n", + " b = nengo.Ensemble(1, 1, noise=process, **ens_args)\n", + " a_voltage = nengo.Probe(a.neurons, \"voltage\")\n", + " b_voltage = nengo.Probe(b.neurons, \"voltage\")\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.15)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_voltage], label=\"deterministic\")\n", + "plt.plot(sim.trange(), sim.data[b_voltage], label=\"noisy\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"voltage\")\n", + "plt.legend(loc=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the neuron without noise (blue)\n", + "approaches its firing threshold,\n", + "but never quite gets there.\n", + "Adding a bit of noise (green)\n", + "causes the neuron to occasionally jitter above the threshold,\n", + "resulting in two spikes\n", + "(where the voltage suddenly drops to zero)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `FilteredNoise`\n", + "\n", + "The `FilteredNoise` process takes a white noise signal\n", + "and passes it through a filter.\n", + "Using any type of lowpass filter (e.g. `Lowpass`, `Alpha`)\n", + "will result in a signal similar to `WhiteSignal`,\n", + "but rather than being ideally filtered\n", + "(i.e. no frequency content above the cutoff),\n", + "the `FilteredNoise` signal\n", + "will have some frequency content above the cutoff,\n", + "with the amount depending on the filter used.\n", + "Here, we can see how an `Alpha` filter\n", + "(a second-order lowpass filter)\n", + "is much better than the `Lowpass` filter\n", + "(a first-order lowpass filter)\n", + "at removing the high-frequency content." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.417955Z", + "iopub.status.busy": "2020-11-25T16:46:58.411731Z", + "iopub.status.idle": "2020-11-25T16:46:58.648836Z", + "shell.execute_reply": "2020-11-25T16:46:58.647929Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process1 = nengo.processes.FilteredNoise(\n", + " dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Alpha(0.005), seed=0\n", + ")\n", + "\n", + "process2 = nengo.processes.FilteredNoise(\n", + " dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Lowpass(0.005), seed=0\n", + ")\n", + "\n", + "tlen = 0.5\n", + "plt.figure()\n", + "plt.plot(process1.trange(tlen), process1.run(tlen))\n", + "plt.plot(process2.trange(tlen), process2.run(tlen))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `FilteredNoise` process with an `Alpha` synapse (blue)\n", + "has significantly lower high-frequency components\n", + "than a similar process with a `Lowpass` synapse (green)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `PresentInput`\n", + "\n", + "The `PresentInput` process is useful for\n", + "presenting a series of static inputs to a network,\n", + "where each input is shown for the same length of time.\n", + "Once all the images have been shown,\n", + "they repeat from the beginning.\n", + "One application is presenting a series of images to a classification network." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.655154Z", + "iopub.status.busy": "2020-11-25T16:46:58.654366Z", + "iopub.status.idle": "2020-11-25T16:46:58.795370Z", + "shell.execute_reply": "2020-11-25T16:46:58.795899Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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aAGR2ZfZgqyRRSDpZ0m2Stko6f4bXl0r6XPH69yQd2vHae4rlt0l6VRXx1K2dKDxGUbXUP888TEYO42eQ1VxPA142SycKSSPAJcCrgSOB0yUdOW21s4CHIuL5wMXAB4ttjwTWAi8CTgb+oXi/rE3mMjunMkkU7nqqVE4tijy6nhj4slnFrVCPBbZGxDYASVcAa4BbO9ZZA/xl8fhK4O/VmmhmDXBFRDwB3Clpa/F+3+31B3/y4C7e9pkbKwh9cTxZTPif+jTj2SSKTAazm83gv33lFu7/xRN1h9LTD37yMM98Rg53QU77+54yBNdRVFFaVgF3dzzfDhzXbZ2ImJD0CPCsYvnGaduumumPSFoHrAPYa+Xh3LHz0QpCXzxHrtyHow/et+4wZtFOFJmMUSSeeO/7xeNc9t0f8+xnLmXfvfaoO5yu9tlzlBNfeEDdYcwulzEKAjTYw705VCsAiIj1wHqAsbGx+Jd3/G7NEQ0AZXbWU+LaU2a986QX8KZjDq43mEFQTOEREWnPdBsx6A2KSgazdwAHdTw/sFg24zqSRoFfAx6Y47a2aHJpUeSRKNpdeBr0o0bfCBHpz1k5BKpIFDcAqyUdJmkJrcHpDdPW2QCcUTw+DbguWnvVBmBtcVbUYcBq4PsVxGRzkcsYRSaD2ZHHUEo+1Eq5yU9aOQTXUZTueirGHM4BrgZGgEsj4hZJFwLjEbEB+Djw6WKw+kFayYRivc/TGvieAN4eEZ54qG8y6XqaksfOmPxJDNloTeEx2Qz2SPlcSA9mz01EXAVcNW3ZezsePw68ocu2HwA+UEUcNk9y11OVmlNdT1YFFS2K9OsxyQdY2mAP1dss2omi5jBmk1nXU+Jh5kOtMYrJ5AsoA/+lO1EMs+xaFGnvjFNRph1mRtqD2YkniiHoenKiGGqZDGa3JX4Ebn+OHqOoSHswO/nTngZ/MNuJYpi1LxJKPVGkHl8hjyjzIWVyemwm5bOMbC64s0VQ1IK2/exRXvt33645mO4OndjG3wFPNoN0r3fuuI5iwGuX/dNqUfzHj25kj4RvQP8/HvkFz95/T5bXHcgicqIYaq0D2qEr9uTOZy6tOZbuJn/WGkN55LEJ9qs5ll6mBrPrDWNgrNh7KTwywnP33bPuUHp6bOcED+2acKKwAVXUfN9y3MG85bhjag6mu2uuexi+BRFpH4I9mF2tZUv3YNm+e3LpmemWTYCb3jv43Y7ptuesD9pHtLSLeXtG7Eg8znaLwoPZFclkUkAp/RjLcqIYZplMCqjiYBGJd+r4gruqKfmyCe3bKw32t+5EYaRea9vdokh7Z/QFdxWTIPlrfFrS3oPKc6IYZpm0KNpSP2REJhcG5iOTrici+UpMWU4UQy2PMYpcdsHdYxT1xjEwlE/X06BzohhmmbQoGu1ElvpZT1NdT2nHmY9MWhRyi8IG2dTtG9PeGdu7YDPxnbHd9ZR2lBnJpEUBqe9B5TlRDLVMWhRTg9lp82B21fJoUTQ8RmEDbarrKe1h4qnTYxM/ZrTD83UUFcmkRSHIIZ+V4kQx1PIYzG5LPcrwHB4Vy6NFISL14bPSnCiGWS6D2crlgrvW77SjzIgayZfNttTLZlmlEoWkFZKukbSl+P20ebEkHSXpu5JukXSTpDd1vPZJSXdK2lT8HFUmHpuvPFoUat83I/md0bPHViqXKTxIfx6yssq2KM4Hro2I1cC1xfPpdgF/FBEvAk4G/pekfTtef3dEHFX8bCoZj81Hdi2KtLnnqWpK/0vHXU9zsQa4rHh8GXDq9BUi4vaI2FI8/ilwP7B/yb9rlcilReHB7KGUyRQeUqS+C5VWNlEcEBH3FI/vBQ7otbKkY4ElwB0diz9QdEldLKnrTREkrZM0Lml8586dJcM2oKNFUW8Ys8kjne2+ZafzRFXy6HpqVWUG+0ufNVFI+rqkm2f4WdO5XrRO+ej6rUpaCXwa+OOIqWrCe4AjgGOAFcB53baPiPURMRYRY/vv7wZJJXK54C6XSQGL32lHmZFW53/dUcxqGOZ6mvXGRRFxYrfXJN0naWVE3FMkgvu7rLcP8FXggojY2PHe7dbIE5I+AbxrXtFbSXmMUeTSoghniorl0aJQ8ndKKa9s19MG4Izi8RnAl6evIGkJ8CXgUxFx5bTXVha/RWt84+aS8dh8KI9DcD5jFK0APUZRkYwuuEs/ynLKJoqLgFdK2gKcWDxH0pikjxXrvBF4BXDmDKfBflbSZmAzsB/wVyXjsfnI5srsluTnevJZTxXL5xA89F1PvUTEA8AJMywfB84uHn8G+EyX7Y8v8/etIonX2pTLwcKzx1YrmxaFz3qygZd+ra193G0mfrL61OyxaYeZkfTLJrSiTL21W5YTxbDLoNa2eyQl7Th946KKZTKFRy4t3jKcKIZe+rW2RiYnnu7+FNOOMxuZTOEBuUS5cE4Uwy6HFkUmXU/NcNdTtdIvmzAc11E4UQy9jGptqe+LPuupWrlM4UEue9DCOVEMuwz6gacmBUw7zI7BbKeKauRxCBaRfNksy4li2OXQDxxP+ZUsD2ZXTJnMHit3PdnAS78fOL8bF6UdZz4yqMQU8ohy4Zwohl0G/cDtA2/aUe6+Fap7niqSwYkW0E5ng/2lO1EMvfQLuJRH31Pi4WUojxaFr6OwwZdBra2dJ1I/PXb3GEXacWYjg7LZ5haFDbj0a23K5Faou++ZXXMYAyX9bz2T22aU4kQx7DKote2ewiPtI/DUYHbaYeYjg1O3wRfc2VDIoEVR/E47ys5pxgf7oNE3OZy6DUAkf6JFWU4Uwy6DdrOyu+Cu5kAGRvqtXcjhdJDynCiGnRqkXmubunFRNoPZ9cYxMDI4dbvNXU824NKvtU11PSW+LzZ90+yK5dH15Htm2+DLoB94d9dTHgdgdz1VJIMTLaA919Ngf+mlEoWkFZKukbSl+L28y3qTHffL3tCx/DBJ35O0VdLnJC0pE48tRAbN+/Z1FIknNN8zu2rpV2KgHWX6cZZRtkVxPnBtRKwGri2ez+SxiDiq+DmlY/kHgYsj4vnAQ8BZJeOx+cqg1pbPjYtacfqCu4pkUDbbPEbR2xrgsuLxZcCpc91QrbmYjweuXMj2VpX0a227b1xUbxyzaRYNM+eJqqRfNsFdT3NxQETcUzy+Fzigy3rPkDQuaaOkU4tlzwIejoiJ4vl2YFW3PyRpXfEe4zt37iwZtk3JoNbWnksn7SjpaPcM9kGjb7LJuIPe8QSjs60g6evAc2Z46YLOJxERmpq97WkOiYgdkp4HXCdpM/DIfAKNiPXAeoCxsbFB/176KP1a29RZT2mH6dljK1d8kBFJf6jp70HlzZooIuLEbq9Juk/Syoi4R9JK4P4u77Gj+L1N0jeAo4EvAvtKGi1aFQcCOxbwP1gZGbUoEh9y392iSPeYlhcVHR6JJwoY/ERRtutpA3BG8fgM4MvTV5C0XNLS4vF+wMuBW6NV/boeOK3X9rbYMqgPtSuWqXfpTM31lHicuZj6HNMun57raXYXAa+UtAU4sXiOpDFJHyvWeSEwLumHtBLDRRFxa/HaecC5krbSGrP4eMl4bL7USH0/zKbrqX3B3WAfMvqpo+spYSKSnzWgrFm7nnqJiAeAE2ZYPg6cXTz+V+AlXbbfBhxbJgYrKYcL7tqD2WmH6a6nqk3VEFLvdITU96GyfGX20MthjKIlEj8Ce/bYqrnrKRVOFMNOJF9jy+U6it0X3NUcyKBQHl1PkHoqK8+JYuhl1PWUeK1tKpGlHWZG8mlRDPoYhRPFsMvh9NgivEg8TqYGswf7oNE3ubQoEg+vCk4UQy/9FgXKo0XhweyqZdSiqDuIReZEMewyaFE0igNG8mMUUzcucqaoRC4tCvKZAn+hnCiGXgYtiqkrs9PeGX0dRdXyaVGkHWF5ThTDLosWRR43Lpo6PTbtMPPROYVH0nx6rA26DO6ZvbtFkXacnj22YtlM4ZFBLivJiWLopd+iyKWGPjV7rPeqimQ0RlF3AIvMRXrYKf1bobbzROqD2W2Z5LX0TQ1mp14+3fVkAy/9wWxFHqfHTg1m59IESl4un6NPj7VBl8Fg9lTFst4wZrV7rierRCanxw7D9+1EMfTyKeapdz35gruq5TGYDemXzbKcKIZdDi2K9llPmZwe6wvuKpJNi2LwxyhK3Y/CBoHgvpvha+fVHUhXI/f/O5B+vbKZ+AEtW9ddCHvsVXcUXTViMvlrfMpyohh2Bx0DN38Rfnh53ZF0JeCO5kp2jS6vO5Q5cYOiIs8+EpbtD7emfYfkx0b25nYdWncYi8qJYtj9wcWtn4Q1m8EJf34V547sWXcoPYVnj63WoS+Hd2+tO4pZXXjlTXxry07+qu5AFlGpMQpJKyRdI2lL8ftpVT5Jvy9pU8fP45JOLV77pKQ7O147qkw8Nph2X0eRdtfO7jGKeuOw/pLSL5tllR3MPh+4NiJWA9cWz58iIq6PiKMi4ijgeGAX8C8dq7y7/XpEbCoZjw2gTMY0p8588XUUwyWD80FKK5so1gCXFY8vA06dZf3TgK9FxK6Sf9eGSPvAm/qNi9pziDpNDBdJPj12FgdExD3F43uBA2ZZfy0wfdT0A5JuknSxpKUl47EBJaV/1pNnjx1ORTWm5igW16yD2ZK+Djxnhpcu6HwSESGp66claSXwEuDqjsXvoZVglgDrgfOAC7tsvw5YB3DwwQfPFrYNmIaUfPN+9wV3zhTDJIeyWdasiSIiTuz2mqT7JK2MiHuKRHB/j7d6I/CliHiy473brZEnJH0CeFePONbTSiaMjY0N+Ndi04kMBgwj3JoYQh7Mnt0G4Izi8RlArxOeT2dat1ORXFCrCnYqcHPJeGxANaTkG/fN8PjEMMqhbJZVNlFcBLxS0hbgxOI5ksYkfay9kqRDgYOAb07b/rOSNgObgf1goE9FtjIyqLUF4W6nIdUc8NHsUhfcRcQDwAkzLB8Hzu54fhewaob1ji/z9214CPjnm+/ljvsfrTuUrrbe/6hbFENIgl2/muTsy26oO5RF4yuzLQuvO3oVm3c8wj2PPF53KF0tWzrKqUc/rT5kA+53Vu/H+F0PJV02y1Lq56bPZGxsLMbHx+sOw8wsK5JujIix+W7nacbNzKwnJwozM+vJicLMzHpyojAzs56cKMzMrCcnCjMz68mJwszMenKiMDOznpwozMysJycKMzPryYnCzMx6cqIwM7OenCjMzKwnJwozM+vJicLMzHpyojAzs56cKMzMrKdSiULSGyTdIqkpqetdkySdLOk2SVslnd+x/DBJ3yuWf07SkjLxmJlZ9cq2KG4G/hD4VrcVJI0AlwCvBo4ETpd0ZPHyB4GLI+L5wEPAWSXjMTOzipVKFBHxo4i4bZbVjgW2RsS2iPgVcAWwRpKA44Eri/UuA04tE4+ZmVVvtA9/YxVwd8fz7cBxwLOAhyNiomP5qm5vImkdsK54+oSkmxch1qrtB/ys7iDmIIc4c4gRHGfVHGe1fn0hG82aKCR9HXjODC9dEBFfXsgfXYiIWA+sL2Iaj4iuYyKpcJzVySFGcJxVc5zVkjS+kO1mTRQRceJC3rjDDuCgjucHFsseAPaVNFq0KtrLzcwsIf04PfYGYHVxhtMSYC2wISICuB44rVjvDKBvLRQzM5ubsqfHvk7SduBlwFclXV0sf66kqwCK1sI5wNXAj4DPR8QtxVucB5wraSutMYuPz/FPry8Tdx85zurkECM4zqo5zmotKE61KvZmZmYz85XZZmbWkxOFmZn1lHSi6Db1R8frS4upP7YWU4EcmmCMr5D0b5ImJJ0203v0wxziPFfSrZJuknStpEMSjfOtkjZL2iTp2x1X+ScVZ8d6r5cUvaa4WUxz+DzPlLSz+Dw3STo7xTiLdd5YlNFbJP1TajFKurjjc7xd0sP9jrGIY7Y4D5Z0vaQfFPv7a2Z904hI8gcYAe4AngcsAX4IHDltnf8K/GPxeC3wuQRjPBT4DeBTwGkJf5a/D+xVPH5bvz/LecS5T8fjU4B/TjHOYr1n0preZiMwlmKcwJnA3/c7tgXEuRr4AbC8eP7s1GKctv6fApcm+lmuB95WPD4SuGu29025RTHj1B/T1llDa+oPaE0FckIxNUgyMUbEXRFxE9DsY1zTzSXO6yNiV/F0I63rWvptLnH+vOPpMqCOszHmUjYB3k9rPrPH+xlch7nGWbe5xPknwCUR8RBARNyfYIydTgcu70tkTzWXOAPYp3j8a8BPZ3vTlBPFTFN/TJ/iY2qdaJ2G+wit02z7ZS4xpmC+cZ4FfG1RI5rZnOKU9HZJdwB/DfxZn2LrNGuckn4TOCgivtrPwKaZ6/f++qIL4kpJB83w+mKbS5wvAF4g6TuSNko6uW/Rtcx5Hyq6bQ8DrutDXNPNJc6/BN5cXNpwFa3WT08pJwqrgaQ3A2PA39QdSzcRcUlEHE7rOpy/qDue6SQ1gA8B76w7ljn4CnBoRPwGcA27W+ipGaXV/fR7tGrrH5W0b50B9bAWuDIiJusOpIvTgU9GxIHAa4BPF2W2q5QTRbepP2ZcR9IorWbUA32JbtrfL6Q6Dcmc4pR0InABcEpEPNGn2DrN9/O8gnpmHJ4tzmcCLwa+Ieku4LeADTUMaM/6eUbEAx3f9ceAl/Yptk5z+d6305rR4cmIuBO4nVbi6Jf5lM211NPtBHOL8yzg8wAR8V3gGbQmNeyu34Mt8xiUGQW20WrCtQdlXjRtnbfz1MHsz6cWY8e6n6S+wey5fJZH0xoEW534d7664/FrgfEU45y2/jeoZzB7Lp/nyo7HrwM2JhrnycBlxeP9aHWvPCulGIv1jgDuoriYOdHP8mvAmcXjF9Iao+gZb9//kXn+06+hVXO4g9ZstQAX0qrxQisTfgHYCnwfeF6CMR5Dqzb0S1qtnVsS/Sy/DtwHbCp+NiQa54eBW4oYr+91gK4zzmnr1pIo5vh5/vfi8/xh8XkekWicotWddyuwGVibWozF878ELqrjM5zHZ3kk8J3iO98EnDTbe3oKDzMz6ynlMQozM0uAE4WZmfXkRGFmZj05UZiZWU9OFGZm1pMThZmZ9eREYWZmPf1/tVKD75SFbWQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "inputs = [[0, 0.5], [0.3, 0.2], [-0.1, -0.7], [-0.8, 0.6]]\n", + "process = nengo.processes.PresentInput(inputs, presentation_time=0.1)\n", + "\n", + "tlen = 0.8\n", + "plt.figure()\n", + "plt.plot(process.trange(tlen), process.run(tlen))\n", + "plt.xlim([0, tlen])\n", + "plt.ylim([-1, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom processes\n", + "\n", + "You can create custom processes\n", + "by inheriting from the `nengo.Process` class\n", + "and overloading the `make_step` and `make_state` methods.\n", + "\n", + "As an example, we'll make a simple custom process\n", + "that implements a two-dimensional oscillator dynamical system.\n", + "The `make_state` function defines a `state` variable\n", + "to store the state.\n", + "The `make_step` function uses that state\n", + "and a fixed `A` matrix to determine\n", + "how the state changes over time.\n", + "\n", + "One advantage to using a process over a simple function\n", + "is that if we reset our simulator,\n", + "`make_step` will be called again\n", + "and the process state\n", + "will be restored to the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.831840Z", + "iopub.status.busy": "2020-11-25T16:46:58.803558Z", + "iopub.status.idle": "2020-11-25T16:46:59.425977Z", + "shell.execute_reply": "2020-11-25T16:46:59.426408Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class SimpleOscillator(nengo.Process):\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None):\n", + " # return a dictionary mapping strings to their initial state\n", + " return {\"state\": np.array([1.0, 0.0])}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " A = np.array([[-0.1, -1.0], [1.0, -0.1]])\n", + " s = state[\"state\"]\n", + "\n", + " # define the step function, which will be called\n", + " # by the node every time step\n", + " def step(t):\n", + " s[:] += dt * np.dot(A, s)\n", + " return s\n", + "\n", + " return step # return the step function\n", + "\n", + "\n", + "with nengo.Network() as model:\n", + " a = nengo.Node(SimpleOscillator(), size_in=0, size_out=2)\n", + " a_p = nengo.Probe(a)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_p])\n", + "plt.xlabel(\"time [s]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can generalize this process to one\n", + "that can implement arbitrary linear dynamical systems,\n", + "given `A` and `B` matrices.\n", + "We will overload the `__init__` method\n", + "to take and store these matrices,\n", + "as well as check the matrix shapes\n", + "and set the default size in and out.\n", + "The advantage of using the default sizes\n", + "is that when we then create a node using the process,\n", + "or run the process using `apply`,\n", + "we do not need to specify the sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:59.449261Z", + "iopub.status.busy": "2020-11-25T16:46:59.439175Z", + "iopub.status.idle": "2020-11-25T16:47:00.469391Z", + "shell.execute_reply": "2020-11-25T16:47:00.470070Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class LTIProcess(nengo.Process):\n", + " def __init__(self, A, B, **kwargs):\n", + " A, B = np.asarray(A), np.asarray(B)\n", + "\n", + " # check that the matrix shapes are compatible\n", + " assert A.ndim == 2 and A.shape[0] == A.shape[1]\n", + " assert B.ndim == 2 and B.shape[0] == A.shape[0]\n", + "\n", + " # store the matrices for `make_step`\n", + " self.A = A\n", + " self.B = B\n", + "\n", + " # pass the default sizes to the Process constructor\n", + " super().__init__(\n", + " default_size_in=B.shape[1], default_size_out=A.shape[0], **kwargs\n", + " )\n", + "\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None):\n", + " return {\"state\": np.zeros(self.A.shape[0])}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " assert shape_in == (self.B.shape[1],)\n", + " assert shape_out == (self.A.shape[0],)\n", + " A, B = self.A, self.B\n", + " s = state[\"state\"]\n", + "\n", + " def step(t, x):\n", + " s[:] += dt * (np.dot(A, s) + np.dot(B, x))\n", + " return s\n", + "\n", + " return step\n", + "\n", + "\n", + "# demonstrate the LTIProcess in action\n", + "A = [[-0.1, -1], [1, -0.1]]\n", + "B = [[10], [-10]]\n", + "\n", + "with nengo.Network() as model:\n", + " u = nengo.Node(lambda t: 1 if t < 0.1 else 0)\n", + " # we don't need to specify size_in and size_out!\n", + " a = nengo.Node(LTIProcess(A, B))\n", + " nengo.Connection(u, a)\n", + " a_p = nengo.Probe(a)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_p])\n", + "plt.xlabel(\"time [s]\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/2d-representation.ipynb b/.doctrees/nbsphinx/examples/basic/2d-representation.ipynb new file mode 100644 index 0000000000..bee00ce87f --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/2d-representation.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2-dimensional representation\n", + "\n", + "Ensembles of neurons represent information.\n", + "In Nengo, we represent that information with\n", + "real-valued vectors -- lists of numbers.\n", + "In this example, we will represent a two-dimensional vector\n", + "with a single ensemble of leaky integrate-and-fire neurons." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "Our model consists of a single ensemble,\n", + "which we will call `Neurons`.\n", + "It will represent a two-dimensional signal." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:03.536957Z", + "iopub.status.busy": "2020-11-25T16:47:03.536077Z", + "iopub.status.idle": "2020-11-25T16:47:04.028750Z", + "shell.execute_reply": "2020-11-25T16:47:04.027863Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "\n", + "model = nengo.Network(label=\"2D Representation\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # and represents a 2-dimensional signal\n", + " neurons = nengo.Ensemble(100, dimensions=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "The signal that an ensemble represents varies over time.\n", + "We will use a simple sine and cosine wave\n", + "as examples of continuously changing signals." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.033684Z", + "iopub.status.busy": "2020-11-25T16:47:04.033164Z", + "iopub.status.idle": "2020-11-25T16:47:04.036677Z", + "shell.execute_reply": "2020-11-25T16:47:04.036197Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes representing the sine and cosine\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the input to the ensemble" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.042550Z", + "iopub.status.busy": "2020-11-25T16:47:04.042050Z", + "iopub.status.idle": "2020-11-25T16:47:04.045467Z", + "shell.execute_reply": "2020-11-25T16:47:04.045046Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # The indices in neurons define which dimension the input will project to\n", + " nengo.Connection(sin, neurons[0])\n", + " nengo.Connection(cos, neurons[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later.\n", + "Let's collect all the data produced." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.050393Z", + "iopub.status.busy": "2020-11-25T16:47:04.049901Z", + "iopub.status.idle": "2020-11-25T16:47:04.053402Z", + "shell.execute_reply": "2020-11-25T16:47:04.052938Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin, \"output\")\n", + " cos_probe = nengo.Probe(cos, \"output\")\n", + " neurons_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.058026Z", + "iopub.status.busy": "2020-11-25T16:47:04.057533Z", + "iopub.status.idle": "2020-11-25T16:47:04.761630Z", + "shell.execute_reply": "2020-11-25T16:47:04.762079Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.768653Z", + "iopub.status.busy": "2020-11-25T16:47:04.767618Z", + "iopub.status.idle": "2020-11-25T16:47:04.987398Z", + "shell.execute_reply": "2020-11-25T16:47:04.987834Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neurons_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Sine\")\n", + "plt.plot(sim.trange(), sim.data[cos_probe], \"k\", label=\"Cosine\")\n", + "plt.legend()\n", + "plt.xlabel(\"time [s]\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/addition.ipynb b/.doctrees/nbsphinx/examples/basic/addition.ipynb new file mode 100644 index 0000000000..5f0e6a4aeb --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/addition.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Addition\n", + "\n", + "In this example, we will construct a network that adds two inputs.\n", + "The network utilizes two communication channels\n", + "into the same neural population.\n", + "Addition is thus somewhat 'free', since the incoming currents\n", + "from different synaptic connections interact linearly\n", + "(though two inputs don't have to\n", + "combine in this way; see the combining demo)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.237356Z", + "iopub.status.busy": "2020-11-25T16:47:06.236493Z", + "iopub.status.idle": "2020-11-25T16:47:06.733169Z", + "shell.execute_reply": "2020-11-25T16:47:06.732236Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model has three ensembles, which we will call A, B, and C." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.739705Z", + "iopub.status.busy": "2020-11-25T16:47:06.739141Z", + "iopub.status.idle": "2020-11-25T16:47:06.742359Z", + "shell.execute_reply": "2020-11-25T16:47:06.742762Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Addition\")\n", + "with model:\n", + " # Create 3 ensembles each containing 100 leaky integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + " C = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "We will use two constant scalar values for the two input signals\n", + "that drive activity in ensembles A and B." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.750663Z", + "iopub.status.busy": "2020-11-25T16:47:06.750148Z", + "iopub.status.idle": "2020-11-25T16:47:06.753262Z", + "shell.execute_reply": "2020-11-25T16:47:06.753663Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes representing constant values\n", + " input_a = nengo.Node(output=0.5)\n", + " input_b = nengo.Node(output=0.3)\n", + "\n", + " # Connect the input nodes to the appropriate ensembles\n", + " nengo.Connection(input_a, A)\n", + " nengo.Connection(input_b, B)\n", + "\n", + " # Connect input ensembles A and B to output ensemble C\n", + " nengo.Connection(A, C)\n", + " nengo.Connection(B, C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.761196Z", + "iopub.status.busy": "2020-11-25T16:47:06.759619Z", + "iopub.status.idle": "2020-11-25T16:47:06.761814Z", + "shell.execute_reply": "2020-11-25T16:47:06.762220Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " input_a_probe = nengo.Probe(input_a)\n", + " input_b_probe = nengo.Probe(input_b)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " C_probe = nengo.Probe(C, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.767503Z", + "iopub.status.busy": "2020-11-25T16:47:06.766706Z", + "iopub.status.idle": "2020-11-25T16:47:07.884914Z", + "shell.execute_reply": "2020-11-25T16:47:07.884450Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data produced by running the model can now be plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:07.893249Z", + "iopub.status.busy": "2020-11-25T16:47:07.890825Z", + "iopub.status.idle": "2020-11-25T16:47:08.201491Z", + "shell.execute_reply": "2020-11-25T16:47:08.201891Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signals and decoded ensemble values\n", + "t = sim.trange()\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded Ensemble A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded Ensemble B\")\n", + "plt.plot(sim.trange(), sim.data[C_probe], label=\"Decoded Ensemble C\")\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_a_probe], label=\"Input A\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_b_probe], label=\"Input B\", color=\"0.75\", linewidth=2.0\n", + ")\n", + "plt.legend()\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"time [s]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check that the decoded value\n", + "of the activity in ensemble C\n", + "provides a good estimate of the sum of inputs A and B." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/combining.ipynb b/.doctrees/nbsphinx/examples/basic/combining.ipynb new file mode 100644 index 0000000000..37b2b9db13 --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/combining.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining\n", + "\n", + "This example demonstrates how to create\n", + "a neuronal ensemble that will combine two 1-D inputs\n", + "into one 2-D representation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.466397Z", + "iopub.status.busy": "2020-11-25T16:47:09.465545Z", + "iopub.status.idle": "2020-11-25T16:47:09.956717Z", + "shell.execute_reply": "2020-11-25T16:47:09.957161Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of three ensembles,\n", + "two input ensembles and one 2-D ensemble\n", + "that will represent the two inputs as one two-dimensional signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.964024Z", + "iopub.status.busy": "2020-11-25T16:47:09.963178Z", + "iopub.status.idle": "2020-11-25T16:47:09.966534Z", + "shell.execute_reply": "2020-11-25T16:47:09.966092Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Combining\")\n", + "with model:\n", + " # Our input ensembles consist of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # The output ensemble consists of 200 leaky integrate-and-fire neurons,\n", + " # representing a two-dimensional signal\n", + " output = nengo.Ensemble(200, dimensions=2, label=\"2D Population\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use sine and cosine waves\n", + "as examples of continuously changing signals." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.972076Z", + "iopub.status.busy": "2020-11-25T16:47:09.970574Z", + "iopub.status.idle": "2020-11-25T16:47:09.972708Z", + "shell.execute_reply": "2020-11-25T16:47:09.973117Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes generating the sine and cosine\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.981208Z", + "iopub.status.busy": "2020-11-25T16:47:09.979649Z", + "iopub.status.idle": "2020-11-25T16:47:09.981788Z", + "shell.execute_reply": "2020-11-25T16:47:09.982192Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, A)\n", + " nengo.Connection(cos, B)\n", + "\n", + " # The square brackets define which dimension the input will project to\n", + " nengo.Connection(A, output[1])\n", + " nengo.Connection(B, output[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.989527Z", + "iopub.status.busy": "2020-11-25T16:47:09.988050Z", + "iopub.status.idle": "2020-11-25T16:47:09.990133Z", + "shell.execute_reply": "2020-11-25T16:47:09.990532Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " cos_probe = nengo.Probe(cos)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms filter\n", + " B_probe = nengo.Probe(B, synapse=0.01) # 10ms filter\n", + " out_probe = nengo.Probe(output, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.995706Z", + "iopub.status.busy": "2020-11-25T16:47:09.994900Z", + "iopub.status.idle": "2020-11-25T16:47:11.096315Z", + "shell.execute_reply": "2020-11-25T16:47:11.095424Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:11.103816Z", + "iopub.status.busy": "2020-11-25T16:47:11.103210Z", + "iopub.status.idle": "2020-11-25T16:47:11.343738Z", + "shell.execute_reply": "2020-11-25T16:47:11.344151Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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S16UzOjpaxowZI6VKlZJ79+65fN0hQ4bIqFGjXDpH1qxZnbaZxeIH4Pa37wdgzTv0psNsS7F88glUq+bChf77D37/nX3oYKorwGqgnaX5q1AYXyu2+6gmoyiUvRC5s6Tt6qAZ02WkZIFydNsmvDTwR3Jm1xkSg4CngTnxD/jxR714Y/ALChYsGBOtmz17dsqVK8fp06ft+iml6Nu3LwUKFOAPB4uBK1eupEqVKlSqVIlXX32Ve/fuATrFxCVLIsSwsDAaNmzIiRMnmDRpEmPHjo2JLO7cuTM9e/YkNDSU0qVLs3jxYgC+/fZbevXqFXOdFi1asGbNGgYMGMCdO3cICQlxObLYL9cIQCeq9BYZMsCNGzoHWrdu0KiR88jl8HCYMAE6doyb1O6B7N4NlSuzFV0gOiOwDqgITK0C7zWBKzYJg3pW68kjuR9J7i2lWjpU6sDHb33FgE//YrVAW+BFdMGcLtZOL70Eb79tspj6gD5/9mHHuR1uPWdIgRDGNRuXqL4mDbUf8fDDeg7fm2TLpheng4PhyhW4dct53969oWvXJJz8xg2oXJm16DWBHMB6tBHY9TB0axXXCAA8Xeppu9P4Cx8MX8/eMzs4VLcES9AJ615FL6THcPmye1LUGlINJg21n3Hnju8zFD/0gHSeCxbomZ5cuRJxsldfZR16mqM4sAIoZGmq2gOdoc3CjYE3mLdvXswiqr8SXCCY6D928dlTOVj0l66v8CZ6ZBCzVD52rC7UU7myr9T0OxL75u5uTBpqP+T2bd8bAoD9+xNuz51bexw5rQN/7x4oxd/z5vE02h1yNdoIjK+pA8Rs4wPOv3uebBmz0Tmkc6p2D3UXAdmyM3C9MO+NuswDnkNXPxtr2yk42Ce6GbyHiElD7a4KZc2UUgeVUkeUUgMctI9VSu2wbIeUUv/ZtN23aVvkDn0SIjJSVxV70Bu5NyhbFmz+TR1SpoyeUoozGo2IgL17oXlzNqHXBAoDq9ClHjcXhj5PxT3Plte28HDWh92pfpqh48R1tOqWhdxP6YX1d9BV0WJQSsca7NrlGwUNHsWkocZ191G0i/ZR4BH0GuVOoHwC/d8Cptt8v5nUa7riPnrtmvYQHD062afwCA9yLd2/36Zz9+4iIFsteXRKgoTbdLZNEjdmwxjZdW6Xz+4rtTDq71HCEKTIW8jT6Oyl38X/R3BQINzgOr52H00p+DL7qDtGBDWAIyJyTEQi0N54rRPo3wFdj9wn3Lmj9ylhaigplCunRw9HjwIrVnAAaAbkQU8HWQOc0w2OPaZntZ70rd2XSvkTqjBgAOhbqy+BWQMJzwtLBulF9y7o/ExWdu5brecV79/3kZYGg2dwhyEojK5pbiWc2OdSHJRSxdHu26tsxJmVUmFKqU1KqWecXUQp1d3SL+zixYvJVvb2bb1PCVNDtuzZoytGJkTNmvDio5sJP3aMJ9FDseVAUaB9O8j0AUTb/ItOfHqi5xROY6QLSMeFfhe48O4FVnVdxUJ0KooX0b8xQPCOs5A1axJdugyGxPHtt9+6rShNUvH2YnF7YJ6I2L5SFRddMedFYJxSqqSjA0VksoiEikhoYGBgshVIqSOCChWgXz+dliIh/qQWzYCr6Hw5jwKH8sDcShBh8QH7t8+/yBBJEZlCUxuBWQNpFNSI8z1eYDFQDh1rsN2203ff+UQ3g8FTuMMQnEa/lFopYpE5oj3xpoVE5LRlfwxYAySQos11rJ5XmTJ58irJZ9IknazOtn5yJu5ykmIcoSAt0QXlF6Br9AJUiBexXDSnyaTpKo98PZvNHWvzB5Ab7Zp7wrcqGQwewx2GYCtQSikVpJTKiH7Y23n/KKXKov+mNtrIciulMlk+50OXzd3nBp2cYvW08kTSOXeglE5Ut369zpAKMIjhFOYU73CODcAP6Dns4fUgT3+Isrz458yUk0nNJ/lG8bSGUjSYsZqq78KfwF30msxlS/Nrv3QmMuJuPHcugyF14rIhEJEooBewFNgP/CQie5VSHymlWtl0bQ/MsaxcWykHhCmldqLXPEeIiFcMQYYMCffzNfnzw/nzUJwTfMjH9Edb1wmAdRZxS2G4alnrqFesHv8N+I8eoT18o3AaJFP6TJwbGc2aUP3bnwBaooPOhnT5jgyZsnC+VeIDiwyGlIpb1ghEZImIlBaRkiIy3CIbLCKLbPoMFZEB8Y7bICKVRCTYsp/mDn0SIrUYAgAVFckJgpgMjEH73fayaT+eS+8LPFSYdV2SHs5ueDBKKXpuuMf+qnoktgmdjqLIdd2ef/FqZu2c5UMNDe5iwYIFKKXcmtbh22+/5cyZM8k+/sSJE/z4449u08cZfhdZbM08mlKnhuIQGckK4A100NgYm6YKb8DuAvrzuf7uyY9ucExAhox03ya0A4aj/aOH27SvGvKKbxQzuJXZs2dTt25dZs92n3e7MQQplNQ0Itjfti3PoufP5hCbGKpYH9hnDRI+3MzRoQYPsH/fOgYAHdH1nq3JAmYshNPXnflHGFIDN2/e5K+//mLatGnMmWOXmDyGMWPGULFiRSpWrMi4ceMA/bCuWLFiTJ/Ro0czdOhQ5s2bR1hYGC+99BIhISHcuXOHEiVK0L9/fypVqkSNGjViUlJ07tyZefPmxZwjWzZdNHzAgAGsX7+ekJAQxo6Nk/zErfhd0rnUYgguT5tGi6VLyQwsRmcUBcj9HvxndX3d/wzM/RXQwWY1anhfT3+iXLl6/LznJ6ZUfJ4jwMvooJiqwLUuHShcsREMG+ZbJVM7ffqAG9IqxyEkBCwPbWcsXLiQZs2aUbp0afLmzcu2bduoFq84yLZt25gxYwabN29GRKhZsyYNGjQgd27H9TyeffZZJk6cyOjRowm1KZCeM2dOdu/ezcyZM+nTp09M3QFHjBgxgtGjRyfYxx2YEUEKJOrzz2n/2mucBhaiM4oCrC5hYwSuFYUlsQFjNWsaBxZv0K58Owq+B72LQz6gFXAGKP/LevjoIx9rZ0gus2fPpn379gC0b9/e4fTQX3/9RZs2bciaNSvZsmWjbdu2MTmCkkKHDh1i9hs3bnxAb+/gdyOCFL9GsGwZg959lxXANMBaHmNpSWj+ok2/sf/aHdq5s4l18jQBKoBN7x6gtpRl9Ujt7/wsOgAmpf6XSlU84M3dE1y5coVVq1axe/dulFLcv38fpRSjRo1KVJbepKaJtj2n9bPtOaKjo4nwchlFMyJISdy6xc9PPslI4HW0d4qVrq3ippR2xMyZsGaN59QzaMrkK0PVCo8TDExHB8bE1DBQCv61N9KGlMu8efN4+eWXOXnyJCdOnODUqVMEBQXZve3Xq1ePBQsWcPv2bW7dusWvv/5KvXr1yJ8/PxcuXODy5cvcu3cvzjSOo1TTc+fOjdnXrl0biJuqetGiRTGppR0d7wmMIUgpXLzInmzZ6ILOcTPOIh7WAB57FU7n1N939tzJrp7O0yE3ahR7jwbPsaD9AgIGw5LWOm31ROB7a2Px4pz876TvlDMkidmzZ9OmTZs4snbt2tlND1WtWpXOnTtTo0YNatasyWuvvUaVKlXIkCEDgwcPpkaNGjRp0oSyZcvGHGOtQ2xdLAa4evUqlStXZvz48TELwN26dWPt2rUEBwezceNGsmbNCkDlypVJly4dwcHBHl0sdjkNtS82V9JQT5yoMwqfP5/sU7ifLVvkaoUK8ihIAZDTlrTHzTvETSm99MjSmENApGlTxymr33zTh/fiR1y6dUmemfOMRIDUB8kCssPyj3CwUWVfq5dq8Kc01MWLF5eLFy965VreTkOdqkiJawTRNWrw8t69nADmoSuMbSgCv5eJ7fN0qadpWrJpzPd79+CPP+DUKSgZL03fl196QWkDeR/Ky68v/ErXKU/zWpDOn9IWnRCw9OpdHNq56gFnMBhSBn5nCFLi1NBotIvoOPTiI8Az7eP2mffcvDjfM2aEgAAoUgQsU4tx2LrV/XoaHPNd18WEF9a1C06h3UqjgdIhj0NUlG+VM6QoTpw4Qb58+Xythh3GEPiSiAj+Vor30fVy37CIX20FF7PFdmtbri1ZMjjPm50zp72sRg2wlEw1eBilFJ/U02s7Y4HfgVHWxgwZ4Ngxn+lmMCQGYwh8yJdzPqQ9Ok5gCmB1KpthyS89qN4gGhRvwNfNv37guSY5SDq6bh0cPOgmZQ0JsrDbStRQqFRUu5MOAjZYG+PP3RkMKQy/NATp02svP18it27xZ6eRXAB+AnIC7zeG7ANj+3QJ6cKazmsSVXS+Rw+oX99eXrYsFC+ugysNnqNxUGNO9T1F1LAPmQIUQ9dkvWJpvx/pXb9wgyEp+J0hiIhIGaOBMbVrsxj4HLAGsk+oCeWCqhM9OBoZIpTMk7Q3yfnzHcv//Rd27nRFW0NiKJKjCI27fEQuYC5wFugKCJAuYyaid+7woXYGg3P8zhBERvrYEFy8yCalGLB7N20Ba3Gx4J5wKxNs6bYlUdGMjsiXT5e7dIaJL/AOTV6GnVVgBLqSnDURSEBIFfOPkEIZPnw4FSpUoHLlyoSEhLB582Zee+019u3zaHmUFINbDIFSqplS6qBS6ohSaoCD9s5KqYtKqR2W7TWbtk5KqcOWrZM79EkInxqCxYu5+uSTvICu7TmN2HWBXQXcc4nPPot1kY1P69buuYYhYZbPFPbUKUlfoAXwLmB17JLevX2nmMEhGzduZPHixfzzzz/s2rWLFStWULRoUaZOnUr58uV9rZ5XcNkQKKXSAV+iU+aXBzoopRz9enNFJMSyTbUcmwcYgk6pUwMYopRynMrPTfjMEKxahbRsyevbt3MGnVY6V7wux3sfd/kySun7GzHCvu2PP+D0aZOczht8Pv4A1wb04VvgYeAF4Aagvv6aZd+bDKUpibNnz5IvXz4yWQqZ58uXj0KFCtGwYUPCwsIAnRZ60KBBBAcHU6tWLc6fPw/AxYsXadeuHdWrV6d69er8/fffPrsPV3BH0rkawBHRxedRSs0BWpO42sNPAstF5Irl2OXo0rDuqwwRj4gIHwWTXbjA9+i54+HoH81KDcv4qESuEm673Hvvaa/FyZPjyosU0fsxY6BvX7ddzhCPdOnSk6vPABgxjh+BhkAf9Ciw6ctD4fmBKSuqMYXQp08fdrg5DXVISEhM7QBHNG3alI8++ojSpUvzxBNP8MILL9Agnu/1rVu3qFWrFsOHD6d///5MmTKFDz74gN69e9O3b1/q1q3Lv//+y5NPPsn+/fvdqr83cMfUUGF0HI2VcIssPu2UUruUUvOUUkWTeCxKqe5KqTClVNjFixeTraxPRgRTp3J8xQreBOoB79k0ZX0fthbxzGUTijAeM8Z5m8FN5M/PP0UzUA8YgE5Q94ul6U7enO7Pu29IFtmyZWPbtm1MnjyZwMBAXnjhBb799ts4fTJmzEiLFi0AqFatGidOnABgxYoV9OrVi5CQEFq1asX169e5efOml+/AdbyVhvo3YLaI3FNK9QC+Axon5QQiMhmYDBAaGprsyQ1fGIKobt3oiF4PmAWkAxaWgddawW0PvhSmT6+ngRytPYeHQ3S0jk42eI6Smw/x56xPGPLeFJYC3YBaQKGbd6FKFTNPF4+E3tw9Sbp06WjYsCENGzakUqVKfBcvn3uGDBlinDjSpUtHlCViPDo6mk2bNpE5c2av6+xO3PEYOI1e+7RSxCKLQUQui8g9y9epxHpMPvBYd+N1Q7B8OZ+ig4u+JrbIzPPPwaWs8PerfxPxQQR3ByWcw9wVvnYSj3bc9SUJwwPIWbAEzfpPpmsbnZ30DtAFnYICQH791XfKGQA4ePAghw8fjvm+Y8cOihcvnsARsTRt2pQvvvgizrGpEXcYgq1AKaVUkFIqI9AeWGTbQSlV0OZrK8A6ibYUaKqUym1ZJG5qkXkMb68RbG7alGHAi5YNQA2FCMtYrE7ROmRIl4FM6TN5TIeePR3LH30UbP7/GzxIkyHfEfwBjAGWob0rAFTbtj7UygC6XnGnTp0oX748lStXZt++fQwdOjRRx06YMIGwsDAqV65M+fLlmeQoxD814CglaVI34GngEHAUGGSRfQS0snz+FNgL7ARWA2Vtjn0VOGLZuiTmeq6koW7SRKRWrWQfniTW/j5NSoIUA7lqkyfaNrW0t5g1y3HK6hEjvKaC37P6+GoZXRNpDpIZZI/1H6FdO1+r5lP8KQ21N0lKGmolqXCOMjQ0VKxuXUmlUSO4f1/n4fEYIrB7N68FBzMdXcbQmv2hYxv4IRg+ffxT+tXpR7qAB5QdcxNRUZApk14XiM/27SYFhbf469+/KFW8HpXQXhGbgJixYHg4FHboK5Gm2b9/P+XKlfO1GmkOR7+rUmqbiITG7+t3S4XeWCPYOrATS4KDmQb0J9YINH9RG4E1ndYwoO4ArxkB0AvH9+87bqtSBf77z2uq+DV1i9Wl/ZS6TAd2oINoYvjrL5/oZDD4nSHwRq6hRz+bRTegAmANHarcE5aU1p9rFanlWQUS4J13HMtr1zbrBd7ipw6/0ALtQTQKPSoAoH17mD7dLz2JUuPMREomqb+n3xmCyEjPLBZ/uOpDGs5oAJMn8zZwHu0jmwldd3h3AXim7DNED4726MLwg/j8c8fyAwegdGnv6uKvBGYNJPLIIaKaaje5zmhvIgC6doVFi5wemxbJnDkzly9fNsbATYgIly9fTpJLq7fiCFIMnpoa+nj9x8hQWMA6vgcGE+sjO7SR3g+qNyjZCeW8xfXrkCOHr7VI+2QoWYpSH3xC+2Xv8yTwAToTLUB0eLhfvaEVKVKE8PBwXAkUNcQlc+bMFCmS+EhVYwjcgQgD18EloAcQgqUwSRF4zJI+4o3QN6hWsJrTU3iTf//VI4C5c2HatLhtbdrAypW+0cvfCMwayJND4fWhurJZG6AuENCrFzsnDSV4t388GDNkyEBQUJCv1fBr/OnFA/CQIdiyhU9W6ZTSV4GZQEbgxXaxXb5s/mWKGQ0ULQpNmsDo0fZtq1bBxo3e18mfaVQSSqCniG5ZZMF7LvlMH4P/YQyBG9hwYj0/oSuNDQUqWeQnc8NHDT/iw/ofuveCbiKLkzLIder45Xql16lbrC4Av7xZhxnoIBybAnXIqlW+UMvgh5ipIRc50LQaJZf/QyugOtpdFOC55/T+wwYp0wiAjiu4fBny5rVvq1wZdu/2vk7+RNl8ZZEh2uI2vVeBtwfuYwLQFp2tVD3+uLHIBq/gdyOCqCjtU+8OJod9Q5nl/9ATuIn2EkoPPPUSXGrekMLZU35wUJ48sG2bvXzPHt/XdfYn/nxvNxtfgUfRuYhuWBuUgo8+8p1iBr/AGIJkcuDSAWZP7MlP6HKE/wOsMXy/z7rP6k6rCX8n3PULeYGqVaF/f8dtlvobBg8ToAJYPukqM4CTxE1VzpAhxrXS4FH8zhDcvw/pXAzoFRHKfVmOabPhLSAU6Av8rz5M+f1/BKjU97N+9pk2kvEpUABefx38pHSrT8mZJRePRUfTG52pdr1NW2S0qXVs8Byp74nlIu4YEfwW9gMHvoChEdpLaBp6SujDtUK3pz9wg5a+IV066NXLXj5pElSo4H19/BGlFEfaaS+i1wBrcvL/+7QR6056MkGWwZ/xO0PgjhFBlj79OH5ZF5kZCFQGOj3jum4pgZEj9ejA4DsmTQ8n1+M6na91deCLDzfQf+zTZvHY4BH8zhC4OiIIWzyZWhvO0QO9JjDIIp9V2Q3KpQCyZIEOHRy3KWWeQ96gcI7CFGwRShdgJLDdIt80/pa21AaDm/ErQxAdrR9kyTUEK46tYP64HryPLrQ8DZ1LKNMHEFwoxG16+hpH7qRWfv/de3r4M9WDnyZ3XcgHdAWsyzfRpqKZ33L9uucSQ/qVIbCmYU7O1NDVO1dpPa0JLVbq6lJvAbWBTYVhXKuv2PLaFjdq6lseekjHFziiZUuYONG7+vgjgxsMpu+guXyJHhFY8xAFbN7sQ60MvmLHDsiZUyeG9ITjhlsMgVKqmVLqoFLqiFJqgIP2d5RS+5RSu5RSK5VSxW3a7iuldlg2j6ZdtBqC5IwIBq8eTMgpvYBXDBhukdfq8gGvV3+dDOm8WQjZ8+TJ49x19K23YPly7+rjb6QLSEeRZs9TsV9X2qLrFhyyNp444TO9DN7n7FldM8RKzpzuv4bLhkAplQ79kvwUUB7ooJQqH6/bdiBURCoD89BTn1buiEiIZWvlqj4JYXWPTM6I4OtNE2k0Cw4Ak4Fs1oZE1jZNjTz8sPO2pk29p4c/U2bkVCYCWdD1C6IBgoI4tHyuT/UyeI+aNeN+T0J26UTjjhFBDeCIiBwTkQhgDtDatoOIrBaR25avm9Bp2L1OckcE285sY9RM+AzoBFifgX/tWOS6C1IKp2BB521KOY49MLiXa48H8zmwDv0SAlC6aXvYv9+HWhm8xalTcb8ntIaXXNxhCAqj106thFtkzugK/GHzPbNSKkwptUkp9Yyzg5RS3S39wpKbtzw5I4L5++bzce9Qvj8JeYAxFnmmDyAoqGqy9EhNnDmjy1jOdfIC2qWL8xKYBvew8pNuVCgIj6NzWVnj1Y/uNaUt0zK3b8OUKd65llcXi5VSHdGBuKNsxMUtxZRfBMYppUo6OlZEJotIqIiEBgYGJuv6SR0R7Dq/i2d/fpYyv8M/6PmvPJa2m0MjKJwj5ecScgc5c8Lzzztu+/572LTJcZvBPdQvXp9a3fVo4D7wBiBAyee6myFZGiZrVuje3TvXcochOA0UtflexCKLg1LqCbTbfSsRuWeVi8hpy/4YsAaoEv9Yd5HUEUG1ydXgCky4A62AmPICEyakucXhxODMk8jMUHiWSvkrIUOFAvNmMwz4DbA6kV5+LO2PSg2exx2GYCtQSikVpJTKCLQH4nj/KKWqAN+gjcAFG3lupVQmy+d8wGOAx7LaJGVEsPr4aqLuR/H0BP0jTQQUQL9+8OabnlIxRZMnD9SoYS/v1k0XtDF4lofatef1AoEEo92XrwN5t+zm3WXv+lgzg7u5FK8uUcaM+rn1ySeeuZ7LhkBEooBewFJgP/CTiOxVSn2klLJ6AY1CO9r8HM9NtBwQppTaCawGRoiIxwxBYkcE526eo/HMxjy0A5YAH2Mz5Bk5EgL8KvwiDitXwtGj9nJTu8A7ZP1mKpOBs8RGtb/f+nPuR5uFmrTEE0/E/X73rq6lMnCg4/6u4pbM/CKyBP3MtJUNtvn8hN1BWr6B2IJeHiexI4IFBxbQZA/sWQhl0FYOgFYe9W5NFWTLprf49OkDa9fqPEWlSnldLf+hRQtqFCpErzNnmAh0BGrehXffKMXoScd8rZ3BRW7fhk6dYOfOuHJP1wbxq1fbxIwIoqKjeP331yk9D86jF+jSA5w8CQsXel7JVIKjYja//qojHw0eJCAAwsP5GCgEdAcigdHfHGfEXyM4esXBcM2Qapg3T2+2TJjg+ev6lSGItKR0T6hUZYb/ZaDsdvgKPRIIBWjdGooV87yCqYiqVcGZF69JTOdhlCLHmDF8AewCxlnEZ4cP5NEvHvWdXgaXiIjQo4H4tGnj+Wv7lSGIiND7TJkct8/ePRvuQ8aF+m3rf9aG+CbaAEC+fPC//9nL//vP66r4H1Wr0gYduTkEOA6M/xOaHMFUM0uFREY6fi5VrQpFvBB+61eG4J7FaTVjRsftPX58kZcm67esL4AcwO18Od1X5DgN8oGDOjxFi9rLDG6mQQPkzBm+ANIRG1vw3QKdF8uQunCWVfTFF71zfb80BI4sb64Rufjme/j1vI4ZeMYif+j0BfvOhjjMnh33+61bJsjMG6iCBfnq5558DPwJ/AQUvAm7fxzL/fsm0Cy1EBbmuALgvHnaCcMb+JUhSGhq6Nrda/wQrmMFvsAmZsDZ8MEQQ5Mm9rLatb2vhz8y5Jmx1OvRhmpAb3Tp1AXTbtHxBf8LeEytVK9uL1u6FNq1814qM78yBE6nhu7codOP8Dt6XSBmWdhUg0oUefM6ji2I7wJncD+Z02ematFqTAYuokunAvT7G87fdJJH3JBi2LjRXibi/ey+fmkI4o8IVpbJzrLDOrfFWxbZ/XweSPGXhnnkEShXLq4sJAQOHNCVlQwepGtXquTOTR90+P7fQNVz0HBAASLvR/pWN0OC1KkT9/vUqb7Rw68MgaOpoTZz27Dg1H3Oof+IrMvC6S7Gi/E2PJDJk+1l5crppHWmlooHKVAAdeUKlVs+SjGgBxAB7P8Sei3pZaKOUyiLF9vLHn/c+3qAnxmC+FNDR64cofjHC/gSHTMQM1VX1STySg516zpvO37ce3r4K9WnLeRLYC8w2iIr+8lkHpv+mA+1MjjizBld9jU+kT4awPmVIbCOCKyGoNS4UqzZAwXR+YRicBTVYUgUf/7pWO7pEHkDlA8sTwvgWfRa1xGg7yaI3rwZjpn0EykJZ2sA+fJ5Vw8rfmkIMmUCwsP59GPYSWzMQAx+ml3UHTz5JHzzjb28USOYNcv7+vgbdzIGMB7IQGxswZapQEmHZT4MPuDsWdi7115+7Rrkzu19fcDPDIHt1NDRKiH8T6AlYBvBva7/C2m+/KSn6d7dcRzBK694Xxd/I8vESRQCPgWWA7YhHhdvJa+yn8G9vPqqvUwEcuSwl3sLvzIE1hHB7kth9Ll0GUVsnYELc6bDoUPU/2yODzVMO8QvuG1l+3bv6uF3dOsGIvTIlJEaQF/giqUpQ76HfaiYAWD9eufTp77ErwzBjYgb1HyiHmeDqrMY+IjYmIGHW3cw+ZPdzGMO1iirVoVDh7yvi79xfv9WvgEuAwMsslx34egxB2ljDV5h3jyoX99e/vTT3tclPm4xBEqpZkqpg0qpI0qpAQ7aMyml5lraNyulSti0DbTIDyqlnnSHPs6YqEqxbMVfvAWEAG/bNmbO7MlL+yXOwuPLlIELJnOHRylUohIh6BHBFMBa5j5zSCgHLx30mV7+zHPPOZb//rt39XCEy4ZAKZUOXdf9KaA80EEpVT5et67AVRF5FBgLfGY5tjy6tGUFoBnwleV8HuHZHef5AF3dKabOALDtnxTwL5EGadfOeZs3Mir6M0op9u5ayVCIE1tQ+AYcPG1Cvr1NgwaO5WfOeFcPZ7hjRFADOCIix0QkApiDzo5rS2vgO8vnecDjSillkc8RkXsichzt8eagKq57eHOhXhN4k9iYge1n/qFalRQwNkuDKOV8gTgyEm7c8K4+/kaFSo3JWrcuX6ILgVtjC66//IIPtfI/7t+Hdevs5fXrQ8GC3tfHEe4wBIWBUzbfwy0yh30sNY6vAXkTeazb6It9zECVglU8dTkD0L6987aEAtAMbmL9elqIxIkt6LgbvhjWPLZ2q8GjWJ1U4rNmjVfVSJBUs1islOqulApTSoVddFYa6wF8jx6u5AS2FALCw92oocERTz0Fp087btu1y/GbksH9xI8teGvoEnj/fd8q5QfcuwcPPeS4LSUFWbrDEJwGbEuRFLHIHPZRSqVHP4svJ/JYAERksoiEikhoYGBgshQNHgD1gIgAGPdxcyjsscGHwYZChWDuXMdtDRqknHnStEyOpb/ZxxaMHEnk4kWmpJwH2b/f1xokDncYgq1AKaVUkFIqI3rxd1G8PosAa96GZ4FVouvpLQLaW7yKgoBSwBY36OSQM8NuooZCpsHwYxcHGZ8MHuNJiz9Yo0b2bZs3e1cXfyRb0xaceTyAQtnjxRa0bJ3wqr4h2YwaBa1axZVlzaoDylat8o1OThERlzfgaeAQcBQYZJF9BLSyfM4M/IyeotwCPGJz7CDLcQeBpxJzvWrVqklyWXhgoey/uD/ZxxuSz6lTIhERIjqOMu72/fci9+/7WsO0z/aRIyUdSDebH/9W3py+VitN4uj/+Y0bvtaJMHHwTFWSCgtdh4aGSlhYmK/VMCSTrl1h+nR7+eDBMGyY9/XxK9asoV+jRowG1gPW9fqF+xfQumx8Zz9DctmzBypViisLCYF//vHt2oBSapuIhNrJjSEweJtr1yBXLsdtqfC/Y+pChB+bFmfgilNkA7YDGQE1BK4NvEaOTD5MeJNGOHkSSpSwl6eE/9vODEGq8RoypB1y5nT+R3HeVFf0LEpR5+d1PF9MxxZ8bhHLMAganJOoaFP03lUcGYGUXpjJGAJDiqJAAV9rkPYpkasEo07q2IKP0At0AJdHwh8Lx/hQs9TPp586lhcv7l09kooxBIYUxxyTANYrBLwXHCe2AGDGnPe4fs8UmU4OYWGOQzMmTvS+LknFGAKDzzh/Ht5+217eoQN88YX39fE3qrRoz6fAMnSgJUCXHXD48mHfKZVKuXkTGjZ03JYa6lwZQ2DwGQ8/rIfSefLYt739Nly5Yi83uI9qBauxvo1O7tUHuAq0PAQze9ZC7tzxrXKpiCtXoEsXuHXLvq13b+/rkxyMITD4lIcegoULHbdt3JgyPC3SKk1KNuGz6Sfokzdu3YLxv0Vx93EHifMNDnnkEV1rID61a8O4cV5XJ1kYQ2DwObVqOZa3aAH9+xtj4EmK5ypOw2eepy86NfvfFnmWjcY9OzEsXardoeNz8SJs2OB9fZKLMQQGn5M+PVy/Dvnz27eNHg1ly3pfJ3+i4MTv7OoWALz9h4MFHEMMN27ApEn28mXLIF8+7+vjCsYQGFIE2bPDuXOO20xpSw+TOTNZv/uOL4G9xMYWLF3yBakx4NRbBAbCggX28iZNvK6KyxhDYEhRDB3qWO4slbXBTbzwAtVaNY4TW3BwIqyb8H8+Vizlcu+evez5572vhzswhsCQoiha1LG8aVPv6uF3ZMrE5Ymj7OoWNOgzlg9WfcCVO8aFy0qLFpAhg+O2mTO9q4u7MIbAkKJwtnC8b5939fBHCuUrQaX+2MUW7J08nL5L+/pQs5RBVJQuNP/77/pzfAYMgEyZvK+XOzCGwJCiKF/euZeQKajlWfJkycPlz4Tp/XLFiS34dS5E3brpW+VSACNG6NGAMz74wHu6uBtjCAwpkr//tpc5y+NicC/Lhx2lxdjX48QW/NDxF7af3e5LtXzO8ePO21as0EVnUivGEBhSJHXqOJZPnuxdPfyRPFny8GGfr+xiC2p+VZXfDv7GkStHfKhdyuPECXj8cV9r4RouGQKlVB6l1HKl1GHLPreDPiFKqY1Kqb1KqV1KqRds2r5VSh1XSu2wbCGu6GNIW+zday/r0UPXOL561fE8rcF9DF27Nk5sQcTH0OerVpT6opSPNfMN9+/by65eTfmZRRODqyOCAcBKESkFrCR2JGnLbeAVEakANAPGKaVy2bT3E5EQy7bDRX0MaYjy5WHdOnt54cI6P9Grr3pfJ38ia/36drEFRyf4UCEfsn8/fPedvdxZgaXUhquGoDVg/Xm+A56J30FEDonIYcvnM8AFINDF6xr8hHr1ICLCcdH7WbO8r4+/0eLyZbu6BUNX4zeBZmfOQPPm+qUkPm3aeF8fT+GqIcgvImctn88BDpIExKKUqoGujHfURjzcMmU0Vinl1PlKKdVdKRWmlAq7ePGii2obUhMZMkDHjr7Wwk/Jk8cutmDIWgj4KIAVx1b4Vjcv8MknsGSJvVwE5s/3vj6e4oGGQCm1Qim1x8EWp9K16FcEp68JSqmCwCygi4hEW8QDgbJAdSAP8J6z40VksoiEikhoYKAZUPgbnTs7lvvJi6lPKTRvXkxswWyLTIbCK1+mwlwKSeTkSXuZ1TvIl0Xo3c0DDYGIPCEiFR1sC4Hzlge89UF/wdE5lFI5gN+BQSKyyebcZ0VzD5iBTo1uMNgREOA4juCXX7yvi9/Rrh2v9u1NDaA3YB2Pv7WFNF9k2llm0bSGq1NDi4BOls+dALvM8kqpjMCvwEwRmRevzWpEFHp9YY+L+hjSMMOH6/UCWy5eNKMCb5ClYWOmA9fQxgBg4F9wp0jaLDIdFQU1asD69fZtWbJ4Xx9P46ohGAE0UUodBp6wfEcpFaqUmmrp8zxQH+jswE30B6XUbmA3kA/42EV9DGmc+DleXn9d/8EaPEyrVlQAPkRPD1nf+LJEwecbPic6ZrY39fP339C1K2zdGlfer1/aTX6oUuPqf2hoqISFmcIZ/krp0nA4Xlndv/92HoRmcBM3brC2aTl6bzrNBWAfkAvo/wSMegxeq/oaU1pN8a2ObsDR3P/w4WkjxYlSapuIhMaXm8hiQ6pj9Gh72WOPeV8PvyN7duqtOcJ09GKgNUH1yBXw5BGYun1qAgenDr7/3rG8XTvv6uFtjCEwpDpatXIsd7SwZ3AvAZkyU/XHH+kPTEd7EgH8+QOkcxB5m9p4+WXH8rTkIeQIYwgMqZLLl+1luXI5L2xjcCPt2zMY7ffdDbhhEf81HdSwtPfEHDsWSqXxrBrGEBhSJXnywAsv2MuHDfO+Ln6HUmTevJnpwCli88rUOg0B0doYpLa1xzt3oEgRe/ns2dCnjxkRGAwpljlz4JVX7OU5c3pfF78jNJTaaFfSrwBrSqj7H0H6+zDln9S1aJwzp71H0MSJqbf0ZFIxhsCQqnGUCOz6de/r4XcEBIAI1eYP5xGgKzq7JMC+L6HH4h7M35d6cjBERtrL3nxT36Y/4Ce3afA3ZszwtQb+wVPNejAVOIKOMQAodQUyRMGzPz/L3/86qDCUgli82PG0z9ix3tfFlxhDYEj13LplL3v1Vf0HPm2a9/XxJ/I+lJdGJ0/SExgLWANxIz6Gyueg7oy6PtQuYa5dg5Yt7eWPPqrXBfwJYwgMqZ6HHoJoJ4Gtr70GW7Z4Vx+/o1gxRp09SxDQGbBWN945Se/7LevHrvO7fKNbArzxhmP5oUPe1SMlYAyBIU2QkFdHzZrGGHiabAUK8O2YMRwH3rWRZ4mA0RtHEzwp2Feq2XHmDLRvDz/+aN/21ltp30PIEcYQGNIM/fo5b4ufksLgfur17cv/Ad8Af1pktz+B7pZsMCklxmDYMJg7114+aBB8+qn39UkJGENgSDOMHOm8rWNH2L7de7r4K/+rX58KaC+iqxbZN4t9qJAD9jjJcTx4cGytAX/DGAJDmqJtWyhY0HFb1apw7Jh39fE3Mv/5J1Pr1OIC0MtGXv4CVDsNp6/7Nn3n8uWwYUNcWcOGOoYgY0afqJQiMIbAkKaYP1/PATujZEm9ZmDwEFmyUOvvjXzYqRM/AtYCJHu/grApsPbkWp+pdvIkNG1qL//tNyhUyPv6pCSMITCkSV5/3XmbWTj2PAOnTCE0a1Z6oouZW1lzYo3P0k+UKGEv27EDsmXztiYpD2MIDGmSiRNh8mRfa+G/ZMiQgZl//slN4DVii5mvWDGFiVsmel0fZ+UlK1Twrh4pFZcMgVIqj1JquVLqsGWf20m/+zbVyRbZyIOUUpuVUkeUUnMtZS0NBpcJCIBu3eBjJzXvwsJMiUtPU65uXUaii5V/ZZEdmwBv//k2F295r/Dv6tXw8MP28pw5IX16r6mRonF1RDAAWCkipYCVxCYijM8dEQmxbLbZ5D8DxorIo2gng64u6mMwxOH992Gqg3op1avDiy96Xx9/o1ujejyFLmJjddaZ8zM8PPphFh/yvDvRvn3QuLHjtrNnPX75VIOrhqA1YE379R26AH2isBSsb0zselKSjjcYEoNSuv6sI+bMgRUrvKuPv5Fl1Tr67V5FLqADcAd4Ya/OUNpytoP8Dm5m/HjH8ujotFmEPrm4agjyi4jVrp4D8jvpl1kpFaaU2qSUesYiywv8JyJRlu/hQGFnF1JKdbecI+yiswk/g8EJCxdCtWr28iZN4N497+vjTzSq2Ihv0SOC/hZZ5P/0fue5nR69tqN1omHD/DN6OCEeaAiUUiuUUnscbK1t+4l2BXA261rcUjD5RWCcUqpkUhUVkckiEioioYGBgUk93ODntGql1wUckTmzd3XxR5rduEFfYCJgnRAqcRVCvglh+dHlbr2WiC404yyr6ODBbr1cmuCBhkBEnhCRig62hcB5pVRBAMv+gpNznLbsjwFrgCrAZSCXUsq6XFME8G20iSHNs22bY7lScMHh/16DW8iWjU/z5iUY6AKcBY6Ph+f3QNPvHTj3u0DevDoRYXyee87/soomFlenhhYBnSyfOwEL43dQSuVWSmWyfM4HPAbss4wgVgPPJnS8weBOqlaFK1cct+XPbzyJPEmmxx9nNnALeAWIBuZaVgjVMEX49XC3XOfqVXtZ4cKO8wsZNK4aghFAE6XUYeAJy3eUUqFKKauvRjkgTCm1E/3gHyEi+yxt7wHvKKWOoNcMTPZ4g8fJnRuOH3fcZpLTeZBvv6VcsWKMA1ZgeVgAlSwRZ0XHFnXp9NeuOZ/7P37crAskhEptRaYBQkNDJczZhK/BkEj27XMcUHT2LBQo4H19/AIRJGtWXrxzh5/QPucNgZfawo+VoWC2giztuJRK+Ssl+dRjx8I779jL9+wxgWNWlFLbLOu1cTCRxQa/pXx5x66ljlIRGNyEUqgffmAyUApoj3Y3/OEXKHcBzt48S+VJlZN82kOHHBsBMEYgMRhDYPBrJkywl927BzlyeF8Xv6FNG7KfO8fPwHV0fEEUEHg7tsvwdcMTfbq1a6FMGcdtf/zhgp5+hDEEBr/moYccPyxu3ID7972vj9+QPz+VhgzhK7Qb4VBg7bdQ51/d/MHqDxiyegjf7vg2wdP8+adOI+0IEWjWzE36pnHMGoHB77l3D+rUgX/+sW87ckSnrjZ4CKXoCkwHlgBPWcTpBkO05TV1w6sbqF20trPDHbJ4MTRv7mZd0wBmjcBgcEKmTDq+4OhR+7ZHH9UPG0dtBjewezcTgcpAR+CkRTxnXmyXC7fiBnhcv64f8qdOOT7ljRvGCCQVYwgMBguPPOK8bcoU7+nhV1SsSJaRI/kZvU7QBrgNPLcPilviAT5c/SHz982POeTHH2HJEihWzP50vXub+gLJwUwNGQw2BAfDrl2O206dgiJFvKuPXxAVBUWKsOT8eVoALwA/Agro8yTMrQjnssOdQXeYMCYT773nPCAgFT7OvIqZGjIYEsHaBCopFi0Kq1Z5Txe/IX16OHeOp4HhwBxglKVp3FI4+zkgkGV4Ft77+Runp9m/3/OqplWMITAYbMiVS6codmYQHn/cPHA8xuHDDACeQxc2+dOmqeg1y4cWr0PgXoeHly3rWfXSMsYQGAzxUArq1XPeXr6893TxKx59FAXMACqh4wuOWJqGrrGkohCg+td2hy72fI2bNI0xBAaDA5RKeL45Ksp5m8EFChYkK7AASAe0RJcufHUH7JoEb20GanwJZRfEOcx4CbmGMQQGQzIoVw7u3vW1FmmQr3R14yBgPnAUaAdEWJo7Whfy27ehzLM/cPs2/Peft5VMexhDYDAkwLlz8PLL9vIjR3SpQ/MQcjPPPKMLQ4SF0eDkSaajUxZ3R88K1TgDMlRv50t2JEsWXYTe4BrGEBgMCZA/P8ycCVWqOG4/dcpME7mdwEBdV7RYMTp26sRQdEHzj+N167IdDl82ecPdgTEEBkMicFbovHJlMz/tCUaP1us0X3yXncHoQjaDgR9s+oxZBjVHlibsjIkpchWXDIFSKo9SarlS6rBln9tBn0ZKqR02211rAXul1LdKqeM2bSGu6GMweIr06Z23LVtm1gvcTb9+ej+QTxnACN6mAg3RZS5X2PS7MhKqT6nOmRtnvK9kGsLVEcEAYKWIlELXmBgQv4OIrBaREBEJARqjI8iX2XTpZ20XkR0u6mMweISZM+Htt3WCOke8/rp39UnL3LZJR32LbIzkPULZwz72URZ4Bths07/8BSg8pjCR9yO9q2gawlVD0Bo9fYdl/8wD+j8L/CEitx/Qz2BIURQvDuPHQ8aMjtu//dar6qRZli3T+YIcMWFOOZaOGkV+4GnAGla29ysIiIZCAzNy5Y6TgtSGBHHVEOQXkbOWz+eA/A/o3x6YHU82XCm1Syk11lrk3hFKqe5KqTClVNjFixddUNlgcI1jxxzLldIRyZHmxTTZPPkkTJ0aV/b88zqm44UXoOC777IcyAQ0Baylp3/+CS6Ogi6v5vWuwmmEBxoCpdQKpdQeB1tr236is9c5DcFRShVEBwwutREPBMoC1YE86GL2DhGRySISKiKhgYGBD1LbYPAYQUHw7LOO2xo2hEaN7B9mhoS5cMFxtTiAuXPjfn8EPbd8B2gCnAXaHtBtC+fAv9f+9ZieaRWXso8qpQ4CDUXkrOVBv0ZEHBaNU0r1BiqISHcn7Q2Bd0WkxYOua7KPGlICy5bpN1hn3L2rax0YEiY6GtKlc95u94g6dw6ATQUL8gRQGB1rUMjSrIZCoeyFONjrINkympzUtngq++gioJPlcydgYQJ9OxBvWshiPFBKKfT6wh4X9TEYvEbTpgnnJBoxwnu6pCaio3V50EuX9HRaQkbg//7PgbBAAShQgFo//MCfwBmgkWUPsOdLuHnxDNk/ze523dMsIpLsDciL9hY6jPbqymORhwJTbfqVAE4DAfGOXwXsRhuA74FsiblutWrVxGBICURHi+h3VsebiMj8+SLffedbPVMS48Yl/JtZt59/fsCJoqNFtm6V5aEhkg2kFEi4zQlWlUAufPCOREdHe+W+UgNAmDh4pprCNAaDi3z/va6a9ccf9m0TJ0KvXvpzKvxTczuLF0PLlg/ut2hR4vpZ2aAUzdDeKisB2+Jlaihc7n+ZPFnyJEnXtIgpTGMweIiOHfWDyxFWI2Dl0CFYvtzzOqU0IiJg48bEPdyHDUuaEQCog/ZCuWj5bFuxIOs9yDsyL3svOK5jYDCGwGBwC+nTx0bDOkMpKFNGry28+KJ39EopvPsu1Knz4H5jxsDgwcm4QFQUtYF1wH2gHrDR0nTzU1g7HSp+XZFFB51YbD/HTA0ZDG5EOS+na0cq/NNLNlWrwvbtD+7n0m9y/Tps28bxxo1pil6UnIcOPgMYUwv+rxnIED/64eNhpoYMBi/gLNjMEU2aeE6PlMKFC7rOc2KMQN++Ll4sRw5o1IhCB/fxN1AOaAV8aWl+ZxO0OAgleyt6Lenl/Dx+iDEEBoMbCQpKfN8VK3T20v79tUtlWqRBA13n+UEULqynhdxBptLleHjSJNagRwO9gDeASOC32XB0Ajw09kvUUEXQ+CBuRdxyz4VTMcYQGAxuZscOmD9fT3O0b59w3927YdQo+PNP2LIFSpaEixdTf8GbQ4f0NNmBA877WBP1BQfDlCluVqBHD7JPnsyvQH/ga6AZYM1ENHIFyDAoHXaC1h+UdPPFUx9mjcBg8CAXL8LDD8OQIdobJinUravz8tes6RndPMGiRZA5c8IR1wADBsCnn3pBobx54coVZgLdgILAXCD+Tzrp42coW60ZtWu2I1PufF5QzDc4WyMwhsBg8BJJWUi2JaX+if77L7RrB7/8ovMrHT2auOO8ej9ffQVvvgnlyrF1/36eB8KBz4C+QPx/kpsZ4PKFExTPVdyLSnoPs1hsMPiYvXv1lElSWb1al8OMsFRwv3wZ5s3Tn69cgQoV4HAyKzZGRsKNGwn3EYHwcPjiC23MLl+Ga9d0au6wMChWLHFGYN06fR6v8sYb+gb27KH6yJFsB1oC/4fOoX8+XvdskbClYyPuRN7h8w2fc+HWhZi2mxE3UcMU4zeN957+wL2oe3y19SuixYMLSY7CjVP6ZlJMGFI7iUmx4Ghbuzb289Kl9uksJkwQOXFCZOFCkbt3Rc6f122//KLb//lHZPHiWD2eeir2WEccPy7y2mtxr/Poo0nXu2VLj/2USQMkGmQ8SCaQvCCzLTJbhYv3Rlq2R3L3R+bvmy+Z/pdJXpz/ojAUKTm+ZIKX2Hp6q3y55Uv3qTyUmO3rrV+7di6TYsJgSDkcPQrHj+s59S++8M41P/gAPrZUgI+O1m/31ukq62Pg2DHYty/pkb0JceCADqRLEQwYAJ99BsB+oDOwBWgLTESvIcSnRG8IPg+LysbKXqr0En1r9eX0jdO0KtOK41eP8822b/hiyxfcjtR1tyI/jOTrrV/zWLHHuHT7Ek1LNo05/tS1U5y/dZ7QQnqWZszGMVy/d53aRWpz4NIB8mfLT4f5HehdszfjN8cdgbgSB2HWCAyGFIgIrF+v3Sy9zcqVsa6dvXrpvP+eqPmUoh4xInqV+ptvoEcPogYNYgwwGMhg2fcGHBWiCxgMue/Cc3vhm+pJv/QnjT9h29ltzGg9gxwjcmh1hggXb13k4dEPP/D4vLfgclY4884ZCmZ3ZLIejDEEBkMKZsYMePVVX2vhHs6f155SSkGfPjB2rK81coIIBOhl0iPAO8BvQGlgJDoYLaH1fTUE0kfD2hkwvzyMiZdCQ0VDgMB9J2m2g8/CzgL6IunuayPz+lYYXl/vp1WFuxn0eaI/0sf0fwJe/n4XlfJXStYtOzMEPp/vT85m1ggMaZUtW0SaNEn+GoIvt3/+Eblyxde/YBKJdxNLQEqDABIKstjB+oF1+6WsY/kndfX+h4p6X7ivnt/P3R9p0AkJz478Vkq3vdUMyfUeciq743OdzmYvO3Bwgwu3a9YIDIZUSceO8MMPvtYiYe7dg4yO5lNSC+PH6+ELEAXMAv6HrolcFXgbeAHInMzTP/EyrJiVfPWuAtOB74B1S5eSq2nTBxzhGOM+ajCkUr7/Xrt53rqlXUhfesnXGsHPP8Pp0zB8uC7JmaqNAEDv3kRE3eOPPQtID3QBDgJT0LWRO6NrHAwkeWUUk2MEooG16EC4wsC7QC7gvCde3h0NExK7Ac+hU39HA6EJ9GuG/l2PAANs5EHAZot8LpAxMdc1U0MGgyYyMu7MwejRsZ/nzRPZutV+xqF587jfa9eO+/2jj0QyZdKfX3019lrBwSKTJ/vsVr1LdLTI4cNidTddAdIaJJ1l2qgiyDCQTSBRbpxfuw2yDKQvSGHLtR4CeQ1kh7WfC+BkashVQ1AOKAOscWYIgHTAUeAR9GL8TqC8pe0noL3l8yTg9cRc1xgCgyGW48dF3nxTZNcux+3794ucPRtXduGCjicAkUWLYp9Fp0/H9lm9WuTOHU9pnYr44w+RL3VcwPlcuWQiSF3LQxqQXCCtQIaCLAQ5CnIvEQ/9myB7QeaA9AdpDJLZcs6MIC3RMQ434x/rAs4MgVvWCJRSa4B3RcRu4l4pVRsYKiJPWr4PtDSNQBcUKiAiUfH7JYRZIzAY3MPlyzodT/x4AkMCREdDunRcTJ+ele3bs+L771mPLtxu+/MVAAKBLMBDlrZbwG10RPNlm74ZgYpAfaCpZZ8VYP9+7dfbtSvkzq1DzHPmTLbqztYI0if7jImnMHDK5ns4OudTXuA/EYmykRd2dhKlVHegO0CxYsWcdTMYDEkgb169r18fevb0rS6phoAAiI4mEGivFO0LFYKRI7k5dy67btzgwODBnDpzhlPAJfQaw230gmw+9AO+PlAcve5Q3rLFLLPMnKmj+l5+GcqW1RkLPcwDDYFSagXauMVnkIgsdL9KjhGRycBk0CMCb13XYPAH1q71tQapDNsMgp99Bp99RjZ0veQ6Xbtq+fXrOsHUhQvQrBlkyqTlt29DunSwdSvkyQPly8OGDZAhAyxbpt3EkpuhMJk80BCIyBMuXuM0UNTmexGL7DKQSymV3jIqsMoNBoMh9ZMjB9SubS9/6CG9r1s3VmYt6Fw9GSHLbsAb7qNbgVJKqSClVEagPbDIsnCxGnjW0q8T4LURhsFgMBg0LhkCpVQbpVQ4UBv4XSm11CIvpJRaAmB52+8FLEXnefpJRPZaTvEe8I5S6gh6zWCaK/oYDAaDIemYyGKDwWDwE0xkscFgMBgcYgyBwWAw+DnGEBgMBoOfYwyBwWAw+DnGEBgMBoOfkyq9hpRSF4GTyTw8Hzry258w9+wfmHtO+7h6v8VFJDC+MFUaAldQSoU5cp9Ky5h79g/MPad9PHW/ZmrIYDAY/BxjCAwGg8HP8UdDMNnXCvgAc8/+gbnntI9H7tfv1ggMBoPBEBd/HBEYDAaDwQZjCAwGg8HP8RtDoJRqppQ6qJQ6opQa4Gt9vIFSarpS6oJSao+vdfEGSqmiSqnVSql9Sqm9SqnevtbJ0yilMiultiildlrueZivdfIWSql0SqntSqnFvtbFGyilTiildiuldiil3Jp+2S/WCJRS6YBDQBN0beStQAcR2edTxTyMUqo+cBOYKSIVfa2Pp1FKFQQKisg/SqnswDbgmbT876yUUkBWEbmplMoA/AX0FpFNPlbN4yil3gFCgRwi0sLX+ngapdQJIFRE3B5A5y8jghrAERE5JiIRwBygtY918jgisg644ms9vIWInBWRfyyfb6ALIRX2rVaeRTQ3LV8zWLY0/3anlCoCNAem+lqXtIC/GILCwCmb7+Gk8QeEv6OUKgFUATb7WBWPY5ki2QFcAJaLSJq/Z2Ac0B+I9rEe3kSAZUqpbUqp7u48sb8YAoMfoZTKBswH+ojIdV/r42lE5L6IhABFgBpKqTQ9DaiUagFcEJFtvtbFy9QVkarAU8Cblqlft+AvhuA0UNTmexGLzJDGsMyTzwd+EJFffK2PNxGR/4DVQDMfq+JpHgNaWebM5wCNlVLf+1YlzyMipy37C8Cv6Clvt+AvhmArUEopFaSUygi0Bxb5WCeDm7EsnE4D9ovIGF/r4w2UUoFKqVyWz1nQDhEHfKqUhxGRgSJSRERKoP+WV4lIRx+r5VGUUlktDhAopbICTQG3eQP6hSEQkSigF7AUvYD4k4js9a1WnkcpNRvYCJRRSoUrpbr6WicP8xjwMvoNcYdle9rXSnmYgsBqpdQu9AvPchHxC3dKPyM/8JdSaiewBfhdRP5018n9wn3UYDAYDM7xixGBwWAwGJxjDIHBYDD4OcYQGAwGg59jDIHBYDD4OcYQGAwGg59jDIHBYDD4OcYQGAwGg5/z/0EyafdiDtdEAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[out_probe][:, 0], \"b\", label=\"2D output\")\n", + "plt.plot(sim.trange(), sim.data[out_probe][:, 1], \"g\", label=\"2D output\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], \"r\", label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"k\", label=\"Sine\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows that the input signal (Sine),\n", + "the output from the 1D population (A output),\n", + "and the 2D population (green line) are all equal.\n", + "The other dimension in the 2D population is shown in blue." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/communication-channel.ipynb b/.doctrees/nbsphinx/examples/basic/communication-channel.ipynb new file mode 100644 index 0000000000..a1f8d61b1f --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/communication-channel.ipynb @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Communication channel\n", + "\n", + "This example demonstrates how to create\n", + "a connection from one neuronal ensemble to another\n", + "that behaves like a communication channel\n", + "(that is, it transmits information without changing it).\n", + "\n", + "Network diagram:\n", + "\n", + " [Input] ---> (A) ---> (B)\n", + "\n", + "An abstract input signal is fed into\n", + "the first neuronal ensemble $A$,\n", + "which then passes it on to another ensemble $B$.\n", + "The result is that spiking activity in ensemble $B$\n", + "encodes the value from the Input." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:12.629613Z", + "iopub.status.busy": "2020-11-25T16:47:12.628756Z", + "iopub.status.idle": "2020-11-25T16:47:13.119872Z", + "shell.execute_reply": "2020-11-25T16:47:13.120340Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.128994Z", + "iopub.status.busy": "2020-11-25T16:47:13.128470Z", + "iopub.status.idle": "2020-11-25T16:47:13.132111Z", + "shell.execute_reply": "2020-11-25T16:47:13.131591Z" + } + }, + "outputs": [], + "source": [ + "# Create a 'model' object to which we can add ensembles, connections, etc.\n", + "model = nengo.Network(label=\"Communications Channel\")\n", + "with model:\n", + " # Create an abstract input signal that oscillates as sin(t)\n", + " sin = nengo.Node(np.sin)\n", + "\n", + " # Create the neuronal ensembles\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # Connect the input to the first neuronal ensemble\n", + " nengo.Connection(sin, A)\n", + "\n", + " # Connect the first neuronal ensemble to the second\n", + " # (this is the communication channel)\n", + " nengo.Connection(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Add Probes to Collect Data\n", + "\n", + "Even this simple model involves many quantities\n", + "that change over time, such as membrane potentials of individual neurons.\n", + "Typically there are so many variables in a simulation\n", + "that it is not practical to store them all.\n", + "If we want to plot or analyze data from the simulation\n", + "we have to \"probe\" the signals of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.138487Z", + "iopub.status.busy": "2020-11-25T16:47:13.137015Z", + "iopub.status.idle": "2020-11-25T16:47:13.139100Z", + "shell.execute_reply": "2020-11-25T16:47:13.139502Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # ensemble output\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the Model!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.144638Z", + "iopub.status.busy": "2020-11-25T16:47:13.143875Z", + "iopub.status.idle": "2020-11-25T16:47:13.574190Z", + "shell.execute_reply": "2020-11-25T16:47:13.572969Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.586066Z", + "iopub.status.busy": "2020-11-25T16:47:13.579768Z", + "iopub.status.idle": "2020-11-25T16:47:13.894015Z", + "shell.execute_reply": "2020-11-25T16:47:13.894417Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.2)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(9, 3))\n", + "plt.subplot(1, 3, 1)\n", + "plt.title(\"Input\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe])\n", + "plt.ylim(0, 1.2)\n", + "plt.subplot(1, 3, 2)\n", + "plt.title(\"A\")\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.ylim(0, 1.2)\n", + "plt.subplot(1, 3, 3)\n", + "plt.title(\"B\")\n", + "plt.plot(sim.trange(), sim.data[B_probe])\n", + "plt.ylim(0, 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots show the idealized sinusoidal input,\n", + "and estimates of the sinusoid that are decoded\n", + "from the spiking activity of neurons in ensembles A and B.\n", + "\n", + "## Step 5: Using a Different Input Function\n", + "\n", + "To drive the neural ensembles with different abstract inputs,\n", + "it is convenient to use Python's \"Lambda Functions\".\n", + "For example, try changing the `sin = nengo.Node` line\n", + "to the following for higher-frequency input:\n", + "\n", + " sin = nengo.Node(lambda t: np.sin(2*np.pi*t))" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/many-neurons.ipynb b/.doctrees/nbsphinx/examples/basic/many-neurons.ipynb new file mode 100644 index 0000000000..3984ee1e76 --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/many-neurons.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Many neurons\n", + "\n", + "This demo shows how to construct and manipulate a population of neurons.\n", + "\n", + "These are 100 leaky integrate-and-fire (LIF) neurons.\n", + "The neuron tuning properties have been randomly selected.\n", + "\n", + "The input is a sine wave to show the effects\n", + "of increasing or decreasing input.\n", + "As a population, these neurons do a good job\n", + "of representing a single scalar value.\n", + "This can be seen by the fact that\n", + "the input graph and neurons graphs match well." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.145135Z", + "iopub.status.busy": "2020-11-25T16:47:15.144281Z", + "iopub.status.idle": "2020-11-25T16:47:15.641456Z", + "shell.execute_reply": "2020-11-25T16:47:15.641907Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import sorted_neurons\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural population\n", + "\n", + "Our model consists of a single population of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.647116Z", + "iopub.status.busy": "2020-11-25T16:47:15.646595Z", + "iopub.status.idle": "2020-11-25T16:47:15.650160Z", + "shell.execute_reply": "2020-11-25T16:47:15.649715Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Many Neurons\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a sine wave as a continuously changing input." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.656944Z", + "iopub.status.busy": "2020-11-25T16:47:15.653883Z", + "iopub.status.idle": "2020-11-25T16:47:15.657848Z", + "shell.execute_reply": "2020-11-25T16:47:15.657408Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(8 * t)) # Input is a sine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.664399Z", + "iopub.status.busy": "2020-11-25T16:47:15.662880Z", + "iopub.status.idle": "2020-11-25T16:47:15.665027Z", + "shell.execute_reply": "2020-11-25T16:47:15.665436Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input to the population\n", + " nengo.Connection(sin, A, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.671595Z", + "iopub.status.busy": "2020-11-25T16:47:15.670056Z", + "iopub.status.idle": "2020-11-25T16:47:15.672228Z", + "shell.execute_reply": "2020-11-25T16:47:15.672630Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms filter\n", + " A_spikes = nengo.Probe(A.neurons) # Collect the spikes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.677701Z", + "iopub.status.busy": "2020-11-25T16:47:15.676920Z", + "iopub.status.idle": "2020-11-25T16:47:15.876936Z", + "shell.execute_reply": "2020-11-25T16:47:15.876502Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 1 second\n", + " sim.run(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.883220Z", + "iopub.status.busy": "2020-11-25T16:47:15.882371Z", + "iopub.status.idle": "2020-11-25T16:47:16.416787Z", + "shell.execute_reply": "2020-11-25T16:47:16.417231Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6Q8MbxtULyj2AhVprR3/KCrY1s17AZKVU5bQGaq1naK1DtNYhJUpkg83gd96BQ4fgk0/AzxpfsOnYRfvx213ruFqzW0eePGYJ6upVGDrU0lQ0vy8PhQRxPjKGEd9vc5OCgpANeP11k6rjk08sS05/7T7Dgo1h9vOHGpdzh3Y3hTMMxQkgyOG8nE2WFj1IseyktT5h+3kIWAE0TD0sm7Fnj6ln27s3tG5taYqNT+TUJZP2onvjcvyvWQV3aHjrqF7dZMRdsACWLbM0lQ4wezQLNobZy6gKQq5i82aT6G/wYGja1NL0ycqD9uOZjzfJUYGqzjAUG4CqSqmKSilfjDFYnLKTUqoGUARY4yAropTysx0XB1oCu1KOzVZobXLH589v3EYdOB0Rzbu/7Sb8aiwlC/oxoZuLKtW5mhdfhCpVzKwiNrkKVwH/5Kenj/4+4A7NBMF9JCQYZ4/ixS1lBZJIKi/w/ZPNaZ3DatBn2VBoreOBZ4Dfgd3Ad1rrnUqpsUopRy+mHsA32uqPWxMIVUptBf4Gxmmts7ehmDsXVqwwM4qSyb/syJh4bnv3L2b+ewSAjx9tlG1SBDsdPz/48EPYu9c8PdloXKEIj4QE4eedh/NXpMa2kMv47DOTLXrSJChc2NIUn5DIkfNRPHFnJRpXyF55nDKDpBm/Ea5eNZvXZcsav+g8yXZ25b5z9PlyPWBSdIS+erfr9XM1DzxgjObevSZ1iY33l+1l2t8H2DU2GxRkEgRXcPGimWXXq2c8Ax2WlRZuDOMFW+Dtuw/WpWdTN1SxtHGzcRSSFPBGGD/epA9fsMBiJBITNTP/PUx+Xy/mD7qNAn655LZOngy1aplEiHPm2MWNKxQhUcNTczfRpWFZOtUvk/41BMETGDvWGIvJky1GImWdibpls3ky0HTIJd9oTuDYMVMmtGdPS5oOgB82n2DF3nOMuK8G9coVdo9+7qByZbNf8fbbZvOuZUsAmlU0cSPL95xl+Z6zKOABMRaCp7J3r0kdPmAA1K9vF8fEJzt3vnJ/DR4OCaJwvhySEDQFkm8hs4wYYX6Os2Qn4cj5q3yz/hgB/t48cUclNyjmZkaONMtOL7xgNvqBvL7W5aYh8zdz6FxkWqMFIefzwguQN6/JtuxAUmobgGL5/XKskQAxFJnjv/9g/nzz9Fzeur74v8/XEXr0IpVLFshR7m5OI39+8w+ydq2JRLUxpafVy3nTsUsuVkwQXMAff8CSJcZlPDDQ0nQ6Itp+XCynlBZIBzEU10NrGD7cPDW/9JKl6XJ0HCcuXQPMPkWupU8fkxhxxAi7u2yn+mX4dejt9i4vLNgqswrBs0hIMN8NlSqlCkANPXKBbh//Zz+vVTr7ZobNDGIorsePP8K6deapuUABS1O9MckBZ7nYTICXl8kDdfCgiUa1UatMAHvebG8/f+uX3e7QThBuDV9/DTt2GCeXFNkZvt+UHIG9ZmQbSgb4pxydoxBDkRHx8fDKK1CzJjz2mKUpZT6jxBzoZuxU2reHtm2N90dEhF3s6B7rnUcRcS2OK9Fx7tBQEJxHTAyMHg0hIdCtW6rmjUdNGp+6ZQvlmHxOGSGGIiNmzzYeDW+/bcnZAnD4/FXAFBwBCMmBQTRORSkzqwgPT7Xhn8SyXWeo/8YyWoxbnma7IOQYPvnEeEKOG2dxhwUTfLvvTCTP312Nn4e0cpOCzkUMRXpcu2aSezVrBl26WJp+2XaK9pNXASbN9pIhrXjl/ppuUDKb0aiRKR4/eTKcSE739WnvxlQukd9+fiU63g3KCYKTuHIF3noL2rUzs2gHEhI1o3/aAUCNHL4v4YgYivSYNs182aV4YtgeFsHT8zYRb1t6qhpYgDplC0lltyTGjjVLdg65bu6tXYofnmpJt0bJ2TIvRcWmNVoQsj+TJsH582nmc3ry6438sNk8JNUoVTBVe05Fvt3S4tIl80dw771w11128Y4TETw+a739fNuYe/DxkltooWJF6N/f5L05etQuLpTXh/cfrs+rHczMy9HHXBByDOfOmWSg3btDkyaWpui4BJY5ZE0uWzjn700kId9yafH++yYc/913LeKh8zdbvuAC/H1crVnOYNQoMwtLEYAEUN32lPXF6kOu1koQss6775pl6bfesojXHQqnxmtLAejVrDyTHq7vUUlBxVCk5MIFkxm1e3dTocoBx4jjgrkln9PNEBRkUnrMmgUHrOnGi+Y3gUfz1x9PY6AgZGNOnYKPPzYekNWrW5pCjyYXK+vSoCwPNso5RYkygxiKlEyebDarRo+2iDcdu8jOk5ft52teaYuQASNHgq8vvPGGRVyyYM72JxdyMRMmQFxcqtLHAL/vNKWB76xWgoblC7tYsVuPGApHkmYT3bpB3bqWppHfb7cff5ubMsTeLKVKwTPPmPodu5JLjJQo6McjIaYg4qcrD/LP/mxQ/1wQrsfp08YltndvE4ntQMS1OLaFmdihWY838ch9S8/7RFlh8mRTGD3FbGLDkQuEXYyynzerVMzFiuVQXnrJ5IJKMau4v15pAN79bQ9Pz93kDs0E4cZImk2MGpWq6Yk5ybVxPDXfm1MMhVKqvVJqr1LqgFJqRBrtfZVS55RSW2yvAQ5tfZRS+22vPs7Q56a4eNHMJh580BQfceChT9ZwNdakDPYkl7dbTvHiMGSIqd+xZ49d3KJysqG9LDEVQnYnaTbx6KOmOJEDy/ecYe2hCwA81Niz9iUcybKhUEp5AdOA+4BaQE+lVK00un6rtW5ge31uG1sUeB1oBjQFXldKFcmqTjdFOrMJRwbeXpGfnm7pOp08geeeA39/iweZj1ce7qtTyn4uy09CtmbiRJPsMo29iX6zzGyib4tgJj5UP1W7p+CMGUVT4IDW+pDWOhb4BuicybH3An9orS9orS8CfwDtrzPG+Vy6lDybqG/9ZR+xpeoAeDgkSEp73iglShgPqLlz4VCyS+wHjzSgfW1jLHp/sT690YLgXs6cMZ5O//tfqtlEdFxyYaJyRTwnZiItnGEoygKOvo5hNllKuimltimlFiqlgm5wLEqpQUqpUKVU6LlzTn4CnTzZJLJLMZsIuxjFXe+tAODJuypTNVCWnW6KF14wGWbHj7eL/H28GNUhOe1JTqzdLuQCJk40CQDTmE3sOJGc/LJUIc/25nPVZvbPQLDWuh5m1jD7Ri+gtZ6htQ7RWoeUKFHCeZpdvmxmE126pJpNhDsE14mXUxYoU8ZEa8+cCWHJ6ZeDiuZjdEezSvnSwm3u0k4Q0ubcOZg+3cwmqla1NB06F0n3T9YAcEe1Etxfp7Q7NHQZzjAUJ4Agh/NyNpkdrXW41jrGdvo50DizY285M2aYpadXXrGIL0XF0nnav/bzqFjZdM0SL71kikBNnGgRPxRSjuaVirF460m5x0L2YupUiI42MUEOJCZq2ry/0n4++I5KHhWFnRbOMBQbgKpKqYpKKV+gB7DYsYNSytHcdgKSKtj8DtyjlCpi28S+xyZzDTExJsFX27ap8rZsDYuwnFcuYS1aJNwgwcHGB33GDLPua6Ogvw9D2lQhJj6RB6f/l/54QXAlV67ARx+ZlYaa1szQv2w/ZT/O7+tFlUDP/27IsqHQWscDz2C+4HcD32mtdyqlxiqlOtm6DVVK7VRKbQWGAn1tYy8Ab2KMzQZgrE3mGr76yoTlj7B69CYkaib/uc9+/svQVnRtmObWiXAjJJVKnTTJIm5asSi+XnnYc/oKS7adtGwSCoJb+Owz4zL/8ssWcUx8AkPmb7af7xzbPldkG1A5cRMxJCREh4aGXr9jRiQkQI0aULgwrF9vSSW+Yu9Z+s7cYD/f+ca95Jc9CufQqxf8/LPJLFs0udjT1Zh4ukz7l/1nIwkM8GPdK+3cqKSQq4mJMdHX1avD8uQiW7tPXebRz9cRftXsXc56vAl3VS/pLi1vCqXURq11yI2Oy72R2d9/bxLWjRhhMRILN4ZZjMT7D9UXI+FMRo6EyEhLbW2A/H7e9GpWHoAzl2PSGikIrmHuXDh5MtVKw5tLdtmNRI1SBWlRubg7tHMLudNQaG0KElWvDl27Wpre+XW3/fjBRmXp5sHRlm6hbl1TX3vKFLNR6ECPJuXtx7Hxia7WTBDMSsOECSZz9N13J4sTNesPJ6+Kf9SrYa4qVpZ7Pqkjy5bB5s3GEydP8i3QWnPB9sTwYKOyTHq4gZsU9HBeeslsaM+ZYxHn9fViak+T2n3TsYtpjRSEW8uiRbB3b6qVhqU7TturWgKULuTZAXYpyZ2GYtw4KFvW5G5xICmNeLH8vkzs7rnh+G7nrrugcWNTKSzBunHdtmZJ8ijoMWOtxT1ZEG45SSsNlSubDNIOHL1w1XKe25ajc5+hWLsWVqyA55839RIc+HaDCRKf2qshXh7uF+1WlDKzin37YLHFk5p8vt4EFc0HwNbjl4hPkCUowUX8/Tds2GD+Nr2sqXrOXo6hoJ83d1YrQbH8vulcwHPJfYZi4kQoUgQGDrSIwy5GMW/9MQID/GhWUdKI33IefNB4lkyYYJ7kHGgYVNh+vPfMFRcrJuRaxo83dVQeeyxV06mIa5Qq5M+sx5uw8bW70xjs2eQuQ3HwIPz4o0lSV8AaJDPsmy0kJGoWDm4hswlX4O0Nw4ebGd6/1iWmHk2TN7U3HbvkYsWEXMn27WbvcuhQk+04BccuXKN04bweW2/ieuQuQ/Hhh+YL6plnUjUdOBdJjVIF7cseggt4/HEoVixVWo86ZQvZj3efupxylCA4nw8+gHz54IknUjXNWXuU3acuEx2bewNBc4+huHgRvvwSevY0SepsJCZqvlx9mEtRcXRqUCaDCwhOJ18+Y7QXL4bdyW7JBfy8OTKuA80qFmX3qcucuRwtexXCreP0aRM78fjjliDQ85ExnLx0jfnrjgFQr1yh9K7g8eQeQzFjBly9apY7HFi57xxjl5iazmUL5y6Xt2zB009D3rzw/vupmmqWDmDzsUs0e+cvJv+53w3KCbmCadNMmdNnn7WIO0z5hxbjlrPr1GXqlSvEi+2ru0lB95M7DEVsrAnwats2VSrxXQ5LG55efCRbUqIE9O0LX38NZ89amhyXoFZJFTzhVhAVZQoTdeqUKpW4Y4aA2PhE/Lxzb9Gy3GEoFiwwIfkpZhMAxy9E2Y/LFpb9Cbfw7LMmv87HH1vE99YOtB+Lg4FwS/jqKwgPN+7yKSicz8d+3LZmzsrp5Gw831BobZY1atY0qSMcWLrjNN9sSC6wV6Kgn6u1E8CkUunQwRSJcUjrUdDfhzUj23BX9RJsPnaJ8EjJASU4kcREs4kdEgKtWtnFCYma7h//x6WoOHo2DWLr6HsYfnfuXXaC3GAoVq406Tqee86SrgNgzOKdALSrWZIj4zrIU6s7GTbMLD19841FXLpQXu6sZioaNn7rTzcoJngsv/5qgj6HD7ek6wi7GEXoUZNCplRAXgrl88n13w2ebygmTTLr4A7pOi5FxdJ1+r+cvhzNPbUCeefBum5UUADM/lHduuYJL0UAXpWSnl8YRnAD778PQUHQvbtFfPBcpP24YfnCLlYqe+IUQ6GUaq+U2quUOqCUGpFG+3Cl1C6l1Dal1F9KqQoObQlKqS221+KUY7PE3r2m9sFTTxnPGhvrDl9gsy2Qq2nFormi8Ei2Rykzq9i2zaRScKCSQ3XB9YcvsHKfbGwLWWTTJpPKZ+hQ8Enei1h3KJx+s0ytm4J+3txWSbI0gBMMhVLKC5gG3AfUAnoqpWql6LYZCNFa1wMWAhMc2q5prRvYXp1wJpMng58fPPmkRXzmcvI6eGCAGIlsQ69eZvb3wQcWcZlC/nbX5Yc/XUOfL9e7QzvBk/jgA5OdYcAAi3jNoXD78dbX78lVqcQzwhl3oSlwQGt9SGsdC3wDdHbsoLX+W2ud5F60Frj1RR7On4fZs82SU2CgpenI+WRPpxaV5Ykh2+Dvb4z6kiWwPzluQinFW13ruFExwaMICzN7YQMGmAqXDlyNiQeg920VyJPL9yUccYahKAscdzgPs8nSoz/wm8O5v1IqVCm1VinVJb1BSqlBtn6h585lYunh88/h2jWzie3Ar9tPsXjrCcoXzcefw++gWAHxdMpWPPmkyer74YcWcYNyhS3nObGEr5BNmD7deDwNHWoRL9pygm1hEVQolo83u8iDiSMunVcppR4FQgDH5D4VbDVcewGTlVKV0xqrtZ6htQ7RWoeUKFEi4zeKjzfRlm3bQu3advGpiGs8NXcT5yNjqVU6gColC2b1IwnOplQpk2Zl5kyTdsVGkRSpncMuXnO1ZoIncO2aydLQqRNUrGgXh12M4tlvtrDu8AUaOGQvFgzOMBQngCCH83I2mQWlVDtgFNBJa213iNdan7D9PASsABpmWaOffjLTyyFDrOLNJ+3HFYpLcF225bnnTMTsZ59ZxK92qGl3Uxz5w3Z3aCbkdL75xgTYpZhNbD0eYT8e0qaKq7XK9jjDUGwAqiqlKiqlfIEegMV7SSnVEPgUYyTOOsiLKKX8bMfFgZbArixrNHUqBAdDx452UXxCIuOX7rGfF/DNXRWqchT160Pr1ub3GBdnFw+4vRJrRrYBYPWB8/b1ZEHIFFqbVD516pgqiw4cc8jQICsNqcmyodBaxwPPAL8Du4HvtNY7lVJjlVJJXkwTgQLAghRusDWBUKXUVuBvYJzWOmuGYssWWLXKZCW1Vanae/oKVUaZbZFyRfLSqX4ZujW+9fvpQhYYNszMCn/4wSIuWdCfTvVNlt9en69zg2JCjuXff833w5AhlgA7MMvSAD8/0yqNgYJTHqu11r8Cv6aQjXY4bpfOuP8A50a7TZ1q0lf36wfA5mMXWbrztL15Qrd6tKhS3KlvKdwCOnY0tYunTIFHHrE0VSiWXCpVEDLNlCmmuuX//mcRxycksnTHaWqVDqBuLk4lnhGe5SR8/jzMmwe9e5s/CKDr9P/4dOUhe5eAvD7pjRayE3nymBTk//1ngqMceKZNFZoEm9+v5H8SMkXS7LR/f8if39L00d8HOHslRlYZMsCzDMXnn5ukcik2sR0pJIYi5/D442Z2OHWqRezn7cVjzYMByf8kZJKPPzZ7FE8/bRFHxyXYa520y+UZYjPCcwxFfLzxj27Txu4Sm1ZVtAB/MRQ5hsKFTaH7+fMhRexMxeLJT4XfbwyTuAohfaKjjUvsAw8YJxcH9p8xeZ2euqsyFYrlT2OwAJ5kKBYtguPHLW5vkWl4xRT0F2+nHMUzz5haFZ9/bhEn7VMAPL9gK2sPXXC1ZkJO4ZtvzLK0w3eD1pqPVxxk3FJTgre7LDtliOcYiilTUrnEXom2GoqhbapIWH5Oo3ZtEzj58cdm1mijoL8PQx383Tcdu5jWaCG3k+QSW7u2cbm2cfzCNcYv3cO/B8Ip6OdtmaEKqfEMQ7F1q3GJffppu0ssQMS1OEu34ffk7uIjOZYhQ8xscdEii3j4PdVZMLg5pQv5s+6wzCiENPjvP1OPJoVL7IFzV+zHYzrVRil5gMwIzzAUSS6x/fvbRVdj4nly7kY3KiU4jY4dzWwxxaY2QJPgotxRtQSr9p2j1uillszAgsCUKWavy6EeDZgZRRL1g8Ql9nrkfEMRHg5z55o/BJtLLEDt13+3/zEMvL0iM3o3dpeGQlbx8jI1RVauNPUqUlC9lImkjYpN4K/dZ1O1C7mUsDD4/vs0XWLDr8YC8FaXOlR2qHcipE3ONxRpuMRGRFmXnF65vyb31C7las0EZ9K/vyk+lcaswnF9+cJViasQbHzyickSm8IlFuDi1ViK5PPh0dsqyLJTJsjZhiIpS2ybNiZ/C6aMYcvxywHo3KAMi59pKX8InkDRoiaidu5cuGDdjyhdOLn41LkrYigEzMPjp58al1iHLLFRsfGcuxLDnLVHKZLPN4MLCI7kbEOR5BLrMJto+/5KImPi8cqjmPRwA+qlqGMg5GCGDDFpor/4wiIuXSi5zO2JS5J+XAC+/TaVS+zVmHhqjf6dJm+bIM1zEtWfaXK2oZg6FSpUME8NwKWoWHvTC/dUt6ekFjyEevXgzjtNYGVCgl0c4O+Nn61kpdSpEOwusbVqmdUGGykdHab2zHpFg9xCzjUUW7eazU1bltgP/thHg7F/2Juj4xIyGCzkWIYMgSNHTLlUG0optoy+hwcblSXs4jWJ0s7trFlj8oOlcIm96PAg+fH/GnFXdUnZkVlyrqGYOtVsbtqyxH74135Lc9VA8WTwSDp3hqCgVJvaeX29qFGqIJEx8fwpnk+5mylToFAhkxzUgfDIZENRunDelKOEDMiZhiI+3mxq9u7N+6HnqP7qb3g7LDMtHXY7HeqWdqOCwi3D29vU1f7rL9hlLV3SIMi4Rw/8KpTfHVLLC7mIEyfSdYlNWnq6u1YgdcoEuEO7HEvONBTnz9tdYqcuP0BMfCLxicnLDTVKBYinkyczcCD4+cFHH1nETSsW5QFbUaMn5mwUD6jcyCefmP2rFC6xpyOimbvuGEFF8zKjd2O8vXLmV5+7cMrdUkq1V0rtVUodUEqNSKPdTyn1ra19nVIq2KFtpE2+Vyl1b6be8Nw5zjRuTmyNWhbxF31C+G9Em3QGCR5D8eLQsyfMng2XLlma7q+THC9z4GykixUT3EqSS2zHjlCpkqXpsS/Xsef0Fe6rU1oeIm+CLBsKpZQXMA24D6gF9FRK1UrRrT9wUWtdBfgAGG8bWwtTY7s20B6YbrtexsTG8lr5NvSbtcEiblszkDKy9pg7GDIEoqJg5kyL+L66pfnxqRZAai8XwcP57juTjt7BJRZMpth9tnTiT99VJa2RwnVwxoyiKXBAa31Iax0LfAN0TtGnMzDbdrwQaKuMWe8MfKO1jtFaHwYO2K6XIbF5vPmzSlNWHzgPQOUS+XnxXkn4l6to1AhatjQBl4nWuiNVA01Kj5MR4iqba0hyia1Z02QbduDQ+asAvNmlDoXyST2am8EZhqIscNzhPMwmS7OP1joeiACKZXIsAEqpQUqpUKVU6MFi5UjMkzzx+GXo7TzdWp4Uch1DhsDBg/CrpVw7Bfy8qVQiPxOW7uWkBODlDtauhY0bU7nEAqzYa4peta0h7rA3S47Z0dFaz9Bah2itQ+K9rMWH/H2uv1oleCAPPghlyqSZ/+nRZhUAmJLCbVrwUKZOTdMl9mj4Vd5cYrzjZFn65nGGoTgBBDmcl7PJ0uyjlPIGCgHhmRybisol8tsrnP3zUuvr9BY8Fh8f4yq7bBns2WNp6tMiGJDAy1zBqVOwYIGpsV7AGj/13rJ9ANxVvYQ7NPMYnGEoNgBVlVIVlVK+mM3pxSn6LAb62I67A8u1CZ9dDPSweUVVBKoC66/3hvl8vfnpqZb88dwdBBXNd73ugiczaBD4+qZylU1K3/LTlpPMXXfUHZoJruLTT9N0iQXYeOQCHeuV5ss+TdygmOeQZUNh23N4Bvgd2A18p7XeqZQaq5TqZOv2BVBMKXUAGA6MsI3dCXwH7AKWAk9rrTP1CFgkv69901LIxZQsmewqGxGRZpcJS/e6WCnBZcTGmtiJ++6DKtZ9ysPnr3IyIpqapQOkBHIWccoehdb6V611Na11Za312zbZaK31YttxtNb6Ia11Fa11U631IYexb9vGVdda/+YMfYRcxpAhEBkJs2ZZxB/2aADAleg44hMSU48Tcj4LFsCZM5YM0mBq0rR+bwUArSWnU5bJMZvZgpAujRtDixZm+cnBVbZzg7K83bUOiVpSSnssU6dCtWpwzz0W8a5TlwFT3bKWpOvIMmIoBM9g6FA4cACWLrWIk8pc9vpsHVdj4t2hmXCr2LAB1q0zGaTzWL/Kdpwwy5D9W1VKa6Rwg4ihEDyDJFfZKVMs4ibBRalbthCHz1/l05UH3aSccEuYOtV4OfXpYxFHXIvj7V93U7tMAKUK+aczWLgRxFAInkGSq+zvv8Pe5M1rrzyKn4e0onaZAKYsP8BhW5SukMM5e9ZUsevbFwKsS0tJcRPFC/i5QTHPRAyF4Dmk4yoLMLRtVQDavL/CxUoJt4QZM4zH0zPPWMQnLl1j07GLQPLvXMg6YigEz6FkSejRw3g/Xb5saWpcwdSq0BqiYmWvIkcTFwcff2w2sKtbc7y1HLecQ+eucnetQPvvXMg6YigEz2Lo0DRdZYvl97Uf7zp5GSEH8+OPcPJkmllik5CoCecihkLwLBo3hubNzUang6usUorfh90BQPdP1vDVmiNuUlDIMlOmQOXKJsjOgbMOhaouRcW5WiuPRgyF4Hmk4ypbzaGO+uhFO12tleAMNm+Gf/816TocXGITEjXrDl8AwNc7D691TFkSR8gKYigEz6NbtzSzyjpWNgsMEI+YHMnUqZAvn0kA6MD4pXsYOn8zAKtebE3dcoXcoZ3HIoZC8Dx8fGDwYDOj2GvN8zTq/poAeCllWdMWcgDnz8O8efDYY1C4sKXpu9DksjbyEOB8xFAInkk6rrID76jEsHZVORkRzfQVEoCXo/j8c4iJSeUSCxATZ92PEpyLGArBMwkMTNdVdvCdlQGY+PteTkdIXe0cQXw8TJ8ObdpA7dqWpoREzTVb3ZFG5Qu7QTnPRwyF4Lmkk1XW38eLT3s3BuDxWRtYdyjcDcoJN8TixXD8eCqXWIAnv95oP/7E9nsVnIsYCsFzCQlJ01UWoFWV4gDsPnWZXp+vc4d2wo0weTIEB0PHjqmalu06A8CQNlUoWVByO90KxFAInk2Sq+zvv1vE+f2S665LTZtszsaN8M8/5nfp5WUXL9t5mrpjzO/17lqBPN26SnpXELJIlgyFUqqoUuoPpdR+289UMfNKqQZKqTVKqZ1KqW1KqUcc2mYppQ4rpbbYXg2yoo8gpKJbNyhdOlVWWYB7awcCUNQhalvIhkyeDAULQv/+FvGbv+ziSrRJx9KjSRD+Pl5pDBacQVZnFCOAv7TWVYG/bOcpiQIe01rXBtoDk5VShR3aX9RaN7C9tmRRH0GwkpRVdulS2LfP0jS1ZyMGtKrImcsxvPGzBOBlS06ehG++gX79UmWJLV0or/04qe6IcGvIqqHoDMy2Hc8GuqTsoLXep7Xebzs+CZwFSmTxfQUh86TjKuvrnYeapc2Xz8x/j3Aq4po7tBMyYvp0SEhItYkdE5/AoXMmZXzPpuUpXzSfO7TLNWTVUARqrU/Zjk8DgRl1Vko1BXwBRwf2t21LUh8opdKNlFFKDVJKhSqlQs+dO5dFtYVcRZKr7MyZcOmSpalZpaL2421hES5WTMiQa9fgk0+gc2eoZK1Ut2LvOc5HxvB21zq8+2Bd8shG0y3luoZCKfWnUmpHGq/Ojv20CXNNN9RVKVUamAM8rrVOckEZCdQAmgBFgZfTG6+1nqG1DtFah5QoIRMS4QZ57jnjKvvZZxZxuSL52DbG1FuWokbZjDlzIDzc/O4cOH4hiifmGJfYFpWLu0OzXMd1DYXWup3Wuk4ar0XAGZsBSDIEZ9O6hlIqAPgFGKW1Xutw7VPaEAPMBJo640MJQioaNDDBWlOmmHoGDgT4+xBUNC//7JeZarZBa7OJ3bAh3H67pWnfmSv2Y1lycg1ZXXpaDCQVrO0DLErZQSnlC/wIfKW1XpiiLcnIKMz+xo4s6iMI6TN8OISFwYIFqZrur1Oa9YcvsPf0FckBlR1Ytgx27zaziRQpOfadiQTghXuq4SVLTi4hq4ZiHHC3Umo/0M52jlIqRCn1ua3Pw8AdQN803GDnKqW2A9uB4sBbWdRHENLnvvugRg14/33zxOpAtcCCxCVo7p28ii9WH3aTgoKdyZOhVCl45BGLeMm2k8xYdZCapQN4po2UOnUVWTIUWutwrXVbrXVV2xLVBZs8VGs9wHb8tdbax8EF1u4Gq7Vuo7Wua1vKelRrHZnlTyQI6ZEnj3lC3bQJVq2yNDUJTt7UTqprILiJ3buNO/PTTxtvNQcmLN3Lxag43ulax03K5U4kMlvIXfTuDcWLw6RJFnH5Yvm4r04pwCSZE9zI5Mng7w9PPGERX42J59iFKJ5pXYWG5aUetisRQyHkLvLmhaeegp9/ThWAN61XI1pVKU54ZEw6g4Vbzvnz8NVX8OijkMK78ZOVxqu+aqAE17kaMRRC7uOpp8ySxuTJFnGePIqSBf04GRFNoswq3MP06RAdDcOGWcSXo+OYuvwAvt556FC3tHt0y8WIoRByH4GB5ol11izjp+9A/aDCnLsSw9glu9yjW27m2jWT6bdDh1Q1J7bbgiH7tayIt5d8bbkaueNC7uS555Ijfx14OCQIgFn/HWHv6StpjRRuFbNmmaWnl16yiMMjY+y/i36tgl2vlyCGQsil1K4N7dub/E8xyXsSeX29GNbOuF3eO3lVeqMFZ5OQYNyWmza1BNiFXYyi8Vt/MnbJLsoWzkuJAlIP2x2IoRByL88/D6dPw7x5FnEehwCvQV+Fcikq1tWa5T5+/BEOHjSzCYf7v/tU8qyufZ1SUg/bTYihEHIvbdtC/fowcaKlAl6FYslpIZbtOsOq/efdoV3uQWuYMAGqVIEuXSxNR8NN/q2mFYsypI0UJnIXYiiE3ItS8PLLJsBr8WK7uFP9MiwY3Jy3upigrivRceldQXAGq1bBhg1mhudlLT505nI0/j55+O6J5hTOJwWm3IUYCiF389BDJoX1u+/a03oopWgSXJRHmpiN7c//Ocyx8Ch3aunZTJhgYib69LGIExI1+89GUiy/7Eu4GzEUQu7G2xtefBHWr4cVKyxNPjY3zMPnr3LHxL+Jjktwg4Iezo4d8OuvMGSICYa0sff0FbpM+5cVe88RE5+YwQUEVyCGQhD69jWxFePGpWoadEdywZxV+yQNudN57z3Il88EQTpw7+RVbD9hYifOS6S82xFDIQj+/iauYtky2LjR0tS2Rkn78SqpV+Fcjh6FuXOhf38oVswu/mFTmP3Y3ycPc/pLmRp3I4ZCEACefBIKFYLx4y3iZpWKsWFUO6qWLEB4pLjJOpUJE4xDwYsvWsTDv9tqP+5Qtwy3V5WKlu5GDIUgAAQEmOWPhQtTJQssUdCPwvl8OHz+KmEXozh4TrLhZ5mTJ+GLL8wGdlCQXZwyZiU6XvaFsgNZMhRKqaJKqT+UUvttP9PM/auUSnAoWrTYQV5RKbVOKXVAKfWtrRqeILiHZ58FPz8TV5GCgv4+7Dl9hVbj/6bt+yulCl5Wef99U5J2xAiLOKUR7n1bBVdqJaRDVmcUI4C/tNZVgb9s52lxzaFoUScH+XjgA611FeAi0D+L+gjCzRMYCP36wezZcOKEpWn3qcuW87NXZIP1pjl/3uTY6tULKle2i6PjEuj9xXr7+Z/D7+S2SsXSuoLgYrJqKDoDs23HszF1rzOFrU52GyCpjvYNjReEW8KLL5p4ihR7FU+1tkYFHz5/1ZVaeRaTJ5uEjCNHWsQ/bz1JVGzyUlNggMRPZBeyaigCtdanbMengcB0+vkrpUKVUmuVUl1ssmLAJa11vO08DCib3hsppQbZrhF67px4nwi3iOBgs24+Y4ZZR7fR+7YKvNS+uv089IiUS70pLl0yqcS7dYNateziM5ejeXHhNvt53xbBFPDzdoOCQlpc11Aopf5USu1I49XZsZ82i7bpLdxW0FqHAL2AyUqpyun0Sxet9QytdYjWOqRECfGCEG4hr7xispmmmFW0qFzcfvzesn1MWrbX1ZrlfKZNg8uXzT22EROfwOItyUa5XrlCjOlUWxIAZiOuayi01u201nXSeC0CziilSgPYfp5N5xonbD8PASuAhkA4UFgplfTYUA44kdZ4QXAplSrBY4/Bp59aZhUNggqz96329G0RDMCU5QfcpGAOJTISPvjAFCZq2NAufvTzdbz9624A7qtTigWDm7tLQyEdsrr0tBhIStDSB1iUsoNSqohSys92XBxoCeyyzUD+BrpnNF4Q3MKoURAfb3z9HfDz9qJo/mTnvPgESS+RaT76yFQUfPVVi3jDkYv242m9GuHn7ZVypOBmsmooxgF3K6X2A+1s5yilQpRSn9v61ARClVJbMYZhnNY6qc7ky8BwpdQBzJ7FF1nURxCcg+Os4tQpS1PHesk1m3eevJxypJAWERHG6N5/P9x2m12csjZ5njyy3JQdyZKh0FqHa63baq2r2paoLtjkoVrrAbbj/7TWdbXW9W0/v3AYf0hr3VRrXUVr/ZDWWnwOhezDqFHG1z/FrKJSiQJ82KMBAJ2n/SsxFZlh8mS4eBHGjrWIa72+1D36CDeERGYLQnpUrgy9exuf/xSziiIOtRG+3XDc1ZrlLC5cgEmToGtXaNzYLj4dEU10nFm6K1ckL+89VN9dGgrXQQyFIGTEq6+avYq33rKIq5cqaD8e8cN2Nh27mHKkkMR778GVK/DGG3bRpahYXlhgcjqVLOjHgsHN6d64nLs0FK6DGApByIjKlWHAABNXceiQXRwY4M9PT7e0nz84/T93aJf9OXsWpkyBhx+GunXt4gZj/2D1AVNidu3ItpQulDe9KwjZADEUgnA9XnsNfHxg9GiLuEFQYfo0T85FFCceUKkZP95EYY8ZYxc57ukMa1dVNrBzAGIoBOF6lCljEgbOmwdbt1qagovntx9HxUimUwvHj8P06fDoo1Cjhl18+Vq8/bhUgL87NBNuEDEUgpAZXnrJ1KsYNcoi9vdJ9vnvP3tDKnfPXM1rr5m8WSk8nXafTnYprlUmwNVaCTeBGApByAxFisDLL8Mvv8Dq1XZx14Zl7QF4oUcvsu6w5IACzMzrq69g6FCokLw8l5CoeeWH7fh652FG78bUK1fYfToKmUYMhSBklqFDoXRpU0PBts7u7+PFO12TN2l7frZWChuBmYEVLpwqQ+z2ExEcOn+V8d3qck/tUu7RTbhhxFAIQmbJl89syv77L3z/vV18LS7e0m363wddrFg2Y9ky83rtNTMTc+D4hSgAapUu5A7NhJtEDIUg3Aj9+hk3zxdfhOhoAKqWLGjp8v2mML7fGOYO7dxPYqKZTQQHm9KyNiKuxbH52EX+tbnEliksm9g5CTEUgnAjeHubDKhHjpi0FECdsoXYOvoee1ZZgOcXbE1zuMfz9ddmf+Kdd0xZWeBKdBz131hG1+n/8c2G41QukZ+C/j5uVlS4EcRQCMKN0rYtdOoEb78Np08DUCifD2M61bZ0e2JOaO7KLnvlitm/adIEHnnELt5xwpo48b46pVOOFLI5YigE4WZ47z2IiUnlLhtSIXlN/vedZ5i+4mDucZl9802TE2vqVMhjvlrCLkbR87O19i79WlZkwO0V3aWhcJOIoRCEm6FqVeMFNXMmbNpkF3/5eBNevDe5ZOqkP/bxyapcsLm9d69Zinv8cWjWzC6e/Od++/GE7vUY/UAtCjskVBRyBmIoBOFmefVVKFYMhgwxm7hAgL8Pd1S1lupduuO0O7RzHVobo5k3L7z7rqUpJj556a1zgzKu1kxwEmIoBOFmKVwYJk6E//6DL5JrbtUtV4h5A5uxflRbKhbPz+FzVzkWHkV4pIeWW1m0yLjDvvEGBAYCsP/MFcYv3cPPW00pWV+vPFK5LgejslJ0RSlVFPgWCAaOAA9rrS+m6NMa+MBBVAPoobX+SSk1C7gTiLC19dVab7ne+4aEhOjQ0FCLLC4ujrCwMKJtLotC5vD396dcuXL4+IgXyk2hNdx1F2zfDnv2QMmSlubvN4ZZPKCOjOvgYgVvMVevQp06JsZkyxaTPBEIHvGLvUu1wALM7tdUMsRmA5RSG7XWITc6zjuL7zsC+EtrPU4pNcJ2/rJjB63130ADm5JFgQPAMocuL2qtF2ZRD8LCwihYsCDBwcEoJdkoM4PWmvDwcMLCwqhYUTYYbwqlTGGj+vXhhRdM2goHWlUtbjmPT0jE28uDJvKvv25chVessBuJlFl0b6tUTIxEDierf7Gdgdm249lAl+v07w78prWOyuL7piI6OppixYqJkbgBlFIUK1ZMZmFZpWZNE2Q2Zw4sX25pCgzw580udezne89ccbV2t47QUBNTMmgQ3HmnXfzJiuTN++n/a8SrHWq5QzvBiWTVUARqrZNqRJ4GAq/TvwcwP4XsbaXUNqXUB0opv/QGKqUGKaVClVKh586dS69PZvUWbMg9cxKjRkGlSvDkk6b+ggOVSySnIn/lxx2eEVsRF2cKOgUGmpoTwIGzV/hhUxjv/7EPMF5O99ctja+3B82gcinX/Q0qpf5USu1I49XZsZ82mx3pbngopUoDdYHfHcQjMXsWTYCipFi2SnH9GVrrEK11SIkSJdLrJgjuIW9e+PRT2LfPeEM50KJyceYNNC6jW49fot/s0LSukLOYNMlEYE+bZjb1gXaTVjH8O7Mfk8/Xi071xcvJU7iuodBat9Na10njtQg4YzMASYbgbAaXehj4UWsd53DtU9oQA8wEmmbt47ifn376CaUUe/bscdo1Z82axcmTJ296/JEjR5g3b57T9BHSoV07GDzYLMf884+lqUXl4rSoXAyAVfvSnhHnGHbuNHsTXbuaFxATn1y0qU/zCmwfc6+lVoeQs8nqnHAx0Md23AdYlEHfnqRYdnIwMgqzv7Eji/q4nfnz59OqVSvmz0+5wnbziKHIQUycaBLi9e0LkdZ04w3LF7YfR0TFcToimqx4HbqF2FhTsS4gAD7+2C7+aPkB+3GR/L54SXlTjyKrXk/jgO+UUv2Bo5hZA0qpEGCw1nqA7TwYCAJWphg/VylVAlDAFmBwFvUB4I2fd7Lr5OXrd7wBapUJ4PUHamfYJzIyktWrV/P333/zwAMP8MYbb6TZb9KkSXz55ZcADBgwgGHDhnHkyBE6duzIjh3GVr733ntERkZSp04dQkND+d///kfevHlZs2YNNWvW5OGHH+a3334jb968zJs3jypVqtC3b186duxI9+7dAShQoACRkZGMGDGC3bt306BBA/r06cNzzz3nxDsjWChQwERr33WXKXQ0bZq96dm21ShfNB8vf7+d+mON49+YB2rRt2UO8jgbPdq4wS5aBIGBaK1ZczCcqQ6GonQhyQzraWRpRqG1Dtdat9VaV7UtUV2wyUOTjITt/IjWuqzWOjHF+DZa67q2paxHtdY5uuLLokWLaN++PdWqVaNYsWJs3LgxVZ+NGzcyc+ZM1q1bx9q1a/nss8/YvHlzutfs3r07ISEhzJ07ly1btpA3r3EzLFSoENu3b+eZZ55h2LBhGeo1btw4br/9drZs2SJGwhXceScMG2bqRS9ebBf7eufhocZB3FEteY9tzaFwNyh4k6xaBRMmmE3sTp0A+C70OL0+X2fv8nL7GjzUOMhdGgq3iKzOKLIl13vyv1XMnz+fZ599FoAePXowf/58GjdubOmzevVqunbtSv78xhPmwQcf5J9//qGT7R8vs/Ts2dP+U778syHvvmtiC/r2NU/g5csDkCePYny3ujR/17jR7jmdQ9xlL1yAxx4znl0fJMfPpswM+8Qdlcgjy04eh0caCndw4cIFli9fzvbt21FKkZCQgFKKiRMnZsoF1dvbm8TE5AnX9WIbHK+ZdOx4jcTERGJjY2/mowjOwN8fvvsOGjWCnj0tAWmlApKXZo6GR3HoXCSVShRwk6KZIDHRGImTJ0298AJG183HLjJn7VF7t3kDm4mR8FDEwdlJLFy4kN69e3P06FGOHDnC8ePHqVixIv+k8H65/fbb+emnn4iKiuLq1av8+OOP3H777QQGBnL27FnCw8OJiYlhyZIl9jEFCxbkyhXrk+e3335r/9m8eXMAgoOD7ctdixcvJi4uLt3xgguoWhU++8zkgnrtNbtYKcXUng3t523eX0l0XEJaV8gejBsHv/xiZhJNjWOi1pqu0/+zdGsSXNQd2gkuQAyFk5g/fz5dba6CSXTr1i2V91OjRo3o27cvTZs2pVmzZgwYMICGDRvi4+PD6NGjadq0KXfffTc1atSwj+nbty+DBw+mQYMGXLMFc128eJF69erx4Ycf8oFtKWDgwIGsXLmS+vXrs2bNGvvyVr169fDy8qJ+/fr2voKL6NEDnnjCBKUtWGAXP5AixuDHzSdcrVnm+OsvY+R69LCUNr0UFZeqq48npSYRLGQpKaC7SCsp4O7du6lZs6abNHItwcHBhIaGUrx48et3zgS56d65hZgYaNMGNm82SzeNGgEwYHYou05GcDLCLDP+9uzt1Cwd4E5NrezbB7fdBqVKwfr19iWn+IRE2k1ayZHw5Ew8q19uTbki+dylqZBJbjYpoDwCCMKtxs8PfvgBSpSAzp1NFTjg8z4h/DeyLRO71wPgqbmb7Ok9jl+I4uSla+le8pYTHg4dOoCXFyxZYjcSAE3f+ctiJAAxEh6ObGbnQI4cOeJuFYQbJTDQuMq2bAkdO8Lff5ugNeChkCAuR8fz5pJdvPXLbmqVDuCl77cBbkpLHhNjIq6PHzdJDitVAkz6kW0nIrhw1ThJfPBIfY6FXyMkuEhGVxM8ADEUguAq6tc3+xSdOpnXb7+ZHFFAv5bBhB65wPcbw5gVE+8+HePjjZfWP//AvHnQogUAu05epvO0f+3dfL3z0KZ6IIXySR2T3IAsPQmCK7nvPlOzYtUqePhhk4UV4wk1+M7KXElhJCKupd40vmUkJECfPvDjj/Dhh8ZgAAs3hnH/FKv33rJhd4iRyEWIoRAEV9Ozp0ntsWQJPPQQ2GJm6gcVpkO90pau9d9YxoGzLkhYEB9vIq7nzYN33oGhQ4mKjWfHiQhecKjQB/BO17oEF8+fzoUET0SWngTBHTz5pHmCHzIEHngAfvoJ8ufnuXZVuRabwPI9yYmY200yKdK2j7mHgv7mKT46LoFtYRE0reiE2IXoaOJ79MR70U/sGTyciaXbMfbSNTpNXU341eSgzVIB/vwytBXFCqRbNkbwUGRG4UQKFHB+dK1kfvVgnnkGZs0yG8atW8PJk1QpWZAv+zZh31v3Mae/Net+q/F/892G4wA8v2ArD3+6hsPnr97w216NiWfeumMmc+2FC9ChA96LfuKNtgNpX6gNf+05S8txyy1GYkK3eqx9pa0YiVyKGIpsjhgKD6dPH+M6u2sXNGkCGzYAZrP49qoleLtrchnViGtxvPT9NoJH/MIv24yL7ZS/9vPVmiMcPBfJfocyq3EJiXy34Tiz/j1sebtD5yJ559fdvPLjdtb/vJKr9RuRsOofnuswnJkhllpkdppWLMpDIeWc/cmFHIRnLj0NG2YSsTmTBg1g8uRMdV2xYgVjxoyhePHi7Nixg8aNG/P111+jlCI4OFhShAtWOnc2aT46dYJWrUxCwWHDIE8e7qtTmmnLD3D+aiwNggqz/vAFy9AfN5+wRHVvGX03aw+FM/jrTXZZUhrz4xeiaPP+StCah7b/SZ1JnxLpl4/ePd5hU9m0Ay77NK/A6AdqS8ncXI5nGopswObNm9m5cydlypShZcuW/Pvvv7Rq1QpIThH+1VdfMWzYMEtep5SMGzeO9957L8M+ggdQr56ZTfTvD88/b3IrffYZRStV4r+RbQE4fP4qrd9bkeFlGoz9I5Xswz/3cyriGt9sOE6JyAu8u3Qq7Q5uYF1QHYY88CJnCxaz9N86+h5+3XGKphWLUjk7JysUXIZnGopMPvnfSpo2bUq5cma63qBBA44cOWI3FJIiXEiTEiVMQaAvvjAzipo14bnn4JVXICCAisXzM61XI6qXKkC7SasyfdkP/tyHf1w0T29YxJPrFuKdmMDYNgOZGfIAWqVefQ7I603PpuWd+MGEnE6W9iiUUg8ppXYqpRJtVe3S69deKbVXKXVAKTXCQV5RKbXOJv9WKeWbFX2yE35+yZt+Xl5exMcn+8dLinAhXZQybqp795pEfOPHQ4UKMHIkHD9Oh3qlqVKyIK/cn5w00rEQUkpKRF7k2dXzWP1Jf178Zw6rgxtwb7+P+LJJ5zSNRLkieWWZSUhFVjezdwAPAuk+3iilvIBpwH1ALaCnUqqWrXk88IHWugpwEeifRX1yBJIiXLguZcvC7NkQGgrt2pnKcuXLmz2MCRMY5HWatkH5aVaxKF/1a0r9coUAeKd9ZbbeG0C/DYuY+80rrJ3eh+f+nceW0tXo/r/xDO46ihPFytKhXmmaVyrGE3dWokPd5NiN1S+3cdcnFrIxWVp60lrvBq73BNIUOKC1PmTr+w3QWSm1G2gD9LL1mw2MAT5O6yKeRFKKcD8/P3sa8oEDB9K5c2fq169P+/bt00wR3rdvX1mqym00bmzSfhw+DHPnmmJIL78MwBcARYrA+ML8AMRfuITf+IsAjAZ0tWrEvvwySxq0o/qdIdy77zxPlcxPmxqBqd9m9WEOnc/RlYiFW4hT0owrpVYAL2itQ9No6w60T6qhrZTqDTTDGIW1ttkESqkg4DetdZ2U17C1DwIGAZQvX77x0aNHLe05JVW2s1OEO4Occu8EG2fPGi+pPXtM4r7LtnKkBQpAUBBUq2ZyNJUpk/F1hFzHzaYZv+6MQin1J1AqjaZRWutFN/qGN4vWegYwA0w9Cle9ryBkO0qWhC5d3K2FkIu4rqHQWrfL4nucAIIczsvZZOFAYaWUt9Y63kHu0UiKcEEQchquiMzeAFS1eTj5Aj2Axdqsef0NdLf16wNkaYaSE6v1uRu5Z4IgXI+susd2VUqFAc2BX5RSv9vkZZRSvwLYZgvPAL8Du4HvtNY7bZd4GRiulDoAFMO2P3cz+Pv7Ex4eLl98N4DWmvDwcPz9/d2tiiAI2RiPqZkdFxdHWFgY0baUzULm8Pf3p1y5cvj4SG0BQfB0btlmdk7Bx8eHihUrulsNQRAEj0OyxwqCIAgZIoZCEARByBAxFIIgCEKG5MjNbKXUFWCvu/XIJhQHzrtbiWyC3Itk5F4kI/cimepa64I3OiinbmbvvZmde09EKRUq98Ig9yIZuRfJyL1IRimVKs1SZpClJ0EQBCFDxFAIgiAIGZJTDcUMdyuQjZB7kYzci2TkXiQj9yKZm7oXOXIzWxAEQXAdOXVGIQiCILgIMRSCIAhChmRrQ6GUaq+U2quUOqCUGpFGu59S6ltb+zqlVLAb1LzlZOI+DFdK7VJKbVNK/aWUquAOPV3B9e6FQ79uSimtlPJYt8jM3Aul1MO2v42dSql5rtbRVWTif6S8UupvpdRm2//J/e7Q0xUopb5USp1VSu1Ip10ppabY7tU2pVSj615Ua50tX4AXcBCoBPgCW4FaKfo8BXxiO+4BfOtuvd10H1oD+WzHT3rifcjsvbD1KwisAtYCIe7W241/F1WBzUAR23lJd+vtxnsxA3jSdlwLOOJuvW/h/bgDaATsSKf9fuA3QAG3Aeuud83sPKNoChzQWh/SWscC3wCdU/TpDMy2HS8E2iqllAt1dAXXvQ9a67+11lG207WYaoGeSGb+JgDeBMYDnpxzPjP3YiAwTWt9EUBrfdbFOrqKzNwLDQTYjgsBJ12on0vRWq8CLmTQpTPwlTasxVQaLZ3RNbOzoSgLHHc4D7PJ0uyjTYGkCEwBJE8iM/fBkf6YpwVP5Lr3wjaNDtJa/+JKxdxAZv4uqgHVlFL/KqXWKqXau0w715KZezEGeNRWaO1XYIhrVMuW3Oh3So5N4SGkgVLqUSAEuNPdurgDpVQeYBLQ182qZBe8MctPd2FmmauUUnW11pfcqZSb6AnM0lq/r5RqDsxRStXRWie6W7GcQHaeUZwAghzOy9lkafZRSnljppThLtHOdWTmPqCUageMAjpprWNcpJurud69KAjUAVYopY5g1l8Xe+iGdmb+LsIw9enjtNaHgX0Yw+FpZOZe9Ae+A9BarwH8MckCcyOZ+k5xJDsbig1AVaVURaWUL2azenGKPouBPrbj7sBybdut8SCuex+UUg2BTzFGwlPXoeE690JrHaG1Lq61DtZaB2P2azpprW8qEVo2JzP/Hz9hZhMopYpjlqIOuVBHV5GZe3EMaAuglKqJMRTnXKpl9mEx8JjN++k2IEJrfSqjAdl26UlrHa+Uegb4HePV8KXWeqdSaiwQqrVeDHyBmUIewGze9HCfxreGTN6HiUABYIFtL/+Y1rqT25S+RWTyXuQKMnkvfgfuUUrtAhKAF7XWnjbjzuy9eB74TCn1HGZju68HPlQCoJSaj3lAKG7bk3kd8AHQWn+C2aO5HzgARAGPX/eaHnqvBEEQBCeRnZeeBEEQhGyAGApBEAQhQ8RQCIIgCBkihkIQBEHIEDEUgiAIQoaIoRAEQRAyRAyFIAiCkCH/BxXHcxKlo1q2AAAAAElFTkSuQmCC\n", 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3K3mTT+RPiq6OX0vH93RPx+Qpv8FJ0dXa+QD59aq2d/6Afl/IvGhzXcWZ9AA/mNbeJwfUnJcSWb7z52eRn0vbGe7s3IBLL+2ynv/1698v/2OfKIomApjIGHs+iqJRAJ4DcA2ArwJ4hzF2TxRFqwGMYYx929RXvXy9Q8eT5wEAWhe8HusqO0vo+hZ5rrcueL0kYwgICDBDVxt0HBX/prXT++sdTvJ6RQCMsV7G2PMFfRDAKwDOAXA1gAcLYQ8i/w+CFfw2ep2kxJhkFj6bv2P0OjQ13pnQO0avw9oDvbHOZVa62LeOBxBzWcyZ4qPG+ubeR6fYOo7iqxe4zt/GmdbcplP2iclX6pqg4lS6rjaI51PkqTXgpHETvD/q6XXru/yD/COeHgBnAOgX+Ei0dT8zZ85U3lwvSpOPIrPwufizblfK18wizibT5NXGUXWKreMovnqB6/xtnGnNbTpln5h8pa4JKq6csSOmXciYZ91O/QvfKIo+DeAfAHyDMfZuFEXiPywsiiLrc6U33/0dVjVOwPot+zCv/wQAYN7bJ7B+yz6sOj/P37VwGua9PeQTJW+76vwJsa2SWfhc/Fm3K+VrZhFnkjt2Ho51Sl7FGACxPq//RKId93NejJV5iq3jKD4qtncew5XNn6lorKmN6/xtHD/TqpyIPlUOVfkWfbwt50QJJGuEyhb1tPWComcd+0lfhT7qGUXR7wF4HMA/M8b+a4HbC+BLjLHewu8FfsUYu8DUz6kTp7LjvfvRuPoJAEDXPUuKdJMEkNADqg+qXJkkoN4HHLItwuQLqAxc82XKN7U+AMV1od7qREW+0jnKv8X/7wBe4YW/gJ8DuBnAPQX5qKJ5AuNHnQoAWNk6NeZUuk7KekD1wSWftn2gs2Pk7sbK1msBAFf23Iftk7+OK3vuAwCtrrJtvAqU2DOObMe7Z11J6u9Q328xacynMo/1aUNZH/36LsHK1qmJfGyf/PW4D1UuTfmm7CdbH8MdaT7t8wUATwHYDeCTAv2fADwD4KcAJgPoRv6jnu+Y+qqXT/sEVAl0F8QDbhe723jlazvE1hoo62PSAfVF6wHeqPk7fKd8bgrr3tONTbs2AUD+8mV+GbuGM0kAJF1l2/jhCtu6mXKg8wH6vKp8Iseh9PGLwAuXeJMuEFdc0q69vF30yZD7VGGgB2gY+lN+Y6wGpWpjjBEvV9fxhrWW88EvV49fW5Nn6p5wrQ31UBdqvvif1nQa++DAB6RLvNNe0Ey9rLmWLnEuB2zrZsqBzgfo86ryiRyHy0XvLpyJt/koft/YUrfxnVfai9llm3pRu4qj1oZ6qAs1f43j+NPGA6Bf4p3mgmbqZc3D5RJnKqgXbFPy5RNHzaHvxe4++8C2R1z2kM9+K1Ub33mlvZhdtqn7RcVluacofC2iKt7519Iz/6P3boz1cXfcnrBlvL9jB06fPdvZ78pTXkuGOHZ+5d7Rezdmdv1eQMBwAj87cj0QOeoZfX/HDjT+3Y/R9Ud/bK0Dl37zrt7ejz7y+kOvUPwd8cpnL4z1C199JWHXEsSxX/jqKwDyc+N6QEAAHfzsyPXAt0ZQ27V1deGl330QWQMVqIrHPhw7HuuMr1Djuoozxat8rpzJP3bFioRftkVU8zt/oHjsR9raodtuunWirKkuT7o4W7yJs0m5DQCn9mIb3pepD5WvUlxaX5o1o+aLmmeffUXdrzrO5ONnSVUPOOfyzh8ATrviCmsdOPbNu/y/b8T3T4Oz/Jk5cyZjjLGNf9rBOLiu4lS2yefKufjrCaa56nyUNdXlSRdnizdxNilzru3FNpQ+qolL60uzZqq1p8bYOJNu87lwFF+5AWAn86y7VfPY56++fRfe2NuHtv98O7ZtfijWN/8/G2NuxMg5+Ph3T2Nu201F9o7HOmMdQNyOg8enfedfz3B9tyPz2zY/FOdi9lXNCZvnhpJPLgEodXmfnHPBmIRPZXPJx8A5ALHO95sYr5IAEq8v9qGK1XHiGE1xWXE234iRc9D9m1+Q5m9aY91aymuv6kOMseVe3D8udULcm3wPi7XD9yxUAjX/Uc9Zs2axG887CwCw6pHHse6GpQmdIgFodZUdkD1Ma07NoygB9X6QdYpt422+NLGl7CNLuI7HdS0pnEvufSRHvdSHmv+oJwDMue5Go26TJl1lUzCwpRsNC6ck9IEt+Rt0uC5yIq+zdZyJd21jGoNuDvIcOecC05pT8+iSV1uOdTk37QWXfeKzp0rRR5ZwHY/rWlI4l9z77CeKbYN8VlQS0J89DtfzmyWq5p1/NX7a59DqpzDpnnkJ/dDq/F8vcl3kRF5n6zgT79rGNAbdHOQ5ci4gIKAY8llRSUB/9jhcz6+Mc9d8sfYf+6xduzZxjyrlQmjdJd0mXWXruOOdAzi1Of/dJDM+bsbzIzpxvDP/fSSnNjfEfs6JPADMmXx5oo0qhsJT2syZfDme7nlBGSvaKn3Rrdcgl8sl5nNqc4PX5emybbpY3SSBcLF6QDF0+8NWE1zqAWXfy2eFS12dkHUOmVPVDFNd+Ldf+3e1/9hHvkSbciG07pJuk66ydRwA4GDB396Cre0PFPGxlHkAi269JtlGEUPiCW0W3XoNtvPXMoxJpS/CNUNzF+blc3m6bJsuVjdJIFysHlAM3f6w1QSXekC+QF6uAQcNdULWNZyyZpjqQgpUzTv/Nd/7AloXfB8A0Nm5Ad3dl2LKlBfR3LwSHU9+A60Lvp+QTY1NccyBrgM4KbraqgMosjl4vzrw1xMvZrehqbEp8boubV2R5rV0c+N9yv3JvMkW19qUQ1mKeQfye6K5eWXKVQqoNYh5l+tCLpdL7BV5P/G9qKsTgLoe6GoBpQbozgwVru1v/PLG3rfe+ri2/8J3zff64gvG+WXjJgmoLyk36SrbxrvGlKJtOV7L1Ia6ZlRbd7m8TgJAuIB+eEK1B3QcYN5jsq6ydRzF5xOXtv3yZYewd+/x2v4L36bGO4t0m/TRVbaNd40pRdtyvJapDXXNqLZLvm1jC6hfmPaJDyfrKlvHUXw+cWna9/Wtqo+/8A0IqBV8r/NwkW6SNs6k+7Qxva5JynpAdQMp/sL3JO9/NQIChjHWdb1ZpJukjTPpPm1Mr2uSsh5Qv6iaxz4BAbWEVY0TinRRrt+yD6vOH5I7dh5OcPP6T8Tt572d1zk/e9bZRTFyP9T2cl88Vhyjbj4B9Y1Q/AMCPPCtpolFuigbf/Q8uhYuSchncSTWY7QBzz53BADwLPJyc9vMZEzBJ/ZDbZ/oi8e2Jceom09AfSMU/4CALJC7G2j5Tl4CWNl6LZC7Gw9PPQbkXsxLIKkr7EkDzxXHKGInDTyHQw0z1bG614v1JYmxoeU7yTkEDAtUzUc9q/HrHQICyGhvANoH8hJI6tUGeWzthb8e5XMIqBnUxRe71TI27dqE5ZctV0oACV1l6zgXXvd64njEsQZkjPmrk1LWqeh6CmgkfK8SNU4H1dh8xhtghGttkDnb+R9x5givP/ACwjv/THDJg5dg9827lRJAQlfZOs6F172eOB5xrAEBAaWHa22QOdv5f639NXxwoA6ucaxVLJu+zChlXWXrOBde93qmcQSkh3h597g7bldy8gXfJs5Vd+V0Y5X1gPTwqQ0UP+dXvrsy/JFXQEClsOeCzyakihOljXPVXTndWGU9oPqBFH/kFd75BwSkxNgVK7DjsU40FeTsq5pxpK0dFwI40taOQcH3fls7ACR877e14+LxRxN+E9+0YgVeemtcHKPieP9imx2S70IgHjcH5wPqH+EvfAMCUmLcHbfj2Se6YgkAe46Oi6Xo23N0XJFvz9FxRX4TP+6O2xMxKo73r3p9cXzcx8H5gPpHeOcfEJABrljSaJSyTrFNPIUz2arxmcYQUH8In/YpYNvmhzC37aaErpIAjJys+9hZ9EEZm26OKqlap4CAeoeqLog6tQ741gdb7O9f/xXvz/mHxz4FPP2zh4t0lbRxsu5jZ9EHZWy2ucrrorIDAuoZqrog6q5nLc3Z1dm+qIrHPife/RAAMLAlf/O9qGchAVh9i+beFr8u1xfNvQ0DW7ox57obY37g7AEtp9IbDjfE8Q2Hh/6qMn49IZ7bLm0o8TZdnI84r8RrFtaK+13W1jd3pn2g2y9UXWXrOBd/PcE0V8ramWyb7roHfM+/KQZI7nd+VsR6IZ4f+RxSdA75bIuxpnP+H376hFtSBVTFO/8Tg/niP9jRE3Ncz0JSfA29DTHH9YbeBgx29GBu200xz3UVp9LFPhp6GxJ9y/E+bSjxNl2cj9i3vC6i32VtfSXV56OrbB3n4q8nmOZKWTuTbdNd90DafajjxP3OzwrXRU51Dim67myLsbZz7ouqeOd/8qhTAACjWifHHNfTyFwuhxmtzXi65wVMAvDS1GOx3J/L4fjUAcyZfDlyBZ1zAJS6HPva+FdxbsNEazudfnCgF+fnPo55apxrv9QxzWhtjucn+sU43fqJbScJeeA+nxxSfT66ytZxLv56gmmulLUz2TbddQ+47iVxz/IaoToD8n4H1OciC108t5MA/NP4l3Buw0Rj2zPOOCN8vYMK7e3t2h85xqW/egVlfrr1E3nf9Q0IKBd0NSFNfSjF+Gy47777cPjw4cp8vUMURScD2AngDcbY0iiKmgD8BMBnADwH4I8YYx9S+urs3IDm5pVF0uQzyfnz52vlJ+zR+HXnfbEfJ0VXxxzXZU5u29M9HZOn/CbRhqJzOdDfjzFjbra2c30dl3hRdnZuSKyFru28L/YXrYm8Np2dGwAg9vnmVRUjciZdZbtwAXro1ouytpR82fKbtkbwfcn3Mt/3aeqD6cy4nlsAuPTSLmt9WL/+/cp9vQOAbwL4HwAeL9g/BfDlgv5DAMtsffCvd/j/OpqV0uRLI7PiXPUs+sh6LFmsiWsMJU82zqSrbBcuQA/delHWlpIvW36zqhHVWB9c+kOlvt4hiqJJAJYA+EsA34yiKAKwAMAfFkIeBNAO4K9tfa090ItrC7fVNzXembBVXFNGMivOVc+ij6zHksWauMSouI7R69CKoXx/q2likU/cB1znsWIMt58v6DoOQKIPzgFI2Drdx86ay6oddY58vXS8ibPlS5V/WZfrQJra4Orz4dLoZv834ItUz/yjKPoZgLsBjALwHwB8FcB2xtj5Bf+5AP6JMXaxqZ9Zs2axQ2vvx5GWy2LurNyuhK3jAuoLYo7lfHNbFWOSAIyczq+LkXUfO2suq3bUObqsoy0/Kgno90KoDUNIc5mLd/GPomgpgMWMseVRFH0JKYt/07d/hLmjRwEA7lo4DW2bn8PmtplYv2Wfkrtr4TSt5LDZAdlDXGOVbpIA8PH5ozDitcEined/7uhR2NY/GO+Dbf2DCU6M4XL2rLMx4rXBBAdAqYttduw8bI3ncLVt3BuHB3HO2aPIbV37p9hUn0rnuZDXludUXGsVD+j3grwv1m/Zp+R0e0/kah2Vusnr9wH8QRRFiwGMBHAGgA0ARkdRNIIx9jHyn5R6g9LZs88diS+gvmvhtPii6Q0d+5XcXQunaSWHzQ7IHuIaq3STBJC44FzUef7jy8uFvSFyYozqQnPx8nOVLrexxfvaFO5w7/vktj7922yqT6krLpaXL7C38bq9IO+LDR37lZxu74nccIZ38WeMfQfAdwCAv/NnjN0URdFmANch/4mfmwE8qutDxMrWqUpb5GVOJ219BpQOqnyJOiV3FF1l6zgTb0Il98v2zmO4svkzFXv9LOCaC1s+qXvL1T9ckcnn/IXivzSKombkC/+ZAF4A8BXG2HFT+2r4YreAAABA7m6g5TtJySHasq8cSHtvLwWqeanmLcqAiqEiz/yzRCj+AVWD9gagfSApY59gy756gWpeqnmLMqBiSFP8q+K7fVwh32Zvsrlu4igyK64UetrYLCVVF6HjK4L5q4sl/xFt2VeOnym/X/rXUM1LNW9RVhko9UCnp5FZ1gNq7IgzR3h/vUPF7+9lHnf4Xvy3F5Ntrps4isyKK4WeNjZLSdVF6PiAAB9Q6oFOTyOzrAfU2JGNIxkbTnf4yrfZm2yuq7g1+2bE9tF7N2LZvKRcc2RGIo7rPKZx8zPKOBun47ne1fZ5Jc91+bVt7URebq+bqzhPLnVrpltfk67LV0BAWlDqgU6Xa4O8303nRHemTJxcQ0y1Q3W+P373Y++vdxjWz/xf+eyFuPDVVxK6KAGQdJWt40x8Wj+Vt81JxwHqNQsIqDfI9YBzgPmcyDqHS31wif/c3lcr8jn/msfYFSuKdFlSdZWt40x8Wj+Vt83JZ/7Vhh2PdWL2Vc3Y8VgnAGj1UuONfX04Z9qYzHxZ9+cTa1o/2cdtUVY7qOfA5Wy41Ady/J13aNvbMKzf+QfUN35w25NY8cMF+MFtTwKAVg9wh2n9ZB+3RRmQDSr1F76ZgnKROEC7/NzElwIHX96Ncy+6JJM4W0yWr1Uu2C6ot10qD/hdHH/FksaENOmlRD2+8wfM6yf7VLlwgW990HEufhN8zplrG1N8w2kjvT/tUzXF/+mfPYy5bTcZJYCErrJtfKlw6JWXMouzxWT5WuWAKhe6nKpyDyChU8EfL4iPGXR6gDtM6yf7VLlwgW990HEufht8zplrG138qJGnTnR+8QKqpvjPue5GkpR1lW3js0Z452+HKhe6nFJyboPPZeGUC71NHFUvt031uc6dIuU1V9kUpKkPOs7Fr0Ol3/n/xWMd3p/2qZriz9/V2aSsq2wbnznaMoyzxWT5WmWEKhe6nFJybsNgR09cYGy6SgJw5qh6uW2qz3XuFCmvucqmIE190HEufi18zplrG0P8wPVfOewxAgBVVPwDArKEz2XhlAvCbRxVL7dd6svUKWuoswMqg6r5tM/atWsBAC0tLcjlcrEUOVm32bZYX97mo/grGaeKcZ2rD0fNlUo3cRQJgKSrbBs/HKCau2ndqOuvkoA+x6KPqlNsFy6tL6v2ALBgwYLa/7TP1q1bAeQnvXXr1liKnKzbbFusL2/zUfyVjFPFuM7Vh6PmSqWbOIoEQNJVto0fDlDN3bRu1PVXSUCfY9FH1Sm2C5fWl1X7tKia4j9//vwiXcXJus22xfryNh/FX8k4VYzrXH04aq5s+2HeF/utsrNzA+bPn4/Ozg0JX2fnhtgv82JbGSLf3LxSGdPXtx1jxlxZxPsiq/7S9iOuI4e8Tqr1FTmxD67LeZs/fz4+YY/Gukr66BTbhUvry6p9GlTNY5/wR14BLuh48jy0LnjdKAEkdJVt43Vwja91UOerirPlRfQDSOgBZtTFH3kFBLigqfHOIrn2QC+uLdgdo9fha6O7Y+7+/imx3Vfw3d+f/8SJyHOIfhX3vBAv8r/74BBGnjbJa06q10zTn6kf1WuZxqObr2y3Ir/2Iv+8IS8AEjowlNOA0iK88w+oG5yV24UjLZcldJUEoNU5qJyJd0VW/WT1WtT5UtZVlxeRC3DHsLvMJWD4Yv2WfVpuVeOEIn1V4wQAwLy3T8Q81+e9fSIRx30ix225jYpX+bikcOu37HNu48vJczHNy8aLtm5ddXkRdVOOKb4AR/heBJDlj+tlLgHDF1O+/biRc9FlafJRpKvPxmXVj+/4fKSNM+kmjuIbjgCwkw2ny1wChi9Wtk41ckV64ZLxh6fmACwBcndjZeu1Cfnw1GNA7sWhuNyLMSfHGHkgEWP12TiD/6nx/5Kub0lun/x15TyT7ZdY10dc66I1B5I6IZ8uvgA3hGf+AfUN1YXslAvaTZyJr1VQ5kNZH9NaA+HS94wRPu0TEKCD7tJx3SXkqkvJdReVV/IC866ngMZ52fZJmQ9lfUxrXKWXvg9HVMUvfI/+9igA+633FOmjm7gA2lqp1tbEmaSNo+qbdm0CWr6T18c06O0xDbHN9ZgXOB2f6EPxY/N7xVy+1LsvpZ8wHwDKdvIaymsct5X1FPk1cdS9Z9vHKrueUBWPfU5rOo19cOADXPLgJdh9825c8mD+60u57iIBOOscKi6AtlaqtTVxJgkU517kqLqP7cPrYqn+LGOyGE/aNbGtURY6tU4A+r0n+jhsdrWh5h/7jD9tPABg2fRlCanibNJH51izbwaO3rsR4+64HUfv3ZhiRnm8v2MHTp89uyS8Ltbmo0Jcg2XzlsXrwiGvnylnKs4lj2lz62r78LpYqj/LmCzGk3ZNbGuUhU7ZX1SfyZZrQpr6kObcqvxnjRjhfZNXVbzzr5Zf+L7y2QsBABe++kqsD1eIa8D1C199pcKjCggoP/jer8b60NbVhZd+90Hk07Zqiv+m7/5UecXbjsc6E7xoq3QTJ0oARVxT1y/w0lvjcPrsK2IdAC4ef9RL/+iNN/B755xT5HvhhQ/xe+ecU9TuozfewOWXnxLbcjwllr+u6BPb6cYqcwcaFxetx4HGxcb1M0lK7kx5DwiQ4VsbXGXu/34kURNEnVIPRNt0Nm3ntqnrCRxoXJLw/dGGO9879O6bo3zWryp+4QsAzz7RReJFW6WbOFGquHF33I49R8cl9D1Hx3nr+0+5TOnjvOzbf8plCVuOp8SqfLo4UZc51XrY1s8kqbqJCwgQ4VsbXKVcE0SdUg+oZ9N2bhv/7sdFvpNOHf1p3/Wrimf+AHDFkkYSL9oq3cS5xGSpu/iybOs6niy4tOtp4kqNbZsfwty2m7Bt80MAkNBlmHwcae5sNrX19VHHJEI1T86JshLwrQ2+0tavSc8iTuUb/Ps+7zt8q+axTzU88w8Y3lh3w1KseuRxrLthKQAkdBkmXz1BNU/OiTKgMqj5T/vUIga2dKNh4ZSEBJDQOVScCtQ4ShuZl8cqyoA85lx3Y0LKui5eh3p45w+o56laq4A8TDWBel5tPMfEUeO8P+0Tir8nBjt60LBwSkICSOgcKk4FahyljczLYxVlQB788YX4GMP0SMP6uKON8KK6GFNbXx/Fr4Bqnqq1CsjDVBOo59XGc4w9/cyJvuOsquKvuvS5WjGqdbJSyrqJM/XrMxYbbxpzNYJygTcH9cJrCrq6utDY2Gj033LLLcjlctpYFZ82NmvOxNt8FL8M10vNdRe6A+oL5asFtppAPa82HgDe3vSO9zP/qir+qkufqxX8HbMsZd3Emfr1GYuNN425GkG5wJsj6wuvu7vtj9/46+liVXza2Kw5E2/zUfwiXC81113oDqgvlK8W2GoC9bzaeADoHTx62GeMQJUVf9VFzQHDF9QLvE2cDyjvePnrhXf+er8KrpeaUy9nD/CA70UAhU8JjQbwMwCvAngFwBwAZwLYAmB/QY6x9TNz5kz2+uvfZ4yxVJLqo+ouPopdqjZZjC3NOtl0Sn5M0saZdBMXUHpQcmHLI2U/+NQBFVeu2uDany5+3LiTDzPP+p32j7w2APglY+yzAKYX/gFYDaCDMTYVQEfBtuJA11+lllQfVXfxUexStclibGnWyaZT8mOSNs6km7iA0oOSC1seKfvBpw6ouHLVBtf+dPFjxpxc/l/4RlHUAOCLAL4KAIyxDwF8GEXR1QC+VAh7EMCvAHzb1l/H6HVoBdDUeCfWHujFtUTZV2jXMXodvja6u4jjEgCeL7S7v39Kkd4nteeQ2/M4sR2H2KcYz6Hq39YnpV95nHKM7nXlPuX1UM3ftGY6XexDzgtFqvbHt5omoqnxzng+Ot3EBZQelFyItphzsRaIPhUnn3WZE/euyJn2rdgPoD+H1PPlUgtknzyWOL7vRPn/yCuKossA3AdgD/Lv+p8DsBLAG4yx0YWYCEAft3WYNWsWO7T2fhxpuQwAcFZuF460XEaSAMicyMu6ynbhSulL69f5fOZPXU+b7iIBaPWA+oKcW1XeTVypa4JLPci6Fqj4NxdcXpE/8hoBYAaAOxhjz0RRtAHSIx7GGIuiiPSvy6rGCUU6VbpwuhjZ3rHzMGbPOhvrt+zDvP4TmD3rbOzYmf/FOudUSONr3tOPc85Wf0eT2PaNw4NFcT6vu37LPqw6f0I8LwCY93Y+Tpyvaf1k20X3ya/q9QPqB5S9RdlLLpwuRmfLZ0R1joDs68QNP3oa8848JcH946ix5f9K5yiKzgKwnTHWWLDnIV/8zwfwJcZYbxRFEwH8ijF2gamvavx6h8bVT6DrniVoXP0EACT0eoJujlzvumdJJYcXEFB1MJ2XcqP3wW/geO9+r6909n7nzxg7EkXRwSiKLmCM7UX+kdSews/NAO4pyEd9X6OSeHhqDsi9iJWt1+LKnvuA3It4eOoxUttJA8/hUMNMso9zh/p+i0ljPmXsY9LAc9j+yYVxnM/rJVCY1/bJXwdydwtzXJJfA4TiHxAQI3d3oiZQ60OaM2qqD9d/1Pue71RSfbFb4bn//QBOAdAJ4Bbkvyb6pwAmA+gGcD1j7B1TP9X4zh/tDQU5MKTXM+R5crt9oHJjCgioNvAzUSX1YdZ972Hn4RPlfecPAIyxXQBUv2xoVXC1hfmr1ToFXU8BjfPoPirnGmvzyZDn6TrvKsOmXZuw/LLlSglAq4vQ8TafL5498iyuOOuKTPtMC+raqNZTJQEk9JoCPxOu9SHNGTWc+TfX/1n4SueAABnUS+JlXYTpsnPqheq1Dp+L26mXqgekQ81/pfPHb70V6+JF4fKl4bJt4wOqG6b8qnQTJ1+uPe6O27Fm3wwAwJp9M3D03o3xRfRrjsxAV9vntXrj5mcAIMFzm+Lj8OEOv/cGzv70Ocq4LF9Hx6ls0zz5Gqtscf25lHOjyqNJim1knSPUAxqq4p3/xSNPYy/97gMASFwULl8arrtEPFwuXpsw5VelmzjVZfOmi7dddRdfqbhyvI6LTfG55IgiAXON0HH1ipp/5z9i/NAlxWNXrFDqKtvGB1Q3TPlV6SbO1eeju/hKxZXjdVxsis8lRzZpe30dF1CMqnjnz5/573isE7Ovak74VFzA8ICYe5XuIgE46yrbFm9qZ+JtvjRtXcZC4Uy2TXeRgH4PiBjONaLm3/lzPPtEV1ESVVzA8ICYe5XuIgE46yrbFm9qZ+JtvjRtXcZC4Uy2TXeRgH4PiAg1wg9VVfzlm+p1XMDwgJh7le4qfXSVDQDjJ+3BlOmLsW3zQxg/qQ9Tpi9G929+Efu3be7F+El9Re10vM3X/+Z+77a2sSTHviCemwjbmrisaxb5Mo0lgIaqeuwTEFArWHfDUqx65HGsu2EpACT0WoNqHqseebzCowqgoG4e+wQEVAoDW7rj6/JUuigBYM51N2JgSzfmXHcjGg7n/8Jz0dzb8m3OHog52Tb5dNzA2QPArt+hYfwEYwzVlvWBLd1YNPe2WOdzA1A0d9MaBdQW0l7mEhBQFxjs6DHqohzs6MHctpti2dCbL6QNvQ1o6G2IOZVt8pliJp+YZo2h2rI+2NGT0LlUzd22XgG1g6p555/L5QDkL2qWdRXHofOZ4rLg0voo/iz6cBmfLpayJmlsas6pPgDKeNkn6qNaJ8f2jNahXx6+NPUYJgGxn0soOJVPZZt8WcZQx2Cak7getjXiPtM623IFuOVb5Ey6j+3CmXibz7fPtKia4r9161YA+cnKuorj0PlMcVlwaX0UfxZ9uIxPF0tZkzQ2NedUHwBlvOwTdf7YYuvWrWhpb4nHuf3gLizCNbFffLwhcyqfyjb5soyhjsE0p63tD8TrYVuj2GdYZ1uuALd8i5xJ97FdOBNv8/n2mRZVU/znz59v1HV+SludbeI+YY/ipOhqfMIeRWfnhpjjEH3zvtivnJPJZ/fvBXBByj70/pOiqxM+bstzPym6GgBt7dLY1JxTfNR4nzEPN7jmnZrTUuXbpPvYKk6uB6pawDmxjc85tbUbN+7k8l/mkiWq8dM+HU+eh9YFr6PjyfMAIKGr7FqDbj4qGRAQMARbbdBxpcDyZYewd+/x8n+lc5bgl3JXC/il0qaLwUt5KXhf33aMGXNlyfoH9PNRzb1SEPcF11USQEJX2TrOBNd4E7b1vYe5Yz5dU21tsK2PLSeq/KkkUD01glIbdFzW6OtbVftf6Sxe4B4QwGG7rNvl8m0dZ4Jr/HCDbX1cLkTX5VbOf8AQ6uJz/uFS7gAVXC5+l3WVreOoY0iDenznD9jXx5YT1wvZA7JB1bzzr7Zn/i5Yv2Uf7lo4Deu37HNuu73zGK5s/oy3XxenGg/n7lo4zXmcAQEB7uBn0KU+bO88hkf+dE4cb6oB37r293s/fveo1y99q+adfy1jQ8d+3LVwGjZ07Pdq/8wB4xXHVr8qTjUezoXiHxBQHvAz6FMfxHhdDTj59NHevwSpjuI/eCQvc3fnZct3hnQOmVPFUNtS2lBQaPfw1GNA7sW8dMShvt9i0phPOfm3T/46ruy5zxynGg/nci9q52JEmhgqb7JddBOnkoCZk3WVreMovlLAdSym+VHXyUUCZk7WfWwbb/O5xCjw8NRj2D7560N1goBDfb9NnF/5bIvn//qPet9zHlQB1fHY5+yT2c7DJ4D2/J/Jo31gSOeQOVUMtS2lDQW+7dKiFK9L6TNNDJU32S66iVNJwMzJusrWcRRfKeA6FtP8qOvkIgEzJ+s+to23+VxiStHW0t+s+97DzsMnavejnkc/VViY+asBAJt2bcJyrve/iOWjL83r0xdj+ehL81whnvtjqWjLuaJYhR+AOkbl27UJEMeki5M4F78yVjNeW78mbnlhfU3z5zG2fkx5SeRTES/mWd4Lsi9uJ+wdcR8ZOUqMSVfZOo7iKwVcx2KaX9r1Naz7pumLi3KozLNlfzjXBWp9kM6c8xmD/myJnLUfxVjefP8R/88hM8Yq/jOycSQTcfHfXmzUTRzFZ5KuPh/OxZ9lrO8cXWMp7X10ih1Qe1Dl0JRnF72e6oKKA7CTedbdqnjnP/608Ql7zb4Zsb5s+jIcvXcjxt1xe6wvm7esKJZLXTxVNm5+RtmvOKYsOKq/q+3zOHrvRqw5oo4V/apYKif70qyVmCNbfuR4Ob9ijNhG9gfULlQ5VOVZtQdsZ0uuGbq9LLZRSZczI55FKqfy8zNmarMEL/kvvO+/Gln+zJw5k4nYc8FntbbOp4uR/WlkVlyl/SYuS2njVLbss/EBwweqPWA79zqdsld9z0xWNcEWt+eCz9b+O38ZY1es0No6ny5G9lPkjsc60VSQ77e140IAR9raASDWBwu+i8cfjeM4ByDWBwt9vfTWOK1f5tL45ddS+XdoOJP0WUtKfmw+Gx8wfKDaA7Zzr9Mpe1WWRwrnnevyeRLrxKCB051pubbYzj4AjF5/R/hityzxg9uexIofLsAPbnsSALS6ylaBEpMVbK+l8nPOJAMChjvEs0CpDTpOB586seYflqHn6F6vT/uEm7wUuGJJI7ZtfghXLGmML4ceP2lPET9+0p6ETyVNMTbOp41tPCo/5+S5czsgIGDoLIg1QKXzc6XidGeYx9pqicwd//Bt78/5h+KvwOyrmvH0zx7G7KuaMfuq/I1FPbt/WcT37P5lwqeSphgb59PGNh6Vn3Py3LkdEBAwdBbEGqDS+blScbozzGNttUTmTjn5hPeXNlXlM/9qwJzrblTaIi9zOunqc/H7jIf6egEBAcVIc06z9v3FYx21/Tl/+dM+Puj/ly6trdJdpI3z0Uvho7SxxaeRJl1lBwSUC5Q9mmVNUHGlONdI8WmfunnsM9jRo7VVuou0cT56KXyUNrb4NNKkq+yAgHKBskezrAkqrlTn2hd189hnVOtkra3SXSUAvDT1GCYV5JzJlyOXy2FGazOe7nkB+3M5HJ86EPPHp+a/80THiz5Z1/nmTL488VqmOPm1+Lj3C2MW5yLOL80a6XSVHRBQLlD2aJr9LteEnOYs8jMHFJ95nU+nz5l8Oc746zO8P+pZN8W/YeEUra3SXSUAbD+4C4twTV7eeg22tj+AlvYWbG9/ADiYj+F8DB0v+Ip0jW/RrdckXssUJ78WHzcOIh6zOBdxfmnWSKer7ICAcoGyR9Psd7km8PMnn8X4HAJFZ17r0+iLbr0Gp59+emW+0jmKorsAfA0AA7AbwC0AJgL4CYDPAHgOwB8xxj5M8zrVgvnz5xulHKdrn8UYfOJETjX2LMY33NDZuQHNzSvR2bkBABI6h4qrBExjEyWAhB5gh6kmUOuDzafC+++/X/47fKMoOgfArwF8jjH2QRRFPwXwCwCLAfxPxthPoij6IYDfMMb+2tRXtf2RV0AAFR1PnofWBa+j48nzACChc6i4SsA0NlECSOgB1YtK3uE7AsBpURR9BOBTAHoBLADwhwX/gwDaARiLPwCsPdCLbzVNxNoD+X/IuC5yIi9CxZUCtrtQTX6dr1xtbD4dsrj/VZcfXW7lfQAM7Y9qQ1PjnQkp6yauEjCNzTaHSkHcE7o9otJlpKkTLueglOdTjjlp3ITKfL1DFEUrAfwlgA8A/AuAlQC2M8bOL/jPBfBPjLGLTf3MmjWLHVp7P460XIazcrsAINZFTuRFqLiA6oEuP7rcyvsAQMwHDD+Ie0K3R1S6jHqsE8du+0N8tHdPeS9ziaJoDICrATQB6AewGcAi3/5WNU5ISB0n2jt2HsbsWfl/+Oa9fQIAMHvW2TG/Y+fhuI1s++DgBx/i3NNOIfnfODyIc84epW3L/bo+3zg8iE/GnKr0HfzgQ5zUdzzRv+11KOO3zUmGak1V675+yz6sOn9CIi88b7rc6viA4Qd5T5hqhajL+239ln2Y13+CtG9luJwdnzPN2/FzPXvW2fjHn+8tOuNyjTna905Fnvm3AVjEGLu1YP8xgDkA2gCcxRj7OIqiOQDaGWP/p6kv32f+jaufQNc9S2IdALruWRLznBP5gOygWlPduqtkQEApodp3AG3fVhrU8XSvWVqRZ/49AK6MouhTyD/2aQWwE0AOwHXIf+LnZgCPpngNI1a2TjXqIifaV/bcF1+CLF6GLF+Mrroo3cSL8LmYPUu/KY46L93aDOlLsLJ1aryOHLp11+UlIKAUMO071R58eGou3sfyXqfWBpOPcmbjmNyLWDv2oLUGXG/szYy0z/z/HMANAD4G8ALyH/s8B/nCf2aB+wpj7Lipn7J/2sd2mXQc53EZdC3A5zJ13aXbQPkvJg8IKAXkPQ2o64TIK/spX32I/vzdynzahzH2XQDflehOALPT9FtyUC7x1tk2vlZAnRflMvNaX4uAAMDtwnqVTfVliv/k3bJqLnP5k/v/BACw/LLl2LRrU+yTbRk2vwnPHnkWV5x1hXeMzqfi08am7dPGP7DoAWzatcm6JpQ1c4UqhyLHdZMMqA+Y8gy414cs4VMLdD7XM6rzrWxd2fvRsY+8Pu5ZNcX/+B35J0O7b96NSx68JPbJtgybP4CGSq6j6rVFjusmGVAfMOUZcK8P9Y7X2l/DBwc+KO9HPbPEx2+9hWXTV6Nx8zMAgDX78jfUq2xZAsCy6cuKfAC0Orc3zzsp/pdU9nOc8sD/xIe3/F/KcdfTO38gv47PHnkWbU99olwL05q56LI8eu9GLJu3rCivPO/cf/TejVhzZEbs4/LovRsx7o7bcfTejQBA0tPass+Fy4ovVR+lWieKzvOsyrdqr6j2jkoC7vtVtsXzI8dV7J3/uytr+/v8Lzp1JGNMfXM9Rdo4WafYNr6eQV0LylqbcuQjbRxVT2vLPhcuK75UfZRqnVx0E+cjfXSVbePLDaT4Pv+qeOc/Yvw4AMDYFSu85I7HOtEkcDse68Tsq5q1OgAcaWvHhYW2s69qjm0IHO93x2OdABBzOqmKsXGl9ps42SeupSpO5psU66/TxTX3zfNYoS8TR9XT2rLPhcuKL1UfpVonF93EUaV4rim1QdRdzoJLTaCeY0rc6NPHeX+9Q8Xf9bMMbvLa+KcdWtumy1Ln08XbYmxcqf2U8VPWhbJeJl1lBwSUGlnWBh9J9fnGnTt2GmO1fpPXts0PKXWVLXNXLGlM+ETbpstS59PF22JsXKn9lPFT1oWyXiad27ZcyrZNN3Eu0tVn48qhZ9WXj5+ybhRJ9Zl0lc0xftKehJ2mNvhIqs83bvCDvtp+5j9z5kz2/16/JP6XV9RVto4LqH5QcqnbCyrdxLlIV5+NK4eeVV8+fsq6USTVZ9JVto2vFyDFM/+q+Kjn5dMuZT/4y2/jotFfAAAc2rMbF61cioEt3Uq7YeEUvLzhcUz63CVoWDglwQ9s6dZKHgNAGyf6OGRbx9UabPNS6SZOtdYy93L/r+M8c96U70N7dmPS5y4past5VaxO8vYqPwBtW5XPxpVSP/rMPhwfdTyzfn38lHUT88PPq+wXz7LJp9sXuj3E99fLGx4vqh2mfUupCaZawVGu+lDJ7/PPBCcGP8TctptwaPVTAIAG5P80ml9QXGQvnIKG3gYM9vagYeGUBD/Y0aOVPIb3pYoTfRyyreNqDbZ5qXQTp1prmZt7j5Bn7jfkuwH5PBe1LfCqWJ3k7VV+ANq2Kp+NK6U+EiMx8r2RmfXr46esmxgTn1fZL55lg0+3L3R7iO+vht7i2mHat5SaYKoVHLVQH6rimf/Jo05BLpfDqNbJGNU6Ob7E+KWpx2JO9Is+WRc50Rbb8r5kPwfl4vF6uIzcNi/T5dYyJ6+pihNzIudUlX+umzixrcyb4kyxNo7aNq2u8+0a+0ZJXs91ztR1AKCNlfNuyqttn8j7Q96bptqg28fUc0E5W9WG6ij+Z5yCrVu35v+lXjglvsR4+8FdMSf6RZ+si5xoi215X7Kfg3LxeLX/q06BbV6my61lTl5TFSfmRM6pKv9cN3FiW5k3xZlibRy1bVpd59v53qsleT3XOVPXAYA2Vs67Ka+2fSLvD3lvmmqDbh9Tz4XpHFUrquKxD+B+ubjt4vEsL0kOsIOSA2oOqXshrXT1+bRNq5eqX5/1tvnT5Mo1hqpTbKqv3lAVv/ANF7gHlAKdnRvQ3LySJDlEW/a5cCpQ4yhI05etLWWOunWirDeHbAe4o+Z/4RsQUAoc6PorNDevJEkO0ZZ9LpwK1DgK0vRla0uZo26dKOvNIdsB5UVVPPMHgLUHehOSyukk16nxosyKc/GXKpbqT+tLI304WVf5mhrvBIBYdoxel7BFvqnxzgQv666c6ocaV+q+bG0pcxRtvtaU9RZzJNu6fNryn6ZO+O7zUtcDF84bvn8gkOXPzJkz2YQnX2CMsViKuonTSa5T40WZFefiL1Us1Z/Wl0b6cLJu8+k4Ex9Ag8u6uuSMmv80dcJ3n5e6HlA51PoXuwHAqsYJCUnldNK3nevr+7x2uWNd18TX5yt9OFm3+Ti3fss+3LVwGgDEusj7SABGzqSX22fTKXMTJQDMe/tEYj05OK/LkS1npdi/afZkuesBhfuPSAHffzWy/En7xW4BAVRM+fbjRt1H2jiTXm6fTafMzbZ2qvUNKA2Q4p1/1TzzR+7upLRxOlvHBZQfttzYdBOnkgTfw1NzRn1l61Qgd3feFnjZFuMSbQo+sZ3YVo4R48R+StVO10Y3d9u6qHyq9SXnyUVSdRM3nOH7r0aWPzNnzmTsu2fk/ynjUtRVnM7WcQHlhy03Nt3EqSTV56P7xqWJKWU7Wxvq3G3r75onF0nVTVyNA/XwzH/T9MXArk1YPn/10KXNEgcgqQsxsb1rEzB9MZYDxkvBKbzNR/GnjS91G9c1ceHFXMUxPKcU3bAXlLL/RSwHAIED8ntr+ehLi3RwvxAr8kW6yWeKSxNTyna2NtS5F2xx/YHitU7kxJZLhYzPtXi+DXq8n/geVNSFrPY/1Z91XyPOHOF9mUvV/JGXfIG77tJm6mXOtkvBKbzNR/GnjS91G9c1ceGp+bHpPpe5U3SOcAl8NpDX0WTbcmi7wN2nJlBsX57qz7qvmr/AHRi6hB1IXs7NL0mWL3AGkNDly9xFXbxoWeyTQ+xHhNiPbeyuc3VFKduo1kRsL6+hal3lXHCfHGvKoyqH8l4QL/fWyXF33B63Ey8BF2OAoYvDVZztsnGfS+KzbJN1nItuu3ydc+K6yzmx5VAlXeuDrg7I+122Vede3tMybH4d0taWmr/AnX/ah3L5cikuZK72S5pLDds8KWtGXXPX3GYhs+Jc/L4XmFcqzkX38ZdS2jiTTrF1nIu/VEA9PPMHKnNht44z8fUG2zxdL/im+Ep1YbfvHvLZaz57Lss2pRxD2rmXMpdZ1geKreNc/FUJ3381svwJn/MPyBrP/Px1rc11E5dG+nAUPct2Ji6NNOkq28YHmIG6+Jx/QECGePaJLq3NdROXRvpwFD3LdiYujTTpKtvGB5QOVfXYh2Pb5ocwt+2mhL5t80MAkNBVto2XcfDl3Tj3Ivtv501xKp8unhqbhvPhfeNcocufSqbBFUsatTbXTVxa6cNR9CzblWL+Jl1l23gd5BoB0GtDFrCdj7Tnn9pPw2kja/+jnuL3+a+7YSlWPfJ4Ql93w1IASOgq28YHVBa6/KlkQIAOco0A6LWhnvD9Lb/GwXf6vT7qWTXFv+Puf4jtl/t/jYtGfwEAcGjPbly0cile3pBP9KTPXYJDe4Y+OyzbA2cPoOFwQxGvxZGPgbMI/wEyxSl8A2+9ifGnTcbxUcet/MBbb6Jh/AScOnhqzHMOQMyLHOff+qAnwZn4zOabArr88Rxze9LnLkHDwikY2NINALGukgASekBtQpdfeQ8AQ3VhYEt3vJ/43nGuAQqIZ1E5VsWZpfipZ12Ol8fF+SV//u/f63qnd5TPHKvmsc9gR0+sz73nJhxa/RQAoAENedmbl4O9PTGnsi9auRSHVj9VxBtB/aSsKU7yNaABeA8Y+d5IK9+Ahrg950WO8zIHAJMxTTkuHW+dh0+cA0z5a+htiO3B3h40LJwS7wuuqySAhB5Qm9DlV94DwFBdGOwY2j9873jVAAXksytCdWYpfupZV8WLfs6fcWrDpx2mlEDVFP+Xph7DnMmX4+meFzCpYAPAnMmXI5fLYUZrM57ueSGO0clJAEa1To45AEVxIne8sx/PjzgQ86JPtGd83IRTm0cnxiz2IfsAVJyX5yG3UfnlOanWQl5DUTdxtryJe4Dn/fjUgcQe4ByPnwQkuFwu/+ViLS0tsS7bJh/F9o3RcaXm08T6xOjW2qaLed0v5Juf/f25ZB3Q1QVbvQDM+xUAnvrN05g3fU4RL9ums8b9cn0R26niVbzO//amd7zfolVN8d9+cBcW3XoNtrc/gEW4BtsP7gIALLr1GmxtfwAt7S15H4/RSVyDhoVTYg5AUZzIAVOwvf0fYz7pE+z2Ibs4TveOs7K8PA+5jcovz0m5FkVraF5jat4Se6CQdyC5B2KOx+MabN26Nea43tLSEuuybfJRbN8YHVdqPk2sT4xurSl6fPYPIs43P/ucE/eHqi7Y6gVgqwnAzv/9AJYu/HIRX2ybzlreL9cXsZ0q3oykv3fw6GFLAy2sxT+Kor8BsBTAW4yxiwvcmQAeAdAIoAvA9YyxviiKIgAbACwG8FsAX2WMPU8ZyLwv9seys3MD5s+fj0/Yo1qfKGU/gNjX3Lwy5nkc1+VYDtnmsfIl1mIfpkuxfS7MzqIP2/jE9eCwzV20VWuo8uvyZMojlydFVxfF8n0hcnweKl2cm9hW9rnYvjE6rtR8mlifGNHW5YTnV6fr8q7aJ5S6IO5t2SfvZ12tkON0HOfl9iq/ClTfuHEnl+7TPlEUfRHAewB+LBT/7wF4hzF2TxRFqwGMYYx9O4qixQDuQL74fx7ABsZY8ZfGSJg1axZb870+tC54HR1PngcAsS5LlU/kOFQxqjhTWxk6Po2P4s+iD+qcfONMtk2n5F0lAZB0ETo+oPSQ196WOxcJ2OsCdY+q/K6ciaf4qb7lyw5h797jpfliN8bYv0ZR1CjRVwP4UkF/EMCvAHy7wP+48Jdn26MoGh1F0UTGmPW5lHjp89dGd8fc2gO9uFbh6xi9Dq0CJ8bd3z8l9okx9/dPwfOFuPv7p2i5tQd60Se0AWDkeR8uPmpbDj4PuS3FL45d9pnWRx6jvEZinNyPKjecbxJ0nqMmyQ/JB4Wk6iJ0fEDpIa+9LXeqC+F1+0PcO/J+U+1D1b7skM6giQPUZ8PEiz65Lxef2G9f33Wl/WI35B/vvCTY/YIecRvA4wC+IPg6AMyy9S9+vQP1gmffy5vTXrxs0l18pWzrM0aXuVLWzyRtnM4OGN5IWxvSXuJezvpg8lXNBe6MMRZFUWZ/LEC94LmSFy+bdBdfKdv6jNHn0nifS7Mpr6myA4Y30taGLC5xT6tn5eN2mgvcSX/kVXjs8zgbeua/F8CXGGO9URRNBPArxtgFURT9qKA/LMdZ+j8KoDvFPAICAgKGI6Ywxsb5NPR95/9zADcDuKcgHxX426Mo+gnyv/AdsBV+APAdfEBAQECAHyif9nkY+V/ujgXwJoDvAvhfAH4KYDLy79ivZ4y9U/io50YAi5D/qOctjLGdim4DAgICAiqIqvhun4CAgICA8iJ8n39AQEDAMEQo/gEBAQHDEKH4BwQEBAxDhOIfEBAQMAxR1uIfRdGiKIr2RlH0WuE7gWT/qVEUPVLwP6P4Wom6AWEtvhlF0Z4oil6MoqgjiqK6/bJ621oIcddGUcSiKJpVzvGVE5S1iKLo+sLeeDmKov9R7jGWC4QzMjmKolwURS8UzsniSoyz1Iii6G+iKHoriqKXNP4oiqK/KqzTi1EUzSB17Punwa4/AE4G8DqAZgCnAPgNgM9JMcsB/LCgfxnAI+UaXzl/iGvRAuBTBX3ZcF6LQtwoAP8KYDsIXxlSiz/EfTEVwAvIf5kiAIyv9LgruBb3AVhW0D8HoKvS4y7RWnwRwAwIX7Ej+RcD+Cfkv2rnSgDPUPot5zv/2QBeY4x1MsY+BPAT5L8ITsTVyH9RHAD8DEBr4W8H6g3WtWCM5Rhjvy2Y2wFMKvMYywXKvgCAvwCwBsDvyjm4MoOyFv8ewA8YY30AwBh7q8xjLBcoa8EAnFHQGwB4f7d9NYMx9q8A3jGExF+oyRjbDmB04ZsXjChn8T8HwEHBPlTglDGMsY8BDAD4TFlGV15Q1kLErcj/y16PsK5F4b+x5zLGnijnwCoAyr6YBmBaFEX/O4qi7VEULSrb6MoLylq0A/hKFEWHAPwC+a+TH45wrScAqugmrwA1oij6CoBZANS3cdQ5oig6CcB/BfDVCg+lWjAC+Uc/X0L+f4P/GkXRJYyx/koOqkK4EcDfMsbWRVE0B8DfRVF0MWPsk0oPrBZQznf+bwA4V7AnFThlTBRFI5D/r9yxsoyuvKCsBaIo+jcA/gzAHzDGjpdpbOWGbS1GAbgYwK+iKOpC/pnmz+v0l76UfXEIwM8ZYx8xxg4A2If8Pwb1Bspa3Ir818yAMfY0gJHIfw3NcAOpnsgoZ/F/FsDUKIqaoig6Bflf6P5ciuFfGAcA1wF4khV+o1FnsK5FFEWXA/gR8oW/Xp/rApa1YIwNMMbGMsYaGWONyP/+4w9YfX5nFOWM/C8ULlKKomgs8o+BOss4xnKBshY9yN97giiKLkS++B8t6yirAz8H8MeFT/1cCeIXapbtsQ9j7OMoim4H8M/I/yb/bxhjL0dR9F+Qv5Dg5wD+O/L/dXsN+V9wfLlc4ysniGuxFsCnAWwu/M67hzH2BxUbdIlAXIthAeJa/DOA/yOKoj0ATgD4FmOs7v53TFyLVQD+WxRFdyH/y9+v1uObRfHLNQu/3/gugN8DAMbYD5H/fcdiAK+h8IWapH7rcK0CAgICAiwIf+EbEBAQMAwRin9AQEDAMEQo/gEBAQHDEKH4BwQEBAxDhOIfEBAQMAwRin9AQEDAMEQo/gEBAQHDEP8/66LlFlOQUtcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Input\")\n", + "plt.xlim(0, 1)\n", + "plt.legend()\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure()\n", + "rasterplot(sim.trange(), sim.data[A_spikes])\n", + "plt.xlim(0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the decoded response of the neural spiking.\n", + "The bottom plot shows the spike raster coming out of every 2nd neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:16.425930Z", + "iopub.status.busy": "2020-11-25T16:47:16.424148Z", + "iopub.status.idle": "2020-11-25T16:47:17.827938Z", + "shell.execute_reply": "2020-11-25T16:47:17.828591Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# For interest's sake, you can also sort by encoder\n", + "indices = sorted_neurons(A, sim, iterations=250)\n", + "plt.figure()\n", + "rasterplot(sim.trange(), sim.data[A_spikes][:, indices])\n", + "plt.xlim(0, 1)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/multiplication.ipynb b/.doctrees/nbsphinx/examples/basic/multiplication.ipynb new file mode 100644 index 0000000000..129802fd97 --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/multiplication.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiplication\n", + "\n", + "This example will show you how to multiply two values.\n", + "The model architecture can be thought of as\n", + "a combination of the combining demo and the squaring demo.\n", + "Essentially, we project both inputs independently into a 2D space,\n", + "and then decode a nonlinear transformation of that space\n", + "(the product of the first and second vector elements)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.243063Z", + "iopub.status.busy": "2020-11-25T16:47:19.242216Z", + "iopub.status.idle": "2020-11-25T16:47:19.737699Z", + "shell.execute_reply": "2020-11-25T16:47:19.738138Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the model\n", + "\n", + "The model has four ensembles:\n", + "two input ensembles ('A' and 'B'),\n", + "a 2D combined ensemble ('Combined'),\n", + "and an output ensemble ('D')." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.746346Z", + "iopub.status.busy": "2020-11-25T16:47:19.745834Z", + "iopub.status.idle": "2020-11-25T16:47:19.749428Z", + "shell.execute_reply": "2020-11-25T16:47:19.748982Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Multiplication\")\n", + "with model:\n", + " # Create 4 ensembles of leaky integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " B = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " combined = nengo.Ensemble(\n", + " 220, dimensions=2, radius=15\n", + " ) # This radius is ~sqrt(10^2+10^2)\n", + " prod = nengo.Ensemble(100, dimensions=1, radius=20)\n", + "\n", + "# This next two lines make all of the encoders in the Combined population\n", + "# point at the corners of the cube.\n", + "# This improves the quality of the computation.\n", + "\n", + "# Comment out the line below for 'normal' encoders\n", + "combined.encoders = Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "We will use two varying scalar values for the two input signals\n", + "that drive activity in ensembles A and B." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.756349Z", + "iopub.status.busy": "2020-11-25T16:47:19.754826Z", + "iopub.status.idle": "2020-11-25T16:47:19.756952Z", + "shell.execute_reply": "2020-11-25T16:47:19.757357Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))\n", + " inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))\n", + "\n", + " correct_ans = Piecewise({0: 0, 1.5: 0, 2.5: 20, 3: 0, 4: 0, 4.5: -20})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the elements of the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.766540Z", + "iopub.status.busy": "2020-11-25T16:47:19.765225Z", + "iopub.status.idle": "2020-11-25T16:47:19.767387Z", + "shell.execute_reply": "2020-11-25T16:47:19.767825Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input nodes to the appropriate ensembles\n", + " nengo.Connection(inputA, A)\n", + " nengo.Connection(inputB, B)\n", + "\n", + " # Connect input ensembles A and B to the 2D combined ensemble\n", + " nengo.Connection(A, combined[0])\n", + " nengo.Connection(B, combined[1])\n", + "\n", + " # Define a function that computes the multiplication of two inputs\n", + " def product(x):\n", + " return x[0] * x[1]\n", + "\n", + " # Connect the combined ensemble to the output ensemble D\n", + " nengo.Connection(combined, prod, function=product)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe the output\n", + "\n", + "Collect output data from each ensemble and input." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.775764Z", + "iopub.status.busy": "2020-11-25T16:47:19.774242Z", + "iopub.status.idle": "2020-11-25T16:47:19.776350Z", + "shell.execute_reply": "2020-11-25T16:47:19.776766Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " inputA_probe = nengo.Probe(inputA)\n", + " inputB_probe = nengo.Probe(inputB)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " combined_probe = nengo.Probe(combined, synapse=0.01)\n", + " prod_probe = nengo.Probe(prod, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.782042Z", + "iopub.status.busy": "2020-11-25T16:47:19.781251Z", + "iopub.status.idle": "2020-11-25T16:47:21.337743Z", + "shell.execute_reply": "2020-11-25T16:47:21.338172Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results\n", + "\n", + "To check the performance of the model,\n", + "we can plot the input signals and decoded ensemble values." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:21.346070Z", + "iopub.status.busy": "2020-11-25T16:47:21.344855Z", + "iopub.status.idle": "2020-11-25T16:47:21.604812Z", + "shell.execute_reply": "2020-11-25T16:47:21.605209Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-25.0, 25.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signals and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded B\")\n", + "plt.plot(sim.trange(), sim.data[prod_probe], label=\"Decoded product\")\n", + "plt.plot(\n", + " sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c=\"k\", label=\"Actual product\"\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-25, 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input signals we chose make it obvious when things are working,\n", + "as the inputs are zero often (so the product should be).\n", + "When choosing encoders randomly around the circle (the default in Nengo),\n", + "you may see more unwanted interactions between the inputs.\n", + "To see this, comment the above code that sets the encoders\n", + "to the corners of the cube (in Step 1 where it says\n", + "`# Comment out the line below for 'normal' encoders`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus step: Make a subnetwork\n", + "\n", + "If you find that you need to compute the product\n", + "in several parts of your network,\n", + "you can put all of the components necessary\n", + "to compute the product\n", + "together in a subnetwork.\n", + "By making a function to construct this subnetwork,\n", + "it becomes easy to make many such networks\n", + "in a single model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:21.623356Z", + "iopub.status.busy": "2020-11-25T16:47:21.612954Z", + "iopub.status.idle": "2020-11-25T16:47:23.414225Z", + "shell.execute_reply": "2020-11-25T16:47:23.413769Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-25.0, 25.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def Product(neuron_per_dimension, input_magnitude):\n", + " # Create the model object\n", + " model = nengo.Network(label=\"Product\")\n", + " with model:\n", + " # Create passthrough nodes to redirect both inputs\n", + " model.A = nengo.Node(output=None, size_in=1)\n", + " model.B = nengo.Node(output=None, size_in=1)\n", + "\n", + " model.combined = nengo.Ensemble(\n", + " neuron_per_dimension * 2,\n", + " dimensions=2,\n", + " radius=np.sqrt(input_magnitude ** 2 + input_magnitude ** 2),\n", + " encoders=Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]]),\n", + " )\n", + "\n", + " model.prod = nengo.Ensemble(\n", + " neuron_per_dimension, dimensions=1, radius=input_magnitude * 2\n", + " )\n", + "\n", + " # Connect everything up\n", + " nengo.Connection(model.A, model.combined[0], synapse=None)\n", + " nengo.Connection(model.B, model.combined[1], synapse=None)\n", + "\n", + " def product(x):\n", + " return x[0] * x[1]\n", + "\n", + " nengo.Connection(model.combined, model.prod, function=product)\n", + " return model\n", + "\n", + "\n", + "# The previous model can then be replicated with the following\n", + "model = nengo.Network(label=\"Multiplication\")\n", + "with model:\n", + " inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))\n", + " inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))\n", + " A = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " B = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " prod = Product(100, input_magnitude=10)\n", + " nengo.Connection(inputA, A)\n", + " nengo.Connection(inputB, B)\n", + " nengo.Connection(A, prod.A)\n", + " nengo.Connection(B, prod.B)\n", + "\n", + " inputA_probe = nengo.Probe(inputA)\n", + " inputB_probe = nengo.Probe(inputB)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " combined_probe = nengo.Probe(prod.combined, synapse=0.01)\n", + " prod_probe = nengo.Probe(prod.prod, synapse=0.01)\n", + "\n", + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)\n", + "\n", + "# Plot the input signals and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded B\")\n", + "plt.plot(sim.trange(), sim.data[prod_probe], label=\"Decoded product\")\n", + "plt.plot(\n", + " sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c=\"k\", label=\"Actual product\"\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-25, 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can use Nengo's built in\n", + "[`nengo.networks.Product` network](\n", + "https://www.nengo.ai/nengo/networks.html#nengo.networks.Product).\n", + "This network works with input of any dimensionality\n", + "(e.g., to compute the dot product of two large vectors)\n", + "and uses special optimizatons to make the product\n", + "more accurate than this implementation." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/single-neuron.ipynb b/.doctrees/nbsphinx/examples/basic/single-neuron.ipynb new file mode 100644 index 0000000000..224cdebc07 --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/single-neuron.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A single neuron\n", + "\n", + "This demo shows you how to construct and manipulate\n", + "a single leaky integrate-and-fire (LIF) neuron.\n", + "The LIF neuron is a simple, standard neuron model,\n", + "and here it resides inside a neural 'population,'\n", + "even though there is only one neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:24.767921Z", + "iopub.status.busy": "2020-11-25T16:47:24.767058Z", + "iopub.status.idle": "2020-11-25T16:47:25.259044Z", + "shell.execute_reply": "2020-11-25T16:47:25.258552Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Neuron" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.265133Z", + "iopub.status.busy": "2020-11-25T16:47:25.264605Z", + "iopub.status.idle": "2020-11-25T16:47:25.267758Z", + "shell.execute_reply": "2020-11-25T16:47:25.268178Z" + } + }, + "outputs": [], + "source": [ + "from nengo.dists import Uniform\n", + "\n", + "model = nengo.Network(label=\"A Single Neuron\")\n", + "with model:\n", + " neuron = nengo.Ensemble(\n", + " 1,\n", + " dimensions=1, # Represent a scalar\n", + " # Set intercept to 0.5\n", + " intercepts=Uniform(-0.5, -0.5),\n", + " # Set the maximum firing rate of the neuron to 100hz\n", + " max_rates=Uniform(100, 100),\n", + " # Set the neuron's firing rate to increase for positive input\n", + " encoders=[[1]],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "Create an input node generating a cosine wave." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.273540Z", + "iopub.status.busy": "2020-11-25T16:47:25.272012Z", + "iopub.status.idle": "2020-11-25T16:47:25.274115Z", + "shell.execute_reply": "2020-11-25T16:47:25.274538Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " cos = nengo.Node(lambda t: np.cos(8 * t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the Network Elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.280861Z", + "iopub.status.busy": "2020-11-25T16:47:25.279344Z", + "iopub.status.idle": "2020-11-25T16:47:25.281469Z", + "shell.execute_reply": "2020-11-25T16:47:25.281873Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input signal to the neuron\n", + " nengo.Connection(cos, neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Add Probes\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.288415Z", + "iopub.status.busy": "2020-11-25T16:47:25.286910Z", + "iopub.status.idle": "2020-11-25T16:47:25.289016Z", + "shell.execute_reply": "2020-11-25T16:47:25.289424Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # The original input\n", + " cos_probe = nengo.Probe(cos)\n", + " # The raw spikes from the neuron\n", + " spikes = nengo.Probe(neuron.neurons)\n", + " # Subthreshold soma voltage of the neuron\n", + " voltage = nengo.Probe(neuron.neurons, \"voltage\")\n", + " # Spikes filtered by a 10ms post-synaptic filter\n", + " filtered = nengo.Probe(neuron, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.294540Z", + "iopub.status.busy": "2020-11-25T16:47:25.293738Z", + "iopub.status.idle": "2020-11-25T16:47:25.468049Z", + "shell.execute_reply": "2020-11-25T16:47:25.468688Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create the simulator\n", + " sim.run(1) # Run it for 1 second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.483140Z", + "iopub.status.busy": "2020-11-25T16:47:25.474689Z", + "iopub.status.idle": "2020-11-25T16:47:25.927310Z", + "shell.execute_reply": "2020-11-25T16:47:25.927753Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[filtered])\n", + "plt.plot(sim.trange(), sim.data[cos_probe])\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure(figsize=(10, 8))\n", + "plt.subplot(221)\n", + "rasterplot(sim.trange(), sim.data[spikes])\n", + "plt.ylabel(\"Neuron\")\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the soma voltages of the neurons\n", + "plt.subplot(222)\n", + "plt.plot(sim.trange(), sim.data[voltage][:, 0], \"r\")\n", + "plt.xlim(0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the input signal in green\n", + "and the filtered output spikes from the single neuron population in blue.\n", + "The spikes (that are filtered) from the neuron\n", + "are shown in the bottom graph on the left.\n", + "On the right is the subthreshold voltages for the neuron." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/squaring.ipynb b/.doctrees/nbsphinx/examples/basic/squaring.ipynb new file mode 100644 index 0000000000..06e1dbdf1a --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/squaring.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Squaring the input\n", + "\n", + "This demo shows you how to construct a network\n", + "that squares the value encoded in a first population\n", + "in the output of a second population." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.108519Z", + "iopub.status.busy": "2020-11-25T16:47:29.107616Z", + "iopub.status.idle": "2020-11-25T16:47:29.599617Z", + "shell.execute_reply": "2020-11-25T16:47:29.600160Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model is comprised of an input ensemble ('A')\n", + "and an output ensemble ('B'),\n", + "from which the squared value of the input signal can be decoded." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.606035Z", + "iopub.status.busy": "2020-11-25T16:47:29.605502Z", + "iopub.status.idle": "2020-11-25T16:47:29.609153Z", + "shell.execute_reply": "2020-11-25T16:47:29.608687Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Squaring\")\n", + "with model:\n", + " # Create two ensembles of 100 leaky-integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A single input signal (a sine wave) will be used\n", + "to drive the neural activity in ensemble A." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.617180Z", + "iopub.status.busy": "2020-11-25T16:47:29.615626Z", + "iopub.status.idle": "2020-11-25T16:47:29.617814Z", + "shell.execute_reply": "2020-11-25T16:47:29.618227Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input node that represents a sine wave\n", + " sin = nengo.Node(np.sin)\n", + "\n", + " # Connect the input node to ensemble A\n", + " nengo.Connection(sin, A)\n", + "\n", + " # Define the squaring function\n", + " def square(x):\n", + " return x[0] * x[0]\n", + "\n", + " # Connection ensemble A to ensemble B\n", + " nengo.Connection(A, B, function=square)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe the Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.624562Z", + "iopub.status.busy": "2020-11-25T16:47:29.623029Z", + "iopub.status.idle": "2020-11-25T16:47:29.625171Z", + "shell.execute_reply": "2020-11-25T16:47:29.625580Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.630720Z", + "iopub.status.busy": "2020-11-25T16:47:29.629933Z", + "iopub.status.idle": "2020-11-25T16:47:30.489720Z", + "shell.execute_reply": "2020-11-25T16:47:30.489232Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run the simulator for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:30.496306Z", + "iopub.status.busy": "2020-11-25T16:47:30.495459Z", + "iopub.status.idle": "2020-11-25T16:47:30.713932Z", + "shell.execute_reply": "2020-11-25T16:47:30.713468Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.2, 1.2)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signal and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded Ensemble A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded Ensemble B\")\n", + "plt.plot(\n", + " sim.trange(), sim.data[sin_probe], label=\"Input Sine Wave\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-1.2, 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plotted output of ensemble B should show\n", + "the decoded squared value of the input sine wave." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/basic/two-neurons.ipynb b/.doctrees/nbsphinx/examples/basic/two-neurons.ipynb new file mode 100644 index 0000000000..22e2ce2c67 --- /dev/null +++ b/.doctrees/nbsphinx/examples/basic/two-neurons.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two neurons\n", + "\n", + "This demo shows how to construct and manipulate\n", + "a complementary pair of neurons.\n", + "\n", + "These are leaky integrate-and-fire (LIF) neurons.\n", + "The neuron tuning properties have been selected\n", + "so there is one 'on' and one 'off' neuron.\n", + "\n", + "One neuron will increase for positive input,\n", + "and the other will decrease.\n", + "This can be thought of as the simplest population\n", + "that is able to give a reasonable representation of a scalar value." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:31.957852Z", + "iopub.status.busy": "2020-11-25T16:47:31.957001Z", + "iopub.status.idle": "2020-11-25T16:47:32.448924Z", + "shell.execute_reply": "2020-11-25T16:47:32.449362Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.dists import Uniform\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.455388Z", + "iopub.status.busy": "2020-11-25T16:47:32.454866Z", + "iopub.status.idle": "2020-11-25T16:47:32.458044Z", + "shell.execute_reply": "2020-11-25T16:47:32.458439Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Two Neurons\")\n", + "with model:\n", + " neurons = nengo.Ensemble(\n", + " 2,\n", + " dimensions=1, # Representing a scalar\n", + " intercepts=Uniform(-0.5, -0.5), # Set the intercepts at .5\n", + " max_rates=Uniform(100, 100), # Set the max firing rate at 100hz\n", + " encoders=[[1], [-1]],\n", + " ) # One 'on' and one 'off' neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "Create an input node generating a sine wave." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.463984Z", + "iopub.status.busy": "2020-11-25T16:47:32.462402Z", + "iopub.status.idle": "2020-11-25T16:47:32.464605Z", + "shell.execute_reply": "2020-11-25T16:47:32.465017Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(8 * t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.471578Z", + "iopub.status.busy": "2020-11-25T16:47:32.470059Z", + "iopub.status.idle": "2020-11-25T16:47:32.472201Z", + "shell.execute_reply": "2020-11-25T16:47:32.472630Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, neurons, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.479280Z", + "iopub.status.busy": "2020-11-25T16:47:32.477737Z", + "iopub.status.idle": "2020-11-25T16:47:32.479912Z", + "shell.execute_reply": "2020-11-25T16:47:32.480327Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin) # The original input\n", + " spikes = nengo.Probe(neurons.neurons) # Raw spikes from each neuron\n", + " # Subthreshold soma voltages of the neurons\n", + " voltage = nengo.Probe(neurons.neurons, \"voltage\")\n", + " # Spikes filtered by a 10ms post-synaptic filter\n", + " filtered = nengo.Probe(neurons, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.485516Z", + "iopub.status.busy": "2020-11-25T16:47:32.484679Z", + "iopub.status.idle": "2020-11-25T16:47:32.661197Z", + "shell.execute_reply": "2020-11-25T16:47:32.661843Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1) # Run it for 1 second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.670568Z", + "iopub.status.busy": "2020-11-25T16:47:32.669708Z", + "iopub.status.idle": "2020-11-25T16:47:33.070203Z", + "shell.execute_reply": "2020-11-25T16:47:33.069748Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+/vhj8D9uCJg6OJkjbvCcPSBbAPLPj0sARk27erbb8hiBzPcz72kvDqAgCEInJm4ZGMJVrNZ0Lcz1NgFoun62Ho0uR8LWkPmEdcOO69PZw/Va4oaAzfvo6wDa3CUgLUjWWSdc2H3wQXA/9trLvQ0AbLwxcOaZwIMPAt98E75tV2DcuKCEzr772teb7+cFC4L/rhAwCT3zs2U+b1pmduZwCUDA3QnE5QC6OjbZ0iGiPkM8bzdHRAAKgiCUEttIIHHKwNgEoM0xoHWFCAHnmgNI67nTwV+/zfXjoq+szJ3DGNYJxCcETNB9pmtsabEPJbdiRXrUEb584cLg/9prZ6/jTJ4cCMTddrOv54waFQiZrp4LWFcHTJgAHHmkvSSKTVCHCcBly9Kda1w/rswfVuYQf2EOoOtHR1QIOKpuJRDde9iczgERgIIgCKWktTV77FyfHEAuXMx8OltPRdrXxwGMKtKc6zBWcXr+2jp78O1dIeyKimwBGBYeN8WdeZ9tnUKAwF0yxQWQKQDDQsDvvRfk/vXubV/PGTQIOOUUYOxYYM6c6O07KxMmBM/DFv4F7PeTBKArB9C8v1EOoBkS9gkBm2kHPiHgqNQJl+jjnyHJARQEQejEtLaGuxQkyChUHNXrlzdaFRXZ4eAoAQVE5/JFhbW4IKX9XDlLUT1/uRvoun5+D1whbHM7wgwBm+FeUyAQy5cDvXplL//xx2B5IhEuAD/5BNhuO/s6GyNHBse66Sb/fTob48YFvad33dW9jUsArrNO9nqbSI96vqYjSJ8fcoRt7zk+XjXgV/qFT0f1iLeVazLPkwMiAAVBEEpJa2vapfANUwHhAtCVmwT4uX6+Qo0v5wIvTo6gq5OITQy6RKu5nB/TzLGkdbYC2kC242cLx7e2BtuRAOQsXJgO/7pyAH/4Ifjbemv7ehsbbBCUi7n//nRHk67EokXAK68AxxwTCCobLgewR4/0UH60PpkMBKD52QpzeG0OIL1fqEeyz/uvWA4g7/nr+pzGQASgIAhCKYkSgFrbXaiwws/ctTAdQFcDFhUCjnILgXTjZHML4zgdZl0/m+sR9lpcbiWQ3evX1QkkLB+Tno8tBPzjj2k3ylxHTJ0a/N9mm+x1YYwaFbyeO+6It19nYNy44P0xYoR7G1cOYP/+2etWrgzuvVlg2xT4rvqaQOZni8oEcSeev898OoG4HEDfH1z8R5aEgAVBEDo5tkbKRwByp8IUebZaZTTvE06Nk5PEt6XQb5yRQqJqCvLwWtg1u0JzLnfUvI9RApAPSUYFhl05gNwBDBOAW22VvS6MTTcNRse4557MIsddgYceArbdFthyS/c2LgeQ5//RetczCiunZDqAQPqzRR1++LautIM4AjCOM25uKw6gIAhCJ8eVp0TLzLBUa2ummxZWusJ0wHxyAG05dHF6IrpCwDbHwrVtnHI1rtdiG6PVFIBmJxBXTmCPHn4O4MKFaQfQFQL+8stAtJjC34dRo4Lz33tv/H07KlOnAh9/DJx0UvS2LgFI95rWU5meqBBwmADknyWb+PRxnaNCwEr5j4pjfoZEAAqCIHRybCFgymkC3K4E7eOaN4WR6RrECQFHNXBxwrpRDZ1PvT9zuWuEhrCi0EB0WRgSgD17ZrtLZg5gMpnpAJqvlfjqq8DNy4VttwV+8YtgrOCwIf86E2PHBuLmuOPCt7MJ6h9/DBeAXKSbbjrg/9myfZbiOoC2MkdxHHczBCwCUBAEoZNjCwH37BlMh7kSNB8mcngjYdYcjOoEEqcWmY8AzKUTiKtx9RGDlZVuB5AwHUBXWZgePdL7mAKQi47W1ugQ8NdfA5tskr3cl1GjAufr4YdzP0ZHoakJeOwx4OCDgb59w7c172dLSzAkn63oNj0jXgbG1ps+6rPl09kKyOypG5Uva45ok4uLLjmAgiAInZwhQ9K9QbUORNfKlZniIqo2WZgj6OodGxZCpQYpKpfJLMwctq0r18mVL+gTArY5MOZrcYXHXSFfVwjY5gCaIWA+CghgF4CrVgHz5+fuAALB6CE77gjceKN9TOXOxL/+FfQAPvlkv+35/aTh+Pr1y15vcwDDOlPRMtdnK0wAmnmnuTqAUT+4zPG2xQEUBEHoxPTtGxT6BTIbKXIAgWwB6CpVYc5HiSGXsxYlAF0Nkm1bW14fdy6ijptLDqCrEwxfR+t9BKBSQUg+KgRsjgJiC1l+/XXwPx8BqFTgAs6cCTz9dO7H6Qg89FAwiscvfxm9rSmoFy8O/vft6xcCps9WdXV8BxAIL6nkk0/L8/5svef5MttoPrwOoISABUEQugC88aKk9Nra9LI4OYB8fZgABOKVfnGFam15fbbyGLzxch3XJhbN7XkIz0f0hYXtmpszXZdkMvserVwZPIuysmgHkBwpHso0HUASgPmEgAHgkEOAYcOA66+3h5k7A999FziAJ52UHrM3DFNQhwlA8xkB6c8WD+fHyQH0cQBdebZA9I+aKBHJw74tLXk/dxGAgiAIpYY3bGYjxUUhzYflAPo6gGaBZGqEotw5vtwnrGubjuOQmI0lxxUmdvXONOejnFQgEIA9emQ+o+XLg9dOxYFp26VLg/+Ud2YLAX/1VfA/XwFYVgZccknQg/aVV/I7Vqm4//7g/pxxht/25v1ctCj4369ftjhctixwbWlMYe4A1tYWNgQM2AUdX+4q4RLHcefTtp76MREBKAiC0FEwGylalk8OoCusZPZopAbRp6Atr9EXlb9kE2ZRAjBM0PFrjhJ9UY12lLAG0gKQL1uxIgj/mq4TCUBynWwh4G+/DQQLd6Fy5fjjg6HTrr8+/2O1N01NwAMPAAceGOTB+uIKAZvrly0LngN/RjYB6NvByvZeihMCNsO+cRxAW/HnPMO/gAhAQRCE0mMLAXfvnl5fXx+IrUTCzwH0CQHHEXrm9rZtuHMY5Yz4hLpcDSCRaw4gv3fmEHskBrhwq6sLBAN3n5YvD3I0ba6TUpm5gaYDOHcuMHgwCkIiAVx0ETB5MvDee4U5Znvx/PPBkHZnn+2/T9wQsCkAubteiBzAqBCw2SPY1YPddvyo/FxxAAVBELoAthCw6QDW1GQPX0brTUfQJwTsMx3HqYuTBB+nofNxAPlxwl5zVAiY5nmHj/r6QIybIWCbyFu6NBCGNJatLQQ8bx6w7rooGKeeCvTp0/lcwL/+NXD+fDp/ELZOIIlE9vMBAjHOXVog210H0s+XaGxM5yPGyQH0qVWZ63CLUT30c0QEoCAIQkfBdCloGQlAmvd1AIHMxsPlZuQjAG2OhKtOoG8nED7NG1SzpiGFncMS8n0EYFVV5n0zBSC/90BaANp6nvK6czYBOHduELYtFLW1wHnnARMmAP/7X+GOW0w+/RSYNAk488zAJYuDmQNo5v/ZQve03JUDaH62KLeT1tO6fHIAfX7USAhYEARhDcOnFzA5gFyokFMRlreUjwMYVQfQlcBuG67K1XhFiUUflzFOCNgmAEnw0Xz37tkCkIs5KtRtywE0BaB57sWLC+sAAsC55wbXeOONhT1usbj11kBknXJKvP1sDiDl/5nPwuy8ExYCJgfQFIBhDiDg7mzkU8IojgMoIWBBEIQuii1MZeYA2gQgCZWGhrSTkksOoE/oibbno3QAdgEYJeqiehq7Gk5XTiI/Ti4hYHKAuAPIt+HhdyAQEpQXyKGOBxzewM+bF/wvpAMIBC7YaacBTzwBzJ5d2GMXmh9+AB59NCj8zAs4+2DLAfQVgIC7E4jpALrcdrMXvc+oNFE96XlvfHEABUEQ1lB8HEAgO1TZ2Jh2Lcz6YC7RlGsI2BzFwBaedTV61HhFNYousRhWKDcqHAwE96a5OQj5Aul7bTqAthxAOh6Q7hhChDmANgFYaAcQCDqDAIG71pG5++7gGfzhD7ntH+UAEq4QcCIR/BFmDqBLALpEHq3zSWWIcvKi6m7yaXEABUEQugC+IWA+r1S6IeONFjVY1GPYJ98uTo9gVz0znxzAsBCwOcpBVNjXp9gzTZPgo+Ukls2Qry0EvGpVdgiY9wym8wCBAOQOoClK5s4N/hfaAQSCnsUnnBDU1qMRSToaq1YB99wDHHZYbnUQbXUATRdR6+DPFQI2xXxYDmBTk/2HBe3Hidvxw/bDKU7nKHEABUEQugBmI1VVlTkygi0EXF2dnucOIDUi3bqFC8A4RWf5tFnPzDcHsLU1He6KOge/pqj1QHR5jm7dgnlqtOlemQ6g6aza8i+TSXvPYCC7EwhdA0ECsBgOIACMHBm8hrvuKs7x8+Xvfw9E8sUX57a/2dljyRJ7CLixMXiv2ULAvKQPPV8zB5AEYVj6QNiwij4926NSJ6JCwOIACoIgdAHM/DIekuKNFBeANkEIpBsGGu/U1Qs4TskJvtwnP892PC4KXWFdWu5zXN/GmItjUwDSftTgmzmB5ACZ4Xets0PATU3B/qYDaIaAe/TIHOe5kGy2GXDooUGYdeXK4pwjV1avDjqp7LorsMsuuR3DrMXY2moPAdNrt4WAuQBsbg6O4coBpPcLlfVxOYA+IWAg+geOKzeW5wuSIykOoCAIQhfCbKRomS0HMMoBpLBnnJIrYdNxeuhGOYRR7obPceOEgE0BaDqC5JaaDqApCM0QPX9Gy5YF02E5gPPnA4MGoaiMGhW4bH/5S3HPE5cHHwwc0CuuyO84dD9to4DQei4AbSFgs56mmQPoctNd7zkgXv5e3NzZKLcwR7wEoFJqPaXUeKXUV0qpb5RSdyilEtF7CoIgCJGENVK2HEAuAAF72IoarShnDvALybp681KD7HIycin9kkza9/OpaWgKQN6gm718zXvlygnkbivvpc2fkTkOMJAdIl64EFhnHRSVnXYChg8PCkOTSCo1jY3AtdcG7t9+++V+HH4/6bVRDiB/FiQATZHOO++Ygp8vM98f5KbbfkzQflGiD7A753E+h67Pb45ECkCllALwLIDntdabAhgKoBbANXmfvQAopWJWkRQEQehg2ELAfJkrBxAIzwEE7I2Gq5SLOR2nFEWU0IxTJ9AVIvZxAG0Fol05gDTqA+Vb1tcHnVwqKrIFIT0PXkqEPyNyAMPKwCxcCKy9NorO9dcHIuiaDtFMB2P+zpsHXHmlfXxkX7ijumhR8N+WA2hzAIHsELDpAEbl09rW0X5xfmjFzYeNctFzxMcB3AdAo9b6IQDQWrcC+AOA3ymlapRSJymlnlVKvdzmEN5oO4hSapZS6kql1EdKqelKqZ+0Le+ulPq7Uuo/SqmPlVKHtC0/SSl1N9v/BaXUXm3TdUqpW5RSUwHsopS6UCk1o+3v923bDFFKfaaUul8p9T+l1KtKqepcb5QgCELRMXsq0jLbUHA8BGwLW/EQMO3X2BgIHKXih4D5NAk0Elrm+lxCwPy4ccVpVIcQW8iX5uk+mbmVQHQImAhzAEshAH/2s6DO3t13A99+W/zzhVFXFwjR3XcH9tkn/+PR/VyyJPhvywEkkW7mAPIfV7YQv5kTaL5f+HuJv8/IsaYfElGdreKIPjp+2Poc8RGAmwOYwhdorVcA+A4A9ePeGsDRALYAcLRSyjXS9SKt9bYA/gqAugFdBuANrfWOAPYGcJNSqrtjf6I7gA+01lsBaABwMoCdAOwM4DSl1DZt220K4C9a680BLANweOSrFQRBaG9sYSpatnq1O1E9qhcw7U8ip6kpmA4TgD4OoK3xieNe5Op02KYrKuy5jVQGh8rAKJVdBoYadC4IuLPKewlHhYBtDiAXJa2tgWvVHgIQCNy2igrgssva53wubrghKP584435uX9ApqC25VwC4TmA5mfLdABp3scB5O8/er/a8m6j3HCfaYIXje5AnUBe11ov11o3AvgUwAaO7Z5t+z8FwJC26V8AGKWU+gTAJADdAKwfcb5WAM+0Te8G4Dmt9SqtdV3bOXZvW/et1voTyzkFQRA6DmE5gCQ4XGVgALsDyOd5w5RIuEu5+EzzUC2HN04tLenpOOHiqG25m0bLEwm7AKTxfUn0AvYQsCkA+X0OywE088tWrAimzR6+3LHSuv0E4LrrBuVWxo0DJk9un3OafPcdcPPNwLHHAjvvnP/xbCH3Xr0y1/mEgGk7MwfQlSNqywHkTjt/zwHxBKBPDqAN/hnLER8B+CmA7fgCpVRPBCLt67ZFPBjdCqACdlZbtlEADtdab932t77W+jMALcb1dWPTjW2h6Ch8r0sQBKF0hOUA2kRIWA6grTHibiAVj/YJ9fJGJkoARsHFm+24/Nw+DSEXgPx4ttdPDqArBOzqXW3ee8DeCxiwlx7hjhUVZ24vAQgEPYKHDAHOOqsgjlFO5weCnMRCYDqA3bsHKQ20DsgWgERYCNh0AF2dhFyfszABmK8bXkR8BODrAGqUUicCqU4XtwAYq7WuL8A1vALgvLbOJmDh21kAtlZKlbWFlHd07P8WgEPb8hG7AzisbZkgCELnwpYDaIYhgWyh4uoFDGQKwMbGtANoE03mNMfViSNfbB0+fJLdzUYXyBymjjuA9JptDg/dG1euJd/eDAETZs9TotQCsKYmKAr92WfAbbe133kB4I03AvfxoouA9aOCejHgAtAVbrc5gA0NgUttCwGbdSBd6RT8/ULvs27d/AWgzeF2/RjqCAJQa60RiKojlVJfAfgSQCOASwt0DVcBqAQwTSn1v7Z5AHgHwLcIHMg7AXzkuL6PAIwF8B8AHwB4QGv9cYGuTRAEofiEhYC5AKRteCeQ5uagEXHVuuMhYJ4DSPAcJiAzlMuxlbAoBNwZtDWQtL7MaK64A0iYbgzVW3OFgBsaMkPA5rBgZi/gsBDwypXBdvw6+X0uhQAEgIMOCopDX3kl8NVX7XPOhgbg9NOBjTcGLi2UVEB2CNjscQ2kn0W3bsF729Z7m7YzBWBUmSCbAKyq8s8BJKJc7bBtCohXSFRrPQfArx3rxiIQYDR/kGO7IWz6QwB7tU03ADjDsr0GcLzjWLXG/K0AbjWWzQLwMzZ/s+1YgiAIJYcaqebm4Ivf5VLYRgKx5fwBmQKQekryHEA6r9mJgqistNfzyzUE7IMtBExUVdlHMuEOIC9vE+YAmvmRPCTYt69fCJgvA9Jjz5qU0gEk7r4b2GILYMQI4O23M4cZLAZjxgDffBO4gCSmCoFS6R6xrnGXSQDSs4jKpQXcnUD4+4V+TFDdQf7+o2Pb0iuSyeBHAe8x30EEoIwEIgiC0FEgcREWAnYVN7aFpgB7JxCCi0EgnU9VUZHtuBHFFIDUSNpcE37dgN0BTCTi5QDyTiBAdicQm/taVxfc+3JWgtYUHYQtBEwCoj1Zd13gr38FPvig+LUBJ08OOn6ccgqw996FP75vCLg2wyfKdPdyzQGkHxNAeA5gMpl5PeZ71yfNQgSgIAjCGoDpUrhCwJS7RyFfHweQh0CbmzNFH58uK0s7Q1Qr0EYxBSBha/xcjSh3AHljTGVgeKNN4W0zBAzYe1eTIOBh4lWr0uLC5ToRZgh4rbWyX0d7cfTRwPHHA1ddBbxVpDT5RYuA444LQr/FyDk0O4GEhYBNB5A+R7bn65sDyH9M8PefWXvTnHb9eAnDZ5s830siAAVBEEqNrwAEMhspLgDNArU039KSngbcAtA1bdLSkpm3Vwx8BKBvDqCZ91henlmwl4eAeS9PnmtZVpaZA+grAGkd0H5FoMP4y1+AjTYCDj88KNFSSFpbgRNPDETgU0/Z70W+hOUARoWAbR166uuD9wO9h6JyALkA5KLPzAEMmwb8xJ3PjywRgIIgCJ2csGHGzDw006UwQ742Z4wLQNOZcAlAFxSmdYWIC4GvA1hWlhmKrapK96rkjbbrtZkhYFtOIIkB/oxMAQhEh4AXLSpN+JfTqxcwYUJwTw49NN1bthBceCHw0kvAnXcCW29duONy6H5S4W0fAUjwfD5bb3q+jW0kEDOflNxkH9GXiwAE0ikZLkQACoIgdBFsJUZ4XiAXgGYnEO74mTl8YQ4gX06EhYBt2xcLU9xxmpqyr9N0A7UOBCt/zWbdPx4CdhWGJmxlemh5lABcuhTo0yfe6y8GP/kJ8MQTwLRpwK9/nX4/5cMNNwTC78ILgTOy+nQWFnJhk8nwHECfEPCqVfayP9xNp7GizR7lQPD+dL1Hw0LAvj+ioj5jUQIxAhGAgiAIpSZODqCZl2Z2bACyS734uH5cTIWFgPlxiGL1Kg1rRGmZSwCaYpBPcwHIHT+qmcgFAYm9XELAPmVLSsHw4cCjjwJvvgkcckh+TuB11wUFn485JhjurZjQ/bQNu0f4hIBtnX6A7HxaHvK1zYd9zqLeu2HLbcewiT1xAAVBEDo5PgLQNtSbmYtEcBFjrvPNAYzCJbYKiUvE8fU2AWtu73qdyWT6XjU3B2E92i9OCNgnB3Dp0uxxa0vJsccCY8cCEycCu+0GzJ4db//VqwO379JLg44fjz6a6YYVAxLhYeMuk0h3CUD6HNEy7vCabrrWmdM8BAyE/wAJCwFHLbcdz3TozWU5IAJQEASho2AbZoxcKuqIQI6fWXQ2zAEMywG0TccNAdM0Xxa1vw9R5wgraeNy/cxGlwQgb/x5j9+wEDAds7U1EBOuEHAyCSxf3nEcQOLEE4O8vdmzgS23BP7+d78OPlOmAD//OfC3vwF/+hPwyCPFry1IRAnAZDJTABJm5ykgeJa2HEAuEum909qa/oHgei/55N1yokK4fH3UZyEHRAAKgiCUGluxWoLnqdmEiulaAOENU9xewD7hKzp+mDgj4rhENtfDDIu5rjOuULQJQOoFDGSKQjMHkJ6bKwRsy1nrKOy/fyDottkmqN239daBm7diReZ2ra1BEenjjgN23BGYNw949lng2muL7/wRPg5gfX1wr31DwFwANjZm9hIHst30OD86bPtwogRcVEqD5AAKgiB0clz5fkDQSNkEIM9do3kizAGMKwBd7kUcd443VDax6IK24Z1afMQs4HZEbfdGqcze01wQmCFgUxQCabHkCgEvXRpMd0QBCAR1+954IxB+TU2BM9i3L7D55sDeewM77RTM77478MILQWePzz8HDjusfa/TJwfQHJPZFgLmjp+ZAxj244nmXe85HwFoE3Iut9wVUi6QA9hOnq0gCIIQia1UBa9NR/NAeJ5flADk01GuX1WVvZNA3PAsiVuev0TlMMxh5+Ic1/Za6Lpt68xG07yPZmFofu/M8YJNAWiOPmE6Vh0pB9CkrAw44YTA4XvvPeDFF4EZM4Jr79kzyBnce2/gV78K5ktFlANo60kPpGv+8R8j5vM1ywIB9s+ZTwg4bNqsYZlIRI9+YwsHiwAUBEHo5HCXoqIiu5GiAsK2HECCO4JRDZNNDPFz8tBqHAfQ5XpEuReJRLgAdIV6w0LAYQ2wq0EHMkPAJAiAYFlLS3Cdvg5gWMiyo1JWBuy6a/DX0fAJAZvDKZo9fvkyEoB8vkeP8PdHLiFgc5p+UMURgFEpFzkgIWBBEIRSYyal82WuEHCYM1GIXsA2YRXlSPiEvaJcPZ5Pxre1CdKwELB5fWEhYI45Mgi/97aepEC6QXflAHb0EHBngYeAa2vtHU/MkXPCQvemAGxtDf9cmfO5lIGxLff5kUXb8OLnkgMoCILQyYlqpPgyGoEgKjRVyBCwbTqXTiAVFdHC0iUWXfvZrr+yMrPIblSuoOteRQlA3xzAzhAC7gxwB9AU064QMC3XOvu52UK+tl7inLAcwLifG9uPIdOJD9tWHEBBEIQugin2CLMRAvxLvZjrXG6YKaBcjpvtHORI5CPkwqajymGY12wLzYXlbfmEgIF0mNqVA+gKAYsDWDiiBKAZAubw3vVAdg4gEO0AhnWQ4u9H14+uqHSIKLEYlvYQExGAgiAIpYY3MGZ4Ech0ocxlRFiY06dDRJiz5joOEIi/KLFoE3I+wpJ3GLGdw9UYhjkzYb2F6fgk3Jqa7Pc+bgiYHMBSdp7oCtBzWb48+16aAtAU6YC/AxjVCYQIc5bjhIBdP3BcYpH/UMsDEYCCIAilhjcctobLJkJ4Q0UhT58QsMv18wkH2xoqlwhzNXS2xst2Dl76JcyRtF1zmMsX1cCb8zZH1lxmlh7hkGPVq1f71cvrqnDH1SWmXZ1AALe7HhYCDnMAfd1AWziY5/JFfcb4tGt9DogAFARB6Ej4CI6yssx8uqhGK8yZi5MDGCckVUix6BsuDsvNihMC5tjC7+bzsBXwpvUUApbwb2GIGnfZ9Sz4sjg/AMJyAMPyAaNG3zF/DPl+hlyf0xwQASgIglBqfFwKjkvg0bIwERPWcYJwOW62hixOx46oUC9f7spJ9Mn7iwrHRTlAUe6rrYdpt26ZHU9ovStnTYgP3c8wAejjAHJ8Pluu+bDUAp8OU1FOos9+eSACUBAEodTEzQE0500BmGsOIF+eTNr3JUi0ufL68nX1XGVcfHIVzYYxLD8yqhOATw6gbXg4vs+yZdIDuBDwkLutxzWQFoBhzy2uA+jKJ/UdFcQm5KJ6+7qO4UqjyAERgIIgCKUmbs6ZSwASUTmANHxcWAjYHK3ANe3TYziq8YojFn06foSFgMN6CAPxBCAPO9pCjkBmDqCQH0oFP0zq6sIdwJqabDcW8AsB+6RTRKVNmMfh03Rdvg43YXPRxQEUBEHoQoR1AiGiQr5hI4EkEulyJmHhYNomyslwhYDj5C+5wsWuBjJq2tZo2xxNIH4dOCBbADY32wUghSzDOi0I8Vi1KniWLgFoPotCOIAcn05UNM/3Ma8n7D1tO0bUD6McEAEoCIJQanxzAKMEHxdtfH8+YoIpAPlyEkmJRDDsGT+2OR3VKzEqrBu3c4lLqNocRVtj7Lo3hcgB5Ms4YTlrQnyUCq+5SPBwfKFzAMNCtHw77kDaRiyJ0/OXT7v2ywERgIIgCKUmqpEyRaEt5w/IdLn4MTmmAOSijy+nEUdcDRLPAfTtMJJrDqCP0AsLx7lcT/M6Kysz6xoCfjmAgD0HkBABWBi4AAxzVF0OoG8vYE7YD4Qw19m8bnPa/CzwzyHhEoD040wcQEEQhC5EHAcwLAeQYzoYPL+PCyOXMxg27RKArhBwlLD0CRfHzQEsLwdWr86+RiCzgTeFNa03sT0jlwPY2hrcVxGAhYGeo68DyPGtAxjlAHJ33PWjwyb6+I8L80dNVKcrchF5eoYIQEEQhE6ObwiYcDmAfHtap1R2g8YdBGpMeMMSZ8QOM7xsm+ZuYZTI5APd+yTJ++Rjccx1/Bg2sWeK7fLy9OvxEYCErUi0EA9+P8MEYFQOIMfH8eNUVWX+gHK952zTPo61KxzMtxUBKAiC0EXwbaTidPrwSU4PcwNtx3YJNZurZ+v5WFlpD1+Z10/b8vPy18OPERVSNkffMB2dqHCvuczV2zdKAIoDmD++AtCVA5hLL2BbGRj6zLhyAPmoPBxX6RefHED+Y8j2Qy0HRAAKgiB0JHzGMI1TBsaWD8enuZiy5SG58vpsDZJLLNq2dbkeLoeESteYjaWtpI05TB7HJjbDQsCmIOfzPmFHQgRgYQm7ny6R7tsLmGOup449gN8PEC4GfXr+utIoxAEUBEHogviGgF1CJcwRjHIAbe5dHEeiosJfALoS2F1ikU+79vMpacOxhYddwtq2zCUAxQEsPvk6gIUYC5jj2zvdZ5of0zbNl4kAFARB6CLEzQGM6wC6Gjifzh6ugeyjXD1b4+Ta1qdOmss55OGwKHeFzhUlADlhIWARgO1LvjmAtmdkOrxhdSBt7yW+LkoMholE2zmiHHcRgIIgCJ2cqBxAVxkYIixvKSoEHGeaGipe/Lmy0p0TRfiKRS4sw4Qe3y9Oz2Wa5x1kOGG9gG0OICdsKDhABGAh4M8tLOSejwMYFgIOey+50i58hzS0TUeFgCUHUBAEoQtR6DIwYYVsXY2NzzBtfFvfsG5UCJj3KDYbTtfQdFE9MsNcG1cond8vs5GNEwLmiAAsHLW12UO9+TyLfEPAtnxSl6CLcvpyDQGLAygIgtCF8OmpyJdFlYGJ0ws4zjQfyN6WA+iT1xd2Dlfen0tkmo2hTw6grdEOE4CmW5hrDqCUgckfup82Me2TA2j7bIWNm03zPg5g3BxAV6crnx9Rtv1yQASgiyuuiF4WZ953XZzpjrBtLtNx1tnmXcvClketE+ITdT/jPKNifJbC1uX7Xg87Zy5EhanMsCM1UtQbMY4AjDPslHls7tTZQsA+7kXY+cLy/lwi0zaqSVQvaF8hbQvBSQ5g6chXANrC9640ANu8uc7l+vnmALrSGmzTUeHlHBAB6GLMmOhlceZ918WZ7gjb5jIdZ51t3rUsbHnUOiE+UfczzjMqxmcpbF2+7/Wwc+ZLWB1A6rFrG/uX48rFAzJDZ/kIQMKnIfPNezKFJRd3JDLDcgB9OqOENehRoXUgXg4goZT9WEJuRInpOCFgcz7MAYwTAibCwr5x81fN44kDKAiC0Mnx7QVMw2DRPImisAHtTQeQEzcHkBd0toVq+fH4NcUJAfNr4MLSpwyMKxzMCXMAwwRgPiHg2lr3MxD8KUYI2JyPW1OTh2PjhoB5igM/pm2afvyJABQEQehC5CoAfca39RWAXNjw6agSLa4cQC4AqfHix3UJRH7d/PVGlYEJ6xDCCcsBDKuvmK8AFPLHVwBGDQXnet5A/BzAqBxU1/6VlfbPL9+GO+1RLnsOiAAUBEEoNVFCghoIs8GwlV+h+bgCkDd8rtpnXPQRrhxAuiZTyBG2RtoMATc2BtPV1dFlYFzXEVYIOqzmmzlvywHkiAAsPmECkONyAKOGULQtC0unqKmJ/tHBz2n+wOHvb9v5+LXb3v+SAygIgtCFCOuJaoZ8wwSgbdokHwHIw1c2d47CxVVVdgFoc9LMcFmUA2i6ftSgctfFFGZxysDwbanDTS5DwYkALCw9e2Yvi/oRxbeJ4wDyHyWmAKyuBhoass9p/shwvb9tAtDm6nGXXRxAQRCELgRvgMhds7l2pgPIBQ/HlY9k4iMAXSFgEoAVFfZxenkDGMcB5NfGX58rBMxzA+vrg+kePcIFoNmgk4gkZ8kmAOlauPskIeD2Jd8cQHOZTQCGjRVtOm41Nen3XPfu9s+cq46g+f7my83zmj+4bNvmgAhAQRCEUhPWSHFMIbViRfC/Vy/3vrnkAIYNfwXE6/nbrZu9obP1pu3ZM7OhixMCTiSAVauCad4Ym8LMFgKm/Uio2XIAzW34drbz8PUiAAsDvTdyzQE0l9nc9jABaK6rqcl8X7hyAAmz40eUA2jrMSwhYEEQhDUYaojq6oL/pgAE0g1NLiFgvk95eXra1vEjypGoqooWgHTcXr0yjxvlAJohYLoftbXp7c3QLC+DY95HU6jx++DahggbCURqABYG+sETJwcwDNtnwzYeNNGnT/b+3AEkJzyRyKzRaUuXcAnAfv2yz8vzW8UBFARB6EL4OoCE2QCutVb2Nrbk9LBz8PArn7blIvqEpOKEgIlevTJDurQfdwD5OXr0yCzDwd0YW26WCQkAasRNBzBKANp6mNqwPR8hPvPnB//XXTd7XS4OoC0EbBNV9N4zxZlS6fdZ9+6Z4o7X6OTvXf7+tglA/mPOlXIRdq0xEAEoCIJQasIaKVvC+2abZc6HOYDV1X416KJEIu9Y0bNnpttBLqEtB7BPn/Ryl8tIjWjfvukGrro6/bq6dUu7L/wc66yTFleVlentu3dPC2CbY0cNsrmO5um1DRiAiRMn4ocffrCHc/k9M8emBfDF119jFQD85CdZ62bPno1zzz0XrVQipwg0NDSgmV5rB+e///0vZs+eHb4RvZb1189ex5+FK08TwJw5c/DdkiXBzNprW7fJYuXKzO05JN66d09/5mpqMt9jvAwSz2Okz7arYxH/DNk+Y3mGgCuiNxEEQRCKSm0tcNZZ0LvsgrEPPYQjjjgCPYYMAUaMAI4+GgCwevVq7LfJJrh+u+2wK4mhceOAiRMDIQQAV1+dFlAHHwy88w5w2mnB+lNOAQ44IFh3xx3AwoXB9O23A4sWBdO33QZQ43jrrcCyZcB66wG//S1w2GGB8DzmmGD4O6WAr78GjjwS2Ggj4OWXgQEDgFGjgn223DLY9txzg8arRw9gu+2Av/4V+PbboFE77TRg332BnXcOjnPOOcG62lrgiCOC4zz1VHD8Bx8Enn4a2GCDYPSVmprgGh5+GHjuOWDwYOChh4Dx44Ntjj4amDYNOOGE4PXcfHPaxdtzT+Coo4ALL8RRRx2F3YYMwflHHw3suGOwfv/9gSOOQMuoUdhns82w5ZZbYuq99wYN9vbbo6WlBY888gh+O2IEyk87DdhtN3z//fe44YYbcPPNN6OiogKtra34yYMPYvjAgXjhyCOzHvmxxx6L9957DyeccAJ23nnnQryLsqipqcFuu+2Gt956qyjHr6+vx1577YV77rkH22+/vXWbZcuW4aWXXsKxxx4beqwd2+69JvHNuOqqq7DOOuvgjJtuAp54InhvmSgFXHghsOmm2c72BRek9lm/TTzqI44Arrwy2KaqKnifbrst3njjDey7776Yf/75GLjxxsH6o44Cpk8Hjj8+mL/xxrQ7fccdwCOPBCK/b99g23PPDd4rRx0FnH128H4+6ijgzDOBWbOCz+NRRwWfF3p///nPwXUMGwYcd1zw95OfBP9POQXo3Tv4UbPHHsH7/KOPwsPVPmit5a/tb7vtttOCIAi5AuBDncf3zmuvvaYB6LPPPjvr2O+++64GoLfffvtiv4w1hlmzZmkAesSIEdb1s2fP1gB03759M5bfcMMNGoB+8MEHU8t++ctfagB64sSJWmutly1bpgHoHj16WI+95ZZbagD6448/zvt1LFu2TM+aNStrOQAdNPNaf//99zqZTOZ9Ls6kSZM0AL377rtnLJ89e7YeNWqUTiaTevjw4RqA/uKLL0KPxa81zjqttf7ss8/0G2+8kbHsgAMO0L/4xS9iH2uLLbbQAPTkyZNDr7ejkMt3Dv1JCFgQBKGDQCGwBgqJMr7++msAwCabbNKu19SVmTBhAgC76wQAc+fOBQCst956Gcu/+uorAMgIry5evBhA4LoBwMq2sGFtW8hYa43x48ejpS1MSM+4Oix3EIGDRs/exVZbbYUhQ4Y410+bNg0DBw7EfffdB601kpQ3mSdNbWH2hJGLdtRRR+H666/HtGnTUtfe2tqKWbNm4ZJLLnHeb1+mTJmCZcuWpeY322wz7LPPPhnbvPTSS3j11VdjH3v69OkA0s+tKyMCUBAEoYOwsC0s28/SE5Aa0o0pLCXkTZQAnDdvHgBgXaPTwdKlSwEAfViv0BVtPVRJONS1hZtpfvz48Tj00ENx4403AkgLwMqIPK6tt94am266aeg2Ublz//vf/wAAkyZNwrXXXovy8nLU1dXhzTffTImkDz/8MCWqFixYEHo8ggSwKQBJ/FZWVmJ1W6i0qqoq9fo///zzjO3jCMJkMontt98eB1A6Qwzoulw0UqeMmNfUWREBKAiC0EEgAbi2JdmcBGAfsxSFkBPLli3DpEmTAEQLwIEDB2YsX9KWJ7kW691L4qK8LVnfdABJpH3//fcAgvw5IplM4sorr0yt47jE3ezZs/H22287Xl0m3Km7++67AQSCdc8998Qvf/lLJJNJ7LDDDjjggAPwzDPPYMCAAZg0aRJef/11jBo1ynlcEneJRAJ1dXUYP358xvkqKyszzk33xBSMtA3x5JNPYuTIkdZzknCeMmWK12vn2O4vZ+rUqalpEYCCIAhCu7GorTOGTeSRAFwTGqb24OWXX0ZLSwtUSA/Q+W1lR3r37p2xnBzAClaSgxxAej6mACThQiFimtda47333sOYMWNw6qmnAgAmTpyYcu04J598cup6hwwZgt1339157bx3MQmsqqoqa9iWnLwPP/wwJYqnTZuG/fbbDzfccAMA4Oqrr8ajjz6acQ5+rFNPPRWHHnooPvvsswxhSNMAMtxADnfeAOCYY47BTTfdlLo/nFVtpX5qQsr7uD4jP/zwg3MfAPjkk09C13c1pBewIAhCB4EEYDkvvtzGzJkzAYgALBTjx4/H2muvjR49ekQ6gCYkAPl+JExomRkCJsfPFIB8W8oPpHw287rGjh0b+br++c9/YsMNN8Tmm2+eWsYdORJ7ZaxsDRdyrrDu5ZdfDgAYMWJEahltW1lZiRkzZgAIRB4dr6ysLDWttU4JPfP9bct5Nc9B0H3uHlLsmbYxIQewl61sEjIF4JrwORMHUBAEoYNAIWCTZDKZEodrQsNUbFavXo1//etf+PWvf43y8nLnPSUH0FxPAjAMlwNodvrgwqhbvmU9EHTA2GGHHTKcNy7wuCAjaFsesnXlJi5btgxKKYwfP94pHPk5+HXQ69Ra49lnn0VlZSVWrVoVKgB5qBzwcwBdz4cEYP/+/a3rRQAKgiAIJYEEoNn4/Pjjj7GP9e6772L06NF47LHHOk0x4Pbi3//+N1auXInDDz88NARMDqD5PMxwL8cMAZNTRULGJgBJABVCABI2AchDwBybkHMJQApN33jjjRn78bAvHUNrbRWDWmuMGjUKLS0tmDt3bur125xv080znVQbvIcwh0LAZkifrmn69OlrVCcrEYCCIAgdBJfLR+VIbOts3HXXXdh1111x1VVXYcSIEdhpp50iE+DXJJ599ln07NkT++67L5RSTiFHDqALHwHoygHk+JaEiQMXetzVo+vj1+7qtBF2XO708Wne85ejtU6FuLkYrKqqChXAUQ6gTdC6HED6gWUT/fPnz8eqVauwWdsoO+IACoIgCO0G5YLlIwA/+eQTXHjhhTj44IOxcuVK/POf/8SXX36JAw880JkbtSbR0tKC8ePH46CDDkJVVZVTAK5YsSIrr88kTADSszQdQFMAmiHgQgkPLsJseX02ARiWA2huy/MJzbAvdwBt5+PXl0gkQgWwSwDSfbW9p0kAmo5iWBoF1XYcOnSoc5uuhgjACMaMGZPTOiE3ou6pa71tubkszrxrms/7bB9nuljb5jsdZ12cbcKWx92mKxDW4MyZM8f7OJdddhl69+6Nhx56CLW1tTjiiCPw9NNPY9q0aTjnnHMKcamdmsmTJ2Px4sU4/PDDU8ts9553AAkTMi7MmnMkcnjPYToeCcDq6mqro+WLTdSVl5dniC3btnx9VA4gDxFH5RaGCWfaXimVlwNIQptDApCX6QHSxbpt1/Xll18CAIYNGxZ67V2KXIcQ6Yp/tqHgEDJkTNg6ITei7qlrvW25uSzOvGuaz/tsH2e6WNvmOx1nXZxtwpbH3aajgDyGglu+fHlqmKr7778/47gjR45Mrbvuuuuc5//88881AH3llVdmrfu///s/DUC/+uqr+b/QTszZZ5+tq6ur9apVq7TWWm+22Wb6yCOPzNqOhuUDoC+44ILU8hUrVqSWv/LKK1prrZPJZGrZtGnTtNZan3766RqAvuSSS7TWWu+///4agH7yySe11jq1/WeffaavueYaDUCPGjVKL126NGO4sjjTq1evTk1/9NFHGoCurq7Wv//97zUAfeutt6bWL1iwIDX94YcfagB68803T13nyy+/nFrPX98zzzyjAehDDz1UjxkzRgPQo0eP1tXV1RqAnjlzZmpb1/Ts2bN1TU2NBqDnzZunx48frwHoTTbZJOu10XOoqqrSWmv94IMPagD66KOP1lpr/emnn2Zsr7XWt912mwagN95444xnOmzYMA1A77jjjlnP+6KLLtJVVVX6xRdf1AD0u+++6/FuKj25fOfQnziAgiAIHQCetxR8r6eZO3cuBg0aZF3Hefzxx1FWVobTTz89a91ll12GoUOH4swzz8yqu7amkEwm8dxzz+GAAw5IOUiuELCrBAwVgQbSz4K7VLTMDB+bThaHh4DzeTa2vD9Xxw7+mm1hXb4t5e6Zx7XlDvLjhk3zELWPA0jhYdMBtI3uQZ+lnj17ZiwPcwC/+uorbLrppqnyOGGfs66CCEBBEIQOQFhpkblz52Lw4MEAwhum559/HrvtthsGDBiQta5bt274y1/+gpkzZ+Lee+/N/4I7Ie+99x6+//77jPAvYL+n1AEkkUhkrOcCkKBewRyXADTPxcVQVVVVXgLQVnTZ1bGDX4ctBMw7SnBh6QoBU+Fpl+gz4dv75ACS4DNzAG0hYOoFzF9DMplMPTvbdX377bfYcMMNU/uIABQEQRDaBZuzRHAB6OLbb7/F9OnTceihhzq32W+//bDvvvvimmuuiRwXtSvy7LPPIpFI4KCDDkotc5WBmTdvHtZaay3U1NQ4BSAt5wLQdAAJPvIHp1gC0OXUmdfp2ta23rWtS1j6TtNrrqqqyro/puNH8zSaiE0A0nubH2vZsmVIJpNZy4k5c+Zg8ODBoWWBuhoiAAVBEDoArhCw1hrz5s3Duuuum7WO8+abbwIAfvGLX4Se59prr8WiRYtw22235XvJnQqtg+LD++23X0Zo0BUCXrBgAQYOHJglCOIKQJrnAtCsy0gCqLKysuAOYFTtPz7NQ8A2hxCAtecvH1XEhY8ArK6uzro3rhAwHcMmAG2OLPUAtlFXV4dly5Zl/MgSB1AQBEFoF1wCcMWKFVi9enUqrOtqmN5++2307t07VcfMxY477ohDDjkEt99+u7Xx7Kr85z//waxZs3DEEUdkrXMJwHXWWSdrfZQAJMyQLx/tgk9zAQRkCjeee+fCJdSi8vNc4WJb0fCo3MK4rp8JdwDNUUFcApCwOdk2B5Dy/2wjv1Ave+4AigAUBEEQ2oWo4rUkRly8/fbb+PnPf+7lxowaNQpLly7FAw88EP9COynjxo1DIpHAYYcdlrE8zAHs379/1vqoHMCoTiA8543m+QgZXAzZCiqb2MRZRUWFNa+PE2eoOJdbaDuuj+hzOYA2B5TuG3UQcTmAvAOJbaQWcgDXXnttEYBtiAAUBEHoALgcQBoGzuZGEStWrMDnn3+OXXbZxetcO++8M/bcc0/ccsstedWd6yy0trbiySefxIEHHphVG86V8+UjAOPkAHIBaPYI5gKQCzcfARgV9o0a/xfwHysYsHcCyUX08Wk6vlIqSwCa99ElAHmB7TAHsF+/flnXZhOAawIiAAVBEDoALgFIDiANYG9rYKdNmwYA2Hrrrb3Pd8kll2Du3LkYN25cLpfbqZg8eTJ++OEHHHvssVnrbA7g6tWrsXz5cqyzzjpeOYDLly/PWmYKFeqAAGSXjeGix+UA8v35tE0AmmPz+gpAVw5gVCeQXESf7fWb7iiQHUo3BSEJQO58h+UA9uvXz+oAKqVSebZhr4kzY8YMHHPMMdhrr71w8803ewn2joQIQEEQhA7AkiVLstwpIDsEHCYAt9xyS+/z/epXv8KWW26JG2+8scuHu8aNG4fa2tqM3r8c8/WT62oT3VRihONTBoafyyUAzRCwLTfQZ9qnRl/UfhxbGZi4OYBh2ELghGskENMB5OdyOYCVlZXo2bOnVQD2798fiUTCOwQ8bdo07LLLLnjttdewcuVK/PGPf8Tuu+9uTRHoqIgAFARB6AAsXboUffr0ARAeArYxdepU9O7dG+utt573+ZRSuOiii/Dpp5/i3//+d45X3fFpamrCM888g0MPPTRrHF7AHgJesGABAKQcQP48li9fnip74goBNzU1OTtwmCLHzAHkYVwf0cfFWZQDyOHX4BsCrqioyBCWNseL78c7lPg6gKYA5OuAbAFoij2ttTMHsF+/flbHl0rAAO6UAE5LSwtOOOEEdO/eHdOmTcOUKVPw7LPPYurUqfj1r3/daQqtiwAUBEHoALgE4MKFC9GjR49UkrvNmZg+fTq23HLL2PlLRx99NPr374/bb7899wvv4LzyyitYunSpNfwL2EPA3AE07+ny5ctTTq1LAPIwpdY6I2RrC3PaHDAzBzCOGDRr9MXp2EGijQvYOM4inzaFrmuavwZTPJn1E6PKwDQ0NFjr/S1ZsgS9e/e2fkZsdTbDHMCnn34a06dPx1133ZUKGx922GF4/PHH8e6772LUqFHOfTsSIgAFQRA6AGEO4Nprrx0amvryyy9Tg9jHoaqqCmeddRZefPFFfPHFFzleecfmiSeeQN++fbH//vs7tzHvKTmAthBwlAAEkCUAXa4WrXd15MgnBMxLu5CYi1MGJipHEEBkCNiVwxgnBzCqU4gZAraF4wH7cyMWLFiQKrPkEwK+7bbbMHTo0KwRZY444gicd955uOOOO/Dqq6869+8oiAAUBEHoAHAByFm4cCHWXntt537Lly/H4sWLsckmm+R03jPPPBOJRAJ33XVXTvt3ZOrq6jBhwgQcccQRGaNgcGwOYFQIuFevXhnbhzmAQHinj7AcwLgOoE0AKqVSQo2LMFsIWCmVGqLNFVq2OYccvoyLOVdv86gQsOkAmp1CTAHI6wLanpv5PJubm7FkyZJUikWUAJw5cyb+85//4LTTTrOWXLrhhhswbNgwnH322R0+FCwCUBAEocQkk0ksW7bM6QDy/D+zYfrmm28AABtvvHFO5+7fvz+OPfZYjB071trBoTPzz3/+E/X19RgxYoRzG1tI8Mcff0T37t3RvXv3rPUrVqxICUBbGNIWAjYFYFgI2JUDGMcNNIUaCUDu3kUJOdu5zXCyLQRs69FsHs/W69q2D5/3DQGTGDeHleMCkEO9g00B6OKZZ54BAGtBcSAoWH3XXXfhm2++wa233hp6rFIjAlAQBKHE1NXVIZlMonfv3gCycwDJAbS5VfkKQAC44IILsGrVKjz44IM5H6MjMnbsWAwdOhQ///nPndu4HEAK/wLp59HU1ITGxsasUGJdXV3KDfIRgD4OIC/mDLg7frhKtHBRScLP5urxaZeQs4WI8xGAZjjYJwRsdq5xdQKheT7cHxD03jaFO+DuZOVyAF944QVss802GDJkiHU9AOy///74zW9+g6uvvhrz5s1zbldqRAAKgiCUGHItzNCi1hoLFy7McCfMhunrr78GkJ8A3GabbbDnnnvirrvu8hp+rDPwzTff4M0338RJJ50U6erYBKDtnlO9P/M5rVq1CrW1tRnz/NhmKRPXSCAAnOHQOJ1AzA4atrw+WwjYp0wMF4BRoWXX9XMhavaCdoWAgez8P8CdA2iWe3GFgE0BGBYCbmxsxAcffIB99tkna53JzTffjJaWFlx99dWR25YKEYCCIAglhjdaQGZj1tzcnBq9wCZkvvnmG/Tv3z9DgOTCBRdcgNmzZ2P8+PF5HaejMHbsWJSVleHEE08M3c4VAiYH0CYAbQ4g3X+b4PPtFOLqEOI77SrREuUAxhkr2OY4uo7rEoA+Dqi5rekORuUAcgHY2NiIpqamUAHIn7eLDz74AKtXr8aee+7p3IbYcMMNcfrpp+OBBx5IufQdDRGAgiAIJcYMW1EDRUVleecQWwg4H/ePOPjggzFkyBDccccdeR+r1LS2tuLhhx/G/vvvnzG6g42oEDAXBC4ByB3AuDmAZig1HzHoKv0SJgDLysqs9fyicgv5dfPj+og+Vw4k345C6rwTiJlH2NzcnJW/yN10m3NrCrw4IeC3334bSinstttuWetsXHbZZaisrMSYMWO8tm9vRAAKgiCUGDMETI0Pdcqg3ECbWJk5c2ZBBGB5eTnOO+88vPXWW/joo4/yPl4pmThxIubMmYOTTz7Za3t+T5PJJBYtWpTR8zqOAwiEh4BtOYB82hUO9QkHkxjiok5rnQrr+4g623GjOoHEFaphDiAJPuq17boHLkfQlgPoem5AIAArKytTn72wEPCUKVOw6aabpj6PUQwcOBDnn38+Hn/8ccyYMcNrn/ZEBKAgCEKJMUPABI0PTA2X6V4kk0nMmzcvq4htrpxyyimora3t9IWhH3roIay11lo45JBDIrc1RfWyZcuQTCbRt2/frPW2HMCmpiY0NzdbHcCqqioA2cOZhZVHiesA2kKypqsY5gD6DBXnGis4KgSciwB0ibyw/MCampqMtImysrKMZaYDaOtlT5+tMAH4ySefxBpvGwBGjhyJHj164PLLL4+1X3sgAlAQBKHEuELAJAC542A2Xq2trRg0aFBBrqNXr1743e9+h3HjxmHu3LkFOWZ7s3DhQjz99NM44YQTUqOnhGGK6sWLFwNAhgAkbE4Sib0ePXpkLevevXukA2i6WLkWfwYyBSBfTg6gT22/sHOUlZVF5gD6XH9YIWwuBltaWjJG9TD3o/vKxd7KlSvRs2fPDKFHTrorBMzDv64cwGXLluHbb7/FNttsY13vok+fPrjwwgvx/PPPY8qUKbH2LTYiAAVBEEpMriHg+fPnA0Bknlscfv/73yOZTHbawtB///vf0dTUhLPPPttre/OemgIQCA8Bk9gzQ8CJRAIVFRXWnMAwAZhPCNhW58+nZy9N24Z/M8+dS+9ic5uwUi8uxy9MAHbv3j21fMWKFejRo4dVuLvKwNjG2TYdwKlTpwJAbAcQCD5TvXv37nC5gCIABUEQSkycEDBvmKjGWKEcQCDovXjEEUfg3nvvdQ6r1VFpbW3Fvffei7322gubbbaZ935hAtAWAubPiXqhmiFgXkQ6LATscgBdzhgQzwHkry0qBBx1Dlfpl7jXybdJJpPOTjBRw8LZQsBmhxwgPATMS/4A7hDwp59+CgD42c9+hrj06tULF198MV544QV88MEHsfcvFiIABUEQSszKlSvRrVu3VOI7dwDLy8tT4UUzPFUMBxAALr74YqxYsaLTFYZ++eWXMWvWLG/3D3CHgHnpHZ5fVlNTk/GcTAeQC0CaDwsBm6LGNiqIuZ2PAPQN6yqlrJ05bOdzuX755ACGuXy+DqApAEl8+wjARYsWpZ413Q8bX3zxBWpqanL+rJ133nno168frrjiipz2LwYiAAVBEErMihUrUnlLQGYO4FprrZXRKJkh4LKysoxRKwrBDjvsgD322AO33357Rlixo3PPPfdgwIABOPTQQ733iQoBm6FEnkemtU45gGYOIBchYWVg8nUAXZ1AaBsa29fclos6Wy5f1Laua/ApAxMmgHkIOCw/0hUCXrVqFWpqaqwC0AwNNzU1YdWqVdYxuE0H8Msvv8TQoUMji4q76NGjB0aOHIlXXnkF77zzTk7HKDQiAAVBEEqMmbdkCkDCFgLu378/KioqCn5NF198Mb777js8/fTTBT92MZg5cyZeeuklnH766SmHzhdTAJaVlWX09OVCwiUAzRxALgDDnCyfUUHM6TgdQlxCLdfevFGlX3yu01cAu4pC03pbCLi+vj5rDOfly5ejR48eKC8vT+0L2DtZuULAX375JYYNG4Z8OOecc9C/f3+MHj06r+MUChGAgiAIJYb3XOQsW7bM2jgR8+fPL2j+H2f48OEYNmwYbr75ZmtJjI7G7bffjoqKCpxxxhmx9rM5gH369EkVIjadJLMnaVgImLZrbGzMEKUNDQ2p+VwcQNe0zQGMEos+YV1baNmns4ePAPStCcjXJRIJAAgNAdMxgOBzZMuj9RWAq1evxrfffouhQ4ciH2pqajBq1Ci88cYbmDRpUl7HKgQiAAVBEEoMhYAJ3kCZRWdNB7DQ+X9EWVkZLrroInz00Ud44403inKOQrF48WI8+OCDOP7442MLYlsOIO8BbAsBEzYH0JYD2NjYmCpJQ/PV1dUAwnvExnUDbQLQFdaNKufiEoDFzAF0rauurs4QgN26dcsKAXMBaAsBm4WegXAByJk1axaSySQ23XTTrHVxOeOMMzBo0CCMHj265D+sRAAKgiCUmFxDwMV0AAFgxIgRGDRoEP785z8X7RyF4N5770V9fT0uuuiinPY3HUAuAPl6WwjYVgaGhyFNwRcmAH3LwESJQZ+8vqhC0XEEYNwyMD6je/BpulemIOQOIGHeeyD8BxYA68ge/D3x3XffAQA22GCDrO3iUl1djUsvvRRvvfUW/v3vf+d9vHwQASgIglBi4oSAqWFqbm7GokWLMGDAgKJdV7du3XDJJZfgzTffxOTJk4t2nnxobGzEXXfdhQMOOCCnEh22ELDpAIaFgH3KwJgCsKGhwSkAfTpRxCkQ7RJhuQhAALEEoM82Ya/f5QAmEglnDiB3AIm6urqMnvRxQ8AkANdff30UglNPPRWDBw8uuQsoAlAQBKHE2BwKrXVWCJg3akuWLAGAjDFri8Fpp52GAQMG4MorryzqeXLl0UcfxYIFC3DxxRfntL9PCNgWSgTSYq+srCxD4JEIoXkuAIFMQegaFSPfHMA4DmCcXsBULDqfHEDfUi/cAeShdMrPrK+vh1IqtT6ZTKYcQDoekDlWs68A5Hz33XdQShUs3aKqqgr/93//h/fffx8vvfRSQY6ZCyIABUEQSowtBNzQ0ICmpiZnDuDChQsBFF8AVldX45JLLsHEiRPx1ltvFfVccWlubsb111+P7bbbDnvvvXdOx/BxAIGgnEp9fX3WcyJxYT676urqjBAwzwF0OYAtLS2psi2mAIwTZk0mk9bh36JcvahtW1tbUwWgfY4bRwCSqLP1ECaHz7yP9fX1qfvMj2WGgE0BSPiGgOfMmYOBAwfG7l0exkknnYQhQ4aU1AWMFIBKqfWUUuOVUl8ppb5RSt2hlEqw9eOUUtOUUn9QSv1EKfWJUupjpdTGxb10QRCEzg81bGYdQBoGzpUDuGjRIgDIKGJbLM444wz079+/w7mAjz76KGbOnIkxY8bkXJ8NSDf2DQ0NaGhosOYA8g4HZg6gWXaEHD5bCDiZTKKxsTHlEPIagb5FkaNcNl670adodJQApPW2oeZcxzWnXa+FRB517HA5gLRft27dUp+DhoaGDKeV8jFtIWCeo8kdwO7du2cIO1cIuFDhXyKRSGD06NGYMmUKJkyYUNBj+xIqAFVwJ54F8LzWelMAQwHUArimbf0AADtorbfUWt8G4FAAT2utt9Faf1PUK2+7PqWUuJiCIHRayPExcwBdApBoTwFILuDrr79e8sR1oqmpCVdddRV22GEHDB8+POfjcFFtGweY1vPOHmHiorm5Ga2trc5OHySWbCFgs0ewT0/bXHrz8mkftzBOj+I4+YlAdp6frVyMGQLmBba500rPKE4I2HT/2ksAAkEnq0022QRXXHFFxtB67UWUeNoHQKPW+iEA0Fq3AvgDgN8ppWoAvApg3TbX7woAvwdwllJqonkgpVSdUuoapdRUpdT7Sqn+bcvXVko9o5T6b9vfrm3LxyilLmb7z1BKDWn7+0Ip9QiAGQAGK6Vuals/XSl1dNv2eymlJimlnlZKfa6Uelzl8xNREAShCNAXPyWpA0Hjs3LlyqzltA5Ih4DbQwACwFlnnYUhQ4bgj3/8Y0kaK5OxY8di1qxZuPLKK/Ny//i+LgEIpDt78FEnbA4gOXpcmKxevTor5GsKQsCdD2hul6sAtO3ncvXyFZa+18xLuzQ3N0NrnVXnzyYAaT0v+cJ7BdOypqYmNDU1OUPALgFIaK0xZ86cogjAiooKXHHFFZg6dSrGjRtX8ONHESUANwcwhS/QWq8A8B2ATQAcDOAbrfXWWusrAdwL4DattS0ZozuA97XWWwF4E8BpbcvvaNtnBwCHA3jA47o3BXCP1npzANsD2BrAVgD2A3CTUmpg23bbIBClPwWwEYBdPY4tCILQbpADaOaR2QRgqULAQNBAX3vttfjkk0/w2GOPtcs5XdTX1+Oqq67CTjvthF/96ld5Hy/MAaT1NgfQlgNoE4A8d80W1qysrERZWVloCDhO3b04Qi1OWDeOsHRdmznNBSAtr6qqAhDcq7KyslSI1pZLycO93AG0ObdEmANobrNo0SI0NjZi8ODB1u3y5bjjjsP222+PkSNHpn5ktBftGT5tAvBC2/QUAEPapvcDcLdS6hMAEwD0VErVZu2dyWyt9ftt07sBGKe1btVaLwAwGcAObev+o7Weq7VOAviEnVMQBKFDQG6amUfmIwB79epV0MT0KI4++mhsv/32uOyyy7LCle3JzTffjLlz5+Kmm27Ky/0DMu8p9ay2hYC5A8jPyYs+A5kCz5YDaBOAJHh8HcA4oVqOTezFEYs+1xM3BMxzAG0CkId8XSFgwhSAQHaZnrghYCoBUywBWFZWhjvvvBPz58/HtddeW5RzOM8dsf5TANvxBUqpngDWB/B1zHM163RQvRUADV5ZBmDnNhdxa631ulrrOgAtxvV1Y9OrPM+5mk3zcwqCIHQIuAAkwhxAYuHChe3m/hFlZWUZ4qsUzJs3DzfccAOOPPJI7L777nkfz5Z3aau96HKSXA5g1MgfPAScSCSglMoS1bmGVvkyF1F5f3HEIs+Xy3VYuOrq6gw3kNbZnFRbCBhARicQui6bACR8QsA//PADABS14Pouu+yCESNG4JZbbsE33xS9+0SKKAH4OoAapdSJAKCUKgdwC4CxWuv60D39eRXAeTSjlNq6bXIWgG3blm0LYEPH/m8BOFopVa6UWhvAHgD+U6BrEwRBKCo2B9AnB3DRokXtLgABYM8998SRRx6Ja6+9Fl999VW7n//SSy9Fa2srbrjhhoIcjztCYR1vbO6SLQfQdACjQsCrV69GVVUVlFJWB5AcXp+Qq03UxYELOUpN4MfjopBPc/LJAaRt+L2yOYB0rT4hYFMA8tfpEwJesGABAKB///7W7QrF9ddfj0QigfPOO6/dysKECsA2x+4wAEcqpb4C8CWARgCXFvAazgewfVspmU8BnNm2/BkAfZRS/wNwbtu5bTwHYBqAqQDeADBSa/1DAa9PEAShaFBD6yMAzRBwsWsAurj99ttRVVWFs88+u11rmE2ePBmPPPII/vCHP2DDDV2eQHy4ACwvL89wY2m9LQTMHUDCFIDNzc1IJpMpwWcOX0YhYKVUSgAlEgkkk0k0NzenwqE+IeCwZflA98d1Da5zcxEZNcJJIpGwhoDNAtquXsAArM/IFQJubm7GqlWrMsQ+reevmQTgOuusY329hWLQoEG4+uqr8dJLL7Vbjm1kSFRrPQfArx3rZgH4GZsfE3KcWjb9NICn26YXATjasn0DgF84DsfPqQH8se2P7z8JwCQ2f67r2gRBEEqFLQQMBMWhy8vLU44HkF0GZquttmqfizQYNGgQrrvuOpxzzjl44okncPzxxxf9nA0NDTj11FOx0UYb4fLLLy/YcU0H0BzqzRYC5u5XWC9guu6w+dWrV6eePXfASEhVVVWhrq7OK+fOtr6QkMPoOq9teVVVFVavXh0aDk4kEigrK8spB5CPA+wbAubL+Qg8tJ72BYAff/wRtbW1GecpFueeey6eeuopXHDBBdh///2LOswjICOBCIIglJSwHEA+6gRfp7UuSQ4g54wzzsAuu+yCc889F3PmzCn6+caMGYOvv/4a999/f0EbYzMH0OUI2crAtLS0oLGxMbQXsEsAkrDnIWAugLgABPxCwGHLCo2PAwggVdIlLDRMOZDkBvoIQFoftxewyxkkzM/bggULih7+JcrLy/Hggw+ivr4eZ555ZtHddRGAgiAIJcQUgNRwkQDkcOejsbGxpAKwvLwcjz76KFpaWnDiiSdmhPsKzcSJE3HTTTfh1FNPxT777FPw43MH0BSAtN42yoQt5GgKQLNjg60QtC0EbLphrrAvd+Vs64uFKwcQCN4bBF0/kB7uzRSDpkike8XvDc2HDQVHz4jnX7rqN7oEIN8GaF8BCAA/+clPcM0112D8+PG45557inouEYCCIAglpLW1FWVlZamG0kcA0himffr0affr5Wy88ca48847MWnSJFx33XVFOcePP/6I4447DkOHDsVtt91W8OObIWCbA0iCobq6GuXl5c5ep0B0yNd0uZqbm1MOGA8BmwIQSAsrM8xKAsqkmCWC6IcLF3u26+HX73ot9PrN7XiJHNMBTCaTGUPBAcHz6NatW8YzigoBuxxAHgJuTwEIAH/4wx8wfPhwXHjhhfjoo4+Kdh4RgIIgCCUkmUxmOEj03yUAAXtv1VJx0kkn4fjjj8fo0aMLPqZpc3MzjjvuOCxduhRPPfWU063JBy48li9f7hSAq1atygojmiNP8GUkVFwCkOd2mh09KG+Or+PTyWQy47pLIQDDzuESgPw10zZhApCvMwUg3VfTkXU5fdxhp22B7F72thBwsTuAmJSVleHhhx/GOuusg9/85jepUjQFP09RjioIgiB4QQKQ43IAaV1HEoBKKdx///3Yfvvtcfzxx2PatGkFOa7WGmeffTZef/11/PWvf8WWW25ZkOOa+DiAQLa4AOwC0OwF7CsAo3IAgUxh5ZrmuJYXivLy8lRYl+Ny/WzTPAeSsOVHJpNJNDU1pQSgGWoHgmdEjiB3+hKJRMa94L3sw0LALS0tWLRoUbs7gEBQjPz555/HwoULcdBBB6Xc5kIiAlAQBKGEmALQJwTckQQgEDTCzz33HHr16oX9998fn332WV7H01pj9OjReOCBB3DZZZfh5JNPLtCVus8HpHsB29aHOYC8VIkpAF05gKawMztB8HxAwkdYcVzLC0VlZaV1JJY418xzAM11tlFSbPUUeUjezKU1y/TECQEvWrQIWuuSCEAA2G677fDkk0/i448/xm9+85vU+61QiAAUBEEoIa2trVkCEAhCwK4SFcuXLwfQcQQgAKy77rp4/fXXoZTCPvvsg48//jin42itMWrUKFx99dU49dRTcdVVVxX4SjMhQdDS0oK6urrQELAZRrS5UOZIIFG9gIG0A8hdP5sD6OMGcmi5zaUrBGbo1nY9UQKwqakpMgfQFNLcWeXHtD2jfATgjz/+CKD4RaDDOOigg/DAAw/gtddew/Dhw7FixYqCHVsEoCAIQgnJJwRsc6tKybBhw/DGG2+gsrISu+++O5599tlY+69cuRJHHnkkbrzxRpx11lm47777rAKjkESJ6rgh4Pr6+pRIiZsDSB0rfESfazmHlvM8PVunjVzhx7UJOPPa8skB5MLZNg8EApC7sS4HEPArA9NeRaCjOPnkk/Hoo4/irbfewk477YQvvviiIMcVASgIglBC8gkBdzQBCAA//elP8cEHH+CnP/0pDj/8cJx44oleSewvvfQStt56azz33HO45ZZb8Je//KVozpVJVFjdJwTMcwBJhJg9e4FAACqlUFGRHofBJYBoXdRyPm3rHMKPbyvLkis8BOxzzb4hYFsZGNMBNOeB7HtvE4CAvXOICYWAAZRsxB3O8ccfj9deew2LFi3Ctttui5tuuim0FI8PIgAFQRBKiE0ANjY2oqWlJVQAVldXFz3HK1cGDhyIt99+G5dffjmeeOIJbLTRRjj33HPx9ttvpxpurTUWLFiARx55BLvtthsOPPBAJBIJTJw4ERdeeGHRnT8iKq+SCwkzvMjrzhFUvNiE168zOz3w+fLycmcdvbjTNgfQJRxpGy5Mo9xCl7D0Ea28vItLALe0tGS9x205gVwA0n32CQF369Yt4/Xy/TpSuSVi7733xkcffYR9990XI0eOxMYbb5zX8UQACoIglBCbAKQ8n7AyMB0p/89GIpHAn//8Z3z22Wc46qijcP/992P33XdH9+7d0a9fP6y11loYMGAAfvvb32LevHm4/fbbMXXqVOyxxx7tep2+IeA4ZWC4C0VwkRgmAMPcwLg9gm0OYJQAtC0DkCWUaH3UcV1lYGydYGzrbD2EXTmA5tjBpnAH3MLQXA8AS5YsAQD07t07a7tSMXjwYEyYMAEvvvgiNtlkk7yOFTkWsCAIglA8zE4gAJwCEAgaNVu9uo7KpptuirFjx+LOO+/Ea6+9hqlTp2Lx4sUoLy/HhhtuiN133x3bbrttu4V7TXx6VtuEBGDviZqLADTFk084OFcBGHa8+vr61H9aRk5bIpFIjTpC98zlLMYNBycSiYyRZMxcQZsAtDmAlH/Jr7G+vt6aY+sSgHybJUuWoLa2tl3qKcblgAMOwAEHHJCXUy4CUBAEoYTEdQBJrHQWAUj07NkThx9+OA4//PBSX0oWPiFgmwNoCwHb5gF7r1/bfJg4zCcE7OvUhYlJEoaVlZVZvXdzzQGk7UnQhe0PpO8j5b9F5QDW19dnjBYSlhtI64F0CLijhH+LgYSABUEQSohNALqK1PIQcEfsANIZiepYQ+FG/pzCOoHQPN+OLwOQlSNohoDNdVHTUYIrrlsYdY5chGXUdFgI3OYA2o5jywE0w8JxBOCSJUs6VPi30IgAFARBKDFmiIqcJVsD1VkdwI4KF9VKKavr6uox2tDQgKqqKpSVlYUKwPLy8owwos0BJAqZA0h5ez69dW0C0BXipeU+IWBfAetyQKMEIJ/XWmflANocQCDIAbSlWPBjiwMoCIIgFBXTASTBwRsuWicCsPDQPe3Vq5c1F9GsGedylwhTANpcKzPMGyaA+Ha2aZuwMt25KKfOJgB9XL9ChYBt0+Z2QLgApHkguPfJZBKNjY1ZzyhODqA4gIIgCELRMAUgOYAuAdiZOoF0dKJEtc0B5IKDjz1LmCFeW8jXnHeFQH07UZjLuVAz3UfbfrZewFHCMp86gK6OL2HiGLDfW5f7SnmF+YSAxQEUBEEQioZLAJohR8pHa2pqEgFYIHgZGFteJReApgMIhOf7+TqAueQA+uT98WPSKCM2t62srCxV8y+OQCxGGZi4IWBXJxEgPcazKwQcVQZm6dKl4gAKgiAIxcOVA2g6gABSxWmlE0jh0FpjxYoVWWMvE2GjRvgKQE5UT99ClIHhrl9lZWWqhEtUmZiootFxRV8+biBtFyUA43TAAdJDLYaFgBsaGtDY2CgOoCAIglAcampqMsYa5Q2XzQHsyMPAdUYoJOgae1kplRJPNgfQJtJNwWH2TI0TAs51WDiXUMt1pBBXCJhctnxHLAm7NxwuABOJRFYHHPNeA9kOYEtLCxobG0MdwMWLFwPoOKOAFAMRgIIgCCVks802w0477ZSad/UcpXVUIsblVgnx4AIwKiRoywEshAPoEoAVFRUZnVLihIML7eq53EISx4Vy/Ww5kC5BbBN7UQ6gUunxmX0EoISABUEQhHaFDy/GcdUIFHKD7nGYA0jkGwL26eiQixvIp0kw5irq+DnMMLJtWVNTk9c5zGPnkgNI0y63EMgW24A9BxAIF4A0DJw4gIIgCEK7QA2QTWwo5R4lRMidsBAwJ24I2FcAxsmHs21nE0K5loyJ6xaG5Raa5wvrBGKbdu3vuq+AXw4gEfYjqiOOA1xoZCg4QRCEDgQ1XDZhwXsIiwNYGKhenE9hYN8QsKsMjG8OIGGKIR83zVaipZAFnc0QMA3Jlk8dQJvLZ1vnIwB9cgAJHwewKwtAcQAFQRA6EFECkBABWBi4qA4TgJWVlSlhU+gcwFxCwC7BZROAcYo7x+lc4lNf0He5T3g8qp6ibRsgOweQCBOArrGhuxIiAAVBEDogthAwR0LAhYPyKsPuqet52ApB5xICtom+XHIAbaLPJdRcNQPDzhGnx7C53DcEnI8DGCcEHPa814RUCxGAgiAIHQhfBzBKIAp+8J7VYQ4gd4uixIWrDAwVYw4rdRImhqhYs207c33cMjBR4WI6tk/v4mKVgWnPEPDy5ctRU1OTGk+5KyICUBAEoQMR1QmE1tnGrBXiwwVBmAA0R2sh4oSAW1tbAcQLAbuIyvvzEWq0nIucKGcxnxCwjwD0cQCJXMvAEGECsL6+vsuXWpJvEEEQhA6IzQEkJP+vcOQiADlhYwH75gD6hoD5tM2Zco3T6xPW5eLUti0/Lm0bZ2QS89j55AC2RxkYoOvX2hQBKAiC0IHwCQF35bykUhJ2XwsRAiZsYU4+Vq+PALRNmwKQjhnl1FVUVESWc+HXRD1/fRxAVw5gmGB05T3mkgNYVlaWVVCdWNPzbEUACoIgdCB8QsDiABaOYoaAba4azZsuFxVUdjlj5jls12iGeqNEHS8aTaIurAyNua1PDiBPVQgTjC5XL8wBtAlA2p//kLLdw0QiYX194gAKgiAIJcHHARQBWDh8BaDLAQzrBUxDjvmEgLkA88kBdIkaLgajhBrhIwD5cW3CMip0bE6H5Qya+7jEoS3cS9vSf9ONjfoMiQAUBEEQSgKF7cJyALt6aKpU5FIGxhYCJmHiEoDdunXL6t3Lh1SLGwImzBCwTdTZOo/EDQHTtj4lYTim68mneV6hy+l05QC6tgdyGwWEEAG4hnPFFVfktE7Ijah76lpvW24uizPvmubzPtvHmS7WtvlOx1kXZ5uw5XG36Uo0NDQAkBBwexFVWse3DIxtWWNjIwC/AsZ8RA2bAKyoqLAKQO6QmR0/4rh6UW5h1LZRI5OY0zw0XFVVlRECNwVdnBCwuY84gCForeWv7W+77bbTgiAIuQLgQ53n9055ebkGoO++++6s42+xxRYagP7d735XzJexRvHb3/5WA9C1tbXW9fvtt58GoC+55JLUsubmZg1AA9Bvvvmm1lrr2bNnp5bV19drrbUeMGCABqAfeeQRrbVOrX/llVf0V199lZrn6x5++GF9yimnaAD6mGOO0f/4xz80AF1TU6Mff/zx1HaPPfaYBqATiYR+9NFHNQC9xRZb6OOPP14D0Oecc47eY489NAD94IMPpvZ7/vnnU9PXXHONBqCPOuooPXDgQA1A/+1vf0utv++++1LTZ555pgagzz777NQyOq953OnTp6emP/3009T0jBkzUtPTpk1LTX/99dd6+PDhGoC+/fbb9dixYzPuze9//3sNQJ9//vlaa6332msvDUBfeumlWmut77//fg1AV1ZWpp7RlVdeqQHorbbaKuN5jh49WgPQO+64o/V5L1myJHXuUaNGxXovlYJcvnPoTxxAQRCEDgSFwsJ6KIoDWDiielbT83Ddc5sDSA6Ybw4gx5UDyN29srIya8ePuA4g34/CulG5fK4RRvhyn+LJYZ1gohxAV+eaqGHg+LFcz3tNcgBFAAqCIHRApBNI++ISBDROcFQvYA6FN+OEgPk6V6cOWzjYVfsv17CuTSya5yNcIeA4pWvoPLYcSBKSgdHlzq00BSJf5soB9BlJRwSgIAiC0O7YBCA1fNIJpHBEOUL19fUA3ALQ1guYKIQD6Mrv8xGGvp1AqqqqUtfKBRNNu0b/yEcAmq/ZlQMJpPMj6T6SWAwrDG0ThXy5SwCKAygIgiCUFFsDVVdXB0AcwEISJQDJAYzTCYSgHt1xBaBtOy7MwsK+vKC0rwPYrVs3q1tJDlx1dXXKhfPp+ZuPA2jrTGIK6XwcQOkEkkYEoCAIQgfE5gCSABQHsPDECQFzXCFGTj4hYH4em9PHj8WHaXM5gFzAkVisqqpKCTzbsGlcIPL1UfUFw/AJAdMxTXFK87nkABISAhYBKAiC0CGxNVA2N0rID98QsMsBpOVhtehMYdK9e/fYIWBeO5CLRH4sM+/P5gDy8ivkpHEhxQUTd9Fs771C5QBWVFRYO4HQ8V0OoG3UD9u12/AJAXf1H1oiAAVBEDoga621VtYy6qnZ1Rum9sQ3BOwSDOXl5RnHsWEKwF69ejm3r6mpsRZF5g4g37+2tjZDMJJgrampsRZ35ue15SjGEYCFCgErpawOoCsEvHLlytR94MeyOXa0DWHL6eRICFgQBEEoKTYBSPTu3bv9LqSLEyUAKTQa5brGEYDmSCCcvn37pgQOFylcmHFRU1tbm5Gft3jx4tRxbA4gx5b35wqj2vJP+/Tpk5oOe/0+2HIA+/btC8AtAM3PAb8vJFjNz1GUiy4CUBAEQSgpYTlKAwcObMcr6dqQ2xblqvrkjLlw9US10bdvX3z//fcAgEGDBllDwD179swIAXOht2TJktRxbDmAtp6/XHS5cgBtwql///7W1xR3GoA1BLz22msDcJfTIQFqc8aXL18OINsBJCErOYAiAAVBEDokYSKBN7xCfpB4GDx4sHU9uVBRDiCFgrngIGFlc9Vo+wEDBmQs79atW2r79dZbL+Xu9e/fPzU9ePDgVAcOLhL79u2but5+/fphgw02yLgOft5hw4alXMXu3bunprkApG3XW289633ggou2HTRoUGp6gw02SE1vuOGGqemNNtooNU2sv/76qWslUT5kyBAAaaFH10jOnykA+b2kHszcpQTSz8Il7vh1+XTw6cwoekMJgFJqIYDZpb4OQRA6LRtordeOs4NSaiWAL4p0PaWmH4BFpb6IItBVXxfQdV9bV31dw7TWOSUFR4/XsgYR94tbEAShAHyhtd6+1BdRDJRSH3bF19ZVXxfQdV9bV35due4rIWBBEARBEIQ1DBGAgiAIgiAIaxgiAAVBEErL30p9AUWkq762rvq6gK772uR1GUgnEEEQBEEQhDUMcQAFQRAEQRDWMEQAdiGUUr9SSn2hlPpaKTXKsv5CpdSnSqlpSqnXlVIblOI6cyHqtbHtDldKaaVUp+jt5fO6lFJHtT23/ymlnmjva8wVj/fj+kqpiUqpj9vekweW4jrjopT6u1LqR6XUDMd6pZS6s+11T1NKbdu2POp+VCmlnmxb/4FSakiRX0pBkO8d+d7pSMj3Tub3Tihaa/nrAn8AygF8A2AjAAkAUwH81NhmbwA1bdNnAXiy1NddqNfWtl0PAG8CeB/A9qW+7gI9s00BfAygd9v8OqW+7gK+tr8BOKtt+qcAZpX6uj1f2x4AtgUww7H+QAAvAVAAdgbwgef9OBvAvW3Tx3SGz6d878j3Tkf6k++dzO+dqGOKA9h12BHA11rrmVrrJgD/AHAI30BrPVFrXd82+z6A9dr5GnMl8rW1cRWAGwA0tufF5YHP6zoNwF+01ksBQGv9YztfY674vDYNgMrx9wIwvx2vL2e01m8CWBKyySEAHtEB7wNYC8ABiL4fhwB4uG36aQD7qnwHWC0+8r0j3zsdCfneYd87SqnQMSNFAHYd1gUwh83PbVvm4hQEvxY6A5Gvrc3uHqy1/ld7Xlie+DyzoQCGKqXeUUq9r5T6VbtdXX74vLYxAE5QSs0F8CKA89rn0oqO7bX/1LLMvB+p/bTWLQCWA+hbvMssCPK9I987HQn53kkT9VmUkUDWRJRSJwDYHsCepb6WQqCUKgNwK4CTSnwpxaACQThmLwTOyZtKqS201stKeVEF4lgAY7XWtyildgHwqFLqZ1rrZKkvTCg88r3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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim.trange()\n", + "\n", + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(t, sim.data[filtered])\n", + "plt.plot(t, sim.data[sin_probe])\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure(figsize=(10, 8))\n", + "plt.subplot(2, 2, 1)\n", + "rasterplot(t, sim.data[spikes], colors=[(1, 0, 0), (0, 0, 0)])\n", + "plt.yticks((1, 2), (\"On neuron\", \"Off neuron\"))\n", + "plt.ylim(2.5, 0.5)\n", + "\n", + "# Plot the soma voltages of the neurons\n", + "plt.subplot(2, 2, 2)\n", + "plt.plot(t, sim.data[voltage][:, 0] + 1, \"r\")\n", + "plt.plot(t, sim.data[voltage][:, 1], \"k\")\n", + "plt.yticks(())\n", + "plt.axis([0, 1, 0, 2])\n", + "plt.subplots_adjust(wspace=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the input signal in green\n", + "and the filtered output spikes from the two neurons population in blue.\n", + "The spikes (that are filtered) from the 'on' and 'off' neurons\n", + "are shown in the bottom graph on the left.\n", + "On the right are the subthreshold voltages for the neurons." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/dynamics/controlled-integrator.ipynb b/.doctrees/nbsphinx/examples/dynamics/controlled-integrator.ipynb new file mode 100644 index 0000000000..6d32fd927b --- /dev/null +++ b/.doctrees/nbsphinx/examples/dynamics/controlled-integrator.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled integrator\n", + "\n", + "A controlled integrator is a circuit that acts on two signals:\n", + "\n", + "1. Input - the signal being integrated\n", + "2. Control - the control signal to the integrator\n", + "\n", + "A controlled integrator accumulates input,\n", + "but its state can be directly manipulated by the control signal.\n", + "We can write the dynamics of a simple controlled integrator like this:\n", + "\n", + "$$\n", + "\\dot{a}(t) = \\mathrm{control}(t) \\cdot a(t) + B \\cdot \\mathrm{input}(t)\n", + "$$\n", + "\n", + "In this notebook, we will build a controlled intgrator with LIF neurons.\n", + "The Neural Engineering Framework (NEF) equivalent equation\n", + "for this integrator is:\n", + "\n", + "$$\n", + "\\dot{a}(t) = \\mathrm{control}(t) \\cdot a(t) + \\tau \\cdot \\mathrm{input}(t).\n", + "$$\n", + "\n", + "We call the coefficient $\\tau$ here a *recurrent time constant*\n", + "because it governs the rate of integration.\n", + "\n", + "Network behaviour:\n", + "`A = tau * Input + Input * Control`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.060341Z", + "iopub.status.busy": "2020-11-25T16:47:36.059463Z", + "iopub.status.idle": "2020-11-25T16:47:36.554755Z", + "shell.execute_reply": "2020-11-25T16:47:36.553814Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "We can use standard network-creation commands\n", + "to begin creating our controlled integrator.\n", + "We create a Network, and then we create\n", + "a population of neurons (called an *ensemble*).\n", + "This population of neurons will represent the state of our integrator,\n", + "and the connections between the neurons in the ensemble\n", + "will define the dynamics of our integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.560204Z", + "iopub.status.busy": "2020-11-25T16:47:36.559527Z", + "iopub.status.idle": "2020-11-25T16:47:36.562211Z", + "shell.execute_reply": "2020-11-25T16:47:36.562606Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Controlled Integrator\")\n", + "with model:\n", + " # Make a population with 225 LIF neurons\n", + " # representing a 2 dimensional signal,\n", + " # with a larger radius to accommodate large inputs\n", + " A = nengo.Ensemble(225, dimensions=2, radius=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Define the 'input' signal to integrate\n", + "\n", + "We will be running 1 second of simulation time,\n", + "so we will use a Python function `input_func`\n", + "to define our input signal for real values of time `t` from 0 to 1.\n", + "We'll define our signal to be a step function using if-then-else code.\n", + "Our piecewise function sits at 0 until .2 seconds into the simulation,\n", + "then jumps up to 5, back to 0, down to -10, back to 0, then up to 5,\n", + "and then back to 0. Our integrator will respond by ramping up\n", + "when the input is positive, and descending when the input is negative." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.566970Z", + "iopub.status.busy": "2020-11-25T16:47:36.566461Z", + "iopub.status.idle": "2020-11-25T16:47:36.569945Z", + "shell.execute_reply": "2020-11-25T16:47:36.569486Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " input_func = Piecewise({0: 0, 0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We include this input function (`input_func`)\n", + "into our neural model like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.577416Z", + "iopub.status.busy": "2020-11-25T16:47:36.575977Z", + "iopub.status.idle": "2020-11-25T16:47:36.578065Z", + "shell.execute_reply": "2020-11-25T16:47:36.578471Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Define an input signal within our model\n", + " inp = nengo.Node(input_func)\n", + "\n", + " # Connect the Input signal to ensemble A.\n", + " # The `transform` argument means \"connect real-valued signal\n", + " # \"Input\" to the first of the two input channels of A.\"\n", + " tau = 0.1\n", + " nengo.Connection(inp, A, transform=[[tau], [0]], synapse=tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Define the 'control' signal\n", + "\n", + "We also need to create a control signal\n", + "that controls how the integrator behaves.\n", + "We will make this signal 1 for the first part of the simulation,\n", + "and 0.5 for the second part.\n", + "This means that at the beginning of the simulation,\n", + "the integrator will act as an optimal integrator,\n", + "and partway though the simulation (at t = 0.6),\n", + "it will switch to being a leaky integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.583436Z", + "iopub.status.busy": "2020-11-25T16:47:36.581993Z", + "iopub.status.idle": "2020-11-25T16:47:36.584215Z", + "shell.execute_reply": "2020-11-25T16:47:36.584636Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Another piecewise step that changes half way through the run\n", + " control_func = Piecewise({0: 1, 0.6: 0.5})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We add the control signal to the network\n", + "like we added the input signal,\n", + "but this time we connect it to\n", + "the second dimension of our neural population." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.590443Z", + "iopub.status.busy": "2020-11-25T16:47:36.588976Z", + "iopub.status.idle": "2020-11-25T16:47:36.591066Z", + "shell.execute_reply": "2020-11-25T16:47:36.591473Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " control = nengo.Node(output=control_func)\n", + "\n", + " # Connect the \"Control\" signal to the second of A's two input channels.\n", + " nengo.Connection(control, A[1], synapse=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Define the integrator dynamics\n", + "\n", + "We set up integrator by connecting population 'A' to itself.\n", + "We set up feedback in the model to handle integration of the input.\n", + "The time constant $\\tau$ on the recurrent weights affects\n", + "both the rate and accuracy of integration.\n", + "Try adjusting it and see what happens!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.598061Z", + "iopub.status.busy": "2020-11-25T16:47:36.596633Z", + "iopub.status.idle": "2020-11-25T16:47:36.598703Z", + "shell.execute_reply": "2020-11-25T16:47:36.599118Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a recurrent connection that first takes the product\n", + " # of both dimensions in A (i.e., the value times the control)\n", + " # and then adds this back into the first dimension of A using\n", + " # a transform\n", + " nengo.Connection(\n", + " A,\n", + " A[0], # -- transform converts function output to new state inputs\n", + " function=lambda x: x[0] * x[1], # -- function is applied first to A\n", + " synapse=tau,\n", + " )\n", + "\n", + " # Record both dimensions of A\n", + " A_probe = nengo.Probe(A, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.604531Z", + "iopub.status.busy": "2020-11-25T16:47:36.603788Z", + "iopub.status.idle": "2020-11-25T16:47:36.995086Z", + "shell.execute_reply": "2020-11-25T16:47:36.994198Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1.4) # Run for 1.4 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:37.039383Z", + "iopub.status.busy": "2020-11-25T16:47:37.038348Z", + "iopub.status.idle": "2020-11-25T16:47:37.421061Z", + "shell.execute_reply": "2020-11-25T16:47:37.421475Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the value and control signals, along with the exact integral\n", + "t = sim.trange()\n", + "dt = t[1] - t[0]\n", + "input_sig = input_func.run(t[-1], dt=dt)\n", + "control_sig = control_func.run(t[-1], dt=dt)\n", + "ref = dt * np.cumsum(input_sig)\n", + "\n", + "plt.figure(figsize=(6, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, input_sig, label=\"Input\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-11, 11)\n", + "plt.ylabel(\"Input\")\n", + "plt.legend(loc=\"lower left\", frameon=False)\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(t, ref, \"k--\", label=\"Exact\")\n", + "plt.plot(t, sim.data[A_probe][:, 0], label=\"A (value)\")\n", + "plt.plot(t, sim.data[A_probe][:, 1], label=\"A (control)\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"x(t)\")\n", + "plt.legend(loc=\"lower left\", frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows the output of our system,\n", + "specifically the (integrated) value stored by the A population,\n", + "along with the control signal represented by the A population.\n", + "The exact value of the integral,\n", + "as performed by a perfect (non-neural) integrator,\n", + "is shown for reference.\n", + "\n", + "When the control value is 1 (t < 0.6),\n", + "the neural integrator performs near-perfect integration.\n", + "However, when the control value drops to 0.5 (t > 0.6),\n", + "the integrator becomes a leaky integrator.\n", + "This means that in the absence of input,\n", + "its stored value drifts towards zero." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/dynamics/controlled-integrator2.ipynb b/.doctrees/nbsphinx/examples/dynamics/controlled-integrator2.ipynb new file mode 100644 index 0000000000..fa122c1133 --- /dev/null +++ b/.doctrees/nbsphinx/examples/dynamics/controlled-integrator2.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled integrator 2\n", + "\n", + "This demo implements a controlled one-dimensional neural integrator\n", + "that is functionally the same as\n", + "the controlled integrator in the previous example.\n", + "However, the control signal is zero for integration,\n", + "less than one for low-pass filtering, and greater than 1 for saturation.\n", + "This behavior maps more directly to the differential equation\n", + "used to describe an integrator:\n", + "\n", + "$$\\dot{x} = \\mathrm{Ax}(t) + \\mathrm{Bu}(t)$$\n", + "\n", + "The control in this circuit is $A$ in that equation.\n", + "This is also the controlled integrator\n", + "described in the book \"How to build a brain.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:38.745045Z", + "iopub.status.busy": "2020-11-25T16:47:38.744195Z", + "iopub.status.idle": "2020-11-25T16:47:39.237348Z", + "shell.execute_reply": "2020-11-25T16:47:39.236848Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "As before, we use standard network-creation commands\n", + "to begin creating our controlled integrator.\n", + "An ensemble of neurons will represent the state of our integrator,\n", + "and the connections between the neurons in the ensemble\n", + "will define the dynamics of our integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.242335Z", + "iopub.status.busy": "2020-11-25T16:47:39.241811Z", + "iopub.status.idle": "2020-11-25T16:47:39.245574Z", + "shell.execute_reply": "2020-11-25T16:47:39.245121Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Controlled Integrator 2\")\n", + "with model:\n", + " # Make a population with 225 LIF neurons representing a 2 dimensional\n", + " # signal, with a larger radius to accommodate large inputs\n", + " A = nengo.Ensemble(225, dimensions=2, radius=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Define the 'input' signal to integrate\n", + "\n", + "We will be running 1 second of simulation time again,\n", + "so we will use the same Python function `input_func`\n", + "to define our input signal. This piecewise function sits at 0\n", + "until .2 seconds into the simulation,\n", + "then jumps up to 5, back to 0, down to -10, back to 0, then up to 5,\n", + "and then back to 0. Our integrator will respond by ramping up\n", + "when the input is positive, and descending when the input is negative." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.253763Z", + "iopub.status.busy": "2020-11-25T16:47:39.252182Z", + "iopub.status.idle": "2020-11-25T16:47:39.254369Z", + "shell.execute_reply": "2020-11-25T16:47:39.254780Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " input_func = Piecewise({0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})\n", + " inp = nengo.Node(output=input_func)\n", + "\n", + " # Connect the Input signal to ensemble A.\n", + " tau = 0.1\n", + " nengo.Connection(inp, A, transform=[[tau], [0]], synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Define the control signal\n", + "\n", + "The control signal will be 0 for the first part of the simulation,\n", + "and -0.5 for the second part.\n", + "This means that at the beginning of the simulation,\n", + "the integrator will act as an optimal integrator,\n", + "and partway though the simulation (at t = 0.6),\n", + "it will switch to being a leaky integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.261376Z", + "iopub.status.busy": "2020-11-25T16:47:39.259799Z", + "iopub.status.idle": "2020-11-25T16:47:39.261955Z", + "shell.execute_reply": "2020-11-25T16:47:39.262382Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Another piecewise function that changes half way through the run\n", + " control_func = Piecewise({0: 0, 0.6: -0.5})\n", + " control = nengo.Node(output=control_func)\n", + "\n", + " # Connect the \"Control\" signal to the second of A's two input channels\n", + " nengo.Connection(control, A[1], synapse=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Define the integrator dynamics\n", + "\n", + "We set up integrator by connecting population 'A' to itself.\n", + "We set up feedback in the model to handle integration of the input.\n", + "The time constant $\\tau$ on the recurrent weights\n", + "affects both the rate and accuracy of integration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.268930Z", + "iopub.status.busy": "2020-11-25T16:47:39.267413Z", + "iopub.status.idle": "2020-11-25T16:47:39.269558Z", + "shell.execute_reply": "2020-11-25T16:47:39.269960Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Note the changes from the previous example to the function being defined.\n", + " nengo.Connection(A, A[0], function=lambda x: x[0] * x[1] + x[0], synapse=tau)\n", + "\n", + " # Record both dimensions of A\n", + " A_probe = nengo.Probe(A, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model and plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.275252Z", + "iopub.status.busy": "2020-11-25T16:47:39.274489Z", + "iopub.status.idle": "2020-11-25T16:47:39.643776Z", + "shell.execute_reply": "2020-11-25T16:47:39.642819Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1.4) # Run for 1.4 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.764078Z", + "iopub.status.busy": "2020-11-25T16:47:39.760404Z", + "iopub.status.idle": "2020-11-25T16:47:40.098616Z", + "shell.execute_reply": "2020-11-25T16:47:40.099016Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the value and control signals, along with the exact integral\n", + "t = sim.trange()\n", + "dt = t[1] - t[0]\n", + "input_sig = input_func.run(t[-1], dt=dt)\n", + "control_sig = control_func.run(t[-1], dt=dt)\n", + "ref = dt * np.cumsum(input_sig)\n", + "\n", + "plt.figure(figsize=(6, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, input_sig, label=\"Input\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-11, 11)\n", + "plt.ylabel(\"Input\")\n", + "plt.legend(loc=\"lower left\", frameon=False)\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(t, ref, \"k--\", label=\"exact\")\n", + "plt.plot(t, sim.data[A_probe][:, 0], label=\"A (value)\")\n", + "plt.plot(t, sim.data[A_probe][:, 1], label=\"A (control)\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"x(t)\")\n", + "plt.legend(loc=\"lower left\", frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows the output of our system,\n", + "specifically the (integrated) value stored by the A population,\n", + "along with the control signal represented by the A population.\n", + "The exact value of the integral,\n", + "as performed by a perfect (non-neural) integrator,\n", + "is shown for reference.\n", + "\n", + "When the control value is 0 (t < 0.6),\n", + "the neural integrator performs near-perfect integration.\n", + "However, when the control value drops to -0.5 (t > 0.6),\n", + "the integrator becomes a leaky integrator.\n", + "This means that with negative input,\n", + "its stored value drifts towards zero." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/dynamics/controlled-oscillator.ipynb b/.doctrees/nbsphinx/examples/dynamics/controlled-oscillator.ipynb new file mode 100644 index 0000000000..501e5e0d61 --- /dev/null +++ b/.doctrees/nbsphinx/examples/dynamics/controlled-oscillator.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled oscillator\n", + "\n", + "The controlled oscillator is an oscillator\n", + "with an extra input that controls the frequency of the oscillation.\n", + "\n", + "To implement a basic oscillator,\n", + "we would use a neural ensemble of two dimensions\n", + "that has the following dynamics:\n", + "\n", + "$$\n", + "\\dot{x} = \\begin{bmatrix} 0 && - \\omega \\\\ \\omega && 0 \\end{bmatrix} x\n", + "$$\n", + "\n", + "where the frequency of oscillation is $\\omega \\over {2 \\pi}$ Hz.\n", + "\n", + "We need the neurons to represent three variables,\n", + "$x_0$, $x_1$, and $\\omega$.\n", + "According the the dynamics principle of the NEF,\n", + "in order to implement some particular dynamics,\n", + "we need to convert this dynamics equation into a feedback function:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " \\dot{x} &= f(x) \\\\\n", + " &\\implies f_{feedback}(x) = x + \\tau f(x)\n", + "\\end{align}\n", + "$$\n", + "\n", + "where $\\tau$ is the post-synaptic time constant of the feedback connection.\n", + "\n", + "In this case, the feedback function to be computed is\n", + "\n", + "$$\n", + "\\begin{align}\n", + " f_{feedback}(x) &= x + \\tau\n", + " \\begin{bmatrix}\n", + " 0 && - \\omega \\\\\n", + " \\omega && 0\n", + " \\end{bmatrix}\n", + " x \\\\\n", + " &=\n", + " \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot x_0\n", + " \\end{bmatrix}\n", + "\\end{align}\n", + "$$\n", + "\n", + "Since the neural ensemble represents all three variables\n", + "but the dynamics only affects the first two ($x_0$, $x_1$),\n", + "we need the feedback function to not affect that last variable.\n", + "We do this by adding a zero to the feedback function.\n", + "\n", + "$$\n", + "f_{feedback}(x) = \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot x_0 \\\\\n", + " 0 \\end{bmatrix}\n", + "$$\n", + "\n", + "We also generally want to keep\n", + "the ranges of variables represented within an ensemble\n", + "to be approximately the same.\n", + "In this case, if $x_0$ and $x_1$ are between -1 and 1,\n", + "$\\omega$ will also be between -1 and 1,\n", + "giving a frequency range of $-1 \\over {2 \\pi}$ to $1 \\over {2 \\pi}$.\n", + "To increase this range,\n", + "we introduce a scaling factor to $\\omega$ called $\\omega_{max}$.\n", + "\n", + "$$\n", + "f_{feedback}(x) = \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot \\omega_{max} \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot \\omega_{max} \\cdot x_0 \\\\\n", + " 0 \\end{bmatrix}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.444248Z", + "iopub.status.busy": "2020-11-25T16:47:41.443379Z", + "iopub.status.idle": "2020-11-25T16:47:41.938115Z", + "shell.execute_reply": "2020-11-25T16:47:41.937594Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.947952Z", + "iopub.status.busy": "2020-11-25T16:47:41.947369Z", + "iopub.status.idle": "2020-11-25T16:47:41.950571Z", + "shell.execute_reply": "2020-11-25T16:47:41.950969Z" + } + }, + "outputs": [], + "source": [ + "tau = 0.1 # Post-synaptic time constant for feedback\n", + "w_max = 10 # Maximum frequency in Hz is w_max/(2*pi)\n", + "\n", + "model = nengo.Network(label=\"Controlled Oscillator\")\n", + "with model:\n", + " # The ensemble for the oscillator\n", + " oscillator = nengo.Ensemble(500, dimensions=3, radius=1.7)\n", + "\n", + " # The feedback connection\n", + " def feedback(x):\n", + " x0, x1, w = x # These are the three variables stored in the ensemble\n", + " return x0 - w * w_max * tau * x1, x1 + w * w_max * tau * x0, 0\n", + "\n", + " nengo.Connection(oscillator, oscillator, function=feedback, synapse=tau)\n", + "\n", + " # The ensemble for controlling the speed of oscillation\n", + " frequency = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " nengo.Connection(frequency, oscillator[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create the input" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.957796Z", + "iopub.status.busy": "2020-11-25T16:47:41.957278Z", + "iopub.status.idle": "2020-11-25T16:47:41.960842Z", + "shell.execute_reply": "2020-11-25T16:47:41.960370Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # We need a quick input at the beginning to start the oscillator\n", + " initial = nengo.Node(Piecewise({0: [1, 0, 0], 0.15: [0, 0, 0]}))\n", + " nengo.Connection(initial, oscillator)\n", + "\n", + " # Vary the speed over time\n", + " input_frequency = nengo.Node(Piecewise({0: 1, 1: 0.5, 2: 0, 3: -0.5, 4: -1}))\n", + "\n", + " nengo.Connection(input_frequency, frequency)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Add Probes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.966140Z", + "iopub.status.busy": "2020-11-25T16:47:41.964618Z", + "iopub.status.idle": "2020-11-25T16:47:41.966749Z", + "shell.execute_reply": "2020-11-25T16:47:41.967153Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Indicate which values to record\n", + " oscillator_probe = nengo.Probe(oscillator, synapse=0.03)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.972218Z", + "iopub.status.busy": "2020-11-25T16:47:41.971445Z", + "iopub.status.idle": "2020-11-25T16:47:43.257727Z", + "shell.execute_reply": "2020-11-25T16:47:43.257252Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:43.263384Z", + "iopub.status.busy": "2020-11-25T16:47:43.262188Z", + "iopub.status.idle": "2020-11-25T16:47:43.662460Z", + "shell.execute_reply": "2020-11-25T16:47:43.661980Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[oscillator_probe])\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.legend([\"$x_0$\", \"$x_1$\", r\"$\\omega$\"])" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/dynamics/integrator.ipynb b/.doctrees/nbsphinx/examples/dynamics/integrator.ipynb new file mode 100644 index 0000000000..f3acad3e04 --- /dev/null +++ b/.doctrees/nbsphinx/examples/dynamics/integrator.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrator\n", + "\n", + "This demo implements a one-dimensional neural integrator.\n", + "\n", + "This is the first example of a recurrent network in the demos.\n", + "It shows how neurons can be used to implement stable dynamics.\n", + "Such dynamics are important for memory, noise cleanup,\n", + "statistical inference, and many other dynamic transformations.\n", + "\n", + "When you run this demo,\n", + "it will automatically put in some step functions on the input,\n", + "so you can see that the output is\n", + "integrating (i.e. summing over time) the input.\n", + "You can also input your own values.\n", + "Note that since the integrator constantly sums its input,\n", + "it will saturate quickly if you leave the input non-zero.\n", + "This makes it clear that neurons have a finite range of representation.\n", + "Such saturation effects can be exploited\n", + "to perform useful computations (e.g. soft normalization)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.020012Z", + "iopub.status.busy": "2020-11-25T16:47:45.018193Z", + "iopub.status.idle": "2020-11-25T16:47:45.510839Z", + "shell.execute_reply": "2020-11-25T16:47:45.509886Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of one recurrently connected ensemble\n", + "and an input population." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.516020Z", + "iopub.status.busy": "2020-11-25T16:47:45.515427Z", + "iopub.status.idle": "2020-11-25T16:47:45.518774Z", + "shell.execute_reply": "2020-11-25T16:47:45.519169Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Integrator\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a piecewise step function as input,\n", + "so we can see the effects of recurrence." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.525563Z", + "iopub.status.busy": "2020-11-25T16:47:45.523963Z", + "iopub.status.idle": "2020-11-25T16:47:45.526138Z", + "shell.execute_reply": "2020-11-25T16:47:45.526552Z" + } + }, + "outputs": [], + "source": [ + "# Create a piecewise step function for input\n", + "with model:\n", + " input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.534790Z", + "iopub.status.busy": "2020-11-25T16:47:45.533316Z", + "iopub.status.idle": "2020-11-25T16:47:45.535481Z", + "shell.execute_reply": "2020-11-25T16:47:45.535957Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the population to itself\n", + " tau = 0.1\n", + " nengo.Connection(\n", + " A, A, transform=[[1]], synapse=tau\n", + " ) # Using a long time constant for stability\n", + "\n", + " # Connect the input\n", + " nengo.Connection(\n", + " input, A, transform=[[tau]], synapse=tau\n", + " ) # The same time constant as recurrent to make it more 'ideal'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.541698Z", + "iopub.status.busy": "2020-11-25T16:47:45.540146Z", + "iopub.status.idle": "2020-11-25T16:47:45.542286Z", + "shell.execute_reply": "2020-11-25T16:47:45.542705Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Add probes\n", + " input_probe = nengo.Probe(input)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.548012Z", + "iopub.status.busy": "2020-11-25T16:47:45.547176Z", + "iopub.status.idle": "2020-11-25T16:47:46.321805Z", + "shell.execute_reply": "2020-11-25T16:47:46.321287Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 6 seconds\n", + " sim.run(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:46.327894Z", + "iopub.status.busy": "2020-11-25T16:47:46.326694Z", + "iopub.status.idle": "2020-11-25T16:47:46.543259Z", + "shell.execute_reply": "2020-11-25T16:47:46.543712Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], \"k\", label=\"Integrator output\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows the response to the input by the integrator.\n", + "Because it is implemented in neurons,\n", + "it will not be perfect (i.e. there will be drift).\n", + "Running several times will give a sense of\n", + "the kinds of drift you might expect.\n", + "Drift can be reduced by increasing the number of neurons." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/dynamics/lorenz-attractor.ipynb b/.doctrees/nbsphinx/examples/dynamics/lorenz-attractor.ipynb new file mode 100644 index 0000000000..1a14408a8e --- /dev/null +++ b/.doctrees/nbsphinx/examples/dynamics/lorenz-attractor.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Lorenz chaotic attractor\n", + "\n", + "This example shows the construction of a classic chaotic dynamical system:\n", + "the Lorenz \"butterfly\" attractor. The equations are:\n", + "\n", + "$$\n", + "\\dot{x}_0 = \\sigma(x_1 - x_0) \\\\\\\n", + "\\dot{x}_1 = x_0 (\\rho - x_2) - x_1 \\\\\\\n", + "\\dot{x}_2 = x_0 x_1 - \\beta x_2\n", + "$$\n", + "\n", + "Since $x_2$ is centered around approximately $\\rho$,\n", + "and since NEF ensembles are usually optimized\n", + "to represent values within a certain radius of the origin,\n", + "we substitute $x_2' = x_2 - \\rho$, giving these equations:\n", + "$$\n", + "\\dot{x}_0 = \\sigma(x_1 - x_0) \\\\\\\n", + "\\dot{x}_1 = - x_0 x_2' - x_1 \\\\\\\n", + "\\dot{x}_2' = x_0 x_1 - \\beta (x_2' + \\rho) - \\rho\n", + "$$\n", + "\n", + "For more information, see\n", + "http://compneuro.uwaterloo.ca/publications/eliasmith2005b.html\n", + "\"Chris Eliasmith. A unified approach to building\n", + "and controlling spiking attractor networks.\n", + "Neural computation, 7(6):1276-1314, 2005.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:47.905113Z", + "iopub.status.busy": "2020-11-25T16:47:47.904235Z", + "iopub.status.idle": "2020-11-25T16:47:48.400376Z", + "shell.execute_reply": "2020-11-25T16:47:48.399450Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:48.413102Z", + "iopub.status.busy": "2020-11-25T16:47:48.412233Z", + "iopub.status.idle": "2020-11-25T16:47:54.317250Z", + "shell.execute_reply": "2020-11-25T16:47:54.318820Z" + } + }, + "outputs": [], + "source": [ + "tau = 0.1\n", + "sigma = 10\n", + "beta = 8.0 / 3\n", + "rho = 28\n", + "\n", + "\n", + "def feedback(x):\n", + " dx0 = -sigma * x[0] + sigma * x[1]\n", + " dx1 = -x[0] * x[2] - x[1]\n", + " dx2 = x[0] * x[1] - beta * (x[2] + rho) - rho\n", + "\n", + " return [\n", + " dx0 * tau + x[0],\n", + " dx1 * tau + x[1],\n", + " dx2 * tau + x[2],\n", + " ]\n", + "\n", + "\n", + "model = nengo.Network(label=\"Lorenz attractor\")\n", + "with model:\n", + " state = nengo.Ensemble(2000, 3, radius=60)\n", + " nengo.Connection(state, state, function=feedback, synapse=tau)\n", + " state_probe = nengo.Probe(state, synapse=tau)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:54.325510Z", + "iopub.status.busy": "2020-11-25T16:47:54.323488Z", + "iopub.status.idle": "2020-11-25T16:47:54.720059Z", + "shell.execute_reply": "2020-11-25T16:47:54.720494Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.figure().add_subplot(111, projection=Axes3D.name)\n", + "ax.plot(*sim.data[state_probe].T)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[state_probe])" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/dynamics/oscillator.ipynb b/.doctrees/nbsphinx/examples/dynamics/oscillator.ipynb new file mode 100644 index 0000000000..e35e4fcd3c --- /dev/null +++ b/.doctrees/nbsphinx/examples/dynamics/oscillator.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple harmonic oscillator\n", + "\n", + "This demo implements a simple harmonic oscillator\n", + "in a 2D neural population.\n", + "The oscillator is more visually interesting on its own\n", + "than the integrator, but the principle at work is the same.\n", + "Here, instead of having the recurrent input just integrate\n", + "(i.e. feeding the full input value back to the population),\n", + "we have two dimensions which interact." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:55.936227Z", + "iopub.status.busy": "2020-11-25T16:47:55.935349Z", + "iopub.status.idle": "2020-11-25T16:47:56.430103Z", + "shell.execute_reply": "2020-11-25T16:47:56.429588Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model consists of a single neural ensemble that we will call `Neurons`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.435146Z", + "iopub.status.busy": "2020-11-25T16:47:56.434626Z", + "iopub.status.idle": "2020-11-25T16:47:56.438195Z", + "shell.execute_reply": "2020-11-25T16:47:56.437736Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Oscillator\")\n", + "with model:\n", + " # Create the ensemble for the oscillator\n", + " neurons = nengo.Ensemble(200, dimensions=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A brief input signal is provided\n", + "to trigger the oscillatory behavior of the neural representation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.446638Z", + "iopub.status.busy": "2020-11-25T16:47:56.445090Z", + "iopub.status.idle": "2020-11-25T16:47:56.447266Z", + "shell.execute_reply": "2020-11-25T16:47:56.447698Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input signal\n", + " input = nengo.Node(Piecewise({0: [1, 0], 0.1: [0, 0]}))\n", + "\n", + " # Connect the input signal to the neural ensemble\n", + " nengo.Connection(input, neurons)\n", + "\n", + " # Create the feedback connection\n", + " nengo.Connection(neurons, neurons, transform=[[1, 1], [-1, 1]], synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Add Probes\n", + "\n", + "These probes will collect data from the input signal and the neural ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.453377Z", + "iopub.status.busy": "2020-11-25T16:47:56.451900Z", + "iopub.status.idle": "2020-11-25T16:47:56.454015Z", + "shell.execute_reply": "2020-11-25T16:47:56.454424Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " input_probe = nengo.Probe(input, \"output\")\n", + " neuron_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.459492Z", + "iopub.status.busy": "2020-11-25T16:47:56.458733Z", + "iopub.status.idle": "2020-11-25T16:47:57.224893Z", + "shell.execute_reply": "2020-11-25T16:47:57.224401Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:57.230805Z", + "iopub.status.busy": "2020-11-25T16:47:57.229637Z", + "iopub.status.idle": "2020-11-25T16:47:57.602591Z", + "shell.execute_reply": "2020-11-25T16:47:57.602132Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neuron_probe])\n", + "plt.xlabel(\"Time (s)\", fontsize=\"large\")\n", + "plt.legend([\"$x_0$\", \"$x_1$\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:57.626048Z", + "iopub.status.busy": "2020-11-25T16:47:57.623125Z", + "iopub.status.idle": "2020-11-25T16:47:57.796240Z", + "shell.execute_reply": "2020-11-25T16:47:57.795804Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data = sim.data[neuron_probe]\n", + "plt.figure()\n", + "plt.plot(data[:, 0], data[:, 1], label=\"Decoded Output\")\n", + "plt.xlabel(\"$x_0$\", fontsize=20)\n", + "plt.ylabel(\"$x_1$\", fontsize=20)\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/learning/learn-associations.ipynb b/.doctrees/nbsphinx/examples/learning/learn-associations.ipynb new file mode 100644 index 0000000000..5ef5ac5985 --- /dev/null +++ b/.doctrees/nbsphinx/examples/learning/learn-associations.ipynb @@ -0,0 +1,546 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning new associations\n", + "\n", + "Being able to learn an input-output mapping\n", + "(or a _heteroassociative memory_)\n", + "is useful for storing and recalling associations.\n", + "This is also a task required by more complicated models\n", + "that require some notion of long-term memory.\n", + "\n", + "In a perfect world, the PES rule could be applied\n", + "to learn this mapping from examples.\n", + "However, when two distinct inputs cause the same neurons to fire,\n", + "their decoded values will depend on one another.\n", + "This leads to difficulty when trying to store\n", + "multiple independent associations in the same memory.\n", + "\n", + "To solve this problem,\n", + "a vector-space analog of Oja's rule,\n", + "dubbed Vector-Oja's rule (or simply _Voja's rule_) was proposed.\n", + "In essence, this unsupervised learning rule\n", + "makes neurons fire selectively in response to their input.\n", + "When used in conjunction with properly-chosen intercepts\n", + "(corresponding to the largest dot-product between pairs of inputs),\n", + "this approach makes it possible to scalably\n", + "learn new associations in a spiking network.\n", + "\n", + "Voja's rule works by moving the encoders\n", + "of the active neurons toward the current input.\n", + "This can be stated succinctly as,\n", + "\n", + "$$\n", + "\\Delta e_i = \\kappa a_i (x - e_i)\n", + "$$\n", + "\n", + "where $e_i$ is the encoder of the $i^{th}$ neuron,\n", + "$\\kappa$ is a modulatory learning rate\n", + "(positive to move towards, and negative to move away),\n", + "$a_i$ is the filtered activity of the $i^{th}$ neuron,\n", + "and $x$ is the input vector encoded by each neuron.\n", + "To see how this is related to Oja's rule,\n", + "substituting $e_i$ with the row of weights $W_i$,\n", + "$x$ for the pre-synaptic activity vector $b$,\n", + "and letting $s = 1 / a_i$ be a dynamic normalizing factor, gives\n", + "\n", + "$$\n", + "\\Delta W_i = \\kappa a_i (b - s a_i W_i)\n", + "$$\n", + "\n", + "which is the update rule for a single row using Oja.\n", + "For more details,\n", + "see [Learning large-scale heteroassociative memories in spiking neurons](\n", + "http://compneuro.uwaterloo.ca/publications/voelker2014a.html).\n", + "\n", + "This notebook will lead the reader through\n", + "a basic example of building a network\n", + "that can store and recall new associations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Configure some example data\n", + "\n", + "First, we will setup some keys (inputs) and values (outputs)\n", + "for our network to store and recall." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.088780Z", + "iopub.status.busy": "2020-11-25T16:47:59.087901Z", + "iopub.status.idle": "2020-11-25T16:47:59.584640Z", + "shell.execute_reply": "2020-11-25T16:47:59.585086Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.591363Z", + "iopub.status.busy": "2020-11-25T16:47:59.590814Z", + "iopub.status.idle": "2020-11-25T16:47:59.594430Z", + "shell.execute_reply": "2020-11-25T16:47:59.593985Z" + } + }, + "outputs": [], + "source": [ + "num_items = 5\n", + "\n", + "d_key = 2\n", + "d_value = 4\n", + "\n", + "rng = np.random.RandomState(seed=7)\n", + "keys = nengo.dists.UniformHypersphere(surface=True).sample(num_items, d_key, rng=rng)\n", + "values = nengo.dists.UniformHypersphere(surface=False).sample(\n", + " num_items, d_value, rng=rng\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An important quantity is the largest dot-product\n", + "between all pairs of keys,\n", + "since a neuron's intercept should not go below this value\n", + "if it's positioned between these two keys.\n", + "Otherwise, the neuron will move back and forth\n", + "between encoding those two inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.599025Z", + "iopub.status.busy": "2020-11-25T16:47:59.598509Z", + "iopub.status.idle": "2020-11-25T16:47:59.602714Z", + "shell.execute_reply": "2020-11-25T16:47:59.602239Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intercept: 0.6952474346952748\n" + ] + } + ], + "source": [ + "intercept = (np.dot(keys, keys.T) - np.eye(num_items)).flatten().max()\n", + "print(\"Intercept: %s\" % intercept)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Build the model\n", + "\n", + "We define a helper function that is useful\n", + "for creating nodes that cycle through keys/values." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.609415Z", + "iopub.status.busy": "2020-11-25T16:47:59.607818Z", + "iopub.status.idle": "2020-11-25T16:47:59.610140Z", + "shell.execute_reply": "2020-11-25T16:47:59.610567Z" + } + }, + "outputs": [], + "source": [ + "def cycle_array(x, period, dt=0.001):\n", + " \"\"\"Cycles through the elements\"\"\"\n", + " i_every = int(round(period / dt))\n", + " if i_every != period / dt:\n", + " raise ValueError(\"dt (%s) does not divide period (%s)\" % (dt, period))\n", + "\n", + " def f(t):\n", + " i = int(round((t - dt) / dt)) # t starts at dt\n", + " return x[int(i / i_every) % len(x)]\n", + "\n", + " return f" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create three inputs:\n", + "the keys, the values, and a modulatory learning signal.\n", + "The model is run continuously in two phases:\n", + "the first half learns the set of associations,\n", + "and the second tests recall.\n", + "\n", + "The learning signal will be set to 0\n", + "o allow learning during the first phase,\n", + "and -1 to inhibit learning during the second phase.\n", + "\n", + "The memory is confined to a single ensemble.\n", + "Roughly speaking, its encoders will hold the keys,\n", + "and its decoders will hold the values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.631486Z", + "iopub.status.busy": "2020-11-25T16:47:59.628361Z", + "iopub.status.idle": "2020-11-25T16:47:59.633725Z", + "shell.execute_reply": "2020-11-25T16:47:59.633298Z" + } + }, + "outputs": [], + "source": [ + "# Model constants\n", + "n_neurons = 200\n", + "dt = 0.001\n", + "period = 0.3\n", + "T = period * num_items * 2\n", + "\n", + "# Model network\n", + "model = nengo.Network()\n", + "with model:\n", + "\n", + " # Create the inputs/outputs\n", + " stim_keys = nengo.Node(output=cycle_array(keys, period, dt))\n", + " stim_values = nengo.Node(output=cycle_array(values, period, dt))\n", + " learning = nengo.Node(output=lambda t: -int(t >= T / 2))\n", + " recall = nengo.Node(size_in=d_value)\n", + "\n", + " # Create the memory\n", + " memory = nengo.Ensemble(n_neurons, d_key, intercepts=[intercept] * n_neurons)\n", + "\n", + " # Learn the encoders/keys\n", + " voja = nengo.Voja(learning_rate=5e-2, post_synapse=None)\n", + " conn_in = nengo.Connection(stim_keys, memory, synapse=None, learning_rule_type=voja)\n", + " nengo.Connection(learning, conn_in.learning_rule, synapse=None)\n", + "\n", + " # Learn the decoders/values, initialized to a null function\n", + " conn_out = nengo.Connection(\n", + " memory,\n", + " recall,\n", + " learning_rule_type=nengo.PES(1e-3),\n", + " function=lambda x: np.zeros(d_value),\n", + " )\n", + "\n", + " # Create the error population\n", + " error = nengo.Ensemble(n_neurons, d_value)\n", + " nengo.Connection(\n", + " learning, error.neurons, transform=[[10.0]] * n_neurons, synapse=None\n", + " )\n", + "\n", + " # Calculate the error and use it to drive the PES rule\n", + " nengo.Connection(stim_values, error, transform=-1, synapse=None)\n", + " nengo.Connection(recall, error, synapse=None)\n", + " nengo.Connection(error, conn_out.learning_rule)\n", + "\n", + " # Setup probes\n", + " p_keys = nengo.Probe(stim_keys, synapse=None)\n", + " p_values = nengo.Probe(stim_values, synapse=None)\n", + " p_learning = nengo.Probe(learning, synapse=None)\n", + " p_error = nengo.Probe(error, synapse=0.005)\n", + " p_recall = nengo.Probe(recall, synapse=None)\n", + " p_encoders = nengo.Probe(conn_in.learning_rule, \"scaled_encoders\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Running the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.639508Z", + "iopub.status.busy": "2020-11-25T16:47:59.638754Z", + "iopub.status.idle": "2020-11-25T16:48:00.683949Z", + "shell.execute_reply": "2020-11-25T16:48:00.684408Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model, dt=dt) as sim:\n", + " sim.run(T)\n", + "t = sim.trange()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plotting simulation output\n", + "\n", + "We first start by checking the keys, values, and learning signals." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:00.739236Z", + "iopub.status.busy": "2020-11-25T16:48:00.730881Z", + "iopub.status.idle": "2020-11-25T16:48:01.143091Z", + "shell.execute_reply": "2020-11-25T16:48:01.142353Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.2, 0.2)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.title(\"Keys\")\n", + "plt.plot(t, sim.data[p_keys])\n", + "plt.ylim(-1, 1)\n", + "\n", + "plt.figure()\n", + "plt.title(\"Values\")\n", + "plt.plot(t, sim.data[p_values])\n", + "plt.ylim(-1, 1)\n", + "\n", + "plt.figure()\n", + "plt.title(\"Learning\")\n", + "plt.plot(t, sim.data[p_learning])\n", + "plt.ylim(-1.2, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we look at the error during training and testing.\n", + "In the top figure, the error being minimized by PES\n", + "goes to zero for each association during the training phase.\n", + "In the bottom figure, the recall error is close to zero,\n", + "with momentary spikes each time a new key is presented." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:01.167634Z", + "iopub.status.busy": "2020-11-25T16:48:01.166621Z", + "iopub.status.idle": "2020-11-25T16:48:01.441342Z", + "shell.execute_reply": "2020-11-25T16:48:01.440914Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train = t <= T / 2\n", + "test = ~train\n", + "\n", + "plt.figure()\n", + "plt.title(\"Value Error During Training\")\n", + "plt.plot(t[train], sim.data[p_error][train])\n", + "\n", + "plt.figure()\n", + "plt.title(\"Value Error During Recall\")\n", + "plt.plot(t[test], sim.data[p_recall][test] - sim.data[p_values][test])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Examining encoder changes\n", + "\n", + "We can also plot the two-dimensional encoders before and after training.\n", + "Initially, they are uniformly distributed around the unit circle.\n", + "Afterward, we see that each key has attracted all of its nearby neurons.\n", + "Notably, almost all neurons are participating\n", + "in the representation of a unique association." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:01.449619Z", + "iopub.status.busy": "2020-11-25T16:48:01.448355Z", + "iopub.status.idle": "2020-11-25T16:48:01.804312Z", + "shell.execute_reply": "2020-11-25T16:48:01.803874Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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1WefjbCilOZkV6fYVC7A5nRXp9g0LsGlYZqYFN4kl3UAOY0z0LMDG+JgF2BgfswAb42N2ECvOlqwr4+GlW9lTEaR35wAzJw2pHzhhTKwswHG0ZF0ZsxdvIFjt3LmirCLI7MUbACzExhO2Cx1HDy/dWh/eOsHqGh5eurWF1sikGgtwHO2pCH+tbKTpxjSVBTiOencOf61spOnGNJUFOI7qLtMLFchIZ+akIS20RibV2EGsOKo7UGVHoU28WIDjbMrIPhZYEze2C22Mj1mAjfExC7BJbSleXdG+A5vU04qqK1qATWppZdUVbRfapI5WWF3Rky2wiFwOPAKkA/NVdd4p828BHuZENYbHVHW+F30bU8+trki/fiDCX4tKCVbX1s8OZKTx/VF9nRCnSHXFmLfAIpIOPI5TQnQo8EMRGRqm6UJVzXMfFl7jvcWLnVvhhgkvQLC6lr8WlTrfidu3d9r7nBe70GOAHaq6U1W/BV7GKSdqTOJUVjoHrLKzAU4Lb5366dnZKVFd0YsAR1s69GoRWS8ifxGRnDDzjWm+YNDZsqY1/k/6r0WlTrsUqK6YqINYrwMDVHU48C7wYqSGVh/YNEsg4Jwqqg2/5Q0VrK512qVAdUUvAtxo6VBVPaCqVe7L+cB5kRZm9YFNs2RmOud5Dx6Mrv3BgylRXdGLAK8FBonIWSLSFrgOp5xoPRHpFfLyKmCLB/0ac7KpU53aTaqkpzVQqFzVaZcC1RVjDrCqHgfuBJbiBHORqm4SkQdE5Cq32XQR2SQiJcB04JZY+zXmNKNGOYXXdu9mzIAu4duo0vub/U67FKiuaNUJTWoJqa749s7DVATOQEUQVToHDxM4XsX6noOYvuy5pBmJZdUJjakTUl1x58//i6H7dtYHeHOPgbwz+AI29RjI9CQJb6wswCb1uNUVH/7ONNod/5Z2x7+lqk1bqtq0bek185yNhTYprapNWw5ndjgtvEvWlUV4h79YgE2rlCr35rYAm5R14/n9Is4rS5F7c1uATcp6cEoukc4Gp0sD54l9xAJsUlqkk6Q1SXz6tCkswCal9YlQBSPSdL+xAJuUlurVMew8sElpqV4dwwJsUl4qV8ewXWhjfMwCbIyPWYCN8TELsDE+ZgE2xscswMb4mJ1GiuDeJRtYsGY3Naqki/DDsTk8OCW3pVfLmJNYgMOYs6iA1/72GW3atKWmTVtqVHlp9S4AC7FJKhbgOiElKS9Z+C4Xh9yGZengcWzscTYL1uy2AJukYgGGk0pSllUJezt2q7+P0sADpfziw5dY33MQT4z7l5ZeU2NOYgexQkpSfh7I5oODirrXiqoI5Vmd2NOxO8O/3M7PVr+SEiUpTerwJMAicrmIbBWRHSJyT5j57URkoTt/jYgM8KJfT9SVpMzJ4ZMvysO3EWFPx+5MOrbbKUlpTJJIVHnRHwPlqvoPwP8D/jPWfj0TUpKypjbyRd6DenZk8MCeKVGS0qSORJUXncyJgmZ/ASaIJME9TU4pSdmQ0QOyU6YkpUkdiSovWt/GLcVyCOjqQd+xaUJJSiBlSlKa1JF0B7ESWl60CSUpgZQpSWlSR0LKi4a2EZE2QCfgQLiFJbS8aCstSWlSR0LKi7qvp7nPfwCs0GSpqhZSkrIhn+8/kjIlKU3qSFR50WeBriKyA/gFcNqpphYTUpIyYohV+XLj9pQpSWlSh5UXhfqRWPuWr6Lo6ypflKQ0qcPKi8bKLUnZY/I69vziNwws3XFaScqKIf+YMiUpTeqwANdxS1J2feS33L2oEA0G60tSBjLSmXvFqWNTjGl5FuBThN5H+JuKIH1S7D7CJrVYgMNI5fsIm9SSdAM5jEmYykooL/f10FjbApvWJeTGDZSUOENjVZ0BPVOnOqcV09MbX06SsACb1iPkxg1kZUGfPs749tpa2LYN7rsP8vNh9mzfnC60XWjTOoTcuIGcHOjW7cRFLGlpzuucHGf+3Lm+uXGDbYFN61B344Z+/Xhjw14OB4+f1uT6sf2cEK9d69y4Ib9ZYysSyrbApnVwb9wQKbwAf16zy/lO3L69b27cYAE2qS/kxg2RwnsSH924wQJsUp9744a1uyqia++jGzfYd2CT+gIB9h06xo5vapxgNsZHN26wLbBJfZmZLG3Tk87Bw9G199GNGyzAplV4tf8YAserGrxxw/Vj+znzfXTjBguwaRUODhnG+p6D6P3N/rAhHnd2V2f67t2+unGDBdi0Cr+8YijPX3xdfYi7HDuEqJKeJowb2IWzCMKuXSdGYvlkOKUdxDKtQt3VZb89oz3Ltm7iB3//hEnH99KjUxZUVjjfeadOdba8PgkvWIBNK3LiMtHLnAmVlc6pokDAFweswrEAm9YrM9O3wa1j34GN8TELsDE+ZgE2xsdiCrCIZIvIuyKy3f3ZJUK7GhEpdh+nVm0wxjRTrFvge4DlqjoIWE7kigtBVc1zH1dFaGOMaaJYAxxa9/dFYEqMyzPGNEGsAe6hqnvd518CPSK0y3RLhq4WkSkNLTCh5UWN8blGzwOLyDKgZ5hZ/xb6QlVVRCKNFO+vqmUiMhBYISIbVPWzcA1V9WngaXBqIzW2fsa0Zo0GWFX/KdI8EdknIr1Uda+I9AK+irCMMvfnThF5HxgJhA2wMSZ6se5Ch9b9nQb896kNRKSLiLRzn3cDxgObY+zXGEPsAZ4HXCYi24F/cl8jIvkiMt9tcy5QICIlwHvAPFW1ABvjgZjGQqvqAWBCmOkFwG3u878BubH0Y4wJz0ZiGeNjFmBjfMwCbIyPWYCN8TELsDE+ZgE2xscswMb4mAXYGB+zABvjYxZgY3zMAmyMj1mAjfExC7AxPmYBNsbHLMDG+JgF2BgfswAb42MWYGN8zAJsTqishPJy56fxBasP3NrV1EBRESxeDCUlIAKqMGKEU7F+1ChfVaxvbSzArdmxYzB3LhQUQFYW9OkDaWlQWwvbtsF990F+Psye7cw3SccC3FrV1PDpXbMpX/kxn2dmgxyDz44BcEagDf88vLezJS4ocEI+Z45tiZNQrOVF/0VENolIrYjkN9DuchHZKiI7RCRSBUOTQO8veIf9K1a54ZWT5h0OHufPa3ax/NOvICcH1q6FdetaaE1NQ2I9iLURmAqsjNRARNKBx4ErgKHAD0VkaIz9mhjtmv8SR9LbnRbeUPsOVzkhbt/e+Y5skk5MAVbVLaq6tZFmY4AdqrpTVb8FXsYpS2paSmUlObu2UxE4o9Gm+w5XQXY2FBfb0ekklIjTSH2A3SGvS91pYVl50QQIBmnXtg3awNb3JGlpzpY6GIzvepkmazTAIrJMRDaGecRlK6qqT6tqvqrmd+/ePR5dmECAs7sFaBNlfqmtdQ5oBQJxXS3TdDGVF41SGZAT8rqvO820lMxMelx0PuPXlPDBwYZLMPc4ox0cPAh5eZCZmZj1M1FLxC70WmCQiJwlIm2B63DKkpqWNHUqfdrWcv2YnIhNepzRjgnnnAlHjzqDOkzSiek8sIh8H/g90B14U0SKVXWSiPQG5qvqlap6XETuBJYC6cBzqrop5jU3sRk1CkaPhoICJ8Thvg+rwu7dTruRIxO/jqZRotrwLlRLys/P14KCgpZejdRVNxJr7VrnVFF29omRWAcPOlve0aNtJFaciUihqkYcR9EQG4nVmmVlOSOs1q1zzvMWF58YC52X5+w2jxxpI7CSmAW4tUtPd8Y75+c753mDQedosx2w8gULsDkhM9OC6zN2PbAxPmYBNsbHLMDG+JgF2BgfswAb42MWYGN8zAJsjI9ZgI3xMQuwMT5mATbGxyzAxviYBdgYH7MAG+NjFmBjfMwCbIyPWYCN8TELsDE+ZgE2xscswMb4WKLKi34hIhtEpFhE7D6xxngk1pva1ZUX/UMUbS9V1a9j7M8YEyKmAKvqFgCJtsqdMcZTibqtrAL/IyIK/EFVn47UUERuB253X1aJyMZErGAY3YCW2mOwvltX30Oa+8ZGAywiy4CeYWb9m6r+d5T9XKiqZSJyJvCuiHyqqivDNXTD/bTbd0FzS07Eyvq2vhPZd3Pfm4jyoqhqmfvzKxH5KzAGCBtgY0z04n4aSUTai0jHuufARJyDX8aYGMV6Gun7IlIKjMMpL7rUnd5bRN5ym/UAVolICfAJ8KaqvhNlFxG/KyeA9W19J33fSV1e1BjTMBuJZYyPWYCN8bGkCXBLD8tsQv+Xi8hWEdkhIvd41He2iLwrItvdn10itKtxP3exiLwWY58Nfg4RaSciC935a0RkQCz9NbHvW0Rkf8hnvc2jfp8Tka8ijS0Qx6Pueq0XkVFe9Btl35eIyKGQz3xfVAtW1aR4AOfinNB+H8hvoN0XQLeW6B9IBz4DBgJtgRJgqAd9/xdwj/v8HuA/I7Q74tFnbfRzAP8KPOU+vw5YmMC+bwEei8Pf+GJgFLAxwvwrgbcBAc4H1iSw70uAN5q63KTZAqvqFlXdmuT9jwF2qOpOVf0WeBmY7EH3k4EX3ecvAlM8WGZDovkcoev0F2CCeDNmNl6/w0apM3joYANNJgN/VMdqoLOI9EpQ382SNAFugrphmYXusMtE6gPsDnld6k6LVQ9V3es+/xLn1Fs4mSJSICKrRWRKDP1F8znq26jqceAQ0DWGPpvSN8DV7m7sX0Qkx4N+oxGvv2+0xolIiYi8LSL/GM0bEjUWGkj8sMw49d8sDfUd+kJV1R0zHk5/97MPBFaIyAZV/czrdU0CrwMLVLVKRH6Ksyfw3RZep3grwvn7HhGRK4ElwKDG3pTQAGsLD8v0oP8yIHRr0NedFlPfIrJPRHqp6l53l+2rCMuo++w7ReR9YCTO98mmiuZz1LUpFZE2QCfgQDP6anLfqhraz3ycYwSJ0Oy/b6xU9XDI87dE5AkR6aaNXILrq13oJBiWuRYYJCJniUhbnIM7MR0Ndr0GTHOfTwNO2xsQkS4i0s593g0YD2xuZn/RfI7QdfoBsELdoy0xarTvU753XgVs8aDfaLwG3OwejT4fOBTy1SauRKRn3TEGERmDk83G/8P0+khfDEfpvo/znaMK2Acsdaf3Bt5ynw/EOWpZAmzC2fVNWP964kjlNpwtnyf943y3XA5sB5YB2e70fGC++/wCYIP72TcAP46xz9M+B/AAcJX7PBN4BdiBMwR2oIe/68b6nuv+fUuA94BzPOp3AbAXqHb/1j8G7gDucOcL8Li7Xhto4GxIHPq+M+QzrwYuiGa5NpTSGB/z1S60MeZkFmBjfMwCbIyPWYCN8TELsDE+ZgE2xscswMb42P8Hj/TCDhyixfcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "scale = (sim.data[memory].gain / memory.radius)[:, np.newaxis]\n", + "\n", + "\n", + "def plot_2d(text, xy):\n", + " plt.figure()\n", + " plt.title(text)\n", + " plt.scatter(xy[:, 0], xy[:, 1], label=\"Encoders\")\n", + " plt.scatter(keys[:, 0], keys[:, 1], c=\"red\", s=150, alpha=0.6, label=\"Keys\")\n", + " plt.xlim(-1.5, 1.5)\n", + " plt.ylim(-1.5, 2)\n", + " plt.legend()\n", + " plt.gca().set_aspect(\"equal\")\n", + "\n", + "\n", + "plot_2d(\"Before\", sim.data[p_encoders][0].copy() / scale)\n", + "plot_2d(\"After\", sim.data[p_encoders][-1].copy() / scale)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/learning/learn-communication-channel.ipynb b/.doctrees/nbsphinx/examples/learning/learn-communication-channel.ipynb new file mode 100644 index 0000000000..1b47b32f34 --- /dev/null +++ b/.doctrees/nbsphinx/examples/learning/learn-communication-channel.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning a communication channel\n", + "\n", + "Normally, if you have a function you would like to compute\n", + "across a connection, you would specify it with `function=my_func`\n", + "in the `Connection` constructor.\n", + "However, it is also possible to use error-driven learning\n", + "to learn to compute a function online." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the model without learning\n", + "\n", + "We'll start by creating a connection between two populations\n", + "that initially computes a very weird function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:03.212235Z", + "iopub.status.busy": "2020-11-25T16:48:03.211323Z", + "iopub.status.idle": "2020-11-25T16:48:03.704389Z", + "shell.execute_reply": "2020-11-25T16:48:03.703475Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal\n", + "from nengo.solvers import LstsqL2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:03.715109Z", + "iopub.status.busy": "2020-11-25T16:48:03.714589Z", + "iopub.status.idle": "2020-11-25T16:48:03.717665Z", + "shell.execute_reply": "2020-11-25T16:48:03.718061Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)\n", + " pre = nengo.Ensemble(60, dimensions=2)\n", + " nengo.Connection(inp, pre)\n", + " post = nengo.Ensemble(60, dimensions=2)\n", + " conn = nengo.Connection(pre, post, function=lambda x: np.random.random(2))\n", + " inp_p = nengo.Probe(inp)\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_p = nengo.Probe(post, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we run this model as is, we can see that the connection\n", + "from `pre` to `post` doesn't compute much of value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:03.723651Z", + "iopub.status.busy": "2020-11-25T16:48:03.722497Z", + "iopub.status.idle": "2020-11-25T16:48:05.127829Z", + "shell.execute_reply": "2020-11-25T16:48:05.128292Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:05.189086Z", + "iopub.status.busy": "2020-11-25T16:48:05.148649Z", + "iopub.status.idle": "2020-11-25T16:48:05.567932Z", + "shell.execute_reply": "2020-11-25T16:48:05.568369Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Add in learning\n", + "\n", + "If we can generate an error signal, then we can minimize\n", + "that error signal using the `nengo.PES` learning rule.\n", + "Since it's a communication channel, we know the value that we want,\n", + "so we can compute the error with another ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:05.578955Z", + "iopub.status.busy": "2020-11-25T16:48:05.577413Z", + "iopub.status.idle": "2020-11-25T16:48:05.579574Z", + "shell.execute_reply": "2020-11-25T16:48:05.580024Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " error = nengo.Ensemble(60, dimensions=2)\n", + " error_p = nengo.Probe(error, synapse=0.03)\n", + "\n", + " # Error = actual - target = post - pre\n", + " nengo.Connection(post, error)\n", + " nengo.Connection(pre, error, transform=-1)\n", + "\n", + " # Add the learning rule to the connection\n", + " conn.learning_rule_type = nengo.PES()\n", + "\n", + " # Connect the error into the learning rule\n", + " nengo.Connection(error, conn.learning_rule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can see the `post` population gradually learn to compute\n", + "the communication channel." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:05.585177Z", + "iopub.status.busy": "2020-11-25T16:48:05.584408Z", + "iopub.status.idle": "2020-11-25T16:48:07.485498Z", + "shell.execute_reply": "2020-11-25T16:48:07.484593Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:07.509427Z", + "iopub.status.busy": "2020-11-25T16:48:07.504278Z", + "iopub.status.idle": "2020-11-25T16:48:08.021500Z", + "shell.execute_reply": "2020-11-25T16:48:08.021026Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(), sim.data[error_p], c=\"b\")\n", + "plt.ylim(-1, 1)\n", + "plt.legend((\"Error[0]\", \"Error[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Does it generalize?\n", + "\n", + "If the learning rule is always working,\n", + "the error will continue to be minimized.\n", + "But have we actually generalized\n", + "to be able to compute the communication channel\n", + "without this error signal?\n", + "Let's inhibit the `error` population after 10 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:08.027274Z", + "iopub.status.busy": "2020-11-25T16:48:08.026766Z", + "iopub.status.idle": "2020-11-25T16:48:08.029835Z", + "shell.execute_reply": "2020-11-25T16:48:08.030281Z" + } + }, + "outputs": [], + "source": [ + "def inhibit(t):\n", + " return 2.0 if t > 10.0 else 0.0\n", + "\n", + "\n", + "with model:\n", + " inhib = nengo.Node(inhibit)\n", + " nengo.Connection(inhib, error.neurons, transform=[[-1]] * error.n_neurons)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:08.035378Z", + "iopub.status.busy": "2020-11-25T16:48:08.034629Z", + "iopub.status.idle": "2020-11-25T16:48:12.552486Z", + "shell.execute_reply": "2020-11-25T16:48:12.551981Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(16.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:12.581514Z", + "iopub.status.busy": "2020-11-25T16:48:12.578742Z", + "iopub.status.idle": "2020-11-25T16:48:13.292767Z", + "shell.execute_reply": "2020-11-25T16:48:13.292332Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(), sim.data[error_p], c=\"b\")\n", + "plt.ylim(-1, 1)\n", + "plt.legend((\"Error[0]\", \"Error[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How does this work?\n", + "\n", + "The `nengo.PES` learning rule minimizes the same error online\n", + "as the decoder solvers minimize with offline optimization.\n", + "\n", + "Let $\\mathbf{E}$ be an error signal.\n", + "In the communication channel case, the error signal\n", + "$\\mathbf{E} = \\mathbf{\\hat{x}} - \\mathbf{x}$;\n", + "in other words, it is the difference between\n", + "the decoded estimate of `post`, $\\mathbf{\\hat{x}}$,\n", + "and the decoded estimate of `pre`, $\\mathbf{x}$.\n", + "\n", + "The PES learning rule on decoders is\n", + "\n", + "$$\\Delta \\mathbf{d_i} = -\\frac{\\kappa}{n} \\mathbf{E} a_i$$\n", + "\n", + "where $\\mathbf{d_i}$ are the decoders being learned,\n", + "$\\kappa$ is a scalar learning rate, $n$ is the number of neurons\n", + "in the `pre` population,\n", + "and $a_i$ is the filtered activity of the `pre` population.\n", + "\n", + "However, many synaptic plasticity experiments\n", + "result in learning rules that explain how\n", + "individual connection weights change.\n", + "We can multiply both sides of the equation\n", + "by the encoders of the `post` population,\n", + "$\\mathbf{e_j}$, and the gain of the `post`\n", + "population $\\alpha_j$, as we do in\n", + "Principle 2 of the NEF.\n", + "This results in the learning rule\n", + "\n", + "$$\n", + "\\Delta \\omega_{ij} = \\Delta \\mathbf{d_i} \\cdot \\mathbf{e_j} \\alpha_j\n", + " = -\\frac{\\kappa}{n} \\alpha_j \\mathbf{e_j} \\cdot \\mathbf{E} a_i\n", + "$$\n", + "\n", + "where $\\omega_{ij}$ is the connection\n", + "between `pre` neuron $i$ and `post` neuron $j$.\n", + "\n", + "The weight-based version of PES can be easily combined with\n", + "learning rules that describe synaptic plasticity experiments.\n", + "In Nengo, the `Connection.learning_rule_type` parameter accepts\n", + "a list of learning rules.\n", + "See [Bekolay et al., 2013](\n", + "http://compneuro.uwaterloo.ca/publications/bekolay2013.html)\n", + "for details on what happens when the PES learning rule is\n", + "combined with an unsupervised learning rule." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How do the decoders / weights change?\n", + "\n", + "The equations above describe\n", + "how the decoders and connection weights change\n", + "as a result of the PES rule.\n", + "But are there any general principles\n", + "that we can say about how the rule\n", + "modifies decoders and connection weights?\n", + "Determining this requires analyzing\n", + "the decoders and connection weights\n", + "as they change over the course of a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:13.298513Z", + "iopub.status.busy": "2020-11-25T16:48:13.297109Z", + "iopub.status.idle": "2020-11-25T16:48:13.299216Z", + "shell.execute_reply": "2020-11-25T16:48:13.299621Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " weights_p = nengo.Probe(conn, \"weights\", synapse=0.01, sample_every=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:13.304740Z", + "iopub.status.busy": "2020-11-25T16:48:13.303969Z", + "iopub.status.idle": "2020-11-25T16:48:14.377928Z", + "shell.execute_reply": "2020-11-25T16:48:14.377437Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:14.405695Z", + "iopub.status.busy": "2020-11-25T16:48:14.400917Z", + "iopub.status.idle": "2020-11-25T16:48:14.908991Z", + "shell.execute_reply": "2020-11-25T16:48:14.908550Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5Z4+Z6Ra0I18NyczM5Pz587RpY86PHzvW8fwOHTqUGPP3pSZbjh6tPDafPQtz50K3biq16ZlnwLBKqNFo/Izs7GyTI//rr2osOrr0/eLiFnnQKo2mepORkUGzZs244QaVeGI3P/6rr5weY/Kpd9lGR2yy5vwO7chXQ3bu3AlgFZEPdFKg3aBBA6Kiohg79lnT2NmzHjPPIzRsCLVrw8aNqlW6bddaf+K662D0aOuxwkLf2KLRaBwjpbRKrVmjxGiYMaP0fZs2bUJ4+GLDcTxloUZTPTFqyBsvrps3t5nQvHnpnaEMdOrkXtvcjXbkqyFpaWkAVhH50vLd27VrR07OQtPjyuLIFxUVsWiROfLVtav6u2cP3HKLj4xywM6du2nb9ndmziy57fx5r5uj0WhK4cSJE+Tn55si8suWqXHjecYZUVFRnDw5FIBZszxkoEZTTcnIyCAmJsb0ePJkmwkG2VhX+OMP/65l0Y58NSQtLY2wsDCaNlVKNJGRYOHT26VNmzamSD6o7q/+zunTpxkyZAgXOpCFmDLFf6Tfjhw5Qp8+z7Jz52V2tycmQn6+l42qBGRmwgsvwMGDvrZEUx3JzlbVcEZHfswYaNzY+QqnkaioKNN9R3rzGo2m7OTn53PgwAGTI9+4MYSGWkyw98P/4otqaWxRyZS3IApYssRDxroB7chXQ3bu3El8fDyTJ6vEL1eaJbRp04ajR4+aHl/jtF+uf3DjjTeycuVKnnzyN4dznnrKiwY54bHHHuP48eNO57Rv7x1bKhOtWsHzz0OzZjo9QeN9jI68MbVm1SoIC3NtX0tHftUqOHLE7eZpNNWSzMxMpJTExsZSrx5cfbXNhK+/tn7899+qGA1UZ0ybSGUK7XnpxWKP2VtRtCNfDTFKTz7+uHp8ww2l72NMw/n662RAOVD+zOzZs/n999+ZOPFHXnvNfpQb7Cy3+YCMjAymTp1KnTrfOp23ezeMGOEloyoJxsCKlPDOO5WnoFlTNcgyREEiIiIoLoZjxzB1yi4N5cibP7Dakddo3INRerJx41YcP65SaU0kJ8Mdd5gfX389DBhgfYBOnUh/4w3Tw3h28TQv+80Kvi3aka9mFBcXk56eTisLT9yVXHGjIy/EJkB9Mfw1AlpcXMxTTz1Fu3bt+Prrq0qd72vn78MPP0QIwYkTjUqdu2CBFwzyc6SUtGx5lOBg61yjRx8FO0qpGo3HsEyt2bWrbPsqR/4d02Nfn4c0mqqCsRlUZmZbAP76y2JjUpL15G/tB9BqTpyI5TX5MBb57XdUO/LVjMOHD5Ofn09sbKxpbODA0veLjY0lKCjIKk/eXyUR586dy7Zt2xg79n2X5h844GGDnFBUVMT333/P8OHXltj29tu56J5t1kgpueuuu0hPb0BhYUiJ7f76mdRUTYyOfOPGjbniirLtqxz5Z+nRQ4kPnDrlZuM0mmpKRkYGQgiaNWsIQI8eDib+8INDpY9GjRrR1kJ3sohAvxX50I58NcOy21lZCA4OplWrVlaO/Pr1bjXNbbz77rs0b96cN98c5tL81FQPG+SEVatWcfjwYfr2vd5qfMcOePjhhowceSGgJWuM/Pzzz3z22VCnc3JzvWSMptqTlZVFw4YNCQoKMtWwbNvm2r6RkZEIIejceSmgHXmNxl2kp6cTHR1NYWEwAJ98Ytiwfbv1RCe6koGBgdS36Nh8jpo6Iq/xD4yOfP36Lcu8r61yzbFjbjPLbezbt48lS5Zwqx19WEepQBdf7FmbnPH7778TGhrK0qVDTGNSmlWE1OtoarXPL79AXp4XjfQT8vLymDTpf4Dz0GejRvDFF96xSVO9sWwGZez+6GpRelBQEI0bN+bkSSWD56+BEY2msmGUnvzxR/W4Xj3DhnbtzJNefbXUXMwmTZpw01AVONpDK+3Ia/wDoyO/cGHZq1XbtGlDWloamzer6m1/jCB9//33SBnLc889azX+8svq77RpkJSUDjzobdPssmDBAgYPHsySJSqF5pdfrLePHj2aWrXyadEi2TR29dXQp483rfQPpk2bxv79d7k09733PGuLRgNmR37duvLtHxUVxYkTuwH47DM3GqbRVGPS09OJiOhiagYVHm5n0pNPUlrL1iZNmrD1xAkA7uUj8g/kuNlS96Ad+WpGeno6DRo0AEJLnWtLmzZtOHfuHOHhKqn85Ek3G1dBpJRMnTqVZs1ethq/6CL43//U/SuugNmzA4H/s5pTVOQlIy04dOgQqampDB5sjsb362c9p2bNmlx00UWcPz+cSy81Lyls3OgtK/2HTz/9lNDQ4VZjv/4KBQUl296mpICh75lG4zGysrKIjIykb9/y7R8VFUVurlrlHDvWjYZpNNWUwsJCMjMzadIk1jRWty5gcMgB1ZjFBZo0acLhw4dNj5v932NustK9aEe+mmFccjLKMd14o+v7GpVrdu3aSe3a/ufIJycns2PHDurUsa7etW3O0rx5c9q3b0/r1lNMY1ZV7V5i6VKVGztr1u2mMdMSoAWXXXYZ2dnZHDhwtOTGakJaWhqrVq0iNNS8kjR+PFx+uUpRsIerucoaTXkxRuSNsnSWqnYl+O47sOkVERUVxaFDh2jUCPbt85iZGjezerWNpKHGbzh48CCFhYU0bx5tGgsJAdasMU8qLBn8sUdkZCRZWVkUh9YEIDDXhaY7PkA78tWMjIwMYmNjTYpL777r+r5GR37nzp2Eh6t25P6Uqz1r1iyEEKSmNrMaf+SRknNHjhzJ/v138eGH6gWMH+8NC61ZsmQJ4eEdWbWqrmnMXjOZ0aNHExoaypEj1u1L/TVfzxNMnz7dcK+Waey++8zb//nnUIl9fvrJw0ZpqjX5+fkcP36ciIgIevdWY48+6mDytm2qYcfEifDll+qLnpNDVFQUhw8f5sgRmD/fZf9C42VSUsyppFOmQN++0Lq1b23S2MeYPhwZae0HWEUeXRSEb9KkCQUFBaz9Sy3vnmwQ6w4T3Y525KsRUkpTRN6I3dwxBzRt2pSwsDB27NjBwYMqvePBB91vZ3mZNWsWXbpcbjX26qslez0ADBo0iPz8fPbtS/eOcXZYtmwZnTuPshqzl7JXq1YtBg4cSGDgPVbjF17oSev8ixkzZtCtWzdOnjSfsoaYM5Lo2zeKHj3upWFDsy73tGnetFBT3bDs6pqUpNrAx8U5mHzeoDy1di3cdhucOwcREUxYsIAii7w+wyE1fkaHDup8k55u3XclLc2/glkas4b8tm1KQ/6iiwwbrrzSPKkMjjzAyaKzpJKAzNY58hofc/ToUc6cOWPlyDuQULWLEIK4uDh2WXQ+Wb3anRaWn3379rFx40Y6d77SavwxByltfQ1JrWFhMz1smX2OHDnC7t27ad8+yTTmzDEfOXIkO3eu4L77zHl+/vLee5pDhw7x77//cskll5jGVq4sedFz880dyc21s/yi0XgAy2ZQZ85AzZpOJhs/rBb5tgAJ69ZhKYCnu7v6H0a1s3XroKWN2FubNnDzzd63SeMYY0T+ww9Vg8X27SkpWWcvumcHkyN/MoscGnNws39+QbUjXxXIy3NJQsZSQ75RI7jzzrI/VXx8PGlpadw5eAcSQcyJzWU/iAeYPXu24Z45TDtjBjhIn6Zhw4YkJCSwdu3fnjfODusMMhetW5u16pxFkEeNUpH7jh1/cTypirJkyRIA8vPNWvu2RcEA48ePRwjBxIlTTWMrVnjcPE01xbarq8NUCynhppscHicCePVVVb1endLlKgtnzuQ73T5vnpcM0bhEeno6TZo0IT9fXTyHhgK//26e0LIlfP65S8cyO/IHKSaAQSx3t7luwW8deSHEXF/bUCkoKIA6dVSOTGEhNGtWUsPQgHHJKSYmhrw8wwfcFZYtU7Iup06R0KoVe/bs4ZI9kwGYld7Z4fN5kz/++IO2bdvy9dcNTGNOJRrz8xnfqROrV69m0iR1tb51q4eNtGDt2rUIIYiJaWMas1foaiQhIYEWLVowd271+1osWbKE+vXr8+KLaiVp1Cj786Kioujbty9r1kw3jbnStVijKQ9ZWarwLTIykqNHVWqNXbZtg82OAx6LgKiUhYB25P2NTZs20aLFp07n+ELxTOMYY/pwnTrqcXQ0YAgGAaoDZI0aLh3L6MgfP55pduJtCtb9AZ868kKIrg5u3YAkX9pWaXj8cXO+1/DhcOAA3HOP3anGiHxYWCynTrmYJvbFFzB4MMTGQng4L7zzDlcVFjI840vznKuvhnznUQtPcu7cOZYvX06TJq9Zjdevb7jz5pswYoS6INm0SY3dcw+v//orjY4dY/9+1dnKSZM3t/Pff/+RkJDAvn2qutXyPGMPIQSjRo1i8eLFREU56GxVRVmyZAmDBg0yPX76acdzx44dy44dqzxvlKbaYxmRP37cIHFnDxdOLDd+PwnQjrw/sWvXLi688EKOH7/B6bwTJ6Aaxlf8lvT0dGJiYhgxQj2+7TbMrV0bNChDBBPq1q1LaGgohw8fZmr9+9WgHzbQ8XVE/j9gMvC2zW0yUM93ZlUiLDvfGOQMHSW+Z2RkUKtWLT76SHm4P/zgwvFvN0gjZqrug4FFRXxvb15oaNkqZ93IqlWryMvLY/lycw71woUGySmASZNgwQJ1QdKli8pX/VJdiOwEril83rRfaQ61O5BSsnbtWnr06GHK4Y+Odr4P8+Zx0cCBnDp1im+/VelAQ1lEfeGH7XXdyN69ew3ttieYxho2dDx/+PDhQA63327OqTF+LTQad5KdnU2NGjWoXbs2J044X1FzhZ78y+nTbjFNU0GKioqYMGECxcXFSFmv1PmvvVbqFI0XKC4uZt++fcTGxrJnj4rfBeRaFKiW0gDKFiGESUt+zjG1xJ+z1/++pL525FOBO6SUg21vgH9WFfgTjrTKiouVg79smdWwUXoyL099mB0E7pWgcUSEimSXBR9dqS5cuLCElrhRJMIVLpr2Aceoh0RwaN0BN1tXkv3795OdnU2PHj1NY6bVA3tMnQqjRjHqnXeYLASHP32fehxjERfyK1eUu6tkZcCotR8e3ts0FhvreH7nzp2JiIjg9Gnzcriluo1G4y6MzaDOnxecP++g2NUyN9fIG2/YPd6/9Ob66+1u0niZKVOmsGZNMl27WisKtHLQEH3FCh0w8AcOHz5Mfn4+MTExbN8O7doB69ebJ7RoUeZjRkZGcujQIU5TG4D8o9qRt+V5Jzbc52BcY2T/fvvjR4/CQw+pCLQFxtyxL75Qj2+7zc6+xcXQsSPk5KhIdlnp1csU7fYWixYtoo9NQryp06LlioUT6qHUYGZO8nxaxiZDek/Hjt3Nz18PJX9w++3w44/qf7hqlVrlmDgRgMD163lESiZMn87ex5Wj2o5UHnrI4yb7jJUrV1K/fmtefTXKNOZwZTQ3l4DsbIYPH87ChQvp2NGcgmTIKtNo3IaxGdQqwynDVhiD+fNVxzIj4eFKy/rxx9X3+9Ahu0V3e/d6zmZ/5r///KOJW15eHi+++BJRUV+waJG5hmncOJg1S91PTMwCrK/cfNFUUGONMX04O7sHZ8+q8kFTUdX998OcOWU+ZpMmTcjOzuaMoYdJ3lH/y3/zqSMvpfxNSrnDwbaZ7ngOIcRIIcQOIcQuIcQTdrbfKITIEUJsMtxudcfzepz16x2HB0r8oiiMuWOGSTR+8zHYuRN27DD/egQGuhxZX4AdvUSjTrKXyM3NZcOGDQwbNsw0lpcHDQ6nwMGDlNXLvYzf1QWQo4skN5CamgrAunWqTfSoURAcDHz9tapJuO46uPdeeP11h/+Lem8+BUA0B7lu+zMes9XXrF27lri4q0puKChQtRmWFcqNGkFUFNfFxtIpJ4cnn9xu2uRQ31ujKSdGR/6goU+bhTqqYuRI68effYapAq9+fWjSBG4t+XPjw3Ijn9Kzp4oh+ZpPPvmFzMx9HDp0ndV4t27KvhMn4L//Ihg82Dp49PbbapsQ8EzVPSX7NUZBj/ff7wJAUbrF7/iNN6rvXBmJiIggOzubxD4qIp+XqyPyXkUIEQh8BIwC2gPXCCHa25n6i5QyyXDzbji5vPz7r+tzpeT8W29R59gxWkZHE0ARU7mBWp9MVmenhAR1UTB0aOnHeu01FrRqxcCYGPb3seNgeZmlS5cipaR7d/WjWasWhARL1cGj1MTzklzNLyol6a233Gypme3btxMVFcVjj6mIjl1llZ9+gj/+cOl4dx552Y3W+Q8nT54kJSWFdu3alNy4e7dSS7roIiUqb+Gpj3z5ZRYDmxYuAmAi33B14XdeslpTXTCm1hgFaeLjLTZu2VJyh+LikmNCcNCmSra6Nxi6+27fPbeUkk8+KXneHTgQ/vc/dT88HEJCBFMGDKAd1jnXxjqJl6vmKdnvMUbkAwOVa/vKdxapNOW8SoyIiCAnJ4cJdylhCp1a4316AruklHuklPnAz8B4H9vkHgIDXZs3dSq8+io1Hn+cDOCxp5/mbR5hgrFk1bK6ypVKzyeeYPnVV7MqM5MDgaoF8par7Zy1CgvVysDSpQ5XCNzBwoULCQvry5gxKt/89ddxniBvKUA+fbrjecHB7jHQDqmpqSQkJJge2/t9LzNu6g519KiKKC345RjFucdY+fhsir/62i3HLivr169HSkn79u1MY4bFDLO3k5Ghmnvs3l1i/wd+UQtw33AT3+FceUKjKQtSSlNEPjtbpd7Wrm0xoWvXkjv16mX3WD9dcYXpfg3OVfuCV6PAiC9Ys2YNO3fuLDF+770lf3JjX3yRFC/ZpXGN9PR0GjVqhBB2XNty/qZHRERQVFREQZ1g5jKSE8GNKmil+6nqjnw0YJkjkWkYs+UyIcRmIcRvQojm3jGtgjjqdGTLxIkl9Poe5P/K95w//wyoplBFRUVMPzWQoSzi0Zwn4IUXrOcGB6u2qkOGmPbzBIsWLeLs2X9Mj6PqnHYeybasSHLWSvXMGdUS7tVX3WClGSklqamphIbarGZUtDd7374q57YCFBebFWGGX92AgEYN6P/WeAJuvbmihy4X/xpWnYKDzYtoCc1OK6fdsk+6A6LPneWDpC88Zp+m+nL8+HEKCwuJiIhg+XILhSwjtkIE+fkOO0aJduYL1dv4oto78kY+/hi2by99njv5/PMvgGSrsaNHweJaS50oTUVYEMxYoqIqeP7WuIX09HRi7akhJCeXHHORyMhIAE6FCUYzl72th5Wyh/fxG0deCNFXCHGtEOIG481LT/0HECulTAQWAt86sO92IcQ6IcS6nJwce1O8i60jP2+eUpuZOtX+/PIwYYJK/LvgApVqcpVyPuMNa8g1a55gCUNZsDgQk46iJW+/rf7u2eM+myzYs2cPe/bsoRObkQi+4FYuu7GOyU67BAebvdWwMHj5Ze4wvJ5VWOQ8Llqkwr/G9VQ3cejQIU6ePMnixaqvdwBFRGf+C4aTRYVo2rRcu0mp2g/s2uX80B5cWLHL2rVrad06jkcesQh1Dhqk0mgslQiccO+m2033Df17NJoKY2wGVadOMzIybL479nJDnEQDIy2++7U4Uy0dedueJmPGKFW1nj3tz/cEeXl5TJ9eMghkUhQ7fBi++QZyc61WQE8EzCEwsHuJ/TTexyjokZMDNTlr3pCYWO5jRkREABAcfIDCQpP2hF/hF468EOI7lHZ8f6CH4eaOb8YBwDLC3swwZkJKmSulNGYlfgl0s3cgKeXnUsruUsrujR228PMitu3kRoyA5s2V8y2lKuyoKFOnwsMPq5zxRx81DRsd+eLiXPPcmjUdC3wv90xb48WLFwOwmc4A3MpX9if+9RfccYc5IrZmjTohBwTA//7H+T59aB0VxQBW0AKDvImdVA13sN0QYgoMVLmVm0hi4ie9ne2iMBYQW0SC7HLiRJltmjJFNQRe1/ZaJIIRlOw5/j73UdgpqczHrghr164lMdEm+uGiA2+Phx+uoEEajQFjMyghmpXcaJkbMn68Otc4wdKRf42nqqUjb5uJZBQX8aai8YIFCzh58qzVmNVP/fjxcNNNSprZgprFxWRm7qdhQ+t9Nd5FSmkVkY/BPVJlRkc+Jyfb5Yxmb+MXjjzKae8npbxbSnmf4Xa/G477HxAvhGgphAgBrgZmW04QQkRZPByH0rb3f9auNd+/6KKS2y06YZaZkSNh40aHmxs3bkx4eDidOn0DWPSfuvde+zssXAgbNpTfHgcsXLiQY6U1eLjpJhg9Gj791Bw2i4uzuqzu2LEjew4dophACrATOTt6VMkQONLtLwNGxZqAgABA0omtJSeNH6/kPy+/XEV/pFQdR+rXV6scjz/u+Anq1XNtGfH0aXWhJiXbp29BIriWnwCYx6gS0+/jQ4K3JbstF780Dhw4wIEDB9i48VnTWNsW5+xP/t//1HL322/DjBnqgtYOc36s2s2zNN7D6MjXrauCFw6b6337bakhvMjISGZaPK5ujnxOjrX4lK/45ZdfqFfP2klPsUyCd6Jk9lvt2jxXozvP8KKHrNOURnZ2NufPn7efWlMBjI58dkXTXz2IvzjyW4Gy6wKVgpSyELgXmI9y0KdJKbcJIV4UQowzTLtfCLFNCJEM3A/c6G47PMJnn6m/8+bZzwm/oZTMJOPy7/PPq+rGX381p8LceiskJTncVQhBXFwc+/dvZvx45UMdP45yqD791P5Ox49bPy4srFCVZ3FxMWsXLaJeafkeU6aUeqyOhmr2e+7ZY9+Rb9hQyRD8+Wd5TLUiNTWVOnXqkJcn6IAD0eSxY5WU4q+/qpbSRhuOHoXeveGNN3je0UUTYOpN7Yz77lMOxsyZXDnnZtdfQN++qtB0/ny11OwhNhouJNPTzdfZW0c8Yn/y3Xerz/DDD8PFF6vaBjscowH79rnbUk11xJhaEx6uHHlToWtamvVEG0Uae0RGRmKpXFm4pgp3eLPhzJlC2wC3Tzh37hyzZs1i9OhLrcYbGesax41zWoN02enT3HcglRd5zjRWpw4Y1BA1XsAoPRkbG0tYGNw2wT3yTw0bNiQgIMD0nfdH/MWRbwSkCCHmCyFmG2/uOLCUco6Uso2UsrWU8hXD2LNSytmG+09KKTtIKTsbusp6ubymHBid14YNHTttQqgIgk2jJIBf6twKH36onOvnnlMO9eWXK0coNxcuu6xUE+Lj40lLSzM1yPjsM1QeqKOz8n33wRGLZr3BwSV1ll3ltdcICAwk/Zh7IqwdOnQAoGPHBfQaUtvxxK1blZPoLJm8FFJTU2nZcihFRYJAiuxPciEZfcDFF9MaeOXhhSU32p5wXnih5ArLAUOG2aWX0oMyOg7Z2ep/N8xzRT/GplmWBH1hI2dhTCOzrQ04q5a4D9pJsE3s4OA912jKQHZ2NkIIwsKUo25KgZ871zypf3+XjmV0FIwM/u4md5np15w7d47u3b8vdZ4bFkJLZfny5Zw+fZpp08yyw6bsSildlgK25PRptSCj8Q6WjnxBAdSUhlQny6Zs5SAwMJBGjRrpiLwLPA9cDLwKvG1x09jD+IFylmIBKvF5vLXa5ot95/FyzBfK0bcXLTJGgEshPj7epNkKFsH1ESPsO+gpKSrhcOdOc7fVhQYndNYspQXepo1rZ76nnip9Tnq6y5IHzZs3p06dOmzdupU5S2oQQRabXphVcuIzz6gC2P8rp+oPypE/d+5BerCWZJJKTkhKstNZpiT9+vXjUM2aHM6fxbs8yAFsnNk331R/CwrUqkvfvmoNWwglwbByZblfg6nNdQUuaEojOTmZli1VLcYFLENiJ4XK0apEZ1UzUfPjj4m32dTl9N9utFJTXcnOzqZhw4ZIqUQHTI78Aw+YJ61Y4dKxTI5CeDgAtakeuTVXXTWZ7dtvLHXeyy97Xlt/3rx51KhRg8JCs0vUqhUqKBBQNjfpm2/MAabnn1clWRrPY3Tko6NjKCiAGsUGR/7+imdpG5tC+St+4chLKZcD24E6hluqYUxjj4svVn+d6aUbufdeigzdBE81bszqOsOpWbOUfVwgPj6e4uJiwsNVhPOcMX05LMw6KmVL27bW3VbPnFGvZ8AAtSztqEj32WchJsY1ZZbGjdXctm1deSkIIejYsSNbDYmaOURwctA4+Ogj+zucOgXryr78feLECQ4dOkR4eH36YCfXPDtbRc4dFQ1bUKNGDQYNGsT8+fN5mHdpZl3DDZMmqZWVeYbC1aIi87L/b79Z/MPsYOgYXDBtmnMjajtZvaggycnJdOyoKuAm2hOSWrXKsaTFW2/BunXU79aN2p06Wa17LGWIUmHS/dQ1FcDYDMqotlJClObXX8t0vMjISP4zBFFakl7lm0L98ccf/PHH0tInohYUa9TwrD3z58+ne3dzJ1dTjKtWLfs7NLNT5Gyg4a8TSCOOGqhz7O23O5yqcSMZGRk0aNCAZ59V/k5MusGFDAur8LG1I+8CQogrgbXAFcCVwL9CiIqth1RljJf4rpRQ16rF31On8iWwccrXzJsvjJkHFcKoXPPMM8ohLXFeW7UKvv/eviylJc6cweJiFY7JzoaXXlLyms4EzS+/HKKiyqVbb3Tk33pLpbVcdRVw5ZX2J3/7LfToUeYcf6NiTb16dfk/HrTeOG+ejURC6YwYMYI025xcS6ZPV7mdoISuXVmjfvdd1ZXyxAmCDRdNGfai4aDSsJ58skw2u8KZM2fYtWsXHTokAdAKG/nSJ5902FwHUL/63ZT41MDBgxkcGmq9/e+/7ReIazQuYmwGZeXIW3rfZVzOj4yMJMUipc5Sy6CqkZeXx0MPPQGUbEB49izcfPNa4DOv2ZOens727dtZudLc1P2WG4vMbVpt+eUXp4WvF/31F3HsJg61YukhATSNDUbFmh9/VI+HrDI0qrQ9/5cD7ci7xv+AHlLKiVLKG1AdWZ/xsU3+j2WXUifsPnKE24DTAUkAbHNQY1kWjI58fr7qT75okc2EPn3guuvMKR5lYedOlZf4118qncUFjXW5M01FwQ4eVE2oykiHDh3Izc2lU6ejgKGOs7Sli9xc59ttMCrWxNSycah373atQNWGiwzO6MiRLqTJnDmjmtKURt26KoIRHq7UfYBvBXwz8Ar7819/3VVzXWbLli1IKUlIUNq/0ZarDffeq5p0ubjcPWjQIFZU9fCmxusYHXnjwlxQEFCBmp3IyEgsK0CC889UyD5/5ptvvmH37pIBnPfeU6fcL7/sQe/e35TYLoSbumDbMH/+fKvHHTvCy5NOlZTyrV9f/TY5CvDYYKyBMixwajyM0ZGvWxdGY7Hi6mhVpQxEREToYlcXCJBSWl7u5OI/tvkvLkpMZmRkEBAQQF6ecohNDS4qQMOGDalXrx4ZGUqfy+lKcsuWZTt427bKUTNGk11ANHe81OkKRuWaAwd2mAeN67mXXGL/NZRRtSU1NZWQkBDu3fCg9YZy/kNat25N165dOXpUqbmswUmUGpx3sn3/ffjpJ2upvMhIOHKE/0aNYuOm/wD4Fs/3aUs2yGf+/Xc/QFKvhiGFLD5e2VkGBg4cCMCMfpfzFo+WMlujcY2srCwiIiJMQfhOnQCLDq1lJTIykqycHKRhlTXi0xdK2aNyUlRUxOTJ/wesKrHNWF4ghOB/DhrxuWM12Zb58+cTGWmuS+rbF2oW21xIde+uVMPiLapu3nlH/XWQ7vkT1wCmkh2NB7HUkA8Lg7FYFCeX1f+wQ2RkJKdOneKcs5RUH+IvzvI8g2LNjUKIG4G/gDk+tqnKkJGRQXR0NAcOqMIsK23ccmKUoNyzx+z4Oszc2LMHMjPB4FR5hAomUbY3SBbu22dOVTl+KlDZ/uOPqji0UyfrnRIT4csvcZXt27cTHxdHl0wLGct//63QldWVV17JWsM6/CjmMpK5FH/2RZmOIZBKVejqq0tGuhs25LoJE8g+qVYfzhJGa2yKXC2Knt1BcnIydevWZcqUuixmKI3OGyLyS5eqsFwZaNiwIYmJiXwYepQvuM2tdmoUGRneURZxxl9/qY/GC17wf8+fP8/JkyeJjIzkyBFV0lKzJmaJXWc1Qg6IjIzk7NmzyADlyAdk+2/0ryLMnj2bXbvuBqzTQm3TmEePHm26f+ut5pQjdzeIKigoYNGiRdSv/6Bp7NJLKSnm/99/JXd+6CFzW2w7tEOlUv7wg5ah9DQ5OTmcO3eOmJgYduyAJOPFk5OOymXB3BQqxy3Hczd+4chLKR8DPgcSDbfPpZSTfGtV1cGybbEQFtq4FcQoQWnE6cVqdDT8/rt7ntiCMfzJ4HqOm1e5SpMmTQgPDycz03yVs3076mq+Rg0VdVm/HgYPtt7xttvUmyqEY+lN4/FSUnjC1lG2bWlYRq64wpzycpz6zGckc6NvVbUFLlJaMdbo0aOZERjKSzzNE7zOHlpbT7j0Umtp0QqSnJxMoqGl9hAsCuKio8t1vKFDh/LPP/9wGpvl/M2by2titWfTpk0sWLCA7dsLiY0tXwaduzh50lzy8Pzzbmn34BTjj3lERATHjhmuwy1rVZqUvSWK0VEoClNpAAd2uyBkUAn58MMPCQ62TiN84YWSirkBAQG8+OIvQFeOHjUv1ru7Wdbq1as5deoUjRqZo7YjRpTjibZssTt8G58DbgkKa5xgVKxp0KCNdVDBDfnx4P9NofzCkQeQUv4upXzYcJvha3v8nisc5CzbIT09nZiYGI4cUT86QUHuMSE+Pp59Fh12duxwMhnUFYSUan3UGIJZvNi83aCuUxqWMdk5jOG+r5Jc2s/pMYUgISGB9PT1prESTUyDg53rCTu5Ws/Ly6Pt7t1cb9nCMDi4wv+MVq1a0b17d6ux9etRPQGMhITY3beAIK7pudvUW8wR4eHhDBzSjdfDhlInOrzkhA0bVKHu+vUlt5WR4uJikpOT6dahA0/wWoWPBzBs2DDy8vI4K2zCfr/84pbjVzeeeeYZunTpwogRI7j8ciUF+/ffqhbdYYdTD1FcDI8/bt134eqrPfucxlzZiIgITpww1EQ+apG2VY5cikhDHdDZJqoB2q4DHpZp8QEZGRksWbKEehZFpM2aKUEye5oHDzwwirCwHezZY46G26SzV5j58+cTEBDNypUW3aDPn4e77jI/dpaSaKRjR7jmmhLDn3OH6f6XXyrxMI37MUphz57dA4Dk3Ya8eDv/k/KgHXknCCFWGv6eEkKctLidEkKc9KVtfssqQ26hpQPshMLCQg4cOGBy5F1QNnSZ+Ph4pJQ0a6aKKJ94wsUda9ZU6jO7d1sXpl59NXzwgeP9Jkzg45EjqVOnDgUpKbRBXTm4ILvuEgkJCWzfvt0kCmTpC5sop5RVWloaH1uoUoxijtsSPm+7zTpl5LnnICPLwhEwSHoeqRdrGjpMJENYQp/rXKvEuvjicZw9O5jHHrO/jAy4rJvtjD179nDmzBlu3rWL13ChX4ALDBw4kKCgIAaOUl7AYxjCx+4O71UD/vrrL15++WWuv/5Wbr55Idu2qfdy8WKl+Hr99a7VVLuDgoICLrroaT77zDrd6swZl1tIlAvjj3lkZCQnThikCi0L48uY/mU8FsDKR9VJpypqyX/77bdALDk5ZnWunTsdzw8PD+fKK68kJcV8XrnvPrUC4y7mzZtHbKzN4v9ff1mn0ixY4NrBbrLfyCsUtbpy220VKqPQOMEYkS8sVMHA8yEG/dDX3BMMMn4//bXg1aeOvJSyv+FvHSlluMWtjpTSTuhPY2rCc/SoS9MPHDhAUVERMTEx5Oa6L60GzMo1d92lTnqGbAjXCA83l/MbfwRr11aqJFJaN1YxMnUq76SlMWjQIILbtSONNsTElOt30y4JCQkcOHAAKZ0oRghRtoTHv/+GRYs4OH8+luW4lz/T3m1LI9dddx3BwdZ58bEtBW8MW6i08F99FQ4fJuH4f0zkG8YzkygOU3/sAG6+2bXnMCrkbN1qJ1fUyEMPwSuvVEhvzVjo2rTQQp6ie/cKXfTUrl2b3r17szU1BYFkMo+xm1aqcPaNN7zneVZyioqKeOihh0hISKBv30+YMsXc2ddyOduV9hbu4IUXXmDu3Ol2t3nSYTI68saIfN26mBvplTOHwugo7C9UepaXMd3OkmDlpbi4mG+++YbGja17QpQmDHbLLbeQn19gNeao1UhZycrKYsOGDbRqZVP7VN6CxgsvtJsvn0J7031nasGa8pOenk79+vWZMUOtPtcNPqvSatwUudQReRcQQrQWQoQa7g8SQtwvhKjnY7P8EylLn2OBccnJGJH3hCMfErKGgIDST8oOMeYX3mChiGLs/mrcvnUre/fuZffu3QwbNszkvLvYiNYlEhISADh9upSzbUyM4222/58LLoALL2T4o9aKKcMnlC6p6Sq1atXi7ru3ERBwh9X4E4uGwd13Q8OGLNm2jVwaMZWJzEZ1+5092/WeTi1atKBNmzZs3aoKa5cz0H5n4Kefdm0p2gHJyckEBARQW1qcmq66qgIfLsWwYcPYu9d8gdGSverOE0/AnXeW+XtVHfnrr79IS0vjxRdf5OuvHV+EekPYISsri7fe+oSgoIqnc5UV44953boRbNtmuK439vT4559yHdPoKFhF/Pr2rYCV/sU///zD3r1Hyckpm+BBv379aNZsMZGR5ouaGW5KvF1o6CzeqpVN48AJE8z3y5ou2LRpiS9AK+O5RuMx9u7dS4zF7/INOZPd2g64Vq1ahIWFaUe+FH4HioQQcaii1+bAj741yU8xFm+0aePSdOOSU8uWLcnNdW9qTYMGDWjQoAG7dqVRq1YFMhVat1aOVFKS/e0dO0KHDiw2pBMNG2aOBJaal18GjI784cPmPPYyq3EYVWyOH3e4VNCIHELC3ZsDO2nSJIKCTpQY37sX7rijgIkT36jwcwwbNoxNm1Te0SCWc8dVx+33H3eg4uAKycnJtG3bltDlFo0J7OY4lY2hQ4cCB02PD2IhGff111ojzgU++ugjmjTpzMqVl9oV8TDixrpnh0ydOpX8/AspLHR8gecJzXFQznZYWBhnzqir4A4dUM5bVJS6lYPg4GAaNGhAVlYWm2PGutFa/2DatGkEBPxkNVai3Gj5cvjqK9i6VXX6PnsWIQTXXDOC3FzrC4DHH6+4TfPmzaNx48Z8/rn5f3bVVTaTyiNGUKMGPOWetECNa+zevZvWrZUIwzAWElDs/mIEf24K5S+OfLGUshC4BPjAoGJTvjNiVceYjuFiVZnRkW/RogWZme6NyAPExcWRlpZGgwbukbW04ttv1YndwKJFi4iKiuLxx83r5i72xHKJ1q1bExgYaOrACk4ayZ48aT938vbbVYqJA435bBFJLo3cVUxvIioqisceSygx3qoVfP55MJmZ1lVibduWmFoqQ4cO5fx5c8vJzz/HfofVCqSqJCcn09nCqT5VN9rl5k/O6NWrF7Vr/8fo0R8THw+vYdORdssWTG06NSXIyspi0aJFNGnyJe+/77yjdMeOZiVGTyClZMqUKSQkOFkZw60BOSuMzaCeflo9jo5GRTFcXd5yQGRkJNnZ2WzpYOtNVm6Ki4uZPn06NWt2sRov0Vx50CC49VYl87typWrkU1zMVVddRaFNROWttypu04IFCxhh0Yjv8GH4/va/K3ZgIyUcefOKnxAuZ8ZqXKCoqIi9e/cSFxdHp07wasO3PfI8/twUyl8c+QIhxDXARMAoHuYeAdCqhnHZzlH7aBvS09OJiopi6lQVAXb3BaVRgvLAAdXd1a0/4DfcgDGJu7i4mMWLFzNs2DD++ssc6Z5uP0W2XISEhNC6dWu2b9/OtGlqzOEybp06jq+K3nvP4fLECqmuPMpZM+uUZ555kpYt7TdSsaU8LeAHDx6MEOdo3Nj8IXrnHVS9gy3z5pX5+CdOnCA7I4NLLK5yivPdI1AeHBzMBRdcwK5d/8fChfAxd5ec5K41e1D//yrUm33mzJkUFxcTH2+dA16vHhw/nketWtYrGp6MyqemprJ9+3YuvHCA03nffeeZ5zc68sbrfSFQJ74KdtqLiIjg8OHD7OngeiO8ysCaNWs4ePASzpwxy3K6LBE6aRJdu3aldevW9O3rvmZ0GzduJCcnh5EjR5rGIiIgaOgF7nmCWrXAkLoDUAvruqvMTPc8jQYyMzMpKCigdevWFBZCuPSMTorxQtsf8RdH/iagD/CKlHKvEKIl4KHTcCXHUAzoas6wUUPe+KPm7sYU8fHx7N+/35SCYjTP3SQnJ3PkyBGGDDHnX69cWeEgWAmMyjVG39Reza0JZ5FiB/mtr/A/hg1zqAhZIUJDQ1mxwo6DakN0tH3fuzTq169P9+7dadnSXCH7yCOoD5Wtbr1ljYOLbNmyha+BK781F8TVCnFflPzCCy9k586dHDmSDggCsFl+veoqc7dGUCHd8ubOX3ghxMWV11S/47fffqNNmzY0bmxdlFKzJtStG8ro0dZLPO5u3GPJPMNFYt++PUxj5ibX5h/aO6xLRtxGVlYWkZGRJrWsJ24/qnQRK9gKvkmTJmRlZRFU3zUZ3srC77//DlifH8aMsZnk6Hs2eTIiK4urrrqKNWvcp21q/Azt2aMumsaPB7HPprHdokW2u5WNYcOUVA0wjSg+avOK6Zzj6wZqVYldBgGQVq1ak5oK4fkGGegBzi/0y4pOrSkFKWWKlPJ+KeVPhsd7pZQVT+qtirxtWDZysZOpsW2xsbN0ieXMCmIseP3kk3TAvdJglhhPvAMHmh358qSHlEZCQgI7d+6kZk0XzrSdOqnizm7d1OMuFkvHdtI0pt/wHhvpWuHfB2dER0ezfXsB99/vOIl53bryH3/QoEFs3LiAuDiLBOT69cG2pfrhw2WWL9m8eTO2mVJBsc3tzi0PRuWdZctUcq60d/p70pByc+qU+o69+KLzgyYnw7ZtJceNtQNVYA391KlTLFu2jEsuuYQzZ6zrPowpDhfZnFg8mVozd+5c2rdvz4YN5oLxCRMgK0tSq5b1l+uMEwGq8mKMyJ85o67l69x8udqwdKnzHUshMjKSrKwsCgrgHR5Sg55K9PcSUkp+//1354ELKaFHD8fbo6J4aPduiouLadXKnOtYkbYV8+fPp1u3bnz5pbpo2r8f6w/L88/D0KHlfwIjhoj/aE5z986nGW1oWN+tG6SmVvzwGpUfDyClSrldF2G4SixD13VXiIiIICcnh2I//E76hSMvhOgnhFgohNgphNgjhNgrhNjja7v8Ghci8kVFRezbt4/Y2FjaGxSw7rzTvWYYHXkplSCwp37A58yZQ9euXalXz7w8W56ocmkkJCRQUFDAyZMuFGwGBMBLL6lo3A8/lPpDvr/15W6y0jlt2wZz8cX2fxgPHSpX40kT/fv3p6CggM6d7RQPWEazk5OVgkMZ2LJlC8KiQDjvw8/L1e7eEa1bt6ZDhw7MmTPTNHYtNlG+/HxV+Gr8cD3/vHNd1aQklRQOsGkTTJ1acvv990OLFuax5GRVhXzjjarAz89Zvnw5hYWFDB8+HIvFEnbsgOuuU/eHDBkCmJWZPJVVdPbsWf7++29GjRpllScdFgYREYKRI5fYzHfv8xcXF5OTk0NERASnT6sMO2Fchry8Yt/vyMhITp06RUZGIUcwpO1V8rqN9evXk5GRQViYOVPW5LPn5anvQEBAqV55o19+4eGmTWnWzLwaWN7mYydOnGDVqlWMGDHCVM4zbBjW7WWfe658B7clNtbq4R+Y06aM6ZuairFr1y5CQkI4dkwpP3XoUVPVErooCOIqERERFBYWctyTUYpy4heOPPAV8A7QH+gBdDf81TjChWrJgwcPUlhYSGxsrCnY4O7cbKMjn5WlHPkKCJY45NixY6xatYrRo0eryAlKWtwT6Slm5RqzBGWpgeWGDeHaa5Uco4Olt/716lGrabS7zCwVyxKKmBgV9JKyYk48KDk4gObN/yq58aGHVKKpkWPHypAMC8F//020xRJ7yN23lVsFxBHjxo1juYXz/BPX0hab7kGbN1s/3rJFdYF1Vj2Zl6dWZCZOhD0WMYj9+1WTs/37VTK1EMq5b9VKFXNbNkTzUxYtWkSNGjWoVct6vSTYooqp2blzXNjU7ES7s3bFkv/++4/8/HwGDRpsGhsyxKw2MnhwF+AF07aVK937/MeOHaOwsNDgdBtS+4zF2Q8+WKFjNzF8OfPzz5KP4eRWyXsc/P777wQFBXH8uDnt6PnnDXdWrMDqyrAU3j54kH9XzKdGDXWOKK/M6cKFCykqKkKIiSZNgtdew9yjxfIcVlHsqN7cx/uAeh/sLeZpysbu3btp1aoVV16pivDrFJ9wuYawLPhzUyh/ceRPSCnnSimzpZS5xpuvjfJLunRRPxyBzpUjwKwhHxsby1lDfwQXdisT9erVo1GjRmRmqjPSk0+WskM5WLBgAcXFxYwcOdrUPfbdd93/PABtDfk6WVlmCUrb9G+nNG5sji5ZdJWLjoszpku6tabSEV26wKxZqla4DL50qTRs2JD27duzfr2D1J09NgtpY8cqh96ZE5yfjwwL430b2SN3NfqyZNy4cRTZ9Ek3RT+dcfXVKtVmzhyYORM+/NB6e7TFRdoLL+AyfrhMa8uiRYsYMGAAvXvbBA9OnlCrFbffDm3asODgRkKCPwPUNa0npPlXGTpbt2hhrkF5/XXzZ0VdaJqf+NJL3fv8xhzZhg0jmTIFcnJQq6MdOlRYQsvoKJw5c44Co9ZDJY7ISyn57bff6NdvvGnsootg9GjDA3vpoU8/rQqTHKxUNZQSUBc3n36qFkPLypw5c6hXrx6vvKIitu3aQcCpE6rnBniu0MvA+zxAFAeJJpPff/foU3mEjRvV961lS/X3rbd824Zj165dJulJgJrnj3vEkffnplD+4sgvFUK8JYToI4Toarz52ii/5OzZMmvIt2gRw5tvek6OLT4+nt27VVSzgvVedpk7dy4NGjTg9OmeJjEUd+rhW9KgQQMiIiLYs2cLdQw1Z2XOfujaVWkhP/64Ke2ipbFIAbev+Dlk3Dil3mnM/HAX/fv3Jzl5pumx1UncMkxrpEED9aOdm6uKv4z5DsOGqTqD0FCERXitgCCGsbDkcdxAz549iYyMJCrKXChwlIYW3kUpjBkDl1yiesVbkmsRd7BNr6nEHDp0iG3btnGhnSZfDbcsV6sVX5i7Ct9UcCcdOpzh558rvvpjj9WrV9O2bVuOHjUrxFheQ3Xq1Ilge59BN2H8ET9xQjkO+fmoego3RHGNjvzIkZurhCO/ZcsWdu3KYPny30xjX39tMWHKFOsdoqNVquJ778HAgXZP8ulAfPyzpsdvvlnymtoZUkrmzp3LoEGXmMbmzEGd5I0VqO7+cRk/vsSQcuObuy2DxxtIKfnkkxzTIoNROOPxx1V21EMP+cam3bt3E2chLBBy7rh25H1EL1Q6zavA24bbZJ9a5I9IqUJALorBGx35VataedAos5Z827aqXsidFwzFxcXMnTuXESNGEBBgXk6o40FhB6NyTe/e6nG5FAY6dICAAHK/+47/AXU79jFtqgRBWKf079+fkyePcM89al3a6rzmzIlq1EgVINWqpZYKFi9WFzw2hFDAYobZOUDFCQgI4LLLLuPo0Qu59lpz2sLRurEeeb7KzrJlywCIiDAXs151FezafJbwHz8tMf9ToN6p7YB0u9StlJLVq1fTt29fq9x3y1KMwL17iYnxXDqKcVm9WbN65kFTjk3FaGK68tlL46hg42CFj+srpk+fjsqYVUyYYPPTZeXVU/Ki5cgRVXcy0NwMKhjYutVcHLFkSclramds2rSJw4cPc+jQ06axBg2wLu5y94XgzJnqdVRipk5dQUzMXdx99xaHc8ohVFZhsrKyOHPmjFVEPuj0cY848tHR0Vx//fVEuTnd0x34hSMvpRxs5+b/yaPeJjVVqWC42IUyPT2dyMhIdu3yQDK5BW3btuXAgQOmLqvlLUKyx4YNG8jOzmb06NFWogKe/H1LSEggNTWVq65SoeaKNP3cdvIkrwLpGWbHtEOHChroY/r37w/AiRMqFWaL5bldCOvCTkfY/ogbyL/zToKDMaVQeYIJEyaQl3ecpk0Xm8auSHut7DnOlsW9VZRVq1ZRq1YtbrzR3ITt6/5f0TqxlsNC5JX7uvMg77ndll27dnHkyBH69OljPz969myIjydt14v8KOw0KnMDxmhceHgDy0GDN1gxjBG/w4cPs71O5S8Rmz59OrVqmRsuPfaYxUbLaMbnn6u/9uoBOneGjz6yGqolJVf0tW7G52pqx1zDZ/bMGbMaVm15Cn79VT344APXDlRWOne2u3QQxhm/X3T5+uuvmTixL/v3fwo4d8vKKFRWYYyKNTEx5hXvgIx0t9dWgVqt/+6770y/f/6EXzjyQohIIcRXQoi5hsfthRC3+Nouv8NYSepirkRGRgaxsbGm3kR163rGrI429tzixv/cnDlzEEIQHT2aV14xjxsb3HqChIQEjh07xrBhqqtNqwosaBi7xEZHm6XyPJH77U1iY2Np2rQpJ07MBlTjHePvIAAZGSry/uqrZT72qaffpaDALX6RQ3r16kV8fDzLl/9sGss6F25deHHzzXb2tOGRR9xjkC8TTEth1apV9LLo3vvKK1Dzu89L3W8sSuLTnXrZq1evBkDKC00CMV98gYrcPvOMVQrDNXItl/GbYb77bMjOziYgIICwsHoAPH1jJhw8qFLEKkhISAj169cnKyuLxblJFT6eL0lLS2PLli00b66i2w0b2rxFli2zjT03HJ0YbfI1t4WEMG3VCKsxV1eB58yZQ9u217N1q7KrZUsIuGi0uZDonntcO1B5sONc7qGVR0Qb3MXy5cu5/fbbAdeK66ZOVad9oyiFpzFqyJ89q2T5anNKfbYqe7SsjPiFIw98A8wHjIukO4EHfWWM32KMWLi49GfUkD9xQj32liPvTv744w969uzJkCENTPrnFjWkHqFdOxV93Lt3OwEBFdPGT01NJSwsjKioeu4xzg8QQjBgwAA2bFDO2tSpcOWVNtGYW24pc1j9gaFDmbNEFcBZSvK7GyEEEyZM4L//zNIqkcbrrA8+UMXKX33lnicbPRquuELdv/9++3OmTAFDEac/cebMGZKTk2nffpRpbMOafJfaAhcZfvifecZ99mzYsIGwsDBWrTKv+Iwdi2q+Zaci/TeuoCZny9XF2BFZWVk0atSI4mL1+kb0NPQJsJEZLC/GplC5ufAXhroNd30WvcgMQ0V/WJhyXkvIgFr2HWjUSPWhcCTf27ix1cMWdiL3rijY5OTksHr1aiIjbzKN/d//YS1t5Mkoi50mLpGGBmajR6t6en/izJkzTJgwgVZliGTdcYf6Vw7zTGZkCbZv305QUBCFhcp1fNSYke0pZ8dP8RdHvpGUchpQDCClLATbtosa0xqcC468UUM+JiaG5oZVRNvaInfRsmVLanmgynX//v2sW7eO0aOvsBp/9FEHO7gJowTljh0pxMaWFGIpC6mpqbRt25aQEPVVM+r5V3b69evHgQPWb8yxYzaThDA3VjI2zXJC/f79ucHQhb1dO+dzK8qtt95KcHAed9+tKrRMv9/33ltSMi4mxsLI+mD4fLjE7NlKuvLrr+ENBz3ubr21woonnuC///6jqKiI9u17msY6HXZQhHz55armwcCFLOIsNd3ZBoDNmzfTqVMn8vPNP1uRkTjNP36al021Lu7A2AzKeCoOKTaEgl1s0FcakZGRHDZoItbjuBr8+WfHO/gp06dPp3v37mzYoJSOrISiCgqs/2f166sLMUc5jLVrKzUbJ1KtrvQLmDVrFsXFktxc8wrT2LEWE0y6mB4iJMTh0sHcueo04U+Lcy+99BL79+/nxhtn2t1+0012hwGzkqen2b59O/Hx8UyYoJbor8Qgzm95zq4G+Isjf0YI0RCDbpgQojdwwrcm+SFlcOQzMzPJz88nLi6O/Hz1O+OORnX2CAgIoEOHDrRq9ZlpzFLEo7zMnDkTgHfeedBq3JNpNQAtWrQgLCyM1NRUDh6En34q/7FSU1Np166dqRGX4SVVeox5gmFh5uiY3T4ZtWurcFkpYdGzKMURI5ZKJJ4gKiqKa665hm+/VYorixfbSQn79lt46ikl9pyZCcuWqRqV1FRzJyRnGPVehVCNb4ydYl991caD8E+MUo+dO5sbYnXLX209KT1dOVk//1zC0arJebbbSPSXFyklycnJJCYmlklC9yleA6TbGkMZHXljUDhUGpah3OjIGwtqT2Go6D91yi3H9haZmZn8+++/dOliXoGycuQta1GWL3etIchLLzlVg3Ll//v7778DxWzbZhF02rfPfN8byep2XutihhBgiFt+8onnTXCFAwcO8O677zJx4kSeeqpkVOWzzywDgyWvPrwl6LB9+3ZT4A2g4RiDqMSoUQ72qJr4iyP/MDAbaC2E+AeYCpShFr2aMEe1d3bFkTfmjsXFxTF5sueLUDp16sSpU8+YisVdSTEujRkzZtCuXTtOnHCz+H0pBAQEmApeje9bTk7Zj3PmzBn27dtHQkI703Gqyopfp06dqFOnDjExZgfdacO7gACnVzEbgVOnPFOg6IiHH36YM2fOMHLkTED9MN19t0WTyRtuUEnhtWqpK4sLLjDv/OWXcNdd9g+claXEle1dvDzzjGq24G/r6HZYtWoV7dq1Y8qUetTgHD9wLRclWxSqpKSoyNdLLzlsUOEuBauDBw9y9OhROnfubHIS5s8HU96gE8I4ayrEryhZWVlERkaaHPnI5b+oO25KdDam1gCcxNBduCK5fT7AGIBZvdrc6dbkyBcVwccfmyeXpYjC5ur+HsIINmjKl3ZdfezYMRYuXGE19uijWF8ceKvqdNo0q3PHEJbSGPUDY1Vr5EPeeecdioqKuPTSkilrZ8+q1hEAP0zJQxLAZ9xOJ6wb6f39t2dtLCgoYNeuXbRr146OHaFP1zwi/rIvolDV8QtHXkq5AbgA6AvcAXSQUm52vpdrCCFGCiF2CCF2CSFKJO0KIUKFEL8Ytv8rhIh1x/N6hG++UX/L4MgHBZUhDaACdOzYkZycHIYMUcmKs2dX7Hi5ubn8/fffDB9ufYa2I8nrEdq3b09KSgqXXaYe2wgnuMQOg/dw4IBZo9xNgTufExQURJ8+fcjKMlc1WWRW2Gf8ePVGpqSYW80WFzOnd29uqFmTm25qBph/JDxN586dufbaa1m27BrT2CefqK7BpTqgNWooFYrvv7d2AF5/XWmKP/qoapbkiGuvLdlQoF8/v1lbt5R6/Oor6MoGruUnywku5T8FUuiWIEKyoUlPYmIiaWkqC2P4cODtt82Tvv5a5SFfYZ2K15Bcjh6tuA1gjsgbI8BNfjOcGNwkWag6xqoI/I9cqwYNTeoqC9OnT6d9+/YcPFjTNPaXsRF0Wpr15LLWFgwfbrr7Ied4BPX//+8/59c7M2bMoKjIWrP4zTex1v93Z2W2M664wvpiBqiFkmTztuqLPXJzc/nss8+4+OI7GT++mdW2li1V/zN27oTrruPau1Rk6na+YDPWqVEXXFC+AJir7N69m8LCQhISEigshITmZ0rfqYriF468ECIQGA0MBYYD9wkhHnbTcT8CRgHtgWuEELZZyrcAx6SUccC7gINEVv8h/YBrjnxoaCivveYdHWJjWsSJE8fdcrw///yToqIiOnWybs3orXTR9u3bk5mZSfv26sxalmadRlJTUwHIzDQXC4WHu8U8v6B///4cPXrY9NilgNbdd1s7gELwZmgoDTuanV5PFzNb8uqrryLtOM8uKS8FBKhQoGWu1+OPu/7k331n/XjVKr+Jvu7cuZOjR4/Sp49aqs4jtJQ9DNhIs7VkLytWOJhbBjZvVnGdgwe78t9/Fs03X3pJ/V2xQqUv/fEHvPsu6d27m/ZtTI5bUv3OnTvHqVOniIiIYNkydZEiAwOhTx/clYhv1JJ/551sZnExp0VtTEVOlYCcnByWL1/O+PGXmi6e3nzTot+aZRVkYGDZJcGmTbN62LSWOaJet67K2nnYjufw9ddfExho3bNCnD6lqjON+FAHMpyT1OQs6WsOlT7Zw0yZMoUzZ86wc+ebJbZ99aWE335TF5c//lhqxOPZZ51urhDG39dDh/qwfTsUnTZUPHtSechP8QtHHvgDuBFoCNSxuFWUnsAuKeUeKWU+8DNgG9MdD3xruP8bMFQI/xYIXJVSr9Q5xrbFGRnqpbhQa1ghjI78cYv8iooEOGbMmEGzZs2IiTGvKAwe7L2IdntDVWp2tjmkUNY0gdTUVAIDAxk0SDWL8UXnO0/Sr18/wBxCmjdPBajLQnFxMRs2bCApyZxW481Vi5iYGN58801gsNX4Dz+AwXfk2DEXPstffw3Tp5dN9aJnz5JjhrQKX2PMj+9rkAYMwcUmSytWsNHig76aPpZB1HKTnJxMTEwMV19tkd9seQFmIZFJdDS5FlfeL/CcWyLyRg35iIgIPvpIOV+iqMitaVLG7q69e6tCchEgVLi5kjBt2jSKiwN47bWXTGNWDZuMEspQjpbZKG89Kcn08MQZ6yKM//s/pSL7f/9nPl/v2LGDlSv/oajI3LE1Pp6SURVvO/IWF72TeZSz1OIQTd3ah6WsSCn58ssvSUy8jS1bwkzjkyaBPHacwVveL7HiZUn37oetHldEKKI0jNLOkyaprq5HFm9SG3pU/h4MZcVfHPlmUspLpZTPSSlfMN7ccNxowFLRNNMwZneOQS3nBOqCwu/YRWtmMY7r7iz9GmfXrl3ExcWZ8pY93YwsIiKCqKgojh0zh77K2l/HyKlTp5g/fz7jx1/MV1+ZHaMlSypoZBkwOvL5+emmMVckzixJTU2ldevW1KypIrZPPuku6/yDXr16ERBg/qesXau6N5aF3bt3c+rUKTp2NEdQQ10M/rqL++67j8suK9ktuXNnmDFDadoPHmxnR0tuvBEuuaSUSXaYNcs6l/7wYcdzvcjq1aupV68ebQ1pHXWwKLi0XUmwofE992A8eTfCDaFwVES+s62qye+/m+/bpLbE9e1rakl1EX/R6aenKmyDMXc9MjKS2rUhGNfFB1zF6MhnZR2mVSuoVXQK1qxx2/E9zY8//ki7dtaOlOnC3PZzU16lpokTTXdrOlBLe/BBVaYyYwa8+eYMAgLqW23/+Sc7KWzubIDiCp+ZxSGGYj6PTrjed62///nnH3bu3MmJE+Zl0bAwlTHIBReU+qM+ZUIKgZijHgsWKM2ArCysGjq6g9TUVJo1M6f+PI0hn3/bNvc+USXAXxz5uUIIN8RtPIcQ4nYhxDohxLocTyZ+OeElnuFj7i51XnFxMbt37yYuLs6Uy+mBjsUl6N69O2fOmE9IH31UPud71qxZnD9/nkOHnjKl0jiS4PYULVu2JDQ0lAYNzMn+ZXXkU1JS6NChQ1nl/ysNtWrVolu3E/TrN7Lcx1hvqCyNiDD/+JdFlcQdCCH44YfviYoqqeV+qSGza+VKFWxfvbrElIoxbpyKIBnb415wgWM9bS+yZs0aevXqxcyZ6ifiByxqVUrpbBjdqhVz6psdpwYVdObPnz/Pjh076NTJpubASWSwbr16vGkRvej392sVrj+wdORPn7Zw5N0oo2VMrcnKyiI93WKDt/T8KsDevXtZtWoVl15qXqEw9YPYtg2TtizYyNiUEQtnskvv3ljH6swcP66+v1OmPMi4cebC28GDoWu+zcWRlKo4xps4SCu6gOXce69vMn2+/PJLwsPDOXbM3JHP1KJhc+lli50eGEohwdTkrMmhv/FG1Ym9UclYSYVISUkx9XwB6IPhf3p36T5SVcNfHPk1wAwhxDkhxEkhxCkhhDuSRQ8AlgmGzQxjducIIYKAulDyl0dK+bmUsruUsntjmwYV3mIqE1nAiFLnHTx4kHPnzhEXF2cS2njvPc/aBtCjRw+ysp7n00/NHm95JC9//PFHmjWLZ/p08w9xGXsLVZigoCDatm1LamqKaayETroT8vLySEtLo3379qYud/7cwa+89O/fn/Xry7FEbmDDhg0EBzfgmmtUClV5gtruIDQ0lG3benPddXOczuvb1zWpuzJj6kiFknF0ZxejMnL69Gm2bdtGr169TMXeVpH1UnpGCCEIt8jlMxYklpeUlBSKioqIjzcf08oEe0nRqMZuMyzP1QEB8M475bbD6MhHRKj/VZAx8ujGK/QIQ/FlVlYWxcWwh5Zqw9NPu+05PMVPBp3ePn3MF1i//orySG2bBga4x/W44Phx+te+gKZNS178mh3hGsycaY5+L1lQaO4m60tq1LCbSreUIaz+aD0LOjxovsD3Anl5eUyfPp3LL7+KkyfVSvgHg6fz0MOibD9+wFlqUYj198KdhbyFhYVs3bqVxMREq1Mn4LbmbJUJf3Hk3wH6AGFSynApZR0ppTvKAv8D4oUQLYUQIcDVKJlLS2YDxrW6y4El0l71m59hp7mdCUvpyZQUlT7a0AvJQj169EBpym4tbapDcnJyWLBgAY0bf2s17unUIHu0a9eOlJQU06pAWbo+p6WlGZrptOfdd9VYVXTk+/Xrx3mbM3RZvj3r16+3Wop3EmT1OPXrB/D996NLnVerFjz2mJuf3PYLauiM6TInTrjtCmPdunUUFxfT21EBpwuhtaSkJN4yLK08xWsVWu02KtZER5tTaz7/HGjWTHWledv+hUJCQgIP2noPFfjHGR35sDDlOURicMLcGJEPDg6mQYMGpqZQRzC8137+kySl5Pvvf6F9+0e46CJ1sv72W2jdGqsUEsCgG1pBDKtCgevXs+L0XjhYslnUxo32vg8SnnvOemjBgorbU14cqB+spztj0v5PKV/Zvn8eYunSpZw6dYqRI82rFzcdMsjNNmjgYC/nhOLAez97VoXqDXUngGoP7mKBwM6dO8nLy6Nz584EBcFjV6SXy76qgr848vuBre52oA057/cC84FUYJqUcpsQ4kUhxDjDtK+AhkKIXSg9ey/Hfl1nwwbzVbGzeo6dO3cCcPx4R9LS4N9/PW2ZorthaTIlxfpXuyz/1d9++42iIsnGjX1MYxde6Bbzykz79u3JyMhAiLKHErYZPJcOFt6/t1NGvEE/Q57rc8+Z5dRcbQIkpWTDhg1WKRPezo+3hzGdxhmTJ6tUm6/dJVtc0QhlvXpuq2hfY8jJTkkZAMBwLByvwkKXCnqTkpJ40iJ9IiXFyeRS2Lx5MzVq1OfCC1V0Ou2Sx7n2OqGadDnJd4uPj2efbTOl0aOV6kY5yM7OJjw8nB07VNL3CjFQbXBzzpxRS/6OO+AqDDr17nB+Pcjq1atJTR1HSspk05jpfGcrGeSOHAsbrds+bdsSGGid+rFiRRi2SAJUQzZLnHSM9TihoXDxxc7nGLsJephZs2ZRq1YtrrzSnOVcM9LOhYZRAz8pSTkXf/zh8JgT+ZYG5NKV9dTgHEKoRtb8/LO60rNcafr1V7j+eqX8VUpQwnhx36xZVw4cgFd/b+N0flXHXxz5PcAyIcSTQoiHjTd3HFhKOUdK2UZK2VpK+Yph7Fkp5WzD/fNSyiuklHFSyp5SSg/WWVeMMWPMTuHmzY57oaSkpBAWFsbBg7ZrTp6lUaNGxMbGkpZmnRZQlly/n376ifBw84mhWTPfdUNt3749Ukpyc+3nYDojJSWFgIAAcnLMqjv+rYVUPpo0aUJcXBzJyQtNY66qlOzdu5fjx4/Tvn2SacxdDYQqwi8G36lRI6Vq6KxJ4M03u7GL4Zkz8IXqNMvrr5deHf3DD7BwofmD5aY2qv/++y9xcXE8+mhtBMXMx6IGwsWr0aSkJCyzoK+4UqgwemZmme1JTk6mXTul8CMoJm7GW+aNTvRo4+KUmsWGUAt1oD//LPeyj7EZ1ECD/x4iDcuiFcn3toOxu2tKCqQbU2tcaHzlSz799FOCg61zvk2+2PPPW0+2LVouDzbLm7/t2MGEGteX/ThS+jbCIoRafZsyBb76irQaTe3Pc/NnzJbi4mJmz57NyJHm73ow+Yh/VpacbGzMNWqUUt6yjLQdOWI19TPuZDV9WE93vuA2BrGUoK8+NcuALV6s0t1GW6yEvvVWqY1bkpOTCQ4O5swZ5cAHFRucjPffd+0FVzH8xZHfCywGQnCv/GSVIjExkebNzZ0Vbc+PRrZt20bbtl148EH17zW3UvY8PXr0YOvWP63GXHXO0tPTWbFiBSdPmr/UrVqpqnlfYFSuOXSo7Nd2KSkptG7dmgsv9IMQs4fp378/K1eaT/iWq6XOWLduHVCDt94yO1ZlLSj2BEFB6vc9J0et4P/yC9x7L7z0kv2VmauuctMTh4XB5eZlbV5/HYe6iSdOqOiV7VVTBUXbpZT8+++/dOhwEQC387l5Yxkkidq2bUuNGjU4ZJkydMcdZdZEl1KyefNmEhKUvO2DJi0aA61bO9y3tWHblOZ2moT/73/qn1oGB8noyJfAWZ5jOYiMjOTw4cNuLw70FLm5uUybNo127WKsxseMlobwqwXt2nnMcf76zBaeiLDfUlxQTHP2eeR53cJNN8HNN3PiDQcJAbNmefTp169fz8GDBxk/fjzjDLkKk3gDYau727eveYndGEAIDVUXyPv3283hbYNqAnY9P7CUIXzKXWb9/j174JFHYO5c6532Ow+eJScn0759e/butVkNc1tUpXLhF468peSkm+UnqxSJiYkcOmTOP3/vPfv61tu2bePoUXPeqD25ak8xYMAA9u/fT82a5i9UeLhr6TVTpkzBVsJ/7Fh3W+g6cXFxBAUFkZm5s8z7btu2jfbt25uid1WZ/v37c8QiEpOfX+p5GFDL8SEhfTh+3HwydrNP5Bbq1IEPPoCnn67B44+XjIz+9husW+emJ7MtJLXXIGr3bisJPisq+IHLzMzk0KFDxMaqlKlPMbeSN3WWdoGgoCA6derEugo2BTh48CC5ubm0adOJ53mOd3jEeoKTPvAtW7ZECMH5C+xEEl59VUX9Nmxw2RaHjryHUmuMAgUvYUg/8Fbn0TIyZcoU8vLy6NXLvGLcpQs0XTsTvvrKevLChbgNO1fQr2Xbz3Wbws3sI8buNn+i2zClDpFGHN9hscJw2WWqqFsI2LvX7c87a9YsAgMDGTNmjKEru+Ql7HRzWrkSjIpUlnnzY8ao5XN38cEHTtWxVF+JkSXV7Pz0O+JpfOrICyHeM/z9Qwgx2/bmS9v8kcTERAoLrQtJbX/njx07xqFDhzh2zNzA1psNdgYbBLfvu28+LVuax0uLyhcWFjJlyhSrpT1QF+u+IiQkhPj4eI4cMauyuLLCnZ+fT1paGh06dDCd84wNKKsi/ezoQbvSCGTVqlW0b59gNXZ9OVbHvckbb9SlY8eS3Rd79HBT8xNbp/D8ebj2Wuv8sosvdh6he/BBlWZTjuWNfw0FNaEh3ZjAVOuNZczj79y5M2+fPl1mGywxdnRt1aodz/Gi9cZJk6Cpg1QEoEaNGjRv3pyzZ+2kBxixaGBXGkZH3nitVZhgcFwtV1HcgJK3PE29ekp4OxTDydMP9WvPnz/Pu+++S//+l/DFF+aLnCuvxL5kpjEtwx24XKAiudHU89ECJ7ndvkIkJPAxd3E1P3MD35GNheqSUTXBA4pWM2fOZODAgUyerJzzdqSWnBQSoi4k7rhDXQQ70oS+/Xb3GPXPP3ZVew4cOMChQ4fIyFCrL42xWALWjrxPMHaImAy8beemsSAxMRFbRRjb36EUQ1VZSIhZScGbHb7bt29Po0aNOHz4ZwwS4QCU9ns+f/58Dhw4x5AhZs99xAjf55V36NCBbds2miSG69UrfTU+LS2NwsJC2rdvb/K3KoF6XLlp27YtDRs25IILPjWNlVYXce7cOUOhq1mWbvlyqF3bU1a6j6ioJnbHPdJeol07+Oknpcu5f79KtSltueP//k/td+21sGNHibxVZ6xZs4bQ0FDy3prFVBxE/V0kKSmJ5SdOsBybVQJDF2hXMBa1xcYmlNz4+uul7t+6dWv27t3Bs7xAMomlzndEQUEBR48eJTIyksREuPiCYwRtNxT1u7lC2xj1P3JEqeL8gcWyZBkuPLzBt99+y6FDh9i82bpW4Z67JWzaZD35T+uUywpTs6bd4e78x1O8QlMOMJ6ZqsDVHhdd5F573EFAAPcFfMQGVOF6a3aXnOPmZcvdu3ezbds2LrzwSl57DZpygBRsJNrGjjV34g0KUlrtji4sP/tM/UjGuGEFJDFRnViLipQz8PLLpmBDVJS66PiGG83ztSPvfaSU6w1/lwMpQIqUcrnx5kvb/JGEhASCbKTObNXVjGopR46ok1z37t6VPQwICOCCCy5g2bJlBAeb82lKK7z/4osvCAxcy2OPmYXny6rA5wmSkpLYs2cPZ8+avffSmlxt3aoutuLiOjqfWEUQQjBgwADS082OVWmawf/99x+FhYW0a2cufHPHed8bJCTYv7p0WwMXe+kboJpFNWzoeuHjzJmQkKB0U6V0yQH4999/6dy5J7fwValzSyMpKQmApxvaaLdv3aps+fJLc1R1+3YV4Tt61OpKefPmzbRo0YK8vPKVTMXFxbF79y5e4lmSSObM4zYZmy5e5GQbCj8iIyPJy4NmRRnlsscVLJtCAazGrODlMwkvO5w/f57XXnuN9u0ncPKk+pHpyb88GfYedaZ/Cz/+aL3DmDFesWttcD9e4WkO0IyZOGhM4aNeMK5w/Ljg1lsfISBgEaftlQr+/LNbm1nMMkSb5s9XDbsOYCdFZvZscCRHa4+AALVEebP9moUyERFhbib27LOsWbOGkJAQ6tWrTy1OMxqL/PoEOxf81QBfR+QRQjwvhDgC7AB2CiFyhBB2krM0ISEhtGvXjoED7zCNPfqo9ZwNGzYQHm6ukrrrLrzOkCFD2LdvHxkZaaax33933BVz7969zJr1A0VF5sK1Bx5wGHDxKl0MrQmfesqcS1taPc3GjRsJDg7m11+VI+8LDXxvM3z4cDIyzM5NaVkdq1atAurw1Ve9TGOVxZF/80374wcPuukJDhwoIa8HlD83Njsbnn1WRY5T7SyZGygoKGD9+vU0qT2RThXoBWFErSDC4JF2pBNDQ+G228w/9Ndeq/JiGzaEp54yTTu1di2jWrdmzz8l05lcIS4ujpycHN57T30gt0TZFAdfe611x1EHWHZ1PXMG6ocY+s3bnoDdgDEib3zOIiyCN24rxig/mzYpRaeHH/6ZjIwMUlLMKVj/0ptXzz4EL9hcMHlKcWHlyhIrPKK0K+qICKe1Fb6mTh24445rKC5Wha9WufIAc+aUqupSFmbNmkViYiL797tZVSIgQF2sryrZMdsKV5wU40WhlKxZs4auXbsSGBhovWIRFYWpg101w9c58g8D/YAeUsoGUsr6QC+gnxDiIV/a5q907tyZnTvN5QNz51oLW6xbt47ExEGmx+64IC4rYwyRlz//nGk1/s8/9uf/3//9H2Bd5Gfbs8NXGB35I0fMJ6PSUoU3btxIhw4dePttpc7Qvr3z+VWBESNUx+HHHlMn3NIc+ZUrVxIW9he7dysnZWnJxox+S40a6vW99pr1B/qqq1QmS4UJDFTOhjsx9ll38mHcunUr586d4+FDNikQx4+XK2+oTp06xMXFsWe/nfQASzp1go0bzY/ffBOEoODFF/lj924+XbqUO16wyYV3cbnOqFxz6JCKvN/1kJ00mO++U3m/39rJozZg6cifPg31Aw3FSa40HSgjRkf+8OHD/hSANzFxopKG/+STGxk9+lr7k9LTzfelVKosnqBfP4iPL9s+27b5feS2W7dudOyoLkhu4Dv2YZMfW5EOaxYcOXKElStXMn78ePbsgQBs8kYXLqxYMzIhoE8fp1NafvIY8xnu8irJpJX7WLZmA9f8cy838o15g6u6x1UQX0fkJwDXSClNoSaDjvv1QOlhkmpInz59TF3/jBgVn/Ly8ti8eTPR0eoD7asfgZiYGLp27crMmdY/tvaCMseOHePLL78sMW4sEvU1TZo0ISIigo0WjoYzR15KycaNG00XAOA7+Uxv0qpVK+Lj41m/Xi1zTpzoePW3oKCA5cuXU1Rkfo8cZZP4KzVqwOOP96FRI2u5OLf5B23K2OBkzhwVya/Aj+6qVatoAVyQOtM8uHIl1K1b7iY+SUlJbN6b5nzSVvvR/2BHV/MvvVR6Ex0DRi35jh3/A6AIB9KHn3+uOk06wNKR73lsPg8tNEjkeqCoI8JwEZeVlWWqrdk7wWKR2rL4yAdYpkzOmWPuxNkXB5Eaf2LHDvc0pPIwQghuueUmQEmnDsRmBcFNeXx//vknxcXFjBs3HrBT5DpsmFueh6NHkefO0btXL7bT1mpTEYGMZL5aOTx9GpKTncrojpEZhJLPmPSPeMgoR3vNNZhknqohvnbkg6WUJZIUpZQ5gP+V6PsBffv2tTteVARLl+6goOB9fvnlNgBuucWblllzySWXsGbNGm65xVzlam9V/8MPP+TMmbJ3TvUWQgi6dOnCJovCrU8+cTz/4MGD5OTkWDnyDz7oOfv8iREjRrB6tbmAwDIoZ8maNWs4ffo0wRbFUv6QRlVWAgICuOaaWM8cPCSk9MKtAweUrNP586o5i1HB5ZprnO93+jScOqWiZU8+qf5RtWuTPX06j9o6pnYUicpCUlISW/aX4siXhcsuK1PluDEiv3//Di6/HIoTOsB996k0Hns46KBqdOQbN45k+lkLZS1buVA3EBwcTMOGDcnKyiLc0Fhz0yUWqSq2nUm9wN69kJGhpIzXr7esEZGEos7f/2BHLtAfqvyNF8XBwWW/QPYh1113HcHBnwGQQaz1xh9/dMsPy6xZs2jWrBnx8V25gmlsxfVC9DJRvz6iRg1ee+01jtLAalMAKlf10UehIKSWKnDt3x+mTnV9teX775USRTXF1468s+orP1SU9j0dO3akdu3aXHSRdY7cSy/BY4/VAcxVpb5UfLn66qsBiI19z2GazJEjR5g8eTJjxpSv06K36NKli6mIGFS+vyOMkfv27bsC6tziyw7g3mTEiBGcO2fO81q3zn595cKFCwkICMDwEQEqh1qNPR5//CKEsFaxcVtPEnuNcyxTOZo2hcmTS6qm/PgjDBrk+Lh16qh0ElDKLy1bwpkzvLBkCfdVUC7SFlXwepgb+JY83FB1X0aFmNq1axMZGcnu3bsJD4fjpwJV98cePezvMHKkubWvBVlZWdSqVYupU20cdw99cI3dXY2OvJXM8PTpHnlOR6SkqMZ8sbHw33/W227hK85Tkwd5t+SOK1d6X3f3p59KjvXu7XKxtz/RuHFjxlo0UjlBuPWECnYxPXfuHAsWLOCyiy7iqVtzeINJ1hOcpJqVl8GDB/NSxy48FXA7l/MrJ6nDYdT58+23VWmF6ad2wgTYudO1upAySuNWNXz96jsLIU7auZ0CT10aVm6CgoLo1asXmZnW6SgvvABbt7a0GnOzKlqZiIuLY+jQoXzxxRc884w5785Sa/vll182RGY/ttrXQVDMZ3Tp0oXCwkLati1dKWDjxo0IITh+XEXk/UwtzqMMHTqUWrXMhXkTJ5ob+Fkyb948evToQY0a6gNao0alWO22S7NmzRg+vIvVmG0zS7fxzz/w4YeuzS0txeaee0o9ROGb75Q6pzSMyjVHRiVQgzxus+wUGxNTdl3xrl3LbENcXBy7du2ibl0Lh7hbN7Vkaa/J1dVXl1g+NGrI77NtDuqBiDyolL7Dhw+bUgxLNPhdsMAjz2uPDh0cb7sKddHzLg+X3FjB1RyXef11FS1Zswar6MDDBptKW6HyY26++WZA5aR2wyalSsoKpdgsWrSIyLNnuWP5Tj76LZI/sZHjdKEIvDy89PVNvFb8LfljQ1i/5CR5mBvdvPIKdLQVe+vWDd56yyO2VBmklPpWxlu3bt2kL3nppZekEEIKUSzVt9n+bdMmn5opZ8yYIQE5ZcoUk03160t59KiU//77rwwICJB33HFHCbv9jV27dklAvv761FJtvPjii2V8fLycPt1/X48nufbaa2Vo6Gem1x4RYb09PT3d8F6+Lu+4o+T2ysi3337ruc/w0aNSnj0r5fHj5rGpU6VcutT5fnv3SvnQQ45PDq7cCgsrbH5xcbFs2LCh7NZthuGwxfJE9nn1uk6fNk90wZ6PuVPKoqIy23DDDTfIZs2ayeeeU4cqcQh7zzdkiNWUoUOHyj59+sjvv7eZXw57XOHaa6+VLVu2lEVFUgYGSvnUU1LKdevMz9uqlUee15LcXCmnTCl0+C9pQbo8RKTj/5mveOklKe+5R90vLvadHW6goKBARkQ0Nb+ltu/xo4+W/aB33y1lbKx84NprrY5VQKDX/n+XX365rF27tszOzpZPPlnyZb3xhpSffGKef/j3lc7PD9UAYJ104JP6xBGu7DdfO/LJyckSkB9++LXDz/XQoT41UUqpfsR79uwpo6OjS9gXHx8vmzaNkZGRRX7/nSwuLpYRERHyuutuMNl46pTjeRMmTJCzZvnv6/Eks2bNkhBleu2Rkdbb33nnHQnItLQ0edNNUjZr5hs73cmxY8dkQMBL/vkZXr9eyubNnf8IevjHcejQobJNm7udH9q4YfZsh/ZczPRyPf8LL7wgAfn223kS1DWOFZs22X/OZ59VF1FSyo4dO8rx48fLtTd/4pWT1WOPPSZDQ0NlcXGxjIyU8rbbDBssvZ7duz32/KdPO/9oNCLb+YSBAz1mW3Xj0UcflYGBF0iQ8glelRnYfJ8nTy7bBYsXv/uOSE1NlQEBAfLee++Vhw87NiMtTcq5c9X9NxMm2p80erTH7fUHnDnyvk6t0ZSDTp06ERsby9y5vzmse3JUy+VNhBB88MEHZGVl0bGjta7w7t2jueuuuWRlWX8EHUlU+hIhBP369WP1anOrd3sr8nv27CE7O5t+/fqZ1PG82VXXHxgxYgT165uLl6OizDWbUkq+++47unTpQnBwHF9/DZmZPjLUjdSrV49Onawbt3ggvbR8dO2q0kTuuqtMDV3Ohrkv1ykpKYmMDOsGUyUyAu66S2ljWzQTuAj4LOQeerGGDXThq23OZewcYVSuCQtTal8llCs7d7avgvPii/D884A5tabHFO805oiOjiYvL4+jR4/SuLGF+qflCf/AAbc/7+nTcOutBdx4o3XX3EgO8yuXs4WO9GAtOTiQR61bV+WiG7uAairMxIkTKSpazv33/8rrPEkM+8jB4vv56KOqKPTTT60LdFatUmlFJ07Au++qjqy+bpVuICEhgTvvvJOPP/6YbduSHc6Lj1d1/AAvbP+QbOxIVP72m4esrEQ48vD1zX8j8lJKOWnSJBkQECD37Nnj9ytN33zzjQwISHApEHDokK+ttc/kyZMlYLLTFCGzYOrUqRKQS5emmOZt2+Z9W33NpEmTpBDXmt4D4xLpqlWrJCDfemuK335Wy8tHH02zek0jR3rvuffvl7JTJyk//1x9f3bulLKgwM7Es2elvOwyZeCQIXa/gI/xhqzLMfnmS+fdZt93330nARkcbF59O3bMyQ7z58sv7rhDArJFC7VPgwblf/41a9ZIQP788xzHn7mrr7Z/QpowQRYUFMhaIL+84QbrbR5M25g2bZoEZHJyshw8WMp+/Sw2Gp//gw+kzMtz6/P+73/HZCjnZDg/ywRS7L4nu2jl+AS+Zo1b7dEounXrJrt06SJvvFG9zTMZ5/yH9NFHS/+xdXSbN88rr+nYsWMyIiJCdus2uMwmptNCnhK1pTx82Cu2+gPo1Jqq58hnZmbKoKAgec0118jLL58p4R75zTdr5IYNUq5d62vrSjJnzrZSv5yTJvlvSuPq1aslIMeO3WGyd/Fi6zl33HGHrF07xuo1bdniG3t9yf79+2VQUJDV+3DypJSXXHKJDA8Pl927W+fdVgVOnDhh9Zp69/bO8xr9ctvbAw842KGwUOXbHztmtcNokFu2mC9Ajx51n41btmyRgAwPP2c6/v79pb2uy2Tr1q1lQkLFPyc5OTkSkO+8867jYz3+uMMT06G//5YH7G3zIP/8848E5Ny5c+UVV0gZGmqx0dIGexGFcpK7dKlsVf8ZeZLazk/Uzm7++ONTBfjggw8kIN9/f7cEKetwQu5t2LX8/ycHtzVtJnj1damL/JplNnXvllOmtLfqgnbk3XzzB0deSimff/55qaLEyEsuuUQW+6sXLFVeqrMv5rp1vrbQOXl5ebJmzZrynnsesLJ73z7znLZt28r27T8s4cBWR+655x6r92HcuAwJX8kuXTZ50x/yKravyxtpws6+U//952TH4mJ15bxsmbx19GjZvHlzmZVV7JH/SX5+vgwNDZXh4bmm4994o/N94uLi5GWXXSZ79lTzExLK//zFxcUyPDxc3nvvvabnL7Fice6clJ9+6ronkZVVfoNcYO/evRKQX375pekpt241bHzxRbd/gQqc1CaU6WZZlK1xG0eOHJHBwcFywoQ3TG91B7ZU+P81nhmm+4/xhrz3NvetxLlCcXGxHDp0pMkkYz68s9tvv3nVRL9BO/JuvvmLI19cXCx//PFH+fHHH8vz5737BSwrhYVSjnOyGlgZGD16tGzdurWV3aNGqW3GH97K+Lo8wdGjR136LUlL87Wl7iMgwHuF208+eUYOGbLQ6Xvbr5+UGzc6P05BQYGsV6+evP76+037/fij++3t1q2bbNv2HZfeG/XZQb788stywgQ1t6Ixii5dushRo0aZnvuZZxxM7Ny59A+tF77YeXl5EpAvvviiDAhQT2klVOROW4qL5dF69Vx73c5u9hQANG7j0ksvlY0bN5b161tccFfwfwbF8gWekR3ZLEHKJ57w/us6cOCgBClDQnbJlJTjvv7q+S3OHHld7FqJEUJwzTXXcNdddxHqS9F4FwgMhFmzYNkyj9RoeYXRo0eze/duJk3KMY0Zdannz58Pjtq/V0PqGwWwnRAVBYY6xCrB4sUHgdFcd91fprE//3T/86SkpPDaa2EsWeK8ffo//0CXLk67nfP3339z/PhxYmLMTdkuusjx/PLSq1cvDhx41qW56wwNYDp27EtyMiQkVLxGr3Xr1uzevdv0eO1aBxPnzavYE7mJkJAQGjduzIEDB5g5U41JaTGhVy/z/WHDYM6c8j9ZQAD1K9LwYs4cyM2tvF3dKgk33ngjOTk5dO9ubmbwSO/yq0MEkw8InuNFPlzWiXfegWdd+4q6laZNo3jzzY0UFw/klltGe9+AKoB25DVe5YILzJ3kQf1IR0b6zp6yMHq0Osk0bPi9aSw3F555BqZPX0Lz5tbtpL3d1NDfKNHExoYLLvCOHd5i0KBmdOy4n127fjSNjR0Ls2e77zmysrIYMOCTMu3zyiuOu83+/vvvhIWF0bVrd9NYnTr251aEPn36cNrFrrH//vsvABdfPJjNm2H79oo/f1xcHHv37qVlS+UNO4x7uBAQ+ZMxFTfIBZo2bcrBgweJilKPrd6+u+8231+8GMaMgYwM1w7cpAlcdhmcOcPZn2ZVzMjTp5WsSIMGFTuOplRGjhxJREQENWs+aRpLqdcXioqc7GVNxpYtHAYe4h0KCQbgqqvUufihh6BmTXdb7RqPPdaFn39+n3Xr/iM6Op577/3CtC0+/gsne2pAO/IaH9GkCYSFKWW8w4d9bY1rtGzZksTERKZNMztq27fDyy/DwoXXERg402r+00972UA/o3592LDB8XYPNQ70KWPHjmXdul/o1KnQNPbDD+47/s03P83RoyW1ZRcvlkRH77C7z/z58OabJceLi4uZMWMGo0aNIji4RskJbqS3Qfryww/NMpQvvgjnz5ecu3btWhISEkyP3XFh0bp1awoKCggJUbqXDi+uQkLU31q14LPP7E65mp8rbpALREdHc+DAAVOg28qRv+EGePJJ6x2++EK1kj53zvmBs7Jg+nSoXZuway92OO0wDiIsltF7D3W21ZQkODiY66+/nrlzf0MIi+WZgAA4eBDO2uk8Pm+eajXdrh3MmsWMRYuIYizv8RCgrv+mTvXSCyiFyy67jJUrV9K8eSM+/PB2goJacccdd5OcfD0FBaoJtO1HXqPQjrzGJ2RklB6x9UduueUW09K/JVKOIz29rQ8s8m+6dLE/vnmzWR+4KjF27FiKioqYMGG+aSw42D3H/uOPP5gz532724YMEWRkOM5TWrRI/S0shLw8dX/p0qUcOnSIK664glOn3GOjI1q3bk2jRo1Yt87ci+G550peYEgpWbt2LT169DSN5eRQYVq3bg1AUZGdKwdLwsJUfsGaNdCiBQBZNWpwNkD9VA5mCYPGeCeFpGnTpo4deYD27a0fv/KKcthK9Li34MMPnT7nEBaz+uONhHOC7tEWEZZ33zXfr1tXRS+uuKLkATQeZeLEiRQUFBAYqKLw8+apcylRUSqc/uefKjfu0CGl5T9ihLrAS0mBceP4/vs1gPkqNijIfO3qD/Ts2ZPVq1dz4sQJTp9O5dNPP6ZmzZoEBameI4765lR3gnxtQFWhoKCAzMxMztsLMVVRatSoQbNmzQguh6fiTyePsnD99dczadIkWrb8k9RUDyQTV0H69lW9SerVU9HhpKTK+/8vjZ49e9K4cWM2bvwBDCkYP/wAn3+ufMTyUlxczFNPPQWMLbHN6GMFBgYyadIZ3nijZJTU4IcycCCsXq3yrT///HMaNGhAYuLFJp+wYcPy2+gMIQR9+vRhzZo1VuPGGhMjGRkZZGVlcfSoarzUq5dL2S6lYnTk8/PzTGN//63eDxtD4QVD8zpDusi7jRuT2qUL7drNYtkbID1Q92CP6OhosrOzDasIwZw5YzNhmJ0aCePyZlqaenObN4cIQ/OmrCy47z6Hz7eZTixlCA2HwjNvwvjxwMn/oEcPFbpt1gymTVOT//e/ir48TTlITEykS5cubNxodt3GjrXIqhozRt1ALXtbkJGRwfr1T9uMedLa8hMeHu5rEyoV2pF3E5mZmdSpU4fY2FiEn3RP8yRSSnJzc8nMzKRly5a+NsdrNGjQgEceeYRXXhkPuJ6bWJ355x8VCQ4IMDuUVZXAwEDGjBnDzJkz2bWrgLg4dZG7Y4fj1QlXmDZtGlu3bjM9btlSpaXZOrmvv16LoUPXM3x4J8B8tbR0Kbz3nnLiAfbv38+MGTO47rpnad/efJBkx00WK0zv3r35448/rMZsT5XTpm0AwvnrL5WKY1XgWQGio6MJCQkhP98caDl0qJSdmjYFKfmqcWMub9qU4mLv5hBHR0cjpeTUqcNA85IReWfFRX37wpEj6n5mpipEfeIJp8/Xmc2ACu4+9phxtLv5nxAfD5dfXtaXoXEzN954Ixs3PgOoIizjv7k0vv/+B+ApqzFPFONrvE8V/1n1HufPn6dhw4bVwokHFWFr2LBhtVqBMPLkk0/SrZtzr8w20ljdCQqq+k68kXHjxnH8+HH27TOnkTgqNnUFKSWvvvoq9etPM43t3es4Un3hhd14/fWpWF5oFhaqYjYjt9/+J0VFd/DNN9YROk8Wng8ePBiAgQPTTWOWjsTff8OkSZcCJ0xj7grMBQYG0qpVK2rWTDWNuXKqzsvL48iRIzRt2pSCAu+uJDU1qAIcOXKQkBA7qTVCQHq6kgSzxdK7a9ECuneHb74p9TmnTfNMsbPGfVx77bUEB79henz2LHzwgfl6a9486NQJCgrM+0gpmTy5hdVxHnlE5Z1rKj/V5KfVO1QXJ95IdXu9RmrVqsWyZcscbl+6VP8YVmcuvPBCQkNDmTXLrAjy2mtgoX5YJv755x+2bLmMY8dcj4Y+/vgtNG7sWO5l3ry7KC62Lppt1EhdcHmKHj16EB4eTtu25kTX7dvNqwA7dpQMv3/5pfuev3Xr1tSubU4Jefzx0vc5bEhVadq0KZ98AidOlLKDG4k2eFkHDhwgPx/eeMPOpJgY5znxoK4id+2yu2keIxjBPGLZS+/ehnQajV/TqFEjxtv8o+6/Xy3C9O6tao+2boVHH1Wpcps2wcqV6zh+/HrfGKzxONqR12jKQe3atdmypeT4zp0waJDXzdH4EbVr12bYsGHMtpBG+f338mvmf/LJJ8BzZdpHCMGFF7Yp0z533FGm6WUmKCiIwYMHs3DhQlasMDvtSUlw3XVw7Jh1Veu77yo/1V20bt2avXvNFzeu5AcfPHgQgMDAVqYiYW9hjMgfKK3xhlFovowsDWrIKOaxgBHUTIjln3+qbu1KVeORRx4BnrEaW7MGDMqtALz/vhKUeOYZuPLKkgU6OhpfdaiyjrwQooEQYqEQIs3w126HGiFEkRBik+HmRsVn71PbAw050tPT+fHHH0ufWA2xFwiLjy85pql+jBs3jr179zJz5s4KHSc7O5tff/21XPt++mnZitCNNZ6e5MILLyQ9PZ2GDa2lMn/8Ed56y9qLfPBB9z5369atXdayN2J0ouvXj3KvMS7QqFEjgoODOXjwIA88oMbsXkzExkJ+Pnz3HbRx7eLtXyH4dLQ5rWry5OqT+lYV6N27N336rCEwsHTt5oULJYcPdygxfv/9nrBM4wuq8lf3CWCxlDIeWGx4bI9zUsokw22c98yrHGhH3jkrV8L335c+T1O9GDtWqcskJ/9uNb5ypb3ZjpkyZQoFBdaJ4rfc4lyf30idOpgcwNJ49VX7qdbuxpgS8NtvJS9Ojhyp59HnNirXvPKKfb19e+zfvx+A2rW937UuICCApk2bkpmZaWoKZRlxtSI4GK6/3roQwgFTv/2W3rIp02Y/aBpr167i9mq8y5NP3k9R0VulzsvLK5kCe+qUd77vGu9QlR358cC3hvvfAhf7zhTvsmzZMgYNGsTll19OQkIC1113HdJQCRMbG8vjjz9Op06d6NmzJ7sMuZM33ngjv/32m+kYxuj+E088wYoVK0hKSuJdSy1hDQD9+qm0gEcfhXfe8bU1Gn8hKiqKnj178uefM9i40Tw+YID9Jkj2KCoq4pNPvgasZSk++8x1BZz33lPR1tJ49FHXjldRmjVrxoABA/j5Z+dNlebNc/9zxxlym1q0+I/u3aFDySBlCfbt20dYWBgBAfXcb5ALtGjRgn379pm6IJeQoLTlzjutP2DvvWeWIwT44w9mzZpF3bq3mobWrIFWrdxmssZLXHTRRcTFla8a3AOL9xofUpXlJyOllEaBscPgqE0dNYQQ64BC4HUp5cyKPvGDDz7Ipk2bKnoYK5KSknjvvfdcnr9x40a2bdtG06ZN6devH//88w/9+/cHoG7dumzZsoWpU6fy4IMP8qcTDarXX3+dyZMnO52jgbdKD4xoqhnjxo3j6aefpmHDg0BT0/jbb7smwz137nz27bPWXX/xxbJH0pw5aTEx8Ntv7mta5QrXXXcdd955J3PnrmHUqN525wwY4P7nNUoD79q1i4gImDOn9H0yMjKIiYnh9GnfFPbHxMSwYsUKUwPVUh15UHJGq1apD8vdd6tbx47w5pucGzqUeVddRZcuV/PPP2p6z57OD6fxT4QQfPrpSIYNywSaubzf5s2es0njGyp1RF4IsUgIsdXOzaqkW6pwtCNF4hgpZXfgWuA9IURrB891uxBinRBiXY47Wg16mJ49e9KsWTMCAgJISkoiPT3dtO2aa64x/V1tFJbWaDRuxZhGMneu9UWwq0WT77//A2Bd2nN9OYQnLr4YFi82a8hbkp6ulAm9yfXXX0+DBg14//1XALjuOrNOXufOkJtbseZZjggNDaV58+bs3r2buXPVWHa283327dtncOTV408+cb9dzoiJiSEzM5PQ0EKgDDr/ffrA3LnqCi04WDUyGD+eKVP+5ezZ5zl6VDWTevBB12Q4Nf7J0KG9uOwy13I7c3Lg+HElTampWlTqiLyU0k5rO4UQIksIESWlPCSEiALsnrKllAcMf/cIIZYBXYASQnFSys+BzwG6d+/utE1JWSLnniLUQmQ6MDCQwsJC02NL2Ujj/aCgIIoNYtfFxcXk5+d7yVKNpmrSoUMHWrZsaZChvN007kpEfc2a/Sxc+IPVWM2aqhFUWREChgwpOT5yZNmP5Q5q1arFE088weOPP86LL77L1q3/AT/x8ss7ue++eLdpx9ujdevW7N69mzffVE2PxoyB//5zPD8jI4Nu3bphSJX3ej+k2NhYioqKOHXqENCcl1+Gl14q//Eee6wLMIhUg5z+66+7w0qNL/nkk/uZPv0cUjruVnbhhUpeVlM1qdQR+VKYDUw03J8IzLKdIISoL4QINdxvBPQDUrxmoY/45ZdfTH/79OkDqB+M9evXAzB79mwKDN0k6tSpw6lTp3xjqEZTiRFCcPHFF7No0SKOHTtuGnelW2mfPs1LjLlDijErC/bvV6nTvqxhf+ihhxg9ejTPPvsw06b9xMsvv8z//udZJx7MjryxeHTdOsdzz549a2gG1ZpJk9SYt52hGMM//cgRF7QyS0FKyfnz1h8+R03FNJWHxo3DyMo6TUzMG8CgEttzcsCmobKmilGpI/Kl8DowTQhxC5ABXAkghOgO3CmlvBVoB3wmhChGXdS8LqWs8o78sWPHSExMJDQ0lJ9++gmA2267jfHjx9O5c2dGjhxJLUNSZmJiIoGBgXTu3Jkbb7yRh1xQRdBoNIprrrmGd99911BIrgoMX3hBpbNcdJH9ffLy8oCSHtaCBRW3JyJC/XVVzcZTBAUFMXv2bBYvXkyDBg3o7qX8nri4OLKzsykqOgc4jmCCSqsBEMJ3uQhGR/7QoT1A/woda8uWLUjZovSJmkpH48aN2bXrEWbMaMXmzc8QG9uZ/PyLueOOIC0rWg2oso68lDIXGGpnfB2GX1Qp5SqgymSMGTWSBw0axCCLrkQffvih1bzHHnuMN2zaBEZGRrJmjbmwzrg9ODiYJUuWeMhijaZq0717d9q0acMPP/yA0ZEHePNNx4680o23ToYvLKx6cnGBgYEMHz7cq89plKA8cCAbUE6ylPbzxI2OfJ06rhcSupsWLZTjvX9/xSPyCxcuBO40PXZlZUhTeQgKCuKKK67giiuu8LUpGi+jr9U0Go3GQwghuP7661m2bJnV+IoV9ucXFcHTT5cspq9qTryvMDrymZnm9/iDD+zPzTC0fn34YRXr+fRTz9pmjxo1atCkSRMyMjK4556KHUs58rXcYpdGo/EftCNfzUhPT6eRrnrRaLzGddddZ3fcUJJixYQJWWRkWKevPf+8B4yqphi15CMilprG1q61P3ffvn0IYV607trVo6Y5JCYmhvT0dHYamgRb9iVwlfPnz/P333+71zCNRuMXaEdeo9FoPEirVq0YNmwYjRpdajX+9tsldcF/+sm63UW7dvDcc562sPpQp04dmjdvzq5dm0lIUGNGB9mWPXv20KDBg6bHLXyUXh4TE0NGRgaPPaYel8eRX7VqFefOuah7qtFoKhXakddoNBoP88ADD3DkyAyrsZ9+Uh0Wn3tOOexDhpRUhxo71lsWVh86dOjAtm3beOEF9diR/GRaWhoNG5qlgiIdtRT0MLGxsezbt4927ZQ88C23lP0YCxYsQAhzrdT27e6yTqPR+JoqW+yq0Wg0/sLo0aOJj48nOHgoKSmLrba9+KL6u317HavxjAyIjvaWhdWH9u3bs2zZMgYNKgLsFx9IKUlLS6NTp0YOI/beIiYmhvz8fKQ8jGWH4LKwcOFCQkL+MjUja9vWffZpNBrfoiPyGo1G42ECAgJ46aWXSElZwosvznRpnxYtdJGrJ2jfvj3nz5+30mbPzbWec+TIEY4fP05kZGMvW1eSloYuYBkZe0hKUmOO8vrtceTIETZu3IgQdd1vnEaj8Tnaka9CBAYGkpSURMeOHbniiis4e/asr03SaDQGrrzySnr16sV7791d6tyrr/aCQdWUDh06ALB79zbT2MmT1nPS0tIAaNpUOfKNfejPt2nTBoCdO3eamoLdequTHWxYvHixoRmUc918jUZTOdGOfBWiZs2abNq0ia1btxISEsKnNnpphYWFPrJMo9EIIZg6dSp5eaV3Sv7+ey8YVE1p164dANu2mR1521Oj0ZFv0UJ58PPne8c2e8TExBAcHExaWhr16qmx48dd33/hwoXUM+6o0WiqHNqRr6IMGDCAXbt2sWzZMgYMGMC4ceNo3749RUVFPPbYY/To0YPExEQ+++wzX5uq0VQb2rRpw+zZs5zO+f57nVLjSerWrUt0dDQpKeYm3raLlzt37iQwMJA6dZQj36CBNy20JigoiFatWrFz507uNizm9Orl2r5SShYtWkRMzJueM1Cj0fgUXezqAR58EDZtcu8xk5Lgvfdcm1tYWMjcuXMZOXIkABs2bGDr1q20bNmSzz//nLp16/Lff/+Rl5dHv379GD58uCkPU6PReJYhQ4bw1lunyMhYxIcfXmIaHzUK5szxoWHViI4dO7J582a+/FKlqSQlWXc63bFjB61ateLsWfUTGR7uGzuNtGnThp07d9KzJ0REuH5hsXv3bjIyMjhx4hpAqSQdPuxBQzUajdfREfkqxLlz50hKSqJ79+60aNGCWww6ZT179jQ56gsWLGDq1KkkJSXRq1cvcnNzTcvIGo3GOzz6aB0++OASWrSA4cPVmM588x5du3Zl27ZtDBhw3jR2+rR5++bNm+nUqRPPPqse16mDT4mPj2fXrl0UFxdTvz4cPerafosWLQKgRo1QAPr3h1q6uatGU6XQEXkP4Grk3N0Yc+RtqWVx5pZS8sEHHzBixAgvWqbRaOyRkQFLl8KCBVBQ4Gtrqg89evSgsLCQvXu3AD0AuOce+PZbOH36NLt27aJ37+c4ZShnCPLxL2WbNm04f/48mZmZNGjQglnOs7NMLFq0iBYtWhAXF8Thw3D//Z61U6PReB8dka9mjBgxgk8++YQCg9ewc+dOzti2l9RoNF6jeXP1t39/39pRnejRQznvKSlmHcepU+GLL2DLli1IGcJ3313nK/NKYFSuSUtLY/VqddFXWhpWUVERS5YsYdiwYdSuLejcWaVvaTSaqoV25KsZt956K+3bt6dr16507NiRO+64Q6vZaDQ+JC5Oddp8/nlfW1J9iI6OJjIykk2brAXZb78dkpOTgdqmsWuu8bJxdjA68qmpqaaxMWOc77Np0yaOHTvGsGHDOH4ctHCNRlM10ak1VYjTlkmeBgYNGsSgQYNMjwMCAnj11Vd59dVXvWiZRqNxhu606V2EEPTo0YN///23xLZVq1KoUeN2zhvS5w2aAT6ladOmNGjQgC1btliNSwlC2N/HmB9ft+4IVq6EceM8baVGo/EFOiKv0Wg0mmrHoEGD2LFjB++8Y105Om3anZw/bw50XHutty0riRCCxMRENm/ebJUeM2mS430WLVpEYmIiY8Y0oLhYR+Q1mqqKduQ1Go1GU+0YNmwYAHXrzrYaz8trb/XY14WuRhITE9myZQtvv11sGnvrLftzz507x4oVK6hRY7JprH59T1uo0Wh8gXbkNRqNRlPt6NSpExERESxatMDXprhEYmIiZ86cITMzs9S5K1euJC+viLVrLzSNrV3rZAeNRlNp0Y68RqPRaKodAQEBjBkzhj/++MPXprhEYmIiALm5G63GZ8woOfevv/4iMPBDq7GbbvKYaRqNxodoR16j0Wg01ZKbbrrJrkiAP9KhQwcCAwPZsmUtN9xgHn/6afP9BQvghx8k06evp6joDqv9LffRaDRVB+3IazQajaZa0r9/f5KSknxthkuEhYXRpUsXVq1axSOPmMdPnDDfHzECrr9esH+/tQb+t99CcLCXDNVoNF5FO/JViMDAQJKSkujYsSNXXHEFZ8+eLdP+6enp/Pjjjx6yTqPRaPwLIQSffvopNWoMB/y/MV7fvn1Zu3Yt7dqZ2wAfOKAkKHNzLWfeabWfjsZrNFUX7chXIWrWrMmmTZvYunUrISEhfPrpp2XaXzvyGo2mutGrVy927/6GDRt2cs89EoD4eNi2DXJyfGycDX379uXs2bNs3ry5xLZGjXxgkEaj8Tnaka+iDBgwgF27dnH06FEuvvhiEhMT6d27t+kHYPny5SQlJZGUlESXLl04deoUTzzxBCtWrCApKYl3333Xx69Ao9FovEPTpk3p0qULH34okBJ27oT27f3POe7Xrx+gVGlKo3ZtpVSTnu5hozQajU/xE4XcKsaDD8KmTe49ZlISvPeeS1MLCwuZO3cuI0eO5LnnnqNLly7MnDmTJUuWcMMNN7Bp0yYmT57MRx99RL9+/Th9+jQ1atTg9ddfZ/Lkyfz555/utV2j0Wg0FaZZs2bEx8czb948pk17gH374NFH7c/t3Bl69PCufRqNxvvoiHwV4ty5cyQlJdG9e3datGjBLbfcwsqVK5kwYQIAQ4YMITc3l5MnT9KvXz8efvhh3n//fY4fP06Qv3Q90Wg0Go1Dxo0bx5IlSxg58hQPPlhye7t2tzFzJvz6q7ct02g0vkB7b57Axci5uzHmyLvCE088wZgxY5gzZw79+vVj/vz5njVOo9FoNBVm3LhxvP3228ydO5crr7yyxPZHHunN+PE+MEyj0fiEKhuRF0JcIYTYJoQoFkJ0dzJvpBBihxBilxDiCW/a6A0GDBjADz/8AMCyZcto1KgR4eHh7N69m06dOjFp0iR69OjB9u3bqVOnDqdOnfKxxRqNRqNxRN++fWnWrBlffvklAHXqqPHo6OXUqnUv1157rQ+t02g03qbKOvLAVuBS4G9HE4QQgcBHwCigPXCNEKK9d8zzDs8//zzr168nMTGRJ554gm+//RaA9957j44dO5KYmEhwcDCjRo0iMTGRwMBAOnfurItdNRqNxg8JCgrirrvuYuHChWzcuJFDh2DOnJUcODCIJ55oQs2aNX1tokaj8SJCSulrGzyKEGIZ8KiUcp2dbX2A56WUIwyPnwSQUr7m7Jjdu3eX69ZZHy41NZV27dq5y+xKQ3V93RqNRuMrjh07RkJCApGRkUyePJlbbrmF4OBgtm7dSlhYmK/N02g0bkYIsV5KaTe7pCpH5F0hGthv8TjTMKbRaDQajV9Sv359vvvuO3bu3MmIESM4f/48v//+u3biNZpqSKUudhVCLAKa2Nn0PynlLDc/1+3A7QAtWrRw56E1Go1GoykTw4cPZ/v27axbt45BgwbRyN9E7zUajVeo1I68lHJYBQ9xAGhu8biZYczec30OfA4qtcbBHIQQFTSp8lDV07I0Go3Gn4mNjSU2NtbXZmg0Gh9S3VNr/gPihRAthRAhwNXA7PIcqEaNGuTm5lYb51ZKSW5uLjVq1PC1KRqNRqPRaDTVkkodkXeGEOIS4AOgMfCXEGKTlHKEEKIp8KWUcrSUslAIcS8wHwgEpkgpt5Xn+Zo1a0ZmZiY5OTluew3+To0aNWjWrJmvzdBoNBqNRqOpllR51RpPYE+1RqPRaDQajUajcTdatUaj0Wg0Go1Go6liaEdeo9FoNBqNRqOphGhHXqPRaDQajUajqYToHPlyIITIATJ89PSNgCM+eu7KiH6/yoZ+v8qGfr/Khn6/yoZ+v8qGfr/Kjn7Pyoav3q8YKWVjexu0I1/JEEKsc1TwoCmJfr/Khn6/yoZ+v8qGfr/Khn6/yoZ+v8qOfs/Khj++Xzq1RqPRaDQajUajqYRoR16j0Wg0Go1Go6mEaEe+8vG5rw2oZOj3q2zo96ts6PerbOj3q2zo96ts6Per7Oj3rGz43fulc+Q1Go1Go9FoNJpKiI7IazQajUaj0Wg0lRDtyGs0Go1Go9FoNJUQ7cj7IUKIkUKIHUKIXUKIJ+xsDxVC/GLY/q8QItYHZvoVLrxnNwohcoQQmwy3W31hpz8ghJgihMgWQmx1sF0IId43vJebhRBdvW2jP+HC+zVICHHC4rP1rLdt9CeEEM2FEEuFEClCiG1CiAfszNGfMQMuvl/6M2ZACFFDCLFWCJFseL9esDNH/0YacPH90r+PNgghAoUQG4UQf9rZ5lefryBfPrmmJEKIQOAj4EIgE/hPCDFbSpliMe0W4JiUMk4IcTXwBnCV9631D1x8zwB+kVLe63UD/Y9vgA+BqQ62jwLiDbdewCeGv9WVb3D+fgGskFJe5B1z/J5C4BEp5QYhRB1gvRBioc33UX/GzLjyfoH+jBnJA4ZIKU8LIYKBlUKIuVLKNRZz9G+kGVfeL9C/j7Y8AKQC4Xa2+dXnS0fk/Y+ewC4p5R4pZT7wMzDeZs544FvD/d+AoUII4UUb/Q1X3jONASnl38BRJ1PGA1OlYg1QTwgR5R3r/A8X3i+NBVLKQ1LKDYb7p1A/htE20/RnzICL75fGgOEzc9rwMNhws1Xt0L+RBlx8vzQWCCGaAWOALx1M8avPl3bk/Y9oYL/F40xKntRNc6SUhcAJoKFXrPNPXHnPAC4zLOP/JoRo7h3TKiWuvp8aM30MS9dzhRAdfG2Mv2BYcu4C/GuzSX/G7ODk/QL9GTNhSHvYBGQDC6WUDj9f+jfSpfcL9O+jJe8BjwP/z955h0V1bX343XQsKIoNUEFUBBsqoMZu1JjEqCmmt5t603sx/aZ6U7703nu/Mcb0GGPvvaEgIAJW7ApK2d8fe870GWZgGAbc7/PMM3P22eecPe2cddZe67eqXKwPqN+XNuQ1Jws/AQlSyj7An1jupjWa2rIS6Cyl7Au8Ckyv3+EEBkKIZsD3wO1SykP1PZ5Ap5rPS//GrJBSVkop04B4IFMI0auehxTQePB56eujCSHEBGC3lHJFfY/FU7QhH3gUAdZ3w/GmNqd9hBAhQAugxC+jC0yq/cyklCVSyuOmxfeAAX4aW0PEk9+gxoSU8pAxdS2l/AUIFULE1POw6hVTLO73wOdSyv856aJ/Y1ZU93np35hzpJQHgNnAeLtV+hrpBFefl74+2jAEmCiEyEeF6Y4WQnxm1yegfl/akA88lgHdhBCJQogw4EJghl2fGcAVptfnAX/Lk7uyV7WfmV387URUHKrGOTOAy03KIoOAg1LKHfU9qEBFCNHeiI8UQmSizqsnrdFg+izeBzZJKf/PRTf9GzPhyeelf2MWhBBthBAtTa8jUSIHWXbd9DXShCefl74+WpBSTpVSxkspE1C2xN9SykvtugXU70ur1gQYUsoKIcTNwO9AMPCBlHKDEOJxYLmUcgbqpP+pECIHlYR3Yf2NuP7x8DO7VQgxEaUQsQ+4st4GXM8IIb4ERgIxQohC4FFUAhRSyreAX4AzgBzgGPCv+hlpYODB53UecIMQogIoBS48WY0GE0OAy4B1prhcgAeATqB/Y07w5PPSvzELHYCPTWplQcA3UsqZ+hrpEk8+L319rIZA/n2Jk/dcoNFoNBqNRqPRNFx0aI1Go9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMA0Ya8RqPRaDQajUbTANGGvEaj0Wg0Go1G0wDRhrxGo9FoNBqNRtMACanvATREYmJiZEJCQn0PQ6PRaDQajUbTyFmxYsVeKWUbZ+u0IV8DEhISWL58eX0PQ6PRaDQajUbTyBFCbHO1TofWaDQajUaj0Wg0DRBtyGs0Go1Go9FoNA2QRmPICyHGCyE2CyFyhBD3O1n/ohBitemxRQhxwGpdpdW6GX4duEaj0Wg0Go1GUwMaRYy8ECIYeB0YCxQCy4QQM6SUG40+Uso7rPrfAvSz2kWplDLNT8PVaDQajUaj0bihvLycwsJCysrK6nsofiMiIoL4+HhCQ0M93qZRGPJAJpAjpcwFEEJ8BUwCNrrofxHwqJ/GptFoNBqNRqPxgsLCQpo3b05CQgJCiPoeTp0jpaSkpITCwkISExM93q6xhNbEAdutlgtNbQ4IIToDicDfVs0RQojlQojFQojJdTZKjUZzUiKlZMGCBRQVFdX3UDQajaZBUFZWRuvWrU8KIx5ACEHr1q29noFoLIa8N1wIfCelrLRq6yylTAcuBl4SQiTZbySEuM5k7C/fs2ePv8aq0WgaATfeeCNDhw6le/furFy5sr6Ho9FoNA2Ck8WIN6jJ+20shnwR0NFqOd7U5owLgS+tG6SURabnXOAfbOPnjT7vSCnTpZTpbdo41eTXaDQaB+bPn89bb73FZZddRkTEWaSn90EI2Levvkem0Wg0Gnc0a9bM5/vMz8/niy++8Nn+GoshvwzoJoRIFEKEoYx1B/UZIUQPIBpYZNUWLYQIN72OAYbgOrZeo9FovOK1114jOjqat956i5CQN5BSpSYNGAAff1zPg9NoNBqNX9GGvBOklBXAzcDvwCbgGynlBiHE40KIiVZdLwS+klJKq7YUYLkQYg0wG5hmrXaj0Wg0NeXIkSP88MMPXHrppURGNmH37lbmdfn5cOWVsGtXvQ1Po9FoNB7wzz//MHLkSM477zx69OjBJZdcgmFKJiQkcO+999K7d28yMzPJyckB4Morr+S7774z78Pw7t9///3MmzePtLQ0XnzxxVqPrbGo1iCl/AX4xa7tEbvlx5xstxDoXaeD02g0JyV///03J06cYPLkybzxhvM+7dtDeTmENJqzsUaj0fiW22+/ndWrV/t0n2lpabz00kse91+1ahUbNmwgNjaWIUOGsGDBAoYOHQpAixYtWLduHZ988gm33347M2fOdLmfadOm8fzzz7vt4w2NwiOv0Wg0gcgvv/xCs2bNWLp0ODff7LrfiRP+G5NGo9FovCczM5P4+HiCgoJIS0sjPz/fvO6iiy4yPy9atMjFHuoG7QPSaDSaOmLu3LkMHz6cqVPdn2rLy/00IA0A774L110Ha9ZAnz71PRqNRlMd3njO64rw8HDz6+DgYCoqKszL1mozxuuQkBCqqqoAqKqq4kQdeWy0R16j0WjqgAMHDrBp0yYGDx5cbV/tkfcfR44c4brr1Ou+fWHbtvodj0ajafh8/fXX5mfjnJ+QkMCKFSsAmDFjBuUmj03z5s05fPiwz46tDXmNRqOpA5YuXQrAoEGDqu2rPfL+obKykokTJ9q0JSTAb7/Vz3g0Gk3jYP/+/fTp04eXX37ZnMB67bXXMmfOHPr27cuiRYto2rQpAH369CE4OJi+ffv6JNlV2Aq4aDwhPT1dLl++vL6HodFoApjHH3+cxx57jAMHDtCiRZTbvnl5yqDU1C2fffYZl112GeB43dOXQo0msNi0aRMpKSn1PYxqSUhIYPny5cTExPhkf87etxBihalwqQPaI6/RaDR1wIoVK0hOTmbePFsjfvZsx77r1/tpUCc5L7/8Mj179nS6budOPw9Go9FofIA25DUajaYOWLduHX369MEUOglAVpaSm7TnrLP8N66TlS1btrB8+XL+9a9/OV3volmj0Wjckp+f7zNvfE3QhrxGo9H4mCNHjpCXl0fv3r2prLS0JydD587Qtq2O4/A333//PQAXXHCB0/UHD/pzNBqNRuMbtCGv0Wg0PmbDhg0A9OrVC/tK3JGRsGuXIDb2Gpt2HdpRt/z111/06dOHNm3ina5ftAieesrPg9JoNJpaog15jUaj8THr1q0zPQ9x2WfAgN3Exz9kXp48ua5HdfJSVlbGggULGD36VCIiXPd76CHX6zQajSYQ0Ya8RqPR+Jh169bRpEkTHnmkjbktJ8e2T3JyMjut3PC5uf4a3cnHokWLOH78OCkpE6rtq6VANRpNQ0Ib8hqNRuNjNm7c6CAflpRk2yc5OZmKCkslqP37/TGyk5MFCxYAEBMzsNq+LkRtNBrNSUhwcDBpaWn06tWLKVOmcOzYsfoekgPakNdoNBofk5OTQ/fu3c3LLVs69klOTrZZrqiAAwfqdlwnK8uXLyc5OZlzz21q096vH9xyywzgInNbdrafB6fRaAKWyMhIVq9ezfr16wkLC+Ott96yWV9RUVFPI7OgDfnGwJVXQuvW9T0KjUYDHD9+nIKCArp162Zus7LpzShD3raw3Lx5dTy4k5Rly5aRnm6ppdK1K9x0k/q8L7igNVBaf4Nr5OTmgillRKNp0AwbNoycnBz++ecfhg0bxsSJE0lNTaWyspJ77rmHjIwM+vTpw9tvv+3XcYX49Wh1iBBiPPAyEAy8J6WcZrf+SuA5oMjU9JqU8j3TuisAI83pSSnlx34ZdG0pL4fzz4fp09Xyjh3QoUO9DkmjOdnJzc2lqqqKrl27mtuqqhz7tWnThpYtd9Cp09+sXTsagBMnHPtpakdxcTHFxcVkZGTw+eeq7Zln4Lzz1OvevXsD+oP3NVLCv/8N77xjWdZoasrtt8Pq1b7dZ1oavPSSZ30rKir49ddfGT9+PAArV65k/fr1JCYm8s4779CiRQuWLVvG8ePHGTJkCOPGjSMxMdG3A3ZBo/DICyGCgdeB04FU4CIhRKqTrl9LKdNMD8OIbwU8CgwEMoFHhRDRfhp67Zg/32LEA4weXW9D0Wg0ihxTVmu3bt1o21a1TZni2E8IQWJiIlVVa81tOtHS9yxfrmY9MjIyzG1BVle+qKgoEhK20KaNpbzurFl+G16jpLJSfcaGEQ+QmQlr17reRqMJREpLS0lLSyM9PZ1OnTpx9dVXA5CZmWk21P/44w8++eQT0tLSGDhwICUlJWT7MUavsXjkM4EcKWUugBDiK2ASsNGDbU8D/pRS7jNt+ycwHviyjsbqO8LDbZd1cKdGU+8YJ3AjtObMM+Gee5z3TUxMZMOGt4HbATh61A8DPMlYs2YNQghefDHT3Gb/Oael9Wblyv8A3wIwZoz2INeG5593bFu2DK67DhYv9v94NA0fTz3nvsaIkbenaVNLvo2UkldffZXTTjvNjyOz0Cg88kAcsN1qudDUZs+5Qoi1QojvhBAdvdlWCHGdEGK5EGL5nj17fDXu2vHrr7bL1iUkNRpNvZCdnU10dDTff9+a3buhaVMQwnnfxMREtm3L59prldV4zTXO+2lqTlZWFp06deK77yx+K/uk4tTUVAoL8/w7sEaMqR6aAytXwqFD/h2LRlPXnHbaabz55puUm6ZUt2zZwlE/emUaiyHvCT8BCVLKPsCfgFdx8FLKd6SU6VLK9DZt2lS/QV2zZw88+WR9j0Kj0diRk5ND165defVVtVxW5rpvYmIiZWVl3HPPbv8M7iQkKyuLHj16YJWywOWX2/ZJTk6mqmqNTVsAiFH4ja++Ujebc+f6Zn+uQsTKy2H4cN8cQ6MJFK655hpSU1Pp378/vXr14vrrr/ermk1jMeSLgI5Wy/FYkloBkFKWSCmPmxbfAwZ4um1A8u23zttbt7aNm9doNH4lOzubbt26sd4Uch0a6rqvEWNZXGzxBh8/7qq3xluklGzevJlu3VLNBblatFAPa5RUqO2F190NWGNg3jzIy4Obb4aLTOqbF13kfhtPeOedKubPL3G5fs0al6s0moDjyJEjDm0jR45k5syZ5uWgoCCefvpp1q1bx/r165k9ezYt7E8ydUhjMeSXAd2EEIlCiDDgQmCGdQchhLWcy0Rgk+n178A4IUS0Kcl1nKktsLnpJsvre++1vN63D84+Wwd4ajT1QFlZmYP05I03uu6fkJAAQGGhpayrk+uGpoYUFRVx9OhRYmP7mducKQN1d6IPWlhYlyOrX2bNUp7xLl3g9dct7cXFtdvvzp3lXH99EIWF7uWQV66s3XE0Go2FRmHISykrgJtRBvgm4Bsp5QYhxONCiImmbrcKITYIIdYAtwJXmrbdBzyBuhlYBjxuJL4GNGlpltfOMuk++shfI9FoNCby8vKQUtpIT7oTkzIM+fx8i0e+sXuC/UlWVhYAiYmWsrrt2zv2a9WqFTExMUyY8IK5zVdhJoHGkSOSMWN8u8/KSigthcxMz+5+Bgyovo9Go/GMRmHIA0gpf5FSdpdSJkkpnzK1PSKlnGF6PVVK2VNK2VdKOUpKmWW17QdSyq6mx4f19R68olkz9TxxIsTEWESRDbSCjUbjd/LylEHepUsXj/o3adKEdu3ambcDZRBpfINhyP/9dx9z299/O+/bvXt3Dh/+ib591XJjvKEqLy9nzBgnkjJWCAGbN3u33yuugCZNYPt2/+hmazQaC43GkD/pmD9fPf/4o3r++mvb9brWu0bjd/Lz8wHYvt1JKVcXJCYmakO+jsjKyiIqKop3321mbjNNgjiQnJzM5s2bzafWxpirMG3aNJYsua3afj16eFdl2Ci0pdFo/I825BsiRpnIc86xtAXZfZVvvgl//un7UmgajcYl27ZtIywsjP/7P/cxwtZ06tSJ7du3myfVGqMnuL7YvHkzPXr0MC+70vMHSEpKYufOnVRVqTupxvY9FBcX8+STHwJhHvUfPhz++af2xz31VOfty5bVft8ajUYb8g0TI1srPd22fedO2+Vx46BfPzQajX/Ytm0bHTt2pLzchXC8Ezp27EhhYSHXX68S1LVH3ndkZWWRnJxsXj7rLNd9DQWhwsJ8AB55RKm6NBbeeOMNTpz4n0N7x46uFWZqW7ypZ0/4/nu4/faVwDk26zIzwVR0V6PR1AJtyDdEDFeRfWXXdu2c9589u27Ho9FoABVak5CQYNbRnjy5+m3i4+MpLS2lvFxVytGVL33D4cOHKSwspGvXVHPbsGGu+xuJx9u25Zvb1q6to8H5mfLyct5++22Cg5Nt2iMjIT8/mmbNVjndbuZM2LvX/b6XL18ObHNoX78eFi1SUp/PPtubli0dY5UyMjx+C5oacumlEBtb36NouAQHB5OWlkavXr2YMmUKx44d82r7/Px8vvjiizoanUIb8g0Ro6KrM4lJZ//Y8ePrdjwajQZQHvnOnTubNeR79ap+m44dVRmLvXtVUaj77qur0Z1cbNmyxfQ8waP+hiFvna9gRDE2dGbPns3evXuprIy0aW/aVGlgX3llFABDhtjeRS5YUL22/BNPPEFo6GqbtldfVd745s3VcmhoKGedlVCbt6CpIZ9/Djt2qNdBQXDllfU6nAZHZGQkq1evZv369YSFhfHWW295tb025DXOMXTjjQon1jjTTDtxwlacOjvb+bYajabGlJWVsXPnTjp37mxue+yx6reLj48HYPv2alyfjZTycvjrL9/vd7NJeuXzzz24mwLat29PeHi4OWEZGk85jm+//ZZmzZoxblwlAH/8oTy1xuXi6aeTSE//L2vWnOaw7V9/OdfeB1i2bBUzZvSivHySuS09XRWZsuecc8YCgh9/nMO//lXbd6Txlg0b1O/5Y69q2musGTZsGDk5Oezbt4/JkyfTp08fBg0axFrT1N2cOXNIS0sjLS2Nfv36cfjwYe6//37mzZtHWloaL774Yp2MK6RO9qqpWyZNUlU8nJ0tk5Ic20BJMBiSlUbxEymVuk15ObRpo9q2bVNCy/v3Oxdc1mg0Ttm+fTsAUvY3twUHV7+d4ZFv3nwdMBhQ6S4ny9/v8cfhySfhttvUqW3UKN/sNysrCyFizcb4wYPu+wcFBdG5c+dG55Gvqqrixx9/ZMKECYSEBJOQAGPHqodB8+bw8cdn0bv3A4SGHqe83DZs84EH4Hk71crCQsjM7AfY5mG5Cg0bO3Ys4eHhzJ49nQ8/HFH7N+YD5s6FIUM8+582dDyZHQxobr/d9+IdaWnw0kseda2oqODXX39l/PjxPProo/Tr14/p06fz999/c/nll7N69Wqef/55Xn/9dYYMGcKRI0eIiIhg2rRpPP/88zaVYH2N9sg3RDqYitRaVY+0wZkb8P33HdtyciA6Gtq2VbfrF16otNkiItQxtm711Yg1mkaP4ck9fNhz6UmAdu3aERISws6dljjj/ft9ObLApLS0lMcff5zvv1dxSC+/rIpnLVzom/1nZWXRuXOCeTnMA7GWhISERueRX79+PXv27OG0005j8WIw3Tc6kJqaylVXXUVFxW8O67KzYfdu22q3Dzywx6Hf4cOujeKmTZsyYsQI/vzzT5wU0vUbUsLUqUr7fsQIuOOO+htLXfLHH39w2mmOMywa7ygtLSUtLY309HQ6derE1Vdfzfz587nssssAGD16NCUlJRw6dIghQ4Zw55138sorr3DgwAFCQvzjK9ce+YZIRYV6dvUjefRRuOQSmD7dord2333qtbBS07C+ETj3XMcqIJs3u/bwazQaG7ZtU4Z4SEiMV9sFBwcTGxtLoZWV1NikD51xzTXXmGJHuwEWd+GQIb4xoDdv3kyXLgMw7HJPrqmJiYmsWLHCvOwqpKQh8bepAtbq1WeTk+P+c3jsscf47LO+9Oy5nxUrrjS3z5ihHqC+m5wc+PTTNg7bN2vm0GTDqFGjmDp1Kn//vY/Ro1uZxue++rGvKS2FadMsy0uX+u/Y/mLFihVMmHAT0dGN4E7UwEPPua8xYuQ94f777+fMM8/kl19+YciQIfz+++91OzgT2iPfENm4UT3ba8db07Ur3H23bdtTT7m+Qjor5Xf4cM3Gp9GchGzbto3g4GCaNm0BeOdVj4+PZ/v27eY89sZuyC9dupQvvviCRx55hIyMAQ7rDV9FTamsrGTLli0kJFhUWjwJn0hISKCkxCLH2Bi+h7///pvExH789pv6Xb7+uuu+cXFx3HHHdaxY4T6I3dlksCc2y8iRIwHYssUy7fLUU9Vv5yt27IAlS/x3vPrirrvuorw8m927dS5cXTBs2DA+N1VB++eff4iJiSEqKoqtW7fSu3dv7rvvPjIyMsjKyqJ58+YcrmNbShvyDZFvvvG8r3XJvYcfdm/823MyzO9rND4iPz+fuLg4KiuVxdiihefbxsfHU1hYSKRJVKSxa8m/9957REY2Y8iQ+0hO7uqw/pJLarf/goICysrK6NjRYnEKD6T9DeWa++8vBhr+91BZWcmcOXMoKfmNzZtVFGV13u/77ruP1q1dFzSTLpxB48ZVP54BAwbQtGlTVqyYZW4rcS1jX2uklHzwwQeMHDmShIS5xMY6vv8lS+B/jvL6DZbVq1czZ84ct3104ffa8dhjj7FixQr69OnD/fffz8emDOKXXnqJXr160adPH0JDQzn99NPp06cPwcHB9O3bVye7amrI+efX/Kp4ww3q8euvWsJSo6kGQ3qyvFx5fz0xHA06duzIjBkzCA+XgGgUnmBXnDhxgq+//pqEhO857bQmTvt88w18/XXNj5GVlQVAXFwXr7YzikL1778aiG3whvzmzZs5dOgQ0BZQce7V0aJFCx566CHuuOMb4HyH9XFxB4GWNm2eiqCFhoYybNgwFiz4gwsvhK++gjVrlN5CaKhn+/CGJ554gkcffYyUlDS2bRvust+55zaOfAiAjz/+mLCwMLdhYdHRjef91jVHrBX/TLRq1Yrp06c7tL/66qtO92GEt9UV2iPf2AkJgf79q+/njtNPV2fak7Cw1PbtSvBHo6mObdu2mYtBeWuUxMfHU1ZWxvHjSlqlMRvyS5Ys4dChQ1RU2FWmxndVqA3pyaZNE73arlOnTgDs2qXyHZ5+2mdDqheW1jAA/IYbbiAp6UUiI992WLdjR0ub5auv9i6VaujQoWzcuBEpLSdWZ6rJtWXRokU8+uijQBVhYSuq7f/VV74fg7+RUvK///2P8ePPtGnv2LG8nkak8QeNxpAXQowXQmwWQuQIIe53sv5OIcRGIcRaIcQsIURnq3WVQojVpscM/47cD7RqVft9nHaampNc5bwCYGMiP38nvXu/TFRUAp06KREfIRqHFJ2mbqioqKCoqIjOnTvz3HPeG+KGBOWBA6pyS0P3BLvjr7/+IigoiObNo2zaMzM9kJXxkKysLFq3bs0ll3gR3wS0bduWkJAQiouVlGhDD0FYunQpzY2qTCiVFk8IDw9n5swPCQ19oNq+bzva+m4ZNGgQACUllroJXhbLrBYpJXfccYe5RsOaNdVPj1VX+KohkJWVRUFBAWPH2hry11/v6FnQHvnGQ6Mw5IUQwcDrwOlAKnCRECLVrtsqIF1K2Qf4DnjWal2plDLN9Jjol0HXFm/OOp98ArfeWjuBZsMbf/RozffRAKisrCQ19TDr19/G4cP5Nuu0Z17jisLCQiorK4mPT6jR9obBUVJSBCglj8bKrFmzGDBgANaRnbGx+FQqLysri27d7C8B1eNMQaghs3TpUjIyMszLzkqPuKJHjx7Mnl19pS63ScTHj8MHHyir8bff4K23yEhORghBSYklzue++3xrWM6dO5clS5bwwAPV34g0JgyVlGHDbBMWbrjBse8vv/hjRBp/0CgMeSATyJFS5kopTwBfAZOsO0gpZ0spjfv+xUC8n8foGwy3sDdCvB06KJHmH36wtA0frh4Aa9cqA/2556rf15QpqjJII72d/+CDDygtda7Pf801J58x/+qrcMEF9T2KwMeQnmzd2jFx0xMMj/zevcqA/OAD34wr0Dhx4gTLli1jxIgRNrMOeXkwZswY4HsABjgK2XhFVlYW27db5FkWLPB8244dOzYKQ76srIw1a9aQmZlpbvMmbwOgf/9ahjs9/riKvQkKUiGaN9xA1L/+Rc/UVPbs2WnutmmTbfHx2vLKK68QExPD7bf/26vtGrrc6KxZs+jevTsxMZZiAdOnq0n5Ll1spwknTPDz4GqIq+TqxkpN3m9jMeTjgO1Wy4WmNldcDfxqtRwhhFguhFgshJhcB+PzHcbVr4nzJDG3WE2xIiXMmaOee/dW+7v7bmW5GZx3nmPw486dSo9+2jSVjfbzz6r988/VVWLfPrV8331wyinw3Xcwa5Y6Q86bp2qDlwdmvF5VVRXPPvusy/VffKHeysnErbd6J5J0smIY8q1aqRjr66/3bvt27doRHBzMwYPZvh5aQLFhwwZOnDhBeno6mzZZ2sPCVMhF06ZX0K3bclas8M74tubAgQPs2rWLoqLe5rbq9M1ZtUqd27BIgdbkFBtIrF27loqKChuPvK9tomr3Z0o6tuGvv1i3YQN377oOsOzAV06Sffv28dNPP3HZZZdx4oT7O5dUNtCX1ebl8HDXfQMdKSWLFy9myJAhmCb4+OADVS0ZICcnnCHMZyjzzNsEerhoREQEJSUlJ40xL6WkpKSEiIgIr7Y76VRrhBCXAumAdbRgZyllkRCiC/C3EGKdlHKr3XbXAdeBJSGqXjCCCZs29X7boCA4+2zlmXcVN3/zzXDLLer1t98qY95ZhVfrKUsp4T//Ua9bt1ZzdoZBPGWKer74YmUJW29jzccfw5Ytanzp9klw/mH27NnkVCO/sH2729UaP7Nhg8rlHjYM/vtf5cmtrISiIvDn39SoBhoVFQvAmDHebR8cHEyHDh0oKioyt1VVeacW2xBYuXIlgCm0xpawsDCGDRvGihXLgHSGDlVeWm9PdZvtamI0bQp9+rjZQEr1I+raFbKziY+P58cff+TBByUPPyw4ccKzqrCBxrp16wDo27evue3QIe/3c+aZFn+NNVdc4cHGbnQdbysvZPKN95HwhrpWrF3rm8JQX3/9NeXl5URH32rTPneumoReSgaiWRNOPXIbGzgXAGF1Q5GTo34K3rJxI/TsqVR43P7e6ojc3Fz27t3LwIGD+PBD1WZd/EsIwXyGqdem9/vXX57JhtYXhizvnj2OVYQbKxEREeZQS4+RUjb4BzAY+N1qeSow1Um/McAmoK2bfX0EnOfueAMGDJD1Rl6elCDlhx/WfB8ffyzljh2u16ekSHnpper1Qw+p4516qnp29pgwwXZ56FDXfY3HeedJuX+/lCdOSFlebruunrj22mtls2bNqh16ZWW9DdHvGO/5wQfreySOvPWWlB07WsYohGq/80617O4n7muuuuoq2b59e7lwoTr2r796sfFHH0k5YYIcOHCgHDNmjPn9HDpUZ8OtN2644QYZFRUljx+vNL/PkSMt6x9//HEJ95vX7drl/TE++ugjCZj38cwz1WxQUmL5EZnORQ+AfOaZIxKk3LPH+zEEArfddpts2rSprKy0fNaFhd7vZ8cOx3PgV19JWVHhYoP775fyqaekjIur/jpg9T356tQ/YsQImZqa6nCoipatZPmZE80Ns9t3cDqGLl1qdtwnnlDbP/CAb96Ht3z22WcSkLNnrzO/FwczoY4+c03dAyyXLmzSxuLvWQZ0E0IkCiHCgAsBG/UZIUQ/4G1gopRyt1V7tBAi3PQ6BhgCbPTbyL3F0OmStZhquvxyaN/e9fqNG+HTT9XrRx5R4TDPP++6/8yZtsvz51c/hu++U97/Tp0ctfreflu1hYUp94YfqKqq4qeffuKUU661aY9mH02wTfCto5oOAcWDD6r6YQZPPQWDBtXbZIkDW7fCv/9tO0MiJfTqBf/3f2r54EH/jaegoIDOnTubc8G98iJfeSXMnEl8XByFhYXmnARfK3kEAitXrqRfv34cOGC59MTEWNYPHjwY+NS87MwTXB1ZWVmEWp1TjGg/BxYuhHXrbCtYm+SGHgIqKlSlIn/+jnzJ2rVr6dmzJ3v3qs/6nnsgzl3AqQvat4e77rJtCwtzk+Q6bZo6gVjNLvmL/fv3M3/+fHr2vA+ACEqJQn2BwQf2EfKzxSwYuXOH+XUmS4g3Refm5tZM08G4JNeX83jJkiU0bdqUL76wJHnLKqnCcW++GW67zdw+laexDmvSNGwahSEvpawAbgZ+R3ncv5FSbhBCPC6EMFRongOaAd/ayUymAMuFEGuA2cA0KWXgGvKGLvApp/jneKGhMHYspKX5/gz1+efmuFQb/v1vVaO9vNw2Zr8OWbZsGTt37iQ7+0Gb9n20Zj29+Ppry3t/4gm/DKleefppePJJ27YlS2BF9XLMdc4117ie+t6wwfLan+EQhiFvJOxVG5NtYPX7T4+I4ILcXE4fry6wjc2Ql1KyceNGevfuzUarM6z1XzwzMxMhis3LV13lebEhg82bN9PV6gfyxhtOOm3aBEOGqBgIUzVXwJy/EwQcP678PTUJR6lvpJSsW7eO3r17066dajOenXLihG2J1U2bbCpyPf648uWcfbZadkhzklKdNM44o1bjrq3c5++//05lZQe+/fZyAFYwgIN2xaucsYRB5GIpHlYTTXnDkH/33foRRVi+fDn9+/fn6FFl1gmq+NfVQdC3L7z+Orzyirnv0zxIAvn+H6SmTmgUhjyAlPIXKWV3KWWSlPIpU9sjUsoZptdjpJTtpJ3MpJRyoZSyt5Syr+n5/fp8H9USEqJqvycn+//Y1q4zf/H++95LLdSAv/76CwgmL89SmtxIbE0kn3PPbWNuP3hQpQ80VnyoAuhzsrNP8L6H/9DFi+t2LAZSSgoKCujUqZN3hvyBA0pRysT9X3zBYydO0HTfFqDxKb3u2LGDw4cP06NHD7ZssbRbTw5GRUXRq1cvm+28NaSzsrLo2rWnefmtt+w67N/v+sdhyh0KAo4cUTcUDdEjv2vXLvbu3Uvv3pZgbbeG/IUXWs7vM2dCaqpqM9GkifLKX321WraZmSsvV0mtDz6oqoBbY+ROgUpkccLvjGMQiwDIrmWu98yZM4mOjjUvp6IyqiOovjBDKBXm1xUVrvsdOOD8N2k9Sf5X9aqdPqWqqop169bRt29fcwJrKKa7LRcfajT7acJRcgdMqZfZE43vaDSG/EnD4cNeuPvqgOxsWLkSEr2rmFhr3J1ZfcDcuXNp395SxvH8820Tr4KDITS0wma9rxkyRE1+1CdSqkiqQKSs7DhXXXWvx/0vvlj9bCZMUFEUdcXevXspKyujU6dO5hzwav+ilZWqTroTzrurBzHsaXQe+SyTgkmPHj3Mqj7W+e8GgwcPpkmTi83L3iT8lpeXk5OTQ2KixYB1KLnRt69y9bshGKjYo5JmG6JH3kh07dzZUtXbqb7Bn39CYaFFmriiAs46y+V+zzxTnSO6GM7r1avV1NfUqY6dIyJs4/Puv185oewYx598ziVA7TzZlZWV/Prrr4wcOdxh3QZ6OtnCkSv4CFCTwlOnOo9gjY5Wmg6HD9vOmln39be047Zt2zhy5Ai9e/c2fzc3XuNeHa41JVzBx3RZ+R2V9zUevf3du+HNN5UeR20ikBsS2pBvaBw+bCsj6W+6doV+/aCbSWs9Lc0/x61D92RFRQULFy4kLs4iV3f5oC22MwHvv0/Jy+9yJy+YGiQPJ31O1VHflOBcvryQhQuVJ0cIuOOO+jkJ/fJL9fqa3lYtrS0rV0JVlSQyMpz581/yatvLL1dx1hdfXH3fmmJIT3bq1Mkcs+8QI3/iBKxfD8uWKePn0Ufd7vMpHmx0hryhJtOiRYq5zVldu8GDB3PsmCXkaMcOxz6uyMvLo7y8nI4dLXHCNrHcv/3mkfRUEPDq5/cymIVeVy4NBNauXQtAfLzlc4iMdNJx3DjoaNEcx1R11UxlpeuDVFSoawHAjz86rv/5Z0tJ7EWLlI78gQPK+rXT8e1CHr9xWq3OLatWrWLfvn0MGTLCYV0X8jzax0f8y/x62jTXszEVFRAVpSYuDHJzbftcdZX/6kEYN269e/dmtykD8K5b3RvyvzGeN7gJgArhWPm1oZKUBDfeCM88A8XF1fdvFLjKgtWPAFWtOf10KTMy6u/4Bnv3Svnmm0rCxV51ZsoUKZ97TsrsbCnvvlvKMWNUe3Gxo2pBbKxjm7NHTeQWPGTZsmUSkOecs96Syf/88y7HAlLew3+lBHn8+ltqdexjx47Js866x+mhcnN98/485fDhwzI2tqfTsbSnWPZgowQpL7/cf2P6+291/FNOWePRz8TVo3Pnuhvj999/LwG5ZMlKi0KGtaLH7Nk1GvQX7xyuu0HXA7feeqts2rSpvO66KreKGRs3bpQQau7TsqXnx/jxxx8lID/9VP1eeve261CD72EUs2r8nuuLK664QrZv315mZam3MWiQlFVVdp0eeaT6979+vZTnny/lwYOOB3n1VefbfPGFZ4MUwmHbp560H6TnPPvssxLekO3bV1h+XzX4vgewzLyYl+d4HPtN7r471/w6gVz5M6fLZhxy+xv3NU888YQEZFGR5biHb3/Q4/d8+PIb/DNQP2D91goK6ns0voOTQLXm5KG+Q2sMWrdW849BQSpu/z//USXkjhxRFYTuvlt57597TsVNHjtmEw/MnDlKN97TmIc69MgvMFWe6dlTabdec2mZGr8LJIJnUaoIFJq8e1VVns0L792rvr+FC6mqquLCC6/ip5+cF6Hq0sVpc53xwgsvUFzsXOKjiDg2odxPn3wCl16qTpV1jeE8XbiwdsLMdTnWgoICAGJiOpvbbLzAL79co/2+eF3g5tzXhKysLHr06IGU7nNekpOTiYqyuI+9SYDcZKoyFRamCtnZiG3ZZ297SGoAi5i5Iisri9TUVJYvV8u33eYk1ejxx6vfUa9e6nzuLLvcVSnWc87xbJCXXurQ9PhDNY+t+eeff4Ab2LnTlZwOSr3FNIPG/Pk4S7hZTganoK4J1vm/rnj+eUuY6Y9M4gx+5Xy+IQz/ZbyuW7eOhIQE7rnHMlvf9H3PhSIqaBwe+dWrV9ss79qFTT5OY0Ub8g2N+g6tccUjj6gScs5090JCHOd1hw9X4TmdOysj/cYbVeiBq8qq33xTZ1IAK1asoEOHDixd2oJWlPDuZ87moJ0T9vN0daa45x4VE7ppk4rncDXW+fPV+33mGUpbteLzGT+ynAFksNRpd3+VDC8tLeW1117j1FOdx8cGWUmVdaSA3M8XehXyUFMiIry3wCMo5WEe99uFtKCggCZNmhAermLeHUIxjLluL4nAzzFMdYxhyFdXTTIoKMimGil4ngS5adMmYmNjuegiu/NQaKhtvLY9V15peX3zzTaryhugkZOdnU337t3NtnJenl0Hb+9s9+xRX8JTT6k7AiFsVG1s8LQ86jvvcNjuWtaGPTW636qoqGDevHk2bQteW+XY8amnlOSxlCopyUVVqwUM5RQWeGTIW9MHFeLyPteQQw2qStWQdevW0adPH3PtxhYcQBy2S+6orLSRoLTmaAf/jbWu+Oeff0i300fOyKgfXRB/ow35hsaRI4HhkfclTZooeayePW094da6cw8/rAzlOmDlypWkpZ3C77/Dv7GXuPCA5GSLePnYsUqD/8474YYblGH/2Weqj5SWUnszZ9L04EGaUcoAVrKUgfRmLQ/xBE1Rnq4ebGLJgrpN8jX48suv2Lv3Co4efdqmff9+eHCsReFjMj9QQGcWMoQV/363zt3yK1Z4NmNz5IgKwZ02DW7gTR7nUe7meZpxmCv5kIKCuhunoVizaZNyeTZpYlpx+LAyeNzNOll7L597zmZVUxqPbM2xY8coKCggOTnZHHbtItcXMGQoXzQvd+/u2XE2btxISkqK+WbBXN3XPln+3HNtf7unnqqeR4yAV1/lQGuLetUUvnU0hAOYffv2sW/fProZeUzYVvgEaibQ3707PPSQZdnO+wnAa695vr+ICCqspT+BruS4LVniipUrV3LYqh7AUzzAKTf3d+wYFWW7HBxs0dS0YwFDCZ0/2+MxTOEbm+WOFCIRHHjtM4/3URMqKirIzs4mNTWV/ftVm7WUJqDqJQQFKe1kJ4nJcf+9larSetDM9BGVlZVcd911tGrVeJJ2vcJVzI1+BGiMfNu2Ul5/ff0dv7a8+qqUvXq572MdXGgfz3fOOT4dzpEjR2RQUJC8996nJUj5IE94HFfo9NG6tet1114r5dix1e5jB+3Mr5/jLp++X1cMHPgvp8M5cUK6HWvV4iV1Nqby8nIZH3+dy8OvbDtOHjfFUluz6foHzJ0+5yJpxL3u3Fk348zIyJCnnXaaDAlRh33uOdOKyZNdf3YTJ0r56aeq37Fj6iGlTZ/PuLhuBlwPbNiwQQLys88+k1dcod6iQ/y6FdOnT5dwns1HtnWr+2NUVVXJ5s2byxtuuM0xPtn+8zfKM/ftq5bfe089jx2rjv/22zb9MzNr8+79y+LFiyUgZ8yYYX4L//dchSrRum9f7c5v7h4vveT1WI89/rjNPv5itGzSxPv3PG3aNAkqfymICufjGzLE+cYff+zyPc3kDJs466Ii5117s8blPsoJ9v4NecHWrVslIN977z3L795+HHYcs6/GDvLANXdJuXt3nY61rjByY4SodPo1NIZq7OgY+UZEoMTI15Sbb1begepwJW/5v//5dDhr166lqqqK1NQ0QHKMJo6drL041c3TuZuLffddJfdWDe3ZZX59Ny/UuWZ9cXExS5bY6monkcNdPE/o+FPdbluxcIlzDUEf8Mknn1JY6FwypAeb6Lf7D8IoZ0/vUXDLLapa1Zdf0qOrxdV7MV8CKu71hva+/e0YGB55w+lrLiq6apXrjX780RIjHBnpVFLkEr6A22/36VjrizyTSzsxMdGsG29MYjkjMzMT+M6mrbqQi6KiIg4fPkx8fJrtCvv4tGuusWha/vSTGoiRv2P6HtrYTQFU7D9MQyHbFIeUkGDxyI+ceZd6j3fe6fmOhg717sDuqoW7IPKhhxhvda4/lb8pPyG93s/s2bPp0SONSI5Rif30A0o387ffnG98+eVw7BgHnWipn8kvdOqkiqPOmeO8Mu4gFrGWvi7HFkIlZcPG1pmO6VZTPE1SUpLH20T+9BOT27SxaWvx3gs2tQMaEl999RUxMTFI6dykdaJ62rhwZeHrRwB65CtMnobHHquf4/uLnTulPHRIvU5Jqda7UBtee+01CcilS4vkJ1xqe5yYGPW8Y4caE0jZqZOUn3wi14Z2dumB8fXjPa6Saxcd8en7tubFF1+UkCxBymQ2yVt42ftx+piKigrZqtVzlq+C3XI5/eWTPCDDKKv55/nnnz4dZ2lpqQRMqhHqEO+/L6Xcs8fx2P/6V7Wf189nnOG43aJFUm7b5tNx+xvjf1ZcXCzvuEPKZs2q36Zjx45yypRLzR9DdWpJv//+uwTkwIF7JVhN/G3ebPt5/vCD48YnTkj5738rl6uUMj8/X/a32qZMhHv1fuuThx9+WAYFBcknnyw3v+WquPjq/xtJSbbLFS482/aPM8+UtXF7XnbZZXKw1UxmS/Z5tf2JEydk06ZNZXLyIvkUU52P8ZNPPNrXN488It+z2xaqZGr8QRlMucNuQznu+bnnu+9q8vFUy5tvvikBWVBQIEHKCI7ZHteF/NkN48c7jjEysk7GWJeUlZXJ5s2by2uuucaflyi/Q3175IUQUUKIZ4QQnwohLrZb56yAtsYZhkpAICa7+pJ27Szv8Z9/HNf70AO8cuVKYmJiaNWqA5dhFcvYqpWKJzRet2unSkT+8gtcdhnXDZhOCOWspJ/PxuKKq/mA3oObKYWFEyfUecmHTJ8+nW7dejGYhWSRwivc5v1ObrzRp8nIP/74I/v2nU8Yx5EI9tCWAazkQZ7mOLXIlfBxxa3tJlmdTuZgbFP+nL3nMy9PiUpv2qQeLih2Fq87eLBKCm/A5OXlER4eTrt27Th2zCqPwJq77rIpI5+Zmcny5QvMy9UVeDYUa5YsUfHt5tzW+fNtO06e7LhxaKiqIhOrqoJ26NCBVVarw2XDiR/Ozs6mc+fOPPSQ8kzfey8IXJwzBg5UamPp6ba/y+efV/HjO3bAG2+o78YZ0dGqEqyU3lXusiIzM5NCq5nMGPZ6VU13+fLlHD16lP27U3iAZ8ztuVPfVWpiRUVOFXKccc4jj/C1XQXaUMrZUNiCjaTSAYsw+WR+4AQeJvYCtGzpeV8v2Lp1K+Hh4cTFxRFEJaXWs8ovv+xydjtx9GgG2jeWlrpWIwpQFixYwOHDh5k0aVJ9D6X+cGXh+/IBfA9MAyYDM0zL4aZ1K/0xBl8+6s0jv22burV85536OX594ez2evBgKadOVd7yWjBw4EA5atQoee2Fh2z370w32YpFi7ZJWCGhSn7F+Z57ZXzxeP75Wr1naw4ePChDQkLkY2fcJZeSXvux/fyzT8Y1ePBg+Qz3yJk48VDX9rF2rZQHDvhknH/99ZcE5N9/z7b1/HTrZjle27Ye7++XX35xPe53322wwZ7nnnuuTE5Olrm50vZzssZYccUVUkpDFxybj8BBC92K66+/XkZHR5v7fvGFlHLmTNvPMC7O4zG3bdvWdtt93nmK64sBAwbIcePGmYc9fbp0Xq9j505TEowVrr6cV15x3N5HLFmyRAZb7XcUs2RR3nGPt3/mmWck3COf4T6b8c2fVzNN+iJXgfAgd9FGtqdYvsM18g/GOO1TNXSo8+1Nv2tfM3nyZJmSkiKllPK8iJ88/o7mzJkjcTbOZcvUObKB8Oijj8qgoCDZsaNtfLygUl7Gx+aZlIYObjzyTht9/QBW2y0/CCwAWmtD3gsWLFBf2S+/1M/x64sBA9T7vvNOx5NO9+413q2RHHfzzTfLbXS03a87i8FE374ZElRyVQeKZCaLbfdRWCjljz+6vChIkMcJtVnetsj1RcT86NKlxu/Znv/973/OT+a1eRQXS/nQQ94bnfv2Sfnyy3LB/Pm+H5Ozx6ZNtf78PvjgAwnIdetUUZhnnpEqcdX6OKee6vH+1q5dK1/AlOTsbMwff1zrMdcH/fv3l+PHj3duY8yb51gs7pdf5D///CMBGR9/2NzszpYeNmyYPOWUYea+08//3Hafn35a7Q26Nf369ZOXjxpl2X7Zspp/AH6iqqpKRkVFyZtuusk87NmzpQqZMBpGjnS9gzVrVCiXPUeOSHnBBeqG4N57pXz4YZ+NuaysTIaGhsrcWEv4z4mEbh5vP378eHkDrzv8V7z4qh04YmRkO3l8yiXO123fbjaANxthmfaPvXtrPigX9O7dW06YMEFKKeU1nX63HOs//3H/Ho8ckcHBwXJyxtfyba51HGtZmc/HWheMHDlSJiXd7jD8mWe/KCXIO3jB9nzTQHFnyPsr2TVcCGE+lpTyKeBdYK7JmNd4wI7FpkIWDXya3WvmzVOFlNLSHNfVotqDkRyXmppKJ6zKtr/9dvXz+MD5508GoIpgdhDLUruJyllZcRwbM9G8fDGfO+zjBGFs2iiRf83iYPZuOg2KVe/VHbm56n0vWlTtGKvjt99+o7cz7X+DL7+EBQtUoi5QMGYMParb6WmnqcxEb8d37bVw22388uijvBPmZkzujnvRReq1E23DQ9iFpG3Y4P0x7CgoKEAIwZEjqphYp07YSvQNHw7ffed8YyfExcVxF7Dt5gznHar7bQQoeXl5dO6c4HzlsGGq8JA1Z5xBelQUXYVgWs/rzDUBXOWaSylZt24d3btbQt1OO2QrB8h55znKD7ohPj6eNfssBdLkWc5rLAQSe/bs4dChQ2bpyXSWMXRyaxUyYfDhh6530KcPDBrk2N60KXz1lQpT+e9/PSsm5SHh4eH07duXqZ07mttC8z0rHFBRUUGb2bN5g5sc1nnxVTvQ9KOPeJ+rnK67kK8cG9u2VZmwvXsD0DImxvmOx42r+aCcIKUkNzeXpKQkCmZl827BaZaV1STKN23alF69enGg6dfOZZf79vXpWOuC48ePs3jxYqKibEMShw2DMwYo+d427AFUcajGir8M+Z+A0dYNUsqPgLsAP5W8afi8dJeqIEnHju47NjYiI1Ul2UsvVf9Qe5Ytq9FuN5gMuYwQq4Ivv/0G113n0fbnnnsuYFuAIoE88+sxY+Dqq6H4/pe5p8NnfMnFvMCdLCGTFhxgOpOYyAyaNQNx6mhadDWpCLgzrA2Sk+GUUzwapyuklPC//7HWWdXc1auVY+PCC9VxTPGvse3bs71JE/493lGL2IyhSnT22Z7HW950E3z/PQDb/5nLtSe81FBft059d59/rlRIliwxr7qBNxjMQqKwUx6x1xavAQUFBXTo0IHBg9VvKCYGWzmWOXO8io2Njo4mMjKS4OAVzjts3lzzwdYTBw8eZP/+/XTu7KZU8T7HisJN+/cnW0ou+f1L/muqpLxnjyoSbc+2bds4cOAAycmW/2NEK7v/kZd1KOLi4igqKuJCk/KR2LkT/v7bq334G0OxxjDkP+JKQg7afbZ22u2BQGZmJn/bq5lVVzkMWLVqFePrqFDgude3cdoeQqVtw6mnKivRyvkT+uCDAPyOneG+cqVPx7hr1y6OHj1KUlISeyZebVnx/vse3ckMHDiQVatm4dSn2wDONevXr6esrIw2bWLNbWEc55Wg2xCmm80q03u7yfFer/HgylXf0B7AeGAzkAPc72R9OPC1af0SIMFq3VRT+2bgtOqOVV+hNa9xoywhul6OHTCUlztOAfboIeUZZ0jZoYNXcaz/93//J5db7edjLvN6OAMHDnQYTnuKZV9WmYfmLrpj3jwXO54+XQ5jjvuNQcpLLlHqEjUgKyvL9X6zs207Hzgg5ahRUubmysmTJ8t27U6VwZTLUsLdj++jj6TMy5PyjTekXLzY+UAefrj692k8ZsxQakLvvKPUR4x2Q4fdmrfflkWDBllCOez3dcstNfrcrBkzZowcZHWMBQusjlPDELiuXbvKCy64QF7R9Fvnn8GYMQ0qVn716tUSkJ999p3tR1NeLuVrr3n0vf/BGPOita63wQ8//CAB2bLlCQlSDk4/YbuPGuhjKyUi5COTV9juK4D58MMPJSC3bNkiw8KkLG7Tx3bsXbvW9xCd8tFHH0nsv3cPdOmff/55B5UZee+9pj9iLfnmm2p/l5vp5vKa0717d8fzzrnn1n5cVsw3hSH+bJ8LsmKFR9sbvxeQ8m6edXyPAc67774rAXn55ftdnuf/Q1JDeTtuIQBCa+oUIUQw8DpwOpAKXCSESLXrdjWwX0rZFXgR+K9p21TgQqAn6mbgDdP+Ao7ObGMbnet7GPVLSIhjqEJWllKT2bFDKcx4yMaNGxlgtTwfL3WTgWuvvdahbScdWEOaeWjucCnVPGkS0xYM55qz1tGCka538Pnnyu1fA+bOnet6pb3wbosWyhuZmMjEiRPZtSufSkLMnlKX/Oc/SjXhxhvVlP3SpY593AmKG6SkQHk5nHWWcstee61SH8nKUstOdNi57jpazppFaOgEzj33LXZgp3N94ED1x60GQ0PewEZQqobixXFxcRQWFrI4/jwuPceJ+/mvv2DGjBrtuz4wNORjYxPMbcnJqBCPm2/2aB/DmEdbU30FZ47a1atXExQUxIEDambk1+un23Zo49y76o74eBUu1bKPnWdTSq/35S+ys7MJDg4mLi5BCVyFhtp2cPY/CQBU3QCYk3GuuW3luy5mpayYM2cOoU3t9NP/+99az1YCMGUKuDtHAs8MmuGyRPGIESMIDf2K663DVtauta1YXksMDfmE1nbnNg9nQocMGWJ+/RvjHTt4MCtSn6xatYqoqChat3Z9rq2iwPw6gP+6taJRGPJAJpAjpcyVUp4AvgIm2fWZBHxsev0dcKoQQpjav5JSHpdS5qE885l+GrdXdKKAAjpV37Gxc+657tfv3u3RbrLWr7dZjsL7gh0XXHCB19sYvPqq+/WnnAJv/5BCVTMnBU6s+fhjJR/nrGS6O1zFym7Z4tbwmTBhAkKomNuXuY3vOYfW7KXsn8WOne1r22dlqf0//bTlrFpdgbOLL1bFlRzqzKMsQlfxqECTJk0YMeI4mze/Tm/WkUQO3zxfoG74CguVxGgNL1ZSShtDvj07SD7PKtbbkxApJ8THx1NUVETbtlBY4sLwciNhGWhs26Zye9q3t5y7mjVD3Xh7SATHmccw2rKLslLHq/Hq1atJSlJhNU05QovwMsvKzz5z6O8JcabqP3srKinCMnXvNsa8nsnOziYxMZGCAmXAxxbbGcP2hn2AkJycTPPmzVkeawlb2b7BvQZlZWUl4u+/ufzoVnPbQzzh24FlujcFPvzEtc9v+PDhlJdfxztczz08y7aWfSA7G7p1c7mNt2zduhUhBJvWxNqucJbn4ISuXbvSpk0b2rffxHp605fVXGItw/zUUz4ba12watUq0tLSCAlxndMWZhUKVV7uj1H5n8ZiyMeBdbYihaY2p32klBXAQVSirSfbIoS4TgixXAixfM+ePT4cuufMYxizcF9p86TBnSevXTtY7MSotEJKSdqaNTZta9xU53NFs2bNOO+8573erls3z5yRwcHBjB7dhDgK3XdcsQJOP115e154wf3NzJIlIATXOktG/fbbai80bdq0YciQLsTEfMV+WnEe37OP1uS0Hli9y2P/fmV8P/igSk597z23WUhhHIdPP4VwL/Sa7Rg7dizr16+nhBhySeKCuzuqPJPZs+GRR5Redg3+03v27KGsrMxsyI/lT8K2WCXQ1tCQj4uLo7i4mMpKyZw5IJ15UR94AO6+W3kM7W5IA42ioiLCwsJo0cJyw9WsGfDoo17tpzvZ7KI9r6c43gGvWrWKEyfU//AIzVW1ToNLLqnRuM2G/N6DFBJvWVGNl7Y+yc7Oplu3bqSkwHh+dewQFub/QXlAUFAQGRkZfFVUyGzTDGRntrndZt26dfxol98zl+G+HVh4OEjJm29IPn9hp+N6N9VsBw8eDBymY8c9PM89EF6L+hcu2Lp1K506deLwHqu4fSk9/p6FEJxyyik0bz6JF16AtfTlKFbnrbouK14LKisrWbNmDWlp/XjuOdUWjWOuzb+DBZ1MvyX7khKNBb8b8kKIU4QQFwshLjce/h5DTZBSviOlTJdSprepwTStL9h2z+u8G3Grz/ebn5/PoEGDaNmyJQ8+OI20NOkLQZS6pTp39s03K2N2wwaV1BgbCx99pLY76yx2btzI2dZKDsBZL9esWNDzz3tf1nrOHM/7jhw5kmI+YQI/cSmfMp8hzjvu3KmM8LvvVl7s/fstmYFHjypPYkkJ/Otfzrfv108pe3jAxIkT2bvXNqzIJNjgHmslhT//VCEyLviGKZQTVuNCMwZjnRWB2ml3Ue7f3+tCKAUFasq2Y3xHHuE/fMIVth1qGMYQFxfHiRMnWLhQeZkGBblI5n7hBRgxwsMP3gWFhSo0wAcKPq4oKioiNjaWtWstXjOnH82HH8JVV6n/7XDXBtk4/uDrr2HhQrW8d+9elXQcFkEGdqFbs2fXeNxGaM2+ffu5jE8tKwI0PEVKaTbkAX7lDMdOAWrIA2RkZLBmzRr2o0JV0ljjNvzNPjRwKRk88Y+PDXkTN9wAl9zZzrZRSrfFGbt06UKrVq0YO1YlvpbtOmBZ6U3FKzds3bqVpKQkZnxb84TfU045hezsbAYMUEW5SrH6fVdWutiq/snJyeHYsWMUF1uKfW285GmHfq0qK/kf5wAqL3n7docuDR6/GvJCiE+B54GhQIbpke52I88oAqylXOJNbU77CCFCgBZAiYfbBgQREVBW5ts4r4qKCiZNmsSmTXtJSbmOp5/ew5o1glNOUeHXAc28ea7XrVgBXbooWbutW9U0/r/+BbfeCjNn0qFXL1sZJVR0Sk3o3Dm++k5WdO0KHTp43n/kyJHAN/zMBD7nUha4MuSt2bZNhY9ce606GQ8froykmBiHsIxvOY9WlMDy5R6PafLkycARBg/2rQG4kRTz64tMaiG1pW/fvjjcfNvPAhQWqouyEB7HzhuGfNf3f+E/PObYoYYVmA0DcupUdcVZerQnJYWl7jax5cABJRnqjB07bEOJ7rhD9R8zBvLzazTe6iguLiYuLo4zTHblK6+4UHdt2lSpbbRpY1YwckZ/VnLhhRIjvHehyaL/tPhfDhKwxHv337QmKiqKZs2a0bLlQrLpTma0SRLxkPcheP5g586dHD16lG7dunG5OYrUjthY5+0BQGZmJuXl5UR9foOl0UX8OcDfs2bZLA9kKSNG1NXovEcIQXp6OitWqJvLYGu1m1rcYFqTk5NDUlISd+xTkrdb8D5sx4iT37dPXU/LrCtnb9wYsHLXRiXn776zXLjbb3L+uQ7AohYU8HZNDfC3Rz4dGCKlvFFKeYvp4QsX8zKgmxAiUQgRhkpetc8GmwFml9l5wN+mTOAZwIVCiHAhRCLQDezdOoGB4QjypdrWZ599xtq1azl0KIfFi58FXjCvu/RS+PFHi+cr4Bg6VE2h33GH8/XGtGt1MfVAP1bWJnqDjh09j7Netcq7ffft25eoKIuX5H2uRkZFKe+2K21rI6Hqiy9UbLkb2bP5DGU/rbzyfHfr1o3U1FTy8gps2rOy8DpkwppiYllCJhfxBVUEM3NmjXdlJigoiDFjxhAaauXZnjLF9Qbb3E/pG+Tn5xMB9Pr5XceVV19dYyFrI6QjLMwSQ37tLR5My69bpy68Eyao/8YJO2XfPXuUIffAA5Y2I3F8506VlFwHUn5FRUXExcWZHRD//jfOa0JY32y5SRSOZQefcDk9USFFCxYsICSkE12POrmprGF4k0FcXBwHD27issvgUHNTxOUXX9Rqn3WFIT3ZvUsXPuZK25UpKfD66/CWE73wAMFIeN2wZ2O1fcvLy2n5xx/m5SYcdZpCU2dkuKjz4NAtg/Wm0LemWIUBnX12rT1yhw4d4om9e3n9ww8ZVqi07S/G+9/mgAEDCAsLY9EidaG38cgDFBQEZJboJmd5QsZ1rl07x3Wo9zB1qs8mRAIGfxvy68FeOqL2mGLebwZ+BzYB30gpNwghHhdCGBV53gdaCyFygDuB+03bbgC+ATYCvwE3SSkDcj7JMOTLytz384aXXnqJNGcXVROTJ8MQDxzA9cbHHyvVEyldFympJmygHTtZTT9PakC55P331V9JiBL3x2pXfW6nPUFBQQwebKmEk013gg4dZO7ZL8Jo+3kF71lKJvfc4/12kydPZteuT2zaUlKAxx5T30dSktPt3CERDGIJiVMvoqgIzjzT+3E5Y+zYsZSXW0KKbtzuRgffw4vW4dWr+daZ9ZCdrYymGmIY8m3bWu74bC48t93muNGJE6qYT8+eFm+8vSFv7OS//+Xo0y87V76JiPBp0SkpJUVFRTaJrqGUg11+CjNmwMiRluXQUDVDUOp8JuIyPmM9vSEvj7lzV1BR4eLmyweGfGFhIU2awP4yKwPHyzAsf2DWkI9zSPFS+TM33lhjJSV/EB8fT2JiIrNnz2ZPqGXKUhbvUE4Zq7oPa779lo+sLoSlNGGqm7+0z3jySfXsYT2B9PR0Kk3hKZusZhsB+OefWg1l69at/BsItfpcvskZ4HoDF0RERDBgwAAWmM4bwfY6+QBNmsDvv9d0qHVCVlaW+VzZmr3czouWlZddpp6tbJu/rebge1Rb1bCB4UqXsi4ewGxgP8rgnmE8/DkGXzzqS0f+zTeVFmpxsW/2t379egnIl19+pVop5wbB8eNSXnyx+zdi9zgw7Q3z4pEjtR/CkiX7ZYcOu1wecs6cmu33//7vRQn/Z7OvZ56RUq5cqRamTPHqfUuQn115pRzeKU9GRtZMin7p0qUSkNHRR5z/Vk45pfpxRERICfI4oVKCfJyHJCiZeF+Sk5MjAdmhQ4n50BW//uF8TA895NE+f+3Y0XHb556r9VjLy8tlUFCQfOihh4yPR55+upRy0SIpN21SnTz5jktKLDs9eFDKQYM82y4zs9bvwWD//v0SkE888arl93HwoHcnmPbtXY61fPx4+VJQpNyGk+8ClFZ9Lbj88stlp06d5D33qN0duPB69WLs2Frtty647777ZGRIiKy8917bz+DGG2v9OfiL6667TkZFRcnf7vzZPP6K8Ej1evJkpe1eWenwPYOUVVX1PXpHCgsLJabxRVMi3+DfPruwfvutbZ2JGLyvlWBw9913y7CwMHVK5pj8GifXk/T0Wo3X12RkZMgxY1R9iU+41HaslZVSFhVJWVYmK196ydzelp3mLrt21fc78A4CSEf+MWAy8DQqhsN4aDzA8Mi7cFJ5zVdffUVQUBAVFVf4Zof1TViYCoDL9qy8N8Cl81U85lln1dp5B0BmZkuKi9uycKFyIP33v5Z1JSVuc/jcMmrUSOBum7bgYFSCamUlfP21yqD1xps65gbmFiRQWmral5cMGDCAuLg4Ro68zHmH995TiiH285hW3tij27ezIiiI4cylL6t5zBRr7kYMokZ06dKF2NhY4uMtsddlw8Y617B/8klLDP2hQ45/uAULYO5cEp0pA919t2Obl4SEhNC+fXsKCwvN4V7h4ShJOW9cSRutQhQuu6xaJScz9hU2a0FRkUo3iolRnrPgYBw/zy5uKr4CrF7NB1c4P0eF/PYbt1WV0gknGWzl5c4lS73AUBCaPFmFzuXtbqJWuMpBqEeys7M5VlFB0LPP2q548cVafw7+YsyYMRw6dIhlzTqb24KPm34v06fD+eer2hRWlKH+JLWZUa0rYmNjiTHJ4+6nFTdR85k6ewwNeYO91FyEY/To0Zw4cYLnn19GGZFcwDeOnQoKPJZ2rmuklGRlZdHDdD4Mwa5Kd1CQCiMMDydo4kRzcy8sCl91VBC4XvCrIS+lnANkAc1Nj02mNo0HGBXGfRVa8+uvvzJkyBCWL68+ltcuryiwcRof58jTF11hjsH2NtylOgYPVkbLvfcqe6q83KtaVQ707t2bFi1skyfNggJBQeoqNnw4tG7tPh7FytC8//7a5ZkHBQUxadIkfvvtN+cdUlKUhndUlHKC7NqlQhL69FHrO3fmm59+Ir2qCUsYxFr6UkUwQ4aoMG9fIoRg2LBhZGdbwqw2bcJ1foWhU9aihWM899ChMGIEyfZXgiZNfDZeQ0veMOQdxEbuvbf6nQwbpuLizzjDuwJSPrSIDEO+VSuVZPn228A551g6XHFF9Zpw7drR84Yb3Pex5+WXfWK8xsXFUVFRQceOyoB5p+wK85gCjWxnDow9ewJaqcae0aNHI4Rg/eYNTOAn553sQih30p7zz/fD4GqAEII+ffoQEZEPgCSIsudfs3SoxYV169at5FnlNXmjhGbP8OHDCQsLY+dOiwE/kMVKntdg9271u7cK5WHkSHW+yMlRifRbtqgL35YtNR+MBxQXF3P48GFSUlS4ko1kZoWdUW8VTtYGi8zwxRfX6RD9ir9Va85HJZJOAc4HlgghPNO70/jUI79v3z5WrlzJqaee6lGS55gx8MEHtT+uX3DmWu/bVz0Lwc6iIgQwI7eaqqQ+IiWl9jZFcHAww4YNo0kTy9l66lTHcxYARU5El1asgH374LnnuDE1lVtTUyksVH9/Z+qMnjJ58mRK7X6QLkU92ra1fDe7dsG6dbz77ruEhtomLQ0eXDfetWHDhnHggOVE7jZf7YYbLIOwvii9847rbbyYCaqOuLg4ioqKzP95h9/PtGmeZaFPnQq/OtETd8exY/DGGx4n/brDYsirmOcW5Xttx33ZZR5JOGVmZnJZhw680SGTcjz4M93qG5leQ0Fo9271Pt5caDqP2Bc6q2eqqqrIcfb7c1MsLRBp3bo16enprF37i2PSpROmM4lRzA5oJZI+ffoAlkJx3023+v2OGVPj/W7dupWq8HCO0JSz+V+tZjGbNm3KKaecwp9//mluW8pA1p37mGPn119XhaJ++MFy99Ctm/KAJycrVaxPPnHczodkmUqm9+jRg7AwCMJKbMJ+etlKdOArLjK/bkya8v4OrXkQyJBSXiGlvBxVQfVhP4+hweLLZNd//vkHKSWnnDKWjz6ytNsaN7YJPVdfXXcVm3/+GYqLfbSzoCB46CHb6e+33lLehF27yMnNBWDJEkvyUYBXogZUye9jx0bSurUlGem005x0NCq8du+uigVJqXTSo6M5duwY727ZQhMrtZu77qr5mEaOHEmLFi2IiLBY7x4lnbVty4aCAhYtyqK83FYiUMqaj8cdw4cPB2yno22OZf0DtC8QNWaMMuyvv971AXwo7WckWRqRNA6CQkKosKrqeP99m8W19OZrzmcQi3iFW8zt27GTabzpJteKSF5gGPJRUW3Vc6md7KeHoUJCCJJvvJGbdhwnjHK62H2PdYWRTFdUVERwsLJTApHi4mIm+1IFoR4599xz2bTpY/ZUEyryDyM4m+nc8N/EgI4c6tOnD2VlFsWaufN946XYunUrzYOC+IGzmc7ZtS5vMG7cONbYJaH36Ssca33cfru6vlrPrNnjq/hfFxiKNSkpKcTHw5XCJLfqzCsZyD8OH+FvQz5ISmkdZFVSD2NosBihNb74j8yZM4cmTZrwxRe2usuffKIkbn/++TBCOBYUqYu4sj17VChFXJzFBq01TzwBp5xiWR40SEnstWnjdAo6JcWhKeAYbgqwv+eev8xtTsUTDHWBP/9UKiZWLFmyhIqKCgYMGGVuq83Me2hoKGeddRbh4ZYwnTfeUM+bNlkUQJ3x3nvvAY5ukboy5Hv27El09BZCQ4+Z244fR/3g777bve67mynwi/iCr8e973J9TYiPj+fQoUO8845SR3Fagy4iQklGggq18UBEuy9ruZCvWcIgbuMVc7uDogb4RJmlqKiIVq1aUVioTl7j7rR4JikrU396D7nmmmuIjFQ/1jyqiav3EYYhX1hYSHw8bN5stTJQDOcdO9g1Y4aj8KC1ClADYopJGnYdfTidX1z2qySY1q09izKrT/oYoYQmJHaGfA1Uok6cOEFcQQFtjx7luClHoLaGvFE475Zb/rFd8eKL3u8sLq5Oi0llZ2fTrFkzYmLak5b7PUHS5Ilz4WY/fPbZdTaWQMDfRvRvQojfhRBXCiGuBH4GN/9UjQ2+DK1ZunQpAwYM4KOPbKehevRQ5/8zzmjOwIH9iIzMtVnv62tXRYVtpbUVK3y7f2fk5OQQHGwbz/zQQ3V/3NrSv39/mjVrxvr11STajRunrOFOnRxWzZs3DyEE06ePNLfVRj8f4JJLLuHgQdubo5ISSE3FZezqsWPH+OSTT4BUm/aoKLjmmtqNxxVBQUEMHTqUuLjx5rasLNQP/rnnahzj/hUXsaLvVb4ZpAnDgCwtLWLQIDdqd2vWqBuRp592K2d3BHiApxzaR/E3b3ADt1oZ9WZ8kP1taMjv3g2J2J5LvP3htW/fnuuvv9q8PIy5zjted523w3RJ27ZtCQkJoaioyBxpdOJS03cdKOE1/fsz4KabHNt9VHTI33Tp0oUBAwbQosUv/MbpvMCdXGpdWddEHomUuFf7DQhSU1MJsppS+wa7k2INjMzi339ngcnj0ZIDQO0N+f79+xMfH8+iRbbXl9adanAeuOsu5Qn3tmiKh+Tm5pKUlMTnnwsGYZXE76KqY1OjXgbqRqoNgZG06yv8nex6D/AO0Mf0eEdK6Z9A5UaAr0JrysvLWb16NRl2QcL21+2xY8dSVpZm0+brGbM77oABVtK3mzer3EIbz1dtWLTIYWc5OTl07mypgFdcXDPVFn8TEhLCkCFDWLXqfzbt3sxizJs3j06dbueLLyxGVGho7cY1ZswY2tsFaBqhuXZV1Fm6VE0SvPvul+zbZ3v3dP31SuCmLmdHhg0bRn7+PK64QnnlC6zrWXlREMseX8f0W3uCo6OVve60plfz5upGxPgBG7NNdjEgm+jMMzxg0xYWNoZ/GMVNvMFmerDsEbvkQh8Y8sXFxcTGxnPDDbACqz9616412t/TT19L27Y7CQnZz3yG0QuLws76uHHKuDamhHxAcHAwHTp0MIcIARy44jb1IhCCbKVUM4127I/v5aRzw+GGG27g4EEVZ303L5gqWltmWC/lU27j5foanldERkbSrVs3mjRR39MhWtieeIzifV5w0OrO/h6eMx2nduMMCgpiypQprF5tq261bx8cifKiFLk1zmpe+IDc3FwSExP5/XcIw1Qvw03OUFBQEN9Z5YsMZlGdjKu+8HtYi5TyeynlnabHD/4+fkPGV6E169evp6ysjODgiTbtjz1m22/s2LFIeZhHHrFcsHxdB+UHu1/Ac88po8U6Wb5WDBqkYsWtyMnJISHBYuh4kGsXMAwfPpwNdgWuPAmVBqioqGDRokVs22YruZia6mIDDwkJCeGSSy5xus74zRoMHapUfJ5+egfgQjGmDhk2bBgAgwapC+Fzz9l1yM2Fw4fV1NBrr+GK5dHRjM3MpDW+K55kjZFkWVRUxJIlqu3VVz3YsGtXFVplZ/V/giUR4tlnVdjT2LG2X07m4xMoXr/P0rBkSa3/8EVFRTRr1h+AaJPnEKhxPERkZBC7drVn6dJoADbQi65kE8923pj4OyQk+Pyu3MhXMNjXwRSuZtVWb7j4Uax5dZ6fB+JbLrnkEtq0sZ1RGMoCYimiO5v5nEs5Ru1vNP1Fnz59iImxzAQebtnRsrK83Ov97bYKx8knAaiVH8LM+eefT0XFzw7tf7bxTOKl7IbbbRvqoHCalJK8vDy6dOlCmDzOOfyP7U2TlUqCG360SixuwjE3PRsefjHkhRDzTc+HhRCHrB6HhRCuNC40dhgSiYcP124/y5apUvXPPWeJqX37bUcJ7EGDBtGsWTN27LBEX95yCz6jqqqKo0dPOF1XV+F1UkqTR7579Z0DkBEexEG7YtWqVRy1C1rft883+vmXX3454FhB1zr8MyfHcs3avdsxlqmuYuOt6d+/P5GRkaxfrwz5+fPtjpuYqP5o/furhE9nBuc773BeVBTtunVjH60B31c/tvbIG0qHLtWA7Bk3ToUJmaZa+nXoz2vcbF49fLgKoRvjRDGj6Fi07RSNt7KPVpSXl7Nr1y46dLBTTjnjDLjyyhrvF2zzirfSlSLifZGb6xRDCtTg0FHTjYKnuvx1iQuPZ3i7lv4dh4+JiIjgmWeedmjfQSzZNLxzd+/evSkosCSSvvmm1cqSElXPQgiPjfoONnUMfDcdOHDgQBITE4iMtL1JzdtqpwZhFcq1JcQyhbp2nF2J8Dq4kO/atYvS0lK6dOnCeYvuoiOFdDxa/RR+dyuP1ZdcTBI5HDjg8+HVC34x5KWUQ03PzaWUUVaP5lLK6kXMNYBKeAsJqb26y/Lly4mOjrZpcxZWGhoaysiRI5k925Jc+ccfTmdyvebAgcO0bz+fAwecZ1ra1xDyFXv37uXQoUPExyfVzQHqmIyMDCLs3dweMnfuXMA25KJly9qPCZTHaeDAqQQF7XdYV1QE556rFMrc4Q9DPiwsjEGDBrFokUXG0+21c9o02+XwcMqvvJLt27eTkGBJuPS17n2TJk2Ijo6mqKjInOjqdS2W4mIK581j9Y59WF/sjf0NGTIEuNpmk+BgbDVNaxEHvnPnTqSUtGvVhs+x8ujNmFHreK527dRNqEFFBZx+eq126RJDCtTAfEP1xx+WwmEBwk9M4A7+zyfe2frmqquuIjTUTbY8tvlVgUyKXbygEMCkSZYGQzrsmAee4u+/p5fd/9JX504hBDfeeCOlpbZyaMGYDPJnnoEPP4RXXmHtlxt4l2uYUWERxXjhXTtzbu1aG/lHX5BrUp0Toi9FBc70l51j/x0MYx7R0S4knBsY/taRTxJChJtejxRC3CqEaOnPMTRkgoKUs9CdEognrF+/nt69e3vUd9SoUeTk2CYyDhpUO7lGKSXDh//Mnj2uy5z+9ZfLVbUixxSP2KZNzWJ065uwsDAGDx5Ms2beewPnzZuHqsdmwZex3Q88cA1VVY5Vr+Lj4X//c7KBCSM0aNw4343FHUOHDmW1VWKB21A1IcC6guKWLeTl5VFVVcWsWVeam+vCcDIMSMPT7HXRspgYZm3dCliSeGfPthRSTUtLo0mTr2w2qahAzUYYLFgAJ5zPmlWHYfx2Lz3KxXxpWeGj0BdrX0Rd5rjExcVx+PBhWrRQ1pKNk+Grr5xv5A+cnISv521e4o5ax0sHAkIIiopcv5GdO9W5pSGQahe/+OKLOE/29kQW7jxL6R3rvAFfcfXVV9OkiW0WsaGMI1u0ZOuwKxHBQfS9KJXreNdGw/3bX5wIBtQ2hMAOw5D/z38GsgNTXKwHCgk97KRujSJS3zgpYtvQ8Pd9+/dApRCiKyrptSM4qmZpXBMWVjsJSCklGzZsoKedLKErRo8eDcCkSWvNbdu21U5d5o8//mDdugtrvoNaYEhPRkYm1MvxfcGIESM4csS7E3hVVRVz5tRtTO+ECRNISUkhPHx99Z2tWLlSGUdW16c6ZdiwYVRZGUHVVkTsYvG806kTW0wFohYvrlsJRCM223DW1aSY6D///ENYmJpVGDnSVpEwNDSUgQNt5WdLSwH7Sr3h4TUy5g1DvnnzWpQ0DgCMMKfvvlM3dPutJ53qU/vwmWccmnpmqrs9O8XDBkubNq5NFB87euuUrl27EhISQtu2OwBVBNWp992LBLjdtGEo1SiY1YDo6GgeeOAWQPDZZ+rk+AQPM437WNbrXw556l+hruVHaYJ0ZVL6MMTGMOSDg0NozmHKgiLh3Xer3a5bt25Y15s9hPoB1YWktr/xtyFfJaWsAM4GXjWp2DSgVMP6Jyysxg4yQF1cDx06RHa25Q7WnepJnz59aNWqFdHRtlqyNa1GL6XkwQcfrNnGPiAnJ4egoCCEqEUZvHpGxclLkpIOmNuqKzedlZXFgQNv1em4goKCeP755zl+3Hvjxp8X5UGDBhFs5cKdNMk2TKM6NvtMUsk9Rmy2EErJyZsxGsyZM4fQUKVO5ezePd1Orq2kBCU5ZD9XHx4Oo0bhDYYh3yK84SQlOsNIPC4rU0ojhYVYkolrczKuDVu3OtXMXbaxKWc4lv9o0Ozb5+g4Gj++9iot/iQsLIyuXbvSr5/lO5ud5MSLXJ0knZVB3JY9bjrWjjvvvJOUlBRuuUVJrR6hOVOZxsDhjrMIy8hEIGmGm1ABH3rlc3NziYuLI0hAH9ZyItizH0J4eDiXJSSYlw21Gx8UsK53/G3IlwshLgKuAGaa2moVLCmEaCWE+FMIkW16jnbSJ00IsUgIsUEIsVYIcYHVuo+EEHlCiNWmR1ptxlPX1NaQNxRP/vpLTZ9fein07eu6f1BQECNHjuRvl0LW3vHCC2tYsaKO4mY8QCW6dubDD1W1ty1bqtkgABk4cCBhYWG0amXR6P3yS5UAOM+FWMXs2bOBur95OeOMM5g8uZbC9HVM8+bNSUtLo0ePJ8xt1dZk+eEHMCWJb968mRgrKbP//KcuRqk8wbt27aK8vJxWrfBaM7ukpIS8vDyaN1c3Lc7un9PS0myWzz3XzQ7d6NQ7o6ioiNDQUCIrrEJAsrJcb1ADvvwSPvjAp7t0wPDIl5Rsp1Mnk8Knu5NmXSOlg3znxPBwKt99n4NHgr0PwQpwoqNVtNfs2Uqu+M474fvv63tU3pOamkpu7nLz8hdHJ8GNN9p2cueRX7ECTj21jkZnS2RkJDNmzCAqyvtqgQ/ypGPjggU+C+Q3FGumnPiccfxJVLnnHo62vSyyrOEoV3xdnb/9ib8N+X8Bg4GnpJR5QohEcFLpwTvuB2ZJKbsBs0zL9hwDLpdS9gTGAy/ZxebfI6VMMz1W13I8dYqvDHkDT7wao0aNosBGcLtGilkA/Pe/4UDLmm3sA5Qh3xcj6b+6BMxAJDIykszMTLZvt8163rHDtWzvn3/+CfgnoPTDDz+kTZvn3fYxJD+XL3fbrc4YNmwYeXkW6b5qDfnJk83FRrZs2UL37t3NcfFX+bYWlJm4uDiklOzYsYOYGO8LQK4yFWPZuVNltzoLzVGG/Pmcd57lprC2OTgGRUVFxMbGErzKyp3q4z/chRfCv/7l0106EGuSyCksLKRZM9NUfH1mkzqR2tiWnMxrR9UP0a48SKNh5Egl7vLCCzWfEa5PUlJS2LrVcv0NCkLJR1njziOfnu40DrCuCgx37dqVlSu911t/mgfZhN37mjABHn3UJ+PKzc2lc+dk/AxDWgAAld5JREFUOu31Pr43JSWFs0OUEy+KQyShcubqsAitX/B3QaiNUspbpZRfmpbzpJT/reVuJwEfm15/DEx2ctwtUsps0+tiYDfgrOh5wOMLQ75t27bmZU+uR0acvDU1MeTz83eyd6/n1X5++qn6Pt6Sk5ODEA2/XPOIESPYtWuWQ7uzQnrl5eVOZ1ScFhjyAS1btmTTJot1JUSRQ5+iIuWgsS4G5k+GDRvG8eN7aN5cSRbk5lazgRWbN2+me/dkqqqUbHldJdxZa8m3aeO9Ib9y5Uqsb96c/de7d+9ORMRPNGv2h7nNfBxnLvyffvJYycao6trnO6sLeAOUU4mMjKR169YUFRXZ5igZ1qS/5+Z37LBZvCI+nm7dunH77WrZF3KyGt+TmppKVZXFYgwORsnFXWiVLzZ4sJLisa+k54TXu73I4MG1r8ztjlatWtZou4usk9sNnnhCiQe8806Nx1NWVkZRURFnlhzmDl5iG504dshz2ZkePXqw0iRT8z7XkEM3Iihl06YaDykg8LdqzRBT+MsWIUSuKaTFi0uoU9pJKY0z207AbUqYECITCAOspCh4yhRy86KhquNku+uEEMuFEMv37Km72LTqCA+vXXKGfaLr229Xv01KSgrt2rXj3HOv5/PPVVtNJJueeGKJV/0nTlTqVb5i37597N+/n+hoZdzU5Qmwrhk9ejRSfuhR36VLl3LYSYyip4WkakLr1q3Nr8vKOjjUKPB1JVRvGWISfr/lFpUkddllnt04Hjp0iJ07dwJK6zA/v44GiK2WfEyMihX25n+3YsUKQkLcGwQhISH07t2b7GzLXZ05mfPJJx096BMnqoB9DyguLqazVcVfudi7/38gYSgIhYVZye9OnaqereJu/cJwW7Wvv3bsoJvV99TYQmsaC4b8YViY+hN//jnqImSfMN2pE1RTL+SS2Fi+bn+7Q8G9usCdjdCiheV18+aWELo1pHFq372UbdjquNHLNa/Iu23bNqSUXPjr1wAU0IkmzT2XrEpJScHefGpNiffSvgGGv90j7wP/BwwFMoB007NbhBB/CSHWO3lMsu4npZSAy0AsIUQHVCjPv6SUxq9uKtDDNI5WwH3OtpVSviOlTJdSprdpU3/O/Np45KWUbNy40WPFGgMhBKNGjWLhwp9o21Z9vKec4v0NxYIFjtN0V1/tpKMVvpwmNqQnmzdXxoVdlFGDYujQoURFRXHaaf9Xbd9PP12Dm79FnTF3rgqJDgsL4mzTJEig3Dy1a9eO7t27s3r1n+Y2T5QEDcWa8PC6ly81DHlrLXlvEl7/+SeBiopEALcep759+7J5899m1UkbAYgtW2yrL4FHlamklBQVFTHE6o5N9K/DO8c6xlAQWrpUhYPt3k2dVK2slqoqm2SJbbNmUVxZSWKipT6E9sgHJsnJyQghEEIZ8ua/UXsPcpceeMBmcW+vXpSVOVbOrguc1ZgxsJ4cCguzNSf/XtOaX7K6ON741yKOJS8vj6FWyyfwLoY/OTnZwZAvpCOXnN2wK73625A/KKX8VUq5W0pZYjyq20hKOUZK2cvJ40dgl8lANwx1p/dWQogo4GfgQSnlYqt975CK48CHQKYv3mhdURtDfu/evRw+fJiuVolSTzrJS3HG6NGj2bFjBzt2WGLlf/zR82Nv377dqdrHyy8rr+auXarK5kMPlRMaasns96UohGHIN2mirKLERN/t29+EhYUxfvx4Vq2q3pCfOdPRiK/F7KbHDBsGySb74pRTVPhKWZl/Cj95wtChQ1m82BJz6knUh/Ebbt++BlqQXtK6dWvCw8MpKirCyK31NLzm4MGD7N5tiVq0D8W1JiUlhb179/LQQ0og/Y037KJn3vJe7ejQoUMcO3qUC+bPtzTWsghUfWJf3fWDD7CNL/SXes1HH9ksbjR5Uw4ftqgP+cO403hPkyZNSExMZOBAWwU4XDkGjRPl6tU2UqNPRkTQtWtXCgvr1zHy0ku2OXYzZ0JcnK2nISgIxxhOww5YuVKpbXhh2O9YvRprPYf3qF4/3ppWrVrR0snn/fah+pHD9hX+NuRnCyGeE0IMFkL0Nx613OcMlAoOpmcH81IIEQb8AHwipfzObp1xEyBQ8fXeiWD7mdoY8nmmq3NiYqL5mmp3o++SUSbpuXXrlpnbfv3V82N/9913qAkYC82bK+9R587Qtq0qc//EE6GcemrdBG9nZ2cjhCA3V+laN8BwXRvOOussdu92jD+3Jje3gKKim2za2raFa6+ty5E5J9BunIYNG8Y+Kxe3J+E+GzduNOlB1/2snBDC7Ak2rj2eRvWtdqcpa4dRKGXXLstNus1s21lnOaplfOG+/EdRURHdgBhT5dNf4ry74AYacXFx7Laaf586FdvkiGXLHDeqC37+2fL6vvvMM0R3322lxhEgs14aR1JSUigpsUjubN6MKtfurEDj5s3qpGQXAxlWVsbmzZezY4ftz6EuWbrUtopuXp5FWOGLL9Qs1aBB8PXXtmEux4+j3sOZZ9ru8IknlIDA55+b9Fw9o/+HlnDSHbRn7PsXeflOoItdhVeAifxEdraTzg0Ef5syA1HW3NPAC6aHe3mL6pkGjBVCZANjTMsIIdKFEO+Z+pwPDAeudCIz+bkQYh2wDogBZ9pJgYMvDPkOHbpQXq60eD2NVU5KSqJjx47k5n7NUNPc1kcfeZ4w+fbbxYAlea5ZM3jtNed9R40aabPsq4zynJwcOnbsyO+/12EZSD9y+umnExQURPfutjFC335reT1tmpPsVw2gDHlrPv0Us5qRK9auXUuPHj349lv/eJcNT7Dhkf/uO/f9DVZ6kclsGPLbt+eb2xxSKn7/3Xb5kkvc7rOoqMhGV/j6okc8Hk8gYigIXXyx1Qdzyy2W1+edp8KQpIS//3b8vHzB0aO2SRnTprFlyxZaWAcqA3Y1vjQBRGpqKjk562ndWnnbTfe5zpPBnBicoNLX9+9PAPBbzYCMDHXfWlSkwmms00IuusgiWtC7t+1v8cILTecS+3ivRx6x3BmYVGQ8odKqrHIpkTXKB+nm4nPt3K+VbRXvBoS/VWtGOXk4SqJ4t88SKeWpUspuphCcfab25VLKa0yvP5NShlpJTJplJqWUo6WUvU2hOpdKKesh8NFzalPZNd90EVi9OglwLODoDiNO/p9/ZnP//ZaklvPPr37bgoICNm+2/akdOgSXX+68/8iRIwFLsuTztb3VM5GTk0NSUpJvdhYAtG7dmnHjxlFaerpN+/nnwyefqOn/WbMcdbvrO9E0UOjSpQvt7eJThw510dnE2rVr6d27t1lSffz4uhmbgeGRNwpmubr5tWeFF6WXO3fuTHh4OLt2WX4rDhKUwcFeJawUFRVxmdXyQVq47NsQMBSEBg+2UqgJCbEk7u3cqeLIHn9czV6MHw85OeqGJywMFi6s3QC+/lp5P4wbNNPvdsuWLSQnJ9t01cmugUtKSgrHjx/n9deVF9pGNt5DZYdSoGXLSNq0UT8LfxIb6z6kPyoKbrrJNhSvXz/Uf8AVXlyQTlidmEqJrFG0XkpKCk2BfXbKQGFH9zvUZ2go+Fu1pp0Q4n0hxK+m5VQhRDXpjhpralgtHVAe+datW3PddTUriTd69GhKSkooKMg3t3lyA6vCamz/ce7+u/3796dZM8ubvN9ZZYAaoDzyab7ZWYBwxRVXsN16ztPcrhKJc3Mdc7erM1ZPFoQQDBs2jObN3/So/8GDBykoKKBPnz7mtk8+qavRKeLi4iguLiYszLvEAm888sHBwXTv3p3duy3Rp06dBVVVThqdc+k119ioBkx73Y+le+sAI/F4/367FCz7bP3HHrO87tZNxR2Ul6u4QSHUw4vP0cyFdjG8pqmjLVu2EB8f0GldGitSU1MBKC5WYn02NdZ691YnbjccbdqUFwBoSrdugRlGdeGFtprCW7fi3vr3cMpdfvIJg6wSzG/mtRoZ8j169OAYsD5QkrV8gL9Daz4CfgcMGYQtwO1+HkODprahNYlWgcp33eXd9kacfGmpdwLv3377Le3bd/K4f0hIiEPYQ22L1Bw4cIC9e/eSm3sxYCub1ZCZNGmSw9R6ddS18dmQGDZsGIcP31h9R2DdunUALFhgMaqsCrzWCfHx8Rw/fpyjRy2x/NXVcDh69ChZXlZQ7dGjh802Ts8x9gaomyqUIXYXZ08KzwUyhiFvKkdioSZvzNspVWfxVF26UFpaSkFBAdHRljSzQYO8H47Gf1jC2FRuw7Rpdh3cGbVFRdxwzjkcjo/n2LHAreA7eHB/mje3KxDirnyqJ4a8lFQ9YanEXUoE/zCqxoY8oM53jaR6mr8N+Rgp5TdAFYCUsgJo4DW1/EttDfnY2D4IAbfe6n3ISqdOnUhKSmLOHMfiQq4oKChg8eLFJCf3qr6zFYMHDwYswd61PWkZijWpqeqf76Mic/VOZGQkN910U/UdTTz+eMOsilhXDDVNT2RmFlTTU4XVAMycmQAoVbW6DlMyDMi9ey1JzWaddxesWbMGaeVt8qTybI8ePci1qoplJ46isL/gZmQod5t9SMDTT5tfHjllLHEUNnhDPjo6moiICA4dyqaVypVXzoWgIO+N+WNeSN0VFMCUKU5XZZuy8zp1sjhJPvjAu6Fo/EuLFi2Ii4ujoMCiqWGT6+nKqC0pgdhYcnJy6NatG0eOBG4IVXBwMAMG9LFtbNJEJSE5IzERzjnH/U5vuolg0zUcoALP4+rt6dixI02aNGHTpk1KWs0eb1Q8AgR/G/JHhRCtMYlaCyEGAQfdb6KxpqaGfFVVFdu2bSMqqh9SwtixNTv+6NGjmWNXJtqdt/w7kzdp+XKLq2jjxuqPM2jQIOCOmgzRKYYhHx+vYu8NXfPGwB13ePY5LVvmvFjnyUyfPn1o3rw5ERGW37SrkufLly+3KXTlhTBMjencuTMAxcUWPcjqDHkVH9+Bpk2ryMz0TD2yR48eVFZWkpqqvOz/+5+TTkYBJIMNG1RMad++lrann7b5kR3sP5pi4hq8IS+EMCceG0JHZqEaNzMTTvGmEqzp+3eGoVjTtq2lTyCGWmhsSU1NJT9/gdkQt5F6dVbxbfNmjLvH7Oxs2rYdzPr1sD6A9fVOOSXNsfHSS1UyuDOVmh9+cL/DN23DHytRghU1caQEBQWRnJysPPKTVCmi+SEjLR3OOEPt+M8/ne8gAPG3IX8nSi4ySQixAPgEuMX9JhpramrI79ixgxMnTtCsWReg5nfzo0aN4uDBg/z662pzW7Nmro2fr7/+mm7druboUctPzUXSuA0ZGRmArXRGbdRrlCEvyMtTsXqNySsdYxXfERPjqNVvkJ7e8CU3fU1wcDAjR45k3TqLx9tVztmSJUvIyBjufGUdYdR8yM21hHRUVxRq5cqVBAfP4ujRILp08Uy+vXv37gBcfbWlaJtDCOmFF7ovPPHXXw53is++piz4hm7IgyXx2MB8Q3WLl5ewAQOUakc1Ep4uMYkWGIb8TTdZEviN2QJN4NKrVy82btzIihUqVM3GIDcucm+9pfIvduwA03/TCA8tL1fnINPXH5CMHZvuemV0dK33/w8jATBF+3pNSkqKMuSHD4eKChY/M9uxk3058gDG36o1K4ERwCnA9UBPKaVnqdoaoOaqNYb05FtvKb2qmnpujDj5tWv/sGl//33Hvvn5+SxdupScHEupSGvpZXe0bNmS5OSONm1//OGiswfk5OQQFfUkH36opuQa6wVv3z4PCwNozJxxxhns32+pVmonZgCoRNdNmzbRrNlljivrkOjoaFq1akVu7lazDe2JIV9ZaZSD9+w4hprT8eNrzG2LHAsxu/YiPPKI02k+w3PWWAx566JQ5o/CapbGhpgY16Wrn3hCKdpMnuz6gB9/7Ni2aZPZS79lyxZiY2OpqrK4JaMadk7xSUHPnj0pLS2loiIfgButU3RONymQDR8O771nkyS61aQssXDhECCwnTKDBtnGyNtEsLg6GbgScrc7EZU2bc0lfM727TWvMdejRw+2bdvGsWPHIDiYu++G2aabAzNr16rCHULAK6/U7EB+wt+qNcHAGcCpwDjgFiHEnf4cQ0MnPFwlu3mbcJ1nM39X8zj79u3bk5KSwuzZs+llFfbu7A/1rUnQXErLhcaJwIpLBg2yvas/44yaCT6AMuTDwy0Cy15I1zYI/vgDbr4Z9ux5l5kz93lVdfdk5/TTTwdeZNKkfwC45x5YvNi2z7Jly5BSEhXlXa6HL+jatSs5OTlm4RJ3hnxZWRkbNljqCgR7WDIhOjqa6OhoCgosbj6n11tnU/+gDFMnVJkuMY3BkI+Pj6e4uJi331YnX7NDxdWHPG9e9SeaH3+0rXNvICXcZ6c4FRVlU6J3y5Yt5pkUg0A27jSKnj17ArBlyzrHlVdfrf7gTqatjZyInTvVdHpdlCrwFREREfTvP9m8bF3gGSHg3XcdtjFmHjhyxLaE9Smn2HR77aFdHKNprW5ae/TogZTSPKsFcEHLPziI3U5vv109P/qo+sOvWlV9bGM94O+//U/AlSiR8OZWD42HGB626pQr7LE35Nu2rfkYRo8ezbx581i0yDI1cO+9Kh/Hmq+//toUIqPwdhps4MCBgG0QnKeVLe3Jzs6mZcvGW7t87Fh49VVVgvrMM1sxcaKqtvfHHzB4sEXuWuNI586d6dkziSNHLEmagwfb9pk7dy5BQUF88EE3q+38M76kpCRycnLMM9LuDPl169ZRaRWD5s0sVlJSktnrBy5umr2sQGNMgTcGlai4uDiOHz/OwIHqQm52hlyslLC48UZVMcd4s0FBnp2oY2Md+w0aZFUtyITdn3jLli2cOHG9l+9CU98YEpQbN25wXCmEy9ATFR5quSMOtErZ9gwd2pmQEIssj43z8Ro3lZ7HjsVcytqOq6dMIXdbMK1b1272KcV0o2St1BXTIZRC7EIGjPC3AwdUbH///jBzZs0PXEf425CPl1KeI6V8VEr5H+Ph5zE0aAxD3luPen5+Ph06dDB79TyJU3fFhAkTOHr0KLNmWSpKHTwIDz1k6bN27VpWrFhBRsa95jZn4TfuGGgqUXjRRavNbTWRoTx06BC7d+8mJqbxGvLOGDBAnRMXLlQqRRrXnHHGGcyd+49NmxDKAQPwxx9/kJFhq+3nLxnirl27UlBQQESEunG2EpdxwL4QlFUkSLUYhrxRGdjpDcOQIeqNe5CwsvrTb9mEMlpq4zgIFAwFoZKSYsDKI9+li/pMXn9dGeVGqI0QFgO9unjb335T05XGj2rpUtv17drBlVeaF0tKSigpacPChRYpVE/DqDT1S1RUFJ06dWK9l9mq2dnZtG9vKVhk+jkGLOnp6VRUHDAv22lk4HTa+KabLNOhJSUqscvEAaBD9+4cPlx7x0C3bt0ICgpSyjUmwsPhG9xUuJw+XT0H4B/N34b8r0KIcX4+ZqOipoa8oSH/yy+1v6ieeuqpxMTE8MUXX9icTL74Aj77TL2+5541gOSNN84zr7cu6+wJvXv3JiIigqNHl5vbXCXVusPwMvbsqX7uVv9djQaA8847j3In3tMPPoB9+/azdGkGbdveY7Oud2//jK1r165UVVWxfXs+4D5cc/ny5bSySgBxpq7miqSkJLZt20azZip8Zpy7M3VQEFzv3Bu8vVs3ugGRGSPMbY0hdtuo7rpnj7o7cnkOfv11SEqCTp1M9empXq964kTV31WRh+uus1lUnsQRNm32M6KawKVXr142IXCeKJLm5OTQuXOqeTkiwP1S6enpwIvm5VGj7BwLEyeayr5a8cYbltcxMWDlmDiBOkf9+GPNQ4MNwsPD6dKli41H/qWX4Ake5mZe5WWceL5MN+MHvFWp8gP+NuQXAz8IIUqFEIeEEIeFEIeq3UpjxjDkvU14zcvLIyEhkUOHYPfu6vu7IzQ0lPPPP5+ffvqJr76yKMscOgSXXQazZh3njz9skwKNoobeHqd///4UFFgC7GqS6GtIT7Zure5gXMzaaU5iMjIyzAox1uzcCePGHUPKV/npp8k262oqOuItxrisw15c3dCuWLGCAQPSCQ5WORPOEnddkZSURGVlJSUlu6rvDOqO4t57HZr/6tuXbaGhNGtmSQKta719f2DxyKtEn9dec9Fx/HjIyVEuPsMAHznSs4NMn67Uf6zp18+2YiyYjECLrujevYGrK65xpGfPnmRlZfH222pmyzok3BXZ2dnEx6s4cqvi0gFL9+7dadYsjHbt8s1tDlEpbr0FtpwA8vIGcuSIcwVLb+nRo4eNR37ECJAE8To3cw/Pudxurf1sWQDgb0P+/4DBQBMpZZSUsrmUshH4avxHTTzy5eXlbN++nbi4ZJ+N46qrrqK0tJQffpjhsG7MGEdJHE+0rJ2RkZHB5s3fmJdnO1GJqg4jSahlSyXT2NgSXTW1RwjBpZdeilLEtfDdd7BiheMc9hVX+M/LbBjy2VaqDs6MyNLSUtavX0+/fgOprIQOHbw7jqFcs23bTs82CAuD//7XshwRAb/+yt8REcTGxnLihLq8eFjmIOBp3749QUFB7Nypiofl5qo8FLecfroKl/F0GnT6dEf1nyeecMhitfbmgmvhHE1g0rNnT06cOEFlpUp0ri7fpqSkhD179pgNeXcqsIFCcHAw/fv3p107S8ytg93y5JNw7rke7a8ceOKJ1Gr7eUqPHj3YsmWLTU6RoZ5bThhP4rzoSqK3J1Y/4G9DfjuwXkp/RZc2Poz42FmzPN9m+/btVFVV0aZN9+o7e8iAAQMYPnw4X33lyi1lS00TAzMzMyktLaVTJ+WKv+su7/eRk5ND+/YdmDpV3WDUVLJK07i59tprCQqqvsDA2Wf7t4JmmzZtaNWqFRutKqndc49jv7Vr11JRUcHRo6rISdOm3h3HMORzc72M0Zg6VRWFKi2F8eMpKioiLi7OPGuQmend7gKVkJAQ2rdvbyNBuWSJFzv4z3+gZUvvDnr22XDmmQ7NGz2pqqcJWHqZJN+ktCjXuFNkMzzHcXHqpr6h1EFJT09n82aLseIwox4SorwlTmb2zJh+/7WMpnEgJSWF48ePs82qQJt1xfeHca7E1SEA75r9bcjnAv8IIaYKIe40HrXZoRCilRDiTyFEtunZacq3EKJSCLHa9Jhh1Z4ohFgihMgRQnwthAi8TAYrjBlab8Jj8k0FRLZvTwO8Tzp1xYMPPkhx8eLqO4KNVKU3GKo399zzpbnt8GFXvZ2Tk5NDXNyp5mXtkdc4IzY2ltNOy6+231tv+VfmTwhBr169qk2OMxJdX39daTh7a8jHxsYSHh5ORYVFS94judenn7Ypc1tYWEh8fLz5ot2Yqo3aa8nffLMXGz/yiCXUpk8f+OorzOoDzrjjDhcldh098pqGRUpKCkII9uyxhGn8x43sh3Hj1q6dkqppKHKu6enpHD9umeG75x4XefLWM3v2TJzIH126cLuPZcJ6mKRcrcNrQkPNxV6xV8wzCDHJhwYS/jbk84BZQBi+k5+8H5glpexm2vf9LvqVSinTTI+JVu3/BV6UUnYF9gMuKngEBoakqjcxp4b05KuvqtAaX8lWjRs3jilTpgBvVNu3phn2Xbt2pWXLlqxbt8hsELg74TkjOzubTp0sslLakNe44r33LqZFi3Zu+zSvB8Fcw5CfOdP1ZOby5ctpY5UA4q0hHxQURJcuXTh0aBHTTKpxBw96tw8ppdkjv8Z0PxDoSXneYFR3dVXnqVoGmZSP/u//4IILHGLfbXAR9L5//352ONOe1zQYmjRpQpcuXWxuyB5/3LW07MaNG2natClNm7Yxbe+PUdaeAQOUU6FZM0uC6DvveLGD++6Dyy/ngehoG6k9a4W8mmIY8tYJr2ARp7FGWhelGjHCsUM94+/Krv9x9qjlbicBRgm8j4HJnm4ohBDAaOC7mmxfHxhGqAfqb2by8vIItipaMnCgm85e8sEHHzBs2HeAox6ULwxmIQQZGRksXbrUXIvGm/yAgwcPsnPnTn76SU2Tdemii6ZoXBMbG8tXX33qcv3ZZ9ePYdqrVy8OHTpEu3bF5jb7AMXly5fTt6/lIlMTr50hQWmEn3lbWOzAgQMcO3aM+Ph4Zs5U3viGkJjnKYZH/pZbariDs89W0h2nmmYIk5NhyhTnfV1o7WpvfOOgV69erFtnWxTKlZG7ceNGUyhIwyqw1rVrV5o3b86UKY+b21zWgnF2d/LYYxARQW5uLl26dDE39+9f+7G1atWKtm3b2njkDfr2NR0eFWuT16IFHxDGdf2+8i6czk/4xaQRQrxkev5JCDHD/lHL3beTUhruiZ2AK3dahBBiuRBisRBisqmtNXBASmmUKywEAlqdtaaGfMeOHRkyRF0/fHk336xZM/755y/mzfvZYd2ECUrFxtkdrjdkZGSwbt06Xn5ZWfDeeESNu+2KCvXBOYst1misGT/etZLC44/XjwKLEVO7bZslNto63vTQoUNs2LCBnj0tWvc1qYJsGPJFReou4dlnvdveCDuRMoXvvlMKUYGud+0N8fHxHDhwgIoKD/QCXREba7v80UfO1TtcSHSpMItGIAN0ktOvXz82b97MzTdbPFOunF8bN26kR49eXHKJWm4oHvmgoCD69etHVpZFRN5lqN3Bg7aeg4MHISKC/fv3s3//fhtD3lf06NHDwSMP8Mwz6vk/PEbO5kqWrV3L1Uzi3VUXYCUeFjD4yzdpuLieB15w8nCLEOIvIcR6J49J1v1MSbSu5p47SynTgYuBl4QQSd68ASHEdaYbgeV7alpe1AcY3mRXldKdoaQnEygvr5uwkqCgIIYOHcqYMbbtffsqWeRJk5xv5ykZGRlUVlbSv7+KAX76aVi71rNt7e+2d3ooyKE5uXnxRRX2vXmzpe2SS8A0G+t3DEN+y5bV5rbHLU4uFi1aRFVVFb17W0rSenOzb5CUlMTRo0e55hp1jjOKlnpKoUkXbsWKPqZl78cQyBgSlHv3eijR6QlNmsDvv8PDD1vabrgBXnB+adywYQNNmlhCqM4+23dD0fiPAQMGIKXk/POXYCjfOvO0Hzx4kKKiIqKjLbNt9RHeV1P69evHaqscGpf1lEJCLJWeXnnFLAtmhAZbG/K+kktJSUlh48aN2OuvnH665dzXLTmIzz47Dij1vECcDfGLIS+lXGF6ngNsBDZKKecYDw+2HyOl7OXk8SOwSwjRAcD07DQNVEpZZHrOBf4B+gElQEshhGHexgNOayFKKd+RUqZLKdPb1LMQeXCwdxfp/Px8EhMTqaio2/jw6dNV6OfDD8Onn1Lz6Wc7Mk2yF0ut9FuNqa/q2LRpE6GhFhfATTf5Zkyaxs3tt6vfWPfucPnl0KqVKnZWX/kV0dHRJCQksHKl5T9geI0A5s2bR3BwMI8+avHIx9tVG/cEQ7lm374cgoOVEI03GB75mBgvA/QbCIYhHxaWD/i4KNgNN1heX3WVS9flunXraNvWUrCmuqKxmsDEiB9fsWIFM0xxCV9+6djPSHLv0MGiOuet+FF90r9/f0qtTiRuVeOGD4cNG2yMh1yTVF9VlSVG3lf/u/79+7N//37zMaz580/L65kzLze/PmkNeQAhxGNCiL3AZmCLEGKPEOIRH+x6BnCF6fUVgENUpxAiWggRbnodAwxB3UxIYDZwnrvtA42QEM898qWlpezYscNsyNel9GLTpkpo4fHH4dJLfadtHBsbS2xsLMuWLeNWJwXX3LFp0yZatrRUl4uJ8c2YNCcPH38cGFUzBw8ezKJFi5xWa50/fz4xMR9RVKRO6VOnwuDBjv2qwyJBuZXmzb1XiDI88k2aNCCXoRcY1V0LCwsZNw7sQpxrR4cOKpzmzz9tStNbI6Vk1apV5OdbNK51zk/DpEOHDnTo0IEVK1aYr5ULFsBpp9n2M9So4uLUdOBtt/lzlLWnvymg/YILVgLgtp6SEJBqqxVvGNnHjnUyt3Xr5puxOXMSGrhyYgRi8r6/YuTvRBnPGVLKVlLKaGAgMEQIUdtyIdOAsUKIbGCMaRkhRLoQ4j1TnxRguRBiDcpwnyalNIJN7wPuFELkoGLmfSTOWHd445E3NFIjIvqwdq13+vOBhJHwalV93iM2bdpEWdlZ5uXGUGFSc3IyaNAgioqKKC21jZ2eMeM4ixdvZteuS81tNU1oT0hIQAjB1q1bOXAAXn3Vu+2Liopo27YtQgRX37kB0rFjR0DNcv7xh2pzkitXc8LCcIhRtCI/P58DBw7YtAU3zo/6pGDAgAGsWLHCJtzE+F0ZrFy5knbt2vPii+riZz1x0xDo0aMHERERtGunSmF/6lpLwCm5ubnExMTQrJnvEwN69epFkyZNWOIkg/XIEefbuAwNqkf8dS9/GXCRlDLPaDCFuFwKXO5yKw+QUpZIKU+VUnYzheDsM7Uvl1JeY3q9UErZW0rZ1/T8vtX2uVLKTCllVynlFCml8wyjACIkxHND3ogv279f3eV662ELFDIzM8nOziYkxPO5/rKyMnJzc2nWTFvvmobPYJOL/cAByxXms89g0qRwjh//wqZvTWNIw8PD6dixIzk5Oea2XV6Egxsa8kYxKGehAg2ZyMhI4uLi2Lp1q1kC3pOk4nnzYP/+2h9/1apVDm1WqnyaBsaAAQPIysri+HEXViPKI9+t29nmUg2eFgkOFEJCQujbty9r1ljKIBcUeL791q1b6dKli7nqqq/HNmDAAKeGvCt8qfrnK/xlyIdKKffaN0op9wC6zqaXBAd7HlpjFINq0aJhx5QYhaFKSvKq6WkhJyeHqqoqwsLUnfy119bJ0DQav5CWlkZUVBR9+75ibrvsMuOV7dWlNrH8hnKNgTfhI4WFhcTFxVFcDElJ7usdNVSMz8d4b9U5R8rLVeivt7OJzli1ahVBQbYxUy7k5jUNgIyMDKSUrFu3wun6Y8eOmaQnLUWIGlJ8vEH//v1tbkKNOhWeYEhPGjNfb73l27ENHDiQVatWccJO1/r22x373nJLYM6A+cuQd6f87evKu40ebz3y4eHh3H+/04K3DYZ0U8zo1q2WcspOVKNsMBRrBg9Wdz3/9391MzaNxh+EhoZy2mmnsWCBM7Fp22nn2hRBtDfkvbnoFhUVER8fT1FR7cYQyCQlJdnMWNxRTXBoebnldW1vbFatWkVV1ULz8scfu+msCXiGDBmCEIKFC+fw2muO69esWUNVVRXvvmtRaWiI4aH9+/fn0KFDhIer6as33/Rsu/LycrZt22ajWDNqlG/HNmjQII4fP87y5ctt2l98EaqqbKc2a6IE5g/8Zcj3FUIccvI4DPgy7/+kYM8eeKP6YqqAMuQ7W11Ra5IAFwhER0fTrVs3tmw5YG6rrrjhhg0bEKIZFRXRtGqlPVeahs9ZZ53Fzp07CQtzHc/RsmXtVB2SkpLYs2cPAweqq5aneTWlpaXs27fPHFoTiOoOviApKYmdO3eya5eKwly8GJxEvABw6622FXa//loZYjNn1uzYK1eutFm+6KKa7UcTGLRs2ZK+ffsyZ84cpwVD586d6/9B1QFGwutNNy02t3kS/peXl0dlZSVJScnmNl8luhqMGjUKIQR/WsvUmFDF1yyznZfXKhC87vCX/GSwlDLKyaO5lFKH1tQheXl5JCYmmpe/+qoeB1NLMjIyOHDAorlXXfjA6tWrkfIw330X4rL0tUbTkDj77LNp0aIFAwa87LLPF1+4XOURhnLNm296J8liSE/GxcVRWhqY6g6+wPh8Cgst0aKDBjnv6ypZ+KmnvD/ujh072GHnvahLFTKNfxgxYgSLFi2itLTcYd2cOXNITq6n4hU+pGfPnoSGhrJ1a765zUXhYhs2mwp5REYqvelp03w/I9GqVSvS09OdGvIzZswAlvL44wf5+OPAjI8HP8pPanxHq1ZuqqPZkZeXR6dOFkO+IcbXGWRmZrJz5zqmT1cX0Pfec9/fugiFRtMYaNasGbfccguLFs13un75clXMpDZ0NVWnyc31roShIT0ZFxfPxo2Bqe7gCwxDPjXV4h0/4SRA1J3HcfFi1+tcsXDhwuo7aRocI0aMoLS0lK1bLdM68+ZBRUUF8+fPJy7OUo58t9MqOYFPeHg4PXv2NItvABw6VP12W7ZsAaC8XIXWjB1bJ8Nj3LhxLF68mH12Hr/p06czcOBAHn64RcB640Eb8g2S00/3rNjLoUOH2LdvH5GRmea2pg24TouR8LpzpzrhffKJ8wsowP79+83Sm1B7L6VGEyg8+OCDpKd3dLrOF7XqDEPVOk7e1f/MGsMjX1Cg5r4bm2KNgfH5FBVtsWk/eNDyurTU9/ruCxYsICIigqFDVViVT4tRaeqNU089lbCwMJYt+9rcNnw4LFmyhMOHQ/n776vM7fVci7JW9O/fn6Ki183L1lWzXbF582ZiYmIQQtWlMBV79TnnnHMOlZWVfPfdd+a2vLw8li1bxqTalqb3A9qQb4BERnpWcdFQrGnf3mL1B2LGtaekpaURHBzM9u2WYsCuZKzWrFkDDDAv61hSTWMhIiKCpUtf5K+/jtr8/ocPh06dXG/nKVFRUcTExNgY8g8/XP12hkc+JERZG08+WfuxBCKtWrWiZcuWNp8PqNlOQ3bzrruq38+aNd4dd+HChfTtO5b584Po2lV5bTUNn6ioKMaMGcP//vc/m/YrrmiBKj7fOOjfvz8lJZbQME8Svzdv3kxycjJPP62W6yrvpl+/fqSkpPDBBx8gTVNpb731FsHBwVxmkQYLWLQh3wCJiPDMkDemsZo3V1f3zz6ry1HVPU2aNKF37942VdhcfQ6LF28AfFE4WKMJPIQQnHpqUzp2VNPtV14J06f7bv/2yjWeJLwWFBQQHR3Ntm3qatvQCtd4g/3nY7Bnj3q2y0l1yrnnen680tJSVq5cycGDTwCqAGyLFp5vrwlszj33XLPjzWDr1l42y95WNQ80jITXyZOzAc/ChLZs2UL37t3NCnVNfF8TClDn05tvvpklS5bw+++/s2vXLt566y0mT55sruYcyGhDvgESGWnx/LjDMOTnzlWqNX371uWo/ENGRgbLli0zL7sqsvLee72Bif4ZlEZTj7RpAx9+CNE+VJg1DFXD2PTkfJOfn0/nzp3Ztw+aN/eNbnqg4sqQN85HntSX2boVXnads2zDsmXLKC8vp23b1gBs3+7pSDUNgfPPP5+W1SSw3XOP29UBT58+fQgKCqJVq9ke9T906BA7d+4kOdmiWFNXhjzANddcQ9euXbn88ssZN24cZWVlPFWTrPR6QBvyDRAjtKY6+ab8/HyaNGnKt98q+QhfXujrC6Vcc8C87MqQ37nTg3KLGo3GKUlJSRQUFPDAA0pJw5ME2m3bttGxYzdeeqnhVpD2lOTkZPLy8rj+elth6VmzHG96WrVyrffurOiMM/766y+CgoJITGzAQdIalxhJ7JDgsk9DVyhq2rQpPXr0YM8ez7RXDcWa5ORk4uOha1fPRT5qQlhYGDNmzKBDhw7s27ePr776yuYmIpDRhnwD5Jtv1PNff7nvl5eXR0KCRboqLq4OB+UnhgwZYrP85JNqmtmaPXv2cPSoD+qhazQnKUlJSVRVVdG0aT5QfZK8lJL8/HzCwobV/eACgNTUVKqqqrjgAtuMvTvvtI3jLS2FkhKlP71iBRQXQ3a27b480dP+448/GDhwIB9/rCwZXQiq8TF16lQGDIgBnnO6vjGoQPXv39+mFoI7YTnDkO/evTvHjrmWePUlKSkprFmzhu3bt3P22WfX/QF9hDbkGyBGbJkpt8wlSnrSx9UT6pmUlBRiY2NJS/sIUHJ7//2vbZ9FixYBlruWhp4boNH4G0OZJS9PhY9U9x8qKSnh6NGjtG/fvq6HFhCkpqYCsGvXWrf9rI2v/v2hQwdH5Y1yR/lwG/bv38+yZcsYMeIMc1sgS+FpakZkZCTz5s3jxx+T+eUXx4u7r1WQ6gOlXFNkXjaSWJ2xbt06QkNDWb26O/v26eu4OxrBT+Pk48MP1bM7hQopJXl5ecTFdfXPoPyEEIIxY8awbdvd5jZDj/bECTjjDHjnnaOARXLzkkv8PEiNpoFjL0G5dat7z7Eh9dq+fbs6H1sgkJycTFBQEBs3bnTbz5nxZR8KfeWV7o/1559/UlVVxYwZDTzbUVMtkZGRTJw4kdNPd0ywbMjS0Qb9+vUDYMiQnQB8+616OGPt2rWkpqZyySXVVH7UaEO+IWJoybrz5JSUlHDkyBHat0/wy5j8ydixY9m/3yLLZVRPXL0afv0Vfv5Za01qNLWhffv2NGnSxCah8/PPXfc3FDc6dDg5PPLh4eF07drVVMLdOQMGOG8PC4OKCphoysX/8kvH8EBrlLa1ZONG5cp/4IEaDlrTIJk5U91EV1fJvCGQlpYGwPjxH5jb3nrLed9169bRp08f8/K999blyBo2Dd6QF0K0EkL8KYTINj07pHQKIUYJIVZbPcqEEJNN6z4SQuRZrUvz93vwFuMPXVHhuo9xYW3XrnPdD8jPjBkzBoAhQ1SlQ0+K1Wg0Gs8RQtClSxcbQ96QgHOG4ZHfti3wpdp8RWpqqluP/KhRrrcNDoY337QsWwlx2XDs2DFmzNhr0+bO6Nc0Ps48s75H4DtatmxJUlISq1db4uSFcOxXUlJCUVERkZGWN//YY34YYAOlwRvywP3ALCllN2CWadkGKeVsKWWalDINGA0cA/6w6nKPsV5KudoPY64VRva6O4+8IT25b1/DyLr2hvbt2zNkyBBKSixf9amnOi973u7kmOnXaHxOUlISOTk5fP+9Wo6Ndd03Pz+f5s2b88QTSh/OJBndqElNTSU7O5uHH3b0qPzxBzzzjPvtrT/PYcNg1y7HPjNnzuT4cdsPXhvymoaMfcKrM4fkunXrACgoUOIW0dF1VwyqMdAYDPlJgJHD/zEwuZr+5wG/SimP1eWg6hJvDPnHHnNeyr2hc8EFF5CVtci8/PffcNttjv3mzHFs02g01ZOUlERubi5nnKGkXN9913Xfbdu2ERt7innZ1XR5Y6JXr15UVlYydOgWh3VDh3ofClFc7Nj2zjvv0Lq1beUnHWJwcrBsmRJzaGz079+fvLw8MjOVBe/sGr3GVPY4KEjVTWijVVfd0hgM+XZSSqPu706gOh/shcCXdm1PCSHWCiFeFEI4VSoVQlwnhFguhFi+xyjfV094asi3sqrI0tjkyqZMmUJICAwe7F6TtoHIwGo0AUdSUhJlZWXmsurupOLy8/OJiUk1L9el3nOgYFSqLCxczMKFtutq8v7tK13+5z87mTXrWsaNG21uy8uDjo3TN6OxIz3ddZ5FQ2aA6U1lZjreABssXbqUDh1iiY1Vbvhnn/XL0BosDcKQF0L8JYRY7+QxybqflFICLrUVhBAdgN7A71bNU4EeQAbQCrjP2bZSyneklOlSyvQ29Xx7aFwk3FVbzM/PJyEhwbwcHFy3Y/I37du358ILL2T16tfqeygaTaPEKIaSZRUc70y5RkrJ1q1biY21WJiNQfO6Orp160bz5s1ZsWIFgwfDF1/AU09BQUHNpALHj4eff4Y//1ThBo891h64gLIyS5ywO6UyjaYhMHjwYEJCQmjW7FNz2+uv2/ZZvHgxO3YU8d57annSJDRuaBCGvJRyjJSyl5PHj8Auk4FuGOq73ezqfOAHKaXZly2l3CEVx4EPsdYtDFA6dFAJIqbZJ6fk5eURG9vXvJyY6IeB+ZmpU6dSXu4ksBQ1tf3oo34ekEbTiDC00q0TOj/4wLHfjh07OHr0KB06JJjbnCWwNTaCgoLo378/y03xDxddpBRlvPGY//vftssTJsC4cXDHHZZZ3x9+iLA6Zq2GrNHUO82aNWPgwIH8/fff5rabb4YdpriKPXv2kJubV0+ja5g0htPCDOAK0+srgB/d9L0Iu7Aaq5sAgYqvX+/7IfqWsDDo0gVMwjQOVFVVkZ+fz4YN95jbTjnFed+GTGpqKs8841wk/rrrdJa7RlMb2rdvT8uWLdm0aZO57fffHftt2aKmyDt0sLiLTwZDHlSYwJo1ayivrqqTC1zNqr72muOsb8+eNTqERhNwjB492nwDbBAfD5WVyhsPuviLNzQGQ34aMFYIkQ2MMS0jhEgXQrxndBJCJAAdAfvUis+FEOuAdUAM8KQ/Bl1bWrd2Lbu4c+dOjh8/Tl5eCtA4vfEGd999N3feuZrWrffZtDdrVk8D0mgaCUIIs8SiqT6U0+IthiHfvr1FerJ1a3+MsP5JT0/n+PHjZpUNb7n1VmjevPp+mZmwYkWNDqHRBByjR4+mqqrKpq2qSuWAfP/9WsASdtMY8wR8TYM35KWUJVLKU6WU3UwhOPtM7cullNdY9cuXUsZJKavsth8tpextCtW5VEp5xN/voSaEh7s25HNzc22WG3uiyAsvpPHssyqx96yz4L771LNGo6kdKSkpbNy4kYvc1FjLzs4mPDyc4mLlRf7rr5PHkB86dCgAc2ooj9Wvn6pMPWGC+35vv31yJBBrTg4GDRpE06ZNufrqG23aBw+Gjz9+0KZtwQJ/jqxh0uAN+ZOVefNg9mzn6+wNeVOtlkaNIfXWogVMm9Y4quBpNPVNamoqe/bs4eabXSt1bdmyha5du/Lgg+pycjLpPXfs2JGuXbsy29XJ2EMefNB9RKepIKZG0yiIiIhgwoQJ/Pjjt5x/vsW3ute29hmdO+sbWE/QhnwDx1lopjLkLUGq3br5bzz1hSHJ6a7arUaj8Q4j4XXzZkucfJ5dHtqWLVvo3r27eTkjwy9DCxhGjRrF3Llzqays9HrbvXv3ctNNNzF0aJrLPnfcUYvBaTQByvnnn8/evXu56qq/XPZxlQeosUUb8g2co0cd23Jzc2nb1jJXO3GiHwdUT0yeDBde2PjDiDQaf2IY8tYJr198YVl/4sQJcnJySEjoY24zbqpPFkaNGsXBgwdZ4UUQu5SSDz/8kK5du/L222/z73//m+++O+S071VX+WqkGk3gcPrpp9OmTRteffWV+h5Kg0cb8g0UowT6EScR/bm5ucTHxzuuaMRERsKXX+piKRqNL+nYsSNRUVGstqoGZS2BuGnTJioqKujYUbnhzz/fzwMMAMaOHUtwcDDTp0/3qH9paSkXXXQRV111FWlpaaxdu5bXXnuNc8+NYtw4634qD6pXr7oZt0ZTn0RGRnLzzTfz888/1/dQGjzakG+g3GjKEXnpJcd1ubm5dOhwchnyGo3G9wghGDBggI1U3AMPwObN6vXatWuB5tx5pypa1BhlbqsjJiaGUaNG8e233yKdVcyy4siRI5x55pl88803PPXUU8yaNcs862HNr79CRMTJN7uhObm44447SExMpHnzKITYVP0GGqdoQ76BcuyYen7hBfv2Y+zYsYOff34AaHwVXTUajX9JT09njV31ubFj1fOaNWsIDbXU0Cst9efIAocpU6aQk5PDsmXLXPY5cOAA48aNY+7cuXzyySc88MADBNudoO++Wz1ryT3NyUDz5s35448/GDduHOef/ywdOljyTJYsqceBNTC0Id9AcVXhLz8/H7CIqGvpJo1GUxsyMjIcCh4ZBvvatWtJ+v/27jw+6ure//jrk33fE7YAYZdFQAwoouIubtBaLWhF69r2She9P6utt63a21p7e1vrUlvrhtYWLVpBKxdFxV0kILILyB6WQEISIHvm/P74foGACUwgyWTC+/l4zGNmvt8z5/uZw5D5fM+c7zl9Ou3ffoQO6Q5r0qRJpKSk8Ic//KHR/Tt37uTcc8+loKCAF198kWuuuabRcuef77Vh9lfXgxLpkPr27cv06dOZNu1pJk06cGKrk9ngKZEPU0MPXFvG7t0HHnsz1iTsf56W1mYhiUgHlJ+f/5VtO3d6K0gvWLCAmJgDSenxmsinpKRw8803889//pOlSw+eSnLz5s2MHTuW5cuX88orr3D55ZeHKEqR9u23v4WPPvKGlmk0QfCUyIepM86Agd7CrUyYcGC7l8jfs/95amqbhiUiHUxeXh5ZWVlf2b5y5UpKSkpYvPiiEETV/tx5552kpaVx7bXXUlZWBsC8efMYM2YMmzZtYtasWVx88cUhjlKk/YqK8haFGjcu1JGEFyXyYWzfd+vcuQe2eYn89/Y/79y5TUMSkQ7GzDj33HPJyhp70Pa//W0l8O2Dth2vPfIA2dnZPPvssyxdupSBAwdy2mmnMXr0aADmzp3LWWedFdoARaRDUiIfxt5/37tv+OW5Zs2X+x/HxbVxQCLSIV1wwQXs3PkeN910YIXX+++/HHj6oHLHcyIPcPHFF/POO++Qn59PZGQkP/vZz1i8eDEj9s0XLCLSwrSQfQdh5q2CtmrV2v3b9s2AICJyLM73p6lx7m1gYpPljvdEHmDMmDHMnDkz1GGIyHHCjjTvrXxVfn6+azivcqiYHfw8I8NRWjqeQOBVwFv1NSGhkReKiDTT6NGjKS0tZeXKxud77tkTPv9c1+WIiLQ0M1vgnPvqzAN0gKE1ZnalmS0zs4CZNfom/XLjzOwLM1tjZnc12N7LzOb5218ws5i2ibzllZTY/iQelMSLSMu55ZZbWLlyJd/97uJG9z/4oJJ4EZG2FvaJPLAUuBx4r6kCZhYJPApcBAwCrjKzfcvpPQD8wTnXF9gF3Ni64YqIhJ+rr76awYMH8+c/397ofs37LCLS9sI+kXfOrXDOfXGEYqOANc65tc65GmAaMMHMDDgHmO6Xmwp8rdWCbWGLG+8YExFpcbGxsbz22mtfmUKxvt4bG9+9e4gCExE5joV9Ih+kbsCmBs83+9sygVLnXN0h28PCiSc2vW/KlLaLQ0SOD3l5ebz44u0MHQrf+x5UVze9yrSIiLS+sJi1xszmAI3NiH63c25GG8VwC3ALQI8ePdrikEfttNPg4YdDHYWIdESJid5FrSIiEnphkcg75847xioKgYY//Ob624qBNDOL8nvl921vLIbHgcfBm7XmGONpFZGRa7j++gf461//GupQRERERKSVHS8/is4H+vkz1MQAk4CZzpt78x3gCr/cdUCb9PC3lC+/hG3b4E9/KqW+fghDhgwJdUgiIiIi0gbCPpE3s6+b2WZgNPBvM5vtb+9qZq8D+L3tU4DZwArgRefcMr+KO4HbzWwN3pj5J9v6PRyL3r2hUyfo0+dToJqhQ4eGOiQRERERaQNhMbTmcJxz/wL+1cj2LcDFDZ6/DrzeSLm1eLPahLXP/UGrSuRFREREjg9h3yMvnsWLF9OtWzcyMzNDHYqIiIiItAEl8h3E559/zrBhw0IdhoiIiIi0ESXyHUBVVRUrVqzQsBoRERGR44gS+Q5g4cKF1NXVMWpU2A/1FxEREZEgKZHvAD7++GMARo8eHeJIRERERKStKJHvAD7++GN69epF586NLX4rIiIiIh2REvkwEQgE+Oc//8mcOXMO2u6c46OPPlJvvIiIiMhxJuznkT9emBl33303vXv35rzzztu/fcmSJWzduvWgbSIiIiLS8alHPkyYGePHj+ftt9+mvLx8//bXX/fWuBo3blyoQhMRERGREFAiH0YmTJhAbW3t/uQd4KWXXuLkk0+mS5cuIYxMRERERNqaEvkwctppp5GXl8ef//xnwFsEqqCggGuvvTbEkYmIiIhIW1MiH0YiIyO59dZbeffdd5k9ezY/+clPSElJ4Zprrgl1aCIiIiLSxnSxa5j5j//4D5566qn9Y+IffPBBMjIyQhyViIiIiLQ1JfJhJiEhgbfeeotHHnmEE044Qb3xIiIiIsepsB9aY2ZXmtkyMwuYWX4TZbqb2Ttmttwv+8MG++4xs0IzW+TfLm676I9Oly5d+NWvfsXkyZMxs1CHIyIiIiIh0BF65JcClwN/OUyZOuA/nXMLzSwZWGBmbzrnlvv7/+Cc+11rByoiIiIi0lLCPpF3zq0ADtsz7ZzbCmz1H+82sxVAN2B5ky8SEREREWnHwn5oTXOZWR5wEjCvweYpZrbYzJ4ys/QmXneLmRWYWcGOHTvaIlQRERERkSaFRSJvZnPMbGkjtwnNrCcJeAn4kXNu3/KojwF9gOF4vfb/29hrnXOPO+fynXP52dnZR/9mRERERERaQFgMrXHOnXesdZhZNF4S/7xz7uUGdW9vUOavwGvHeiwRERERkdYWFj3yx8q8AfRPAiucc78/ZF+XBk+/jnfxrIiIiIhIuxb2ibyZfd3MNgOjgX+b2Wx/e1cze90vNgaYDJzTyDSTvzWzJWa2GDgbuK2t34OIiIiISHOZcy7UMYQdM9sBbAjR4bOAnSE6djhSezWP2qt51F7No/ZqHrVX86i9mk9t1jyhaq+ezrlGL9BUIh9mzKzAOdfowlfyVWqv5lF7NY/aq3nUXs2j9moetVfzqc2apz22V9gPrREREREROR4pkRcRERERCUNK5MPP46EOIMyovZpH7dU8aq/mUXs1j9qredRezac2a552114aIy8iIiIiEobUIy8iIiIiEoaUyLdDZjbOzL4wszVmdlcj+2PN7AV//zwzywtBmO1KEG32bTPb0WAdgZtCEWd7YGZPmVmRmTW6+Jl5HvLbcrGZjWjrGNuTINrrLDMra/DZ+nlbx9iemFl3M3vHzJab2TIz+2EjZfQZ8wXZXvqM+cwszsw+NbPP/fa6t5Ey+o70Bdle+n48hJlFmtlnZvZaI/va1ecrKpQHl68ys0jgUeB8YDMw38xmOueWNyh2I7DLOdfXzCYBDwAT2z7a9iHINgN4wTk3pc0DbH+eAR4Bnm1i/0VAP/92CvCYf3+8eobDtxfA+865S9smnHavDvhP59xCM0sGFpjZm4f8f9Rn7IBg2gv0GdunGjjHObfHzKKBD8xslnPukwZl9B15QDDtBfp+PNQPgRVASiP72tXnSz3y7c8oYI1zbq1zrgaYBkw4pMwEYKr/eDpwrplZG8bY3gTTZuJzzr0HlBymyATgWef5BEgzsy5tE137E0R7SQPOua3OuYX+4914X4bdDimmz5gvyPYSn/+Z2eM/jfZvh17sp+9IX5DtJQ2YWS5wCfBEE0Xa1edLiXz70w3Y1OD5Zr76R31/GedcHVAGZLZJdO1TMG0G8A3/Z/zpZta9bUILS8G2pxww2v/pepaZDQ51MO2F/5PzScC8Q3bpM9aIw7QX6DO2nz/sYRFQBLzpnGvy86XvyKDaC/T92NCDwI+BQBP729XnS4m8HC9eBfKcc0OBNzlwNi1yrBbiLZ89DHgYeCW04bQPZpYEvAT8yDlXHup42rsjtJc+Yw045+qdc8OBXGCUmQ0JcUjtWhDtpe9Hn5ldChQ55xaEOpZgKZFvfwqBhmfDuf62RsuYWRSQChS3SXTt0xHbzDlX7Jyr9p8+AZzcRrGFo2A+g+JzzpXv++naOfc6EG1mWSEOK6T8sbgvAc87515upIg+Yw0cqb30GWucc64UeAcYd8gufUc2oqn20vfjQcYA481sPd4w3XPM7G+HlGlXny8l8u3PfKCfmfUysxhgEjDzkDIzgev8x1cAb7vje0GAI7bZIeNvx+ONQ5XGzQSu9WcWORUoc85tDXVQ7ZWZdd43PtLMRuH9XT1ukwa/LZ4EVjjnft9EMX3GfMG0lz5jB5hZtpml+Y/j8SY5WHlIMX1H+oJpL30/HuCc+4lzLtc5l4eXS7ztnLvmkGLt6vOlWWvaGedcnZlNAWYDkcBTzrllZnYfUOCcm4n3R/85M1uDdxHepNBFHHpBttkPzGw83gwRJcC3QxZwiJnZP4CzgCwz2wz8Au8CKJxzfwZeBy4G1gAVwPWhibR9CKK9rgC+Z2Z1QCUw6XhNGnxjgMnAEn9cLsBPgR6gz1gjgmkvfcYO6AJM9WcriwBedM69pu/IJgXTXvp+PIL2/PnSyq4iIiIiImFIQ2tERERERMKQEnkRERERkTCkRF5EREREJAwpkRcRERERCUNK5EVEREREwpASeRERERGRMKREXkREREQkDCmRFxEREREJQ0rkRURERETCkBJ5EREREZEwpEReRERERCQMKZEXEREREQlDSuRFRERERMKQEnkRERERkTCkRF5EREREJAwpkRcRERERCUNK5EVEREREwpASeRERERGRMKREXkREREQkDCmRFxEREREJQ0rkRURERETCkBJ5EREREZEwFBXKg5vZOOCPQCTwhHPuN4fsjwWeBU4GioGJzrn1/r6fADcC9cAPnHOzD1enmU0BfgT0AbKdczv97WcBM4B1/mFfds7dd7i4s7KyXF5e3tG/cRERERGRICxYsGCncy67sX0hS+TNLBJ4FDgf2AzMN7OZzrnlDYrdCOxyzvU1s0nAA8BEMxsETAIGA12BOWbW339NU3V+CLwGzG0knPedc5cGG3teXh4FBQXNeLciIiIiIs1nZhua2hfKoTWjgDXOubXOuRpgGjDhkDITgKn+4+nAuWZm/vZpzrlq59w6YI1fX5N1Ouc+29ebLyIiIiIS7kKZyHcDNjV4vtnf1mgZ51wdUAZkHua1wdTZmNFm9rmZzTKzwY0VMLNbzKzAzAp27NgRRJUiIiIiIq1HF7vCQqCnc24Y8DDwSmOFnHOPO+fynXP52dmNDlMSEREREWkzoUzkC4HuDZ7n+tsaLWNmUUAq3kWvTb02mDoP4pwrd87t8R+/DkSbWVZz34yIiIiISFsKZSI/H+hnZr3MLAbv4tWZh5SZCVznP74CeNs55/ztk8ws1sx6Af2AT4Os8yBm1tkfd4+ZjcJrk+IWeYciIiIiIq0kZLPWOOfq/CkhZ+NNFfmUc26Zmd0HFDjnZgJPAs+Z2RqgBC8xxy/3IrAcqANudc7Vw/5pJg+q09/+A+DHQGdgsZm97py7Ce8E4XtmVgdUApP8kwURERGRjq++Dmp2Q0QUxCSB178pYcCUszZffn6+0/STIiIiElacg22LYcPHUFgAhQth91aorThQJiIK4lIhMQdSukJqN0jJ/erj2KTQvY+jFQhARTHs3gJ7iqBmL9RWQl2l1zY4/76BfdsBep8N2f0PrbXVmdkC51x+Y/tCuiCUiIiISFirq4GyTVC6AUo3wi7/ft/zylKISfB6uuNSvSQ4pRuk5nq3lG5+gtwNomJbPr5APWz6FFbMhBWverECJHeBbifDgIsgNgVikyFQB5W7vNveHVC2GbYtgb1FX603LvWQBL8bJHWC5M4H7hOzISKy5d/TkTgHu9bBlkWw5TPYusj7d9m9Feprjr7er/8lJIn84SiRFxERETnUvt7bvUVe7+2eogaPt/vJ+kYo38L+HlvwerRTcyGtB/S7AOLTvV7fmr1eglxeCJsLoLLkq8dMzPYS4OgEL6mPivPuE7Mh+wTI6u/dJ3c+/PCX6t2wdi6smu3d9hZBZAz0OQfOusvrWU4NZnZuX1219z7Lt3jxl20+8Li80EuWK3Z+9XURUZDU2T956QpJOV4cEVHeLTIaLBIiIvz7yAb3EQc/j4iG5E6Q2t07aYiOOxDbztVQtBy2L/OS9i2LoKrU2x8ZA52GQPdTIKWL99rkLt7JRmwSRMdDVLx3PPDb1Ro8xntuBjGJwbdZG1EiLyIiIsev6j1e8le4EHaugpJ1Xm/u7m3gXX53sKg4b9hJWg/oNRbSe3qP0/z7lK7B9ULXVPjJ8GYvMS4r9B7v3g51VV6CWr0baqtg3XtQVXbgtXFpkDMQMnp7JwqBem94SM1eKF4D25ZCoNbrae97Lgy8DPqeD3EpR9dGUbGQ0cu7NaWu2jvB2b0d9mzz2m/31gMJ//alsHaHNx4/UOfF5wJHFw94CbpFeG21T0Q0dBoEg78GXU/ybtkDISrm6I/TzimRFxERkeNDfa3Xa1u4wEvctyyEHSsPJJSJOV5y3OtMr1c9MQeSsr3e232PY1Na5mLQmATI6uvdjsQ575eAHStgxxdQtMKLe+1cqCjxeraj4rxe6ozeMPpW6He+1wsdGX3ssQYjKtY/oekR/GsCAe9kKVB/yH0j2+trvRODss3eCU/1Hm9fbIr3nnMGQVa/tnu/7YQSeREREemYdm+HzZ96Y8Q3fer1vO/rwY3P8MaIDxzv3XcbAYntdBkZM29YSXIn6H1WqKNpORERQETwyXcwJz3HGSXyIiIiEhrOQclab4x12Savt3XPdm+YRm2lNwQjKtYbwxwd512QGZviXTgam+zdYhK9cnVV3tCSXeuh+Etv3HTZRu84kTHQZTjk3wi5J3uJe1pPTbMoYU+JvIiIiLSduhpYPRtWvu6N/S7ffGBfXKp3cWRMgjdUJCLKG0teUeJfMLrHGzdes6fp+vcNteg+Ek65xRte0mVY68wIIxJiSuRFRESk9W1dDAufhaXTvdlb4tO9sei9bocep3pjq2OTg6srUO8n9Xu8XvjIqAO99nFp6mmX44YSeREREWkd9bXe3OWfPg4bP/Z62U+4BIZd5U2BGHmUaUhEpNd7H5fasvGKhBkl8iIiItKydm+DBVNhwdPeTCPpeXDBr+Ckb3k98SLSIpTIi4iIyLFzzut1//Sv3iqigTrocy5c+gdvYaRQrPAp0sEpkRcREZGjV1ECS1+CBc94i/7EpcKo78DIGyGzT6ijE+nQlMiLiIhI89RVw6rZsPgF7z5QC51PhMseghOv9GadEZFWp0ReREREjsw5b1GlxdNg6ctQVeqteHrKd2DYJC+RF5E2pUReREREmlayzut5/3wa7FrnTfM48DIYNhF6nXX0M8+IyDHT/z4RERE5WKAeVr8B85+ANXMAg15nwNgfe0l8sPO9i0irUiIvIiIiHudg+Ssw516v9z25C5z1EzjpGkjNDXV0InIIJfIiIiICGz+BN/4LNs+HnMFw5VRv8abI6FBHJiJNUCIvIiJyPCv+Eub8wluBNbkLjH8Ehl+ted9FwoASeRERkePR3mJ49wEoeBIiY+Hsu2H0rRCTGOrIRCRISuRFRESOJ7VVMO/P8P7/Qs0eGHGdNw4+uVOoIxORZlIiLyIi0pjty+CLWVC0HCpLvW0JGZCQBYn+LSEL4tMbDEMx/86O8Pwo4omIgvgMb/jL0Uz5WFcNnz0H7/8ByjdD/3Fw3r2Qc8JRBCMi7YESeRERkYYKF8Lsn8LGj73naT29pN05KF4DFcVeT3aoRMV7iy91GQa5I6HnaEjr0XT5ihJvHvgPH4LdW7zXfO1P0Hts28UsIq1CiXxHsGsDrPo/2PKZ9we7uhyqd0OgDlzA+/KJSYDU7tD3PBj8dYhPC3XUIiLtS30tvHUvfPQIJOXAhb+GoZMgMfOrZWuroGIn7N0JlbsA5/2tBe9xg7sDzw/Z3+z4arzj7VwFWxbB5/+A+X/19qXkQm4+dBri/WrgAlC+xZuBZuPH3vdBzzHw9ceg19gGvxCISDgz547yD8pxLD8/3xUUFIQ6DAgE4MMHYe5voL4akjp7Xz5xqd5iHRFRB37ura2E7cuhbCPEJHljIk/9HqR1D+lbEBFpF/buhGlXw6Z5kH+DN+QkLiXUUR1eoN4b9rPhY9jwodeZU7rhwH6L8Hrue58FQ77h9eCLSNgxswXOufxG9ymRb752k8h/8hj8313eKnvn3wcZvQ9f3jnvD/0nj8HSl7xtQy6H076vP/AicvwqWQd/uxzKt8LXHvWS3nBVUwFVZV4nTnzG0Y2lF5F2RYl8C2sXifzeYnj4JOg6Aib/q/k/k5Zu8mYtWPCMN9az11gY8wPoc65+chWR48eWRfD8lRCohatfhO6jQh2RiMhBDpfIR7R1MA2Z2Tgz+8LM1pjZXY3sjzWzF/z988wsr8G+n/jbvzCzC49Up5lN8bc5M8tqsN3M7CF/32IzG9GKb7nlzL0fqvfAuPuPLvFO6w4X/gpuWwbn3QM7voC/fQMeOw3mPX5ghgYRkY5qzRx45hKIioUbZiuJF5GwE7JE3swigUeBi4BBwFVmNuiQYjcCu5xzfYE/AA/4rx0ETAIGA+OAP5lZ5BHq/BA4D9hw8CG4COjn324BHmvJ99kqnINl//IuWs0ZeGx1xafB6bfBj5bAhD95S3HPugP+dwC8fAuseQvq61okbBGRdqPgKXj+m5DeC258A7IHhDoiEZFmC+XguVHAGufcWgAzmwZMAJY3KDMBuMd/PB14xMzM3z7NOVcNrDOzNX59NFWnc+4zf9uhcUwAnnXeGKNPzCzNzLo457a26LttSaUbvdkSeo5uuTqjYuCkb3m3LYtg4VRYMt2bsiwxxxszOvRKbyiPht6ISLiqr/NnpnkI+l0AVzzlTQ4gIhKGQpnIdwM2NXi+GTilqTLOuTozKwMy/e2fHPLabv7jI9UZTBzdgIMSeTO7Ba/Hnh49DjNfb1vYstC779pKo4C6DvduF94Pq2fD4he9JbznPQbpeTBogndTUi8i4WTLInj1B7D1c8i/ES76rS4GFZGwpr9gQXLOPQ48Dt7FriENpnABRMZ48wW3pui4A0l7ZSmsmAnLXoGPH4UP/+jNSz9wvLc/dyREhPSSCxGRxlXugvf/Fz7+EyRkwpXPwKCvqSNCRMJeKBP5QqDhJOa5/rbGymw2syggFSg+wmuPVOfRxNG+FC6EzkO94TBtJT4NRlzr3SpKvAWols/wFiP55FFvyfCBl8GwSeqpF5HQCwS86XaXvAifPQ81u+GkyXDBLyE+PdTRiYi0iFAm8vOBfmbWCy9xngRcfUiZmcB1wMfAFcDbzjlnZjOBv5vZ74GueBeqfgpYEHUeaiYwxR9PfwpQ1q7HxwfqvZ+HT7omdDEkZMDwq71bVTmsmg3LX4GFz8Knj0POYBgx2dsflxq6OEWk4wgEoHQ97FgFVaVQVwV1NeDqvf3OQV2lNzVv8RpvRdPKEu/Xy4Hj4fQfeYsjiYh0ICFL5P0x71OA2UAk8JRzbpmZ3QcUOOdmAk8Cz/kXs5bgJeb45V7EuzC2DrjVOe+veWN1+tt/APwY6AwsNrPXnXM3Aa8DFwNrgArg+rZpgaO04wuo3QvdTg51JJ64FO8i2KFXeouQLJkOnz3nLVT19q8g/3o49T8gpUuoIxWRcLNztTek74v/g21LvET9SKLiIbMP9B8Hvc6EARd5vyiKiHRAWhDqKIR0Qagl0+GlG+F7H0OnQ2frbEe2fAYfPexNk2mRXg/9Gf8Jqbmhjkyk/XIOvnzbS143L4DqMoiIhpSu3jUx3UZ4J/EZvTvu8LX6Om/l6Q//CEXLvG1dR0CP0ZBzAmSf4I1zj4rz5n+PiDzw2qh479oeEZEO5HALQuli13Czd6d3n9QptHEcSdeTvGndzvmZN83bwufgs7/BiOvgjNu9xEREDti2FGZ+35uVKjYVuo+EToOhvhrKNntTws7zl7lI6gx5YyBnkHehefdTwj+Bra/1prt973ewa5134nLR/8AJl0BqtyO/XkTkOKREPtxUlgAWPj8VZ/SCS//gLTr13u9gwdPeWPr8G7xtye38hESkLSx8Fv79/7z/15c9BMOu+urF7PV1sGMlFBbAuvdg06dezzV4vdPdT4HeY6HXWd70sQ17qtuzumpY9Hf44PfeGhldhsGkv0P/izQTlojIEWhozVEI6dCaf/+n9+V95/rQHP9Y7VoP7/0PLPqHdxHayBthzA8hKSfUkYmExry/wKwfQ59z4fLHITEr+NdWlcGGj2Hdu7D23QNDUWJTIe90P7Ef661a2t6G4tRWedfTfPAHKC+Ebvkw9sfeIk3tLVYRkRA63NAaJfJHIaSJ/D+/7V309f0FoTl+Syn+0uuhXzwNImNh+FXeRbFZ/UIdmUjb+eTP8H93wgmXwhVPH/uUsnuKvN76fYl96QZve2SMNxwvqZN3gXpENEREeb32kfseR3nlUrpCWk9I7wlxaRCTANGJXtmISK+c+Y8tonlJd6AeNhd4U9d+/g/vF8bup3oJfJ9zlMCLiDRCiXwLC2kiP3W8N+3ajW+E5vgtbeca+OiP8PkL3ljg/uNg9K2Qd4a+1KVj++Qxb3anlkriG7NrvZfYF6/xkvzd26BmjzcePVAPgVoI1B14XlcFFTubdwyLaJDYN0jwIyK9E4PELIhOgNpKbxaaukrvZOCES2Dkzd4vB/q/LiLSJF3s2pFUlHSsmV+y+sL4h+Gcn0PBk/DpX2HqZd5c9KNuhqHfhJjEUEcp0rI+fhRm/9RbRO2Kp71e8daQnufdmqO2Eko3eePVq8ugpgJqK7yEP1DnJfyu3pvX3dU3eF4PLnDw8/pq7wL9uiqITfFO0HNP9oYRhct1PiIi7Zh65I9CSHvkfz8Iep8NX3s0NMdvbbVV3kqMnz7uDSGKTYWTvgUjb/Lmhg6lumooWeclOKUboLL0QMISFeutbpvSxbtP7uwNS1BPoxzqo0fgjbu9RYqueKr1kngREekQ1CPfkVQUQ0IHXl48Og5GXOstpb5pntdD/+nj8MmfvPmz+5wDWQO8FWNjEr3xu7ZvZgs/aTY75LG/LyrW+zUjKja4WGr2ejODbPjIu22e7/UwBismGdK6Q1oPSPXv07p7CX50gvde62uherc33KFyl/fvW1Hi34oP3KrKvLijE7z3nZrr9bRm9PZWq+x8IkTHBx+bhMZHD8Mb/wWDvgbfeEJJvIiIHBMl8uGkpsL7iTohM9SRtD4z6HGqd9v9a+/CuGX/gvf/1/v5/lgkd2mQWPvJdWoPwEHZJtiyyJvLu2iFN5TAIrwp8Ubd7N3vuxAwIdMfExzhDUfYvQ12b/Vu5Vu9ub9LN3q3jR97yXgwImO9ccUJGRCf4R0zLtVL+msrvMR/52pY/eaBEwuL9OYU7z7Su3iwxylenPpFoP348I/w5s9h8Nfh8r8qiRcRkWOmRD6cVBR79/EZoY2jrSV3gtN/5N1qq7xhLft6sWsqAOetiAlffQwHntfs9RL10k1eHYUFsPwVL1lvKC7NW9BqzA+hx2nQfZQ308fhRMd7c+Zn9Gq6TGWpl9xXl3sJeW2VdzFgbBLEJEF8upe8RycEl4AHAt60fVs/9048ChfA4n9CwVPe/qTOXuw9TvWS+y5Djz55rN4DRcu94U7bl3r3pRu97ZHRXvyxSd7JQ/YAb/XNLkMheyBEhuDPzO7tXozbFnvx7inyPjPVu70515OyITHH+/fqfKK3+FB6Xuuc+DgH7z4Ac++HwZf7Sbz+9IqIyLE74reJmcU656qPtE3awL5E/njokW9KdJyXKLaUQL3Xk1660ZtlI6mT10vfGgldfFrLXuAXEeEP3ekOAy/1tgXqvYR74yfe0KSN82DFTG9fVLw3PKnHKV5i332kd/LQUH2td7Kx4wvYvsRPhpdCyVr2nxjFpkLnId5833GpUF/jnSRVlXmzpKx9x9u275hdh3vH7TbCu2/JXwoC9d6MLPuS9n3x7i06UCa1h7cyaFKONxSprspL7IvXwtLpB37hic+AnqcduHUeeuyLKtVVw+t3eKuyDrvau7BbSbyIiLSQYL5RPgZGBLFNWltliXd/PCfyLS0i0kvyOsoS8BGRB8bMj7rZ21a+5UBiv2kefPCgd5EuQHJXbxiPmTcuv7zw4KFL6b28pH3oRL/eId6wpMMl4vV1XkK/dZH3K0HhApj/BHxc5e1PyPQTe//WZRgkZh+5zvLN3voDxV96vezbl8L25d50huDNjZ4zEPqdf6ANOg3+6slKQzUVsGOFdwKwaT5s+BBWvubti0n2fs3oeRr0HOP9StOcKSJ3roaXb4Ytn8EZ/wnn/ExDnUREpEU1mcibWWegGxBvZiex/+pBUoCENohNDlWxL5E/zobWyLFJ6QpDLvdu4PWeFy7wkvqS9d684c55w2H2jf/P7OslwbHJzT9eZJQ3rWhWXzjxCm9bfa33S8G+xL5woTfGf18v/74FixIyvTnGwTvZqCr3LgKuKj34BCMuzRu6k3/DgaQ9q3/z52KPSThwQnHyt71t5VsOXOC84UN4615ve1Q85OZ7SX3P0yB3pPf6Q5Vu9C/Q/rO3f9LfvTnTRUREWliT00+a2XXAt4F8oOFci7uBZ5xzL7d6dO1UyKafnPc4zLoD7viyecu4i7RH1bu98f3blnjDm/YUwd4dBxJ2M2/u8fh075be0xsak9HHm96zrXq39+70Llbe8BGs/8CLF+f9AtBthHcCFJ/u/WK27/2A9yvG+fd5Q3pERESO0lFNP+mcmwpMNbNvOOdearXoJHj7xsjHpYU0DJEWEZvsreqZd3qoIzm8xCxv4aaBl3nPq8q8aw82fAAbPoYvXvcuZI5P85L6sXfC8Ku9ay1ERERaUTBj5F8zs6uBvIblnXP3tVZQ0oSKYi+J18VyIqETlwr9L/BuIiIiIRRMRjgDKAMWAJqpJpQqSzQ+XkRERESA4BL5XOfcuFaPRI6solgz1oiIiIgIABFHLsJHZnZiq0ciR1ZRokReRERERIDDTz/pT81AFHC9ma3FG1pjgHPODW2bEGW/ihJvBUoREREROe4dbmjNpW0WhQTn+wXefNwiIiIictw73PSTGwDMrLGrK3e3WkTStOh47yYiIiIix71gxsgvBHYAq4DV/uP1ZrbQzE5uzeBERERERKRxwSTybwIXO+eynHOZwEXAa8B/AH9qzeBERERERKRxwSTypzrnZu974px7AxjtnPsEiG21yEREREREpEnBzCO/1czuBKb5zycC280sEgi0WmQiIiIiItKkYHrkrwZygVf8Ww9/WyTwzdYKTEREREREmnbERN45t9M5933n3En+bYpzbodzrsY5t+ZYDm5m48zsCzNbY2Z3NbI/1sxe8PfPM7O8Bvt+4m//wswuPFKdZtbLr2ONX2eMv/3bZrbDzBb5t5uO5T2JiIiIiLSFJhN5M3vQv3/VzGYeejvWA/tDcx7Fu3h2EHCVmQ06pNiNwC7nXF/gD8AD/msHAZOAwcA44E9mFnmEOh8A/uDXtcuve58XnHPD/dsTx/reRERERERa2+HGyD/n3/+ulY49CljjnFsLYGbTgAnA8gZlJgD3+I+nA4+YmfnbpznnqoF1ZrbGr4/G6jSzFcA5eEOCAKb69T7WOm9NRERERKR1HW5BqAX+/btmFg/0cM590YLH7gZsavB8M3BKU2Wcc3VmVgZk+ts/OeS13fzHjdWZCZQ65+oaKQ/wDTM7E2+u/Nuccw3rAMDMbgFuAejRo0eQb1FEREREpHUccYy8mV0GLAL+z38+vCWG1rQjrwJ5zrmheHPmT22skHPucedcvnMuPzs7u00DFBERERE5VDCz1tyDN2ylFMA5twjo1QLHLgS6N3ie629rtIyZRQGpQPFhXtvU9mIgza/joGM554r9IToATwBarVZERERE2r1gEvla51zZIdtcCxx7PtDPn00mBu/i1UN7+mcC1/mPrwDeds45f/skf1abXkA/4NOm6vRf845fB36dMwDMrEuD440HVrTAexMRERERaVXBLAi1zMyuBiLNrB/wA+CjYz2wP+Z9CjAbb076p5xzy8zsPqDAOTcTeBJ4zr+YtQQvMccv9yLehbF1wK3OuXqAxur0D3knMM3M/hv4zK8b4AdmNt6vpwT49rG+NxERERGR1mZeZ/VhCpglAHcDFwCGN1b+v51zVa0fXvuUn5/vCgoKQh2GiIiIiHRwZrbAOZff2L5geuS7OOfuxkvmRURERESkHQgmkX/KzHLxxp+/D7znnFvSumGJiIiIiMjhHDGRd86N9S8cHQmcBfzbzJKccxmtHZyIiIiIiDTuiIm8mZ0OnOHf0oDX8HrmRUREREQkRIIZWjMXWADcD7zunKtp1YhEREREROSIgknks4AxwJl4UzUGgI+dcz9r1chERERERKRJwYyRLzWztXgrpuYCpwHRrR2YiIiIiIg0LZgx8muBlXjj4h8DrtfwGhERERGR0ApmaE1f51yg1SMREREREZGgBTO0Rkm87FdbH+CLbbv5csceyitr2bcucHJcFL2ykuiVlUhqvEZeiYiIiLS2YHrk5ThXVx9g7hc7eKFgEx+u2UlFTf1hy+emxzO2fzZj+2czpm8WibH6mImIiIi0NGVY0qTdVbU8/eF6np+3ge3l1WQlxfKNEbmM6pXBgM7JpCfEYOaVLa2oYe2OvazbuZcFG3bxymeFPD9vI/HRkYwf1pWbz+xF35zk0L4hERERkQ4kmItdb29kcxmwwDm3qMUjkpCrqq3nuY838Ke5a9hVUcvY/tncO74H5w7MIToyotHXZCXFHpSo19QFKNhQwsxFW5ixaAv/XLCJK07O5e6LB5GaoKE3IiIiIsfKnHOHL2D2dyAfeNXfdCmwGMgD/umc+21rBtge5efnu4KCglCH0eLq6gNMX7CZB+esZlt5FWf2z+aOCwZwYm7qMdVbsreGx+au4ekP15OZFMNvvjGUswfktFDUIiIiIh2XmS1wzuU3ui+IRP494GLn3B7/eRLwb2AcXq/8oBaOt93raIl8XX2A/1u2jd+/uYq1O/ZyUo807hx3Aqf2zmzR4ywtLOP2FxexavserhrVnZ9fOpj4mMgWPYaIiIhIR3K4RD6YMfI5QHWD57VAJ+dcpZlVN/EaaSOBgKO8qpbSilrKKmsJ+Cdm+07PGp6nRRjERUcSGxVBfcCxaVcFH60p5t9LtrK1rIq+OUn8ZfLJXDCoE7Zv8HsLGtItlVe/fzq/f3MVf3l3LZ9tLOXP15xMXlZiix9LREREpKMLJpF/HphnZjP855cBfzezRGB5q0UmTXLO8dLCQqYv2MSCDbuorT/8ryqHExMZweg+mdwzfjDnDexEZETLJ/ANxUZF8pOLBnJq70xue2ERlz38Af9z5TDGDencqscVERER6WiOOLQGwMxGAqf5Tz90znWccSVHIZRDa6rr6vnJy0t4eWEh/TslMbZ/Nl1S40lLiCYlLprIyAOJ+L5H+3rX6wMBqmsDVNcFMIOuafEM7ppCQkxoJi/avKuCW59fyOeby7jlzN7cceGAJi+mFRERETkeHevQGoCFQOG+8mbWwzm3sYXik2Z4+sP1vLywkB+d148fnNOPiFbuQW9NuekJvPjd0fz3ayt4/L21LNpYyiNXn0ROSlyoQxMRERFp947Y/Wlm3we2A28Cr+Fd6PpaK8cljaipC/D0h+sY0zeTH53XP6yT+H1ioyL55deG8MdJw1lSWMbFD73PjEWFBPNLkYiIiMjxLJhxDD8EBjjnBjvnhjrnTnTODW3twOSrZiwqZHt5Nbec2SfUobS4CcO7MWPKGLqmxfPDaYv4xmMf8eby7dTVB9rk+FW19WwprWTdzr0U76kmENCJhIiIiLRvwQyt2YS3AJSEkHOOv76/lhM6J3Nmv6xQh9Mq+ndK5l//MYZp8zfyp3e+5OZnC8hOjuXcE3IY3j2Nk3qk0ysrkZioox9HXx9w7NxTzfqde1m2pZylW8pYVljOmh17qG+QvKclRHNqr0yuOqUHZ/bLapVZfERERESORTDzyD8JDMAbUrN/uknn3O9bN7T2KxQXu9YHHNMXbCIrKZZzB3Zq02OHQm19gDnLt/OvzwqZt66Essra/ftioyJIiY8mNioCMzCMfXm24V3cuz/ttgPbKmvq2V5eRV2DhD0nOZYh3VIZ3DWFrmnxxEZFUF5Zy/Kt5bzzxQ527K5mePc0fnvFUPp3OrByrYiIiEhbONYFoX7R2Hbn3L0tEFtY6mgLQrV3gYBjXfFeFm0sZUtpJbur6yivrKWmLoCD/ePpvccN59B33mN/Q2xUBJ1T4+iSFk9uejyDu6Qc9sLamroAr3xWyP2zVrC3up5ffm0wE0f2aMV3KiIiInKwY0rk5auUyB9fivdU86MXFvH+6p3ccmZvfnLRCRpqIyIiIm3iqKafNLMHnXM/MrNXOdDJuZ9zbnwLxijSbmUmxfL0t0dy32vLefy9tVTU1HHf+CEdYtYgERERCV+Hu9j1Of/+d611cDMbB/wRiASecM795pD9scCzwMlAMTDRObfe3/cT4EagHviBc2724eo0s17ANCATWABMds7VHO4YIvtERUZw7/jBxMdE8pd31xIVEcEvLhuknnkREREJmSYTeefcAv/+3dY4sJlFAo8C5wObgflmNtM5t7xBsRuBXc65vmY2CXgAmGhmg4BJwGCgKzDHzPr7r2mqzgeAPzjnppnZn/26H2vqGK3xniW8mRl3jTuBunrHkx+sIy0hmh+d1//ILxQRERFpBYcbWrOERobU7NMCc8mPAtY459b6x5sGTAAaJvITgHv8x9OBR8zrAp0ATHPOVQPrzGyNXx+N1WlmK4BzgKv9MlP9eh9r6hhOFw9II8yMuy8eSFllLQ/OWU1KXDQ3nN4r1GGJiIjIcehwQ2su9e9v9e/3DbW5hsMk+M3QDW+O+n02A6c0VcY5V2dmZXhDY7oBnxzy2m7+48bqzARKnXN1jZRv6hg7j/qdSYcWEWH85vIT2VNVx32vLSclPporTs4NdVgiIiJynGlyZR3n3Abn3AbgfOfcj51zS/zbncAFbRdi+2Bmt5hZgZkV7NixI9ThSIhFRUbwx6uGc3rfLO58aTGzl20LdUgiIiJynAlmiUwzszENnpwW5OuOpBDo3uB5rr+t0TJmFgWk4l2Q2tRrm9peDKT5dRx6rKaOcRDn3OPOuXznXH52dnaz3qh0TLFRkfxl8smc2C2V7//9Mz5cox9xREREpO0Ek5DfCPzJzNab2QbgT8ANLXDs+UA/M+tlZjF4F6/OPKTMTOA6//EVwNv+2PWZwCQzi/Vno+kHfNpUnf5r3vHrwK9zxhGOIXJEibFRPHP9SHplJXLzswUs2LAr1CGJiIjIcSLoBaHMLBXAOVfWYgc3uxh4EG+qyKecc78ys/uAAufcTDOLwxubfxJQAkxqcCHr3XgnFHXAj5xzs5qq09/eG2/6yQzgM+Aa51z14Y7RFC0IJYcqKq/im3/5mJ17aph6wyhO7pneKsfZUlrJ/PUlrN2xl/XFe9lUUkF1XYD6gPf/OCU+muykWLKSYshJiaNTShydU+LolBJLp9Q4kmOjNGWmiIhIGDmmlV39BP4XwJn+pneB+1oyoQ83SuSlMdvKqpj0+McU7a7m0W+N4OwBOS1Sb2FpJdMLNvPvJVtYtX0PAGbQNTWenpkJxEdH7l+cqqyilp17qtmxp5rdVXVfqSs+OtJL6v0kPykuiqgIIyoiguq6esqr6iivrKW8qpbyylr2VNext7qeipo6zIzICCMmMoKemQkM6JzMwM4pnN4vixM6J+sEQUREpBUcayL/ErAUb8pGgMnAMOfc5S0aZRhRIi9NKSqv4vpn5rNy225+evFAbhiTd1QJbiDgeHf1Dp7/ZANvryzCASPzMrhgUCdO7Z1J35wk4qIjD1tHZU0928ur2F5exbbyKorKq9nmP/du1VTU1FMXCFBX74iNiiA1Pprk+GhS4qJIiYsmMTaSxNgoEmK8Y9UFHNW1Ab7csYcvtu2maHc1AD0yEhg3pDNXnpxLv07JzX6/IiIi0rhjTeQXOeeGH2nb8USJvBzOnuo6bnthEW8u385ZA7K5b/wQemQmBPXabWVVvLRwM9Pmb2RTSSVZSTFMHNmdq0b1IDc9uDraUlF5FXNWFDF72TY++nIntfWOM/plccPpvRjbL3v/LwUiIiJydI41kf8YuMM594H/fAzwO+fc6BaPNEwokZcjcc4x9aP1/Hb2F9QFHFecnMtVI3swuGvKV5LbovIq3l+9kxmfb+GD1TsIOBjVK4PJp/bkwsGdiYlqiUmiWl/J3hr+8elGpn60nqLd1QzumsIdFw5gbP9sDbsRERE5SseayA/HG1aT6m/aBXzbOfd5SwYZTpTIS7C2lVXx4JxV/OuzQqrrAmQmxtA7O5HE2Cj2VNWxsaRi//CUbmnxfGNENy4fkUteVmKIIz96NXUBZn6+hQfnrGLzrkpG9crgznEDOLlnRkhjKq2oYVdFLQ5HQnQUcTERpMXHhM2JkoiIHJ+OKZFvUEkKgHOuvAVjC0tK5KW5SitqmLOiiI+/LKawtIK91fUkxUbRLT2eEzonc2rvTAZ1+WpvfTirqQswbf5GHnprDTv3VHPuCTncedEJ9G/lMfR19QE+XV9CwfpdLNpUyuLNpezcU9Nk+aykGDqnxtElNZ5uafF0TYujW1qCfx9PVlJsh/p3ERGR8HKsPfK/Bn7rnCv1n6cD/+mc+6+WDjRcKJEXCV5FTR1Pf7ieP7/7JXur6/hmfnduO78/nVLiWuwYgYBjwcZdvPr5Fl5fspWde2owg77ZSQzNTSMvM4G0xBjSE6KJMKOypp6K2npK9tSwrbySLaVVbCurorC0kj3VB8/2ExMVQW5aPN3S4+mekUD39AQGdklmWG4a6YkxLfYeREREGnOsifxnzrmTDtm20Dk3ogVjDCtK5EWab9feGh55Zw3PfryeyAjj5jN6c8uZvUmOiz6q+pxzLCks49XPt/Da4q1sLasiNiqC8wZ24tKhXTi9X1az63bOUV5Vx5bSyv23zbu826ZdFWwqqWBXRe3+8nmZCYzpm8VFQ7pwWp9M9dyLiEiLO9ZEfjEw0jlX7T+Px1uwaXCLRxomlMiLHL2NxRX87o0vmPn5FpLjorh6VA8mjuxO7+ykI742EHCs3Lab15ds5dXFW9hQXEF0pDG2fzaXDevKuQM7kRQb1arxl1fVsrSwjM83lbFgwy4++nInFTX19M1J4ofn9uPSoV10ca+IiLSYY03k7wQuA572N10PzHTO/bZFowwjSuRFjt3SwjIee/dLZi3ZSsDBoC4pnNEvi0FdU+iaFk9sVASlFbUU761ma1kVCzeUUrChhNKKWiIMxvTN4rKhXblwcGdSE46uV78lVNXWM2vpVv7y7lpWbtvNKb0y+N2Vw+ie0f6mCxURkfBzzBe7mtk44Dz/6ZvOudktGF/YUSIv0nK2l1cxY1Ehb60oYuHGXdTWN/43KS8zgZF5GYzslcHZA3LITo5t40gPrz7geGH+Ju5/fQUAv584nPMHdQpxVCIiEu5aIpHvCfRzzs0xswQg0jm3u4XjDBtK5EVaR01dgHU791K0u4qq2gCp8dFkJcWQlRxLylGOpW9rm0oqmPL3hSzdUs4fJw3n0qFdQx2SiIiEscMl8kccTGpmNwO3ABlAH6Ab8Gfg3JYMUkQkJiqCAZ2TGdC5daeobE3dMxJ4/uZTueGZ+fzgH58RGxWpnnkREWkVwayEciswBigHcM6tBnJaMygRkXCWFBvFM9eP5MTcNKb8fSEF60tCHZKIiHRAwSTy1c65/aupmFkUENwqUiIix6mEmCie/vZIuqbF892/LaSovCrUIYmISAcTTCL/rpn9FIg3s/OBfwKvtm5YIiLhLyMxhr9MPpm91XVM+ftn1NYHQh2SiIh0IMEk8ncBO4AlwHeA14HjdlVXEZHm6N8pmfsvP5FP15fwP7O/CHU4IiLSgRzxYlfnXMDMXgFecc7taP2QREQ6lq+d1I2CDSU8/t5aRvRIZ9yQzqEOSUREOoAme+TNc4+Z7QS+AL4wsx1m9vO2C09EpGP42aWDGJqbyp0vLWZbmcbLi4jIsTvc0Jrb8GarGemcy3DOZQCnAGPM7LY2iU5EpIOIjYrkwYnDqakLcMf0zwkENGeAiIgcm8Ml8pOBq5xz6/ZtcM6tBa4Brm3twEREOpre2UncfclA3l+9k2c/Xh/qcEREJMwdLpGPds7tPHSjP04+PJZYFBFpZ751Sg/OHpDN/bNWsnr7cbtAtoiItIDDJfI1R7lPRESaYGY8cMVQEmOjuO3FRdTUaUpKERE5OodL5IeZWXkjt93AiW0VoIhIR5OTHMf9l5/I0sJy/vjWqlCHIyIiYarJRN45F+mcS2nkluyc09AaEZFjcOHgznwzP5fH5n5JwfqSUIcjIiJhKJgFoUREpBX8/LLBdEuP57YXF7G7qjbU4YiISJhRIi8iEiJJsVH84ZvDKdxVyS9fWx7qcEREJMwokRcRCaH8vAy+d1YfXizYzOxl20IdjoiIhJGQJPJmlmFmb5rZav8+vYly1/llVpvZdQ22n2xmS8xsjZk9ZGZ2uHr9VWof8ssvNrMRDeqqN7NF/m1ma793EZFD/fDc/gzplsJPXl7C9nKt+ioiIsEJVY/8XcBbzrl+wFv+84OYWQbwC7zVZEcBv2iQ8D8G3Az082/jjlDvRQ3K3uK/fp9K59xw/za+5d6iiEhwYqIieHDicKpq6/n20/Mp13h5EREJQqgS+QnAVP/xVOBrjZS5EHjTOVfinNsFvAmMM7MuQIpz7hPnnAOebfD6puqdADzrPJ8AaX49IiLtQt+cZP58zcms3r6bG59RMi8iIkcWqkS+k3Nuq/94G9CpkTLdgE0Nnm/2t3XzHx+6/XD1NlUXQJyZFZjZJ2b2taYCNrNb/HIFO3bsOOybExE5Gmf2z+bBScP5bGMpE//yCVvLKkMdkoiItGOtlsib2RwzW9rIbULDcn6vumvp4zej3p7OuXzgauBBM+vTRH2PO+fynXP52dnZLRmqiMh+lw7tylPfHsnG4r1c9Mf3mbVkK96fMxERkYO1WiLvnDvPOTekkdsMYPu+oS3+fVEjVRQC3Rs8z/W3FfqPD93OYeptqi6cc/vu1wJzgZOO8i2LiLSIM/tn8+r3T6dbWjzfe34hE//yCXOWb6euPhDq0EREpB0J1dCamcC+WWiuA2Y0UmY2cIGZpfsXuV4AzPaHzpSb2an+bDXXNnh9U/XOBK71Z685FShzzm31644FMLMsYAygyZxFJOR6Zyfxyq1j+OXXhrC+eC83PVvAyf89h+ue+pTfv7mK2cu2sWRzGUW7qwgE1GMvInI8slD8ZGtmmcCLQA9gA/BN51yJmeUD33XO3eSXuwH4qf+yXznnnva35wPPAPHALOD7zjl3mHoNeARvdpsK4HrnXIGZnQb8BQjgndQ86Jx78kjx5+fnu4KCgpZoChGRI6qtD/D2yiLeXlHE55tLWbV9Nw1z96gIIzs5lk4pcXROiaNzahx9cpIYnpvGCV2SiY7UkiEiIuHKzBb4w8C/uk9jL5tPibyIhNLe6jq+3LGHbWVVbC+vYlt5FdvLq73HZd5td3UdALFREYzMy+CiEztzwaDOZCfHhjh6ERFpDiXyLUyJvIi0Z845CksrWbSplM82lvL2yiLW7dxLhMEpvTKZMLwrFw3pQmpCdKhDFRGRI1Ai38KUyItIOHHO8cX23by+eCuvLt7Kup17iY40xvbPYfzwroztn01qvJJ6EZH2SIl8C2sska+trWXz5s1UVWl59fYuLi6O3NxcoqOVuMjxxznH0sJyZiwq5NXFW9heXk2EwQmdUzihczKZSTEkxUaTGBtJVIQd9FrvcqOGzw+uu+HT+oCjtt5RUx+gtj5AwEGnlFj6ZicxNDeN+JjIVnqHIiIdixL5FtZYIr9u3TqSk5PJzMz8ypedtB/OOYqLi9m9eze9evUKdTgiIVUfcCzcuIv3V+3gs02lrCnaQ1llLRU19a16XO/XgGwmjuzBeQNz9DdTROQwDpfIR7V1MB1VVVUVeXl5+kJq58yMzMxMtDqvCERGGCPzMhiZl3HQ9rr6AHtr6g+a1vLQLp9DO4HcQfu8mXSiIo3oyIj9s+ZsL69i5bZyPlpTzGuLtzJnRQHDuqfx4wsHMKZvVku+NRGR44IS+RakJD486N9J5PCiIiNIjW/5KSu7psXTNS2ec07oxF0XncDLCwt5cM4qvvXEPM7ol8V/XTKIAZ2TW+RYlTX1LN5cyvrivZRV1mIY3TPiGdY9jS6p8S1yDBGRUFMiLyIibS4qMoJvjuzO+OFdeX7eRh56azUXP/Q+15zSg9vO709aQkyz6qutD/D5plI+XFPMR1/u5LONpdQ0shKuGZzRL5s7xw1gcNfUlno7IhImdlfVUrB+F59tKuXLoj3s2FNNRU0dFdX11NQHaPhjo3MOB/u3/eKyQVx0YpeQxN0UJfIdSGRkJCeeeCK1tbVERUVx7bXXcttttxER0TqLwaxfv55LL72UpUuXHnUdd999N88++yy7du1iz549+7dXV1dz7bXXsmDBAjIzM3nhhRfIy8vjmWee4Y477mDChAk88cQTANx///08+eSTREZG8tBDD3HhhRdSWVnJ6NGjWb58OVu2bCErSz/bi7RHcdGR3Hh6Ly4/qRt/mLOK5z7ZwMsLC7n2tJ5865SedE1rvPe8pi7Aym3lfLK2mI++LObTdSVU1NRjBoO7pnD9mDxO6Z1Bv5xk0hNjqA84NhTvZc6KIv4+bwNfe/RD7rhwADef0Vu/0ol0cKUVNbyxfDuzlmzlgzU7qa13RBj0zEwkOzmWnOQ4EjIjiYmMAAPD9l/Mb3gdAIaRkxIX0vfRGCXyHUh8fDyLFi0CoKioiKuvvpry8nLuvffe0Abmq6urIyrq4I/cZZddxpQpU+jXr99B25988knS09NZs2YN06ZN48477+SFF14AYOLEiTzyyCMALF++nGnTprFs2TK2bNnCeeedx6pVq/a3RV5eXpu8NxE5NumJMdw3YQjfOqUnf3xrFX+a+yWPvvMlw7qncVL3NLKSYgg42FZexYqt5SzbUk5Nndfj3ic7kW+MyGVM30xO7Z3ZZG/+0Nw0huamcf1pefzk5SX8+vWVbCmt4ueXDiIiQsm8tB3nHJW19ZRW1FJaUcvuqlpq6x219QFq6gPU1TscjqgII8KMyIgDt8TYKHKSY8lKiiUuWrM/NWVrWSVzVhTxxrJtfPxlMXUBR7e0eL59Wh5nD8jhpB7pHWL2LCXyreDeV5exfEt5i9Y5qGsKv7hscNDlc3JyePzxxxk5ciT33HMPgUCAu+66i7lz51JdXc2tt97Kd77zHQAeeOAB/va3vxEREcFFF13Eb37zGxYtWsR3v/tdKioq6NOnD0899RTp6eksWLCAG264AYALLrhg//Hq6+sbrX/u3Ln87Gc/Iz09nZUrV7Jq1aqD4jz11FMbjX/GjBncc889AFxxxRVMmTLlKxfX7Ss3adIkYmNj6dWrF3379uXTTz9l9OjRQbeViLQfAzon86dvncyG4r3MXLSF91bv4MWCTftn0klLiKZ/TjLXje7JsO5p5PfMoHNq83rJ0hNj+NO3RvCr11fw5AfrALhnfPB/X0WC4ZxjS1kVK7aU88X23azbuZeNxRVs2lVB8Z6aRod+NVfnlDhO6pHGST3SGJmXwdDcNCKP05PSmroAy7eW887KIuas2M4yPw/Ly0zg5jN7c/GQLgzpltLhfoFTIt+B9e7dm/r6eoqKipgxYwapqanMnz+f6upqxowZwwUXXMDKlSuZMWMG8+bNIyEhgZKSEgCuvfZaHn74YcaOHcvPf/5z7r33Xh588EGuv/56HnnkEc4880zuuOOO/cd68sknG60fYOHChSxdurRZ0z0WFhbSvXt3AKKiokhNTaW4uLjRcg1PBnJzcyksLDyq9hKR9qNnZiLfP7cf3z+3H855c9IDxES1zFDBiAjjvy4ZiHPw1Ifr6J6RwI2na0paOXrbyqpYuHEXCzfsYnFhGSu3llNeVbd/f6eUWHpmJHJanyyyk2NJS4gmLT6atIQYUuKiiI7yZniKivBmezLzpojdd6vz7/dW11G0u4qi8mrW7NjDwo27mLV0G+Cd6J7RL5tzTsjm3IGdSIkLzXope6vr2FhSwfbyKnbuqWHnnmoqauqpqw9QF/B+edg3K9a+xNqaGtJi5j82YqMiiIuOJDYqgqq6evZW11FWWcvyLeUs9X+lizAY0SOduy46gfMG5tAnO6nDJe8NKZFvBc3pOW8rb7zxBosXL2b69OkAlJWVsXr1aubMmcP1119PQkICABkZGZSVlVFaWsrYsWMBuO6667jyyispLS2ltLSUM888E4DJkycza9asw9YfExPDqFGjNGe7iBw1MyMmquW/iM2Muy8ZSGFpBf/97+V0S4tj3JD2dSGbtE+BgGP51nLmrSth4cZdfLZhF1vKvAUhY6IiGNw1hcuGdeWELikM6pLMgM4pJMW2Xsq1Y3c1n6wtZu4XO3h31Q5e/XwLMZERnNk/m0uHduHcgTkkt3BSX1Vbz5qiPazavpv1xRVsLN7LhpIKNpVUsHNPTaOviYwwoiON6IiI/cPZ9l1QijswjW3Di0wdzrt3fOVXjMgIIyk2in45SVx7ak+G90hjdO9MMpNiW/S9tmdK5DuwtWvXEhkZSU5ODs45Hn74YS688MKDysyePbtFjtVU/XPnziUxMbHZ9XXr1o1NmzaRm5tLXV0dZWVlZGZmNllun82bN9OtW7fmvwEROS5FRhgPTjyJq/76CT+ctoh/3BLHiB7poQ5L2qFNJRV8sGYnH6zZyUdrdrKrohaAbmnxjOiZzk090hnRM51BXVJa7JejYGUnx3LZsK5cNqwrgYBj0eZS/r14K/9evJU5K7YTExXBWf2zuWRoF84d2KlZJxW19QHW79zLF9t3s2rbbu9++x7WF+/dP5tLhEGX1Hh6ZCRw3sBOdM9IoEdGAl1S48j2x/MnxEQec894IOCorgtQXVe/v2e+I/e2B0OJfAe1Y8cOvvvd7zJlyhTMjAsvvJDHHnuMc845h+joaFatWkW3bt04//zzue+++/jWt761f2hNRkYG6enpvP/++5xxxhk899xzjB07lrS0NNLS0vjggw84/fTTef755/cfr6n6j9b48eOZOnUqo0ePZvr06ZxzzjmN/mcdP348V199Nbfffjtbtmxh9erVjBo16qiPKyLHn/iYSJ68Lp/LH/uIm6YW8NL3TqNXVvM7IKRjqasPsHBjKW+t2M5bK4tYU+TNrNYpJZZzTujE6f0yGd07q9nXaLS2iAhjRI90RvRI5+6LB/LZpl28tngrry/ZyhvLtxMbFcGJ3VLp1ymJvjnJdE6JIyE2kqqaeipq6imrrGVD8V7WFVewfudeCksrqfeHwUQY9MpKZGCXZMYP68qAzsn075REj4zENjl5iYgw4mMiO8RFqi1FiXwHUllZyfDhw/dPPzl58mRuv/12AG666SbWr1/PiBEjcM6RnZ3NK6+8wrhx41i0aBH5+fnExMRw8cUX8+tf/5qpU6fuv9i1d+/ePP300wA8/fTT3HDDDZjZQRe7NlX/kfz4xz/m73//OxUVFeTm5nLTTTdxzz33cOONNzJ58mT69u1LRkYG06ZNa/T1gwcP5pvf/CaDBg0iKiqKRx99lMhI/QcXkebJTIpl6vWjuPyxj7juqU956XunkZ18/Pw8L56yylreXbWDt1dsZ+6qHZRW1BIdaZzSK5OrR/XgzP5ZYTXmOiLCOLlnBif3zOBnlwxiwcZdzFqyjaVbyvi/pdvYVbGp0dclxUaRl5XA0NxUJgzvSu/sRAZ0SqF3dqJmymlnrLGZQOTw8vPzXUFBwUHbVqxYwcCBA0MU0fHjmWeeoaCgYP/0k0eSl5dHQUHBV+aR17+XiDTms427uOqvn9C/UzL/uPlUEltxXLO0D2t37OFtf6aT+et3UR9wZCTGcPaAHM4dmMMZ/bJafHx5e1G8p9pfEKme+OhIEmIiSYqNIiMxJmxOVo4HZrbAOZff2D79hZKwEh8fz6xZs7jpppv2LwjVmH0LQtXW1rbaglgi0vGc1COdh68awXeeK+D6p+fzxLfzQzbzh7SOipo6Ctbv4r1VO3h7ZRFrd+4F4ITOyXznzN6cO7ATw7sfH9M4ZibFHlcXhnZE6pE/CuqRD3/69xKRw3n18y3c9sIi+uYk8dBVJ9G/U3KoQ5KjUB9wrNu5l2Vbyli+pZyFG3exaFMptfWOmMgITu2TyXkDczh7QA7dMxJCHa5Io9QjLyIi0gyXDetKSnw0t72wiEsf+oCrRnXnyvzuDOicTHSkfuVrj+oDji+27WbpljKWFZaxdEs5K7aW719MLCYygoFdkrnx9N6M7pPJyLx0EmKUBkl40ydYRESkEWP7Z/PmbWdy/6yV/OPTTUz9eAPRkUZCTNRX5r7e/7wVRUYYWUmx9MlO4pReGVw2rGu7mzGlLe2trmPRplLmry9hwYZdfLaxlD3V3gJMiTGRDOqawjfzuzO4awqDu3qztOgkTDoaJfIiIiJNyEyK5XdXDuMnF53Au6t2sLpoDxXVdY2uRtnaQ6pr6x07dlezbEsZc1Zs5/5ZK7hkaFd+fOGA42JYyPbyKuavL6Fg/S4WbNjF8q3l1AccZjCgUzJfO6kr+T0zGJqbSl5m4v4Fh0Q6MiXyIiIiR5CZFMvlI3JDHcZ+63fu5R/zNzL1o/XMXraNm8/oxffO6tuqq4e2tZ17qvlwzU7eW7WTeeuK2byrEoC46AiGd0/je2P7kJ+Xzkk90kmN1wXJcnzSb0wdSGRkJMOHD2fw4MEMGzaM//3f/yUQCBz5hUdp/fr1DBky5JjquPvuu+nevTtJSUkHba+urmbixIn07duXU045hfXr1wPe9JPZ2dncdNNNABQXF3P22WeTlJTElClTDqpj3/ZDL0wWEQl3eVmJ/OSigbz9n2dx8ZDOPPrOl4x78D0WbNgV6tCOmnOOpYVlPPB/K7n04ffJ/+85/HDaIt5auZ0hXVP5r0sG8sqtY1hyz4VMu2U0/+/CAZw1IEdJvBzXOs6puxAfH8+iRYsAKCoq4uqrr6a8vJx77703tIH56urqiIo6+CN32WWXMWXKFPr163fQ9ieffJL09HTWrFnDtGnTuPPOO3nhhRcAmDhx4v555OPi4vjlL3/J0qVLWbp06UF1vPPOO5x11lmt94ZEREKsa1o8D046iWtO7cmPXljEN//yMbef35/vju0TNtMnFpVX8dLCQl5euJnVRXuI8lcm/X8X9OeMftkM6ZYaNu9FpK0pkW8Ns+6CbUtats7OJ8JFvwm6eE5ODo8//jgjR47knnvuIRAIcNdddzF37lyqq6u59dZb+c53vgPAAw88wN/+9jciIiK46KKL+M1vfsOiRYv2r+zap08fnnrqKdLT01mwYAE33HADwEEru9bX1zda/9y5c/nZz35Geno6K1euZNWqVQfFeeqppzYa/4wZM7jnnnsAuOKKK5gyZQqNTZWamJjI6aefzpo1a4JuGxGRjiY/L4PXf3gGP315Cf8z+ws+XLOTBycNJye5fV4M65zj4y+L+du8DbyxbDt1AUd+z3R+9fUhXHpiV1IT1MsuEgwl8h1Y7969qa+vp6ioiBkzZpCamsr8+fOprq5mzJgxXHDBBaxcuZIZM2Ywb948EhISKCkpAeDaa6/l4YcfZuzYsfz85z/n3nvv5cEHH+T666/nkUce4cwzz+SOO+7Yf6wnn3yy0foBFi5cyNKlS+nVq1fQsRcWFtK9e3cAoqKiSE1Npbi4uAVbR0SkY0mJi+bhq07izH7Z/HzmUi556AP+OGk4p/XJOvKL20hZRS3TF27m+XkbWLtjL2kJ0Vw/Jo+rT+lJr6zEUIcnEnaUyLeGZvSct5U33niDxYsXM336dADKyspYvXo1c+bM4frrrychwZvxICMjg7KyMkpLSxk7diwA1113HVdeeSWlpaWUlpZy5plnAjB58mRmzZp12PpjYmIYNWpUs5J4ERE5OmbGN0d2Z1j3NP7j+QVc88Q8fnRef249u2/Ihqc45/h8cxl/+2QDr36+heq6ACN6pPH7bw7j4hO7EBcdGZK4RDqCkCTyZpYBvADkAeuBbzrnvnKFjpldB/yX//S/nXNT/e0nA88A8cDrwA+dc66pes3sBOBpYARwt3Pudw2OMQ74IxAJPOGca39Z+FFau3YtkZGR5OTk4Jzj4Ycf5sILLzyozOzZs1vkWE3VP3fuXBITm9/L0q1bNzZt2kRubi51dXWUlZWRmZnZIrGKiHR0AzonM3PK6dz9ryX8/s1VfLquhN9dOaxN553fvKuCGYu28PLCzXy5Yy8JMZF84+RcrjmlJ4O6prRZHCIdWahmrbkLeMs51w94y39+ED8p/wVwCjAK+IWZpfu7HwNuBvr5t3FHqLcE+AGwP4H3jxEJPApcBAwCrjKzQS30HkNqx44dfPe732XKlCmYGRdeeCGPPfYYtbW1AKxatYq9e/dy/vnn8/TTT1NRUQFASUkJqamppKen8/777wPw3HPPMXbsWNLS0khLS+ODDz4A4Pnnn99/vKbqP1rjx49n6tSpAEyfPp1zzjln/7zNIiJyZImxUfxh4nAe+MaJzF9fwnm/f5e/vreWipq6Fj9WZU09SwvLmL5gM3dOX8zZv5vL6Q+8w//M/oLMxFjuv/xE5v30XH799ROVxIu0oFANrZkAnOU/ngrMBe48pMyFwJvOuRIAM3sTGGdmc4EU59wn/vZnga8Bs5qq1zlXBBSZ2SWHHGMUsMY5t9ava5pfx/Jjf4ttr7KykuHDh1NbW0tUVBSTJ0/m9ttvB+Cmm25i/fr1jBgxAucc2dnZvPLKK4wbN45FixaRn59PTEwMF198Mb/+9a+ZOnXq/otde/fuzdNPPw3A008/zQ033ICZHXSxa1P1H8mPf/xj/v73v1NRUUFubi433XQT99xzDzfeeCOTJ0+mb9++ZGRkMG3atCbryMvLo7y8nJqaGl555RXeeOMNBg3qEOdjIiLHxMyYOLIHp/bO5OczlvGr11fw6Nw1XHJiF845IYcRPdJJT4wJqq7Kmnq2llWyrayKzaWVfFm0h9VFe1hdtJvNuyrZNx9BSlwUo3plcPWoHowb0vm4WKxKJFSssZlAWv2gZqXOuTT/sQG79j1vUOb/AXHOuf/2n/8MqMRLzn/jnDvP334GXrJ+6ZHqNbN7gD37htaY2RXAOOfcTf7zycApzrmDJyT39t0C3ALQo0ePkzds2HDQ/hUrVjBw4MCjbxQJyjPPPENBQcH+6SeP5KyzzuJ3v/sd+fn5B23Xv5eIHI8WbCjhqQ/W884XRVTU1AOQHBdFTnIsOclxxMdEEmFGZATsra5nV0UNpRW17Kqo2V9+n5jICHpnJ9I3J4l+Ocn065REv5wk+mQnaVVVkRZkZgucc/mN7Wu1HnkzmwN0bmTX3Q2f+GPbW/xsoqXrdc49DjwOkJ+f3/ZnPwJ4c+XPmjWLm266iSeeeOKwZc8++2zWrl1LdLSmMRMRATi5ZwYn98ygqraeRZtK+XxTKVvLqijaXcWO3dXs2F1HfcBRH3AkxEbSKSWOAZ2TSU+IISMxhs4pcXRJi6Nrajy56fFERWpdSZFQarVEfl+PeWPMbLuZdXHObTWzLkBRI8UKOTBMBiAXrze+0H/ccHuh/ziYeg89Rvcm6pJ2aOLEiUycODGosu+8804rRyMiEp7ioiM5tXcmp/bWJAIi4SxUp9Izgev8x9cBMxopMxu4wMzS/YtcLwBmO+e2AuVmdqo/fObaBq8Ppt6G5gP9zKyXmcUAk/w6jkoohilJ8+nfSURERDqCUCXyvwHON7PVwHn+c8ws38yeAPAvcv0lXrI9H7hv34WvwH8ATwBrgC/xLnQ9XL2dzWwzcDvwX2a22cxSnHN1wBS8k4YVwIvOuWVH84bi4uIoLi5WktjOOecoLi4mLq59rnYoIiIiEqyQXOwa7vLz811BQcFB22pra9m8eTNVVVUhikqCFRcXR25ursbOi4iISLsXkotdjzfR0dFavVRERERE2owuNxcRERERCUNK5EVEREREwpASeRERERGRMKSLXY+Cme0ANhyxYMvKAna28THDndqsedRezaP2ah61V/OpzZpH7dU8aq/mC1Wb9XTOZTe2Q4l8mDCzgqauWJbGqc2aR+3VPGqv5lF7NZ/arHnUXs2j9mq+9thmGlojIiIiIhKGlMiLiIiIiIQhJfLh4/FQBxCG1GbNo/ZqHrVX86i9mk9t1jxqr+ZRezVfu2szjZEXEREREQlD6pEXEREREQlDSuTbGTMbZ2ZfmNkaM7urkf2xZvaCv3+emeWFIMx2I4j2+raZ7TCzRf7tplDE2V6Y2VNmVmRmS5vYb2b2kN+ei81sRFvH2J4E0V5nmVlZg8/Xz9s6xvbEzLqb2TtmttzMlpnZDxspo89YA0G2mT5nPjOLM7NPzexzv73ubaSMvid9QbaXvicPYWaRZvaZmb3WyL529fmKCuXB5WBmFgk8CpwPbAbmm9lM59zyBsVuBHY55/qa2STgAWBi20cbekG2F8ALzrkpbR5g+/QM8AjwbBP7LwL6+bdTgMf8++PVMxy+vQDed85d2jbhtHt1wH865xaaWTKwwMzePOT/pD5jBwumzUCfs32qgXOcc3vMLBr4wMxmOec+aVBG35MHBNNeoO/JQ/0QWAGkNLKvXX2+1CPfvowC1jjn1jrnaoBpwIRDykwApvqPpwPnmpm1YYztSTDtJQ04594DSg5TZALwrPN8AqSZWZe2ia79CaK9pAHn3Fbn3EL/8W68L8JuhxTTZ6yBINtMfP7nZo//NNq/HXqxn74nfUG2lzRgZrnAJcATTRRpV58vJfLtSzdgU4Pnm/nqH/T9ZZxzdUAZkNkm0bU/wbQXwDf8n/Cnm1n3tgktbAXbpnLAaP9n61lmNjjUwbQX/s/NJwHzDtmlz1gTDtNmoM/Zfv6wh0VAEfCmc67Jz5i+J4NqL9D3ZEMPAj8GAk3sb1efLyXy0tG9CuQ554YCb3LgLFqkJSzEWzp7GPAw8Epow2kfzCwJeAn4kXOuPNTxhIMjtJk+Zw045+qdc8OBXGCUmQ0JcUjtWhDtpe9Jn5ldChQ55xaEOpZgKZFvXwqBhmfCuf62RsuYWRSQChS3SXTtzxHbyzlX7Jyr9p8+AZzcRrGFq2A+g+JzzpXv+9naOfc6EG1mWSEOK6T8cbgvAc87515upIg+Y4c4Upvpc9Y451wp8A4w7pBd+p5sRFPtpe/Jg4wBxpvZerzhuueY2d8OKdOuPl9K5NuX+UA/M+tlZjHAJGDmIWVmAtf5j68A3nbH72IAR2yvQ8bejscbfypNmwlc688scipQ5pzbGuqg2isz67xvbKSZjcL7m3rcJgx+WzwJrHDO/b6JYvqMNRBMm+lzdoCZZZtZmv84Hm+yg5WHFNP3pC+Y9tL35AHOuZ8453Kdc3l4OcXbzrlrDinWrj5fmrWmHXHO1ZnZFGA2EAk85ZxbZmb3AQXOuZl4f/CfM7M1eBfhTQpdxKEVZHv9wMzG480MUQJ8O2QBtwNm9g/gLCDLzDYDv8C7+Ann3J+B14GLgTVABXB9aCJtH4JoryuA75lZHVAJTDpeEwbfGGAysMQfkwvwU6AH6DPWhGDaTJ+zA7oAU/1ZyyKAF51zr+l7sknBtJe+J4+gPX++tLKriIiIiEgY0tAaEREREZEwpEReRERERCQMKZEXEREREQlDSuRFRERERMKQEnkRERERkTCkRF5EREREJAwpkRcRERERCUNK5EVEREREwtD/B5UWj+rHy23vAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])\n", + "plt.ylabel(\"Decoding weight\")\n", + "plt.legend((\"Decoder 10[0]\", \"Decoder 10[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:14.914096Z", + "iopub.status.busy": "2020-11-25T16:48:14.912685Z", + "iopub.status.idle": "2020-11-25T16:48:14.914800Z", + "shell.execute_reply": "2020-11-25T16:48:14.915207Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Change the connection to use connection weights instead of decoders\n", + " conn.solver = LstsqL2(weights=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:14.920176Z", + "iopub.status.busy": "2020-11-25T16:48:14.919392Z", + "iopub.status.idle": "2020-11-25T16:48:16.357905Z", + "shell.execute_reply": "2020-11-25T16:48:16.357422Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:16.380843Z", + "iopub.status.busy": "2020-11-25T16:48:16.367216Z", + "iopub.status.idle": "2020-11-25T16:48:16.918153Z", + "shell.execute_reply": "2020-11-25T16:48:16.917680Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Connection weight')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])\n", + "plt.ylabel(\"Connection weight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For some general principles governing how the decoders change,\n", + "[Voelker, 2015](http://compneuro.uwaterloo.ca/publications/voelker2015.html)\n", + "and [Voelker & Eliasmith, 2017](\n", + "http://compneuro.uwaterloo.ca/publications/voelker2017c.html)\n", + "give detailed analyses of the rule's dynamics.\n", + "It's also interesting to observe qualitative trends;\n", + "often a few strong connection weights will\n", + "dominate the others,\n", + "while decoding weights tend to\n", + "change or not change together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is `pre_synapse`?\n", + "\n", + "By default the `PES` object sets\n", + "`pre_synapse=Lowpass(tau=0.005)`.\n", + "This is a lowpass filter with time-constant $\\tau = 5\\,\\text{ms}$\n", + "that is applied to the activities of the pre-synaptic population $a_i$\n", + "before computing each update $\\Delta {\\bf d}_i$.\n", + "\n", + "In general, longer time-constants\n", + "smooth over the spiking activity to produce more constant updates,\n", + "while shorter time-constants adapt more quickly\n", + "to rapidly changing inputs.\n", + "The right trade-off depends on\n", + "the particular demands of the model.\n", + "\n", + "This `Synapse` object can also be\n", + "any other linear filter (as are used in the `Connection` object);\n", + "for instance, `pre_synapse=Alpha(tau=0.005)`\n", + "applies an alpha filter to the postsynaptic activity.\n", + "This will have the effect of delaying the bulk of the activities\n", + "by a rise-time of $\\tau$ before applying the update.\n", + "This may be useful for situations\n", + "where the error signal is delayed by the same amount,\n", + "since the error signal should be synchronized\n", + "with the same activities that produced said error." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/learning/learn-product.ipynb b/.doctrees/nbsphinx/examples/learning/learn-product.ipynb new file mode 100644 index 0000000000..e7ec8110d4 --- /dev/null +++ b/.doctrees/nbsphinx/examples/learning/learn-product.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning to compute a product\n", + "\n", + "Unlike the communication channel and the element-wise square,\n", + "the product is a nonlinear function on multiple inputs.\n", + "This represents a difficult case for learning rules\n", + "that aim to generalize a function given many\n", + "input-output example pairs.\n", + "However, using the same type of network structure\n", + "as in the communication channel and square cases,\n", + "we can learn to compute the product of two dimensions\n", + "with the `nengo.PES` learning rule.\n", + "\n", + "The product is a trickier function to learn than\n", + "the communication channel and the square.\n", + "We will also try the `nengo.RLS` learning rule\n", + "and see how `PES` and `RLS` compare in terms of\n", + "learning the product of two dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:18.465204Z", + "iopub.status.busy": "2020-11-25T16:48:18.464316Z", + "iopub.status.idle": "2020-11-25T16:48:18.962699Z", + "shell.execute_reply": "2020-11-25T16:48:18.962193Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the model\n", + "\n", + "Like previous examples,\n", + "the network consists of `pre`, `post`, and `error` ensembles.\n", + "We'll use two-dimensional white noise input and attempt to learn the product\n", + "using the actual product to compute the error signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:18.991146Z", + "iopub.status.busy": "2020-11-25T16:48:18.990284Z", + "iopub.status.idle": "2020-11-25T16:49:19.357410Z", + "shell.execute_reply": "2020-11-25T16:49:19.358916Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " # -- input and pre popluation\n", + " inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)\n", + " pre = nengo.Ensemble(120, dimensions=2)\n", + " nengo.Connection(inp, pre)\n", + "\n", + " # -- post populations\n", + " post_pes = nengo.Ensemble(60, dimensions=1)\n", + " post_rls = nengo.Ensemble(60, dimensions=1)\n", + "\n", + " # -- reference population, containing the actual product\n", + " product = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(inp, product, function=lambda x: x[0] * x[1], synapse=None)\n", + "\n", + " # -- error populations\n", + " error_pes = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(post_pes, error_pes)\n", + " nengo.Connection(product, error_pes, transform=-1)\n", + " error_rls = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(post_rls, error_rls)\n", + " nengo.Connection(product, error_rls, transform=-1)\n", + "\n", + " # -- learning connections\n", + " conn_pes = nengo.Connection(\n", + " pre,\n", + " post_pes,\n", + " function=lambda x: np.random.random(1),\n", + " learning_rule_type=nengo.PES(),\n", + " )\n", + " nengo.Connection(error_pes, conn_pes.learning_rule)\n", + " conn_rls = nengo.Connection(\n", + " pre,\n", + " post_rls,\n", + " function=lambda x: np.random.random(1),\n", + " learning_rule_type=nengo.RLS(),\n", + " )\n", + " nengo.Connection(error_rls, conn_rls.learning_rule)\n", + "\n", + " # -- inhibit errors after 40 seconds\n", + " inhib = nengo.Node(lambda t: 2.0 if t > 40.0 else 0.0)\n", + " nengo.Connection(inhib, error_pes.neurons, transform=[[-1]] * error_pes.n_neurons)\n", + " nengo.Connection(inhib, error_rls.neurons, transform=[[-1]] * error_rls.n_neurons)\n", + "\n", + " # -- probes\n", + " product_p = nengo.Probe(product, synapse=0.01)\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_pes_p = nengo.Probe(post_pes, synapse=0.01)\n", + " error_pes_p = nengo.Probe(error_pes, synapse=0.03)\n", + " post_rls_p = nengo.Probe(post_rls, synapse=0.01)\n", + " error_rls_p = nengo.Probe(error_rls, synapse=0.03)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(60)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:19.365545Z", + "iopub.status.busy": "2020-11-25T16:49:19.363517Z", + "iopub.status.idle": "2020-11-25T16:49:21.164897Z", + "shell.execute_reply": "2020-11-25T16:49:21.165317Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plots(start_ix=None, end_ix=None):\n", + " sl = slice(start_ix, end_ix)\n", + " t = sim.trange()[sl]\n", + " plt.figure(figsize=(14, 12))\n", + " plt.suptitle(\"\")\n", + " plt.subplot(4, 1, 1)\n", + " plt.plot(t, sim.data[pre_p][sl], c=\"b\")\n", + " plt.legend((\"Pre decoding\",), loc=\"best\")\n", + " plt.subplot(4, 1, 2)\n", + " plt.plot(t, sim.data[product_p][sl], c=\"k\", label=\"Actual product\")\n", + " plt.plot(t, sim.data[post_pes_p][sl], c=\"r\", label=\"Post decoding (PES)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(4, 1, 3)\n", + " plt.plot(t, sim.data[product_p][sl], c=\"k\", label=\"Actual product\")\n", + " plt.plot(t, sim.data[post_rls_p][sl], c=\"r\", label=\"Post decoding (RLS)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(4, 1, 4)\n", + " plt.plot(t, sim.data[error_pes_p][sl], c=\"b\", label=\"Error (PES)\")\n", + " plt.plot(t, sim.data[error_rls_p][sl], c=\"r\", label=\"Error (RLS)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.ylim(-1, 1)\n", + "\n", + "\n", + "plots()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine the initial output\n", + "\n", + "Let's zoom in on the network at the beginning:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:21.169798Z", + "iopub.status.busy": "2020-11-25T16:49:21.168848Z", + "iopub.status.idle": "2020-11-25T16:49:21.705631Z", + "shell.execute_reply": "2020-11-25T16:49:21.706066Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plots(end_ix=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows that when the network is initialized,\n", + "it is not able to compute the product.\n", + "The error is quite large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine the final output\n", + "\n", + "After the network has run for a while, and learning has occurred,\n", + "the output is quite different:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:21.710676Z", + "iopub.status.busy": "2020-11-25T16:49:21.709720Z", + "iopub.status.idle": "2020-11-25T16:49:22.267036Z", + "shell.execute_reply": "2020-11-25T16:49:22.267455Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plots(start_ix=38000, end_ix=42000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that it has learned a decent approximation of the product,\n", + "but it's not perfect -- typically,\n", + "it's not as good as the offline optimization.\n", + "The reason for this is that we've given it white noise input,\n", + "which has a mean of 0; since this happens in both dimensions,\n", + "we'll see a lot of examples of inputs and outputs near 0.\n", + "In other words, we've oversampled a certain part of the\n", + "vector space, and overlearned decoders that do well in\n", + "that part of the space. If we want to do better in other\n", + "parts of the space, we would need to construct an input\n", + "signal that evenly samples the space." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/learning/learn-square.ipynb b/.doctrees/nbsphinx/examples/learning/learn-square.ipynb new file mode 100644 index 0000000000..14b3486be7 --- /dev/null +++ b/.doctrees/nbsphinx/examples/learning/learn-square.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning to square the input\n", + "\n", + "This demo shows you how to construct a network\n", + "containing an ensemble which learns how to decode the square of its value." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:23.785879Z", + "iopub.status.busy": "2020-11-25T16:49:23.785033Z", + "iopub.status.idle": "2020-11-25T16:49:24.278388Z", + "shell.execute_reply": "2020-11-25T16:49:24.277895Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "This network consists of an ensemble `A` which represents the input,\n", + "an ensemble `A_squared` which learns to represent the square,\n", + "and an ensemble `error` which represents the error\n", + "between `A_squared` and the actual square." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.290340Z", + "iopub.status.busy": "2020-11-25T16:49:24.289814Z", + "iopub.status.idle": "2020-11-25T16:49:24.292888Z", + "shell.execute_reply": "2020-11-25T16:49:24.293277Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " # Create the ensemble to represent the input, the input squared (learned),\n", + " # and the error\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " A_squared = nengo.Ensemble(100, dimensions=1)\n", + " error = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # Connect A and A_squared with a communication channel\n", + " conn = nengo.Connection(A, A_squared)\n", + "\n", + " # Apply the PES learning rule to conn\n", + " conn.learning_rule_type = nengo.PES(learning_rate=3e-4)\n", + "\n", + " # Provide an error signal to the learning rule\n", + " nengo.Connection(error, conn.learning_rule)\n", + "\n", + " # Compute the error signal (error = actual - target)\n", + " nengo.Connection(A_squared, error)\n", + "\n", + " # Subtract the target (this would normally come from some external system)\n", + " nengo.Connection(A, error, function=lambda x: x ** 2, transform=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A single input signal (a step function)\n", + "will be used to drive the neural activity in ensemble A.\n", + "An additional node will inhibit the error signal after 15 seconds,\n", + "to test the learning at the end." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.301089Z", + "iopub.status.busy": "2020-11-25T16:49:24.299562Z", + "iopub.status.idle": "2020-11-25T16:49:24.301692Z", + "shell.execute_reply": "2020-11-25T16:49:24.302099Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input node that steps between -1 and 1\n", + " input_node = nengo.Node(output=lambda t: int(6 * t / 5) / 3.0 % 2 - 1)\n", + "\n", + " # Connect the input node to ensemble A\n", + " nengo.Connection(input_node, A)\n", + "\n", + " # Shut off learning by inhibiting the error population\n", + " stop_learning = nengo.Node(output=lambda t: t >= 15)\n", + " nengo.Connection(\n", + " stop_learning, error.neurons, transform=-20 * np.ones((error.n_neurons, 1))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe the Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.309447Z", + "iopub.status.busy": "2020-11-25T16:49:24.307933Z", + "iopub.status.idle": "2020-11-25T16:49:24.310033Z", + "shell.execute_reply": "2020-11-25T16:49:24.310443Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " input_node_probe = nengo.Probe(input_node)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " A_squared_probe = nengo.Probe(A_squared, synapse=0.01)\n", + " error_probe = nengo.Probe(error, synapse=0.01)\n", + " learn_probe = nengo.Probe(stop_learning, synapse=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.315550Z", + "iopub.status.busy": "2020-11-25T16:49:24.314776Z", + "iopub.status.idle": "2020-11-25T16:49:28.949480Z", + "shell.execute_reply": "2020-11-25T16:49:28.948998Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:28.981149Z", + "iopub.status.busy": "2020-11-25T16:49:28.980342Z", + "iopub.status.idle": "2020-11-25T16:49:29.631296Z", + "shell.execute_reply": "2020-11-25T16:49:29.630750Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signal\n", + "plt.figure(figsize=(9, 9))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_node_probe], label=\"Input\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.plot(\n", + " sim.trange(),\n", + " sim.data[learn_probe],\n", + " label=\"Stop learning?\",\n", + " color=\"r\",\n", + " linewidth=2.0,\n", + ")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.ylim(-1.2, 1.2)\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_node_probe] ** 2, label=\"Squared Input\", linewidth=2.0\n", + ")\n", + "plt.plot(sim.trange(), sim.data[A_squared_probe], label=\"Decoded Ensemble $A^2$\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.ylim(-1.2, 1.2)\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(\n", + " sim.trange(),\n", + " sim.data[A_squared_probe] - sim.data[input_node_probe] ** 2,\n", + " label=\"Error\",\n", + ")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that during the first three periods,\n", + "the decoders quickly adjust to drive the error to zero.\n", + "When learning is turned off for the fourth period,\n", + "the error stays closer to zero,\n", + "demonstrating that the learning has persisted in the connection." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/learning/learn-unsupervised.ipynb b/.doctrees/nbsphinx/examples/learning/learn-unsupervised.ipynb new file mode 100644 index 0000000000..afc8299f56 --- /dev/null +++ b/.doctrees/nbsphinx/examples/learning/learn-unsupervised.ipynb @@ -0,0 +1,623 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unsupervised learning\n", + "\n", + "When we do error-modulated learning with the `nengo.PES` rule,\n", + "we have a pretty clear idea of what we want to happen.\n", + "Years of neuroscientific experiments have yielded\n", + "learning rules explaining how synaptic strengths\n", + "change given certain stimulation protocols.\n", + "But what do these learning rules actually do\n", + "to the information transmitted across an\n", + "ensemble-to-ensemble connection?\n", + "\n", + "We can investigate this in Nengo.\n", + "Currently, we've implemented the `nengo.BCM`\n", + "and `nengo.Oja` rules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:30.967555Z", + "iopub.status.busy": "2020-11-25T16:49:30.966604Z", + "iopub.status.idle": "2020-11-25T16:49:31.459416Z", + "shell.execute_reply": "2020-11-25T16:49:31.459924Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.464776Z", + "iopub.status.busy": "2020-11-25T16:49:31.464278Z", + "iopub.status.idle": "2020-11-25T16:49:31.468851Z", + "shell.execute_reply": "2020-11-25T16:49:31.468412Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bienenstock-Cooper-Munroe learning rule.\n", + "\n", + " Modifies connection weights as a function of the presynaptic activity\n", + " and the difference between the postsynaptic activity and the average\n", + " postsynaptic activity.\n", + "\n", + " Notes\n", + " -----\n", + " The BCM rule is dependent on pre and post neural activities,\n", + " not decoded values, and so is not affected by changes in the\n", + " size of pre and post ensembles. However, if you are decoding from\n", + " the post ensemble, the BCM rule will have an increased effect on\n", + " larger post ensembles because more connection weights are changing.\n", + " In these cases, it may be advantageous to scale the learning rate\n", + " on the BCM rule by ``1 / post.n_neurons``.\n", + "\n", + " Parameters\n", + " ----------\n", + " learning_rate : float, optional\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " pre_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " post_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the post-synaptic activities.\n", + " If None, ``post_synapse`` will be the same as ``pre_synapse``.\n", + " theta_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the theta signal.\n", + "\n", + " Attributes\n", + " ----------\n", + " learning_rate : float\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " post_synapse : `.Synapse`\n", + " Synapse model used to filter the post-synaptic activities.\n", + " pre_synapse : `.Synapse`\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " theta_synapse : `.Synapse`\n", + " Synapse model used to filter the theta signal.\n", + " \n" + ] + } + ], + "source": [ + "print(nengo.BCM.__doc__)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.474052Z", + "iopub.status.busy": "2020-11-25T16:49:31.472636Z", + "iopub.status.idle": "2020-11-25T16:49:31.475913Z", + "shell.execute_reply": "2020-11-25T16:49:31.475453Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Oja learning rule.\n", + "\n", + " Modifies connection weights according to the Hebbian Oja rule, which\n", + " augments typically Hebbian coactivity with a \"forgetting\" term that is\n", + " proportional to the weight of the connection and the square of the\n", + " postsynaptic activity.\n", + "\n", + " Notes\n", + " -----\n", + " The Oja rule is dependent on pre and post neural activities,\n", + " not decoded values, and so is not affected by changes in the\n", + " size of pre and post ensembles. However, if you are decoding from\n", + " the post ensemble, the Oja rule will have an increased effect on\n", + " larger post ensembles because more connection weights are changing.\n", + " In these cases, it may be advantageous to scale the learning rate\n", + " on the Oja rule by ``1 / post.n_neurons``.\n", + "\n", + " Parameters\n", + " ----------\n", + " learning_rate : float, optional\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " pre_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " post_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the post-synaptic activities.\n", + " If None, ``post_synapse`` will be the same as ``pre_synapse``.\n", + " beta : float, optional\n", + " A scalar weight on the forgetting term.\n", + "\n", + " Attributes\n", + " ----------\n", + " beta : float\n", + " A scalar weight on the forgetting term.\n", + " learning_rate : float\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " post_synapse : `.Synapse`\n", + " Synapse model used to filter the post-synaptic activities.\n", + " pre_synapse : `.Synapse`\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " \n" + ] + } + ], + "source": [ + "print(nengo.Oja.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create a simple communication channel\n", + "\n", + "The only difference between this network and most\n", + "models you've seen so far is that we're going to\n", + "set the decoder solver in the communication channel\n", + "to generate a full connection weight matrix\n", + "which we can then learn using typical delta learning rules." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.487126Z", + "iopub.status.busy": "2020-11-25T16:49:31.485595Z", + "iopub.status.idle": "2020-11-25T16:49:31.487762Z", + "shell.execute_reply": "2020-11-25T16:49:31.488188Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(t * 4))\n", + "\n", + " pre = nengo.Ensemble(100, dimensions=1)\n", + " post = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " nengo.Connection(sin, pre)\n", + " conn = nengo.Connection(pre, post, solver=nengo.solvers.LstsqL2(weights=True))\n", + "\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_p = nengo.Probe(post, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.494299Z", + "iopub.status.busy": "2020-11-25T16:49:31.493510Z", + "iopub.status.idle": "2020-11-25T16:49:32.604983Z", + "shell.execute_reply": "2020-11-25T16:49:32.604533Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Verify that it does a communication channel\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What does BCM do?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:32.611449Z", + "iopub.status.busy": "2020-11-25T16:49:32.610942Z", + "iopub.status.idle": "2020-11-25T16:49:32.614474Z", + "shell.execute_reply": "2020-11-25T16:49:32.614030Z" + } + }, + "outputs": [], + "source": [ + "conn.learning_rule_type = nengo.BCM(learning_rate=5e-10)\n", + "with model:\n", + " weights_p = nengo.Probe(conn, \"weights\", synapse=0.01, sample_every=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:32.619334Z", + "iopub.status.busy": "2020-11-25T16:49:32.618527Z", + "iopub.status.idle": "2020-11-25T16:49:45.327257Z", + "shell.execute_reply": "2020-11-25T16:49:45.328774Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:45.335300Z", + "iopub.status.busy": "2020-11-25T16:49:45.333356Z", + "iopub.status.idle": "2020-11-25T16:49:46.064242Z", + "shell.execute_reply": "2020-11-25T16:49:46.064677Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Connection weight')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The BCM rule appears to cause the ensemble\n", + "to either be on or off.\n", + "This seems consistent with the idea that it potentiates\n", + "active synapses, and depresses non-active synapses.\n", + "\n", + "As well, we can show that BCM sparsifies the weights.\n", + "The sparsity measure below uses the Gini measure of sparsity,\n", + "for reasons explained [in this paper](https://arxiv.org/pdf/0811.4706.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:46.070507Z", + "iopub.status.busy": "2020-11-25T16:49:46.070008Z", + "iopub.status.idle": "2020-11-25T16:49:46.075946Z", + "shell.execute_reply": "2020-11-25T16:49:46.075339Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting sparsity: 0.14271014651392688\n", + "Ending sparsity: 0.46467866311030237\n" + ] + } + ], + "source": [ + "def sparsity_measure(vector): # Gini index\n", + " # Max sparsity = 1 (single 1 in the vector)\n", + " v = np.sort(np.abs(vector))\n", + " n = v.shape[0]\n", + " k = np.arange(n) + 1\n", + " l1norm = np.sum(v)\n", + " summation = np.sum((v / l1norm) * ((n - k + 0.5) / n))\n", + " return 1 - 2 * summation\n", + "\n", + "\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What does Oja do?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:46.082789Z", + "iopub.status.busy": "2020-11-25T16:49:46.081261Z", + "iopub.status.idle": "2020-11-25T16:49:46.083477Z", + "shell.execute_reply": "2020-11-25T16:49:46.083932Z" + } + }, + "outputs": [], + "source": [ + "conn.learning_rule_type = nengo.Oja(learning_rate=6e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:46.089110Z", + "iopub.status.busy": "2020-11-25T16:49:46.088314Z", + "iopub.status.idle": "2020-11-25T16:49:59.534545Z", + "shell.execute_reply": "2020-11-25T16:49:59.536171Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:59.543178Z", + "iopub.status.busy": "2020-11-25T16:49:59.540913Z", + "iopub.status.idle": "2020-11-25T16:50:00.252425Z", + "shell.execute_reply": "2020-11-25T16:50:00.252863Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting sparsity: 0.12262675709408066\n", + "Ending sparsity: 0.27264319863455877\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Oja rule seems to attenuate the signal across the connection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do BCM and Oja together do?\n", + "\n", + "We can apply both learning rules to the same connection\n", + "by passing a list to `learning_rule_type`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:00.258838Z", + "iopub.status.busy": "2020-11-25T16:50:00.257328Z", + "iopub.status.idle": "2020-11-25T16:50:00.259494Z", + "shell.execute_reply": "2020-11-25T16:50:00.259972Z" + } + }, + "outputs": [], + "source": [ + "conn.learning_rule_type = [\n", + " nengo.BCM(learning_rate=5e-10),\n", + " nengo.Oja(learning_rate=2e-9),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:00.265078Z", + "iopub.status.busy": "2020-11-25T16:50:00.264291Z", + "iopub.status.idle": "2020-11-25T16:50:15.772601Z", + "shell.execute_reply": "2020-11-25T16:50:15.773077Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:15.777130Z", + "iopub.status.busy": "2020-11-25T16:50:15.776240Z", + "iopub.status.idle": "2020-11-25T16:50:16.493206Z", + "shell.execute_reply": "2020-11-25T16:50:16.493604Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting sparsity: 0.14517215817668105\n", + "Ending sparsity: 0.42952806258413734\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combination of the two appears to accentuate\n", + "the on-off nature of the BCM rule.\n", + "As the Oja rule enforces a soft upper or lower bound\n", + "for the connection weight, the combination\n", + "of both rules may be more stable than BCM alone.\n", + "\n", + "That's mostly conjecture, however;\n", + "a thorough investigation of the BCM and Oja rules\n", + "and how they interact has not yet been done." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/learning/lmu.ipynb b/.doctrees/nbsphinx/examples/learning/lmu.ipynb new file mode 100644 index 0000000000..d27d835741 --- /dev/null +++ b/.doctrees/nbsphinx/examples/learning/lmu.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Legendre Memory Units in Nengo\n", + "\n", + "Legendre Memory Units (LMUs) are\n", + "a novel recurrent neural network architecture, described in\n", + "[Voelker, Kajić, and Eliasmith (NeurIPS 2019)][paper].\n", + "We will not go into much of the underlying details of these methods here;\n", + "broadly speaking, we can think of an LMU as a recurrent network\n", + "that does a very good job of representing\n", + "the temporal information in some input signal.\n", + "Since most RNN tasks involve computing\n", + "some function of that temporal information,\n", + "the better the RNN is at representing the temporal information\n", + "the better it will be able to perform the task.\n", + "See the [paper][] for all the details!\n", + "\n", + "In this example we will show how an LMU can be used\n", + "to delay an input signal for some fixed length of time.\n", + "This is a simple sounding task, but performing an accurate delay\n", + "requires the network to store the complete history of the input signal\n", + "across the delay period.\n", + "So it is a good measure of a network's fundamental temporal storage.\n", + "\n", + "[paper]:\n", + "https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:17.881325Z", + "iopub.status.busy": "2020-11-25T16:50:17.880407Z", + "iopub.status.idle": "2020-11-25T16:50:18.373362Z", + "shell.execute_reply": "2020-11-25T16:50:18.372467Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from collections import deque\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.filter_design import cont2discrete" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our LMU in this example will have two parameters:\n", + "the length of the time window it is optimized to store,\n", + "and the number of Legendre polynomials used to represent the signal\n", + "(using higher order polynomials\n", + "allows the LMU to represent higher frequency information).\n", + "\n", + "The input will be a band-limited white noise signal,\n", + "which has its own parameters\n", + "determining the amplitude and frequency of the signal.\n", + "\n", + "Feel free to adjust any of these parameters\n", + "to see what impact they have on the model's performance." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.378135Z", + "iopub.status.busy": "2020-11-25T16:50:18.377573Z", + "iopub.status.idle": "2020-11-25T16:50:18.381153Z", + "shell.execute_reply": "2020-11-25T16:50:18.380716Z" + } + }, + "outputs": [], + "source": [ + "# parameters of LMU\n", + "theta = 1.0 # length of window (in seconds)\n", + "order = 8 # number of Legendre polynomials representing window\n", + "\n", + "# parameters of input signal\n", + "freq = 2 # frequency limit\n", + "rms = 0.30 # amplitude of input (set to keep within [-1, 1])\n", + "delay = 0.5 # length of time delay network will learn\n", + "\n", + "# simulation parameters\n", + "dt = 0.001 # simulation timestep\n", + "sim_t = 100 # length of simulation\n", + "seed = 0 # fixed for deterministic results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to compute\n", + "the analytically derived weight matrices used in the LMU.\n", + "These are determined statically based on\n", + "the `theta`/`order` parameters from above.\n", + "It is also possible to optimize these parameters using backpropagation,\n", + "using a framework such as [NengoDL](https://www.nengo.ai/nengo-dl)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.389214Z", + "iopub.status.busy": "2020-11-25T16:50:18.387257Z", + "iopub.status.idle": "2020-11-25T16:50:18.392467Z", + "shell.execute_reply": "2020-11-25T16:50:18.392859Z" + } + }, + "outputs": [], + "source": [ + "# compute the A and B matrices according to the LMU's mathematical derivation\n", + "# (see the paper for details)\n", + "Q = np.arange(order, dtype=np.float64)\n", + "R = (2 * Q + 1)[:, None] / theta\n", + "j, i = np.meshgrid(Q, Q)\n", + "\n", + "A = np.where(i < j, -1, (-1.0) ** (i - j + 1)) * R\n", + "B = (-1.0) ** Q[:, None] * R\n", + "C = np.ones((1, order))\n", + "D = np.zeros((1,))\n", + "\n", + "A, B, _, _, _ = cont2discrete((A, B, C, D), dt=dt, method=\"zoh\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will set up an artificial synapse model\n", + "to compute an ideal delay\n", + "(we'll use this to train the model later on).\n", + "And we can run a simple network containing\n", + "just our input signal and the ideal delay to see what that looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.418077Z", + "iopub.status.busy": "2020-11-25T16:50:18.406020Z", + "iopub.status.idle": "2020-11-25T16:50:18.923747Z", + "shell.execute_reply": "2020-11-25T16:50:18.924215Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class IdealDelay(nengo.synapses.Synapse):\n", + " def __init__(self, delay):\n", + " super().__init__()\n", + " self.delay = delay\n", + "\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None, y0=None):\n", + " return {}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " # buffer the input signal based on the delay length\n", + " buffer = deque([0] * int(self.delay / dt))\n", + "\n", + " def delay_func(t, x):\n", + " buffer.append(x.copy())\n", + " return buffer.popleft()\n", + "\n", + " return delay_func\n", + "\n", + "\n", + "with nengo.Network(seed=seed) as net:\n", + " # create the input signal\n", + " stim = nengo.Node(\n", + " output=nengo.processes.WhiteSignal(\n", + " high=freq, period=sim_t, rms=rms, y0=0, seed=seed\n", + " )\n", + " )\n", + "\n", + " # probe input signal and an ideally delayed version of input signal\n", + " p_stim = nengo.Probe(stim)\n", + " p_ideal = nengo.Probe(stim, synapse=IdealDelay(delay))\n", + "\n", + "# run the network and display results\n", + "with nengo.Simulator(net) as sim:\n", + " sim.run(10)\n", + "\n", + " plt.figure(figsize=(16, 6))\n", + " plt.plot(sim.trange(), sim.data[p_stim], label=\"input\")\n", + " plt.plot(sim.trange(), sim.data[p_ideal], label=\"ideal\")\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to build the LMU.\n", + "The full LMU architecture consists of two components:\n", + "a linear memory, and a nonlinear hidden state.\n", + "But the nonlinear hidden state is really only useful\n", + "when it is optimized using backpropagation (see\n", + "[this example in NengoDL](https://www.nengo.ai/nengo-dl/examples/lmu.html)).\n", + "So here we will just build the linear memory component." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.931125Z", + "iopub.status.busy": "2020-11-25T16:50:18.930395Z", + "iopub.status.idle": "2020-11-25T16:50:18.932972Z", + "shell.execute_reply": "2020-11-25T16:50:18.932506Z" + } + }, + "outputs": [], + "source": [ + "with net:\n", + " lmu = nengo.Node(size_in=order)\n", + " nengo.Connection(stim, lmu, transform=B, synapse=None)\n", + " nengo.Connection(lmu, lmu, transform=A, synapse=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On its own the LMU isn't performing a task,\n", + "it is just internally representing the input signal.\n", + "So to get this network to perform a function,\n", + "we will add an output Ensemble\n", + "that gets the output of the LMU as input.\n", + "Then we will train the output weights of that Ensemble\n", + "using the PES online learning rule.\n", + "The error signal will be based on the ideally delayed input signal,\n", + "so the network should learn to compute that same delay." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.944347Z", + "iopub.status.busy": "2020-11-25T16:50:18.940475Z", + "iopub.status.idle": "2020-11-25T16:50:18.946103Z", + "shell.execute_reply": "2020-11-25T16:50:18.946522Z" + } + }, + "outputs": [], + "source": [ + "with net:\n", + " ens = nengo.Ensemble(1000, order, neuron_type=nengo.SpikingRectifiedLinear())\n", + " nengo.Connection(lmu, ens, synapse=None)\n", + "\n", + " out = nengo.Node(size_in=1)\n", + "\n", + " # we'll use a Node to compute the error signal so that we can shut off\n", + " # learning after a while (in order to assess the network's generalization)\n", + " err_node = nengo.Node(lambda t, x: x if t < sim_t * 0.8 else 0, size_in=1)\n", + "\n", + " # the target signal is the ideally delayed version of the input signal,\n", + " # which is subtracted from the ensemble's output in order to compute the\n", + " # PES error\n", + " nengo.Connection(stim, err_node, synapse=IdealDelay(delay), transform=-1)\n", + " nengo.Connection(out, err_node, synapse=None)\n", + "\n", + " learn_conn = nengo.Connection(\n", + " ens, out, function=lambda x: 0, learning_rule_type=nengo.PES(2e-4)\n", + " )\n", + " nengo.Connection(err_node, learn_conn.learning_rule, synapse=None)\n", + "\n", + " p_out = nengo.Probe(out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can run the full model\n", + "to see it learning to perform the delay task." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.954797Z", + "iopub.status.busy": "2020-11-25T16:50:18.954024Z", + "iopub.status.idle": "2020-11-25T16:50:37.853937Z", + "shell.execute_reply": "2020-11-25T16:50:37.854356Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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bhRn6iI3LidpdkDYUdNGej2eNgbWvgtmsKBMLBAKBQCAQCAQRQI2Z53RgjyzL+2RZ7gE+AOb66H8p8L4K9xUIDktKqlvRaSQK0z0bp0MyFM/svrq2SA7LmdpdkOWjrlf6UDB2Qkt55MYkEAgEAoFAIIg4b7zxBhUVFUGfX1paynvvvafaeNQwYPOBQw77ZZY2NyRJGgQMBn5yaI6VJGmdJEmrJEn6jbebSJL0W0u/dbW1tSoMWyDoG0qq2xicoSda5/nPLzk+inR9NPvr2iM8MguGTmjY7zn/1Ur6cOVn/e7IjEkgEAgEAoFA0CcciQZsIFwCfCLLssmhbZAsy1OBy4B/SZI01NOJsiy/IsvyVFmWp2ZmZkZirAJBWNhX28bQTM/5r1YGZ+jZW9tHBmz9XkCGjOFOzUazkaauJmXHmhtbvzeiQxMIBAKBQCAQhM7TTz/NuHHjGDduHP/6178oLS1l3LhxtuNPPvkkDz30EJ988gnr1q3j8ssvZ+LEiXR2dlJYWMjdd99NUVER06dPZ8+ePQDMmzePTz75xHaNhARlvnvvvfeydOlSJk6cyDPPPBPy2EPOgQXKgQEO+wWWNk9cAtzi2CDLcrnl5z5Jkhaj5MeKWbHgiMRklilr7OTksdk++w3O0LO4pI8iDRpLlZ9pQ2xNzd3NHP3B0QCsvXwtsYk5EJ0AdcIDKxAIBAKBQBAsj695nF0Nu1S95qi0Udwz/R6vx9evX8/rr7/O6tWrkWWZGTNmcOyxx3rse8EFF/Cf//yHJ598kqlTp9rak5OT2bp1K2+99Ra33347X3/9tdf7PfbYYzz55JM++wSCGh7YtcBwSZIGS5IUjWKkuqkJS5I0CkgFVjq0pUqSFGPZzgBmAztczxUIjhSqWrroMZkZlOZbnGlIZgK1rd20dhkiNDIHGvcrP9MG25r+tupvtu0ecw9IkuKFrd8T6dEJBAKBQCAQCEJg2bJlnHvuuej1ehISEjjvvPNYunRpQNe49NJLbT9XrlzZS291CdkDK8uyUZKk3wPfA1rgNVmWt0uS9DCwTpZlqzF7CfCBLMuOxS1HAy9LkmRGMaYfc1QvFgiONA7WK6VxBqbF++w32KI+vL+unfEFKeEeljONpRCbDHGptqbvSr+zbUtIykb6MChbG9mxCQQCgUAgEBxB+PKURpKmpibMZrNtv6ury2d/SZLctnU6ne0aZrOZnp6eMIxUpRxYWZa/lWV5hCzLQ2VZftTS9hcH4xVZlh+SZflel/NWyLJcJMvyBMvPV9UYj0DQXzlkqe06KN23ATsgLQ6A8sbOsI/JjcZSSB3s9bCMZQ0qdZCiQmw2ee0rEAgEAoFAIOhfzJkzhy+++IKOjg7a29v5/PPPOf3006mpqaG+vp7u7m6ncN/ExERaW1udrvHhhx/afs6cOROAwsJC1q9fD8D8+fMxGAxezw8FNXJgBQKBnxxoaEerkchNjvXZryBVMXDL+sKAbdgPOUVeD89+fzY/X/QzGSkDwWyElgpIGeC1v0AgEAgEAoGg/zB58mTmzZvH9OnTAbj++uuZNm0af/nLX5g+fTr5+fmMGmUvpzhv3jx+97vfERcXZwsXbmxsZPz48cTExPD++0qF1BtuuIG5c+cyYcIETjvtNPR6JaJw/PjxaLVaJkyYwLx587jjjjtCGr/kHNF7eDB16lR53bp1fT0MgSBgbn1/I5sPNbHk7uN77Vv00PecOymfh+eO67WvaphN8LdsmHkLnPxXAP675b88u/FZp243TbiJmxNHwdvnwrxvoXB25MYoEAgEAoFAcBizc+dORo/2Ua6wn1NYWMi6devIyMhQ7ZqePhNJktZbqtU4EekyOgLBr5qDDR29hg9bKUiNj7wHtqUCzAabgNN3+79zM14BNJIGUgYpO00HIzlCgUAgEAgEAsGvGGHACgQR5GB9OwN6EXCyMiA1jrLGjjCPyAWrAnFqIQDLK5Z77Pbd/u/oSchSdoQBKxAIBAKBQPCrobS0VFXva6AIA1YgiBBt3UYaOwwMSA3MAxvRMP/GA8pPi3d1R71nUfC9zXt5cftrkJgrDFiBQCAQCAQCQcQQBqxAECGqmpVw4LwU3wJOVgpS4+joMdHYEcFasC0Vys+kfABKGku8dm3oaoCUgdB0IBIjEwgEAoFAIDhiOBx1iMJFoJ+FMGAFgghR0aTU08pNjvOrf0Gq0i+iYcQtZaDPwqTR8vT6p312LWstQ04eIAxYgUAgEAgEggCIjY2lvr5eGLEoxmt9fT2xsf45eECU0REIIkalxQPbWwkdK46ldMYXpIRrWM40l0NyPosPLeb1ba/77Lqmag0fpkzikmZLLViNNjJjFAgEAoFAIDiMKSgooKysjNra2r4eSr8gNjaWgoICv/sLA1YgiBCVzV1IEmQn+WfAWg3dquaucA7LmZYKSB+Kwexf2PJeyQSyCdpqICk3zIMTCAQCgUAgOPyJiopi8ODBfT2MwxYRQiwQRIjKpi4yEmKI1vn3Z5cSH0W0TkN1SyQN2HJIykeSJLdDc4fOdWuTo5QC1bRWhHtkAoFAIBAIBAKBMGAFgkhR2dLld/gwgCRJ5CTFUhkpD2xXC3S3QFKeUufVgek50z0ateYoy+/TIgxYgUAgEAgEAkH4EQasQBAhKps6AzJgAXKSYqmKlAfWaoQmF2AwOYcQj00fi4QnA9ZSEqilMtyjEwgEAoFA4ML2imaeW7SbkurWvh6KZ2QZtn4CK/4D3W19PRrBEYIwYAWCCFHV3OW3ArGVnOTYyIUQt5QrP5PyuGfpPbbmUwtP5dbJt3r0wMq6aNBEiRBigUAgEAgizLbyZs57YQVPLSzhN88vZ09NPzRil/wTPr0OfrgP3jlfEX0UCEJEGLACQQRo7TLQ2m0M3AObrIQQR0Rm3WbA5js13zHlDqI0UUzOmgzA+cPPtx3rMRsgMUd4YAUCgUAgiDB//Wo7ibE6vr71aKJ1Gv72zc6+HpIzjQcUA3bseTD3BTi0CjZ/0NejEhwBCANWIIgAViXhnAAN2OykWHqMZpo6/FMFDomWCkCCRGc14Yy4DADmDpvLwgsW8tCsh2zHuk3dkJRnN34FAoFAIBCEnW3lzawtbeSW44cxLj+Za2cPZnFxLbv7Uyjx+tfBbIRTHoGJl0H2OFj5HyWsWCAIAWHACgQRoMJiwOalBBhCbCm5ExEhp+YySMgCXbRTc4w2xj4efY7TsYUHFmJIyIZW4YEVCAQCgSBSfLzuEDE6DedNUmpnXjp9IBoJ5m/uJyk9ZjNseh+GnwrJBSBJMPVaqNmh/BMIQkAYsAJBBKhq7gQIKoQYiEwebEsFJOUHHK78b02LEkIsVlQFAoFAIAg7siyzaFcNc4ZnkhwfBUBmYgwzBqfzzdZ+sqBcuQnaqmDsufa20eeApIHtX/TVqARHCMKAFQgigNWDmpUYnAEbESXilgpIysNoNtqaHpr5UK+nVWhkMLRDV3MYBycQCAQCgQBgX107ZY2dHDcy06n9lLHZ7Ktt51BDRx+NzIE9iwAJhp1ob0vIhAEzYM+PfTYswZGBMGAFgghQ09pNuj6aaF1gf3JZiTFIUoRCiNuqIDGHN3e8aWvKTcj12NWxTqxGZymlI8KIBQKBQCAIO78U1wJw7AhnA3bm0HQAVu2rj/iY3NjzI+RNBH2Gc3vhHMU7Kxa9BSEgDFiBIALUtnaTmRjTe0cXorQa0vUxVIfbgDV2Q2cjJGTz86Gfbc0mL3L3EzMn2rZr5W5lo6Wf5N0IBAKBQHAEs/5AI/kpcQxIi3dqH5GVSJo+mpV9bcAau6FiAxQe7X5s8ByQzXBgZeTHJThiEAasQBABaoI0YAFykmPCH0LcVqP8TMgmWmMXcTLLZo/dh6cOt21vaN6jbAgPrEAgEAgEYWfToSYmDkxxa9doJKYXprGutDHyg3KkahuYeiB/qvuxgulK/fiDwoAVBI8wYAWCCFAXggGblRhLXVu3yiNywcGAbe6xh/WYZM8e2Lun3e39GgKBQCAQCMJCTWsX5U2dTBqQ4vH4hAEpHGzooDkS5fe8Ub6eeo2Gxkxlsfvz3Z/z3MbnKHqziA/3fgnZY5UwYoEgSIQBKxCEGVmWqW3tDljAyUpmQgy1reE2YKuUH3FJ7G7cbWv2ZsBGa51L7RCTJAxYgUAgEAjCzOZDyiLzRC8G7PiCZAC2lvdhjmn5Oo4bVMAxCy4B4C8r/sIrW14B4G+r/6bkxlZsFNULBEEjDFiBIMw0dxroMZmD9sBmJsZQ396DyRzGB31bNQC/tO5zah6ZOtLrKccPON6+k5Blu4ZAIBAIBILwsPlQE1qNxNi8ZI/Hx1nat5Q3RXBULpSvt20uK1/mfjxvkiLi1Lg/goMSHEkIA1YgCDNW72mwBmxGQjQms0xjR4+aw3KmrQaQeHTz87amLVdtYWDSQK+nPHDUAwDk6HNAnyU8sAKBQCAQhJldVS0MzdQTF631eDw5PorC9Hi2HOojD2xPB0s6ym27N/14k3ufvEnKz/INERqU4EhDGLACQZipsRiwWUF7YJXQ47CGEbdWQXw6UQ6hwZIk+R5XfCbnDD0HraQVHliBQCAQCCJASXUbw7MTffYZm5fMrqqWCI3IhboSbsnJ9NmlPjEbNDqo3h6hQQmONIQBKxCEmVA9sNbzwirk1FYDCdnE6+J77+vAuqp1lLeV8562S3hgBQKBQCAIIx09Rg41djAiy7cBOzw7gQMNHXQZPOtYhJXaXb12Of+bSyB9mF99BQJPCANWIAgzNa1KCZzgPbDKeWH1wLZVQWI2+ih9QKdVtCu1X//RtgO6m8HQGY7RCQQCgUDwq2dPTRuyDCOyE3z2G56ViCwr/SNOzY5eu9R31UPmSKjZGYEBCY5EhAErEISZ2tZuYqM0JMTogjo/Mgas4oEtbiwO/TqCIwJZlukymJD7s0qk2QzGMCt0CwQCQT+hpFoxSHsLIbYauH1hwJZVb/GvY+ZoaCwVC9+CoAhuRi0QCPzGWkKnt5xSb+ijtcRFacNnwMqykr+akA2hpsy010LqIFWGJegbZFnmi03l/PvH3ZTWd5CTFMtvjxnCvFmFaDTBfYdVp7sNfvobbHoXulugYDqc8ggMPKqvRyYQCARhY3d1K9FaDYXpvtN9CjP06DQSJdWtERqZnRt69oJnfSlnskYBMtSVQO6EcA9LcIQhPLACQZipae0OOv8VFDGlzMQYasOVA9vZCKYexYC1cPHIiwO+jAGEkNMRwNMLS7jjw80kx0Vxx0kjGJql5+Gvd3DHR5vCW8rJX7qa4c2zYfVLMPJ0OOaP0FoJb5wJ2z7t69EJBAJB2CipbmVIph6d1vf0PUqrYXCG3uaxjRjdbbTgX95tXVIO26KjoUbkwQoCR3hgBYIwU9vazdBM3/kqvZGZGBM+D6wl7PdPdcttTfcfdX/Al9kcG8PUfmzANnX08OIve6lr7eGyGQOYMiitr4fkTtl6WPcaxKfBrNsgwbeSo9p8sbGc537aw8VTB/D384rQaiRkeRjP/7yHJ38oIS8ljntOGxXRMTkhy/D5TVC1BS55F0adqbTPuhXevxQ+/x0kFcDAGX03RoFAIAgT++raGZfvuf6rKyOyE9lWEdlSOm01W2nR+uN+hfOW301jfg5ba0UerCBwVPHASpJ0miRJxZIk7ZEk6V4Px+dJklQrSdImy7/rHY5dLUnSbsu/q9UYj0DQn6hp7SYrKXgPLEBmQkz4VIjbqgD4usHPvBUHbppgr++mlem3ObAtXQbOe3EF/1u6nx92VHHxy6v4aVc/M7b3LYbXToWd82HVC9z7/oks37sgYrevau7igS+2MXVQqs14BSUC4Jbjh3Hp9IG8uHgvq/bVR2xMbmx6F4q/gZP+ajdeAWKT4eJ3ICkPPrsBetr7bowCgUAQBgwmM+WNnb2GD1sZmqnnUEMHPUZzmEdm5+Xtb/rdt7G7Sdmo3xuewQiOaEI2YCVJ0gLPA6cDY4BLJUka46Hrh7IsT7T8+5/l3DTgQWAGMB14UJKk1FDHJBD0F7qNJpo7DWQmhGjARsADGww3T7zZtq2JTe63IcR/nb+DA/UdvHPdDJbfewIjshO56+MtNLT39PXQFLpa4NMbIH0o3L4F5n3LN9Eyv1t2d8SG8NQPxXQbzTx10QSb8WpFkiQeOGs0A9Li+NNnWyM6IbLR3cb2xQ8jD5gOR93sfjw+Dea+AE0HYPFjkR+fQCAQhJGKpk6MZplBaf5VCxiUrscsQ3lT5ESSjJ2NgZ/UsF/9gQiOeNTwwE4H9siyvE+W5R7gA2Cun+eeCiyUZblBluVGYCFwmgpjEgj6BXVtioEUsgc2MYbGDkN4DAeVjE5NXFq/9MCWVLfy2cYyrj96MDOHppMUG8XTF0+gsaOHV5bs6+vhKax+Cdpr4DcvQFyqcwhs+Yaw376kupVPN5Rx1cxBDEr3PDmKj9bx8Dnj2F/XzodrD4Z9TK4sWnQvl6TF8PHYk3l83T9p6bErjhU3FLOiYgUUzoaJl8Pql6G5LOJjFAgEvw7aDe3sbtwd0XseqO8AYJCfHtjCDKVfaX3kIlK03e5KkOcMPYc1l6/hm3O/8XxSwz4lPUQgCAA1DNh84JDDfpmlzZXzJUnaIknSJ5IkDQjwXCRJ+q0kSeskSVpXW1urwrAFgvBT06LUgA1FxAkgw+LBrW8Pgxe2tQp0caFfJy6lX3pgX/plL/FRWm48dqitbVROEmePz+OtlaW0dBn6cHQoJQRWPg8jz4D8KQDOpWuWPhX2Ibz8yz5io7Tccvwwn/2OG5nJ9MI0nv1pD509/gl1qEJPBwf3/gDAs6XzeWfnO1y94GqO/+h4Zr03iwu+uoAbF95oGeS9gCy8sAKBQBUMZgOnf3o6iw4ssrX94ac/cN788zDLkYtGOWAxRL0tMrpi7XegLnIGrKbL3YC9YvQVxOniSIlN8XySob1fLn4L+jeRUiH+CiiUZXk8ipfV/yB5C7IsvyLL8lRZlqdmZkZW2EQgCBarBzZDhRBiCFMt2PZaJ7Ggh2Y+FNDpOo2iBVcXl9jvDNjWLgPfbq1k7qR80vTRTseuO3owHT0mvtpc0Uejs7DrG+hqghk32pqcJkXFC8L6cq9p7eKrzRVcMKWAVJfPyBVJkrjr1JHUtnbz8fpDPvuqytaP0BgU70NztyJKsqdpD3WddbQaXMpEpAyEKfNg8/vQXB65MQoEgiOS5u5mytrKuH3x7RxoOQDA+pr1AHSbIleH+kB9B7FRGrL8XBBP10eTEKOj1OK5jQS6TmfRqE/O/oTR6aMB0Eo+xJ0a+kk0lOCwQQ0DthwY4LBfYGmzIctyvSzL1r/y/wFT/D1XIDicqbcIL6X3dwNWbzdgA61X+8QxTwBwW2exYmj1o1Cgb7ZU0mUwc+GUArdj4wuSGZmdyEfr+jjUdNO7kDwQCo/h+9LveWTlI5hxMGBlE2z5MGy3f3fVQXpMZubNKvSr//TBaUwamMJry/ZHpKxOt7GL5zc8y5PpAcgjzLwFZDOseSV8AxMIBL8KqjvsC7NnfX4W35V+h9FsBKDbGDkDtrS+g4Fp8X7X45YkiUHp8TbPbbgxdLfxYYyzR3pAon2Kr5E8mxwyCANWEDBqGLBrgeGSJA2WJCkauASY79hBkqRch91zAKtm9vfAKZIkpVrEm06xtAkERwT1FpGg9F48W72RkaCcX98WBtGh9jpnA5bADNiEKIcSQcYu6Ilw3TkffLO1ksEZeiYOSHE7JkkS507OZ/OhJsoaI7dC7URHA+z7BcZfBBoNd/1yFx+VfMQXe76wddmZXwTbPw/L7c1mmU/WlzFneAZDAij1dN3Rgymt72DRzvB53HtMPRS9WcTUd6fxUnSAYd6phTD6bFj/OnT3n++jQCDwjCzLdBkimJYQAA8uf9Bp/4+//NG2bZSNERvHwYZ2v8OHrRSm6225s+HmnU0vuJXQiY+y5+t688CaJK0wYAUBE7IBK8uyEfg9iuG5E/hIluXtkiQ9LEnSOZZut0mStF2SpM3AbcA8y7kNwCMoRvBa4GFLm0BwRFDf1oM+WktslH910byRrrfmwIbJgI3PsO1GawMztp0MWOv1+gFt3UZW72vgpNFZXr3Kp4zJBuDHHX0U+rxnkeJhHXmGU/PDKx+2bV8U3Qzl66GlUvXbr97fQHlTJxd48FD74rSxOeSnxPHGilLVx2TFUaQpKI66Bbqaw+q9FggEoVNa185JT//C6L98xwNfbMMcgciOQPAVJtxl7IrIGMxmmQP1HQxK80/Aycqg9HgONnRgNIU/V7elxXc0kzcPrDllADSIUjqCwFAlB1aW5W9lWR4hy/JQWZYftbT9RZbl+ZbtP8myPFaW5QmyLB8vy/Iuh3Nfk2V5mOXf62qMRyDoLzS0d4ccPgwQF60lPlprC0lWDVmGjjrQZxCtUQzXaTnTArrEiLQRzg39xIBdtruWHpOZE0dne+0zJDOBoZl6FobRk+iTkgXsT8rimm3/ocPQyyp5ifo1YT/fWIY+WsspY3ICOk+n1XDJtAGs2FvPwTCt7le3h/h/MmA6ZI+DjW+rMyCBQKA6RpOZ372znob2HuZOyOPtVQd4dVk/K6viI0z4uY3PRWQINa3ddBvNDMoI3ANrNMtUNIXf0O7ucK4RPiLVeW7gzQNrTB0sPLCCgImUiJNA8Kukvr3HTTwoWNL00erXLe1uAVMP6DOYkDWByVmTyYrPCugSOknn3NDRPwzYRTtrSIrVMWWQ79zJk8Zks3pfA23dkQsFA8BkhD0/8kx2Puuq1/H8pue9d00dBCXqZld0GUws2FrFaeNyiYsOPELggqkFaCT4aF14xJwu+eaSgM/5+eDPtFlD2CUJJl0JFRuhaqvKoxMIBGrwxaYKdlW18ui5RTxz8UROGJXFv34sobmjj9XhHZBdDDNHFh5YGJEx2BSIA/TADkyPXCmdzW0HnPZPGXSK0763SKie1IFKLdh+pJ8h6P8IA1YgCCP1bT22/NVQSU+IoU5tA9biLd1MD2ur1rKhJvCao1qN3fgxgSIK1Q9Ysbee2cMyiNL6fszNGZaJ0SyztjTC2QtVm6GrGVmfDsBbO97y2nViikzTwZWK0asSS3fX0dptZO7EvKDOz02O49gRmXyyviwi4Wn+cNvPtzH7g9k8te4pbv/5diW3WBsNG4QXViDoj7y7+gDDshI4fVyOonJ+ykjae0xhWxgLlObq7RzA+3vXYDawtyn84a/lTZ0AFKQGVvJugMXgLWvsVH1MrmwxOisQ3zD+Brc+14y7hrdPd34ez2n4WVlM72oK5/AERxjCgBUIwkh9e7dqHth0fbT6IcQWA/b9RnU8VM+lJvcLA7assYPypk5mDE7rte+UQalEaSVW7vW+yh4WDqwAwBznn7ruMTlJtJetUu32P2yvIjFWx8yh6UFf4+JpA6hq6WLJ7r7/P7dils28sf0NFh1cBPFpMOosJQ/WEJlcNYFA4B97alrZeLCJS6cPtHnnxuQlMa0wlffXHnSuh91HHP1d75Egr259lW/3fRvWcVRYDNi8lMAM2OzEGHQaifKmyAsVesp5vXPKnUzMmkicp9rzTf1j0UJweCAMWIEgTMiyTEN7jyo5sKAYsKqHEFvCfb+pW6/K5TbExUN7hA1BD1i9qdMH926cxUVrmTQwtW8M2LShyH6KZsmSxM2rHlLl1iazzE+7ajhhVFavHmpfnDg6mzR9NJ9t6MfVzyZdoazs7xYC9wJBf+LHnUp96zOLcp3az5mYz77adnbXHB4K4l/t+4p7lt4T1nuUN3WRro8OWBBSp9WQkxwbEQ+sIzdPuNnn8TWXr3FvbDoYptEIjkSEASsQhImWLiMGkxxyCR0raQnR1Lf1qLsqrZK3dELmBOVy2qh+4YFds7+BpFgdI3MS/eo/c0g62yuaI5d3ZTYrBuygWZhl/8NvN3RVYzKHXmpiw8FG6tt7OHmMd4Erf4jSajizKJcfd1armkN8zxIVJ4ODjwV9Fmz9WL1rCgSCkPlpVw1jcpPISY51aj91bDaSpNTx7lNaq/r2/g5UNHUG7H21kp8SR3mYDdi6plKn/ctGXxb4RZqFB1bgP8KAFQhUprGrkaI3i/hi9zfEF/6HCuMKVa6boY+hx2RWV2xIJcXgzbWbASjR0S9EnFbvb2BaYRpaPwu+HzUkHbMM6w5EKA+2dqfiFRw0m+UVywM6deLbE0O+/Q/bq4jWajh2RGbvnXth7sQ8ugxmFu5QZ7K3r2kf3+5XMRxPq4Nx50HJD9DZpN51BQJB0LR0GVh/oJHjR7k/g7ISY5k8MJXFxTV9MDI7C9e/0Kf3d6SiqZP8IA3YgtT4sHtgd5Q5v8cCXWjfFZ8oQogFASEMWIFAZRYfWgzAkxsfQBtXxscHH1flutZc2vo2FcOI2+sg2u6lfO3U11S4Zt96YOvautlX2840P/JfrUwYkIxGgk2HmsI3MEcs+a8MmhmZ+7nw484aZg5NJzE2KuRrTR6YSn5KHF9uqlBhZPDEuid8Hh+bPpZXTn4lsIsWXQSmbtj1dQgjEwgEarHpYBMms8zMIRkej88ems7W8maaO/tGjViWZe488LnHY3dOuTPiYwnJA5saR3VrFz3G8Intratcbdt+fM7jpMSmBHR+RVI2NB3ovaNAYEEYsAKByvxlxV/Cct10i5pxvZp5sJYasFZy9bk+Ontn9WXKy+uC6Jw+z4HdWqYoIU4ckOL3OfHROkblJEXOgK3YCPpMSBkUmfs5cKC+nf117ZwwKrBySd7QaCTOmZjH0t111KkgMtbQ6dsL/t6Z7zEzbyarL1vN5aMv9++i+ZMhdTBs+Sjk8QkEgtBZf6ARjaQsHnpi1rAMzDKs2tc375NPdn/i9diJA0+M4EigpdNIe4+JvJTY3jt7oCA1DlmGyubweWFfr/jZtn3GkDMCPt+szxAhxIKAEAas4LCloqmTpxeW8OHag5jMfa9WCHDnYs8rs/PXPBPytTMsYlCqKhG319Kut3sqvRUa7434KEWqf7O5Q/HA9qF65JayZiQJxuV7nhh5Y+LAFDYdbMIcie9SxUbIm6TUKg2C5u7m3jt5YeluJcR7znDPno9g+M3EfExmmW+3hi9nLUefQ7Qm2qZsGR8Vj4Tvz6/ozSIOtRxSPueiC2H/kn6V1yYQ/FrZcLCREdmJXqNAJg1MIS5Ky4o9fZOS8u2e+W5tL570IuuuWMfApIF8f767KNz7u95XRaPAlTKLgnDQIcSW88KdBxsKxvh0IeIkCAhhwAoOSyqaOjn3heU8u2g393y6lfu/UKcMTKh4K2p+387XYHdoBc+tIcSqKhG319PqUMbFsaZrMOw2toDZoNR06yO2ljcxNDOBhBhdQOdNGpBCa7eRfXVhVr7saYfaXZA3iV0Nu9wOPzjzQe6fcT/XjruWgoQCj5c4+oOjg779st115KfEMThDH/Q1XBmZk8ionERVwoh3Nux0a0uOSWbBeQtYfflqp/bUWOW7e0zBMV6vt7JypbJRdCEgw7bPQh6jQCAIHrNZZtPBJqYM8l5CLEanZcqgVNaWNkZwZHbk7la3tqPzjyZGqywk5yXksWyuc0rC31f/nS/3fqn6WCqalBJgwYYQF6RGrhZssJji06CzEboPD+VpQd8jDFjBYckjX++gpdPIgj/M4cZjhvD+mkMs7Ue1KD3RMf82xXgJElsOrKoGbC1/w77CbX05h4IMqolDBcOWsmbGB+h9BZg0UJlMbTjYpPKIXKjaCrKZ5qyRXPjVhW6HhyQP4eJRF3PHlDu4csyVqt7aaDKzfG8dc4Zn2OouqsU5E/NYf6CRQw3B1xvsMnqu1brgvAXoNDp0GudFiWvGXcNfZv6F5054zus1bSrPmSMgdwJsFWHE/Ynt9dspaSyx7Td2NXpc2BEcOeyra6O122h75npj0sAUdlW10NGjonChn3gyYF1J9pAC0trT+3mBEmwNWCs5ybFIEpQ19S8D9tpx19q2e+Is72wRRizwE2HACg47iqtaWbCtit8dO5TRuUncecoI8lPieOHnvX09NJ+8pOuAze8HfX5slJaEGJ16Ik6yDB11HMIukhGKATs+czwAz6Um95kBW9XcRU1rN0UFgRuwQzL0JMbq2BhuA7Z8AwDHrHvY7dBxA45jXMY4277aRubmsmZau4zMGR66+rAr50zIA2D+5sC9sM3dzRS9WcS0d6d5PJ4Y7bkcUpQmigtHXGgLK/bEtrpt9p2iC5Xw7fr+/azwh05jJ+2G4BfE+guXfH0J588/H1AWGy786kKPCzuCI4edlYqRNyY3yWe/SQNTMMt2XYNI0dzdzIbu4BSQVS1zZ6GiqZNonYaMhOBK8kXrNOQkxVLWGPziYji4Y8odtu0Ss8W4FmHEAj8RBqzgsOO91QeI1mm4cqay+hmj03LVzEGs3FdPSbX6q59q8XpKEqz5b0j5oekJ0dS3q5QD29UEZiNmh8l/KAbsltotAPw3JbnPlIi3lDUBMD4IA1ajkRhfkMy28jBPlio2QmIeZtwVIZ874TmitfZJSm85noGybHcdkgSzhqarel1QwtSmFabyxcbygCdxHxWHzyvqFNI37nxAOiLEnE795FSOeu+ovh6Gqjyw/AGqO6pt+6sqV9HSo6Qj1HbUsqF6Q18NTaAiu6pa0Gkkhmb5TmOYUJACwMZIietZuHHhjW5tKTEpfp0ro74BW24poRPKgmY4a8F2OUSWxemC8xJrYiyLGcKAFfiJMGAFhxU9RjOfbyzn9HE5tpBagPMmFyBJ8HUfFT7vMHTYDDhfGGp3QY17jp+/pOmj1cuBtagFlxrsBptqHr8+qgW7tbwZjQRjcgM3YAHG5iVTXNWKwRS+cgNUbOSizAS/uqptwC7dXcv4/GRS9cGt5PfG3In57K5pY0dlYDnQvr5388bO8+saH5z5AR+d9REbrvRh5CTlweA5ShhxHwqNhUJLTwvN3c00dvdNbmC4qOmoYf5eu3BOS08LN/xwA3/46Q8AXPz1xVz93dV9NTyBihRXtTIkU0+MzrfmQnpCDAPT4tkU7qgYF7bXb3drW3rJUo99E3XxTvsvbn5RdS+sUkInOAViK/mpcWHLga2rs4f8PzTzoYDOtabJpCbmgzZaGLACvxEGrOCwYm1pAy1dRs4an+fUnpkYw/TCNL7b1jcG7APLH+Dyb3sv6fF4eirsdFc39Jd0fQx1aoUQu3hJzx5ytjrX9XDtSLG1vJnhWYnERQcnRjU2L4kek5m9tWESkuhuhfrd7JTdcz2fOvYptzarYaeGIdvaZWDjoSaOVlF92JUzinLRaaSAxZxavIh+JUYl8n9T/8+va4zNGMvo9NFEadxVTR9c8aB9p+hCaNhnC+U+3Jj9/mw3ES9ZljGaI58nqCYnfuxcmuRf6/8FwN4mJdy7trN/axwI/GdnZSujcnyHD1uZNDCFjYf672LNzxcu4uI2u2HYaewMSSXeExVNXeQlB+fZtFKQGkdVSxfGMCzOnrfot7btQEvo3Dhe8Xb/e+OzkDxA5MAK/EYYsILDioU7qonRaTh6mPsk/NSxOZRUt4UkIhMsjiIkvvgwKRFKvgv6Pun6aPXK6Lh4SVXNt+yjHNiSqlZG53rOl/SHsXnKpGp7eZhUlGuLPTbn6HM4pfAUt3ZrOJbHeqcBqjWuK23EZJaZPTR8BmyaPppjR2Qyf1NFQOWIXt/+usf2z+d+rsq4Ptv9GT0my8LP6HOUlf6tH6ty7b6mvK2cB1c8yKS3J/X1UALGV8mRj0uU/5/G7kaPHjHB4UlLl4Hypk5G+fmcLspPprqlW5Ua0/7i+ib8+Gzvz4qY6AQGxznX1DbK6i0mGUxmqlu7yA1SwMlKbnIcJrNMbRg+x05z8IvqjguODck50FyuxpAEvwKEASs4rPhpVw2zh2V49LBZPUsr90a+8LmrOqovTJWbobMpqPukJyghxKqEKLl4Sc8ffn7o17RdO/IGbEuXgYrmLkbkBG/ADs5IIDZKw/aKMBmwNTs8Nmu8PIpPH3w6N0+8mVsn3crfj/6707HS/YsCuvWq/fVEaaVelT9DZe6kfKpauli9vyHoazw+53G2Xr2VbH12UOdfNuoyt7aXNr+kbMSlwIhTYdunYDp8vJYms4mmria39tM+PY3P96hj6EcSWZb514Z/+dX3kq8vCe9gBGGnx9SD0WykuErRqRjtpwd2tEXoaWeAaQmh4Ph2/eisjxiVNspnf01irtO+mtEQNa3dyDLkJYcWQmwNQa5s9qz0rgZPzXok4HMc507LY+OgJfRSbIJfB8KAFRw2VDR1crChw6P3FWB4VgIZCdGs2Bt546nT6Dm35MrCR9wKnn+WEAcHVgR1nzR9NEazTEunCi/IdmdDf1KWih6cPggh3m0R8BqRFbwBq9VIjMpJYkdlmIScanZCVLxbszfvt06j46YJNxEfFc/ZQ51DvM9efX9ACxlr9jcwviAl6PBqfzl5dDb6aC1fbvJvJb2u0/3vNdAwNFcuHnWxW9t/t/7X7skrugjaa2D/LyHdJ1IsOriIy7+9nDkfzvHZr+jNIqa9M40VFSvoMHTw+JrH6TD0L+VRK89seIY3tr8R8Hm2skiCw4op70zhym+vZHe1EjkyPNs/HYC+MGCd7p8+utc+dfEpTvsm2XtkQaBUtygGZ3ZSaAZsTpLiwa1sCp8BO8iPz8oVRwP2z10ltLZVgY/IDIHAijBgBYcNa0sVj870wWkej0uSxMyhGazYWx8WKXtf2MITXZiYNYG8BOd83YaoWNi/JKj7ZCQoKsGqKBG310KMXexI1RDijsh7wUssE6ORIXhgQQkj3lHREp7vUM1OyBzp1hyIB98Rf9V7O3qMbC1rZoaXvx01iYvWcurYHL7dWkm3sfeJyMVfuRuboTIkeQgDEwe6tT+3wVIvdvgpyne/n4cRLzq4iI+KP+L2n2/3O4y2y9TFjQtv5KUtL/HOznd4d+e7YR5lcLy/M7iSYmoaB4LwU9xQTNGbRQBsq9/G/ro2YnQav3M60/TR5CbHsiNcUTEq8ErdGqd9NT2w1c3qGLB2D6y6Qk7f7PvGth3Me8y1BNobSXporQp5XIIjH2HACg4b1pY2kBCjs63IemLqoFRqWrvDGibjCU8CI11VZ5Gf7G4wGJJyoXxdUPdJt9SBq1dDibijjk69egaNVU0Q6BMPbHFVK/HRWvJDzBUak5dES5cxPIqNNTsha4xbc7zO3SvrDxtrN/rVb8OBJoxm2evij9qcMzGPli4jP+/q/XtQ0xlcvcXeyNHnuLUtr1iuTKajYmHM2bDzK+jx7qFs7jSwfE8dX2+p4IuN5SzdXRvRXLzbf76dR1YFHpYHsKJcifLoL+JO66vXU9NRgyzL3Ln4TrpMwT2jN9VsUndggrBywVcXOO3vr2unMF2PRuP/guno3KSAlc2DxhD4cz9f7xxCXN1e7aVn4Ng9sMGXuANIjosiLkqr+tzosTWP2bZ91eP2F1lChBEL/CK4ZX+BoA9Yu7+RyYNS0fp48Vnrf24+1EReiIZMKFw+8HFe2mmyeUwdadWnw65VYOwBXWDlTKylg+rVUCJur6VbnwGo87KN1TqsEHfUg9kMmsitkZVUtzI8OzGgiZEnxuYp36HtFS0MSAvOsPRIRwO0VbFKnwgOEcp/mPwHzhx8ZlCX/GbfN9w26TY3L78rq/fXo9VITC2MjAF79LAMMhKimb+5nNPGuRuSkeC+Gffx/Kbn+eHAD27HjGYjuqKLYOM7iqjauPMwm2V217Sx8WAjGw42suFgE3tqPAtlTRiQwrWzCzl7fF7I3zdveEtL8JfiRkUwLBx1KYNh3nfzSItNo6Er+NxogGu/vxaAVZetQh/lu46ooP+xv66d4QGmeYzJTeKXklq6DCZio8KbAvHzrsCjMsZmFFHebq+A8EnJJ0zPna7KeKpauonSSk5lA4NBkiRyk2NV98A2dTfZtnWSb5Pi9eX7eemXvYzJTeLpiyZ6LOdmRIKWMmCaquP0i+42+PIWOLgSpl1P8bhziNHFUphcGPmxCHpFGLCCw4LmTgPF1a2cNT7XZ7/RuUlEaSU2lzVzepHvvuFibsHx6M3DgRJS490f0O917OdPpm6o2Q55geWdqhtCXM/PSUmgUkSeU26a2QhdTRAfGYMJFAP2+JFZvXfshVE5iWgk2FHZoq7xZan/+5XB2eN4fdH1IV12RcUKLhhxgc8+q/c1MC4viYSYyDzydVoNZ43P4701B2nuMJAc717aJtwMSRnCU8c9ZQtfdKSlq5OKqCKGxGRRtvB/PLQyj61lzbR2K97K1PgoJg1M5TcT85g4IJXspBi0Gonqlm42HGzksw1l/OGDTby98gBPXTSBQenqGFKyLDP+rfFcOOJCmwpvqJj3/MgXcTl0tY5ny6FOEmN1nD0+j3H5wdVKDoVQjVdHNtduZlbeLNWuJ1CHstYydtTvYHDyYPY373c7frChg1PGBvZcHZOXhMkss7u6jaKC8H5vb9vwz5CvMT5zPF3GLmJ1oYX9AtS0dJGVGKtKik9uSmxYo9O0Gu+LCz9sr+KvX+1g0sAUlu+t546PNvH6vGlIksQvF//CsR8eC0C7Ruo7D+zXdyhlDgccBT8/ygWlbwOw9eqtfTMegU+EASs4LNheobisJgxI8dkvNkrL6NwkNh9qCv+gPDDMYOL+Yx7nsQX7SIrVEa1TPJDXjLuG17fZS4WU67Tkl68P2IC1GsQNanhgO+oozBkGrerL1ldpteR01EfMgK1v66aurSfk/FdQvkOF6XpKLGqZqlG7kzKdlp8bdwZ9iUePfpT7lt3n1NZb2FaXwcSmQ01cPWtQ0PcNhgumFPDGilI+21jGNbMH+33es8c/G8ZRKUz7x3eYDPHcpZvJTV3zidNWcc7EoUwemMrkQakUpsd7nDAOyUxg5tB0bjp2KJ9tLOeRr3fwm+eX88pVU5kWrHfbbIbG/VCxEUOlEhKulvEK8HL7blil1MHVlD5FZ4+Jl3/Zx++OHcrdp44Mmwc53Ny48Ea2Xr2VLbVbyEvIIyMufOWhBP5z/vzz6TB6D8s3mGQGZwS24GNNG9pR2Rx2AzYYXKMcHl/7OAtKF/DuGaHnn1e1dJETogKxldzkOJbtDp/Ipbd3kcks89iCXYzKSeTD387krZWl/O2bnazcV8+soRmkxdqfnR8lJfJAX5TSqdgEWz+CY/4Ix/0ZXj8NqOztLEEfInJgBYcF1rqc1jqdvphQkMLW8uaA6lCqxZW6TGKj4qhv7yHdIXz4zil3MjR5qG3/tAH5UO5f/qIj0ToNSbG60HNgzWZor0OOTQntOg5cW3StbfvkgfkRzYO1CjiNyA7dgLVep6RaZQO2ZicX5OfRamwP+hLnDD2He6ff69QmuVUtdGbToSZ6TGZmDE4P+r7BMC4/mQkDUnh39UGvgljtBvfPYnjq8JDuK8syq/bV89evtnP8k4s99rl6Vj4vXD6Zc675ExoJXi3awaPnFnH+lAIGZ+h79XZoNBIXTCngy1tmkxofzdWvrWHDwUb/B9nTDps/hI/nsfTfw9j/0nT49DrWbvJcD1ctNt1zFOseOIlLpw/kpV/28ui3wS+mBEI4lYMv//ZyUWanH+HLeLUSqAE7KC2euCgtxVWB1b4OhRm5M9h05Sa/+v5+0u8piHFewNpSu0WVcVS3dIWc/2olNzmWmtYujKbw/D16E3FatLOafXXt/OHE4UTrNFxx1CDS9dG8vrzU84VaysIyPp+s/I8i7DfrViX16di7Iz8GQUAIA1ZwWLCtopnc5Fgno9AbY/KSaOsOkwiPB97e8bZt+7yc2YDiEUx3ye94+vinnU+sDi4sJT0hJnQDtqsJZBOGGHUMPoCkaJfFhQjWgrUam6oZsDmJlNa302VQUfG0ZqcSHuVAQpR/pSQccfuce2HN/gYkieA9hCFw+YyB7KlpY42XmrA3/3izW5vjanwgGE1m3l9zkJOfWcIlr6zivdUHKUz3nMN8xcwcjhmZyIAho5CGnQQb3gqqJmxhhp4PfnsUmYkxXPP6WkrrelmcaKuB7/4MT42Gz38LB1Zwc5qecwryaL72W36XHd5Fhh0fXkRStIa/nzuOq2cO4tVl+/0udxQKgQhJ3T75dr76zVcBXb+6Qz3RHEF40SbsYFB6YPoUGo3EsKwEdteovKjoglX4DGDu0Lk+Q2IdGZI8hPePfy4sY6pp6SYrUT0PrFlWasuGA28e2C82lZOREM3JY5S63rFRWs6bnM/i4hqaOw3uJ0Q6hLi7DXZ9A+POg9hkytvKuadioe1wpKtaCPxDGLCCw4LtFS02cZ3esIaR7qqKjGrhE2ufsO/kTgCgob3HTXRhQOIA5xNri4Oqd5amj6Y+VCVUi3F5UApjSYqIemBbSYrVqbZSPSonEbOMVxGfgJFlWw6sI2+c9kbAlzoq9yin/d5CiFfvr2dUTlKf5KGePT6PxFgd76056PH4hpoNTvtvn/428R7q5PbG0t21nPqvJfzps63ER2t58sIJbPzLybx+zXTOHOIukDX3y7nMfH8mn+3+DKZeA62ViphTEGQlxfL2tTMAuPndDZ4XPUwGWPoUPDsJ1rwMw0+Ga76DO3fZujxb/lNQ9wc4ffDpfvW7hHKe+PxiJEnigbPGMGlgCg/N3x7686QX/DVgjxtwHNcVXUd+Yn5YxyPoO+IHvMXn+94K+LzhWQm2GrLhYm/z3uBPTipQbyAW2ruNtHYbVQwhDk8pHSueooFauwz8uLOGs8bnodPa31VnFOViMMn8uMPD4lOkQ4iLF4Chg0W5I3hi7ROc8/k5fLv/W9vh70o+jex4BH4hDFhBv6ejx8je2jbG5fvnebJ64YrVzmH0B4sBW9fWYyt5YyVK42JAGLugYV/At0jTR9MQqge2QzFgHz70TS8dQ7lH5GrBllS3MjInUbVatqp/h9pqoNPdCzkyzb0mbG9kxmc67UteahAD9BjNrD/QGJH6r56Ii9Zy/uQCFmytspWD8EVKTEpA1+8ymHho/naufHUNZhleuXIKX94ymwumFBAfrYSznTH4DK/nLy5bDMNPhcQ8xbAMkoHp8Txz8QR2VLbwd9ew3Lo98OopsOhhGHIc3LwaLngVBs10Uun+qMS/mr6eeOKYJ3rvZOHttmLYvwSdVsPj54+nrdvIUwtLgr63Pxhl7wbsgFh3r3OUJorH5zxOQYJvo0B4Rg5PdjXs6r2TC8OzE6lq6aKly4PHTiXkIEro2FChhIwrapXQsZJrqQVb0RQeISdPi6kr9tbTYzS7CSJOHJBCVmIMv5R4WOhuqwoqIiZodn8P+ixu3/4ib+94mx6z8zu1pnyNlxMFfYkwYAX9np2VLcgyjPPTA5sQo2NAWhy71M5h9ECPq/GQMRyzWaaxo4d0vR8vnertAd8zIyE69BBiF+/ov477V2jX83iPyIQQy7JMcVWrauHDAIXp8URrNerlwdbsUOc6FhxXuiUfRd+3ljfTZTD3mQELcM3sQoxmM68uc1ckdSWQcgX1bd1c/PJK3lhRyrWzB7PgD3M4ZWyO2yKGLw+1LMug1cFRv4P9S6B8vd/3d+WEUdlcO3swb608YA+Z3vcLvHKcItJ04RtwybuQMSzoe6jG/Fuhp4MR2YlcNn0gH6091Hv4c5AYzAZ+Pviz1+MfnDufi0ZcBMDphXZP8hlDzuCzuZ8xLt29brLjtQWHH8HUCx2epaRbhNMLK3cGkMfugqfIkVC/n1U2A1a9EGKAKpWUiK//3q6g/+zxz5Ic4z5HW76njvhoLZMHpjq1S5LEzKHprNhbjyzLDEtxeC7KZsWIjQSyDPsWc4KP+YMchKNBEH6EASuIGK09rZhlM+ur19Pc3dz7CRZ2VCpGxBg/BJysjMxOUl9F1gNf7PnCuUGjpbnTgMks9163TdJ4DCvtjTR9NI3tPaGJVLkYlwWJ6oc/Wb284aamtZuWLqOqBqxOq2FoVgLFahmwtbtQQTfahuME8M/bX6Gs1bPoxer9ihd8eh8asIPS9Zw1Po93Vx2guUMdg+NQQwcXvLSS4upW/nvVVP5y9hiv9SF9iVyZZEu479RrITYZlj7tta8/3HXqCApS47j30y30bP4E3jkfUgbA75bD2HNDunZvvHfGe/53bixl2Xd/wGQ2ccsJw9BpJZ5dtDss43p588vcv/x+r8eTopN4YOYDbL16K2cMcfaWx+niuHPqXV7PnfLOFNXGKYgcWinwWq7DsxUDdk8482BDMGCjtdFsHjyPa5rsqUsrK1aGNJyaFiW0Xy0DNilWR3y0lgqVQohXV622bR8/8HiPfZbtrmPG4DRbRQZHZg1Np66tmz01bbx00kvOByMVRly9HdprqZW9v5vMwoDtlwgDVhAROgwdzHp/Fv9Y/Q/mfTeP2366ze9z91S3khCjs+Vv+MOonET21bXTbQxjjifwyKpH3Nqs3lHXEGLASVb/2IEFSi3YAEnTx2A0y6GFUrkYsMGsiHvilZNf8XqPcGEN81XTgAUYmZ2g3iJIzQ52JDvXqA00XNYR17ING2s8K1qv2d/A8KwEv8TPwslNxw2lvcfEGytKAeg0dnLOF+cEda0dFS2c9+IKGtp7ePf6o2zCIN7wpYBrC0GNSYTpv4VdXwe1qGQlPlrHP84rYk7jZ0R9fj0UTINrFkCye05na08rV357ZdD3snLduOsASI/zXwBq0uBB3NS4ildW/YOsxFgumz6I+ZsrVPPMOFLeFtpEdFrONL4+54te+62vDt57LgidirYKjzWXPbHwwMLeO7lQkBpPbJTGpjgfDkLxwAJossewLtb+rO1NIb431PbASpJEbnIslWEKIXaloqmTfXXtHD080+PxmUOU0ler9tW718yNlBLxvsW9djG310ZUlFLgH8KAFYQVa/hYm0F56XxQ/AEQWA7M7po2hmUlBJTfOCInEZNZZm9NeMLiPPH37OMAbIIonkKIHQ2PBg1QHXhoqVXdOKQw4o46ViTZJ7xqGbDdJgcxmAg98O0KxIEr+vpiRE4iFc0q5VzV7KQ51VnE6+Ozg6/16WqUeTLSTGaZdaWNfep9tTI6N4lTx2bzypK91LZ2s7F6I/ub7SHFtw06mw/O/KDX66zcW8/FL69Ep5H45HczmTIotddzfAkI2TywADNugpgkWPhgr9f0iiwz5+CL/DXqTRbJU6n9zfsQl+Khm8ys92exqXZT8PeykK1XDHjH78BVhY8zI2eG13OMlufQC7s/5PJvL2ferEJMsszbq0pDHo8rajxbMhJyeu3zh5//EPJ9BMGzvd7/xVijbMQUoIChViMxNDOB3WoJ63kgVAOWzJFU6wL3LnujuqWLhBgdCTGey9MEQ15KHJV+6BGowboDyufpLYVlQFoc6fpoNpc1u5fgiZQS8aFVdKT6rpEuA1QEXvZQEF6EASsIKy9uepHbfr6NFRUrnNoDCSEqqW6z5b/4y7BMpf++usjVjcvLHAdgE1jy5IF1y5ltLAVjYIaoNTQ5JCGn9lq26+0h2Z5yV4LBaRIToRDikupWMhKiVfcyjrR4dHeHGkYsy1Czi7sku6jVdeOuI0ff+6TcX5wMMQs7K1to6zb2CwMW4N7TR9NtNPP0wmI0GudXzw2z7mdsxlif53+7tZKrX1tDTnIsn908i+F+etx95aGtqlxl39GnwzF3KYIee4NQBDYZYf7vYelTtIy5nJsNt/P0Ys/qy5/s/iSgS98/436Gpw5n3RXrSIxSfu+zhpwF2J+l1sUxyZjGHUefxv9O/R8/X+Q999TKltotfHXodcaOXs87a3fS2aNu1Iqvz///pvyfX9fQR+mZnDHeZ58uY2Qm5QLPBCqoNfHtiXQYeq8Z68iI7MTQn8deMJqNPN0WYl3klEEMMtoXkm5e5F4mLBDUrAFrJScplsqmyJQY3HyoiRidxlYZwhVJkhhfkMyWsianOaE5OiFyIcTlG/hbhu935PtJibywPbw1ugWBo4oBK0nSaZIkFUuStEeSpHs9HL9TkqQdkiRtkSRpkSRJgxyOmSRJ2mT5N1+N8Qj6D4daDwFQ1+lszLQaWvnH6n/0+tJrbO+hrq3blv/iL9ZC6ftrw+eBdfR43NDUzJShSv5WndWA9ZADOyR5iHODbIKmAwHd12oY17eFYsDWYXYQnciIywj+Wg441TXtqFeMtzBTXN3mV/hwdXs1P5T+QGVbJTPfm8mexj0++1uvuSvUMOLmMuhppQv792VoytCQLvmHyc7epp3Vm9z6WIWE+osBOzhDz9WzCvlg7SGKK10WlqJ9l855e2Upt7y3gfEFyXz8u5k2MRJ/CEhIZcbvILUQvvk/6Ang2dHTAR9eDhvfgWPvJenC57li5hA+XHuIXVVNGEz2MTR1NfHwyof9vzZKGO1n53xGjDaGN09/k5sn3MyfZ/yZK0ZfwW+G/QaAXZVK7l1ibJStXEVGXIZfi4UvbX6JA/LHdCd/yIJtlQGNzRtf7vmSC+ZfwIL9CzweP3fYucwbN8/v6z1xnO/85G5Tt+19Izg8eHlLYMrfw7ISqGzuojUMSsRO0UPBotHSHe38bFpTGbyCbXVLt2rhw1ZyU+Kobeumx+g9tSJQTkz0/D7bdKiJovxkorTeTY2ighT21LTRbbDPFQ4m50YmhLilElrKKe3Fa16r0/Ji89bwj0cQECEbsJIkaYHngdOBMcClkiS5ygZuBKbKsjwe+ARw1PzvlGV5ouVfcElRRyBm2cxXe78KOMymv2H1DP17w7/djr236z2q2n0rze2pVSa6w7MCy2+Mi9aSlxzLvjApawK0dNvFGq7tMEGyIoTUYDEsUz0YsB7z1Op9G1Ju17CEJte3h/DCba+jJVr9vEhHJUbZbISuJtXv4YjZLLO72j8F4nnfzeP/fvk/Pir5iDZDGx8Wf+izf0FqHPpobeh5sB5yKt3CpQLk+qLrnfY/2Pu5W581+xsoSI0LyNgLN3eePIJBafE8/8s2v/qbzTL/+HYnD3y5nRNHZfH2dTNIie9FHM2FWK0yARyROsLj8aI3i+yTV10MzH0eGvbD93/27wZtNfDGmVDyPZz5FBz/J5Akfn/CMOKitNzww41MfmcyALsbdzPnwzkBjR9Aq7FPsIanDuemiTeRGJ3IPdPvIVqrfB4frFOMt8RY53Jdo9JG+X2fuNgePlqnjhF4//L7KW4s9nrcV26yJ6yh0r444zPvJZME4aWmo8atbUTqCC4ZeQkAo6IvdTv+2rbXArqH9TmvWn1uB9QqyZQe7Sw2+dr2wH5HR6qau1Q3YPOSY5WgoFb1Ihb+MXKeW5vBZGZbeTMTBqT4PHdCQTJmGXZW2Odq2xNTIhNCbFGdl6L9dJCIsl39CjU8sNOBPbIs75NluQf4AJjr2EGW5Z9lWbbGiqwCwiB5emQx4a0J/HnZn3lo5UN9PZSgMZgNbK3zvWrVafIdymKVzB8WYAgxwOBMfVgNWMec3Jj04WDZb2jvJjkuyuuq44xce26aCQI2YFP1ygS1IRQPbEcdb5nUD/F1nAS0aqSw58GWN3XS0WPyGqLkSFmbsqL7v63/A6C5x7cStiRJjMhJDF2JuHYnrv4Ct5rAQeBLIESWZdaWNvQb76sVfYyOpy+eSGfaq732be0y8Pv3N/Dykn1ccdRAXrpiCnHRgeeXHT/weP48489OAmquOKmiFx4Ns/8A69+AVS/6vnjlFvjfScoixSXvwjT7wkKaPpp5swtpkpU896fXP81588/za8yXjbrMaZGit3DzssYOFm/vIUGTzX0znA3vP077o1/3BEhP0LK+4Tv21bX03jlE9FH6sN9DEH4MZgNPr3+ax9c+7nbMaDZy31H3sfXqrcR0nBDyvcJZSmdLxareO/nBI/mnOe0Ho7gMyjO8plV9AzbHIoZZqaJgW2zKQLe24qpWuo1mJvZiwBYVKOlL2yvt79nW2MTIhBCXrwONDvx8FpVXbQrveAQBoYYBmw84LtmWWdq8cR3gGFMUK0nSOkmSVkmS9BtvJ0mS9FtLv3W1tR4KHx+hfLHnCya8NYGfDgaRk9XH/Hv9v3v1sJY0lPg8vrumlfhoLfkpgXuRBmfo2V/bFrZi989ueNa2rcuweznq2ns85r9acRRX6dSnQ11g5StidFoSY3TBiziZzUp4r0psKWvi+jfXcv2baylrtOc0nVWQF3YD1i8BJ1mGde6r4Av2L+j1uzEyO5HiqtbQvkM1O/k40/mROCFzQvDXs/Dted96Pbavrp369h6mF/YvAxZwqwf48tEvuPVZuruWM55dyvfbq7nvjNE8MnecLSw2UDSShktHXequculAm6HNOdT4xL/A6LPhu3th8eNKfqsjhi5Y9gz870Qw9cA138CoM92ue/3Rg23br2/zL4cqMy6Te6bfw8i0kQA8eeyTxGh9R0u8vrwUSdbx6dlfMafA2cM7JdteamZW3iyf16nu2UVs7mc8tuwtv8bqiVe2vOIk0OWJwqRCtzB4weHJd/u/8/rddlyEqWjsQiK0yJMBafHE6FSsz+3A7365U5XrJOdMdNoP1oBtaO/BYJJVz4HNs8ylKlTMg5WS8tzaNh5qAujVgM1MiCFNH+2U2/xo5x4quuoC1gcJmLJ1kD0OSePfu+X8RTeEdzyCgIioiJMkSVcAU4F/OjQPkmV5KnAZ8C9JkjwG08uy/Iosy1NlWZ6amelZkvtIwTVnyyybDzuFRZPZxJs73uy13x+X/NFnTdg9FgVijSZwOfohGQm0dBlDU+v1wUclH9m2pezRtu2Gth6P+a9WHPNNO9KGQP3egO+dnhAdvIhTZ6NSKFwFiqtaueSVVWw61MSmQ028usxeL61Rqw27kJPVO+pT0GfFc1Qt8FxLcv7e+by13ftkfUR2Io0dBupC8XbX7KA0wW5Ibr16K5nxoT/DXGv3Hmix51Jb81+n9TMPLCg5oI7c+FYHf/1qOx+uPcgLi/dw3gvLufLVNUhIfHTjUdxwzJCAFMh98fW5X3tsn/vFXC7/5nJ7g0YL578K4y+BxX+Hl2bDkidhw1vw/X3w7CT48SEYfopS4zXfcz3Sz/d79/p646eLfkIjaTit8DQ+PvtjThl0is/+LV0GPlx7iDPH59omp96Yk+9f+PL6Q2VB1Zlu7m7muY3P9Voi6dpx1zqlG/jLm6e9yTmDTvXZ59FVjwZ8XbWRZdljTuUvh35x+js9EvCl8n320LMBJRWgrKmT89N7j7zwhVWJ2JpapCaupcmCfuZkjnTaDTZdpNpSAzZH7RxYiwc21JJZD614CICz2zpA7/4+21XZQlKsjoJU388kSZIYka3UXHcsLXdjdia0+XaAhEz1dsgd7/Z/7412NfKkBaqhhgFbDjjWhyiwtDkhSdJJwH3AObIs274FsiyXW37uAxYDk1QY02FNa4/n1cU7F6uzQhgJXtnySu+dLPgST9hd3WZTFA6UwZkWIacwhhHbyLC/tOrbuz2W0LEyd5g9wr4jZUDAIcSghCcGnQPbrk4EgyzLPPDFNuKitHx96xy+vW0OMVEuL/0we2B3V7eRlxxLUqyXkNzGA/DTI5w80HNQyP3L7+ef6/7pNdd8RKhKxGYT1JbwPr7DlYPF0cNx1udn2bbX7m8gIyGaIRn9K0yz3dDulgM6Z0QG764+yD2fbuWJ74rp6DHx4NljWHjnMUwZpK4BPihpkFfF7Z0NLrnKuhg49yW46C1l+6dHYP6tsPolyBkHV81Xwob13uuvflISmNqwK6PSRvU6kf5gzUHauo3cMGeIz36B0NplZG1pQ8Dn+ZvXGqxxMDl7Mo8e96TPPtZSbX3JcxufY+o7U51UdldVruL3P/3e6e/0SMCf/8s6i2jQ4PQ0njr2qZDuNzw7ISwhxK5kx/eec+2R1EKn3WDLSFVbclSzVDZgE2OjSIjRhRxC/OnuTwH4Vh+nLPi5sCeA8ocjshMpqWrltVPtkVLVOm14w4jb66CzATJHsaV2S/juIwgbahiwa4HhkiQNliQpGrgEcFITliRpEvAyivFa49CeKklSjGU7A5gNBF4Y8wijsctzLbJgin/3FYsOLvK7r7fwzI4eI1UtXQzJDG4SPiQCSsQAhT0GyBhu229o7yHNRwix4wvNlDJAWWXsDsxAStPHBK9CrJJXdMnuOtaUNnD7ySPISY4lKymWP8w627lTmA3Y4qpWRvjKf13yT/BjAvHHJZ7zBEfkKIsnQefBNpaCMXwlC34/8fce29eUNjCtME01z6VaPLnO3fh44fIpbHnwFJbdczxbHjqF724/hmtmDyZGxXqKjnw590v/O0sSjJkLNy6Bew7A7VvhzxVw+ccw5NheTw9UqChQDCYzry8v5aghaYzL770Ulr+eBp1W4qFV9/Pi5l5ygB3oNnXzp2V/8tnnhAFKHqRadae9UfRmEe/uDNz7HSw/HfyJ6vZq2/6Xe5Xv2C9lv9jabvjh1xN+KCFRmFRo2y+zhKsWpMb1Wi6rN4ZlJlDe1El7t3fPb6i8edqbTMuZFtzJLsbc96XfB3WZaouBac1ZVZPc5FjVQohNXt4xe2vb/NYuGZGdSHuPCY3JvhhokCRoCaMBW1tMD7BYE5iita+IA0FkCfktIsuyEfg98D2wE/hIluXtkiQ9LEmSNY7on0AC8LFLuZzRwDpJkjYDPwOPybL8qzZgO42dLClb4vV40ZtF/HLoF6/H+wu+1CddudNL7smBemX1elB6cAZsfkocUVqJvWGuBXt/QxNYRAzMZpmGdt8hxGA3PNZqLQ/DgJWIQwghdvHAnjzo5KAu897qA6Tro7l4qj0A46qpU9CasuydwhhCbDSZ2VPro4ROVzPmbZ9SNCDL83EHvC0OZSbEkBofFXzOlQcFYjXRelj5rmjqpKyxk2n9MP/VW7pAbJSWgtR47550FUmPS/eZU7q7cTflbR4mTnEpyt+5zv+cNH8N2Jsm3OT3NR35dmsllc1d/PaY3r2vZw5xz9H1xrCsBMoMy3hhk3t+sjeWlS1jeflyr8c3XbyCxGjlbzXYvEArc3rJ5QV4bM1jId3DX2RZ5g8//4GrFlzl9v2+e8ndERlDXyHLMg8sf8CtfcOVG/hi7he2/bJGqwEbH7KAnbWk3t4whBFbmZw9OaTzX40d2XunXrCGEGeqXN8clFI6aok4jZPcQ4SbOnqoa+vx24C1ijCW1tqjyoySpJSgCxd1xfw7LYVbdzmHtc/MnQngNVLg94s8LxoLIk9oGfUWZFn+FvjWpe0vDtsneTlvBVCkxhiOFKa/O73XPr//6fdsvbr/1qT6Zt83AfXfUrsFg9ng9mIrtYT+Dg4yDFKn1TAwLT4sHljHurZZ8dmgVcbe1GnALHuuAeuItaTHowe+4hJQ8mDz/I+et+bAyrIcuJfNxSv6x6n+q5RaqWvrZtHOGq6ZXUi0zr4OJkkSuqh2TNZ5exg9sAcaOugxmr0bsNs+w+gQwhcMkiQx3CLkFBQ1O3ksLbX3fipiDf3sbwrEEH6PpL88fszj3P7z7R6PWZWC1XjG+vv73jzx5oC8naAYD/9duo8hmXqOG+F7kcb6u/jK93ZkVFY8+z2Ug5VlGRnZowc1xodhn6yJRhubyP9N/T/0Ufpe83p7Y0beLJZWrAjpGmphlJVFyIr2Co7+4GjePv1tt88nXEKCoCya1rZ1k5kQE5RWRChYf3dXXPM+q+saSKGVvJQ4DC7ndBm7fAqsuTIsy15KZ3xBSmADjhDTc6ZDqX0R32g2BpwLW9XSRbo+2un9qhZ5ybHsqFAnreUSvfvimbXM0VA/079GWP5PS6rt7+tYsxxmD2wJFVHOz6yPz/6YoSlDbXPRqbumsq56nVOf5RXeF+kEkSWiIk4C3zgaRYcz1jIlnhiZ6nll8pn1z7i1ldo8sIGLfVgZlK7nUKP6IZyOXnJdst0D2WDJS03rZdXUzXPWWBrQ/dP00RjNMi2dQYSzuCgQ66MDXyBYtLMao1nm3EnuFbHOH36uw73C95225qWO9GbA7pzP3szQ8wJHZieyuzpINevanbybHFgN41BZs7+BhBgdo3OTeu8cYfpLXesTB54Y1uubZTNvbn+T6o5qj8etobQA5w8/H4CFFyzk47M/9vseq/c3sK28heuPHqK64aLrXO+x/drvr2XCW+4K2s9vep4F+xd4OENBo1O8NKmxqfxpxp+I0obmhTu10LeQU6QwmAxuIYX/XPdPJ/X9d3e+S1N3k1Mfk9nklB8bLHtr2zj5mV+Y8fdFnPP8Mmpa1CuN4g89JvcoIEchHgA2vc/Vy09kU+yNJPx0H1GSsyHnS8TRE4PS44nSSuwOQy1YgMfmqOC5dxFy8jd035GaFvVL6FjJTY6jrq2HbmPoz+NSD+rwVgPWXw9scnwUmYkx7HPwqg+Vw5wDW1eM1qX+66i0UU6OlJdOfomj848O3xgEISEM2H5Ch6GD4z86vq+HoQom2fNDMS02jWdPeNY2YXNka627t6O0rp2MhGgSQwgrHJAaR1lDh+or4B8X2yeacWl2I8mqVtubB1bn+BLXZwVswFrL9AQl5NReixybYttNig7c0Fm0s4a85FhG57obZ9cWzbNtm9vCV/KquKoNSfLykuxug9JlXJTgv4HvreTTiJxEWruNQYVcGWoimxFhrf86ZVAq2gh7Y3rDYDKwuGxxXw/DJ72p5/rL2qq1HvN9rcRiF4ixqvHm6HMYlTbK2ylu/G/pPtL10Zw32VfVOmcuGHEB5ww9h6UXL2X5pd49CV81bbdtdxmU53nRm0U2b8SlX1/q1P+lzS8xf6+T9IUTkodQ91DI0eew9XLPRnakaO5uZvI7k3l1q3MIoqsgzGNrHuOYD49xanty3ZPMeG8GB1oOuC3qmGWzTam4savRo5EIikbEDW+to6nDwJ0nj2BfbTu3vLchKPXoYPE0tgXnOSxkHFwFX95MafRIvok6GVa/hG79G079T/vUuW5qb0RpNQzO0Ksr5ORQJiuQUHuvZDgbsMHkTVa1dKleQsdKXkpoSsSOkSVSjPscYG9tG9E6DQWp/jsfBqfrKa23R8tptdHQEs4Q4t2YYnwb2DHaGPcFGUG/QRiw/YQNNRv6egiqcaj1kFvbOUPP4ZeLfyEvIY/TB5/udnxT7Sa3ttL69qDzX60MSIuntdtIU0dgifq9sa1+m21bnzbMtm3NS03rxYB19MBuTMuDpsBKK6RZVI6DyoNtr+Pn5ODDS7uNJpbtqeP4UVkew5ez9dnEaxPJ7I7C0BI+A7akupWBafHERXuYHO9bzNLowB5vJ39yskdvwIisIIWcTAaMLrnNg5IGBXYNP3j79Ldt27Wt7ZRUt/XL8OG11Wvd2hKiglMYDxeO9UsXHVgU8MKXyWyi3dDu8RnoiKPCrzchLl/sqWnlx501XHHUIGKj/DcO46PiefToR0mJTUGv8+/ZurjY/W/Y8fnXZ+iiGasNf3TDW9vf4un1T/PattecDJHaDuVzeXnLywFf8+t9Sjmnsz4/i4lvT2RPo/058fq21znr87N4Y9sbHPPhMUx5Z4pHA+j15aXsq23n2UsncduJw3no7LGsLW3k660eYr/DhCcDNsHq1ZJl+PYuSC7grwkP8G7m/8Gwk4lZ/LhTf6NsZFtdYN+nYVkJ7KlRrxZsVa1y/3NSxqhzwbQhnN5u97A/ve7pgC9R3dIdFgEncKwFG5wB6+ikmJgxzu34npo2hmToA1pELcyIZ39dhy1PXqeLCV8ObHcbNB/iR5NnwVRHPDlkhJBT/0AYsP2AFza9wE0/Bifi0R/x9Mf96NH22nzeBDxcJ4ul9e0UqmDAAhxsCD1cyxux6Y4ldCweWB8qxOAcUnSVriFwD6ze6oENwoDtqKc41ndtNl+sK22ko8fECaO8591NyZlEj0ZC292gTGTCQEl1q/f81z0LuTnHfXxj0n1PULqM7i/0oEvp1O9FdvlbkFDfK+qo6rlum+Ih648CTrFa98nYV+d+1QcjUdh45Uansg2u3L74dr4r/S6gaz625jGOeu8o/rryrz77lTUaeGza+3x6zqdB1UN97qc9xEdruWpm8AsingTAPPHV5gqfx1dWrOz1GuFSHZbjUsJyXdv1ZZl/rvsnr297nWfWP8PRHyjhhFtqt3Du/HN7Ods7bT3O3sNLv7nUlkK0sWYjAE+tt4vITH1nKvctu8+2395t5L9L93HCqCxmD1Pqil8wpYAhmXr+t3RfWHNubWMwtPPVPh9/v8ULoGorHPdnStu05CTHwUkPIvW0srzwCqeul35zqZeLeGZYViIHGzps0QGhUNVexcnfXwlATBDRSB7RRfOE2a6o+0HxB9R01Pg4wRmDyUx9ezdZieEKIVauG6wS8acln9q2x3pQa94TgAKxlcEZCdS1dYPlHanVxSrpToYwqPjX73ZrenjWwx67etIxsNbAFfQtwoDtBwQq3gH9J5fMFdcX59j0sTbBIiuTsydzQ5F7SQHHiVBHj5Hqlm4GZwSf/wow0GLAHmoMjwGbazQiZdg9sPVtSkhvarxvA9at1m9zOZj89xLbQoiDKaXTXocxKngDds3+BjSSb5GgNVWraY7qYWOMFnNnU9D38kaP0cz+unZGZHt5SR5wn1TfN+M+3j/zfZ/XfXeXe+mNVH00WYkxFFcFGLJWs4MWTfgfsY45O6/seYponYbxBb2XVIk0V393tVtbRlxGH4xEQafR9Voqo76z3udxRy7++mK/a5DqNWm8tbSV4SnDe+/swp6aNuZvruCqmYWkh6hQuu6Kday+bLXPPovb7uf9ne71bK9ecDWtPa38duFvvZ778JjrANCEaaphCED8JxhcJ6/thnYeXPEgl397eUjXdRU/6jJ12VKIPFUhMMkmpxDtb7ZU0tRh4ObjhtraNBqJa2YVsqWsme0VLSGNzx/u+uUu/r3h305tTuko616FpALM4y6guqWL7ORYyCmCQUeTtPmDkKIvhmclYJZhnwoCjY7REmvaSkO+no3MkYw02RcsfYXYu1Lb2o0sh6eEDig5sACVzcEZh3ua7BEDUoJzvdwug4myxs4gDFhlrmb9k1vbVY0ZoMX3AlpQ1Ja4Nc0pmOOho2cD1lomS9C3CAP2MKWhK/Ai85HA1fv6wVkf8Ok5nzq1aSQNt02+jbRYZwOo22TP5wy1hI6VcHhgazpq0EpaJsdk8kNZta2EDighvclxUUR5EDZwxPF3BUA2BRQuYw1RbggyB7YnKvgX4/oDjYzKSfKZm2z9/Z5IS6V4X2nQ9/LG3to2jGaZkTkeVsw7GqDOvYzTJaMu6dUT9Pq21z22j8hODLyUTs0OTh7of36iGuyVS5lYkBJQWKnAO/ub97O70X213hM76v3Ld75r6l38YcYVrN7fwHfbPOdd++LZRbuJi9Jyw5zBAZ/rSow2plcPsCb2EH9f4+5R3lCzgec3Pe/1vOOkBGaOuQRwV6VVi8z4TJ/HH1vzmO1ZtLN+J0+ufTIg76QZ98nrZ7s/C2yQAVD0ZpFfgj+fbChjSIaeKYOcFc7PGp+HTiPx9ZbwhxFvrXPXrbBFVLRUwt6fYMIlNHSZMZplshMtiy3jL4KGfSw/1vt3pzespXT2qFBKx3EB8EBH4H+PXskcxdBOu4EdSNhplUWMK1w5sHHRWlLjo6gIMgf2w+IPbduySw7s3to2ZNl/AScrhZZqEyaHv8+d0VHhCSOuKwaXSMDkGM+LvvdMu4e5Q+c61TUW9A+EAXsY4FhPzUpvIWp9xaE237lfjvx04U9O+/oou7EaagkdKwkxOtL00RxqUC8M5cSPT8Qkm2jpaVWMVwdFzfr2nl7Dh8FLGHUAYcQxOi0JMbrAQ4jNZuhsIN6yUn7xyIsDOt1oMrPhYCPTCv0rDbMrJpotxYHVuPWHEl8KxId8e5R6o76zntWVztcYkZ3I7prWwARSPNSAnTtsbkhj84dpgyNbtudI5qOSjzhv/nm2+qZGs5Gn1z3N9rrt/HvDvyl6M/AqcFePvZorZgxmVE4ij3y9g84e/6NpNh5sZP7mCubNCt37qgbv7nSPWLDyxPHPkB2fzTVjr+HFkwKPMvKHx+c8ztNFt3g9/u7Od5n6zlTe3P4mNyy8gTd3vEmrwf+FqEiE4gbCtrptlDV2sGZ/A+dPKXDTIEjVRzN7WAYLtoXfgPWUDmFblN72ieJKm3CpTSjI5k0cfTZodGh2+u+RdGVwhh6NBHuCrc/tgOO7ODlaxciVzJFOixElje5eP29Y1aTDFUIMSh5sZZAhxI7ILl+DQBWIrVjTxeI0dsdGlU4XnlI6tcVUpdsXAO+YcofX+sTZ+mz+dvTfOHvo2eqPQwW+3PMlRW8WUddZx19X/tVzDfMjFGHA9kNcZbuHpgzl23Odyuz225I7X+/92u++vnKw1CihY2VAWjyHwpADu0fugjTnMi31bd29KhADnDzoZPfGgIWcogMXcepsBNnMCy2KaMWlowLLPdpZ2UpHj4mpAeRYHjgU2O/lD7uqWonSSp4XOA6uBC8vI4C/zf4bn5/zudfjx310HNf/cL1T28icBLoMZr9D0Y1mI5e1O6uRnj/8fK4bd51f54fC+IHBh4dHktFpo/t6CH7z1Pqn+GrvV5z1+Vm8vv11LvnmElu5MFmWnfIT/UGn1fDw3HFUNHfx3E/+eXjNZpmHvtpBVmIMNx8/rPcT+piYgulIksSdU+9kSEro5aw8kRKbwsmTbuTYXrIvnlz3pE353ZuqrytGs9E91aOPufSbS3lvk7KYckZRrsc+J47O4kB9BwfqQw+v9YWrARujdVhQKV4A2UWQMYxqmzfRYozFp8GAGbBnkdP5gaRFxei0FKbrVSml4xiV88SxT4R8PRuZI3H8jRYeWOj3qdUtStRAuEKIQQkjDkZZ3xXXxfi9NW1opMCdD7FRWvKSYxmjudPW1qLRhMkDW8KVifbFhctH954ScH3R9ZycOVX9sYSI1Rv+xZ4v+KTkEx5Y/gDlbeXsa9pHh6GDx9c8TqcxDHnE/QBhwPZD5g6dy1unKwXnrYpsA5IGOPUxmNVV1VWLT0rsuVLXjru21/6OCpyOITZqlNCxMiA1Lmw5sKQPddptaO/pVYEYPITUaaKCKqUTcA5su7OiaLSm97E6suGgotrnGrrmjVSTiea6Stq71VXtK6lqZUhGguci74fW0pE73qnpjdPesG3PHTaXYanD+Oycz2x/Z55w9L5YhZyKq/yb0La2V7PV5b84V5/rUbVZDa4YbRdFWdvo3SvWV7iqO39z7jc+BZT6G7Is8+dlf/a4uv3uznf9zm9zXOWfPjiN8ycX8NIve1lX2ntKyDurD7D5UBP3nDaKhJjwhOSqSbiEm9yQJP4z+EIG9/h+J9Z3KfnM2+q20dTV5LGPLMusqVzDgv0LuHvJ3Rz30XEqDzZ0Vuw/yOAMvVcD4WiLqNOyPeFd5G7sdlZwtRkBnY1K+ZwRpwB2Y8yppumQ46HKeYEvEJEjgKFZCaoYsI5e0tQYFaNX0ofRHaQGQlVLFzqNRFovWhqhkJcSG7SIkyOpsc6f2Z7aNgamxROjCzyNpTBDT2W9fSFEG5OovgFrMkDDPqqwPy/8eVZpJA1ZyXbRvPXVfVvGy4p1LmmdP2+s2chpn57G3C/n8s7Od3hn5zs+I2UOZ4QB24d0GDo8Toj0UXomZU1iwxUb+OXiXzye218NWMeX2h1T7ui1/40TbmRW3iwA2o32FWM1SuhYGZgWT3ljJ6Zw1Mdz8cAqBmwQoX0pA4JSIg44hLjDeVITpQ1sgWBbeTMZCdE2FcPeOKajk2S5hTV+TNADYVdVKyNzPIQPm81QtYUZ0c6G+pTsKW5dh6cOZ1LWJK/3cJTPH25VIvZ3wlTn7lW7tqj3BZ1gcRRK+3C3b6GqvuCCry5w2h+YNNBebuMwwFG0xJXH1z7u9ZgjTx77JN+c+41T20PnjCE/NY4/fLCJRh9/yyXVrTz6zU6OG5kZUN3XQFFLJdu68Boxxl3A7xub/Op660+3csk3l3g89vmez7nuh+u4e8ndAXnMIsmu+gMcP9K7AvzgDD15ybEs2x3ZKK3bJ9+ubOz9SdF0GH4qoBhjkgSZiQ7vxaHuNe/9yf11ZHhWAqV17RhM7nnKgXDt9/bnclKMSirEAFFxJOmCiyCrbukiOykWTRhreecmx9HSZaQthMXlQq37M3xPTeAKxLbrZeidIgd+0OvVDyFu2Acu+cjeKmO4YtbZFxTmfTdPzVEFjdWAteoRODqCrAJUwgMrUJ2bF93sVsT7/OHn20KIo7RRXuPyy1rDWOA5SCrbgsu7sRq6dy6+k9LmUkARcVIjfBiUEGKjWQ5acc8nDgas2SzT0N5Dhh85sACfneMgBpIyCBqDCSEOUMSp3XlSE+iEdXtFC2Pykv32JJq0UWRpWlmhojegtctAeVOnZwO2cT/0hL4qD87qgwkxOvJT4vz2wJpq3UWkvP0tq4GG8KqxhkpVu4riKIchV4y+glMLTyU3wTnsMzE2imcvmURtWzfXvLHWY6RCdUsX17y+lsRYHU9cMD5sXnzAzcAOlqeOfar3TmqSPQYpeUDv/SyUt5Xz88Gf+Xrf17y9422K3iyi6M0iHlzxYBgHqQ5RWZ+zqtt7yLokScwalsGqffVhyeE1mAxuKUxzh861fy9LfoC4NChQwi1rWrpI18c4CxvmTgQXY9FTvU1fDM9OwGiWQw6VdpzcxwdpcHrjjvjAVcYBalq6yQqTgJOVvBTlnRFoHmyHwR7NFu+iAm40KdUBhgZpwA5Ijaexw+6c+UVrUCo0qEltMVVaZ4PV32gRT4rEfYnJbGJtlXt9dStWw7y/Vi0JFWHA9iGeQhCuGXeNXxMUg9ng9CDpD3SZgsunsOYmAbyy5RUaO1upbu1iQKo6L5OBKioRu33mKfaQkqZOA2YZv0KIQfEA2kJ4UwsD9sCm6WNoaO8JbJLi4oENZDLcYzSzu6aVsXm9r1IPS1Fy9Iy6GIYldLF8j//lSHqjpFoxUD3WgK3c7LaO7/j9CgTXCdXIHP+ViM0uBuycfM8S/WqhN0502m/pCX8ZDYF/jEkfwz3T7/F6fNLAVP5z6SS2ljdz/osr2OFQAmVdaQPnvbCCxo4eXps3LayiLqCkqvRWasofAjVGVGHgUQF1v+3n2/jT0j/xxFoV8x4jxKH2PRh8lF2bMiiVxg6DTc1fTR5Y8YCt5I+Vvx39N2VDlqF0KQw+BiwaF1UtXeQkuxhjWh3kO0fF1HY4R830xvAsa31udRYsQf1FxsyscUGdV93SRXaY/9atpXQCVSJ+buNztu1bCpy1PA42dGAwyQzNDNKATVPG9MeJ/7C1mdX2wNYVsyfa/v8cyKJFX5Z988SFX1/o87h1fhfORc++RBiwfYinh6WvB+jmqzY77bsKzfQlJrOJlza/FNS5jurDX+37ijsX/xFZtpfACRWrAVumghKxowDIo7X1LiV0FG+ovwYsKIqkAB3J+dDZAF3+Gx4ZCdEYTDItXQGEALl4YDPjfJehcKSkuhWDSfbLgH3njHdIiErApIthYEwHOypbAhec8jEOgFGePLBVWzhpgHOI5fJLlwd1H9fV1uHZCeyr9S9kzVznrDj5nxP/E9QY/GX1vkZks/3ZceuiW8N6v1C4ZaJ31di+4MwhZ3Jq4alsuWoL88bOU+26Cy9YyOarNvtlEJ4yNoc3rplGVUsXZzy7lNP+tYSTnv6FC15aiVmW+fC3MxlfkKLa2HwxLmMcqy9bTWJUcGHAU7OnMjU78mInUoAGbDh587Q3w36Pt3Z4z9+fNDAFgI2HGr32CZafDv7k/WDTASXks9AuRFnV3EVOkgdjrGAab1fa81491Yn2xdDMBCQpgLQOPwg0paZXMkfxw8HADTAlhLh/emD3Nu21bY/IdNaa2GupyxtsCLHVaZEi2QX+DD0t0NXs7ZTAqdvNohS7IRpIOt5FIy9y2v98t3cxyEjQW4m3XQ27IjSSvkEYsH2Ik2qfBV/18jSSho1XbrTte6rD1ldcueBKvt3/be8dPZCtdy6EvathOwAFqeqoqeYmx6KRUEXI6ZZF9sn3MK0eou1GtlVQKSOA8hZWL/xjLZb/y6aDfp9rrwUbgGHYXgexKbbdQFbmrJ6hsXm9lxrQR+nJT8jHqI0mQ6MYnKv2qeOFLa5qJT5aS36K+/dDrtxCjYN4xGNzHuu1zqU3XA3YkdmJ9JjMfoWsmertBmy0JjrsgjYr99Wjk+wLJ9vqtoX1foHgGrUQSD3ESPDYnMd48tgnkSSJgy3+//31hizLaCSN3//3c4Zn8stdx/PHU0eSkxzLoLR47j9zND/eeSxFBSqW9/CDYP9mtl69lddPe51YXeRD2qXEnIjf0xNvnPYGk7MnO6VnrL7Me2mvYMOt5++d73XyPTwrkYQYHRsPNgV1bV94mrfYOLBC+Tlolq2pprWbLE8G7IDpDOwJflEzzvIOUNOADTZaxyuZI0lzCN9cVbmq11M6e0y0dBk9f2Yqkp0UiyQF7oGdlG3XjTDHpzsdC7aEjhXrnK+yyf692BITo24YcW0xn8Tan8mBhAWnxaYxS293WvxlxV/UG1cY+L70+74eQlgRBmwf4mliY2trr4Pl/4aVLzitPrkauLIsI8syTR0BhpKqjKsxfd7w85w7GHu8ehddPwez5fdQywOr02rISYqlXAXFvS11duXEIQnOOVdWQaVAPLBWykyWsQWguGc3YAPIg+2oA31wYTDbK5pJiNExyM//l+LGYn6WW4kzNBIfrWXlXvUM2BHZiR4FLt5u3uG0f8bgM4K+j+uLza5E3MuEqb0eU4f9d/W1KKUGzR0Gtle0kBadZ2vrMavj7Q4Vg8nAjPdmOLX1SXipn/x0yId3KUCC+T2T46O45fhhvHHNdF6dN43r5wxBHwHF4WWXLOPni34O6RqfnP1J753CiGNI7ZZT+k510yoY5ygME611fidsvXorH531EY/Necy2iJgem06cLo57pnkPN3dkX/M+nt/4vMdjWo3EhAHJYTFgfVK6HOJSIVPxoHUbTTS093j2wOZPIc0cWk7h8KwEdqtQC9aKr9J+QZExAp3DtOyx1Y/1eoq17JDHz0xForQashJjAvbAOi7y6RLznI7tqWkjKzGGpCCrR6Tpo4mP1lLR5PL+UiuM2Gx2E1h00iLxA22UOuKianDWkLP86reyYmWYR9I3CAO2D/GUp6aRNNB0CF4+Fhb+Bb7/E/zvJOjwrOK6ZF8xp/5rCRMfXsiJT/3CtnIVQy1C4K+z/mrfWfc6/HMYPDYAPrq61zBZsww6jaTqAzwvJY7yRnVFnGJTC532rQasP3VgrVjDp9c2WUI9mg/5fW66Re04oFI67XWg9z9s2JEdlS2MyvFsOPpid08T0walslIFD6wsyxRXtzLSU/5razULo5wnRP54mP9zwn944wT3iWC3yXlhYFhWAhqJ3vNga3ZgdLhtuA3Y1fvrkWW4c8Jfe+8cYTwp9PY3D6wAkmOSQ87vcjXSIs3k7MkAvFPVgLTBe3htOPBkvFsXZnP1uWglreKZzZps+5xHp4/mzCFnMid/DicNPIl3z3yXNZev8amM7sqr217l632ea69PKEhhZ2UL3UZ1F4x8RhQcWA4DZ4GlfEyNtZ6pp3d5fBqkDeG1KHspukDVUodnJ7Kvrj08FQbUICYBrYO42N7mvT46K7jVzQ0jgdaCbe5utn3f/tnYQZaDBggoJXSC9b6C8r4ekBpPeaP93SuDeqV0WsrB4BxBFWiNak20Oo6VUGg3tDv9X/TG9vrtFL1ZxFHv9Z80CzUQBmwf4ZgI70i8JhY++y10NcENP8NV86FhP3xjL+48PsOed3D712/S3Gng/04eQbfRzGX/XcUhFcSKVGP75/D17ZA3EWbdCju/go/nKSthXugxd5KXEodWRQn5/NQ4KtRWIXbIfwVosBiSqQEYsHE6exhsjzY6IAM2zaJ2HFApnfY6cAn78QdZlimpbvOs/NsL96cnc2xhDHtq2qhpDa1wem1bNw3tPYzwNI7anU6F42fnz/brmscOOJYpA45xa39h0wtO+7FRWgal6/0yYOcW2Femw6k+DEr4cIxOw8kjxzi172/eH9b7+oOrUF1mXCaXjrq0j0bTO8NTg1MNdWThBQu5Y8od5CeEr9xNJPn5op85rfC0XvupYcCuLW3gjg83MeeJn5jw1x845ZlfePirHeyr7T1MNCs+i61Xb2XCsDNgy4c8PvPhkMfjiWStYlgc3W1/2owscfdeWw29L+Z+gSRJTMmewpunv+nm6Y7VxfLM8c/Yvi+BlpP509I/eWwfnZuE0SzbwjrDxcmDLEI+LZWKCrxD+LDVGPOqqJs9TjnHwmNrevdQOjIsK4Eeo7l/zXlcyRzptPvd/u98dq9utdbNDW8OLAReC7bdwfg7Kcp5HiHLMntDKKFjpSA1jrJG+zzhy8QE9Tywde7VAQJF6mMPbIehg6PeO4qjPzi6984utBt6T386nBAGbB/xypZXnPbvnX4vKy9dSeyehXBwBZz2D8ifDEOOhTn/pxiC5RsAuHS0fQJoTPmad66bwa0nDue9G2Ygy/Dnz/tJbmxnI3x1O+RPhcs/gVP+Bmf8E/Yugo3eV8jN9NjU6NQiPyWOyqYu1VZqj+rsdFIgBqhv7yY5Lsq5XEAvOK5mtyfnKd53P0kPJgc2yBDiurYemjsNQb2cuiSJmZb0tFX7QqsHu92Wh+tBSKq2hK2x9pf+8vLAxJvGRaU47e+o3+HWZ0R2AsW9GbCVW5x2LxhxgZeO6rBybz1TC1OJ0WnROYQtnj///LDe1x/qO5297j9d9BN5CXleevc9d025K6jzHI3VHH0O14679rBXfkyPUyaoGkljK+3mysdnf2zbTogKfuJa29rNTe+s58KXVrJoZzXj81P4zcQ88lLieHtVKac8s4R/fLvTP2/i1Gugu4Uz2js4f7i6fwNxMjxWroRQyvoMnkoYx3WGGPjuHpbVG1gx2V6GZ2iK4lkM9HtQkFAQ8Lje2PaGW8rD6FzlGbmzUr0QW3AvvZYUbXkWVyjzEwqm2Y5VWcNhvdUNzynC3GovsVXRVhHQWIZb3kfB5sEearW/b2+bdFtQ1+iVzFFOu39c8kefhkSNzeiPjAe2ornT7/Qzx7QIXaJzSbDqlm7auo0hG7AD0uIpc4iWm5+gV88DW1vCz/GhzS0ltcPMA0QNI7SyrdJn+Z3DBWHA9gFdRncv1GWjLiMhSg+LH4eMETDxcvvBWb9X8kqW/xuAEwac4HSuNlaRnx+Uruf2k0ewdHcdS3cHJkmvJi+caPFcrXhO8SSf9QxYC0BPvRYGzICfHoUe+6rp66e+TlacvTi7WiV0rOSlxGE0yyF7AK2c09oOqa4GbE9A4cPgPBkwJecF5IGNjdKij9b6H0JsNkNHPXWxgXtRd9cok6BgXk4yMDyhi8QYXch5sNstIfJjPBqwuzijw/5ZWEv5+MtbU/7stL+zYafbhGpEdiKlde10GbxPpK9vcDacw+mJq2ntYldVK7OGKosSZw0923YsEHXFcPDd/u9o7FZfBTWczMybybEFxwZ83s0TbwZ8i/Ucbvz3lP9y55Q7SY1J9dpnVJp9ch6s8NOOihbO+c8yFu2q4Y+njmT1n0/i+csn89e543jjmumsuPdEzp9cwMtL9nHpK6t6f4YPnKm8Q9e9pnq+9R2doDnhAQBMmSM55fz3uf36dXDtDyTHZ5L46XWw8EEwm3nxpBd54cQXnKJs/CHFQWTPX55a/xSLDy12ahucoSc2SsPOSnVLarka5DdNuEnZqNgEkgZyimzHqi0hxF5LwmSPQ+vgcV5VucotdcMXw2wGbHBGenGD3SOnRvSFRzJHMrPT2ctZ01HjpbPitY6N0pAUG/7c97yUOLoMZpo6/HtXONUTdTFgbQJOQZbQsVKQGkebaz1stQzYumL+nuF/BJrZLPPUD8XM+PuPXPTSSnZXt7pFNkVae0aNhdGzvziba7+/VoXR9C3CgO0D/rXhX077Q5KHKF/KQ2ugZrsSauu4yhOTCBMuhV3fQHu9Ww7KZyX2JPQrjhpITlIsryzZF85fwSdzCuaAoRPW/g/GzIVcB6l1SYITHoD2Gtj6ka15as5UxmXYa6apJeBkJd+ibhdIuIwrbg8qFw9sQ1tPUAJOVoyJ+QE/qNMSov0XcepsBNlMTVTgoUl7Q1AXLI2OQtdZz/TBaazcW9f7CT7YXtFCYXq8Z5GIuhK0sXbD9vTBpwd07ahCdy/T85ucc2NHZCdilmFfrZdVUGM3q7XOk+ZwKhAvLVE+z2NHKHnNfW20OvLHJX/s6yEEjCRJAZc8+r8p/8c5Q89h69Vbgzbi+iM5+hxbXXJfk6Y7ptxBlCYqqFD5daUNXPTySmQZPr95FrccP4y4aGcPR2ZiDI9fMJ4XLp/MrqpWLn55FZW+0kEkCabdAOXrSOsObcFyROoIluefa9vXHXMP+kKlprPVQw3AwBlw/Y8w5RpY/i+Yfyup0UnKuzBCuHpmtBqJkdmJ6huwDouuL570or2KQOUmyBjppMxf3dJFtE5DSryX70bOOIb3OD+z3tnxjt9jSYyNIicplj1B1oJ1DNcOm7hc5iiiXKYOToagC9Ut3RaF4PBHcORZPOP+plc5fUYuqt97QljkdqTAk/NCpRDixtqdVAUQIffUwmKe+2kPY3KT2FfXxqX/XeVWRi/SooRqaEgEskjUnxEGbB/gWrDb9hDd+DZE6WHsue4nTboSzAbY+jHtLu9kM/Y/qBidlstnDGTp7jr2+pE3pDYPz7LkHe38SlFPnnqde6fCoyG7CFa/rBQ+t+D4e6hVQseKteRKWQhCTssrHD1rEiQ7h3rVt3cHbsA6vKN6ErKgtUpRbPaTNH2M/zmwHYqxYwjCA7unpo2EGF1Awlp/P/rv9p32OmYOTae0vsP35LMXtlU0ey3jU1VfzFca+x9HYnSAv2dMIomy86Rh/t75TvvWHGCvebA17mHHYTVgd9eSro9mjCVcUAgkRR7VlUsPIxrae7h23LVsuHJDwOduK2/mmtfXkpUYwxe3zO61PNcZRbm8fd10alu7uejllbb8So9MugLi07m5spS7p90d8NisZHa1E7fs37b9gtShjM8Yz8OzHuaBox5w7qyLUaKNjr0XNr0DX97iU+vBHwKJ3vD0tz86N4mdlS2qeokchZZsxqwsKx7YvIlOfa01YL0aY8kDSHZ5TjvWWveH4dkJQYcQmx0MyXHp43z0DIGMEfyu0Vlc05fRo9SAjUwZqtwUa9ka/xZ6nL5jrgrEtW0kxurITAwtd9dj+lhLhdNcMVhWt/qvC1Fc1cpLv+zjwikFvDZvGh/89ijau03UtzvPXw43A7a0uVSdgfQDhAHbD5BlGQxdSp7r2HMVj6sr2WMUafpdX7NwRw2mbruSrGvuyyXTB6LTSHy0zv9wVLWwKa5ueAtSC6HQwwq0JMG0a5XJfpU9X9Dx96gyqhufbzVgK/x8UHuiocshfzMuRZmwOB5v7yE9gBqwYFchBvjW3AjIAa02Zuij/Q8hblcM2CuKXw1kiICSYzQ0KyGgVeFjChyEkToUAxYIOoy4ucPAoYZOxuZ7CB/uaGAxzkIeF464MOB7fJZ7Bok+Jp2F6Xp0Gsl7HmzlZremcKkQm80yS3fXMWd4hk0ZOpCadgJ1+DV85q55j6NSlDDRN5YHJxS2p6aNq15bQ1JcFO9cP8N7jqQLUwal8c71M2ho6+Hq19bQ0uUl4iA6Hmb8jpjdP3Bl2iSePu7pgHPR78qew993rSZq3PlsuWIjH531ETPzZiJJEucOP9fp2W1DkuD4P8Fxf4bN78OPD7r38QPrWN86/S2uGnOVX+d4ir4YnZtEY4fBFsobCkazkdnvz6bNYDcWbYvvrVVKVFXuBKdzFGPMxztRkhQhJwcCFbEalpXA3to2zEHoWyw7sMi27VqPXjXiUiiKTqNIa5/X+YqUqWntjpgBa/XA+ruo/MyGZ+w7bh5YRcApVM+x1QN7Sd7T9kZjF3QEN2+w0V6Pucd5ocP1uebIy0v2EqPTcN+Zo5EkiWFZidx47BDq911OkUPJum/2fRPauALEHwP2tTbvv9fZX5zt9djhhjBg+wBP4jDsXwI9bZ69r1ZGnQEHVvDz5hISe+zhjq45tZmJMcwelsE3WyojEp9f1W4XYZAkCdrrFTn9ootscvpujPkNaHSw1V5+wHEl6z/bH/BwUvDoY3SkxEdR3hS8WqHTw85FCMlslhUDNkAP7J1T7mRGrlInc2iaRa0wECVifbT/Ik7tzp7/4wYc5/d99tS0BZzbkhzj4FFpr2d0ThIp8VFBG7DbK5VV7HGePDV1JW7TnmAMx5xhp/Lfymqvx6N1GoZk6r3WHpQr3A3YcLGjsoX69h6OGWFfzHJ9ua2u7D85mffNuK+vhxAwabFp/G7C73z2cUx9ONI5c8iZSg3Tc95ltuYNXlteSm1rYMZRWWMHV766Go0k8c71M8hLCSzaZuKAFF66cgp7atq48a313oWdpl0P0Ynw0yOcPOhkv2pCT8ycaNu+etV7pA09Gc59GUmrY3T6aP8Heezdyv1XPAurXvT/PAsPznyQrVdvJSs+ixMHnuzXOZ4WUoZnh5Yj6khbT5tb6T/b/KJyk/Izd6LTcb+8iZmjGOqgKRCoATs8K5GOHlNQVQa+OKAoAl+aFebyIpkjMRns4/vxwI8e52ayLFPV3EV2iF5Mf8lIiCFKK1Hu58K+kzCiSw7s3tr2kPNfAZLjokiK1VHVYv+82iQpoHmRR+pKcH1SfH2u5zI0Na1dfLW5goumDiAl3j6nu2HOEPRSAaPb7To03iqKhIOSxhJqOz3r26y93O70mVZ7gLdH3xipYfUZwoCNMEazkbI2D3mOxd8oL9vBPnJmRp4Bsom4Az/zm6EX25o/3f2pW9ezJ+RR1tjJpkNNKozaN+/tfM+2rUEDu78H2awY3N6IT4NhJ8G2T21hVuYQw616Iy85LiQPrFMoqEspmqZOA2aZgEOIE6MTbYXrzXFpSmMAebBKDmyPfwsVHc75p75WHx1p7jRQ09ptmwwFw6LmYjQaiRmD04KuB7u93JcCcXGA0x4vDJjBWJcFTtcw4hHZiR49sJtqNnFi/U9ObTNyZ3DSwJPUGJkbv5QoL7I5w+0GrE1QxcL1P1wflnsHw8UjL+69Uz/jm3O/4ZaJt3B6oT2f2hpZ8MP5P7DskmVMyZ7SV8OLGFavitaici1JEnefNpIug4mnF5b4fZ3a1m6u+N9q2ruNvHXtdAZnBFeSYs7wTJ64YDwr99Vz18dbPHvf4tPgmLug5DvY+5NfnvK02DT7zqDZcNGboA2iDJYkwelPwKiz4Ls/wbbPej/HCwnyELqqzuaM3FsAvIpCeQpltBoUe8NUSsdm1FdsAiQnASdZlqlq6eo97SRjBDUhlMyzGekB5sE6LvZdMiTMXqnMUYzotOcov7rtVX46+JNbt9ZuI50GU8Q8sBqNRE5ybHBpPQ4e2OZOA7Wt3QwNMf/VSn5qvJNY298y0qA5xDzYumLMDt7hL3/zJQOTBnrs+u2WSgwmmSuOcj6uj9Fx0dQB/HjInjbSbYxcPun588/3KL50Q9ENxOocvjPJA5m4w3e5Joi8AJXaCAM2wly94Gq3NpNsguIFMOxEt7BUJ/Im0xOVzEy2ccY437kxp4zNJkor8d22Kp/91OD17a/btguTCxWxqcQ8t9VYN8bMVcJlq5WyP6HkKvlDfmoc5SHkwDqpcboYsFYhpfSEwEWcrDl0d234p9IQYCmdHpOZVlfVPk+0OxuO3kpjuKKGuuD7XcrvNHNIOmWNnUHV7dta3kxucqznMO3aYv6RYZ98+vu7uREd71QGAuC+Zc6ew5HZiRxq6KTd5TN/efNL1Er2SfJVY67if6f8j6hgJsB+sKSkljG5SU45R6PTR3PLxFts+1qpb/IzD7QccNrP1ecelmVlEqKV7/wjRz9CrKX+522TbmPDlRvITch1jjI4gjl10KlcMvIS7ppqLzM0JDOBq2YW8sHag6wt7b08VnOngateW0N1SzevXzPds5J4AJw3uYB7Tx/FV5sr+Ns3Oz1Pxo66SUll+eYuzAb3Z84HZ37gtH98vIOuwaXvQ1QIWgwaLZz/Pxh4FHx+oxJlFQTbKlowNM5mziAlPPfmCTd77FffWe/2GWQmxpAYq2OvN9G5ALh/+f1O+1uu2kJGnCUSqXKTovwcY39HtHQZ6TKY/fDAjiDZh6hRb4zIUkJzd1UF5mX+sPhD23aUizdRdbJGcV+ts/Lw9vrtbt1qequbGwbyU+KC0wZJsIdcq6VAbB9TLPXN9s+gWquFpoOhXbS2BLNDreohyUO8dv1mayUjsxMZluWeznfZjIFUme1zwVaDumWqguG2yfYSUOmx6TD5Kihd2ut5kc7fVRthwEaYLXVb3NrMhk5oq1Y8rL7QaCiOHc8s3U7G5fueOCXFRjGtMM3mpQkXjnXUAMYkD4O9P8PI05RVaF8Ms3imSn5QdlMDK3sSKPkpcZQ3+V/zzBWjbDdYzC4GrDUPNV0f+IvHychIyIZm/x/U1vs1+JMH21EHDiUa5g6b69c9QlEgHpiorGCaLMIcMy3lXoLxwm442MikgSmeD7oUKH/q2KcCvr4ND1EQZa1lTH1nKvua9jHCm5BTt/P+iQNPDH4MvdDWbWT9gUan8GErEVHW7IWzPj/Laf9wyxP95OxPeOY4e75XjDaGgkS7cROM4u7hTJQ2ivuOuo/UWOeSOneeMoKBafHc/sEmmjq8P4Pq27q56tXV7Klp5ZWrpjBlkPfSPIFw4zFDuGZ2Ia8t38/LnpT3dTFwznPQsA95zStuh3P0di/SsqOe4Dc//ct+MDY0AxtQDOBL34e0ofD+ZW41ov1ha3kzsVEaTh9+FF/+5kuuHHOlx37/3fpfxr81nku+vsTWJkkSQzMTbAZGKPxS9ovTvtOClAcBJ6vIVnZv+c0ZIzCGsLiVHB9Ffkoc2yuae+/sQHO3vb9Gn+WjpwrkTCDWZd7x363/detmKzsUIQ8swMC0+KAWlG2lEQltjuCJvJQ4ahpjydUrCwvr4mJpaPA/0sMTcu0u/pXa+990TUsXa0sbOaPI86LG0MwECrP8L8WjFp7KbwK8ffrbtu3vzv+OL3/zJYw7z69rPrbmMVXG1lcIA7YfcF2iJfdx6PE++8myzMKO4QygGm1LmVNYmyeOHZHJrqrWkFRfe6O+08UQqdgAhnYYeoLnExxJyIK8ybD7B4+HN1QHrmzpi/wUpb5YS1fgKm7V7dXcssju2TLEpTgdtyoBB1NGxylvMbkg4BBix/v7pL3WKXc3WuPfWPfWtRGt1QRV2shqTGlNyu84IjuBdH00qwLMg61p6aKssZPJA71MfOt2O+2GVM7Eg/DY3UvuptvUzWe7P7OFMG+vcMkH62py2g+0/mMgrNxbj9Esc8yIDPeD/TAqKFxCVuFiZNpIThrkHPptNWDD+f96uJEQo+NfF0+kprWLa99YS6sHUaWS6lYufGklu6paefHyKU4h76EiSRIPnDmGsyfk8diCXXy63sOzc/AxMOdO5L3uIZvR2mjeOf1tFoy6ieSProLkAbw+50kWnLdAtTESlwpXfKoYxO9eAI2lAZ2+vbyFMblJaDUSQ5KH9Kp67erZs4ochY3WKmirchNwqmpWJty9hhAn5WN0SM8JZoF5bF4SO1yex4Egh1tJPHsMuETDnDfc3ciwGv2BqP2HyoDUeGpau33WNncl3UWtf09tG9G64OYInshLiaOly0ie3r5oWNUUWmnI1vrdNEi9f7eW7lZSrU4c7X1R46QxOV6PhYs7f7rfY/vYjLG27fyEfCUqKH0o5E3q9ZqOUQiHI8KAjSBLytxDiN454x0urK9RFIYTfK8C7q1tY2GHpdj2geU8OudR27HPd3/u1v+4kcr1fikOnxfWrUSINWxh0Gz/LjD8FChbC+31boqSV3/nHm4dCtZasMGEEW+r22bbzjQaOcPl5WM1IIMJIXaqyZU8IOAQYsA/Iaf2OkwOnmN/QzoP1HVQkBaHNog8JetkRGfqAVlGkiSOGpLOir3u4W6+2HCwEYBJngxYY0/oAg+OFEzjxA7n1c6tdUqYu0k2kZ8SR0p8lNuKv7mzyWk/INGXAFm0s5qEGB1TB6W5HXP0KoF7Ldu+IC8hr/dO/Zy/H/13njnuGa95U79WJg1M5blLJ7G5rJlz/rOcH7ZX0dJloLSunad+KObs55bR3GngnetncNIY9ZVeNRqJJy8cz+xh6dz96RYWbK1073T8/chDjnNr1patZ8KixylY8CcomA7XfMvUIac6edtVITkfrvgMjN3w1m/8fsabzTLbK5opcom4ska2+MPQzARqWru9KzaHilV53YOAE+BbhRhAkjA4GJCBijgBjM1LZn99u1tahy+qO+xifWFTILYSFaeEWDvgKf3A6oGNZAix1egsa/TfC/uxbqjT/p6aNoZk6IOaI3jCKuzmqM8mtXj4u/aXnnbMLfbFLV/6H8v31pEaH2UrTeeJU8aEOeTchZ931fBL6UaPx7xGA409jw/Kq0iKUscr3h8RBmwEqelwzoHQaXRMSB0NB1f5Fm+ysHxPPbvkAZhiUuDAcnSS3avxlxV/ces/IjuB3OTYsIYRuxVELl2myOLHu0+sPTLsRECG0qUcauhANocvNC/PVkonNI/047WNxKc5P8CtIbyp8YEbsE61/lIGKB5YP407q8e3vu3/2zvv8LbK8/3fr4YtyUvy3jNObGdvEhISMtgQVgtlhZbRFlrohpa29MuvpaULWkqhdFBW2VAoGwKEERKy42w7jvfeU7bG+/vjaOtIOpKOJNt5PteVy9I57zl6Y0s653mf57lvCUICoz0Y1gVfutfQO4ritNDEVuw3IwpudZTYrihLQ/ugEfU90i+Yexr7EadUYI6Yhc5AkyAaJhdqDX6pmym6y2Q1gTGGObkpONjivuL/uTX0DEAwWK0c7x/pxJpZGYhTeX+FXzzjYqTFOz9/j+x/JCrz8ofrd9VUJSkuySsrSwicMycH/7lxOUwWK25+cjfm/eJdrP39R3jwg1psqMrCW99ZjaXFEq8JIRCvUuKRaxZjQYEet/5nD57b6dGGoVDAepqzd7RiXPi+Vj2xSbhmbfgFsPk16detUMisAK5+ARjtBf51NtAVuCSyrnsEIxMWr5ahNy59A49u9C6JFsNe1imnkNNFZRc5n9gFnHLmuY1xBrCBs4lf1xQ5HoeageUcONIm/TvY1c82Ki0BHr+fpw8/7TWkY9CIJI0KurjofV/aA9imXun3RRnJ7gs8tTabPbmw2/tMuKxHKIbaQveC7a5Bi8q5SPLChS+IDuOcY1ttD1aWOa3pxOfnnmluGQ5TYMoPnUNG3P7sXijjgry/mHkOZk9MoEApT1Z8MkIBbBTxXPV58cIXnSW3JWf4OMrJroY+ZCXroMxfDLTsDZhBY4xhRVkadpzsjZjamKsi2iNnPgg07gCKgxDQyV0IqBOAhs8EIYEIBrB2L9iWMAPYBZoMQOl+gekZGUeyRiUaUATCbSU2pQCwjHtZ3vjC3gMrtYT4QFxwv1/OORp7RlAYZmmQknOHCnIofrB7GvowJy8Z8SqRUq/e0Lwo/ZFYvFZ0u73ce3ZeMo61D+FoTw3u/OROvHrwCbdxmdrI9VTtb+5H9/A4NlaKZw0YYzg9P0QRKxnoHuv22uZVqUFMO5aXpuHDH6zFv65fgjvPrcCvLpmDrT9ci4euWoTMpMiXRCZp1HjyhmVYVZ6BO16qxv/97xAmzM6FLZVNTO3cgnX456If4bHc8xD3pX8D3z0IrPquILoUaQqWAde/DlgmgH9sAI78z+/wgy1ClcfcfO9s3YrcFe5e2y48f+x5x+JyWYaw+ChHH6wdV6E4tO0D0mZ4+dd3DI4jRauGRh3493ptrvP/EYrYm90b3LOtwx9SVfhlI9s9gJ2wTuB/J9z//pJsh2SmIFW4L2oMpg/WRfTKaLKgqW9UNgEnwJlsGDM5I9h2bpJ8X+RF93Fcmeecs2cvv5267hG0Dxqxcob/HldPbYlzXjoHf9gVhu6GH+5/7ziMJivUSu9FersFoyjp5YChGFZjdBbWYwHdVUQRzy/m3MRcmzIhk1Ryu6ehD4uK9EDeIqDzMDDh/oUjJpSyvCQVvSMTEemBOdTt3mtzOjSAeSy4AFapFi7q9Z+iqXcUxnY/PrhhkpYQhziVIqQA1gqXGyG9d/lWz/AE0sXUcYPEnGwrtZRYYqaNU0IXpwxcQmy1AKM9GLepRF4+83JJ5+8ensDIhAVFaaEFsPaFk48SdBiwqQiWpicgMylespCT0WTBgZYB38IvvXV4OdGZIX7xwhfFxwVDyWrc3e09P/uFa05uCiYsVnzp9UvxRt0b+Onu37mNe+7CyPWWvH+kA0oFw9pZvnsJvzHPv3dpJHEVkbFz8YyLoz8RIuqolQqsq8jCN9aU4erlRSgKsXIjVHRxKvzjuiW4fmUxHvusHhf95VNsqxUWVE7LOQ23L7odd51+D5KX3IAlG+8TfNc1UVaSzpkH3Pg+kFYKPHcN8PLNPi1CqlsGEK9S+AwOfIm0/b/t/w9/3fdXAIJIj1rJUCvjPUCci5qrmIATAGkWOnbSZzkevtfwXtDzyU7WIDUhLighp/T44KuRwiJnHt5rdP877+9y9w0XAtjolQ8DQEZiPDRqRUAhpy/avnA+cbHQqesaAefyCTgBQGZSPJQKhnGzM4D9VnYm0Nfg5yg/dLmLPPrS/9hlU1M/rdR/ACuWEPr3oX+HNjc/dA4a8dLuFlyxtMBNRBQARhu+iZ8u/qPvgxkDys+CZcL/5344wP7JjCwBLGPsHMbYMcZYLWPsTpH98Yyx52z7dzDGil32/di2/Rhj7Gw55jNZ8Vzx06q0Qs9oduCS285BI1r6bSI2eYsBbgHa3RUN3cSAbCwrET6IX5zsC3P23nzU/JH7huZdws8CP6tCYhSvAjoPo7erDZqJhZLFhYJFoWAOJeJgcf3dMkOx1/6u4XGky2A+/r8RWzYxiJ7O1IS4wAHsaC/ArdDpBNEftxIwPzT2CvYLoZYQu37pftEuXADtlQGfS+yD3VXfhwmzFStniAgWAUDfSdyd4bzgzEqdJT4uGHIX4VyRquz/1v4X7SPtAVXAHfYSEWDLkU4sLTa4Gax7UpBc4PY8mn5vrr1lds4pOSdqr0+c2sSpFPjFRbPx9+uWYMhoxlX/2IFNf/kUT37eiDWZVyBJLYO6cLgYioGvvQOs/j5w6L/An+YDL3xVeDzsbDU62DKAypxkqJTit2p3n3a3z5foH+8HAKiUChSlJaC+O3wrHTuOiorhTmCo1UvACbAFY4EUiO2klzsenhwIvqKGMSYIOQVRQlypE4Kwx0uvCvr1QiJ7LrIt7gsOnveEHYPjyIpCtYLbHBhDgUEXMAN7w7s3OJ+4ZGDtCyNyBrAqpQLZyRoYzR73tEEKoNk53rnP7bkva7t9Tf1I0apRGsCfOkOXgZWpc0KaSzA8/nk9zFYrblxd4pWgso7l47V93tdaN8rPdiRfXC3QXPnuR9+VZa6xIOwAljGmBPAQgHMBVAH4CmOsymPYDQD6OOczANwP4D7bsVUArgQwG8A5AP5qO9+0xKuMzmoBmncDhSsCHmsXsVlUZBCUewGgZbfbGLEAtjhNh4ykeHxxMnjbkkAoPN8+LbuBlMKAYlRe2DK2Ce1fIN+gjZhvJmCz0glBxKl1uNX5RF/stb97aBwZMmRgh+w+wEEEsGkJcegO1AM7LHzRmeOFmzep/qD13cJFrTDEDKzrl67Vxbbg9LJ0dA+P40hbYA+1T2u7oVYyLPPVQ9cbnjqhKKo4JPj4XP5h1x9QlKpDolZC2bbMNPWO4mj7EDb4KB/2xYQ1+nO1s/ua3YEHEYTMbKzKwpbvr8EvLqzCmMmCu187hI33f4yKn72NdX/4CF997Av84rVD+PdnJ7G7oQ8Wa5Tlu1XxwPqfA9/aCSy9ETjxAfDCZuD35cBvCsH/dga+2vp/+K7iWWDv06LZp5zEHHx8hbi/rOuiVVGqDg1BaA64YrKYcPaL7rkFxyKzDwEnwBbASl3U9VgUDsV2qyo3Gcfbh2GySDvWMipk2zL0JUG/VkhoDYBH9daEdcJx38Y5R+dQEEG/jBSk6tAUzH2RSwB7onMYCgaUBAj6giUnRYMU4/nuG/vrQzrXZRPu/ea+ep73NvZjfoE+YBm7SqHC39beD0UEF4YtVo6XdrfgzFmZopUsiwvT8e7hdv8nKV6FcVvccWbBmdh37T6vIXaByqmIHBnYZQBqOed1nPMJAM8C8DSY3ATgcdvjFwGsZ8I7ZBOAZznn45zzkwBqbeebduzr3OdlBI7Ow0L/a37g/7JdxGZ2bjKQlAUk5wMtu92k2D1LDABhdW1ZSWpk+mA9P+Mtu4Xy5mDJXQSotMjt34N8gy6i/XK5ek1IIk4P7HnA+USkhLhreBzpISgQezKhUAJxicFZ6UjJwI4Iq/p1VuEmRqqtSUPvKBQMyDeEZh1icTGo5y69GOsqM6FgwNuHAnwBA/isthsLCw1IiPcx5wj0wALw6cu8tXkrLNwMVuwtnBZp3j8iLEQEG8B6ia1FCLHXcSs3JIgoolErcf3pJXjnO2dg6w/X4reXz8NXVxVjZmYS2gfH8fyuJvzif4dx2cPbsPze9/HnLTWiVkARxVAEnPsb4Ie1wA3vA2ffC8z9EsZUelTwOqzufBp49RbgT/OAR9cCh191E7MxaAz4SsVXvE7rquZblJaAhp7RkO4B9nftR+tIq9u2xDhbtq11n/DTQ6DIbLGia2gc2VKDMbX79eVo79Gg5znb1tZxrD3woijnHP+sF/pPVS7BWMTJmQ+ty5/g5ZqX8f2Pvg8A6Bs1wWTh0oN+GSlM1aG5N4j3h2sGtnMYBak6Sb3OwZCr12Kgz/1eyxTKtd7i/XkWC2BHxs043jGEBQV6aedNyMS3+gO/10JlR10P2geNuGRRnpeDyZWFZ2N9ZRYOtgz6t8lUa/AVtdBqlK5NF7XgGjHJV5kRbeSIFPIAuKaLmm3bRMdwzs0ABgCkSTx2WvBZ62feG5tsPQX5SwIe7yVik7cQaNkDjdJ5gRCz6QGEPti2AcFHM1JcWnwe0N8QWgCrigPPXYDS8cMoSNW6ZQd9/Z9CJU8veJ6Nm6V7nnlhKHJ7ajRZMGQ0IyOMC4/d03dr89bgrXQS4wMHsMNdGFAo8Ie6lwBI94Bt7BlBTopWXDxJAq6LKtZxZwCbnhiPpcWpePugf2n8nuFxHGwdwOllPkpyrdaQy4oCUnEBLhny7g8ZM49h0VMhvM9l4O2D7SjPTESxhNXus4udWZP/HPlPJKflwBjE+5YgogVjDEVpCfjykgL8+NxKPHLtYrx1+2oc+r+z8cVd6/HgVxZiXr4ef3zvOM554BPstPXCRRWlGihYCqy4FTj/D3hv8cNYO3E/jt5QA9z6BXDWrwDjIPD8dcAzVwptITZ+svwneHDdg26nc81iFqfrMGayoGso+IWsr77zVd872/YBqWVefcTdwxOwcmkKxGJc8foVQR+z0BZ47G0M3C61o30HThqF3mhlNAPY/KX4dae7ENEHTR/gP0f+E5Rqs+zTMmgxNG5G/6jExRsXP/maziGUZyb5GRwauXqtw0vYzp39IVTziFRoiWVYq1sGYOXO91FAFArMUEauHeGVvS1IjFdhQ2UW7vviPrd9Ny79nsOnNpBN5rUlF6L6ZCN0E5G7/48VU0bEiTF2M2NsF2NsV1dX5GxhIoWolUXzTiAhw6t8xhOzxYrqlgEsKHARHcieD/SdRLaLEMGPP/mx6PGLbN6Z+5v7g522X1z7N76mt63A5i0O6VzGrMWoQD0KU1RuGdg7Pr4jrDl6kqsXLg5t/cYAI/3gkYG1l++GI+J060JB1XFf1z4gJR8YaPR/gAtpCXHoGZnwv3o63IG7050luHqNXtK563tGQxZwAjxLiN1XK8+Zk43jHcN+BcbeO9wBzoENVT7K0odawSOVXUzKwt26mdhsmhwZxM5BI76o78X586TdcL1T/47jcTS8YEdNo1j1euRE2AhCbhhjyEzS4ML5ufjX9Uvx0jdXQKVkuPofO/De4QD9ZRHmUOsg4lQKlOcYgIxZwMpvCeXGZ/8aOPEh8K9zgEFnZnRtwVq341878RqGJoTvXLuKfEMwSrNSaNsvKuAUSjB2ljo83YB8gxYZSfHY3RA4gH3q8FOOx9rEbD8jZSZ/GdaOegcSv/7i12i3/c6i6QFrx2Gl48ML1rWSCoBDsdtkseJk9wjKs+T3Gs3TazDhUQ7+Lgvh/dt5xO3pPSvvER22r6kfADBfagALYFliZPJtE2Yr3j7YjnPmZItmtrMSc1GemYi0hDh8EWixrWSN8LP+kwjMNLbIEcC2AHBVDMm3bRMdwxhTAUgB0CPxWAAA5/xRzvkSzvmSjAzf6ptTiuadQP5SQS3MD3XdIxg3W909MLOFBvJrDPNQmVrp9/hZ2UmIVymw3/YBlYueMWdfrbLzCMAUor0wUuhImo14ZkYla3ALYI2WMAJNEfIMMljpeFzwum0esOEEsPFKl2P1wWVgUxPiMGG2YtififtIJwZcrH/08XpJ527sHQ1LSfS2hbc5HvNxd4GNc+ZkgzHgtX2tnoc5ePNgOwpTdb5NxXtP4qWkyCmdKis3IcFFVCWWvFndBs6B8+dG10RdKk1D3u/Zs4rOisFMCCI0Fhel4r+3nI7KnGTc8vTu2GRibVQ3D6AyOwlqVwEnhRJYcQtwzUtC8PrUZQ5/bTHu/ETQ1LSL8Mkp5ISRHkGrQUTAyR6MSVYhBvDr/HPDmg5jDIsLDdjT2B9w7NbmrY7HSfFRFPXKXQCFj/5Le7YxJyW0dp1wsC9w+BJy6jE67/WyufP92NAzApOFY2YEAlifvweRkmC/eCgQ942LL3AcbBlAQaoWqQnSF6zjPMQS5WJnfS+Gxs04e7Zwr2myOv/P+Urhs+xoD6wL8B2VuwiIS7I5nkwv5AhgdwIoZ4yVMMbiIIgyveYx5jUAm22PLwfwARfSRa8BuNKmUlwCoBzAFzgFuGPBt4GeWiGADcBhm7fZ7FyXMp2s2QAAdddRXFt1rd/j1UoF5uSlOFaY5GDUNIpnjz3reK7qOChI4ceH9kV2Ik4IwovHDrttFxOmCod8vfBFHYqQkwOF+8em21aWFU4JsWtPRl9iBmDs93tj4or9C9dvGfFwF+DSiyilB3bQaELvyERYGdirKp0Kj+YJ9/9PTooWq8sz8PyuJphFhDd6hsexrbYb587J9i2q0HcS++MjuGI978tQBKErV5pSij+u9SNtHwZvVLdhVlYSyrPkL9eSA/WY943Bj5eLV4UQxGTFkBCHJ766DAUGHb7+5G5HNjGacM5xsHXAt9p5yWrgiieFm/NXvuHoid177V63YR83f4zavlrkGbRQKljIQk6uPLrxUeFBm+21RBatO+0Z2BTp381xqTNwe29/WHNbVKRHY+9oYFHDWKHWgnn0C9tp6x+DggkWMtHGfo33tcAxanK+b/6lqXA8Pt4hVE9FqoRYlCCtdJo69rk9d61McuVI2yAqs4NbzFClRCaA/fBoJ+JUCpxu86N17VO9ONMZMywrSUVL/xiafWTOAQBKFVC00hHAbizaGJE5x4KwA1hbT+u3ALwD4AiA5znnhxhj9zDG7F4d/wSQxhirBfA9AHfajj0E4HkAhwG8DeBWzn0Ym00zrtHa+igLAgs4HWodQJxKgdIMl0xTSgEQnwx0HEL9YH3Ac8zP16O6ZUA0UAiFX27/pfuGzsNA7sKQz1c7nox2bkBa/4GQFAilkp2iAWPBZWBdS3OvgvcNhaOEOIwLj2tG9HNmu/hKFHJKs4lH9fgLYEc6wW3qzlIFnBptNzvFYQSwroyJ+I1dtawQbQNGfCTSx/H8rmaYrRyXL873fdLeOihdvsZuWXCLLHN1kJAORYb/CgdXlmUvi8gFon3AiJ31fbhAYvmwGJEWclKf2Oq1LZJ2QgQRKVJ0ajx63RKMTphx50sHompDBQANPaMYMpox159dV9mZwMZ7gKOvA/uEHnex7/Zbt9wKtVKBPL1WlhLiFbk2dXaHgJN4BlapYEhLCOKamFqKpcbwFgvs7VJ7JJQRxwwfop1tA0ZkJml8WiZFEl2cCjkpGtR1iQewrgFUgb7U8bimYxiMAWU+fIrDIc9XANtTE9R5zjMedHsuVrFoNFlwsnsEFb4qvXzA9H7uTcLgg2OdOK00Dbo4Fba1bMPghLN6TZPsfM2lNmeGgFUHJWcIv7fBVvxx7R9RvbkaZ+SfEYmpRxVZPimc8zc55zM552Wc81/Ztv2cc/6a7bGRc/4lzvkMzvkyznmdy7G/sh03i3P+lhzzmez8df1fBQEnppQU9B1uG0SFZykRY0IWtuMQVuauDHiO+QUpMJqsjhWzcKnp9/gSGel2lDWHQnPfGA6ymVC37cGcdPfz1A/Uh3xeT+JUCmQlaYIKYF2/vBcneq+42YUx0oIoPfHEVR1uKM72xS2xjNh+k9A77C8D2ymIhMCjXNkP9tX6wlR5SnSV4yNu6pkAsL4yEzkpGvz1o1q3m8QJsxVPbW/AaaWp/jOOvSdhjXfO75vzvynLXF1RSFhksnPbotsCDwqBN6sFsavzgghg1+SvcXu+5KklMJojl01iNe9G7NwEEW1mZCbiznMq8OGxLrzqp80hElS3CJZjgfymcdotQNEq4O07hSobAC9d9JLbkIEJ4VxFaTo09MhYQty2DzCUAFq91672gXFkJsVDqfDfHuVGagkUYa4TzMlLgVrJJPXBxoyCpbi729vWsG3AiBx99AWc7JRlJPrUo7jyjSudT5KdfZ/HO4dQYNBBGye/+2WyVoWEOCUu0D/svqP7uPgBYoiUG2tV3oHx8Y4hWDlQlRNkJjkCGdiGnhHUdY1g3SyhVfKF4y+47U9Nm+l4PCs7CXEqBQ62DMAvpbZ7gTrnIvOy7Klv+DJlRJymE/p4vdD/mjUbiPMfHHDOcah1ULDP8cQWwOYl5Do27WrfJXoeuzS4XEJOniq2KnBHWXMoNPeNolFbCfSdxJ1zvoEsndMmZH/X/pDPK0aeITgv2DGzc+z6DG/12e7hcSRrVLLJyD9Q94rwQKKQk72EuGfET4Zt2JmBlRrA1ttudkL1gPXEYDICHllYtVKB29aXY09jP96sdlrqPL2jAS39Y/j6mjL/J+2tgyVeuOhIzSwHi9Ig3ScwKS4y5b3/O9CKypzkoFa6xcqu20b8qz6HzEAzeNs+t01/WfeXyLwWQUSJ61YUY25eCu57+yjGJqJXHHawdQBxSgVmBmoXUCiACx8ATKPAR78GAJTry92G2Bdgi21WOsEw/wnv7KoDHwJOANA5ZERmsGq6mhTEuagZd491B3c8BNuk+fl6bK+T3/deNopOx2VD3gsJLQOjyImBB6yd0owE1HWNBK42SHbeb9Z2DKM8U/7sKyBcv3L1WvQOePxOuoPIwIrY7lwxy1vh+qjNj74iyBJipLhnYLtHw++Z/+CooLlxZoUgXNk55q7BcV7ZRY7HaqUCldlJqG4OEMBmzga0qW59sNdVXRf2XGMNBbBRwDMAGzePAS17JPW/tg0Y0T9qEhexyZoNjA8ifsT5Re9L8r4wVQe9Ti2bkNOB7gNuz9MtViArvAxsn2EuAKBgsMPtS0ZuX9g8vTaoDKyrkJRYMNM9PBFW+bAnZxWfDSjU0jOwgUqIrRZgtNvRAyvVl7OxZxTpifFI9OW/KpGb590MALAwJmTqPfjS4nzMyUvGT16pxqHWAexu6MV9bx/F6vJ0rJ3pR7CNcwz31+M1LpTXSLUGChap779ProiMyl9d1zD2NvZj04LcwINdmJ/hffPJvMybZWL/s9ic41SKvmv5XVhTsMbPAQQx+VEoGH56fiXaBoz456fedhyR4mDLgCO7EpD0cmDJDcDux4DOo6ILV48eeBRFaToMjJnQPxrAcs0Fn+08o71Af6No+TAgtDxkh6CmOyu52PH4zOfPDPp4AFhVno4DLQNB/T+jSlI2WEYl/gJ3MciuhD8hIzlC388SKMtIxNC4ObDVki1oM1msqOsejqgmQ45eizbPHvSeWukn6HJXIL5t4W0oTin2Gna4bRBatdIhZiWZ5Dwwl4D/+rduDO54ET442onSjASHeOba/LVu+z29XGfnpeBg64D/hQeFQuibP/mxowrOp67IFIIC2Cgw5CFekzo2AEwMSQpgD9kEnKpyRUqJsoSAz9AXONBhjGF+vl5WISc7KjBBmTchtH43zjma+8ZgybSJG7Tvdwsa5BZyyjNo0TYwBqtVWr3S8T6XkhUPCx1AKCEOR4HYzl3L7wIAvFz7Clr1uYLCowR0cSpo1ArfJcSjPQC3ot0qXAjaR9rFx3nQ0DsSloCTnU1lmwAAZvtcPFApFXjoqkXQqBU4/8+f4rKHP0dGUjz+8KX5/r9kR3txBM6L7ahZZosIG1ICWMZVkq2JguWlPc1QMODShcFJ9n9tztfw8Ab38quWYVGR9/DgHMZ9T6NL5VzoiGQfO0FEk+WladhQmYVHP67zr/QuE5xzHGwZDFw+7MraOwG1Dvj4d6K7H9z7oOOGuF5iFta18sjOuSU2pWB7tYUP14H2QWNQCsQOgqh28cWqGengHNh2YhJnYUvXYk2TexKAaU/g1b6bYjQhODRWTvjog3VgKyFu6BmFycIjloEFBCudVs9kQzAlxB4KxGV68Yquo+2DmJWdBEUwJe8AoEnGt10C/obhIIJrEUbGzdhR14t1s5yLwTq1/3uwuXkpGDKafSpIOyhZAww2u/niXj/7ejeniKkGBbARpmesB998370vr3TA5i8nwTP1UOsAGAMqskVWuTJtzeid7sq9jx18TPRcc/NSUNM5DKNJ3lKoMyzKsMqHe0cmMGayICPD5onbth9KF+XXn2/7uQyzdJKn18Jk4eiUaOr+nQ+/43wiEsB2D4+HpUBsZ9OMTY7HNcnpQVnppCXE+87A2mxgWk3SVI3tNITpAWvHvmJoZgwYEfdwLkpLwBu3rcad51bgrvMq8b9vrQpcgtZbB6XLGkRVWlXYcxVDSgDLOZNNIM0Vi5Xjpd0tWDMzI+iSPAVTYKZhptu2Oz6R11cZAB76+CdYmuJ+Y788Z7nsr0MQseLb62Zg0GjGf3YEp4AaCk29YxgYM/kXcPJElwos+Rpw6GWgtw6z07yvx1kpAGCW3AfbPOQtInj3iruFB34EnEYnzBgymoMvIQaA1NLAYwIwv0CPxHgVPqkJvgQ5apSdCYjoEVh47LLGpbb2FH++7ACARKG960ibkFypCLZvNAhyU7QOm0I7x02DQgWAFNqrHQ+T1ElYV7jOawjnHEfahlAZpICTnQVqg+vZQjqHnW0nejBhsWJdhTOADZTAmWNLbh1sGfQ7DqVrhZ91Hzk2fX/J93HTvNgtmoQLBbAR5qo3rnJ7PidtDtCyW1AQTpsR8PgjbYMoSUtAglgZZ3wikJzvtSL1x93iNh5VucmwWDlqZBJysvPbluawBZwAIN+gEy6IbQciWt7g9IINLmNXajIDCZle27uGx5EhQwbWtTdVlZAlOQMLCBY+Pkt/RoQAdqOtf1dKn6bRZEHbgBFFMgg4qZjw3rUwAMMdPselJ8bjG2vKcNMZpdDrJJQD952EwuWCIdXbNlgWZQm/N7vVUbmhHI9ufBT3rrrXcaPI4ayWkJPParvRPmjE5YtDE4vwVAGeiIAS8SP1r3tt87XSTRBTkfkFeqyakY6/f3JS9gVgT+wCTvPygwhgAWDFrULryacPiIrJ3bv3O0iq/Cm2Nn8g6XTbWrc5HpemlKJ6czUS1LbrQds+YbFZl+p1XMeg8B0TWga2GCUTQfp8eqBWKnBaaRo+qekK2M/5s6XyL+hJouh04W81ichJ1kCrVooqEduv4QAEWxYI1zu1kkXEQseOmJVOu0opvYy43Znlnp/po9x90IiBMRMqQwzEzQnubU7hfD98cLQTifEqLCl2fq5cA9gFIgtT5VmJUDBBiMovqaVC9vykt1vAVIUC2AhitprROuKuXvjMBc8IAWzuQi8/UTFqOob9CzlkzAS6jmFpduByZLsQ1KHWAA3fQRJvmQi7/xUA8g1aIHse0HcSSov7qluvUT5Debs8e3OQXrDnWOK9/mZGkwVDRjPSE8Pvv3TN9CkTM4GhdsAsbUU2MykenUM+FGZtGdgEjbBSqJZw4WyylaPImYF9JikJGPIdwAZNbx2U3LnQoQzCrzUYKlIrsOfaPQ5Ror+u/ytW5K7AhWUX4pENjzjGfXZC/hX/F3c3I0WrxvpK74UTKSiYAt9b/D3H8zGzEU8feVqu6QET3otA5xSfI9/5CWKS8M21ZegaGscbByIkhGbjQEu/NAEnT5KygQVXAQeew0p9hdfuQz2Cncjhgc8lne73u37veFw34NH/27rXZ/mw3Tc3OxRBIn0h7u0Kv/R3Q2UmmvvGcLjN/6Lil6uuCfu1QiI+UehJFGFgfABDE0MwiSjoRhKFgqEkPUE0A5sUl4RUKHCnxfmePNQ6gPJMiX3aISKmyvxaYoK0MmLjANBX73jqFoS7YHfmCPrzZiMzyV2bIlQLJ845PjrWidXl6Y7f6TNHn8EDex4AAPypfxxPXvCs13EaW+9uTWeAAJYxoYz45CeAdXq0+FAAG0Ee2veQ90bTGNBxSFL58ITZiobeUczw12OQPgvoPo5/bfxHwPMVGHRIjFdFJFMUrgIxYMuM2kqS2JB7n+aERb7SGnsAG4yQEwBcrvXOgtk9YOUoIXZFlZgNgAs9CxLITI73XRJtC2CVttVzKUI+drVKOQJYe8B8PD4OGJbWfyuJ3pNgLqufP18hb6m5K2qFGivzVqJ6czWyE5ziG/ZKASV0+Fzmnque4XG8fbAdmxbkhqVw7ak2+JsvfhPu1JwcfMlrk5hIBkFMdVaWpaEsIwFPbo9sGXF18wAqckIMDJbdJJSm7nva54JeizlMsbmRHkHAyYcFoD2AzQpBxAmGIlROhH+tP2t2NpQK5rAfc6VztFPkiBhQtUl086pnV2HlMytx65ZbozwhwZblWLt7INQ23Ia+8T5snACuThQUrjnnOOzLHUNG7PdqerXzmvtOYgLQcdjXIU7aq9GpdPkM+Ljtqe0UAli/99l+KEurwOpR571kqAvZR9qG0DZgxJku/a/37rjX8Xhtsu+qphmZSdIsMkvXAGO9QMfBwGOnABTARpB/VLsHlavyVgk1+VazpAC2vmcEFiv3/8HKmClI6EsIdBQKhqqc5ICrkoHoN/Y7HpeqkoRSmPSZvg8IQHPfGFK0aiRr1I4AVjnofuGRczUyIV4FvU4tyUrH1TczSV/ktd/enyGHiJMrNcz2/5XYB5uRqEH/qAnjZpHylZFOQKXB9k7BYklKeXaDIwMbfgmx243UsIw3D30n8VqKs8zONbCMFslxybh53s3YaLgbO+t7xX//IfLcriZMWKy49jTv910weKoWygbn4F/8zWuzTiWP7RJBTCYYY7jmtCLsa+oPbFsRIlYrR3XLQHD9r65kzQYKVwA7/4kz8s4I6RT/rf0vfrn9l74HtO0VfvoIYNsH7AFsCBnYpBwoZCitTU2Iw2mlqXizut2rjPjZo95ZrJhQcQHea2pDptK7TBYAPm+TlimXk6qcZLQPGtEz7FwMP+ulswAAz8VZHQrEHYPj6BmZiHgAm52iAWPAuWn3uO9wKQ32SdsBPGDQO576Wrg/0TUMvU6NtIQQq+hSClFkct6fflYb2kL2h8eEe6O1Fc5FedeqPEX6LJ/HzsxKRH33CCbMATKrJbbvhGlSRkwBbBSpTK0UyocBSQGspJUh+5u6S5oyW1VuMo60DcIiUYFXjJdqnFmXTLMFyJgFKEO/6DT3jQrlwwCQmAkkZkMx4K6W+vNtP5dV2VSqlY6rgJNKLIC1ZT3lDmCf6bBdvAakZ2ABeAkeABAM7hMy0TIslLP/eNmPA56voWcESRoVDLrwbybc/FmH5MzA1uElJm8/d7AwxvDthd/G2TPnwWiyYk9DvyzntVg5nt7eiJVlabLYFHx74bdlmJU7psbPcS3zXpC4qvIqkdEEMfW5dFE+tGolnopQFrahdxRDRnPw/a+uLL0R6DuJn+X4trHyd/3/2Wc/w3PHnvN9/lZbAOvDQqdjcBwJcUokaUK4diiUYHpnpZPFGvqC4AXzcnGyewR7PZwX/l7995DPKSsJ6cguXIU7gtTiiCRVtoD0SJuPclSbB+zhNmEBZ3aoCy0SiVcpkZEYj75BLTJ1Lm007dUOOxhfdLTuxP+SnAvwPgPYzmGUZSSGrruSkg+ry7kPNPdj0Bh8wuWDo52Yl5+CzCTnwo/bPa+fJFF5ViLMVo76QAJtyblAWjlQRwEsESRWbhX8X5NygeScgONrO4fBmODP5ZMMWwDb7S4XXtMnbvZclZuM0QmLZCVCMYZNzqDh3v5R5xxCpKV/zBnAAkDOPKzrdQ90dnXskrX0J0+vlZSB/az1M8djpb7Ya3+XbaVSTh9YAFAo4wAwyUJOmbbX7/T0TAME4aRE56reWcVnBTyfXYFYDjEt1wD2/XGZemDHh4CRLkyEqfonF8tLU6FUMHxcI66yHCxbjnSgpX8M160IL/tq56tzxP2hw6Hvi79hv8b9fb8oc5GbGBlBTCdStGpsWpCL1/a3RsRSxy7gNDdPH/pJKi8CdOnIqH4FCzIWiA7Z1lQtut1XwPjY2S7OBq37gNQyQCs+x45BY2jZVzsuSv+uHuzBctH8XCTGq/DU55FXjg6ZJV/F6T3SFqmjgV2J1x6gemGz0DnYMgjGELJybzDk6rVoGxjH7Ytud2yrNw8HXNzfMLzL7bmve5kTXcMoywij0iwlXxCotBPXii/qgtNs6R2ZwJ7GPrfyYU/7Tb8BrE1IK6CQEyCUETdsA6LcYx0JKICNEGLqdxxcyMDmLZJ0jtrOYeTptdDG+SkBTEgHtKlefld/PyC+yugUcgq9jNi1rDajr8mZBQ4BuwdsvsGl7DBnPtK7avDyec+4jXXL5IVJnkHIwAZSKXSFGbyDCXu/T6bMASwHBFEOqSXEttcXVSIeaocpMbjy2sbeUVkUiAH3v9vbCmPAlVNJuIgzTAaSNWosL0nF+4flCdD/va0eOSkabKjMkuV8UoS7gmKoA+z4W16bI2VlRBCThcsX52PMZMFbIv2V4VLd3I94lQLlWWF4a6rigLlfAo6/gz8uF9cFuOWjqzH38bk41utx3yCSnbxr+V1Ykr3EuaF1n8/yYUBQdQ0vgHVeZ0dNoWcnE+JVuHRRHl4/0OZb4DDWzDoPSj/X5u1t2/HEoSdgskYn2EhNiENOigaHRe4PlZw7Atg9jX0oz0xEopg7hszk6bVo7R9zW1y5sCDXfxmxUVqJf//oBLqHJ0LufwUAJOWAu2RgE0r/HHQf7NbjneAcbmKNfUYPMagM3wHsjMxEMAZpfbAlZwCmEWc16BSGAtgI8eThJ722cdMY0HtCUvkwIASwkj5YGbO8VNneqve+uQSElRq1koUcwHLO8dSRp1y3+P1gBaJv1ITRCYt7BjZ7HsAtSB1yz2ZJ8eOUSp5ei9EJC/pHg7gwiHjAdgyOIz0xDmqlvB+luoE6IKUAGGiUNN5ediIq5DTUhi3x0jOpZosVTb3yeMB68k6CFiYfXrBB0VuH2DnmibOxKgs1ncOoC+SjF4D9Tf3YdqIHm1cWQyXz+8pO63Br4EF+uO+dr+OC3Ayv7d9b8j2R0QQxfVhcZEBxmg4v72kJPDhIDjQPoCo3OfzryYKvAJYJZNRtxWXll/kc9kmLu6CTZ0ALePhQDncKeht+AlghAxvGgq6hCDf2CwHIiCn0SjEA+NrpJbByjge3eNuurEwpD+vcsqBUw7ToZp+7b3r3Jvxu1+/w/LHnozalqpxk0fvD6wcGgZQ8WK0cexr6sLjI20IpEtiTDV5+qG1+AtjmXV6bxEqIT9gsg/xWOQZCqYI1zn2x/9MgPYi3HOlEemK8w9MVALY2e5T5Juf7PF6jViLfoMXJbgmfl+LVABhw4sOg5jgZoQA2Qni9+QBcnWTLVEoIYC1WjhNdw5gh5YOVPtMrA+uLOJUC5ZlJIQs51fSLlCaHkYG1KxC7Z2DnAQDSeuvxixW/cGyWswc23xCcEnGBDw/YjkGjW8+CrOgLJGdg0xPjwJhIAGsaA4z94Bq95JdtGzDCbOURCWABYExiUO6X3pPoV0ZInChENlYJ2dL3wszC/vWjWiRrVLh6ufeCiVxc+MoFIR87PNiMp0ZOYFTEBkz2TC9BTDIYY7h0UT4+r+txXL/kwGrlOBiOgJMr2fOAzNnAvmewoWiDz2F/2vMnnOg/4VD5f7/xfa8xbtm/1n3Cz9wFoufjnKNzcBxZoVjo2NEXYe64MJ9Rc3i/3+L0BFy5rADPfNGIgy0DbhVX5+avDevcctFUdnXAMa6Z6HHLOOY+Phev1r4akfnMy9ejtmsYA2Mmt9/XbQMjQKKwSDtoNGNJkSEir+9JbooG42YrxkzulVsPNIonaQAATTsknftEmArEdrhHAFvTOYzGHmnvXZPFio+Pd2FdRQYUCmeQXZHqYYUVwHazOC1BWmugLhXIXwocf1vS/CYzFMBGgJeOv4Tjfe4Z0U1lm5DVXQeA+fzyd6WlbwzjZqv0DOyY9Jr7qtxkHG4dCKp81o7XMUwBpPmW9w6EmwesnZRCIC4R6DyMwmTnTby8Ik46t9cPhEWpEv0C6Rg0huZ3JwGenA8Mtkjy7FIpFUhLiPMuIR4SytxUujTJr2sXApBDgViM52q8rVeCprcOFm10VoClkm/QoSonGW8fCl2oqqZjCO8c6sDmlcWhiaBIZMJqwiWvXhLSsfXbHxTdvihTWmsEQUx1LlkolFL+d698Wdi67hGMTFjkCWAZA+ZfCbTswqr4LDxz/jM+h1786sVY/NRinwGRhbv0xbbuBcCEAFmEvlETJixWZIdZQpxgu+b50vIIhu9vnIX0xHh8+5m9aBt03uArbdeP/tEJ/GdHI6795w6c/psPsP4PH+HuVw861JQjTeMgx9Jjl+MHPb79QxWjPcCWe4BH16LvzwsAAA/u+E1E+hiXFhvAuVAm/PX3vu6cQ0o+oFBit83ndHG0Aliblc6c5HVu2/+JPnQOe5fxW6wW8MbtXtvFemBPdA0jTqlwT6CEgGcGFrDivSPSFrJ31vdi0GjGugr3BMnX3vlaUHMoTkvAye4Raff1s84F2vYBA/JXkUQTCmBlpmGwAb/4/BfoH+932740e6lQc55eDmgCX6Bqu4RmbEkBbJAZ0Nm5yegenhDvlwwWQwmgCr1cyM0D1o5CAWRWAR2H3D6MsgawQWZgW318UjoGx8Mrl/Lg72c5e5BOaHWAZUIQYZJAemI8ujx7fWyqvwqt9IuNnB6wdv649o+Oxz0jMvSJ9p2ERe/tyxtrLpifg72N/aiXUsojwoMf1EKjVuD6lcXyTkyE2v7a4BexjAP4U/1rorvuXX2v6HaCmG4UpOqwvCQVL+1pCWkhWIw9jUJgsKBAL8v5MO/LwgLz/mcktd/89LOfim6/qsJFVbz5C2HBXCMu3hOWhY4dQxF0NpXkn372UzfNjZBOlxCHv1y1EO0DRlz+8KeO7bvbLbjpiV1Y9qst+Mkr1WjpH8PSYgOK0xLwzM4mbLx/K7aF6OkZDM19o/jAugS6Et+ZctW2vwCf3g/EJYIVrgAAWI39wL/OBkaDEwwKxIJCPZQKhl31vWgacqkAs7VQba/rQXpifMQqtDyxB7CdgyavRdLvvPdNt8/f8b7jWPDkArzf411eXJjkXdF0omsYJekJUCrCE6u0xLn/LmZm6fDeYWkL2W8caINWrcQZM50tOVub3Cs412jzAp6nKE2HIaMZfVLa4irOF34ee1PSHCcrFMDKjC+/0k1lF9kEnKT3vwISA1hbD6qGSWuon5UtKJYdbZegWObB9z7y6HELU4HYzQPWlazZNrNl55eTVw9EGBh0amjVSklKxL4wWazoGRmXtYQ4S+cU7fnbwCHhgVQl4mSNdwnxoNDreO/JVwBIy5I19o4iTqVAloz/rw2FzouzKswbEgBAbz2sKb57QmLFpQvzoWDAi7uDV5asbh7Aa/tbccOqEqTJbMsEADfOvdFrW9CfqU8fwHYfwh1uNgcEMc25bHE+TnaPYE9jvyzn29PQhxStOrx+PFeSsoGydcD+56AM8VYvTZMGndp2c261Ak07gYLlPsfbRQ3DCmATMpCkdHpyirVjBcuS4lT856bl0MQ5fw9PbW/HgeZ+XLuiCK9/exW2fG8NHrhyIf55/VK8+50zkJOiwVcf24mDLZHx/LXT3DeGxHgVlLMv9DnGMPsy4HtHgetfh+Lc+wAAVo0eaD8IPHUpYJYhGWFDF6fCnNxkfH6yAc3DLtcxfSEsVo5Parpwxsx0WRwKpOBs9zJCo3J/X1UPnnB7f1R3Cera/9M424vmZczDwxsexq0Lb/U6t2SdmQBY1O5evmdWpGFnfR/6R/0rdZgtVrx9sB3rKzOhi3NeV3+5w92H+cEq333SdkrShSxwQCsdQGg7TC0Djvkpw54CUAArM/G+spEDzcBIZ1ABbHpiHPQ6CebKyfmASosnDSvcNu/uEFcZq8gWVk+PtgffB9s45Oxf/Gd7j19pbykICsQiRt5ZswHjAPiIcwX03JfPRdeoPDYljDHkGQR1O1+4ruy9XnCp1/6uoXFwDllLiIuSXRQY7SXL/VKFnOJFSoiFVcDOcWF1/7rZ1wU8T333CApTdW79GOHierErsYR5XvM4MNAES4qwKjkvfR5+s/o34Z1TJrJTNFhdnoGX9jQH5bXMOcdv3j4Cg06Nr68JvSTfH9fPvt5r24Q1CCmswVZg+8M+d1P/K3Eqcd7cHGjUCryyVx4blN0NfVhUqJf1exfzvwIMNmO4aVtIh2cluKigdx8Dxgf8BrDttgA2rGsiY9AnOjNOP9j6g9DP5cLCQgNevsU590e+fD623bkeP7ugCnPyUtyuUcXpCfjPTafBoIvDt/6zB6MT8lsm2WnuG0W+QQurH0u4u9o/wNyXN2Bn+05HJZpVqQYu/5dQ1v2euNp0qKwoS0d1a4/juZJzQF+M6pYB9I2asGamt4BfpEjRqqGLE5INYtn4uz69CwCwo22HY0H2wwRnRvSGOTdgVd4qr+vTuNmCxt7R8Cx0bKxIX+D2/IxZqbBYOd495L/abHtdL3pGJnDBvFy37Z73uUxCoqjYHsBKqf5iTMjCnvxY9gx+NKEANlo07xR+BmGhI3llSKEA0mcgwUPw5897/iw6PDUhDplJ8SFlYF1ZNjYiQwZ21HcAC3jZpbiVtIRJnl7rt4R43hPOPp+ijLle+52rzfJly1xLvcxK24qcxAxshi2AtboGTkNtgMr5+5XipdvYO4riCPW/AoB2IjyVXvQ3YpxxfKdXEGq4dva1OL/0fBlmJg9fWVaAtgEj3gmiF/bdwx34rLYH315X7l2NIBMp8SlYnOW+gDYxGkSJ3Hs/h5WL+0QSxKlGYrwKZ1Vl4/UDbRg3h/e5GBg1oaZzGEuKZe7rn3UeEJeEBSe9VVkD8Y3538Bf1v3FucEujFN4ms9j2geMYCx8WzlVhKprXq4V9Be+qczChvI5fktH0xPj8cCVC1DfM4qHPzoRkfkAQFOvYCNoD0z9VUl9a8u38M+D/wQAWGEFKi8Alt0M7Pgb0LZftjltrMqC2eU+Io5zQF+I9w93QMGA1eXRC2AZY8i1WelcV+W9AD84MYgtjVtw47s34k97/uS1f13hOq9tgNAqZeVAmQwZ2E0zL8UvupwBvz55CKXpCQErsV7e24zEeBXWznL/fboqh/+po1vIlgagwKCDggH1EsWjMPdywGoCDr0ibfwkhAJYmfHZD9O0A1DrfIofeJ4j6NKG9JlI7q1326RU+FZprchJxrEwA1jhdWX2gLWTafOT9AhguZ9VymCxy7P7mpsbohY6dg/YyIg4mQFAow9o2G0nMykeZitHn2vZylCbUEpmI03jX8yJc476npGI9rdYxsIsyeqtw3aNBnXjQvClZJNNjTgbJekJePijE5L64waNJvz81YOoyE7CtSu8vYbl5JENj7g9H9/7lI+R7liPvwNT9Qv419yzIzEtgpiSXLooD/2jJnx4NLzKIHv/66JCmYVx4nRA1SYojwSvWPulmV9Chs7lxrrpC0CXBqSW+jymfcCI9MT4sG2AVPrIfA/ev/t+AIBBK03U8LTSNGxakIu/fVznt1orVIR7oFEUpGqRnSBcpzcWbcSFpeLlxKPmUTxzVBDkGhgfQMtwC3DmXcLf5Z27ZJvXwgI9UhOc19V7u3pgTSnEf/e14PQZ6UhNkFAZKCO5ei1aB8awvmg9Vuau9Nr/nQ+/AwAYMkm/p7W36clSsp+ch6oJ533XlW9cicsW5+OL+l6fGdH+0Qm8fqANFy/MhUbtfg/jKpxWrs0E1IHvMeNUCuQZtNL1N7LnARmVwIHnpI2fhFAAKzNiQkN3LrtTCGDzFgPKwNmVruFxDBrN0ix07KTPREqfe6ZO5acntiI7CTWdwzBbQhNGWhhv63dLD91LTdQD1o5WDyTng3sGsDIJZgBCBrZ3ZEK0PMjimWny4QELyFtC7IrZag7KSkfUC3aoHUh2lqdsLNro9xydQ+MwmqwojmAAax4PP4BVu7wN5PQHlgOlguHmM0pR3TKA948Eznj/6vUj6Boax28umye7n7AnGpUGl8+83PHctPuxwKbvY3341tbvY1FJIT5UUQaWIOysmpGO9MT4sMuIdzf0QalgmF8ggwKxJ/OvBCaGsWfhT5GkTpJ8mE7lcQ1o2iGUD/vpfWwfNIanQGxDaSgO+xz+qFNIv4/44dmzYLVy/P2TOtnn0TdqwsiEBfkGHc7IPwP/PuffuKryKiTGSbv3O/elc4V7pTN+ANR/Aoio74aCQsGwttJZDbDUOI4d/Ylo7hvDZYuirz2Rp3e2e81Omy3LOe0WOqUylBBDk4wKhfvnpaKoBwoGPLtT/P7txd3NmDBbcdUy98Wa1uFWvOTi1KAy+F4w8kSylQ4gfI7P/Alw2i2AjPfV0WRy3flNA6zwDgivLrtYMF320zviSm2HXcBJ+sVGCCQ5nlvhVAJV+wmWZ2UlYcJsldbwbcPVD+5xdRmQlOtTjVAKoh6wrmTN9g5g5czA2tTtxFZW3bzvAJ8esCoFQ6qUPuUQMHOzYCkkWcRJKNty64P1yMAGEl6wKxAXRrCE2DQemgexg96TYC6iCY2DMvjKyszli/MxIzMR97x+CEaT76DvhV1NeG5XE76xpkw+9dEAdLuUDd+TwDC85R7fg61W4JVv4JM44X1zoOeg6LAFGQvknCJBTAlUSgUuXpCLD452om8kiH5yD3Y19KIyJ8lNyEU2ik4HUgqgPvACvrP4O5IO2XrFVvcgaqQb6KkV/CP90DFoDE/AyUakMrB2zGqRRXMf5Bt02LQgD89+0YTeMP7GYjjvgYT5LM5aDAVT4DuLviPpeMf90KLrAG0q8Mkf/R8QBJ3qZx2PmUKN+z7tR06KBufNzZHtNaSSp9ege3jC77U0WGq7hpGn18r2mWMpBbhC4aygeOr4Izh3Tg6e2t6AAQ9lYKPJgr9/UodlJamoynW/h/6o6SO356ogbCqDstIBgKqLgNkX+12UmsxQACszVjHPzpbdALdID2C7QjBXtokpVU2YoFIIH0h/oioVOUJwfKRNesnFw/ucAi6s57hD/ThURD1gXcmaDT4YOZ8qu5WOmBesawD7I6NS1AO2fdCIzKR4eUU3ABjihS9Bk8XkzMBK+ELKsCnXOjKwnAODbUCS9AuOfUEjkhlYi9kITIRhUN9bB+4SlPeM9fgZHBvUSgXuuWg2mnrH8IvXDomO+fh4F+7670GsKE3D9zaG91kKhiO9RxyPt2s1uKD9LXQdEvHm5Rx4+86AhucffOkDPHnek3JPkyCmBJcsyoPJwvF6tbcnpRTGJizY09CPFaXSvbqDQqEQLHVObMGXc9dIOiRV49GLe9Km9Fpyht/j2geNyE4JXxOCGYrxRGvoftqB4EEEsADwjTWlGDNZ8Pwu+TQ4AKH/FRD6F11xKD9LOcdgExCXACz/OlDzDtArT6Z4X/dOx+NOnoZ9zYO445wKxKmiHzbk+kk2+OMny3/ic9+JrmFZ+l8dpBRAO+5MCI2aR3HrmTMwPG7Gw1vde6gf31aPjsFxfGeDdwWjZ5JGlTZD8hSK0nQYNJoxMCa/P/BkhAJYmXm97nXvjXbxgwL/q5d2ajuHkRivCk4cKLUMAAO6a6Cw/VntgawYMzIToVSwoPpg3UzFu2vC6n8FfHjAupI1G7C6r7jJXUIMiHvButohabXiwhqdg+PIlGG12ZOFmQsB2DOwBcDEEGDsD3icvZTZ3psL4wBgHnPLwAaisWcUKgVz/G4igZkxITMcKn0nYXX5PyXHh14FEElWzkjHrWeW4dmdTfj1m0dgspXrc87x7BeNuOmJXSjLSMRfr14EVYRLh13xLMHqUSmxbtcvgNotjm3WsX5YX74Z+OJvsJ7mbT/gilufHEGcYlTlJGNWVhJe3hNaGfGuhl5MWKxYOSNd5pm5MO9KgFuBgy+ienN18MfXbQXik4GcBT6HGE0W9I+aZCkhhqFIEA6KEBYJPYWulGclYVlxKp7b2STrPYgjA5sa+vX2vFfOEx4svFbw/d0j/2JiB9Lxk/MqcPHCwH6kkcAZwAZnwWeximdsOeeo6xqRRYHYQUo+Usac1WVmqxlVucn40uJ8/P2TOuyqF9R+D7UO4I/vHceGyiysLPP+zP/mC3dHBWWa9MVtezWjWFJmOkIBrMyIBo2NO4Rmaa00gYbaTmFlKCifrTidkK3rPu7oCfSXgY1XKVGanhCUErGb5cbEsCwZWFEPWDtZswGPX4GZyydnn5WsgVrJHKugrrhmYJU68ZXx9kGjrArEntT01aA30fbafQ0Bx2vUShh0aucqpT1IDDIDm2fQRjSg2qrTSham8sJiBvoaYE1y2jtMNhEnV763cRauXl6Iv31ch/V/2IrbntmLDX/cijtfrsbiIgOevnE5DFEWxPD1vbLjxa/gtcfWAM9di0XPrcKXej4GzrwLhxZf6fNceYmxuaEhiMkCYwyXLsrD3sZ+nJQqoOLCZ7U9UCkYlsmtQOxKxkwgdxGwXxAAOivh78Edf3IrULwKUPpeFO9wWOjIsPipNSBeLWN2zAOrMvjr9hVLC3CyewTb6+SzHWnqG/V/DySRC165AH3xOqD8LGDf04AlvAzckZ4jbs+XzV+Am8+IjL2bFFzbvb4656uSj/PSMrHRPmjE6IQFpXJ5LgNASj5WDPU7ntrvIX96QRUKDFpc968v8L3n9+HKR7cjNSEO9146R9JpFUFkYPMdVYVhVLhNISiAlZlvzv+mI8ORqE7Ew+seApq/AAqWST5HbedwcAJOdtJnAt3HHTeootlgF2ZlJ0n2gh23jGNbq4eXXNgZ2DH/mb60GZjw6OO1+3zJgVLBUGDQiTa9uwawKp33KhnnHG39Y46VQTlxDdLfNdqCUIllQTkpWrQN2FYpB4Ty60Gd9Bujhp5RFEWo//Wxsx8DAOzQaoBQS8MHmwGrCTuZ8+8jZ1+03CgVDL+8eA4evXYxClN12NvUh6xkDf745fl48oblUVdz9MeNOVm4S9ELdByEhTEcj48D1vwIV715tc9jbph7QxRnSBCTk00L8sAY8EoIWdhtJ7qxsFCPhPgI9L+6Mv9KoL0a6DiE0jTnNS1OEeA7qK9BcAMoXet3mP26I0sGFoDKZeFVjqynq8BmXkrwPbbnzc1BkkaFF3bLV0YsWOiI30N8fMXHks/TMNiAS1+7FFi0GRjucKumCYXa/lq35woREctokp2iAWNAc/8YkuKSkK6VVq3g636xrku45ytLlzcDO3tiAmdnrwAAnBw4ic7RTqRo1Xj25hVYNSMdHx7txJIiA57/+grJ7hWJQfzuCygDS4SDUqHEkqwlAICb5t2EVYpEoZSzeLWk4weNJnQOjQfX/2onfSbQUwvmkrZ8/NDjPodXZCehuW8MQ8bAq3UdI05D5rdKrhUehOkB29g7isJUP70eSjXUye4ZHlOYK4ueFKXpHMJFbq/jEsAuy/Iu/R4YE9QDI1FqG++yOmzW6oUHfSclHZur1zgzsAOCuNHpn94OIHC2zGGh4+9vEgb20mhhbiEGsL3C7+HfLv05swzhvQ8jDWMMZ83OxlM3LscnP1qH/9x0Gi5dlO/XgzCSrMkP0Ad3217J5ypJLglzNgQx9clO0WDVjHS8vLfF3Yc7AH0jEzjYMiBaSig7cy4DFCpg/7PIN2hhHhYyO6vyVgEAHlj7gPhxdR8KP0v8f284M7DyVCVpXLxgXT3ZQ6XP2Od4fPO8m4M+XhunxNmzs/HeoQ7ZxITqe0ZQ7COIMmiCs1TqHusGZmwQrPcOvRzWvDwVqBWp0rOAkUCtVCAryXlv86+z/yXpOF8Z2LouuwKxjBlYW6CpNDvLnGv7hIWA7BQNHr1uCfb+/Cw89tVlKBC5x+oe68bghHtCqZIHt6iVrFUhKV5FASwROvYPjZIpgbqPhI2l0sQT7N5U5aEEsGkzANOo2x/197t+73N4RbbQO3i8I3AZsevqZe5AK6BJARJC732zWrkQwAYQC1qZNh8FFucNgYnLHcAmoLF31GuF99o3r3U8zsqa73WcvW82EgHsz077meNxr2kYSMxyBG6BcM/ANgMu5bUapf8Vv/5RE4aM5oh5wLr5Eg+GWELskYnO1GVidb60xSFC4JLySySP7Rr17W9549wbsSR7iRxTIogpzyUL89DcN4ZdDX2BB9vYcrQTVg6sq/BWuZedhHRgxkag+gXkp8RjrHkz7ln4HOZmzAUA5CTm4PZFt3svCB59U7g5D7Bg3W677sihQgwAWXp5g6bbPrjN8difPog/LpiXg6FxMz4+Hp7vLwBMmK1o6h1FqZ8s4HVV1wV3UlUcUHmB8DczBdcv6opG5f43ZGHYJcqF6+J8SUoJbllwS8BjMrTi96gnukaQEKeUtwXMtuAyS+H8ewYjxnXm82fi9GdOd9umVAV3f8kYQ36qjkqIidBZX7geALAid4XQO5JZBSRKu0DZA9iQM7AA8uL1kobPyhaUiKX0wbquZCnsAk5hSG93Do1jwmz1n4EFwLLn4Ns9TpVZuTOwhak6DI+b0eMhj++2EpbqnWVqsa1wRaKE2HXl9e/VfwcMJdIDWL0GA2Mmwdu2vwlwyWCfGDjh50inAnGkSojdCDkDWwe4ZKgf2fCITBM6tbig9AJJ4144/oLPfdT/ShBOzp6dDV2cMigxp/cOtyM7WYO5eRHwfxVj/pXAUBtKh3cDXI3BYR2+NudreOWiV1CVVoUb596IFy960Tl+fFhYhK+4IOD1vn3QiMR4FZLC7Oe0w1KLZTmPnQPdBwAAN8UXhHyO02ekw6BT440QFaddaeobhZUDJX4C2B8u/SGqN1djdZ60RVortwKzLxWEH2vfD3lu1d1Ooa8rBoeAVOlepJEiz6BzUyH+6mz/vbAPrX8IF8+4WHTfia5hlGYEqTMTiMQsQKHC9SqnPsfW5q3hnTIuCCtNG/kGraiuy3SEAtgIsCR7Cao3V2NmYqFgLB2g9MaV2s5hxKkUoiUGAbEFsI/knC1peL5Bi8R4lSQlYrdMbvexsAWcGnttfqOB/p9Zs3HOyCh+Pet6AMBPPv2JrCqAxenC64uVETsQEd9yZGB9KSjLSWqp9BLiFBe1voEmQdhLIva/SSQtdOzwUDOwffU4mebsX/InVEb4Zlm27578gfEBx2PPkiZXJizyeiISxFQmIV6F8+bm4NV9rV6+j2IYTRZ8fLwbG6oyZbdi88nMcwBtKvSHHodGrUBT7ygUTIEZBh/ZzhNbAMs4MOu8gKfukFvU0FAs26lceyH/Ph56D6taqcA5c7Lx/uHwy4hP2vowfZUQu/KrVb/CucXnBhx3+we345mJdsETNowy4of2PeR4vJglCCKhMUbIwBodJfqeWWJPzsg/w2eAWtc1glI5FYgBQKEEkvOgcLm3+Uf1P8I6pf2+NxjyDVo093lXFU5HKICNJE07ALNRcvkwIASwpekJofXHJWYC8SlI75eW3WKMCUJOAbxgTVYTPm351LlhpCtsAacGR7YvcADLACx36cWXU4m4MDXBbT6iiHwJtvaPIV6lQFo0RHhSSwTRI1PgVbUcm5VO28CYUEKcIj2Are8eBWMIbfEkSH7LQ1Ry7K3D00nO+bmVJROScW0J8GTVs6scj58+8rTPca594gRBAF89vRhjJgue3dkYcOyWI50YM1lw9mzpNmdho9YAS74KdvRNLE0ZdCxa+uTgy4AuDShcEfDUbQNGh5WbLOiDF1ryxcHug47HM+PD6zc+b24ORiYs2BpmGbG94slfCbEdg8aA3675LfZe61+f4KPmj3Dvzt8AlRcCx94OqYy439OyLykK5e0SyNNrMWGxontkPKzzGE0WtA6MoTQ9AirXKQWhOyyIkJ67KOhj8g06jEwIllbTHQpgI8nxdwBlnCA/LxG7hU5IMAaklwPdx90272zf6eMApxKxv9Ua0ZvdMAWcmnpHoVSwwCW4iVmALg3qLuf/6XjfcZ/+XsFSkKoFY+4ZWH8393Za+gUFZVlLUHxQp7P5nEqw0rH/Ptt7h4HBVkdfhhQaekeQnayBRh25oLAgSQiojys5MC7dwgkAwDnQexKmOOfngzKwoZEYF/7F+/KZl8swE4KYPszOTcFppal4fFs9zBb/15EXdjchJ0UTHQEnV5beBChUuI697T+AHe0Fjr0JzP2yX/scOx0DRtn6XwE4RHHkoHHIuaBwbf76sM51WmkakjQqbDnSEXiwH+q6R6DXqaHXSV8El9y7W3EBYBoB6j8NPNaD7W3b3Z6zhCgusPjBrbosDE52j4BzyJ+BBYT7LRkDWBiCF0l0WulM/zJiCmAjBefA0deF8uF4aXXsRpMFTX2joVno2EmfCXTXYEHGAsemt0++7XN4ZXYSBo1mp/CPCKLBYnp4JcQNvaPI1WugDuQ3yhiQNRvxXcccm658/Ur86OMfhfX6duJVSuSmaN0u5L6U61xp6TdGtHz47GJnGfibo7aSJwllxFnJgtz8YFcTwC1BlRALFjqRzb4m2Xo64jkPvg92qB0wj2FY7bzghyrGcapzVtFZuGflPXh4w8MhnyNBHYVeaYKYYtywqhStA0a8tr/V55j2ASM+Pt6FSxflRV+NPDkHmHMpzhh5GyN9Hb4Xr6tfACwTwELfNlp2rFaOzqFxRwWQLHiUrYZTEnnXp3c5n2jC6zdWKxVYOysTW450whKE4rQnJ7tG/Pa/hsMdre/i55mZMB/1b6UoxqjZY1EjaZIEsLbF+RaRwEwbhNiR3UInYgHsoO/PfdCogq/ws1vpNJ0CQk5hBbCMsVTG2HuMsRrbT69mQcbYAsbY54yxQ4yxA4yxK1z2/ZsxdpIxts/2b0E485lUdB4G+huAivMlH1LXJawMhSTgZCe9HBhqw5PrnD0MDUO+M3ezbErE/vpgXTOSl2gLAZUm7NXRgBY6rmTNgabzqNumdxveDev1XSlM1TnKeQD3gH1f1e2ix7T0jTlWBCPBdxd/1/HYGGd7HQlesHEqBdIT4zHRY/ubu2Rgzy3x30NzsjtyF1Q7diXkPZr44JWIewRJ+neHnB51KkYBbCgwxnBJ+SVIVEegjIogTmHWV2Ridm4y/vjecYybxRdDH/tMWIy8YkmM/DVXfx9qqxFftbyIPrFSQ4sZ2P4wkLcYyJ4b8HTdI+MwW7lsHrBiSFlYlgK3W9OFwYbKTPSMTGBfU3/I56jvidz19s2Gd/BKggZ7T74rJFOC4O5tdzserxgbc4iSxpr8VOE+SCww++LqL9yeV6VV+TzPCbuFTiRKiA1FQuLAhQ8bPwzpVM8qQ7Ooy3NkYCmADcSdALZwzssBbLE992QUwHWc89kAzgHwAGNM77L/h5zzBbZ/+8Kcz+Th8KsAGDArcOO9ndquMBSI7dgzoz01jk072nYIHmEiSFEidr1w3GPSAWnlQsN6GDT2BBfAMnPkyiGK03Vo7BHPwCpF1PeMJgu6h8cjmoF1VXi1KuOEVWOJSsS5KRqHByxPdmZg715xt69D0Dsygd6RCZTJ6Ysmgn2ldEShCD4DayuNn+niPUoZ2PBQstA+xz9aKk8FBEFMNxQKhjvPrUBz3xj+/Vm91/6e4XE8tb0BF8zLDWgjFzEyZqG19HJco3wPHXXV3vsPviRU/Kz6nqTTdQwIfYmylhADeE43FypbAOZqgxMOK4o2hH2OtTMzoVIwvB9iGfHwuFD1JqX/1RcPrnsw8KDRbqBtf8ivcVd3H9QZFSEfLyfJGjUMOrVbtdwvT/8l/rr+r27j3rzkTb8+sXVdw8jTa6GNi0CrlIjw2G0fBn7ferat3Tg4gtkZgReOxEjRqpGsOTW8YMMNYDcBeNz2+HEAF3sO4Jwf55zX2B63AugEELqB6FTAagX2PyOINwVRflHbOQwF8y+rHhB7ANtd47b5mjevER2eolUjN0WDo+2+1Ubfa3jP+aQrfAViu22NXUApIFmzw3q9QBSmJqBnZAJDRmEl2q5YeEdPn2gPQmsEPWDFsMJqs9IJnIEFBC/Y+BGhjOWFLmf/s7+ST7uxd6QDWFfRpcaeo35GitBdA6gTcHqBUxRNwagLIhxC+f1t+8o2XFt1beCBBHGKsro8AxursvCH9457XVt/9eYRjJutuG29vD6nwWI8/Q6MQIucLbcBrvZ0Y/3Aez8DchZIUh8GbKKBgLwiTgCq0udgkVEIjj9p+USWc2Yn5oR9jhSdGstKUvH+4dAC2JoOIWEwMyt4m5R7V92LVze96ma35wsGBhx7K+jXcByvihe1EYwVhWkJbsmGTTM2efnAFyQX+L/X6Y6AArEd2/3ib/OlfW7seCr6KyxmoZoyRPINOgpgJZDFObcbYrUDyPI3mDG2DEAcAFdDyl/ZSovvZ4z51GBnjN3MGNvFGNvV1RW+iXREafgU6G8EFogHjb440TmMwlRdeCI6qSWAQgV0H3cI5gBAy7DvbFdFTrLfEuL/+/z/nE/6G8NWILZ/AUnOwGZUAEyJHGVkVqvttjH13cK87BlYJZioCFKDbQUw0v2idixWi2Cl0+vfx9VOvkGLlLFm8MRsHB+ql3TMiSgFsK4B00e9B/2MFKH7OJA+A+MuX/bxShltG05Fgmy/u3zm5Y4+ZoIgfPPrS+ciWaPG1x7bidrOIXDO8feP6/DynhZ8Y00ZZmTG9nOUk1+Mn5hugL7/IPDSDcDEiCCs98JmYLQHuPABQCHtFrF9UNDQkDuAhb5I0EuwMZmsQTZUZqGmc9i/g4EP7PdbFbYWrmC4sOxClOpLUZIiIbDMqASOhx7AqgwlYVfbyUlRqg4NveK/7wfWPoDHzn7M7/Gcc8FCJ1KtUsm5gDIO51rd70v2dOzBx80f+zxs6dNL3Z4rAaHSMUQEL1gqIQZj7H3G2EGRf5tcx3Hhm8XntwtjLAfAkwC+yrkjX/5jABUAlgJIBXCHr+M5549yzpdwzpdkZEzyBO7nfxW8QysvCOqwms6h8MqHAUCpFsoYumsklwfOyk5CbecwJszeqolGs6e4Ew9rZQhw+o1KDgDVGiC9HPez8FdOxSjPEn7ntV3CReWDxg8AAEqtQfh9elDfbbcAio6IDQcXgvi+BklWOoVpOhSgDaaUYkmKyoDQfx2nUkTc13ZTmfNrwzraE9zBPTVoTi1E20gbUjWpqN5cTTY6YcKCjGDlUv8miOlOemI8nvjaMsEq54FPcNqvt+BXbx7B2bOz8N2N4VUxyUFCvAo7davxevatQsvTHyqBP1YBJz8GLnoQyF0o+Vwt/WOC/kKCzAuKhmJHCTFguxZOEjZUCvma9490Bn3ssY4h6OKUDsXYUEiOS0b1ZpHyb1cKlwslxBLadcYt45j7uHvZalpGZKvfgqUwVYfWfiNMIgrf64vWY0n2Er/Hdw2NY3jcjNJILdQrlII+TF+92+bNb2/GrVtulXwaZZj32XkGLVr6xybVgk8kCBjAcs43cM7niPx7FUCHLTC1B6iin2TGWDKANwDcxTnf7nLuNi4wDuAxAMvk+E/FlPaDworXabcAaulfTmaLFSe7R0K30HHFpkQsNXipyE6C2codWThXOkdF/qRhWug02lbQgvIbzZqN2V3SMpDBUpSWAJWCoaZD+P/bM84qXZro+PruESTGq5CeGAUPWNgywhkzAXCv0nAxClN1KGHtGNAVSn4PnOgaRklaiP7DQbDBpf/IMtYn/cCJUaC/CeeO7seHTR+i1xiijyzhhj2ALTcEvliqFCp8peIrkZ4SQUwbqnKT8e531+DmM0pxWmkafnvZPDx89eLoKw/7oCBVh2dUFwFfeweougiYfQlww/vAgquCOo8gaqiBQu7/l6EYrioHoXzvV3c5g7z7UwP72UqlME2HWVlJePdQe9DHHmsfQnlWkvy/Lw++0yVk/X778Z244vUr0G/s9ypXtXPzuzd7bYvPmhPR+QVLYZoOFit3tHEFy4lIKhDbMZRI1isBgK5R74pSnSIeSAg9UZdv0GH0FPCCDbeE+DUAm22PNwN41XMAYywOwCsAnuCcv+ixzx78Mgj9s0HWFE5ChtuBzCpg2U1BHdbYOwqThYdnoWMnvRzoPQGLrZczEJU5QhmLWB+sa8nnkvhMgCmAtPB6dxp7R5GiVSNFG4SHZ9ZsoXw5AqiVChSnJ6Cm0z2Ar9eIB9j1PaMoTtdFxQMWsDX424UUXOyEfFGcxJHBBtCuzA0igB1BWWZ0bVGsE8NC2ZoUek/AT4EHESYKl0vBfavvEx2z55o9qEyrjNaUCGJakJEUjzvOqcCfrlyILy8tiHjQEgwFBp1QEVV4GrDpL8BFfwbyFwd9ntb+scCe7qGQlAOFS5XITe8Gd18FAB82OVVgFQnyeu5urMrCzvpe9I2IB4W+ON4xhFlZ8mQBnzz3SawrWCe6b8A0jNqMUjzZuw+Hew5j9XOr8Y33vyE6doZe5L4u07eabyywt5359S/2g0OBOJKtUoZirwysnT0de7y2Xf4/dz/1r/MUXK4tECwkQ8Suz9ISYqA/VQg3gP0NgI2MsRoAG2zPwRhbwhj7h23MlwGcAeB6Ebucpxlj1QCqAaQD+GWY84k9MzYA39wmlBAHQW2nDArEdtJnApYJfKv8isBjAZSmJyBOpcCRNu8+WNcMbJbZJHw4VeGVCTX2jgXfP5oVmiKbVMozE3HCI4D91CreF1zfMxK18mEAaB9pB1LLAKYEugILH+VZhbb0emsWPmr6KOD4cbMFjb2jEe9/9eTPqXo0NH8ubbBNgZiQF/siDAfH8xc8jytmXYFzSs7xO5YgiOmBvSTTLFKSGQyt/cbIiBoqFFDEOa+1tf21fgaL4+YqkJApy7TsnDU7C1YOfHBUehlx9/A4uocnQhJwEmNB5gLcOPdGn/u/leJeKbazfafoONEqnMzJtWBpv29s6AktgK3rGoFWrUROBO2ekFoCjA9Cq/T+PGx+e7Pj8eGew9jett2rquBbPd3Qhqkzk++w0qEA1iec8x7O+XrOebmt1LjXtn0X5/xG2+OnOOdqF6sch10O53wd53yurST5Gs65dw3rVCSEG70auQNYABdociUNVykVmJmViCNt3hlY1w+cxjjsVDkOg8aekeDKhwFRJWKzxAyzFMozE1HfM+Lm26cUEQgyWaxo7htDSRQD2G2t2zBsnQDSyiQFsOoBoXxl2wRH33jgMt3GnlFYrDzqASwAvFDzsrSB3TUIWnGICEhJSglKU0pxx9I7UJlWiZ+e9lMomAJnF58NAPj0yk/x2sWv4dMrP43xTAmCkJvCVKEks23AU+tCOhNmKzqGjJHJwAJI9BCNG5rwLTgphmvJrCJR3gB2bl4KspM1ePew9DLi6uYBAMCcvBTZ5jEnfQ5uW3gbLiu/zGtfi0VasLe9bbvb82d7jIIo0SQiK0mDOJUi5AxsbdcwSjMSIlsFYVMi/kr+mX6HXfH6FeIVBUNtYevM2BeTprsXLHlQTBJOdA4jJ0WDJE0QZbW+sJf4emStfK28AUBldjIOtw76bfq2jg+GHcDaA8CiYAPY5FxAo8cjifMdm16vez2subhSlpkIKwdOdDkvjoPcO0Bu7huDxcpRHCkVOx88sv8R4XcvoYQYPUKv8OEx5+94SZZvcQNnWU10/08AAOOAtHHdxwVxBEJW4pXxePXiV7Esx11+4Hdn/A57r92LlPgUlKSUICVevpstgiAmBwVhlmQCQMegEZxHzlbuewb3kuaVz6wM6njXhe7TSs+VZU52GGM4a3YWth7vwtiENIG7fU39UDAh+JVzHjfNuwkZOml9k2KLAFsatzge/z9jHGZnLQyrjDUSKBQMBQatm5VOMNR0DMmW+faJzQv29tQluHPZnV67P2/9HGPmAJnRjPAy33qdGro4JZUQE9GhpnNYnuwrAOhShQZwjwC2frDe5yGVOcnoGZlA19C4zzGCmFB4pQ3NfWMwW3nwPQiMAdlzcXqfc6XTlxhBKJTbLA3+c/h5x7ZEjd5r3MluIdgrjrIB/bhlXOiD7a0DzAH+3z0nMKhKQ+OgczHi3lX3+hx+rH0YjMmU/ZeASuGU5eBSA9jOI27lTG9dGro1ABEYxpjb34kgiOlHQaoQdIYTwNrLFCOlYJ+YWoYLhoO3qrFjdlmIVgfZ2iWFs6qyYTRZ8Wltt6Tx+5v7UZ6ZhIR4+b9fKwwVksb9ac+f3J5f86a75aNiqA3IC74XOhoUpSU4rAyDYdBoQtuA0eE6ETFsAayivx5XV17ttfu2D27DsqfF9Wp/mWdr38mU9nf0BWMMeXotWqiEmIg0VitHbeewI4iSBZsSsSv3fH4PusfEv2TtQk6HXcqIHz/0uNuYTIslbA9YewBYEkoGM2s20HHY8VTOnrzSjAQoGNAw4OxleWjDw17jjtuUimX9W/mgMMmZcfxv7X+FAJZbgJ4AfUBdRzCQNAMDY84Ld1aCb4vmI22DKE5LgC4uOgGLWuFSZSAlgDWPC4sxLoqIWTq/ltMEQRBEAHJStFApWFiekXZF2EiVEMNQjJ91h646b7IISqzZ1shkE5eXpiJJo5KkRsw5x/6mfswviExFy/qi9fj3Of8OOG7Y5N6tt79rv9tzxvmkDWALU3Vo6h0N2iLG7jIxM9L3bnE6IDHLp5CT0eK7XF8x1A6otIC+OOxp5NusdKYzFMBOAlr6xzBmssi7MpReDvR4W66c+bx4XX6VLYB1FXL6/a7fOx5nKnW4pW8g7Nr8OruMeagBrCn0lVh/aNRKlGYkomvI+eWSnZDtNe5Y+xByUjRI0clQ6h0A1wDdaDE6+4DbD/g+yGoBOo/Ckl7pJtrrqibtydH2QVRkRz4gt+PmPTrWH/iArmOA1ezWB+3v/0MQBEEERqlgyDdow8rA2m+Sc1IiJIxjKIYuRD9Lk9WEV08I5hhuC6cyolYqsL4iE1uOdsJi9T/Pxt5R9I2aML9AH5G5AMDirMWYlzHP7xh7WfULx1/w8n4FgEGlAshbFJH5hUthqg7D42Z0DwdXgVfTIdzbRryEGBD6YHvqgj6MDbYKlomK8O9v8iiAJaKBXYG4XM4SzvSZwGgP/rzyV167xFauUnRq5Om1okJOALBeqYc6MQvQ6sOaVl33CPQ6NQwJIXioeniStY8E77/mj7l5KegY9P+BP9oehR4KX6TPBNQ6oHWf7zF99YB5DAn5cyVpHo2Mm9HQO+rIwEcDrcq5Us8nBoHxANptHYK71jGt8/NBASxBEET4FNgyWqHS2j+G9MR4aNRKGWflgqEo5EN/vePXjse/SJnvZ2R4nDU7G70jE9hxssfvuB11QiZ5SVFqxOYCAH9Z9xe/++32eo/se0R0/47kdKEVbRJSZrtPtmt3SOV4xzC0aqVDoTei+EggBUIx2Bp2/6udPL0O/aMmDI/LJ3Y62aC7wElATaewMiRrD2KakCmdz7w/rGYRcSIAqMxJ8hnA8rE+WRSIT3aNhJZ9BYQSWpfA5dEDj4Y9H1fm5KXAYrOgEcNkseJE53DUspU3zLnBfYNSBWTPBdr2+T6o4xAAILV0IdTKwKvWxzqGwDmimoF17a18MiU5sDBVxyFApcHln37fsYksXQiCIMKnIFWHpjB65Vr6x5Cnj6AtiSYlaFtCOy8cf8HxeGm6/6xkOJw5KxMJcUr8d2+L33GfnehGemI8Zka4D9Og8f/74pyjZbgFnWM+7H9k9suVkxkhBrA1nUOYkZkYHR/mjFnASBcwGlzpu8LYL5t1kb0nfTr3wVIAOwmo6RhGRlI89LoQspK+sJX6pg56B2T2nhBPKnOScaJrGEaTBSMepbrc2B+2gBMAnOweQUl6iF/ecTrBDzVCzMtPAddX+9xf3z2CCYsVs6IU7F1Sfon3xpwFQNt+oVRYjM7DABiUWZXIMwT26z1qKxmPZgb2jmV3uG/oOuL/gI6Dbl/q35gvbsROEARBBEdhqg69IxMYMorfFwSipX8sYgJODmzCOHZCsdBjEVSx18Ypcd7cHLxZ3e5TjZhzjm0nerCyLC0qC7DFycU+973f+L64hYuNmenetoWThZxkDbRqpaNyUSrHO4YiL+Bkx57s6T6O6s2+7ylFkSuAtfWkt/RPXysdCmAnAbVdw/KWDwOC5Ygy3kuJGPBu4LdTmZMMKxc+6GueW+O2T2GeCDsDOzJuRvugMTy7Fg8/2J4x/yU7wVAVIIg72CoIDkUz2PMidwFgGvUS6HLQtl/wi43TIT81cAB7uG0AifGq6JTV2NhYtBE6lYuKc6efAJZzoL3a7e9uNIfuWUgQBEE4KbRZ6TSEYE3COUdr/xhyU6IbwNpLYP3xYeOH7htSCmSckDeXLsrH8LjZpyfsodZBdA2NY9WM6GQ3ByfEq+nsNA01+dx31pzr5J6ObCgUDGWZCUEFsANjJnQMjkev/cslgA0GBedCpaEMFFAGlog0nHPUdshooWNHoRT8YLtrkORhBP7DrT8UPaTSIeQ0KNi2uKACDzsDW98jZHVDUiC2k+3eB9s/3h/GjNwJJGu/t7EfujhlzHpgX619FchdKDxp2eU9gHOgeSeQvxQAkGcQMvrfnHebz3Pua+rHvPyUqJfkKpmzX2p/517fA/vqMWjsxdz+jx2bAnqoEQRBEJKwX49PdgcvkNg1PA6jyRr5BdC0GfhmvzMgkxLA3vahx3Uvwj7iy0tSkW/Q4ukdjaL736xug1LBsKEqOgr6d6+4O+hjrhpnOBeJKM1aIP+EZGRGRiJOBBHAOgWcopSB1RcCKo2jPWpp9lJJh82xqmRbaElPjEecUoHmaSzkRAFsjOkYHMfQuFn+DCwglBF3H8dz5z/ntnlP5x70Gr1r8wsMGujiFG5KxHZmTJiAzPDKSuwKxGEFsB5CTnIqC37c/LHf/Xsb+zE/Xw9lNHoobDx13lOOxz/97KeCjZEuDaj/1Htwf4PQd5G/BACQoxcC2DSl+Ire2IQFR9uGsLBQL/u8A6FwUdl7daTB98CW3ahTu/+NQykfIwiCILwJJ4BttGVti9LCuKZLIa0ct/T1O55KCWC9SMyUbz4iKBQM168sxhcne7Gvqd9tn9XK8fqBNqwoTUNqKAKWIbCucB02Fm0M6phb2xvx29k3T3oP8LKMRLQOGDEiUaDoUKuw+BG16jlHAknIwN6/9v6Ah/zSakBueqUsCsSA8H7M1WsoA0tEDqeAUwSyeukzgb56pKq9Ly6XvXaZ17aFTy1AWuFb2N7hXnrzjfhCXMJ1QGJGWNOxXyDDC2Ddg+hfbv9lOFNy43c7f+d4fFPFj932jU1YcKRtMOrB3vwMD+VEhQIoXg2c/ETIuLrStFP4acvA/qfuXgDAiU7xL7CDrQMwWzkWFshv7h6IeKWzvPkFrQIY8qEo3bwTZpW7QMhl5d7vXYIgCCJ4NGol8vRa1AUpigM4y44L03QBRoZJ2gy3p2ufX4tRU5Alz1GoMrpyWSGSNCo89KG7V/vWmi409o7iS0vyIz4HV+5bfR/SNGmSxycrtcDsiyM3IZmwVyzakyKBONQ6gLSEOGQnR1BszJP0mY4ANiU+sO8vH2gGcuQVGsszaNFMASwRKezmyhFpLk+fCXAr4ga8lfG6x7rxo60/cjy3W+v0qz9EC3/dbez8oX4wGRrLT3aPIE+vDU9u36O84vO2z2GyhiY+4YmrNYtpyD1ruf1kD8xWjuWl0i8GEaNkNTDYDPR6+IzVfSQoNtoy5SNm4b1V0yF+od/b2AcAWBCDDOwPl3qUsTeLlEQDQgDr0Xs9N8Pbt44gCIIIjZL0hJAysA29o2AMUSghdhdvHDOP4Yv2LyL7miGQGK/CN9aU4b3DHfjwmKDwa7Vy/HlLDbKS43HunJyozketVOOFC18IPBDAI529wPyvhKz4HE3sAWxtl3e1oBgHWwZRlZsc3VapjFlAXwNgkhhAmo1AjrxWT3n66e0FSwFsjDnWPoTUhDikRaKsxKZErO49YH4xPwAAKGJJREFUKarc+lb9W47HriU5Fo/qHMVAU9jlw4DgdxuWgBMAMIZr4J6tbh1uDe+cNqxmZ9/vthp3oaCtx7qgUSuwvCS23mgWqwUoPVN4cvwd5w7OgRNbgLJ1gFLl9vc81i5+Y7Kzvg+FqTqkJwYWe5Kb9QXr3Tc07/QeNDECtB2AxfY+JgiCIOSnNCMBdV0joh7x/mjsGUFuihbxqgh5wNrR6oEE9wqwYMT8LtBEL/N54+oSlGUk4PvP78f2uh78/t1j2NvYjx+cNQtxqujfcmfoMiT1YC4zMWDNjwKOmwwUpSVArWQ42h44gB03W3C8Ywhz8gJnQWUlowIAB7qOAgDeuewdv8PVnAPZMmdg9Tp0DY3DaPLhWjHFoQA2xhxpH0RlTlJkVobsZTfdx3Hrglv9DnUTbWLuFzFmNgJZVWFNxWrlqO0cRrkMpdJr0tw/5BZfljJBYnIRB9rd0OdYubJaOd4/0oHTStMiZ9Yuke9+9F1hNTp7LnDoZeeO1j3AUBswYwMAYP4TzpW89oEJdA+7i3KZLFZsP9GD06OkiOiJUuHxexTLwNZ/BlhNsHgIdxEEQRDyUZKegKFxM7qHJ4I6rqF31KFiHHE8yoh/+LG4GKUYvy6NXttJvEqJf25eijilAlc+uh1//egEvrwkH5cvjm75sCv/OvtfAceoL3kESMqOwmzCJ06lwMysJBxu9a+0DAhVjmYrx5zcKAew9nLgtgMAgDStePXeipwVuCWpEueMmWSz0LFjr4xoG5iezg0UwMYQs8WKY+1DqMyOUGN5fCKQnO+wXFmVt8rn0J98+hPHY6XGPaNZOWECMsMLYJv7xjBmssiiAleVv9rt+X9P/Dfsc46YRtAy7i5s9dLuZgDAjpO9aO4bw8UL8sJ+nXD5sMnWnzznMiFraVO5w96nBdW7ygu9juHWeHx+wt1uaH9TP4bGzTijPDYBrGu5NgD8cvQoMOFR6nxiC6DSwEwZWIIgiIhRmmHvKQyuD7apdxRFke5/teMRwPrDK5NsKJF5Mv4pTk/Au987A3/40nw88bVluO+yeVFX+g+aygtiPYOgmJ2bjEOtgwGrBg62DDjGRxV9MRCfLFgbAlAxpzCWfUEhXhmPR896FN8cMUGZWQGo5K2Gy5vmVjoUwMaQ+p5RjJutkVVGS3cqoS3JWuK1e1/nPnDOsaVxi+jh/84+C3qrNWxvquM2GfNyGSxokgtX4lsuioSPHXws7HPe/O7Nbs/XVWTiX5+dRM/wOP68pQYGnRpnz47N6mRVmsjiwcJrAbUO+PBXQH8TsO9pIajVeK8yJscnOvpx7Hx0rAsKBqwsi00A68lziTrgpIsKNOdAzXtA0Urc/vEPYjcxgiCIaU5pCErEw7aMbcQFnOykzcA9XdJ83+c94VGKmRrdABYAkjVqXLY4H2fMzJj0wWv15upYTyFo5uSloHdkImB2sbplAEnxquhVCthRKISS4HYhA+u6aF+QJGi5LMpcJNzrtO6Tvf8VEHpgAaClP3iP56kABbAx5EibUP5QkRNBX9H0mUIGlnPRL9Fr37rW+8veBdZbL5iIx4eXOT3eaQ9gZRCrMhSDq+RVkzvQfcDxODchFz88exZGJyw447cf4vO6Hnz/rFnQxsWmfFhUdTchHVj9PeDwq8BDywGmBNbcIXr8mhl52HqsC2ZbczPnHK/tb8XKsnSk6OSzIQqWA9cdcN9w/G3n47Z9QO8J7CtyX3TJ0IanhE0QBEG4k6vXIk6lQF0QAazDQic1whY6dtLLofXItoll30RbimTy1pzKbL9qe6ynICuzbSXBhwKUEe9u6MOCQj0UUbQ/dJAzH2g/CFgtYIzhprk3IT8xH9kJ2bh/7f343ZrfAT21wFgvkL9M9pfPTtFAwTBtlYgpgI0hR9sHoVIwh6JaREifCUwMAUPtQQs0AEB5T4MsAk41HcPISdEgWSNDwMQYrkyaFf55fLC2YC0qc5Lxr81LcVppGn56fiWuXh5ZE3R/fHnWl8V3rPo+sO5nQOka4NqXAUOR15BLyy/F+fPy0DMygQ+OClnYL072orF3FJsW5EZy2gHxWlA5+LIg3AQA+/4DKONw7cln3Yb8ed2fozQ7giCIUwOlgqE4TSfZlgQAGnqEsbEsIbZw72B1wurex6viAJSxW6idLCR42Cl+c/43sTBzYYxmEz6CdoyzRFiMgTETjnUMYUlRjMQ3c+YB5jFHFeRti27DW5cJ4qkbijYI9jqNtoWFwtNkf3m1UoHs5OnrBUsBbAw50jaEsozEyCr42fsHu4/BbJVm+mxneerZSOqpC1vACRBKiOUoH7ajz3PPzIUSnPvCXuqxqjwd/7x+KW5cXTo5S4AUCuCMHwBfecbnl192QjbWV2QiO1mDv39SJ0j6f1CDtIQ4nD8vupL+ARkfAPY8CQy2AnueAOZ+yWtIunZylDwTBEFMJ0rTE4Pqga3tFMaG7SwgFUMJGNyvw8d6j4FzjkcPPIrGwUYA8LLV+7EyKzrzm2JcMuMSPHHuE1OyfBgAdHEqzMpKwt6mfp9j9jb2gXNgSXGMrIHyFgs/xVwW7DRtF6yL0iKj9ZFv0KF5mlrpUAAbQ460CQrEESXL5pnZdkC8l9IPCYMAuAXIWRDWFCw2BeKZcmaaPebkahsTLptnb5btXLHANZjXqXRQKRW4fUM5dtb34fwHP8VntT24bX05dHEqP2eJDrfMv8X5pPRMYMv/Af8+H2AKbK88y2u8QTP5PeoIgiCmGjOzk1DfMyLZcqO2axh5em30riOqOCDJfdH1yjeuxDsN7+DBvQ/i6+99HSaLCQNG94zcPEN4+h3TiVSNkIk8cN0B5CROsgXsEFhWkord9b2O9ihPdjf0QalgWFCgj+7E7KTNEOyf6j/zPaZxB1CwXEhIRIA8g5YysIS89I8KzecVkRRwAoCENKH/o20fVnuo9wZiXqftwpS7IKwpNPYKYlUzZczAIte99MXMg8su+2Jt7unITpjcUvKHew773f/YIaeolUYp9ApfubQAt6wtw+CYCd9YU4brVniXG8eCOelOi5wn5p4FFJ6GQYUSz67+Om7a/jOv8fHK6HvWEgRBTHcqs5Ng5UK7jxRqO4dRFsn2JxHy9KVe2364VbDTGTWP4qo3r8J5r5zntp8ZiqMxtSnBf87/Dx5Y+8DkrCgLgWUlqRiZsPjsg912ogdzcpOREB+jxXrGgKKVQMM28f0DLUBPjTAmQuTptWgfNPoM8qcyFMDGiGpb3f7caJgr58x3SHlLRc0SkNp/BFyXDiSHZx9zrF34cpmZLWMAqy/E3S7qc2e96J2tCwWlzOJQkWBwwr9ogauidHFKMQCh3/RH51TgszvX4c5zKybNBcw1IP3dgYfx0NwNuDovB786+VIMZ0UQBHFqMct2fT7SHthb02rlONE1jBkZ0Q1g5+Quw5Ot7aL7TBYTjvYe9d5BAayDvMQ8rC9aH+tpyMayEiGj/MXJXq99/aMT2NvYhzUzYyz8WHQ6MNAI9Dd67zthu1ebsSFiL59n0MJi5WgfnH5esBTAxoj9trr9OVEJYBcISmfGwBcmOyqFEhX8BEbT5wmrSGFwqHUQSgVDhZwBLGM4S+8sie41en+BTSd+s/o3jseKAB/bMbOzXGR5zvKIzUkO4pRxbs8f2f8I6gfrYzMZgiCIU5SitARo1Aocax8KOLalfwxGk1UeV4FgyJqDBeMToruGTD7mLSJuSEwPMpM0KMtIwMc1XV77Pq3thpUDa2ZlxmBmLhSvEn6e+NB7X817QFIukBm+zowvHFY607CMmALYGLGvaQClGQlI0UZBHc/uL9VeLbkPVq1gKGctaIwPv7H8YMsAZmQkQqOWV6xKke8u5CQqny+FcWfJ1GQtUT2/9HzH40DZ05q+mkhPRzZcvdECcc/KeyI4E4IgiFMXpYJhVlYSjkrIwNbaxJ4i6qAghu1G/5YM6YqtmQZv9WJi+rCxKhufn+jBwKi7eNfbB9th0KkxPz8KSSJ/ZFYB+iLgyP/ct0+MALVbgPKNYSeJ/JFnsHvBUgBLyADnHPub+7EgXx+dF7T3sLbtw3MXPCfpECWsUDErdpnCX7081DqI2bny9/oqCtx9s944+UZI56mre8/x+PrZ14czpagQTNA32clNlG7lc0b+GRGcCUEQxKlNRXYyjrQNBVT1r7X1yUa7hBj6QiAuCTdZpb3uGVxLwn/TnHPnZMNs5XjvSIdj26DRhPcOd+DC+blQKWN8v8QYUHURUPcRMNbv3H70DcA0AszzYZMoE5SBJWSlfdCIrqFxzIvWylBipqDe17oPAPDihS8GPERhy2a+3ROeBH3nkBGdQ+OYHYlSaQ8hp7/s/UtIp6lr+dzxuDKtMqwpRYPr375e0rgrZl0R2YnIQLo2HU+c+4SksZ72CARBEIR8VOQkoXdkAl3D437HHWodQE6KBoaEOL/jZIcxIGs2VJ1HJA0/JyVyfvHE5GBefgqK0nT4z44Gx7ZX9rRg3GzFJQvD02+RjTmXA1YTsO9p57ZdjwEphUBh5AScAECjViI9MZ4ysIQ87G8SBJzmR1PaO2cB0LoHADArdZZbT6Una/LX4EFVIYbis/BZlwb9o+I9J1Kwq8NFJAMb737OtpG2kM5zqPugHNOJKsMTgZUiU+JjXDojkXGL/5slO1Pl/0MQBDEVqbS5IvhSdbVzMEJVVZLIngO0H8TsAO1QP+rpw4UurTfE9IQxhq+dXoI9jf3YerwLQ0YTHtl6AkuKDLGzz/Ekd4Eg5rTtL4IWTe37QOM2YMUtEbPPcSXPoKUAlpCHvY19UCuZ42IRFQqXC0JOw0Kz+/ml52NT2SbcOPdGt2FnFZ2Fv6x7EHNbqjGesxQAw+6GvpBf9rDtQlgVgYsdh/8yJ6n8Y7xJlvNEk6eOPCW6fX+XU236prk3RWs6YVGZGjjrvSx7GbQqbRRmQxAEcWoyNy8FCgbsbez3OWZ0woy6rmFU5cZoQTFvCTAxhD/OucXvsGsHh5z6H8S05oqlBSjLSMB3n9uHq/6+Ax2DRvz4vMpJ47YAANjwC2C4HXj8QuDlm4G0cmDx9VF56Xy9Fs1UQkzIwfaTvVhQoJdd1Mgv9jKFRme57C9X/RK3L7rdbZiVWwW576E2pMw6A2olw8760APYA839KErTIVkjv1iVVqXFiiR3XzgrD87raslTSwIPmoQ8tO8h0e3XvHmN47FmClgCAUJmdesVW/2OSY6L0Wo/QRDEKUJCvAqzspOxt9H3Nf9I2xCsHJgTqwxs/lIAgKLtgP9xKo0QJBDTHo1aiUevW4ICgxZdQ+P4w5fnY3HRJOt9LlgGXPwwMNwhWDt95VlAHZ1FeXsG1mqVJ+kzWaAANsoMj5txsGUAy0vSovvCuQuFL3SXAFaMKyuuBBq3AwDUJSsxNy8FO+tDs6jhnGNPYz8WFUbui+SmeV93e373truDOl5q+epkp66/Drd/cHvggZMUJfO/mHNuyblRmglBEMSpy8JCPfY19fu82T1o87CPiK6FFNLKAK0BrN23t/2b1hwgaw6gVEVxYkQsKctIxKvfWoXtP1mPSxbmx3o64sy/Evj+UeCmD4D06Klj5xu0mDBb0T0yPe537VAAG2V21ffCYuU4rTTKAawqTii9adjmd9jynOVCbX58MpBZhaXFqTjQ3A+jKXiLmua+MXQNjWNRBFfCEvXFbs//W/tfjJpGQzrXZFcgfnXTqz733b3tbnzQ9EEUZyMvaoXvDP2y7GU4q/isKM6GIAji1GRRoQFDRjNOdInrLOys70V2sga5KTGq8GEMyF8K3iYewP6/0+5GQct+oGBye6ATRLSYrkrEFMBGmR0ne6FSMCwq0kf/xYtWAu0HhCZyFx7d+CjW5q/Fn8/8M8A5cOIDwXxZocTy0lSYLDykPlj7MYsjmIGtTK/C/2lnum37676/hnQunVonx5QihqcdwOOHHo/RTORHqRDPwF4x6wrcf+b9UZ4NQRDEqcmiQj0AiLYOcc7xxcleLCtJjW1/Yf4ymHtPiO66WJsPmI3C/Q5BENPWCzasAJYxlsoYe48xVmP7KRqpMMYsjLF9tn+vuWwvYYztYIzVMsaeY4xFWZM9+mw91oVFhQbo4mJQ2lK6BuBW4KR7v+GK3BV4cP2DOLPwTEHoqb8RmLEBALC8JA1qJcPHx7uCfrndDX1IiFNiVnaSLNP3xaWVX3F7PmIekXagh9ddoDLWWOPp//r7Xb93PPa8mZiTNicqc5ILX7/7VXmrqP+VIAgiSpSkJyA3RYOtxzu99jX2jqJzaBxLS1JjMDMXStf63mevMitcEZWpEMRkx56BnW5CTuFmYO8EsIVzXg5gi+25GGOc8wW2fxe5bL8PwP2c8xkA+gDcEOZ8JjWt/WM43DaI9ZWZsZlAwXJAkwIce9v3mJr3hJ+2ADYhXoXFRQZ8XNMd9MvtbujDwkIDlIoIr9R6XMwsVmnlzjtrXnN7fk3lNT5GTg7Egrz2kXYAAIP779gz2J3sqBQqzNB794RUBbBKIAiCIOSDMYY1szLxWW0PTBZ3UcRPa4X7gNNiHcDmLUK+OgU/ii/BTIOzAuv+tfcDdR8BGZVAQpTbtAhikpKkUSNZo6ISYg82AbDXMT4O4GKpBzIhZbQOwIuhHD8V2XJUWNFcX5kVmwko1UJgWvMOYPWh1nv8LSB9JmAocmxaXZ6BI22D6BwySn6p/tEJHGkfxJLiKCjBJaS7PbVwaQHsc4eedDxWKVSTvoRYrMx244sbYbFasKdzj9v2n6/4ebSmJRvXVV3n9rx6czUydTFa7CEIgjhFWTsrA8PjZuzyKCN+73AHClN1mJGZGKOZ2VAogbJ1uLbpMB5Z9xC+Nudr2HftPmzIXAI0fAbMOie28yOISUaeQUclxB5kcc7bbI/bAfiKzDSMsV2Mse2MsYtt29IA9HPOzbbnzQDyfL0QY+xm2zl2dXUFX846GXjjQCtK0hNQlpEQu0nMPBcY6QKatnvvG2wDTn4CzL7EbfMZ5RkAgM9qpWdhP6vtAefA6vL0wINlZtQ0KslO553BY47HW760JZJTkgWtSovNVZu9ti94coHXtlmps6IwI3mZlzEv1lMgCII45Vk1Ix26OCX+u7fFsW1g1IRttT3YWJU1Ofw1Ky8ERrqQ0X4Q3138XWGB99hbgNUMzDov1rMjiElFvkF76mVgGWPvM8YOivzb5DqOc84B+DIZKuKcLwFwFYAHGGNlwU6Uc/4o53wJ53xJRkZGsIdHjZPdI/jVG4dh9ii9aeodxfa6Xly2KC+2X/6zzgXiEoG9T3vvO/gSAA7M/ZLb5tm5yUhLiMOWI949Mb74tLYLSfEqzM/XhzffEHi/8X2c+fyZeLnmZZ9jRsfdFRYne/+rndPzTg845rySqXnxLtOXoXpzNe5bfR+eveDZWE+HIAjilCQhXoUL5uXgfwdaMWQ0AQBe3tuMCYsVlyz0mWeILrPOBbSpwG4XMcM9jwOpZYLjAkEQDvL0ghcs59PHCzZgAMs538A5nyPy71UAHYyxHACw/RSNcDjnLbafdQA+ArAQQA8APWPMrmaUD6BF7PipxPGOIfz9k5N4o7rNbfszXzSCMeCSRTH2p4pPBOZcChx6BTAOOLdbLcDOfwgm4enu5t8KBcNZs7Px4dFOSXY6nHN8fLwbK8rSoFLGphez19jr1xO2uX7yZ1zFkFIefd8Z90VhJpHjvNLzMDttdqynQRAEccpy7WnFGJ2w4OGPTmBk3Iy/ba3DokI95sTK/9UTVTyweDNw5H9A617gxIeCz/3SGwDF1NKAIIhIk2/QYnjcjMExc+DBU4RwP+WvAbDXNG4G4GVUyRgzMMbibY/TAZwO4LAtY/shgMv9HT/V2FiZhfLMRPz1wxOw2IzA+0Ym8MTnDThvTo5DDSymLLkBMI0Anz/k3HboFaDvJLDy26KHnDc3GyMTFklqxLWdw2jpH8PqmZM3U95+4n235zrV5O5/tZOty471FAiCIIhpztz8FFy6KA8Pbz2B8//8CTqHjPjJeZWxnpY7p38HSMwEnroceGEzkFoq3N8QBOGGPfZo6huN8UzkI9wA9jcANjLGagBssD0HY2wJY+wftjGVAHYxxvZDCFh/wzk/bNt3B4DvMcZqIfTE/jPM+cQchYLh9g3lONYxhIc/qoXVyvHTVw/CaLLgtvXlgU8QDXIXAFUXA5/9GWg7AAy1A+/cBWTPBSouED3ktNI06HVqvHWwPeDp36xuB2PA2VUxEqsKwN7Ovbi16yPH8zX5a6BWqmM3oSCYYZiBB9c9GOtpEARBENOcey+Ziy8vLoAuToU/XbkQS4pjrD7siVYPXPMykFEB5C4Crn4RUGtiPSuCmHRMRy/YsMxIOec9ANaLbN8F4Ebb420A5vo4vg7AsnDmMBk5f24O3p7Xjt+/exxP72hE24ARPzpnVsT9UIPi3N8CTTuAf50NKOMAiwm4+gVB3U8EtVKBs6uy8b8DrRgeNyMx3vdb562DbVhalIrM5OhdSCpSK3C096iksX/94vduz6ea5Uxe4iTpQSIIgiCmLRq1EvddPsnF9bLnAF99I9azIIhJTb5BqDKcTkJOU+vOfYrAGMP9VyzAD86aiaqcZPzu8nn45pqgdasiS1IWcMO7Qj/sjPXA194GcvxfqK5cVoDRCQte3ee7VflY+xCOtg/h3LnRLXV94MwH8PV5X5c0dnvPAbfnU0XAyY4UhWWCIAiCIAiCMOjU0KqVlIElAqNWKvCtdZOkZNgX+kJg00OBx9lYUKBHVU4yntreiKuWFYqqKT+9owFxKgU2LYhuljAvMQ/fWvgt/O3A3/yOG58Y9to21TKw5I1KEARBEARBSIExhrxpZqUzte7ciZjCGMPmlUU40jaID495C04PjJrw8p4WXDA3B6kJcTGYYWCufOUir21Tpf/VjkFj8LkvNyE3ijMhCIIgCIIgJjt2K53pAgWwRFBcuigfRWk6/O6d415et498fAIjE2bcdEZpjGbnzZhZ+LD2G/vxTt1bqDV6qyir2NQrRLh90e2i25flTLuWcoIgCIIgCCIM8gxaNE8jFeKpd+dOxBS1UoE7zqnALU/vwYMf1OK7G2cCAA61DuAfn9Rh0/xcVOYkx3iWTpY9HTig++7i70ZhJvJisgrm8ldXXo2Xa152BOo/O+1nsZwWQRAEQRAEMcnI02vRN2rC6IQZuripH/5N/f8BEXXOm5uDSxfm4U9bajBhsWJWVhJ+9eYRGHRx+PmFs2M9vaC4b/V9SNOmxXoaQVOcXAwAOC3nNHzQ+IEjgI1TTs7SbYIgCIIgCCI25NutdPrGUJ41iVxRQoQCWCIkfnPZPIABD390AgBQkp6AR69dPGl7X31xXul5sZ5CSJxTfA5mGmaiTF+Ge3fcG+vpEARBEARBEJMUewDb3E8BLHEKE6dS4I9fXoDb1pWjb3QCs3NTEKeaWi3VGdqMWE8hZBhjKNML1kyLsxbj9brX8bs1v4vxrAiCIAiCIIjJRp5e8IJtniZKxFMr4iAmHcXpCVhYaJg0weuzFzyLTWWbJI194MwHIjuZKPGLlb/AKxe9gnOKz4n1VAiCIAiCIIhJRmZSPDRqBeq7R2I9FVmYHFEHQcjE7LTZuLLiSklj52XMi/BsokO8Mh4zDDNiPQ2CIAiCIAhiEqJQMJRlJKK2czjWU5EFCmCJaQcDCzhmfsb8KMyEIAiCIAiCIGLPjEwKYAliSnPX8rtiPQWCIAiCIAiCiArlmYlo6R/DyLg51lMJGwpgiWlHRWpFwDGVaZVRmAlBEARBEARBxJ4ZmYL68ImuqZ+FpQCWmHYoFUrsuWaP6L5EdSKqN1dHeUYEQRAEQRAEETtmZCYCwLQoI6YAlpiWqJVqpMSneG1fkbsiBrMhCIIgCIIgiNhRlKaDWslQMw0CWPKBJaYtVm51e/7s+c86vFMJgiAIgiAI4lRBrVSgOC0BNR0UwBLEpIVz7vZ8dvrsGM2EIAiCIAiCIGJLRU4y9jT0xXoaYUMlxMS0xTMDSxAEQRAEQRCnKnPzktHSP4bekYlYTyUsKIAlpi1LspfEegoEQRAEQRAEMSmYm6cHAFS3DMR2ImFCJcTEtOV3Z/wOLcMtSIpLgpIpYz0dgiAIgiAIgogZc/KSAQDVzf1YMzMjxrMJHQpgiWmLTq1DuaE81tMgCIIgCIIgiJiTpFGjND0BB5qndgaWSogJgiAIgiAIgiBOARYU6LG7oQ9WKw88eJJCASxBEARBEARBEMQpwIqyNPSMTOBYx1CspxIyFMASBEEQBEEQBEGcApw+Ix0A8Fltd4xnEjoUwBIEQRAEQRAEQZwC5Oq1KE1PwMc1UzeAJREngiAIgiAIgiCIU4SrlhdidMIS62mEDAWwBEEQBEEQBEEQpwg3ri6N9RTCgkqICYIgCIIgCIIgiCkBBbAEQRAEQRAEQRDElIACWIIgCIIgCIIgCGJKQAEsQRAEQRAEQRAEMSWgAJYgCIIgCIIgCIKYEoQVwDLGUhlj7zHGamw/DSJjzmSM7XP5Z2SMXWzb92/G2EmXfQvCmQ9BEARBEARBEAQxfQk3A3sngC2c83IAW2zP3eCcf8g5X8A5XwBgHYBRAO+6DPmhfT/nfF+Y8yEIgiAIgiAIgiCmKeEGsJsAPG57/DiAiwOMvxzAW5zz0TBflyAIgiAIgiAIgjjFCDeAzeKct9ketwPICjD+SgDPeGz7FWPsAGPsfsZYvK8DGWM3M8Z2McZ2dXV1hTFlgiAIgiAIgiAIYioSMIBljL3PGDso8m+T6zjOOQfA/ZwnB8BcAO+4bP4xgAoASwGkArjD1/Gc80c550s450syMjICTZsgCIIgCIIgCIKYZqgCDeCcb/C1jzHWwRjL4Zy32QLUTj+n+jKAVzjnJpdz27O344yxxwD8QOK8CYIgCIIgCIIgiFOMcEuIXwOw2fZ4M4BX/Yz9CjzKh21BLxhjDEL/7MEw50MQBEEQBEEQBEFMU8INYH8DYCNjrAbABttzMMaWMMb+YR/EGCsGUABgq8fxTzPGqgFUA0gH8Msw50MQBEEQBEEQBEFMUwKWEPuDc94DYL3I9l0AbnR5Xg8gT2TcunBenyAIgiAIgiAIgjh1CDcDSxAEQRAEQRAEQRBRgQJYgiAIgiAIgiAIYkpAASxBEARBEARBEAQxJaAAliAIgiAIgiAIgpgSUABLEARBEARBEARBTAkogCUIgiAIgiAIgiCmBBTAEgRBEARBEARBEFMCCmAJgiAIgiAIgiCIKQEFsARBEARBEARBEMSUgAJYgiAIgiAIgiAIYkpAASxBEARBEARBEAQxJaAAliAIgiAIgiAIgpgSUABLEARBEARBEARBTAkogCUIgiAIgiAIgiCmBBTAEgRBEARBEARBEFMCCmAJgiAIgiAIgiCIKQEFsARBEARBEARBEMSUgAJYgiAIgiAIgiAIYkpAASxBEARBEARBEAQxJaAAliAIgiAIgiAIgpgSUABLEARBEARBEARBTAkogCUIgiAIgiAIgiCmBBTAEgRBEARBEARBEFMCCmAJgiAIgiAIgiCIKQEFsARBEARBEARBEMSUgAJYgiAIgiAIgiAIYkpAASxBEARBEARBEAQxJaAAliAIgiAIgiAIgpgSUABLEARBEARBEARBTAkogCUIgiAIgiAIgiCmBBTAEgRBEARBEARBEFOCsAJYxtiXGGOHGGNWxtgSP+POYYwdY4zVMsbudNlewhjbYdv+HGMsLpz5EARBEARBEARBENOXcDOwBwFcCuBjXwMYY0oADwE4F0AVgK8wxqpsu+8DcD/nfAaAPgA3hDkfgiAIgiAIgiAIYpoSVgDLOT/COT8WYNgyALWc8zrO+QSAZwFsYowxAOsAvGgb9ziAi8OZD0EQBEEQBEEQBDF9iUYPbB6AJpfnzbZtaQD6Oedmj+0EQRAEQRAEQRAE4YUq0ADG2PsAskV23cU5f1X+Kfmcx80AbrY9HWaMBcr8xpJ0AN2xngRBgN6LxOSA3ofEZIDeh8Rkgd6LxGRgKrwPi8Q2BgxgOecbwnzhFgAFLs/zbdt6AOgZYypbFta+3dc8HgXwaJhziQqMsV2cc5+iVgQRLei9SEwG6H1ITAbofUhMFui9SEwGpvL7MBolxDsBlNsUh+MAXAngNc45B/AhgMtt4zYDiFpGlyAIgiAIgiAIgphahGujcwljrBnACgBvMMbesW3PZYy9CQC27Oq3ALwD4AiA5znnh2ynuAPA9xhjtRB6Yv8ZznwIgiAIgiAIgiCI6UvAEmJ/cM5fAfCKyPZWAOe5PH8TwJsi4+ogqBRPN6ZEqTNxSkDvRWIyQO9DYjJA70NiskDvRWIyMGXfh0yo5CUIgiAIgiAIgiCIyU00emAJgiAIgiAIgiAIImwogJUBxlg9Y6yaMbaPMbbLti2VMfYeY6zG9tMQ63kS0xsf78PfMcaOMsYOMMZeYYzpYzxN4hRA7L3osu/7jDHOGEuP1fyIUwNf70PG2Ldt34uHGGO/jeUciemPj2vzAsbYdvs2xth0bKcjJhmMMT1j7EXb998RxtiKqRqvUAArH2dyzhe4yFHfCWAL57wcwBbbc4KINJ7vw/cAzOGczwNwHMCPYzc14hTD870IxlgBgLMANMZuWsQphtv7kDF2JoBNAOZzzmcD+H1MZ0ecKnh+H/4WwP9xzhcA+LntOUFEmj8BeJtzXgFgPgRx3SkZr1AAGzk2AXjc9vhxABfHbirEqQrn/F2bEjgAbIfgt0wQseJ+AD8CQOILRKz4JoDfcM7HAYBz3hnj+RCnJhxAsu1xCoDWGM6FOAVgjKUAOAM2xxfO+QTnvB9TNF6hAFYeOIB3GWO7GWM327Zlcc7bbI/bAWTFZmrEKYTY+9CVrwF4K8pzIk5NvN6LjLFNAFo45/tjOzXiFELsO3EmgNWMsR2Msa2MsaUxnB9xaiD2PvwOgN8xxpogVAFQdRQRaUoAdAF4jDG2lzH2D8ZYAqZovBKWjQ7hYBXnvIUxlgngPcbYUdednHPOGKOMAxFpvN6HnPOPAYAxdhcAM4CnYzpD4lRB7DvxJxDKhwkiWoi9D1UAUgGcBmApgOcZY6WcLBmIyCH2PrwcwHc55y8xxr4MISu2IaazJKY7KgCLAHybc76DMfYneJQLT6V4hTKwMsA5b7H97ITgi7sMQAdjLAcAbD+pTImIKD7eh2CMXQ/gAgBX000aEQ1E3otrIKz+7meM1UMoZd/DGMuO2SSJaY+P78RmAC9zgS8AWAGQoBgRMXy8DzcDeNk25AXbNoKIJM0AmjnnO2zPX4QQ0E7JeIUC2DBhjCUwxpLsjyFkGA4CeA3CFxRsP1+NzQyJUwFf70PG2DkQeg4v4pyPxnKOxKmBj/fiTs55Jue8mHNeDOFCuohz3h7DqRLTGD/X5v8CONO2fSaAOADdMZomMc3x8z5shbCwBwDrANTEZobEqYLtetvEGJtl27QewGFM0XiFSojDJwvAK4wxQPh9/odz/jZjbCeE0qQbADQA+HIM50hMf3y9D2sBxEMoWwKA7Zzzb8RumsQpgOh7MbZTIk5BfH0nxgH4F2PsIIAJAJupMoWIIL7eh8MA/sQYUwEwAhDTrSAIufk2gKdt34N1AL4KIZk55eIVRt/bBEEQBEEQBEEQxFSASogJgiAIgiAIgiCIKQEFsARBEARBEARBEMSUgAJYgiAIgiAIgiAIYkpAASxBEARBEARBEAQxJaAAliAIgiAIgiAIgpgSUABLEARBEARBEARBTAkogCUIgiAIgiAIgiCmBBTAEgRBEARBEARBEFOC/w8NsBbByijrkwAAAABJRU5ErkJggg==\n", 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\n", 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mz54NwPXXX8+sWbN48MEHmT17NklJSYwdO7Zz/5UrV3LzzTcTGBjYmS5cWVnJ5MmT8ff35623tA6pN9xwAytWrGDKlCksX76c4OBgACZPnozRaGTKlCmsXLmSO++8s1/jV6wzegeHmTNnqunp6QM9DCGEEEIIIYRwycGDBxk3btxAD6PP0tLSSE9PJyYmRrdz2vs3URRle3u3GiuebqMjhBBCCCGEEEL0iaQQn2iqcmHn6xA7FiacDxYLrIUQQgghhBCiP3Jycgb0/SWAPZFUF8ALy6CuWHtedQwW9i/HXAghhBBCCCG8haQQn0jWPgxN1XDz9zD+PPjuMag4OtCjEkIIIYQQQlgYjHWI3MXVfwsJYE8Ulcdgzzsw+0ZImAjLHwdVha3PD/TIhBBCCCGEEO0CAgIoLy+XIBYteC0vLycgIMDpYySF+ESx+23t6+wbta9hidoa2F1vwqkPg4//wI1NCCGEEEIIAUBycjL5+fmUlpYO9FC8QkBAAMnJyU7vLwHsiWLPOzBsEUSkdG2bdBHsfRey18Po0wZubEIIIYQQQggAfH19GTZs2EAPY9CSFOITQUU2VGTB2HOstw9fAv5hcPCjARmWEEIIIYQQQuhJAtgTQeZa7evIpdbbffxhxMmQtU5bDyuEEEIIIYQQg5gEsCeCrG8hYihEDbd9LW0R1ORDpVQjFkIIIYQQQgxuEsAOdmYzHPsehi8GRbF9fdhJ2tejGzw7LiGEEEIIIYTQmQSwg11Fltb7NXm2/ddjRkNwHBzb7NlxCSGEEEIIIYTOJIAd7PK3aV+TZ9p/XVEgaQYU7PDcmIQQQgghhBDCDSSAHezy08EvVJtpdSRpOpQfgaYaz41LCCGEEEIIIXQmAexgV5AOSdPAYHS8z5Dp2tfjuzwypJ+CupY6KpsqB3oYQgghhBBC/KRIADuYtTZD8X4tRbgnQ6ZpXyWNWDenvX8aJ71z0kAPQwghhBBCiJ8UCWAHs7LDYG6F+Ik97xccrbXZkRlY3dS21A70EIQQQgghhPjJkQB2MCs5qH2Nn9D7vvETu/YXumlpa6GgrmCghyGEEEIIIcRPggSwg1nxfjD4QvRIuy+XNJRQ09JeuCluHJRnamnHol9yqnM6H9+36T6Wv7+clraWgRuQEEIIIYQQPxESwA5mJQe16sNGX7svL31vKaetOk17EjdOSzcuz/TgAE9M56w+p/PxxvyNgKQUCyGEEEII4QkSwA5mJQcgfnyPu9Sb6rUHce37eXkacUZRLVe9sJUr/vsD+wurB3o4NlrNrVbPG1obAFjy7hKqm71vvEIIIYQQQpxIJIAdrJqqoTpPm1ltV2+qZ9Irk1h7bK3t/tEjweCjBb1eqrK+hZ+9tJUDx2s4UlLH1S/+SHFN00APy0rl/vcdvyZtdYQQQgghhHArCWAHq5JD2te4rgJOmVVaevAd6+7oWvvawccPokd59Qzs8xuyKK1t5tXrZvPWDXOpa27lr19lDPSwurS1oqx73OHLRqWHXrxCCCGEEEKIfpMAdrAqO6x9jR3duemeDfd0Pl7w1gLbY+LGee0MbH1zK2/+kMuZkxKZmBTOyLgQrpyTyvs7Ciiq9pJZ2Iw1qFU5Dl82GOTHSQghhBBCCHeSK+7BqiJLq0AcnkqDqQFTm8lhO5fShlLtQcwoqMr1ykrE32WUUNvcylVzh3Zuu2ZeGm1mlfd35A/gyLoU73mDquBYh68b5MdJCCGEEOKEo6rqQA+hZ6qq/fmJ8BnoAYg+Ks+EyDQw+jDn9WmMjLDfSgfgmi+u4bPzP0OJHgmqGSpzIHaMx4bqjM/3FhET4s+stKjObWkxwcwdHsWq7fn8YskIFEUZuAE217Ks+QBE+w/cGIQQQgghhFvVNJn4fO9xNh4pY29BNSU1zTS1thHq78Ow2BBmDo3k3ClDmJISMXCDLM+Cvasgd7O2rLChDFAgKBriJ8CIU2DSRRCaMHBjdCMJYAer8myr/q8d61/tyavNY8m7S1i/4Kn2YzO9KoBtaTXzXUYJ509LwmiwDlLPmpTIAx/tJ6u0npFxIQM0QjAd3djrPhmVGSSGJHpgNEIIIYQQQk9VDS08ty6LV7fk0GQykxgewLTUCE4dF0iAr5GaJhMZRbW8tuUYL246yoKR0dx/1njGJYZ5bpDHd8M3f4CstYACCRNh5DIIiQNUqC2Gwp3w1e9g7R9g1vWw5LcQ4MExeoAEsIOR2QwV2TDiZKcPqWiqgOjh2pPyLDcNrG/2FlTR0NLGolG26bmnjIvngY/2s/Zg8YAGsN9lvNfrPrd9ext7r9nrgdEIIYQQQgi9bDhcyq/f3U15fTPnT03iZ/PTmJIcbjf7r7rRxHvpeTy3LosVz37PA+eM52qLJXBu0dYK6/8MG56AwAg45QGYeiWEOZg4KT0Mm/8OW5+HjM/hklchcbJ7x+hBsmhvMKothNZGiB7h2nGBkRAUo83AepGtRysAmJUWafNaUkQg4xPDWHuwxNPDsvJAZfqAvr8QQgghhNDfi5uO8rOXfiQq2JdPb1vIU5dOZWpKhMOla+GBvly/aDhf33kSC0ZG88DqfTz99WH3rZNtqYc3L4YNf9GC1l/tgpPuchy8glbkdcWzsHINtLXAy2dB7lb3jG8ASAA7GHXMoEaNILs627Vjo0d43Qzs1uwKRsWFEB1if33potEx7MyrpLGlzcMja9dYiUE1D8x7CyGEEEIIt3huXSaPfHqA5RMS+OiXC5kwJNzpY6ND/HnhmllcPCOZZ9Ye4bUfjuk/wJYGeO0CyF4H5/wdzvunNgPrrKHz4OdfQXAsvHExlB3Rf4wDQJcAVlGU5YqiZCiKkqkoyr12Xl+pKEqpoii72v9cb/HaNYqiHGn/c40e4znhdcygRo/gQLmLbXGiR2oVjL2E2ayy/Vgls4ZFOdxn7rBoTG0qO3IrPTgyC4W7qHO2RY5ZAl0hhBBCCG/30a4C/vJFBudOGcKzV0wj0M/o8jmMBoXHL5zMsnFx/OGTA/yQXa7fAM1mWH0z5G2FC1+AGX0Mk8KT4WerwegLb14KzXX6jXGA9DuAVRTFCPwTOAMYD1yuKMp4O7u+o6rq1PY/L7QfGwX8HpgDzAZ+ryiKbR6psFaRDT6BEDqETQWbXDs2egTUHveab95jFQ3UNbcyNTnC4T4z0yIxKLBVzw8FF9Tn/+j0vm1e2mdXCCGEEEJoDh6v4f9W7WH2sCieuHgyPsa+h0RGg8LfLptGSmQgd723m/rmVn0GuekpOPARnPYITLywf+eKSIVLXtFiiG9+r8/4BpAeM7CzgUxVVbNVVW0B3gZWOHns6cDXqqpWqKpaCXwNLNdhTCe28iyIGg4GA59lf+basVHt62a9ZBZ2f2E1AOOHOK6OFhrgy4Qh4fzQvlbWkw5VHGJuzutO7z/1y8vdOBohhBBCCNEfza1t3PnOLsICfPnXldPx93F95rW7EH8fnrx4CgVVjTz5VUb/B1m4E9b9CSZcAPNu7f/5ANIWwtxfwLYX4NgWfc45QPQIYJOAPIvn+e3burtQUZQ9iqKsUhQlxcVjURTlRkVR0hVFSS8tLdVh2INYeWaPBZzOG3me3e2TXplEQ3j7P6+XrIPdX1iDj0FhVHzPFYZnD4tiV14VLa2eTdE9XHnY5WOWvy/3YIQQQgghvNGz32ZyqKiWv1w0yWH9lb6YmRbF5bNTeW3LMbJL+5Hp2GaCD2+B4Dg466/goJhUn5xyP4QOgS/vG9TL3jxVxOkTIE1V1clos6yvuHoCVVX/o6rqTFVVZ8bG2rZb+ckwm6EqFyLTHO6yMGkh4f72F6FXBrZvr3LDQvM+OFBYw6j40F7vfk1NiaCl1UxGUa2HRqZRHaRa/3LqLx0eU1BX4K7hCCGEEEKIPsqraODfG7I5b+oQThkbr/v571w2Gn8fA3/+4lDfT5L+EpQehLOehCDHNWL6xC8Ilj4AhTtg/wf6ntuD9AhgC4AUi+fJ7ds6qaparqpqc/vTF4AZzh4ruqkvhbZmLZe9m1XnrOLts9/m9LTT2XTZJi4dc6nt8f7BEBQNlTnuH6sT9hfWMKGH9OEOU1MiANidX+XeAVlobmvm/m1/stn+4bkfcvOUmwGYnTDbY+MRQgghhBB996fPD2JUFO49Y5xbzh8b6s8NJw3ny/3FfZt0aaiA7x6D4UtgzJm6jw+AyZdB/EStr6y7Wv+4mR4B7DZglKIowxRF8QMuAz623EFRFMtGRecCB9sffwmcpihKZHvxptPatw1aO3IruePtnVTWt7jnDarbM67DU1ift97qJX+jPxOiJ3Q+Nyq2s5pm1QwRQ6Fy4GdgS2ubKatrZnxi7wFscmQgkUG+7PFgAFtQa/9eysjIkQDsvWYvL57+osfGI4QQQggh+mZ3XhVr9hZx8+IRJIQHuO19rpmXRqCvkX+v78NyvU1PQ3MNnP4nfVOHLRkMcPbf4JLX3PcebtbvAFZV1VbgVrTA8yDwrqqq+xVFeVhRlHPbd/uVoij7FUXZDfwKWNl+bAXwCFoQvA14uH3boFVe18LqXYUcq2hwzxt0BLARKdz6rfWiboNi/d95zohzbA5vM7dB5FCvSCHOLNHSc3tb/wqgKAqTkyPYk1/t7mFZvKnn3koIIYQQQrjPs99lEh7oy88XDXPr+0QG+3HZ7BQ+3l1IYVWj8wc2VGjpwxMugHh7DV10lDIL4sa69z3cSJc1sKqqrlFVdbSqqiNUVX20fduDqqp+3P74t6qqTlBVdYqqqierqnrI4tiXVFUd2f7nf3qMZyAlRwYCkF/ppgC2Sgtgf2wqsXkp0CfQ6vnEmInsvHqn1TYzZm39bFUemNvcM0YnZZdpAezw2N4DWIApyeEcLq6loUWn8uS9UOxEsLuu3uWR9xZCCCGEEPo4VFTD1weKWTk/jRB/H7e/33ULhtFqVnk3Pa/3nTts/Te01MGi37hvYCcITxVx+snoCmBduOPiiuo88A/nwfQ/W22O8I8gNsi2uJWPwfqH1GxuTyE2m6Cm0D1jdFJWST0BvgYSw5xL45iSEoFZhX0FNW4emcZeAGs02KZl771mryeGI4QQQggh+uD5dVkE+xm5dkGaR94vJSqIRaNieHdbHm1mJ9aZttTD1n/BmLOcnn2tbq7m9m9vp7Kpsp+jHXwkgNVZaIAvEUG+7puBrc6H8GSbSrfLhi5z6nBtBnao9mSA04izy+oYHhOCweBcru6kJK2CckfvWHdTWvpeAr2m7LCWri2EEEIIIQZMaW0zn+09zsUzU4gI8vPY+142K5XC6iY2HHGi/efe96CpGubf5tS5d5XsYuHbC/k271ue2PYEp686nQe/f5BJr0zioc0Pde63rWgby99fToPJTXHJAJEA1g2SIwPdNwNblQcRKTabLxx1oVOHX//l9Ww2td+pGeBCTtml9QyPDXZ6/9hQf6KD/Th43DMzsN0rNc+Mn+n0oQs+u5C/bPuLzgMSQgghhBCueDc9D1ObytXzhnr0fU8dH09UsB/vb8/veUdVhW0vaJWBU+c6de7Psj/rfPxJ9icU1hfyYeaHALx/5H0WvLWASa9M4qn0pyioKyCzKrPPfw9vJAGsGyRHBJHnziJO4bYBbJif40q+d8+6u/NxZXMlN219CBTDgLbSaTK1kVfZ4PT6V9AKOY1LDOPgcc/0gj1YsNXq+X9P+6/DfVeMWMFFoy+y2vZR1kduGZcQQgghhOhdm1nlza25zB8RzQgXrjn14OdjYPnEBNYeLOm5fkt+OhTthZnXOV0VuKmtqcfXa1q0yZ595fsA2JC/wblBDxISwLpBSpQ2A6vq3VupuRaaqiA82eYls2p2eFh0QLTtxrCkAU0hPlbegKrCCBdmYAHGJoSSUVxLa5vjv69e/i93tdXz7uuJLf1x4R/5/bzfW22TFGIhhBBCiIGz/nAJBVWNXDXXs7OvHc6enEijqY3vDvWQRpz+EviFwORLnD7v6szVLo3j33v+7dL+3k4CWDdIjgyiudVMWZ3OvWDbKxC/0JRj81KAj+NCSPHB8bYbB7gXbHZpewXiGNfuho1LDKOl1czRsnp3DMshX4Ovy8cog7S3luidqc3Mm1tzuePtnTz8yQH2FXiwvZOzagph7SPw/vWw6WnU+vIeb3QJIYQQJ5r3txcQHezHqePtXAt7wJxh0cSG+vPpHgeFU5vr4MBqmHgh+Ie6dSwn0iys++tI/wRZttKJDfXX78TVWg79MwVrOze9eeab1LfWkxCc4PCwGfEzCPULpbbFIvU2cihkfavf2FyU117kKjUqyKXjxiVqqdIHi2oZFe/eH3RLr57xqsvHGOT+0AmppsnENS/9yM7cKhLDA6iob+Gl74/y84XD+O0ZY/ExesH/e+4P8MbFWlXDsCRa977HtKyXAKmaLYQQ4qehpsnE1weLuWJ2Kr4D9LvZaFA4c2ICb2/Lo6GllSC/bqFXxhowNcCUy5w6X2NrI7PfmN2nsRwoP8BJySfRam7tMatwMPCCK60TT3KkFpTl6V3IqToXAKPS9d8W6hfK3MTeF3w/v+x5q+fv+5ig9jiY3FRsqhd5FY2EBvgQHuTazObIuBB8jYrnCjm1Sw1LdfkYAzqnkIsBp6oqv3prJ/sKqvnH5dPY8tulpN+/jJ/NG8qLm47yyzd3YPJAenuPqgvgjUsgJA5uS4c795Jx5Ztdr+98Y+DG5sVqWmpoNXumx7QQQgj3+2JfES2tZlZMHTKg4zhtQgLNrWY2Z5bbvrjnXa22TYpzxZv+vuPvfR7HP3f9k1f2v8K016aRU53T5/N4Awlg3cByBlZXVXlg8MWodN01CfZ1bg1pXFCc1fOHyn/oOucAyKtsICXStdlX0BbEj4gNcX8A29L1f/fDFT/0WCTLEUXSNU847+8oYF1GKb87cxznTNF+IYYG+PLwiok8ePZ4vtxfzN2r9ui//t0Vn96p9Xm+8j2IGg5Am0Xl8t1f/ppdW/8xUKPzSq3mVha8tYBHfnhkoIcihBBCJ6t3FpAWHcTUlIgBHcestChC/H1Ye6jE+oW6Ui0bctLFYHAuJKts7l/P1yfTnwQgozKjX+cZaBLAukGwvw9RwX76t9KpzofwJFrMXWtrY4NinTo0ITiBuMA42xfaZ3U9Lb+ykZSowD4dOz4xzO0BbGt5VudjZ28SdFfT1jSwgYzQlanNzNNfH2ZKcjg/m5dm8/p1C4fxm1NH8+HOAv7x7QCVq8/dCke+hMX3dAavAG1qV0GxqxLjuPrQf6Bw1wAM0Dt1rA3+OPPjAR6JEEIIPRRVN7Elu5wVU5MGvCaJn4+BhSNjWJdRYn1duP8DUNt6Ld506aeXcvf6u3ls62NW7XN+yiSAdRO39IJ10ELHWWcOP9N24wDMwKqqSn4fZ2BBWwdbXNNMRb3ORbIs1JQd7NNxCtYfkhVNFXoMR3iB1TsLKKhq5I5lozEY7P8yvPWUkZw/LYmnvj7MV/uLPDxCYOOTEBQNs29ge/F2rvn8Gp7b9Rw3fHWD7b5vXwF1Jbbbf0JqWmqobKpEbU/3b1UlhVgIIU4Ea/YeR1Xh3AFOH+5wytg4jlc3WbeC3PeB1vs1blyPxx4oP8DnOZ/z1qG3dBuPOsiXuUkA6yZaAKtzCnF1PkS4vhazg836LsXYWRjKk0rrmmkymUlxsYBTh7GJWvGmQ0Xum4VVqvUJ7E9+92RdziMG3ls/5jIqLoQlYxxnPSiKwp8umMTk5HDufGcXGUWe6VkMaDejjnzNmvHLmLtqKSu/WMmOkh38a/e/aG5rttn9nz5NXP/eGdBq+9pPxeK3F3PSOyfxSdYnAz0UIYQQOvrqQBGj40M83vvVkSVjtWuH7zLabxzXlUDeVhh3zsAMaHDHrxLAuktyZBD5lY2YzTp9h7RpRZfUsKQ+n8JkNllvCEvSZnU9LK9Cm5nuawrxmIT2APa4+4KDJVkv9+m45FDrHr2D/Q6X0GSW1LEjt4qLZyb3mooU4GvkP1fPJNjfh+te3kZprYcCxN1vAypP1mVQb+q9zdTz4cFsNbTAJ7fDTzTVvWPG9Q9b/jDAIxFCCKGXivoWfjxawekTHHfo8LS40AAmJoWx4XB7P9iMNYAKY892eEyDqcFtN1j1nM0dCBLAuklKZCAtrWbK6nS6eK0pANVMU1hi56ZzR5zr0imMitHquTk8aUBmYDtmpvuaQhwb4k9UsJ9bZ7f6Wn7pf6f/z2Zbm7nNzp5iMPlwZz5Gg8J505y7gZQQHsCL18yivL6ZG19Lp8nk5u8BVYXdb0HaIkqbXUxb3/0WbPyre8blxbKrswd6CEIIIdzgm4PFmFU4bbz3BLAAC0bEsDO3isaWNjj4KUQMhfgJdvctbyxnzptzuG/TfW4Zy46SHW45r6dIAOsmurfSaQ80a4OiOjedOvRUl07xy2m/tHq+Osh/QNbAdqwNTors2wysoiiMiQ/lULF7Alh76ZbOig+OZ8GQBVbbbGa+xaDzzYESZqdFERca4PQxk5LD+dulU9mZW8Vv3t1Nqzvb65Qdhoos1PErXD5UnXgRfPsI7HnPDQPzTllVWdzy9S12X3tp30seHo0QQgg9fbW/iKSIQCYmud5Bwp3mjYimpc3MrsxcOLpeSx92kNWVV+v+6/Ovj33t9vdwFwlg3aQjOCuo0imAbQ80Xy/f3rlpScoSl07RvRVMVUCwNrPr4RnCvIoGYkL8bJs5u2BsYiiHi2r1S9G28Ketf9L1fBLADm55FQ1kFNeydJydKt69WD4xkfvPGsdne49z+9u73Ncj9vAXAFxV9JXLh/4leQQ1Q+fDhze2pyGf+M776DwK6wvtvvbK/lc8PBohhBB6qW9uZcORMk4dHz/g1Ye7m5UWhY9BoXTHJ9DW0mP68Laibf1+vymxUxgX5bhA1GNbH+v3ewwUCWDdJClC516w1XnUKwr/y17dr9PMSpjV+Vj1D9fKd9ce7+fgXJNX2dA5Q91XYxNCaTS1kVuhc6EsIN4/svPx8rTlLh+fGJJo9bylzX3VkoX7fdvet23puPg+HX/9ouGdQeyVL2yluKZJz+EBoGZ8zgtJo9hTecjlY1/PeIsFhnxy0ubAhzfD2keg7adbjbd7JXEhhBCDx/rDpbS0mr1q/WuHYH8fpqZEEJX3FQTHQspsh/v+feffXT+/RdtHH4MPDy94mN/P/73D/QOMzmeVeRsJYN0k2N+HyCBfCnRLIc6jOdi5nq89+ccp/+h8bA5on5H18DrY41VNfU4f7jAmQRv7ITesg002dgXXEf4RLh9/96y7uXbitZ3PTT/hKq8ngnUZJQyPCWZYTN/6AYMWxD596RT25ldz6lPr+c+GLGqadJqZb6qmvGAbz/j17/vsqeQRMO1KrRXPf5bA4S89np3hDcqbyimssz87K4QQwrt9c6CYiCBfZqVF9r7zAFg4LIwpTem0jDwdDMbeD3BCWlgaTy15ivfO6VoKtOOqHQwPH86EaPtrbGFwt47rew6n6FWSnr1gq/JoCR8CaAVaXl7+cp9OY3l3ptU/tPPcpM7t5wCdo6oqx6ubOGWs6+mYlkbHh6AokFFUy/KJ+t5lUxq6iuD0Jf0k0CeQc4afw//2aQWdmsqPQKh39CETrmkzq2zLqWSFDn3kzp+WzKSkCB759ACPrTnEX786zJzh0UxNDmf8kDDGJoSRGhXksMesQ3k/4qP2PzX5u/wNmK7age/IZfD1g/DmJRCSAGkLIWm61qsufiIER/f7vbzd5Z9dzvpL1w/0MIQQQrjAbFbZcKSUxaNj8TF65xzdqWG5hCqN7AqZx9RurzW1NnH+R+czJW5Kr+eZEjuFe2bdQ3RgNGF+YYT4WbcLcub6tah+APrV60QCWDdKjggis7ROn5NV52OKHQntFUZnxM/o9ykDOmZ0PdhKp6axlUZTGwnh/UtbCPLzYWhUkFt6wZoaSjsf9zWd0DIt45z1t7I3bW+/xyU87+DxGuqaW5k9LKr3nZ0wMi6EV66bze68Kj7cWcAP2eU8+10pHUu5g/yMjE8M44LpyVw4Iwl/Hyfuzh77Xre7uEvfW8qGyzbAmDO1Ev/7P4TcLbBvVddOIQmQOgdm36gFt4OM6kTLoIomFys5CyGEGHAHjtdQVtfC4tH9z1h0lzF1WzGpRr5pGmsTwB6rOUZ+XT75db1nRr5+5ut2twf6BNLYaj15tmLECj7K+qiPI/ZOEsC6UVJkIOsOl6Cqav8WkqsqVOdTnjYLdMxGbVUMEBjl0QD2eI32Q5UY3r8UYtD6wbqjlY6pobzf5wjwsQ7Q6031VrPfYnDYelQLZPQKYDtMSYlgSkoEAI0tbRwuruVQUQ0Hj9fyQ3Y59324l/99f5Rnr5je2ffYoWObaUuYDJT2vJ8TKpsr+eMPf+T+uffDhPO1P6A1XC/e3/5nHxz5Cg58BJMvg7OfBr/+rWn3lK9yvuL/NvyfU/vevf5u/rL4L24ekRBCCL2sy9BqViwa5b0BrE/WWvb7jWdznu0FvUr/C5O+d8577CndY7XtofkPcc/sewj1C8VkNjH9ten9fp+B5p3z6yeI5MhAmkxmKur7WcSnvgxaG7m6YpM+A2tnVs0QnuzRNbDHq7UCNv2dgQVtHWxOeb3WT0tH/67a0/tOvfD38bd6/vMvf97vcwrP+/FoOalRQbrccHEk0M/IlJQILp2VykPnTuDz2xfx4jUzqWo0cfHzm9lXUO344JYGKNjBP8Lt3xxZnrac38z4jUvjeSfjHaqaqqhrscgeCYmDESfD/Fvh/OfhzgNw0t2w5x14/UJtHIPAP3b+Q/vcc8LnOZ87NVsrhBDCO6w/XMqkpHBiQ/1733kg1ByH4r2UJCxiX0GNTY/4X679pYMDu7y8/GU+WuF4NnVo2FDOGXGO1TYfgw+hftrNcB+la+6yp/Wx3k4CWDfqqETc71Y61XkcN+qTImjp2V3PsjUsyqO9YIvaA9hEHQLYcQmhmFU4UqLfLGx5YzklatcNh7igvq3V7V7ZbX/5/n6NS3ieqmrrX2el6Tv72htFUVg6Lp4PbplPaIAv17+STmmtg9SLgnQwm3i/IcfmpbOGn8UTi5/gmgnXuDyGRe8sYt5b8xzv4BsAp/wOLnxBSzH+5FdapoiX8zG4lnRU0lDippEIIYTQU3WjiR25VV6dPkzWtwAEjDudljYze9tvULe0tdDS1uLU75y0sDSGRwzv8xAsM0LHRI3p83kGmgSwbtTRKqbfhZyq86hwQwAL8De1XEsh9tDF5/HqJgwKutwd60it1LMScWVTZefjPy36EysnrOzTeXwNvjqNSAyUvIpGKupbmD40YkDePyUqiP/+bCaVDS08+NE++zsVbLe/HXh80eOA9S+rHVftcOmmTK+N1CddpAWye9+D/R84fd6BYlRc+xzVI51LCCGE+32fWUabWWXxGC8OYDO/hpAExkzWbhCn52jXnHPemMOSd5c4dQpns4hOdBLAulFHq5h+t9KpygM3XUjta63G1FIHTVVuOX93RdWNxIb646tDdbih0cEE+Bp0XQd7rCan8/FZw87C2MfiON7WPFu4ruPO6KSk8AEbw/ghYfxq6Sg+31fEd4fs3Jkt3AURqTabT0o+yer57dNv59wR5+Jr9CUh2Pmq3TXNThRJW/hrSJwKn98LTfoXVRNC/LRlVGR0VvUHqG7uYVmF+Mlan1FKaIAP09rrS3idtlbI+g5GLiMqxJ/hscGk52h1NlrVVmpbnLuWNZl1asE3yEkA60bhgb6E+vuQX9nP9WHV+Rh83VckxazgsXWwx6ubSNBpPaHRoDA6Xt9CTnesu7PzsQShP217C6rxNSq9F1FysxsWDSctOognvsywXZN5fBcMmWZzzKKkRVbPr590PY8ufBSAVrPzfd8u++wyJr0yiVZzK4veXsSn2Z/a7mQwwllPQX0J/Pgfp8/tDS4cdaFNsG9pU4G+dQeEEK675NNLeGr7U9S21HKo4hAL317IJ1mfDPSwhBdRVZX1h0tZNCrGa9vnULhDmywauRSAmUMj2Z5bidns/ATVygkrSQxO7PdQnlv6HAA/G/+zfp9roHjp//KJIykyUJc1sIaQeH0GBDxx0hNWz9tQPLYO9nh1E4lh/V//2mFMfKhbWum4w/G64wM9BOGCfQXVjEkIda6VjRv5+Ri47ZRRHDhew9cHirteaKyEyhzOac22OaanINWVALZDbm0uVc1VPLntSfs7JM+AUafDlmehWf/K4HpoM7eRUZlhte3nk37OP5f+k5dOf8luevEftvzBU8MTQjjQkTI5/635vJfxHgAb8jcM5JA84nh1I794YzunPb2ev36VganNC1NHj3wNL5yq/cn4YsCGcbi4jqKaJu9e/5r5DSgGGL4EgJlDo6hqMPHmvs96PfS1M17j3tn38puZv9FlcmVR8iL2XrOXEREj+n2ugSIBrJslRwbqsga2JqTrh/LUoaf263TLhy23en7U19djM7BF1U26VCDuMCYhlLK6FsrqdOwv5Ca5tbkDPQThJFVV2VtQPaDpw5ZWTB1CUkQgr2zJ6dpYuAuAHJNtOl1PQerIiJGAVqnQ6fdfvQKA8qZysqtsA2YAFv1GC6r3vte5afuxCv746QHWH+5/i5/+mvraVJttKcFJsP1lZu37jDOTHM/ECiE8r7q5mrcOvWW17d3D7wJa0ZsTWWV9Cxf9awvrM0qJDvbnH99m8pt3d3tXZfTDX8EbF0NjhTaz+NZlWnu1AdDRPmfx6L4V3vSII19D8iwI0gpDzkiLBODjzDU9HvbeOe8xNW4qV4670u1DHEwkgHWz5MggXdbA/kot7Hz61JKn+jkqa5clJUC1+4Or2iYTdc2tDInQL4AdmxAG4JZ+sHqzLBAlvFteRSPVjSYmekkA62M0cPnsFL7PLCe7tL29zfFdDvc/OfVkh689NP8hXl7+Mi+e9iI3Tr6RO6bf4dJY3s542/4LKbMhfhJsewlUlY1HSrn4+S28sOko17z0Ix/tKnDpfTxizV3wye2w6Wl+t3MNd028caBH1C9ljWWS9ixOGL/f/Hse2/qY3dea27z/pnV/PPLpAUprm3njhrm8deNc7jptNB/vLuSDHV7yOdpUA6tvgfiJcNNGuGkDJM2Aj26F2uLej9fZ+sOljE0I1XWCRFf1ZVC4E0Yu69w0PCaY8GCobuw5K6qj/Y2wJgGsmyVFBFLb3Ep1Yx8XXbfUozZWUKfq2+vUhgdmYIs6e8Dq11NzbKL2g33wuPenEf/fhv/jSOWRgR6GcII3FHDq7pKZKfgYFN7e1p7u76CA04PzHuxxdjXQJ5AZ8TOID47ntmm38fNJrvUo3lO6hzaznc8jRYGZK6F4Ly2527h71R5GxoWw/f5lzBgayYMf7ae2yXuKT6yf9xdIfxHm3Qq/+IHgpjquObpzoIfVL+euPpdbvrlFqlSKE0JPN32b25rZUbyjT0sivN2hoho+3FXAtQvTmNpekOgXS0YyJSWCJ77MoLHFzdeDztjyT2gog3OfAb8g8A2EC/4DpgZY9yePDqW+uZVtORXenT6c9R2gdq5/Ba3Oijn1Xgpbt/R4aFSAZ1v5DRYSwLpZcnsl4j4XcqrOZ2uABxoye2AN7HEde8B2iAnxJybET5cZWL17Pn52/mfcPetuq23d1+AJ75RRVINBgdHx3nPnMy4sgMWjY/l0d6FW9KHkACRMttnv4tEXu3Uc+8v3c+u3t9p/cdLFYPQj69tXOV7dxO/PmUB0iD8PnTOB6kYTr//gPWn0UVueg5AEOOUBiBsHc26Cfd7fCsiebUXbWPre0s4qlifiRb346elprV96cTrXfHENz+9+3oMj8oxXNucQ4GPklsVd6xMNBoV7lo+hqKaJD3Z6ZsmXQ60tsO0FGH2GNuvaIXoETL8Gdr7u0VnYzVnlmNoGQfucoGhItC266Mgloy/Bz+BHoI9+kz4nEglg3azfrXSq8mgweOC/yZMzsDoWcQJtHWxGcf8D2Fu+uaXz8Yz4GT3s6ZzUsFTmD5lvtc3uzJXwOoeL60iLDibAd2ALOHV31uRECqub2H2sGMqzUGMGpgn5poJN7C/bzzWfX2OdyhcQDiNOITrvCyYmhjB/RDQAk5LDmT0sive253nFGq6xYcPg6AaYcyP4tn8ezbkZFIUxvhFW+x4sP+j5Abroui+vs7oBJ20WxIlAofdiNUerj3pgJJ5T19zKR7sKOWdKIhFBflavzRsezfjEMF7ZnDOwn6MZn2mzr7Out31t7i/AbILtL3tsOOsPlxDkZ2TmUC+dqTSbIXMtjFgKLlzPPzDvAbZf7bjX+0+dLpGRoijLFUXJUBQlU1GUe+28/mtFUQ4oirJHUZS1iqIMtXitTVGUXe1/PtZjPN4kOVJrf9PnQk7VuRg88Dml1hVBq3vXlBTVaAFsXJi+M8pjE8I4XFxLmwulyO0pqi/qfPzg3Af7OywAmwpvBkXuGQ0Gh4trGRUfMtDDsLFsfDx+RgM/bvsR1DY2Bfjqct5Xlr/CaUNPY/fPdjt9zIObH2RHyQ4OVRyy2l6UvJw4cyk3jai0mkG5YFoS2aX17CsY+HR/pakKUGDypV0bwxJh2GLauvXEHozBYG1LLdnVDoptCTFIVDRV9LpPVXOV+wfiQV/sK6KhpY1LZ9kuD1EUhZXz0zhcXEf6sQGsqbHrLQhPgRF2ai3EjNTWeW5/WQvc3ExVVdZllDJ/RAx+Pl56fVW0Wwv4Lda/9uaeWfe4cUAnhn7/byuKYgT+CZwBjAcuVxRlfLfddgIzVVWdDKwC/mLxWqOqqlPb/5zb3/F4m8ggXwJ9jX1vpVOdj8Hijk1MYIwu41pzgXXVszfDQqDGvcUBSmubiQjy1b0tyZiEUJpMZo6V1/frPDUtXRfWvkZ9AoPu8msHOPVH9KrJ1EZOeT1jvCh9uENYgC8njY6h8MhOfhkfyy+y3tTlvNPjp/PXJX/FoBi4d7bNPUi7DlceBqC5242vDxqm0KIaOYWtVttPm5CAosC3h/RN1XeGTeZDYzWkLYTwZOvt41cws946m2Mwric9ddWprFi94oQvdCNObM7chPmx6Eequt10Gsy+2l9EYngA01Mj7L5+5uRE/H0MfLK70O7rbtdcB9nrYNy5Wg9we6ZcDrWFkNvz2k49HC2rJ7+y0bvTh498o30dcYrV5p5m0WcnznbniE4IetyumA1kqqqarapqC/A2sMJyB1VVv1NVtWMR6A9At6uGE5eiKCRFBvZ9DWxVHnVB0Z1P9SqjnRKaYvX805Bgt6+DLatrJiZE//W8YxO0QEPPSsS+BvcEsM/tfo7qZtu2J8J7ZJXWYVZhlBcGsAAnj40jpjGTDUHuWRdz5bgreffsd53ev1W1Xm/5dXYjh/wmEpy7zmp7VLAfk5MjWH/Y8wHszpJuxZlam2D0ctsdRy7l7vJKnhlyeucmy6UF3ujH4z86fE2WLIifguu+uo6vcr4a6GH0W5OpjY1Hylg2Lt7h+t8Qfx+Wjotjzd7jtA5EX9isb6GtGcac4XifMWeAbxDse9/tw1mXobVoW+LNBZwyv4Eh0yDEeoxtPRRndSZ9/qdOjwA2CbCMfPLbtznyc+Bzi+cBiqKkK4ryg6Io5zk6SFGUG9v3Sy8tHfiegq5Ijgzs1wzsPW7KZFyQtKDzsQpQ7d4AtrS2mVg3BLCj4kJRFDikYwDrZ/Trfac+amztZ1sl4VZHirU2NWMSvDOAPWlULHlRh936HuOix/Hb2b91al9VVTGrZkxtJqobTOzOq6JqyElakalq66yOxaNj2ZVXRXWDZ9Nyr/3yWtuN3e6GAxCRim/MaIYf71r3Wmeqc+PI+mdzwWZ+/pXjKtLePHYhHKlrqSO9KN3p/Y9UHuE363/jxhF5xveZZTSa2jh1fHyP+507ZQhldS1sPdp7irXuMtZAYCSkznO8j1+wFsQeWA1t7i0ot/5wKcNjg0mJCnLr+/RZYyXk/2g3fdgb6kEMZh5NGFcU5SpgJvCExeahqqrOBK4A/qYoygh7x6qq+h9VVWeqqjozNtaL77TYkRQR2I81sNZBpZ4zg88tfa7zsRnFMzOwofoHsIF+RoZFB3OoSL+1dX4G9wWwsg7Wux0ursXHoJAWHTzQQ7ErJSqIikD3ByZXjLuCm6fc3Ot+24q2ccknlzD99el8n1WGWYXoqWdqL2Z9a7XvghHRmFVIPzYAF16WjL5a5WF7hi2muWiPZ8fTRzd9c1OPry99b+mgTIEWP23z3ppn/6bTCW7D4VICfY3MHR7d436LR8fh52Ng7UEPZ7OoqvaZPmIpGH163nfs2dBQDgXuK0LUZGrjh+xy726fk70eVLPdALanGVjROz2upAsAy3zU5PZtVhRFWQb8DjhXVdXOhTmqqha0f80G1gHO15geJJIjg6hqMFHf7OKdqLZWqLFe53DpmEsd7Ow6g2LoSlMw+kKVe1tclNY2ExPinsBwTEJov1OIl8Z0fev5GHr5cO4HuaD0boeLaxkeG+y9BSFaGghRm6w2XTL6Er69+Fs2XrpR17f65dRf9rrPi/te7GwPtTmrjGA/I2MmzYHQRK11gIXJyREYDQo7cgewAAmg+IVqfWvtSZlNaEv/1tN7wl+2/aX3nYCm1qbedxJCDLitRyuYmRbZ6++eQD8j84ZHsy7DwwFseSbUFcOwRb3vO+IUUIxwxH2p3VuPVtDcavbuADbzG/APh6SZVpv3lu51+jNc2KfHFdo2YJSiKMMURfEDLgOsqgkrijIN+Dda8FpisT1SURT/9scxwALggA5j8iqdrXRcTSOuPQ7d7tAE+OjbgqYjUFONvm5NIW5saaO+pY1YN8zAghbAHqtooKGl7+kq4YpWkEBBcWsKsdx1826Hi+u8dv0rAGWHbT6475p1F7FBsUQEROj+dn87+W9O77szt4qpqRH4+Bi1C5ijG7W79u0C/YyMSwxlx7Eq3cfpCsWvh9n15FkMaW3j/oSuCpubCjZ5VaGYYzXHeO3Aa07tK0sWxIki0j+yx9ePVB7x0Ej0V1nfwqGiWuYMc64VzClj48guqyenzIM323I2aV+HLux938AISJ0LR75023DWZ5Ti72PodcZ6wKhqe/ucJTYz1lesuYL3Dr83MOM6QfQ7gFVVtRW4FfgSOAi8q6rqfkVRHlYUpaOq8BNACPBet3Y544B0RVF2A98Bj6uqesIFsMntAazLhZyq82h28zrujhYRqtHPrTOwZXXapLs7ijiB1kpHVbvWL/ZFa0s9Q0yt7LngK13TfOOC4qyeS2EV79Xc2kZeZQMjY72vhU6n8kyM3dbOGBX39atdmrqUnVfv5IsLv+h130NFtUxPbb/ITJ0HjRVQmmG1z/TUSHbnVw1MAZJ2o6MdpA8DRKZBcCyKxefhLd/cwm3f3ub+gTnJlaBUMj7EiWJaXM8Jepd8comHRqK/jvWsc5wMxk4eo11XfOfJWdicTRASD9G2K/3Mqpl/7/63dZHKUadC0V6bTEK9rD9cwpzh0V7Xr71TyQGtGrML7XM6JAQnuGFAJxZdrtJVVV2jqupoVVVHqKr6aPu2B1VV/bj98TJVVeO7t8tRVXWzqqqTVFWd0v71RT3G422SI9pnYF1dB1uVR55P15rXOQlz9ByWlQxzA1V1heCm4KqkVgtg3TUD21GJuD/rYD+uPkiJjxGC9GlV1GH1itVWz1sHQXriT1VueQOqCsNivHP9K6AFsN02uTPlveP8SSFJRAX0PDvQZjYzraP9w9D52tfczVb7TEuNoKGljczSgSkwNKPZxO8W/9nxDooCybMwVB2z2pxTk+PegbnAlRsW3jRuIfrqhkk38KdFf+pxn+4V0QeTrUfLCfA1MDk53Kn9U6ODGB4b3FmF1+1UFY59r7Ufs1h+8cbBNyhtKGV36W6e3fUsD37/YNcxo07Tvmau1X04eRUNZJXWe3/1YbAJYHsq3vTWad+w95q9hPp5cRaYl/DSRV4nlpgQf/x8DK4XcqrO4+OQrgvpx096XOeRWdvm56OlLbtBxwysO6oQA6RGBRHoa+xzJeJffPMLAFoVpffiBC7q/kF001rvbsvxU3a0PR3LuwPYLIy+1uPzVGGw3qsmmpmW0j4DGzVcu1t/zLoX4PhE7QLt4HH9iq71pHs10+n+Mfj79lKxMmESSp31zIY3tTVw5YbFdV9ex66SXe4bjBA6aTA18OzOZ+2+Ni56HEG+QZ0zU/fMuof759zvyeG51Y5jlUxJjsDfx/mbUwtGxLAtpwKTJ7JZqvO060OL6sMv7H2Bx398nJu/uZncGi1jZXfpbg5XHtYyP+LGaxMCHanHOtpwRAvcvbr/a+Y3EDcBwoZYbW4127/RUnv4ATKLBu9NGE+TANYDDAZFq0Ts4hpYtSqXT0K7gp8Ao77rX7t7JirCbZWIS2vdm0JsMCiMjg/pcyGnjQX6Fr/pSVFTORkVGZLa54U6Atg0rw5gMzH4D8zdWTM9f8+mxQQSGdy+flxRtIudY9YzsMNjg/EzGjh4XL+2V440mBpsqplelLDAwd4W4icyrcl7ix+5esNid+luN43kxFbTUkODqY893IXLXtj7Av/e82+7r3X8vlx1zio+O/8zrhp/FeEBzs1WeruWVjMHj9cyJSXCpePmDo+moaWNvQUe6C1f2N5Le8j0zk3P7HgGgMOVh7n/e+1mQnlTORd+fCE3f32z9jsgbSHkWNdC0MP6jFKSIwMZ7q2/q5vrtJu3I5dabd56fCsHKw7aPSTAEMTu/CoPDE7z2JqD3LNqcFTct0cCWA/pSyud+qpjlBm77vq7s7AQQIXB6LZ1sB0zsNFuqkIM2jrYQ0W1Xtlba9U5q6yeX/TJRby076UBGo33e2X/Kyx4awFljWUefd+jZfVEB/sRHqhfuypdqSqUZ7GaruBvaOD0Hg7Q++17/tmamBRifXd56AKoybf6XPE1GhgVH+KRGdg3D71ps23I0JN6PzBhIsNM1nfCFUdViwfAQ5sfstn2tyV/c7i/N1YiPlxcywsbs3ltSw7Hq72w0JS5jQVvLeDUtxfBvg/A5H3/hieantZ2dwSw4f7hpIalAmA4QS5hM4pqaWkzO50+3GHOcG1Jx5ascncMy1rBDjD4QsJEgF5/N2853p55M2wR1BRA5VHdhtLSamZzltY+x5s+l63kbASzySZ9+PqvrufKNVfaPWRiUgS786o8MDjNd4dKOq/NB6MT46d/EEiODHR5DWxbt4Xv7ghgLx59cedjkwJUuy+AjQzyxdfovm+5MQmhVNS3UOqFP5BhfmE22w5VHBqAkXi3nSU7+SjzI55Mf5Kalhqe3v60zT6qqlJQZ9OpSxfZZfXenT5cXwbN1nfbhzR5rriQihbA/nHBH+2+XuDzMtNem9Y1a5U6V/uau9Vqv3GJYR6Zge1eMG3TsTxImNT7gRFpKH7Whby8KYU4vdg6LXr7VdtZOnSpg72hqc17gi+zWeXRzw5w2tMb+ONnB3ngo/0s/ss6XtiYPeA3H49UHtGCqJpCeFFbv1djboFV1zLpzVn8c8OJk7I6WPxz6T+ZFjeNRUm2rVtOlCymPQVVAExJjnDpuJgQf0bHh/BDtgcC2MKdED8BfPxRVZWT3z2592MA0tr/347ql+W2LaeCuuZWloyJ633ngXL4C/AL6fod6IQpSZHsL6zxSEp4bZOJzNI6l2f9vYkEsB6SFBFIWV0zTSYniySpKqZa91ykW4oM6CpL36wobk0hdlf6cIeOQk797QfrKd50Qewtfvb5zzpTkQBMbSabfT7N/pTl7y9nW9E2CusKrS56d5futnuMs3K8PYCtyLLZtCffA+lj7Tr+rWMC7Rc6y2zQ0oVrWtpnV+PGgU8AFO6w2m9sQihldc2dSwvcxTLV9pqAVML9wiA8pYcjOg40aOu3LHjDnf5Wcyt/2PIHm+0dNzc7/l+eWPyE1eve1Ernkc8O8N+NR7lqbio/3reU7+5awuIxsfzxs4M8/c3AtUFpbG3kgo8v4LrPr+GSVcspLreonn3VBwA8f/QjyNs2QCN0r+oGEztyK6lztV+9m6WFpfHqGa8S4hcCFdlwfDeYu2ZjTwR78qqJDPLt7FjhinnDo0nPqaSl1Y1Bj9kMhbtgiFYFuqHVhbT6mNEQHKfrOtivDxQT4Gtg4Uh9C27qxmyGjM+19GEf5657/3HKP5iaGklzq9kj17B7C6pRVSSAFb1LjnKxF2xjJSaLtK8zh53pjmFZVbNUFcWNKcQtbqtA3GGMFwewjW22/+/ecEHs7bYVb6PN3EZ1czUlDSV8eOTDzvV8qzNXc/r7p3f2UsuqyuKqNVfx1Pan+vRedc2tlNQ2e/361+6KapooqfHMDFvHDOyYqDH845R/ONyvoz0XRl9InKKln1kYn6hlJPSnargzLH/GrqhthPiJVhU0e9SeKtd5Li+44ZRenM6qw6scvv7JeZ+w/tL1TI+zTisf6JnNDl8fKOZ/3+dw7YI0HlkxkbiwAIbFBPPvq2Zwycxk/r72CB/vdk/Ljd50zNbvqzjIQaPKqjldaX57wi0Kxbx1KdQWeXp4bvX+9nzmPb6WC57bzMI/f8umI55dutETRVGgtQU+uAn+Pg3+fRK8dBrUlzFvyLzeTzAI7M6vYlJyRJ+uCeYOj6bR1MYed66drMjWMn+StM8Vl3rZ67wOVlVVvj5QzMKRsQT6eWn7nMKdUFcMY85yave7Z93NkpQlnTPwnlgHuztPu/E9OWnw3gSSANZDkiK0qpdOpxFX5dJi8WF2wagL3DGsrgvNdmU17kshdvcMbHSIP7Gh/i6nJnpifVhKiO2sjzdcEHu7ssYyHv/xcRa+vZCl7y3lwc0PsjpzNQDZVdmAlnYMUN6opVFZpmZ/fvRz7t/kXNpfR0N4ry0KAVCeycLU5M6nZ6deBcBuD83CdgRCvgZflqQscbjfmR+c2RU0DZmuzZq0dc3sjIzX0nOzSjzXSsdQdlgLYJ0VM4bJTd61HMFs7nmWJcQvhKiAKJvU6YkxLvy93aSxpY3ffbiXcYlh/PaMcVYX6waDwmPnT2J6agS/+3Cv8zd6dWRTGCsytfOh1Zq1lnpYfYvuRWkGypf7i7hr1W6mJEfw3JXTSQgL4IZX0zs/Dz2pe2CUEppCfFA8fHwb7HkbFt4JZ/1V6y36ztWdM7GDWWNLG0dK6pji4vrXDh19Yzv6yLpFtwJOn2Z96trxaQu1CsYV2f0eysHjtRRUNXLqeC9OH874DBSj1gfXCR2fPSlRgUQG+XpkHezuvCqGRgd1FV0chCSA9ZCO1BCnCzlV5WKy+AXvrmDn0jGXWj1/U61yyy/m0tpmt8/Agpaa6Oqszrq8dW4ZiyVfo21RIE+1PhksTl91ut3tb2e8bfW8uU0LKvaV77Pa3jE7aHlhfPeGu/ko6yOa25o7A1xHBkcF4iyqLdaRPzD/NxgNiscKP3T8G3dkblw+9nKH+7564FXtQdIMaG2E0q7Ki7Eh/oT6+5Dt5otky58xc2uDzaxqj2JG8VxxVyud0kYP9VvsQW9VoDt0DwS2FQ182usrW3IoqW3mD+dOwM/H9rPPx2jgmcum0WZWefiT/R4fn9ptPeXzu5+3v+Npf4Ssb2Hf+x4YlXtV1rdw3wd7mTgknP9dO4szJyXy8rWz8TEqPPDRvt5PoLO3Dr1l9XzNBWvwy/hCC14X3wvLHoJZ18NZT2n9pXe9zn1z7rM6pqWtxYMj7r9DRTW0mVUmDOlbABsV7MeI2GC2H6vUeWQWju8Coz/EjgXgTz/23I+3w4HyA9qDjtY7eT/2eyjfHCxGUeCUsfH9PpfbZHyu9UEPsu6bfqzmmIMDNIqiMCUlonN21J325Fe5vOba28gVtIfEhwXgY1AoqHJy7UB1Hi0emKDr6KnWydwK3fof9ld9cysNLW1un4EFmJgUTkZRrfNrjYGBmgiVGVhrhfV9Sx1UUHjz4Jtc/9X1nc+7u3XtrSx5d0mP5+kMYKO9O4C1FOTvw+j4UI+V3u+YVe0IDO+bcx+TY6bY3bczaGpPO7NMI1YUheFxIWSVuncG1vJ7wd+sujgDO4pws4rBi35OnS1akxSSxPkjz+98/mHmh+4aklMaWlp5fn0Wi0fHMntYlMP9UqKC+OXJI/lyfzEbj3j2hoF65Gvndpx5nZYW/9UDWquMQeyZtUeoajTxl4smE+Cr3ZRKCA/glyePZOORMnbmujEo6qa62c5Fe2sLfHkfxE+Ck+7q2j71Cm02cONfbWZhv8z50s0j1VfHkqdxiX1vjTZzaBTbj1ViNrspK6DkAMSOAaOPS+vpn9v1nPYgdiz4h0PeD/0eytcHipmWEuGRCZE+qTiq/XuNOaNz07GaY3yS9Qlnf3i23UOGhg3tfDw5OYIjJbXUu3EteklNE4XVTYN6/StIAOsxRoNCYkSACzOwebT4eeZC2iY9uVrfQk4dZbpj3NhCp8P01EhazapLfdE8FUj++1Tr/nayBlbT3NbMX9P/2ufjFUWxmqVtbG3kms+vYdIrXdVmfziu/eKsaHKcZnW0rJ4h4QHeu67GbKa42rYVwdSUcPbkV3tknWNHpVtfQ1dGwQXDbuj5oKjhEBAOBdutNo+ICSa71L0zsJaz7tEqWlEpZ4Ulg08gs/yi9R9YHzkbwCqKwsMLHmbjpZ7rb92Tj3YVUtVg4tZTRva6788XDmNodBCPfnbQfRfkdhxP/49T+71zeBWc8QTUFsKWZ908KvcpqW3irR9zuXB6EuMSravkXz13KGEBPry8Ocdj47lr/V22G3e/pV2PLHtIW0/fQVFg0W+gMofJDdY3Ee7bZD0j6+0OFdUS5GckJTKoz+eYMTSS6kaT+24IFh+A+Al8kvUJV3x2hevHGwyQMsumGr2rCqsa2VtQzbLx3jz7ukb7ahHAnv3h2Q6/L585+RkWJi3sfD41JRyzCvvc2Nu3Y8lRX9PWvYUEsB6UFOFCK52qXK6O7fql4s5g5/fzft/1Piq6F3LqCGA9ccdsWmoEADtcSKfxVAA7J2GO9fu2eH6NkTd6//D7vLz/5T4f/3HWxxy1COz2lu1lR8kOu/sufmexw0Avt6KBlKi+X0S4XV0xD0bY3tSanBxBdaOJY+UuVIbso0fmP8Lai9dap8Q3DbO7b+fPlaJosyXdKhEPjw3meHWT2+40VzdX88qBV7o2RI8CXxeqfBoMED0STO7/d3VW9wD21zN+zc1Tbna4v+X/00D1glVVlVe3HGNsQigzh0b2un+Ar5FfnzqaQ0W1fLHfQ8WSyo5wAflO7frHrX+E1DnUjjkDtjwHjVXuHZubvLjpKKY2M79YYntTIdjfh3OmDOHL/UXUNvW9qrsrsqqss0u+OH8NbHpKq3w70k6LqNHLITiOCZkbOG/keR4ZoztkFNUyKj4Ug6Hv1yEz0rSfq3R3pBE3VEBdEQ0xo7hv031kVtkWEnTEat+Uudoyksa+j3HN3uMAnDExsc/ncLt9H2gZA1HDndr9lNRTrJ5P9kAhp915VRgNSp/T1r2FBLAelBQR5Hxxim79WB21rdCD5TqxcqNR9wC2tFZbk+KJFOKYEH+GRgexw4XUJ8tfnN1nSfVkNFjP7BkaPNC7bRBwqaKhDroXLutQUNlIcj/ugrtddR6bg2wDsEntVQT3Fbp/3Yyv0Ze4IOviGVklTgR4Q6ZCyUFo7SqKNCJWK+R01E3rYD/O+th6gyuzrx1iRqK0eE8A2/1n5dqJ1/LLqb90uL/lTPmsN2a5bVw92ZlXxcHjNfxsXprTN2LPnjyEEbHBPPPNEbfPwm4p3MKkz1wrkni0+ijzW/ZzTkwQLYNwFra5tY330vM5fUKCwzX/F85Ipslk5sv9xZ4ZU5t1wbSkkgyozIH5t9mvHG70gcmXwOEvMbZ5V+sfZ6mqSkZxLWPj+54+DFrhwahgP/esgy3W1qOrsWNcPrSgroDvC77XnqS238DvRxuqT3YXMikp3Htb3VXmQEE6TLqwz6eICfEnKSLQrYUZd+VVMSY+1HuzzZwkAawHJUcGUlTT5Fy/rm79WIeF25/l0Nv7YSG6pxCXts/AxnlozcL01Eh25FY5nVL53G5tncbVrX7MHzLfnUOz8mH5rq5+mT9hni5m1f1CCaCl1UxxbVOf+vB5jIMbSyPjQvAxKBw8PjDfS9ml9fg3T7PZvi5/XdeThEna+vrSrt6aw9sDWHelvdn8/MeMdv0k0aNggGYu7XG1QI2PwcdNI3Hex7sK8fMxcM4U52dNjAaFXy0dRUZxLZ/vc+8s7I1f3+jyMStWrwAgx8+XF/e/POhmYb8+UExFfQuXzU51uM+0lAgSwgJYe9AzAazN78Ltr0BgFIy1v24QgHHngtlEUrP33GRyRWldMxX1LZ0tAPtKURSmp0a6J4AtaS/EFNuHG4DAzd+0Z4gkzdAq8/ZxHeyx8np251e79Dnicfu0ftFM6F/XkKkpEW4rzGg2q+zOr2Jqe7biYCYBrAclRQaiqlBU3csFUVMNNFV5ZEx2Vem8Bra2GUXRquV5wvTUCEprm51fb9zudF/3l2V/+yzriro/FPa/qMFg9/iPj3v0/RpbG1mbu5ZJr0zqTD0+Xt2IqjKoAtiZ8TMBLeVyRGyIy+2j9JJVWsck/+t73im+fT1ycVdl06HRQRgUyHLTOtiOismdYka5fI62qJFgcR5PpVM6Ypk9MD56vPagMgdeOQeengg//Mtq/4GudN5mVvl0z3FOGRNHaIBtJfaenD15iNYjdkOW1/Sx7WD5vXXQqML2lwduMH3wzrY8kiICWTjScWaXoiicPDaWjUfKMLW5t13Np9l22rJkrIEpl4NPDze+k2dCYCTX1lpfU9WbBsfynI4CTmP7GcACzEyL5GhZfeeSLd0U74fASMwhsb3v68Didxaj+gZB4uQ+r4P9pL0/9FmTh/R5HG637wNIngWRQ3vdNSU0hfvn2G/xNyUlnPzKRsr1/r8EjpbXU9vUytRBXoEYJID1qK5WOr3cLazO41uLVMG7ZtopbuBOeqcQ1zUTGeSHj9Ez327TUrX1IK6kEQNEd6/I7AYTYiZYPTc1D0zQ4S3WZK/x+HsufW8pr+7XWrzsK9MCqo6bHUmDKICND+4qZDF+SBgHCj0/A9vaZuZYeQNj4uI4Pc22DdLL+17WHkSPAJ9ArX9juwBfI0MiAjlW7qGLzT4EsC8etK5BPP+d6b32YnWnAJ+AzseJwYnQ0gCvrtD67IanwBf3ajNXXmJrdjlldc2cO9X1i06jQeHnC4exJ7+aH93Z47KflOAY2PpvrWLuIFBc08SmzDIumpGMsZd1l0vGxFHX3Mq2HPf++3+dY6cCtLkVpl/d84EGI4xchk/mNwwL68pSm/vmXJ1H6B4dAWx/Z2CBzvXlus/ClhyEuAmYu98QtHDDpBvYe81eh69XNFVwsOKgtg62YDu0uXYjUFVVPtpVyMyhkSRFeOnv6JJDULwXJjqXPvzReR9x6dhL7b7W0d5mjxvSiHflVmnvMcgrEIMEsB6VHKGtr8vvbR1sVR63x3fd7VqaaqeAgTtV5+naC7astplYD6x/7TAuMYzQAB+2ZLm2xjQ6xDN39lJCUzof3/vDQx55T2/zwt4XuG3tbU73k9NbR5GnVrO2dqrjplJ/KkG6nUUAG2AM4J5Z93Q+H5cYSlFNExX1nr2Izq9spKXNzPDYYB5b+JjN63/d3l5d2mCE+PFWASxos7C5Fe5J/7OctRvd3KIVZHLB/sJqnttjZkSL9cXWx3sKdBlfX/gZumWxbHpam4G97E1Y+RmkLYKv7rcqlPLS6S91Pn7n0DseGqnm073HCfIzcvKYvmW3XDg9mahgP/67MVvnkbUz93/9vRI1QqtIvP8DHQbkfp/vPY6qwjlTev99t2BkDEaDwg8u/i51leXa6ABjAF+2xUPceOfWrY9YCg1lPDP5NjeO0D0yimqJCfEnWofro4lJ4fgZDS4VsOyVqmoBbPz4HiugnznsTACeOOkJh/vsKN4BKbO1nuBFe1waxo7cKo6U1HHhjGSXjvOoHa+CwRcmXuTU7j6K4+UdE5PCMSjaWlW97c6vItjPyMi4EN3P7WkSwHpQQngABoXeU1u7zbR4Yh3T2KixnY9L2hr7VSmuu7K6ZmJCPZM+DNqd+3nDo/k+q8yp/SdHa7OigWGeCWC7p/V5W3qcJzyz4xnW5a+jqrlqQMfRURSnoLIRg6L9jHoti7Xpv575ayIDuiq6jk/UCjl5eh1sdpm2fnVEbAh+Rj/mJtrOfHR+fydM0gJYi+/31Kgg8twUwGZXdwU9/6szgL9rsxwvbjyKyTeMO+utL9z+te7wgP3MVjZ1fS6r5lb48T8w7hxIW6hVTV7+ODTXwNauljCzErqKN/1x6x/55tg3Hhmrqqp8e7CExaNj+1wsJNDPyNVzh/LNwRIyS/RbK72/bD/fHPuGmqPrmdTUzzS94Fitz+XmZ3W98esua/YVMTo+xKkL2BB/H8YnhvGjG2dgW9paWJu7tvP5yYnzGJKbDuPPc+4EQ+cBMKw8x2pzgxdVD3cko7hWl/Rh0DJaJiaF6VuJuCoXWmohznEA+/yy5xkZqd0cXD5sOc+c/Izd/f687c+Q2v77wcU04rd/zCXYz+jUTZcBYWrSWj6NPQucTLXuqaBdsL8Po+Lc0999d14Vk5LDe82+GAwkgPUgPx8D8WEBvbfS6VaB2Ki4v1LY62e+3vn45oRYXdOIS+uaPVKB2NLCUTHkVTSS60RrkWifIG2GJsQzvcX8jdb/Fk1t3lMk5qfmD1v+gKnNRH5lIwlhAfh6KM3dZapKcW3XzJ+h20f3uETtIsjjAWz7+tURsVpVyOsn2a6F7ZjlJn6itra/puvvkRIVRFldi1ta6XyY+WHn4zAXZ18r6lv4ZE8hF89MxS8yzeq1jOIa9xRL6cX6vPU8tOWhrg01hdq/51yLKsQJE2HkqdqaTIvZxZ9P/Hnn4zvX3en2sQIcOF5DUU0TJ4/tX22Bq+cNxc/HwIubbHsg99Vln13GnevuZMGm29kb4Ph3050z7uQXU37R47nW5n1L9rTLtfTBo+t1G6M7lNQ0sS2ngjMnOV8IZ2ZaJDtzq5wrPtkH3auFL27zBVQYv8K5E0QMhdBEyLWuJ/GXbX/RaYTuYTarHCmuY1S8fjNhM9Oi2JtfTZNJp8r+pYe0r3HjHAaw3QOxU1JPIdDHfprvj/X5EJ7qUiGnmiYTn+wp5NypQwjxH/iidHYd+hQaK2DGNTYvvbTvJTsH9G5KSji785wvRuqMJlMbB47XnBDpwyABrMclRwb2vga2WxGl2KC+L553lmVQdcTPT7cAVlVVympbPJpCDDB/hFacYlNm77OwebUFGEG7k+4BTy952ur5gcL+Nfc+0YwIH+HR93tg8wPkV3l5C536MnKVriCv+0VDdIg/8WH+Hl8Hm1VaR1SwHxFBWoZF95szYFF4KGGy9tUijTi1ve+uu9KIO0W7tv71mwPFmNpULpqRDN0C2EBfAx/s9Hwa8esHu24yDg0bym9qW7Reg6ndZr2nXqGltOZs7Nz0bsa7nhpmp+8OlQCwZEz/PldjQvy5cHoy7+/I179AjQOnDj2VjZdu5NoJ13Lj5N6rFK848iJvxiTC93/3wOj67sv9RagqLgWws9OiaG41s7fAPW09Om9wtTur4JA2ox031sER3SgKpM6D3C0MCe6aobPMVvBGRTVNNJraOtuJ6WHG0Eha2szs0+v/quwIAI0RqWwtsr5O6WjR5UqhuJ9/9XOtnU7uVqezFd5Lz6fJZObyHipmD7gf/6PdSBm2xOalp7c/bbPt1qm39nrKyckRVDaYXC5G2pODx2swtalMkwBW9EVSRGDvvWB1LqLUJ5X63O2ub2mj0dRGjIda6HQYERtMQlgA3/cSwObV5JHZUMhBfz+PzcBaroEFWPndbRTVu7dVxGByyZhLHL727tnahXh0QDQXjup7rzVLu0p2UVDZ6PUFnK5L7Pr+TLBTcGxcYhgHPDwDm1Va3zn7Cti9S9+qdszAtlfNLeqqROyxANbFFjqf7ztOcmQgE4aEQZR1C7OFY334bM9xWt1cmbW76uaui9J/L3qC1JwtWouR7qloY84A/zDY+17nplqTdbG4Nh3Wfvbm20MlTE4OJy60/2n51y8aRkurmVe3HNNhZL1bOWElEQERKIqC0WDkycVP9nrMn0J9IWstFB/wwAj75vN9RYyIDWa0C31HZ6ZFAbD9mHvSiG2qhR/73vnZ1w6p86CmgKuHdbXcqW5xf1/s/ujofz08Vr+eph2FnLbl6BS8l2dCYBR/2P13frvxt52bH57/MFPjpgK22UAACj2kp6bOhboibe1+L0xtZl7cmM3stCgme2vV3GNbIG8rzPultozDCc4E/VPbg0w918F2tOaRGVjRJ0mRgRRVN/V88aNzH9Y+qdCnaEZZrXbH3NMzsIqisHh0LBsOl9Lc6vhi7VitxQVRiPvb6IA2tl1X77LaVt7o3iIZg8VTS55iUfIiAK4Z35WOc9fMu3j9zNc7Zx5jAmN4YO4DVsfGBfXt/6+groDjNdVe3kLH+sL9pOSTbHYZnxhGZkldj9/vessurWN4TNcMQkygbVuOzt6l/qHajKFFAY+OANZd62ABflFZBTHOpxDXNbfyfWY5yyckaN9vkWksq+8aX1JiMdWNJna0V3P0FMsLfWPO91qV1nHn2u7oGwgjl8GRr8FBxeQWs3uLfVXUt7Azr6rPxZu6GxEbwrJx8by2JYfGlv59fzsTvE+OnWz1PNTXyYDPNwi2PNuXYbldbZOJH49WcOp416rtx4b6MyQ8gH0F+t4cy6rKYtIrk9hb2pWRcUv8QkCFMWe6drKk6QAYa7tuBHv779Ts9v7Xlp+f/RUd4s/w2GDS9VqzXJ4J0SP5LPszq82KonQWF+1+Qx7s/37qlKqtWe6e8m3Px7sKKaxu4uYlw50fs6d9/zetX/G0q2xemvTKJLuHOKo+bGlMQih+PgZd+8Huzq8mLtSfhDAvrvXhAglgPSw5MohWs0pxrYNUKFMj1Jd6dlD2VOgzA9uR8uXpGViA5RMTqG1uZXOm419kVms1AiLcP6h2RoP1umZzm2eqx1Y3mNh+rJLMkjrdC9EcLq7ltR+O8eqWHA4XO24PlFOd4/A1s2ompbWNvSe/wF3jfsbU2KmcNfwsrplwDVNip3TO8BkUA0aDke1Xbe88tse7vr0IHvOg95bnB6duao1LDKPVrOpa7KYn1Y0myupaGBHXNYOQGpZqs/7psa0W1YnjJ1r1gg0P9CU0wEf3GdjCusLOx7dU1bg0A7stp4KWNjNLOoKvyDT+UNb1GTI6PhQfg8J3GSW6jbc33dMsfXK3ap9XSTPsHzDqNKgr7rxZ0L3foGU/WXf4PrMMVe1/+rClG08aTmWDiVU78vt1np0lO3t8PcwvzGZbx42zEeEjuHvW3Y5nUKZdBXvehZrj/RqjO3yfWUarWeXkPvyfTEgKZ1+hvjOaG/I3APBJ9ied226qadQyoRImOzrMvvgJoBhJa+gKsu0tZ/Am2WX1BPkZiQ/Td5yz06JIP1aJ2azD7/f2ALY7BYUrxl7BD1f8QGKIbTr6owsf5dlT7N/IUWPGQkA45G7p8a2bW9v429rDjE0I1e1GmO5yt8LhL2DuLeDn/Ey6vc+Y7nyNBiYOCdO1lc6uvCqmpkT0WEBqMJEA1sM6LpIdFnLqtv713tn3untIdjXoFMCWtgfqMSGeq0LcYf7IaEL9ffhin+P03FA/izvrTqZ/uIN6fJdbz99kauOhj/cz69FvuPBfm1n21HoWP7GOt37Mpa2fv+iKa5q45qUfOe3pDTyweh8PfrSf057ewD2r9tidDbx7w90OzzXy2yfgmSnw0mnw5CheK6vh8WEXd77ePej2M3Z9X9mkornIq9fAOrGsoKOX4JFizwSwjmYQhoZZN3H/+phFj8e48drNMZP2+acoilta6Zz+vkVPWp9ACHO+/cLW7Ap8jQrTh0ZoGyLTCLP4GRkVlcbMtMjONZ6e8PCWhzlUcajzuU/+1q7Kw/aMXKZ9PfIVYFtHocXNN8y2ZJcT6u/DpKRw3c45Ky2SKSkRvLgxu1+fWZ0p7Q6cnHKy7XsnzOLSMZfy/KnPc/X4qx0WVpxU+gWobfDjv/s8PndZl1FKqL8P04dG9r5zN5OSwjlaVk+dG4qtWTJmf6d977r6u9g3EGLHMq+y63d9RmWGzqPTV3ZpPcNignUPJmamRVHdaOJIf29kNtdC7XFM0bazn4qioCgKwb62QVuTqY2csmYaG+1nLbyR8ZbWD7aXAPbVzcfIq2jkd2eN0z/gaqjQemeXZfa9lZaqwpf3QUiClj7c/S0cVMG+e5bj65/uJidHsLegWpflKtUNJo6W1Z8w6cMgAazHdaQpOizkVJ3LC+Fdd2dGRrhWPVMvO1tKdWnM3jEDGzsAM7D+PkZOGRfHVweKHFZQfGaH/ZLvnnbl7qfcVva/ydTGdS9v4+XNOVw4I4mXVs7k8QsmERPix28/2MsF/9rsVLVme77cX8Tyv21g69Fy7lk+lk33nMzme0/h5sUjeCc9j1vf3GlzJ9jRhcWXJbWMKM2E0x6FK96DJfdpPeheOAU+vxdam4kOjAZgYdJC2xO0v82y1GV9+rt8kPsUuTVesP7cDrWy93GlRQfja1TI6GH2W09ZpfbXcNmbCX/j4Bvag7hxgAqlXd8DqVHu6wULQPQIly6If8guZ3JyBEF+7RUvw1PAImDxN/qzaFQsh4pqPdZ39/Ojn1tvqMrTAlhHQmK1Wayj2izXkpQlXDqmK22t+4yu3n7IKmf2sCh8dKzqrSgKNy4aTk55A18fKO7zecwO0qo7BPjYptf5GHy4f+79nWvP/7XsX45PMO4cSH9JCwC8hKqqrMsoZdHomD5VWp+YFIaq4v4icU1VMOrUvh2bOAWO7+5XJo4nHS3TAli9zUrrWAfbzzTi8iwAHquzXdNt79+4or6FB1bvY+Yfv+G0pzfwize22+wDkFmVqa2DLTsM9fZrlOSWN/C3bw6zZEwsi0bpWFwzPx1eORf+Mhz+fRI8OwOeGAGf3N5ZsMpp216AgnRY+oDN7GtxfTFz3pxj97Crx1/t9FtMTYmg0dTW/5sRwI48bV30iVLACSSA9bghvc3AVhzlmaiIzqdzEu3/ELhbs4IuxaRKa5tRFIgK8vwMLMB5U5OobDDxzUH7FzwdaUzeYM6bcxyumeiPP605yOascv568RT+dMFkThkbz2WzU3n/lvk8c9lUskvrOPPvG/lol/OVVRtaWvntB3u56bXtJEUG8ulti7hlyQiSI4MYEhHIvWeM5cGzx/P1gWJe+t56Nt9ROf4hij/c8B3MvxVGnwZL7oHbtsPsG2Hrv+CFZSQ01vLNRd/wy6lddzyvGX8Nt0+/nV9N/xWgpS/1xdf5n3DX+rv6dKy7NVf3/rPo52NgWEwwRzwUwGaX1uFrVEiJsp65tne3/PEfH9cexI3TvpZ2zSamRAWRX9GoT8qbPTHOVyCub25lb0E1c4dHdW00+kJ41wyuWTUzZ5j2er8vEp3UsS68Q6BqhqELej5o6Hztgq21BYNi4IZJN3S+dOqqPgYJTiiuaSK7rJ55I6J1P/fpE+JJiQrkvxv7VqOhsqmSw5WHe9zn9um393qenn4vm+fdCk3VsPN1h/t42qGiWopqmlgyum+pmBOHaDPpulW3xUHGjGKE4bYz4E5JnAJ1xcyJm9a/gXlAc2sb+ZUNDNexAnGH1Kgg4kL9+78OtjwTgFWl6TYvdf+M351XxWlPb+CtH3NZPjGBZy6byu/OnOD43EPna1/zbDswNLe2cee7uzAYFB49X6frIVXVKoS/sEy7ebr4HrjkNVjxnLbcYtdb8M/Z8MkdUO/E2uniA/DVAzBiKUy90ublZav6dhO9uyk6FnJKz6nAaFCYmhrR73N5CwlgPSzA10hsqL/j0thOVGbzhBZF0aWQU2ldC1FBfrreiXfFSaNjSYoI5M2tPQcA9wZ6tnVLT+pN9XyS9UnvOzphW04Fr2w5xsr5aVw4wzqNUlEUVkxNYs2vFjE6PoTb397Fr9/ZRW1Tz+vjduVVcfY/NvH2tlxuXjyCD25ZwMg421/E1y5IY9m4eJ78KoPimia+y/2OSz5xXGGYi16yqfiKfyic+QRc9pa2DvTfJxF/cA1GizVod826i+snXc+KkSvYe81egowBvJx6AZsKbNeSP77o8R7/bl7Zk1dVaa52bt3f6PhQD87A1pEaFWQzo+NoBiSnOkcr4mT0g5Kuu/qpUUG0tJkpqtHn397mZ8eFFjq78qpoM6vMHtYt+LJopfPo1keZkKQV2PjxqGcC2AnRXReD6yIW4e8Xrq0n7snQ+dDaqKXKAb5GX3cOsdOWLO0CcO5w/QNYH6OBny8YxvZjlX2qinvhxxfy1+1/tfvaYwsf48sLv7ReVtIHrUOmaoVqtjwHbe6d6XZWx3rtxX1ckxwXFkBsqL+uVc67LwcJUoGU2RAY0bcTJk4B4HdJp/VvYB6QW96AWYXhbpiBVRSFWWlR/a9EXJ4JDj7LLT/j9+ZXc/l/fyDA18Anty3kyYunsGJqEtfNnsdvZvyGmfGzrI796MjnFASNBaM/HNts9VpLq5k739nF9mOV/OmCSfrVpdjwJHz9AIw/F25Lh5N/qz2ediVc8B+4cz/Mvgl2vAr/mAZb/gkmB7+PKrLhjYu0dbwr/mlbBb4Hl4+93KVhp0UHER3sp8uN0m05lUwcEtaVWXQCkAB2ACRFBJJf5SBlTqe1p/31RFSELq10yuqaByR9uIPRoHDprBQ2ZZbZFBayTNnNNXpP2tEjPzzCfZvus6rO2BeqqvKXLw4RH+bPPcsd99RLiQri3ZvmcceyUazeVcAZz2zkk92FNjNiFfUtPPrZAS547nsaW9p44/o53HvGWPx87H+MKIrCg2ePp82s8rdvjvDbTb/lYMVBm/3+UF7Fy6HTIa2HGaWxZ8ItW7QLnE/vgDcuhuL9tvsV7oRXz2XG+r8RnrqQq0ZcwF0zu2ZVzxp+Fm+c+YbDt3E0OzygGiv5fYRzVQPHxIeSV9FIQ4v7L5yzS+vt9jCcHj/d7v7nrD5Hm82MGQ0lXTOwHRcphb21F3PSfZvu63z8TsFxlwo4dRTMmNx97WZECn+v1m7sHKo4RFljEdNSIjw2A2vJp2gvDJnae1p0R7XPY98DXX0b3W1LVjnhgb6MT+y9UElfXDwzhbAAH/67wfXfT6WNjgsknjPiHIaEDHH4urNMZhPMvw2qc+HgR/0+nx7WZZQyPjGM+H5UHx0dH+LW7A5/c1vf04cBYscAkFar3UCJCojqae8Bld3eQscdKcQAM9MiKahq7L1lY0/KM7XlE3Z0/J4sqm7iule2ERnkxwe3zGecxc+8oiisnLiS/y1/yerYVho45Z8fcCxgLI1Z36OqKqqqsv1YBZf+Zwtr9hZx/1njOHty/38WAdi7Cr77I0y+FC56Wbsp3l1ILJzxONyyWSuM9+V98I8Z8ON/u2ZkTY1aVsV/T4GWerjqfQhzvp8ywH1z7ut9JwuKojB7WFS/b5S2tJrZnVfV2RLrRHHihOKDSFp0kOO7Y5VHwT2faS4p8fHBXJ7d7zscpbXNxHi4hU53V88dyn82ZPP014f511VdVTv/sfMfnY/PjJ7q8XF9e/G3nPLeKTbbO0rWN7T2b13g95nlbMup5JEVEwj0s190pIOP0cAdy0azaFQs932wl9ve2snjnx9i/ohowgN9ySmvZ1NmGc2tZi6dmcJ9Z40jLKD3C+LU6CAunZXCu9vyiZpg/ybBvKYWEpc+Zvc1K2GJcNWHsPV5WPcn+Nd8SJ4FQ6aDaoaC7VC4Q7szeu4/YNrV3NN+d7S8qZyqpirAfg/VDl4ZwFYdY22wcwWmRsV3FXJyZ7GG1jYzx8obWDrOtnfyr2f8mgtGXkBxQzE3f3Oz7cGxYyHvx86nnYXtqhqZqfM400ytLrXQ2VegtVOKDO625CE8hdCGCgjX/r4m1cTsYVH887tM6ptbCfZ3769SH0PX+Y2lGTDHzr9rdyFxWgXR9nYVlgXP3GlLdjlzhkVhMLjnpmCwvw9XzxvKc+uyyCiq7Sxe1h96FktsNbfC6DO0mf8Nf4Xx5w9ogcDqRq3y/E0n9a8Vyai4UN5Nz0NVVV2K6lgVdwMeLq2AM/oRwAZFaRWM25cnVDRV0GZus6n47w2y2+sHDNOxB6ylWe2BSnpOBUlTk/p2krIj/D0qErC9VjWZTZjNKv+3ajd1Ta18dOsC4ly4ObJ4bASf7UvjhrpPmfn7j2lWAqhrbiUq2I9nr5imX/BaeUxLC06ZC+c+2/vPYdxYuPpDyF4Hax+GNXdpfwIjobkOzCZImqnN2kZbZ+1lV2UTHRhNTbO+68RnD4vi831FFFQ19nlGel9hNc2t5s4+wScKmYEdAMNiQiisbqTJ1K36mapSZ9Hv8cxhLvZC66fPzrfu9fV9ef9mAGHgZ2ABIoP9uG7hMD7fV8TW7K71DUX1XRULp8Tov/a0N/Yq+Fnqb1XdlzfnEBPixyWz7N9FtWfG0EjW3L6If1w+jXGJoXyXUcobW3PJLq3nkpkpfH3nSTx+4WSngtcO1y4YRkubGUftSRMnXQbhTv6SNRhg3i/g9t1wygNaBcHdb8Hed8FghFMfhjv2wfSfWaX2/HrGr3l4wcOAbeqapbzaPO/rH9itMvkZaWc43LXjYt7dacT5lY20tJltCjiBFmyNjBzJxBgHKa5x47TZqfYiN0MsAli9Baiq3TYQjuwtqGZysp3KueEpGC2+b1rNrUxLjcCs6rsu0JFw/64xGdpaYIiT6/ySZ2k3dlTVpq2I3m20QPs/zK1ocMv6V0s3LBpOiJ8Pf/1Kn0qz9npZ9uYP8/9gd7vJbNI+p5bcCyX7Yf8H/R1ev3yfWUabWeXksf1rRTIqPoSGljbdfk73l1tn0Ez0CYeEfv4ejh1rtb6+osnzGRLOOFpWR0yIv0u/R10xNiGUEH+fvmeIqCqUZ/FfO8HrwqSFnDb0NN7YeoyNR8r43VnjGB3f802k0ZHWWTA3LE7hyosvxVdp47Yx1Vw0I5knLprM+v9bol/wqqrwUXutjAv+Az4u3MAbvgSuXws3b4Klv4cJ52u1Oa5eDdd/YxO8Aqz4aAUXf3Ix5310nt1TpoWlufo3ALQAFmBbP2Zht7dPmM1IkwBW9NOw2GBUFXLK661fqCvmP8FdH2iOSvW7S2pYqtXztjrH7WecoaoqZXXNA9JCp7ubThrO0Oggfv3u7s7KoVbtFEJsZ5LcLcjXfW1biqqb+PZQMRfPTMHfx7XvI6NB4ZwpQ3jhmlmk37+Mg48s59u7lvDwiomMjHN9tmNEbAiLRsXQ4mhp7czrXD4nQVFw0l1w43fw2zy4N1f7xbLgdgjoOXWxtxsDV3x2hevjcSeLYmonp5zMo4scF6lKjQrC38fg9kJO2WVaVUR7KcQdHPbKjBuvfW2vRBzs70NEkK9uKcRWYwgdYj9lzI6qhhZyKxqYaK/1S3iy1S9Ls2pmUlIEoAW97mRqM/HA9w90PjeiQuJU5w4eMh3qS6Cm0Oal5jYHvcj74cej2s2fOd3XEOssIsiP6xcN56sDxezWocBJXyrXXjDqArvbT373ZA6WH4QJF2jrlL97FNrc23e3J98dKiEswKff1UdHtX/261ER1V7F/YARS1xaT2hX7FirCue/Wf+b/p3PTY6W1du9+acXH6OBaakRpPd1HWxdCbTY/g65d/a9/GvZv2gx+fHElxksGBnNlXNS7ZzAWvduGtd9eR2hI+cBCiuTi3jo3AlcPDOFUD0D+n3vQ85GOO0RiBza+/7dKYp2Q2XRr+Hsp2HZQzDi5B6/R4/XH6fFbL8y/cvLX3Z9DMDYhDBCA3zY2o8AdltOBWnRQcSF9n0JgTeSAHYAdCzcP1raLYCtOEq5RUEUT6V8WbJseG+oK+57jyygrrmVJpN5wFOIQbtI/tulUymra+bKF7aSXVpHW/vfLdhsRg2OpaS2ie3HKvguo4RKD7XH6IlqsrigV1Wozoesb7U1Hcc299jm6L30PMwqXD6r918unnDRjGTa7MSNfzGFdBbf8LS4IPszEoX1hZ3pxl7BIoC9cfKNPa5lNBoURsaFkOHmXrBZJdpn14geLsIcBgVx7euxLQo5DQkPdFyZvY9ubw12MX1YS/2y27s0PBmjxfdvS1sLsaH+DAkP0LXRvD3dK6Ub/COsikr1qGOmtnCHzUuPbXUibd9FO45VEeLvo0tab2+uW5hGZJAvT3yZ4fRssqObwnr3mbzk00s4XJ0JJ/9OK/oyQBWJVVVl3eFSFo2O7XchxVHthfr0uDn2y7W2fTP9R55uZ08XxY2Flq7PvszKzP6f0w2yS+vdUsDJ0uy0KDKKa6lu6MPNk3L7LWWuHKdV3H3q6wzqW9p46JwJff7ZafUPgfgJvfaD7ZOWBvj6Qa2V2PSf6X/+bnr7/IkOiO5sAegqo0ErytVxc9BVZrNK+rFKZgw9sda/gk4BrKIoyxVFyVAUJVNRFJvFJIqi+CuK8k7761sVRUmzeO237dszFEXR4RPM+3Us3O9YyN+pMkdLeWu3NHWpJ4cFaEVuOhw1KlDjfGuV7srqtABroFOIO0xLjeS/P5tJYVUjy55aT3qu1lqn3mBg9t/3MfvRtVz4ry1c+79tzH7sG/746QGaHeW9eoC66ufwyjnw0hmof06DpyfAa+fD+z+H/50Bz0yG/avtHvvZ3uPMSoskNdp9s7yuWDYu3m5A05Y61+NjiQmMYWL0RB6Z/4jDfS785EIPjqgX1V0pxA5nNS2MiQ/1yAxsVLAfET20x3I40x2RBj6B1oWcIgMprNK3AnRsXalLFYg7ZlI7WoZYCU/GYPH36Zi9nJQc7tYZ2MzKTL489qXVNmPiZOdnqhImaq1JCnfavLS92H6fxv7YfqySqSkRGN20/tVSaIAvt50yik2ZZXy5v/e+sNXN1bSp1p/nBsXAeSPPc0u7uuzqbBhzhrb+7ttHoMHz6az7C2sorW1myej+99KMDPYjJsSfIzrcHEsvtm3N4jtSh+udWOtihbUm7+nF26G6wUR5fYvbCjh1mDUsClXtY6uvcseBf2ZJLW9uzeXquUM7ay70xl5tiS9zvtQKzeVu1T9DYcs/tWvXM/6sLS1ys2d3Pdvj64nBrhV76m72sCiySuspq3M9ayajWOtX7u5lHQOh3wGsoihG4J/AGcB44HJFUcZ32+3nQKWqqiOBp4E/tx87HrgMmAAsB55rP98JLdjfh/gwf47aBLBHeTes6wOhe+8/T7AsePBkdKTrzZ0tlNZqP2zeMAPb4aTRsXx950n8YslIVKUrjenc2WN4eMUE/nftLN6+cS4XTk/mhU1H+fnL6bS0Dkxhn5uig/lPayl7aWFyYhjbltyJ6WcfYb5lC1z6OoQmwHvXwNb/WB2XU1bPoaJalk/s34emnoL9fTD62H74qs6mQurIx+DDW2e/xfyk+Q73KWko8eCIemExA+vMsoJR8aEcr26iutF9aYtZpfU9zr6C/bGqqqqtD4wdYzUDmxQRSEFVo67rMgOa612qQJxRVMOQ8ADbAk4APv4YLe6gm9ovuCYnR3C0rN5t/9bnf3w+nx/93GqbIWGy8yfwDdRStu0EsP1dY99dfXMrh4pqmO7BQiE/mzeUsQmhPPzJ/l4rb3+Z86XNtp1X7+SRBY/0uUJzTz1jd5Xs0m40nPUkNFZqQayHrT+sVV3ua/uc7kbHh3BYhxTi7v5pjtGK7/VXtwDWGx1tXzrmjh6wlqalRhDoa2RTZpnrB5dnam1u7Hj220wCfI38aqnzNwftBbD3bboPhi8GUz3kb3N9jI4018KWZ7VCakMd/47X06rDqxy+lhaWxj+W/sPh687o6Dve0aLMFd+3//8vGCkBrD2zgUxVVbNVVW0B3gZWdNtnBfBK++NVwFJFyztYAbytqmqzqqpHgcz2853whseEkF3a7ReBF7TQsbnoLOu56XtPOu4WecsMbIe4sADuOn0MQyK7LlQfOGcCP5uXxslj4pg7PJrHL5zMXy6czKbMMh759EAPZ3Ovf/jUs2G6ttZqY2gY0zfexm8zXoFx58B1X8GYs+CLe7Sqee2+2K+tXV4+0XG1XU8xtZlYk70GVVUxY5vybO5rz7+fkPzarh6wTs3AJuiX6udIdmkdw2N6vgCzt8b77Yy3tQdx46Gkq6VSUkQgdc2t1DT1r/1Pbk1XsB+oqi6lEB8urutxRsEQ2vXz1DED21HwyROFnDrFjXNt/6RpWgCrqqxesbpzs95FnHbnVWFWYXpqhK7n7YmP0cAj502ksLqJv3zhuKCTqc3EIz/YBpDO/Dz15PpJ1zsstvjGwTe0Gx0Jk7Sq0en/g5zv+/V+rvruUAkTk8J0W/s2Ila7btH7e+ekUefqc6KgKAiK4f+8qK97dx3Xfe6egfX3MTJ7WBQbjzhuHeVQWabdQkXHyuv5eHchV80dSpS9G30OXDzmYvsvpC0CxQBZ37k+RkfSX4KmKjjp//Q7pwOqqrI6czVNrY6zh945+x1iAmP69T6TkyMID/TtvCHlik2ZZYyIDSYxXKeeul5EjwA2CbAsk5nfvs3uPqqqtgLVQLSTx56QhsUG252BHWjdA9iGUtu+nc7qCGC9aQbWUm9tai6ZlcLPFw7jtR+OWVUv1tOH537Y6z5vH3rb6vmao2u4as1VZNXlwYX/hajh8PGvUJvree/we6zZl8vk5HD9moD3w792/4t7Nt7Dd3n2f0FNiJ7g4RFZi287h6jmCwny8Y5UaxuNVZwR31WUypkZ2I6KkIfdtA62usFEWV0LI+J6vwD78kLrWa/Htj5GelG6FoTVFXWmVQ7RoRdsU2sTZ33YtQRCC2Cdm4FtM6tkldYxOr6HoNyi0NuBCu2mVsd6WXcXcrLi6ixT4lRtBrDqmFXl8/y6fF0DkR25WsGYaamerXQ5Ky2Ka+YN5eXNOXx7yH4q8UNbHnL7OOxVGZ3++nTezXgXTr4PoobBBzd4LJW4usHEjtxKlozuX/VhS2kxwdQ2tXYWQtTNyH60z+kuegSR9VWdT91Rbbs/jpbVYzQopEa5/3fOolExZJXWc7zaxc/VctsA9u5Zd/OvdVn4GA1cv3CYS6ebmzjXppATAIERWt/VbJ0CWFMjbH4Whp8MyTN6398F/9v3P5a9t8xq29fHvuaB7x9weC35+pmv61Ks02hQWDQqhvWHS136fm5pNbM1u4KFI/sXQHurQVPESVGUGxVFSVcUJb20tA93lLzM8JhgKhtM1sWCKrIHbkDtut+RvqV8c5/PVVrbjEHBpTt1njQ7QZvsX6A4vmi967QxJEUE8tiag275RejMwv6OdVtf5XzVuW136W7O++g8Pjz2lVYhr+oYG9c9yMNbHibD9Ban9LNlgl6KG7QLytu/s02127X8bUZGOj9D5g5tFacxMeRcIgO8tLx8dZ7VU2dmjJIiAgn2M3LYTTOwWe0ViHubgQX7a39ya3O7ZhHbW14MidBmiPpTyKl7mlqAwQ/Ckp06NreigeZWc2elVbssAtjndz8PaNVwh4QHcOi4vr3/HNmSk6elX7uiI+W4eL9ND+T3Dr+n08i09a+j4kIID3RPa5Ce/PbMcYxLDOM37+62zWwCPsn6xGabXjfPOorYTI61n9r9yA+PaJWwL3pJq+76/vUeqUq8MbMUswonj9UnfRi0HvYAOeV971H+fYGdWeh4HW9kRg0nuK5rCYie3+N6yC6tJyUyED8f919+LxylBS4bj7iQRtxmgsqjfBVkfQN8VsypvL8jn8tmpbjU87WDyezge374yVqrr8Yql89pY8drWtX1k+7q96kyKzOZ88acznaLT21/qvN6psPBip4nePwM+l37Lh4dS2ltMwdc+F2zI7eSRlMbCySAdagAsGyilty+ze4+iqL4AOFAuZPHAqCq6n9UVZ2pqurM2Fj9PpAHik0hp4YKaOia5fv9vN8PxLBsKsrtUPreaqGsrpmoYH+PFPToCz+jH7FtKs9HOs5aD/Qz8qulI9mdX813Gfqvi3Rm7VVtezn7gjrbH431+eth2EkcHb6Qb7PXAKAYGlg0yvs/sIxx3ZfKe5bZrFJQ2UhyRGCf2mh4hMX6V3BuBlZRFEbFh7otgM0u7VjD1fsMrKIorBhhvaLkyW1PdgWw7WnESZHtM7CuzhT0ICAsuffG9e06/q1G9TQDG2Z/TfmYhFAOFXmmWExIWLLTbYE6dfxbF++3eWlvWf97fYP2s7Qzr4oZHlz/ainA18i/rpyOQVH42Us/Ws3kf5f7nd31vq+e8aou750UoiWNRfo7/rtvLtisVYQ+66+QtRY+urVfFf6d8d2hUsIDfZmaot//SVr7dUtO9+wxF9z8zc22G/WsAh01gpPLu35X2ksdH0jZZfVuTx/uMCY+lNhQfza5EsBW5YK5lX/VdxVyGh05mrd/KEdV4abFfUvPbjXbLg9pM7dprWlUs9bypj9aW+D7Z7SiaUMX9Pk0Wwq3UNNSwzsZ79DQ2sC1X1zLsZpjna9bpgu/sPeFHs/V17X19ixuL8TmShrxuoxSfAwKc0/AAk6gTwC7DRilKMowRVH80Ioyfdxtn4+Ba9ofXwR8q2rTWR8Dl7VXKR4GjAJ+1GFMXq9jAX/n3eKyI7QA8X7hTI6ZzEWjLxq4wXXXx5Sn0lrv6AHryP6yfbRghuCeZysvmJ7MkPAAXtqUo/sYfAw+/T7H3evv5lw1l/eDfDrPOTk5ot/ndTudW1e4qrSumZY2M8mRgdw35z7bHUz69yV1VV6JdYBhcDIgGx0f4sYAtg5fo0KKkylw3WeNa021EJYE/mGdAWxMsD9+RkO/ZmC7BykBkc6nuR3pDGCdm4G1NDYxjKzSOrcXe/NT6VuRGv8QiBzWGcAuGNJ1cbc6c7XjmREXZJfVU9VgYrqH04ctpcUE879rZ1HVYOL8577v7A/7q+9+ZXd/vdrU3TzlZv528t96zKa56ZubtAczroFTHoA9b8Nbl2up3TpSVZUzPziTj458xPrDpSwaFaPrDeSUyCAMirYW0mtFD7e5HektacRms0pOWb3bCzh1UBSFhSNj2JRZhtns5L9BewVi1afr5+No9VHe+jGX86cl9Xlp0h3T77DZVt9aD8mzwC+k/+tg97wNNfna2lcnri1MZhM3fX0Tu0t3d26rbq7mxq9v5Nff/bozoye/Lp+zPzy7c59Zb8wCnOuj3dfWOfbEhQUwPjGMdYecD2C/OlDE3OHRhOnZX9eL9PvqWVXVVkVRbgW+BIzAS6qq7lcU5WEgXVXVj4EXgdcURckEKtCCXNr3exc4ALQCv1RVdeD6lnhQalQQ/j6GzotMU8lBlqQmU9tSTUPNwK+FtXQsdyNDx3avy9W70roWryvg1KHN3MbBikNgNEJIzwGsr9HAZbNTeerrw+SU1XfehdaDZQC78+qdTHttmkvHN7c1szZ3rdW22JBAfPvZ808vjmY2R6kDv9Q9vz1YSo4MslvxuzJnPZGjlnt6WFZWl1jfzzM4ec9xdHwo76bnU17XTLTOa9CzSutIjQpy+nvMbtqzomjBWHsAazAoDIkIoKAfa2C7pxAHRjk/U3C4uI6kiEBC/Hv4lRhinX6rqiqKojA2IRRTm0p2WR1jE8IcHNx//yqpgCkupg93iJ/QGcB273/8nz3/4ZdTbftyuqJj/asnKxDbMzk5glW3zOPnL6dz/nPfc/XcoQ73LaltIqOoliPFdRwpqeNoWR21Ta20mVViQvwZHhvM/BHRLBkTR4Cv48wHX4MvS1OXUm+q56ntTzncr+P7hZPugsBIWPN/8OxsWHIvTL5Uu9FgydymLSsq3q8VUyzNgKpj2o01gxFCh8CQadQMnccWGjl92OnUmerIq83jgc0PUFP3J04eo+9SEj8fA0mRgRztRwqx20UNt9m0v3w/E2MmDsBgrBXVNNFoavPYDCxoM3cf7ixgV36VczeY2jtPqBazhyazCVObmVuW9L041vJhyxkSMoQr11zZue3PP/6ZRxc+CsMWw+EvtX73fbmx3dYKm57W1vs72Y6psK6QzYWbKagr4I0z36C8sZw//fgnALKqs6hpcZyqu79sP28eetPh699d8h2t5lbdlyYtGx/PP749QklNU69p3JkldWSX1rNyfpquY/Am/Z/+AVRVXQOs6bbtQYvHTYDdMmSqqj4KPKrHOAYTo0FhVHxIZ+rZlsLvqW2/IOxIGfUWzx96kz/1IYAtq21mhAc/qF1x09c3dT1xMLNi6dJZKTyz9gjvpOdxz3L9SvUbFSPBvsHcPv32Ps3G2rsLODRQ5wIb/eCoyXlgxU12t3tSfqV2EdaRvtrdSZv/jzUJE0gJTbH7uicoTdbFgeylYdkzJkGbScwormW+zgFsdmk9I1yYQXCY9hw3Dg5+0nnRMqS9lU5fdZ+BDY11fl3dkZK6ntOHAUKtPyde2f8KKyeuZFyiFrQeOl7r1gBWMZtcr0DcIX4CZKyBlgabQP9Y9TEHBzlvx7FKwgN9Ge4Fn/djE8JYc/si/vLFIV7fmkuQg5h/9qNdN/4ignwZERtCQlgABoNCWV0z76Xn8+qWY0QG+fKzeWnctHg4QX6OP6ODfYNJvyqdTfmbuGPdHTavby/ezsyEmdqTWT/Xitd89hv47Nfw+T1az97gODC3auv4yo6AZXXTiFRtJj04DtpaoDIHDn/B3fExfB8UyITqIi4/pKU0qqj4Rm7ipNHLbMbRX2nRwf1KIe7u7bPe7n0nV7QHsOP9YzjQrKXO6pFloIeOwp2e/Dk5eWwcPgaFL/cVORfAlmdiCoyk0GK9p9qUylmTh/R75rj776+Psz7WAthxZ0PGZ1q19KTprp/4wGrtZs+lrzsdAFc0aZmFhXWFXPP5NWRVZ3W+VtZYRlmj47Tryz67rMdz97fqsCPnTE7k72uP8Nne41y7oOcMo68OaGt3l43r/fp2sNIlgBV9Mzo+tHNtwgGLHx5vY252vcKmqqqU1jUT46UzsFuLtnY96WUGFiA+LICFI2P4dE8hd58+xmFg5ipFUfjhih/6fPy2Itv+aYeadvRnSB6xudSPqoYWIoIGLsW8I1jqKSWqoK5gQAPYf7dZr7sO83cuQBrTUYm4qJb5I/T7ZdraZianvJ6lLvxSnDtkLu8eftf2hbjxsOMVrbBNaDxDIgL71vKhnWWaoJ9ZxT/OuRtNHRWIe1s3HhxivQY2vTidlRNXMiwmGF+jwsGiGs5zYxF9BSC2HwGsaobSQySHWhe20uPifkduJdNTIzB4Sb2D8EBfHjlvApfMDeGqr+zv8+DZ4xmbEMrohFCig/1sPtNNbWZ+yC7ntS3HtJuX2/J48uIpnYVx7PE3+lPZbD8t2OZm45CpcP03Wg/MQ5/C8d1QexyMvhCaqM1KxY2H+PFaNW0/O0FPYyX5q8+FlipMn/2aquQhnS8FJHzKKR98yhtnvuGwwFRfpEUHszqvoGtGuR9ujZrOhBidK9EHhENQDKFtXcl8L+97mWmnuJbd5A4dS8Y8lUIM2s/CvBHRfLm/iHvPGNv7/1l5Jr+Oi6Gprevmidms8MuT+9+ayOEN2NHLQTFqPweuBrBmM2x4UsvoGXNWj7s2tTZR3VzNslVdN3ZMZpNV8OrNRsWHMjYhlE/39BzAqqrKx7sKmZIS0Vnh/0TkHXmGP1FjE0IpqW2msr6Ff5q9t7Ky0ux6hc3a5lZaWs3EemkLHStOBLAAZ05KIK+ikf2F7qs4GhUQ1e9z1NNKUd1xHUbTf45SiFUV0nP0Xf/lqvzKRiKDfAluTxs9dahtKwe1rX99SfW06+pdhPuHO7VvbKg/kUG+ZOjcSie/shFTm+pUAacO9v5dAe3CHKBEa0mTFBFISW1zn9eSWgawM5qbINq5CtcFlY20tJoZ0cvfaUhoErc3d6XVdQQkvkYDI+NCOXTcvZkzia2tEDOqbwfHt6dPFu/n+knXW73k7Ky+I9WNJg4X1w3o+ld7/rvnv1z1lf3+oi8vf5nrFg5j/sgYYkL87V7U+xoNLBoVy39+NpP3b5lHaIAPV7+0lae/PtzjmkpHhVs+OPKB1kLKkqJAymw49WH42Udw80a44Vu48j04/VGYdqVW/Mle8AoQGElr+2s+J91rd5dNBZtoNbfy1qG3dLlZ0Z9WOoV1hVbPzXbSfXURPQKjRQ2DquYq97yPi7LL6gn0NRIf5tnrotMnJJBT3uBca7XyTNYZrG+2hAf56JJd0qraftaoqqr1701bAAc/df2khz6F0oOw6K5ei/bNemOWVfA6GJ0zZQjbj1X2uA59X0ENh4pquWiGc1X4BysJYAfQmPYPhIwCFyrEDQClD+XNS2vbe8CGem8Rp069FHHqcNr4BIwGhc/2ui84fP2M13U5z/+t7d+aNr3Yu2BK8AnFz2jgxxzP9EN0JL+ykeTIrkJE3VMrAczlmTbbPKbZOiAyGnqvQNxBURRGx4eSUaTvzZas9hmEkXE6zCDE2QawqgpF1Y6bwvfETNf/n9knwOlqvccqtAuB1Kjeg/JrA9M6H7dZlGsYlxDKIZ3/rbtL8ouAgD5eREamgW8QFO+3WarQ36BmV3uxpIGqQOzIj0WO60HOiHetR+SMoVF8fOtCLpyezDNrj3D3qj2Y2uzfaHG0FOSrY19x7ZfXsjZ3LfUm/VJwi+u1VM/Hmu234Xt5/8v8YcsfeGzrY7x50PG6PWf1p5XO6e+fbvW8LchN1VGjhhPgBUX4ujvaXoHYmZnrBlMDG/I36PK+p42PR1Ho/dqluU7LAugmoQ9tc+yZnTCbuYlzrba9m9GenTPuXCjL6KyL4BRVhQ1PQNQImHiBLmPsrw/P/dCt579wejI+BoVXtzhe+vFueh5+PgbOnTzE4T4nAglgB9DY9nVqx48eGOCR9GyP0cyOo9+4dExZRwA7GGZguxfPcCAy2I+5w6NYe7C49537qHt6X1/tqjqiy3n669Ns2zuqry/8CxOTwtiVW+X5AVkoqGwg2WL968WjbZfpm8sOe3JI1qryet+nB2MSQjlcXKdrBc6OAHaEEz1gLXVvYr+5YDMEx0BwbGcA25Hq1Nd1sJZ/T5Ov82lTuRXahfjQ6N6rKhsjUri9Vpt5WpTUVfhrbGIoxTXNfZqVcpoLVZVtGIza+tnifTYvOVNNsyfbj1ViUGBKSkS/zqM3vZZ5dAj0M/LERZO5Y9ko3tuez2/e3U2bncquC5MW9nieO767g7lvzuWcD8/hrUNvuTSG43XHWZe3DoBdJbuobantnNXaVLDJ7jGNrY2szlwNaFVW+2totHajR49KxPZuGuoiajgXV3hfVlt2aX2P2StljWVMemUS/9/eeYe3VV5//Ptqemh4y9uO45Xh7EEmEEKAQEjCXiHsAmWVltX2RymrUEahdDJKw96rrAAhgTCylzNtx473nrLlJen+/riSrD3vlZz4fJ4nj6W79Ma+uvee95zz/b5Y+iJu+uYm/HLDL1HRGfokaoomCgvGJ+H9nXXe1Yg9TNjOSisJeQwAP7lz35z7HJY9vPVhtPe3AxNXARIZsOd1/w9Y/hXQtA9Y9Gv+GucF0c41O/Lj8kX3tk/VRuGskjS8s70WvYOuGe2OviG8t7MOK6akQxtzYqoPW6EANoKkqJWIi5HDUO/qzxdJfjXzV/jHaf+wva+Ry7H2+18FdIy2Xv5BbrSqEFu5KcBkz8mFyShr7kWjgH6V9jDGsPfKvb43PE6JN5mhy5qPKZlxKK3vhtFDFkNsOI5DfVe/Q//rgowFKF3raFvDtUduIsDq6xssRalq9A4a0RBkRtMdR1v6kKRSBnxjXHfWOof3NluRlIm2Gff0OH6WP9jvln3GbUmsZ/VZZ2raDVBIJf5lGbSZOK+D70t+eufT2NbIf2ahpee4XCTrIgBAQggBLMAHsK2HAQDvrXjPtnhx5uKQDru7phPFqRpbKf5owZNi98tnvBz0MRljuGNpIe49qxif7G3A7z4sdZkg0iq1WJG3wuexjvUcw6NbH/W43mg2Ytl7y7Dyo5UwmU0YNg1j2fvLcOu3t+LTyk+x5os1mP/m/IDG/0LpC3jtYGhVPtZJv1Asr6yYxDKdiMtBvF0PrDsf4HAzaDShrtPgVcCptJW//zy761nsauG1LIQqf75wVibqu/rxc2W7543cBLAXjLsBd8++W5AxAO7/Fi2GFkCVzPfC7n0LMPlRFcJxwKbHeHGzKRf53DzUVgl/+HCluNlXK9cuHAf9oBEvbXZ1LHnph0r0D5tw48kileePIiiAjSCMMRTp1JC3BVAyEQaumXyNW1uRQGjV8w/N1gysyWziDeVHiR+bldNlgQncLCrgzaQ3l4lX9i1hEizJWhL6gYIo/Rabs6XxgESCaVlx6B82oaJV2B5Nf2nvG8LAsNkhA+uOSJYQl3ceCWl/q5CTkGXER1t7A+p/taJReCh9TZkItBwGzGakafm/RUOQGdi7vx95yLoy00PfrRtqOgzITIj2T4BIk4k488iky0NbHgIw4h9b3iLi+RyfG9r+ycVAXytg6EBRwog0r8e/jR+YzBx213RhRk5caGMTAU8ZWJsScAjcePJ43HJqPt7aXovnvnW9Rtw09aagjlunr0PJuhKUrCvB9Feno7GvEZXdlTjUcQjPlz5v2+6+zfd5OYp3Ht/+OO767q6g94+SS5GsVtpsyPylrNO1mkW0rFhcNiYNjQRB4ci++aKm3QAz513AyV2biLu+0WA4Y1IqtNFyvLGtxvNG7RWAk27FHxbfCrlUuEye21Yd67Lpa/hr1OHPfB9o//tAwy7g5Ht44TMvPL7tca+TRccb07LicHZJGv713VHUdoyU8le19eGFzVU4d2q6d0/zEwQKYCPMpHQtEvsiWKYYANU9/tsttPUOQcKAeIvK7H8P/Be3bbzNxbM0UuhieBXVnNg0H1s6UpyqRrJaie9CUEv1hydPeRKfrPoktINUCdM/IyR3Zi8HAEzJ5MWI9lr658KN9eErI9572eitMcaITQQoBkILPAtsAaxwQdXR1t6ALHR8kjIBGO4DuqoRrZAiIVYRVMb48W2PO7xnyYV+71vdbkBOgu/yYQCANtPhplmnrwMApGujEKuQokLAANZlsi+UEmKAD2AB3k/UjlCyYOUtevQOGkdd/ysADJnEtRP79bJCnDcjA09/XYYvnHoLszRZ+Op8D/LHXjjrg7PcLl/zxRr8a++/ghqnO7489mVI5cQZcdGo6wqsB/b8T853WSZeBtZROd6jlVcYqbRY6HjzgHUnejjsTzbSD6LkUlwyOwtflDZ6tkFqrwC04VfdtwXp+Uv5ibofn+EzrJ4YHgC++SOgKwGmXurz+K8deg3vl78vyFhHC/ctL4ZcynD9KzvQ3DOAxu5+3PDKDkTLpfjd2UGq1R9nUAAbYaZlxyGfc5wRC6XESUjilHEO78/58By/xSfaegeRqFJCaslq1PfWAwDf6zAKaLZ4nClUqQHtxxjDooIk/FjR5r2XJETkEjnGacfh09WfBu0p1lO+XuBRBYbz3zpneBjyrDkAeCsGdZQMe+tC78kKBqsHrK8MLACgcY+4g/GA3M4D9pGFgVtla6PlSNNGoUygstaOviF0GoZ9qvUGhE3Iia9CSdNGBZWBfe2QU1lkkn8BLMdxqOkwINvvANbRJmdlPu+PzRjD+BSVoAGs/cN9stEYegbW+juxlBFbean0paAzVDureSXx0aZADPA2R87MSZ0j2PEZY3h0dQlmZMfhV+/swf56x2tZmioNV0680u/jfVbpOeskRvnjwrcWYuargYlZWcmMjw44A+sO0TKj6jS+n9KCaIFyAFg9YMd5uX5KmOsj+f72/Vh3YJ2brQPn2kXjIJNK8I9NHiqL2iugV/nffhEM6SpXYaErPr+CF6ySyoCFd/J+sBVekh2bnwS6a4AzHvbZ++qi/H2CkBkfg79fPgPV7QYseOxbLHp8I+q7+vHPK2ZAJ5Do1miHAtgIMz1Zgu5oxxufECVOQuBOTfHa9df6tW+rfhBaTTtK1pU4XECEFtYIGVXgJs8LxiehyzCMI2L2u1nI0eTg09Wf4qmTn8K2y7fhokK+12NV/iqf+y7oimwG9p97/+nw/opuPZA2DQAgkTBMzYzDvrqu8A8MI/1bGX4EsIO1ntVMxYQZumyvg33wLkpV43CTMOepTcBJCAVigJ8Ms3q1tvA6AOlx0WjsEqBnV+Of+mKnYRi9g0ZkJ/oZlGscA1h70bX8FBXKW4S7JtirA6+vbQi9B1abxSsRO2VgG/oasLFmY1CH3FXdhSSVwv8JgBOMKLkU/14zC/ExCtz8+i70DDhmy87JO8fvY9nUWMPIkDm4LHVmfAwauvrdili5o6mvye1yk1mkwFIiBbSZWCDlK31GQwlxZWsvklRKaKI8l7u6C2D/secfeHLHk4KMIUUdhcvnZuO9nXWu1U8cB66tHP/Qi/uMFiuPxQ+XuAqO/dLqnDD1Ur6vdf1vAaMbgbn6ncDmp4GplwF5p/j8vKvXXx3iiEcviwqS8eltC3HdojxcvzgPn9+2SFDf99EOBbARJnO4EpG/tLonMcpV4v5Au3+CU229g5CreFn/+3+6H++WvSvo2ARDlRzwLrNzea/WHWGygYmVx2JZ7jJEy6JtAgglSSX4+dKffe5r7HH/4BAOnGe9L2FxvPKshSmZWhxu1GNgOPyz43Wd/dBEybw+TFj5S21oYkrBktUberVCkU6Noy29gohlHbVkF/ODLCH+eOXHDu/fK3uPt7qJyx4RcgoyA+uCnxNlViVVv0uIZUqPtlsFKbwSsXMQEyz2AaxcFgUEWC3igkTCZ2HbXHurB0zBTRrsqunE9Oz4UTcx6UlrQQwxn2S1En+7bDrqu/px3/uOok7+/F6OdR+zWeFEAsNw4HY4mfHRGDZxaNH7d96c/p5jT/p47XgAQEF8kL7G/hCXjcRhPgAqbSv1sbH4VLX1eRRwquisQMm6EtTqPSvPf1j+oSA+vr86vRBJKiV+8+5eh2uVSd8MNtSL1xKPhfwZvvDqZy5TAMuf4q9T3/zRcV13HfDWFXyG/UzvPa3VPdX4qeEnAUY7uhmfrMK9ZxXjnjOLkeulPP1EhALYCMOaD6Dbh/lypPjbaX8Let9W/SBUSj44sL8oH+k4grb+NtH7k7zxq428ovIZvX1BZWCzEqKh0yix/Vin0EPzif0DmEqhsj0IeOKpn/4g9pA84jLrnTbF4e2UTC2MZg6HGsX1z3RHfZejB6w9zoHW10MR8GkeMoAbDP33UqhTY8hkxjEBLC+OtvZCKZPY7G4CJS/OURXRllVImQg0j1jp6AeNIQWBMyT+i1dYLXSy/bDQsaEdybran+NWb1yhyojv+f6ekTdxOXwAGirJRS4ZWCC4EtWOviFUtfWNyvLhv+1xf+/6/dzfi/J5M3MScNcZRfistBGvbRnRihivHe8zSFvx0QosfW+pTXU23Cx8y7vtjzusrRfBlhG/uvxVvLfiPbfWZYIRl407OsN/b/FEZWufx/7Xz6r48nFvmdb7f7pfkFJiTZQcf7l4Gqra+rDmpW3YX9+NI016PPH6/xy2UyvU2LUmvOektdUMhcuAOb8Atvwd+PI+oKsGOPIl8NIy3h/9sreAaO/XnXM+PAe/+PoXoo315mk3215/vPLjUdP6N5YYnZHTWKJhN25JdT+jH2lSYwOb8bfODnIch7beIaiVrtmtd8rewanvnIqrvrxKiCEGxTc1vKftelWsx2yKNxhjmJWbELYMrD3W2X3rzP6/Tvcu7PFa80/oG+5D71B41X45jnMVCUmb6vB2Yho/C3uoUfxSbGfqOg0ey4fz4vJwz+yR4KFFygBDmP/W3bVYpx1Rh02ISgjqMEWpwgk5HbU8gEn9Uev1E8OwgQ9g28sB45AtOA6ljPjG5Ll+b1vTbglgAymBtQtg7bNtBdYAtlmY75qDr2eo5cNWkouAnnrASSBswBj473t3jbX/NU6IkQnGTw0/4fl9z7ssL11b6jKJIiQ3LMrDKUXJeOjTQ7Z+WLlUjudPdx3LaGLYPIw/b/8z7t18r9/7WCf/rFoC3nDOho/X5EKtUKMooUjczH1cDmL1kcts29NtGEZ735BHBXdrKXW/0fuEgFAaIgvyk/D3y2egsqUX5zz3A8545nv0NTpObKXGpkIuCa+P6Jnvn8lb6gDAmX8CZl8PbPkH8EwJ8ObFgFQBXP05kOrdl9ad/3wgPHvqsz5FNO2VxvPi8kZN699YggLYCGIym9BZH5n+On/x98H5y6ovMePVGfi88nO8uO8VDJnM0ER59oAtbStFVberh1XYUQU3eTA7Jx4N3QOoF6LcMQCsGVirWmFqbCrYUIa3XXDSGydh3pvzRB+bPR9WfOiqOO0UwGbGR0OtlIU9A8txHOo6+70KOJ2cebLjguYwezV31WB79IgQg0KqCOow+SkqSBgE6dc+2tobcv/ruePPdXhv5Ix8AGs2Au0VNi/YUMqIiwN4kKjuMECnUSJKHoBKqX0Aa1cRkZUQA4VMImgfrI1QFYitWJWI2xyV7x/e+nDAh9pV0wmZhGFKZpwAAxMOMbMu3pBIGJ6+aBoSYhW45Y1d6B3ks9ru+hrFoiRxqu+N3PDqwVe9ikg5E4gXrH7Y8fvw0er/edhSYOKyIR0F/q8AUNXuW4HYH+r0dYJZEZ4xKRXf3X0qnrpwKv58wRTcO1sOSEee2RamB56ZF4K3Dr/Fv5BIgbOfBG78ETj7KeDi14FfbnWp5HKHvzZTq/NXu12+JHsJxmk9X3PdtdcR4YcC2Ahy76Y7sVjl2KSukgtoUSEALy17ya/tvqrmLQPu2XwP/rrnSchUBzDIWrzu45BhiBTBBrDj+MB+e1V4M3O2DKwlgG3VD0J/7Cqcnx64Sq2YuO09cQpgJRKG4jR12APYLsMwDEMmjyXEgKsfX1u4J5q6vHj1BUCUXIrcxNiQvWAHjSbUdhhCttC5drKjCNyCNxfwVjoA0HLQloFt6A4+gI1Pm+73tgEpEFvxkIGVShjGJwurRAwAqUIoEFuxs9JxmaQJkJ3VnZiYrkG0IvIWJb6Ykuz7oVcIEmIV+Oul01HTYcDvP+T7YeOV8bhiwhV4d4W4OhBqjEdCdFxIx1h3YB30Q3r8efufUbLOc5YrSi5Fksq3F+yweRh7W/aGNKagicuGbHTEr6i0COC5y8ByHIePj37sstwdm+o24a0jbwk2roRYBc6fmYmLZmUhVl8FJIxUKAya3AgoCYgnDQ+X1qPUycDs64AJ5/AaBAJycdHFAe8TK4/FNxd+I+g4iOCgADaCfFn7rcsyd+pskcReZdPKwrcW4pOj3ssrorNexQ+t3m/YnpQJw4WE4wB5cP18xakaxCqktjK6cGHLwFpKrw40dIMzqXHm+AWYpRs9JSxKqeON5tO2fl54wYkJaRocbtKLaknkjM0D1ksvp3PW5Nyjr3nYUhy+bBROfKIoVY2yEMtaj7UZYOYQsoWOWzXQpELe8qLlIFLUUZBKWNAZ2ESTyeEhzBc17QZkJwT4f9J4rnjglYhDD2DtSwWv7+oRroQ4Locvw2s97KJxEIhSq9Fkxt7a7lHZ/+qOM3LOCNtnzRmXgDuWFuKjPQ14d2cdGGO4Z849KE4oFvVzZ6fO9WgX8+KyF/06xpM7nsTj2x7Hqwdf9bmtP1Y693x/D27ecLPXbUQjLnvUPOBWtvZBKmFurzVHOo+gY8D/ifA9LXsEHJkd7eXgEkc0NcS2HlIp3E+Gmn3Imn5d/TXKO8vdruM4DhWdHiyC3G0fQIZ+QcYCAHwVkdWhY0bKDKgV/msuEMIyWr7fhIUug/B+b6Hgrkele7Abv/vhdw7LgilreeXgK/iu9rugxxYqCjem4f4ilTBMytBin5P3n9gsSOcvohMS+KzVgQY+szYxXYOXz3wZZ+edHdbx+EtOyhS3yrAT0jToHTSi1o9eKqGo7/LtAetsKK9HeL+XO/TCldcX6tQ41t4XktqztQS5UBfazdrtA4NMASTmAy2HIJUwpGqiAuqBtbeAkTMpIPfPA29g2ISmngHkBCLgBPB2NBac/z8FKSrUdfbDMBTa+WI/gXKhvle4EmKpDEgscCvkFEgAe7hJj/5hE6aPsv5XT6yZuCasn/fLU/MxLy8Rf/j4ACrsSsr/tOhPgn7OW+eMZONWFi9wuQ8/uvBRfHjuh5ib5n9fuL9e73wA6/26/XX1135/ruCo08DsrACD6fMWisq2XmTFR0Mhc33kDlSfQoySdM44hLbuGhgT80eWCVSqHChms/fr0J2b7sR5n5znsnzINISndjyF1Z+4Lwv2h6nJU7F24lqX5R+c+wHmpfFtWPb2kuvOWoefLj3xlY5HKxTAjjK+K2sNaPtBowkbDjXjg111aAyh7M4TEi+nyOuHXrepCQdrT3DLt7cEtZ8QhBLAAsCUDC0ONvRgWACLEn85c9yZ2HLZFkxI5APY/fXdyE2MsdnBPLbosbCNxRvtA05iE2nue7MmpvFCReEsI7ZmDbwFsG4xCWOP4g/ywZGHyC/O+yKkYxWlqsFxQHkIWdgjTT2QSphHERJ/GR83HrmaXNcVKRNsfcZp2qiAestv23ib7bU8gF5h68N34CXEIxnYf+79p4NFmFXIqbI1NNXnB356wPaagfFWQ0KRXOTWSieQAHaXpfJkZs7oysC6sxlRK9Rht/mRShieuWQaYhRS/PL13bbJo8L4QkGO/96K91C6thTJivEYbl+C2bG/wJKcxbas2cVFF+PnS3/GivErkB+f7+NojlhFDn2RER+N+q5+j9Uz+qHwi/M5IJE6VEu8dii8VTT2VLb2Ic9D+0Wgz06b6zcLMSQHXt31N5yalYpK1cj32VcmVAimJLmW9geb+X1066NYd9C3SvOMlBlulz+x+Am8tvw1/Gb2b1zWFcQX4JSsUwC46jgQkYMC2Egx7PqApuxehc9LG/0+xNbKdix58jtcu24H7nxnLxb/eSPe3CZM75wVbzf+x7Y9huu/uh5A8EbhzpmucKJkMt8beWFKVhwGjWYcaQrvjTpWPhJEHGzswcR0jcP6V856JazjcceP9T86LvCgGliUqoaEAQfDqERc19kPlVIGbbRnhUV3531/834xh+X4+YMjvw93ZfyBYFUiPhxCH+yRpl7kJcVCKQut31HCJO57AVMmAV3VwKAe6XHRQffASqX+ZV8BoLo9CAsdwEW5/J0j79heF+j4h9RQhZy+tbSX/F/UeECT7ndW2S+Si4HOamDIMXt2wf8u8NtOZ1d1J1LUSq9l+JHAnT1bpO4xOk0UnrpoKo406/Hgp7xNlLcJ4UAoSigCALy7ow4DLcvwu5OvAjCSNVuas9RjiaZQpGt5L9j2PveWeDU9wj6LBIVdALuxdqOXDcXDbOa8esB6E8+SMtfrbfdg94hSr0D81MDfr1/RH3b4HLH59+n/dlkWbDJkZ/NOv7ZbdxYf5F4x4QrkafOQGpuKF5e9iDPHnemy7YPzH8TTpzwNAMjR5KB0banorQCE/1AAKzTGIeDw54CP8ou+KtfS2XPHXYjvy9r88kD8orQRl7+4FUqZBP+5aha+uH0R5o9Pwn0flGL9AeF6S33d/K3edcFedDhwqOyuDGrfUJFJQnsYn5LB28CUhrmM2IphyIiaDgOKUx0D2Okp/ovYhIM7OjoB3WS366LkUoxLig17BjYzPtrr5Iy78/6fe/8p5rBsDA/24tUY4YRxchNjES2X4mAIv+MjzT22QDhUomRRUMudjpVqOT+aDyA9LhpN3QN+9UUfaHNUh5YEIPJRHYyFDuDix9rWP+ITnJMYC5mEhZTttmejoU44AScryYUAOKC9HP8987+2xVXdVegZ8u8c2VXThRnZ8WHPbPrikS2uYnbhVAF25pSiFPxicR7e2FqDz/Y1QimACM1fT/0rAGDYZMYbW2swLy/RJq62KHMRACA1xrMFnr39hy+8lZGmavlJlaZu96W57u7r684M3cc0IOyqJU7PPj28n22hvqsfg0az2wzs/rb9eL/8fY/77rlyj9vlaz4XtiSeDfPXwk8aRyaew1F27+67+UH5B0EdK5Bn0NK1pbhnzj2Ikcfg6wu+9lhiv7pgNU7Picx5Q/iGAlih2fM68NalQO1Wr5tdsu2PLsvOmZKJIZMZn+3znoX9obwNt7+1B1MytfjolgVYUqzDhDQN/r1mJqZkavHbD0r9CoL9we+bfwjtEis/WhmRfgtFiB5nOYkx0ETJsK+uS5gBBUhFSy84zv++xEj1tFzTO+hVWGdiOl+KHS7qOg0+M0dxyjjEKeMcljX31Io4qhE+O/iGoMeTShgmpmts3pSB0jdoRG1HP4pC7H+1Z2LiRMcF1hLzhj1Ij4vCsIlDW69vFczbN97u8F6nSvd7DDUdBsQqpEiMDc6iyIp9ACuXSpCbFBuSkJNheCQzuhsDwvW/WrFTIp6pm+mwyp9sZat+EDUdBszIiRN2XCHCcRz+V+lq0RLJKh8A+M0ZRZiWFYd7398H82AC/u+k/3NYf30JX8WUH+e91Hf3mt14cdmLODX7VADAJ3saUN/Vj+sWjZwfl0+4HF+e/yVytbkejxPI78PIec7Ip1kCWE/VEr/94bcuy6ytL2FDM3I98EeYSgwq2/h2AnftF5tqNwV1zIa+hhBG5IrEqRpjYcbCsEyEO6v9A7wXrqdnFXfLvzr2FSq7Km1eut6Yljwt4DESoxcKYAXmcNY0PJ2sQ8ePf/G8EcfhmMnxASdDlYHp2fGYkKbByz9WefwC763twg2v7kBecixevmqOrfcR4LNZj6wqQYdhCP/adFSQ/4+/M+zebnT+cLRLmPH6wr609W95F4V0LMZ4D8R9dZHJwFpLl91lxt4/13VWd+2XruIEYuB87rKkIl48xgMT0tSo7+oXbNLFF/Vd3j1gAUAulWPzJY69Rp8P1Ik5LBtDIqhzT07X4EBDT1Bqz2XNns+zYFmWu8z2umugi1eojk0BGvciXWu10vEtuuJcSvfnJc/6PYaaDgOyE2MFzyIWpIRmpTP3jZFsQJzJBCTkCjAqOxLGA0wKtB72va0bRmv/qycrkkhnieVSCZ67dDrAgNve2o1V4y9AQXyBbb1VFGZpzlKvx5FJZLZMkdnM4Z/fHUVxqhpLilMctslQefcFD+T3MePVGR6fRdIs31N3GVhPVVUKSWiTRQGjGWm/aOkXtuzWX7xZ6Px7n2sJbSSQDDsGsNGy8LQGeCqpd9fLDrhvVfv1d7/Gyo9X+szA3jXrLvznjP8EPkhi1EIBrMAcMzTjZZUSHZUbgDYPct5ueunWTFwDxhiuXTgOZc29+HK/60NsZWsvrv7vdiSqFHjlmjnQxrhmEEsytThzUipe21KNvsHwKKeWrCsJ2dNVbM8xKzd+cyMA4NaOLuQK0MswJVOLI036kBReg6WsWQ+lTOK2BLIwvhDvrXjPYdnult1hGdfM1xyzOjafTw8UpvCBkVBll97o7h+GfsCIjEAFnMJJv/DWTJMztDAMmWzZgECwTpQ4l6qHgr0H6aK3F+HDio+A9GlA4x6kxVkyO34IOTk/tMRF+28wz3vACn8eFKSoUN3eh0Fj6NeEh9rahc/AyhRA4nibEvGD8x+0rfJHQGXHsQ4oZBJMStcKO64Qse9FBoCrZXxgp1EId94GS1ZCDB4/fwr21nXjoU8PQmanv3Du+HOhVWpxbp7/4jCflTaioqUXN50y3u+A9KmTn8K6M9fZNBQSo/z7rng6JxJjFZBLmUMGdnPdZtT01ODfe10DsxkpM9xm3ERF41iR0TkQXts7gBdwUitlSFYFVz5+yzTxhS4HhhzvC/IQq9P8xVOF38zXZrrtZ/emtVLfW+9x3YyUGbhy0pWQS8Pz/yLCAwWwArO3lTftfjE+Dvjpr+432ve2yyJrWc+qaeko0qnx8GeH0GknjlDZ2os1L20DA/DKNXORovEs6nHdojz0DBjx4W7PX+hAePbUZ5EW6+rhKSShZnADRQIAqhRfm/lkSqYWRjMX1h5OK0eae1GgU0Eqcf8AYz/LHy76jf2us6e+AlidNYAVX8ip3qZAHGDfo5X+LuEG44Gv2vfZXrsT8QiGkkw+2AimjPhIsx4xCmngqs1ecH6Qvf+n+/ky4tbDyIjlg9JgvWD9wWzmUNNhQE5icKrK+UrPD//5OjXMHFAVxGSBMxlGo/ABLMArEVsC2FX5q2yL/RHj21bVgWlZcYiShzkYccOweRgflH+AP/78R5S2lTqsG5dUgrtm3YV/nf6vCI3OkeUlabhhcR5e3VKNtt6RrGWmOhM/XPIDsjRZLvucNe4sAEC8ciTbPTBswuNfHsaENA3OmeJ/yfyy3GWYoZuBS4ouwe0zbsfaSf5V5JR1lrldLpEwpGqjHDKwN2+4GWd/eDY2Vm9w2f72Gbe7LBMdpwDW2f4vHFS29SIvOfhKj/gokSsd+jtR4fRVDlbTJFCkEimeWPyE23X2dk6XfXYZXip9KWhl5Hnp84LajxjdUAArMFb7kD3qBBh3vwo0H3TcYKgP2OWqEmudiZJJJfjT+SVo1Q/iyv9sw/dlrVj30zGs/sdPGBg2Yd01czDOg5qdlZk58SjSqQULYJdkLxG9kf3Lqi9FPb4zn6liBAlgJ6bxgcGhMKroWilr0nvtf3U3u+mvSEuwuDVZT5nousyOzPhoRMulKAtDBtZqnRJsMDbcfMD3RiGydXCk1E2oADY/WQWlTBJUAHu4UY8CnRoSDxMlgpE2DeDM0PaUIVouRYMPL9jbv3V8ID5fU+T3RzXrBzBkNAcu4GTh1Sl34pM6931oVisdISoKtCYzkCBGAFsMdFQCxkGHB2tffWS9g0bsb+jB3HEJwo8pQHY178KMV2fgDz/9Ae+Vvee6QeJ4XDnpSp8lteHknjOLsWyiDscOnYd89TS8ttyztcsby9/Anxf/Ga+e9apDS8iLmytR19mP3589wePkpTfkUjmuK7kO5xWch7TYNDw4/0HcOfNOj9vfucnzujRNNBrdlBD3m10rquz9M8OGNhPv1Y1oirjYu4UBbxY6oVCyrkQYpeC2CiidysTDqZdh7el2ZvHbi9E10AXDsAGlbaV4ZtczQbldfLb6M9ww5YZQh0mMQiiAFZgoi41Dvbkf03MzgS/uBuwfCrb+G03Drg82qbEjqoEzsuPxj8tnoLq9D1f+Zxv+8MkBFKSo8MHN8zE5w7+yrZXT07GzuhO1Hd6Nxv1F7Bm5cHu09UgkQGxyyMfJjI+GSikLewa22zCMpp6BgIV1Lv/scpFGxLOhxnXm3VcGViJhyE9RhWw94g9WD9hg7T+e3f+CkMPxiVAldzKpBBPSNAErZpvNHPY3dGNyurBlmDEyN4GjRciJNe5FelyUT19rq9WMlcke/P3cUROsArEFVWI+xg2PVI3YP1iNS4qFhCEkIScrsQo1EC1CBia5GOBMQLuj9sCy95d59e/cWd0Jk5nD3HH+l2qLxfam7d430PifnQwXVn/YkpR8HNh1GTo73Fc2la4tRUkybz02LWUakmP4e9XBhh48u6EcZ5ekYUF+Ukhj0Sq1+OqCr7C6YLVXwZ763nrU6muxvWk7pqyb4lCGm+bH99TKOK0IEzG+iElCkd3jl7uyVDExDBnR2D3g1kLHXZD4zCnPAABePuNlfHshf31bkr3E4/FbDa2hD7K9HAqnsdhXZYiNUqrEB+e6Vx5e9PYinP/J+bb3zr8zfwLabE12RJXICfGgv6rAuDxwHtsMfPMH3lanqRT4/gn8LsfVzNy+JwwAlk7U4cd7l+DVa+fg89sW4d0b5wVU7nbuVP7m/VkAvrLesF44PJlA+6J07Uh516tnuVcD/L7u+6COHQxmJgEEsDSQSBgmpKnDHsCWWYK9Qh/COs7Zh2M9x8QaEgDg7SOu5fHQupbGOVOgU4XFT7e+qx/RcikSglSeresNj5CTFVmIXsX2lGTwas+BCDlVdxigHzBiSqaw/Y4x8hh8ft7njgu1mUBMItC4h/eCDbCEeOp4Vx8/T1RbJvZyAvWAtaJ19Oat6q6yvY6SS5GTGIuKECdkxkHBW+iIIUKUbMlWuxFyenn/yx5321rZDpmEjQoFYl+TO9wos/ixEqOQ4b9Xz0FBigo3vLoTGw41+7Vft2EYt765C9poBR5a5d6WLFh8TVAv/2A5Xt7/MjhwtjYpgLfSaZN+ivVV61Gyzr3XtxW1QjgROL+RSBwmMsSuQHKmstWqQOyagf3z9j+7LDst5zTsXrMbs1Jn2SYtkqKTHJ6f7BFCoIxrLUOVwrE3dEHGgpCPGwje2p3s77nO/dhuKy+IMQMFsAKzMH2h44JZ1wI/PYfyf87G9tfOBqK02Ma59ka5uxCpo+RYVJCMiemagC9UmfExmJCmwcbDwijvzUmdA0CYC9u0lGk4J+8cl+W/3PBLNImgwOoOs4AzchPSNDjcpA9K4TVYbArEPjKwH638KAyj8cxvjSoX30x3FOrUaNEPotsgrhJxXacBGT48YO3J0eRgWc6IYu6GQf8eNoPG6JgheObUZwQ79OQMDfSDRlvw5g/WjK2/lR+BkKV2nNgwg+OzsA28EnG9jxJiZwpSA8vASiUM6UFm4hGlAZQjWWl7Kx0AyE9RBVUSb3+cpYNGccqHASAxH2CSgJWIt1V1oCRTixhFBMpBnfCVVYmUbZg/xMcq8Pp1c1GkU+O6V3bg398d9Tre3kEjbnh1B2o6DPjbZdODnoDzhD+ZrM31vCr7gGnkexkd3QlZ0tf4zfe/EXQ8gqIZmcR1Vi0XG08WOoZhg8eqs0BKrYWwiNrcuivkY4SL+W/Ot70+2nUUD215KIKjISINBbACszDTMYB9J38uuHOewXmxg7gmWQtc+3XYxnJqUTJ2VHcKYk9yavap+OnSn3DDlBvwx/muHrbueOJk9835gOcLr5gqgbua7S7UfgRV/jIhTYPeQSNqO4Up1/aHsmY91EqZzYvPE1Ey7+vF5tKkmb43AlCo42eoy0QuI67r7EdWAP2vn67+FE+d8pSII3Ki29FrVkjfxGlZfCnqrmr/v2P767uhkEn89hoOhamvTAUyZgEtB5CjMqOtd1AQJV931HQYkB4XBbk0hOuAXRa23+iYLS5IUeFYWx+GjIH1bNmXxdYa+/gMrBjIo/ljuwlgXyh9Ab1DrsF3/5AJe+u6MGcU9L8CwvWHR4q4GAXe/sVJWD45DX/64jAufWELJsRPddnuUGMPLvzXz9hR3YknL5yKk/KEL98OpLdwyE4lPVHlX7AVUfsSTTrUwWn/hExlay8Yg4tuiVC9uP6ohvuiR+/Yy7/1sq0hHzMYzh3vvwo3AFz5xZUijYQ4XqAAVmCcS/4e2vowfk4bERcZUIXed+kvS4pTYDJz2FzW5ntjP7CWAJ1XcJ5fEvynZp2KW6bdgvvn3Q8AePqUp/HOObzVweKsxW73ufarawUZqzvsrX5SJcIpqk5M4zMx4SwjPtKkR2GqOuIeh/a4FZTwIeBkpcBipVMmshJxbYcheAViC+Y+Yb5PbumqcXirlIZe5m6lIEUFTZQMOwIIYPfVdWFCmia0QC8QsucCnBkTOV751J3HpDtiWGD2CNUdBuQkBKdAbMMus+Os1FqgU8Fo5lDdHpgS8bambbbXX8ZGi6NAbCW52KZEPClxksOqeW/OQ8dAh8Oy3bWdGDZxOGkU9L8CrrY5zoTdsiUIYhQy/O2y6Xh0dQkONvRg288XIM/wJB79/BCeWH8Ya17aiuV/3YxW/QBeWjsLK6eJI0gVSLZ68Mv7gLfXAN8+DFW5f77Ls1NnBzu00NFmQB+hJ92Kll5kxEW7KHa7s6kJprrNk1+qv3DGYWwydTksi5GHdn8MlkcWPhLQ9uEuBydGHxTACoy7m+Ydm+6wvf7rbg/WOiIwLSsOmigZNpcL0OjvRKZ6JPtgX2Jpj0KiwC+m/gIXFl4IADg953RbRunM3DOx8aKNLvt4ExAJFfs+n4fihbuhFqWqIWHAwTApEXMch7Jm7wrE3nCXXRGCcz9yM4PqQ8DJSkZcNGIUUlG9YLv7h9EzYERWiN6fppaDvjcKFrsA9oVlL0AhFa5UUCJhmJETjx3HOnxvDF7A6UB9D0oywuijmTkbAEOege/58qVEbEUSYLBS22FAVpACTjbsMrDP7X7OYZV1QiZQISeXni6xMrAA3wfbXgGYhrF83HKX1ZVdlQ7vtxxth4QBM3NFtvXwkxp9jdf1o8H/1R8YY7hsbja+u+tU3H3GZJiNsXj5xyr8Y9NR1Hf149YlBfj6VyfjlKLQVfM9MTlpMorii/B/J/2fz23NmTNR3bwHJbVv4/6hfT63d/YjDzsax6Dfl9K2kJQ397q9T7vLeP9raeB2T3/d/Vdcs/6aoMYGAJ/vfwXrY0exJ3qI/G3J3yI9BEJEKIANA/blZa8edBQwuqDwAqw/f70onyuTSjA7NwHb/HxgDYSJiSOZNXc2BRcXXewzO5gQlYB8bZ7gY/OE/SxzQdx4wY4bJZdiXFIsDjaEZ0awVT+ITsMwinT+SfM7WyDNe3MeSlvdi0KEgnPG5ubOLr8zsBIJQ0GKStQMbMgesBaGRQ1gq20vo2XCP1jMzk1AeUsvugy+1TjLWvTQDxptpcdhIUoL6CYjqXM3AM9esLtbdju81wbglagfGEZH31DwAk62D/WcDRufrAJjAljpiNUDC/AZWLMR6KjEosxFLqudr9/fl7dZJkUDy3YLBcdx2Na4DUc6juCe7+/xuu0D8x7AqVnu7TlGK/GxCtx8Sj7+d+tClD+yHJWPLse3vz4Fd55eiHiBe16diZHH4L1z38NFRRf53PZo9kxcn+6/L3xRgv/2VqKgyYDc7t4fLs/5YZMZlW28V7szwQTRZ+We5bLsx/offatxe6G1PbAe+OOJ03NOx8lZJ/vekDhuoQA2wkxOnIx0lXhy/3PGJaCytQ+teldftlC4a9ZdNuVkZ9GBHFyK3831bRguYRJ8uOpj6DjH01CsLKx9Bpap/b8B+8PEdG3YSoiPNPunQGzlntmuD3sba12z30Jz06AsIK/dAp1aVC/Y2hA9YK28WStiH7tdBtZoFv5Ba2YOH+jt9KOMeHsVPyExJzfMPY/ZcxHdshtSmNxadFR0Vrj0P70YQI9dtcVCJyfkDKyjCNWh9kO219EKKbLiY0K3htJk+t4mWOyUiMdpxyFbne2w2l4kqcswhH11XVhUEL4WGGc21GzAtV9diwv+dwE+r/rc43Yvz30A5xeeP6raK4IhUuP//LzPvarCvnn4TTT2+edu8MRizzoYYUOTjkdaR3pOh03iCgVaqW7vw7CJQ2GK6306mCD68cWPY++Ve3F9yfVCDA8AwPocRa3+tOhPgh07Unx+3ue4vuR6v55BieObkAJYxlgCY+xrxli55afLNDhjbBpj7GfG2AHG2D7G2MV26/7LGKtijO2x/JsWyniOR8R4SLXHKrixXeAsrFwqx03TbgIAnJZ9GjZdtMlWKhyrlAV081U4CQ0d7hBnVtChzyeAwMofJqSpUd/Vj+5+8W+O/ioQW9HF6lwEtZxFZ0RBNykgC5BCnQptvYPo7BPHq8/qAZsVRAb21um32l5v7fVeuhgK/XYZWDGuDVMz4yCXMr+qMrYd60SqJirkkmtvOJcslqwrQX/mLLChXpwU0+BWibhtwLEH+dLoXIeWBl9YvbGFLCEGgD2texzeF6SoUBFgCfH5BSOeh0/2SQCpiGq/SRY7N0sf7O0zbndYbS+092NFO8wcsLgwcgFss8G3AnhpmxGzis4Lw2hOXLLUWZia7ComFQyn5ZwmyHFCQpOBs/pGBBbFfuayYp2MdVdC7JyB9SfYYoxBwiS4bcZtwgwQAOt1/E6dkXOGYMcOhpToFJvjRbBkqbNw24zbkBg9Onr1CfEINQN7L4ANHMcVANhgee+MAcCVHMdNAnAmgGcYY3F26+/iOG6a5d+eEMczKsjV5Pq9rdjlLJMztIiWS7GtSvgy4kmJk1C6thSTkiYhMToRcglf5qRSBtaPplA4lthcs/6agFQR/cWq2BdtNgMqnaDHnhBGIaeyZj2SVEokqvwX+HG2dxKz19iGn/2vVgp04go51XUaEKuQIi4m8BLIqyddbXu9nxMv+J8jG7GRytHkCH78aIUU07Pi8UO5dyEqjuOwraods8cliJoJWl2w2mXZ+2Y+O3x61EG3JcTO1wZDbGAZ4pA9YK346K3L16lQ2doHo8n/a9n75e/bXp+hFrm9QhELxGXblIidvUDt/+6by1uhjpJhqsB+wADwzz3/RMm6Emyu22xbNmQawq82/grbGrehe7Abx7qP+ac6PH6JOL65Y4woaejq9f8+/d9uxYrCTmwyYDeOcJUQlzXrwRhvqeWMs3pwINY5AHDNZMe+166BroDHBwCs1zEDG2nhsw0XbcBLZ7yEkiTvnsIEAYQewK4EsM7yeh2AVc4bcBxXxnFcueV1A4AWAJGbxg0DgTx4zkufJ+JIALlUghk5caIEsM5IOD6g0kYFlrF5atHjLst6h4UvJbXOvMo4CB7AhlOJ+EhzL4pS/et/teJ8g2wfaMegSdiycns+qmsIOIC1zlSXBZi18pe6zn5kxscEFZDZl1PqJQzoE8YGwYFhx2xjamyq8J8B4OSiZBxo6EGL3rNAUk2HAc09g5gjsmCPXCJHfly+wzJpdDyQWoL55t1uS4idA9ivOgPrSa5uNyAhVgF1qL2cmnQk2tn8OI+rIEWNIZMZNQH47jogpgKxFTslYmdrM+t7juOwubwNC8YnQSaCGvU/9v4DAHDzhptxqP0QtjZuxczXZuKbmm9w84absfCthVjx0Qo8ueNJr8eJMZuB8cdX3+to5Zbpt4R8DF2MsPfYoJFIALuWoXBlYMube5GdEINohWtQeMH/LnB47zx55IuT0k5yeB+UH6pxCMxJUd+Xt3K4EKoCgDixCfVs1XEcZ22GaALg9YrFGJsDQAHgqN3iRyylxX9hjHlMKTHGbmCM7WCM7WhtFV5VV0hWjF/h13ZPnfwU8sIgYjQ9Kx5HmvXoHxJXfe/09Msx2LoUSzL8+/9bGZc6w2WZGCb0bxx+AwAgBwdEC/tgnqJWIiFWgcMiKxGbzRzKg1AgdvaD/aH+B/zi618IOTQHxg8b/RZwspKujYJKKUO5SBlYXnk2uHJYlxt76yH3G4ZCd53wx3TDyZYy0O+92GttPMzPzIej59G53y5KGgXkL8X4gQPo6XKdeGvucyx7u9HSyuAvgigQA4BMiVe7Rx6GnR9CCyyZl0CViG2IqUBsJbkIaCsHTEa3Qk4AbwVS39WPRYVJog/nok8vwnVfXWd7bz/J5mvC7YmWdiCPAlghiJWHaDGF0RMMAQDUI5OBoVrP+EtZs96mRu6LQJ93nAPYoFqCOo7ikHx0Wk0F+jc6a5yrwBVx4uPzCsMY+4Yxtt/Nv5X223H8N9Djt5AxlgbgVQBXc5xtqvo+AMUAZgNIAOBRWpDjuOc5jpvFcdys5OTRncA9I9e/PoJlue7tZ4RmSqYWJjOHg41ufDoFpLsfGGpbijRtYNlBdzc6oUuI7W8QUibhZ2UFhDGG4lQ1DjWJm4Gt7+qHYcjkd/+rN3Y273TxrwyWIx1HXBcmFwd0DMYYCnTiKBFzHId6SwY2GFyyti3CB7Bbj4koDmXHxDQNklRKbDrS4nGbb4+0Ii8pFrlJoT/I+mLleIdbCe7/6X4MjDsZUpgwdXgPegYcH2Ye+PkBh/dXTboqoM+r7ugLXcDJQpZd763zNWu8JYANtA/WhpgKxFaSiwHTINBV7VLuubN5J76t+RZfHeQnDE4rHiUZNQ9Ik4uAWOp7Gy2M1gD26i+v9rKhMAwZzahq60Ohn04BgT7vON+PhsxB6Ea0HMIn6sCe1cJFoFny60uux4PzH8TfT/u7SCMiRiM+rzAcxy3lOG6ym38fA2i2BKbWANXtExFjTAPgMwC/4zhui92xGzmeQQAvAwite3sU4aunYUaKa9ZRLKZmxQEA9taKG8BalY5T1P73Znqid7jXQdUzVOyFoWIhzqzjhDQNjjTpYTILnz22YhVwKggigL16suuNe1fzrpDHBADf1n7ruECdDkTHBXycwhS1KF6wPf1G6AeNISkQ/3H+H0fetAovNHbboRcEP6Y7JBKGJcXJ2HSk1W1VRt+gEVsq27GkWDzfSXsWZCzA86c/77Ds1orXMaTQYrl0Gxq6+tFiaMFnlZ+57Ctj0oAelIdNZjR0DYTe/2pF6zmAVSllyIiLDr6iIFwlxADQetilx/TpnU/j9o23Y/2BJkzLikOqNvS+SCv1vfW4dv21WHdgne+N/USRPV+wYxF8hdjDCx7Gr2b+Kqj9nUvSI4o6DQmWXvRmQzM6+obw5f4m2/1UaI6198Fo5vyulEqOdpOUaT4IHPzEY7vK3NS5ttdbG7cGPkgR7mFCEUyWfHXBaizOXCzCaIjRSqhTZJ8AWGt5vRbAx84bMMYUAD4E8ArHce85rbMGvwx8/+z+EMczajgz90yv67VK4cUwPKHTREGnUWJfXZeon2MNYJMCEBey8tas3zu8X/7Bclz06UXuM3tBcNGnI/52SpGEJYpT1Rg0mnGsvU+U4wN2Fjp+zuzac+fMO/GNxLFkXahZ8h/qfrC9juEQcP+rlQKdCu19Q2jrFbY/d8RCJ/jAZXX+iOBQW4vwlypOBOEyT6yaloHeQSO+OeSq7PrVwSYMGc1YNkmcHlx3OHvebmnairezZ2OZZAdaWttwy4ZbcO/me2EYduwnlQX4XW7o6ofJzAlTQgwAmkxEe5mwyk9R+V1CvLHGydoqXngRLxdsSsSHwRjDz5f+7LLJvrpuLJskbPZ11UersK1pm8++Vn85X9+L6dOu8b0h4TfLcpdhZf5KF8Egf4m0IJAD6lQs6Ru5L5/8xEbc+NpOnPHM97j/4/2CTzpbtTD8CWD/ftrfsSR7ycgCjgO+/gPwz3nAO2uAZ6cAR75w2e/FM150eB9o1pJrcryHbbxIfGs9f/G3itHKqMr2E2Ej1L/6YwBOZ4yVA1hqeQ/G2CzGmPXbdRGAxQCucmOX8zpjrBRAKYAkAA+HOJ5RgycT9bPGnYULCy/E/fPuD+t4pmTGYV+duBnYtt5BqJUyt6IFvtCkTne73B/rhEDJk4szeRAOJeLyZj0y4qKDFqCROpX1PrTlIZz8duhm3/va9tler29oDTqALUoVR4m4TgAPWPuyref7j/EPGgIiRt+3J07KS0SaNgrv7XTtu31vZx2yEqIxW2QBJ3uce7QB4B2FHtFsCPIjn6C8qxwA8H3d9w7bBFp6J5gHrBVtJp5u4TUZqrqrXFZbrXT8eUDe3+40KaIMvU3AJ1EaXk3ZIuSkUrifGDtDwMmMxt5GDJg8C4gFSqk5Cw9I0iBL8uxdSgjDvXPcGU24xy/V6HChTsPlPSMTSWnaKLx9w0m4ekEuXvm5Gk9/LcxEuZWDDT1QSCUocDPR7KxWvjhzsWNJ8O5XgR+fAWZcCVz9JT/J9PYaoG6n189c8s6SgK6H73aWOrwfFYrRFhZnLkbp2lK8sMy/qiS1IgzXSmLUEVIAy3FcO8dxp3EcV2ApNe6wLN/Bcdx1ltevcRwnt7PKsdnlcBy3hOO4EktJ8hUcx4kjPxoBluUuw/bLt7ssf2DeA7h/3v1IihZfEMOeqZlaVLb1iepT2qofRHKQ5cNZiYV4sd81uOgYEF49+YEU92IloZKfooJUwkQVcjrS3Ov2pugvct1kl2Wh/o6db5pxQ/1BB7DWGWuhy4hD8YB1x5uxCqBPWDE5k93vcXKi699JSCQShkvnZOO7slaHCZfDTT34saIdF87MEtU+xxmFVOGyLCpKi8PmLBQcfdlWjnjX93c5bONsR+ELqyJwtmAlxBkoHuT7zz4++jGGTI69aAU6FQaNZtR3+hZZ6R4Ud4LRI8lFXssJC1JUGJ8sXK/csvcF1n6o/gmYEJhwIBEYV068EgBwWfFlPrdNjOL7kAO1hhEVtQ4KuwnC6dO/xdy8RPxhxSRcNCsTf994FLtqOgX7uAMNPShKVUPuRrXbq42PoQP4+n4gZyFwzrNAzjzgivd5FeUPrgeMniuTOgc7Ud9b798ADR14yEneYFT9vSw4i1XZo5AosPOKnXh3xbtIiQlPuwsxuqC8u4hEyaLw7op3MU7L9zI9MO8BxMgFenAKkCmZcQCA/fXiPSS16geRFEL/69zU2S7L/rX3X6EMyS0xTv6NQhEll2J8cqxoGVijyYyjrb0hCThJA1QG9oepr7iRvA8ygE1RK6GJktlKpYWirrMf6igZtEF4wNrz58V/HnnTEph9iy/Mdhp4+fH5XrYUhrXzcqFSyvDE+iPgOA4cx+HJ9UcQq5DiynlhKF+1w53vpEKqwBvKC2EaqvXYExVMAKuQSaBTC9TPqc2E/Rn1UcVHDqvzLSqk5S3ez+dLPr0Ebx952/Y+rN2DycVAaxlgdp+9WT1DmOvlP/f+E19XCytUliCNAsABUy7yuS0RPL+Z9RvsvXIvGGN4eMHD+NOiP9nWZagykNh1t+39bTNuAwBoFeFrk/KJOg0yu+vr59Uf2F7fv2ISUtRKPPDJAZgFKCXmOA77G7oxKV3jdr19qe9fTvmL48od/wH6O4GzHhsRmoxJAFb8Beg4Cmz5p8/P9oumfS6LRmMA6w2FVAGFVIHihMDEIokTBwpgRaY4oRgL0hcAQMSCVwC2i+nBBvHKW1t7B5EcRP+rjfRpLosK4wuDP54nBPaAtac4VYPDIglDVHcYMGQ0ByXgZEUarjK7pKKgdmOMoVCnFtxKp7bDEFL/q5UvqkZ6karqXPsFg8XQ1waTXcZzeor7knoh0cbI8avTC/Ht4RY8+vkhPLH+CL451ILblxYgLsY1Iyom7jKw+9r24cPM97A0W7gJp+r2PmQnxEAiEShE1GRCZvfQuLlus8Nqa7WEr2vCgfYDwownGJKLAWM/0OlaAg0Aq6cL8/v/x55/4M5NdwpyLCvfdTEg6yQgcbygxyUcYYzZ+gxX5q/EKZmn2Na9u+JdZMaO3KfPKzgPpWtLIZeOnpJUqFMh9xDcqZQy3H1mMfbVdbvVBAiUhu4BdBmG3QawHQMdeGrHU7b34+PszlvTMLD9Jd4KKrXEccf8pUDBGcAPfwEGPV9L/C4hbnQTwLLjL4AlxjYUwIaBW6ffittn3I5lOeGxzXFHokqJFLVSVJuXUEqIAQBp01wWefImDAkRA9gJaRrUd/Wj2yB8qbY1qAslAyuTB98D6g635cdxOYAy+JLDwlQ1ypp7Be0Jre00hNT/asW+pGlns/eepEB4xM4aJj02HavyVwl2bG9cPT8XF8/Kwgubq/CPTUdx3vQMXLtQfG9qZ9xlYMWgpqMf2UL1vwJAbLKDkNSmuk0OqzVRcmQlRAdclXFZ0iwhRucf1oflZr4H96xcR0/FNK2w1wxBaTsCTL0k0qMYc1iDWaVUCbVCjRRN6M4DYjIs18DEeQ7QVk1LR3ZCDP6+6WjI950Dliq3iemuGehHtjyCd8vetb13yHpWfQfoG4DZ17nsBwA4+R5goAvY8bLHz35066N+jbGr0fHe9eD8B0eX6JYfjKaeXSIyUAAbBmLkMbiu5LqIXyAmpGlwSKT+zIFhE/QDxtACWDdlpxIxTlGVeP0SxWl8cHlYhImCI029YIzvtQ0WoWdZl73nZlImyPJhK4UpKnT3D9tUrUPFbOZQ3W7AOAE8TWfblbkP6htDPp6Vxp5a2+szcs8Im6qiRMLw+AVT8OUdi/D5bYvw9MXTIBUqOxkASmlw141XznrF7205jkONJQMrGBIJZJp0r5tMSNXgYAABrIzjcM+k60Mdmf+kTACYBGjiRV2un+L42UJO1AiOQg1MPj/SoxhzKKVKFMYX4rFFjwHgnQ4Gq2/D35aMTh/Onyo70GmK87heJpXgFyfnYW9tF36udG9b4y8HGnrAGDAhzXWi2blH3iEIO/ARfz7nL3V/4MyZwLiTgZ//Dhjd+77+3Pgz9rTswZ+2/gn9Rs999z+2jQg4KaVKrC5Y7XHb0Yo74T9ibEEB7BhiQpoGFS16DBmFt+uwBhshlRArVbjP4PjwLIqQjIgZ2IkiKhGXteiRFR8TlMqzFcYYrogr8b2hnwyaHIPMuzu6R7wlg8Qq5CRUH2yzfgCDRrMg3p/2DyB9fS2CKREbnexhwk1xqgYTPfRshQOZRIZcTW5A+3x+3ucBlVp39A2hb8gknAesBak2y+v6iekaVLX1wTDkn81FFMeBJYYxCy6P5pVOLQGss3rsS6UvoWcouOuZ0WzE24ffxt/3CBPYnJp1Ks4aZ5chnrGGV1ImwopUIsX7576PpTl8sKVTKzFkSMfk+Lk+9owMX5Q2oot5V1U/f0Ym4mLkeG1LdUifdaChG3lJsYhRuJksdnqcsWVgTcPA4U+BorMAuZfAbN4tQG8TcMTVE9vKmi/W4I3Db+Dtw2+732CoDy39IwKEzvfw0czyccttr6mEmKAAdgwxIU2NYROHo63Ciz236HlbhOQQS4mWxDn2TlZ2VYZ0PGf+0tEHKMTrRU5RKxEfIxelD7asSe+3Mbo37im5wWVZbU+tqw9lEKzp7gZCFIoqtFnpCHOeHmvjg8PcxNAzsBMSR7LL2+QABMrC7h5osr3O0ngPiE5EGGP43+r/4cH5D/q9T5Y6sN9TtVWBWMgMLABoM3HykOeJjAlpGnAccMTPa4KMg6iTbG5JLQEsvpCHmxy/d5vrN2P1R8FlaJ7b/Rwe3vqwIGJ8ydHJeGzRY45CanPCmKkmPKLT8EFXc8/oC4Y4jsM3h1ogV6d53S5KLsWFMzPx1YFmtPQEZ/PEcRx213Rhalac2/XMKYK1ZWBrt/HiTb7UtPNPA+Ky+V5ZH7hTOx40DeIXX16NpxPcj2+0Yz+5dvfsu71sSYwFKIAdQ1h9SsUob23q5m9cqZoQyzqcxH/WHVwX0uE6BjpQ3lmO1NhUzJWosVQmrrclY8xSqi3s73jIaEZVWx8KQ7DQseHGSmfVx6tw28bbQj82AKSEloFNUimREKsQTMipup03sBcicJEwCV5axj88bI2OAloOhXxMZy4ouEDwYx4viNn7W2P1gBU4AwtNBu5qGRF/ueRTx55Ma1WGv2XEciYFwmhhBIC/JvTUgTN04MXvj7msbulvCao38D/7/xPy0Fbnr8bHKz/Gp6s/5YUQu+28ixPC369NuJJiDWD1wvn7CkVZcy/aegehSfI94XXZ3BwYzRze3l7rc1t31HQY0N43hJk57p8znFXTbQFs1Xd8Gf+4xd4/QCIFZl4FHNsMtJbhmwu+8bipu+/rwfaD+KlT+HuWmLy2/DXba2tF3h/n/9GrxQ4xNqAAdgyRlxQLhUwiSh9ss2XGMtQAVpfuaqUTCud9fB7O++Q8NPU1YatZH5bMRnGqBkea9TAJIMlvpaqtD0Yzh6JUAQy7Y109iIfM7ntqvOFsyD4vKpW/CSeFrhxdkKISrIT4WLsBcilDepwwYjQ5mhGLmYM13wtyTHvC6b862mCMQS0Xx5T+WHsfGIMgatQOaDMht3swdVYUzoyPhjpK5vekliQSdhYWIaed2zZjd437ygd/BWKCxd4b3X4iY3zceOTF5Y2o+H/zR/y7pQPPzP6dqOMh/EdnqbwKNnMpJj8dbQMApGTkIs408j2t09e5bDsuKRYL85Pw5raaoO7fO6t5L9kZ2a4BLMdx+L7O8X5h00Wp+p4XsYyO8/0h09cAEjmw4z/QxXp+nuHgOn53Ynl52tE9CTQ1eSr+76T/Q7wy3pbBds5kE2MTCmDHEDKpBIU6lSj9mc09A1DIJIgL0WeTpU7G3qoagUYFtA84CTL4KCMSgglpagwMm3HMkvkTgjJLMFeQIs7DfaB0DXRhb+teh2XPQwfEj+N76kKkKFWNCoGUiKvb+5CVECOYOJG9eMQnzVtCP6AHQY6xihnC9+gDfAY2TROFKLnAYnraTL7s1wPWqgx/LcwuUkXAEsYSwG7+/lvkJ7u/xrx15C20GFp89sx9Xf01ntv9XMC9dY8sfMT22j6YPS37tJGNjnwBlL6D+TNvxmkTSX14tGAVbxyNJcQ/VrQjJzEGcSnZyB4eKav97Q+/dbv95XOz0dA9gE1HWgL+rF01nVApZW5bfWr1jlndqclTefG6wV6gbjuQd7J/H6JK4UuN974JDPd7bLt4bvdzLsukbsQB3QW6o42Lii7C95d8b7v3Biv6R5xYUAA7xihOFUeJuKlnADqNMvTsUfw4SJz8cj+r9CxYEDCacASwllJtAX/PZc16SCUMecmh93F6wzmr6ok1X6zB2i/XOi5sORSyArGVAp0a+kEjGrtDn9GvbjcI0v9qxT6AfX2oIeTjHa75LuRjnEj47WUYIMfa+5Aj4HlgQ+voBeuOiWm8P7TZTVZn2OxouXV9Vvjt1oajk9ApSUCOsRJPnjff43anvXsabvjKtYfeislswp2b7sTz+57HrNd8WwHFK92XWlp73W6dfisy1Zn8wtYy4MNf8OXOi+/yeWwifChlUiTEKmyVWKMFk5nD1qp2zMtLBNSpOKnf9/iWTtQhSaXEm9sCn0jfWd2F6dlxbidLnQPF03NO51/UbgHMRiA3AMvAWVfzljoHP/ZLsf6vu/6Kr459BWOXq0DVhARh7tnh4I4Zd+CmqTdhWW7kLCmJ0QMFsGOMCWkatPUOCmZRYqWpeyD0/lcAkEhcRICe3fVs6Me1ovZueSEE+SkqSCVM0Ex3WbMeOYkxwmePnHB+mHZZbxrG0a6jONZzzHVlR6VgAWyhxSqoLMQyYo7jUN3eJ2jfo0LipH4YYpb4ws13hrT/WOO26bfh6wu+Dni/mg4DcpNEEHDTZMDXt3JimgaGIZNNSMqeTbWbHN6zxPBmYHsGhnHDKzuwbzgTS+KaMTUrEU8sfsLj9rtadnlc9+ftf/a4zh2FCYWYm8or19qXBV416SpcUHgBrphwBb+gfhewbgVfOnnJ64CMFEhHGylq5ajLwFa29kI/YMSs3ARAnYZL9SP3E0/VPXKpBBfNysS3h1vQ0OXZisaZbsMwjjT1eOx/dQ40bYJEdTsAMCBrjt+fhdxFQMJ4YMfLfgWwL5S+gF9/92tcvPnXDssfXfgo7p93v/+fG2FUChVunnazo38uMWahAHaMMSFVHJ/SFv2gTcghZFIdRYYELXEJQwY2Si5FXlKsoL/jsuZeFAmgQGzlXzPvc7vcVwD7xI4nsOrjVe5XcmbhAlidVYk4tAC2rZe3ThEyA+tcZdDfXh70scTKNh7PzE3zbsVx/ZTrkRqbGtAx9QPDaOsdQnaCCBnYKA0kCu/fTatFUWl9t8s6l3MgflzIQ2rRD+CdHbV4+NODuOe9fXjwfwfx3IZyvLG1BusPNGFndSe2VXXgb9+W47SnvsN3Za1IKpiNuN6jwHA/zhx3ZlCfe6gjMIGYif0GwPL9YTtHBPtU1Vvwh+wViKn8Hvj4l8CLS3kBm7X/A+JzgxobIS46TZTNjWC0sKe2CwAwLSsOUKdCavco4a1V4dI52eCAgMScfjraBjMHLMx31ZgAAOfHGFv/a/1O/r6pDOD+zhgv5lS7BVJ9k8/NPTEvfR5i5eJWdRGEWNA0xhjDKgJ0pEmPRQXJghyT4zg0dQ9gSXGKIMeDbjJOrf0UG2P5bElTX/AXaBfCkIEF+Ey3VdAhVAaGTahu78OKqcKNfV7RecDOP7ksn//mfPxv1f+Qq811u9+elj3eD5wsTAAbH6tAsloZspWOVYFYcOVZO+Z8dj5K15b63tANRrN/3qBjiT8v/jPmvO4+GzE1eWpQx6xut1opiXMeqDQZAEbOVY7jHCY6ilLVUMok2FPThXOdvsf2mUe12Qz48JX1hn5gGI9/eRhvb6/FsIlDlFwCTZQchiETegfdn2vz8hLx0tpZmKTngMqXgMZ9QHZwfp7+ZIOsvFXfiKKqGhxUJ+LeuGiU7P0QyLLcQ14/f2RDeSxfMnnq74CYhKDGRYhPilrpt1VUuNhT2wW1Uoa8pFhAwiCR2X3/vcyLZyXEYFFBMt7eXotbl+RDJvV9Xm+uaINKKfNooeNsa7MibwVfvVO3Ayhe7nYfr0y7HPj2IcQe+xEA3xfq3Hf+111/RYYqw+Mh7HvNCeJ4gwLYMUaiSolktVLQPtieASP6h03ClBADQMpEPNvShinjsm2LTGbTyIxlKIQhAwsAxWlqfLK3Ad39w9BGhyZsVdHSCzMHQTOwEnkUJpuk2C917Xm987s7cWnxpVg+brnL7KzX0h2JDEjMF2yMhTpVyFY6x9qF84AVAwpgXYmWjYiAvbjsRVz31XW29/Y+gIFQ02G10BHnPGDaTMB42PbezJkdxiqXSlCSocWeWtdJLYegj0kAaXC35fJmPa5dtwP1Xf24ZHYWrjgpB8WpalsgPWg0oaNvCO29Q2jt5R90J6ZpbB6e0Ft6Vut3ANlzkanKRF2vq1IrAPQN97nN3AybvFdwWLlXP4xJSx4GJq3GFLUOnwMAx+GrzgoM9DYCpyqBvlZeMV03GVAKYB9GiIpOE4XW3kGYzJxggnmhsreuC1OytJBYxiNTpQDgr7m+ql8um5ONG1/biY1HWnH6RN/uBT9WtOGkvATIPQS7zvoSKoUK6KgC+juAjJl+/G+ciE0EJpyLxYe/xr3nPAiJTOmiFP5C6QuBH5cgjhOohHgMUpyqxpFm4cpbrdL5KRqBlOFSJriIpLf2twpzbFVgpYfBMiLkFPrv2apeai1DFApO7n7CobyzHA/+/KBb2wyvAUTyBEF70wpS1Chr7nUrfOMvVW29kEoYMuKFsdCxYhPgCBFnX8D/rfqfIMc93rl9xu1Ylb8K6bFO2cogReKsiuDZYmXitZkOb490HnHZZFpWHPY39GDI6Pjg3GwY8ZD9nSS461NZsx6XPL8F/cMmvPOLk/DI6hJMSNM4/L6UMinStNGYnKHFqUUpOLUoZSR4BQB1KqDJtPTkec+m3rLhFof3/977b5SsK8G+tn1ex/nr9k6USvJx+dU/AifdCKjtAgPGkJZQgHHZi/kM8IRzgOyTKHg9TtBplDCZObT3jY4+2IFhEw436jE1M862TKIe+X7tb9/vdf/TJqQgWa3EG1tdhY+cqWrrQ3W7wXP5MBwzsFdPupp/Ub+T/xlMAAsAM68CG+jG5UYFlQITYw4KYMcgRTo1ypt7BfMpbRLIA9ZGTIKL3c3p753ut0KuV8Ik/jEh1RLAClBSdbCxBzEKKXIShH34viLZu2iEiwURfJQIOvUuh0pRqhr9wybUByCk4UxFSy9yEmM8zooHy1MnPyXIcV7b/1+H955Kt8ca15Vch4cWPOTS/75m4pqgjlfdZkCSSgmVUqSiI00Gpg+M9P9ds/4al02mZcdhyGh2KbN8bNtjttdnJ00L+KM7+oZw9cvbIZUwvHXDSZiZE0KZbcaMkYdqL+xo3oEvj32JZe8twzkfnoO/7fmbf8dPLgYufYvPHhEnFFYNjJZRIuR0oKEHRjPnUNIrDWACWy6V4OJZWdhU1oq6TlfxNXs+L20EACyb5Pn49s8vt824jX9RvwuQRbkIV/pN7kIgsQDY8TLmpAYgAkUQJwAUwI5BilLVGDQK51PaZLE6SdUKFMACbi/oLx94Wbjji4xOo0R8jFwQJeKDjT0oTlXbyqCE4pyCVdjpxXPX/obbNdAFw7DBbQnxfxY/hQdb221ekkJRqAtdibiipRf5ycJncJwzgY36+oCP0T3YjX+UPi/UkE5I7APYS1L/4egJGgDVHcIqUbugzcJ/Gkd8I/uG+1x696dZHqTty4idvZQDFXAymznc/tZutPYO4oUrZ2F8qOd65iygqxroa/O56UM/P4TGvkZU9/jOUFnhplwIyMjD8UTEms0fLVY6By333skZWtsyWYA+8JfM4fvR39rmXczpi/2NmJ4dh/Q4z5U++9tGMr62+2jTPkA3CZAG2WZkFXOq24ZUfRt2XeFZIZwgTjQogB2DWMtbhRJcsN6wdEJlYAFA5xrA1und92N5IpIKr4wx3nM3xN8xx3E41Nhj+5sJim4yvOWjB4wjDyKL3l6EFR+tQIvB1dx9tkmK1b19fK+agOSnWATHggxgh01mVLcbkJ8ifgni5Z9dGvA+IXsmjwFSY1OREp0CWet16NYH/x2objeIG8DGZbkISjiXh2fERSNJpcRuizIqAFzx+RWOOyXkBfSxr22txubyNjywYpJH8ZiAyLD0wVrKiL3RMxT45FxslHuLEeL4R2dpIRotVjpHmnqgjpIh3W5iXaJJx1w/vGCtZMbH4LRiHV75+Rh6Btz3d5c367G/vgfLJ3sPjh/e+jAA4MapN/ILOA5oORh89tXKtMt4obMfng5IRI0gjnfobB+D5KeoIGHC9GcC/A1LGy0X1qM0ZZLLokDtdPqNjqWnd8QIJzDkDxPSNDjS1BNSqXZdZz/0A0bB+18BAKoUINazcjQHDmWdZTahoRZDi3v/1yaLAq/AGVhttBwZcdFBC45Vt/fBaObCEsC2DgqjOE04opQqseGiDRgfO9smyBUoA8MmNHYPiCvk5SbwlDHHkJYxhunZcdjlTZ08gAC2rtOAx784jMWFybh0TvDKxQ6kT+PF2Gq32hadlHZS0Ic7RTXy/7l3zr04r+C8UEZHjGKSVEowNtJSFGmONOkdRMwAAOpUxJgDm9i+/bQC9AwY8fIPx9yuf3VLNRRSCc6b4Vnt1x5bqW9vC2BoD33iNyYBmHsDsP8DSFpde+8J4kSFAtgxSJRcitykWEH6MwH+hiVY/6sVNxnYIdNQQIe46H8X2V6/Xd+Ia1PmhzysQChOU2Ng2GyzcgkGaxmUKBlYANBNwjcDWrerKrsrcf4n5+Ovu//qcfd1Z64DmvcDmgxRLC4mpWtwwI13pj9UtPC2JmIFsM+c8kxI+29v3C7MQMYAuUmxQX+Pam0KxCJmYFU6Pgtix8dHP3bZ7KS8RBxrN6Chqx8/1f/kepz4HL8/8g8fHwAAPLp6snDZfEUskD4DOLYZt0znhZp+O/e3QR+OtY4oM18+4XLvKubEcY1cKkFirAKto8ALluM4HG7S22wDbagDF0krydTi9Ik6vLi50qU8uq13EO/vrMM5U9KQqPJcGr+5brPtte070GwpKXbzrBMw828DFLFg3zzg9y5vLH8j9M8liAhCAewYhVciFq6EWCdk/ysAJBViwqBjwPpp5acBHaJGP9LfKQHCZqFjZaIl6AzFsuhQYw8Y4/9eoqCbBF1LmdtV+iF+3BtrNnrcfYZuBp+BFTj7amVyhhaVbX0ePSy9YQ1gQ+4L9ECf0TGgsi+59sWweRh3bLpD4BGduOQmxqC5ZxCGocDPg6o2qxewiBlYxoCEPCxkI5/xftn7LpvNy+PFi34+2o6/7/27w7oXezi/+0N/rGjDhsMtuO20AmTGCxyYj1sE1O/CWekLUbq2FOO0gfXl2iOxlFFnq7N9bEmcCKSoo0aFiFND9wD0A0YUpTpN/KrTAqzj4vnt8gkYMpnxuw/3g+NGjvDUV2UYMJrxyyXeq7tu3nCz7bVNyb/lIP/TTbVZwMQkAKf+Fihf7/cuSupFJ45zKIAdoxSnalDTYUBfEIGBM03dA9CpBb4YyqNxBiec9Qnj4KJsLDa2Uu2m4Eu1Dzb0YFxSLGIUImUuUksAk/cHDrdlw1aG+4G2chEDWOskQOC/w4qWXqRroxArkvKss2n8b3/wP1P1t92uqq223ijChdwkPjCsDqKM+GgrH8COTxbZZiIxDw/1jEy6tQ+044ntTzj04henqhEfI8fPle3QKBwfrlP8VKA2mTk88tkhZMZHY+18//YJiHGLAc4E1GwJ+VCXpZ+CdWeuwytnvSLAwIjRjk6jRPMoyMAesdxzXSZ+VTpwQVQrjEuKxV1nFOGbQ8344/8OYtBowutbq/HmthpcPT83oElSm59980G+ckMoRe65NwJZvsv9V+StwMMLHkZhfKEwn0sQEYIC2DFKUaoaHBeawisADBpNaNEPCu6zCQDXxJVgfbcwpygDAE26r80EJUouRV6yKiQl4oNiCThZCbX/puUQ/7ArsICTlcnpfHnz/iDKiCtaezFexP7XQaNjAOstU+1MVXeVy7Kbpt4U8phOVKz9q8GUEVe09EKnUUIdFaTSp78k5CGpY6TqY9A0iFcOvoJDHYdsyyQShrnjEvHz0XYoJI4SalI/FYg/3F2Pg409uPvMYmF1B6xkzQWkCqDqu6APcZskCXsbujFn6eOYoZuBxGiyzRkLjJYMrLU9yqWEWKkCJxn5zhiG/Z8Qu3bhOFw1Pxf//ekYJvzfl/jdh/uxqCAJd59ZHNDYbL3xLQd4BWKhkEiBS17Hnwai8MvOLpfVayauwb1z7sWjix7FyvyVwn0uQUQICmDHKNaZyVCViK0WOhle5OODhekmI7nDf4sGr8cCAG2mIMcKhEnpGuyvDy6A7ewbQl1nvy2IE4XkYkAegxfjgvSQa9zD/0ybKtiQ7EnRRCFJpQz4d2g0mVHe3ItCnUil1wAGTI6ZBnujel8wuGYBSEHSM9b+1aq2YDKwvaKVkTuQMB4wuyqV2pccAsC88Ymo7+qH0eQYfJr96H/tHzLhyfVHMDUrDiumiFRRIo8GMmcDVd8HfYjLK3dDsvjX5Pc6xtBplGjrHYTRFDkHAAA43KhHRlw0NG4mrTjpSLvT3Dfm+n1MxhgeOHcSXrt2Lq5fnIcnLpiC/1w1GwpZYNdtmUQGmIxAy+HQFYidiU3COddsxo0LHnBZdffsu3H5hMuF/TyCiCD0xDRGyYqPQYxCGrKQU30nr/QrRgAL3UTInDpWKrsq/drVuR9RolQDSvGCGU9My4pDU88AGrv7fW/sxN66LtsxREMqA9Kmwdzmvg/WG2qFGqjfCcQkAvG5wo/NwuQMDQ40BJaBPdbeh0GjGZPEUG+2EC1zPec31mx08f90B1noBIY6So4klSLgDCzHcWEMYN0rCDvbeZ1SlAzAjO8bvnbczo8A9sXNlWjqGcDvz54g7jmUvxRo3At0B+5vDADRmixgzi8EHhQx2knWRMHMAe19gQkuCk1ZsxsBJwuczJt5nG8WFiThvrMm4MJZWZBLfT9CO09gSZkU6KziW3eEzMBakUcBs6/F2+e8LfyxCWIUQQHsGEUiYSjQqUPOwNZ3WQJYEUqIkTLRJU+18mP/Sl+qexwztywI9UEhsAafe2q6At53b203GONVEEUlcyZUbUcD3u2aydcA9buAjJm8iI1ITE7XorylFwPDJt8bWzjQILJ6M4CLCi9yWXbbxttw+ee+Z7mdM7APL3hYsHGdqOQmxtoEmfyltXcQ+gGj+P2vAJA4HgAwK8bRTsPZ/isnMRaJ+f913d/HJFCrfhD/+u4ozpikw+xc4RW/HSg+m/955POAd32nvhFs6R/4B2liTGHVwohkGbHJzKGyrc/jd/7kqPA+Czj7QUslUsA6YZwkXh/qxMSJyFSFv+qMIMIFBbBjmAmpahxu6nGZIQyEhi4+05kqtAoxwD/Qucly/VD/g9fdzJwZF/zvAodlqWr/7SmEZGK6BgqpBHtquwLed09tJwpSVFCJJEJkI2MmSvr78I9pdwa02xnpC/ke2IyZIg2MZ3KGBiYzF1C1wMHGHiikElEzb3KpHMvHLXdZ3mJo8bmvc/aMepJ8k5MYG7CI09EWPuDNTwlD9YVKB8hj8LJ6usNid9fXIbljxcMt+gGMS/X+PXp2QxkGjWbcE2DPXVAkFQKJ+cDhzwAA75zzDh5d+KjXXV5Y8jd83GnGhKTJwCTyex2LpFjs9JztZsJJQ1c/hoxm5Hm49l8cPyWs43GuwJBL5EB7Bf8mUVxv+igZTSIRJy4UwI5hilLV6DQMo1Uf/GxpfZcBKWollDIRxEQkUiDF9WHtpm9u8hp0O6vD/r6rDzFxkbFxUMqkmJiuwe4AM7Acx2FvXTemZMaJMi4HMmYBABYNuPbveSO+owYAJ3oAW2L5HewNYBLgYEMPCnSqgPuTAsX54cRvTIH9rgneSqepZwD9Q/5n4o+2WqyUUsKQgbVY6aDdsZrh+X3Po2Rdia2twWh27JX+NeLxi6gcMInnc/Voay/e3FaLy+Zme3wwFxTG+Czssc2AoQMTEidgxfgVttVKqavq/EnVu5DXVQec/hDg5f9CnLjoNJYMbAjPFKFSaanSyEty/51n6jRoTf5fQ0LFJQPLpLxyf2wyEB0n6mf/dQnv4b4ka4mon0MQkYDuMmMYa49IKH2wDV0DSBej/9WKB4+0Ka9McQlUrTgHt5xxEIjLEnxo/jI9Ow776rswHICwRV1nPzr6hjBVzP5XK9pMPntUv9PvXfas2QNVs8XHLn2GSAOzHF4bhVRNFHZUd/q1PcdxONggsnqzhSFT4L1eQ6YhfF23SfjBnOBYAzdrUOoPR1t7EaOQIlUTpkxEYv5IeaCFzfWbAQBbGrege7Ab896Y57D+qrYWn6WEj39xGNFyKW47rUDY8Xqj5ELAbARK37Ut+v3c3+OCwguw44odmJHCf+8/X/05Np7zPrD5aaDwTN5HlhiTJKmUYCyyGdhKy/XB40SPOhWy4IvOAsZ5kjNOGcdnYBPF/y5nqbNQurYUzy55VvTPIohwQwHsGKbYYvIdSh9sfVe/OP2vVlImeFzVN+y+H85ZDVYCANpIBrDxGBg2B/R7tpYcTwtHBpYxPgtbvwPnjj/Xr12kEikf8Mbniq40yhjDzNx47DzW4df2zT2DaO8bwsQwBLCzUmcFvI81oCECo1DHP5CWt/j/Papo4QWcwiaalTIB6DzmdtWt396K2zfe7qJeDX0jkOT5Yfb7slZ8dbAZN56chySVwH7b3kgtAdKmAbteASyTghcXX4w/zPsDAODlM1/GnjV7kKXJQtKPfwOG+oClfwzf+IhRh1wqQWKsIqIZ2Kq2PqiVMiSpPIg1qVNxvt7/SbBQsa+4OHf8uZBL5XwG1tIzTxBEcFAAO4ZJiFUgWa0MOgPLcRwfwIqZgdVNxBMtbW5tR9xlv9r627DgzQWux4lkBtaSRd1d418GEQC2H+tAjEKK4rQwKSdnzQHaK/DItNv9257jgJqfec/IMDAzOx4N3QNo6PKt5mz9PU/LjhN5VMClxZfii/O+cFjm7ly1Z1PtJof32erIlLcfb+QmxUIuZTjS5P/D56FGz2qkopAyAYDn9M7OZg9VDh4qTQaGTfj9R/uRlxSL6xa5VzkWlZlrgeb9QPWPLqskTMJPZFX/DOxaB8y72W3LBzG24L1gI5mB7UNecqznSSt1Gk4aCN/47DOwZ+SeAfR3AoY2r5NWBEH4hgLYMU6xRcgpGNr7hjBkNIsbwKZMwpl9Buwrusll1aeVnzq8r+yqxH2b73PZjoEDtJELEjLjo5GmjcKWSv8yiACwtbIDM3Pi/ZLpF4RcS9nfMT+zg23lQF8rkONmskAEZuXGAwB2+lFGvKumEwqpRFQLHSsyiQyZakelRw4cDrUfclh256Y78ebhNzFsGsZHFR85rJNKROgfPwGRSyXIS1KhvNm/CbdW/SDaegfDUkpuI9lzxYg3mmPdi7k8sf4IajoMeHj1ZETJI3CeTL0UUKUCmx5zv36wF/jkVr7C5RTXay8x9kjRKNGsj2wJsdc+cZUOswZGMsTOPelCY98DuzhzMdBmFXCiAJYgQiGkp2PGWAJj7GvGWLnlZ7yH7UyMsT2Wf5/YLR/HGNvKGKtgjL3NGAvNoIsImOJUNcpbeoMyHrd6wIraA6tK4X1Gmw+4rFp3YB2mvTINz+x8BgBvsbOlcYvLdhKJAohNEm+MPmCMYd74RPxc2Q6z2XfzTUffEI4063FSnriluQ6kTQUUaqDKcwCbobKzB7EGurkLRR4Yz4Q0DaLlUj8D2C5MztCIIyzmJxd96mix83X113h066PY07rHZdu7Z98dplEd/xToVCjzs4TYOjE3IZwZ2IQ8QBrYbayTU+GBbztceve/3N+Il36owpqTcjB/fISuX/JoYNGd/Pd9//uO6zgO+PQOoOMosPLvgCIMQlnEqEenjoqYjY5hyIiG7gGPAk4AAKUKTDFyTTAYA1M2D5QdzTscF1gViCkDSxAhEWp6514AGziOKwCwwfLeHf0cx02z/LNvsnscwF84jssH0Ang2hDHQwRIUaoGQ0YzjgVoTwEANR38PlkJIgawjAEpE4GWQ/j30n87rOoa7IKJM+Gl/S95VSWWRMeL6lPqD/PHJ9kCU19sq2oHAMwdJ7LXoz1SGZAz3yEDu/fKvdh40UYAQKYqE1+c9wUWpFsyrtU/8pmZhPCUNcqlEszMicdPR9u8bjdkNKO0vhszst3OpYUVd+eksyIlACzMCM8kwIlAoU6N2o5+9A36zpocahTfC9gFqQxILEAC/J88McQX44sDzXhxc5Vt2dcHm3HbW3swNSsOvz8nuKyuYMy6llca//RXQOM+fplpGPjs17zA06m/BfJOjuwYiVGDTqNEW+8gTH5M1grNsTb+mcSnUredL7xhWNwA9q7v7nJc0F4OMKlP32eCILwTagC7EsA6y+t1AFb5uyPjGxSWAHgvmP0JYSi2ZCeCEXKyBrDZCTGCjskFSwA7P+0kj5s49xU6EBPGQNAD88bz2dSfjrb73HZLZQei5JLwWOjYM24R0F6B/y56Cm+d8xYkTIKk6CQ8fcrTePPsN8EYw3OnPYetl24Bjv0I5C4I68TA4sIklDX3orHbcx/s3rouDBnNtpLjSOIuWP31pl9HYCQnDoU6/npV0eK7D/ZQox6pmijEx4a5sCdlAm7p89+mI61oNpaXpOKRzw/hwn/9hMtf3ILrX9mBQp0K/71qdkQrCQDwQfkF/+ErNF46HXjrcuDvc4EdLwHzbwMW/Say4yNGFcmaKJg5oL03/FnYyjb+ujDOWwYWcAhge4fCJ+gEgG+/ic8FpPLwfi5BnGCEGsDqOI5rtLxuAqDzsF0UY2wHY2wLY2yVZVkigC6Os0nG1gHIcLs3AMbYDZZj7GhtbQ1x2ISV/BQVJAw4EkQf7LG2Pug0SsQoZCKMzA7dRGC4D+iq9rjJbRtvc1kmY/y4CrWRV/vLiItGbmIMfqzwnkHkOA4bj7Rg7rhE0T1MXbD0wc7sacOkxBFRmdNzTkdcVBwA3oQ9pv0o0NsE5J0a1uEtLkwGwKuyemJzWSskDJgX5pLLhxc87LKsobfBZVnPkOP3LC02TbQxnYhYlYj9mXA71NgTPhE0e1KKcWFLDb4+9yOfm26oqYckdTL+esl0/HZ5MfQDRrT3DuFXSwvx3o3zwx98eyI+F7juG6DkAqDlIKBOAy55A1j2UMSrW4jRhU7NK2U3R6CMuLKVdyYIJIDd375fzCG50l5B5cMEIQA+n5AZY98wxva7+bfSfjuOr5fzVDOSw3HcLACXAXiGMRZwRMFx3PMcx83iOG5WcnJyoLsTHoiSSzEuKRaHgsjAVrcbkJMQhr4nq0Jny8GAdjs7+zR8V12HSTpxfUr95ZSiFPxQ0ea1/LGyrQ/V7QYsnZASxpFZSJ0CqNOBI597365sPf+z8Azxx2RHkU4NnUaJ77wEsN+Xt2FaVhy00eGd3XYWcgKAsz88GwCw/th6j/utGL9CtDGdiOQkxkKllKG0vtvrdgPDJlS09IbFSsmFtGkAgGirWIsXUkwmILUEMqkENywejy/vWIwv71iM25cWREa0yRuaNL7X9bbdwNWfAcVnR3pExCgkxeK5HAkv2GNtfUjXRiFa4eO7o07FM21dAIBWg3gJkf1tI8Hx5MTJgNkMdFTyftEEQYSEzwCW47ilHMdNdvPvYwDNjLE0ALD8bPFwjHrLz0oAmwBMB9AOII4xZk3fZQKoD/l/RARMcaomqBLiY+19yEkUuXwYGLFmaD6Ik7yUETvDDfYgwWwOW5+mL86YlIoho9lrALbxMP8VOrU4AgGsRAIULwcqNgDDXuxqytcD6TN4ga0wwhjDqUUp+O5IKwaGXUs0O/uGsK+uC4sKwj/BlR/n+YHkN995LrG8aaqrujbhGamEYXKGBnvrurxut7++G0Yzh+mR6IVO5yfMpE37vG72VvxCQBbt0UKHII5HdBo+AxsJL9iaDgOy/GlpUqUiv59vgdrX5v17KhQx8higtxkwDlD/K0EIQKg1ip8AWGt5vRbAx84bMMbiGWNKy+skAAsAHLRkbDcCuMDb/oT4FKWqUdNh8EsYxYphyIgW/WB4AlilGojLAVoOQiX3Ic5gBzdoCcpHiWH47Nx4xMfIsf5Ak8dtvtjfhOJUNTLjw/B7dUfx2cCwATi60f367nqgbgdQdFZ4x2Xh3Knp6BsyYcMh17myL/Y3wcwBp0/01MkgHlql1u3yOn2dx31ePetVyCQil9+fgEzNisOhxh4MGj33me6u6QIATLN4MIeV2EQgLgeShr0eN7l9xu2Y1FwGZMzge0wJ4gQhSaUEY5HJwNZ2GvzT5FCn2soFvepnhIiUjWSCFVLFSBsUBbAEETKhBrCPATidMVYOYKnlPRhjsxhjL1q2mQBgB2NsL/iA9TGO46y1oPcAuJMxVgG+J/alEMdDBEGRRcipzE9/RWBEwCknMUzWCSkTgZaDuH/e/X7vcq0yFlFpwwAAIaBJREFUk1f7i4ucB6w9MqkEyyam4puDzeh1M1lQ3d6HndWdWDnNYyu4+OQsBKITgH1vuV9f+g4Aju+FiwBz8xKRolbiw92ugeEne+uRlxQbFv9XfznrA8+B/uSkyWEcyYnD1Mw4DJs4HG70fL3aXduJrIRoJFv68cJOxgxIvQSwKmkU0LQPyJwVxkERhPjIpRIkxirCnoEdGDahuWfQvwysOhVcGFq37YX84pXxQOcx/k1cjvgfThAnOCEFsBzHtXMcdxrHcQWWUuMOy/IdHMddZ3n9E8dxJRzHTbX8fMlu/0qO4+ZwHJfPcdyFHMdFxjxsjBOMErFVrj4sGViAF3JqK0e8zL/PK04oxvieNj54HUVqfxfPyULfkAmf7HEV+Hl/Zx0YA1ZNT4/AyCzIFMC0y4DDnwG9TllOsxnY8waQdVLEyrKlEoYLZ2Viw+EWVLaOqEeWN+uxpbIDq6dngB0nojKUfQ2OqZasqrcy4t01XZieFUEl6oyZkHbXeFwt62kCTENA5uwwDoogwkOKOgotYc7A1ll86f2y9VOnOQi2/Gnrn0QZ04Bx5Hdw39z7gE5LBnaUTKoTxPFMmGVOidFIVnwMYhRSHA4ggK1u59X+wiLiBPAZWM4EtJX5tXlqbCovljBK+l+tTM+Kw4Q0Ddb9dAxmO588w5ARr26pxmnFKUjTiuir6w8z1gJmI7DdqSCi/Cv+9z/rmsiMy8JV88dBIZXguW9HRHKe+7YCUXIJLj8pcjPbsXL/vwvU+xo86doo6DRKbKvqcLu+ur0Pjd0DmJkTwQA2c7ZXJ1hpx1HLdnPCMhyCCCcpGmXYM7C1nQHY+ql0iLW7/75x+A1RxvTbH35re61WqPkSYnUaII8S5fMIYixBASwBiYShUKfG4QCsdCpaepGkUkIbE6bsps4idNLsnxLxhQUXjMoAljGGG0/Ow5FmPT7YPaJZ9tLmKnQahnHTKaOgXze5EJhwLvDz30aysCYjsPFhQJsNTD4vssNTK3HdonH4cHc93txWg3e21+KTvQ24flEeEiJoO3LFhCv83jYhKvLexMcrjDEsGJ+En462O0wCWdlczltVLSwIr5WSAxkzwRQqJDD356Os9TAv3qQOf782QYiNTh0V9h7YWktbU5Y/+hFKFXRS8avHGvsaHRd0HqPyYYIQCApgCQB8GfGRJj14bS3flLX02jwZw0JiPiCRAy0H8ZdT/uJ1071X7sVibQEw2AMkF4VpgP6zYko6pmXF4Y//O4ADDd34saINz31bgXOmpGFmzigJbE77A2AaBt6/FujvAr6+H2gqBc54eFSUZN92WgFOykvAfR+U4u7392HOuAT88tTIWhP8Ysov8NCCh/Da8td8bhtItpZwZUF+Ejr6hnDIzaTbD+VtyIiLRp4vL0gxkcqB3IX4rsvsdrW2pQwYH14fZYIIFykaJdp6B2FyM8EkFrUdBihlEv/73tWpSECY2zg6q0nAiSAEggJYAgAv5NRpGEarH2U/HMeholmPgpQwBrBSOZBUCDQfwNKcpV43lTDJiGdsyoQwDC4wJBKG5y6djmi5FGf/9Qdc/uJWZCfG4MGVo0jUJykfWPEscOwH4PEcYMvfgdnX85nZUYBSJsUr18zFXy+djmcvmYbXrp0bcd9MuVSOVfmroJT6foA6O488NEPBml39vqzNYfnAsAk/VrRhUUFS5Huh804FOirx3/l/wt2z77YtnhSbiUV9vcD4JREcHEGIR4omCmYOaO8NXxmx1ULH7++9OhUKPyfsg2HQ5PR/Nw4CPfVAPGVgCUIISEWEADCiRHyoSW8zIvdEY/cA+oZMyNepwzG0EdKnAWXrAS83nW8v/JZ/0XKY/5k8+gJYAMhKiMHnty/C29troZRJcPHsLKijIp/ZdGDapUBSAXDkCyBtKjBhBRDpoMAOhUyCc6dGUPDKA0XxRViVvwofVXzkdv3689fzkyxE0Og0UZiSqcWn+xocyu43HWmFftCI5SVpERydhaIzgS/vwcymI5i56NdYlb8Kg6ZBJH12D6/0PW5xpEdIEKKQYsmCNvcM+nyeEIrajn7/+l+tqFMR1V0Nr83qIdBqGPF7X5azDOiuA8BRBpYgBIKeoggAQHEqbz1yuNF3H6zVbqcwnBlYgLecMLSNSNG7ITkmmX/RcghQ6XhPxlFKkkqJX56aj+sW5Y2+4NVK5izgtP8DJp47qoLX0QxjDBcUurcZkkvkSFeNvqD7eGTltAwcaOhxsP/6cHcdEmMVmD9+FHzv43OBrLnAvncAjoNaoUYSk/MTQhNXjopSfIIQA50laG3Rh6cPluM41HYYkBUfgACiSgeFadj2tm+4T9gx2ekcn557OlnoEITAUABLAAASYhXIiIvGntoun9tWtPD2JQXhzsBaLSfqdvjetuUgkFws7ngIwgODRvelcydnnhzmkZy4rJyWDqVMgue/rwQAVLb24quDzbhodhZk0lFya5u+Bmg9DJR/zb/f/hIw3AfMvCqiwyIIMdFpRjKw4aC7fxj6QaN/HrBW1GmYPjASYJd3lgs6Jhc9EWsASyXEBCEIVEJM2JiVG4+fjraD4zivfSQHG3qQrFaGX/E1eQIgjwXqttsWbbxoI6Jl0fjLzr+MlGWazUDrEWDGleEdH0FYKEpwLx7GIXyiJic6SSol1pyUg//8WIWF+Ul4fWs1YhUyXL0gN9JDG2HqJcDmJ4Ev7wU4M/D9k0DBGXw7BEGcoCSplGAsfBnY2g6rB2xgJcS/7OzG2xp+Il4ucEWEy7W+qxqQKngbHYIgQmaUTFMTo4FZOfFo1Q/abgae2FffjamZ2jCNyg6pDMiY4RDAJkUnIVYei9+f9Hv8dq7Fc629gs9ypI4iUSRiTKFVarHvyn14+5y3HZabOfeqtERw3HF6IQp1atzx9h7sqO7EI6snI0U9ijwWpXJg1T958ZY3LwaiNMDZT0V6VAQhKnKpBImxirBlYGsCsdCxok6FyjxyPZZLhA1ge4d6R95w4DOw2ixAElmxQYI4UaAAlrBhtXDZUd3hcZu+QSOOtvaiJCMuTKNyInMW0LQPy3PO8LxN/U7+Z8as8IyJINzAGMPExIn4bPVnNtscf22qCP9QKWX44Ob5eO7S6fjs1kVYOS0j0kNyJWc+cNNPwHkvADf+CMRlRXpEBCE6yeootITJC7a20xLAJgTQA6tOg33IyiCsxsMt395iez07dTZZ6BCEwFAAS9goSlVDrZRhR3Wnx20ONPSA44CSTE0YR2ZH1lzAbMRj2Suw78p97rep3wkoVLyCLkFEmGxNNh5d+Gikh3HCEqOQYcXUdExMj9A1yR8SxwNTLhrVonIEISQ6jRItftjyCUFNhwHxMfLAxBBVOgCARsK3Qv3mu98IOqa2/hGLr8ToRL6EmPpfCUIwKIAlbEglDNNz4rGtynMGdl9dFwBgckYESogBIGcBwKRgVd957tOt3wmkT6dSHWLUYOuHIiFngiDGAClqJZrDlYG1eMAGhFIFKNR4SF0CAKjsrhRhZECeNg8Y6Ab6O0mBmCAEhAJYwoHFBUmoaOlFfZf7PtitVR3IToiJXJ9ZlIZXIz660f36IQPQVMr3yhLEaMESv0rokksQxBhAp4lCW+8gTGbx2yaCCmABQJ0KWX+X4OPZ07LH9jpPm8eXDwNUQkwQAkJPU4QDJxfyPqqbjrS4rDOZOWytbMe8vAiXwY0/FWjYDRjcZIprfgbMw0Du4vCPiyA8YAYvFuJN3ZsgCOJEIUUTBTMHtPeKW0ZsMnOo7+oPTMDJijoVkv6RlimhNAqe2PGE7fVlEy4jCx2CEAEKYAkH8lNUyIiLxqYjrS7rDjX2oGfAiHnjIx3ALgHAARUbXNdVbgIkciBnXrhHRRAemZM6B+mx6bhhyg2RHgpBEITopKjD4wXb3DOAYROH7CAzsPYBbFlnmSBjMpqNttfZ6my+/xWgDCxBCAgFsIQDjDGcNiEFm8tboR8Ydlj3zaFmMAbMz49wAJsxC1CnA/vfd11XsYEXelLEhn9cBOEBrVKL9Resx8TEiZEeCkEQhOjoNHybkdhesDYLnUAUiK2odDDaBbAmziTImOzt0nSxOr6EWKkFouMFOT5BEBTAEm5YNT0DA8NmfFHa5LD8i9ImzM5JiLzPokQCTD4PqPjGsYy45TDQcgCYsCJyYyMIgiCIMU64MrC1wXjAWlGnwcCNZEttYnshYp+BBcCXEMdnC3JsgiB4KIAlXJieFYe8pFi8vq3G1hOyr64LR5r1OHtKWoRHZ2HKRXyv6+5XR5btfRNgEmDS6siNiyAIgiDGOMmWAFbsDGxthwESBqTHBZGBVaei306XQKge2IquCscFXeQBSxBCQwEs4QJjDNctysPe2i58e5gXc/rXd0ehUspw3oyMCI/OQtpUIO8U4Ke/AQM9vET9jpeB4nMAtS7SoyMIgiCIMYtcKkFirEL8DGxnP9K00VDIgnicVafizD6D7a196W+wHO447LjAbAa6ashChyAEhgJYwi0XzspEXnIs7nm/FA9/ehCflzbh+kV5gRmFi82S/wMMbcC7a4F3rwKG+4CT74n0qAiCIAhizJOiiUJrGDKwQfW/AoA6DdF2WVchSogv/fRSxwW9zYBxgDKwBCEwFMASbpFLJfj3FTOhkDK8+EMVlk3U4aZTxkd6WI5kzgKWPwlUfQ8c+wE4+ykgdXKkR0UQBEEQYx6dRil6BramwxBc/ysAqByrtUzm0EWcjJxT/yspEBOEKMgiPQBi9FKgU2PTXaeirXcwuP6ScDD7Wr7nlTFS+CMIgiCIUUKKWomDDT2iHX9g2IQW/SCygrHQAQClClCobW/XfrkWpWtLBRqdBasHLJUQE4SgUAaW8IpCJhm9wauVmAQKXgmCIAhiFKHTRKGtdxAmszDiSM7UdfL9q0F5wFpRpzq8HTYNe9gwSDotGdg4UiEmCCGhAJYgCIIgCIIQlBS1EmYOaO8Vp4y4tqMfQJAesFacAthbv701lCG50lUNqNMAeYTtBwniBIMCWIIgCIIgCEJQUjR80NaiFyeArbF6wAqYgf2x4cdQhuRK5zHqfyUIEaAAliAIgiAIghCUFIsXbHOPOErEtR0GRMklSFYpgz+ISodrevqEG5QdN0y5gS8hpv5XghAcCmAJgiAIgiAIQdGJnIGt7TQgMz4GjLHgD6JOw3K9OEJTt5bcAPTUA/EUwBKE0FAASxAEQRAEQQhKssgZ2JqO/tAEnAC+hFgEjamLiy4GuusAcFRCTBAiQAEsQRAEQRAEIShyqQSJsQpRvGA5jkNdhwFZ8SG6JKhTEUL+1iOJ0YlkoUMQIkI+sARBEARBEITgpGii0KoXPgPbZRiGftAYmoATAKjTkGgyCTKmd8vetb0+2H4QGFLwbygDSxCCQxlYgiAIgiAIQnBS1EpRMrC1nQIoEAOASodEsxkXa4pDHtODPz9oez01eSpvoSNV8DY6BEEICgWwBEEQBEEQhODoNEq0iJCBtVrohNwDq1QBCjXShUnC2jhr3Fl8CXFcNiChR22CEJqQvlWMsQTG2NeMsXLLz3g325zKGNtj92+AMbbKsu6/jLEqu3XTQhkPQRAEQRAEMTpIUUehVT8Ik1lYpaTajn4AAmRgAUCtg2bIEPpx7MhQZZCFDkGISKjTQvcC2MBxXAGADZb3DnAct5HjuGkcx00DsASAAcBXdpvcZV3PcdyeEMdDEARBEARBjAJ0GiXMHNDeK2wZcW2nAfExcqiUAki5qNMgGei2vd1QsyHgQ3CcmwC9q5osdAhCJEINYFcCWGd5vQ7AKh/bXwDgC47jhJ3qIgiCIAiCIEYVKSJ5wdZ2GITJvgKAOhVRAyNesHdsvANV3VUBHcJewAkAMNAN9HdSBpYgRCLUAFbHcVyj5XUTAJ2P7S8B8KbTskcYY/sYY39hjCk97cgYu4ExtoMxtqO1tTWEIRMEQRAEQRBio7MEsI3dwvbB1nYYQu9/taJOxbL2JodFhuHA8iwPbXnIcUFnNf8zYVwoIyMIwgM+A1jG2DeMsf1u/q20347j6yc8NjkwxtIAlABYb7f4PgDFAGYDSABwj6f9OY57nuO4WRzHzUpOTvY1bIIgCIIgCCKCpGutAWy/YMc0mTnUdfYLF8BqMiEzDeLKggtti8ycObRjWj1gyUKHIETBZ/MAx3FLPa1jjDUzxtI4jmu0BKgtXg51EYAPOY4btju2NXs7yBh7GcBv/Bw3QRAEQRAEMYpJUikhlzI0dAmXgW3s7ofRzAlXQqzNBACYB0fKiM0QKIClEmKCEIVQS4g/AbDW8notgI+9bHspnMqHLUEvGGMMfP/s/hDHQxAEQRAEQYwCJBKGVG0UGrqEy8AKZqFjRZsBAHiv7lvbIreiTH4yL20eH8BGxQHRcaGNjSAIt4QawD4G4HTGWDmApZb3YIzNYoy9aN2IMZYLIAvAd077v84YKwVQCiAJwMMhjocgCIIgCIIYJaRrowUtIa4VPIDNAgAMmG0Fgmg2NAd9uOeXPc8HsFQ+TBCiEZL+OMdx7QBOc7N8B4Dr7N4fA5DhZrsloXw+QRAEQRAEMXpJj4vGtqoOwY5X02GAVMKQZumvDZmYREDmeKzffPcbnJF7hl+7723d67qw8xiQWiLA4AiCcEeoGViCIAiCIAiCcEt6XBSaegZgMgdflmtPbUc/MuKiIZMK9AjLGKDJwDnShKB231iz0XGB2QR01VAGliBEhAJYgiAIgiAIQhTStNEwmTm0CuQFWyOkhY4VbQamDRmD2vWl/S85LuhpAMzDFMAShIhQAEsQBEEQBEGIQkZcNACgXiAhp9oOg3AKxFa0WTD3dwpzLLLQIQjRoQCWIAiCIAiCEIW0OOG8YHsHjWjvG0JWQnTIx3JAkwHzoN5hUd9wX8CHSYlOAbqq+TfxZKFDEGJBASxBEARBEAQhCumWDKwQVjqCKxBb0WZilV6PuUlTbYse+OkBj5u/W/YuTn77ZBe7HYlEwmdgmcSmbkwQhPBQAEsQBEEQBEGIgiZKDpVShoaugZCPJV4Am4FYjsOLk39pW3Ss55jHzR/8+UF0DHSgqa/JYfnS7KV8AKvNBKRyYcdIEIQNCmAJgiAIgiAI0UiPixIkA1sjWgBryZZ219kWtRhafO4mlUgd3v9m1m/IA5YgwgAFsARBEARBEIRopGmj0dgtTAZWHSWDNlrg7KYmg//ZMxLAdgx0oGPAu3+tyWyyvZZL5HxASwEsQYgOBbAEQRAEQRCEaKTHRQuWgc1OiAFjTIBR2aFUAVFxDhlYADj57ZO97rbs/WW21xw4YLAX6GulAJYgRIYCWIIgCIIgCEI00rVRaO8bwsCwyffGXhDFA9aKNhPorg9694XpC+0UiHOFGRNBEG6hAJYgCIIgCIIQDSGUiM1mDrWd/cJ7wFrRZgLdtUHv/sTJT4x4wMblCjIkgiDcQwEsQRAEQRAEIRrZiXzQWdsZfADb2DOAIaMZOYkiBbDxuUBnNd5b8Z7D4se3Pe7X7lGyKKD9KP8mYZzAgyMIwh4KYAmCIAiCIAjRsJb9WlWEg+FYWx8AYFxSrCBjciE+FxjSo0iZ5LD4tUOv+X+MjqNAdAIQkyDs2AiCcEAW6QEQBEEQBEEQJy7JKiWUMonNxzUYKsMRwAIjZcBuGDQN4t0j73o+RvtRIHG8oMMiCMIVysASBEEQBEEQoiGRMGQlxKCmPbQMbLRcCp06SsCR2RGXw//srHJZpR/SAwBeLH0Rj293LSm+sPBC/kX7USAxX5zxEQRhgwJYgiAIgiAIQlSyE2JCKiGuautDblIsJBKBLXSsxFsD2GMuq+a/OR9dA134195/ud1VrVADQ32AvoEysAQRBiiAJQiCIAiCIEQlOyEGtR0GcBwX1P7H2vowLkkkAScAUMQCsSkeS4jXfLHG+/4dlfzPBApgCUJsKIAlCIIgCIIgRCUrIQb6QSO6DMMB72s0mVHTYRCv/9VKfC7QeQxpsWkuq471HPO4m0quAtor+DdUQkwQokMBLEEQBEEQBCEqoSgR13X2w2jmkJsYjgC2GkqpMqDdrpp0lZ2FTp7gwyIIwhEKYAmCIAiCIAhRCSWArbIoEOclhyGA7akD4H+Z8/bLt0MulfMBrDoNUKpEGx5BEDwUwBIEQRAEQRCikpUQDSC0ADYsGVjOjNzoFL93iZJZVJE7jlL/K0GECQpgCYIgCIIgCFGJUciQpFIGZaVT1dYHdZQMCbEKEUZmh8UL9uHc1YHv214BJFL5MEGEAwpgCYIgCIIgCNHJTYxBVXtfwPuVt+iRn6ICYyJZ6Fix9K9qe5pQurbU//362gBDO5BUJNLACIKwhwJYgiAIgiAIQnTGJ6tQ2dob8H7lzb0oTFGLMCIn1KmAQg20HgEA3Db9Nv/2aznE/0wpFmlgBEHYQwEsQRAEQRAEITr5KSq09Q6hyzDk9z7tvYNo7xtCgS4M4kiMAcmFQFsZAEDC/HxMbj3M/0yeINLACIKwhwJYgiAIgiAIQnTGp/AiTEcDyMKWNfPbFurCkIEFgKRCoK08sH1aDgFKDaBJF2dMBEE4QAEsQRAEQRAEITrjk/ks6tEW//tgy1v0AMIcwOobgIEecP7a6bQeBpKL+QwuQRCiQwEsQRAEQRAEITqZ8TFQyCSoCCgDq4c6SgadRiniyOxIKuR/tpcjShrlddNvL/wW4Dg+A0v9rwQRNmSRHgBBEARBEARx4iOVMOQlxeJoS2AlxIU6tfgKxFaSLUrCrWW4eMrFkElkkEvkeODnB1w3jUkGeluA/g7qfyWIMEIZWIIgCIIgCCIsjE9W+d0Dy3Ecypv1KAyHgJOV+FxAIgPayiCXyHFJ8SU4v/B8fLzyY8Qr4yGT8Lmfcdpx/PakQEwQYYcysARBEARBEERYGJ+iwhf7GzEwbEKUXOp126aeAXQahlEUrv5XAJDKgYTxNiViK3lxefj+ku/R2NuINw+/iTtm3sGvaN7P/0yZFL4xEsQYJ6QMLGPsQsbYAcaYmTE2y8t2ZzLGjjDGKhhj99otH8cY22pZ/jZjTBHKeAiCIAiCIIjRS3GqGmaO93b1RWldNwCgJFMr9rAc0U0EmkrdrkpTpeHOWXeOWOw07AHUaYBaF77xEcQYJ9QS4v0AzgPwvacNGGNSAH8HcBaAiQAuZYxNtKx+HMBfOI7LB9AJ4NoQx0MQBEEQBEGMUialawAA+xu6fW67v6EHEgZMTAtzAJs6BeiqBvo7fW/buBdImyb6kAiCGCGkAJbjuEMcxx3xsdkcABUcx1VyHDcE4C0AKxnfjb8EwHuW7dYBWBXKeAiCIAiCIIjRS1Z8DNRKGQ74E8DWdyM/RYVohfdSY8FJm8L/9JCFtTHYy5cap08TfUgEQYwQDhGnDAC1du/rLMsSAXRxHGd0Wk4QBEEQBEGcgEgkDBPTNTjQ0ONz2/313ZicEebsKwCkTuV/Nu7zvl3zfgAcZWAJIsz4FHFijH0DINXNqt9xHPex8EPyOI4bANxgedvLGPOV+Y0kSQDaIj0IggCdi8TogM5DYjRA5+Eog/3S9zbbAfzlYtGH4p4/3grgVj+2OyvQI9O5SIwGjofzMMfdQp8BLMdxS0P84HoAWXbvMy3L2gHEMcZkliysdbmncTwP4PkQxxIWGGM7OI7zKGpFEOGCzkViNEDnITEaoPOQGC3QuUiMBo7n8zAcJcTbARRYFIcVAC4B8AnHcRyAjQAusGy3FkDYMroEQRAEQRAEQRDE8UWoNjqrGWN1AOYB+Iwxtt6yPJ0x9jkAWLKrtwBYD+AQgHc4jjtgOcQ9AO5kjFWA74l9KZTxEARBEARBEARBECcuPkuIvcFx3IcAPnSzvAHAcrv3nwP43M12leBVik80jotSZ2JMQOciMRqg85AYDdB5SIwW6FwkRgPH7XnI+EpegiAIgiAIgiAIghjdhKMHliAIgiAIgiAIgiBChgLYEGGMFTHG9tj962GM3cEYS2CMfc0YK7f8jI/0WIkTFy/n4ROMscOMsX2MsQ8ZY3GRHitxYuPpXLRb/2vGGMcYS4rgMIkTHG/nIWPsVst18QBj7M8RHipxAuPl3jyNMbbFsmwHY+xEbKcjRhGMsV9Zrnn7GWNvMsaiLAK7WxljFYyxty1iu8cFVEIsIIwxKXgroLkAfgmgg+O4xxhj9wKI5zjunogOkBgTOJ2HRQC+5TjOyBh7HADoPCTChf25yHFcNWMsC8CLAIoBzOQ4brT7zxEnAE7XxDwAvwNwNsdxg4yxFI7jWiI6QGJM4HQevgDgLxzHfcEYWw7gbo7jTonk+IgTF8ZYBoAfAEzkOK6fMfYOeG2i5QA+4DjuLcbYvwDs5Tjun5Ecq79QBlZYTgNwlOO4agArAayzLF8HYFWkBkWMOWznIcdxX1mUwAFgC3i/ZYIIF/bXRAD4C4C7AdDMKRFO7M/DmwA8xnHcIABQ8EqEEfvzkAOgsSzXAmiI2KiIsYIMQDRjTAYgBkAjgCUA3rOsP65iFQpgheUSAG9aXus4jmu0vG4CoIvMkIgxiP15aM81AL4I81iIsY3tXGSMrQRQz3Hc3sgOiRiD2F8TCwEsspTNfccYmx3BcRFjC/vz8A4ATzDGagE8CeC+SA2KOPHhOK4e/HlWAz5w7QawE0CXXZKjDkBGZEYYOBTACoSlbvxcAO86r+P4Om3KOBCi4+k8ZIz9DoARwOuRGBcx9rA/FxljMQB+C+D+yI6KGGu4uSbKACQAOAnAXQDeYYyxCA2PGCO4OQ9vAvArjuOyAPwKwEuRGhtx4mPR4VkJYByAdACxAM6M6KBChAJY4TgLwC6O45ot75sZY2kAYPlJZUpEOHA+D8EYuwrAOQAu56jpnQgf9ufiePA3zr2MsWPgS9l3McZSIzg+YmzgfE2sA9/zxXEctw2AGQAJihFi43wergXwgeX1uwBIxIkQk6UAqjiOa+U4bhj8ubcAQJylpBjg78v1kRpgoFAAKxyXwrFs8xPwFyhYfn4c9hERYxGH85Axdib4nsNzOY4zRGxUxFjEdi5yHFfKcVwKx3G5HMflgg8iZnAc1xTJARJjAud780cATgUAxlghAAUAEhMjxMb5PGwAcLLl9RIA5WEfETGWqAFwEmMsxlJxchqAgwA2ArjAss1xFauQCrEAMMZiwZ8ceRzHdVuWJQJ4B0A2gGoAF3Ec1xG5URInOh7OwwoASgDtls22cBx3Y4SGSIwR3J2LTuuPAZhFKsSEmHi4JioA/AfANABDAH7Dcdy3ERskccLj4TxcCOBZ8CXtAwBu5jhuZ+RGSZzoMMb+COBi8O1kuwFcB77n9S3wbRW7AVxhFbgb7VAASxAEQRAEQRAEQRwXUAkxQRAEQRAEQRAEcVxAASxBEARBEARBEARxXEABLEEQBEEQBEEQBHFcQAEsQRAEQRAEQRAEcVxAASxBEARBEARBEARxXEABLEEQBEEQBEEQBHFcQAEsQRAEQRAEQRAEcVxAASxBEARBEARBEARxXPD/QY4mNJjt1PEAAAAASUVORK5CYII=\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(net) as sim:\n", + " sim.run(sim_t)\n", + "\n", + "# we'll break up the output into multiple plots, just for\n", + "# display purposes\n", + "t_per_plot = 10\n", + "for i in range(sim_t // t_per_plot):\n", + " plot_slice = (sim.trange() >= t_per_plot * i) & (\n", + " sim.trange() < t_per_plot * (i + 1)\n", + " )\n", + "\n", + " plt.figure(figsize=(16, 6))\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_stim][plot_slice], label=\"input\")\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_ideal][plot_slice], label=\"ideal\")\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_out][plot_slice], label=\"output\")\n", + " if i * t_per_plot < sim_t * 0.8:\n", + " plt.title(\"Learning ON\")\n", + " else:\n", + " plt.title(\"Learning OFF\")\n", + " plt.ylim([-1, 1])\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the network is successfully learning to compute a delay.\n", + "We could use these same principles to train a network\n", + "to compute any time-varying function of some input signal,\n", + "and an LMU will always provide\n", + "an optimal representation of that input signal.\n", + "\n", + "See these other examples for some other applications of LMUs:\n", + "\n", + "- [State of the art performance on the psMNIST task using LMUs in NengoDL](\n", + "https://www.nengo.ai/nengo-dl/examples/lmu.html)\n", + "\n", + "\n", + "As well as [the original paper][paper] for more information on LMUs.\n", + "\n", + "[paper]:\n", + "https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/networks/basal-ganglia.ipynb b/.doctrees/nbsphinx/examples/networks/basal-ganglia.ipynb new file mode 100644 index 0000000000..45c0643818 --- /dev/null +++ b/.doctrees/nbsphinx/examples/networks/basal-ganglia.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The basal ganglia\n", + "\n", + "The basal ganglia\n", + "according to [Stewart 2010](\n", + "http://compneuro.uwaterloo.ca/files/publications/stewart.2010.pdf)\n", + "is an action selector\n", + "that chooses whatever action has the best \"salience\" or \"goodness\".\n", + "Its really interesting behaviour manifests itself\n", + "when it interacts with the thalamus and other components of the brain,\n", + "but in this example we will only show the basal ganglia's basic behaviour.\n", + "It will choose between three actions\n", + "that we'll pretend are \"eating\", \"sleeping\" and \"playing\"." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.141750Z", + "iopub.status.busy": "2020-11-25T16:50:41.140864Z", + "iopub.status.idle": "2020-11-25T16:50:41.635135Z", + "shell.execute_reply": "2020-11-25T16:50:41.635584Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Network\n", + "\n", + "Here we create the basal ganglia and the action input node." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.644344Z", + "iopub.status.busy": "2020-11-25T16:50:41.642908Z", + "iopub.status.idle": "2020-11-25T16:50:41.819549Z", + "shell.execute_reply": "2020-11-25T16:50:41.820069Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Basal Ganglia\")\n", + "with model:\n", + " basal_ganglia = nengo.networks.BasalGanglia(dimensions=3)\n", + "\n", + "\n", + "class ActionIterator:\n", + " def __init__(self, dimensions):\n", + " self.actions = np.ones(dimensions) * 0.1\n", + "\n", + " def step(self, t):\n", + " # one action at time dominates\n", + " dominate = int(t % 3)\n", + " self.actions[:] = 0.1\n", + " self.actions[dominate] = 0.8\n", + " return self.actions\n", + "\n", + "\n", + "action_iterator = ActionIterator(dimensions=3)\n", + "\n", + "with model:\n", + " actions = nengo.Node(action_iterator.step, label=\"actions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Connect the Network\n", + "\n", + "Connect the input to the basal ganglia and connect the probes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.826012Z", + "iopub.status.busy": "2020-11-25T16:50:41.825510Z", + "iopub.status.idle": "2020-11-25T16:50:41.828561Z", + "shell.execute_reply": "2020-11-25T16:50:41.828956Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(actions, basal_ganglia.input, synapse=None)\n", + " selected_action = nengo.Probe(basal_ganglia.output, synapse=0.01)\n", + " input_actions = nengo.Probe(actions, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Simulate the Network and Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.836860Z", + "iopub.status.busy": "2020-11-25T16:50:41.836007Z", + "iopub.status.idle": "2020-11-25T16:50:45.393115Z", + "shell.execute_reply": "2020-11-25T16:50:45.392223Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " # This will take a while\n", + " sim.run(6)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:45.415990Z", + "iopub.status.busy": "2020-11-25T16:50:45.408677Z", + "iopub.status.idle": "2020-11-25T16:50:45.692484Z", + "shell.execute_reply": "2020-11-25T16:50:45.692010Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[input_actions].argmax(axis=1))\n", + "plt.ylim(-0.1, 2.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.title(\"Index of actual max value\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[selected_action].argmax(axis=1))\n", + "plt.ylim(-0.1, 2.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.title(\"Basal ganglia selected max value\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the maximum index\n", + "is found at 0, then 1, then 2\n", + "or \"eating\", \"sleeping\", then \"playing\".\n", + "Note that if you zoom in enough on the basal ganglia values,\n", + "you'll be able to see a bit of a delay between finding max values.\n", + "If you read the aforementioned paper,\n", + "you'll see that this is expected and matches previous experiments." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/networks/ensemble-array.ipynb b/.doctrees/nbsphinx/examples/networks/ensemble-array.ipynb new file mode 100644 index 0000000000..727fed3e2e --- /dev/null +++ b/.doctrees/nbsphinx/examples/networks/ensemble-array.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ensemble array\n", + "\n", + "An ensemble array is a group of ensembles\n", + "that each represent a part of the overall signal.\n", + "\n", + "Ensemble arrays are similar to normal ensembles,\n", + "but expose a slightly different interface.\n", + "Additionally, in an ensemble array,\n", + "the components of the overall signal are not related.\n", + "As a result, network arrays cannot be used\n", + "to compute nonlinear functions that mix the dimensions they represent." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:47.036723Z", + "iopub.status.busy": "2020-11-25T16:50:47.034583Z", + "iopub.status.idle": "2020-11-25T16:50:47.528328Z", + "shell.execute_reply": "2020-11-25T16:50:47.527819Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:47.546842Z", + "iopub.status.busy": "2020-11-25T16:50:47.546314Z", + "iopub.status.idle": "2020-11-25T16:50:47.549499Z", + "shell.execute_reply": "2020-11-25T16:50:47.549895Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Ensemble Array\")\n", + "with model:\n", + " # Make an input node\n", + " sin = nengo.Node(output=lambda t: [np.cos(t), np.sin(t)])\n", + "\n", + " # Make ensembles to connect\n", + " A = nengo.networks.EnsembleArray(100, n_ensembles=2)\n", + " B = nengo.Ensemble(100, dimensions=2)\n", + " C = nengo.networks.EnsembleArray(100, n_ensembles=2)\n", + "\n", + " # Connect the model elements, just feedforward\n", + " nengo.Connection(sin, A.input)\n", + " nengo.Connection(A.output, B)\n", + " nengo.Connection(B, C.input)\n", + "\n", + " # Setup the probes for plotting\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A.output, synapse=0.02)\n", + " B_probe = nengo.Probe(B, synapse=0.02)\n", + " C_probe = nengo.Probe(C.output, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:47.555746Z", + "iopub.status.busy": "2020-11-25T16:50:47.554945Z", + "iopub.status.idle": "2020-11-25T16:50:50.730511Z", + "shell.execute_reply": "2020-11-25T16:50:50.730951Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:50.737109Z", + "iopub.status.busy": "2020-11-25T16:50:50.736321Z", + "iopub.status.idle": "2020-11-25T16:50:50.940064Z", + "shell.execute_reply": "2020-11-25T16:50:50.939434Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[sin_probe])\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.plot(sim.trange(), sim.data[B_probe])\n", + "plt.plot(sim.trange(), sim.data[C_probe])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots demonstrate that the ensemble array\n", + "works very similarly to a standard N-dimensional population.\n", + "However, this is not true when it comes to computing functions.\n", + "Ensemble arrays cannot be used\n", + "to compute nonlinear functions that mix the dimensions they represent." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/networks/integrator-network.ipynb b/.doctrees/nbsphinx/examples/networks/integrator-network.ipynb new file mode 100644 index 0000000000..78ab3b0616 --- /dev/null +++ b/.doctrees/nbsphinx/examples/networks/integrator-network.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrator\n", + "\n", + "This demo implements a one-dimensional neural integrator.\n", + "\n", + "This is the first example of a recurrent network in the demos.\n", + "It shows how neurons can be used to implement stable dynamics.\n", + "Such dynamics are important for memory, noise cleanup,\n", + "statistical inference, and many other dynamic transformations.\n", + "\n", + "When you run this demo, it will automatically\n", + "put in some step functions on the input,\n", + "so you can see that the output is integrating\n", + "(i.e. summing over time) the input.\n", + "You can also input your own values.\n", + "Note that since the integrator constantly sums its input,\n", + "it will saturate quickly if you leave the input non-zero.\n", + "This makes it clear that neurons have a finite range of representation.\n", + "Such saturation effects can be exploited\n", + "to perform useful computations (e.g. soft normalization)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.105687Z", + "iopub.status.busy": "2020-11-25T16:50:52.104804Z", + "iopub.status.idle": "2020-11-25T16:50:52.598006Z", + "shell.execute_reply": "2020-11-25T16:50:52.597511Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of one recurrently connected ensemble,\n", + "and an input population." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.605371Z", + "iopub.status.busy": "2020-11-25T16:50:52.604866Z", + "iopub.status.idle": "2020-11-25T16:50:52.608053Z", + "shell.execute_reply": "2020-11-25T16:50:52.608452Z" + } + }, + "outputs": [], + "source": [ + "tau = 0.1\n", + "\n", + "integrator = nengo.networks.Integrator(tau, n_neurons=100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a piecewise step function as input,\n", + "so we can see the effects of recurrence." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.614140Z", + "iopub.status.busy": "2020-11-25T16:50:52.612656Z", + "iopub.status.idle": "2020-11-25T16:50:52.614776Z", + "shell.execute_reply": "2020-11-25T16:50:52.615188Z" + } + }, + "outputs": [], + "source": [ + "with integrator:\n", + " input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.620954Z", + "iopub.status.busy": "2020-11-25T16:50:52.619108Z", + "iopub.status.idle": "2020-11-25T16:50:52.621486Z", + "shell.execute_reply": "2020-11-25T16:50:52.621898Z" + } + }, + "outputs": [], + "source": [ + "# Connect the input\n", + "with integrator:\n", + " nengo.Connection(input, integrator.input, synapse=tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.627541Z", + "iopub.status.busy": "2020-11-25T16:50:52.626065Z", + "iopub.status.idle": "2020-11-25T16:50:52.628176Z", + "shell.execute_reply": "2020-11-25T16:50:52.628578Z" + } + }, + "outputs": [], + "source": [ + "with integrator:\n", + " input_probe = nengo.Probe(input)\n", + " integrator_probe = nengo.Probe(integrator.ensemble, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.633661Z", + "iopub.status.busy": "2020-11-25T16:50:52.632900Z", + "iopub.status.idle": "2020-11-25T16:50:53.414720Z", + "shell.execute_reply": "2020-11-25T16:50:53.415146Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(integrator) as sim:\n", + " # Run it for 6 seconds\n", + " sim.run(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:53.421312Z", + "iopub.status.busy": "2020-11-25T16:50:53.420138Z", + "iopub.status.idle": "2020-11-25T16:50:53.633156Z", + "shell.execute_reply": "2020-11-25T16:50:53.632721Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[integrator_probe], label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[input_probe], \"k\", label=\"Input\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows the response to the input by the integrator.\n", + "Because it is implemented in neurons,\n", + "it will not be perfect (i.e. there will be drift).\n", + "Running several times will give a sense of\n", + "the kinds of drift you might expect.\n", + "Drift can be reduced by increasing the number of neurons." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/quirks/config.ipynb b/.doctrees/nbsphinx/examples/quirks/config.ipynb new file mode 100644 index 0000000000..36b3077cac --- /dev/null +++ b/.doctrees/nbsphinx/examples/quirks/config.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context matters, membership doesn't\n", + "\n", + "When you create a Nengo object\n", + "and leave the parameters as their default values,\n", + "we determine the default values dynamically\n", + "based on `Config` objects.\n", + "Every `Network` has an associated `Config` object,\n", + "and you can make new `Config` objects for additional flexibility.\n", + "\n", + "Nengo keeps track of the current context.\n", + "The following example works based on the context." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:54.960374Z", + "iopub.status.busy": "2020-11-25T16:50:54.959462Z", + "iopub.status.idle": "2020-11-25T16:50:55.274081Z", + "shell.execute_reply": "2020-11-25T16:50:55.274520Z" + } + }, + "outputs": [], + "source": [ + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.280607Z", + "iopub.status.busy": "2020-11-25T16:50:55.280025Z", + "iopub.status.idle": "2020-11-25T16:50:55.284376Z", + "shell.execute_reply": "2020-11-25T16:50:55.283674Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0\n" + ] + } + ], + "source": [ + "with nengo.Network() as model:\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " subnet = nengo.Network()\n", + "\n", + " with subnet:\n", + " a = nengo.Ensemble(10, 1)\n", + " print(a.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The radius of `a` is `2.0`\n", + "because the current context includes both\n", + "`subnet` and `model`.\n", + "`subnet` does not change the default value of the radius,\n", + "but `model` does, so it uses the `model` default of `2.0`.\n", + "\n", + "Here's a similar example." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.291687Z", + "iopub.status.busy": "2020-11-25T16:50:55.290070Z", + "iopub.status.idle": "2020-11-25T16:50:55.293362Z", + "shell.execute_reply": "2020-11-25T16:50:55.292916Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "with nengo.Network() as model:\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " subnet = nengo.Network()\n", + "\n", + "with subnet:\n", + " a = nengo.Ensemble(10, 1)\n", + " print(a.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this example looks nearly identical,\n", + "the difference is that `a` is created\n", + "in the context of `subnet` only;\n", + "`model` is not part of the config context.\n", + "Because of that, when `a` is created,\n", + "Nengo sees that no default is set in `subnet`\n", + "and uses the global default value of `1.0`.\n", + "\n", + "This may seem counterintuitive\n", + "since `subnet` is a member of `model`;\n", + "it's stored as a sub-network of the `model` network.\n", + "However, Nengo objects are not aware of their parents.\n", + "This allows `subnet` to be used the same way\n", + "whether it's the top-level network\n", + "or whether it's nested twenty layers deep,\n", + "but it also means that we can't set defaults\n", + "based on network membership." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Context details\n", + "\n", + "The configuration context is stored in the\n", + "`nengo.Config` class as `nengo.Config.context`.\n", + "`context` is a thread-local list.\n", + "We add new contexts to the end of that list\n", + "at the start of `with` blocks,\n", + "and pop contexts off of that list\n", + "at the end of `with` blocks." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.302961Z", + "iopub.status.busy": "2020-11-25T16:50:55.301762Z", + "iopub.status.idle": "2020-11-25T16:50:55.305358Z", + "shell.execute_reply": "2020-11-25T16:50:55.305766Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# No context\n", + "len(nengo.Config.context)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.311374Z", + "iopub.status.busy": "2020-11-25T16:50:55.309874Z", + "iopub.status.idle": "2020-11-25T16:50:55.313043Z", + "shell.execute_reply": "2020-11-25T16:50:55.312609Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "True\n" + ] + } + ], + "source": [ + "# Model context\n", + "with model:\n", + " print(len(nengo.Config.context))\n", + " print(nengo.Config.context[0] is model.config)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.319110Z", + "iopub.status.busy": "2020-11-25T16:50:55.317617Z", + "iopub.status.idle": "2020-11-25T16:50:55.320773Z", + "shell.execute_reply": "2020-11-25T16:50:55.320352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "# Subnet, Model context\n", + "with model:\n", + " with subnet:\n", + " print(len(nengo.Config.context))\n", + " print(nengo.Config.context[0] is model.config)\n", + " print(nengo.Config.context[1] is subnet.config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are not sure what context you're in,\n", + "but you want to know what the defaults are\n", + "in the current context,\n", + "use `nengo.Config.all_defaults`.\n", + "You can optionally pass in the type that you're interested in." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.326818Z", + "iopub.status.busy": "2020-11-25T16:50:55.325287Z", + "iopub.status.idle": "2020-11-25T16:50:55.328512Z", + "shell.execute_reply": "2020-11-25T16:50:55.328091Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIF()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 2.0\n", + " seed: None\n" + ] + } + ], + "source": [ + "with model:\n", + " with subnet:\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.333738Z", + "iopub.status.busy": "2020-11-25T16:50:55.332323Z", + "iopub.status.idle": "2020-11-25T16:50:55.335436Z", + "shell.execute_reply": "2020-11-25T16:50:55.334997Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIF()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 1.0\n", + " seed: None\n" + ] + } + ], + "source": [ + "with subnet:\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note above that the radius changes\n", + "despite the fact that both situations occur\n", + "in the context of `subnet`!\n", + "Configuration outside matters if no default\n", + "has been set in the immediate context." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/usage/config.ipynb b/.doctrees/nbsphinx/examples/usage/config.ipynb new file mode 100644 index 0000000000..27d2196f31 --- /dev/null +++ b/.doctrees/nbsphinx/examples/usage/config.ipynb @@ -0,0 +1,1067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The `config` system\n", + "\n", + "Nengo objects have many parameters\n", + "that can be modified.\n", + "Some of these parameters\n", + "are critical characteristics of that object,\n", + "and others are hints or suggestions\n", + "that a backend can use or ignore.\n", + "\n", + "Nengo's `config` system is designed\n", + "to make setting large numbers of parameters easy,\n", + "and to allow backends\n", + "to introduce additional parameters\n", + "without changing core Nengo objects." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:56.655264Z", + "iopub.status.busy": "2020-11-25T16:50:56.654376Z", + "iopub.status.idle": "2020-11-25T16:50:56.981942Z", + "shell.execute_reply": "2020-11-25T16:50:56.982346Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nengo\n", + "import nengo.params\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting default parameters\n", + "\n", + "The `config` system aids in\n", + "setting many parameters\n", + "with a **hierarchy of defaults**.\n", + "When you create a Nengo object,\n", + "any parameters not specified\n", + "will be given the value `nengo.Default`.\n", + "This value tells Nengo\n", + "to use the default value\n", + "with the highest precedence\n", + "in the hierarchy.\n", + "Every `Network` has an associated\n", + "`config` object,\n", + "on which defaults can be set.\n", + "The network hierarchy is traversed\n", + "from most to least specific\n", + "and the first network with a default\n", + "set for that particular parameter\n", + "is used. For example:\n", + "\n", + " with nengo.Network() as net:\n", + " with nengo.Network() as subnet:\n", + " with nengo.Network() as subsubnet:\n", + " ens = nengo.Ensemble(10, 1)\n", + "\n", + "When filling in defaults for `ens`,\n", + "the hierarchy looks like\n", + "\n", + " └── net <- least specific\n", + " └── subnet\n", + " └── subsubnet <- most specific\n", + "\n", + "so defaults set in `subsubnet`\n", + "will take precedence over those in `subnet`,\n", + "which take precedence over those in `net`.\n", + "\n", + "If no default has been set in the\n", + "network hierarchy,\n", + "then the parameter default\n", + "is used.\n", + "These defaults are specified\n", + "when the Nengo objects are created.\n", + "We can investigate these defaults\n", + "by printing the class attributes\n", + "associated with them." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:56.986750Z", + "iopub.status.busy": "2020-11-25T16:50:56.986224Z", + "iopub.status.idle": "2020-11-25T16:50:56.989977Z", + "shell.execute_reply": "2020-11-25T16:50:56.990412Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumberParam('radius', default=1.0, optional=False, readonly=False)\n", + "1.0\n" + ] + } + ], + "source": [ + "# Get all info about the radius\n", + "print(nengo.Ensemble.radius)\n", + "# Just get the default\n", + "print(nengo.Ensemble.radius.default)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can inspect which defaults\n", + "have been overridden in a\n", + "particular `config` object\n", + "by printing it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:56.996244Z", + "iopub.status.busy": "2020-11-25T16:50:56.994685Z", + "iopub.status.idle": "2020-11-25T16:50:56.997941Z", + "shell.execute_reply": "2020-11-25T16:50:56.997507Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No parameters configured for Connection.\n", + "No parameters configured for Ensemble.\n", + "No parameters configured for Node.\n", + "No parameters configured for Probe.\n" + ] + } + ], + "source": [ + "model = nengo.Network()\n", + "print(model.config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To configure a parameter\n", + "(i.e., change its network-local default),\n", + "set it as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.004191Z", + "iopub.status.busy": "2020-11-25T16:50:57.002355Z", + "iopub.status.idle": "2020-11-25T16:50:57.005804Z", + "shell.execute_reply": "2020-11-25T16:50:57.005358Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters configured for Ensemble:\n", + " radius: 1.5\n" + ] + } + ], + "source": [ + "model.config[nengo.Ensemble].radius = 1.5\n", + "print(model.config[nengo.Ensemble])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this network, the default radius will be 1.5." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.013382Z", + "iopub.status.busy": "2020-11-25T16:50:57.011825Z", + "iopub.status.idle": "2020-11-25T16:50:57.015026Z", + "shell.execute_reply": "2020-11-25T16:50:57.014596Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normal network: ens.radius = 1.0\n", + "Configured network: ens.radius = 1.5\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1)\n", + "print(\"Normal network: ens.radius = %s\" % ens.radius)\n", + "\n", + "with model:\n", + " ens = nengo.Ensemble(10, 1)\n", + "print(\"Configured network: ens.radius = %s\" % ens.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if a radius is explicitly passed in,\n", + "it will always be used." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.022488Z", + "iopub.status.busy": "2020-11-25T16:50:57.020901Z", + "iopub.status.idle": "2020-11-25T16:50:57.024173Z", + "shell.execute_reply": "2020-11-25T16:50:57.023740Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normal network: ens.radius = 2.0\n", + "Configured network: ens.radius = 2.0\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1, radius=2.0)\n", + "print(\"Normal network: ens.radius = %s\" % ens.radius)\n", + "\n", + "with model:\n", + " ens = nengo.Ensemble(10, 1, radius=2.0)\n", + "print(\"Configured network: ens.radius = %s\" % ens.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When networks are nested within one another,\n", + "the most specific network configuration is used.\n", + "For example, if you create an Ensemble\n", + "without specifying a radius,\n", + "it will first check the network\n", + "that the Ensemble is a part of;\n", + "if that network has not configured a default,\n", + "then it will check the network\n", + "that that network is part of,\n", + "and so on." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.036480Z", + "iopub.status.busy": "2020-11-25T16:50:57.034896Z", + "iopub.status.idle": "2020-11-25T16:50:57.038117Z", + "shell.execute_reply": "2020-11-25T16:50:57.037696Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating e1 in subsubnet\n", + " radius = 2.0\n", + " neuron_type = LIFRate()\n", + "Creating e2 in subnet\n", + " radius = 1.5\n", + " neuron_type = LIFRate()\n", + "Creating e3 in model\n", + " radius = 1.5\n", + " neuron_type = LIF()\n" + ] + } + ], + "source": [ + "with model:\n", + "\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + "\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"Creating e1 in subsubnet\")\n", + " e1 = nengo.Ensemble(10, 1)\n", + " # Uses subsubnet.config value for radius\n", + " print(\" radius =\", e1.radius)\n", + " # Uses subnet.config value for neuron_type\n", + " print(\" neuron_type =\", e1.neuron_type)\n", + "\n", + " print(\"Creating e2 in subnet\")\n", + " e2 = nengo.Ensemble(10, 1)\n", + " # Uses model.config value for radius\n", + " print(\" radius =\", e2.radius)\n", + " # Uses subnet.config value for neuron_type\n", + " print(\" neuron_type =\", e2.neuron_type)\n", + "\n", + " print(\"Creating e3 in model\")\n", + " e3 = nengo.Ensemble(10, 1)\n", + " # Uses model.config value for radius\n", + " print(\" radius =\", e3.radius)\n", + " # Uses nengo.Ensemble default for neuron_type\n", + " print(\" neuron_type =\", e3.neuron_type)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that each `config` object\n", + "only knows about the defaults set on itself." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.047055Z", + "iopub.status.busy": "2020-11-25T16:50:57.045500Z", + "iopub.status.idle": "2020-11-25T16:50:57.048742Z", + "shell.execute_reply": "2020-11-25T16:50:57.048294Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subsubnet:\n", + "Parameters configured for Ensemble:\n", + " radius: 2.0\n", + "\n", + "subnet:\n", + "Parameters configured for Ensemble:\n", + " neuron_type: LIFRate()\n", + "\n", + "model:\n", + "Parameters configured for Ensemble:\n", + " radius: 1.5\n" + ] + } + ], + "source": [ + "with model:\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + "print(\"subsubnet:\")\n", + "print(subsubnet.config[nengo.Ensemble])\n", + "print(\"\\nsubnet:\")\n", + "print(subnet.config[nengo.Ensemble])\n", + "print(\"\\nmodel:\")\n", + "print(model.config[nengo.Ensemble])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want a more global picture of the defaults\n", + "in the *current context*, you can query the `Config`\n", + "class itself (all `config` objects are instances of `Config`).\n", + "\n", + "To query all parameters, print `Config.all_defaults()`.\n", + "You may pass a Nengo object class to this function\n", + "to filter the results.\n", + "For example, to get all defaults set for `Ensemble`,\n", + "use `Config.all_defaults(nengo.Ensemble)`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.059045Z", + "iopub.status.busy": "2020-11-25T16:50:57.057506Z", + "iopub.status.idle": "2020-11-25T16:50:57.060784Z", + "shell.execute_reply": "2020-11-25T16:50:57.060350Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In 'model' context:\n", + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIF()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 1.5\n", + " seed: None\n", + "Current defaults for Probe:\n", + " attr: None\n", + " label: None\n", + " sample_every: None\n", + " seed: None\n", + " solver: ConnectionDefault\n", + " synapse: None\n", + "Current defaults for Connection:\n", + " eval_points: None\n", + " function_info: None\n", + " label: None\n", + " learning_rule_type: None\n", + " scale_eval_points: True\n", + " seed: None\n", + " solver: LstsqL2()\n", + " synapse: Lowpass(tau=0.005)\n", + " transform: None\n", + "Current defaults for Node:\n", + " label: None\n", + " output: None\n", + " seed: None\n", + " size_in: None\n", + " size_out: None\n", + "In 'subnet' context:\n", + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIFRate()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 3.0\n", + " seed: None\n", + "In 'subsubnet' context:\n", + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: Direct()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 2.0\n", + " seed: None\n" + ] + } + ], + "source": [ + "with model:\n", + " print(\"In 'model' context:\")\n", + " print(nengo.Config.all_defaults())\n", + "\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " subnet.config[nengo.Ensemble].radius = 3.0\n", + " print(\"In 'subnet' context:\")\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))\n", + "\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].neuron_type = nengo.Direct()\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"In 'subsubnet' context:\")\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default value for a particular parameter\n", + "can be queried from the global context\n", + "with the `nengo.Config.default` function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.066272Z", + "iopub.status.busy": "2020-11-25T16:50:57.064776Z", + "iopub.status.idle": "2020-11-25T16:50:57.067991Z", + "shell.execute_reply": "2020-11-25T16:50:57.067538Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function default in module nengo.config:\n", + "\n", + "default(nengo_cls, param)\n", + " Look up the current default value for a parameter.\n", + " \n", + " The default is found by going through the config stack, from most\n", + " specific to least specific. The network that an object is in\n", + " is the most specific; the top-level network is the least specific.\n", + " If no default is found there, then the parameter's default value\n", + " is returned.\n", + "\n" + ] + } + ], + "source": [ + "help(nengo.Config.default)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.077229Z", + "iopub.status.busy": "2020-11-25T16:50:57.075692Z", + "iopub.status.idle": "2020-11-25T16:50:57.078881Z", + "shell.execute_reply": "2020-11-25T16:50:57.078454Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subsubnet:\n", + " default radius: 2.0\n", + " default neuron_type: LIFRate()\n", + "\n", + "subnet:\n", + " default radius: 1.5\n", + " default neuron_type: LIFRate()\n", + "\n", + "model:\n", + " default radius: 1.5\n", + " default neuron_type: LIF()\n" + ] + } + ], + "source": [ + "def print_defaults():\n", + " def_radius = nengo.Config.default(nengo.Ensemble, \"radius\")\n", + " def_type = nengo.Config.default(nengo.Ensemble, \"neuron_type\")\n", + " print(\" default radius: %s\" % def_radius)\n", + " print(\" default neuron_type: %s\" % def_type)\n", + "\n", + "\n", + "with model:\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"subsubnet:\")\n", + " print_defaults()\n", + " print(\"\\nsubnet:\")\n", + " print_defaults()\n", + " print(\"\\nmodel:\")\n", + " print_defaults()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Defaults are filled in immediately\n", + "\n", + "One important feature about the defaults hierarchy\n", + "is that defaults are filled in **immediately**.\n", + "When you create a Nengo object,\n", + "the attributes are filled in with the **current**\n", + "defaults that are set.\n", + "Changing the defaults after object creation\n", + "will not update objects already created." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.085793Z", + "iopub.status.busy": "2020-11-25T16:50:57.083367Z", + "iopub.status.idle": "2020-11-25T16:50:57.088084Z", + "shell.execute_reply": "2020-11-25T16:50:57.088499Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e1.radius = 1.5\n", + "Changing default radius to 2.0\n", + "e1.radius = 1.5\n", + "e2.radius = 2.0\n" + ] + } + ], + "source": [ + "with model:\n", + " e1 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"Changing default radius to 2.0\")\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " e2 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"e2.radius =\", e2.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Resetting to default\n", + "\n", + "If you ever wish to reset a value\n", + "back to the default,\n", + "you can remove it from the `config` object you modified." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.096565Z", + "iopub.status.busy": "2020-11-25T16:50:57.095132Z", + "iopub.status.idle": "2020-11-25T16:50:57.098387Z", + "shell.execute_reply": "2020-11-25T16:50:57.097955Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e1.radius = 2.0\n", + "Resetting radius back to default\n", + "\n", + "No parameters configured for Ensemble.\n", + "\n", + "e2.radius = 1.0\n" + ] + } + ], + "source": [ + "with model:\n", + " e1 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"Resetting radius back to default\")\n", + " del model.config[nengo.Ensemble].radius\n", + " print(\"\\n\" + str(model.config[nengo.Ensemble]) + \"\\n\")\n", + " e2 = nengo.Ensemble(10, 1)\n", + " print(\"e2.radius =\", e2.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Making new `config`s\n", + "\n", + "Typically, several Nengo objects\n", + "will share a set of parameters,\n", + "but won't make sense to encapsulate in a network.\n", + "One method of having those objects share parameters\n", + "is to use dictionary unpacking." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.106435Z", + "iopub.status.busy": "2020-11-25T16:50:57.105040Z", + "iopub.status.idle": "2020-11-25T16:50:57.108291Z", + "shell.execute_reply": "2020-11-25T16:50:57.107864Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 1.5 1.5\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " hippocampus_args = {\"radius\": 1.5, \"neuron_type\": nengo.LIFRate()}\n", + " e1 = nengo.Ensemble(100, 2, **hippocampus_args)\n", + " e2 = nengo.Ensemble(150, 3, **hippocampus_args)\n", + " e3 = nengo.Ensemble(200, 4, **hippocampus_args)\n", + "print(e1.radius, e2.radius, e3.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An alternative method that can be very useful\n", + "for large networks and for more readable models\n", + "is to create a new `config` object\n", + "to encapsulate those parameter settings." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.117222Z", + "iopub.status.busy": "2020-11-25T16:50:57.115711Z", + "iopub.status.idle": "2020-11-25T16:50:57.118916Z", + "shell.execute_reply": "2020-11-25T16:50:57.118493Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 1.5 1.5\n" + ] + } + ], + "source": [ + "in_hippocampus = nengo.Config(nengo.Ensemble)\n", + "in_hippocampus[nengo.Ensemble].radius = 1.5\n", + "in_hippocampus[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + "\n", + "with nengo.Network():\n", + " with in_hippocampus:\n", + " e1 = nengo.Ensemble(100, 2)\n", + " e2 = nengo.Ensemble(150, 3)\n", + " e3 = nengo.Ensemble(200, 4)\n", + "print(e1.radius, e2.radius, e3.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced: adding new parameters\n", + "\n", + "This section is targeted to those\n", + "implementing new backends\n", + "or large libraries of networks\n", + "(like, for example, `nengo.SPA`).\n", + "\n", + "Often, you want to associate some kind of\n", + "metadata with a Nengo object,\n", + "or a type of Nengo objects.\n", + "For example, in backends\n", + "that communicate with specific hardware,\n", + "it can be helpful to mark certain nodes\n", + "as being time-dependent,\n", + "or to assign certain ensembles\n", + "to a particular portion of the hardware memory.\n", + "\n", + "Python allows us to make new attributes\n", + "on Nengo objects.\n", + "However, we highly discourage this activity,\n", + "because a Nengo object should be\n", + "a backend-agnostic part of a model.\n", + "The parameters pre-defined on Nengo objects\n", + "make up the parameters that all backends\n", + "should deal with in some way.\n", + "\n", + "For this reason, we raise a warning\n", + "when creating a new attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.124825Z", + "iopub.status.busy": "2020-11-25T16:50:57.123649Z", + "iopub.status.idle": "2020-11-25T16:50:57.126471Z", + "shell.execute_reply": "2020-11-25T16:50:57.126848Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/travis/build/nengo/nengo/nengo/base.py:106: SyntaxWarning: Creating new attribute 'memory_location' on ''. Did you mean to change an existing attribute?\n", + " SyntaxWarning,\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1)\n", + " ens.memory_location = 0x1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So how should backends associate arbitrary information\n", + "with Nengo objects?\n", + "The `config` system!\n", + "\n", + "We saw above that we can create new `config` objects\n", + "by specifying which Nengo objects they can configure.\n", + "We can also create new parameters\n", + "on those `config` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.132209Z", + "iopub.status.busy": "2020-11-25T16:50:57.130715Z", + "iopub.status.idle": "2020-11-25T16:50:57.132878Z", + "shell.execute_reply": "2020-11-25T16:50:57.133278Z" + } + }, + "outputs": [], + "source": [ + "my_config = nengo.Config(nengo.Ensemble)\n", + "# memory_location must be a positive integer\n", + "my_config[nengo.Ensemble].set_param(\n", + " \"memory_location\",\n", + " nengo.params.IntParam(\"memory_location\", default=None, optional=True, low=0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can set that parameter\n", + "for the `nengo.Ensemble` class as a whole,\n", + "or with individual instances." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.141063Z", + "iopub.status.busy": "2020-11-25T16:50:57.139492Z", + "iopub.status.idle": "2020-11-25T16:50:57.142761Z", + "shell.execute_reply": "2020-11-25T16:50:57.142328Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e1 will be stored at 0x1000\n", + "e2 will be stored at 0x2000\n" + ] + } + ], + "source": [ + "# Make the network (this code is backend-agnostic)\n", + "with nengo.Network():\n", + " e1 = nengo.Ensemble(10, 1)\n", + " e2 = nengo.Ensemble(10, 1)\n", + "\n", + "# Set backend-specific parameters\n", + "my_config[nengo.Ensemble].memory_location = 0x1000 # Set Ensemble default\n", + "my_config[e2].memory_location = 0x2000 # Set value for e2\n", + "\n", + "print(\"e1 will be stored at 0x%x\" % my_config[e1].memory_location)\n", + "print(\"e2 will be stored at 0x%x\" % my_config[e2].memory_location)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Parameter` types for the most common Python objects\n", + "are available in `nengo.params`,\n", + "as well as other types that Nengo uses frequently,\n", + "but it is possible to implement your own\n", + "in order to do additional processing\n", + "like validation.\n", + "See the `nengo.params` source\n", + "for examples." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.148200Z", + "iopub.status.busy": "2020-11-25T16:50:57.146836Z", + "iopub.status.idle": "2020-11-25T16:50:57.151547Z", + "shell.execute_reply": "2020-11-25T16:50:57.151119Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['BoolParam', 'DictParam', 'EnumParam', 'FunctionParam', 'IntParam', 'NdarrayParam', 'NumberParam', 'ObsoleteParam', 'ShapeParam', 'StringParam', 'TupleParam']\n" + ] + } + ], + "source": [ + "print([cls for cls in dir(nengo.params) if cls.endswith(\"Param\")])" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/usage/delay-node.ipynb b/.doctrees/nbsphinx/examples/usage/delay-node.ipynb new file mode 100644 index 0000000000..5362f28449 --- /dev/null +++ b/.doctrees/nbsphinx/examples/usage/delay-node.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Delaying a connection with a node\n", + "\n", + "Nodes allow for all sorts of advanced behavior\n", + "that is typically done by modifying the code of a neural simulator.\n", + "In Nengo, the `Node` object allows us to run custom code.\n", + "\n", + "In this example, we will implement\n", + "an `n`-timestep delayed connection by using a node." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:58.845063Z", + "iopub.status.busy": "2020-11-25T16:50:58.844186Z", + "iopub.status.idle": "2020-11-25T16:50:59.336196Z", + "shell.execute_reply": "2020-11-25T16:50:59.335253Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:59.349624Z", + "iopub.status.busy": "2020-11-25T16:50:59.349069Z", + "iopub.status.idle": "2020-11-25T16:50:59.352654Z", + "shell.execute_reply": "2020-11-25T16:50:59.352199Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Delayed connection\")\n", + "with model:\n", + " # We'll use white noise as input\n", + " inp = nengo.Node(WhiteSignal(2, high=5), size_out=1)\n", + " A = nengo.Ensemble(40, dimensions=1)\n", + " nengo.Connection(inp, A)\n", + "\n", + "\n", + "# We'll make a simple object to implement the delayed connection\n", + "class Delay:\n", + " def __init__(self, dimensions, timesteps=50):\n", + " self.history = np.zeros((timesteps, dimensions))\n", + "\n", + " def step(self, t, x):\n", + " self.history = np.roll(self.history, -1)\n", + " self.history[-1] = x\n", + " return self.history[0]\n", + "\n", + "\n", + "dt = 0.001\n", + "delay = Delay(1, timesteps=int(0.2 / 0.001))\n", + "\n", + "with model:\n", + " delaynode = nengo.Node(delay.step, size_in=1, size_out=1)\n", + " nengo.Connection(A, delaynode)\n", + "\n", + " # Send the delayed output through an ensemble\n", + " B = nengo.Ensemble(40, dimensions=1)\n", + " nengo.Connection(delaynode, B)\n", + "\n", + " # Probe the input at the delayed output\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:59.357985Z", + "iopub.status.busy": "2020-11-25T16:50:59.357182Z", + "iopub.status.idle": "2020-11-25T16:50:59.830021Z", + "shell.execute_reply": "2020-11-25T16:50:59.829183Z" + } + }, + "outputs": [], + "source": [ + "# Run for 2 seconds\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:59.851476Z", + "iopub.status.busy": "2020-11-25T16:50:59.845061Z", + "iopub.status.idle": "2020-11-25T16:51:00.215978Z", + "shell.execute_reply": "2020-11-25T16:51:00.216408Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe], lw=2)\n", + "plt.title(\"Input\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[B_probe], lw=2)\n", + "plt.axvline(0.2, c=\"k\")\n", + "plt.title(\"Delayed output\")\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/usage/network-design-advanced.ipynb b/.doctrees/nbsphinx/examples/usage/network-design-advanced.ipynb new file mode 100644 index 0000000000..35e937d632 --- /dev/null +++ b/.doctrees/nbsphinx/examples/usage/network-design-advanced.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Additional tips and tricks for designing networks\n", + "\n", + "This tutorial assumes that you have read\n", + "the `network_design` tutorial,\n", + "and have designed a network or two.\n", + "Here, we will give a few advanced tips and tricks\n", + "for designing networks that can be reused flexibly.\n", + "In particular, these tips will use the\n", + "`config` system, so we will also assume that\n", + "you have gone over the `config` tutorial.\n", + "\n", + "Briefly, the general principles covered\n", + "in this tutorial are\n", + "\n", + "0. Accept `**kwargs` to pass through network arguments\n", + "0. Accept a config argument for groups of parameters\n", + "\n", + "We will demonstrate these principles\n", + "using the two examples from the `network_design` tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:14.598778Z", + "iopub.status.busy": "2020-11-25T16:51:14.594056Z", + "iopub.status.idle": "2020-11-25T16:51:15.093338Z", + "shell.execute_reply": "2020-11-25T16:51:15.093757Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "\n", + "def test_integrators(net):\n", + " with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, net.pre_integrator.input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + " plt.figure()\n", + " plt.plot(sim.trange(), sim.data[input_probe], color=\"k\")\n", + " plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\")\n", + " plt.plot(sim.trange(), sim.data[post_probe], color=\"g\")\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Accept a `**kwargs` argument\n", + "\n", + "The standard `nengo.Network` accepts a number of arguments,\n", + "including the widely used `seed` and `label` arguments.\n", + "Sometimes it is helpful to be able to\n", + "set these on your custom networks too.\n", + "While there is nothing wrong\n", + "with explicitly passing these arguments along,\n", + "it is less typing to use the Python `**kwargs` construct.\n", + "This special argument allows a function\n", + "to accept any number of keyword arguments\n", + "which we can then pass into the `Network` constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:15.110886Z", + "iopub.status.busy": "2020-11-25T16:51:15.106529Z", + "iopub.status.idle": "2020-11-25T16:51:16.283314Z", + "shell.execute_reply": "2020-11-25T16:51:16.283766Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def Integrator(n_neurons, dimensions, tau=0.1, **kwargs):\n", + " with nengo.Network(**kwargs) as net:\n", + " net.input = nengo.Node(size_in=dimensions)\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " nengo.Connection(net.ensemble, net.ensemble, synapse=tau)\n", + " nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)\n", + " return net\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " # Make both integrators use LIFRate neurons\n", + " net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net.pre_integrator = Integrator(50, 1, label=\"pre\")\n", + " net.post_integrator = Integrator(50, 1, label=\"post\")\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:16.288096Z", + "iopub.status.busy": "2020-11-25T16:51:16.287536Z", + "iopub.status.idle": "2020-11-25T16:51:16.292129Z", + "shell.execute_reply": "2020-11-25T16:51:16.291550Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pre integrator label: pre\n", + "post integrator label: post\n" + ] + } + ], + "source": [ + "print(\"pre integrator label:\", net.pre_integrator.label)\n", + "print(\"post integrator label:\", net.post_integrator.label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Accept a config argument for groups of parameters\n", + "\n", + "Often, you will not want to use the\n", + "network-level defaults for all of your objects.\n", + "Some objects need certain things overwritten,\n", + "while others need other values overwritten.\n", + "Again, it is possible to deal with this issue\n", + "by adding more and more parameters,\n", + "but this quickly gets out of hand.\n", + "Instead, add a small number of arguments\n", + "that optionally accept a `config` object,\n", + "which allows for setting multiple parameters at once.\n", + "\n", + "In the coupled integrator network example,\n", + "we make two connections.\n", + "We have to be careful changing the defaults\n", + "for those connections, as they are wildly different;\n", + "one is a recurrent connection from an ensemble to itself,\n", + "while the other is a connection from a node to an ensemble.\n", + "We will accept a `config` object for the recurrent connection\n", + "to make this easier." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:16.309167Z", + "iopub.status.busy": "2020-11-25T16:51:16.299592Z", + "iopub.status.idle": "2020-11-25T16:51:17.420431Z", + "shell.execute_reply": "2020-11-25T16:51:17.419984Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def ConfigurableIntegrator(n_neurons, dimensions, recurrent_config=None, **kwargs):\n", + " net = nengo.Network(**kwargs)\n", + " if recurrent_config is None:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)\n", + " with net:\n", + " net.input = nengo.Node(size_in=dimensions)\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " with recurrent_config:\n", + " nengo.Connection(net.ensemble, net.ensemble)\n", + " tau = nengo.Config.default(nengo.Connection, \"synapse\").tau\n", + " nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)\n", + " return net\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " # Make both integrators use LIFRate neurons\n", + " net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net.pre_integrator = ConfigurableIntegrator(50, 1)\n", + " # Give the post_integrator a shorter tau (should make integration fail)\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.01)\n", + " net.post_integrator = ConfigurableIntegrator(\n", + " 50, 1, recurrent_config=recurrent_config\n", + " )\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Longer example: double integrator network\n", + "\n", + "Recall in the previous tutorial that\n", + "we created a model\n", + "that released a lever 0.6 to 1.0 seconds\n", + "after pressing a lever.\n", + "Let's use the above principles,\n", + "and the `config` system in general,\n", + "to improve the code constructing this model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:17.448485Z", + "iopub.status.busy": "2020-11-25T16:51:17.433384Z", + "iopub.status.idle": "2020-11-25T16:51:20.132378Z", + "shell.execute_reply": "2020-11-25T16:51:20.133210Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def controlled_integrator(n_neurons, dimensions, recurrent_config=None, **kwargs):\n", + " net = nengo.Network(**kwargs)\n", + " if recurrent_config is None:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)\n", + " with net:\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)\n", + " with recurrent_config:\n", + " nengo.Connection(\n", + " net.ensemble,\n", + " net.ensemble[:dimensions],\n", + " function=lambda x: x[:-1] * (1.0 - x[-1]),\n", + " )\n", + " return net\n", + "\n", + "\n", + "def medial_pfc(\n", + " coupling_strength,\n", + " n_neurons_per_integrator=200,\n", + " recurrent_config=None,\n", + " tau=0.1,\n", + " **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " with net:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(tau)\n", + " net.pre = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)\n", + " net.post = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)\n", + " nengo.Connection(\n", + " net.pre.ensemble[0], net.post.ensemble[0], transform=coupling_strength\n", + " )\n", + " return net\n", + "\n", + "\n", + "def motor_cortex(\n", + " command_threshold, n_neurons_per_command=30, ens_config=None, **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " if ens_config is None:\n", + " ens_config = nengo.Config(nengo.Ensemble)\n", + " ens_config[nengo.Ensemble].encoders = Choice([[1]])\n", + " ens_config[nengo.Ensemble].intercepts = Choice([command_threshold])\n", + " with net:\n", + " with ens_config:\n", + " net.press = nengo.Ensemble(n_neurons_per_command, dimensions=1)\n", + " net.release = nengo.Ensemble(n_neurons_per_command, dimensions=1)\n", + " return net\n", + "\n", + "\n", + "def double_integrator(\n", + " mpfc_coupling_strength,\n", + " command_threshold,\n", + " press_to_pre_gain=3,\n", + " press_to_post_control=-6,\n", + " recurrent_tau=0.1,\n", + " **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " with net:\n", + " net.mpfc = medial_pfc(mpfc_coupling_strength)\n", + " net.motor = motor_cortex(command_threshold)\n", + " nengo.Connection(\n", + " net.motor.press,\n", + " net.mpfc.pre.ensemble[0],\n", + " transform=recurrent_tau * press_to_pre_gain,\n", + " )\n", + " nengo.Connection(\n", + " net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control\n", + " )\n", + " nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)\n", + " return net\n", + "\n", + "\n", + "def test_doubleintegrator(net):\n", + " # Provide input and probe outside of network construction,\n", + " # for more flexibility\n", + " with net:\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)\n", + " pr_press = nengo.Probe(net.motor.press, synapse=0.01)\n", + " pr_release = nengo.Probe(net.motor.release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "for coupling_strength in (0.11, 0.16, 0.21):\n", + " # Try the same network with LIFRate neurons\n", + " with nengo.Config(nengo.Ensemble) as cfg:\n", + " cfg[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net = double_integrator(\n", + " mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0\n", + " )\n", + " test_doubleintegrator(net)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/usage/network-design.ipynb b/.doctrees/nbsphinx/examples/usage/network-design.ipynb new file mode 100644 index 0000000000..67444a7e18 --- /dev/null +++ b/.doctrees/nbsphinx/examples/usage/network-design.ipynb @@ -0,0 +1,996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Designing networks\n", + "\n", + "`Networks` are how Nengo encapsulates\n", + "functionally complementary objects.\n", + "Networks can contain\n", + "ensembles, nodes, connection, even other networks.\n", + "Models are often made much more readable\n", + "my grouping objects into networks.\n", + "Tools for visualizing networks\n", + "will often use the network structure\n", + "to produce cleaner images.\n", + "\n", + "Here, we will go over some general principles\n", + "of network design. In the first section,\n", + "we will go over these principles with a simple example.\n", + "In the second section, we will apply\n", + "these principles to a more complicated example.\n", + "\n", + "Briefly, the general principles we will\n", + "go over in this tutorial are\n", + "\n", + "0. Group related objects in networks with `with`\n", + "0. Reuse networks by defining functions\n", + "0. Parameterize functions for more flexible reuse\n", + "\n", + "We will demonstrate these principles\n", + "with a network consisting\n", + "of two connected integrators\n", + "as an example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:01.433967Z", + "iopub.status.busy": "2020-11-25T16:51:01.433109Z", + "iopub.status.idle": "2020-11-25T16:51:01.939708Z", + "shell.execute_reply": "2020-11-25T16:51:01.940154Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "\n", + "def test_integrators(net):\n", + " with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, net.pre_integrator.input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + " plt.figure()\n", + " plt.plot(sim.trange(), sim.data[input_probe], color=\"k\")\n", + " plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\")\n", + " plt.plot(sim.trange(), sim.data[post_probe], color=\"g\")\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Group related objects in networks with `with`\n", + "\n", + "All Nengo objects must be created\n", + "in the context of some network.\n", + "We use the `with` keyword to denote\n", + "the network context in which\n", + "a Nengo object is created.\n", + "\n", + "These `with` statements can be nested\n", + "to group objects in appropriately named networks." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:01.953677Z", + "iopub.status.busy": "2020-11-25T16:51:01.952320Z", + "iopub.status.idle": "2020-11-25T16:51:01.954503Z", + "shell.execute_reply": "2020-11-25T16:51:01.954918Z" + } + }, + "outputs": [], + "source": [ + "# Example: two connected integrators\n", + "# (See integrator example network for more details)\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " with nengo.Network() as pre_integrator:\n", + " pre_input = nengo.Node(size_in=1)\n", + " pre_ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(pre_ensemble, pre_ensemble, synapse=0.1)\n", + " nengo.Connection(pre_input, pre_ensemble, synapse=None, transform=0.1)\n", + " with nengo.Network() as post_integrator:\n", + " post_input = nengo.Node(size_in=1)\n", + " post_ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(post_ensemble, post_ensemble, synapse=0.1)\n", + " nengo.Connection(post_input, post_ensemble, synapse=None, transform=0.1)\n", + " nengo.Connection(pre_ensemble, post_input)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:01.966524Z", + "iopub.status.busy": "2020-11-25T16:51:01.962510Z", + "iopub.status.idle": "2020-11-25T16:51:03.397887Z", + "shell.execute_reply": "2020-11-25T16:51:03.397430Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Test the integrators by giving piecewise input and plotting\n", + "with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, pre_input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(pre_ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(post_ensemble, synapse=0.01)\n", + "with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], color=\"k\", label=\"input\")\n", + "plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\", label=\"pre integrator\")\n", + "plt.plot(sim.trange(), sim.data[post_probe], color=\"g\", label=\"post integrator\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `with` statements\n", + "are not just cosmetic;\n", + "Nengo objects are stored\n", + "in the network that they're constructed in." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:03.401866Z", + "iopub.status.busy": "2020-11-25T16:51:03.401372Z", + "iopub.status.idle": "2020-11-25T16:51:03.404425Z", + "shell.execute_reply": "2020-11-25T16:51:03.404818Z" + } + }, + "outputs": [], + "source": [ + "assert pre_input in pre_integrator\n", + "assert pre_input not in net\n", + "assert pre_integrator in net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Reuse networks by defining functions\n", + "\n", + "Often, the same network is constructed\n", + "more than once, resulting in duplicated code.\n", + "We can reduce this code duplication\n", + "by defining a function that\n", + "creates the network and returns it.\n", + "\n", + "Even if you only use the network once,\n", + "it can be helpful to define all of\n", + "your subnetworks in functions\n", + "to keep your code cleaner.\n", + "Determining when a network\n", + "should be defined in a function\n", + "is informed by experience\n", + "designing large networks\n", + "and personal preference.\n", + "\n", + "### Store useful objects on the network\n", + "\n", + "A corollary to putting networks in functions\n", + "is that any objects that you might want\n", + "access to outside of that function\n", + "should be stored on the network.\n", + "Functions that create networks\n", + "should only return that network.\n", + "While all objects in that network\n", + "are stored somewhere\n", + "(e.g., ensembles are stored in a `net.ensembles` list),\n", + "that list can be large and difficult to search.\n", + "Therefore, we recommend storing objects\n", + "on the network itself\n", + "to make them accessible outside of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:03.415550Z", + "iopub.status.busy": "2020-11-25T16:51:03.412406Z", + "iopub.status.idle": "2020-11-25T16:51:03.418288Z", + "shell.execute_reply": "2020-11-25T16:51:03.418690Z" + } + }, + "outputs": [], + "source": [ + "# Example: two connected integrators. Now with functions!\n", + "def Integrator1():\n", + " with nengo.Network() as integrator:\n", + " integrator.input = nengo.Node(size_in=1)\n", + " integrator.ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=0.1)\n", + " nengo.Connection(\n", + " integrator.input, integrator.ensemble, synapse=None, transform=0.1\n", + " )\n", + " return integrator\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator1()\n", + " net.post_integrator = Integrator1()\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:03.426287Z", + "iopub.status.busy": "2020-11-25T16:51:03.425532Z", + "iopub.status.idle": "2020-11-25T16:51:04.753958Z", + "shell.execute_reply": "2020-11-25T16:51:04.753503Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# This function does the same as the previous example\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Parameterize functions for more flexible reuse\n", + "\n", + "Now that your network is created in a function,\n", + "you can add parameters to your function\n", + "to change how your network is constructed.\n", + "This could be a huge change,\n", + "allowing you to compare two different approaches\n", + "to some component of your model,\n", + "or it could something straightforward\n", + "like changing the number of neurons\n", + "in the ensembles in the network.\n", + "\n", + "Unlike other languages, Python allows you\n", + "to add two different types of parameters:\n", + "**positional** and **keyword** arguments.\n", + "Positional arguments must be passed\n", + "to your function.\n", + "\n", + "```python\n", + ">>> def my_network(arg1, arg2):\n", + "... return \"%s %s\" % (arg1, arg2)\n", + ">>> my_network()\n", + "TypeError: my_network() takes exactly 2 arguments (0 given)\n", + ">>> my_network(0, 0)\n", + "'0 0'\n", + "```\n", + "\n", + "Keyword arguments are assigned a default value,\n", + "and so do not have to be passed to your function.\n", + "\n", + "```python\n", + ">>> def my_network(arg1=0, arg2=0):\n", + "... return \"%s %s\" % (arg1, arg2)\n", + ">>> my_network()\n", + "'0 0'\n", + ">>> my_network(1)\n", + "'1 0'\n", + "```\n", + "\n", + "Order does not matter if you explicitly\n", + "label the argument in your function call.\n", + "\n", + "```python\n", + ">>> my_network(arg2=2, arg1=1)\n", + "'1 2'\n", + ">>> my_network(arg2=2)\n", + "'0 2'\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:04.759315Z", + "iopub.status.busy": "2020-11-25T16:51:04.758795Z", + "iopub.status.idle": "2020-11-25T16:51:04.762327Z", + "shell.execute_reply": "2020-11-25T16:51:04.761898Z" + } + }, + "outputs": [], + "source": [ + "# Example: two connected integrators. Functions have parameters!\n", + "def Integrator2(n_neurons, dimensions, tau=0.1):\n", + " with nengo.Network() as integrator:\n", + " integrator.input = nengo.Node(size_in=dimensions)\n", + " integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=tau)\n", + " nengo.Connection(\n", + " integrator.input, integrator.ensemble, synapse=None, transform=tau\n", + " )\n", + " return integrator" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:04.776047Z", + "iopub.status.busy": "2020-11-25T16:51:04.769777Z", + "iopub.status.idle": "2020-11-25T16:51:06.032163Z", + "shell.execute_reply": "2020-11-25T16:51:06.031695Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try 50 neuron ensembles\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator2(50, 1)\n", + " net.post_integrator = Integrator2(50, 1)\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:06.046560Z", + "iopub.status.busy": "2020-11-25T16:51:06.039875Z", + "iopub.status.idle": "2020-11-25T16:51:07.322939Z", + "shell.execute_reply": "2020-11-25T16:51:07.322488Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try shorter tau (spoiler: it fails to integrate well)\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator2(50, 1, tau=0.01)\n", + " net.post_integrator = Integrator2(50, 1, tau=0.01)\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Longer example: double integrator network\n", + "\n", + "While the coupled integrator model above\n", + "seems like a toy example,\n", + "it is in fact a critical component\n", + "of many models that capture\n", + "neurophysiological and behavioral data.\n", + "Let's looks at a more complicated network\n", + "that uses coupled integrators,\n", + "and show how the network design principles\n", + "discussed above improve model organization.\n", + "We will look at a simplified version\n", + "of the network described in\n", + "[Bekolay et al., 2014](\n", + "http://compneuro.uwaterloo.ca/files/publications/bekolay.2014a.pdf).\n", + "\n", + "This network uses two coupled integrators\n", + "in the context of a simple reaction time task.\n", + "The subject presses down a lever,\n", + "and should release that lever\n", + "between 0.6 and 1.0 seconds later.\n", + "In order to time the interval,\n", + "the lever press starts the coupled integrators,\n", + "which are tuned such that\n", + "the second integrator should reach\n", + "a high point by around 0.6 seconds.\n", + "The second integrator\n", + "effects the lever release.\n", + "\n", + "We'll start with a completely disorganized network.\n", + "Even if you've read the paper,\n", + "the code is quite hard to follow." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:07.345944Z", + "iopub.status.busy": "2020-11-25T16:51:07.341259Z", + "iopub.status.idle": "2020-11-25T16:51:08.285722Z", + "shell.execute_reply": "2020-11-25T16:51:08.285269Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Network() as net:\n", + " tau = 0.1\n", + "\n", + " pre_integrator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " pre_integrator,\n", + " pre_integrator[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " post_integrator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " post_integrator,\n", + " post_integrator[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " nengo.Connection(pre_integrator[0], post_integrator[0], transform=0.16)\n", + " press = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + " release = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + "\n", + " nengo.Connection(press, pre_integrator[0], transform=tau * 3)\n", + " nengo.Connection(press, post_integrator[1], transform=-1 * 6)\n", + "\n", + " nengo.Connection(post_integrator[0], release)\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)\n", + "\n", + " pr_press = nengo.Probe(press, synapse=0.01)\n", + " pr_release = nengo.Probe(release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(pre_integrator[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(post_integrator[0], synapse=0.01)\n", + "\n", + "\n", + "def test_doubleintegrator(net):\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "test_doubleintegrator(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the goal is to release the lever\n", + "0.6-1.0 seconds after the press.\n", + "The blue \"Press\" line in the upper plot\n", + "represents the activity of neurons\n", + "in motor cortex which effect the lever press.\n", + "If we assume that the press occurs at 0.2 seconds,\n", + "then the release window is between 0.8 and 1.2 seconds\n", + "(shown in green).\n", + "This will occur if the \"Post Integrator\" (see lower plot)\n", + "is sufficiently high during this window.\n", + "If you rerun the cell above several times,\n", + "you can see that it usually achieves this,\n", + "but not all of the time.\n", + "\n", + "Looking at the code, it's difficult to tell\n", + "how we achieve this result.\n", + "It's also difficult to see what parameters\n", + "needed tweaking.\n", + "Rather than just showing one parameter set that works,\n", + "we should investigate reasonable ranges for\n", + "each parameter.\n", + "It would be easy to think that there is only\n", + "one parameter, `tau`, in the model as presented,\n", + "but this is not the case.\n", + "\n", + "Let's start with the first network design principle:\n", + "group objects into networks with `with`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:08.298563Z", + "iopub.status.busy": "2020-11-25T16:51:08.293408Z", + "iopub.status.idle": "2020-11-25T16:51:09.273316Z", + "shell.execute_reply": "2020-11-25T16:51:09.272746Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Network() as net:\n", + " tau = 0.1\n", + "\n", + " with nengo.Network() as mpfc:\n", + " with nengo.Network() as pre_integrator:\n", + " pre_ensemble = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " pre_ensemble,\n", + " pre_ensemble[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " with nengo.Network() as post_integrator:\n", + " post_ensemble = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " post_ensemble,\n", + " post_ensemble[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " nengo.Connection(pre_ensemble[0], post_ensemble[0], transform=0.16)\n", + "\n", + " with nengo.Network() as motor:\n", + " press = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + " release = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + "\n", + " nengo.Connection(press, pre_ensemble[0], transform=tau * 3)\n", + " nengo.Connection(press, post_ensemble[1], transform=-1 * 6)\n", + "\n", + " nengo.Connection(post_ensemble[0], release)\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)\n", + "\n", + " pr_press = nengo.Probe(press, synapse=0.01)\n", + " pr_release = nengo.Probe(release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(pre_ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(post_ensemble[0], synapse=0.01)\n", + "\n", + "test_doubleintegrator(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This exhibits some of the hypotheses of this model;\n", + "specifically, that the medial prefrontal cortex\n", + "contains these integrators,\n", + "and that they can be involved\n", + "in control of motor actions.\n", + "\n", + "Let's do both of the remaining principles\n", + "by making some parameterized functions\n", + "to get a sense of all of the knobs\n", + "that be be tweaked in this model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:09.298866Z", + "iopub.status.busy": "2020-11-25T16:51:09.293621Z", + "iopub.status.idle": "2020-11-25T16:51:10.310287Z", + "shell.execute_reply": "2020-11-25T16:51:10.309847Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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aQ6NtnkZl+Apz8tX+r4ptc3ZytoIShS3jEIZ//jxkZdn+oCsoSJGsDF9haer71Le2BIWN4RCNtllZ2ruPj3V1mEJeSCcx0aoyFA7ML/f+gl7qcXW20anfFFbDIQw/O1t7t9WpDQujYvgKc6OXeoN1ZydnnFHhHEVxTArpCCEGCSFOCiHOCCGmG9k/TghxTQhxIPf1iPmllowyfEV1RqfX5S8vGrrIekIUNk+ZNXwhhDMwD+gPxAJ7hBCrpZTHihRdLqV80gIayyQnNzmgMnxFdSSvhu/v7k9Nz5pWVqOwZUyp4XcDzkgpo6WUWcAyYKhlZZUPe6rhu7trA65UDF9hLnRSq+GPaD3CykoUto4pMfwGwPlC67HATUbKjRBC9AJOAVOllOeLFhBCTAQmAjRq1Kj8akvAngzfyQn8/FQNX2E+8vLf20s3TCe9E4E5gbhK6/7D1nGtw/Hjx62qoTQ8PDwIDg7G1YzGZq5G29+ApVLKTCHEY8Bi4LaihaSU84H5AF26dJFmuna+4bvYSRN0zZoFE7YoFJUlr4bvLOzD8ANzAqkXVA+/AD+rzrGbpcuitndtq12/NKSUxMXFERsbS2hoqNnOa0pI5wLQsNB6cO62wuLipJR5eVm/AjqbR55pXL2qvbu7V+VVK06tWnDtmrVVKByFned3AvZTw3eVrlY3e1tHCEFgYCAZZs7BYorh7wGaCyFChRBuwGhgdRFx9QqtDgGq9HfShQtarblGjaq8asUJCoLr162tQuEo5GXJlNJsP5otjjL7srHEZ1RmEERKmSOEeBJYDzgDC6WUR4UQrwORUsrVwFNCiCFADhAPjDO70lLIztbi4vZCrVpw+LC1VSgcjcSMRGtLUNg4JkW9pZRrgbVFtr1aaPklwGpJt7OytJmk7IW8kI5KkayoLNEJ0fnLqdlq7kxTCfQOpHXb1qCH1q1bs3jxYrzsIRlXJXGIXDpZWfbRQyePoCAtPXJamrWVKOydE9dP5C+7OtnRP4GV8fT0ZNM/mzhy5Ahubm588YXhTGE5eYN7HAyHMXx7q+GDarhVVJ7raQWNQaPajrKiEvulZ8+enDlzhi1bttCzZ0+GDBlCmzZt0Ol0PP/883Tt2pX27dvz5ZdaW8mlS5fo1asX4eHhtGvXjm3btqHT6Rg3bhzt2rUjLCyMjz76yMp3ZRw76chYOtnZ9lfDB83wQ0KsKkVh50QlRAFwZ/M7cXexk25qhXjpOU8OHzJv76Kw9jrefj/dpLI5OTn88ccfDBo0CIB9+/Zx5MgRQkNDmT9/Pv7+/uzZs4fMzEwiIiIYMGAAP//8MwMHDuSVV15Bp9ORlpbGgQMHuHDhAkeOHAEg0UZHVjqE4dtbDb92btffvO6kCkVFScpMomv9rkzsPNHaUuyK9PR0but+Gy5OLvTs2ZMJEyawc+dOunXrlt/vfcOGDRw6dIgVK1YAkJSUxOnTp+natSvjx48nOzubu+++m/DwcJo0aUJ0dDRTpkxh8ODBDBgwwJq3VyIOYfgZGfbTBx+gQQPt/cKF0sspFKWRmZNJRk6G0dmu7AVTa+LmJi+GX3TglXehibGllHz66acMHDiw2PFbt25lzZo1jBs3jmnTpvHQQw9x8OBB1q9fzxdffMGPP/7IwoULLX4f5cUhYvj2MoF5HnXraikWYmOtrURhr2w/t52RK0Zy8cZFuzZ8W2bgwIF8/vnnZOcO5T916hSpqamcPXuWOnXq8Oijj/LII4+wb98+rl+/jl6vZ8SIEcyaNYt9+/ZZWb1x7L6Gr9Np89naU48qV1fN9FUNX1FR5u+bn79sj7F7e+CRRx4hJiaGTp06IaWkVq1arFq1ii1btvDee+/h6uqKj48P3377LRcuXODhhx9Gr9cyl7799ttWVm8cuzf89NxfhPZUwwcIDlY1fEXFcXEq+Nd1d1aGX15ir8eSpcsy2Na7d2969+6dv+7k5MRbb73FW2+9ZVBu7NixjB07ttg5bbVWXxi7D+nkpZrImyvWXmjQQBm+ouK0Dmqdv2wvSdMU1sfuDT9vPlt76qUDWg3//HlttK1CUR5iEmNIzkzOX8/UZZZSWqEowO5DOpm5z7o99dIBaNIEbtzQkqjlDcRSKEzhqXVPGaw38G1gJSUKe8PuDT8vF7691fBbtNDeT51Shq8wncIZMVsEtmBE6xF0q9/NiooU9oQK6ViJ5s2199OnratDYV/kTXYCWu6c7sHd7SYPvsL62L3h54V07M3wQ0K0GbpOnbK2EoU9ka3Lzl/W6XWllFQoimP3IZ28Gr69xfBdXaFpUzh2zNpKFPZEtr7A8E/EnSilpKI0CqdHDg0N5bvvviMgIKDE8jNnzsTHx4fnnnuu6kRaALuv4ecZvj0lT8ujY0fYv9/aKhT2RGHDV1ScwumRa9asybx586wtqUqwe8O31146AJ07w7lzarpDhenk6LQ87U1qNOHlW162shrHoHv37lzIHfYeFRXFoEGD6Ny5Mz179uTEieK/okoq89tvv3HTTTfRsWNH+vXrx5UrVwD4+++/CQ8PJzw8nI4dO3Ljxg0A3nvvvfzUy6+99lqV3KvDhHTsLYYP0KmT9h4ZCbnZWRWKUsmr4Q9rOYybg2+2sprK89LGlzh81bzzfYbVDuPtfqalNtDpdPz1119MmDABgIkTJ/LFF1/QvHlzdu/ezeTJk9m0aZPBMSWVueWWW9i1axdCCL766iveffddPvjgA95//33mzZtHREQEKSkpeHh4sGHDBk6fPs2///6LlJIhQ4awdetWevXqZdbPoijK8K1It25aw+2WLcrwFaaRZ/iuznYYw7Qh8tIjX7l0hdatW9O/f39SUlLYuXMnI0eOzC+XmWk4qK20MrGxsYwaNYpLly6RlZWVn2Y5IiKCadOmcf/99zN8+HCCg4PZsGEDGzZsoGPHjvnnPX36tDL8srBnw/fxge7d4c8/YfZsa6tR2AN5IR1Hmc7Q1Jq4ucmL4fsIHwYOHMi8efMYN24cAQEBHDhwoMTj9Hp9iWWmTJnCtGnTGDJkCFu2bGHmzJkATJ8+ncGDB7N27VoiIiJYv349UkpeeuklHnvsMcvcYAk4RAzfxQWc7bQr8sCBWsOtyqujMIW8Gn7h5GmKiuPl5cUnn3zCBx98gJeXF6Ghofz000+ANsjt4MGDBuX9/PxKLJOUlESD3MkuFi9enH9MVFQUYWFhvPjii3Tt2pUTJ04wcOBAFi5cSEpKCgAXLlzgahXMiGT3hp+dbZ+1+zxGjdLy6Sxdam0lCntAhXTMT8eOHWnfvj1Lly5lyZIlfP3113To0IG2bdvy66+/FitfUpmZM2cycuRIOnfuTFDePKbAnDlzaNeuHe3bt8fV1ZXbb7+dAQMGcN9999G9e3fCwsK455578htzLYndVxPsbXrDojRrBhER8PnnMHWq9mtFoSiJvJCOquFXjqLpkX/77bf85XXr1hUrnxeeAa3fvrEyQ4cOZejQocW2f/rpp0Y1PP300zz99NPlkV1p7L6Gn5lp34YP8OKL8N9/MH9+2WUV1RtVw1dUBrs3fHuv4QPceSf07w/PPw+HDllbjcKWydE7VqOtomoxyfCFEIOEECeFEGeEENON7HcXQizP3b9bCBFidqUl4AiGLwR88w0EBECfPvDzzypPvsI4ebl07D2kI9UDXiaW+IzKfGqEEM7APKA/EAvsEUKsllIWzgIzAUiQUjYTQowG3gFGmV1tLsmpmSSlZ+OdCqnZ4OIF6XaSRypDDylZxbf714I//oL774cRY7Rsmv37Q9u20LAh1KwJnp7ay8MDECDQvixKeynsi6TkHLylkQckb3+qNsWbLtODdDvNkpnhlENSfDK+/v4IKz6kUi/Q2ahvSCmJj4/D1dWD3I48ZkGU9S0ihOgOzJRSDsxdfylX0NuFyqzPLfOPEMIFuAzUkqWcvEuXLjIyMrJCom+d+RpbxesVOlahcAjeuQ7pgdZWUSFq1Mhm5sxYmjXLwMnug8qWQa+HM2c8mDkzmISEouE7sVdK2aUi5zXld2ED4Hyh9VjgppLKSClzhBBJQCBgkCVGCDERmAjQqFGjiugFYHTXAcSv9sXDU1uvXw8C7eTZT0yCHt1NK6vTQVISJCRok7VnZxe8oCDsU/hdAuS9K+yOs4kxeDiXPkGzt6xDkycvo9Wr7JMDsdrLmiRmJtGjYQ/riiiDl42kS3r++Yqfr0oDgVLK+cB80Gr4FT3PpMERcC6C4GCzSasyYrNhkm0/Ywor8vmezwn2M+XBjra4FkcnNjmWSV3t75+xMoZvyg+qC0DDQuvBuduMlskN6fgDcRWXpVAoFApzY4rh7wGaCyFChRBuwGhgdZEyq4Gxucv3AJtKi98rFAqFouops9EWQAhxBzAHcAYWSinfFEK8DkRKKVcLITyA74COQDwwWkpZ6m9OIcQ14GwltAdRpI3ATrBH3faoGexTtz1qBvvUbY+aAVpKKX0rcqBJhm+LCCEiK9pSbU3sUbc9agb71G2PmsE+ddujZqicbtUpSqFQKKoJyvAVCoWimmDPhm+vqcbsUbc9agb71G2PmsE+ddujZqiEbruN4SsUCoWifNhzDV+hUCgU5UAZvkKhUFQTbN7wbTk1c0mYoHmaEOKYEOKQEOIvIURja+gsSlm6C5UbIYSQQgird2kzRbMQ4t7cz/uoEOKHqtZoDBOekUZCiM1CiP25z8kd1tBZRNNCIcRVIcSREvYLIcQnufd0SAjRqao1GtFUlub7c7UeFkLsFEJ0qGqNxihLd6FyXYUQOUKIe0w6sZTSZl9oA72igCaAG3AQaFOkzGTgi9zl0cByO9DcB/DKXZ5kbc2m6s4t5wtsBXYBXWxdM9Ac2A/UyF2vbQ+fNVrD3KTc5TZAjA3o7gV0Ao6UsP8O4A+0zN03A7vtQHOPQs/G7bag2RTdhZ6jTcBa4B5TzmvrNfxuwBkpZbSUMgtYBhSdNHIokDdF/Aqgr7Bmkm0TNEspN0sp03JXd6HlJ7I2pnzWAG+gzXeQUZXiSsAUzY8C86SUCQBSyqtVrNEYpuiWgF/usj9wsQr1GUVKuRVtJH1JDAW+lRq7gAAhRL2qUWecsjRLKXfmPRvYzv+iKZ81wBRgJWDyM23rhm8sNXODkspIKXOAvNTM1sIUzYWZgFYrsjZl6s79id5QSrmmKoWVgimfdQughRBihxBilxBiUJWpKxlTdM8EHhBCxKLV4KZUjbRKUd5n39awlf/FMhFCNACGAZ+X5zj7nifNzhFCPAB0AW61tpayEEI4AR8C46wspby4oIV1eqPV3rYKIcKklInWFGUCY4BFUsoPcich+k4I0U5Kqbe2MEdECNEHzfBvsbYWE5kDvCil1JcnoGHrhl+e1MyxNpKa2RTNCCH6Aa8At0opM6tIW2mUpdsXaAdsyX3A6gKrhRBDpJQVm7qs8pjyWceixWWzgf+EEKfQvgD2VI1Eo5iiewIwCEBqM8l5oCX7soWQVEmY9OzbGkKI9sBXwO1SSntJ694FWJb7vxgE3CGEyJFSrir1KGs3TpTRcOGCNtNDKAWNW22LlHkCw0bbH+1Ac0e0Rrvm1v6My6O7SPktWL/R1pTPehCwOHc5CC3kEGgHuv8AxuUut0aL4QsbeE5CKLkBdDCGjbb/WluvCZobAWeAHtbWWR7dRcotwsRGW5uu4UttusQngfUUpGY+Wjg1M/A12s/dM+SmZraeYpM1vwf4AD/lfkOfk1IOsZpoTNZtU5ioeT0wQAhxDNABz0sr1+JM1P0ssEAIMRWtAXeczP3vthZCiKVoobGg3LaF1wBXACnlF2htDXegGWga8LB1lBZgguZX0dr8Psv9X8yRNpBB0wTdFTuvlZ8hhUKhUFQRtt5LR6FQKBRmQhm+QqFQVBOU4SsUCkU1wWqNtkFBQTIkJMRal1coFAq7ZO/evdellLUqcqzVDD8kJITISGt131YoFAr7RAhxtqLHqpCOQqFQVBOU4SsUCoU5STwPGUnWVmEUZfgKhUIBmklHbTLctv/7AvPOSoX1r0BGsrYeF6W9J8UaHjOnHXzZq2A9LgpOFMo3mBQLV4/DnPawq4TcZxlJoNeBXg/ndsHpPyE2ErZ9UPH7w/Zz6SgUCkXlSY0DJyeQEvYugu5Pgoubtu/KUfBrAD8/Cqc3wMQtWnmfWvDrE5pZj1kKB5fCP3O116DZsG46hI2Ewz9B+APQajDaoGggIQaO/wa+9eGr27RtwxfA8dXa9jzWTde+ELxqQtRmSE+AuNMW+xisNtK2S5cuUjXaKhSKCqPLhswbmllu+xD2LYanD8K/C+DwChg6F1ZNgsDmcLDIRGc+dSCgEXjXhpMmZPuu2RTioyxzH+VE/C95b0XTP6gavkKhsF2SLsC5f7Qac/MBkJUCNULgw9YFZcb+Bn/9T1v+8la4dEBbnpvribFGEqOmXNFepmIjZl9ZlOErFArLknodnN3AI3cCLynh2K9wdgf8txWunYCgliCcYNwa8KyhhTXmdTM8z6Y3jJ9/8V0Fy3lmb2lqhGhfQqYw+gdYdp+23G4E6LIMwzoP/Ax7v9Hi9u1GgKsXhPQEzwCt3M+PauW6jAc3H2BWhWWrkI5CobAMmSmw8TXY8xW4eEDPZ2Hzm9ZWZRr+jcC/gfbrIo+uj8KeBdDxARg6T4vzJ50HJPjWg8VD4PpJCO0Fff4P6rTVGnp962gNtRnJUKcNZGdATgasmaZ9cdw2A0qbxCThLBxZCRHPgJMTQogKh3TKNHwhxELgTuCqlLKdkf0C+BgtLWoaWhrXfWVdWBm+QuFArH4KGveA9qM084qPhk86Wvaa96/UDPf3Zwq2DXhT61lz7Tg4u4OuyNxCj23TTPjqUa2hde8iiN4C/sEQfh8ENtMaZXs9DysnaL9EOj8MzfpC67solfQEuHYSGt1s5hs1xNKG3wtIQZuc2Jjh34E23+YdwE3Ax1LKm8q6sDJ8hcIOSY0D70CtwfTEGi3MEnfGPOdu1h9itmu13m6Pwv7vtF4wXR+F+bfC1WNauVcTtB43ecz0196fOwNegdq+hLPg3xDO7YR64Vr4JfUqNL2tHPd6XTP/22aAk7N57tEMWNTwcy8QAvxeguF/CWyRUi7NXT8J9JZSXirtnMrwFQo7IidL67K4/H64433N5HdXeB4OGPyhFtIozMwyBivlGXvRcpHfaOGTht2KH+OAVMbwzdFoW9JM9cUMXwgxEZgI0KhRIzNcWqFQmI28yt+y++DkWmg9BG5/B07+YWjOa58z/Zx3fQIX92uNs/FRWg37wV+0fQ27af3U0+PB3a/sczXtC7VbF9/exeoTa9kNVdpLR0o5H5gPWg2/Kq+tUChKIPkibPg/rWGwTju4ckTbfny19ioPzxyBgIZw6ZAWgukwGjqP1falJ4Cbb0HZumHau3egaed+8OfyaVEUwxypFexypvqqJFun52JiOgM++pvFO2OsLUdR3flhlBYe0WXDn69qfdqPrNT25Zm9Kdw8GV6MgU4PaV0qH9+umT1Avfaa2RfGswY4q57g1sQcMfzBwJMUNNp+IqUsM5hWnWL4vd7dzLn4tPz1mNmDrahGUW3R62F+L7h8uGLH93kFAhpr3Qyb9DarNIXpWDSGb48z1dsSOr00MHuArBw9bi4qb52iiki5qmVw3Luw/GZ/64tarL1ZP8toU1QpZRq+lHJMGfsl8ITZFDkYExYXH9YdeTaeHk2DrKBG4fBkpcH6lyEtDkZ8pZn9nGI/zI3TcrDWwHrjorb+Uiy4+5Z+jMKuUAE1C7LzzHW2nLxWbPv1lCwrqFE4LFJC5NfgEQAX9mnD9AFm1Tbt+A5jtEFHobkpfaM2QeMIcHG3iFyF9VCGbwEuJ2WwbM855mwsSHPap2Ut2gcH8PFfp/l44ymGdKhvRYUKh0Gvh/3fwppnTSsf3A1i/9WG/je6SRuU5FGkS2R5Bicp7Apl+BZgwEd/k5yRY7Dtxdtb0bSWDx//dZqoa6lIKRGl5c9QKExhyT0Q9VfZ5QbM0nLAC6GNIPUKLD1/i8IhUS2HZmbzyasGZv/Ubc2ImT2YVnX9cHV24uGIEADiU1VYR1EBEs7Ct3dD/H/a6NeyzL7NUC0VQY8pBQbvHaTMvpqiavhmJCktm4e/MWykfbB7iMF6t5CafLMjhrWHLxXbp1CUStRm+O5ubfmT8LLLvxijjWB1UvU6hYZ6EsyEXi/p8PoGg22P3BJKLV/Dhq86/h4AzPj1aJVpU9g5q5+CQz8VmH1JtB0GE/7Ulr0CtYFONpT0S2F9VA3fTCz595zB+q9PRNA+2L9Yudq+queDohycWKtN3bdvsfH9A2ZpI14LG/vUY+DqWTX6FHaFMvxKotNL3lxznIU7/jPY3qqer9FG2eAaXtTz98DTTdW8FEU4uEyb7QipdbVMvWY8UVmddjDkE2jQ2fh5/BtYVKbCflGGX0l+P3SxmNkDuLuUbOi9mtdieeR5UjJz8HFXf4Jqzck/tIbY+Gj490vTjnlotekJxxSKQii3qQRv/H6Mr7cXmH2HhgH8+kREmcf5e7kCMGTudjY929tS8hTWRpet1dRd3LT1jCQ4ugraDIHYvXDiN23GpfLw9CFl9ooKowy/gqRl5RiY/f8Nbk3/NnVMOtY9N49O9LVUi2hT2AjzukHiOXg1TjP+Oe0hIxF+e6rkYwIaaVPv7ZqnfUE06wdN+kDL27UJrH1Ne8YUCmMow68gYxbszl/+7P5O3BFWz+Rjh3VswKebtGnhkjOy8fNwNbs+hQ0QH629b/sA/nq97PKNb4GH12jLPafBrs+gywRw97GcRkW1QnXLrADvrDvBwfOJ+esDTKzZ59GkVsE/8Fdbo80lS2FLpMUXLJdl9j2fg66PwNhCk404u0LE08rsFWZFGX45SEzLYsXeWD7fEpW/rVtoTVycy/8xLnhIS2f9yaYznItLK6O0wqbJSC6YHhC0jJXvhpZ+zLD5MHmXZva9noPBH6g+8wqLo0I65aDbm3+RpdPnrz9wcyNm3R1WoXMVjvf/F5dKo0CvSutTWIG4KPi0U8F675ch7J6yj+swSnvvO8MyuhQKIyjDN5HMHJ2B2U/q3ZRJvZua5dxrD10iM1vHgLZ1zXI+hYXJSIYLe42PfN3ylvZSKGwQZfgm8NW2aGatOW6w7cVBrcx2/uWR51keeZ4TbwzCzdkJJyeV2MpmkRLebwE56WWX7fOKNpDqwA9arvl6HbReOw3LnAFUobAIKoZfBnEpmcXMfnhH84xk/Pz+TgbrrWas4+VfyjcFXY5OT3ahXx72QHqWjr+OXzGp7L5zCaw/etnCikrgzEbILmTsp/+E/wWUbvZthxcsBzWHHk/C5J1w+2wIHwO9X4SmfSwmWaEoDWX4ZTDtx4MG6+ENA3hvZAeznPv2sHrEzB5sEM9ftuc8B84n8k9UHG//cZwFW6P532+GidairqVwNTmD6ymZDPhoK93e3EhyRjaPLN6T33vo/fUnWZg7TiA+NYuMbB3GJqzP0en58u8oLidlABiUib6Wwpd/RxF9LYXfDl402rh8IyOb2ITi2y8mpnPqyg0OnE8kI1tnsG/OX6eYsDiSf/+LL3ZcUYZ/tpPHvtvL9ZRMkjOyyyxfElk5etKycoptT0rL5siFpPz7zk9bHbsXvh8Bi+6EY6thzXNa7vnSeOJfTvb8hK9aLyQzfBy0GESOTk96VsH9SymN3kdcSiYJJaTMllKy5eRVo3+/8nA+Po3TV26UuF+vl+j1lbuGLfL7oYssLZLrypL8cfhS/v+erSEq+xBVlC5dusjIyEirXNtUFmyN5s21BbX7wWH1mFekVm4O4lOz6PTGn/nrrer6cuJy8X/MMd0a8fIdrQibuaHYvqn9WvDRxlNlXuvj0eHMWnOcSbc2JSzYnw1HL7Ng23/cf1MjXJ2dWLQzhqHh9RneKZi31hznZBGD+PLBzni5OSMQ1PFz5/aPt5GjlzzbvwUpWTl4u7nwVN/mhExfk39MkI8bbwxtx42MHD788xSXk7Uvl3u7BNO5cQ1eXHmYkEAvbgoNpIa3G0E+btzZvj41vd1o8X9/GFx/aHh9BrWty6Ql+xjTrSEP3NyYCwnprDl8CWcnwYA2dfBwdebTTWeYeVdbJv+wl36t63DgfCL7zyVyaOYA2s/cQA0vV7Y834cO/9M+y+EdGzA8ezWXT+6hYz0Pml4t/hkX5mfdLfyo641eCpbWW8qfbd/hmK4hn+SOr+jXug7/N7g1zyw/wIHzifnPzve7zvJ/q46wcdqt/HnsCmlZOTw7oGX+5xUzezCg9Qh77qdD9G5Zi/9bdQSAcT1CaFTTi+AanlxPyaJLSA1a1PElOSObjzeeZu/ZBJ7p15z5W6OZ2r8F205dY3D7+rSsq81LW/QaALEJaby//iQz7mxD51kbAXh/ZAcCPF3p06o2h2ITWbL7HK/d1YZ1Ry4zJLw+UsKnm07zaM8mnI1L49cDF/nz+GX+mtYbV2dhkEMqNiGN4BpexCakMerLXXzzcFdCg7xxLdSzbXd0HOcT0hnQtg6+7i75x+v0krs+3c6U25pxe6FxLpk5Ol5aeZg1hy+xb0Z/vN1dDCYUipi9iUY1vVg68eYS7xsgNTOHpf+eY1jHBvh5upKZo+fUlRvU8HIjNMg7X39iWjaNAr1KHC9z5EISIUHe+WlSCl/v71PX+GbHfywc2zU/VJuWlYOXW0E0XUqJXoKzkVBujk5PeraOlMwcsnL0NA70RgixV0rZxaiYMlCGb4QbGdn0fm8LcYVqXDc3qcmih7vh4WqZrnOHY5O4a+52i5y7qrm5SU12RZdde7clPMnguMf4UstkSleGZ83kqAwByt/OEuTjZtb5jAO93QgL9jc6b3IeQmhKC1fcX7mjtUFFpiR8PVy4UWTmtiZB3kRfL3mE+JAO9fnveipP9GnGt//EsDMqzvi53V3oFlqTzSevGmhzc3YiW69HSnjw5sZ8t+ssAJ0b12Dv2QQAnETB/dT182BQu7os2hmDs5NAV+hkbev70aSWD78d1CZlH9u9MRuPX2VIeH06BAfw+Pd7Aajh5Uqrun78E12gdcFDXWjXwI/ub28CoHltH2YOaUtSejYerk6cupJCWmZO/hd8cA1Pvh7blYFztuaf48N7O/DiykNk6yS7XupLXX8P3l13gs+2RBHRLJD6/p4cvpDEics3uCm0JtduZBJ9PZXVT0bw5d/RXEvJpGktH4NfJ4Pa1uXLh7oowzcnM1cfZdHOmPz1Tc/eSv0AT4uZfR5bTl5lXJEJVBSW4y2XBfRyPswzWZN5zvUnbnYq2QRHZ/0fu/RtqlCdwtGo6+eR/+u2Mpx9584KG76K4RchI1vHsYvJBtua1PKxuNkD9G5ZmxNvDKJ1PT8+Hh2ev31Qoe6az/Zvkb8c9dYdvDWsYBzAU7c1Mzhfz+ZBNK/tQ++WtQy2P3JLwaCgaYXON+GWUB6/tSkjOwdzZ/t6dCiUzz+sgWFu/xZ1fIpl+mxeu/io0EHl6Gr64b3F20YCvd1MOrboRDNdQ2oYLddGxOBBJnNdP+Y+l80Ei+uscH/dqNm/nz2SEZmv0TJjkV2bfZCPG6O7NrS2jGqPOcy+sqhumYX49cAFnl52wGDbz5N7VKkGD1dn/ni6JwBDwwt6A+09G09okA81vd1Y/M9Zrqdk4uwkGNOtIQlpWbg6Cyb2akrbBv4cv5TMM/1aGJw3OSPbID66ZPc50rN1PNW3OZtOXOXA+URm3Fnc1Pp+sIVW9fyYd18nEtOy+HJrNPd1a0TDmtpAsT0x8bSq68ulpAwaBHgy49cj/LzvgrbvlX7U8nVHp5c4OwmydXqav6LF5FdO6sGExXt49c42NA70pmENT2r7eTC8UzB6vWTgnK2cvprCPy/1Jfp6Cg9+/S/v3dOeE5dvcCkxnVcGt8HNxYnz8Wn8eewKY3uEkJKRw+XkDC4mptO9aSDXUzLx9XDFz0O777j17xD4j2l95Pc7hzH11QU85yQ4G5fKm2uOM6BtXe7qUI/41CychOCmt0qeT3blpB4cv5RMv9Z10EvJ/K3RtK3vx8guDVmy+yyv/HKEx3o14fawevx3PYUOwQH8GBlLHT93RnZpyPojlzkXn0Z8alZ+WOOrh7pwW6vavLb6KF5uzjzSswnrjlzit4OXmHtfR+74ZFuxkNGgtnWZMzqczBw9Z66mEHk2gU6NAnimXwtCAr3p9d5mAFycBDmlNNiO7BxMq3p+vPH7MUALl1xMTKdpLR8izybwZJ9mzN2shTda1vHlv7hUsnL0+c9B1zc3GpzvsV5NaNfAn2s3MnFxFrz661GeH9iS99afBOCNu9sRfS2FO8LqMfKLfwyOW7jjP7J1BVoLH1fHz53dL/djzaFLPPHDPqP38tawMIPecH9O7cWLKw/Rp2VtPvhTawdrU8+P8wlp+SEtH3cXUjJzGNOtUX6I5YGbG/H9rrIbgx+/tSlf/B1VZrmqQIV0crmRkV2sMfTj0eEGpmsrJKVnk63TE+RT8dmzMnIbgoJ83MnM0ZGjk3ibKTf/1eQMXJydqGmkdn4xMZ0D5xPLlWyu0ty4os0YtflN04/pMUWbTaoUPt54mo82nuKNoW1JzdLx6V+n+fXJW0jJzCG8YUDlNBdi8c4Y3l13gkMzBxpt2DPG5aQM/o2JZ0iH+qWWe3/9STo0DODWFrX4fEsU43qE0PfDLVxPyWLJIzfxxA/72PJcbwK8tL9lWlYO//4XT++WtQGt4XPu5jM83bc5rWasA+C/t+9ACMH209e5kpzBiM7B3MjI5rMtUQwNr4+3m0t+hQEgW6fnYmI6jQO9SUjN4lJSBm3q++Xve/23Y7Sp74ezk+DeLsZ/qZy8fIPxi/YwZ3Q4XUNqAnDqyg1+ijzPgm3/4eHqxBO9m9GrRS065P5tjDXmFt6Wo9MjhMj/zBPTsvD3dCVLp0enl3i5aY3FUddSGfnFThLSCnpfrXoiAh93Fy4lpdOzeS2ir6Xw7T9n6dk8iL6t6+RfZ90zPRk0Z1v+cTW93VjxeHdCg7zZcuoaxy4m0yE4gHoBHjQJ8ubbf84yLiJUxfAry4xVR/JrUgD+nq4cfG2AFRUpKowuG7Z/BN0mwsGlsG568TIeAVqq4jzG/gYh2i8rTv6hpSV2KT2cJKVk4/Gr9G1V26EGy+X1CDH2hV0aSenZCIHdZH89ciEJJyHyv1yg5B49ZZGRrSNbp8fXxHu/nJSBkxPU9vUgR6fnyMVkkysJqpdOJcnI1uXXTgDGR4QyvFMD2hWJWyvshIPL4JfHSi/z0GpIiNFy04ffD3d/ViXSFLZNbEIaTkJQP8B25wSujOFX6xi+lNp8tOlFBga9epf9NtBVa06ug1Pr4NKBksv4NdBGuja6GfS5P8FrlpHZUlFtCK7h2EkMq7XhX7uRyVdFRsQtzx2sobADLh2CL3tqk4TosmD/d8XLBDSC+34Ez5rw29Mw8E0IzE1616wf3L8SmvSuUtkKhbWo1oZ/9Uamwfr+Gf2pUc64pcJKnP6zINVB5NfF93d7DLpPhhohBdvuW1a8XPN+FpGnUNgi1dbwLydlcOenhiNbldnbMFJqjaw7P9WyTv46peSyD/4CTW+rMmkKhb1gkuELIQYBHwPOwFdSytlF9o8D3gMu5G6aK6X8yow6zYZeL3lvw0mDWasAbmtV20qKFCUS/58WX4/+G47+DHsXlX1MrxeU2SsUJVCm4QshnIF5QH8gFtgjhFgtpTxWpOhyKeWTFtBoNq4mZ9DNyGCZ3S/3NXlEp8LCHFyuxeLrhmmTeJdGh/vg4A9QqxX41oML++C2V6pGp0Jhh5hSw+8GnJFSRgMIIZYBQ4Gihm/zGEvk5ObiRG1fd4MMf4oq5tivkJ2hTfv3y0RtW8y20o/pcB8M+1x7KRQKkzDF8BsA5wutxwI3GSk3QgjRCzgFTJVSni9aQAgxEZgI0KhRo/KrrSQJaYbDznu1qMW349XsQ1bnx4e093bDSy/XcjDcPU/LVd+4alNeKBSOgLmSp/0GhEgp2wN/AouNFZJSzpdSdpFSdqlVq5axIhYl+lpBWtd3RoQps7c2iedg4aCC9TeCSi47eincsxA8a2g9a9wcu7+0QmEJTKnhXwAKJ7AIpqBxFgApZeFYyVfAu5WXZl42HL1skDphVNeq/4VRLYmPhuSLcHoDtB0GKybAjUuQXXyWrBK56XFodYflNCoU1QRTDH8P0FwIEYpm9KOB+woXEELUk1Jeyl0dApQ9u0IVotdLJn6319oyqg/pifBhGxj9PXw3rGD7jo9NO37iFlg6RvtieOhXNTBKoTATZRq+lDJHCPEksB6tW+ZCKeVRIcTrQKSUcjXwlBBiCJADxAPjLKi53Px9qmBGoDp+7lxJziyltKJSnP4Tjq6C7FTYVHq2SQOGfga/ToaIZ6B+R3j2hKUUKhTVFodPnnbqyg1GfLaTG5k5LHq4Kz2aBqGXskomNKkWxEVBWjxciNSSke3+wrTjGnWHuz4Gd19IuQr1wy2pUqFwGFTytBJIy8phwEfaHJPPDWiRn8NbUUmy0kA4QeRCWP+S6ceN/gGW5UYDxxdkJ8Wv9JztCoXCPDi04Z+4fCN/+ZGeTayoxEG4chTcfGDeTZCTbtoxk3dpA6h6vQABDaFJH8i8UfZxCoXC7Dis4Wfl6FmSO/3Ythf6qBCOqXxzB4TcArVbQ5u7QQitl80nnUwz+dptICNZ6y/v5qOdZ8inBfsfWmUp5YpSyM7OJjY2lowM68+rqjANDw8PgoODcXU134QyDmv4s/84wcp9sQA0sOHJDGyOszu0F0Dncablr8ljRhw4O+wjZdfExsbi6+tLSEiIGlVuB0gpiYuLIzY2ltBQ883XYK6BVzbH0YtJ+cuONP2c2dFla33ls9O1V2FKM/uGhQZbj/5BC90os7dZMjIyCAwMVGZvJwghCAwMNPsvMof9Dz12MRmAAC/7mF/Tavw4Fk6uAb9gSI41/biH18GaaVo8vlX55v9UWAdl9vaFJf5eDmn4xy8lcyMzh4Ft6/DJmI7WlmNb6HVaDxshtBzzJ7VJm00y+6nH4L+tkHoNnJzgrjkWlapQKMyLw4V0pJTc/rGWaXFirya4u6jG2nzS4mFWHVjxMGx9H/4XUPYx/WbC4zug44PgWxfCx0DEU5ZWqnBAnJ2dCQ8Pp127dowcOZK0tHKk1yiCj49PmWXmzJlTqWuYSkxMDD/88IPFr2MOHM7w41ILMmKGBHpbUYmVybyh9ZbJyQJdDnwzGN4N1SbuPvoLbHqj5GNfvgQvX4QnI+GWqVC3HQydC07qy1NRcTw9PTlw4ABHjhzBzc2NL74wHKSXk5Nj1uuZ0/BL01YRwzf3vZqKw4V0Yq4XZMT096zG8fu3GwJSS1MQdi+c3V7mIQDUCC3IRBnU3GLyFNbjf78dzW/jMhdt6vvx2l1tTS7fs2dPDh06xJYtW5gxYwY1atTgxIkTHD9+nOnTp7NlyxYyMzN54okneOyxx0o8z5YtW5g5cyZBQUEcOXKEzp078/333/Ppp59y8eJF+vTpQ1BQEJs3b2bDhg289tprZGZm0rRpU7755ht8fHxYu3Yt06ZNw9vbm4iICKKjo/n999+ZOXMmUVFRREdH06hRI95++20efPBBUlM1j5k7dy49evRg+vTpHD9+nPDwcMaOHcukSZOYNGkSkZGRuLi48OGHH9KnTx8WLVrEzz//TEpKCjqdjr///rvSn3t5cTjD/y/X8Id3aoCLs8P9gCmbjCSt3zy5KTMu7tdexnh8B5xYA1veggdXQUhP1dNGYXFycnL4448/GDRIS429b98+jhw5QmhoKPPnz8ff3589e/aQmZlJREQEAwYMKLVr4v79+zl69Cj169cnIiKCHTt28NRTT/Hhhx+yefNmgoKCuH79OrNmzWLjxo14e3vzzjvv8OGHH/LCCy/w2GOPsXXrVkJDQxkzZozBuY8dO8b27dvx9PQkLS2NP//8Ew8PD06fPs2YMWOIjIxk9uzZvP/++/z+++8AfPDBBwghOHz4MCdOnGDAgAGcOnUq/14PHTpEzZo1LfTplo7D/XfviYnH39OVd0e0t7YUy6DXQ9I5yEwB/wbafK+hvbQ0B8kXIfLrko9tdSe0HwWBzbSyddtBnbbQfiTUVCORqwvlqYmbk/T0dMLDwwGthj9hwgR27txJt27d8g19w4YNHDp0iBUrVgCQlJTE6dOnSzX8bt26ERwcDEB4eDgxMTHccsstBmV27drFsWPHiIiIACArK4vu3btz4sQJmjRpkn/+MWPGMH/+/PzjhgwZgqenNo4nOzubJ598kgMHDuDs7Jxv4kXZvn07U6ZMAaBVq1Y0btw4v2z//v2tZvbggIb/Y2QsnRoFOFbt/vAK2PwWPLIR9n0LG18r/zmeOqBNCJ5HnTbauxDK7BVVQl4Mvyje3gVtbVJKPv30UwYOHGjyed3d3fOXnZ2djcbHpZT079+fpUuXGmw3pqckbR999BF16tTh4MGD6PV6PDw8TNZo7HzWwIFcEc7FaQ003u4O8j2WkaylGv59GsRHwbFVppt9gy4w+AOYfh5mJhmavUJhowwcOJDPP/+c7OxsAE6dOpUfMy8vvr6+3Lih5W26+eab2bFjB2fOnAEgNTWVU6dO0bJlS6Kjo4mJiQFg+fLlJZ4vKSmJevXq4eTkxHfffYdOpyt2HdB+vSxZsiRf/7lz52jZsmWF7sHcOIgzapy6on3o4yMcxNz+mQt/v1Ow/vvU0sv3fBb6vmpZTQqFBXnkkUeIiYmhU6dOSCmpVasWq1atqtC5Jk6cyKBBg6hfvz6bN29m0aJFjBkzhsxMbT6MWbNm0aJFCz777DMGDRqEt7c3Xbt2LfF8kydPZsSIEXz77bf55QHat2+Ps7MzHTp0YNy4cUyePJlJkyYRFhaGi4sLixYtMvgVYk0cKh9+yHRtENGGqb1oUcfXrOeuUpIvwZe9IPVq2WXv+hh86kLLQWWXVVRbjh8/TuvWra0twyZJSUnBx8cHKSVPPPEEzZs3Z+rUMipXVYSxv5vKh1+ExoF2OMF1WjwcWg7rppt+zOPboW6Y5TQpFNWABQsWsHjxYrKysujYsWOp3UDtHYcx/NRMraGmaS1v+xxd+66JYaiRi6FxBLj7gKvKAqpQVJapU6faTI3e0jiM4V9O1rLKPXlbMysrKScp1+DE7yXvD+4KHR+A8PvBuRoPJFMoFJXGYQz/UGwiAHX8yt9Vyqrs/QY2v2m4rcXtWg3ezQcGf6glKlMoFIpK4jCGP3X5QQDq2ovhSwlrn4M9XxluD+4KY5Zq/eMVCoXCjDiM4edR199ODD/lqqHZ934Zek5TYRuFQmExHCpW4OvhgpebnXyHXT9puN77RWX2CoemsumRS8tKGRMTQ7t27co8x1tvvVWua1aUAwcOsHbt2iq5VnlwCMPPytED8GjPCqQIOLgMblw2reyJtfDPZ5AUC+f3aNtysiB2r7as18GeryEnU0tYdn4PxEXBkZVaCCctXisnJSy+S1vuPA4e2VR+3QqFnVFWeuSyMEfeeXMafmkpjiti+FWRMtlOqsOlcz1FGzlXy9eE0WwJZ+HUOrjpMUiNg18eg6CWWhqC6C2w7X14dBPUbguuHppJf3YzjPoeluVm0lv/kvbe8zk4/BMkntUGQCG0af/WTCt+3RXjtffQXnDHBwXbB75dkI5YoagK/pgOlw+b95x1w+D22SYXz0uPHB8fz/jx44mOjsbLy4v58+fTvn17/v77b55++mlAm+pv69atxdIQl9SVctGiRaxevZq0tDSioqIYNmwY7777LtOnT89P4Na2bVuWLFnC999/zyeffEJWVhY33XQTn332Gc7Oznz99de88847BAQE0KFDB9zd3Zk7dy7jxo3Dw8OD/fv3ExERwejRo3n66afJyMjA09OTb775htDQUF599VXS09PZvn07L730Ev379zd6n0VTMBfN9WNuHMLwf9l/AYBaPqUYft6I4i9ugcxkcPcrSBt8/SQsvrOg7ILbCpbb3QMpV+Dr/sXPue39guXfnjZN7H9bYV7u8O27PlZmr6h2FE6P/Nprr9GxY0dWrVrFpk2beOihhzhw4ADvv/8+8+bNIyIigpSUFDw8PIqlIS6NAwcOsH//ftzd3WnZsiVTpkxh9uzZzJ07Nz9h2vHjx1m+fDk7duzA1dWVyZMns2TJEvr168cbb7zBvn378PX15bbbbqNDhw75546NjWXnzp04OzuTnJzMtm3bcHFxYePGjbz88susXLmS119/ncjISObOnQvAlClTjN4nGKZgtjQOYfjvrdfi4cVq+Loc2Pkx+DXQpvSLO12wb9Xjpp38yAozqTRCox6WO7dCURLlqImbE2PpkW+66SZWrlwJwG233UZcXBzJyclEREQwbdo07r//foYPH56f/thU+vbti7+/PwBt2rTh7NmzNGzY0KDMX3/9xd69e/Pz56Snp1O7dm3+/fdfbr311vw0xiNHjjRIhTxy5EicnbXBnUlJSYwdO5bTp08jhMhP+laU7du3G71PMEzBbGns3vDzRtgCBPq4aQvXTsKVIwVhlKqkxe1w6o+CdTcfCGoB/V6DH8fCkE9h32LtF4ZKS6yoRpSUHtkY06dPZ/Dgwaxdu5aIiAjWr19frmuZmjJ57NixvP322wbby0rWVjjF8YwZM+jTpw+//PILMTEx9O7du1w6i57P0ti94R+/VDBVW3ANLy13/MoJlT+xuz9kJsGt06FJb22yEZ86MKu2tj/8AWg7TBs41XYYNO8PWWngV0/bf+YvbdLvOoUmm5h+VntvM6Ty+hQKByAvlfCMGTPYsmULQUFB+Pn5ERUVRVhYGGFhYezZs4cTJ07QsGFDgzTEFcHV1ZXs7GxcXV3p27cvQ4cOZerUqdSuXZv4+Hhu3LhB165deeaZZ0hISMDX15eVK1cSFmY8Z1VSUhINGjQAtLaDPEpKmVz0Pqsauzf8i0kZuJPFwgfaaz1iSjP7h37VzPvsTqjdWutRs+kNePEsnNlYcOyQudDpQePneDUeTm+AFoO0wVHN+xXs8/AvWG7Wt9L3plA4OjNnzmT8+PG0b98eLy8vFi9eDGgTkG/evBknJyfatm3L7bffjpOTk0Ea4orkv5k4cSLt27enU6dOLFmyhFmzZjFgwAD0ej2urq7MmzePm2++mZdffplu3bpRs2ZNWrVqlR8eKsoLL7zA2LFjmTVrFoMHD87f3qdPH2bPnk14eDgvvfRSifdZ1ZiUHlkIMQj4GHAGvpJSzi6y3x34FugMxAGjpJQxpZ2z0umR0xPgzF/sOR6F15EltHU6CzdPhl2fFZRpc7eWaKx+OFw6CF0fKX0Ea0YSOLuppGQKh0OlRy4feSmTc3JyGDZsGOPHj2fYsGFVrqPK0yMLIZyBeUB/IBbYI4RYLaU8VqjYBCBBStlMCDEaeAcYVRFBpqDT6XB+JwSArlAwmiDP7O/7EVoUmSKtYbeyT+xh/FtcoVBUL2bOnMnGjRvJyMhgwIAB3H333daWZBZMCel0A85IKaMBhBDLgKFAYcMfCszMXV4BzBVCCGmh2VWiPrmTFiXtfD4KvIMscVmFQlFNeP/998suZIeYMtK2AXC+0Hps7jajZaSUOUASEFj0REKIiUKISCFE5LVr1yokWEpJi6Sdxnf2m6nMXqEoAWvNbqeoGJb4e1VpagUp5XwpZRcpZZdatWpV6BxCCLj3W+M7b6kekxgoFOXFw8ODuLg4Zfp2gpSSuLg4PDzMmwzSlJDOBaDwiIXg3G3GysQKIVwAf7TGW8vQZijymSMk7fqWJJ/mNN74KLQdbrHLKRT2TnBwMLGxsVT0l7Wi6vHw8Cj3gLOyMMXw9wDNhRChaMY+GrivSJnVwFjgH+AeYJOl4vd5iICGBAx6hQCAW+615KUUCrvH1dWV0FATp9FUOCxlGr6UMkcI8SSwHq1b5kIp5VEhxOtApJRyNfA18J0Q4gwQj/aloFAoFAobwqSBV1LKtcDaItteLbScAYw0rzSFQqFQmBOHyIevUCgUirIxaaStRS4sxDXgbCVOEQRcN5OcqsIeNYPSXZXYo2awT932qBmgpZTStyIHWi2XjpSyYv0ycxFCRFZ0eLG1sEfNoHRXJfaoGexTtz1qBk13RY9VIR2FQqGoJijDVygUimqCPRv+fGsLqAD2qBmU7qrEHjWDfeq2R81QCd1Wa7RVKBQKRdVizzV8hUKhUJQDZfgKhUJRTbB5wxdCDBJCnBRCnBFCTDey310IsTx3/24hRIgVZBbVVJbmaUKIY0KIQ0KIv4QQja2hsyhl6S5UboQQQgohrN6lzRTNQoh7cz/vo0KIH6paozFMeEYaCSE2CyH25z4nd1hDZxFNC4UQV4UQR0rYL4QQn+Te0yEhRKeq1mhEU1ma78/VelgIsVMI0aGqNRqjLN2FynUVQuQIIe4x6cRSSpt9oeXuiQKaAG7AQaBNkTKTgS9yl0cDy+1Acx/AK3d5krU1m6o7t5wvsBXYBXSxdc1Ac2A/UCN3vbY9fNZoDXOTcpfbADE2oLsX0Ak4UsL+O4A/AAHcDOy2A809Cj0bt9uCZlN0F3qONqGlvbnHlPPaeg0/f7YtKWUWkDfbVmGGAnkzAq8A+gpR2sS1FqdMzVLKzVLKtNzVXWgpp62NKZ81wBtoU1hmVKW4EjBF86PAPCllAoCU8moVazSGKbol4Je77A9crEJ9RpFSbkVLjlgSQ4FvpcYuIEAIUa9q1BmnLM1Syp15zwa2879oymcNMAVYCZj8TNu64Ztttq0qxBTNhZmAViuyNmXqzv2J3lBKuaYqhZWCKZ91C6CFEGKHEGKXEGJQlakrGVN0zwQeEELEotXgplSNtEpR3mff1rCV/8UyEUI0AIYBn5fnOKulVlCAEOIBoAtwq7W1lIUQwgn4EBhnZSnlxQUtrNMbrfa2VQgRJqVMtKYoExgDLJJSfiCE6I6WfrydlFJvbWGOiBCiD5rh32JtLSYyB3hRSqkvT0DD1g3f9mbbKhtTNCOE6Ae8AtwqpcysIm2lUZZuX6AdsCX3AasLrBZCDJFSVji3RyUx5bOORYvLZgP/CSFOoX0B7KkaiUYxRfcEYBCAlPIfIYQHWrIvWwhJlYRJz76tIYRoD3wF3C6ltKZ3lIcuwLLc/8Ug4A4hRI6UclWpR1m7caKMhgsXIBoIpaBxq22RMk9g2Gj7ox1o7ojWaNfc2p9xeXQXKb8F6zfamvJZDwIW5y4HoYUcAu1A9x/AuNzl1mgxfGEDz0kIJTeADsaw0fZfa+s1QXMj4AzQw9o6y6O7SLlFmNhoa9M1fGmHs22ZqPk9wAf4Kfcb+pyUcojVRGOybpvCRM3rgQFCiGOADnheWrkWZ6LuZ4EFQoipaA2442Tuf7e1EEIsRQuNBeW2LbwGuAJIKb9Aa2u4A81A04CHraO0ABM0v4rW5vdZ7v9ijrSBDJom6K7Yea38DCkUCoWiirD1XjoKhUKhMBPK8BUKhaKaoAxfoVAoqgnK8BUKhaKaoAxfoVAoqgnK8BUKhaKaoAxfoVAoqgn/D95mF/MCus1iAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def controlled_integrator(n_neurons, dimensions, tau=0.1):\n", + " with nengo.Network() as integrator:\n", + " integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)\n", + " nengo.Connection(\n", + " integrator.ensemble,\n", + " integrator.ensemble[:dimensions],\n", + " function=lambda x: x[:-1] * (1.0 - x[-1]),\n", + " synapse=tau,\n", + " )\n", + " return integrator\n", + "\n", + "\n", + "def medial_pfc(coupling_strength, n_neurons_per_integrator=200, tau=0.1):\n", + " with nengo.Network() as medial_pfc:\n", + " medial_pfc.pre = controlled_integrator(n_neurons_per_integrator, 1, tau)\n", + " medial_pfc.post = controlled_integrator(n_neurons_per_integrator, 1, tau)\n", + " nengo.Connection(\n", + " medial_pfc.pre.ensemble[0],\n", + " medial_pfc.post.ensemble[0],\n", + " transform=coupling_strength,\n", + " )\n", + " return medial_pfc\n", + "\n", + "\n", + "def motor_cortex(command_threshold, n_neurons_per_command=30):\n", + " with nengo.Network() as motor_cortex:\n", + " ens_args = {\n", + " \"dimensions\": 1,\n", + " \"encoders\": Choice([[1]]),\n", + " \"intercepts\": Choice([command_threshold]),\n", + " }\n", + " motor_cortex.press = nengo.Ensemble(n_neurons_per_command, **ens_args)\n", + " motor_cortex.release = nengo.Ensemble(n_neurons_per_command, **ens_args)\n", + " return motor_cortex\n", + "\n", + "\n", + "def double_integrator(\n", + " mpfc_coupling_strength,\n", + " command_threshold,\n", + " press_to_pre_gain=3,\n", + " press_to_post_control=-6,\n", + " recurrent_tau=0.1,\n", + " seed=None,\n", + "):\n", + " with nengo.Network(seed=seed) as net:\n", + " net.mpfc = medial_pfc(mpfc_coupling_strength)\n", + " net.motor = motor_cortex(command_threshold)\n", + " nengo.Connection(\n", + " net.motor.press,\n", + " net.mpfc.pre.ensemble[0],\n", + " transform=recurrent_tau * press_to_pre_gain,\n", + " )\n", + " nengo.Connection(\n", + " net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control\n", + " )\n", + " nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)\n", + " return net\n", + "\n", + "\n", + "def test_doubleintegrator_wprobes(net):\n", + " # Provide input and probe outside of network construction,\n", + " # for more flexibility\n", + " with net:\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)\n", + " pr_press = nengo.Probe(net.motor.press, synapse=0.01)\n", + " pr_release = nengo.Probe(net.motor.release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "net = double_integrator(mpfc_coupling_strength=0.16, command_threshold=0.85)\n", + "test_doubleintegrator_wprobes(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this does add some complexity and length,\n", + "the effort is worth it,\n", + "as we can now do simple parameters sweeps\n", + "to investigate the effects\n", + "of changing various parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:10.325996Z", + "iopub.status.busy": "2020-11-25T16:51:10.323149Z", + "iopub.status.idle": "2020-11-25T16:51:13.128838Z", + "shell.execute_reply": "2020-11-25T16:51:13.129272Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for coupling_strength in (0.11, 0.16, 0.21):\n", + " net = double_integrator(\n", + " mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0\n", + " )\n", + " test_doubleintegrator_wprobes(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `coupling_strength` changes how quickly\n", + "the \"Post Integrator\" rises,\n", + "which causes the release to occur\n", + "at different times.\n", + "Of the values tested,\n", + "only a coupling strength of 0.16\n", + "effects the release during the release window\n", + "(for this particular network seed).\n", + "\n", + "We will looks at some more techniques\n", + "for cleaning up this network code\n", + "and making it more flexible\n", + "in \"Additional tips and tricks for designing networks\"." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/usage/rectified-linear.ipynb b/.doctrees/nbsphinx/examples/usage/rectified-linear.ipynb new file mode 100644 index 0000000000..2c527c5e80 --- /dev/null +++ b/.doctrees/nbsphinx/examples/usage/rectified-linear.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding new objects to Nengo\n", + "\n", + "It is possible to add new objects\n", + "to the Nengo reference simulator.\n", + "This involves several steps and the creation\n", + "of several objects.\n", + "In this example, we'll go through these steps\n", + "in order to add a new neuron type to Nengo:\n", + "a rectified linear neuron.\n", + "\n", + "The `RectifiedLinear` class is what you will use\n", + "in model scripts to denote that a particular ensemble\n", + "should be simulated using a rectified linear neuron\n", + "instead of one of the existing neuron types (e.g., `LIF`).\n", + "\n", + "Normally, these kinds of frontend classes exist\n", + "in a file in the root `nengo` directory,\n", + "like `nengo/neurons.py` or `nengo/synapses.py`.\n", + "Look at these files for examples of how to make your own.\n", + "In this case, because we're making a neuron type,\n", + "we'll use `nengo.neurons.LIF` as an example\n", + "of how to make `RectifiedLinear`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:21.402613Z", + "iopub.status.busy": "2020-11-25T16:51:21.401749Z", + "iopub.status.idle": "2020-11-25T16:51:21.900434Z", + "shell.execute_reply": "2020-11-25T16:51:21.899908Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import tuning_curves\n", + "\n", + "\n", + "# Neuron types must subclass `nengo.neurons.NeuronType`\n", + "class RectifiedLinear(nengo.neurons.NeuronType):\n", + " \"\"\"A rectified linear neuron model.\"\"\"\n", + "\n", + " # We don't need any additional parameters here;\n", + " # gain and bias are sufficient. But, if we wanted\n", + " # more parameters, we could accept them by creating\n", + " # an __init__ method.\n", + "\n", + " def gain_bias(self, max_rates, intercepts):\n", + " \"\"\"Return gain and bias given maximum firing rate and x-intercept.\"\"\"\n", + " gain = max_rates / (1 - intercepts)\n", + " bias = -intercepts * gain\n", + " return gain, bias\n", + "\n", + " def step(self, dt, J, output):\n", + " \"\"\"Compute rates in Hz for input current (incl. bias)\"\"\"\n", + " output[...] = np.maximum(0.0, J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use `RectifiedLinear` like any other neuron type\n", + "without making modifications to the reference simulator.\n", + "However, other objects, including more complicated neuron types,\n", + "may require changes to the reference simulator.\n", + "\n", + "## Tuning curves\n", + "\n", + "We can build a small network just to see the tuning curves." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:21.910143Z", + "iopub.status.busy": "2020-11-25T16:51:21.909328Z", + "iopub.status.idle": "2020-11-25T16:51:22.100888Z", + "shell.execute_reply": "2020-11-25T16:51:22.100418Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Firing rate (Hz)')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " encoders = np.tile([[1], [-1]], (4, 1))\n", + " intercepts = np.linspace(-0.8, 0.8, 8)\n", + " intercepts *= encoders[:, 0]\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " neuron_type=RectifiedLinear(),\n", + " max_rates=nengo.dists.Uniform(80, 100),\n", + " encoders=encoders,\n", + " )\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(A, sim)\n", + "plt.figure()\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2D Representation example\n", + "\n", + "Below is the same model as is made in the 2d_representation example,\n", + "except now using `RectifiedLinear` neurons insated of `nengo.LIF`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:22.112905Z", + "iopub.status.busy": "2020-11-25T16:51:22.112112Z", + "iopub.status.idle": "2020-11-25T16:51:22.606898Z", + "shell.execute_reply": "2020-11-25T16:51:22.607360Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"2D Representation\", seed=10)\n", + "with model:\n", + " neurons = nengo.Ensemble(100, dimensions=2, neuron_type=RectifiedLinear())\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)\n", + " nengo.Connection(sin, neurons[0])\n", + " nengo.Connection(cos, neurons[1])\n", + " sin_probe = nengo.Probe(sin, \"output\")\n", + " cos_probe = nengo.Probe(cos, \"output\")\n", + " neurons_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.01)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:22.613628Z", + "iopub.status.busy": "2020-11-25T16:51:22.612517Z", + "iopub.status.idle": "2020-11-25T16:51:22.799865Z", + "shell.execute_reply": "2020-11-25T16:51:22.799413Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neurons_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Sine\")\n", + "plt.plot(sim.trange(), sim.data[cos_probe], \"k\", label=\"Cosine\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples/usage/tuning-curves.ipynb b/.doctrees/nbsphinx/examples/usage/tuning-curves.ipynb new file mode 100644 index 0000000000..d158fc3ece --- /dev/null +++ b/.doctrees/nbsphinx/examples/usage/tuning-curves.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tuning curves\n", + "\n", + "One of the most common ways\n", + "to tweak models\n", + "and debug failures\n", + "is to look at the **tuning curves**\n", + "of the neurons in an ensemble.\n", + "The tuning curve tells us\n", + "how each neuron responds\n", + "to an incoming input signal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-dimensional ensembles\n", + "\n", + "The tuning curve is easiest\n", + "to interpret in the one-dimensional case.\n", + "Since the input is a single scalar,\n", + "we use that value as the x-axis,\n", + "and the neuron response as the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.072197Z", + "iopub.status.busy": "2020-11-25T16:51:24.071310Z", + "iopub.status.idle": "2020-11-25T16:51:24.568207Z", + "shell.execute_reply": "2020-11-25T16:51:24.567647Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.utils.ensemble import response_curves, tuning_curves" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.575636Z", + "iopub.status.busy": "2020-11-25T16:51:24.574831Z", + "iopub.status.idle": "2020-11-25T16:51:24.786325Z", + "shell.execute_reply": "2020-11-25T16:51:24.786764Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Input scalar, x')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_1d = nengo.Ensemble(15, dimensions=1)\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_1d, sim)\n", + "\n", + "plt.figure()\n", + "plt.plot(eval_points, activities)\n", + "# We could have alternatively shortened this to\n", + "# plt.plot(*tuning_curves(ens_1d, sim))\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each coloured line represents the response on one neuron.\n", + "As you can see, the neurons cover the space pretty well,\n", + "but there is no clear pattern to their responses.\n", + "\n", + "If there is some biological or functional reason\n", + "to impose some pattern to their responses,\n", + "we can do so by changing the parameters\n", + "of the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.793268Z", + "iopub.status.busy": "2020-11-25T16:51:24.792367Z", + "iopub.status.idle": "2020-11-25T16:51:24.964251Z", + "shell.execute_reply": "2020-11-25T16:51:24.964673Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Input scalar, x')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens_1d.intercepts = Choice([-0.2]) # All neurons have x-intercept -0.2\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens_1d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, there is a clear pattern to the tuning curve.\n", + "However, note that some neurons start firing at\n", + "-0.2, while others stop firing at 0.2.\n", + "This is because the input signal, `x`,\n", + "is multiplied by a neuron's *encoder*\n", + "when it is converted to input current.\n", + "\n", + "We could further constrain the tuning curves\n", + "by changing the encoders of the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.970856Z", + "iopub.status.busy": "2020-11-25T16:51:24.969990Z", + "iopub.status.idle": "2020-11-25T16:51:25.144028Z", + "shell.execute_reply": "2020-11-25T16:51:25.144436Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Input scalar, x')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens_1d.encoders = Choice([[1]]) # All neurons have encoder [1]\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens_1d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us an ensemble of neurons\n", + "that respond very predictably to input.\n", + "In some cases, this is important to the\n", + "proper functioning of a model,\n", + "or to matching what we know about\n", + "the physiology of a brain area or neuron type." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2-dimensional ensembles\n", + "\n", + "In a two-dimensional ensemble,\n", + "the input is represented by two scalar values,\n", + "meaning that we will need three axes\n", + "to represent its tuning curve;\n", + "two for input dimensions, and one for the neural activity.\n", + "\n", + "Fortunately, we are able to plot data in 3D.\n", + "\n", + "If there is a clear pattern to the tuning curves,\n", + "then visualizing them all is (sort of) possible." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:25.153023Z", + "iopub.status.busy": "2020-11-25T16:51:25.152164Z", + "iopub.status.idle": "2020-11-25T16:51:28.096268Z", + "shell.execute_reply": "2020-11-25T16:51:28.096699Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Firing rate (Hz)')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_2d = nengo.Ensemble(15, dimensions=2, encoders=Choice([[1, 1]]))\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_2d, sim)\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "ax = plt.subplot(111, projection=\"3d\")\n", + "ax.set_title(\"Tuning curve of all neurons\")\n", + "for i in range(ens_2d.n_neurons):\n", + " ax.plot_surface(\n", + " eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn\n", + " )\n", + "ax.set_xlabel(\"$x_1$\")\n", + "ax.set_ylabel(\"$x_2$\")\n", + "ax.set_zlabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But in most cases, for 2D ensembles,\n", + "we have to look at each neuron's tuning curve separately." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:28.104268Z", + "iopub.status.busy": "2020-11-25T16:51:28.103479Z", + "iopub.status.idle": "2020-11-25T16:51:29.731048Z", + "shell.execute_reply": "2020-11-25T16:51:29.731465Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens_2d.encoders = nengo.Default\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_2d, sim)\n", + "\n", + "plt.figure(figsize=(12, 12))\n", + "for i in range(4):\n", + " ax = plt.subplot(2, 2, i + 1, projection=Axes3D.name)\n", + " ax.set_title(\"Neuron %d\" % i)\n", + " ax.plot_surface(\n", + " eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn\n", + " )\n", + " ax.set_xlabel(\"$x_1$\")\n", + " ax.set_ylabel(\"$x_2$\")\n", + " ax.set_zlabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## N-dimensional ensembles\n", + "\n", + "The `tuning_curve` function accepts\n", + "ensembles of any dimensionality,\n", + "and will always return `eval_points` and `activities`.\n", + "However, for ensembles of dimensionality\n", + "greater than 2, these are large arrays\n", + "and it becomes nearly impossible\n", + "to visualize them.\n", + "\n", + "There are two main approaches\n", + "to investigating the tuning curves\n", + "of ensembles of arbitrary dimensionality." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clamp some axes\n", + "\n", + "In many cases, we only care about\n", + "the neural sensitivity to one or two dimensions.\n", + "We can investigate those dimensions specifically\n", + "by only varying those dimensions,\n", + "and keeping the rest constant.\n", + "\n", + "To do this, we will use the `inputs` argument\n", + "to the `tuning_curves` function,\n", + "which allows us to define\n", + "the input signals that\n", + "will drive the neurons\n", + "to determine their activity.\n", + "In other words, we are specifying\n", + "the `eval_point` parameter\n", + "to generate the `activities`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:29.740532Z", + "iopub.status.busy": "2020-11-25T16:51:29.739761Z", + "iopub.status.idle": "2020-11-25T16:51:29.913121Z", + "shell.execute_reply": "2020-11-25T16:51:29.913529Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$x_0$')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_3d = nengo.Ensemble(15, dimensions=3)\n", + "\n", + "inputs = np.zeros((50, 3))\n", + "# Vary the first dimension\n", + "inputs[:, 0] = np.linspace(-ens_3d.radius, ens_3d.radius, 50)\n", + "inputs[:, 1] = 0.5 # Clamp the second dimension\n", + "inputs[:, 2] = 0.5 # Clamp the third dimension\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_3d, sim, inputs=inputs)\n", + "\n", + "assert eval_points is inputs # The inputs will be returned as eval_points\n", + "\n", + "plt.figure()\n", + "plt.plot(inputs.T[0], activities)\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"$x_0$\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that these tuning curves\n", + "are much more broad than those\n", + "in the 1-dimensional case.\n", + "This is because some neurons are not\n", + "very sensitive to\n", + "the dimension that are varying,\n", + "and are instead sensitive\n", + "to one or two of the other dimensions.\n", + "If we wanted these neurons\n", + "to be more sharply tuned\n", + "to this dimension,\n", + "we could change their encoders\n", + "to be more tuned to this dimension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Response curves\n", + "\n", + "If all else fails,\n", + "we can still get some information\n", + "about the tuning properties\n", + "of the neurons in the ensemble\n", + "using the **response curve**.\n", + "The response curve is similar to the tuning curve,\n", + "but instead of looking at the neural response\n", + "to a particular input stimulus,\n", + "we are instead looking at its response\n", + "to (relative) injected current.\n", + "This is analogous to the tuning curves\n", + "with the inputs aligned to the\n", + "preferred directions of each neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:29.921051Z", + "iopub.status.busy": "2020-11-25T16:51:29.920279Z", + "iopub.status.idle": "2020-11-25T16:51:30.097695Z", + "shell.execute_reply": "2020-11-25T16:51:30.098114Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'x along preferred direction')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_5d = nengo.Ensemble(15, dimensions=5)\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*response_curves(ens_5d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"x along preferred direction\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.doctrees/nbsphinx/examples_advanced_functions-and-tuning-curves_10_1.png b/.doctrees/nbsphinx/examples_advanced_functions-and-tuning-curves_10_1.png new file mode 100644 index 0000000000..2d98536a52 Binary files /dev/null and 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b/_images/inheritance-47436b5f83567651f4c8f6967a4351f2ed9adcfd.png differ diff --git a/_images/inheritance-47436b5f83567651f4c8f6967a4351f2ed9adcfd.png.map b/_images/inheritance-47436b5f83567651f4c8f6967a4351f2ed9adcfd.png.map new file mode 100644 index 0000000000..74cf2098c9 --- /dev/null +++ b/_images/inheritance-47436b5f83567651f4c8f6967a4351f2ed9adcfd.png.map @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + + diff --git a/_sources/advanced.rst.txt b/_sources/advanced.rst.txt new file mode 100644 index 0000000000..d975d6877b --- /dev/null +++ b/_sources/advanced.rst.txt @@ -0,0 +1,11 @@ +*************** +Advanced topics +*************** + +.. toctree:: + :maxdepth: 2 + + connections + nengorc + improving-performance + utils diff --git a/_sources/backend-api.rst.txt b/_sources/backend-api.rst.txt new file mode 100644 index 0000000000..f073e1c049 --- /dev/null +++ b/_sources/backend-api.rst.txt @@ -0,0 +1,167 @@ +***************** +Nengo backend API +***************** + +Nengo is designed so that models created with the +:doc:`Nengo frontend API ` +work on a variety of different simulators, or "backends." +For example, backends have been created to take advantage of +`GPUs `_ and +`neuromorphic hardware `_. + +Reference backend +================= + +.. autosummary:: + :nosignatures: + + nengo.Simulator + nengo.simulator.SimulationData + +.. autoclass:: nengo.Simulator + +.. autoclass:: nengo.simulator.SimulationData + +The build process +================= + +The build process translates a Nengo model +to a set of data buffers (`.Signal` instances) +and computational operations (`.Operator` instances) +which implement the Nengo model +defined with the :doc:`frontend API `. +The build process is central to +how the reference simulator works, +and details how Nengo can be extended to include +new neuron types, learning rules, and other components. + +`Bekolay et al., 2014 +`_ +provides a high-level description +of the build process. +For lower-level details +and reference documentation, read on. + +.. autosummary:: + :nosignatures: + + nengo.builder.Model + nengo.builder.Builder + +.. autoclass:: nengo.builder.Model + +.. autoclass:: nengo.builder.Builder + +Basic operators +--------------- + +.. automodule:: nengo.builder.operator + + .. autoautosummary:: nengo.builder.operator + :nosignatures: + +Signals +------- + +.. automodule:: nengo.builder.signal + + .. autoautosummary:: nengo.builder.signal + :nosignatures: + +Network builder +--------------- + +.. automodule:: nengo.builder.network + :exclude-members: nullcontext + + .. autoautosummary:: nengo.builder.network + :nosignatures: + :exclude-members: nullcontext + +Connection builder +------------------ + +.. automodule:: nengo.builder.connection + + .. autoautosummary:: nengo.builder.connection + :nosignatures: + +Ensemble builder +---------------- + +.. automodule:: nengo.builder.ensemble + + .. autoautosummary:: nengo.builder.ensemble + :nosignatures: + +Learning rule builders +---------------------- + +.. automodule:: nengo.builder.learning_rules + + .. autoautosummary:: nengo.builder.learning_rules + :nosignatures: + +Neuron builders +--------------- + +.. automodule:: nengo.builder.neurons + + .. autoautosummary:: nengo.builder.neurons + :nosignatures: + +Node builder +------------ + +.. automodule:: nengo.builder.node + + .. autoautosummary:: nengo.builder.node + :nosignatures: + +Probe builder +------------- + +.. automodule:: nengo.builder.probe + + .. autoautosummary:: nengo.builder.probe + :nosignatures: + +Process builder +--------------- + +.. automodule:: nengo.builder.processes + + .. autoautosummary:: nengo.builder.processes + :nosignatures: + +Transform builders +------------------ + +.. automodule:: nengo.builder.transforms + + .. autoautosummary:: nengo.builder.transforms + :nosignatures: + +Decoder cache +------------- + +.. automodule:: nengo.cache + + .. autoautosummary:: nengo.cache + :nosignatures: + +Optimizer +--------- + +.. automodule:: nengo.builder.optimizer + + .. autoautosummary:: nengo.builder.optimizer + :nosignatures: + +Exceptions +========== + +.. automodule:: nengo.exceptions + + .. autoautosummary:: nengo.exceptions + :nosignatures: diff --git a/_sources/changelog.rst.txt b/_sources/changelog.rst.txt new file mode 100644 index 0000000000..d9e113ec68 --- /dev/null +++ b/_sources/changelog.rst.txt @@ -0,0 +1 @@ +.. include:: ../CHANGES.rst diff --git a/_sources/citation.rst.txt b/_sources/citation.rst.txt new file mode 100644 index 0000000000..4469ea44a7 --- /dev/null +++ b/_sources/citation.rst.txt @@ -0,0 +1 @@ +.. include:: ../CITATION.rst diff --git a/_sources/config.rst.txt b/_sources/config.rst.txt new file mode 100644 index 0000000000..1f013be72f --- /dev/null +++ b/_sources/config.rst.txt @@ -0,0 +1,77 @@ +******************************* +Setting parameters with Configs +******************************* + +Building models with the :doc:`Nengo frontend API ` +involves constructing many objects, +each with many parameters that can be set. +To make setting all of these parameters easier, +Nengo has a ``config`` system and +pre-set configurations. + +The ``config`` system +===================== + +Nengo's ``config`` system is used for two important functions: + +1. Setting default parameters with a hierarchy of defaults. +2. Associating new information with Nengo classes and objects + without modifying the classes and objects themselves. + +A tutorial-style introduction to the ``config`` system +can be found below: + +.. toctree:: + + examples/usage/config + +``config`` system API +--------------------- + +.. automodule:: nengo.config + + .. autoautosummary:: nengo.config + :nosignatures: + +Preset configs +============== + +Nengo includes preset configurations that can be +dropped into your model to enable specific neural circuits. + +.. automodule:: nengo.presets + + .. autoautosummary:: nengo.presets + :nosignatures: + +Quirks +====== + +.. toctree:: + + examples/quirks/config + +Parameters +========== + +Under the hood, Nengo objects store information +using `.Parameter` instances, +which are also used by the config system. +Most users will not need to know about +`.Parameter` objects. + +.. automodule:: nengo.params + + .. autoautosummary:: nengo.params + :nosignatures: + + nengo.dists.DistributionParam + nengo.dists.DistOrArrayParam + nengo.learning_rules.LearningRuleTypeParam + nengo.learning_rules.LearningRuleTypeSizeInParam + nengo.neurons.NeuronTypeParam + nengo.processes.PiecewiseDataParam + nengo.solvers.SolverParam + nengo.synapses.SynapseParam + nengo.transforms.ChannelShapeParam + nengo.transforms.SparseInitParam diff --git a/_sources/connections.rst.txt b/_sources/connections.rst.txt new file mode 100644 index 0000000000..729bd14668 --- /dev/null +++ b/_sources/connections.rst.txt @@ -0,0 +1,375 @@ +******************** +Connections in depth +******************** + +The `.Connection` object encapsulates different behaviors +depending on the attributes of the connection. +It can be helpful in debugging network behavior +to know what is happening under the hood +for different types of connections. + +The biggest determiner of what happens +in a connection is the ``pre`` object. +When the ``pre`` object is an `.Ensemble` +with a neuron type other than `.Direct`, +Nengo will create a decoded connection. +When the ``pre`` object is anything else, +Nengo will create a direct connection. + +The ``post`` object +is only used to determine +which signal will receive the data +produced by the connection. +If you're not sure what your connection +is doing, interrogate the ``pre`` object first. + +Decoded connections +=================== + +Decoded connections are any connection +**from an ensemble to any other object**. +The following are all examples of decoded connections: + +.. testcode:: + + with nengo.Network() as net: + ens1 = nengo.Ensemble(10, dimensions=2) + node = nengo.Node(size_in=1) + ens2 = nengo.Ensemble(4, dimensions=2) + + # Ensemble to ensemble + nengo.Connection(ens1, ens2) + # Ensemble slice to node + nengo.Connection(ens1[0], node) + # Ensemble to neurons slice + nengo.Connection(ens1, ens2.neurons[:2]) + +The important thing about decoded connections +is that they do not directly compute the +``function`` defined for that connection +(keeping in mind that passing in no function +is equivalent to passing in the identity function). +Instead, the function is approximated +by solving for a set of decoding weights. +The output of a decoded connection +is the sum of the pre ensemble's neural activity +weighted by the decoding weights +solved for in the build process. + +Mathematically, you can think of a decoded connection +as implementing the following equation: + +.. math:: \mathbf{y}(t) = \sum_{i=0}^n \mathbf{d}^{f}_i a_i(x(t)) + +where + +- :math:`\mathbf{y}(t)` is the output of the connection at time :math:`t`, +- :math:`n` is the number of neurons in the pre ensemble +- :math:`\mathbf{d}^{f}_i` is the decoding weight associated + with neuron :math:`i` given the function :math:`f`, +- :math:`a_i(x(t))` is the activity of neuron :math:`i` given + :math:`x(t)`, the input at time :math:`t`. + +Note that the length of the :math:`\mathbf{d}` and :math:`\mathbf{y}` vectors +is the same, and is specified by the dimensionality of +the output of the function :math:`f` when applied to input :math:`x`. + +While the equation above is straightforward, +there are several important implications +that one should keep in mind when using decoded connections. + +- Decoders will be automatically solved for in the build process. + + Solving for decoders makes up the majority of build time, + so if building your networks takes a long time, + look at your decoded connections and + try lowering the number of neurons + or using different `.Solver` types. + +- The function passed to the connection + is used to determine decoders. + It will never be run during the simulation. + + When you define a function in a node, + it will be execute on every simulation timestep. + That may lead you to think that the function + passed to a connection is executed on every timestep, + but that is *not* the case for decoded connections. + The function passed to the connection will be executed + in the decoder solving process determine an error + to minimize, but never during the simulation. + +- The characteristics of the ``pre`` ensemble + are critically important to performance. + + If you determine that your decoded connection + is not approximating the desired function well, + examine the ``pre`` ensemble. + The decoded value depends on the activity + of the ``pre`` ensemble; + does it represent its input reliably? + If not, then a function of that input + cannot be well approximated. + If you think that your function may be incorrect, + switch the ``pre`` ensemble to use + the `.Direct` neuron type, + which does not use decoders. + If that function looks correct, + move on to a simpler neuron type + like `.RectifiedLinear` until you + can determine why your function is not + being approximated well. + + Concrete examples of how the properties of ``pre`` ensemble influence the + desired function can be found in [1]_, [2]_. + +Direct connections +================== + +Any connection that is not a decoded connection +is a direct connection. + +For simplicity and consistency, +Nengo exposes the same interface +for decoded and direct connections. +In all cases, data from the ``pre`` object +is sent to the ``post`` object, +with an optional ``synapse`` filter. +In decoded connections, +weights are automatically determined +through decoder solving. +In direct connections, +weights can be manually specified +through the ``transform`` argument. [3]_ + +The most common example of a direct connection +is a neuron-to-neuron connection. +These connections are the types of connections +you see in most neural simulators, +and can be used to reproduce networks +written in other simulators like +`Brian `_: + +.. testcode:: + + with nengo.Network() as net: + ens1 = nengo.Ensemble(10, dimensions=1) + ens2 = nengo.Ensemble(20, dimensions=2) + + # Neuron to neuron + weights = np.random.normal(size=(ens2.n_neurons, ens1.n_neurons)) + nengo.Connection(ens1.neurons, ens2.neurons, transform=weights) + +Note that it does not matter that the dimensionality of ``ens1`` +does not match the dimensionality of ``ens2``. +It only matters that the ``transform`` +is of the correct shape, +which in the case of neuron-to-neuron connections +is ``(post.n_neurons, pre.n_neurons)``. + +In the vast majority of cases, +the above description is all you need to know. +Below we give some additional examples, +focusing on situations that differ from the description above. + +Nodes and `.Direct` ensembles +----------------------------- + +In connections from nodes and ensembles +using the `.Direct` neuron type, +the ``function`` argument is valid +and will result in the function being applied +to the input on every timestep. +This is in direct contrast to decoded connections, +in which the function is executed +during the build process and *not* during the simulation. + +Examples: + +.. testcode:: + + with nengo.Network() as net: + node = nengo.Node(output=[1]) + ens1 = nengo.Ensemble(1, dimensions=2, neuron_type=nengo.Direct()) + ens2 = nengo.Ensemble(10, dimensions=1) + + # Node to LIF ensemble + nengo.Connection(node, ens2, function=lambda x: x**2) + # Direct ensemble to LIF ensemble + nengo.Connection(ens1, ens2, function=lambda x: x[0] * x[1]) + +Passthrough nodes +----------------- + +When creating large networks, +it is often helpful to use passthrough nodes +to route signals from place to place +without introducing unnecessary ensembles. +For example, the `.EnsembleArray` network +is often used to represent a high-dimensional vector +with many lower-dimensional ensemble. +The high-dimensional vector is still available +as ``EnsembleArray.output`` through the use +of a passthrough node that collects the output +of all the lower-dimensional ensembles. + +Unlike other types of nodes, +we explicitly disable the ``function`` argument +when connecting from passthrough nodes. +The reason for this is to ensure that users know +they are making a direct connection +and not a decoded connection. +The output of a network like `.EnsembleArray` +can usually be treated the same way +as the output of an `.Ensemble`, +except for the case of applying a function +to the output, +since decoders are not used to approximate +the function in the case of networks +using passthrough nodes. + +As an example, +consider using an `.EnsembleArray` to compute a product: + +.. testcode:: + + with nengo.Network() as net: + ea = nengo.networks.EnsembleArray(40, 2) + product = nengo.Ensemble(30, dimensions=1) + + # Passthrough node to ensemble -- raises error + nengo.Connection(ea.output, product, function=lambda x: x[0] * x[1]) + +.. testoutput:: + :hide: + + Traceback (most recent call last): + ... + nengo.exceptions.ValidationError: Connection.function: Cannot apply functions \ + to passthrough nodes + +If this example did not raise an error, +the product would be computed nearly perfectly, +despite the fact that that computation +is impossible to decode from the ensembles +of the ensemble array. +Consider that the product +requires information from both dimensions of the signal +(i.e., the dimensions interact nonlinearly). +In order for nonlinearities to be decoded, +some neurons must encode information from +the nonlinearly-interacting dimensions. +Since the ensemble array represents each dimension independently, +no neurons will encode information from multiple dimensions, +and therefore the product cannot be approximated +by the ensemble array. + +If you are aware that the function +will not be approximated but directly computed, +and you desire this behavior, +you can enable it by modifying the node so that it is +no longer a passthrough node, +but instead computes the identity function: + +.. testcode:: + + with nengo.Network() as net: + ea = nengo.networks.EnsembleArray(40, 2) + product = nengo.Ensemble(30, dimensions=1) + + # Make the node non-passthrough + ea.output.output = lambda t, x: x + # Node to ensemble -- no error + nengo.Connection(ea.output, product, function=lambda x: x[0] * x[1]) + +If you're designing networks +that may have arbitrary function +applied to the output, +you should implement a way to make +decoded connections from the ensembles +in your network. +See the `.EnsembleArray.add_output` method +for an example of how that might be implemented. + +Neuron-to-ensemble connections +------------------------------ + +As noted above, +a decoded connection is implemented by +solving for a set of decoding weights +and then weighting a sum of activities by those decoders. +If you already know the decoding weights +you want to use on a connection, +then you can skip the decoder solving step +by using a direct connection +from the neurons of an ensemble to another object. + +In the example below, +we make two equivalent connections, +one using a decoded connection +and one using a direct connection: + +.. testcode:: + + with nengo.Network() as net: + ens1 = nengo.Ensemble(20, dimensions=1, seed=0) + ens2 = nengo.Ensemble(15, dimensions=1) + + # Decoded ensemble to ensemble connection + conn1 = nengo.Connection(ens1, ens2, function=lambda x: x + 0.5) + + with nengo.Simulator(net) as sim: + decoders = sim.data[conn1].weights + + with net: + # Direct neurons to ensemble connection + conn2 = nengo.Connection(ens1.neurons, ens2, transform=decoders) + +.. testoutput:: + :hide: + + ... + +In the above example, the shape of ``decoders`` is ``(1, 20)``. +If you run this example and probe the output of ``conn1`` +and ``conn2``, you will see that their output is the same +(as long as a seed is set on ``ens1``): + +.. testcode:: + + with net: + probe1 = nengo.Probe(conn1, "output", synapse=0.01) + probe2 = nengo.Probe(conn2, "output", synapse=0.01) + + with nengo.Simulator(net) as sim: + sim.run(0.1) + + assert np.allclose(sim.data[probe1], sim.data[probe2]) + +.. testoutput:: + :hide: + + ... + +Both ``conn1`` and ``conn2`` can have learning rules applied, +so this type of direct connection can be useful +when saving the weights in a learning network +and loading it up in the future. + +.. [1] Gosmann, Jan. Precise multiplications with the NEF. + Waterloo, Ontario, Canada: University of Waterloo; 2015. + Available from: https://zenodo.org/record/35680 +.. [2] Gosmann, Jan, and Chris Eliasmith. “Optimizing Semantic Pointer + Representations for Symbol-Like Processing in Spiking Neural Networks.” + PLoS ONE 11, no. 2 (February 22, 2016): e0149928. + `doi:10.1371/journal.pone.0149928 + `_. +.. [3] Note that decoded connections + also accept the ``transform`` argument. + In the case of decoded connections, + the ``transform`` is a linear operation + that is applied after the function + is applied to the input. + In most cases, slicing the input + or including the transform + in the function is recommended. diff --git a/_sources/contributing.rst.txt b/_sources/contributing.rst.txt new file mode 100644 index 0000000000..809643e9ab --- /dev/null +++ b/_sources/contributing.rst.txt @@ -0,0 +1,153 @@ +********************* +Contributing to Nengo +********************* + +Please read our +`general contributor guide `_ +first. +The instructions below specifically apply +to the ``nengo`` project. + +.. _dev-install: + +Developer installation +====================== + +If you want to change parts of Nengo, +you should do a developer installation, +and install all of the optional dependencies. + +.. code-block:: bash + + git clone https://github.com/nengo/nengo.git + cd nengo + pip install -e '.[all]' --user + pre-commit install + +If you are in a virtual environment, you can omit the ``--user`` flag. + +How to run unit tests +===================== + +Nengo contains a large test suite, which we run with pytest_. +To run these tests do + +.. code-block:: bash + + pytest --pyargs nengo + +Running individual tests +------------------------ + +Tests in a specific test file can be run by calling +``pytest`` on that file. For example + +.. code-block:: bash + + pytest nengo/tests/test_node.py + +will run all the tests in ``test_node.py``. + +Individual tests can be run using the ``-k EXPRESSION`` argument. Only tests +that match the given substring expression are run. For example + +.. code-block:: bash + + pytest nengo/tests/test_node.py -k test_circular + +will run any tests with ``test_circular`` in the name, in the file +``test_node.py``. + +Plotting the results of tests +----------------------------- + +Many Nengo tests have the built-in ability to plot test results +for easier debugging. To enable this feature, +pass the ``--plots`` to ``pytest``. For example + +.. code-block:: bash + + pytest --plots --pyargs nengo + +Plots are placed in ``nengo.simulator.plots`` in whatever directory +``pytest`` is invoked from. You can also set a different directory: + +.. code-block:: bash + + pytest --plots=path/to/plots --pyargs nengo + +Getting help and other options +------------------------------ + +Information about ``pytest`` usage +and Nengo-specific options can be found with + +.. code-block:: bash + + pytest --pyargs nengo --help + +Writing your own tests +---------------------- + +When writing your own tests, please make use of +custom Nengo `fixtures `_ +and `markers `_ +to integrate well with existing tests. +See existing tests for examples, or consult + +.. code-block:: bash + + pytest --pyargs nengo --fixtures + +and + +.. code-block:: bash + + pytest --pyargs nengo --markers + +.. _pytest: https://docs.pytest.org/en/latest/ + +How to build the documentation +============================== + +The documentation is built with Sphinx, +which should have been installed as part +of the :ref:`developer installation `. + +However, one additional requirement for building the Jupyter notebooks +that we include in the documentation is Pandoc_. +If you use a package manager (e.g., Homebrew, ``apt``) +you should be able to install Pandoc_ through your package manager. +Otherwise, see +`this page `_ +for instructions. + +After you've installed all the requirements, +run the following command from the root directory of ``nengo`` +to build the documentation. +It will take a few minutes, as all examples are run +as part of the documentation building process. + +.. code-block:: bash + + sphinx-build -vW docs docs/_build + +Depending on your environment, +you might have to set the Jupyter kernel +used to build the examples. +To set the kernel, use this command. + +.. code-block:: bash + + sphinx-build -vW docs docs/_build -D nbsphinx_kernel_name= + +.. _Pandoc: https://pandoc.org/ + +Getting help +============ + +If you have any questions about developing Nengo +or how you can best climb the learning curve +that Nengo and ``git`` present, please head to the +`Nengo forum `_ +and we'll do our best to help you! diff --git a/_sources/converting.rst.txt b/_sources/converting.rst.txt new file mode 100644 index 0000000000..cc90d8c77c --- /dev/null +++ b/_sources/converting.rst.txt @@ -0,0 +1,285 @@ +************************************** +Converting from Nengo 1.4 to Nengo 2.0 +************************************** + +On this page, we'll go over the changes between Nengo 1.4 and 2.0. +They will first be reviewed heuristically in the section Big Changes, before +being broken down practically in Changes to Common Functions + +Big changes +=========== + +Objects instead of strings +-------------------------- + +In the old API each object had to be assigned it's own unique string. + +In the new Nengo, you can use strings to identify objects called ``labels``, +but they are not unique. Instead, if you want to identify an object, you just +make sure to assign it a variable in your network + +No Origins and Terminations +--------------------------- + +Previously, each object had a set of origins and terminations, +which determined how the object produced output and +accepted input, respectively. +These two things have been collapsed into a single +Connection object, which contains +the logic of the origin and termination +in one place. + +Because the model is defined separately +from when it's built, +the performance advantages of having +origins and terminations can be accomplished +during the build phase of the model instead. + +Only Ensembles, Nodes, Networks and Probes +------------------------------------------ + +Many other objects have been removed, +in order to start with a very minimal +set of objects allowing a new user to get up and running without having +to spend all the effort of memorizing a large API. + +Basically: + +- Anything made with neurons is an Ensemble. +- Anything not made with neurons (inputs, interfaces) are Nodes. +- Probes are how you get data out of Nodes and Ensembles after simulating. +- Networks are dumb containers + for Ensembles, Nodes, Probes, and other Networks. + +A power user can easily divide his code and stop from repeating themselves +by encapsulating code that appears in multiple places in a Network. + +Model and Simulator separation +------------------------------ + +There is now a clear separation between +model definition and model creation/simulation. +The motivation behind this is to allow +for testing models as they are being created. +For example, you can create a model, +add a node and an ensemble, +and the create a simulator based +on that model and run it +to make sure that your node and ensemble +are doing what you think they're doing. +Then, you can continue adding new objects +to your model---this will not be reflected +in the simulator that you've already created, +but you can create a new simulator +with this updated model and run it +without having to rerun your script +from the top. +Basically, it allows for a more +iterative and interactive modelling process, +and makes it more explicit which +decisions are made manually and which +are automatically determined +when the simulator is created. +Additionally, this means that the +simulator timestep (dt) is not +defined until the simulator is created, +meaning that you can run the same model +with different timesteps to see if +there is a marked functional difference. + +Changes to common functions +--------------------------- + +Many commonly used functions have been +simplified or changed to be more explicit. + +Making ensembles +---------------- + +Old API example:: + + nef.Network.make('A', 40, 1, mode='spike') + +New API example: + +.. testsetup:: + + net = nengo.Network() + net.__enter__() + +.. testcode:: + + A = nengo.Ensemble(40, 1, neuron_type=nengo.LIF(), label='A') + +See `nengo.Ensemble` for the full API specification. + +Making ensemble arrays (i.e., network arrays) +--------------------------------------------- + +Network arrays were very tightly coupled +with the old API. In the new API, +they have been decoupled and are just dumb containers, which +you can easily import. +The functionality should still be identical, +though the syntax has changed. + +Old API:: + + nef.Network.make_array(name, neurons, length, dimensions, **args) + +New API: + +.. testsetup:: + + neurons = 1 + n_ensembles = 1 + dimensions_per_ensemble = 1 + ens_args = {} + +.. testcode:: + + nengo.networks.EnsembleArray(neurons, n_ensembles, dimensions_per_ensemble, **ens_args) + +See `nengo.networks.EnsembleArray` for more information. + +Changes to common functions +=========================== + +Making nodes +------------ + +Previously, there were several different ways +to provide input to a Nengo model: +``SimpleNode``, ``FunctionInput``, and others. +All of these use cases should be covered +by `nengo.Node`. + +In the old API, you could create your own +``SimpleNode``, or create a ``FunctionInput`` with:: + + nef.Network.make_input(name, values, zero_after_time) + +In the new API, you create a node with: + +.. testsetup:: + + output = [0] + +.. testcode:: + + nengo.Node(output) + +where ``output`` is either a constant value +(float, list, NumPy array), a function, or +``None`` when passing through values unchanged. + +See `nengo.Node` for more information. + +Making inputs +------------- + +In the old API, inputs were defined as:: + + # Piecewise example + net.make_input("contextinput", {0.0:[0, 0.1], 0.5:[1, 0], 1.0:[0, 1]}) + # Periodic white noise + net.make_fourier_input('fin1', base=0.1, high=10, power=0.5, seed=12) + +Inputs are just nodes whose sole function are to output a function. + +Many of the :doc:`examples` use function output nodes. + +Terminations and Origins +------------------------ + +Practically, to convert from one to the other, consider this table +that uses an example ensemble called ``ens`` who's input needs to be +transformed by a two-dimensional identity function, ``[[1,0],[0,1]]``. + +Nengo 1.4:: + + ens.addDecodedTermination("term_name", transform=MU.I(2)) + +Nengo 2.0: + +.. testsetup:: + + ens = nengo.Ensemble(2, 2) + +.. testcode:: + + # first create a simple pass-through node + term_name = nengo.Node(size_in=2, label="term_name") + # now connect the pass-through node to the ensemble + nengo.Connection(term_name, ens, transform=np.eye(2)) + +Same, thing but instead of a decoded origin, we want one that connects +directly to the ensemble's neurons. + +Nengo 1.4:: + + ens.addTermination("term_name", transform=MU.I(2)) + +Nengo 2.0: + +.. testcode:: + + # first create a simple pass-through node + term_name = nengo.Node(size_in=2, label="term_name") + # now connect the pass-through node to the ensemble neurons + nengo.Connection(term_name, ens.neurons, transform=np.eye(2)) + +One more time, but with an output and no transform. + +Nengo 1.4:: + + ens.addDecodedOrigin("origin_name") + +Nengo 2.0: + +.. testcode:: + + # first create a simple pass-through node + origin_name = nengo.Node(size_in=2, label="origin_name") + # now connect the pass-through node to the ensemble + nengo.Connection(ens, origin_name, transform=np.eye(2)) + +Connecting things +----------------- + +A lot of the complexity of the old API +has been pushed down to the constructors +of the connection object. +In general, old API calls of the form:: + + nef.Network.connect(pre, post) + +are now: + +.. testsetup:: + + pre = nengo.Ensemble(10, 2) + post = nengo.Ensemble(10, 2) + +.. testcode:: + + nengo.Connection(pre, post) + +However, there are some changes in the additional arguments. +The old API used ``weight``, ``index_pre`` and ``index_post`` +as a shortcut to define ``transform``; +in the new API, only the ``transform`` can be specified. +There are many NumPy functions that make transforms +easier to specify. +Additionally, we now utilize Python's slice syntax +to route dimensions easily: + +.. testcode:: + + nengo.Connection(pre[0], post[1]) + +The keyword argument ``pstc`` has been renamed to ``synapse``. + +.. testcleanup:: + + net.__exit__(None, None, None) diff --git a/_sources/examples.rst.txt b/_sources/examples.rst.txt new file mode 100644 index 0000000000..0d2bddc4c0 --- /dev/null +++ b/_sources/examples.rst.txt @@ -0,0 +1,118 @@ +******** +Examples +******** + +Introductory tutorials +====================== + +These are tutorial-style examples that focus on +introducing the basic principles of Nengo. + +Representing values +------------------- + +.. toctree:: + :maxdepth: 1 + + examples/basic/single-neuron + examples/basic/two-neurons + examples/basic/many-neurons + examples/basic/2d-representation + examples/basic/combining + examples/basic/addition + +Computing functions +------------------- + +.. toctree:: + :maxdepth: 1 + + examples/basic/communication-channel + examples/basic/squaring + examples/basic/multiplication + +Building dynamical systems +-------------------------- + +.. toctree:: + :maxdepth: 1 + + examples/dynamics/integrator + examples/dynamics/controlled-integrator + examples/dynamics/controlled-integrator2 + examples/dynamics/oscillator + examples/dynamics/controlled-oscillator + examples/dynamics/lorenz-attractor + +Advanced examples +================= + +These examples illustrate some of the more advanced uses of Nengo. + +Nodes +----- + +.. toctree:: + :maxdepth: 1 + + examples/usage/delay-node + +Processes +--------- + +.. toctree:: + :maxdepth: 1 + + examples/advanced/processes + +Ensembles +--------- + +.. toctree:: + :maxdepth: 1 + + examples/usage/tuning-curves + examples/advanced/izhikevich + +Connections +----------- + +.. toctree:: + :maxdepth: 1 + + examples/advanced/inhibitory-gating + examples/advanced/functions-and-tuning-curves + +Learning +-------- + +.. toctree:: + :maxdepth: 1 + + examples/learning/learn-communication-channel + examples/learning/learn-square + examples/learning/learn-product + examples/learning/learn-unsupervised + examples/learning/learn-associations + examples/learning/lmu + +Networks +-------- + +.. toctree:: + :maxdepth: 1 + + examples/networks/ensemble-array + examples/advanced/matrix-multiplication + examples/networks/basal-ganglia + examples/networks/integrator-network + +Under the hood +-------------- + +.. toctree:: + :maxdepth: 1 + + examples/usage/rectified-linear + examples/advanced/nef-summary + examples/advanced/nef-algorithm diff --git a/_sources/examples/advanced/functions-and-tuning-curves.ipynb.txt b/_sources/examples/advanced/functions-and-tuning-curves.ipynb.txt new file mode 100644 index 0000000000..b46581d20d --- /dev/null +++ b/_sources/examples/advanced/functions-and-tuning-curves.ipynb.txt @@ -0,0 +1,488 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Improving function approximation by adjusting tuning curves\n", + "\n", + "This tutorial shows how adjusting the tuning curves of neurons\n", + "can help implement specific functions with Nengo.\n", + "As an example we will try to to compute\n", + "the Heaviside step function,\n", + "which is 1 for all $x > 0$ and 0 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The naive approach\n", + "\n", + "As a first pass, we can try to implement the Heaviside step function\n", + "using an ensemble with default parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_neurons = 150\n", + "duration = 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def stimulus_fn(t):\n", + " return (2.0 * t / duration) - 1.0\n", + "\n", + "\n", + "def heaviside(x):\n", + " return x > 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as model_naive:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_naive = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model_naive) as sim_naive:\n", + " sim_naive.run(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = sim_naive.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that this approach does work,\n", + "but there is room for improvement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating the tuning curves\n", + "\n", + "Let us take a look at\n", + "the tuning curves of the neurons in the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_naive))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "About half of these neurons are tuned to fire more for smaller values.\n", + "But these values are not relevant\n", + "for the Heaviside step function,\n", + "since the output is always 0\n", + "when input is less than 0.\n", + "We can change all neurons to be tuned\n", + "to fire more for larger values\n", + "by setting all the encoders to be positive." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as model_pos_enc:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens = nengo.Ensemble(\n", + " n_neurons=n_neurons, dimensions=1, encoders=nengo.dists.Choice([[1.0]])\n", + " )\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_pos_enc = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model_pos_enc) as sim_pos_enc:\n", + " sim_pos_enc.run(duration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting tuning curves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_pos_enc))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the resulting decoded Heaviside step function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = sim_pos_enc.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\")\n", + "plt.plot(t, sim_pos_enc.data[p_pos_enc], label=\"pos. enc.\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compared to the naive approach,\n", + "the representation of outputs lower than 1 is less noisy,\n", + "but otherwise there is little improvement.\n", + "Even though the tuning curves are all positive,\n", + "they are still covering a lot of irrelevant parts of the input space.\n", + "Because all outputs should be 0 when input is less than 0,\n", + "there is no need to have neurons tuned to inputs less than 0.\n", + "Let's shift all the intercepts to the range $(0.0, 1.0)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intercept distributions\n", + "\n", + "Not only can the range of intercepts be important,\n", + "but also the distribution of intercepts.\n", + "Let us take a look at the Heaviside step function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xs = np.linspace(-1, 1, 100)\n", + "plt.figure()\n", + "plt.plot(xs, heaviside(xs))\n", + "plt.ylim(-0.1, 1.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function is mostly constant,\n", + "except for the large jump at 0.\n", + "The constant parts are easy to approximate\n", + "and do not need a lot of neural resources,\n", + "but the highly nonlinear jump\n", + "requires more neural resources\n", + "for an accurate representation.\n", + "\n", + "Let us compare the thresholding of a scalar in three ways:\n", + "\n", + "1. With a uniform distribution of intercepts (the default)\n", + "2. With all intercepts at 0 (where we have the nonlinearity)\n", + "3. With an exponential distribution\n", + "\n", + "The last approach is in between\n", + "the two extremes of a uniform distribution\n", + "and placing all intercepts at 0.\n", + "It will distribute most intercepts close to 0,\n", + "but some intercepts will still be at larger values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "threshold = 0.0\n", + "\n", + "with nengo.Network() as model_dists:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens_uniform = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Uniform(threshold, 1.0),\n", + " )\n", + " ens_fixed = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Choice([threshold]),\n", + " )\n", + " ens_exp = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Exponential(0.15, threshold, 1.0),\n", + " )\n", + "\n", + " out_uniform = nengo.Node(size_in=1)\n", + " out_fixed = nengo.Node(size_in=1)\n", + " out_exp = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens_uniform)\n", + " nengo.Connection(stimulus, ens_fixed)\n", + " nengo.Connection(stimulus, ens_exp)\n", + " nengo.Connection(ens_uniform, out_uniform, function=heaviside)\n", + " nengo.Connection(ens_fixed, out_fixed, function=heaviside)\n", + " nengo.Connection(ens_exp, out_exp, function=heaviside)\n", + "\n", + " p_uniform = nengo.Probe(out_uniform, synapse=0.005)\n", + " p_fixed = nengo.Probe(out_fixed, synapse=0.005)\n", + " p_exp = nengo.Probe(out_exp, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model_dists) as sim_dists:\n", + " sim_dists.run(duration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us look at the tuning curves first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_uniform, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Uniform intercepts\")\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_fixed, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Fixed intercepts\")\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_exp, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Exponential intercept distribution\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us look at how these three ensembles\n", + "approximate the thresholding function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = sim_dists.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\", c=\"gray\")\n", + "plt.plot(t, sim_dists.data[p_uniform], label=\"Uniform intercepts\")\n", + "plt.plot(t, sim_dists.data[p_fixed], label=\"Fixed intercepts\")\n", + "plt.plot(t, sim_dists.data[p_exp], label=\"Exp. intercept dist.\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the fixed intercepts\n", + "produce slightly higher decoded values close to the threshold,\n", + "but the slope is lower than for uniform intercepts.\n", + "The best approximation of the thresholding\n", + "is done with the exponential intercept distribution.\n", + "Here we get a quick rise to 1 at the threshold\n", + "and a fairly constant representation of 1\n", + "for inputs sufficiently above 0.\n", + "All three distributions perfectly represent values below 0.\n", + "\n", + "Nengo provides the `ThresholdingEnsemble` preset\n", + "to make it easier to assign intercepts\n", + "according to the exponential distribution,\n", + "and also adjusts the encoders and evaluation points accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as model_final:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " with nengo.presets.ThresholdingEnsembles(0.0):\n", + " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_final = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model_final) as sim_final:\n", + " sim_final.run(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = sim_final.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_final.data[p_final], label=\"final\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## The takeaway\n", + "\n", + "Adjusting ensemble parameters in the right way\n", + "can sometimes help in implementing functions more acurately in neurons.\n", + "Think about how your function maps from\n", + "the input vector space to the output vector space,\n", + "and consider ways to modify ensemble parameters\n", + "to better cover important parts\n", + "of the input vector space." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/advanced/inhibitory-gating.ipynb.txt b/_sources/examples/advanced/inhibitory-gating.ipynb.txt new file mode 100644 index 0000000000..dddaf8547c --- /dev/null +++ b/_sources/examples/advanced/inhibitory-gating.ipynb.txt @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inhibitory gating of ensembles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "Our model consists of two ensembles (called A and B)\n", + "that receive inputs from a common sine wave signal generator.\n", + "\n", + "Ensemble A is gated using the output of a node,\n", + "while Ensemble B is gated using the output of a third ensemble (C).\n", + "This is to demonstrate that ensembles can be gated\n", + "using either node outputs, or decoded outputs from ensembles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_neurons = 30\n", + "\n", + "model = nengo.Network(label=\"Inhibitory Gating\")\n", + "with model:\n", + " A = nengo.Ensemble(n_neurons, dimensions=1)\n", + " B = nengo.Ensemble(n_neurons, dimensions=1)\n", + " C = nengo.Ensemble(n_neurons, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "As described in Step 1, this model requires two inputs.\n", + "\n", + "1. A sine wave signal that is used to drive ensembles A and B\n", + "2. An inhibitory control signal used to (directly) gate ensemble A,\n", + " and (indirectly through ensemble C) gate ensemble B." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(np.sin)\n", + " inhib = nengo.Node(Piecewise({0: 0, 2.5: 1, 5: 0, 7.5: 1, 10: 0, 12.5: 1}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the different components of the model\n", + "\n", + "In this model, we need to make the following connections:\n", + "\n", + "1. From sine wave generator to Ensemble A\n", + "2. From sine wave generator to Ensemble B\n", + "3. From inhibitory control signal to the neurons of Ensemble A\n", + " (to directly drive the currents of the neurons)\n", + "4. From inhibitory control signal to Ensemble C\n", + "5. From Ensemble C to the neurons of Ensemble B\n", + " (this demonstrates that the decoded output of Ensemble C\n", + " can be used to gate Ensemble B)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, A)\n", + " nengo.Connection(sin, B)\n", + " nengo.Connection(inhib, A.neurons, transform=[[-2.5]] * n_neurons)\n", + " nengo.Connection(inhib, C)\n", + " nengo.Connection(C, B.neurons, transform=[[-2.5]] * n_neurons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later.\n", + "Let's collect all the data produced." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " inhib_probe = nengo.Probe(inhib)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " C_probe = nengo.Probe(C, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of Ensemble A\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], label=\"Sine input\")\n", + "plt.plot(sim.trange(), sim.data[inhib_probe], label=\"Inhibitory signal\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of Ensemble B and C\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded output of B\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], label=\"Sine input\")\n", + "plt.plot(sim.trange(), sim.data[C_probe], label=\"Decoded output of C\")\n", + "plt.plot(sim.trange(), sim.data[inhib_probe], label=\"Inhibitory signal\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/advanced/izhikevich.ipynb.txt b/_sources/examples/advanced/izhikevich.ipynb.txt new file mode 100644 index 0000000000..6acd95bda2 --- /dev/null +++ b/_sources/examples/advanced/izhikevich.ipynb.txt @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using the Izhikevich neuron model\n", + "\n", + "The [Izhikevich neuron model](\n", + "http://www.izhikevich.org/publications/spikes.htm)\n", + "is a quadratic integrate-and-fire type model\n", + "with a recovery variable.\n", + "It is able to replicate several characteristics\n", + "of biological neurons while remaining\n", + "computationally efficient.\n", + "\n", + "The Izhikevich neuron model is implemented in Nengo.\n", + "To use it, use a `nengo.Izhikevich` instance\n", + "as the `neuron_type` of an ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import tuning_curves\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network(seed=0) as model:\n", + " u = nengo.Node(lambda t: np.sin(2 * np.pi * t))\n", + " ens = nengo.Ensemble(10, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(u, ens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the usual decoded output and neural activity\n", + "that can always be probed,\n", + "you can probe the voltage and recovery terms\n", + "of the Izhikevich model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " out_p = nengo.Probe(ens, synapse=0.03)\n", + " spikes_p = nengo.Probe(ens.neurons)\n", + " voltage_p = nengo.Probe(ens.neurons, \"voltage\")\n", + " recovery_p = nengo.Probe(ens.neurons, \"recovery\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulating this model shows that we are able\n", + "to decode a time-varying scalar with\n", + "an ensemble of Izhikevich neurons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, sim.data[out_p])\n", + "plt.ylabel(\"Decoded output\")\n", + "ax = plt.subplot(2, 1, 2)\n", + "rasterplot(t, sim.data[spikes_p], ax=ax)\n", + "plt.ylabel(\"Neuron\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing you might notice is a slight bump in the decoded value\n", + "at the start of the simulation.\n", + "This occurs because of the adaptive nature of the Izhikevic model;\n", + "it is easier to initiate the first spike than it is to ellicit\n", + "further spikes.\n", + "\n", + "Let's use a constant input and\n", + "look at the first 100 ms of the simulation in more detail\n", + "to see the difference between the first spike and subsequent spikes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def izh_plot(sim):\n", + " t = sim.trange()\n", + " plt.figure(figsize=(12, 10))\n", + " plt.subplot(4, 1, 1)\n", + " plt.plot(t, sim.data[out_p])\n", + " plt.ylabel(\"Decoded output\")\n", + " plt.xlim(right=t[-1])\n", + " ax = plt.subplot(4, 1, 2)\n", + " rasterplot(t, sim.data[spikes_p], ax=ax)\n", + " plt.ylabel(\"Neuron\")\n", + " plt.xlim(right=t[-1])\n", + " plt.subplot(4, 1, 3)\n", + " plt.plot(t, sim.data[voltage_p])\n", + " plt.ylabel(\"Voltage\")\n", + " plt.xlim(right=t[-1])\n", + " plt.subplot(4, 1, 4)\n", + " plt.plot(t, sim.data[recovery_p])\n", + " plt.ylabel(\"Recovery\")\n", + " plt.xlim(right=t[-1])\n", + "\n", + "\n", + "u.output = 0.2\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.1)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Those neurons that have an encoder of -1\n", + "receive negative current, and therefore\n", + "remain at a low voltage.\n", + "\n", + "Those neurons that have an encoder of 1\n", + "receive positive current, and start spiking rapidly.\n", + "However, as they spike, the recovery variable grows,\n", + "until it reaches a balance with the voltage\n", + "such that the cells spike regularly.\n", + "\n", + "This occurs because, by default,\n", + "we use a set of parameters\n", + "that models a \"regular spiking\" neuron.\n", + "We can use parameters\n", + "that model several different\n", + "classes of neurons instead." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intrinsically bursting (IB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens.neuron_type = nengo.Izhikevich(reset_voltage=-55, reset_recovery=4)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chattering (CH)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens.neuron_type = nengo.Izhikevich(reset_voltage=-50, reset_recovery=2)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fast spiking (FS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Low-threshold spiking (LTS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens.neuron_type = nengo.Izhikevich(coupling=0.25)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resonator (RZ)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1, coupling=0.26)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caveats\n", + "\n", + "Unfortunately, Izhikevich neurons can't necessarily\n", + "be used in all of the situations that LIFs are used,\n", + "due to the more complex dynamics illustrated above.\n", + "\n", + "The way that Nengo encodes and decodes information\n", + "with neurons is informed by the tuning curves\n", + "of those neurons.\n", + "With Izhikevich neurons, the firing rate\n", + "with a certain input current `J` changes;\n", + "the spike rate is initially higher due\n", + "to the adaptation illustrated above.\n", + "\n", + "We try our best to generate tuning curves\n", + "for Izhikevich neurons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network(seed=0) as model:\n", + " u = nengo.Node(lambda t: np.sin(2 * np.pi * t))\n", + " ens = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(u, ens)\n", + " out_p = nengo.Probe(ens, synapse=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens, sim))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But these are not as accurate and clean\n", + "as LIF curves, which is detrimental\n", + "for function decoding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u.output = lambda t: np.sin(2 * np.pi * t)\n", + "with model:\n", + " square = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(ens, square, function=lambda x: x ** 2)\n", + " square_p = nengo.Probe(square, synapse=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(t, sim.data[out_p], label=\"Ensemble 1 (sin wave)\")\n", + "plt.plot(t, sim.data[square_p], label=\"Ensemble 2 (sin^2)\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of these weird dynamics\n", + "can be overcome by using Izhikevich\n", + "neurons with different parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "square.neuron_type = nengo.Izhikevich(tau_recovery=0.2)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(t, sim.data[out_p], label=\"Ensemble 1 (sin wave)\")\n", + "plt.plot(t, sim.data[square_p], label=\"Ensemble 2 (sin^2)\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generally, however, Izhikevich neurons are most useful\n", + "when trying to match known physiological properties\n", + "of the system being modelled." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/advanced/matrix-multiplication.ipynb.txt b/_sources/examples/advanced/matrix-multiplication.ipynb.txt new file mode 100644 index 0000000000..583b974f00 --- /dev/null +++ b/_sources/examples/advanced/matrix-multiplication.ipynb.txt @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matrix multiplication\n", + "\n", + "This example demonstrates how to perform\n", + "general matrix multiplication using Nengo.\n", + "The matrix can change during the computation,\n", + "which makes it distinct from doing static matrix multiplication\n", + "with neural connection weights (as done in all neural networks).\n", + "\n", + "Note that the order of operands in matrix multiplication matters.\n", + "We will be computing $A \\cdot B$ which is equivalent to $(B \\cdot A)^{\\top}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N = 100\n", + "Amat = np.asarray([[0.5, -0.5], [-0.2, 0.3]])\n", + "Bmat = np.asarray([[0.58, -1.0], [0.7, 0.1]])\n", + "\n", + "# Values should stay within the range (-radius, radius)\n", + "radius = 1\n", + "\n", + "model = nengo.Network(label=\"Matrix Multiplication\", seed=123)\n", + "with model:\n", + " # Make 2 EnsembleArrays to store the input\n", + " A = nengo.networks.EnsembleArray(N, Amat.size, radius=radius)\n", + " B = nengo.networks.EnsembleArray(N, Bmat.size, radius=radius)\n", + "\n", + " # connect inputs to them so we can set their value\n", + " inputA = nengo.Node(Amat.ravel())\n", + " inputB = nengo.Node(Bmat.ravel())\n", + " nengo.Connection(inputA, A.input)\n", + " nengo.Connection(inputB, B.input)\n", + " A_probe = nengo.Probe(A.output, sample_every=0.01, synapse=0.01)\n", + " B_probe = nengo.Probe(B.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "plt.figure()\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"A\")\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[A_probe])\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"B\")\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[B_probe])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # The C matrix is composed of populations that each contain\n", + " # one element of A and one element of B.\n", + " # These elements will be multiplied together in the next step.\n", + "\n", + " # The appropriate encoders make the multiplication more accurate\n", + " # Check the \"multiplication\" example to see how multiplication\n", + " # can be implemented in neurons.\n", + " c_size = Amat.size * Bmat.shape[1]\n", + " C = nengo.networks.Product(N, dimensions=c_size)\n", + "\n", + "# Determine the transformation matrices to get the correct pairwise\n", + "# products computed. This looks a bit like black magic but if\n", + "# you manually try multiplying two matrices together, you can see\n", + "# the underlying pattern. Basically, we need to build up D1*D2*D3\n", + "# pairs of numbers in C to compute the product of. If i,j,k are the\n", + "# indexes into the D1*D2*D3 products, we want to compute the product\n", + "# of element (i,j) in A with the element (j,k) in B. The index in\n", + "# A of (i,j) is j+i*D2 and the index in B of (j,k) is k+j*D3.\n", + "# The index in C is j+k*D2+i*D2*D3.\n", + "transformA = np.zeros((c_size, Amat.size))\n", + "transformB = np.zeros((c_size, Bmat.size))\n", + "\n", + "for i in range(Amat.shape[0]):\n", + " for j in range(Amat.shape[1]):\n", + " for k in range(Bmat.shape[1]):\n", + " c_index = j + k * Amat.shape[1] + i * Bmat.size\n", + " transformA[c_index][j + i * Amat.shape[1]] = 1\n", + " transformB[c_index][k + j * Bmat.shape[1]] = 1\n", + "\n", + "print(\"A->C\")\n", + "print(transformA)\n", + "print(\"B->C\")\n", + "print(transformB)\n", + "\n", + "with model:\n", + " nengo.Connection(A.output, C.input_a, transform=transformA)\n", + " nengo.Connection(B.output, C.input_b, transform=transformB)\n", + " C_probe = nengo.Probe(C.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at C\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[C_probe])\n", + "plt.title(\"C\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Now do the appropriate summing\n", + " D = nengo.networks.EnsembleArray(\n", + " N, n_ensembles=Amat.shape[0] * Bmat.shape[1], radius=radius\n", + " )\n", + "\n", + "# The mapping for this transformation is much easier, since we want to\n", + "# combine D2 pairs of elements (we sum D2 products together)\n", + "transformC = np.zeros((D.dimensions, c_size))\n", + "for i in range(c_size):\n", + " transformC[i // Bmat.shape[0]][i] = 1\n", + "print(\"C->D\")\n", + "print(transformC)\n", + "\n", + "with model:\n", + " nengo.Connection(C.output, D.input, transform=transformC)\n", + " D_probe = nengo.Probe(D.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[D_probe])\n", + "for d in np.dot(Amat, Bmat).flatten():\n", + " plt.axhline(d, color=\"k\")\n", + "plt.title(\"D\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/advanced/nef-algorithm.ipynb.txt b/_sources/examples/advanced/nef-algorithm.ipynb.txt new file mode 100644 index 0000000000..5a150738dc --- /dev/null +++ b/_sources/examples/advanced/nef-algorithm.ipynb.txt @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The NEF algorithm\n", + "\n", + "While Nengo provides a flexible,\n", + "general-purpose approach to neural modelling,\n", + "some aspects rely on the Neural Engineering Framework (NEF)\n", + "to help specify network behavior.\n", + "The theory behind the Neural Engineering Framework\n", + "is developed at length in\n", + "[Eliasmith & Anderson, 2003: \"Neural Engineering\"](\n", + "https://mitpress.mit.edu/books/neural-engineering)\n", + "and a short summary is available in\n", + "[Stewart, 2012: \"A Technical Overview of the Neural Engineering Framework\"](\n", + "http://compneuro.uwaterloo.ca/publications/stewart2012d.html).\n", + "\n", + "However, for some people,\n", + "the best description of an algorithm\n", + "is the code itself.\n", + "With that in mind,\n", + "the following is a complete implementation of the NEF\n", + "for the special case of\n", + "two one-dimensional populations with a single connection between them.\n", + "You can adjust the function being computed,\n", + "the input to the system,\n", + "and various neural parameters.\n", + "\n", + "This example does not use Nengo at all.\n", + "It is Python code that only requires\n", + "Numpy (for the matrix inversion)\n", + "and Matplotlib (to produce graphs of the output)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The NEF is a method for building large-scale neural models\n", + "using realistic neurons.\n", + "It is a neural compiler:\n", + "you specify the high-level computations the model needs to compute,\n", + "and the properties of the neurons themselves,\n", + "and the NEF determines the neural connections\n", + "needed to perform those operations.\n", + "\n", + "This script shows how to build a\n", + "simple feed-forward network of leaky integrate-and-fire neurons\n", + "where each population encodes a one-dimensional value\n", + "and the connection weights between the populations\n", + "are optimized to compute some arbitrary function.\n", + "This same approach is used in Nengo,\n", + "extended to multi-dimensional representation,\n", + "multiple populations of neurons, and recurrent connections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To change the input to the system, change `input`.\n", + "To change the function computed by the weights, change `function`.\n", + "\n", + "The size of the populations and their neural properties\n", + "can also be adjusted by changing the parameters below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import math\n", + "import random\n", + "\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "dt = 0.001 # simulation time step\n", + "t_rc = 0.02 # membrane RC time constant\n", + "t_ref = 0.002 # refractory period\n", + "t_pstc = 0.1 # post-synaptic time constant\n", + "N_A = 50 # number of neurons in first population\n", + "N_B = 40 # number of neurons in second population\n", + "N_samples = 100 # number of sample points to use when finding decoders\n", + "rate_A = 25, 75 # range of maximum firing rates for population A\n", + "rate_B = 50, 100 # range of maximum firing rates for population B\n", + "\n", + "\n", + "def input(t):\n", + " \"\"\"The input to the system over time\"\"\"\n", + " return math.sin(t)\n", + "\n", + "\n", + "def function(x):\n", + " \"\"\"The function to compute between A and B.\"\"\"\n", + " return x * x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create random encoders for the two populations\n", + "encoder_A = [random.choice([-1, 1]) for i in range(N_A)]\n", + "encoder_B = [random.choice([-1, 1]) for i in range(N_B)]\n", + "\n", + "\n", + "def generate_gain_and_bias(count, intercept_low, intercept_high, rate_low, rate_high):\n", + " gain = []\n", + " bias = []\n", + " for _ in range(count):\n", + " # desired intercept (x value for which the neuron starts firing\n", + " intercept = random.uniform(intercept_low, intercept_high)\n", + " # desired maximum rate (firing rate when x is maximum)\n", + " rate = random.uniform(rate_low, rate_high)\n", + "\n", + " # this algorithm is specific to LIF neurons, but should\n", + " # generate gain and bias values to produce the desired\n", + " # intercept and rate\n", + " z = 1.0 / (1 - math.exp((t_ref - (1.0 / rate)) / t_rc))\n", + " g = (1 - z) / (intercept - 1.0)\n", + " b = 1 - g * intercept\n", + " gain.append(g)\n", + " bias.append(b)\n", + " return gain, bias\n", + "\n", + "\n", + "# random gain and bias for the two populations\n", + "gain_A, bias_A = generate_gain_and_bias(N_A, -1, 1, rate_A[0], rate_A[1])\n", + "gain_B, bias_B = generate_gain_and_bias(N_B, -1, 1, rate_B[0], rate_B[1])\n", + "\n", + "\n", + "def run_neurons(input, v, ref):\n", + " \"\"\"Run the neuron model.\n", + "\n", + " A simple leaky integrate-and-fire model, scaled so that v=0 is resting\n", + " voltage and v=1 is the firing threshold.\n", + " \"\"\"\n", + " spikes = []\n", + " for i, _ in enumerate(v):\n", + " dV = dt * (input[i] - v[i]) / t_rc # the LIF voltage change equation\n", + " v[i] += dV\n", + " if v[i] < 0:\n", + " v[i] = 0 # don't allow voltage to go below 0\n", + "\n", + " if ref[i] > 0: # if we are in our refractory period\n", + " v[i] = 0 # keep voltage at zero and\n", + " ref[i] -= dt # decrease the refractory period\n", + "\n", + " if v[i] > 1: # if we have hit threshold\n", + " spikes.append(True) # spike\n", + " v[i] = 0 # reset the voltage\n", + " ref[i] = t_ref # and set the refractory period\n", + " else:\n", + " spikes.append(False)\n", + " return spikes\n", + "\n", + "\n", + "def compute_response(x, encoder, gain, bias, time_limit=0.5):\n", + " \"\"\"Measure the spike rate of a population for a given value x.\"\"\"\n", + " N = len(encoder) # number of neurons\n", + " v = [0] * N # voltage\n", + " ref = [0] * N # refractory period\n", + "\n", + " # compute input corresponding to x\n", + " input = []\n", + " for i in range(N):\n", + " input.append(x * encoder[i] * gain[i] + bias[i])\n", + " v[i] = random.uniform(0, 1) # randomize the initial voltage level\n", + "\n", + " count = [0] * N # spike count for each neuron\n", + "\n", + " # feed the input into the population for a given amount of time\n", + " t = 0\n", + " while t < time_limit:\n", + " spikes = run_neurons(input, v, ref)\n", + " for i, s in enumerate(spikes):\n", + " if s:\n", + " count[i] += 1\n", + " t += dt\n", + " return [c / time_limit for c in count] # return the spike rate (in Hz)\n", + "\n", + "\n", + "def compute_tuning_curves(encoder, gain, bias):\n", + " \"\"\"Compute the tuning curves for a population\"\"\"\n", + " # generate a set of x values to sample at\n", + " x_values = [i * 2.0 / N_samples - 1.0 for i in range(N_samples)]\n", + "\n", + " # build up a matrix of neural responses to each input (i.e. tuning curves)\n", + " A = []\n", + " for x in x_values:\n", + " response = compute_response(x, encoder, gain, bias)\n", + " A.append(response)\n", + " return x_values, A\n", + "\n", + "\n", + "def compute_decoder(encoder, gain, bias, function=lambda x: x):\n", + " # get the tuning curves\n", + " x_values, A = compute_tuning_curves(encoder, gain, bias)\n", + "\n", + " # get the desired decoded value for each sample point\n", + " value = numpy.array([[function(x)] for x in x_values])\n", + "\n", + " # find the optimal linear decoder\n", + " A = numpy.array(A).T\n", + " Gamma = numpy.dot(A, A.T)\n", + " Upsilon = numpy.dot(A, value)\n", + " Ginv = numpy.linalg.pinv(Gamma)\n", + " decoder = numpy.dot(Ginv, Upsilon) / dt\n", + " return decoder\n", + "\n", + "\n", + "# find the decoders for A and B\n", + "decoder_A = compute_decoder(encoder_A, gain_A, bias_A, function=function)\n", + "decoder_B = compute_decoder(encoder_B, gain_B, bias_B)\n", + "\n", + "# compute the weight matrix\n", + "weights = numpy.dot(decoder_A, [encoder_B])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Running the simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v_A = [0.0] * N_A # voltage for population A\n", + "ref_A = [0.0] * N_A # refractory period for population A\n", + "input_A = [0.0] * N_A # input for population A\n", + "\n", + "v_B = [0.0] * N_B # voltage for population B\n", + "ref_B = [0.0] * N_B # refractory period for population B\n", + "input_B = [0.0] * N_B # input for population B\n", + "\n", + "# scaling factor for the post-synaptic filter\n", + "pstc_scale = 1.0 - math.exp(-dt / t_pstc)\n", + "\n", + "# for storing simulation data to plot afterward\n", + "inputs = []\n", + "times = []\n", + "outputs = []\n", + "ideal = []\n", + "\n", + "output = 0.0 # the decoded output value from population B\n", + "t = 0\n", + "while t < 10.0: # noqa: C901 (tell static checker to ignore complexity)\n", + " # call the input function to determine the input value\n", + " x = input(t)\n", + "\n", + " # convert the input value into an input for each neuron\n", + " for i in range(N_A):\n", + " input_A[i] = x * encoder_A[i] * gain_A[i] + bias_A[i]\n", + "\n", + " # run population A and determine which neurons spike\n", + " spikes_A = run_neurons(input_A, v_A, ref_A)\n", + "\n", + " # decay all of the inputs (implementing the post-synaptic filter)\n", + " for j in range(N_B):\n", + " input_B[j] *= 1.0 - pstc_scale\n", + " # for each neuron that spikes, increase the input current\n", + " # of all the neurons it is connected to by the synaptic\n", + " # connection weight\n", + " for i, s in enumerate(spikes_A):\n", + " if s:\n", + " for j in range(N_B):\n", + " input_B[j] += weights[i][j] * pstc_scale\n", + "\n", + " # compute the total input into each neuron in population B\n", + " # (taking into account gain and bias)\n", + " total_B = [0] * N_B\n", + " for j in range(N_B):\n", + " total_B[j] = gain_B[j] * input_B[j] + bias_B[j]\n", + "\n", + " # run population B and determine which neurons spike\n", + " spikes_B = run_neurons(total_B, v_B, ref_B)\n", + "\n", + " # for each neuron in B that spikes, update our decoded value\n", + " # (also applying the same post-synaptic filter)\n", + " output *= 1.0 - pstc_scale\n", + " for j, s in enumerate(spikes_B):\n", + " if s:\n", + " output += decoder_B[j][0] * pstc_scale\n", + "\n", + " if t % 0.5 <= dt:\n", + " print(t, output)\n", + " times.append(t)\n", + " inputs.append(x)\n", + " outputs.append(output)\n", + " ideal.append(function(x))\n", + " t += dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x, A = compute_tuning_curves(encoder_A, gain_A, bias_A)\n", + "x, B = compute_tuning_curves(encoder_B, gain_B, bias_B)\n", + "\n", + "plt.figure()\n", + "plt.plot(x, A)\n", + "plt.title(\"Tuning curves for population A\")\n", + "\n", + "plt.figure()\n", + "plt.plot(x, B)\n", + "plt.title(\"Tuning curves for population B\")\n", + "\n", + "plt.figure()\n", + "plt.plot(times, inputs, label=\"input\")\n", + "plt.plot(times, ideal, label=\"ideal\")\n", + "plt.plot(times, outputs, label=\"output\")\n", + "plt.title(\"Simulation results\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/advanced/nef-summary.ipynb.txt b/_sources/examples/advanced/nef-summary.ipynb.txt new file mode 100644 index 0000000000..00abd03430 --- /dev/null +++ b/_sources/examples/advanced/nef-summary.ipynb.txt @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NEF summary\n", + "\n", + "The Neural Engineering Framework (NEF)\n", + "is one set of theoretical methods that are used in\n", + "Nengo for constructing neural models.\n", + "The NEF is based on [Eliasmith & Anderson's (2003) book](\n", + "https://mitpress.mit.edu/books/neural-engineering) from MIT Press.\n", + "This notebook introduces the three main principles\n", + "discussed in that book and implemented in Nengo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Uniform\n", + "from nengo.processes import WhiteSignal\n", + "from nengo.utils.ensemble import tuning_curves\n", + "from nengo.utils.ipython import hide_input\n", + "from nengo.utils.matplotlib import rasterplot\n", + "\n", + "\n", + "def aligned(n_neurons, radius=0.9):\n", + " intercepts = np.linspace(-radius, radius, n_neurons)\n", + " encoders = np.tile([[1], [-1]], (n_neurons // 2, 1))\n", + " intercepts *= encoders[:, 0]\n", + " return intercepts, encoders\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 1: Representation\n", + "\n", + "### Encoding\n", + "\n", + "Neural populations represent time-varying signals\n", + "through their spiking responses.\n", + "A signal is a vector of real numbers of arbitrary length.\n", + "This example is a 1D signal going from -1 to 1 in 1 second." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], lw=2)\n", + "plt.title(\"Input signal\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These signals drive neural populations\n", + "based on each neuron's *tuning curve*\n", + "(which is similar to the current-frequency curve,\n", + "if you're familiar with that).\n", + "\n", + "The tuning curve describes how much\n", + "a particular neuron will fire as a function of the input signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "intercepts, encoders = aligned(8) # Makes evenly spaced intercepts\n", + "with model:\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " max_rates=Uniform(80, 100),\n", + " encoders=encoders,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(A, sim)\n", + "\n", + "plt.figure()\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can drive these neurons with our input signal\n", + "and observe their spiking activity over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure()\n", + "ax = plt.subplot(1, 1, 1)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "ax.set_xlim(0, 1)\n", + "ax.set_ylabel(\"Neuron\")\n", + "ax.set_xlabel(\"Time (s)\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decoding\n", + "\n", + "We can estimate the input signal\n", + "originally encoded by decoding the pattern of spikes.\n", + "To do this, we first filter the spike train\n", + "with a temporal filter that accounts for\n", + "postsynaptic current (PSC) activity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)\n", + " intercepts, encoders = aligned(8) # Makes evenly spaced intercepts\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " max_rates=Uniform(80, 100),\n", + " encoders=encoders,\n", + " )\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "scale = 180\n", + "plt.figure()\n", + "for i in range(A.n_neurons):\n", + " plt.plot(sim.trange(), sim.data[A_spikes][:, i] - i * scale)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(scale * (-A.n_neurons + 1), scale)\n", + "plt.ylabel(\"Neuron\")\n", + "plt.yticks(\n", + " np.arange(scale / 1.8, (-A.n_neurons + 1) * scale, -scale), np.arange(A.n_neurons)\n", + ")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we mulitply those filtered spike trains\n", + "with decoding weights and sum them together\n", + "to give an estimate of the input based on the spikes.\n", + "\n", + "The decoding weights are determined\n", + "by minimizing the squared difference\n", + "between the decoded estimate and the actual input signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms PSC filter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded estimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The accuracy of the decoded estimate increases\n", + "as the number of neurons increases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(15, 3.5))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "eval_points, activities = tuning_curves(A, sim)\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "\n", + "ax = plt.subplot(1, 3, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded esimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any smooth signal can be encoded and decoded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(WhiteSignal(1, high=5), size_out=1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded esimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 2: Transformation\n", + "\n", + "Encoding and decoding allow us to encode signals over time,\n", + "and decode transformations of those signals.\n", + "\n", + "In fact, we can decode arbitrary transformations of the input signal,\n", + "not just the signal itself (as in the previous example).\n", + "\n", + "Let's decode the square of our white noise input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(WhiteSignal(1, high=5), size_out=1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " Asquare = nengo.Node(size_in=1)\n", + " nengo.Connection(input, A)\n", + " nengo.Connection(A, Asquare, function=np.square)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " Asquare_probe = nengo.Probe(Asquare, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[Asquare_probe], label=\"Decoded esimate\")\n", + "plt.plot(sim.trange(), np.square(sim.data[input_probe]), label=\"Input signal squared\")\n", + "plt.legend(loc=\"best\", fontsize=\"medium\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes])\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the spike trains are exactly the same.\n", + "The only difference is how we're interpreting those spikes.\n", + "We told Nengo to compute a new set of decoders\n", + "that estimate the function $x^2$.\n", + "\n", + "In general, the transformation principle\n", + "determines how we can decode spike trains\n", + "to compute linear and nonlinear transformations of signals\n", + "encoded in a population of neurons.\n", + "We can then project those transformed signals\n", + "into another population, and repeat the process.\n", + "Essentially, this provides a means of\n", + "computing the neural connection weights\n", + "to compute an arbitrary function between populations.\n", + "\n", + "Suppose we are representing a sine wave." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: np.sin(np.pi * t))\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.title(\"A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear transformations of that signal\n", + "involve solving for the usual decoders,\n", + "and scaling those decoding weights.\n", + "Let us flip this sine wave upside down\n", + "as it is transmitted between two populations\n", + "(i.e. population A and population -A)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " minusA = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(A, minusA, function=lambda x: -x)\n", + " minusA_spikes = nengo.Probe(minusA.neurons)\n", + " minusA_probe = nengo.Probe(minusA, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.plot(sim.trange(), sim.data[minusA_probe])\n", + "plt.title(\"-A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(2, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(2, 2, 4)\n", + "rasterplot(sim.trange(), sim.data[minusA_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"-A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nonlinear transformations involve\n", + "solving for a new set of decoding weights.\n", + "Let us add a third population connected\n", + "to the second one and use it to compute $(-A)^2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " A_squared = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(minusA, A_squared, function=lambda x: x ** 2)\n", + " A_squared_spikes = nengo.Probe(A_squared.neurons)\n", + " A_squared_probe = nengo.Probe(A_squared, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 6.5))\n", + "plt.subplot(3, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(3, 2, 3)\n", + "plt.plot(sim.trange(), sim.data[minusA_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"-A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(3, 2, 5)\n", + "plt.plot(sim.trange(), sim.data[A_squared_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"(-A)^2\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(3, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(3, 2, 4)\n", + "rasterplot(sim.trange(), sim.data[minusA_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"-A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(3, 2, 6)\n", + "rasterplot(sim.trange(), sim.data[A_squared_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"(-A)^2\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 3: Dynamics\n", + "\n", + "So far, we have been considering the values\n", + "represented by ensembles as generic \"signals.\"\n", + "However, if we think of them instead\n", + "as state variables in a dynamical system,\n", + "then we can apply the methods of control theory\n", + "or dynamic systems theory to brain models.\n", + "Nengo automatically translates from standard dynamical systems descriptions\n", + "to descriptions consistent with neural dynamics.\n", + "\n", + "In order to get interesting dynamics,\n", + "we can connect populations recurrently (i.e., to themselves).\n", + "\n", + "Below is a simple harmonic oscillator\n", + "implemented using this third principle.\n", + "It needs is a bit of input to get it started." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: [1, 0] if t < 0.1 else [0, 0])\n", + " oscillator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(input, oscillator)\n", + " nengo.Connection(oscillator, oscillator, transform=[[1, 1], [-1, 1]], synapse=0.1)\n", + " oscillator_probe = nengo.Probe(oscillator, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(3)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[oscillator_probe])\n", + "plt.ylim(-1.2, 1.2)\n", + "plt.xlabel(\"Time (s)\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(sim.data[oscillator_probe][:, 0], sim.data[oscillator_probe][:, 1])\n", + "plt.grid()\n", + "plt.axis([-1.2, 1.2, -1.2, 1.2])\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$x_2$\")\n", + "hide_input()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/advanced/processes.ipynb.txt b/_sources/examples/advanced/processes.ipynb.txt new file mode 100644 index 0000000000..5478b9b012 --- /dev/null +++ b/_sources/examples/advanced/processes.ipynb.txt @@ -0,0 +1,563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Processes and how to use them\n", + "\n", + "Processes in Nengo can be used to describe\n", + "general functions or dynamical systems,\n", + "including those with randomness.\n", + "They can be useful if you want a `Node` output\n", + "that has a state (like a dynamical system),\n", + "and they're also used for things like\n", + "injecting noise into Ensembles\n", + "so that you can not only have \"white\" noise\n", + "that samples from a distribution,\n", + "but can also have \"colored\" noise\n", + "where subsequent samples are correlated with past samples.\n", + "\n", + "This notebook will first present the basic process interface,\n", + "then demonstrate some of the built-in Nengo processes\n", + "and how they can be used in your code.\n", + "It will also describe how to create your own custom process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interface\n", + "\n", + "We will begin by looking at how to run an existing process instance.\n", + "\n", + "The key functions for running processes\n", + "are `run`, `run_steps`, and `apply`.\n", + "The first two are for running without an input,\n", + "and the third is for applying the process to an input.\n", + "\n", + "There are also two helper functions,\n", + "`trange` and `ntrange`,\n", + "which return the time points corresponding to a process output,\n", + "given either a length of time or a number of steps, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `run`: running a process for a length of time\n", + "\n", + "The `run` function runs a process\n", + "for a given length of time, without any input.\n", + "Many of the random processes in `nengo.processes` will be run this way,\n", + "since they do not require an input signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a process (details on the FilteredNoise process below)\n", + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "\n", + "# run the process for two seconds\n", + "y = process.run(2.0)\n", + "\n", + "# get a corresponding two-second time range\n", + "t = process.trange(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(t, y)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"process output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `run_steps`: running a process for a number of steps\n", + "\n", + "To run the process for a number of steps, use the `run_steps` function.\n", + "The length of the generated signal will depend on the process's `default_dt`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "\n", + "# run the process for 1000 steps\n", + "y = process.run_steps(1000)\n", + "\n", + "# get a corresponding 1000-step time range\n", + "t = process.ntrange(1000)\n", + "\n", + "plt.figure()\n", + "plt.plot(t, y)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"process output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `apply`: running a process with an input\n", + "\n", + "To run a process with an input, use the `apply` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process = nengo.synapses.Lowpass(0.2)\n", + "\n", + "t = process.trange(5)\n", + "x = np.minimum(t % 2, 2 - (t % 2)) # sawtooth wave\n", + "y = process.apply(x) # general to all Processes\n", + "z = process.filtfilt(x) # specific to Synapses\n", + "\n", + "plt.figure()\n", + "plt.plot(t, x, label=\"input\")\n", + "plt.plot(t, y, label=\"output\")\n", + "plt.plot(t, z, label=\"filtfilt\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"signal\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that Synapses are a special kind of process,\n", + "and have the additional functions `filt` and `filtfilt`.\n", + "`filt` works mostly the same as `apply`,\n", + "but with some additional functionality\n", + "such as the ability to filter along any axis.\n", + "`filtfilt` provides zero-phase filtering." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing the time-step (`dt` and `default_dt`)\n", + "\n", + "To run a process with a different time-step,\n", + "you can either pass the new time step (`dt`) when calling the functions,\n", + "or change the `default_dt` property of the process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "y1 = process.run(2.0, dt=0.05)\n", + "t1 = process.trange(2.0, dt=0.05)\n", + "\n", + "process = nengo.processes.FilteredNoise(\n", + " synapse=nengo.synapses.Alpha(0.1), default_dt=0.1, seed=0\n", + ")\n", + "y2 = process.run(2.0)\n", + "t2 = process.trange(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(t1, y1, label=\"dt = %s\" % 0.05)\n", + "plt.plot(t2, y2, label=\"dt = %s\" % 0.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `WhiteSignal`\n", + "\n", + "The `WhiteSignal` process is used to generate band-limited white noise,\n", + "with only frequencies below a given cutoff frequency." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as model:\n", + " a = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, seed=0))\n", + " b = nengo.Node(nengo.processes.WhiteSignal(1.0, high=10, seed=0))\n", + " c = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, rms=0.3, seed=0))\n", + " d = nengo.Node(nengo.processes.WhiteSignal(0.5, high=5, seed=0))\n", + " ap = nengo.Probe(a)\n", + " bp = nengo.Probe(b)\n", + " cp = nengo.Probe(c)\n", + " dp = nengo.Probe(d)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[ap], label=\"5 Hz cutoff\")\n", + "plt.plot(sim.trange(), sim.data[bp], label=\"10 Hz cutoff\")\n", + "plt.plot(sim.trange(), sim.data[cp], label=\"5 Hz cutoff, 0.3 RMS amplitude\")\n", + "plt.plot(sim.trange(), sim.data[dp], label=\"5 Hz cutoff, 0.5 s period\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.legend(loc=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the 10 Hz signal (green)\n", + "has similar low frequency characteristics\n", + "as the 5 Hz signal (blue),\n", + "but with additional higher-frequency components.\n", + "The 0.3 RMS amplitude 5 Hz signal (red)\n", + "is the same as the original 5 Hz signal (blue),\n", + "but scaled down (the default RMS amplitude is 0.5).\n", + "Finally, the signal with a 0.5 s period\n", + "(instead of a 1 s period like the others)\n", + "is completely different,\n", + "because changing the period changes\n", + "the spacing of the random frequency components\n", + "and thus creates a completely different signal.\n", + "Note how the signal with the 0.5 s period repeats itself;\n", + "for example, the value at $t = 0$\n", + "is the same as the value at $t = 0.5$,\n", + "and the value at $t = 0.4$\n", + "is the same as the value at $t = 0.9$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `WhiteNoise`\n", + "\n", + "The `WhiteNoise` process generates white noise,\n", + "with equal power across all frequencies.\n", + "By default, it is scaled so that the integral process (Brownian noise)\n", + "will have the same standard deviation regardless of `dt`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 1))\n", + "\n", + "t = process.trange(0.5)\n", + "y = process.run(0.5)\n", + "plt.figure()\n", + "plt.plot(t, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One use of the `WhiteNoise` process\n", + "is to inject noise into neural populations.\n", + "Here, we create two identical ensembles,\n", + "but add a bit of noise to one and no noise to the other.\n", + "We plot the membrane voltages of both." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 0.01), seed=1)\n", + "\n", + "with nengo.Network() as model:\n", + " ens_args = dict(encoders=[[1]], intercepts=[0.01], max_rates=[100])\n", + " a = nengo.Ensemble(1, 1, **ens_args)\n", + " b = nengo.Ensemble(1, 1, noise=process, **ens_args)\n", + " a_voltage = nengo.Probe(a.neurons, \"voltage\")\n", + " b_voltage = nengo.Probe(b.neurons, \"voltage\")\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.15)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_voltage], label=\"deterministic\")\n", + "plt.plot(sim.trange(), sim.data[b_voltage], label=\"noisy\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"voltage\")\n", + "plt.legend(loc=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the neuron without noise (blue)\n", + "approaches its firing threshold,\n", + "but never quite gets there.\n", + "Adding a bit of noise (green)\n", + "causes the neuron to occasionally jitter above the threshold,\n", + "resulting in two spikes\n", + "(where the voltage suddenly drops to zero)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `FilteredNoise`\n", + "\n", + "The `FilteredNoise` process takes a white noise signal\n", + "and passes it through a filter.\n", + "Using any type of lowpass filter (e.g. `Lowpass`, `Alpha`)\n", + "will result in a signal similar to `WhiteSignal`,\n", + "but rather than being ideally filtered\n", + "(i.e. no frequency content above the cutoff),\n", + "the `FilteredNoise` signal\n", + "will have some frequency content above the cutoff,\n", + "with the amount depending on the filter used.\n", + "Here, we can see how an `Alpha` filter\n", + "(a second-order lowpass filter)\n", + "is much better than the `Lowpass` filter\n", + "(a first-order lowpass filter)\n", + "at removing the high-frequency content." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "process1 = nengo.processes.FilteredNoise(\n", + " dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Alpha(0.005), seed=0\n", + ")\n", + "\n", + "process2 = nengo.processes.FilteredNoise(\n", + " dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Lowpass(0.005), seed=0\n", + ")\n", + "\n", + "tlen = 0.5\n", + "plt.figure()\n", + "plt.plot(process1.trange(tlen), process1.run(tlen))\n", + "plt.plot(process2.trange(tlen), process2.run(tlen))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `FilteredNoise` process with an `Alpha` synapse (blue)\n", + "has significantly lower high-frequency components\n", + "than a similar process with a `Lowpass` synapse (green)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `PresentInput`\n", + "\n", + "The `PresentInput` process is useful for\n", + "presenting a series of static inputs to a network,\n", + "where each input is shown for the same length of time.\n", + "Once all the images have been shown,\n", + "they repeat from the beginning.\n", + "One application is presenting a series of images to a classification network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = [[0, 0.5], [0.3, 0.2], [-0.1, -0.7], [-0.8, 0.6]]\n", + "process = nengo.processes.PresentInput(inputs, presentation_time=0.1)\n", + "\n", + "tlen = 0.8\n", + "plt.figure()\n", + "plt.plot(process.trange(tlen), process.run(tlen))\n", + "plt.xlim([0, tlen])\n", + "plt.ylim([-1, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom processes\n", + "\n", + "You can create custom processes\n", + "by inheriting from the `nengo.Process` class\n", + "and overloading the `make_step` and `make_state` methods.\n", + "\n", + "As an example, we'll make a simple custom process\n", + "that implements a two-dimensional oscillator dynamical system.\n", + "The `make_state` function defines a `state` variable\n", + "to store the state.\n", + "The `make_step` function uses that state\n", + "and a fixed `A` matrix to determine\n", + "how the state changes over time.\n", + "\n", + "One advantage to using a process over a simple function\n", + "is that if we reset our simulator,\n", + "`make_step` will be called again\n", + "and the process state\n", + "will be restored to the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SimpleOscillator(nengo.Process):\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None):\n", + " # return a dictionary mapping strings to their initial state\n", + " return {\"state\": np.array([1.0, 0.0])}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " A = np.array([[-0.1, -1.0], [1.0, -0.1]])\n", + " s = state[\"state\"]\n", + "\n", + " # define the step function, which will be called\n", + " # by the node every time step\n", + " def step(t):\n", + " s[:] += dt * np.dot(A, s)\n", + " return s\n", + "\n", + " return step # return the step function\n", + "\n", + "\n", + "with nengo.Network() as model:\n", + " a = nengo.Node(SimpleOscillator(), size_in=0, size_out=2)\n", + " a_p = nengo.Probe(a)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_p])\n", + "plt.xlabel(\"time [s]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can generalize this process to one\n", + "that can implement arbitrary linear dynamical systems,\n", + "given `A` and `B` matrices.\n", + "We will overload the `__init__` method\n", + "to take and store these matrices,\n", + "as well as check the matrix shapes\n", + "and set the default size in and out.\n", + "The advantage of using the default sizes\n", + "is that when we then create a node using the process,\n", + "or run the process using `apply`,\n", + "we do not need to specify the sizes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class LTIProcess(nengo.Process):\n", + " def __init__(self, A, B, **kwargs):\n", + " A, B = np.asarray(A), np.asarray(B)\n", + "\n", + " # check that the matrix shapes are compatible\n", + " assert A.ndim == 2 and A.shape[0] == A.shape[1]\n", + " assert B.ndim == 2 and B.shape[0] == A.shape[0]\n", + "\n", + " # store the matrices for `make_step`\n", + " self.A = A\n", + " self.B = B\n", + "\n", + " # pass the default sizes to the Process constructor\n", + " super().__init__(\n", + " default_size_in=B.shape[1], default_size_out=A.shape[0], **kwargs\n", + " )\n", + "\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None):\n", + " return {\"state\": np.zeros(self.A.shape[0])}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " assert shape_in == (self.B.shape[1],)\n", + " assert shape_out == (self.A.shape[0],)\n", + " A, B = self.A, self.B\n", + " s = state[\"state\"]\n", + "\n", + " def step(t, x):\n", + " s[:] += dt * (np.dot(A, s) + np.dot(B, x))\n", + " return s\n", + "\n", + " return step\n", + "\n", + "\n", + "# demonstrate the LTIProcess in action\n", + "A = [[-0.1, -1], [1, -0.1]]\n", + "B = [[10], [-10]]\n", + "\n", + "with nengo.Network() as model:\n", + " u = nengo.Node(lambda t: 1 if t < 0.1 else 0)\n", + " # we don't need to specify size_in and size_out!\n", + " a = nengo.Node(LTIProcess(A, B))\n", + " nengo.Connection(u, a)\n", + " a_p = nengo.Probe(a)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_p])\n", + "plt.xlabel(\"time [s]\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/2d-representation.ipynb.txt b/_sources/examples/basic/2d-representation.ipynb.txt new file mode 100644 index 0000000000..6b9036e883 --- /dev/null +++ b/_sources/examples/basic/2d-representation.ipynb.txt @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2-dimensional representation\n", + "\n", + "Ensembles of neurons represent information.\n", + "In Nengo, we represent that information with\n", + "real-valued vectors -- lists of numbers.\n", + "In this example, we will represent a two-dimensional vector\n", + "with a single ensemble of leaky integrate-and-fire neurons." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "Our model consists of a single ensemble,\n", + "which we will call `Neurons`.\n", + "It will represent a two-dimensional signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "\n", + "model = nengo.Network(label=\"2D Representation\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # and represents a 2-dimensional signal\n", + " neurons = nengo.Ensemble(100, dimensions=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "The signal that an ensemble represents varies over time.\n", + "We will use a simple sine and cosine wave\n", + "as examples of continuously changing signals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes representing the sine and cosine\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the input to the ensemble" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # The indices in neurons define which dimension the input will project to\n", + " nengo.Connection(sin, neurons[0])\n", + " nengo.Connection(cos, neurons[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later.\n", + "Let's collect all the data produced." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin, \"output\")\n", + " cos_probe = nengo.Probe(cos, \"output\")\n", + " neurons_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neurons_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Sine\")\n", + "plt.plot(sim.trange(), sim.data[cos_probe], \"k\", label=\"Cosine\")\n", + "plt.legend()\n", + "plt.xlabel(\"time [s]\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/addition.ipynb.txt b/_sources/examples/basic/addition.ipynb.txt new file mode 100644 index 0000000000..a2dbe91a7c --- /dev/null +++ b/_sources/examples/basic/addition.ipynb.txt @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Addition\n", + "\n", + "In this example, we will construct a network that adds two inputs.\n", + "The network utilizes two communication channels\n", + "into the same neural population.\n", + "Addition is thus somewhat 'free', since the incoming currents\n", + "from different synaptic connections interact linearly\n", + "(though two inputs don't have to\n", + "combine in this way; see the combining demo)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model has three ensembles, which we will call A, B, and C." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Addition\")\n", + "with model:\n", + " # Create 3 ensembles each containing 100 leaky integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + " C = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "We will use two constant scalar values for the two input signals\n", + "that drive activity in ensembles A and B." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes representing constant values\n", + " input_a = nengo.Node(output=0.5)\n", + " input_b = nengo.Node(output=0.3)\n", + "\n", + " # Connect the input nodes to the appropriate ensembles\n", + " nengo.Connection(input_a, A)\n", + " nengo.Connection(input_b, B)\n", + "\n", + " # Connect input ensembles A and B to output ensemble C\n", + " nengo.Connection(A, C)\n", + " nengo.Connection(B, C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " input_a_probe = nengo.Probe(input_a)\n", + " input_b_probe = nengo.Probe(input_b)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " C_probe = nengo.Probe(C, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data produced by running the model can now be plotted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the input signals and decoded ensemble values\n", + "t = sim.trange()\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded Ensemble A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded Ensemble B\")\n", + "plt.plot(sim.trange(), sim.data[C_probe], label=\"Decoded Ensemble C\")\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_a_probe], label=\"Input A\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_b_probe], label=\"Input B\", color=\"0.75\", linewidth=2.0\n", + ")\n", + "plt.legend()\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"time [s]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check that the decoded value\n", + "of the activity in ensemble C\n", + "provides a good estimate of the sum of inputs A and B." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/combining.ipynb.txt b/_sources/examples/basic/combining.ipynb.txt new file mode 100644 index 0000000000..d959b3236e --- /dev/null +++ b/_sources/examples/basic/combining.ipynb.txt @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining\n", + "\n", + "This example demonstrates how to create\n", + "a neuronal ensemble that will combine two 1-D inputs\n", + "into one 2-D representation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of three ensembles,\n", + "two input ensembles and one 2-D ensemble\n", + "that will represent the two inputs as one two-dimensional signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Combining\")\n", + "with model:\n", + " # Our input ensembles consist of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # The output ensemble consists of 200 leaky integrate-and-fire neurons,\n", + " # representing a two-dimensional signal\n", + " output = nengo.Ensemble(200, dimensions=2, label=\"2D Population\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use sine and cosine waves\n", + "as examples of continuously changing signals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes generating the sine and cosine\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, A)\n", + " nengo.Connection(cos, B)\n", + "\n", + " # The square brackets define which dimension the input will project to\n", + " nengo.Connection(A, output[1])\n", + " nengo.Connection(B, output[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " cos_probe = nengo.Probe(cos)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms filter\n", + " B_probe = nengo.Probe(B, synapse=0.01) # 10ms filter\n", + " out_probe = nengo.Probe(output, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[out_probe][:, 0], \"b\", label=\"2D output\")\n", + "plt.plot(sim.trange(), sim.data[out_probe][:, 1], \"g\", label=\"2D output\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], \"r\", label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"k\", label=\"Sine\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows that the input signal (Sine),\n", + "the output from the 1D population (A output),\n", + "and the 2D population (green line) are all equal.\n", + "The other dimension in the 2D population is shown in blue." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/communication-channel.ipynb.txt b/_sources/examples/basic/communication-channel.ipynb.txt new file mode 100644 index 0000000000..6385c9bad2 --- /dev/null +++ b/_sources/examples/basic/communication-channel.ipynb.txt @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Communication channel\n", + "\n", + "This example demonstrates how to create\n", + "a connection from one neuronal ensemble to another\n", + "that behaves like a communication channel\n", + "(that is, it transmits information without changing it).\n", + "\n", + "Network diagram:\n", + "\n", + " [Input] ---> (A) ---> (B)\n", + "\n", + "An abstract input signal is fed into\n", + "the first neuronal ensemble $A$,\n", + "which then passes it on to another ensemble $B$.\n", + "The result is that spiking activity in ensemble $B$\n", + "encodes the value from the Input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 'model' object to which we can add ensembles, connections, etc.\n", + "model = nengo.Network(label=\"Communications Channel\")\n", + "with model:\n", + " # Create an abstract input signal that oscillates as sin(t)\n", + " sin = nengo.Node(np.sin)\n", + "\n", + " # Create the neuronal ensembles\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # Connect the input to the first neuronal ensemble\n", + " nengo.Connection(sin, A)\n", + "\n", + " # Connect the first neuronal ensemble to the second\n", + " # (this is the communication channel)\n", + " nengo.Connection(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Add Probes to Collect Data\n", + "\n", + "Even this simple model involves many quantities\n", + "that change over time, such as membrane potentials of individual neurons.\n", + "Typically there are so many variables in a simulation\n", + "that it is not practical to store them all.\n", + "If we want to plot or analyze data from the simulation\n", + "we have to \"probe\" the signals of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # ensemble output\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the Model!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 3))\n", + "plt.subplot(1, 3, 1)\n", + "plt.title(\"Input\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe])\n", + "plt.ylim(0, 1.2)\n", + "plt.subplot(1, 3, 2)\n", + "plt.title(\"A\")\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.ylim(0, 1.2)\n", + "plt.subplot(1, 3, 3)\n", + "plt.title(\"B\")\n", + "plt.plot(sim.trange(), sim.data[B_probe])\n", + "plt.ylim(0, 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots show the idealized sinusoidal input,\n", + "and estimates of the sinusoid that are decoded\n", + "from the spiking activity of neurons in ensembles A and B.\n", + "\n", + "## Step 5: Using a Different Input Function\n", + "\n", + "To drive the neural ensembles with different abstract inputs,\n", + "it is convenient to use Python's \"Lambda Functions\".\n", + "For example, try changing the `sin = nengo.Node` line\n", + "to the following for higher-frequency input:\n", + "\n", + " sin = nengo.Node(lambda t: np.sin(2*np.pi*t))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/many-neurons.ipynb.txt b/_sources/examples/basic/many-neurons.ipynb.txt new file mode 100644 index 0000000000..7baec85f20 --- /dev/null +++ b/_sources/examples/basic/many-neurons.ipynb.txt @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Many neurons\n", + "\n", + "This demo shows how to construct and manipulate a population of neurons.\n", + "\n", + "These are 100 leaky integrate-and-fire (LIF) neurons.\n", + "The neuron tuning properties have been randomly selected.\n", + "\n", + "The input is a sine wave to show the effects\n", + "of increasing or decreasing input.\n", + "As a population, these neurons do a good job\n", + "of representing a single scalar value.\n", + "This can be seen by the fact that\n", + "the input graph and neurons graphs match well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import sorted_neurons\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural population\n", + "\n", + "Our model consists of a single population of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Many Neurons\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a sine wave as a continuously changing input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(8 * t)) # Input is a sine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input to the population\n", + " nengo.Connection(sin, A, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms filter\n", + " A_spikes = nengo.Probe(A.neurons) # Collect the spikes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 1 second\n", + " sim.run(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Input\")\n", + "plt.xlim(0, 1)\n", + "plt.legend()\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure()\n", + "rasterplot(sim.trange(), sim.data[A_spikes])\n", + "plt.xlim(0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the decoded response of the neural spiking.\n", + "The bottom plot shows the spike raster coming out of every 2nd neuron." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For interest's sake, you can also sort by encoder\n", + "indices = sorted_neurons(A, sim, iterations=250)\n", + "plt.figure()\n", + "rasterplot(sim.trange(), sim.data[A_spikes][:, indices])\n", + "plt.xlim(0, 1)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/multiplication.ipynb.txt b/_sources/examples/basic/multiplication.ipynb.txt new file mode 100644 index 0000000000..5323600efe --- /dev/null +++ b/_sources/examples/basic/multiplication.ipynb.txt @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiplication\n", + "\n", + "This example will show you how to multiply two values.\n", + "The model architecture can be thought of as\n", + "a combination of the combining demo and the squaring demo.\n", + "Essentially, we project both inputs independently into a 2D space,\n", + "and then decode a nonlinear transformation of that space\n", + "(the product of the first and second vector elements)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the model\n", + "\n", + "The model has four ensembles:\n", + "two input ensembles ('A' and 'B'),\n", + "a 2D combined ensemble ('Combined'),\n", + "and an output ensemble ('D')." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Multiplication\")\n", + "with model:\n", + " # Create 4 ensembles of leaky integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " B = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " combined = nengo.Ensemble(\n", + " 220, dimensions=2, radius=15\n", + " ) # This radius is ~sqrt(10^2+10^2)\n", + " prod = nengo.Ensemble(100, dimensions=1, radius=20)\n", + "\n", + "# This next two lines make all of the encoders in the Combined population\n", + "# point at the corners of the cube.\n", + "# This improves the quality of the computation.\n", + "\n", + "# Comment out the line below for 'normal' encoders\n", + "combined.encoders = Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "We will use two varying scalar values for the two input signals\n", + "that drive activity in ensembles A and B." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))\n", + " inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))\n", + "\n", + " correct_ans = Piecewise({0: 0, 1.5: 0, 2.5: 20, 3: 0, 4: 0, 4.5: -20})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the elements of the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input nodes to the appropriate ensembles\n", + " nengo.Connection(inputA, A)\n", + " nengo.Connection(inputB, B)\n", + "\n", + " # Connect input ensembles A and B to the 2D combined ensemble\n", + " nengo.Connection(A, combined[0])\n", + " nengo.Connection(B, combined[1])\n", + "\n", + " # Define a function that computes the multiplication of two inputs\n", + " def product(x):\n", + " return x[0] * x[1]\n", + "\n", + " # Connect the combined ensemble to the output ensemble D\n", + " nengo.Connection(combined, prod, function=product)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe the output\n", + "\n", + "Collect output data from each ensemble and input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " inputA_probe = nengo.Probe(inputA)\n", + " inputB_probe = nengo.Probe(inputB)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " combined_probe = nengo.Probe(combined, synapse=0.01)\n", + " prod_probe = nengo.Probe(prod, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results\n", + "\n", + "To check the performance of the model,\n", + "we can plot the input signals and decoded ensemble values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the input signals and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded B\")\n", + "plt.plot(sim.trange(), sim.data[prod_probe], label=\"Decoded product\")\n", + "plt.plot(\n", + " sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c=\"k\", label=\"Actual product\"\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-25, 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input signals we chose make it obvious when things are working,\n", + "as the inputs are zero often (so the product should be).\n", + "When choosing encoders randomly around the circle (the default in Nengo),\n", + "you may see more unwanted interactions between the inputs.\n", + "To see this, comment the above code that sets the encoders\n", + "to the corners of the cube (in Step 1 where it says\n", + "`# Comment out the line below for 'normal' encoders`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus step: Make a subnetwork\n", + "\n", + "If you find that you need to compute the product\n", + "in several parts of your network,\n", + "you can put all of the components necessary\n", + "to compute the product\n", + "together in a subnetwork.\n", + "By making a function to construct this subnetwork,\n", + "it becomes easy to make many such networks\n", + "in a single model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def Product(neuron_per_dimension, input_magnitude):\n", + " # Create the model object\n", + " model = nengo.Network(label=\"Product\")\n", + " with model:\n", + " # Create passthrough nodes to redirect both inputs\n", + " model.A = nengo.Node(output=None, size_in=1)\n", + " model.B = nengo.Node(output=None, size_in=1)\n", + "\n", + " model.combined = nengo.Ensemble(\n", + " neuron_per_dimension * 2,\n", + " dimensions=2,\n", + " radius=np.sqrt(input_magnitude ** 2 + input_magnitude ** 2),\n", + " encoders=Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]]),\n", + " )\n", + "\n", + " model.prod = nengo.Ensemble(\n", + " neuron_per_dimension, dimensions=1, radius=input_magnitude * 2\n", + " )\n", + "\n", + " # Connect everything up\n", + " nengo.Connection(model.A, model.combined[0], synapse=None)\n", + " nengo.Connection(model.B, model.combined[1], synapse=None)\n", + "\n", + " def product(x):\n", + " return x[0] * x[1]\n", + "\n", + " nengo.Connection(model.combined, model.prod, function=product)\n", + " return model\n", + "\n", + "\n", + "# The previous model can then be replicated with the following\n", + "model = nengo.Network(label=\"Multiplication\")\n", + "with model:\n", + " inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))\n", + " inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))\n", + " A = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " B = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " prod = Product(100, input_magnitude=10)\n", + " nengo.Connection(inputA, A)\n", + " nengo.Connection(inputB, B)\n", + " nengo.Connection(A, prod.A)\n", + " nengo.Connection(B, prod.B)\n", + "\n", + " inputA_probe = nengo.Probe(inputA)\n", + " inputB_probe = nengo.Probe(inputB)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " combined_probe = nengo.Probe(prod.combined, synapse=0.01)\n", + " prod_probe = nengo.Probe(prod.prod, synapse=0.01)\n", + "\n", + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)\n", + "\n", + "# Plot the input signals and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded B\")\n", + "plt.plot(sim.trange(), sim.data[prod_probe], label=\"Decoded product\")\n", + "plt.plot(\n", + " sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c=\"k\", label=\"Actual product\"\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-25, 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can use Nengo's built in\n", + "[`nengo.networks.Product` network](\n", + "https://www.nengo.ai/nengo/networks.html#nengo.networks.Product).\n", + "This network works with input of any dimensionality\n", + "(e.g., to compute the dot product of two large vectors)\n", + "and uses special optimizatons to make the product\n", + "more accurate than this implementation." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/single-neuron.ipynb.txt b/_sources/examples/basic/single-neuron.ipynb.txt new file mode 100644 index 0000000000..0767d66979 --- /dev/null +++ b/_sources/examples/basic/single-neuron.ipynb.txt @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A single neuron\n", + "\n", + "This demo shows you how to construct and manipulate\n", + "a single leaky integrate-and-fire (LIF) neuron.\n", + "The LIF neuron is a simple, standard neuron model,\n", + "and here it resides inside a neural 'population,'\n", + "even though there is only one neuron." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Neuron" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from nengo.dists import Uniform\n", + "\n", + "model = nengo.Network(label=\"A Single Neuron\")\n", + "with model:\n", + " neuron = nengo.Ensemble(\n", + " 1,\n", + " dimensions=1, # Represent a scalar\n", + " # Set intercept to 0.5\n", + " intercepts=Uniform(-0.5, -0.5),\n", + " # Set the maximum firing rate of the neuron to 100hz\n", + " max_rates=Uniform(100, 100),\n", + " # Set the neuron's firing rate to increase for positive input\n", + " encoders=[[1]],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "Create an input node generating a cosine wave." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " cos = nengo.Node(lambda t: np.cos(8 * t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the Network Elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input signal to the neuron\n", + " nengo.Connection(cos, neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Add Probes\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # The original input\n", + " cos_probe = nengo.Probe(cos)\n", + " # The raw spikes from the neuron\n", + " spikes = nengo.Probe(neuron.neurons)\n", + " # Subthreshold soma voltage of the neuron\n", + " voltage = nengo.Probe(neuron.neurons, \"voltage\")\n", + " # Spikes filtered by a 10ms post-synaptic filter\n", + " filtered = nengo.Probe(neuron, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create the simulator\n", + " sim.run(1) # Run it for 1 second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[filtered])\n", + "plt.plot(sim.trange(), sim.data[cos_probe])\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure(figsize=(10, 8))\n", + "plt.subplot(221)\n", + "rasterplot(sim.trange(), sim.data[spikes])\n", + "plt.ylabel(\"Neuron\")\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the soma voltages of the neurons\n", + "plt.subplot(222)\n", + "plt.plot(sim.trange(), sim.data[voltage][:, 0], \"r\")\n", + "plt.xlim(0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the input signal in green\n", + "and the filtered output spikes from the single neuron population in blue.\n", + "The spikes (that are filtered) from the neuron\n", + "are shown in the bottom graph on the left.\n", + "On the right is the subthreshold voltages for the neuron." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/squaring.ipynb.txt b/_sources/examples/basic/squaring.ipynb.txt new file mode 100644 index 0000000000..cfd65248a1 --- /dev/null +++ b/_sources/examples/basic/squaring.ipynb.txt @@ -0,0 +1,157 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Squaring the input\n", + "\n", + "This demo shows you how to construct a network\n", + "that squares the value encoded in a first population\n", + "in the output of a second population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model is comprised of an input ensemble ('A')\n", + "and an output ensemble ('B'),\n", + "from which the squared value of the input signal can be decoded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Squaring\")\n", + "with model:\n", + " # Create two ensembles of 100 leaky-integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A single input signal (a sine wave) will be used\n", + "to drive the neural activity in ensemble A." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input node that represents a sine wave\n", + " sin = nengo.Node(np.sin)\n", + "\n", + " # Connect the input node to ensemble A\n", + " nengo.Connection(sin, A)\n", + "\n", + " # Define the squaring function\n", + " def square(x):\n", + " return x[0] * x[0]\n", + "\n", + " # Connection ensemble A to ensemble B\n", + " nengo.Connection(A, B, function=square)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe the Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run the simulator for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the input signal and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded Ensemble A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded Ensemble B\")\n", + "plt.plot(\n", + " sim.trange(), sim.data[sin_probe], label=\"Input Sine Wave\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-1.2, 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plotted output of ensemble B should show\n", + "the decoded squared value of the input sine wave." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/basic/two-neurons.ipynb.txt b/_sources/examples/basic/two-neurons.ipynb.txt new file mode 100644 index 0000000000..3b7e7665d4 --- /dev/null +++ b/_sources/examples/basic/two-neurons.ipynb.txt @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two neurons\n", + "\n", + "This demo shows how to construct and manipulate\n", + "a complementary pair of neurons.\n", + "\n", + "These are leaky integrate-and-fire (LIF) neurons.\n", + "The neuron tuning properties have been selected\n", + "so there is one 'on' and one 'off' neuron.\n", + "\n", + "One neuron will increase for positive input,\n", + "and the other will decrease.\n", + "This can be thought of as the simplest population\n", + "that is able to give a reasonable representation of a scalar value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.dists import Uniform\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neurons" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Two Neurons\")\n", + "with model:\n", + " neurons = nengo.Ensemble(\n", + " 2,\n", + " dimensions=1, # Representing a scalar\n", + " intercepts=Uniform(-0.5, -0.5), # Set the intercepts at .5\n", + " max_rates=Uniform(100, 100), # Set the max firing rate at 100hz\n", + " encoders=[[1], [-1]],\n", + " ) # One 'on' and one 'off' neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "Create an input node generating a sine wave." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(8 * t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, neurons, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin) # The original input\n", + " spikes = nengo.Probe(neurons.neurons) # Raw spikes from each neuron\n", + " # Subthreshold soma voltages of the neurons\n", + " voltage = nengo.Probe(neurons.neurons, \"voltage\")\n", + " # Spikes filtered by a 10ms post-synaptic filter\n", + " filtered = nengo.Probe(neurons, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1) # Run it for 1 second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = sim.trange()\n", + "\n", + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(t, sim.data[filtered])\n", + "plt.plot(t, sim.data[sin_probe])\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure(figsize=(10, 8))\n", + "plt.subplot(2, 2, 1)\n", + "rasterplot(t, sim.data[spikes], colors=[(1, 0, 0), (0, 0, 0)])\n", + "plt.yticks((1, 2), (\"On neuron\", \"Off neuron\"))\n", + "plt.ylim(2.5, 0.5)\n", + "\n", + "# Plot the soma voltages of the neurons\n", + "plt.subplot(2, 2, 2)\n", + "plt.plot(t, sim.data[voltage][:, 0] + 1, \"r\")\n", + "plt.plot(t, sim.data[voltage][:, 1], \"k\")\n", + "plt.yticks(())\n", + "plt.axis([0, 1, 0, 2])\n", + "plt.subplots_adjust(wspace=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the input signal in green\n", + "and the filtered output spikes from the two neurons population in blue.\n", + "The spikes (that are filtered) from the 'on' and 'off' neurons\n", + "are shown in the bottom graph on the left.\n", + "On the right are the subthreshold voltages for the neurons." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/dynamics/controlled-integrator.ipynb.txt b/_sources/examples/dynamics/controlled-integrator.ipynb.txt new file mode 100644 index 0000000000..1431fe1cd7 --- /dev/null +++ b/_sources/examples/dynamics/controlled-integrator.ipynb.txt @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled integrator\n", + "\n", + "A controlled integrator is a circuit that acts on two signals:\n", + "\n", + "1. Input - the signal being integrated\n", + "2. Control - the control signal to the integrator\n", + "\n", + "A controlled integrator accumulates input,\n", + "but its state can be directly manipulated by the control signal.\n", + "We can write the dynamics of a simple controlled integrator like this:\n", + "\n", + "$$\n", + "\\dot{a}(t) = \\mathrm{control}(t) \\cdot a(t) + B \\cdot \\mathrm{input}(t)\n", + "$$\n", + "\n", + "In this notebook, we will build a controlled intgrator with LIF neurons.\n", + "The Neural Engineering Framework (NEF) equivalent equation\n", + "for this integrator is:\n", + "\n", + "$$\n", + "\\dot{a}(t) = \\mathrm{control}(t) \\cdot a(t) + \\tau \\cdot \\mathrm{input}(t).\n", + "$$\n", + "\n", + "We call the coefficient $\\tau$ here a *recurrent time constant*\n", + "because it governs the rate of integration.\n", + "\n", + "Network behaviour:\n", + "`A = tau * Input + Input * Control`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "We can use standard network-creation commands\n", + "to begin creating our controlled integrator.\n", + "We create a Network, and then we create\n", + "a population of neurons (called an *ensemble*).\n", + "This population of neurons will represent the state of our integrator,\n", + "and the connections between the neurons in the ensemble\n", + "will define the dynamics of our integrator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Controlled Integrator\")\n", + "with model:\n", + " # Make a population with 225 LIF neurons\n", + " # representing a 2 dimensional signal,\n", + " # with a larger radius to accommodate large inputs\n", + " A = nengo.Ensemble(225, dimensions=2, radius=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Define the 'input' signal to integrate\n", + "\n", + "We will be running 1 second of simulation time,\n", + "so we will use a Python function `input_func`\n", + "to define our input signal for real values of time `t` from 0 to 1.\n", + "We'll define our signal to be a step function using if-then-else code.\n", + "Our piecewise function sits at 0 until .2 seconds into the simulation,\n", + "then jumps up to 5, back to 0, down to -10, back to 0, then up to 5,\n", + "and then back to 0. Our integrator will respond by ramping up\n", + "when the input is positive, and descending when the input is negative." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " input_func = Piecewise({0: 0, 0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We include this input function (`input_func`)\n", + "into our neural model like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Define an input signal within our model\n", + " inp = nengo.Node(input_func)\n", + "\n", + " # Connect the Input signal to ensemble A.\n", + " # The `transform` argument means \"connect real-valued signal\n", + " # \"Input\" to the first of the two input channels of A.\"\n", + " tau = 0.1\n", + " nengo.Connection(inp, A, transform=[[tau], [0]], synapse=tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Define the 'control' signal\n", + "\n", + "We also need to create a control signal\n", + "that controls how the integrator behaves.\n", + "We will make this signal 1 for the first part of the simulation,\n", + "and 0.5 for the second part.\n", + "This means that at the beginning of the simulation,\n", + "the integrator will act as an optimal integrator,\n", + "and partway though the simulation (at t = 0.6),\n", + "it will switch to being a leaky integrator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Another piecewise step that changes half way through the run\n", + " control_func = Piecewise({0: 1, 0.6: 0.5})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We add the control signal to the network\n", + "like we added the input signal,\n", + "but this time we connect it to\n", + "the second dimension of our neural population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " control = nengo.Node(output=control_func)\n", + "\n", + " # Connect the \"Control\" signal to the second of A's two input channels.\n", + " nengo.Connection(control, A[1], synapse=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Define the integrator dynamics\n", + "\n", + "We set up integrator by connecting population 'A' to itself.\n", + "We set up feedback in the model to handle integration of the input.\n", + "The time constant $\\tau$ on the recurrent weights affects\n", + "both the rate and accuracy of integration.\n", + "Try adjusting it and see what happens!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create a recurrent connection that first takes the product\n", + " # of both dimensions in A (i.e., the value times the control)\n", + " # and then adds this back into the first dimension of A using\n", + " # a transform\n", + " nengo.Connection(\n", + " A,\n", + " A[0], # -- transform converts function output to new state inputs\n", + " function=lambda x: x[0] * x[1], # -- function is applied first to A\n", + " synapse=tau,\n", + " )\n", + "\n", + " # Record both dimensions of A\n", + " A_probe = nengo.Probe(A, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1.4) # Run for 1.4 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the value and control signals, along with the exact integral\n", + "t = sim.trange()\n", + "dt = t[1] - t[0]\n", + "input_sig = input_func.run(t[-1], dt=dt)\n", + "control_sig = control_func.run(t[-1], dt=dt)\n", + "ref = dt * np.cumsum(input_sig)\n", + "\n", + "plt.figure(figsize=(6, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, input_sig, label=\"Input\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-11, 11)\n", + "plt.ylabel(\"Input\")\n", + "plt.legend(loc=\"lower left\", frameon=False)\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(t, ref, \"k--\", label=\"Exact\")\n", + "plt.plot(t, sim.data[A_probe][:, 0], label=\"A (value)\")\n", + "plt.plot(t, sim.data[A_probe][:, 1], label=\"A (control)\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"x(t)\")\n", + "plt.legend(loc=\"lower left\", frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows the output of our system,\n", + "specifically the (integrated) value stored by the A population,\n", + "along with the control signal represented by the A population.\n", + "The exact value of the integral,\n", + "as performed by a perfect (non-neural) integrator,\n", + "is shown for reference.\n", + "\n", + "When the control value is 1 (t < 0.6),\n", + "the neural integrator performs near-perfect integration.\n", + "However, when the control value drops to 0.5 (t > 0.6),\n", + "the integrator becomes a leaky integrator.\n", + "This means that in the absence of input,\n", + "its stored value drifts towards zero." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/dynamics/controlled-integrator2.ipynb.txt b/_sources/examples/dynamics/controlled-integrator2.ipynb.txt new file mode 100644 index 0000000000..c223a7e927 --- /dev/null +++ b/_sources/examples/dynamics/controlled-integrator2.ipynb.txt @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled integrator 2\n", + "\n", + "This demo implements a controlled one-dimensional neural integrator\n", + "that is functionally the same as\n", + "the controlled integrator in the previous example.\n", + "However, the control signal is zero for integration,\n", + "less than one for low-pass filtering, and greater than 1 for saturation.\n", + "This behavior maps more directly to the differential equation\n", + "used to describe an integrator:\n", + "\n", + "$$\\dot{x} = \\mathrm{Ax}(t) + \\mathrm{Bu}(t)$$\n", + "\n", + "The control in this circuit is $A$ in that equation.\n", + "This is also the controlled integrator\n", + "described in the book \"How to build a brain.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "As before, we use standard network-creation commands\n", + "to begin creating our controlled integrator.\n", + "An ensemble of neurons will represent the state of our integrator,\n", + "and the connections between the neurons in the ensemble\n", + "will define the dynamics of our integrator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Controlled Integrator 2\")\n", + "with model:\n", + " # Make a population with 225 LIF neurons representing a 2 dimensional\n", + " # signal, with a larger radius to accommodate large inputs\n", + " A = nengo.Ensemble(225, dimensions=2, radius=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Define the 'input' signal to integrate\n", + "\n", + "We will be running 1 second of simulation time again,\n", + "so we will use the same Python function `input_func`\n", + "to define our input signal. This piecewise function sits at 0\n", + "until .2 seconds into the simulation,\n", + "then jumps up to 5, back to 0, down to -10, back to 0, then up to 5,\n", + "and then back to 0. Our integrator will respond by ramping up\n", + "when the input is positive, and descending when the input is negative." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " input_func = Piecewise({0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})\n", + " inp = nengo.Node(output=input_func)\n", + "\n", + " # Connect the Input signal to ensemble A.\n", + " tau = 0.1\n", + " nengo.Connection(inp, A, transform=[[tau], [0]], synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Define the control signal\n", + "\n", + "The control signal will be 0 for the first part of the simulation,\n", + "and -0.5 for the second part.\n", + "This means that at the beginning of the simulation,\n", + "the integrator will act as an optimal integrator,\n", + "and partway though the simulation (at t = 0.6),\n", + "it will switch to being a leaky integrator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Another piecewise function that changes half way through the run\n", + " control_func = Piecewise({0: 0, 0.6: -0.5})\n", + " control = nengo.Node(output=control_func)\n", + "\n", + " # Connect the \"Control\" signal to the second of A's two input channels\n", + " nengo.Connection(control, A[1], synapse=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Define the integrator dynamics\n", + "\n", + "We set up integrator by connecting population 'A' to itself.\n", + "We set up feedback in the model to handle integration of the input.\n", + "The time constant $\\tau$ on the recurrent weights\n", + "affects both the rate and accuracy of integration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Note the changes from the previous example to the function being defined.\n", + " nengo.Connection(A, A[0], function=lambda x: x[0] * x[1] + x[0], synapse=tau)\n", + "\n", + " # Record both dimensions of A\n", + " A_probe = nengo.Probe(A, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model and plot results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1.4) # Run for 1.4 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the value and control signals, along with the exact integral\n", + "t = sim.trange()\n", + "dt = t[1] - t[0]\n", + "input_sig = input_func.run(t[-1], dt=dt)\n", + "control_sig = control_func.run(t[-1], dt=dt)\n", + "ref = dt * np.cumsum(input_sig)\n", + "\n", + "plt.figure(figsize=(6, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, input_sig, label=\"Input\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-11, 11)\n", + "plt.ylabel(\"Input\")\n", + "plt.legend(loc=\"lower left\", frameon=False)\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(t, ref, \"k--\", label=\"exact\")\n", + "plt.plot(t, sim.data[A_probe][:, 0], label=\"A (value)\")\n", + "plt.plot(t, sim.data[A_probe][:, 1], label=\"A (control)\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"x(t)\")\n", + "plt.legend(loc=\"lower left\", frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows the output of our system,\n", + "specifically the (integrated) value stored by the A population,\n", + "along with the control signal represented by the A population.\n", + "The exact value of the integral,\n", + "as performed by a perfect (non-neural) integrator,\n", + "is shown for reference.\n", + "\n", + "When the control value is 0 (t < 0.6),\n", + "the neural integrator performs near-perfect integration.\n", + "However, when the control value drops to -0.5 (t > 0.6),\n", + "the integrator becomes a leaky integrator.\n", + "This means that with negative input,\n", + "its stored value drifts towards zero." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/dynamics/controlled-oscillator.ipynb.txt b/_sources/examples/dynamics/controlled-oscillator.ipynb.txt new file mode 100644 index 0000000000..8fc7f7530b --- /dev/null +++ b/_sources/examples/dynamics/controlled-oscillator.ipynb.txt @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled oscillator\n", + "\n", + "The controlled oscillator is an oscillator\n", + "with an extra input that controls the frequency of the oscillation.\n", + "\n", + "To implement a basic oscillator,\n", + "we would use a neural ensemble of two dimensions\n", + "that has the following dynamics:\n", + "\n", + "$$\n", + "\\dot{x} = \\begin{bmatrix} 0 && - \\omega \\\\ \\omega && 0 \\end{bmatrix} x\n", + "$$\n", + "\n", + "where the frequency of oscillation is $\\omega \\over {2 \\pi}$ Hz.\n", + "\n", + "We need the neurons to represent three variables,\n", + "$x_0$, $x_1$, and $\\omega$.\n", + "According the the dynamics principle of the NEF,\n", + "in order to implement some particular dynamics,\n", + "we need to convert this dynamics equation into a feedback function:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " \\dot{x} &= f(x) \\\\\n", + " &\\implies f_{feedback}(x) = x + \\tau f(x)\n", + "\\end{align}\n", + "$$\n", + "\n", + "where $\\tau$ is the post-synaptic time constant of the feedback connection.\n", + "\n", + "In this case, the feedback function to be computed is\n", + "\n", + "$$\n", + "\\begin{align}\n", + " f_{feedback}(x) &= x + \\tau\n", + " \\begin{bmatrix}\n", + " 0 && - \\omega \\\\\n", + " \\omega && 0\n", + " \\end{bmatrix}\n", + " x \\\\\n", + " &=\n", + " \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot x_0\n", + " \\end{bmatrix}\n", + "\\end{align}\n", + "$$\n", + "\n", + "Since the neural ensemble represents all three variables\n", + "but the dynamics only affects the first two ($x_0$, $x_1$),\n", + "we need the feedback function to not affect that last variable.\n", + "We do this by adding a zero to the feedback function.\n", + "\n", + "$$\n", + "f_{feedback}(x) = \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot x_0 \\\\\n", + " 0 \\end{bmatrix}\n", + "$$\n", + "\n", + "We also generally want to keep\n", + "the ranges of variables represented within an ensemble\n", + "to be approximately the same.\n", + "In this case, if $x_0$ and $x_1$ are between -1 and 1,\n", + "$\\omega$ will also be between -1 and 1,\n", + "giving a frequency range of $-1 \\over {2 \\pi}$ to $1 \\over {2 \\pi}$.\n", + "To increase this range,\n", + "we introduce a scaling factor to $\\omega$ called $\\omega_{max}$.\n", + "\n", + "$$\n", + "f_{feedback}(x) = \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot \\omega_{max} \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot \\omega_{max} \\cdot x_0 \\\\\n", + " 0 \\end{bmatrix}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tau = 0.1 # Post-synaptic time constant for feedback\n", + "w_max = 10 # Maximum frequency in Hz is w_max/(2*pi)\n", + "\n", + "model = nengo.Network(label=\"Controlled Oscillator\")\n", + "with model:\n", + " # The ensemble for the oscillator\n", + " oscillator = nengo.Ensemble(500, dimensions=3, radius=1.7)\n", + "\n", + " # The feedback connection\n", + " def feedback(x):\n", + " x0, x1, w = x # These are the three variables stored in the ensemble\n", + " return x0 - w * w_max * tau * x1, x1 + w * w_max * tau * x0, 0\n", + "\n", + " nengo.Connection(oscillator, oscillator, function=feedback, synapse=tau)\n", + "\n", + " # The ensemble for controlling the speed of oscillation\n", + " frequency = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " nengo.Connection(frequency, oscillator[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create the input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # We need a quick input at the beginning to start the oscillator\n", + " initial = nengo.Node(Piecewise({0: [1, 0, 0], 0.15: [0, 0, 0]}))\n", + " nengo.Connection(initial, oscillator)\n", + "\n", + " # Vary the speed over time\n", + " input_frequency = nengo.Node(Piecewise({0: 1, 1: 0.5, 2: 0, 3: -0.5, 4: -1}))\n", + "\n", + " nengo.Connection(input_frequency, frequency)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Add Probes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Indicate which values to record\n", + " oscillator_probe = nengo.Probe(oscillator, synapse=0.03)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[oscillator_probe])\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.legend([\"$x_0$\", \"$x_1$\", r\"$\\omega$\"])" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/dynamics/integrator.ipynb.txt b/_sources/examples/dynamics/integrator.ipynb.txt new file mode 100644 index 0000000000..2af965349b --- /dev/null +++ b/_sources/examples/dynamics/integrator.ipynb.txt @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrator\n", + "\n", + "This demo implements a one-dimensional neural integrator.\n", + "\n", + "This is the first example of a recurrent network in the demos.\n", + "It shows how neurons can be used to implement stable dynamics.\n", + "Such dynamics are important for memory, noise cleanup,\n", + "statistical inference, and many other dynamic transformations.\n", + "\n", + "When you run this demo,\n", + "it will automatically put in some step functions on the input,\n", + "so you can see that the output is\n", + "integrating (i.e. summing over time) the input.\n", + "You can also input your own values.\n", + "Note that since the integrator constantly sums its input,\n", + "it will saturate quickly if you leave the input non-zero.\n", + "This makes it clear that neurons have a finite range of representation.\n", + "Such saturation effects can be exploited\n", + "to perform useful computations (e.g. soft normalization)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of one recurrently connected ensemble\n", + "and an input population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Integrator\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a piecewise step function as input,\n", + "so we can see the effects of recurrence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a piecewise step function for input\n", + "with model:\n", + " input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the population to itself\n", + " tau = 0.1\n", + " nengo.Connection(\n", + " A, A, transform=[[1]], synapse=tau\n", + " ) # Using a long time constant for stability\n", + "\n", + " # Connect the input\n", + " nengo.Connection(\n", + " input, A, transform=[[tau]], synapse=tau\n", + " ) # The same time constant as recurrent to make it more 'ideal'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Add probes\n", + " input_probe = nengo.Probe(input)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 6 seconds\n", + " sim.run(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], \"k\", label=\"Integrator output\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows the response to the input by the integrator.\n", + "Because it is implemented in neurons,\n", + "it will not be perfect (i.e. there will be drift).\n", + "Running several times will give a sense of\n", + "the kinds of drift you might expect.\n", + "Drift can be reduced by increasing the number of neurons." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/dynamics/lorenz-attractor.ipynb.txt b/_sources/examples/dynamics/lorenz-attractor.ipynb.txt new file mode 100644 index 0000000000..4e70b4cae2 --- /dev/null +++ b/_sources/examples/dynamics/lorenz-attractor.ipynb.txt @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Lorenz chaotic attractor\n", + "\n", + "This example shows the construction of a classic chaotic dynamical system:\n", + "the Lorenz \"butterfly\" attractor. The equations are:\n", + "\n", + "$$\n", + "\\dot{x}_0 = \\sigma(x_1 - x_0) \\\\\\\n", + "\\dot{x}_1 = x_0 (\\rho - x_2) - x_1 \\\\\\\n", + "\\dot{x}_2 = x_0 x_1 - \\beta x_2\n", + "$$\n", + "\n", + "Since $x_2$ is centered around approximately $\\rho$,\n", + "and since NEF ensembles are usually optimized\n", + "to represent values within a certain radius of the origin,\n", + "we substitute $x_2' = x_2 - \\rho$, giving these equations:\n", + "$$\n", + "\\dot{x}_0 = \\sigma(x_1 - x_0) \\\\\\\n", + "\\dot{x}_1 = - x_0 x_2' - x_1 \\\\\\\n", + "\\dot{x}_2' = x_0 x_1 - \\beta (x_2' + \\rho) - \\rho\n", + "$$\n", + "\n", + "For more information, see\n", + "http://compneuro.uwaterloo.ca/publications/eliasmith2005b.html\n", + "\"Chris Eliasmith. A unified approach to building\n", + "and controlling spiking attractor networks.\n", + "Neural computation, 7(6):1276-1314, 2005.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tau = 0.1\n", + "sigma = 10\n", + "beta = 8.0 / 3\n", + "rho = 28\n", + "\n", + "\n", + "def feedback(x):\n", + " dx0 = -sigma * x[0] + sigma * x[1]\n", + " dx1 = -x[0] * x[2] - x[1]\n", + " dx2 = x[0] * x[1] - beta * (x[2] + rho) - rho\n", + "\n", + " return [\n", + " dx0 * tau + x[0],\n", + " dx1 * tau + x[1],\n", + " dx2 * tau + x[2],\n", + " ]\n", + "\n", + "\n", + "model = nengo.Network(label=\"Lorenz attractor\")\n", + "with model:\n", + " state = nengo.Ensemble(2000, 3, radius=60)\n", + " nengo.Connection(state, state, function=feedback, synapse=tau)\n", + " state_probe = nengo.Probe(state, synapse=tau)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = plt.figure().add_subplot(111, projection=Axes3D.name)\n", + "ax.plot(*sim.data[state_probe].T)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[state_probe])" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/dynamics/oscillator.ipynb.txt b/_sources/examples/dynamics/oscillator.ipynb.txt new file mode 100644 index 0000000000..7730970c8f --- /dev/null +++ b/_sources/examples/dynamics/oscillator.ipynb.txt @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple harmonic oscillator\n", + "\n", + "This demo implements a simple harmonic oscillator\n", + "in a 2D neural population.\n", + "The oscillator is more visually interesting on its own\n", + "than the integrator, but the principle at work is the same.\n", + "Here, instead of having the recurrent input just integrate\n", + "(i.e. feeding the full input value back to the population),\n", + "we have two dimensions which interact." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model consists of a single neural ensemble that we will call `Neurons`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Oscillator\")\n", + "with model:\n", + " # Create the ensemble for the oscillator\n", + " neurons = nengo.Ensemble(200, dimensions=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A brief input signal is provided\n", + "to trigger the oscillatory behavior of the neural representation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input signal\n", + " input = nengo.Node(Piecewise({0: [1, 0], 0.1: [0, 0]}))\n", + "\n", + " # Connect the input signal to the neural ensemble\n", + " nengo.Connection(input, neurons)\n", + "\n", + " # Create the feedback connection\n", + " nengo.Connection(neurons, neurons, transform=[[1, 1], [-1, 1]], synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Add Probes\n", + "\n", + "These probes will collect data from the input signal and the neural ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " input_probe = nengo.Probe(input, \"output\")\n", + " neuron_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neuron_probe])\n", + "plt.xlabel(\"Time (s)\", fontsize=\"large\")\n", + "plt.legend([\"$x_0$\", \"$x_1$\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = sim.data[neuron_probe]\n", + "plt.figure()\n", + "plt.plot(data[:, 0], data[:, 1], label=\"Decoded Output\")\n", + "plt.xlabel(\"$x_0$\", fontsize=20)\n", + "plt.ylabel(\"$x_1$\", fontsize=20)\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/learning/learn-associations.ipynb.txt b/_sources/examples/learning/learn-associations.ipynb.txt new file mode 100644 index 0000000000..22605e438f --- /dev/null +++ b/_sources/examples/learning/learn-associations.ipynb.txt @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning new associations\n", + "\n", + "Being able to learn an input-output mapping\n", + "(or a _heteroassociative memory_)\n", + "is useful for storing and recalling associations.\n", + "This is also a task required by more complicated models\n", + "that require some notion of long-term memory.\n", + "\n", + "In a perfect world, the PES rule could be applied\n", + "to learn this mapping from examples.\n", + "However, when two distinct inputs cause the same neurons to fire,\n", + "their decoded values will depend on one another.\n", + "This leads to difficulty when trying to store\n", + "multiple independent associations in the same memory.\n", + "\n", + "To solve this problem,\n", + "a vector-space analog of Oja's rule,\n", + "dubbed Vector-Oja's rule (or simply _Voja's rule_) was proposed.\n", + "In essence, this unsupervised learning rule\n", + "makes neurons fire selectively in response to their input.\n", + "When used in conjunction with properly-chosen intercepts\n", + "(corresponding to the largest dot-product between pairs of inputs),\n", + "this approach makes it possible to scalably\n", + "learn new associations in a spiking network.\n", + "\n", + "Voja's rule works by moving the encoders\n", + "of the active neurons toward the current input.\n", + "This can be stated succinctly as,\n", + "\n", + "$$\n", + "\\Delta e_i = \\kappa a_i (x - e_i)\n", + "$$\n", + "\n", + "where $e_i$ is the encoder of the $i^{th}$ neuron,\n", + "$\\kappa$ is a modulatory learning rate\n", + "(positive to move towards, and negative to move away),\n", + "$a_i$ is the filtered activity of the $i^{th}$ neuron,\n", + "and $x$ is the input vector encoded by each neuron.\n", + "To see how this is related to Oja's rule,\n", + "substituting $e_i$ with the row of weights $W_i$,\n", + "$x$ for the pre-synaptic activity vector $b$,\n", + "and letting $s = 1 / a_i$ be a dynamic normalizing factor, gives\n", + "\n", + "$$\n", + "\\Delta W_i = \\kappa a_i (b - s a_i W_i)\n", + "$$\n", + "\n", + "which is the update rule for a single row using Oja.\n", + "For more details,\n", + "see [Learning large-scale heteroassociative memories in spiking neurons](\n", + "http://compneuro.uwaterloo.ca/publications/voelker2014a.html).\n", + "\n", + "This notebook will lead the reader through\n", + "a basic example of building a network\n", + "that can store and recall new associations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Configure some example data\n", + "\n", + "First, we will setup some keys (inputs) and values (outputs)\n", + "for our network to store and recall." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_items = 5\n", + "\n", + "d_key = 2\n", + "d_value = 4\n", + "\n", + "rng = np.random.RandomState(seed=7)\n", + "keys = nengo.dists.UniformHypersphere(surface=True).sample(num_items, d_key, rng=rng)\n", + "values = nengo.dists.UniformHypersphere(surface=False).sample(\n", + " num_items, d_value, rng=rng\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An important quantity is the largest dot-product\n", + "between all pairs of keys,\n", + "since a neuron's intercept should not go below this value\n", + "if it's positioned between these two keys.\n", + "Otherwise, the neuron will move back and forth\n", + "between encoding those two inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "intercept = (np.dot(keys, keys.T) - np.eye(num_items)).flatten().max()\n", + "print(\"Intercept: %s\" % intercept)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Build the model\n", + "\n", + "We define a helper function that is useful\n", + "for creating nodes that cycle through keys/values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cycle_array(x, period, dt=0.001):\n", + " \"\"\"Cycles through the elements\"\"\"\n", + " i_every = int(round(period / dt))\n", + " if i_every != period / dt:\n", + " raise ValueError(\"dt (%s) does not divide period (%s)\" % (dt, period))\n", + "\n", + " def f(t):\n", + " i = int(round((t - dt) / dt)) # t starts at dt\n", + " return x[int(i / i_every) % len(x)]\n", + "\n", + " return f" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create three inputs:\n", + "the keys, the values, and a modulatory learning signal.\n", + "The model is run continuously in two phases:\n", + "the first half learns the set of associations,\n", + "and the second tests recall.\n", + "\n", + "The learning signal will be set to 0\n", + "o allow learning during the first phase,\n", + "and -1 to inhibit learning during the second phase.\n", + "\n", + "The memory is confined to a single ensemble.\n", + "Roughly speaking, its encoders will hold the keys,\n", + "and its decoders will hold the values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model constants\n", + "n_neurons = 200\n", + "dt = 0.001\n", + "period = 0.3\n", + "T = period * num_items * 2\n", + "\n", + "# Model network\n", + "model = nengo.Network()\n", + "with model:\n", + "\n", + " # Create the inputs/outputs\n", + " stim_keys = nengo.Node(output=cycle_array(keys, period, dt))\n", + " stim_values = nengo.Node(output=cycle_array(values, period, dt))\n", + " learning = nengo.Node(output=lambda t: -int(t >= T / 2))\n", + " recall = nengo.Node(size_in=d_value)\n", + "\n", + " # Create the memory\n", + " memory = nengo.Ensemble(n_neurons, d_key, intercepts=[intercept] * n_neurons)\n", + "\n", + " # Learn the encoders/keys\n", + " voja = nengo.Voja(learning_rate=5e-2, post_synapse=None)\n", + " conn_in = nengo.Connection(stim_keys, memory, synapse=None, learning_rule_type=voja)\n", + " nengo.Connection(learning, conn_in.learning_rule, synapse=None)\n", + "\n", + " # Learn the decoders/values, initialized to a null function\n", + " conn_out = nengo.Connection(\n", + " memory,\n", + " recall,\n", + " learning_rule_type=nengo.PES(1e-3),\n", + " function=lambda x: np.zeros(d_value),\n", + " )\n", + "\n", + " # Create the error population\n", + " error = nengo.Ensemble(n_neurons, d_value)\n", + " nengo.Connection(\n", + " learning, error.neurons, transform=[[10.0]] * n_neurons, synapse=None\n", + " )\n", + "\n", + " # Calculate the error and use it to drive the PES rule\n", + " nengo.Connection(stim_values, error, transform=-1, synapse=None)\n", + " nengo.Connection(recall, error, synapse=None)\n", + " nengo.Connection(error, conn_out.learning_rule)\n", + "\n", + " # Setup probes\n", + " p_keys = nengo.Probe(stim_keys, synapse=None)\n", + " p_values = nengo.Probe(stim_values, synapse=None)\n", + " p_learning = nengo.Probe(learning, synapse=None)\n", + " p_error = nengo.Probe(error, synapse=0.005)\n", + " p_recall = nengo.Probe(recall, synapse=None)\n", + " p_encoders = nengo.Probe(conn_in.learning_rule, \"scaled_encoders\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Running the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model, dt=dt) as sim:\n", + " sim.run(T)\n", + "t = sim.trange()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plotting simulation output\n", + "\n", + "We first start by checking the keys, values, and learning signals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.title(\"Keys\")\n", + "plt.plot(t, sim.data[p_keys])\n", + "plt.ylim(-1, 1)\n", + "\n", + "plt.figure()\n", + "plt.title(\"Values\")\n", + "plt.plot(t, sim.data[p_values])\n", + "plt.ylim(-1, 1)\n", + "\n", + "plt.figure()\n", + "plt.title(\"Learning\")\n", + "plt.plot(t, sim.data[p_learning])\n", + "plt.ylim(-1.2, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we look at the error during training and testing.\n", + "In the top figure, the error being minimized by PES\n", + "goes to zero for each association during the training phase.\n", + "In the bottom figure, the recall error is close to zero,\n", + "with momentary spikes each time a new key is presented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train = t <= T / 2\n", + "test = ~train\n", + "\n", + "plt.figure()\n", + "plt.title(\"Value Error During Training\")\n", + "plt.plot(t[train], sim.data[p_error][train])\n", + "\n", + "plt.figure()\n", + "plt.title(\"Value Error During Recall\")\n", + "plt.plot(t[test], sim.data[p_recall][test] - sim.data[p_values][test])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Examining encoder changes\n", + "\n", + "We can also plot the two-dimensional encoders before and after training.\n", + "Initially, they are uniformly distributed around the unit circle.\n", + "Afterward, we see that each key has attracted all of its nearby neurons.\n", + "Notably, almost all neurons are participating\n", + "in the representation of a unique association." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scale = (sim.data[memory].gain / memory.radius)[:, np.newaxis]\n", + "\n", + "\n", + "def plot_2d(text, xy):\n", + " plt.figure()\n", + " plt.title(text)\n", + " plt.scatter(xy[:, 0], xy[:, 1], label=\"Encoders\")\n", + " plt.scatter(keys[:, 0], keys[:, 1], c=\"red\", s=150, alpha=0.6, label=\"Keys\")\n", + " plt.xlim(-1.5, 1.5)\n", + " plt.ylim(-1.5, 2)\n", + " plt.legend()\n", + " plt.gca().set_aspect(\"equal\")\n", + "\n", + "\n", + "plot_2d(\"Before\", sim.data[p_encoders][0].copy() / scale)\n", + "plot_2d(\"After\", sim.data[p_encoders][-1].copy() / scale)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/learning/learn-communication-channel.ipynb.txt b/_sources/examples/learning/learn-communication-channel.ipynb.txt new file mode 100644 index 0000000000..220e749ee1 --- /dev/null +++ b/_sources/examples/learning/learn-communication-channel.ipynb.txt @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning a communication channel\n", + "\n", + "Normally, if you have a function you would like to compute\n", + "across a connection, you would specify it with `function=my_func`\n", + "in the `Connection` constructor.\n", + "However, it is also possible to use error-driven learning\n", + "to learn to compute a function online." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the model without learning\n", + "\n", + "We'll start by creating a connection between two populations\n", + "that initially computes a very weird function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal\n", + "from nengo.solvers import LstsqL2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)\n", + " pre = nengo.Ensemble(60, dimensions=2)\n", + " nengo.Connection(inp, pre)\n", + " post = nengo.Ensemble(60, dimensions=2)\n", + " conn = nengo.Connection(pre, post, function=lambda x: np.random.random(2))\n", + " inp_p = nengo.Probe(inp)\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_p = nengo.Probe(post, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we run this model as is, we can see that the connection\n", + "from `pre` to `post` doesn't compute much of value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Add in learning\n", + "\n", + "If we can generate an error signal, then we can minimize\n", + "that error signal using the `nengo.PES` learning rule.\n", + "Since it's a communication channel, we know the value that we want,\n", + "so we can compute the error with another ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " error = nengo.Ensemble(60, dimensions=2)\n", + " error_p = nengo.Probe(error, synapse=0.03)\n", + "\n", + " # Error = actual - target = post - pre\n", + " nengo.Connection(post, error)\n", + " nengo.Connection(pre, error, transform=-1)\n", + "\n", + " # Add the learning rule to the connection\n", + " conn.learning_rule_type = nengo.PES()\n", + "\n", + " # Connect the error into the learning rule\n", + " nengo.Connection(error, conn.learning_rule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can see the `post` population gradually learn to compute\n", + "the communication channel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(), sim.data[error_p], c=\"b\")\n", + "plt.ylim(-1, 1)\n", + "plt.legend((\"Error[0]\", \"Error[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Does it generalize?\n", + "\n", + "If the learning rule is always working,\n", + "the error will continue to be minimized.\n", + "But have we actually generalized\n", + "to be able to compute the communication channel\n", + "without this error signal?\n", + "Let's inhibit the `error` population after 10 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def inhibit(t):\n", + " return 2.0 if t > 10.0 else 0.0\n", + "\n", + "\n", + "with model:\n", + " inhib = nengo.Node(inhibit)\n", + " nengo.Connection(inhib, error.neurons, transform=[[-1]] * error.n_neurons)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(16.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(), sim.data[error_p], c=\"b\")\n", + "plt.ylim(-1, 1)\n", + "plt.legend((\"Error[0]\", \"Error[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How does this work?\n", + "\n", + "The `nengo.PES` learning rule minimizes the same error online\n", + "as the decoder solvers minimize with offline optimization.\n", + "\n", + "Let $\\mathbf{E}$ be an error signal.\n", + "In the communication channel case, the error signal\n", + "$\\mathbf{E} = \\mathbf{\\hat{x}} - \\mathbf{x}$;\n", + "in other words, it is the difference between\n", + "the decoded estimate of `post`, $\\mathbf{\\hat{x}}$,\n", + "and the decoded estimate of `pre`, $\\mathbf{x}$.\n", + "\n", + "The PES learning rule on decoders is\n", + "\n", + "$$\\Delta \\mathbf{d_i} = -\\frac{\\kappa}{n} \\mathbf{E} a_i$$\n", + "\n", + "where $\\mathbf{d_i}$ are the decoders being learned,\n", + "$\\kappa$ is a scalar learning rate, $n$ is the number of neurons\n", + "in the `pre` population,\n", + "and $a_i$ is the filtered activity of the `pre` population.\n", + "\n", + "However, many synaptic plasticity experiments\n", + "result in learning rules that explain how\n", + "individual connection weights change.\n", + "We can multiply both sides of the equation\n", + "by the encoders of the `post` population,\n", + "$\\mathbf{e_j}$, and the gain of the `post`\n", + "population $\\alpha_j$, as we do in\n", + "Principle 2 of the NEF.\n", + "This results in the learning rule\n", + "\n", + "$$\n", + "\\Delta \\omega_{ij} = \\Delta \\mathbf{d_i} \\cdot \\mathbf{e_j} \\alpha_j\n", + " = -\\frac{\\kappa}{n} \\alpha_j \\mathbf{e_j} \\cdot \\mathbf{E} a_i\n", + "$$\n", + "\n", + "where $\\omega_{ij}$ is the connection\n", + "between `pre` neuron $i$ and `post` neuron $j$.\n", + "\n", + "The weight-based version of PES can be easily combined with\n", + "learning rules that describe synaptic plasticity experiments.\n", + "In Nengo, the `Connection.learning_rule_type` parameter accepts\n", + "a list of learning rules.\n", + "See [Bekolay et al., 2013](\n", + "http://compneuro.uwaterloo.ca/publications/bekolay2013.html)\n", + "for details on what happens when the PES learning rule is\n", + "combined with an unsupervised learning rule." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How do the decoders / weights change?\n", + "\n", + "The equations above describe\n", + "how the decoders and connection weights change\n", + "as a result of the PES rule.\n", + "But are there any general principles\n", + "that we can say about how the rule\n", + "modifies decoders and connection weights?\n", + "Determining this requires analyzing\n", + "the decoders and connection weights\n", + "as they change over the course of a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " weights_p = nengo.Probe(conn, \"weights\", synapse=0.01, sample_every=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])\n", + "plt.ylabel(\"Decoding weight\")\n", + "plt.legend((\"Decoder 10[0]\", \"Decoder 10[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Change the connection to use connection weights instead of decoders\n", + " conn.solver = LstsqL2(weights=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])\n", + "plt.ylabel(\"Connection weight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For some general principles governing how the decoders change,\n", + "[Voelker, 2015](http://compneuro.uwaterloo.ca/publications/voelker2015.html)\n", + "and [Voelker & Eliasmith, 2017](\n", + "http://compneuro.uwaterloo.ca/publications/voelker2017c.html)\n", + "give detailed analyses of the rule's dynamics.\n", + "It's also interesting to observe qualitative trends;\n", + "often a few strong connection weights will\n", + "dominate the others,\n", + "while decoding weights tend to\n", + "change or not change together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is `pre_synapse`?\n", + "\n", + "By default the `PES` object sets\n", + "`pre_synapse=Lowpass(tau=0.005)`.\n", + "This is a lowpass filter with time-constant $\\tau = 5\\,\\text{ms}$\n", + "that is applied to the activities of the pre-synaptic population $a_i$\n", + "before computing each update $\\Delta {\\bf d}_i$.\n", + "\n", + "In general, longer time-constants\n", + "smooth over the spiking activity to produce more constant updates,\n", + "while shorter time-constants adapt more quickly\n", + "to rapidly changing inputs.\n", + "The right trade-off depends on\n", + "the particular demands of the model.\n", + "\n", + "This `Synapse` object can also be\n", + "any other linear filter (as are used in the `Connection` object);\n", + "for instance, `pre_synapse=Alpha(tau=0.005)`\n", + "applies an alpha filter to the postsynaptic activity.\n", + "This will have the effect of delaying the bulk of the activities\n", + "by a rise-time of $\\tau$ before applying the update.\n", + "This may be useful for situations\n", + "where the error signal is delayed by the same amount,\n", + "since the error signal should be synchronized\n", + "with the same activities that produced said error." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/learning/learn-product.ipynb.txt b/_sources/examples/learning/learn-product.ipynb.txt new file mode 100644 index 0000000000..1dadf8b748 --- /dev/null +++ b/_sources/examples/learning/learn-product.ipynb.txt @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning to compute a product\n", + "\n", + "Unlike the communication channel and the element-wise square,\n", + "the product is a nonlinear function on multiple inputs.\n", + "This represents a difficult case for learning rules\n", + "that aim to generalize a function given many\n", + "input-output example pairs.\n", + "However, using the same type of network structure\n", + "as in the communication channel and square cases,\n", + "we can learn to compute the product of two dimensions\n", + "with the `nengo.PES` learning rule.\n", + "\n", + "The product is a trickier function to learn than\n", + "the communication channel and the square.\n", + "We will also try the `nengo.RLS` learning rule\n", + "and see how `PES` and `RLS` compare in terms of\n", + "learning the product of two dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the model\n", + "\n", + "Like previous examples,\n", + "the network consists of `pre`, `post`, and `error` ensembles.\n", + "We'll use two-dimensional white noise input and attempt to learn the product\n", + "using the actual product to compute the error signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " # -- input and pre popluation\n", + " inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)\n", + " pre = nengo.Ensemble(120, dimensions=2)\n", + " nengo.Connection(inp, pre)\n", + "\n", + " # -- post populations\n", + " post_pes = nengo.Ensemble(60, dimensions=1)\n", + " post_rls = nengo.Ensemble(60, dimensions=1)\n", + "\n", + " # -- reference population, containing the actual product\n", + " product = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(inp, product, function=lambda x: x[0] * x[1], synapse=None)\n", + "\n", + " # -- error populations\n", + " error_pes = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(post_pes, error_pes)\n", + " nengo.Connection(product, error_pes, transform=-1)\n", + " error_rls = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(post_rls, error_rls)\n", + " nengo.Connection(product, error_rls, transform=-1)\n", + "\n", + " # -- learning connections\n", + " conn_pes = nengo.Connection(\n", + " pre,\n", + " post_pes,\n", + " function=lambda x: np.random.random(1),\n", + " learning_rule_type=nengo.PES(),\n", + " )\n", + " nengo.Connection(error_pes, conn_pes.learning_rule)\n", + " conn_rls = nengo.Connection(\n", + " pre,\n", + " post_rls,\n", + " function=lambda x: np.random.random(1),\n", + " learning_rule_type=nengo.RLS(),\n", + " )\n", + " nengo.Connection(error_rls, conn_rls.learning_rule)\n", + "\n", + " # -- inhibit errors after 40 seconds\n", + " inhib = nengo.Node(lambda t: 2.0 if t > 40.0 else 0.0)\n", + " nengo.Connection(inhib, error_pes.neurons, transform=[[-1]] * error_pes.n_neurons)\n", + " nengo.Connection(inhib, error_rls.neurons, transform=[[-1]] * error_rls.n_neurons)\n", + "\n", + " # -- probes\n", + " product_p = nengo.Probe(product, synapse=0.01)\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_pes_p = nengo.Probe(post_pes, synapse=0.01)\n", + " error_pes_p = nengo.Probe(error_pes, synapse=0.03)\n", + " post_rls_p = nengo.Probe(post_rls, synapse=0.01)\n", + " error_rls_p = nengo.Probe(error_rls, synapse=0.03)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(60)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plots(start_ix=None, end_ix=None):\n", + " sl = slice(start_ix, end_ix)\n", + " t = sim.trange()[sl]\n", + " plt.figure(figsize=(14, 12))\n", + " plt.suptitle(\"\")\n", + " plt.subplot(4, 1, 1)\n", + " plt.plot(t, sim.data[pre_p][sl], c=\"b\")\n", + " plt.legend((\"Pre decoding\",), loc=\"best\")\n", + " plt.subplot(4, 1, 2)\n", + " plt.plot(t, sim.data[product_p][sl], c=\"k\", label=\"Actual product\")\n", + " plt.plot(t, sim.data[post_pes_p][sl], c=\"r\", label=\"Post decoding (PES)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(4, 1, 3)\n", + " plt.plot(t, sim.data[product_p][sl], c=\"k\", label=\"Actual product\")\n", + " plt.plot(t, sim.data[post_rls_p][sl], c=\"r\", label=\"Post decoding (RLS)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(4, 1, 4)\n", + " plt.plot(t, sim.data[error_pes_p][sl], c=\"b\", label=\"Error (PES)\")\n", + " plt.plot(t, sim.data[error_rls_p][sl], c=\"r\", label=\"Error (RLS)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.ylim(-1, 1)\n", + "\n", + "\n", + "plots()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine the initial output\n", + "\n", + "Let's zoom in on the network at the beginning:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plots(end_ix=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows that when the network is initialized,\n", + "it is not able to compute the product.\n", + "The error is quite large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine the final output\n", + "\n", + "After the network has run for a while, and learning has occurred,\n", + "the output is quite different:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plots(start_ix=38000, end_ix=42000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that it has learned a decent approximation of the product,\n", + "but it's not perfect -- typically,\n", + "it's not as good as the offline optimization.\n", + "The reason for this is that we've given it white noise input,\n", + "which has a mean of 0; since this happens in both dimensions,\n", + "we'll see a lot of examples of inputs and outputs near 0.\n", + "In other words, we've oversampled a certain part of the\n", + "vector space, and overlearned decoders that do well in\n", + "that part of the space. If we want to do better in other\n", + "parts of the space, we would need to construct an input\n", + "signal that evenly samples the space." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/learning/learn-square.ipynb.txt b/_sources/examples/learning/learn-square.ipynb.txt new file mode 100644 index 0000000000..e3f18a66a8 --- /dev/null +++ b/_sources/examples/learning/learn-square.ipynb.txt @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning to square the input\n", + "\n", + "This demo shows you how to construct a network\n", + "containing an ensemble which learns how to decode the square of its value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "This network consists of an ensemble `A` which represents the input,\n", + "an ensemble `A_squared` which learns to represent the square,\n", + "and an ensemble `error` which represents the error\n", + "between `A_squared` and the actual square." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " # Create the ensemble to represent the input, the input squared (learned),\n", + " # and the error\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " A_squared = nengo.Ensemble(100, dimensions=1)\n", + " error = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # Connect A and A_squared with a communication channel\n", + " conn = nengo.Connection(A, A_squared)\n", + "\n", + " # Apply the PES learning rule to conn\n", + " conn.learning_rule_type = nengo.PES(learning_rate=3e-4)\n", + "\n", + " # Provide an error signal to the learning rule\n", + " nengo.Connection(error, conn.learning_rule)\n", + "\n", + " # Compute the error signal (error = actual - target)\n", + " nengo.Connection(A_squared, error)\n", + "\n", + " # Subtract the target (this would normally come from some external system)\n", + " nengo.Connection(A, error, function=lambda x: x ** 2, transform=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A single input signal (a step function)\n", + "will be used to drive the neural activity in ensemble A.\n", + "An additional node will inhibit the error signal after 15 seconds,\n", + "to test the learning at the end." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input node that steps between -1 and 1\n", + " input_node = nengo.Node(output=lambda t: int(6 * t / 5) / 3.0 % 2 - 1)\n", + "\n", + " # Connect the input node to ensemble A\n", + " nengo.Connection(input_node, A)\n", + "\n", + " # Shut off learning by inhibiting the error population\n", + " stop_learning = nengo.Node(output=lambda t: t >= 15)\n", + " nengo.Connection(\n", + " stop_learning, error.neurons, transform=-20 * np.ones((error.n_neurons, 1))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe the Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " input_node_probe = nengo.Probe(input_node)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " A_squared_probe = nengo.Probe(A_squared, synapse=0.01)\n", + " error_probe = nengo.Probe(error, synapse=0.01)\n", + " learn_probe = nengo.Probe(stop_learning, synapse=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the input signal\n", + "plt.figure(figsize=(9, 9))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_node_probe], label=\"Input\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.plot(\n", + " sim.trange(),\n", + " sim.data[learn_probe],\n", + " label=\"Stop learning?\",\n", + " color=\"r\",\n", + " linewidth=2.0,\n", + ")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.ylim(-1.2, 1.2)\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_node_probe] ** 2, label=\"Squared Input\", linewidth=2.0\n", + ")\n", + "plt.plot(sim.trange(), sim.data[A_squared_probe], label=\"Decoded Ensemble $A^2$\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.ylim(-1.2, 1.2)\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(\n", + " sim.trange(),\n", + " sim.data[A_squared_probe] - sim.data[input_node_probe] ** 2,\n", + " label=\"Error\",\n", + ")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that during the first three periods,\n", + "the decoders quickly adjust to drive the error to zero.\n", + "When learning is turned off for the fourth period,\n", + "the error stays closer to zero,\n", + "demonstrating that the learning has persisted in the connection." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/learning/learn-unsupervised.ipynb.txt b/_sources/examples/learning/learn-unsupervised.ipynb.txt new file mode 100644 index 0000000000..2978fb95a9 --- /dev/null +++ b/_sources/examples/learning/learn-unsupervised.ipynb.txt @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unsupervised learning\n", + "\n", + "When we do error-modulated learning with the `nengo.PES` rule,\n", + "we have a pretty clear idea of what we want to happen.\n", + "Years of neuroscientific experiments have yielded\n", + "learning rules explaining how synaptic strengths\n", + "change given certain stimulation protocols.\n", + "But what do these learning rules actually do\n", + "to the information transmitted across an\n", + "ensemble-to-ensemble connection?\n", + "\n", + "We can investigate this in Nengo.\n", + "Currently, we've implemented the `nengo.BCM`\n", + "and `nengo.Oja` rules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(nengo.BCM.__doc__)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(nengo.Oja.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create a simple communication channel\n", + "\n", + "The only difference between this network and most\n", + "models you've seen so far is that we're going to\n", + "set the decoder solver in the communication channel\n", + "to generate a full connection weight matrix\n", + "which we can then learn using typical delta learning rules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(t * 4))\n", + "\n", + " pre = nengo.Ensemble(100, dimensions=1)\n", + " post = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " nengo.Connection(sin, pre)\n", + " conn = nengo.Connection(pre, post, solver=nengo.solvers.LstsqL2(weights=True))\n", + "\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_p = nengo.Probe(post, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Verify that it does a communication channel\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What does BCM do?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conn.learning_rule_type = nengo.BCM(learning_rate=5e-10)\n", + "with model:\n", + " weights_p = nengo.Probe(conn, \"weights\", synapse=0.01, sample_every=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The BCM rule appears to cause the ensemble\n", + "to either be on or off.\n", + "This seems consistent with the idea that it potentiates\n", + "active synapses, and depresses non-active synapses.\n", + "\n", + "As well, we can show that BCM sparsifies the weights.\n", + "The sparsity measure below uses the Gini measure of sparsity,\n", + "for reasons explained [in this paper](https://arxiv.org/pdf/0811.4706.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sparsity_measure(vector): # Gini index\n", + " # Max sparsity = 1 (single 1 in the vector)\n", + " v = np.sort(np.abs(vector))\n", + " n = v.shape[0]\n", + " k = np.arange(n) + 1\n", + " l1norm = np.sum(v)\n", + " summation = np.sum((v / l1norm) * ((n - k + 0.5) / n))\n", + " return 1 - 2 * summation\n", + "\n", + "\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What does Oja do?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conn.learning_rule_type = nengo.Oja(learning_rate=6e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Oja rule seems to attenuate the signal across the connection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do BCM and Oja together do?\n", + "\n", + "We can apply both learning rules to the same connection\n", + "by passing a list to `learning_rule_type`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conn.learning_rule_type = [\n", + " nengo.BCM(learning_rate=5e-10),\n", + " nengo.Oja(learning_rate=2e-9),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combination of the two appears to accentuate\n", + "the on-off nature of the BCM rule.\n", + "As the Oja rule enforces a soft upper or lower bound\n", + "for the connection weight, the combination\n", + "of both rules may be more stable than BCM alone.\n", + "\n", + "That's mostly conjecture, however;\n", + "a thorough investigation of the BCM and Oja rules\n", + "and how they interact has not yet been done." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/learning/lmu.ipynb.txt b/_sources/examples/learning/lmu.ipynb.txt new file mode 100644 index 0000000000..d56f9e7684 --- /dev/null +++ b/_sources/examples/learning/lmu.ipynb.txt @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Legendre Memory Units in Nengo\n", + "\n", + "Legendre Memory Units (LMUs) are\n", + "a novel recurrent neural network architecture, described in\n", + "[Voelker, Kajić, and Eliasmith (NeurIPS 2019)][paper].\n", + "We will not go into much of the underlying details of these methods here;\n", + "broadly speaking, we can think of an LMU as a recurrent network\n", + "that does a very good job of representing\n", + "the temporal information in some input signal.\n", + "Since most RNN tasks involve computing\n", + "some function of that temporal information,\n", + "the better the RNN is at representing the temporal information\n", + "the better it will be able to perform the task.\n", + "See the [paper][] for all the details!\n", + "\n", + "In this example we will show how an LMU can be used\n", + "to delay an input signal for some fixed length of time.\n", + "This is a simple sounding task, but performing an accurate delay\n", + "requires the network to store the complete history of the input signal\n", + "across the delay period.\n", + "So it is a good measure of a network's fundamental temporal storage.\n", + "\n", + "[paper]:\n", + "https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from collections import deque\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.filter_design import cont2discrete" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our LMU in this example will have two parameters:\n", + "the length of the time window it is optimized to store,\n", + "and the number of Legendre polynomials used to represent the signal\n", + "(using higher order polynomials\n", + "allows the LMU to represent higher frequency information).\n", + "\n", + "The input will be a band-limited white noise signal,\n", + "which has its own parameters\n", + "determining the amplitude and frequency of the signal.\n", + "\n", + "Feel free to adjust any of these parameters\n", + "to see what impact they have on the model's performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters of LMU\n", + "theta = 1.0 # length of window (in seconds)\n", + "order = 8 # number of Legendre polynomials representing window\n", + "\n", + "# parameters of input signal\n", + "freq = 2 # frequency limit\n", + "rms = 0.30 # amplitude of input (set to keep within [-1, 1])\n", + "delay = 0.5 # length of time delay network will learn\n", + "\n", + "# simulation parameters\n", + "dt = 0.001 # simulation timestep\n", + "sim_t = 100 # length of simulation\n", + "seed = 0 # fixed for deterministic results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to compute\n", + "the analytically derived weight matrices used in the LMU.\n", + "These are determined statically based on\n", + "the `theta`/`order` parameters from above.\n", + "It is also possible to optimize these parameters using backpropagation,\n", + "using a framework such as [NengoDL](https://www.nengo.ai/nengo-dl)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute the A and B matrices according to the LMU's mathematical derivation\n", + "# (see the paper for details)\n", + "Q = np.arange(order, dtype=np.float64)\n", + "R = (2 * Q + 1)[:, None] / theta\n", + "j, i = np.meshgrid(Q, Q)\n", + "\n", + "A = np.where(i < j, -1, (-1.0) ** (i - j + 1)) * R\n", + "B = (-1.0) ** Q[:, None] * R\n", + "C = np.ones((1, order))\n", + "D = np.zeros((1,))\n", + "\n", + "A, B, _, _, _ = cont2discrete((A, B, C, D), dt=dt, method=\"zoh\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will set up an artificial synapse model\n", + "to compute an ideal delay\n", + "(we'll use this to train the model later on).\n", + "And we can run a simple network containing\n", + "just our input signal and the ideal delay to see what that looks like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class IdealDelay(nengo.synapses.Synapse):\n", + " def __init__(self, delay):\n", + " super().__init__()\n", + " self.delay = delay\n", + "\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None, y0=None):\n", + " return {}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " # buffer the input signal based on the delay length\n", + " buffer = deque([0] * int(self.delay / dt))\n", + "\n", + " def delay_func(t, x):\n", + " buffer.append(x.copy())\n", + " return buffer.popleft()\n", + "\n", + " return delay_func\n", + "\n", + "\n", + "with nengo.Network(seed=seed) as net:\n", + " # create the input signal\n", + " stim = nengo.Node(\n", + " output=nengo.processes.WhiteSignal(\n", + " high=freq, period=sim_t, rms=rms, y0=0, seed=seed\n", + " )\n", + " )\n", + "\n", + " # probe input signal and an ideally delayed version of input signal\n", + " p_stim = nengo.Probe(stim)\n", + " p_ideal = nengo.Probe(stim, synapse=IdealDelay(delay))\n", + "\n", + "# run the network and display results\n", + "with nengo.Simulator(net) as sim:\n", + " sim.run(10)\n", + "\n", + " plt.figure(figsize=(16, 6))\n", + " plt.plot(sim.trange(), sim.data[p_stim], label=\"input\")\n", + " plt.plot(sim.trange(), sim.data[p_ideal], label=\"ideal\")\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to build the LMU.\n", + "The full LMU architecture consists of two components:\n", + "a linear memory, and a nonlinear hidden state.\n", + "But the nonlinear hidden state is really only useful\n", + "when it is optimized using backpropagation (see\n", + "[this example in NengoDL](https://www.nengo.ai/nengo-dl/examples/lmu.html)).\n", + "So here we will just build the linear memory component." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with net:\n", + " lmu = nengo.Node(size_in=order)\n", + " nengo.Connection(stim, lmu, transform=B, synapse=None)\n", + " nengo.Connection(lmu, lmu, transform=A, synapse=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On its own the LMU isn't performing a task,\n", + "it is just internally representing the input signal.\n", + "So to get this network to perform a function,\n", + "we will add an output Ensemble\n", + "that gets the output of the LMU as input.\n", + "Then we will train the output weights of that Ensemble\n", + "using the PES online learning rule.\n", + "The error signal will be based on the ideally delayed input signal,\n", + "so the network should learn to compute that same delay." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with net:\n", + " ens = nengo.Ensemble(1000, order, neuron_type=nengo.SpikingRectifiedLinear())\n", + " nengo.Connection(lmu, ens, synapse=None)\n", + "\n", + " out = nengo.Node(size_in=1)\n", + "\n", + " # we'll use a Node to compute the error signal so that we can shut off\n", + " # learning after a while (in order to assess the network's generalization)\n", + " err_node = nengo.Node(lambda t, x: x if t < sim_t * 0.8 else 0, size_in=1)\n", + "\n", + " # the target signal is the ideally delayed version of the input signal,\n", + " # which is subtracted from the ensemble's output in order to compute the\n", + " # PES error\n", + " nengo.Connection(stim, err_node, synapse=IdealDelay(delay), transform=-1)\n", + " nengo.Connection(out, err_node, synapse=None)\n", + "\n", + " learn_conn = nengo.Connection(\n", + " ens, out, function=lambda x: 0, learning_rule_type=nengo.PES(2e-4)\n", + " )\n", + " nengo.Connection(err_node, learn_conn.learning_rule, synapse=None)\n", + "\n", + " p_out = nengo.Probe(out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can run the full model\n", + "to see it learning to perform the delay task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(net) as sim:\n", + " sim.run(sim_t)\n", + "\n", + "# we'll break up the output into multiple plots, just for\n", + "# display purposes\n", + "t_per_plot = 10\n", + "for i in range(sim_t // t_per_plot):\n", + " plot_slice = (sim.trange() >= t_per_plot * i) & (\n", + " sim.trange() < t_per_plot * (i + 1)\n", + " )\n", + "\n", + " plt.figure(figsize=(16, 6))\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_stim][plot_slice], label=\"input\")\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_ideal][plot_slice], label=\"ideal\")\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_out][plot_slice], label=\"output\")\n", + " if i * t_per_plot < sim_t * 0.8:\n", + " plt.title(\"Learning ON\")\n", + " else:\n", + " plt.title(\"Learning OFF\")\n", + " plt.ylim([-1, 1])\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the network is successfully learning to compute a delay.\n", + "We could use these same principles to train a network\n", + "to compute any time-varying function of some input signal,\n", + "and an LMU will always provide\n", + "an optimal representation of that input signal.\n", + "\n", + "See these other examples for some other applications of LMUs:\n", + "\n", + "- [State of the art performance on the psMNIST task using LMUs in NengoDL](\n", + "https://www.nengo.ai/nengo-dl/examples/lmu.html)\n", + "\n", + "\n", + "As well as [the original paper][paper] for more information on LMUs.\n", + "\n", + "[paper]:\n", + "https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/networks/basal-ganglia.ipynb.txt b/_sources/examples/networks/basal-ganglia.ipynb.txt new file mode 100644 index 0000000000..4550c9c27a --- /dev/null +++ b/_sources/examples/networks/basal-ganglia.ipynb.txt @@ -0,0 +1,153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The basal ganglia\n", + "\n", + "The basal ganglia\n", + "according to [Stewart 2010](\n", + "http://compneuro.uwaterloo.ca/files/publications/stewart.2010.pdf)\n", + "is an action selector\n", + "that chooses whatever action has the best \"salience\" or \"goodness\".\n", + "Its really interesting behaviour manifests itself\n", + "when it interacts with the thalamus and other components of the brain,\n", + "but in this example we will only show the basal ganglia's basic behaviour.\n", + "It will choose between three actions\n", + "that we'll pretend are \"eating\", \"sleeping\" and \"playing\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Network\n", + "\n", + "Here we create the basal ganglia and the action input node." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Basal Ganglia\")\n", + "with model:\n", + " basal_ganglia = nengo.networks.BasalGanglia(dimensions=3)\n", + "\n", + "\n", + "class ActionIterator:\n", + " def __init__(self, dimensions):\n", + " self.actions = np.ones(dimensions) * 0.1\n", + "\n", + " def step(self, t):\n", + " # one action at time dominates\n", + " dominate = int(t % 3)\n", + " self.actions[:] = 0.1\n", + " self.actions[dominate] = 0.8\n", + " return self.actions\n", + "\n", + "\n", + "action_iterator = ActionIterator(dimensions=3)\n", + "\n", + "with model:\n", + " actions = nengo.Node(action_iterator.step, label=\"actions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Connect the Network\n", + "\n", + "Connect the input to the basal ganglia and connect the probes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(actions, basal_ganglia.input, synapse=None)\n", + " selected_action = nengo.Probe(basal_ganglia.output, synapse=0.01)\n", + " input_actions = nengo.Probe(actions, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Simulate the Network and Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " # This will take a while\n", + " sim.run(6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[input_actions].argmax(axis=1))\n", + "plt.ylim(-0.1, 2.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.title(\"Index of actual max value\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[selected_action].argmax(axis=1))\n", + "plt.ylim(-0.1, 2.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.title(\"Basal ganglia selected max value\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the maximum index\n", + "is found at 0, then 1, then 2\n", + "or \"eating\", \"sleeping\", then \"playing\".\n", + "Note that if you zoom in enough on the basal ganglia values,\n", + "you'll be able to see a bit of a delay between finding max values.\n", + "If you read the aforementioned paper,\n", + "you'll see that this is expected and matches previous experiments." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/networks/ensemble-array.ipynb.txt b/_sources/examples/networks/ensemble-array.ipynb.txt new file mode 100644 index 0000000000..e2b53b25be --- /dev/null +++ b/_sources/examples/networks/ensemble-array.ipynb.txt @@ -0,0 +1,105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ensemble array\n", + "\n", + "An ensemble array is a group of ensembles\n", + "that each represent a part of the overall signal.\n", + "\n", + "Ensemble arrays are similar to normal ensembles,\n", + "but expose a slightly different interface.\n", + "Additionally, in an ensemble array,\n", + "the components of the overall signal are not related.\n", + "As a result, network arrays cannot be used\n", + "to compute nonlinear functions that mix the dimensions they represent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Ensemble Array\")\n", + "with model:\n", + " # Make an input node\n", + " sin = nengo.Node(output=lambda t: [np.cos(t), np.sin(t)])\n", + "\n", + " # Make ensembles to connect\n", + " A = nengo.networks.EnsembleArray(100, n_ensembles=2)\n", + " B = nengo.Ensemble(100, dimensions=2)\n", + " C = nengo.networks.EnsembleArray(100, n_ensembles=2)\n", + "\n", + " # Connect the model elements, just feedforward\n", + " nengo.Connection(sin, A.input)\n", + " nengo.Connection(A.output, B)\n", + " nengo.Connection(B, C.input)\n", + "\n", + " # Setup the probes for plotting\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A.output, synapse=0.02)\n", + " B_probe = nengo.Probe(B, synapse=0.02)\n", + " C_probe = nengo.Probe(C.output, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[sin_probe])\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.plot(sim.trange(), sim.data[B_probe])\n", + "plt.plot(sim.trange(), sim.data[C_probe])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots demonstrate that the ensemble array\n", + "works very similarly to a standard N-dimensional population.\n", + "However, this is not true when it comes to computing functions.\n", + "Ensemble arrays cannot be used\n", + "to compute nonlinear functions that mix the dimensions they represent." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/networks/integrator-network.ipynb.txt b/_sources/examples/networks/integrator-network.ipynb.txt new file mode 100644 index 0000000000..a769f69e0d --- /dev/null +++ b/_sources/examples/networks/integrator-network.ipynb.txt @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrator\n", + "\n", + "This demo implements a one-dimensional neural integrator.\n", + "\n", + "This is the first example of a recurrent network in the demos.\n", + "It shows how neurons can be used to implement stable dynamics.\n", + "Such dynamics are important for memory, noise cleanup,\n", + "statistical inference, and many other dynamic transformations.\n", + "\n", + "When you run this demo, it will automatically\n", + "put in some step functions on the input,\n", + "so you can see that the output is integrating\n", + "(i.e. summing over time) the input.\n", + "You can also input your own values.\n", + "Note that since the integrator constantly sums its input,\n", + "it will saturate quickly if you leave the input non-zero.\n", + "This makes it clear that neurons have a finite range of representation.\n", + "Such saturation effects can be exploited\n", + "to perform useful computations (e.g. soft normalization)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of one recurrently connected ensemble,\n", + "and an input population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tau = 0.1\n", + "\n", + "integrator = nengo.networks.Integrator(tau, n_neurons=100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a piecewise step function as input,\n", + "so we can see the effects of recurrence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with integrator:\n", + " input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Connect the input\n", + "with integrator:\n", + " nengo.Connection(input, integrator.input, synapse=tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with integrator:\n", + " input_probe = nengo.Probe(input)\n", + " integrator_probe = nengo.Probe(integrator.ensemble, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(integrator) as sim:\n", + " # Run it for 6 seconds\n", + " sim.run(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[integrator_probe], label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[input_probe], \"k\", label=\"Input\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows the response to the input by the integrator.\n", + "Because it is implemented in neurons,\n", + "it will not be perfect (i.e. there will be drift).\n", + "Running several times will give a sense of\n", + "the kinds of drift you might expect.\n", + "Drift can be reduced by increasing the number of neurons." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/quirks/config.ipynb.txt b/_sources/examples/quirks/config.ipynb.txt new file mode 100644 index 0000000000..133da98921 --- /dev/null +++ b/_sources/examples/quirks/config.ipynb.txt @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context matters, membership doesn't\n", + "\n", + "When you create a Nengo object\n", + "and leave the parameters as their default values,\n", + "we determine the default values dynamically\n", + "based on `Config` objects.\n", + "Every `Network` has an associated `Config` object,\n", + "and you can make new `Config` objects for additional flexibility.\n", + "\n", + "Nengo keeps track of the current context.\n", + "The following example works based on the context." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as model:\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " subnet = nengo.Network()\n", + "\n", + " with subnet:\n", + " a = nengo.Ensemble(10, 1)\n", + " print(a.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The radius of `a` is `2.0`\n", + "because the current context includes both\n", + "`subnet` and `model`.\n", + "`subnet` does not change the default value of the radius,\n", + "but `model` does, so it uses the `model` default of `2.0`.\n", + "\n", + "Here's a similar example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as model:\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " subnet = nengo.Network()\n", + "\n", + "with subnet:\n", + " a = nengo.Ensemble(10, 1)\n", + " print(a.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this example looks nearly identical,\n", + "the difference is that `a` is created\n", + "in the context of `subnet` only;\n", + "`model` is not part of the config context.\n", + "Because of that, when `a` is created,\n", + "Nengo sees that no default is set in `subnet`\n", + "and uses the global default value of `1.0`.\n", + "\n", + "This may seem counterintuitive\n", + "since `subnet` is a member of `model`;\n", + "it's stored as a sub-network of the `model` network.\n", + "However, Nengo objects are not aware of their parents.\n", + "This allows `subnet` to be used the same way\n", + "whether it's the top-level network\n", + "or whether it's nested twenty layers deep,\n", + "but it also means that we can't set defaults\n", + "based on network membership." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Context details\n", + "\n", + "The configuration context is stored in the\n", + "`nengo.Config` class as `nengo.Config.context`.\n", + "`context` is a thread-local list.\n", + "We add new contexts to the end of that list\n", + "at the start of `with` blocks,\n", + "and pop contexts off of that list\n", + "at the end of `with` blocks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# No context\n", + "len(nengo.Config.context)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model context\n", + "with model:\n", + " print(len(nengo.Config.context))\n", + " print(nengo.Config.context[0] is model.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Subnet, Model context\n", + "with model:\n", + " with subnet:\n", + " print(len(nengo.Config.context))\n", + " print(nengo.Config.context[0] is model.config)\n", + " print(nengo.Config.context[1] is subnet.config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are not sure what context you're in,\n", + "but you want to know what the defaults are\n", + "in the current context,\n", + "use `nengo.Config.all_defaults`.\n", + "You can optionally pass in the type that you're interested in." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " with subnet:\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with subnet:\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note above that the radius changes\n", + "despite the fact that both situations occur\n", + "in the context of `subnet`!\n", + "Configuration outside matters if no default\n", + "has been set in the immediate context." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/usage/config.ipynb.txt b/_sources/examples/usage/config.ipynb.txt new file mode 100644 index 0000000000..61bbb67265 --- /dev/null +++ b/_sources/examples/usage/config.ipynb.txt @@ -0,0 +1,590 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The `config` system\n", + "\n", + "Nengo objects have many parameters\n", + "that can be modified.\n", + "Some of these parameters\n", + "are critical characteristics of that object,\n", + "and others are hints or suggestions\n", + "that a backend can use or ignore.\n", + "\n", + "Nengo's `config` system is designed\n", + "to make setting large numbers of parameters easy,\n", + "and to allow backends\n", + "to introduce additional parameters\n", + "without changing core Nengo objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nengo\n", + "import nengo.params\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting default parameters\n", + "\n", + "The `config` system aids in\n", + "setting many parameters\n", + "with a **hierarchy of defaults**.\n", + "When you create a Nengo object,\n", + "any parameters not specified\n", + "will be given the value `nengo.Default`.\n", + "This value tells Nengo\n", + "to use the default value\n", + "with the highest precedence\n", + "in the hierarchy.\n", + "Every `Network` has an associated\n", + "`config` object,\n", + "on which defaults can be set.\n", + "The network hierarchy is traversed\n", + "from most to least specific\n", + "and the first network with a default\n", + "set for that particular parameter\n", + "is used. For example:\n", + "\n", + " with nengo.Network() as net:\n", + " with nengo.Network() as subnet:\n", + " with nengo.Network() as subsubnet:\n", + " ens = nengo.Ensemble(10, 1)\n", + "\n", + "When filling in defaults for `ens`,\n", + "the hierarchy looks like\n", + "\n", + " └── net <- least specific\n", + " └── subnet\n", + " └── subsubnet <- most specific\n", + "\n", + "so defaults set in `subsubnet`\n", + "will take precedence over those in `subnet`,\n", + "which take precedence over those in `net`.\n", + "\n", + "If no default has been set in the\n", + "network hierarchy,\n", + "then the parameter default\n", + "is used.\n", + "These defaults are specified\n", + "when the Nengo objects are created.\n", + "We can investigate these defaults\n", + "by printing the class attributes\n", + "associated with them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get all info about the radius\n", + "print(nengo.Ensemble.radius)\n", + "# Just get the default\n", + "print(nengo.Ensemble.radius.default)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can inspect which defaults\n", + "have been overridden in a\n", + "particular `config` object\n", + "by printing it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "print(model.config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To configure a parameter\n", + "(i.e., change its network-local default),\n", + "set it as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.config[nengo.Ensemble].radius = 1.5\n", + "print(model.config[nengo.Ensemble])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this network, the default radius will be 1.5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1)\n", + "print(\"Normal network: ens.radius = %s\" % ens.radius)\n", + "\n", + "with model:\n", + " ens = nengo.Ensemble(10, 1)\n", + "print(\"Configured network: ens.radius = %s\" % ens.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if a radius is explicitly passed in,\n", + "it will always be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1, radius=2.0)\n", + "print(\"Normal network: ens.radius = %s\" % ens.radius)\n", + "\n", + "with model:\n", + " ens = nengo.Ensemble(10, 1, radius=2.0)\n", + "print(\"Configured network: ens.radius = %s\" % ens.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When networks are nested within one another,\n", + "the most specific network configuration is used.\n", + "For example, if you create an Ensemble\n", + "without specifying a radius,\n", + "it will first check the network\n", + "that the Ensemble is a part of;\n", + "if that network has not configured a default,\n", + "then it will check the network\n", + "that that network is part of,\n", + "and so on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + "\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + "\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"Creating e1 in subsubnet\")\n", + " e1 = nengo.Ensemble(10, 1)\n", + " # Uses subsubnet.config value for radius\n", + " print(\" radius =\", e1.radius)\n", + " # Uses subnet.config value for neuron_type\n", + " print(\" neuron_type =\", e1.neuron_type)\n", + "\n", + " print(\"Creating e2 in subnet\")\n", + " e2 = nengo.Ensemble(10, 1)\n", + " # Uses model.config value for radius\n", + " print(\" radius =\", e2.radius)\n", + " # Uses subnet.config value for neuron_type\n", + " print(\" neuron_type =\", e2.neuron_type)\n", + "\n", + " print(\"Creating e3 in model\")\n", + " e3 = nengo.Ensemble(10, 1)\n", + " # Uses model.config value for radius\n", + " print(\" radius =\", e3.radius)\n", + " # Uses nengo.Ensemble default for neuron_type\n", + " print(\" neuron_type =\", e3.neuron_type)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that each `config` object\n", + "only knows about the defaults set on itself." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + "print(\"subsubnet:\")\n", + "print(subsubnet.config[nengo.Ensemble])\n", + "print(\"\\nsubnet:\")\n", + "print(subnet.config[nengo.Ensemble])\n", + "print(\"\\nmodel:\")\n", + "print(model.config[nengo.Ensemble])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want a more global picture of the defaults\n", + "in the *current context*, you can query the `Config`\n", + "class itself (all `config` objects are instances of `Config`).\n", + "\n", + "To query all parameters, print `Config.all_defaults()`.\n", + "You may pass a Nengo object class to this function\n", + "to filter the results.\n", + "For example, to get all defaults set for `Ensemble`,\n", + "use `Config.all_defaults(nengo.Ensemble)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " print(\"In 'model' context:\")\n", + " print(nengo.Config.all_defaults())\n", + "\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " subnet.config[nengo.Ensemble].radius = 3.0\n", + " print(\"In 'subnet' context:\")\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))\n", + "\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].neuron_type = nengo.Direct()\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"In 'subsubnet' context:\")\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default value for a particular parameter\n", + "can be queried from the global context\n", + "with the `nengo.Config.default` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(nengo.Config.default)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def print_defaults():\n", + " def_radius = nengo.Config.default(nengo.Ensemble, \"radius\")\n", + " def_type = nengo.Config.default(nengo.Ensemble, \"neuron_type\")\n", + " print(\" default radius: %s\" % def_radius)\n", + " print(\" default neuron_type: %s\" % def_type)\n", + "\n", + "\n", + "with model:\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"subsubnet:\")\n", + " print_defaults()\n", + " print(\"\\nsubnet:\")\n", + " print_defaults()\n", + " print(\"\\nmodel:\")\n", + " print_defaults()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Defaults are filled in immediately\n", + "\n", + "One important feature about the defaults hierarchy\n", + "is that defaults are filled in **immediately**.\n", + "When you create a Nengo object,\n", + "the attributes are filled in with the **current**\n", + "defaults that are set.\n", + "Changing the defaults after object creation\n", + "will not update objects already created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " e1 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"Changing default radius to 2.0\")\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " e2 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"e2.radius =\", e2.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Resetting to default\n", + "\n", + "If you ever wish to reset a value\n", + "back to the default,\n", + "you can remove it from the `config` object you modified." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " e1 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"Resetting radius back to default\")\n", + " del model.config[nengo.Ensemble].radius\n", + " print(\"\\n\" + str(model.config[nengo.Ensemble]) + \"\\n\")\n", + " e2 = nengo.Ensemble(10, 1)\n", + " print(\"e2.radius =\", e2.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Making new `config`s\n", + "\n", + "Typically, several Nengo objects\n", + "will share a set of parameters,\n", + "but won't make sense to encapsulate in a network.\n", + "One method of having those objects share parameters\n", + "is to use dictionary unpacking." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network():\n", + " hippocampus_args = {\"radius\": 1.5, \"neuron_type\": nengo.LIFRate()}\n", + " e1 = nengo.Ensemble(100, 2, **hippocampus_args)\n", + " e2 = nengo.Ensemble(150, 3, **hippocampus_args)\n", + " e3 = nengo.Ensemble(200, 4, **hippocampus_args)\n", + "print(e1.radius, e2.radius, e3.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An alternative method that can be very useful\n", + "for large networks and for more readable models\n", + "is to create a new `config` object\n", + "to encapsulate those parameter settings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "in_hippocampus = nengo.Config(nengo.Ensemble)\n", + "in_hippocampus[nengo.Ensemble].radius = 1.5\n", + "in_hippocampus[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + "\n", + "with nengo.Network():\n", + " with in_hippocampus:\n", + " e1 = nengo.Ensemble(100, 2)\n", + " e2 = nengo.Ensemble(150, 3)\n", + " e3 = nengo.Ensemble(200, 4)\n", + "print(e1.radius, e2.radius, e3.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced: adding new parameters\n", + "\n", + "This section is targeted to those\n", + "implementing new backends\n", + "or large libraries of networks\n", + "(like, for example, `nengo.SPA`).\n", + "\n", + "Often, you want to associate some kind of\n", + "metadata with a Nengo object,\n", + "or a type of Nengo objects.\n", + "For example, in backends\n", + "that communicate with specific hardware,\n", + "it can be helpful to mark certain nodes\n", + "as being time-dependent,\n", + "or to assign certain ensembles\n", + "to a particular portion of the hardware memory.\n", + "\n", + "Python allows us to make new attributes\n", + "on Nengo objects.\n", + "However, we highly discourage this activity,\n", + "because a Nengo object should be\n", + "a backend-agnostic part of a model.\n", + "The parameters pre-defined on Nengo objects\n", + "make up the parameters that all backends\n", + "should deal with in some way.\n", + "\n", + "For this reason, we raise a warning\n", + "when creating a new attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1)\n", + " ens.memory_location = 0x1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So how should backends associate arbitrary information\n", + "with Nengo objects?\n", + "The `config` system!\n", + "\n", + "We saw above that we can create new `config` objects\n", + "by specifying which Nengo objects they can configure.\n", + "We can also create new parameters\n", + "on those `config` objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_config = nengo.Config(nengo.Ensemble)\n", + "# memory_location must be a positive integer\n", + "my_config[nengo.Ensemble].set_param(\n", + " \"memory_location\",\n", + " nengo.params.IntParam(\"memory_location\", default=None, optional=True, low=0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can set that parameter\n", + "for the `nengo.Ensemble` class as a whole,\n", + "or with individual instances." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make the network (this code is backend-agnostic)\n", + "with nengo.Network():\n", + " e1 = nengo.Ensemble(10, 1)\n", + " e2 = nengo.Ensemble(10, 1)\n", + "\n", + "# Set backend-specific parameters\n", + "my_config[nengo.Ensemble].memory_location = 0x1000 # Set Ensemble default\n", + "my_config[e2].memory_location = 0x2000 # Set value for e2\n", + "\n", + "print(\"e1 will be stored at 0x%x\" % my_config[e1].memory_location)\n", + "print(\"e2 will be stored at 0x%x\" % my_config[e2].memory_location)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Parameter` types for the most common Python objects\n", + "are available in `nengo.params`,\n", + "as well as other types that Nengo uses frequently,\n", + "but it is possible to implement your own\n", + "in order to do additional processing\n", + "like validation.\n", + "See the `nengo.params` source\n", + "for examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print([cls for cls in dir(nengo.params) if cls.endswith(\"Param\")])" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/usage/delay-node.ipynb.txt b/_sources/examples/usage/delay-node.ipynb.txt new file mode 100644 index 0000000000..3a737b72f9 --- /dev/null +++ b/_sources/examples/usage/delay-node.ipynb.txt @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Delaying a connection with a node\n", + "\n", + "Nodes allow for all sorts of advanced behavior\n", + "that is typically done by modifying the code of a neural simulator.\n", + "In Nengo, the `Node` object allows us to run custom code.\n", + "\n", + "In this example, we will implement\n", + "an `n`-timestep delayed connection by using a node." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Delayed connection\")\n", + "with model:\n", + " # We'll use white noise as input\n", + " inp = nengo.Node(WhiteSignal(2, high=5), size_out=1)\n", + " A = nengo.Ensemble(40, dimensions=1)\n", + " nengo.Connection(inp, A)\n", + "\n", + "\n", + "# We'll make a simple object to implement the delayed connection\n", + "class Delay:\n", + " def __init__(self, dimensions, timesteps=50):\n", + " self.history = np.zeros((timesteps, dimensions))\n", + "\n", + " def step(self, t, x):\n", + " self.history = np.roll(self.history, -1)\n", + " self.history[-1] = x\n", + " return self.history[0]\n", + "\n", + "\n", + "dt = 0.001\n", + "delay = Delay(1, timesteps=int(0.2 / 0.001))\n", + "\n", + "with model:\n", + " delaynode = nengo.Node(delay.step, size_in=1, size_out=1)\n", + " nengo.Connection(A, delaynode)\n", + "\n", + " # Send the delayed output through an ensemble\n", + " B = nengo.Ensemble(40, dimensions=1)\n", + " nengo.Connection(delaynode, B)\n", + "\n", + " # Probe the input at the delayed output\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run for 2 seconds\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe], lw=2)\n", + "plt.title(\"Input\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[B_probe], lw=2)\n", + "plt.axvline(0.2, c=\"k\")\n", + "plt.title(\"Delayed output\")\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/usage/network-design-advanced.ipynb.txt b/_sources/examples/usage/network-design-advanced.ipynb.txt new file mode 100644 index 0000000000..fb01669b35 --- /dev/null +++ b/_sources/examples/usage/network-design-advanced.ipynb.txt @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Additional tips and tricks for designing networks\n", + "\n", + "This tutorial assumes that you have read\n", + "the `network_design` tutorial,\n", + "and have designed a network or two.\n", + "Here, we will give a few advanced tips and tricks\n", + "for designing networks that can be reused flexibly.\n", + "In particular, these tips will use the\n", + "`config` system, so we will also assume that\n", + "you have gone over the `config` tutorial.\n", + "\n", + "Briefly, the general principles covered\n", + "in this tutorial are\n", + "\n", + "0. Accept `**kwargs` to pass through network arguments\n", + "0. Accept a config argument for groups of parameters\n", + "\n", + "We will demonstrate these principles\n", + "using the two examples from the `network_design` tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "\n", + "def test_integrators(net):\n", + " with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, net.pre_integrator.input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + " plt.figure()\n", + " plt.plot(sim.trange(), sim.data[input_probe], color=\"k\")\n", + " plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\")\n", + " plt.plot(sim.trange(), sim.data[post_probe], color=\"g\")\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Accept a `**kwargs` argument\n", + "\n", + "The standard `nengo.Network` accepts a number of arguments,\n", + "including the widely used `seed` and `label` arguments.\n", + "Sometimes it is helpful to be able to\n", + "set these on your custom networks too.\n", + "While there is nothing wrong\n", + "with explicitly passing these arguments along,\n", + "it is less typing to use the Python `**kwargs` construct.\n", + "This special argument allows a function\n", + "to accept any number of keyword arguments\n", + "which we can then pass into the `Network` constructor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def Integrator(n_neurons, dimensions, tau=0.1, **kwargs):\n", + " with nengo.Network(**kwargs) as net:\n", + " net.input = nengo.Node(size_in=dimensions)\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " nengo.Connection(net.ensemble, net.ensemble, synapse=tau)\n", + " nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)\n", + " return net\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " # Make both integrators use LIFRate neurons\n", + " net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net.pre_integrator = Integrator(50, 1, label=\"pre\")\n", + " net.post_integrator = Integrator(50, 1, label=\"post\")\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"pre integrator label:\", net.pre_integrator.label)\n", + "print(\"post integrator label:\", net.post_integrator.label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Accept a config argument for groups of parameters\n", + "\n", + "Often, you will not want to use the\n", + "network-level defaults for all of your objects.\n", + "Some objects need certain things overwritten,\n", + "while others need other values overwritten.\n", + "Again, it is possible to deal with this issue\n", + "by adding more and more parameters,\n", + "but this quickly gets out of hand.\n", + "Instead, add a small number of arguments\n", + "that optionally accept a `config` object,\n", + "which allows for setting multiple parameters at once.\n", + "\n", + "In the coupled integrator network example,\n", + "we make two connections.\n", + "We have to be careful changing the defaults\n", + "for those connections, as they are wildly different;\n", + "one is a recurrent connection from an ensemble to itself,\n", + "while the other is a connection from a node to an ensemble.\n", + "We will accept a `config` object for the recurrent connection\n", + "to make this easier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ConfigurableIntegrator(n_neurons, dimensions, recurrent_config=None, **kwargs):\n", + " net = nengo.Network(**kwargs)\n", + " if recurrent_config is None:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)\n", + " with net:\n", + " net.input = nengo.Node(size_in=dimensions)\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " with recurrent_config:\n", + " nengo.Connection(net.ensemble, net.ensemble)\n", + " tau = nengo.Config.default(nengo.Connection, \"synapse\").tau\n", + " nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)\n", + " return net\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " # Make both integrators use LIFRate neurons\n", + " net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net.pre_integrator = ConfigurableIntegrator(50, 1)\n", + " # Give the post_integrator a shorter tau (should make integration fail)\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.01)\n", + " net.post_integrator = ConfigurableIntegrator(\n", + " 50, 1, recurrent_config=recurrent_config\n", + " )\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Longer example: double integrator network\n", + "\n", + "Recall in the previous tutorial that\n", + "we created a model\n", + "that released a lever 0.6 to 1.0 seconds\n", + "after pressing a lever.\n", + "Let's use the above principles,\n", + "and the `config` system in general,\n", + "to improve the code constructing this model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def controlled_integrator(n_neurons, dimensions, recurrent_config=None, **kwargs):\n", + " net = nengo.Network(**kwargs)\n", + " if recurrent_config is None:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)\n", + " with net:\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)\n", + " with recurrent_config:\n", + " nengo.Connection(\n", + " net.ensemble,\n", + " net.ensemble[:dimensions],\n", + " function=lambda x: x[:-1] * (1.0 - x[-1]),\n", + " )\n", + " return net\n", + "\n", + "\n", + "def medial_pfc(\n", + " coupling_strength,\n", + " n_neurons_per_integrator=200,\n", + " recurrent_config=None,\n", + " tau=0.1,\n", + " **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " with net:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(tau)\n", + " net.pre = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)\n", + " net.post = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)\n", + " nengo.Connection(\n", + " net.pre.ensemble[0], net.post.ensemble[0], transform=coupling_strength\n", + " )\n", + " return net\n", + "\n", + "\n", + "def motor_cortex(\n", + " command_threshold, n_neurons_per_command=30, ens_config=None, **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " if ens_config is None:\n", + " ens_config = nengo.Config(nengo.Ensemble)\n", + " ens_config[nengo.Ensemble].encoders = Choice([[1]])\n", + " ens_config[nengo.Ensemble].intercepts = Choice([command_threshold])\n", + " with net:\n", + " with ens_config:\n", + " net.press = nengo.Ensemble(n_neurons_per_command, dimensions=1)\n", + " net.release = nengo.Ensemble(n_neurons_per_command, dimensions=1)\n", + " return net\n", + "\n", + "\n", + "def double_integrator(\n", + " mpfc_coupling_strength,\n", + " command_threshold,\n", + " press_to_pre_gain=3,\n", + " press_to_post_control=-6,\n", + " recurrent_tau=0.1,\n", + " **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " with net:\n", + " net.mpfc = medial_pfc(mpfc_coupling_strength)\n", + " net.motor = motor_cortex(command_threshold)\n", + " nengo.Connection(\n", + " net.motor.press,\n", + " net.mpfc.pre.ensemble[0],\n", + " transform=recurrent_tau * press_to_pre_gain,\n", + " )\n", + " nengo.Connection(\n", + " net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control\n", + " )\n", + " nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)\n", + " return net\n", + "\n", + "\n", + "def test_doubleintegrator(net):\n", + " # Provide input and probe outside of network construction,\n", + " # for more flexibility\n", + " with net:\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)\n", + " pr_press = nengo.Probe(net.motor.press, synapse=0.01)\n", + " pr_release = nengo.Probe(net.motor.release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "for coupling_strength in (0.11, 0.16, 0.21):\n", + " # Try the same network with LIFRate neurons\n", + " with nengo.Config(nengo.Ensemble) as cfg:\n", + " cfg[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net = double_integrator(\n", + " mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0\n", + " )\n", + " test_doubleintegrator(net)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/usage/network-design.ipynb.txt b/_sources/examples/usage/network-design.ipynb.txt new file mode 100644 index 0000000000..462b8e05d7 --- /dev/null +++ b/_sources/examples/usage/network-design.ipynb.txt @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Designing networks\n", + "\n", + "`Networks` are how Nengo encapsulates\n", + "functionally complementary objects.\n", + "Networks can contain\n", + "ensembles, nodes, connection, even other networks.\n", + "Models are often made much more readable\n", + "my grouping objects into networks.\n", + "Tools for visualizing networks\n", + "will often use the network structure\n", + "to produce cleaner images.\n", + "\n", + "Here, we will go over some general principles\n", + "of network design. In the first section,\n", + "we will go over these principles with a simple example.\n", + "In the second section, we will apply\n", + "these principles to a more complicated example.\n", + "\n", + "Briefly, the general principles we will\n", + "go over in this tutorial are\n", + "\n", + "0. Group related objects in networks with `with`\n", + "0. Reuse networks by defining functions\n", + "0. Parameterize functions for more flexible reuse\n", + "\n", + "We will demonstrate these principles\n", + "with a network consisting\n", + "of two connected integrators\n", + "as an example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "\n", + "def test_integrators(net):\n", + " with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, net.pre_integrator.input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + " plt.figure()\n", + " plt.plot(sim.trange(), sim.data[input_probe], color=\"k\")\n", + " plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\")\n", + " plt.plot(sim.trange(), sim.data[post_probe], color=\"g\")\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Group related objects in networks with `with`\n", + "\n", + "All Nengo objects must be created\n", + "in the context of some network.\n", + "We use the `with` keyword to denote\n", + "the network context in which\n", + "a Nengo object is created.\n", + "\n", + "These `with` statements can be nested\n", + "to group objects in appropriately named networks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: two connected integrators\n", + "# (See integrator example network for more details)\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " with nengo.Network() as pre_integrator:\n", + " pre_input = nengo.Node(size_in=1)\n", + " pre_ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(pre_ensemble, pre_ensemble, synapse=0.1)\n", + " nengo.Connection(pre_input, pre_ensemble, synapse=None, transform=0.1)\n", + " with nengo.Network() as post_integrator:\n", + " post_input = nengo.Node(size_in=1)\n", + " post_ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(post_ensemble, post_ensemble, synapse=0.1)\n", + " nengo.Connection(post_input, post_ensemble, synapse=None, transform=0.1)\n", + " nengo.Connection(pre_ensemble, post_input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test the integrators by giving piecewise input and plotting\n", + "with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, pre_input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(pre_ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(post_ensemble, synapse=0.01)\n", + "with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], color=\"k\", label=\"input\")\n", + "plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\", label=\"pre integrator\")\n", + "plt.plot(sim.trange(), sim.data[post_probe], color=\"g\", label=\"post integrator\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `with` statements\n", + "are not just cosmetic;\n", + "Nengo objects are stored\n", + "in the network that they're constructed in." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert pre_input in pre_integrator\n", + "assert pre_input not in net\n", + "assert pre_integrator in net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Reuse networks by defining functions\n", + "\n", + "Often, the same network is constructed\n", + "more than once, resulting in duplicated code.\n", + "We can reduce this code duplication\n", + "by defining a function that\n", + "creates the network and returns it.\n", + "\n", + "Even if you only use the network once,\n", + "it can be helpful to define all of\n", + "your subnetworks in functions\n", + "to keep your code cleaner.\n", + "Determining when a network\n", + "should be defined in a function\n", + "is informed by experience\n", + "designing large networks\n", + "and personal preference.\n", + "\n", + "### Store useful objects on the network\n", + "\n", + "A corollary to putting networks in functions\n", + "is that any objects that you might want\n", + "access to outside of that function\n", + "should be stored on the network.\n", + "Functions that create networks\n", + "should only return that network.\n", + "While all objects in that network\n", + "are stored somewhere\n", + "(e.g., ensembles are stored in a `net.ensembles` list),\n", + "that list can be large and difficult to search.\n", + "Therefore, we recommend storing objects\n", + "on the network itself\n", + "to make them accessible outside of the function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: two connected integrators. Now with functions!\n", + "def Integrator1():\n", + " with nengo.Network() as integrator:\n", + " integrator.input = nengo.Node(size_in=1)\n", + " integrator.ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=0.1)\n", + " nengo.Connection(\n", + " integrator.input, integrator.ensemble, synapse=None, transform=0.1\n", + " )\n", + " return integrator\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator1()\n", + " net.post_integrator = Integrator1()\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This function does the same as the previous example\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Parameterize functions for more flexible reuse\n", + "\n", + "Now that your network is created in a function,\n", + "you can add parameters to your function\n", + "to change how your network is constructed.\n", + "This could be a huge change,\n", + "allowing you to compare two different approaches\n", + "to some component of your model,\n", + "or it could something straightforward\n", + "like changing the number of neurons\n", + "in the ensembles in the network.\n", + "\n", + "Unlike other languages, Python allows you\n", + "to add two different types of parameters:\n", + "**positional** and **keyword** arguments.\n", + "Positional arguments must be passed\n", + "to your function.\n", + "\n", + "```python\n", + ">>> def my_network(arg1, arg2):\n", + "... return \"%s %s\" % (arg1, arg2)\n", + ">>> my_network()\n", + "TypeError: my_network() takes exactly 2 arguments (0 given)\n", + ">>> my_network(0, 0)\n", + "'0 0'\n", + "```\n", + "\n", + "Keyword arguments are assigned a default value,\n", + "and so do not have to be passed to your function.\n", + "\n", + "```python\n", + ">>> def my_network(arg1=0, arg2=0):\n", + "... return \"%s %s\" % (arg1, arg2)\n", + ">>> my_network()\n", + "'0 0'\n", + ">>> my_network(1)\n", + "'1 0'\n", + "```\n", + "\n", + "Order does not matter if you explicitly\n", + "label the argument in your function call.\n", + "\n", + "```python\n", + ">>> my_network(arg2=2, arg1=1)\n", + "'1 2'\n", + ">>> my_network(arg2=2)\n", + "'0 2'\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: two connected integrators. Functions have parameters!\n", + "def Integrator2(n_neurons, dimensions, tau=0.1):\n", + " with nengo.Network() as integrator:\n", + " integrator.input = nengo.Node(size_in=dimensions)\n", + " integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=tau)\n", + " nengo.Connection(\n", + " integrator.input, integrator.ensemble, synapse=None, transform=tau\n", + " )\n", + " return integrator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try 50 neuron ensembles\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator2(50, 1)\n", + " net.post_integrator = Integrator2(50, 1)\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try shorter tau (spoiler: it fails to integrate well)\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator2(50, 1, tau=0.01)\n", + " net.post_integrator = Integrator2(50, 1, tau=0.01)\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Longer example: double integrator network\n", + "\n", + "While the coupled integrator model above\n", + "seems like a toy example,\n", + "it is in fact a critical component\n", + "of many models that capture\n", + "neurophysiological and behavioral data.\n", + "Let's looks at a more complicated network\n", + "that uses coupled integrators,\n", + "and show how the network design principles\n", + "discussed above improve model organization.\n", + "We will look at a simplified version\n", + "of the network described in\n", + "[Bekolay et al., 2014](\n", + "http://compneuro.uwaterloo.ca/files/publications/bekolay.2014a.pdf).\n", + "\n", + "This network uses two coupled integrators\n", + "in the context of a simple reaction time task.\n", + "The subject presses down a lever,\n", + "and should release that lever\n", + "between 0.6 and 1.0 seconds later.\n", + "In order to time the interval,\n", + "the lever press starts the coupled integrators,\n", + "which are tuned such that\n", + "the second integrator should reach\n", + "a high point by around 0.6 seconds.\n", + "The second integrator\n", + "effects the lever release.\n", + "\n", + "We'll start with a completely disorganized network.\n", + "Even if you've read the paper,\n", + "the code is quite hard to follow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as net:\n", + " tau = 0.1\n", + "\n", + " pre_integrator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " pre_integrator,\n", + " pre_integrator[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " post_integrator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " post_integrator,\n", + " post_integrator[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " nengo.Connection(pre_integrator[0], post_integrator[0], transform=0.16)\n", + " press = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + " release = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + "\n", + " nengo.Connection(press, pre_integrator[0], transform=tau * 3)\n", + " nengo.Connection(press, post_integrator[1], transform=-1 * 6)\n", + "\n", + " nengo.Connection(post_integrator[0], release)\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)\n", + "\n", + " pr_press = nengo.Probe(press, synapse=0.01)\n", + " pr_release = nengo.Probe(release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(pre_integrator[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(post_integrator[0], synapse=0.01)\n", + "\n", + "\n", + "def test_doubleintegrator(net):\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "test_doubleintegrator(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the goal is to release the lever\n", + "0.6-1.0 seconds after the press.\n", + "The blue \"Press\" line in the upper plot\n", + "represents the activity of neurons\n", + "in motor cortex which effect the lever press.\n", + "If we assume that the press occurs at 0.2 seconds,\n", + "then the release window is between 0.8 and 1.2 seconds\n", + "(shown in green).\n", + "This will occur if the \"Post Integrator\" (see lower plot)\n", + "is sufficiently high during this window.\n", + "If you rerun the cell above several times,\n", + "you can see that it usually achieves this,\n", + "but not all of the time.\n", + "\n", + "Looking at the code, it's difficult to tell\n", + "how we achieve this result.\n", + "It's also difficult to see what parameters\n", + "needed tweaking.\n", + "Rather than just showing one parameter set that works,\n", + "we should investigate reasonable ranges for\n", + "each parameter.\n", + "It would be easy to think that there is only\n", + "one parameter, `tau`, in the model as presented,\n", + "but this is not the case.\n", + "\n", + "Let's start with the first network design principle:\n", + "group objects into networks with `with`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with nengo.Network() as net:\n", + " tau = 0.1\n", + "\n", + " with nengo.Network() as mpfc:\n", + " with nengo.Network() as pre_integrator:\n", + " pre_ensemble = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " pre_ensemble,\n", + " pre_ensemble[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " with nengo.Network() as post_integrator:\n", + " post_ensemble = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " post_ensemble,\n", + " post_ensemble[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " nengo.Connection(pre_ensemble[0], post_ensemble[0], transform=0.16)\n", + "\n", + " with nengo.Network() as motor:\n", + " press = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + " release = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + "\n", + " nengo.Connection(press, pre_ensemble[0], transform=tau * 3)\n", + " nengo.Connection(press, post_ensemble[1], transform=-1 * 6)\n", + "\n", + " nengo.Connection(post_ensemble[0], release)\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)\n", + "\n", + " pr_press = nengo.Probe(press, synapse=0.01)\n", + " pr_release = nengo.Probe(release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(pre_ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(post_ensemble[0], synapse=0.01)\n", + "\n", + "test_doubleintegrator(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This exhibits some of the hypotheses of this model;\n", + "specifically, that the medial prefrontal cortex\n", + "contains these integrators,\n", + "and that they can be involved\n", + "in control of motor actions.\n", + "\n", + "Let's do both of the remaining principles\n", + "by making some parameterized functions\n", + "to get a sense of all of the knobs\n", + "that be be tweaked in this model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def controlled_integrator(n_neurons, dimensions, tau=0.1):\n", + " with nengo.Network() as integrator:\n", + " integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)\n", + " nengo.Connection(\n", + " integrator.ensemble,\n", + " integrator.ensemble[:dimensions],\n", + " function=lambda x: x[:-1] * (1.0 - x[-1]),\n", + " synapse=tau,\n", + " )\n", + " return integrator\n", + "\n", + "\n", + "def medial_pfc(coupling_strength, n_neurons_per_integrator=200, tau=0.1):\n", + " with nengo.Network() as medial_pfc:\n", + " medial_pfc.pre = controlled_integrator(n_neurons_per_integrator, 1, tau)\n", + " medial_pfc.post = controlled_integrator(n_neurons_per_integrator, 1, tau)\n", + " nengo.Connection(\n", + " medial_pfc.pre.ensemble[0],\n", + " medial_pfc.post.ensemble[0],\n", + " transform=coupling_strength,\n", + " )\n", + " return medial_pfc\n", + "\n", + "\n", + "def motor_cortex(command_threshold, n_neurons_per_command=30):\n", + " with nengo.Network() as motor_cortex:\n", + " ens_args = {\n", + " \"dimensions\": 1,\n", + " \"encoders\": Choice([[1]]),\n", + " \"intercepts\": Choice([command_threshold]),\n", + " }\n", + " motor_cortex.press = nengo.Ensemble(n_neurons_per_command, **ens_args)\n", + " motor_cortex.release = nengo.Ensemble(n_neurons_per_command, **ens_args)\n", + " return motor_cortex\n", + "\n", + "\n", + "def double_integrator(\n", + " mpfc_coupling_strength,\n", + " command_threshold,\n", + " press_to_pre_gain=3,\n", + " press_to_post_control=-6,\n", + " recurrent_tau=0.1,\n", + " seed=None,\n", + "):\n", + " with nengo.Network(seed=seed) as net:\n", + " net.mpfc = medial_pfc(mpfc_coupling_strength)\n", + " net.motor = motor_cortex(command_threshold)\n", + " nengo.Connection(\n", + " net.motor.press,\n", + " net.mpfc.pre.ensemble[0],\n", + " transform=recurrent_tau * press_to_pre_gain,\n", + " )\n", + " nengo.Connection(\n", + " net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control\n", + " )\n", + " nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)\n", + " return net\n", + "\n", + "\n", + "def test_doubleintegrator_wprobes(net):\n", + " # Provide input and probe outside of network construction,\n", + " # for more flexibility\n", + " with net:\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)\n", + " pr_press = nengo.Probe(net.motor.press, synapse=0.01)\n", + " pr_release = nengo.Probe(net.motor.release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "net = double_integrator(mpfc_coupling_strength=0.16, command_threshold=0.85)\n", + "test_doubleintegrator_wprobes(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this does add some complexity and length,\n", + "the effort is worth it,\n", + "as we can now do simple parameters sweeps\n", + "to investigate the effects\n", + "of changing various parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for coupling_strength in (0.11, 0.16, 0.21):\n", + " net = double_integrator(\n", + " mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0\n", + " )\n", + " test_doubleintegrator_wprobes(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `coupling_strength` changes how quickly\n", + "the \"Post Integrator\" rises,\n", + "which causes the release to occur\n", + "at different times.\n", + "Of the values tested,\n", + "only a coupling strength of 0.16\n", + "effects the release during the release window\n", + "(for this particular network seed).\n", + "\n", + "We will looks at some more techniques\n", + "for cleaning up this network code\n", + "and making it more flexible\n", + "in \"Additional tips and tricks for designing networks\"." + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/usage/rectified-linear.ipynb.txt b/_sources/examples/usage/rectified-linear.ipynb.txt new file mode 100644 index 0000000000..564f5dc014 --- /dev/null +++ b/_sources/examples/usage/rectified-linear.ipynb.txt @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding new objects to Nengo\n", + "\n", + "It is possible to add new objects\n", + "to the Nengo reference simulator.\n", + "This involves several steps and the creation\n", + "of several objects.\n", + "In this example, we'll go through these steps\n", + "in order to add a new neuron type to Nengo:\n", + "a rectified linear neuron.\n", + "\n", + "The `RectifiedLinear` class is what you will use\n", + "in model scripts to denote that a particular ensemble\n", + "should be simulated using a rectified linear neuron\n", + "instead of one of the existing neuron types (e.g., `LIF`).\n", + "\n", + "Normally, these kinds of frontend classes exist\n", + "in a file in the root `nengo` directory,\n", + "like `nengo/neurons.py` or `nengo/synapses.py`.\n", + "Look at these files for examples of how to make your own.\n", + "In this case, because we're making a neuron type,\n", + "we'll use `nengo.neurons.LIF` as an example\n", + "of how to make `RectifiedLinear`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import tuning_curves\n", + "\n", + "\n", + "# Neuron types must subclass `nengo.neurons.NeuronType`\n", + "class RectifiedLinear(nengo.neurons.NeuronType):\n", + " \"\"\"A rectified linear neuron model.\"\"\"\n", + "\n", + " # We don't need any additional parameters here;\n", + " # gain and bias are sufficient. But, if we wanted\n", + " # more parameters, we could accept them by creating\n", + " # an __init__ method.\n", + "\n", + " def gain_bias(self, max_rates, intercepts):\n", + " \"\"\"Return gain and bias given maximum firing rate and x-intercept.\"\"\"\n", + " gain = max_rates / (1 - intercepts)\n", + " bias = -intercepts * gain\n", + " return gain, bias\n", + "\n", + " def step(self, dt, J, output):\n", + " \"\"\"Compute rates in Hz for input current (incl. bias)\"\"\"\n", + " output[...] = np.maximum(0.0, J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use `RectifiedLinear` like any other neuron type\n", + "without making modifications to the reference simulator.\n", + "However, other objects, including more complicated neuron types,\n", + "may require changes to the reference simulator.\n", + "\n", + "## Tuning curves\n", + "\n", + "We can build a small network just to see the tuning curves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " encoders = np.tile([[1], [-1]], (4, 1))\n", + " intercepts = np.linspace(-0.8, 0.8, 8)\n", + " intercepts *= encoders[:, 0]\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " neuron_type=RectifiedLinear(),\n", + " max_rates=nengo.dists.Uniform(80, 100),\n", + " encoders=encoders,\n", + " )\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(A, sim)\n", + "plt.figure()\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2D Representation example\n", + "\n", + "Below is the same model as is made in the 2d_representation example,\n", + "except now using `RectifiedLinear` neurons insated of `nengo.LIF`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"2D Representation\", seed=10)\n", + "with model:\n", + " neurons = nengo.Ensemble(100, dimensions=2, neuron_type=RectifiedLinear())\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)\n", + " nengo.Connection(sin, neurons[0])\n", + " nengo.Connection(cos, neurons[1])\n", + " sin_probe = nengo.Probe(sin, \"output\")\n", + " cos_probe = nengo.Probe(cos, \"output\")\n", + " neurons_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.01)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neurons_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Sine\")\n", + "plt.plot(sim.trange(), sim.data[cos_probe], \"k\", label=\"Cosine\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/examples/usage/tuning-curves.ipynb.txt b/_sources/examples/usage/tuning-curves.ipynb.txt new file mode 100644 index 0000000000..1c1ddd6031 --- /dev/null +++ b/_sources/examples/usage/tuning-curves.ipynb.txt @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tuning curves\n", + "\n", + "One of the most common ways\n", + "to tweak models\n", + "and debug failures\n", + "is to look at the **tuning curves**\n", + "of the neurons in an ensemble.\n", + "The tuning curve tells us\n", + "how each neuron responds\n", + "to an incoming input signal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-dimensional ensembles\n", + "\n", + "The tuning curve is easiest\n", + "to interpret in the one-dimensional case.\n", + "Since the input is a single scalar,\n", + "we use that value as the x-axis,\n", + "and the neuron response as the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.utils.ensemble import response_curves, tuning_curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_1d = nengo.Ensemble(15, dimensions=1)\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_1d, sim)\n", + "\n", + "plt.figure()\n", + "plt.plot(eval_points, activities)\n", + "# We could have alternatively shortened this to\n", + "# plt.plot(*tuning_curves(ens_1d, sim))\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each coloured line represents the response on one neuron.\n", + "As you can see, the neurons cover the space pretty well,\n", + "but there is no clear pattern to their responses.\n", + "\n", + "If there is some biological or functional reason\n", + "to impose some pattern to their responses,\n", + "we can do so by changing the parameters\n", + "of the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens_1d.intercepts = Choice([-0.2]) # All neurons have x-intercept -0.2\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens_1d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, there is a clear pattern to the tuning curve.\n", + "However, note that some neurons start firing at\n", + "-0.2, while others stop firing at 0.2.\n", + "This is because the input signal, `x`,\n", + "is multiplied by a neuron's *encoder*\n", + "when it is converted to input current.\n", + "\n", + "We could further constrain the tuning curves\n", + "by changing the encoders of the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens_1d.encoders = Choice([[1]]) # All neurons have encoder [1]\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens_1d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us an ensemble of neurons\n", + "that respond very predictably to input.\n", + "In some cases, this is important to the\n", + "proper functioning of a model,\n", + "or to matching what we know about\n", + "the physiology of a brain area or neuron type." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2-dimensional ensembles\n", + "\n", + "In a two-dimensional ensemble,\n", + "the input is represented by two scalar values,\n", + "meaning that we will need three axes\n", + "to represent its tuning curve;\n", + "two for input dimensions, and one for the neural activity.\n", + "\n", + "Fortunately, we are able to plot data in 3D.\n", + "\n", + "If there is a clear pattern to the tuning curves,\n", + "then visualizing them all is (sort of) possible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_2d = nengo.Ensemble(15, dimensions=2, encoders=Choice([[1, 1]]))\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_2d, sim)\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "ax = plt.subplot(111, projection=\"3d\")\n", + "ax.set_title(\"Tuning curve of all neurons\")\n", + "for i in range(ens_2d.n_neurons):\n", + " ax.plot_surface(\n", + " eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn\n", + " )\n", + "ax.set_xlabel(\"$x_1$\")\n", + "ax.set_ylabel(\"$x_2$\")\n", + "ax.set_zlabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But in most cases, for 2D ensembles,\n", + "we have to look at each neuron's tuning curve separately." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ens_2d.encoders = nengo.Default\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_2d, sim)\n", + "\n", + "plt.figure(figsize=(12, 12))\n", + "for i in range(4):\n", + " ax = plt.subplot(2, 2, i + 1, projection=Axes3D.name)\n", + " ax.set_title(\"Neuron %d\" % i)\n", + " ax.plot_surface(\n", + " eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn\n", + " )\n", + " ax.set_xlabel(\"$x_1$\")\n", + " ax.set_ylabel(\"$x_2$\")\n", + " ax.set_zlabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## N-dimensional ensembles\n", + "\n", + "The `tuning_curve` function accepts\n", + "ensembles of any dimensionality,\n", + "and will always return `eval_points` and `activities`.\n", + "However, for ensembles of dimensionality\n", + "greater than 2, these are large arrays\n", + "and it becomes nearly impossible\n", + "to visualize them.\n", + "\n", + "There are two main approaches\n", + "to investigating the tuning curves\n", + "of ensembles of arbitrary dimensionality." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clamp some axes\n", + "\n", + "In many cases, we only care about\n", + "the neural sensitivity to one or two dimensions.\n", + "We can investigate those dimensions specifically\n", + "by only varying those dimensions,\n", + "and keeping the rest constant.\n", + "\n", + "To do this, we will use the `inputs` argument\n", + "to the `tuning_curves` function,\n", + "which allows us to define\n", + "the input signals that\n", + "will drive the neurons\n", + "to determine their activity.\n", + "In other words, we are specifying\n", + "the `eval_point` parameter\n", + "to generate the `activities`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_3d = nengo.Ensemble(15, dimensions=3)\n", + "\n", + "inputs = np.zeros((50, 3))\n", + "# Vary the first dimension\n", + "inputs[:, 0] = np.linspace(-ens_3d.radius, ens_3d.radius, 50)\n", + "inputs[:, 1] = 0.5 # Clamp the second dimension\n", + "inputs[:, 2] = 0.5 # Clamp the third dimension\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_3d, sim, inputs=inputs)\n", + "\n", + "assert eval_points is inputs # The inputs will be returned as eval_points\n", + "\n", + "plt.figure()\n", + "plt.plot(inputs.T[0], activities)\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"$x_0$\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that these tuning curves\n", + "are much more broad than those\n", + "in the 1-dimensional case.\n", + "This is because some neurons are not\n", + "very sensitive to\n", + "the dimension that are varying,\n", + "and are instead sensitive\n", + "to one or two of the other dimensions.\n", + "If we wanted these neurons\n", + "to be more sharply tuned\n", + "to this dimension,\n", + "we could change their encoders\n", + "to be more tuned to this dimension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Response curves\n", + "\n", + "If all else fails,\n", + "we can still get some information\n", + "about the tuning properties\n", + "of the neurons in the ensemble\n", + "using the **response curve**.\n", + "The response curve is similar to the tuning curve,\n", + "but instead of looking at the neural response\n", + "to a particular input stimulus,\n", + "we are instead looking at its response\n", + "to (relative) injected current.\n", + "This is analogous to the tuning curves\n", + "with the inputs aligned to the\n", + "preferred directions of each neuron." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_5d = nengo.Ensemble(15, dimensions=5)\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*response_curves(ens_5d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"x along preferred direction\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/frontend-api.rst.txt b/_sources/frontend-api.rst.txt new file mode 100644 index 0000000000..f621796b1b --- /dev/null +++ b/_sources/frontend-api.rst.txt @@ -0,0 +1,106 @@ +****************** +Nengo frontend API +****************** + +Nengo Objects +============= + +.. autosummary:: + :nosignatures: + + nengo.Network + nengo.Ensemble + nengo.ensemble.Neurons + nengo.Node + nengo.Connection + nengo.connection.LearningRule + nengo.Probe + +.. autoclass:: nengo.Network + +.. autoclass:: nengo.Ensemble + +.. autoclass:: nengo.ensemble.Neurons + +.. autoclass:: nengo.Node + +.. autoclass:: nengo.Connection + +.. autoclass:: nengo.connection.LearningRule + +.. autoclass:: nengo.Probe + +Distributions +============= + +.. automodule:: nengo.dists + + .. autoautosummary:: nengo.dists + :nosignatures: + :exclude-members: DistributionParam, DistOrArrayParam + +Learning rule types +=================== + +.. automodule:: nengo.learning_rules + + .. autoautosummary:: nengo.learning_rules + :nosignatures: + :exclude-members: LearningRuleTypeParam, LearningRuleTypeSizeInParam + +Neuron types +============ + +.. automodule:: nengo.neurons + + .. autoautosummary:: nengo.neurons + :nosignatures: + :exclude-members: NeuronTypeParam + +Processes +========= + +.. automodule:: nengo.processes + + .. autoautosummary:: nengo.processes + :nosignatures: + :exclude-members: PiecewiseDataParam + + nengo.Process + +.. autoclass:: nengo.Process + +Solvers +======= + +.. automodule:: nengo.solvers + + .. autoautosummary:: nengo.solvers + :nosignatures: + :exclude-members: SolverParam + +Solver methods +-------------- + +.. automodule:: nengo.utils.least_squares_solvers + + .. autoautosummary:: nengo.utils.least_squares_solvers + :nosignatures: + +Synapse models +============== + +.. automodule:: nengo.synapses + + .. autoautosummary:: nengo.synapses + :nosignatures: + :exclude-members: SynapseParam + +Transforms +========== + +.. automodule:: nengo.transforms + + .. autoautosummary:: nengo.transforms + :nosignatures: + :exclude-members: SparseInitParam, ChannelShapeParam diff --git a/_sources/getting-started.rst.txt b/_sources/getting-started.rst.txt new file mode 100644 index 0000000000..f71ced6b73 --- /dev/null +++ b/_sources/getting-started.rst.txt @@ -0,0 +1,310 @@ +*************** +Getting started +*************** + +Installation +============ + +To install Nengo, we recommend using ``pip``. + +.. code:: bash + + pip install nengo + +``pip`` will do its best to install +all of Nengo's requirements when it installs Nengo. +However, if anything goes wrong during this process, +you can install Nengo's requirements manually +before doing ``pip install nengo`` again. + +Installing NumPy +---------------- + +Nengo's only required dependency is NumPy, +and we recommend that you install it first. +The best way to install NumPy depends +on several factors, such as your operating system. +Briefly, what we have found to work best +on each operating system is: + +- Windows: Use Anaconda_ or + the `official installer `_ and + unofficial binaries (www.lfd.uci.edu/~gohlke/pythonlibs/) +- Mac OS X: Use Anaconda_ or Homebrew_ +- Linux: Use a package manager or install from source + +For more options, see +`SciPy.org's installation page `_. +For our recommended options, read on. + +Anaconda +^^^^^^^^ + +If you're new to Python and just want to get up and running, +Anaconda_ is the best way to get started. +Anaconda provides an all-in-one solution +that will install Python, NumPy, +and other optional Nengo dependencies. +It works on all operating systems (Windows, Mac, Linux) +and does not require administrator privileges. +It includes GUI tools, +as well as a robust command line tool, ``conda``, +for managing your Python installation. + +Package managers +^^^^^^^^^^^^^^^^ + +If you are comfortable with the command line, +operating systems other than Windows +have a package manager that can install Python and NumPy. + +- **Mac OS X:** Homebrew_ has excellent Python support. + After installing Homebrew, ``brew install python`` and ``pip install numpy``. +- **Linux:** Linux distributions come with a package manager + capable of installing Python and NumPy. + In Debian, Ubuntu, and other distributions with ``apt`` use: + ``sudo apt-get install python-numpy``. + In Fedora and others distributions with ``yum`` use: + ``sudo yum install python-numpy``. + For other package managers, + try searching the package list for ``numpy``. + +From source +^^^^^^^^^^^ + +If speed is an issue +and you know your way around a terminal, +installing NumPy from source +is flexible and performant. +See the detailed instructions +`here `_. + +Installing other packages +------------------------- + +While NumPy is the only hard dependency, +some optional Nengo features require other packages. +These can be installed either through +Anaconda, a package manager, or through +Python's own package manager, ``pip``. + +- Additional decoder solvers and other speedups + require SciPy and scikit-learn. +- Running the test suite requires + pytest, Matplotlib, and Jupyter. +- Building the documentation requires + Sphinx, NumPyDoc and nengo_sphinx_theme. + +These additional dependencies can be installed +through ``pip`` when installing Nengo. + +.. code-block:: bash + + pip install nengo[optional] # Additional solvers and speedups + pip install nengo[docs] # For building docs + pip install nengo[tests] # For running the test suite + pip install nengo[all] # All of the above + +.. _Anaconda: https://www.anaconda.com/products/individual#Downloads +.. _Homebrew: https://brew.sh/ + +Usage +===== + +Everything in a Nengo model is contained within a +`nengo.Network`. To create a new ``Network``: + +.. testcode:: + + import nengo + net = nengo.Network() + +Creating Nengo objects +---------------------- + +A Nengo object is a part of your model that represents information. +When creating a new object, you must place it within a ``with`` +block in order to inform Nengo which network your object +should be placed in. + +There are two objects that make up a basic Nengo model. +A `nengo.Ensemble` is a group of neurons that represents +information in the form of real valued numbers. + +.. testcode:: + + with net: + my_ensemble = nengo.Ensemble(n_neurons=40, dimensions=1) + +In this case, ``my_ensemble`` is made up of +40 neurons (by default, Nengo uses leaky integrate-and-fire neurons) +and it is representing a one dimensional signal. +In other words, this ensemble represents a single number. + +In order to provide input to this ensemble +(to emulate some signal that exists in nature, for example) +we create a `nengo.Node`. + +.. testcode:: + + with net: + my_node = nengo.Node(output=0.5) + +In this case, ``my_node`` emits the number 0.5. + +In most cases, however, we want more dynamic information. +We can make a `nengo.Node` using a function as output +instead of a number. + +.. testcode:: + + with net: + sin_node = nengo.Node(output=np.sin) + +This node will represent a sine wave. + +Connecting Nengo objects +------------------------ + +We can connect nodes to ensembles +in order to represent that information +in the activity a group of neurons. + +.. testcode:: + + with net: + nengo.Connection(my_node, my_ensemble) + +This connects ``my_node`` to ``my_ensemble``, +meaning that ``my_ensemble`` will now represent +0.5 in its population of 40 neurons. + +Ensembles can also be connected to other models. +When the dimensionality of the objects being +connected are different, we can use Python's +slice syntax to route information from +one node or ensemble to another. +For example: + +.. testcode:: + + with net: + two_d_ensemble = nengo.Ensemble(n_neurons=80, dimensions=2) + nengo.Connection(sin_node, two_d_ensemble[0]) + nengo.Connection(my_ensemble, two_d_ensemble[1]) + +This creates a new ensemble that represents +two real-valued signals. +By connecting ``sin_node`` to ``two_d_ensemble``, +its first dimension now represents a sine wave. +Its second dimensions now represents the same +value as ``my_ensemble``. + +When creating connections, +we can specify a function that +will be computed across the connection. + + +.. testcode:: + + with net: + square = nengo.Ensemble(n_neurons=40, dimensions=1) + nengo.Connection(my_ensemble, square, function=np.square) + +Functions can be computed over multiple dimensions, as well. + +.. testcode:: + + def product(x): + return x[0] * x[1] + + with net: + product_ensemble = nengo.Ensemble(n_neurons=40, dimensions=1) + nengo.Connection(two_d_ensemble, product_ensemble, function=product) + +Probing Nengo objects +--------------------- + +Once you have defined the objects in your model +and how they're connected, +you can decide what data you want to collect +by probing those objects. + +If we wanted to collect data from +our 2D Ensemble and the Product of those two dimensions: + +.. testcode:: + + with net: + two_d_probe = nengo.Probe(two_d_ensemble, synapse=0.01) + product_probe = nengo.Probe(product_ensemble, synapse=0.01) + +The argument ``synapse`` defines the time constant +on a causal low-pass filter, +which approximates a simple synapse model. +The output of ensembles of spiking neurons +can be very noisy, so a filter is recommended. + +Running an experiment +--------------------- + +Once a model has been constructed and we have probed +certain objects, we can run it to collect data. + +To run a model, we must first build a simulator +based on the model we've defined. + +.. testcode:: + + sim = nengo.Simulator(net) + +.. testoutput:: + :hide: + + ... + +We can then run that simulator. +For example, to run our model for five seconds: + +.. testcode:: + + sim.run(5.0) + +.. testoutput:: + :hide: + + ... + +Once a simulation has been run at least once +(it can be run for additional time if desired) +the data collected can be accessed +for analysis or visualization. + +.. testcode:: + + print(sim.data[product_probe][-10:]) + +.. testoutput:: + :hide: + + ... + +.. testcleanup:: + + sim.close() + +For more details on these objects, +see :doc:`the API documentation `. + +Next steps +========== + +* If you're wondering how this works and you're not + familiar with the Neural Engineering Framework, + we recommend reading + `this technical overview `_. +* If you have some understanding of the NEF already, + or just want to dive in headfirst, + check out :doc:`our extensive set of examples `. +* If you want to see the real capabilities of Nengo, see our + `publications created with the NEF and Nengo `_. diff --git a/_sources/history.rst.txt b/_sources/history.rst.txt new file mode 100644 index 0000000000..2ed0d85304 --- /dev/null +++ b/_sources/history.rst.txt @@ -0,0 +1,189 @@ +************* +Nengo history +************* + +Some form of Nengo has existed since 2003. +From then until now, +students and researchers have used Nengo to create models +which help us understand the brain. +Nengo has evolved with that understanding. + +We're currently in what could be called the fifth generation +of Nengo development. Many of the design decisions of this generation +are a result of lessons learnt from the first four generations. + +**Summary** + +Generation 1: NESim (Matlab) -> Nemo (Matlab) + +Generation 2: NEO (Java) -> Nengo (Java) -> Nengo GUI (Piccolo2D) + +Generation 3: Nengo scripting layer (Jython) -> Nengo Theano backend (Python) + +Generation 4: Nengo API -> Nengo reference implementation (Python, Jython, Theano, PyNN) + +Generation 5: Nengo 2.0 + +Generation 1 +============ + +When Chris Eliasmith and Charles H. Anderson released the book +Neural Engineering [Eliasmith2003]_, +they described a framework for creating models of spiking neurons +called the Neural Engineering Framework. +With the book, they provided a set of Matlab scripts +to facilitate the use of these methods in theoretical neuroscience research. + +NESim (Neural Engineering Simulator) was that set of Matlab scripts, +along with a basic graphical user interface that allowed users +to set the parameters of simulations. +Its primary developers were Chris Eliasmith and Bryan Tripp. + +NESim offered fast simulations by leveraging the computational power +of Matlab. And while the GUI may not have been pretty, +it enabled researchers to quickly test out ideas before +making a full model. The use of Matlab also meant that researchers could +do their simulation and analysis in the same Nemo. + +However, there were some downsides to NESim. It was difficult +to communicate with other simulation environments. +The GUI, while functional, was not very eye-catching or dynamic. +The object model of Matlab is limited. +And, even though NESim is open source software, +Matlab itself is not, meaning that NESim +was not accessible to everyone. + +.. [Eliasmith2003] Eliasmith, Chris, and Charles H. Anderson. Neural engineering: + Computation, representation, and dynamics in neurobiological systems. + MIT Press, 2003. + +Generation 2 +============ + +The desire for a more robust object hierarchy +and to interact with other simulation environments +resulted in the creation of NEO (Neural Engineering Objects) +in 2007 by Bryan Tripp. NEO was later renamed to Nengo, +also a short form of Neural Engineering Objects. + +Nengo was created in Java, which encourages the use +of deeply nested object hierarchies. +This hierarchy of objects allowed Nengo to be flexible; +it could implement the same types of simulations as +NESim did, but it could also have those simulated objects +communicate with other types of objects. +Also, despite a common misconception about the speed of Java, +simulations in Nengo maintained the speed of the Matlab NESim. +Java, like Matlab, is multi-platform, meaning it would work +on any modern operating system, but unlike Matlab, +is available at no cost to the end user. +Java is common enough that, for most, installing Nengo +is quite easy. + +Nengo was a robust neural simulator, but required modelers +to be proficient in Java. To overcome this, +in the summer of 2008, a graphical user interface +was created to make model creation and simulation +easy for anyone. + +Check out `a demo video of this interface `_. + +The GUI leveraged the Java graphical toolkit Piccolo2D.java, +which makes it easy to make zoomable interfaces that +can play well with the Java Swing ecosystem. +The new GUI made it easy for beginners to become familiar +with the methods of the NEF, and gradually transition +to writing models outside of the GUI. + +While Nengo and its GUI introduced many people +to the NEF, its deeply nested object hierarchy +proved difficult for many people to use productively. +While the GUI provided easy access for beginners, +the transition to being an expert user was difficult. +Additionally, while Java is cross platform and free to download, +it is not open source (though an open source version exists). +And while Java can simulate many networks quickly, +efforts to leverage non-standard computing devices +like GPUs were difficult to implement +in a cross-platform manner. + +While new versions of Nengo followed, +this version remains in use to this day. +`nengo.ca `_ +contains documentation for this version, +now referred to as Nengo 1.4. + +Generation 3 +============ + +Shortly after the GUI was released, +Terry Stewart began making it possible +to make simulations using Nengo +through a simple Python scripting interface. +The interface was originally known as ``nef.py`` +due to the first implementation's file name, +but it quickly became the preferred way +for modelers to create models due to its simplicity, +and therefore was incorporated with the rest of Nengo. +While the scripting interface used Python syntax, +it was still able to operate within the Java ecosystem +thanks for a Java implementation of Python called Jython. + +Although this boost in productivity allowed for the creation of Spaun, +the simulation speed was still much to be desired. Because of that, +a project that had the same interface as the ``nef.py`` scripts, +but with an implementation that used Theano. + +However, there were concerns that the Jython +and the Theano backed implementations would soon +diverge, fracturing the population of people building +nengo models. + +Generation 4 +============ + +In order to deal with the fracturing of the Nengo community, +the decision was made to standardize the API that +had been evolving since the introduction of ``nef.py``. +Because the name "Nengo" was now well known, +the name stuck, and the API was called the Nengo API. + +Through a grueling weekend of meetings, +the CNRG tentatively decided on an API +that any software claiming to be "Nengo" +would have to implement. In addition to the API, +the CNRG would produce a reference implementation +in Python with as few dependencies as possible, +change the Jython version to conform to the new API, +and in Theano to continue the work making a fast backend. + +Although these three implementations may choose to +implement their own specific capabilities, +since they all conform to the Nengo API, +they can all run the vast majority of models +that a modeler would want to run. + +Generation 5 +============ + +Issues with the implementation of Theano led to +a new effort to create an OpenCL-backed version of Nengo. +With so many possible backends, the Nengo API +switched focus to being able to provide a consistent +front-end experience while being able to run models +on different backends. The Nengo API, and a NumPy-backed +reference simulator, matured into what we have now released as +Nengo 2.0. + +Since standardizing on the scripting frontend of Nengo 2.0, +new backends have begun development, for the BlueGene, Neurogrid, +SpiNNaker and other hardware. + +We hope that, in this generation, +we have made all the right compromises such that +we can build large models with concise, expressive code, +and that we can create backends that can build and simulate +those models much more quickly than before. +Further, by making this API available, +we hope to be able to interact even further with +the rest of the neuroscience packages written in Python. diff --git a/_sources/improving-performance.rst.txt b/_sources/improving-performance.rst.txt new file mode 100644 index 0000000000..6ded252bdd --- /dev/null +++ b/_sources/improving-performance.rst.txt @@ -0,0 +1,197 @@ +********************* +Improving performance +********************* + +Once you start creating larger models, Nengo might become a little bit slow. +This section walks you through different things that might help you to get the +best performance out of Nengo. + +In a nutshell +============= + +To improve build time +--------------------- + +1. Set a seed on the top level model to enable decoder caching: + +.. testcode:: + + net = nengo.Network(seed=1) + +2. Disable the operator graph optimizer: + +.. testcode:: + + with nengo.Simulator(net, optimize=False) as sim: + ... + +.. testoutput:: + :hide: + + ... + +3. Reduce the number of neurons in very large ensembles, or consider using the + `.RandomizedSVD` solver. + +To improve run time +------------------- + +1. Enable the operator graph optimizer + (and install `SciPy `_): + +.. testcode:: + + with nengo.Simulator(net, optimize=True) as sim: + ... + +.. testoutput:: + :hide: + + ... + +2. Consider switching to the `nengo_ocl `_ + backend if you have a powerful GPU. + +To lower peak memory consumption +-------------------------------- + +1. Disable the operator graph optimizer: + +.. testcode:: + + with nengo.Simulator(net, optimize=False) as sim: + ... + +.. testoutput:: + :hide: + + ... + +2. Reduce the number of neurons in very large ensembles. + Consider replacing them with + multiple smaller ensembles (`.EnsembleArray` is useful for that). + +3. Reduce the number of probes or their sampling intervals + (with the ``sample_every`` argument). + +Build and run time performance +============================== + +The two main determiners of how long your model takes to run are the +build time and the run time. Build time is the time required to +translate the model description into the actual neural network that gets +simulated. A build happens when you create the simulator with +``nengo.Simulator(net)``. Run time is how long it takes to simulate this +network for the desired amount of simulation time. A run happens when you +call ``sim.run``. + +Some of the techniques described below +will influence one of these variables, while others will +reduce one variable at the cost of increasing another. +While the run time is usually the most important variable, +sometimes the memory consumption can be the main problem. + +Getting the best performance for your model depends on your model +and your computing environment. + +Decoder caching +=============== + +*Influences build time.* + +A significant amount of build time is spent on finding the NEF +decoders. Because of that, it is possible to cache the decoders. The first +build of a model will still take about the same time (technically a bit longer +because the computed decoders will be stored), but all subsequent builds of the +same model can load the stored decoders and will be faster. + +To enable the decoder caching, set a seed on the network like so: + +.. testcode:: + + with nengo.Network(seed=1) as net: + ... + +There are :doc:`a few configuration options ` for more +advanced control of the cache. The most important might be the possibility to +control the path where the cache files are stored. On high performance +clusters, certain file systems might provide better performance. + +Operator graph optimizer +======================== + +*Influences build time, run time, and memory consumption.* + +By default, Nengo optimizes its internal data structures +(the "operator graph") to access memory in a linear manner. +However, this can increase build time significantly +and in some cases it can be better to turn this +optimization off to speed up the build at the cost of slowing the run. +To turn the optimizer off, +set the simulator's ``optimize`` argument to ``False``: + +.. testcode:: + + with nengo.Simulator(net, optimize=False) as sim: + ... + +.. testoutput:: + :hide: + + ... + +Another situation where it is helpful to disable the optimizer is when the peak +memory usage is too high. The optimizer can use up to three times as much +memory as would be required without the optimizer. Note that limiting the +number of optimization passes does not noticeably reduce memory consumption. + +SciPy +===== + +*Influences run time.* + +To gain the maximum performance gain from the operator graph optimizer, +install `SciPy `_. +When the operator graph optimizer is deactivated, +installing SciPy has no effect on performance. + +nengo_ocl +========= + +*Improves run time.* + +If you have a powerful GPU, you have the option to switch to the `nengo_ocl +`_ backend. It will utilize your GPU, +which is optimized for the sorts of calculations done by Nengo. +Build times with ``nengo_ocl`` are usually comparable to Nengo, +but run times can be significantly faster. + +Model structure +=============== + +*Influences build time, run time, and memory consumption.* + +Some aspects of the model structure, apart from the size of the model, +influence performance. Ensembles with many neurons will take a long +time to build and consume a lot of memory. Sometimes it is +feasible to split large ensembles into multiple smaller ensembles (the +`.EnsembleArray` is helpful for that). Alternatively, using the +`.RandomizedSVD` decoder solver can reduce the build time. + +However, be aware that many small ensembles will take longer to simulate if the +operator graph optimizer is deactivated. + +Limiting probed data +==================== + +*Influences memory consumption.* + +All data that gets probed in the model has to be stored in memory. +Depending on how long the simulation runs and how many things are probed, +this might consume a significant amount of memory. By reducing the number +of probed objects, the memory consumption can be reduced. An alternative +is to not record a value for every time step. Probes accept a +``sample_every=`` argument to reduce the number of recorded samples. + +Note that in most cases, +probing data does not noticeably affect run time. diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000000..c74046feba --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,54 @@ +***** +Nengo +***** + +Nengo is a Python library for building and simulating +large-scale neural models. +Nengo can create sophisticated +spiking and non-spiking neural simulations +with sensible defaults in a few lines of code: + +.. testcode:: + + import nengo + import numpy as np + import matplotlib.pyplot as plt + + with nengo.Network() as net: + sin_input = nengo.Node(output=np.sin) + # A population of 100 neurons representing a sine wave + sin_ens = nengo.Ensemble(n_neurons=100, dimensions=1) + nengo.Connection(sin_input, sin_ens) + # A population of 100 neurons representing the square of the sine wave + sin_squared = nengo.Ensemble(n_neurons=100, dimensions=1) + nengo.Connection(sin_ens, sin_squared, function=np.square) + # View the decoded output of sin_squared + squared_probe = nengo.Probe(sin_squared, synapse=0.01) + + with nengo.Simulator(net) as sim: + sim.run(5.0) + plt.plot(sim.trange(), sim.data[squared_probe]) + +.. testoutput:: + :hide: + + ... + +Yet, Nengo is highly extensible and flexible. +You can define your own neuron types and learning rules, +get input directly from hardware, +build and run deep neural networks, +drive robots, and even simulate your model +on a completely different neural simulator +or neuromorphic hardware. + +.. toctree:: + :maxdepth: 2 + + getting-started + user-guide + examples + contributing + project + +* :ref:`genindex` diff --git a/_sources/license.rst.txt b/_sources/license.rst.txt new file mode 100644 index 0000000000..68c5792f07 --- /dev/null +++ b/_sources/license.rst.txt @@ -0,0 +1 @@ +.. include:: ../LICENSE.rst diff --git a/_sources/nengorc.rst.txt b/_sources/nengorc.rst.txt new file mode 100644 index 0000000000..78662dc4a2 --- /dev/null +++ b/_sources/nengorc.rst.txt @@ -0,0 +1,8 @@ +******************* +Nengo configuration +******************* + +.. highlight:: text + +.. automodule:: nengo.rc + :noindex: diff --git a/_sources/networks.rst.txt b/_sources/networks.rst.txt new file mode 100644 index 0000000000..4e8b52c7e0 --- /dev/null +++ b/_sources/networks.rst.txt @@ -0,0 +1,62 @@ +***************** +Reusable networks +***************** + +Networks are an abstraction of a grouping of Nengo objects +(i.e., `.Node`, `.Ensemble`, `.Connection`, and `.Network` instances, +though usually not `.Probe` instances.) +Like most abstractions, this helps with code-reuse and maintainability. +You'll find the documentation +for the reusable networks included with Nengo below. + +You may also want to build your own reusable networks. +Doing so can help encapsulate parts of your model, +making your code easier to understand, +easier to re-use, and easier to share. +The following examples will +help you build your own reusable networks: + +.. toctree:: + + examples/usage/network-design + examples/usage/network-design-advanced + +You may also find the :doc:`config system documentation ` useful. + +Built-in networks +================= + +.. Note: we don't use autoautosummary here because we want the canonical + reference to these objects to be ``nengo.networks.Class`` even though they + actually live in ``nengo.networks.module.Class``. + +.. autosummary:: + :nosignatures: + + nengo.networks.EnsembleArray + nengo.networks.BasalGanglia + nengo.networks.Thalamus + nengo.networks.AssociativeMemory + nengo.networks.CircularConvolution + nengo.networks.Integrator + nengo.networks.Oscillator + nengo.networks.Product + nengo.networks.InputGatedMemory + +.. autoclass:: nengo.networks.EnsembleArray + +.. autoclass:: nengo.networks.BasalGanglia + +.. autoclass:: nengo.networks.Thalamus + +.. autoclass:: nengo.networks.AssociativeMemory + +.. autoclass:: nengo.networks.CircularConvolution + +.. autoclass:: nengo.networks.Integrator + +.. autoclass:: nengo.networks.Oscillator + +.. autoclass:: nengo.networks.Product + +.. autoclass:: nengo.networks.InputGatedMemory diff --git a/_sources/project.rst.txt b/_sources/project.rst.txt new file mode 100644 index 0000000000..ffc994eec5 --- /dev/null +++ b/_sources/project.rst.txt @@ -0,0 +1,12 @@ +******************* +Project information +******************* + +.. toctree:: + :maxdepth: 2 + + changelog + citation + history + converting + license diff --git a/_sources/spa.rst.txt b/_sources/spa.rst.txt new file mode 100644 index 0000000000..af9bd7ed99 --- /dev/null +++ b/_sources/spa.rst.txt @@ -0,0 +1,6 @@ +***************************** +Semantic Pointer Architecture +***************************** + +.. deprecated:: 3.0 + Use the `NengoSPA `_ project instead. diff --git a/_sources/user-guide.rst.txt b/_sources/user-guide.rst.txt new file mode 100644 index 0000000000..f5ebc6df8d --- /dev/null +++ b/_sources/user-guide.rst.txt @@ -0,0 +1,13 @@ +********** +User guide +********** + +.. toctree:: + :maxdepth: 2 + + frontend-api + backend-api + config + networks + spa + advanced diff --git a/_sources/utils.rst.txt b/_sources/utils.rst.txt new file mode 100644 index 0000000000..d84650957c --- /dev/null +++ b/_sources/utils.rst.txt @@ -0,0 +1,170 @@ +********* +Utilities +********* + +Nengo provides various utility functions in the ``nengo.utils`` module. +In general, anything in this module +should not be considered part of the core API. +This functionality may move or change +with less notice than we provide for the core API, +and it is not as thoroughly tested. + +These utilities are mainly designed for internal use within Nengo, +but we expose them as they may be useful to some users. + +Builder +======= + +.. automodule:: nengo.utils.builder + + .. autoautosummary:: nengo.utils.builder + :nosignatures: + +Cache +===== + +.. automodule:: nengo.utils.cache + + .. autoautosummary:: nengo.utils.cache + :nosignatures: + +Connection +========== + +.. automodule:: nengo.utils.connection + + .. autoautosummary:: nengo.utils.connection + :nosignatures: + +Ensemble +======== + +.. automodule:: nengo.utils.ensemble + + .. autoautosummary:: nengo.utils.ensemble + :nosignatures: + +Filter design +============= + +.. automodule:: nengo.utils.filter_design + + .. autoautosummary:: nengo.utils.filter_design + :nosignatures: + +Functions +========= + +.. automodule:: nengo.utils.functions + + .. autoautosummary:: nengo.utils.functions + :nosignatures: + +Graphs +====== + +.. automodule:: nengo.utils.graphs + + .. autoautosummary:: nengo.utils.graphs + :nosignatures: + +IPython +======= + +.. automodule:: nengo.utils.ipython + + .. autoautosummary:: nengo.utils.ipython + :nosignatures: + +Lock +==== + +.. automodule:: nengo.utils.lock + + .. autoautosummary:: nengo.utils.lock + :nosignatures: + +Magic +===== + +.. autofunction:: nengo.utils.magic.decorator + +Matplotlib +========== + +.. automodule:: nengo.utils.matplotlib + + .. autoautosummary:: nengo.utils.matplotlib + :nosignatures: + +NCO +=== + +.. automodule:: nengo.utils.nco + + .. autoautosummary:: nengo.utils.nco + :nosignatures: + +Network +======= + +.. automodule:: nengo.utils.network + + .. autoautosummary:: nengo.utils.network + :nosignatures: + +Numpy +===== + +.. automodule:: nengo.utils.numpy + + .. autoautosummary:: nengo.utils.numpy + :nosignatures: + +Paths +===== + +.. automodule:: nengo.utils.paths + + .. autoautosummary:: nengo.utils.paths + :nosignatures: + +Progress bars +============= + +.. automodule:: nengo.utils.progress + + .. autoautosummary:: nengo.utils.progress + :nosignatures: + +Simulator +========= + +.. automodule:: nengo.utils.simulator + + .. autoautosummary:: nengo.utils.simulator + :nosignatures: + +stdlib +====== + +.. automodule:: nengo.utils.stdlib + + .. autoautosummary:: nengo.utils.stdlib + :nosignatures: + +Testing +======= + +.. automodule:: nengo.utils.testing + + .. autoautosummary:: nengo.utils.testing + :nosignatures: + +Threading +========= + +.. automodule:: nengo.utils.threading + + .. autoautosummary:: nengo.utils.threading + :nosignatures: diff --git a/_static/basic.css b/_static/basic.css new file 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* + * Sphinx JavaScript utilities for all documentation. + * + * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + +/** + * make the code below compatible with browsers without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + */ +jQuery.urldecode = function(x) { + return decodeURIComponent(x).replace(/\+/g, ' '); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

' + _('Hide Search Matches') + '

') + .appendTo($('#searchbox')); + } + }, + + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + }, + + /** + * make the url absolute + */ + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + }, + + /** + * get the current relative url + */ + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + }, + + initOnKeyListeners: function() { + $(document).keydown(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box or textarea + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' + && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) { + switch (event.keyCode) { + case 37: // left + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + case 39: // right + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + } + } + }); + } +}; + +// quick alias for translations +_ = Documentation.gettext; + +$(document).ready(function() { + Documentation.init(); +}); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000000..64c53cff69 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,12 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '3.1.1.dev0', + LANGUAGE: 'None', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false +}; \ No newline at end of file diff --git a/_static/favicon.ico b/_static/favicon.ico new file mode 100644 index 0000000000..d034ac95e6 Binary files /dev/null and b/_static/favicon.ico differ diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000000..a858a410e4 Binary files /dev/null and b/_static/file.png differ diff --git a/_static/graphviz.css b/_static/graphviz.css new file mode 100644 index 0000000000..8ab69e0148 --- /dev/null +++ b/_static/graphviz.css @@ -0,0 +1,19 @@ +/* + * graphviz.css + * ~~~~~~~~~~~~ + * + * Sphinx stylesheet -- graphviz extension. + * + * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +img.graphviz { + border: 0; + max-width: 100%; +} + +object.graphviz { + max-width: 100%; +} diff --git a/_static/jquery-3.5.1.js b/_static/jquery-3.5.1.js new file mode 100644 index 0000000000..50937333b9 --- /dev/null +++ b/_static/jquery-3.5.1.js @@ -0,0 +1,10872 @@ +/*! + * jQuery JavaScript Library v3.5.1 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2020-05-04T22:49Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + return typeof obj === "function" && typeof obj.nodeType !== "number"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.5.1", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), +function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +} ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.5 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2020-03-14 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem.namespaceURI, + docElem = ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +}; +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the master Deferred + master = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + master.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( master.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return master.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); + } + + return master.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var + rkeyEvent = /^key/, + rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, + rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + return result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + + which: function( event ) { + var button = event.button; + + // Add which for key events + if ( event.which == null && rkeyEvent.test( event.type ) ) { + return event.charCode != null ? event.charCode : event.keyCode; + } + + // Add which for click: 1 === left; 2 === middle; 3 === right + if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { + if ( button & 1 ) { + return 1; + } + + if ( button & 2 ) { + return 3; + } + + if ( button & 4 ) { + return 2; + } + + return 0; + } + + return event.which; + } +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px"; + tr.style.height = "1px"; + trChild.style.height = "9px"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( + dataPriv.get( cur, "events" ) || Object.create( null ) + )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) { + xml = undefined; + } + + if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { + jQuery.error( "Invalid XML: " + data ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ) + .filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ) + .map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script + if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/backend-api.html b/backend-api.html new file mode 100644 index 0000000000..aa53db892d --- /dev/null +++ b/backend-api.html @@ -0,0 +1,4145 @@ + + + + + + + + Nengo backend API — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Nengo backend API

+

Nengo is designed so that models created with the +Nengo frontend API +work on a variety of different simulators, or “backends.” +For example, backends have been created to take advantage of +GPUs and +neuromorphic hardware.

+
+

Reference backend

+ ++++ + + + + + + + + +

nengo.Simulator

Reference simulator for Nengo models.

nengo.simulator.SimulationData

Data structure used to access simulation data from the model.

+
+
+class nengo.Simulator(network, dt=0.001, seed=None, model=None, progress_bar=True, optimize=True)[source]
+

Reference simulator for Nengo models.

+

The simulator takes a Network and builds internal data structures to +run the model defined by that network. Run the simulator with the +run method, and access probed data through the +data attribute.

+

Building and running the simulation may allocate resources like files +and sockets. To properly free these resources, call the Simulator.close +method. Alternatively, Simulator.close will automatically be called +if you use the with syntax:

+
with nengo.Network() as net:
+    ensemble = nengo.Ensemble(10, 1)
+with nengo.Simulator(net, progress_bar=False) as sim:
+    sim.run(0.1)
+
+
+

Note that the data attribute is still accessible even when a simulator +has been closed. Running the simulator, however, will raise an error.

+

When debugging or comparing models, it can be helpful to see the full ordered +list of operators that the simulator will execute on each timestep.

+
with nengo.Simulator(nengo.Network(), progress_bar=False) as sim:
+    print('\n'.join("* %s" % op for op in sim.step_order))
+
+
+
* TimeUpdate{}
+
+
+

The diff of two simulators’ sorted ops tells us how two built models differ. +We can use difflib in the Python standard library to see the differences.

+
# Original model
+with nengo.Network() as net:
+    ensemble = nengo.Ensemble(10, 1, label="Ensemble")
+sim1 = nengo.Simulator(net, progress_bar=False)
+
+# Add a node
+with net:
+    node = nengo.Node(output=0, label="Node")
+    nengo.Connection(node, ensemble)
+sim2 = nengo.Simulator(net, progress_bar=False)
+
+import difflib
+
+print("".join(difflib.unified_diff(
+    sorted("%s: %s\n" % (type(op).__name__, op.tag) for op in sim1.step_order),
+    sorted("%s: %s\n" % (type(op).__name__, op.tag) for op in sim2.step_order),
+    fromfile="sim1",
+    tofile="sim2",
+    n=0,
+)).strip())
+
+sim1.close()
+sim2.close()
+
+
+
--- sim1
++++ sim2
+@@ -0,0 +1 @@
++Copy: <Connection from <Node "Node"> to <Ensemble "Ensemble">>
+@@ -4,0 +6 @@
++SimProcess: Lowpass(tau=0.005)
+
+
+
+
Parameters
+
+
networkNetwork or None

A network object to be built and then simulated. If None, +then a Model with the build model must be provided instead.

+
+
dtfloat, optional

The length of a simulator timestep, in seconds.

+
+
seedint, optional

A seed for all stochastic operators used in this simulator. +Will be set to network.seed + 1 if not given.

+
+
modelModel, optional

A Model that contains build artifacts to be simulated. +Usually the simulator will build this model for you; however, if you +want to build the network manually, or you want to inject build +artifacts in the model before building the network, then you can +pass in a Model instance.

+
+
progress_barbool or ProgressBar, optional

Progress bar for displaying build and simulation progress.

+

If True, the default progress bar will be used. +If False, the progress bar will be disabled. +For more control over the progress bar, pass in a ProgressBar +instance.

+
+
optimizebool, optional

If True, the builder will run an additional optimization step +that can speed up simulations significantly at the cost of slower +builds. If running models for very small amounts of time, +pass False to disable the optimizer.

+
+
+
+
Attributes
+
+
closedbool

Whether the simulator has been closed. +Once closed, it cannot be reopened.

+
+
dataSimulationData

The SimulationData mapping from Nengo objects to the data associated +with those objects. In particular, each Probe maps to the data +probed while running the simulation.

+
+
dgdict

A dependency graph mapping from each Operator to the operators +that depend on that operator.

+
+
modelModel

The Model containing the signals and operators necessary to +simulate the network.

+
+
signalsSignalDict

The SignalDict mapping from Signal instances to NumPy arrays.

+
+
+
+
+
+
+property dt
+

(float) The time step of the simulator.

+
+ +
+
+property n_steps
+

(int) The current time step of the simulator.

+
+ +
+
+property step_order
+

(list) The ordered list of step functions run by this simulator.

+
+ +
+
+property time
+

(float) The current time of the simulator.

+
+ +
+
+clear_probes()[source]
+

Clear all probe histories.

+
+

New in version 3.0.0.

+
+
+ +
+
+close()[source]
+

Closes the simulator.

+

Any call to Simulator.run, Simulator.run_steps, +Simulator.step, and Simulator.reset on a closed simulator raises +a SimulatorClosed exception.

+
+ +
+
+reset(seed=None)[source]
+

Reset the simulator state.

+
+
Parameters
+
+
seedint, optional

A seed for all stochastic operators used in the simulator. +This will change the random sequences generated for noise +or inputs (e.g. from processes), but not the built objects +(e.g. ensembles, connections).

+
+
+
+
+
+ +
+
+run(time_in_seconds, progress_bar=None)[source]
+

Simulate for the given length of time.

+

If the given length of time is not a multiple of dt, +it will be rounded to the nearest dt. For example, if dt +is 0.001 and run is called with time_in_seconds=0.0006, +the simulator will advance one timestep, resulting in the actual +simulator time being 0.001.

+

The given length of time must be positive. The simulator cannot +be run backwards.

+
+
Parameters
+
+
time_in_secondsfloat

Amount of time to run the simulation for. Must be positive.

+
+
progress_barbool or ProgressBar, optional

Progress bar for displaying the progress of the simulation run.

+

If True, the default progress bar will be used. +If False, the progress bar will be disabled. +For more control over the progress bar, pass in a ProgressBar +instance.

+
+
+
+
+
+ +
+
+run_steps(steps, progress_bar=None)[source]
+

Simulate for the given number of dt steps.

+
+
Parameters
+
+
stepsint

Number of steps to run the simulation for.

+
+
progress_barbool or ProgressBar, optional

Progress bar for displaying the progress of the simulation run.

+

If True, the default progress bar will be used. +If False, the progress bar will be disabled. +For more control over the progress bar, pass in a ProgressBar +instance.

+
+
+
+
+
+ +
+
+step()[source]
+

Advance the simulator by 1 step (dt seconds).

+
+ +
+
+trange(dt=None, sample_every=None)[source]
+

Create a vector of times matching probed data.

+

Note that the range does not start at 0 as one might expect, but at +the first timestep (i.e., dt).

+
+
Parameters
+
+
sample_everyfloat, optional

The sampling period of the probe to create a range for. +If None, a time value for every dt will be produced.

+
+

Changed in version 3.0.0: Renamed from dt to sample_every

+
+
+
+
+
+
+ +
+ +
+
+class nengo.simulator.SimulationData(raw)[source]
+

Data structure used to access simulation data from the model.

+

The main use case for this is to access Probe data; for example, +probe_data = sim.data[my_probe]. However, it is also used to access the +parameters of objects in the model; for example, encoder values for an ensemble +can be accessed via encoders = sim.data[my_ens].encoders.

+

This is like a view on the raw simulation data manipulated by the Simulator, +which allows the raw simulation data to be optimized for speed while this +provides a more user-friendly interface.

+
+

Changed in version 3.0.0: Renamed from ProbeDict to SimulationData

+
+
+ +
+
+

The build process

+

The build process translates a Nengo model +to a set of data buffers (Signal instances) +and computational operations (Operator instances) +which implement the Nengo model +defined with the frontend API. +The build process is central to +how the reference simulator works, +and details how Nengo can be extended to include +new neuron types, learning rules, and other components.

+

Bekolay et al., 2014 +provides a high-level description +of the build process. +For lower-level details +and reference documentation, read on.

+ ++++ + + + + + + + + +

nengo.builder.Model

Stores artifacts from the build process, which are used by Simulator.

nengo.builder.Builder

Manages the build functions known to the Nengo build process.

+
+
+class nengo.builder.Model(dt=0.001, label=None, decoder_cache=None, builder=None)[source]
+

Stores artifacts from the build process, which are used by Simulator.

+
+
Parameters
+
+
dtfloat, optional

The length of a simulator timestep, in seconds.

+
+
labelstr, optional

A name or description to differentiate models.

+
+
decoder_cacheDecoderCache, optional

Interface to a cache for expensive parts of the build process.

+
+
buildernengo.builder.Builder, optional

A Builder instance to use for building. Defaults to a +new Builder().

+
+
+
+
Attributes
+
+
configConfig or None

Build functions can set a config object here to affect sub-builders.

+
+
decoder_cacheDecoderCache

Interface to a cache for expensive parts of the build process.

+
+
dtfloat

The length of each timestep, in seconds.

+
+
labelstr or None

A name or description to differentiate models.

+
+
operatorslist

List of all operators created in the build process. +All operators must be added to this list, as it is used by Simulator.

+
+
paramsdict

Mapping from objects to namedtuples containing parameters generated +in the build process.

+
+
probeslist

List of all probes. Probes must be added to this list in the build +process, as this list is used by Simulator.

+
+
seededdict

All objects are assigned a seed, whether the user defined the seed +or it was automatically generated. ‘seeded’ keeps track of whether +the seed is user-defined. We consider the seed to be user-defined +if it was set directly on the object, or if a seed was set on the +network in which the object resides, or if a seed was set on any +ancestor network of the network in which the object resides.

+
+
seedsdict

Mapping from objects to the integer seed assigned to that object.

+
+
sigdict

A dictionary of dictionaries that organizes all of the signals +created in the build process, as build functions often need to +access signals created by other build functions.

+
+
stepSignal

The current step (i.e., how many timesteps have occurred thus far).

+
+
timeSignal

The current point in time.

+
+
toplevelNetwork

The top-level network being built. +This is sometimes useful for accessing network elements after build, +or for the network builder to determine if it is the top-level network.

+
+
+
+
+
+
+add_op(op)[source]
+

Add an operator to the model.

+

In addition to adding the operator, this method performs additional +error checking by calling the operator’s make_step function. +Calling make_step catches errors early, such as when signals are +not properly initialized, which aids debugging. For that reason, +we recommend calling this method over directly accessing +the operators attribute.

+
+ +
+
+build(obj, *args, **kwargs)[source]
+

Build an object into this model.

+

See Builder.build for more details.

+
+
Parameters
+
+
objobject

The object to build into this model.

+
+
+
+
+
+ +
+
+has_built(obj)[source]
+

Returns true if the object has already been built in this model.

+
+

Note

+

Some objects (e.g. synapses) can be built multiple times, +and therefore will always result in this method returning +False even though they have been built.

+
+

This check is implemented by checking if the object is in the +params dictionary. Build function should therefore add themselves +to model.params if they cannot be built multiple times.

+
+
Parameters
+
+
objobject

The object to query.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.Builder[source]
+

Manages the build functions known to the Nengo build process.

+

Consists of two class methods to encapsulate the build function registry. +All build functions should use the Builder.register method as a +decorator. For example:

+
class MyRule(nengo.learning_rules.LearningRuleType):
+    modifies = "decoders"
+    ...
+
+@nengo.builder.Builder.register(MyRule)
+def build_my_rule(model, my_rule, rule):
+    ...
+
+
+

registers a build function for MyRule objects.

+

Build functions should not be called directly, but instead called through +the Model.build method. Model.build uses the Builder.build method +to ensure that the correct build function is called based on the type of +the object passed to it. +For example, to build the learning rule type my_rule from above, do:

+
with nengo.Network() as net:
+    ens_a = nengo.Ensemble(10, 1)
+    ens_b = nengo.Ensemble(10, 1)
+    my_rule = MyRule()
+    connection = nengo.Connection(ens_a, ens_b, learning_rule_type=my_rule)
+
+model = nengo.builder.Model()
+model.build(my_rule, connection.learning_rule)
+
+
+

This will call the build_my_rule function from above with the arguments +model, my_rule, connection.learning_rule.

+
+
Attributes
+
+
buildersdict

Mapping from types to the build function associated with that type.

+
+
+
+
+
+
+classmethod build(model, obj, *args, **kwargs)[source]
+

Build obj into model.

+

This method looks up the appropriate build function for obj and +calls it with the model and other arguments provided.

+

Note that if a build function is not specified for a particular type +(e.g., EnsembleArray), the type’s method resolution order will be +examined to look for superclasses +with defined build functions (e.g., Network in the case of +EnsembleArray).

+

This indirection (calling Builder.build instead of the build +function directly) enables users to augment the build process in their +own models, rather than having to modify Nengo itself.

+

In addition to the parameters listed below, further positional and +keyword arguments will be passed unchanged into the build function.

+
+
Parameters
+
+
modelModel

The Model instance in which to store build artifacts.

+
+
objobject

The object to build into the model.

+
+
+
+
+
+ +
+
+classmethod register(nengo_class)[source]
+

A decorator for adding a class to the build function registry.

+

Raises a warning if a build function already exists for the class.

+
+
Parameters
+
+
nengo_classClass

The type associated with the build function being decorated.

+
+
+
+
+
+ +
+ +
+

Basic operators

+

Operators represent calculations that will occur in the simulation.

+

This code adapted from sigops/operator.py and sigops/operators.py +(https://github.com/jaberg/sigops). +This modified code is included under the terms of their license:

+

Copyright (c) 2014, James Bergstra +All rights reserved.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met:

+
    +

  1. Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer.

    2. +

  2. Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the +distribution.

    3. +
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +“AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.builder.Operator

Base class for operator instances understood by Nengo.

nengo.builder.operator.TimeUpdate

Updates the simulation step and time.

nengo.builder.operator.Reset

Assign a constant value to a Signal.

nengo.builder.operator.Copy

Assign the value of one signal to another, with optional slicing.

nengo.builder.operator.ElementwiseInc

Increment signal Y by A * X (with broadcasting).

nengo.builder.operator.reshape_dot

Checks if the dot product needs to be reshaped.

nengo.builder.operator.DotInc

Increment signal Y by dot(A, X).

nengo.builder.operator.SparseDotInc

Like DotInc but A is a sparse matrix.

nengo.builder.operator.BsrDotInc

Increment signal Y by dot(A, X) using block sparse row format.

nengo.builder.operator.SimPyFunc

Apply a Python function to a signal, with optional arguments.

+
+
+class nengo.builder.Operator(tag=None)[source]
+

Base class for operator instances understood by Nengo.

+

During one simulator timestep, a Signal can experience

+
    +

  1. at most one set operator (optional)

    2. +

  2. any number of increments

    3. +

  3. any number of reads

    4. +

  4. at most one update

    5. +
+

in this specific order.

+

A set defines the state of the signal at time \(t\), the start +of the simulation timestep. That state can then be modified by +increment operations. A signal’s state will only be read after +all increments are complete. The state is then finalized by an update, +which denotes the state that the signal should be at time \(t + dt\).

+

Each operator must keep track of the signals that it manipulates, +and which of these four types of manipulations is done to each signal +so that the simulator can order all of the operators properly.

+
+

Note

+

There are intentionally no valid default values for the +reads, sets, incs, +and updates properties to ensure that subclasses +explicitly set these values.

+
+
+
Parameters
+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
Attributes
+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+property incs
+

Signals incremented by this operator.

+

Increments will be applied after sets (if it is set), and before reads.

+
+ +
+
+property reads
+

Signals that are read and not modified by this operator.

+

Reads occur after increments, and before updates.

+
+ +
+
+property sets
+

Signals set by this operator.

+

Sets occur first, before increments. A signal that is set here cannot +be set or updated by any other operator.

+
+ +
+
+property updates
+

Signals updated by this operator.

+

Updates are the last operation to occur to a signal.

+
+ +
+
+init_signals(signals)[source]
+

Initialize the signals associated with this operator.

+

The signals will be initialized into signals. +Operator subclasses that use extra buffers should create them here.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
+
+
+
+ +
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.operator.TimeUpdate(step, time, tag=None)[source]
+

Updates the simulation step and time.

+

Implements step[...] += 1 and time[...] = step * dt.

+

A separate operator is used (rather than a combination of Copy and +DotInc) so that other backends can manage these important parts of the +simulation state separately from other signals.

+
+
Parameters
+
+
stepSignal

The signal associated with the integer step counter.

+
+
timeSignal

The signal associated with the time (a float, in seconds).

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets [step, time]

    2. +

  2. incs []

    3. +

  3. reads []

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
stepSignal

The signal associated with the integer step counter.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
timeSignal

The signal associated with the time (a float, in seconds).

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.operator.Reset(dst, value=0, tag=None)[source]
+

Assign a constant value to a Signal.

+

Implements dst[...] = value.

+
+
Parameters
+
+
dstSignal

The Signal to reset.

+
+
valuefloat, optional

The constant value to which dst is set.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets [dst]

    2. +

  2. incs []

    3. +

  3. reads []

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
dstSignal

The Signal to reset.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
valuefloat

The constant value to which dst is set.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.operator.Copy(src, dst, src_slice=None, dst_slice=None, inc=False, tag=None)[source]
+

Assign the value of one signal to another, with optional slicing.

+

Implements:

+
    +

  • dst[:] = src

    • +

  • dst[dst_slice] = src[src_slice] +(when dst_slice or src_slice is not None)

    • +

  • dst[dst_slice] += src[src_slice] (when inc=True)

    • +
+
+
Parameters
+
+
dstSignal

The signal that will be assigned to (set).

+
+
srcSignal

The signal that will be copied (read).

+
+
dst_sliceslice or list, optional

Slice or list of indices associated with dst.

+
+
src_sliceslice or list, optional

Slice or list of indices associated with src

+
+
incbool, optional

Whether this should be an increment rather than a copy.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets [] if inc else [dst]

    2. +

  2. incs [dst] if inc else []

    3. +

  3. reads [src]

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
dstSignal

The signal that will be assigned to (set).

+
+
dst_slicelist or None

Indices associated with dst.

+
+
srcSignal

The signal that will be copied (read).

+
+
src_slicelist or None

Indices associated with src.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.operator.ElementwiseInc(A, X, Y, tag=None)[source]
+

Increment signal Y by A * X (with broadcasting).

+

Implements Y[...] += A * X.

+
+
Parameters
+
+
ASignal

The first signal to be multiplied.

+
+
XSignal

The second signal to be multiplied.

+
+
YSignal

The signal to be incremented.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs [Y]

    3. +

  3. reads [A, X]

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
ASignal

The first signal to be multiplied.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
XSignal

The second signal to be multiplied.

+
+
YSignal

The signal to be incremented.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.operator.reshape_dot(A, X, Y, tag=None)[source]
+

Checks if the dot product needs to be reshaped.

+

Also does a bunch of error checking based on the shapes of A and X.

+
+ +
+
+class nengo.builder.operator.DotInc(A, X, Y, reshape=None, tag=None)[source]
+

Increment signal Y by dot(A, X).

+

Implements Y[...] += np.dot(A, X).

+
+

Note

+

Currently, this only supports matrix-vector multiplies +for compatibility with NengoOCL.

+
+
+
Parameters
+
+
ASignal

The first signal to be multiplied (a matrix).

+
+
XSignal

The second signal to be multiplied (a vector).

+
+
YSignal

The signal to be incremented.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs [Y]

    3. +

  3. reads [A, X]

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
ASignal

The first signal to be multiplied.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
XSignal

The second signal to be multiplied.

+
+
YSignal

The signal to be incremented.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.operator.SparseDotInc(A, X, Y, tag=None)[source]
+

Like DotInc but A is a sparse matrix.

+
+

New in version 3.0.0.

+
+
+ +
+
+class nengo.builder.operator.BsrDotInc(A, X, Y, indices, indptr, reshape=None, tag=None)[source]
+

Increment signal Y by dot(A, X) using block sparse row format.

+

Implements Y[...] += np.dot(A, X), where A is an instance +of scipy.sparse.bsr_matrix.

+
+

Note

+

Requires SciPy.

+
+
+

Note

+

Currently, this only supports matrix-vector multiplies +for compatibility with NengoOCL.

+
+
+
Parameters
+
+
A(k, r, c) Signal

The signal providing the k data blocks with r rows and c columns.

+
+
X(k * c) Signal

The signal providing the k column vectors to multiply with.

+
+
Y(k * r) Signal

The signal providing the k column vectors to update.

+
+
indicesndarray

Column indices, see scipy.sparse.bsr_matrix for details.

+
+
indptrndarray

Column index pointers, see scipy.sparse.bsr_matrix for details.

+
+
reshapebool

Whether to reshape the result.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs [Y]

    3. +

  3. reads [A, X]

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
A(k, r, c) Signal

The signal providing the k data blocks with r rows and c columns.

+
+
indicesndarray

Column indices, see scipy.sparse.bsr_matrix for details.

+
+
indptrndarray

Column index pointers, see scipy.sparse.bsr_matrix for details.

+
+
reshapebool

Whether to reshape the result.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
X(k * c) Signal

The signal providing the k column vectors to multiply with.

+
+
Y(k * r) Signal

The signal providing the k column vectors to update.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.operator.SimPyFunc(output, fn, t, x, tag=None)[source]
+

Apply a Python function to a signal, with optional arguments.

+

Implements output[...] = fn(*args) where args can +include the current simulation time t and an input signal x.

+

Note that output may also be None, in which case the function is +called but no output is captured.

+
+
Parameters
+
+
outputSignal or None

The signal to be set. If None, the function is still called.

+
+
fncallable

The function to call.

+
+
tSignal or None

The signal associated with the time (a float, in seconds). +If None, the time will not be passed to fn.

+
+
xSignal or None

An input signal to pass to fn. +If None, an input signal will not be passed to fn.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets [] if output is None else [output]

    2. +

  2. incs []

    3. +

  3. reads ([] if t is None else [t]) + ([] if x is None else [x])

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
fncallable

The function to call.

+
+
outputSignal or None

The signal to be set. If None, the function is still called.

+
+
tSignal or None

The signal associated with the time (a float, in seconds). +If None, the time will not be passed to fn.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
xSignal or None

An input signal to pass to fn. +If None, an input signal will not be passed to fn.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+

Signals

+ ++++ + + + + + + + + + + + +

nengo.builder.signal.is_sparse

Check if obj is a sparse matrix.

nengo.builder.Signal

Represents data or views onto data within a Nengo simulation.

nengo.builder.signal.SignalDict

Map from Signal -> ndarray.

+
+
+nengo.builder.signal.is_sparse(obj)[source]
+

Check if obj is a sparse matrix.

+
+ +
+
+class nengo.builder.Signal(initial_value=None, shape=None, name=None, base=None, readonly=False, offset=0)[source]
+

Represents data or views onto data within a Nengo simulation.

+

Signals are tightly coupled to NumPy arrays, which is how live data is +represented in a Nengo simulation. Signals provide a view onto the +important metadata of the live NumPy array, and maintain the original +value of the array in order to reset the simulation to the initial state.

+
+
Parameters
+
+
initial_valuearray_like

The initial value of the signal. Much of the metadata tracked by the +Signal is based on this array as well (e.g., dtype).

+
+
namestr, optional

Name of the signal. Primarily used for debugging. +If None, the memory location of the Signal will be used.

+
+
baseSignal, optional

The base signal, if this signal is a view on another signal. +Linking the two signals with the base argument is necessary +to ensure that their live data is also linked.

+
+
readonlybool, optional

Whether this signal and its related live data should be marked as +readonly. Writing to these arrays will raise an exception.

+
+
offsetint, optional

For a signal view this gives the offset of the view from the base +initial_value in bytes. This might differ from the offset +of the NumPy array view provided as initial_value if the base +is a view already (in which case the signal base offset will be 0 +because it starts where the view starts. That NumPy view can have +an offset of itself).

+
+
+
+
+
+
+property base
+

(Signal) The base signal, if this signal is a view.

+

Linking the two signals with the base argument is necessary +to ensure that their live data is also linked.

+
+ +
+
+property dtype
+

(numpy.dtype) Data type of the signal (e.g., float64).

+
+ +
+
+property elemoffset
+

(int) Offset of data from base in elements.

+
+ +
+
+property elemstrides
+

(int) Strides of data in elements.

+
+ +
+
+property initial_value
+

(numpy.ndarray) Initial value of the signal.

+

Much of the metadata tracked by the Signal is based on this array +as well (e.g., dtype).

+
+ +
+
+property is_view
+

(bool) True if this Signal is a view on another Signal.

+
+ +
+
+property itemsize
+

(int) Size of an array element in bytes.

+
+ +
+
+property name
+

(str) Name of the signal. Primarily used for debugging.

+
+ +
+
+property nbytes
+

(int) Number of bytes consumed by the signal.

+
+ +
+
+property ndim
+

(int) Number of array dimensions.

+
+ +
+
+property offset
+

(int) Offset of data from base in bytes.

+

For a signal view this gives the offset of the view from the base +initial_value in bytes. This might differ from the offset +of the NumPy array view provided as initial_value if the base +is a view already (in which case the signal base offset will be 0 +because it starts where the view starts. That NumPy view can have +an offset of itself).

+
+ +
+
+property readonly
+

(bool) Whether associated live data can be changed.

+
+ +
+
+property shape
+

(tuple) Tuple of array dimensions.

+
+ +
+
+property size
+

(int) Total number of elements.

+
+ +
+
+property strides
+

(tuple) Strides of data in bytes.

+
+ +
+
+property sparse
+

(bool) Whether the signal is sparse.

+
+ +
+
+may_share_memory(other)[source]
+

Determine if two signals might overlap in memory.

+

This comparison is not exact and errs on the side of false positives. +See numpy.may_share_memory for more details.

+
+
Parameters
+
+
otherSignal

The other signal we are investigating.

+
+
+
+
+
+ +
+
+reshape(*shape)[source]
+

Return a view on this signal with a different shape.

+

Note that reshape cannot change the overall size of the signal. +See numpy.reshape for more details.

+

Any number of integers can be passed to this method, +describing the desired shape of the returned signal.

+
+ +
+ +
+
+class nengo.builder.signal.SignalDict[source]
+

Map from Signal -> ndarray.

+

This dict subclass ensures that the ndarray values aren’t overwritten, +and instead data are written into them, which ensures that +these arrays never get copied, which wastes time and space.

+

Use init to set the ndarray initially.

+
+
+init(signal)[source]
+

Set up a permanent mapping from signal -> data.

+
+ +
+
+reset(signal)[source]
+

Reset ndarray to the base value of the signal that maps to it.

+
+ +
+ +
+
+

Network builder

+ ++++ + + + + + + + + +

nengo.builder.network.build_network

Builds a Network object into a model.

nengo.builder.network.seed_network

Populate seeding dictionaries for all objects in a network.

+
+
+nengo.builder.network.build_network(model, network, progress=None)[source]
+

Builds a Network object into a model.

+

The network builder does this by mapping each high-level object to its +associated signals and operators one-by-one, in the following order:

+
    +

  1. Ensembles, nodes, neurons

    2. +

  2. Subnetworks (recursively)

    3. +

  3. Connections, learning rules

    4. +

  4. Probes

    5. +
+

Before calling any of the individual objects’ build functions, random +number seeds are assigned to objects that did not have a seed explicitly +set by the user. Whether the seed was assigned manually or automatically +is tracked, and the decoder cache is only used when the seed is assigned +manually.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
networkNetwork

The network to build.

+
+
progressProgress, optional

Object used to track the build progress.

+

Note that this will only affect top-level networks.

+
+
+
+
+

Notes

+

Sets model.params[network] to None.

+
+ +
+
+nengo.builder.network.seed_network(network, seeds, seeded, base_rng=numpy.random)[source]
+

Populate seeding dictionaries for all objects in a network.

+

This includes all subnetworks.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
networkNetwork

The network containing all objects to set seeds for.

+
+
seeds{object: int}

Pre-existing map from objects to seeds for those objects. +Will be modified in-place, but entries will not be +overwritten if already set.

+
+
seeded{object: bool}

Pre-existing map from objects to a boolean indicating whether they +have a fixed seed either themselves or from a parent network (True), +or whether the seed is randomly generated (False). +Will be modified in-place, but entries will not be +overwritten if already set.

+
+
base_rngnp.random.RandomState

Random number generator to use to set the seeds.

+
+
+
+
+
+ +
+
+

Connection builder

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.builder.connection.BuiltConnection

Collects the parameters generated in build_connection.

nengo.builder.connection.get_eval_points

Get evaluation points for connection.

nengo.builder.connection.get_targets

Get target points for connection with given evaluation points.

nengo.builder.connection.build_linear_system

Get all arrays needed to compute decoders.

nengo.builder.connection.build_decoders

Compute decoders for connection.

nengo.builder.connection.solve_for_decoders

Solver for decoders.

nengo.builder.connection.slice_signal

Apply a slice operation to given signal.

nengo.builder.connection.build_solver

Apply decoder solver to connection.

nengo.builder.connection.build_no_solver

Special builder for NoSolver to skip unnecessary steps.

nengo.builder.connection.build_connection

Builds a Connection object into a model.

+
+
+class nengo.builder.connection.BuiltConnection[source]
+

Collects the parameters generated in build_connection.

+

These are stored here because in the majority of cases the equivalent +attribute in the original connection is a Distribution. The attributes +of a BuiltConnection are the full NumPy arrays used in the simulation.

+

See the Connection documentation for more details on each parameter.

+
+
Parameters
+
+
eval_pointsndarray

Evaluation points.

+
+
solver_infodict

Information dictionary returned by the Solver.

+
+
weightsndarray

Connection weights. May be synaptic connection weights defined in +the connection’s transform, or a combination of the decoders +automatically solved for and the specified transform.

+
+
transformndarray

The transform matrix.

+
+
+
+
+

Create new instance of BuiltConnection(eval_points, solver_info, weights, transform)

+
+ +
+
+nengo.builder.connection.get_eval_points(model, conn, rng)[source]
+

Get evaluation points for connection.

+
+ +
+
+nengo.builder.connection.get_targets(conn, eval_points, dtype=None)[source]
+

Get target points for connection with given evaluation points.

+
+ +
+
+nengo.builder.connection.build_linear_system(model, conn, rng)[source]
+

Get all arrays needed to compute decoders.

+
+ +
+
+nengo.builder.connection.build_decoders(model, conn, rng)[source]
+

Compute decoders for connection.

+
+ +
+
+nengo.builder.connection.solve_for_decoders(conn, gain, bias, x, targets, rng)[source]
+

Solver for decoders.

+

Factored out from build_decoders for use with the cache system.

+
+ +
+
+nengo.builder.connection.slice_signal(model, signal, sl)[source]
+

Apply a slice operation to given signal.

+
+ +
+
+nengo.builder.connection.build_solver(model, solver, conn, rng)[source]
+

Apply decoder solver to connection.

+
+ +
+
+nengo.builder.connection.build_no_solver(model, solver, conn, rng)[source]
+

Special builder for NoSolver to skip unnecessary steps.

+
+ +
+
+nengo.builder.connection.build_connection(model, conn)[source]
+

Builds a Connection object into a model.

+

A brief summary of what happens in the connection build process, +in order:

+
    +

  1. Solve for decoders.

    2. +

  2. Combine transform matrix with decoders to get weights.

    3. +

  3. Add operators for computing the function +or multiplying neural activity by weights.

    4. +

  4. Call build function for the synapse.

    5. +

  5. Call build function for the learning rule.

    6. +

  6. Add operator for applying learning rule delta to weights.

    7. +
+

Some of these steps may be altered or omitted depending on the parameters +of the connection, in particular the pre and post types.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
connConnection

The connection to build.

+
+
+
+
+

Notes

+

Sets model.params[conn] to a BuiltConnection instance.

+
+ +
+
+

Ensemble builder

+ ++++ + + + + + + + + + + + + + + + + + +

nengo.builder.ensemble.BuiltEnsemble

Collects the parameters generated in build_ensemble.

nengo.builder.ensemble.gen_eval_points

Generate evaluation points for ensemble.

nengo.builder.ensemble.get_activities

Get output of ensemble neurons for given evaluation points.

nengo.builder.ensemble.get_gain_bias

Compute concrete gain and bias for ensemble.

nengo.builder.ensemble.build_ensemble

Builds an Ensemble object into a model.

+
+
+class nengo.builder.ensemble.BuiltEnsemble[source]
+

Collects the parameters generated in build_ensemble.

+

These are stored here because in the majority of cases the equivalent +attribute in the original ensemble is a Distribution. The attributes +of a BuiltEnsemble are the full NumPy arrays used in the simulation.

+

See the Ensemble documentation for more details on each parameter.

+
+
Parameters
+
+
eval_pointsndarray

Evaluation points.

+
+
encodersndarray

Normalized encoders.

+
+
interceptsndarray

X-intercept of each neuron.

+
+
max_ratesndarray

Maximum firing rates for each neuron.

+
+
scaled_encodersndarray

Normalized encoders scaled by the gain and radius. +This quantity is used in the actual simulation, unlike encoders.

+
+
gainndarray

Gain of each neuron.

+
+
biasndarray

Bias current injected into each neuron.

+
+
+
+
+

Create new instance of BuiltEnsemble(eval_points, encoders, intercepts, max_rates, scaled_encoders, gain, bias)

+
+ +
+
+nengo.builder.ensemble.gen_eval_points(ens, eval_points, rng, scale_eval_points=True, dtype=None)[source]
+

Generate evaluation points for ensemble.

+
+ +
+
+nengo.builder.ensemble.get_activities(built_ens, ens, eval_points)[source]
+

Get output of ensemble neurons for given evaluation points.

+
+ +
+
+nengo.builder.ensemble.get_gain_bias(ens, rng=numpy.random, dtype=None)[source]
+

Compute concrete gain and bias for ensemble.

+
+ +
+
+nengo.builder.ensemble.build_ensemble(model, ens)[source]
+

Builds an Ensemble object into a model.

+

A brief summary of what happens in the ensemble build process, in order:

+
    +

  1. Generate evaluation points and encoders.

    2. +

  2. Normalize encoders to unit length.

    3. +

  3. Determine bias and gain.

    4. +

  4. Create neuron input signal

    5. +

  5. Add operator for injecting bias.

    6. +

  6. Call build function for neuron type.

    7. +

  7. Scale encoders by gain and radius.

    8. +

  8. Add operators for multiplying decoded input signal by encoders and +incrementing the result in the neuron input signal.

    9. +

  9. Call build function for injected noise.

    10. +
+

Some of these steps may be altered or omitted depending on the parameters +of the ensemble, in particular the neuron type. For example, most steps are +omitted for the Direct neuron type.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
ensEnsemble

The ensemble to build.

+
+
+
+
+

Notes

+

Sets model.params[ens] to a BuiltEnsemble instance.

+
+ +
+
+

Learning rule builders

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.builder.learning_rules.SimPES

Calculate connection weight change according to the PES rule.

nengo.builder.learning_rules.SimBCM

Calculate connection weight change according to the BCM rule.

nengo.builder.learning_rules.SimOja

Calculate connection weight change according to the Oja rule.

nengo.builder.learning_rules.SimVoja

Simulates a simplified version of Oja’s rule in the vector space.

nengo.builder.learning_rules.SimRLS

Calculate connection weight change according to the RLS rule.

nengo.builder.learning_rules.get_pre_ens

Get the input Ensemble for connection.

nengo.builder.learning_rules.get_post_ens

Get the output Ensemble for connection.

nengo.builder.learning_rules.build_or_passthrough

Builds the obj on signal, or returns the signal if obj is None.

nengo.builder.learning_rules.build_learning_rule

Builds a LearningRule object into a model.

nengo.builder.learning_rules.build_bcm

Builds a BCM object into a model.

nengo.builder.learning_rules.build_oja

Builds a BCM object into a model.

nengo.builder.learning_rules.build_voja

Builds a Voja object into a model.

nengo.builder.learning_rules.build_pes

Builds a PES object into a model.

nengo.builder.learning_rules.build_rls

Builds an RLS (Recursive Least Squares) object into a model.

+
+
+class nengo.builder.learning_rules.SimPES(pre_filtered, error, delta, learning_rate, tag=None)[source]
+

Calculate connection weight change according to the PES rule.

+

Implements the PES learning rule of the form

+
+\[\Delta \omega_{ij} = \frac{\kappa}{n} e_j a_i\]
+

where

+
    +

  • \(\kappa\) is a scalar learning rate,

    • +

  • \(n\) is the number of presynaptic neurons

    • +

  • \(e_j\) is the error for the jth output dimension, and

    • +

  • \(a_i\) is the activity of a presynaptic neuron.

    • +
+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
pre_filteredSignal

The presynaptic activity, \(a_i\).

+
+
errorSignal

The error signal, \(e_j\).

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate, \(\kappa\).

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs []

    3. +

  3. reads [pre_filtered, error]

    4. +

  4. updates [delta]

    5. +
+
+
Attributes
+
+
pre_filteredSignal

The presynaptic activity, \(a_i\).

+
+
errorSignal

The error signal, \(e_j\).

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate, \(\kappa\).

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.learning_rules.SimBCM(pre_filtered, post_filtered, theta, delta, learning_rate, tag=None)[source]
+

Calculate connection weight change according to the BCM rule.

+

Implements the Bienenstock-Cooper-Munroe learning rule of the form

+
+\[\Delta \omega_{ij} = \kappa a_j (a_j - \theta_j) a_i\]
+

where

+
    +

  • \(\kappa\) is a scalar learning rate,

    • +

  • \(a_j\) is the activity of a postsynaptic neuron,

    • +

  • \(\theta_j\) is an estimate of the average \(a_j\), and

    • +

  • \(a_i\) is the activity of a presynaptic neuron.

    • +
+
+
Parameters
+
+
pre_filteredSignal

The presynaptic activity, \(a_i\).

+
+
post_filteredSignal

The postsynaptic activity, \(a_j\).

+
+
thetaSignal

The modification threshold, \(\theta_j\).

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate, \(\kappa\).

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs []

    3. +

  3. reads [pre_filtered, post_filtered, theta]

    4. +

  4. updates [delta]

    5. +
+
+
Attributes
+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate, \(\kappa\).

+
+
post_filteredSignal

The postsynaptic activity, \(a_j\).

+
+
pre_filteredSignal

The presynaptic activity, \(a_i\).

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
thetaSignal

The modification threshold, \(\theta_j\).

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.learning_rules.SimOja(pre_filtered, post_filtered, weights, delta, learning_rate, beta, tag=None)[source]
+

Calculate connection weight change according to the Oja rule.

+

Implements the Oja learning rule of the form

+
+\[\Delta \omega_{ij} = \kappa (a_i a_j - \beta a_j^2 \omega_{ij})\]
+

where

+
    +

  • \(\kappa\) is a scalar learning rate,

    • +

  • \(a_i\) is the activity of a presynaptic neuron,

    • +

  • \(a_j\) is the activity of a postsynaptic neuron,

    • +

  • \(\beta\) is a scalar forgetting rate, and

    • +

  • \(\omega_{ij}\) is the connection weight between the two neurons.

    • +
+
+
Parameters
+
+
pre_filteredSignal

The presynaptic activity, \(a_i\).

+
+
post_filteredSignal

The postsynaptic activity, \(a_j\).

+
+
weightsSignal

The connection weight matrix, \(\omega_{ij}\).

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate, \(\kappa\).

+
+
betafloat

The scalar forgetting rate, \(\beta\).

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs []

    3. +

  3. reads [pre_filtered, post_filtered, weights]

    4. +

  4. updates [delta]

    5. +
+
+
Attributes
+
+
betafloat

The scalar forgetting rate, \(\beta\).

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate, \(\kappa\).

+
+
post_filteredSignal

The postsynaptic activity, \(a_j\).

+
+
pre_filteredSignal

The presynaptic activity, \(a_i\).

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
weightsSignal

The connection weight matrix, \(\omega_{ij}\).

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.learning_rules.SimVoja(pre_decoded, post_filtered, scaled_encoders, delta, scale, learning_signal, learning_rate, tag=None)[source]
+

Simulates a simplified version of Oja’s rule in the vector space.

+

See Learning new associations for details.

+
+
Parameters
+
+
pre_decodedSignal

Decoded activity from presynaptic ensemble, \(a_i\).

+
+
post_filteredSignal

Filtered postsynaptic activity signal.

+
+
scaled_encodersSignal

2d array of encoders, multiplied by scale.

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
scalendarray

The length of each encoder.

+
+
learning_signalSignal

Scalar signal to be multiplied by learning_rate. Expected to be +either 0 or 1 to turn learning off or on, respectively.

+
+
learning_ratefloat

The scalar learning rate.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs []

    3. +

  3. reads [pre_decoded, post_filtered, scaled_encoders, learning_signal]

    4. +

  4. updates [delta]

    5. +
+
+
Attributes
+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ij}\).

+
+
learning_ratefloat

The scalar learning rate.

+
+
learning_signalSignal

Scalar signal to be multiplied by learning_rate. Expected to be +either 0 or 1 to turn learning off or on, respectively.

+
+
post_filteredSignal

Filtered postsynaptic activity signal.

+
+
pre_decodedSignal

Decoded activity from presynaptic ensemble, \(a_i\).

+
+
scalendarray

The length of each encoder.

+
+
scaled_encodersSignal

2d array of encoders, multiplied by scale.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+class nengo.builder.learning_rules.SimRLS(pre_filtered, error, delta, inv_gamma, tag=None)[source]
+

Calculate connection weight change according to the RLS rule.

+

Implements the Recursive Least Squares (RLS) learning rule of the form

+
+\[\begin{split}g_i &= \sum_j P_{ij}(n-1) r_i \\ +P_{ij}(n) &= P_{ij}(n-1) + - g_i g_j / \left( 1 + \sum_{ij} r_i P_{ij}(n-1) r_j \right) \\ +\Delta \omega_{ji} &= \frac{\kappa \delta_t}{n} e_j \sum_{k} P_{ik}(n) r_k\end{split}\]
+

where \(r_i\) are the filtered presynaptic activities, \(e_j\) are the +errors, \(\kappa\) is the learning rate, \(\delta_t\) is the simulator +timestep, and \(n\) is the number of presynaptic neurons.

+
+
Parameters
+
+
pre_filteredSignal

The filtered presynaptic activity, \(r_i\).

+
+
errorSignal

The error signal, \(e_j\).

+
+
deltaSignal

The synaptic weight change to be applied, \(\Delta \omega_{ji}\).

+
+
inv_gammandarray

The inverse activity matrix \(P_{ij}\).

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs []

    3. +

  3. reads [pre_filtered, error]

    4. +

  4. updates [delta, inv_gamma]

    5. +
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.learning_rules.get_pre_ens(conn)[source]
+

Get the input Ensemble for connection.

+
+ +
+
+nengo.builder.learning_rules.get_post_ens(conn)[source]
+

Get the output Ensemble for connection.

+
+ +
+
+nengo.builder.learning_rules.build_or_passthrough(model, obj, signal)[source]
+

Builds the obj on signal, or returns the signal if obj is None.

+
+ +
+
+nengo.builder.learning_rules.build_learning_rule(model, rule)[source]
+

Builds a LearningRule object into a model.

+

A brief summary of what happens in the learning rule build process, +in order:

+
    +

  1. Create a delta signal for the weight change.

    2. +

  2. Add an operator to increment the weights by delta.

    3. +

  3. Call build function for the learning rule type.

    4. +
+

The learning rule system is designed to work with multiple learning rules +on the same connection. If only one learning rule was to be applied to the +connection, then we could directly modify the weights, rather than +calculating the delta here and applying it in build_connection. +However, with multiple learning rules, we must isolate each delta signal +in case calculating the delta depends on the weights themselves, +making the calculation depend on the order of the learning rule +evaluations.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
ruleLearningRule

The learning rule to build.

+
+
+
+
+

Notes

+

Sets model.params[rule] to None.

+
+ +
+
+nengo.builder.learning_rules.build_bcm(model, bcm, rule)[source]
+

Builds a BCM object into a model.

+

Calls synapse build functions to filter the pre and post activities, +and adds a SimBCM operator to the model to calculate the delta.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
bcmBCM

Learning rule type to build.

+
+
ruleLearningRule

The learning rule object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same BCM instance.

+
+ +
+
+nengo.builder.learning_rules.build_oja(model, oja, rule)[source]
+

Builds a BCM object into a model.

+

Calls synapse build functions to filter the pre and post activities, +and adds a SimOja operator to the model to calculate the delta.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
ojaOja

Learning rule type to build.

+
+
ruleLearningRule

The learning rule object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same Oja instance.

+
+ +
+
+nengo.builder.learning_rules.build_voja(model, voja, rule)[source]
+

Builds a Voja object into a model.

+

Calls synapse build functions to filter the post activities, +and adds a SimVoja operator to the model to calculate the delta.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
vojaVoja

Learning rule type to build.

+
+
ruleLearningRule

The learning rule object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same Voja instance.

+
+ +
+
+nengo.builder.learning_rules.build_pes(model, pes, rule)[source]
+

Builds a PES object into a model.

+

Calls synapse build functions to filter the pre activities, +and adds a SimPES operator to the model to calculate the delta.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
pesPES

Learning rule type to build.

+
+
ruleLearningRule

The learning rule object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same PES instance.

+
+ +
+
+nengo.builder.learning_rules.build_rls(model, rls, rule)[source]
+

Builds an RLS (Recursive Least Squares) object into a model.

+

Calls synapse build functions to filter the pre activities, +and adds a SimRLS operator to the model to calculate the delta.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
rlsRLS

Learning rule type to build.

+
+
ruleLearningRule

The learning rule object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same RLS instance.

+
+ +
+
+

Neuron builders

+ ++++ + + + + + + + + + + + +

nengo.builder.neurons.SimNeurons

Set a neuron model output for the given input current.

nengo.builder.neurons.build_neurons

Builds a NeuronType object into a model.

nengo.builder.neurons.build_rates_to_spikes

Builds a RatesToSpikesNeuronType object into a model.

+
+
+class nengo.builder.neurons.SimNeurons(neurons, J, output, state=None, tag=None)[source]
+

Set a neuron model output for the given input current.

+

Implements neurons.step(dt, J, **state).

+
+
Parameters
+
+
neuronsNeuronType

The NeuronType, which defines a step function.

+
+
JSignal

The input current.

+
+
outputSignal

The neuron output signal that will be set

+
+
statelist, optional

A list of additional neuron state signals set by step.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets [output] + state

    2. +

  2. incs []

    3. +

  3. reads [J]

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
JSignal

The input current.

+
+
neuronsNeuronType

The NeuronType, which defines a step function.

+
+
outputSignal

The neuron output signal that will be set.

+
+
statelist

A list of additional neuron state signals set by step.

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.neurons.build_neurons(model, neurontype, neurons, input_sig=None, output_sig=None)[source]
+

Builds a NeuronType object into a model.

+

This function adds a SimNeurons operator connecting the input current to the +neural output signals, and handles any additional state variables defined +within the neuron type.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
neurontypeNeuronType

Neuron type to build.

+
+
neuronNeurons

The neuron population object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same NeuronType instance.

+
+ +
+
+nengo.builder.neurons.build_rates_to_spikes(model, neurontype, neurons)[source]
+

Builds a RatesToSpikesNeuronType object into a model.

+

This function adds two SimNeurons operators. The first one handles +simulating the base_type, converting input signals into rates. The second +one takes those rates as input and emits spikes.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
neurontypeRatesToSpikesNeuronType

Neuron type to build.

+
+
neuronNeurons

The neuron population object corresponding to the neuron type.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same NeuronType instance.

+
+ +
+
+

Node builder

+ ++++ + + + + + +

nengo.builder.node.build_node

Builds a Node object into a model.

+
+
+nengo.builder.node.build_node(model, node)[source]
+

Builds a Node object into a model.

+

The node build function is relatively simple. It involves creating input +and output signals, and connecting them with an Operator that depends +on the type of node.output.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
nodeNode

The node to build.

+
+
+
+
+

Notes

+

Sets model.params[node] to None.

+
+ +
+
+

Probe builder

+ ++++ + + + + + + + + + + + + + + +

nengo.builder.probe.SimProbe

Mark a signal as being probed.

nengo.builder.probe.conn_probe

Build a “connection” probe type.

nengo.builder.probe.signal_probe

Build a “signal” probe type.

nengo.builder.probe.build_probe

Builds a Probe object into a model.

+
+
+class nengo.builder.probe.SimProbe(signal, tag=None)[source]
+

Mark a signal as being probed.

+

This performs no computations, but marks signal as being read. This is +necessary for the rare case in which a node with constant output is probed +directly without that signal being otherwise used.

+
+
Parameters
+
+
signalSignal

The probed signal.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs []

    3. +

  3. reads [signal]

    4. +

  4. updates []

    5. +
+
+
+property signal
+

The probed signal

+
+ +
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.probe.conn_probe(model, probe)[source]
+

Build a “connection” probe type.

+

Connection probes create a connection from the target, and probe +the resulting signal (used when you want to probe the default +output of an object, which may not have a predefined signal).

+
+ +
+
+nengo.builder.probe.signal_probe(model, key, probe)[source]
+

Build a “signal” probe type.

+

Signal probes directly probe a target signal.

+
+ +
+
+nengo.builder.probe.build_probe(model, probe)[source]
+

Builds a Probe object into a model.

+

Under the hood, there are two types of probes: +connection probes and signal probes.

+

Connection probes are those that are built by creating a new Connection +object from the probe’s target to the probe, and calling that connection’s +build function. Creating and building a connection ensure that the result +of probing the target’s attribute is the same as would result from that +target being connected to another object.

+

Signal probes are those that are built by finding the correct Signal +in the model and calling the build function corresponding to the probe’s +synapse.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
probeProbe

The connection to build.

+
+
+
+
+

Notes

+

Sets model.params[probe] to a list. +Simulator appends to that list when running a simulation.

+
+ +
+
+

Process builder

+ ++++ + + + + + + + + +

nengo.builder.processes.SimProcess

Simulate a process.

nengo.builder.processes.build_process

Builds a Process object into a model.

+
+
+class nengo.builder.processes.SimProcess(process, input, output, t, mode='set', state=None, tag=None)[source]
+

Simulate a process.

+
+
Parameters
+
+
processProcess

The Process to simulate.

+
+
inputSignal or None

Input to the process, or None if no input.

+
+
outputSignal or None

Output from the process, or None if no output.

+
+
tSignal

The signal associated with the time (a float, in seconds).

+
+
modestr, optional

Denotes what type of update this operator performs. +Must be one of 'update', 'inc' or 'set'.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets [output] if output is not None and mode=='set' else []

    2. +

  2. incs [output] if output is not None and mode=='inc' else []

    3. +

  3. reads [t, input] if input is not None else [t]

    4. +

  4. updates [output] if output is not None and mode=='update' else []

    5. +
+
+
Attributes
+
+
inputSignal or None

Input to the process, or None if no input.

+
+
modestr

Denotes what type of update this operator performs.

+
+
outputSignal or None

Output from the process, or None if no output.

+
+
processProcess

The Process to simulate.

+
+
tSignal

The signal associated with the time (a float, in seconds).

+
+
tagstr or None

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.processes.build_process(model, process, sig_in=None, sig_out=None, mode='set')[source]
+

Builds a Process object into a model.

+
+
Parameters
+
+
modelModel

The model to build into.

+
+
processProcess

Process to build.

+
+
sig_inSignal, optional

The input signal, or None if no input signal.

+
+
sig_outSignal, optional

The output signal, or None if no output signal.

+
+
mode“set” or “inc” or “update”, optional

The mode of the built SimProcess.

+
+
+
+
+

Notes

+

Does not modify model.params[] and can therefore be called +more than once with the same Process instance.

+
+ +
+
+

Transform builders

+ ++++ + + + + + + + + + + + + + + + + + + + + +

nengo.builder.transforms.multiply

Matrix-matrix multiply, interpreting vectors as diagonal matrices.

nengo.builder.transforms.build_dense

Build a Dense transform object.

nengo.builder.transforms.build_sparse

Build a Sparse transform object.

nengo.builder.transforms.build_convolution

Build a Convolution transform object.

nengo.builder.transforms.ConvInc

Apply convolutional weights to input signal.

nengo.builder.transforms.build_no_transform

Build a NoTransform transform object.

+
+
+nengo.builder.transforms.multiply(x, y)[source]
+

Matrix-matrix multiply, interpreting vectors as diagonal matrices.

+
+ +
+
+nengo.builder.transforms.build_dense(model, transform, sig_in, decoders=None, encoders=None, rng=numpy.random)[source]
+

Build a Dense transform object.

+
+ +
+
+nengo.builder.transforms.build_sparse(model, transform, sig_in, decoders=None, encoders=None, rng=numpy.random)[source]
+

Build a Sparse transform object.

+
+ +
+
+nengo.builder.transforms.build_convolution(model, transform, sig_in, decoders=None, encoders=None, rng=numpy.random)[source]
+

Build a Convolution transform object.

+
+ +
+
+class nengo.builder.transforms.ConvInc(W, X, Y, conv, tag=None)[source]
+

Apply convolutional weights to input signal.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
WSignal

The convolutional weights (a.k.a. the kernel).

+
+
XSignal

The input signal.

+
+
YSignal

Output signal to be incremented.

+
+
convConvolution

The Convolution object being applied.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+

Notes

+
    +

  1. sets []

    2. +

  2. incs [Y]

    3. +

  3. reads [W, X]

    4. +

  4. updates []

    5. +
+
+
Attributes
+
+
WSignal

The convolutional weights.

+
+
XSignal

The input signal.

+
+
YSignal

Output signal to be incremented.

+
+
convConvolution

The Convolution object being applied.

+
+
tagstr, optional

A label associated with the operator, for debugging purposes.

+
+
+
+
+
+
+make_step(signals, dt, rng)[source]
+

Returns a callable that performs the desired computation.

+

This method must be implemented by subclasses. To fully understand what +an operator does, look at its implementation of make_step.

+
+
Parameters
+
+
signalsSignalDict

A mapping from signals to their associated live ndarrays.

+
+
dtfloat

Length of each simulation timestep, in seconds.

+
+
rngnumpy.random.RandomState

Random number generator for stochastic operators.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.transforms.build_no_transform(model, transform, sig_in, decoders=None, encoders=None, rng=numpy.random)[source]
+

Build a NoTransform transform object.

+
+ +
+
+

Decoder cache

+

Caching capabilities for a faster build process.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.cache.get_fragment_size

Get fragment size in cross-compatible way.

nengo.cache.safe_stat

Does os.stat, but fails gracefully in case of an OSError.

nengo.cache.safe_remove

Does os.remove, but fails gracefully in case of an OSError.

nengo.cache.safe_makedirs

Try to make directories, but continue on error.

nengo.cache.check_dtype

Check that array is a standard dtype.

nengo.cache.check_seq

Check that all objects in list are fingerprintable.

nengo.cache.check_mapping

Check that all values in dict are fingerprintable.

nengo.cache.check_attrs

Check that all attributes of obj are fingerprintable.

nengo.cache.Fingerprint

Fingerprint of an object instance.

nengo.cache.CacheIndex

Cache index mapping keys to (filename, start, end) tuples.

nengo.cache.WriteableCacheIndex

Writable cache index mapping keys to files.

nengo.cache.DecoderCache

Cache for decoders.

nengo.cache.NoDecoderCache

Provides the same interface as DecoderCache without caching.

nengo.cache.get_default_decoder_cache

Get default decoder implementation based on config settings.

+
+
+nengo.cache.get_fragment_size(path)[source]
+

Get fragment size in cross-compatible way.

+
+ +
+
+nengo.cache.safe_stat(path)[source]
+

Does os.stat, but fails gracefully in case of an OSError.

+
+ +
+
+nengo.cache.safe_remove(path)[source]
+

Does os.remove, but fails gracefully in case of an OSError.

+
+ +
+
+nengo.cache.safe_makedirs(path)[source]
+

Try to make directories, but continue on error.

+
+ +
+
+nengo.cache.check_dtype(ndarray)[source]
+

Check that array is a standard dtype.

+
+ +
+
+nengo.cache.check_seq(tpl)[source]
+

Check that all objects in list are fingerprintable.

+
+ +
+
+nengo.cache.check_mapping(mapping)[source]
+

Check that all values in dict are fingerprintable.

+
+ +
+
+nengo.cache.check_attrs(obj)[source]
+

Check that all attributes of obj are fingerprintable.

+
+ +
+
+class nengo.cache.Fingerprint(obj)[source]
+

Fingerprint of an object instance.

+

A finger print is equal for two instances if and only if they are of the +same type and have the same attributes.

+

The fingerprint will be used as identification for caching.

+
+
Parameters
+
+
objobject

Object to fingerprint.

+
+
+
+
+

Notes

+

Not all objects can be fingerprinted. In particular, custom classes are +tricky to fingerprint as their implementation can change without changing +its fingerprint, as the type and attributes may be the same.

+

In order to ensure that only safe object are fingerprinted, this class +maintains class attribute WHITELIST that contains all types that can +be safely fingerprinted.

+

If you want your custom class to be fingerprinted, call the +whitelist class method and pass in your class.

+
+
Attributes
+
+
fingerprinthash

A unique fingerprint for the object instance.

+
+
+
+
+
+
+classmethod supports(obj)[source]
+

Determines whether obj can be fingerprinted.

+

Uses the whitelist method and runs the check function associated +with the type of obj.

+
+ +
+
+classmethod whitelist(typ, fn=None)[source]
+

Whitelist the type given in typ.

+

Will run the check function fn on objects if provided.

+
+ +
+ +
+
+class nengo.cache.CacheIndex(cache_dir)[source]
+

Cache index mapping keys to (filename, start, end) tuples.

+

Once instantiated the cache index has to be used in a with block to +allow access. The index will not be loaded before the with block is +entered.

+

This class only provides read access to the cache index. For write +access use WriteableCacheIndex.

+
+
Parameters
+
+
cache_dirstr

Path where the cache is stored.

+
+
+
+
+

Notes

+

Under the hood, the cache index is stored as a pickle file. The pickle file +contains two objects which are read sequentially: the version tuple, +and the index dictionary mapping keys to (filename, start, end) tuples.

+

Examples

+
from nengo.cache import CacheIndex, WriteableCacheIndex
+
+to_cache = ("gotta cache 'em all", 151)
+
+# create index file
+with WriteableCacheIndex(cache_dir) as index:
+    index[hash(to_cache)] = ("file1", 0, 1)  # set an item
+
+# read from index
+with CacheIndex(cache_dir) as index:
+    filename, start, end = index[hash(to_cache)]
+
+
+
+
Attributes
+
+
cache_dirstr

Path where the cache is stored.

+
+
index_pathstr

Path to the cache index file.

+
+
versiontuple

Version code of the loaded cache index. The first element gives the +format of the cache and the second element gives the pickle protocol +used to store the index. Note that a cache index will always be written +in the newest version with the highest pickle protocol.

+
+
VERSION (class attribute)int

Highest supported version, and version used to store the cache index.

+
+
+
+
+
+ +
+
+class nengo.cache.WriteableCacheIndex(cache_dir)[source]
+

Writable cache index mapping keys to files.

+

This class allows write access to the cache index.

+

The updated cache file will be written when the with block is exited. +The initial read and the write on exit of the with block are locked +against concurrent access with a file lock. The lock will be released +within the with block.

+
+
Parameters
+
+
cache_dirstr

Path where the cache is stored.

+
+
+
+
+

Examples

+
from nengo.cache import WriteableCacheIndex
+
+to_cache = ("gotta cache 'em all", 151)
+key = hash(to_cache)
+with WriteableCacheIndex(cache_dir) as index:
+    index[key] = ("file1", 0, 1)  # set an item
+    del index[key]  # remove an item by key
+    index.remove_file_entry("file1")  # remove an item by filename
+
+
+
+
+remove_file_entry(filename)[source]
+

Remove entries mapping to filename.

+
+ +
+
+sync()[source]
+

Write changes to the cache index back to disk.

+

The call to this function will be locked by a file lock.

+
+ +
+ +
+
+class nengo.cache.DecoderCache(readonly=False, cache_dir=None)[source]
+

Cache for decoders.

+

Hashes the arguments to the decoder solver and stores the result in a file +which will be reused in later calls with the same arguments.

+

Be aware that decoders should not use any global state, but only values +passed and attributes of the object instance. Otherwise the wrong solver +results might get loaded from the cache.

+
+
Parameters
+
+
readonlybool

Indicates that already existing items in the cache will be used, but no +new items will be written to the disk in case of a cache miss.

+
+
cache_dirstr or None

Path to the directory in which the cache will be stored. It will be +created if it does not exists. Will use the value returned by +get_default_dir, if None.

+
+
+
+
+
+
+static get_default_dir()[source]
+

Returns the default location of the cache.

+
+
Returns
+
+
str
+
+
+
+
+ +
+
+get_files()[source]
+

Returns all of the files in the cache.

+
+
Returns
+
+
list of (str, int) tuples
+
+
+
+
+ +
+
+get_size()[source]
+

Returns the size of the cache with units as a string.

+
+
Returns
+
+
str
+
+
+
+
+ +
+
+get_size_in_bytes()[source]
+

Returns the size of the cache in bytes as an int.

+
+
Returns
+
+
int
+
+
+
+
+ +
+
+invalidate()[source]
+

Invalidates the cache (i.e. removes all cache files).

+
+ +
+
+shrink(limit=None)[source]
+

Reduces the size of the cache to meet a limit.

+
+
Parameters
+
+
limitint, optional

Maximum size of the cache in bytes.

+
+
+
+
+
+ +
+
+remove_file(path)[source]
+

Removes the file at path from the cache.

+
+ +
+
+wrap_solver(solver_fn)[source]
+

Takes a decoder solver and wraps it to use caching.

+
+
Parameters
+
+
solverfunc

Decoder solver to wrap for caching.

+
+
+
+
Returns
+
+
func

Wrapped decoder solver.

+
+
+
+
+
+ +
+ +
+
+class nengo.cache.NoDecoderCache[source]
+

Provides the same interface as DecoderCache without caching.

+
+ +
+
+nengo.cache.get_default_decoder_cache()[source]
+

Get default decoder implementation based on config settings.

+
+ +
+
+

Optimizer

+

Operator graph optimizers.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.builder.optimizer.optimize

Optimizes the operator graph by merging operators.

nengo.builder.optimizer.OpMergePass

Manages a single optimization pass.

nengo.builder.optimizer.OpInfo

Analyze and store extra information about operators.

nengo.builder.optimizer.OpsToMerge

Analyze and store extra information about a list of ops to be merged.

nengo.builder.optimizer.OpMerger

Manages the op merge classes known to the optimizer.

nengo.builder.optimizer.Merger

Base class for all op merge classes.

nengo.builder.optimizer.ResetMerger

Merge Reset ops.

nengo.builder.optimizer.CopyMerger

Merge Copy ops.

nengo.builder.optimizer.ElementwiseIncMerger

Merge ElementwiseInc ops.

nengo.builder.optimizer.DotIncMerger

Merge DotInc ops.

nengo.builder.optimizer.SimNeuronsMerger

Merge SimNeurons ops.

nengo.builder.optimizer.SigMerger

Merge signals.

nengo.builder.optimizer.groupby

Groups the given list by the value returned by keyfunc.

+
+
+nengo.builder.optimizer.optimize(model, dg)[source]
+

Optimizes the operator graph by merging operators.

+

This reduces the number of iterators to iterate over in slow Python code +(as opposed to fast C code). The resulting merged operators will also +operate on larger chunks of sequential memory, making better use of CPU +caching and prefetching.

+

The optimization algorithm has worst case complexity \(O(n^2 + e)\), +where \(n\) is the number of operators and \(e\) is the number +of edges in the dependency graph. In practice the run time will be much +better because not all \(n^2\) pairwise combinations of operators +will be evaluated. A grouping depending on the operator type and view +bases is done with dictionaries. This grouping can be done in amortized +linear time and reduces the actual worst-case runtime of the optimization +algorithm to \(O(gm^2 + e)\), where \(g\) is the number of groups +and \(m\) is the number of elements in a group. Moreover, information +about memory alignment will be used to cut the inner loop short in +many cases and gives a runtime much closer to linear in most cases.

+

Note that this function modifies both model and dg.

+
+
Parameters
+
+
modelnengo.builder.Model

Builder output to optimize.

+
+
dgdict

Dict of the form {a: {b, c}} where b and c depend on a, +specifying the operator dependency graph of the model.

+
+
+
+
+
+ +
+
+class nengo.builder.optimizer.OpMergePass(dg)[source]
+

Manages a single optimization pass.

+
+
+perform_merges()[source]
+

Go through all operators and merge them where possible.

+
+
Parameters
+
+
only_merge_ops_with_viewbool

Limit merges to operators with views.

+
+
+
+
+
+ +
+
+perform_merges_for_subset(subset)[source]
+

Performs operator merges for a subset of operators.

+
+
Parameters
+
+
subsetlist

Subset of operators.

+
+
+
+
+
+ +
+
+perform_merges_for_view_subset(subset)[source]
+

Perform merges for a subset of operators with the same view base.

+
+
Parameters
+
+
subsetlist

Subset of operators. These need to have the same view base (can be +None if it is None for all) for their first signal in +all_signals.

+
+
+
+
+
+ +
+
+merge(tomerge)[source]
+

Merges the given operators.

+

This method will also update op_replacements, sig_replacements, +and the internal list of merged operators to prevent further merges +on the same operators before all required operators and signals have +been replaced.

+
+ +
+ +
+
+class nengo.builder.optimizer.OpInfo[source]
+

Analyze and store extra information about operators.

+
+ +
+
+class nengo.builder.optimizer.OpsToMerge(initial_op, merged, merged_dependents, dependents)[source]
+

Analyze and store extra information about a list of ops to be merged.

+
+ +
+
+class nengo.builder.optimizer.OpMerger[source]
+

Manages the op merge classes known to the optimizer.

+
+ +
+
+class nengo.builder.optimizer.Merger[source]
+

Base class for all op merge classes.

+
+
+static merge_dicts(*dicts)[source]
+

Merges the given dictionaries into a single dictionary.

+

This function assumes and enforces that no keys overlap.

+
+ +
+ +
+
+class nengo.builder.optimizer.ResetMerger[source]
+

Merge Reset ops.

+
+ +
+
+class nengo.builder.optimizer.CopyMerger[source]
+

Merge Copy ops.

+
+ +
+
+class nengo.builder.optimizer.ElementwiseIncMerger[source]
+

Merge ElementwiseInc ops.

+
+ +
+
+class nengo.builder.optimizer.DotIncMerger[source]
+

Merge DotInc ops.

+
+ +
+
+class nengo.builder.optimizer.SimNeuronsMerger[source]
+

Merge SimNeurons ops.

+
+ +
+
+class nengo.builder.optimizer.SigMerger[source]
+

Merge signals.

+
+
+static check(signals, axis=0)[source]
+

Checks that all signals can be concatenated along a given axis.

+

For views, this includes also a check that the signals have a common +base and agree on the strides.

+

In comparison to the check_* functions, this function does +not throw exceptions and allows for either signals or signal views.

+
+ +
+
+static check_signals(signals, axis=0)[source]
+

Checks that all signals can be merged along a given axis.

+

If this is not possible, or any signals are views, a +ValueError will be raised.

+
+ +
+
+static check_views(signals, axis=0)[source]
+

Checks that all signal views can be merged along a given axis.

+

If this is not possible, or any signals are not views, +a ValueError will be raised.

+

signals must be ordered by the offset into the base signal.

+
+ +
+
+static merge(signals, axis=0)[source]
+

Merges multiple signals or signal views into one contiguous signal.

+

Note that if any of the signals are linked to another signal (by being +the base of a view), the merged signal will not reflect those links +anymore.

+
+
Parameters
+
+
signalssequence

Signals to merge. Must not contain views.

+
+
axisint, optional

Axis along which to concatenate the signals.

+
+
+
+
Returns
+
+
merged_signalSignal

The merged signal.

+
+
replacementsdict

Dictionary mapping from the old signals to new signals that are +a view into the merged signal. Used to replace old signals.

+
+
+
+
+
+ +
+
+static merge_signals(signals, axis=0)[source]
+

Merges multiple signal into one contiguous signal.

+

Note that if any of the signals are linked to another signal (by being +the base of a view), the merged signal will not reflect +those links anymore.

+
+
Parameters
+
+
signalssequence

Signals to merge. Must not contain views.

+
+
axisint, optional

Axis along which to concatenate the signals.

+
+
+
+
Returns
+
+
merged_signalSignal

The merged signal.

+
+
replacementsdict

Dictionary mapping from the old signals to new signals that are +a view into the merged signal. Used to replace old signals.

+
+
+
+
+
+ +
+
+static merge_views(signals, axis=0)[source]
+

Merges multiple signal views into one contiguous signal view.

+
+
Parameters
+
+
signalssequence

Signals to merge. Must only contain views.

+
+
axisint, optional

Axis along which to concatenate the signals.

+
+
+
+
Returns
+
+
merged_signalSignal

The merged signal.

+
+
replacementsdict

Dictionary mapping from the old signals to new signals that are +a view into the merged signal. Used to replace old signals.

+
+
+
+
+
+ +
+ +
+
+nengo.builder.optimizer.groupby(lst, keyfunc=<function <lambda>>)[source]
+

Groups the given list by the value returned by keyfunc.

+

Similar to itertools.groupby, but returns a dict, and does not depend +on the order of the input list.

+
+ +
+
+
+

Exceptions

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.exceptions.NengoException

Base class for Nengo exceptions.

nengo.exceptions.NengoWarning

Base class for Nengo warnings.

nengo.exceptions.ValidationError

A ValueError encountered during validation of a parameter.

nengo.exceptions.ConvergenceError

A RuntimeError raised when an algorithm does not converge.

nengo.exceptions.ReadonlyError

A ValidationError occurring because a parameter is read-only.

nengo.exceptions.BuildError

A ValueError encountered during the build process.

nengo.exceptions.ObsoleteError

A feature that has been removed in a backwards-incompatible way.

nengo.exceptions.MovedError

A feature that has been moved elsewhere.

nengo.exceptions.ConfigError

A ValueError encountered in the config system.

nengo.exceptions.SpaModuleError

An error in how SPA keeps track of modules.

nengo.exceptions.SpaParseError

An error encountered while parsing a SPA expression.

nengo.exceptions.SimulatorClosed

Raised when attempting to run a closed simulator.

nengo.exceptions.SimulationError

An error encountered during simulation of the model.

nengo.exceptions.SignalError

An error dealing with Signals in the builder.

nengo.exceptions.FingerprintError

An error in fingerprinting an object for cache identification.

nengo.exceptions.NetworkContextError

An error with the Network context stack.

nengo.exceptions.Unconvertible

Raised a requested network conversion cannot be done.

nengo.exceptions.CacheIOError

An IO error in reading from or writing to the decoder cache.

nengo.exceptions.TimeoutError

A timeout occurred while waiting for a resource.

nengo.exceptions.NotAddedToNetworkWarning

A NengoObject has not been added to a network.

nengo.exceptions.CacheIOWarning

A non-critical issue in accessing files in the cache.

+
+
+exception nengo.exceptions.NengoException[source]
+

Base class for Nengo exceptions.

+

NengoException instances should not be created; this base class exists so +that all exceptions raised by Nengo can be caught in a try / except block.

+
+ +
+
+exception nengo.exceptions.NengoWarning[source]
+

Base class for Nengo warnings.

+
+ +
+
+exception nengo.exceptions.ValidationError(msg, attr, obj=None)[source]
+

A ValueError encountered during validation of a parameter.

+
+ +
+
+exception nengo.exceptions.ConvergenceError[source]
+

A RuntimeError raised when an algorithm does not converge.

+
+ +
+
+exception nengo.exceptions.ReadonlyError(attr, obj=None, msg=None)[source]
+

A ValidationError occurring because a parameter is read-only.

+
+ +
+
+exception nengo.exceptions.BuildError[source]
+

A ValueError encountered during the build process.

+
+ +
+
+exception nengo.exceptions.ObsoleteError(msg, since=None, url=None)[source]
+

A feature that has been removed in a backwards-incompatible way.

+
+ +
+
+exception nengo.exceptions.MovedError(location=None)[source]
+

A feature that has been moved elsewhere.

+
+

New in version 3.0.0.

+
+
+ +
+
+exception nengo.exceptions.ConfigError[source]
+

A ValueError encountered in the config system.

+
+ +
+
+exception nengo.exceptions.SpaModuleError[source]
+

An error in how SPA keeps track of modules.

+
+ +
+
+exception nengo.exceptions.SpaParseError[source]
+

An error encountered while parsing a SPA expression.

+
+ +
+
+exception nengo.exceptions.SimulatorClosed[source]
+

Raised when attempting to run a closed simulator.

+
+ +
+
+exception nengo.exceptions.SimulationError[source]
+

An error encountered during simulation of the model.

+
+ +
+
+exception nengo.exceptions.SignalError[source]
+

An error dealing with Signals in the builder.

+
+ +
+
+exception nengo.exceptions.FingerprintError[source]
+

An error in fingerprinting an object for cache identification.

+
+ +
+
+exception nengo.exceptions.NetworkContextError[source]
+

An error with the Network context stack.

+
+ +
+
+exception nengo.exceptions.Unconvertible[source]
+

Raised a requested network conversion cannot be done.

+
+ +
+
+exception nengo.exceptions.CacheIOError[source]
+

An IO error in reading from or writing to the decoder cache.

+
+ +
+
+exception nengo.exceptions.TimeoutError[source]
+

A timeout occurred while waiting for a resource.

+
+ +
+
+exception nengo.exceptions.NotAddedToNetworkWarning(obj)[source]
+

A NengoObject has not been added to a network.

+
+ +
+
+exception nengo.exceptions.CacheIOWarning[source]
+

A non-critical issue in accessing files in the cache.

+
+ +
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/backend_api.html b/backend_api.html new file mode 100644 index 0000000000..ba53e99f56 --- /dev/null +++ b/backend_api.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/changelog.html b/changelog.html new file mode 100644 index 0000000000..eac4bb807e --- /dev/null +++ b/changelog.html @@ -0,0 +1,1479 @@ + + + + + + + + Release history — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Release history

+
+

3.1.1 (unreleased)

+

Fixed

+
    +

  • Operator graph step order will now be deterministic. (#1654)

    • +
+

Removed

+
    +

  • Removed nengo.utils.graphs.graph (this was a small utility function for building +graphs that was only used in tests). (#1654)

    • +
+
+
+

3.1.0 (November 17, 2020)

+

Added

+
    +

  • Added a new example notebook for Legendre Memory Units. +(#1589)

    • +

  • Added the step_order attribute to nengo.Simulator, which contains an +ordered list of the operations run on each timestep. +(#1615)

    • +

  • Added the make_state method to NeuronType, which initializes the +neuron type’s state variables. (#1609)

    • +

  • Added the spiking attribute to NeuronType, which exposes whether +a neuron type is spiking or non-spiking. (#1609)

    • +

  • Added the negative attribute to NeuronType, which indicates whether +the neuron type can have negative outputs. (#1609)

    • +

  • Added the Tanh neuron type to simulate hyperbolic tangent neurons. (#1609)

    • +

  • Added the RatesToSpikesNeuronType, which is a base class for neuron types +that convert a rate-based type to a spiking one. (#1609)

    • +

  • Added the RegularSpiking neuron type, which emits regularly-spaced spikes +at the rate specified by its base type. (#1609)

    • +

  • Added the StochasticSpiking neuron type, which emits spikes based on stochastic +rounding to roughly match the rate specified by its base type. (#1609)

    • +

  • Added the PoissonSpiking neuron type, which emits Poisson-distributed spikes, +as are commonly used to match biological spiking statistics. (#1609)

    • +

  • Added the PositiveNeuronType test argument to run tests on all neuron types +for which negative is not True. (#1609)

    • +

  • Added the QuasirandomSequence distribution, which is similar to +Uniform but spreads points across the space evenly. (#1611)

    • +

  • Added the ScatteredHypersphere distribution, which is similar to +UniformHypersphere but spreads points across the space more evenly. (#1611)

    • +

  • Added the RLS (recursive least-squares) learning rule, which is an online +version of the least-squares method typically used for offline decoder-solving. +(#1611, example)

    • +

  • Added the SimProbe operator, which marks a signal as being probed. (#1653)

    • +
+

Changed

+
    +

  • Nengo is now compatible with Python 3.8. (#1628)

    • +

  • The default Connection transform is now None, meaning that there will be +no transform applied. This only changes behavior when learning on a +neuron-neuron connection with the default scalar transform. In that situation +there are now no weights to apply learning to, so this will result in an +error. The old behaviour can be obtained by setting transform=1. +(#1591)

    • +

  • Network list attributes (e.g. .ensembles, .connections, .probes) are now +read-only, to prevent users from accidentally overwriting them with their own data. +(#1545, +#1608)

    • +

  • The NeuronType.step_math method has been renamed to NeuronType.step. +(#1609)

    • +

  • Neuron types can now create arbitrary state variables without needing to register +a new build function. The state class attribute declares the neuron type’s +state variables and their default initial values. All __init__ methods accept +an initial_state dictionary for users to override the default initial state +values. (#1609)

    • +

  • The nl and nl_nodirect test arguments have been renamed to AnyNeuronType +and NonDirectNeuronType. (#1609)

    • +

  • Weight solvers (i.e. those with weights=True) are now allowed on all connections. +For connections that are not between Ensembles, though, weight solvers have the +same effects as solvers with weights=False, and a warning will be raised. +(#1626)

    • +

  • Various improvements to simulation speed. (#1629)

    • +

  • EnsembleArray now raises an error if add_output would +overwrite an existing attribute. (#1611)

    • +

  • The encoders and eval_points of Ensemble are now sampled from +ScatteredHypersphere by default. (#1611)

    • +
+

Deprecated

+
    +

  • NeuronType.step replaces the NeuronType.step_math method, +which will be removed in Nengo 4.0.0. (#1609)

    • +

  • Connection.is_decoded is deprecated, as the definition of whether a Connection +is decoded or not was ambiguous. Instead we recommend directly checking the pre/post +objects for the properties of interest. (#1640)

    • +
+

Fixed

+
    +

  • Fixed a bug when comparing equality with Ensemble.neurons or +Connection.learning_rule objects. +(#1588)

    • +

  • Fixed a bug preventing unpickling an Ensemble. +(#1598)

    • +

  • Fixed a bug in which unpickling a Simulator would rerun the optimizer. +(#1598)

    • +

  • Fixed a bug where the LstsqDrop solver errored when solving for zero weights. +(#1541, +#1607)

    • +

  • Fixed a bug in the validation of Choice distributions. (#1630)

    • +

  • Fixed a bug where a Signal did not register as sharing memory with itself. +(#1627)

    • +

  • Fixed a shape error when applying PES learning to a neuron-to-neuron connection with a +slice on the post-synaptic neurons. (#1640)

    • +

  • Fixed a shape error when applying PES learning to a neuron->ensemble connection with +a weight solver. (#1640)

    • +

  • Fixed a shape error when applying PES learning to an ensemble->neuron connection. +(#1640)

    • +

  • Fixed a shape error when applying PES learning with a slice on the pre-synaptic +object. (#1640)

    • +
+
+
+

3.0.0 (November 18, 2019)

+

Added

+
    +

  • Added progress bar support for Jupyter Lab >=0.32. +(#1428, +#1087)

    • +

  • We now warn that the progress bar is not supported in Jupyter Notebook <5. +(#1428, +#1426)

    • +

  • Added support for convolutional connections. +(#1481)

    • +

  • Added version tracking to documentation, so that documentation from old +versions remains available. +(#1488)

    • +

  • Added support for sparse connections. +(#1532)

    • +

  • Added a fail_fast setting to test operators when they are first +added to the model. See configuration options +for details. (#1532)

    • +

  • Added a --memory option for pytest that prints the total memory +consumed by the tests when they complete (Linux and Mac OS X only). +(#640)

    • +

  • Added a bit precision setting to change the number of bits allocated +to each value tracked by Nengo. +(#640)

    • +

  • Added a Simulator.clear_probes method to clear probe data. +This method can be used before pickling to reduce the pickle file size. +(#1387)

    • +

  • Nengo tests now use the allclose fixture from pytest-allclose, +which makes it possible for backends to change test tolerances. +(#1563)

    • +

  • Nengo tests now use the rng and seed fixtures from pytest-rng. +(#1566)

    • +

  • Nengo tests now use the plt fixture from pytest-plt. +(#1566)

    • +

  • Added a nengo_simloader pytest option for specifying a callable that +takes a pytest request and returns a callable to be used +as Simulator in the Nengo test suite. +(#1566)

    • +

  • Added more content to the API reference documentation. +(#1578)

    • +
+

Changed

+
    +

  • Python 2 is no longer supported. The oldest supported Python version is 3.5. +(#1520, +python3statement.org)

    • +

  • Nengo no longer supports Python 3.4. +Official 3.4 support ended in March 2019. +(PEP-429, +#1514)

    • +

  • Replaced the dt argument to Simulator.trange with sample_every +because dt would return values that the simulator had not simulated. +dt is now an alias for sample_every and will be removed in the future. +(#1368, +#1384)

    • +

  • Dense connection transforms (this includes all previously supported values +for Connection.transform) will now be represented internally as +nengo.Dense objects. Arrays/scalars can still be passed as transform +values, and they will be automatically converted to the equivalent +nengo.Dense object. Retrieving the value of my_conn.transform will +return that Dense object. The original input array can be retrieved +through my_conn.transform.init. +(#1481)

    • +

  • nengo.solvers.NoSolver(w, weights=True) now expects w to have shape +(pre.n_neurons, function_d), +rather than pre.n_neurons, post.n_neurons). That is, with NoSolver +you are always specifying the values for the decoders, and encoders/transform +will be applied automatically to those decoders (as occurs with +all other solvers). Note that this does not affect +NoSolver(..., weights=False) (the default). +(#1481)

    • +

  • Increased minimum NumPy version to 1.11.0. See our +instructions for installing NumPy +if you need to upgrade. +(#1481)

    • +

  • Solvers are now explicitly marked as compositional or non-compositional +depending on whether they must act on full connection weight matrices +when solving for weights. +(#1507)

    • +

  • Solvers no longer take encoders as an argument. Instead, encoders will +be applied to the targets before the solve function for non-compositional +solvers and applied by the Transform builder for compositional solvers. +(#1507)

    • +

  • Example Jupyter notebooks have been upgraded to notebook format 4. +(#1440)

    • +

  • Switched documentation to new +nengo-sphinx-theme. +(#1489)

    • +

  • The settled_firingrate function has been moved from +nengo.utils.neurons to nengo.neurons. +(#1187)

    • +

  • Added new pytest config option, nengo_test_unsupported (replacing the +previous Simulator.unsupported functionality). +(#1521)

    • +

  • Switched to nengo-bones templating system for TravisCI config/scripts. +(#1514)

    • +

  • The NeuronType.current and NeuronType.rates methods now document +the supported shapes of parameters and return values. +(#1437)

    • +

  • PES learning updates are now applied on the next timestep rather than +the current one. +(#1398)

    • +

  • The NdarrayParam now accepts a dtype argument to check that +data assigned to that parameter matches the given Numpy dtype. +DistOrArrayParam accepts an analogous sample_dtype argument. +(#1532)

    • +

  • We no longer test operators when they are initially added to the model, +which speed up build times slightly. To re-enable this testing, +enable the fail_fast RC setting. +(#1532)

    • +

  • LinearFilter now uses state space representations internally, +which is faster and potentially more accurate. +(#1535)

    • +

  • The default value of y0 in Synapse.filt is now 0 instead of +the initial value of the input signal. This allows unstable filters +(e.g., integrators) to be used with filt. +(#1535)

    • +

  • LinearFilter now accepts the discretization method as an argument, +rather than having it specified in make_step. +(#1535)

    • +

  • The synapse_kwargs argument to FilteredNoise has been removed. +(#1535)

    • +

  • Processes with internal state now declare that state by defining a +make_state method and accepting a state parameter in make_step. +(#1387)

    • +

  • Simulator is now pickleable, allowing its state to be saved and loaded. +(#1387)

    • +

  • Renamed utils.testing.allclose to utils.testing.signals_allclose, +to differentiate it from the allclose fixture. +(#1563)

    • +

  • The default intercepts value has been changed to Uniform(-1, 0.9) +to avoid high gains when intercepts are close to 1. +(#1534, +#1561)

    • +

  • The --simulator and --neurons pytest command line arguments are now specified +by nengo_simulator and nengo_neurons entries in the pytest config file +instead. +(#1566)

    • +

  • The nengo_test_unsupported option now uses pytest nodeids for the test names +(the main change is that this means a double :: between file and function names). +(#1566)

    • +

  • Signals will now raise an error if their initial value contains NaNs. +(#1571)

    • +

  • The builder will now raise an error if any encoders are NaN, +which can occur if an encoder has length zero. +(#1571)

    • +

  • Renamed simulator.ProbeDict to simulator.SimulationData. +(#1574)

    • +

  • Increased minimum numpy version to 1.13. +(#1577)

    • +

  • Documentation pages that had underscores in their filenames have been +renamed to have hyphens instead. +(#1585)

    • +
+

Deprecated

+
    +

  • Deprecated the nengo.spa module. Use the +Nengo SPA +project instead. +(#1465)

    • +

  • The A and B inputs to the Product and CircularConvolution +networks are officially deprecated. Use input_a and input_b instead. +(#887, +#1179)

    • +

  • nengo.utils.compat will be removed in the next minor release. +(#1520)

    • +

  • Deprecated utils.numpy.rmse. Call utils.numpy.rms on +the difference between two arrays instead. +(#1563)

    • +
+

Removed

+
    +

  • Networks no longer accept the net argument. To set network arguments +like label, pass them as keyword arguments instead. +(#1179)

    • +

  • Removed generate_graphviz utility function. It can now be found in +nengo_extras. +(#1187)

    • +

  • Removed functions for estimating firing rates from spikes. They can now +be found in nengo_extras. +(#1187)

    • +

  • Removed the probe_all function. It can now be found in +nengo_extras. +(#1187)

    • +

  • PES.correction is no longer probeable. +(#1398)

    • +

  • The internal rng and seed fixtures have been removed. Use the +external pytest-rng package instead. +(#1566)

    • +

  • The internal plt fixture has been removed. Use the +external pytest-plt package instead. +(#1566)

    • +

  • The internal logger fixture has been removed. Use pytest’s +log capturing instead. +(#1566)

    • +

  • Removed nengo.log and nengo.utils.logging. Use the standard Python +and pytest logging modules instead. +(#1566)

    • +

  • The internal analytics and analytics_data fixtures have been removed. +Use pytest’s cache fixture +instead. +(#1566)

    • +

  • The RefSimulator fixture has been removed. Use the Simulator fixture +and the nengo_test_unsupported configuration option instead. +(#1566)

    • +

  • Removed find_modules and load_functions from nengo.utils.testing. +Backends wanting to run Nengo test should use pytest --pyargs nengo +instead. +(#1566)

    • +

  • Removed nengo.tests.options. It is no longer necessary to use +-p nengo.tests.options when running Nengo tests. +(#1566)

    • +

  • Removed nengo.conftest. Use pytest configuration options instead. +(#1566)

    • +

  • Removed support for legacy cache files. +(#1577)

    • +

  • Removed the nengo ipynb progress bar extension. This is no longer needed in more +recent ipynb versions. +(#1577)

    • +

  • Removed the deprecated *_tau (e.g. pre_tau) parameters from learning rules. +Use *_synapse instead. +(#1577)

    • +

  • Removed the deprecated neuron_nodes argument from networks.EnsembleArray. +Use EnsembleArray.add_neuron_input/add_neuron_output instead. +(#1577)

    • +

  • Removed the deprecated progress.updater config option. +Use progress.progress_bar instead. +(#1577)

    • +

  • Removed the deprecated nengo.synapses.filt/filtfilt functions. +Use the Synapse.filt/filtfilt methods instead. +(#1577)

    • +

  • Removed the Python 2 compatibility code from utils.compat. +(#1577)

    • +

  • Removed utils.connection.target_function. Target points can be passed +directly to the Connection.function argument instead. +(#1577)

    • +

  • Removed utils.functions.piecewise. Use nengo.processes.Piecewise instead. +(#1577)

    • +

  • Removed utils.testing.Mock. +(#1578)

    • +
+

Fixed

+
    +

  • FrozenObjects can control parameter initialization order when copying, +which fixed a bug encountered when copying convolutional connections. +(#1493)

    • +

  • Fixed an issue in which reshaped signals were not having their offset +values preserved, causing issues with some node functions. +(#1474)

    • +

  • Better error message when Node output function does not match the +given size_in/size_out. +(#1452, +#1434)

    • +

  • Several objects had elements missing from their string representations. +These strings are now automatically generated and tested to be complete. +(#1472)

    • +

  • Fixed the progress bar in recent Jupyter Lab versions. +(#1499, +#1500)

    • +

  • Some higher-order LinearFilter synapses had unnecessary delays +that have now been removed. +(#1535)

    • +

  • Models using the SpikingRectifiedLinear neuron type now have their +decoders cached. (#1550)

    • +

  • Optional ShapeParam/TupleParam can now be set to None. +(#1569)

    • +

  • Fixed error when using advanced indexing to connect to an Ensemble.neurons +object. +(#1582, +#1583)

    • +
+
+
+

2.8.0 (June 9, 2018)

+

Added

+
    +

  • Added a warning when setting gain and bias along with either of +max_rates or intercepts, as the latter two parameters are ignored. +(#1431, +#1433)

    • +
+

Changed

+
    +

  • Learning rules can now be sliced when providing error input. +(#1365, +#1385)

    • +

  • The order of parameters in learning rules has changed such that +learning_rate always comes first. +(#1095)

    • +

  • Learning rules take pre_synapse, post_synapse, and theta_synapse +instead of pre_tau, post_tau, and theta_tau respectively. +This allows arbitrary Synapse objects to be used as filters on +learning signals. +(#1095)

    • +
+

Deprecated

+
    +

  • The nengo.ipynb IPython extension and the IPython2ProgressBar +have been deprecated and replaced by the IPython5ProgressBar. +This progress bar will be automatically activated in IPython and +Jupyter notebooks from IPython version 5.0 onwards. +(#1087, +#1375)

    • +

  • The pre_tau, post_tau, and theta_tau parameters +for learning rules are deprecated. Instead, use pre_synapse, +post_synapse, and theta_synapse respectively. +(#1095)

    • +
+

Removed

+
    +

  • Removed nengo.utils.docutils in favor of using +nbsphinx. +(#1349)

    • +
+
+
+

2.7.0 (March 7, 2018)

+

Added

+
    +

  • Added amplitude parameter to LIF, LIFRate, +and RectifiedLinear which scale the output amplitude. +(#1325, +#1391)

    • +

  • Added the SpikingRectifiedLinear neuron model. +(#1391)

    • +
+

Changed

+
    +

  • Default values can no longer be set for +Ensemble.n_neurons or Ensemble.dimensions. +(#1372)

    • +

  • If the simulator seed is not specified, it will now be set +from the network seed if a network seed is specified. +(#980, +#1386)

    • +
+

Fixed

+
    +

  • Fixed an issue in which signals could not be pickled, +making it impossible to pickle Model instances. +(#1135)

    • +

  • Better error message for invalid return values in nengo.Node functions. +(#1317)

    • +

  • Fixed an issue in which accepting and passing (*args, **kwargs) +could not be used in custom solvers. +(#1358, +#1359)

    • +

  • Fixed an issue in which the cache would not release its index lock +on abnormal termination of the Nengo process. +(#1364)

    • +

  • Fixed validation checks that prevented the default +from being set on certain parameters. +(#1372)

    • +

  • Fixed an issue with repeated elements in slices in which +a positive and negative index referred to the same dimension. +(#1395)

    • +

  • The Simulator.n_steps and Simulator.time properties +now return scalars, as was stated in the documentation. +(#1406)

    • +

  • Fixed the --seed-offset option of the test suite. +(#1409)

    • +
+
+
+

2.6.0 (October 6, 2017)

+

Added

+
    +

  • Added a NoSolver solver that can be used to manually pass in +a predefined set of decoders or weights to a connection. +(#1352)

    • +

  • Added a Piecewise process, which replaces the now deprecated +piecewise function. +(#1036, +#1100, +#1355, +#1362)

    • +
+

Changed

+
    +

  • The minimum required version of NumPy has been raised to 1.8. +(#947)

    • +

  • Learning rules can now have a learning rate of 0. +(#1356)

    • +

  • Running the simulator for zero timesteps will now issue a warning, +and running for negative time will error. +(#1354, +#1357)

    • +
+

Fixed

+
    +

  • Fixed an issue in which the PES learning rule could not be used +on connections to an ObjView when using a weight solver. +(#1317)

    • +

  • The progress bar that can appear when building a large model +will now appear earlier in the build process. +(#1340)

    • +

  • Fixed an issue in which ShapeParam would always store None. +(#1342)

    • +

  • Fixed an issue in which multiple identical indices in a slice were ignored. +(#947, +#1361)

    • +
+

Deprecated

+
    +

  • The piecewise function in nengo.utils.functions has been deprecated. +Please use the Piecewise process instead. +(#1100)

    • +
+
+
+

2.5.0 (July 24, 2017)

+

Added

+
    +

  • Added a n_neurons property to Network, which gives the +number of neurons in the network, including all subnetworks. +(#435, +#1186)

    • +

  • Added a new example showing how adjusting ensemble tuning curves can +improve function approximation. +(#1129)

    • +

  • Added a minimum magnitude option to UniformHypersphere. +(#799)

    • +

  • Added documentation on RC settings. +(#1130)

    • +

  • Added documentation on improving performance. +(#1119, +#1130)

    • +

  • Added LinearFilter.combine method to +combine two LinearFilter instances. +(#1312)

    • +

  • Added a method to all neuron types to compute ensemble +max_rates and intercepts given gain and bias. +(#1334)

    • +
+

Changed

+
    +

  • Learning rules now have a size_in parameter and attribute, +allowing both integers and strings to define the dimensionality +of the learning rule. This replaces the error_type attribute. +(#1307, +#1310)

    • +

  • EnsembleArray.n_neurons now gives the total number of neurons +in all ensembles, including those in subnetworks. +To get the number of neurons in each ensemble, +use EnsembleArray.n_neurons_per_ensemble. +(#1186)

    • +

  • The Nengo modelling API document +now has summaries to help navigate the page. +(#1304)

    • +

  • The error raised when a Connection function returns None +is now more clear. +(#1319)

    • +

  • We now raise an error when a Connection transform is set to None. +(#1326)

    • +
+

Fixed

+
    +

  • Probe cache is now cleared on simulator reset. +(#1324)

    • +

  • Neural gains are now always applied after the synapse model. +Previously, this was the case for decoded connections +but not neuron-to-neuron connections. +(#1330)

    • +

  • Fixed a crash when a lock cannot be acquired while shrinking the cache. +(#1335, +#1336)

    • +
+
+
+

2.4.0 (April 18, 2017)

+

Added

+
    +

  • Added an optimizer that reduces simulation time for common types of models. +The optimizer can be turned off by passing optimize=False to Simulator. +(#1035)

    • +

  • Added the option to not normalize encoders by setting +Ensemble.normalize_encoders to False. +(#1191, +#1267)

    • +

  • Added the Samples distribution to allow raw NumPy arrays +to be passed in situations where a distribution is required. +(#1233)

    • +
+

Changed

+
    +

  • We now raise an error when an ensemble is assigned a negative gain. +This can occur when solving for gains with intercepts greater than 1. +(#1212, +#1231, +#1248)

    • +

  • We now raise an error when a Node or Direct ensemble +produces a non-finite value. +(#1178, +#1280, +#1286)

    • +

  • We now enforce that the label of a network must be a string or None, +and that the seed of a network must be an int or None. +This helps avoid situations where the seed would mistakenly +be passed as the label. +(#1277, +#1275)

    • +

  • It is now possible to pass NumPy arrays in the ens_kwargs argument of +EnsembleArray. Arrays are wrapped in a Samples distribution internally. +(#691, +#766, +#1233)

    • +

  • The default refractory period (tau_ref) for the Sigmoid neuron type +has changed to 2.5 ms (from 2 ms) for better compatibility with the +default maximum firing rates of 200-400 Hz. +(#1248)

    • +

  • Inputs to the Product and CircularConvolution networks have been +renamed from A and B to input_a and input_b for consistency. +The old names are still available, but should be considered deprecated. +(#887, +#1296)

    • +
+

Fixed

+
    +

  • Properly handle non C-contiguous node outputs. +(#1184, +#1185)

    • +
+

Deprecated

+
    +

  • The net argument to networks has been deprecated. This argument existed +so that network components could be added to an existing network instead of +constructing a new network. However, this feature is rarely used, +and makes the code more complicated for complex networks. +(#1296)

    • +
+
+
+

2.3.1 (February 18, 2017)

+

Added

+
    +

  • Added documentation on config system quirks. +(#1224)

    • +

  • Added nengo.utils.network.activate_direct_mode function to make it +easier to activate direct mode in networks where some parts require neurons. +(#1111, +#1168)

    • +
+

Fixed

+
    +

  • The matrix multiplication example will now work with matrices of any size +and uses the product network for clarity. +(#1159)

    • +

  • Fixed instances in which passing a callable class as a function could fail. +(#1245)

    • +

  • Fixed an issue in which probing some attributes would be one timestep +faster than other attributes. +(#1234, +#1245)

    • +

  • Fixed an issue in which SPA models could not be copied. +(#1266, +#1271)

    • +

  • Fixed an issue in which Nengo would crash if other programs +had locks on Nengo cache files in Windows. +(#1200, +#1235)

    • +
+

Changed

+
    +

  • Integer indexing of Nengo objects out of range raises an IndexError +now to be consistent with standard Python behaviour. +(#1176, +#1183)

    • +

  • Documentation that applies to all Nengo projects has been moved to +https://www.nengo.ai/. +(#1251)

    • +
+
+
+

2.3.0 (November 30, 2016)

+

Added

+
    +

  • It is now possible to probe scaled_encoders on ensembles. +(#1167, +#1117)

    • +

  • Added copy method to Nengo objects. Nengo objects can now be pickled. +(#977, +#984)

    • +

  • A progress bar now tracks the build process +in the terminal and Jupyter notebook. +(#937, +#1151)

    • +

  • Added nengo.dists.get_samples function for convenience +when working with distributions or samples. +(#1181, +docs)

    • +
+

Changed

+
    +

  • Access to probe data via nengo.Simulator.data is now cached, +making repeated access much faster. +(#1076, +#1175)

    • +
+

Deprecated

+
    +

  • Access to nengo.Simulator.model is deprecated. To access static data +generated during the build use nengo.Simulator.data. It provides access +to everything that nengo.Simulator.model.params used to provide access to +and is the canonical way to access this data across different backends. +(#1145, +#1173)

    • +
+
+
+

2.2.0 (September 12, 2016)

+

API changes

+
    +

  • It is now possible to pass a NumPy array to the function argument +of nengo.Connection. The values in the array are taken to be the +targets in the decoder solving process, which means that the eval_points +must also be set on the connection. +(#1010)

    • +

  • nengo.utils.connection.target_function is now deprecated, and will +be removed in Nengo 3.0. Instead, pass the targets directly to the +connection through the function argument. +(#1010)

    • +
+

Behavioural changes

+
    +

  • Dropped support for NumPy 1.6. Oldest supported NumPy version is now 1.7. +(#1147)

    • +
+

Improvements

+
    +

  • Added a nengo.backends entry point to make the reference simulator +discoverable for other Python packages. In the future all backends should +declare an entry point accordingly. +(#1127)

    • +

  • Added ShapeParam to store array shapes. +(#1045)

    • +

  • Added ThresholdingPreset to configure ensembles for thresholding. +(#1058, +#1077, +#1148)

    • +

  • Tweaked rasterplot so that spikes from different neurons don’t overlap. +(#1121)

    • +
+

Documentation

+
    +

  • Added a page explaining the config system and preset configs. +(#1150)

    • +
+

Bug fixes

+
    +

  • Fixed some situations where the cache index becomes corrupt by +writing the updated cache index atomically (in most cases). +(#1097, +#1107)

    • +

  • The synapse methods filt and filtfilt now support lists as input. +(#1123)

    • +

  • Added a registry system so that only stable objects are cached. +(#1054, +#1068)

    • +

  • Nodes now support array views as input. +(#1156, +#1157)

    • +
+
+
+

2.1.2 (June 27, 2016)

+

Bug fixes

+
    +

  • The DecoderCache is now more robust when used improperly, and no longer +requires changes to backends in order to use properly. +(#1112)

    • +
+
+
+

2.1.1 (June 24, 2016)

+

Improvements

+
    +

  • Improved the default LIF neuron model to spike at the same rate as the +LIFRate neuron model for constant inputs. The older model has been +moved to nengo_extras +under the name FastLIF. +(#975)

    • +

  • Added y0 attribute to WhiteSignal, which adjusts the phase of each +dimension to begin with absolute value closest to y0. +(#1064)

    • +

  • Allow the AssociativeMemory to accept Semantic Pointer expressions as +input_keys and output_keys. +(#982)

    • +
+

Bug fixes

+
    +

  • The DecoderCache is used as context manager instead of relying on the +__del__ method for cleanup. This should solve problems with the +cache’s file lock not being removed. It might be necessary to +manually remove the index.lock file in the cache directory after +upgrading from an older Nengo version. +(#1053, +#1041, +#1048)

    • +

  • If the cache index is corrupted, we now fail gracefully by invalidating +the cache and continuing rather than raising an exception. +(#1110, +#1097)

    • +

  • The Nnls solver now works for weights. The NnlsL2 solver is +improved since we clip values to be non-negative before forming +the Gram system. +(#1027, +#1019)

    • +

  • Eliminate memory leak in the parameter system. +(#1089, +#1090)

    • +

  • Allow recurrence of the form a=b, b=a in basal ganglia SPA actions. +(#1098, +#1099)

    • +

  • Support a greater range of Jupyter notebook and ipywidgets versions with the +the ipynb extensions. +(#1088, +#1085)

    • +
+
+
+

2.1.0 (April 27, 2016)

+

API changes

+
    +

  • A new class for representing stateful functions called Process +has been added. Node objects are now process-aware, meaning that +a process can be used as a node’s output. Unlike non-process +callables, processes are properly reset when a simulator is reset. +See the processes.ipynb example notebook, or the API documentation +for more details. +(#590, +#652, +#945, +#955)

    • +

  • Spiking LIF neuron models now accept an additional argument, +min_voltage. Voltages are clipped such that they do not drop below +this value (previously, this was fixed at 0). +(#666)

    • +

  • The PES learning rule no longer accepts a connection as an argument. +Instead, error information is transmitted by making a connection to the +learning rule object (e.g., +nengo.Connection(error_ensemble, connection.learning_rule). +(#344, +#642)

    • +

  • The modulatory attribute has been removed from nengo.Connection. +This was only used for learning rules to this point, and has been removed +in favor of connecting directly to the learning rule. +(#642)

    • +

  • Connection weights can now be probed with nengo.Probe(conn, 'weights'), +and these are always the weights that will change with learning +regardless of the type of connection. Previously, either decoders or +transform may have changed depending on the type of connection; +it is now no longer possible to probe decoders or transform. +(#729)

    • +

  • A version of the AssociativeMemory SPA module is now available as a +stand-alone network in nengo.networks. The AssociativeMemory SPA module +also has an updated argument list. +(#702)

    • +

  • The Product and InputGatedMemory networks no longer accept a +config argument. (#814)

    • +

  • The EnsembleArray network’s neuron_nodes argument is deprecated. +Instead, call the new add_neuron_input or add_neuron_output methods. +(#868)

    • +

  • The nengo.log utility function now takes a string level parameter +to specify any logging level, instead of the old binary debug parameter. +Cache messages are logged at DEBUG instead of INFO level. +(#883)

    • +

  • Reorganised the Associative Memory code, including removing many extra +parameters from nengo.networks.assoc_mem.AssociativeMemory and modifying +the defaults of others. +(#797)

    • +

  • Add close method to Simulator. Simulator can now be used +used as a context manager. +(#857, +#739, +#859)

    • +

  • Most exceptions that Nengo can raise are now custom exception classes +that can be found in the nengo.exceptions module. +(#781)

    • +

  • All Nengo objects (Connection, Ensemble, Node, and Probe) +now accept a label and seed argument if they didn’t previously. +(#958)

    • +

  • In nengo.synapses, filt and filtfilt are deprecated. Every +synapse type now has filt and filtfilt methods that filter +using the synapse. +(#945)

    • +

  • Connection objects can now accept a Distribution for the transform +argument; the transform matrix will be sampled from that distribution +when the model is built. +(#979).

    • +
+

Behavioural changes

+
    +

  • The sign on the PES learning rule’s error has been flipped to conform +with most learning rules, in which error is minimized. The error should be +actual - target. (#642)

    • +

  • The PES rule’s learning rate is invariant to the number of neurons +in the presynaptic population. The effective speed of learning should now +be unaffected by changes in the size of the presynaptic population. +Existing learning networks may need to be updated; to achieve identical +behavior, scale the learning rate by pre.n_neurons / 100. +(#643)

    • +

  • The probeable attribute of all Nengo objects is now implemented +as a property, rather than a configurable parameter. +(#671)

    • +

  • Node functions receive x as a copied NumPy array (instead of a readonly +view). +(#716, +#722)

    • +

  • The SPA Compare module produces a scalar output (instead of a specific +vector). +(#775, +#782)

    • +

  • Bias nodes in spa.Cortical, and gate ensembles and connections in +spa.Thalamus are now stored in the target modules. +(#894, +#906)

    • +

  • The filt and filtfilt functions on Synapse now use the initial +value of the input signal to initialize the filter output by default. This +provides more accurate filtering at the beginning of the signal, for signals +that do not start at zero. +(#945)

    • +
+

Improvements

+
    +

  • Added Ensemble.noise attribute, which injects noise directly into +neurons according to a stochastic Process. +(#590)

    • +

  • Added a randomized_svd subsolver for the L2 solvers. This can be much +quicker for large numbers of neurons or evaluation points. +(#803)

    • +

  • Added PES.pre_tau attribute, which sets the time constant on a lowpass +filter of the presynaptic activity. +(#643)

    • +

  • EnsembleArray.add_output now accepts a list of functions +to be computed by each ensemble. +(#562, +#580)

    • +

  • LinearFilter now has an analog argument which can be set +through its constructor. Linear filters with digital coefficients +can be specified by setting analog to False. +(#819)

    • +

  • Added SqrtBeta distribution, which describes the distribution +of semantic pointer elements. +(#414, +#430)

    • +

  • Added Triangle synapse, which filters with a triangular FIR filter. +(#660)

    • +

  • Added utils.connection.eval_point_decoding function, which +provides a connection’s static decoding of a list of evaluation points. +(#700)

    • +

  • Resetting the Simulator now resets all Processes, meaning the +injected random signals and noise are identical between runs, +unless the seed is changed (which can be done through +Simulator.reset). +(#582, +#616, +#652)

    • +

  • An exception is raised if SPA modules are not properly assigned to an SPA +attribute. +(#730, +#791)

    • +

  • The Product network is now more accurate. +(#651)

    • +

  • Numpy arrays can now be used as indices for slicing objects. +(#754)

    • +

  • Config.configures now accepts multiple classes rather than +just one. (#842)

    • +

  • Added add method to spa.Actions, which allows +actions to be added after module has been initialized. +(#861, +#862)

    • +

  • Added SPA wrapper for circular convolution networks, spa.Bind +(#849)

    • +

  • Added the Voja (Vector Oja) learning rule type, which updates an +ensemble’s encoders to fire selectively for its inputs. (see +examples/learning/learn_associations.ipynb). +(#727)

    • +

  • Added a clipped exponential distribution useful for thresholding, in +particular in the AssociativeMemory. +(#779)

    • +

  • Added a cosine similarity distribution, which is the distribution of the +cosine of the angle between two random vectors. It is useful for setting +intercepts, in particular when using the Voja learning rule. +(#768)

    • +

  • nengo.synapses.LinearFilter now has an evaluate method to +evaluate the filter response to sine waves of given frequencies. This can +be used to create Bode plots, for example. +(#945)

    • +

  • nengo.spa.Vocabulary objects now have a readonly attribute that +can be used to disallow adding new semantic pointers. Vocabulary subsets +are read-only by default. +(#699)

    • +

  • Improved performance of the decoder cache by writing all decoders +of a network into a single file. +(#946)

    • +
+

Bug fixes

+
    +

  • Fixed issue where setting Connection.seed through the constructor had +no effect. (#724)

    • +

  • Fixed issue in which learning connections could not be sliced. +(#632)

    • +

  • Fixed issue when probing scalar transforms. +(#667, +#671)

    • +

  • Fix for SPA actions that route to a module with multiple inputs. +(#714)

    • +

  • Corrected the rmses values in BuiltConnection.solver_info when using +NNls and Nnl2sL2 solvers, and the reg argument for Nnl2sL2. +(#839)

    • +

  • spa.Vocabulary.create_pointer now respects the specified number of +creation attempts, and returns the most dissimilar pointer if none can be +found below the similarity threshold. +(#817)

    • +

  • Probing a Connection’s output now returns the output of that individual +Connection, rather than the input to the Connection’s post Ensemble. +(#973, +#974)

    • +

  • Fixed thread-safety of using networks and config in with statements. +(#989)

    • +

  • The decoder cache will only be used when a seed is specified. +(#946)

    • +
+
+
+

2.0.4 (April 27, 2016)

+

Bug fixes

+
    +

  • Cache now fails gracefully if the legacy.txt file cannot be read. +This can occur if a later version of Nengo is used.

    • +
+
+
+

2.0.3 (December 7, 2015)

+

API changes

+
    +

  • The spa.State object replaces the old spa.Memory and spa.Buffer. +These old modules are deprecated and will be removed in 2.2. +(#796)

    • +
+
+
+

2.0.2 (October 13, 2015)

+

2.0.2 is a bug fix release to ensure that Nengo continues +to work with more recent versions of Jupyter +(formerly known as the IPython notebook).

+

Behavioural changes

+
    +

  • The IPython notebook progress bar has to be activated with +%load_ext nengo.ipynb. +(#693)

    • +
+

Improvements

+
    +

  • Added [progress] section to nengorc which allows setting +progress_bar and updater. +(#693)

    • +
+

Bug fixes

+
    +

  • Fix compatibility issues with newer versions of IPython, +and Jupyter. (#693)

    • +
+
+
+

2.0.1 (January 27, 2015)

+

Behavioural changes

+
    +

  • Node functions receive t as a float (instead of a NumPy scalar) +and x as a readonly NumPy array (instead of a writeable array). +(#626, +#628)

    • +
+

Improvements

+
    +

  • rasterplot works with 0 neurons, and generates much smaller PDFs. +(#601)

    • +
+

Bug fixes

+
    +

  • Fix compatibility with NumPy 1.6. +(#627)

    • +
+
+
+

2.0.0 (January 15, 2015)

+

Initial release of Nengo 2.0! +Supports Python 2.6+ and 3.3+. +Thanks to all of the contributors for making this possible!

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/citation.html b/citation.html new file mode 100644 index 0000000000..abce6f7c39 --- /dev/null +++ b/citation.html @@ -0,0 +1,378 @@ + + + + + + + + Citation — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Citation

+

If you would like to cite Nengo in your research, please cite this +paper:

+
+

Bekolay, Bergstra, Hunsberger, DeWolf, Stewart, Rasmussen, Choo, +Voelker & Eliasmith. (2014) Nengo: a Python tool for building large-scale +functional brain models. Frontiers in Neuroinformatics 7.

+
+

A BibTeX entry for LaTeX users is:

+
@article{
+  Bekolay2014,
+  title = {Nengo: a {Python} tool for building large-scale functional brain models},
+  author = {Bekolay, Trevor and Bergstra, James and Hunsberger, Eric
+            and DeWolf, Travis and Stewart, Terrence and Rasmussen, Daniel
+            and Choo, Xuan and Voelker, Aaron and Eliasmith, Chris},
+  journal = {Frontiers in Neuroinformatics},
+  pages = {1--13},
+  volume = {7},
+  number = {48},
+  year = {2014},
+  issn = {1662-5196},
+  doi = {10.3389/fninf.2013.00048}
+}
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/config.html b/config.html new file mode 100644 index 0000000000..f511cb16dc --- /dev/null +++ b/config.html @@ -0,0 +1,916 @@ + + + + + + + + Setting parameters with Configs — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Setting parameters with Configs

+

Building models with the Nengo frontend API +involves constructing many objects, +each with many parameters that can be set. +To make setting all of these parameters easier, +Nengo has a config system and +pre-set configurations.

+
+

The config system

+

Nengo’s config system is used for two important functions:

+
    +

  1. Setting default parameters with a hierarchy of defaults.

    2. +

  2. Associating new information with Nengo classes and objects +without modifying the classes and objects themselves.

    3. +
+

A tutorial-style introduction to the config system +can be found below:

+
+ +
+
+

config system API

+

Configuration system to set defaults and backend-specific info.

+

The idea here is that a backend can create a Config and ConfigItems to +define the set of parameters that their backend supports. +Parameters are done as Python descriptors, so backends can also specify +error checking on those parameters.

+

A good writeup on descriptors (which has an example similar to Parameter) +can be found at +https://nbviewer.ipython.org/urls/gist.github.com/ChrisBeaumont/5758381/raw/descriptor_writeup.ipynb

+ ++++ + + + + + + + + + + + + + + +

nengo.config.ClassParams

A class to store extra parameters and defaults on configured classes.

nengo.config.InstanceParams

A class to store parameter value on configured objects.

nengo.Config

Configures network-level defaults and additional parameters.

nengo.config.SupportDefaultsMixin

Mixin to support assigning Default to parameters.

+
+
+class nengo.config.ClassParams(configures)[source]
+

A class to store extra parameters and defaults on configured classes.

+

This is used by Config to associate defaults and new Parameter +instances with existing objects. It should not be instantiated outside of +Config.configures.

+
+
Parameters
+
+
configuresclass

The class with which to associate new defaults and parameters.

+
+
+
+
+
+
+update(d)[source]
+

Sets a number of parameters at once given a dictionary.

+
+ +
+ +
+
+class nengo.config.InstanceParams(configures, clsparams)[source]
+

A class to store parameter value on configured objects.

+

In contrast to ClassParams, the only thing that can be done with +InstanceParams is get and set parameter values. Use the corresponding +ClassParams to set defaults and create new parameters.

+
+ +
+
+class nengo.Config(*configures)[source]
+

Configures network-level defaults and additional parameters.

+

Every Network contains an associated Config object which can +be manipulated to change network-specific defaults, and to store +additional parameters (for example, those specific to a backend).

+

A Config object can configure objects of any class, but it has to be +told the classes to configure first. This is either done on instantiation +of the Config object or by calling the configures method. +This sets up a mapping between configured class and a ClassParams +object that sets the default values for that class. Attempting to +configure an instance of a configure class will create a mapping from +that instance to an InstanceParams object to configure additional +parameters for that instance.

+
+
Parameters
+
+
*configures

The classes that this Config instance will configure.

+
+
+
+
+

Examples

+

To configure defaults on a network:

+
net = nengo.Network()
+net.config[nengo.Ensemble].radius = 1.5
+with net:
+    ens = nengo.Ensemble(10, 1)
+ens.radius == 1.5  # True
+
+
+

To add a new parameter to a Nengo object:

+
net.config[nengo.Ensemble].set_param(
+    'location', nengo.params.Parameter('location')
+)
+net.config[ens].location = 'cortex'
+
+
+

To group together a set of parameters:

+
gaba = nengo.Config(nengo.Connection)
+gaba[nengo.Connection].synapse = nengo.Lowpass(0.008)
+with net, gaba:
+    conn = nengo.Connection(ens, ens)
+conn.synapse == nengo.Lowpass(0.008)  # True
+
+
+

To configure a new type of object:

+
class SynapseInfo:
+    label = nengo.params.StringParam('label', default=None)
+gaba.configures(SynapseInfo)
+gaba[SynapseInfo].label = "GABA"  # Set default label
+
+
+
+
Attributes
+
+
paramsdict

Maps configured classes and instances to their ClassParams +or InstanceParams object.

+
+
+
+
+
+
+static all_defaults(nengo_cls=None)[source]
+

Look up all of the default values in the current context.

+
+
Parameters
+
+
nengo_clsclass, optional

If specified, only the defaults for a particular class will +be returned. If not specified, the defaults for all configured +classes will be returned.

+
+
+
+
Returns
+
+
str
+
+
+
+
+ +
+
+static default(nengo_cls, param)[source]
+

Look up the current default value for a parameter.

+

The default is found by going through the config stack, from most +specific to least specific. The network that an object is in +is the most specific; the top-level network is the least specific. +If no default is found there, then the parameter’s default value +is returned.

+
+ +
+
+configures(*classes)[source]
+

Start configuring a particular class and its instances.

+
+ +
+ +
+
+class nengo.config.SupportDefaultsMixin[source]
+

Mixin to support assigning Default to parameters.

+

Implements __setattr__ to do so. If the inheriting class overrides +this method, it has to call the mixin’s __setattr__.

+

This mixin may simplify the exception depending on the value of the +simplified rc option.

+
+ +
+
+
+

Preset configs

+

Nengo includes preset configurations that can be +dropped into your model to enable specific neural circuits.

+

Configuration presets for common use cases.

+ ++++ + + + + + +

nengo.presets.ThresholdingEnsembles

Configuration preset for a thresholding ensemble.

+
+
+nengo.presets.ThresholdingEnsembles(threshold, intercept_width=0.15, radius=1.0)[source]
+

Configuration preset for a thresholding ensemble.

+

This preset adjust ensemble parameters for thresholding. The ensemble’s +neurons will only fire for values above threshold. One can either decode +the represented value (if it is above the threshold) or decode +a step function if binary classification is desired.

+

This preset:

+
    +

  • Sets intercepts to be between threshold and radius with an +exponential distribution (shape parameter of intercept_width). +This clusters intercepts near the threshold for better approximation.

    • +

  • Sets encoders to 1.

    • +

  • Sets evaluation points to be uniformly distributed between +threshold and radius.

    • +

  • Sets the radius.

    • +
+
+
Parameters
+
+
thresholdfloat

Point at which ensembles should start firing.

+
+
intercept_widthfloat, optional

Controls how widely distributed the intercepts are. Smaller values +give more clustering at the threshold, larger values give a more +uniform distribution.

+
+
radiusfloat, optional

Ensemble radius.

+
+
+
+
Returns
+
+
nengo.Config

Configuration with presets.

+
+
+
+
+
+ +
+ +
+

Parameters

+

Under the hood, Nengo objects store information +using Parameter instances, +which are also used by the config system. +Most users will not need to know about +Parameter objects.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.dists.DistributionParam

A Distribution.

nengo.dists.DistOrArrayParam

Can be a Distribution or samples from a distribution.

nengo.learning_rules.LearningRuleTypeParam

nengo.learning_rules.LearningRuleTypeSizeInParam

nengo.neurons.NeuronTypeParam

nengo.processes.PiecewiseDataParam

Piecewise-specific validation for the data dictionary.

nengo.solvers.SolverParam

A parameter in which the value is a Solver instance.

nengo.synapses.SynapseParam

nengo.transforms.ChannelShapeParam

A parameter where the value must be a shape with channels.

nengo.transforms.SparseInitParam

nengo.params.DefaultType

Placeholder object used to represent default values for a parameter.

nengo.params.is_param

Check if obj is a Parameter.

nengo.params.iter_params

Iterate over the names of all parameters of an object.

nengo.params.equal

Check if two (possibly array-like) objects are equal.

nengo.params.Parameter

Simple descriptor for storing configuration parameters.

nengo.params.ObsoleteParam

A parameter that is no longer supported.

nengo.params.BoolParam

A parameter where the value is a boolean.

nengo.params.NumberParam

A parameter where the value is a number.

nengo.params.IntParam

A parameter where the value is an integer.

nengo.params.StringParam

A parameter where the value is a string.

nengo.params.EnumParam

A parameter where the value must be one of a finite set of strings.

nengo.params.TupleParam

A parameter where the value is a tuple.

nengo.params.ShapeParam

A parameter where the value is a tuple of integers.

nengo.params.DictParam

A parameter where the value is a dictionary.

nengo.params.NdarrayParam

A parameter where the value is a NumPy ndarray.

nengo.params.FunctionInfo

nengo.params.FunctionParam

A parameter where the value is a function.

nengo.params.FrozenObject

An object with parameters that cannot change value after instantiation.

+
+
+class nengo.params.DefaultType(name)[source]
+

Placeholder object used to represent default values for a parameter.

+
+ +
+
+nengo.params.is_param(obj)[source]
+

Check if obj is a Parameter.

+
+ +
+
+nengo.params.iter_params(obj)[source]
+

Iterate over the names of all parameters of an object.

+
+ +
+
+nengo.params.equal(a, b)[source]
+

Check if two (possibly array-like) objects are equal.

+
+ +
+
+class nengo.params.Parameter(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

Simple descriptor for storing configuration parameters.

+
+
Parameters
+
+
namestr

Name of the parameter.

+
+
defaultobject

The value returned if the parameter hasn’t been explicitly set.

+
+
optionalbool, optional

Whether this parameter accepts the value None. By default, +parameters are not optional (i.e., cannot be set to None).

+
+
readonlybool, optional

If true, the parameter can only be set once. +By default, parameters can be set multiple times.

+
+
+
+
Attributes
+
+
coerce_defaultsbool

If True, validate values for this parameter when they are set in a +Config object. Setting a parameter directly on an object will +always be validated.

+
+
equatablebool

If True, parameter values can be compared for equality +(a==b); otherwise equality checks will just compare object +identity (a is b).

+
+
+
+
+
+
+hashvalue(instance)[source]
+

Returns a hashable value (hash can be called on the output).

+
+ +
+ +
+
+class nengo.params.ObsoleteParam(name, short_msg, since=None, url=None)[source]
+

A parameter that is no longer supported.

+
+ +
+
+class nengo.params.BoolParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A parameter where the value is a boolean.

+
+ +
+
+class nengo.params.NumberParam(name, default=Unconfigurable, low=None, high=None, low_open=False, high_open=False, optional=False, readonly=None)[source]
+

A parameter where the value is a number.

+
+ +
+
+class nengo.params.IntParam(name, default=Unconfigurable, low=None, high=None, low_open=False, high_open=False, optional=False, readonly=None)[source]
+

A parameter where the value is an integer.

+
+ +
+
+class nengo.params.StringParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A parameter where the value is a string.

+
+ +
+
+class nengo.params.EnumParam(name, default=Unconfigurable, values=(), lower=True, optional=False, readonly=None)[source]
+

A parameter where the value must be one of a finite set of strings.

+
+ +
+
+class nengo.params.TupleParam(name, default=Unconfigurable, length=None, optional=False, readonly=None)[source]
+

A parameter where the value is a tuple.

+
+ +
+
+class nengo.params.ShapeParam(name, default=Unconfigurable, length=None, low=0, optional=False, readonly=None)[source]
+

A parameter where the value is a tuple of integers.

+
+ +
+
+class nengo.params.DictParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A parameter where the value is a dictionary.

+
+
+hashvalue(instance)[source]
+

Returns a hashable value (hash can be called on the output).

+
+ +
+ +
+
+class nengo.params.NdarrayParam(name, default=Unconfigurable, shape=None, dtype=None, optional=False, readonly=None)[source]
+

A parameter where the value is a NumPy ndarray.

+

If the passed value is an ndarray, a view onto that array is stored. +If the passed value is not an ndarray, it will be cast to an ndarray +of dtype and stored.

+
+
+property coerce_defaults
+

bool(x) -> bool

+

Returns True when the argument x is true, False otherwise. +The builtins True and False are the only two instances of the class bool. +The class bool is a subclass of the class int, and cannot be subclassed.

+
+ +
+
+hashvalue(instance)[source]
+

Returns a hashable value (hash can be called on the output).

+
+ +
+ +
+
+class nengo.params.FunctionInfo(function, size)[source]
+

Create new instance of FunctionInfo(function, size)

+
+
+property function
+

Alias for field number 0

+
+ +
+
+property size
+

Alias for field number 1

+
+ +
+ +
+
+class nengo.params.FunctionParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A parameter where the value is a function.

+
+ +
+
+class nengo.params.FrozenObject[source]
+

An object with parameters that cannot change value after instantiation.

+

Since such objects are read-only (“frozen”), they can be safely used in +multiple locations, compared, etc.

+
+ +
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/connections.html b/connections.html new file mode 100644 index 0000000000..57013bad4a --- /dev/null +++ b/connections.html @@ -0,0 +1,687 @@ + + + + + + + + Connections in depth — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Connections in depth

+

The Connection object encapsulates different behaviors +depending on the attributes of the connection. +It can be helpful in debugging network behavior +to know what is happening under the hood +for different types of connections.

+

The biggest determiner of what happens +in a connection is the pre object. +When the pre object is an Ensemble +with a neuron type other than Direct, +Nengo will create a decoded connection. +When the pre object is anything else, +Nengo will create a direct connection.

+

The post object +is only used to determine +which signal will receive the data +produced by the connection. +If you’re not sure what your connection +is doing, interrogate the pre object first.

+
+

Decoded connections

+

Decoded connections are any connection +from an ensemble to any other object. +The following are all examples of decoded connections:

+
with nengo.Network() as net:
+    ens1 = nengo.Ensemble(10, dimensions=2)
+    node = nengo.Node(size_in=1)
+    ens2 = nengo.Ensemble(4, dimensions=2)
+
+    # Ensemble to ensemble
+    nengo.Connection(ens1, ens2)
+    # Ensemble slice to node
+    nengo.Connection(ens1[0], node)
+    # Ensemble to neurons slice
+    nengo.Connection(ens1, ens2.neurons[:2])
+
+
+

The important thing about decoded connections +is that they do not directly compute the +function defined for that connection +(keeping in mind that passing in no function +is equivalent to passing in the identity function). +Instead, the function is approximated +by solving for a set of decoding weights. +The output of a decoded connection +is the sum of the pre ensemble’s neural activity +weighted by the decoding weights +solved for in the build process.

+

Mathematically, you can think of a decoded connection +as implementing the following equation:

+
+\[\mathbf{y}(t) = \sum_{i=0}^n \mathbf{d}^{f}_i a_i(x(t))\]
+

where

+
    +

  • \(\mathbf{y}(t)\) is the output of the connection at time \(t\),

    • +

  • \(n\) is the number of neurons in the pre ensemble

    • +

  • \(\mathbf{d}^{f}_i\) is the decoding weight associated +with neuron \(i\) given the function \(f\),

    • +

  • \(a_i(x(t))\) is the activity of neuron \(i\) given +\(x(t)\), the input at time \(t\).

    • +
+

Note that the length of the \(\mathbf{d}\) and \(\mathbf{y}\) vectors +is the same, and is specified by the dimensionality of +the output of the function \(f\) when applied to input \(x\).

+

While the equation above is straightforward, +there are several important implications +that one should keep in mind when using decoded connections.

+
    +

  • Decoders will be automatically solved for in the build process.

    +

    Solving for decoders makes up the majority of build time, +so if building your networks takes a long time, +look at your decoded connections and +try lowering the number of neurons +or using different Solver types.

    +
    • +

  • The function passed to the connection +is used to determine decoders. +It will never be run during the simulation.

    +

    When you define a function in a node, +it will be execute on every simulation timestep. +That may lead you to think that the function +passed to a connection is executed on every timestep, +but that is not the case for decoded connections. +The function passed to the connection will be executed +in the decoder solving process determine an error +to minimize, but never during the simulation.

    +
    • +

  • The characteristics of the pre ensemble +are critically important to performance.

    +

    If you determine that your decoded connection +is not approximating the desired function well, +examine the pre ensemble. +The decoded value depends on the activity +of the pre ensemble; +does it represent its input reliably? +If not, then a function of that input +cannot be well approximated. +If you think that your function may be incorrect, +switch the pre ensemble to use +the Direct neuron type, +which does not use decoders. +If that function looks correct, +move on to a simpler neuron type +like RectifiedLinear until you +can determine why your function is not +being approximated well.

    +

    Concrete examples of how the properties of pre ensemble influence the +desired function can be found in 1, 2.

    +
    • +
+
+
+

Direct connections

+

Any connection that is not a decoded connection +is a direct connection.

+

For simplicity and consistency, +Nengo exposes the same interface +for decoded and direct connections. +In all cases, data from the pre object +is sent to the post object, +with an optional synapse filter. +In decoded connections, +weights are automatically determined +through decoder solving. +In direct connections, +weights can be manually specified +through the transform argument. 3

+

The most common example of a direct connection +is a neuron-to-neuron connection. +These connections are the types of connections +you see in most neural simulators, +and can be used to reproduce networks +written in other simulators like +Brian:

+
with nengo.Network() as net:
+    ens1 = nengo.Ensemble(10, dimensions=1)
+    ens2 = nengo.Ensemble(20, dimensions=2)
+
+    # Neuron to neuron
+    weights = np.random.normal(size=(ens2.n_neurons, ens1.n_neurons))
+    nengo.Connection(ens1.neurons, ens2.neurons, transform=weights)
+
+
+

Note that it does not matter that the dimensionality of ens1 +does not match the dimensionality of ens2. +It only matters that the transform +is of the correct shape, +which in the case of neuron-to-neuron connections +is (post.n_neurons, pre.n_neurons).

+

In the vast majority of cases, +the above description is all you need to know. +Below we give some additional examples, +focusing on situations that differ from the description above.

+
+

Nodes and Direct ensembles

+

In connections from nodes and ensembles +using the Direct neuron type, +the function argument is valid +and will result in the function being applied +to the input on every timestep. +This is in direct contrast to decoded connections, +in which the function is executed +during the build process and not during the simulation.

+

Examples:

+
with nengo.Network() as net:
+    node = nengo.Node(output=[1])
+    ens1 = nengo.Ensemble(1, dimensions=2, neuron_type=nengo.Direct())
+    ens2 = nengo.Ensemble(10, dimensions=1)
+
+    # Node to LIF ensemble
+    nengo.Connection(node, ens2, function=lambda x: x**2)
+    # Direct ensemble to LIF ensemble
+    nengo.Connection(ens1, ens2, function=lambda x: x[0] * x[1])
+
+
+
+
+

Passthrough nodes

+

When creating large networks, +it is often helpful to use passthrough nodes +to route signals from place to place +without introducing unnecessary ensembles. +For example, the EnsembleArray network +is often used to represent a high-dimensional vector +with many lower-dimensional ensemble. +The high-dimensional vector is still available +as EnsembleArray.output through the use +of a passthrough node that collects the output +of all the lower-dimensional ensembles.

+

Unlike other types of nodes, +we explicitly disable the function argument +when connecting from passthrough nodes. +The reason for this is to ensure that users know +they are making a direct connection +and not a decoded connection. +The output of a network like EnsembleArray +can usually be treated the same way +as the output of an Ensemble, +except for the case of applying a function +to the output, +since decoders are not used to approximate +the function in the case of networks +using passthrough nodes.

+

As an example, +consider using an EnsembleArray to compute a product:

+
with nengo.Network() as net:
+    ea = nengo.networks.EnsembleArray(40, 2)
+    product = nengo.Ensemble(30, dimensions=1)
+
+    # Passthrough node to ensemble -- raises error
+    nengo.Connection(ea.output, product, function=lambda x: x[0] * x[1])
+
+
+

If this example did not raise an error, +the product would be computed nearly perfectly, +despite the fact that that computation +is impossible to decode from the ensembles +of the ensemble array. +Consider that the product +requires information from both dimensions of the signal +(i.e., the dimensions interact nonlinearly). +In order for nonlinearities to be decoded, +some neurons must encode information from +the nonlinearly-interacting dimensions. +Since the ensemble array represents each dimension independently, +no neurons will encode information from multiple dimensions, +and therefore the product cannot be approximated +by the ensemble array.

+

If you are aware that the function +will not be approximated but directly computed, +and you desire this behavior, +you can enable it by modifying the node so that it is +no longer a passthrough node, +but instead computes the identity function:

+
with nengo.Network() as net:
+    ea = nengo.networks.EnsembleArray(40, 2)
+    product = nengo.Ensemble(30, dimensions=1)
+
+    # Make the node non-passthrough
+    ea.output.output = lambda t, x: x
+    # Node to ensemble -- no error
+    nengo.Connection(ea.output, product, function=lambda x: x[0] * x[1])
+
+
+

If you’re designing networks +that may have arbitrary function +applied to the output, +you should implement a way to make +decoded connections from the ensembles +in your network. +See the EnsembleArray.add_output method +for an example of how that might be implemented.

+
+
+

Neuron-to-ensemble connections

+

As noted above, +a decoded connection is implemented by +solving for a set of decoding weights +and then weighting a sum of activities by those decoders. +If you already know the decoding weights +you want to use on a connection, +then you can skip the decoder solving step +by using a direct connection +from the neurons of an ensemble to another object.

+

In the example below, +we make two equivalent connections, +one using a decoded connection +and one using a direct connection:

+
with nengo.Network() as net:
+    ens1 = nengo.Ensemble(20, dimensions=1, seed=0)
+    ens2 = nengo.Ensemble(15, dimensions=1)
+
+    # Decoded ensemble to ensemble connection
+    conn1 = nengo.Connection(ens1, ens2, function=lambda x: x + 0.5)
+
+with nengo.Simulator(net) as sim:
+    decoders = sim.data[conn1].weights
+
+with net:
+    # Direct neurons to ensemble connection
+    conn2 = nengo.Connection(ens1.neurons, ens2, transform=decoders)
+
+
+

In the above example, the shape of decoders is (1, 20). +If you run this example and probe the output of conn1 +and conn2, you will see that their output is the same +(as long as a seed is set on ens1):

+
with net:
+    probe1 = nengo.Probe(conn1, "output", synapse=0.01)
+    probe2 = nengo.Probe(conn2, "output", synapse=0.01)
+
+with nengo.Simulator(net) as sim:
+    sim.run(0.1)
+
+assert np.allclose(sim.data[probe1], sim.data[probe2])
+
+
+

Both conn1 and conn2 can have learning rules applied, +so this type of direct connection can be useful +when saving the weights in a learning network +and loading it up in the future.

+
+
1
+

Gosmann, Jan. Precise multiplications with the NEF. +Waterloo, Ontario, Canada: University of Waterloo; 2015. +Available from: https://zenodo.org/record/35680

+
+
2
+

Gosmann, Jan, and Chris Eliasmith. “Optimizing Semantic Pointer +Representations for Symbol-Like Processing in Spiking Neural Networks.” +PLoS ONE 11, no. 2 (February 22, 2016): e0149928. +doi:10.1371/journal.pone.0149928.

+
+
3
+

Note that decoded connections +also accept the transform argument. +In the case of decoded connections, +the transform is a linear operation +that is applied after the function +is applied to the input. +In most cases, slicing the input +or including the transform +in the function is recommended.

+
+
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/contributing.html b/contributing.html new file mode 100644 index 0000000000..da2153c68d --- /dev/null +++ b/contributing.html @@ -0,0 +1,474 @@ + + + + + + + + Contributing to Nengo — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Contributing to Nengo

+

Please read our +general contributor guide +first. +The instructions below specifically apply +to the nengo project.

+
+

Developer installation

+

If you want to change parts of Nengo, +you should do a developer installation, +and install all of the optional dependencies.

+
git clone https://github.com/nengo/nengo.git
+cd nengo
+pip install -e '.[all]' --user
+pre-commit install
+
+
+

If you are in a virtual environment, you can omit the --user flag.

+
+
+

How to run unit tests

+

Nengo contains a large test suite, which we run with pytest. +To run these tests do

+
pytest --pyargs nengo
+
+
+
+

Running individual tests

+

Tests in a specific test file can be run by calling +pytest on that file. For example

+
pytest nengo/tests/test_node.py
+
+
+

will run all the tests in test_node.py.

+

Individual tests can be run using the -k EXPRESSION argument. Only tests +that match the given substring expression are run. For example

+
pytest nengo/tests/test_node.py -k test_circular
+
+
+

will run any tests with test_circular in the name, in the file +test_node.py.

+
+
+

Plotting the results of tests

+

Many Nengo tests have the built-in ability to plot test results +for easier debugging. To enable this feature, +pass the --plots to pytest. For example

+
pytest --plots --pyargs nengo
+
+
+

Plots are placed in nengo.simulator.plots in whatever directory +pytest is invoked from. You can also set a different directory:

+
pytest --plots=path/to/plots --pyargs nengo
+
+
+
+
+

Getting help and other options

+

Information about pytest usage +and Nengo-specific options can be found with

+
pytest --pyargs nengo --help
+
+
+
+
+

Writing your own tests

+

When writing your own tests, please make use of +custom Nengo fixtures +and markers +to integrate well with existing tests. +See existing tests for examples, or consult

+
pytest --pyargs nengo --fixtures
+
+
+

and

+
pytest --pyargs nengo --markers
+
+
+
+
+
+

How to build the documentation

+

The documentation is built with Sphinx, +which should have been installed as part +of the developer installation.

+

However, one additional requirement for building the Jupyter notebooks +that we include in the documentation is Pandoc. +If you use a package manager (e.g., Homebrew, apt) +you should be able to install Pandoc through your package manager. +Otherwise, see +this page +for instructions.

+

After you’ve installed all the requirements, +run the following command from the root directory of nengo +to build the documentation. +It will take a few minutes, as all examples are run +as part of the documentation building process.

+
sphinx-build -vW docs docs/_build
+
+
+

Depending on your environment, +you might have to set the Jupyter kernel +used to build the examples. +To set the kernel, use this command.

+
sphinx-build -vW docs docs/_build -D nbsphinx_kernel_name=<kernelname>
+
+
+
+
+

Getting help

+

If you have any questions about developing Nengo +or how you can best climb the learning curve +that Nengo and git present, please head to the +Nengo forum +and we’ll do our best to help you!

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/converting.html b/converting.html new file mode 100644 index 0000000000..c1d518538e --- /dev/null +++ b/converting.html @@ -0,0 +1,574 @@ + + + + + + + + Converting from Nengo 1.4 to Nengo 2.0 — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Converting from Nengo 1.4 to Nengo 2.0

+

On this page, we’ll go over the changes between Nengo 1.4 and 2.0. +They will first be reviewed heuristically in the section Big Changes, before +being broken down practically in Changes to Common Functions

+
+

Big changes

+
+

Objects instead of strings

+

In the old API each object had to be assigned it’s own unique string.

+

In the new Nengo, you can use strings to identify objects called labels, +but they are not unique. Instead, if you want to identify an object, you just +make sure to assign it a variable in your network

+
+
+

No Origins and Terminations

+

Previously, each object had a set of origins and terminations, +which determined how the object produced output and +accepted input, respectively. +These two things have been collapsed into a single +Connection object, which contains +the logic of the origin and termination +in one place.

+

Because the model is defined separately +from when it’s built, +the performance advantages of having +origins and terminations can be accomplished +during the build phase of the model instead.

+
+
+

Only Ensembles, Nodes, Networks and Probes

+

Many other objects have been removed, +in order to start with a very minimal +set of objects allowing a new user to get up and running without having +to spend all the effort of memorizing a large API.

+

Basically:

+
    +

  • Anything made with neurons is an Ensemble.

    • +

  • Anything not made with neurons (inputs, interfaces) are Nodes.

    • +

  • Probes are how you get data out of Nodes and Ensembles after simulating.

    • +

  • Networks are dumb containers +for Ensembles, Nodes, Probes, and other Networks.

    • +
+

A power user can easily divide his code and stop from repeating themselves +by encapsulating code that appears in multiple places in a Network.

+
+
+

Model and Simulator separation

+

There is now a clear separation between +model definition and model creation/simulation. +The motivation behind this is to allow +for testing models as they are being created. +For example, you can create a model, +add a node and an ensemble, +and the create a simulator based +on that model and run it +to make sure that your node and ensemble +are doing what you think they’re doing. +Then, you can continue adding new objects +to your model—this will not be reflected +in the simulator that you’ve already created, +but you can create a new simulator +with this updated model and run it +without having to rerun your script +from the top. +Basically, it allows for a more +iterative and interactive modelling process, +and makes it more explicit which +decisions are made manually and which +are automatically determined +when the simulator is created. +Additionally, this means that the +simulator timestep (dt) is not +defined until the simulator is created, +meaning that you can run the same model +with different timesteps to see if +there is a marked functional difference.

+
+
+

Changes to common functions

+

Many commonly used functions have been +simplified or changed to be more explicit.

+
+
+

Making ensembles

+

Old API example:

+
nef.Network.make('A', 40, 1, mode='spike')
+
+
+

New API example:

+
A = nengo.Ensemble(40, 1, neuron_type=nengo.LIF(), label='A')
+
+
+

See nengo.Ensemble for the full API specification.

+
+
+

Making ensemble arrays (i.e., network arrays)

+

Network arrays were very tightly coupled +with the old API. In the new API, +they have been decoupled and are just dumb containers, which +you can easily import. +The functionality should still be identical, +though the syntax has changed.

+

Old API:

+
nef.Network.make_array(name, neurons, length, dimensions, **args)
+
+
+

New API:

+
nengo.networks.EnsembleArray(neurons, n_ensembles, dimensions_per_ensemble, **ens_args)
+
+
+

See nengo.networks.EnsembleArray for more information.

+
+
+
+

Changes to common functions

+
+

Making nodes

+

Previously, there were several different ways +to provide input to a Nengo model: +SimpleNode, FunctionInput, and others. +All of these use cases should be covered +by nengo.Node.

+

In the old API, you could create your own +SimpleNode, or create a FunctionInput with:

+
nef.Network.make_input(name, values, zero_after_time)
+
+
+

In the new API, you create a node with:

+
nengo.Node(output)
+
+
+

where output is either a constant value +(float, list, NumPy array), a function, or +None when passing through values unchanged.

+

See nengo.Node for more information.

+
+
+

Making inputs

+

In the old API, inputs were defined as:

+
# Piecewise example
+net.make_input("contextinput", {0.0:[0, 0.1], 0.5:[1, 0], 1.0:[0, 1]})
+# Periodic white noise
+net.make_fourier_input('fin1', base=0.1, high=10, power=0.5, seed=12)
+
+
+

Inputs are just nodes whose sole function are to output a function.

+

Many of the Examples use function output nodes.

+
+
+

Terminations and Origins

+

Practically, to convert from one to the other, consider this table +that uses an example ensemble called ens who’s input needs to be +transformed by a two-dimensional identity function, [[1,0],[0,1]].

+

Nengo 1.4:

+
ens.addDecodedTermination("term_name", transform=MU.I(2))
+
+
+

Nengo 2.0:

+
# first create a simple pass-through node
+term_name = nengo.Node(size_in=2, label="term_name")
+# now connect the pass-through node to the ensemble
+nengo.Connection(term_name, ens, transform=np.eye(2))
+
+
+

Same, thing but instead of a decoded origin, we want one that connects +directly to the ensemble’s neurons.

+

Nengo 1.4:

+
ens.addTermination("term_name", transform=MU.I(2))
+
+
+

Nengo 2.0:

+
# first create a simple pass-through node
+term_name = nengo.Node(size_in=2, label="term_name")
+# now connect the pass-through node to the ensemble neurons
+nengo.Connection(term_name, ens.neurons, transform=np.eye(2))
+
+
+

One more time, but with an output and no transform.

+

Nengo 1.4:

+
ens.addDecodedOrigin("origin_name")
+
+
+

Nengo 2.0:

+
# first create a simple pass-through node
+origin_name = nengo.Node(size_in=2, label="origin_name")
+# now connect the pass-through node to the ensemble
+nengo.Connection(ens, origin_name, transform=np.eye(2))
+
+
+
+
+

Connecting things

+

A lot of the complexity of the old API +has been pushed down to the constructors +of the connection object. +In general, old API calls of the form:

+
nef.Network.connect(pre, post)
+
+
+

are now:

+
nengo.Connection(pre, post)
+
+
+

However, there are some changes in the additional arguments. +The old API used weight, index_pre and index_post +as a shortcut to define transform; +in the new API, only the transform can be specified. +There are many NumPy functions that make transforms +easier to specify. +Additionally, we now utilize Python’s slice syntax +to route dimensions easily:

+
nengo.Connection(pre[0], post[1])
+
+
+

The keyword argument pstc has been renamed to synapse.

+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples.html b/examples.html new file mode 100644 index 0000000000..ad40a5b663 --- /dev/null +++ b/examples.html @@ -0,0 +1,478 @@ + + + + + + + + Examples — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Examples

+
+

Introductory tutorials

+

These are tutorial-style examples that focus on +introducing the basic principles of Nengo.

+ +
+

Computing functions

+
+ +
+
+ +
+ +
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/functions-and-tuning-curves.html b/examples/advanced/functions-and-tuning-curves.html new file mode 100644 index 0000000000..f2d7ad9093 --- /dev/null +++ b/examples/advanced/functions-and-tuning-curves.html @@ -0,0 +1,1066 @@ + + + + + + + + Improving function approximation by adjusting tuning curves — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Improving function approximation by adjusting tuning curves

+

This tutorial shows how adjusting the tuning curves of neurons can help implement specific functions with Nengo. As an example we will try to to compute the Heaviside step function, which is 1 for all \(x > 0\) and 0 otherwise.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

The naive approach

+

As a first pass, we can try to implement the Heaviside step function using an ensemble with default parameters.

+
+
[2]:
+
+
+
+n_neurons = 150
+duration = 2.0
+
+
+
+
+
[3]:
+
+
+
+def stimulus_fn(t):
+    return (2.0 * t / duration) - 1.0
+
+
+def heaviside(x):
+    return x > 0
+
+
+
+
+
[4]:
+
+
+
+with nengo.Network() as model_naive:
+    stimulus = nengo.Node(stimulus_fn)
+    ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)
+    output = nengo.Node(size_in=1)
+
+    nengo.Connection(stimulus, ens)
+    nengo.Connection(ens, output, function=heaviside)
+
+    p_naive = nengo.Probe(output, synapse=0.005)
+
+
+
+
+
[5]:
+
+
+
+with nengo.Simulator(model_naive) as sim_naive:
+    sim_naive.run(duration)
+
+
+
+
+
[6]:
+
+
+
+t = sim_naive.trange()
+plt.figure()
+plt.plot(t, sim_naive.data[p_naive], label="naive")
+plt.plot(t, heaviside(stimulus_fn(t)), "--", c="black", label="ideal")
+plt.xlabel("t")
+plt.ylabel("Output")
+plt.legend(loc="best")
+
+
+
+
+
[6]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7faba6c411d0>
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_7_1.png +
+
+

We see that this approach does work, but there is room for improvement.

+
+
+

Investigating the tuning curves

+

Let us take a look at the tuning curves of the neurons in the ensemble.

+
+
[7]:
+
+
+
+plt.figure()
+plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_naive))
+plt.xlabel("Input")
+plt.ylabel("Firing rate [Hz]")
+
+
+
+
+
[7]:
+
+
+
+
+Text(0, 0.5, 'Firing rate [Hz]')
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_10_1.png +
+
+

About half of these neurons are tuned to fire more for smaller values. But these values are not relevant for the Heaviside step function, since the output is always 0 when input is less than 0. We can change all neurons to be tuned to fire more for larger values by setting all the encoders to be positive.

+
+
[8]:
+
+
+
+with nengo.Network() as model_pos_enc:
+    stimulus = nengo.Node(stimulus_fn)
+    ens = nengo.Ensemble(
+        n_neurons=n_neurons, dimensions=1, encoders=nengo.dists.Choice([[1.0]])
+    )
+    output = nengo.Node(size_in=1)
+
+    nengo.Connection(stimulus, ens)
+    nengo.Connection(ens, output, function=heaviside)
+
+    p_pos_enc = nengo.Probe(output, synapse=0.005)
+
+
+
+
+
[9]:
+
+
+
+with nengo.Simulator(model_pos_enc) as sim_pos_enc:
+    sim_pos_enc.run(duration)
+
+
+
+

The resulting tuning curves:

+
+
[10]:
+
+
+
+plt.figure()
+plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_pos_enc))
+plt.xlabel("Input")
+plt.ylabel("Firing rate [Hz]")
+
+
+
+
+
[10]:
+
+
+
+
+Text(0, 0.5, 'Firing rate [Hz]')
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_15_1.png +
+
+

And the resulting decoded Heaviside step function:

+
+
[11]:
+
+
+
+t = sim_pos_enc.trange()
+plt.figure()
+plt.plot(t, sim_naive.data[p_naive], label="naive")
+plt.plot(t, sim_pos_enc.data[p_pos_enc], label="pos. enc.")
+plt.plot(t, heaviside(stimulus_fn(t)), "--", c="black", label="ideal")
+plt.xlabel("t")
+plt.ylabel("Output")
+plt.legend(loc="best")
+
+
+
+
+
[11]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7faba4e76a90>
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_17_1.png +
+
+

Compared to the naive approach, the representation of outputs lower than 1 is less noisy, but otherwise there is little improvement. Even though the tuning curves are all positive, they are still covering a lot of irrelevant parts of the input space. Because all outputs should be 0 when input is less than 0, there is no need to have neurons tuned to inputs less than 0. Let’s shift all the intercepts to the range \((0.0, 1.0)\).

+
+
+

Intercept distributions

+

Not only can the range of intercepts be important, but also the distribution of intercepts. Let us take a look at the Heaviside step function:

+
+
[12]:
+
+
+
+xs = np.linspace(-1, 1, 100)
+plt.figure()
+plt.plot(xs, heaviside(xs))
+plt.ylim(-0.1, 1.1)
+
+
+
+
+
[12]:
+
+
+
+
+(-0.1, 1.1)
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_20_1.png +
+
+

This function is mostly constant, except for the large jump at 0. The constant parts are easy to approximate and do not need a lot of neural resources, but the highly nonlinear jump requires more neural resources for an accurate representation.

+

Let us compare the thresholding of a scalar in three ways:

+
    +

  1. With a uniform distribution of intercepts (the default)

    2. +

  2. With all intercepts at 0 (where we have the nonlinearity)

    3. +

  3. With an exponential distribution

    4. +
+

The last approach is in between the two extremes of a uniform distribution and placing all intercepts at 0. It will distribute most intercepts close to 0, but some intercepts will still be at larger values.

+
+
[13]:
+
+
+
+threshold = 0.0
+
+with nengo.Network() as model_dists:
+    stimulus = nengo.Node(stimulus_fn)
+    ens_uniform = nengo.Ensemble(
+        n_neurons=n_neurons,
+        dimensions=1,
+        encoders=nengo.dists.Choice([[1]]),
+        intercepts=nengo.dists.Uniform(threshold, 1.0),
+    )
+    ens_fixed = nengo.Ensemble(
+        n_neurons=n_neurons,
+        dimensions=1,
+        encoders=nengo.dists.Choice([[1]]),
+        intercepts=nengo.dists.Choice([threshold]),
+    )
+    ens_exp = nengo.Ensemble(
+        n_neurons=n_neurons,
+        dimensions=1,
+        encoders=nengo.dists.Choice([[1]]),
+        intercepts=nengo.dists.Exponential(0.15, threshold, 1.0),
+    )
+
+    out_uniform = nengo.Node(size_in=1)
+    out_fixed = nengo.Node(size_in=1)
+    out_exp = nengo.Node(size_in=1)
+
+    nengo.Connection(stimulus, ens_uniform)
+    nengo.Connection(stimulus, ens_fixed)
+    nengo.Connection(stimulus, ens_exp)
+    nengo.Connection(ens_uniform, out_uniform, function=heaviside)
+    nengo.Connection(ens_fixed, out_fixed, function=heaviside)
+    nengo.Connection(ens_exp, out_exp, function=heaviside)
+
+    p_uniform = nengo.Probe(out_uniform, synapse=0.005)
+    p_fixed = nengo.Probe(out_fixed, synapse=0.005)
+    p_exp = nengo.Probe(out_exp, synapse=0.005)
+
+
+
+
+
[14]:
+
+
+
+with nengo.Simulator(model_dists) as sim_dists:
+    sim_dists.run(duration)
+
+
+
+

Let us look at the tuning curves first.

+
+
[15]:
+
+
+
+plt.figure(figsize=(12, 4))
+
+plt.subplot(1, 3, 1)
+plt.plot(*nengo.utils.ensemble.tuning_curves(ens_uniform, sim_dists))
+plt.xlabel("Input")
+plt.ylabel("Firing rate [Hz]")
+plt.title("Uniform intercepts")
+
+plt.subplot(1, 3, 2)
+plt.plot(*nengo.utils.ensemble.tuning_curves(ens_fixed, sim_dists))
+plt.xlabel("Input")
+plt.ylabel("Firing rate [Hz]")
+plt.title("Fixed intercepts")
+
+plt.subplot(1, 3, 3)
+plt.plot(*nengo.utils.ensemble.tuning_curves(ens_exp, sim_dists))
+plt.xlabel("Input")
+plt.ylabel("Firing rate [Hz]")
+plt.title("Exponential intercept distribution")
+
+plt.tight_layout()
+
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_25_0.png +
+
+

Now let us look at how these three ensembles approximate the thresholding function.

+
+
[16]:
+
+
+
+t = sim_dists.trange()
+plt.figure()
+plt.plot(t, sim_naive.data[p_naive], label="naive", c="gray")
+plt.plot(t, sim_dists.data[p_uniform], label="Uniform intercepts")
+plt.plot(t, sim_dists.data[p_fixed], label="Fixed intercepts")
+plt.plot(t, sim_dists.data[p_exp], label="Exp. intercept dist.")
+plt.plot(t, heaviside(stimulus_fn(t)), "--", c="black", label="ideal")
+plt.xlabel("t")
+plt.ylabel("Output")
+plt.legend(loc="best")
+
+
+
+
+
[16]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7faba49d56d8>
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_27_1.png +
+
+

We see that the fixed intercepts produce slightly higher decoded values close to the threshold, but the slope is lower than for uniform intercepts. The best approximation of the thresholding is done with the exponential intercept distribution. Here we get a quick rise to 1 at the threshold and a fairly constant representation of 1 for inputs sufficiently above 0. All three distributions perfectly represent values below 0.

+

Nengo provides the ThresholdingEnsemble preset to make it easier to assign intercepts according to the exponential distribution, and also adjusts the encoders and evaluation points accordingly.

+
+
[17]:
+
+
+
+with nengo.Network() as model_final:
+    stimulus = nengo.Node(stimulus_fn)
+    with nengo.presets.ThresholdingEnsembles(0.0):
+        ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)
+    output = nengo.Node(size_in=1)
+
+    nengo.Connection(stimulus, ens)
+    nengo.Connection(ens, output, function=heaviside)
+
+    p_final = nengo.Probe(output, synapse=0.005)
+
+
+
+
+
[18]:
+
+
+
+with nengo.Simulator(model_final) as sim_final:
+    sim_final.run(duration)
+
+
+
+
+
[19]:
+
+
+
+t = sim_final.trange()
+plt.figure()
+plt.plot(t, sim_final.data[p_final], label="final")
+plt.plot(t, heaviside(stimulus_fn(t)), "--", c="black", label="ideal")
+plt.xlabel("t")
+plt.ylabel("Output")
+plt.legend(loc="best")
+
+
+
+
+
[19]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7faba4928588>
+
+
+
+
+
+
+../../_images/examples_advanced_functions-and-tuning-curves_31_1.png +
+
+
+
+

The takeaway

+

Adjusting ensemble parameters in the right way can sometimes help in implementing functions more acurately in neurons. Think about how your function maps from the input vector space to the output vector space, and consider ways to modify ensemble parameters to better cover important parts of the input vector space.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/functions-and-tuning-curves.ipynb b/examples/advanced/functions-and-tuning-curves.ipynb new file mode 100644 index 0000000000..6d05d1b61f --- /dev/null +++ b/examples/advanced/functions-and-tuning-curves.ipynb @@ -0,0 +1,803 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Improving function approximation by adjusting tuning curves\n", + "\n", + "This tutorial shows how adjusting the tuning curves of neurons\n", + "can help implement specific functions with Nengo.\n", + "As an example we will try to to compute\n", + "the Heaviside step function,\n", + "which is 1 for all $x > 0$ and 0 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:02.119301Z", + "iopub.status.busy": "2020-11-25T16:46:02.118470Z", + "iopub.status.idle": "2020-11-25T16:46:03.027487Z", + "shell.execute_reply": "2020-11-25T16:46:03.027973Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The naive approach\n", + "\n", + "As a first pass, we can try to implement the Heaviside step function\n", + "using an ensemble with default parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.032018Z", + "iopub.status.busy": "2020-11-25T16:46:03.031456Z", + "iopub.status.idle": "2020-11-25T16:46:03.034622Z", + "shell.execute_reply": "2020-11-25T16:46:03.035023Z" + } + }, + "outputs": [], + "source": [ + "n_neurons = 150\n", + "duration = 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.040175Z", + "iopub.status.busy": "2020-11-25T16:46:03.038711Z", + "iopub.status.idle": "2020-11-25T16:46:03.040889Z", + "shell.execute_reply": "2020-11-25T16:46:03.041294Z" + } + }, + "outputs": [], + "source": [ + "def stimulus_fn(t):\n", + " return (2.0 * t / duration) - 1.0\n", + "\n", + "\n", + "def heaviside(x):\n", + " return x > 0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.049765Z", + "iopub.status.busy": "2020-11-25T16:46:03.048648Z", + "iopub.status.idle": "2020-11-25T16:46:03.052168Z", + "shell.execute_reply": "2020-11-25T16:46:03.051596Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network() as model_naive:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_naive = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.059630Z", + "iopub.status.busy": "2020-11-25T16:46:03.058877Z", + "iopub.status.idle": "2020-11-25T16:46:03.460874Z", + "shell.execute_reply": "2020-11-25T16:46:03.460415Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_naive) as sim_naive:\n", + " sim_naive.run(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.466625Z", + "iopub.status.busy": "2020-11-25T16:46:03.465327Z", + "iopub.status.idle": "2020-11-25T16:46:03.649214Z", + "shell.execute_reply": "2020-11-25T16:46:03.648725Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_naive.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that this approach does work,\n", + "but there is room for improvement." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating the tuning curves\n", + "\n", + "Let us take a look at\n", + "the tuning curves of the neurons in the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:03.688439Z", + "iopub.status.busy": "2020-11-25T16:46:03.654083Z", + "iopub.status.idle": "2020-11-25T16:46:04.024975Z", + "shell.execute_reply": "2020-11-25T16:46:04.025423Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Firing rate [Hz]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_naive))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "About half of these neurons are tuned to fire more for smaller values.\n", + "But these values are not relevant\n", + "for the Heaviside step function,\n", + "since the output is always 0\n", + "when input is less than 0.\n", + "We can change all neurons to be tuned\n", + "to fire more for larger values\n", + "by setting all the encoders to be positive." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.033810Z", + "iopub.status.busy": "2020-11-25T16:46:04.033310Z", + "iopub.status.idle": "2020-11-25T16:46:04.036526Z", + "shell.execute_reply": "2020-11-25T16:46:04.036926Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network() as model_pos_enc:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens = nengo.Ensemble(\n", + " n_neurons=n_neurons, dimensions=1, encoders=nengo.dists.Choice([[1.0]])\n", + " )\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_pos_enc = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.042363Z", + "iopub.status.busy": "2020-11-25T16:46:04.041583Z", + "iopub.status.idle": "2020-11-25T16:46:04.373382Z", + "shell.execute_reply": "2020-11-25T16:46:04.372905Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_pos_enc) as sim_pos_enc:\n", + " sim_pos_enc.run(duration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting tuning curves:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.379060Z", + "iopub.status.busy": "2020-11-25T16:46:04.378232Z", + "iopub.status.idle": "2020-11-25T16:46:04.689463Z", + "shell.execute_reply": "2020-11-25T16:46:04.689881Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Firing rate [Hz]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens, sim_pos_enc))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the resulting decoded Heaviside step function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.709140Z", + "iopub.status.busy": "2020-11-25T16:46:04.701129Z", + "iopub.status.idle": "2020-11-25T16:46:04.875204Z", + "shell.execute_reply": "2020-11-25T16:46:04.875623Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_pos_enc.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\")\n", + "plt.plot(t, sim_pos_enc.data[p_pos_enc], label=\"pos. enc.\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compared to the naive approach,\n", + "the representation of outputs lower than 1 is less noisy,\n", + "but otherwise there is little improvement.\n", + "Even though the tuning curves are all positive,\n", + "they are still covering a lot of irrelevant parts of the input space.\n", + "Because all outputs should be 0 when input is less than 0,\n", + "there is no need to have neurons tuned to inputs less than 0.\n", + "Let's shift all the intercepts to the range $(0.0, 1.0)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intercept distributions\n", + "\n", + "Not only can the range of intercepts be important,\n", + "but also the distribution of intercepts.\n", + "Let us take a look at the Heaviside step function:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:04.894226Z", + "iopub.status.busy": "2020-11-25T16:46:04.893311Z", + "iopub.status.idle": "2020-11-25T16:46:05.016106Z", + "shell.execute_reply": "2020-11-25T16:46:05.015630Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.1, 1.1)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.linspace(-1, 1, 100)\n", + "plt.figure()\n", + "plt.plot(xs, heaviside(xs))\n", + "plt.ylim(-0.1, 1.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function is mostly constant,\n", + "except for the large jump at 0.\n", + "The constant parts are easy to approximate\n", + "and do not need a lot of neural resources,\n", + "but the highly nonlinear jump\n", + "requires more neural resources\n", + "for an accurate representation.\n", + "\n", + "Let us compare the thresholding of a scalar in three ways:\n", + "\n", + "1. With a uniform distribution of intercepts (the default)\n", + "2. With all intercepts at 0 (where we have the nonlinearity)\n", + "3. With an exponential distribution\n", + "\n", + "The last approach is in between\n", + "the two extremes of a uniform distribution\n", + "and placing all intercepts at 0.\n", + "It will distribute most intercepts close to 0,\n", + "but some intercepts will still be at larger values." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:05.031799Z", + "iopub.status.busy": "2020-11-25T16:46:05.023763Z", + "iopub.status.idle": "2020-11-25T16:46:05.033616Z", + "shell.execute_reply": "2020-11-25T16:46:05.033999Z" + } + }, + "outputs": [], + "source": [ + "threshold = 0.0\n", + "\n", + "with nengo.Network() as model_dists:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " ens_uniform = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Uniform(threshold, 1.0),\n", + " )\n", + " ens_fixed = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Choice([threshold]),\n", + " )\n", + " ens_exp = nengo.Ensemble(\n", + " n_neurons=n_neurons,\n", + " dimensions=1,\n", + " encoders=nengo.dists.Choice([[1]]),\n", + " intercepts=nengo.dists.Exponential(0.15, threshold, 1.0),\n", + " )\n", + "\n", + " out_uniform = nengo.Node(size_in=1)\n", + " out_fixed = nengo.Node(size_in=1)\n", + " out_exp = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens_uniform)\n", + " nengo.Connection(stimulus, ens_fixed)\n", + " nengo.Connection(stimulus, ens_exp)\n", + " nengo.Connection(ens_uniform, out_uniform, function=heaviside)\n", + " nengo.Connection(ens_fixed, out_fixed, function=heaviside)\n", + " nengo.Connection(ens_exp, out_exp, function=heaviside)\n", + "\n", + " p_uniform = nengo.Probe(out_uniform, synapse=0.005)\n", + " p_fixed = nengo.Probe(out_fixed, synapse=0.005)\n", + " p_exp = nengo.Probe(out_exp, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:05.039143Z", + "iopub.status.busy": "2020-11-25T16:46:05.038364Z", + "iopub.status.idle": "2020-11-25T16:46:05.470922Z", + "shell.execute_reply": "2020-11-25T16:46:05.470457Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_dists) as sim_dists:\n", + " sim_dists.run(duration)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us look at the tuning curves first." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:05.493289Z", + "iopub.status.busy": "2020-11-25T16:46:05.487102Z", + "iopub.status.idle": "2020-11-25T16:46:06.474469Z", + "shell.execute_reply": "2020-11-25T16:46:06.474022Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_uniform, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Uniform intercepts\")\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_fixed, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Fixed intercepts\")\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(*nengo.utils.ensemble.tuning_curves(ens_exp, sim_dists))\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"Firing rate [Hz]\")\n", + "plt.title(\"Exponential intercept distribution\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us look at how these three ensembles\n", + "approximate the thresholding function." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:06.480901Z", + "iopub.status.busy": "2020-11-25T16:46:06.480042Z", + "iopub.status.idle": "2020-11-25T16:46:06.683018Z", + "shell.execute_reply": "2020-11-25T16:46:06.683432Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_dists.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_naive.data[p_naive], label=\"naive\", c=\"gray\")\n", + "plt.plot(t, sim_dists.data[p_uniform], label=\"Uniform intercepts\")\n", + "plt.plot(t, sim_dists.data[p_fixed], label=\"Fixed intercepts\")\n", + "plt.plot(t, sim_dists.data[p_exp], label=\"Exp. intercept dist.\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the fixed intercepts\n", + "produce slightly higher decoded values close to the threshold,\n", + "but the slope is lower than for uniform intercepts.\n", + "The best approximation of the thresholding\n", + "is done with the exponential intercept distribution.\n", + "Here we get a quick rise to 1 at the threshold\n", + "and a fairly constant representation of 1\n", + "for inputs sufficiently above 0.\n", + "All three distributions perfectly represent values below 0.\n", + "\n", + "Nengo provides the `ThresholdingEnsemble` preset\n", + "to make it easier to assign intercepts\n", + "according to the exponential distribution,\n", + "and also adjusts the encoders and evaluation points accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:06.692487Z", + "iopub.status.busy": "2020-11-25T16:46:06.691367Z", + "iopub.status.idle": "2020-11-25T16:46:06.694038Z", + "shell.execute_reply": "2020-11-25T16:46:06.693603Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network() as model_final:\n", + " stimulus = nengo.Node(stimulus_fn)\n", + " with nengo.presets.ThresholdingEnsembles(0.0):\n", + " ens = nengo.Ensemble(n_neurons=n_neurons, dimensions=1)\n", + " output = nengo.Node(size_in=1)\n", + "\n", + " nengo.Connection(stimulus, ens)\n", + " nengo.Connection(ens, output, function=heaviside)\n", + "\n", + " p_final = nengo.Probe(output, synapse=0.005)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:06.699268Z", + "iopub.status.busy": "2020-11-25T16:46:06.698394Z", + "iopub.status.idle": "2020-11-25T16:46:06.994952Z", + "shell.execute_reply": "2020-11-25T16:46:06.995598Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model_final) as sim_final:\n", + " sim_final.run(duration)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:07.001264Z", + "iopub.status.busy": "2020-11-25T16:46:07.000374Z", + "iopub.status.idle": "2020-11-25T16:46:07.166273Z", + "shell.execute_reply": "2020-11-25T16:46:07.166652Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim_final.trange()\n", + "plt.figure()\n", + "plt.plot(t, sim_final.data[p_final], label=\"final\")\n", + "plt.plot(t, heaviside(stimulus_fn(t)), \"--\", c=\"black\", label=\"ideal\")\n", + "plt.xlabel(\"t\")\n", + "plt.ylabel(\"Output\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## The takeaway\n", + "\n", + "Adjusting ensemble parameters in the right way\n", + "can sometimes help in implementing functions more acurately in neurons.\n", + "Think about how your function maps from\n", + "the input vector space to the output vector space,\n", + "and consider ways to modify ensemble parameters\n", + "to better cover important parts\n", + "of the input vector space." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/advanced/functions_and_tuning_curves.html b/examples/advanced/functions_and_tuning_curves.html new file mode 100644 index 0000000000..a9428a58d9 --- /dev/null +++ b/examples/advanced/functions_and_tuning_curves.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/advanced/inhibitory-gating.html b/examples/advanced/inhibitory-gating.html new file mode 100644 index 0000000000..41b965e331 --- /dev/null +++ b/examples/advanced/inhibitory-gating.html @@ -0,0 +1,779 @@ + + + + + + + + Inhibitory gating of ensembles — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Inhibitory gating of ensembles

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the network

+

Our model consists of two ensembles (called A and B) that receive inputs from a common sine wave signal generator.

+

Ensemble A is gated using the output of a node, while Ensemble B is gated using the output of a third ensemble (C). This is to demonstrate that ensembles can be gated using either node outputs, or decoded outputs from ensembles.

+
+
[2]:
+
+
+
+n_neurons = 30
+
+model = nengo.Network(label="Inhibitory Gating")
+with model:
+    A = nengo.Ensemble(n_neurons, dimensions=1)
+    B = nengo.Ensemble(n_neurons, dimensions=1)
+    C = nengo.Ensemble(n_neurons, dimensions=1)
+
+
+
+
+
+

Step 2: Provide input to the model

+

As described in Step 1, this model requires two inputs.

+
    +

  1. A sine wave signal that is used to drive ensembles A and B

    2. +

  2. An inhibitory control signal used to (directly) gate ensemble A, and (indirectly through ensemble C) gate ensemble B.

    3. +
+
+
[3]:
+
+
+
+with model:
+    sin = nengo.Node(np.sin)
+    inhib = nengo.Node(Piecewise({0: 0, 2.5: 1, 5: 0, 7.5: 1, 10: 0, 12.5: 1}))
+
+
+
+
+
+

Step 3: Connect the different components of the model

+

In this model, we need to make the following connections:

+
    +

  1. From sine wave generator to Ensemble A

    2. +

  2. From sine wave generator to Ensemble B

    3. +

  3. From inhibitory control signal to the neurons of Ensemble A (to directly drive the currents of the neurons)

    4. +

  4. From inhibitory control signal to Ensemble C

    5. +

  5. From Ensemble C to the neurons of Ensemble B (this demonstrates that the decoded output of Ensemble C can be used to gate Ensemble B)

    6. +
+
+
[4]:
+
+
+
+with model:
+    nengo.Connection(sin, A)
+    nengo.Connection(sin, B)
+    nengo.Connection(inhib, A.neurons, transform=[[-2.5]] * n_neurons)
+    nengo.Connection(inhib, C)
+    nengo.Connection(C, B.neurons, transform=[[-2.5]] * n_neurons)
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later. Let’s collect all the data produced.

+
+
[5]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin)
+    inhib_probe = nengo.Probe(inhib)
+    A_probe = nengo.Probe(A, synapse=0.01)
+    B_probe = nengo.Probe(B, synapse=0.01)
+    C_probe = nengo.Probe(C, synapse=0.01)
+
+
+
+
+
+

Step 5: Run the model

+

In order to run the model, we have to create a simulator. Then, we can run that simulator over and over again without affecting the original model.

+
+
[6]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(15)
+
+
+
+
+
[7]:
+
+
+
+# Plot the decoded output of Ensemble A
+plt.figure()
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded output")
+plt.plot(sim.trange(), sim.data[sin_probe], label="Sine input")
+plt.plot(sim.trange(), sim.data[inhib_probe], label="Inhibitory signal")
+plt.legend()
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f7295dc5198>
+
+
+
+
+
+
+../../_images/examples_advanced_inhibitory-gating_12_1.png +
+
+
+
[8]:
+
+
+
+# Plot the decoded output of Ensemble B and C
+plt.figure()
+plt.plot(sim.trange(), sim.data[B_probe], label="Decoded output of B")
+plt.plot(sim.trange(), sim.data[sin_probe], label="Sine input")
+plt.plot(sim.trange(), sim.data[C_probe], label="Decoded output of C")
+plt.plot(sim.trange(), sim.data[inhib_probe], label="Inhibitory signal")
+plt.legend()
+
+
+
+
+
[8]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f7295613c18>
+
+
+
+
+
+
+../../_images/examples_advanced_inhibitory-gating_13_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/inhibitory-gating.ipynb b/examples/advanced/inhibitory-gating.ipynb new file mode 100644 index 0000000000..ccfa560c27 --- /dev/null +++ b/examples/advanced/inhibitory-gating.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inhibitory gating of ensembles" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.432474Z", + "iopub.status.busy": "2020-11-25T16:46:10.431599Z", + "iopub.status.idle": "2020-11-25T16:46:10.925564Z", + "shell.execute_reply": "2020-11-25T16:46:10.925052Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "Our model consists of two ensembles (called A and B)\n", + "that receive inputs from a common sine wave signal generator.\n", + "\n", + "Ensemble A is gated using the output of a node,\n", + "while Ensemble B is gated using the output of a third ensemble (C).\n", + "This is to demonstrate that ensembles can be gated\n", + "using either node outputs, or decoded outputs from ensembles." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.931988Z", + "iopub.status.busy": "2020-11-25T16:46:10.931419Z", + "iopub.status.idle": "2020-11-25T16:46:10.934990Z", + "shell.execute_reply": "2020-11-25T16:46:10.934540Z" + } + }, + "outputs": [], + "source": [ + "n_neurons = 30\n", + "\n", + "model = nengo.Network(label=\"Inhibitory Gating\")\n", + "with model:\n", + " A = nengo.Ensemble(n_neurons, dimensions=1)\n", + " B = nengo.Ensemble(n_neurons, dimensions=1)\n", + " C = nengo.Ensemble(n_neurons, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "As described in Step 1, this model requires two inputs.\n", + "\n", + "1. A sine wave signal that is used to drive ensembles A and B\n", + "2. An inhibitory control signal used to (directly) gate ensemble A,\n", + " and (indirectly through ensemble C) gate ensemble B." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.940994Z", + "iopub.status.busy": "2020-11-25T16:46:10.939493Z", + "iopub.status.idle": "2020-11-25T16:46:10.941616Z", + "shell.execute_reply": "2020-11-25T16:46:10.942034Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(np.sin)\n", + " inhib = nengo.Node(Piecewise({0: 0, 2.5: 1, 5: 0, 7.5: 1, 10: 0, 12.5: 1}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the different components of the model\n", + "\n", + "In this model, we need to make the following connections:\n", + "\n", + "1. From sine wave generator to Ensemble A\n", + "2. From sine wave generator to Ensemble B\n", + "3. From inhibitory control signal to the neurons of Ensemble A\n", + " (to directly drive the currents of the neurons)\n", + "4. From inhibitory control signal to Ensemble C\n", + "5. From Ensemble C to the neurons of Ensemble B\n", + " (this demonstrates that the decoded output of Ensemble C\n", + " can be used to gate Ensemble B)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.950930Z", + "iopub.status.busy": "2020-11-25T16:46:10.949450Z", + "iopub.status.idle": "2020-11-25T16:46:10.951548Z", + "shell.execute_reply": "2020-11-25T16:46:10.951987Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, A)\n", + " nengo.Connection(sin, B)\n", + " nengo.Connection(inhib, A.neurons, transform=[[-2.5]] * n_neurons)\n", + " nengo.Connection(inhib, C)\n", + " nengo.Connection(C, B.neurons, transform=[[-2.5]] * n_neurons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later.\n", + "Let's collect all the data produced." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.959360Z", + "iopub.status.busy": "2020-11-25T16:46:10.957898Z", + "iopub.status.idle": "2020-11-25T16:46:10.960002Z", + "shell.execute_reply": "2020-11-25T16:46:10.960400Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " inhib_probe = nengo.Probe(inhib)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " C_probe = nengo.Probe(C, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:10.965546Z", + "iopub.status.busy": "2020-11-25T16:46:10.964788Z", + "iopub.status.idle": "2020-11-25T16:46:14.587431Z", + "shell.execute_reply": "2020-11-25T16:46:14.586937Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:14.593579Z", + "iopub.status.busy": "2020-11-25T16:46:14.592823Z", + "iopub.status.idle": "2020-11-25T16:46:14.932696Z", + "shell.execute_reply": "2020-11-25T16:46:14.933120Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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H5+hwJBrfOdKYomURQSVXvLo6ILra/salL63gIs1Yr/Z/jjHeq3jQDEAYSbq8jL+42ryCvQpyl8CgS8HW+tn0bULTjAf+riVQ1XCqdNOq8mjpfso4LYcYUc4S52Sbz381yeN1/mR0Av/TxxApqpisbSbrwEmqdd3j9Srqc6FtI1v0ZA7TxrznbSG6KySNh5yvvFenoj77VhrunIGXerfewZeBvRx2/+DRapTgn8V1/1jDRVo65TKMlfpwAI8M1p7N89eOZL0+iBMy2uXWeXvFXiAwut7+EBFSXuWgC8WMFrtY7EhzbW+wBexuLp2GGDwTjmyB43vNL7sBAuF3dTZun9P2byAkEvpMMcWeFtNrkhHv7+EenhL8WmzOP8m6vUVcaNvICn04lbRhGTs3sBPCEsdoLtQ2EoKd9H3ezaTnaXzdRfW3JTuZatuEJqSrdwfQM/bsVMgeOo9Blxn/t3u3lR8ILh1TzkFK2PEt9D3fyFvvTWyhMOASY1UtR/1U6WahBL8W2wtOMVTsI14UsVhPBeDDX4z3qg1L9FRiRRmpYpdX6w12MvYd543le5imZZAvu5AtewEw74ZR/OGKYfX294hIduxlJOra6Z1JOIqzOLwZSvIN/70VDLoUKorhwDqPVaEEvxYSyYVaBroU/OAYBcCEvt7z4+7986Ws0odSJW1MsWW6tgdi19vX+Mlra4igknO0LSxxjKYmNGrmyJ71FrkxfeJVbfpfBPvXGHnYPYw/uNlai1vntP0bQED/i5vd1SP0mWLMx/DgA18Jfi0e+3QLF9oy2Cj7U0Ss1+sXQlBKFOn6QKaoNAteZ5K2lUhRxRJn7+6LpgbrPeUF6XcR6HbYs8xDFSgaZee3kDgWor0w96IhwttDr4mwa7HHqlCC76Si2hiwG67l8YMjBTBad97mjZtTWaqnMFg74FoJK1DwVV/x2j1FAJynbaZMhrNeNxacH9nEYL3HziRxLITHGJN/vIVvXpZW4fb4UGmhkQ65/zRzDGor/acZaTZO7vdI8Urwnfzs7fVM0rYAsEIfARj+W28zbWh3lukpAAzT8oDA6Hr7sltq/iojKuZcbTNr9CFUEWqdMbZQ6DvVaOV5eJDbl6+J16npUfU931IzXA8cD826VYLvZP3e40y2bea4jGarTLbUll0ynoOyM8M174TnBTvbD58iURyht3aEFc5QXEvpdxGcKvB6MrWgJvd7iOwEPVKstaNLfyODqofcOkrwgROnqwDJZG0LK/XhSMu/FsEyRwoDxT4A9hcFxtKHvurS2VdUxmRn7265s3e355mmIjU8fB41GRo96Mutja9el9bg1jnoujHhqe9UY9arlQhhrITV0zPeBauVzSeY9OwPDBb7iRPFrhveapbpI4kURjyuXff/G9LX3VKTtc3kyy7skT349O6JzaTS8PC5xPSA7sONKf6exLcvSZtok5uqcBucLoS+F5hvUFuY9ABMme2RopXgA2VVDs7VNgOw3GG94Gc/fTFr9CE4nLM5bUJdJk8Sgp0J2jaWO4YDgtReza9w5PE5ZH2mwoH1UBUYvTufJvd743/fqU3vFwAoJXEyWdvMdj2RQoyb/av7zrHMlqiwEEqJIo/ugPW9TLPwxZm2Dl2SInKJEeUsdy500zxeOI8+5xkpc/ev8XhVvnhdWotbLp3dP0DXIRDj/ag8bxMgUuIeEVQyRtvhcufkzZ3BsHjvx+HXpkt0ONv1RABs9nJLbTEDX40IWb7zKOfatuCQgtX6kFYc6eHzSZpgTMLZ86PHqvDVa+IOrXYdVp02HqpWR+d4CSX4wDhtO+HC7hsRGk5+Oi6J7TIJgIjCrGb2VrSVJxduY7K2hSzZlxKiefiiAVabZBDWzojJ3+s5wVcA+9aAoyoo3DmgBB+ACVo2VdLGBn2gz9zwt09KZo80XDphRzZZbI05+GI0yPHjxxgu9rBKN/Ll3H9B/xYc5aUz6X0eFGyGMs8m0fPF69Ja2uyWyltu9KSSJpprkI+iBB8Yr2WTKftRQXgLb3jP0yEqDDtGDpfIABB8X4zSKSguJ03bSYjQWdMad47AO7NT+5xnVJS3wiPFB6RLp7XnlLcSEtIgLMozBvkYQS/4877JYJjYy1p9MLGRFs6wbICLhnQDILQ4z5j6rTCV2+ZvYLyWQ5W0sVFvzYPeS0IZnwph0R714wc1FSVwKBOSrQvQ8DZBLfjHSivJWPmts4U3lLdv9eKSdi2gV+darY69y60zxCx8zHOw/fApxmvbXL27m8f3auGRXjoRW6iRTMvD1z4QXDptYv9akA4l+MFCUWkV47UcKmUIm/R+2Dy4bm1bqDFHD41W2RM9QDRlDHf27gBum5TcouO85dEBDD9+0S4oPuitGoOHvBWG/z5hrNWWeA1TBF8IMV0IsUMIkSuEqDdFTAhxqxDiqBAi0/l3hxn1uktBcXkd/72P6T01roOKuBGwb5XFtriHL/qL07Qd2IRkjT4UgD5x0S0/2FuK33uy8X/fatOL9sVxFXdp1TkFmf8eTBB8IYQNeAW4BBgC3CCEaGgE7N9SyhTn31vu1usuX28u4N75P9Zp4Q3raW3s/dnU/HbLugyD43vg1GFrDXITX3Md1O7d+Szdhhrpkj34wA+IiVetPYeKEijIhORzPWKPr2JGC38skCul3COlrAI+AmaZUK5HyTxwwtXCW+uM0Gg6f4r30VyC75wf4IFWXrDywuKddXp3H93ZuqUsvSaRmg2Sxqtrbzb714DUg8p/D+YIfjxwoNb7fOe2s/mJEGKzEOITIURiQwUJIe4UQqQLIdKPHj1qgmmNs/NIKRO0bCplCBv1/oTafEvs4YwbpDymL4S2Uze9icz/PsvZuzMe9uP7eG8py1bTayIc2wGnj5larC+62bxG3gqwhUGCbwVqeBpvDdp+CSRLKUcAi4F3G9pJSvmGlDJNSpkWF+fZZcZOVVQzXsshU/ajkjD+e3cTy9lZhTDakVILMWZd+rng+4pL58Dxslq9u8FtKMHL51EzKcgLeXX8lVb/tvJWQnxw+e/BHME/CNRusSc4t7mQUhZJKSudb98CUk2ot804dMmu/Yec8fdGC29YfIyVJjWIqwUmJfSaBIXZHp91GQzsKypjfK3eXVvwqtu75ygIifD7B77PUFFsLGfYO7j892CO4G8A+gshegshwoDrgYW1dxBC9Kj19nIgx4R628wz3+QwWtuFTUjWOdcv9cWIBVdYphRGtx4JB9ZZalNb8aXv9+mvtjFW20GW7EslYUzq1xZ3jhfPJ8TpejB54DYQXTotOqcDGwz/fdIEzxvkY7gt+FJKO3AvsAhDyD+WUm4TQjwthLjcudv9QohtQogs4H7gVnfrdYeFWYdI1XZglxqZvhyh4eymSl0asy5tYX4dnukL0SAV1Q72HTnOULGXDN3ImzRrZENDTj5Gr0lweIsRXWIyvuJqc4dWncOBtSA0IyQzyAgxoxAp5TfAN2dt+32t148Dj5tRlxkcPVVJWuhOcmQSZURYbU7j1LTwAUIjDNHfp/y47rD/eBkjxW7ChIMN+kAARiS2MhxXWDBpuNcEo1V6YD30v9DbtQcW+9caK4qFt7faEq8TlDNtQ7CTou0m3XnD3zox2VqDGkE72w3Sa6IRO1xZaok97uAr7gMBpGk7AVwt/EHd2zB+423FTxgDWoi5PTzfuCSm0qzr0FENBzMgsXVhuIFC0Am+lJIhYh9RotIl+L+Z0ZZIDe/h8oT0mgi6HfI3WGpPW/EF18HK3GOkaTvYpcdTTCtm1tbBgvMIa2cM3npg4NYXXG3u0uJzOLwFqssgaZxnDfJRgk7wM/adcLXw0p0tvFCbb34NZwZtnT/mxHGG71FFa7SZp7/cSqq203XtrVzKstUkTTBap9X+vwKaZexfa/xXLfzgYFdhKanaDvJlF47QyZWC2DcxFF/W9L3D20O3YZC/3kKb2oavuHT6iUPEijIypCH4PWLbNoZjSZs4abyxzm2BOSug+co1MZNmz+nAWohNglg/GKj3AEEn+Iu2FpCm7XQN2P35Kt9Z1vBsRM3Eq9rd1cSxkJ8OusMiq/ybNG0HcKZ31zEqzEpzWkdNVkeTQ3N9wdXmLi06Bylh/7qgdedAEAr+7l3b6CZOugbsukSHW2xR49S0VuoK/jioKjUmYSlaTZq2g6MyhjzZnYcvGtD2/ElWaGR0HHTqY0TqKFrPyX1Qeti4h4KUoBP8NFHjvx9IWIhvn36NFkm9ligleqaV52l8ZeJVmthJhj4QENwyIbnN5Uir3CGJ44xrb8JAq69cEzNp0qWz33nPBOGEqxp8W/FMxqFL0rSdlMhIdsoE1j9xgdUmNY3TpaPXbk526AXtuhqzBf0MK6NBquw6r365il5aocudExvV1iUtJZYt35UwBk4fhRN5phUZCFE6LeLAWiPVdFffjsrzJEEl+H2f+IZUbSeb9P7oaHTwcf9tTWtFr30/CmG08v2shW8181ftZfOa74Az8fd+SY07Qrl1Ws/+tc75DDarLbGMoBL8GEoZpB1wDdj6Oq4e99kNsMRxcGIvlHo2hXQgcarCzhhtBxUylK2yN326tHOvQGmRO6TrYAhrb8oDPyCjdBpzU5WfhMIcI9IpiAkawXfoktHaLgBXSJ6vI1xx+Gd9UNPK87PwTCujQUor7aQ6E6ZVE8K957uXQ8myM9FsRg4YP7v2nqZZt1T+BkAqwbfaAG8xZ+E20rSdzoRpfa02p0W4Gvhn/5h7jDQWX1ZunRbz0eodDBX7XP77q0YnuFmihX7vxLFwZBtUnrLOBn9j/1oQNiMfVRATNIL//tp9jBK55Mgkyong0en+4NZx+vDP3hwaAT1T/MqPa7X7YLjYS6hwtDn/fX0sPJ/EsUYitYMZbhVj9TXxKvkboPswI0VFEBM0gq+hM0Lb40qHPK53J4stap6aiVcNNiYTx8HBjWCv8qpN7mClSydFywUgU+/H1IHurqYmvbsAytnEpwHCtAd+wE+80nU4tMn5vQU3QSP4fcUh2otyl+Cn9vIDwXf+b/DHnDgWHJVGMihFk7y7Oo8ULZcDehxFxDKwLdkxfYnIDhA3yK96eJZybCdUlgRl/vuzCRrBr2nhbZK+vOBJXWoiDnS9ga63h6bZeworJ/k8uXAbKdpuMqUxdnP9mMRmjmgey9vEiWONgVu9nsOvxQTkxKuGzqkmu2yQLVjeEEEh+J9k5DNK7KJYRrFXdrfanFbQQC6dGmJ6GEmg/ETwwboJPl05QbwoYpPTf5/sbkim9XJvuPQqio3Wq5sEwsSrJs/hYDpExEIn/wjW8CRBIfiP/CeLUdpusvS+SDSiwvxj4oXQarJlNkLiWNWtbwGjXP57c254IYS1PnyoNQHLfx74lpGfYUTnaEEhd00SFN9AFBUMEAdc7pytcy622KKWUXNxGhWXhDFw6hCUHPKWSW3GqoiQsio7KVouVdLGNplsWrn15kZ4m859IbKj0XpVuKj3O6s6DYXb1ICtk6AQ/OFiLzYh2eQcsG1zhkRv09jEqxpqBqHy/eOmtyIaZMjvF5EidpMje1FJGNOHmuHSk6YkL3MLIYxWa757oZkQ4FE6hzYZIazKfw8EgeCfOF3FKOcM2yw/mXBVQ81jSW9MXLoPB1uYauU1gRGOu9v1sH/9ZnMm3ljewgej1Xo0xy/XOPYaNY2hIJ9wVUPAC/7/fbKZFG03eXo3ThDDOf26WG1Si2k0l04NIeGG6JvQyvM0VkWEDBD5tBOVrnBcMxD4xLCtIWLSGWPeBgJx4lW9czqYDh17Q7vO1hjkYwS04OefKGNJzhFStFyX//6c/n4k+M7/TQbexacaN7w/rIBlgUq6JlzJvnSJNi87aqO9Lm9S02p1s4cXCC6dRslPV/H3tQhowf8kI5/uFNFdnHC18O48t4/FVrWcM8nTmrgh49Og+rSRCVBRh+TZX5Micjkho8mT3Vn/xIXmFCysd+EDRqu1Y2+/GcPxOsUH4VSBGrCtRUAL/v+2Hq4Xkuc3A7accYM0KS41rRcf9+Nb5T4YpeU6r70w9dr7hOCDcf3bmFMn4Cde1dwTasDWRUAL/vbDp0jRcqmUIeTIXlab02rOZMtsYqdOfYzwPNXKq0c0ZfQXB03139cg8ZEJS/FpRiu2+KDVlvge+elGUEP3YVZb4jMEtOADpGi72SaTqSLUhKRZ1tCkS6cmPO/gRu8Z1Ea86Ss+cLyM4dpeNCHJlP24Z4qZEVrGeTh8IVTHhB6eTzy43KTBc8hPh+4jjOAGBRDggm/DwQhxJkPm/NvGWmxR66jpnTZ7P6rwvHqc+5eljBJn3Hk/SXU3//3ZCOy+IPiu0NzWu3UCOkrHYYeCTDVgexYBLfiDxAEiRZVHuvTewCX4ze2YkOZWeF6gMkrLZbfeg2KiaRcWYlq5Z9Ya9gHB96PQXK9SmA3VZcp/fxYBK/hSyloZMvvyxKWDLLao9dQIvsPRjLCYFJ7nabwb/mdc/5oMmV3bm9mtN87DJ1r4YPTw/CU010PU+23VZMhUE67qELCCf+B4OSkil2MyhgOyK3dO9q9ZtgA2p+JXNyf4UZ2MwVsfHrj1dkRIPMeIE8Wu3p3p0VmyBQ9ib5HQttDcQIzScXEwA6I6Q8dkqy3xKQJW8Cf/damRA90ZkufPVDtakPM8vu3heYHGm8v3kKLtBvCMO6+m5+ULLh3wmx6eV8lPN+6JQH6otYGAFfwYTtNf80xInreptLdE8FN9PjzPW9Egz3ybwyhtFxUylO0yib9dN9Ij9fhElA64HZobEFE6tV065Sfh2A7lv2+AgBX8EdoewL9WuGqM6pYIi49PwPJmRIiURjjuVtmbakK4cpTZEToAwncE3xWaq3p4Qgg45AxRTlD++7MJSMGvqHaQInLRpWCz3pedf7zEapPcoqq6BS38mvA8H/bje4Mqu04IdoaJvaYteNIYPiP4YLgvCnOg8pTVllhPTcRSz9HW2uGDBKTg3/FuOilaLrtlT04RRViIf55mTTe1uiXrltaE5/lwK88bUTqvLdvNILGfCFFNpt6PH359ngdqMc6j0u5DUTEJaYBsU2huICRPq+OWOpgOXQYYi70r6uCfStgMK3OP1sqh4v9Ut8SHD2fC8xx2zxrUBrzl0jlaWnEmf5LsR6jN/J94zThgaaUPCb5r4LblD/yAnHglMUIylf++QQJS8BNFIZ3FKTbJ/labYgrNhmXWkJBmTDY5ut2zBvkwWw6WkKLlclTGki+70KmdeSmRa6gRytIKH3qw+kForlc4uQ/KilT8fSMEpOCPEjUhef7tv6/pplZUt7AlqcLzyDpwkhSx2xmdJWgXbt4M2zMY16W00ocEH9ocmhsQLp2ac6hxaamUCg1iiuALIaYLIXYIIXKFELMb+DxcCPFv5+frhBDJZtTbEFV2nRQtlzIZzg6Z6Lf++9ocL6tu2Y6d+kBkp6Bu5cVSSl+tgE16X2aO7OmROmomLJ32NcFPUJkzObgRQiKh61CrLfFJ3FZDIYQNeAW4BBgC3CCEGHLWbj8HTkgp+wF/A551t97GOFxs+HC3yN44sHmqGq9yrKSyZbHSPhye541ZnfuLys5MuJL9eP4az8TfG6ciKCgu90j5bSa+daG5gTjTVhzMgJ6jwOaJnp3/Y8a3MhbIlVLuARBCfATMArJr7TMLmON8/QnwshBCSA/M+DhdUcT73YrZK7sTIf/FIz8uNbsKr5F/Kh+A0ioHt8zfQPvm3BMCLj0RzyVFS3hkwUoqtSgvWNky9jiOUKFX8siPj3isjqOnKjnRM4tfiy5s1jfyxKpHPVJPub2MmIhQ3l2zj+yCEp8RzhC9mudEKMsWf83nmYnN7l8lSwD4KOcTMo74XiOhNewpNubd2As2syL2Kj77l++nC2+K5M5R/N/F5uf/MkPw44EDtd7nA+Ma20dKaRdCFAOdgWO1dxJC3AncCZCUlNQmY0TlCTaHtaNY6iR2OMnOE/4dl5zadQztQvux60g5h5p4PNY8O9vbezEDiTi4ke0hI7xkZfMcdfRA7xjHzhM7PVZHQXEFseHFbCMSXR5l5wnPtMCT2icxtd9UlmZEsPNIqU/NVN0letO1ZCvb7SXN7uuQDuyR/TgRWubR6+ItOpQnE6nvZ2V5L3IKSvw6BslTtvtUv0dK+QbwBkBaWlqb7qKkhCHk7n4GgHfvP5chPWPMM9AfKBsBf3mK5yfa4dwpVlvj4o9fdeXD9YNZ+IvpHqsjefZXLAr/JYsdaTxmv5OFd87wWF0AD07waPFt49vzYeN7fP/gOc26NU5X2hn6ZAU3DR/kl8kFa3PoZDn/+OtjaKHLmXP3LRDridnV/o8ZI5oHgdr9xwTntgb3EUKEALFAkQl116N2bHHQiT04w/P6+pwfXwjw9MTUXuIInUQpmbIvPz+nt2cr81XinaG5hdnN7qqJmrz+njbK8xw/XUWKlktFRBzExFttjs9ihuBvAPoLIXoLIcKA64GFZ+2zELjF+fpq4AdP+O8VThLSjMknPvQVa0J4fMGQFNcKV/34zaWDPVqXz9KKnEo1Qw8+sZCLmxw/XUWKyKW86yiVIbMJ3BZ8KaUduBdYBOQAH0sptwkhnhZCXO7c7Z9AZyFELvAwUC900ywCIabYbRLGQOkRKM632hIXQgiPP39StN2UyXB2ygTz89/7Cx2TjTzwLVgBq6aFHwB6T+nJQnprR3Co/DlNYooPX0r5DfDNWdt+X+t1BXCNGXU1R2SojbHJnbh+bPNRCgFL7QlYHXzje9CEZx/GC7MOBVw4bpsQwjkBq/kWvs35UPSpJHBtJLIwEwChJlw1if/PSjoLIQQf3zWBq0YH8aBNt2FgC/epCViGS8dz5T/y4XoGi30Bkz/JLRLS4OgOqChucjctQFw6ZVV2stYuQZcCTbXwmyTgBF8BhIRBj5E+JfjGoK3nhGWw2Ee4sJOp9+P2SUE6YFtDfCogjVmnTSACZND23GeXMlLsZpeMJ7J9B6vN8WmU4AcqCWOgIBMcLUzL4GE87cOvvaTh7ecke64if6AVOZU04f8rXhWdrmSkZuRPCg+AVCqeRH07gUpCKtgr4Mg2qy0BzrgPPCUuKVouR2QHCuhEXPtwj9ThN0R2MPLBt2Dg1qZ5PnrK09QOx/WVWc++ihL8QKUmr0r+BmvtcOLpmO8UkevKkBkeEsSDtjXEtyw0VwiBo4XLLfgqtcNxFU2jBD9Q6ZAE7eJ8ZgKWJwcITxw7TG/tiLrha5OQCmXHjPzwTRAILp3a4biKplGCH6gIYfjxfWTg9swAofni8tDf/glAplQROi5qVnxq5vp7Y0Kcp1HhuC1HCX4gE58KRbug/ITVlrgmP5qtLb/7fGutBev7kNaro7kV+Ctdhxp54Zvp4Xk6XNbTVFeWM1jsY5Pq3bUIJfiBjGuavfVuHU/N6nx/7T5SxG52ygROE8knd080twJ/xRYCPVOaHcPRhH9PvLrn+Xdd4biK5lGCH8j0HA2IFkVreBrP+fClMyRPuXPqEZ8KBZvBXtXoLpom/NqH3/O0kSQuU+/L3VPUb6A5lOAHMhExEDfIJ9a4rcliarbg9xJH6ChKyVL++/okjAFHJRzZ0ugu/u7SSdFyOSw7cpjOPDbd/AVDAg0l+IFOQqoxcGdxK+5MZkbzyiyttKuQvKaocek10cPTPDwD2pOUVzlIEblkqd5di1GCH+jEp0H5cTi+x1IzzvjwzROXo6cq64TkzRjew7SyA4KYeIju3qQfX/hxC//vX65V4bitRAl+oOMjA7eaB6J07no/o05I3j1TVUuvDkIY178Jl55NCHQ/VfztGcsAFY7bGpTgBzpxgyG0neXx+DX56c10H+w9crxOSF5cdJCnVGiI+FSjd1d2vMGP/dWl49AlKdqZcNwLBnW12iS/QAl+oGMLgZ6jLB+4rclwYmZjckitDJkAXWMizCs8UKiZgNVID89fXTol5dV1wnFvHJ9ktUl+gRL8YCDBGZ5XXWGZCcIDPvwUrWbAVnXpG6XnKBBao358TfPP1Aoa1AnHHZ2kJty1BCX4wUB8GujVcLjx8DxP44nkaSO13a6QvDuCddHy5giPNtx6jbj0NCFw+KHgb8/JoqMoJVMavbsOUWEWW+QfKMEPBlqxsLWncA3amrjM4ShXhky4S026aZyEVMOl04Cw2/zUpfPhf/8LqHDc1qIEPxiI6WmE6Fk4cGt2C3/bzlyStSNk6P0BCNXUT7lREsZAxUko2l3vI0+vROYpUrWdnJKRKkNmK1F3SbAQn2ptbvyaiVcmKf6L73wAwEan4MdGhZpSbkDSxNoImvC/1Aq7jpxitLaLTXo/dDT+c9cEq03yG5TgBwsJaUZu9NPHLKne7ORpqdouqqSNrVL57pslbiCERTfo0tOEQPezBVCu+NsiBon9bJLGw35McieLLfIflOAHC65WnjVuHTN9+EWllYzWdrJV9qaSMBWD3RyazYjWaeDaC4HfDdpO73AQm5Aud56i5SjBDxZ6poCwWTZwa6YPf/rzSxgp9pChDwDgwQsHuF9ooJMwBo5sheryOpttfpgts+epzehSqAHbNqAEP1gIawfdhsKB9ZZUL0xKj1zt0OlZkUu4qHb57wd2b++ueYFPwhjQ7XAos85mf8uWuSW/mNHaLnbKBEpoZ7U5focS/GAicZzRrXfYvV61WROvHLokVdsF4Grhh4Won3GzJI41/h9YW2ezv6VW+HBdHqO1XWx0tu6znpxmsUX+hbpTgomk8VB92ujaexnNxPTIo7Wd5MsuFKJmV7aYdl2gcz/Yv67OZn9LrbA+fS2xooyN0njYx0aq6KzWoAQ/mEgcZ/w/sK7p/TyAWVE6n2Tkk6rtcrXuLxuhUiK3mMTxRgu/VljOtkPFZOQ1nFjN16iodtTr3SlahxL8YKJDIsQkwP61ze9rMmYscSil5JXPf6SHOO7y37/809FmmBccJI0zFrQv2uXaVO2QnK5yWGhUy5n96WZGi10cl9Hsld15+CIl+q1FCX6wkTTOEHwv+22FcD89cu/HvyFV2wmgQvLaQpJzgpIFD3wz+DzzEKnaTufDXnDf+SpKp7UowQ82EsfDqUNQfMCr1dakR3b3OZOq7aRMhrNdJvGbSwe7bVdQ0bkfRHWu49I7b0AcIxNiLTSqZeQdO00spfTTDrHR6c6paUQoWo4S/GAjyenH3+9dP75mQgsfYJS2iyy9L3ZCuGCwmnDVKoQwxnH2r3Ftsmn+MWh714IMRin/vdsowQ82ug41ptkf8G63via3mTt6H0ElQ8U+NjpT4vaJizbBsiAjcZyxAlZpIWA8iO1+oPjbD58iVduFXWpsVuk02owS/GDDFmJMwvFyC98MH/5IsYdQ4XAN2CraQI0f3+nWCdEEDj9JppMmdpIjkyhHrWzWVpTgByNJ441Y/Ipir1XpbmqFzzcdZIy2HV0KNugDGR7v+35nn6RnCtjCXQO3NpvA4Qct/FDsjNJ2sV5X4zbuoAQ/GEkcB0ivpks+M2jbNnF5ZWkuY7Xt7JCJlBDtV7NDfYqQcCORWo3gC/8Q/OFiDxGimvX6IACuHBVvsUX+iRL8YCQhzVjn1ItuHXdb+HsKi0nVdrJeHwjAo9MHmWVa8JE0DgqyoLrccOn4+MMz88BJxmnbAdigDyQ2MpS/XD3CYqv8EyX4wUh4e+g+3KsDt670yG0Ul6Eij3aikvX6YK4aFc95A+JMtC7ISJpgrHF8cCOaJnA4fFfwpZRc8coqxmjb2aXHc5wY0n97IaE2JV1tQX1rwUrieMjPAEe1V6oTbrTw846dZqyzhbdeH8gL16WYaFkQ4kqxsZb9RWUcKq7w2RTJ6/ceR0MnTdvhcudoKv6+zbgl+EKITkKIxUKIXc7/DWazEkI4hBCZzr+F7tSpMImaRGoFm71SnTst/Jd+2MU4bTt79O4cVQnT3CeqE3QZCPtWs96ZRyf/RHkzB1nDziOnGCz2EyPKWacPYsWjU7FpSvDbirst/NnA91LK/sD3zvcNUS6lTHH+Xe5mnQozSD7H+J+3wivVtbWFX1Ht4LONB+q08BQmkHwO7F+LDSOPjq+6SH73xTbGajkAbNAHkdgpymKL/Bt3r/Is4F3n63eBK9wsT+Etorsarby8lV6prq1LHD7w0Sb6i4N0FKVs0Acx96rhHrAuCEk+B6pKGS72AsbCMr7KGG0HB/Q4CuhstSl+j7uC301KWeB8fRjo1sh+EUKIdCHEWiHEFY0VJoS407lf+tGjR900TdEsyecY0+y9sCBKW1v4i7Ydcfnv18lBXDU6wWzTghNnD2+8lg3AM9/kWGlNE0jGattZL1XvzgyaFXwhxBIhxNYG/mbV3k8aztnGbudeUso04KfAi0KIvg3tJKV8Q0qZJqVMi4tTURgex9nKoyDT41W5kx55nJbDIdmJfBmnVrcyC2cPb7zTXXL8dJXFBjVMX3GILqKEdfog4jtEWm2O3xPS3A5Sygsb+0wIcUQI0UNKWSCE6AEUNlLGQef/PUKIZcAoYHfbTFaYRvK5xv+8FUZsvgdp+xKHkjHaDtbqgzkzfUthCr3PJe3oAkKwk1NQYrU19dB149oDrHcO2Crcw93m0kLgFufrW4Avzt5BCNFRCBHufN0FmARku1mvwgyi4yBukFf8+K4Wfitdxb3FYbqLE2pKvSdIPodoUcFwsZeSCu+vc9wcfZ74hglaNoWyA3myO5qKznEbdwV/LnCREGIXcKHzPUKINCHEW859BgPpQogsYCkwV0qpBN9XcEZreDoe37XEYSuPm6QZ6++u1ofQPrzZDqmiNfSq8eMbbp3MAyctNKYhJBO1bazWh6B6d+bgluBLKYuklBdIKftLKS+UUh53bk+XUt7hfL1aSjlcSjnS+f+fZhiuMIkaP/6hTI9WI9rgw39/7T4mats4KDuTJ7vzxAzVyjeV6Dh26vGugdv8E2UWG3SGaofOAJFPnChmlT7ManMCBjUCFuz08k48vtYGH/7vP9/MBC2b1Y6hgOD6MYkesi542d1uFGnaDkKw+9Tkq+9zCs/07hxDLbYmcFCCH+xEx0HcYI/78c+08Fu2f1mVnSFiHx1FqauFp5a0M5+c8JG0E5UMF3uZ++12q81xcdeCDCZq28jTu3GQOH6iwnFNwa+cotXV1eTn51NRUWG1KYHFhBeg6jRkZ59RZpOpdui8eXkPOutF5OScBCAiIoKEhARCQ0Pr7V9cXl3Lf69aeJ5i7JSZ8PmfmKhtY5PDdxaWseFgnJbDVw5jwZY/XancOmbgV4Kfn59P+/btSU5OVq09Myk/CSf2QudEI5OmB6iodsCRUyR1iqJDVBhSSoqKisjPz6d37/pL1q3fe5xJ2jZ26fEU0pF7p/bziF3BTkSHbmzTe3GubQuvOK7g/TV53Dwh2VKbcgtPMULsIUaUu3p3EaE2S20KFPzKpVNRUUHnzp2V2JtNeHtAQOUpr1UphKBz586N9tYe+SidMdoOVjlb97+ephau9gSpvTqyQh9BqthJO8r53RfbrDaJC19YzkTNsGONPoShPWMstihw8CvBB+XH9QiaDcKioNJzk29cK17V3tbEtRwldhElKl3uHHXdPYMQgh/1EYQKhytaxxeYpG0lW+/FcWIY1F0Jvln4neArPER4DFSXez4/fgsGbbcdKmaSbSsOKVirD+HDX4z3rE1BToY+gDIZzmTNO6mym6LS7iCCSlK1Xa7e3e9nDrHYqsBBCX4rsdlspKSkMHToUEaOHMnzzz+P3trpo60gLy+PYcNaN2B166238sknn7SuohrffS23TmZmJt98803ryjmLF198kbKyMlcTvyVBOj9/J50pWhabZH9KaEdMpF8NNfkdP53Yn7X6YM7VtgDWZs58+OMsJmjZhItqluvGMoaxkfUH9RVtQwl+K4mMjCQzM5Nt27axePFivv32W5566imrzXKf0CgQNo8JfksdMvknyrCXHGGktodljpEAasELD/Po9IEs10fQRztMgijkq82HLLPl680FTNUyKZPhav0DD+C3TaenvtxG9iFzfc5Desbw5MyWhwB27dqVN954gzFjxjBnzhx0XWf27NksW7aMyspKfvWrX/HLX/4SgGeffZYFCxagaRqXXHIJc+fOJTMzk7vuuouysjL69u3L22+/TceOHcnIyOD2228HYNq0aa76HA5Hg+VLKbnvvvtYvHgxiYmJhIWFNWhvY/VNmTKF5557jrQ+nTl2aB9po85j586d/P73v6e8vJyVK1fy+OOPk5OTw+7du8nNzeXYsWM8+uij/OIXv2DZsmU899xzfPXVVwDce++9pKWlUVJSwqFDh5g6dSqdO3fhpQWfNfudnvPsUq7SsgBYqqcAMLCbZyKHFAZRYSGs0I11BiZrW3jo3125cpRVce+SKVomq/ShVNLw71jRdlQL30369OmDw+GgsLCQf/7zn8TGxrJhwwY2bNjAm2++yd69e/n222/54osvWLduHVlZWTz66KMA/OxnP+PZZ59l8+bNDB8+3NVTuO2225g3bx5ZWVl16mqs/M8++4wdO3aQnZ3Ne++9x+rVqxu0tbH6XETEgLQDkrCwMJ5++mmuu+46MjMzue666wDYvHkzP/zwA2vWrOHpp5/m0KHGW4P3338/PXv2ZOnSpXy35Hvn1uadOlNsWRyVsWTLXvz16hFqwNYL7JY9yZddXH785NlfU2l3eN2OPqKAJO0oP+pG727Jw5O9bkMg47ct/Na0xL3Fd999x+bNm13+8+LiYnbt2sWSJUu47bbbiIoylmfr1KkTxcXFnDx5kvPOOw+AW265hWuuuYaTJ09y8uRJJk82fug333wz3377bZPlL1++nBtuuAGbzUbPnj05//zz69nWWH11qPHjy8Z9uLNmzSIyMpLIyEimTp3K+vXr6dChQ7PfjStKpxm9t+FgsraZxY5UJBqje6k1bL3BR3dOYPnbw5lpW0sodqoJobTCTni0d+Pfp2qZACxz9u5U0jRz8VvB9xX27NmDzWaja9euSCmZN28eF198cZ19Fi1aZEpdjZXvrp89JCTEGHi2hVHh0JpU5bNb20KIM8c7aTC2vgX37daDxYwUu+kgTrtu+PYR6ifqDaLDQ/heH81PQ5YyTsthpT6cIyWVdIgK89oYyus/7uY8LYudejz50lgAqUOUGrA1E+XScYOjR49y1113ce+99yKE4OKLL+a1116jutoIbdy5cyenT5/moosuYv78+Ua0CnD8+HFiY2Pp2LEjK1YYScvef/99zjvvPDp06ECHDh1YudLIbfPBBx+46mus/MmTJ/Pvf/8bh8NBQUEBS5curWdrY/UBJCcnk5GRAcAn3y43WviOatq3b8+pU3UnY33xxRdUVFRQVFTEsmXLGDNmDL169SI7O5vKykpOnjzJ999/79q/oTIaIvtQCZfNW8lUWyZ2qbHCOcOya/uIZo9VuE+oTWOVPoxyGcaFmvFbuPSlFTz/3Q6v2fDSt5mM03JcD/sVj06lS3S41+oPBlTzqZWUl5eTkpJCdXU1ISEh3HzzzTz88MMA3HHHHeTl5TF69GiklMTFxfH5558zffp0MjMzSUtLIywsjEsvvZRnnnmGd9991zWI2qdPH+bPnw/A/Pnzuf322xFC1Bm0baz8K6+8kh9++IEhQ4aQlJTEhAkTGrS9sfoeeeQRrr32Wt544w1mTHfWV1HC1KlTmTt3LikpKTz++OMAjBgxgqlTp3Ls2DF+97vf0bNnTwCuvfZahg0bRu/evRk1apSrzjvvvJPp06fTo0dPXlrwWaMe/IVZxljAhdpGMuQASojm/vNVOgVvMaBbNBWEs0IfzkW2DObYbwEEb63cy6PTPR8tszn/JOdqWwgXdtdgfWKnKI/XG2yI1i855x3S0tJkenp6nW05OTkMHqxyonsUKaEwG0IioXOfOh/NmTOH6OhoHnnkkVYXa3foZBeU0LNDZJ1WW801ffy/W1i1YQPLwx/iD9U38U/Hpez986VqwNaL5BaW8o+/P8VfQ9/g0spnyJbJAOTNneHRer/IPMgDH2XyQuirTNUySat8DQc2j9cbqAghMpxriNdDuXQUdRHCiNapPAW6B6I0GmhfXP3aaj5cv5+LtQ0ALNLHOE1RYu9dJD84RqFLwUVOt443eOCjTEKxc4G2kSWO0TiwMf/WMV6rP5hQLh1FfcJj4fQxYyWsiFjX5jlz5rS9zCZm2qbvOwHAdNsGtujJrgE7hXfRJRQRy0bZnwttGfzd8ROP1+lwLpAwXssmVpTxP+fDfuqgrh6vOxhRLXxFfcKjjVm35SdNK7K5tnpXTpCq7WKRw7jhLxrSzbS6FS0jyekzX+xIZbiWRzxHPV7nB+v2ATBd28BpGc5K5wQwhWdQgq+oj9CMln1FcZMx+a0s1Pm/4TGjaTZjvKamhfe361JMqlfRUiJCbax4dCrf6GMBuMy21uN1FpZUoqEzzZbOUj1Fza71MErwFQ0T2RGkAyrMyZFfI/dn5+XSnV36S7V17NZ7kCvj6RYTTnS48jZaQWKnKA7IbmzS+zHTtgaA4jLPZVB9eWkuaWIHcaKY7xzKb+9plOArGsbl1jlharGFp+pOyjpUXEF3ihiv5bDQMREQrHviQlPrVLSO8BCNLx0TGKbl0UccYuTT31FSYb7ov7NqLwBX2FZyWoazWB8NQESokiVPob7ZVvKnP/2JoUOHMmLECFJSUli3bh1gxMhnZ7u/gER6ejr333+/2+WczbJlyxrNsdMgQjNa+ZXF5kTrNODE150hwZfbVqMJyef6JPfrUbjNuf278JVjPLoUzNSMVv7dC8yP2pnzZTZhVDPDto5F+hjKiWD+bWP4/tdTTK9LYaD6za1gzZo1fPXVV2zcuJHw8HCOHTtGVVUVAG+99ZYpdaSlpZGW1mAIrVssW7aM6OhoJk6c2PKDIjtA2THDlx/VyXSbth4sBuBK2yo26f3YJ7uz5vH6eYAU3uXF60cx7MlC1umDudy2mr87rmJVbpFH6pqqZRIryvjCMYnfzhjM1IEqOseT+K/gfzsbDm8xt8zuw+GSuY1+XFBQQJcuXQgPNyYOdenSxfWZK8VwWhrR0dE88MADfPXVV0RGRvLFF1/QrVs3VyqG/fv3A0au+EmT6rZqa6canjNnDvv372fPnj3s37+fBx98kPvvv5+8vDymT59OamoqGzduZOjQobz33ntERUWRnJxMeno6Xbp0IT09nUceeYR33nmH119/HZvNxoIFC5g3bx7nnntu899HWDRooYZbx03BP7uBb3c680OxM1jbz++rbwGgR2ykW/Uo3Cc6PIQLB3fli50TmRv6FiPFbrJkP06WVdEhytxB1Vm2VRyVMazUh3GpypvkcZRLpxVMmzaNAwcOMGDAAO655x5+/PHHBvc7ffo048ePJysri8mTJ/Pmm28C8MADD/DQQw+xYcMGPv30U+64445m69y+fTuLFi1i/fr1PPXUU648Ojt27OCee+4hJyeHmJgYXn311UbLSE5O5q677uKhhx4iMzOzZWIPxiSsqE7GWrf2qpYd0wJKK+1kFxhrGUSJSuxS42uHWsbQl5hz+VC+coynTIZzvc3IzXThC8tNK3/N7iI6cIoLtI185ZiAAxsT+nRp/kCFW/jvI7WJlriniI6OJiMjgxUrVrB06VKuu+465s6dy6233lpnv7CwMC677DIAUlNTWbx4MQBLliyp4+cvKSmhtLSU6OjoRuucMWMG4eHhhIeH07VrV44cOQJAYmKiq3dw00038dJLL7Up5UGzRHWG0iNQVgQxPdpcTO1Zs3uOlhrbkLSjgsV6KkXENnaowgJ6xkZSShRfOcZzuW01f7TfxLFSOHC8jISOkW2eBe3QJRn7TnDDm2v5uW054cLOvx1TAaN9ofAs/iv4FmGz2ZgyZQpTpkxh+PDhvPvuu/UEPzQ01HVD2Gw27HY7ALqus3btWiIiWp4BssZ9dHZZDaUpBuqkKm4wTXFrCQk38uSXFUH77qbelbGc5jQ6HziMqJxHpg0wrWyFe2jOlMgfOaZybciPzLSt4SPH+Zz7F6O1P31od+44tzdpya1z9b2yNJcXFu8EJDfaviddH8B2mQRAQkflzvM0yqXTCnbs2MGuXbtc7zMzM+nVq1eLj582bRrz5s2rc3xb2b9/P2vWGBEU//rXvzjnnHOAuqmOP/30U9f+LU1T3CBRnUGvNlw7JtJJnMKOjVW6sZjN2N6dTS1f4R7Th3Zno+zPDj2BG2w/UHvS3P+2Hebq19e0elWs7YeN39AELZs+2mEW2I2H/YvXpajcSV5ACX4rKC0t5ZZbbmHIkCGMGDGC7OzsVuWXeemll0hPT2fEiBEMGTKE119/vc22DBw4kFdeeYXBgwdz4sQJ7r77bgCefPJJHnjgAdLS0rDZzqxWNHPmTD777DNSUlJcOfFbTESsMXhbWthme+sVSRXRooLTRCCdP8Oxvc2PBFK0nddvTgUE7zsuYqS2hzRRPzf+wN/+j037G56rUWl3cOhkOZ9m5HPfh5soLKngmy2HAfiZ7TtOyGi+dc7qvWJUvMfOQ3EGlR7ZD8nLy+Oyyy5j69at3qv01BE4dQi6DISwtuUp35x/0vU6URwlhtMs22fn5wuP0CM2gjWPX2CSsQqzSJ79NRFUsjr8PjL0gfyi+tcN7rd5zjSiw0JcrqCaYxuijzjEkrD/4xXHLJ63Xwt4PgVzMKHSIyvcp11nYzKWCa38MOx0oJTjxKA7f4J/v35UM0cprOCWCb2oIJz3HdO4yJZBX3Gwwf3ufC+dPk+cWWrzqS+3NVrmnbavqCKEd+zGUp3JndVCJ95CCb4fkpyc7N3WPYAWAlFdoOIEVLs3GBwnTiKBY/JMZI5y5/gmT80ylpp81z6NChnKPSFfNLjf2j3HAaNVnzz7a+avymtwvx4UcZVtBf92THFFZn19fwvDhBVuowRf0XKiuxqt/FMFbS4ijGo6cooTtKcaY4zh0ekDzbJQ4SGOE8M7jou5UlvFILG/zeU8FPIJEsEb9stc29qpRHleQwm+ouXYQqFdV6g4CVWn21REd3ECieCI7Ojads8UtXatL7P4ockAvGa/nFNE8ljIh20qZ5DYz9W25bzjuJiDGIvc/OUnI0yzU9E8SvAVrSO6q+HeKc431r9tzaFU0EGc5hgdsDtb912iVf5zX6d/t/bYNEEx0bxsv4KptiwubOUSiAKdOaHvUkIUr9hnAbD7mUu5dkyiJ0xWNIISfEXr0GwQmwDVZXC6FQO4uoN4cZRKGcrRWr77iFBbEwcpfIUtc6YB8I5jOjl6In8K/ScxtLyXd6Pte8ZrOTxj/yklGDPLbZqKu/c2SvBbSVNpEGpITk7m2LFj9bYvXLiQuXONlBC33norn3zySb19aqdHbnVKYzc5dOgQV199dfM7RnQw1r0tKWiRa+ed+fO595e3Ey7s5Msu6M5UagkdVXSGvxAVZvjZqwnh0epf0oVi/hz6Jo2tYFabAeIAj4f8i+WO4XzsmALAkzOHeNBaRWMowfcil19+ObNnz25yn7S0NF566SWgbYJfk3qhLfTs2bPBh1A9hIAOiYZP//hecDSTWK3yFNgrKA+P4zQRziIEndopd44/kTd3Brv+dAlbZB+etV/PDNt6HrD9t8ljulDMG6EvcJpIHqm+CxBcPyaR68ckecdoRR3cGh4XQlwDzAEGA2OllOmN7Dcd+DtgA96SUrqd+ezZ9c+y/fh2d4upw6BOg3hs7GMt2nfZsmXMmTOHLl26sHXrVlJTU1mwYIFrevi8efP48ssvqa6u5j//+Q+DBg3inXfeIT09nZdffhkwkqnNnTuXkpISXnjhBS677DJXeuSXX365XkrjxMREbr/9do4dO0ZcXBzz588nKSmJW2+9lYiICDZt2sSkSZP48ssvWb16NXFxcei6zoABA1izZg1xcXEu+3/88UceeOABwBDf5cuXU1RU5JrQVVZWxq233srWrVsZOHAghw4d4pVXXqmb/vnLhUSGCr54dx7dBo3jy28X88c//pGqqio6d+7MBwsW0K2dMNIr28KJ7BTPCDV93q8JtRltxDcclzFQy+eh0E/RhORF+1WuGdM1JIijvBP6LF3FSW6qepxCOjJzZE/mqoFay3C3hb8VuApoNG+qEMIGvAJcAgwBbhBCBER/btOmTbz44otkZ2ezZ88eVq1a5fqsS5cubNy4kbvvvpvnnnuuwePz8vJYv349X3/9NXfddVedZGcNpTS+7777uOWWW9i8eTM33nhjnZWx8vPzWb16NS+88AI33XQTH3zwAWA8VEaOHFlH7AGee+45XnnlFTIzM1mxYgWRkXUTV7366qt07NiR7Oxs/vCHP7jy80Ct9M+btzB5ygW8+f5/4OgOzhnZn7Urf2RTRjrX/+QK/vL0b4zZuaFRRgI2JfYBweQBcYDg0eo7+dh+Hg+E/JePwv7Iudpm2lNGT45xp+1LvgmbTVdxglurHmWjNBLjTeqr8iVZiVstfCllDtTP3HgWY4FcKeUe574fAbMAt9YDbGlL3JOMHTuWhIQEAFJSUsjLy3MlMbvqqqsAIz3yf//bcLf32muvRdM0+vfvT58+fdi+vekey5o1a1xl3XzzzTz66KOuz6655hpX7pzbb7+dWbNm8eCDD/L2229z22231Str0qRJPPzww9x4441cddVVrvOoYeXKla4ewLBhwxgx4kyrrE7657HjWbxoEYRFk5+dwXU330ZB4TGqqqrpnRQPMQnQrguIfU2em8J/eO/2sRQUlzPhzz/wqP1O0uUAHgv5iPfD6nbclzuG84T95+TLruTNncGx0ko6KzeepXjDhx8PHKj1Pt+5rR5CiDuFEOlCiPSjR496wTT3aCx1ce3Pzt5em8ZSHLeFdu3auV4nJibSrVs3fvjhB9avX88ll1xSb//Zs2fz1ltvUV5ezqRJk5p92NSmXvpnXYfOfbnv6Ze591f3sGX9Sv7x6stUyBCIjlMt+wCkR2wkc68aDgg+dkxlYuU87qj6NX+uvoHfVt/GtMpn+Vn14+TLM0sWdokOVxkxLaZZwRdCLBFCbG3gb5bZxkgp35BSpkkp0852QQQi//nPf9B1nd27d7Nnzx4GDqw74/TslMYTJ07ko48+AuCDDz5ocuWqO+64g5tuuqlOy782u3fvZvjw4Tz22GOMGTOmnuBPmjSJjz/+GIDs7Gy2bGl+OcniklPE9x0C7bvx7kef0uDK5YqAoW/XMxFrlYSxRE9lV/+fk9/vp+yUKr7eF2nWpSOlvNDNOg4Cta9+gnNb0JOUlMTYsWMpKSnh9ddfr7cwysyZM7n66qv54osvmDdvHvPmzeO2227jr3/9q2vQtjEuv/xybrvttgbdOWCsp7t06VI0TWPo0KFccsklFBScSZlwzz33uFJBDxo0iKFDhxIb2/SqVHPmzOGaa66hY8eOnH/++ezdu7cV34bC3wipFUe/7amLjRUxneGbFdUOIkJtPPnFVv637bBVJirORkrp9h+wDEhr5LMQYA/QGwgDsoChzZWZmpoqzyY7O7veNkXDbNiwQZ5zzjltPt5ut8vy8nIppZS5ubkyOTlZVlZWmmWeC3VN/Rdd1+Xzi7bLgpPlVpuiqAWQLhvRVXfDMq8E5gFxwNdCiEwp5cVCiJ4Y4ZeXSintQoh7gUUYYZlvSykbz52qcJu5c+fy2muvuSJ12kJZWRlTp06luroaKSWvvvoqYWFqwE1xBiEED09Tie/8CbUAisJS1DVVKMwloBZA8dUHlKL1qGupUHgXvxL8iIgIioqKlFAEAFJKioqK6g1UKxQKz+FXKw8kJCSQn5+PP8ToK5onIiKi3oQvhULhOfxK8ENDQ+ndu7fVZigUCoVf4lcuHYVCoVC0HSX4CoVCESQowVcoFIogwWfj8IUQRwF3Uix2AeovO+U7+Lp94Ps2+rp9oGw0A1+3D3zLxl5SygaTkfms4LuLECK9sckHvoCv2we+b6Ov2wfKRjPwdfvAP2wE5dJRKBSKoEEJvkKhUAQJgSz4b1htQDP4un3g+zb6un2gbDQDX7cP/MPGwPXhKxQKhaIugdzCVygUCkUtlOArFApFkBBwgi+EmC6E2CGEyBVCzLbanrMRQiQKIZYKIbKFENuEEA9YbVNDCCFsQohNQoivrLalIYQQHYQQnwghtgshcoQQE6y2qTZCiIec13erEOJDIYTlaUGFEG8LIQqFEFtrbeskhFgshNjl/N/RB238q/M6bxZCfCaE6GChiQ3aWOuzXwshpBCiixW2NUdACb4Qwga8AlwCDAFuEEIMsdaqetiBX0sphwDjgV/5oI0ADwA5VhvRBH8H/ielHASMxIdsFULEA/djLPs5DGOlt+uttQqAd4DpZ22bDXwvpewPfO98byXvUN/GxcAwKeUIYCfwuLeNOot3qG8jQohEYBqw39sGtZSAEnxgLJArpdwjpawCPgJmWWxTHaSUBVLKjc7XpzCEKt5aq+oihEgAZgBvWW1LQwghYoHJwD8BpJRVUsqTlhpVnxAgUggRAkQBhyy2BynlcuD4WZtnAe86X78LXOFNm86mIRullN9JKe3Ot2sBS3NqN/I9AvwNeBTw2UiYQBP8eOBArff5+JiY1kYIkQyMAtZZbMrZvIjxw9UttqMxegNHgflOt9NbQoh2VhtVg5TyIPAcRkuvACiWUn5nrVWN0k1KWeB8fRjoZqUxLeB24FurjTgbIcQs4KCUMstqW5oi0ATfbxBCRAOfAg9KKUustqcGIcRlQKGUMsNqW5ogBBgNvCalHAWcxnpXhAunH3wWxoOpJ9BOCHGTtVY1jzRitH22dSqE+A2GS/QDq22pjRAiCngC+L3VtjRHoAn+QSCx1vsE5zafQggRiiH2H0gp/2u1PWcxCbhcCJGH4RI7XwixwFqT6pEP5Espa3pGn2A8AHyFC4G9UsqjUspq4L/ARIttaowjQogeAM7/hRbb0yBCiFuBy4Abpe9NHuqL8XDPct43CcBGIUR3S61qgEAT/A1AfyFEbyFEGMZA2UKLbaqDEEJg+J5zpJQvWG3P2UgpH5dSJkgpkzG+vx+klD7VOpVSHgYOCCEGOjddAGRbaNLZ7AfGCyGinNf7AnxoUPksFgK3OF/fAnxhoS0NIoSYjuFivFxKWWa1PWcjpdwipewqpUx23jf5wGjn79SnCCjBdw7s3AsswrjBPpZSbrPWqnpMAm7GaDlnOv8utdooP+Q+4AMhxGYgBXjGWnPO4Ox5fAJsBLZg3GeWT70XQnwIrAEGCiHyhRA/B+YCFwkhdmH0TOb6oI0vA+2Bxc775XUftNEvUKkVFAqFIkgIqBa+QqFQKBpHCb5CoVAECUrwFQqFIkhQgq9QKBRBghJ8hUKhCBKU4CsUCkWQoARfoVAogoT/B4D8FblgXUMZAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of Ensemble A\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], label=\"Sine input\")\n", + "plt.plot(sim.trange(), sim.data[inhib_probe], label=\"Inhibitory signal\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:14.939304Z", + "iopub.status.busy": "2020-11-25T16:46:14.938560Z", + "iopub.status.idle": "2020-11-25T16:46:15.277444Z", + "shell.execute_reply": "2020-11-25T16:46:15.277867Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of Ensemble B and C\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded output of B\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], label=\"Sine input\")\n", + "plt.plot(sim.trange(), sim.data[C_probe], label=\"Decoded output of C\")\n", + "plt.plot(sim.trange(), sim.data[inhib_probe], label=\"Inhibitory signal\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/advanced/inhibitory_gating.html b/examples/advanced/inhibitory_gating.html new file mode 100644 index 0000000000..2d9ed2384c --- /dev/null +++ b/examples/advanced/inhibitory_gating.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/advanced/izhikevich.html b/examples/advanced/izhikevich.html new file mode 100644 index 0000000000..6bd82c295f --- /dev/null +++ b/examples/advanced/izhikevich.html @@ -0,0 +1,961 @@ + + + + + + + + Using the Izhikevich neuron model — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Using the Izhikevich neuron model

+

The Izhikevich neuron model is a quadratic integrate-and-fire type model with a recovery variable. It is able to replicate several characteristics of biological neurons while remaining computationally efficient.

+

The Izhikevich neuron model is implemented in Nengo. To use it, use a nengo.Izhikevich instance as the neuron_type of an ensemble.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.utils.ensemble import tuning_curves
+from nengo.utils.matplotlib import rasterplot
+
+
+
+
+
[2]:
+
+
+
+with nengo.Network(seed=0) as model:
+    u = nengo.Node(lambda t: np.sin(2 * np.pi * t))
+    ens = nengo.Ensemble(10, dimensions=1, neuron_type=nengo.Izhikevich())
+    nengo.Connection(u, ens)
+
+
+
+

In addition to the usual decoded output and neural activity that can always be probed, you can probe the voltage and recovery terms of the Izhikevich model.

+
+
[3]:
+
+
+
+with model:
+    out_p = nengo.Probe(ens, synapse=0.03)
+    spikes_p = nengo.Probe(ens.neurons)
+    voltage_p = nengo.Probe(ens.neurons, "voltage")
+    recovery_p = nengo.Probe(ens.neurons, "recovery")
+
+
+
+

Simulating this model shows that we are able to decode a time-varying scalar with an ensemble of Izhikevich neurons.

+
+
[4]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1.0)
+
+t = sim.trange()
+plt.figure(figsize=(12, 6))
+plt.subplot(2, 1, 1)
+plt.plot(t, sim.data[out_p])
+plt.ylabel("Decoded output")
+ax = plt.subplot(2, 1, 2)
+rasterplot(t, sim.data[spikes_p], ax=ax)
+plt.ylabel("Neuron")
+
+
+
+
+
[4]:
+
+
+
+
+Text(0, 0.5, 'Neuron')
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_6_1.png +
+
+

One thing you might notice is a slight bump in the decoded value at the start of the simulation. This occurs because of the adaptive nature of the Izhikevic model; it is easier to initiate the first spike than it is to ellicit further spikes.

+

Let’s use a constant input and look at the first 100 ms of the simulation in more detail to see the difference between the first spike and subsequent spikes.

+
+
[5]:
+
+
+
+def izh_plot(sim):
+    t = sim.trange()
+    plt.figure(figsize=(12, 10))
+    plt.subplot(4, 1, 1)
+    plt.plot(t, sim.data[out_p])
+    plt.ylabel("Decoded output")
+    plt.xlim(right=t[-1])
+    ax = plt.subplot(4, 1, 2)
+    rasterplot(t, sim.data[spikes_p], ax=ax)
+    plt.ylabel("Neuron")
+    plt.xlim(right=t[-1])
+    plt.subplot(4, 1, 3)
+    plt.plot(t, sim.data[voltage_p])
+    plt.ylabel("Voltage")
+    plt.xlim(right=t[-1])
+    plt.subplot(4, 1, 4)
+    plt.plot(t, sim.data[recovery_p])
+    plt.ylabel("Recovery")
+    plt.xlim(right=t[-1])
+
+
+u.output = 0.2
+with nengo.Simulator(model) as sim:
+    sim.run(0.1)
+izh_plot(sim)
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_8_0.png +
+
+

Those neurons that have an encoder of -1 receive negative current, and therefore remain at a low voltage.

+

Those neurons that have an encoder of 1 receive positive current, and start spiking rapidly. However, as they spike, the recovery variable grows, until it reaches a balance with the voltage such that the cells spike regularly.

+

This occurs because, by default, we use a set of parameters that models a “regular spiking” neuron. We can use parameters that model several different classes of neurons instead.

+
+

Intrinsically bursting (IB)

+
+
[6]:
+
+
+
+ens.neuron_type = nengo.Izhikevich(reset_voltage=-55, reset_recovery=4)
+with nengo.Simulator(model) as sim:
+    sim.run(0.4)
+izh_plot(sim)
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_11_0.png +
+
+
+
+

Chattering (CH)

+
+
[7]:
+
+
+
+ens.neuron_type = nengo.Izhikevich(reset_voltage=-50, reset_recovery=2)
+with nengo.Simulator(model) as sim:
+    sim.run(0.4)
+izh_plot(sim)
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_13_0.png +
+
+
+
+

Fast spiking (FS)

+
+
[8]:
+
+
+
+ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1)
+with nengo.Simulator(model) as sim:
+    sim.run(0.4)
+izh_plot(sim)
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_15_0.png +
+
+
+
+

Low-threshold spiking (LTS)

+
+
[9]:
+
+
+
+ens.neuron_type = nengo.Izhikevich(coupling=0.25)
+with nengo.Simulator(model) as sim:
+    sim.run(0.4)
+izh_plot(sim)
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_17_0.png +
+
+
+
+

Resonator (RZ)

+
+
[10]:
+
+
+
+ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1, coupling=0.26)
+with nengo.Simulator(model) as sim:
+    sim.run(0.4)
+izh_plot(sim)
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_19_0.png +
+
+
+

Caveats

+

Unfortunately, Izhikevich neurons can’t necessarily be used in all of the situations that LIFs are used, due to the more complex dynamics illustrated above.

+

The way that Nengo encodes and decodes information with neurons is informed by the tuning curves of those neurons. With Izhikevich neurons, the firing rate with a certain input current J changes; the spike rate is initially higher due to the adaptation illustrated above.

+

We try our best to generate tuning curves for Izhikevich neurons.

+
+
[11]:
+
+
+
+with nengo.Network(seed=0) as model:
+    u = nengo.Node(lambda t: np.sin(2 * np.pi * t))
+    ens = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())
+    nengo.Connection(u, ens)
+    out_p = nengo.Probe(ens, synapse=0.03)
+
+
+
+
+
[12]:
+
+
+
+with nengo.Simulator(model) as sim:
+    plt.figure()
+    plt.plot(*tuning_curves(ens, sim))
+
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_22_0.png +
+
+

But these are not as accurate and clean as LIF curves, which is detrimental for function decoding.

+
+
[13]:
+
+
+
+u.output = lambda t: np.sin(2 * np.pi * t)
+with model:
+    square = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())
+    nengo.Connection(ens, square, function=lambda x: x ** 2)
+    square_p = nengo.Probe(square, synapse=0.03)
+
+
+
+
+
[14]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(2.0)
+
+t = sim.trange()
+plt.figure(figsize=(12, 3))
+plt.plot(t, sim.data[out_p], label="Ensemble 1 (sin wave)")
+plt.plot(t, sim.data[square_p], label="Ensemble 2 (sin^2)")
+plt.legend(loc="best")
+
+
+
+
+
[14]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f86b29f5b70>
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_25_1.png +
+
+

Some of these weird dynamics can be overcome by using Izhikevich neurons with different parameters.

+
+
[15]:
+
+
+
+square.neuron_type = nengo.Izhikevich(tau_recovery=0.2)
+
+with nengo.Simulator(model) as sim:
+    sim.run(2.0)
+
+t = sim.trange()
+plt.figure(figsize=(12, 3))
+plt.plot(t, sim.data[out_p], label="Ensemble 1 (sin wave)")
+plt.plot(t, sim.data[square_p], label="Ensemble 2 (sin^2)")
+plt.legend(loc="best")
+
+
+
+
+
[15]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f86b0c832e8>
+
+
+
+
+
+
+../../_images/examples_advanced_izhikevich_27_1.png +
+
+

Generally, however, Izhikevich neurons are most useful when trying to match known physiological properties of the system being modelled.

+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/izhikevich.ipynb b/examples/advanced/izhikevich.ipynb new file mode 100644 index 0000000000..c903636e2d --- /dev/null +++ b/examples/advanced/izhikevich.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using the Izhikevich neuron model\n", + "\n", + "The [Izhikevich neuron model](\n", + "http://www.izhikevich.org/publications/spikes.htm)\n", + "is a quadratic integrate-and-fire type model\n", + "with a recovery variable.\n", + "It is able to replicate several characteristics\n", + "of biological neurons while remaining\n", + "computationally efficient.\n", + "\n", + "The Izhikevich neuron model is implemented in Nengo.\n", + "To use it, use a `nengo.Izhikevich` instance\n", + "as the `neuron_type` of an ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:16.604912Z", + "iopub.status.busy": "2020-11-25T16:46:16.604037Z", + "iopub.status.idle": "2020-11-25T16:46:17.098505Z", + "shell.execute_reply": "2020-11-25T16:46:17.098936Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import tuning_curves\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:17.106886Z", + "iopub.status.busy": "2020-11-25T16:46:17.106372Z", + "iopub.status.idle": "2020-11-25T16:46:17.109391Z", + "shell.execute_reply": "2020-11-25T16:46:17.109782Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network(seed=0) as model:\n", + " u = nengo.Node(lambda t: np.sin(2 * np.pi * t))\n", + " ens = nengo.Ensemble(10, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(u, ens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the usual decoded output and neural activity\n", + "that can always be probed,\n", + "you can probe the voltage and recovery terms\n", + "of the Izhikevich model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:17.116456Z", + "iopub.status.busy": "2020-11-25T16:46:17.114914Z", + "iopub.status.idle": "2020-11-25T16:46:17.117067Z", + "shell.execute_reply": "2020-11-25T16:46:17.117485Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " out_p = nengo.Probe(ens, synapse=0.03)\n", + " spikes_p = nengo.Probe(ens.neurons)\n", + " voltage_p = nengo.Probe(ens.neurons, \"voltage\")\n", + " recovery_p = nengo.Probe(ens.neurons, \"recovery\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulating this model shows that we are able\n", + "to decode a time-varying scalar with\n", + "an ensemble of Izhikevich neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:17.124339Z", + "iopub.status.busy": "2020-11-25T16:46:17.123515Z", + "iopub.status.idle": "2020-11-25T16:46:18.200139Z", + "shell.execute_reply": "2020-11-25T16:46:18.199618Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Neuron')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, sim.data[out_p])\n", + "plt.ylabel(\"Decoded output\")\n", + "ax = plt.subplot(2, 1, 2)\n", + "rasterplot(t, sim.data[spikes_p], ax=ax)\n", + "plt.ylabel(\"Neuron\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing you might notice is a slight bump in the decoded value\n", + "at the start of the simulation.\n", + "This occurs because of the adaptive nature of the Izhikevic model;\n", + "it is easier to initiate the first spike than it is to ellicit\n", + "further spikes.\n", + "\n", + "Let's use a constant input and\n", + "look at the first 100 ms of the simulation in more detail\n", + "to see the difference between the first spike and subsequent spikes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:18.208888Z", + "iopub.status.busy": "2020-11-25T16:46:18.208010Z", + "iopub.status.idle": "2020-11-25T16:46:19.364513Z", + "shell.execute_reply": "2020-11-25T16:46:19.364915Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def izh_plot(sim):\n", + " t = sim.trange()\n", + " plt.figure(figsize=(12, 10))\n", + " plt.subplot(4, 1, 1)\n", + " plt.plot(t, sim.data[out_p])\n", + " plt.ylabel(\"Decoded output\")\n", + " plt.xlim(right=t[-1])\n", + " ax = plt.subplot(4, 1, 2)\n", + " rasterplot(t, sim.data[spikes_p], ax=ax)\n", + " plt.ylabel(\"Neuron\")\n", + " plt.xlim(right=t[-1])\n", + " plt.subplot(4, 1, 3)\n", + " plt.plot(t, sim.data[voltage_p])\n", + " plt.ylabel(\"Voltage\")\n", + " plt.xlim(right=t[-1])\n", + " plt.subplot(4, 1, 4)\n", + " plt.plot(t, sim.data[recovery_p])\n", + " plt.ylabel(\"Recovery\")\n", + " plt.xlim(right=t[-1])\n", + "\n", + "\n", + "u.output = 0.2\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.1)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Those neurons that have an encoder of -1\n", + "receive negative current, and therefore\n", + "remain at a low voltage.\n", + "\n", + "Those neurons that have an encoder of 1\n", + "receive positive current, and start spiking rapidly.\n", + "However, as they spike, the recovery variable grows,\n", + "until it reaches a balance with the voltage\n", + "such that the cells spike regularly.\n", + "\n", + "This occurs because, by default,\n", + "we use a set of parameters\n", + "that models a \"regular spiking\" neuron.\n", + "We can use parameters\n", + "that model several different\n", + "classes of neurons instead." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intrinsically bursting (IB)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:19.371147Z", + "iopub.status.busy": "2020-11-25T16:46:19.370371Z", + "iopub.status.idle": "2020-11-25T16:46:20.587713Z", + "shell.execute_reply": "2020-11-25T16:46:20.588147Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(reset_voltage=-55, reset_recovery=4)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chattering (CH)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:20.594352Z", + "iopub.status.busy": "2020-11-25T16:46:20.593577Z", + "iopub.status.idle": "2020-11-25T16:46:21.814639Z", + "shell.execute_reply": "2020-11-25T16:46:21.814126Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(reset_voltage=-50, reset_recovery=2)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fast spiking (FS)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:21.820727Z", + "iopub.status.busy": "2020-11-25T16:46:21.819929Z", + "iopub.status.idle": "2020-11-25T16:46:23.058824Z", + "shell.execute_reply": "2020-11-25T16:46:23.058365Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Low-threshold spiking (LTS)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:23.064772Z", + "iopub.status.busy": "2020-11-25T16:46:23.063979Z", + "iopub.status.idle": "2020-11-25T16:46:24.314362Z", + "shell.execute_reply": "2020-11-25T16:46:24.314756Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(coupling=0.25)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resonator (RZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:24.320902Z", + "iopub.status.busy": "2020-11-25T16:46:24.320104Z", + "iopub.status.idle": "2020-11-25T16:46:25.554105Z", + "shell.execute_reply": "2020-11-25T16:46:25.553384Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens.neuron_type = nengo.Izhikevich(tau_recovery=0.1, coupling=0.26)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.4)\n", + "izh_plot(sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caveats\n", + "\n", + "Unfortunately, Izhikevich neurons can't necessarily\n", + "be used in all of the situations that LIFs are used,\n", + "due to the more complex dynamics illustrated above.\n", + "\n", + "The way that Nengo encodes and decodes information\n", + "with neurons is informed by the tuning curves\n", + "of those neurons.\n", + "With Izhikevich neurons, the firing rate\n", + "with a certain input current `J` changes;\n", + "the spike rate is initially higher due\n", + "to the adaptation illustrated above.\n", + "\n", + "We try our best to generate tuning curves\n", + "for Izhikevich neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:25.562824Z", + "iopub.status.busy": "2020-11-25T16:46:25.561209Z", + "iopub.status.idle": "2020-11-25T16:46:25.563524Z", + "shell.execute_reply": "2020-11-25T16:46:25.563973Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Network(seed=0) as model:\n", + " u = nengo.Node(lambda t: np.sin(2 * np.pi * t))\n", + " ens = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(u, ens)\n", + " out_p = nengo.Probe(ens, synapse=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:25.569407Z", + "iopub.status.busy": "2020-11-25T16:46:25.568630Z", + "iopub.status.idle": "2020-11-25T16:46:26.665408Z", + "shell.execute_reply": "2020-11-25T16:46:26.664964Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens, sim))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But these are not as accurate and clean\n", + "as LIF curves, which is detrimental\n", + "for function decoding." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:26.673320Z", + "iopub.status.busy": "2020-11-25T16:46:26.671844Z", + "iopub.status.idle": "2020-11-25T16:46:26.673970Z", + "shell.execute_reply": "2020-11-25T16:46:26.674392Z" + } + }, + "outputs": [], + "source": [ + "u.output = lambda t: np.sin(2 * np.pi * t)\n", + "with model:\n", + " square = nengo.Ensemble(30, dimensions=1, neuron_type=nengo.Izhikevich())\n", + " nengo.Connection(ens, square, function=lambda x: x ** 2)\n", + " square_p = nengo.Probe(square, synapse=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:26.680590Z", + "iopub.status.busy": "2020-11-25T16:46:26.679809Z", + "iopub.status.idle": "2020-11-25T16:46:29.348655Z", + "shell.execute_reply": "2020-11-25T16:46:29.349074Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(t, sim.data[out_p], label=\"Ensemble 1 (sin wave)\")\n", + "plt.plot(t, sim.data[square_p], label=\"Ensemble 2 (sin^2)\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of these weird dynamics\n", + "can be overcome by using Izhikevich\n", + "neurons with different parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:29.356034Z", + "iopub.status.busy": "2020-11-25T16:46:29.355141Z", + "iopub.status.idle": "2020-11-25T16:46:31.950101Z", + "shell.execute_reply": "2020-11-25T16:46:31.950536Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "square.neuron_type = nengo.Izhikevich(tau_recovery=0.2)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "t = sim.trange()\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(t, sim.data[out_p], label=\"Ensemble 1 (sin wave)\")\n", + "plt.plot(t, sim.data[square_p], label=\"Ensemble 2 (sin^2)\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generally, however, Izhikevich neurons are most useful\n", + "when trying to match known physiological properties\n", + "of the system being modelled." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/advanced/matrix-multiplication.html b/examples/advanced/matrix-multiplication.html new file mode 100644 index 0000000000..28264a5697 --- /dev/null +++ b/examples/advanced/matrix-multiplication.html @@ -0,0 +1,873 @@ + + + + + + + + Matrix multiplication — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Matrix multiplication

+

This example demonstrates how to perform general matrix multiplication using Nengo. The matrix can change during the computation, which makes it distinct from doing static matrix multiplication with neural connection weights (as done in all neural networks).

+

Note that the order of operands in matrix multiplication matters. We will be computing \(A \cdot B\) which is equivalent to \((B \cdot A)^{\top}\).

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+
[2]:
+
+
+
+N = 100
+Amat = np.asarray([[0.5, -0.5], [-0.2, 0.3]])
+Bmat = np.asarray([[0.58, -1.0], [0.7, 0.1]])
+
+# Values should stay within the range (-radius, radius)
+radius = 1
+
+model = nengo.Network(label="Matrix Multiplication", seed=123)
+with model:
+    # Make 2 EnsembleArrays to store the input
+    A = nengo.networks.EnsembleArray(N, Amat.size, radius=radius)
+    B = nengo.networks.EnsembleArray(N, Bmat.size, radius=radius)
+
+    # connect inputs to them so we can set their value
+    inputA = nengo.Node(Amat.ravel())
+    inputB = nengo.Node(Bmat.ravel())
+    nengo.Connection(inputA, A.input)
+    nengo.Connection(inputB, B.input)
+    A_probe = nengo.Probe(A.output, sample_every=0.01, synapse=0.01)
+    B_probe = nengo.Probe(B.output, sample_every=0.01, synapse=0.01)
+
+
+
+
+
[3]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+plt.figure()
+plt.subplot(1, 2, 1)
+plt.title("A")
+plt.plot(sim.trange(sample_every=0.01), sim.data[A_probe])
+plt.subplot(1, 2, 2)
+plt.title("B")
+plt.plot(sim.trange(sample_every=0.01), sim.data[B_probe])
+
+
+
+
+
[3]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7fc3b5bc47f0>,
+ <matplotlib.lines.Line2D at 0x7fc3b3b1c668>,
+ <matplotlib.lines.Line2D at 0x7fc3b3b1c748>,
+ <matplotlib.lines.Line2D at 0x7fc3b3b1c7f0>]
+
+
+
+
+
+
+../../_images/examples_advanced_matrix-multiplication_3_1.png +
+
+
+
[4]:
+
+
+
+with model:
+    # The C matrix is composed of populations that each contain
+    # one element of A and one element of B.
+    # These elements will be multiplied together in the next step.
+
+    # The appropriate encoders make the multiplication more accurate
+    # Check the "multiplication" example to see how multiplication
+    # can be implemented in neurons.
+    c_size = Amat.size * Bmat.shape[1]
+    C = nengo.networks.Product(N, dimensions=c_size)
+
+# Determine the transformation matrices to get the correct pairwise
+# products computed.  This looks a bit like black magic but if
+# you manually try multiplying two matrices together, you can see
+# the underlying pattern.  Basically, we need to build up D1*D2*D3
+# pairs of numbers in C to compute the product of.  If i,j,k are the
+# indexes into the D1*D2*D3 products, we want to compute the product
+# of element (i,j) in A with the element (j,k) in B.  The index in
+# A of (i,j) is j+i*D2 and the index in B of (j,k) is k+j*D3.
+# The index in C is j+k*D2+i*D2*D3.
+transformA = np.zeros((c_size, Amat.size))
+transformB = np.zeros((c_size, Bmat.size))
+
+for i in range(Amat.shape[0]):
+    for j in range(Amat.shape[1]):
+        for k in range(Bmat.shape[1]):
+            c_index = j + k * Amat.shape[1] + i * Bmat.size
+            transformA[c_index][j + i * Amat.shape[1]] = 1
+            transformB[c_index][k + j * Bmat.shape[1]] = 1
+
+print("A->C")
+print(transformA)
+print("B->C")
+print(transformB)
+
+with model:
+    nengo.Connection(A.output, C.input_a, transform=transformA)
+    nengo.Connection(B.output, C.input_b, transform=transformB)
+    C_probe = nengo.Probe(C.output, sample_every=0.01, synapse=0.01)
+
+
+
+
+
+
+
+
+A->C
+[[1. 0. 0. 0.]
+ [0. 1. 0. 0.]
+ [1. 0. 0. 0.]
+ [0. 1. 0. 0.]
+ [0. 0. 1. 0.]
+ [0. 0. 0. 1.]
+ [0. 0. 1. 0.]
+ [0. 0. 0. 1.]]
+B->C
+[[1. 0. 0. 0.]
+ [0. 0. 1. 0.]
+ [0. 1. 0. 0.]
+ [0. 0. 0. 1.]
+ [1. 0. 0. 0.]
+ [0. 0. 1. 0.]
+ [0. 1. 0. 0.]
+ [0. 0. 0. 1.]]
+
+
+
+
[5]:
+
+
+
+# Look at C
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+
+
+
+
[6]:
+
+
+
+plt.figure()
+plt.plot(sim.trange(sample_every=0.01), sim.data[C_probe])
+plt.title("C")
+
+
+
+
+
[6]:
+
+
+
+
+Text(0.5, 1.0, 'C')
+
+
+
+
+
+
+../../_images/examples_advanced_matrix-multiplication_6_1.png +
+
+
+
[7]:
+
+
+
+with model:
+    # Now do the appropriate summing
+    D = nengo.networks.EnsembleArray(
+        N, n_ensembles=Amat.shape[0] * Bmat.shape[1], radius=radius
+    )
+
+# The mapping for this transformation is much easier, since we want to
+# combine D2 pairs of elements (we sum D2 products together)
+transformC = np.zeros((D.dimensions, c_size))
+for i in range(c_size):
+    transformC[i // Bmat.shape[0]][i] = 1
+print("C->D")
+print(transformC)
+
+with model:
+    nengo.Connection(C.output, D.input, transform=transformC)
+    D_probe = nengo.Probe(D.output, sample_every=0.01, synapse=0.01)
+
+
+
+
+
+
+
+
+C->D
+[[1. 1. 0. 0. 0. 0. 0. 0.]
+ [0. 0. 1. 1. 0. 0. 0. 0.]
+ [0. 0. 0. 0. 1. 1. 0. 0.]
+ [0. 0. 0. 0. 0. 0. 1. 1.]]
+
+
+
+
[8]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+
+
+
+
[9]:
+
+
+
+plt.figure()
+plt.plot(sim.trange(sample_every=0.01), sim.data[D_probe])
+for d in np.dot(Amat, Bmat).flatten():
+    plt.axhline(d, color="k")
+plt.title("D")
+
+
+
+
+
[9]:
+
+
+
+
+Text(0.5, 1.0, 'D')
+
+
+
+
+
+
+../../_images/examples_advanced_matrix-multiplication_9_1.png +
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/matrix-multiplication.ipynb b/examples/advanced/matrix-multiplication.ipynb new file mode 100644 index 0000000000..f8cb0f10b4 --- /dev/null +++ b/examples/advanced/matrix-multiplication.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matrix multiplication\n", + "\n", + "This example demonstrates how to perform\n", + "general matrix multiplication using Nengo.\n", + "The matrix can change during the computation,\n", + "which makes it distinct from doing static matrix multiplication\n", + "with neural connection weights (as done in all neural networks).\n", + "\n", + "Note that the order of operands in matrix multiplication matters.\n", + "We will be computing $A \\cdot B$ which is equivalent to $(B \\cdot A)^{\\top}$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:33.454016Z", + "iopub.status.busy": "2020-11-25T16:46:33.453163Z", + "iopub.status.idle": "2020-11-25T16:46:33.944879Z", + "shell.execute_reply": "2020-11-25T16:46:33.943974Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:33.968799Z", + "iopub.status.busy": "2020-11-25T16:46:33.968236Z", + "iopub.status.idle": "2020-11-25T16:46:33.971978Z", + "shell.execute_reply": "2020-11-25T16:46:33.971338Z" + } + }, + "outputs": [], + "source": [ + "N = 100\n", + "Amat = np.asarray([[0.5, -0.5], [-0.2, 0.3]])\n", + "Bmat = np.asarray([[0.58, -1.0], [0.7, 0.1]])\n", + "\n", + "# Values should stay within the range (-radius, radius)\n", + "radius = 1\n", + "\n", + "model = nengo.Network(label=\"Matrix Multiplication\", seed=123)\n", + "with model:\n", + " # Make 2 EnsembleArrays to store the input\n", + " A = nengo.networks.EnsembleArray(N, Amat.size, radius=radius)\n", + " B = nengo.networks.EnsembleArray(N, Bmat.size, radius=radius)\n", + "\n", + " # connect inputs to them so we can set their value\n", + " inputA = nengo.Node(Amat.ravel())\n", + " inputB = nengo.Node(Bmat.ravel())\n", + " nengo.Connection(inputA, A.input)\n", + " nengo.Connection(inputB, B.input)\n", + " A_probe = nengo.Probe(A.output, sample_every=0.01, synapse=0.01)\n", + " B_probe = nengo.Probe(B.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:33.979505Z", + "iopub.status.busy": "2020-11-25T16:46:33.978472Z", + "iopub.status.idle": "2020-11-25T16:46:34.693888Z", + "shell.execute_reply": "2020-11-25T16:46:34.694291Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "plt.figure()\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"A\")\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[A_probe])\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"B\")\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[B_probe])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:34.743106Z", + "iopub.status.busy": "2020-11-25T16:46:34.742582Z", + "iopub.status.idle": "2020-11-25T16:46:34.746964Z", + "shell.execute_reply": "2020-11-25T16:46:34.746522Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A->C\n", + "[[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]]\n", + "B->C\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 0. 1.]\n", + " [1. 0. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "with model:\n", + " # The C matrix is composed of populations that each contain\n", + " # one element of A and one element of B.\n", + " # These elements will be multiplied together in the next step.\n", + "\n", + " # The appropriate encoders make the multiplication more accurate\n", + " # Check the \"multiplication\" example to see how multiplication\n", + " # can be implemented in neurons.\n", + " c_size = Amat.size * Bmat.shape[1]\n", + " C = nengo.networks.Product(N, dimensions=c_size)\n", + "\n", + "# Determine the transformation matrices to get the correct pairwise\n", + "# products computed. This looks a bit like black magic but if\n", + "# you manually try multiplying two matrices together, you can see\n", + "# the underlying pattern. Basically, we need to build up D1*D2*D3\n", + "# pairs of numbers in C to compute the product of. If i,j,k are the\n", + "# indexes into the D1*D2*D3 products, we want to compute the product\n", + "# of element (i,j) in A with the element (j,k) in B. The index in\n", + "# A of (i,j) is j+i*D2 and the index in B of (j,k) is k+j*D3.\n", + "# The index in C is j+k*D2+i*D2*D3.\n", + "transformA = np.zeros((c_size, Amat.size))\n", + "transformB = np.zeros((c_size, Bmat.size))\n", + "\n", + "for i in range(Amat.shape[0]):\n", + " for j in range(Amat.shape[1]):\n", + " for k in range(Bmat.shape[1]):\n", + " c_index = j + k * Amat.shape[1] + i * Bmat.size\n", + " transformA[c_index][j + i * Amat.shape[1]] = 1\n", + " transformB[c_index][k + j * Bmat.shape[1]] = 1\n", + "\n", + "print(\"A->C\")\n", + "print(transformA)\n", + "print(\"B->C\")\n", + "print(transformB)\n", + "\n", + "with model:\n", + " nengo.Connection(A.output, C.input_a, transform=transformA)\n", + " nengo.Connection(B.output, C.input_b, transform=transformB)\n", + " C_probe = nengo.Probe(C.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:34.754128Z", + "iopub.status.busy": "2020-11-25T16:46:34.753354Z", + "iopub.status.idle": "2020-11-25T16:46:36.107713Z", + "shell.execute_reply": "2020-11-25T16:46:36.107182Z" + } + }, + "outputs": [], + "source": [ + "# Look at C\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:36.113240Z", + "iopub.status.busy": "2020-11-25T16:46:36.112412Z", + "iopub.status.idle": "2020-11-25T16:46:36.251075Z", + "shell.execute_reply": "2020-11-25T16:46:36.251483Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'C')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[C_probe])\n", + "plt.title(\"C\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:36.265512Z", + "iopub.status.busy": "2020-11-25T16:46:36.264981Z", + "iopub.status.idle": "2020-11-25T16:46:36.269083Z", + "shell.execute_reply": "2020-11-25T16:46:36.268597Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C->D\n", + "[[1. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 1.]]\n" + ] + } + ], + "source": [ + "with model:\n", + " # Now do the appropriate summing\n", + " D = nengo.networks.EnsembleArray(\n", + " N, n_ensembles=Amat.shape[0] * Bmat.shape[1], radius=radius\n", + " )\n", + "\n", + "# The mapping for this transformation is much easier, since we want to\n", + "# combine D2 pairs of elements (we sum D2 products together)\n", + "transformC = np.zeros((D.dimensions, c_size))\n", + "for i in range(c_size):\n", + " transformC[i // Bmat.shape[0]][i] = 1\n", + "print(\"C->D\")\n", + "print(transformC)\n", + "\n", + "with model:\n", + " nengo.Connection(C.output, D.input, transform=transformC)\n", + " D_probe = nengo.Probe(D.output, sample_every=0.01, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:36.276268Z", + "iopub.status.busy": "2020-11-25T16:46:36.275485Z", + "iopub.status.idle": "2020-11-25T16:46:37.657663Z", + "shell.execute_reply": "2020-11-25T16:46:37.656788Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:37.668436Z", + "iopub.status.busy": "2020-11-25T16:46:37.662395Z", + "iopub.status.idle": "2020-11-25T16:46:37.842650Z", + "shell.execute_reply": "2020-11-25T16:46:37.841945Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'D')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[D_probe])\n", + "for d in np.dot(Amat, Bmat).flatten():\n", + " plt.axhline(d, color=\"k\")\n", + "plt.title(\"D\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/advanced/matrix_multiplication.html b/examples/advanced/matrix_multiplication.html new file mode 100644 index 0000000000..2cba66aae4 --- /dev/null +++ b/examples/advanced/matrix_multiplication.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/advanced/nef-algorithm.html b/examples/advanced/nef-algorithm.html new file mode 100644 index 0000000000..2be945c81e --- /dev/null +++ b/examples/advanced/nef-algorithm.html @@ -0,0 +1,934 @@ + + + + + + + + The NEF algorithm — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

The NEF algorithm

+

While Nengo provides a flexible, general-purpose approach to neural modelling, some aspects rely on the Neural Engineering Framework (NEF) to help specify network behavior. The theory behind the Neural Engineering Framework is developed at length in Eliasmith & Anderson, 2003: “Neural Engineering” and a short summary is available in Stewart, 2012: “A Technical Overview of the Neural Engineering +Framework”.

+

However, for some people, the best description of an algorithm is the code itself. With that in mind, the following is a complete implementation of the NEF for the special case of two one-dimensional populations with a single connection between them. You can adjust the function being computed, the input to the system, and various neural parameters.

+

This example does not use Nengo at all. It is Python code that only requires Numpy (for the matrix inversion) and Matplotlib (to produce graphs of the output).

+
+

Introduction

+

The NEF is a method for building large-scale neural models using realistic neurons. It is a neural compiler: you specify the high-level computations the model needs to compute, and the properties of the neurons themselves, and the NEF determines the neural connections needed to perform those operations.

+

This script shows how to build a simple feed-forward network of leaky integrate-and-fire neurons where each population encodes a one-dimensional value and the connection weights between the populations are optimized to compute some arbitrary function. This same approach is used in Nengo, extended to multi-dimensional representation, multiple populations of neurons, and recurrent connections.

+

To change the input to the system, change input. To change the function computed by the weights, change function.

+

The size of the populations and their neural properties can also be adjusted by changing the parameters below.

+
+
[1]:
+
+
+
+%matplotlib inline
+
+import math
+import random
+
+import numpy
+import matplotlib.pyplot as plt
+
+dt = 0.001  # simulation time step
+t_rc = 0.02  # membrane RC time constant
+t_ref = 0.002  # refractory period
+t_pstc = 0.1  # post-synaptic time constant
+N_A = 50  # number of neurons in first population
+N_B = 40  # number of neurons in second population
+N_samples = 100  # number of sample points to use when finding decoders
+rate_A = 25, 75  # range of maximum firing rates for population A
+rate_B = 50, 100  # range of maximum firing rates for population B
+
+
+def input(t):
+    """The input to the system over time"""
+    return math.sin(t)
+
+
+def function(x):
+    """The function to compute between A and B."""
+    return x * x
+
+
+
+
+
+

Step 1: Initialization

+
+
[2]:
+
+
+
+# create random encoders for the two populations
+encoder_A = [random.choice([-1, 1]) for i in range(N_A)]
+encoder_B = [random.choice([-1, 1]) for i in range(N_B)]
+
+
+def generate_gain_and_bias(count, intercept_low, intercept_high, rate_low, rate_high):
+    gain = []
+    bias = []
+    for _ in range(count):
+        # desired intercept (x value for which the neuron starts firing
+        intercept = random.uniform(intercept_low, intercept_high)
+        # desired maximum rate (firing rate when x is maximum)
+        rate = random.uniform(rate_low, rate_high)
+
+        # this algorithm is specific to LIF neurons, but should
+        # generate gain and bias values to produce the desired
+        # intercept and rate
+        z = 1.0 / (1 - math.exp((t_ref - (1.0 / rate)) / t_rc))
+        g = (1 - z) / (intercept - 1.0)
+        b = 1 - g * intercept
+        gain.append(g)
+        bias.append(b)
+    return gain, bias
+
+
+# random gain and bias for the two populations
+gain_A, bias_A = generate_gain_and_bias(N_A, -1, 1, rate_A[0], rate_A[1])
+gain_B, bias_B = generate_gain_and_bias(N_B, -1, 1, rate_B[0], rate_B[1])
+
+
+def run_neurons(input, v, ref):
+    """Run the neuron model.
+
+    A simple leaky integrate-and-fire model, scaled so that v=0 is resting
+    voltage and v=1 is the firing threshold.
+    """
+    spikes = []
+    for i, _ in enumerate(v):
+        dV = dt * (input[i] - v[i]) / t_rc  # the LIF voltage change equation
+        v[i] += dV
+        if v[i] < 0:
+            v[i] = 0  # don't allow voltage to go below 0
+
+        if ref[i] > 0:  # if we are in our refractory period
+            v[i] = 0  # keep voltage at zero and
+            ref[i] -= dt  # decrease the refractory period
+
+        if v[i] > 1:  # if we have hit threshold
+            spikes.append(True)  # spike
+            v[i] = 0  # reset the voltage
+            ref[i] = t_ref  # and set the refractory period
+        else:
+            spikes.append(False)
+    return spikes
+
+
+def compute_response(x, encoder, gain, bias, time_limit=0.5):
+    """Measure the spike rate of a population for a given value x."""
+    N = len(encoder)  # number of neurons
+    v = [0] * N  # voltage
+    ref = [0] * N  # refractory period
+
+    # compute input corresponding to x
+    input = []
+    for i in range(N):
+        input.append(x * encoder[i] * gain[i] + bias[i])
+        v[i] = random.uniform(0, 1)  # randomize the initial voltage level
+
+    count = [0] * N  # spike count for each neuron
+
+    # feed the input into the population for a given amount of time
+    t = 0
+    while t < time_limit:
+        spikes = run_neurons(input, v, ref)
+        for i, s in enumerate(spikes):
+            if s:
+                count[i] += 1
+        t += dt
+    return [c / time_limit for c in count]  # return the spike rate (in Hz)
+
+
+def compute_tuning_curves(encoder, gain, bias):
+    """Compute the tuning curves for a population"""
+    # generate a set of x values to sample at
+    x_values = [i * 2.0 / N_samples - 1.0 for i in range(N_samples)]
+
+    # build up a matrix of neural responses to each input (i.e. tuning curves)
+    A = []
+    for x in x_values:
+        response = compute_response(x, encoder, gain, bias)
+        A.append(response)
+    return x_values, A
+
+
+def compute_decoder(encoder, gain, bias, function=lambda x: x):
+    # get the tuning curves
+    x_values, A = compute_tuning_curves(encoder, gain, bias)
+
+    # get the desired decoded value for each sample point
+    value = numpy.array([[function(x)] for x in x_values])
+
+    # find the optimal linear decoder
+    A = numpy.array(A).T
+    Gamma = numpy.dot(A, A.T)
+    Upsilon = numpy.dot(A, value)
+    Ginv = numpy.linalg.pinv(Gamma)
+    decoder = numpy.dot(Ginv, Upsilon) / dt
+    return decoder
+
+
+# find the decoders for A and B
+decoder_A = compute_decoder(encoder_A, gain_A, bias_A, function=function)
+decoder_B = compute_decoder(encoder_B, gain_B, bias_B)
+
+# compute the weight matrix
+weights = numpy.dot(decoder_A, [encoder_B])
+
+
+
+
+
+

Step 2: Running the simulation

+
+
[3]:
+
+
+
+v_A = [0.0] * N_A  # voltage for population A
+ref_A = [0.0] * N_A  # refractory period for population A
+input_A = [0.0] * N_A  # input for population A
+
+v_B = [0.0] * N_B  # voltage for population B
+ref_B = [0.0] * N_B  # refractory period for population B
+input_B = [0.0] * N_B  # input for population B
+
+# scaling factor for the post-synaptic filter
+pstc_scale = 1.0 - math.exp(-dt / t_pstc)
+
+# for storing simulation data to plot afterward
+inputs = []
+times = []
+outputs = []
+ideal = []
+
+output = 0.0  # the decoded output value from population B
+t = 0
+while t < 10.0:  # noqa: C901 (tell static checker to ignore complexity)
+    # call the input function to determine the input value
+    x = input(t)
+
+    # convert the input value into an input for each neuron
+    for i in range(N_A):
+        input_A[i] = x * encoder_A[i] * gain_A[i] + bias_A[i]
+
+    # run population A and determine which neurons spike
+    spikes_A = run_neurons(input_A, v_A, ref_A)
+
+    # decay all of the inputs (implementing the post-synaptic filter)
+    for j in range(N_B):
+        input_B[j] *= 1.0 - pstc_scale
+    # for each neuron that spikes, increase the input current
+    # of all the neurons it is connected to by the synaptic
+    # connection weight
+    for i, s in enumerate(spikes_A):
+        if s:
+            for j in range(N_B):
+                input_B[j] += weights[i][j] * pstc_scale
+
+    # compute the total input into each neuron in population B
+    # (taking into account gain and bias)
+    total_B = [0] * N_B
+    for j in range(N_B):
+        total_B[j] = gain_B[j] * input_B[j] + bias_B[j]
+
+    # run population B and determine which neurons spike
+    spikes_B = run_neurons(total_B, v_B, ref_B)
+
+    # for each neuron in B that spikes, update our decoded value
+    # (also applying the same post-synaptic filter)
+    output *= 1.0 - pstc_scale
+    for j, s in enumerate(spikes_B):
+        if s:
+            output += decoder_B[j][0] * pstc_scale
+
+    if t % 0.5 <= dt:
+        print(t, output)
+    times.append(t)
+    inputs.append(x)
+    outputs.append(output)
+    ideal.append(function(x))
+    t += dt
+
+
+
+
+
+
+
+
+0 0.0
+0.001 0.0
+0.5000000000000003 0.10169046205375874
+1.0000000000000007 0.5600060592004141
+1.5009999999999455 0.9016353842815601
+2.0009999999998906 0.9053721164863286
+2.5009999999998356 0.4807937869520167
+3.0009999999997805 0.13653211370079643
+3.5009999999997254 0.03712836245916179
+4.000999999999671 0.37309816783966915
+4.500999999999838 0.8510836666141086
+5.000000000000004 0.9670552038667225
+5.500000000000171 0.6562015670959472
+6.000000000000338 0.24694255703539536
+6.500000000000505 0.03460342314695294
+7.000000000000672 0.3003104797831265
+7.500000000000839 0.704584764277374
+8.000000000001005 0.9772856245237679
+8.500000000000728 0.7757873426223485
+9.000000000000451 0.36021073760393646
+9.500000000000174 0.05867723255776759
+
+
+
+
+

Step 3: Plot the results

+
+
[4]:
+
+
+
+x, A = compute_tuning_curves(encoder_A, gain_A, bias_A)
+x, B = compute_tuning_curves(encoder_B, gain_B, bias_B)
+
+plt.figure()
+plt.plot(x, A)
+plt.title("Tuning curves for population A")
+
+plt.figure()
+plt.plot(x, B)
+plt.title("Tuning curves for population B")
+
+plt.figure()
+plt.plot(times, inputs, label="input")
+plt.plot(times, ideal, label="ideal")
+plt.plot(times, outputs, label="output")
+plt.title("Simulation results")
+plt.legend()
+plt.show()
+
+
+
+
+
+
+
+../../_images/examples_advanced_nef-algorithm_9_0.png +
+
+
+
+
+
+../../_images/examples_advanced_nef-algorithm_9_1.png +
+
+
+
+
+
+../../_images/examples_advanced_nef-algorithm_9_2.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/nef-algorithm.ipynb b/examples/advanced/nef-algorithm.ipynb new file mode 100644 index 0000000000..416e089260 --- /dev/null +++ b/examples/advanced/nef-algorithm.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The NEF algorithm\n", + "\n", + "While Nengo provides a flexible,\n", + "general-purpose approach to neural modelling,\n", + "some aspects rely on the Neural Engineering Framework (NEF)\n", + "to help specify network behavior.\n", + "The theory behind the Neural Engineering Framework\n", + "is developed at length in\n", + "[Eliasmith & Anderson, 2003: \"Neural Engineering\"](\n", + "https://mitpress.mit.edu/books/neural-engineering)\n", + "and a short summary is available in\n", + "[Stewart, 2012: \"A Technical Overview of the Neural Engineering Framework\"](\n", + "http://compneuro.uwaterloo.ca/publications/stewart2012d.html).\n", + "\n", + "However, for some people,\n", + "the best description of an algorithm\n", + "is the code itself.\n", + "With that in mind,\n", + "the following is a complete implementation of the NEF\n", + "for the special case of\n", + "two one-dimensional populations with a single connection between them.\n", + "You can adjust the function being computed,\n", + "the input to the system,\n", + "and various neural parameters.\n", + "\n", + "This example does not use Nengo at all.\n", + "It is Python code that only requires\n", + "Numpy (for the matrix inversion)\n", + "and Matplotlib (to produce graphs of the output)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The NEF is a method for building large-scale neural models\n", + "using realistic neurons.\n", + "It is a neural compiler:\n", + "you specify the high-level computations the model needs to compute,\n", + "and the properties of the neurons themselves,\n", + "and the NEF determines the neural connections\n", + "needed to perform those operations.\n", + "\n", + "This script shows how to build a\n", + "simple feed-forward network of leaky integrate-and-fire neurons\n", + "where each population encodes a one-dimensional value\n", + "and the connection weights between the populations\n", + "are optimized to compute some arbitrary function.\n", + "This same approach is used in Nengo,\n", + "extended to multi-dimensional representation,\n", + "multiple populations of neurons, and recurrent connections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To change the input to the system, change `input`.\n", + "To change the function computed by the weights, change `function`.\n", + "\n", + "The size of the populations and their neural properties\n", + "can also be adjusted by changing the parameters below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:39.143254Z", + "iopub.status.busy": "2020-11-25T16:46:39.142398Z", + "iopub.status.idle": "2020-11-25T16:46:39.469298Z", + "shell.execute_reply": "2020-11-25T16:46:39.469761Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import math\n", + "import random\n", + "\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "dt = 0.001 # simulation time step\n", + "t_rc = 0.02 # membrane RC time constant\n", + "t_ref = 0.002 # refractory period\n", + "t_pstc = 0.1 # post-synaptic time constant\n", + "N_A = 50 # number of neurons in first population\n", + "N_B = 40 # number of neurons in second population\n", + "N_samples = 100 # number of sample points to use when finding decoders\n", + "rate_A = 25, 75 # range of maximum firing rates for population A\n", + "rate_B = 50, 100 # range of maximum firing rates for population B\n", + "\n", + "\n", + "def input(t):\n", + " \"\"\"The input to the system over time\"\"\"\n", + " return math.sin(t)\n", + "\n", + "\n", + "def function(x):\n", + " \"\"\"The function to compute between A and B.\"\"\"\n", + " return x * x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:39.501235Z", + "iopub.status.busy": "2020-11-25T16:46:39.496079Z", + "iopub.status.idle": "2020-11-25T16:46:41.604526Z", + "shell.execute_reply": "2020-11-25T16:46:41.604026Z" + } + }, + "outputs": [], + "source": [ + "# create random encoders for the two populations\n", + "encoder_A = [random.choice([-1, 1]) for i in range(N_A)]\n", + "encoder_B = [random.choice([-1, 1]) for i in range(N_B)]\n", + "\n", + "\n", + "def generate_gain_and_bias(count, intercept_low, intercept_high, rate_low, rate_high):\n", + " gain = []\n", + " bias = []\n", + " for _ in range(count):\n", + " # desired intercept (x value for which the neuron starts firing\n", + " intercept = random.uniform(intercept_low, intercept_high)\n", + " # desired maximum rate (firing rate when x is maximum)\n", + " rate = random.uniform(rate_low, rate_high)\n", + "\n", + " # this algorithm is specific to LIF neurons, but should\n", + " # generate gain and bias values to produce the desired\n", + " # intercept and rate\n", + " z = 1.0 / (1 - math.exp((t_ref - (1.0 / rate)) / t_rc))\n", + " g = (1 - z) / (intercept - 1.0)\n", + " b = 1 - g * intercept\n", + " gain.append(g)\n", + " bias.append(b)\n", + " return gain, bias\n", + "\n", + "\n", + "# random gain and bias for the two populations\n", + "gain_A, bias_A = generate_gain_and_bias(N_A, -1, 1, rate_A[0], rate_A[1])\n", + "gain_B, bias_B = generate_gain_and_bias(N_B, -1, 1, rate_B[0], rate_B[1])\n", + "\n", + "\n", + "def run_neurons(input, v, ref):\n", + " \"\"\"Run the neuron model.\n", + "\n", + " A simple leaky integrate-and-fire model, scaled so that v=0 is resting\n", + " voltage and v=1 is the firing threshold.\n", + " \"\"\"\n", + " spikes = []\n", + " for i, _ in enumerate(v):\n", + " dV = dt * (input[i] - v[i]) / t_rc # the LIF voltage change equation\n", + " v[i] += dV\n", + " if v[i] < 0:\n", + " v[i] = 0 # don't allow voltage to go below 0\n", + "\n", + " if ref[i] > 0: # if we are in our refractory period\n", + " v[i] = 0 # keep voltage at zero and\n", + " ref[i] -= dt # decrease the refractory period\n", + "\n", + " if v[i] > 1: # if we have hit threshold\n", + " spikes.append(True) # spike\n", + " v[i] = 0 # reset the voltage\n", + " ref[i] = t_ref # and set the refractory period\n", + " else:\n", + " spikes.append(False)\n", + " return spikes\n", + "\n", + "\n", + "def compute_response(x, encoder, gain, bias, time_limit=0.5):\n", + " \"\"\"Measure the spike rate of a population for a given value x.\"\"\"\n", + " N = len(encoder) # number of neurons\n", + " v = [0] * N # voltage\n", + " ref = [0] * N # refractory period\n", + "\n", + " # compute input corresponding to x\n", + " input = []\n", + " for i in range(N):\n", + " input.append(x * encoder[i] * gain[i] + bias[i])\n", + " v[i] = random.uniform(0, 1) # randomize the initial voltage level\n", + "\n", + " count = [0] * N # spike count for each neuron\n", + "\n", + " # feed the input into the population for a given amount of time\n", + " t = 0\n", + " while t < time_limit:\n", + " spikes = run_neurons(input, v, ref)\n", + " for i, s in enumerate(spikes):\n", + " if s:\n", + " count[i] += 1\n", + " t += dt\n", + " return [c / time_limit for c in count] # return the spike rate (in Hz)\n", + "\n", + "\n", + "def compute_tuning_curves(encoder, gain, bias):\n", + " \"\"\"Compute the tuning curves for a population\"\"\"\n", + " # generate a set of x values to sample at\n", + " x_values = [i * 2.0 / N_samples - 1.0 for i in range(N_samples)]\n", + "\n", + " # build up a matrix of neural responses to each input (i.e. tuning curves)\n", + " A = []\n", + " for x in x_values:\n", + " response = compute_response(x, encoder, gain, bias)\n", + " A.append(response)\n", + " return x_values, A\n", + "\n", + "\n", + "def compute_decoder(encoder, gain, bias, function=lambda x: x):\n", + " # get the tuning curves\n", + " x_values, A = compute_tuning_curves(encoder, gain, bias)\n", + "\n", + " # get the desired decoded value for each sample point\n", + " value = numpy.array([[function(x)] for x in x_values])\n", + "\n", + " # find the optimal linear decoder\n", + " A = numpy.array(A).T\n", + " Gamma = numpy.dot(A, A.T)\n", + " Upsilon = numpy.dot(A, value)\n", + " Ginv = numpy.linalg.pinv(Gamma)\n", + " decoder = numpy.dot(Ginv, Upsilon) / dt\n", + " return decoder\n", + "\n", + "\n", + "# find the decoders for A and B\n", + "decoder_A = compute_decoder(encoder_A, gain_A, bias_A, function=function)\n", + "decoder_B = compute_decoder(encoder_B, gain_B, bias_B)\n", + "\n", + "# compute the weight matrix\n", + "weights = numpy.dot(decoder_A, [encoder_B])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Running the simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:41.633060Z", + "iopub.status.busy": "2020-11-25T16:46:41.626818Z", + "iopub.status.idle": "2020-11-25T16:46:43.244630Z", + "shell.execute_reply": "2020-11-25T16:46:43.245267Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.0\n", + "0.001 0.0\n", + "0.5000000000000003 0.10169046205375874\n", + "1.0000000000000007 0.5600060592004141\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5009999999999455 0.9016353842815601\n", + "2.0009999999998906 0.9053721164863286\n", + "2.5009999999998356 0.4807937869520167\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0009999999997805 0.13653211370079643\n", + "3.5009999999997254 0.03712836245916179\n", + "4.000999999999671 0.37309816783966915\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.500999999999838 0.8510836666141086\n", + "5.000000000000004 0.9670552038667225\n", + "5.500000000000171 0.6562015670959472\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.000000000000338 0.24694255703539536\n", + "6.500000000000505 0.03460342314695294\n", + "7.000000000000672 0.3003104797831265\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.500000000000839 0.704584764277374\n", + "8.000000000001005 0.9772856245237679\n", + "8.500000000000728 0.7757873426223485\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.000000000000451 0.36021073760393646\n", + "9.500000000000174 0.05867723255776759\n" + ] + } + ], + "source": [ + "v_A = [0.0] * N_A # voltage for population A\n", + "ref_A = [0.0] * N_A # refractory period for population A\n", + "input_A = [0.0] * N_A # input for population A\n", + "\n", + "v_B = [0.0] * N_B # voltage for population B\n", + "ref_B = [0.0] * N_B # refractory period for population B\n", + "input_B = [0.0] * N_B # input for population B\n", + "\n", + "# scaling factor for the post-synaptic filter\n", + "pstc_scale = 1.0 - math.exp(-dt / t_pstc)\n", + "\n", + "# for storing simulation data to plot afterward\n", + "inputs = []\n", + "times = []\n", + "outputs = []\n", + "ideal = []\n", + "\n", + "output = 0.0 # the decoded output value from population B\n", + "t = 0\n", + "while t < 10.0: # noqa: C901 (tell static checker to ignore complexity)\n", + " # call the input function to determine the input value\n", + " x = input(t)\n", + "\n", + " # convert the input value into an input for each neuron\n", + " for i in range(N_A):\n", + " input_A[i] = x * encoder_A[i] * gain_A[i] + bias_A[i]\n", + "\n", + " # run population A and determine which neurons spike\n", + " spikes_A = run_neurons(input_A, v_A, ref_A)\n", + "\n", + " # decay all of the inputs (implementing the post-synaptic filter)\n", + " for j in range(N_B):\n", + " input_B[j] *= 1.0 - pstc_scale\n", + " # for each neuron that spikes, increase the input current\n", + " # of all the neurons it is connected to by the synaptic\n", + " # connection weight\n", + " for i, s in enumerate(spikes_A):\n", + " if s:\n", + " for j in range(N_B):\n", + " input_B[j] += weights[i][j] * pstc_scale\n", + "\n", + " # compute the total input into each neuron in population B\n", + " # (taking into account gain and bias)\n", + " total_B = [0] * N_B\n", + " for j in range(N_B):\n", + " total_B[j] = gain_B[j] * input_B[j] + bias_B[j]\n", + "\n", + " # run population B and determine which neurons spike\n", + " spikes_B = run_neurons(total_B, v_B, ref_B)\n", + "\n", + " # for each neuron in B that spikes, update our decoded value\n", + " # (also applying the same post-synaptic filter)\n", + " output *= 1.0 - pstc_scale\n", + " for j, s in enumerate(spikes_B):\n", + " if s:\n", + " output += decoder_B[j][0] * pstc_scale\n", + "\n", + " if t % 0.5 <= dt:\n", + " print(t, output)\n", + " times.append(t)\n", + " inputs.append(x)\n", + " outputs.append(output)\n", + " ideal.append(function(x))\n", + " t += dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:43.269258Z", + "iopub.status.busy": "2020-11-25T16:46:43.252984Z", + "iopub.status.idle": "2020-11-25T16:46:45.885376Z", + "shell.execute_reply": "2020-11-25T16:46:45.885917Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

NEF summary

+

The Neural Engineering Framework (NEF) is one set of theoretical methods that are used in Nengo for constructing neural models. The NEF is based on Eliasmith & Anderson’s (2003) book from MIT Press. This notebook introduces the three main principles discussed in that book and implemented in Nengo.

+
+
[1]:
+
+
+
+%matplotlib inline
+import numpy as np
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.dists import Uniform
+from nengo.processes import WhiteSignal
+from nengo.utils.ensemble import tuning_curves
+from nengo.utils.ipython import hide_input
+from nengo.utils.matplotlib import rasterplot
+
+
+def aligned(n_neurons, radius=0.9):
+    intercepts = np.linspace(-radius, radius, n_neurons)
+    encoders = np.tile([[1], [-1]], (n_neurons // 2, 1))
+    intercepts *= encoders[:, 0]
+    return intercepts, encoders
+
+
+hide_input()
+
+
+
+
+
[1]:
+
+
+
+Show Input + +
+
+
+

Principle 1: Representation

+
+

Encoding

+

Neural populations represent time-varying signals through their spiking responses. A signal is a vector of real numbers of arbitrary length. This example is a 1D signal going from -1 to 1 in 1 second.

+
+
[2]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(lambda t: t * 2 - 1)
+    input_probe = nengo.Probe(input)
+
+
+
+
+
[3]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1.0)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[input_probe], lw=2)
+plt.title("Input signal")
+plt.xlabel("Time (s)")
+plt.xlim(0, 1)
+hide_input()
+
+
+
+
+
[3]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_4_1.png +
+
+

These signals drive neural populations based on each neuron’s tuning curve (which is similar to the current-frequency curve, if you’re familiar with that).

+

The tuning curve describes how much a particular neuron will fire as a function of the input signal.

+
+
[4]:
+
+
+
+intercepts, encoders = aligned(8)  # Makes evenly spaced intercepts
+with model:
+    A = nengo.Ensemble(
+        8,
+        dimensions=1,
+        intercepts=intercepts,
+        max_rates=Uniform(80, 100),
+        encoders=encoders,
+    )
+
+
+
+
+
[5]:
+
+
+
+with nengo.Simulator(model) as sim:
+    eval_points, activities = tuning_curves(A, sim)
+
+plt.figure()
+plt.plot(eval_points, activities, lw=2)
+plt.xlabel("Input signal")
+plt.ylabel("Firing rate (Hz)")
+hide_input()
+
+
+
+
+
[5]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_7_1.png +
+
+

We can drive these neurons with our input signal and observe their spiking activity over time.

+
+
[6]:
+
+
+
+with model:
+    nengo.Connection(input, A)
+    A_spikes = nengo.Probe(A.neurons)
+
+
+
+
+
[7]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+plt.figure()
+ax = plt.subplot(1, 1, 1)
+rasterplot(sim.trange(), sim.data[A_spikes], ax)
+ax.set_xlim(0, 1)
+ax.set_ylabel("Neuron")
+ax.set_xlabel("Time (s)")
+hide_input()
+
+
+
+
+
[7]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_10_1.png +
+
+
+
+

Decoding

+

We can estimate the input signal originally encoded by decoding the pattern of spikes. To do this, we first filter the spike train with a temporal filter that accounts for postsynaptic current (PSC) activity.

+
+
[8]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(lambda t: t * 2 - 1)
+    input_probe = nengo.Probe(input)
+    intercepts, encoders = aligned(8)  # Makes evenly spaced intercepts
+    A = nengo.Ensemble(
+        8,
+        dimensions=1,
+        intercepts=intercepts,
+        max_rates=Uniform(80, 100),
+        encoders=encoders,
+    )
+    nengo.Connection(input, A)
+    A_spikes = nengo.Probe(A.neurons, synapse=0.01)
+
+
+
+
+
[9]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+scale = 180
+plt.figure()
+for i in range(A.n_neurons):
+    plt.plot(sim.trange(), sim.data[A_spikes][:, i] - i * scale)
+plt.xlim(0, 1)
+plt.ylim(scale * (-A.n_neurons + 1), scale)
+plt.ylabel("Neuron")
+plt.yticks(
+    np.arange(scale / 1.8, (-A.n_neurons + 1) * scale, -scale), np.arange(A.n_neurons)
+)
+hide_input()
+
+
+
+
+
[9]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_13_1.png +
+
+

Then we mulitply those filtered spike trains with decoding weights and sum them together to give an estimate of the input based on the spikes.

+

The decoding weights are determined by minimizing the squared difference between the decoded estimate and the actual input signal.

+
+
[10]:
+
+
+
+with model:
+    A_probe = nengo.Probe(A, synapse=0.01)  # 10ms PSC filter
+
+
+
+
+
[11]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[input_probe], label="Input signal")
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded estimate")
+plt.legend(loc="best")
+plt.xlim(0, 1)
+hide_input()
+
+
+
+
+
[11]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_16_1.png +
+
+

The accuracy of the decoded estimate increases as the number of neurons increases.

+
+
[12]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(lambda t: t * 2 - 1)
+    input_probe = nengo.Probe(input)
+    A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))
+    nengo.Connection(input, A)
+    A_spikes = nengo.Probe(A.neurons)
+    A_probe = nengo.Probe(A, synapse=0.01)
+
+
+
+
+
[13]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+plt.figure(figsize=(15, 3.5))
+
+plt.subplot(1, 3, 1)
+eval_points, activities = tuning_curves(A, sim)
+plt.plot(eval_points, activities, lw=2)
+plt.xlabel("Input signal")
+plt.ylabel("Firing rate (Hz)")
+
+ax = plt.subplot(1, 3, 2)
+rasterplot(sim.trange(), sim.data[A_spikes], ax)
+plt.xlim(0, 1)
+plt.xlabel("Time (s)")
+plt.ylabel("Neuron")
+
+plt.subplot(1, 3, 3)
+plt.plot(sim.trange(), sim.data[input_probe], label="Input signal")
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded esimate")
+plt.legend(loc="best")
+plt.xlabel("Time (s)")
+plt.xlim(0, 1)
+hide_input()
+
+
+
+
+
[13]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_19_1.png +
+
+

Any smooth signal can be encoded and decoded.

+
+
[14]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(WhiteSignal(1, high=5), size_out=1)
+    input_probe = nengo.Probe(input)
+    A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))
+    nengo.Connection(input, A)
+    A_spikes = nengo.Probe(A.neurons)
+    A_probe = nengo.Probe(A, synapse=0.01)
+
+
+
+
+
[15]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+plt.figure(figsize=(10, 3.5))
+plt.subplot(1, 2, 1)
+plt.plot(sim.trange(), sim.data[input_probe], label="Input signal")
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded esimate")
+plt.legend(loc="best")
+plt.xlabel("Time (s)")
+plt.xlim(0, 1)
+
+ax = plt.subplot(1, 2, 2)
+rasterplot(sim.trange(), sim.data[A_spikes], ax)
+plt.xlim(0, 1)
+plt.xlabel("Time (s)")
+plt.ylabel("Neuron")
+hide_input()
+
+
+
+
+
[15]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_22_1.png +
+
+
+
+
+

Principle 2: Transformation

+

Encoding and decoding allow us to encode signals over time, and decode transformations of those signals.

+

In fact, we can decode arbitrary transformations of the input signal, not just the signal itself (as in the previous example).

+

Let’s decode the square of our white noise input.

+
+
[16]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(WhiteSignal(1, high=5), size_out=1)
+    input_probe = nengo.Probe(input)
+    A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))
+    Asquare = nengo.Node(size_in=1)
+    nengo.Connection(input, A)
+    nengo.Connection(A, Asquare, function=np.square)
+    A_spikes = nengo.Probe(A.neurons)
+    Asquare_probe = nengo.Probe(Asquare, synapse=0.01)
+
+
+
+
+
[17]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(1)
+
+plt.figure(figsize=(10, 3.5))
+plt.subplot(1, 2, 1)
+plt.plot(sim.trange(), sim.data[input_probe], label="Input signal")
+plt.plot(sim.trange(), sim.data[Asquare_probe], label="Decoded esimate")
+plt.plot(sim.trange(), np.square(sim.data[input_probe]), label="Input signal squared")
+plt.legend(loc="best", fontsize="medium")
+plt.xlabel("Time (s)")
+plt.xlim(0, 1)
+
+ax = plt.subplot(1, 2, 2)
+rasterplot(sim.trange(), sim.data[A_spikes])
+plt.xlim(0, 1)
+plt.xlabel("Time (s)")
+plt.ylabel("Neuron")
+hide_input()
+
+
+
+
+
[17]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_25_1.png +
+
+

Notice that the spike trains are exactly the same. The only difference is how we’re interpreting those spikes. We told Nengo to compute a new set of decoders that estimate the function \(x^2\).

+

In general, the transformation principle determines how we can decode spike trains to compute linear and nonlinear transformations of signals encoded in a population of neurons. We can then project those transformed signals into another population, and repeat the process. Essentially, this provides a means of computing the neural connection weights to compute an arbitrary function between populations.

+

Suppose we are representing a sine wave.

+
+
[18]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(lambda t: np.sin(np.pi * t))
+    A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))
+    nengo.Connection(input, A)
+    A_spikes = nengo.Probe(A.neurons)
+    A_probe = nengo.Probe(A, synapse=0.01)
+
+
+
+
+
[19]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(2)
+
+plt.figure(figsize=(10, 3.5))
+plt.subplot(1, 2, 1)
+plt.plot(sim.trange(), sim.data[A_probe])
+plt.title("A")
+plt.xlabel("Time (s)")
+plt.xlim(0, 2)
+
+ax = plt.subplot(1, 2, 2)
+rasterplot(sim.trange(), sim.data[A_spikes], ax)
+plt.xlim(0, 2)
+plt.title("A")
+plt.xlabel("Time (s)")
+plt.ylabel("Neuron")
+hide_input()
+
+
+
+
+
[19]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_28_1.png +
+
+

Linear transformations of that signal involve solving for the usual decoders, and scaling those decoding weights. Let us flip this sine wave upside down as it is transmitted between two populations (i.e. population A and population -A).

+
+
[20]:
+
+
+
+with model:
+    minusA = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))
+    nengo.Connection(A, minusA, function=lambda x: -x)
+    minusA_spikes = nengo.Probe(minusA.neurons)
+    minusA_probe = nengo.Probe(minusA, synapse=0.01)
+
+
+
+
+
[21]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(2)
+
+plt.figure(figsize=(10, 5))
+plt.subplot(2, 2, 1)
+plt.plot(sim.trange(), sim.data[A_probe])
+plt.title("A")
+plt.xticks(())
+plt.xlim(0, 2)
+
+plt.subplot(2, 2, 3)
+plt.plot(sim.trange(), sim.data[minusA_probe])
+plt.title("-A")
+plt.xlabel("Time (s)")
+plt.xlim(0, 2)
+
+ax = plt.subplot(2, 2, 2)
+rasterplot(sim.trange(), sim.data[A_spikes], ax)
+plt.xlim(0, 2)
+plt.title("A")
+plt.xticks(())
+plt.ylabel("Neuron")
+
+ax = plt.subplot(2, 2, 4)
+rasterplot(sim.trange(), sim.data[minusA_spikes], ax)
+plt.xlim(0, 2)
+plt.title("-A")
+plt.xlabel("Time (s)")
+plt.ylabel("Neuron")
+hide_input()
+
+
+
+
+
[21]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_31_1.png +
+
+

Nonlinear transformations involve solving for a new set of decoding weights. Let us add a third population connected to the second one and use it to compute \((-A)^2\).

+
+
[22]:
+
+
+
+with model:
+    A_squared = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))
+    nengo.Connection(minusA, A_squared, function=lambda x: x ** 2)
+    A_squared_spikes = nengo.Probe(A_squared.neurons)
+    A_squared_probe = nengo.Probe(A_squared, synapse=0.02)
+
+
+
+
+
[23]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(2)
+
+plt.figure(figsize=(10, 6.5))
+plt.subplot(3, 2, 1)
+plt.plot(sim.trange(), sim.data[A_probe])
+plt.axhline(0, color="k")
+plt.title("A")
+plt.xticks(())
+plt.xlim(0, 2)
+
+plt.subplot(3, 2, 3)
+plt.plot(sim.trange(), sim.data[minusA_probe])
+plt.axhline(0, color="k")
+plt.title("-A")
+plt.xticks(())
+plt.xlim(0, 2)
+
+plt.subplot(3, 2, 5)
+plt.plot(sim.trange(), sim.data[A_squared_probe])
+plt.axhline(0, color="k")
+plt.title("(-A)^2")
+plt.xlabel("Time (s)")
+plt.xlim(0, 2)
+
+ax = plt.subplot(3, 2, 2)
+rasterplot(sim.trange(), sim.data[A_spikes], ax)
+plt.xlim(0, 2)
+plt.title("A")
+plt.xticks(())
+plt.ylabel("Neuron")
+
+ax = plt.subplot(3, 2, 4)
+rasterplot(sim.trange(), sim.data[minusA_spikes], ax)
+plt.xlim(0, 2)
+plt.title("-A")
+plt.xticks(())
+plt.ylabel("Neuron")
+
+ax = plt.subplot(3, 2, 6)
+rasterplot(sim.trange(), sim.data[A_squared_spikes], ax)
+plt.xlim(0, 2)
+plt.title("(-A)^2")
+plt.xlabel("Time (s)")
+plt.ylabel("Neuron")
+hide_input()
+
+
+
+
+
[23]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_34_1.png +
+
+
+
+

Principle 3: Dynamics

+

So far, we have been considering the values represented by ensembles as generic “signals.” However, if we think of them instead as state variables in a dynamical system, then we can apply the methods of control theory or dynamic systems theory to brain models. Nengo automatically translates from standard dynamical systems descriptions to descriptions consistent with neural dynamics.

+

In order to get interesting dynamics, we can connect populations recurrently (i.e., to themselves).

+

Below is a simple harmonic oscillator implemented using this third principle. It needs is a bit of input to get it started.

+
+
[24]:
+
+
+
+model = nengo.Network(label="NEF summary")
+with model:
+    input = nengo.Node(lambda t: [1, 0] if t < 0.1 else [0, 0])
+    oscillator = nengo.Ensemble(200, dimensions=2)
+    nengo.Connection(input, oscillator)
+    nengo.Connection(oscillator, oscillator, transform=[[1, 1], [-1, 1]], synapse=0.1)
+    oscillator_probe = nengo.Probe(oscillator, synapse=0.02)
+
+
+
+
+
[25]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(3)
+
+plt.figure(figsize=(10, 3.5))
+plt.subplot(1, 2, 1)
+plt.plot(sim.trange(), sim.data[oscillator_probe])
+plt.ylim(-1.2, 1.2)
+plt.xlabel("Time (s)")
+
+plt.subplot(1, 2, 2)
+plt.plot(sim.data[oscillator_probe][:, 0], sim.data[oscillator_probe][:, 1])
+plt.grid()
+plt.axis([-1.2, 1.2, -1.2, 1.2])
+plt.xlabel("$x_1$")
+plt.ylabel("$x_2$")
+hide_input()
+
+
+
+
+
[25]:
+
+
+
+Show Input + +
+
+
+
+
+
+../../_images/examples_advanced_nef-summary_37_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/nef-summary.ipynb b/examples/advanced/nef-summary.ipynb new file mode 100644 index 0000000000..4a87c90366 --- /dev/null +++ b/examples/advanced/nef-summary.ipynb @@ -0,0 +1,2046 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NEF summary\n", + "\n", + "The Neural Engineering Framework (NEF)\n", + "is one set of theoretical methods that are used in\n", + "Nengo for constructing neural models.\n", + "The NEF is based on [Eliasmith & Anderson's (2003) book](\n", + "https://mitpress.mit.edu/books/neural-engineering) from MIT Press.\n", + "This notebook introduces the three main principles\n", + "discussed in that book and implemented in Nengo." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.154169Z", + "iopub.status.busy": "2020-11-25T16:46:47.153329Z", + "iopub.status.idle": "2020-11-25T16:46:47.654677Z", + "shell.execute_reply": "2020-11-25T16:46:47.654182Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Uniform\n", + "from nengo.processes import WhiteSignal\n", + "from nengo.utils.ensemble import tuning_curves\n", + "from nengo.utils.ipython import hide_input\n", + "from nengo.utils.matplotlib import rasterplot\n", + "\n", + "\n", + "def aligned(n_neurons, radius=0.9):\n", + " intercepts = np.linspace(-radius, radius, n_neurons)\n", + " encoders = np.tile([[1], [-1]], (n_neurons // 2, 1))\n", + " intercepts *= encoders[:, 0]\n", + " return intercepts, encoders\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 1: Representation\n", + "\n", + "### Encoding\n", + "\n", + "Neural populations represent time-varying signals\n", + "through their spiking responses.\n", + "A signal is a vector of real numbers of arbitrary length.\n", + "This example is a 1D signal going from -1 to 1 in 1 second." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.660281Z", + "iopub.status.busy": "2020-11-25T16:46:47.659424Z", + "iopub.status.idle": "2020-11-25T16:46:47.663042Z", + "shell.execute_reply": "2020-11-25T16:46:47.662595Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.732381Z", + "iopub.status.busy": "2020-11-25T16:46:47.718269Z", + "iopub.status.idle": "2020-11-25T16:46:47.868457Z", + "shell.execute_reply": "2020-11-25T16:46:47.868850Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], lw=2)\n", + "plt.title(\"Input signal\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These signals drive neural populations\n", + "based on each neuron's *tuning curve*\n", + "(which is similar to the current-frequency curve,\n", + "if you're familiar with that).\n", + "\n", + "The tuning curve describes how much\n", + "a particular neuron will fire as a function of the input signal." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.873763Z", + "iopub.status.busy": "2020-11-25T16:46:47.873257Z", + "iopub.status.idle": "2020-11-25T16:46:47.876501Z", + "shell.execute_reply": "2020-11-25T16:46:47.876888Z" + } + }, + "outputs": [], + "source": [ + "intercepts, encoders = aligned(8) # Makes evenly spaced intercepts\n", + "with model:\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " max_rates=Uniform(80, 100),\n", + " encoders=encoders,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:47.882684Z", + "iopub.status.busy": "2020-11-25T16:46:47.881661Z", + "iopub.status.idle": "2020-11-25T16:46:48.067499Z", + "shell.execute_reply": "2020-11-25T16:46:48.067965Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(A, sim)\n", + "\n", + "plt.figure()\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can drive these neurons with our input signal\n", + "and observe their spiking activity over time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.073896Z", + "iopub.status.busy": "2020-11-25T16:46:48.073403Z", + "iopub.status.idle": "2020-11-25T16:46:48.076478Z", + "shell.execute_reply": "2020-11-25T16:46:48.076861Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.083129Z", + "iopub.status.busy": "2020-11-25T16:46:48.082331Z", + "iopub.status.idle": "2020-11-25T16:46:48.444215Z", + "shell.execute_reply": "2020-11-25T16:46:48.444607Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure()\n", + "ax = plt.subplot(1, 1, 1)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "ax.set_xlim(0, 1)\n", + "ax.set_ylabel(\"Neuron\")\n", + "ax.set_xlabel(\"Time (s)\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decoding\n", + "\n", + "We can estimate the input signal\n", + "originally encoded by decoding the pattern of spikes.\n", + "To do this, we first filter the spike train\n", + "with a temporal filter that accounts for\n", + "postsynaptic current (PSC) activity." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.452444Z", + "iopub.status.busy": "2020-11-25T16:46:48.451936Z", + "iopub.status.idle": "2020-11-25T16:46:48.455422Z", + "shell.execute_reply": "2020-11-25T16:46:48.454971Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)\n", + " intercepts, encoders = aligned(8) # Makes evenly spaced intercepts\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " max_rates=Uniform(80, 100),\n", + " encoders=encoders,\n", + " )\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.462529Z", + "iopub.status.busy": "2020-11-25T16:46:48.461757Z", + "iopub.status.idle": "2020-11-25T16:46:48.740973Z", + "shell.execute_reply": "2020-11-25T16:46:48.741396Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Zc4/4TiwdlUp1W69Q0i6HjXnDk1hf2KQuvHr9TM6JJzbcYW4/LwWfX2GTNqp22G3MG57MttIW8RtdnKcq5nWawXPIEvOGJ7Ovqk24mHTlrbcd6bIzfUgCnxQGRueLRibT1uNhR4U6wJk9NBGn3Sb2OuSlRZMa4xKfr9NuY+6wJDaXNNPd78NltzFnWCK7KtvEGsiivBT8SuD3t0jrh/6dWqCl39fTI5KRJPhgv5qePyIJm6QODh2yjQUjk3F7/Xx0qAGHrD7Dzj4vm4qbcMg25mpp3SDMHX5qs4DTugagKMq7iqKMUBRlqKIovzmd1zoRjD/69/cHK6gTQf/i7q/uEJs4jl8noIhWHmUB1Yj+I+9x+0xKImTb2j1tKWkWERxH6zPA23trQi6k6n2MdMq8t68upKtIbycx0hkUDWMsH5wYwTshyt0+P3na4uv7+2tD1h2dHkN5c4/JpaU/j3nDk+js97KpKNj1MX9EMv1eP+uOBI/azx+bxrbSFtPGJI/Pz+DECEanx/DBgfqgOheOT6e8uUe4afT8C8al09HnDfI3XzA2nR63T8zEdIN2wdh0bfdqwBXikG0sGZVCfnmgTx6fn6wEtT8faQZY/74uG51GdVsvB2s7DEYhBYcs8eHBgGy4U2b+8GRWFzSYQhGXjUmlq98rXAdqXhp+BT46VC/yJufEkRTlZNXBuoChGJ2KJMG7+2pNeQDv7a81GQX1c60T97h4VCrN3W62lbXg8SmEOWQWjUzmw4P1dGqL7DNyE4kNd/DO3lrTs+n3+nlP+43mJkeRlxbNyj019HtVmaWjUvErsGK3uschPTaMKTnxrBAyEueOSaO0qZvdlW3YbRJL8lJxyjZe36l6oqPDHCwckSxCaJ12G+ePTWftkUZaut04ZRvnjU2js8/Lvup2HLKNC8al4/MrYm/FjCEJJEe72Kh9J0emRpOXFi0imbITIpicHUdJo6orMuLCmT44QawXpUSHMXuoqsSdssSs3ETiIxy4vX6cso2FI5MJd8jUtvfhtNtYnJdCmMMm+rtsdBqnwme+CPy/RJ8+Dk6MEF+CT4vbp5CXFo0kwRsh4rOPdl2AKTnxrNxTY1LIIeW9fkakRpES7eKt3ce+hsencM5o9ccQSikbr3/ZpAyKG7tDrhcImckZNHe7xWYcI26fgtOu/ihWH2oIcgPpbpVLJmawrawlKNrH7fWTGR+hPYfakNE0F45PR7ZJJkOply0YkUy0y27a2KSXzR6aSGKk0/S8dAV68YRB2vMx13NoP/CdFa3ijBZ9pH/+OLUf+vdEv87ivBQinbLI168xf0QyseEOXt1ZZe7XsCQSI528puXrP+wLx6fjV+B1PV8bcV44Pp3tZa2UatEqDlnivLFpOGUbr+RXiXbjI5wsyUvlzd3V9Ht9uH0KDtnGxRMG0djZzzrDKPucUanERTh4ZUeV6O/0IQkMTozg5fxKocRddpmLJwzio4MN1HeoLo2s+AjmDE3itZ1VYuY1JCmS6YMTeDW/SkS75CRGMmNIgriGU7ZxzqhUolx2XsmvEsr9ssmZtPd6xAAgwilz8YR0PjhQR3OXqnRn5iaSFhPGi1q0nUOWuGxSBrsr2yhs6MQp2xibEcPwlChe0SLOnLKNSydlcKS+i+q2XvUZj0vHKdtYd6QRh2wjNsLBwpHJwh3okCUunRQITHTINi6ZOAi31095c48YYSdoa4cOWWJ0egxDkyPFwCDMIXPx+EGBNuxqGzpO7TMJXEMypV12GxdPSAfUkE+7bOPcMapS9/kVIpx2loxSZxESEOG0s2CEGixjk2DW0EROhbPOADhlG5dNymRLaTNlJziCN7WhKbHZQxN5bUfVcZW5fl2AL03NpLnbLUZtx5IPc8gsnziIjw83HDV8VHcJjEqPYVxGLM9vqwgZDaRf/5KJg3DabTy7JfgFQvpo8ZzRacRFOEzb5o3tOGUbl0/OpNfjCwr3FOXaj+qZAW2o02qJq6Zkcri+ky2GtRjdPZQaE8bivBRe2l4R5E6Jctm5fHIG7+ytFZucdOUV7pC5cmomHx6sF1E6er0JWXGMz4zl6c3lpgVWp93GVVMzsUkSz2wpM9XJjAvnnFGpoh96fmy4g0snZbByTw1tPW5T366ckskH++to6OwT+RFOmS9Ny2L1oXpq2nqFmyMvLYZJ2XE8v61CHDzmlNX+2G0SL2yrEEYqIdLJuWPTeGNXtXCLOGSJa6Zn0aJ9nzw+Py67OnpOinLxwrbKQN/C7Fw2KUPrm/rcXHYbV03NYmtpi9hs5JBtXD0tC7fPz0vb1Qhup2zjyimZVLb0ilmMU7Zx5dRMSpq6xcjXIUt8aWoW5c09bChqwmm3Ee6UuXBcOiv2VGv1JOYOSyI1xsWb2sjdZbdxxeRM+r1+Vh2sx2m3IdskLpucISKbHLKN5RMzsEmwsagZh2xDkiQun5wpvj8Ou42LxqWLzWq6wl+UpypL3U14mVHhayNqHadsY1JWnAjJddhtqlEdryrojl4vkiSZ2nDabeY2ZYlLjArfLnGhVl+Xv2BcukHexnlj1LQ+a9Tb043MwPSFmsE5VsTeiXKWGQAFhyxx7fQsnLItaOv9ibWhKrGvzhlCdVvvCc0C9FHXuWPSGJIUyd8/LjpmPLvHp2C3Sdw8ezCKAv9eXxJSTt/A4pQlbpk3hKKGLj4M4b/XR7Up0S6umpLJazuqg9xFupslOszOTTNz+DDEzltdaU7JiWdydhyPrS81uYo82tR7cFIkS0el8uzWctMsQVdol07KICHSabovY8THLXOH0NrjEQZGNw4O2cbNswfj9vl5enOZaFMvu2nWYACe1M5nEREiso2bZw2mqKFLLHzqI+b02HDOG5PGS9srae8N+MOddhs3zcqhtcfDazurxOzGIdu4YWYO/V4/T20qN13/+hnZeP0KT24sM137uunZKMDjG0rxeBWhpG6YkUNJYzcfHWrQvps2UqLDOHdMGi9sq6Chs18cOXD9jGzaez08tUk1qrqvPCshnH+tK6bf68MhS5oSz2R1Qb2Ig7fbJK6bno3b5+fRT0pEf6+akonTbuPBj46IZ5+XFsPUnHhhRB12dQaSHO0SYcROuzq6jo9wiM/IqblHEiOd9Lh9ot83zsoRRlpX7jfOzBGfu0O2MTErjglZcabv2vUzssX/nXYbabFhQnHq36kvTQ0YAKdsIz7SyeWT1LxW7eC5W+blmr5fuqtKrxPmkJk2OF58XyRJEnX0aLebZqvfK91QXj8j0H+7TWKctnajt6nvedDTKdFhYge2064a9JRoNTLQ5VAN1a8vHctD10wE1NkZBPZ26OsKegTihePSuXV+Lt9aOJRT5awyAPo0OyUmjGunZ/P6zuqQ54McC12JLc5LYcygGB5eXXjM4w3UOuoPIMwh882FQzlQ08HKo7hr9H46tC/S8okZPL+1IuRsxah8LhyXTnZCBA98cDjovBqjMrp1fi4+ReGBVYdD9tEp27hp9mAinDL3v3soyE2jK69vLhxGRUsPzxhmE/roVi0fSluPh7+tKTJdw6H96L42dwhrDKcbGvs4fUgCk7PjePCjQjr6AtvkHXYbuclRXDAujX+vL6GmrVfcq0OWyIgL57JJmTy9uZyypm6htJ12GxdNSCcnMYLfvntI+MedcqCvnf1e/ram0PRMZw1NZGpOPH/5sFActOaUbYxKj+HcMak88kkx1W194hq5yVEsnziIJzaUitGZQ7aRlRDBVVMyeWZzOcWNXTi1Z3TJxEHkJkXy+/cL6DUoze8uGU53v5e3dteIvJm5icwZlijcX05ZVabfXzKC/dUdVLb0Ctmvz8slymXngwP1OGQJSZIYnhptGpnKNomUmDBunJkjZmJ6v+5cNkLI6Z/XNxcMNeVFuuzcNiAv3Kl+vyEQJj02I1aENXdpfv+bNYWqf6aSJPH9pcOBwOFomfER5Ggjcf1z+vbiYUBgk15ilEu0rR+29q1F6vX1IyqmaeU6dtnGNxaoCl4P6PjnDVO4ZloWMzV3ypVTVCMyVwuzHJocxS+Xj+Hv108GID7SKWYOknadd747lx+eN1Kkt/x4CX+7bhJ2re/rfriIf980VYRSf3jHAh6/eSoRTtUw3DAzh+UTM0Sbe+5bxpvfnqPei01i3Q8WsuauhYD62f3kglEM184nOhXOKgPg0RaQQP0yxYQ7+MEre0Ie8HXUNjQlJkkS9140mqrWXv7w/uHj1AkolcsnZTA+M5ZfrDgg3Bih5PUf4w/OHYlTtnH363uDFmaNI2O7bOO+i0dT2NDF39YUBrUH6g88JzGSW+YN4eX8KtMRusY+JkW5uGPpCFYXNJj87W6vIp7f0lEpzBuexJ9WHRFhlPqoGtRw2csnZ/Dv9SWBXZy+wPP/2twhZCdEcM+b+0ybdZyaQvj5JWNo7u7n128fFJ+PU/uR//j8USgK/OSNfWLarD+vH543Eocscffre4ULySnbcNllfnrBKAobuvjrmiLNWKntjc2I5UtTsnhyY5kIETZ+xk1d/dy3Qg1g0+vcff4oPD6/ODJCV1I/OHckAL9YqeVr8neeMxK7LJFf3iqUk0O2cff5eRQ1dNHr8Ym2R6ZF86Wp6hYaPbwQ4O7zRon/68/x0kkZjBkUA0Bbr+oeiotw8h1NWRoDEO4yKHadUKPI2UOTxP3YtRDb6wwjcl3Z6jMu9bmo8jdoo/vJ2XGi7GcXj9buS+1ndJhDXDc2XI1iWzgimYy4cOHfBnjzW3P4xSVjhCHIS4vhqimZppH/E1+Zxv2Xj2OU1nZOYiRvfGs2P79kjJDJv2cpH94xX6TvPi+PV26bxZQcdeSfFOXid1eMJyZM7UuYQ2b/L87lN5eNFXVumjWYmbkBf/tjN01l/y/OFekxg2L51sJhIp0WG8ZFhrWBhEinafYRG+FgyahAeiCx4Q7RH/2+hmgzgv8mZ5cBMCigpCgXv750LHuq2vnRa3tPKD5/YBszcxP58uzB/GdTWUi/urGOTVItt1228Ycrx9Pt9nLLU9tDbroyXiMtNoyfXTyaLSUt/GzFAZOP3zgyBlgyKpUrJmfy8Joi02KoUbkDfH/JCEakRnH7czvFLtWAjPrj/vKcwUzJiedHr+1lR3kg4kVXDJIk8dvLxmGXJW55Kp+mrn5TOcBPLxhFSnQYX386n8qWHq1cbT/MIfPHK8dT1drLt5/fJUaHeh/HZ8Zx+8JhvJxfJVx1ellWQgT3XDSatYcb+cXKA6ay1JgwfrF8LFtKWvjjB4dN93TOaP35FJKvxV3r/OSCUQyKCxenU+r3MSErjm8vCvyw9TpDkiL5yQUGhawp78z4CH5hUD7Gz/FXy1WFYvTdLhuTJhSaMYT1pxcG2tYZlxnLFZrfO1rb0yLbJB68eiKAcCsAfG1urjh/RycnMZI/XDFerNGAOor+3eXj1D5qu5EB8u9dypu3zxEj2jCHzDvfncsPzh0pRrHhTpk3b5/DLXOHEKmdThnmkNl17zk8+ZXpoq2xGbEc+uV5Jl/4D8/L48ivzxfnBkmSOsp98svThEx8pJObZw8WfQD441UT+MOVE0Q6ymXn2unZ2Aw3Oyk7XizcgvpbN46WJUli2uAEU7sDiXLZxeg9FDabJPr+eeYsMwAB/yuoIX3/t2wEb+yq5panth/zjJpAGwElBuoPdXFeCve8uZ/73zsUMm7fOPIFdSTz8DWT2F/TwZX/3BT0ajfvgH5eNTWL2xYM5fmtFXzruZ2GBVDzyBjgN5eNZfqQBL7/0m4e/OgIbq/f5L8G9Yf7+M3TCHPIXPmvTbyzt1aMpHUZh2zjkRunkBYTxvWPbeWl7RW4veb7yEqI4J/XT6GytYcr/rmJLSXNpn4nRrl49KYpdPd7ueKfm+ju95rqz8hN5NeXjuWTI43c+fIe0/UB7jhnBOePTRPRTcayG2Zkc/OsHENER6DsyimZYpoPgY1ikiTx60vHMi1HdQu0Gs6/j41w8OhNU0RaV+h6P/TD1iKdgR/9l2cPZp4Whx1tGK1dPS2LZdpoLy48oIiumJLJneeM4I6l5pH4Ly4ZyzXTskyLidFhDrb8eAnvf3+eSfaBq8bzznfninBagOGp0az/4SKTQZJtEvt/cS7rf7jIVP9L07L4s2YwdK6Zns2Oe5YK3zNATJiDiQP88mMGxXK7wRiCupP1notGm5RpfKRTjOx1wjUDYUSftenYZZtJkVucfj7/JuxTMFARA3x78XBiI5z8auVBFj2wlmunZ3PpxAxGD4oRisOIx+DmAFXx/OuGKdy34gCPrCvh7T213Dgrh/PHppGdEIEkSXi8imlkDOrI7z9fmcb3XtzNRX9dz/KJGVw1JZMpg+ND9vNH540kMdLJHz4oYOOfmrhuRjZTtF3JRtkwh8zTX53Oj17by4MfFfLmrmrDYrFZeb/+rdnc9uwObn9+pziuwvijTIpy8eo3Z3P7czv50Wvqm46yEwILXKCGoT13y0y+8/xOatr7iI8w//DHDIrl1W/O5pvP7qChsx+Xw3xf10zPJirMzo+1NymFGcplm8Rfr53EXa/s4a3dNSQbRri6m8jlkHn0kxLSY8NM7d59Xh4JEU72VbeblFO4U+apr07nvhX7xa5unby0GNbctYCtpS3CN6v344Pvz6epq9+kyCRJ4umvTqejz2saDUqSxKM3TaWzz2MyDKD69wcS7pT53RXjg/LTYsNIG3BfkiQxZlBskGzWgM8F1JDBiIQT+4knRrmOL2TxhUM6VjTK/5rR4ycpz7/z8Wlr/1dvH8Tj87Pi23ODysqbu3lg1RHe1za3xITZyUuLITsxgkGxYUSHOYgOs3P36/v45sKh/Oi8vKA2tpQ08+dVR9imbRJKjw1jWEoU6wubiHbZ2WfwGeq09bh5eHURr+RX0tnvJcxho8/j5/JJGUEjNVDPA3/gg8N8eKheuK3+eu0kU2yxztrDDTy0ulAc0lXwq/PEaZQ6Xp+fZ7eU89iGUlq63eTfs9Sk/EA95fK1nVU8sOow84cn88erJjCQ7n4v/1xbTFpsmPADG+n3+nhzVzVzhiWZoiR06jv6WH2ogWumZYUcBXb3e4k8ypTbE8JgWlicLUiStENRlKknVfdMMgCu9OFK+s0PntZrzB2WxLO3zDhqeXNXP+sLm9ha2kxxQzflLd00dPZjfEz3XDhKhIqForSpmw2FjeSXt1LW1M2R+i6m5MQf87o9bi8bi5rZXNzM4foOrpueY/KZDqSho4/1hU0cru/kmwuGEm/weQ7kSH0n7b2eoIgIIz6/Qrfba1p4srCwOPP5whiAkWMnKv96ddVpvcao9JigafXx8GvKsaPPS6/by5CkqJDuIQsLC4v/NadiAM6oNYDoMDuLDDvzzhRsNklzAVmjYwsLiy8OluPUwsLC4izFMgAWFhYWZymWAbCwsLA4S/n8G4DeNkwhOodWQn/XUcUtLCwsLFQ+fwagpyWg8Lsa4fc5sOHParqjBl66AV6+KSD/1u3wwrXmNuoPgDf0OTwWFhYWZwufLwPQUQN/GAIbH1TT/doRChu0tE/b2l+8OlBn17Nw+N2A0ejvgn/Ohle/am67uxk6jv+2LgsLC4svCqfNAEiS9IQkSQ2SJO3/rzXapyn8TX9V/9UVvm4IfO7gOjqd2isgvdp5PwVvm8v/NhX+HLy7l5ZSqNt3cv21sLCwOIM5nTOA/wDn/Vdb9GunJfZorys0Kny/z5x2D3hbTq162NhRXT+92tup+ge8H+BvU+FfwUdHAKorqWh16DILCwuLM5zTZgAURfkEaDmu4KfBqOB93sAMAKClxFzeoJ7HTox2drhuAIwy3cHvvaVmtzmtG52OmiBR/jkHnr1cNT7Hork4MHuxsLCwOEM489cAGg5Bq3bWvlHhNxaYlXnNbnN5zS71X1nbvVu7O7iN2l2B/ydoL8aozjdf36WdvFi5LUTnlEAfj8VfJx99FmFhYWHxGfGZGwBJkm6VJClfkqT8xsbGYIF/zISHtKNyjQq/ajv4DO6c8o1m907Zeq2OpvArtoDfP6ANg7J3qee9U73DfP3kkYHrDSRZO3+9YnPomzPSdvQXxlhYWFh8FnzmBkBRlEcVRZmqKMrU5OTkowv2d5kVfsnagHKPSILSdQHlnpALZRs0hd8PjkjVx1+/z2wAig1HT+ttlW9S6w2kYktwXnTa0ctC0V51YnIWFhYW/wM+cwNwwpRvDCjp1LGqAfD0qunh56hrAC0lanrYUnWhuOGgqvCHL1XzS9YGDMCgSeqovm9ABFFPc8B9ZMyv2Rm8ZqCvD5StD200wLw+ULbh09yxhYWFxWnldIaBvgBsBkZKklQlSdLXTqqhcO0M++I1AWU84lzoa4PKrWp6mKbgj3xgThd9pBqNuGxIGjmgjfNA8RlcRW4YvgyQoOjDwPV9HojJAMVvztfrAHTVB7uOjPV1Cj8MLWNhYWHxGXA6o4CuVRQlXVEUh6IomYqiPH5SDUlaFw+tDPj4h58LNjvsf11Np0+E+CEBBZ2QC4Mmw4HXVSUtOyHvAihdH4jmGTxXXeA9uEJN+zyqSydzKhx+L3B9Xz9kToOoVHM+qG1nTlf7UrAydP+NLqfD7wWHp1pYWFh8Rpz5LiCfByJToKNaHcEDRKfCsHOgU1PmdieMuzJQR3bA2CvU0E+/VzUA465SR/x7X1JlnJEw9jI4tEJbX9AMxZjL1Yih+gPa9d1gD4O8i9QZRm+ruW9RKTBkPhx4M7QbSJ8BjLoYPN1Q+MF/8+lYWFhYnDSfAwPghtHLwRUDe15Q82QnTLgmICO7VAUv0k4Ye7kh7YDUMZAyJmBEZCdMuBY8PXDwTfBqBmD81WBzwM5ntOt71PpTbgZvL+x5ydw32QETr1ejfIo+Ct1/gNyFEJ0OO/5zig/EwsLC4r/D58MAhMWoSlZHdsLI8w1pRyBcE1QFHjNInSUAeLTjH2Z8w9xG1gzVKGx4UFXusgMiE9XR+q5n1YPnfG6wuyB9AmRMgW2PBEb1+qxh1CUQlQZb/hG6/wD2cPX6JWsDm9IsLCwsPkPObAPg96luG9kJc74byJedqlKereU5tRj+W9fCxBsgIlFNz/8/rTxC/dc0a3CAJMGCH0BzoeYqcqllC34I7i71lFGfR70ewPwfqJFGO59S0/rswO6EWd+Cko8DMwwd3VjITpjyFXUms/pX5iOsLSwsLD4DzmwDIJSnNqIfoR0t5AhX/132K7i3SVXAoIZ2Xvp3sGm3lT0TvrkJZnxTTdtdcO794IhQ9w4AjFquju5BjSwCSBkFE6+DLf9UD5rTdxOPOA9y5qoKvK0yMAMAmHGbuhD97g/M7yPQZwCyA8LjYOGP1cXq/a/9t56ShYWFxUlxhhsAXXlqSvbaF+GHpQGFDOb/hyJ1DDjCAulZ34Kf1AR2/tpscOk/1f8nG04DPfc3auQPBCJ3JAkueVidLbxys7pnQO+b3aWWtZTAm7epZxWFuofpt0LGVFjxXcsVZGFh8ZlyhhsAg/sEVAUckXDq7UqSOZ06Bn5UDlMN7wgIj4cvaQvBKQbDkDgULntEPXtI8Zs3eg2ZD8t+o4asvngddDcF34Nsh2ueU9v/z8WBvQsWFhYW/2POcANgcJ+cbsLjgg1D5hR1tjB1wB62URfB1c+q/0/INZfN+hZc8IC6FvD36bDud2q+8R6i0+Cr70NsJjz/JXjxeijbePTdxBYWFhanAftn3QETihKI2AF1IRYCo+fPAmdk6Py8C+CndeoegYFM/zrkzIEPfhIIDXVEmGXisuDWj2Hjw7D5r+oLasLjIXsWJA1XDUtEEoTFqn+OCLDJqiGx2dVIJ5us/gUxwJANNGwnK2NhYfGFQlLOoGiUqYNkJf/WqOCCKx43b/T6PNFwSD1baPw1gcXpgbh7oOAdNYqoKh9aS4/9djMLCwsLDekXHTsURZl6UnXPKAOQl6Xk//sOc6Y9DCbfCK7oz6ZTnwV+n7rzubcV+tqht019laVfewmO36vK+D3qOoSRoM8zxOd7UjIWFhZnItL8u07aAJxZLqCoVJh352fdi88em6weYBeX/Vn3xMLC4oznrpOueWYvAltYWFhYnDYsA2BhYWFxlmIZAAsLC4uzFMsAWFhYWJwBuH1u3MeI/vP5fTT1Nv1Xr2kZAAsLC4tT5JOqT/Dqr4g9CqvLV9NveK/5ltot7GoIvH72/NfOZ/Eri0W6pK2EX27+pWj32UPPsujlRVR2VAKgKAq/3PzLU+q3ZQAsLCw+F/gVP7VdtaY8n9/HweaDQXJ7Gs3nbCmKwu6G3aa8T6o+4aGdD5nyfrrhpzx36DmRbupt4txXz+Vwy2GR917pe1y54kr0EPq9jXu5ffXt/Cn/T0LmuUPPMe6pcXj86lEwJW0lfH/t9/nZxp8Jma+v+jo3vXeTSDf0NtDe3y7a/UP+H3jlyCtsq9sGwIEm9SVV+fX5AHR6OnnlyCshn9WJYhkACwuLk6bH08OB5gNB+cVtxZS1lwXlb6jeQI/H/FrUN4ve5PlDz5vyStpKuO2j2+hyB07WfavoLZa9toy9jXtF3htFb3D121ezvmq9yFtRvIIb3r2BD8sD7+B+v+x9bnzvRlYWB17devvq23ls32Om/qwoXsHvtv1OpPPr86npruHhXQ+LvPs23cfh1sPUdqvGSNH20ayrWidk9PspbisGwKf4gmR0dCOhU99TD8CgyEGmNobEDlGfTXsJwHFnHCfC6XwpfJYkSR9LknRQkqQDkiR973Rdy8LibKC9v/2oZfXd9SHzXyp4iR31O4Ly/YqfN4vexOMzK5/m3mYmPzOZ7XXbTflNvU2Me2ocH5SZDy+8f9v9XPP2NTT0NJjyL33rUi5+82JTXmtfK9/86Jt89+PvmvLv3Xgv92+7H+Om1Cf2P8HG6o28X/a+yCvvKAfMSrTD3QGohkVHV+ifVH0i8nTXy9rKtSIv1hULwKGWQwzEr22wjHfFA1DYWijKhscPBxCGT5et7KwMktFnJ7qy7vZ0B12rpK3ElC5oKQAgNUI9jbiorQgAh3aemG4QBn52J8PpnAF4gbsURRkNzARulyRp9Gm8noXFaae2q1b84ENR0FLA/qb9IcsUReHnm34e5IrQqe6q5jurvxNS0W+s3sjcF+cGKWaAzTWbWfrqUlZXrA4q+/XWX/Pl978clL+lZgv3bryXB/IfMOWXtJfg8Xv44/Y/mvJ1A/OXHX8Jmb+vaV/IezL6vPX/b63dGlJWH1ED5MaphyzqyhBgUJQ6Ij7SckTkJYQlBMnpit3otgm3q+8QOdIaqDsifgQQcK0YqeioAAKjc2PfhsQMMdUzKmKfdjpwWmSaScbtDyzuGp8JBIyEfi+6QfIqqtEoai0yXUc3AMY2T5bTZgAURalVFGWn9v9O4BCQcbquZ/HZ4Vf8QSPAT0Nzb7OY1p4oayvXmkZlR6PH08O3V387pDvCSJe7izkvzDGNGgfS1tfGsteW8YftfziqzFUrr+Lad64NWdbr7eW1wte48b0bQ5a/euRV1lat5fmC54PK9NHlm0VvBpU19zWrZYXBZToDjZY+mhx4vw6bmh80KtbOBazuqjZl6yPdoxm9Q82BdoyuDuNoX5bUAw2Nrh2X9nY+Y33djVLQGlD2epuHWwPKXo+kMebpcmUdZSIvxhmj9r15f1Cf9NG9Ubnr7ihJOzhRlzEqYr19fcQfqh39e5sUrr6USn/W4tlr96zXOdJ6BK/fK+6hpruG9v72INfRyfA/WQOQJGkwMAkIMv2SJN0qSVK+JEn5jY2N/4vuWPyXWVm8kiWvLGFfY+hR4PG4ZdUtLH9zuRg9nQjfWfMdLl9x+XHlDjQfYF3VOn60/kfHlKvuqqbD3cG9G+89qky3V52+GxcJj0Yog3i8H2x2tHr0h1ER6sS54oDQI219dLu3Kbiejj5q1NGVaVVX1VH7aPw8jArMGKqoK++Bi65RjqigfGPbRneJ7ts29l+/XkFLgVDMel5ddx193j5TXrenm053p+k6fsVPa19rUP8bexpNcrrx0p+JMc+o3PWRun7/B5oO4PP7TG3rn51uAI60HqHf12+6d/2Z6M9XnxHqMrsaduFX/CLd5+vjcMth03PfUb/jjHcBASBJUhTwGvB9RVE6BpYrivKooihTFUWZmpycfLq7Y3EaqOmqAeDd0ndPqr7u4zRO40+U4/0InNpR4gMjRY5GS1/LCV3raG4gu6Qer5Vflx9c36AE6rrrgsr1xcSBytRYt7S9lIEHOOplLX0tR10YNIYbgvlejC4nYx/1z2VgvvFZGpWoUUZ3w5gMgOGauxt3i//r97OnISCrK163301pR2lQH3RFa8zT79GYp69/GPP0KBq9P5WdldR21ZoU7Na6rUH1ttRuMeV1ejopaCkIKaO35fF72Fm/85jtFLQU0NbXhsfnIdoZTVt/G4WthaY6m2s34/F7CLeHEyaHsb1u+5m9CAwgSZIDVfk/pyjK66fzWhafHUna+5VDLTaeCJNTJgPql/zTcjTfs45R6fR6e48qZ/wxNfc2h27L8IM8mvtJ911vrw/21RuvoSuhUH3tdHfSpr+fOsS1B47mjfdoNKLGEfzAtQOTQjQYK2MfdUU1UN7ow9fze729prUNvU9barcIGWPbm2o2if/ryn5f077AiN1wvY3VG01yNskmFn11OWOefh0JKUgOMOXpi7ybajYJmeTwZApbC2noaRD3kRKRwuaazaJeSnhKUL2xiWPZUrsFRVFUmYgU7DY7m2s2i3bGJ41ne912PH4PHr+HySmTUVDYVrcNj9/D3EFzxTN2+9wkhScxLG4YW2u34vF7iLBHMCllElvrtp7ZawCS6ih7HDikKMqfT9d1LD579C/3oZZDJzUtjXGpvlj9B/ZpOJ7RMP7wd9bvPCE5o+I7mszR+qrPDDZWbwweqRueTai1BmP766vXm8qMo9O1VWtNZUbFaoxyGdiesQ2j8lhTuSb0dQxt6deQJTnoGlGOKOw2u+mePH4PSeFJdLo7hWHQr5kcnsyG6g2iTY/fw7C4YSgoAeXs82C32cmNzRXtenwebJKNaanTxPPR73HWoFnCUOh58zLnsbFG/Rz0a83LmMfG6o3CxTIyYSQpESlsrNko6i3IWgCon7HIy1zAgeYD6kjd7yE1MpVRCaPYVLNJPLP5mfNp6WvhSOsRPH4Psa5YJqVMEqN3gLmZc+n2dIsZ06SUSUQ6IoVMZnQmOTE5Iu2wOZiZPpNdDbvocnfhkB1MT59OYWuhmHmfCqdzBjAHuBFYLEnSbu3vgtN4PYvPiIFT1ZOtv7N+5zFdMDpGxfph2YdBivZofVtVvuqE5AaGOp6MTG13bdDsRC+LckSxrnJdUDSIXh7tjOaj8o9Clg2OGczq8tVHLzNEAun509Om0+3pNs0CxIg0eTzrqtaZlDHA0uyl7GrYJWYiuvw5Oeewv3m/WOPw+FRFNy11mik80+13Mz9zvskw6G0syV5Ce397wI3j8zAheQKJYYkinl9XfvMy5pFfn0+3pxuv34vT5mRuxlyK2oqo6arB4/MgSzLzM+ZT0VlBaXupuM7CrIXUdddxpPWIUNILsxbS3NfMoeZDuH1unLKTOYPmsLlmswjRHJM4hqTwJNZWrhXPY2HWQhQUPqn+BK/Pi8PmYPag2exu2E1jr7qmMD9rPhISqytWi/7PGTSHgpYCsTg8P0N9JqvKVuFX/ITZw5g9aDary1fjU3w4ZAcLMxeypXYLzb3NOGUn8zLn0e/rZ03lGhw2B4uyFgEn73I1cjqjgDYoiiIpijJeUZSJ2t+p99jijEP/kYTbw3m7+O1PXd/r8xLtiMareHm/9P3jy2vhcSkRKRS3F5uiPY7Wt+Hxw/mw/MOjnrWiK41xSePYUL1BxJeHkpkzaA77m/ebFjKNMouyFokfeai+XDDkAnq8PWLEOrD8/MHns6F6gylmXC87b8h57G/ebxr9GdstaisSceViJJwxj3B7uMkA6gr/3Jxzae9vF7tN9TrLBi/Dp/j4qOKjoHwIGEBd0S3OXkxpe6lwQemGYWb6TN4ve19dLDWMsF2ySygwj9+DS3axOHsxa6vW0uXuwuP34JSdLM1ZitfvZVXZKnGtJTlLADX4QM9bmrMUm2QTeXbJzpLsJdglu8gDWJazDKfNyYriFaLu+UPOp8vTJTaOuWQX5w0+j3VV68TZO5NTJpMZlcmK4hW4/W5Rz6t4xeayjMgMpqdP5+2St3H7AjIQiN5KDE9kXsY83ip6C1Ajfy4cciGt/arry2lzcn7u+Xj9XjbWbMRhczA9bTqJYYn0+/px2BwMjRvK8Pjhx4xYO1GsncAWp4z+47o492LWVK4JqTyPV3904mhGxI9gRfGKY47oIaCIL8q9CLtkZ0XxiuP2bfnQ5XS6O/m48uNjyl0y9BI8fg/vlbx3VJkLcy9EQhI/4oEyCWEJzM2Yy8qSlaZRvl5/9qDZJIQl8OqRV011deN08dCLcfvdpl2r+j1fknsJNslmOgJAL7sg9wLskl2U6flRzijOG3we75W+Jz4bvS9LcpYQ54rj5cMvm+pMSpnEsLhhvHz4ZeHTBshLyGNc0jheOfKKyHfIqqJzyS5eORzId9qcXD78cuq660wulviweM4dfC5vl7xNj6dHKOLLh19Or7eXd0vfFXkTkieQG5vLa4WviWtlRWcxPW06bxa9KZRiSkQKczPm8lbRW/T5+nDIDhLCEliQtYCVJSvp9fZit9mJC4tjcfZi3il9hx5Pj1CwqRGp4vNw2Bzie7CiSP1uOWUnlwy9hG2126joqMAu2xmZMJK8hDwxunfIDi7OvZjKzkq2123HYXMwKGoQ09KmifBZu83ORbkX0enpFO3Oy5yHpMXZOmwORieMZnDMYJG22+zCkOiDgguG/HecKZYBsDhl9Gn4VSOvot/Xz4sFL366+n4PdtnOlSOu5EDzgZALpAPlQfUlnzP4HF478tpRd8nqCm1+5nyyorN4cv+TIQ2M3uaklEmMTRzL0wefDgpL1WVyYnJYmLWQFw+/GHSsga64rh91PS19LbxT8k5Q/XB7ONfkXcP66vWm/Q9GpTc2cSzPHnpWrCl4/B4kJDKjM1mUtYhXj7waCIXU2h0UOYhlg5fxRtEbYhQNqhK5Ju8aer29vFH4hqlOlCOKK4ZfwceVH6suFWOdkddwqOUQexr3COPksDn40sgvUdpeypbaLWKkG+uKFUq9pa8FBQWHzcHCrIUkhSfx/KHnTW1fOeJKuj3dgZG47GBM4hhGxo/khYIX6Peqil2SJC4ffjl7Gvewq2EXdpsaZXXZ8Muo6qpidflqsafh8mGX09DbwDsl74iY+suHX05LXwtvFb1lymvvb6emuwan7ES2yVw2/DKhpB02B3kJeYxKGEVDb4PIu2SYanyb+5px2pyiLR2HzcE5OecQ44xR3TmG6xllFmYtxCbZRNopOzlvyHmAGsklSRJXj7waCOwRuGL4FUDgmIhLh13KfwPLAFicMl6/V/xoFmQu4OmDT4uY7BNBV3yXDbuMpPAk/rnnnyfk13fYHNwy7hZ6vD08ffDpo/YNIEwO4ytjv8KB5gNBC6ymNmUHXxv3NSo6K4J8rMbrfnXsV2nvb+eFgheC70V2MCNtBnkJeTy27zExC9CNkUN2cPXIq3HJLv6x+x9Bz0GSJG4eczPlHeW8XfJ2UNmNo2+krb+NZw89a+qX3WbnptE30e3p5on9T5j6OzpxNNPSpvHE/ifodHcG+qIZB1mS+duuv5mew0VDLyIhLIEHdz4oFnAdNgfnDT6P1IhUHt75sDAAADePuZleby//3PNP0YbD5uDG0TeysWajiPxx2BxMTJ7IpJRJPLL3EfH9kSSJW8bdQlFbEStLVop2rxh+BfGueI60HhF55w4+l+zobBp6G0To7aLsRQyLG0ZLX4tQsPMy5jEqYZRwsQDMTJ/J+KTxQGA96YZRN4hyh6z25RsTviHyJEkiIyqDi4eqx1u09bcBcNmwy4SM3WYnwhHBDaPVtorb1Wit8wafF2hbU/j69fSop29O+CagrsmA2WgADIsfxoTkCVyUexGgbiLTZwmngmUALE4ZXTkBfGvit+h0d5pORjxufZ9aP8wexq3jb2V73fZjunV0pe6QHYyIH8H5Q87nyf1PBoVH6n3TZZcPXc7gmMH8dutvQ47cAeHPHpc0jgfyHzCFYxplJqZMZGHWQh7Z+whVnYHNVPq9SJLEnVPupLKzksf2PRZUPyEsga+N/RoflH3ApupNgbraaHbZ4GWMTxrPn/P/LHZ96mVTUqewJHsJj+59lOquanUGZbMjSRJjksZwYe6FPHXgKRGqqte7a+pdtPa18tddfzX1JS0yjZtG38TKkpUixNNhcxDpiORbE77FjvodAZ+1rH5O3538XfY372dr3Vax12JE/AguGXoJLx1+SbQBcF3edUEuFv356D52XXbZ4GWMShgFBNwdUc4ovj7+6wCmUfrtE28HEKN0m2TjO5O+AwQUtCRJIk8PA5Ykie9MVvP0Ix70GQwENrctzgoczaxz6/hbgcBxDmH2MH4+6+fMzZgrjM71o64HAsdB2G12Hlz4IAsyFxBmDwPg9om3c9mwy7ggV3XlDIkdwq4bd7E4W71mhCOCJ859gqfOe0pc+5nzn+H+efeL9LMXPMsvZv8iqI+fBssAWJwyRuU0OnE0N4++mdcKXzP5sI9bX1MAV4+8mkkpk7h/2/2ms1xM8obRK8CPpv2ISEckd629K2j9wajonLKT+2bdR01XDfduvNe0mcvYpk2ycd+s++hwd/CDT34QFMeu3+tPpv8Em2TjzrV30uPpCfjEtX7NGjSLi3Iv4tG9j7K+ar2pLwBfGfsVBscM5scbfizcL3qZTbLx05k/Vfuw7gf0entFGcAPpv0Au2Tnjo/voKO/w1R255Q7iXBEcNe6u0zXG5M4hutGXccLBS+ImYXuUvn6+K+THZ0tInnE6HvEFUxMniiONNDzL8q9iBnpMwBMx2zcMeUOoQj15xtmD+NnswLHIOttTEyZKEa0urK3STah1PQjLgCuybsGgKzoLJF3/pDzyYrOYkziGJG3KGsRoxJGMToxcOzYvMx5TEieIM7+AXUWcM+Me/jhtB+KvN/M/Q2/nP1LpqdNB1RDseqKVfxr6b+ETFZ0Fk+e+yR/nP9HkXfFiCv459J/inSMM4b3r3ifhxcFThBdkrOEvy35m3g2EY4Ifjnnl+TE5AgZ/bPQmZY2jcmpk0VaP4JCJ9YVGzRT+LRYBsDilNFHoDrfnvRtZqTN4Gcbf8ZrR147/qLuAMX3h/l/INIRya0f3hry4LSBijQxPJEHFjxAeWc5t3xwi+lkzIHGYmraVO6ccieryldx9/q7g/zoutzIhJHcN+s+ttRu4fsff1/1qQ9oKz0qnT/M/wOHWw9z64e30tDTIHzfOvfOvJfhccO5Y+0dIrRTf1Zh9jAeWvwQHp+HL7//ZUrbS011RyeO5p6Z97C5djOvHnnVtKCcEZXB/fPu53DrYV4+8rJpk1tKRAp/mB84r8j42dw15S4mpUwSaw+6Uol0RPLnhYHtOvoo2G6z88cFAWVn/Jx+N089Nnlq6lRRnhieyN8W/w0IHG0B6hrM8qHLkZCIdkaL/Ptm3ceXRnxJjIQBRiWO4s8L/yza16+79bqtvHBhwOUmSRIrL13JMxc8Y8p78aIXee4C83Edz5z/DC9d9JIp7+q8qxmVOEqkXbKLy4ZfJgw8qJ/xnIw5pnpT06aSHZPNsciIyiAxPPGYMmcClgGwOGV0t4eOU3by4KIHmZ4+nZ9v/jnf/Oib7Gvcd1RDYJxBgDp1fmzZY0Q6Irn5/Zv59ZZfm90sA5Q1wIz0GTy06CHKOsq49K1LeXL/k6YDs4yyN4+5me9N/h7vlb7HpW9dyrsl79LjVV1Cxn5cOuxS7p15LxuqN3DJm5eIDVDGtuZnzueBBQ9wqPkQ57+uRmoYFW6EI4JHlz3K8LjhvFX8VtA1cmNzeezcx3D73Oxs2CncKTqXD7+ce2bcAwTvZF6QtYAHFphP89SZNWgWf174Z3JicsiJDowyHbKDfyz5B+mR6aREpJjqjEwYyfMXPM/d0+82jTbTItN457J3+O3c35ruLSk8ia3XbeVXc39lamde5jy2X7+dRdmLTPm/nvtr8m/IJ8IRIfLC7GHcO+te0+gc1P0GF+ZeaMqLcESIIyZ0ZJts+jxANU4DR9OSJAXlWYB0vNHZ/5KpU6cq+fnHjgCxOPP4wbofUNBSwMrLzC4fr9/LCwUv8M/d/6TT00lOTA7T06aTl5BHVnQWaZFpxDhjuOTNS7gw90J+MuMnpvod7g7+tutvvHT4JRRFYWzSWKamTsUpO3lk7yP8fcnfmZ8531SnvKOc+7fdz8bqjbhkF7HOWBp6G9h7096gKfT2uu38ZstvxGIdwLbrt4nD1XT2N+3nV1t+Jc7A2XDNhiBFVNJews82/ow9jXv45exfctnwy0zlbp+bx/c/zuaazfxr6b9MShDU6I+/7PgL8a547px6Z9Az3te4jw53R9BoFNTz5Jt6m5iePj2o7Gj4FT993r6gflh8/pAkaYeiKFOPLxmi7plkAOKGxSlz/zj3s+6GxaekoaeBzOhMXr8k9HFPne5O3it9j3VV69hRvyPkSzG+PObL3DX1rpD167rreKvoLTbVbGJv017hi3/m/GeYmDIxZJ2DzQdZWbySLbVbCLeH8/yFwUcsg6oIN1Zv5N3Sd/H5ffx+/u+DDAWo0SL59fnUdtdyydBLQralKAqVnZWkR6UHjUotLE4XXxgDkJaXptz06E3HF7Q445ibMfeoitGI/u6Ays5K6nvq6ejvoNvTzXlDzjMt8B0Nj99DdWc1bf1tTEieEFJZW1icTXxhDIDlArKwsLD4dJyKAbAWgS0sLCzOUiwDYGFhYXGW8oUzAIrHg+IOnPjobW0V4YeK34+35fjHDVtYWFicDXzuDICiKDQ/+R98bW0AtL/9DofyRuHvUeO4i849l8KFavyxr7OTwlmzafyTeixBy9NPUzh7Du6KCrW8o4O2114PGAivl75DgZdQK36/kLWwsLD4ovG5MACe6mq8TeqZIe6iIhp+/3uq7rgDgJan1LMy+g6qMdremlp82ihfNwrNjz0OQO8e9Z2jPTvVN0M1PvQwtT/9Kd2bNom2Si+7nN7duwFoe/kVipedK9IAzf/5D32Hj4h0f0kpPdsDL9pQvF46PjS/pKRn5y48DcEvCbewsLD4LPlcGICiJUspnDtPTdjULvfuUJW4K3cIAP0lJaY63qYmFI/59YSuIaqsu6TU1Fa/Nur31KtHCHRrCt1dqY7+uzQDoSgKDb/7PaXLl4s2Sy65hPIbbxIKv+2NN6j+zndpeyVwXnv5dddRfM4ykfbU11O0eAn9ej+0tnv37AnaLasbPgsLC4v/Np8LA6CjKIpQ6rqf356qnrjXX1hkku07fBjFHTAAit+PFKbu8HSXqsbCkZ6u1i1W087MTLW8qDhkmgEGBQCvuinJW6ueKqjHpfdsM7+EW+kPnOPStW4dnpoamv4VOECqZ+tWyq6+htanA8ca9x06ROHcebS99pqpraZH/0372+8wkJ5du1B85jPs/W437qqqIFkLCwuL0/lS+DBJkrZJkrRHkqQDkiSd2rmlgLeuzrTAq7jdKD5VAfcfVk+OtEVHa+kjKJ6ArKeyUtQVI2/thR/9JZqC10bf+mxC8aunGfYXq+V+o0HRjIGckAAEXFC2CHVrfX9hYVD//ZoRsKeoZ7D0HwnI6Aai44PAa/t8repZ4a0vvWxqp/HPf6bm//7PlNdfVET5tdfR8Ic/mPIbHniA4qXnBC1+K34/DX/+C57q6pD97N1/ICjfWNfCwuLzz+mcAfQDixVFmQBMBM6TJGnmqTTYd/Cgya3TX1ws0n2HD6MoCraoKLXscIFpBtBXcFjIusvK8LvdgXRRsWl20V9UpKbdA9IGg6IbBafmgtINgF83MpoBULQZAhgUvtdstACwqacv9hcUBOcZFqaN+I3GUOt7+1vmc/T7C9Rr9O0b8ILymhqaH32U8q9+Najdpn/8k7Irr6TvcPBxzB2rVlEweswxZxU9u3bh6zzxF8JYWFh8NpzOl8IriqJ0aUmH9ndS2471UXXfgQMmA9B38KAY1fvb29VRvlbeu3efWXbf3kDa56PfYEz83d14qmuEQlV6e9XZhkHeW1s74NqqUpZku9Y31QAIGb8fX1dXUH9NMooiZgWiLz09gagk3d3l8YQ8SdNoLHRDo0dH6bjyRornYUIbxXvKg6OcvM3qukPP1q1BZd3rNwDQuerDoDJQZwfl115H+XXXhSzXaXjoIYrOPfeYMgPpO3QIb3Pz8QUtLCxOiBM2AJIkzZYk6TpJkm7S/06gjixJ0m6gAfhQUZQgjSJJ0q2SJOVLkpTf2NgIgLe5mUN5o+h4/wNVyK4q2d49e00uoJ7t+SYF27N9uyh3l5biqa0xyxrqdhtkAXryt5vbyt9hGvH37NhhWgPQI4MCBmcvit9vVvh795rSvXvVKCTjdXUlbpTzVFYG54UIR+3ds1f83yjr6+oS/7c5nZrsHlNdo7xxlgLgzMnR+jvAaACuYUPVsn17g8qM7Q5ckxlI8z//hae84lMp9NLLLqdw/oITlrewsDg2J2QAJEl6BngAmAtM0/6Oe/aEoig+RVEmApnAdEmSxoaQeVRRlKmKokxNTk4GwFNbB0Ddr3+tymgKs2fnTvzd6kmSjqwserZtQ/F4cGRmIsfHC4MQPnWKKr9lCwAR06bRe+AAvo525MREnLm59OSrslJEBHJsrLpo6/GAw4EtOlq0DWCLiaF761aTy6Vba1vvm6+1VXX7GBRr95atJkXbvXmzydUE0L11m9qOyZDlB+dpUU8AUri6mN27a1fgObrNhkfk6wZqzx6T795kqAoGuHq0heSBbiMAxa/ORPr2HMUAGJ7RsdxA9jR18b5n27ajyoTE57PWICws/kuc6AxgKjBHUZRvKYryHe3vuyd6EUVR2oCPgfOOI2rCp4VAKh4PruHDUXp7hXKMnDsHT00N7rJyJKeTiKlT1RmAx0P4hAlIERF0b1TDNyPnzAavl578fCHbu2Mn/r5+bC4XEdOnCYVvc7mImDqV7m2q8pbCwoiYNo0egzIPnzIFT0UFnupqFI+HsDHqK+m6N20WRiJs9GhV4Wt1XKNH4a2pxVNREVhATkoSexBMRmHjhuC8DYEXmev53Zs2iagfk6zWpjHf39lJ3/79QfkQrIR1Y+IuLw9ePNbqeWpq8LW3MxCz0doRVK7jGjFc7eunNQCEXmC3sLD49JyoAdgPpH2ahiVJSpYkKU77fzhwDlBwzEoaRteLu6oKfD4i58wBWabrk08AiJqjvhijb+9eJIeDiBkz1IgWrxdbRAQRU6bgqVFdQBHTp4PDgbemVpOdjr+rSzUIDgcR06bjqaqiv6hYlHvKK1Tj4nAQOUMtd5eWqdeep+5J6N6yRZ2BZGfhHDJEVciaAoxcMJ++/fvxNqpGLGq++uISo1GIWriA3h078Pf2inuOnD2bro2qYtflIqZNC+T5/eD14szNxdfWJkbpxtlK59q14vn53W4kpxNsNroM+UZF3fXxxwOev6Fs7bqjfjZdn6xnIGZDtimoPNAxdSbRs2nzcV8ZOZDuTZs/lbyFhUVoTtQAJAEHJUn6QJKkFfrfceqkAx9LkrQX2I66BvD2iVzMqEQ6tbBIOSGB8IkT8WjRJ668UTiy1fdySg4H0UsWizqSw0n00qUiLcfFEzl7lpCNWrAAyeHAXawq/KjFat3ujRvVthapR0l0rV6tli9cCEDH22r3w0aPwp6WRueHH6mzBIeDyLlz6dm6FV9LK9hsRC9YAIpC5yq1/67hw3FkZND50Wpxf9ELF6J4PKrh0EbdUYsX429vV102mrIN5O0V/vqohQvBZqNz3TrTM4s+ZynuouJAlI7Hgz05mfBJk0yGQb9e+NQp9OzYgVcLOdXbkiIisKen0/nRRyE/Gzk5ic4PgxeCTZ/dqlVHddeICKzycvqPHAkpY5I3tNO5+qNjSFpYWJwoJ2oAfg5cCvwW+JPh76goirJXUZRJiqKMVxRlrKIovzzRThmVSNvr6lumJIeDmAsDL46WnA5izlM9Sv6uLhzp6Ti1BUrJ4SB66RKDrJOYZWrEibu4GDkqisi56pvHJLsdZ2YGYePHC1lnTo5w60hOJ87sbMLGjRMKT3I6ibnwAro2bMDb0IDkcBB74QUobjcd776L5HAQNmECjsxM2t54w1DnQro3bcJTo24ai5wzBzkhgfYVKwMKfMlipLAw2lesCOQtXoTkctHx9kqhuO1JSURMnUrHu++a1hVilqk7jjvee088S9WoLaT/4CHcZWWmZxxz7nng99O1JjALUDwebA4H0UuW0L1xI76ublOZ5HIRvXQpXevX4+81v6dWGLIFC/DW19O727z4bGzHNXIkyDId77wbUsYkrxk+KTyc3vwdIfcvWFhYfDpOyAAoirIO1X0Trf0d0vJOC0I5XXA+bi3eXnI4iDn/fCFjczqJuUBN60oteok66vdUVmJPTBSyksNumiEAxJyvGg93ebmWVtvSZxh62966Oi1tND5OYi+6CLxelP7+gMLPzsbX0oLkcCBJEjEXXyTWMSSHg9hLLga/n/a31JeDS2FhxFx0IV1r1uBtUiOg5IQEos85h4533sWvKV49r/3td/B3d4n24q64HE95hVj7AHAOHUrE1Km0vfqqiEqSnE5iLr4EZJm2V181PePwSZNw5ubS+vJL5ufvdBB70YUo/f10vL3SVKZ/FkpvLx3vmpW3MFrLliG5XLS/+SahUDwe7KkpRM6cScfbbwdFIgXJa4Yl7rJLAWhfeUKTSQsLi2NwolFAXwK2AVcBXwK2SpJ05enqlK5EYi8LvFhbcjiwx8eb0q6RI8FmI2Kmur8s/vrrsEVHE63Fl2c8/BCuUaOQ4+OR4+KIu/YaEm+9FVAVlJHY5ZcMSC8/arnkcODKywvMEmQ7kiQRd/nlgDojAYi79FJznWHDCJ8wQS2XZVFH8XhoffY5Vc5uJ+6Ky/F3dtJumP3EXXE5/o4OU170smXYoqNpff4FEX0jORzEXf0lPOUVdG/ajOJWFbYjNYXoxYtoe/0N/H194hlLTgfx11xD35699O7bL56/btTCxoyh5dlnTXsT1HWTabhGjKDl6WdMPnzhIoqPJ/aSS2h/662QR3Cr7TiJu+ZqPDU1Id1JZnn1/pxDcomYMYPW5583RWVZWFh8ek7UBfRTYJqiKDcrinITMB2497T1SlMijkGD1MVfwN/ZAcDQVR+Q+pMfY4uMRJIkRu7eRfbjj6nyKSmM3L6NyBnTAdUdkvvG6yIWPv2++0i5Uz1F1BYWRsZfH2bQnx4AwJ6QQOI3vkHi129R00lJxF11JbFXXC7Knbm5av9sNiRJEsZEP0I6/rprTbfhzMkRR0XoW+ASv6HW0UMtw/LyiFwwXxwFIckyETNmEDZunJidSA4HETNnEjZ2LI0PPSzybOHhxF93HZ3vv0/f/n0iP3rZMuxpaTQ+/DCKx43kUF9QnnDzzfhaWmh55pmAAXA4iL10ObboaBr/9le1q5qSlySJ+BtvwF1UTOf77weVJdx0I/2HD9O1erW4Z11RSw4HCV/5Mkp/Py1PPhn0EQvX1OLFOAcPpumRR4POMRoor7eb9I1b8TY00D7gjCQLC4tPx4kaAJuiKMbzjJs/Rd1PjfHHnnrPT7FFRxMxbRoAzuxsEm4K7EGzOZ1IsnxS14k55xxiL7xQpFPu+D4pd90l0um/+hWDfvMbkc555mkSv3kbYSPV3bXR5ywldvlykm7/FgByTAypP/0p8ddfL+pkP/kk4ZMmEabtyNUXlI0kf/vbprQkSSR/NxBlK2mzheTvfS+Q51SVeuLXvootJoa2V17V8p3YXC6Sv/Md+vbupXvTZmEAIqZOJWrBApof/TeeKnWzmeRwIsfEkHTbbXSv+4TOtWuFcgaIvfhiXHl5NPzxAXUPhrFs+XKcw4ZS//s/iLUA42fnys0ldvlymv/zVNBprcKQyDJJ3/k2/QUFtL7wYtCzERjajZg1i/ApU2h88CFrZ7CFxSlwokr8fS0C6MuSJH0ZeAc4/srdSWJSIkOGMHL7NsInTDhdlzth7ImJpHzve0jazmTJZmPQ738nQlIBEm68gbR77xHpsJEjGPzC89iTkkSdYas/Ivs//xEy4ePGEbVwIfZB6SIvat5cHBkZputHzZuLa9QoNaFFxcgxMaTefbeQEcr50uWET1E3xBkXTFN/fDeKz0fjgw+Z5ONvvAHX8OHU/uSneCorkRzqrEmSZdLuvQdPXR21P7tPDSvV6kgOB2n33Iunqoq6n//ctBitG6iUH/wftogIqu+4U2ziA0xGJuaCC4icM4eGP/1JHJcxEN3dIznV2Uf6L36Ov6eHmh/dHXTst4WFxYlxXAMgqecbPww8AozX/h5VFOVHp6tTfoM/+4uIIyODyJkzTHmZ//wHw1atMuUNff89hq0zr7Xn/OdJ4q+7jsjZs0Ve7GWXEjZW3WQtlLMsk/FH9WRQPToKwDl4MOm/+LlI64ra5nSS8dCDKP399B08KBalASKmTCH5e9+j45136HzvffEeBYDImTNI+vbttL+1gvpf/walr8/UD3tSEhkPPEB/YSGVt30TX4fqyjMaAEmSGPS7+5Hj4qi49Rv0Hgg+idQ4KABwDRtG6s/upXvDBqp/+ENxppKFhcWJYz+egKIoiiRJ7yqKMg54/X/QJ9N0/2xBkiRx5pHI0xZvjcixsaT9zLz8IkkSg19+CX9nJ5JBOTsGDWL4+k+CnmPsxRfja2mh/d13kbXTUwFcublkPfZvyq+9TpwHpJN469fxd3XR/O9/B7ldkr71Lfxd3bQ8+SStzz0n+q4TNW8ug37/e2p+/GNKLr6E1Lt/hNLXZ5KxJyeT/di/qfj61ym/5loSb72VhC/fjKwd7z3QAADEX3UV/o4OGv74AKWFhaTe/WMi58wW72SwsLA4Nsc1ABo7JUmapijK9uOLnjqhfuwWx0ay2ZBjY4Py7dr5SgNJuPlmEm6+OSg/YtKkoFkHqEYm5a47CRs3VrjATGU//AHh48dRfcedANiiok0ysRdfhDMnm9p77hUyA9txDR3KkJdfpv6399P097/T/OSTRC9coEZ5aYvoA78TiV/7Gq5hw6j75a+ovOUWnEOGELVwIRFTJuMcMgRHZiY2lyvkM7CwONuRTmQbviRJBcAwoBzoBiTUycH4/2ZnxqemKu9efQ3u0hL6C4vI27fXMgKfM3xdXbiLigifODFkueL10rlmDR3vvEv8tdcGucJ0evcfoO2lF+lauw5vY8Adlf30U0ROnx4k7+/vp+Odd2l/8016d+0yrQvYIiOxxcQgR0cjhYepYbuyDHZZ/b/dbnJrCULNJKSBIqFmG0FCIe/RwuK/QdZfH96hKMpxD+cMxYkagJxQ+YqilJ/MRY/GuLg45S3tnB3nkFwyHnrQms6f5SiKgqeigv7iEnytrcQuvyRo5jAQf28v/UeO4K6owF1Zib+9HV9HJ77ODpR+N4rXA16feraSzwseb/B5RKF+Fycjc3KvwLCwOGGGvv32aTcA2aHyFUUJPqT+FJg6daqSn5//32zSwsLC4guNJEknbQBOdA3gHdShjASEAUOAw8CYk7mohYWFhcVnzwkZAC0CSCBJ0mTgW6elRxYWFhYW/xNOajevoig7gdCrdxYWFhYWpw3Fr7DmmUM0Vhz9jXsnyokeBnen4e//JEl6Hqg5bkULCwsLCwD8foUNLxfS2aJuliw/0MyzP9uM16OegfXeI/t4++/q8emefh9/v20NhzapR8cX72rg77etobfLTV+Ph0Mba3n196e+XnqiM4Bow58LdU1g+TFrWFhYWHzO6e0ynzjb1+XB0x84tHD7O6Uc3lIr0ise2sWapw+J9Cv3bxdKvbmqiz1rKnn/UfXU3c1vFNPe0EtTlXp6cMmuRsr3qZss+7rVMOZPXlTf160bgpojbfh9auCO/u+pcKLvA/iFoii/AP6oKMpvFEV5TlGUvlO+uoWFhcX/gK7WviCXSeneJtrqe0R68xtF/P22NSLdUN7BE/+3gcLt9SLv8f9bzwu/3CrS21aW8tF/Agq/8lCrUNZqG51CqdtkNaS9qUrtR3xaBACttYE+APT3eFC0V6Z63eqZX/GpmmxdDz5v6LfsnQwn6gKaJUnSQbR3+kqSNEGSpH/813phYWFhcRx8Hj+VB83vlmhv7OH1B3bQ2xkYqTdXd/HId9bS3hhQrK/+fgcv/3a7ab/Hu//Yy3P3bRHpnR+oUe3dbf2mfw9uNHu7O5uDx75+v3k07vOYlbSn3xcYuXvVf6MTwgBoqe02yTZXd5uUvKIouCLVDbFt9T2iPqgzklPhRF1ADwLnoh4DjaIoe4D5J1JRkiRZkqRdkiRZr3CysDiLaazoDBq91hS18eSPNgiXh877j+7jmXs2mfL2rKlkxcO7KdvXJPIOfFJDbVE7B9YHTrwt2d2I1+Nn/ycBxa0r8/ZG8ytMjcQmh4t+Atgd6jHzzTXdQbJGNxBAe4N5FB+s1LtM9+7z+MWewWZtRiA7bAbZgJLvbnML49Fa143PF2inuabrqPdzIpxwFJCiKJUDso7+9g4z3wMOHVfKwsLijEHxK0GjWiNle5uCRrkAR7bX8ffb1uDuNb/is6/bw8u/3c5HT5qP+967upKedrdJqQMU72yko6nPrDS1/1cdbhV5cZprpKkyoAhjksK1vIDLRyj38uDImX6tr/HpkQA0aAZAV7S9HcFvnjO2DQg/vk7jwPJKs/Frqe3Gr6WbqlVjER7l0NJd+I1K3mA8Wmq7Tc+9ufp/YwAqJUmaDSiSJDkkSfo/TkCpS5KUCVwIPHYKfbSwsDhBervcIqrkaHg9Pja8Uhg06jby5I828Pofd4Qsa63r5p1/7GXNM8Eq4IA26q4pajNf0632qWhHgyk/KUs9jTaUYgazIg2PVt9RYVK+2kkxjQYFrLt5jEo5NkU1FPXl6nHkRuOmj/hlu2RKH8vVovdLP6lG75PdadPS6rWd4XZNvsvUXqPBIPR2uOlqDRi7hrIO0wygobxDGAuv22+6r7qSDk6FEzUAtwG3AxlANTBRSx+PB4EfAkddtZAk6VZJkvIlScpvNBz6ZWFxttPR1MvrD+ygu/3E33XwxP9t4PU/7jymTNWhVvasrmTtc4ePKtPb6aG+tCP4jCQC0SdHttUHlSVnq6fANpSZFZNRoRnbtNlVFTRwgVb3j9cbFJx+XeNoX1eMHY29Qqnriravy2MYdat5uqExjrD1vup9bChX79voatHzdIPTUGbur66I9T7WlbSb+ldX0m5qr6awDZ8hiqemKBDd01zVZVrTGChbdUhdBwmLclA7wNB+Wk40CqhJUZTrFUVJVRQlRVGUGxRFOea7+CRJughoUBQl9DAi0PajiqJMVRRlavJRji62sDgbObK9ntqidnZ98OmO3DreBiGHS/VtVxW0HFMOQvvMjeGHAw2E3nZdaceAOgHl194QaFNX1o2VnSLyBSA82qG10x7URn+PV4RnGg1Lq+Z3N7pa9Gehy9WXdeD1+EyjcV2J6u33tLtpqzdH21QfaVX7p1WrOtyK3xfw49eVtpsWehsrO+nr9uDzKUg2iZaabrpaVUMemxyuhnN6/UQnhOEIk6k50obP4yc5OxpFUa8HkJITTV1JO95+H+ExTiLjXFQeUssyR8aLNk+WYxoASZJ+doy/470Ufg5wiSRJZcCLwGJJkp49pd5aWJxFxCSqo+DqwtbjSAbT0Xz0xU5dSfX3eE1KNxQ1hW1BeUbFaAyjhED0S11xu0npG+sY3UP6qNjr9pvcPbrCrj7SJvpobKOqoNVUH6DiQIvp/gAqDup5ftG/2qJ2c1tH2vB5/fi8ChGxqpup8lCrMBIxSWFUFbSKUXhMcjjdbf00a777jJFx+L2KUNrZYxJACRiN7NEJWv/UMXPW6AQ6W/poa+jF7rSRPjROjPIHDYvDZpMCSj4vAa/bT11JO7JdYtDwOOG6yxgZz6lyvBlAd4g/gK8Bx3wlpKIoP1YUJVNRlMHANcAaRVFuOLXuWlicPRhdHsdT1GAejesKMhRGpdl0lEVE3Xcdqh2jYtcV1cC2Pf0+aosDo3fjSL18f8B5IEbi0oB8wwKsviirPw9XhF3E1uv141IjKNcUrK7cEzOihNL1efwMGh6HzS5RcbBF9CdnbCLefp9qsLx+4tMiNYXfItrJGZNIQ0UnPe1uUUe9D3XhOnNkPDabJAxQ+tA47A6bSKflxuAIk4UxyhqlGoSGsg5sdhsZI+JoretB8Su4Iu2kDI4Rs5nMPFXJt9b1IMuqrE5KTjSuyBM9zzM0xzQAiqL8Sf8DHgXCga+gjuhzT+nKFhYWx8Q4Sm04yiKpEaORKNvbdFQ5o/ujdHfodTddAZftawpaVDYq85Jd5kVdv1fB7rAh222U7Go05KvtxSaHU3GgWSwK+7x+HC6ZtCExpj77vH6yxyQgSYgIIZ/Hj2STyBmbSPmBZvx+RTyjweOTqC1qw93nFXlDJiRRX9ahrgX4FFwRdtKHxlFxoFncX/aYBCSbRPmBZnxeP7JdInNUAlWHW3H3qdFBOeOSQIHSPer9xKdGEJMURrF2f64IB+nDYinaoa6JOMJkMvLiKd6pPhu7UyZrVIKYHSVnRxOtze5ku40hE5LEfdtkiSETA+moeBdpuTFqmd3G4PGBMrtTFsboZDmRl8InSJL0a2Av6umhkxVF+ZGiKA3HqSpQFGWtoigXnUI/LSzOOoyujCPb6o4rb1woLD/QfNQoH32UHhHrpDC/IfRCr1chJScaT59PjGRFfU3BZo9OoKawjR5DmKTP58cV6SBrdAIluxtF23rfhk1Jwev2U6nNLPxePza7RM64JBrKO8U5OX6fQmSsi7TcWEp2NWqLsgqyLDF4XBJ9XR5qCtvw+9T6Q8Yn4fcplO5uxO9TkCTInZgMChTtbFCvI9vInZhMS023MKhhUQ6y8uIpzK/XDICNYVNS8PT5KN6pKvhBw+OIindRsFnd4avKpIrFaNluY9jUVHo7PSI9fGoq/T1eU1pHttsYOll917fX7SM+LZKoeFegbFJKSNnu1j4iY12GMokh409t3fR4awB/BLYDncA4RVF+rijKp3dIWlhYfGp0RZs1SlNQBtdLKPRR9ojpqfi9ihiBHk0ub2YabfU9QYvG+h6ArNEJhEU6goyPbphGTE9FUTAdleD3Ksh2iWFTUuhq7Rcx+/o1s0YnEB7t4JC2u9bnU5DtNkZMSwUpsOvW5/Vjs9sYMSONlppu6kvVUEjZYWPwhCSc4XYObqjB51Xrpw+LJTY5nEObakXdpKwoEjMi1Tyf2q8R01KxyRIHN6gbx2TZxshZaXS1qD59m2wjY0Q8kbHOQHiow8aI6WnC5y/bJUbMMCp0iaGTk03pgaP6nHGBkbrssDFMU+ot2iazIRPU+j0dbrFnQW3LRu4ktczdp86axi7IAMDhsjPEMCM4GY43A7gLGATcA9RIktSh/XVKknRqAagWFhbHRFe0o+dm0NvpOapC19FdM2m5sSQMimTf2qqQawf6aHz4tDTsLpk9a8x7PPXrOlwyebPTKdndREdTYFFZN0zJ2TGk5cay9+NKEYLp86kj7aGTkwmLcrB3TZXpmg6XzOg5gyjbq7bp9/qRZRsxSeFkj05UlbpPXZCVZYkR01NxuGQOfFKNz6dgkyUcTpmR01Mp2dVId1s/smxDkiTyZqdTfaSN5uouZFlCkiRGzR5EQ1kHHY292Ow2wqIcDB6fJNYubHYbuROSxZqHbJew2SRGzkwT9xuUtttIHBQlYv4lWSI8yknCoEjx/JxhdtKHxorn5XDKIrJJtkukDI7WnqH677iFqlLX90VMv3gIAM4IOzGJ4cQkhYlF3/lXj+CGX80kIsYpdg+fLMdbA7ApihKuKEq0oigxhr9oRVFiTunKFhYWx0T4sicmkTAokvx3y4+5GKz7tWW7jcnLsmmu7g7aYQuB0XhEjJMxcwdRuL3BrOC1dmyyjfGLMpGA3R8FjIRuIGx2iYlLs+ho6qNY2+ClzwDsDpkx8wZRtq+J5uoucU3ZbmPM/AwkSWLnB+WqUtc2YI1bmEFPu5tDG2s115ANZ5idvFnpHNlWT0tNF7K2b2Dswkz8Pj9FOxpE/dFzBmF3yVQcaBGKMW9WGq4Iu7g2wKRzAm+4lWUJu1Nm/KJMIBDVNH5RlumZJaRHir0Jui9/6gWDAVC05zHjEnVZNEw7t0dPR8apbpvr7pvJhbePx+6QkSSJW/4yn8vumgxAfFok33h4ASOmqYZm2oVDuO3vC3E41bDa6385i+XfnwiAZJOITY4I+lxPhlMzHxYWFqcN3ZctyzamXjCY1tpu05k3A9FnADa7xPBpqcQmh7PxtSKx4BokJ0tMXJqNbJdY/3Kh8Nfri8SyXSI6IYxRc9LZ/0m12IHqMyjzIROTScyMYtPrRXj6fWIGADBxaTauCDvrXy4UdWyy2uaYBRkc3FBDQ3mHUMw5YxNJHxbLtrdL8Xr8YmfulPNzsDls1Ba1ixM1E9Ijxahcj86JiHEyQVPkuj/eFeFg4lJV4bfVqe6WtNxYcU3JprY3YYmq8KMTVfdLZJyLnLGJphH24ptHqdfWRvqTl+Vw3jfGMmyK6g7KnZjMjb+epa49oIZpfvWPc0U6LMrB4HEBl40r3C72TYC6qGtElgPXttnUGc1/G8sAWFicoei+bFAXTzPz4tn0ejGtdcGHk0FgBmCTJWyyjQXXj6S9oZdNrxWZ2zXMFKLiXUy/OJeyvU0c2lhrKtcV+czlQ3FF2PnoPwdNm51ssuouWXDNCLpa+/nkxcP4DIo7LNLBzEtyqT7cys5VFeKaANMvHIIr0kF7Q69oT5Ik5l41nD5tF6x+/chYF9O00XZHU+AkTn2EbWTSuTlBeROWZBGTFGZy41z146lkjIgjMSNK9PWm385m8U15QubCb43n638OnHmZOTKeb/5jESk5qvNDskkMnZRiMhIxSeHCqIB6fMXpUNz/LSwDYGFxhuLX/OCgKsfFN43C7rSx8uE9pqOOdfSRvT5yzMpLYMKSLPatqyb/3dKgEb7uOpmwOJOs0Qmse/4wJbsaDSN8TZFHOVj65dG0VHfx/qP76O8JRLsApA+LY9qFgynYXEdVQatQ3ABj5meQOzGZZm32oJeFRTlY9tUxgHm3cUpODDOWq4rd6O6aeE42MUlhIi4eICo+jEvvmMS5Xx8r8lzhdq792QzhLgF13eHGX89m1OxBIi8xI4pL75xMRIxT5EUnhOEMC8TVSzYpyMdus525yvxkOLVdBBYWFqcNny8wAwBVQV38nYm89dAuXvldPrMvH8aoWelixClmAIY6s68YRk+Hm60rSmms7GLuVcMDI3ytnk22ce4tY1j51z289+g+Bmux5UZFnjM2kYU35LH22QKxOUx3x4Dqs+5ud3NwQw29hoPTJEliyZdHUXNPG31dHhxhATdH1ugEzrt1rGnEDDD53Byi4s3K3maTuP6Xs4KeUajdsAmDIoWbxuLYWAbAwuIMRY2QMSvH5Oxorrp7Kh89eYiPnylgx3tljJiRRubI+MDI3VDHZpM45yujScqMYtvbpZTtbTK5XHRcEQ6W3zGJja8WceATLUTSbh79jp4ziKg4Fx8/W0B/j9c0OpZsEguvH0lydjRRcS5TPWeYnS//bg7tjb24ws0qR49xNyJJEiNnpAXlf9FG32cCUqhNIJ8Vo4aPU55+cMVn3Q0LizOCkt2N9Pd4uek3s4PKFEWheGcj+9dVUV3YJg4pA1h+xyQyQ4yMO5p62bumij1rKolNDueGXwWPqEE9+bJoRwNTzsvBFeEIKvf0++jtdItz9y0+WyRJ2qEoytSTqnsmGYDs5JHKj67452fdDQuLM4accYlcdPuEY8r0dXuoLW6nubqL/m4P0y/JFeGDofD7/CiYo0wsPr98YQzA1KlTlW3btn/W3bCwOGOQJM7oKBKLz55TMQBn3BqA5eezsLCw+N9gzQEtLCwszlIsA2BhYWFxlvK5NQBVB/dTW6S+09Tv9/Hho3+jtTawTd7j7hcbX7pamtm+4jUUf+A0Rb//2C/OtrCwsPii87kxAKW7d/D0D76Nz6uesf3SL+7m+Z/eBUBbXR17V7/Pa/ffB0B/TzcP33gFW994GYC1zzzOJ889SXXBQQAOrf+Yv1y7nI4m9bzvmiOH+Pst19HTob7BqLGijP/c9S2Rbqmp4pkffU+k2+pqeebu79Hdpm6IaW+o55m7v0dXa+Dc9PaGejx9gW3rit+Pz2s+nz3UAvyZtChvYWHxxeZzYwA+/s8jNFaU0VJtPrpW8fvRgyTa69Vzy/t71G3ym199HgCHSz3Fr6myHIDiHdsAqNi/B4DdH7xDX2cHJVr+jnfepLmqgiObNwCw872VNJQVc/CTNar8h+/SUFrMvtUfALB/7Yc0lBaz+4N3RL8e+87XeOFnPxDp9/7xFx68/jKRbigr4c/XXEzF/r0i729fuZq3//I7kfb7fex8bwWe/oAheefhP7L+hacC968o7FuzyiQTCq/HQ1td7TFlLCwszi7OWAPQ09HOn66+iOIdWwFIyFBP9NOVuE5bQ52YFej4tbTfp7p5YlPU0/paaqq0ttST/3RjoqebtXTigHTCIPWEweYqLZ2eYSqPS03XyitM/WgsLxX/P7T+YwAxa9DvY/eqt4VMf083R7ZuFOmyPTv5+D+Psvapx0RewcZ1bHvzFZGuKz7CqkceZtUjfxV5fd1dPHzzVZTt3iHy1j/3JI9/7+vi+gCVB/byxB23mYxHQ1kJb/7xV3jc/aZ7Kd+3G3ev+fyZrtYW2hvqTXl+v4/60mIsLCzOfE6rAZAkqUySpH2SJO2WJCn/09Tt0BTLumeeACAuVVXiDWUlJrnG8lJ8noBrxd3XazIIfr8PRVF9/7rCtjvVA6B0ha3PEJo1pWx3uUzldoe6G1I3GJK2gUZvT4/T1tNGN46+1hCdqB4J26Apx7Ao9RTCpkqz0QBE/x1OtR9Vh/aHkFHv2WZTN/yU79stylT3Uy8fPRHYVNdYUQZAfWngZMiNLz9La02VcI0BbHjhKYrzt1JhaM/d28Orv76H1357n6kPT3zvVh77ztdMeXs+fI9n7/6emF2Ja730DM/++I6g+6grLqRg0ydB+c1VlRTlbw3KB2itqwmZ39fVFWS4LCwsjs7/YgawSFGUiZ92o4JkU7umL+zqJwPqo+qwyCgtXWZS+E0V5SZfe1tdnZgR6ApeT7dUa28r0uR1Ba4bFN0AePV0dYW5vlauX79Vm2H4fYH+6G6XhAx1FqGPjgfWMaLPVPzaonVLCBndcOjt9GrrExAwSLpLDCApWz0mt7EsMCtJzlHfOmScqSRkqjMto6HV77/myCFTH/SZQ193l8jr7VBfFFdqmH0AbHn9JepLCk3rIgDP/eQO3nnoD0GzuBfv+yFv/fFXQesmVQf388T3bmXfx6sYyN+/dg1P/+DbQfkAO955iyNbNoQsO7BuNYXbN4csA/Uz02eTFhZfJM5YF5Dxh68oilAQDWUl6ghbMxANZcVCIYM6wjaly4qFAutua6W7rRWfpqDbGurw9PUJ+Y7GBjz9feJaPe1tuPt68Wt96e/upr+nWxgIn9dLb1enaQbS095mUma6wtdnHfoMwNjHgf77+pKiIJmBCiiUjD76NebpkU/6bKKuuFCUhUfHas8ooOzDo9RX1BmNwsDPYiDGNqMSEkz3qWOT1ZlKXUkhoWjSZijimtozHTjj6+tRz8I/sHZ1yHaOts6x9ul/s9KwvmLk/X/8hRUP/CZkWVdrC8/e/T1WPfrXkOV6/Vd+9dOjlit+Pyv+/FvK9u46qoxO2d5d9LS3HVcO1M+lvyf0uwEsLE6E020AFGCVJEk7JEm6NZSAJEm3SpKUL0lSfkNVpfBRG5VoW12NaaTbXl8nlHLN4UN4PW4hW334IF6Dwqo9UmBSiNUFBwJtKwq1RYeFQQBVmRnl64oKTX2pLTpiThcWmNI1hYfNBqD4iOl+dAXoMxmpUlNYan0IGX12ImvuqFAyTeVlAKb7b9UUoq7EdcMBgZmKMc9oaIWc8bOoDyjYRG22UFd42CDrC6oPkDJYPeO95kiBKT81d5iWfyh0/mGzvKwZEmOfB2KMxhrIwHUMI8aZjI63XzWqB9Z+dNR6B9atpmL/nqPOEtx9fRRu3cRrv7n3qG2Aalxf+829PHWUWcxAVv7l9/ztK1d/6six3s4OnvvJHUHraRZnH6fbAMxVFGUycD5wuyRJ8wcKKIryqKIoUxVFmaq4+3n7wd8DDFCyh4XCB6g4sBef10tkXDx9XZ1C2cSlplN1cJ8YPdpkmcqD+/B5vbgiI3G4wqg8uF+US5JNLTemtbYD5Ya0zVyOJFF5YJ9phFx5YK9JYVYc2Acg+t/Z1EhbfZ1p1lCl9TGQ3h/0DKoPHdDaUZVMlZb2+QLtVBfoMmYDZWyrs7lRhL8KN1RtNb1dnVqe2l57fZ0Iex34Wejosxp9P4ZRtq+r06RQneHhQfUBImLjgGDDEB4do+WbDYPevtfdH+Q20hlYx1Q24PrHq2e8xvEUrXF9xYjRJXisNnS5E50BFOdvAQKuzBOl5kgBdcWFrH7cOnjxbOe0GgBFUaq1fxuAN4Dpx6ujL3galVjZ3l14vV7iB2USGRcvlHDOuIkAlO/dCUDO+Il0tbYIn/ngCZNprCiju60VhyuMQSNHUX1oPz6vl/DoGFJzh4q2nOERpAzJFQrd7nQZyj3YZDtpw0aINEBm3hhzetRYNa0p99iUVBrLSujpaMfr8RIVr7pHKvbvFoolMi6eiv27xf1GxMbRVFlOd1uryeiV79slFrRdEZG0VFfS2dJkMiT6QrBRaZVrboeQeYa6FSHqhswzLO7q+VWHArMqkzE8uC9ItrrggGm2o4+aqwoOmJSj3k714YMD8gN9aRwwy9DjgY2L2jq6CypUmSsy8qhlxvsJtRYDEKl9rpUH9oUsN/Z5YNTU0eSONYvRSRs2AlC/T58GZ5ga9BAquMDi7OK0GQBJkiIlSYrW/w8sA07oG6f64dUfXmJmNuV7d+HzuLHb7WSNGa8qMEUhPj2DmOQUEUufrRkEPfwxZ/xkUBTK9+5CdjjIzBtDY0UZXS3NyHY7maPHUVt4hP7uLmSt7drCggHpQHn2mAnUFRfS29mplY+jobSEnvZ2kCRyxk2ksbxU/HiHTFLXvSsP7MPv9ZCUM4So+AQq9u0Ryn3wxClUFxykv1v15Q6ZqNap2L9HKITBEyZTsX8vnr5+U7sV+wIyOeMnUXVwP163W+QlZGRRtncXit+P3+slOimZyPgE4Yv2eT2ERccQFhklFm39mjEMi4qmbI9qWHVDYXe5KNu9Q6wr+LxeZIcDd2+PGD3rn5vd6aJ0Z+BkV+PMoLbwiCFfle9qbjKtA+jy3a0tA9xRAYVcsisQWGY0EqWGfB3Zoc5W9MGCERFJFcJH7z+K8TOiByQYDd6nbQPMBqDywN6jyulExSdqbR5f9mjXGbgob3F2cTpnAKnABkmS9gDbgHcURXn/RCoa3TJDp0ynp72NuqIj2OwOcsZPordTjTSx2e1CYQKk5OQSm5JKiaZ4MvJG44qMxNPfhyzbGTJ5GqBOnW12B4MnTMbv81KcvxXZbidn/CR8Xi8lO7cjO9Rr+X1einds08onovj9FOdvUcvHTUJR/BTlb8Fud5AzYRIAhds2ATBo5Gic4RGU7spXlaXdQfa4iZTv241H8y3nTp6Gz+ulTFNM6cNHEBYVTenuHUI5Dpk0DXdvD1WHVAWTmjuM8JhYSnfvEMold/J0vB43VYf2CyU5dMp0ejvaqS8txuf1Ync4GDx+EhV7d+H3+fB5vTicLrLHT6Jsz05tsd2D3ekkZ/wkSjVlryuMYVNn0t3WKhSy3+thyMQp2GS7eOZ+rxckiSGTplC6K18oZp/XS+aosUg2GyU7t4nPzOf1kpQ9WPtcAmGffq9X3X8hSaZ8vS9RCYmmfMXvB0UhMj6B1tpqsWYSaE99JrWFh+lqaTaV6W3WlxTR0dQQsgygaFvoSCHddVO5f0/IRVnjLEJ324Rsx3AtfbPisdCvW7F/D163+zjSxv4YXZSfznhYfLE4bQZAUZQSRVEmaH9jFEUJHWYRgoKNn4gv6dCpM5BsNrrbWpHtdoZNnSnk7A4HI2bOFWl5QNoZFsawabP0/pAyOFds2pLtdrJGjyM8Jpa+7i5kh4PsMeMJj46hu60Vm1YeERtHV0szNrudjLzRRMYnaGkHg0bkEZWYRFdzEza7nbShI4hJThWbvhxOF8Onz6Jw2ybcvT3IdjsjZs6hr6uTYk0JDpkwBVdkJAfWqbuM7Vqdou1b6NNmBUOnTMPucHJQl3E4GT59FsU7ttKn+e4HT5iEMzycw5vXG4yn+uyObN2Iz6O6sYZOmUFfdxfl+1S3k2y3M3TyNLpbW6g5fEgYqqGTp9HT3kb14YNCeQ6bNhMkiUJNEfq8XsKiYsgcPZbi/C0iWku228mdNI2u1hbqtEVwv9dLREwsmXljKM7fGjAMHg8xScmkDRthivv3eb1EJSYxaHheSAMwYuZcGsqKhcLWleyIGXMAKNoeULR6v0TZgP0Ffq9XvTeClbze7qCRo6k8uE+si5hlVPekz+s19XVgn6OTkinbsyvkYrPxWjZZpnjH1uOOzvV23b09lO4+8W02RkNTsHHdCdez+OJxxoWBpuYOo3DbJvq61B9JdGIyQyZOAdRF2LCoKNKGDlfTskzm6DGirmy3ix+5mnYwctY8QF3olCSJETPV8v6ebmyyLORVBSmLcr/Xi02WGT7DkLbJjNQMjNfdj2SzifY9fX3qu0xnzxORTLLdTt6cBbh7e2hvqEe22xk8YTJhUdHUHikAScLucjFy5jxqDqv+Z5vdzqi5C/H09VKo7QoOj4ll6LSZYpewzW5n1LxFePv7OawdV+GKiGTEzLkc2bJBhEpGJSQyZNJUDn6yBo+7H9nuYMjkaYRFRnFg3Wp8Xi82u51h02fhcIWxf+1HwigMmzYLR1g4+9d+ZBh1JzFkwmQOrPsIv9+Hz+NBttsZPW8RrbU1VGtrAbLdzvAZs7E7Xez/+EP1+Xo92LTn0VxVIRaD1edsZ+TMudSXFIoNa3o7uqLXZx16X/LmqPEEB9atNuXHpqSRPnwkB9etFkZGX2dIzhlCwqBMYaB1fF4vSVk5JGUP5tCGtUFlAKPmLEDx+zmyZSMD8Xm9ZOaNJjoxOaRC1dsYPW8Rfp9XzBCPKjd/Cd7+fop2hN4IF5D3MGjkaCJi44L6fbx6AFmjx1GUvwV3X+8J17X4YnHGGYCxi5bh7e/nwFpVcch2O2MWLgUCUS6j5i0C1GgJm00WIYN2l4tUzTiAOpLKHmt+nd7oBUtEXYDR8xcDgUW3UfMWm8u1a+mjNl1eDw/Uy/XdxnoaVEWdPXaCWPy12e2qUZqtBUMpCpIkMWp+oI5st5M5aizRScki1FG22xmjXVdPZ4wYRWxKqvC9y3YHo+cvxt3by6FPPhZyYxcupbu1hbLdO5AdduwOB3lzF1C0fTOdLU3IdjvOsHBGzJrL4c3r6elox2a34wgLY+SseRzZvEFsMpPtdsYuXkZXSzOlu3YElPSMOTjDI9i75gMxg3BFRDJy1lwKNq5Td2d71Py8OfNxuMLY+5HqDdTXEcYsXIrscLDnw/cA1WUj2+2MWbAEu9PFnlXvinxQj+vIGT+JvR+9j9/nE6Na2W5nwjkX0FJTJfzooszhYMI551Nz5FBgH4W2sG6z25mw9HzqigtNEUm6Uk4bNoKUIUPZ9d4K06myA++hdM9OWmqqTeX69QeNHEViZja73lsZMhpIj/DKHjeBmORUdr//dpDMwOs6XC5GzV1Acf42Opubjik/8J7GLlZ/awc/+fg4NSy+qJxxBiBr9DjSho0QI0Gb3c7QKWrwkH5mz4RzLmDuNTcxcdmFAFz9899x9X2/wxkWjiRJzL/hqwC4oqKQ7Xau/sXvufoXanhpYkYWsSmpZOSpM4f04SOBwHEQg0bkmfozMK0bGx09vl0nKSsH2R540ZpNlpl84aVAYBfulAsuMdXJGDla7HyWkJBsNqZqddQ21JmDHuVik2Ukm40pBhnZoRqOlMFDRVim7HCQO3k6Mcmpoh2AiedehN/ro/ZIgejrpHMvwtPXqy6Y23W5C/H097FtxWtqe9pnEZWYxNbXXxKjekdYGGMXLuXwpk9oqaoQ9SeccwHu3l52vbcSn6bQneERjJ6/mEMb1tKuneMk2+2ER8cwctY8Dqz7iK7WFrxeLza7g7CoKPLmzOfg+o/VqCdNednsDiaeexFdLc0c2rDWkG9n5Kx5hEXHsF3rt89gHMYsXIojLDxEmYPRCxbjDI8gf+Xr4rnqBkd2OJhy4aW01FSZFp91GdluZ9K5FyLb7ex4+w1TuT7ilu0Opl50GY3lpSEXnMUCusPB1IsupebIoWNG6uiz1MnnLwcU8gdc92jo95w5aizpw0eyfcWrRw2ptfhic8YZANnhYObl1wTS2qj5639/kqvvu1/kzbjsS0TGxQOq8s4cPVbUmXbx5dz54kqx+zUzbwyZeQFX0VcffFS0JUkS33r8Bb728L9F+rZHnuGWvz4u5G975Bm+9tC/Temv/OURkf7mo8/ylb/8S6Qv+9HPcbjCxCFyE5aeB6iL0gDx6RnEpaULeUmSOOfr6uYf/YygcUvONZVLNhsLrv+K6VmNXbzM8JwcSJLEzCsHPjs7M6+4GgjMoBIzsoQLpVub6aTmDhN+cH22kzpkKMNnzBbROfoMZtYV16ob6DwecU7S9Euvwma3U3lwHza7mpc+fCS5U6azfcVr6mxNMwwzLvsSNpuNDS8+I5QnwMwrrsHv9bLplefM+ZdfjeL3sfGlZwMKW5YZOnkaqbnD2fDi06LPst2O3elkxqVXUbZnp2kx3abNTCaffwmHN6+n+vAh08zBGRbOlAsvpXDbpqC9GLLdzshZc4lLS2fds0+YFnZ9Hg82u4OI2DjGLV7Gvo9XmSKXfJ5AG3lzFxKdlKy1YVa6foMxGrvoHCJi41j3zONHfXeFbjxjklMYNXcRez58N2j2EQr9Onbtt9bR2MDuD44927D4YnLmGQC7nVwtWkdNq8okJilZbBo6EY71Im19BK0THhUtjAmosfn6CaJ62qiwI+PiSRiUIdIRsXFC2YO6H+G7T79KdGISAM7wCL795MssvOkWIXPzA//gtkeeEelxi5dxy18fJ3P0OEA1apfc9RPGLzlPyEy56DKu+MkvxTqFw+nikjt/woiZc8X9GBfJ9V3DY+YvQXY4GDV3oSibc/WNQODsHoC5194MqJvVRN41Nwfa0z6LsQuXkpSlni2kK97IuHimL78SgI7GQKz7/Ou+jFc7okK2q+GW0YlJTL3kcgo2rqOrtUUYjPi0QUw672L2rf5ArJmA6teffMFyDqz9iMJtm8TnJ9lsLLz5Frpamln9+D+0awRmOfHpg/jw0b8J955eNv3SK4lKSOSDfz1Eb6e6iK4bp2mXXE50UjKrHn1YPfbDYABku4NFX76V1poqNr38HIBp4Rtg9pduICwyilWPPCyO5tA368l2B3aHg8Vf/gZNFWVsfeMljBhnMQ5XGAtvuoW64kLyV4Ye2fu0WRLAvOtuxu50suqRh0zG6Wj19OsMmTSV3MnT2PDiM0GRUxZffM5IA6CPyq+697diA8/nHVdEhOle7A6HyeiAunHMaLiGT5/NObcGjgWQJInBEyYLRQwwfMZsLr7jblFPkiS++eizXPrDe4WcTZb5zn9e4fzb7zRd60v33c+XfvZbkZeYkcXy/7uHy+4OnPqZMCiDOV+6Qbhp9PYu+K76roPk7CFCdsalX8LudIlD5kDdxzFfm7m4DVEtMy+/Rmxk6jdExcy55kaStbBQPcIJYPZV15MyeChNFWWmIxcy88YwffmVYhOXfs92h4MLv/tDetpbeeN3P9fKVCXtDAvngm/fRVtdDW898GtTmcMVxgXfvov2hnpW/uV3Yn+G3m7upGmMX3Ie2956lb2rPxB9kTX3WnhUNMtu+x71JUW8/7c/4/N6TAoX1GiqMQuWsPnVF8QiNphnGwB5cxYwfMZs1r/wlFjsN2KcJUXGxbPkq9+kuuAgq/718DEPrwu4pNTf2tJbbscRFsYbv/v5Ca8jWHwxOOMMgP4jCY+KJnvs+M+4N59PImLjGDplhilPtttNsx5Q11v0NRCdYdNmkjtpmilv5hXX8L1nXscVESHykrMH853/vGxyVdlkmdufeJFrf/lHU/1J51/COV//NtMuucLUn+V3/ZT49EHq+oaGw+li+Q/uxRkeTpY2GwL12InlP7gn5P3OufpG0rW1God2lDeobq3zv32XiMoyGuCsMeNZesvtAfeWHFi3yRw1lnNu/Q7l+3az4cWn1XLDus7ir36DwROn8OGjfxUzD2P5sKkzWHjTLRzZupFXfnUPzdrJrca1oaW33E722PG8/4+/sO7ZJ/D09wXWGzQ5SZI4//Y7GTQ8j7cf+j0bX3rGFO/v8/lMbY6au5A5X7qBg+s/5rX776O9IXAarBGjqwnUGdllP/qZekbQT++kZNf2kPUsvnhIZ9IrCLMS4pSS2jqxIGtx9qJoEVID6elop6ulOWjx3e/zUbZ3JznjJpmUIqibqja+/CwX3H6n2HSmU7BxHZ889x8uvuPuIGNYvGMrH/zrYfq7u/n2Ey/iCAt8L31eD2ufflz4zhfe9HWmXLjcVP/Q+o9Z/cS/xOawrz74CPHpAdeh1+Ph4ycfYe/q94mMiyciLp7GshJuuP9BU7CBx93PR//+Owc/WUNUYhLjF5/L0KkzePXX9zBi5hyW3nK76bp7V3/A2qf+jeL3M3LOfPJmzycjb7T4XW18+Vm2vP4Sd76wwvSMmyrKWPmX39FSU0XmqLGMXXQOOeMniSg2izMTSZJ2fNrj9kXdM80AlDc2f2HcPhaff3o62ulobBB7TwZSXXCQfWs+YNryK8Wb5Ix0tTSz+bUXaKqs4Mqf/jLk4Kbq0H62vfmKOI7jqw89SnzaoCC5iv172PbWq6YIoknnX8ziL38jSLazuYktr73IoQ1r8fT3YZNlYlPSiE8fRGdTIy211Xz/2eC1BZ/Xw+4P3mXXByvF+ySi4hOISxtETHIKYZFROCMicUVEIDsc2GwyNrus/ivL2GQ7IZffBmRKhBA62XpnOcOnz/piGIDspASlvLH5mAu4FhZfVLpaW2ivrxPRYkeVa2mmYv8e6kuKGD1/cVBoshFPXx/VBQeoKjhIa00VrXU1dLe1kjAok6t/Hvr9CKAerdFQVkLlgb00VVbQVl9DR1Mj7p4e6x0EZxj/9/I7XwwDMHnSJGXnruO/NMPCwuKzw+/34e7txe/1qpvw/D78Xv3f4P0EQRomhM45ET10JumqM4m03GEnbQDsxxf532G5fiwsznxsNlmcgGrx+eaMiwKysLCwsPjfYBkACwsLi7MUywBYWFhYnKVYBsDCwsLic4Di8dFfGngfha/LTecnn+590AOxDICFhYXFGYDiV1B8gaPGvS19tH9QhuJXo5/aV5XT+Mhe3NXq0SkdH5TT/m7pKV3TMgAWFhYW/2P6S9po+OceFG9A4df/KZ+63weO4Wh7u4TOjyvFqN/frR4V4q5UD3CUXKceNXlaDYAkSXGSJL0qSVKBJEmHJEmadTqvZ2FhYXGm4Xf7qH94J/3lgZN321aW4C7vEKN5AG9zH74Ot9jvIEepZzV5atWNd/akcDVd36OWxzpPuW+newbwEPC+oih5wATg0Gm+noWFhcVpx9PQg6/THZTfu7+Juj/lC7cNgLexF09NN60vHxZ5jrRItZ3a4F3Vvlbt+PRY9WBDr6bw9VMw9LTiO/WNcafNAEiSFAvMBx4HUBTFrShK2+m6noWFhcV/E09dd0glD1D/5x3U/ib4nc1tK0tUha8rbQgo7ubAcei6cvcYZgA67ir1GHTdiHjqVCOheLV0g9b2mWwAgCFAI/CkJEm7JEl6TJKkyNN4PQsLC4vjovj8dG+vO+4Iuv7BnSGVvKktv7kNZ6a6Q9pdHojWMSpqXV5/r7Su7CHg0nFXdZnquWu71b5qaX+XB297v2nB+GQ5nQbADkwG/qkoyiSgG7h7oJAkSbdKkpQvSVJ+Y2PjwGILCwuL/yq9e5tofa2Qzo8rTkj+WGcQeRt7TGl9ZO8uC/j7TZE9urw+mq/vxt9vfnmPu6LDXM/rx1PTZVow7i9pFzOCU+F0GoAqoEpRFN2EvopqEEwoivKooihTFUWZmpycfBq7Y2FhYQGSU1V7vQebT0jeW99z1LL+0g5TWh/hGxd8jTMNPaJHKHe/MU8b8Vd04u/3mhR+X1Eris+PFCZji7DTX9QGPv8pRwKdNgOgKEodUClJkv6WjSXAwdN1PQsLC4sTQtPHnpruEzphtL+4LTjTrqrO/hJzma60fa39eJp61UyDAegraNXkFGyRdiSHjb7DLSLPkRYBfoX+4nbwKdiiHDjSI+kvbAOfguSw4cqNpb+4DcXrR7Kfmgo/3VFA3wGekyRpLzAR+O2xxS0sLCxOLyaXjGFhNght8bb3cGtwmebD7ytoRfEYfPE+RWjVPm2GoRsFZ06Mqrg9Pm30bleVeWGbVtePKzdONQpHWlF8CpLdhmt4PP3lHfh7vUiyDdewOHxt/XjqepDkU3t3ymk1AIqi7NbcO+MVRblUUZQQT9LCwsLif4fRd967N/S6o+JXxEyhv6gVn7YJS5T5wZkdjeL20VcUUGuKz489MRxHeqRwMekGJ3xMIorHT19Rm6bcJcJGJuBt6sVT1626eFwyrmFx9B5sRvH4kGSJ8NEJ4FPo3dekpZNAAnd5h5iJnCzWTmALC4uzC80lI8e76NnVENoNpCvtCcngh959TUH1w0bEI4XJ9O4NlCk+BUm2ET4mEXd5B96WPpO8LdJOz476gNz4JLBJdOfXgx8kWSJicgr+Djd9h1tBtuHMjkGOVxeXkW3IMU5cubEAZ/YMwMLCwuJMQx+RR05Nw9vYi7uiM1hGmyU4M6Owp0bQvbVWGAq9vuSSiZiUQs/exsB+Aa8f7BIR09JAgq6ttaItySkTMSWV3oMt+Nr6QJaQo5yE5SXQvV19/zJ2G+GjEpHC7Sj96gxAsklETEoBwN+lXidiopr2Nvae0rOwDICFhcVZhR5tEzElFVuEnc6PK0PIaErebiN6bgae2m7hq9frS7KNqNmDwKfQtaVWlEmyDXusi/DRifRsr8Pf49HakoiclgZ+BU9NN5Ksqt+omekoWiioJEtIdhuR01IB8Gq7giOnpwHg71FfuRk+4b8TMWkZAAsLi7MLTbnbIuxEzcugr6CF/rJ2k4gI3ZTV0bcc4wyczKkvIssSjuQIwsck0rW+Cp+2OUt3y0QvzMLf46XjI22/gWzDkRxBxERVeeuGwTU8TpzzgxZGGj0/U+1Hn6rw7XFhRC/KIuacHLXvTpnEL48h8YZRp/QoLANgYWFxViFcMrJE1OwM5DgXra8W4ncbNmRpkTuSbEOy24i9MBdPdRddG6oN9VX1GXvBEBS/QutbxeoGL80AODOjiZicguIOjO4BYpYNBsDXpc0MJInYi3MBsEWoB8DJUU4Srssj8caAgo89dzAxS7JFOjwvgfCxSaf0LCwDYGFhcVYhwkBtEjaXTPyVw/E299Ly4mEx8g+4eVSlHT4+ibDRibS/XyqieyS7WmZPDCf23CH0HWzGXdkpDANA7IW54v96vj0hjKSvjiXpK2NEWfjIBNJ+MJWIyakiL2J8MuFjTk3BHw/LAFhYWJxd+NVRuiSpCjxsWDxxF+XSd7CZpv/sV49kFi4gVUVKkkTC1SNwpEXS/naJVhaIwImaO4iIqaryNh4gJ0c6SP3+ZGLPH4LkCKjbsBHxuLJjTN2yJ4afclTPp8X+P72ahYWFxWeM4lVMo3SAqDkZSE6Z1jeLqHsgH2d2NGAOs7S57CR/fTyNj+zFU9ct3DWgGoj4y4djTwzDkWo+89KRFimOfz7TOKMMgOJV1LhZCwsLi9OEv8cj3DdGIqel4cqNpf29Unr3a26eAWft2MLtpHx3Ep66bhzpZqUu2SRiFmXzeeKMMgCe+m7q/rD9+IIWFhYWp4Ac5wqZb08MJ/GG0fja+3FXduIaEhskI9kknIOiTncX/yecUQbAHu8i/qoRn3U3LCwsvuA4UiOOWS7HugiPDW0kvkicUQbAFuEgckrq8QUtLCwsLE4ZKwrIwsLC4izFMgAWFhYWZymfOwNQXl6Oz6furOvt7eX/2/v2oKqu+9/POnsfOCgIyEMeIm/wBQISRIckatRwI0mT9iYTo9Mmsb9fkiZ3kl7ndjrTTm96O52k88u1ye10arx5NR3zEGMNNjEaNI2aiMaoKA8RAfEgqIg85M05Z90/Nt/FXvvsA6T3F0TcnxlH1uu71l7nnO9nfb/ftdaura0VZc3Nzbh8+bJIV1ZWor9f21XU39+PiooKUXb58mU0NTX57KejowMul3YM2+VySf2MBZfLJfq1YMGChcmKW4oA2tra8Pbbb+PTTz8FAPzjH//Atm3b0N6u3ce9detWbNmyBQDQ3d2N4uJivP/++wCAzz//HDt27IDTqV38tGXLFrzxxhtC9okTJ3Dw4EEAgNvtxquvvori4mIAQFlZGbZt24Zz584BALq6ulBcXCyU/MDAAA4dOiSI6b333sPLL78sZNfX1+Orr74S6StXrkhk1NbWhrNnz4767E6nc9S3F3V0dKC7u3tUGRYsWLCgxy1BAKRY6f+qKu3Nkn192lWoV69elep7PB6xem9sbAQAkdZbCAAwNKTdx1FSUoIDBw6Acy76qampkeoQeZSVlaGyshLHjx8HAHzzzTfYv38/jh07BkBT+ADQ26u9S/Tdd9/F559/Lsbw5ptvYseOHSL9zjvv4IMPPhDpGzdu4JVXXkFLS4vo98033xQEBQB79uzB4cOHRfrVV1/FK6+8ItINDQ145513pLkrKSlBW9vIe1DLy8tx+vRpkW5ra8PevXtFG3r2gYEBac6MRNTc3IyGhgZYsGDh1sKkJ4BDhw7hd7/7HYaGhuAZfg0bKf7wcO2ejNZW+a0+7e3tkhIDgJkzZwIArl27JuUb2964ccOr7YwZM6S2oaGhUluHwwEAQmH7+fkB0Fb6AGCz2aT69BwkjwiGyp1OJ7q7u3HgwAEAI8RXXV0txnT06FGUlpbCF3bv3o0LFy6IPtra2nDixAls27ZN1Pn73/+OnTt3ivTevXtx5MgRQZoA8Oc//xkvvfSSSLvdbvz2t7+VyGjr1q3461//KvVfW1uLb7/9Vsp7/fXXBUkC2ny9+OKLEoEPDQ3h66+/FmQIaPN1+vRpMW+UV1FR4fVZWbBgYfyY9ARAK9QrV65IP3bOOex27Si20QIw1h0aGhL3ftAK2KikCZcvX5aUz8DAgJBFClpRFClNsigdEREhyaY0WR9jpQMCAkyfy0hWALwUILmBIiMjJZn0/NevX/eSMTio3V0SFBQkjRvQXEv6fqgukZMePT094u9t27Zh9+7dQmlzztHS0iLcdwBQV1cHAMKSAjSrZN++fZLLrKqqCjt37sShQ4dEXm1tLXbs2IEvv/xSGsPmzZvxwQcfSHknT57Eyy+/LBFIX18fXnrpJWGtEXbu3Ildu3ZJeZxzHD58GDduyC8O6enpwYkTJ7wsIo/Hg66uLhjBOReLFwsWJgO+NwJgjKUzxk7p/nUxxl74rnJmzdLOBbS0tEiKubOzUyglUljTp08Xab1ivHbtmmhLSjQ4OFhqGxYWZtpWn7527ZrkIqLVtVE2WQQkm/oiZRwSEiKlycKgNMnv7OyU0marXSMpkAyyUowyzWAchxnRUN5ocsgC0oMIR698CcbPABiZu4sXL4o8VdWOq+iVNZHw+fPnJZldXV1e8ZTS0lL09/dL1l9bWxsGBgbwySefSHVPnz6NU6dOSXnt7e0oLS2VrCcA2LdvH0pKSoRrkHDs2DFs3rzZax7Ly8vxhz/8wWvRwTnHu+++K7njCKdOncLu3bu98gGN2IwuTcKVK1ckQtajra1NuCeN4JwLi9TC1Mf3RgCc8xrOeRbnPAvAYgC9AP7+XeUEBmpHrpubmyXlc/nyZWll7nK5xCpXX2ZMd3Z2oq+vTygk+gGRkjG2NRKCnngGBwclC8HlcmFwcFCkSbaxL19pUqB6ohsaGpL6N65CjUqXZNKqlMr1MkgxkAVFdWgcegVlrKOXQ0FwsmCam5tFGZEx5enbkZVi1h99hmPlGZ/XCH3cIjo6GgBw6dIlkUduOX1MRA99QJ3m0tgXfWeMu8nIcjOSE7Wn2BLB4/Ggvr5ecscRdu3ahW+//VZYXnp8/PHHYtODEX/5y1/w2muvmZb96U9/wubNm03LSktL8fvf/176DurhdDpRUlJiSuiANlcHDx70+p7q0dfXh8rKSp/lBDNXroX/XEyUC+geAHWc88Yxa0JTGvQFo/+NBOB0OkXa4/FI5U6nU/oC6+sC2g+W0pcuXYLb7fZKm9U1S49GTFeuXJEIoaWlBR6Px2ea2hoJSP8spIhJ+RjjDqRwqY2xD32badOmSW3046a/yVoxU+Sk0IwkAYzEXEjp6p/BKKu/v1+UU153d7dQvFTW398vvg/6z95MGenJiJ5Br6j1z2HWXk8WRtcjgSwYIwmTNWnMpznRj200+cCIpaMfjxG+doeZkQbB5XKZtiPFbGbNAdrOuxMnTni5Jwnt7e04cOAA/va3v/ns+8CBAyguLpZiTWbje+2117B9+3afdQifffaZT7IzQ2lp6Xfa1g1on9HXX3896pzeipgoAngUwPtmBYyxf2eMHWeMHW9tbUVrayu2bt0qApz047h69arwn6qqKs4DkOKjtL+/P3p7e8Vq0d/fX5SpqgqbzSbVHRoaQktLi+ino6NDrAoZY7hw4YIo8/Pzk84h6Psl6Ot7PB6JMAYHB6W+BgYGJMIYHBz0skDM5JNsYMRVQnUaGxslN9XAwACuXr0qyTC2IReG3pIxWiekPI1kaJQjXpw9/D8pLn07szz6vPR59DmY5ekJxcxlpVeY9Ay+lLreCqD4iy+y0Nc1Wm4EenajotcvZvTQy6eYC4GsF6ObSa+8jW30MItFEGj7tB6zZ2uvItS74PQga89XOY3LF0EAI9YXfZfNQJ+v0VoyQ1lZGdrb2326tYw4fPiwlztvLDQ0NGDfvn1SDGsq4HsnAMaYH4AHABSblXPOt3LOcznnuREREeKDp90ilOaci6BhcnIympub0dfXh6CgIERERAhFmZSkvYGH/MVJSUloa2tDZ2cnHA4HYmJi0NjYCJfLJepSW/qxUdvk5GR0dnaira0NqqoiLi5OUvBRUVFSOjw8HA0NDXC73UhISJAIhGIZlKYdTJSm1SG1BzSi06dnzJiBhoYGseq12Wy4fPkyenp64Ha7MWPGDPT09KC1tRVut1v80PQy9M9HedevX0dHR4dpHb0l0dvbO6qc7u5ur1hBc3Mz+vv7R21HYzTmkYIYbx4w4i7SKyi9ZUOLCCPJjqc9ICtifSxIH9zV5+tXjJTf1dUlHRT0JR8YseyM+aO10WO0VbaZEqcYjK92tFHAl/I2br4YTcZo4x6PHAKR9miEYobvEusgi3s8hHQrYSIsgP8C4ATn3NuBawL9KpRcFzNmzICqquIgVlJSEjweDxobG6EoCuLj43Hx4kW43W5EREQgMDBQIgtAUzqqqiI+Ph6XLl1Cf38/goODERYWJgghJiYGAQEBwndLBFFXVyf6aW1tRVdXFxRFQWJiIpxOp/A3Jycnw+l0YnBwENOmTUN0dLRQ8NQXpUNCQhAWFiaUc3BwMMLDwyVCSUlJQWNjo1AiKSkpaGlpET781NRUMT79s5LM0NBQzJw5E/X19ZJMen632y3Nj9vtRmBgICIjIyUlTTuK9HJo3ihGMWfOHFGH2jkcDng8HumZgoOD0dTUJJFCaGioeAbKs9vt4nOgPEVRvPJsNpvI83g8YgXa0NAgfuBUl3NuSj56X73emiElPVZd/WdgzNefjxhPvnFXkt6y01s9vmQBskvLTCkSyZmVkVz6PRlBssnS9NUe8O22ojpNTU0+YwlmlqYvREVFAfCeBzPoxzyWXD30W9B9jflWxEQQwDr4cP+YwegWIFdNQkKCWGUlJiZCURQMDAxAURQkJycLJamqKpKTk8WPZfbs2XA4HHC5XKIuKQpFUZCUlIT6+nr09/dDVVUkJCQI0zgmJgbTp09Hf3+/aAsAZ8+eFQTgdrtx/vx5kXa5XGhvbxeym5qa0N3dDVVVkZiYiAsXLgh5CQkJaGxsFOnExESRBjQFPzQ0JFZjKSkpACD8l3FxcfD39xfEGB4ejpCQENTV1cHtdosxNDY2CpJKSUkB51wo5aioKAQGBgrlTm0uXrwo4hfUj54A0tLS4Ha7hWIKCwvDzJkzJUWekJAAPz8/nD9/XmrHOZeskrS0NKk/AEhPT0d9fT1cLpeU19DQYJqnD5bHx8dL8+Z2uxEWFgaHwyHmjurOnj1b9EP5s2fPhsfj8SKlmTNnSs9CLsiAgADxGejr+/n5SStGync4HKb5AHDu3DlJwejdg3qFrW9TU1Nj2sasjHMulKCxL33b/v5+UwuBynt6erxcWca+ffnZ9X2YyTDKMQbTjaDnqaurG/W0PCCTo560x4J+PL7iI7civlcCYIxNB7AagPf2Bh/QT3RVVZVQSqT8AM3ko7TNZpPKFEXB/PnzRdput2Pu3LkiHR8fL0xGqkvWhrGtqqqYN28eAO2LExMTg+DgYPT19QlF6e/vjytXrgiCIJOdZHk8HnR3d4v00NAQ2traRHpwcBCtra2ir6GhIbGNMTU1FaqqisBcQkICpk+fLrYL2u12pKSkiGslFEXB3LlzUVdXh56eHqiqivT0dAwODgqFk5iYCIfDgcrKSrjdWlwkNTUV586dE0REyv3cuXNwu92w2+1ITk5GTU2NWFUnJSVBVVWcPXtWfEapqamor69HX1+fUI6JiYk4d+6cULAJCQnw9/cX7QBNibvdbtTW1kp5g4ODUgyE8vQWRXp6Olwul0QoycnJksVIz5CSkoLa2lopKD5v3jzRD9WNj4+Hw+GQ2lPdgYEBqa7dbkdqaqo0dnr2lJQUScnq58mYT/J7e3u93EyJiYmw2+3S9lZqk5CQYNoG0OIH3d3dprGPqKgo9Pb2eil5t9uNoKAgqKoqHTzUl9vtdthsNtOdPHrLrLKyclQrwWaziVP9vuoA8CnHWPf69es+d4WZya2qqhqTMHy1myr4XgmAc97DOQ/jnHeOtw1NtL+/P06fPi1W7gsWLBB19OmWlhbY7XbhrmGMib8BTYmTUifFm5aWRuNDfHy82A2jL6M0te3v7wdjTKSHhoagqqpELna7Henp6WIc0dHRYgcKuZBoeyRZAPq+ScGTaTpt2jQhj+QvWLBArAQVRUFmZqY03oyMDLjdbjidTkFS06ZNEz9WPz8/zJ8/X6RJxuDgoLBsEhISEBQUhDNnzgillZmZiZ6eHkEkDocD8+bNQ0VFBQYHB0Udt9stiFtVVWRkZKCrq0us4ugZqqqqBJFSf+Xl5ZJi9/f3x6lTpyRLweFwSPVSU1MREBAg5TkcDqSlpaGiokJYC/RZ9vT0CAsJ0Cwi6ocsQ1o0VFdXS9t809LSYLfbBQHTd3PevHno6+uT3FNExt3d3VLcgvJ7e3u93FHp6elQFEVSrGQBp6SkoLq62mu31Ny5c03bABqhGBX1aO2onBZY1dXVphZCYGAgkpKSTBUoyc/MzERHR4dPK4EIciySSEtLQ0dHx6i7oNxuN2JjY2Gz2XDmzBmf9fRyIyMjcf36dZ8WiK92wcHBOHPmzJRxA026k8A00dnZ2ejs7ER9fT0URRGBI0BW1PRBECHcuHEDdrtdBG1ICepBSryzsxM2m020HRgYgL//yFuAbDYb4uPjpbYZGRkARoKjCxcuBDCy5Y7SDQ0NYIyJ+mQF6Psypm02m2hP/VN7ehZ9ud4tRemYmBhxCExRFFPyzMzMFPNGxESHwBRFEf3SqpYssICAAHG9g6IoWLRokdjCSX2HhYUJpa0oCtLT0+FwOMRpX1VVsWjRIgwNDeHMmTOiv8zMTJw/fx6dnZ1gjMHPzw8ZGRmorq4WMQ9/f39kZGSgqqpK5Pn5+SEzMxNnz54Ve88VRUFOTg56e3tRU1MjxpKWloZp06bhxIkTEllkZmZKMqn94OCgsJQAzfLMyMhARUWFsHJI7vTp08XcUP68efOkZ9fPybRp06R8QCP8+fPno7y8XLjsqE1OTg56enqEFaAfk1kbQDuLMXfuXJw6dUp8P0drp+8vMzMTN27c8Ap6UnlGRgY6Ojq83Cgkf8GCBVBV1es6EL2MhQsXorOz09QVY5Rz4sQJrzr6uoGBgUhJSRGLxtHqAtrvWFEUnDx50mdds3bZ2dno6uqaMndfTVoCWLhwobQ6BoCf/vSnyM3NhZ+fHxwOBx588EE8+uijAICsrCwsX74c+fn5AIBnn30WK1aswPTp06GqKtatW4d169YB0FaNq1atwvLlywEAixcvBjASHNu4cSPmzJmD0NBQKIqCRx55BGvXrgWgxQX00CtgQFtRhoeHY8WKFZJs+gHm5uYCGDlFfMcddwAY2QaZl5cnZNlsNhHopXRcXJxwM9lsNskKIStlyZIlAEaCcNQHzeWcOXMEodpsNthsNjFO2sWzePFiiSRUVUV2drb4fIhY9WTDGENubi6cTid6e3uhKArsdjsWLVoktiNS/xEREejr6xM7lbKzs+HxeHDy5Enxeefk5MDlcqGsrEyMMycnB263G0eOHBHyzPKSkpIQHByMI0eOwOVyQVVVqKqKrKws1NTUSNd6UPujR4+KvLi4OERERKCsrEwoFEVRkJubC5fLJUiESDY7Oxu1tbVoa2sT+Xa7HdnZ2Th79qzYZaWfy5qaGrS3t0vy8/LyMDAwgPLycgAjyjI5ORmhoaEoKyuTtvn6akNlS5YsQX9/v7BajGWDg4OSEtSTVHBwMMrKyqAHlS9YsACBgYFizvXlgHaAc9GiRSgvL/e6pVZvkQUFBeHrr7+GEXoSy8rKQnl5uc/DZSQvLy8P3d3dpieqzeRmZmbi1KlT47pFV09I06dPl64quZUxaQnA4XBg2bJlAEaCLrNnz0ZRUZFQ1FlZWUL5KYqC5cuXi8M5oaGhuPvuu0Xd9PR04U6x2WwoKCgQWy+joqKwceNGobTj4uLw5JNPCkU7f/58SYm+8MILeOqpp4SsJ554Aj/84Q/FOJ577jksWrQIgHYIacOGDXjooYcAaKbnfffdJ+pHRkZizZo1+NGPfgRA211DB6tI3v333y+5lmicZDoXFhYiMjJSWDo5OTkARrYQ0i4eAOIsREFBAYCRG0uJeGj+w8LChJVFK0T6PGhcejm0P37x4sWCuEm5G9sxxnD33XcDGDlNHB4e7mVdxcTEeFl60dHRSEtLE2mbzYZZs2Zh7ty5QgHS2O688040NTWhqalJkMrSpUths9kksoiOjkZqaqq4XZXGeNddd+Hq1aviagiycpKTk3H48GFBcgCQn58PVVWxf/9+oZAAYMmSJWCM4cCBA175iqKI+iR/9uzZmDNnDr788kvhfqLnWbZsGZqamoRVo28TFxeHgwcPSi4rItvY2FhJnrGvQ4cOSTueiNTy8/PR2NgoBXP1JLZkyRLU1dWZBqcVRcHSpUvh8XikiwPNZNTX13utqM3k6O+CMpOXnJyM6OhoHDp0yOcWT73cgoICuN1u6VZdXzDqpfr6+u+87XQyYtISgKIoQunSHvrvE3FxcWIP9FgICQkRZwYALbCs98UbkZKSIlbKgKZs6cANoCnIhIQEkd60aZMgGEBTqmS9AJqyWb9+vXAHhYSE4Gc/+5nYDufn54enn34aTz75pGjz85//HPfff79wcd1xxx1Ys2aNUPwBAQF47LHHsGHDBtFm5cqVAEZOtpKZDUDIycrKwqJFi4Tl5efnh7vuugvAyCGk4OBg8XzkmtMH2wlECnoQ2elB4/JVj5Rsdna2GDsphKCgICxdutSr7j333OOVt2DBAkRFRQnlRPmrVq1CX18fLly4IPICAwOxbNkyVFVV4ezZs+I5Q0JCkJ+fj9OnT4vdYoB2pmPp0qWoqKgQCpaIZ82aNejp6fEijZycHISHh+Ozzz6T3FWMMRQWFoobZPW/IcYY7r33Xty4cQP//Oc/pTIAuPfee0VfAKT+7rjjDoSFhWHPnj1eLilAI7Hg4GB88sknXltuFUVBeHg4cnNz8c0333gFovUyQkJCJBlGOWFhYcjNzcWxY8dMt26SPMYYVq9ejfb2dq9LAn3JzcnJwdGjR0eNMRjb5ebmIjg4GLt3777l702a1ATg7++PTZs2CTfP7QKHwyERjBGMMbFDyBeioqKE8gM0JUxuHgBiRamPraSlpUk7qqKiovDLX/5SWBQA8Nhjj+Gpp54S7RRFwUMPPSTOAQAaweXn50uKdv369Xj44YcFmdtsNmzatAlPP/20qBMeHo777rtPuMkAbcW/bNkyyRUWFRWF/Px8KT4za9YsZGdnC9k0tgcffBAApFOiRFBUh2TSc5JlZbPZRHt93ejoaGH56IOIBQUFmDVrFlwul7jIj/oLCwtDd3e35Ma48847ERERIVkjgGbpLlmyBEePHpWsDEVR8MADD6CzsxMfffQRgBFCjY2NRV5eHo4ePSpcOtRuzpw5yM3NxZEjR4R7hMpiY2OxZMkSHDt2TATSqUxVVRQVFaG9vR27du0Su6eo3M/PD0VFRWhtbRX3AxkJZuXKlQgKCsKHH34onl0vw263o6ioCNeuXcPHH3/stTNKT9AzZszA9u3bvVxBenlJSUnIysrC4cOHfe5i0stdtWoVAgMDTeX6aufv74/7778fbW1t0phvRUxqAgC0FRtt27Qw8XA4HMKNBmhKcTRyojqFhYWIjY0VebT7Ry8rKChIWC2EvLw8FBUVSXlr1qzB+vXrpbzCwkI88cQTUl5RUREeffRRicTi4uKwfv164XKjsTz//PN46KGHxPcMANauXYu1a9dKQfOoqCgUFRWJsxCEFStWIC4uTorZ2O12PPzwwwBGLjEENGvpkUceAQCJtP38/EQ+AGksq1evFgSnX2XOmTMHhYWF4kyMvs2aNWsQHx8v/Pb6ssLCQsTFxYmVsVlfu3btQktLi1SWmJiI1atXo7q6Gtu3bxebGQipqalYuXIlzpw5g507dwqipToBAQFYt24d+vr68NZbb4mzPXoZKSkpuOeee1BRUYGPPvpIOiRI9RwOh5ccglHe2rVrERsbi+LiYq/ruo1yaXy9vb14++23fV5hYWynH/POnTu9Xpp0q8D3EvImwTjRFiyMF4qiSNtyCXrrgRAaGiq55ai9PtZDyM3NlawSqrtx40avuuHh4XjhhRe8VoWzZs3Cc88957VDJSIiAs888wyOHTsmWWyqqmL9+vXYu3evtBMM0NwmAwMD+Oqrr8TuLX2bN954A1evXpUIS1VVbNiwAW+88QZaW1tFfEjfrri4WDrPQFi2bBkYY9i3bx8455LVCGiWDGMM+/fvl86kEKKjo/GTn/wE7733Hl5//XV4PB5xFQqhoKAAjDGUlpbi4sWLYi5Gk5OXl4e8vDwvArDb7diwYQM+/PBDlJSUoKKiAgUFBeLgplFuTEwMfvzjH+P999/Hli1bhFyKEQLy2QWzMTc2NuLuu+9GRkaGNO+THWy8ByEmArm5ufyPf/wj9u/fj1/96ldSMNSCBQsyOOeSRUXweDxoaWlBdHS0pLCozOl0Ii4uzrSssrISkZGRpnG35uZm7NmzB/PmzZMC+wSn04nt27fD5XJh06ZNXi7Knp4efP755zh16hTS0tLw2GOPecloamrCp59+Klxrzz//vBdRk5zy8nKxui8oKMCqVau8nuf48eP44osv0NfXJ87g9PT04PHHH5fiboC2Vbu0tFTIjYyMRFxcHMLDw3Hy5ElcvXoVL774oulz79mzB83NzWIHWWxsLGbOnImQkBA4HA44HA74+/uLTRj6f/+/YIx9yznPHbumSdvJRACxsbH8qaeegsfjwW9+85v/lMmxYMHCxIFzLk7V+0JXVxdUVRW7xcxkOJ1OXLt2DdnZ2aYkB2jneM6cOYOLFy96baTQY2hoCNXV1aitrUVtbS1cLheeffZZL2Ixyq2vrxeXGQLaOY1f/OIXo465qqoKFy5cEBcyjgfGufL1vL7yf/3rX08NAmCM3QAwta7b+9cRDuDamLWmPqx5GIE1FyOw5mIE6ZzzoLGreWOyxQBq/lUmm2pgjB235sKaBz2suRiBNRcjYIwdH7uWOSwfiwULFizcprAIwIIFCxZuU0w2Ath6swcwiWDNhQZrHkZgzcUIrLkYwb88F5MqCGzBggULFiYOk80CsGDBggULEwSLACxYsGDhNsWEEwBjrJAxVsMYO88Y+6VJuT9j7MPh8qOMsYSJHuNEYRxz8d8ZY1WMsdOMsf2MsXgzOVMBY82Frt6PGGOcMTZltwCOZy4YY48MfzcqGWPvTfQYJwrj+I3MYYx9wRg7Ofw7ue9mjHMiwBh7izF2lTFW4aOcMcb+z/BcnWaM5ZjVk0AviZ6IfwAUAHUAkgD4ASgHMN9Q52cAtgz//SiADydyjJNsLlYAmDb89zO381wM1wsCcBBAGYDcmz3um/i9SAVwEkDocDryZo/7Js7FVgDPDP89H8CFmz3u73E+7gKQA6DCR/l9APYAYADyARwdS+ZEWwB5AM5zzus554MAPgDwA0OdHwD46/DfOwDcw3ydgb61MeZccM6/4JzTPcZlAGZjamI83wsA+B2APwDon8jBTTDGMxf/BuDPnPN2AOCcm19heetjPHPBAdCNeMEAxveS31sQnPODAK6PUuUHAN7lGsoAhDDGRr26d6IJIBaAU5duGs4zrcM5dwHoBBCGqYfxzIUeG6Gx+1TEmHMxbM7Gcc4/mciB3QSM53uRBiCNMfYVY6yMMVY4YaObWIxnLl4EsIEx1gTgUwD/bWKGNinxXXXKpLsKwoIJGGMbAOQC8H5l1m0AxpgNwGYAj9/koUwWqNDcQMuhWYUHGWMZnPOOmzmom4R1AN7hnP9vxthSAH9jjC3knN+6b2mZQEy0BXAJQJwuPXs4z7QOY0yFZta1TcjoJhbjmQswxlYB+BWABzjnt+ZbJ8bGWHMRBGAhgH8yxi5A82+WTNFA8Hi+F00ASjjnQ5zzBgDnoBHCVMN45mIjgO0AwDk/AsAB7aK42xHj0il6TDQBfAMglTGWyBjzgxbkLTHUKQHwk+G//yuAA3w4wjHFMOZcMMayAbwOTflPVT8vMMZccM47OefhnPMEznkCtHjIA5zzf/kSrEmM8fxGdkFb/YMxFg7NJVQ/gWOcKIxnLi4CuAcAGGPzoBFA64SOcvKgBMCPh3cD5QPo5Jy3jNZgQl1AnHMXY+w5AHuhRfjf4pxXMsb+F4DjnPMSAG9CM+POQwt4TMkXAo9zLv4DQCCA4uE4+EXO+QM3bdDfE8Y5F7cFxjkXewGsYYxVAXAD+B+c8ylnJY9zLjYB+L+MsZ9DCwg/PkUXjGCMvQ+N+MOHYx7/E4AdADjnW6DFQO4DcB5AL4AnzCXpZE7RubJgwYIFC2PAOglswYIFC7cpLAKwYMGChdsUFgFYsGDBwm0KiwAsWLBg4TaFRQAWLFiwcJvCIgALFixYuE1hEYAFCxYs3Kb4fzv2cGwJvfW1AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "scale = 180\n", + "plt.figure()\n", + "for i in range(A.n_neurons):\n", + " plt.plot(sim.trange(), sim.data[A_spikes][:, i] - i * scale)\n", + "plt.xlim(0, 1)\n", + "plt.ylim(scale * (-A.n_neurons + 1), scale)\n", + "plt.ylabel(\"Neuron\")\n", + "plt.yticks(\n", + " np.arange(scale / 1.8, (-A.n_neurons + 1) * scale, -scale), np.arange(A.n_neurons)\n", + ")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we mulitply those filtered spike trains\n", + "with decoding weights and sum them together\n", + "to give an estimate of the input based on the spikes.\n", + "\n", + "The decoding weights are determined\n", + "by minimizing the squared difference\n", + "between the decoded estimate and the actual input signal." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.745655Z", + "iopub.status.busy": "2020-11-25T16:46:48.745173Z", + "iopub.status.idle": "2020-11-25T16:46:48.748619Z", + "shell.execute_reply": "2020-11-25T16:46:48.748195Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms PSC filter" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:48.754420Z", + "iopub.status.busy": "2020-11-25T16:46:48.753658Z", + "iopub.status.idle": "2020-11-25T16:46:49.078797Z", + "shell.execute_reply": "2020-11-25T16:46:49.079215Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded estimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The accuracy of the decoded estimate increases\n", + "as the number of neurons increases." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.087828Z", + "iopub.status.busy": "2020-11-25T16:46:49.086482Z", + "iopub.status.idle": "2020-11-25T16:46:49.088675Z", + "shell.execute_reply": "2020-11-25T16:46:49.089083Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: t * 2 - 1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.097764Z", + "iopub.status.busy": "2020-11-25T16:46:49.096696Z", + "iopub.status.idle": "2020-11-25T16:46:49.787411Z", + "shell.execute_reply": "2020-11-25T16:46:49.787902Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(15, 3.5))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "eval_points, activities = tuning_curves(A, sim)\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "\n", + "ax = plt.subplot(1, 3, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded esimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any smooth signal can be encoded and decoded." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.795816Z", + "iopub.status.busy": "2020-11-25T16:46:49.795070Z", + "iopub.status.idle": "2020-11-25T16:46:49.797625Z", + "shell.execute_reply": "2020-11-25T16:46:49.797188Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(WhiteSignal(1, high=5), size_out=1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:49.805169Z", + "iopub.status.busy": "2020-11-25T16:46:49.804385Z", + "iopub.status.idle": "2020-11-25T16:46:50.346763Z", + "shell.execute_reply": "2020-11-25T16:46:50.346342Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded esimate\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 2: Transformation\n", + "\n", + "Encoding and decoding allow us to encode signals over time,\n", + "and decode transformations of those signals.\n", + "\n", + "In fact, we can decode arbitrary transformations of the input signal,\n", + "not just the signal itself (as in the previous example).\n", + "\n", + "Let's decode the square of our white noise input." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.355536Z", + "iopub.status.busy": "2020-11-25T16:46:50.354365Z", + "iopub.status.idle": "2020-11-25T16:46:50.358031Z", + "shell.execute_reply": "2020-11-25T16:46:50.358663Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(WhiteSignal(1, high=5), size_out=1)\n", + " input_probe = nengo.Probe(input)\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " Asquare = nengo.Node(size_in=1)\n", + " nengo.Connection(input, A)\n", + " nengo.Connection(A, Asquare, function=np.square)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " Asquare_probe = nengo.Probe(Asquare, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.366502Z", + "iopub.status.busy": "2020-11-25T16:46:50.365674Z", + "iopub.status.idle": "2020-11-25T16:46:50.956715Z", + "shell.execute_reply": "2020-11-25T16:46:50.957153Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(1)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input signal\")\n", + "plt.plot(sim.trange(), sim.data[Asquare_probe], label=\"Decoded esimate\")\n", + "plt.plot(sim.trange(), np.square(sim.data[input_probe]), label=\"Input signal squared\")\n", + "plt.legend(loc=\"best\", fontsize=\"medium\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 1)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes])\n", + "plt.xlim(0, 1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the spike trains are exactly the same.\n", + "The only difference is how we're interpreting those spikes.\n", + "We told Nengo to compute a new set of decoders\n", + "that estimate the function $x^2$.\n", + "\n", + "In general, the transformation principle\n", + "determines how we can decode spike trains\n", + "to compute linear and nonlinear transformations of signals\n", + "encoded in a population of neurons.\n", + "We can then project those transformed signals\n", + "into another population, and repeat the process.\n", + "Essentially, this provides a means of\n", + "computing the neural connection weights\n", + "to compute an arbitrary function between populations.\n", + "\n", + "Suppose we are representing a sine wave." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.965051Z", + "iopub.status.busy": "2020-11-25T16:46:50.964551Z", + "iopub.status.idle": "2020-11-25T16:46:50.967527Z", + "shell.execute_reply": "2020-11-25T16:46:50.968216Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: np.sin(np.pi * t))\n", + " A = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(input, A)\n", + " A_spikes = nengo.Probe(A.neurons)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:50.975228Z", + "iopub.status.busy": "2020-11-25T16:46:50.974404Z", + "iopub.status.idle": "2020-11-25T16:46:51.671040Z", + "shell.execute_reply": "2020-11-25T16:46:51.670576Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.title(\"A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(1, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear transformations of that signal\n", + "involve solving for the usual decoders,\n", + "and scaling those decoding weights.\n", + "Let us flip this sine wave upside down\n", + "as it is transmitted between two populations\n", + "(i.e. population A and population -A)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:51.677512Z", + "iopub.status.busy": "2020-11-25T16:46:51.677019Z", + "iopub.status.idle": "2020-11-25T16:46:51.680129Z", + "shell.execute_reply": "2020-11-25T16:46:51.680539Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " minusA = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(A, minusA, function=lambda x: -x)\n", + " minusA_spikes = nengo.Probe(minusA.neurons)\n", + " minusA_probe = nengo.Probe(minusA, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:51.689640Z", + "iopub.status.busy": "2020-11-25T16:46:51.688871Z", + "iopub.status.idle": "2020-11-25T16:46:52.574321Z", + "shell.execute_reply": "2020-11-25T16:46:52.574974Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.plot(sim.trange(), sim.data[minusA_probe])\n", + "plt.title(\"-A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(2, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(2, 2, 4)\n", + "rasterplot(sim.trange(), sim.data[minusA_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"-A\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nonlinear transformations involve\n", + "solving for a new set of decoding weights.\n", + "Let us add a third population connected\n", + "to the second one and use it to compute $(-A)^2$." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:52.581507Z", + "iopub.status.busy": "2020-11-25T16:46:52.581009Z", + "iopub.status.idle": "2020-11-25T16:46:52.584468Z", + "shell.execute_reply": "2020-11-25T16:46:52.584057Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " A_squared = nengo.Ensemble(30, dimensions=1, max_rates=Uniform(80, 100))\n", + " nengo.Connection(minusA, A_squared, function=lambda x: x ** 2)\n", + " A_squared_spikes = nengo.Probe(A_squared.neurons)\n", + " A_squared_probe = nengo.Probe(A_squared, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:52.596134Z", + "iopub.status.busy": "2020-11-25T16:46:52.595308Z", + "iopub.status.idle": "2020-11-25T16:46:53.754257Z", + "shell.execute_reply": "2020-11-25T16:46:53.754682Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)\n", + "\n", + "plt.figure(figsize=(10, 6.5))\n", + "plt.subplot(3, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(3, 2, 3)\n", + "plt.plot(sim.trange(), sim.data[minusA_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"-A\")\n", + "plt.xticks(())\n", + "plt.xlim(0, 2)\n", + "\n", + "plt.subplot(3, 2, 5)\n", + "plt.plot(sim.trange(), sim.data[A_squared_probe])\n", + "plt.axhline(0, color=\"k\")\n", + "plt.title(\"(-A)^2\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.xlim(0, 2)\n", + "\n", + "ax = plt.subplot(3, 2, 2)\n", + "rasterplot(sim.trange(), sim.data[A_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(3, 2, 4)\n", + "rasterplot(sim.trange(), sim.data[minusA_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"-A\")\n", + "plt.xticks(())\n", + "plt.ylabel(\"Neuron\")\n", + "\n", + "ax = plt.subplot(3, 2, 6)\n", + "rasterplot(sim.trange(), sim.data[A_squared_spikes], ax)\n", + "plt.xlim(0, 2)\n", + "plt.title(\"(-A)^2\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Neuron\")\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Principle 3: Dynamics\n", + "\n", + "So far, we have been considering the values\n", + "represented by ensembles as generic \"signals.\"\n", + "However, if we think of them instead\n", + "as state variables in a dynamical system,\n", + "then we can apply the methods of control theory\n", + "or dynamic systems theory to brain models.\n", + "Nengo automatically translates from standard dynamical systems descriptions\n", + "to descriptions consistent with neural dynamics.\n", + "\n", + "In order to get interesting dynamics,\n", + "we can connect populations recurrently (i.e., to themselves).\n", + "\n", + "Below is a simple harmonic oscillator\n", + "implemented using this third principle.\n", + "It needs is a bit of input to get it started." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:53.762833Z", + "iopub.status.busy": "2020-11-25T16:46:53.762328Z", + "iopub.status.idle": "2020-11-25T16:46:53.765389Z", + "shell.execute_reply": "2020-11-25T16:46:53.766009Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"NEF summary\")\n", + "with model:\n", + " input = nengo.Node(lambda t: [1, 0] if t < 0.1 else [0, 0])\n", + " oscillator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(input, oscillator)\n", + " nengo.Connection(oscillator, oscillator, transform=[[1, 1], [-1, 1]], synapse=0.1)\n", + " oscillator_probe = nengo.Probe(oscillator, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:53.773456Z", + "iopub.status.busy": "2020-11-25T16:46:53.772439Z", + "iopub.status.idle": "2020-11-25T16:46:54.633754Z", + "shell.execute_reply": "2020-11-25T16:46:54.633303Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(3)\n", + "\n", + "plt.figure(figsize=(10, 3.5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sim.trange(), sim.data[oscillator_probe])\n", + "plt.ylim(-1.2, 1.2)\n", + "plt.xlabel(\"Time (s)\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(sim.data[oscillator_probe][:, 0], sim.data[oscillator_probe][:, 1])\n", + "plt.grid()\n", + "plt.axis([-1.2, 1.2, -1.2, 1.2])\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$x_2$\")\n", + "hide_input()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/advanced/nef_algorithm.html b/examples/advanced/nef_algorithm.html new file mode 100644 index 0000000000..9c0ccac361 --- /dev/null +++ b/examples/advanced/nef_algorithm.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/advanced/nef_summary.html b/examples/advanced/nef_summary.html new file mode 100644 index 0000000000..092e66b2c1 --- /dev/null +++ b/examples/advanced/nef_summary.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/advanced/processes.html b/examples/advanced/processes.html new file mode 100644 index 0000000000..36cdae6878 --- /dev/null +++ b/examples/advanced/processes.html @@ -0,0 +1,1135 @@ + + + + + + + + Processes and how to use them — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Processes and how to use them

+

Processes in Nengo can be used to describe general functions or dynamical systems, including those with randomness. They can be useful if you want a Node output that has a state (like a dynamical system), and they’re also used for things like injecting noise into Ensembles so that you can not only have “white” noise that samples from a distribution, but can also have “colored” noise where subsequent samples are correlated with past samples.

+

This notebook will first present the basic process interface, then demonstrate some of the built-in Nengo processes and how they can be used in your code. It will also describe how to create your own custom process.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

Interface

+

We will begin by looking at how to run an existing process instance.

+

The key functions for running processes are run, run_steps, and apply. The first two are for running without an input, and the third is for applying the process to an input.

+

There are also two helper functions, trange and ntrange, which return the time points corresponding to a process output, given either a length of time or a number of steps, respectively.

+
+

run: running a process for a length of time

+

The run function runs a process for a given length of time, without any input. Many of the random processes in nengo.processes will be run this way, since they do not require an input signal.

+
+
[2]:
+
+
+
+# Create a process (details on the FilteredNoise process below)
+process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)
+
+# run the process for two seconds
+y = process.run(2.0)
+
+# get a corresponding two-second time range
+t = process.trange(2.0)
+
+plt.figure()
+plt.plot(t, y)
+plt.xlabel("time [s]")
+plt.ylabel("process output")
+
+
+
+
+
[2]:
+
+
+
+
+Text(0, 0.5, 'process output')
+
+
+
+
+
+
+../../_images/examples_advanced_processes_4_1.png +
+
+
+
+

run_steps: running a process for a number of steps

+

To run the process for a number of steps, use the run_steps function. The length of the generated signal will depend on the process’s default_dt.

+
+
[3]:
+
+
+
+process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)
+
+# run the process for 1000 steps
+y = process.run_steps(1000)
+
+# get a corresponding 1000-step time range
+t = process.ntrange(1000)
+
+plt.figure()
+plt.plot(t, y)
+plt.xlabel("time [s]")
+plt.ylabel("process output")
+
+
+
+
+
[3]:
+
+
+
+
+Text(0, 0.5, 'process output')
+
+
+
+
+
+
+../../_images/examples_advanced_processes_6_1.png +
+
+
+
+

apply: running a process with an input

+

To run a process with an input, use the apply function.

+
+
[4]:
+
+
+
+process = nengo.synapses.Lowpass(0.2)
+
+t = process.trange(5)
+x = np.minimum(t % 2, 2 - (t % 2))  # sawtooth wave
+y = process.apply(x)  # general to all Processes
+z = process.filtfilt(x)  # specific to Synapses
+
+plt.figure()
+plt.plot(t, x, label="input")
+plt.plot(t, y, label="output")
+plt.plot(t, z, label="filtfilt")
+plt.xlabel("time [s]")
+plt.ylabel("signal")
+plt.legend()
+
+
+
+
+
[4]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7ff27681e048>
+
+
+
+
+
+
+../../_images/examples_advanced_processes_8_1.png +
+
+

Note that Synapses are a special kind of process, and have the additional functions filt and filtfilt. filt works mostly the same as apply, but with some additional functionality such as the ability to filter along any axis. filtfilt provides zero-phase filtering.

+
+
+

Changing the time-step (dt and default_dt)

+

To run a process with a different time-step, you can either pass the new time step (dt) when calling the functions, or change the default_dt property of the process.

+
+
[5]:
+
+
+
+process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)
+y1 = process.run(2.0, dt=0.05)
+t1 = process.trange(2.0, dt=0.05)
+
+process = nengo.processes.FilteredNoise(
+    synapse=nengo.synapses.Alpha(0.1), default_dt=0.1, seed=0
+)
+y2 = process.run(2.0)
+t2 = process.trange(2.0)
+
+plt.figure()
+plt.plot(t1, y1, label="dt = %s" % 0.05)
+plt.plot(t2, y2, label="dt = %s" % 0.1)
+plt.xlabel("time [s]")
+plt.ylabel("output")
+
+
+
+
+
[5]:
+
+
+
+
+Text(0, 0.5, 'output')
+
+
+
+
+
+
+../../_images/examples_advanced_processes_11_1.png +
+
+
+
+
+

WhiteSignal

+

The WhiteSignal process is used to generate band-limited white noise, with only frequencies below a given cutoff frequency.

+
+
[6]:
+
+
+
+with nengo.Network() as model:
+    a = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, seed=0))
+    b = nengo.Node(nengo.processes.WhiteSignal(1.0, high=10, seed=0))
+    c = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, rms=0.3, seed=0))
+    d = nengo.Node(nengo.processes.WhiteSignal(0.5, high=5, seed=0))
+    ap = nengo.Probe(a)
+    bp = nengo.Probe(b)
+    cp = nengo.Probe(c)
+    dp = nengo.Probe(d)
+
+with nengo.Simulator(model) as sim:
+    sim.run(1.0)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[ap], label="5 Hz cutoff")
+plt.plot(sim.trange(), sim.data[bp], label="10 Hz cutoff")
+plt.plot(sim.trange(), sim.data[cp], label="5 Hz cutoff, 0.3 RMS amplitude")
+plt.plot(sim.trange(), sim.data[dp], label="5 Hz cutoff, 0.5 s period")
+plt.xlabel("time [s]")
+plt.legend(loc=2)
+
+
+
+
+
[6]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7ff2766972b0>
+
+
+
+
+
+
+../../_images/examples_advanced_processes_13_1.png +
+
+

Note that the 10 Hz signal (green) has similar low frequency characteristics as the 5 Hz signal (blue), but with additional higher-frequency components. The 0.3 RMS amplitude 5 Hz signal (red) is the same as the original 5 Hz signal (blue), but scaled down (the default RMS amplitude is 0.5). Finally, the signal with a 0.5 s period (instead of a 1 s period like the others) is completely different, because changing the period changes the spacing of the random frequency components and thus creates +a completely different signal. Note how the signal with the 0.5 s period repeats itself; for example, the value at \(t = 0\) is the same as the value at \(t = 0.5\), and the value at \(t = 0.4\) is the same as the value at \(t = 0.9\).

+
+
+

WhiteNoise

+

The WhiteNoise process generates white noise, with equal power across all frequencies. By default, it is scaled so that the integral process (Brownian noise) will have the same standard deviation regardless of dt.

+
+
[7]:
+
+
+
+process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 1))
+
+t = process.trange(0.5)
+y = process.run(0.5)
+plt.figure()
+plt.plot(t, y)
+
+
+
+
+
[7]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7ff2765fbba8>]
+
+
+
+
+
+
+../../_images/examples_advanced_processes_16_1.png +
+
+

One use of the WhiteNoise process is to inject noise into neural populations. Here, we create two identical ensembles, but add a bit of noise to one and no noise to the other. We plot the membrane voltages of both.

+
+
[8]:
+
+
+
+process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 0.01), seed=1)
+
+with nengo.Network() as model:
+    ens_args = dict(encoders=[[1]], intercepts=[0.01], max_rates=[100])
+    a = nengo.Ensemble(1, 1, **ens_args)
+    b = nengo.Ensemble(1, 1, noise=process, **ens_args)
+    a_voltage = nengo.Probe(a.neurons, "voltage")
+    b_voltage = nengo.Probe(b.neurons, "voltage")
+
+with nengo.Simulator(model) as sim:
+    sim.run(0.15)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[a_voltage], label="deterministic")
+plt.plot(sim.trange(), sim.data[b_voltage], label="noisy")
+plt.xlabel("time [s]")
+plt.ylabel("voltage")
+plt.legend(loc=4)
+
+
+
+
+
[8]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7ff2766b1438>
+
+
+
+
+
+
+../../_images/examples_advanced_processes_18_1.png +
+
+

We see that the neuron without noise (blue) approaches its firing threshold, but never quite gets there. Adding a bit of noise (green) causes the neuron to occasionally jitter above the threshold, resulting in two spikes (where the voltage suddenly drops to zero).

+
+
+

FilteredNoise

+

The FilteredNoise process takes a white noise signal and passes it through a filter. Using any type of lowpass filter (e.g. Lowpass, Alpha) will result in a signal similar to WhiteSignal, but rather than being ideally filtered (i.e. no frequency content above the cutoff), the FilteredNoise signal will have some frequency content above the cutoff, with the amount depending on the filter used. Here, we can see how an Alpha filter (a second-order lowpass filter) is much +better than the Lowpass filter (a first-order lowpass filter) at removing the high-frequency content.

+
+
[9]:
+
+
+
+process1 = nengo.processes.FilteredNoise(
+    dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Alpha(0.005), seed=0
+)
+
+process2 = nengo.processes.FilteredNoise(
+    dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Lowpass(0.005), seed=0
+)
+
+tlen = 0.5
+plt.figure()
+plt.plot(process1.trange(tlen), process1.run(tlen))
+plt.plot(process2.trange(tlen), process2.run(tlen))
+
+
+
+
+
[9]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7ff276587b38>]
+
+
+
+
+
+
+../../_images/examples_advanced_processes_21_1.png +
+
+

The FilteredNoise process with an Alpha synapse (blue) has significantly lower high-frequency components than a similar process with a Lowpass synapse (green).

+
+
+

PresentInput

+

The PresentInput process is useful for presenting a series of static inputs to a network, where each input is shown for the same length of time. Once all the images have been shown, they repeat from the beginning. One application is presenting a series of images to a classification network.

+
+
[10]:
+
+
+
+inputs = [[0, 0.5], [0.3, 0.2], [-0.1, -0.7], [-0.8, 0.6]]
+process = nengo.processes.PresentInput(inputs, presentation_time=0.1)
+
+tlen = 0.8
+plt.figure()
+plt.plot(process.trange(tlen), process.run(tlen))
+plt.xlim([0, tlen])
+plt.ylim([-1, 1])
+
+
+
+
+
[10]:
+
+
+
+
+(-1.0, 1.0)
+
+
+
+
+
+
+../../_images/examples_advanced_processes_24_1.png +
+
+
+
+

Custom processes

+

You can create custom processes by inheriting from the nengo.Process class and overloading the make_step and make_state methods.

+

As an example, we’ll make a simple custom process that implements a two-dimensional oscillator dynamical system. The make_state function defines a state variable to store the state. The make_step function uses that state and a fixed A matrix to determine how the state changes over time.

+

One advantage to using a process over a simple function is that if we reset our simulator, make_step will be called again and the process state will be restored to the initial state.

+
+
[11]:
+
+
+
+class SimpleOscillator(nengo.Process):
+    def make_state(self, shape_in, shape_out, dt, dtype=None):
+        # return a dictionary mapping strings to their initial state
+        return {"state": np.array([1.0, 0.0])}
+
+    def make_step(self, shape_in, shape_out, dt, rng, state):
+        A = np.array([[-0.1, -1.0], [1.0, -0.1]])
+        s = state["state"]
+
+        # define the step function, which will be called
+        # by the node every time step
+        def step(t):
+            s[:] += dt * np.dot(A, s)
+            return s
+
+        return step  # return the step function
+
+
+with nengo.Network() as model:
+    a = nengo.Node(SimpleOscillator(), size_in=0, size_out=2)
+    a_p = nengo.Probe(a)
+
+with nengo.Simulator(model) as sim:
+    sim.run(20.0)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[a_p])
+plt.xlabel("time [s]")
+
+
+
+
+
[11]:
+
+
+
+
+Text(0.5, 0, 'time [s]')
+
+
+
+
+
+
+../../_images/examples_advanced_processes_26_1.png +
+
+

We can generalize this process to one that can implement arbitrary linear dynamical systems, given A and B matrices. We will overload the __init__ method to take and store these matrices, as well as check the matrix shapes and set the default size in and out. The advantage of using the default sizes is that when we then create a node using the process, or run the process using apply, we do not need to specify the sizes.

+
+
[12]:
+
+
+
+class LTIProcess(nengo.Process):
+    def __init__(self, A, B, **kwargs):
+        A, B = np.asarray(A), np.asarray(B)
+
+        # check that the matrix shapes are compatible
+        assert A.ndim == 2 and A.shape[0] == A.shape[1]
+        assert B.ndim == 2 and B.shape[0] == A.shape[0]
+
+        # store the matrices for `make_step`
+        self.A = A
+        self.B = B
+
+        # pass the default sizes to the Process constructor
+        super().__init__(
+            default_size_in=B.shape[1], default_size_out=A.shape[0], **kwargs
+        )
+
+    def make_state(self, shape_in, shape_out, dt, dtype=None):
+        return {"state": np.zeros(self.A.shape[0])}
+
+    def make_step(self, shape_in, shape_out, dt, rng, state):
+        assert shape_in == (self.B.shape[1],)
+        assert shape_out == (self.A.shape[0],)
+        A, B = self.A, self.B
+        s = state["state"]
+
+        def step(t, x):
+            s[:] += dt * (np.dot(A, s) + np.dot(B, x))
+            return s
+
+        return step
+
+
+# demonstrate the LTIProcess in action
+A = [[-0.1, -1], [1, -0.1]]
+B = [[10], [-10]]
+
+with nengo.Network() as model:
+    u = nengo.Node(lambda t: 1 if t < 0.1 else 0)
+    # we don't need to specify size_in and size_out!
+    a = nengo.Node(LTIProcess(A, B))
+    nengo.Connection(u, a)
+    a_p = nengo.Probe(a)
+
+with nengo.Simulator(model) as sim:
+    sim.run(20.0)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[a_p])
+plt.xlabel("time [s]")
+
+
+
+
+
[12]:
+
+
+
+
+Text(0.5, 0, 'time [s]')
+
+
+
+
+
+
+../../_images/examples_advanced_processes_28_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/advanced/processes.ipynb b/examples/advanced/processes.ipynb new file mode 100644 index 0000000000..bc03e5b286 --- /dev/null +++ b/examples/advanced/processes.ipynb @@ -0,0 +1,908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Processes and how to use them\n", + "\n", + "Processes in Nengo can be used to describe\n", + "general functions or dynamical systems,\n", + "including those with randomness.\n", + "They can be useful if you want a `Node` output\n", + "that has a state (like a dynamical system),\n", + "and they're also used for things like\n", + "injecting noise into Ensembles\n", + "so that you can not only have \"white\" noise\n", + "that samples from a distribution,\n", + "but can also have \"colored\" noise\n", + "where subsequent samples are correlated with past samples.\n", + "\n", + "This notebook will first present the basic process interface,\n", + "then demonstrate some of the built-in Nengo processes\n", + "and how they can be used in your code.\n", + "It will also describe how to create your own custom process." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:56.358265Z", + "iopub.status.busy": "2020-11-25T16:46:56.357412Z", + "iopub.status.idle": "2020-11-25T16:46:56.850162Z", + "shell.execute_reply": "2020-11-25T16:46:56.849656Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interface\n", + "\n", + "We will begin by looking at how to run an existing process instance.\n", + "\n", + "The key functions for running processes\n", + "are `run`, `run_steps`, and `apply`.\n", + "The first two are for running without an input,\n", + "and the third is for applying the process to an input.\n", + "\n", + "There are also two helper functions,\n", + "`trange` and `ntrange`,\n", + "which return the time points corresponding to a process output,\n", + "given either a length of time or a number of steps, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `run`: running a process for a length of time\n", + "\n", + "The `run` function runs a process\n", + "for a given length of time, without any input.\n", + "Many of the random processes in `nengo.processes` will be run this way,\n", + "since they do not require an input signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:56.860486Z", + "iopub.status.busy": "2020-11-25T16:46:56.857504Z", + "iopub.status.idle": "2020-11-25T16:46:57.105882Z", + "shell.execute_reply": "2020-11-25T16:46:57.106294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'process output')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create a process (details on the FilteredNoise process below)\n", + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "\n", + "# run the process for two seconds\n", + "y = process.run(2.0)\n", + "\n", + "# get a corresponding two-second time range\n", + "t = process.trange(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(t, y)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"process output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `run_steps`: running a process for a number of steps\n", + "\n", + "To run the process for a number of steps, use the `run_steps` function.\n", + "The length of the generated signal will depend on the process's `default_dt`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.115005Z", + "iopub.status.busy": "2020-11-25T16:46:57.113583Z", + "iopub.status.idle": "2020-11-25T16:46:57.341441Z", + "shell.execute_reply": "2020-11-25T16:46:57.341840Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'process output')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "\n", + "# run the process for 1000 steps\n", + "y = process.run_steps(1000)\n", + "\n", + "# get a corresponding 1000-step time range\n", + "t = process.ntrange(1000)\n", + "\n", + "plt.figure()\n", + "plt.plot(t, y)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"process output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `apply`: running a process with an input\n", + "\n", + "To run a process with an input, use the `apply` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.350510Z", + "iopub.status.busy": "2020-11-25T16:46:57.347483Z", + "iopub.status.idle": "2020-11-25T16:46:57.656443Z", + "shell.execute_reply": "2020-11-25T16:46:57.656866Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.synapses.Lowpass(0.2)\n", + "\n", + "t = process.trange(5)\n", + "x = np.minimum(t % 2, 2 - (t % 2)) # sawtooth wave\n", + "y = process.apply(x) # general to all Processes\n", + "z = process.filtfilt(x) # specific to Synapses\n", + "\n", + "plt.figure()\n", + "plt.plot(t, x, label=\"input\")\n", + "plt.plot(t, y, label=\"output\")\n", + "plt.plot(t, z, label=\"filtfilt\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"signal\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that Synapses are a special kind of process,\n", + "and have the additional functions `filt` and `filtfilt`.\n", + "`filt` works mostly the same as `apply`,\n", + "but with some additional functionality\n", + "such as the ability to filter along any axis.\n", + "`filtfilt` provides zero-phase filtering." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing the time-step (`dt` and `default_dt`)\n", + "\n", + "To run a process with a different time-step,\n", + "you can either pass the new time step (`dt`) when calling the functions,\n", + "or change the `default_dt` property of the process." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.666527Z", + "iopub.status.busy": "2020-11-25T16:46:57.663882Z", + "iopub.status.idle": "2020-11-25T16:46:57.866899Z", + "shell.execute_reply": "2020-11-25T16:46:57.866478Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'output')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.FilteredNoise(synapse=nengo.synapses.Alpha(0.1), seed=0)\n", + "y1 = process.run(2.0, dt=0.05)\n", + "t1 = process.trange(2.0, dt=0.05)\n", + "\n", + "process = nengo.processes.FilteredNoise(\n", + " synapse=nengo.synapses.Alpha(0.1), default_dt=0.1, seed=0\n", + ")\n", + "y2 = process.run(2.0)\n", + "t2 = process.trange(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(t1, y1, label=\"dt = %s\" % 0.05)\n", + "plt.plot(t2, y2, label=\"dt = %s\" % 0.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `WhiteSignal`\n", + "\n", + "The `WhiteSignal` process is used to generate band-limited white noise,\n", + "with only frequencies below a given cutoff frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:57.881169Z", + "iopub.status.busy": "2020-11-25T16:46:57.880313Z", + "iopub.status.idle": "2020-11-25T16:46:58.072700Z", + "shell.execute_reply": "2020-11-25T16:46:58.072286Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Network() as model:\n", + " a = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, seed=0))\n", + " b = nengo.Node(nengo.processes.WhiteSignal(1.0, high=10, seed=0))\n", + " c = nengo.Node(nengo.processes.WhiteSignal(1.0, high=5, rms=0.3, seed=0))\n", + " d = nengo.Node(nengo.processes.WhiteSignal(0.5, high=5, seed=0))\n", + " ap = nengo.Probe(a)\n", + " bp = nengo.Probe(b)\n", + " cp = nengo.Probe(c)\n", + " dp = nengo.Probe(d)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(1.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[ap], label=\"5 Hz cutoff\")\n", + "plt.plot(sim.trange(), sim.data[bp], label=\"10 Hz cutoff\")\n", + "plt.plot(sim.trange(), sim.data[cp], label=\"5 Hz cutoff, 0.3 RMS amplitude\")\n", + "plt.plot(sim.trange(), sim.data[dp], label=\"5 Hz cutoff, 0.5 s period\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.legend(loc=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the 10 Hz signal (green)\n", + "has similar low frequency characteristics\n", + "as the 5 Hz signal (blue),\n", + "but with additional higher-frequency components.\n", + "The 0.3 RMS amplitude 5 Hz signal (red)\n", + "is the same as the original 5 Hz signal (blue),\n", + "but scaled down (the default RMS amplitude is 0.5).\n", + "Finally, the signal with a 0.5 s period\n", + "(instead of a 1 s period like the others)\n", + "is completely different,\n", + "because changing the period changes\n", + "the spacing of the random frequency components\n", + "and thus creates a completely different signal.\n", + "Note how the signal with the 0.5 s period repeats itself;\n", + "for example, the value at $t = 0$\n", + "is the same as the value at $t = 0.5$,\n", + "and the value at $t = 0.4$\n", + "is the same as the value at $t = 0.9$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `WhiteNoise`\n", + "\n", + "The `WhiteNoise` process generates white noise,\n", + "with equal power across all frequencies.\n", + "By default, it is scaled so that the integral process (Brownian noise)\n", + "will have the same standard deviation regardless of `dt`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.082305Z", + "iopub.status.busy": "2020-11-25T16:46:58.080852Z", + "iopub.status.idle": "2020-11-25T16:46:58.220578Z", + "shell.execute_reply": "2020-11-25T16:46:58.220119Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 1))\n", + "\n", + "t = process.trange(0.5)\n", + "y = process.run(0.5)\n", + "plt.figure()\n", + "plt.plot(t, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One use of the `WhiteNoise` process\n", + "is to inject noise into neural populations.\n", + "Here, we create two identical ensembles,\n", + "but add a bit of noise to one and no noise to the other.\n", + "We plot the membrane voltages of both." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.231328Z", + "iopub.status.busy": "2020-11-25T16:46:58.230209Z", + "iopub.status.idle": "2020-11-25T16:46:58.403907Z", + "shell.execute_reply": "2020-11-25T16:46:58.403125Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process = nengo.processes.WhiteNoise(dist=nengo.dists.Gaussian(0, 0.01), seed=1)\n", + "\n", + "with nengo.Network() as model:\n", + " ens_args = dict(encoders=[[1]], intercepts=[0.01], max_rates=[100])\n", + " a = nengo.Ensemble(1, 1, **ens_args)\n", + " b = nengo.Ensemble(1, 1, noise=process, **ens_args)\n", + " a_voltage = nengo.Probe(a.neurons, \"voltage\")\n", + " b_voltage = nengo.Probe(b.neurons, \"voltage\")\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(0.15)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_voltage], label=\"deterministic\")\n", + "plt.plot(sim.trange(), sim.data[b_voltage], label=\"noisy\")\n", + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"voltage\")\n", + "plt.legend(loc=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the neuron without noise (blue)\n", + "approaches its firing threshold,\n", + "but never quite gets there.\n", + "Adding a bit of noise (green)\n", + "causes the neuron to occasionally jitter above the threshold,\n", + "resulting in two spikes\n", + "(where the voltage suddenly drops to zero)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `FilteredNoise`\n", + "\n", + "The `FilteredNoise` process takes a white noise signal\n", + "and passes it through a filter.\n", + "Using any type of lowpass filter (e.g. `Lowpass`, `Alpha`)\n", + "will result in a signal similar to `WhiteSignal`,\n", + "but rather than being ideally filtered\n", + "(i.e. no frequency content above the cutoff),\n", + "the `FilteredNoise` signal\n", + "will have some frequency content above the cutoff,\n", + "with the amount depending on the filter used.\n", + "Here, we can see how an `Alpha` filter\n", + "(a second-order lowpass filter)\n", + "is much better than the `Lowpass` filter\n", + "(a first-order lowpass filter)\n", + "at removing the high-frequency content." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.417955Z", + "iopub.status.busy": "2020-11-25T16:46:58.411731Z", + "iopub.status.idle": "2020-11-25T16:46:58.648836Z", + "shell.execute_reply": "2020-11-25T16:46:58.647929Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "process1 = nengo.processes.FilteredNoise(\n", + " dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Alpha(0.005), seed=0\n", + ")\n", + "\n", + "process2 = nengo.processes.FilteredNoise(\n", + " dist=nengo.dists.Gaussian(0, 0.01), synapse=nengo.Lowpass(0.005), seed=0\n", + ")\n", + "\n", + "tlen = 0.5\n", + "plt.figure()\n", + "plt.plot(process1.trange(tlen), process1.run(tlen))\n", + "plt.plot(process2.trange(tlen), process2.run(tlen))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `FilteredNoise` process with an `Alpha` synapse (blue)\n", + "has significantly lower high-frequency components\n", + "than a similar process with a `Lowpass` synapse (green)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `PresentInput`\n", + "\n", + "The `PresentInput` process is useful for\n", + "presenting a series of static inputs to a network,\n", + "where each input is shown for the same length of time.\n", + "Once all the images have been shown,\n", + "they repeat from the beginning.\n", + "One application is presenting a series of images to a classification network." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.655154Z", + "iopub.status.busy": "2020-11-25T16:46:58.654366Z", + "iopub.status.idle": "2020-11-25T16:46:58.795370Z", + "shell.execute_reply": "2020-11-25T16:46:58.795899Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "inputs = [[0, 0.5], [0.3, 0.2], [-0.1, -0.7], [-0.8, 0.6]]\n", + "process = nengo.processes.PresentInput(inputs, presentation_time=0.1)\n", + "\n", + "tlen = 0.8\n", + "plt.figure()\n", + "plt.plot(process.trange(tlen), process.run(tlen))\n", + "plt.xlim([0, tlen])\n", + "plt.ylim([-1, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom processes\n", + "\n", + "You can create custom processes\n", + "by inheriting from the `nengo.Process` class\n", + "and overloading the `make_step` and `make_state` methods.\n", + "\n", + "As an example, we'll make a simple custom process\n", + "that implements a two-dimensional oscillator dynamical system.\n", + "The `make_state` function defines a `state` variable\n", + "to store the state.\n", + "The `make_step` function uses that state\n", + "and a fixed `A` matrix to determine\n", + "how the state changes over time.\n", + "\n", + "One advantage to using a process over a simple function\n", + "is that if we reset our simulator,\n", + "`make_step` will be called again\n", + "and the process state\n", + "will be restored to the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:58.831840Z", + "iopub.status.busy": "2020-11-25T16:46:58.803558Z", + "iopub.status.idle": "2020-11-25T16:46:59.425977Z", + "shell.execute_reply": "2020-11-25T16:46:59.426408Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class SimpleOscillator(nengo.Process):\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None):\n", + " # return a dictionary mapping strings to their initial state\n", + " return {\"state\": np.array([1.0, 0.0])}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " A = np.array([[-0.1, -1.0], [1.0, -0.1]])\n", + " s = state[\"state\"]\n", + "\n", + " # define the step function, which will be called\n", + " # by the node every time step\n", + " def step(t):\n", + " s[:] += dt * np.dot(A, s)\n", + " return s\n", + "\n", + " return step # return the step function\n", + "\n", + "\n", + "with nengo.Network() as model:\n", + " a = nengo.Node(SimpleOscillator(), size_in=0, size_out=2)\n", + " a_p = nengo.Probe(a)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_p])\n", + "plt.xlabel(\"time [s]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can generalize this process to one\n", + "that can implement arbitrary linear dynamical systems,\n", + "given `A` and `B` matrices.\n", + "We will overload the `__init__` method\n", + "to take and store these matrices,\n", + "as well as check the matrix shapes\n", + "and set the default size in and out.\n", + "The advantage of using the default sizes\n", + "is that when we then create a node using the process,\n", + "or run the process using `apply`,\n", + "we do not need to specify the sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:46:59.449261Z", + "iopub.status.busy": "2020-11-25T16:46:59.439175Z", + "iopub.status.idle": "2020-11-25T16:47:00.469391Z", + "shell.execute_reply": "2020-11-25T16:47:00.470070Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class LTIProcess(nengo.Process):\n", + " def __init__(self, A, B, **kwargs):\n", + " A, B = np.asarray(A), np.asarray(B)\n", + "\n", + " # check that the matrix shapes are compatible\n", + " assert A.ndim == 2 and A.shape[0] == A.shape[1]\n", + " assert B.ndim == 2 and B.shape[0] == A.shape[0]\n", + "\n", + " # store the matrices for `make_step`\n", + " self.A = A\n", + " self.B = B\n", + "\n", + " # pass the default sizes to the Process constructor\n", + " super().__init__(\n", + " default_size_in=B.shape[1], default_size_out=A.shape[0], **kwargs\n", + " )\n", + "\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None):\n", + " return {\"state\": np.zeros(self.A.shape[0])}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " assert shape_in == (self.B.shape[1],)\n", + " assert shape_out == (self.A.shape[0],)\n", + " A, B = self.A, self.B\n", + " s = state[\"state\"]\n", + "\n", + " def step(t, x):\n", + " s[:] += dt * (np.dot(A, s) + np.dot(B, x))\n", + " return s\n", + "\n", + " return step\n", + "\n", + "\n", + "# demonstrate the LTIProcess in action\n", + "A = [[-0.1, -1], [1, -0.1]]\n", + "B = [[10], [-10]]\n", + "\n", + "with nengo.Network() as model:\n", + " u = nengo.Node(lambda t: 1 if t < 0.1 else 0)\n", + " # we don't need to specify size_in and size_out!\n", + " a = nengo.Node(LTIProcess(A, B))\n", + " nengo.Connection(u, a)\n", + " a_p = nengo.Probe(a)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[a_p])\n", + "plt.xlabel(\"time [s]\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/2d-representation.html b/examples/basic/2d-representation.html new file mode 100644 index 0000000000..eefe132fb1 --- /dev/null +++ b/examples/basic/2d-representation.html @@ -0,0 +1,720 @@ + + + + + + + + 2-dimensional representation — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

2-dimensional representation

+

Ensembles of neurons represent information. In Nengo, we represent that information with real-valued vectors – lists of numbers. In this example, we will represent a two-dimensional vector with a single ensemble of leaky integrate-and-fire neurons.

+
+

Step 1: Create the network

+

Our model consists of a single ensemble, which we will call Neurons. It will represent a two-dimensional signal.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+model = nengo.Network(label="2D Representation")
+with model:
+    # Our ensemble consists of 100 leaky integrate-and-fire neurons,
+    # and represents a 2-dimensional signal
+    neurons = nengo.Ensemble(100, dimensions=2)
+
+
+
+
+
+

Step 2: Provide input to the model

+

The signal that an ensemble represents varies over time. We will use a simple sine and cosine wave as examples of continuously changing signals.

+
+
[2]:
+
+
+
+with model:
+    # Create input nodes representing the sine and cosine
+    sin = nengo.Node(output=np.sin)
+    cos = nengo.Node(output=np.cos)
+
+
+
+
+
+

Step 3: Connect the input to the ensemble

+
+
[3]:
+
+
+
+with model:
+    # The indices in neurons define which dimension the input will project to
+    nengo.Connection(sin, neurons[0])
+    nengo.Connection(cos, neurons[1])
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later. Let’s collect all the data produced.

+
+
[4]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin, "output")
+    cos_probe = nengo.Probe(cos, "output")
+    neurons_probe = nengo.Probe(neurons, "decoded_output", synapse=0.01)
+
+
+
+
+
+

Step 5: Run the model

+

In order to run the model, we have to create a simulator. Then, we can run that simulator over and over again without affecting the original model.

+
+
[5]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(5)
+
+
+
+
+
[6]:
+
+
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(sim.trange(), sim.data[neurons_probe], label="Decoded output")
+plt.plot(sim.trange(), sim.data[sin_probe], "r", label="Sine")
+plt.plot(sim.trange(), sim.data[cos_probe], "k", label="Cosine")
+plt.legend()
+plt.xlabel("time [s]")
+
+
+
+
+
[6]:
+
+
+
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+Text(0.5, 0, 'time [s]')
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+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/2d-representation.ipynb b/examples/basic/2d-representation.ipynb new file mode 100644 index 0000000000..bee00ce87f --- /dev/null +++ b/examples/basic/2d-representation.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2-dimensional representation\n", + "\n", + "Ensembles of neurons represent information.\n", + "In Nengo, we represent that information with\n", + "real-valued vectors -- lists of numbers.\n", + "In this example, we will represent a two-dimensional vector\n", + "with a single ensemble of leaky integrate-and-fire neurons." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "Our model consists of a single ensemble,\n", + "which we will call `Neurons`.\n", + "It will represent a two-dimensional signal." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:03.536957Z", + "iopub.status.busy": "2020-11-25T16:47:03.536077Z", + "iopub.status.idle": "2020-11-25T16:47:04.028750Z", + "shell.execute_reply": "2020-11-25T16:47:04.027863Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "\n", + "model = nengo.Network(label=\"2D Representation\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # and represents a 2-dimensional signal\n", + " neurons = nengo.Ensemble(100, dimensions=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "The signal that an ensemble represents varies over time.\n", + "We will use a simple sine and cosine wave\n", + "as examples of continuously changing signals." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.033684Z", + "iopub.status.busy": "2020-11-25T16:47:04.033164Z", + "iopub.status.idle": "2020-11-25T16:47:04.036677Z", + "shell.execute_reply": "2020-11-25T16:47:04.036197Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes representing the sine and cosine\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the input to the ensemble" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.042550Z", + "iopub.status.busy": "2020-11-25T16:47:04.042050Z", + "iopub.status.idle": "2020-11-25T16:47:04.045467Z", + "shell.execute_reply": "2020-11-25T16:47:04.045046Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # The indices in neurons define which dimension the input will project to\n", + " nengo.Connection(sin, neurons[0])\n", + " nengo.Connection(cos, neurons[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later.\n", + "Let's collect all the data produced." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.050393Z", + "iopub.status.busy": "2020-11-25T16:47:04.049901Z", + "iopub.status.idle": "2020-11-25T16:47:04.053402Z", + "shell.execute_reply": "2020-11-25T16:47:04.052938Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin, \"output\")\n", + " cos_probe = nengo.Probe(cos, \"output\")\n", + " neurons_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.058026Z", + "iopub.status.busy": "2020-11-25T16:47:04.057533Z", + "iopub.status.idle": "2020-11-25T16:47:04.761630Z", + "shell.execute_reply": "2020-11-25T16:47:04.762079Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:04.768653Z", + "iopub.status.busy": "2020-11-25T16:47:04.767618Z", + "iopub.status.idle": "2020-11-25T16:47:04.987398Z", + "shell.execute_reply": "2020-11-25T16:47:04.987834Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tJxtXD8Rep6V1i7H0qz8g2mQPhCX7+GJvo2ZJh2Js6VkWgLmtizK/bdHIOwrBNreK1Og0l3WFq9P97GZ8cubn/f7YDVWTJGthuQk/6P2nxymzQ7ut0GDWp9fSFYBMnyWV8r/CsOeQOrexq6fPJei4L9KphRC0c7fjZk9n6uaxYfDBYKqtDOSZn8F4o/jqehO9qa+j1RuYsOsWfmcvoivyA5V3rWZVkTrU7DibE64e0R7nliEZ4xtFbtHHhyr50pEh+aeWes0C6aPd18/BmaG1etOq5TgCAzUkq1mN4+368PZD3FQUlExLlkf+NgmuWmaCtGdQxOc5Kn3qvvka/+2btSRkKxvlLumdVWxs5siyhg6cfWZs7e+8q4XNXb4v5jiy59oLfGf9jWO5Mrx79prWLcYyosb/CIhh9M2sVkXY1bc8P5VMOIXjFrQtyqkhkWua/OdUNnfq/DyLbfkrUG7lLO67l4ZnkebzSQmMLI/8bWTC/xau5WN/jrb/QqMF0H5HpE1CCNp72HGxaxIyJxPUWxPEoP0a9COSwUczjBn398djaC8m7ZnF6Yz5qfPzzBhb9QAHfq1IffeM8RPfN6hRID0Zkjtyc3RNLv1ePcp9/O2d+KXeb/xW5xcKPr9LQP6CjP51djxHKn0vWR45cZZHTnhS5jCWQyjaIfbnsrEH9xbGx/3vw6nZcGJ6hF3yplZzunMS+u3RMPlkCFdf6/knODcpJsVt+dmYKF5e0LAhma9fZ0r5tswt3QxFRP83vUMZV0Y2SBgFtGLiZGeDkx3Mbf0DK0895lS4uvsACMGmQlW5lDEv8/8dx9DpfTmlf03p6aOsqgDeN9s9GF5ei9tzpi8EtSd+0yGyPHL8lUe23BZ+gcYRv8cV5zRQbSQMewWdDkTY5GAjmFfPkfn1HDj4UE/JRQHc6ukceSnFQBPUeD9xgkCPony884D2Tf5gTpkWMSZ7gCJZXeI+DhOqUygDa7qWolr+qMs8PEyVmUZt/+RwzuKUnjmGoLbtwURVByXT2LdvHytWrMDDw4OSJUvy9u3bbyqP7OnpGWV55C+d/3vLI3t6en7ze/yvPHLq1KnDyiPHF8tt4St649BMlQn+pgkBtg6Qpbhx3P1nM3q7FrXDLY2KJuuDKLU4gM0f81J14Suwd4a7++CfZtBuG+SoGDfxLF2KrktXXiVLS+e243mYKuYhpueGVeOvA3epVTD6G6MJ2axWRXjyLpCa0yP/Z/O3d6Jr42H0PbGGfqtX8uzyDS7PXk7dSt92084qfGNL3FRkeeT4K49suS18vTbiWPx4Vi6rDee7JCFrchW1Vwfyz09p4cUVeHLKuIN3HPxVVxQYMwY6duRklkL82G7aF5N9lpSOpElqz/hGhaKcGZsYONqpyZs+KY8m1GFBFMM3FaFiernW9GwwiNS3r5K3SW3w8or/QKUviq48slZrLGF+9+5dAgICqF69OkuXLg27efnu3bsI5YqBKMsjA1GWR/78/BUqVGDdunXo9XpevHgRZVXK6K4Hn8ojA19VHlmj0fD27VuOHDlC8eLFyZYtW1h5ZF9fXw4ePBjjOb6X5bbw9SHxV9Rs0GOwcTD29V9dB/92AyBLchXHfk7Cj2sDab05iOcfS/Jbm9rGGQJex6HCgBhPGyO9nredupNq+SIM7drTMW0jdOqof53D6+Zn7E7jgiI/emT6/msmMEIIasQwfHNn/vK8cU7Bwk1j0JYshWH7DuxLyEqb5ibLI1tAeeS4FuvSCtv7wp3d0P9u3AX1tT7r4gnWKbTbEsT6Gzp+KWXHnzXsjR/rfr0Fyb5jdIxGA23bwsaNzCvZhJdDRrLs1ONod9/Trzz50icjMESHg40alcqybmT2XnOJ7VeeR7s9l88Tlm34AxeNP0M6jGXW/F/jMbqERZZWMC9zl0eWXTqm8FnL3d5GsKaJI31L2vHX6RD+t1ODQVFgWn7QBn3buQMDoV492LiRMZU7MbHSzzEm+3K5UpMzjTNgHO1iackeYErTwuz7pQL5opkhfD91Vhq3mcrzpGmYsmQIh2asQKONZiF7SbJgskvHFKoMB88pEV5SCcFfNe1xsoUJx0PQ6GBxAwfU2/tC4wVfd96AAGOy9/Tk17q/sLlg1Wh3fTi+DiF6Aw62ibOf/ls42KrJky4pQ+vkj1CDP7zXSVPR8qcJrFz3O2V/68S2tx9pNrpnPEcqWbuRI0ea9fqW28K/tsE4Dt/cmi0LeyiEYFwVe0ZVsmf5FS1t/g1Ce2kt6HVfPo+/P9SuDZ6evP17UYzJ/tGEOqhUwiqSfXhlcqaKcfs7p+S0ajWeW2lz8OO4vlyfvjCeIpOkhMFyE765tdkEP7SHAo0ivCyEYERFeyZWtWftdR2tNwehG5USzi+J/lwfP0KtWnDyJCErVlH0YdoYL/35sC9rYaNWMebHgmRL5cSm/5WJch8/B2fatBjLpYx5yf9rd14tXR3lfpJkiSy3S8fcclUzfkVjUDl7bFTQf38wjrYalop+qHb8An2vQArXTzsGBBhb9mfOYPhnDXkuxrzox8J21j0KpW2pbLQtlQ2AVZ1KktTBhoZzTkTYx9/eiQ7NRrFi/QgKd+5ASMqk2DVsYI5wJSleWXYL3/YbiqWZkkPUSy3+VsbYvbPiipZeuzQoigIz3I2jfDZ3g+BglEaN0Z88RY+6/cnxhWSfP0MyqrvFbrFxS1Iud2rcs7hEeTM30M6Rn5uN5HZaV5QmTSHcuGdJslSWm/BT5YI8Nb+8X3zod91YY7/XhUibfq9gx8Aydvx9XsvA/cGEDZO9vAbatEHs38fgWr3Yla9ctKdf0bEED8fXYXffOCgUZ4E29yjDmB8jz7T9aJ+Eds1H8yhFRpQGDfjzjyXMPXLfDBFan3HjxlGgQAEKFy6Mh4cHZ86coXPnzty8edPcoVk0y034igFUCeSmpUMyY4391Llg8FMY8R7cfwKM/e0Tq9nTs7gtU0+FMNYzxDiDdrvGOPSySmc2FK4R7aknNC5EhTxpLHK4ZVxxsrOhyQ9RTzjzdUxG2xZjeeiQgi4Te7N9xW6evpN19U3p1KlT7Nixg4sXL3L16lUOHDhAlixZWLRoEW5ubl8+gfTdLDfhG3QgEkjCD88hmbG+T8NPJVuFEMys7UA7d1tGHAlmyapAuKyFCnYcjWFmaK60zrQqkXDq1ydkTnY2eE2sS49KOSNte+OcgjYtxhJg58iyDSNpOWwdWy7Juvqm8uLFC1KnTo29vXEt5dSpU5MxY8YIJYadnZ0ZNmwY7u7ulCpVilevjKXG37x5Q5MmTShevDjFixfnxIkT0V5Hisxyb9r6PoEsJc0dRfRUKmPhNV0I3NuLal0bFtV34JW3jq4P9aTPY0OdSvYcEAMprpnLG1wiHH6kfyVSOZuvVlBiNbBWPsrkTE2bxWcivP4iWRraNx/FhtWDWL5+BE3sHHG2r0w1S74n0q8fXL4ct+f08IBoygX/p0aNGowePZo8efJQrVo1WrRoEVaT5j8BAQGUKlWKcePGMXDgQBYuXMjw4cPp27cvv/zyC+XKlePJkyfUrFmTW7duxe17sGCW28IH8H1q7gi+zMYO8hprb9je0bHhrYK7AzR7pOPcc2P1vFyqiK3Nw/0r4Zo6CUkdzDSxLJErlzvquuJ307jSpcnvZPnwisWbRtNz8QlO3PeJ5+gsn7OzMxcuXGDBggWkSZOGFi1asGzZsgj72NnZhS0IUrRoUbxCi98dOHCAXr164eHhQYMGDfDz88Pf3z+e30HiZZkt/P/KjOaM/Qox8UKlwq/CdpKNq0jSzGp2NnagzIpA6v4TyMlOSViTchx1g8dxQ8nO5RHVcXGSLfvYql0wPbuvv4z0+tksBelX7zfmbJ3EzO1TaKu2YXW3spT+wqSuROkLLXFTUqvVVKpUiUqVKlGoUCGWL18eYbutrW3YfBK1Wo1OZ5ycaDAYOH36NA4ODvEesyWwzBa+IXTmakK5afsFQTdvI35syBPndNDSkfQp1Oxp44RBgdqrA3kXpLDTfhgZeCuTfRz5u01RvCbW5cH4OpG27c5XjlHVulLz3mmGHl5Cq4Wn0eoNLDr2kBCd6RbbthZ37tzh3r17Yc8vX75MtmzZvurYGjVqMGvWrAjHSl/PMhO+EloYS5XwP8CcO3+PF+WqotUZaNdsNL/Z9gAgTyo121o58uSDgWYbAtHqFU459DZztJZHHc3opuVF67OkaAM6n99Ky8t7yD1sN2N33mLJiUfxHKHl8ff3p3379ri5uVG4cGFu3rz51TVmZs6cyfnz5ylcuDBubm7MmzfPtMFamISfEb9HWAs/gb89rZb0nduS1u8NrVqOxytlJvwNzmGby2SxYWF9B9pv0dBnt4a5dR0Qp+dBqe5mDNrylMmZipMP3kZ6fVyVTuR494wx+//GK2VGTmctjPd7OWQztooWLcrJkycjvX7kyJGwx+H75Zs2bUrTpk0B44iedevWmTxGS2WZLfxEkvAN/fqR5coZBtXuw8XMEWta+ynGWbXt3O0YVNaOeRe0zDmnhT2DItXbl2JnUftiHPytIg8/697Rq9T0bjgQrxQZ+fvfCWR7/5xVp5+g08tuHSlxstCEn7C7dP458wTfmXNQzZ3L/BKN2VLg083l9zjzwJCB37TdeZazJQDjq9rTMK8N/fZo2Pcg9I/Ztj7mCN0iOdnZkDONMyqVYP5nSyZ+tE9CpyYjUIRgycbRJNP4k2vYbjNFKkmxY6EJP+HetH3kE8DmWWtx+qUfR7IXZVLF9hG261FTNeRP9huKkbbReCjWCZUQrGrsSIG0KppvCOSOjx4uLo/mClJs1CyQntYlI05me5IiA90bDSWL70tmbZuMyiAXT5ESJwtP+AmrhX/V25ezRy8x79/xPHVJR58GAzB89kfp1JAq1CqQnlxpnbF1TgX1pkGrdTjbCba1dMJOLWi8Pgj/EAWW1YNZRY2re0lxZmid/LQqkYWfwiX+s1kKMqJ6dyo+usgvx//h9MPIff6SlNDJhB9Prnr70vyvgxTo2QE7nZYujX/Hz8E50n4Zkjsyr21RDvwabuZh3loAZHNRsbapI7d9DHTcGoTyyBPe3ocxqY31d6Q4kcTehgmNCzO+USF29vlUtG6tRy3WFq5B71PrWDxgOp5335gxSkn6dhaa8BNeH77nndeM3fc3bq8e0qfBAB6myhxpnyY/RH4tTI/TULwLVbLbMLGqPRtu6ph2KuTT9msbTBC1VCBjckY1KBD2/I/q3bmSPjfTdvzJyCmbzRiZJH07C034CaMPP1inZ9T2Gzx848/TqbNpev0gs8q05EjO4hH2G9WgAF4T6/Jnc/foT5Y2P9SdCjaO9C9jR5P8Ngw6EMwRr9D3+vyy8UuKc+3LuIY9Drax43+NhhCitmXev+NZuucKeoP8dPWtXr58ScuWLcmZMydFixalTp063L1795vOUadOHXx9fU0ToIWy8IRv3hb+5ovPWHrCi94DFjN6/zw8XYswo2zLCPt0LpeddqW/bpYhAMNfIoRgaUNHcqdS0XxDEN5+Bjg9BxZUTBz1gxKh5R1LhD1+niwtvRsMJOc7b9L268lCzwdmjCzxURSFRo0aUalSJR48eMCFCxeYMGFCWEXMr7Vr1y5cXFxME6SFkgnfhAJD9CTT+PP3lgm8dUpOv/r9I92kHV7P7dvXoK3yO0ntBf+2cCRIp9B0fRAh+tBW5geZ8E3BNVXE1cZOunowqWJ76t45wZvRE3nzMZgXH4LMFF3icvjwYWxtbene/dMEQnd3d8qVK8eAAQMoWLAghQoVCptg9eLFCypUqICHhwcFCxbk2LFjALi6uuLj44OXlxf58+enS5cuFChQgBo1ahAUZPxdPHjwgFq1alG0aFHKly/P7du34/8NJyAJp5M7LiWQhB8QFMKfO/8iw0cfWvw0kXdOESdMNS0aQ599TH5oDze2kI9rLGvoSNMNQQzaH8xftRzg2J+QLeoFvKXvZ6OO3DZaUKIxPzy/zeCjy2jW043LGfOysXtpnOxscMuYzAxRfpt+/frFeS0aDw8Ppn+hKNv169cpWrRopNc3b97M5cuXuXLlCj4+PhQvXpwKFSrwzz//ULNmTYYNG4ZerycwMPJs53v37rFmzRoWLlxI8+bN2bRpE23atKFr167MmzeP3Llzc+bMGXr06MGhQ4fi6u0mOhbawv/vpq15+vB7rL7AhvNPUaZMpfr9M4yr3ImLmSLOpJ3YuBBjGkZedu+rOKeB/x0HoImbLb1L2DH9TAjb7mjh/gE4PD62b0H6TCYXRzqXy06nctk/vSgEA2v35WXS1MzeOolkGn+azjtFnZnHzBdoInb8+HFatWqFWq0mXbp0VKxYkXPnzlG8eHGWLl3KyJEjuXbtGkmTRl6jOHv27Hh4eACfyin7+/tz8uRJmjVrhoeHB926dePFixfx/K4SljhpAgshagEzADWwSFGUiZ9t7wBMAf4r7D5bUZRFcXHtKJmxhf8hSMuuay95s+sAaw4sYUe+8iwrWj9se8okduz/pQKpnO3j7JpTqttz4qmODluCuNxdTdajk6BUD3B0ibNrSMbut3Ne71h8/FMBNT8HZ3o3GMiG1QOZsms63RoNAyF48jaQrKliXnTe3L7UEjeVAgUKsHHjxq/ev0KFCnh6erJz5046dOjAr7/+Srt27SLs89/qWWAspxwUFITBYMDFxUVW1Awn1i18IYQamAPUBtyAVkKIqBamXKcoikfol+mSPXxK+PG8xKFGq6f+rOMkD/rIjO1TeeqSjkG1ekO4PvqLv1ePu2T/hy/UmoS9e2PWNXVCZ4CWG4PQ6hWYlA0Ojo6b60hhcqROEum1yxnzMrFiB2reO02HC9sBqDDlcHyHlmhUqVKF4OBgFixYEPba1atXcXFxYd26dej1et68eYOnpyclSpTg8ePHpEuXji5dutC5c2cuXrz4VddJliwZ2bNnZ8MG45BlRVG4cuWKSd5TYhEXXTolgPuKojxUFCUEWAs0jIPzfj8ztPD9NFry/b6HJ28DmLRnJqkDfOndYBAB9p9aeTt6l4vhDN9BCGPlzPozyZVSxYL6jpzy1vP74WDj9mN/xu31JFI52+M1sS5eE+vSt2rusNcXF/+RAzmLM/TwEgq9uBfDGSQhBP/++y8HDhwgZ86cFChQgCFDhvDTTz9RuHBh3N3dqVKlCpMnTyZ9+vQcOXIEd3d3ihQpwrp16+jbt+9XX2v16tUsXrwYd3d3ChQowNatW034zhI+ocRyhqYQoilQS1GUzqHP2wIlFUXpFW6fDsAE4A1wF/hFUZQYh5MUK1ZM+W9B42927wCsbgKdDkCW4l/eP5a8fAK4+uwDfdZcos2lXYzdN5cxlTuxuESjsH1qF0zP320i36iKM6EVNLttD2LBRS27WztRK5cNjHhvXD9XinOKovD30QdM3nMHAJcgP3Yu7YtOraZehxm0r+VO/5p5zRxlRLdu3SJ//vxf3lFKFKL6fQohLiiKUiyq/eMrE2wHXBVFKQzsB6Ks/CWE6CqEOC+EOP/mTSymrcfzxKtKU4/QZ80l8r7x4veDCzmSvShLin/6kLOjdznTJnuAKsMBmF7LgUJpVbT9N4jnHw2we6Bpr2vFhBD0qJQr7LmvYzJ6NxhIpg+vGb9nNrMP3SMoRBZakxKOuEj4z4As4Z5n5tPNWQAURXmrKEpoPwOLgCizn6IoCxRFKaYoSrE0adJ8f0Rm6NJx0GqYuW0yfg5J6F+3H4r49KMtmCke6tdXGAAqGxxtBeubORKoVWi/JQjD2QWwd5jpr2/Ffq2eJ+zxxcz5+atca+rfPkajG4dZfPyhGSOTpIjiIuGfA3ILIbILIeyAlsC28DsIITKEe9oAuBUH141ePCb8688+ADD80GLy+jzhl7q/4ZMkBQCTmxbm6IBKJo8hTLJMAORLreavmg4ceKhnxukQODU7/mKwQn3C9eUD/F2qKWczuzF6/9+sW+/J/dcfOXLnNcG6hNHaj203rpQwfM/vMdYJX1EUHdAL2Isxka9XFOWGEGK0EKJB6G59hBA3hBBXgD5Ah9heN0bxkPCDdXpe+2m4+dyPWndO0ObybuaVbMLx7EXC9mleLAvZUkUe1WEyP++CJosB6PKDLQ3z2jD4YDBXX+lhmhvc+Df+YrEy+3+pQNcKOQAwqNT8Uq8/CoLp26dSc+phOiw9x8htN80cJTg4OPD27VuZ9BM5RVF4+/YtDg4O33RcrG/amkqsbtpeWQf/doXeFyFVzrgNLFSD2ce56v2BdB992LukF14pMtCs9WS0alsACmdOzrZecTwq52td3wQbO/ImwEDheQGkchSc65IER1sBIz+YJyYr8cpPQ8nxBwFocPMoM7dPYVq51sws2woAr4l1zRkeWq0Wb29vNBqNWeOQYs/BwYHMmTNja2sb4fWYbtrK0grf6ar3B1AUpuyagZ1eS796/cOSPUDvKrljONrECjQGRSHNpk4sbehI7dWBDD4QzIzaDnByNpTp9eVzSN8lXbJPLa5tbhWp9PA8fU6s4birBxcz5eeRTwBpk9qTxN48//VsbW3Jnj37l3eULJJljtczYcKfsvc2zeadBKDtpZ1U8LrEuMqd8Epp7D+/MLwaXhPrUt0tXZxf+6sJAYWaQtl+1MplQ58Sdsw8G8Le+zrYNwyCP5ovNitwdWSNsMd/VO/Oi2RpmL59Ks7BgVSeeoTWi86YMTrJmsmE/43mHH7AOa/35HjrzdDDSzmcoyirPWoDxuUJ47JkQqxVHwUjPzCxmj0F0qjosDWINwEGmJD5U70hKc4lc7Bld9/ygHER9H71fiOT3xtGHpgPwOWnvsw6KCdnSfHPQhO+aVe8stHrmLbzT4Js7RlYu29Y6YQMyR1Ncr3YcrQV/NPEkXdBCl22a4w37B55mjssi5Y/w6dqmRcyuzG7dAuaXj9IrTsnAPhz/7ct9iFJccFCE75pJl6demBcuLrXqXV4vLjH0Jo9eeOcEic7NX82i2G1qgSgcDo1E6ras/WOjhVXtLCpk1wH18QWtft032xWmRZcTZ+LcXvnkCrA13xBSVbNwhN+3LXwrzz1pdXC07g/v0Ovk+vYVKAye/KWBWB4XTeafG9t+/iQqxoA/UrZUSGbmj57NDx98QaW1//CgVJsVAt3H0entuG3Or/gHBLE+L2zQVFwHbwTrd5gxgglayMT/ldqOOcEDloN03ZO42XSVIysblytp3LeNDQpminOrmMSbTbBTxtQhS6NqDdAp21BKLJbx+TG/vhpzYN7abIxtXxbat47TaMbxmqauYftTjATsiTLZ6EJP2778MfuME6YGXJkKTnfPWNAnV+MN+Oq5WbpzyWwtzHvYulfJU8NaLeNHClUTK3hwP6HeuZf0BqLrskbuCbTplQ2Ho6vg0cWFwAWF2/I2cxujDown/R+PgA88glg/fmnGORi6JKJWWjCj7s+/KAQPYuOP6Ks12XaX9zJwuI/cipbYQBaFM/yhaMTmBwVAehW1JbqOdT036fh4XsDPD5h5sAsm0ol2NKzLBd/r45BpaZ/nV+wMeiYvHsGKAq1ph9j4MarbLvy3NyhShbOchO+UEVYeOR7PHkbSP4Re0gSHMik3TO4nzIzUysYV9rxmlg3wY7KiVGfS4jc1VncwBG1CjpsCcKwrB74PjF3ZBYvZRI7pjV350mKDIyv3IkKXpf46cqesO391l02X3CSVbDMhK/oY92dExiiC1u1aMiRpWT4+JYBdfoRbGMXFxGaT8oc0GYTWZKrmFnLgWNPQgus/dPC3JFZhcY/GG/ur/KojadrEYYdWkwW35dh2/feeIledu1IJmKZCd+gi3XCdxuxF4AyXpdpc3k3i4r/yKVM+QB4NKFOrEM0uyaLaeduS/08Ngw9FMztm9fg2dctHSfFzvFBlWlVMiuDavdBL1RM3fkXQjGO1um28gJLTzz6whkk6ftYaMKPXQv/zEPjePskwYFM3j2TBykzMa1cawAejq+DiGVXUYJQqClCCBbUd8DJVtB+SxC6+ZXMHZVVyJzCiQmNC/MiWRpGVetGSe8bdDz/qaL464/BMRwtSd/PQhO+LlY3bFssOA3AoKPLyej3hgG1+xFsa0+zoplRqSwg2f9n6HPSO6v4u64DZ58ZmHoyBK5thJAAc0dmFTqWzc6mglXYn6sE/T1XkvX9CwD233xFzb88ZQljKc5ZcML/vha+9/tAAEo/vkK7SztZXLwhFzPnJ30yB6Yk8Nm030xtvB/RvIAtTfLbMPJIMHfmdzDWzpdMbnjd/JwaWpXhNXqgVdswac9MhGLgkU8Ad1595I1s6VuldevWsXbtWpP8wZcJP5zlJ70oN+kwTiFBTN49k4cpMvJn+TYAbOpRJq6jNL9w5Zxn13EgiZ2g4zYN+sD3YJAzQE1NpRJkSO6ITZYsjKnSidJPrvHT5U+jdnyDtGaMTjKH58+f0717d+bOnSsT/lf7zj78P7bdAGDQ0WVk+vCagXX6orF14NjAymRySYRDML9G36ugsiG9s4rpNe05+VTPnHMhMDqFuSOzGscGVmZDoep4uhZhyJGlZPR7DUCNvzy58tTXvMFJ8UZRFP7XvTuaoAAWL1yIShX36dlCE/639eFr9QaGb7kGQKknV2l/cSdLizXgfOYCLOlQjCwpnUwVqfmlyAZDjGvOtylsS53cNgw5GGyckKUoEOxv5gAtn0olQAiG1uqFUBQm7JkdVtiu4Rw5Kc5arJvcj23btzO2gorcx/uY5BoWnPC/voW/69oLVp1+gmOIhsm7ZuDlkoEpFdoyuHY+quQz40Im8cXWAUZ+QAjBvLoOqAV02R6EMjI5TMgEH1+ZO0KLN/bHgngnT8fESh2o+OgiTa8fDNt29tE7M0YmxYfXr1/Ta9QsSmRS0a+UHTw9bZLryIQPvPUPAWCg53Iyf3jNgNCunG6hi1JbkyzJjbV2Dj3Ss+hiaB/yRznl39TalMqG18S6rCpShzOZC/D7wYWk/WgcHtx8/imuP5NrEVuy3r178zFEYUkDR9QmHAlooQn/2/rwR++4yQ/et2h/YQfLi9bjXBZjhUOLGG//LboeAaDLD7ZUdlXTf78Gbz8DLKwC/m/MG5uVWNqxJINq98Fer2XcvrlhXTv1Zx83c2SSqWxbt4L169fzewV7CqQ1bSFGC034X9+HHxiiw06nZdKemTxPlpopFdpR3S0dw+vmN3GQCVDGIjDoMUIIFtZ3RGeA7js0KAa9cS1cyeQq5U3LLz3qMrV8G6rfP0P9W8YS1ooCd1/JtYgtilaD30svevyvG4XSqhhY1vRlWyw44X9dC99txF7+d3oDud8+ZXiNngTaObKgbVE6l7e+7hwAbI2jkXKmVDG+ij077+n455oOrq4D/9dmDs46NPTIxJJiDbmcIQ8jD8wnZaCxO6fx3JOya8eSjEvHkPp5eP5ew6IGjtipTd+jYNUJf/7RB+TyeULPU+v5160SR3IWo3XJrNbXlROejT203w5ArxJ2lM5sXCHrlb8BpuY2c3DW4+HkBgyo3RfnkEBG758HgH+wjnqzjnPivo+Zo5NiTVE48UTH3PNa+pa0o0Sm+FlTw0ITvh7El3+Ak3beYNLumfjbOzGmahdKZE/JuEaF4iHABC57BQDUKsGShg4EhCj02q0xbnv7wIyBWReX4h7MLNOKerePUf3ep1Ebj98GmjEqKVa0QRD0nmA/Hzpv15AtuWBMFfvI+1UYYJLLW2jC/3If/t1XH2lzaRdFn99mdNUuvHNKzvpupeMpwMQjX2o1f1S0Z+NNHVtua2HWD7CsHgTKoYKmtqF7GeaXbMKtNK6M3vc3zsHGRL/nxks+auQs3ERpcQ2Y5Mr4Bpm57WNgXj1HnO0+61Eo/xtUGW6Sy1twwo+5S6fDqI0M9FzB0ew/sMWtUvzElZj0PAtOqQHoX8YO93Qqeu7S8EGjgNcxmJzdzAFaB53ahsG1epPO/x0Djy4HwPPuGzosPcfSE49kgbXEZMcv8PIq11/rmXA8hDaFbamVK4o8ZWu6iZ4WmvBjHpapGAyM3TsXlWJgWM2eIAReE+vGY4CJQJq80M84+9hWLVjUwJGX/gpDDmo+7XNrh5mCsx53xtbiSsa8LC3WgHaXdlLU27i+8oXH7xm1/SaXZOmFxMHvOZxfgt6g0HmbhuQOgr9qhuvKGfoCaoyFnFWgdC+ThWGhCT/6Fn7rRafp8+Ngqjw8z9Ty7fBObgUzab+XnROU/B8AxTKq6VvSjr/Pazn+JHTN4HWt4cUVMwZo+ext1GRM7sCf5dvgnSwtE/fMwk73qTvnv0mDUgJ2azv80xyAuee0nHmmZ3pNB1I7habf4a+N/9fK9Ia2/xpnvpuIhSZ8fbR9+DeuPeKPgwu4nCE3y4rWA2BiY3mjNlq1J8LID+BanjGV7XF1EXTZriFYF9qVML+CeeOzAv1r5iXQzpHhNXqQ++1TepxeH7aty4rzfJBVNROuaW6wrg28vMaTDwaGHNRQK5eanwrZGPvp2+8wjoyLJxaa8KNu4V956svwQ4tJrvFncK0+GEL/KORK6xzfESY+bTaRpO4Y5tV15LaPgQnHw9VqPzDKfHFZgUZFMrGuaymO5CzGFreK9Di1gVw+nxadb7PojBmjk6JlMICfsTChoij8b6exO3ReXUfj0O8KAyB7+XgNyWoS/oGbr5jSfzZNrx9kXsmm3E776aZj0WyyFPAX2dhD6d7ULF+U1oVsGX8shJtv9MZtx6eBVhPz8dJ3E0JQMkcqPAdUZnTVrvjbOzFxz6ywdXCvyclYCc/r2+BzJ+zp2us6dt3TMa6KPdlcVCbtp4+J1ST83ouOM37vbB6kzMTsMi0AWNSuGI8mWMgatfFBpYJunvzVIjfJ7I1dO4b/RomMS2f8Ry6ZTNZUTjSp6cHYKp0o9uwWrS/tDttmMMjROgnK3JIwtxQAPoEG+uzRUCKTil4lQssn1BxnlrAsNOFHHKUTFKLnl+OryfrhFUNq9SbYxvhDr+aWTib775Bm2A3++qUFJ5/qmXc+XP/x3JIQ9N58gVmBZA62bC5QBU/XIgw6uoz0fsZZt7MO3TdzZFKYdw8jPP1tXzC+GoVF9R1RF2hgvCdmJhaa8CNOvBo+fCmdzm9ltUctzoZWwvzT0tanjWdt2rWneg41gw+EVtT8j5es6mhKXSrkoEaB9Ayt2RO1YmDMfmNFzb8O3DV3aNJ/ZhYJe7jvgY4VV7QMLmtHoXRqKNbRjIFZdMI3tvB1mmA6LR+PTxIXJlb6GYCaBdLRpGhmc0aY6Ik8NZlXz1hRs+cuzacJQOvawBPTLN4ggYOtmnltiuLtkp5p5VpT/f5Z6twxrop14OYr/IN1Zo7Qyp2YEfYwIESh244g8qZSMaxtNRjkZRxnb0YWnfA/BGn5s1Y33F4/4vfq/+OjfRIA/m5d1MwBWgAhyDH6NmPqZWHbHR2bboVLNEcmmC8uK6BSCUbWd2NpsYZcTZ+LUQfmkUzjT+cV5+m75pK5w7Nu+0eEPRxxOBgvX4WF9R1wyFUWHM0/OMQiE77BoEMv1DTsu5R+x/9hV54y7MtjrJMzuUlh4xqiUuwlz0TfDQ8pmj87vXdreB8U2sp/eAQ+PDNraJauQ9ns6FVqBtfqQ4pAP4YeXgLAwduyhLXZPL8c9vDcMz3Tz4TQvagt5Zv1gIqDzBdXOBaX8LV6A8HBISw8/piJe2ahsbHjj+r/C9vevHgWM0ZneWxsbFi4ZDlvAhQG7g83NPMvN7i20XyBWYH9v1TgZrocLCzRmJZX91H68VVzh2S9fO7DgooAaPUKnbcHkd5ZMLFHQ6g9+asXZDK1OEn4QohaQog7Qoj7QojBUWy3F0KsC91+RgjhGhfXjYpWb0CNnjyXH1Lq6XXGVe7EG2fzf5SyZEVKlee3pqVZdEnLUa9wXTubOpkvKCuQO11SvCbWZXrZVni5ZGD83lnYa4N5Issnx7+b/4Y9nHoyhKuvDMyt40Dyn9dDAhoJGOuEL4RQA3OA2oAb0EoI4fbZbp2A94qi5AL+AibF9rrR0RsUbD7qKHvwIiezFmZ94eph27b3Kmeqy1q9PxbuIEcKQdcdGjS6cGPCn8hZoKaWNEVShtTqRfb3L+h7cg0Vphxm2YlH+MkSyvEntDvn7ls9o44G09TNhoZ/30xQyR7ipoVfArivKMpDRVFCgLVAw8/2aQgsD328EagqTDQAXq/Xo9oThFpvYGhoJcz/FMqc3BSXlACn5CmZv2ord98aGOsZruzCkhrmC8pKTGxcmFPZ3FlfqBpdz2zG7dVDRm6/SdmJh8wdmnV45Am3d2BQFLpu1+BoC7PaFYEUruaOLJK4SPiZgKfhnnuHvhblPoqi6IAPQKrPTySE6CqEOC+EOP/mzZvvCkac3gd3dBwpXwKvlJ/C2NUnfmtWWKNqderTwcOWSSdCuPpK/2mDrNluUhXzpgFgXOVOvHdMxoQ9s1Ab9HzUyCGa8WJ5fQAWX9Ry9LGeqdUdSF++rZmDilqCummrKMoCRVGKKYpSLE2aNN91DqdK9WjXaTR9iv4a9pp75uS4ZUwWV2FKMZg6dykpHARdtgeh/2+6/+2d5g3KwtmqVSxuX4wPjkkZWa0b7i/v0eH8NgBcB+9k342XsvSCKehCwpL9848GBuzXUNlVTccitlC0g3lji0ZcJPxnQPihL5lDX4tyHyGEDZAceBsH147EVq3irmsZAtWOYa8lc7Q1xaWkKKQq3ZqZ9V04+8zA7LOhtdrXtYbLa8wbmIWrki8tADvzlWN/rhL0P7aKLL4vAei68gLT5UzcuDevnLE7B+i9W4NGB/PrOSAazgH7pGYOLmpxkfDPAbmFENmFEHZAS2DbZ/tsA9qHPm4KHFJMuDbbrJ+K4J7FJez5Xy08THUpKQotph+jbm4bhh0Kxss3tOzClu7mDcrChd0SE4Lfq/dAp1Ixbu+csO60/bfk+Pw4pdeFVcP895aWzbd0jKxkT+5UauNiJglUrBN+aJ98L2AvcAtYryjKDSHEaCFEg9DdFgOphBD3gV+BSEM341Jx15Rs7VmW/1XKydgfC5LaOf4WGJBApC/A3OUbEQK67wj6VHbh4GjzBmbhLv5uHJH2MllqJlXsQAWvSzS+Ybxxe+uFHx8Ctfyx9ToarT6m00hfEvQelhmXRPXVKPTcpcE9nYrfSttBrurg9qN544uBSKiLIBcrVkw5f/68ucOQYmF2r9r0nrOHVY0caV04tFvNjJUCrcGxe29ou/gsQjGwYfUgcr71plrnv3mbxIUOZVxZdtKLsT8WpE2pbOYONfGa4QHvHwHGBs3Ci1pO9y9A8XHnTbo84dcSQlxQFKVYVNsS1E1bybL8b8p6SmdW03ePhjcBhi8fIMVa+dzGwQ6KUDG4Vm+ctEH8fmghAMtOegF8upkufbuRycOS/bHHOuZf0NKvpB3FJ11LEMn+S2TCl0xG7ZiUhfUd8AtW+GVv6Nj8O7tjPkiKtQO/GtcZvp86K3NKt+DHm0ep9OBc2PY/tt2g+bxT5grPImh0Cl22a3B1EYxefiDBTbCKjkz4kkkV+GkMQ8vbsfqalt33tLCmpfEjcYic/m8qOdM4Y6s2JqC/SzXlTuqsjNs7lyTBn37mZ73emSu8xCtc9/c4z2DuvDUwr64jSbIVieGghEUmfMm0Sv6PIeXsyZ9aRfedGvxDFONH4vEZzB2ZxRJCcG9cHQC0aluG1OpNho8+9D+2MsJ+bz4GR3W4FJ0HBwG49krPxBMhtClsS81cNmDvbObAvp5M+JJp2TpgP+YDixs48PSDwvBD4ZJMuHKyUtyb3KQwABcz5Wd50Xq0v7CDIs8+rTtcfNwBc4WW+NzeCauaoDcYu3JcHAR/1bSH4d9XEcBcZMKXTE8ISudKRc/itsw8E8IZ79Ap/wsqwgdv88ZmwZoXz8KUpsakP7V8W14kTc3EPTOx1X8qqrbsxCO5StbXWPsTAHPPaTnzTM9fNe1J7aSC0PWxEwuZ8KX4IWB8VQcyJRN03q4hRB/aH/pXAfPGZeGaFcvCvz3KEGDvxPCaPcjr84Tupz+tUzBy+01GbrthxggTAY1xKPGTDwaGHNRQM6ea1oVsE+UQY5nwpfhRqDlJ7QV/13Xg+msDk46HfNr2T0vzxWUFimRNwZU/anA4Z3G25q9Ir1PryOnzqd7h+4CQGI6WODweRVH4304NCjCvniMmKvZrcjLhS/Gj9iQY9Jh6P/enZUEbxh4L5tab0Bmfd3eDv5z6b0rJHW3Jk86Z0VW7EGjryMQ9sxCKcW7EwduvOf3QJKWtLMOZeay7oWPXPR1jK9vj6qKCpkvMHdV3kQlfih8qNTi6QOlezKjlgLOdoMt2DYb/hrqtaSWTfjx4m8SFMVU7U/zZTVpf3hP2essFpzl6942sqhmFt4EG+uzWUDyjij4l7aBkdyjYxNxhfReZ8KX45ZyWtG0WMq2GPSee6pl/PvQG4rPzMDW3eWOzcEkdjOUtzpevh6drEQYdWUp6P5+w7e2XnGXhsYfmCi/hubkVRian//5g3gUpLKzviFoloNZEc0f23WTCl+Jf7hq0c7eleg41gw5o8PYLV3bh7EJjnXEpzs1t/QND6+RjYpPCDK3ZE7ViYMz+uREmFF3x9jVfgAnN+nYceKhj2WUtA8va4Z5eDb0vJppZtVGRCV+Kf04pEaP8mF/PEb0CPXZqPlXU3NUfxn7f4jdSzNIlc6BrhZyUyZUab5f0/FmuDdXvn6XOnRNh+xy984YdV5/LippAQIhCtx1B5Eqp4vcK9jDEG1LlNHdYsSITvmQ22Se/YExle7bf1bHhphwLHp/m/PQDy4o14Er63Iw6MI9kGn8AAkL09PrnEuN23jJzhGZ2ZS3DDwXz8L3CwvoOOHbbn2AXNfkWMuFL5mPvTJ+SdhTLqKL3bg3vgsLdMDTIFqYpVc6XBr1KzeDavUkR6MfQwxFHnaw689hMkSUMJ2d2YsaZEP5XzJZKrjaQubi5Q4oTMuFLZmVTZzKL6jvyNlCh/z7Npw2jU4LfC/MFZuGc7Gy4MLwarlXKsKBkY1pe3Ufpx1fCtifQZTLihebYXDpu1ZAluWBSNQdIkhZUlpEqLeNdSIlXqe64z/NnYFk7ll7Wsv9BuK6dafmsO/OYWCpne/5uU5QZZVrxKEUGJuyZjWPIpz+6G84/5aNGa3VDNUcN+YU7bw0sqOdIUnsBafKaO6Q4IxO+lCD83jAfeVOp6Lw9iI/B4RLMKBfZvWNirSvlY1Dtvrj6vmCA54qw1wdsvEqhkfvosOxcDEdbgNs74cgkuLCM87+XYsrJEH72CK2ECdB8RczHJyIy4UsJgmOvYyxtaKyoOXC/JuLG0Snh6VnzBGYFRtR3Y/3qQSwtWp8OF7ZT/On1CNs97yauipDfbO1PcGQ8IVv60HHBOdImEfxZI9zqVU4pzRdbHJMJX0oYHJJRutd8fi1tx7wLWg4+/GzUzt5h5onLikyv8jNPXdIxefcMHLSaLx9gYSYcC+HaawPz6jmQwjF0rL2LZa39KxO+lHAUacOYg77kyZOHTts+69rxPgv+Ft7SNLPzExriM30O2d+/oL9nxMVSznm9s+gyyldf6Rl7LJhWBW1okNf204b228wXlAnIhC8lKI6OjixZsoQnHxQGH/islbmioXmCshK2ahWFWv/I8h/q0vH8Nop63wzb1mzeKXqsvmjG6ExAr4M3d9AZFDpuDSKFg2Bm7dCunPK/Gcsfp3A1a4hxTSZ8KcEpW7Ysv/TtzdzzWg4/CteqfH0D3slaL6ZkZ6Oi7s7lPEuelim7pkfo2rG4vvwxqWBOCf48GcKFFwZm13EwLmpi5wzlfjV3dCYhE76UII0ZP5HcuXPTaVuQcR3c/xxJvIWrEovU6VMxsHYfcrx/zm/HVkXY5jp4p5miMo3bPnr+OBJMo3w2NHOzgfz1YeizRLVO7beQCV9KkJycnFiyZAlevgpDwnftXF0HT86YLzArYVe9GiuL1KHTua384B2xzEKxsfsZvuWamSKLOzqDQoctGpxsYU4dB+OiJkU7mDssk5IJX0qwypUrR98KqZh9TstRr3BdO0tqmC8oK7G4fTEmVuzA82RpmLJ7OvbaT4vP+/iHsOr0EzNGFwcWVmHKiRDOPNMzp44jGZKGpsKkGc0bl4nJhC8laOM2XiRnCkHHbUEEhO/a8ZxqvqCsgI1aRYC9EwNr9yHnu2f8eny1uUOKO7pgrl48yx9HgmnmZkPLgqETrEr1gHRu5o3NxGTClxI0pzRZWbpwPo/eKww5+KmVyaEx5gvKSjRwz8hJVw9We9Siy9l/+eFZxK6d5Se9zBNYbHzwJmRUGtr9G0QKR8Hcug6f1qetMda8scUDmfClBK98ky70rpmHWWdDIk7IenzSfEFZgektPFjUrhhbWvVFlykTf+6cFqHWzh/bbvDaLxFN0DLo4a8CjD4azJVXBhbWDx2V02SxcQimSm3uCE1OJnwpUZiw8QL5MrnQYWsQvprQrp2ltUEXHPOB0ndTqQTV3NKxYWAt3s5ZQLb3Lxl6JGIZ5RLjD/Lz0rO8+BBkpii/wfG/OOOtY8LxEDp42H6aYJVI16f9HjLhS4mCk7MzK7fs56W/Qq9d4VqVU/OYLygrkqZeTVaVbkTbS7uo9OB8hG2H77yh75rL5gnsaykKQXtH036LhkxJBdNrhk6wylEpUS9Z+K1kwpcSjWLFivF782KsvqZl/Y3Qxc81vvDgkFnjsgY2ahXtDq3mSYbsTN49A5cgvwjbzybk0gtvH8CY1Aw9GMydtwaWNHQkuUNokm+31byxxTOZ8KVEZeigAZTIpKL7jiCefwxd/HxlI/B9at7ArIGDA+m2bsAl6CPj9s6JtFZBwT/24qfRmim4aNzaAbN+4MhDDdPPhNCzuC3VcoSOyulsfQ0FmfClRMXGvRkrZ4xBo4OOW4M+LX4+vaB5A7MS9sWLMr3cT9S9c4Ifbx6JtL1nQqu3s641HzQKHbYYFyOfVC20KydNPshc1LyxmYFM+FLiIgR5mgxlasMM7H2gZ975cC3Kkcnh0DiwwtK+8WleySacz5Sf0fvnkcEvYn2dY/d8zBRV9Hru0uDtp7CykQNJ7ASobKGdZVXB/Foy4UuJ0v9a1KRmTjW/7dNw9224FbE8J8O5heYLzAos/LkkW/pNQG3QM3XXXwjFEGH7az8NAQmhPz/gLauvall9TcuIivaUymwDGYvACB9Ims7c0ZmFTPhSoiR+nMuS5atxtBW0/TcIXfh1V88skMsimlDV/OkY278hz0eMp+zjq/x8fnuE7SXGH6TAH3u59+qjeQJ8fgkursRrmCs9dgVRJouaoeXtjNs6HTBPTAmETPhS4mTrSMZSTZhX14GzzwyMOhJuPP6HJ7C5q/lisxK5h/WDevUYdnwF+V9HLltd/S9Pbr3wi3ygqS2ohH5LT9r+G4SiwKpGjtioQkflqG3iP54ERCZ8KfFS29BsfQA/l0rNuGMhHAlfYO36RtnKNzUhYMkS1ClTsPrA9AizcP9Te8ax+Isn2B9CAgCYeDyE40/0zK3rQPYUMs39J1Y/CSFESiHEfiHEvdDvKaLZTy+EuBz6ZZ13SyTTEIJZ+x6SJ5WK1puD8AkM1588OqXxRu6bu+aLz9KlSQOrVpHC+xEjDi6Ichfls+GbJjMhE0zMxtlnxhr3rQra0LpQuOUK89ePnzgSsNj+6RsMHFQUJTdwMPR5VIIURfEI/WoQy2tKUgRJkiZlTRNHfAIVOm3TRE4wT2TNHZOqWhUxaBCtru6j7q3ILfoN573jLRR/TQg/bQokUzLB3LqOxsJolYZAv+vGmjlWLrYJvyGwPPTxcuDHWJ5Pkr5LkQxqJlezZ9sdHXPOfTb5Z3tf8wRlTUaPRilZkplH/ibzh1cRNg3cdNX019ca/9D32Knhka/CqkaOuPw3m7bSYHDJAjb2po8jgYttwk+nKMqL0McvgejGOjkIIc4LIU4LIX6M7mRCiK6h+51/88bC1s+UTCuDO31K2lE3tw3992m48vKz/nvZn29atraINWtQAzO2TcFGH3FYpuvgnbgO3sn91yYauTMuHcsua1l5VcuICvaUzxZ6c7bFqpiPszJfTPhCiANCiOtRfDUMv59i/BwdXWddNkVRigE/AdOFEDmj2klRlAWKohRTFKVYmjRpvvW9SNas61HEr7dYumAeKR0FLTd9tmDKxxfRHyvFjezZYd48ij6/Td8Ta6Lcpdo0T56+C4zb6wb7c+O1np67NFTJrmZ4BbtP23JWjdtrJXJfTPiKolRTFKVgFF9bgVdCiAwAod9fR3OOZ6HfHwJHgCJx9g4kCYwjRpJnIk2lTqycPpI7Pgb67gk3auSvAhDka7bwrEarVvDzz/Q6vZ7Sj69EuUv5yYfj5loGA2zqTMCojDTbEEQye8Hqxo6oVeGqX6ptoz/eCsW2S2cb0D70cXsgUuk5IUQKIYR96OPUQFngZiyvK0nRqtppBEMaFWbxJS3LL4d82rBrAOgTwAxQSzdrFiJvXmZum0Laj29Nc40XV2B0Cri2gV67Ndz2MbC6sSPpnUNTWoWBMOylTPifiW3CnwhUF0LcA6qFPkcIUUwIsSh0n/zAeSHEFeAwMFFRFJnwJZMatf4ClStX5n87NVx7Fdp/f209jEll3sCsQZIksHEjqQlh9rZJkfrzYy3YH07MBGD55RCWXdYyvIIdVf+rgvmHL1QZBraOcXtdCxCrhK8oyltFUaoqipI7tOvnXejr5xVF6Rz6+KSiKIUURXEP/S7HRkkmZ2Njw5o1a3BxEDRZH8QHTTyNBZeMChRALFxICe+bDPBcEWnz7EP30Gi/80b6ujZwfSO33ujpsUtDxWxq/qgYbgSOFS1o8q3kFDTJYqVLl451m7fz8L2BTtvClVI+MhHmlDJvcNbgp5+gRw+6nd1MzbsR50JM3XeXfL/vQas3RHNwNBQFHh7mY7BC4/VBJLEV/NMkXL+9ldfK+RKZ8CWLVr56XSZ2rMSmWzqmnw7tzz8yAd7cMiZ+ybSmTYPixflr9wxc3z2LtDn3sN3fdr6TM1EUhQ5bg7j31sC6po5kTBqaxvrfgyzF4yBoyyUTvmTxfmtcgh/z2TDwQDAnnoTrTz4ywXxBWQt7e9iwAUcne9YfmIa99jsWnddq4OV1mF8R9o9g8okQNt/SMbm6PZWzhyuG5pw27uK2UDLhSxZPlO7B0oaOZEsuaLYh3NKIAK9vmy8wa5EtG2LVKtJ43WX83tmRlkb8Yu38CZlgXll4cZn9D3QMPRRMiwI2/FIq3Hj7Ee9MELjlkQlfsnwpsuHiINjcwgm/YIXG64LQ6EKTztyS5o3NWtSuzYeBQ2ly4zCdzm2JsKnAH3tjPtZg/IPg5Wug1aYg3NKoWNwgtE5Oy3+g1TpQqU0UuGWRCV+yDr0vUjidmhWNHDnzTM//doYrsjYyOTw6Brrv6G6QvprL+NG8rFaXoUeWUuHhhQjbdl97EeOonSCtQpP1gegMCpubOxqXKux9EfLVhby1TB26xZAJX7IOqXJCx7007j6cPyraseyylhlnwk3KWl4PxqaFD5FvLEpxRKUi/b9r0eV3Y9a2yRFu4v5v9UXaLDoTcX+NHxydjKIodNoWxMUXBlY2ciR3KjXUn2H8nUrfRCZ8yXpkLQWVhzKioj2N8tnw275gDjz8rP/4Lzc4/TfcPwDvH0fqb5ZiydkZ+x3b0KvULNw8FufgT3V1zj9+z1VvX+OTgLcwMQscHsdYzxDWXNcxoao99fOGzpwt1Dz+Y7cAMuFLVkc1+DErGjnilkZF8w2B3H/32VjwPYNhVROYURh29DNLjBYte3bWDfyT7O+eMX37FFShlUyTEkjhRdm4emAlTMkBwPobWkYcCaaduy2DytpBowUw8gPYOZnzHSRaMuFL1scxBc6tV7CtpRMqIaj3TyDvgqJpyV9YZpzGf19O6IlL/xvZmVHVulLtwTkGH1kGQC5h7OIpfLwXAOee6Wm/JYiyWdQsqOdgvEnr3sJcIVsEEW/Lj32jYsWKKefPnzd3GJKl0uvg6ESOrZxAtZWBlMqsZl8bJ+xtYpiWP/JD/MVnBXQ6PTeb/UzhLSvR1XbEpsSnQmfefgZKLAzA3gbOdk5CmiQq+HkPZCttxogTByHEhdBy9JHIFr5kndQ2UGU45ee/ZvmPjng+1vPz1iAMMTWAHp8EXUj026VvYmOjpuD6JQQWy4XNniC4Y1ypzC9Yof6aQPxDFLa3cjIm+17nZbKPAzLhS9bNLgktf5vKhKr2rLmuY/ihGIZmLq0NY+XCPHHi3UMYmRyVz02cqr+CDCrYFETIUx2N1wVy/bWBDc2cKJjWOL5+7cVX319sTQojE74kle7BoMVH6PqDLROOh7DggmzFm9ztXcbv88uDnYBWThicoMPyQA4+0rO4gQN22Yvyu7YDA7RdGXzoA/l+32PemC2ATPiSBIhspZhz9AW1a9agx65gttzWRr9zgIkW9bAmn5cwdlYx0NWGNXqY4CRol8uGHfrSrNTXYIO+UthuspUfOzLhS1IoG+dUrN+4iWLFS9DiXz0HPx+j/58pOeDQONmfHxvvH0d4+tepYP68oqNXfhv6BNvwbKUT+wM9Ih2W7/c9PPcNiqcgLY9M+JIUjrOzM7t27SJPnjw03KRwxrZs1Dt6ToYNHeQ6ud8qJAD0Wjg7P+ylxRdD+HVfME3dbGjfoiT/azycNG/esWjTWBy0mkinWHTsEYuPPyKhjjBMyOSwTEmKwosXLyhXrhzv37/Hc/taCu5vFv3OI96DSradvsrI5BGe/nNNS5vNQdTMpWZLCyfsx/oRojPQr+kwZm2bzDHXInRpMhxtFGvTru9WmhLZU8ZX5ImGHJYpSd8oQ4YMHDhwAEdHR2o068D9pDGskDU6BTy/BFfXx1+AiZHf8whPN9/S0u7fICq5qtl8+jH2w70BsLNR0Xh8P4bU7EWlRxeYvn0qakPkvvvbL/3iJWxLIhO+JEUje/bs7N+/H61WS8Vp17lboH/0Oy+oBJu7wK0d8RZfovLIE6blD3u6656WlhuDKJFJzbZWTjimygQOycK2V3NLx6Rd03kzajx175xgxrYpkRZDH7H1BmN23GTvjZfx9jYSO5nwJSkGbm5uHD58GK1WS6We07nt84VRIutaw4Xl8RNcYqDXwZJasLx+2Evb72hptC6IwulU7G7thLND5O4aACEEaUYM4fXI8dS7c5yZ2yZHSvqLjz+i28oLnLjvY9K3YSlkwpekLyhYsCCHDx9GL2yotFrhZuHfYz5gex84Mx9m/hA/ASZkuwfAk1NhTzfc0NJ4vTHZ72ubhOQOArocivEUaf8YgmHaNOrcPcmcrROx1UceMtv689LKUpTkTVtJ+kq3bt2iSpUqGAwG9v9ek8I+W798ULNlUKCRyWNLMLb3Bdsk4JgCDo+NsGnV1RDab9FQOrOanT85kbzLVshZ+atPHfzXdOx//YX9uUrQq+Fggm3sIu2zvVc5CmVOHsXR1iOmm7Yy4UvSN7hz5w5Vq1bF/+NHtjXWUSGbzZcParwQ7u2HH/821vCxNDt/A+/z8OJytLssuBBC9x0aKmdXs62lE0l+WgYFG3/zpYbX7MnYfXM5k6UgXRoPx8/BOdI+jybUMVbWtFJylI4kxZG8efNy8uRJ0mfIQI01Ora8SP/lgzZ3gWvr4c4u0wdoDucWRZvsFUXhj8Mauu3QUDu3DTtaORmXJ/yOZA8wdu8cetcfQJFnt1n3z2DSfow86zn7kF3MP/rgu85v6WTCl6RvlDVrVo4fP46HRxGaLLrPwg8Vvu7A9W1hYRXTBhffRkbffaLVK3TapmG0Zwg/e9iypYUjjvUmQOeDsbpky2kDWTFsNll9X7J51QByvPWOtM+E3bdxHbyTm8/l0M3wZMKXpO+QOnVqDh48SM2aNek6fQeDD2hiLq38n2cXYEJW49KJigK7B8PrW6YPOK7NKxdjsv8YWuJ46WUtf1S0Y3EDB2zVAkp2h8xR9jZ8tbK5UtN5dDcG9pyOo1bDplUDKP34apT71pl5DO/3gVFus0Yy4UvSd0qSJAlbt26le/fuTDoRwo9rg/gY/BVJP/gDjHIxJv8zfxuXU0yIQgJgZ38I/vjpNUUBzynw8lq0h91/Z6DU4gAOPNSzqL4DIys5IH65bqxpr1LHWXh/Tu7M2TU78Uniwor1v9P6UtRdZuUmHWbLpWd8CNLyIUhL1xXnee0XuWSDNZA3bSUpDsydOpY+A38nX2oV21o5kSPFt7SlBAx7AbaOJovvuxz7Ew6OhoqDofyvsK0PXF0b4yF77utotSkQlRCsG96MagOWGytj2tibLs4PH9C3aoV6925WFKnL6Kpd0EVzc1ytEugNCm1KZWXsj4VMF5MZyZu2kmRiPfoPZ+/+AzwPcabYchVb3eZA9q/s20eBcenBYPjyrqYSEgjaIGOrfmRy2DvMmOwBHh6GsWljTPYGRWHi8WDqrA4kWzZXzu9aRbWh68DWwbTJHiB5ctTbt+PXqy/tLu3kn7VDSe8X9UQsvcHYwF11+gmLjz/izEPrKnUtW/iSFIcePHhA8+bNuXjxIv3+14lJqdZjp/6GIYJFO8DHV1BjDKTObXzt9k7IWhqcTFgobGRysE8Gwd9+k/Olv4H2W4LY90BPi2pFWbzlKEmSJDFBkF9mWLWawE5dCFHb8lvdXzics/gXjxnfqBCpnO2onj8dKtWXf1fBOj1rzjyhbWlX1F+x/5coioIQgmn77jD3yAPWdC2FnVqFexaX7zqfHIcvSfEoODiYAQMGMGvWLIoXL84/v9Ug161Z336imhPAvSVMzg7JMoPmA1QaBO6tIEnquA06hhuwMdl7X0e7LUH4BSv89Vtruk1YadYx8IqiUKXbQuZsnYjb60csKN6IPyu0jXKS1udSJrFj+c8lvjhx66/9d5lx8B6TmxSmefEssYr3qrcvDWaf4J/OJfnps9nCXhPrftc5ZZeOJMUje3t7Zs6cyaZNm7h37x6Ff57GDMffvm4UT3h7h8COfsbHft4Q8hH2DYcpOeHUXPB98mnfh0eN3TLfQhsEV9bCk28vS/AxWKHXriBqrQ4kjZPgXJckdJ+4yuwTnoQQTBvejHP/7GKVR226nvuX7cv64f78zhePfRcQQv3Zx9Fo9Sz0fMiVp758CPpUxuG5bxBBIfqw1/yDo1kgJwp+Gi2PfAIivX7snrHr6fNkbyqyhS9JJvTs2TO6devGzp07KVeqOIuL3SRPqrgbqYJLNmizCWaHNugqDICMP8Ct7ZCzChQOreOvCw69ATsQnp6FLCVh4ve1Tnff09JthwZvP4U+rWoyYfrfODonN22X03dwHbyTig8vMGHPLNL5v2NBicZML9uKYNtvv6eQPpkDL/00lMieErcMyVh20osR9dzoWC47iqIwZsctqrmlpUzOiJ+8jtx5TfrkDnRbeYHHbwPZ0rMsS44/4uKT93i/j3nlLlO08GXClyQTUxSFlStX0rdvXwI/+vJrKTuGVbDHue5Y2P+FQmyx1XQpPDgEl1bG+lRPPxgYdEDDmus68qdWsXhCf0p3nhQHQZpOuUmH+PDSh2GHFtPy6j6eJE/H2Cqd2Ze7VOR1db/D2q6laLPoDLrQm8E3RtWky4rzuGdxoWq+tDSdd+oLZ4ieTPiSlIi9ePGCwW2qsuLQLTKmScGkP2fw0/2eqBJ43ZeAEIXJvnWZsmwLigIDKyRjaPEg7PuehzR5zB1ejF77afhp0Rnuv/Znhas/6X4fSF6fJ3i6FmFslU7cTeNqsmvnTJOEB28id+N8LZnwJckCnDp1it69e3PhwgUK5MvDqN+60ahudVTzy5k7tAgCtQrzz4cw+bTCS78QWrZsycSJE8mWLZuxi8jUwy3j0H8jYYICg5nYsC+/HltF0uBAtuevwMyyLXmQKnY3X01B3rSVJAtQunRpzp49y9q1a9GjommX3yhSszUrr4QQrDN/A+xdkMLkE8Fkn6fi133BuGVPz4kTJ1izZo0x2UOiSvZA2M1kRyd7+m+fxaHtJ7jfsSfV7p9h3+KezNg25atu7MaHPG+8KPfokknOLVv4kmRGer2etWvXMnbsWG7fvk3aJIJuRW3pWMQOV5f4a48pisKFrF34e9c1/tm2H40OqlevzoimHpTrMBLsnOItlvg0YbknKebOoN3VPThpArmUIS9FJg8n/0VHguwc4i2OJIqeZk/P0uzsDgo8uMK9VFnI/ebxd91nMFmXjhCiGTASyA+UUBQlygwthKgFzADUwCJFUSZ+6dwy4UvWRFEUDiyfzMwJw9l5V4cClMmbllY5/Pgxnw2Zk8V98lcUheuvDWy4qWXdTQN3fXQkSZKENq2a879efXF3d4/zayY0fhot848+4JcS6Vnfexz1PTeR9OkjdI5OPClfnXFJC3Mimzsa27hP/nY6LdWeXWWW+h6q7dsQfn6QIwf3m7RF1fFncuTL9l3nNWXCzw8YgPlA/6gSvhBCDdwFqgPewDmglaIoN2M6t0z4klVa1wYvJw/W3tDxzz//cO2asUhZ/uwZqJ7mLeWyqvkhg5ocKcSnMe8VBoLn5C+eWqtXuOVj4OILPYce6Tn4SMfzjwoqlYpKlSrRvHlzWrZsSfLkVrxilMEAnp6wZg1s3Ajv3qG1seVspvzkb9OYN27uNDsbHOXCK1+STONPK4f3lHhxB7ujRyj76jYqjQZcXKBRI2jVCqpWBVXs/rib/KatEOII0Sf80sBIRVFqhj4fAqAoyoSYzikTviTBzZs32b17N/v37+fo0aNoNMYqj8mdnciTLz/ZsmUjS+YspLyxlCSqYJxsBToDaHQKgVp4kb0J3ifW8/iDgdtvBSE64yLsqVOnokr5MlQtX4oGrTqSPv1XLORibUJC4MgRtLv3ELJnH0lu3wjb5J0sDU9d0iOyZkWTLgNnfLQE2joQoralbu4UFExlx+OHLwh89AT906dk831Jlg+vPp27YEFjcq9RA6pVA7svzwT+WuZO+E2BWoqidA593hYoqShKryj27Qp0BciaNWvRx48fxzo2SbIUwcHBXO+diosv9FzK8jMPHjzgyZMnPHnyhMDAqGfZpkqVisyZM5E5uS0FSlbBo0gRPDw8yJs3L6pYtiStjo8PXLrE6yMneXT4FMWFP6qnT1CePUNEVfhOrYYMGTBkysQOP3tupc3OoMEt4YcfIG1ak4UZU8L/4gKbQogDQFR//ocpivIVqzh/PUVRFgALwNjCj8tzS1JiZ29vT9HeyyjqlNI4izYcrVZLQEAAgYGB2NjY4OjoiIODA7a2tmaK1gKlTg3Vq5O2enXCp2uhKKDRQEAABAeDgwM4Ohq/q1SogGxPfXEJ0kKeNOaKHviKhK8oSrVYXuMZEH6Qa+bQ1yRJ+laFmkb5sq2tLS4uLri4uMRvPJJxJI2jo/ErGt9b+TKuxcdnunNAbiFEdiGEHdAS2BYP15UkSZLCiVXCF0I0EkJ4A6WBnUKIvaGvZxRC7AJQFEUH9AL2AreA9Yqi3IjunJIkSZJpfLFLJyaKovwL/BvF68+BOuGe7wKiXnBSkiRJihfyNr0kSZKVkAlfkiTJSsiEL0mSZCVkwpckSbISMuFLkiRZiQRbHlkI8QaITW2F1IBPHIWTGFjb+wX5nq2FfM/fJpuiKFFO6U2wCT+2hBDno6snYYms7f2CfM/WQr7nuCO7dCRJkqyETPiSJElWwpIT/gJzBxDPrO39gnzP1kK+5zhisX34kiRJUkSW3MKXJEmSwpEJX5IkyUpYXMIXQtQSQtwRQtwXQgw2dzymJoRYIoR4LYS4bu5Y4osQIosQ4rAQ4qYQ4oYQoq+5YzI1IYSDEOKsEOJK6HseZe6Y4oMQQi2EuCSE2GHuWOKLEMJLCHFNCHFZCBGnC3tbVB++EEIN3AWqA94YF19ppSjKTbMGZkJCiAqAP7BCUZSC5o4nPgghMgAZFEW5KIRIClwAfrTw37MAkiiK4i+EsAWOA30VRTlt5tBMSgjxK1AMSKYoSj1zxxMfhBBeQDFFUeJ8spmltfBLAPcVRXmoKEoIsBZoaOaYTEpRFE/gnbnjiE+KorxQFOVi6OOPGBfWyWTeqExLMfIPfWob+mU5rbUoCCEyA3WBReaOxVJYWsLPBDwN99wbC08E1k4I4QoUAc6YORSTC+3euAy8BvYrimLp73k6MBAwmDmO+KYA+4QQF4QQXePyxJaW8CUrIoRwBjYB/RRF8TN3PKamKIpeURQPIDNQQghhsV14Qoh6wGtFUS6YOxYzKKcoyg9AbaBnaLdtnLC0hP8MyBLueebQ1yQLE9qPvQlYrSjKZnPHE58URfEFDgO1zByKKZUFGoT2Z68FqgghVpk3pPihKMqz0O+vMS4hWyKuzm1pCf8ckFsIkV0IYQe0BLaZOSYpjoXewFwM3FIUZZq544kPQog0QgiX0MeOGAcm3DZrUCakKMoQRVEyK4riivH/8SFFUdqYOSyTE0IkCR2IgBAiCVADiLMReBaV8BVF0QG9gL0Yb+StVxTlhnmjMi0hxBrgFJBXCOEthOhk7pjiQVmgLcZW3+XQrzrmDsrEMgCHhRBXMTZs9iuKYjVDFa1IOuC4EOIKcBbYqSjKnrg6uUUNy5QkSZKiZ1EtfEmSJCl6MuFLkiRZCZnwJUmSrIRM+JIkSVZCJnxJkiQrIRO+JEmSlZAJX7IKQggXIUSPcM8zCiE2muA6I4UQz4QQo2PYJ2fo3AH/6PaRJFOQ4/AlqxBaZG2HqUtICyFGAv6Kokz9in39FUVxNmU8khSebOFL1mIi8F/LeooQwvW/RWOEEB2EEFuEEPtDF5/oJYT4NXThjdNCiJSh++UUQuwJrWJ4TAiR70sXFUJUDDcb+NJ/0+YlyRxszB2AJMWTwUDB0GqT/7X4wyuIscyyA3AfGKQoShEhxF9AO4ylehcA3RVFuSeEKAnMBap84br9gZ6KopwIre6piZu3I0nfTiZ8STI6HLqYykchxAdge+jr14DCocm6DLDBWLsNAPuvOO8JYJoQYjWwWVEU7ziOW5K+mkz4kmQUHO6xIdxzA8b/JyrA979PCF9LUZSJQoidQB3ghBCipqIoFlvlUkrYZB++ZC0+At/dfx66wMojIUQzMJZoFkK4f+k4IURORVGuKYoyCWOVyy/2+0uSqciEL1kFRVHeYmxhXxdCTPnO07QGOoWWrr3B162X3C/0mlcBLbD7O68tSbEmh2VKUhySwzKlhEy28CUpbvkDXb9m4hXwKt6ikiRkC1+SJMlqyBa+JEmSlZAJX5IkyUrIhC9JkmQlZMKXJEmyEv8Hj2QOR855+fEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neurons_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Sine\")\n", + "plt.plot(sim.trange(), sim.data[cos_probe], \"k\", label=\"Cosine\")\n", + "plt.legend()\n", + "plt.xlabel(\"time [s]\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/2d_representation.html b/examples/basic/2d_representation.html new file mode 100644 index 0000000000..0ac7689d7c --- /dev/null +++ b/examples/basic/2d_representation.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/basic/addition.html b/examples/basic/addition.html new file mode 100644 index 0000000000..42d2ab8f2e --- /dev/null +++ b/examples/basic/addition.html @@ -0,0 +1,735 @@ + + + + + + + + Addition — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Addition

+

In this example, we will construct a network that adds two inputs. The network utilizes two communication channels into the same neural population. Addition is thus somewhat ‘free’, since the incoming currents from different synaptic connections interact linearly (though two inputs don’t have to combine in this way; see the combining demo).

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+
+
+
+
+

Step 1: Create the Model

+

The model has three ensembles, which we will call A, B, and C.

+
+
[2]:
+
+
+
+# Create the model object
+model = nengo.Network(label="Addition")
+with model:
+    # Create 3 ensembles each containing 100 leaky integrate-and-fire neurons
+    A = nengo.Ensemble(100, dimensions=1)
+    B = nengo.Ensemble(100, dimensions=1)
+    C = nengo.Ensemble(100, dimensions=1)
+
+
+
+
+
+

Step 2: Provide Input to the Model

+

We will use two constant scalar values for the two input signals that drive activity in ensembles A and B.

+
+
[3]:
+
+
+
+with model:
+    # Create input nodes representing constant values
+    input_a = nengo.Node(output=0.5)
+    input_b = nengo.Node(output=0.3)
+
+    # Connect the input nodes to the appropriate ensembles
+    nengo.Connection(input_a, A)
+    nengo.Connection(input_b, B)
+
+    # Connect input ensembles A and B to output ensemble C
+    nengo.Connection(A, C)
+    nengo.Connection(B, C)
+
+
+
+
+
+

Step 3: Probe Output

+

Let’s collect output data from each ensemble and output.

+
+
[4]:
+
+
+
+with model:
+    input_a_probe = nengo.Probe(input_a)
+    input_b_probe = nengo.Probe(input_b)
+    A_probe = nengo.Probe(A, synapse=0.01)
+    B_probe = nengo.Probe(B, synapse=0.01)
+    C_probe = nengo.Probe(C, synapse=0.01)
+
+
+
+
+
+

Step 4: Run the Model

+

In order to run the model, we have to create a simulator. Then, we can run that simulator over and over again without affecting the original model.

+
+
[5]:
+
+
+
+# Create the simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 5 seconds
+    sim.run(5)
+
+
+
+

The data produced by running the model can now be plotted.

+
+
[6]:
+
+
+
+# Plot the input signals and decoded ensemble values
+t = sim.trange()
+plt.figure()
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded Ensemble A")
+plt.plot(sim.trange(), sim.data[B_probe], label="Decoded Ensemble B")
+plt.plot(sim.trange(), sim.data[C_probe], label="Decoded Ensemble C")
+plt.plot(
+    sim.trange(), sim.data[input_a_probe], label="Input A", color="k", linewidth=2.0
+)
+plt.plot(
+    sim.trange(), sim.data[input_b_probe], label="Input B", color="0.75", linewidth=2.0
+)
+plt.legend()
+plt.ylim(0, 1)
+plt.xlabel("time [s]")
+
+
+
+
+
[6]:
+
+
+
+
+Text(0.5, 0, 'time [s]')
+
+
+
+
+
+
+../../_images/examples_basic_addition_11_1.png +
+
+

You can check that the decoded value of the activity in ensemble C provides a good estimate of the sum of inputs A and B.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/addition.ipynb b/examples/basic/addition.ipynb new file mode 100644 index 0000000000..5f0e6a4aeb --- /dev/null +++ b/examples/basic/addition.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Addition\n", + "\n", + "In this example, we will construct a network that adds two inputs.\n", + "The network utilizes two communication channels\n", + "into the same neural population.\n", + "Addition is thus somewhat 'free', since the incoming currents\n", + "from different synaptic connections interact linearly\n", + "(though two inputs don't have to\n", + "combine in this way; see the combining demo)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.237356Z", + "iopub.status.busy": "2020-11-25T16:47:06.236493Z", + "iopub.status.idle": "2020-11-25T16:47:06.733169Z", + "shell.execute_reply": "2020-11-25T16:47:06.732236Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model has three ensembles, which we will call A, B, and C." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.739705Z", + "iopub.status.busy": "2020-11-25T16:47:06.739141Z", + "iopub.status.idle": "2020-11-25T16:47:06.742359Z", + "shell.execute_reply": "2020-11-25T16:47:06.742762Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Addition\")\n", + "with model:\n", + " # Create 3 ensembles each containing 100 leaky integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + " C = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "We will use two constant scalar values for the two input signals\n", + "that drive activity in ensembles A and B." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.750663Z", + "iopub.status.busy": "2020-11-25T16:47:06.750148Z", + "iopub.status.idle": "2020-11-25T16:47:06.753262Z", + "shell.execute_reply": "2020-11-25T16:47:06.753663Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes representing constant values\n", + " input_a = nengo.Node(output=0.5)\n", + " input_b = nengo.Node(output=0.3)\n", + "\n", + " # Connect the input nodes to the appropriate ensembles\n", + " nengo.Connection(input_a, A)\n", + " nengo.Connection(input_b, B)\n", + "\n", + " # Connect input ensembles A and B to output ensemble C\n", + " nengo.Connection(A, C)\n", + " nengo.Connection(B, C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.761196Z", + "iopub.status.busy": "2020-11-25T16:47:06.759619Z", + "iopub.status.idle": "2020-11-25T16:47:06.761814Z", + "shell.execute_reply": "2020-11-25T16:47:06.762220Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " input_a_probe = nengo.Probe(input_a)\n", + " input_b_probe = nengo.Probe(input_b)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " C_probe = nengo.Probe(C, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model\n", + "\n", + "In order to run the model, we have to create a simulator.\n", + "Then, we can run that simulator over and over again\n", + "without affecting the original model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:06.767503Z", + "iopub.status.busy": "2020-11-25T16:47:06.766706Z", + "iopub.status.idle": "2020-11-25T16:47:07.884914Z", + "shell.execute_reply": "2020-11-25T16:47:07.884450Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data produced by running the model can now be plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:07.893249Z", + "iopub.status.busy": "2020-11-25T16:47:07.890825Z", + "iopub.status.idle": "2020-11-25T16:47:08.201491Z", + "shell.execute_reply": "2020-11-25T16:47:08.201891Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signals and decoded ensemble values\n", + "t = sim.trange()\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded Ensemble A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded Ensemble B\")\n", + "plt.plot(sim.trange(), sim.data[C_probe], label=\"Decoded Ensemble C\")\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_a_probe], label=\"Input A\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_b_probe], label=\"Input B\", color=\"0.75\", linewidth=2.0\n", + ")\n", + "plt.legend()\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"time [s]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check that the decoded value\n", + "of the activity in ensemble C\n", + "provides a good estimate of the sum of inputs A and B." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/combining.html b/examples/basic/combining.html new file mode 100644 index 0000000000..345a01352a --- /dev/null +++ b/examples/basic/combining.html @@ -0,0 +1,742 @@ + + + + + + + + Combining — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Combining

+

This example demonstrates how to create a neuronal ensemble that will combine two 1-D inputs into one 2-D representation.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

Step 1: Create the neural populations

+

Our model consists of three ensembles, two input ensembles and one 2-D ensemble that will represent the two inputs as one two-dimensional signal.

+
+
[2]:
+
+
+
+model = nengo.Network(label="Combining")
+with model:
+    # Our input ensembles consist of 100 leaky integrate-and-fire neurons,
+    # representing a one-dimensional signal
+    A = nengo.Ensemble(100, dimensions=1)
+    B = nengo.Ensemble(100, dimensions=1)
+
+    # The output ensemble consists of 200 leaky integrate-and-fire neurons,
+    # representing a two-dimensional signal
+    output = nengo.Ensemble(200, dimensions=2, label="2D Population")
+
+
+
+
+
+

Step 2: Create input for the model

+

We will use sine and cosine waves as examples of continuously changing signals.

+
+
[3]:
+
+
+
+with model:
+    # Create input nodes generating the sine and cosine
+    sin = nengo.Node(output=np.sin)
+    cos = nengo.Node(output=np.cos)
+
+
+
+
+
+

Step 3: Connect the network elements

+
+
[4]:
+
+
+
+with model:
+    nengo.Connection(sin, A)
+    nengo.Connection(cos, B)
+
+    # The square brackets define which dimension the input will project to
+    nengo.Connection(A, output[1])
+    nengo.Connection(B, output[0])
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later.

+
+
[5]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin)
+    cos_probe = nengo.Probe(cos)
+    A_probe = nengo.Probe(A, synapse=0.01)  # 10ms filter
+    B_probe = nengo.Probe(B, synapse=0.01)  # 10ms filter
+    out_probe = nengo.Probe(output, synapse=0.01)  # 10ms filter
+
+
+
+
+
+

Step 5: Run the model

+
+
[6]:
+
+
+
+# Create our simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 5 seconds
+    sim.run(5)
+
+
+
+
+
+

Step 6: Plot the results

+
+
[7]:
+
+
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(sim.trange(), sim.data[out_probe][:, 0], "b", label="2D output")
+plt.plot(sim.trange(), sim.data[out_probe][:, 1], "g", label="2D output")
+plt.plot(sim.trange(), sim.data[A_probe], "r", label="A output")
+plt.plot(sim.trange(), sim.data[sin_probe], "k", label="Sine")
+plt.legend()
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f82eea0b4e0>
+
+
+
+
+
+
+../../_images/examples_basic_combining_13_1.png +
+
+

The graph shows that the input signal (Sine), the output from the 1D population (A output), and the 2D population (green line) are all equal. The other dimension in the 2D population is shown in blue.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/combining.ipynb b/examples/basic/combining.ipynb new file mode 100644 index 0000000000..37b2b9db13 --- /dev/null +++ b/examples/basic/combining.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining\n", + "\n", + "This example demonstrates how to create\n", + "a neuronal ensemble that will combine two 1-D inputs\n", + "into one 2-D representation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.466397Z", + "iopub.status.busy": "2020-11-25T16:47:09.465545Z", + "iopub.status.idle": "2020-11-25T16:47:09.956717Z", + "shell.execute_reply": "2020-11-25T16:47:09.957161Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of three ensembles,\n", + "two input ensembles and one 2-D ensemble\n", + "that will represent the two inputs as one two-dimensional signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.964024Z", + "iopub.status.busy": "2020-11-25T16:47:09.963178Z", + "iopub.status.idle": "2020-11-25T16:47:09.966534Z", + "shell.execute_reply": "2020-11-25T16:47:09.966092Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Combining\")\n", + "with model:\n", + " # Our input ensembles consist of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # The output ensemble consists of 200 leaky integrate-and-fire neurons,\n", + " # representing a two-dimensional signal\n", + " output = nengo.Ensemble(200, dimensions=2, label=\"2D Population\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use sine and cosine waves\n", + "as examples of continuously changing signals." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.972076Z", + "iopub.status.busy": "2020-11-25T16:47:09.970574Z", + "iopub.status.idle": "2020-11-25T16:47:09.972708Z", + "shell.execute_reply": "2020-11-25T16:47:09.973117Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create input nodes generating the sine and cosine\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.981208Z", + "iopub.status.busy": "2020-11-25T16:47:09.979649Z", + "iopub.status.idle": "2020-11-25T16:47:09.981788Z", + "shell.execute_reply": "2020-11-25T16:47:09.982192Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, A)\n", + " nengo.Connection(cos, B)\n", + "\n", + " # The square brackets define which dimension the input will project to\n", + " nengo.Connection(A, output[1])\n", + " nengo.Connection(B, output[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.989527Z", + "iopub.status.busy": "2020-11-25T16:47:09.988050Z", + "iopub.status.idle": "2020-11-25T16:47:09.990133Z", + "shell.execute_reply": "2020-11-25T16:47:09.990532Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " cos_probe = nengo.Probe(cos)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms filter\n", + " B_probe = nengo.Probe(B, synapse=0.01) # 10ms filter\n", + " out_probe = nengo.Probe(output, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:09.995706Z", + "iopub.status.busy": "2020-11-25T16:47:09.994900Z", + "iopub.status.idle": "2020-11-25T16:47:11.096315Z", + "shell.execute_reply": "2020-11-25T16:47:11.095424Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:11.103816Z", + "iopub.status.busy": "2020-11-25T16:47:11.103210Z", + "iopub.status.idle": "2020-11-25T16:47:11.343738Z", + "shell.execute_reply": "2020-11-25T16:47:11.344151Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[out_probe][:, 0], \"b\", label=\"2D output\")\n", + "plt.plot(sim.trange(), sim.data[out_probe][:, 1], \"g\", label=\"2D output\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], \"r\", label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"k\", label=\"Sine\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows that the input signal (Sine),\n", + "the output from the 1D population (A output),\n", + "and the 2D population (green line) are all equal.\n", + "The other dimension in the 2D population is shown in blue." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/communication-channel.html b/examples/basic/communication-channel.html new file mode 100644 index 0000000000..5a56ca499d --- /dev/null +++ b/examples/basic/communication-channel.html @@ -0,0 +1,727 @@ + + + + + + + + Communication channel — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Communication channel

+

This example demonstrates how to create a connection from one neuronal ensemble to another that behaves like a communication channel (that is, it transmits information without changing it).

+

Network diagram:

+
[Input] ---> (A) ---> (B)
+
+
+

An abstract input signal is fed into the first neuronal ensemble \(A\), which then passes it on to another ensemble \(B\). The result is that spiking activity in ensemble \(B\) encodes the value from the Input.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

Step 1: Create the Network

+
+
[2]:
+
+
+
+# Create a 'model' object to which we can add ensembles, connections, etc.
+model = nengo.Network(label="Communications Channel")
+with model:
+    # Create an abstract input signal that oscillates as sin(t)
+    sin = nengo.Node(np.sin)
+
+    # Create the neuronal ensembles
+    A = nengo.Ensemble(100, dimensions=1)
+    B = nengo.Ensemble(100, dimensions=1)
+
+    # Connect the input to the first neuronal ensemble
+    nengo.Connection(sin, A)
+
+    # Connect the first neuronal ensemble to the second
+    # (this is the communication channel)
+    nengo.Connection(A, B)
+
+
+
+
+
+

Step 2: Add Probes to Collect Data

+

Even this simple model involves many quantities that change over time, such as membrane potentials of individual neurons. Typically there are so many variables in a simulation that it is not practical to store them all. If we want to plot or analyze data from the simulation we have to “probe” the signals of interest.

+
+
[3]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin)
+    A_probe = nengo.Probe(A, synapse=0.01)  # ensemble output
+    B_probe = nengo.Probe(B, synapse=0.01)
+
+
+
+
+
+

Step 3: Run the Model!

+
+
[4]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(2)
+
+
+
+
+
+

Step 4: Plot the Results

+
+
[5]:
+
+
+
+plt.figure(figsize=(9, 3))
+plt.subplot(1, 3, 1)
+plt.title("Input")
+plt.plot(sim.trange(), sim.data[sin_probe])
+plt.ylim(0, 1.2)
+plt.subplot(1, 3, 2)
+plt.title("A")
+plt.plot(sim.trange(), sim.data[A_probe])
+plt.ylim(0, 1.2)
+plt.subplot(1, 3, 3)
+plt.title("B")
+plt.plot(sim.trange(), sim.data[B_probe])
+plt.ylim(0, 1.2)
+
+
+
+
+
[5]:
+
+
+
+
+(0.0, 1.2)
+
+
+
+
+
+
+../../_images/examples_basic_communication-channel_9_1.png +
+
+

These plots show the idealized sinusoidal input, and estimates of the sinusoid that are decoded from the spiking activity of neurons in ensembles A and B.

+
+
+

Step 5: Using a Different Input Function

+

To drive the neural ensembles with different abstract inputs, it is convenient to use Python’s “Lambda Functions”. For example, try changing the sin = nengo.Node line to the following for higher-frequency input:

+
sin = nengo.Node(lambda t: np.sin(2*np.pi*t))
+
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/communication-channel.ipynb b/examples/basic/communication-channel.ipynb new file mode 100644 index 0000000000..a1f8d61b1f --- /dev/null +++ b/examples/basic/communication-channel.ipynb @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Communication channel\n", + "\n", + "This example demonstrates how to create\n", + "a connection from one neuronal ensemble to another\n", + "that behaves like a communication channel\n", + "(that is, it transmits information without changing it).\n", + "\n", + "Network diagram:\n", + "\n", + " [Input] ---> (A) ---> (B)\n", + "\n", + "An abstract input signal is fed into\n", + "the first neuronal ensemble $A$,\n", + "which then passes it on to another ensemble $B$.\n", + "The result is that spiking activity in ensemble $B$\n", + "encodes the value from the Input." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:12.629613Z", + "iopub.status.busy": "2020-11-25T16:47:12.628756Z", + "iopub.status.idle": "2020-11-25T16:47:13.119872Z", + "shell.execute_reply": "2020-11-25T16:47:13.120340Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.128994Z", + "iopub.status.busy": "2020-11-25T16:47:13.128470Z", + "iopub.status.idle": "2020-11-25T16:47:13.132111Z", + "shell.execute_reply": "2020-11-25T16:47:13.131591Z" + } + }, + "outputs": [], + "source": [ + "# Create a 'model' object to which we can add ensembles, connections, etc.\n", + "model = nengo.Network(label=\"Communications Channel\")\n", + "with model:\n", + " # Create an abstract input signal that oscillates as sin(t)\n", + " sin = nengo.Node(np.sin)\n", + "\n", + " # Create the neuronal ensembles\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # Connect the input to the first neuronal ensemble\n", + " nengo.Connection(sin, A)\n", + "\n", + " # Connect the first neuronal ensemble to the second\n", + " # (this is the communication channel)\n", + " nengo.Connection(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Add Probes to Collect Data\n", + "\n", + "Even this simple model involves many quantities\n", + "that change over time, such as membrane potentials of individual neurons.\n", + "Typically there are so many variables in a simulation\n", + "that it is not practical to store them all.\n", + "If we want to plot or analyze data from the simulation\n", + "we have to \"probe\" the signals of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.138487Z", + "iopub.status.busy": "2020-11-25T16:47:13.137015Z", + "iopub.status.idle": "2020-11-25T16:47:13.139100Z", + "shell.execute_reply": "2020-11-25T16:47:13.139502Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # ensemble output\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the Model!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.144638Z", + "iopub.status.busy": "2020-11-25T16:47:13.143875Z", + "iopub.status.idle": "2020-11-25T16:47:13.574190Z", + "shell.execute_reply": "2020-11-25T16:47:13.572969Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:13.586066Z", + "iopub.status.busy": "2020-11-25T16:47:13.579768Z", + "iopub.status.idle": "2020-11-25T16:47:13.894015Z", + "shell.execute_reply": "2020-11-25T16:47:13.894417Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.2)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(9, 3))\n", + "plt.subplot(1, 3, 1)\n", + "plt.title(\"Input\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe])\n", + "plt.ylim(0, 1.2)\n", + "plt.subplot(1, 3, 2)\n", + "plt.title(\"A\")\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.ylim(0, 1.2)\n", + "plt.subplot(1, 3, 3)\n", + "plt.title(\"B\")\n", + "plt.plot(sim.trange(), sim.data[B_probe])\n", + "plt.ylim(0, 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots show the idealized sinusoidal input,\n", + "and estimates of the sinusoid that are decoded\n", + "from the spiking activity of neurons in ensembles A and B.\n", + "\n", + "## Step 5: Using a Different Input Function\n", + "\n", + "To drive the neural ensembles with different abstract inputs,\n", + "it is convenient to use Python's \"Lambda Functions\".\n", + "For example, try changing the `sin = nengo.Node` line\n", + "to the following for higher-frequency input:\n", + "\n", + " sin = nengo.Node(lambda t: np.sin(2*np.pi*t))" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/communication_channel.html b/examples/basic/communication_channel.html new file mode 100644 index 0000000000..534d322281 --- /dev/null +++ b/examples/basic/communication_channel.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/basic/many-neurons.html b/examples/basic/many-neurons.html new file mode 100644 index 0000000000..2e7d54440f --- /dev/null +++ b/examples/basic/many-neurons.html @@ -0,0 +1,773 @@ + + + + + + + + Many neurons — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Many neurons

+

This demo shows how to construct and manipulate a population of neurons.

+

These are 100 leaky integrate-and-fire (LIF) neurons. The neuron tuning properties have been randomly selected.

+

The input is a sine wave to show the effects of increasing or decreasing input. As a population, these neurons do a good job of representing a single scalar value. This can be seen by the fact that the input graph and neurons graphs match well.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.utils.ensemble import sorted_neurons
+from nengo.utils.matplotlib import rasterplot
+
+
+
+
+

Step 1: Create the neural population

+

Our model consists of a single population of neurons.

+
+
[2]:
+
+
+
+model = nengo.Network(label="Many Neurons")
+with model:
+    # Our ensemble consists of 100 leaky integrate-and-fire neurons,
+    # representing a one-dimensional signal
+    A = nengo.Ensemble(100, dimensions=1)
+
+
+
+
+
+

Step 2: Create input for the model

+

We will use a sine wave as a continuously changing input.

+
+
[3]:
+
+
+
+with model:
+    sin = nengo.Node(lambda t: np.sin(8 * t))  # Input is a sine
+
+
+
+
+
+

Step 3: Connect the network elements

+
+
[4]:
+
+
+
+with model:
+    # Connect the input to the population
+    nengo.Connection(sin, A, synapse=0.01)  # 10ms filter
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later.

+
+
[5]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin)
+    A_probe = nengo.Probe(A, synapse=0.01)  # 10ms filter
+    A_spikes = nengo.Probe(A.neurons)  # Collect the spikes
+
+
+
+
+
+

Step 5: Run the model

+
+
[6]:
+
+
+
+# Create our simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 1 second
+    sim.run(1)
+
+
+
+
+
+

Step 6: Plot the results

+
+
[7]:
+
+
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(sim.trange(), sim.data[A_probe], label="A output")
+plt.plot(sim.trange(), sim.data[sin_probe], "r", label="Input")
+plt.xlim(0, 1)
+plt.legend()
+
+# Plot the spiking output of the ensemble
+plt.figure()
+rasterplot(sim.trange(), sim.data[A_spikes])
+plt.xlim(0, 1)
+
+
+
+
+
[7]:
+
+
+
+
+(0.0, 1.0)
+
+
+
+
+
+
+../../_images/examples_basic_many-neurons_13_1.png +
+
+
+
+
+
+../../_images/examples_basic_many-neurons_13_2.png +
+
+

The top graph shows the decoded response of the neural spiking. The bottom plot shows the spike raster coming out of every 2nd neuron.

+
+
[8]:
+
+
+
+# For interest's sake, you can also sort by encoder
+indices = sorted_neurons(A, sim, iterations=250)
+plt.figure()
+rasterplot(sim.trange(), sim.data[A_spikes][:, indices])
+plt.xlim(0, 1)
+
+
+
+
+
[8]:
+
+
+
+
+(0.0, 1.0)
+
+
+
+
+
+
+../../_images/examples_basic_many-neurons_15_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/many-neurons.ipynb b/examples/basic/many-neurons.ipynb new file mode 100644 index 0000000000..3984ee1e76 --- /dev/null +++ b/examples/basic/many-neurons.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Many neurons\n", + "\n", + "This demo shows how to construct and manipulate a population of neurons.\n", + "\n", + "These are 100 leaky integrate-and-fire (LIF) neurons.\n", + "The neuron tuning properties have been randomly selected.\n", + "\n", + "The input is a sine wave to show the effects\n", + "of increasing or decreasing input.\n", + "As a population, these neurons do a good job\n", + "of representing a single scalar value.\n", + "This can be seen by the fact that\n", + "the input graph and neurons graphs match well." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.145135Z", + "iopub.status.busy": "2020-11-25T16:47:15.144281Z", + "iopub.status.idle": "2020-11-25T16:47:15.641456Z", + "shell.execute_reply": "2020-11-25T16:47:15.641907Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import sorted_neurons\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural population\n", + "\n", + "Our model consists of a single population of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.647116Z", + "iopub.status.busy": "2020-11-25T16:47:15.646595Z", + "iopub.status.idle": "2020-11-25T16:47:15.650160Z", + "shell.execute_reply": "2020-11-25T16:47:15.649715Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Many Neurons\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a sine wave as a continuously changing input." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.656944Z", + "iopub.status.busy": "2020-11-25T16:47:15.653883Z", + "iopub.status.idle": "2020-11-25T16:47:15.657848Z", + "shell.execute_reply": "2020-11-25T16:47:15.657408Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(8 * t)) # Input is a sine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.664399Z", + "iopub.status.busy": "2020-11-25T16:47:15.662880Z", + "iopub.status.idle": "2020-11-25T16:47:15.665027Z", + "shell.execute_reply": "2020-11-25T16:47:15.665436Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input to the population\n", + " nengo.Connection(sin, A, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.671595Z", + "iopub.status.busy": "2020-11-25T16:47:15.670056Z", + "iopub.status.idle": "2020-11-25T16:47:15.672228Z", + "shell.execute_reply": "2020-11-25T16:47:15.672630Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01) # 10ms filter\n", + " A_spikes = nengo.Probe(A.neurons) # Collect the spikes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.677701Z", + "iopub.status.busy": "2020-11-25T16:47:15.676920Z", + "iopub.status.idle": "2020-11-25T16:47:15.876936Z", + "shell.execute_reply": "2020-11-25T16:47:15.876502Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 1 second\n", + " sim.run(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:15.883220Z", + "iopub.status.busy": "2020-11-25T16:47:15.882371Z", + "iopub.status.idle": "2020-11-25T16:47:16.416787Z", + "shell.execute_reply": "2020-11-25T16:47:16.417231Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6Q8MbxtULyj2AhVprR3/KCrY1s17AZKVU5bQGaq1naK1DtNYhJUpkg83gd96BQ4fgk0/AzxpfsOnYRfvx213ruFqzW0eePGYJ6upVGDrU0lQ0vy8PhQRxPjKGEd9vc5OCgpANeP11k6rjk08sS05/7T7Dgo1h9vOHGpdzh3Y3hTMMxQkgyOG8nE2WFj1IseyktT5h+3kIWAE0TD0sm7Fnj6ln27s3tG5taYqNT+TUJZP2onvjcvyvWQV3aHjrqF7dZMRdsACWLbM0lQ4wezQLNobZy6gKQq5i82aT6G/wYGja1NL0ycqD9uOZjzfJUYGqzjAUG4CqSqmKSilfjDFYnLKTUqoGUARY4yAropTysx0XB1oCu1KOzVZobXLH589v3EYdOB0Rzbu/7Sb8aiwlC/oxoZuLKtW5mhdfhCpVzKwiNrkKVwH/5Kenj/4+4A7NBMF9JCQYZ4/ixS1lBZJIKi/w/ZPNaZ3DatBn2VBoreOBZ4Dfgd3Ad1rrnUqpsUopRy+mHsA32uqPWxMIVUptBf4Gxmmts7ehmDsXVqwwM4qSyb/syJh4bnv3L2b+ewSAjx9tlG1SBDsdPz/48EPYu9c8PdloXKEIj4QE4eedh/NXpMa2kMv47DOTLXrSJChc2NIUn5DIkfNRPHFnJRpXyF55nDKDpBm/Ea5eNZvXZcsav+g8yXZ25b5z9PlyPWBSdIS+erfr9XM1DzxgjObevSZ1iY33l+1l2t8H2DU2GxRkEgRXcPGimWXXq2c8Ax2WlRZuDOMFW+Dtuw/WpWdTN1SxtHGzcRSSFPBGGD/epA9fsMBiJBITNTP/PUx+Xy/mD7qNAn655LZOngy1aplEiHPm2MWNKxQhUcNTczfRpWFZOtUvk/41BMETGDvWGIvJky1GImWdibpls3ky0HTIJd9oTuDYMVMmtGdPS5oOgB82n2DF3nOMuK8G9coVdo9+7qByZbNf8fbbZvOuZUsAmlU0cSPL95xl+Z6zKOABMRaCp7J3r0kdPmAA1K9vF8fEJzt3vnJ/DR4OCaJwvhySEDQFkm8hs4wYYX6Os2Qn4cj5q3yz/hgB/t48cUclNyjmZkaONMtOL7xgNvqBvL7W5aYh8zdz6FxkWqMFIefzwguQN6/JtuxAUmobgGL5/XKskQAxFJnjv/9g/nzz9Fzeur74v8/XEXr0IpVLFshR7m5OI39+8w+ydq2JRLUxpafVy3nTsUsuVkwQXMAff8CSJcZlPDDQ0nQ6Itp+XCynlBZIBzEU10NrGD7cPDW/9JKl6XJ0HCcuXQPMPkWupU8fkxhxxAi7u2yn+mX4dejt9i4vLNgqswrBs0hIMN8NlSqlCkANPXKBbh//Zz+vVTr7ZobNDGIorsePP8K6deapuUABS1O9MckBZ7nYTICXl8kDdfCgiUa1UatMAHvebG8/f+uX3e7QThBuDV9/DTt2GCeXFNkZvt+UHIG9ZmQbSgb4pxydoxBDkRHx8fDKK1CzJjz2mKUpZT6jxBzoZuxU2reHtm2N90dEhF3s6B7rnUcRcS2OK9Fx7tBQEJxHTAyMHg0hIdCtW6rmjUdNGp+6ZQvlmHxOGSGGIiNmzzYeDW+/bcnZAnD4/FXAFBwBCMmBQTRORSkzqwgPT7Xhn8SyXWeo/8YyWoxbnma7IOQYPvnEeEKOG2dxhwUTfLvvTCTP312Nn4e0cpOCzkUMRXpcu2aSezVrBl26WJp+2XaK9pNXASbN9pIhrXjl/ppuUDKb0aiRKR4/eTKcSE739WnvxlQukd9+fiU63g3KCYKTuHIF3noL2rUzs2gHEhI1o3/aAUCNHL4v4YgYivSYNs182aV4YtgeFsHT8zYRb1t6qhpYgDplC0lltyTGjjVLdg65bu6tXYofnmpJt0bJ2TIvRcWmNVoQsj+TJsH582nmc3ry6438sNk8JNUoVTBVe05Fvt3S4tIl80dw771w11128Y4TETw+a739fNuYe/DxkltooWJF6N/f5L05etQuLpTXh/cfrs+rHczMy9HHXBByDOfOmWSg3btDkyaWpui4BJY5ZE0uWzjn700kId9yafH++yYc/913LeKh8zdbvuAC/H1crVnOYNQoMwtLEYAEUN32lPXF6kOu1koQss6775pl6bfesojXHQqnxmtLAejVrDyTHq7vUUlBxVCk5MIFkxm1e3dTocoBx4jjgrkln9PNEBRkUnrMmgUHrOnGi+Y3gUfz1x9PY6AgZGNOnYKPPzYekNWrW5pCjyYXK+vSoCwPNso5RYkygxiKlEyebDarRo+2iDcdu8jOk5ft52teaYuQASNHgq8vvPGGRVyyYM72JxdyMRMmQFxcqtLHAL/vNKWB76xWgoblC7tYsVuPGApHkmYT3bpB3bqWppHfb7cff5ubMsTeLKVKwTPPmPodu5JLjJQo6McjIaYg4qcrD/LP/mxQ/1wQrsfp08YltndvE4ntQMS1OLaFmdihWY838ch9S8/7RFlh8mRTGD3FbGLDkQuEXYyynzerVMzFiuVQXnrJ5IJKMau4v15pAN79bQ9Pz93kDs0E4cZImk2MGpWq6Yk5ybVxPDXfm1MMhVKqvVJqr1LqgFJqRBrtfZVS55RSW2yvAQ5tfZRS+22vPs7Q56a4eNHMJh580BQfceChT9ZwNdakDPYkl7dbTvHiMGSIqd+xZ49d3KJysqG9LDEVQnYnaTbx6KOmOJEDy/ecYe2hCwA81Niz9iUcybKhUEp5AdOA+4BaQE+lVK00un6rtW5ge31uG1sUeB1oBjQFXldKFcmqTjdFOrMJRwbeXpGfnm7pOp08geeeA39/iweZj1ce7qtTyn4uy09CtmbiRJPsMo29iX6zzGyib4tgJj5UP1W7p+CMGUVT4IDW+pDWOhb4BuicybH3An9orS9orS8CfwDtrzPG+Vy6lDybqG/9ZR+xpeoAeDgkSEp73iglShgPqLlz4VCyS+wHjzSgfW1jLHp/sT690YLgXs6cMZ5O//tfqtlEdFxyYaJyRTwnZiItnGEoygKOvo5hNllKuimltimlFiqlgm5wLEqpQUqpUKVU6LlzTn4CnTzZJLJLMZsIuxjFXe+tAODJuypTNVCWnW6KF14wGWbHj7eL/H28GNUhOe1JTqzdLuQCJk40CQDTmE3sOJGc/LJUIc/25nPVZvbPQLDWuh5m1jD7Ri+gtZ6htQ7RWoeUKFHCeZpdvmxmE126pJpNhDsE14mXUxYoU8ZEa8+cCWHJ6ZeDiuZjdEezSvnSwm3u0k4Q0ubcOZg+3cwmqla1NB06F0n3T9YAcEe1Etxfp7Q7NHQZzjAUJ4Agh/NyNpkdrXW41jrGdvo50DizY285M2aYpadXXrGIL0XF0nnav/bzqFjZdM0SL71kikBNnGgRPxRSjuaVirF460m5x0L2YupUiI42MUEOJCZq2ry/0n4++I5KHhWFnRbOMBQbgKpKqYpKKV+gB7DYsYNSytHcdgKSKtj8DtyjlCpi28S+xyZzDTExJsFX27ap8rZsDYuwnFcuYS1aJNwgwcHGB33GDLPua6Ogvw9D2lQhJj6RB6f/l/54QXAlV67ARx+ZlYaa1szQv2w/ZT/O7+tFlUDP/27IsqHQWscDz2C+4HcD32mtdyqlxiqlOtm6DVVK7VRKbQWGAn1tYy8Ab2KMzQZgrE3mGr76yoTlj7B69CYkaib/uc9+/svQVnRtmObWiXAjJJVKnTTJIm5asSi+XnnYc/oKS7adtGwSCoJb+Owz4zL/8ssWcUx8AkPmb7af7xzbPldkG1A5cRMxJCREh4aGXr9jRiQkQI0aULgwrF9vSSW+Yu9Z+s7cYD/f+ca95Jc9CufQqxf8/LPJLFs0udjT1Zh4ukz7l/1nIwkM8GPdK+3cqKSQq4mJMdHX1avD8uQiW7tPXebRz9cRftXsXc56vAl3VS/pLi1vCqXURq11yI2Oy72R2d9/bxLWjRhhMRILN4ZZjMT7D9UXI+FMRo6EyEhLbW2A/H7e9GpWHoAzl2PSGikIrmHuXDh5MtVKw5tLdtmNRI1SBWlRubg7tHMLudNQaG0KElWvDl27Wpre+XW3/fjBRmXp5sHRlm6hbl1TX3vKFLNR6ECPJuXtx7Hxia7WTBDMSsOECSZz9N13J4sTNesPJ6+Kf9SrYa4qVpZ7Pqkjy5bB5s3GEydP8i3QWnPB9sTwYKOyTHq4gZsU9HBeeslsaM+ZYxHn9fViak+T2n3TsYtpjRSEW8uiRbB3b6qVhqU7TturWgKULuTZAXYpyZ2GYtw4KFvW5G5xICmNeLH8vkzs7rnh+G7nrrugcWNTKSzBunHdtmZJ8ijoMWOtxT1ZEG45SSsNlSubDNIOHL1w1XKe25ajc5+hWLsWVqyA55839RIc+HaDCRKf2qshXh7uF+1WlDKzin37YLHFk5p8vt4EFc0HwNbjl4hPkCUowUX8/Tds2GD+Nr2sqXrOXo6hoJ83d1YrQbH8vulcwHPJfYZi4kQoUgQGDrSIwy5GMW/9MQID/GhWUdKI33IefNB4lkyYYJ7kHGgYVNh+vPfMFRcrJuRaxo83dVQeeyxV06mIa5Qq5M+sx5uw8bW70xjs2eQuQ3HwIPz4o0lSV8AaJDPsmy0kJGoWDm4hswlX4O0Nw4ebGd6/1iWmHk2TN7U3HbvkYsWEXMn27WbvcuhQk+04BccuXKN04bweW2/ieuQuQ/Hhh+YL6plnUjUdOBdJjVIF7cseggt4/HEoVixVWo86ZQvZj3efupxylCA4nw8+gHz54IknUjXNWXuU3acuEx2bewNBc4+huHgRvvwSevY0SepsJCZqvlx9mEtRcXRqUCaDCwhOJ18+Y7QXL4bdyW7JBfy8OTKuA80qFmX3qcucuRwtexXCreP0aRM78fjjliDQ85ExnLx0jfnrjgFQr1yh9K7g8eQeQzFjBly9apY7HFi57xxjl5iazmUL5y6Xt2zB009D3rzw/vupmmqWDmDzsUs0e+cvJv+53w3KCbmCadNMmdNnn7WIO0z5hxbjlrPr1GXqlSvEi+2ru0lB95M7DEVsrAnwats2VSrxXQ5LG55efCRbUqIE9O0LX38NZ89amhyXoFZJFTzhVhAVZQoTdeqUKpW4Y4aA2PhE/Lxzb9Gy3GEoFiwwIfkpZhMAxy9E2Y/LFpb9Cbfw7LMmv87HH1vE99YOtB+Lg4FwS/jqKwgPN+7yKSicz8d+3LZmzsrp5Gw831BobZY1atY0qSMcWLrjNN9sSC6wV6Kgn6u1E8CkUunQwRSJcUjrUdDfhzUj23BX9RJsPnaJ8EjJASU4kcREs4kdEgKtWtnFCYma7h//x6WoOHo2DWLr6HsYfnfuXXaC3GAoVq406Tqee86SrgNgzOKdALSrWZIj4zrIU6s7GTbMLD19841FXLpQXu6sZioaNn7rTzcoJngsv/5qgj6HD7ek6wi7GEXoUZNCplRAXgrl88n13w2ebygmTTLr4A7pOi5FxdJ1+r+cvhzNPbUCeefBum5UUADM/lHduuYJL0UAXpWSnl8YRnAD778PQUHQvbtFfPBcpP24YfnCLlYqe+IUQ6GUaq+U2quUOqCUGpFG+3Cl1C6l1Dal1F9KqQoObQlKqS221+KUY7PE3r2m9sFTTxnPGhvrDl9gsy2Qq2nFormi8Ei2Rykzq9i2zaRScKCSQ3XB9YcvsHKfbGwLWWTTJpPKZ+hQ8Enei1h3KJx+s0ytm4J+3txWSbI0gBMMhVLKC5gG3AfUAnoqpWql6LYZCNFa1wMWAhMc2q5prRvYXp1wJpMng58fPPmkRXzmcvI6eGCAGIlsQ69eZvb3wQcWcZlC/nbX5Yc/XUOfL9e7QzvBk/jgA5OdYcAAi3jNoXD78dbX78lVqcQzwhl3oSlwQGt9SGsdC3wDdHbsoLX+W2ud5F60Frj1RR7On4fZs82SU2CgpenI+WRPpxaV5Ykh2+Dvb4z6kiWwPzluQinFW13ruFExwaMICzN7YQMGmAqXDlyNiQeg920VyJPL9yUccYahKAscdzgPs8nSoz/wm8O5v1IqVCm1VinVJb1BSqlBtn6h585lYunh88/h2jWzie3Ar9tPsXjrCcoXzcefw++gWAHxdMpWPPmkyer74YcWcYNyhS3nObGEr5BNmD7deDwNHWoRL9pygm1hEVQolo83u8iDiSMunVcppR4FQgDH5D4VbDVcewGTlVKV0xqrtZ6htQ7RWoeUKFEi4zeKjzfRlm3bQu3advGpiGs8NXcT5yNjqVU6gColC2b1IwnOplQpk2Zl5kyTdsVGkRSpncMuXnO1ZoIncO2aydLQqRNUrGgXh12M4tlvtrDu8AUaOGQvFgzOMBQngCCH83I2mQWlVDtgFNBJa213iNdan7D9PASsABpmWaOffjLTyyFDrOLNJ+3HFYpLcF225bnnTMTsZ59ZxK92qGl3Uxz5w3Z3aCbkdL75xgTYpZhNbD0eYT8e0qaKq7XK9jjDUGwAqiqlKiqlfIEegMV7SSnVEPgUYyTOOsiLKKX8bMfFgZbArixrNHUqBAdDx452UXxCIuOX7rGfF/DNXRWqchT160Pr1ub3GBdnFw+4vRJrRrYBYPWB8/b1ZEHIFFqbVD516pgqiw4cc8jQICsNqcmyodBaxwPPAL8Du4HvtNY7lVJjlVJJXkwTgQLAghRusDWBUKXUVuBvYJzWOmuGYssWWLXKZCW1Vanae/oKVUaZbZFyRfLSqX4ZujW+9fvpQhYYNszMCn/4wSIuWdCfTvVNlt9en69zg2JCjuXff833w5AhlgA7MMvSAD8/0yqNgYJTHqu11r8Cv6aQjXY4bpfOuP8A50a7TZ1q0lf36wfA5mMXWbrztL15Qrd6tKhS3KlvKdwCOnY0tYunTIFHHrE0VSiWXCpVEDLNlCmmuuX//mcRxycksnTHaWqVDqBuLk4lnhGe5SR8/jzMmwe9e5s/CKDr9P/4dOUhe5eAvD7pjRayE3nymBTk//1ngqMceKZNFZoEm9+v5H8SMkXS7LR/f8if39L00d8HOHslRlYZMsCzDMXnn5ukcik2sR0pJIYi5/D442Z2OHWqRezn7cVjzYMByf8kZJKPPzZ7FE8/bRFHxyXYa520y+UZYjPCcwxFfLzxj27Txu4Sm1ZVtAB/MRQ5hsKFTaH7+fMhRexMxeLJT4XfbwyTuAohfaKjjUvsAw8YJxcH9p8xeZ2euqsyFYrlT2OwAJ5kKBYtguPHLW5vkWl4xRT0F2+nHMUzz5haFZ9/bhEn7VMAPL9gK2sPXXC1ZkJO4ZtvzLK0w3eD1pqPVxxk3FJTgre7LDtliOcYiilTUrnEXom2GoqhbapIWH5Oo3ZtEzj58cdm1mijoL8PQx383Tcdu5jWaCG3k+QSW7u2cbm2cfzCNcYv3cO/B8Ip6OdtmaEKqfEMQ7F1q3GJffppu0ssQMS1OEu34ffk7uIjOZYhQ8xscdEii3j4PdVZMLg5pQv5s+6wzCiENPjvP1OPJoVL7IFzV+zHYzrVRil5gMwIzzAUSS6x/fvbRVdj4nly7kY3KiU4jY4dzWwxxaY2QJPgotxRtQSr9p2j1uillszAgsCUKWavy6EeDZgZRRL1g8Ql9nrkfEMRHg5z55o/BJtLLEDt13+3/zEMvL0iM3o3dpeGQlbx8jI1RVauNPUqUlC9lImkjYpN4K/dZ1O1C7mUsDD4/vs0XWLDr8YC8FaXOlR2qHcipE3ONxRpuMRGRFmXnF65vyb31C7las0EZ9K/vyk+lcaswnF9+cJViasQbHzyickSm8IlFuDi1ViK5PPh0dsqyLJTJsjZhiIpS2ybNiZ/C6aMYcvxywHo3KAMi59pKX8InkDRoiaidu5cuGDdjyhdOLn41LkrYigEzMPjp58al1iHLLFRsfGcuxLDnLVHKZLPN4MLCI7kbEOR5BLrMJto+/5KImPi8cqjmPRwA+qlqGMg5GCGDDFpor/4wiIuXSi5zO2JS5J+XAC+/TaVS+zVmHhqjf6dJm+bIM1zEtWfaXK2oZg6FSpUME8NwKWoWHvTC/dUt6ekFjyEevXgzjtNYGVCgl0c4O+Nn61kpdSpEOwusbVqmdUGGykdHab2zHpFg9xCzjUUW7eazU1bltgP/thHg7F/2Juj4xIyGCzkWIYMgSNHTLlUG0optoy+hwcblSXs4jWJ0s7trFlj8oOlcIm96PAg+fH/GnFXdUnZkVlyrqGYOtVsbtqyxH74135Lc9VA8WTwSDp3hqCgVJvaeX29qFGqIJEx8fwpnk+5mylToFAhkxzUgfDIZENRunDelKOEDMiZhiI+3mxq9u7N+6HnqP7qb3g7LDMtHXY7HeqWdqOCwi3D29vU1f7rL9hlLV3SIMi4Rw/8KpTfHVLLC7mIEyfSdYlNWnq6u1YgdcoEuEO7HEvONBTnz9tdYqcuP0BMfCLxicnLDTVKBYinkyczcCD4+cFHH1nETSsW5QFbUaMn5mwUD6jcyCefmP2rFC6xpyOimbvuGEFF8zKjd2O8vXLmV5+7cMrdUkq1V0rtVUodUEqNSKPdTyn1ra19nVIq2KFtpE2+Vyl1b6be8Nw5zjRuTmyNWhbxF31C+G9Em3QGCR5D8eLQsyfMng2XLlma7q+THC9z4GykixUT3EqSS2zHjlCpkqXpsS/Xsef0Fe6rU1oeIm+CLBsKpZQXMA24D6gF9FRK1UrRrT9wUWtdBfgAGG8bWwtTY7s20B6YbrtexsTG8lr5NvSbtcEiblszkDKy9pg7GDIEoqJg5kyL+L66pfnxqRZAai8XwcP57juTjt7BJRZMpth9tnTiT99VJa2RwnVwxoyiKXBAa31Iax0LfAN0TtGnMzDbdrwQaKuMWe8MfKO1jtFaHwYO2K6XIbF5vPmzSlNWHzgPQOUS+XnxXkn4l6to1AhatjQBl4nWuiNVA01Kj5MR4iqba0hyia1Z02QbduDQ+asAvNmlDoXyST2am8EZhqIscNzhPMwmS7OP1joeiACKZXIsAEqpQUqpUKVU6MFi5UjMkzzx+GXo7TzdWp4Uch1DhsDBg/CrpVw7Bfy8qVQiPxOW7uWkBODlDtauhY0bU7nEAqzYa4peta0h7rA3S47Z0dFaz9Bah2itQ+K9rMWH/H2uv1oleCAPPghlyqSZ/+nRZhUAmJLCbVrwUKZOTdMl9mj4Vd5cYrzjZFn65nGGoTgBBDmcl7PJ0uyjlPIGCgHhmRybisol8tsrnP3zUuvr9BY8Fh8f4yq7bBns2WNp6tMiGJDAy1zBqVOwYIGpsV7AGj/13rJ9ANxVvYQ7NPMYnGEoNgBVlVIVlVK+mM3pxSn6LAb62I67A8u1CZ9dDPSweUVVBKoC66/3hvl8vfnpqZb88dwdBBXNd73ugiczaBD4+qZylU1K3/LTlpPMXXfUHZoJruLTT9N0iQXYeOQCHeuV5ss+TdygmOeQZUNh23N4Bvgd2A18p7XeqZQaq5TqZOv2BVBMKXUAGA6MsI3dCXwH7AKWAk9rrTP1CFgkv69901LIxZQsmewqGxGRZpcJS/e6WCnBZcTGmtiJ++6DKtZ9ysPnr3IyIpqapQOkBHIWccoehdb6V611Na11Za312zbZaK31YttxtNb6Ia11Fa11U631IYexb9vGVdda/+YMfYRcxpAhEBkJs2ZZxB/2aADAleg44hMSU48Tcj4LFsCZM5YM0mBq0rR+bwUArSWnU5bJMZvZgpAujRtDixZm+cnBVbZzg7K83bUOiVpSSnssU6dCtWpwzz0W8a5TlwFT3bKWpOvIMmIoBM9g6FA4cACWLrWIk8pc9vpsHVdj4t2hmXCr2LAB1q0zGaTzWL/Kdpwwy5D9W1VKa6Rwg4ihEDyDJFfZKVMs4ibBRalbthCHz1/l05UH3aSccEuYOtV4OfXpYxFHXIvj7V93U7tMAKUK+aczWLgRxFAInkGSq+zvv8Pe5M1rrzyKn4e0onaZAKYsP8BhW5SukMM5e9ZUsevbFwKsS0tJcRPFC/i5QTHPRAyF4Dmk4yoLMLRtVQDavL/CxUoJt4QZM4zH0zPPWMQnLl1j07GLQPLvXMg6YigEz6FkSejRw3g/Xb5saWpcwdSq0BqiYmWvIkcTFwcff2w2sKtbc7y1HLecQ+eucnetQPvvXMg6YigEz2Lo0DRdZYvl97Uf7zp5GSEH8+OPcPJkmllik5CoCecihkLwLBo3hubNzUang6usUorfh90BQPdP1vDVmiNuUlDIMlOmQOXKJsjOgbMOhaouRcW5WiuPRgyF4Hmk4ypbzaGO+uhFO12tleAMNm+Gf/816TocXGITEjXrDl8AwNc7D691TFkSR8gKYigEz6NbtzSzyjpWNgsMEI+YHMnUqZAvn0kA6MD4pXsYOn8zAKtebE3dcoXcoZ3HIoZC8Dx8fGDwYDOj2GvN8zTq/poAeCllWdMWcgDnz8O8efDYY1C4sKXpu9DksjbyEOB8xFAInkk6rrID76jEsHZVORkRzfQVEoCXo/j8c4iJSeUSCxATZ92PEpyLGArBMwkMTNdVdvCdlQGY+PteTkdIXe0cQXw8TJ8ObdpA7dqWpoREzTVb3ZFG5Qu7QTnPRwyF4Lmkk1XW38eLT3s3BuDxWRtYdyjcDcoJN8TixXD8eCqXWIAnv95oP/7E9nsVnIsYCsFzCQlJ01UWoFWV4gDsPnWZXp+vc4d2wo0weTIEB0PHjqmalu06A8CQNlUoWVByO90KxFAInk2Sq+zvv1vE+f2S665LTZtszsaN8M8/5nfp5WUXL9t5mrpjzO/17lqBPN26SnpXELJIlgyFUqqoUuoPpdR+289UMfNKqQZKqTVKqZ1KqW1KqUcc2mYppQ4rpbbYXg2yoo8gpKJbNyhdOlVWWYB7awcCUNQhalvIhkyeDAULQv/+FvGbv+ziSrRJx9KjSRD+Pl5pDBacQVZnFCOAv7TWVYG/bOcpiQIe01rXBtoDk5VShR3aX9RaN7C9tmRRH0GwkpRVdulS2LfP0jS1ZyMGtKrImcsxvPGzBOBlS06ehG++gX79UmWJLV0or/04qe6IcGvIqqHoDMy2Hc8GuqTsoLXep7Xebzs+CZwFSmTxfQUh86TjKuvrnYeapc2Xz8x/j3Aq4po7tBMyYvp0SEhItYkdE5/AoXMmZXzPpuUpXzSfO7TLNWTVUARqrU/Zjk8DgRl1Vko1BXwBRwf2t21LUh8opdKNlFFKDVJKhSqlQs+dO5dFtYVcRZKr7MyZcOmSpalZpaL2421hES5WTMiQa9fgk0+gc2eoZK1Ut2LvOc5HxvB21zq8+2Bd8shG0y3luoZCKfWnUmpHGq/Ojv20CXNNN9RVKVUamAM8rrVOckEZCdQAmgBFgZfTG6+1nqG1DtFah5QoIRMS4QZ57jnjKvvZZxZxuSL52DbG1FuWokbZjDlzIDzc/O4cOH4hiifmGJfYFpWLu0OzXMd1DYXWup3Wuk4ar0XAGZsBSDIEZ9O6hlIqAPgFGKW1Xutw7VPaEAPMBJo640MJQioaNDDBWlOmmHoGDgT4+xBUNC//7JeZarZBa7OJ3bAh3H67pWnfmSv2Y1lycg1ZXXpaDCQVrO0DLErZQSnlC/wIfKW1XpiiLcnIKMz+xo4s6iMI6TN8OISFwYIFqZrur1Oa9YcvsPf0FckBlR1Ytgx27zaziRQpOfadiQTghXuq4SVLTi4hq4ZiHHC3Umo/0M52jlIqRCn1ua3Pw8AdQN803GDnKqW2A9uB4sBbWdRHENLnvvugRg14/33zxOpAtcCCxCVo7p28ii9WH3aTgoKdyZOhVCl45BGLeMm2k8xYdZCapQN4po2UOnUVWTIUWutwrXVbrXVV2xLVBZs8VGs9wHb8tdbax8EF1u4Gq7Vuo7Wua1vKelRrHZnlTyQI6ZEnj3lC3bQJVq2yNDUJTt7UTqprILiJ3buNO/PTTxtvNQcmLN3Lxag43ulax03K5U4kMlvIXfTuDcWLw6RJFnH5Yvm4r04pwCSZE9zI5Mng7w9PPGERX42J59iFKJ5pXYWG5aUetisRQyHkLvLmhaeegp9/ThWAN61XI1pVKU54ZEw6g4Vbzvnz8NVX8OijkMK78ZOVxqu+aqAE17kaMRRC7uOpp8ySxuTJFnGePIqSBf04GRFNoswq3MP06RAdDcOGWcSXo+OYuvwAvt556FC3tHt0y8WIoRByH4GB5ol11izjp+9A/aDCnLsSw9glu9yjW27m2jWT6bdDh1Q1J7bbgiH7tayIt5d8bbkaueNC7uS555Ijfx14OCQIgFn/HWHv6StpjRRuFbNmmaWnl16yiMMjY+y/i36tgl2vlyCGQsil1K4N7dub/E8xyXsSeX29GNbOuF3eO3lVeqMFZ5OQYNyWmza1BNiFXYyi8Vt/MnbJLsoWzkuJAlIP2x2IoRByL88/D6dPw7x5FnEehwCvQV+Fcikq1tWa5T5+/BEOHjSzCYf7v/tU8qyufZ1SUg/bTYihEHIvbdtC/fowcaKlAl6FYslpIZbtOsOq/efdoV3uQWuYMAGqVIEuXSxNR8NN/q2mFYsypI0UJnIXYiiE3ItS8PLLJsBr8WK7uFP9MiwY3Jy3upigrivRceldQXAGq1bBhg1mhudlLT505nI0/j55+O6J5hTOJwWm3IUYCiF389BDJoX1u+/a03oopWgSXJRHmpiN7c//Ocyx8Ch3aunZTJhgYib69LGIExI1+89GUiy/7Eu4GzEUQu7G2xtefBHWr4cVKyxNPjY3zMPnr3LHxL+Jjktwg4Iezo4d8OuvMGSICYa0sff0FbpM+5cVe88RE5+YwQUEVyCGQhD69jWxFePGpWoadEdywZxV+yQNudN57z3Il88EQTpw7+RVbD9hYifOS6S82xFDIQj+/iauYtky2LjR0tS2Rkn78SqpV+Fcjh6FuXOhf38oVswu/mFTmP3Y3ycPc/pLmRp3I4ZCEACefBIKFYLx4y3iZpWKsWFUO6qWLEB4pLjJOpUJE4xDwYsvWsTDv9tqP+5Qtwy3V5WKlu5GDIUgAAQEmOWPhQtTJQssUdCPwvl8OHz+KmEXozh4TrLhZ5mTJ+GLL8wGdlCQXZwyZiU6XvaFsgNZMhRKqaJKqT+UUvttP9PM/auUSnAoWrTYQV5RKbVOKXVAKfWtrRqeILiHZ58FPz8TV5GCgv4+7Dl9hVbj/6bt+yulCl5Wef99U5J2xAiLOKUR7n1bBVdqJaRDVmcUI4C/tNZVgb9s52lxzaFoUScH+XjgA611FeAi0D+L+gjCzRMYCP36wezZcOKEpWn3qcuW87NXZIP1pjl/3uTY6tULKle2i6PjEuj9xXr7+Z/D7+S2SsXSuoLgYrJqKDoDs23HszF1rzOFrU52GyCpjvYNjReEW8KLL5p4ihR7FU+1tkYFHz5/1ZVaeRaTJ5uEjCNHWsQ/bz1JVGzyUlNggMRPZBeyaigCtdanbMengcB0+vkrpUKVUmuVUl1ssmLAJa11vO08DCib3hsppQbZrhF67px4nwi3iOBgs24+Y4ZZR7fR+7YKvNS+uv089IiUS70pLl0yqcS7dYNateziM5ejeXHhNvt53xbBFPDzdoOCQlpc11Aopf5USu1I49XZsZ82i7bpLdxW0FqHAL2AyUqpyun0Sxet9QytdYjWOqRECfGCEG4hr7xispmmmFW0qFzcfvzesn1MWrbX1ZrlfKZNg8uXzT22EROfwOItyUa5XrlCjOlUWxIAZiOuayi01u201nXSeC0CziilSgPYfp5N5xonbD8PASuAhkA4UFgplfTYUA44kdZ4QXAplSrBY4/Bp59aZhUNggqz96329G0RDMCU5QfcpGAOJTISPvjAFCZq2NAufvTzdbz9624A7qtTigWDm7tLQyEdsrr0tBhIStDSB1iUsoNSqohSys92XBxoCeyyzUD+BrpnNF4Q3MKoURAfb3z9HfDz9qJo/mTnvPgESS+RaT76yFQUfPVVi3jDkYv242m9GuHn7ZVypOBmsmooxgF3K6X2A+1s5yilQpRSn9v61ARClVJbMYZhnNY6qc7ky8BwpdQBzJ7FF1nURxCcg+Os4tQpS1PHesk1m3eevJxypJAWERHG6N5/P9x2m12csjZ5njyy3JQdyZKh0FqHa63baq2r2paoLtjkoVrrAbbj/7TWdbXW9W0/v3AYf0hr3VRrXUVr/ZDWWnwOhezDqFHG1z/FrKJSiQJ82KMBAJ2n/SsxFZlh8mS4eBHGjrWIa72+1D36CDeERGYLQnpUrgy9exuf/xSziiIOtRG+3XDc1ZrlLC5cgEmToGtXaNzYLj4dEU10nFm6K1ckL+89VN9dGgrXQQyFIGTEq6+avYq33rKIq5cqaD8e8cN2Nh27mHKkkMR778GVK/DGG3bRpahYXlhgcjqVLOjHgsHN6d64nLs0FK6DGApByIjKlWHAABNXceiQXRwY4M9PT7e0nz84/T93aJf9OXsWpkyBhx+GunXt4gZj/2D1AVNidu3ItpQulDe9KwjZADEUgnA9XnsNfHxg9GiLuEFQYfo0T85FFCceUKkZP95EYY8ZYxc57ukMa1dVNrBzAGIoBOF6lCljEgbOmwdbt1qagovntx9HxUimUwvHj8P06fDoo1Cjhl18+Vq8/bhUgL87NBNuEDEUgpAZXnrJ1KsYNcoi9vdJ9vnvP3tDKnfPXM1rr5m8WSk8nXafTnYprlUmwNVaCTeBGApByAxFisDLL8Mvv8Dq1XZx14Zl7QF4oUcvsu6w5IACzMzrq69g6FCokLw8l5CoeeWH7fh652FG78bUK1fYfToKmUYMhSBklqFDoXRpU0PBts7u7+PFO12TN2l7frZWChuBmYEVLpwqQ+z2ExEcOn+V8d3qck/tUu7RTbhhxFAIQmbJl89syv77L3z/vV18LS7e0m363wddrFg2Y9ky83rtNTMTc+D4hSgAapUu5A7NhJtEDIUg3Aj9+hk3zxdfhOhoAKqWLGjp8v2mML7fGOYO7dxPYqKZTQQHm9KyNiKuxbH52EX+tbnEliksm9g5CTEUgnAjeHubDKhHjpi0FECdsoXYOvoee1ZZgOcXbE1zuMfz9ddmf+Kdd0xZWeBKdBz131hG1+n/8c2G41QukZ+C/j5uVlS4EcRQCMKN0rYtdOoEb78Np08DUCifD2M61bZ0e2JOaO7KLnvlitm/adIEHnnELt5xwpo48b46pVOOFLI5YigE4WZ47z2IiUnlLhtSIXlN/vedZ5i+4mDucZl9802TE2vqVMhjvlrCLkbR87O19i79WlZkwO0V3aWhcJOIoRCEm6FqVeMFNXMmbNpkF3/5eBNevDe5ZOqkP/bxyapcsLm9d69Zinv8cWjWzC6e/Od++/GE7vUY/UAtCjskVBRyBmIoBOFmefVVKFYMhgwxm7hAgL8Pd1S1lupduuO0O7RzHVobo5k3L7z7rqUpJj556a1zgzKu1kxwEmIoBOFmKVwYJk6E//6DL5JrbtUtV4h5A5uxflRbKhbPz+FzVzkWHkV4pIeWW1m0yLjDvvEGBAYCsP/MFcYv3cPPW00pWV+vPFK5LgejslJ0RSlVFPgWCAaOAA9rrS+m6NMa+MBBVAPoobX+SSk1C7gTiLC19dVab7ne+4aEhOjQ0FCLLC4ujrCwMKJtLotC5vD396dcuXL4+IgXyk2hNdx1F2zfDnv2QMmSlubvN4ZZPKCOjOvgYgVvMVevQp06JsZkyxaTPBEIHvGLvUu1wALM7tdUMsRmA5RSG7XWITc6zjuL7zsC+EtrPU4pNcJ2/rJjB63130ADm5JFgQPAMocuL2qtF2ZRD8LCwihYsCDBwcEoJdkoM4PWmvDwcMLCwqhYUTYYbwqlTGGj+vXhhRdM2goHWlUtbjmPT0jE28uDJvKvv25chVessBuJlFl0b6tUTIxEDierf7Gdgdm249lAl+v07w78prWOyuL7piI6OppixYqJkbgBlFIUK1ZMZmFZpWZNE2Q2Zw4sX25pCgzw580udezne89ccbV2t47QUBNTMmgQ3HmnXfzJiuTN++n/a8SrHWq5QzvBiWTVUARqrZNqRJ4GAq/TvwcwP4XsbaXUNqXUB0opv/QGKqUGKaVClVKh586dS69PZvUWbMg9cxKjRkGlSvDkk6b+ggOVSySnIn/lxx2eEVsRF2cKOgUGmpoTwIGzV/hhUxjv/7EPMF5O99ctja+3B82gcinX/Q0qpf5USu1I49XZsZ82mx3pbngopUoDdYHfHcQjMXsWTYCipFi2SnH9GVrrEK11SIkSJdLrJgjuIW9e+PRT2LfPeEM50KJyceYNNC6jW49fot/s0LSukLOYNMlEYE+bZjb1gXaTVjH8O7Mfk8/Xi071xcvJU7iuodBat9Na10njtQg4YzMASYbgbAaXehj4UWsd53DtU9oQA8wEmmbt47ifn376CaUUe/bscdo1Z82axcmTJ296/JEjR5g3b57T9BHSoV07GDzYLMf884+lqUXl4rSoXAyAVfvSnhHnGHbuNHsTXbuaFxATn1y0qU/zCmwfc6+lVoeQs8nqnHAx0Md23AdYlEHfnqRYdnIwMgqzv7Eji/q4nfnz59OqVSvmz0+5wnbziKHIQUycaBLi9e0LkdZ04w3LF7YfR0TFcToimqx4HbqF2FhTsS4gAD7+2C7+aPkB+3GR/L54SXlTjyKrXk/jgO+UUv2Bo5hZA0qpEGCw1nqA7TwYCAJWphg/VylVAlDAFmBwFvUB4I2fd7Lr5OXrd7wBapUJ4PUHamfYJzIyktWrV/P333/zwAMP8MYbb6TZb9KkSXz55ZcADBgwgGHDhnHkyBE6duzIjh3GVr733ntERkZSp04dQkND+d///kfevHlZs2YNNWvW5OGHH+a3334jb968zJs3jypVqtC3b186duxI9+7dAShQoACRkZGMGDGC3bt306BBA/r06cNzzz3nxDsjWChQwERr33WXKXQ0bZq96dm21ShfNB8vf7+d+mON49+YB2rRt2UO8jgbPdq4wS5aBIGBaK1ZczCcqQ6GonQhyQzraWRpRqG1Dtdat9VaV7UtUV2wyUOTjITt/IjWuqzWOjHF+DZa67q2paxHtdY5uuLLokWLaN++PdWqVaNYsWJs3LgxVZ+NGzcyc+ZM1q1bx9q1a/nss8/YvHlzutfs3r07ISEhzJ07ly1btpA3r3EzLFSoENu3b+eZZ55h2LBhGeo1btw4br/9drZs2SJGwhXceScMG2bqRS9ebBf7eufhocZB3FEteY9tzaFwNyh4k6xaBRMmmE3sTp0A+C70OL0+X2fv8nL7GjzUOMhdGgq3iKzOKLIl13vyv1XMnz+fZ599FoAePXowf/58GjdubOmzevVqunbtSv78xhPmwQcf5J9//qGT7R8vs/Ts2dP+U778syHvvmtiC/r2NU/g5csDkCePYny3ujR/17jR7jmdQ9xlL1yAxx4znl0fJMfPpswM+8Qdlcgjy04eh0caCndw4cIFli9fzvbt21FKkZCQgFKKiRMnZsoF1dvbm8TE5AnX9WIbHK+ZdOx4jcTERGJjY2/mowjOwN8fvvsOGjWCnj0tAWmlApKXZo6GR3HoXCSVShRwk6KZIDHRGImTJ0298AJG183HLjJn7VF7t3kDm4mR8FDEwdlJLFy4kN69e3P06FGOHDnC8ePHqVixIv+k8H65/fbb+emnn4iKiuLq1av8+OOP3H777QQGBnL27FnCw8OJiYlhyZIl9jEFCxbkyhXrk+e3335r/9m8eXMAgoOD7ctdixcvJi4uLt3xgguoWhU++8zkgnrtNbtYKcXUng3t523eX0l0XEJaV8gejBsHv/xiZhJNjWOi1pqu0/+zdGsSXNQd2gkuQAyFk5g/fz5dba6CSXTr1i2V91OjRo3o27cvTZs2pVmzZgwYMICGDRvi4+PD6NGjadq0KXfffTc1atSwj+nbty+DBw+mQYMGXLMFc128eJF69erx4Ycf8oFtKWDgwIGsXLmS+vXrs2bNGvvyVr169fDy8qJ+/fr2voKL6NEDnnjCBKUtWGAXP5AixuDHzSdcrVnm+OsvY+R69LCUNr0UFZeqq48npSYRLGQpKaC7SCsp4O7du6lZs6abNHItwcHBhIaGUrx48et3zgS56d65hZgYaNMGNm82SzeNGgEwYHYou05GcDLCLDP+9uzt1Cwd4E5NrezbB7fdBqVKwfr19iWn+IRE2k1ayZHw5Ew8q19uTbki+dylqZBJbjYpoDwCCMKtxs8PfvgBSpSAzp1NFTjg8z4h/DeyLRO71wPgqbmb7Ok9jl+I4uSla+le8pYTHg4dOoCXFyxZYjcSAE3f+ctiJAAxEh6ObGbnQI4cOeJuFYQbJTDQuMq2bAkdO8Lff5ugNeChkCAuR8fz5pJdvPXLbmqVDuCl77cBbkpLHhNjIq6PHzdJDitVAkz6kW0nIrhw1ThJfPBIfY6FXyMkuEhGVxM8ADEUguAq6tc3+xSdOpnXb7+ZHFFAv5bBhB65wPcbw5gVE+8+HePjjZfWP//AvHnQogUAu05epvO0f+3dfL3z0KZ6IIXySR2T3IAsPQmCK7nvPlOzYtUqePhhk4UV4wk1+M7KXElhJCKupd40vmUkJECfPvDjj/Dhh8ZgAAs3hnH/FKv33rJhd4iRyEWIoRAEV9Ozp0ntsWQJPPQQ2GJm6gcVpkO90pau9d9YxoGzLkhYEB9vIq7nzYN33oGhQ4mKjWfHiQhecKjQB/BO17oEF8+fzoUET0SWngTBHTz5pHmCHzIEHngAfvoJ8ufnuXZVuRabwPI9yYmY200yKdK2j7mHgv7mKT46LoFtYRE0reiE2IXoaOJ79MR70U/sGTyciaXbMfbSNTpNXU341eSgzVIB/vwytBXFCqRbNkbwUGRG4UQKFHB+dK1kfvVgnnkGZs0yG8atW8PJk1QpWZAv+zZh31v3Mae/Net+q/F/892G4wA8v2ArD3+6hsPnr97w216NiWfeumMmc+2FC9ChA96LfuKNtgNpX6gNf+05S8txyy1GYkK3eqx9pa0YiVyKGIpsjhgKD6dPH+M6u2sXNGkCGzYAZrP49qoleLtrchnViGtxvPT9NoJH/MIv24yL7ZS/9vPVmiMcPBfJfocyq3EJiXy34Tiz/j1sebtD5yJ559fdvPLjdtb/vJKr9RuRsOofnuswnJkhllpkdppWLMpDIeWc/cmFHIRnLj0NG2YSsTmTBg1g8uRMdV2xYgVjxoyhePHi7Nixg8aNG/P111+jlCI4OFhShAtWOnc2aT46dYJWrUxCwWHDIE8e7qtTmmnLD3D+aiwNggqz/vAFy9AfN5+wRHVvGX03aw+FM/jrTXZZUhrz4xeiaPP+StCah7b/SZ1JnxLpl4/ePd5hU9m0Ay77NK/A6AdqS8ncXI5nGopswObNm9m5cydlypShZcuW/Pvvv7Rq1QpIThH+1VdfMWzYMEtep5SMGzeO9957L8M+ggdQr56ZTfTvD88/b3IrffYZRStV4r+RbQE4fP4qrd9bkeFlGoz9I5Xswz/3cyriGt9sOE6JyAu8u3Qq7Q5uYF1QHYY88CJnCxaz9N86+h5+3XGKphWLUjk7JysUXIZnGopMPvnfSpo2bUq5cma63qBBA44cOWI3FJIiXEiTEiVMQaAvvjAzipo14bnn4JVXICCAisXzM61XI6qXKkC7SasyfdkP/tyHf1w0T29YxJPrFuKdmMDYNgOZGfIAWqVefQ7I603PpuWd+MGEnE6W9iiUUg8ppXYqpRJtVe3S69deKbVXKXVAKTXCQV5RKbXOJv9WKeWbFX2yE35+yZt+Xl5exMcn+8dLinAhXZQybqp795pEfOPHQ4UKMHIkHD9Oh3qlqVKyIK/cn5w00rEQUkpKRF7k2dXzWP1Jf178Zw6rgxtwb7+P+LJJ5zSNRLkieWWZSUhFVjezdwAPAuk+3iilvIBpwH1ALaCnUqqWrXk88IHWugpwEeifRX1yBJIiXLguZcvC7NkQGgrt2pnKcuXLmz2MCRMY5HWatkH5aVaxKF/1a0r9coUAeKd9ZbbeG0C/DYuY+80rrJ3eh+f+nceW0tXo/r/xDO46ihPFytKhXmmaVyrGE3dWokPd5NiN1S+3cdcnFrIxWVp60lrvBq73BNIUOKC1PmTr+w3QWSm1G2gD9LL1mw2MAT5O6yKeRFKKcD8/P3sa8oEDB9K5c2fq169P+/bt00wR3rdvX1mqym00bmzSfhw+DHPnmmJIL78MwBcARYrA+ML8AMRfuITf+IsAjAZ0tWrEvvwySxq0o/qdIdy77zxPlcxPmxqBqd9m9WEOnc/RlYiFW4hT0owrpVYAL2itQ9No6w60T6qhrZTqDTTDGIW1ttkESqkg4DetdZ2U17C1DwIGAZQvX77x0aNHLe05JVW2s1OEO4Occu8EG2fPGi+pPXtM4r7LtnKkBQpAUBBUq2ZyNJUpk/F1hFzHzaYZv+6MQin1J1AqjaZRWutFN/qGN4vWegYwA0w9Cle9ryBkO0qWhC5d3K2FkIu4rqHQWrfL4nucAIIczsvZZOFAYaWUt9Y63kHu0UiKcEEQchquiMzeAFS1eTj5Aj2Axdqsef0NdLf16wNkaYaSE6v1uRu5Z4IgXI+susd2VUqFAc2BX5RSv9vkZZRSvwLYZgvPAL8Du4HvtNY7bZd4GRiulDoAFMO2P3cz+Pv7Ex4eLl98N4DWmvDwcPz9/d2tiiAI2RiPqZkdFxdHWFgY0baUzULm8Pf3p1y5cvj4SG0BQfB0btlmdk7Bx8eHihUrulsNQRAEj0OyxwqCIAgZIoZCEARByBAxFIIgCEKG5MjNbKXUFWCvu/XIJhQHzrtbiWyC3Itk5F4kI/cimepa64I3OiinbmbvvZmde09EKRUq98Ig9yIZuRfJyL1IRimVKs1SZpClJ0EQBCFDxFAIgiAIGZJTDcUMdyuQjZB7kYzci2TkXiQj9yKZm7oXOXIzWxAEQXAdOXVGIQiCILgIMRSCIAhChmRrQ6GUaq+U2quUOqCUGpFGu59S6ltb+zqlVLAb1LzlZOI+DFdK7VJKbVNK/aWUquAOPV3B9e6FQ79uSimtlPJYt8jM3Aul1MO2v42dSql5rtbRVWTif6S8UupvpdRm2//J/e7Q0xUopb5USp1VSu1Ip10ppabY7tU2pVSj615Ua50tX4AXcBCoBPgCW4FaKfo8BXxiO+4BfOtuvd10H1oD+WzHT3rifcjsvbD1KwisAtYCIe7W241/F1WBzUAR23lJd+vtxnsxA3jSdlwLOOJuvW/h/bgDaATsSKf9fuA3QAG3Aeuud83sPKNoChzQWh/SWscC3wCdU/TpDMy2HS8E2iqllAt1dAXXvQ9a67+11lG207WYaoGeSGb+JgDeBMYDnpxzPjP3YiAwTWt9EUBrfdbFOrqKzNwLDQTYjgsBJ12on0vRWq8CLmTQpTPwlTasxVQaLZ3RNbOzoSgLHHc4D7PJ0uyjTYGkCEwBJE8iM/fBkf6YpwVP5Lr3wjaNDtJa/+JKxdxAZv4uqgHVlFL/KqXWKqXau0w715KZezEGeNRWaO1XYIhrVMuW3Oh3So5N4SGkgVLqUSAEuNPdurgDpVQeYBLQ182qZBe8MctPd2FmmauUUnW11pfcqZSb6AnM0lq/r5RqDsxRStXRWie6W7GcQHaeUZwAghzOy9lkafZRSnljppThLtHOdWTmPqCUageMAjpprWNcpJurud69KAjUAVYopY5g1l8Xe+iGdmb+LsIw9enjtNaHgX0Yw+FpZOZe9Ae+A9BarwH8MckCcyOZ+k5xJDsbig1AVaVURaWUL2azenGKPouBPrbj7sBybdut8SCuex+UUg2BTzFGwlPXoeE690JrHaG1Lq61DtZaB2P2azpprW8qEVo2JzP/Hz9hZhMopYpjlqIOuVBHV5GZe3EMaAuglKqJMRTnXKpl9mEx8JjN++k2IEJrfSqjAdl26UlrHa+Uegb4HePV8KXWeqdSaiwQqrVeDHyBmUIewGze9HCfxreGTN6HiUABYIFtL/+Y1rqT25S+RWTyXuQKMnkvfgfuUUrtAhKAF7XWnjbjzuy9eB74TCn1HGZju68HPlQCoJSaj3lAKG7bk3kd8AHQWn+C2aO5HzgARAGPX/eaHnqvBEEQBCeRnZeeBEEQhGyAGApBEAQhQ8RQCIIgCBkihkIQBEHIEDEUgiAIQoaIoRAEQRAyRAyFIAiCkCH/BxXHcxKlo1q2AAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Input\")\n", + "plt.xlim(0, 1)\n", + "plt.legend()\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure()\n", + "rasterplot(sim.trange(), sim.data[A_spikes])\n", + "plt.xlim(0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the decoded response of the neural spiking.\n", + "The bottom plot shows the spike raster coming out of every 2nd neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:16.425930Z", + "iopub.status.busy": "2020-11-25T16:47:16.424148Z", + "iopub.status.idle": "2020-11-25T16:47:17.827938Z", + "shell.execute_reply": "2020-11-25T16:47:17.828591Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# For interest's sake, you can also sort by encoder\n", + "indices = sorted_neurons(A, sim, iterations=250)\n", + "plt.figure()\n", + "rasterplot(sim.trange(), sim.data[A_spikes][:, indices])\n", + "plt.xlim(0, 1)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/many_neurons.html b/examples/basic/many_neurons.html new file mode 100644 index 0000000000..6ff6df93c4 --- /dev/null +++ b/examples/basic/many_neurons.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/basic/multiplication.html b/examples/basic/multiplication.html new file mode 100644 index 0000000000..b7ab84a48f --- /dev/null +++ b/examples/basic/multiplication.html @@ -0,0 +1,860 @@ + + + + + + + + Multiplication — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Multiplication

+

This example will show you how to multiply two values. The model architecture can be thought of as a combination of the combining demo and the squaring demo. Essentially, we project both inputs independently into a 2D space, and then decode a nonlinear transformation of that space (the product of the first and second vector elements).

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.dists import Choice
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the model

+

The model has four ensembles: two input ensembles (‘A’ and ‘B’), a 2D combined ensemble (‘Combined’), and an output ensemble (‘D’).

+
+
[2]:
+
+
+
+# Create the model object
+model = nengo.Network(label="Multiplication")
+with model:
+    # Create 4 ensembles of leaky integrate-and-fire neurons
+    A = nengo.Ensemble(100, dimensions=1, radius=10)
+    B = nengo.Ensemble(100, dimensions=1, radius=10)
+    combined = nengo.Ensemble(
+        220, dimensions=2, radius=15
+    )  # This radius is ~sqrt(10^2+10^2)
+    prod = nengo.Ensemble(100, dimensions=1, radius=20)
+
+# This next two lines make all of the encoders in the Combined population
+# point at the corners of the cube.
+# This improves the quality of the computation.
+
+# Comment out the line below for 'normal' encoders
+combined.encoders = Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]])
+
+
+
+
+
+

Step 2: Provide input to the model

+

We will use two varying scalar values for the two input signals that drive activity in ensembles A and B.

+
+
[3]:
+
+
+
+with model:
+    # Create a piecewise step function for input
+    inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))
+    inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))
+
+    correct_ans = Piecewise({0: 0, 1.5: 0, 2.5: 20, 3: 0, 4: 0, 4.5: -20})
+
+
+
+
+
+

Step 3: Connect the elements of the model

+
+
[4]:
+
+
+
+with model:
+    # Connect the input nodes to the appropriate ensembles
+    nengo.Connection(inputA, A)
+    nengo.Connection(inputB, B)
+
+    # Connect input ensembles A and B to the 2D combined ensemble
+    nengo.Connection(A, combined[0])
+    nengo.Connection(B, combined[1])
+
+    # Define a function that computes the multiplication of two inputs
+    def product(x):
+        return x[0] * x[1]
+
+    # Connect the combined ensemble to the output ensemble D
+    nengo.Connection(combined, prod, function=product)
+
+
+
+
+
+

Step 4: Probe the output

+

Collect output data from each ensemble and input.

+
+
[5]:
+
+
+
+with model:
+    inputA_probe = nengo.Probe(inputA)
+    inputB_probe = nengo.Probe(inputB)
+    A_probe = nengo.Probe(A, synapse=0.01)
+    B_probe = nengo.Probe(B, synapse=0.01)
+    combined_probe = nengo.Probe(combined, synapse=0.01)
+    prod_probe = nengo.Probe(prod, synapse=0.01)
+
+
+
+
+
+

Step 5: Run the model

+
+
[6]:
+
+
+
+# Create the simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 5 seconds
+    sim.run(5)
+
+
+
+
+
+

Step 6: Plot the results

+

To check the performance of the model, we can plot the input signals and decoded ensemble values.

+
+
[7]:
+
+
+
+# Plot the input signals and decoded ensemble values
+plt.figure()
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded A")
+plt.plot(sim.trange(), sim.data[B_probe], label="Decoded B")
+plt.plot(sim.trange(), sim.data[prod_probe], label="Decoded product")
+plt.plot(
+    sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c="k", label="Actual product"
+)
+plt.legend(loc="best")
+plt.ylim(-25, 25)
+
+
+
+
+
[7]:
+
+
+
+
+(-25.0, 25.0)
+
+
+
+
+
+
+../../_images/examples_basic_multiplication_13_1.png +
+
+

The input signals we chose make it obvious when things are working, as the inputs are zero often (so the product should be). When choosing encoders randomly around the circle (the default in Nengo), you may see more unwanted interactions between the inputs. To see this, comment the above code that sets the encoders to the corners of the cube (in Step 1 where it says # Comment out the line below for 'normal' encoders).

+
+
+

Bonus step: Make a subnetwork

+

If you find that you need to compute the product in several parts of your network, you can put all of the components necessary to compute the product together in a subnetwork. By making a function to construct this subnetwork, it becomes easy to make many such networks in a single model.

+
+
[8]:
+
+
+
+def Product(neuron_per_dimension, input_magnitude):
+    # Create the model object
+    model = nengo.Network(label="Product")
+    with model:
+        # Create passthrough nodes to redirect both inputs
+        model.A = nengo.Node(output=None, size_in=1)
+        model.B = nengo.Node(output=None, size_in=1)
+
+        model.combined = nengo.Ensemble(
+            neuron_per_dimension * 2,
+            dimensions=2,
+            radius=np.sqrt(input_magnitude ** 2 + input_magnitude ** 2),
+            encoders=Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]]),
+        )
+
+        model.prod = nengo.Ensemble(
+            neuron_per_dimension, dimensions=1, radius=input_magnitude * 2
+        )
+
+        # Connect everything up
+        nengo.Connection(model.A, model.combined[0], synapse=None)
+        nengo.Connection(model.B, model.combined[1], synapse=None)
+
+        def product(x):
+            return x[0] * x[1]
+
+        nengo.Connection(model.combined, model.prod, function=product)
+    return model
+
+
+# The previous model can then be replicated with the following
+model = nengo.Network(label="Multiplication")
+with model:
+    inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))
+    inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))
+    A = nengo.Ensemble(100, dimensions=1, radius=10)
+    B = nengo.Ensemble(100, dimensions=1, radius=10)
+    prod = Product(100, input_magnitude=10)
+    nengo.Connection(inputA, A)
+    nengo.Connection(inputB, B)
+    nengo.Connection(A, prod.A)
+    nengo.Connection(B, prod.B)
+
+    inputA_probe = nengo.Probe(inputA)
+    inputB_probe = nengo.Probe(inputB)
+    A_probe = nengo.Probe(A, synapse=0.01)
+    B_probe = nengo.Probe(B, synapse=0.01)
+    combined_probe = nengo.Probe(prod.combined, synapse=0.01)
+    prod_probe = nengo.Probe(prod.prod, synapse=0.01)
+
+# Create the simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 5 seconds
+    sim.run(5)
+
+# Plot the input signals and decoded ensemble values
+plt.figure()
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded A")
+plt.plot(sim.trange(), sim.data[B_probe], label="Decoded B")
+plt.plot(sim.trange(), sim.data[prod_probe], label="Decoded product")
+plt.plot(
+    sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c="k", label="Actual product"
+)
+plt.legend(loc="best")
+plt.ylim(-25, 25)
+
+
+
+
+
[8]:
+
+
+
+
+(-25.0, 25.0)
+
+
+
+
+
+
+../../_images/examples_basic_multiplication_16_1.png +
+
+

Alternatively, you can use Nengo’s built in `nengo.networks.Product network <https://www.nengo.ai/nengo/networks.html#nengo.networks.Product>`__. This network works with input of any dimensionality (e.g., to compute the dot product of two large vectors) and uses special optimizatons to make the product more accurate than this implementation.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/multiplication.ipynb b/examples/basic/multiplication.ipynb new file mode 100644 index 0000000000..129802fd97 --- /dev/null +++ b/examples/basic/multiplication.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiplication\n", + "\n", + "This example will show you how to multiply two values.\n", + "The model architecture can be thought of as\n", + "a combination of the combining demo and the squaring demo.\n", + "Essentially, we project both inputs independently into a 2D space,\n", + "and then decode a nonlinear transformation of that space\n", + "(the product of the first and second vector elements)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.243063Z", + "iopub.status.busy": "2020-11-25T16:47:19.242216Z", + "iopub.status.idle": "2020-11-25T16:47:19.737699Z", + "shell.execute_reply": "2020-11-25T16:47:19.738138Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the model\n", + "\n", + "The model has four ensembles:\n", + "two input ensembles ('A' and 'B'),\n", + "a 2D combined ensemble ('Combined'),\n", + "and an output ensemble ('D')." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.746346Z", + "iopub.status.busy": "2020-11-25T16:47:19.745834Z", + "iopub.status.idle": "2020-11-25T16:47:19.749428Z", + "shell.execute_reply": "2020-11-25T16:47:19.748982Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Multiplication\")\n", + "with model:\n", + " # Create 4 ensembles of leaky integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " B = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " combined = nengo.Ensemble(\n", + " 220, dimensions=2, radius=15\n", + " ) # This radius is ~sqrt(10^2+10^2)\n", + " prod = nengo.Ensemble(100, dimensions=1, radius=20)\n", + "\n", + "# This next two lines make all of the encoders in the Combined population\n", + "# point at the corners of the cube.\n", + "# This improves the quality of the computation.\n", + "\n", + "# Comment out the line below for 'normal' encoders\n", + "combined.encoders = Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide input to the model\n", + "\n", + "We will use two varying scalar values for the two input signals\n", + "that drive activity in ensembles A and B." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.756349Z", + "iopub.status.busy": "2020-11-25T16:47:19.754826Z", + "iopub.status.idle": "2020-11-25T16:47:19.756952Z", + "shell.execute_reply": "2020-11-25T16:47:19.757357Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))\n", + " inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))\n", + "\n", + " correct_ans = Piecewise({0: 0, 1.5: 0, 2.5: 20, 3: 0, 4: 0, 4.5: -20})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the elements of the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.766540Z", + "iopub.status.busy": "2020-11-25T16:47:19.765225Z", + "iopub.status.idle": "2020-11-25T16:47:19.767387Z", + "shell.execute_reply": "2020-11-25T16:47:19.767825Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input nodes to the appropriate ensembles\n", + " nengo.Connection(inputA, A)\n", + " nengo.Connection(inputB, B)\n", + "\n", + " # Connect input ensembles A and B to the 2D combined ensemble\n", + " nengo.Connection(A, combined[0])\n", + " nengo.Connection(B, combined[1])\n", + "\n", + " # Define a function that computes the multiplication of two inputs\n", + " def product(x):\n", + " return x[0] * x[1]\n", + "\n", + " # Connect the combined ensemble to the output ensemble D\n", + " nengo.Connection(combined, prod, function=product)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe the output\n", + "\n", + "Collect output data from each ensemble and input." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.775764Z", + "iopub.status.busy": "2020-11-25T16:47:19.774242Z", + "iopub.status.idle": "2020-11-25T16:47:19.776350Z", + "shell.execute_reply": "2020-11-25T16:47:19.776766Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " inputA_probe = nengo.Probe(inputA)\n", + " inputB_probe = nengo.Probe(inputB)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " combined_probe = nengo.Probe(combined, synapse=0.01)\n", + " prod_probe = nengo.Probe(prod, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:19.782042Z", + "iopub.status.busy": "2020-11-25T16:47:19.781251Z", + "iopub.status.idle": "2020-11-25T16:47:21.337743Z", + "shell.execute_reply": "2020-11-25T16:47:21.338172Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results\n", + "\n", + "To check the performance of the model,\n", + "we can plot the input signals and decoded ensemble values." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:21.346070Z", + "iopub.status.busy": "2020-11-25T16:47:21.344855Z", + "iopub.status.idle": "2020-11-25T16:47:21.604812Z", + "shell.execute_reply": "2020-11-25T16:47:21.605209Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-25.0, 25.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signals and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded B\")\n", + "plt.plot(sim.trange(), sim.data[prod_probe], label=\"Decoded product\")\n", + "plt.plot(\n", + " sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c=\"k\", label=\"Actual product\"\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-25, 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input signals we chose make it obvious when things are working,\n", + "as the inputs are zero often (so the product should be).\n", + "When choosing encoders randomly around the circle (the default in Nengo),\n", + "you may see more unwanted interactions between the inputs.\n", + "To see this, comment the above code that sets the encoders\n", + "to the corners of the cube (in Step 1 where it says\n", + "`# Comment out the line below for 'normal' encoders`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus step: Make a subnetwork\n", + "\n", + "If you find that you need to compute the product\n", + "in several parts of your network,\n", + "you can put all of the components necessary\n", + "to compute the product\n", + "together in a subnetwork.\n", + "By making a function to construct this subnetwork,\n", + "it becomes easy to make many such networks\n", + "in a single model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:21.623356Z", + "iopub.status.busy": "2020-11-25T16:47:21.612954Z", + "iopub.status.idle": "2020-11-25T16:47:23.414225Z", + "shell.execute_reply": "2020-11-25T16:47:23.413769Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-25.0, 25.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def Product(neuron_per_dimension, input_magnitude):\n", + " # Create the model object\n", + " model = nengo.Network(label=\"Product\")\n", + " with model:\n", + " # Create passthrough nodes to redirect both inputs\n", + " model.A = nengo.Node(output=None, size_in=1)\n", + " model.B = nengo.Node(output=None, size_in=1)\n", + "\n", + " model.combined = nengo.Ensemble(\n", + " neuron_per_dimension * 2,\n", + " dimensions=2,\n", + " radius=np.sqrt(input_magnitude ** 2 + input_magnitude ** 2),\n", + " encoders=Choice([[1, 1], [-1, 1], [1, -1], [-1, -1]]),\n", + " )\n", + "\n", + " model.prod = nengo.Ensemble(\n", + " neuron_per_dimension, dimensions=1, radius=input_magnitude * 2\n", + " )\n", + "\n", + " # Connect everything up\n", + " nengo.Connection(model.A, model.combined[0], synapse=None)\n", + " nengo.Connection(model.B, model.combined[1], synapse=None)\n", + "\n", + " def product(x):\n", + " return x[0] * x[1]\n", + "\n", + " nengo.Connection(model.combined, model.prod, function=product)\n", + " return model\n", + "\n", + "\n", + "# The previous model can then be replicated with the following\n", + "model = nengo.Network(label=\"Multiplication\")\n", + "with model:\n", + " inputA = nengo.Node(Piecewise({0: 0, 2.5: 10, 4: -10}))\n", + " inputB = nengo.Node(Piecewise({0: 10, 1.5: 2, 3: 0, 4.5: 2}))\n", + " A = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " B = nengo.Ensemble(100, dimensions=1, radius=10)\n", + " prod = Product(100, input_magnitude=10)\n", + " nengo.Connection(inputA, A)\n", + " nengo.Connection(inputB, B)\n", + " nengo.Connection(A, prod.A)\n", + " nengo.Connection(B, prod.B)\n", + "\n", + " inputA_probe = nengo.Probe(inputA)\n", + " inputB_probe = nengo.Probe(inputB)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)\n", + " combined_probe = nengo.Probe(prod.combined, synapse=0.01)\n", + " prod_probe = nengo.Probe(prod.prod, synapse=0.01)\n", + "\n", + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)\n", + "\n", + "# Plot the input signals and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded B\")\n", + "plt.plot(sim.trange(), sim.data[prod_probe], label=\"Decoded product\")\n", + "plt.plot(\n", + " sim.trange(), correct_ans.run(sim.time, dt=sim.dt), c=\"k\", label=\"Actual product\"\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-25, 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can use Nengo's built in\n", + "[`nengo.networks.Product` network](\n", + "https://www.nengo.ai/nengo/networks.html#nengo.networks.Product).\n", + "This network works with input of any dimensionality\n", + "(e.g., to compute the dot product of two large vectors)\n", + "and uses special optimizatons to make the product\n", + "more accurate than this implementation." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/single-neuron.html b/examples/basic/single-neuron.html new file mode 100644 index 0000000000..2fa50d3b14 --- /dev/null +++ b/examples/basic/single-neuron.html @@ -0,0 +1,758 @@ + + + + + + + + A single neuron — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

A single neuron

+

This demo shows you how to construct and manipulate a single leaky integrate-and-fire (LIF) neuron. The LIF neuron is a simple, standard neuron model, and here it resides inside a neural ‘population,’ even though there is only one neuron.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.utils.matplotlib import rasterplot
+
+
+
+
+

Step 1: Create the Neuron

+
+
[2]:
+
+
+
+from nengo.dists import Uniform
+
+model = nengo.Network(label="A Single Neuron")
+with model:
+    neuron = nengo.Ensemble(
+        1,
+        dimensions=1,  # Represent a scalar
+        # Set intercept to 0.5
+        intercepts=Uniform(-0.5, -0.5),
+        # Set the maximum firing rate of the neuron to 100hz
+        max_rates=Uniform(100, 100),
+        # Set the neuron's firing rate to increase for positive input
+        encoders=[[1]],
+    )
+
+
+
+
+
+

Step 2: Provide Input to the Model

+

Create an input node generating a cosine wave.

+
+
[3]:
+
+
+
+with model:
+    cos = nengo.Node(lambda t: np.cos(8 * t))
+
+
+
+
+
+

Step 3: Connect the Network Elements

+
+
[4]:
+
+
+
+with model:
+    # Connect the input signal to the neuron
+    nengo.Connection(cos, neuron)
+
+
+
+
+
+

Step 4: Add Probes

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later.

+
+
[5]:
+
+
+
+with model:
+    # The original input
+    cos_probe = nengo.Probe(cos)
+    # The raw spikes from the neuron
+    spikes = nengo.Probe(neuron.neurons)
+    # Subthreshold soma voltage of the neuron
+    voltage = nengo.Probe(neuron.neurons, "voltage")
+    # Spikes filtered by a 10ms post-synaptic filter
+    filtered = nengo.Probe(neuron, synapse=0.01)
+
+
+
+
+
+

Step 5: Run the Model

+
+
[6]:
+
+
+
+with nengo.Simulator(model) as sim:  # Create the simulator
+    sim.run(1)  # Run it for 1 second
+
+
+
+
+
+

Step 6: Plot the Results

+
+
[7]:
+
+
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(sim.trange(), sim.data[filtered])
+plt.plot(sim.trange(), sim.data[cos_probe])
+plt.xlim(0, 1)
+
+# Plot the spiking output of the ensemble
+plt.figure(figsize=(10, 8))
+plt.subplot(221)
+rasterplot(sim.trange(), sim.data[spikes])
+plt.ylabel("Neuron")
+plt.xlim(0, 1)
+
+# Plot the soma voltages of the neurons
+plt.subplot(222)
+plt.plot(sim.trange(), sim.data[voltage][:, 0], "r")
+plt.xlim(0, 1)
+
+
+
+
+
[7]:
+
+
+
+
+(0.0, 1.0)
+
+
+
+
+
+
+../../_images/examples_basic_single-neuron_13_1.png +
+
+
+
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+
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The top graph shows the input signal in green and the filtered output spikes from the single neuron population in blue. The spikes (that are filtered) from the neuron are shown in the bottom graph on the left. On the right is the subthreshold voltages for the neuron.

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+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/single-neuron.ipynb b/examples/basic/single-neuron.ipynb new file mode 100644 index 0000000000..224cdebc07 --- /dev/null +++ b/examples/basic/single-neuron.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A single neuron\n", + "\n", + "This demo shows you how to construct and manipulate\n", + "a single leaky integrate-and-fire (LIF) neuron.\n", + "The LIF neuron is a simple, standard neuron model,\n", + "and here it resides inside a neural 'population,'\n", + "even though there is only one neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:24.767921Z", + "iopub.status.busy": "2020-11-25T16:47:24.767058Z", + "iopub.status.idle": "2020-11-25T16:47:25.259044Z", + "shell.execute_reply": "2020-11-25T16:47:25.258552Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Neuron" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.265133Z", + "iopub.status.busy": "2020-11-25T16:47:25.264605Z", + "iopub.status.idle": "2020-11-25T16:47:25.267758Z", + "shell.execute_reply": "2020-11-25T16:47:25.268178Z" + } + }, + "outputs": [], + "source": [ + "from nengo.dists import Uniform\n", + "\n", + "model = nengo.Network(label=\"A Single Neuron\")\n", + "with model:\n", + " neuron = nengo.Ensemble(\n", + " 1,\n", + " dimensions=1, # Represent a scalar\n", + " # Set intercept to 0.5\n", + " intercepts=Uniform(-0.5, -0.5),\n", + " # Set the maximum firing rate of the neuron to 100hz\n", + " max_rates=Uniform(100, 100),\n", + " # Set the neuron's firing rate to increase for positive input\n", + " encoders=[[1]],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "Create an input node generating a cosine wave." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.273540Z", + "iopub.status.busy": "2020-11-25T16:47:25.272012Z", + "iopub.status.idle": "2020-11-25T16:47:25.274115Z", + "shell.execute_reply": "2020-11-25T16:47:25.274538Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " cos = nengo.Node(lambda t: np.cos(8 * t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the Network Elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.280861Z", + "iopub.status.busy": "2020-11-25T16:47:25.279344Z", + "iopub.status.idle": "2020-11-25T16:47:25.281469Z", + "shell.execute_reply": "2020-11-25T16:47:25.281873Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the input signal to the neuron\n", + " nengo.Connection(cos, neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Add Probes\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.288415Z", + "iopub.status.busy": "2020-11-25T16:47:25.286910Z", + "iopub.status.idle": "2020-11-25T16:47:25.289016Z", + "shell.execute_reply": "2020-11-25T16:47:25.289424Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # The original input\n", + " cos_probe = nengo.Probe(cos)\n", + " # The raw spikes from the neuron\n", + " spikes = nengo.Probe(neuron.neurons)\n", + " # Subthreshold soma voltage of the neuron\n", + " voltage = nengo.Probe(neuron.neurons, \"voltage\")\n", + " # Spikes filtered by a 10ms post-synaptic filter\n", + " filtered = nengo.Probe(neuron, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.294540Z", + "iopub.status.busy": "2020-11-25T16:47:25.293738Z", + "iopub.status.idle": "2020-11-25T16:47:25.468049Z", + "shell.execute_reply": "2020-11-25T16:47:25.468688Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create the simulator\n", + " sim.run(1) # Run it for 1 second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:25.483140Z", + "iopub.status.busy": "2020-11-25T16:47:25.474689Z", + "iopub.status.idle": "2020-11-25T16:47:25.927310Z", + "shell.execute_reply": "2020-11-25T16:47:25.927753Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[filtered])\n", + "plt.plot(sim.trange(), sim.data[cos_probe])\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure(figsize=(10, 8))\n", + "plt.subplot(221)\n", + "rasterplot(sim.trange(), sim.data[spikes])\n", + "plt.ylabel(\"Neuron\")\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the soma voltages of the neurons\n", + "plt.subplot(222)\n", + "plt.plot(sim.trange(), sim.data[voltage][:, 0], \"r\")\n", + "plt.xlim(0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the input signal in green\n", + "and the filtered output spikes from the single neuron population in blue.\n", + "The spikes (that are filtered) from the neuron\n", + "are shown in the bottom graph on the left.\n", + "On the right is the subthreshold voltages for the neuron." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/single_neuron.html b/examples/basic/single_neuron.html new file mode 100644 index 0000000000..46e35c23a8 --- /dev/null +++ b/examples/basic/single_neuron.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/basic/squaring.html b/examples/basic/squaring.html new file mode 100644 index 0000000000..8a5918d2ba --- /dev/null +++ b/examples/basic/squaring.html @@ -0,0 +1,726 @@ + + + + + + + + Squaring the input — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Squaring the input

+

This demo shows you how to construct a network that squares the value encoded in a first population in the output of a second population.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

Step 1: Create the Model

+

The model is comprised of an input ensemble (‘A’) and an output ensemble (‘B’), from which the squared value of the input signal can be decoded.

+
+
[2]:
+
+
+
+# Create the model object
+model = nengo.Network(label="Squaring")
+with model:
+    # Create two ensembles of 100 leaky-integrate-and-fire neurons
+    A = nengo.Ensemble(100, dimensions=1)
+    B = nengo.Ensemble(100, dimensions=1)
+
+
+
+
+
+

Step 2: Provide Input to the Model

+

A single input signal (a sine wave) will be used to drive the neural activity in ensemble A.

+
+
[3]:
+
+
+
+with model:
+    # Create an input node that represents a sine wave
+    sin = nengo.Node(np.sin)
+
+    # Connect the input node to ensemble A
+    nengo.Connection(sin, A)
+
+    # Define the squaring function
+    def square(x):
+        return x[0] * x[0]
+
+    # Connection ensemble A to ensemble B
+    nengo.Connection(A, B, function=square)
+
+
+
+
+
+

Step 3: Probe the Output

+

Let’s collect output data from each ensemble and output.

+
+
[4]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin)
+    A_probe = nengo.Probe(A, synapse=0.01)
+    B_probe = nengo.Probe(B, synapse=0.01)
+
+
+
+
+
+

Step 4: Run the Model

+
+
[5]:
+
+
+
+# Create the simulator
+with nengo.Simulator(model) as sim:
+    # Run the simulator for 5 seconds
+    sim.run(5)
+
+
+
+
+
[6]:
+
+
+
+# Plot the input signal and decoded ensemble values
+plt.figure()
+plt.plot(sim.trange(), sim.data[A_probe], label="Decoded Ensemble A")
+plt.plot(sim.trange(), sim.data[B_probe], label="Decoded Ensemble B")
+plt.plot(
+    sim.trange(), sim.data[sin_probe], label="Input Sine Wave", color="k", linewidth=2.0
+)
+plt.legend(loc="best")
+plt.ylim(-1.2, 1.2)
+
+
+
+
+
[6]:
+
+
+
+
+(-1.2, 1.2)
+
+
+
+
+
+
+../../_images/examples_basic_squaring_10_1.png +
+
+

The plotted output of ensemble B should show the decoded squared value of the input sine wave.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/squaring.ipynb b/examples/basic/squaring.ipynb new file mode 100644 index 0000000000..06e1dbdf1a --- /dev/null +++ b/examples/basic/squaring.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Squaring the input\n", + "\n", + "This demo shows you how to construct a network\n", + "that squares the value encoded in a first population\n", + "in the output of a second population." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.108519Z", + "iopub.status.busy": "2020-11-25T16:47:29.107616Z", + "iopub.status.idle": "2020-11-25T16:47:29.599617Z", + "shell.execute_reply": "2020-11-25T16:47:29.600160Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model is comprised of an input ensemble ('A')\n", + "and an output ensemble ('B'),\n", + "from which the squared value of the input signal can be decoded." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.606035Z", + "iopub.status.busy": "2020-11-25T16:47:29.605502Z", + "iopub.status.idle": "2020-11-25T16:47:29.609153Z", + "shell.execute_reply": "2020-11-25T16:47:29.608687Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Squaring\")\n", + "with model:\n", + " # Create two ensembles of 100 leaky-integrate-and-fire neurons\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " B = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A single input signal (a sine wave) will be used\n", + "to drive the neural activity in ensemble A." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.617180Z", + "iopub.status.busy": "2020-11-25T16:47:29.615626Z", + "iopub.status.idle": "2020-11-25T16:47:29.617814Z", + "shell.execute_reply": "2020-11-25T16:47:29.618227Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input node that represents a sine wave\n", + " sin = nengo.Node(np.sin)\n", + "\n", + " # Connect the input node to ensemble A\n", + " nengo.Connection(sin, A)\n", + "\n", + " # Define the squaring function\n", + " def square(x):\n", + " return x[0] * x[0]\n", + "\n", + " # Connection ensemble A to ensemble B\n", + " nengo.Connection(A, B, function=square)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe the Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.624562Z", + "iopub.status.busy": "2020-11-25T16:47:29.623029Z", + "iopub.status.idle": "2020-11-25T16:47:29.625171Z", + "shell.execute_reply": "2020-11-25T16:47:29.625580Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:29.630720Z", + "iopub.status.busy": "2020-11-25T16:47:29.629933Z", + "iopub.status.idle": "2020-11-25T16:47:30.489720Z", + "shell.execute_reply": "2020-11-25T16:47:30.489232Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run the simulator for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:30.496306Z", + "iopub.status.busy": "2020-11-25T16:47:30.495459Z", + "iopub.status.idle": "2020-11-25T16:47:30.713932Z", + "shell.execute_reply": "2020-11-25T16:47:30.713468Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.2, 1.2)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signal and decoded ensemble values\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[A_probe], label=\"Decoded Ensemble A\")\n", + "plt.plot(sim.trange(), sim.data[B_probe], label=\"Decoded Ensemble B\")\n", + "plt.plot(\n", + " sim.trange(), sim.data[sin_probe], label=\"Input Sine Wave\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylim(-1.2, 1.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plotted output of ensemble B should show\n", + "the decoded squared value of the input sine wave." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/two-neurons.html b/examples/basic/two-neurons.html new file mode 100644 index 0000000000..ee7f392ede --- /dev/null +++ b/examples/basic/two-neurons.html @@ -0,0 +1,749 @@ + + + + + + + + Two neurons — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Two neurons

+

This demo shows how to construct and manipulate a complementary pair of neurons.

+

These are leaky integrate-and-fire (LIF) neurons. The neuron tuning properties have been selected so there is one ‘on’ and one ‘off’ neuron.

+

One neuron will increase for positive input, and the other will decrease. This can be thought of as the simplest population that is able to give a reasonable representation of a scalar value.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.dists import Uniform
+from nengo.utils.matplotlib import rasterplot
+
+
+
+
+

Step 1: Create the neurons

+
+
[2]:
+
+
+
+model = nengo.Network(label="Two Neurons")
+with model:
+    neurons = nengo.Ensemble(
+        2,
+        dimensions=1,  # Representing a scalar
+        intercepts=Uniform(-0.5, -0.5),  # Set the intercepts at .5
+        max_rates=Uniform(100, 100),  # Set the max firing rate at 100hz
+        encoders=[[1], [-1]],
+    )  # One 'on' and one 'off' neuron
+
+
+
+
+
+

Step 2: Create input for the model

+

Create an input node generating a sine wave.

+
+
[3]:
+
+
+
+with model:
+    sin = nengo.Node(lambda t: np.sin(8 * t))
+
+
+
+
+
+

Step 3: Connect the network elements

+
+
[4]:
+
+
+
+with model:
+    nengo.Connection(sin, neurons, synapse=0.01)
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later.

+
+
[5]:
+
+
+
+with model:
+    sin_probe = nengo.Probe(sin)  # The original input
+    spikes = nengo.Probe(neurons.neurons)  # Raw spikes from each neuron
+    # Subthreshold soma voltages of the neurons
+    voltage = nengo.Probe(neurons.neurons, "voltage")
+    # Spikes filtered by a 10ms post-synaptic filter
+    filtered = nengo.Probe(neurons, synapse=0.01)
+
+
+
+
+
+

Step 5: Run the model

+
+
[6]:
+
+
+
+with nengo.Simulator(model) as sim:  # Create a simulator
+    sim.run(1)  # Run it for 1 second
+
+
+
+
+
+

Step 6: Plot the results

+
+
[7]:
+
+
+
+t = sim.trange()
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(t, sim.data[filtered])
+plt.plot(t, sim.data[sin_probe])
+plt.xlim(0, 1)
+
+# Plot the spiking output of the ensemble
+plt.figure(figsize=(10, 8))
+plt.subplot(2, 2, 1)
+rasterplot(t, sim.data[spikes], colors=[(1, 0, 0), (0, 0, 0)])
+plt.yticks((1, 2), ("On neuron", "Off neuron"))
+plt.ylim(2.5, 0.5)
+
+# Plot the soma voltages of the neurons
+plt.subplot(2, 2, 2)
+plt.plot(t, sim.data[voltage][:, 0] + 1, "r")
+plt.plot(t, sim.data[voltage][:, 1], "k")
+plt.yticks(())
+plt.axis([0, 1, 0, 2])
+plt.subplots_adjust(wspace=0.05)
+
+
+
+
+
+
+
+../../_images/examples_basic_two-neurons_13_0.png +
+
+
+
+
+
+../../_images/examples_basic_two-neurons_13_1.png +
+
+

The top graph shows the input signal in green and the filtered output spikes from the two neurons population in blue. The spikes (that are filtered) from the ‘on’ and ‘off’ neurons are shown in the bottom graph on the left. On the right are the subthreshold voltages for the neurons.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/basic/two-neurons.ipynb b/examples/basic/two-neurons.ipynb new file mode 100644 index 0000000000..22e2ce2c67 --- /dev/null +++ b/examples/basic/two-neurons.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two neurons\n", + "\n", + "This demo shows how to construct and manipulate\n", + "a complementary pair of neurons.\n", + "\n", + "These are leaky integrate-and-fire (LIF) neurons.\n", + "The neuron tuning properties have been selected\n", + "so there is one 'on' and one 'off' neuron.\n", + "\n", + "One neuron will increase for positive input,\n", + "and the other will decrease.\n", + "This can be thought of as the simplest population\n", + "that is able to give a reasonable representation of a scalar value." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:31.957852Z", + "iopub.status.busy": "2020-11-25T16:47:31.957001Z", + "iopub.status.idle": "2020-11-25T16:47:32.448924Z", + "shell.execute_reply": "2020-11-25T16:47:32.449362Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.dists import Uniform\n", + "from nengo.utils.matplotlib import rasterplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.455388Z", + "iopub.status.busy": "2020-11-25T16:47:32.454866Z", + "iopub.status.idle": "2020-11-25T16:47:32.458044Z", + "shell.execute_reply": "2020-11-25T16:47:32.458439Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Two Neurons\")\n", + "with model:\n", + " neurons = nengo.Ensemble(\n", + " 2,\n", + " dimensions=1, # Representing a scalar\n", + " intercepts=Uniform(-0.5, -0.5), # Set the intercepts at .5\n", + " max_rates=Uniform(100, 100), # Set the max firing rate at 100hz\n", + " encoders=[[1], [-1]],\n", + " ) # One 'on' and one 'off' neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "Create an input node generating a sine wave." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.463984Z", + "iopub.status.busy": "2020-11-25T16:47:32.462402Z", + "iopub.status.idle": "2020-11-25T16:47:32.464605Z", + "shell.execute_reply": "2020-11-25T16:47:32.465017Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(8 * t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.471578Z", + "iopub.status.busy": "2020-11-25T16:47:32.470059Z", + "iopub.status.idle": "2020-11-25T16:47:32.472201Z", + "shell.execute_reply": "2020-11-25T16:47:32.472630Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(sin, neurons, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.479280Z", + "iopub.status.busy": "2020-11-25T16:47:32.477737Z", + "iopub.status.idle": "2020-11-25T16:47:32.479912Z", + "shell.execute_reply": "2020-11-25T16:47:32.480327Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " sin_probe = nengo.Probe(sin) # The original input\n", + " spikes = nengo.Probe(neurons.neurons) # Raw spikes from each neuron\n", + " # Subthreshold soma voltages of the neurons\n", + " voltage = nengo.Probe(neurons.neurons, \"voltage\")\n", + " # Spikes filtered by a 10ms post-synaptic filter\n", + " filtered = nengo.Probe(neurons, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.485516Z", + "iopub.status.busy": "2020-11-25T16:47:32.484679Z", + "iopub.status.idle": "2020-11-25T16:47:32.661197Z", + "shell.execute_reply": "2020-11-25T16:47:32.661843Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1) # Run it for 1 second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:32.670568Z", + "iopub.status.busy": "2020-11-25T16:47:32.669708Z", + "iopub.status.idle": "2020-11-25T16:47:33.070203Z", + "shell.execute_reply": "2020-11-25T16:47:33.069748Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = sim.trange()\n", + "\n", + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(t, sim.data[filtered])\n", + "plt.plot(t, sim.data[sin_probe])\n", + "plt.xlim(0, 1)\n", + "\n", + "# Plot the spiking output of the ensemble\n", + "plt.figure(figsize=(10, 8))\n", + "plt.subplot(2, 2, 1)\n", + "rasterplot(t, sim.data[spikes], colors=[(1, 0, 0), (0, 0, 0)])\n", + "plt.yticks((1, 2), (\"On neuron\", \"Off neuron\"))\n", + "plt.ylim(2.5, 0.5)\n", + "\n", + "# Plot the soma voltages of the neurons\n", + "plt.subplot(2, 2, 2)\n", + "plt.plot(t, sim.data[voltage][:, 0] + 1, \"r\")\n", + "plt.plot(t, sim.data[voltage][:, 1], \"k\")\n", + "plt.yticks(())\n", + "plt.axis([0, 1, 0, 2])\n", + "plt.subplots_adjust(wspace=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top graph shows the input signal in green\n", + "and the filtered output spikes from the two neurons population in blue.\n", + "The spikes (that are filtered) from the 'on' and 'off' neurons\n", + "are shown in the bottom graph on the left.\n", + "On the right are the subthreshold voltages for the neurons." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/basic/two_neurons.html b/examples/basic/two_neurons.html new file mode 100644 index 0000000000..ea276b975d --- /dev/null +++ b/examples/basic/two_neurons.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/dynamics/controlled-integrator.html b/examples/dynamics/controlled-integrator.html new file mode 100644 index 0000000000..68765d9eb3 --- /dev/null +++ b/examples/dynamics/controlled-integrator.html @@ -0,0 +1,798 @@ + + + + + + + + Controlled integrator — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Controlled integrator

+

A controlled integrator is a circuit that acts on two signals:

+
    +

  1. Input - the signal being integrated

    2. +

  2. Control - the control signal to the integrator

    3. +
+

A controlled integrator accumulates input, but its state can be directly manipulated by the control signal. We can write the dynamics of a simple controlled integrator like this:

+
+\[\dot{a}(t) = \mathrm{control}(t) \cdot a(t) + B \cdot \mathrm{input}(t)\]
+

In this notebook, we will build a controlled intgrator with LIF neurons. The Neural Engineering Framework (NEF) equivalent equation for this integrator is:

+
+\[\dot{a}(t) = \mathrm{control}(t) \cdot a(t) + \tau \cdot \mathrm{input}(t).\]
+

We call the coefficient \(\tau\) here a recurrent time constant because it governs the rate of integration.

+

Network behaviour: A = tau * Input + Input * Control

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the network

+

We can use standard network-creation commands to begin creating our controlled integrator. We create a Network, and then we create a population of neurons (called an ensemble). This population of neurons will represent the state of our integrator, and the connections between the neurons in the ensemble will define the dynamics of our integrator.

+
+
[2]:
+
+
+
+model = nengo.Network(label="Controlled Integrator")
+with model:
+    # Make a population with 225 LIF neurons
+    # representing a 2 dimensional signal,
+    # with a larger radius to accommodate large inputs
+    A = nengo.Ensemble(225, dimensions=2, radius=1.5)
+
+
+
+
+
+

Step 2: Define the ‘input’ signal to integrate

+

We will be running 1 second of simulation time, so we will use a Python function input_func to define our input signal for real values of time t from 0 to 1. We’ll define our signal to be a step function using if-then-else code. Our piecewise function sits at 0 until .2 seconds into the simulation, then jumps up to 5, back to 0, down to -10, back to 0, then up to 5, and then back to 0. Our integrator will respond by ramping up when the input is positive, and descending when the input is +negative.

+
+
[3]:
+
+
+
+with model:
+    # Create a piecewise step function for input
+    input_func = Piecewise({0: 0, 0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})
+
+
+
+

We include this input function (input_func) into our neural model like this:

+
+
[4]:
+
+
+
+with model:
+    # Define an input signal within our model
+    inp = nengo.Node(input_func)
+
+    # Connect the Input signal to ensemble A.
+    # The `transform` argument means "connect real-valued signal
+    # "Input" to the first of the two input channels of A."
+    tau = 0.1
+    nengo.Connection(inp, A, transform=[[tau], [0]], synapse=tau)
+
+
+
+
+
+

Step 3: Define the ‘control’ signal

+

We also need to create a control signal that controls how the integrator behaves. We will make this signal 1 for the first part of the simulation, and 0.5 for the second part. This means that at the beginning of the simulation, the integrator will act as an optimal integrator, and partway though the simulation (at t = 0.6), it will switch to being a leaky integrator.

+
+
[5]:
+
+
+
+with model:
+    # Another piecewise step that changes half way through the run
+    control_func = Piecewise({0: 1, 0.6: 0.5})
+
+
+
+

We add the control signal to the network like we added the input signal, but this time we connect it to the second dimension of our neural population.

+
+
[6]:
+
+
+
+with model:
+    control = nengo.Node(output=control_func)
+
+    # Connect the "Control" signal to the second of A's two input channels.
+    nengo.Connection(control, A[1], synapse=0.005)
+
+
+
+
+
+

Step 4: Define the integrator dynamics

+

We set up integrator by connecting population ‘A’ to itself. We set up feedback in the model to handle integration of the input. The time constant \(\tau\) on the recurrent weights affects both the rate and accuracy of integration. Try adjusting it and see what happens!

+
+
[7]:
+
+
+
+with model:
+    # Create a recurrent connection that first takes the product
+    # of both dimensions in A (i.e., the value times the control)
+    # and then adds this back into the first dimension of A using
+    # a transform
+    nengo.Connection(
+        A,
+        A[0],  # -- transform converts function output to new state inputs
+        function=lambda x: x[0] * x[1],  # -- function is applied first to A
+        synapse=tau,
+    )
+
+    # Record both dimensions of A
+    A_probe = nengo.Probe(A, "decoded_output", synapse=0.01)
+
+
+
+
+
[8]:
+
+
+
+with nengo.Simulator(model) as sim:  # Create a simulator
+    sim.run(1.4)  # Run for 1.4 seconds
+
+
+
+
+
[9]:
+
+
+
+# Plot the value and control signals, along with the exact integral
+t = sim.trange()
+dt = t[1] - t[0]
+input_sig = input_func.run(t[-1], dt=dt)
+control_sig = control_func.run(t[-1], dt=dt)
+ref = dt * np.cumsum(input_sig)
+
+plt.figure(figsize=(6, 8))
+plt.subplot(2, 1, 1)
+plt.plot(t, input_sig, label="Input")
+plt.xlim(right=t[-1])
+plt.ylim(-11, 11)
+plt.ylabel("Input")
+plt.legend(loc="lower left", frameon=False)
+
+plt.subplot(2, 1, 2)
+plt.plot(t, ref, "k--", label="Exact")
+plt.plot(t, sim.data[A_probe][:, 0], label="A (value)")
+plt.plot(t, sim.data[A_probe][:, 1], label="A (control)")
+plt.xlim(right=t[-1])
+plt.ylim(-1.1, 1.1)
+plt.xlabel("Time (s)")
+plt.ylabel("x(t)")
+plt.legend(loc="lower left", frameon=False)
+
+
+
+
+
[9]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7fbc34ac8cf8>
+
+
+
+
+
+
+../../_images/examples_dynamics_controlled-integrator_15_1.png +
+
+

The above plot shows the output of our system, specifically the (integrated) value stored by the A population, along with the control signal represented by the A population. The exact value of the integral, as performed by a perfect (non-neural) integrator, is shown for reference.

+

When the control value is 1 (t < 0.6), the neural integrator performs near-perfect integration. However, when the control value drops to 0.5 (t > 0.6), the integrator becomes a leaky integrator. This means that in the absence of input, its stored value drifts towards zero.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/dynamics/controlled-integrator.ipynb b/examples/dynamics/controlled-integrator.ipynb new file mode 100644 index 0000000000..6d32fd927b --- /dev/null +++ b/examples/dynamics/controlled-integrator.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled integrator\n", + "\n", + "A controlled integrator is a circuit that acts on two signals:\n", + "\n", + "1. Input - the signal being integrated\n", + "2. Control - the control signal to the integrator\n", + "\n", + "A controlled integrator accumulates input,\n", + "but its state can be directly manipulated by the control signal.\n", + "We can write the dynamics of a simple controlled integrator like this:\n", + "\n", + "$$\n", + "\\dot{a}(t) = \\mathrm{control}(t) \\cdot a(t) + B \\cdot \\mathrm{input}(t)\n", + "$$\n", + "\n", + "In this notebook, we will build a controlled intgrator with LIF neurons.\n", + "The Neural Engineering Framework (NEF) equivalent equation\n", + "for this integrator is:\n", + "\n", + "$$\n", + "\\dot{a}(t) = \\mathrm{control}(t) \\cdot a(t) + \\tau \\cdot \\mathrm{input}(t).\n", + "$$\n", + "\n", + "We call the coefficient $\\tau$ here a *recurrent time constant*\n", + "because it governs the rate of integration.\n", + "\n", + "Network behaviour:\n", + "`A = tau * Input + Input * Control`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.060341Z", + "iopub.status.busy": "2020-11-25T16:47:36.059463Z", + "iopub.status.idle": "2020-11-25T16:47:36.554755Z", + "shell.execute_reply": "2020-11-25T16:47:36.553814Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "We can use standard network-creation commands\n", + "to begin creating our controlled integrator.\n", + "We create a Network, and then we create\n", + "a population of neurons (called an *ensemble*).\n", + "This population of neurons will represent the state of our integrator,\n", + "and the connections between the neurons in the ensemble\n", + "will define the dynamics of our integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.560204Z", + "iopub.status.busy": "2020-11-25T16:47:36.559527Z", + "iopub.status.idle": "2020-11-25T16:47:36.562211Z", + "shell.execute_reply": "2020-11-25T16:47:36.562606Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Controlled Integrator\")\n", + "with model:\n", + " # Make a population with 225 LIF neurons\n", + " # representing a 2 dimensional signal,\n", + " # with a larger radius to accommodate large inputs\n", + " A = nengo.Ensemble(225, dimensions=2, radius=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Define the 'input' signal to integrate\n", + "\n", + "We will be running 1 second of simulation time,\n", + "so we will use a Python function `input_func`\n", + "to define our input signal for real values of time `t` from 0 to 1.\n", + "We'll define our signal to be a step function using if-then-else code.\n", + "Our piecewise function sits at 0 until .2 seconds into the simulation,\n", + "then jumps up to 5, back to 0, down to -10, back to 0, then up to 5,\n", + "and then back to 0. Our integrator will respond by ramping up\n", + "when the input is positive, and descending when the input is negative." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.566970Z", + "iopub.status.busy": "2020-11-25T16:47:36.566461Z", + "iopub.status.idle": "2020-11-25T16:47:36.569945Z", + "shell.execute_reply": "2020-11-25T16:47:36.569486Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " input_func = Piecewise({0: 0, 0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We include this input function (`input_func`)\n", + "into our neural model like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.577416Z", + "iopub.status.busy": "2020-11-25T16:47:36.575977Z", + "iopub.status.idle": "2020-11-25T16:47:36.578065Z", + "shell.execute_reply": "2020-11-25T16:47:36.578471Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Define an input signal within our model\n", + " inp = nengo.Node(input_func)\n", + "\n", + " # Connect the Input signal to ensemble A.\n", + " # The `transform` argument means \"connect real-valued signal\n", + " # \"Input\" to the first of the two input channels of A.\"\n", + " tau = 0.1\n", + " nengo.Connection(inp, A, transform=[[tau], [0]], synapse=tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Define the 'control' signal\n", + "\n", + "We also need to create a control signal\n", + "that controls how the integrator behaves.\n", + "We will make this signal 1 for the first part of the simulation,\n", + "and 0.5 for the second part.\n", + "This means that at the beginning of the simulation,\n", + "the integrator will act as an optimal integrator,\n", + "and partway though the simulation (at t = 0.6),\n", + "it will switch to being a leaky integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.583436Z", + "iopub.status.busy": "2020-11-25T16:47:36.581993Z", + "iopub.status.idle": "2020-11-25T16:47:36.584215Z", + "shell.execute_reply": "2020-11-25T16:47:36.584636Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Another piecewise step that changes half way through the run\n", + " control_func = Piecewise({0: 1, 0.6: 0.5})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We add the control signal to the network\n", + "like we added the input signal,\n", + "but this time we connect it to\n", + "the second dimension of our neural population." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.590443Z", + "iopub.status.busy": "2020-11-25T16:47:36.588976Z", + "iopub.status.idle": "2020-11-25T16:47:36.591066Z", + "shell.execute_reply": "2020-11-25T16:47:36.591473Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " control = nengo.Node(output=control_func)\n", + "\n", + " # Connect the \"Control\" signal to the second of A's two input channels.\n", + " nengo.Connection(control, A[1], synapse=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Define the integrator dynamics\n", + "\n", + "We set up integrator by connecting population 'A' to itself.\n", + "We set up feedback in the model to handle integration of the input.\n", + "The time constant $\\tau$ on the recurrent weights affects\n", + "both the rate and accuracy of integration.\n", + "Try adjusting it and see what happens!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.598061Z", + "iopub.status.busy": "2020-11-25T16:47:36.596633Z", + "iopub.status.idle": "2020-11-25T16:47:36.598703Z", + "shell.execute_reply": "2020-11-25T16:47:36.599118Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a recurrent connection that first takes the product\n", + " # of both dimensions in A (i.e., the value times the control)\n", + " # and then adds this back into the first dimension of A using\n", + " # a transform\n", + " nengo.Connection(\n", + " A,\n", + " A[0], # -- transform converts function output to new state inputs\n", + " function=lambda x: x[0] * x[1], # -- function is applied first to A\n", + " synapse=tau,\n", + " )\n", + "\n", + " # Record both dimensions of A\n", + " A_probe = nengo.Probe(A, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:36.604531Z", + "iopub.status.busy": "2020-11-25T16:47:36.603788Z", + "iopub.status.idle": "2020-11-25T16:47:36.995086Z", + "shell.execute_reply": "2020-11-25T16:47:36.994198Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1.4) # Run for 1.4 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:37.039383Z", + "iopub.status.busy": "2020-11-25T16:47:37.038348Z", + "iopub.status.idle": "2020-11-25T16:47:37.421061Z", + "shell.execute_reply": "2020-11-25T16:47:37.421475Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the value and control signals, along with the exact integral\n", + "t = sim.trange()\n", + "dt = t[1] - t[0]\n", + "input_sig = input_func.run(t[-1], dt=dt)\n", + "control_sig = control_func.run(t[-1], dt=dt)\n", + "ref = dt * np.cumsum(input_sig)\n", + "\n", + "plt.figure(figsize=(6, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, input_sig, label=\"Input\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-11, 11)\n", + "plt.ylabel(\"Input\")\n", + "plt.legend(loc=\"lower left\", frameon=False)\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(t, ref, \"k--\", label=\"Exact\")\n", + "plt.plot(t, sim.data[A_probe][:, 0], label=\"A (value)\")\n", + "plt.plot(t, sim.data[A_probe][:, 1], label=\"A (control)\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"x(t)\")\n", + "plt.legend(loc=\"lower left\", frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows the output of our system,\n", + "specifically the (integrated) value stored by the A population,\n", + "along with the control signal represented by the A population.\n", + "The exact value of the integral,\n", + "as performed by a perfect (non-neural) integrator,\n", + "is shown for reference.\n", + "\n", + "When the control value is 1 (t < 0.6),\n", + "the neural integrator performs near-perfect integration.\n", + "However, when the control value drops to 0.5 (t > 0.6),\n", + "the integrator becomes a leaky integrator.\n", + "This means that in the absence of input,\n", + "its stored value drifts towards zero." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dynamics/controlled-integrator2.html b/examples/dynamics/controlled-integrator2.html new file mode 100644 index 0000000000..45ace0b7c1 --- /dev/null +++ b/examples/dynamics/controlled-integrator2.html @@ -0,0 +1,759 @@ + + + + + + + + Controlled integrator 2 — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Controlled integrator 2

+

This demo implements a controlled one-dimensional neural integrator that is functionally the same as the controlled integrator in the previous example. However, the control signal is zero for integration, less than one for low-pass filtering, and greater than 1 for saturation. This behavior maps more directly to the differential equation used to describe an integrator:

+
+\[\dot{x} = \mathrm{Ax}(t) + \mathrm{Bu}(t)\]
+

The control in this circuit is \(A\) in that equation. This is also the controlled integrator described in the book “How to build a brain.”

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the network

+

As before, we use standard network-creation commands to begin creating our controlled integrator. An ensemble of neurons will represent the state of our integrator, and the connections between the neurons in the ensemble will define the dynamics of our integrator.

+
+
[2]:
+
+
+
+model = nengo.Network(label="Controlled Integrator 2")
+with model:
+    # Make a population with 225 LIF neurons representing a 2 dimensional
+    # signal, with a larger radius to accommodate large inputs
+    A = nengo.Ensemble(225, dimensions=2, radius=1.5)
+
+
+
+
+
+

Step 2: Define the ‘input’ signal to integrate

+

We will be running 1 second of simulation time again, so we will use the same Python function input_func to define our input signal. This piecewise function sits at 0 until .2 seconds into the simulation, then jumps up to 5, back to 0, down to -10, back to 0, then up to 5, and then back to 0. Our integrator will respond by ramping up when the input is positive, and descending when the input is negative.

+
+
[3]:
+
+
+
+with model:
+    # Create a piecewise step function for input
+    input_func = Piecewise({0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})
+    inp = nengo.Node(output=input_func)
+
+    # Connect the Input signal to ensemble A.
+    tau = 0.1
+    nengo.Connection(inp, A, transform=[[tau], [0]], synapse=0.1)
+
+
+
+
+
+

Step 3: Define the control signal

+

The control signal will be 0 for the first part of the simulation, and -0.5 for the second part. This means that at the beginning of the simulation, the integrator will act as an optimal integrator, and partway though the simulation (at t = 0.6), it will switch to being a leaky integrator.

+
+
[4]:
+
+
+
+with model:
+    # Another piecewise function that changes half way through the run
+    control_func = Piecewise({0: 0, 0.6: -0.5})
+    control = nengo.Node(output=control_func)
+
+    # Connect the "Control" signal to the second of A's two input channels
+    nengo.Connection(control, A[1], synapse=0.005)
+
+
+
+
+
+

Step 4: Define the integrator dynamics

+

We set up integrator by connecting population ‘A’ to itself. We set up feedback in the model to handle integration of the input. The time constant \(\tau\) on the recurrent weights affects both the rate and accuracy of integration.

+
+
[5]:
+
+
+
+with model:
+    # Note the changes from the previous example to the function being defined.
+    nengo.Connection(A, A[0], function=lambda x: x[0] * x[1] + x[0], synapse=tau)
+
+    # Record both dimensions of A
+    A_probe = nengo.Probe(A, "decoded_output", synapse=0.01)
+
+
+
+
+
+

Step 5: Run the model and plot results

+
+
[6]:
+
+
+
+with nengo.Simulator(model) as sim:  # Create a simulator
+    sim.run(1.4)  # Run for 1.4 seconds
+
+
+
+
+
[7]:
+
+
+
+# Plot the value and control signals, along with the exact integral
+t = sim.trange()
+dt = t[1] - t[0]
+input_sig = input_func.run(t[-1], dt=dt)
+control_sig = control_func.run(t[-1], dt=dt)
+ref = dt * np.cumsum(input_sig)
+
+plt.figure(figsize=(6, 8))
+plt.subplot(2, 1, 1)
+plt.plot(t, input_sig, label="Input")
+plt.xlim(right=t[-1])
+plt.ylim(-11, 11)
+plt.ylabel("Input")
+plt.legend(loc="lower left", frameon=False)
+
+plt.subplot(212)
+plt.plot(t, ref, "k--", label="exact")
+plt.plot(t, sim.data[A_probe][:, 0], label="A (value)")
+plt.plot(t, sim.data[A_probe][:, 1], label="A (control)")
+plt.xlim(right=t[-1])
+plt.ylim(-1.1, 1.1)
+plt.xlabel("Time (s)")
+plt.ylabel("x(t)")
+plt.legend(loc="lower left", frameon=False)
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f3d6c452c88>
+
+
+
+
+
+
+../../_images/examples_dynamics_controlled-integrator2_12_1.png +
+
+

The above plot shows the output of our system, specifically the (integrated) value stored by the A population, along with the control signal represented by the A population. The exact value of the integral, as performed by a perfect (non-neural) integrator, is shown for reference.

+

When the control value is 0 (t < 0.6), the neural integrator performs near-perfect integration. However, when the control value drops to -0.5 (t > 0.6), the integrator becomes a leaky integrator. This means that with negative input, its stored value drifts towards zero.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/dynamics/controlled-integrator2.ipynb b/examples/dynamics/controlled-integrator2.ipynb new file mode 100644 index 0000000000..fa122c1133 --- /dev/null +++ b/examples/dynamics/controlled-integrator2.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled integrator 2\n", + "\n", + "This demo implements a controlled one-dimensional neural integrator\n", + "that is functionally the same as\n", + "the controlled integrator in the previous example.\n", + "However, the control signal is zero for integration,\n", + "less than one for low-pass filtering, and greater than 1 for saturation.\n", + "This behavior maps more directly to the differential equation\n", + "used to describe an integrator:\n", + "\n", + "$$\\dot{x} = \\mathrm{Ax}(t) + \\mathrm{Bu}(t)$$\n", + "\n", + "The control in this circuit is $A$ in that equation.\n", + "This is also the controlled integrator\n", + "described in the book \"How to build a brain.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:38.745045Z", + "iopub.status.busy": "2020-11-25T16:47:38.744195Z", + "iopub.status.idle": "2020-11-25T16:47:39.237348Z", + "shell.execute_reply": "2020-11-25T16:47:39.236848Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network\n", + "\n", + "As before, we use standard network-creation commands\n", + "to begin creating our controlled integrator.\n", + "An ensemble of neurons will represent the state of our integrator,\n", + "and the connections between the neurons in the ensemble\n", + "will define the dynamics of our integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.242335Z", + "iopub.status.busy": "2020-11-25T16:47:39.241811Z", + "iopub.status.idle": "2020-11-25T16:47:39.245574Z", + "shell.execute_reply": "2020-11-25T16:47:39.245121Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Controlled Integrator 2\")\n", + "with model:\n", + " # Make a population with 225 LIF neurons representing a 2 dimensional\n", + " # signal, with a larger radius to accommodate large inputs\n", + " A = nengo.Ensemble(225, dimensions=2, radius=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Define the 'input' signal to integrate\n", + "\n", + "We will be running 1 second of simulation time again,\n", + "so we will use the same Python function `input_func`\n", + "to define our input signal. This piecewise function sits at 0\n", + "until .2 seconds into the simulation,\n", + "then jumps up to 5, back to 0, down to -10, back to 0, then up to 5,\n", + "and then back to 0. Our integrator will respond by ramping up\n", + "when the input is positive, and descending when the input is negative." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.253763Z", + "iopub.status.busy": "2020-11-25T16:47:39.252182Z", + "iopub.status.idle": "2020-11-25T16:47:39.254369Z", + "shell.execute_reply": "2020-11-25T16:47:39.254780Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create a piecewise step function for input\n", + " input_func = Piecewise({0.2: 5, 0.3: 0, 0.44: -10, 0.54: 0, 0.8: 5, 0.9: 0})\n", + " inp = nengo.Node(output=input_func)\n", + "\n", + " # Connect the Input signal to ensemble A.\n", + " tau = 0.1\n", + " nengo.Connection(inp, A, transform=[[tau], [0]], synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Define the control signal\n", + "\n", + "The control signal will be 0 for the first part of the simulation,\n", + "and -0.5 for the second part.\n", + "This means that at the beginning of the simulation,\n", + "the integrator will act as an optimal integrator,\n", + "and partway though the simulation (at t = 0.6),\n", + "it will switch to being a leaky integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.261376Z", + "iopub.status.busy": "2020-11-25T16:47:39.259799Z", + "iopub.status.idle": "2020-11-25T16:47:39.261955Z", + "shell.execute_reply": "2020-11-25T16:47:39.262382Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Another piecewise function that changes half way through the run\n", + " control_func = Piecewise({0: 0, 0.6: -0.5})\n", + " control = nengo.Node(output=control_func)\n", + "\n", + " # Connect the \"Control\" signal to the second of A's two input channels\n", + " nengo.Connection(control, A[1], synapse=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Define the integrator dynamics\n", + "\n", + "We set up integrator by connecting population 'A' to itself.\n", + "We set up feedback in the model to handle integration of the input.\n", + "The time constant $\\tau$ on the recurrent weights\n", + "affects both the rate and accuracy of integration." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.268930Z", + "iopub.status.busy": "2020-11-25T16:47:39.267413Z", + "iopub.status.idle": "2020-11-25T16:47:39.269558Z", + "shell.execute_reply": "2020-11-25T16:47:39.269960Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Note the changes from the previous example to the function being defined.\n", + " nengo.Connection(A, A[0], function=lambda x: x[0] * x[1] + x[0], synapse=tau)\n", + "\n", + " # Record both dimensions of A\n", + " A_probe = nengo.Probe(A, \"decoded_output\", synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model and plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.275252Z", + "iopub.status.busy": "2020-11-25T16:47:39.274489Z", + "iopub.status.idle": "2020-11-25T16:47:39.643776Z", + "shell.execute_reply": "2020-11-25T16:47:39.642819Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim: # Create a simulator\n", + " sim.run(1.4) # Run for 1.4 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:39.764078Z", + "iopub.status.busy": "2020-11-25T16:47:39.760404Z", + "iopub.status.idle": "2020-11-25T16:47:40.098616Z", + "shell.execute_reply": "2020-11-25T16:47:40.099016Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the value and control signals, along with the exact integral\n", + "t = sim.trange()\n", + "dt = t[1] - t[0]\n", + "input_sig = input_func.run(t[-1], dt=dt)\n", + "control_sig = control_func.run(t[-1], dt=dt)\n", + "ref = dt * np.cumsum(input_sig)\n", + "\n", + "plt.figure(figsize=(6, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, input_sig, label=\"Input\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-11, 11)\n", + "plt.ylabel(\"Input\")\n", + "plt.legend(loc=\"lower left\", frameon=False)\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(t, ref, \"k--\", label=\"exact\")\n", + "plt.plot(t, sim.data[A_probe][:, 0], label=\"A (value)\")\n", + "plt.plot(t, sim.data[A_probe][:, 1], label=\"A (control)\")\n", + "plt.xlim(right=t[-1])\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"x(t)\")\n", + "plt.legend(loc=\"lower left\", frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows the output of our system,\n", + "specifically the (integrated) value stored by the A population,\n", + "along with the control signal represented by the A population.\n", + "The exact value of the integral,\n", + "as performed by a perfect (non-neural) integrator,\n", + "is shown for reference.\n", + "\n", + "When the control value is 0 (t < 0.6),\n", + "the neural integrator performs near-perfect integration.\n", + "However, when the control value drops to -0.5 (t > 0.6),\n", + "the integrator becomes a leaky integrator.\n", + "This means that with negative input,\n", + "its stored value drifts towards zero." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dynamics/controlled-oscillator.html b/examples/dynamics/controlled-oscillator.html new file mode 100644 index 0000000000..dc531f6cc2 --- /dev/null +++ b/examples/dynamics/controlled-oscillator.html @@ -0,0 +1,764 @@ + + + + + + + + Controlled oscillator — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Controlled oscillator

+

The controlled oscillator is an oscillator with an extra input that controls the frequency of the oscillation.

+

To implement a basic oscillator, we would use a neural ensemble of two dimensions that has the following dynamics:

+
+\[\begin{split}\dot{x} = \begin{bmatrix} 0 && - \omega \\ \omega && 0 \end{bmatrix} x\end{split}\]
+

where the frequency of oscillation is \(\omega \over {2 \pi}\) Hz.

+

We need the neurons to represent three variables, \(x_0\), \(x_1\), and \(\omega\). According the the dynamics principle of the NEF, in order to implement some particular dynamics, we need to convert this dynamics equation into a feedback function:

+
+\[\begin{split}\begin{align} + \dot{x} &= f(x) \\ + &\implies f_{feedback}(x) = x + \tau f(x) +\end{align}\end{split}\]
+

where \(\tau\) is the post-synaptic time constant of the feedback connection.

+

In this case, the feedback function to be computed is

+
+\[\begin{split}\begin{align} + f_{feedback}(x) &= x + \tau + \begin{bmatrix} + 0 && - \omega \\ + \omega && 0 + \end{bmatrix} + x \\ + &= + \begin{bmatrix} + x_0 - \tau \cdot \omega \cdot x_1 \\ + x_1 + \tau \cdot \omega \cdot x_0 + \end{bmatrix} +\end{align}\end{split}\]
+

Since the neural ensemble represents all three variables but the dynamics only affects the first two (\(x_0\), \(x_1\)), we need the feedback function to not affect that last variable. We do this by adding a zero to the feedback function.

+
+\[\begin{split}f_{feedback}(x) = \begin{bmatrix} + x_0 - \tau \cdot \omega \cdot x_1 \\ + x_1 + \tau \cdot \omega \cdot x_0 \\ + 0 \end{bmatrix}\end{split}\]
+

We also generally want to keep the ranges of variables represented within an ensemble to be approximately the same. In this case, if \(x_0\) and \(x_1\) are between -1 and 1, \(\omega\) will also be between -1 and 1, giving a frequency range of \(-1 \over {2 \pi}\) to \(1 \over {2 \pi}\). To increase this range, we introduce a scaling factor to \(\omega\) called \(\omega_{max}\).

+
+\[\begin{split}f_{feedback}(x) = \begin{bmatrix} + x_0 - \tau \cdot \omega \cdot \omega_{max} \cdot x_1 \\ + x_1 + \tau \cdot \omega \cdot \omega_{max} \cdot x_0 \\ + 0 \end{bmatrix}\end{split}\]
+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the network

+
+
[2]:
+
+
+
+tau = 0.1  # Post-synaptic time constant for feedback
+w_max = 10  # Maximum frequency in Hz is w_max/(2*pi)
+
+model = nengo.Network(label="Controlled Oscillator")
+with model:
+    # The ensemble for the oscillator
+    oscillator = nengo.Ensemble(500, dimensions=3, radius=1.7)
+
+    # The feedback connection
+    def feedback(x):
+        x0, x1, w = x  # These are the three variables stored in the ensemble
+        return x0 - w * w_max * tau * x1, x1 + w * w_max * tau * x0, 0
+
+    nengo.Connection(oscillator, oscillator, function=feedback, synapse=tau)
+
+    # The ensemble for controlling the speed of oscillation
+    frequency = nengo.Ensemble(100, dimensions=1)
+
+    nengo.Connection(frequency, oscillator[2])
+
+
+
+
+
+

Step 2: Create the input

+
+
[3]:
+
+
+
+with model:
+    # We need a quick input at the beginning to start the oscillator
+    initial = nengo.Node(Piecewise({0: [1, 0, 0], 0.15: [0, 0, 0]}))
+    nengo.Connection(initial, oscillator)
+
+    # Vary the speed over time
+    input_frequency = nengo.Node(Piecewise({0: 1, 1: 0.5, 2: 0, 3: -0.5, 4: -1}))
+
+    nengo.Connection(input_frequency, frequency)
+
+
+
+
+
+

Step 3: Add Probes

+
+
[4]:
+
+
+
+with model:
+    # Indicate which values to record
+    oscillator_probe = nengo.Probe(oscillator, synapse=0.03)
+
+
+
+
+
+

Step 4: Run the Model

+
+
[5]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(5)
+
+
+
+
+
+

Step 5: Plot the Results

+
+
[6]:
+
+
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[oscillator_probe])
+plt.xlabel("Time (s)")
+plt.legend(["$x_0$", "$x_1$", r"$\omega$"])
+
+
+
+
+
[6]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7ffaa9b39908>
+
+
+
+
+
+
+../../_images/examples_dynamics_controlled-oscillator_11_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/dynamics/controlled-oscillator.ipynb b/examples/dynamics/controlled-oscillator.ipynb new file mode 100644 index 0000000000..501e5e0d61 --- /dev/null +++ b/examples/dynamics/controlled-oscillator.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Controlled oscillator\n", + "\n", + "The controlled oscillator is an oscillator\n", + "with an extra input that controls the frequency of the oscillation.\n", + "\n", + "To implement a basic oscillator,\n", + "we would use a neural ensemble of two dimensions\n", + "that has the following dynamics:\n", + "\n", + "$$\n", + "\\dot{x} = \\begin{bmatrix} 0 && - \\omega \\\\ \\omega && 0 \\end{bmatrix} x\n", + "$$\n", + "\n", + "where the frequency of oscillation is $\\omega \\over {2 \\pi}$ Hz.\n", + "\n", + "We need the neurons to represent three variables,\n", + "$x_0$, $x_1$, and $\\omega$.\n", + "According the the dynamics principle of the NEF,\n", + "in order to implement some particular dynamics,\n", + "we need to convert this dynamics equation into a feedback function:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " \\dot{x} &= f(x) \\\\\n", + " &\\implies f_{feedback}(x) = x + \\tau f(x)\n", + "\\end{align}\n", + "$$\n", + "\n", + "where $\\tau$ is the post-synaptic time constant of the feedback connection.\n", + "\n", + "In this case, the feedback function to be computed is\n", + "\n", + "$$\n", + "\\begin{align}\n", + " f_{feedback}(x) &= x + \\tau\n", + " \\begin{bmatrix}\n", + " 0 && - \\omega \\\\\n", + " \\omega && 0\n", + " \\end{bmatrix}\n", + " x \\\\\n", + " &=\n", + " \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot x_0\n", + " \\end{bmatrix}\n", + "\\end{align}\n", + "$$\n", + "\n", + "Since the neural ensemble represents all three variables\n", + "but the dynamics only affects the first two ($x_0$, $x_1$),\n", + "we need the feedback function to not affect that last variable.\n", + "We do this by adding a zero to the feedback function.\n", + "\n", + "$$\n", + "f_{feedback}(x) = \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot x_0 \\\\\n", + " 0 \\end{bmatrix}\n", + "$$\n", + "\n", + "We also generally want to keep\n", + "the ranges of variables represented within an ensemble\n", + "to be approximately the same.\n", + "In this case, if $x_0$ and $x_1$ are between -1 and 1,\n", + "$\\omega$ will also be between -1 and 1,\n", + "giving a frequency range of $-1 \\over {2 \\pi}$ to $1 \\over {2 \\pi}$.\n", + "To increase this range,\n", + "we introduce a scaling factor to $\\omega$ called $\\omega_{max}$.\n", + "\n", + "$$\n", + "f_{feedback}(x) = \\begin{bmatrix}\n", + " x_0 - \\tau \\cdot \\omega \\cdot \\omega_{max} \\cdot x_1 \\\\\n", + " x_1 + \\tau \\cdot \\omega \\cdot \\omega_{max} \\cdot x_0 \\\\\n", + " 0 \\end{bmatrix}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.444248Z", + "iopub.status.busy": "2020-11-25T16:47:41.443379Z", + "iopub.status.idle": "2020-11-25T16:47:41.938115Z", + "shell.execute_reply": "2020-11-25T16:47:41.937594Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.947952Z", + "iopub.status.busy": "2020-11-25T16:47:41.947369Z", + "iopub.status.idle": "2020-11-25T16:47:41.950571Z", + "shell.execute_reply": "2020-11-25T16:47:41.950969Z" + } + }, + "outputs": [], + "source": [ + "tau = 0.1 # Post-synaptic time constant for feedback\n", + "w_max = 10 # Maximum frequency in Hz is w_max/(2*pi)\n", + "\n", + "model = nengo.Network(label=\"Controlled Oscillator\")\n", + "with model:\n", + " # The ensemble for the oscillator\n", + " oscillator = nengo.Ensemble(500, dimensions=3, radius=1.7)\n", + "\n", + " # The feedback connection\n", + " def feedback(x):\n", + " x0, x1, w = x # These are the three variables stored in the ensemble\n", + " return x0 - w * w_max * tau * x1, x1 + w * w_max * tau * x0, 0\n", + "\n", + " nengo.Connection(oscillator, oscillator, function=feedback, synapse=tau)\n", + "\n", + " # The ensemble for controlling the speed of oscillation\n", + " frequency = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " nengo.Connection(frequency, oscillator[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create the input" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.957796Z", + "iopub.status.busy": "2020-11-25T16:47:41.957278Z", + "iopub.status.idle": "2020-11-25T16:47:41.960842Z", + "shell.execute_reply": "2020-11-25T16:47:41.960370Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # We need a quick input at the beginning to start the oscillator\n", + " initial = nengo.Node(Piecewise({0: [1, 0, 0], 0.15: [0, 0, 0]}))\n", + " nengo.Connection(initial, oscillator)\n", + "\n", + " # Vary the speed over time\n", + " input_frequency = nengo.Node(Piecewise({0: 1, 1: 0.5, 2: 0, 3: -0.5, 4: -1}))\n", + "\n", + " nengo.Connection(input_frequency, frequency)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Add Probes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.966140Z", + "iopub.status.busy": "2020-11-25T16:47:41.964618Z", + "iopub.status.idle": "2020-11-25T16:47:41.966749Z", + "shell.execute_reply": "2020-11-25T16:47:41.967153Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Indicate which values to record\n", + " oscillator_probe = nengo.Probe(oscillator, synapse=0.03)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:41.972218Z", + "iopub.status.busy": "2020-11-25T16:47:41.971445Z", + "iopub.status.idle": "2020-11-25T16:47:43.257727Z", + "shell.execute_reply": "2020-11-25T16:47:43.257252Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:43.263384Z", + "iopub.status.busy": "2020-11-25T16:47:43.262188Z", + "iopub.status.idle": "2020-11-25T16:47:43.662460Z", + "shell.execute_reply": "2020-11-25T16:47:43.661980Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[oscillator_probe])\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.legend([\"$x_0$\", \"$x_1$\", r\"$\\omega$\"])" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dynamics/controlled_integrator.html b/examples/dynamics/controlled_integrator.html new file mode 100644 index 0000000000..897ef62419 --- /dev/null +++ b/examples/dynamics/controlled_integrator.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/dynamics/controlled_integrator2.html b/examples/dynamics/controlled_integrator2.html new file mode 100644 index 0000000000..ec2ccf14bc --- /dev/null +++ b/examples/dynamics/controlled_integrator2.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/dynamics/controlled_oscillator.html b/examples/dynamics/controlled_oscillator.html new file mode 100644 index 0000000000..889e0b7e2a --- /dev/null +++ b/examples/dynamics/controlled_oscillator.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/dynamics/integrator.html b/examples/dynamics/integrator.html new file mode 100644 index 0000000000..2a227c0163 --- /dev/null +++ b/examples/dynamics/integrator.html @@ -0,0 +1,738 @@ + + + + + + + + Integrator — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ + + +
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+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Integrator

+

This demo implements a one-dimensional neural integrator.

+

This is the first example of a recurrent network in the demos. It shows how neurons can be used to implement stable dynamics. Such dynamics are important for memory, noise cleanup, statistical inference, and many other dynamic transformations.

+

When you run this demo, it will automatically put in some step functions on the input, so you can see that the output is integrating (i.e. summing over time) the input. You can also input your own values. Note that since the integrator constantly sums its input, it will saturate quickly if you leave the input non-zero. This makes it clear that neurons have a finite range of representation. Such saturation effects can be exploited to perform useful computations (e.g. soft normalization).

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the neural populations

+

Our model consists of one recurrently connected ensemble and an input population.

+
+
[2]:
+
+
+
+model = nengo.Network(label="Integrator")
+with model:
+    # Our ensemble consists of 100 leaky integrate-and-fire neurons,
+    # representing a one-dimensional signal
+    A = nengo.Ensemble(100, dimensions=1)
+
+
+
+
+
+

Step 2: Create input for the model

+

We will use a piecewise step function as input, so we can see the effects of recurrence.

+
+
[3]:
+
+
+
+# Create a piecewise step function for input
+with model:
+    input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))
+
+
+
+
+
+

Step 3: Connect the network elements

+
+
[4]:
+
+
+
+with model:
+    # Connect the population to itself
+    tau = 0.1
+    nengo.Connection(
+        A, A, transform=[[1]], synapse=tau
+    )  # Using a long time constant for stability
+
+    # Connect the input
+    nengo.Connection(
+        input, A, transform=[[tau]], synapse=tau
+    )  # The same time constant as recurrent to make it more 'ideal'
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later.

+
+
[5]:
+
+
+
+with model:
+    # Add probes
+    input_probe = nengo.Probe(input)
+    A_probe = nengo.Probe(A, synapse=0.01)
+
+
+
+
+
+

Step 5: Run the model

+
+
[6]:
+
+
+
+# Create our simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 6 seconds
+    sim.run(6)
+
+
+
+
+
+

Step 6: Plot the results

+
+
[7]:
+
+
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(sim.trange(), sim.data[input_probe], label="Input")
+plt.plot(sim.trange(), sim.data[A_probe], "k", label="Integrator output")
+plt.legend()
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f87837805c0>
+
+
+
+
+
+
+../../_images/examples_dynamics_integrator_13_1.png +
+
+

The graph shows the response to the input by the integrator. Because it is implemented in neurons, it will not be perfect (i.e. there will be drift). Running several times will give a sense of the kinds of drift you might expect. Drift can be reduced by increasing the number of neurons.

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+
+ + +
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+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/dynamics/integrator.ipynb b/examples/dynamics/integrator.ipynb new file mode 100644 index 0000000000..f3acad3e04 --- /dev/null +++ b/examples/dynamics/integrator.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrator\n", + "\n", + "This demo implements a one-dimensional neural integrator.\n", + "\n", + "This is the first example of a recurrent network in the demos.\n", + "It shows how neurons can be used to implement stable dynamics.\n", + "Such dynamics are important for memory, noise cleanup,\n", + "statistical inference, and many other dynamic transformations.\n", + "\n", + "When you run this demo,\n", + "it will automatically put in some step functions on the input,\n", + "so you can see that the output is\n", + "integrating (i.e. summing over time) the input.\n", + "You can also input your own values.\n", + "Note that since the integrator constantly sums its input,\n", + "it will saturate quickly if you leave the input non-zero.\n", + "This makes it clear that neurons have a finite range of representation.\n", + "Such saturation effects can be exploited\n", + "to perform useful computations (e.g. soft normalization)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.020012Z", + "iopub.status.busy": "2020-11-25T16:47:45.018193Z", + "iopub.status.idle": "2020-11-25T16:47:45.510839Z", + "shell.execute_reply": "2020-11-25T16:47:45.509886Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of one recurrently connected ensemble\n", + "and an input population." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.516020Z", + "iopub.status.busy": "2020-11-25T16:47:45.515427Z", + "iopub.status.idle": "2020-11-25T16:47:45.518774Z", + "shell.execute_reply": "2020-11-25T16:47:45.519169Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Integrator\")\n", + "with model:\n", + " # Our ensemble consists of 100 leaky integrate-and-fire neurons,\n", + " # representing a one-dimensional signal\n", + " A = nengo.Ensemble(100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a piecewise step function as input,\n", + "so we can see the effects of recurrence." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.525563Z", + "iopub.status.busy": "2020-11-25T16:47:45.523963Z", + "iopub.status.idle": "2020-11-25T16:47:45.526138Z", + "shell.execute_reply": "2020-11-25T16:47:45.526552Z" + } + }, + "outputs": [], + "source": [ + "# Create a piecewise step function for input\n", + "with model:\n", + " input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.534790Z", + "iopub.status.busy": "2020-11-25T16:47:45.533316Z", + "iopub.status.idle": "2020-11-25T16:47:45.535481Z", + "shell.execute_reply": "2020-11-25T16:47:45.535957Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Connect the population to itself\n", + " tau = 0.1\n", + " nengo.Connection(\n", + " A, A, transform=[[1]], synapse=tau\n", + " ) # Using a long time constant for stability\n", + "\n", + " # Connect the input\n", + " nengo.Connection(\n", + " input, A, transform=[[tau]], synapse=tau\n", + " ) # The same time constant as recurrent to make it more 'ideal'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.541698Z", + "iopub.status.busy": "2020-11-25T16:47:45.540146Z", + "iopub.status.idle": "2020-11-25T16:47:45.542286Z", + "shell.execute_reply": "2020-11-25T16:47:45.542705Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Add probes\n", + " input_probe = nengo.Probe(input)\n", + " A_probe = nengo.Probe(A, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:45.548012Z", + "iopub.status.busy": "2020-11-25T16:47:45.547176Z", + "iopub.status.idle": "2020-11-25T16:47:46.321805Z", + "shell.execute_reply": "2020-11-25T16:47:46.321287Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 6 seconds\n", + " sim.run(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:46.327894Z", + "iopub.status.busy": "2020-11-25T16:47:46.326694Z", + "iopub.status.idle": "2020-11-25T16:47:46.543259Z", + "shell.execute_reply": "2020-11-25T16:47:46.543712Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MucvXufH4r3jJ3p/1DiVg3548z3V6jHI7PwYOHMjPP/88A+C6det6fLYr8tOuXTsGwDExMRwVFeV1m4EDBzIAbtq0aUDHCvT8BpDApeRU23fpuDp37pzb+ujRo9GrVy+dohEluc6TDwCpqalo2rSpTtEIIzn5YzIubJjtVrZ8+XJlOSsrK6D979+/HwBw/PjxUrcpPp5rS95obN6lc/Pr7Pr16z1e1/sh2jIs09O8efOU5dtvv13HSMyv+Lwye5dOYmIinujVWe8wTMHWCb9I0dn+yCOPuJUys9uwTD3I7eKehg0bpncIlmGFKTuysrIQGxurdximYdmEf/78eXzzzTfIz88vdRtmIO96jsdV8bS0tGCHV0Hm/2AK4zJrs+Lq1asIDw9Xfb8dO3ZUbV+HDx/G1KlTlfXBgweje/fuZb7nxRdfxNatW1WLwZUqCZ+IehLRj0R0gohe9PJ6FSJa5nx9HxFFq3Hc0uzfvx/16tVD165dERYWhqSkJGzZsgWvv/46mBnXr19HRkYG2FGImU/c4/H+Bg0aBDM8n0mXjnfXr19XlkeOHKljJOZm9i4db/dj9PrTIDgcDuzevRvbtm3DuXPnkJubC2bGL7/8gk8++QTfffcdmBkrVqzAhg0bMHv2bDz11FO4evUq0tLSsHfvXuUiZ25uLqZNm4YbN264Xfy8fv26MuInPDwcx44dQ3Z2Nk6fPg1mxoABAwAALVu2xL///W/lfUuWLMHGjRu9XlCNj4/H9evXMW3aNHTr1i04v7TSrub6+gMgBMBJAE0BhAE4AKBFiW1GAvjQufxXAMt82be/o3QQwJXxefPm+XXMYDh65jI3Hv8Vxx88o3cohuP6Nzt9+rTe4ZhS+sUcbjz+K176nflG6TgcDo/PbuTwebwzJVPTOBISEjgzU9tjlgdBHqXTAcAJZk4FACJaCuBRAEddtnkUwGTn8goAs4mInMEZhsHCUVpei7/9GVuPZZS9sQm0bXQLHu/YWJV9/eMf/8BHH30EANi3bx+ioqJU2a8vdqRkYu2BM5odL1hy8opmHjXjtaKSo+f2nsjAX+Z9hzk7TmJ1kjaz2RYJBVLTAah7zBpVQ/FSn5aq7hNQ507bBgBOu6ynASjZCaZsw8wFRHQZQF0A50vujIiGAxgOAI0aNVIhPN+UNdxKL/VrV8Nd9Wvh56xr+DnL+w0gZnHpej62HstQLeG//fbbSsIfMGCApv9ZL9pzCjuOZ6JeDX0v6qshuu5vcGf9mnqH4bOLFy9iwYIFHqPqmtxaE80jauBkRjZOZmTrFJ166tQIC8p+DTcOn5nnApgLALGxsZp9ips1a6bVoXxWu1plrBttjeFm//nyENYdOlf+hj4qOdVCQUGB2zTKwcQA7rytFtY+d78mxxNFfv75Z0RHR3uUJyQk4NaaVbFxbBftgzIZNS7apgNo6LIeBc/vN8o2RBQKoDaAwO6EKEPJKXSnT5+OdevW4YsvvgAAt0cV3n///UG7Ii6Cq02bNsry5MmT9QtEaMLb3zg+Ph7t27fXPhiTUqNJ9D2AGCJqgqLE/lcAg0tsswbAEADfAhgAYGsw++8TEhKQn5+P1atXIyIiAg8++KDy2pUrVxAaGoqNGzdi3rx5+OMf/xisMEQJav/Bd+7cqYzUmDp1qmbTVxvtWo9dLFy40G29cePGHvfPiLIFnPCdffKjAGxA0Yidj5n5CBH9H4quFq8B8BGAT4joBIALKPpPIWjCwsIQFhaGwYNL/r8D1KxZ1F955oz5L7rZXfHfUljfrl273NbnzJmD4cOH6xSNeakyDp+Z45m5OTPfzsxTnWWTnMkezJzLzH9m5mbM3KF4RI+wj2CNBHH99vbGG28E5RjemHXsutkUFhZi9OjR6NzZ/VqWJHv/GO6irbCuYHSFbNiwQblYGxcXh27dugX9Vnvp0NFOWFgYHA6HW1n//v11isb8LDu1grCHkJAQTJs2TVl3fTKWMLfPP//cI9m//vrrWLlypU4RmZ8kfKGJYHaBvPjizdk85s6dG7wDuZAeneDaunUrBg4c6FbWokULvPDCCzpFZA2S8IWlbNiwQe8QhApcr80UO3ToUFAf/2cHkvCFZoLZ93306M2ZPIJ98VZGZQYHM8PhcHi96z03NxeVKkm6CpT8BoUmgt0uu+uuu5TluLg4XLx4MbgHlJam6v72t78hJCQEzZs3dyvfvn277s+msApJ+MKS/vKXv+gdgqigRYsWeS3v0kWmTFCLJHyhmWB3hZw/f3Muvk2bNgXtONKjo77SLravW7dO40isTcbhC8uoW7eu3iEIP40YMcKj7Pz58/I3VZm08IUm9Bhd8d133wVt39KDr47z5897PTcWLlwoyT4IJOELzWgx6ViPHj2UZTWfTepKJk9Tz6pVqzzK1qxZg6eeekqHaKxPEr6wlOXLl7utX758WadIRHmuXbvmdU6cPn36yHj7IJGELyylVq1aSEtLU9ZbtlT/MXGAjMoMVHZ2NmrUqOFWtmfPHvn2FGSS8IXl1K9fX1lOT9fy+abCV96mtpY+++CThC80o1XbreQdma+++qpGRxa+2Lx5s9fyJk2aaByJ/UjCF5ZUp04dZXnixIk6RiJc5ebmonv37m5lBw4cQEFBASpXrqxTVPYhCV9oQus+78TERLf13NxcVfcvXfgVd+XKFbeZTQHg4sWLaN26NUJCQnSKyl4k4QvtaHg9Ljo62u1W/caNG6u2b7mu6J/atWvj7bffdiu75ZZb9AnGpiThC8t68sknleWMjAwZAaKTo0ePok2bNh7lKSkpOkRjb5LwhSaC9UzbMo9Zoh9pxowZQdu3KF3Lli1x8OBBt7INGzYgJiZGp4jsSxK+sDTXefLj4uJ0jMR+CgoK8Prrr3uUnz17Fg8//LAOEQmZPE1oRo8OFdd58tXCMl+mT+bPn4/x48e7lRUWFsqDTHQkv3mhCT17QJ5++mll+cqVK6rsUzp0yrd06VKPMkn2+pLfvrA817nWa9eurWMk9pGTk4NvvvnGrez777/XKRpRLKCET0R1iGgTER13/vvbUrYrJKIk58+aQI4pzMsoo2Ty8/MDer9BqmFYycnJqF69ukd5bGysDtEIV4G28F8EsIWZYwBsca57c52Z2zp/+gZ4TCEqzLV7oW3btvoFYnGpqalo0aKFR/m1a9d0iEaUFGjCfxRA8d0tiwA8FuD+hEXp3eft+oxb15E7/pJRmZ7S09Nx++23e5RfunQJv/nNb3SISJQUaMKPYOazzuVzACJK2a4qESUQ0V4ieqysHRLRcOe2CZmZmQGGJ4zESD0h9957r9/vlS4dTw6HA1FRUR7lzCzXTQyk3IRPRJuJ6LCXn0ddt+OiDtrSPgqNmTkWwGAAs4jIsxlwcz9zmTmWmWPr1atXkboIUab4+Hhlee/evYa5pmAF3ubCKSws1CESUZZyEz4zP8TMv/PysxrAr0RUHwCc/2aUso9057+pALYDaKdaDYQpGKEL5JFHHnFb/+9//+v3vvS4c9ioSt5FCxS17GUIpvEE+hdZA2CIc3kIgNUlNyCi3xJRFedyOIBOAALvRBXCDz/++KOyPHXqVB0jsYapU6d6zJPjbYSOMIZAE/50AN2J6DiAh5zrIKJYIprv3OYuAAlEdADANgDTmVkSvg0ZoQelefPmbusnTpyo8D7kTtub/vOf/3iUZWdn6xCJ8EVAUyswcxaAB72UJwAY5lzeA6BVIMcR5mfUycZiYmL868s3ZnU0dfbsWbf1iIgIrFq1SqdohC9kLh1hO8nJyW5z7OTm5qJq1ao6RmQ+2dnZiIyMdCtLS0tDaKikFCOTqypCM0bpCrnzzjvd1mfPnl2h9xuha0pvgwcPdls/ceKEJHsTkIQvbOnXX39Vlv/1r3/pGIn57NmzB2vXrlXWq1Sp4vWGK2E8kvCFJozW5X3rrbcG9H6j1UdLnTp1cluXSdHMQxK+0IzRukJcn6dakQnVDFYNTX399dceZa1ayZgMs5CEL2zr2LFjynLXrl31C8REevfu7bau1vMFhDYk4QttGLAPJCLi5tRPe/bswZYtW3x+r0FHmQbVlClT3NYdDgdq1qypUzTCH3JZXQinRYsW4cEHPW4rsb2LFy+iTp06bmV16tQx7L0VonTSwheaMWLfd9++Nx/P8Mknn/j2JiNWJIi8XeDOysrSIRIRKEn4QhNGnWzsyy+/9Ot9Rq2P2g4ePIiCggK3soULF+oTjAiYJHxha0SEyZMnK+vyZKabfvjhB4+J0RYsWIAhQ4aU8g5hdJLwhXYM2hUyadIkZblWrVrlbm+UO4aD6aeffkL79u09yocOHap9MEI1kvCF7blefHQ4HNi0aZOO0RhD06ZNPcr8mVlUGIskfKEJow/ouHHjhrL88MMPl7u90esTiDlz5niU7dmzR6ZPsAAZlik0Y+SukLCwMJ+3Ndodw2r68ccf8cwzz7iVnT592uvzaoX5SAtfCC82bNigdwiaIyKPmUTfeustSfYWIglfaMIMPSBXr15Vll955ZUyt7Val05ubq5H2aJFizBmzBjtgxFBIwlfCKcaNWooy7t373Z7/q2VvfPOO6hWrZpH+VNPPaVDNCKYJOELzZih7zs2NlZZLtm9UcwE1UBWVhYuXLiA/Px8EBHmzp2LtLQ0j+1ycnIwevRoj/KkpCQNohRak4QvhIuSE6j59bxblTAzNm7c6BZDRkYGLl68qKwnJiYqI4wKCgpw8uRJ7NmzB+Hh4ahbt65yMXrEiBFo2LAhhgwZAiJSfqpXr+5x3I0bN3rccCWsQUbpCE2Ypc+7Vq1aeOuttzB27FgAwKefforHH3/cYzs1plYoLCxEfn6+2/N0161bhwsXLqBRo0Z44IEHlPInn3wSRITFixcHdMzy3r9gwQJ07949oGMI45KELzRjhq4QoKg1XJzwn3jiCY+E70+rn5lx8uRJNGvWDL/88gtq1qyJwYMHY/369T693+eJ3QKg57cZoQ3p0hGihJIXMJ955hkUFhb6vb/8/HxMnToVMTExeOKJJ9C4cWPUqVPH52SvhZIPNhHWJAlfaMJss0umpKQoy3PmzEFoaCgyMjKUMm9dVAUFBTh//jwuXLiA6dOn44svvgARISwsDP/9738BAEuWLAl67GXp0qULRo0a5VHu+lByYV0BdekQ0Z8BTAZwF4AOzJxQynY9AbwNIATAfGaeHshxhTmZqcsgJibGoywiIgKZmZmldk1Vrlw5uEGVIiwsDDk5OQgNLfo4p6am4syZM9i4cSMaNmyI/Px8hIaGYtiwYcq8QVOnTkWVKlVw+fJleZCJjQTawj8MoD+AHaVtQEQhAN4D8AiAFgAGEVGLAI8rRNCtXLnSo6xevXo48uX7AICjR49i2bJlyogXNb377rsAiub1YWblm0G3bt2wdOlStGzZEhMmTAAz48aNGwgJCcHp06fx3nvvoUmTJujUqRNefvllDBs2DM8++yyefvpptxhr1aqFKlWq4NZbb0W9evVUjV0YF6nR6iKi7QBe8NbCJ6J7AUxm5h7O9QkAwMzTyttvbGwsJyR4/dIgTObNjT/ivW0nkDrtj3qHUiGlJfLGsQ/i5wTfn4FbluXLl6Nq1aro06ePKvsT9kZEicwc6+01LUbpNABw2mU9DUDH0jYmouEAhgNAo0aNghuZEOU4fvy41+6diib7Hj16oGXLlpgxYwYKCwtRqVIlFBQUID8/3+tYeCGCodyET0SbAdzm5aWJzLxa7YCYeS6AuUBRC1/t/Qv9mPGP2axZMyQlJaFt27Y+v+fUqVN4//330a9fP3Ts2BHnzp1D/fr1ldeL+9rDwsIqNEunEIEqN+Ez80MBHiMdQEOX9ShnmRCm0KZNG+zbtw8dO5b6xRQfffQRevToASJCZGQkXnvtNeU112QvhJ60GJb5PYAYImpCRGEA/gpgjQbHFQZi9nEgHTp0gMPhwPz585WyBg0aYPLkyXA4HPj73/+OBg0aIDIyUscohShboMMy+wF4F0A9AF8TURIz9yCiSBQNv+zFzAVENArABhQNy/yYmY8EHLkwHRONyvSKiPCPf/wDa3LvRO1qlbH47x30DkmICgko4TPzKgCrvJSfAdDLZT0eQHwgxxJCCBEYudNWaMNiN/dYqzbCLiThCyGETUjCF6KizH4xQtiWJHyhCat1gVish0rYhCR8IYSwCUn4QlNmmjGzNOavgbArSfhCCGETkvCFJqzW522x6gibkIQvNGWBHh1L1EHYkyR8IYSwCUn4QhNme6ZteeSxgMKMJOELIYRNSMIXmrJC9zdbohbCjiThC01YrQfEYtURNiEJXwghbEISvtCUJe60NX8VhE1JwhdCCJuQhC80YbU+b6tdkxD2IAlfaMoKvSHSpSPMShK+EELYhCR8oQnrdYFYrkLCBiThCyGETUjCF5qyQv+3BaogbEoSvtCE1SYbs1h1hE0ElPCJ6M9EdISIHEQUW8Z2p4joEBElEVFCIMcUQgjhn9AA338YQH8Ac3zYthsznw/weMLkrDDxmBXuFhb2FFDCZ+ZkwHpf14UQwoq06sNnABuJKJGIhpe1IRENJ6IEIkrIzMzUKDwhKkaaOMKMym3hE9FmALd5eWkiM6/28Tj3M3M6Ed0KYBMRHWPmHd42ZOa5AOYCQGxsrHx3thjpDRFCP+UmfGZ+KNCDMHO6898MIloFoAMArwlfCCFEcAS9S4eIqhNRzeJlAA+j6GKvsBGrXeaxWn2EPQQ6LLMfEaUBuBfA10S0wVkeSUTxzs0iAOwiogMAvgPwNTOvD+S4QgghKi7QUTqrAKzyUn4GQC/nciqANoEcRwgjMct1iPz8fKSlpSE3N1fvUEQQVK1aFVFRUahcubLP7wl0HL4QwqDS0tJQs2ZNREdHy9Bpi2FmZGVlIS0tDU2aNPH5fTK1gtAEWWwgoxnqk5ubi7p160qytyAiQt26dSv87U0SvtCUWbpDymKmu4Ul2VuXP39bSfhCCGETkvCFJqzW0LRafYKlRo0aqu/z1KlT+PTTT1Xfrx1IwhdCmIokfP/JKB2hKTP1f5fGjNchXl57BEfPXFF1ny0ia+GlPi192nb79u2YPHkywsPDcfjwYbRv3x7/+9//QESIjo7GwIEDsW7dOlSrVg2ffvopmjVrhqFDh6J3794YMGAAgKJvC9nZ2XjxxReRnJyMtm3bYsiQIRg7dqyq9bIyaeELTVitB0S6dCpu//79mDVrFo4ePYrU1FTs3r1bea127do4dOgQRo0ahTFjxpS5n+nTp6Nz585ISkqSZF9B0sIXwgZ8bYkHU4cOHRAVFQUAaNu2LU6dOoX7778fADBo0CDlX0niwSMtfKEpM3aHlGSBKuiiSpUqynJISAgKCgqUddchhsXLoaGhcDgcAACHw4G8vDyNIrUuSfhCCN0tW7ZM+ffee+8FAERHRyMxMREAsGbNGuTn5wMAatasiatXr+oTqMlJl47QhNX6vM1wp62ZXLx4Ea1bt0aVKlXw2WefAQCefvppPProo2jTpg169uyJ6tWrAwBat26NkJAQtGnTBkOHDpUuoAqQhC80ZYXuEHmmre+ys7MBAF27dkXXrl2V8tmzZ7tt969//QuvvfaaW1lERAT27t2rrBe/XrlyZWzdujVIEVubdOkIIYRNSAtfaMJyXSAWq46eTp06pXcItiEtfCGEsAlJ+EJTVuj/Nn8NhF1JwheasN4oHSHMRxK+EELYhCR8oSlLdIdYohLa8GV65FmzZiEnJyfoseg1y+bChQtx5swZv9+vZtyS8IUQulIz4btO11CSP4mzrP35ykgJX4ZlCuEHsz06cMyYMUhKSlJ1n23btsWsWbN82ra06ZHfffddnDlzBt26dUN4eDi2bduGjRs34qWXXsKNGzdw++23Y8GCBahRowbi4+Mxbtw4VK9eHZ06dUJqaiq++uorTJ48GSdPnkRqaioaNWqEadOm4cknn8S1a9cAFN3kdd9993lMq/zss8/i2WefRUJCAkJDQzFz5kx069YNCxcuxBdffIHs7GwUFhbim2++cavLzJkz8fHHHwMAhg0bhjFjxuDUqVPo3bs3Dh8+DACYMWMGsrOz8bvf/Q4JCQl4/PHHUa1aNXz77be46667dJsOWhK+0JQFBulIj46f9u/fjyNHjiAyMhKdOnXC7t278fzzz2PmzJnYtm0bwsPDcf78eUyZMgWbN29G9erV8dprr2HmzJmIi4vDiBEjsGPHDjRp0kSZXbPY0aNHsWvXLlSrVg05OTnYtGkTqlatiuPHj2PQoEFISEjA9OnTMWPGDHz11VcAgDfffBNEhEOHDuHYsWN4+OGHkZKSAgD44YcfcPDgQdSpU8ftOImJiViwYAH27dsHZkbHjh3RpUsX/Pa3v/Va5wEDBmD27NmYMWMGYmNjlfLi6aAXL16MMWPGKDF5UzLuQEjCF8IGfG2JB1NZ0yMX27t3L44ePYpOnToBAPLy8nDvvffi2LFjaNq0KZo0aQKgaBrluXPnKu/r27cvqlWrBgDIz8/HqFGjkJSUhJCQECWJl7Rr1y4899xzAIA777wTjRs3Vrbt3r27R7Ivfk+/fv2UeX369++PnTt3om/fvhX6Xeg1HXRACZ+I3gDQB0AegJMA/sbMl7xs1xPA2wBCAMxn5umBHFeYj9m6QMpjrdpoo6zpkYsxM7p3765MoFasvO6o4gQMAG+99RYiIiJw4MABOBwOVK1atcKxuu7PF65TOQNAbm5umdvrNR10oBdtNwH4HTO3BpACYELJDYgoBMB7AB4B0ALAICJqEeBxhRAW4Trd8T333IPdu3fjxIkTAIBr164hJSUFd9xxB1JTU5VpGIqnU/bm8uXLqF+/PipVqoRPPvkEhYWFHscBgM6dO2PJkiUAgJSUFPzyyy+44447yoy1c+fO+PLLL5GTk4Nr165h1apV6Ny5MyIiIpCRkYGsrCzcuHHDrfvF23TOek0HHVALn5k3uqzuBTDAy2YdAJxg5lQAIKKlAB4FcDSQYwtz6vf+boSYvLWfdjEHrRrU1jsMyxg+fDh69uyJyMhIbNu2DQsXLsSgQYNw48YNAMCUKVPQvHlzvP/++8o0yb///e9L3d/IkSPxpz/9CYsXLy5zWuWRI0fi2WefRatWrRAaGoqFCxe6fQvx5u6778bQoUPRoUMHAEUXbdu1awcAmDRpEjp06IAGDRrgzjvvVN4zdOhQPPPMM8pFW0C/6aBJrVvdiWgtgGXM/L8S5QMA9GTmYc71JwF0ZOZRpexnOIDhANCoUaP2P//8syrxCX2lZmZj1ubjKHD52mtmf/19IzzQvJ7eYZQpOTkZd911l95hqCY7Oxs1atQAM+Of//wnYmJiTDkXfnR0NBISEhAeHh7wvrz9jYkokZljvW1fbgufiDYDuM3LSxOZebVzm4kACgAsqXDEJTDzXABzASA2NlYGRFhE03o18M6gdnqHIUxs3rx5WLRoEfLy8tCuXTuMGDFC75BMp9yEz8wPlfU6EQ0F0BvAg+z960I6gIYu61HOMiGE8NnYsWNN2aIvSc/poAO6aOscfRMHoC8zl3ar3PcAYoioCRGFAfgrgDWBHFcI4RsrzE4qvPPnbxvoKJ3ZAGoC2ERESUT0IQAQUSQRxTuDKgAwCsAGAMkAljPzkQCPK4QoR9WqVZGVlSVJ34KYGVlZWRUechroKJ1mpZSfAdDLZT0eQHwgxxJCVExUVBTS0tKQmZmpdygiCKpWrarcyOYrudNWCIuqXLmycmeqEIDMlimEELYhCV8IIWxCEr4QQtiEanfaBgMRZQLw91bbcADnVQxHL1apByB1MSqr1MUq9QACq0tjZvZ6G7ihE34giCihtNuLzcQq9QCkLkZllbpYpR5A8OoiXTpCCGETkvCFEMImrJzw55a/iSlYpR6A1MWorFIXq9QDCFJdLNuHL4QQwp2VW/hCCCFcSMIXQgibsFzCJ6KeRPQjEZ0gohf1jsdfRPQxEWUQ0WG9YwkUETUkom1EdJSIjhDRaL1j8hcRVSWi74jogLMuL+sdUyCIKISI9hPRV+VvbVxEdIqIDjln7U3QO55AENEtRLSCiI4RUTIR3avavq3Uh+98YHoKgO4A0lA0F/8gZjbd83OJ6AEA2QAWM/Pv9I4nEERUH0B9Zv6BiGoCSATwmEn/LgSgOjNnE1FlALsAjGbmvTqH5hciGgcgFkAtZu6tdzz+IqJTAGKZ2fQ3XhHRIgA7mXm+8xkiv2HmS2rs22otfOWB6cycB6D4gemmw8w7AFzQOw41MPNZZv7BuXwVRc9FaKBvVP7hItnO1crOH1O2mogoCsAfAczXOxZRhIhqA3gAwEcAwMx5aiV7wHoJvwGA0y7raTBpYrEqIooG0A7APp1D8ZuzGyQJQAaATcxs1rrMQtET66zwZHkGsJGIEolouN7BBKAJgEwAC5xdbfOJqLpaO7dawhcGRkQ1AKwEMIaZr+gdj7+YuZCZ26Lo+cwdiMh0XW5E1BtABjMn6h2LSu5n5rsBPALgn84uUTMKBXA3gA+YuR2AawBUuxZptYQvD0w3KGd/90oAS5j5C73jUYPzq/Y2AD11DsUfnQD0dfZ9LwXwByL6n74h+Y+Z053/ZgBYhaLuXTNKA5Dm8q1xBYr+A1CF1RK+PDDdgJwXOj8CkMzMM/WOJxBEVI+IbnEuV0PRAIFjugblB2aewMxRzByNos/JVmZ+Quew/EJE1Z2DAeDs/ngYgClHtzHzOQCniegOZ9GDAFQb3GCpRxwycwERFT8wPQTAx2Z9YDoRfQagK4BwIkoD8BIzf6RvVH7rBOBJAIecfd8A8G/ns47Npj6ARc4RYZUALGdmUw9ptIAIAKuK2hUIBfApM6/XN6SAPAdgibPRmgrgb2rt2FLDMoUQQpTOal06QgghSiEJXwghbEISvhBC2IQkfCGEsAlJ+EIIYROS8IUQwiYk4QshhE38P5cIaietLyHQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[A_probe], \"k\", label=\"Integrator output\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows the response to the input by the integrator.\n", + "Because it is implemented in neurons,\n", + "it will not be perfect (i.e. there will be drift).\n", + "Running several times will give a sense of\n", + "the kinds of drift you might expect.\n", + "Drift can be reduced by increasing the number of neurons." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dynamics/lorenz-attractor.html b/examples/dynamics/lorenz-attractor.html new file mode 100644 index 0000000000..db341004ef --- /dev/null +++ b/examples/dynamics/lorenz-attractor.html @@ -0,0 +1,700 @@ + + + + + + + + The Lorenz chaotic attractor — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

The Lorenz chaotic attractor

+

This example shows the construction of a classic chaotic dynamical system: the Lorenz “butterfly” attractor. The equations are:

+
+\[\begin{split}\dot{x}_0 = \sigma(x_1 - x_0) \\\ +\dot{x}_1 = x_0 (\rho - x_2) - x_1 \\\ +\dot{x}_2 = x_0 x_1 - \beta x_2\end{split}\]
+

Since \(x_2\) is centered around approximately \(\rho\), and since NEF ensembles are usually optimized to represent values within a certain radius of the origin, we substitute \(x_2' = x_2 - \rho\), giving these equations:

+
+\[\begin{split}\dot{x}_0 = \sigma(x_1 - x_0) \\\ +\dot{x}_1 = - x_0 x_2' - x_1 \\\ +\dot{x}_2' = x_0 x_1 - \beta (x_2' + \rho) - \rho\end{split}\]
+

For more information, see http://compneuro.uwaterloo.ca/publications/eliasmith2005b.html “Chris Eliasmith. A unified approach to building and controlling spiking attractor networks. Neural computation, 7(6):1276-1314, 2005.”

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+import nengo
+
+
+
+
+
[2]:
+
+
+
+tau = 0.1
+sigma = 10
+beta = 8.0 / 3
+rho = 28
+
+
+def feedback(x):
+    dx0 = -sigma * x[0] + sigma * x[1]
+    dx1 = -x[0] * x[2] - x[1]
+    dx2 = x[0] * x[1] - beta * (x[2] + rho) - rho
+
+    return [
+        dx0 * tau + x[0],
+        dx1 * tau + x[1],
+        dx2 * tau + x[2],
+    ]
+
+
+model = nengo.Network(label="Lorenz attractor")
+with model:
+    state = nengo.Ensemble(2000, 3, radius=60)
+    nengo.Connection(state, state, function=feedback, synapse=tau)
+    state_probe = nengo.Probe(state, synapse=tau)
+
+with nengo.Simulator(model) as sim:
+    sim.run(10)
+
+
+
+
+
[3]:
+
+
+
+ax = plt.figure().add_subplot(111, projection=Axes3D.name)
+ax.plot(*sim.data[state_probe].T)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[state_probe])
+
+
+
+
+
[3]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7fe072b0a4e0>,
+ <matplotlib.lines.Line2D at 0x7fe072b0a550>,
+ <matplotlib.lines.Line2D at 0x7fe072b0a5f8>]
+
+
+
+
+
+
+../../_images/examples_dynamics_lorenz-attractor_3_1.png +
+
+
+
+
+
+../../_images/examples_dynamics_lorenz-attractor_3_2.png +
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/dynamics/lorenz-attractor.ipynb b/examples/dynamics/lorenz-attractor.ipynb new file mode 100644 index 0000000000..1a14408a8e --- /dev/null +++ b/examples/dynamics/lorenz-attractor.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Lorenz chaotic attractor\n", + "\n", + "This example shows the construction of a classic chaotic dynamical system:\n", + "the Lorenz \"butterfly\" attractor. The equations are:\n", + "\n", + "$$\n", + "\\dot{x}_0 = \\sigma(x_1 - x_0) \\\\\\\n", + "\\dot{x}_1 = x_0 (\\rho - x_2) - x_1 \\\\\\\n", + "\\dot{x}_2 = x_0 x_1 - \\beta x_2\n", + "$$\n", + "\n", + "Since $x_2$ is centered around approximately $\\rho$,\n", + "and since NEF ensembles are usually optimized\n", + "to represent values within a certain radius of the origin,\n", + "we substitute $x_2' = x_2 - \\rho$, giving these equations:\n", + "$$\n", + "\\dot{x}_0 = \\sigma(x_1 - x_0) \\\\\\\n", + "\\dot{x}_1 = - x_0 x_2' - x_1 \\\\\\\n", + "\\dot{x}_2' = x_0 x_1 - \\beta (x_2' + \\rho) - \\rho\n", + "$$\n", + "\n", + "For more information, see\n", + "http://compneuro.uwaterloo.ca/publications/eliasmith2005b.html\n", + "\"Chris Eliasmith. A unified approach to building\n", + "and controlling spiking attractor networks.\n", + "Neural computation, 7(6):1276-1314, 2005.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:47.905113Z", + "iopub.status.busy": "2020-11-25T16:47:47.904235Z", + "iopub.status.idle": "2020-11-25T16:47:48.400376Z", + "shell.execute_reply": "2020-11-25T16:47:48.399450Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:48.413102Z", + "iopub.status.busy": "2020-11-25T16:47:48.412233Z", + "iopub.status.idle": "2020-11-25T16:47:54.317250Z", + "shell.execute_reply": "2020-11-25T16:47:54.318820Z" + } + }, + "outputs": [], + "source": [ + "tau = 0.1\n", + "sigma = 10\n", + "beta = 8.0 / 3\n", + "rho = 28\n", + "\n", + "\n", + "def feedback(x):\n", + " dx0 = -sigma * x[0] + sigma * x[1]\n", + " dx1 = -x[0] * x[2] - x[1]\n", + " dx2 = x[0] * x[1] - beta * (x[2] + rho) - rho\n", + "\n", + " return [\n", + " dx0 * tau + x[0],\n", + " dx1 * tau + x[1],\n", + " dx2 * tau + x[2],\n", + " ]\n", + "\n", + "\n", + "model = nengo.Network(label=\"Lorenz attractor\")\n", + "with model:\n", + " state = nengo.Ensemble(2000, 3, radius=60)\n", + " nengo.Connection(state, state, function=feedback, synapse=tau)\n", + " state_probe = nengo.Probe(state, synapse=tau)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:54.325510Z", + "iopub.status.busy": "2020-11-25T16:47:54.323488Z", + "iopub.status.idle": "2020-11-25T16:47:54.720059Z", + "shell.execute_reply": "2020-11-25T16:47:54.720494Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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zbhd5xJiS5uMPLFg7jl4gGmctJ373zB0vBSCj+njvV05a/IOM59bQBkwMsaJFefGgG1x+wjZBCKt7KZpPXxDqh6jY4le9Mcqqk3+/q4+W/++9XzwGx/jHb80DYG9UuVY5x/3n+pjApEfyI1xYLzI55MXmF4QVteU5ubwJkjeYza3mygTcDnxeMQIwYq+QLdUo9UtcVypk97dWG3nun36NS2qMCWWVH/2/37F+nKwQM6xrwdbhsTD4Y4zY8wD91bokyXuHWFgvcmg0YP21fWJA8tuJh/9VH4dXrTf45HkPfqXM8//nx6y9PrsodLiRae7TTKKm9gmSn1FW+IdvW8zv55qR3zdOJhgJunEHxdYyomxS/1zJihZsLYcb8jjwOIVmPmgTpL+8lxpo5Kg9LY978He/yIIaw1dY4L988D5rx5CpncAo64UK42EPynWvoOIIsJgucdFqA5luSTvC+bUCM1Gfbvd8XaTG40ub2JkZupfX8hWifpeQebr8hOxisZZpnF0PSfJzz9AXriV1iDFlvc8d1SoE4pxdFaT+xidPg2+Y2+PimEmrwRxo5mRhcjWFSq2xY6kaGJD89kGu4h4RLfzrvRc4pwpd86yyxJcfs2AOJkkhPMPXjifwOG1Mz+4HRCRvWUmRbzZClWsNIj6nTgrPnBCXfPNqAJGTHwmJFnhcAXxopLCXtvjSjmD4oB6hXVRjTCsJvnLMotHb6PUQv5ZKQ2ExXeLVt07C0BzOegmFBvfNW1Qk5VaEEZY/xsJagakhn+iBAK4LVzm9mcYl3WZY5ORHgm7xf1eQgCIW65W9kpfXfZxm+MDXRY3skhojrqRZz2T49mkLRVNVFR3jvph+3WaH/eAbJqKK9/J0P2mzwhp4o3pg9MyDOzc4ZEDy2wVJgk9+KwC/+4lHOKcKRcassszPfchC9CfVGJFpTidyjAQ92LUGmBfNKATdDmtbQknygVEupYrcNjMk2uFdQUZdQpFxod/mJYOZ1Uq2TDwgScGPVxXH2lOt8F95p3gMjPDQgvgQL9nHmVUEwWetSAyzSxAc57NHhYqjWm+AP46i1hhzlfnB+ZS1c8ktg2+YfFUlma8wHfXq+eUZb5kLawVq/XoH6ekakX6LS5J3B/AjruOeycunL4jOXk+ID3xDkPyRa0XPQlxJ8Tf3WBBHFNdF+i44xke/LxaN6SEf+GP4qikA64s/iEjeN6yT/KgMmnYAA5LfLlzQNLcG69G8dwJVsTFrs3jxtYijEZri0UsZzq8VwOUDV4BpV45suaZvFbsitwyKnaIjrJGCT3zfN8S0R9xUfXc0GqYIrWRLjISapOCsF7EpeyySj2lFdEXhX+8V1+UZT7qdsFLAT5HPPrzY+xjZRQiOU6iIXdMP3Typd60+OVbS9dE9kU+Af4QL6+KaiXSNIPkJV4FaQ2Ux3ecCa0jXJLLlJsm7/Hjkor1XrmfqgqiVAU+aE+/bi558CyDkrPecsDDCT+92HdMtPFwOG/hiOEoiwMuW+uhY1WyGByS/myALKbNP17e5b3nutSihKW4LiJugZ2SUOg++GMslcVne8CRN8hUYIW4ThPCIlQEduWXwx/n6KXFOsqMRb5RgQ7x+PmlhsTBCIwW1bXsfQKnkGQ64904OF2D1uP6lXBDHp0XqbExZ4+jFHv4zjbqI5EPjPHAuBcB140EIiUacodoq3z+7Zk2RlFuBwAjnNXKZHvKByw92F3Gt6Nf39dQW7aorRKZUY9jfvJ7OWhG7Tdk76Zr0gt4AtVao8NT9w9hCYpc9aU8xNWRBn677+Y8ScDu4eUob5egfhkqWp80GONlXumYdfMMspcVnZkDyuwHSCjZ2kIcvCAK4eToCQ7Mc9qQAeuf+NI38CU0t8cqbNSMkp59o6hEcNoVjVqK/nPA5kZHFC67T/Md9UZyVFEGPQ+wS+oFGCjlbkHKtwUiwmZOnkmU05N472mppTKZp45P5Ms+7dgRFcx+8NVzgI9/rMfYtnxBeN8FxXRXlsNv0SP5ZE+J3WNqZ5TWSXzNE8ooC3igRRby+b4M4LSe/VhcE53PZxfddAZRKjljAtXcWbY3kVVXlTCLP/rhf7wZ/xX4bC+vF3ukuTczQ8I9SqTV4ygFtZJ+2ozoSqfWnmCok9Zx80O0g4N656VDbQvKKovy9oigriqI8YvheVFGULyuKclJ7HNqO33XFIjUvDMWcXh5eSGFT4PqJEISniNXFdvD9X+vRLZm6AOFpXfd8ndTOpi+grJ/lQDxgbdRebhkCoyQ1SeMNk9pxvFGU4jpzw37m+yUFLZJfqQpS0NM1Lj9U8owEPXuHFGRN49afpFStczqRF9dS8wIK18TPc+Uu23Pdz1/sxmZkykwb+HHIL97/hfUe10FVxaLtjwunSLdDFNIBvEN4a2lcDlv/i3YxBa4AP1gQ95rezOYOQEXUg/ZEg1spI1xdw1NcSpdIF6tcMxIQ5Gxz4C0J8v5Gr45Vzbp7sR6iUm8wG9V8ZrT5rCfnRf+EpVpXtSSmtvmGWEqXGA3vXBQP2xfJ/yPwkg3fextwl6qqB4G7tP/vXSRPCztZ4OGLaQ6NBvG5HBCewpZbxE69u9RNVbVIfoaTy1mG/S4hawO47pUAjIXc1vKH2vb+YqpIyOMgKFvUfVEoJJkd9nGu7+19GmwOloviljEWXinnGA25904Od/FB8Rie4vhSlnpDFSSvRX8/clC8Bw90i9y0+atFryB1vdnF5QNXkBgiku65xa/koFbUI/npqA9F0eR2PrFoz0Z9zFvZERihdS9L5d4PyV2jy6+R/B5Jv0kpbHhKNwI7PBbUBumMccuQ+Bt7NpTlVsDp50JeXHt90dYmO/36QfG5tNQrUtRUVb5hljIlxnYwVQPbRPKqqn4d2KgHexXwQe3rDwI/vB2/64rF2ll9yMeJpSzXjmleLuEpUBuMIgih47YwnxD+JJEZvnsmyYG4oTkiJtwP5y+JaCJd7KLsaDR0W9pLqSITRj8MXwxKafYPuVlYL/Y3zUlzoExozVXNwmsQ6mVG/Q5hXLYXJkRJe+jgmN4dfGg0CE7xYTx86u8B+I1/e7DzMbQc7rmqIPdX3Dze/FlwFHdxhbGQhxO9NO4GS4MLawWmjflj7xAU1piJ+riw3qdRmeZDtKpdz+GAFlC4AmJntlcWbfn++Yb54qPimkwPaQQdHCVYFRF8z5rGuW9DIM5F7X2eiGjErKX0RqMRAGu9D/L+0tI1O5mPh53NyY+qqiolCEvA6A7+rsuLakls5yIzZEpVLqVLTXLVCj4vmRZb+2+f7uBhrWnkG6Ep5pMFasaBFX6Rx/2ZWwXxf6Wb5r6UEiMDA6N85dhKa67PL7aW1wTL1Btqf9OcCqvgj+nF47iekxfb1gl/XZgu5vpoCLlSIVMt8Ws5ncjjstuakRtgq1oosGUvgc3BI+tiF7XPaCMbGIPcCofHghzvZTGhkZTqj3NyJddyHviiUFxjasjbO+2zEZrNsIw8jYVXaiVG/Pa9sWhLjXx0v74z1gutwXGU3DIHRwJ89qgFtVS9xnwyj92miF4F0O2fo07xPp5JWNhRaSKNujfKSrbMWNht/e/ZBJ6QwqsqxN2mAm9FUd6iKMp9iqLcl0hYSEVciZDbe5tdz5mPyzyblpN99X7x5x/vFLlpA5/P1gQRS9kdIMaMAc+fEsfoakilkULNJ26++4wpBe2G3OcV5N5XXj6fBF+MlWwZt8NGyKMtHi6x8Iy7xSK2JxQZqXmxsDq9nFrJMRfziaIpwM0/BuFpXnHTOG6HvfMxHvpXaNT4m6+LLuXZYSPJj0BuicNjQU6u5Lq7SGq+NSsNsSMYkik8EPngQpKpiJdsqUaq0McCq43+S+bKhL1OIQcEkZMHxn0NVNWgzNqtOPrv4jEwwt9ojVB6uiswCtlFqvUGqUK1ew9Kbhn2P5szq3mmhryG9ysINgfu8hqTEa81Xyit5pNSwtQb6u5I13TAsqIo4wDao6m/raqqf6uq6h2qqt4Rj++Mn/KOY10zrZp9Go9o0rqnHtA62DRFxvX+DAF3F1WLFsnP10S1/l2vubH5M21O60RReM5c6qaJ1mZXJhsiXfQLzznQ/JlPnNOUS0SifeXlC0nwRVnJCI28/kHR5rzG3SKFtCfyuKsnIXYIgNOJnCjUSQRGIbvELVMhLqaKnWWxmo2tHKPXsqMKjkF2mUOjQSq1RvdUQVbs2haq4n2+cTLc/Jk3Co0ah8Qt059RmSFdo6dqQN+ZjXvkor3Lr6emZsJpIpMMjUNxnemQoMGHFzrIYht1ffzi/Gq+dVemKGKxza+yP+63JmXV3EmXauK+2s3pmk8Bb9K+fhOwifEpuwQZbXs/dpMeLehbQncAfMPY0ue4eTrMA+c7FOtS58Ed4iGtyH/QSCyaqsN27ptcOxakWOliSaBJ4y6pYkewzxhBapF8WM3gstv0/KIlaOmalm5X0E2fhh17xKRMVSFxHGIHKdfqnEvmW+sj4SloVLl9WJCg9AJvQ3gGbvxRVBWCG+VxgVGo5rkuKj5+XfPyuSVQ7JzJCyKYHTakazSrilmveM8te+GAXnhdzZWJGa+ntjOL75WdWSEJs0/X+xGmoway1z4Pv3xnBNiw6zUitwJqAzU4ztnVPHPGz5Q8Tn6VmaiPBSufqcIqKDYulsU1HdsN6hpFUf4Z+A5wWFGUBUVRfhZ4N/BCRVFOAi/Q/r83sXZGfHDdAfbH/HicNmHeJRHdD2tnODIe4tFLGXPfGE1Z8767RLQeNA5t8EbEY13Iv7puCTWp19mqIIAn7Ys2f6YN9bYVVqnUG/qC1BONhmYzLHKIukYe9Eg+bBc3965vhc+vCgKMH+ZcskBDpTWS12os1/mETPYhs+ivXoX0BWrhWQCec+1I68+1ASQHvDkUhe55+cwlCI4xv17CYVNaB0to1gajTrE7PG81/VYrC9WOd0gjeUMkr13PYadI06xmd3m6Zv0cRGZZ0na/P3r7dPNn2s72UFDcs+/9SgePf21XlnbGKFTqQmdvhD8GhVXmhv2kCtXeabPsEvhi3H9BpF13RbpGVdUfU1V1XFVVp6qqU6qq/j9VVZOqqj5fVdWDqqq+QFXVPTgfTsPaWUHkiEj2OYc2fKij+2HtLJmiiI6+8IjJ4F9NI+9x2nA7TC6LzQl2J9eOBVlYL3b2TskugSfC2VQNm2JQAQB4IqDYmzpwqyhnhFTMFyWRLTeVNaCTgquaI+Jzsrjb/Wu0dBexg3qDUUs+XSN5T2GRQ6NBHrqQaj/G+jlA5SLiPmhszLlrKQRPWRBD10U7I8Y4LqaECsNhdE3UGnG81TRjIQ9nrabfdG+juLiexkVbS9dEpH10fhcv2tWiIOjoPhZS4lrePmto19GG8wyVL4qndyoya42OF2oRgPZI3heD/KoekX+9l+Zesyy+57i4DsOBPVB43fNYOwND+6jWG1xYK7ZuqQGG9kF6gd94nojs/uOBi60/1zTyJf8EpWqjNecnMfd0yC1zeEykRzoSgzbA+0wiz0zU11octAknQ/Kr/PTT5/A67dYMzzTztYorTLpYbVoagE7yVHKMh727Pyevk/xh3d2xJeUlpws9/O/cMBnmnhOJ9vdQm+t7rCrI/K3PPtD6c63GQnaJQ6OBzsV4gOVHwTfMf/zgYns6Rks3kE9ovQ8WI3ktJ1xxD5Mp1VojeS1d41aL+F12PQLelZDKmqE5vUmpRZ0UEZ9HqaYqVTuQvOZb81hW3Adtn08tXSM9or57poOCTiJzCUIT7I/7mRv2Yd9Bm2EYkPzWUSmImyC6j0upIpV6gwPG7T1oUb7KaF0U0dp810spKGdYtglS0O0MjAiMQXZJn6X64e92aKs//llYPcHpRI798UD7z7WoYybqo1itW1NPaM0baVXc3GbpGspZYgHX7u+SXD0BTh+EJplfzRMLuAj7DKkzj1b4PPklRrUdTVtDkzby8URZpAOuHd8w/zbYJPnDo0HmkwXzAR3S3vakmO/bFjwEmiQ/N+y3XkjXJHzLNXE9vS5DzUAjeco5xsK7vItZW2wZmuM/fyACq3Fj/tsTBqcfMpeEBQkdahCp82Bz8LYviR34xMZZrMFRqGS5MS4Cqp6yU43kHziXIuJzdX/uNmBA8lvFJW22Y2CUB7Wtu95sIaGlclg7ww2TIR5fyrZu4deEzG5B859/xU3jtCEoSH5KS7+Ypnw0qH4x2ODAxtwh6PnD8bC4US+lLERqOa2bjwhA07EQWkg+HnSzuttz8qsnRPOZzcZ8Mt+aqgGhpph7JkzcKhqkgP/2sYdbn3PpAQAeWHNx7ViwfTCFdwgcHshc5NBYkHpDNfeE13ZQ1dv/CwA/fMtk68/dIZHGKySZjflYzVW6Wy1IaM04ibog9LDXsIhpEkoqWUaCHpK7OV2jfa4Y2kdKm+bUku5SFAhPQuo8v/Z80XBouqtKnYfILBGfuO/bIm+tE9quyV3/7b4uE9wqBRHUBcdZypSekOlbA5LvgYba4CPHPkKh2mErvPyoeIxfq98gc7HOJB/SCqotTVFaiuDuNZFjndq4SIBQ2DSq2IpJnnkw1h7VAZQFUaRv+lnKtUarKkTCH9O392DRvVAr5i43tOn0RpK32UU0pJF8Iltuz0FfQfinx/6JDzz8gc5PSJzQ5ZPnkgXz9zl2CFaP80PaYtzSuAbw0D8DcGJ5g/xSQlFEXj6f0Hdmpuk3LU3wqON6wORaKYpuVSHzxJaieS2SX22Ic2uRZeqLdo7hgGt3N7etnxX3pj+G027jOYdNJNqRGUhf4CbNVdKU5DMXITyJ3+3osMvW+jyzSzx5X7RpR2IG7ZpW/OLeefahnZeND0i+B97ypbfw7u+/myd/9MnmT5j/unicvI1PPiiq8G3Vcl8U3GFYO8Nvv1SM8/uPHxhW+5VjYHPyj4+LCME0Rydzwenz3DId4eRKjkJlQ9Sm3UCX6uKGNU3X+OOQT+p5RUsdeppW+6Km1W4pvIIghnKG8ZCHSr3Bej9NOU8gao0af3Lvn/C+H7yP1aJJcaxSEE1psUOUqnUW06X2IhuIge2lNEo+we2zQzxycUNzms1BefIpXEqXuMNY6DMiMAqZS8xp1+H/3HWq/TlauiHjFRH8W561v/05ngiU0nqu2ZLCppAExcZSRVzHFlJy+sRMhHKWWODK3pn96b1/yl3n7ur8hOQpGN4PitJu8SERmoTMIsMBN2Mhj7lWfu0M1dAsC+tFDpvNYtUiebKLPO1AjPVCpbPMOSPSRmt2kco7OBo0f942YkDyPfC9pe91f8KxT4tHu1OXD+qNQhKKAkMzkHicG7WIoaX4mjhOLXqAGl3sRjUlAKnz3DwVod5QeXSjI6W2PT1dE9GBabrGF4NyGo9SYzLi5eyqhQaa3BJ4oyzlG9gUQwu8hDsIpQwxLcK/UqO/85lmHeP3vv177U9IagPOY4d0C+COkTzA6nHdXvbzsi2+kodGje/ZbgHgBmOUbMTQPlif11M5Z8wMxjSSf6QwhE3BfGfmi0JhzbAzs0Dy2qSpS5kqLruNYSPJK4q2aIudWbZ8ZQ70vnfpXj742Af5ta/9WmfxgGYaWKzUSeYrTJjp0UOTQm1UKXD77BCfeuhS68/LWSgkWXGKCN6UlA01loOjAVSVziMZNyh1WgrBO4QByfeBXMXkwrkCcOSHARiPeMy3cyBmhs5/o+Vb+ocn8TiX7CJa+60XHTJ/vYzkUxe4aVoQR5t8LyU6bx8pRIj4nObbRk0rLzv0TMllI7LLEBxjJSMaZ9p2Gp4QlDN6U82V2hB1ItXUQYdcofYnrDZJXqY99sdMiFUn+RP8408/CYBf+MgDLcf4ejICGOyiN2JoTkR1teaCaKrScYd4eNXG3LC/tfdCIjACuWWCHiexgMvatCltLOFiusho2N0+RNod0kkerszehwvZC/rXp1Imu6BaWeTSY4d5bFFE56adpcMHABVS55nUGhhb7IJXHgfgtCo+n7rxoBGesKix5Jb0JsaTK53UbyK4O1US98WA5K8A+Bw+5kJzADyWfKz1h6W0aCqZuIVqvcG5ZKG1G9SII68Sj9USf/LamwD48HfPCb/r9bPcdVFE8W988qz5670RkfJJnWck6GE8bLK1fOhfAPjbB8Ri1LajgCbJF1bZH/NzJpHvLaPMLUFglESu3JqPl/CEoZTWf9amHrpCcCknorQbhm/gTNqkESxxXKQqhg/wpUdFiqqlQ1IiNAGuICRO8KyDG3KqK+Ie+dq6qK/4Ow2DGJoVvQeZBX5VK/q1SSTX52FoluMrOb3I24bAqIjMEaZxlvzMs0J7n8xVWrtdJbT0myTFK/F6LuabhmKPrD7S/oTVE2Joy8h1+mjESbMJUFrfA6nzPOMa8dl4/z2GuQ8roub2QGkCn8veLqoAsfvRhBGzw34cNoWTyx0i+ewiuMOczYDbYWuVI+8QBiTfBdVGlUKtwJ1jdwImEcPSUfE4ckT3rDHjVaBZnDn9VV51q4j2/9dnj8FFMeD7MVWQe9eijVYkArhpKtzeUq8dS8WmqwnaYNBW74v5yZVrvf1JcisQHNMaZ8xIPgKltN6N2ZddwhOIlcIKfqefW0dv5XTqdPvi9r3/K4jX4Sav1TtMJW6KIhQ4q8dbouAHzq/D8qOodhfz6hi3aLI8U0iNduo8zzooyOWrj2+wd1qfpxGZ41wyz0GzXDCI+6qUhmqRm6fCFklejCVcypTMgxItXSOvdVevpMuEc5lzjPvHcdvdnE6ZDOORPvKxQ/qO9/pxk9SZZg9O8hR3zIn6yUeNU7+WHwNXgH8+rjI37G/f9UgExyG7hMthYy7m7zwnQJNPnk+K2QAdj7eNGJB8F6TLgrgPRA7gtru5mNvQxLRwr3icvlPXm5vKHwFuf7N4zC23NCjd+4UPA/Ct+g0c6vRBlohM60ZmN01FmE8WSBvJ3Bul6hMyzB+5fcr8GD6Zrknq0WHXkYKNhra9H+Niqmjus6FF8h6nnbDXecWaWq0UVhjxjTDuH6dcL+vXV0e5+T6s5SvcabSE2Ij4YT01879++AYAXvPX34aL9/NAdZYaDt7xius6vz4iBkuzfo7rJwT5fO+soSm80YD1cyzZxmiomKdqoBk85FZYyZZJ5ivdLaRrFcgnUANjXFgzVw8ddTk4X07pzT19WVI/QTifOc9caI6Z0Azzmfn2Jyw/JnZlsUN6/SvkNdlV+YZFkJI8KYb8bMTKYzTi17Kcq+p9EabQjOtA1MK+3MkOPHMRQhOcWys8IakaGJB8V0gSiHqizIZm22+m7/+dePQO6YU6U/kjQFz7wN/zJwD8/g8JWdyTEh8H4BLD/PvPP637CUVmxBa+0eDmqQgAXzuhRX/1KhTXODUupki97MYx82PoOfkEh7T8Ylf3wuwiNKqU/JOs5SttuvFqo8qNa3dx44gXVFVMFLpCTa0ShQQj3hFsirjt71++v/lDTUHEbT8FiALmnFnRVSJ2SHxgS2l+4ikiKldowPnv8EhjThxqpsvEy9CksJhIncfrsmO3KXz2YYOneW4J6mW+uizOoeOuwEDyL7lBXPOu0bymwMq5RynXGq1eOMCJ9RO8sXGBl7vXCWizR6/EdM1CboHp4DQHwgfMI/nFByF2GJweDo8F2R/zm6cvFUWTxIoF+8fuFIvv146viGa05Ue55BaqpufLWclm0CJ5aNoUmNamMouowXEuDEj+ysB6SSgnwu4wU4EpPaerI9OUQZ5O5Bj2u8xz1iAsBUA3O3rT0+aQFvsl1QkorZ2VZogdErMh0+e5bTYC0BwerG1Pv3hG7CiecU0H/a0nLBpo8gmG/S6GfM7OSgDQi7nLdkEgG2/ML81/Sf86nVtiNORheQtdkuX6zu0CMpUMIXeIp02IxTRfMxSdHxGLLTe8lny5RiJbbm+EMmJc1FX0ZjjgmTaRvqtj5859UXNSkbCL0ZCsC0XUNZpyRh/+sSgarL66KO6Jp+wfNj+OtNLNLfE0bbj0qW7XUxtLuKKI524MSr56/qv612fSZ8SEqE1ez1QpRam2/QtEQ22QKWcY8gwxHZxmMb9IrWGQE6uquC4TtwKi30Gq2kzhcAtRhKrqNZg3/8O9otZRXOMDx8X3XnpDh8AJ9K5XyllefL143oPnU63PqVcht0zJO0auXBuQ/JUAGclH3BHG/GMs5w1bMBn5PennAKE3N/WcMWJa09prnhrzv74PgG8f/E3m3/3y3ickVR1rZ/St5Ye+c04MnXj0PwH4cknsGFxmJmcgIhfNv0ZRFK4ZCXSP5DWv/HN1sWhsvDGNBczHV37AiNYQtRnc+MEbuePDd/Dhxz68qdf3Qq6aI+AMMBEQNZGWRVuzImD6yXrTkalGXmLiNkCBc98BYP7dL+dJtuMA/EXtNfzrW57S+4TSF/TF5cfuFOqpd3/+8ebPgMcaYpfQ0d9E12gvMRH24nXau19PTd1xoR4B2ouRxhTWJ05+grGQh6VNRPIfPfZRnvmvz+RJH3lS36/thVw1h4pKyBViKjhFXa2zXDB8NjOXhCxSE0QspovMdiPU+LXiMZ/gvzyj2YtQXxJF1+OquDZdjcT09Nu83gD37i883vqc7BKg6iMhd9piWGJA8l2QKqcAGPIMMeIbIVvNNjtfl7RW9utfDcDZjcMEzPDSPxaPn/oV8fh+EVE+74febO2E9CKR2J6+6IjYPv79N8/CN94DwAU11vs4gVG9i3Uk5OHe+S4DqdfnAYWT5QiAnqeVWC2uYkcQ0OOrjxIPCZK3ZHxmgDES++N7/7iv11pFrpLD7/TjtrsZ9gy3KDR48KPifXF6+YyWNjFVY0j4ooAK9zQdtH/F8QmYuJWH3v367lG8hEtTzKgqP3KHIBL5u5n/BqrDyxJRft6sCUrCHwe7G9IXsNkUDoz4u5O8ZsZ1WrueG//Gi7mLzDrEeVVrJW1n1j/J/9H3/0j/+uHEw12e2T8yWu0k6AoyGRDSxotZQ71MTmobv4XlTImGauI3Y8Thl4rHiw+0BEf/56MicHq8Md3bREw3O7uop8DaroO2azuaEyR/Y6ceim3GgOS7QJJ82B3G6xAX7tGkZmPw+GfF49iNukJln1nzkRHjt4jHM18T4/QkQh2KtRshmy6+/r8BeKeW1//Dzx2Dfc/kPKNkCPDnr7+5+3FCE3pTxrKmnOiYskmdg9AE86kaIY+j1ecEEQ0fCUwzUqtxbP04I0HR9dpR3dMB57MdDNe2CfVGnUKtQFAj1jH/GEt5zf+nURdpMJ9IYZzR3ouehXCJ5UfhgQ+Jrw3pm56QBmNLD7dMjsqVa5C+SE7xAUrnoiuINGBkWt8dXhMPcLobyT/2CQDOZGxEfE7dZkNiKb/EjDvKvkqVxeyCnq7pd9E24vj68U2/1gzZitCgh9whfVfWIoq49ANRdB27kW9qtr++TlJWgKjYUfMvbwTgS7/+LAB+svEJAFIE+c5vP6/7SelSzHMt325537Tmtq+tCnIfRPJXAFLlFG67G6/Dy5HhI4ChIUqTK+IJ6Y0zXbf30Kqv/N9dorNer9d00TI6UWjA6a+Sb4jt5Ktv7aCsAV79yVfzi+qSXhv4/14gNNodLWW1oQsX1gttUTzAo6uPEnAFua5S5fHMvG7psNin7O5sSstNR8RuJVVK9fX6XpD594BTEHcLyUu53dN+FQCv085kxGuutjDiR/5BPL7/ac3d2ev7SDU99+3iUSv6yZzvLb/3Wbh4H6uIwu0vPveA6csL1QI3fvBG3h8O6CQ/F/NzKV1ipVP0rRWdz3co/F3IXmDSG2O2WuVc9hxjulWF9UVbVVWCziCvP/x6gO7WA5tApiIi+ZArxIimJlspGOSnlx4UKRiXT4+mj2x0AjViSCN5VTQnHhoNcjDuZ1gRi0ks4G51XjWDLICvHAPgd18u0qb3G6dNPfxvAHzxooObpyPtxnU7hAHJd0GqnCLs1lZdv/gAJooJXd4mlRjSL8RSIeW/blAC/I8+Z6lIKWZVyNre/ZobebbtIQAKuM2bdzQ8nHiYU6lTfKO6SqqchkqeGzT53oNmwy9ARCZDs5xPFpojDQ3IVrOU1DqHKhXmiyuMhkVk2K/sTub2f/6mnwcwl8VtAXJxDrhaSV5VVXjkP8STxsRc3YX1Ytf3UccNr2n/3nWvtH5SY1rx9uM/C8Cfv/4WAK5RxAL88cKt/NRTZzsODH/Ll98CwF+ra6gayUu/oo7X8+L9cP1rOL2Sa0sv1ho1ctUcUU+U6VqNU9nzula+n5RNspQkW82yL7wPr8NLqb69xVcjybvtbobcQ02SbzTgwvf0XbOcumVqCSFhDL40s7kvv7JZV7rvd1/Q+6QURaTOzn0LaBbK3/6fR5vP0Trea6qdfd2UW9uMAcl3QaqcYsgtoqlh7zAKiiD5hXuFplortlzQFBFmkW4b/DH45fuFTcFvLwgXx35w4PniUVNfvOHOGX4p/G0A/rj6Bu75red2fOld55sR1V8OheHSgwxpzVf/+4smW+paGTKXqAanObOa1weWSNQbIvK5fvh6Zqo16qjYXSJy0VUiFrGUXyLijnDdsIiAtp3kqxrJy0jeN0ahViBbzTY9a0ZF+uvcWsG8s9EMP/Wp5te/ZtJ52Q2xgy3/9TjtxAJunqGpdD5ef1b7wBEDHko8pH/9MVsBKgWed+0IigKPm7kpahYKFdXGpXSJwxta9OVCGPJESWtqMLsrBfRH8lKgMOGf4GX7XmbeeLYFyJy8tKYY9Y82C69LD4vPpmaZXNccUXvWSOScgAc+KB4/+jrx+PPfMH++AXefv5u3f+PtYkdQETvGI+NyuI8hdeYbJrFfBAYvONJFjrnNGJB8F6TLaSLuCABOm5O4Ny62+J/+/8QTxkTu+8Ja0TRf3RGxa+DXH2nauvaDadF9y3/8F/1bTyqK6OHf3vWbXTvo/v6Rv9e/vt/j1hUcMkJv+yCmzgMqF7QxdtMbInmZAhkPzTBdFYXTfH0Zt8PW31Bpmo1Kk4FJHDYH8+n5vl7fC3okL0lem860lF8S9sIHngeKQk6XT1ok+f3PhnemxT9pItcPZKpAw72/83x+1/kRAF78tNu7FwwNOO10QfoCAbcDVYX3fNlkXqmWlloYfirQ7sMic91BX5znFMT1UxxCbdMPyUuHz5g3xsGhg6TKKZKlHtOS+oDc9YW0IfIjvpEmyX/rL8SjFgwtpku8/EYLNa/Xi/ecz/yakDpKSKlsB6iqyq/e/at8+syneXOgLvoQqiVsNkXfDebLNeHhX0hyrCDOuWsPxTZjQPJdkK1k9UIdaBFDflkYcgFcI26kM6s59nXbDm4nZPFVjjY79+3mz3pEKx67h0NDh3DanAzXG8KuAHT1RhsxP/oJAI7lxLb+SXOtHaCSOIOeIcY0B81EMcHkkLd/ki8KknfYHEwHpzmXOdf7RX1Aj+RdzUgeYCk1D8tHYUTUXB7Xun+PTHQwFtsmnFg/QUNtwM1vEN/QCvmKwQRPFtbNIFVeb7lJpGy+5vPqcleJNl//hJD0PVYXO9CNO7NMVVOt+EeZrolF+3RW7BaOLXYZUbgBiaIYMhPzxtgfFvfW2fRZy6/vBWlO5nOIhXjUN9qUNz+qpd6ueT7VeoPzHbp627Dvmc2v/6emULvhR3q+7NuXmp+/+8sJYYtx7psAvOd1twCI7tcfiFrNpy+InbPVxXs7MCD5LihUC/iczRtkzD/GUmFJ5PxGb9RTLfOrhR3PsT26+iiJwoYB3Pf8CfyDJv962Z92fX2pVqJULzETnOGFsy/kvNMBp74MwK1aVPHphxZbX6QRzu89KLayG3PyMvILuAKMOMViuFRYYjLiZaFP/5pkMcmwR+QxTbuLtwiznDzA0rmviSdoLp9ycdrJRpUbP3gjr/3Ua7n5QzfDta8Q39SUHfyZptl+1V91PcZCTkghD0Y0czOnQ1d2yBSP3g0toXktfT8TIeh2tFnv6pF8YJxZbWdWaYgI/hMPbrD06ALZRBj1RnX1y7HkMcuv74W4N07AGdBTMKO+UdbL66KRLjIDU3eC3cnCepF6Q7W+K3vWf239/2v+tudLjCQPWnvjqtgx3a59rn7tXx+kuiC6q79Yv8PauWwjBiTfBcVaUZdOgriZlvJL4kIuiw9MqVrnUrrYvTtyi1jKL/GGz76B5/378yjWivCb2lb87j9sPunOn+t6jEt5UcyTW+hFh4OcVgiS2/Y/3ti8kTwFwwdZ1Xbqjg1qAJ3knQGctRLRhspKYYWpIV9fJmWqqrJWWmPYK0h+X2gf5zPn9Zz/dmBjTj7ujWNTbCxlNMvam0QO9nQij02xWF/ZBNpSYmM3NL8+c4++sHLzj3U9zkJWkPxUcIo3HH49wXpDj9Sl4dknfrChQ/ubopfiB4sVrp8MteWpmzuzCF5XkJDiJF8Vc27Niu6dkC6n8dg9uO1uJvyC5KUceTtQrBV1QQTQVNisnhA73IMvBJqGb50kqLVGjffc9x4qdc3u+Xm/2+xd+JUHLNXLqo0qfqefa6NicX7c5YQzdwO0pE6dxz4BQIYAH/+Fp1r8S7cHA5Lvgo0kP+Yfo1grklMUeM5vA6LAqKomE9y3ES/82Av1rz9y7COihdqIH/l7ekES8rOmnqXLFE+5xNbRSN6VmjbKTlXh8c/Q0FQ8ZppxSZxBVxAaNUZVO8v5ZaaGvCTzlfbJVZ3OrZql2qgS9Yh00Fx4jkqj0tqstEXkq6J+4HeK62S32Yl6oqytnxb+6T7xu0+v5JiJ+joqWraKjbnpry98HZ7xG+I/H/qh5g96EIzMQY/5x5gKTpO120hri/ZTNXuDlgEY2uLSCIxx9GJaH1xthB7Ju4LgHWJEcbBSWGE1V2mfftUFmUpGL4o67U7G/eNNueo2oC348ovPw/J5bUrbiCjey4Hazzk0YnqcW//pVv7h0X/g9g/f3vzm2xdEfWW4c8HbiEu5S0wGJnnrTW8V52B3wIkv6D9/5PdfTJzWZsPbZ7sY3+0ABiTfAQ21QaleaovkAZYcDn2bPb8qcqOWt4RbxF88oBWWZLHvnWm44bU9Xyd150OeIQ5ExA18yuWAovi+JPE/+7KmstE6eks1QQ7vevWNbcdsIYVrXsCoKshHdvxZlVEazw1Euga2V2EjZXweezNFEfPGWC2stLhPnk7kzMcmamioDbGb2iROrItd2BsOi1z83RfuhhdsmFL1jt5FymQxiYJCxB1hKiD6Ii42tGKpIULXB3tr+u1vqeI6+k16AKQ0UZJ8vCHUZM88aKGLesNxZFEUYNw/ru8ktwNtwZdWX1k5/hnxjdmnA8JqJBZwmXpCbdxRtTmSWsQ3Ln6DmDfGneOaHXlYa3DThqUH3A6+NiuCsFeXf5/H/+dLNvV7toIByXeANFbaGMkDzLtcuo+MFZ+TX77rl3nfA+/bqVO1hPWyiCYi7giTgUlcioN5pxMuiYlG//oWsYX8m3s0Dxetc/PXUiKNYRb5taRAvEOMVipauka8Z1bz8kaPIEAf0rKdxddyrYzD5sBuiJBjnmGW7XbYJzocVVVlYb3YNR9/84du5s6P3MnT/rmHY2gHnFoX+dq33vzWlpQD70zD7yzB7ywL87IeWCutMeQZwmFzMBkUrf0LxQRUxX377tcIMv/Id7X38Lt/DcDfr98CwC88pz1SzVayKChit+OLEq8LT5ina8M09AWjB4yRPMB4YJzF3PbtyjaSvEzXLCc0Cau2KzvVZcHeuLP43NnPbepc7Iodr8NL0BVkwj/BiSmt2/w7f6k/x6+5nf7n//rl7t3LO4QByXdAoSYidDOSvxiMgUOkOuaTecJep64334gHVx7knoV7+MDRD2yq+CRNtN5w+A06KcjUQz8wRss2xSYUCXa7rrc3nv/8ah6WjtJweLinIW5as+68tu19uUCqnCIWEpGkZZKvCJKXxBD1RHHanK2mU1tEuV5uieIBJusqlxx2ka5BeMjnyrWO+Xg9d0vzb+8Xp9OniXqiDHuH2R/ez5mUYUKV0wtOa63uyWJST29J/5YFpwPOC8O0H9W8cP5IGp6d+CIA32wI8u90PQOugLBi9g0zUS2TKCQYDQlistr7kCm3kvyEf4LlwnKrU+QWUKwV8Tia71PAFcDv9Iv72YAziVzHJii5o3r/C94PwA+W+7Cj0FCoFqg2qtwQE3WV6dA0FxUt3fmNPxOPxz7TfIGFxXsnMCD5DpBbciPJx71iK5b1NXNq53r4jv/k539S//p1n3ld3+chO/meOfVM/uBpfwBsTo62Xl7HaXPqsrOp0AznPX49kgf4jReK3clz/vRrcO/fYauVKOPiD199g9khyVVyuGwuXHYXBEYZrWn6YnsWh02xLKPUvUg0YlAUBafNqX8QtwOlegm3vdVFMG5zkbHbKT/tl4Fmd+S+mPn1/PrC11v+/5VzX+n7PC7lLunplf3h/ZuWFiZLTTVS0BUk7Apx0eHQzbmMhlrvv+sY5MV9VMXBC64zz1FnK9kmOftijBXzwu3RL3YH560MCcckXRMYp67Wdf38VrExjQow6o6y4rDr1hTJXJn1QrXjZ/PTZz4NwI0xseh9fv7zfZ+HrK/EvGKnMxWYYsHoofPOMPzrj4uvf/G7fR9/uzAg+Q6QJG+MGOyKjVi9QdLdvHHmk/kdVdbID8aIb4TpoIjOjEOMrUJ278p87URggiW7DR77pN7K/SvPEwXZX7ELrXFCFTuHN2qDFDYiW83qkkRC44xo2upEcZmJiNeywkbvYDQQQ6FW4FsXv9XPn9gV5Vq55VoCxMvi/Fa1QvaHtdRG1G9uKfv4moiK//r5IvXx2TOf7fs8lvJLeqFwf3g/6+V11kp9WlugRfLeZrAxFZxmwReC89/Tv/eFXxPa76mv/VrLa9+j2SdsREtfiH+YkbLYMbo8Ii133spoQdrTNVJh0zaPYZMo1Uptu7IRm1tE8kd+GIBHLol7qtOuTBb1w+4wN8d7GPp1QLLYSvKzoVnWSmuk3/Tp9iePdJkStsMYkHwHmOXkyS4Sq9VYdYhtV6XW4OJ6kbkOyhrZsPKsqWfpxcR+CVpG8nFvnKmgiADPZ/p3bFwvrRP2NHPAY/4x1pQGFdDb+hVF4d/f+lR+0/kxAF5W/iP+8xef1rElPFfJNUkhOMFoXUgeZfHV6vY+WzWkfTRISdp2wTSSL4o0UUJrApoIi2t9UwcL2G9c/AZ2xc4zpwR5to2D7AFVVVnKLzHuFx2Y+yOiUaglZWMRa6U1PZIHkbJZcHlEukZbtK/Vmp1eaRdR5EvK7+bH7pxuc56UyFQyzWsQGGVEu56FRhK/y25pZ1Zr1MhX8y0kL9Oc26WWKtfbF+zReoMlhx3iYje6qJ3rTR2GhdgVO08aE173h4cOAxtMzizA2NkLzbTZUngUnv3fxZPC0/B7qb6Ou90YkHwHmJL86kmG63USiIj1wnqBhkrHLaEk9FceeCU/dUSYmT26+mhf55EoJnAoDoY8Q3gdXka8I1uK5CWkUmjZ4YCvNT3Rn7T4L/rXd//+6/VGKTNkq1mCWhMUoXFGa4IUZPHVaromV8nhsDlaSPjZU89GQaFat+5+2A2lWjvJj5wVO4WlgijC/d03ReqkkzVEoVrQyeVFsy/i2Fp/NZZ0OU2pXtJJ70BYFD+Ng1esQA6YNxZuJwOTLKpl1FIKvvI/9O/P/3JzmtGhm57CH72mc5t+rprTJaYEx4lp13O1uCq6mC3szIw2wBJxX1w/znagWCu2R/KXHmbNbqfhEud/bq2Aw6YwHjbX9y/mF/XF9snjYphPv4V+GcnLxbZlMXvu20Ux/dcf6dmJvtMYkHwHSMldCzEkTzFar7OiqUqkxXCndI28aWaDs7x0n+hMvfvC3X2dR6KQYNg7rM8lnQpObZrkpXoFDB2fDrtoBa9VhK/6F4X+n9ihFo9zM8hCHQCBUfwqBBQny/llJoe8rGTLlGu9G5rkxCYjRv2jqKibSmWYwSz6G9PSNSt5axHcWmmNV+wX0lmXXRSq+9F/y0KyXGDH/GN4Hd6+8/Jyh2h8z0Z8I1TVBms2G3z7/zSf/HeaD/qtP8H7fuzWrsct1op6zYbgGEONBg7FxmpxlZmon3MWcvJGh0iJoDOI2+5u79jeBFRVFemaDdcy5PBSUxT9fplfzTMT9ZkO+6g1aqwUVvTPgDTF6/dztVpaRUHRpb8zQZHW3G5Ljq1iQPIdICP5lpspeZoR1UaynKLaqOoa+U6RvByEMROa0bfBRudAK0gUE7pEDGA6OK13O/aDVCml34xgIPm4Nm3qf8XhDwxNGr98b89jtqRr7E5RfFWEKmbY70JVhVa5F/LVfDOClN/T3PweTDzY8/VW0JauqZUJNRq4sbFSWKHeULHbFH6pg3d7qVYiU8noBC0X7QdXrJ+fJCCpilEUhengdN/kYrSTkJDX4YJTW5jfe5Mo/En0sEmADdLE4Dg2YNjuI1FIsD/u52wy3+6HswEbHSJB/J1xb5yVYn/pEDNUGhVU1DaSd2upJZn6Op1ot1KWWMov0VAbeiQ/7h/HoTj6J/niqi5jBYh4Ivid/m1t/NoODEi+A2Qk77UbtntrpxlxR1FRSRaTzCfzBD0Ooh3kk+cz54l5YzqBPWfqOXoEaBUrhRU95weC5FeKK3015NQbddKVtHkkf8eb21/wq9bkZLnKhgg8NE68IXYfcW3Iwn3GoQkdkK/m2yJ5uYXeLpRrGySUa2dRgJjDR6KY4FJK+JxsHGwtIZt55PsmZXP95Jk3kjywqW5QKaE1vmeHhkQuevU6zQvHOKHoZ62pgEo1g2rFNww2J3HFSaKYYCbqo1JrsNJjfq9ZugbETqPfnHenc4TWpjaqRW7Mive2UCtQb6jMrxa4psNkL2mgJhdsh83BeGC8b5JPFpO6FYeEtD65kjAg+Q6QN1MLKWcuMaoR7nJhmflkgblhf8fC5LnMOX0LB7AvvI+L2Yt9ebKYRfJAX9F8tpKloTZaInmvw0vYHWapsNxK6v99HqLWplYVaoXWCDw4QbxaaemSzBR759TNInmpQNmuPG65XsbtMETy2qzTuCvEanGVM6uCODtFf/L9lu9/1BMl6on2NbZQ/i1GYhjzj/VdkJRNaMb3TPdvOfzC1if/6AdhuvcwbVVVWyN5RYHgOHFVdL3Kju4zq11GC2KergGDg+sWYbrDTi8Q1T5TS/klLqWKVOoN9nVIo8rF1ngdNrOjMlqRS4z6Rre1v2M7MCD5Dqg2BDm1bPFzy4xo5LNSWOFcMt/VzmAhu6CTAgiJVaVR0Qt9vVCpV0iX07o+H0TqB/rLHxq7XY0Y82mumtH9TYsEr3Wf67YUSGicWDnPanEVn8tOPOjW6xbd0FLw0xBxR7Ardo6vbc980LZzLYr3JB6aJVFM6PLATp3LRkMwiVHfaF8yz/XSOg6bo015kqlkmgPiLUA3EjOokWTaYKWwIq7j/1gTqo7rf9jSMWuNGnW13kqewTHitSqJQoJrRkRU3HV+LO0eQRKjvlFWCitbHh5iWitLXyCq+dR87uznOKst2J1Ub3pjoEGIsBmSz1QyhF2t6p3tWsy2EwOS7wDZ3ahH8vUa5FYY0Uh7MbfEwnqxY+RXrVdZKa7oVqvQ9GT53uL3TF+zEXJbaRbJ93NDSgdA400N4obcbDGs2qhSa9Q2kMI48bLoAkyX0ySyZf7tvt47DrNI3qbYsCt2YR+7DWhL12jpjHhoikQhwflkHrfDpo+724iVwgoOm6NFtpir5vqKwtOVNGFXuGXnp6fN+tjim0XyNsVG3BvX7xls9r5UHWYd3gTHiJWLpMophv12fC47p3vUWCTJGy26QdzDlUZl0x4xEvJ+aLnvUhfwaIvHbGhWtxrp9NnUgx5PRP/edHCabCXb1/mly+lWawpETWKluKLvaK4EDEi+A+TNpJN8IQmoDIWmcdqcnF67pHlVd4j8NL9v400go8A/v//PLZ2DJGBjTj7sDhN0BfuL5EvtNzUI7f1m86Tlmnh/WkghNEFcK4Aligl9Mk6v6M2M5AGuHb5WP/etolQvtaZr8qvgDhEPTJCr5phPppga8naUTyaKCeLeeAtBy4EYVi0O0uV0W65aFv/6InmTSB6EVHGzqQLTNEhwnHhevP/JUpL9cb+e1uoEOS1MV+lo0P1ltpjKkMFXayS/AIqNudAc+Wqes6t5fC57xwV7vbSO1+FtuXflZ9NqGlRVVdPreWNcdNBeSdH8gOQ7oFKvYFNseuWcvCBcJTDCiG+E8xkRwXVS1sj8qzFdI6O264c7T/wxwiySl8fcTCS/MV0z4hthrbSmp6b6gZmrI4FRYgaS/5mni9F2yXyl7fVGbBzOIhH1RHk02V9fgRlUVW33rikkhd+O9t5+5eTJrlHqSmFF13tLvOKAKHJa7eRMFBItqTcwRPIWU3hgHsmDSIlsdmdmZuOBN0K8Ir6fKCbYHwv0TNcUq0Xcdnfzc2M4N+i/4WgjTGtl6QsQHGfYO8xaaY35VdGF3qlWtrFnBJqL7f2amVjP86iXqDQqbbUH2T27XdLf7cCOk7yiKC9RFOW4oiinFEV5207/vu1CtVHFZTPcSBrJ44+JmZKatrpTJC8vsvwQS0Q9UctbObNCHQg97naQfNwX15VC/UJ+2FqiY4eHuKGBxuUQt9dnH+6c0pDWvRsjPxDR2naYWtUaNRpqozVKLSTBN6yTvM3Z/ZqsFlfbCHo60F8RPFVulbGCWGgVlL7SPvlqHrtib2sI2srOzFRNNnywZdE+EA9wKV2kVO0sHOi0K5Pvs55O2iQ6RvLhKZw2J/cv3898ssD+LvMd1kprbbtaGclbTQ9udE6VkOm87RIMbAd2lOQVRbEDfwW8FDgC/JiiKEd28nduFyr1Ck67of07r100f5wR3wjr5QQBt4NYwFwSKS/yRmK4Y/QOy6QqC0Qb836TgUkWc4tiRqgFZMoZHIqj3dRJ+uNvQvJlur2ffnIzXVNI8OxD4m9vGV5hchwV1ZQYjgwfoVArbMp1s+V3mBXrTt8Flx5gxCvIR3FkePvLOlsprBRW2q7leEBLtViMwtfL622k0DIg3iJkE9rGSHXEN0KumuuriCth5tVEIK5bVawUVtgf96Oq6IVNMxRqhbb7DJqfg62ma9rSqCBIPjSJXREulOeS2a5DfFKl9kg+5AoRdAUtL5KS5Dd+Nre7u3c7sNOR/J3AKVVVz6iqWgH+BXjVdv+Sz33749z4wRtZ/J9RfvXtb2fubZ/hr+4+taVjluvlVlIoaBfNFyPujVOorzEz7O24JUwUEjhsjrabYC48x3Jh2VKKJFVOEXQGcdpavUbG/GPU1JrlxSJXzZmSgm5tsIkPnmnkZ3fgU1V8qsJqcVX3grm/i1ZeFvzMInlJDFvtlNSLdRsiX8LT+odScWSIeM0X7HK9TKaSaUvXDLmHcNlcliykG2qDTDnTRvLQv4zSrK8ADDLKTUTzpumakSNE6w0cKKwUVnTb3tOJzimbTpG80+4k6oluOV1Tbphcy+Ia+GM8bUJ4/Ku2AuORzpbN6+X1th0VGGY4W0AnqWjAGcDr8G5LT8B2YadJfhIw5hUWtO/pUBTlLYqi3Kcoyn2JxOY+zHcf/yAAL5qZ5H2uv2Le8+N87ktfZO5tn+Ul7/265WEHRrSna1ZBsYnhGL5RGkqF6Whn9UKimGDY07QjkJgKTFFX65Yit1Q51bZIALpix6pBVidSkAXdzUQdppGfPG6twnJhGZtN4RnXxDqaREGzRd8sJ79dW/y21JKqgs0BN/4oAWcAl82DzZHh0FjQ9PVykdkYySuKQtAV1P3wuyFbyaKiml7PMf9YX4U6MxsI2Nr7ZR7Jj2DzhInZ3KwUVvTo+LFLnVNbhWrBdMGW57fVBdtU9VZKgzeq//2KPa9PJzPDeql9RwVCUmz1OrTMUjBAUcS0ru8uXj5r4Y247IVXVVX/VlXVO1RVvSMej/d+gQne9RP/rn8tExifdb+dec8befnq/+OG3/sCc2/7LGs9CoBGVOqV1i1hYRW8UbDZ9IguEurcdbqcX9YbeoyQTnVWCNqs2QKa+UOreXkZyW/EkGcIh+LYVE5eRscbTb+47pWM1uosa4vYtWNBji9l9XmbG9E1kvdt7xZfj/7KGWjUwBdFURR89iEUR5aJsHn0t9Ft0IgbYjdYmnok2/07kfxSfsmyhtysrwC2pmAxNeQDGJoj1hB1G69LpEP++munOx6nUDMvosPWagYbz1O/77SUJt4hvXalODIcGjVfsMv1MoVaoaXrWD8/X9yy6ZxZ17FEQ220FZ4vJ3aa5C8C04b/T2nf21Y4nW5+7safA+Chn/hoy89+xfEJ5j0/zrznjdz2P7/ES977dbNDtKFcL7fn5P3iQ+5WxFavrqQ6vn65sKzPnjRCEvQ9F+7peQ7r5fW2AhE0Z1pajdg6baFtio2oN7qpyK8jKRx+GSP1OkktIrp+MkS51ugovesWycvIOVlMCp/0d4bFv9Nf7e9cN+bktfmbaH7sLiLYnBmGA+aSu42+4UaM+8ctqWtktL+xeUYeo1Qv6QXyXshVzBdt+X6tFvrfmXW8nsMHGa6UdSGBmeGXEYVqwfReA22m7hZz1W2FV62pDV9U//sVR66jfFLWucw+V3KRtSKJ7aRwAnjaxNP6vgbZSnbbJmdtxE6T/L3AQUVR9imK4gLeAHxqJ37R6w+/HoD3nfqY6Pj7yf9se86858d5fCnD3Nt6D3uoNCq4bcYOyZROCtWyiBJ8/s4FqE5RuFTbyJukGzodw+f04XP4LEdFbR4zBsS8se3b3gOoDaL1Omvah/nIuCC1Tlt80yYcDQFnALfdzepX/gf8/YuaP/inVwuyr1gryEpNv56ukSTv0yK/egSHK9uRwOQEoI0qJxA7s2w121MxJQm8UyQP1gvgndI1fqcft92tn28/MM3JA8QPEy3nSGrX87++WHivpzvYVXQqvIK415KlJPVv/nlzwb5oTbIo0d6/0lyw5SI8FCrhMBlvCM1GqKi7PZK/bfQ2wNp1kL0KnXZUq6XVvkj7ef/2PP7igb+w/Px+sKMkr6pqDfhl4IvAMeDfVFXduvDZBHJrf9/yfeIbB57XbNW/sTl2b94jxnF94ZHuW+xqvdqarimlwBsBoKKRvMttvuI31Abr5XXTD7TM0X/i1Cd6/Ult9sBGjPpHLZN8p0geRPS3mcivYzHzhtcyXK9TpEGhWmB/3I/LYeOxRXMSlORiFskrisJwpcSqo8Pw43dN6AMyuqFN01+UJC8+6LVKAOzpjukSGcmbFetkfaRXyqaTGgP6H6rRqcaiKApj/rFNuZTq79HGRTt+mFi9zlppjVqjxiHN9Ovksvm9v3HIdsuhfHHx2fjqHzS/+YHntbpl9kB7JC+v5ZC4x1UnLlfnAKpTYyA0O9KtkHy2msVpc5rWpEZ8IzTUhuX6Q71Rp1QvdaxlbBU7npNXVfVzqqoeUlX1gKqqf7hTv8em2JgMTJrfYK/9APx0c4ajjxJv/fAD7c8zoE1CWUyBdmOs5VTUmo9iw7zhQW5tW17fJ6r1Kvlq3pQUQKRsrOSCoXPkB5vfQptKKAGcXuLeZgHQabdxeDTYOZKvdo7kqeQZrlZYt9ngVx5oLtpvN/zdfzDUM5etR/Id0jWFoh9VqeoTqjZitbhKxB1pUzlBk+R7pWyskLzlSL5DugZE6mczlr7FWhEFpVVsABC/lrFanZoqiF7muu8+bv47OjW2AcR/8M8A5ou2RaIv18s4bc6moEGma7xitKVaC6LaO6dbJMlvlFBCsyHKymLbYrO9AfJePp3uXLswQleqdVgct4rLXnjdTrxk7iUUa0XzbdLs0yAudNCPeX4GgEO/23l4b5uE0hDJL2fKqPUQ6Yo5OcrIT07+2YjXHnxtj7+kmcPtFMn3I/fKV/P4XeaR/LB3mPXyel/OmNAl8gNGtbqDLL4eGQ/x2GLGlIz1SN4sivn+Bxiu11mKzsBw8728/Y+/xYvKf6z//9rf/kTXUYOVxgZFhiGSV1WVTE787k5EnSwlTfPxYCD5fA+S167nRskdiAY5p81pieQr9QqVRqXjoj3sHd5cIV2bgdsmCR6+huG6YUKUplpJ5tpFDLVGjUqj0jEijc1/B4DEy/6kuWD/wrebr7/nz3qfZ5u0ublgl6p16tUgQX/ne0Gma8x2ZXFvHKfNaWkn1I3kD0dFSstqf0e3utR2YE+RvPSROJ3qsIL+fLPo6qVEpdag2kH10SKhrFehktMj+ZVMCac61DFdYmZlaoT8IHW7CTp11EmM+8dZLa727NCr1quU6+WOpBD3xvX0Uj/oGPkBcY34EknhIHlkIsRavsJypv1cu+XkuftdTNTqrNSbKqY/+vwxkvkKJ9RpLjREiu5vnH/OM/6488StNtmdjP48EdYLVSplEUV2up7JYrLFmMyIIfcQHrunp1oqU84QcAZMVRc2xaYrbHqhW8EPRMflZlrqzWbgipOzE3OKz1WymERRFJ68L8oJk3RNx7w+wPq83j276m8SbCZ8iD+r/ggAjrv/oGe9rFwvt6ZRi2ug2METJpEto9ZClNVUx9enyikUFNPF1m6zC997CzuhTDXTdXcM1ndmXT8D24A9RfI3DItBDg+sdEjFGFrwv+gSg3b/5AuPmz61JV1T0nTQMpLPlvA7Ojd2mA2HMOKO0TvEcbpI3STJbzRAkpDdlr1G13WyfpXQZYp9Gip1jPyA6C0/CUDy1JcAuH5C/A2PXmrXk3cs4NbKUC8zZHOSreYo18uoqsrf3NOchzr9jkcAeI5dTNu68Z1fND1XneTlglTJg9MPNhuXUkXUqji/TmmrZClJ1Gt+LRVFYSIwYSkn3yn1BtZ3Zp3MySSGvcMUa8W+u17byNN4TC1aXtXSGIfHgpxYzrXtzLqm3r78e3o3tHyf6w2Vm975Jf5P/TX6095s/wJPedddXc+zzTLaOwSKwnKmhFoLka93XuSkRt5uM6/zDHuHLdWoukXysknOak6+6252G7CnSF5uk7oWJH9HkNmMTVyAD3zDfL5mpVFpkoIkeS2SX86UiThjHc295Ha5E8nrnaZdiFXP4ZpI7sDQyFTqfkPKPHOnqGMzBlkgIr+2oquG0NSdANyVPgHAteOCRM3y8sWqKNRtbBrjnNjG22efAYjF7Pc//Zj+4/l3vxycHgiI879NOUG2VDNNCbWlayp50NJXS+kSal18WDcTyYNI2fSK5NOVtGn0KDHm255IXt5z/Sps2qyYDYg/5VcBSCw9CMCh0SC5co1L6VLL82REanpuj30CtyoWJ6nmOvD2z+k/brzunwB4p/NDLGVKPL5kXsOp1Cvt6RptBsJSpoRaC1KqFzoucuslc1myhNWhH91IXlEU4r64ZdVa18VxG7CnSD7sDgtP7W4rqLN5I/sRK6hZo07LzVRMiUcZyadLxLwjqKimv2u1tIrT5uz4oZZNUt1upm6SO2iSfK8IUm/a6FCo26y2ulgrmubjAWzaDsil5b4Dbgdzwz5ThU1Hyd3SwwAcvk7UL9bKa/zjt+cBuPu3ntN83us+BMA/uv4EMG+5b0vXGEh+MV0E1cGwJ2aaiy1UCxRqhY45eYAJ/0TPnHymnOm4KwOI+UQB3IotM3SJ5LXFqN+8fNvkLAPcN7+RcL3OyrrYRcni68aUTcd0jeFvinvjrBZXObXSfO2xP3gJtiM/pP/fTp2XvPcbnc+zJZJf01VSy5kyDW1X1olg18vrpkVXCTncpBey1WzHwAn6EzR0U5htB/YUyYPwhuk5Lf35/wOAnwgIueX7TTr4WjpeDRX8fLlGtlxjQkuXmBF1oiBG9nXytZGRfLcqvtRdd8rJy4LfY8nHTH8uoQ997lSo8wxjV+x9d0mWaqWOJC/xXa8XslrxdSJkSvKdHCj5srhG0cgcAKeTzSi3xXxqRsyCDSkiGnrD37a3k8vdVivJi/djMV3CYVMY94+Zfri7aeQlJgITpMvp7jWWHpF8zBOj2qj21NvL69mt4Qj6tzZos2JuOeg1xFUbCW23ImWUJ5ZaSb5jAfGsVgubfYboyygkeMF7DPUxrZOWG0Ru/tX2bwKYul2ap2sEya9kStjV7vWV9ZK5b43EiG+EQq3QsyGqm8IJRJD2/aXvdz2GxCAn3yemAlO9LQNuexMAv117P4Cpt01LTl5rSccd1AcZz4Q1kjdJuZj5hhvhsrsY849xIdPZliBdTmNX7B0/zJIwehmddWu/BlFsinqifcsou5KCEd/5S0AobM4lC2RLredbqBbwOjvf3DL98H+/KfLuf/GGWzo+N0KWVRPVh4zkdQlkJadH8olsmVjAzVgnktci4m7pGmlV0U1G2amxbeMx5LCZTuh1PXX/mj49YjoWXuVxXSES2j0S8bkYDbk5viGS72hRcVLUZnjBO9si3NPvelnzea8UzUB/6vwbAN7xiUfazqOtdlBYN0TyJYY93f2YuvWegDVJbK1Ro1ArEHSa76agKde1YlUxyMn3iYnABIlionvhSbMnIHaY22eH2lwSVVVtzcnLYzl9LGdEHvJgVMgEzaLxRDHR5ljYdp7+ia6RvCzUddoNgGje6JV7lTncblHHZrTyvSL5nzj8Y/gaDXhcqCWOaMXXY4vtW/y2CEbKOZ/2q3pkOp8S79WrbpmkDS8UzTW/Mnyv9jtao+FKvYLD5mjm/Q3pmmS+wnDAhcPmMNU1W4nkZRG80/WUU4S6kYsk515ps17XU/oR9b1o1zqnawDizhArNmBRpNEOjQY7pmvayEqrrzB2o+ZfkwBU3vy0udYuY7fxb1L59/vbF7y2nHyxNSc/2sWJU1VVUqVUx1oZNHfZ3XZCvVKgAC/f/3JxerXO/lYSek6+S7CzFew5kpcXsFdExFN+CVLnGPHbuO/ceks03zbEu6KRvCugk/xcdJiAM2BaLFsrrXW9kUAQQ1eS77G9B0EMvfKH3dqvJTZD8sV6sWvkFw+MUbDZKKyfhUbdYG/QqrAxTdec0tQV+VVcdhdhVwTF0WX7fIvoYn7KuCCMt/3H0ZYfl+vlVqmngeRXcyKSlwvAxvezn0i+0w4yW81SV+td1TVW0yzyenaK5DfrR9StkA4wMn4bq3Y7je9/AIDDo0FOLueoN5qRakey8sdg5Hpweoj74lQaZbCV+S/P3Nf+i7SUzUGfONbGSLglkq+WRACmkfxKpsx4SEhazXYy2WqWmlqztNh2E0V0cqA0op/ayCCS7xOHhg4BFjSqk7dBrcQzQuJD/YPzzWi+vVCnFfNcPlY0rfdIyMOYf6xtW1dr1EiVu0cL0Izkq3XzdEsvyR1oXa89uvN6SShByCg3E/l1IwXdC95uh6+8k9GQm2G/qy0vb1p4lemxJ/0sAF57GMWe4y/feKv5L/PHYOQIRxoiEn/oQqrlx9VGtXVBMpK8lq553szzAHhktTVFoCulOkgoQQQWLpur4xY/Xere8wAGku+RZslVc7hsro5yR6B1oLdFtOW6Nx5z5EbqisLaQx8G4NBYkHKtwYW15o65Y7omeRpiB4HmjsjmyDA1ZEJqt78ZgHfcIIr2GwvpprUyQ7pmLOQl5o1xMnWy7dB6t2uXnHzcF0dB6VqjkrupbukauZO3ormX71uvGtdmsedIXm9N7tXyPynMiF4YER/MhxeaEaaU3Ok53GoBUMDhIZEr43bYCLodzIZmOZ8933JYK5EfCDfKhtroeBNYInn/GIlComu3ar6ax6bYuhLysGdYGEf10fXaTY0BTdJacdjh4X9DURTT4qtpuiat1Sq0DmW1FsDuXuV517bOum3BxG0oy+05XDCxqKgKkldVldV8hVjApUtJN5JsspTsaGkgYVNsTAQmOpJ8p/GLRjjtToY9wz0L4L0KfrA5PyLZ99AJeq7fYYd6jcOawsaYlzdViTQa4noOCV+YiEt8LkaGOth+j4lB2LcmRZrvX77fWrdqaVKUNsOeCNlSlXylzmjIjdvuNnX0tELyTpuTIc9Q16BHFzN0uQ5y4pgVpU6hWjCXEW8T9hzJS3L5x0f/sfsTI3PgChAviOjvawYvjvZIviAiP0XRt/eKolCqlziTPtOypZQf0o2zXTdC/rxjK30PbTaID15d8xTphEKtgN/ReagxiDxkQ230Fc33jPykNNNuh1zT3uDEUq6ly9jU6yR1XigmtBxttqTiclbwubp4dEf3QX6Fn7xO/J3G39FSXwE9ks+Wa1RqDWIBN/vD+4GmXYOElesAWCL5Xov2qH+0Z79CJy95I4a9w5tK15h1L0vIyDRht8OlBzhoorApVAvYFFvrcTILUK/AkEjNpHNiQf+h2zpEwZpMOXhRqG/+7putfSwtkbx0IXUF9G7qsbCH62PXm97LvZoUJWLeWFdy1lNm3Ujeb53ku5m6bQf2HMnLTrZummQAbDaIX4uyIoYE3Dvfnq7RSayaB42I1vIVon5xk8lUi1EjrU8R6lF4nQqIwq2ZNruhNizl9a2Me8tX8z31t3Jh7Kcdvpe6RieFiPg7qVU4MhGiUm+0bMFNb/DUBYiIMQSlap1UKopqy3dXKkREpPiMkHj/jxvIp4UY6jWolcAV0P1XhgMuAs4AAWegLa+eKCa6auQlxv3jHbXyupd8L5L3jfbsPO7kQGlE3BdnvbRuacSkRLluLZJfcdhh9QQ+l4OZqK8tkvc5fK0Bxfq8eIwKkr+wIj6fwUAXYcTk7V3Ps53kfaxotbKRoEe3/NjoYSUj+Z71Mn/3epmVdE3QGcRj91iL5LvYM28H9hzJAzx98unWbvCR62D5UcKe1ghRl9zJLX6lAC5BlMmcUGMAvPn6NwOtW3yZfpEfik4Y94+joJjeTNmKKBB1U3RAs6mqW/RnheTl9rVfku+Wrgm5QrhsLla92gfh4X/lmhFtRuiK+HCqqtohXbMAYUHy/3rvBRq1MHUq3TXkml7+WkVs779/tvm3tNhGSy27y89qTkR/cmc2HZzmfKY1/fZQ4iGOrrYWcs0wGZhkrbRmqqbo5kBphJVGHDnEuxvi3jgqKmtFa9dTVdWeOzN5LybsdvjkLwFCL3/ChORbkNaCGO16HrtYBdVBuZHqfEJa2vDXnjqEy25raVZsSddI1ZvLz3JWkPxoyM2o33xnKu/vbuka0Ei+S7rXSrpGURRhFmeh87hYLe5YIxTsUZKfCkxZ82KJXwvFNX7lqWJlL1bEzaW3wRtvJm2LbIzkparCOIZvMb+Iw+boGS047U7ivripIsNqXl/KvboRQ6Eq0jXdIMnnC/Nf6Po8I8q17qSgt3bHrxHf+MLb2B8LoChwUut2LNVLqKjtxJC9BCGhV/7umSRqVZxf12J6SFyLibIY4F7plK6pGEhe63mQi/ZUsLXHQtopy1RON3TzlZeLUzc1BohFO1PJ6OkAM1iK5GXR22LKRp8N0CWSd9q0Qdz2pufLodEgZxJ5KjXxXpv2PEiS167PQwtp3Eqkux3HnW8B4MZAmkq9wYX15sJpmq5x+llKi79hNOTRJ6dtvF/WSmv6IJpumAhMkK1mOzZEWVHXQLO7txcGkfwmMOYfI1PJ9DZpCotUwkGXuBAXNLvatnRNJQ8uH6qqksyXGdZIfiY0g0NxcCbdNM1ayC4w6hu1VETppJWXq383RQeIbadDcXQl+Ww129FmWGImNAN0LwwaUWvUqKm1nh+WmDdGQk7dreTwuuxMRrycSYgPp2kbfKUgvIKCooD+lWPLqLWmC2JH2Owwcj3OepGQx8FFAzGYbvGdfla1mb9xbezfZGCS+cy8XoCWH9DXHW4OnemEbpbD0oGyW/EWmnWarvngLrMBJGTUbXVn1nFe7waM+EZIDGuyx7WzHB4LUmuozCeb17Ntwc5cFBO4nB7W8xXmkwUi7uHu5KeJIsaqYsH94qOCrGuNGnW1bthhN9M1y5kSQbcDv9vRcYebLCV7Bl/Q298/W8nisXt6Xs+YN2apAN6x63ubsCdJ3vJAYy1aPJgV9gZSDtZWeK0WwOkjU6pRqjaoadpgh83BVHCK+fS8fsgvn/uypSHdILTyZsU6q5G8TbFRU2v83dG/6/icTDnTk7ylz46MXHtBvj+9Ol7NpHz7Yn7OavNeTY2Zstqip5F8ta7qH8xeZmxEpiF9kakhHxdTTZJvSdeYRPJyZyY/aHLhlefeK/UGYsEG80K6FaUUNGsj3bb4Vgqv/abfrJJ83Btnxam9j49/hrlhcR7z8nqaRaTZZf1aPrSQAmAiMNKd/Ib2gWLjgE1cB6nFb6+VtTYpjoTE93WSzrVH8r1SNdDcwXeyJchWsz17WED45JxOn+7Z9VqoFQYk3y9kemKj5rkNo9cDEPYLsjovSX6jhFJT18joMOBu5vAPRA5wKnWq5bA3xW+ydJ4T/gmW88tt0kUrXZZWUagWekZ+IHYFVj3l9cHYXXLyYJgf+xSRwyV9kf0ayct8PGyQ3GU0kgyN6wqZREr8np5b39AkZBaYHPK2RPKd0jXJfJkhn1OfB3r7qCj4yespi+K9lFIgip0Om8N0gbfS2AZN2V2n4ERVVfKVfM+cvAwOLJN8rXe6BrTmu4Y2E+CR/2B2WFw3+bkxTdfkliAo3r+jmkx539BY91SS0wOhKTy5C4S9TmEih8GDqO1aiibFsbA4/5ArRNAZbNtV9fKtkbg+JnihEzlnK9meqRpoBmuyJtMJxWpxx7pdYY+S/JHhI0Bz5e8Ilx88YXylZXwue5Pk2yJ5oa6Rw4ufsr9JvtdEruF89jzlellvPHrO1HMsnedEYIKaWmvfVhaT2BRbR5thI16676VMB6c7/txK5AeC5K06F+qk0COSH/GNkK1kKR15pfjGxfvZHw+QK9dYzpTNm2c0QzOC47pC5g13XIPP4evtxxIYheI6syEbC+sF/UPaopM3EMNqtkIs0Fyorhu+DmiS/KnUKRw2h57O6gabYutYsFsrWosgpT1CJylmuV6mptZ6Ltpy0LtV/xp90e4Vyfviwl4b4NIDhL1Ogm4HC9qCKuW6Lcgu6XbQf/ZlYT09ERC1h64Db4ZmYX2edLHKh78riuGmbqI2BzhcJPOt19JM0rpaXLWklIp5Y9gUW8egJ1POWCL5t978VkA4qHbDIJLfBPRGHCuzLkOTKJlLzER9bema1py8n/WC+L7c3gNcM3QNDbXBfHpen0hlpVAHzVz4xptxrbTGkHuo42ADI6QlgVnUUW/UKdSsRfL9WBtIUujWdSmPCbAa0D5YP/iwHv3NJ/PmroVyG++P67nel904Tswb670IZcX7+CT1YfKVur4otxbrZPeyiORl0RWahbS/eEAYZZ1OnWYuNNcz9yoxEZjgYr49kl8prnQ1rJPwOryE3eGOOXndt8bC9RzxjVh2Fu04lH0DpGonef2rIDSFoihMDnn1z02hVmhvhMqtQHC09TiavLbr9QyOQ671/OV5tjQpGgQRQ77mtdzo8V9v1EmVU5Z6Hhw2BzFPrGNOPlVOdfWkl9C9/XvctwOd/CbgtAslwMWshdx4eApS55mO+trSNc1tYaGF5I030zVhoR753NnP8d4H3gvAWKD39h4653GTpaTlVM2ob5RirWgqL9QjZQvyrLgvbjmSl7n7Xjem/DCv1rToubCq2wSfSeTNPTv00Xxhjl4U29ybpyJiEeqVk7/1p7TfK25rGWF2VNfkWqO/jTiVOsWBiPmcXjNM+CfanEUbaoNkMWkprw9iqpDUc2+E7kPUo5AOm7uevdJvetdrZFw0OZUyTEd9zUi+uiEiLayCWofAmK7AOTwatObTExiB3Ao//+z9uOw23TQQ2gUR1XqDbKnWEnxNBiY5lTqlp0KTpSQNtWEpkgehtOo067WXJ72E/Ax3q7E01IYovA4klP1jKjBlbdqRFjFMGfK4LTp5VdXTNamCiAwjvmZkNxeeA+DTpz/NvUvCBfFI9Iilcxzzj6GgtJmprRV7N0IZjwHmSoBetrRGxLwxstWspeJrtyHeRkhSWMovwbWvgHKWCW0Q9NGLKfNFqLgOnjDY7KQLVWwKhH1O3Ye8K8JCpjeqiMVhwXA9OxVeN5L8G699IyCir4u5i32R/HRwmvXyeouv/Fppjbpa79kcJ5GtZPnSuS+Z/kwfGNKlCUdCWlVYgeVIXvqx+LUAZPUEU0NeLmipsbZIXk+9jeoB1JueNmdtUE1gBKoFJrwNKvUGqUJVbz5sEUQYgy8DyctmSMkBUuY8pQ2Z74UR34jpGFHpZGklkrfyd1oNmLaCPUvyo/7e3YOAyOPmE4wHHOQrdbKlamvur1YGtQEuH+v5Cl6nHY+zmUaRg5mNUUk3CwEjXHYX4/7xtgacfiJ53avHRIrZa06sEXIba0VbbfXGlIXE1eKqMKhaO4sTEVn98/cvmKtriindVfDMap7bZsTXI76R3ufmE3/DkCJy+VJhU6lXWr3kgZLiIVuuEQu0ppzkovn73/l9QNRcrELWRoxpAr0D2kK6Bnora6C72ZzEZtJvPSWU2vVMuLXrlXic6SEfhUqdlVyBWqPWHskD+OO6ouracYuRvGYLMOUUO9SVbNk8J+/0saZJYaOGHbacoywHCEmSnwn2rq+AiLCNjxLFWpFKo2Ipko+4Izhsjq5/Z0dTt23EniX5Mb+YmdnTtD84CqjMeAUhLGdKrTl5XablZ61QadkSSrxw9oUtv7cfzIRm2kh+rbRmKXcI3YccyBSOFWWHpTypBn1734MUQu5Q09s8dggaVVgXHzq7TemcrtGipO+fXdMj7WHvMPlqvrs/t1Zc9a4dw+eyN3dmjUpr9GdzkNQ2LBsj+Vdd8yoA7r5wNwAHIwe7/o1GyMKpcVclP+BWI/lfukUokcyKklZmA0jI96vbtCoJWUjvla6JeqLYFTsrNMDuhpVjTA0Jwj+VEPdNy4KtDQDHN8yHvjMPwPSQj6gnik2xdV+EAoLkx+3iHl7OlPT3ZOMYR+lbM+Rv7rAlmcvP1vnMeeyKXb9GvXDHmFgkNiqUZDHWSk+JoiiMeLvbgRerHUYmbiP2LMmP+8cp1Us95UsERFFoQruZltLl1py8oeEiVai2pGokfv32X9e//vPn/Hlf5zkTnOFCrpnHLVQLFGtFy+kaaXNrFslnNMteKxptGWla8dqwmq6xKTZhx5y/BMMaWSZP8prbJhkNus3HnhXXwTukj36T2morPt8SyrFPMRnxsrBeoKE2qDVqbcSQzLcX0aHd10Sm46xA1li+NN9Mt8hIXkbBvSD/TrPF1spsAAl9WLyF4qvVdI3dZhfmZyVt0U4cZzoqFuiza4IMW9I1eS2C9cf1iV3Dfpe1aWQayccQn9+VbLlDJ7qPlJauGQk2z3/EN4LX4dUj+YXsAuP+cctF9E5y1pTmfGlFLQVice+WZuynbrZZ7FmS13PVvfLyHkGAE4XHATHYWUbyDpujpeFivdBawZeYDk7zhdd+gc+++rPcELuhr/OcDk6TLqf1xUhvhLKYrrEpNsYD46Y5+X4iefl+9fKnB0O6xt47+hjxjYibfFjLba+dYTLiZSlTIl8p4LF7WlVEpRR4h/R8+i8+V6RL9PxmrxREdD/4YkIrnyrqJlXNwmsOXAG9vjJksjP770/67wB86KUf6vn3GSGjdaN0Vyq8rBb8dGWYyWLbj7qmr52ZRbUUCPJLFBIQPwSrJ5jUIvmL6RSwYVeWXwXFDp4II0E3N06GsWmToFaLq3z85Mc7/yIt+Io0xOKxki215+Q1Tyn9WhoCMEVRmAnO6N3o57PnLUlhJTo1VPUTyUNvlVPH4efbiL1L8h38K9oQFXLHsEcQjcz9uWwukVs3FOpShaopKYCo5vdzE0lMh0Qe92xaWKrqjVAW0zVAM1reAD0nb4HkpaFYP5NsrAw5iPu0rlfvENhd8PhnGQ97aaiwWsi0RzDFdfBGdFnetWOiyKjLMXspbGaeBnYXkxFB8qZ53BalVHtk9xNHfoKjbzrKrSMdhpR0wW0jt7V8qFcLqwy5h1r97LtARvJmpmhyZ2ZFox3TZp1auZ5W+x5AXM+V4or43KQvEHSo+F12Lmkk37LLKKyKOonNxj0nEnqzkiX4hkGx4SolCbodrGTKJk2K+ZYFO+xtfY/Pps/y7UvfBkROvls/yUboXkQbgh4rnvRG9KolDXLyW0Av/wkdWrHOWV7H57Kzlq9QaVRMW6eFFtfah9UqpMnZF+e/CFj3rTFi3D/eFnGAiOS7DQM3QlEUy8U6q23woFkbFBKgKELJVCszEREf9tV8qnUBUlW98Co18rJ13urkJPzDUFhlIuwhVaiSLYsFqZUY/AalVO/otR/MhGb0FAFoGnmL+XiAfWHhDWNWe8hUMvidfr3Y3w36omjhelrtYAbDziw8DWoDJbfEWNjDcl6QX8sCVEiCb1jvXHYY5rm+/vDru0fDNjv4YpBbJh5ys5Ittd93muotkSsRMXQuS8jaxfG142Qqmb5IPuKO4HV424InKwNgjBjxjXStjcic/CBdswkMe4dx2By9Sd7pFQ0VhTWifpcgeZMOybrDR6ZU3XZSODx0GAAVUSCWDVX9RPLj/nESxUTbKMFMJUPIFbKs9rFK8jJdYyWSl5Pt0+U0zDwVcstMajLKtdKGzsFyVuiqvUNcXC/idth09UvEHcFlc/WuGXijUK8w5hXEspQRKY7WSD6gR/IR7/Yu2jPBGZKlpJ4qSxQSlpU1IAgs6ol2tKC2EsWDVvTuoeyQKNfL2BW7pXx13BsnVU5RCWkFzNQFxsNeVgvi721JJRXWwDfMUlrcL88+1Hwfhr3DpMvp7l2vgRHIJxgNeljOlDuma9byFd000AhpBf4H3xGD3q0qa0AEPeP+8bZem/XSOnbFbvk66I6gHYIT07rUNmPPkrxNsTHq6z1pBxDRXz5BLOBmNVc21VXnGi5UFaLbHMkrisKhoUO6yZncpvfjWzPmH0NFbftb0+W0JfmkRNQb7Z0OoTnE24rTZl0VBdRTqVOi8SxzicmwSzu/DSSvN0JFOL9WYDraHEChKApRb7S3H4smvxx3iwhpKSuu30bZ3XdOix3Txuhvq5DdzsfXjgPiw201Hy8x6hs1DU5y1ZxlcrEptuYuqgdKtZKlXRkYGqLc2u4wvcBY2EOqpJG8ayPJD+lSVpm/B5FOVVF7F19zy4xokXxL6q1Rh1oRnH7W81VT1dtrDr4GgIdXHwbg5pGbLf2NEhutp0FE8mF32PKoPv396rDY7vQQb9jDJA+dPyxt8MehsEos4GI1JyL5jema9Zq4iTrl5LeCG2M38kjyEVRV5WsXvgZgWQUATfXMY8nHWr4vI3mrsDobtFQrWR46/MoDwremWCsKAlbr+HLnGfI5KdRy5iSvFV5noq03vtfhbZObtkEb6jziENdtRZK8rTUnH9rmCF5CNtvcfeFuCtUCK8WVvtIE0JT/bkS2krXUCNVyHAtBTq+pUEboE78cWsoofZ7xsIespvxpu57eIS5Jko80SV6qmLruzPwjkEswEnSzkim32hoYBoZ0EkRsVJVZVaxJTAUEyRtl2KlyypJGXkK+X52Kr6a9ItuMPU3ynT4sbfBEoJhi2O8mmRMFnhYHSiBVEzf1dqdrQBBDupzmZOokI74Rbhu5ra/XXxsVA6835v0y5UxfkfyIb4T18npPY7d+Ir+W7apGwGQXGQ15KDc2DMCQg5m9QyysF3QNtsTZ9FkeTDzY/RdqkXwEQTrJgrh+Lek3l598ucbts9Y/rFZxaOgQbrubUq3EfGYegP0Ra15GEuP+cVOp6PH145ZTb4A1vx96z+s1QpfaVtIiZ65F8qqtiA1ba0RaSoEnovcrTBhIXi4GJ9dPdv5lWiQ/GnRTrjXIlA39GRVJ8r6WQT4b8ekf/jQAv/fU37P09xkxHZwmV821GJWtl9YtdbtK9BKADNQ1W8SYf4zlwnJb11obPGEopYkFhZtdS7pGI85kRZDEdhdeoWmD8J7730OymNQbMaxCanrvOn9Xy/etWtxKSG11rzxuqV6yfFPKSOZC9gKMa9vlQpJ40E1NLbSenxbJZ20BMqVaG8k/feLpvX+hVrAOIbpe1wtmkXyAdLG67fl4EGmlcr3Mv5/4d12+ty+0r69jjPnHyFazbROi0uU09y/fb/k4UU/UkrVBPyQv0w+L+UXh35+6wHjYg2Ir4THOd62WxCxdb4SLqSKxgLulU1wWmLuO6QyMQL3MuFc8J1UUxO6yu/TPpSqlzR1Ifi48x9E3HeVHDv2Ipb/PCKmWM+4e+43kfU4fIVeoI8kXagXcdrclM8LNYs+TfK1Rs5DHjUBJRPL1hkqhWm41J0MhWRJvldm2cKt40tiTAPjWxW9RV+u6r7lVyCh142K2XrJmpCShb8V75HFLtZIluR00lRAfOPoB3XKWzCLRgIKqVE3TNZfK4tjTQ63pmjvH7wTadywt0CJ5VyWN32VnvSgiJZfdJdQ7lZyurgnvwIJtxL88/i8AzIZm+3qdmTJMpgxee/C1lo8ji5sbC/IbUa5ZT9dE3BFCrpBYtMNTkL7AWMiLYivhthlUXHJX5hEkP7lhwZYt/13TNZpWftIhFuxUsYhDcYh8uBbJlxQP1bq6I8GXLNQa1VL9RvKgWax0SNfs9FQo2Oskb1Urr6drxI2Sr5Zac/JOH+vFzs0zW8VGDXW/6RoQXh1G8stX8+SqOX0UmhVY7XrtJycvMR2cFukaVwBS54gExILUkq4ppgC4VBLH3qirtjTxSyN5CmtEAy7SJQPJ18pCvaPlcaM7sGADvOsZ7wLEEHBov769IEneKN+T2/q+Gnq0+79X12upbj39JqW2yWISwjOQXmA85AZ7CTsbPIhARPLrRSYjHtPjdA0o/OJ+HLGJfo9MqdimesvUxXnvRPA1GZzErth1kldVte9IHsR16NSpXaju7HxX2Oskb1Ur742AWifmEkReqpU35HB9rBeqOO0KftfObKve8ZR36F/3S6DQPiRBfrBlCsYKrGqrS/X+SP7Fcy/GrtiFVj4wCrllgtoWXFE3+NbY3SwVxZZ/NNT6O/RW/W7WBk4POH1QXCfqc5Epad2cBouKqkOYau3Egg3w8v0v179+wcwL+n69NJ0zTjaTkkwrg2QkrAx6B63wanFnBtoYwMKKiOSrBSJKFru9jKIayEqL5FUZyUfaiWzEO9K9w1qL5KOqOFa2YthBagFNqiY+p90sozcLp83JRGCC81mRrslUMtTVumWNvETPSH4HNfIwIHkBbfsVtYstYLlWMfXHGPK5+ip89YPXHX4dR990lKNvau90tILJwCTLhWVdwy6JsB+SH/IMYVfsvUm+j3QNiEhmMb8oUg4BoZgI+YXdQLlsIFpNjfHg+RQA8WDrB9cqaeEdguI6Q34XWa1Y57Q7dWIoqOK4Gzsktws2xcbrDr0OgD9+1h/3/Xr5dxobomT3shUfIgm587GyM7PSCCUxFZwS9tgRoRpS0hdwOss0aoZ7QovkUw0f5VrDlOTH/GOW/Gs8pVX8Lju5crFVIw9k6uIamnlKbQdmQ7N6JC+DqH52xyCuw1ppzTRtZjoXd5uxp0k+4o7gtrutRfJABHHjtOnkXf62yTNXGqR0T96QeiTfxw1pU2wMe4a3PZIf849RrpdFEdAfh/wKPo9Q8JQqhvdU861ZK0g//9bb0/KAdm9URPJ+F7lyeySfU8W57+T1fMdT38HRNx215AezEYqiEHQGeTjxsP49vRO6Dxmg7v9uIZK3mq4BcT3XSmuU/Zr+P7OIzVGiWjXcE1okv1QR35scao9WdcuLTvBGAQUKSUZCHvLVcru0uSpJfmeu5Vxojvn0PA21ofPIVMCaJ71EN0HDFZ+TVxTlRxVFeVRRlIaiKHds+NlvK4pySlGU44qivHhrp7np87OmFdYi+YhNkEAbyTs7O1BeKZD5dBltyEje6kQiiWHvcG91TZ+RvLRVOJ06Lbbg2SUcTqF5LpSMkXwKvBFK1To3T0fajuNxeLqOx9PhjejpmlzFYL6lkXy2oXXRXsHXM1vNtgytkFLIfhqrQq4Qbrt720le+rosSJuCY5+moeQplQ3H0CL5hZL4nlkkH/PGyFebYyDbYLOBOwTlDLGAi1Kt1DbGMVndOdUbCBVQqV5iOb+s80g/NhXQvTZoOvx8m7HVSP4R4DXA143fVBTlCPAG4HrgJcBfK4qycxqhLhjzWdDKa5G8p5bB47RR3eg/7hIyrU5a3CsBB4eEle8nT38SENHukHuorw8vGLxJuqDfwqscrJ4qp4SfSTmD2kgBUCgaPpxauuYbJ1f1cXEbMeqzMAzGF4XCGkN+ly7REyQviCFdu/JJ/kWzLwKaqhq5u+qnE1pRFJE/7zHruJ++BzAMR1HFQl0tJKlTIl900WhojUNaJH8uJ/pLNqproBmAdN05ekJQShP1u1oXIy1dkyw7hC2SZ2eupVxUP/r4R/nEqU8A/TdVdXN4veLTNaqqHlNV9bjJj14F/IuqqmVVVc8Cp4A7t/K7NotRv4WuVy2SV0ppYgE3NbXaKqF0CjXGTm0JtwPS60Z63ywXlvvOHQKWrCBK9f4i+Zbc8MQtABQ0ov7APYYbv5hC1a7FieVsx2P1TtcM6ekaFJH7d9qceiSvdy9fwdfzlpFbgKa1bbKYxOvw9r21j3ljrBW7S4j76XiFJslfyC1AYJS0X5BevebVffoppsAVZCFTJeB2EPK0m6pZSr95wlDKEPW7qZoYB65W7IS9Tuy2namVPWX8KQDMZ+b1jnIrBnFGdCP5fDVvyTp6K9ipnPwkYJxovKB9rw2KorxFUZT7FEW5L5Ho7bPRL8b8YySKCd1X3BRaJE8pxXDATV2ttjRDqdJm+AqO/BRF4SnjT9GjguX8cl9FV4nxwDjpcrqjFl1VVYq1Yl+koDv65S7BiIjqs8VVVNUOamskX7QL3fzvvvw602ON+jorFXR4h6C4RszvQrFpfvKGdM1a9cqP5KU7qTTIWi2tMuwZ7rvwP+ztPutVXs9+Fg8ZUNxz4R7ILbN2Tmzk1bpPNyMT9ZUIC+tCWWN23vo4wW47R7eI5Id8TmrqhjSq3cVqobGji7VUvki7EXldNnMMo95eIlvJWpr0tRX0JHlFUb6iKMojJv9etR0noKrq36qqeoeqqnfE4/3luqxgzD9GQ2103xK6gqDYoJgi6neiUmup4lftHmoN9YqO/ACui17HqdQpqo2qiOQ3QfIyt98p+pOe3v2QvKIoTAYmhSJDszbIFtewq15A+/DXKlDNs66KG15aDG/EqG+0o1JBhzcKjRoxd1WP5I2F12TFjsthw+u8LBlES5AdobJr9rHkY5sig5g3xnJhueMYzHK9TF2t9yXjk4T9ncXvALCq1QvUapDFtKYIKqY6NkJJWCoMe8JQTuu7MrvSKog4k8gTcPcXWfcLoxnZVqLuT5/+dMv/y/Uy1Ua1Lz+izaAnyauq+gJVVW8w+ffJLi+7CBhdmaa07z3hsNQQZbPp1gZDPjsoaouEsoSmxriCc/IAh6OHqTaqPJx4mFQ51XfRFZougmZDK2Dz0+Wng9MsZBf01Fh27aSprnq1Lshmo6WBhExBdc0zaw1RUSUPSh0Fm2gb10h+pexgyOfcMTnsdmA6OI3L5hLunQjfnsfXHt/UcbKVbMcxmHLHZmXmgBG3j94u8tWHX07SK0iqUQ+yuCGSv7heMC26QpMwjQXmNnhCUMqIAEupNXd+VZFGfWwxw6OXeoz43CL+6aX/pH/9X5/0Xzd1jDH/mOgVMUDaVlz2SH6T+BTwBkVR3Iqi7AMOAt/fod/VFTIfZmwsMYUnAqUUEb94S5w2p9YGn6eA7Kq7crf3ADfFbgLgzV94M9D/Bxeac0rNtpbQ1G73W9CVtq1qWHRsZhQY8oRQFKjVG7qlwXJVSu7MicGS9lvbLUSULIpSw65okZ4k+ZL9it+VOWwODkQOcCx5TFefPGfqOX0fR+7MOqVsNkvy10WvY7W4ijo0xxrCTtqhGki+mKLqCpEp1TpeS7nIyqHpptCCr6jfhaLUoGG4li4RELzwSP871n5wU/wmnj/zfF4892KePP7kTR1jKb+k74Il+hnnuBVsVUL5akVRFoCnAp9VFOWLAKqqPgr8G/AY8AXgl1RVMxZ/giFzaPIN7QhvBIopQtr9qOCEegXUut48cyUXXqGplZd4wWz/3ZbSMbFTcamfgSEt5xaYolgrkqymwRMhq9bwO4OoKqJYp5H8QsnDsN+Fz2X++y0N9NYi+UA9C0oNGwbZncPLerGxY41Q24nD0cN8b+l7HFs7BjRHRfYDmRLplK7USd7RH8lLo7SvkmfBphJw+hkJRFiS6ZpSirxNkFenSB6E8mouNNf5F2kSyiGfE5QqjYYWDVfyqE4figKHRnc23QHw3ue+lz999p9u+vWvP/x6QOTgJeTXVmcEbBZbVdf8p6qqU6qqulVVHVVV9cWGn/2hqqoHVFU9rKrq57d+qpuDz+nD7/R33K7q0CL5oHY/Vqo2w8AQSfJXNjFsTD/IXUw/cNvdBJyBjqZuclSclSHeRkhFxkJW5OUz9TJDHuFAubBe0HXV83lXx1QNGKwNuvrXiEjeXl7H5Wxgwxj9dfYfv9Igm27+7ujfAfDyfS/v9nRT9PIjkiTfb2u9nLpUcnmZdzqY808wEfYa0jVpMqpYOCa6kPyp9VO6JbMpPGFQGww7qyi2GvW6di2rBeoOL6q6c53L24mnjj8VaHW0lJ8xq/NiN4s93fEqMeobbZvw0gYtkpckX642h3hnZev0LriZ7nn9PQD89A0/veljRD3Rjj7km47kDR25qi/KslpmKig8Wi6mSnokfybnZMqkO1Ii5ArhsXt6yO40++JSBpej0czj6iRfZch/5V/LZ0w+A4BvXvwm0CzG9oNe6S05fq7fdM2zp5+NTbEx3yhx2ulknyfOWNjLUqYE9SpUCySq4sPUbdGWun95X7VBu5ZD9iIoNWp1jbIqeSo2cZ9c6TtsaDqRGhc0ORQ86u5Pd98vrgqSnwnOtJh3mUKL5P0ad5WrNl2Lm6q7UJTdETFEPVGOvukov3H7b2z6GKP+0Y7GUTKS30y6BuDBxIMkLz1AEZUDQyI//5mHLukkfzzj6EoKiqIw6h/tnpOXhaxKDqejLqSaoMthr/TGNgnZRCaxGSMrn9NH0BnsuChuNifvd/qZCc7wfy/eRcLh4EbvKONhD4vpEmpJ7JofTIgMbTfzsF+85ReBLjMMPMKrx9cQqbdKVV7LAiVF3IPDgSv/Wk6HplFQ2rzpAcIe635Em8FVQfI9PTJA3EzFFF6n6LQsVZpqjPWqg4i3fRr8XsVMcEZ33tsIPZLvoxkKmna7HzvxMRZiIu9/TVSQ/Fq+AqUUKgrJmqcryYOITi2RfDmH09GgYSjW1R0+6rtADgtiQZOD3mVUvxl0awjcLMlDa/H9FkeEsZCHSq1Bel3k/8sOkWvu1qjU01lUm2xWL6VQFLVJ8pU8RU0QYTbE+0qD2+5m3D/eEsmny2lsiu3KLrzuFsR9cQva6gg0qnjs4qYvVZqRfLLi2BWR33Zh3D8uDKi0mZpGbDZdY8TFKTEhajowxXXjIbFDKq5TdwVpYCPq767c6WltYLOB0w+VHHZ7nUa9SQxVrZawG0ge4GM/9DEe/qmHef8L3r/pY3Qbg7nZnDzAW256i/71wYZNV9Esr4gF+P7lHhPZsOAsqkluywWRPixXm+mavCqu4fAO2AzvBGZCMy2RvJzBbHUo+GZxVZD88TXhvCCbN0whp71UUwAUys3pM4mSneEexLOX0C2Pu1kJpRGPNsT7OuEeYmrIy4X1AhTXSSMimgMj3aPKEd8IK8WV7mMd3UEoZ7DZatQMJF9RBBFd6UV0I7aq55djMM0g5ZmbcUJ80Zzw1wnWGziKa+yPieu2mhC/K6v6uGGy+/jJntYGWk6+rKXzihU5XrBAtrF7InnQbIuz5/TGtLXSWt/e9JvBVUHyz556NgD1RhcVp5YXK2lNObmiovuPL5ccuyLvt13oZhwlC3Wb2WL+zpN/B4B/Sj9KvFbDW1hnJurj/FoBtZhirSFIolO3q8Sob7T3WEd3AMo5FFudet1BsVKHarHZ87BLiGE7IHdmZsXNfDWPx+7p249F4uibjvLt9Trkk7qK5uhp4WiSwc8jFzNdXx9wBfA7/Z2bFbXPZUW71sWSTRR26xUydRcBt6NlduyVjNnQLNlKVvcjupi7qDt67iSuCpKXM1OzVXPTK0BEfkCpLG7KbFHRI/nFou2qStd0azeXXXp+V/853Fdd03TCSDgckF9hbthHqdqgmktSdoQ4PBrs+aG1NDzEHYRKDkWpoap2kvmyIPmGiODHQptPN+02SCIxEx/ka/mtTybyx6Cwik+bmnbmovg9GdXHS2/oLeMd8Y10FkZoOfmyDL7K6LWyVM25q4IvqbCRKZuLuYub8sLpF1cFyctW+K4KG420ylqDQrqAnpNfKtp2zZZwOyBJdDFn7prnsXtER3CfaLNCKKyxPy52BPX8OktVL4fHejeGyOvZNS/vCkA5i6pUQXWSzFWgWiCvySmvpkVbKpsWcgttP8tX85squrbAF4P8qp5WCmnDd7L4ePdrb+r58snAZGfnU6cH7C7KWp9LoaxQLYlAY73i2FWfS6OMMlfJkS6nByS/XXDb3cS8se5aeY3ki1qkmso3dfJ51X1VkULYHSbkCnEhe6HtZ9lqdkuk8MXXfpEXTzyTh8+eh+Ia+7Q8bjW/xmLFo/+/G+Qi1NUS2R2CcpYGohV+NSci+UzNuau2+NsB3dHS5P4vVAtbJ3ktkgd4/R3ThJQ8DVWhYvdZkh1PBia7B2CeMGVth602nKTSgvATFWfPIv2VhInABHbFzvnMef1abOxS3wlcFSQPIprpTvIioizLAcE5VY/kS7iI7pIK/nZhOjhtSvL5Sn5LhkoTgQn+9Fl/IrwnC2uMhz2Aiq+eJa36efL+3o0hw95hnDan6U5DhzsI5Sx1tYKqOkhmC1Avk9llW/ztQMwbw21367bFRuSr+a2Pn/PFIC/UL88+HCdMngw+yhaNTMb942QqmY721rhDlMtaqlV1sJ4SOe3TqYaeItoNcNqcTAWnmM/M67uqfkcJbgZXDcmP+kdb5Ett0IirpHncFCs2qsUsdbsXFRuxqyiSB8010mR7n6vmth75ufxgd0NxDUVRCFDEoTRIqQHunOtN8jbFxkRggkv5LtGfW6Rrqo0yqA6SKREJrtfsu2qLvx1QFIWJwIRpkLMt6Rr/MFSyUCvzjIMxwkqetOrntbdZI7BuNQMAPGEq2udSVR1k0ikACnh44Pz61s79Ccaob5Qvn/uyvuAO0jXbiEdWH2G5sKxLANugSbVKlYLmWmijXMhS03TVI6GrK5KfCk6xmFtsG7ayLZNsFEUb0Seiv/e8UuQq0/gtN5yN+8e7b/G1wmu5XsbrcJNMpQBYK9sZCV49RVeJycCkebqmtg3pGp82dza/Ssjj5NpwgzR+/vRHe+fjoemv03F4iCdEWUuj0nCQzYoFu6C6+emn92/1cDnx/SVhxnvX+btwKA7C7p3tdoWriOR/+JofBrr4yjs8YHNQqhVx2QQJVEs5KtrX8auMGKaD09TUWpu9wbZE8gC+YX2L/6J9IrJ+948/y/LLO5GWDleAer1CtVEl4PaxpkXyq2U78eDVtWCDSAssZHeo8OrXSF7Ly18bqXPTNbOW9f3jAeFh1MlKQ0Ty2rBv1Ukpr5E8HiYju+tz+Zu3/yYgPPRrau0JmWlw1ZC8lFF2JHlFEbm/WlFv9KmVchTx4LQrpjMq9zL0OZ4b8vLbNpPSN6xH8nJgiN1n3aipm/YbENdS+wCF3V7SGVGsS5btjFyNJB+cIlttHx6Sr26DhNIQyQP6VCirGPWNYlfsndNv7hDlqtiB+11uSgUR1Rdw7wpzMiN+8shPPuG/86oheWm728u9sFgv6VK/elm0Tk8N+a7oKUI7gRZrYAOyla2pa3T4Y5DXtudaN6M+a9cC9DxuR2II6iQ/5PWTy4rCXREXsauQ5M0UNg21QbFW3MZIXi7a6b6upcPm6K6V94Qpa8Z446EQ6bTW/aq6udaC5PZKgt1m120M/ur5f/WE/M6rJjyVXZxdxwC6Q5TrFXxO4e/cKOfJ1F1Mj24x0tmFGPGN4LK5WiL5aqNKppIh6tkGa1RfrEkKOslb99WWpLWYW2R/eH/7E9wBneSjPj+LlQK4oYhbU/RcXTCSvHS3lJYG/Q4MaYNP2AWTX4VGA4prze9ZRNcaiydMpSF8p6bCQaqLIpK3uf27LpIHeOinHnpCf99VE8m77W6inmhPki81qvicXuJBN2qlwHrNyXQPV8S9CJtiYzI42ULyKS2tsi0k749BOQO1sj4wpB+Sl5F8x7y8IZIf9vvxKsJsraS6uGFy54tdVxomgxrJG2SUWzEna4EnAopdkHshCY0aBPobWDMZmOyaky9p13J/LEIpL3ZlifLukU9eTlw1JA8iBdF9Co0gebfdLYi9okXy0asvkod2rbz0iol6t4nkQUR/+VXhGum0vpjGvXEciqNz9OcK6sQwEgjiRczXtLl8V52EEsSwlaAr2CKLzdc2bzPcAptNqKXyq5DTgqhgf3NXxwPjLBeWqTZMnGLdIQo2Bbti46bJGC61RFF10bi66GvTuKrepdnQrKnCQIc7RFmt43F4GAl6sNeLFFQ3h5+AGZJXIiTJS9c8OQx6eyJ5kT4jvyKiP39/23u7zc6of7R7Tl7zMR8NBvAgIvlIOHzV1VckpgJTLSSvp2u2RS2l1ViykuTH+3r5hH+Chtow9yPyhCkqCl67m5umIvgpUcDN773ySPtzB2jDVUXyY/4xEsVEZ195d5CSWsdj9zAScuOjRBE314zsrKn/lYrp4DTFWlGP4PVIfjtIPqCRfC4htvl9pGokurbDG9I1E6EQHkVc80dWKps63b2AycCkabpmW0g+NA6Zi02SD/QXyXdtiPKEKNps+GwuZod9eJUyRdwciF+dn8t+cVWR/HRwmoba6Ox54glRQsXj8PC626fwUaaAu+ekor2KjTLKteI2krxfNMCQXxER4CZSQBOBiS4kH9DTNV6nB68WyYdD3f3N9zKmglNcyl3SffilnDLk2ob3JDgB2eUtk7xpXt4TpqAoeBUHiqIQddYoqG6edSi+1bO+KnBVkbxubNWp+OoOUVIU3IqdG8a8OJQGr77z0NW7vdfMk3SSL63hUBzbQwoyks8nILcCwf4KdSC2+IligkrdJDp3+ilrUjWP3cMvPUOQyOd/80WbPuXdjsnAJJVGRZ8TIFMjUnm2JQTHILcsonnvkHCP7ANS4mxaSHeHKCoKPkUUWp+7z8+h6f7vl6sVVyXJf/vSt82f4BENNF5sugPlRLy/XPFewlRgCgVFr2OsldaIeqLbs+i5/OD0iXRNfrVJ+n2ga/Rns1HSCrluu5u4uw4o2FxX564Mmu/XifUTAKwUV3DanNsznSg0AWodFh/sW1kD2jXyxs1N5zxhCjYbXjkmr5LTXWMH6I2riuRlZDqfnjd/gjtE0abgVtEdKHFdncoaAJfdJYzdtKHea6W17VHWSFQL8N2/grpBRtkHehlblbVo0m13iwEwLr/obL5KMe4XxdD7l+8HRCQ/4hvZnkV7+IB4vPSDTe3KQChsTAvp7qAovKraeZYy+jCRAXrjqiJ5l11I58zG2gHUXH5qioJHVfWpUGxHUWoXw+fw8ZkznwGakfyOYOLW/l/Sg+RLTtHZ6na4RfS3VT34LoessXxv8XuAMAST5mBbxohB6bJJkl8prOjn1gKbnYLdgU9TeVHO6GMBB+iNq4rkJR5MPGj6/bK2vfeoDX2+69UcyUNzmpOcqbqtJP/ktza/nnlq3y8f8Y1gU2wdG6LKDkHyHrtH7Bqu8mvpcXg4OHSQIY9QMslIfltgTLf5N7dwrJdE57OZ+q1os+FtaIPbS+kByfeBq47kXzr3Ut2AbCNKkhQaDUMkf3UTw8v2vQyAexbu4WLuIp8989ntO7jb0H8wNNf3y502J6O+0Y6dkmWH2Lnp6ZqrfFcG4FAcfH3h66iqur0kb0R0c/a/v33nbwOQKLZbDhdsNnz1OjTqYlc2SNdYxlVH8oeihyjXy3ojiBElhxhV5q5XDTn5q1uL+8ypZwLwkWMfAeC1h167fQffZ7AW7lONIdFNRlmyO3CoonGKav6qj+QBbooLj/dja8co1ArbS/I/91WYeybc8TOberm0HDb1vUfFW6+KVA0MIvk+cNWRvCw+mUV/ZY3kvfWarq652olhLjQHwL1L9wLwmmtes30H3/cseN2H4Fce2PQhJvydJ0SV7Q68Mo9bKVz1uzKAZ089G4D3/eB9wDbJJyUmb4c3f2bTL5ej8DaSfK1Ro6KAr1YRqRrQh/wM0BtXHcl3k90VNR2uu1ZpkvxVTgwblRfXx67f3l9w5FVNZcYmMBGYYKWwYprHLdhsBpLPX/W7MoA7x+8E4FsXvwXAgcjm3/vtxrh/3LTGIqe5+WploayBQbqmD1x1JC8VBmYyynJDNNV4ahVDumaQx/3Aiz6gfy29sK8UTAQmOnYxFxUbvkYdVFXL4w5IfmM96mDk4GU6k3Y47U7GfGNt/lIyteqtFAfpmk3gyvrEPgEY9gzjdXhN835yypCnWhxE8gY8ZfwpHH3TUY6+6ejlPpU26DszkyaaggLehgq10qCBxoD3POc9+td225Vl12s2cFyP5CuFQbpmE7hqhoZIKIrScT5oSZs+46kUm5H8gOSvaEz62yceSRRo4Gs0oJwdpGsMeOHsC6/IBRuE9cLXFr6Gqqp6qrBQ0yL5Rh2y2mI+iOQt46qL5AFmgjOcz5xv+76M5N3VgiAFh1d4ZQ9wxUJ6nshWfSOKqCInn18V0bz76rSM3k2YC8+RLqfJVXP69/RIvtGAlDbfwD0geau4KhlsNjzL+ex56o16y/fLdeFU6C3nB80zuwROu1BEffjYh9t+llfr+BsNWDsjvjFI11zx8NiFlPZ06rT+PZmT96kqpDWSH6RrLOPqJPngLNVGta1YJ9M17nJu0DyzB1BoVPGrKqxqUf5AkXHF49YRYW9hHB7STNeoIpJ3+kBb3AfojauS5GdCMwCcy5xr+b5eeC1lB2qMXQQ59EJOsJIo1Mtii79yTHyjz+HSAzzxmA4J9ZuxwS1TEYqaUKMhIvnBYt0XrkqSnw3NArTl5fXCaykjRtINSGFX4LUHRReuHE8IgvAL9SI+VYHEgOR3C8xm0coB8hFZeB0UXfvClkheUZT/rSjK44qiPKwoyn8qihIx/Oy3FUU5pSjKcUVRXrzlM91GxL1xvA6vaSTvUmzY6mXIXBLDiQe44iF7H4z66lK9RENtiChfj+QH13M3YCow1aKWWi+v43N4hQU4DPLxfWKrkfyXgRtUVb0JOAH8NoCiKEeANwDXAy8B/lpRlCtGkKsoCjPBmTaSL9fLuBUt15c6N4j8dgmeNPYkoFVGKeeX+lxBaNTENwfXc1dgKjjVsmCnSinhnOmJiG8M0jV9YUskr6rql1RV1T5BfBeY0r5+FfAvqqqWVVU9C5wC7tzK79puzIRm9GEYEqVaCY/NUNAZkMKugGyIMpK8VGT4JTGgDLb5uwSzoVnmM/P6WMf18joR9xCMXCeeMFC99YXtzMn/DPB57etJ4ILhZwva99qgKMpbFEW5T1GU+xKJdovRncJcaI6L2YvUZJQHZCtZAsbmpwHJ7wp4HV7i3rg+ixaaxbqg7n+vwhXW3TmAOeSYzpPrJwERyUc8keZw8MjsZTqz3YmeJK8oylcURXnE5N+rDM/5HaAGfKTfE1BV9W9VVb1DVdU74vEnbvr6TGiGmlprqeKnK2nC2kCFAXYXZkOzLem3tdIaAEP+0ct1SgNsEk+feDog7JABUuUUQ+4h8EbEE8ZuukxntjvR09ZAVdUXdPu5oihvBl4BPF9tatguAtOGp01p37tiIBU25zLndEllppxhxDgOLbr/cpzaAJvAdHCaT5/5tN4OL6cMRf2bG0U3wOXDVHCKoDPI42uPA2LBjrgj8MK3wfgtcOOPXtbz223YqrrmJcB/A35IVVXjFI5PAW9QFMWtKMo+4CDw/a38ru3GTLBdK58upwkbB1XPPu2JPq0BNokR3wi1Ro10WRhYSZIfGtGskW2D5pndAkVROBw9LAabVAsUagUxdtIThjt+emA10ie2alD2l4Ab+LJmJvRdVVXfqqrqo4qi/BvwGCKN80uqqta7HOcJR9QTJeAMtJJ8JU3IFYIf/xgsPzrwOtlFuCF2AwDns+eJeCKslddw2pz4D78cfv1RCE/1OMIAVxKujV7Lx058TO9KHx2k3TaNLZG8qqrXdPnZHwJ/uJXj7yQURWlR2FQbVfLVPGF3GA6+UPwbYNdApt/mM/PcFL+J9dI6Q54h4WQ4IPhdh+uGr6NUL/GpU58CQEHp8YoBOuGq3vfMBpvFuow2jCA8cLfblZgOTuOwOXRjq/XSutjiD7ArcW30WgA+efqTADx5/MmX83R2Na5qkp8JzbCYX6RSr+i53LBrQPK7EQ6bg/3h/RxfPw4Ikh9yD5RSuxX7w0L0sFpcBUSX+gCbw1VN8rOhWRpqg/nMvD4MWk6MH2D3oVwv862L30JVVdZKa6JLcoBdCYetNZO8cdbwANZxVZO8nHf5lz/4S72NejJg2rM1wC6AtKldLiyzXFgeFOt2Od773PcC8MGXfPDynsgux1VN8s+deS4Ad1+4m4u5i7hsLmLe2GU+qwE2i5fvfzkA77n/PVQbVaYCg4LrbsbzZ57P0Tcd5bbR2y73qexqXNUk7zRopxeyC0wGJ7EpV/VbsqtxTUSIvT5/VrhryCa3AQa4mjFgNA3H1o4NUjW7HBt3YQfCBy7TmQwwwJWDq57kX3/49YBwMJSudwPsXvzUkZ/Svx6k3gYYYEDy/NYdv6V/HXQNOlx3O+T1fMdT3jFQZAwwAFu3Ndj18Dg8vHTupXx+/vP82bP/7HKfzgBbhKIoHH3T0ct9GgMMcMVA2Tj8+HLijjvuUO+7777LfRoDDDDAALsKiqLcr6rqHWY/u+rTNQMMMMAAexkDkh9ggAEG2MMYkPwAAwwwwB7GgOQHGGCAAfYwBiQ/wAADDLCHMSD5AQYYYIA9jAHJDzDAAAPsYQxIfoABBhhgD+OKaoZSFCUBnOv5RHPEgNVtPJ3dgMHffHVg8DdfHdjK3zyrqqrp+KwriuS3AkVR7uvU8bVXMfibrw4M/uarAzv1Nw/SNQMMMMAAexgDkh9ggAEG2MPYSyT/t5f7BC4DBn/z1YHB33x1YEf+5j2Tkx9ggAEGGKAdeymSH2CA/7+duwm1oo7DOP79cm9RGvRCIHUVdCHFJQgjwhIisoVRZKsoKCRa9qIRhLVp2yKiFtHGLCExwoQkohcsaCeRBvlSJBZ6TVOIXmhj0tPiTHCRWnXm/HP8fTYz81/MPIcz5znn/GeYUspZquRLKWXABlHy6hr1G/WQurF1nr6pS9RP1QPqfnV960yToE6pe9X3WmeZFPUydbv6tXpQvbl1pj6pT3bn9D51m3pR60x9UDerJ9V988auUD9Wv+2Wl4/jWOd8yatTwCvAncAs8IA62zZV784ATyWZBVYCj54HrxlgPXCwdYgJexn4IMm1wPUM+PWrM8ATwI1JrgOmgPvbpurNG8Cas8Y2AruSLAd2ddv/2Tlf8sBNwKEkh5OcBt4C1jbO1Kskx5Ps6dZ/Y/TBn2mbql/qYuAuYFPrLJOiXgrcCrwGkOR0kp+bhurfNHCxOg0sAH5onKcXST4DfjpreC2wpVvfAtw7jmMNoeRngKPztucYeOHNpy4FVgC7G0fp20vA08CfjXNM0jLgFPB6N021SV3YOlRfkhwDXgCOAMeBX5J81DbVRC1KcrxbPwEsGsdOh1Dy5y31EuAdYEOSX1vn6Yt6N3AyyRets0zYNHAD8GqSFcDvjOkv/P9RNwe9ltGX29XAQvXBtqnayOje9rHc3z6Ekj8GLJm3vbgbGzT1AkYFvzXJjtZ5erYKuEf9ntF03O3qm20jTcQcMJfk739p2xmV/lDdAXyX5FSSP4AdwC2NM03Sj+pVAN3y5Dh2OoSS/xxYri5TL2R0oWZn40y9UmU0T3swyYut8/QtyTNJFidZyuj9/STJ4H/hJTkBHFWv6YZWAwcaRurbEWCluqA7x1cz4AvN/2AnsK5bXwe8O46dTo9jJy0lOaM+BnzI6Gr85iT7G8fq2yrgIeAr9ctu7Nkk77eLVHryOLC1+wFzGHi4cZ7eJNmtbgf2MLqDbC8DfbyBug24DbhSnQOeA54H3lYfYfTI9fvGcqx6rEEppQzXEKZrSiml/Isq+VJKGbAq+VJKGbAq+VJKGbAq+VJKGbAq+VJKGbAq+VJKGbC/AKVWm2Cd199hAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.figure().add_subplot(111, projection=Axes3D.name)\n", + "ax.plot(*sim.data[state_probe].T)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[state_probe])" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/dynamics/lorenz_attractor.html b/examples/dynamics/lorenz_attractor.html new file mode 100644 index 0000000000..72ad492373 --- /dev/null +++ b/examples/dynamics/lorenz_attractor.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/dynamics/oscillator.html b/examples/dynamics/oscillator.html new file mode 100644 index 0000000000..40bc67be1f --- /dev/null +++ b/examples/dynamics/oscillator.html @@ -0,0 +1,747 @@ + + + + + + + + A simple harmonic oscillator — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

A simple harmonic oscillator

+

This demo implements a simple harmonic oscillator in a 2D neural population. The oscillator is more visually interesting on its own than the integrator, but the principle at work is the same. Here, instead of having the recurrent input just integrate (i.e. feeding the full input value back to the population), we have two dimensions which interact.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the Model

+

The model consists of a single neural ensemble that we will call Neurons.

+
+
[2]:
+
+
+
+# Create the model object
+model = nengo.Network(label="Oscillator")
+with model:
+    # Create the ensemble for the oscillator
+    neurons = nengo.Ensemble(200, dimensions=2)
+
+
+
+
+
+

Step 2: Provide Input to the Model

+

A brief input signal is provided to trigger the oscillatory behavior of the neural representation.

+
+
[3]:
+
+
+
+with model:
+    # Create an input signal
+    input = nengo.Node(Piecewise({0: [1, 0], 0.1: [0, 0]}))
+
+    # Connect the input signal to the neural ensemble
+    nengo.Connection(input, neurons)
+
+    # Create the feedback connection
+    nengo.Connection(neurons, neurons, transform=[[1, 1], [-1, 1]], synapse=0.1)
+
+
+
+
+
+

Step 3: Add Probes

+

These probes will collect data from the input signal and the neural ensemble.

+
+
[4]:
+
+
+
+with model:
+    input_probe = nengo.Probe(input, "output")
+    neuron_probe = nengo.Probe(neurons, "decoded_output", synapse=0.1)
+
+
+
+
+
+

Step 4: Run the Model

+
+
[5]:
+
+
+
+# Create the simulator
+with nengo.Simulator(model) as sim:
+    # Run it for 5 seconds
+    sim.run(5)
+
+
+
+
+
+

Step 5: Plot the Results

+
+
[6]:
+
+
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[neuron_probe])
+plt.xlabel("Time (s)", fontsize="large")
+plt.legend(["$x_0$", "$x_1$"])
+
+
+
+
+
[6]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f22a9bec6a0>
+
+
+
+
+
+
+../../_images/examples_dynamics_oscillator_11_1.png +
+
+
+
[7]:
+
+
+
+data = sim.data[neuron_probe]
+plt.figure()
+plt.plot(data[:, 0], data[:, 1], label="Decoded Output")
+plt.xlabel("$x_0$", fontsize=20)
+plt.ylabel("$x_1$", fontsize=20)
+plt.legend()
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f22a205ac18>
+
+
+
+
+
+
+../../_images/examples_dynamics_oscillator_12_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/dynamics/oscillator.ipynb b/examples/dynamics/oscillator.ipynb new file mode 100644 index 0000000000..e35e4fcd3c --- /dev/null +++ b/examples/dynamics/oscillator.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple harmonic oscillator\n", + "\n", + "This demo implements a simple harmonic oscillator\n", + "in a 2D neural population.\n", + "The oscillator is more visually interesting on its own\n", + "than the integrator, but the principle at work is the same.\n", + "Here, instead of having the recurrent input just integrate\n", + "(i.e. feeding the full input value back to the population),\n", + "we have two dimensions which interact." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:55.936227Z", + "iopub.status.busy": "2020-11-25T16:47:55.935349Z", + "iopub.status.idle": "2020-11-25T16:47:56.430103Z", + "shell.execute_reply": "2020-11-25T16:47:56.429588Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "The model consists of a single neural ensemble that we will call `Neurons`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.435146Z", + "iopub.status.busy": "2020-11-25T16:47:56.434626Z", + "iopub.status.idle": "2020-11-25T16:47:56.438195Z", + "shell.execute_reply": "2020-11-25T16:47:56.437736Z" + } + }, + "outputs": [], + "source": [ + "# Create the model object\n", + "model = nengo.Network(label=\"Oscillator\")\n", + "with model:\n", + " # Create the ensemble for the oscillator\n", + " neurons = nengo.Ensemble(200, dimensions=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A brief input signal is provided\n", + "to trigger the oscillatory behavior of the neural representation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.446638Z", + "iopub.status.busy": "2020-11-25T16:47:56.445090Z", + "iopub.status.idle": "2020-11-25T16:47:56.447266Z", + "shell.execute_reply": "2020-11-25T16:47:56.447698Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input signal\n", + " input = nengo.Node(Piecewise({0: [1, 0], 0.1: [0, 0]}))\n", + "\n", + " # Connect the input signal to the neural ensemble\n", + " nengo.Connection(input, neurons)\n", + "\n", + " # Create the feedback connection\n", + " nengo.Connection(neurons, neurons, transform=[[1, 1], [-1, 1]], synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Add Probes\n", + "\n", + "These probes will collect data from the input signal and the neural ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.453377Z", + "iopub.status.busy": "2020-11-25T16:47:56.451900Z", + "iopub.status.idle": "2020-11-25T16:47:56.454015Z", + "shell.execute_reply": "2020-11-25T16:47:56.454424Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " input_probe = nengo.Probe(input, \"output\")\n", + " neuron_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:56.459492Z", + "iopub.status.busy": "2020-11-25T16:47:56.458733Z", + "iopub.status.idle": "2020-11-25T16:47:57.224893Z", + "shell.execute_reply": "2020-11-25T16:47:57.224401Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " # Run it for 5 seconds\n", + " sim.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:57.230805Z", + "iopub.status.busy": "2020-11-25T16:47:57.229637Z", + "iopub.status.idle": "2020-11-25T16:47:57.602591Z", + "shell.execute_reply": "2020-11-25T16:47:57.602132Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Jz1QaeQC41si+3hmqrj64VcNqOmrKiqykoziyGJ9fTcWgL86LvHYAcNP3iuNIJpjskfHrt4nCyQMG7frJ++dYMnTuc8D644GocZ2OfJs4bswvrXdfGHzpzS8khx6+p2mZzaJI11s8Yf176eHvCq5apoZO4lAubnpAnkEGkyfTRQFzIUJlA/9wtxh3bCkxNezoUnI+lE7+EWG4ZMGVhAziVjbQH3CcyTdw1cRCBIj4wlyymqrCg8d1L5qYlyyiG2ZcAcDJ5fHF6ORqan4e/v0fnt6tydqPTUH37NY6aPcGOJQbX7BuWM/Mb0ck6zqktwoMM9bywyZA912qTNWg3Hp0YX4Gv/CoSD1eHVGHXRd0Dc4NZcs/+7AIKb7o8ULQkBk7u/q88vH7PWHYZYwIEAs3AGzfO5/vxD4x+MV6B5lYCOGg+HMPZuPYrrbHumC5giy6Sh+w/n1qHQBDtyiM6lpmmtIBgMMLcVRbPW/TDWW6qJXBzx4yxyynxCKUjoWnhh1bTMzPw184Ph1bSK2LrW75AnaqbXR6AzOWMDavpQS2q23vq6Zl2mPu6Pj7yRUgGDUN/umdOpZTEdNblbh6NYXTO3Xv7i8JzoFuHehP7E5zR8XCaai1PmDUBoxSTQBww4EMzuTr3p8vQLT2TK0JLloingOSq4Jugug69/B2zQy4Szz+UBablZb3iRQA8IjR0nTtccP35HNaG6bPSsdBxvkA4PsN2qk+j/Tf8jmg1xwq6wLC088eAXbu9/77DOwLg5+vd7CUGnrVC8kI+gNuZgy4hrxxUqvWvw9FhLEwvG07SmclPYeiMMmTWxn8WE4Es8oXsFtr41Yj4DiJo4sJlJtdlBset2EsbwxppVEEQ+KhrFwwYxqHLFJZjxrxBs8XI5k1NEk1BQJikTQW0Ud2pqkmQBj8ZrePi16njMrArNy1STAmaDBDr+n0rthd3DBhWK9bT4Nz4KGtOew+tr4xblQllq8BdkX2y/liA+3eYOqcnTQ8frlQeQpZZzLq4TMGHLgZeGioe3TfpQqede2K6dkDwPNvEgVaD81jtybjGgsTrMDqDcCW7+G7gqyylVhMhs33PYEMsqTsK/iQOYBQfRORUADZ+LQXDQwN/raXPXdLZwEwa8PKmDBglQs4m2/gQHbaqALA0aU5pWaWLwxppUlkxLxk74JJegKAWc/geX+DnfuAYGTawwfEImBQFI/s1HFyZZpqunpegduWwb8//zenf7d0jWncTu/UkYwEpxwLOVe5IHgGzoWHP2pUzXldbZ6vbxiZOE8YUYoFhGQ5gPloSZXPAys3TGfnhGImddfq9nGh1Jw6X9cZsZkH57EQmQb/+Pj7qzeI89X32LkysE8NvriwngVudR4+AKQPItbawUoqOuZFjEIa/N2ah1vb6iWjPiBi/fvMQfDKBWxXW6Zhn4Sstj3rZYC0VQY6VeudB2B40kODf8CidkHqIp0veGzwyxtiXlbnbPlaoHgW5WYXhXpnKsgNwPRgz3hdRCc9+8msJgBIrwH1HWAwwOndOk6sJKfus6NLCTAGnNn1eOFuFkW2l9XivXBciAp26mbNxDVr4x7+WjqGSDDgffwKELu1SaMKiIwYAGiWzFTop109XqdyZDGBWDhgKsh6iuIZ4VRM0sArNwjKbiSzyUvsC4Ofr3fGuLkFg3PNe2VYjTQ9RDP2Y9LrSHd3bAO2gEg1BIAdL2UfqltAet3+95nD4KUNdPvc/P5JSMPqadFOWQaT1R7+hWIDmVjIMrawmo4iGgp4X52s2nnkjgDtCja3xCJ/KDe9SC4lIwgHGTYrHpfJb98rjIRVckD6gMgsql7E6Z0arlqeppqioSDW0rE5VplbxLCShibW9v3YqrawlIwgGhqv/A0YmWAbXi/cnIt4jNVOTe5GCqfNTK+jE3GioKFiO5fGRMUzYrc4qbskM/0mCum8wp43+JxzFCc8/AUjPbPc9Gjb9Pk/FEcbzx0AkDmIzKCMgyn7MQuJCIIBhp2ah4aieklt8LOHwOrbCKNnuxhl4iHEw0Fc8tLgm0bCrnbhENBroVLYMuUwJhEIMBxZTHhfPVo+ryiiE/PdOi/oE6uspkCAYS0Tw6bXHH55Q5yXoAUlaHj93e2HcKHUxHGLnQcgYiHeU2BG2uXytdO/kwv62c/i0w/umLvYSRxZTHi7gwSA+q7YRVotkGYG0cNmBtrRxelzdjiX8Pa+lyg8ar3zkBlYjfkIEex5g19p9tAb8DGDL7MqPDP40cx0zvYkDKN7VdT+pg4EGJa9ThmtbWliC4fAwLGKoq2HzxjDgVzM1LTxBFLz3s7DN97vFTcs+XuJ9UwMl7yMeXRbYjFaPGn9eyMW0tgRmTxW2UNyXptezgsQO0nVzgNAdes0OAcO5awzwQ7m4t4Hk3ceEFlVVrUeR75dHPtCz2pgVZEL4V17nvor00Gt5rV4AgAD8o/g0d06EpEgllPTFN5iKuJ9LIZz4eFbGfzsYSFqKDPFPMaeN/h5o+hqaeRipqMhBAPMOw4/uQxc/93KIZ2Y2NoeiahvnpV01DuD3+8JXtcuXRQwDcgBlseqTbooIB7ILS8pivIFgAWNlFWreQlPOlC9YOvhAyLF1dMgt4zHWNETgOnhD0rnEA8HkYmFLIetZ2Pe7zyqF+3nlTkEsACaO2eM77c+Z4cND99T9VNVnCiSAJIr4MWz2K218axrrWXPjy4mUGn1vM0Ek3pMVh5+KCqei/J53L9ZxcmVlG1sDYC3FfDNItCuWBt8U13XN/gzYSicNvReGWPIxcMoeXVzNfLTwmQTKDKRmXAgrOYDV1JR7yid+o6owEwrPHzDkz7ICrbbbQBYTkWR95JqqlwQD1zQ2mDKeWW6O0qDv56NeltTISUC7BbJ5CoQCCNUvYAD2ZitkZAePrfxaB2Dc+Hh280rGAbSB9DeFYbCqlANEIa12+fe7j50u8jsETR3zqDdG+DakarkURyZh0ifqugQELui0jmcLzRwzep0zAMAnmvoN533cl52GToS6QPD+9Bj7HmDL6tsR4O2gKB1Sl5QOv2eyDiJLyqHbQ/Ejb4c0Bh8Lz38msZ4AebDcDRUQCpqY3whdki7tY53Bqy8YU/nAEByFTwQxkGWx0EbegIQHn5/wL3TRior6hYAMxc/3rxkW0AHCA+/1R14Rxs2CkYmjE1sAQDSBxCobyLArLOagOH7nsYX7AKjEpmD4EYl7mQxmISk7Tylm0pnxQJtJ5iWPQJeOofNSsvWqZCB3C0vF0idwX/264ey3B5jzxv8woSsgkQuHvZm+9g0BJhGVRUtcKkrbvRFXlKOW0lHsVvreOOxSi/BjjYBgGgajUAKJyNq8arlZBSd/gBVr6oOK4pMGAAIBNCOr+EAy9vWBwAwheg8eyDNYLJibtkjyLQ3bY0qIAw+AO88aSldoFq80+uINrexko4iFLR+tOW8PA1E1rc1mWCHEK4LqsxKOBCAuah7GlAunrUWDZTIHQXKG+B8gDWbhAUpZT4fg3/M+vdHvx049jTr37nEnjf4krZZmCh/zyUiKDU98Arrhj550pqblNhsBtDgUaQHarnVlVQU/QH3Jr5g0hOKhxHAbmAZB4Ml5RgZA/EslbW6pTZeAKrRNRxkeduHERgaMM8eyNq2CJrF7FNseeYgVgY7ln0NJA54bVhl6q9qIUqvI9XdHetYNjWvjMcptr2O4KQ1RYeRXhVJNG0zwUSviqC3tQvVS/YxDwDIHATjfSyhartbS0dFhtq2l/Gr4hkgsTwUTbuM2PMGv9LqIhxkiE90/cnFwyjWPfDwTYOv9vC3K23kkUG8o/akVwwPyBMev7YFgKkLwgDs8gyWmVpFUUpTeMLjt2tCE0Yzr2JoBQeQt/UKgaFMhWcB5dqWdvFuRpaxhDLWFTEPGTTd8sqwSg9fZcDS60gPqjiUsn+sZYqtZwaf4vAYi9S18epUDr4EYwxHFxPeBkerW+rdrZFxdYRtm46D1bzWMlFseZk5J/tTXAHsfYPf7CITC08F13KJiDf8al00dNAZie1qC5VAFkyTX+upnk5tSyhRWuVtj2Crn0KOq3ceMgbi2bwArcHfwRLWWAnxsP1tKnceu14FlGvbam8VQCm0hCjr4WjMPpC3mo6CMa89fKaem7FjuiZhHydijGE962Eqa13KiiiupRF3uF4xL0DEYzyjwNo1kYNPmNcaK9rWLQDAaibmPaVjx9/PGXvf4Ld6yFho1+QSYdTaPXT7A3dfQKR0dqpt1EMLw/E2kPUCnuj81HdE0EqBVrePzV4a6Z565yGDWp6Uv1O0hwBsDdKIsq5lw2mJaCiIbDzsscHXLEQB8fuDgYLtmHAwgOVU1NvYQmpVuXi3wiIT7HqmTulbz8S823nIlouq+8xYiK6Oqw3+eiaGzbJH11EG31XBZIPqPB6tIGNRyS3haepvvysSFnyDPx8ID386+yTnVfFVfUfkk8dyymH5egftyIK2gk4afE9aMNZ3tVTTTrWNPM8g0q+LZimKeXnGZdZosYULXSNVTrNILqci3hn8ut7gbw2EYV2Gele0nol55+FXFSmZcl4xEaBcDavPxVom6p0nTdKREtf5UEgTv0pHUai3vakRoDgViWX0EcTxiHohWk2L1F9PMtRqW0ICQxWLmSM8MfiMsRcyxh5gjD3MGHu9Ytz3M8Y4Y+yUF99LQaXVtfTwpWKl61z8+o4wqpOaGBPI1zroRheFEVbcONl4GAHmkYdf29bvPGptFGAEKDWLkWetIYke/tmWsc2WtJkN1rMxXCx5YMD6XXEONLuijZ5YiLKDknZe3nn4iipbA5t9sRAtaRai5VTUO6VYAqXTDcZR4QmsQpMJlopgwD0SNaTsvAMBFAKLOBhUz2stE0Wj0/emTwXx3p8XXBt8xlgQwB8CeBGAGwG8kjF2o8W4NICfB/AFt9/pBJWmtcHPmXo6Lm+uel5rVAFhwAeJZZFL3bGvtg0GGHKJCAqe3PS7+thCpY08Tw/HK7CcinrjSVc3xa5IUbvAOcfphpGOqfHw19Ixb+Yl/36Nh3+2JQx+qKmel7cevqLK1sBmk6HGY9qFaCklDJgnjVBqO0AkDYTtU2e3Ki1s8xyWNQZfJix4c48ZWU2aa7nJc1hjJeUYmcHjSWLAY93gA3gKgIc556c55x0A7wdwm8W4Xwfw2wDmoERkj3KzZ8nPyTRN15k6jd3pFn0TqLd7aHb7CKQM46sxYAuJsHsPrNcG2mWSh1+UBr+hN/jeBG2NnYdiV1Tv9LHRNeb19fcrP24hGfGGAiMGky/UA2giNuSvbbCejaHc7KLptk0eJfURQ3ou2bWPLQCjfRc8CsDrssBqHWzzHDI99Q5SatnsVj24lpVLQCSlfDZ7/QEu9rJY4urzJbPEPOHxzXtM7yTOA14Y/EMAzo+83jDeM8EYuxXAEc75h1UfxBh7NWPsLsbYXTs76oeJCkHpWHD4cXFzua62bZa0/L30WCIZ44Gta6iTpAdbbjN7SMPhV1ooMknpqG/8xWTYm+12Y1d7wxfrHWwjJ14w61S+4bwiqHf6aHVdGla5EGsNawuVoD4Av+pVxlWDeC2rbeSRRbilXrgPGimInlS11nf0Br/axg5yiLc1DoU8XzUvDOumdl6FhliI0l318yjrQLa8kC2vXIDItlLHr+aFuQdtGWMBAO8E8Mu6sZzzd3POT3HOT62suF8BW90+Or2BpYcvFTNLbg1YqwTEF5RDpPGO5oy/SeNJLyQ9qBGgpMtBePiDuGFINJSO8KS77oNX9V1ReKKAOGcMzcQhJV0ADOWuXcdjpPdFyLhqRhaG59gGsk+wFPCbGXVCJgzEtayGFsB0C1HGw0Y7hDjRbq2NbZ5DuLmjjF+ZDYC88PAJ6bW7VWHwo92yMmFBigp6siOqGguRXUOiOcMLg38BwGj/vMPGexJpAI8D8CnG2BkA3w7g9ssRuJU9a604/HQ0BMYExz8zOBdb7XhOOUwaonhOevj61EzXwVFifcBOtY1YelHI22oWosVEBJ3+AHW3FIUMdCsgdxKD+CIhs8mjlpU1/SLJOcduvYNOdEm7QMqMK9fVyTVCABLCgDcji9r7aynpYRGdTjgNQ4Mf6DWFSqQN0tEQoqEAtr3wpElUUxvbMJy1mv3inYqGkIwEveHwCc7OPOGFwf8SgGsYYycYYxEArwBwu/wl57zMOV/mnB/nnB8H8HkAL+Wc3+XBdytRaYqglFUP2UCAIR0NuUvL7DZFOzINpSONV3rB2MYRUjOLDZdCZfKh16h47tY6WEzHxS6FaMBc8+WNgvaml+eMJZe050t6+J4Y/EgKiNgX4dTaPXR6A/QTy5evpoIYW9itttGJLovzNbBflLPxMIIB5n4h6nXEDpfA4VfDxn1Y3bIdJ6paY9j2Ik6kq7KFMPg73OivqzD4gAjcekLpEJydecK1weec9wD8LIA7ANwH4AOc83sYY29ljL3U7ee7gfTw0zaa5dlEGJWWi0yFVkkcNR6+fOBzmaxoUdcsKccvJCLoD7i7uRE9/GLD6AaWWCbXCLgyYL22qIBMauSkDUormNTPy9T5cU2d6HPwTSOZXBHneGBfuGdWAXsxL4DksfYTy0ISW3HOAgFm7CK9opr0iQHdmDH3mr3BBwRf7jqVtdsUCQu6eoqK2HmIeanliFczUe+CtoSsvnnBEw6fc/4Rzvm1nPOTnPPfNN57E+f8douxz74c3j0AVA2DadekIhsPu/PwpeHWcPilRhcBBqTjYbEbkAuFDTwxrPUdsbhoBJoKtY7wkAmGVSqOukoZld+h6x/Q6CDAgHB6iRBMFhSFJx6+hieXRjKUWRcFNAoaLBERujUF15TONhBOKnceA0MiOiCNnJbWibjn8M2FSMeVtzGQYzQGfzXtgYdPTH28WGqiE1smzWstE3NP6QwGImibO6IfOyfs6UrbmmHwrRpgA0AmFnbH4TeNvGICpZNLRBAIMLE4aDx8Twy+pE0UXXy6htzxQiIiDLCO0kl4QOk4MPjZeBiB5LLgfXv23+lZsRpBVkEayciC8dBWLirHr2ai3hgwTVZTqdlFf8ARkplgGorCk+IrQswDEDuPQMagVzSNPZZSkcs2r3y9bSwKTEk1AYa8QtVlQ5tWCRj0tE7FPLGnDX7VoHTsGnu49vCJlE6x0RnKM8dzQw19G3jClTeL2p2H/NsXkmFhgHWUjhdCZWSD3xU7Ctnwo3LBdqwsVnMd6CYE+iSlk1w+NPw/Cgh9GJdUQENfQCevSWzBKM7SefipiPugrTSsirlxzrFVaSOVWQRCMS11spSMotToutO4Iqax7lY7WEjFxb2oo3TSUbS6A3c0K/He//pGCV87X5r9exTY0wZflkKnbCidTCxs8vwzQXrqGg+/1Oialb1ILGvz8D0JQjYL2oIwmZKajYfFw9EsKDlpqQ3uamtr3vSaoG1dUk2SotAHlF1RJ722EYBU0wAFg9JJL9EC8MupqAccvt7gy4UoJRMDNB7+UjLqPmhLiC1U2z3U2j0cXkwacQ+aU+HK2SHGFnbrbZH7n1rVFtENUzNdLN7yXtHEr95+xwP477ffM/v3KLCnDb5cjVMR+6CtNx6+nsM3PfzUijZ/exiEdEmdaOZVNJvDGEFbPhjSVBYwtcFd3fTG7obi4SfCw0WLsCtyHfMASKmP6WgI0bSsqSBkXHmRYkugwAAgs7AsYjcED7/a7qHdc5FiW9sGohllnUTJCL7n5LXULZBJD+596RxonIp8rSO+j7C7XfOi7wJxXsJBVEuaz4o9bfBrrR5S0ZDgzi2QiYXQ6g5mv+mbRQBM3PQKlEf1fOKLwugpuMB4OIhIMOCuIxfBK5T1AeJhNAwKMWXU1bwA/WIkPXw5ThO4XXKbdUIM9BXqHeGFRtNAIEwKdEt+fSZwTqJ05GK3mIoanrTa4OfMwkMXDg+h6Erew7kEzbAOG+24vMciKSCSsB3S6Yl+w0upKM3gm3o6bpwdGtUkKOD5FGbtaYNfbXVtUzKBYX6+zNd3jGYJiGW1SpnlZteUckA8J7I7FAJqjDFk4i4CyoO+WIyIXqGgTqTB11TbJiLuqoAbeUGBBe2vi5zbQjJy+Tx8YqCvUDfSWBkTDy4hG4a7UYA0A316CgzAMONKQ+ksekEbEmQVpKzEgnQqdMV9pofvxpOmF/YtpSKk6yhlH1zFr3wPf76otXu2AVtgWIE7M4/fKmkDtt3+ALV2b1j8JT1WTaZONu6iKKxVBsC1HL5s4p5NhIc3oYYrzyUi7uQoGnntQtTs9NHuDYTximaNKmC9h+/KkyZ6X/l6x+z+hdSaNrvDdQCeSk/UBdUUCQVE3EPr4RvzcnMtCVlNUi30QM4Ijmo4fFNAzZWHv0MOci8lI+I6tkpKeYVkJIhIKOAycy4vdh5h+5adra6QYV70PXznqLZ6Sg9fGvyZDStBOE166eaKLccruHLAZQaRNI4K+WFAPOxBo+LYNHQaDyyXCJvc/2xzy2uNasHceYTF7im+SPLwXXnSxAyKQr093G6n1rRZOotuOWlioM/cEQHCCBOrgF3t1mpb2hRDeQ8vSa68U1Wm2GZiogq44MrDp2Q1iTksSwoMUJ4zxpj72gVCLEZSRgdyav2oWbG3DX67h5SidZkUVZvdky7pA7bGZw89/Nzw/yrgyuBL46jL0ml2kYsb/X6JHP5CIoxm14UyZaOgp5rqI7wvYAT7NAY/5bL4qlEQnHwkZTuEcz7k8OW85l1TQQxyF+ojBl9SFIo4kSkPPusCSZRVqDS7iIQCiIWDJHrOrAJ27eHrArZiQVmWHD5AKPCLuFuICLtb6UxJfSivsbcNfqsrvFcbDDn8WQ2rXjhNGu1swhmlI6gTF/Ma/S4b7FbbpqIjQlHRyEKz5R42jpl195HXLkTSCEljSfHwJc0ys6GQD6OiUK3a7qHb50NKJ5Y16DN7uDf4xvXQ7NYKo1RTclVoPCnmljMVRmelmqSUtN7DN50dolOx5EY8cDAgBbnlfbKUuoxxooa+5eiUs+Mx9rTBr2kpHfG72Q1+SUvpmDx53Bml4+rmMikdtcHfqbVNSVoAgjYgBG2BGT1DmXFC9HLMVNakvnbBtWElBLllnr+UckAsK6qAFbULrmsqmsQ01vpIZgeBooiEAkhFQyjMSumYUtL6IHfOocHPuNndEqtZd2tt8xw4y1BzQYHV82TRQJ/DnwHVliZoG5NB2xmydDgnBW3ljZtzSOksJiNozNrUg0rpyGpWCUJ6mqtOYZ2a8Dw1xkt6neMUha6QyKXOD2HnIb3OMQ8fXAh12SASCiAdDbnz8IMRpY4OIP5ukwYgUhS5RHjuHv5WtY11o+GKOS9dYkDczbyIVbZGDj5jbLh70pwvkaE247zM9Fo9NSe/ax7Yswa/1x+g2e3bVtkCQCwcRDQUmM2b6NSFJ6EtuhqpZgUERxwIESgdF/GFRkFktkSzymGF+ojkA2BUAeuzdMS8ZrjxiVW2prroqGfYyCs9aZOicGNYdVRTfWIhMuk5zW7NTfP36qYIDiuopmanj1Z3MNx5yL+D4LHOvEASZBUAcc6GVBOtWE3o6cxKGcqsJrVhzdfbw1gM9TomRbFapzeD7EOnDvRapJTMYIApmQk32LMGv2F4xioPHxCGeCZKhyicVp7U5GeMpJgpx8/E4zcL4iZW1Af0+gNUWt1xrpCgmJkzg32zLES0TJhSQ9RPhILG/OMLogq4U7X9PyZFMbOHrw8mywD8UBdJcr9qQ7GUjGB3VgE1QucmmbM+7eHr4zEzUxRmGqu+IGxhNBYDpq8RMIr7BrOk2BI9/GKjO1wgQxERvyLKg89EZxLTfgsNQYHZFYu6xd41+G1h8BM2sgoSM/OFROG0UrODVHTEeMn/ozESslBrZg9fm5LZBecj9AQw9KSV2R1ubnp6xsni6LyIHpigKGY4X4OBsUjStIfMRdKsAlbPayUdnb2QqEnJahqRyADonHQiPDtF0SgIMTRFNWu7N5FTHjT4cm3KaNToBzHLvU/z8ItTu1t9JpirxADi7rY0ml47B+xZg1/vCM86GVU3wM7GZxRQIwqnjWUpSMRyZEpnJi6zWdTSE5I2kbo9AMRD0muJ7acN4hFBg81kWM0iIn2WztjOg5jZNLPsQ7ssdhCEnUeAYZj5laB5+AtuMq4aehG8wmRWUyQJBKOEmgoXMhmkBVKqsY7uIvWyD6ZhnWUxqtN2kWNBbsBIsSWq2M5yzojzmqJZPcaeNfhkDz82Y0UrUTit3LAw+PEFOqUzE91UIDdWX5ykdABSps5MC5EDSmdx9KYne/gzBtXIgm4jfQ2AEUpHbSiyiTBKzRmbvzuoWzANvqyr0Ow8FpMRVFu92aSIGw6ciuSkYdXPa/T/O5vXrtC2CkVth4z1gZAg9E52JWpoUjr6e39eAVtgDxt808OPEDz8WbR0zFz3nHJYuWmhi0GgdGTe/mzxhZLW+5KL3FiD95gR5G3ZN5oGXFTbNvIiYB2jBJOdUzoLM8+LntU0di3l36HLholH0OkN0Oo6NKzdFtCtaxfv/KTBB0ge64IbATWCGmthMsgNiP9DKHACZqROHCiLjhU3JfSd1cwU21n0dIgSGVP3vsfYswa/YRj8hCZoOzOHT9XCn5HSSUdDCAbYjA+j3sOvTFYAA8N2iG374CjgIp2vkRcLkSLjBBA01hilI4OW1UvK/zdz2py589BTTWM7oqCxeBEWIvn/HcFBDn4wwMw0YwAkw+pKT4fQb6Ew40LkOjiqLW6yoJoIO49cIgI2a2e1xq625SjnfDpV2mPsWYNfNygdiodfbXWdZwS0SgALanvGWnL48QVRBalIM2SMzUY39drCK0zoJB9kAHLU4Bsyz221hz8zJ02ILbR7fdQ7/XHvK74gzjXBAxPVsA49aaJhLdQ70xWQ8QWyAdtxmqlDXIgKRke1scwOJ4Z1VhqMUP0LwII6UcuDu6J0CMVNZlbT5LzaFaBvf18HAwwLiRlTWeW8FM5OvdNHpz/41ufwGWMvZIw9wBh7mDH2eovf/xJj7F7G2NcZYx9njB3z4ntVaHYMDp+QljngQK3jkNZplgQ1o7iAnHPB4VtROuBaw5pLRJxz+ERZhXJT5PuOpa1Kz0iTNjczpUNouzjU6B95GBkj0WALyRkpCqJ8QanRndY4ies9Q1nN7NiAUbOaahY0gDSsCsycYss5qTI5X2sjwCw8/EFXuYuMhYNIRoKzZ8No5iUX3rEqc7N2QU+Dzebh5/UCeFYUmMdwbfAZY0EAfwjgRQBuBPBKxtiNE8PuBnCKc/4EAH8L4Hfcfq8OVA7frLadxbBq6JxWd4BOf2BN6cjPUCA7S8WhA4OfiYVEpaGENHgafRgpkew4CEkw+FM6OubcFkjZMKOfQUZ9h7TdLlg1piCk85lFYU6L1ag6OlapfJKiIHjSjs9Xqyx6Omh2Hjs1kV4bHNt5GEZPt/tIzSBURqxmnQpyA2Q9nZlbQzZ2ybIK3+oc/lMAPMw5P8057wB4P4DbRgdwzj/JOW8YLz8P4LAH36tEo0PPwwdmyHcnyCqYtEl80njlhp+hwExFYTI2oDX4vemFKJIU1InG4C8kwugNOOodh7IPzSKZBpgOdFOCkDNSFITtdrPbR6c3mDasBEonN2sRnQMOf0p7Jb4ojLLiWs68QMp5aa7lbq2NpeREtgxRxmAxGXWeDdOuGtId+qIrYCJ+RZ7XjBpXpGDyfJUyAW8M/iEA50debxjv2eHHAfyz1S8YY69mjN3FGLtrZ0edq6tDrd1DOMhEQwgFhgJqM1A6hBx8ANYcvvwMBXJGOp+zedE8/FKjg+ykkWAMiGX0Hn58RsPaKOgXydE+u6OgePjJGSkKBwHIKX6VQOlkZjX4RBG8YmNEslmC4LHGwkHEw8HZruPod9ig1OhM79TInvQMhpWsIdVBJjZRDEktVkvNWLtA6AMxb6VM4DIHbRljPwLgFIC3W/2ec/5uzvkpzvmplRV1ybYOor2hfqXMuvLwqXy0DaVD8PCde4U0g1+xCiYDJMnfmfqhdptArzlbZgcwX0rHQWzBciFqlYG+vcMgMmhmCMA3CqLkP2RvAAYDLmQCrDx8QJuLP1MqK7HBjmWrPqKzs5CYxeDLe19fZW5JgQF6qsmQo3CU5NFri3jdFVbKBLwx+BcAHBl5fdh4bwyMsecB+DUAL+Wcu+giQENNo5QpMVTMnMGwUrXwpzx84//pPHyjCtjRzSUXkVkqgAGSwZcPiyNOmmwk7CidBYLxmtHgE9JYiw0b7yvhLO7hCISdR6Ul2jrOasBmKlZz0GBn+t6nUSdCQM1hnIhYGzNVye1gXovJCPoD7mzxpnYtq3fA2ERtjMfwwuB/CcA1jLETjLEIgFcAuH10AGPsFgD/B8LYq1NAPIKun63ETAVOg4F4wJ1q4UtQPfyEaNtXdSLf3CwaSpkZ9dyaXWTjFucnltXOS3LSjjxDopEoNrpIRoKIhiaC7fEFIZ6mSJuLR4KIhWeQfSAFkyeE08wvpRrWGeg5wkJkWc06Oi+CAZtpgQSUc7PPUKMrU7Z7AzMWR0KdJuhWbFjIF0QSQhuIKKDmKDWTWHRVbIgOdME5CacBHhh8znkPwM8CuAPAfQA+wDm/hzH2VsbYS41hbweQAvA3jLGvMsZut/k4z6DTwpdIRUJgzCGl06kK7RWn3a4kwnGRFaJtZD4D3SSzhxRKmQPDQ5kKJgMGdaKelymR7OSmp263rXLd5bwAQkDZIRVgphjqGr7bePimgJpGXmEmek7v4ZuZHTN7+DNQOk1Dflvh8DSMnPKpeywYErLdVMPq5FoSpTuKdRv5gsQSSaAPcEhnSu0gnbKoVRaYx/BEdJlz/hEAH5l4700jPz/Pi+9xgma3P80DWyBgVCg68vDJVbYjTcJHQZRINrM7mh0chb0q4dTcNAtRtd3DgFvsPABH0s3OPHxabKFoFegb/X/NojL45Zg66dREXjjRw586Zwmax5pLRHC+0FCOmUKjACycUA6RKYJTvG8sC4DNz8PXyG/b7jwAcc6ImU3lZneML1aiWQTAtNIdopLb4t4n1S7M0BrSpHSurFImsKcrbXtIalIyJTJxhwE1qnCaVa67RDznQDHToWHVzGur0gIArMlORGPzWtDmb0vteUeGgphxUrAK9AFDw6o1YA4LY8iBvg6SkeB01pe5EOkLduaRPTT08CfOWSBo3GN6A1ZuijiAo3kR02stF29CZ7WZ+kE0CsLYB+xrbzq9Aeqdvo2Hr1+IZtIfIsavCvXuXKtsgT1s8BudPhKaoisJIZHskCcHSCmGtilWBK58ZkqHsBABFnw0IP6mfkdk1Wjm5nheACld1PJhdKCYOdvDqFc9taaaaBLJuUTEDLCS0O8J+kor9yD+1ql8dzk33QKZCINzh/cYoTtYwY5qIs5L0qCO7zFCSiZgc+8TFiIzJdkRnVkAwAj2Yv6Ujm/wITJ1nN1YJXEkZMLYRtwJAmrypncU7CNUAEv6KmOVtkpVpkw65KRbJaHRHo4rhxUnm584nNeiU413B1TTlBcNiIWboPOTiwvDSqYOHew8oqEA4lb3OkUxc5Zq24a+gE5rWLWUzgwNgBwE320Xb811TMdCCDCnHn5eGHvFzgOY6A42J+xhg9/T6uhIOK5oJXa7EoFRG4Mfz9E9fCcPI6E+QKagWi5GxAyiXNwhVy4fRkU1q2i72LPhV+kSySUnFAU10GenU27q/OgMq0PFTGJW01R3sFE4kPx1lJpJoJps6xYAkhxFdiR+5WhexPRa23m1SsDAPjMoEGDOG8cQhOaanT7avYHv4c+CTm+Abp9rdXQkZvbwCdSJZWAUIHn40ZCohCR7E4OBEbTVFV1N9NkdBdGwZp2mGVKKm5qytNzipo8aQUjtvEQqa41K0UnDQwio2dJzhGrboZ4O8ZwRlTKVNADBYx3WLjikwQietG1OeXxRBMt79uU4sXAAkWDAcw/fts4DEAskH2gzwRzLKxAE3QqqHZGH2JMGv0nU0ZHIJhy2OWwWgUAYCKszZywrDSXiOa1EMuCQK2+XAXAyh5+OWZwfB0VhZUfB5JLeSKhKywMBkiftOO4hDauGBhMevt21JGjPm0FIqodPpZoU9xiB0jEVM6kGjFgxXW50kInZ5JQTlCkZY8gmHN5jBGlks57CLragmRcgA/De7oguh1ImsEcNvlTKpHP4IbS6A7R7xCIPKZymoCcGRhNmpYdPkEh2ZPDNnUdOOazS7CIRCSIctLj8ThqGO2nbR6hMti1uGp2b17ULrZIoUgvaOweyobath09onjGkTrzl8NUe/gLQbSgD8I4VM8ntIDULEUCSMSB70t2mqI/R6dUoYwv06mRnHL5eSvpyKGUCHuXhf6uB2u1KQhqJSrOHlTRhkSAIp1VbPXC7XHdgXDFTYQidGXwn0siqhQgkDr8/4Ki1eyTNIjRLwIEnKodob/pYjl4YQ+V+CXULlWYXnEMRj1kEtu5RfsaCU0rHwbWcKuyTGG2ybhMsT0SCiAQD9MpRomSzpXyB+aVSqMxDZUqzylZHzXURCQUQD4vnvNvtYmNjA61WC+itAi/4AFAMAbX7bD/jP90URqubxn332Y8Zw9N+T2giKcYnOn380UsPIFa/hPvu2yJ9bCwWw+HDhxEO02mgPWrwad2uJEYlkseaItiBEBi11dGRMDXxS4DiozLxMDaKxIIdopHYrrbt/85oWmSdEHvulho0kTpSBoVuW+ug+bsjD19L59jkuo/Oi5zd4YDS0XRUky3xlAsRIOaWOWg5hDEmMq7IOw9aMLnc7NoHk8156ZUp77uo3gEP50Wv5F5MRMzamI2NDaTTaRw/fhys3wG2B0DuqNIjv1RuYrfWwfUHM9Y1NqMY9IHNFpA+AKTXbYft1toIl5q44UDGeuc9Ac458vk8NjY2cOKEujhvFHuT0jHaG1qmqllAGnwyj0+gJ4YtBO1ueuP/e6mJTxROs5StlTCzTtTzyjkxrLLtIpHSsVULJChmOtaeJ11LRSofIAp2unVlEDIQYM7kFQgd1eqdPnoDrthFUjObHLTtIxYRFRsd+4XIgUQyWROfmDk3STW1Wi0sLS0Jwy3TJgfqgH8wwMA5BykRTGb8BNS+tcwqCxF1dBhjWFpaEjsTB9iTBr9hdrsiVtrGHHqFbrTwJUY9fAXmQelU2z2krAK2EhTDapaYE+ZGzGpS5pQDoLQ5dNzQhnItZe62zpMmnDN6Wqa+nsLcEaliCwChetRBii1Zc14R86B6+ElRBUzqUUyVO7GIeZheOgsCYMq0TABmIJqU+isXD43B7w04ggGm3zGMwMlYiT1p8KW6JFVmdMjhO/CkiY087LN0pBhYSTu3eqdPvOlpFcC1Vg8ZlcGn6Pw44crlvAgGTBm0IjR/j4WlYqYDA0aUL7A3YA4C3WQPn57rbn+P0RaiBSdyFA09ddLrD1C1q6cAgHAMCCe1ctdLTgLK5u5WraNjW0AHiN1UIKj18EOmwSc8k5zo4fc52bt3gz1p8M3CIpVRG4Ejgz/oA62Kew6fmP6YNTtyET3pcAIIqeMQWiVRkofvQECNqD1U1IlHxRdEnrSu+XucmEUhlTIJjTwARfYQuQF2hB5MJhTr2CplShCVPBecZJ00C0AkpWzKMpTuUFxLQspoZiSZQgty3YJi5wEIw6yldITZ7Dny8NX0cm8wMD93ntibBt/MM6d5+GabQ0qxTsvIdXdL6YQTIpdfq4nvgKIg5Lp3+wM0u32koopzQ+DwHVUBO8gpVxaeEJu/LyQjtIWoXRUPJKEgjDHF/UQUUMvFw/S0zEbBQSqfzbykxjuBwy81iY12CDo6Rd3OAzCqbdWUjqPYWn1XPFO6IHdTc48FglpKJzQHSueHvve78blPfxIA8N/+23/Dz/3cz+k/ewbsySydSquHeNhC2dAG0ZCgAUhG1YGsQjQUQCxss7KTg6MONEWcCKepGiUTPPxoKIhEJEgzrA6E065fVzRuIdJg5MIYRz1QFY0pHHD4XlJNZV0wGQCpyXoijP6Ao9rq2ad4ShB2HiUdBQaQqoAdxdYIO7VKqye6g9nM6y3/eA/uPbctdpHhXdvP4QAa7R6iocB4X1wL3LgcxH9/aljr4f/Mr7wB/+t3fwuBdgV33303br99Pi1D9qbBb3ZNr52KTIxY1ecgOKT0cORneBmEJAT65Nbdduch5yW5csU2c4EahPQip3z0/+tSRuNhPLRd08/LTW/WUZApHRGP6fQGamek2xIFU9o0VsK1jC9qufLR4iutwXeko6M6Z0tA8YzycxxRrY5kFVTFTUwpDW6MACAMvxaci2YxTL0w3PKUpwGc453vfCc+9alPIRgMol6v47WvfS0ikQie/exn44d/+Icp36jE3jT4LUVhkQ2ycaK8ggMPX/kgys/wMq+8VQIWr9LMi+J9LUBUAZeVD1GGmjIqG1Mo2i5qc8oBB3EPYmaTg96syvMVThgdzOhFYatpi14EDudVbHSQjobUedsUxczEsG3fcSSVYylNWcwgt1VHNQfzcnTve1DJ/d9fchNQuQDUdkSRoCIL5psXylhMRnAwp1Z/RfGs0A1SYDDgeODeb2J7axOrK8tIpwUt9Xd/93d42ctehpe85CV4+ctf7onB36Mcfs9xI+AM2Ug48FZ1c5CetAJe3/QlXYoh4ExPhxxbyCl3C7V2D70BV3vSDoTdyhTZBzNdVK8LozxfjBkCatRuSZpz5qDhe05FzQGkojCzKT2VBqNSTUracFHc+337uNkwTuSNh6/NtgIMrp0LWkeBUIDROXympnM2LlzEG/6fV+PP3/+3SKVS+OhHPyre39jAkSOi31cwSKsp0mFvGvxWl5yhI0H28Mla+D39LsNRcNSboK02lQ8gB0fJhUSk7bZciBQPo4N5dXoDtLqatDli8xOlLowExbBSDb6DnYdWe4Xk4Yu/raALKMumLITsIcvWnmPzMgLSimsZCQWQiARpchQEBU+lRr+EDK4Siq9oWTp9JX/faDTwAz/w/fjlN/4GbrzxBrzxjW/EW97yFgDA4cOHsbGxIT6GkgJKgCcGnzH2QsbYA4yxhxljr7f4fZQx9tfG77/AGDvuxffaoaJqPGKDTIzY5pBI6VRIHn5W6+FL3Q/t3LotoWKo7bNLMKwOagS8DiYrOeRwDAjFvaPBmjSDT+pElFjU74jMVFaNJ01ufkKhDQ1hN8Vuh+zhy3kRsnRy8bC6MMhBUZj2fBEb0cuYh/JaMrrBJ3n4vKfM0EkkEvj4p/4NT33mdyIYDOCZz3wmPve5zwEAvu/7vg8f/OAH8ZrXvAYveclL9N9FgGsOnzEWBPCHAL4LwAaALzHGbuec3zsy7McBFDnnVzPGXgHgtwG83O1326HZbCITVYsoTUJIGBDSMpslUucm0qIjKR3OlXwhybA66LMrUgxVefg5cSRw0mSDT6QB9AaMIq8wzGxat+rbK9EoCJ19rVJmjzavwmn1vEz9IarB1y9ExxY1ze0Ti8J4tSu2RUnpaAihANMXXzVpVFOZsiMyA92aloKUYrVOndSIvtTo2Gv0S5jyCrrUzACaXYK6rsbDB4b5/JOFV8lkEn/6p3+q/w4H8MLDfwqAhznnpznnHQDvB3DbxJjbALzX+PlvATyXzVIXTMClfBFfGLwSv/6179BetFFkDEpHm4tMEE7rDziqbUIcIZYVlXjtqnIYyeATq2zLjQ6y8TACqqo+kysvKT8rEw+j3RugpbvxCdlDwxRDisFXz2vYAJtgwBK0nQdtXh7JUVCppnpH3zCDoPHOmOziRJwXIZis3RGRtecJHr6DOo9sXJFeCzikdDQ0C+eGwafp6Cjn5RG8MPiHAJwfeb1hvGc5hnPeA1AGMFVVwhh7NWPsLsbYXTs7OzNNZik4Iib0oZ8h/7+s0XO01tF4+YTAaIXsrRqf4wV1Qk0XVbVdlCBKJJOpE8IiSUoXBZzFPSiLJDmnnOCxNgpK6iQZCSIcZHrD2iyKgqmIvfc+bAdJoJoAEo+vbYLiSEeH6OETagTIMQ9KJbfufDkw+IMBVycG8AEArg3a9h5jBt8zcM7fzTk/xTk/tbKyMtNnRHIHgF89I1587a/I/88s8tDeXCV9k/AW0XjJNMWWWiqAlEFEjC2UGl1kdTd9OEaq0JQPtXJuxLaLZUpsAXAk+6A9Z8RWfeIzCfPqt5XNRqQnXdbJKxBiHrIqXL/zkJ60vtpW60k7yR7SLkRSE18voKanmqgUGGEhIlI6wQADBzBQGXy5aCgoQ0B4+EHGEJgP6TEGLwz+BQBHRl4fNt6zHMMYCwHIAlBfaTeILwCrN4mfidFtchk3QTiNzEdLTpWQmqnNd3fg4WvnBZAMK8mTJrZdLDU7iIQCiIU1t6SXipmEFMPNstgxrur6JMSJHitFXsFBiiEpmEyY10LSgSdNqVvQ3WPhhIiFESidSquLnko8kBzkJnj4jAmPnODhAxp5BSdKmcH5G3vAG4P/JQDXMMZOMMYiAF4BYLIu+HYArzJ+fhmAT3Byb7wZ8dTXiuMOrSuNrMzVGwl1MdLoZ2hTQx0YfM+Ctrqccom4PutkyJUr5kb0vmRWkza0Q1DyTEdlsxH3lI50AGz7B0g4EFDTc9IlB1Wj7jl8OS+tJn4jL/SfIinbIe1eH41OX9+blTHh5RO6XnGueS699PABkoCaDLAqUzOJBr8/uDxKmYAHBt/g5H8WwB0A7gPwAc75PYyxtzLGXmoM+2MAS4yxhwH8EoCp1E3Psf54ccw/Qho+2uZQCUIAUn6GtkzdAYevlUg2PXy1PKyoGiXc9ARxK5LOj5OHkbrz6DZEGqoNAgGmp8EGA0P1NKf8uoq5eBOCtoA3mU2k7mCEFEPA+PuY9louJIXOj9IPaxg7IsWiTKrzkKDUCBgLh5LWISYskDx8gCSg5q2Hf3mUMgGPpBU45x8B8JGJ99408nMLwA948V1kLJwQ+hWbXwdufKl2uHygldRJvycaJXtG6Rif40AieSllQy3IZtyKFLDBgKNM2W4D4mHcVu+OSJQOsbF6iZJTPvo5rRIQtm8Zp60CblcgVE/VC2Sl1UUwwJDQdU+jUjqJML62QeHwb1EOkfUU+iBkUPyNhKBtt6/pUexASlobiwFIxWpmBzOdUxGKK1OlzZ0H1cPvqxdlmocvtfDV905/wBELPUY8/G9ZxDJCV2bnAdJw6Y0rOXxJvRBTDLVeoUnplEhz0xpWamN1ipcTy2kXonQsBMY0EskOpChIXiExZTRrSP7agtgwo9IUzWK0VJODdoLFhkb2oUlXpNTuIoFhBpFmXgDU8YWGPuahlWwem9eSdudBqjQnVdkSg+8AURPfMPh9goevydLp9/llydAB9rLBB4CF40DpHGloKmIYL4qR0GZQdBGieIWBoCj80VZoEjr/ULpwSeE0qifdKinTDANG+bwXlI7QHiJ6haOfa4NcPKxeiMzFW+/hk/oqEDn8XCKiln3oNoFeixS0DQYYTULEic6PKoPIgVImeSHyQkCtvguk1Fl95CA3IAw+11M6DEzd9WpgVNkqnIUB5+hz3+B7g+wRoHxePw4G7xvTZMNQi5uoAUgAiGe1Hv6KQePsVFUPY4kunEZ5GGM5oN9RphmKz4rQKB2CnDSJ0nGgp0Obl9rgk0TwANFlLJwkp4zaLt4Oioi08gUSJA+fEIB3oIVPMqyJJfH3KowmzeDvAEm1wS/UHew8AkGRQ6+YF2NMr6dDqLKVMYDvf/ELcOeddwLwG6DMjtwRsWXs1IGIRvYVIlPHC+NVdqLlQ6BO5E1fVdJNJWD5WuXnmDo6JOokN/xcRQFQNh7W86ualnjd/gD1Tp+eLirnpUAuQZgXQNKrIZ0vOTctpTM0+JbSug5kFcjzSixp4zHahYhzkoc/lCCm7NYWhWFtlWw/l5Ri28gDS1crv4pE6fzz64HNbwiZhl5LLOAKHfvjnZ6oWA/ZGPVuQzyT3/d/bD9DGvw3/Lc34Td/89exvb091wYoe9vDzx0TxxLNy9d6hQ6E08gGn6CJL3VvqqoWjCQP3+B9KdQJOaBMOGfEmIeyC5eEE0pH1baPSM+RiogkEoQgpPFZtpy0oxRD4rwI3aXMedldy3bFaAepy8En1lMAIzUC9tcySKENW2VShg5AvMeILU4YY7peKUo6BxjGAJ71zGeCGw1Q3v/+9yMYDOL06dP48R//cbzsZS8jzJmGve3hZ416sPJ5YPV6/fB4WN3X1klOOflhXAC271cOSRoys7W2Ym4ODKtjD1+BbCKMi2UF7eNEKZOySEYzABhJ54dz2Lfto1InFL0aCS+avzugdA7lFMJwo0gsAN060GvbNriX5942aEvU0SnVRc9YGtU0Um27dNJ2WDYRttdFGgyEwSd2elPuPF70NnFsV4H8w2LXoOiRu7VbR6c/wLVrNmM2v6n8/wDMGMB9996DS5cuYWlpyWyActVVV+GP//iPPTX4e9zDNwx+6SxpeCZG5X1zys8pNx3o8cf0HH44KDwmW0qn2zICfep5kfVqAEcevjbuQZwXaVcUCJCqbbUea7OoVT2VSpmOPGlidylb6oQonOZo50EovgoHA0hHQ/ZBW6JSZqnZoaVkEucFAEupKPJ2efjE9NpivYN4OGjfY3oUDvR0lHn4XC+c1htw7Gxt4j+/6kfxoQ99aKwByjywtw1+al1UBjqgdJTGq1USJeEKPhpwEOgDDIOv1tIBgJV0FNvVtv28AJKXk9K1xJNwUBRWUqUZUnRhTB0dDz3pOCE4Gl9QbrlNqonMleupE20Qkqw572DnQZRXyCYUfZ0b1Hk5iHkQJZKXkorqZGK2VbHRpZ8vLwz+YCDiE5qgbbVWxy+9+kfxjne8AzfcMN4AZR7Y2wY/EACyh8mZOtrqTEKuO+dE/XSJWFY0LunZGHMDC6psGGpxU5OYCQOQPfxcPIzegKPRsUljo3S7asrYgpNFUi1HoRVQc9T02uFCpCB2Y+Eg4uGgvTJlsyD644btA+Wtbh+t7sBTDx8Qf6f9AikpnSmR2zGUG4QuXBIOFDNtqSbiAuloR0TWxGcYcG4dJ+K0KttILI6/uP1OvOAFzweAsQYo+XweP/3TP427774bv/Vbv0WbuwZ7m8MHBK1DzMXPjui7W279CLnu9U4f/QGfzbCm12yHKekmoodfplazAuSisGFj7q4ZazDBOSmYXHZSFCPnRjT4tpk6FL2apsN5xReN/gb2zUYAQ4rY1pM2Uh+9ki8AHHWXsj1fRKXMYqODWxI52ryiGWEQdXo6CYViphNpZFLAFiIzhwXoAmqcI4CJ6+WgytZOR2dpaQnvete7aHMmYm97+ACQO0qmdCTvbltt60Dml56lQ0szVNJNxPoAso4O4LgozDKo1m0KyWCiYfUy7iEzkWyLryjBZErD91GQm6wrJJK9VMo050UvCrPdeTTyAJjyHuOcO8seks3fCTo/zW4fTatdJDG91tG8AEfVtpa0jhOlzMtUdAXsB4OfPQrUNrWUCTAikWwnoEYSTnMQGAWGDxBB8rdsO6+SOBKCyeR5AaSiMFPrxMpjdVBlm46GEKLEFuTnEfvH2jbmpgSTZWWyU4qCUORkn6VTIssXeO3hLybCCk+6KORKFB5rs9tHpz+gz0vOTTevpCLQTW5E7yDmAZAE1JR6Ok48fOp97wH2vsGXmTrlDe1QbZGHl1r4ElSv0PDwLYOjxJzyaqur7mU7CUJRmLJtH9XgNxw2nZfZMAquXGad2HPSJbr+CvlaOqBOVFw5cedB9vDDcSEupluIkhFUWj1rVVZC6uOw6MrhtaTq/FidMzN+ZX/OpGgg+XwBDj18i/Nl6uhQlDJ9D9875I6KI4HHNyWSVZSOV8JpEqaR0Hn4IXT6NhosRJmAaquHVNTJw5hzwOFbPYweC6eZ81owGnOrewFLyd8p9LsiL10nJe0kXVTOCyAIuymajZAoHYccPmB40up7THrSlnMjODuFmkOqSc5LY/DXMqJ24FLJQhKbUMldaXUx4PY7NUsnimTwhfm09vAlpaMXTptVC3+WliJ73+CPFl9poJRI7nWEkSCmGHrt4Su15wnSyH0jk8axh09ozgIojIT8HAXIXbgkHPRpLVjOi656momF6B6YA0qnZLdbmweHD7j3pFtl7QKZrwvadCnl0OBrruOyoSVl7eHT21Ra7TxisRjy+fz0tQiEyJRO30oxc9AHwNRy5S6E0zjnyOfziMWIxXcG9n6WTuagiLgTArfDJigK4+U1pRPNiPkRKzRLzQ7WsxMXmbDzqBkVxM4Mvj4bJhYOIhYOWC9EDjz8a9fsuyhNYTQIuXDcdtiCXT9Uska/g1Q+gCzstpCIoD/gqLZ74ztBolJmqdFBLBygFRFJJBa0hnVJ1WykVdZqNQ0FypwYfEMimXPbzCSlPLjLBfLw4cPY2NjAzs7O+C9aZfGvqFa73Ck1UY+EkJ9cTBoFcS3L9hpG/QHHVrmFdiKMwmSGGwGxWAyHDx929H/2vsEPhoHMIRKlo2xzSAyMysUiRTWsgYDBldMKiey323qZX8AB1QSQKlrF3GyoEwe6MPPx8CN4eLtmPy+vuoNJBENGZhOt+KpU745fDweyCo6MKiAWya17lENkdynLTB0HDd+17SAn5yXpuVjGckg6KnZZlvc+SQvfPsgdDodx4sSJ6f/0xT8C7vgV4FceAlKrtp/9k7/9CTz5+CJ+7+U3jP/ir34IKJ4BXvvvtv/3wa0qfvJ9n8b//KFb8OQbDir/Bq+w9ykdgCyTHA0Jb9VST8eBh592QgMAtApNlXwtwcN3pFMuEV8UXkqnoRyWs8s6aRaNHqj2SqWcc2dic3JewLD60wYiOGoxr9qmOKbs6x6AGRYigCSgZkudzGuBBBxlw0z1tuVceOHJZeX/l84OqX/A6LwAZWomYwzZuE1RWCOvz2qitoN0OC9A7Iqsd5F6ZVH5/xadLt4usD8Mfu6Ig1x8m/JyQjYAAGdVthIk0S3J4Vttt0t0tcBZbnqCx2p9zvTyBTKVbx7zWkiEUWv30O5NcLH1XXHUaKiLYLJTT5ogkZy0KQpzoKMzk4ev0Z43U1lrE/dYqyQKyjRVtpWWkO5w5uwYn0motrUsCmvsAgn1QjTbvS+F3fSZTZYG39HO4zFi8Blji4yxOxljDxnHqb+QMXYzY+xzjLF7GGNfZ4y93M13zoTcUaByQfSk1UAoZir4aK9z3QFH2jCzevjypl+kVhsC5IId0bbPhtKhNmVxcs7k30p4GAELKWLptVHK8R1fS0oXJ5tiNSc6Ok6uo/xMPgDa9jGZaCiIZCQ4vVszlTLVBr/a6tGL5ySIu7VFq6Kwflfw7JqdR6nRRYA5jF+Z89rVzsvaw9fHFmSNiCMKzCXcevivB/Bxzvk1AD5uvJ5EA8CPcs5vAvBCAL/PGMu5/F5nyB4RHkr1onaorZ6OA0rHEU8OkAx+IhJEOMisvRxKe0On8gUA3ZNO2lA6DnqNOlokgyFiY24biqJZFFo1CqVMs+G7EwoMcNYEZdJQzEML35wXvdp2aiEyF0g9peOImgPGJZIVsPSkzXmpF6JSUwTfA7PsPHSyD1bCbsRmMcPCPofnzAXcGvzbALzX+Pm9AL53cgDn/EHO+UPGzxcBbANQ76W9hoPiK3sPvySO2qbX8/HwBY9pwUlLaWSNhy8fFkceK9FIZONCKmAqta1Z1BoJsyWeUy9HUhQKSC94SniL0Kqv3BS52469LweKmVOLN2EXyTlHqelA+XF0XqPfYYOFZHh6gZQUGIGTnim2ABCqgC0MqzT4BA9/XvNaSEbQ6PTR6o7Qhu0qqVlMudF1nm3lEm4N/hrn/JLx8yYAZRSMMfYUABEAj9j8/tWMsbsYY3dNpUm5QWpdHKub2qGZmE13nVYJiKRF1o8CjjM7AHFjtCtii6pALhGe5vCJO49So4NMzIF8AeCIK+/2OeqTWieEgFphFn4VEIvkrMHRRl4EVwnzcmzw44uCZlDkcIeCAaRjoenFu5EXGv2KIHe13UN/wOma86PzAoj0nB0Fpvakd2ttrGac5YULB4qRPPxifaJ2wVyI1AZ/pp1aOC5aHBI8fGAilVXSQISFyPF1dAnt088Y+xfG2Dct/t02Oo6LK2Fb+sUYOwDgfQD+E+fcMnLEOX835/wU5/zUyoqHm4A03eCrA5A55f/lnM/O4QN6Bci4RYUmMV202OjO5kUDpGwYYIKikJkdGiMh/89MnPSsGizNAn1esyxE4NpraRn3kPNSKWXWZ6iyBciL91LSgisnGrBCvYNFp/MKBEmL92IyjI7R+9jpvEpGw3fHkDUCynlZGHwzKcA+nROQVNPlo3MAQh4+5/x5dr9jjG0xxg5wzi8ZBn3bZlwGwIcB/Brn/PMzz3ZWxBeExniN4OHHw6i2exgM+DjnRwiMtroDdHoDZ6mPcn6AWFQUN28uEcbFyfJyoodfdFpEBIhy9UiK1DgDEJ7UEflmpwb0O3rD6lQXRiK+COw+pBySs+PKG4VhBbYNzJS5WSgd+R2K3Y1lKqvm/wAzZpwAw3tslqyTRh4IxZQa/d3+AJVWz7lTAZAWbzMeU+sgJYuU6rTYQqnZwdWrDgr7zHktzGbwa4YZ1CxERacaUh7ALaVzO4BXGT+/CsCHJgcwxiIA/h7An3HO/9bl980GxoSXT/TwORdb5zFQAqMyCON4u00VULNogmJ6+HrJh5m8nFlL8ok0QKHeQZrahWsUBG0Y+6wTws5j5tgC7VouWnrShJxyR824RxDLGRXdeq58KpW1URBGlaDRP1PGiRNPujFJnTBSn13Hu25ApO3WLH1YE5b3fn1n+P8VcNQv2SO4NfhvA/BdjLGHADzPeA3G2CnG2HuMMT8I4JkAfowx9lXj380uv9c50geA6iXtMFs9nWZJG7B1LKsgQfS+clbNnIkefqXVm82bIJTkWzbmJhr8crOLnFPjBTiIe0wY1kFf0C3aAKRhwGbNhiHQTdOetJ5qGt5jDuclK7p11ElK0nMj57W+O7+dB2A4FbpgsgVtWN8Vz45Cr6bd66Pa7pmyEY6QOai1GfJz8zUrSkft4W9X21hNO4x5uIQraQXOeR7Acy3evwvATxg//zmAP3fzPZ4gvQ5s2+taSIxKJI9t+kkt8WblV6kCamHUO310egNEQsZaTcwpd9RYfRQED3/FELfarozQTWbutt5QzBS4Gs06UZS+i5TRkYexWQLASZ2bYuEA4hGHGRQJ2uK9nIpit9YG5xxMes6NvH5eZmxhlsWbEPdIDCkKU7OJsCOSBm8mw5pYBC59jTSv/GRwlBBXAIYLmSPITDCVzk88jACz8PCjWSAUtf3obn+AcrPrTGjOA+yPSltAePgVvYdvK5HsINd9Zg9f2ynJYm6NAsCM7lQ2kPIFM21rCUZC3vSlGTz8kpOm16OgctKTWScOqKaZSt4dUDrt3mDYC3jQN9JYaTGPma4lYfG2DHQTDOvMFBhA4/BN6eZRw5rX8vfDhcje+NoiviDiUF17aZFAgGFhsviqvkMKJAOXt+gK2G8Gv1PVaqhLAbUxSoeY6y7/j2MDFjVS03Sa+DEL1UApDxuwv5SNTh+9AZ+N0iEYiUCATResOalmncWwEgt2pgqJzGbcGiGwemc24xXNkrjyKSqgVQbASefLcXqthIPMpnFPWk81zRzkBkiaTZlYCKEAm05/TNLmNZMnLR08p4Hu+o6ev3dDgbnA/jL4AFDdUg4bSiSPBG2pue5G0Nax9xUIkJQps3ELg0/Ybrt6GBP6vHI5t/F56XceAGYrIgLIGixT7QSJzbgLjc5s54uofioNkNSQp1azlmbR95EgcOWLk1x5ryNiJcQ01pl2a4TFmzFmYVj1Ojqu7n1ptJ3KK9R3gZTa4LualwvsI4Mvc/HVtI5lm0OicFqp0UUwwIZpY04Q1wdHhz13JwzYPFX5iHnl0wbfyDhR7DxM+YJZqSb5PQrkEhFUWt1ho2kipSMyKGZ8GAnVtpJiMD184o5ISCPPmNlB8PBziQgYG/HwqRRYQ6RLRkMzVI06qBEwDetgIMZrqJPdWtv8v44h8+g1mTpT8goUD3/WOg+X2D8GP2s0CtDIK6QiIQTYBE/uQDgtFw8Pg3BOQJAKsPbwHWy3Zw1cye/RzM3pzqPS6oJzIDtrZgdhXgsJkWZrzq1JCyYX6jN6+HJuxHaCBYeGdWYKDBCLd7chKEobBAMMuXh46OE7WCBdna/R77LBGFfeLAoxOIKHHwow5/pWwDAZQJeaObYQ9Q0paY2HP2slt0vsH4MvL57MkbVBIMCQjk0YLzKlM2NgFCBKJCs4fAXce/gg9Ny1oHQIAVvAob6PRCQhCoEIRgIYCUI2CoZGv30hjllE5MrDV9MAktLZnaJ09PUBrjx8gBQgHS5EUr5A5+HPUMltzosuVGbm4Tuo/l1IOhROkzBths7DDyNf72AwMKrLwfUGX/b/nSUl2QX2j8GPpES1rcZAAIaAmhWlo/PwG13nVbYSBIM/1T+WKF/gysMnGom1dAyXys3hG4QiIikeNvNNn1giSySPeaw6+QIzg2LGeSWXh1WgNkhEQoiHg0Pt+TrNsJbqLjl8gEA3jRh8Yu+AQr3tXFZBgtgLeKxYjXi+8vXObHQOIPR0Immth7+SioJzYKvaGim60mQP1TtIx2akwFxg/xh8xgwDofa8AJGpY+3h63uzzuStys/WGPxwMIBUdER0q1M35Av0AchwkCE9a2wBIFAUYbS6g2GFJiGnXGbPOC4ikiB40qN55QCM1EdqNeuM80quiHlZNSkfwVIqMuTK67vCKYmo5Quq7d7s+isOWkM6NfjF+gxtFyXMe0y/eJeaRjyG6OHna213tElqVWvwD+aEzPZutUOust2ttc3m7JcT+8fgA0aptF6FU0gkj2TpkHuguuRXW2Vtk5ZsPGxmA5EDajURgJwttkDLdx+LLxB3HuVZ01glEsv6kvzUhMEn5Ei7bj2XXBELcbuiHLaUHDX49Nzt2Q0r0cNPjVAn9R2RZkpoFD7zAhkMi2wuws6Dc8NRIGY1FeodLLkxrASDPyb7QBROy9dc7DxcYH8Z/NQaUFOnZQJGm8NJSieaVZZwAzPqbktI70unmJkYUfM0A5D6be3MXk4sB1KNwGgGUatMaom3ZVTmLs9SFAOIz68TPfxRA0bNoHDj4QPauS2losgbWSSob+uNhMH3z+wZUrXnDeqEcz5sIajItmr3+mh0+u50YQhCZSY91+iMCKfNkdIBhMHXcPhjtCGR0nGVFOAC+8vgJ1e0QVvAgsNvlYC42rvvDziqs/SzlSBSJ8upKDalhIF8QAgyATPfXGaNgD6dDzC8UOLOI1/rIBoKzB73IHD48UhwgivXV2e6zqCQD7vmXluc5Mo1C5FM4VyetRzfQTZMb8DFLpcwr5m6qU0isUQuVtutGYY1lhWKrjbo9AaotnruDGuS4OGPyj7Ud4BASBvvy9fb7nYeM2J/GXyCwiJgkXHSLOmbl7ulJ4gGfzUdHcmgoHn4rr0JSkn+KFdu9madk6yCRHJJVE/32uq5ScPaa4uerkQPf+a5mR6+2uAvpSLI1wxPuratLdYxc8pnNRRhQ+JYU3y1NEqDEagmT6pG44vahWglLf7u3VqbtFPzpLgptSYcPsU9lo2HEQwwFOrGvDQ7osGAo1DvzL5wu8D+MviEPGRAXMB2bzBsW9YsaldsmXEybw8/O9oEhShQ5trgU4Kjo0bCSa9RNx1/iPIKZjofUcWwUO/OXkQEDHcQOoOfjKDTH6DW6hgyATqD79LDB8TfrrmWCwmHBr8uYwtuKB19FbAU6dup0gz+kAJzY/D1i3cgwLCYFIs3aUdktM/0Ofx5g5pTbqhKmsVXJOE0l14hMVMhlwij2e2LbJhGXgTUFMFkqcrnyuBnjwDlC8ohpjbMmMHXZem4iHkAQ8NKMfhj/Kpe58RVfrRciHQcvhG7KO5uiSIiLaXTnr2ISIJAa8p5FeodkeRAyDgBZkz7lSBQOrlEGOEgw3a1bRhWqqyCC+rErLZVx/6WkhGxINe2SZlDALDoUzpzBrGR85SEAaHblWsec5ZsmEZezEsRTC56UdGXWtUaiVg4iEQk6MjDF0Uxl8OwGgbfUas+F+crFBHXRcfhGwaymjfkPggc/lJqxiIiCUqzETmv0q6gzGSVug3OFYTo2dFF+5RSLeKLokuagjphjGElFTU8/O3LR+kA2uy+5VRU7CgIOw9zp+Z7+HMG2YuWW1ojxbBV0uvoNF3ymDGpskhtBNEl9oz1QIY1uSJSDLtN5TDTk27kReAqmlaO3621TV52tnnRPHyzcpSaU+4mxdCcm96TltlJzeJF0rx2a+3ZZH5HkVjSFoXJ3VqrYMxLCg/aoFDvIBkJIhFx0V6DWHy1ko4iX6mLa55aV47ddaPRL2FSOurArYzHCOE0WraVH7SdN4iUjuQKC/W24Pz7HX3PWNlcelaKIhA0OhKpH0aZkrdba5Oaccuby5XHStQUMfPKCdWs3f4AxUbXXfGJg5L8RqePbsXYls/bwwcMg0+Le3TKxnnVGIrdesd9wwxC8WEsHEQ6GkK7TDtflaYHvVmJmU0r6Sh68jpqzleh3kYwwNzRhmRKJ4pGrQx062Sq6XI3PwH2ncGnUTppk8PvkWUVSs0uGIO7G5+w3V4eDVxRml5LD99VoI+mQyQ8fDkvWockVwY/vgCA6XVrpMda3hY6OtGMcnyx7qKATiK5TFogAaBfNcYROPwVt15halVoz2v6Qiyno+hLGkOTxlp2oyElkTFoo4o6VrSSjg2Nb1rt4RcMxVNXFFg4JmpwdJROOoJYtyReECgdxi6/Uiaw7ww+jScf4/Dl4qDx8MsN0Yw76JZf1dBNK6MePqHXaEF6+G45fIBQcRgVhpzSEs+LDIpAUFxTYsFOr2LwvoqdR6c3QL3Tn11HR4KQDRMLB4WUNjV3u+aBh2/2hdhUDltORcAaNAqs0uq6CyQDI/eY2pNeSUcRbRF3RF5Vs6ZWtPNaS8ewBKNokrBwLyQi7mzFjHBl8Blji4yxOxljDxlHW6KbMZZhjG0wxv6nm+90hUhSCKhpPHypZ19p9UYCfeqLWG66EE6TIOioZ+IhRIIB7FYbwlBoeMyCmTLngcHX8JjLhjYMJwinDWMLLj1WClduGMlBbVvbIWmYbeUBpdMoaKUyFpMRBJv6atZ6u4dmt++e95VBSI3BX0lHEWzSsq0K9Y67egpg+HxpPOmVdBQrKIkX2nvfgwUSEOdMc48dyMawxAwpDULzctc7tRnh1sN/PYCPc86vAfBx47Udfh3Ap11+nzswRmo0Inm/cc0OvS6M620tweAzxrCciqBZ2hXyBfIBtkGh3kYmFkJ4lpZ4EsSHcTEZQac3MHYexNiCW086tartYraaHmnGrQ3YerBAAsb3cO3uYykVQaytn5erJuGjMBsB6Tz8KBKdXUGDBu2vEeccG8UmDi+4yNABBHUSy2qditV0FCtET/piqYm1TMzdvOT3aHa3a2MGXz2vS+UmDuQ8mNcMcGvwbwPwXuPn9wL4XqtBjLEnAVgD8DGX3+ceBFVKYCQAaVI6eqlf9wbf4PA1KovL6Sj6FeOB1WYEeKDZEYqKh1EXuEpFEUIPrFnQ6sIMq1k9MGA1vbcKAJHmNqHptQcUGDBSbavn8RPdor7KVlJgbrKagKHB152zVBQL/TwGmYPKceVmF41OHwe9MGAEGYOVdBSrrIhOdEEpqzAYcGxX297MK7Wmndd6JjZciDT32Ga5hQPZx6bBX+Ocy56BmxBGfQyMsQCA3wXwK7oPY4y9mjF2F2Psrp0dvebNTIgvDgOxCiylItittofVf5q0zEulFtYzcfdz67eF7LECy6kouDS+Gg/flY7OKJJ6EamlZASLMIKBGgO2UxMZFK6zYVJrWg8/Fg4iHQFS7W1g4ZhyrNQpWnf7QJr52/rsjlyf7uHPLDQnEc0Aobjew09HcYAV0Imr76+dqliIVr3wpAn1HiupKFZYGY2Ivvdvf8C9kSBOrQhJDkWFfjIawoFwFa1AQitxvVvreLPzmAFag88Y+xfG2Dct/t02Oo5zzgFYuaavBfARzrm6t6D4jHdzzk9xzk+trKgfgJkRX9DSJoAwqiL1sSh0yhXeBOccxYZHKXOANti3kooKbxUA0uoHMl/zyOCnVrWUzlIqghUmt9tqD3+70say2yIiQBjKXhNo15TDTiRaYODaBXKrIgzYWsaDbBhAf86SQazyPHj2iHLcUEfH5bVkTNwzOg4/FcUaK6AW1Rj8mgfBd4n0urYFqfDwS6gE1TvuvFvdoVHIe0bj8BwOV1EOqB1DeR1NmvEyQ2vwOefP45w/zuLfhwBsMcYOAIBxtDojTwXws4yxMwDeAeBHGWNv8/BvcIYEjdIRBr9jtBBU31xC6mDgnveV2+3iWfXc0gbvC+ipE688fIJM7IFsHCusZIxXG4rtatubm54oVHZNwiga01BgW5UW0rGQuyKi0e/RePgHI00EGEcrqktj9cjgAyJTp3pJOeRAimGFVVAMqj1p6eF7EoTMHgEqF5WUZiwcxGqgjIJ9fggAcX8BHi1ESdrifSBQxC5y6nlVpMF/bAZtbwfwKuPnVwH40OQAzvkPc86Pcs6PQ9A6f8Y5VwV35wsih7+ciqLc7GLQKGhTMocl3G5zkQ+JIyEXfxkl8HASiNr3ZuWcmz09XSOp9/AXkyMevobS2aq03HvRANngXyUNvoY6EfPyYCGKpIQypY77DYmdSSWglt/eqba9a4mXPaz1pA+FRAByk6udnYslQXMcyLmkMwHhJAy66ueTc6yghB2oz9eForjeh3Mug8nA8F7WLN6rg12c7eszdABg1Yt7fwa4NfhvA/BdjLGHADzPeA3G2CnG2HvcTm4uiC8KCqDTUA5bThu52zV9k3BP9MCBIaWjySJaTkWxwkroxtXGq9buodvn3uUia3jMYIDhaNSIP2h2HjvVtiiicQuzQlNNgx0KV0nz8mwhYoy0K1oLifOVH9gv3GJebe943+xh4UkPBvZDumLe53s55UdtlptIx0JmKrMrUHZFzSIi6GrndbHcBGMexGIAGqXDOTK9PM530+j27c/rdlU8P9+ylI4KnPM85/y5nPNrDOqnYLx/F+f8JyzG/1/O+c+6+U7XIHqEMtjDHanyuTX4dD2RVVZCM0YVj/LCk6bl4h8KV9FiMeXOo9sfIF/veLOtJdYIrAaEwa+H1Yv3VqWNNa8exuSqlitf6wm9mo3pfIcxbFc9WogAsZMcdJXnjFXEvE631VXJm5UW1r1aiCiBboOKOttRz+tiqSliXSEPakvlva+6lq0SQryLbZ4zaS4rbFfaYMwjqmkG7K9KW2AkLU29PZMGP9DUd0gaNoBwSenI3p4ab3U5JYpPaiFN4MorqgkgByHXgxUUNfyqDFx5liMN6CV/UUKHB7HbtTeaIpWvhTWvUuZyR4DiGfWQgaAvzrTVQnNbFY9iHoDgygE1rWMY1nvr6nltllveeNHAUJVTdc4qYl4PNlKicYwNNopNHFrwgGYCRMJGclUt+2Bkiu3w3LAjnQXOFRo4kIkh5KYuxgX2n8GX9Iymd+xKKoowegh3K4QGEB50/JFIrWi9VZmaVtJkBMhUvsvp4a+yEra5ml/d8jJwFYoKKqxwRjksNyhiBzlsVTu2Y4qNDrp9jjWvAmrZI8IrVBimSLuIGuK4ULMfwznHTrXtHe8rDWv5vP2YykW0A3E8UlFnUV3yMqc8dxRgQaB0zn6MsRCd7+WGjYAscKHkQTHYKLKHBA1mB2Mx2OQL2CzbG/ytSsubeMeM2H8GXzYL0Rj85XQECzKnnKDrHmAecPiAUdWn9lYzoS4yrIFdqA3+ZlkErjx5IIl6OrlBCZf6GQwG9gZs2/CAPDNgCye0olvpXgE7PGtqt1tBemaeceXpA4I6UVF01UuoBLJKr7BQ76DTH3hHNWWN5ABVU5vKRdSjq9iudUT1tAU6vQF2am0c9MqABYKCblLtPCoXwMGwzXO4WLaW6x4MOC6WmjjkpWHNHAJKigWycBoAcI6vKg3+ZtlDCmwG7F+Dr8mESURCOBwxcrs1Hv5OrY3FZNQbMaSk3sNnBn1xaaD2pC+WWwgHmTcpc8TK0WSvhJ1BZtgtzAIyU8E7w7quTTOMtXawwxewpTCsMmXOM0pH1kioqlqLZ1EOr+KSwkjI33lSNQoIkbZISmNYL6KbWAfnsD1nm+UWOId3Bh/QZxAVz6KbXEcHYTNDaBLb1Ta6fe4dpQMAC8eB0ln7QHfhUfBwAoXQsu3i3esPcL7YwBE3jWJcYv8Z/PiiaDSi8QgB4HjCuHAaDn+n6mFD4pS+vFzGHy501fyq1BJxXdwEjMjEKubW7yHWLSGPjMnTW2G70kKAedjTM6PZbgMI1LdQCiwoA2ree/iGLEFFsRhVN1FPHLY1XgBMj3E965EBY8zI1NFw+IaswsWStSd9wXjfU086d0RNNRXPADlRLX2haL1b2zDeP+y1we+17BfvygWwzCGsZ+K2Hv7FUgvdPseJZd/gXz4EQ+JB1ARGAeBIxLihCB6+q85No0itiQ5bqspRw+CfaatT+S6VWjjolZEADJlYhcGv74CBY5dncUFlwCotrKSj3gWusodFRy47yYyO6JBUjq6baXFWuFQWC5FnRTFSh8bOsA76QG0LPLWO3VobrW7fel7GQnTQS/0VFXUyGADVS4gsCOrHjjqRC4HnHn7lojg3ViieQXj5BKKhADaK6oXosJfzWjwhjoVHrX9fuQBkDmI9G7M1+I/mRQru8aWkd/NyiP1n8AEhkavJ6gCAA2HD6Go4/N2qhwZ/wbixVDsQw+A/3FTfOBfLTe9oAMDQ01GcNyPYtsGXcV7Jlbe95THNIKSNATPm1UwcNmkby3mVm1hORd0pi44ifUDsJu248rpQPA3lDhrfb20oLhSbCAeZty3xVNRJfQcY9JBcEZ603e5DGlxP77HsYWDQs06B7LaA6iWwhRM4upiwjcfIeXlK6SyeFMfCI9a/r24Kg5+J4bzNzuPMrjD4J5Z9g395QWg/BwBrwRr6CCilFcwMCq8Caibvq0gbrW1jgAAeqcdtg6P9AReqfF56Oel1UqbCLltRGvwLxYa3XqH0pO2224ZURS97xJaeAIRh81TFMBgycvFtzpnxfnzxsPH91nM7V6jj8ELC24YZ2SPCsFsV0hnXMbJwCAuJsILSaWA1HfWm+tec11FxzD88/bvyeQAcWDiGo4sJnLfx8DeKTSwmI+7lMcbmdUR0S8tbGPxuUyye2SM4vpzEpXLLcrf26G4dyUjQO+dwBuxPg59YJhn8JVZBkafQU6gVlxpddPoDbykdQE2dVDfRiiygM2AoNa2Do7u1NnoD7i0NkDsqHjq77XbJ0ADKHbH1cjjnuOB1BoXs4mTHlRvziq9ehYvlFpod6/k/slPDyVU1TeYYuSPWRgIw388evAbAkIqYxNl8A8eWPOZ9ZaaO1U5SBsAzB3EwF7c1+J7mukssi3OBogV1IvPzF47j6FICZ/N1S4fH8/sLEIv34gmbhWgDAAeWrjb5eSu66Uy+jmNLSTBFx7V5Y38afEL7OQDI8TJ2edasWLXCTs3DnHJgmP6oyjqpbZuyCnbB0bnxq4OePa1TuQREM1heXrbdbhfqHbS6A28NReagQZ3YBPtK54BQHAcOCu/RajEaDDh2ax5TTQCwdI19Xrnx/uIRYeSsqBPOOc7s1r3nfSUNZmXw5S4ucwiHcnHbhejfH8mj3bWXEZgJ5rW0mNeIwT+5kkKj08eWRUzmQrHhvcEHBK1jpF+OYed+ccwdMa+TpG9GcWa3fkXpHGA/G/xuQ6s7n+4XkecZ06hbwXP1u1hOaJarik9qW+gnxMJw36WK5RBT1MrLoK3Jldtw0rVNIL2Oo4sJnM03LCsh5bw8XYiCYeHl252z4hkgdxSHjXQ4K491tyZS+TyrGpXIHhaLd8/CaSidBeKLiCayWElHcaE0vRBtVdqod/q4asVjQ5EzqJPdh6Z/d8d/FcfEMg4txLFRbE5dy3pbtG703FkNhkXrQitPungGCMWA1JqZ2ni+MH4t+wOOi6WWt/eXxJJh8CdTM+/4NXHMHTUN+sM740kX3f4A54tNHL+CGTrAfjX4CZrgVrxbNFIM7T18mfXhGaXDmCFfq8jdrm0jvih460rLumfqpfIcAmq6rJPqJpBaw9HFBKqtHsoWdJPM+PA0ewgQBszK+wKEYV04ZqZbnt6ZXuhPzyugtnwtwAfWBiz/CLB0NQDg+FICZ3anDf5pw3Bctewx1ZQ9Kjhpq0Wyb9zvgQBOLCfR6PTN2gkJuUt6zbNPejsvADh0K3Dx7un3ZUomYzhmGPwz+fFreXqnhma3jxsOqFOWZ8LSSZGaObkrkhk82cPIJSJYTkXx4FZ1bMhGsYn+gF/RDB1gvxp8s4hIbfAjrQLyPCM6X9nA044/EqpcfM6B2hai2XUwBtu88oulFhKRoPu2i6PQabAUHgUWjpnelxWtc8nwrj3v6bl6o9haW8kYFM8BuWNmYP2t/3Tv1JBH52Xwl64y5mDBSe8+aHLWRxeTlufrEWNennv4gYCoAn7wo9O/W7wKuOn7AAAnV8RC8/D2uMd6Ni/menQeRURLV4t7bLIBfPGM2bHs8EIc4SAzr5uE5M6vWvF4gQTsM3UaBeDkc8yXV60k8cjE+fpWyNAB9q3BNzx8FY/fayPQqWCXZ9VFRNU2EpGgN/KwEqk14OJXrH/XLAKDLgKZdSynoqYBncTFUhMHsjFvA0TxBSCStm7Q0iyKKtzla00jYGnwyy1EggH3rQ0nsXyNkMuYrKBuFoWsc+6oqZx47dq0MTiTryMSDHi/8zA8eGxNLDKtssjEMgz+saUENistkyqROL1TQzwcnF85vuSfJQYDYWxzYnG/xghiPzThsT64WTXmPQcDtnqDWIx2Hxyf18iOKBQM4PjStGF9wJjnXAyrvJYP3Tkyr76Y58oN5ls3rKfx4FZtjAaTC9Nx3+BfAZga6oqccsPDrgTVBn/LKCLyFOl1sa22SpmTVE9qFSeWklNbWolL5ab3PCZjwPLVwO4D07+T1MDCCaWH/8hOHUeXEt5U/45CPoyT1IlcnAzP8LnXryIYmL7tL5VaOJDzqCp5FLGs2BnlJ7jyXWOeS8LgX7smKIhJKuBRI9Dn+bwA4NSPi2N/hHrb+KK49yJiPivpKLLxMB6aMKz3b1ZxfCnh7Q5SYvVGcRy9zyoXRB8LeZ0hPOlJrvzLZ4s4sZz0psvbJExKcyTNtnRW0Dyr15tvXb2WRrPbH8vUkbtKz6rLZ8T+NPgJgsE3aIt67ICSw59LB/q1m8TRKoNC0inZIzi2lDC31pO4WPa4ylZi+bqhsbKc12GkoiEspyI4ZzG3B7equG59TvwqMG3w5UJklOMfWUzgzG4d/Yl0vo1iY35e9OKJ6fiC9F6XrwUAk3N+YHPc4J/LN+YX6JOd3C58efjeNz8ojkY9CGMMV60kp+IeH/7Gpfk14jav5Qh18s+/Ko4jRZAnV1I4vVNH1dBt4pzjK2eLuPWoWlRwZjAGXPN84J6/G773lT8Tx5WhwV82jPoff2aaxruSKZnAfjX4kSQQTmplAgCAJ1eUHv6leRhWmUFRsqBOzFz3Yzi0EMdOrT2lZtju9bFTbXvPkwNiq1+x4FdHFiJAGNZJD7/e7uFcoYHr1+Zg8GUQcsrDNx46w8N/wuEsmt0+HhnxDHv9Ae69VMGNB9VNNWbG0jXAzoPj8YX8Q0AgZAb8jiwkkIgEcf+IwW91+zhXaMyP933CK8RxNFMnHBfzuvmHzbeuXU3j/s2KSVHIYHzSSxpzFJGkkH649LXhezKQfPw7zLced0iIB359Qyjfnsk3kK938KRjczL4ABA0PHS5+777z8VxdUjpPONaESNsdMQzIosQ/8Mth+Y3LyL2p8FnzFBYVGTCGAY/klmzLTzpD7jo+OO1h79oBPqssk7M1LRVHF1MgHNMBa62ymKBmktqmsyFfuiO8fd37hfppAZdZlX6LumKa+fh4dsVxmzdI7STjD4IkjoZ5X4f2Kqi1R3g5iM57+cFiB1buzxeJ7D7oBDkCgpKJBBguGYtPebh332uhN6Az8+ALV0tVDM3vz587/wXgfXHC6liA7cczaHY6Jq7STnHH/n2o/OZFwAcfep4pk5jFzj0pDFdq6efFD9/9XwJAHDXGSFDfer4HA3+4VPi+Om3CyqsviMcjejwnk5FQ7jpYMZ8Ll/9PrGDmtv95QD70+ADeoNfvQSwALJL67hQms5DBkTudn/AvW9okFoHQnFroabSWaNRBDMbPHz57HjT57mlPgLArUbP+u2JIGT+EWDlOjMx++hiAhdLzbH+ntLgXz8Pgw8IAzZZ1br5DWD9ceZLM397pPhKGoxbjszJUKw/YTgXQHj69/2j2SVJ4uRyEp87nTfvtS+dKYAx4EnH1J3NZkYgIOYmDWuvDZz7HHD0aWPDrjYCt797p6ChPvOQcIZuPKCW53aFg7eIBXLrHrF7vHg3cP2Lx4ZkE2Fcu5bCFx4Vhv5LZwrIxEK4eh4ZOhI3/Qdx/NpfAWf+Tfx8w4unhj37uhV86UwRlVbXrJV5+ZOPzG9eROxvg6/SKS+dAzKHsb6YQas7QNGiu45Zzeq1hx8IWPO+cl4GHy1538l893/+hqjSnQulc/AWcZyUMSicHgq/QSgCDjhwdiSofP9mFfFwEEe87EQ0iqWrgZ0HhOECxLZ790HhsRrIxsNYTEbw0NbQw//quRIWkxEcWZxTJyIZk/n028VRLkrXPn9smGyV+ZVzJQDAFx8t4Lq19HwCoxKHbgU27hLqrJ//X+I9g/6SeMLhHADgH78mgpV/8ImHce1ayvud7SgeJ9JC8ZnfA37POH83vGRqWDAQwKcf3MH5QgMfuGsDK+nofALcEgvHxbFyAXifYfyf95apYamouGZPePPHzPdiYQ81h2aEK4PPGFtkjN3JGHvIOFq6SIyxo4yxjzHG7mOM3csYO+7mez2BLG6yaz9XuQhkDpi0iBWtM9Qpn8ONv3jVtMEfDASvGRJZQelYGJlYaKpCc9eQgpiLYY2mBE/fGcmOaBSEN7ZynfmWDMzed2lIUTywWcW1a6n5PZC5owDvA6f/VbzeuV9IQaw9bmzYrUdz+Jsvb5ie9FfPl/DEw9n5BdQixnWQnvTZz4jjs98wNuxnvlNkoPz7w7vo9gf4yrkinnJiTt69xFXPBsCBv/8p4F/eLN679UfHhow2Av+p990FYNrJ8BwyI+YbfzN8T+rsjOCWozkAwDN+55MA4K2Qmx0mxRQnFkgA+MlnnBh7/af/6cnznBEZbj381wP4OOf8GgAfN15b4c8AvJ1zfgOApwDQdPi4DEivC3mFdtX695WLQOagqclhpSdyUXYimgd1sniVoHRGy7i37xHHEZ2dq1dTeHBzPDXtw18Xvx99UD3F8rXA5jeHrx/5hDgeunVsXqNzAeaYoWN+6XPFcec+cfzs/yeOIx4+ALNm4r5LVWxVWnhou4bHH5ojPTGK2g7wb+8UcZiRFENgyPH+7p0P4v/86yNodPrz531lEPT+fxq+F56+n597vZDyuOMeQUO98cU3zndegLEYGbj6eZZDfuO28cX8g695muU4T/G6EUfs9dZyHqFgAH/wylvM19953eq8Z0WCW4twG4D3Gj+/F8D3Tg5gjN0IIMQ5vxMAOOc1zrm9du7lQmpdHK14fM6FwU8fNLvmWMn9/vG/iQufS8xhy714FdBvj0vryrk+/zfMt244kMF9lyqWMYa5IZYVi4+kTiQ3fXB4g8vt60fvEXP++kYJu7UO0rE50hOSUrrzTeIo0+cmDOtzblgz5/befz8DQJzHueI5bxTHd1wt4jDHnj4lRBMKBpCOicXoHR8TfPlzrp+zoZg07s99k+Wwd/7gzWOvX/yEg3Oa0Ah+xLh+t/xH4Ec+aDkkEGD4y5/8NgDA77/8ZsQjl8HDZwx4c1n8i9k7Ci994kGcedv34Mzbvmf+cyLCrcFf45xLF24TwJrFmGsBlBhjf8cYu5sx9nbGmOVVYYy9mjF2F2Psrp0dfYMSV0hLg2+hStksiiKP7CHkEoI2sSpwkjo2c6ECZKbOaBDy3OfFcYQrP7QQR7XdMysMZaXmf3nBkF7xHMy4bR41glZf/lMx3/g4oyeDs93+AB824gpPPj5HimL0Olz8qvX7AL7n8UJO+Q8+/hD+16fE+X3uDVa3rod4+i+Mv37+r1sOe+9/fsrY65zXFclW+NWzQrTv+94DPOOXLYdkE2H8f6+4GQDwhf/63PnPCRCZQm8uA7f9T+Wwp51cxpm3fQ++91sg7fFbHVqDzxj7F8bYNy3+3TY6jgsX08rNDAF4BoBfAfBkAFcB+DGr7+Kcv5tzfopzfmplZcXp3+IMUkPdKtddps9lDhmFJylLwa3VTBTf/fj1+czPTM0cMfif/9/imBoapycaATWpdSIzduaWUw4Az3qdOH7mnYK/b5UtA8ySX33/l87jA18S5/QFN83ZsD7px8Tx3c8SRwvP0KqRyNzoL/NLQ8DL/0L8fPI5w0DuBG49umAqr37jzc+3HOM54jng9WeBJ/yActhtNx/Cmbd9z/wKrnzMHdq7nHP+PM754yz+fQjAFmPsAAAYRytufgPAVznnpznnPQD/AOBWi3GXFxnD4EsvdRQyl9ugAq5anq407PUH2Cg055dxkjG8lX//HyPvHRD8+Yg0gMzR/hOjqk+mGJ6aZ/GJDM6e/exwfq/4y6lhr3mWOH9v/IdvmllOc680fOHbxl+ftPZG/+1132n+/MlfefYcJzSCG14sPNb/+PfKYV/8tefhzNu+Z770l499Cbduze0AjMRsvArAhyzGfAlAjjEmXfbnAJiWK7zciBjViz2LoipJoxhe9snVFDYrLdRGhK3OF5vo9AdmcNJzSKMuPefKRbEQPe77x4ZJrlym8t1xzyauXk1dPmPxmXeK43XfPfWryTTHP5ugK+aCcBz4wfcBT3ylMK42C8yRxYTJr15pBUMfPi4X3Br8twH4LsbYQwCeZ7wGY+wUY+w9AMA570PQOR9njH0DAAPwRy6/1xscf8ZU8QsAYVgzh810upOGNO2jI16+qVM+zyKPJ7xcHCsXhzncsojHAp8/ncc9Fyv49qvmnMoHAK/53PDnF/yWpWFljOGrb/ouAMBNBzN45rVzpukkbnwp8B/edXm+y4ePxxBciWFwzvMApvbMnPO7APzEyOs7AdhbqiuFhePAg3dMv59/eCjghGGK4X2XKnj8YRGVn5t++igO3Ax8/a8FbXLXn4j3RnS3Jf761d+Ol7/783jFu0VQ9weedBkq+tZuBH7xHhHAzdhnbOQSkW+pLAUfPvYz9m+lLSAMfn17vNUh54bBH6byySYQf/CJocjUu/5VUC0L80jJlDj1n8VRVkACQHg6YDaptfLEy6XZkT2sNPY+fPj41sL+NviyNdmoZk0jL7JORgy+DDRuVUShFefcVNCcaxBy0ri/8q8th4WCATNV7rIUnvjw4eMxif1t8A0t8rFGCxMZOhI/9cyrwMDQ6Q1QMKQLbjXSDueK1xmL0fN/A7juhbbD1jIxnHnb98xXGtaHDx+PacxJ0PoxgqVrBAe9PdLmzWxMMW7wn3gkh46hmy7bu7322eNj5oLEosg28eHDhw+X2N8GPxwTjYlHpX4v3g1Es0Du+NhQWUT0Z587g7CRMvmMa5fhw4cPH48V7G+DD4hsE6kFAwAXvgIcvHmsuAkADhgCaX/3lWHbwcuizOfDhw8fHmF/c/iAaJhceFRk6vR7wPZ9wAF9BukPfdscu/348OHDxxzgG/yDtwLgognE/f8kFCpHGhKP4vT/O6wmfdPlkIf14cOHDw/hUzrHniqOH339kMsf1eEeQSDA/CIiHz58PGbhe/iy+fBo4DZ7+MrMxYcPHz7mCN/gA0I3R+LHPnzl5uHDhw8fc4Rv8AHgF78JRAwRNNnyzYcPHz72GHwOHxBKj//1gn6cDx8+fDyG4Xv4Pnz48LFP4Bt8Hz58+Ngn8A2+Dx8+fOwT+Abfhw8fPvYJfIPvw4cPH/sEvsH34cOHj30C3+D78OHDxz6Bb/B9+PDhY5+Acc6v9BwswRjbAXDWxUcsA9j1aDqPBey3vxfw/+b9Av9vdoZjnPMVq198yxp8t2CM3cU5P3Wl53G5sN/+XsD/m/cL/L/ZO/iUjg8fPnzsE/gG34cPHz72CfaywX/3lZ7AZcZ++3sB/2/eL/D/Zo+wZzl8Hz58+PAxjr3s4fvw4cOHjxH4Bt+HDx8+9gn2nMFnjL2QMfYAY+xhxtjrr/R85g3G2J8wxrYZY9+80nO5XGCMHWGMfZIxdi9j7B7G2M9f6TnNG4yxGGPsi4yxrxl/81uu9JwuBxhjQcbY3Yyxf7rSc7lcYIydYYx9gzH2VcbYXZ5+9l7i8BljQQAPAvguABsAvgTglZzze5X/8TEMxtgzAdQA/Bnn/HFXej6XA4yxAwAOcM6/whhLA/gygO/d49eZAUhyzmuMsTCAzwD4ec7556/w1OYKxtgvATgFIMM5f/GVns/lAGPsDIBTnHPPi832mof/FAAPc85Pc847AN4P4LYrPKe5gnP+aQCFKz2PywnO+SXO+VeMn6sA7gNw6MrOar7gAjXjZdj4t3e8NQswxg4D+B4A77nSc9kr2GsG/xCA8yOvN7DHDcF+B2PsOIBbAHzhCk9l7jDoja8C2AZwJ+d8r//Nvw/gdQAGV3gelxscwMcYY19mjL3ayw/eawbfxz4CYywF4IMAfoFzXrnS85k3OOd9zvnNAA4DeApjbM9SeIyxFwPY5px/+UrP5QrgOzjntwJ4EYCfMWhbT7DXDP4FAEdGXh823vOxx2Dw2B8E8Bec87+70vO5nOCclwB8EsALr/BU5omnA3ipwWe/H8BzGGN/fmWndHnAOb9gHLcB/D0EVe0J9prB/xKAaxhjJxhjEQCvAHD7FZ6TD49hBDD/GMB9nPN3Xun5XA4wxlYYYznj5zhEYsL9V3RScwTn/A2c88Oc8+MQz/EnOOc/coWnNXcwxpJGIgIYY0kAzwfgWQbenjL4nPMegJ8FcAdEIO8DnPN7ruys5gvG2F8B+ByA6xhjG4yxH7/Sc7oMeDqA/wjh9X3V+PfdV3pSc8YBAJ9kjH0dwrG5k3O+b1IV9xHWAHyGMfY1AF8E8GHO+Ue9+vA9lZbpw4cPHz7ssac8fB8+fPjwYQ/f4Pvw4cPHPoFv8H348OFjn8A3+D58+PCxT+AbfB8+fPjYJ/ANvo99B0Nt8tmX6btuZIzdZdQOqMb9HGPsty/HnHzsX/hpmT72HBhjtZGXCQBtAH3j9U9xzv/iMs7lgwD+hnP+fs24GICHAdxqVFj68OE5fIPvY0/DKM3/Cc75v1yB7z4A4B4ABznnLcL4PwLwAOf8HXOfnI99CZ/S8bHvYDSYeJ7x85sZY3/DGPtzxljVaDxxLWPsDUZjmfOMseeP/N8sY+yPGWOXGGMXGGO/YfRhsMJ3AfjKqLFnjP2q8f+qRqOe546M/xSEHLAPH3OBb/B9+ABeAuB9ABYA3A0hzRGAkNZ+K4D/MzL2/wLoAbgaQpb5+QB+wuZzHw/gAfmCMXYdhPTHkznnaQAvAHBmZPx9AJ7o9o/x4cMOvsH34QP4N875HYYW098AWAHwNs55F0Kp8ThjLMcYWwPw3RByzHWDa/89CHEvK+QAVEde9wFEAdzIGAtzzs9wzh8Z+X0VQNbTv8yHjxGErvQEfPj4FsDWyM9NALuc8/7IawBIATgI0Wnq0kjSTQDjTXdGUQSQli845w8zxn4BwJsB3MQYuwPAL3HOLxpD0gDKrv4SHz4U8D18Hz7oOA+R8bPMOc8Z/zKc85tsxn8dwLWjb3DO/5Jz/h0AjkF0NhpNxbwBwNfmMG8fPgD4Bt+HDzI455cAfAzA7zLGMoyxAGPsJGPsWTb/5U4Atxopl2CMXccYew5jLAqgBbF7GG3f9ywA/zzHP8HHPodv8H34cIYfBRABcC8EZfO3EFr1U+CcbwH4BIDbjLeiAN4GYBfAJoBVAG8AzDz87wbw3jnO3cc+h5+H78PHHMEYuxHCiD+FKx42xtjPATjCOX/dZZucj30H3+D78OHDxz6BT+n48OHDxz6Bb/B9+PDhY5/AN/g+fPjwsU/gG3wfPnz42CfwDb4PHz587BP4Bt+HDx8+9gl8g+/Dhw8f+wT/P7HLZsQb/HSAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neuron_probe])\n", + "plt.xlabel(\"Time (s)\", fontsize=\"large\")\n", + "plt.legend([\"$x_0$\", \"$x_1$\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:57.626048Z", + "iopub.status.busy": "2020-11-25T16:47:57.623125Z", + "iopub.status.idle": "2020-11-25T16:47:57.796240Z", + "shell.execute_reply": "2020-11-25T16:47:57.795804Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data = sim.data[neuron_probe]\n", + "plt.figure()\n", + "plt.plot(data[:, 0], data[:, 1], label=\"Decoded Output\")\n", + "plt.xlabel(\"$x_0$\", fontsize=20)\n", + "plt.ylabel(\"$x_1$\", fontsize=20)\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/learning/learn-associations.html b/examples/learning/learn-associations.html new file mode 100644 index 0000000000..e58637e327 --- /dev/null +++ b/examples/learning/learn-associations.html @@ -0,0 +1,915 @@ + + + + + + + + Learning new associations — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Note

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+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

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+ + + + +
+

Learning new associations

+

Being able to learn an input-output mapping (or a heteroassociative memory) is useful for storing and recalling associations. This is also a task required by more complicated models that require some notion of long-term memory.

+

In a perfect world, the PES rule could be applied to learn this mapping from examples. However, when two distinct inputs cause the same neurons to fire, their decoded values will depend on one another. This leads to difficulty when trying to store multiple independent associations in the same memory.

+

To solve this problem, a vector-space analog of Oja’s rule, dubbed Vector-Oja’s rule (or simply Voja’s rule) was proposed. In essence, this unsupervised learning rule makes neurons fire selectively in response to their input. When used in conjunction with properly-chosen intercepts (corresponding to the largest dot-product between pairs of inputs), this approach makes it possible to scalably learn new associations in a spiking network.

+

Voja’s rule works by moving the encoders of the active neurons toward the current input. This can be stated succinctly as,

+
+\[\Delta e_i = \kappa a_i (x - e_i)\]
+

where \(e_i\) is the encoder of the \(i^{th}\) neuron, \(\kappa\) is a modulatory learning rate (positive to move towards, and negative to move away), \(a_i\) is the filtered activity of the \(i^{th}\) neuron, and \(x\) is the input vector encoded by each neuron. To see how this is related to Oja’s rule, substituting \(e_i\) with the row of weights \(W_i\), \(x\) for the pre-synaptic activity vector \(b\), and letting \(s = 1 / a_i\) be a dynamic +normalizing factor, gives

+
+\[\Delta W_i = \kappa a_i (b - s a_i W_i)\]
+

which is the update rule for a single row using Oja. For more details, see Learning large-scale heteroassociative memories in spiking neurons.

+

This notebook will lead the reader through a basic example of building a network that can store and recall new associations.

+
+

Step 1: Configure some example data

+

First, we will setup some keys (inputs) and values (outputs) for our network to store and recall.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+
[2]:
+
+
+
+num_items = 5
+
+d_key = 2
+d_value = 4
+
+rng = np.random.RandomState(seed=7)
+keys = nengo.dists.UniformHypersphere(surface=True).sample(num_items, d_key, rng=rng)
+values = nengo.dists.UniformHypersphere(surface=False).sample(
+    num_items, d_value, rng=rng
+)
+
+
+
+

An important quantity is the largest dot-product between all pairs of keys, since a neuron’s intercept should not go below this value if it’s positioned between these two keys. Otherwise, the neuron will move back and forth between encoding those two inputs.

+
+
[3]:
+
+
+
+intercept = (np.dot(keys, keys.T) - np.eye(num_items)).flatten().max()
+print("Intercept: %s" % intercept)
+
+
+
+
+
+
+
+
+Intercept: 0.6952474346952748
+
+
+
+
+

Step 2: Build the model

+

We define a helper function that is useful for creating nodes that cycle through keys/values.

+
+
[4]:
+
+
+
+def cycle_array(x, period, dt=0.001):
+    """Cycles through the elements"""
+    i_every = int(round(period / dt))
+    if i_every != period / dt:
+        raise ValueError("dt (%s) does not divide period (%s)" % (dt, period))
+
+    def f(t):
+        i = int(round((t - dt) / dt))  # t starts at dt
+        return x[int(i / i_every) % len(x)]
+
+    return f
+
+
+
+

We create three inputs: the keys, the values, and a modulatory learning signal. The model is run continuously in two phases: the first half learns the set of associations, and the second tests recall.

+

The learning signal will be set to 0 o allow learning during the first phase, and -1 to inhibit learning during the second phase.

+

The memory is confined to a single ensemble. Roughly speaking, its encoders will hold the keys, and its decoders will hold the values.

+
+
[5]:
+
+
+
+# Model constants
+n_neurons = 200
+dt = 0.001
+period = 0.3
+T = period * num_items * 2
+
+# Model network
+model = nengo.Network()
+with model:
+
+    # Create the inputs/outputs
+    stim_keys = nengo.Node(output=cycle_array(keys, period, dt))
+    stim_values = nengo.Node(output=cycle_array(values, period, dt))
+    learning = nengo.Node(output=lambda t: -int(t >= T / 2))
+    recall = nengo.Node(size_in=d_value)
+
+    # Create the memory
+    memory = nengo.Ensemble(n_neurons, d_key, intercepts=[intercept] * n_neurons)
+
+    # Learn the encoders/keys
+    voja = nengo.Voja(learning_rate=5e-2, post_synapse=None)
+    conn_in = nengo.Connection(stim_keys, memory, synapse=None, learning_rule_type=voja)
+    nengo.Connection(learning, conn_in.learning_rule, synapse=None)
+
+    # Learn the decoders/values, initialized to a null function
+    conn_out = nengo.Connection(
+        memory,
+        recall,
+        learning_rule_type=nengo.PES(1e-3),
+        function=lambda x: np.zeros(d_value),
+    )
+
+    # Create the error population
+    error = nengo.Ensemble(n_neurons, d_value)
+    nengo.Connection(
+        learning, error.neurons, transform=[[10.0]] * n_neurons, synapse=None
+    )
+
+    # Calculate the error and use it to drive the PES rule
+    nengo.Connection(stim_values, error, transform=-1, synapse=None)
+    nengo.Connection(recall, error, synapse=None)
+    nengo.Connection(error, conn_out.learning_rule)
+
+    # Setup probes
+    p_keys = nengo.Probe(stim_keys, synapse=None)
+    p_values = nengo.Probe(stim_values, synapse=None)
+    p_learning = nengo.Probe(learning, synapse=None)
+    p_error = nengo.Probe(error, synapse=0.005)
+    p_recall = nengo.Probe(recall, synapse=None)
+    p_encoders = nengo.Probe(conn_in.learning_rule, "scaled_encoders")
+
+
+
+
+
+

Step 3: Running the model

+
+
[6]:
+
+
+
+with nengo.Simulator(model, dt=dt) as sim:
+    sim.run(T)
+t = sim.trange()
+
+
+
+
+
+

Step 4: Plotting simulation output

+

We first start by checking the keys, values, and learning signals.

+
+
[7]:
+
+
+
+plt.figure()
+plt.title("Keys")
+plt.plot(t, sim.data[p_keys])
+plt.ylim(-1, 1)
+
+plt.figure()
+plt.title("Values")
+plt.plot(t, sim.data[p_values])
+plt.ylim(-1, 1)
+
+plt.figure()
+plt.title("Learning")
+plt.plot(t, sim.data[p_learning])
+plt.ylim(-1.2, 0.2)
+
+
+
+
+
[7]:
+
+
+
+
+(-1.2, 0.2)
+
+
+
+
+
+
+../../_images/examples_learning_learn-associations_13_1.png +
+
+
+
+
+
+../../_images/examples_learning_learn-associations_13_2.png +
+
+
+
+
+
+../../_images/examples_learning_learn-associations_13_3.png +
+
+

Next, we look at the error during training and testing. In the top figure, the error being minimized by PES goes to zero for each association during the training phase. In the bottom figure, the recall error is close to zero, with momentary spikes each time a new key is presented.

+
+
[8]:
+
+
+
+train = t <= T / 2
+test = ~train
+
+plt.figure()
+plt.title("Value Error During Training")
+plt.plot(t[train], sim.data[p_error][train])
+
+plt.figure()
+plt.title("Value Error During Recall")
+plt.plot(t[test], sim.data[p_recall][test] - sim.data[p_values][test])
+
+
+
+
+
[8]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f1712d779b0>,
+ <matplotlib.lines.Line2D at 0x7f1712daef98>,
+ <matplotlib.lines.Line2D at 0x7f1712d3c080>,
+ <matplotlib.lines.Line2D at 0x7f1712d3c128>]
+
+
+
+
+
+
+../../_images/examples_learning_learn-associations_15_1.png +
+
+
+
+
+
+../../_images/examples_learning_learn-associations_15_2.png +
+
+
+
+

Step 5: Examining encoder changes

+

We can also plot the two-dimensional encoders before and after training. Initially, they are uniformly distributed around the unit circle. Afterward, we see that each key has attracted all of its nearby neurons. Notably, almost all neurons are participating in the representation of a unique association.

+
+
[9]:
+
+
+
+scale = (sim.data[memory].gain / memory.radius)[:, np.newaxis]
+
+
+def plot_2d(text, xy):
+    plt.figure()
+    plt.title(text)
+    plt.scatter(xy[:, 0], xy[:, 1], label="Encoders")
+    plt.scatter(keys[:, 0], keys[:, 1], c="red", s=150, alpha=0.6, label="Keys")
+    plt.xlim(-1.5, 1.5)
+    plt.ylim(-1.5, 2)
+    plt.legend()
+    plt.gca().set_aspect("equal")
+
+
+plot_2d("Before", sim.data[p_encoders][0].copy() / scale)
+plot_2d("After", sim.data[p_encoders][-1].copy() / scale)
+
+
+
+
+
+
+
+../../_images/examples_learning_learn-associations_17_0.png +
+
+
+
+
+
+../../_images/examples_learning_learn-associations_17_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/learning/learn-associations.ipynb b/examples/learning/learn-associations.ipynb new file mode 100644 index 0000000000..5ef5ac5985 --- /dev/null +++ b/examples/learning/learn-associations.ipynb @@ -0,0 +1,546 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning new associations\n", + "\n", + "Being able to learn an input-output mapping\n", + "(or a _heteroassociative memory_)\n", + "is useful for storing and recalling associations.\n", + "This is also a task required by more complicated models\n", + "that require some notion of long-term memory.\n", + "\n", + "In a perfect world, the PES rule could be applied\n", + "to learn this mapping from examples.\n", + "However, when two distinct inputs cause the same neurons to fire,\n", + "their decoded values will depend on one another.\n", + "This leads to difficulty when trying to store\n", + "multiple independent associations in the same memory.\n", + "\n", + "To solve this problem,\n", + "a vector-space analog of Oja's rule,\n", + "dubbed Vector-Oja's rule (or simply _Voja's rule_) was proposed.\n", + "In essence, this unsupervised learning rule\n", + "makes neurons fire selectively in response to their input.\n", + "When used in conjunction with properly-chosen intercepts\n", + "(corresponding to the largest dot-product between pairs of inputs),\n", + "this approach makes it possible to scalably\n", + "learn new associations in a spiking network.\n", + "\n", + "Voja's rule works by moving the encoders\n", + "of the active neurons toward the current input.\n", + "This can be stated succinctly as,\n", + "\n", + "$$\n", + "\\Delta e_i = \\kappa a_i (x - e_i)\n", + "$$\n", + "\n", + "where $e_i$ is the encoder of the $i^{th}$ neuron,\n", + "$\\kappa$ is a modulatory learning rate\n", + "(positive to move towards, and negative to move away),\n", + "$a_i$ is the filtered activity of the $i^{th}$ neuron,\n", + "and $x$ is the input vector encoded by each neuron.\n", + "To see how this is related to Oja's rule,\n", + "substituting $e_i$ with the row of weights $W_i$,\n", + "$x$ for the pre-synaptic activity vector $b$,\n", + "and letting $s = 1 / a_i$ be a dynamic normalizing factor, gives\n", + "\n", + "$$\n", + "\\Delta W_i = \\kappa a_i (b - s a_i W_i)\n", + "$$\n", + "\n", + "which is the update rule for a single row using Oja.\n", + "For more details,\n", + "see [Learning large-scale heteroassociative memories in spiking neurons](\n", + "http://compneuro.uwaterloo.ca/publications/voelker2014a.html).\n", + "\n", + "This notebook will lead the reader through\n", + "a basic example of building a network\n", + "that can store and recall new associations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Configure some example data\n", + "\n", + "First, we will setup some keys (inputs) and values (outputs)\n", + "for our network to store and recall." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.088780Z", + "iopub.status.busy": "2020-11-25T16:47:59.087901Z", + "iopub.status.idle": "2020-11-25T16:47:59.584640Z", + "shell.execute_reply": "2020-11-25T16:47:59.585086Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.591363Z", + "iopub.status.busy": "2020-11-25T16:47:59.590814Z", + "iopub.status.idle": "2020-11-25T16:47:59.594430Z", + "shell.execute_reply": "2020-11-25T16:47:59.593985Z" + } + }, + "outputs": [], + "source": [ + "num_items = 5\n", + "\n", + "d_key = 2\n", + "d_value = 4\n", + "\n", + "rng = np.random.RandomState(seed=7)\n", + "keys = nengo.dists.UniformHypersphere(surface=True).sample(num_items, d_key, rng=rng)\n", + "values = nengo.dists.UniformHypersphere(surface=False).sample(\n", + " num_items, d_value, rng=rng\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An important quantity is the largest dot-product\n", + "between all pairs of keys,\n", + "since a neuron's intercept should not go below this value\n", + "if it's positioned between these two keys.\n", + "Otherwise, the neuron will move back and forth\n", + "between encoding those two inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.599025Z", + "iopub.status.busy": "2020-11-25T16:47:59.598509Z", + "iopub.status.idle": "2020-11-25T16:47:59.602714Z", + "shell.execute_reply": "2020-11-25T16:47:59.602239Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intercept: 0.6952474346952748\n" + ] + } + ], + "source": [ + "intercept = (np.dot(keys, keys.T) - np.eye(num_items)).flatten().max()\n", + "print(\"Intercept: %s\" % intercept)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Build the model\n", + "\n", + "We define a helper function that is useful\n", + "for creating nodes that cycle through keys/values." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.609415Z", + "iopub.status.busy": "2020-11-25T16:47:59.607818Z", + "iopub.status.idle": "2020-11-25T16:47:59.610140Z", + "shell.execute_reply": "2020-11-25T16:47:59.610567Z" + } + }, + "outputs": [], + "source": [ + "def cycle_array(x, period, dt=0.001):\n", + " \"\"\"Cycles through the elements\"\"\"\n", + " i_every = int(round(period / dt))\n", + " if i_every != period / dt:\n", + " raise ValueError(\"dt (%s) does not divide period (%s)\" % (dt, period))\n", + "\n", + " def f(t):\n", + " i = int(round((t - dt) / dt)) # t starts at dt\n", + " return x[int(i / i_every) % len(x)]\n", + "\n", + " return f" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create three inputs:\n", + "the keys, the values, and a modulatory learning signal.\n", + "The model is run continuously in two phases:\n", + "the first half learns the set of associations,\n", + "and the second tests recall.\n", + "\n", + "The learning signal will be set to 0\n", + "o allow learning during the first phase,\n", + "and -1 to inhibit learning during the second phase.\n", + "\n", + "The memory is confined to a single ensemble.\n", + "Roughly speaking, its encoders will hold the keys,\n", + "and its decoders will hold the values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.631486Z", + "iopub.status.busy": "2020-11-25T16:47:59.628361Z", + "iopub.status.idle": "2020-11-25T16:47:59.633725Z", + "shell.execute_reply": "2020-11-25T16:47:59.633298Z" + } + }, + "outputs": [], + "source": [ + "# Model constants\n", + "n_neurons = 200\n", + "dt = 0.001\n", + "period = 0.3\n", + "T = period * num_items * 2\n", + "\n", + "# Model network\n", + "model = nengo.Network()\n", + "with model:\n", + "\n", + " # Create the inputs/outputs\n", + " stim_keys = nengo.Node(output=cycle_array(keys, period, dt))\n", + " stim_values = nengo.Node(output=cycle_array(values, period, dt))\n", + " learning = nengo.Node(output=lambda t: -int(t >= T / 2))\n", + " recall = nengo.Node(size_in=d_value)\n", + "\n", + " # Create the memory\n", + " memory = nengo.Ensemble(n_neurons, d_key, intercepts=[intercept] * n_neurons)\n", + "\n", + " # Learn the encoders/keys\n", + " voja = nengo.Voja(learning_rate=5e-2, post_synapse=None)\n", + " conn_in = nengo.Connection(stim_keys, memory, synapse=None, learning_rule_type=voja)\n", + " nengo.Connection(learning, conn_in.learning_rule, synapse=None)\n", + "\n", + " # Learn the decoders/values, initialized to a null function\n", + " conn_out = nengo.Connection(\n", + " memory,\n", + " recall,\n", + " learning_rule_type=nengo.PES(1e-3),\n", + " function=lambda x: np.zeros(d_value),\n", + " )\n", + "\n", + " # Create the error population\n", + " error = nengo.Ensemble(n_neurons, d_value)\n", + " nengo.Connection(\n", + " learning, error.neurons, transform=[[10.0]] * n_neurons, synapse=None\n", + " )\n", + "\n", + " # Calculate the error and use it to drive the PES rule\n", + " nengo.Connection(stim_values, error, transform=-1, synapse=None)\n", + " nengo.Connection(recall, error, synapse=None)\n", + " nengo.Connection(error, conn_out.learning_rule)\n", + "\n", + " # Setup probes\n", + " p_keys = nengo.Probe(stim_keys, synapse=None)\n", + " p_values = nengo.Probe(stim_values, synapse=None)\n", + " p_learning = nengo.Probe(learning, synapse=None)\n", + " p_error = nengo.Probe(error, synapse=0.005)\n", + " p_recall = nengo.Probe(recall, synapse=None)\n", + " p_encoders = nengo.Probe(conn_in.learning_rule, \"scaled_encoders\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Running the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:47:59.639508Z", + "iopub.status.busy": "2020-11-25T16:47:59.638754Z", + "iopub.status.idle": "2020-11-25T16:48:00.683949Z", + "shell.execute_reply": "2020-11-25T16:48:00.684408Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model, dt=dt) as sim:\n", + " sim.run(T)\n", + "t = sim.trange()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plotting simulation output\n", + "\n", + "We first start by checking the keys, values, and learning signals." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:00.739236Z", + "iopub.status.busy": "2020-11-25T16:48:00.730881Z", + "iopub.status.idle": "2020-11-25T16:48:01.143091Z", + "shell.execute_reply": "2020-11-25T16:48:01.142353Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.2, 0.2)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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qdNuhrEiigErncXnpOrfWfQhBArY2JWMeQdY4LzrnQ14FYifkuGRMI0Ig6ThJ90haJemMNvc/LGmlpNskXStpXsu9zZJuqf+WDn82JzJMP31KYVZDQ3tiexEJe6LT9Z7FkiYD5wPHAGuB5ZKWmtnKFmf/CQyY2UZJHwD+AXhXfe9pMzuo23D0Nb67i5epQOVVVH0+c4FjBBV5xbuJFsGhwCozW21mzwGXAQtaHZjZ9Wa2sT69CdirAX8nDhZWQ0Xg3TUU5qtJyHGsrwkhmA2saTlfW18bifcDV7acz5C0QtJNkk4c6SFJi2t3K9avX99VgFMjTwEocIzAd0KZB55dQ+VVbnKMcdddQ9uCpPcAA8CbWy7PM7N1kvYFrpN0u5ndN/xZM1sCLAEYGBiYYCVbtAhSY252EBMsaTZCWXE28otxE7llHTCn5Xyv+toLkHQ0cBZwgpk9O3jdzNbV/68GfgQc3ECY+oqhFrtXE7qwprsb3l0zMUYQjJMmhGA5MF/SPpKmAQuBF1j/SDoYuIBKBB5puT5T0vT6eDfgTUDrIHPQNeVlVGFRPqWksIpGjuajXXcNmdkmSacBVwGTgYvM7E5J5wArzGwp8FlgB+DbqnLob8zsBOA1wAWStlCJ0rnDrI3yYGgHqSid8mZwdUFzqSm77YBXGNV+BHnFu5ExAjNbBiwbdu3sluOjR3juRuDAJsIQjEBmCbavcXvX3jVUb/+DbomZxQlQS0UxKACnD+2zU2V5FY1YdC4YJ87mfaFAiRiyCkjrrff39fY/6JoQggS4ziMoloL2I6jxKY/LS9tuW6H2kBCCFLjPNi2txlZoDbnYMYq0RNdQMC62blUZpEBD/7j47IBj12OBYwQ5ZuQQghR455Ui+3A9X7rXxEEfbyN9TXxCCBKgWHSuDJxnkEf6SkV+whdCkIR6hzK3JQDyS7ijUfXhFjS5aihhuSQwJ3+DJgkhSErU2JLh+qqdWgQl9tc7kZv0hRAkQEODeU41tiL7cB3wnjkYi84lIr/8FEIQBI1RYqEY5EDS/QhKZbBFcMuaJ1h782+S+n3kk8+yy/TNTEvqqx82VBsvz2ro4Sef458Tp68D1z1ZLRbmtNCeC0Z2cQ0hSMD206vXfNWdD3PnHbcn9fuSqU8xb0cxN6mvheK8H8H9j/6ei7+TNn19aMrDHFhcKZJf11Bxn9CDnbabCsDX338om16WdrHV1Z8/F2xLUj89Mav3I/BsETiNEXzkra/kzw8+KqmfN371B/AEZbUIMiSEIAV1wbDr9tNhxxlJvb6fHOsvIzNkSOk6s9jnje84Yxo7Jk5fUyaVWfjnNmcjBoszp6oblyQFjhS41o+1OQomHiEEKfEoKPKquIzJ4GCx7zSCkhadKyyB1eQW6xCCJHjW2Mqb+SmsrG0bXeeJlLnrUnQNtUHScZLukbRK0hlt7k+XdHl9/2ZJe7fcO7O+fo+kY5sIT1Aursaj8h0jcCGv8rAzMhS9roVA0mTgfOCPgP2BkyXtP8zZ+4HHzGw/4DzgM/Wz+wMLgdcCxwFfqn8vLxxt23OruUwIMiwogq3kmKOasBo6FFhlZqsBJF0GLABWtrhZAHy8Pr4C+KIk1dcvM7Nngfslrap/72cNhCugEoK9Nt4Fn355Yn/h4Skv57x9v4IpXQ/kFoOPQGH95Y5dj4Pf9tw5pI7/Rmbwhb2/wKPT90rq71HPbYIpeclBE0IwG1jTcr4WOGwkN2a2SdITwK719ZuGPTu7nSeSFgOLAebOjelRnfK1Se9gy8xXcsT83ZL6++Sqm9hz/Qp+fNcDMDWtSeP/kJi5vedc6nJaBMtfehTP/O4x3nVI2orG5ifW8ZKV32HVL1dyx4y06euYSWLG1Lw6LibMPAIzWwIsARgYGJhgOa0OrkMt9dZJr+HKl7+ZI459XVJ/Nzz9SXZav4IvvvtgDtkvbSHB59LP1wDcZxZ7+L9h6p5cOOMU3nXsm5P6u+m+nzJ55XdYcNDL+cofp51Ex7cvgYceSetnj2mizb4OmNNyvld9ra0bSVOAnYBHO3w26ALh1GXtKtVenpdnQeM1S8VnJd98aUIIlgPzJe0jaRrV4O/SYW6WAovq45OA66wy+F4KLKytivYB5gP/0UCY+pS8+hVHY3BtfKkkm/pBnIWoCJzjmtlyGl13DdV9/qcBVwGTgYvM7E5J5wArzGwp8FXgknoweAOVWFC7+xbVwPIm4C/MbHO3Yeo7HGuImaXX/qbAmcXVdhfp/d9qh+cQ9wxbfI2MEZjZMmDZsGtntxw/A7xzhGc/DXy6iXAE7fFJt+Ut+lZi15AfUcNpkphZnAS/wTyvVTiHlnpwKxMLLChc0pcPri2C2uecCCEoAJeBtRL7pLythpzw8b3EMaDeEUKQFIcam1fZNOR/cSZL/l1TBTBYuSknxr0lhCAF3jW20sYIIGYWJ0KSu+66kFmLN4Qg6ClF7YVQ4qJzbtTmyWE11AghBClxGswrrw/XmYL2IyhvkfM8CSFIQnlZxbUP18zLZx8yrKGOxdZVdWM8pglCCDJHmfVl9jUFdg3JaUKZL/nFN4QgBY77EbzA+5R+evbhFktJol/FdVJJUe4hIQRBhpiv1VBy5XW0GnLz2ZnMWtohBClxSjwuE8qs5BZBOYPF4NtR4jdzPS9CCDIns4pLf1PozGIPfFc5z+99hxCUgEuDYLBF4ICb1VB5i85Jcp2w6LbMeWadYiEESckr8QT9RjnpqxypTUMIQQoK3I9gMMZFJTA381HfwWKf3cLKEb0UFJVPS6W82lNpVkPlMWSQXdAs7l4SQpCEAvcjcJ/5WRCOm9dXO5Sl9zYaBM0SQlAA5c38dKLAmcVeeGpfji2+roRA0i6SrpZ0b/3/zDZuDpL0M0l3SrpN0rta7n1N0v2Sbqn/DuomPMGL8WvBOlp1eGfU2I8gASXFtfd02yI4A7jWzOYD19bnw9kIvM/MXgscB/xvSTu33P+omR1U/93SZXj6E+/9CArx84XEfgQpED7mo7FVZbN0KwQLgIvr44uBE4c7MLNfmtm99fEDwCPA7l36G3SIX3KtWwT+ipCO6BoqhPy+b7dCsIeZPVgfPwTsMZpjSYcC04D7Wi5/uu4yOk/S9FGeXSxphaQV69ev7zLYTngtAeCSbvPLLH2PhzGC91aokc4aYcpYDiRdA+zZ5tZZrSdmZhqlQ1jSLOASYJGZbakvn0klINOAJcDpwDntnjezJbUbBgYGxvf1f/A3sObmcT3aFRt/m95Pdzxnfjqbj172bpg8Yp2meTY9m86voCKvnqGxhcDMjh7pnqSHJc0yswfrgv6REdztCPwbcJaZ3dTy24OtiWcl/RPw19sU+m1l2ktgxk499aItM3aCOYfBTnOSe+21H0GR9bR5/wVedbxPwbzz8TD38OTeCi+rNMeS2NsYoQeMKQRjsBRYBJxb///d4Q4kTQO+A3zdzK4Ydm9QREQ1vnBHl+EZnSP/tqc/3694blWZ2byb0dllHzj5Uu9QBME20+0YwbnAMZLuBY6uz5E0IOnC2s2fAkcAp7QxE/2mpNuB24HdgE91GZ5gGO52LC4mJYVtVemMp1VaWA01Q1ctAjN7FDiqzfUVwKn18TeAb4zw/JHd+B90RnFN9yAZRbX4hsivayhmFueOu1VHkDuuy1BnWCh7EEJQAJ5jBD54WQ2Vh9daVu5klr5CCDLHb4zAav+jxhY0j//GR3kRQhD0iLxqTEF7JK89sdN7mTMhBJmjKqf6+e9WSIQQ5Y23eXJe6SuEIAiCcSOn/Qi2tkLKXFKxaUIICsCj6W5h1RH0Es8xggwJIcicMjNKWA2lxKVO7tkggOzSVwhBAXiuFx/kjlOBmFlB7E0IQeZEfgl6jevGNCXugNcDQggKwCejeo8RhAKmoFyrHW//myWEIHOKnPmZYY0teCGD62cVtQNeDwkhCHpEWHWUQPV9SyuN84tvCEHmuM38DIIeMtT1GE2CRgghCHrCYPb0SWBhPpqSInviMktfIQQFUGRGDZLgPVgst42P8iKEIOgJ/lZDQSriC098QggKoLiMmmGNrV/xskqzWHSuUboSAkm7SLpa0r31/zNHcLe5Zb/ipS3X95F0s6RVki6vN7oPGkROOcV/T9kgFT5boXqSX3y7bRGcAVxrZvOBa+vzdjxtZgfVfye0XP8McJ6Z7Qc8Bry/y/AEQZAQ7xq5yxhBhnQrBAuAi+vji4ETO31QVVX1SOCK8TwfdIbwnVnsQ1gN5Y67SXRm6atbIdjDzB6sjx8C9hjB3QxJKyTdJOnE+tquwONmtqk+XwvMHskjSYvr31ixfv36LoMdBEETCKeOEjnOI8iwFTJlLAeSrgH2bHPrrNYTMzON/FXmmdk6SfsC10m6HXhiWwJqZkuAJQADAwP5fYkeUeUXx/0I8qo4BX1ChmWxK2MKgZkdPdI9SQ9LmmVmD0qaBTwywm+sq/9fLelHwMHAvwA7S5pStwr2AtaNIw5BX1LnVDc771CgVPgUyt7f19v/Zum2a2gpsKg+XgR8d7gDSTMlTa+PdwPeBKy0ytTgeuCk0Z4Puqe8MYIgFWVapeXXHOlWCM4FjpF0L3B0fY6kAUkX1m5eA6yQdCtVwX+uma2s750OfFjSKqoxg692GZ5gGN5dM2E+mj8u5qPeCTszxuwaGg0zexQ4qs31FcCp9fGNwIEjPL8aOLSbMARj41NnqjOqV36NgiJrhpah9gpAZukrZhZnjtt+BLX6+Nh5RyskJT5v27EgznCkOoQg6A2Z1ZiC9nh95pi53iwhBJkjeS0BYEP++xBCFPSSvNJXCEGQHxk23fsaV6u0+NZNEEKQOV4zP800eODge5CKIvfEzlB8QgiCPIkximR4FothNdQMIQQF4DKhzHMtmAxrbP1KZuVhsYQQ5E7k1KDHeBgjuO6Al2F3ZwhB0BMGxwhcGgSVz14eF0W5bzmvmIcQZI7bMsGeZFhj62c8O/9iHkEzhBAEPWHQaMhnjCBIRfQ85kEIQeb4TSgLgl4SCtQkIQRBT9gyNEbg1HEQVdVkuG054Ulm6SuEIHPySq5Bv+G2H8GgebKL594q1DwhBEFPGMyojmZDQSJ8NpK3Yf8H3RBCEGRKtIVS4PWWt84j8CKv9BVCkDmSfFqyQ/sRePgdtcSUlLflRH7pK4QgCILx47UfQWaDtd50JQSSdpF0taR76/9ntnHzh5Juafl7RtKJ9b2vSbq/5d5B3YQneDHVhDLHJQC8xgiioEiGZ6PPbUJZZumr2xbBGcC1ZjYfuLY+fwFmdr2ZHWRmBwFHAhuBH7Y4+ejgfTO7pcvwBAE5Nt2DPiLDrsduhWABcHF9fDFw4hjuTwKuNLONXfobdEg1oczD4/q/DDNNsBWv/QiszVEwfroVgj3M7MH6+CFgjzHcLwQuHXbt05Juk3SepOldhifoE/zL/7ya7n2N+7cOumXKWA4kXQPs2ebWWa0nZmYapUNY0izgQOCqlstnUgnINGAJcDpwzgjPLwYWA8ydO3esYAc1wslqaGiMwKFA9lehYvDbvN7TfDS/9DWmEJjZ0SPdk/SwpFlm9mBd0D8yyk/9KfAdM3u+5bcHWxPPSvon4K9HCccSKrFgYGAgvy+RGUMfKArl7PGZUBY0SbddQ0uBRfXxIuC7o7g9mWHdQrV4oKraeCJwR5fhCfqM2Eowb7wndIXVUDN0KwTnAsdIuhc4uj5H0oCkCwcdSdobmAPcMOz5b0q6Hbgd2A34VJfhCYYjL/PRFx95+B70nmj0TXzG7BoaDTN7FDiqzfUVwKkt578CZrdxd2Q3/gf9zOAYgXMwgp7iP0ZQ4tKnzRMzizNHOC8T7JZpQoFSkV+x2Al5pa8QgqAnxBIAQW/x3hM7L0IIMkcqtcYWpMBvQplnqs4vR4UQBHkSLZJk+GyFqhf8l977vNJXCEHmuNXYvBcFC5LgN1hc+x/pqxFCCEqgpLySoUVHv+Pzxh1r5BmmsRCCoCfEVpVl4LdD2YuPgvETQpA5cppQ5k9efbj9jGcF2Xtmcy6EEAQ9wczJvC/DZnvwYnwGqId8d/S7N4QQZI7bfgRBGWRmPVMqIQRBT3C36ogCKmu2KBada5IQgsxRcQZ2ZcXWm7yKww7JsIkdQhD0BNdFwYLkpO6zHxyDyrFQ9iCEIHMya8FuA8VGPCmRvvIghKAAPCwsto4RpPY4aogeJH/tg9NUXMrj/NJYCEEQBOPGawmToFlCCArAZ48wb6sOH29LJf10kcF5KmE11AQhBEFm5NdsD/qMDLsfQwgyR5LPDmVtjoL80JDxTmKrIe8WZ2Z0JQSS3inpTklbJA2M4u44SfdIWiXpjJbr+0i6ub5+uaRp3YQnCLaSV9M96DfySl/dtgjuAN4B/HgkB5ImA+cDfwTsD5wsaf/69meA88xsP+Ax4P1dhicYht/qkHlllKA9bl85sz56b6Z087CZ3QVV98MoHAqsMrPVtdvLgAWS7gKOBN5du7sY+Djwf7sJU/BCJgluXfs4rz37B0n9PWTLA5wwBab98ztgUlfJbNsY2hEnej1TMGlSlfdf94kfJhWFSVue5fYpMO+282DllxL6DDz3e5j/1rR+9pgUOXQ2sKblfC1wGLAr8LiZbWq5PnukH5G0GFhcnz4l6Z5xhmc34LfjfLbf6Nu4rAQuAeDJTpw3H49PfAT4SKM/2SF9+03GQV/HZRuFp+G4fBve49Iq6TYe89pdHFMIJF0D7Nnm1llm9t0uArRNmNkSYEm3vyNphZmNOJ4xkcglLrnEAyIu/UoucelVPMYUAjM7uks/1gFzWs73qq89CuwsaUrdKhi8HgRBECQkRUfqcmB+bSE0DVgILLXK3ux64KTa3SIgWQsjCIIgqOjWfPSPJa0FDgf+TdJV9fWXS1oGUNf2TwOuAu4CvmVmd9Y/cTrwYUmrqMYMvtpNeDqk6+6lPiKXuOQSD4i49Cu5xKUn8ZDvlm9BEASBN2FjFwRBUDghBEEQBIWTrRCMtKxFy/3p9bIWq+plLvZ2COaYdBCPUyStl3RL/XeqRzg7QdJFkh6RdMcI9yXpH+u43ibpDanD2AkdxOMtkp5o+SZnpw5jp0iaI+l6SSvr5WL+exs3ff9dOozHhPgukmZI+g9Jt9Zx+UQbN82WX2aW3R8wGbgP2BeYBtwK7D/MzZ8DX66PFwKXe4d7nPE4Bfiid1g7jM8RwBuAO0a4fzxwJdVcoTcCN3uHeZzxeAvwfe9wdhiXWcAb6uOXAr9sk8b6/rt0GI8J8V3q97xDfTwVuBl44zA3jZZfubYIhpa1MLPngMuABcPcLKBa1gLgCuAojbFWhgOdxGPCYGY/BjaM4mQB8HWruIlqnsmsNKHrnA7iMWEwswfN7Bf18e+oLPuGz/Dv++/SYTwmBPV7fqo+nVr/DbfqabT8ylUI2i1rMTxRDLmxysT1CSoT1n6ik3gA/EndZL9C0pw29ycKncZ3InB43bS/UtJrvQPTCXX3wsFUNdBWJtR3GSUeMEG+i6TJkm4BHgGuNrMRv0kT5VeuQlAS3wP2NrPXAVeztZYQ+PELYJ6ZvR74AvD/fIMzNpJ2AP4F+JCZdbRAVD8yRjwmzHcxs81mdhDViguHSjqgl/7lKgQjLWvR1o2kKcBOVMte9BNjxsPMHjWzZ+vTC4FDEoWtF3Ty3foeM3tysGlvZsuAqZJ2cw7WiEiaSlV4ftPM/rWNkwnxXcaKx0T7LgBm9jjVCgzHDbvVaPmVqxC0XdZimJulVMtaQLXMxXVWj7z0EWPGY1hf7QlUfaMTlaXA+2orlTcCT5jZg96B2lYk7TnYXyvpUKp81m+VDKCyCKKa0X+XmX1+BGd9/106icdE+S6Sdpe0c328HXAMcPcwZ42WXwkXik+HmW2SNLisxWTgIjO7U9I5wAozW0qVaC5RtbzFBqpCtq/oMB5/KekEYBNVPE5xC/AYSLqUynJjN1VLk3yMaiAMM/sysIzKQmUVsBH4M5+Qjk4H8TgJ+ICkTcDTwMI+rGQM8ibgvcDtdZ80wN8Ac2FCfZdO4jFRvsss4GJVm3pNolqW5/u9LL9iiYkgCILCybVrKAiCIOiQEIIgCILCCSEIgiAonBCCIAiCwgkhCIIgKJwQgiAIgsIJIQiCICic/w8GOuPpesIpdwAAAABJRU5ErkJggg==\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.title(\"Keys\")\n", + "plt.plot(t, sim.data[p_keys])\n", + "plt.ylim(-1, 1)\n", + "\n", + "plt.figure()\n", + "plt.title(\"Values\")\n", + "plt.plot(t, sim.data[p_values])\n", + "plt.ylim(-1, 1)\n", + "\n", + "plt.figure()\n", + "plt.title(\"Learning\")\n", + "plt.plot(t, sim.data[p_learning])\n", + "plt.ylim(-1.2, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we look at the error during training and testing.\n", + "In the top figure, the error being minimized by PES\n", + "goes to zero for each association during the training phase.\n", + "In the bottom figure, the recall error is close to zero,\n", + "with momentary spikes each time a new key is presented." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:01.167634Z", + "iopub.status.busy": "2020-11-25T16:48:01.166621Z", + "iopub.status.idle": "2020-11-25T16:48:01.441342Z", + "shell.execute_reply": "2020-11-25T16:48:01.440914Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train = t <= T / 2\n", + "test = ~train\n", + "\n", + "plt.figure()\n", + "plt.title(\"Value Error During Training\")\n", + "plt.plot(t[train], sim.data[p_error][train])\n", + "\n", + "plt.figure()\n", + "plt.title(\"Value Error During Recall\")\n", + "plt.plot(t[test], sim.data[p_recall][test] - sim.data[p_values][test])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Examining encoder changes\n", + "\n", + "We can also plot the two-dimensional encoders before and after training.\n", + "Initially, they are uniformly distributed around the unit circle.\n", + "Afterward, we see that each key has attracted all of its nearby neurons.\n", + "Notably, almost all neurons are participating\n", + "in the representation of a unique association." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:01.449619Z", + "iopub.status.busy": "2020-11-25T16:48:01.448355Z", + "iopub.status.idle": "2020-11-25T16:48:01.804312Z", + "shell.execute_reply": "2020-11-25T16:48:01.803874Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "scale = (sim.data[memory].gain / memory.radius)[:, np.newaxis]\n", + "\n", + "\n", + "def plot_2d(text, xy):\n", + " plt.figure()\n", + " plt.title(text)\n", + " plt.scatter(xy[:, 0], xy[:, 1], label=\"Encoders\")\n", + " plt.scatter(keys[:, 0], keys[:, 1], c=\"red\", s=150, alpha=0.6, label=\"Keys\")\n", + " plt.xlim(-1.5, 1.5)\n", + " plt.ylim(-1.5, 2)\n", + " plt.legend()\n", + " plt.gca().set_aspect(\"equal\")\n", + "\n", + "\n", + "plot_2d(\"Before\", sim.data[p_encoders][0].copy() / scale)\n", + "plot_2d(\"After\", sim.data[p_encoders][-1].copy() / scale)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/learning/learn-communication-channel.html b/examples/learning/learn-communication-channel.html new file mode 100644 index 0000000000..87d3b42f72 --- /dev/null +++ b/examples/learning/learn-communication-channel.html @@ -0,0 +1,990 @@ + + + + + + + + Learning a communication channel — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Learning a communication channel

+

Normally, if you have a function you would like to compute across a connection, you would specify it with function=my_func in the Connection constructor. However, it is also possible to use error-driven learning to learn to compute a function online.

+
+

Step 1: Create the model without learning

+

We’ll start by creating a connection between two populations that initially computes a very weird function.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.processes import WhiteSignal
+from nengo.solvers import LstsqL2
+
+
+
+
+
[2]:
+
+
+
+model = nengo.Network()
+with model:
+    inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)
+    pre = nengo.Ensemble(60, dimensions=2)
+    nengo.Connection(inp, pre)
+    post = nengo.Ensemble(60, dimensions=2)
+    conn = nengo.Connection(pre, post, function=lambda x: np.random.random(2))
+    inp_p = nengo.Probe(inp)
+    pre_p = nengo.Probe(pre, synapse=0.01)
+    post_p = nengo.Probe(post, synapse=0.01)
+
+
+
+

If we run this model as is, we can see that the connection from pre to post doesn’t compute much of value.

+
+
[3]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(10.0)
+
+
+
+
+
[4]:
+
+
+
+plt.figure(figsize=(12, 8))
+plt.subplot(2, 1, 1)
+plt.plot(sim.trange(), sim.data[inp_p].T[0], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[0], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[0], c="r", label="Post")
+plt.ylabel("Dimension 1")
+plt.legend(loc="best")
+plt.subplot(2, 1, 2)
+plt.plot(sim.trange(), sim.data[inp_p].T[1], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[1], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[1], c="r", label="Post")
+plt.ylabel("Dimension 2")
+plt.legend(loc="best")
+
+
+
+
+
[4]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7fb933af3ac8>
+
+
+
+
+
+
+../../_images/examples_learning_learn-communication-channel_6_1.png +
+
+
+
+

Step 2: Add in learning

+

If we can generate an error signal, then we can minimize that error signal using the nengo.PES learning rule. Since it’s a communication channel, we know the value that we want, so we can compute the error with another ensemble.

+
+
[5]:
+
+
+
+with model:
+    error = nengo.Ensemble(60, dimensions=2)
+    error_p = nengo.Probe(error, synapse=0.03)
+
+    # Error = actual - target = post - pre
+    nengo.Connection(post, error)
+    nengo.Connection(pre, error, transform=-1)
+
+    # Add the learning rule to the connection
+    conn.learning_rule_type = nengo.PES()
+
+    # Connect the error into the learning rule
+    nengo.Connection(error, conn.learning_rule)
+
+
+
+

Now, we can see the post population gradually learn to compute the communication channel.

+
+
[6]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(10.0)
+
+
+
+
+
[7]:
+
+
+
+plt.figure(figsize=(12, 12))
+plt.subplot(3, 1, 1)
+plt.plot(sim.trange(), sim.data[inp_p].T[0], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[0], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[0], c="r", label="Post")
+plt.ylabel("Dimension 1")
+plt.legend(loc="best")
+plt.subplot(3, 1, 2)
+plt.plot(sim.trange(), sim.data[inp_p].T[1], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[1], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[1], c="r", label="Post")
+plt.ylabel("Dimension 2")
+plt.legend(loc="best")
+plt.subplot(3, 1, 3)
+plt.plot(sim.trange(), sim.data[error_p], c="b")
+plt.ylim(-1, 1)
+plt.legend(("Error[0]", "Error[1]"), loc="best")
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7fb9338b2588>
+
+
+
+
+
+
+../../_images/examples_learning_learn-communication-channel_11_1.png +
+
+
+
+

Does it generalize?

+

If the learning rule is always working, the error will continue to be minimized. But have we actually generalized to be able to compute the communication channel without this error signal? Let’s inhibit the error population after 10 seconds.

+
+
[8]:
+
+
+
+def inhibit(t):
+    return 2.0 if t > 10.0 else 0.0
+
+
+with model:
+    inhib = nengo.Node(inhibit)
+    nengo.Connection(inhib, error.neurons, transform=[[-1]] * error.n_neurons)
+
+
+
+
+
[9]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(16.0)
+
+
+
+
+
[10]:
+
+
+
+plt.figure(figsize=(12, 12))
+plt.subplot(3, 1, 1)
+plt.plot(sim.trange(), sim.data[inp_p].T[0], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[0], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[0], c="r", label="Post")
+plt.ylabel("Dimension 1")
+plt.legend(loc="best")
+plt.subplot(3, 1, 2)
+plt.plot(sim.trange(), sim.data[inp_p].T[1], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[1], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[1], c="r", label="Post")
+plt.ylabel("Dimension 2")
+plt.legend(loc="best")
+plt.subplot(3, 1, 3)
+plt.plot(sim.trange(), sim.data[error_p], c="b")
+plt.ylim(-1, 1)
+plt.legend(("Error[0]", "Error[1]"), loc="best")
+
+
+
+
+
[10]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7fb931c93278>
+
+
+
+
+
+
+../../_images/examples_learning_learn-communication-channel_15_1.png +
+
+
+
+

How does this work?

+

The nengo.PES learning rule minimizes the same error online as the decoder solvers minimize with offline optimization.

+

Let \(\mathbf{E}\) be an error signal. In the communication channel case, the error signal \(\mathbf{E} = \mathbf{\hat{x}} - \mathbf{x}\); in other words, it is the difference between the decoded estimate of post, \(\mathbf{\hat{x}}\), and the decoded estimate of pre, \(\mathbf{x}\).

+

The PES learning rule on decoders is

+
+\[\Delta \mathbf{d_i} = -\frac{\kappa}{n} \mathbf{E} a_i\]
+

where \(\mathbf{d_i}\) are the decoders being learned, \(\kappa\) is a scalar learning rate, \(n\) is the number of neurons in the pre population, and \(a_i\) is the filtered activity of the pre population.

+

However, many synaptic plasticity experiments result in learning rules that explain how individual connection weights change. We can multiply both sides of the equation by the encoders of the post population, \(\mathbf{e_j}\), and the gain of the post population \(\alpha_j\), as we do in Principle 2 of the NEF. This results in the learning rule

+
+\[\Delta \omega_{ij} = \Delta \mathbf{d_i} \cdot \mathbf{e_j} \alpha_j + = -\frac{\kappa}{n} \alpha_j \mathbf{e_j} \cdot \mathbf{E} a_i\]
+

where \(\omega_{ij}\) is the connection between pre neuron \(i\) and post neuron \(j\).

+

The weight-based version of PES can be easily combined with learning rules that describe synaptic plasticity experiments. In Nengo, the Connection.learning_rule_type parameter accepts a list of learning rules. See Bekolay et al., 2013 for details on what happens when the PES learning rule is combined with an unsupervised learning rule.

+
+
+

How do the decoders / weights change?

+

The equations above describe how the decoders and connection weights change as a result of the PES rule. But are there any general principles that we can say about how the rule modifies decoders and connection weights? Determining this requires analyzing the decoders and connection weights as they change over the course of a simulation.

+
+
[11]:
+
+
+
+with model:
+    weights_p = nengo.Probe(conn, "weights", synapse=0.01, sample_every=0.01)
+
+
+
+
+
[12]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(4.0)
+
+
+
+
+
[13]:
+
+
+
+plt.figure(figsize=(12, 12))
+plt.subplot(3, 1, 1)
+plt.plot(sim.trange(), sim.data[inp_p].T[0], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[0], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[0], c="r", label="Post")
+plt.ylabel("Dimension 1")
+plt.legend(loc="best")
+plt.subplot(3, 1, 2)
+plt.plot(sim.trange(), sim.data[inp_p].T[1], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[1], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[1], c="r", label="Post")
+plt.ylabel("Dimension 2")
+plt.legend(loc="best")
+plt.subplot(3, 1, 3)
+plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])
+plt.ylabel("Decoding weight")
+plt.legend(("Decoder 10[0]", "Decoder 10[1]"), loc="best")
+
+
+
+
+
[13]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7fb933cc2860>
+
+
+
+
+
+
+../../_images/examples_learning_learn-communication-channel_20_1.png +
+
+
+
[14]:
+
+
+
+with model:
+    # Change the connection to use connection weights instead of decoders
+    conn.solver = LstsqL2(weights=True)
+
+
+
+
+
[15]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(4.0)
+
+
+
+
+
[16]:
+
+
+
+plt.figure(figsize=(12, 12))
+plt.subplot(3, 1, 1)
+plt.plot(sim.trange(), sim.data[inp_p].T[0], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[0], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[0], c="r", label="Post")
+plt.ylabel("Dimension 1")
+plt.legend(loc="best")
+plt.subplot(3, 1, 2)
+plt.plot(sim.trange(), sim.data[inp_p].T[1], c="k", label="Input")
+plt.plot(sim.trange(), sim.data[pre_p].T[1], c="b", label="Pre")
+plt.plot(sim.trange(), sim.data[post_p].T[1], c="r", label="Post")
+plt.ylabel("Dimension 2")
+plt.legend(loc="best")
+plt.subplot(3, 1, 3)
+plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])
+plt.ylabel("Connection weight")
+
+
+
+
+
[16]:
+
+
+
+
+Text(0, 0.5, 'Connection weight')
+
+
+
+
+
+
+../../_images/examples_learning_learn-communication-channel_23_1.png +
+
+

For some general principles governing how the decoders change, Voelker, 2015 and Voelker & Eliasmith, 2017 give detailed analyses of the rule’s dynamics. It’s also interesting to observe qualitative trends; often a few strong connection weights will dominate the others, while decoding weights tend to change or not change together.

+
+
+

What is pre_synapse?

+

By default the PES object sets pre_synapse=Lowpass(tau=0.005). This is a lowpass filter with time-constant \(\tau = 5\,\text{ms}\) that is applied to the activities of the pre-synaptic population \(a_i\) before computing each update \(\Delta {\bf d}_i\).

+

In general, longer time-constants smooth over the spiking activity to produce more constant updates, while shorter time-constants adapt more quickly to rapidly changing inputs. The right trade-off depends on the particular demands of the model.

+

This Synapse object can also be any other linear filter (as are used in the Connection object); for instance, pre_synapse=Alpha(tau=0.005) applies an alpha filter to the postsynaptic activity. This will have the effect of delaying the bulk of the activities by a rise-time of \(\tau\) before applying the update. This may be useful for situations where the error signal is delayed by the same amount, since the error signal should be synchronized with the same activities that +produced said error.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/learning/learn-communication-channel.ipynb b/examples/learning/learn-communication-channel.ipynb new file mode 100644 index 0000000000..1b47b32f34 --- /dev/null +++ b/examples/learning/learn-communication-channel.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning a communication channel\n", + "\n", + "Normally, if you have a function you would like to compute\n", + "across a connection, you would specify it with `function=my_func`\n", + "in the `Connection` constructor.\n", + "However, it is also possible to use error-driven learning\n", + "to learn to compute a function online." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the model without learning\n", + "\n", + "We'll start by creating a connection between two populations\n", + "that initially computes a very weird function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:03.212235Z", + "iopub.status.busy": "2020-11-25T16:48:03.211323Z", + "iopub.status.idle": "2020-11-25T16:48:03.704389Z", + "shell.execute_reply": "2020-11-25T16:48:03.703475Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal\n", + "from nengo.solvers import LstsqL2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:03.715109Z", + "iopub.status.busy": "2020-11-25T16:48:03.714589Z", + "iopub.status.idle": "2020-11-25T16:48:03.717665Z", + "shell.execute_reply": "2020-11-25T16:48:03.718061Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)\n", + " pre = nengo.Ensemble(60, dimensions=2)\n", + " nengo.Connection(inp, pre)\n", + " post = nengo.Ensemble(60, dimensions=2)\n", + " conn = nengo.Connection(pre, post, function=lambda x: np.random.random(2))\n", + " inp_p = nengo.Probe(inp)\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_p = nengo.Probe(post, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we run this model as is, we can see that the connection\n", + "from `pre` to `post` doesn't compute much of value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:03.723651Z", + "iopub.status.busy": "2020-11-25T16:48:03.722497Z", + "iopub.status.idle": "2020-11-25T16:48:05.127829Z", + "shell.execute_reply": "2020-11-25T16:48:05.128292Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:05.189086Z", + "iopub.status.busy": "2020-11-25T16:48:05.148649Z", + "iopub.status.idle": "2020-11-25T16:48:05.567932Z", + "shell.execute_reply": "2020-11-25T16:48:05.568369Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Add in learning\n", + "\n", + "If we can generate an error signal, then we can minimize\n", + "that error signal using the `nengo.PES` learning rule.\n", + "Since it's a communication channel, we know the value that we want,\n", + "so we can compute the error with another ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:05.578955Z", + "iopub.status.busy": "2020-11-25T16:48:05.577413Z", + "iopub.status.idle": "2020-11-25T16:48:05.579574Z", + "shell.execute_reply": "2020-11-25T16:48:05.580024Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " error = nengo.Ensemble(60, dimensions=2)\n", + " error_p = nengo.Probe(error, synapse=0.03)\n", + "\n", + " # Error = actual - target = post - pre\n", + " nengo.Connection(post, error)\n", + " nengo.Connection(pre, error, transform=-1)\n", + "\n", + " # Add the learning rule to the connection\n", + " conn.learning_rule_type = nengo.PES()\n", + "\n", + " # Connect the error into the learning rule\n", + " nengo.Connection(error, conn.learning_rule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can see the `post` population gradually learn to compute\n", + "the communication channel." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:05.585177Z", + "iopub.status.busy": "2020-11-25T16:48:05.584408Z", + "iopub.status.idle": "2020-11-25T16:48:07.485498Z", + "shell.execute_reply": "2020-11-25T16:48:07.484593Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:07.509427Z", + "iopub.status.busy": "2020-11-25T16:48:07.504278Z", + "iopub.status.idle": "2020-11-25T16:48:08.021500Z", + "shell.execute_reply": "2020-11-25T16:48:08.021026Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(), sim.data[error_p], c=\"b\")\n", + "plt.ylim(-1, 1)\n", + "plt.legend((\"Error[0]\", \"Error[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Does it generalize?\n", + "\n", + "If the learning rule is always working,\n", + "the error will continue to be minimized.\n", + "But have we actually generalized\n", + "to be able to compute the communication channel\n", + "without this error signal?\n", + "Let's inhibit the `error` population after 10 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:08.027274Z", + "iopub.status.busy": "2020-11-25T16:48:08.026766Z", + "iopub.status.idle": "2020-11-25T16:48:08.029835Z", + "shell.execute_reply": "2020-11-25T16:48:08.030281Z" + } + }, + "outputs": [], + "source": [ + "def inhibit(t):\n", + " return 2.0 if t > 10.0 else 0.0\n", + "\n", + "\n", + "with model:\n", + " inhib = nengo.Node(inhibit)\n", + " nengo.Connection(inhib, error.neurons, transform=[[-1]] * error.n_neurons)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:08.035378Z", + "iopub.status.busy": "2020-11-25T16:48:08.034629Z", + "iopub.status.idle": "2020-11-25T16:48:12.552486Z", + "shell.execute_reply": "2020-11-25T16:48:12.551981Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(16.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:12.581514Z", + "iopub.status.busy": "2020-11-25T16:48:12.578742Z", + "iopub.status.idle": "2020-11-25T16:48:13.292767Z", + "shell.execute_reply": "2020-11-25T16:48:13.292332Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(), sim.data[error_p], c=\"b\")\n", + "plt.ylim(-1, 1)\n", + "plt.legend((\"Error[0]\", \"Error[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How does this work?\n", + "\n", + "The `nengo.PES` learning rule minimizes the same error online\n", + "as the decoder solvers minimize with offline optimization.\n", + "\n", + "Let $\\mathbf{E}$ be an error signal.\n", + "In the communication channel case, the error signal\n", + "$\\mathbf{E} = \\mathbf{\\hat{x}} - \\mathbf{x}$;\n", + "in other words, it is the difference between\n", + "the decoded estimate of `post`, $\\mathbf{\\hat{x}}$,\n", + "and the decoded estimate of `pre`, $\\mathbf{x}$.\n", + "\n", + "The PES learning rule on decoders is\n", + "\n", + "$$\\Delta \\mathbf{d_i} = -\\frac{\\kappa}{n} \\mathbf{E} a_i$$\n", + "\n", + "where $\\mathbf{d_i}$ are the decoders being learned,\n", + "$\\kappa$ is a scalar learning rate, $n$ is the number of neurons\n", + "in the `pre` population,\n", + "and $a_i$ is the filtered activity of the `pre` population.\n", + "\n", + "However, many synaptic plasticity experiments\n", + "result in learning rules that explain how\n", + "individual connection weights change.\n", + "We can multiply both sides of the equation\n", + "by the encoders of the `post` population,\n", + "$\\mathbf{e_j}$, and the gain of the `post`\n", + "population $\\alpha_j$, as we do in\n", + "Principle 2 of the NEF.\n", + "This results in the learning rule\n", + "\n", + "$$\n", + "\\Delta \\omega_{ij} = \\Delta \\mathbf{d_i} \\cdot \\mathbf{e_j} \\alpha_j\n", + " = -\\frac{\\kappa}{n} \\alpha_j \\mathbf{e_j} \\cdot \\mathbf{E} a_i\n", + "$$\n", + "\n", + "where $\\omega_{ij}$ is the connection\n", + "between `pre` neuron $i$ and `post` neuron $j$.\n", + "\n", + "The weight-based version of PES can be easily combined with\n", + "learning rules that describe synaptic plasticity experiments.\n", + "In Nengo, the `Connection.learning_rule_type` parameter accepts\n", + "a list of learning rules.\n", + "See [Bekolay et al., 2013](\n", + "http://compneuro.uwaterloo.ca/publications/bekolay2013.html)\n", + "for details on what happens when the PES learning rule is\n", + "combined with an unsupervised learning rule." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How do the decoders / weights change?\n", + "\n", + "The equations above describe\n", + "how the decoders and connection weights change\n", + "as a result of the PES rule.\n", + "But are there any general principles\n", + "that we can say about how the rule\n", + "modifies decoders and connection weights?\n", + "Determining this requires analyzing\n", + "the decoders and connection weights\n", + "as they change over the course of a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:13.298513Z", + "iopub.status.busy": "2020-11-25T16:48:13.297109Z", + "iopub.status.idle": "2020-11-25T16:48:13.299216Z", + "shell.execute_reply": "2020-11-25T16:48:13.299621Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " weights_p = nengo.Probe(conn, \"weights\", synapse=0.01, sample_every=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:13.304740Z", + "iopub.status.busy": "2020-11-25T16:48:13.303969Z", + "iopub.status.idle": "2020-11-25T16:48:14.377928Z", + "shell.execute_reply": "2020-11-25T16:48:14.377437Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:14.405695Z", + "iopub.status.busy": "2020-11-25T16:48:14.400917Z", + "iopub.status.idle": "2020-11-25T16:48:14.908991Z", + "shell.execute_reply": "2020-11-25T16:48:14.908550Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])\n", + "plt.ylabel(\"Decoding weight\")\n", + "plt.legend((\"Decoder 10[0]\", \"Decoder 10[1]\"), loc=\"best\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:14.914096Z", + "iopub.status.busy": "2020-11-25T16:48:14.912685Z", + "iopub.status.idle": "2020-11-25T16:48:14.914800Z", + "shell.execute_reply": "2020-11-25T16:48:14.915207Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Change the connection to use connection weights instead of decoders\n", + " conn.solver = LstsqL2(weights=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:14.920176Z", + "iopub.status.busy": "2020-11-25T16:48:14.919392Z", + "iopub.status.idle": "2020-11-25T16:48:16.357905Z", + "shell.execute_reply": "2020-11-25T16:48:16.357422Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:16.380843Z", + "iopub.status.busy": "2020-11-25T16:48:16.367216Z", + "iopub.status.idle": "2020-11-25T16:48:16.918153Z", + "shell.execute_reply": "2020-11-25T16:48:16.917680Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Connection weight')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 12))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[0], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[0], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[0], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[inp_p].T[1], c=\"k\", label=\"Input\")\n", + "plt.plot(sim.trange(), sim.data[pre_p].T[1], c=\"b\", label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p].T[1], c=\"r\", label=\"Post\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., 10])\n", + "plt.ylabel(\"Connection weight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For some general principles governing how the decoders change,\n", + "[Voelker, 2015](http://compneuro.uwaterloo.ca/publications/voelker2015.html)\n", + "and [Voelker & Eliasmith, 2017](\n", + "http://compneuro.uwaterloo.ca/publications/voelker2017c.html)\n", + "give detailed analyses of the rule's dynamics.\n", + "It's also interesting to observe qualitative trends;\n", + "often a few strong connection weights will\n", + "dominate the others,\n", + "while decoding weights tend to\n", + "change or not change together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is `pre_synapse`?\n", + "\n", + "By default the `PES` object sets\n", + "`pre_synapse=Lowpass(tau=0.005)`.\n", + "This is a lowpass filter with time-constant $\\tau = 5\\,\\text{ms}$\n", + "that is applied to the activities of the pre-synaptic population $a_i$\n", + "before computing each update $\\Delta {\\bf d}_i$.\n", + "\n", + "In general, longer time-constants\n", + "smooth over the spiking activity to produce more constant updates,\n", + "while shorter time-constants adapt more quickly\n", + "to rapidly changing inputs.\n", + "The right trade-off depends on\n", + "the particular demands of the model.\n", + "\n", + "This `Synapse` object can also be\n", + "any other linear filter (as are used in the `Connection` object);\n", + "for instance, `pre_synapse=Alpha(tau=0.005)`\n", + "applies an alpha filter to the postsynaptic activity.\n", + "This will have the effect of delaying the bulk of the activities\n", + "by a rise-time of $\\tau$ before applying the update.\n", + "This may be useful for situations\n", + "where the error signal is delayed by the same amount,\n", + "since the error signal should be synchronized\n", + "with the same activities that produced said error." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/learning/learn-product.html b/examples/learning/learn-product.html new file mode 100644 index 0000000000..2c18283165 --- /dev/null +++ b/examples/learning/learn-product.html @@ -0,0 +1,773 @@ + + + + + + + + Learning to compute a product — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Learning to compute a product

+

Unlike the communication channel and the element-wise square, the product is a nonlinear function on multiple inputs. This represents a difficult case for learning rules that aim to generalize a function given many input-output example pairs. However, using the same type of network structure as in the communication channel and square cases, we can learn to compute the product of two dimensions with the nengo.PES learning rule.

+

The product is a trickier function to learn than the communication channel and the square. We will also try the nengo.RLS learning rule and see how PES and RLS compare in terms of learning the product of two dimensions.

+
+
[1]:
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+%matplotlib inline
+
+import nengo
+from nengo.processes import WhiteSignal
+
+
+
+
+

Create the model

+

Like previous examples, the network consists of pre, post, and error ensembles. We’ll use two-dimensional white noise input and attempt to learn the product using the actual product to compute the error signal.

+
+
[2]:
+
+
+
+model = nengo.Network()
+with model:
+    # -- input and pre popluation
+    inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)
+    pre = nengo.Ensemble(120, dimensions=2)
+    nengo.Connection(inp, pre)
+
+    # -- post populations
+    post_pes = nengo.Ensemble(60, dimensions=1)
+    post_rls = nengo.Ensemble(60, dimensions=1)
+
+    # -- reference population, containing the actual product
+    product = nengo.Ensemble(60, dimensions=1)
+    nengo.Connection(inp, product, function=lambda x: x[0] * x[1], synapse=None)
+
+    # -- error populations
+    error_pes = nengo.Ensemble(60, dimensions=1)
+    nengo.Connection(post_pes, error_pes)
+    nengo.Connection(product, error_pes, transform=-1)
+    error_rls = nengo.Ensemble(60, dimensions=1)
+    nengo.Connection(post_rls, error_rls)
+    nengo.Connection(product, error_rls, transform=-1)
+
+    # -- learning connections
+    conn_pes = nengo.Connection(
+        pre,
+        post_pes,
+        function=lambda x: np.random.random(1),
+        learning_rule_type=nengo.PES(),
+    )
+    nengo.Connection(error_pes, conn_pes.learning_rule)
+    conn_rls = nengo.Connection(
+        pre,
+        post_rls,
+        function=lambda x: np.random.random(1),
+        learning_rule_type=nengo.RLS(),
+    )
+    nengo.Connection(error_rls, conn_rls.learning_rule)
+
+    # -- inhibit errors after 40 seconds
+    inhib = nengo.Node(lambda t: 2.0 if t > 40.0 else 0.0)
+    nengo.Connection(inhib, error_pes.neurons, transform=[[-1]] * error_pes.n_neurons)
+    nengo.Connection(inhib, error_rls.neurons, transform=[[-1]] * error_rls.n_neurons)
+
+    # -- probes
+    product_p = nengo.Probe(product, synapse=0.01)
+    pre_p = nengo.Probe(pre, synapse=0.01)
+    post_pes_p = nengo.Probe(post_pes, synapse=0.01)
+    error_pes_p = nengo.Probe(error_pes, synapse=0.03)
+    post_rls_p = nengo.Probe(post_rls, synapse=0.01)
+    error_rls_p = nengo.Probe(error_rls, synapse=0.03)
+
+with nengo.Simulator(model) as sim:
+    sim.run(60)
+
+
+
+
+
[3]:
+
+
+
+def plots(start_ix=None, end_ix=None):
+    sl = slice(start_ix, end_ix)
+    t = sim.trange()[sl]
+    plt.figure(figsize=(14, 12))
+    plt.suptitle("")
+    plt.subplot(4, 1, 1)
+    plt.plot(t, sim.data[pre_p][sl], c="b")
+    plt.legend(("Pre decoding",), loc="best")
+    plt.subplot(4, 1, 2)
+    plt.plot(t, sim.data[product_p][sl], c="k", label="Actual product")
+    plt.plot(t, sim.data[post_pes_p][sl], c="r", label="Post decoding (PES)")
+    plt.legend(loc="best")
+    plt.subplot(4, 1, 3)
+    plt.plot(t, sim.data[product_p][sl], c="k", label="Actual product")
+    plt.plot(t, sim.data[post_rls_p][sl], c="r", label="Post decoding (RLS)")
+    plt.legend(loc="best")
+    plt.subplot(4, 1, 4)
+    plt.plot(t, sim.data[error_pes_p][sl], c="b", label="Error (PES)")
+    plt.plot(t, sim.data[error_rls_p][sl], c="r", label="Error (RLS)")
+    plt.legend(loc="best")
+    plt.ylim(-1, 1)
+
+
+plots()
+
+
+
+
+
+
+
+../../_images/examples_learning_learn-product_4_0.png +
+
+
+
+

Examine the initial output

+

Let’s zoom in on the network at the beginning:

+
+
[4]:
+
+
+
+plots(end_ix=2000)
+
+
+
+
+
+
+
+../../_images/examples_learning_learn-product_6_0.png +
+
+

The above plot shows that when the network is initialized, it is not able to compute the product. The error is quite large.

+
+
+

Examine the final output

+

After the network has run for a while, and learning has occurred, the output is quite different:

+
+
[5]:
+
+
+
+plots(start_ix=38000, end_ix=42000)
+
+
+
+
+
+
+
+../../_images/examples_learning_learn-product_9_0.png +
+
+

You can see that it has learned a decent approximation of the product, but it’s not perfect – typically, it’s not as good as the offline optimization. The reason for this is that we’ve given it white noise input, which has a mean of 0; since this happens in both dimensions, we’ll see a lot of examples of inputs and outputs near 0. In other words, we’ve oversampled a certain part of the vector space, and overlearned decoders that do well in that part of the space. If we want to do better in +other parts of the space, we would need to construct an input signal that evenly samples the space.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/learning/learn-product.ipynb b/examples/learning/learn-product.ipynb new file mode 100644 index 0000000000..e7ec8110d4 --- /dev/null +++ b/examples/learning/learn-product.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning to compute a product\n", + "\n", + "Unlike the communication channel and the element-wise square,\n", + "the product is a nonlinear function on multiple inputs.\n", + "This represents a difficult case for learning rules\n", + "that aim to generalize a function given many\n", + "input-output example pairs.\n", + "However, using the same type of network structure\n", + "as in the communication channel and square cases,\n", + "we can learn to compute the product of two dimensions\n", + "with the `nengo.PES` learning rule.\n", + "\n", + "The product is a trickier function to learn than\n", + "the communication channel and the square.\n", + "We will also try the `nengo.RLS` learning rule\n", + "and see how `PES` and `RLS` compare in terms of\n", + "learning the product of two dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:18.465204Z", + "iopub.status.busy": "2020-11-25T16:48:18.464316Z", + "iopub.status.idle": "2020-11-25T16:48:18.962699Z", + "shell.execute_reply": "2020-11-25T16:48:18.962193Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the model\n", + "\n", + "Like previous examples,\n", + "the network consists of `pre`, `post`, and `error` ensembles.\n", + "We'll use two-dimensional white noise input and attempt to learn the product\n", + "using the actual product to compute the error signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:48:18.991146Z", + "iopub.status.busy": "2020-11-25T16:48:18.990284Z", + "iopub.status.idle": "2020-11-25T16:49:19.357410Z", + "shell.execute_reply": "2020-11-25T16:49:19.358916Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " # -- input and pre popluation\n", + " inp = nengo.Node(WhiteSignal(60, high=5), size_out=2)\n", + " pre = nengo.Ensemble(120, dimensions=2)\n", + " nengo.Connection(inp, pre)\n", + "\n", + " # -- post populations\n", + " post_pes = nengo.Ensemble(60, dimensions=1)\n", + " post_rls = nengo.Ensemble(60, dimensions=1)\n", + "\n", + " # -- reference population, containing the actual product\n", + " product = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(inp, product, function=lambda x: x[0] * x[1], synapse=None)\n", + "\n", + " # -- error populations\n", + " error_pes = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(post_pes, error_pes)\n", + " nengo.Connection(product, error_pes, transform=-1)\n", + " error_rls = nengo.Ensemble(60, dimensions=1)\n", + " nengo.Connection(post_rls, error_rls)\n", + " nengo.Connection(product, error_rls, transform=-1)\n", + "\n", + " # -- learning connections\n", + " conn_pes = nengo.Connection(\n", + " pre,\n", + " post_pes,\n", + " function=lambda x: np.random.random(1),\n", + " learning_rule_type=nengo.PES(),\n", + " )\n", + " nengo.Connection(error_pes, conn_pes.learning_rule)\n", + " conn_rls = nengo.Connection(\n", + " pre,\n", + " post_rls,\n", + " function=lambda x: np.random.random(1),\n", + " learning_rule_type=nengo.RLS(),\n", + " )\n", + " nengo.Connection(error_rls, conn_rls.learning_rule)\n", + "\n", + " # -- inhibit errors after 40 seconds\n", + " inhib = nengo.Node(lambda t: 2.0 if t > 40.0 else 0.0)\n", + " nengo.Connection(inhib, error_pes.neurons, transform=[[-1]] * error_pes.n_neurons)\n", + " nengo.Connection(inhib, error_rls.neurons, transform=[[-1]] * error_rls.n_neurons)\n", + "\n", + " # -- probes\n", + " product_p = nengo.Probe(product, synapse=0.01)\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_pes_p = nengo.Probe(post_pes, synapse=0.01)\n", + " error_pes_p = nengo.Probe(error_pes, synapse=0.03)\n", + " post_rls_p = nengo.Probe(post_rls, synapse=0.01)\n", + " error_rls_p = nengo.Probe(error_rls, synapse=0.03)\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(60)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:19.365545Z", + "iopub.status.busy": "2020-11-25T16:49:19.363517Z", + "iopub.status.idle": "2020-11-25T16:49:21.164897Z", + "shell.execute_reply": "2020-11-25T16:49:21.165317Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plots(start_ix=None, end_ix=None):\n", + " sl = slice(start_ix, end_ix)\n", + " t = sim.trange()[sl]\n", + " plt.figure(figsize=(14, 12))\n", + " plt.suptitle(\"\")\n", + " plt.subplot(4, 1, 1)\n", + " plt.plot(t, sim.data[pre_p][sl], c=\"b\")\n", + " plt.legend((\"Pre decoding\",), loc=\"best\")\n", + " plt.subplot(4, 1, 2)\n", + " plt.plot(t, sim.data[product_p][sl], c=\"k\", label=\"Actual product\")\n", + " plt.plot(t, sim.data[post_pes_p][sl], c=\"r\", label=\"Post decoding (PES)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(4, 1, 3)\n", + " plt.plot(t, sim.data[product_p][sl], c=\"k\", label=\"Actual product\")\n", + " plt.plot(t, sim.data[post_rls_p][sl], c=\"r\", label=\"Post decoding (RLS)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(4, 1, 4)\n", + " plt.plot(t, sim.data[error_pes_p][sl], c=\"b\", label=\"Error (PES)\")\n", + " plt.plot(t, sim.data[error_rls_p][sl], c=\"r\", label=\"Error (RLS)\")\n", + " plt.legend(loc=\"best\")\n", + " plt.ylim(-1, 1)\n", + "\n", + "\n", + "plots()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine the initial output\n", + "\n", + "Let's zoom in on the network at the beginning:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:21.169798Z", + "iopub.status.busy": "2020-11-25T16:49:21.168848Z", + "iopub.status.idle": "2020-11-25T16:49:21.705631Z", + "shell.execute_reply": "2020-11-25T16:49:21.706066Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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PqCV5NkLReQXANc369bhtlrRTFOWooijLFUXJbasiRVF6K4qyX1GU/ZyB1jO0IV8zZrTdy+eIP/+khHQNG6rbHj0yRDQmSBCCXlYJMQeHbES/YusNZRCZMpFT/alTwJYtvjtPoFizBrh4kX6nkcyYAbz5Zvw1+TtxgkZktAEr3OHaNettqVNTDp7kyWn9jTcoKtvcudRZlSaN6ofWqxclHWUYhtESGQlMnAj88APw3nvW+7WmylWqAJUr+000Q/BXMIK1APIJIcoA+BfAr7YKCSFmCCEqCSEqZTHaSziB8Kvmyt6+7b5ZScGC1BOttf9mRSd+sWQJvazGjqX1bdsSTgjaf/+l3C2+fr20bk2N0EWLfHueQNAqbrzeaEUHoJGw+/dJGY9vyF5QdzufAGqI5Mljvf2NN8h80NEIpYxoB1CCaIZhGC2ffQYMHGh//9tvWweJadHCtzIZiRGKzg0A2hGaXHHbXiKECBdCPI9bnQXAYkCMMYqTJymh3Pr13kVCkskPAeDcOfrQMqGP2Ux5lQB6ua1Zo2ZZj+9cugT89ReZXPna5yxFCmqELllCDp3xkezZja8zSxa6XtWrA48fG19/oBCCRlnSpgUKFXL/eMscaPv2UefERx/R+q1b9pVDy3v9zz/jpyLJMIx7REZSZ7itsP4tWtDI/cyZ9D5u3Fi//48/Qsc02whFZx+Awoqi5FcUJSmATgB0up+iKDk0qy0BxKPAdcHD48f0weveXW965glJklDDUFFoODNVKuDuXWPkZPzPtWv0X3boQKFpJbJ3fuZMNSdHfGTBAjWJZ8uW/jnna69RlJqvvvLP+fzBa6+py+3aGV+/DJiyZw+wbp3x9QeKtm3Jvn3wYM+CNxw9qi4fOECjQkmS6EfeXX1+mzcnGS5dcl8OhmHiDxs3qp0er75K1jt9+pBp97ffAvnzk8mrolC+s4cP9UF8hljFWA5OvFZ0hBCxAN4H8A9IgVkqhDihKMpXiqLIJsUHiqKcUBTlCIAPAPT09ryMNW+/TXMZ9clb8uUD+vVT17Nl457AUCQmhjKlA2qiWFsMGABcvuwXkfyONmdJ4cL+OWfGjDQfMyZ0er6csWMHzcePN+49o0XrQxJfzCnDwymUeaZM9Ix5wrlzZIJ244Y+FHdYmBpWOmtW997P2nc7wzAJj02b1GWzmd4xU6dSVMgSJazLp09PViDynTNunF/E9BpDfHSEEH8JIYoIIQoKIUbHbRsuhFgTt/yZEKKkEKKsEKKuEOK0EedlVP78U23Etm5tXL0//qg3Y+Okc6HFrl3UE/Pdd3pTxvHj1eXOnWm+YAHZ6cbHIAUy6uCbb/rGt8QWUtEBgDNn/HNOX6JtRHsaNcwZ2bKpPoHxZQRZhpKePp2CA7jLjz9SR0WWLLZ9caTiLoT9iHVHjlhHa2NzZIZJuNy/TwEIEsclmdF+rxyRKJG+syUU8FcwAsbHNG9O84EDgdw2Y9p5zsGD1CsN0CgP52MIDebPB5o2Vdc3bCDTxufPybZfOsoXKaKWWbkS6NLFv3L6g6RJ6VrMmeO/c2bIoC6fO+e/8/qKZ8/UZcuwo0aSNi2ZsB075rtz+BMZ1tmTkOa3blECZ4BymtlCmzpg9WrbZcqUIXO3Ll3oWWjdOn6bqjIM45jatWn+66/A11+7lyA+Rw7nZYIJVnTiGVWqGF9n2rR6e/xT7GEV9Dx/DnTrpjp058tH/hXZs1NDByB/nRkzgE8/1R+7eLHejyc+cPeu70Yh7KHtvZe5TEKZvXvVZZlvyxcoCplI/PorRckLdWR0Q3c7oMxmdTRdUYDZs22XS5YMGDaMlj/8UK+QWjJvHoW4zpSJcq7NneueTAzDhD5CUOCqVKmA9u3p/eGO8iK/bf37+0Y+o2FFJ8SJjVVNF+rVo8arLyhQQHV8ZfO14Eebeb1NG3I8tnSCTpSIkjSmSAFMmaLf17x5/OnxNZmoZ9yXSUJtkT8/2TsD8UPRad+e5nPn+j5qnQyD7MinLFS4epWSerqj6Ny/T1GPoqJo/dNPHYdE//prddnRSFiiRDRJ/6q33oo/vlAMwzjHZKKAJgDwxReeRedVFOqImTTJWNl8BSs6Ic6GDcD587T844++a4AkTqyGOI2vDuvxCe0oTb58zsv36wf07q2u37gBDB1quFh+JyYG+O036hBw5ToYTZ8+QI0a8UPRkaZT/ohaV6sWhQH/5Rf6D0OVmBhSdNz1m+zYkUKhAxT9aMQI58dIG/vq1Z2X1YYG59w6DJNwGDVKTfipNa92F0XxfYeXUbCiE+IcPKgu24qSYSSpUlFj8cgR356H8Q6TSd8TniuXa8e9+aZ+/dgxyuYeyowdq/6uvHkDI0PBgpSPINSJiKCRPledVr2lbl0ywbx50z/n8wX589PzqPWDcwXtqEyPHkDy5M6Pkb5ArtC7t5o7q1s3ffQlhvEEIdSAJX/+ycEughXts+6NohNKsKITwphMwN9/07K/fCpq1ACWLbNuFDPBw/Dh+nVXM7FXq6aPrLVzJ5nchDK3bqnLvnSgd0SuXCSHZYjpFy+AadNCZ8Tixg3Kr+AvpKlXKJvKSuWjVi3XjxGC8lVIHJmsaUmeHPj4Y/LZcRZmOmVKsgCQ2PP/YRh7mEzAyJFqcKJEichn49gx6hCRQTSY4EJrwu6vTqtAw4pOCPPuu8D27dQYLVvWP+f89lua//IL8Pvv/jkn4zqxsZTluE0bCmm8ZYt7jSyAjtMqR7dvGyqiX5Ev8vbtfZP3xRVeeYUaBZbhkidPBvr29W8kOE95/pwaNP5UdPLkofm1a/47p5EcOkTzypXdG02MiKDnGKAAImFhrh+bNy/9V5UquZe7SYb0ZhhX2b4d+PJLaofI0ZuffgLu3aPlVauA/fvJl0M6v8eH6JOhzoMH6nK2bIGTw5+wohPCyF44fzqT5sqlRl2bPNl/52WcExMDlC9PDaUuXchcRoaQdIciRWh0RxLKH6fHj6mHe8mSwMlQoADN//lHv13mPPnvP8pnEMxIp1Me0XEdmWvCMqqhM2QP+bRpwNat7h0rRy0PHgSOHnVeft06mmuDlwQ7ly6RaRQTWGS74/hx8oOU1K9P8/BwUvLDwshUsmRJ9004GWO5do3eC337Ahs3qgGm4jus6MQD/J11vVgx6sU5eBB4+tS/52bsM20afXQAoG1b7+rSfpBOnvSurkAyeTKZ9ATSabJBA5q/+SaF85YJWaWis2AB5TXS9rQFE1FRamM9fXr/nTd1arIhD8URHa3pWL167h0rFZ1cuawjJTqjYkXVn6dDB8pi7ojGjcnc7cwZNcJbsFO2LJlGCUGN7fgQgjwUuHqVovvFxFAACzlCffYs8N57jo/V5mgJxec5viAtNTp2pPdSqAQT8BZWdEKUlSvV5UD0knTuTErO4sX+PzdjjRDAgAHqurcvsL591RGI994LzdxJ0oTCmb+Cr0mcWE3o++67QLNmNOJm2St99SpFNIyO9ruIDtE2lhs39u+58+QJzUAOMpdNly7uOfxGR6u50DwxtUyaFDh9mpbPnwdatXJ+TK1aZO4mQ84GO7Jz7dkzSpDdqBF9D1nh8S1vvEH+n8mSUUP5hx88q0cb3ZPxH48eqcppqPveugsrOiHKgAHkpDp6NDB/vv/PLz/GvXsDQ4b4//yMHq3pyYIF3tcXFkYNiJQpad1dE5pgQDqCu5Px2VfIUR2AzIUWLlRHdiQnTlCUrp49/SqaU6SP1r17FHnRn1SrRiYWa9f697zeIpWGunXdO65mTXXZ0w4sbehowHn0Kxl2Pdh9xf75R9+Bc+AA+YQANILdqBE5wwe6YyO+Ik1I5fWVPmiWVK0K/Pyzum7Z6RaKnWbxARnJccKEhBOEQMKKTggSHk7Dv4MHA59/HpibNkUKdfm774A7d/wvA6Mig0Rs3Ur5R4xCKgsff+w4EWEwIsMSuxpe2xc8eUI9nx9+SOtaReGzz/S9m1270jyQ/kS2CA8nE6pAhCJt3JhMZVq2BHbv9v/5PaVOHZonTuzecfv303zECM/f68mSUSJQibPEv1mz0nzu3ODO92SZ1FheY8syMt8bYyzSpFKLLcuB//0PaNKEltu3VzveUqQAxowhk92pUyl3G+MftmxRI+W2aBFQUQICKzohyL59NC9fPrByaMmenROJBoLYWAoWMHUqrRsdfS99ejV0ebA7zGt58QLYto2Wc+YMjAxCUO/mxx/Tev78emVhzBjqkbblAxVMduz371Oj253oX0ZRv76qLMhQ+sGOttOnRg3Xj5PBZd56i8L2eoM2dLQzRUdrIte8OSWhDkaeP9evJ08O/PqrahaaKBGNQFevzqGNjeSvv6itYekLPHs2+elOmwYsX05pJ/bto1G1PHlo5Prnn9UAGf/7n9pm6dePvln374emtUCoUbcucOECLQcqn1xAEUIE5VSxYkXBWHPnjkzLJURkZGBlWbNGiOzZVXnGjw+sPAmRDz9Urz/gu/NUqiREkya+q99ofvhBvSbR0YGRYc0a/X/ToYMQiRPTcqJE+rLHjwvx1ltq2RUrAiOzJZcvkzyFCgVOhpgYIapXFyJUPgkTJtA127LFveNKlaLjBgwwRg55L61f73rZQD8z9nj8WC9flix0X0jWrBHiwgUhcudWy+zaFTh5g5UVK4SYPl2Iv/8Wwmx27Zg33rC+P9y9Rx88EMJkEuL2bX09+fLRPDzc7Z/CuIjZrF7vmTMDLY1vAbBf2NAneEQnxNDmrtGajwWCFi30ozihnG8lVFm0SF0eNcp358malXrotmwJDRv4M2fU5WTJ/H/+e/co6lWWLORM/9dfwKuvqiNwlr3sJUtSD6lMcNquHZmFyPC/gSAyUu2NlaGSA4EM5nDggD4BbDASHU0R6ho0oBw47iB9Zb780hhZpOmmK2GmL1/WR9Q7f94YGYzC8t12/rzeLLBFCwrjrjX3q16dQlHLwBAJmefPaZS7XTsKLtO0KTBvnuNj9uwBVq+md1mWLDRic/kyvcPcHd3PkIFG3LJlo/+yeHHaLtsPs2aRn1VEhJs/jHGKNDkcPx7o1SuwsgQKVnRCDBlxJliSdWobkY8fB06OhIr2+r/xhu/OI+3469alD9Y33/juXEZgy57cn5w7R43eX34hk7WmTVUTunLl7Dv1y+ssCWSuKqmQ9ewZ+IAO0jwp2M3Xbt2iBuX//ue6qZ/JBPToAfzxBzX20qUzRpYcOYCCBSmxozPy5tUrDn//HRwKwuXLZDo7f76anyV/fiBtWtvlly2jsOSSAgVoXSZgTUiYzXQfFi1KpmSWObCkP5g9WremaetWoGFD+r7kzeu9CevQoWSuq+08+fRTipwX7AExQhHZ6Ve0aGDlCCSs6IQYV65Qz1ubNoGWxJqbN62zvzO+RftR95XtbWysdYSdzz/3zbmMQvb8+zscMkAfcRl1SOsflCMHzZ89o55mWyRKpHfS/ftvtWfe38hAFO++C6RJExgZJKVL03sv2EMgy/vOMvKZI3btUnvXZdAKo6hZkxQdV0ZhtXl0Bg+m/z3Q5M9Pfh23bgHdugGbNjkOSlG4MHVy7N2r3y79WhMCJhO9Nz75hEb8z56l77Jl58+UKZSUd+VK8mnSJh4fPlxvodGjh/FyDh1qvW3evNCwGAglZETWBOmbEwcrOiHEn3+S83Kw3bAyWdjatTQ0zS8q/6E1gZKJAo2mXTvgyBHb+44cCb4gFCYTKRu9e/vf9EsIMkOTDVdtg1cbKMKRadDAgfr1zp2pEerv50oqOoEK5qBFUch8xpljfaCRio5Ual1BPj/LlunDSxvBa69RA3fHDudlly1TR00A+8+8v7BMoFu0KI0oW456WpIsGVC5sn7b4cPkML9woaEiBh1mM3VCvf66dZ4bWz3616/TKOKPPwKbN9O2mBhKDKqlUSPjZW3bVn3HSA4cCB5rlfiCHNHJli2wcgQSVnRCCBke8NGjgIphxbRp+nUe1fEPT55QhKf33vOtsuEou3q5ctTrGkwcP05mlEY3Gl3BcvQlSxZ1OU0atZfUURLMTJn069u2UTQpGULcX6xdS6Mo7jTafUmmTMGt6ERFqeajuXO7ftyKFTSXIXmNRObxccXvp2lTfcS1EiWMl8cVZOJcy3wrBQq4V8/u3Wq+tz17KNRxly6GiBiUFC9OZmVjx6rbXn+d5sWKkblx4cL2j5fvLpmbyB9+eTlzWucTW7WK2jrcYeo9MTFqBMeEljtHCys6IUJUlJrp3Vc9997Qv7+6XKsWv6T8gRwVaNDAN6N816877skL1henDCtdq5b/zy1NZBYupA4JS3v2LFnIF8KROZo9/4PFiw0R0WX27qVe1yRJ/HteewS7ovPvv+qypbJqj/BwatgBejNUoyhQgBq7J064fszo0TSPjjZeHlfIm5c6T8aPV7elTet8JMeSqlVJwSlenMJQSyxHEUKZS5dIMXz2DDh9mrblyaMmHc6dm0bmNm8mc3dboewlp09TuY8+ovXNmykx+caNPv0JUBR95+38+eTbyMGNvEeGlAbILDqhkoB/emgxYoS6vHp14OSwx+TJarK5s2eDb9RJcuIEsHNnoKXwnpMnVV8OR710nnL2LH0ktY03Sx4+VJeDSbHdto0aS3ny+Pe8MTHAoEHk9NuunW2n8kSJaITEkaJjKwkfYJyTuis8f06jhcFkJpspk7U5UzBx8CDNZS+6M+7eVXPYdOrkG5kA6gi5dUttCDvj88/JJ2rNGjX4TSCQJky//+5dcBGZiFcSnxKKFihAz6hWSe7fX02SnCYNUKaMakKbODF9A6Vy3b8/dZhlykQjQd26qfWkTUvR1erV8/3vSJfOOlHyyJFsHeItUrGVymtChRWdEEGaJr32WvBGzyhcWO05C9ae18qVKYnf06dks92oUWhG5CldWnXKLVjQ+PobNNCvlyxJvWy//gq8/751+Z49gaRJKfx0IDl0iP5XdxI1GsXGjWSSNmECXQt75MzpvFdZG9Vu/nygVCmyX3/yxBhZnSH/f3dMsHxNpkwUjCVYE1pevkwNzD//dK28tlzTpj4RCQCZayVJ4l7kvGPHaD58uG9ksoflu/ibb2gkwptRxU8/Jd+jChWonjffpNDpoYzZDMycaXtfixakbJcqRX6KlpQoAbRsCfzzD72rChZUr28gR7ssAyzNmGHtr8i4jhBqp66lz1WCw1ZynWCYOGGont69KeHThQuBlsQxf/xBcqZLFzwJ22JjKVmZNnHWqFHq8pdfCvHrr0IUKCDEpUuBltY1fJkk1GTS11+vnhDr1qn758xR923Zoi/7+uvGy+MOUo4RI/x3TrNZiIkThWjZUoj06Z0nW2zbVoj8+Z0n7HvyRE06+fvv9LsUhZJ4+hp5Ha9e9f25Hj92rZz2mX361LcyeUKtWkLUqOFa2b//FqJ2bf8liG3a1LV7TiKvdZIkdB/6i2PH9O+T2Fhj6jWZaN6tG9W7Zo0x9QaKVausk3h+953n9RUubF1fmjT+TzBpKQMgxF9/CTFtmn/liA/MnEnXL1OmQEviP8AJQ0ObO3eoF99dh0x/I/02Hj/2jXOtJyROTL4SWkfUYcPU5REjKHzmxYuqI2Ywow2veviwsXXfvatPHBgWRiMV2jDN2ohGr76qLmfIYO1AHCi0OUF8zd69FBZ4zRq6Ns4SlKZLR7b1n33muFyaNEDt2rQsr7MQejNWXxATQ/OvvvLNiM7t2+qI7759dD2+/556k7//3n5SO61PmGW480Bz7RqZTLrqwN20KeUnkVhGCTOa1q3pnpPmxc4YOpTMVmNi/Gvqq70mgPc5WyTSP2HqVDINNfq96W9s+U95Y/4o/QJTplS3PX0KvPOO53V6g9Yi5PXXgT59giOvUyixYAHNLYNFJURY0QlyTCZKlrd6tZo5O5jRNpIDnUB02jR9srxFi5wfs3hx8Gdfl2FfZ8/Whyw2gtWr9Xb5tswktYnnYmPVRnjDhvSfjxwZeIVRq4D5Gq05mSt+QdI85LvvXD+HNnqbr330pO+VL4JNCEE+SpkzAx06qEr7J5+Q2dcnn9B9/eef9O6zh9bJNhiYNInm7jYMZTh+X5sIli9Pc0fO6JaULk3zZs2Ml8cejqIRGkHq1GRivWGDtVIVSlia+KVP75lP4tOn1NFw4AB19j17BkREGCKiR7x4Qb8tY0br92OwdW4EO1euUMLY9u0DLUngYUUnyDl0SLXlDkTyQ3cpVky/7qoDrNHcvQv07es8xPDgwfr169fJh6JPH+r9CzbMZnJ6TpRI7zjqLSYThaIdN876fJZoldnTp6nRcOkS2Xo/eEChbN9/33FD1RdIBe3jj/3jQCvRKsauNDY++YTm9oIO2CJRIrK5ByjQhy87EaTDvy8UHW1DdtkySlRoi+bN6TotW6Y2vLR+T1OmGC+bN5w9S4qBVA5cxV8jj/K93L8/BZpwBRnlzGSikTdf+zJ+9x05v8tr4s7z4Q7lytHoW506wetL6gxtIJjy5T0fodq3T+2okXlWUqXySjSvSJJEHcWT6TQke/fSOz6YAt8EI3fvAkWK0Dc5WFIDBBpWdIKcK1do3qyZfZOOYEJR6OMhP6zFi/tfhrNngVatbO/TvsTbtaNIM2YzMGcOOX0vXUr7pk+nqGauJNrzJ/PmkZOm2Wxs2N+DBymQgKVpS/fu1mUVBVi/npZPnABSpKDRRssP5I4dqhmUP5D5fnxtBmTJ/Pnq8ttvOy9fvz6ZnwnhXuPx4EEaNQOoQe2r8L/S/NAXio6rplMAhRfu0AH47Tda796dnKcB6oEOpsiOZ896FhTEX4pOmjQ0v3EDGDXKtWMUhZQBgPLRaANkGI3JBAwZQu81Ofpk6ZxuFFplVIaiDzVkGHuAnhNPoyMePaouy3eLJYFSLDJl0jfUFy4kEzttqHDGmj17gHPnaJkVHYIVnSBmzhw1Ad28ec5t/4OFjBn1IwP+6tl/8ICuWdGiej+WxInV0K/vvUfzLl3UvCSKQr1HXbrQMK/sOQcoyt2xY2oOo0AjzdbKlTO23r171eV06WiUIiKCGh+2kCZz3bqpeRZkT7Fs8NWu7d+oTTKMrC/CbdsjKop8GXLkANat05v1OUL2lrtjJpkkiRo959o1oFIl35jfrFxJDQojzf9u3aJ7duhQ633ORiZlvqgkScgXSuJNyGEjGTeOso9XrepaeZmBHjDOB8Ud3DFfmzFDXfZlREWZvR2gUfZTp/QdCEYiQy8DFKExlNi4kUar581Tt7n6ztFiMlFn3vHj6rb69dVlbRTA5csDE5k0USKSUXLgAM3jU3hwX6Btq/jThDuosRWhIBimYIu6FhXl/3PKqCOpUrkeLSdYMJuFKF5c/Q1TpvjmPDExaiS6zJmtI7YsXSrE+fO0/9QpiuITFaVG4bFFjRq2o7+MGuWb3+AOH33km6hTuXMLERYmRJ8+FNHHGdrIbG+9Rds+/pjWa9ZU99WpY6yc9oiOpvNVrOjfZ2XAADrvkiXuHffXX3TcZ5+5d9zDh9b3pZE8fUp1VqlibL3ffWctd8uWQjRoIERkpBCnT9O9Z+u5a9aMor8dO0Z1vfUWbX/lFSGePzdWTk8oUoTkuXLFtfLa37Zzp29l07J9O52zdGnXj9FGqqxWzXeyDRpE5/BH1MsXL4QYN079XTdu+P6cRmHr+YiIcL+eRYv0dRQtqt8fGSnEO++o+99/3xj5PeHmTSEyZFBl6d8/cLIEO8uXq9fJWfTP+AjsRF0LuEJjbwomRWfpUrXR7C+0Dcm8ef13XiOZN0//Mm3c2PiHr00bqjtPHusPQJEintW5YoUQuXLpXxpy8meoVVsAJJuRXLtG9ebI4d5xUulKmZL+1wkTaF02/uUkG6i+5MwZOtevv/r+XFpKlRIie3ZSuN3BbBYidWohevZ0/5wxMaSU+kLR+fprY+s1mYQYO9b6Odq+3bpsdDTdg0uWCHH2LDW+bCl1u3er6ydPGiOnp+zbR3JMnuxaealIAkLMmuVb2WwxaJAQyZM77uix5MQJISpX9vx96ojISCHefdc397IztPfV+vX+PbcnyM4cOZ04IcSmTZ7VNX26Ws+nn9ouExWllqlRQ4h79zyX3VuSJhUvO30B6hQxKvS4lkuX9N/4L74QoksX48/jK3zVARYq+FTRAdAEwBkA5wEMsbE/GYAlcfv3AMjnrM5gUnQGD6Yr5a/cHLGx+hu2RAn/nNdoXrywbqjs2GFc/WfPWtcPCNGrlxBDh6ojOZ5y44Z13deuGSO7J9StK2z2vnmD2az2kG/e7P7x06bRsZcv0327ahX1vmuvWZkyxslriyNHKEcIIMS2bb49l5affqJzjh3r2fE5ctDxt2+7f6w278XOnUJcvOiZDJZ89hnV2b69MfV9+60qZ5YsNE+UyPXjLUdXnz2j0R+5LvMMBYpSpdT73xUOHlRl/+MP38pmC9nAHTbMveP69KERc6OR+eEA/+dK0d5Xffv699zuEhmptkOMaMh+/71aj6POR+35UqSgb3og2LBBiO7d1dFTgEacvCEigt6bkZHUZsibV/991Y6ehwKPH7OiY0/R8dpHR1GUMAA/AWgKoASAzoqilLAo9jaAh0KIQgAmAHAjsGrguXmT5tpIJ77kzh39eoYM/jmv0SRJYp2Z+Z9/jKm7fn3bWZ8B8tUYNcoz52At0pGvWTM1NPWjR/Qa8Td37qi2/Vr7bG/ZsIH8mgCgYkX3j5dRxm7eJH+DVq0oVO6FCxTaEiCfAF/6U/z2G0WYAfznnxMdTcEqAM/Dd0r/HE+ca7Uhf1991XYYcHf55x9yOFcUY+6x48f1PlpduwJffEGR1FylTBn9+pIllOujeXNat3xX+pOoKNXHQev34QjpJAz415dMIh3xXQ1IIEmfnp7hrl1tR2L0FK1TvfSf9BfjxpHPWIUK+v8lGJk+nXJMSSZO9K4+6e8yZoxj31/t/xMVRZHxApF+oX596/fkzJme1xcTA1SvTnkJU6cGZs1SAz+dOUP+u9p2V82awOXLVCYQ339X0AaWkOHumThsaT/uTACqA/hHs/4ZgM8syvwDoHrccmIA9wEojuoNphGdNGlULfnhQ9+fb+9evWa+cqXvz+kr7t1TfTfkNHasZ34UZjONqvXqZT3Ssny5EFu3Gu+fER5OpkLSpwIQ4ocfjD2HK4wc6VoPnLtI8zNPe4AOHVKPv3lTv09r+lCokNei2uXDD/3fiyVHcxYu9LyOTz6hOj74wP1jX7wQYvRoY3vw5GjO/Pne1yWEEIkT6+WbOtX9Op49o1HCWrXUeipUEOLuXVquXdsYWT3h0iX3r73WNDBQ/kVZs9L1dIcePVS5V6wwThbtyGSgePtt+sY/fhw4GZzxwQfqdbJl9ukOJ08KoSj0XXYFy29tqVLend8bypfXyzJmjGff/DfesP5drk7vv09tsmDzm54/n+Q7fTrQkgQO+GpEB8ArAK5p1q/HbbNZRggRC+AxgEyWFSmK0ltRlP2Kouy/FyxhrqBPoKiN5uUrrmmu5tq1lNU6VMmcGWjZUr/tk0/U8MS2iI62nevhv/8oR8usWfrtEyZQqOhatYzPvZAxI0VtS5dO3TZokP9G9yS3blG4TSGMib53/z71XMmoXZ7mJdGGr7TscUueXA23fP6873rC7t+n/+jyZd/UbwsZatebUNbffQfUqKH2rrpDkiTWPeDaHj1POH2awsJ36eJdPRERlK/FMlKTJ9cqZUoaJdSOBF+4QO9hRQlcni5AHU1ascL1Y86epYh7t27p8wL5k9decz9/jPa/W7TIuIStL17QvHZtY+rzhPfeo2/87NmBk8EZt2+ry9pvkTs8eUJh7T/7jN7FH33k2nGWo5XaSG3+pk8fmnfsSPPPP6d2gStcu0bvpMhI62h7a9aoSZmd5UKbMoVCn5cpQwnJ3Yli6EvkM53JqmXNBFV4aSHEDCFEJSFEpSzaVOABRjsM6E4eCE/RZgD2ND5+MCFfHNpG8fHjQNu2+gSCly5RIsRMmSjh1YULeoVn/37b9WtDzvqKtGn1666+XI3i3j01JLERVK9OjdqDB4EfflDNsNxF+5jaCtU6bZpqvuTpORwhBIWhLVjQv89K4sRkPlWokHf1VK1K4VI9aThamrR6G2r60iUy5fCGGzcoZ4tWcW7UiBoDlSp5Xm/y5Gpo7cePqfNEiMCarslzu5qRfvt2MrPMkwfInt13cjkjUyb3Ox769KF3MkDPeaFCninoWqQpUNOm1KEXKCpVIuVPG0o72Lh1i57Nzz4DSlg6BrjIN98AX30FrF5N/1/OnK4dt307dTAGA++8QyZ0CxfSNwwgM0yZ9NQe+/bRc5ckie2EqPXrqx082nvRUeLp48fJpK1kSep0CfT9IxM9h6qrgy8xQtG5ASC3Zj1X3DabZRRFSQwgHYCQyUksHygAuH7d9+fTxokPhB230eTNC/zxB/Dnn6qfyccfU76OggXp+m7eTC/yatWox+XqVXoZ16tHoz+nT1MjyhJ/Jdx75RVKjCmRSRX9wY0bwO+/G9uok7lJSpTwzjY+keYNcuyYdYLQJEnUPA/TpgHPnnl+LlvI/EjSR8cfvHhB96dMbOgNXbtSL6MnuSEUhZR/OTqgHQn2hOvXyb/KG2z5K/XpY0zi4GHDbCv7UVHe1+0JMqdV/vyulZd+Fa+95hNxXCZ7dupASpRIzYHljESJKDmwNtdKpUrePc/yujVvriY0DRTVq9M3pl694PLBMJvp2/nff+SLN2aM/p3rDtrRT3cU7bx5gR499NsCmaw3eXK6BjKv07//0ijXrl32j1m9Wr/+6qvUeTh7Nn23UqakxKsXLtBIzalT1Am7YQOVr1pVvWb2RtTefVfvR+VPJk0iZTRdusDk5gp6bNmzuTOBfG4uAsgPICmAIwBKWpTpB2B63HInAEud1RtMPjra6F49evj2XDdvClGsmHt2y4cOBbd9sSU5c1rbvZYr556d7P799L/4MwfC48cUWeyVV0iGPn38c96GDY23Y5f1jRtnXF2AEL/8Yr1fG1547Vrvz6dlxgyq15XcP0Yh3we2fqu73LtHdU2a5F09BQoI0bmz58fLKEze5oqyDPNudJSmJEmoXm1ejaFDjT2HK8ydS+fOmtW18pcuUdkCBTzLe2Ik2mhSb7zh3rGbN+v/X1ejzWkxm4WYONF37wRP0EYh80c4fFfR+kZ52/bIk0d9Pnv3dv94y29wMKCVJ1Uq++W00doyZ3YvVPapU0I8ekTRMR89ovtX1te3r/V1CQ/3/ne5S7D9L4ECPg4v/TqAswAuABgat+0rAC3jlpMDWAYKL70XQAFndQaTohMZqd5EiuI7J7TLl9XzdO9u7dwtBH0kR44UYvFiylcSHk7lmzXzjUy+YNgwtcHi7tSoEYUvDiQyzDMgxPDhvj1XTIx6rgMHjKtXvqjdzf9ii0uXSDZ7L9obNyh3h/aDbcQzZDZTgtA0adzLC+ItMk+Qt07BQpDinCiR++F+LWnQgGRq2lSI//5z/3j533ibhyhjRt9+dCtWpHofPBDi889pOXt2Unj9Sf36dG5XE6t6qlj4Cm/k2bFDPX7fPveP174rAAq5HWh++UWVZ968QEtDaJO1AhRsx1NkqoQffqB6PMmJY9mJEYgk6pZYtg9koJ7nz6ljcu5ctWP1889pnxHfnidP6Jpev267neJq8mAj0LZPXc3nFV+xp+gY4qMjhPhLCFFECFFQCDE6bttwIcSauOVoIUR7IUQhIUQVIcRFxzUGFylSqPaXQgA7dvjmPGfOqMslSuh9WuS5v/oKGDkS6NSJhiul49muXRQu888/fSObkYwcSeZpKVO6fky2bDRv1sx78xpvyZxZXf7qK2PDrVoifTemT6cwqEbx8CGF5zbC9C9fPpKtRQtat3RWzZmTzIukjf+vv5Lpl7fs2EF+Ak+fem7O4S7nzqk+e0b4BIWFUcALb8NvyzDMf//tnnO8Jd48W0+ekJ346NHAt9967zNki7/+ArZtIzv0Dz6gbbdv073szE7fSKRv2oIF7h1npJ+dN/z8M809eQ6rVlWXPYkZJMMTy3dPMJhna30NvTUBNQKTiUzWJMuXU7AdT5GmW40bUz3ab5ir1KxJc/ne86f5tj3OniXfGvn/yXDYOXOSGdebb6rpQaRvjhEBi9KkoXO88gq9844dI/8pSd68ZAapbdP5CukO8NtvFASGsSaoghEEM9oPlLTNNhoZxx1QGy7nzpGz9Sef0MM1dqztY9OnBwYPJnvnPn2AXr18I6MRhIXRR+7RI/LpKFbMcflXXyWfkp071agrgeTbb/U+B9qIOEZz4gTNPclxY4uoKPpg3btnjN+ElmHDaP7DD7b3L1+u/tdGRK2THxF5Xn+g/a+Nim6TPbv3jauyZdXlkydJ2YiMdL8eV/PBWBIdDWzaRMu5cwOffupdw8weWbOqDa5s2fSOt9qcH77m7Fmgbl33g1HIDptA07s3dZZ58hyGhZFfBEAdT3v3un7s3buqAnzuHHXepU7tvgxGU62aei+dPeuf6KqOWLBAjVa6dClFFfWGlSupo8nTQAYA5a3Zvp2+w4oC9O3rm84MdyhcmNpHhw/T+oED1KawFVVQ+440kgwZgFKl1I4XyebN5Fvra2RONU/f3QkCW8M8wTAFk+maEGSmAgiRMqUQ/fv75hzvvkvnKFWK8kcIobctdXcKFSpUIHlXrKB5yZK0/ckTMqXxp1mSO8jcOjt2+Kb+kyfV/9Iou/6hQ62H+Y2kYUPyMbPHpk10biN8g4oUIRNII8zvXEXrG2IU3bpRfd6YZO7bZ/38p08vxJ07zo81mbw3R9Ged/16z+rwBK1vny9zNWm5coXON2aMa+WfPFFl/Pln38rmDn37CpEpk2fHarOwZ8zo+nGNGxv/TjMKk0k1SQSEOHo0cLJoc895YooqOX5ciJYtqZ7u3Y2TT35HEiemnFbBQKZMZMZatap67Zo3p7bbkSP+kWHCBOv3cI8elHdNtumMRp7n3Dnf1B9KwJemawkBOdSbJo1nw/WusHYt0KGDGgUEsB/Oets2dblcOd/I4y9kT1qyZGR+IsNIp0kDdO/uP7Mkd8mXj+Y1arifl8IVvviC5qlT2w6J6QmyB27zZmPy8VhStixF9zGZbO+X//XHH1PvoKdcukTPRrNm/ou8B/gmf5LMCfHjj57XUa4c9dLXqaNue/SIeoKdjUBHRND8hx8oopG7CKFf92cehyZN1OXz5yn0tK9Zs4bmrvSyHz2qz0FjmVMskGTMSO8tT/KAaKOkuROBS5tOwKh3mlEkSqTvlXc1Ip0v2LOHRtz/+ou+L54yfrx6vxppNjlqFI00xcaSuSxg/R7wN4UL04i7NoJlo0Z0/aSFjK955x1gwAB6JmTY+V9/pdQKo0YZfz7tNZfRTRlrgrQJGXxIRSdlShp+N5onT8iWVBuy1lY45fr11fjtq1YB69aRuRpgnYDOXmMz2OjaleYFCtAH1JPGViCQig4ALF5sfP0XLpCZiFEJ2h48IOVi8GB9g9hIZCNXe220aE2N7OVFcobZrOZ7GTfOszo8wVcf8mbN6P+Q4VI9IXFi8ruQ7wLJ9u3OO0JkSNQkSdw/b8OGqqImkb5Y/mDQIP26rXem0Vy4QI10V37n8uWqieW2bYHNn2NJxow0L1nS/W+a1s/BHR9FaaYmG9/BRoMGau4Uf9xLtrh5k8JJd+5MOYa88SnR/gZbSbi9oU0b+lYfOUJtkUSJVH+YQGAZZn7DBgr57E9SpaIw8unSkbmgNpT8N9+4nqTVVWTy9DFj9OkvGD2s6LhIhgxq/HZfjOisWkXzEiUoX0qtWtY9ALNnUy9KyZK03qoVORcOG0b2s3Pm6MufPEnKTqDyTLhKz57UCDfaZ8TXaF8sRjjXawkPJ7vjAQOMS4QplZBq1Yypzxay8XT9OgUJsETrBOupX4r2412woGd1eIJ2dNXT7OT2KFWKfEy8vY/s5XSJjbW9/dAh9T3jbq+nyUSNiWXL1G337vnX5yJzZsqzJenYkUa2fJnQ9/Zt1xUWbZAJVxM0+gvp6wSoOX48wbKDzRF37gBvvaUGLgk2UqZUR3LGjVMDJ/gLIdROIm/zLb3zDvDPP+q60c9l4sRA6dI0atSmDW3zh/O9LWJjrUeu69d37940mgoV6D2kTaQ9cSJ1TsfEkE/28uWUl81TevemuSedVAkJVnRcJFEiaghERhqv6Dx4QFm/y5ShHpzly+kBmT6d9h88qH4gZENSS7JkFHygSxdylJeUKUMvo5QpAz+s7AxvsvmeO2fdWzV1Kr2AfY2Ue+xY4xJ6xsaqCoEvHCjddaB2B23vri1FRmuu4qmiIwM0SIdof3DggBpIYfly40d1ZeeFt0pto0a2tx88aFvZkVH9AKB2bffOZWnGt327Z9GcvEX7jBw/Tp0+cpTYaK5do9FbV0cx7t8nBWfpUv8q5a5QqRIFkahalXqbJ0927/hvv6Vvy4sXzjvTHj6kjqybNwMfNdMd/B3Fas8eNemyNx1/ly+rvf0NG9L3cOhQr8Wz4vXX9evR0cafwxmxsXoTVonRnY+e0q6d/rvXtCl1lOXLRwmWBw70rF7tO6h1a28kjP+wouMGxYpRJuj7940NKfzpp/RQTphAmrnWjhkgczZX7Ws//pgaYbLhJJEjRvGNu3fJhCR5cmrMCUF2+v36kVlL5sy+7ZW7fVsd2bE0o/EUramaUVnUtYquLxtc2lGutm3pY205siP9hDxVdGbMoPNozTx9jfaZbNnS+J5CbcZ5b0xOU6emzhhtaFqAGrNffWVdXior7kQs27CB/kPtvTl0qHe+BN6QNSvw4Yf6bb4axZYNKlfN8+7fJzPL9u19I4+3JEtGo4kAjR67w6efAlOm0HL37o6V/4ULyXcPoGhvwY40jV2xwj9+VeHh1Okgw36vXeudmePUqepy374UrdQXPlHvvkuhnaWfpK1RfF+zbZs6Cvf66+p7Lm9eSmURDJ28O3cCP/2krmvfT1eukLLmjpxCqO+UCRN823kZL7AVoSAYpmCLuiaEEJ9+KkRYGEW4+PJL4+otXlyIFi3UdRklxZvoTu3bW0f/ePLEGHmDhSNHhJg9W/8bZQQry6lPH0oy6AsOH6boT6lTU6Iyb5k/n2SeO9f7uoQQYvVqSiQJCPHNN8bUaY+YGCFGjdJf+w0brMu98w7tu33b/XNkz07/s7/YulVNEvvjj747T4cOdI7z542pTxtByl5UNPlfuRNtzbK+f/4xRl5v2LKFZNFmKl+71thzPHtG9fbs6Vo0OyGEKFFCiNatjZXDaL77Tr1mjx65d+y9e+qxpUrZL/fmm1Tm1CnvZPUXT54Icfas+tt8EaFSi4wUFhYmRK9e3tW1bh3VVb6875KbWyKTnVeqJMTu3f45p6R1a/V/un2bJu37ychE295i631cpIgQadMK0akTlZk6VYiNGx3Xs22benywJLgNBmAn6lrAFRp7UzAqOpahA40gOloIRaHM6PPm0cc0UyYKBZkihRCffOJZvbduCZEli17eEyeMkTlYsBdW29HkqxCPK1dS/d5krxaCFIWqVSlkqxFKkxBC1Kyp/v6ZM42p0xnLl6vnXLnS/v633nKv3qlT6biffjJETJfwV2fBrl10jjVrjKnv7l0hFi60/RwIQeF0e/cWIl069+q1rOv0aWPk9QYZin3BAlWufv2MPcfBg1TvokWulf/0Uyr/2WfGymE0z5+rSrEnjcJhwxx/E8PDhUialELPhxpz5tDvunDBt+fRPk8LF3pXV8GCqtLvL8LDVfmrVxciNtb354yJEWLgQDpnmzb6fdrruWmT72VxlQUL6P+VHX2W04wZ6rKj8OvyOwg4V4oSEqzoGMCiRcZ+4E0mId54g+r66CN93X/95b28JhPJXLky1WlkHP1Ac+yY9UuiXDmK7//0qRAvXgjx7bfWZWrW9E3+hkeP1NG+6dM9r+e//6iO2bONk61ePfX3+yvnwd276jltjYKYzbSveXP36i1SRIi8eX3fw6pFe//4sof00SM6x5tvGluvrQ+q2ayO8jVubP/YmzfVUbfHj/Ujxe++a8x7yggsc9UAQvzvf8bVL3usAdfzVcjyK1YYJ4evOHKEZF282P1jo6L0DTUtZrOaH85Wh0ews369+ttcHcVzF7OZOju97ZA0m4X44w/Kn5U7N30H/cXz59bvmDFjfPe+vH1biPHj1XNZKjpbtlC+Q3c6JvzJTz/Zfi9bTtev06RFfjvlveKvUbtQgBUdA9i/X20YGNFTJ5MnAjRyI5ebNDFGXol2KDc+YEvJqVnTupz2hVCqlL78pEnGy1WmjFr/w4ee1TFlCh1/7ZpxclWsqDYA/Ym8FvYU7Hr1SDk9e9a1+mRHw4ABhonoEvJ3FC/u+3O1aydEqlTGJkG19QHVJj794gvrY27eJFNMWaZYMXWEAiBlM9g+sFK2zz+nzp1cuYS4ccOYuqV5V44crpX/+29VHqNGZn1JRIQqrye98aNHq8eHh6vbd+5Ut9+8aZy8/uLCBVX+iRN9c8/fvKl/Nj3txPnzT7UObzrbPMXWe8YXpopmMyUq1Z7H1mih7GzzxbfeW0wm6iRKkYJklN99e5P2vtMm62X02FN0OBiBG5QqRcECKlQg5969e72rb9cumrdqpY8/r3VMNoJs2YAePUj2R498kwfIH1y8SE6V2mSpAEXBs9wGUP6Bjz6iHCVLlujzEQwYQCG8z583Tr4RI9RlT6NnHTlCkfWMSv41bhxFDOvRQw1F6S927CCHWlv/DUD35eHD5NjtSojNXr1oLhOx+QMZGKBiRc8SK7pL27YU8ERGljOCwYOtcyxoI6Zt3UrOsjEx5DB+5Qo59MpEogBt/+47dX3SJO/ye/gCGSDi9m0KynD9Oj1HmzZ5H6EvMpLmroSt/v13iqwEUBLWQIa4dRWto7on3zVttL9Mmci5/uFDup8BcpzOkcM7GQOBNh/Yhx+q/6tRPH2qhh0fNYqSBnuayFkbdj8QqRr++kt9R0tmzjQuH1FsLAWwOXLEOoKkrcANmTJRVMDx4/XRJYOBRInoXvrhB3r2evRwXl6G7pZRf2fP9q2M8Qpb2k8wTME4oiME9RK++SYNlaZM6Z0taq9eQmTNKkSjRqqG3revcb2QWizN7kKR//3Puqdj2DDXj5c29trpp5+oN/OXX4zprbt2Ta175073jj1/no6rW9d7OYTQ9/CNGGFMne4ie3r/+sv6+n7xhSrfxYvO68qaVYiSJf3bQ/799yRfpUr+OZ+8B4wefXv+nHqk79whU0JXzCZsTV9+SSa7wTaaIwSNJBQqRCPuyZNby37kiOd1v/22a6M52lFkgHw8QoWJE0nmJEk8C9xy/Lj6u1euFKJjR3XdZDJcXL/x1Vf6/zRTJiGuXjWm7jZtvLvmWj7/XJXRSIsAd9i+3fq5K1vWmLq1vmDaad48+0E0tP6pwc7du+Rb/fvvFJjA8ndOnEjlpC/nn38GVt5gBDyiYwyZM1PI0EePqJdvwgTP6jGbKVlf/vzU8y356SffJJazzJguhPHn8DXaENt169I1/Ppr148vX14NcSr5+WfK69CzJ/0f3pIrl9qL+eqr7h1buTLNixb1Xo5794BmzdR1y3wH/qJqVfX8O3fq92lHmJzlPHj6lEYiu3b1bw/59u00N+I/cYUCBegds3u3sfUmTUqjmFmz0giyp3z6KV2LYBvNAWgktGRJGskZPNh6f3i453VfuuTaKK1lbiFv8oP5mwEDaCQvJoZ6mN3NiVKihLrcpg2NoksShXBL44sv9O/y8HDKi2QER4/SyMPz597fK9evq8uBSk5bowawaJH63gRoBGblSu/C5gP6kPnVqtGo1YQJQLdu9hM4Z8umLttLmhwsZMlCI1Nt2gC//mr9nv74Y2qvyCTEWbL4X8ZQJYRfP4FBKjpp09K6ND9zl48+IpOi7NnVnAzah9JoUqemxJoSrVlKKHD5sj579/ffe9bYKlpUbQSlT08fGtk4MSr3hjbnijsvV/l73nvPexm0WaKrVVOVKH9Tty5Qrx4tW5oP5MqlfhDr1HFsviYVWn8nXXz4kBog06b553yKQsqh0YqOFq2Zh6u5b5o3J5k8NavxFzlykNlfrlzWSQQ9begcPUrmb4ULOy738CGZ6gBq8tVgSxLqjA8+oPnatdSAdAdFsd3AtpXMMdSYO5feURJv8sVs304mat99R+/Ejh296zgQAtiyBZg3j97zQgRWsezUid4r334LVKlC29q2pVw78v3mSOm5dw9Yt44U7b/+ot+zZw+ZOWfMSKZwu3aRKbFl/ixLhgxRl9et8/aX+Y+kSSn3oTYJeWwstQ3mz6d1X+RFirfYGuYJhilYTdc6dhSicGEyAQHIedgdYmIovr0cjqxUiebp0hmXP8Me2sgoadIER1hYV9E6Rg8Y4L3pjNlsPRRuVN4Nbb0nT7p2jMlEOTfatjVGhnHjVDOLQCPzjwAU0ENLbKy67+hR28evWGH/eF9TuDA98/7k66/pt3oa0MIV5PVs105dfu89ilpoaTLx0Ue+D69rFN27q3Lfvy/EBx+o68uWeVZntWp0/IQJjss1bKiea8sW/0YGNJKuXek3JErkfl6dmBi9c/2sWcYG1ggk2veYO+92LTKqpnby1kx17ly1LnejWPoabTRE7TRokBC1agkxebIQkZFqeZNJiBo19GW1IZe7dnVfBq1JpfZcoYD2+2g5vfWWEKVL2343P3ni/29lMACOumYM/fuTkiAEhWctV86942ViO8vplVeMl9UWY8eq5/ziCyEOHRJi3z7/nNtdzGaSr18//bUyinPn9PUaFakmNlaIf/+lOjt1EuLKFcflX7ygRgVgXO6PN9+kPErB4kshr7EtPwftf2CZuPLWLf1+Xzb+LfnhBzqnv8Oyb9xI5x092jf1m0zq9dy5k0JGyxCmZjOFLF2/njpkOnYMLf8KbfLL4cPVSJmA5zmkMmQQok4d575h2bOr5wqlTiRbyIig7oTmff99ih519Cgdmzix7+QLFFmzqv/x5MnuH2/53V+40PucM507q/UFYwoJew11OVWoQOW0v8PeZK8zzBExMerxmzcb+tP8wp499C577z3b12TmTFLgIiMppPj160K8/jrt80UqjWCGFR2DkM7JDx8K0acPLR8/7vrx9hL4eZsN2VW0PSwyh4+RyoORyHwYAI18Zc7sXvABV9DmxwCocWtEwy46Wq3TWbjwU6e8e5FbIkO61qnjfV1GIUcuAQr3eeuWuu/hQ3Wf1mF8xQq6dnJfWJh/ZdY+J/7EbCYn2rx5fVO/zPjuKJN9qBIbqyYNfeMN6kSQ/6MniqNUtMePd15WO+r84oX75womtLlxwsNd6zCx9V0LhdDa7vD0KTmNp07tfkJO7XuuVCkKgGMEffuq9X7wgTF1GkmzZs4VGG0QH3sdwd6Mxnz5JdXz5ZfG/S5/YxnoxHIqWFCInDn12w4dopx8wdLh6WtY0TGIpUvpqu3aJcSQIbRsmazKFnv3Usb7MWNs36T+NHP49VfKi5EmTfB+kEwmfe4bX+YFkJGu5PTll8ZEvZImSOnT23/RyAgqAPXcGEGmTFSfo0SQ/sbS7OO11/T7e/Sg7Tt20Pq9e2rZokUD86KW19GoCEvuIDtUChY0Psv4kiVU94EDxtYbTNSpQ79Rjo7Jyd1cKDKxn7MOiMmT9eeJD7Rtq/6eJEnsNzSvXtXn4ZENU0CIFi38K7O/ePVV9bc+fqw3z7PVUTZ7tv76fPKJMXLs2KGvd/duY+o1kufPycTKUSO9RAnH+41QCitU8F5hCiTLltG1SJlSNe8rVMjxdStSRF32VcLbYIIVHYOQjeL+/SkcpLyJvv/e8XFVq1K5WrWsb0ZPhsC9RfpwyMkXib08JTpan/UYcG/UzBPmz7f+X4x4MUj74nnzbO/X+vM8fuzduS5epNEcmWXb34k1nSFHEgDqEb13j/wfzp5VR6H++ovKJk2qlv3hB//K+fixGvZ15Ej/nluyeLH6+41OtDhkCJkVhaoPiSsUK0bXrmJFMh2VnQ6A62FZly2jRkXJks7Las1uQimktCOmTtW/D7dvpxEDrTmMduTHXgMrPvLuu/rfW748bZcjEwsWqGXlCKPRDXchyIcQIJO6YOus1HL+vLUJuqvTlCnG+HnJzqOffvK+rkAgr8e6dTTKevEiWeho322OpqVLyZoCoI7H+AgrOgZSpowQLVvSctGi1i/0e/fI5OH4cbohnz+n3gRZTmvnO2hQYH7DX3/pH4L16wMjhy1kbyBA5mr+yglg6bNjhPInTV9at7bd06e1u/UWrewVKgRfz5XZLESVKiRf8uRkWwxQfqQTJ2i5b19rB9Z16/wrp9a81Oh8Nq6ydq0qg5FKvgyiIv0M4ytr1qiKjhBkty6vpyvmPVqTN1f85sqXD64RVCPYtk3/HMrOOhmU4elTIQYPtt2okr6gefIEx3soOppG1hcvNqY+bYAUObVrp1pJNGhAnTfaPC4A5bCaOtW4UdosWajeUPAJM5ko2Mm8ec4b5Tdvku/wtm3Gnd9sJvPn994zrk5/oTVbs5UP7OpV+rY2aGD/msoOUMD3ga8CBSs6BlKvHg0dCkG9fZYNVelYLnu2bCWv0z7QgUBrGgSQI2kwEBmpl8ubJH+eIB3Qjez5SZeO6qtZU282I81ijFA0tclQc+RwP1qSv3jyRO1Zk1OePPRRs3w26talubNgDkby6JFehkB1ADx7JkTGjCTD8uXG1bt6NdVplOlMMPPWW/Rbu3TRN66aNaOIbI6QZouAvnfeFsuXU7lAjf75ksuX9SOxAPmmPnigjnpqpy1b6BkymYT49lt1uxG+h66wZw/1cC9Zom6TSRgBChhhBLGxpDB/8AFZA1h+4ytX1q+nTEnXzahRlxcv1IiCzqIBBiM7d5I1wz//0OhY797UWO/Rw7cBZ157jUziQ81n5f599V66e9d+ObOZnsHYWLVTq39/221Pfwb28Res6BhIhw40kiOE3lZXDq86661o1cr6mEAhQ06nSBFYOSSHD6vXJn/+wMig/a9GjTK2Pm3Ulxw5aFvBgt6fQ2tK0aOH9/X5Ejl6o51smXTeuSPE7dv+lc0yvPLFi/49v5YDB1Q5jAjvLM0WAO/NJEMBS/PXY8eEqF5dXX/61PZxT5+qZQYOtB+c5NkzvQJ17pzvfksgiY3VWyE4mrSj71rfxzFjfCefyUS+KZYO7a1aUdS4xInVbenS+UYGZ9flxAljz7dunVq3vzsDQ5lp0+ia+doU3miOHVP/b3eCJT14QOW13xI5/fQTtbfu3fOd3P7GnqLDCUM9IFMmNTttuXLqdsuEiJb06AGcOQOsXk3rZjMl0QokSZMCb71FyTLHjKFHIFCcPQsMG6auByqT8eXL6vKwYUCzZsDdu8bUPW6culyqFM2/+87z+h4/pmRx2izJuXN7Xp8/KF4c6NtXv23bNv36228DWbP6NomuLR49UpfTpg3stcyYUV0+ftz7+ubMoXmbNmrC4/hM//76a1iqFFCihLo+a5bt486fp3mNGsDo0faTL372GdC9Oy3nzAkUKuS9zMFIWBhw9SowdixQpozjstqEofnyqcubNwMDB1LiWW8ZPRrYsIGWY2JIvmrVrJ/V1auBzp3135HHj4F+/YB33wXCw72XRdK/v+P92mthBPv20fzDD4HSpY2tOz4jEyT//Xdg5XCXmzdp/t9/7iWDzZCByleoALRsqd/Xrx+1X0uWNEzM4MWW9hMMUzCP6Mgh+TNnyNRq4EBVS7Y1nC+n4cP1YXaDhSlTVJkCFZ7SbCYbdylH7tyBMxuS4X21/920aWTG8cUX7oefliEfmzWjvEsbNqiJGlu18k5WbcJHgBKqPXjgXZ3+4upV289JsmSBMy14++3geT61fiJG+PLVqkVmG/HVEdUWn32m7wnVJlcE1OAXkl27VPNVZ73w2mdvyxaf/YSg4sIFMoW5ft36+f3nH+vyb70lRLZs+nLuMHKkaoZ2/Ljef08b8MCTyUj/O7NZjWb4wQcUDKNgQbJKMMpcTmIyUTuibFlj600IvHhB/wtAPmeXLgVaItf45Rfh9cj+w4c08vnhh9bPwrlz5B8baAsjbwGbrhmHjBAlQ+Q6i28up19/9fyF70u02ZpTpjQ+nK0zWrbUX5eCBQOfpNBsJrthrVzp07vWALLk6lVKIGqplADe+19I87dgu6dc5c4dNR+VdjLSCdUdmjal86dNG5jzWyL9dAAyM/CUBw/oXvnf/4yTLRTQBhjJmJE+5NqOKYAiVwmhb7hXquRc2e7aVS3vbxPLYCAmhvzounUjBdEe0dH6ZKrO/KO0yGMuXqS5ZZ4QbycjO1RiYihh7dOn9A2NjaUIdUZHN5wzh2QPRLTW+IAM0wyQyXcoINOSGBXYw9JUvHx5mv/xR2grO/YUHTZd84Bq1cj0Q5o4KArw5InjY7ZvB0wm38vmCa+9RsP4S5cCkZHA4sX+Oe+zZ0D58sCaNeq2LVuAkyfdG571BYpCZn0AULEizaVZ09Wr7tWVOzfQoAHw4IF++/HjQLt27st2/Tpw4oQqp+SXX9yvK9BkzQpMnUr33Jw5wKVLtP3MGf/LMmgQmTS88op6fQPNtWvq8oQJnteRMSNw6xbQuLExcoUKhQoBf/5Jyw8eAKlTk/lodLRqdnbuHHDqFLBnj3rcxIn6Z0vLgwf0nnr6VN2WNasvpA9uEicGNm0C5s2jb6I9kiUD6tZV1z/5hEylbfHPP0Dr1sCLF2SWJpEmv9KER8u+fcDWrdbfrRQpgIsX6fsyfTpQsCCZG2r57Tf6lhthxpY4Mf221KnJnC4sDEiVin6/kYwZQ9+k9983tt6EgtbdICIiYGK4xblz5DKRIoUx9f37L9Cokbp+6BDNmzcHvvzSmHMEFba0n2CYgnlERwghRoygcH1RUeo22dNStqx1z9GFC2S6po3IFmyYTJS4q3p1/zioaZNlAmQuFkzcvy/Exx+rI3hy8jQam+w1ASgBn6ekSkV1dOyolysYk8W5S0xMYJ4P7ajswIH+PbcztOZWnoQFlWYPPXqEXrQhIzCZaKRaXsOhQ6nnXTsiA6hJC2XSWnvIZH2ZM1M44VDNy+FPpLl37tzq9ZZh4y9epITalmaFGzY4H5GpXl1/nsWL1eADMreNFmleZjmtXUumccGcVPHBAwpPDQjx1VeBliZ0iY1V//fs2Y0J9OJLrl+ne9qVxPTu8PSpmucvWCwqvAVsumYs8qWsfUg2baJtNWrQDZQ3r3rjSHvibNkoD8/w4YGS3DGjR6syb9zom3N89hnVrfXJAfwXgtQTli1TFZVBg9yz7R0+nJIUasNJJ0rkee4Dy5fSRx+R0hPMCePcQf6ub77x3zm1ZkvBZhKiVcLsJZ61h/ajnpB8cyzZvNl5o9mZgn3xIpkiacuOH++3nxDSREVRh5EQej9Wbc4qy6l4cf26NDls21aI+vUp7LU9E+eFC22nbjCbKT+a1i9VO6VMSSGqg5E33lDlnDYt0NKENteuqW2yYsUCLY1jZs8mObURW43CVloHOa1aZfz5fA0rOgbz77909bZuVbdFRFCm9wMHaP3ZMyFKl9Y7tk+fHhh5XSUqSojPPydZR4wwvgc4Otr2Q9W1q7Hn8RXaj6+znl+Jo0bVpk3q/eIqlnVs3+7+7whmLl1Sf5s79vzeUKyYek6jkgoayYsXlOyucGH3FJYbN5w34BMKV664pujYyi9hzw9TNt4Z17HXi+xsiooiBcaod4KjxJXB2KOtfUctWxZoaUIfaRkRzO/GgwcpgFHy5L7znT54kBT/6dOpc1H7HMyf75tz+gpWdAzm5EnXboSnTykTL+C7ERJfkDaterO/845x9WqdgwEaHZFJ5kIBy6h6ZjNFh3vyRF8uIoJGcbQ5OexNqVLRMY4SgWmxPD6+jORokTlQvHHAdwd5LQcMIKUiGCld2v3/XJtIltEHd7A3jRundkjFxpIZ78yZtstGRAT294QiN2/av/baqKTayRtTX2fIhK/a6aOPyLRuxw6KHBdok7b58/Xy/flnYOWJD1g+x1o3hGAhTx6Sz585BS3NO69e9d+5vcWeouOVy7eiKBkVRflXUZRzcfMMdsqZFEU5HDetsVUm1ChQgJwMDx92XC5NGnKEbNAAqFfPL6IZzsyZxgVSuHJFv54xI1C7NpAunTH1+5qvv9avv/YaOfWlTUsOsZKPP6b8O+vXW9cxebJ+PWVK4NdfyaH56FH7537+nHIxaXn8WA2aEJ949VWaa53xfcW//1L+j+7dyQk9SRLfn9MTtI7Ut2+7dowsN3268fKEIleuUPALS2ROK4Ce3ffeo1xpffpQjqp33rFdX6pUvpEzPpMjh+37t2xZYO9efS61qCjKT2f53TASWwFhJkwAmjShvCtz5lA+r7NnfSeDM2bMAJInB775htaNzsuTEHn9dXW5Xj1y9D91KnDy2EIG5PBngIAOHYCfflLXhwwBChcG7t3znwyGY0v7cXUCMBbAkLjlIQC+s1Muwt26g31ERwjyxalWzf7+589Vrbh/f//JZQRS7g4daP73397Xefeutd31lCne1+tv7twhO29bPY+TJgnx229C1Kmj9sRo90+YoI4CyW25cqlhjX//nXyVbJkM2rJnj69ofWZ8OXyuzTjdr5/vzmMEe/eqspYo4TwM6D//CNG6NZW/eNE/MoYK2hDTlSrRNsvgKPamtm3j//Pna7SmgBERZDKjDcO8bh2NovkLKcv33wuxYIH9/75PH//1cMfGUuoHk0mILFlUy4r4lMk+kERGkqO/9v8dPTrQUqk8ekQy9e0bmPNb3vs//0xm0/v3B0YeV4AvTNcAnAGQI245B4AzdsrFS0Xnk0/oCmbNanv/2bPqTRKoRJyeIuX+7Td1uVkzIQ4d8qy+Bw/0pje7dlFDLFjNhJwRGem4MVSxovW2r79Wj4+IEKJkSesy3brRfM4c/fn+/NP2eeIrWqfvkiV9d56tW9XztGvnu/MYhTYXV7Nm9nN0PH6sv08CnZcq2Jg1i67Lu++qZqenT7um6AwYEP+fP38QTNdQK8v9+87vAUd5g4xi7Vo6V/XqNB81yvfnTIhYKrLBwvDhJFOgojpq/TstJ1fN7P2NPUXH22wl2YQQt+KWbwPIZqdcckVR9iuKsltRlNb2KlMUpXdcuf33QmCcrFIlmt+9a70vNhbo1Utd79LFPzIZRZ8+9PsKF1a3/fmnPh+Cq7x4AXTuDBw7pm6rVo1MvoLVTMgZzuLZHzhgvU2bGyhVKsqjY2nG9ttvND94kPL2HDlC682a6ctVqADs2uWWyCFFypTq8q1bvstBJXMjAUCePL45h5G0aKEu//kn0LYt5RHRsmIFULWquj5gQODzUgUbPXqQmejUqWReDACZM6v7GzZUl4sUUZe7dQM+/5zyWsicT4xnrF1LeXOCgcWLgX79aDlTJuDyZcrBY4/q1el+WbuW3sMyV5NRPHyonl++5wsVMvYcDKHNzXTrlv1y/uTFC2oL5MwJ9O4dGBly5LCfS2zFCjX3TkhgS/vRTgA2ADhuY2oF4JFF2Yd26nglbl4AwGUABZ2dNxRGdLRmL1rn4OPH9T36oc7Ro/o8FE2auH5sTIwQLVqox376aWiGLbRF375kepc8ue1ejzp1aF/BgrQ+aJB1HRERQnTp4rj3cPVq622hFNjCU7S/96OPfHMOGXlp1izjsk77mp49bY/s7dhBgT1Sp1a3uxMGnSETP2kSGBurjoTt3CnEmjWBk4vxP5ahxG1N2qA9RmE5ovTffzS6E8oZ64Od+/fV/Fh9+9LIeSDZv59k+eWXwMpx9Srlbvv3XyHGjFGtmIK1bYtAmq5ZHPMLgDeclQsFRUcbKlk2KEwmfRjIYLwZPCFXLv1vevzYdrnt29XGQWSkPklm3br+k9ef3LxJcfmfPhXiww/V39umDdmiP3pEZlHXr9s+PjZWnyOhQQPnH9gTJ/z7GwOB5W/2NO+QPUwmNfR7sA7F2+LOHduKjlzOnl1d9lVIUoaJ79gLKW5viojw3kR0yxZ9nQUKGPNbGOf066e/9oE0q5fJi4PxOy9lC8YOV3uKjrcGDWsA9Ihb7gFgtWUBRVEyKIqSLG45M4AaAE56ed6gIFkydVg5f36K1JIyJXD6tFpm+PDAyGY0lqZT6dIBu3frt/37L0Uh+/FHMjVKmVI/vPnjj76XMxDkyAHkygWkTk33gIzm0r07Df2mSwcsXw688ort48PCgGXLgPnzyXxi9Woya9NGYCtfnuZffUXXvUQJ3/6mYKJjR5oXL25svVu3Av/9R8uhEvUPoOh8c+dSNBzJkiXq8p07NJ81i+4thmHcR1Ho3f7RRxR5dN48vSm3JalTA127khlbdLRr54iJAbZto+/HjRtAnTr6/QnpPR9ovv9eH83u8ePAyGEyASfjWsjBaK64fbu6rDXxDWYUUoI8PFhRMgFYCiAPgCsAOgghHiiKUgnAe0KIXoqivArgZwBmAIkATBRCzHZWd6VKlcT+/fs9ls1fmExA4sS29zVpAvz9t3/l8RUvXlCo5CVLqEEOkN1/ypT0gmjZ0n5D/vhxemHbs/dkbCMEXd/oaPL5SZmS/AUSir9F8eL0fDVqpIa79OJ1ZcXEidSIKVgQOH/euHr9yeHDqhKs5epVIHduv4vDMPGahw+BgQOBX35xXK5kSfruOSN/fvIHAiis79Kl+v2ffAJ8950nkjKekCKFqqSePetYsfUFhw6R/y1AIcXthbUPNAcOkA93mjTk5xosbRJFUQ4IISpZbfdG0fEloaLoAPSiyp9fv61aNWDDhviZZ+Hvv/Ux6AFSYmzdSrt20bVgPOfJE8rTk1D5+WfKawJQkA8jRimuXgXy5qUcRM+fe19fIBk5Up9noXp1YOfOgInDMPGeP/+kYASjRgF//GG7zIkT1FmjKNQhkSIFdVSFh9MIcuLE9huIyZNT/du2WbctGN9RpowaNGnPHqBKFf+ef9Qo4IsvaHn/fqBiRf+e3x3OnaP7t0CB4OnEtqfoBIkeFtrky6c30apRg0Y/4qOSAwBNm9IIjhZ7+rLsnWA8JyErOQD1av3vf7ScODGZSHqL/Ji8eOF9XYFGmpV+9RWZ2MgRV4ZhfEOzZhTZUEZT3b3bepSnZEmaoqJo1LVYMWoYZslCHSwTJ9quu0ULOubaNVZy/M3ff5OFCkAJMmXUU3+xZo26rE1iHIwULkzWEMGi5DiCR3QMJCqKtPCqVeNntnotz54B06YBgwfrt6dOTb1VN24Av/5KfioM4y137gDZs9Ny+vRkQuINTZpQaNsWLfQfl1DlwAGgXDn2yWEYfyIE+dnI73337hQWuGhR4MwZ9+u7fx/ImDE0Go/xldOn9f6gY8cCb76pDz/vC86epftm2DCgdevgHs0JVth0jfEJzZurOQSSJydfB5OJcgBYOlYyjDdoP/67dgHXrwNvvOH68Vu3ArNnA1eukEmIHHnV5uxhGIbxFLMZePCAzNRSp7ZdJiyMTKQs85CkSgVERPheRsYxZrN1h1GZMr4d3Xn+HKhXj8zljh0zPvBOQsGeomPHjZ5hXKN2bVJ0xoyh6FgyIEEoJF9kQpfq1WnuTj9N3br68u++y0oOwzDGkSiR457/8eOBNm3o+7hiBUXXGjmS9oVUAsZ4TKJEwMqV9D9JtBFQfcH8+eRXOW0aKzm+gEd0GK8wm6lnnRUbxtfYMudw5/WVKJFa/uRJ/qAwDOM75Pvq0SMybQsLs23Sfvw4+TqkSOFX8RgHPH9OFipafNlU7tWLlKv799ls0Rs4GAHjExIlYiWH8Q8FC9K8dGl1288/u3681hyBlRyGYXzJ0aPkB5guHSkx9vx2S5ViJSfYSJaMoub276+uL1lC/lhGc+MG5XMrVYqVHF/Big7DMCHBpk0U2ahoUXXbe++5Fjntt98oNDUADBrkE/EYhmFeUro05QBjQpP69YHJk4F9+2iEp1MnYM4c4+p/8oS+RblyUSCCHDmMq5vRwz46DMOEBHnyAD160BC/luTJ1ZwVAPWQ5csH7NhB4VmzZlXLrl1LATQYhmEYxhnahMwPHhhT582b1gnW40Oqg2CFR3QYhgkpkiTRrwuhz63z9980ejNtGtCvn76sDGLAMAzDMM4IC6NOMwD4/HNSSG7d8izRdEwMRaVdvNh635073snJ2IcVHYZhQooJE4BKce6GnTvTfNIkYNYsUnrkByg2Fli2TD2uZ08gUya/isowDMOEOK++qi5/8AGQMyfl1nGXwoWBKlXIssCSSZM8l49xDEddYxgmpClXTs1xULgwhTyfNcu6XJC+6hiGYZgg5+BBfRLPFClIOcmb1zVfrIgIIE0a6+2rVgFNm8b/JPP+gPPoMAwTL6ldW1V0zp2jiWEYhmGMokIFYOlSoEMHWs+eHejdm5Zd6US7ccP29pYtOdqar2HTNYZhQpqvviIfHVs2zk2a0Pyzz/wrE8MwDBO/aN9eXb50SV1+9EhdnjQJWLdOXY+JoYSgd+/q6/roI6BPH1Zy/AGbrjEME2/o0EH1y0mZEti9W593h2EYhmE8xZZi0qIFsGYNKT8FCtA2sxl4/JgCGEybBmTJAty7R/sKFWLLA1/ACUMZhon3TJ2qLj97xkoOwzAMYxza0RrJ2rX0vZFKDkC+oxkykJIDqEpOnjzAvHk+F5PRwD46DMPEGzJnJifR7t0DLQnDMAwT32jcGLh2DcidW7+9RAn9+tGjto/fuJFGdBj/wSM6DMPEKyIjgenTAy0FwzAMEx/JlQsYOFC/7epV1461FXmN8S2s6DAMwzAMwzCMi/zwA/ng7NkDpE3rvHyePDRnRcf/sOkawzAMwzAMw7hB2rSUAPTWLeC334AaNYCwMODvv4HixYFUqSj09J07QNWqwIYNFCSH8S8cdY1hGIZhGIZhmJCFo64xDMMwDMMwDJNgYEWHYRiGYRiGYZh4Bys6DMMwDMMwDMPEO1jRYRiGYRiGYRgm3hG0wQgURbkH4Eqg5dCQGcD9QAsRj+Hr63v4Gvsevsa+h6+x7+Fr7Hv4GvsWvr6+J9iucV4hRBbLjUGr6AQbiqLstxXNgTEGvr6+h6+x7+Fr7Hv4Gvsevsa+h6+xb+Hr63tC5Rqz6RrDMAzDMAzDMPEOVnQYhmEYhmEYhol3sKLjOjMCLUA8h6+v7+Fr7Hv4Gvsevsa+h6+x7+Fr7Fv4+vqekLjG7KPDMAzDMAzDMEy8g0d0GIZhGIZhGIaJd7CiwzAMwzAMwzBMvCPBKzqKojRRFOWMoijnFUUZYmN/MkVRlsTt36MoSj7Nvs/itp9RFKWxXwUPIVy4xgMVRTmpKMpRRVE2KoqSV7PPpCjK4bhpjX8lDx1cuMY9FUW5p7mWvTT7eiiKci5u6uFfyUMHF67xBM31PasoyiPNPr6PnaAoyhxFUe4qinLczn5FUZTJcdf/qKIoFTT7+B52AReucZe4a3tMUZSdiqKU1ey7HLf9sKIo+/0ndWjhwjWuoyjKY837YLhmn8N3DOPS9R2subbH4969GeP28T3sAoqi5FYUZXNcu+yEoigDbJQJnfexECLBTgDCAFwAUABAUgBHAJSwKNMXwPS45U4AlsQtl4grnwxA/rh6wgL9m4JtcvEa1wWQMm65j7zGcesRgf4NwT65eI17Aphi49iMAC7GzTPELWcI9G8KtsmVa2xRvj+AOZp1vo+dX+NaACoAOG5n/+sA/gagAKgGYE/cdr6HjbvGr8prB6CpvMZx65cBZA70bwj2yYVrXAfAHza2u/WOSaiTs+trUbYFgE2adb6HXbvGOQBUiFtOA+CsjTZFyLyPE/qIThUA54UQF4UQLwAsBtDKokwrAL/GLS8HUF9RFCVu+2IhxHMhxCUA5+PqY/Q4vcZCiM1CiMi41d0AcvlZxlDHlfvYHo0B/CuEeCCEeAjgXwBNfCRnKOPuNe4MYJFfJIsnCCG2AXjgoEgrAPMEsRtAekVRcoDvYZdxdo2FEDvjriHA72KPcOE+toc37/EEg5vXl9/DHiCEuCWEOBi3/BTAKQCvWBQLmfdxQld0XgFwTbN+HdZ/5ssyQohYAI8BZHLxWMb96/Q2qJdAklxRlP2KouxWFKW1D+SLD7h6jdvFDTEvVxQlt5vHJnRcvk5xppf5AWzSbOb72Hvs/Qd8D/sGy3exALBeUZQDiqL0DpBM8YXqiqIcURTlb0VRSsZt4/vYQBRFSQlqYK/QbOZ72E0UctcoD2CPxa6QeR8nDuTJGUaLoihdAVQCUFuzOa8Q4oaiKAUAbFIU5ZgQ4kJgJAxp1gJYJIR4rijKu6BRynoBlim+0gnAciGESbON72MmZFAUpS5I0XlNs/m1uHs4K4B/FUU5Hde7zrjHQdD7IEJRlNcBrAJQOLAixUtaANghhNCO/vA97AaKoqQGKYofCiGeBFoeT0noIzo3AOTWrOeK22azjKIoiQGkAxDu4rGMi9dJUZQGAIYCaCmEeC63CyFuxM0vAtgC6llg9Di9xkKIcM11nQWgoqvHMgDcu06dYGEuwfexIdj7D/geNhBFUcqA3hGthBDhcrvmHr4LYCXYVNsjhBBPhBARcct/AUiiKEpm8H1sNI7ew3wPO0FRlCQgJWeBEOJ3G0VC5n2c0BWdfQAKK4qSX1GUpKAHwzIi0hoAMmrEGyDHNhG3vZNCUdnyg3pk9vpJ7lDC6TVWFKU8gJ9BSs5dzfYMiqIki1vODKAGgJN+kzx0cOUa59CstgTZ3ALAPwAaxV3rDAAaxW1j9LjyroCiKMVADpi7NNv4PjaGNQC6x0X7qQbgsRDiFvgeNgxFUfIA+B1ANyHEWc32VIqipJHLoGtsM+oV4xhFUbLH+flCUZQqoHZYOFx8xzDOURQlHcgyZLVmG9/DLhJ3f84GcEoIMd5OsZB5Hydo0zUhRKyiKO+D/oQwUJSkE4qifAVgvxBiDejP/k1RlPMgB7hOcceeUBRlKajBEgugn4WpCgOXr/H3AFIDWBb3/r8qhGgJoDiAnxVFMYM+Bt8KIbiBaIGL1/gDRVFagu7VB6AobBBCPFAU5WvQRxYAvrIY6mfg8jUG6P2wOK4zRML3sQsoirIIFJEqs6Io1wGMAJAEAIQQ0wH8BYr0cx5AJIA34/bxPewiLlzj4SAf1Klx7+JYIUQlANkArIzblhjAQiHEOr//gBDAhWv8BoA+iqLEAogC0CnufWHzHROAnxDUuHB9AaANgPVCiGeaQ/kedp0aALoBOKYoyuG4bZ8DyAOE3vtY0X+PGYZhGIZhGIZhQp+EbrrGMAzDMAzDMEw8hBUdhmEYhmEYhmHiHazoMAzDMAzDMAwT72BFh2EYhmEYhmGYeAcrOgzDMAzDMAzDxDtY0WEYhmEYhmEYJt7Big7DMAzDMAzDMPEOVnQYhmEYhmEYhol3sKLDMAzDMAzDMEy8gxUdhmEYhmEYhmHiHazoMAzDMAzDMAwT72BFh2EYhmEYhmGYeEfiQAtgj8yZM4t8+fIFWgyGYRiGYRiGYYKYAwcO3BdCZLHcHrSKTr58+bB///5Ai8EwDMMwDMMwTBCjKMoVW9vZdI1hGIZhGIZhmHgHKzoMwzAMwzAMw8Q7WNFhGIZhGIZhGCbeEbQ+Oowb3L8PREQAjoI3CAF8+y3QsSNQoIDfRGMYhmEYhnGHmJgYXL9+HdHR0YEWhQkykidPjly5ciFJkiQulVeEED4WyTMqVaokOBiBhiVLgNdfB9Kksd6XKhUQGUnKDABcvw6YzUCePLRNUYA7d4Ds2YFChYBz5/wrO8MwDMMwjItcunQJadKkQaZMmaAoSqDFYYIEIQTCw8Px9OlT5M+fX7dPUZQDQohKlsew6VoocPYs0KkT0KOH7f2Rkfr13LmBvHmBdeuAlCmB8+cBk4n23bnjW1kZhmEYhmG8IDo6mpUcxgpFUZApUya3RvpY0QkWLl8G/v0XuHoV+OYbICaGtq9YATRtSst797pX5/vvA9HRwPHjwPPntM1SKWIYhmEYhgkyWMlhbOHufcGKTqDIkwdo3Fhdr1QJaNQI6N4d+PxzIGlS2v7GG8DFi7QcHm5dz7Nn6nJsLPDggbp+/TrNo6JI4QHUkR2GYRiGYRjGLqtWrYKiKDh9+rTTshMnTkSkF53Jv/zyC95//32Pj3eVOnXqeJSn8tGjR5g6daoPJPItrOj4E5MJ2LKFlq9dA9avV/dJJebECfvHR0cDBw6o6ytWAKlTq+u5cwOZMqnrchTnyRMyf2MYhmEYhmFcYtGiRXjttdewaNEip2W9VXS8ITY21ufnYEWHcc64cUDdusCGDeo2s1lf5v59dblaNes6KlWioAIffAB06KDfd/u27fMePQq0bu2RyAzDMAzDMAmNiIgIbN++HbNnz8bixYtfbjeZTPj4449RqlQplClTBj/++CMmT56Mmzdvom7duqhbty4AILWmI3r58uXo2bMnAGDt2rWoWrUqypcvjwYNGuCOE9/pkSNHolu3bqhevToKFy6MmTNnAgC2bNmCmjVromXLlihRogSio6Px5ptvonTp0ihfvjw2b94MAIiKikKnTp1QvHhxtGnTBlFRUS/rtifjnTt30KZNG5QtWxZly5bFzp07MWTIEFy4cAHlypXD4MGDPb+wfobDS/uKJ08oGlpYmLpN9ghoh0AjImh0xxZ79tjefucO8OOPrsty6pR+/e23gQkTgLRpXa+DYRiGYRjGz3z44Yc4fPiwoXWWK1cOEydOdFhm9erVaNKkCYoUKYJMmTLhwIEDqFixImbMmIHLly/j8OHDSJw4MR48eICMGTNi/Pjx2Lx5MzJnzuyw3tdeew27d++GoiiYNWsWxo4dix9++MHhMUePHsXu3bvx7NkzlC9fHs2aNQMAHDx4EMePH0f+/Pnxww8/QFEUHDt2DKdPn0ajRo1w9uxZTJs2DSlTpsSpU6dw9OhRVKhQwen1+eCDD1C7dm2sXLkSJpMJERER+Pbbb3H8+HHD/wtfwyM6vsBkAtKlA955h4IKPHgALF4MHDlC+7UjL3/8AZQqZcx5X3kFKF/eevuuXfr1OXMAJw84wzAMwzBMQmXRokXo1KkTAKBTp04vzdc2bNiAd999F4kT01hBxowZ3ar3+vXraNy4MUqXLo3vv/8eJxy5LMTRqlUrpEiRApkzZ0bdunWxNy44VZUqVV6GWd6+fTu6du0KAChWrBjy5s2Ls2fPYtu2bS+3lylTBmXKlHF6vk2bNqFPnz4AgLCwMKRLl86t3xhM8IiO0dy4AUybRstz59LUsCFFVJOcOaMuDxvmuL7Fiym0tCukSwfUqwccOqRuS5nSdqS1AweAtWtpf/36rtXPMAzDMAzjR5yNvPiCBw8eYNOmTTh27BgURYHJZIKiKPj+++9drkMbHUwbDrl///4YOHAgWrZsiS1btmDkyJFu1aVdT5UqlcvyuCNjfIJHdIzmrbeA0aP127Q+OQCwfLm6fOkSkCKF/fo6diQTthYtnJ87fXqgdm39tvHjbZddswZo2RJo0IDy7DAMwzAMwzBYvnw5unXrhitXruDy5cu4du0a8ufPj//++w8NGzbEzz///DIAwIO4aLdp0qTB06dPX9aRLVs2nDp1CmazGStXrny5/fHjx3jllVcAAL/++qtL8qxevRrR0dEIDw/Hli1bULlyZasyNWvWxIIFCwAAZ8+exdWrV1G0aFHUqlULCxcuBAAcP34cR48edSpj/fr1MS2u095kMuHx48dWvy9UYEXHaJ48sd4mhONjZJ4cAGjVikZaLl0CpB1klSqkxABAjhzAkCHAF1/QeqVKQK5caj1NmwIffUSjSr/8ArgwRInGjfXR3BiGYRiGYRIoixYtQps2bXTb2rVrh0WLFqFXr17IkycPypQpg7Jly75UInr37o0mTZq8DEbw7bffonnz5nj11VeRI0eOl/WMHDkS7du3R8WKFZ3680jKlCmDunXrolq1avjiiy+QM2dOqzJ9+/aF2WxG6dKl0bFjR/zyyy9IliwZ+vTpg4iICBQvXhzDhw9HxYoVXx5jT8ZJkyZh8+bNKF26NCpWrIiTJ08iU6ZMqFGjBkqVKhVSwQgU4awRHiAqVaokPInzHXAaNdKbqUnatgXWrVPNyF55hczc5DGTJgFZswL2bD0vXyblZfRoIHFiitY2ahTQpw+ZrK1cSTl3tMEPAFKYChRwLnfatMDjxy7/TIZhGIZhGF9w6tQpFC9ePNBiBAUjR45E6tSp8fHHHwdalKDB1v2hKMoBIUQly7I8omMkjx/bVnIAYMkSSu4ZFykDRYqo+27eBIoVs6/kAEC+fMB335GSAwCJEgHDhwNZslBy0Y4drZUcAMiQQV125A8UT20zGYZhGIZhmIQJKzpG8fSp3vcGUJWeNGlUBSVZMpo3bw789BMtDxniO7m0IaS//ppGhmxh4ejGMAzDMAzDBJaRI0fyaI4XcNQ1IxCCwjpfuKDf3qAB5bDRjqrEhQFE8uRA3740+ZJEFrps3ry2yykK8OWX5NNjYZfKMAzDMAzDMKEGKzpGsGePquRYhpIuVkxfdtgwMhOLi2keNKRMCcgQh0Hqt8UwDMMwDMMwrmKI6ZqiKE0URTmjKMp5RVHs2mEpitJOURShKIqVs1BIs2cPzW/dAtavBypXViOhWZI+PTBlit6kzNesXAls366uHzoEvP22uj5gACU1lbC/DsMwDMMwDBPieK3oKIoSBuAnAE0BlADQWVGUEjbKpQEwAMAeb88ZdBw7RkEBsmen9b17gWvXAiuTltatgRo11PVy5YD331fXs2TRl/e1OR3DMAzDMAzD+BgjRnSqADgvhLgohHgBYDGAVjbKfQ3gOwDxb7jg1i0gd+5AS+EeMnZ7p07Wis7cucCiRbaPM5tpYhiGYRiGiaeEhYWhXLlyKFWqFNq3b49ImR7ERS5fvvwyx44zevbsieWWAa0MRFt/r169cPLkSUPqjYqKQu3atWEymXD58mWkSJEC5cqVQ4kSJfDee+/BbDbrtstp3rx5AIA5c+agdOnSKFOmDEqVKoXVq1cDAD7++GNs2rTJEBmNUHReAaAdvrget+0liqJUAJBbCPGno4oURemtKMp+RVH237t3zwDR/IAQwJ07lAMnlMiVCzh+HFiwwLbs//sfEBsLvHih3964sRpBjgk806YBK1YEWgqGYRiGiVekSJEChw8fxvHjx5E0aVJMnz7drePdUXT8yaxZs1CihJXhlUfMmTMHbdu2RVhcepOCBQvi8OHDOHr0KE6ePIlVq1bptsupe/fuuH79OkaPHo3t27fj6NGj2L17N8rEJbnv378/vv32W0Nk9Hl4aUVREgEYD2CQs7JCiBlCiEpCiEpZLEcZgpWBA4EDByiEdKhRsiRFZbPnT5QkCYXD7txZ3bZhAwcrCCb69qVEsQzDMAzD+ISaNWvi/PnzePDgAVq3bo0yZcqgWrVqOHr0KABg69atL0crypcvj6dPn2LIkCH477//UK5cOUyYMEFXnxAC77//PooWLYoGDRrg7t27L/cdOHAAtWvXRsWKFdG4cWPcunULAHD+/Hk0aNAAZcuWRYUKFXDhwgUIITB48GCUKlUKpUuXxpIlS5zWX6dOHezfvx8AkDp1agwdOhRly5ZFtWrVcOfOHQDAhQsXUK1aNZQuXRrDhg1D6tSpbV6XBQsWoFUrayOuxIkT49VXX8X58+ftXtO7d+8iTZo0L+tOnTo18sdFJs6bNy/Cw8Nx+/ZtB/+KaxjRNX8DgNZuK1fcNkkaAKUAbFEoV0t2AGsURWkphNhvwPkDR0wMMHEiLYfaiI6WPHkc71+8GKhbF+jRQ91mNluHrvYV4eEUjjtVKv32hQuBbNmA+vVpfe9e4JdfgNGj9SG9tQhB9UnTvVDjwQPAZLI2N2QYhmGY+MiHHwKHDxtbZ7lyavvNCbGxsfj777/RpEkTjBgxAuXLl8eqVauwadMmdO/eHYcPH8a4cePw008/oUaNGoiIiEDy5Mnx7bffYty4cfjjjz+s6ly5ciXOnDmDkydP4s6dOyhRogTeeustxMTEoH///li9ejWyZMmCJUuWYOjQoZgzZw66dOmCIUOGoE2bNoiOjobZbMbvv/+Ow4cP48iRI7h//z4qV66MWrVqYdeuXTbrt+TZs2eoVq0aRo8ejU8++QQzZ87EsGHDMGDAAAwYMACdO3e2O5L14sULXLx4Efny5bPaFxkZiY0bN+Krr74CQIpTuXLlXu7/8ccf8eqrryJbtmzInz8/6tevj7Zt26JFixYvy1SoUAE7duxAu3btXPqf7GFES3UfgMKKouRXFCUpgE4A1sidQojHQojMQoh8Qoh8AHYDCH0lBwCuXqV5nz6UjDNUkY3mIkXsl3n3XX1i0ydPfCtTVBTw+++0nDkzULGius9kAn77DejShXIVnTlD20eMIFMuR3adq1fT79VGoQslcuQIbaWaYRiGYUKAqKgolCtXDpUqVUKePHnw9ttvY/v27ejWrRsAoF69eggPD8eTJ09Qo0YNDBw4EJMnT8ajR4+Q2ImJ/7Zt29C5c2eEhYUhZ86cqFevHgDgzJkzOH78OBo2bIhy5cph1KhRuH79Op4+fYobN26gTVyew+TJkyNlypTYvn37y3qyZcuG2rVrY9++fXbrtyRp0qRo3rw5AKBixYq4HJdUfteuXWjfvj0A4H//+5/NY+/fv4/06dPrtkmFpkaNGmjWrBmaNm0KwNp0rWbNmggLC8O6deuwfPlyFClSBB999BFGyjQnALJmzYqbN286vI6u4PWIjhAiVlGU9wH8AyAMwBwhxAlFUb4CsF8IscZxDSFM3A2BDh3sjyCEAopCIbLz56docb//TqMilhw6pC4/eEA+PFu3Al5q2zYZMgSYPBn47z9al8rMrVtAzpz6ssWKkfwREbQ+diwpQOnS6cs9fgwMHkzLy5YBr71mvNy+RAjVZ6pjR+Dzz9V9W7cCdeqQ0v3DD0CKFAERkWEYhmEMxcWRF6ORPjquMGTIEDRr1gx//fUXatSogX/++cejcwohULJkSezatUu3/enTpx7V54wkSZIgztoKYWFhiI2NdfnYFClSINoiHYlUaFxFURRUqVIFVapUQcOGDfHmm2++VHaio6ORwoC2jCG2R0KIv4QQRYQQBYUQo+O2Dbel5Agh6sSL0RwAOHuW5nE2hSFNlSo00lGhAvDJJ7bLhIerywULUvk33iDlw13OngXu37e/X46Waeu+eBHo3992+apV1VGavXspX9HKlfoyX30FSHtRNx7moEF7/ZcupaF3SZ06NJ82jZK/3tBajzIMwzAM4y01a9bEggULAABbtmxB5syZkTZtWly4cAGlS5fGp59+isqVK+P06dNIkyaNXQWlVq1aWLJkCUwmE27duoXNmzcDAIoWLYp79+69VHRiYmJw4sQJpEmTBrly5Xrp3P/8+XNERkaiZs2aL+u5d+8etm3bhipVqtit31WqVauGFXGBjhYvXmyzTIYMGWAymayUHVe5efMmDh48+HL98OHDyJs378v1s2fPolSpUh7VrcVPThbxlG3byJTImY9LqJE2LWDRmwDAvkLjiaJTtChN9kienOaffaZuK1jQvQhjbdsCBw/SKMjNmzRyJfG16Z0vcMcpz83oMAzDMAzDOGbkyJE4cOAAypQpgyFDhuDXX38FAEycOBGlSpVCmTJlkCRJEjRt2hRlypRBWFgYypYtaxWMoE2bNihcuDBKlCiB7t27o3r16gDIlGz58uX49NNPUbZsWZQrVw47d+4EAPz222+YPHkyypQpg1dffRW3b99GmzZtUKZMGZQtWxb16tXD2LFjkT17drv1u8rEiRMxfvx4lClTBufPn0c6SwuZOBo1aoTtLrgCSJM2OU2ePBkxMTH4+OOPUaxYMZQrVw5LlizBpEmTAJCCd/78eVSqVMktuW0ihAjKqWLFiiLoKV5ciNatAy2F71i+XAgymHI8rVpF5adOFWLrVtfqlsfaYuZM187ryrRunRCtWtFyv35ChIUJUbo0re/aZchl8gk3bggxdqwQZrMQJpMQs2a5d13atRPiwgWq6/x5mj97JkRUlP4833wjROrUQsTG+vf3MQzDMIwdTp48GWgREjTPnj0TZrNZCCHEokWLRMuWLW2WO3DggOjatavh5//999/FsGHD7O63dX+A3GWs9AlOiOIpJhOZQbVsGWhJfId0ev/6a+DoUfJrscW5czTv25fmzsJPO9v/44+O94eF0fWXMsqwiQ0aUPhrLS1bqn4tmzaRf490EqxenfYlSeL4fIGgVy/g77+BevWAyEhad4cVK2j68EOyb96xA6hRA8ibV/UtA9QRs2fPaCSPYRiGYZgEzYEDB/D+++9DCIH06dNjzpw5NstVqFABdevWhclkeplLxwhiY2MxaJDTrDQuwYqOp9y6ReGlCxQItCS+47XXgGPHKN/ORx+p23/8kUyjTpyg9d279T4v+/YBR45QQtL27YGyZYHUqclcTAi9YrFzJ/nSjBtHAR0yZbIdOjlDBuDhQ1pOnZoUgJUrqfzdu8DmzeSncvQoKQgFClCQCG3C01OnSJZTp9RtSZMCs2eTQiRtaV+8cGxW52sePSJTO4B+kwyy4AhFURVI7bWSTpzSZ+fKFdvHR0ayosMwDMMwDGrWrIkjR464VNZW2GpvkRHfjIB9dDzl8WOaW4TWi1coClCqFM2zZ1e39+tHowOSnTvVqGgABTZ45x1g0iRSltKkAb7/nqKgpU8PTJmilq1Rg5QcgBrn58+r/jmSLFmogS7z+AgBLFlC5aXfTcaMNC9TBvj0U1KwtHajcdl2kSkTYOk4N306+VoVKECyFyvmn2AFMTHAF19QlDgZaOD+fVJU5AvGUslJmVJdHjNGDYTx/vtA+fK0bCOmPbQOkXEJwXRERnr0ExiG8R+nT5/G+PHjcd9RIBeGYRjmJazoeIpsOKZJE1g5/EVc7HYApFxIx7QUKWh0y1lkDG0kt08/dVzWMjpIr150nT/5hOZNmtCoUPr0atJSbaABiVYZ69SJ5tHRwNy5+nL79qmKjWxAXL/uWEYjWLQIGDWKrsfbbwMzZ5Ly4gjtaNdnn6mhvVOkAKZOpWVbMe+XLlWXs2cnRXDdOnVbVJRnvyEUkJ5LtlixIn6bnzIhSXR0NL799ls8f/785bbTp0+jUqVKGDRoECpXrhxA6RjGPwhnZu5MgsTd+4IVHU9JaIqOpSmXHEEx6oPboIF+PV8+SgAKqKZuJUrQSNqSJWq5n36iMMuFCzuuv3t3KjNmDNCzJ2ARAcWKixfdEN5DTp5Ul2/dAnr3di5XqlT6delvlCIFUK0aNegbN7Y+zjKm//LlQFwiLwA0orN9u+OQ36FK69b2TRHfeANYu5ZG1xxx/z6ZcTKMH5g+fTo+++wz/Bjnr2g2m1G8eHE8e/YMAF4m9WOY+Ery5MkRHh7Oyg6jQwiB8PBwJLe0/HEA++h4SkJTdABq/Eufl1deoXnBgmRqtn69Wi5bNtvmUY5o3RooUkQdlYiKUkdZtBmGLUduatXSJzK1R86cat4jwHlCzSlTgPr1gb/+0isEZjP998+eWScudYfr14F799w/zlLRkR+BZMnUbZ74jUVEkN9TiRLkW2VrhCxUiIqiUbIvvqDnc40mndfx46ToVq5MOYckDx9SYIu1a+l+Gj5cX2eFCpRMlz+6jB+QeSkmT56MuXPnWuXAKFu2bCDEYhi/kStXLly/fh33PPlOMvGa5MmTI1euXC6XZ0XHUxKioqNNjNq+PUXy+uILUnq0DeMuXYDx49X1NGmAyZOp/KxZtK1oUaBZM7VcrlykWEhFJ21atZc9sRe36YcfAr/9Zt1w1/qxfPopKXHaqHIy2ejrrwM//0yjLX/8AbRooZYRgnr5p06lAA3O5JSN5Fu3gNy59fv27tWvh4eTqd7s2TTK88cfwMaN5KMzbJgaUEFGOXn0SD3WUhlyhVGjaH7yJJkDSoVn5UqaDxsGPH/uXEEESAn0RAaj+PVX+j+SJgXq1lW3T51K/mUAcOCAPtfQgwek6EgztkGD6DcIQffOtWu0Xa4zjA+Ris61uPsuW7Zsuv0xzkYgGSbESZIkCfLHh2TsTMBh0zVPkYpO6tQ4fPjwy2y1CYbkyamhKEd2JGPGUDhqSVQU9Zb37AmULq1uL1aMAhQUL07rGTPqzc9at7Y9ouMuEybYNsdq3Bj480+KHPftt6Rk2ePdd4F58yhBrBYhaN/06ZSYVDJ5st68TtKlC1CpEpnROSNdOhpx2LiRlDWZmXjYMLq+y5fTeufONLf0kcqVS3+9ZZ0AyWDJpk369ZMnga5dybRrxAgKKpEypfMRjX37KCre2rVOf6LPkJHlUqTQm5tJJccW27eTIi5JnZoUmkSJgPfeU7fv2UOjegzjQ245ScL8SNuxwTAMw9iFFR1PkVHX0qZF+fLl0aZNG7YlBYAhQ/SRwZInV0cd8uZVt6dLR43IDz+k9SJFqNyXX1Jv+jffGKPoOOL112nkAnDuo9GjBzmua0mUiCKkAfqQ1QMGqMEPtCxaRArRrl3W+xJZPIphYeSbVK8erWfOTEqGpS9TqVJ0L3btqt9+9SqF2h47liLgAUDVqjSX+ZGcsXChqthImQ8eJLMue419qVhogx8AJIeln5CnnDypVywB+v969AA+/lg1CXzxQs3x5Ix33iFlzhY//6wuV69O/41lwAyGMZAnT568XB4no1JqePToER49eoQZM2bwd4dhGMYRtrKIBsNUsWJF11OoBoIPPhAibVohhBAABABx9+5dcefOnZfZZBMUMraVpFEjIUqW1Jcxm4Xo2JHKjRihbouJsV3n0KFUdvZsn4isIypK/Q1p09J8504hRo1St9uaypen+YABQixYIETPntbX4uRJIerXt19HpUpClCmj32Ykjx8L0bmzEHfvCjFtGs21clpOQ4Y4/s2AEDlyCPHggfW5WrRQy3TuTNvMZmN/l6xr2TKab9xoW8YmTZz/jpw5rbfVqeP8uDJljPktDKPht99+EwDEq6+++vK7cu3atZfL2qlgwYICgNi9e3egxWYYhgk4APYLG/pEwBUae1OwKzqXqlYVT3PkEMeOHXv54enfv78AIFKmTJnwlJ1Ll4T47z/n5U6cECJrViGuXHFe9tkzIb77zr4iZDQDB5KSkycPPRqXLwuxYoXzRi8gRLJk1tuEEGLRIvuKAiBEkiSkCPTvT+vvvCPE/v2+/60REfZ/y+bN6vKaNfbLTZyorzMy0rrMhg3W18RVNm6kyRJX/g/tlDix/X1ly1pvGz7ceZ3lynl65RnGLqVLlxYARPr06V9+V549e2ZT0ZHT+vXrAy02wzBMwLGn6LDpmif064d8e/bg0K1bKK3xg5ChQCMjI3FTZrZPKOTLZ9/0R0uJEhSRLU8e52VTpiSHfF+Zrlnyww9kBrZ6NdCnDwUMcDV8tibfxUs2bQIGD7ZdvnZtmsfEkC/I99+TH9DPPwMVK3omvztozQvTpCETwyxZKGeRTK4K6IMvWCJzDR04QMEp9u2zLqMNSgEA8+e7LmP9+nrfqY0bgUaNXD9e0qMHqSfacN61atH8q6/UbfKerF7deZ1aU8Nnz2z//wzjJjJ8tNYHJ4UmAMiRI0cwefJk3TF33I1wyTAMk4BgRcddYmJeRgZ74qDYgQMHsGXLFr+IxBhMuXL0HydKRMrOixfApUvUoLd02geAjh31ioOkfn3rxKOvv05z6S8jSZYM6NbNfxG9tOe5c4eitt29S0lLZY4kS9q316+PGwf8/Tc5+S9fTpHOLPnrL/36558DFy6QMimEffm0+xSFruP48cC//9o/5osv1OVNm8gPrFgxCg4BUOCLbt1Ied66lfyMtMlC164lxVQbqU1iGUpce/1SpwbKl6flI0eAJ47eDAxjm6dPn+KiJn9X1apVsXHjRiiae61MmTLIp40YCaBbt25Yrw3vzzAMw7yEw0u7y+HDLxfjXOWhKArZAWpo1aoVAFhtZ0KQJEloxCpfPjVoQc6cgBy169bNdpQ1S9avV8NrZ8pEuXrsJbL0B6tXU84dWyGj9+yhUR6AlLjISAocERVFitDChRQs4vXXgYYNqZyMBOeIdOmAtm0pUMKpUxTkIE0aYMsWUkjkSEl4uP64oUP1IzK26NqVlNEuXUhZefiQlBkZDAOgUTOJpVJZsiQFTQBIIcuTh0YpTSYgRw59EIcDBygsuQwTfuoUXY9y5YCaNa0j9DGME7paBBQpUqQI6sUFI1m0aJHDSGtvv/32y1DUDMMwjAorOm5y7/hxZIlbNsXNixQpgmbNmmG8pZkOE/9IkgTYsIGinWXPTttcMXUCaITnxAmKSFa2rN5ELBBoRzMsqVJFXT52DLh8GXj1VTVs9PHjauQzW6MshQoB58+r6+XKAbdvA9HR6nYZWlzSvDnQpAkta3q2AegVFHvkzEkhwyWKoldy7NGmDSlz2rKjR+vL2OqwkEqRRJr5/fefddnDh0kpypTJuTyMT/nhhx+QK1cudOzYMdCi6NizZ49u/bzm+emkieKY1UbUxBw5cvhOMMYpixYtwv79+zFixAgkSZIE0dHRmD9/PqKjo/H666+jZMmSgRaRYRIuthx3gmEK1mAE/7333kuH5KVxzqAXLlwQZrPZpqPoli1bAi0y4yt27xZi5UpadsUpXgghTCYhLlwImMiG0ayZ/d+ZNy8FJqhWTYgMGYR44w06pl8/ITJmdHyN+vUTonJlIT75xLVr+uWXQrx4IcSpU77/zStWCLF6tev/tUQGfqhb1/cyMk6R7+ZgI2fOnLpvR0nLqJUalixZIsqWLfuybLZs2QQAsd8fgUwYK7T/W+XKla3aAUuWLBGRkZGBFpNh4jXgYATGkEzjdDwjbl6gQAEoioLENpzm69Sp4x/BGP9TtSolNrVF+vQ0nzSJnOFnz6b1RInIXCzU+e03Sg4rmTiR5qlS0ehPihRklvbgAbBsGe3LmJHMyRzx008U1MBytMQeKVLQKFuxYu7+Avdp25ZGwbSjXfYoW1Ydldq9m+YXLvhONqOIiKD/zFsGDwZWrvS+HoN5rnl/f/HFF3hH5pgKAmJl3rA4HCWh7tChAw4fPoxhw4YBUAMSrLDM9cX4HGEx2rvPRlCWjh07YtSoUf4SiWEYDYYoOoqiNFEU5YyiKOcVRRliY/9ARVFOKopyVFGUjYqi5DXivIHAFOc7kALABot9p06dws6dO/0uExMETJqkT9o5JO4xqF8f+OUXRHXujHbt2uHs2bOBkc9oMmQAPvtMTUYqnfFNJvvH5MtnbQJmmejUGT17ApUqUVQ8wPXkp0YyYwaZ8Tni6FGgYEEyxbt7l7YFQlZnREZSklXp/1GyJJnX/fcfBdnQRo988sTapNAWQlCgirZt9dujoynwRQC5rgkOMmrUKMyaNQsrg0QhS5s2LQAgPDwcQggUKlTI6TFff/01+vfv/3K9YMGCPpOPsebChQuoLSNoOsGRjxXDML7Da0VHUZQwAD8BaAqgBIDOiqKUsCh2CEAlIUQZAMsBuNhdG3yIx4/xAkCXt96y2leoUCEUiA+99Yz7fPABjXIAQLVqwKBBwLlz1HAE8M8//+D333/H22+/jdGjR8NsNgdQWAOZNo0CB2SJ81xz9LvKlbPe5shPCCB/qOPH1fURI2jEZ/x4ivLmrqJkBGXLAjt2qOtTplCgie+/t/YJ+ucfNQqbLX+hW7f0gQtmziQF0l/Mm0dh1b/9ltavXqX5p5+SL9mWLeT39PAhheR21JC+fJkUXctAEgCF4O7bl/zaLl82+Ee4zv379622tW3bFkeOHMH8+fPx7NkzvHjxwu9y/fLLLzh//jzGjh2LjPaiHtohQ4YML5dNjjoagpVbt4BWrYwZSfQzAwcOxH+2fPJsEOaKvyDDMIZjxIhOFQDnhRAXhRAvACwG0EpbQAixWQgRGbe6G0AuA84bEFLfuoUHAGbOmoW7d+/i3r17uv2yV05So0YNP0rHBJwHDyi0ceLE5JAfh2xgbd++HcOGDcOGDZbjgSFKWBiZpMn73pmi88Yb+m1t25KSaIsFC2hETOvIK535kycH3n/ftWADvqJSJZr360eR5z7+GLBlbjRgAM21pq3Pn5MiUbq0mlNp8GCgd29V6fAHERE0Dw/XB4/YtYvm589TkIjmzSl0NmA9Kvf4MSn1+fMD33yjDwghKVYMmDtXrduWMuQHnj59anN7uXLl0K1bN2TOnBnJkiXDVanw+YkDBw4AAP73v/+5faxW0YmOjjZMJp9hMpHiK5kyBVizhuZCqO+Q3buBOXMCI6MD1qxZg2XLlmHHjh1Ys2aNy8dt2bIFHTt29Pu9xTAJHSMUnVcAaONaXo/bZo+3Afxta4eiKL0VRdmvKMp+SwUiWCh88SI2JU8ORVGQJUsWZM6cWbdfm9ytRYsWdj+sTDwlQwZduOZ58+ahSZMmughKABAhG5jxBanoOOpRTpTI2vcmLIzM/n791ap4+5UrcVyO5kiFJnVqA4Q1iG3bAMsRAhluWoutZKKffkqmYbLBX6cOmXtpj7l6Ffjf/8gMzlfIRte8eUDhwtb7T52iudYkVxtlz2Si6HlFitD6F19Qg1WLEPp8Uh99BGTOTFH4/Iyz97FUFM6cOePdiQ4dclmZ27RpE6ZMmYJ8+fLhlVccfTptk176A0LvgxS0vPMOPcdSYZYjWHfu0AhxWBglFq5eHXj7bdfrvX2bIi1u3268zHEIIdCqVSt06NABzZs31+374IMPsG/fPixevBh//PEHFi1apNt/7NgxLF26FHnz5sXGjRt9JiPDMHr8Gl5aUZSuACoBsGnUKoSYgTgf/0qVKgVfAhqzGcljYxEhzXSckCFDBhw9ehRLlizBlClTsHnzZpsBC5j4y9SpU7Fnzx4clKGY44h3ik6qVDTX+AvYJF8+YNQoGpHRKkUWIbpjMmbE8uXLcenSJezfv5/Cch865L+Eqq6QIoV1DqI8eeyX37GDeq4zZCDlTsvWrfr1zp1VZ/5bt4DNm72XV8v58+RHJs207JlrSUVHS+PGpHyZTJQ76NYt/f79+2meLBnw9Kn1KJ700/n3X8pBJdm6lRq5r73m/u9xwqNHj1CoUCGEu6h8WAYG0HHtGpn0FSgAdOhAQUkWL6agGDduUJjzChVoBPPQIafnql+/PgAglXyG3OSZZnQkJEZ05MjelSv0Pjh2jNbv3AF++YWWtffFixdA0qR0v+3daz+cv1Rwxo937R6KiKCgMYsWWSdDtsMTTTJgrc/NsWPHUKpUKQBAJTnSC+Ds2bMYMWKEVT3z589/+b8zDONbjGh13wCg7cbMFbdNh6IoDQAMBVBbCBEC3U7W/L1yJZoCSOliLoz06dPj4cOHL3MghIeHI1u2bD6UkAk2ZFZzyxFKGSVp69atSJ06NSpWrOh32QwlUSIahXCmyCsKJf+0RKswnD+PPcePA61bw2w2Y9WqVTh27Bi++OILY2X2BZredQDUcNcqdK1awSW0DvIyX5OR9O1rO/9RnTpkiicbftJczRKZA8qR6eDz55QEdv162/uvXSPTpOrVaVRIRqj0JslydDSZPqVMqds8fPhwKyUnTZo0yJIlCy7aCLDw0FZ0wIgI8hGzzJe2ciVQuTKZWGp78WVy6UePKFGuEyXdUyuG6pqG//Dhw6EoystobEFJihSUeHj/frq35WjuqVMUHMOSKVOAgQNpNPjzzylQhi1FRr57HCmpWq5fp2dz0CCXFZ1v7ZiVSiXHkiJxI51FihTRBaKxjNTGMIzvMMJ0bR+Awoqi5FcUJSmATgB0hquKopQH8DOAlkKIuwacMyB0i+uZTO1i9KSsWbPqeoC0y0z8JDo6Gh07doSiKJg6dSp2y9DCFhw6dAjPnz9HnTp1dD2AIU3SpKTweIKmYXosMhI148J2m0wmtGnTBsOHDzdAQD+gKOSjJbl/H/A2maMH5kwOOXuWRsgkWuf3ESNoBMZVHxp7porJktH8n3/sH7tmDZkmyQh63nDtGslctiwpFRZYRryaMGECLl++/FLJWSsT4cbxjy25t2yxVnIkR4/qlRyARi3v3qURvG++sSt63rwUhPTuXc8+jRUqVIDZbEbKuGfIpQ6Bv/9W/bBWraL/XcujR64rDO4iFfd//wVOn1a3nzxpu/ygQaTEfP45rWsjV167psoplW5LuZcts07iGxmpmoQ+fuyy6JaKzvjx47Flyxa75Tt27IjFixfjyy+/1G2Piopy+ZwASF6pONvCbAZiYtyrM6FTrZr1fc/ET2wl13F3AvA6gLMALgAYGrftK5BiA1Ak5jsADsdNa5zVGYwJQ3PHJQP8p2NHh+WGDh0q+vXrJ2bMmKFLGrZv3z4/ScoEivnz59tMHGtr+t///he0yQv9TmTky2SbLVu2fHldSpUqFZrX6OxZIaZPp2WTSYiePR0nGF25UohGjWzvGzDA9jn++EOIGzfsy2AyCXH/vhDPnwsxcqQQv/4qxPnz1vUnSyZEjhy0fOSIevzYsa4lR7U1lSnjetkUKeh8lslWzWYhfv5ZiEeP1G3Xrwvx+DEtnz4tRM2aQly9al2n2Uy/fedOMWfOHKtnz2w2x52S1qOiosTAgQN1Zf78809hMpnUc8+caX2euXOFOHjQ9u/KmFGIY8doOVEim3/R1q1bRVhYmAAgunbtal1gwwYhevUSon9/+/+zkJdPld0hz57pr7Wt6w7QPesNc+bo7ydJ6tRUf5o0nt1bEydSPWfP0vo339D60qW03rCh/nyWv08ISkysrfPHHx3+lDvDhol7Y8da3Uc3HD1/GtavX2917NKlS106VgghRKdOJOeDB7b3Dx4sxKuvul4fY/u+YEIa2EkYaoii44sp2BQds9ksisY9GLfli9YJq1ev1r3YNm7c6GMpmUDz888/O1Ru7E0JHrNZ/FWmjKgACEVRXl6X4sWLx49rNGiQdYNt3z4hvv+eGp5CCNG0qe2GXa9e1vXFxqr7nz8XYvNmIV680Jfp3Zv2ywagvWn3biFy5qTly5fV481mIWJihFi7Vl8+a1b7dU2bRvNRoxyfM29edTlxYrVxDQhx6JAQDx+qCkTz5qpMAClRQtB2QIgOHazrv3VLiI4dhQBEW/L3tPm8LVu2TAwdOlQIIcSRI0dE4sSJX5ZJC4gP6tZVz/311/pzVKum7mvYkLaVLCnEihXq/p071fJapWn5cmFOlEg8AUQPQPSsVUuYHjyg6/DOO6QElCqlP58TXHqfaK+zLGcpX0SE941AWUfy5LQ+cKAQdesKMX8+bS9USC/HlCmO7xft1KOHEL//LsSkSbTepg2dY+5cWq9Xz/LCWP+WevWs69Veo3v3bNYBQHzzzTcvr/Pz589duhx79+61ugfLli0rxLVr1AkRp3jb5PBhVcZz52jbvn2k9Etef506LLT3GOMYb+9xH3Ls2DExZMiQlx0yjGuwouMlT58+FeXlg7F6tUvH7Nu3T/dimy57eJl4y8SJE+0qM46m8+fPB1r0gGEymUS1atWcXqOQZt8+faNqzRrrMgcO2G7YJUtG8y+/FOKnn2j0Jzxc3f/hhzT/6CN9fXK/M6XDbBZi/Hhajoiw/xtko10qULYmk0mI/ftJYZLbChemec2a1PgHhChfXt2fOLF+lEFOgwery5cu6evMnFldLlrU+ti9e4Vo0UIIQKx+5RWX76Xnz58LACIdIG7Junr3FqJbNxqV0Z6jRg31wDfeoG116tB6hQpCNGsmxLp1avmzZ2nfw4e2r13//qSg2bu2TrD8jXfu3LEudPy4vs7169Xlli2pzO3btG5nFMouL14IcfMmLW/frtb7+LH1bxk9Wl2ePl2IZcsc36OOprZt6Zw//qhuO3ZMe2Gsr1+7dtb1vHhB/80nn9B6eLhVHQDErFmzxIEDB8S4ceNcvjRnz561+n9q1qwpRK1aVPdvvwnx77/qAb17k6KulR+gZ1Aqq7lzq+Xlc/XLL6zsuEJMjMvPVSDInj27ACDuWSrcjEPsKTpG+OgkCO7fv4+XXgQWjq72KFasmG79vffe89jhlAkNbDkxT5w4EV27dsWwYcMwZcoUHD58GNOnT9eVKVSokE2n6ITAjRs37PoyaXEYCSvYqVSJPqtx/hjIZSOVWIUKwPLl1ttlyOARIyhQQJs2QK9e6n5pmy99LjZuBMaMUfdrQ0PbQlEo5LMQavQ8W1SvThHW3nmH1osXpxDb2qSviRIBFSsC2qArnTpR3du2kSyxsZQfSRIba/u8P/2kLufPD2ifGW1Yb1uhoG/deul7VDzOB2PFihUAgJIAMHw4yaTlzh0kffoUXbp0wTIAL0NAzJhByYAtc0RVrqwuS/lliO4UKSgwgjas9pgxwIULFLXOFmfP6st7iWV4YwDkm6OlUSN1WeaEkSG4bfnb3b1LPlUWPk/Ik4d89HLmBC5dAm7eVPfZCnqhDSbw7rtqwmFPuH6dQktr/XBKl6b/y54Pma3tXbqQP5UMgf/hhwCAuRa5fDJlyoQKFSpg0KBBLosogxDVrFkTHTp0AABERkaq/kHdulEuLsmMGcCSJcDPP+srWrBA/V+uXQNkNE+ZbLVnT+vw7ow12qAX7vpLGcyBAwcwc+ZM5M2b96UvofThinfRWQOFLe0nGKZgG9FZu3ataCh7AHbscPk4AKJhw4Yve3H27NnjQymZQNO/f/+X/3WnTp0EALFw4UKrcjExMVY9fNu3bw+AxIFn48aNVtfC1hThaLQhVJC+K7Ln2xJtD7vlVLy4/ZEAgMymhHCtF/yNN8iH5eJF93/DiRNUR7FitC5HehRFX07bo2/Jixd68zVXpjhTNIdT9+4079GDRlwA8RQQTRo1EsJsFgDEDVm2alUh0qalEYwXL2hb/vy2fXHkNHw4zcuU0ZsKVqpE21etovX69clHx53flymTEJ9+Sstt2ljvnzjRYW/99OnTxcqVK3XPzF9//aUv1KuXYxmuXyfTQYBGEi0pUYL2Wb7TtHVMny7ErFnq+vffW58nNpZGuC5coOMjIsicrXZttcxbb6m+KY6mokWFKFjQersc5ZDTmDFCbNumPi8uTnU0ywDEtm3b7P4Hjrh165Z48uSJMJlMomPHjiJfvnxClC2rP5/ZbNvnzNF0/77+XnPBnyvBox05PXPGr6c2mUzCZDKJq1evik2bNume13/jRvXSp08vAIjDhw/7VbZQB2y65jl37twRAERX+WCcPOnysdHR0SI2NvalGdtqF83emNDj2bNnolGjRi9fWpcvXxbt2rWz20BPkSKF7iW3ZcsWP0scWMqWLSu+/vpr3TV48803xY0bN146aGuncK0pSahy4QKZidmzvb5503ZjpkIF+w0drY/K8+f2y23cqC5PmOD5bzhzRpVJCCGOHqX1pk315QAhChRwXJe2QQxQ8AF3GnmW06RJQoSF2d73xRciSZIkIkZR9Nstg0CkT2+//vBwId58kxpKWv74g+qJiaH1Zs30x/Xo4d7vGDrU9vYvvxRizx7af+SIEA0aqD5ecWTOnPnlMzNp0iR1hz2TOe2UKZOqjKdMSUFCrl2j+1XrF9a/PzWwhRBi+XKr6ywmTLB/DntKflQUKXLr1qkK47Nntk0lkyRRl6V/GUDKtjf3j43pgmY5A4xpfA4YMECkTp1amC2DdmjN7zyd+vXzWj67PHokxIwZjn2KQgFtUJa//xbi6VPqSND6AxrMs2fPRO3atUWePHlExYoVha3OvBkzZoiYmBiRIUMGAUBs3brVZ/LER1jR8YJjx44JAOIHQJiSJ1c/Zm5w/fp1AbCfTnzjyZMnAoDInz+/roExbdo0p8fmzp1b95Jr0qSJHyQODh48eGDzRT9y5EghhBD9+vWz2jd16lQR48Gz5w8ePnwoFi1aZExl2kbL3LnkjNy3r/2GTZ06+oaevZEEbd1z5ngun9ksxLBham+82SzEggVWDW7x8KFjnx8hhJg8WS+j1rfj9ddp0u7Pnt1xI2/NGsejYnLEx9GULZv9fa6i9QHp2dNuw39O3NxsOSIhHesdTa+9RvN//tGd+sKFC7bfQ3362K8rXz7H5/rpJyHu3NFvS5PGehtAIzFffknLVavq93ky2vD8ubUyVb06BYBo0EC/ffNm10dEFEX/7LgwHereXZUrIoJGGW1FlpOYTDaVgg0dO4rXAHE5XTq3zi8AipBYt679/X37WssRG0vPo7fI63XkCP226Gjv6wwEsnPG1vTHHxTAxQAePXokJkyYIEwm08t2pLOpTZs2ImPGjAKAWGPLl5Oxiz1Fh310XEDaSxYFYC5c2HlSRBtkibNB9jRXAhOcXL58GQBw6dIl3I/zG8iZMyfee+89p8dqM5oDwLp16wyXL1g5KnNYWCBzikyaNAlr1ujScaFv375W+SiChe7du6Nz5844f/6895VFRwOjRpEfRM+elBvGUa6ZuOSzAOhTLe31HZE5s+fyKQrw9ddAgQLq+v/+Z+27mD69Y58fwDr3R9q06vKffwLdu+v3v/qqupwyJTBhAvk/SXLl0vs6WDJvnmN5AP319JSkSWmePz8wdy6QO7fNYu8AOLxjB5Ry5fQ7LBPP2kL6DFnkPEqruYbJzGZA+v45+l2HD5N/ij369QNmz9Zve/pU74sluXKF/EdSpAAKFtTvS53a/jnskTQpUKiQuv7665Twdv16yoUiGTCAks66kndqxw7yiXLRz+Zh3Pe7nPb3zJlDfjRTp6rbIiLIp+7RI+CHH0iWnj3puZwy5eV/UH/JEvwH4LGjHD5Ll6rLb75J13TSJGD7dse5uaZOBWrXpudD1v/VV/T/WvpWuYv0g7p3D/j4Y8oVZc8PKtDs3k25r9auBbS5pYQAHH2fmzcHOnTw/loB6NSpEz766CPs2bPHdhJiG6xcuRIP4t7hjx8/xtOnT3H79m2vZUnQ2NJ+gmEKphEdGQP/ICDMzZp5XE/y5MnF4MGDxcaNG8VHlhGSmJBk165dNntlXGHMmDEeHRfqnD59WrRr1+7lb65UqZJo1KiRSJYsmbh9+7aurOX1qSOjWgUZ+fPnFwDE5s2bxblz50Tt2rXF1atXjTvBlSv6Xsfvv7feZmPaliePui6Eurxzp3GyecPTp9TLX748mXddvKiXVwgKfy1z+jRvTn4c772n7o+NfZmb5eLu3ZSPJq6OWfaujaNRG830NG7+Xe3a4utatcQzy1Ere2jNCYUgczMb9QMQV65cEaJLF/2+vXudyydHSyy+JdHR0S+fl4syRHXbtvpjtRHt5Oie5eiZAVPk4cNCVKlCo2yffmo/D4wzTp+2vi+E0I9SjR1re/uDB9bPiiWnTqn72rQRQz/7TFf+Qf36QqRKRWHiZV6sFClof79+ZNLoaLRQmkolTaqLmHjEXvlbt/SRwX74QS+v5f1ibzp0SB8WvXBhel4iI93/D0wm2+c4fFiIXbuEyJOHzBLPnXOc38tf2JK1VSv9qLGcbIX2/+8/ta65cylC3uXLQgwZQtfQCSaT6eVzOHbsWNGqVSth+T2rUKGCqFq1qtV2OU2ZMkUULVpUJJS2gbeATdc8Z+nSpQKICzfau7fH9WTNmlVkyZLl5U18+vRpA6VkAsEff/xh9XJKZsuJ1w67d++2evFd1+ZHiIdYXq/+DsxZtm7dKt58882XZYsXL+5HSV1HhgPVTu9pG+Pe8uiR+gGePZu2aRtCgBC5cll9rHsAoiogJr/+Oh0j98lQx8HGvXu2G6ORkUJ07Uo5fWyxbZsQnTuLPHHmoLKOnPYagDNnUsNVrmfKZOX4PqNXL5EVEG00/6nLPpbacOJCUMPPQoa1yZOL/2RjqnNn2v7++2QSaDarOYnsTdIfpWhRq9O/7DixN508qS7/8QcdtHCha41nNybDOiZkaHHLkNdvvmm7Yfr0KTVMP/9c3Sb9eoYNs65fk1/oZpMm1teub18yGXvnHfI70+5r316I775zfC02bbK5/Za98rIhLRXSb7/Vy7ttm/UxxYpZb5PKmHaaOJHmu3a59x/YC1Mvw99b3ptGc/u2PuS3EHSdtNtu3CDzRSHs/xfvv68u9+9P/m6ahNUvJ9kBoPWXa9xYvdeOHKHluAACu3fvFms17yfLb7utaePGjWLWrFl2948ePfrlMufUcY49RYdN11zg0aNHSAIgK+B4yNgJqVOn1oWXPnDggNeyMYHhxYsXGDlyJK5cuWK176AM+ekC6dKlszp25syZXssXjEyfPh1Llix5uZ7bjjmPllq1aqF9+/Yv16Ojo30im7dEasOVxmE2mxEREYGnMlyvN2hNfjp2pLnWhLZiReDaNTwrUYLW33gDSJMGpwDsATBs+3YAgAgLo/3emK75kjRpbG9PkYJCPDdvbnt/zZrAwoW4FheeuROAWw0bYuWePdgoy2hDWufJQ2GJhwyh9ZkzgWHD1P2pUyN5rVq4C2Cl5jRaszCHSHO6MmVobmHi9QmAiTVq4DUZZlma8JUtSyaBiuLYvAagptcrr6ihtp89A168AICXpi920f4OGdr59df1ZZo1o3nWrPqyzswRNWzZssXlsg5JnpzmKVLot48ZAwwdSiGttSGrU6cGtm4FRo9Wt929S9fq66+t61cUCjk8aBBanzsHACgM4GWQ7EyZ6JpduqSaAkpu3bI2wbTEzrdehjCvb7lDPqfSBFKGmJfUrEm/T8uqVer99ttvNLcVOlmaIHbvTr/7r79o/dw51dRNS0QEmaaOH2/zN1jJBtC9aTTZs1ubSn78Mf03UVHAkyf0PNStS6Hw7TF/vrr8zTdkJpwiBb0DtNz9P3tnHV619cbxb6hRXIq7D3cbDhuwHzYYOmy4DhsMGMMHw204bMPddbh7gba4FC9eo0L1vr8/zk1ucm+utVfacj7Pc56bnCQnb05ykyOvvAf8/JTPkPj9CQjQuU0/cABEhBo1aqBFixbA3buI/+sv1JCrVRqhUKFC8DLxLparNu7fv99seRwjqPV+kkJKSjM6c+fOpWJij37NmgSXo298vmDBAhtKyXEkK1euJACUOnVqxT29JQ9UZwEfPnwgAFSsWDGpjMmTJ9tJaucCvdEq0UPd4MGDTR538+ZN6ZhMmTJReHg43bt3z0FSW4abm5vB9bm6upKnpydlzZrVNicR30HykT1tnn/JkhQUFERlAXoHsJFNrTtlMQUEBNDC9u1pFUAxFkZ0dwoAG0W1AtFJhfz/+PDhQwoJCSEAtFF0AiPWofj8xMczr0sajdJj3fnzdP78eYN7ekTP8N8kz56xmTiR16/pStOmRADN0n/uV65k5z182LAuAOb2Wquep0i//85mOUSPaDLV6vTp0xsf1SZirrUB5eye6Cpc3IeIaMgQXV7NmkqPZ9p0FWzmqxJA8dq8PbChOq6o7lWhgm3KM0GuXLmk+71VvEZTXuQA884cTKS72ne/wf0hIpoyha2PHWsoqBhguGJFXYDUu3eZgw+5VzFzqVw59gwARJUrs5nigQN1z4Up5x6m0s6dtr0x+nVDpHsW799nKoTGZFm/nqnFurqq1zORclYnTRqm5qZfjuihcd48okmTiACKGzNGERw+ShtYOBXUZ2l2bt5M32iX4+PjFe8ZY8eIKSIigoKDg+mj6PGQowB8RifhREdHo7C4om9caQX6o77cMUHyRQzsJZ9h2L59O8qUKWNVOV5eXggJCcFff/0l5QmCYBMZkzJff/01/qc/gmyE8uXLS8shISFIly4dSpYsiYcPH9pLPKuJVRnRjYuLw+fPnxEYGIjUqVPjzZs3iTtJnjxsxF/+fGgNyK/cu4ehQ4fiFoAcAD6lSwcIAtLKRt+fPXuGX/fuRR8AgZY4LLAxixcvRt26dVW3RUREoECBAjh69ChrUvzxh8XlPnr0CG5ubti9ezdSiyP/ADJlyoSMGTOCiPBjv37Kg8TZxFSpgKZNWZ26uzMD5I8fgVq1UETlXa82c2eUAgUA+YxtrlzwrVMHJwEsgl5A6T592CixfjDRKlWA775jhvP69yxDBhYUVaNhhvEAc+AgCMC//6Kkudmnt2/ZaL4Y5BRQyisyYwbQpQtbTptWmr2QzzcNA5v5ugEgM4Ctw4ejo3bbmTNnEh/sN0sW5khCP+CpHfj06ZO0LI2n68t/9Khy/dkz1jYIDNQF1DWHdqa6ZNGiyJMnD2aq7dOmDftt29ZwW9myQK9ewPbtgPjdKVkS+Pln044l9PHzA4oXZ8vXrwPe3syhQa1a7PmwQkNBwQ8/sN+5c9lM061bwP376vteu8ZmjuRMnsycMOjz6RPbdveubibtyhVALUCuSP78zDGISK1ahoFVZe8OREYCe/caliPOzn/6xGZQARw8fRpVtcGDe0LrAASAODe9efNm3Lt3Tyqija8vjgHY+csvSBUdjcxaRwNZAYQB+F3r1MUTgL7rjqtXr+LXrFlxzstLN7vEMY9a7ycppKQ0ozNx4kTqLfboE2FgnCNHDkXvvHfv3jaUkuNI/vjjD+k+Zs2alWbOnJkoHdrIyEipvF9++YXat29Pd+7csaHEljF58mQ6ePCgXcqWP/vbtm2jLVu2EACaPn262WOXLFliMLrl7OC7uXLlovnz59OxY8cUz4K+nGISg8ElmIgIQyPYY8eIABoIUMuWLVXP21RrcyBPvr6+dODAAXptLKaJHRDPHRYWRp8/f5byFy9eTIsXLyYAlDt3bqvLPXjwoOp1R6vNWh04wAy5LUCjNyMGqAf/tYYpU6ZIZVkcAFf+Xtm/n6hqVfYtKlqUaRhYOsL+009sRsbUcyi3JZIjxjeSxRzKLiu7pJmR6M4W1rmzef/+vULuetrReTp6lNlliNcsDzgppmrVWCGivVW3bkRbthh3fPH33+z3228pf/78BGMzDQkhLs50PChTSftOUU2tW+tmAi1Jak44RMLC2Izn27csv0ULlq/RKGymKCKCxVjSr7fWrdVnOdWS+J4TY2wZC/oOENWpY97teN++RC1bEgE0V2afKd/n1w4dCACFhIRQnNaddQOA6Pvv2T47dkj/5dQATcqThwigT7lzU01tiID/ZM9iMYCuyh1e3LrF7Kx++YXNRtuL/v1ZQORTpxweXNVawJ0RJJwxY8bQWPEPYqnXHRWuXLlChQsXVrxI//77bwoKCkqy8UE46owZM0a6h7aKf+Ph4aF4Nnr06GGTcq1BPLc9DB/Tpk0rlX/w4EHSaDS0ceNGipFHmDfBX3/9paifkydP2lxGSxHjJ8lT27ZtKTg4mE6fPk1lypQx2H5ANPq2Mce0Bqv/+9//VBuZffv2Vc0HQCVUDNnthfy8RYsWpRs3big8hEmNBSvZuXOn6rXZSuYMGTLQjh07CGAB/RLz3xg2bFji5btwgX2LqlYlOnTI8kanJcjV9+SIXuC+/lra7iYr28XI8yUmNze3hF+vA4iOjqa9e/eqP0OfPul2FK9Z3hAXU+3abJ+GDdm6qIb4/DlR9uyG+4sqYQ0aSF4bxW2XL1+23MOfKeTn+/57ot69zT8npmJVzZjBnDwArPE7dSpTC5s1i+jPPy17Di9fNnSkIqa4OFZ/8iC+t24pve6JqUwZw3rt2lW3LPe8J/5nz5xhnRRj8X8+fmSdKrGz9fEja+CLcaGMpLVq+fL4SjNmEAG0UBB0MbY2bNDt6+JCGrljDVmaD9ButfLPn9d5U5w1i3m702jY4MfVq+y8/v5MjgkT2PN465Z1cSD1Azrb6L1qL3hHJxEMHz6c5rm5Me8iNmgABgcHG4z8Dhs2jOIscFmY0oiMjFSM7hojJiYmyXQGr127RpkyZZLu3fr1621Srv5o4ogRI2xSrjkiIyPp5MmTFBMTI527Zs2atHHjRtq8eTNdv36dNm3aRHv37rW4U6KGu7u7VH5CIj5v2LBBUT97xOjpTuDOnTsGjaLz589L20+dOmWwvW8iPDaaYu3atSYbmTNmzDC53d4EBAQoGvjmkrUY81pkC96/fy/pxIvltmvXLsHl9ezZkwDQy5cvEy7UiRPs092mjbIBKAYQNZYsZexY5kJXjkZDMSNHUtj58xTdqxeRti4G5spFfSy4p66urknWa9SePXsIALm4uChkVm2DyOtSv37F/bX1oxj9Dgtjg6QPH+rceJ8+zX7r1KEff/yR5B0dmw10iWUGB+tcSoeGWtYhUUvPn7PG/4YN6m0hcwF9xaQfSBZgdjHyjoqYpk1TLyNDBqLUqZlnusuXWR3LZyRfvKB4b2/ymzkz8fWo0bAZO2vqauFCIi8v1gkZOZIIoE9DhrAgswDR9OnK/cuXV65XrGiyfE327OyZM7aPj496fu/ebIBk0ya2z/jxhjNCZ84YD0qbhOEdnUQwcOBAWuvhwf7ENiJjxoyqH4SjR4/a7BxJlVOnTkmOGDJnzkx58+YljUZDjx49MnqMu7s7lSpVylEimkT/nj1+/NhmZbu6ukrl/vjjjzYr1xRt27YlAFSrVi2zDZZxVhqJi8hjCgAgb29vq8vYt28fibMBAGjdunUJksUWHD58WHE9TZo0ofj4eGn77du3VevvyZMnNpdl4cKFUvlyQ2p5B8yZHZ2ffvrJ7HMlT5Y0iOPi4qhLly508eJFmj17tuL4zJkz0xj9hnoikauWWiqjHB8fH6pWrRqVL1+evvrqq8QJExfHGicfPrCRaYANwhkbJdcmVVU+K6hQoQIBoD+mTiVBWw/169eX6sTX11fhVEU/devWLXHXbSe6dOliIOv27dvV6+vBA53ak37slYIFWX54OHMnbYzYWDZrcOcOO65/f4qIiKC//vqLNg4ZQkW0MlSwheMFseMhR202ypJk6UCjRkN08KD15avNehlLcscDU6cqzy/mh4RIWgD7jbmlt4bAQBaHyNrrKlCAqF8/tty3L1HbtmxZnI1RG6AQBKKXL9X/xwm5d8ZS8+bsd/Nm9TpUS0kYYx0d7ozAAqKjo5FVEJhBpI2INxJN+JDo6jGF8fbtW/ytdWvZoEEDDBs2DCtWrEBwcDBevXqFVKlSoVixYtigdf2ob9wdExODu3fvOlxuObGxsWjUyMARKPLnz2+zcyxYsADZs2dH9erVE2+8bgElS5bEjh07AAAXLlwwu/+0adMQqBeJ3RhXrlzB2bNnERsbq3C2AEBhJG8pbm5uimPlRsOO5sWLF9JyhQoV8N9//yFVKt3rtGTJkpg7dy6uXLmCtjJD4sKFCyN37tyJN86WIY+43UTfmB1AxYoVjR6bFB1fvHr1CkOHDjUw/D9z5gwCAgIAAE+fPsWGDRvQtm1b/CMa4mvp168f/vzzT5vKJHdyAMDi/4DIgQMHcPXqVfj6+iK9MRfaluLiwiLde3kBHh7A8uXA1atKd+MA4OODpTLXuIl5nwQGBsLHxwcA8Pv48SBtvvw7Vq5cOaRLp28+rWPdunVYuHBhgmWwFxqt8bictm3bwl107SyneHHg66/Z8qFDwIcPwKhRbF18J6RNy1wcG8PVlRm+lyoFnDwJzJ+PNGnSYPDgwRBq1IC/drc0WqP0ROHrC9y+rcxL6H9e//kyhiBY5wwBYM+xNc6ZypbVLeu5HN+mfQ9HpkoFf39WmzZxXJMlC3MKou/i3BzPnwMrVrDl16+ZO3IA2LWL/TZsqNtXdPVOBOTOrVrcrYkTlRmlS1snj5wDB9jvy5cJLyMZwDs6FhAdHY3MgE07On2MeGZRfbmmANq0aYPevXvjgPjHAtBfJU7EmjVr8PjxY7i7u6NgwYI4fvw4XmljYzibxYsX4+TJk9J69erV8e7dO6kBbgsGDRqEd+/eIVu2bHj27JnqR9iW3DfmBccEo8QPuwl2796NGjVqoF69eli2bBmGDRum2O4ixomwAk/tB0bsWIaqxXxwEPKOThaV90KqVKkwYsQIVKtWDVu2bFFse/PmjaJzkljksblat26Nx48fY8CAAbh27RqICGnSpMHcuXMBAI0bN1YcywbBbE9oaCgmT56MUqVKWd0hHTp0KBYtWqSIJxUbG4v69eujdu3aePHiBX755RcAwOvXr3Hv3j0UK1YM//77LwDgwYMHtrsQLfodwmzZshkdrFLj8ePH0nKiOzr69Ouni58yfrwuv3hxlK9XD/kA5ALzbJcQiMhorI8c2rgmZbUNT3HQBGDvAH303wPOJCwsDK1atcKmTZsU+b/99pvlhXh5ATNnMo+I+l68LKFBA4W3L3k9J2QwyIDs2Y03hJs0QfDx4+gry0oDQPzXadKnB27eZF739L3MmUPuvU8+EHH0KFCiBFu+elWXL3p9M0XJkgCA6MmTsUv2Hd6/ciVu374t/R87aTTwAuD/7Jk0QBEVFYWgoCAcToTXvs+fP+POnTvqsYlkrBQEoFMn9Y1nzjDvc3IqVNAtV6+uW05l2DxfB6DSiBHKzJMnAVl8ugQREMA6mo8fs06WOW7cYHGH7Nw+sRlq0zxJISUl1bV27drRXQ8P5mXDRsTFxdGHDx8oT548iinzRo0a0c2bN212nqRC7ty5CQD9/vvvRlUbAFCaNGlo8ODBRrc7ipiYGKpbt67CU5a+LJesjSxtBaIHrQEDBtDz589tWvbnz59pzZo1CnWyqlWrSsuTJk0yeY86dOhAvnJDSxXk+3/77bfS8vjx4xPsoS4+Pp5mzJhBHz58oJw5czpMtU8fjUZD1atXl66pZMmSZo/Rr0NTaprWcPDgQapcubJUrtxOSE5cXBxt2rTJQIUQABUsWJA+fPhgE3lEypcvL5Xv6elpcM5ffvnF5DMGgGbNmiWVJ7eJqlixosG+165dI39/fwJAQ4YMsem1iOjbVQYEBFh8bJMmTaTjWrVqZRf5JERdf2LxhcTzXrx4MUHFvXjxwug9evDgAa1fv17xf5a/q+XvdXH50KFDScJeZ+nSpQbXM2zYMKfKdOPGDUmWJk2a0KBBg+jQoUOJLvfmzZs0fPhw2rhxI719+5bu37hBWzduJABUSqaWJJ7bDaCr584l/IRytSuNhujaNaZuqdEQxcQw1T0inSpYvXrm1azu3qUNuXKRAFAtWf7/ZHI/efJE9TmtW7culStXjgDQzZs3jdpDx8TEKFSQ5XTs2JEAUJzomMpI8nRzY+p05q5Hdl3SstaWR/z/6u/rX68ekUZDt2R5koqlpedTS999p/Omp2Y/JU9ERCVLsuUk1lYFt9FJOC1btqTXbm7MPaeN0Wg0VK1aNYM/phxfX1/y9fWlN2/eUL9+/Swy3k9qiK61xZdFQtPBgwfp77//Nig/MjKSgoODSaPR0OLFiyk0NDRR8l64cIEAUI4cOSg6Opo+f/4sydCjRw96//59oso3xzfffGP0eUgsonG63Ih9xIgR0vLdu3epePHiZu/F6dOnjdonGTvm33//tck1tG/fngDQnDlzbFKeNbx+/VpxTc2bNzd7zIABAwiANLBx7dq1RMtx9epVg/q1JJDquXPnVO/Nf//9l2iZRMw9O0REK1asMLmPp6cn/fPPP9S+fXsaP368yX3v379PRKxBFykaXduYlStXUo0aNaRzWjrQIR9EAEBdunSxi3wSnz8zOxEt4rvsn3/+saqYe/fu0c6dO1WfMzGpBS4cM2YM9e/fn4iIpk2bRrt376bvvvtOcdz48eMTd402QG1AZ9KkSU6VSe70Qkw5cuRIdLnyTrr8GQZAeWSNWHn+jBkzqEWLFtSrVy/6JPc8ZwkREazM334zvV///mw/MTjnsmVEc+eqNrADAwMV8m354w8a8fPPijxLvluALlhvYGCgQhzAuC2ZeGwBMHfqBNBDgKqmT080bBjd+/lnmgTm1IL+/ZfJXbGiujvu0qXZb5s2umC/AHO8sG8fkZ+feFKW2rVjvxs2SLKsA6gTZI5NVOpMkbQBTg2SmtODNGmMlzNsmG7Zht8MWwB7dnQANAXwAMBjAGNUtnsA2KrdfgVAQXNlJqWOTpMmTSg8VSqi4cPtUr7aH/H58+fUvHlzev/+vfSS+uGHH9gffMsWu8hhD/z8/BTXValSJYteROaSRqOhV69eSS+qKlWqSB8qgLn6TQyrVq2SztW4cWOp3A4dOtiiWsxSu3ZtxfVa/aExgnyEt0ePHtKyfIQ9KChIMvjXT23atDHIE73hxcbGkkajoXv37hm9bzNmzLDJdfws+8A5mhMnTiiuyZKBhzlz5iie/+PHj9OkSZPo8uXLCZZDfB/Ik6Ud8Pj4eIPZZFs9Z69evTJ6//v06UNLliwhIsM4NXXq1Enw++BFIuKbWYuPjw8BLBaUJejLOnDgQDtLqOTWrVvSuU+dOmXxceIxohMQeerVq5fFzz4RUaNGjQzKsFeH1BL27dtHDRs2NJBp/vz5TpNJRF+m7Nmz04MHD2jq1KlWzYSFhYXRnDlzKC4uzuR/Jy3UOzryNG3aNOvvV3g4kZHZEYnoaKInT1jDvkABFlOHVQJL4oxPv37SjK2t0qxZswgA7dq1iyIjIyk0NNToN+X06dMGx5cEKCvYgAyRcjaODh9mctevr/R+Js5cvXmjPEH37ix/+3ZlvhjHiYjo1SspWy5Hv379lHVWuLBu+cwZ3TIR877Xs6ey46Lm1e7XX813nKDreCUVYK+ODgAXAP4ACgNwB+ALoJTePgMBLNcudwSw1Vy5Samj8634cOp797ARolqXPIkqXv369TPYtmbNGrvIYQ/UvPCkSpVKsd6yZUvq2rWrVS+ply9fEsBGcMLDww22FylSxKhMcXFxtG3bNqNT1ESGMVvE5CiVC/1R4IR4KVPj2bNnqtfVunVrxTUWKVKEAENPbCtXrlQ9/tKlSwSAfv31V5P3zVaxb4YMGWL0o2RvxE7w0KFDqbYYO8MM27dvJwCS3OJ6QuWXzzACrDF6/Phxq8rQ9yQGJD6IsVqQTQDUqVMnKl68uMH+33//PQG6QJzm1CaNJbWZBXsREhJCAOu0WzIzpy/rb+ZGuW2M/D9ft25dmjRpEu3atcvsceIx+rMxgwcPppiYGHorNkotQK0Te/r0aXrz5g0NHTrU4aEDjD1HSaGjc/LkSRo1apQkU/r06SlLliwEwCDA78GDB1UDEcfHx0uz9BMnTjT7/zkD0Pdm9ilWrJijqkBqSEeEh0uDmb6+vgl6N5hLYntk8+bNUt7Tp0+JiOjixYuqqrfy5OLiQkRKFU86f55dQ/XqTF3P1ZVo9Gi2HBJieL3Pn7OZEn1Pfx8/MpfZBtWjlGHPnj10tUsXCuvbl3WiDh0iun5d6YlRJDaWuePOmJHlq3nIE4Oymkta77lJBdixo1MTwBHZ+lgAY/X2OQKgpnbZFcBHAIKpcpNSR2dsiRKsqpYutUv5r169Ij8/P5owYYLBA+zm5maQt2zZMrvIYQ/kutnGkjgae+/ePeqgjSacKlUqyZ1p27ZtqU2bNhaVJa83YyxZsoQAqKrAaTQaWrt2LY0bN061XEeh757YVm7Hz5w5o3pdPj4+5OXlJV2jGMDu4cOHFBYWRjNmzKD9+/cbdVUst0EQ05w5c6RgjoIg2DRO1KBBgxx+T0RmzpxJACgsLMziYzQaDZ09e5aePn1KAOjrr79OkPxv374lQDkbB1g+sq6P/j1LbPDbsLAwgzKfPXtmdH+NRkOXL1+WBhBiYmKkkVZrkqPVedOnTy+9m6dPn666T2xsrCKwsJhm2iKuhxXExsYadFYAUFxcHPn7+xvsHxMTY7TDevDgwQTJ0L9/f5P3L6HlJgS5nVr69OkVcvzxxx8Ok8MU4qyhfrpy5QoRkfR/UXuHnDhxgkqUKGH2P/Pvv/9a/T9zGGAN6bJly0rnFVUwHZXU3I6bqpeIiAjdemgokacnkRgk2sYDpOJ59Aciy5UrZ7hzrVpEZcsa5r94QXTxotJGSExiUGJ5qlVLtywGxf3lF5teV2KBHTs6bQGslq13BbBYb5/bAPLK1v0BeKmU1ReANwDv/Pnz279WLEW8uUeO2P1Uxhqi+ilbtmz07t07xbG2NlpPKI8fP5bihYhxYfr37y91VPQDs8nVZa5du0YAi0Z++/Zt2rBhgzTzombLZCqJHcL//vuPevfuTRcuXKCLFy9KHcqJEycazNDoq9o57UVPurgVAGjHjh02KXP9+vUG1yQGPP3w4YNkc3PmzBmqW7euQYBQeVBTeSwofb1vgNkEyD9OtkRuSOxoxowZk+AAiPKgrGKKj48nb29vsw4ePnz4QPPmzVMcK86YJhT9DnWLFi0SXBYRewfJy2vTpk2Cyrly5QpNnjyZNm/eTL1791aUmTlzZoM6dLRxe+nSpRXyqCEfIQZY57RXr16Jth9MKPp1Jto9yWM7bdQaqBtraF8QY8hYyefPnyWVT7VG+N69e211mUYJCQmhjx8/0ocPH6TzlilThgAWBBYAnT171u5yWILagAHAZoLPnj1LADOql78DR40aRVOmTLH4+0hk+EyIKonGUkBAgDTbIXLz5k2b2BwqAGtzyZ9VNc0X/ZQ2bVoCmF2TOAtmy7RixQppEEAeQ0onNih37ty2rQsVrl69Snv37pWC3YrJ3d3dcOf4eGYHZIrly3Xt3BkzWMfs+HGmghcSwlJMjG6fO3eImjYlypHDcBbKiSA5dHTkKSnN6MTkycOqykaeksxRr149gz+Y2sehXr169MMPP9DDhw/pyJEjBIB2797tEBlNIconNuqmalX+NBoNde3alU6dOkVRUVHk5+dHW7duVRwbGRlJrVu3Vm30iXY48nT8+HHVIIlikqsAiClv3rzScsGCBSk+Pp4uX75Mu3btkurR1MfBUch1hpcsWaI6+mot8obyw4cP6dtvv7XaA9iePXvow4cPRtXYxLRz506FvY4t0Wg0kmc6W9kvWUJwcDDVrVuXsmbNmuAyEvp8qRna2sIphlhWgwYNKEOGDFSjRg2rn4moqCgaOnQobdu2TSHfgAEDEi0fETPSvn//PtWvX58WLVrk1P8lkc65BABKly4dnTp1SpqxvHjxIp0/f56WL1+ukHHEiBEOl1OOfp2JgxPnz5+n6OhoCgwMlIIHy69PTMuWLbNZh1J/tNwR3y2xESxXj5SrnkZFRdldBms4ePCgQWd53rx51L17dwKY7Y6Yrz/AYEkiYrYnBw4ckBro+gNhrVu3psKFC5v8v9niP/jzzz9Tp06d6OXLl0x7AcqOjqkkfs/z5ctHsbGxtGDBAoqMjKThw4dbXSemUpUqVRSDFPodTSKis2fP0iuZLY29uXLlikJGQRASVlBkJKvztWtN76e9L/T6NdGNG7rguUkEcNW1RHD3LpuiM2dYZyMCAgIM/mSmbB+mTJki2fRMnjzZITKaQpRLVFuaO3euTcr9+++/Da49ICCAmjdvnqgXWFBQkLSs/6KfNm2a0Re8I+jWrZvi/Ldu3UpQObdv36bo6GjJFioxRvAiaqoPx44dk5ZPnDghqVrZo+7Ee/Xzzz/bvGxj2OJZEB0TGGt8WHJud3d3m9k17Nixg9q0aaPwiGjNTMy7d++kxpc8jRs3jkLU9NFtwLt37+jdu3dO+1+qzXgcOHCAoqKipHX9DpmzbSvXrVsn2UTJ05EjR6RBg06dOqk+lx4eHjaVRfT8KP++6duf2Bq160qsqqa90bfFGz58uKoThb179xp9p4geT+VJ7uJco9HQ1KlTydfXl4KDg6lmzZq0fPlyKliwIPn4+NBXX32lWu6dO3eIyDYdHbGMnDlzsrJgvqPTqlUrApi6oXisHHEmuF27djR9+nSj5Rw6dEjq7IlJPhgqpv379yvKl3vfdCbyby7AZrf/97//0S/2UCvbvp3oq6/Y7E4SBHbs6LgCeAKgEHTOCErr7TMISmcE28yVm6Q6Og5G7hkLYCpWaqOYYho8eLDkhcrZxpRqcTqW2si2SaPRUEREhMLNZFRUlGpHZ8OGDSZfkvJ0//59aVneCM2SJQsRkcIewtHoPwsJkeHw4cMEQOFlyxb8888/BDBd5r59+9KYMWOIiCh16tQEMAcK0dHRdqs7ufczRxkzJ+Y+yGnRooXqs2hqdkq+nz1sUuR2FI0bN1Zsu3Hjhqp6UalSpVSv4+7duzaXT41x48ZR9erVHXIuOfLnWkybNm2SnKQAbCZbXH748KFJ5yeOZOTIkQZyi8v6TlDEVLBgQZvKoKZCK7pRDgkJodWrV9tcHVHtuixxDe9s5HaRaja7AAw6jmIqV64crVmzhgAWoy8gIIBevnxp1ftDbUZHbJcQJb6jo9+ZA0CnFy+mr8x8t4mIgoKCKDY2lmrXrm1gxyra+26QuWTOlCmTajnyNkVMTIxkAzp69GgpX+zYici/zc5Gfj0K729fGDDS0TEMvWolRBQHYDDYrM09sE7MHUEQpgiC0FK7298AsgqC8BjACABjEnvelIyrqytiYmLQs2dPACwKd5kyZYzu/+rVKwQGBgJgEdmdiTxKu0iaNGlsUrYgCEiTJg2yZMmCzp07AwA8PDxQoEABg32LFClicblfffWVtDxy5Ehp2cPDAwDwjzyys4NxdXU1yLM0Iruvry9KliyJ06dPAwACAgJsKRo02qjIrq6uWLFiBf78808ALIo9AGTMmBHu7u4AWLR7W1OzZk1pOTQ01OblmyKxz4TafQWAjx8/quZPmDBBWq5Tp44U8duWZMqUSVr+8OGDok4rVaqEVq1aAWB1LQgCpk+fjrt376qWVdySSOc24I8//sDly5cdci457u7uuHXrFnr37i3lffr0Ce/fv5fW169fLy0XKVLE6e9mkdKlSyvW5c/c/fv3VY9ZsGCBTWXIkCGDQd67d+8AAH379kXv3r3h7e1t03MmV2rVqoVHjx6hRYsW0rtVnzFjdE2qAgUKYNu2bQCAqKgoKT937tzInTs38ubNa9X74+nTp6r5kydPhiAI0rql3yV95P8ZkfqDB0N8EuXfLTc3NwCQ/neZM2eGq6srzp07h2+//VZRRnR0NAAgffr0AIDAwEC8ePECderUMThflixZFOfIkSMHACAsLAzZsmUDoGwnAMbf4c6mUqVKzhYhyWGTNy8RHSKi4kRUhIimafMmENE+7XIUEbUjoqJEVI2IntjivCkZNzc36Y+bO3du1K5dG23btlXdd8+ePdi0aRMA9sd8//493r596zBZ5QQFBRnk2aqjI2ft2rWIiIgAAMycORN///03bt++LW0vWrSotBwSEpKgc8g/BufOncOtW7cSJmwiyZ07t2I9derUGDVqlNnj5s6di/v372PmzJl2kYsNoBh2rsUPnthwJiKbN5QAwNPTE2vXrgUABAcH27x8feT3v3379okqS/xPrF69WpEvv45Lly4hPj4eV65cwdSpU6V8T0/PRJ3bGPLn/ebNm8iUKRMmTpyo2IeI8Pr1awDAuHHjDMrYsWMHFi9eDBcXF7vImJQoU6YMGjRoIK2fPXsWlStXltYfPnwoLSeVTg4AVK1aVbEuv49hYWEG+/fv31/q5NqK6tWrq+aHhoZKjXSxoZoYPn/+jLi4uESX42yKFi1q8T1wd3dH9uzZAbA6FJ898X1tLZMmTZKW06VLZ3S/hA42GetIAcD169eRO3duHD9+HFeuXEFMTAyCgoKwfPlys+UuWLAAPXv2RJMmTQCwzkz69Oktao9069YNHh4eaN68OXx9fXH9+nXV/3C7du0wY8YMs+XZG/kgFUcFtWmepJC+ZNU1OTdv3lRM4cPMdK58qtUZqMm0Z88eh52/ffv2BDBPVhs2bKCzZ88qAqbJvSXJk9xAtVmzZgSASpYs6TC5TWHMQYI5Jk+erHrc4sWLbSLXqVOnVMsT4xLoe2yzB6Ju+gHRjaedkHtBskUclHfv3tGgQYMMvCudOHFC4UlHLY6WvdRtftaLMi4muYxhYWFGXYw7653jTP777z+T7+MePXo41DjZEtTijplKgwYNspssps578eLFBJUZHBxMQUFBUvnNmjWT7Dv1PXclB9U1ETHwq5r9iDyVKFGC7ty5QwBzWCAarIuBehNz/pcvX9JXX31Fa9asMQiiqa/aZQ411+uOeJ+IMeM6dOigcHqULl26ZPsOCw0NpYcPH37x72PYy0bHXol3dNTRf5DlgRMBKIyCHY2ari3gWCPc6Oho1UB2oiz67n1LlChB9+/fp8+fP1P58uVp4sSJknFj6dKlHSa3KTQaDe3Zs8egk2YuUrWaAeaMGTNsKpuPj4+BLv3Vq1dp/PjxNj2PMeTu2G/cuGG381SvXl06j7G4KQlBLV6JfmwP/ZRQl83mCAgIoAkTJtDChQsV55O7oBbjAPGODkMt6Ko8TZs2zdkiqqIvp2jYDYB69uxJt27donXr1hFg39gypuouoa6exYEWNfvG6dOn0549e2j27NnJrqMTFxdHbdu2Vbzz1JwU1axZU3LUkSFDBiIievjwoV1csMtDIGzdupXmzJlDfn5+FjmWMNfJsdf75OLFi+Ti4mIg48ePHxWu1pMbas97dBJy/ewIeEcnheDn50cHDhyg69ev05MnTyRDebVgmo5GbgQHQPJ6YgsPX4lFXidyGeWNY/FDIAYUtbUBbmIRXYzL4xCpodFoaMuWLTR06FCDZ8LR8UbsjTjKCYBmz55tt/PY8+NhyQdfnjp27GjT8+uj7xVO7pHI29ubd3T0+PTpk6IO5J7NxKjuSQ1A5+FKfC+8ffuWWrRoQbdv3yYi5lhm9erVdp2ZNeUC2JogyQBo5MiR0jIAVe+lq1atIiKi3bt3J7uOjhzxeuSOdNq3b0+enp504cIFiouLo+LFixuEb7A1ckdA8k5P1apVpdlOeYfi4sWLFBERQf7+/kbve9++fb/o90limD59usJ75rNnz1LcN98UvKOTQrl9+zY1bNiQgoODDVwhOzpa+KFDh6RziyMjogqBs5G/OMV4CgBUg6yK6lCOCPxlDaJXLLl6Ynh4uLR93LhxdO7cOcqWLZvqB+TcuXNOlN4+xMfHS+qKdnGnqUWsw4oVK9qtbGNJ/37quzm1Nc+fP6fcuXPT6tWrpXqVN6Z4R8cQX19fmj17NkVEREhezURvT0mRjx8/UmhoqBR81ZkEBATQ7du3DWYy9+7dS7dv31adoZcj9/RJRCaf1dOnTxMR0blz5whg7pqTI7NmzaLixYtTdHQ0Va9enY4fP+4UOeSdEv1UuXJlafnu3btS3B8PDw+T75BZs2Z98e+TxBAREaFo4wAw+x9KKfCOzhfA1q1bFQ939+7dHdKbX7duHdWvX58KFSoknfvjx492P681NGzYkPr06UNEpIiMHRYWZrDvtWvXCAAVLVrU0WKaJDo6mh48eECrVq2S5N+wYQNpNBpF/A61NHPmTGeLb1cKFixIXbp0sVv58o+3vcouUqSI6r2Tf7Tu3btn8/ObQh79Wy1lzJiRd3T0CAsLo+nTp0sBRJMygYGBdo9fYylFixZVfcZy5cpFL168oAkTJlB8fDwdOXKEHj9+LB0nD6wsurw3lt68eSMdt2/fviQXJDS5cfbsWapWrZrZwRpLUr58+QgALVy4kNq1a0fdu3d39uUlW7Zv366o2wULFjhbJIdgrKOTdFzBcBJN2rRpFetr167FoUOH7HpOIkK3bt1w+vRphfcUNfehzuTEiRNYuXIlAMDLy0tyi6lfZwBQsGBBAMCgQYMcJp8luLu7o3jx4goPK126dMFXX31l1steSveClSNHDod4GixVqpTNy2zTpg0aNWqEe/fuqW6X3zu5G1RHoO/xTyRjxowAgBUrVqBy5cpYsmQJzp8/70jRkizp0qXD2LFjk8V/LkuWLMiVK5ezxQCge6b0efPmDfLnz48pU6bA19cXTZo0kVzoxsXF4b///pP2FUMy6PPkyRMEBgYiZ86cUl6LFi2kEAKchFGnTh1cuXIFM2bMQPv27TF8+HCLjitbtixmzZqlyBO9ykZHR2Pbtm1Ys2aNrcX9YtD3jmcsDMCXAu/opCDEl/+RI0ekvMjISLueU80daWRkpOTvPqly69YtrFmzRhEHQMTLywuRkZF2if1iCxo3bowff/xRWn/48CF8fX1NHpMcGl2JoVChQjh+/LjkctyWyGNXzJs3z+bl79y5E8ePH4ebm5vkCvW7777D8OHD0bBhQxw+fFja19EdHdFNbcmSJaU8IsKbN2+wb98+tG/fHt7e3hg4cCBq1arlUNk4KQvR7W/58uWN7vPq1SsALGYRAIwdOxYdOnQwWe6aNWtQqFAhh/93viRGjx6NrVu3Yu7cuYiPj8elS5ekbSVKlACgHCQaOnSoIkTClClTUK1aNQCOi8GVkhFjB4mYax+kdJJmxCNOgsiVKxfTR5Rh7xgCajFq7BXnw5aULl3aIHCenKR8DRkyZMCqVauk2EkAMH/+fNV906dPj7CwMEVwzZSIOCo9btw4m8fsWbZsmbTs5eVl07L1OXjwIOLj46VAq/o4OkidGDuiVatWuHfvnhRrx9PTEy1atHCoLJyUjfjO7d69O0aMGKG6z/Xr16XlmJgYHDt2zGSZ+t9Djn0RBAGCIKB69epYv349GjVqhLRp02L+/PkYNGgQfvnlF6xbt07q9ISEhMDV1VXSrChZsqTJji7HMvQHNq9cuYITJ06gUaNGTpLIufAZnRSOWtRhW5LQYJycxKEf9Oz06dOKdbHhv3XrVoSEhBgN0JdSECNliyO9tkKj0Ugze44Iyubi4qLayVm7di169Ohh9/Pr06RJE2TKlAm9evXCpUuX4O/v73AZOF8GYhDeb775xug+kydPlpbr16+vOlItNqLV1JI5jkEQBHTp0gW5cuVChgwZMHHiRHh5eWHx4sVYs2YNatSoAYCpK8rvE+/k2AY1NdBvvvkGUVFRTpDG+QhJdcSjSpUq5O3t7Wwxki23b99G2bJlAbDp/alTp9pcfenUqVNYu3Yt1q5di549eyJz5syoW7cuWrZsadPzcNRRU7sTuXv3Ll6/fv1FjeAUKlQItWvXxvr1621W5syZMzFmzBgAwOPHj1GkSBGblc3hcHSIbRFBEHDkyBGUKlUKLVu2hI+Pj1XlfPPNN2jYsCHatGkjqU1xOF8aly5dQv369RETEyPlnTt3DrVr13aiVPZFEITrRFTFIJ93dFIu8obwgQMH0KxZM5uVHRAQgLx580rr169fl2yEOI7h8uXLOHHiBI4ePYqzZ88qtsXHx0tqR18KVatWhZeXl8KmJbHI/0NJ9V3J4aRU6tWrZ/Buk5MlSxYEBQUBAJYuXYqBAweiWbNmOHDggKNE5HCSLKGhoZg1axamT58OAJg+fTpGjBiRYp1wGOvofFktoS8YW3qkOnPmjKKTAzhGrYejpEaNGhg3bpw04yCSIUOGL66TAzD7mcDAQGeLweFwbITcuch3331nsL1bt27Ssugwo0oVg3YOh/NFkjFjRhQqVEha/+2335A6dWp8/PgRGo3GiZI5li+vNfSF0rt3b6xatUphWJ1Qbt68aZBnzA0tx/589913iIqKQkhICP7++298+PDB2SI5BS8vL3z8+NEuZW/dutUu5XI4HOM8f/5cWp4wYYLB9hw5ckjL9evXx+nTpzF+/HiHyMbhJAfkqmsi2bJlwy+//OIEaZwD7+ikYG7fvo2dO3dK63379sXAgQMTXa5aR0f0xsRxDh4eHsiYMSN69uxp1GNXSidr1qw27+iIbjrFGA8cDsdxiI4FGjVqJMU3A5hTgg4dOqBdu3aK/evVq5fiXelzONYgOnjYv3+/It/W3kmTMty9dArGnAvlhBAZGYl169YBAAoUKICoqCiD4FQcjjPw8vJCWFgYYmJibNbZ8/DwQOfOnb9IVUAOx9ns3r0bz549Q6VKlRAdHS3ly2d32rRp88UHRORwjFGrVi1ER0fD3d0dEyZMwJQpU6Rt8fHxmDt3LgYMGGAQeyclwb/eXyDyD4a1XL58GQDrRD179gzPnz/HnTt3bCUah5NgxBg3O3bssEl5gwYNwsePH3lHnsNxElmyZJGc3Hh4eODHH3/EoUOHFPvs3LkT9+7dc4Z4HE6yQBz469q1qyJ/165dGD16NH7//XdniOUweEfnC+DgwYOK9dSpU1ulx3z//n0EBAQgLi4O//33HwBg+/btANjHJ6V68OAkL8TYQZ07d05UZ15k6dKlALhaJoeTVNi4caOqUwIOh2OePHnyKNZF5z2RkZHOEMdh8I7OF8D//vc/FC9eXJH3xx9/WHx8yZIlkTdvXsyePRuzZ88GwDx7cThJie+++w6dO3cGAFy9ejVRZcldSYvuazkcDofDSa54enoia9as0vqrV6+k/JQM7+h8IdSqVcsgLzY21qoytmzZIi2rRd7lcJyJu7s7Zs6cCQDw8/NLVFnBwcEAgPz58xu47+ZwOBwOJzlSuHBhafnPP/8EwDs6JhEEIYsgCMcEQXik/c2ssk8FQRAuCYJwRxAEP0EQOiTmnJyEsXTpUly4cAFDhgyR8goWLGi2QRgfHy8ty/dNmzat7YXkcBJJ7ty5kTFjxkTr7Ive26ZPn458+fLZQjQOh8PhcJzK1q1bpfhTYiydtWvX4u+//3amWHYlsTM6YwCcIKJiAE5o1/WJBNCNiEoDaApggSAImRJ5Xo6VpE6dGl9//bUi0vvr168xcOBACIJg1A4hJCRENV9eDoeTVBAEAXny5MGbN28QFRWlUEGzFI1Gg5UrVwIAd0TA4XA4nBRDoUKFsHbtWtSsWVPKe/fuHXr37u1EqexLYjs6rQCs1S6vBfC9/g5E9JCIHmmXXwN4DyBbIs/LSSD6Db8LFy4AMO6JjdsncJIb2bJlw40bN+Dp6Yk1a9ZYffzNmzcxd+5cALyjw+FwOJyUx5cU5D2xHZ0cRPRGu/wWQA5TOwuCUA2AOwD/RJ6Xk0Dc3NyMbgsLCwPAgrNlypQJxYoVw6lTpxT7eHl5YdGiRXaVkcNJDNmyZcOzZ88AQBEw11I+fPggLXMVTQ6Hw+GkNPQ9sAFA1apVMXXqVJPHffr0CZs2bcK///6LESNGJEhrwtGYDRgqCMJxADlVNo2TrxARCYJg9IoFQcgFYD2A7kSkMbJPXwB9AWYEzLE948aNQ3BwMP755x+DbU2aNMHFixdx8uRJAEBoaCj69eun2Gfnzp2oW7euQ2TlcBKCGE8HYJ0eaxEdEQB8RofD4XA4KQ+1GR1vb294e3tj/PjxiIqKgouLi8Hg+IABA7Bp0yYULFgQqVOnxrx58xwlcoIxO6NDRN8QURmVtBfAO20HRuzIvFcrQxCEDAAOAhhHRJdNnGslEVUhoioJaaBwzJM5c2ajRmeXLl1Cr169TB7Pva1xkjo1atSQluXONCxFrq7JZ3Q4HA6Hk9LIkcO4AlZ8fDzSp0+Pr7/+2mDbkydPAADPnj1DqVKl7CafLUms6to+AN21y90B7NXfQRAEdwC7AawjItuELOckmvXr1+PMmTN48OCBIl9tpsfd3R3Lly9H9+7dUbp0aUeJyOEkiE6dOknL79+rjr2YhHd0OBwOh5OSyZ49OwBg4MCBBtuePXuGuLg4eHt7G2yTO6KqXLmy/QS0IYnt6MwA8K0gCI8AfKNdhyAIVQRBWK3dpz2AugB+EgTBR5sqJPK8nETSpUsX1K1b1yLVHn9/f/Tr1w9r1qyBq6tZbUcOx6m4u7tLy58+fbLq2MjISKxdu1Za5x0dDofD4aQ0vvvuO5w+fRqLFy/G58+fsXDhQmnbxo0bpeWgoCApsGhUVJSio9OqVSvHCZwIEtVqJaJAAI1U8r0B9NYubwCwITHn4diP9OnTq+YXLlxYmqJUM1rjcJIyq1atwoABAxAREWHxMXFxcTh27Bj8/f1RqVIl1K1bF2nSpLGjlBwOh8PhOB5BEFCvXj0ALPyIfNB74sSJ0nLhwoURGhqKb7/9FseOHVOUkStXLscIm0iEpOoxoUqVKqQ2bcaxPREREahbty5u3Lgh5Wk0GjRo0ABnzpxJFl41OBx9unTpgkuXLsHf3zInj2XKlMGdO3cAsBhTyeUlzuFwOBxOYvD390fRokWtOiY+Ph6pUiVWMcx2CIJwnYiq6OcnHQk5TiNt2rQYMWIEGjduDADw8PCAIAg4duyYVSPiHE5SIl26dAgPD7do39jYWKmT4+rqKukvczgcDoeT0ilSpAimTZtmcoBP3wNbUurkmCJ5SMmxO507d8aRI0fw5s0bvH79GgB7qLnqDie5Yk1H5+PHj9Jyrly54OLiYi+xOBwOh8NJcvz2229S+0+fmTNnokuXLg6WyDZwy3KOgpw51UImcTjJj7Rp0yIyMhIrV65EvXr1UKJECaP7yoOE8v8Ah8PhcDg63N3d8eeff8LDwwMdO3ZMUIw6Z8FndDgcTopEo2Fxifv166eIrRMXFyfZnb19+xaenp44ePCgtJ13dDgcDofzpaIWZuSrr75Cjhw5sGzZMtSrVy/ZxNABeEeHw+GkUOR2NiEhIQgODkZwcDDc3NykaM7Hjx9HVFQUxo8fr3och8PhcDhfEj169ECbNm0AAGvXrsWVK1fQtGlTJ0uVcLjqGofDSZH0798f+/btw/HjxwEAFy9eRL58+QAAI0eORHx8vBQdOj4+XjouU6ZMDpeVw+FwOJykwj///IOePXuiWbNmzhYl0fAZHQ6HkyJxc3PDsWPH8P79ewAsCFpMTIy0ffTo0YiNjTU4LkOGDA6TkcPhcDicpEbGjBlTRCcH4DM6HA4nhePl5QUA2Lx5M1xdla+8qKgog/15R4fD4XA4nJQBn9HhcDgpGkEQpOXLly8rtoWEhBjszzs6HA6Hw+GkDPiMDofD+WJ49OiRYj0oKEhazpgxI/Lnz49WrVo5WiwOh8PhcDh2gM/ocDicL5b58+dLy7Vr14afnx+yZs3qRIk4HA6Hw+HYCt7R4XA4HKjb63A4HA6Hw0m+cNU1DofzRfPVV1+hZMmSmDhxorNF4XA4HA6HY0P4jA6Hw0nxjB071ug2IsKuXbtQvnx5B0rE4XA4HA7H3vCODofDSfFMnz7dwBGBSJEiRRwsDYfD4XA4HEfAOzocDueLQIyno8/q1asdLAmHw+FwOBxHwDs6HA7niyBjxoyK9ZEjR+Lo0aPIlSuXkyTicDgcDodjT7gzAg6H80UgDxyaNm1azJ4924nScDgcDofDsTeJmtERBCGLIAjHBEF4pP3NbGLfDIIgvBIEYXFizsnhcDiJRR4olMPhcDgcTsoksaprYwCcIKJiAE5o140xFcDZRJ6Pw+FwEszSpUuxfv16uLu7O1sUDofD4XA4diaxqmutANTXLq8FcBrAaP2dBEGoDCAHgP8AVEnkOTkcDidBDBgwwNkicDgcDofDcRCJndHJQURvtMtvwTozCgRBSAVgLoCRiTwXh8PhcDgcDofD4ViE2RkdQRCOA8ipsmmcfIWISBAEUtlvIIBDRPRKbgxs5Fx9AfQFgPz585sTjcPhcDgcDofD4XBUMdvRIaJvjG0TBOGdIAi5iOiNIAi5ALxX2a0mgDqCIAwEkA6AuyAI4URkYM9DRCsBrASAKlWqqHWaOBwOh8PhcDgcDscsibXR2QegO4AZ2t+9+jsQUWdxWRCEnwBUUevkcDgcDofD4XA4HI6tEIgSPnEiCEJWANsA5AfwHEB7IgoSBKEKgP5E1Ftv/5/AOjqDLSj7g7bMpIIXgI/OFiIFw+vX/vA6tj+8ju0Pr2P7w+vY/vA6ti+8fu1PUqvjAkSUTT8zUR2dLwlBELyJiHuMsxO8fu0Pr2P7w+vY/vA6tj+8ju0Pr2P7wuvX/iSXOk6s1zUOh8PhcDgcDofDSXLwjg6Hw+FwOBwOh8NJcfCOjuWsdLYAKRxev/aH17H94XVsf3gd2x9ex/aH17F94fVrf5JFHXMbHQ6Hw+FwOBwOh5Pi4DM6HA6Hw+FwOBwOJ8XBOzocDofD4XA4HA4nxfHFd3QEQWgqCMIDQRAeC4JgEMhUEAQPQRC2ardfEQShoGzbWG3+A0EQmjhU8GSEBXU8QhCEu4Ig+AmCcEIQhAKybfGCIPho0z7HSp58sKCOfxIE4YOsLnvLtnUXBOGRNnV3rOTJBwvqeL6sfh8KghAi28afYzMIgvCPIAjvBUG4bWS7IAjCIm39+wmCUEm2jT/DFmBBHXfW1u0tQRAuCoJQXrbtmTbfRxAEb8dJnbywoI7rC4IQKnsfTJBtM/mO4VhUv6NkdXtb++7Not3Gn2ELEAQhnyAIp7TtsjuCIAxV2Sf5vI+J6ItNAFwA+AMoDMAdgC+AUnr7DASwXLvcEcBW7XIp7f4eAAppy3Fx9jUltWRhHTcAkEa7PECsY+16uLOvIaknC+v4JwCLVY7NAuCJ9jezdjmzs68pqSVL6lhv/58B/CNb58+x+TquC6ASgNtGtv8PwGEAAoAaAK5o8/kzbLs6/lqsOwDfiXWsXX8GwMvZ15DUkwV1XB/AAZV8q94xX2oyV796+7YAcFK2zp9hy+o4F4BK2uX0AB6qtCmSzfv4S5/RqQbgMRE9IaIYAFsAtNLbpxWAtdrlHQAaCYIgaPO3EFE0ET0F8FhbHkeJ2TomolNEFKldvQwgr4NlTO5Y8hwbowmAY0QURETBAI4BaGonOZMz1tZxJwCbHSJZCoGIzgIIMrFLKwDriHEZQCZBEHKBP8MWY66Oieiitg4B/i5OEBY8x8ZIzHv8i8HK+uXv4QRARG+I6IZ2OQzAPQB59HZLNu/jL72jkwfAS9n6KxjeTGkfIooDEAogq4XHcqyvp15gowQiqQVB8BYE4bIgCN/bQb6UgKV1/IN2inmHIAj5rDz2S8fietKqXhYCcFKWzZ/jxGPsHvBn2D7ov4sJwFFBEK4LgtDXSTKlFGoKguArCMJhQRBKa/P4c2xDBEFIA9bA3inL5s+wlQjMXKMigCt6m5LN+9jVmSfncOQIgtAFQBUA9WTZBYgoQBCEwgBOCoJwi4j8nSNhsmY/gM1EFC0IQj+wWcqGTpYppdIRwA4iipfl8eeYk2wQBKEBWEentiy7tvYZzg7gmCAI97Wj6xzruAH2PggXBOF/APYAKOZckVIkLQBcICL57A9/hq1AEIR0YB3FYUT0ydnyJJQvfUYnAEA+2XpebZ7qPoIguALICCDQwmM5FtaTIAjfABgHoCURRYv5RBSg/X0C4DTYyAJHidk6JqJAWb2uBlDZ0mM5AKyrp47QU5fgz7FNMHYP+DNsQwRBKAf2jmhFRIFivuwZfg9gN7iqdoIgok9EFK5dPgTATRAEL/Dn2NaYeg/zZ9gMgiC4gXVyNhLRLpVdks37+Evv6FwDUEwQhEKCILiD/TH0PSLtAyB6jWgLZthG2vyOAvPKVghsROaqg+ROTpitY0EQKgJYAdbJeS/LzywIgod22QtALQB3HSZ58sGSOs4lW20JpnMLAEcANNbWdWYAjbV5HCWWvCsgCMJXYAaYl2R5/Dm2DfsAdNN6+6kBIJSI3oA/wzZDEIT8AHYB6EpED2X5aQVBSC8ug9WxqtcrjmkEQciptfOFIAjVwNphgbDwHcMxjyAIGcE0Q/bK8vgzbCHa5/NvAPeIaJ6R3ZLN+/iLVl0jojhBEAaD3QQXMC9JdwRBmALAm4j2gd3s9YIgPAYzgOuoPfaOIAjbwBoscQAG6amqcGBxHc8GkA7Adu37/wURtQRQEsAKQRA0YB+DGUTEG4h6WFjHQwRBaAn2rAaBeWEDEQUJgjAV7CMLAFP0pvo5sLiOAfZ+2KIdDBHhz7EFCIKwGcwjlZcgCK8ATATgBgBEtBzAITBPP48BRALood3Gn2ELsaCOJ4DZoC7VvovjiKgKgBwAdmvzXAFsIqL/HH4ByQAL6rgtgAGCIMQB+Aygo/Z9ofqOccIlJGksqF8AaA3gKBFFyA7lz7Dl1ALQFcAtQRB8tHm/AcgPJL/3saD8HnM4HA6Hw+FwOBxO8udLV13jcDgcDofD4XA4KRDe0eFwOBwOh8PhcDgpDt7R4XA4HA6Hw+FwOCkO3tHhcDgcDofD4XA4KQ7e0eFwOBwOh8PhcDgpDt7R4XA4HA6Hw+FwOCkO3tHhcDgcDofD4XA4KQ7e0eFwOBwOh8PhcDgpDt7R4XA4HA6Hw+FwOCkO3tHhcDgcDofD4XA4KQ7e0eFwOBwOh8PhcDgpDt7R4XA4HA6Hw+FwOCkOV2cLYAwvLy8qWLCgs8XgcDgcDofD4XA4SZjr169/JKJs+vlJtqNTsGBBeHt7O1sMDofD4XA4HA6Hk4QRBOG5Wj5XXeNwOBwOh8PhcDgpDt7R4XA4HA6Hw+FwOCkO3tHhcDgcDofD4XA4KY4ka6PzRXDjBuDiApQv72xJOBwOh8PhcJIEsbGxePXqFaKiopwtCieJkTp1auTNmxdubm4W7c87Os6kcmX2S+RcOTgcDofD4XCSCK9evUL69OlRsGBBCILgbHE4SQQiQmBgIF69eoVChQpZdAxXXXMEYWGAmxuwZ4+zJeFwOBwOh8NJ0kRFRSFr1qy8k8NRIAgCsmbNatVMH+/o2AMiIDJSt/70KRAXB/z+O1uPiwPmz3eObBwOh8PhcDhJHN7J4ahh7XPBOzr2YM0aIG1a4MkTth4Tw35jY9nv+vXAiBG6/ePiHCoeh8PhcDgcDoeT0uEdHXuwaxf7vXWL/YaGst+HD4GzZ4G7d5X7FyniONk4HA6Hw+FwOGbZs2cPBEHA/fv3ze67YMECRMq1eaxkzZo1GDx4cIKPt5T69evD29vb6uNCQkKwdOlSO0hkX3hHxx64u7Pf6Gg2izN0qG5bvXrAunXK/V+8YHncKQGHw+FwOBxOkmDz5s2oXbs2Nm/ebHbfxHZ0EkOcAzSDkmtHh3tdswceHuy3Z08gMBC4c0e5/f17w2O6d2cdog4dgHHjgHz57C8nh8PhcDgcThJm2LBh8PHxsWmZFSpUwIIFC0zuEx4ejvPnz+PUqVNo0aIFJk+eDACIj4/H6NGj8d9//yFVqlTo06cPiAivX79GgwYN4OXlhVOnTiFdunQIDw8HAOzYsQMHDhzAmjVrsH//fvzxxx+IiYlB1qxZsXHjRuTIkcOoHJMmTYK/vz8eP36Mjx8/4tdff0WfPn1w+vRpjB8/HpkzZ8b9+/fh5+eHAQMGwNvbG66urpg3bx4aNGiAz58/o0ePHvD19cVXX32Fz58/S2Ubk/Hdu3fo378/nmhNMJYtW4ZFixbB398fFSpUwLfffovZs2cn5hY4DN7RsQdiRyciAhg4UH2f5s1ZDJ29e3V5ISHAihUsHTnC3E9nzWp3cTkcDofD4XA4Ovbu3YumTZuiePHiyJo1K65fv47KlStj5cqVePbsGXx8fODq6oqgoCBkyZIF8+bNw6lTp+Dl5WWy3Nq1a+Py5csQBAGrV6/GrFmzMHfuXJPH+Pn54fLly4iIiEDFihXRrFkzAMCNGzdw+/ZtFCpUCHPnzoUgCLh16xbu37+Pxo0b4+HDh1i2bBnSpEmDe/fuwc/PD5UqVTJ77UOGDEG9evWwe/duxMfHIzw8HDNmzMDt27dt3um0N7yjY0uIgFWrAEvc3uXIAbx8aXx7kya6MjkcDofD4XC+QMzNvNiLzZs3Y6jW9KBjx47YvHkzKleujOPHj6N///5wdWVN6CxZslhV7qtXr9ChQwe8efMGMTExFsWDadWqFTw9PeHp6YkGDRrg6tWryJQpE6pVqyYdf/78efz8888AgK+++goFChTAw4cPcfbsWQwZMgQAUK5cOZQrV87s+U6ePIl1WjMLFxcXZMyYEcHBwVZdZ1KBd3RsycWLQL9+pvfJmRN4+5Y5INBXaVNj1y6gdm0ge3bbyMjhcDgcDofDMUpQUBBOnjyJW7duQRAExMfHQxAEq9S15G6Q5XFffv75Z4wYMQItW7bE6dOnMWnSJKvKkq+nTZvWYnmskTElwZ0R2JJPn8zvU7s2+82UCfjmG7as75xAzg8/ACVLJlo0DofD4XA4HI55duzYga5du+L58+d49uwZXr58iUKFCuHcuXP49ttvsWLFCskBQFBQEAAgffr0CAsLk8rIkSMH7t27B41Gg927d0v5oaGhyJMnDwBg7dq1Fsmzd+9eREVFITAwEKdPn0bVqlUN9qlTpw42btwIAHj48CFevHiBEiVKoG7duti0aRMA4Pbt2/Dz8zMrY6NGjbBs2TIAzCYpNDTU4PqSCzbp6AiC0FQQhAeCIDwWBGGMif1+EASBBEGoYovzJjm0Bl1GefIEWL0aGD4c6NIFmDQJePUK+PFH5X7ZsinXtX8iDofD4XA4HI592bx5M1q3bq3I++GHH7B582b07t0b+fPnR7ly5VC+fHmpE9G3b180bdoUDRo0AADMmDEDzZs3x9dff41cuXJJ5UyaNAnt2rVD5cqVzdrziJQrVw4NGjRAjRo1MH78eOTOndtgn4EDB0Kj0aBs2bLo0KED1qxZAw8PDwwYMADh4eEoWbIkJkyYgMqVK0vHGJNx4cKFOHXqFMqWLYvKlSvj7t27yJo1K2rVqoUyZcpg1KhRllemkxEokTYggiC4AHgI4FsArwBcA9CJiO7q7ZcewEEA7gAGE5FJJ95VqlShhPj5dhohIcxbminXe6bqWpw+LFqULT96ZFh+xoyJlZLD4XA4HA4nSXPv3j2U5NosAFjHKF26dBg5cqSzRUkyqD0fgiBcJyKDiRRbzOhUA/CYiJ4QUQyALQBaqew3FcBMAClPCfDTJyBzZtOdHHMsWACcP886OFqjMQWZMgH/+x/rBE2bptz24QPw9GnCz83hcDgcDofD4aQwbNHRyQNA7j7slTZPQhCESgDyEdFBUwUJgtBXEARvQRC8P3z4YAPRHMTMmcr1EyeAjx+Z4wEAeP2adUZMMXQoUKsWWx40CAgONnRscPgw+/39d+DxY11+oUJA4cIJl5/D4XA4HA6Hk+SYNGkSn81JBHZ3RiAIQioA8wD8Ym5fIlpJRFWIqEo2fTuVpAoRMH26br1MGaBhQxb/xtcXuHkTyJULsFAPEwCbtcmUCWjc2Pg+Z87oliMirBabw+FwOBwOh8NJydiioxMAIJ9sPa82TyQ9gDIATguC8AxADQD7UoxDgjlzlOuZM+uWs2cHKlRIeNlt2gBaDxoGpEoFxMQA8fG6vFatgHfvEn4+DofD4XA4HA4nhWCLjs41AMUEQSgkCII7gI4A9okbiSiUiLyIqCARFQRwGUBLc84Ikg2//sp+69dnv/KOji348UfgyBHD/DdvgPz5lR7b9u1jtj4cDofD4XA4HM4XTqI7OkQUB2AwgCMA7gHYRkR3BEGYIghCy8SWn+QpV46pmg0fztZLlbL9ORo3VjogEATm4e3dO2DbNuW+adLY/vwcDofD4XA4HE4ywyY2OkR0iIiKE1ERIpqmzZtARPtU9q2fYmZzABY758cfgebNgb//ZrFx7MEvWhOnqVNNu6lOZJRcDofD4XA4nC8dFxcXVKhQAWXKlEG7du0QGRlp1fHPnj2TYuyY46effsKOHTsSIqbV5ffu3Rt37941c4RlfP78GfXq1UN8fDyePXsGT09PVKhQAaVKlUK3bt0QGxsLADh9+jSaN29ucPyBAwdQsWJFlC9fHqVKlcKKFSsAAIsXL8Y///xjExnt7owgRfP4MQsCmiULs5np2RPw8LDPuTw8AI2GzeSYIiAAqFwZOHfOPnJwOBwOh8PhpHA8PT3h4+OD27dvw93dHcuXL7fqeGs6Oo5k9erVKGUj7aN//vkHbdq0gYuLCwCgSJEi8PHxwa1bt/Dq1Sts09c6khEbG4u+ffti//798PX1xc2bN1FfawbSs2dP/PXXXzaR0dUmpXypFCvGfrNkccz5xKCi+jLIg4vOm8d+jx0D6tRxjFwcDofD4XA49mDYMMDHx7ZlVqhglU1znTp14Ofnh6CgIPTs2RNPnjxBmjRpsHLlSpQrVw5nzpzB0KFDAQCCIODs2bMYM2YM7t27hwoVKqB79+4YLpo4ACAi/Pzzzzh27Bjy5csHd3d3adv169cxYsQIhIeHw8vLC2vWrEGuXLnw+PFj9O/fHx8+fICLiwu2b9+OwoUL49dff8Xhw4chCAJ+//13dOjQwWT59evXx5w5c1ClShWkS5cOQ4cOxYEDB+Dp6Ym9e/ciR44c8Pf3R+fOnREREYFWrVphwYIFCA8PN6iXjRs3qnbmXFxcUK1aNQQEBBhsEwkLC0NcXByyZs0KAPDw8ECJEiUAAGnSpEHBggVx9epVVKtWzeL7pAaf0bEFFSs679zGnB98+uRYOTgcDofD4XBSGHFxcTh8+DDKli2LiRMnomLFivDz88P06dPRrVs3AMCcOXOwZMkS+Pj44Ny5c/D09MSMGTNQp04d+Pj4KDo5ALB79248ePAAd+/exbp163Dx4kUAbJbj559/xo4dO3D9+nX07NkT47SaPJ07d8agQYPg6+uLixcvIleuXNi1axd8fHzg6+uL48ePY9SoUXjz5o3R8vWJiIhAjRo14Ovri7p162LVqlUAgKFDh2Lo0KG4desW8ubNq3psTEwMnjx5goIFCxpsi4qKwpUrV9C0aVOj9ZolSxa0bNkSBQoUQKdOnbBx40ZoNBppe5UqVXDOBtpJfEYnMaRLx+LltHSwz4VSpYC7d9nvqFFAu3aG+7x961iZOBwOh8PhcGyNk7zJfv78GRW0IULq1KmDXr16oXr16ti5cycAoGHDhggMDMSnT59Qq1YtjBgxAp07d0abNm2Mdg5Ezp49i06dOsHFxQW5c+dGw4YNAQAPHjzA7du3MLg4wQAAqxlJREFU8e233wIA4uPjkStXLoSFhSEgIACtW7cGAKROnRoAcP78eamcHDlyoF69erh27ZrR8vVxd3eXbGcqV66MY8eOAQAuXbqEPXv2AAB+/PFH1YClHz9+RKZMmRR5/v7+qFChAp4+fYpmzZqhXLlyJuth9erVuHXrFo4fP445c+bg2LFjWLNmDQAge/bsuH//vsnjLYF3dBIKERAdDXTqpK5SZk/OnWO2OGXLsvXYWMDNTbfdzY25n+ZwOBxOioGIEBcXBzf5+57D4dgF0UbHEsaMGYNmzZrh0KFDqFWrFo6ohQWxACJC6dKlcenSJUV+WFhYgsozh5ubGwRtG9bFxQVxcXEWH+vp6YmoqChFnmij8/HjR9SqVQv79u1DSzOTAWXLlkXZsmXRtWtXFCpUSOroREVFwdPT07oLUoGrriUUHx/WwbB13BxLyJJF18kBAFdZfzVVKqBDB+DsWRZQlMPhcDjJjnh5MGgtAwYMgLu7u828EXE4HOuoU6cONmoDuZ8+fRpeXl7IkCED/P39UbZsWYwePRpVq1bF/fv3kT59eqMdlLp162Lr1q2Ij4/HmzdvcOrUKQBAiRIl8OHDB6mjExsbizt37iB9+vTImzevNMsSHR2NyMhI1KlTRyrnw4cPOHv2LKpVq2a0fEupUaOGNHO1ZcsW1X0yZ86M+Ph4g84OAHh5eWHGjBn4888/jZ4jPDwcp0+fltZ9fHxQoEABaf3hw4coU6aMVXKrwTs6CWXlSvYrOiRwNtevA2fOsJkecbRv3DhA5WPJ4XA4nKTLxYsX4erqqhjV7dWrl+R6tVevXs4SjcP5opk0aRKuX7+OcuXKYcyYMVi7di0AYMGCBShTpgzKlSsHNzc3fPfddyhXrhxcXFxQvnx5zJ8/X1FO69atUaxYMckNc82aNQEwVbIdO3Zg9OjRKF++PCpUqCDZ16xfvx6LFi1CuXLl8PXXX+Pt27do3bo1ypUrh/Lly6Nhw4aYNWsWcubMabR8S1mwYAHmzZuHcuXK4fHjx8iYMaPqfo0bN8b58+dVt33//feIjIyU7GxOnDiBvHnzSunmzZuYNWsWSpQogQoVKmDixInSbA4AXLhwQVLhSwwCmYrJ4kSqVKlC3t5JONxOs2bA/fuAv7+zJTHk+HFA/nBcvAhY+ZBzOBwOxzlMmTIFEydORP/+/dG1a1dUqlRJocIhCILCaJfDSWncu3cPJUuWdLYYXyyRkZHw9PSEIAjYsmULNm/ejL179xrsd+PGDcyfPx/r16+36flv3ryJefPmGS1X7fkQBOE6EVXR35fP6CSE9++BEycArb/vJMc33wClS+vWv/7aebJwOBwOxyo8tPHYli9fjlq1amH69OmK7ZUqVXKGWBwO5wvh+vXrqFChAsqVK4elS5di7ty5qvtVqlQJDRo0UFW1TQwfP37E1KlTbVIWd0aQEPz9mSOCNm2cLYlx8uQB7tzRrb98CeTL5zx5OBwOh2MRgYGBinX9D35oaKgjxeFwOF8YderUga+vr0X79uzZ0+bnt4XKmgif0UkIwcHsVxvkaPny5ejRo4cTBVJBG7hKIn9+58jB4XA4HKv48OGDye0hISGOEYTDcSJJ1bSC41ysfS54RychiB0drce1AQMGYM2aNUlLZ/p//wNmzlTmcS9sHA6Hk2SJi4vD3bt3FRHIz549a7BfaGgo7t27h7Zt26p6POJwkjupU6dGYGAg7+xwFBARAgMDpThClsBV1xKCXkdH5OXLl3j48CHq1asHd3d3Jwimx+DBwKxZgKgG0a8f8PffzAU1h8PhcJIUo0ePxrx581CqVCkpr5iKZ8/Y2Fhpn0GDBqFBgwYOk5HDcQR58+bFq1evzM5ucr48UqdObTYgqxze0UkIWrWBPxYvRr4iRaTsdu3a4dq1awDYh8jV1cnVmyYN8PEjsGQJ6/SsWQOMGgXIPqIcDofDSRr8999/AICAgAApL7OZWG3R0dF2lYnDcQZubm4oVKiQs8XgpAD40H5CePoUgQDGT52Kn376ScoWOzkA8ObNG8fLZYzixXXLDx86Tw4Oh8PhGCUyMhKA0tmA6IENYAH2NmzYoDgmSX1rOBwOJ4nBOzoJ4eJFXDKzy9mzZ1V9jjuFTJl0y7yjw+FwOEmOwMBAPHv2TFpv3LgxHj16pNgnbdq0BoH7evbsKUUw53A4HI4SrrpmLTExwKNH8JFlCYJgYDDXpUsXAEnEa4jWOxwA4MED58nB4XA4HFXat2+vWM+ZMyeKFi0KgKm0hYWFGT22f//++OGHH+wqH4fD4SRH+IyOlbw+fx6Ij4d8XqRYsWIYP36802QyS+HCwN27QO3aytg6HA6H84Uxb948bN261dliGHD37l3F+gPZoFSTJk3Qtm1bAED27NkNjuW2DM5l8+bNGDlyJCIjIxETE4PQ0FCsXLkS8+fPx71795wtHofzRSMkiRkHFapUqULe3t7OFsOAE7/+ikazZ6M2gAvaPH9/fxQqVAipVLyZXb58GdWrV3eojEaZMgWYNAl4/RrImdPZ0nA4HI7DEQQBQBKZbZeRN29ehROCYsWK4aERVeOdO3di4sSJuKMduMqXLx8iIiJw6tQplCtXziHycnSIzxQA1K5dG1evXkWMLJzDkSNHUL9+/aThjZXDSaEIgnCdiKro5/MZHStJGxEBAHgnyytcuLDiRSenRo0aDpDKQurUAYiACxf4zA6Hw/niiI2NlZb/+ecfzNSPNeZE9L2nmbLx/OGHH3D79m3069cPAAttEBQUhE2bNtlVRo55zp8/r+jkAGxGbsaMGU6SiMP5srFJR0cQhKaCIDwQBOGxIAhjVLaPEAThriAIfoIgnBAEoYAtzusM6B3r4rxX2Xbjxg0cO3bMsQJZQ+HC7LdtW6BMGefK8oURHx+P3377jXtI4nCcyOvXr6XlXr16YcyYMbhy5YoTJdKRLVs2AKzTEhsbi5IlS5o9Zvny5Rg0aJC0Ltr0cBzDhw8fMHbsWIv2lT97HA7HcSS6oyMIgguAJQC+A1AKQCdBEPQDtdwEUIWIygHYAWBWYs/rLFJ9/IhoACWrVTPYVrFiRZQuXdrxQllK3ryAfOp8zhwpJhDHvhw9ehR//vknBg8eLMXK4HA4jkUt+GCdOnXw+fNn3L17FxqNxglSMWcD9+7dw6RJk5A3b16rYrBlknnVTGrqeCmdYcOGWTxTkyZNGjtLw+Fw1LDFjE41AI+J6AkRxQDYAqCVfAciOkVEkdrVywAsD2maxEj79i1eArh0+TL8/Pxw69Ytxfb06dMr1qupdIichosLIO+IjRoFWDgaxUkc77Qzgbt27cJ3332HS5fMOCiPjgYEAfj3XwdIx+F8Gah5LouNjUWaNGlQunRptGnTBsWKFcOnT58cKteRI0cAAG3atLH6WHlAUR481P4EBATA398fz58/t0pV8P79+1i6dCnvjHI4DsYWHZ08AF7K1l9p84zRC8BhG5zXKeR7+RI33N0hCALKli2LMnoqYGnTppWWmzZtiri4OEeLaJq8en3MkBDASaOYXwI3b97EokWLDEaS3759a/rAwED2O2oU+z10CPjmGyAqyg5SJk02bNigCJzI4SSW8PBwk9v37t2Lx48fO1Sd7dWrV/jvv/+QN29elC1b1urj5TM6vKNjf/LmzYsKFSrg+++/V+QXKVIEmzdvxpAhQzBt2jQMHTpUsf3w4cMYNGgQUqVKhefPnztQYg7ny8ahzggEQegCoAqA2Ua29xUEwVsQBG81FQOnExmJjJGReK0XsE2O3ClBlixZ8OnTJzx79gx//fWXIyQ0T8OGyvU3b9hMz8KF9j83EZtBunkTOHkSePzY/ud0MmPGjMHQoUOxceNGRX6UuQ6LNkI6Pn9mvwsXAidOAJ6eQPfurC5PnEixndQ7d+6ga9eu6NGjh7NF4aQAiAgbNmzAunXrLN7fUdSoUQP37983CARqKXJZzb5XOIlCrN/w8HD4+PhI+fPnz8fDhw/RsWNHLFy4EL/99hvmzp2LRo0aqZYze7ZqE4jD4dgBW3R0AgDkk63n1eYpEAThGwDjALQkItVhJyJaSURViKiKaJiZlAjSqqnFWeiaOUOGDAgNDUWhQoUwZMgQBAcH21M8yxgyBFi1Srd+5gz7nTQJMKdOlRh+/BH4/ntgxgygUiWgUSOgWDGmnqU/CnnrFtCzJ5DUZsMSgDgj4evrq8gXO/IxMTHqs37iyLPY0ckn+4utWwekSsVmeMSI6EFBQHy8TWV3JmKD4vnz53j9+jX8/PycLBEnObN06VJ07doVO3bssGh/U8E5bY3oUjpC69HTWuS2HxcvXsTNmzdtIhfHkIsXL6rmDxs2zCC8hIuLC5o3by4ty3Hk88XhfOnYoqNzDUAxQRAKCYLgDqAjgH3yHQRBqAhgBVgnR81hWbKgR+PGAIBU+fNbtH/WrFkVKkuO1vtWJVUqoHdvICYGOHpUlx8SAnz9tW3P9fw5cP06W968Gdi3T32/oCDm7vqwVqOxd29mm3LggOG+Gg2bzUjC3Lp1C8uWLUNgYCA+ix0VPd6/Z3+DNGnSGKg/IjwcqFiRLROxujHW6WvfHrh6FciaFRgxgtV3Mnd2EBkZiT59+gAANBoN8ufPj/LlyztZKk5y5saNG4r1kiVL4uzZs/D09ATAGqpygoKCHCUaMmTIAAAJVmfq2LEjduzYATc3Nxw6dAiVKlWypXgcGfozNCVKlDDpda1r165o2LAhpk2bpsiPT0GDUhxOkoeIEp0A/A/AQwD+AMZp86aAdWwA4DhY6BkfbdpnrszKlStTUqMNa3bS6iFDTO4HgADQwoULpWUA5Ofn5yBJLSQ6mkh7TVLSaGxXfs6crEx3d8PzyFOlSrrlxYuJGjXSrd++rSsvJoblTZ5sOxltzMWLFxX33FTav3+/tEzTpxPdu8fuSePGhnX0ww/G669TJ/br5qbLi4lxdlVYzZ07d6hQoUI0c+ZMqV7Kli2rqyN9Bgwg2rOHLYvPxrRpjhWak+QJCAgw+O/FxsYSke5d/fr1a+rYsaNin/fv3ztEvpo1axIAcnd3T1Q5adKkMf5f4dgE/efo0aNHFh0nf9eL6cOHD3aWlsP5sgDgTSr9CZvY6BDRISIqTkRFiGiaNm8CEe3TLn9DRDmIqII2tbTFeR2Np/Y3Z6FCJvd7/fo1Xrx4AX31uyQxoyNHLUqzLXS89+9nxvOiwb1e8DQD5KOtgwcz2xORhQuBLVuAcuV0M1ATJyZeRjtx7949i/dt3749ADZKgN9+A0qWZDY48pk2kZ07ga++ArSzigo2b2a/smCIWLnScqGTCHPnzsXTp0+xUia7UXe/8fHAsmVMHRIARFUQvZFTDmfgwIEGeaL75nLlygEAcuXKhQYNGij2ad26Nby9ve0mFxGhUaNGkgfGy5cvJ6q8SNGuj2NzJk6ciBEjRsDFxQWdO3eW8i1VsVezv+rVq5fN5OOkLOLj45OGqUMKwaHOCJIz8fHxUkfnOzMuQHPlyoV8+fIhe/bsivwk19FRwxYytmwJNGsG5MqV+LJWrQI6dWJ2O1p956SMMVU1Y/sWBXBQnmnKucD9+zq1PXMBXwcPBq5cSTZe2m7cuCEZ9/r7+0v5riEhmAzARf8A0SudiNjRSaHOGTgJJ8RErLCzZ8/i5UvmNLRHjx6YNGmStO3ChQuoWrWq3eT6/PkzTp48CQBo27YtKorqqjbg4MGD5nfiWMyUKVMwf/58xMfHo1q1amjZko3VimqH5lDr6IghBzgcfUaMGIEsWbJY1Z7gGId3dCwkKCgIoslnqnTpLDqmYMGCivXff/896fvQt6U73zdvjG/TizeUUlAbVa1bt65i/euvv0ZOACUAWO1MVmzIz5nDbJtu3za+b40aQDLw7hMREYHKlSsb2FEAwLyAAEwAc9Wo+O/oNxJE5w1c952jh5ubGwDDGGcAa4Dm1brcd3Nzw0QHzhbLXV3LwxLYgubNmzvURXZKR3yGAGZ7u3v3bgQHByu8rJpCraPjrqZRwfkiiYuLUzgsWrt2LYBkMjieDOAdHQsJCgqSZnTg6WlqVwn9js6NGzfw6NEjm8plc8SOzv371s0GvHrFDOGfPFHm//CD+v6y2A8S1avrlseMMX2+q1eZt7bu3VmjX+TmTWDqVDYTtHixRaInmuBg4K+/gLg4FDh9GkXBOjFb0qWDC4C+ffvi8uXL8Pf3x4sXL3Du7Fm8AXAfwC5T5S5YwH5Hj2a/+fPrZnRSpQIyZ2YBYL/7zngZEyboVAiTKG9MdIhF9xgdAMSK3qRiY3XqegBw8CCg/TB8sTM6RMlm9s7RREdHw9PTEy9evLBo/zOiJ0o7I+/o2MM4feTIkTyujg0oVqwYYmVqwVmzZkWqVKkU8YvMkTdvXnTo0AHz5s3DCa1qNldN4gDAH3/8gTx58qBChQqS6rvowY93dGyEmuFOUkhJzRnBgAEDaFICDPanTp1Ku3btorZt2xIAOnPmjB2lTADr1xMVKaIzYj96lKh/f7Y8eLD542NiiJo10x3fsKHSUH7kSKJXr4iCgpT5Y8cSFSpEdO0aUcuWLG/WLKIWLYhmz2Zlm3JgoJ8ePya6cMEw/9Mn2zpY0Cc2lqhDB3auYcOIADoI0Cvt+YsD9N9//ymPCQxUvYan/frp1i9eZHIHBrJjXrxgy7dvM2cFERG68qKjiZYs0R27ZYth+adP268OEsjz58/p06dPNG3aNIWRboMGDXRG1frJz4+obVvjz0GqVKzwgACiDRuce4GO4uVL9j8DiEJDnS1NkuHRo0d08+ZNAkDNmjUjIp0xuTn8/f0JABUtWtQusmk0GoXzg4EDBya6TH9/f3rz5o3iv7R06VIbSPtlI69PAHTx4sVEl9mrVy/KkyePDaTjJCeCg4Pp5cuX9PDhQ9q/fz/9/PPPimdLbCtkyZKFAJC3t7eTJU5ewIgzAqd3aIylpNTRET96MwGK9/BIUBl+fn4EgLZu3Wpj6WxAdDTRr78aNhpr1TJ/7MmT6g3OBg3Yr9wLlrht2DBlQ71VK5av3zD991+dRzExLVhgXQdIXwY1wsKIgoMtrCw9atc2ON8d2fLhKVNII3a0Vq9m+Xv3qsp5ddEi3bq1+Pvrjg0LMyy/a1ei/PmJrl5N2HXaGI1GQwCoUqVKihf90KFDiYgorbs75bLk3so99Ilp+3aiYsVI6uimZDQa5bXre4EKC2Oe6aKi2L6PHxO9fescWR1IfHy84rn65ptviIho+vTpdOjQIYvKaNWqFZUrV84u8t2/f18h3/Xr121W9ujRo6VyFy1aZLNyjVKqFNHff9v/PA4kKCiIpkyZQtHR0VJdlitXjgDQs2fPEl3+r7/+Su7u7hQeHk4nTpywgcScpM779+8NOs36qU+fPvTu3Tupo8OfDesw1tHhqmsWINpdpAFAqVMnqIyc2iCjb5OiGpG7O9C/v2F+jhzmjzXmZUyMNSSf3r9/nzkVmD8fkAW5gxhoTaYHDQD46Sdg40Zl3rffmpdJH3mwTj8/4MULoG1bnUF7pUpMDcxagoKA8+cNskvJlptWrw7hxQsWQ6h3b5bZqpVqcTWGDGELZrz6qSLXAZfbkC1bxn537mTXPXUqC0JKZP05bIgYJFG0yykP4AcAQw8eBEJCcLl5c7w2V0jx4oDWvkJBu3aAqCL64IFD7Hb8/f0xbNgwx8fH0P//7d8PlC0LaOM0YfBg5pkudWpg3DigaFEgTx7HyugEbmmDO4uIQXnHjh2L70ypeor4+6N+YCD8/PxQrVo1o4EiE4roAAEAzp07Z9PYNwMGDJCW7WYHQsRie0VFAXfvAinMg9jvv/+OCRMmYLSoNgzgxIkTCAwMRIECBSwvKCZGVZ22ePHiiImJQbp06dCoUSNuT5VC8ff3R4cOHRARESF980yxatUqlC9fXrL94qprtoF3dCxA1KX2BCDIG+hWkCVLFgCODURnFfrGkpkzA69fsw/ajh1K18VyjDkvEANcyhvdJUqoewsTP/K5cxtuEwTmPWzJEmbDU7w4CyZqSWNFxNublbNwIVC+PFCgAGv4L1rEtouN4j/+ABo2VB5LxDzRtWvH7Ibk7ovPnjV/7iZNgIIFjXZu7gE4A+AbABqA2TmpdJ7MYsz7T//+QOXKgOgkISyMdTJLljQehNQB3LlyBXUBjAaQFSy41g4AhR4/Bn76CWW03qiMkioVs8dSMfJVULUqYCKgn61o164dFi5ciAcPHtj9XArkrtgBFjT29m1A68GO7tzRbVu9mv1+AQ4bjh07pliPs+RZv31b958YPhzDzp9HTQDXrl2T7CpsxdOnT6VlSz13WYrc6YKLi4G/QtsweTIbmErhnsMWaO0kFy1aBC8vL+k7bjEeHoDMHbVIuXLlUBTAL9r1GjVq4NHWrcCKFYmSl5O0+O6777Bt2zZcv37dpPdHOW/fvkWgdhD206dPePnypaqjHo4VqE3zJIWUlFTXDh8+TABoI0CaIkUSXE6aNGloxIgRtH37dmratKlOpSkpIAZcFFPNmkz9Z+dOtj5livpxaipv+fLp7FY2bTJ/7rg4ooTYLlmrwqafhg4l2r/fMF8bTJAOHGDro0Ypt9eqRbRwoSIvDqDZ1pz7m2/o4m+/GUxdJ4qqVYn++Yctr19PtGMHW5YHIJUHFTV2T+3Mo99+U9TF1ooVrbtvv/+uK+ztW6LMmU3vnyuX3a+pYMGCBIC2bdtGBw4csLk6kgHx8UTHjxu/5j/+IHr/nm4YC9abQrl48SJVr16dvv76a8X/qkaNGqYP3LWL1Yu7O3sflShBBNCP2uPr1atH2bNnt0mQx+joaBozZowk21sbqxLGxMRIZdtNdS1bNlZfR46oP1M9e1JE6dL08eNH+5zfzowfP17x/Pj7+1t+cPnyzG5V/k2Vc+QIaZ4/p4i0aYkAyiS++8X08aN97Uo5DiE2NlZ6fn799VdJ9VGeunbtSp06daL0AGWFoSrbX3/9RS4uLolvG3whgNvoJJwtW7YQANoHEFWsmOBycubMSYIgSA+xj4+PDaW0AfKXbZcuyvWffjLcv3lz9UZUaCjRDz+w5e3b7Sfv/v3MaL9JE925Dx+2vMEsOkHQTy9fsvLbtbO4rFfae7rNkv1lRqje3t6KF1uqVKno/v37tq0nfTsn+fU7Az05rhUvbvk9AwztUB48MH/M1q3MIcOdO3a5pBw5chh8pPr372+XcxGRusMJa9L790RTp7IOUwpC1G0HQK1bt5aWq1Spotzx5k2izp2ZfSIRUZ8+urp58ICodGkigIbp3dN9+/YlSj7ROUL69OmpaNGi9ObNm0SVZ4yMGTMSAPrzzz/tUj7lz08E0J2+faV6e/78OY0cOZICAgKkvJIlS9rn/HZk+/btint++PBh9R19fYmGDFH+h+LidM/R69e6ZSKiZ890jn4KFJC2FYWK45V58+x+nRzbcuzYMVq/fr20fvz4cYNvgjwVBejUqVP0zz//0AHtfc+gt88ff/whLcensHe1PTDW0eGqaxYQqlXPygyou0W2kDRp0oDdC8ZtUzFQnEmuXICXlzJvzRqlrnF8PHDggHKfGjWYTUSGDExlCEiYvYmlNG8O1KwJrF8PTJnC1OuaNrX8eH35RcaOBYYMAbZv1+UVLcrOVVYZ+SbGwwMAsEa7LoX3GjIEqF9fWe7IkexXpk6iHz9Do9Fgs9x1si0wFr37v/+A589tey4TLJg/H5d/+skgv8rDh8YPUvu/Zc2qXC9cGOjWjdlfXbigXk7Xrux+lC5tqbhWoRbYTaPRICwszHZ61kuWsOsbPBjo2FGXL3fLrsJWAAaKW23aAOPHA+fO2Ua2JIJcPWT48OGSykdcXBxw6hSzD5w4EahYkdn/XbrEdpbHQ6leHdCq/MlDPqcCUMTbW131Ly7OomDLF7TPZ1hYGAoXLizZbtqaDx8+AFC6sLYpWhXuKytXSlnFCxTAnDlz0L17dynv0b17rL4+frSPHHbgt99+U6wXLlxYfcc2bZj6s7+/Lo7X8eO67aKdnEjPnsDy5WxZ9t71AmCgED93rtVyc2yMvz9g6tskQ6PR4Ntvv0XXrl0BALGxsfjmm2+M7v8jgEcAqk2fjmLv3qGZNj8fmK1qBe36p0+fIL6Zdu0yGYyCYwq13k9SSElpRmfu3LlUXhxp0XrvSQh58+ZV9Nbnz59vOyFtwaVLRLduEUVGEk2YYDgKvGEDU+14+1Z9FP3ePV1Z8fFsxMsZGBvFTp064SPg166plu/q4kLZABK09zRUnCWaM4do+nRlGfv2sV/RfTYRffz4kaAd+RSfi8mTJ9u2PmbPNn5drVszNQlRXc+OjLG2zvfvJ/r8WVofAtA7QaDPERH05MkT4yeaNo3Iw4OpgHz7LTte7kI9PNzm1+bq6mowYpc6dWpyc3OjjBkzJrzg6GiiK1eI7t83Wk+aHj1M1uMggMoCFJc9u+4YQWDLFy7YrA6ciagGnDp1aqn+Hz58SKGhoVQNoNBcudTrZ/NmohMnmDqqyvbV2lHWOIAuiPlq6mDiPTCjcvTXX39J8g0ZMsQeVSGRPn16yYuhKjExTMX4/HnDbcuXs3c8EfNcKP/PaDSkqVPHoK6OAuQK0CFPTynPFyD6+We2Lve0mYTJlSuX4n8casxluzgTnSED+61SRVknovq26Km1fHnVZ2xrly5USj8/Z06HXS/HCOK9MENMTAz5+vpKz0urVq0IAGUE6CeA+gNURvY8HTlyhFbL7nVkvnzScoQsfyhA67XL2bXHRoSGUnT//vTp8mUHVEDyA1x1LeFMnz6dDooPYNasCS4na9asihfob7/9ZkMpbYxa4zhNGl2jsX59XX69es6WVsnff+tkky8XLqxbFt1fm0udOrF4QvIGTIsWRAAV0mvYbtu2jahjR3bc8uVMjeHSJV1ZL1+yDqBeYyg8PFyyAwNAU2xtO7Npk+lrLFqU/b5/b9vzypHr8qulUqV0y66uCrfQmtmzaRMMp/4fPnxo2bn1n2U7qK/py6af8uTJY70tRlyczgZOdMEupn//lZZfaeNY7StblgoBlAUgTdWqRACtdHWlVFoZLp4+TbNdXJTlHD3KGu4OaISuXbuW2rZtq7rt8+fPVK1aNTp37pzV5Yru/w8cOCCpbAGgd69fM7fxU6YYf+4mTjT5XF7QNlQM3oPnzjE7KLHDLW4zE8do0aJFknwLFy60+lqtIX/+/Kxepk9nz4/43J8+zTo34sALwGKdiURFsbxs2di6hwdRxoxE168TZcpksr78Tf3HbeCW2RGkS5dO8d/VhIez+F36VKpk2TckTx7m4l2r7meQli6lzlmzKvMyZCD67Tf2PMXFOb4S7MW7d0Tjx9tWZbZ2bdYesTXivRAJCGC2r3oMGzZMelYGAlRJu7xZ7z6L+xARvZa3nyxItbTH+syZQwQWq4+IiLp1Y/bUQUEszt6SJUQjRnyxNl68o5MIJk2aJPWsKUuWBJeTPn16xQu0T58+NpTSxixfbtmf8Mcfk+YHLCZGFzNGlHXxYt3yunW6IIvydOCArmE0ebL6Czk2lmZMmiTdx3Tp0tHvv//OdGhFJwWrVun2F0c/Tbzcw8LCpPLGjh1Lw4cPp6dPn9qmLq5fZ+fv2dP0vTx9mq62akW3//rLNuclYvY0N24YxD96VbmyYb2rNbq0TJLVt5guWzqqpR9X6MABm1xakyZNaOvWrYrRPLkNnn46duyYdScw1aj88IE5m5g8mS7+/jsRQHNkMYnOa/frAVCTJk0IABUsWJAy6ZUTJc5EDBigPPedO6xRYkOkhqPeR3jHjh20YcMGAkAFChSwulzR+YOYCgFUBaC4779n16Z1LGCX9PPP4sWxZOZdKNe5NzpTkBiOHmXON8LDpRhVk0XZTHX45E52ZDY2imvTS8+trSv5rHgSRf4elhqmX3/N5A8LY++OmTPZAISlHR1zqWNHGqrf0ZGnrl2dXS3qHDxIZK2dsajxkBDnQ8HB7NjNm5X58mfVVsTG6soND2edzWrV2PpXXym0V0QnAy6ye9atWze6o+ckZ6D4PH38aPUzMgrMWYHPjz8SAXQaIJo0SbdPhQpKR0NXrti2PpIJvKOTCMaOHUtLU6Vi1TV4cILLOXr0qMFL1OrGj6MQZwFKlCAaO1b3B9L3cJUcIrF/8w1TZ9JomCqQ2BjRaCSjY/r+e+Z8wELGjRsn3cMGDRroNsTHMw9Onz/r8kJDDQ3oVdB/Nvr27WuxPGZ59YoZYAOsIfTypemXa2LRaNgsgVjeuHGKF/6B/fspftIkOtSpE0W+fs3qy82NaM0ao0UuXLhQUT+nTp2yXB7ROYaYEqmqFxERYXC/6tSpQwEBAbRz506DbQAsDlQpYezedOig2G379u1UEKA2WpUJAPRCu29dgHr16mUgy3CtMfR7uVe2sWPZaP6GDWw9AZ0O05fDzp0vXz5q3rw5RUVFKfLlI57WsGfPHuXxtkgLFhC1aWOQ/0S/g960KdGgQbr1mzdNyjpixIgEX6dFiOpR169Tzpw5CWCBrqX7a+qaxf+En5/yWTOy/0Lt7xtZXiNT5Verxso/cIDo7l37XH8iuHDhgsGzGPfdd7Z5nhKb7MiKFStowYIF1h+YENlq1GDHqKlLmiImRueA5euvEy+HOfQ8g1KPHmxmTlwfOVLaVfTwWFC2/5utW1WDibcHdB3kfPl06u2id1sT6bI1z8v+/batj2QC7+gkgl9++YXWuboylQUb2DLcunWLvLy8pJeph4cHrVixwgaS2pAPH9iMRnAwW1+0iMjTk43gjBjBHp3ixZP/FOmdO0R9+1qlHvD69WuqXbu2dP+WLl1qE1EePnyo+Mj+8ssvNilX4t07dt+mTWPr6dKpviQjAJo4cSL5+fmRt7c3vXv3jnx8fOjVq1fGXaLrq+SNGaMst1AhadnqToqWtWvXKurHKg9YK1Yo5Tl7lqh9e4WKnDXo3ysA9N9//0nb1TzuzLPGk9Ldu8Y/YnqjoeJsiIeHh3Sujdp9U4PZfOnLkt9Y2b1727yBFR4eLsmonypXrmyQZy3//vuvdGxqcw0AUyPn48ax0WKAecwS1VABiilfnlwAGmGu/JMnTcrau3dvAkC+9rJfFDs63t5Up3ZtagzQIbEzmz69admXLGFlnDplUWPqN4AKFihAbrK8ArLl7WrHEemW9WduHzxwyvfk+vXrlD59eoUNbWqAduureVqT/PzYDJC+/dzq1cwOdtUq68ojYnUjH0CzEQn638m9yxER+fuz+6dm//junU41Vnw+z5617nxDhujO16aN/gWYfFeFP3tGdPu2decrW9bwHuh5Bz377bdEZ8/SAe1z00B//7p1Td/TypVZPQYGGmodiO8dV9eEPX8bN7LriIrS/aeuXUv+7TUz8I5OIhg8eDAdcnNjf1IbkSlTJpJ/3F1dXalUqVL0+PFjm50jqfLy5Uu6ePEiERH16tWLBg4caPaYn376icaMGWNv0SxCX4f7trUvURNkyJBBKrd79+42K1ciJET3svPxUX1J3tGevzpAdQBKmzatJNPKlSvVy61enXWEPTyY3YKRF3BVbTlXRbVCKxBH7gsVKkQAaKP4MreEuDiipUt1shQsyH7//ttqOYiITpw4QfoNdHGGgkjnRthsI16uzhgZyRoKGo3pj5hebJIlS5ZI5Ys2KqkBygddB0xfjizWNLASiXwWw5JkKatWraIXL17Q/PnzpWPz6cvv5cV+y5dn9SZXyV2+nDU6w8LUGwCPHrG4VMeOUdiTJwSABpirr507lWVoy33z5g2NGjWK/ve//1Hx4sUTUZtmkDUkg8TYQJam8uWJSpY0OYsjplNgnZpatWqRfBatTLFi0jK0+ymOlW0nQDfAtG0bW9+3jzXOjh+3Xx3p0bVrVwJAlQHKo5X7qNxmMCFJRH8WTbSDDA5mDWHxPWQu9elDtGwZW379mpVx/z7R06dstiMRJKij8+aNTrbHj3XLLVqw+0fEVCBFt+21arE8sbMguuz+/Nlw8Fh0iKQUUpf69VPfpiUkJERSa963bx89tfZdFh3NtED074GJMAhpAeqpn2/KDvj774levNCdU6NhKor16in3E9UmVVJ8zpzGy1+2jA3iAcyW8ORJtmyvuFpJBN7RSQR9+vShy25uNjV407fXEZNJLznJmJiYGHqmVRlzc3MjABQQECBdd4ECBQgA3TSi+mFXdQ8L0Wg0BupTACjchl682rdvTwDoq6++oiZNmtisXGMc7t5dejnekr0o5Q1haF/kAAu8GKc/+xUdrXzJ/u9/qi/f7rI6S0jn8NChQwSAypYtSwBo+fLl1l+wvlwqxqWWIJ9dKl26tMH2yMhI+v7772nWrFmKZ8XT05PGjx/PZsbOnmUyiLZGBQuyhrn+bI58FuK77wzONW3aNKn8b7/91uD5FD37yZOHuYYVwDws2oAuXbqovuuMpYiICDp27JjB7OHnz5+lZ098d9SoUYO6detGACgVoGvUuLuzUVlvb7ZetiwrZPdutm7loElcXBwBoH7m6mz1at1BLi5MZZKIFixYIF1fpUqVElyXRMQaycWKGdq8rF6tk+PAAUP1m0SmT7Jl0ctkgwYNCGCzO5QjBxUtWpTyA1RYdj87AfSrsXJv3CAaOJA91wCzfxA7WgcOsNFuO8xiyOncuTNl0MoTqpXZaIw1gMXSE5fv3mWzBVmyKPcREQdX/vc/dS+k/v5MQ2LqVDbDbMm9EGd0xfXChRN1/fJv66ZNm+jy5cumg5mHhzNbMPH88+apy6m1J5HSs2c6hww7d7LGffXq7FkWOzvi/xVgeT4+Oq92YhLtCUNDld8aLVWqVCEAFB0dzTwbitvj49l/JyjIdIWIKmrmAlHLUi+AHljzf5J3cvQJDGT2xaNHEz1/bjDDelQ7Sxjj6Wn4nIoeD2fOZAM1AFORW7eOLTugTeFMeEcnEXTv3p3uu7kZTpkmgho1apDaR36kTPczJTFo0CACQHfv3lW9bjG1bNmSPn/+TN26daN///2X4uPjSaPRKF7GzkJudA6AihQpQvttrAsbHh5OT58+pSZNmlA1UafdjqSRvSQB0HSVl3Jt7e8PYB2eR6VKscjfIm/fmnyp3/nlF0qnd58fiK5rreDIkSMkNm4B0KxZs6y/4H79lPIl0NBXblReu3Ztk/tGR0cbPOfBwcE6j2p//KHsLA4frpQxd26Dj7mc4cOHS+Xu2LGD9u/fL9XPHa2nrYYNGxrIYNEHOREfxidPntDKlSut7uiMGjWKANDWrVulssR3QM+ePSkmJoa2bt2qOCZdunS0XD56fu4cmy0Q1TWnTxcLYrN4CWg8A6A+anUkb2zNmSM/QLpnP//8syRr3bp1E1ynRMSMwAHmOp2IPTvTplneyJI/Y8bcbquko3rvCgBUrVo1xbtZHmxz8ODB0nIn/fJGjbJcVktsJz990gV6VuPePWZfKxuU0mg0tH37dvrf//5Hd2TnG2uqgXv0KJttKFaMaNgwXflXrzLvjteusVkWEVFFzcLgwU9atKDdlvwn5UGyjbwXLCEqKkpx//QHHFQx4ordIOmrbsnX161TDuisXctm8/78U5f39i0LVK5XrqZbN3r06JGyYw+w9Xv3CGCaCO9evaJfxXcsQBQURJq0aQ3rKy6OfRd8fFgnSNzfSs9oViVrOXFCOjby/Xt6ClBnaAesunbVlSvvcO/fb3he/cDJKQze0UkEHTt2pLcuLkS9etmszHfv3hnYHACgMWPGmB5NSabkyZOHANDMmTMNrlme6tSpQ6tXr1bkiV5NHN3R2bFjhyJyub6s00RbFzvQtm1bAkDX7OSpSIyyDDCPLqJHmKFmXtByP/8ST5+aPGaqdpYKADVu3JjSp0+foFmwoKAgSps2LZ04cYI8PDxomLyhYfmFE4kfOzHFx7ORRCvqunPnztI1fSs2OE2g/+w8e/ZM19GZNo05YdCvO3E0smlTox/I+Ph4RUfipBEbkffv31ObNm0oMDBQ2reV9n4HmPMedewY+1XxhmfAuXNEe/Yo/ueVZN7gLEmiqqS8Iyuf/R04cKDBMZs2bSJfUbUHYGozIsHBNtFNB0Df6tdNYKD6zEnbtop71rx5c0nWZvJBAjVu3mSjuGFhOvfV69Yxr5FEuvuRPj3R9u0WNawuAPT6p59YQ2jfPtbgz52bzZro24uozMheBygT2ExNfVm9nzhxgpo1a8Y67rJ6Algkd3H5O3l5Fy4wd8sWyC2lO3eYkxdvb9ahyZOHddJEo3Yxhg0Ru9fDhunqi4h1cgCiv/5i6mQFCii89Vksh7Xv49BQ1hC10IPhpUuXyAU6Rw8EUKzWcYjJJPLypc6uVkZERATdvHmToqOjpfZFYGCg5JERYJoJ4nJNgD4XL67uNMLSuvrqK+vusRVpCUBh+mqQ2lRG+zsXoEqy2EV/y/+na9YQHTrEHK+ITnr0U+PGRDNmKPKOpE9PMSbel//p5xUpQtSlC7N5fvaMqcJu2mTdM6QlP0ATChXS3gLdfzD+jz905zPj/t3AkUMKg3d0EkGbNm3osyCwUSgbc+bMGcqSJYviwa1evbrqvhqNhu7Jg3ImI0QPQP369SP9Roo16dOnT/RRz0ZBHwPVqgTw9OlTkjdK5LNKAGjLli127ZB+88030rk6duxo07KPHTtGAMjPz086h3hfzNogyFJwYCAr8M4dg20fZMuZZPW2Wq7akwjq169PABJm0xYRoRwFk9sUEbHR/u3bjToeiYiIUBj+57QguJ9o6Cw6IfH19VV3by5PGg1T8Xj/XimflsDAQEWATADkY4G7V/0ZlmYAXTJlqC52tPTtT9TQHmPufxwYGKgaZFWeevToQQEBAXT+/Hnat2+f6j4NwTrpt2/fls79dNs283ImAFGtdKi8bojYjJyJ+3hIO8OWAazx9ZM5zQDxWNFDldzdc2wsMzS24n9KADUH65SYZPZsZitDJM24iCPgs43co3cqDXip86BdbtCgAf1Ss6YkS+rUqSnAXGwveTLV0E+VSlln/v6sQ67/f2nenK3LHLBs7tGDFgHU25q6tPP3V4wJJe98NWzYUOl+Xy3Vq8cGbMT1P/5gdiZahy2tZB4Zp0yZoqriWrhwYWl5tLacd23b0hP5O1Y/CLZaMvN/MJtkz4o8afRV2CxIO2XLs4ztJ4tJpkjibLYsbzNAJ4yU86RFCxIA6uTiQiQGZK5Rw2bPRnR0NMVo7bHk9y1MHitQHAQxljJksG0MoyQG7+gkgtbih/7PP+1Svr5jAnE0TDSo69u3L7Vs2VIyur2SjHykR0dH05w5c6RRWnkDPjFJn/3799Ps2bPp5cuXBIDWrl2bKLk3bdoknevUqVN0/fp1ad3olL4NERvy8ufBVogj7L///rtU/m+//UaA0gbhpJmPyEeA+nXrptSr1t4bFyP3bZU8vlAiEDtmqVOnTnghorzyKf7gYJ274B072H4REWxkb84cos+fDRwNmG1Aks5G46uvviIAdO7cOYqQd7bEJHcQoS+rno3g8uXLDerX0thLz549M/xPGUsVKrDflSvZjI2pZ1F7jKn/rpeXFxGpxywxllxcXNTfA9r0/Nw5nbyyWVhbEhwcTH5+fjRP5iqdiJjthYn6+59W1u7a9TMlSpg+kX4ZMu9vBoFj9ZNo/C1L0TlykDtAbm5u9MlSL4Pakex/tWWMMXJfQkJCDA59//69NBMeGRlJMTEx1EdmVA2Aypp5t1ic0qY1rDPR8Fr+HypSJGHlV63KbIjEdVO2FTZAPgs2CyxeipeXF2ni4yly4UI2g2VM1hs31POJFPG9UqVKRY0AmqF3Lz0ByqZdniY7fjFAL5s2JWrUyPi5s2dnjm727VPGoDGXVq1Sqk+WKMFsFuX7jBxJdPgwvXj2jHxs9dxYkkR7SFneZkAXPB5KL4zXZWqbdPUqy69Z0y7Pify+3fb2ZvZQt26xjQ8esAC/pq7NRBiH5IxdOzoAmgJ4AOAxgDEq2z0AbNVuvwKgoLkyk1JHp6uok5pAD03mUPuAbNy4UfELgLJmzUoAaH0CDaidwcSJE1WvL7EpKiqKpk2bRhs2bCAiwzpMrMHvsmXLVM+rNoJpD+S679Y0YM0REhIilZkjRw5puWfPngSA0gAUX78+zciShSon8ANhatbuTxsNFgwdOlT3UUkoorGmPD14oDOMnj2b7SdX7Vm92sA+xJKZPVGVq06dOgSADhw4QGfU6k+jYU4Jxo5VFvDqlcITkUajUbjDLVq0KNWuXVsa8bOEixcvKq7D7L0VbYXWrmU2EWoxtLT7ppWVmwU64/WnT59S8KlTrJ5J979t27YteXt7U5o0aax6DxjIKNri2JHnz5/TKoCutWqleu1qyROgH7XLj/PmNV74rVuGx+vHgBKTWmNmwADlevv29OjRI6m++vTpQ+/evaOwsDDTFxkdTT209+34uHHkqlfvd+/epcmTJ1s8q91WNkoPgHLqy23MoN2IC3zFMymbzSOAGXGLy2PGsBlbcYTdTDomN6A/eVKn8igGPLah4xlj1KxZU1HXWbJkoZEjRxIAinj+XCmzvJNgRKXq/v37Rv83mWR5l2T3Z4W5utq8mei//3Tr+jG39Pc35j0sOFjpyjlfPsOAmnv3EhHR3bt36boF99BmqVkziomJoXMTJpC/1sh/M0DztdsXaOuqgqsr0ePHinqm2Fhm95MAW1RL0L+fBlosYnBgMVi5WurVi6nHpiDs1tEB4ALAH0BhAO4AfAGU0ttnIIDl2uWOALaaKzcpdXRGlSnDqurcObuUv2TJEmrdujVlz55denBFmxbRVkOe/vnnH7vIYQ/kKj7G0pUrV+jZs2fUsWNHKc/d3d3kMStWrJCW1Tyh5ciRw6hMjx8/phw5ckhe4NSQu6xVfCAcxJAhQxTnPZOQSNIq3Lp1S/W6fv31V8U1ii6cxZfiUIDuyYNLmkhiOVmzZpWe41SpUtGff/5ps2jwcuPuRGHqWgYOZCO4evl//fUXAaAbN25YPLsaEBBApUqVosOHDxMAWrVypcKLFQFM5cMMGo2G1q5dS48fP1bcv/ei21orkI8eA6CmAI014UJVSvLAefpo80WvW6I3q/e9erGZL734GwEBAVSpUiV6pbX/8b16lX4w8b8vK1vOoy9X795W10FCiImJoVSpUlGXLl3oxx9/1M2SmGhMF4ROLdS/YEH1go2NhDdurJ5fvbph3oMHShe1K1bQhw8fpDpr06YNAczu0Rxq9T9s2LAEqU830Mp6Xf4u1aYHAD18+FDpzUxMxgIbix0PeZJ5kDSZVqygh0a2LZo4Ubd+44byIhxoO3vy5EmpnuTfwgd6HhnPpElj9nr1NUYKAhSm3dZQ5X4ASpUvg+ThwRwFyD2v1aunvADRuyHAnGfExLCZ8eBgnYOBn35i+86dq9s3a1ZdGZ07EwF0cPRoIiK6du0aXdPut00ru3hcZYCumKmHtXrr27S/cUb2f752LbVo0UJ67xB0DgA66f0viEjxP7M3+v/LokWLUqpUqXTxGMXO/7p1poODazuRKQV7dnRqAjgiWx8LYKzePkcA1NQuuwL4CEAwVW5S6ujszp6d4gGD+BW25sGDBxZ5KGrXrl2yibdjrsMCKGdJRPsRgAXMTJcuHb148YI+fvxIJUuWNFuWPIkGsjExMRQkcyk5btw4AkBTp041kDc0NJRq1aplVMXOUeh76jpw4IBNyj148KDBNXXt2pUiIyNp3rx51KlTJyLSGU+Hjh1L9PPP9PLlSwoJCSH/DRuovpGXZkuAysvKXbJkieJ+2hLRi1+iy3VzM91QKFrUsDGk9XJmzeyJyPv37wkAZVY7lwWBZ318fFSfy4SqNjZr1kxRTpvWrXXyyEeIJ09Wr5/795nhPBEzttXmV9KW11gMFJsnD9tHHqNFDa0Dhrnff29wjS21x/2oIkd81aoJuv6Ekjt3bkmuChUqsEwTsw/VAZqcOjURQO/047GJNm6iNzVLUvbsRD//rMwT40ppNKzTdPCg1Dg/d+6cQX0GBATQhAkTFM9OYGAgbd26lWJiYlSfsyNHjiSovg4dOkS1AYX3RVHuTtAG//34kTW85LGOiJTX2Lw5s0MgUqqUAYYzO0ZS9I0bdEMlfza0DmbEPNERhBNQUy0FtGqy4jN/5QpVsuB6ATYzNxqGjfpftdtdZXkuAF02Vl6HDjoh4+PZe+HXXw093t27p3tO9Vm/nm0THUloNCyuDsDcI4sEB9NwreyxsbG0ceNGqTPzvewZitUuVzFRBz2hG8wR84pof39W2V9N9ToTmOMZcaBLv10guqB3RDtBDLT8zz//KORIkyaNbicxZp58cAkg+uUX5Xoy0hAyhz07Om0BrJatdwWwWG+f2wDyytb9AXiplNUXgDcA7/z589u/ViwhLo7OFy5MJ8WPmZ1Ri3lhLHl7e0tTljExMTRr1iz6bOeYA5bw77//0saNGyUD/g4dOtCdO3ekF0S1atWk+CIZM2ak6Oho6dgHDx4QoHQCIFK9enWL60Zehth5HDlyJE2ePJkmTZpEAPPedOHCBYXsly9fNlmmI5HPWm1KoKcWfVauXCmVKdpNbVMx3g4JCaHDYlA3GZGRkQSAflfx7lJCr642bNhA3t7edqk7uZ1MouyXbt0iKlPG0DGA/szGwYOKIHL/uLqyGV5/f8vPNXYsaTZuJDfAoIESlj49LZswweThZ8+epdq1ayvqeN68eTR//vyEXz+RVFbBggWpQYMGFOflxWaXRKP3BQuUAQLVkt5o/CmAJgH0WHTVmyMHU72TH6M2Qq61A4jZtYs+TZ5Moa9f0/Pnz+myGH/DWPr++0TVgbXI30Xp06dnmVWrMllkARTlo8jvRNevDRowlRHR1m/oUF0j0tQ1ytOPP7KgmvI8M3aJ+u+yxo0bEwDJFpRIN1O6YcMG1fdfYlVoZ8+eTWfOnKGvv/6a8gKUW1vunj17dDuFhuquiYh1OFau1NWXiFxFDVC6aG/XzmjdPb1zhyppn08x786OHdS7d29mc/TDD0SFChl1RuII4uLiyM3NjYoVK6ao/7Vr19L2Pn1oCFgj1x3MVtLUsyKA2Vipbdvj6koHAXonyxM7A2dV9g+oWtVAdX7GjBk0fvx45QV8+MCOMRZ78PlzxWqwn5/ynmsRr1t0ZiSq1zXW5ucCKI/MIcsMuXtqbSqp9wyL+QCbcQaYB8ruetvU0tGjR4mIaOHChQobSbm8gwcPTuBdt5yYmBiKjIyk3bt3K+Rzc3NTP0BeJ1u2GD4LKcTTb7Lo6MhTUprRISJdBGcHsGjRIoM/mBiYTS2dPHlS6kTMnDnTYXIaQ5Tr8+fPBICmy/TmDx8+LKnZaDQa1Y7Zrl27VA3+9Rt5AIvH8vXXXxutm1y5chndJqbo6GiaNm0adejQwah3J2d0dOSzOqNHj7aJyqLc7W9MTAytW7fOaucKcXFxpNEPEgqmEiGvq3379ilUrGyNqLKYELUtA/z9ldcTGqprvIuxB8Sga/opNpZo3Djz7pe1+z+XHeut/c1rQR3pP4sVKlRI0KySPp6engSA/ve//0llG3SsNRrLG+FqycvLIPAdiYbsfn66iPH6HpcmTDDrvpwAZoTvQPQdQYwYMYJC7t8n+vdfmjRpkiSXSaN7cbRbnEUzZezdqZNu+bvvmFvrmBjlPvLOggr6z0+2bNkIAJ09e5aeP39Ohw8fph49ehAAhRoxwJxBBJkLtGgF8hg7AGjXrl26jfHxzE5t3TrThUydqrx+ItZB//135sbXSF2KarnyRq+iExUX59DvvTHi4+Ppxo0binqaNm2a5P1PTFn0ri9Mb/0bE8+gRjvLqJZ6ADRBL97RYe05b8jU+oy+3/futVgLJq2rq3QOMZbgq1evDJ7ZC9p96sjyRE2PPOKssd51eOiVIebL80Q32/XAgt+qffuXLl2qGHiVxwR0FnK3+3JZgoKClCEcypZl/5ewMHWnFe/fs8C1Pj5O7eAnFnt2dFK86pqjUZvVmTp1quqfDwCNHz+eRo8eLb0InY0olxjvIrEjziK+vr7UsmVLhXrfhw8fJD1aeZLH3jGX3r17Jy3//fffim1Xr141eIk4Ev0Rm4Q4ooiLi6PFixfTy5cvqVChQiQIgk0ayOJL8qTWNiE7mGG+OFN05swZRdwWW7Nt2zYCmFvURCN34azVCafvv2frP/7I1vfuVW8UHDqka4wSsZgZ3t66sh89Mmo/kA+6UW3AtGMD+XMwVt9ZQSLw9/endevWKf5XzZs3N9zx+XM2sq5WB+ZS5sw6lRUxPX9OdOkSWxZjlJUqpdzHUpeyU6bYrD4srTP998jmzZvp06dPBIDOaeUSzMm9dq1l1/fggW5ZNgNOPj5Ep08zT1VmRmVv375Np0+fpu7duyvkPnDgAGXOnJkAKNwQy5OnXKXIBugP5tWpU4cuXrxoXSF//aWsIzkaDRu8KFqUPVNt2lB3gIbpXVeIaHeYhBGdEAGgAQMGGAzsuek9K9kS8v9USSOHD6fQ0FD6MX9+yR5mhOy8YrBsW7zfAdA86GI0xcfHqw5sntfKUUv73FapUkWyZyovqoQuX657J4O507527ZpOTm0S19++fUsXLlyQ1lu0aEH16tUzOLe+2qY9v23WIA/6CkAaIM2XL5/6AeKMaZ06zKkEYBjAN5liz46OK4AnAApB54ygtN4+g6B0RrDNXLlfckdHo9HQDz/8INm3rF692qgXMIB50hE7FYsWLXKq7GpR4JcvX27z84hlx8bGqnZ0du7cabS+9JN8FufPP/+UlrNqDSO3bNnitBeavsG4tTJoNBpatWpVosowRoyHBy0DqFeXLnRx+XJJ/UR8bn18fOyqtyxXM0y0ymZMDFGaNMzDmohoNDtxIls3FstCjC3RpQvz1CSqWRGZDYyof19MxYgS98mQIYNN3Y2LyO2evvnmG8W2nTt30uTJk9nKuXP07OxZihsxwvKGU4YMRH37smWxA3nrls4dcvHizMDZXDleXuy3QQNp9uduzZoO8YYlRz+uFsAGIZ48eUIA8144XesZ0OT1pEplevs//zBbBnnE9kQi2iiKSR64unTp0qrvyDJlytig1nTI3ffrv5eePHlCffr0MT8YI2vMkrbRbQq187Vv3JjNjiVhxJhuptKB8uVpDUDVtOu/lSxJv6k9T/Lgw6aSODtCRPnz5yeAqYnJz/n7778rvk8JJTQ01OB65M+o6EAD0KnT1dE736JFiwycC20oU4bmgQ1AEDFvdt27d6e/8uWjxXrPnKh9ArBO1ogRIwzkeKU3Y2+La7cV8rqTtxWNcvw4m23Tc25hq3eMs4Cd3Uv/D8BDMJW0cdq8KQBaapdTA9gO5l76KoDC5sr8kjs6Iv379yeAzYjI47jop3r16kkBwGw1e5JQxDg28rTOnApCAli0aBGV0Maj+OOPPwzOKZ/yt9aJgZjyyF72APN+4wz05bJU1UycDVJz92wLVq9eTQDoJ9F7jpZUqVIRAHqiNeZt1qwZbdmyxSbnlCP/0Lx9+9bm5UverkSVIG1HJx6g52KjHSBq2FD9Y2HC283zDBkoK2Bgd/bo0SNVUapWrSrt07BhQ9tfK5EUS0ls2Pr4+EgzTPLnRpwB/fHHH3X5liZBIDpyhC17eCjd41qS6tZlo/VJQKc8PDyc5syZI9XBkiVL6Pjx4wb/tWwAaYzNBppLYsdXVFNzcUm03OJMqJjmzp0rLbu5uam+C+/fv5/o88rZrw2iqvZeEkfTzXqaFGe5Gje26Jxq51OduUyidO7c2aLvVqFChWj37t0GKm0kvvfFZVOBPYsUkc5r6fcyoVoC+h4k5alEiRIUFRVFGTJkIAB0SOsgZdOkSWbL/emnnwgAHROdV2ipW7euwTMnv04ikmyIBw8eTI0aNSJAfbbdlt/TxGCs/ixCzfV3eDizs9KG70gugAcMTX6cOHGCANDp06dJo9FIRvSm0pQpU+jhw4c2/zBZir5OMQDavn27Xc8ZFxdH169fp/DwcOmcwcHB0rKoTmJtkqtEvXjxgj58+GDX6zCGmge4pk2bmj2uV69eiXsBmkF0bNCzZ09FvniOQAeMlIpG0w/sEa/g2jVmP6GdLTp74gTNBygHQLELFyas4apN57SBQ/WNvq9du0ZEbMZg27Zt9PnzZ9q7d6/V9z4hjB8/3uA5+V5r5C+uazQahW669DxZmv75R6euZi5pPbAp0rff2uXaE8rmzZsV90Xtv5YtWzalnZEYl00t6Qf8lKuprV7N1CATiTyujiVpwIABiT6nPuHh4VJMKXl68eKFtHxe9MplDI2GaNkykwFiX79+LdkqqF1bcuroGJsF009FixaVvOz1FGdAxUREdPasLiagmN+vH5s5/PVXtl6jhnTenTt3Sl6+KlasaPS81tpKxsbGUmhoqEJjQu15IGKzfO/fv2e2U3fuWFR+cHAwzZ8/36CDIv+fenh4SPnyb2NISAg1adKErly5Qp8/fzZqn/bXX39JKnzOpFatWqqDFBZx+rThe+jJE10MHl/fZGO3wzs6yRT9SNbmXnL68VAcjZpMe8wYydoS0WuQRqOh8+fP0+PHjxUj/z/88IOqjPIgkGLwzFKlSjlMblOcPn1aVWZzqM10AUytzxZcuXKFxMa6nPTp0xPA1ArtjTgyvHXrVrueR95xHjVqlE5dTUwVKljWeE+blgig6P79ac6cOQb61ceOHVO4DJWrbYipRYsWdrnGYcOGEQBpRk5MctWS0NBQaQBGtaMjN5pXS7GxSnWJVKmYRzb5Pt27M+cOUVFMfVAeMFOMVp5EUJvBkafRo0ez4JyixzkPD51HKrU0YYJhw9TGiJ4TjSXx/yuOog8aNMguchCZ/p5ZbbOj5e3bt/RG2/kBQJUrV5ZmrUTNh+TY0REDUhYpUsRkvZUoUYLu3btHAOtkP1m2jAigt2rfs86dieT319eXPXcqgZ3DwsIoKCiImjRpQgcPHjQIROrn52fV9YjfWVPJHojv1OHDh0sdKSKSPNwlV+RxfKyqP5mXSCnVr69c1xvMTKrwjk4KQf9BFiMmi6lbt252fUmYIiIiQvVl5cgAp/Hx8apT6KIssbGxCtnq1q1LL1++pJiYGKpRowbNmDFD6iCULl3aYXKb48qVKwYOFiIjI00eM23aNIN7sdSCWC3WoKYydufOHYfZip09e1a6NkuDdyaEUqVKSeeZNWsW0evXRLVr6z4EFy7obHPUkosLc3nbogVbFx0ekOF/OrXMXapaatOmjV2u8ePHj/TXX3/RkiVLDBow4rIxewFFw3zrVsP4Q+3aMa8+RDpbnJw52Xrv3sp9r15VCqbR6CJ9t2xpl2tPKGo2ifL0p7zBOH26LgilitdCOnCAqGNHu3d0iAyfOdHGEwD16tWL3r17Rxs3biTAvp48TdXd2bNnE1Wm/rtevB/nz5+nxYsXU3Lr6MTHx1O/fv0UDnLkaodiql27ttTozZAhAxGRdYGab9+2WDW0UqVK0nk3b95M06dPpxs3blBAQIDZY811cuzVhvHz86PixYsb2ENGREQ4RAvBXsjtYcUUFRVlyYGWDdJpbZ2IiGjTJqIZM+x3MQmEd3RSCPfv36fjx4/TnTt36OXLl5IHnSxZsjjkJWEKfbU18eNpzwaopcjrRC7jzZs3DfYVG3oFjUUwdxIlSpSQPl7m7vHu3bulEXp5MuXVKzly69Yt6dpmz55tt/PI61DqSMsj2T97plyXx+FZvVoXfHDwYJYnGvbrlW1J6tixo92uk4gUdicA824kLsvjIslTBYCKyp9HcWaiUCHW8dFn+3ad/cnz50rX3WrBkGfNYtvsOLuQUPSDa8pnjcWgxarIGxF167I8MXbRjBlEw4bZTWYAiqCnGo2GwsPDqW3btnTv3j0iYuqTW7ZssevMrNwuTD+JMUssAWABpsVlAKruiVevXk1EOvtFe82O2hvxeuSzKt27d6esWbPSlStXKD4+nipXrky7d++2qxxyR0AVKlSQlitVqkRHjhwhAPT69Wtp/xs3btDnz59V742YREcAzmjDJHfmz5+vGOyW171JxPdQo0ZEYrwvtSS2H8T1JOCGXQ7v6KRQ/Pz8qGbNmhQYGEjTp09XvDAs6s3bkMOHD0vnFg3RrRpJsiPyF6dcNUffUwsR0Z49e6SGQFJCjHExatQoSX65Y4JZs2aRt7e3YvZBnhI6QpqUiYuLo9atWxOgi79gD8Q6rFixov4GlkRbCnH9zBndshwxOOSsWQZlG0tivBMxKWKO2IEnT55QhgwZJG99Y8aMkc7du3dvk7JKfPrEvKy9e2f5iTt1IipQQGmXIrJzJ6s3PcPipIKvry/NmTOHIiIiaMiQIQQwb2YmEZ+Ply+VcVzs4FFPn6CgIPr06RN5eHg4vUEZEBBAd+7coXTp0imepX379tHz589ZEE8TyD3gEen+T/JZKjGdOnWKiIhOnTpFgH3V8uzJ1KlTKWfOnBQdHU2lS5emQ4cOOUWOPn36GH0XVKtWTVp+/vy5NFCXI0cOk+8Q0RGAs5/L5EpERIT0vxaT2Zmq169ZIjKcYZend++YSrG4fveu/S/ICnhH5wtAbhgLgGY4aGrx4MGDNGDAAIU3E2cZ7hujSJEi1KxZMyJiBqqinPo2UEQkqQYUkXmeSQp8/vyZfHx8JCcAgM5g15wKTVIIJGtP8ufPT926dbNb+fKRSgWnT7MAhbodWXr1iujgQRYkU44YzX3OHIOyxfhD+kneAPTx8bHbNaqhFstCnkR7Dps0TEzNNmo0BtHUkyrBwcE0ZswY816ofHwM1fQczLt371QHe5xB4cKFVZ+xkiVL0sePH2mV1vW7r6+vwvBd7mxGP+6Yfnr58iURsc7Rpk2bzKr/ckxz9OhRybbFmveEWsqUKRMBoIULF1KNGjUMXNxzLEffs+Jff/1l+cHTprFvVN26hh2dtm2V66tXJwkPmCK8o/MFcOjQIYOXx8mTJ+1+XrWXlqNnk6xFlFNNlUuMNjxq1CgnSGYeueMEgI2yq7n1lid7qnUlBapUqUJNmjSxW/liPYpeyEzsyJKxUfl375gBsMxzUP78+cnV1dWokbinp6e0/MaEhyl7oB+FXT/Nnz+fANCwYcOShPchTvJFbu+hn8RZ6tu3bxPAPIsRsQ6LXH1VDHyqn27cuEGP1VQiOTahe/fuVKBAAfr+++/NdmjEQR191WpxfZZstpuTMPTbglbNXK5ebbyjo5aSEMY6OqnASTEULlwYADB//nwp7/3793Y9Z2RkpEGev78/PDw87HrexHLq1Cn8/vvvEATBYFvu3Lnh7++PP//80wmSmadOnTooX768tL569Wo8ePDA5DGurq72Fsup5MuXD48ePbJL2ez9yZg1a5bpnefPB9q1A1IZebVmzw5s2ACUKiVlPX/+HLGxsfD09ESePHkAAKlSpUKVKlUAAMuWLZP2zZw5cwKvImFky5ZNkkck9v/t3Xd4U+XbB/DvQ9lDBMreKCoKyCgiigxRWTIUFRQRBAQVUEQZiqLieEFFUcGfooCKCCqoDBmyEaRA2UugrDIq0rJaVkfu9487aZI2bdMmadL0+7muczVn5smTk9NzP+skJiImJgYTJkzAkCFDICL45JNP8OCDD+Zo2ii4FC1aFABQvHjxNOv27t0LADh58iQAIDIyEgAwefJk1KtXL2W7c+fOpdn39ddfR8OGDXHDDTd4Pc2kvv32Wxw9ehSzZ8/G6dOnsWDBggy3Hz9+vNN9Sps2bVCjRg0AQOnSpX2Z1Dwh9W9oz5497u9s/R3C8X/Nrl1eSJX/MNAJIjfffDPOnDmDoUOHpixzdSPvTefPn0+zzBZwBbJWrVrhnXfeSXd9rVq1EBISkoMpcl/FihWxZs0ap2VLlizJcB/bP5FgFRoaisOHD+PLL7/0+rF///13APoPuHbt2hlvPHQo8PPP2X6vnTt34tixY7h27RrCw8MhIujdu3fK+pwuQChWrBgAOF1T8ufPjzJlymDYsGEB+xuh3KdAgQIAgDFjxqS7za5UN1wzZszI8Jjnzp3L8DpP3lWoUCGUK1cOHTp0wEsvvYQdO3bg2LFjaNWqFfbu3YvWrVsD0HsVQAPYI0eOYPny5Rg8eDC+++479OnTx4+fIDgULFjQaX7v3r2IjY11b+eaNfVv5872ZXXq2F87FuJZv8dAx0AnyISGhjrNuyrh8iZXgQ753nXXXec0/9FHH7nc7osvvsCff/6JLl265ESy/ObRRx8FkHnAl1UigocffhgAcPbsWa8e25XSpUujWrVqyJ8/v1MQ8dBDD/n8vV2pXr06AKB9+/aYPHky1q9f75d0UPBr1qwZAKBu3brpbrN69WoA+n/u3XffxaZNm9Js41ioc/3113szieSmfPny4eOPP0b9+vVRrVo1rFq1CnXq1ME333yDvn37onnz5gCAOnXqpHxfISEheOqpp1h44gWOrRAAbdkTGhoKi8WS+c533gkcPgz07Wtf5vid7NkDVKoEjB4N5JL/ByZ1hgSKsLAwiYiI8Hcyci3HkpGYmBiUKVPG6+9x9epVrFy5Eh07dkRoaChiYmLQs2dP/PDDD15/L0oro9q6n376CZs3b8Z7772XpnQnWFWuXBnt2rXD1KlTvXbMTZs2oWnTpgCA+fPno1OnTl47dlaICCwWS47fBCQmJuLIkSO46aabcvR9Ke+Ji4vDwYMH0bBhQ4wYMQI33HADnnvuuSwfp1WrVoiNjcWoUaPwxBNP+CClRIHNYrFg7NixePvtt52W7927F3Uca2cy8+CDwKlTwNatgO1+48oVoHBhL6bWe4wxW0QkLM1yBjrBy/FGeOfOnU5tmb2hWbNmCA8PBwBs2LABd955p1ePTxkbNWoUfv31V1SqVClNU7arV68GfD8pb6tfvz5q1aqV0tTMGxx/QxaLxedNQYnI7t5778WqVavc2rZfv36YOnUq2rdvj0WLFvk4ZUSB7++//8aYMWOwYsUKAMDUqVPR17GmJivWrgV++037oQao9AIdNl3LI3bs2OG1Y507dw7dunVLCXIANhHwh3HjxmH//v0um3rktSAHAMqUKeN+O+RsYJBDlLOuXr2a4fqnn3465fU999wDACgcoKXNRDntrrvuQrdu3VLmp0+fjlGjRqVp2uaWFi0COsjJCAOdPGLcuHG4ePGiV/oZfP/99/j111+dlqXuG0Q5wxiDDz/8EGPHjsWkSZNQuXJlLFu2zN/J8ovSpUv7rB9N9+7dfXJcIkrfmTNnUl4vX748zfpKlSqlvH7kkUfQrVs3fPzxxzmSNqLcwDaaIQCsW7cO48ePx5AhQ1JGMswLGOgEsUGDBqW83rNnD0qWLJkyZK0nXPUT8EUfIHJPkSJF8MYbb2DQoEE4ceIE7rvvPn8nyS98WaOT6bDSROR1hw4dSnltG3rdkWNz7GLFimHOnDlBP8IkUVbEx8cDsA/2Aeiw7I7zwY6BThCbNGlSmirKI0eOeHzcIUOGpFnGZj3kb7ZAx5v9DitVqoQ+ffqgWrVqXjsmEbln9uzZaNCgAbZt2+ZUeyMiOHr0aMpoi0TkWu/evfHGG2+k9NOxuXjxop9SlPOC+ymC5JKIZDswOXbsWMrrjRs34uDBg0hOTvZW0oiyrUyZMkhKSkJcXFya4bez48SJEzh16hRKlCjhhdQRUVY99thjeOyxxwAgZWhcW59E29Dn69evR3R0tH8SSBTgihcvjrFjxwIAIiIinFr1REVFoXr16lizZg1atGjhryT6HAOdPGjVqlVo0qRJtm7gtm/fDgD48ssvcccdd+COO+7wcuqIssfWfPLJJ5/E/PnzPT5e1apVAdgfmklE/pMvXz789ddfKQ+btLnrrrv8lCKi3KV8+fJO87Z+b9OmTQvqQIdN1/KAxMREp/k2bdpkaSz1b7/9FsuXL0dMTAxGjhyZcgyiQHLrrbcCABYsWODUidlTCQkJXjsWEWVf8+bNUbZsWX8ngyhXSt3PLS4uDkDwj1TIQCcPyJ8/P2655RanZSdPnnR7/6effhr3338/Ro0ahf379wMAm/NQwGnatGnKiHOunpieFY7NMUuWLOnRsYiIiPzNGIMqVaqkzO/ZswcAAx0KEv369QNgb44DAFeuXMnSMX777beU197oA0HkbY0aNQIA7Nu3z6Pj2GqEBg4ciOHDh3ucLiIiIn9z7G7w9ddfA9CRW4OZR4GOMaa0MWaZMeag9W8pF9s0MMZsMMbsMcbsNMbwgRR+8PLLL+Py5ct46KGHUpYVLVoUCxYswOXLl3H+/HmX+zk2e3N8RkmwlwBQ7lS6dGmUK1cOBw4c8Og4586dAwC0bNky6P8JEBFR3jB9+nRMmzYNxYsXT1k2btw4p4fvBhtPa3RGAVghIrUBrLDOp3YZwFMichuAdgAmGmOu9/B9KYuMMS5v2MaOHYtixYqhVKk0MSoApBsAcThpClTly5fHmTNncOrUqZSRmrIiMTERb775JgAOREBERMHjuuuuw9NPP42GDRs6Lf/222/9k6Ac4Gmg0wXAd9bX3wHomnoDETkgIgetr08B+A8AexMGiIiIiAzX++pJ80S+EhoaivDwcFSuXBkTJ07M8v5btmzBL7/8AoCBDhERBZ+KFSv6Owk5xtNAp7yI2Aaw/xdA+Yw2NsbcAaAggEMZbUe+k1GTM9sIHI0bN4YxBtdff31K526bW265BTNmzPBpGok8ERoain///ReADqWeVbZmawADHSIiCj6uAp06depg1ChXDbPsLly4gBkzZuCbb75x+fD4QJTpc3SMMcsBVHCxarTjjIiIMSbdR5IbYyoCmAGgt4i4bE9ijBkAYAAAPoncR1577TXEx8fjiy++SLPugQcewIYNG7B161YAekKnPpGnTJmCe+65J0fSSpQdoaGhKa+zMxQtAx0iIgpmrgKdf/75B//88w/GjRuH5ORk5MuXL003hWeffRazZ89GtWrVULRo0ZxKrkcyrdERkftEpK6LaR6A09YAxhbI/OfqGMaY6wD8AWC0iIRn8F5TRCRMRMI4Vr5vlCxZEpMnT3a5Ljw8HGPGjMl0f6JA1rhx45TXjsNEu8uxXxoDHSIiCjYVKriqv1AWiwUVKlTAvffem2bd0aNHAQBRUVFZeh6jP3nadG0+gN7W170BzEu9gTGmIIDfAHwvInM8fD/yksmTJ2PRokXYvn270/J33nnH5fZvvfUWWrZsmeap1ESB5sknn0x5nZ0Hh7JGh4iIgpntWYjdu6cdCPnkyZOIiYnB6tWr06xzrOGpX7++z9LnTZ4GOuMA3G+MOQjgPus8jDFhxphvrNs8BqAFgD7GmO3WqYGH70seev7559G+fXunh0elZ9++fXjzzTexevVqFCpUKAdSR5R9jufoxYsXs7RvcnIyFi5cmDLPQIeIiIJNly5dMGvWLPzwww+Ijo7GhAkTUtY5DlJlsVicRi91DHS6du2aI2n1lEeBjojEikgbEaltbeJ21ro8QkT6W1//ICIFRKSBw7TdC2knL7BF9RlhLQ7lNi+//DIA4NKlS1nab/Xq1QgP19a1N954Y65pg0xEROSukJAQ9OjRA/nz50eFChWc+rY+/PDDKa/btGmDwoULY+rUqahbt65TK6BKlSrlZJKzzdMaHcrlChYsiJ07d6Jy5cpOy5OSklJu8vjMHMptPvroIzz++OOIj493e593330XPXr0AAAcPHgQBw8eRL58vEQSEVFwq1u3rsvlq1evRmJiIvr37489e/bg8uXLKetKly6dU8nzCP+LE+rVq4f+/fs7LQsJCcHBgwexY8cOP6WKyDPFihVzu0ZHRPDGG28gJiYGAEd9JCKivKNBgwaoV69elvbJnz/TgZsDAgMdAgCMHj0aV65cwZIlS1Ka7lSqVCnXdDYjSi0rgY7jg3FDQ0NRsGBBXyWLiIgooOTLlw87d+50GnXU0f/93/+hQ4cOOZsoL8kd4Rj5XIECBVCgQAG0bdvW30kh8gpboHP+/HkUL148w9InW00OkLeeGE1ERGST3iNEChYsiGHDhmHRokUYOHBgrioEZ6BDREGpWLFiSE5ORqlSpdC3b19MnTo1zTYigu+//x5Vq1ZNWVauXLmcTCYREVHAuOGGG3Do0CGnZYUKFUKbNm1gsVhyXb9tNl0joqDkWEszbdo0ABrYjB8/HsePHwcALF68GH369MFzzz2Xsm358uVzNqFEREQBYvfu3SmPaShVqhQefvhhPPXUUwBy5+BUDHSIKCg988wzTvPbtm3D/v37MWrUKDz44IM4e/YsYmNjAQAHDhxI2S63jCRDRETkbYULF8bPP/+MMmXK4MSJE5g7d65bjyIJVAx0iCgo1alTB+vWrcPIkSMBAHv37sWVK1cAADt37kSZMmWQmJiYZr/cfEEnIiLyVOfOnRETExMUz5JjoENEQevuu+/GW2+9BQB48skncfr0aaf1rgKd6667LieSRkRERD7GQIeIglrhwoVTXr/22mtO6xwffmbDQIeIiCg4MNAhojxj9+7dTvOunhnAwQiIiIiCA4eXJqI8I3VTNceR2Ro2bIhevXrhoYceyulkERERkQ8w0CGiPOuLL75IeV2hQgW89NJLfkwNEREReRObrhERASnPDSAiIqLgwBodIsrTbrvtNnTt2hUDBgzwd1KIiIjIi1ijQ0RB73//+1+66xISEvDuu++iWrVqOZgiIiIi8jUGOkQU9J599llERka6XFenTp0cTg0RERHlBAY6RJQnlClTxuXyqVOn5nBKiIiIKCcw0CGiPKFkyZJO80OGDMHixYsRGhrqpxQRERGRL3EwAiLKE4wxTvOfffaZn1JCREREOYE1OkSU55w7d87fSSAiIiIf86hGxxhTGsBPAGoAOArgMRFxeQdhjLkOwF4Av4vIYE/el4goO7p3746yZcvi+uuv93dSiIiIyMc8bbo2CsAKERlnjBllnR+ZzrbvAFjr4fsREWXb7Nmz/Z0EIiIiyiGeNl3rAuA76+vvAHR1tZExpjGA8gD+9PD9iIiIiIiIMuVpoFNeRKKtr/+FBjNOjDH5AEwA8EpmBzPGDDDGRBhjIs6cOeNh0oiIiIiIKK/KtOmaMWY5gAouVo12nBERMcaIi+2eB7BIRE6kHvUoNRGZAmAKAISFhbk6FhERERERUaYyDXRE5L701hljThtjKopItDGmIoD/XGzWDMA9xpjnARQHUNAYEy8io7KdaiIiIiIiogx4OhjBfAC9AYyz/p2XegMR6Wl7bYzpAyCMQQ4REREREfmSEcl+CzFjTBkAPwOoBuAYdHjps8aYMADPikj/VNv3gQY6mQ4vbYw5Yz1moAgFEOPvRAQx5q/vMY99j3nse8xj32Me+x7z2LeYv74XaHlcXUTKpl7oUaCTlxhjIkQkzN/pCFbMX99jHvse89j3mMe+xzz2PeaxbzF/fS+35LGno64REREREREFHAY6REREREQUdBjouG+KvxMQ5Ji/vsc89j3mse8xj32Peex7zGPfYv76Xq7IY/bRISIiIiKioMMaHSIiIiIiCjp5PtAxxrQzxuw3xkQaY9I838cYU8gY85N1/UZjTA2Hda9al+83xrTN0YTnIm7k8TBjzF5jzE5jzApjTHWHdcnGmO3WaX7Opjz3cCOP+xhjzjjkZX+Hdb2NMQetU++cTXnu4UYef+KQvweMMecd1vE8zoQxZpox5j9jzO501htjzGfW/N9pjGnksI7nsBvcyOOe1rzdZYz52xhzu8O6o9bl240xETmX6tzFjTxuZYy54HA9GOOwLsNrDLmVv8Md8na39dpb2rqO57AbjDFVjTGrrPdle4wxL7rYJvdcj0Ukz04AQgAcAlALQEEAOwDcmmqb5wF8aX3dA8BP1te3WrcvBKCm9Tgh/v5MgTa5mcetARS1vn7OlsfW+Xh/f4ZAn9zM4z4AJrnYtzSAw9a/payvS/n7MwXa5E4ep9p+CIBpDvM8jzPP4xYAGgHYnc76DgAWAzAA7gSw0bqc57D38vguW94BaG/LY+v8UQCh/v4MgT65kcetACx0sTxL15i8OmWWv6m27QRgpcM8z2H38rgigEbW1yUAHHBxT5Frrsd5vUbnDgCRInJYRBIAzAbQJdU2XQB8Z309B0AbY4yxLp8tItdE5AiASOvxyFmmeSwiq0TksnU2HECVHE5jbufOeZyetgCWichZETkHYBmAdj5KZ26W1Tx+HMCsHElZkBCRtQDOZrBJFwDfiwoHcL0xpiJ4DrstszwWkb+teQjwWpwtbpzH6fHkOp5nZDF/eR3OBhGJFpGt1tdxAPYBqJxqs1xzPc7rgU5lAMcd5k8g7ZeZso2IJAG4AKCMm/tS1vOpH7SUwKawMSbCGBNujOnqg/QFA3fzuJu1inmOMaZqFvfN69zOJ2vTy5oAVjos5nnsufS+A57DvpH6WiwA/jTGbDHGDPBTmoJFM2PMDmPMYmPMbdZlPI+9yBhTFHqDPddhMc/hLDLaXaMhgI2pVuWa63F+f745kSNjzJMAwgC0dFhcXUROGmNqAVhpjNklIof8k8JcbQGAWSJyzRgzEFpLea+f0xSsegCYIyLJDst4HlOuYYxpDQ10mjssbm49h8sBWGaM+cdauk5ZsxV6PYg3xnQA8DuA2v5NUlDqBGC9iDjW/vAczgJjTHFooDhURC76Oz3ZlddrdE4CqOowX8W6zOU2xpj8AEoCiHVzX3Izn4wx9wEYDaCziFyzLReRk9a/hwGshpYskLNM81hEYh3y9RsAjd3dlwBkLZ96IFVzCZ7HXpHed8Bz2IuMMfWh14guIhJrW+5wDv8H4DewqXa2iMhFEYm3vl4EoIAxJhQ8j70to+swz+FMGGMKQIOcmSLyq4tNcs31OK8HOpsB1DbG1DTGFIT+MFKPiDQfgG3UiEegHdvEuryH0VHZakJLZDblULpzk0zz2BjTEMBX0CDnP4flpYwxhayvQwHcDWBvjqU893Anjys6zHaGtrkFgKUAHrDmdSkAD1iXkTN3rhUwxtwC7YC5wWEZz2PvmA/gKetoP3cCuCAi0eA57DXGmGoAfgXQS0QOOCwvZowpYXsNzWOXo15RxowxFaz9fGGMuQN6HxYLN68xlDljTEloy5B5Dst4DrvJen5OBbBPRD5OZ7Nccz3O003XRCTJGDMY+iWEQEdJ2mOMGQsgQkTmQ7/sGcaYSGgHuB7WffcYY36G3rAkARiUqqkKwe08/hBAcQC/WK//USLSGUAdAF8ZYyzQfwbjRIQ3iKm4mccvGGM6Q8/Vs9BR2CAiZ40x70D/yQLA2FRV/QS38xjQ68Nsa2GIDc9jNxhjZkFHpAo1xpwA8CaAAgAgIl8CWAQd6ScSwGUAT1vX8Rx2kxt5PAbaB/UL67U4SUTCAJQH8Jt1WX4AP4rIkhz/ALmAG3n8CIDnjDFJAK4A6GG9Xri8xvjhIwQ0N/IXAB4C8KeIXHLYleew++4G0AvALmPMduuy1wBUA3Lf9dg4/z8mIiIiIiLK/fJ60zUiIiIiIgpCDHSIiIiIiCjoMNAhIiIiIqKgw0CHiIiIiIiCDgMdIiIiIiIKOgx0iIiIiIgo6DDQISIiIiKioMNAh4iIiIiIgg4DHSIiIiIiCjpeCXSMMdOMMf8ZY3ans94YYz4zxkQaY3YaYxp5432JiIiIiIhc8VaNzrcA2mWwvj2A2tZpAID/eel9iYiIiIiI0vBKoCMiawGczWCTLgC+FxUO4HpjTEVvvDcREREREVFq+XPofSoDOO4wf8K6LNpxI2PMAGiND4oVK9b4lltuyaHkERERERFRbrRly5YYESmbenlOBTpuEZEpAKYAQFhYmERERPg5RUREREREFMiMMcdcLc+pUddOAqjqMF/FuoyIiIiIiMjrcirQmQ/gKevoa3cCuCAi0ZntRERERERElB1eabpmjJkFoBWAUGPMCQBvAigAACLyJYBFADoAiARwGcDT3nhfIiIiIiIiV7wS6IjI45msFwCDvPFeRERERESBJjExESdOnMDVq1f9nZSgVbhwYVSpUgUFChRwa/uAGoyAiIiIiCg3OnHiBEqUKIEaNWrAGOPv5AQdEUFsbCxOnDiBmjVrurVPTvXRISIiIiIKWlevXkWZMmUY5PiIMQZlypTJUo0ZAx0iIiIiIi9gkONbWc1fBjpERERERBR0GOgQEREREQWBkJAQNGjQIGUaN26cT99v6NChWLt2LQCgVatWuPnmm3H77bfj7rvvxv79+52W29L0yCOPAAD279+PVq1aoUGDBqhTpw4GDBgAANi1axf69OnjlfRxMAIiIiIioiBQpEgRbN++PcNtkpOTERISku68u/vFxsYiPDwcEydOTFk2c+ZMhIWFYcqUKRg+fDjmz5/vtNzRCy+8gJdeegldunQBoAEOANSrVw8nTpxAVFQUqlWrlmm6MsJAh4iIiIjIi4YOBTKJN7KsQQPAIabIkho1aqB79+5YtmwZRowYgVGjRjnNiwjef/99iAg6duyI8ePHAwCKFy+OgQMHYvny5Zg8eTKaN2+ecsy5c+eiXbt2Lt+vRYsWTgGQK9HR0ahSpUrKfL169VJed+rUCbNnz8aIESOy94Gt2HSNiIiIiCgIXLlyxanp2k8//ZSyrkyZMti6dSt69OjhNN+iRQuMHDkSK1euxPbt27F582b8/vvvAIBLly6hadOm2LFjh1OQAwDr169H48aNXaZjwYIFToFLz549U9I0fPhwAMBLL72Ee++9F+3bt8cnn3yC8+fPp2wfFhaGv/76y+P8YI0OEREREZEXZbfmxVMZNV3r3r27y/nNmzejVatWKFu2LAANStauXYuuXbsiJCQE3bp1c3m86OjolH1sevbsiSJFiqBGjRr4/PPPU5a7arr29NNPo23btliyZAnmzZuHr776Cjt27EChQoVQrlw5nDp1Kkuf3RXW6BARERERBblixYplOO9K4cKF0+2/U6RIkTTPtJk5cya2b9+O33//HVWrVs30+JUqVULfvn0xb9485M+fH7t37wagzyQqUqRIpvtnhoEOEREREVEedccdd2DNmjWIiYlBcnIyZs2ahZYtW2a6X506dRAZGZnt912yZAkSExMBAP/++y9iY2NRuXJlAMCBAwdQt27dbB/bhk3XiIiIiIiCgK2Pjk27du0yHWK6YsWKGDduHFq3bp0yGIFtJLSMdOzYEV999RX69++f6ba2Jm0AEBoaiuXLl+PPP//Eiy++iMKFCwMAPvzwQ1SoUAEAsGrVKnTs2DHT42bGiIjHB/GFsLAwiYiI8HcyiIiIiIgytW/fPtSpU8ffychRzZs3x8KFC3H99dd77ZjXrl1Dy5YtsW7dOuTPn7ZOxlU+G2O2iEhY6m3ZdI2IiIiIiLJswoQJiIqK8uoxo6KiMG7cOJdBTlax6RoREREREWVZ06ZNvX7M2rVro3bt2l45Fmt0iIiIiIgo6DDQISIiIiKioMNAh4iIiIiIgg4DHSIiIiIiCjoMdIiIiIiIgkBISAgaNGiQMmX2DB1PDR06FGvXrgUAtGrVCjfffDNuv/12NGnSBNu3b0/ZrkaNGoiJiXHa9/Tp03jwwQdx++2349Zbb0WHDh0AAGfOnEG7du28kj6OukYp4uOBkSOB1q2BUqWAxo2B668HkpOBfPkAY/ydQiIiIiJKT5EiRZwCDFeSk5MREhKS7ry7+8XGxiI8PBwTJ05MWTZz5kyEhYVh+vTpGD58OJYtW5bu8caMGYP7778fL774IgBg586dAICyZcuiYsWKWL9+Pe6+++5M05URBjqEuXOBI0eAo0eBL77QyeaGG4CoKKB2beCvv4DSpf2WTCIiIqLcYehQIJOAI8saNAAcgoqsqFGjBrp3745ly5ZhxIgRGDVqlNO8iOD999+HiKBjx44YP348AKB48eIYOHAgli9fjsmTJ6N58+Ypx5w7d266NS/NmjXDhx9+mGGaoqOj8cADD6TM169fP+V1165dMXPmTI8DHTZdy4POnQN27QJmztRamkceAYYPByZPBho1Au64w77toUNAYiKwdy9QpgywahWwZYv/0k5ERERErl25csWp6dpPP/2Usq5MmTLYunUrevTo4TTfokULjBw5EitXrsT27duxefNm/P777wCAS5cuoWnTptixY4dTkAMA69evR+PGjV2mY8mSJejatWuGaR00aBD69euH1q1b47333sOpU6dS1oWFheGvv/7KRg44Y41OHnP6NFChQvrrV68GSpTQ1zt3ArGxwP79wHPP6bJ779W/U6cCffv6NKlEREREuVM2a148lVHTte7du7uc37x5M1q1aoWyZcsCAHr27Im1a9eia9euCAkJQbdu3VweLzo6OmUfm549eyIhIQHx8fGZNqFr27YtDh8+jCVLlmDx4sVo2LAhdu/ejbJly6JcuXJOgU92sUYnjzh8GLjtNucg59lngX/+ASwWrbFJTrYHOQBQv77213n2WeDUKcDx99GvH/Ddd4BIzn0GIiIiIsqeYsWKZTjvSuHChdPtv1OkSBFcvXrVadnMmTNx+PBh9O7dG0OGDMn0+KVLl8YTTzyBGTNmoEmTJikDG1y9ehVFihTJdP/MMNDJAy5fBoYN02AGAJ56Smtq/vc/4OabtflanTo64EB6KlYEfvwR6NUL+PRTXdanD/DDDz5PPhERERH5yB133IE1a9YgJiYGycnJmDVrFlq2bJnpfnXq1EFkZGSa5cYYvPPOOwgPD8c///yT7v4rV67E5cuXAQBxcXE4dOgQqlWrBgA4cOAA6tatm81PZOeVQMcY084Ys98YE2mMGeVifR9jzBljzHbr1N8b70sZi4oCmjYFihUD5s0DSpYEIiO1JiY7gwrkywd8/z3wwgsa9AAaNFWvDvzxh3fTTkRERERZk7qPzqhRaW7L06hYsSLGjRuH1q1b4/bbb0fjxo3RpUuXTPfr2LEjVq9e7XJdkSJF8PLLLzsNSFC/fn1UqVIFVapUwbBhw7BlyxaEhYWhfv36aNasGfr3748mTZoAAFatWoWOHTu696EzYMTDtkfGmBAABwDcD+AEgM0AHheRvQ7b9AEQJiKD3T1uWFiYREREeJS2vOzMGWD0aODrr3U+JASIiNABO7xl0yagXTsd3KBoUeDsWaBQIe8dnyinvPKKDrTRqxewaBHw0kvA/fcD+dmLkYiI3LRv3z7UqVPH38nIUc2bN8fChQtx/fXXe/W4LVq0wLx581CqVKk061zlszFmi4iEpd7WGzU6dwCIFJHDIpIAYDaAzMNA8joRbVa2eDHQqZM9yImNBZKSvBvkADo627FjWrtz+bLWGhHlJosWafPNCROArVs1wFm2DOjQAShQAKhZE9i82d+pJCIiCkwTJkxAVFSUV4955swZDBs2zGWQk1XeCHQqAzjuMH/Cuiy1bsaYncaYOcaYqq4OZIwZYIyJMMZEnDlzxgtJy1u2bNFh2zt0ADZu1GW//OLbZ9+UKAE8+ihQq5YOVtCkCXD+vO/ej8gbEhOBtm2Bjh2BAweAIkU06BkxAmjRwr7d0aPOw60TERGRXdOmTZ2ef+MNZcuWzXRoanfl1GAECwDUEJH6AJYB+M7VRiIyRUTCRCQs9XB1lL7ly4EqVYDnn3defviwPiPH1/LnBzZsAG65RZvHdevGYIcC2+zZwJ9/alO1FSuAbduA9u2B8eO1RmfNGuftz53zTzqJiCh38bRLCGUsq/nrjUDnJADHGpoq1mUpRCRWRK5ZZ78B4PrpQuS2a9f0pmzOHO1LcPKkvYnNypXApUva7CanlCuno7q9846+f6lSwIULOff+RO5askQH0ShXDpg+XZ8NdfPN9vUFC2qtjsUC/PyzLitdWs9rIiKi9BQuXBixsbEMdnxERBAbG4vChQu7vY83utpuBlDbGFMTGuD0APCE4wbGmIoiEm2d7QxgnxfeN886d06DnPHjnZf36AF07qzPvvEHY4DXX9dBCT75BLj1VmDPHsDL/dOIsm3dOv2NlC4NLFigg3SkxxitER0wAJgyBfjgA6BhQ63BdHzeFBEREQBUqVIFJ06cALtf+E7hwoVRpUoVt7f3ONARkSRjzGAASwGEAJgmInuMMWMBRIjIfAAvGGM6A0gCcBZAH0/fN6/69199po2j33/Xh3vmZA1ORv7v/3SQgl9/1Rqe11/XGh4if0pO1sEGEhO1Vsc6gmWGjAG++gooX17P5dKlgRo1gCNHfJ5cIiLKZQoUKICagXIzRgC8MLy0r3B4aWf//gt89pkGETaffqo3XZ07+y1Z6bJYNPjas0fn//hDB0kg8pdu3TT4/vZboHfvrO174YKez7aBZRITOfQ0ERFRoPDl8NLkQ+vX60M6K1a0BzmffKJD4Q4ZEphBDqAPF12zxh7cDB7MUnDynyNHNMgZNUr752RVyZI6eIHtOVEjR3o3fUREROR9DHQC2LFjQPPm9tLnKlWAmBgdQrphQ21Wk6HkZODqVd3pyBHtcX3vvcCOHfb177yjQ075QJkyWpPz3nv69rVqMdgh/5g2Tf/27OnG7yYdN99sP38//hjYtcs7aSMiIiLfYNO1AHTtGtC0qT0e6dUL6N8fuOce601aQoI+zdB2x5aYCFy8COzcqdHRiRP6AJ2dO9N/k7JlAcfOciNHAqdPa1XM6NF6/DJlgKJFPf48qZv9tG6tDzW1lY6T94joV3/tmt7c79unQyk3aJB3B4U4e1ZPZUAfnJvRAATu2LVLz2cAOH5cCyCIiIjIf9JrusZAJwAtXAh06qSvX3tNa0QAAKdOAe+/D3z5pbZZa9IEuHIFmD/fHhVlJjRUa3jcUamSHjc0NMufwZVHH9XhsAFtCrR6NXDbbRpT+drJk8DatcBjjwHffacjbh0/riPVvfKKBgjnz+f+QRNGjtTRwVKzNb0qXhyoUyf7tRq5TWIi0LKlPudp6lSgb1/vHLd7dx16+qOPgJdf9s4xiYiIKHsY6OQSFy/qTWkIkrDx1Xlo1L02TNsHtLYlI23aAA8/rA8HOX5ca3a2btU7svvv1+CmaFEt2k9MBPbv18fBFygA/PefRiGjR2tw07Gj87FLlAAOHtShpzy0cKF2Bp87177s55/17b1h/37tu3T+vFZaFS+uyZ86Vdfff78+ENJRgwa6/dGj+vDI1q018Dl0CKhd2zvp8iUR7X8yfLi9aVX37sBPP6W/z8iRwLhxOZM+f/rhB60R/fJLYOBA7x67QQP9Sf31l+e1RERERJR9DHRygTNnNE4phKvY1Pg51N/ybdqNrrtORyP480/grruAuDjtPPDII95NzPr12kHIpmRJ7c/TqZMO9eahSpWA6Gj7/I03apz23HN6+L/+Aho1AooV04+4aZOOPNezp25/8SIwaZLGcnPn2gOSgwfde/9nn9VtV6xIu65oUa3ZOXlSuy81aODJJ/WtmBj96tes0Ti0cmUNcG68Ude//77mdUQEMHmy875PP601Esbk/pqs9HTvrg/6tLXK9KYJE7Q28LrrgBEjNJDyUuUnERERZQEDnQB36RLw1lvA/I/2Y3HBrqiV8I+uKFVK2yJFRuqdVE6Oz75hg9YMzZ1rb3MGABUqABs3AtWqZfvQkZEavKxeDXz9dfrb3XQTcOCAff6JJ7QGY9asjI+/eLFWVt13n86fPw888IDe8G/erEEUoC3ztm3TB0l26KBZ7Niyr3594IUXtNJr1KjAavJ18SIQFqYBW716mp+ZPSx4wwYNHNu31yHAAb1RX7NGjxFMNRM7d2qQ+tJLGpR429WrWvm5cqXO254ZRURERDmLgU4AS07Wm8x9+4B9hRvglqvW/jZvvw2MGePfxNlcvqxtwmzDVwEaMYSlOaey5NIlvRF9+mmtoMqKH3/Ufa5d0/gvPFwDlqFDtVUeAKxapbU+L7+s2508qaO/ZeTbb4FBg7Qvy5Yt9uWvvAK8+25gDKKQlKSf4/hxHS75s8+00s1dV69qEHn8uH3ZrbdqH5aXXvJ+7UdO27hR+2PFx2tQ7VaN1fr1GtmWKJGl97I9nwfQ97rhhqynl4iIiLKPgU6AOhsruCk0Fh9gBO4u/Q9uPrtBHzrz6aeBebe5YQPw5pva0eWuu/QO+9ZbdRzpbt08qvJISNB+Mb/+CkycqDUoZcroW02apKXl//4LPPOM1ly0auW1T5VGcrLeJEdEaJpS9+9YuDBtV6ac9OSTwMyZWku1dGn2jiECnDun/Za2brUvf+45rfUZOzZnKxC9JTkZuP127a/0++/6+ZxWHjoEVK0KfPONVmUlJGiEsm+fPrBq5Urglluy9J41a2ofr4YNnfOSiIiIfI+BTiBKTsaRxt1Qc8c8+7J77gFmzACqV/dfutzx2mv2J5jaLFmid96B1L7LS0aM0NZ7js8B2rlTa+Jy0ubNeopcu6Z9mfbty7y5WmaSk4HYWO3DM3as87r27TXYnDpVv9acGCHPU/PmAV27al+lxx5LtfKzz4AXX9QxoU+cSP8gjRppRP3ss26957Zt9uaQ/g6CiYiI8hoGOgEmIQH4uuUPGBTey77w2DEtac4NgYLFooHN1Kn2djuA3hz27q3jRgfhg1vOnNHmXX/+qd9hrVo6GMBbb9mby/nSkCFau9Whgw6T7e3O7z//rDVo336rzeNSq15da9oWLND+TzffrE214uLsrRg3b9av3h8j1iUn63DSR47ozyl/foeVp05pv7LkZJ1/6ilgyhT9Ehs31mHaP/1U2z4COorD0aNuR3dnz9prx1q10maTRERE5HsMdAJM3I0NUOLQDiQiPwogSZuBrV/v72Rlz+XLOqpA6mLshx7SgQxyQ+CWRUePAsOGAb/9pvNVquhzenzV1GvUKH3w58mTOvCdY2zpCwsX6kNqn3tOBzuYOdO9/R55xD5uRdWq2q/pzz+1VmjKFN/2b7p2TWtw5s/XwQeGDUu1QceOwKJF+nrkSH1AVUiI7hgSYo+Knn9eoyTbtrt3a+DuBsdnMT3xhJYDeFrjRkRERBljoBNANqy3oFlzHd7qWK3WKDxxHMo3r537x/hdskSrNjZutC/77jst9j91SgOfIHPwoHbqtxk2zD5ks7ckJzvXTBw+nLN9ZywWfT7RTTdpE62PPtKhwGNidNCGrOjWDfj+ex3C29scH5ZqsaT6DjZs0DZ/yck6AkOVKpkf8OGHNZK97jr9sG7W7MyfD3Tpoq8HDdL+ZiI6AEQWxzkgogycOwdMn65lFYMH67PEChQAPv/c3ykjCi7//af/t4sX1xFfx4/Xpu2OTyHxNwY6AWLXTkHBBnVws+zXBePG6R1aMLlyRe8yK1XS/0Q23nw0fYBxbPHkzYdTLl0K9OunNTmFCun7ePvBl564dg0oWFADi8hIrc2pXBno0wfYvl2buIWG6vN8bF1ibrxRR8Hr1Uufk+Qtt9yiAdlvv2kfnRR//w3ce68ONLBunSbQXePHa3VawYLaNs3NBG/bpgNG7N2rwU2FChoUJyVp5VFMjD4Oa8wY/V5PndJyjnXrtOlgQoL+hKZN0y5FQ4fqwBj33ef/MUpOn9bnfX3+udZsvv8+EBWlTRWDsPKWAsTBg/bHBmzbZm8sEBubdtu6dXUUyhYttDJ25UrtUlq8uDYx5nlKlLnNm/UaX7Ik0LZt2vWlS7v+/flLeoEORCQgp8aNG0uwOX82WUQLd0UAufbRZyLXrvk7Wb6zaJHT5xVAZMYMkcceEzl1yt+p87rPPhMpWFCkUCGRgQNFVqzw7HgzZtizrVUrkX//dWOny5dF2rUTGTFC5K+/RO68U2TVKhGLxbPEeOjKFT3Vf/5ZJF8+/UxNmnjn9LdYRBYv1mNOnJhq5T//6IrQUJFNm7J+8AsX7F/C9ddrXrrpwIG0pz8g8t57Ik8/ra9LlBCpWtWeJ5lN3buLvPuuyNWrWf8o2RUToz/XXbtEevdOP20NG4qULy9yww069esnEhWlx/Dz6Ue52N9/i5w8mf55V7++e78d29SmjV6HEhL8/cmIAltmv6V+/QLrdwQgQlzEE34PaNKbgjHQeb/RLylnSNzWA/5OTs5JStL/LK5+KZUri8TH+zuFXrN0qf2jFS4sEheXveMMHGg/zvLlmWyclCRy7JjIiy9mfFU6ciQgrkpHjzona+JEz26EP//cfqzIyFQrmzXTFZ9+mv03OH9e5JNP9Dh3352lXfftEwkJydqN2KBBIqNG2QMhV9sYo0HH4cPZ/1jusFhEatbMWvodp0KFRDp1EmnfnsEOZc3y5SJDhrg+r8aNE3nlFZHRo0WSk0V27xZ54gmRZ5/Vn+rKlSIbNojs2CFSsWL65+fXXwfEJZEooOzbJ9K8uf13UreuyIkTImvWiFy6pL+/AwF4C8tAx882LT0rV1FQTptyYomJ9Xdy/GPYMD3lypd3/m9Tr57Ixx/7O3Vec/q0VqjYPt7SpSIXL2a+36VLIrNnO2dNt26Z7GSxiDzwgOsAskABkV9+cV5+ww16xfLzXWdkpCandGl70mbMyNoxEhJEFiwQKVJE9x87NtUGthrFNCuy6c039XhZPFctFg3uEhOdT3nb67ffFgkP11qa//6z33glJenfOXNEoqP1pqx3b5FbbnH+Stet887HcxQTI3L77WlPq9dfF4mI0KwYMkQryRo3Ftm7V6dHHtEbzZdfFmndOu0pOXiwyPjxIl984fsgjXIvWw2tbbrlFj1vfvghe+fNsWNaE7p3r56XtvIP21S+vMjx497/HES5ycWLIl27Ov82Tp603i589ZW9mUqAllox0PGjqGMW+bLyWBFAYqbP93dy/C8mRuT550UWLnR9N9Wrl8iXX4ps2aJ3fb//rq8dHT7sXvTgJ0lJemNn+0itW2vTts2b0263ebPerHbp4pwNO3Zk8iYnTjjv0K6d1urExemdcXS0/U1eeSVtBBUANWnJydq6zpYsW1OnzMTFiVSrpvvcfrsGCE5sEWOpUt47T86dsyc0m3fp//4rcvasvr58WeTMmawf4/RpkcmTRaZOtSenf3+tePLUsmV6M/nkk/Zjjx4tsn9/2nM3MwkJImPGiMydm/Ynbgt8Zs+2B3REK1dq0zLbOdK+vZZXeNuFCyKrVzvXBgMaYBHlRVFR9t9B1VJxEtXkIYn5ba22WXZsh/3cc9oU/IsvRGIDq9CegY6fnIm6LGdNKftJEsx9crIjKUn/67Rv7/puyHG67TaRvn3tEUStWiI//igya5b+/ftv/UGuW6clDuvWaW3H6dMie/Zo54jkZL0jTE7WH/D48XqX/OqrIgcPOqctOdmjj3blipbGp/4YNWtqEBMervfhqdf/9ZcbTd6SkkQefFB3KFtWP19mLBbn4prSpTW6OnrUo8/pqeRk/QptyWrYUFuI7d2bdtt160Seesq+baFC2iLPyerVvrtzWbnSHlReuODdY2fDpEnOtWILF2bvOElJWqLteB527KjBmDesX69NFJcv1xjxt9/s73P33RpMvfKK1nolJOhXePq0ft+rV2tTCltNl+1nuXWrbjN2bPabiPrKtWt6WbFYRH79VQPTAC6X8bsnn9QaQlvtbLFiWjCUEwXHJ09qDaTtfOzd2+NLP7khQCsF8pT4eJEBA7SRgjaTtsjact3EUrBg5vdjgMgdd/j7IzhhoOMHO3eKTMZzKSeFZcBAfycpcCUlaUPrY8f0pn3uXJEaNezF9o5TsWKZ/wBbtbK/7tXL/rprV+0847jt44/r3xo1dNq2TdvolCyZ9WJsFy5dEmnbNvMkf/CBm/9g160TyZ9fd+rcOXv/McaN07zNl0/kmWcCoqH6vHkiRYva86NyZf0qHn5YS/5//dU5v1591cVBHNuHuYqUvOGLLzTfXn/dN8fPhocesn/se+7RAMBdf/+d9lx87z3f34gMGKDvVaaM699D6v5Jt9yi6QLsv6cCBezrJ0/WNHujZiu7oqNFXntNpFIl15+pUycXgXkeduWKXvJt+VOpkpYseyvAzopDh5y/t1Gj9H94bnHmjDYJ/v57/wZqv/0mUr26SMuWIj16aPfINWu0YcbgwdqEcORIkeuu0wDzqad0m6NHWbub0376X6yUxWlZivtlA5qmf3PSq5fIXXfZ5594wv46MdHfHyMFA50cdumSSNPrdcSnP0s+krPDJAWbpCSRd94RGTpUr5i2q3itWiJNm+qdsDulD9mdxozRu7KvvnLRRiprwsO1VZ7t0NOnZ/HG7OuvndO2Zo1H6UlpN9asWUCcozt2ZPxVlCqlfZ7++SedA3z8sW5YsqRv79SbNdMaxgD5z2yxOFdkAdpccs4cvfFInRWJidrZdNiwtLWKV67kbNrj47U84ZFHXH/nVavqgHeZ/UwLFtTaoQIF/NNR1rGJXr58IrVr2+e7d3det317zqcv0HzzjQ6qYcuXYsX0XHVLQoKe1BaLvT1oYqL9f8Px41rVl8WIyWIReeEF5/Pqzz+zdAi/+PZb17+J9u1Fpk3TfxMTJnj/kmixaEv0y5e13MdVd9GsTgMHirzxhkifPlqA46vr0cmT2uXks880b44fF5k5U/8///OPc17t3q1NhYPFu++K3FgqJv0voWRJjUKnT3cuBI2N1ZYMFovIkiUBV2rDQCcHnTx0Rb4s+7rsw82SlL+QnNufheJVcl9Cgr004cIFfb1xo1YDLF2qr0eO1NqPOXNE1q7V4qMjR7Q6IC7OfgfSrp3WcIwfb/+xO96pOE59+2oxuAd9XCIjNYlu27dPh+OypWHZsqwV26cnPFzb29qKUz/4wO8371euiLz0kkjx4hpLvPOO/WNnOGDBpk1aW3f//b4vZbK1tfv8c9++TxZduiRSrlzaU/aGG0RuuklrFCZNcr4hKVZM5H//E/nwQ5E//vBv+vfs0e5jMTE6qMFbb9lvOL74QuSjj/RGpFEjbSpepYrI++/rdqk/86xZOZPmbduca5aKFNHuc6lFRdnTaYz+nAMkTk5hy+vkZC3T2btXxzN5+GF7N8lz5/SykV1Hj6Y9R/v1c2PH//7TDIyOtrfXvOce+0GKFtWTuVo1LQQD9GTPBsfueHffrc0tA22wgt27RVq00Oa7WQ0mOnXSJtKXLmX9fS9c0HM5MtK5kN9xqltXm6Lef7/+Xlu21HvnVat0NLzdu/W6PmqUyA+fxshXX1qc+rQ6Tvfco/tPmmRvYHHxoue/nTvucC+vatSwv/ZnbbE3bN0qMqrXCfkQL6fN5Pnztcp8925/JzPbGOjkAItFSzY+rPWF/QQaM8bfyaKMuCriSkjQIuGYGC3a2bBBg6RHH017FWzUSGuafFl7YLGIhIXZ39MXxdVDh9qP37u39kgPMBlmcVycyK236p1vdnr4Zycxd96pHa5ysoNIcrI9I44d07uOI0f0DmDRIpGLFyUmRrufrVwp0qFDxrUhZcrkruY56UlK0gEZUo/01q2bBnW+GuFtwgTn93vjjczLH1atErn5Zvs+vuhsnxUWi55WBw+KVKigP6GSJV2fL7YWs4AWRGzfbi/wzej3eeWKTlFRzuU1btVs/f23Rhv33Zf1O3pAO/01a6Yjaly+rKXSbnSYsnXHc5wCwZUrWh7lmK7HH9dCinXrdP327dpUzDZQZEZTt27uP2LMsS+l49SkiTYn3b5drz1OYmNF1q2TazN/0cLDUaP07/XX6zXbdmJt3SoJn0yST+v8T56sskqOlguTPiV/FUCkIk5KYVyWgrjq1Cfx8GEN1jZt0vNv2zatFfz7by1s+OADvb5FRuqyH37QoNrx36njdNNNaVu2u5oGDcpekOhP1/47LzdjnxzAjc4fZulS+8BFuRwDnRzwwQcid+JvOY2ymrW9etmHWKLgcOWKBq8NGjhfLJ59VosqfRHwrF1rvwp/9pn3j2+zc6dz0XRuKtmxPUMoJ9uZ2HrUt2mTM43iHZ9FNXiw6//APXpoUe306SkB33//aW3N4sVaOv/qq1qj8Mwzmbyf47ls6/zyf/+n6fj6azefYJvztmzRn6hjtjRt6v33OXTIfvxy5bQC2V1Xr+pDY6tU0b5I27alHQslp6T3rBrbVLq0NtupUsX1+sKFtT9d/vwa+G3cqBXOgBYSf/hh2n3at89kwKbkZI2g3ngj/YR9+qn+j50yRe/qH39c28P17Zv5nartWpHJ7zYmxnk495gY7+Z9Vq1bZ78RL1lS/x1kNg7Nnj0afG/dqoHmnj2us6N4cS23e+cdbYa2eLE+g2j7du2S0amT8/Y33KDnrUsWixa+pNemzs0pqVARmVBolNOyGjjsySFTpnz59LyOj9dp1ix70L9liwZsx49rbdK774rUqeP6OBUraq1VoFqxQiT8qclOiX4Pr+rv5dw5fyfPqxjo+NjlyyI18tuHLUpYsMTfSSJfa9ZMJ8e6bUBrfrwV8Nju2EJCcqbe/JtvnD/LRx8FXvsaR+fP29Patm3Ov7+tT9Ds2b45fnKy1ty4M5pF6qlsWd1/6VL7A3pWrtQhwTLrj2V7QGrHjvbBOlJPFSpox5revfXO1t93gal8+62OYn/TTZrc4cO9F4869sdp2TL7rUgPHrQfp2jRnO8ml5Rkf//QUC3x3rnT/tynqCjne6GFC3Xdrl3Zv8Fs08bFzbnFoud5ixbanLh+ffsOd97pHOS/+WbG1WAXL2pEP3q03o2LaFBUoYJGbY7t5sqVy7TfZXi4VgoBWgPqabfI7Dh/3nmkwgEDPBvA9ehRreHYuFH/hbk7yBag/WZsXaRSXL1qr0apUCHzg8yYoaMmGKPNyVM/W8E2uWiXd6nAdbJ6QoTcdd0uaYzNWQp8bKdVdsoLr17VPjxTpqR9PB2g53RCQgCNAHn1qmz7ZJXMQnenhJ69s73fClV8jYGOD1ksIh+F/l/KiZTw8GP+ThLlhORknc6eFfnpJ+eR3lau9PzYn35qP15O9QWJi9Mi3kaN0l7JR4507xgWi95c2/4TJydrfni7V+natc5jK/tjuOdr1+zv73ZPajfNm+ec/yVK2Jt6ANoGo0wZkXvv1fF5mzRx/26lWDEtpixXTpstNmmiI/jdfbf7x3B1UxIVpcHxoEHezQsPON4gLvFC+dP27fbjzZ3rxg6ZFHo4li24HEnQR+bPt7/va69lEASmTn9yssjly5KUpKXgFYtdkE6NTkjnzlohXLOm69NjyJB0WpWeO5d+f0hAIw2LRftPpn6eWnbNnm2vGrnrLq3ejI3NsFBnxQrdpWNH7yTBXf/9Z28ymD+/lkF4u+zp3DmtoF28WIOfiRO1xqNePS1jmT5dm2q5fN/U1ae2qVs37fd5330aWT39tAY3jp1TL12yt/mfMkWrSUV0lJS339bAafFifQJ3jRo6goqr9xo7Vv5+ZY4cm7JEvvvO/lvauFELIbL972fhQj3vHnvMPhrBnj0iV67IsGcuyovPJ8jChWmT89JLfhzbZ/BgiXnzM9ldoY1Too7d21vk1KmAeH6erzDQ8RFLXLxE1WqZcjJZGjTgIPx5VUKC3uQVKqTFo0OH6jDOWRUVZW8nUL++f4qIkpP1P0TqmoQWLfTGYOBAHc83Olrr99es0RLU1LVbffrYXzdooPny5Zc6+EOGIwu4oW5d+7Fzol9OeiZO1DTcdJP9H3V2HT0q0rNn2kb4TzzhulG4401oYqJ+B8uXux4zOqvT0qX6njEx9qZEInrHEBenwf2pUzq6lav9A+SZYVevaqm1LVlFimgznuxwrMkYMcLFBm++qUXejz+u+dKmjd7srVqV4XGjo+3H9XBgR7esWeP8VaW5xMyZo7V1LVpoqXuZMtqxaPhw++AlX38tMnmyWGydwPbts+9/4oTInj2SmKinkFNfnJgYPb/ffFPvCG1VboAOsffYY/bfka8LL55/3jkj8uXTtlvpsDXzs41H40uXL2tZhq3fme35ayni47VzyoULWhW3caO9VvX0aX14VZZGvHGWYTAVGanXccchRG3TRx/57nEF8fH2DmTpBcd33y1xpy7a78NsN/bR0XqN3LVLn7v34otaSHn+vF5Hd+zQdmjDh6cd3TS9qVUrkaVL5bHH0q7q1Mk3WZDGhQtiib8knw/YKfM7TUmTkDWhD8myGdF54rlFPg10ALQDsB9AJIBRLtYXAvCTdf1GADUyO2ZuCHSSYs7JltL2qDnqyz/4FCzSwQIcm0ecOuX+vo6PJwYCo5NgdLTWGDz+uH00I8deyZ5M2S0UOHFC9y9aNDDG6l2wQIt7K1XyrMTM8YGugGdDh1ks+o88MlKPu3u33oxeu6ZB6uuv20cdrFZN/2k/9ZQub9o0a99NfHzaRuyDB+tAHgHCsXVMw4aaBVmJxRxH4gJS3ctt2mQfqj29af36DI+/ZIluVr26Bge+ulfctcs+QMXgwQ7xyfnzOoTds89m77ecL59e90aOtC+rWVM7uXTsqOeYrQ2Yq+mGG0QiInzzodNz7Jjrzkdbtui6VL+B1MPfe+Exa+l6/33n8zXl1uLCBT05bL9dxzG6AW3mnPoakpDgnWogi0WHaHQ11Nv48TnzVNyzZ+0/3Pj49Ju9eXuqXVvb+N11V5oh2ywPtJUrLe6X6U0my7TOv0kDbBVAW7FHRtqT7WlDjzQuXRJL4cJyrVDxlLTsxS2ytqgWUB6es8Uvz6TyF58FOgBCABwCUAtAQQA7ANyaapvnAXxpfd0DwE+ZHTegAp24OElcudZpUdKp0xJVr33KyRX5s5eq1Ck4HD+ud1K2i+GqVVoFn96dVVycc7EzoMWGgcjWY3PRIntzqUaNtLhz+HD9ZxcZqe16qlfXdbYSshtTjfhim4wRKV8+4zFc//lHOxA7luQFUi/Q33/XNGWn2db27VpFYLs57N3bd/1+fOnwYa1Rsn0/112nTVsCYPRJx2fJ2qa77tIWkOk5dUqb7QwaZH+A6QcfpNrohx/SHrhQIX0ORf/+9vMC0OGR07nhdKzVsU3elpCgpxaglTYiopHOlClpmy326KE1uh98oDW4K1Zo7WXr1vqZNm2y1/jOmZO9wo9PP9XfsD8fOmh7GExCgn7hqYfdOnnSqfBi3z69VAFa0eXt2CwhwXm0tO6PWeT090v0Tjm9ZmKVK2ttWHr5XLastg6oW1dr3f/4I+u1z5s2aQ1l6mN37pzzD99KbetWbep78mTaByFld7rnHq1hX7lSj+soKUl/MwMHOg9x7jB9ghdlJP5PihZOdqq09Erjg0OHRO65Ry60dB4hYjvqS6sbogLh+d9+4ctApxmApQ7zrwJ4NdU2SwE0s77ODyAGgMnouIES6CQnWeRoidtEALm075icPXlZ9lZo5XRyJf2+wN/JpEDVr1/ai+D779uf8Oj4kBjbVLJkcDZ/vHhRP/eZM/q6bFnX/2B++klLB8+c0WF9nnoq7ShjNWrocE4B9FRmuXzZnj53gxSLRW+uHG8Sc8MTCjOzcqWW/DqO4udpsz4vOH1as7dWLedWlh9/bB9k0NZlICEhbcH1Sy85HGzTJr3RsXW+njVLa3Ojo9P+fh1HnypSJN3RC44dc06XN5/Hl5Bg7z/TubN1oWNHndSTO+37kpLsv8G4OA3Wb7hBz+ktW0R+/VXzY8cOzdidOzVzA7nlw6JFIrff7pwXrVtrkyyHdG/bpj/b0qW9d2rv3+/cPXLMGLEPYedqGjpUq3ts7R0PH9aDxMRoAGlrVpvRNG2a1uYlJuo5bGsueOKEnh+VK2uTrtT7DRwYuGPTWyz6G7P1Fx07Vudnz9a+WL//bh+q/8IFDWQ/+kjP+ZiYrNV+xcVps+zGjbVGP1U+LQp5UKJQRaJQRZbjXmlUdF/a51BlVrUcHq6fITxckrfvlCN32NvKbUWDlNfTp/tuKP3cwJeBziMAvnGY7wVgUqptdgOo4jB/CECoi2MNABABIKJatWq+zxV3JCfLjlpd05y88aaYfIN+sntTLhtMnXKWxaJDvLhTgjR9elB3FEzDYtGSwEmTtFQ8dY2Wq+m227R5X6DeKO3bZ69xeughvUlxxXYT4fjAkief1AECAvWzZcePP9r7UpUsqUXVFStqFUmG4wu7yRYo2uza5fY4z/HxaZ+7k9G0YoV1x7g4LcZ3vKn59NPM3zA5WTu9A3ojnU6z1IsX9VJgO/QLL7j1cTKUkOD8UNU531/SAgTHB/p07apvfvWq1j7ndTt2pC2tTzWe8o8/2ld50uXQcYALQFufRR1NluT7H3BecdNN+mUeOaIFZe5o3Ni+f/Xq9nMw9eRYKOH42nEUtbAwHb6eXLt6VX/XL7/sOo+tUyNEyPJinWX9A2/J/GGrRAA5P/ZTkX37JOZonFxd/pdEPzJY/qzQK9ML08/9lkjcZ1PF8s1Uf396v8sVgY7jFCg1OiIisf85jMEJyH7UFoslgIYRpMAXHa3T6dP2NsUlS+qjo9u2DZiO234VF6clbrbfWpkyzhd1Xz5DyJuOHnVO94gR+oh5Ef2M58/b20HZpkBqgucLjs1JHPsUtG6tv4f02nNs3Ki1AseOaYnr9OkaEI4fn/affocO9tfVqunITd99pyXW69alm7SlS9O/j6hRQwt5U8aBWLPG+cZv2DDt05KV51HMmKH7tmmT7lMHk5Od0+Fp94r//c9+rN27xbkTed++WnhAaVksaYfcf/JJpxo729cJaBcOW58Md128qDVsKT8JrJCYirfp/wbbwnvv1d9Cdk6E+Hht9vv55/aaxKtX0+/f4lj48uij9r5KAfrcrIC2fbuOFPf3384D83hhipi0Qc5HBOk40dnEpmse2jl+kewe8Z0srPacLB7NEg3ygMXiu57GwWDPHvuNRFSUNssYPDhnniPkLa6ekli5ctpl990XXDU4GTlxQvuqxcen6cwrhQtLSq/Z337TYCKjPgfZmXr21GZJ6TQLXbNG7//WrNE4YMUKkcOHLFpsP2SIPpyyRAl7k8tKlbLfxHTqVA34Bg9Od5OVK3UUcEBbJ2X3Seznz2tXKcBaUZOUJPLww7rgxRe1qROlLzFR2zY6DqTw4INODwMfNMj5VHv/fa2wTf3TtljszdxiYkS++sr+3dStK7J6UqoHFNmCaF//vzh5UmvVz53TQrctW3w70kJetWePDsxh/X4nFXlZRuF9eQNvp7lerUczmYdO8nG9aTLilnnSt/5m+e6z8/L+W9ck8VoQNm33Al8GOvkBHAZQ02EwgttSbTMo1WAEP2d23EALdIiIsiwuTktSW7bUOxrHvjgREdm/e83tEhO1+VbqIWId+0Y8+KAGJ7b5GTN0hKxy5fQhGSNGaM3O2bPaRPDtt7U4ffx43a9tWx0EY9Ys5842hQrpw0hsMgpW/vrLOX2hodo8MSbG6UY3W2z99554It0a3dQDKDzzTNbeYtcuHSEa0O4cIqK1A4A2XaOsSUiwB4mAZu64cSInT8quXWkf89KkiciECdpic9Ike1nHkiX2AAcQ+WHkTkmq6FAQctdd2geMgtPOnSLLl4vFouUM4eEilXFcBjeLkGPvfi8TJ2r5nq1bK7nH18NLdwBwwNokbbR12VgAna2vCwP4xTq89CYAtTI7JgMdIgoqtv9YkyeLfPGFf9MSSK5c0UE5bEOX33ijfZSj5GSR997zTr+AqVOda4k6dNC+DkWLainr669rv6GpU/WvLUKwTXPnerdk/dgxfSIjoM+sSWf46a++ck6Gu6dOeLh9n0cftS6MiNAFZcuyuVp2xcc7tzUDdGjtf/8ViY+XBQucn+ub0dQNv0js7a2z9wVTUNm92/+D1+V26QU6RtcFnrCwMImIiPB3MoiIKCfExQFnzwLVqgHG+O59vv4aGDDA/e1//hlo3RoIDfV+Wq5eBYoUsc9fvuw8bxUXB8yZA/Ttq/O//ALcfjtQsyaQP3/aw27dCgwfDqxcqfNr1gAtTs4CnnhCF+zfD9x0k5c/TB6zeDGQmAi89x6waZN9+YYNQP/+wPjxeHNTR6xbBzz4IDBsmH2T2+sm46ta49F0/mjnY7ZvD8yaBZQsmTOfgSiIGGO2iEhYmuUMdIiIKM8QAZKSgCFDgK++Sn+7Z54BXngBqFvXt+lZuhRo105fr1ypQVU6unQB5s9Pu/y224CuXYH4eODHH4EzZ3R56dJ6zz2uazhM61bAtWvAhAnOd93kuR49gJ9+Srv80UeBW2/Flb6DMPqFOITJZtzVpjBq7P5DA24AuOsuICwM6NcPqF8/Z9NNFEQY6BAREdlcuwb8+SfQvDnwySfAddcBlSsDDz+sgVCxYjmXluhooFIlfb14sT3wceG994B339XKoIxs2ADUqwcUK5ysnzEqCtixwzc1U3ndmTPApEkaOJ8+7f5+o0frl0lEHmOgQ0REFKjuuQdYt05fX7oEFC2a4eZRUUCJEhqrDR0K/PsvUKGCNpMqV04DIpw6pcEbAEybBjz9tE8/AgE4eVKD1oQEICICGDcOWLjQvv6224Du3YGXX870OyYi9zHQISIiClQWi7Y/W7BAO+B89hnQooXbuyYmAoUKpVrx9tvAW29p349Fi7ydYnLHpUvAvn365dx0k4sviYi8Ib1AJ58/EkNEREQO8uXTDjiffqpNzFq21CZtbu6a5v554kQNclq3ZpDjT8WKaR+cevUY5BD5AQMdIiKiQPHCC8D77+vrJ57Qkdiy6osvgJde0tcvv+y9tBER5TIuBqYkIiIiv3n1VWDPHmDmTK0ReP11oHFjbdqWEcfmb4COSHDnnb5OLRFRwGKNDhERUaD58ktgxgx9/e67wEMPaU3Nli3a0f2ff+zbiuiDdpo3twc5q1czyCGiPI81OkRERIGmeHHgySeBGjV0RDYAGDTIeZtWrYA6dYCDB4Hly3VZz57Ad98BISE5mVoiooDEQIeIiChQ2Z6BM3OmNmlztHq1TjYTJwK9ezPIISKyYqBDREQUyKpWBfr3ByIjtZnaH3/oIAWVKgFvvgkcPQr066cP0CEiohQMdIiIiAJdaCjwzTf2+cREDXoKFvRfmoiIAhwDHSIiotymQAF/p4CIKOBx1DUiIiIiIgo6DHSIiIiIiCjoMNAhIiIiIqKgw0CHiIiIiIiCDgMdIiIiIiIKOgx0iIiIiIgo6DDQISIiIiKioMNAh4iIiIiIgg4DHSIiIiIiCjoMdIiIiIiIKOgw0CEiIiIioqDDQIeIiIiIiIIOAx0iIiIiIgo6HgU6xpjSxphlxpiD1r+l0tku2Riz3TrN9+Q9iYiIiIiIMuNpjc4oACtEpDaAFdZ5V66ISAPr1NnD9yQiIiIiIsqQp4FOFwDfWV9/B6Crh8cjIiIiIiLymKeBTnkRiba+/hdA+XS2K2yMiTDGhBtjuqZ3MGPMAOt2EWfOnPEwaURERERElFflz2wDY8xyABVcrBrtOCMiYoyRdA5TXUROGmNqAVhpjNklIodSbyQiUwBMAYCwsLD0jkVERERERJShTAMdEbkvvXXGmNPGmIoiEm2MqQjgv3SOcdL697AxZjWAhgDSBDpERERERETe4GnTtfkAeltf9wYwL/UGxphSxphC1tehAO4GsNfD9yUiIiIiIkqXp4HOOAD3G2MOArjPOg9jTJgx5hvrNnUARBhjdgBYBWCciDDQISIiIiIin8m06VpGRCQWQBsXyyMA9Le+/htAPU/eh4iIiIiIKCs8rdEhIiIiIiIKOAx0iIiIiIgo6DDQISIiIiKioMNAh4iIiIiIgg4DHSIiIiIiCjoMdIiIiIiIKOgw0CEiIiIioqDDQIeIiIiIiIIOAx0iIiIiIgo6DHSIiIiIiCjoMNAhIiIiIqKgw0CHiIiIiIiCDgMdIiIiIiIKOgx0iIiIiIgo6DDQISIiIiKioMNAh4iIiIiIgg4DHSIiIiIiCjoMdIiIiIiIKOgw0CEiIiIioqDDQIeIiIiIiIIOAx0iIiIiIgo6DHSIiIiIiCjoMNAhIiIiIqKgw0CHiIiIiIiCDgMdIiIiIiIKOgx0iIiIiIgo6DDQISIiIiKioONRoGOMedQYs8cYYzHGhGWwXTtjzH5jTKQxZpQn70lERERERJQZT2t0dgN4GMDa9DYwxoQAmAygPYBbATxujLnVw/clIiIiIiJKV35PdhaRfQBgjMloszsARIrIYeu2swF0AbDXk/cmIiIiIiJKj0eBjpsqAzjuMH8CQFNXGxpjBgAYYJ2NN8bs93HasiIUQIy/ExHEmL++xzz2Peax7zGPfY957HvMY99i/vpeoOVxdVcLMw10jDHLAVRwsWq0iMzzNFWORGQKgCnePKa3GGMiRCTdfkjkGeav7zGPfY957HvMY99jHvse89i3mL++l1vyONNAR0Tu8/A9TgKo6jBfxbqMiIiIiIjIJ3JieOnNAGobY2oaYwoC6AFgfg68LxERERER5VGeDi/9kDHmBIBmAP4wxiy1Lq9kjFkEACKSBGAwgKUA9gH4WUT2eJZsvwjIJnVBhPnre8xj32Me+x7z2PeYx77HPPYt5q/v5Yo8NiLi7zQQERERERF5VU40XSMiIiIiIspRDHSIiIiIiCjo5PlAxxjTzhiz3xgTaYwZ5WJ9IWPMT9b1G40xNRzWvWpdvt8Y0zZHE56LuJHHw4wxe40xO40xK4wx1R3WJRtjtlsnDmKRDjfyuI8x5oxDXvZ3WNfbGHPQOvXO2ZTnHm7k8ScO+XvAGHPeYR3P40wYY6YZY/4zxuxOZ70xxnxmzf+dxphGDut4DrvBjTzuac3bXcaYv40xtzusO2pdvt0YE5Fzqc5d3MjjVsaYCw7XgzEO6zK8xpBb+TvcIW93W6+9pa3reA67wRhT1RizynpftscY86KLbXLP9VhE8uwEIATAIQC1ABQEsAPAram2eR7Al9bXPQD8ZH19q3X7QgBqWo8T4u/PFGiTm3ncGkBR6+vnbHlsnY/392cI9MnNPO4DYJKLfUsDOGz9W8r6upS/P1OgTe7kcarthwCY5jDP8zjzPG4BoBGA3ems7wBgMQAD4E4AG63LeQ57L4/vsuUdgPa2PLbOHwUQ6u/PEOiTG3ncCsBCF8uzdI3Jq1Nm+Ztq204AVjrM8xx2L48rAmhkfV0CwAEX9xS55nqc12t07gAQKSKHRSQBwGwAXVJt0wXAd9bXcwC0McYY6/LZInJNRI4AiLQej5xlmsciskpELltnw6HPWiL3uXMep6ctgGUiclZEzgFYBqCdj9KZm2U1jx8HMCtHUhYkRGQtgLMZbNIFwPeiwgFcb4ypCJ7Dbsssj0Xkb2seArwWZ4sb53F6PLmO5xlZzF9eh7NBRKJFZKv1dRx0xOTKqTbLNdfjvB7oVAZw3GH+BNJ+mSnbiA6VfQFAGTf3paznUz9oKYFNYWNMhDEm3BjT1QfpCwbu5nE3axXzHGOM7SG+PI/d43Y+WZte1gSw0mExz2PPpfcd8Bz2jdTXYgHwpzFmizFmgJ/SFCyaGWN2GGMWG2Nusy7jeexFxpii0BvsuQ6LeQ5nkdHuGg0BbEy1Ktdcj/P7882JHBljngQQBqClw+LqInLSGFMLwEpjzC4ROeSfFOZqCwDMEpFrxpiB0FrKe/2cpmDVA8AcEUl2WMbzmHINY0xraKDT3GFxc+s5XA7AMmPMP9bSdcqardDrQbwxpgOA3wHU9m+SglInAOtFxLH2h+dwFhhjikMDxaEictHf6cmuvF6jcxJAVYf5KtZlLrcxxuQHUBJArJv7kpv5ZIy5D8BoAJ1F5JptuYictP49DGA1tGSBnGWaxyIS65Cv3wBo7O6+BCBr+dQDqZpL8Dz2ivS+A57DXmSMqQ+9RnQRkVjbcodz+D8Av4FNtbNFRC6KSLz19SIABYwxoeB57G0ZXYd5DmfCGFMAGuTMFJFfXWySa67HeT3Q2QygtjGmpjGmIPSHkXpEpPkAbKNGPALt2CbW5T2MjspWE1oisymH0p2bZJrHxpiGAL6CBjn/OSwvZYwpZH0dCuBuAHtzLOW5hzt5XNFhtjO0zS0ALAXwgDWvSwF4wLqMnLlzrYAx5hZoB8wNDst4HnvHfABPWUf7uRPABRGJBs9hrzHGVAPwK4BeInLAYXkxY0wJ22toHrsc9YoyZoypYO3nC2PMHdD7sFi4eY2hzBljSkJbhsxzWMZz2E3W83MqgH0i8nE6m+Wa63GebromIknGmMHQLyEEOkrSHmPMWAARIjIf+mXPMMZEQjvA9bDuu8cY8zP0hiUJwKBUTVUIbufxhwCKA/jFev2PEpHOAOoA+MoYY4H+MxgnIrxBTMXNPH7BGNMZeq6ehY7CBhE5a4x5B/pPFgDGpqrqJ7idx4BeH2ZbC0NseB67wRgzCzoiVagx5gSANwEUAAAR+RLAIuhIP5EALgN42rqO57Cb3MjjMdA+qF9Yr8VJIhIGoDyA36zL8gP4UUSW5PgHyAXcyONHADxnjEkCcAVAD+v1wuU1xg8fIaC5kb8A8BCAP0XkksOuPIfddzeAXgB2GWO2W5e9BqAakPuux8b5/zEREREREVHul9ebrhERERERURBioENEREREREGHgQ4REREREQUdBjpERERERBR0GOgQEREREVHQYaBDRERERERBh4EOEREREREFnf8H+crpVmfIGxAAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plots(end_ix=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot shows that when the network is initialized,\n", + "it is not able to compute the product.\n", + "The error is quite large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examine the final output\n", + "\n", + "After the network has run for a while, and learning has occurred,\n", + "the output is quite different:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:21.710676Z", + "iopub.status.busy": "2020-11-25T16:49:21.709720Z", + "iopub.status.idle": "2020-11-25T16:49:22.267036Z", + "shell.execute_reply": "2020-11-25T16:49:22.267455Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plots(start_ix=38000, end_ix=42000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that it has learned a decent approximation of the product,\n", + "but it's not perfect -- typically,\n", + "it's not as good as the offline optimization.\n", + "The reason for this is that we've given it white noise input,\n", + "which has a mean of 0; since this happens in both dimensions,\n", + "we'll see a lot of examples of inputs and outputs near 0.\n", + "In other words, we've oversampled a certain part of the\n", + "vector space, and overlearned decoders that do well in\n", + "that part of the space. If we want to do better in other\n", + "parts of the space, we would need to construct an input\n", + "signal that evenly samples the space." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/learning/learn-square.html b/examples/learning/learn-square.html new file mode 100644 index 0000000000..ac60ec0bf9 --- /dev/null +++ b/examples/learning/learn-square.html @@ -0,0 +1,760 @@ + + + + + + + + Learning to square the input — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Learning to square the input

+

This demo shows you how to construct a network containing an ensemble which learns how to decode the square of its value.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

Step 1: Create the Model

+

This network consists of an ensemble A which represents the input, an ensemble A_squared which learns to represent the square, and an ensemble error which represents the error between A_squared and the actual square.

+
+
[2]:
+
+
+
+model = nengo.Network()
+with model:
+    # Create the ensemble to represent the input, the input squared (learned),
+    # and the error
+    A = nengo.Ensemble(100, dimensions=1)
+    A_squared = nengo.Ensemble(100, dimensions=1)
+    error = nengo.Ensemble(100, dimensions=1)
+
+    # Connect A and A_squared with a communication channel
+    conn = nengo.Connection(A, A_squared)
+
+    # Apply the PES learning rule to conn
+    conn.learning_rule_type = nengo.PES(learning_rate=3e-4)
+
+    # Provide an error signal to the learning rule
+    nengo.Connection(error, conn.learning_rule)
+
+    # Compute the error signal (error = actual - target)
+    nengo.Connection(A_squared, error)
+
+    # Subtract the target (this would normally come from some external system)
+    nengo.Connection(A, error, function=lambda x: x ** 2, transform=-1)
+
+
+
+
+
+

Step 2: Provide Input to the Model

+

A single input signal (a step function) will be used to drive the neural activity in ensemble A. An additional node will inhibit the error signal after 15 seconds, to test the learning at the end.

+
+
[3]:
+
+
+
+with model:
+    # Create an input node that steps between -1 and 1
+    input_node = nengo.Node(output=lambda t: int(6 * t / 5) / 3.0 % 2 - 1)
+
+    # Connect the input node to ensemble A
+    nengo.Connection(input_node, A)
+
+    # Shut off learning by inhibiting the error population
+    stop_learning = nengo.Node(output=lambda t: t >= 15)
+    nengo.Connection(
+        stop_learning, error.neurons, transform=-20 * np.ones((error.n_neurons, 1))
+    )
+
+
+
+
+
+

Step 3: Probe the Output

+

Let’s collect output data from each ensemble and output.

+
+
[4]:
+
+
+
+with model:
+    input_node_probe = nengo.Probe(input_node)
+    A_probe = nengo.Probe(A, synapse=0.01)
+    A_squared_probe = nengo.Probe(A_squared, synapse=0.01)
+    error_probe = nengo.Probe(error, synapse=0.01)
+    learn_probe = nengo.Probe(stop_learning, synapse=None)
+
+
+
+
+
+

Step 4: Run the Model

+
+
[5]:
+
+
+
+# Create the simulator
+with nengo.Simulator(model) as sim:
+    sim.run(20)
+
+
+
+
+
[6]:
+
+
+
+# Plot the input signal
+plt.figure(figsize=(9, 9))
+plt.subplot(3, 1, 1)
+plt.plot(
+    sim.trange(), sim.data[input_node_probe], label="Input", color="k", linewidth=2.0
+)
+plt.plot(
+    sim.trange(),
+    sim.data[learn_probe],
+    label="Stop learning?",
+    color="r",
+    linewidth=2.0,
+)
+plt.legend(loc="lower right")
+plt.ylim(-1.2, 1.2)
+
+plt.subplot(3, 1, 2)
+plt.plot(
+    sim.trange(), sim.data[input_node_probe] ** 2, label="Squared Input", linewidth=2.0
+)
+plt.plot(sim.trange(), sim.data[A_squared_probe], label="Decoded Ensemble $A^2$")
+plt.legend(loc="lower right")
+plt.ylim(-1.2, 1.2)
+
+plt.subplot(3, 1, 3)
+plt.plot(
+    sim.trange(),
+    sim.data[A_squared_probe] - sim.data[input_node_probe] ** 2,
+    label="Error",
+)
+plt.legend(loc="lower right")
+plt.tight_layout()
+
+
+
+
+
+
+
+../../_images/examples_learning_learn-square_10_0.png +
+
+

We see that during the first three periods, the decoders quickly adjust to drive the error to zero. When learning is turned off for the fourth period, the error stays closer to zero, demonstrating that the learning has persisted in the connection.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/learning/learn-square.ipynb b/examples/learning/learn-square.ipynb new file mode 100644 index 0000000000..14b3486be7 --- /dev/null +++ b/examples/learning/learn-square.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning to square the input\n", + "\n", + "This demo shows you how to construct a network\n", + "containing an ensemble which learns how to decode the square of its value." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:23.785879Z", + "iopub.status.busy": "2020-11-25T16:49:23.785033Z", + "iopub.status.idle": "2020-11-25T16:49:24.278388Z", + "shell.execute_reply": "2020-11-25T16:49:24.277895Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Model\n", + "\n", + "This network consists of an ensemble `A` which represents the input,\n", + "an ensemble `A_squared` which learns to represent the square,\n", + "and an ensemble `error` which represents the error\n", + "between `A_squared` and the actual square." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.290340Z", + "iopub.status.busy": "2020-11-25T16:49:24.289814Z", + "iopub.status.idle": "2020-11-25T16:49:24.292888Z", + "shell.execute_reply": "2020-11-25T16:49:24.293277Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " # Create the ensemble to represent the input, the input squared (learned),\n", + " # and the error\n", + " A = nengo.Ensemble(100, dimensions=1)\n", + " A_squared = nengo.Ensemble(100, dimensions=1)\n", + " error = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " # Connect A and A_squared with a communication channel\n", + " conn = nengo.Connection(A, A_squared)\n", + "\n", + " # Apply the PES learning rule to conn\n", + " conn.learning_rule_type = nengo.PES(learning_rate=3e-4)\n", + "\n", + " # Provide an error signal to the learning rule\n", + " nengo.Connection(error, conn.learning_rule)\n", + "\n", + " # Compute the error signal (error = actual - target)\n", + " nengo.Connection(A_squared, error)\n", + "\n", + " # Subtract the target (this would normally come from some external system)\n", + " nengo.Connection(A, error, function=lambda x: x ** 2, transform=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Provide Input to the Model\n", + "\n", + "A single input signal (a step function)\n", + "will be used to drive the neural activity in ensemble A.\n", + "An additional node will inhibit the error signal after 15 seconds,\n", + "to test the learning at the end." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.301089Z", + "iopub.status.busy": "2020-11-25T16:49:24.299562Z", + "iopub.status.idle": "2020-11-25T16:49:24.301692Z", + "shell.execute_reply": "2020-11-25T16:49:24.302099Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " # Create an input node that steps between -1 and 1\n", + " input_node = nengo.Node(output=lambda t: int(6 * t / 5) / 3.0 % 2 - 1)\n", + "\n", + " # Connect the input node to ensemble A\n", + " nengo.Connection(input_node, A)\n", + "\n", + " # Shut off learning by inhibiting the error population\n", + " stop_learning = nengo.Node(output=lambda t: t >= 15)\n", + " nengo.Connection(\n", + " stop_learning, error.neurons, transform=-20 * np.ones((error.n_neurons, 1))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Probe the Output\n", + "\n", + "Let's collect output data from each ensemble and output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.309447Z", + "iopub.status.busy": "2020-11-25T16:49:24.307933Z", + "iopub.status.idle": "2020-11-25T16:49:24.310033Z", + "shell.execute_reply": "2020-11-25T16:49:24.310443Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " input_node_probe = nengo.Probe(input_node)\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " A_squared_probe = nengo.Probe(A_squared, synapse=0.01)\n", + " error_probe = nengo.Probe(error, synapse=0.01)\n", + " learn_probe = nengo.Probe(stop_learning, synapse=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:24.315550Z", + "iopub.status.busy": "2020-11-25T16:49:24.314776Z", + "iopub.status.idle": "2020-11-25T16:49:28.949480Z", + "shell.execute_reply": "2020-11-25T16:49:28.948998Z" + } + }, + "outputs": [], + "source": [ + "# Create the simulator\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:28.981149Z", + "iopub.status.busy": "2020-11-25T16:49:28.980342Z", + "iopub.status.idle": "2020-11-25T16:49:29.631296Z", + "shell.execute_reply": "2020-11-25T16:49:29.630750Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the input signal\n", + "plt.figure(figsize=(9, 9))\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_node_probe], label=\"Input\", color=\"k\", linewidth=2.0\n", + ")\n", + "plt.plot(\n", + " sim.trange(),\n", + " sim.data[learn_probe],\n", + " label=\"Stop learning?\",\n", + " color=\"r\",\n", + " linewidth=2.0,\n", + ")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.ylim(-1.2, 1.2)\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(\n", + " sim.trange(), sim.data[input_node_probe] ** 2, label=\"Squared Input\", linewidth=2.0\n", + ")\n", + "plt.plot(sim.trange(), sim.data[A_squared_probe], label=\"Decoded Ensemble $A^2$\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.ylim(-1.2, 1.2)\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(\n", + " sim.trange(),\n", + " sim.data[A_squared_probe] - sim.data[input_node_probe] ** 2,\n", + " label=\"Error\",\n", + ")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that during the first three periods,\n", + "the decoders quickly adjust to drive the error to zero.\n", + "When learning is turned off for the fourth period,\n", + "the error stays closer to zero,\n", + "demonstrating that the learning has persisted in the connection." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/learning/learn-unsupervised.html b/examples/learning/learn-unsupervised.html new file mode 100644 index 0000000000..6db23690d1 --- /dev/null +++ b/examples/learning/learn-unsupervised.html @@ -0,0 +1,1010 @@ + + + + + + + + Unsupervised learning — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Unsupervised learning

+

When we do error-modulated learning with the nengo.PES rule, we have a pretty clear idea of what we want to happen. Years of neuroscientific experiments have yielded learning rules explaining how synaptic strengths change given certain stimulation protocols. But what do these learning rules actually do to the information transmitted across an ensemble-to-ensemble connection?

+

We can investigate this in Nengo. Currently, we’ve implemented the nengo.BCM and nengo.Oja rules.

+
+
[1]:
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+%matplotlib inline
+
+import nengo
+
+
+
+
+
[2]:
+
+
+
+print(nengo.BCM.__doc__)
+
+
+
+
+
+
+
+
+Bienenstock-Cooper-Munroe learning rule.
+
+    Modifies connection weights as a function of the presynaptic activity
+    and the difference between the postsynaptic activity and the average
+    postsynaptic activity.
+
+    Notes
+    -----
+    The BCM rule is dependent on pre and post neural activities,
+    not decoded values, and so is not affected by changes in the
+    size of pre and post ensembles. However, if you are decoding from
+    the post ensemble, the BCM rule will have an increased effect on
+    larger post ensembles because more connection weights are changing.
+    In these cases, it may be advantageous to scale the learning rate
+    on the BCM rule by ``1 / post.n_neurons``.
+
+    Parameters
+    ----------
+    learning_rate : float, optional
+        A scalar indicating the rate at which weights will be adjusted.
+    pre_synapse : `.Synapse`, optional
+        Synapse model used to filter the pre-synaptic activities.
+    post_synapse : `.Synapse`, optional
+        Synapse model used to filter the post-synaptic activities.
+        If None, ``post_synapse`` will be the same as ``pre_synapse``.
+    theta_synapse : `.Synapse`, optional
+        Synapse model used to filter the theta signal.
+
+    Attributes
+    ----------
+    learning_rate : float
+        A scalar indicating the rate at which weights will be adjusted.
+    post_synapse : `.Synapse`
+        Synapse model used to filter the post-synaptic activities.
+    pre_synapse : `.Synapse`
+        Synapse model used to filter the pre-synaptic activities.
+    theta_synapse : `.Synapse`
+        Synapse model used to filter the theta signal.
+
+
+
+
+
[3]:
+
+
+
+print(nengo.Oja.__doc__)
+
+
+
+
+
+
+
+
+Oja learning rule.
+
+    Modifies connection weights according to the Hebbian Oja rule, which
+    augments typically Hebbian coactivity with a "forgetting" term that is
+    proportional to the weight of the connection and the square of the
+    postsynaptic activity.
+
+    Notes
+    -----
+    The Oja rule is dependent on pre and post neural activities,
+    not decoded values, and so is not affected by changes in the
+    size of pre and post ensembles. However, if you are decoding from
+    the post ensemble, the Oja rule will have an increased effect on
+    larger post ensembles because more connection weights are changing.
+    In these cases, it may be advantageous to scale the learning rate
+    on the Oja rule by ``1 / post.n_neurons``.
+
+    Parameters
+    ----------
+    learning_rate : float, optional
+        A scalar indicating the rate at which weights will be adjusted.
+    pre_synapse : `.Synapse`, optional
+        Synapse model used to filter the pre-synaptic activities.
+    post_synapse : `.Synapse`, optional
+        Synapse model used to filter the post-synaptic activities.
+        If None, ``post_synapse`` will be the same as ``pre_synapse``.
+    beta : float, optional
+        A scalar weight on the forgetting term.
+
+    Attributes
+    ----------
+    beta : float
+        A scalar weight on the forgetting term.
+    learning_rate : float
+        A scalar indicating the rate at which weights will be adjusted.
+    post_synapse : `.Synapse`
+        Synapse model used to filter the post-synaptic activities.
+    pre_synapse : `.Synapse`
+        Synapse model used to filter the pre-synaptic activities.
+
+
+
+
+

Step 1: Create a simple communication channel

+

The only difference between this network and most models you’ve seen so far is that we’re going to set the decoder solver in the communication channel to generate a full connection weight matrix which we can then learn using typical delta learning rules.

+
+
[4]:
+
+
+
+model = nengo.Network()
+with model:
+    sin = nengo.Node(lambda t: np.sin(t * 4))
+
+    pre = nengo.Ensemble(100, dimensions=1)
+    post = nengo.Ensemble(100, dimensions=1)
+
+    nengo.Connection(sin, pre)
+    conn = nengo.Connection(pre, post, solver=nengo.solvers.LstsqL2(weights=True))
+
+    pre_p = nengo.Probe(pre, synapse=0.01)
+    post_p = nengo.Probe(post, synapse=0.01)
+
+
+
+
+
[5]:
+
+
+
+# Verify that it does a communication channel
+with nengo.Simulator(model) as sim:
+    sim.run(2.0)
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[pre_p], label="Pre")
+plt.plot(sim.trange(), sim.data[post_p], label="Post")
+plt.ylabel("Decoded value")
+plt.legend(loc="best")
+
+
+
+
+
[5]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f2a3f652240>
+
+
+
+
+
+
+../../_images/examples_learning_learn-unsupervised_6_1.png +
+
+
+
+

What does BCM do?

+
+
[6]:
+
+
+
+conn.learning_rule_type = nengo.BCM(learning_rate=5e-10)
+with model:
+    weights_p = nengo.Probe(conn, "weights", synapse=0.01, sample_every=0.01)
+
+
+
+
+
[7]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(20.0)
+
+
+
+
+
[8]:
+
+
+
+plt.figure(figsize=(12, 8))
+plt.subplot(2, 1, 1)
+plt.plot(sim.trange(), sim.data[pre_p], label="Pre")
+plt.plot(sim.trange(), sim.data[post_p], label="Post")
+plt.ylabel("Decoded value")
+plt.ylim(-1.6, 1.6)
+plt.legend(loc="lower left")
+plt.subplot(2, 1, 2)
+# Find weight row with max variance
+neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))
+plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])
+plt.ylabel("Connection weight")
+
+
+
+
+
[8]:
+
+
+
+
+Text(0, 0.5, 'Connection weight')
+
+
+
+
+
+
+../../_images/examples_learning_learn-unsupervised_10_1.png +
+
+

The BCM rule appears to cause the ensemble to either be on or off. This seems consistent with the idea that it potentiates active synapses, and depresses non-active synapses.

+

As well, we can show that BCM sparsifies the weights. The sparsity measure below uses the Gini measure of sparsity, for reasons explained in this paper.

+
+
[9]:
+
+
+
+def sparsity_measure(vector):  # Gini index
+    # Max sparsity = 1 (single 1 in the vector)
+    v = np.sort(np.abs(vector))
+    n = v.shape[0]
+    k = np.arange(n) + 1
+    l1norm = np.sum(v)
+    summation = np.sum((v / l1norm) * ((n - k + 0.5) / n))
+    return 1 - 2 * summation
+
+
+print("Starting sparsity: {0}".format(sparsity_measure(sim.data[weights_p][0])))
+print("Ending sparsity: {0}".format(sparsity_measure(sim.data[weights_p][-1])))
+
+
+
+
+
+
+
+
+Starting sparsity: 0.14271014651392688
+Ending sparsity: 0.46467866311030237
+
+
+
+
+

What does Oja do?

+
+
[10]:
+
+
+
+conn.learning_rule_type = nengo.Oja(learning_rate=6e-8)
+
+
+
+
+
[11]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(20.0)
+
+
+
+
+
[12]:
+
+
+
+plt.figure(figsize=(12, 8))
+plt.subplot(2, 1, 1)
+plt.plot(sim.trange(), sim.data[pre_p], label="Pre")
+plt.plot(sim.trange(), sim.data[post_p], label="Post")
+plt.ylabel("Decoded value")
+plt.ylim(-1.6, 1.6)
+plt.legend(loc="lower left")
+plt.subplot(2, 1, 2)
+# Find weight row with max variance
+neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))
+plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])
+plt.ylabel("Connection weight")
+print("Starting sparsity: {0}".format(sparsity_measure(sim.data[weights_p][0])))
+print("Ending sparsity: {0}".format(sparsity_measure(sim.data[weights_p][-1])))
+
+
+
+
+
+
+
+
+Starting sparsity: 0.12262675709408066
+Ending sparsity: 0.27264319863455877
+
+
+
+
+
+
+../../_images/examples_learning_learn-unsupervised_16_1.png +
+
+

The Oja rule seems to attenuate the signal across the connection.

+
+
+

What do BCM and Oja together do?

+

We can apply both learning rules to the same connection by passing a list to learning_rule_type.

+
+
[13]:
+
+
+
+conn.learning_rule_type = [
+    nengo.BCM(learning_rate=5e-10),
+    nengo.Oja(learning_rate=2e-9),
+]
+
+
+
+
+
[14]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(20.0)
+
+
+
+
+
[15]:
+
+
+
+plt.figure(figsize=(12, 8))
+plt.subplot(2, 1, 1)
+plt.plot(sim.trange(), sim.data[pre_p], label="Pre")
+plt.plot(sim.trange(), sim.data[post_p], label="Post")
+plt.ylabel("Decoded value")
+plt.ylim(-1.6, 1.6)
+plt.legend(loc="lower left")
+plt.subplot(2, 1, 2)
+# Find weight row with max variance
+neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))
+plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])
+plt.ylabel("Connection weight")
+print("Starting sparsity: {0}".format(sparsity_measure(sim.data[weights_p][0])))
+print("Ending sparsity: {0}".format(sparsity_measure(sim.data[weights_p][-1])))
+
+
+
+
+
+
+
+
+Starting sparsity: 0.14517215817668105
+Ending sparsity: 0.42952806258413734
+
+
+
+
+
+
+../../_images/examples_learning_learn-unsupervised_21_1.png +
+
+

The combination of the two appears to accentuate the on-off nature of the BCM rule. As the Oja rule enforces a soft upper or lower bound for the connection weight, the combination of both rules may be more stable than BCM alone.

+

That’s mostly conjecture, however; a thorough investigation of the BCM and Oja rules and how they interact has not yet been done.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/learning/learn-unsupervised.ipynb b/examples/learning/learn-unsupervised.ipynb new file mode 100644 index 0000000000..afc8299f56 --- /dev/null +++ b/examples/learning/learn-unsupervised.ipynb @@ -0,0 +1,623 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unsupervised learning\n", + "\n", + "When we do error-modulated learning with the `nengo.PES` rule,\n", + "we have a pretty clear idea of what we want to happen.\n", + "Years of neuroscientific experiments have yielded\n", + "learning rules explaining how synaptic strengths\n", + "change given certain stimulation protocols.\n", + "But what do these learning rules actually do\n", + "to the information transmitted across an\n", + "ensemble-to-ensemble connection?\n", + "\n", + "We can investigate this in Nengo.\n", + "Currently, we've implemented the `nengo.BCM`\n", + "and `nengo.Oja` rules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:30.967555Z", + "iopub.status.busy": "2020-11-25T16:49:30.966604Z", + "iopub.status.idle": "2020-11-25T16:49:31.459416Z", + "shell.execute_reply": "2020-11-25T16:49:31.459924Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.464776Z", + "iopub.status.busy": "2020-11-25T16:49:31.464278Z", + "iopub.status.idle": "2020-11-25T16:49:31.468851Z", + "shell.execute_reply": "2020-11-25T16:49:31.468412Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bienenstock-Cooper-Munroe learning rule.\n", + "\n", + " Modifies connection weights as a function of the presynaptic activity\n", + " and the difference between the postsynaptic activity and the average\n", + " postsynaptic activity.\n", + "\n", + " Notes\n", + " -----\n", + " The BCM rule is dependent on pre and post neural activities,\n", + " not decoded values, and so is not affected by changes in the\n", + " size of pre and post ensembles. However, if you are decoding from\n", + " the post ensemble, the BCM rule will have an increased effect on\n", + " larger post ensembles because more connection weights are changing.\n", + " In these cases, it may be advantageous to scale the learning rate\n", + " on the BCM rule by ``1 / post.n_neurons``.\n", + "\n", + " Parameters\n", + " ----------\n", + " learning_rate : float, optional\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " pre_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " post_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the post-synaptic activities.\n", + " If None, ``post_synapse`` will be the same as ``pre_synapse``.\n", + " theta_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the theta signal.\n", + "\n", + " Attributes\n", + " ----------\n", + " learning_rate : float\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " post_synapse : `.Synapse`\n", + " Synapse model used to filter the post-synaptic activities.\n", + " pre_synapse : `.Synapse`\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " theta_synapse : `.Synapse`\n", + " Synapse model used to filter the theta signal.\n", + " \n" + ] + } + ], + "source": [ + "print(nengo.BCM.__doc__)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.474052Z", + "iopub.status.busy": "2020-11-25T16:49:31.472636Z", + "iopub.status.idle": "2020-11-25T16:49:31.475913Z", + "shell.execute_reply": "2020-11-25T16:49:31.475453Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Oja learning rule.\n", + "\n", + " Modifies connection weights according to the Hebbian Oja rule, which\n", + " augments typically Hebbian coactivity with a \"forgetting\" term that is\n", + " proportional to the weight of the connection and the square of the\n", + " postsynaptic activity.\n", + "\n", + " Notes\n", + " -----\n", + " The Oja rule is dependent on pre and post neural activities,\n", + " not decoded values, and so is not affected by changes in the\n", + " size of pre and post ensembles. However, if you are decoding from\n", + " the post ensemble, the Oja rule will have an increased effect on\n", + " larger post ensembles because more connection weights are changing.\n", + " In these cases, it may be advantageous to scale the learning rate\n", + " on the Oja rule by ``1 / post.n_neurons``.\n", + "\n", + " Parameters\n", + " ----------\n", + " learning_rate : float, optional\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " pre_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " post_synapse : `.Synapse`, optional\n", + " Synapse model used to filter the post-synaptic activities.\n", + " If None, ``post_synapse`` will be the same as ``pre_synapse``.\n", + " beta : float, optional\n", + " A scalar weight on the forgetting term.\n", + "\n", + " Attributes\n", + " ----------\n", + " beta : float\n", + " A scalar weight on the forgetting term.\n", + " learning_rate : float\n", + " A scalar indicating the rate at which weights will be adjusted.\n", + " post_synapse : `.Synapse`\n", + " Synapse model used to filter the post-synaptic activities.\n", + " pre_synapse : `.Synapse`\n", + " Synapse model used to filter the pre-synaptic activities.\n", + " \n" + ] + } + ], + "source": [ + "print(nengo.Oja.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create a simple communication channel\n", + "\n", + "The only difference between this network and most\n", + "models you've seen so far is that we're going to\n", + "set the decoder solver in the communication channel\n", + "to generate a full connection weight matrix\n", + "which we can then learn using typical delta learning rules." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.487126Z", + "iopub.status.busy": "2020-11-25T16:49:31.485595Z", + "iopub.status.idle": "2020-11-25T16:49:31.487762Z", + "shell.execute_reply": "2020-11-25T16:49:31.488188Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " sin = nengo.Node(lambda t: np.sin(t * 4))\n", + "\n", + " pre = nengo.Ensemble(100, dimensions=1)\n", + " post = nengo.Ensemble(100, dimensions=1)\n", + "\n", + " nengo.Connection(sin, pre)\n", + " conn = nengo.Connection(pre, post, solver=nengo.solvers.LstsqL2(weights=True))\n", + "\n", + " pre_p = nengo.Probe(pre, synapse=0.01)\n", + " post_p = nengo.Probe(post, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:31.494299Z", + "iopub.status.busy": "2020-11-25T16:49:31.493510Z", + "iopub.status.idle": "2020-11-25T16:49:32.604983Z", + "shell.execute_reply": "2020-11-25T16:49:32.604533Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Verify that it does a communication channel\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2.0)\n", + "\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What does BCM do?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:32.611449Z", + "iopub.status.busy": "2020-11-25T16:49:32.610942Z", + "iopub.status.idle": "2020-11-25T16:49:32.614474Z", + "shell.execute_reply": "2020-11-25T16:49:32.614030Z" + } + }, + "outputs": [], + "source": [ + "conn.learning_rule_type = nengo.BCM(learning_rate=5e-10)\n", + "with model:\n", + " weights_p = nengo.Probe(conn, \"weights\", synapse=0.01, sample_every=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:32.619334Z", + "iopub.status.busy": "2020-11-25T16:49:32.618527Z", + "iopub.status.idle": "2020-11-25T16:49:45.327257Z", + "shell.execute_reply": "2020-11-25T16:49:45.328774Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:45.335300Z", + "iopub.status.busy": "2020-11-25T16:49:45.333356Z", + "iopub.status.idle": "2020-11-25T16:49:46.064242Z", + "shell.execute_reply": "2020-11-25T16:49:46.064677Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Connection weight')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The BCM rule appears to cause the ensemble\n", + "to either be on or off.\n", + "This seems consistent with the idea that it potentiates\n", + "active synapses, and depresses non-active synapses.\n", + "\n", + "As well, we can show that BCM sparsifies the weights.\n", + "The sparsity measure below uses the Gini measure of sparsity,\n", + "for reasons explained [in this paper](https://arxiv.org/pdf/0811.4706.pdf)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:46.070507Z", + "iopub.status.busy": "2020-11-25T16:49:46.070008Z", + "iopub.status.idle": "2020-11-25T16:49:46.075946Z", + "shell.execute_reply": "2020-11-25T16:49:46.075339Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting sparsity: 0.14271014651392688\n", + "Ending sparsity: 0.46467866311030237\n" + ] + } + ], + "source": [ + "def sparsity_measure(vector): # Gini index\n", + " # Max sparsity = 1 (single 1 in the vector)\n", + " v = np.sort(np.abs(vector))\n", + " n = v.shape[0]\n", + " k = np.arange(n) + 1\n", + " l1norm = np.sum(v)\n", + " summation = np.sum((v / l1norm) * ((n - k + 0.5) / n))\n", + " return 1 - 2 * summation\n", + "\n", + "\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What does Oja do?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:46.082789Z", + "iopub.status.busy": "2020-11-25T16:49:46.081261Z", + "iopub.status.idle": "2020-11-25T16:49:46.083477Z", + "shell.execute_reply": "2020-11-25T16:49:46.083932Z" + } + }, + "outputs": [], + "source": [ + "conn.learning_rule_type = nengo.Oja(learning_rate=6e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:46.089110Z", + "iopub.status.busy": "2020-11-25T16:49:46.088314Z", + "iopub.status.idle": "2020-11-25T16:49:59.534545Z", + "shell.execute_reply": "2020-11-25T16:49:59.536171Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:49:59.543178Z", + "iopub.status.busy": "2020-11-25T16:49:59.540913Z", + "iopub.status.idle": "2020-11-25T16:50:00.252425Z", + "shell.execute_reply": "2020-11-25T16:50:00.252863Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting sparsity: 0.12262675709408066\n", + "Ending sparsity: 0.27264319863455877\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Oja rule seems to attenuate the signal across the connection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do BCM and Oja together do?\n", + "\n", + "We can apply both learning rules to the same connection\n", + "by passing a list to `learning_rule_type`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:00.258838Z", + "iopub.status.busy": "2020-11-25T16:50:00.257328Z", + "iopub.status.idle": "2020-11-25T16:50:00.259494Z", + "shell.execute_reply": "2020-11-25T16:50:00.259972Z" + } + }, + "outputs": [], + "source": [ + "conn.learning_rule_type = [\n", + " nengo.BCM(learning_rate=5e-10),\n", + " nengo.Oja(learning_rate=2e-9),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:00.265078Z", + "iopub.status.busy": "2020-11-25T16:50:00.264291Z", + "iopub.status.idle": "2020-11-25T16:50:15.772601Z", + "shell.execute_reply": "2020-11-25T16:50:15.773077Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(20.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:15.777130Z", + "iopub.status.busy": "2020-11-25T16:50:15.776240Z", + "iopub.status.idle": "2020-11-25T16:50:16.493206Z", + "shell.execute_reply": "2020-11-25T16:50:16.493604Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting sparsity: 0.14517215817668105\n", + "Ending sparsity: 0.42952806258413734\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[pre_p], label=\"Pre\")\n", + "plt.plot(sim.trange(), sim.data[post_p], label=\"Post\")\n", + "plt.ylabel(\"Decoded value\")\n", + "plt.ylim(-1.6, 1.6)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.subplot(2, 1, 2)\n", + "# Find weight row with max variance\n", + "neuron = np.argmax(np.mean(np.var(sim.data[weights_p], axis=0), axis=1))\n", + "plt.plot(sim.trange(sample_every=0.01), sim.data[weights_p][..., neuron])\n", + "plt.ylabel(\"Connection weight\")\n", + "print(\"Starting sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][0])))\n", + "print(\"Ending sparsity: {0}\".format(sparsity_measure(sim.data[weights_p][-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combination of the two appears to accentuate\n", + "the on-off nature of the BCM rule.\n", + "As the Oja rule enforces a soft upper or lower bound\n", + "for the connection weight, the combination\n", + "of both rules may be more stable than BCM alone.\n", + "\n", + "That's mostly conjecture, however;\n", + "a thorough investigation of the BCM and Oja rules\n", + "and how they interact has not yet been done." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/learning/learn_associations.html b/examples/learning/learn_associations.html new file mode 100644 index 0000000000..023e0d2fd9 --- /dev/null +++ b/examples/learning/learn_associations.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/learning/learn_communication_channel.html b/examples/learning/learn_communication_channel.html new file mode 100644 index 0000000000..810ba449e2 --- /dev/null +++ b/examples/learning/learn_communication_channel.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/learning/learn_product.html b/examples/learning/learn_product.html new file mode 100644 index 0000000000..43fd49a0d8 --- /dev/null +++ b/examples/learning/learn_product.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/learning/learn_square.html b/examples/learning/learn_square.html new file mode 100644 index 0000000000..2f7b2c9030 --- /dev/null +++ b/examples/learning/learn_square.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/learning/learn_unsupervised.html b/examples/learning/learn_unsupervised.html new file mode 100644 index 0000000000..53495a5e5a --- /dev/null +++ b/examples/learning/learn_unsupervised.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/learning/lmu.html b/examples/learning/lmu.html new file mode 100644 index 0000000000..6704e331fe --- /dev/null +++ b/examples/learning/lmu.html @@ -0,0 +1,878 @@ + + + + + + + + Legendre Memory Units in Nengo — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Legendre Memory Units in Nengo

+

Legendre Memory Units (LMUs) are a novel recurrent neural network architecture, described in Voelker, Kajić, and Eliasmith (NeurIPS 2019). We will not go into much of the underlying details of these methods here; broadly speaking, we can think of an LMU as a recurrent network that does a very good job of representing the temporal information in some input signal. Since +most RNN tasks involve computing some function of that temporal information, the better the RNN is at representing the temporal information the better it will be able to perform the task. See the paper for all the details!

+

In this example we will show how an LMU can be used to delay an input signal for some fixed length of time. This is a simple sounding task, but performing an accurate delay requires the network to store the complete history of the input signal across the delay period. So it is a good measure of a network’s fundamental temporal storage.

+
+
[1]:
+
+
+
+%matplotlib inline
+
+from collections import deque
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+from nengo.utils.filter_design import cont2discrete
+
+
+
+

Our LMU in this example will have two parameters: the length of the time window it is optimized to store, and the number of Legendre polynomials used to represent the signal (using higher order polynomials allows the LMU to represent higher frequency information).

+

The input will be a band-limited white noise signal, which has its own parameters determining the amplitude and frequency of the signal.

+

Feel free to adjust any of these parameters to see what impact they have on the model’s performance.

+
+
[2]:
+
+
+
+# parameters of LMU
+theta = 1.0  # length of window (in seconds)
+order = 8  # number of Legendre polynomials representing window
+
+# parameters of input signal
+freq = 2  # frequency limit
+rms = 0.30  # amplitude of input (set to keep within [-1, 1])
+delay = 0.5  # length of time delay network will learn
+
+# simulation parameters
+dt = 0.001  # simulation timestep
+sim_t = 100  # length of simulation
+seed = 0  # fixed for deterministic results
+
+
+
+

Next we need to compute the analytically derived weight matrices used in the LMU. These are determined statically based on the theta/order parameters from above. It is also possible to optimize these parameters using backpropagation, using a framework such as NengoDL.

+
+
[3]:
+
+
+
+# compute the A and B matrices according to the LMU's mathematical derivation
+# (see the paper for details)
+Q = np.arange(order, dtype=np.float64)
+R = (2 * Q + 1)[:, None] / theta
+j, i = np.meshgrid(Q, Q)
+
+A = np.where(i < j, -1, (-1.0) ** (i - j + 1)) * R
+B = (-1.0) ** Q[:, None] * R
+C = np.ones((1, order))
+D = np.zeros((1,))
+
+A, B, _, _, _ = cont2discrete((A, B, C, D), dt=dt, method="zoh")
+
+
+
+

Next we will set up an artificial synapse model to compute an ideal delay (we’ll use this to train the model later on). And we can run a simple network containing just our input signal and the ideal delay to see what that looks like.

+
+
[4]:
+
+
+
+class IdealDelay(nengo.synapses.Synapse):
+    def __init__(self, delay):
+        super().__init__()
+        self.delay = delay
+
+    def make_state(self, shape_in, shape_out, dt, dtype=None, y0=None):
+        return {}
+
+    def make_step(self, shape_in, shape_out, dt, rng, state):
+        # buffer the input signal based on the delay length
+        buffer = deque([0] * int(self.delay / dt))
+
+        def delay_func(t, x):
+            buffer.append(x.copy())
+            return buffer.popleft()
+
+        return delay_func
+
+
+with nengo.Network(seed=seed) as net:
+    # create the input signal
+    stim = nengo.Node(
+        output=nengo.processes.WhiteSignal(
+            high=freq, period=sim_t, rms=rms, y0=0, seed=seed
+        )
+    )
+
+    # probe input signal and an ideally delayed version of input signal
+    p_stim = nengo.Probe(stim)
+    p_ideal = nengo.Probe(stim, synapse=IdealDelay(delay))
+
+# run the network and display results
+with nengo.Simulator(net) as sim:
+    sim.run(10)
+
+    plt.figure(figsize=(16, 6))
+    plt.plot(sim.trange(), sim.data[p_stim], label="input")
+    plt.plot(sim.trange(), sim.data[p_ideal], label="ideal")
+    plt.legend()
+
+
+
+
+
+
+
+../../_images/examples_learning_lmu_7_0.png +
+
+

Now we are ready to build the LMU. The full LMU architecture consists of two components: a linear memory, and a nonlinear hidden state. But the nonlinear hidden state is really only useful when it is optimized using backpropagation (see this example in NengoDL). So here we will just build the linear memory component.

+
+
[5]:
+
+
+
+with net:
+    lmu = nengo.Node(size_in=order)
+    nengo.Connection(stim, lmu, transform=B, synapse=None)
+    nengo.Connection(lmu, lmu, transform=A, synapse=0)
+
+
+
+

On its own the LMU isn’t performing a task, it is just internally representing the input signal. So to get this network to perform a function, we will add an output Ensemble that gets the output of the LMU as input. Then we will train the output weights of that Ensemble using the PES online learning rule. The error signal will be based on the ideally delayed input signal, so the network should learn to compute that same delay.

+
+
[6]:
+
+
+
+with net:
+    ens = nengo.Ensemble(1000, order, neuron_type=nengo.SpikingRectifiedLinear())
+    nengo.Connection(lmu, ens, synapse=None)
+
+    out = nengo.Node(size_in=1)
+
+    # we'll use a Node to compute the error signal so that we can shut off
+    # learning after a while (in order to assess the network's generalization)
+    err_node = nengo.Node(lambda t, x: x if t < sim_t * 0.8 else 0, size_in=1)
+
+    # the target signal is the ideally delayed version of the input signal,
+    # which is subtracted from the ensemble's output in order to compute the
+    # PES error
+    nengo.Connection(stim, err_node, synapse=IdealDelay(delay), transform=-1)
+    nengo.Connection(out, err_node, synapse=None)
+
+    learn_conn = nengo.Connection(
+        ens, out, function=lambda x: 0, learning_rule_type=nengo.PES(2e-4)
+    )
+    nengo.Connection(err_node, learn_conn.learning_rule, synapse=None)
+
+    p_out = nengo.Probe(out)
+
+
+
+

Finally, we can run the full model to see it learning to perform the delay task.

+
+
[7]:
+
+
+
+with nengo.Simulator(net) as sim:
+    sim.run(sim_t)
+
+# we'll break up the output into multiple plots, just for
+# display purposes
+t_per_plot = 10
+for i in range(sim_t // t_per_plot):
+    plot_slice = (sim.trange() >= t_per_plot * i) & (
+        sim.trange() < t_per_plot * (i + 1)
+    )
+
+    plt.figure(figsize=(16, 6))
+    plt.plot(sim.trange()[plot_slice], sim.data[p_stim][plot_slice], label="input")
+    plt.plot(sim.trange()[plot_slice], sim.data[p_ideal][plot_slice], label="ideal")
+    plt.plot(sim.trange()[plot_slice], sim.data[p_out][plot_slice], label="output")
+    if i * t_per_plot < sim_t * 0.8:
+        plt.title("Learning ON")
+    else:
+        plt.title("Learning OFF")
+    plt.ylim([-1, 1])
+    plt.legend()
+
+
+
+
+
+
+
+../../_images/examples_learning_lmu_13_0.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_1.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_2.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_3.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_4.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_5.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_6.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_7.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_8.png +
+
+
+
+
+
+../../_images/examples_learning_lmu_13_9.png +
+
+

We can see that the network is successfully learning to compute a delay. We could use these same principles to train a network to compute any time-varying function of some input signal, and an LMU will always provide an optimal representation of that input signal.

+

See these other examples for some other applications of LMUs:

+ +

As well as the original paper for more information on LMUs.

+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/learning/lmu.ipynb b/examples/learning/lmu.ipynb new file mode 100644 index 0000000000..d27d835741 --- /dev/null +++ b/examples/learning/lmu.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Legendre Memory Units in Nengo\n", + "\n", + "Legendre Memory Units (LMUs) are\n", + "a novel recurrent neural network architecture, described in\n", + "[Voelker, Kajić, and Eliasmith (NeurIPS 2019)][paper].\n", + "We will not go into much of the underlying details of these methods here;\n", + "broadly speaking, we can think of an LMU as a recurrent network\n", + "that does a very good job of representing\n", + "the temporal information in some input signal.\n", + "Since most RNN tasks involve computing\n", + "some function of that temporal information,\n", + "the better the RNN is at representing the temporal information\n", + "the better it will be able to perform the task.\n", + "See the [paper][] for all the details!\n", + "\n", + "In this example we will show how an LMU can be used\n", + "to delay an input signal for some fixed length of time.\n", + "This is a simple sounding task, but performing an accurate delay\n", + "requires the network to store the complete history of the input signal\n", + "across the delay period.\n", + "So it is a good measure of a network's fundamental temporal storage.\n", + "\n", + "[paper]:\n", + "https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:17.881325Z", + "iopub.status.busy": "2020-11-25T16:50:17.880407Z", + "iopub.status.idle": "2020-11-25T16:50:18.373362Z", + "shell.execute_reply": "2020-11-25T16:50:18.372467Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from collections import deque\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo\n", + "from nengo.utils.filter_design import cont2discrete" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our LMU in this example will have two parameters:\n", + "the length of the time window it is optimized to store,\n", + "and the number of Legendre polynomials used to represent the signal\n", + "(using higher order polynomials\n", + "allows the LMU to represent higher frequency information).\n", + "\n", + "The input will be a band-limited white noise signal,\n", + "which has its own parameters\n", + "determining the amplitude and frequency of the signal.\n", + "\n", + "Feel free to adjust any of these parameters\n", + "to see what impact they have on the model's performance." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.378135Z", + "iopub.status.busy": "2020-11-25T16:50:18.377573Z", + "iopub.status.idle": "2020-11-25T16:50:18.381153Z", + "shell.execute_reply": "2020-11-25T16:50:18.380716Z" + } + }, + "outputs": [], + "source": [ + "# parameters of LMU\n", + "theta = 1.0 # length of window (in seconds)\n", + "order = 8 # number of Legendre polynomials representing window\n", + "\n", + "# parameters of input signal\n", + "freq = 2 # frequency limit\n", + "rms = 0.30 # amplitude of input (set to keep within [-1, 1])\n", + "delay = 0.5 # length of time delay network will learn\n", + "\n", + "# simulation parameters\n", + "dt = 0.001 # simulation timestep\n", + "sim_t = 100 # length of simulation\n", + "seed = 0 # fixed for deterministic results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to compute\n", + "the analytically derived weight matrices used in the LMU.\n", + "These are determined statically based on\n", + "the `theta`/`order` parameters from above.\n", + "It is also possible to optimize these parameters using backpropagation,\n", + "using a framework such as [NengoDL](https://www.nengo.ai/nengo-dl)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.389214Z", + "iopub.status.busy": "2020-11-25T16:50:18.387257Z", + "iopub.status.idle": "2020-11-25T16:50:18.392467Z", + "shell.execute_reply": "2020-11-25T16:50:18.392859Z" + } + }, + "outputs": [], + "source": [ + "# compute the A and B matrices according to the LMU's mathematical derivation\n", + "# (see the paper for details)\n", + "Q = np.arange(order, dtype=np.float64)\n", + "R = (2 * Q + 1)[:, None] / theta\n", + "j, i = np.meshgrid(Q, Q)\n", + "\n", + "A = np.where(i < j, -1, (-1.0) ** (i - j + 1)) * R\n", + "B = (-1.0) ** Q[:, None] * R\n", + "C = np.ones((1, order))\n", + "D = np.zeros((1,))\n", + "\n", + "A, B, _, _, _ = cont2discrete((A, B, C, D), dt=dt, method=\"zoh\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will set up an artificial synapse model\n", + "to compute an ideal delay\n", + "(we'll use this to train the model later on).\n", + "And we can run a simple network containing\n", + "just our input signal and the ideal delay to see what that looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.418077Z", + "iopub.status.busy": "2020-11-25T16:50:18.406020Z", + "iopub.status.idle": "2020-11-25T16:50:18.923747Z", + "shell.execute_reply": "2020-11-25T16:50:18.924215Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "class IdealDelay(nengo.synapses.Synapse):\n", + " def __init__(self, delay):\n", + " super().__init__()\n", + " self.delay = delay\n", + "\n", + " def make_state(self, shape_in, shape_out, dt, dtype=None, y0=None):\n", + " return {}\n", + "\n", + " def make_step(self, shape_in, shape_out, dt, rng, state):\n", + " # buffer the input signal based on the delay length\n", + " buffer = deque([0] * int(self.delay / dt))\n", + "\n", + " def delay_func(t, x):\n", + " buffer.append(x.copy())\n", + " return buffer.popleft()\n", + "\n", + " return delay_func\n", + "\n", + "\n", + "with nengo.Network(seed=seed) as net:\n", + " # create the input signal\n", + " stim = nengo.Node(\n", + " output=nengo.processes.WhiteSignal(\n", + " high=freq, period=sim_t, rms=rms, y0=0, seed=seed\n", + " )\n", + " )\n", + "\n", + " # probe input signal and an ideally delayed version of input signal\n", + " p_stim = nengo.Probe(stim)\n", + " p_ideal = nengo.Probe(stim, synapse=IdealDelay(delay))\n", + "\n", + "# run the network and display results\n", + "with nengo.Simulator(net) as sim:\n", + " sim.run(10)\n", + "\n", + " plt.figure(figsize=(16, 6))\n", + " plt.plot(sim.trange(), sim.data[p_stim], label=\"input\")\n", + " plt.plot(sim.trange(), sim.data[p_ideal], label=\"ideal\")\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to build the LMU.\n", + "The full LMU architecture consists of two components:\n", + "a linear memory, and a nonlinear hidden state.\n", + "But the nonlinear hidden state is really only useful\n", + "when it is optimized using backpropagation (see\n", + "[this example in NengoDL](https://www.nengo.ai/nengo-dl/examples/lmu.html)).\n", + "So here we will just build the linear memory component." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.931125Z", + "iopub.status.busy": "2020-11-25T16:50:18.930395Z", + "iopub.status.idle": "2020-11-25T16:50:18.932972Z", + "shell.execute_reply": "2020-11-25T16:50:18.932506Z" + } + }, + "outputs": [], + "source": [ + "with net:\n", + " lmu = nengo.Node(size_in=order)\n", + " nengo.Connection(stim, lmu, transform=B, synapse=None)\n", + " nengo.Connection(lmu, lmu, transform=A, synapse=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On its own the LMU isn't performing a task,\n", + "it is just internally representing the input signal.\n", + "So to get this network to perform a function,\n", + "we will add an output Ensemble\n", + "that gets the output of the LMU as input.\n", + "Then we will train the output weights of that Ensemble\n", + "using the PES online learning rule.\n", + "The error signal will be based on the ideally delayed input signal,\n", + "so the network should learn to compute that same delay." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.944347Z", + "iopub.status.busy": "2020-11-25T16:50:18.940475Z", + "iopub.status.idle": "2020-11-25T16:50:18.946103Z", + "shell.execute_reply": "2020-11-25T16:50:18.946522Z" + } + }, + "outputs": [], + "source": [ + "with net:\n", + " ens = nengo.Ensemble(1000, order, neuron_type=nengo.SpikingRectifiedLinear())\n", + " nengo.Connection(lmu, ens, synapse=None)\n", + "\n", + " out = nengo.Node(size_in=1)\n", + "\n", + " # we'll use a Node to compute the error signal so that we can shut off\n", + " # learning after a while (in order to assess the network's generalization)\n", + " err_node = nengo.Node(lambda t, x: x if t < sim_t * 0.8 else 0, size_in=1)\n", + "\n", + " # the target signal is the ideally delayed version of the input signal,\n", + " # which is subtracted from the ensemble's output in order to compute the\n", + " # PES error\n", + " nengo.Connection(stim, err_node, synapse=IdealDelay(delay), transform=-1)\n", + " nengo.Connection(out, err_node, synapse=None)\n", + "\n", + " learn_conn = nengo.Connection(\n", + " ens, out, function=lambda x: 0, learning_rule_type=nengo.PES(2e-4)\n", + " )\n", + " nengo.Connection(err_node, learn_conn.learning_rule, synapse=None)\n", + "\n", + " p_out = nengo.Probe(out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can run the full model\n", + "to see it learning to perform the delay task." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:18.954797Z", + "iopub.status.busy": "2020-11-25T16:50:18.954024Z", + "iopub.status.idle": "2020-11-25T16:50:37.853937Z", + "shell.execute_reply": "2020-11-25T16:50:37.854356Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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SiUTyX4IMYCUSiUQikYwabIFptjVQHcxAANsTtDkNobVCBKmDW+jYiM+BFpmBlUgkkkAgA1iJRCKRSCSjhupWEZhmxke43G8LbCtbuoI2pyG0VUJclvv98TnQ2wo9rcGbk0QikfyXoEkAqyjKeYqiFCmKUqwoyoMu9j+lKMoe67/DiqK0OOwzO+x7T4v5SCQSiUQiOTGpbeshNsJAdLhrn8mM+Ah0ClSGMgPbVj1MADtGPLZWBmc+EolE8l/EiF2IFUXRA38BVgIVwHZFUd5TVfWgbYyqqt93GH8PMNfhFN2qqs4Z6TwkEolEIpGc+FS3dpMR5zr7CmDU60iPiwhdDazZBB21XgawxyF9enDmJZFIJP8laJGBXQQUq6p6TFXVPuBV4FIP468HXtHguhKJRCKRSE4yatp6yXAjH7aRlRAZugC2oxZQh5cQg3QilkgkJwXPPfccVVVVfh9fWlrKyy+/rNl8tAhgswHHT+gK67YhKIqSB4wD1jlsjlAUZYeiKFsVRbnM3UUURfmWddyO+vp6DaYtkUgkEolktFEzTAYWRABbGaoAts16ExfrIYCNSQedURo5Sf4rMJktdPb2h3oanjF1Q39vqGdxwnIyBrC+cB2wWlVVx+ZteaqqLgBuAH6vKMoEVweqqvoPVVUXqKq6IDU1NRhzlUgkEolEEkT6zRbq23vdGjjZyIgLp7atB1VVgzQzB2wBrKcMrE4HsZnQXhOcOUkkIaCrr5/H3i9gzmOfMuNna7joTxvYdqwx1NNypr4IXrgUfpUJ/5sDq78B7bWhntWo4Mknn2TmzJnMnDmT3//+95SWljJz5kz7/ieeeIJHH32U1atXs2PHDm688UbmzJlDd3c3Y8eO5f7772fWrFksWrSI4uJiAFatWsXq1avt54iJiQHgwQcfZMOGDcyZM4ennnpqxHMfcQ0sUAmMcXidY93miuuAuxw3qKpaaX08pijKl4j62KMazEsikUgkEskJRH1HLxYV0ocJYNPjIugxWWjr6Sc+0hik2VnxJoAFiE2H9urAz0ciCQEdvf3c+szX7Cpv5vK52YxNjmb1zgpu/Nc2/nDdXC6cnRnqKULlTnjhMtAZYNkPoK8Tdj4HFdth1YeQMGa4MwSF33z9GwqbCjU959SkqTyw6AG3+3fu3Mmzzz7Ltm3bUFWVxYsXs3z5cpdjr7rqKv785z/zxBNPsGDBAvv2+Ph49u/fzwsvvMD3vvc9PvjgA7fXe/zxx3niiSc8jvEFLTKw24FJiqKMUxQlDBGkDnETVhRlKpAIbHHYlqgoSrj1eQpwGnBw8LESiUQikUhOfmqGaaFjIzU2HID69hA4EbdXgSECIhM9j4tJt9bLSiQnF6qq8pO39rPneAt/vWEeT14zh3vPmsRH313GnDEJ/OD1PRysagvtJDsb4ZUbxN/ptzfAWY/A+b+B2z6C7mZ4/eb/aknxxo0bufzyy4mOjiYmJoYrrriCDRs2+HSO66+/3v64ZcuWYUZry4gzsKqq9iuKcjewBtADz6iqWqAoys+BHaqq2oLZ64BXVWe9zzTg74qiWBDB9OOO7sUSiUQikUj+e7AFsOnD1MCmxYr9dW29TEyLDfi8nGirEvJgRfE8LjYDSjcGZ04SSRD5+EAN7+2t4kfnTOb8WQOZ1phwA3+7aT4X/nED963ey3t3L0WvG+bvJFB89CPoboJvrhswVQPIng+X/RVeuwk2/RGW3xea+TngKVMaTFpaWrBYLPbXPT2eFwgVh89A23ODwWA/h8Vioa+vLwAz1agGVlXVj1RVnayq6gRVVX9l3faIQ/CKqqqPqqr64KDjNquqOktV1Xzr47+1mI9EIpFIJJITj9o2ccM0nIlTWpzIwNa1hyCD0l4zvHwYICYDelrAFMJ+tRKJxvT2m/nfjw8xNSOWO1dMHLI/NTacRy6eTkFVG698XR6CGQLHt0PBW7D0B5Axa+j+aRfD9Ethw/9Ba0Xw5zcKWLZsGe+88w5dXV10dnby9ttvc/7551NXV0djYyO9vb1Oct/Y2Fja29udzvHaa6/ZH0899VQAxo4dy86dOwF47733MJlMbo8fCcE2cZJIJBKJRCJxSX1HL3qdQmJUmMdxabG2ADYEwWFHLcSkDT8uNn1gvERykvDq18c53tTNjy+Y5ja7euGsTBbkJfKXL4rp67e4HBNQ1j4qJPxL7nE/5pxfgsUEG38frFmNKubNm8eqVatYtGgRixcv5o477mDhwoU88sgjLFq0iJUrVzJ16lT7+FWrVvHtb3/bbuIE0NzczOzZs/nDH/5gN2b65je/yfr168nPz2fLli1ER0cDMHv2bPR6Pfn5+aPGxEkikUgkEolkxDR29JEUHYZuGNlhTLiBSKOe2rYQZGA76yHai24IsVZpZXsNJOYFdk4SSRAwW1T+tfEY8/MSWT7Z/d+AoijcfeZEVj27nbd3V3DtwtzgTbJyF5RthHN/DeEx7scl5EL+dbDrBTj9voEFp/8ifvCDH/CDH/zAadu9997LvffeO2TslVdeyZVXXum07b777uM3v/mN07b09HS2bt1qf23bbzQaWbduHVohM7ASiUQikZzEWCwqb++u4MuiulBPxT2dDbDt7+iaj5ESEz7scEVRSIsLD76EuL8Xeloh2osMbIwtAytb6UhODj4/VMvxpm5uP23csGOXT05lRlYcz2wsDW67q21PQ1gMzL1p+LFLfwDmPtj5bODnJdEUGcBKJBKJRHIS8/u1h/n+a3tZ9ex2Pto/Ctu6mE3w/CXw8f08UHkP4yK7vDosLTacurYgS4g7G8RjdMrwY2MzxKPsOSk5SXhxaxmZ8RGcO2P4bKWiKNy4OI+i2nZ2H28J/ORAOA8feAvm3AgR8cOPT54A41fArhfBYg749E4mSktLSUnx4nMwQMgAViKRSCSSk5SGjl7+/tUxzp2RztSMWH63pii42RBv2Pca1BXA8geIUTu4vPddrw5Li42gPtgZ2E5rFtubGtioFFD0MgMrOSmoa+thU3EDV83PwaD3Lny4ZE4WUWF6Xg2WmVPBW6Kudd4t3h8zfxW0VcBR7eStksAjA1iJRCKRSE5SPjlQQ2+/he+vnMwdy8ZT0tDJnmBlQ7xl32uQPAlW/Jiv1Dksal8LluGNX1Jjw+2uxUHDnoH1ogZWpxOBbrsMYCUnPu/vq8aiwqVzsr0+JibcwMWzs/hgXzXdfUHIcO5fDWnTIWOm98dMuQCikmHPfwI3LzeMusXEEOLrz0IGsBKJRCKRnKSsKahhfEo0U9JjOWdGOka9wpqCUSRp7WqC0k0w/RI6+8y8Z1pMfF8tVO8Z9tC0uHA6+8x09fUHfp42OqwZWG8CWBB1sNKFWHIS8O6eSmZmxzExzYMxkgsumZNFV5+Z9YcDXIPfXAbHt8Ksq3w7zhAmWuocXgN93pUvaEFERASNjY0yiEUEr42NjUREeG6f5oh0IZZIJBKJ5CSkx2Rm27Embl2Sh6IoxEUYyc9JYOuxxlBPbYCS9aCaYfL5NHb0sclizZyUboTseR4PTYkWZk+NHX1EJQXpdqazXjx6G8BGpw4cI5GcoJQ1drKvopWHLpjm87GLxyWRFB3GB/uqOW9mZgBmZ6XgbfE408cAFmD6ZbDjGTjyKcy4TMtZuSUnJ4eKigrq6+XnA4iAPicnx+vxMoCVSCQSieQkpKCqlT6zhQVjk+zbThmfzN/WH6Wjt5+Y8FFwC3B8OxgiIDOf+spO6kikK3YcUaUb4bShrRwcSYoWvWIbO/sYkxQVjNmKYNQY5bk9hyPRKVB3KLBzkkgCzOeHRPb0HC/MmwZj0Os4d0YG7+6ppMdkJsKo13p6gsOfQMZs/1pW5Z0matYPvhO0ANZoNDJu3PBuzhLXSAmxRCKRSCQnITvLmgGYl5to3zY/LxGzReVgVVuopuXM8W2QNRcMYTR2CEOmnowFULkThpHWJceIALapM4hGTp313jkQ24hOga6GYf8vEsloZl1hHRPTYshLjvbr+AtnZVplxAHKNnY1ic+Syef5d7zeANMuFjJiU5Dr6iV+IQNYiUQikUhOQnaXtzAmKZLU2IG+qjOy4gCRnQ05/b1QvRdyFgLQ0NEHgD47XwR9w5gfJTtIiINGR513PWBtRKVAfw/0dQZuThJJAOno7WdbSSNnTvXhfT+IxeOTiAk3BK4XdfFaUC0w+Vz/zzHlfDB1Qdkm7eYlCRgygJVIJBKJ5CTkUHUbs7KdeyGmxoaTEhNGwWjIwDYcFi0vsuYA2DOwkblzxf6a/R4PT4oZkBAHjc4G7+tfYSBbK+tgJScoG4/UYzKrIwpgjXodSyem8GVRfWBMiw6vEYtFWZ7r5j0ydhnow0UwLBn1yABWIpFIJJKTjB6TmbKmLialxTptVxSF6Vnxo0NCXFcoHlOFMUxDRy9xEQbCsmaL7TV7PR4eHaYn3KCjKagBbB3E+BLAWsd2jSLjLInEB74orCc2wsD8vMThB3vgjKmpVLf2UFTbrtHMrFgsIuicdI5oXeUvYVEwbpkwcpKMemQAK5FIJBLJSUZxXQeqCpPTY4fsm5wWw7GGDiyWENdl1h8CnQGSJwJCQpwSEw4RcRA/BhqOeDxcURSSo8OCJyG2WHzPwEbZMrANgZmTRBJgtpY0snhcMkb9yEKG5ZNFBvfLIo3VCLUHoKcFxi8f+bkmroTGYmg6NvJzSQKKDGAlEolEIjnJOFInshyT04e65Y5LjabHZKG6LcRmJXWFkDRB9GEEGjt77cZMJE8QN5LDkBQTRmOwTJx6WkTLH58kxMniUUqIJScgVS3dlDV2ceqE5BGfKyM+gmmZcXxRqHEdbOkG8Th26cjPNWmleDwiZcSjHRnASiQSiURyklFU04FRrzA2Zahr6DjrtpL6EBsL1R2EtKn2ly1dJhKibAHsRBHADudEHB0ePAlxV5N4jEzyPM4Ru4RYZmAlJx62ntGnjPfhPe+BZZNS2F3eQnefWZPzAaJndOI4iPe+h6hbkidAQq7oTy0Z1cgAViKRSCSSk4ziug7GpUS7lP2NTxFZ2ZKGjmBPawBTDzSX2utfAZq7+kiMMooXyROhp3XY2tGgSoi7rQFslA8382HRYIiUEmLJCcmWo43ERxqZlhGnyflOHZ9Mn9nCrvJmTc6HxQylm0TtqlaMPV04EVss2p1TojkygJVIJBKJ5CSjvKnTbc/G9LhwIo16jjWEMAPbUg6okDQeAFVVae4ykeiYgYVh62CTooMoIfYnAwvCiVgGsJITEFH/moROp2hyvgVjE9HrFLYc1cjUrGY/9LYKB2GtGLcMupuhrkC7c0o0RwawEolEIpGcRKiqSnlTF7lJUS73K4rCuJRoSkIZwDaXisfEsQB0m8z09VscJMQTxOMwdbDJMeH0mCx09fUHZp6O2DOwPrqxRqdICbHkhKOypZvjTd2a1L/aiI0wMjM73i5NHjGlG8WjFvWvNmznKtmg3TklmiMDWIlEIpFITiLqO3rpMVncBrAAuUlRHG/qCuKsBjEogG3uMomXNglx/BhQdNBS5vE0ydFWA6hgyIj9zcBGyQys5MRjR6l4vy8cq039q41Txyezt6JFm0Wn41shIQ/iskZ+LhvxOaKm1hYcS0YlMoCVSCQSieQkwhaYegpgsxMjqWrpQR3GJClgNJeK2tAY0Vqj2WrEZM/A6o0QmwWtFR5Pk2QNYINi5NTdBIoeIuJ9Oy46RfaBlZxw7DneQoRRx9SMoa24RsIp45MwmVV2lmlQB1uxE8YsGvl5BjN2KZRtFDW2klGJDGAlEolEIjmJKLcGsGM8BLBZCZF0m8z2zGfQaS4V2VdF1Na1DM7AgsiEtBz3eBpb252g1MF2NUFkon3OXhOdItrohGqxQCLxg93lLczOTsAwwv6vg1k4NgmDFnWwrZXQXgXZC7SZmCPjThcmcjX7tT+3RBNkACuRSCQSyUnE8aZuAHISI92OyU4Q+yqbu4MypyHYAljbyy6RQU20ZlQBSBgDreUeT5McHQ4ESULc3eSbA7GNqBTo74G+ELctkki8pLffzMGqNubmJmh+7uhwAzOz49lROsIMbMV28ZizcOSTGkzeaeLx+Dbtzy3RBBnASiQSiURyElHe1EVGXAQRRr3bMbbgtrIlBHWwqjokgG3pskmIHTOwY6CtyqOMLzFajLcFwAGlq8n3+leAKKsJjpQRS04QDla10We2MGdMQkDOPz8vkb0VLfT1j6BVTeUO0IdBxkztJmYjPhvismUAO4qRAaxEIpFIJCcRnhyIbWTZMrAtPcGYkjOdDWDqHJSBFRLihEiHDGx8Dlj6ob3G7aliwg0YdEpwpNDdzX5mYK3H2FyMJZJRzp7jLQDMzfXRcdtL5ucl0ttv4WB1m/8nqdgBmflgCNduYo6MWQTHvw7MuSUjRgawEolEIpGcRFQ2d5PtQT4MotY00qgPjYTYJgtOGGPf1NzVR0y4gTCDw21JQq51vPs6WEVRSIgy2mtoA4q/GdhIaxDQrYFpjUQSBHaXt5AZH0FGfERAzj8/T/xN+G3kZDZB1e7AyIdtjFksPntaKwN3DYnfyABWIpFINKC9x8Tm4obQtiYZjqYSKPkKettDPRNJgLBYVGrbesgc5sZTURSyEyNDIyFuqxKPcdn2TS1dJmf5MAgJMQxr5JQQFWaXIAeU7ibfe8DCQNDbJTOwkhODPcdbAiYfBkiPiyA7IZJd5X4GsLUFoq48e762E3PE5m5cIbOwoxFDqCcgkUgkJzqfHKjmvjf20d4r+tp9Y+k4HrpgGjqdj26lgcLcD588CNv/KV5HJMDlf4cp54V0WhLtaejspd+iDhvAgjByqgqFhNhFANvc1UdiVJjzOFuG1kMGFkQ2OeA1sH1d4obZrxpYm4RYZmAlo5+Wrj7Km7q4YXFuQK8zLy/R3mvWZ6p2icdABrAZs0Wrr+Nfw4zLA3cdiV/IDKxEIpGMgM3FDdz18m4mpsfw7G0LuemUXP69sYQnPi0K9dQGWPMTEbwu/jbc8LqoPXz9ZlnfcxJS0yoC0vS44QPY9LhwattCFMDqjAPmRoga2CEZ2LBoCI/zWAMLEB8ZFngJsa1+1Z8a2IgE6zlkACsZ/RysEnWpM7LiAnqd+bkJVLf2UNXiRxlDzX4Ij3eqo9ccvRGy50kjp1GKDGAlEonET9p7THz3tT2MT4nmhdsXccaUNH5x6UyuWziGv60/6r88SkuOrIWv/w6n3AXn/wYmnwu3vAOxGfDOnWAKQQAjCRi2ADYz3nMNLIggt6GjF7MlyP1J26ogLhN0A7cgLa4ysCDep+3VHk+XGIwaWJv8158MrCEMwmKlhFhyQlBgDWCnZwY4gM0Tf0t+1cFW74OMWb73ZPaVMYugei+YQtRuTOIWGcBKJBKJn/xpXTENHb08cXU+sREie6QoCg9fNJ202HAe/6gQVQ1ycOBIfx98fD8kT4SzHx3YHpkIF/0eGoth53MhmpwkENRYM6remK+kxYZjUaGxozfQ03KmrcpJPgzQ3NlH4uAMLEBMOnTUejxdYnRY4CXEI8nAgqidlS7EkhOAgqpWMuIiSI4JkLuvlamZsUQa9b4HsBazqIHNmBWYiTmSs0g4oVftDvy1JD4hA1iJRCLxg8aOXp7fXMrlc7PJH2R2ERNu4K4zJvJ1aRNbjoaw9+OB1dB0FM75pcgCOTLxLBi7DDY+Cf1BDmAkAaO6tQejXiE52kU2cxBpVplxXXuQf//tVRCXZX/Zb7bQ1tNPgtsM7HASYiO9/RZ6TO77xY6YkWRgQSwaSQmx5ASgoKot4PJhAKNex+yceHb7qlRqLIb+bsicHZiJOWIzcpLlNqMOTQJYRVHOUxSlSFGUYkVRHnSxf5WiKPWKouyx/rvDYd+tiqIcsf67VYv5SCQSSaB5YUsZvf0WvrNigsv91y4cQ1J0GC9sKQvyzKxYLLDpj5A2Aya7MWta+n2R3Tr0fnDnJgkYNa09pMVGeGUglhYrMixBrYNVVZGBjc20b2rtFvJflxlYWwDrQclgkx4HNAs70gxsZJKUEJ8kWCwqnxyo4fXtx2nvCUL7Jn/oaoIdz8LhTz3+7Qymx2TmaH1HUAJYgDljEjhU3U5fv8X7g6r3iceMIASw0SminZfNNEoyahixC7GiKHrgL8BKoALYrijKe6qqHhw09DVVVe8edGwS8DNgAaACO63HymVKiUQyaukxmXlhSykrp6czMS3W5Zhwg56r5+fwr40l1Lb1eGWqoylH10H9Ibj8H+7rhMafIUwwdjwDs64K6vQkgaGmdfgWOjbSQ5GB7W4Wbr5ODsTWANZV1jgmA8y90NMy0E91ELbAt7nT5FXtr190WW9LRpKBbQnRYpZEM/rNFr790i7WHhKy9qfXH2X1nUtI8kLxEDSajsEz50OHVbkwf5UoGfGiXrSwph2LCtODFMDmj0mgz2yhsKaN2TkJ3h1Usw/0YZA6JaBzs5M9Hyp2BudaEq/RIgO7CChWVfWYqqp9wKvApV4eey7wmaqqTdag9TNA9nWQSCSjms8P1dHcZeLmU/I8jrt+US5mi8pbu0LQCH3PS+Jm25P9v04nbm7KNkHj0aBNTRI4atp6vKp/BUix1rjVtQUxgLW30BmQENt6uLo1cQJod18Ha5Met3QHOAMbFjtUiu8tUUlSQnwS8K+NJaw9VMtDF0zj+dsXUdHSzY/f2hfqaQ1gscCbd4hFn9vXwJJ7hM/B/tVeHV5Q1QrAjKz4AE5ygNk54jp7j7d4f1DNPkibJlyCg0HWPGgth4764FxP4hVaBLDZgGOTtgrrtsFcqSjKPkVRViuKMsbHY1EU5VuKouxQFGVHfb18E0kkktCxeudxMuMjOG1iisdxY1OiyR+TwEf7Pbuoak53MxR+CLOvGf6Ge6Y183rw3cDPSxJQVFWlurWbDC+z/WEGHUnRYdS2B1FC7CKAtWdgPQaw7v+GbO13AupE3NUkjJj8JTIJuluEAY3khKSxo5c/fn6Es6el883Tx7N8cir3njmRNQW17CwbJfLw/a9D5U4473HIPQXO/rmQ2n7+c2HqNwwHq9qIjTCQkxggJcMgshMiSY4OY29Fq3cHqKpooRMM+bANW69ZKSMeVQTLxOl9YKyqqrMRWdbnfT2Bqqr/UFV1gaqqC1JTUzWfoEQikXhDXVsP6w/Xc8W8bPRe1BleMDOD/ZWtlDd2BWF2Vg68CeY+mHPD8GMTxogv6IPvBHxaksDS1t1Pj8nidQYWRB1scDOwVjWCiwxsfKQrF2JrAOvBiTg4NbDNA/1c/SEyEVChx8sbdcmo45Wvy+nqM/Pg+QPS1duXjiM+0sizm0pDNzEbqgpb/gxp02H2tWKbTgdnPCQyiEUfDXuKgqo2pmfGoQS6PY0VRVHIH5PgfQa2rQq6GoMbwGbmg6ITCwOSUYMWAWwlMMbhdY51mx1VVRtVVbV9Q/4LmO/tsRKJRDKa+HB/NRYVLp+b49X4C2YJs5qPDgQxC7v/TXET4+2X/PTLRK+7pmMBnZZW9JjMbCpuoLiuPdRTcU9bNRSvDapxT3Wb6FXoUwAbF0FdMDOw7dWAItrjWGnr6QfcBLCx6Q7HuSYoGdieVohM8P94m/mTlBGfkPSbLby0tZxlk1KcfA+iwgxcOS+HNQU1wW9HNZjj20R2ctG3nOtdJ62E+DHDtkyzWFSKatqDVv9qY3ZOPMX1HXT09g8/uPaAeAxGCx0b4TGQOhUqZQZ2NKFFALsdmKQoyjhFUcKA64D3HAcoipLp8PIS4JD1+RrgHEVREhVFSQTOsW6TSCSSUcm6wjrGp0YzMS3Gq/FjkqKYkRXH54c897LUjO5mcSMz5QLvm7xPu1g8Hh79H78lDZ2c89RX3PivbZz95Fc88u4BLJYQ9tp1xd5X4fez4KUrxWPhh0G5rC2T6othWHqwM7DtNRCd6lS/1mZ1IY6JcOErGR4LYTEea2AjjHoijDp7Jjcg9LRCxAjqAm3mT9KJ+IRk67Ematp6uGFR7pB9Vy/IwWRW+fRgkD7j3bH3VTBGi9IRR3R6yL8OStZDZ4Pbw483d9FtMjMl3bUxYaDIH5OAqsKBSi/UCXVWf9i0aYGd1GCy5gkJcSj7ukucGHEAq6pqP3A3IvA8BLyuqmqBoig/VxTlEuuwexVFKVAUZS9wL7DKemwT8AtEELwd+Ll1m0QikYw6uvr62XasiTOmpPl03Iopqewqb6EtGC0Xjq4D1QyTzvH+mKRxkDxRZAxHMT0mM//z4g46evv5243zWLVkLC9sKeP3nx8J9dQGqNwJ794l6s9uehNSJsMbq4IiP6u3ugmnWs2ZvCEtLpz6jt7gLQJ01DllXwHae/qJDTe4l+THpHvMwIKQETcHMgPb2zbCANZaP9stb3FORD4+UE2kUc8KF5/9UzNiGZMUyacFnvsVBxSLGQo/ENnWsOih+6deBKoFDn/i9hSHazsAmJwR5ADW6j7slYy4rhBis0amhvCH7HlCuiydxEcNmtTAqqr6kaqqk1VVnaCq6q+s2x5RVfU96/Mfq6o6Q1XVfFVVz1BVtdDh2GdUVZ1o/fesFvORSCSSQLCpuJE+s4Uzp/oWwC6fnIbZorK52P3qt2Yc/lRke3IW+HbcxLOhdBOYugMzLw14aWsZh2s7+L9r8jl/ViY/u3g6V8zL5i9fFHOwqi3U0xOr8x8/CNFpcO2L4md605vi9bv3gDmwCxgNVgljSqz3AWxqTDhmi0pLd5D6WXbWQYyzj0Vbj4k4V/JhGzHpHjNHIJyIAy4hHkkNrJQQn7CYLSprCmo5c2oakWH6IfsVReGc6RlsKm70TgYbCMq3Qmc9THfTBCQzH+JyoNB9HezhWlGSMclLdZFWJEWHkZMYyT5vjJzqD0Ha1MBPajDZ88SjlBGPGoJl4iSRSCQnPOsK64gO07NwrG+9IOfmJhAbbmD94QA7qFvMUPyZCJx0Q2+0PDLhLOjvhrLNgZnbCOkxmfnbl0dZNinFngFXFIVHLppOQqSRX344uPV4CDj2BVR8DcvvH8i4RSXB+b+BugLY9UJAL1/f3kukUU+0i5tsdyRbs7VBq99zkYFt6zYR60o+bCM6RdyceyAh0hg4CbG5H/o6IHwEtYG294OUEJ9w7DneTENHL+fOzHA75owpafSZLewoDdHvt/BD0Ie7V94oCkw6G0o3iPezC47UtpMVH0FsRJDa0ziQPyaBPcNlYC0WqD8MqUGWDwOkzRA/X2nkNGqQAaxEIpF4gaqqfFlUx9JJKYQZfPvoNOp1nDYxhfVF9aiBrKGp2i1kTr7Ih22MPU18QRd/rv28NODzQ3U0dvbxzWXjnbYnRIXxP8vHs/loIzvLQpzd2vk8RCXDnBudt0+9ELIXwKbfBzQL29DRS0psmE8OosnRwsG3sTOA9aM2VFUEsNEuMrCebpqjU6DLcwY2MdoYOBfiXmt2fyQS4oh4QJES4hOQjUcaURQ4fZL7tmnz8xIx6hW2HgvR7/fYl6JsIdxD9nTsMvFernHdt/ZwbQeTglz/aiM/J57Klm67isQlLaVikTUUGVhDmDCOqtod/GtLXCIDWIlEIvGCwpp2qlt7fJYP21g6KYWq1h7KAtlO5/AaYfc/8Szfjw2LhtzFUPKV9vPSAE+9d29cnEdilJGn1x8NwcysdDWJNhWzrh7ae1dRRFa2pRwOvBWwKdR39JLiQ/0rOGZggxDA9rSCuRdinP+G2rr7iYv0lIFNFT9fN5kjEAsZrYGSQfe0iMeRBLA6vajbkxLiE45NRxuYkRVHgqs+xVYiw/TMGZPA1mONQZyZlY56ofAYv9zzuLFLxWPpxiG7zBaV4voOJqcHVz5sw1YHu6+ixf2gOmv1oR8Z2A0VG1hTOkKTwux5ULVH9nIeJcgAViKRSLxgXWEdgM8GTjZOGS9kx9tKAniDc2QN5CwaqLfzlbylok3BKLvJbunq46sjDVw213Xv3ehwA9ctyuXzQ7XUtAaxJYwjhR+K3rv517neP+kcSBo/bCuLkdDQ3ueTgRNAcowtAxsECbFNBjxYQjxsBjYVUD1mL4WE2BQYhYOtd6uXAWxNZw0mi4tgOjJRSohPMLr6+tld3sxpE9xnX22cMj6Z/ZWtwa+DLVkvHset8DwuNgOSJ7kMYMsaO+nrt4QsAzszOx6dAnuPe6iDtTkQp05xP8YN3/n8O/xo/Y/8nJ2V7Plg6oT6opGdR6IJMoCVSCQSL/iyqI6Z2XGk+dCixJEJqTGkxISxrSRAN7DtNaKX62Q/5MM2xp4GqMIQZBTx1ZEGzBaVldPT3Y65buEYLCq8vuN4EGfmwJFPhTtm5hzX+xUF5q+C8s0BuwGq7+j1ycAJhHuvogQpA9thbTMyWELcPYyJU1SyePRg5JQYFUa/RaU9EMGDDwFsl6mLlatX8sutvxy6MzJJSohPMLaXNmMyqyxxofwYzMKxSZgtqnduulpSsh7C4yFrzvBj806F41uHtIOxORAHu4WOjehwAxPTYtjrKQNbXyj62UaMrE/tfw79h4vfvtj3A7NsRk6yDnY0IANYiUQiGYaWrj52ljX7nX0FYTi0aFwS2wJVI3XkM/HoT/2rjewFog7WxQp9KPmyqI7EKKNdZuaKvORolk5M4bXtx4PfF9ZsEjVok1Z67r2bfwPojKJWVmP6zRaau3zPwOp1ColRYcHJwHYIFYNjBtZiDTrjPJo4WQNeD0ZO8VEiAG4NhBOxlwHsH3f9kcUvLwbgrSMupOKRiaNO3SDxzJajjRj1CgvHJg47dnaOeH94DMICQckGIQ/2xrgve754Pzcdc9p8xOpA7G1/80CQn5PAvopW9yqKukJI9a3+tb6rnvXH19tff1LyCY9//TilbaW+TzB5ojByq5JOxKMBGcBKJBLJMKw/XI9FhTP8rH+1sWhsEpUt3VQ0B6AO9sgaiMuG9Jn+n8MYIdrvlG3Sbl4jxGJRWV9Uz+mTU933CbVy9YIcKlu62R5sJ9Dj24Q5yqSVnsfFpMLkc+HAas3rqJo6+1BV31ro2EiKDgtOBtYuIR74O+rs60dV8ZyBjU5xPt4F8dbjA1IHO0wAe7ztOLOen8U/9//TaXtlR6XzwMgE6G7Rfn6SgLG7vJnpWfFEhXlYYLGSEBVGXnIU+zzJYLWmswGaS4SBkzfYsoiDzIgO13WQkxhJdPjw/89AMXtMAk2dfVQ0u2jlZjFDw2GvDJxUVaWoqYjG7kbOfONM7l53t33ffV/dZ39e1uZjT1edTmS5ZSudUYEMYCUSiWQYviyqJyk6zGMG0BsWjxdSSM2zsP19cPTL4TOA3pC3REiRe0ZBX1XgSF0HjZ19LPVCwnf2tHQijDre31cVhJk5ULJBmGeNO334sTOvFFJajRcJ6tpFBtXXDCwIJ+KguBB31IKiF1JaK209QvI7fA0swmHbDYENYD27EP9ymwu5MHD/+vudN0QkDBhCjWKKatq58m+bmf7IJ3z7xZ3UtYeortwTu/8DT86A302E9b8VLVY0pt9sYV9FK3PHJHh9zOycBM9GRFpTsUM85iz0bnzaNDBEDAnCjtS2MzlE8mEbc6zfry4z2E0lwgDOg4GTqqo8ueNJnt77NFe9fxV/2v0nj9e76O2LfJ9k9nzhE2EahX8T/2XIAFYikUg8YLaI9jnLvcgADseU9FjiI43aGzmVb4G+9pHJh23knQaqRWQVRwG21jje9N6NDjdw9rR0PtpfQ79Z+xtatxzfJvoEemPyM/k8MEbD/tWaTsHWfiI11r1TqjtSYsKD0wfW1kJHN3Dr0WYNOD32gY1MFAsEIc3AKhDmWl65ucp17+R9Dft4/+j7AxsiE0QwHIBgSysqW7q57h9bKGvs4pL8LL48XMcN/9wWGGm2v+x5Gd79DsRni7KHL34Fnzyo+WWKatvpNpmZm5vg9TH5OfFUtfZQ3x6kvsoV20FngMx878brjZAx20kG22+2cKy+k0khciC2MSUjljC9jn0VLjLY9YfEo4cMbGNPI88WPMtf9/4VgDePvDnsNT8p+cS3SWbNA0u/CGIlIUUGsBKJROKBPcdbaO4yjVg+DKDTKSzIS9S+X+mRT0EfBuOGaaPgDWMWiSzZKAlgd5Q1kRwtpHnecHF+Fk2dfWw+GqR2FhazyIKMWeTd+LAomHoBHHpPZM41wnbD7GsbHbBKiIOSga0TMmoHbAGsRwmxzpq1DWUAGxHnFHjbsKieg9G1ZWsHXkQkACr0BlFi6iMPv72f3n4Lr//PKTx+5WyeXbWIkoZOHvugINRTE7TXwMcPiIW2VR/C9a/A4jvh679D0ceaXmp3eQsA83KHr3+1MduaRdxf2aLpXNxSsV2UjYR59/kIiHYw1XvtZQxlTV30mS1MSgttBjbMoGN6Vhx7XJlg1Vtb6KT47kDsCUdJsVdkzxeP0sgp5MgAViKRSDzwZVEdep3C8kmpww/2gnl5iRyt76SlS8OA4cinwsTDUxN7bwmLhvQZcPzrkZ9LA3aVNTM/LxHFS2n08smpxIYbeG9vkGTEdQdF9tvbGjSAmVcJM59jX2g2jQZrDas/AWxyTBgtXabAZ60761y00PFCQgwic+vBhTjwAazr7HqXyXM9e0OPw5wjE8TjKK2D3VnWzBdF9Xz3rEmMTxWfJadOSObO5RN4a1el9gtv/rD5T9DXCZf8SWQTFQVW/hzSpovA1qzd739XeTMpMeHkJEZ6fczUTBEEHqpu12webrGYRSDlrXzYRvpMMHVBcykAx+o7AZiQGq3xBH1nzpgEDlS2Yh5sxNdQLDwePHzH9Vv8cyA/8/Uz6ejr8G5wXBbEZMg62FGADGAlEonEA+sK65ifm2h3OR0pNjnabq1aLTSVCHOLSedqcz4Q2cTKnSFv2F7f3ktpYxcLvHAAtRFh1LNyRjqfFtTQ1x8Eqaat5dCYxd4fM+FMERAVvK3ZNOrbe4kK0/tlwpJsDXqbtFxUcUVHHUQ7KxkGMrDDzDs6xWMAGxWmx6BTgh7A3vjRjU6vP7/6cy6dcKn99b76ffSZrT/XiATr+Vq0n6MG/POrY8RHGrnplDyn7XeumEBydBhPfXY4RDOz0tMq+ijPvAKSJwxsN4TB2Y9BSxnsflGzy+0pb2FuboLXi2cgFmKyEyIpqglCAFtfCH0dfgSw08VjrciqH6sXwZtt0SKUzM6Jp6vPTHHdoICy8YhwAfbAn3f/2a9r1nfXU9DopcJAUUQGW2ZgQ44MYCUSicQNtW09FFS1sWKqNtlXEK0CdMqAPG3EHPlUPA7ngOsLOQvFjZFNthUibFIyXyR8ABfNzqStp59Nxe4DHs2o2CGyigm53h9jCIOpF0HhR9CvTa1cQ0cvqX44EIMwcQLhZBwwVNUqIR4UwPZYA9hhM7Ap0OX+96koCvGRxgAGsAkudx1rHWhH8ocz/kBaVBq/XOps6lTdWS2ejOIMbFNnH2sP1XLdwjFDFkGiww38z/LxbCxu4EBlCOXPB98Vn0uL7xy6b9JKUQ+76Y+a1Bi3dps41tDJHB8MnGxMzYgNTgBrC6JyFvh2XOo0QBHqEUQGNiUmzK5iCCX51p+3Uy9dVRUZ2JRJbo/rNffy7tF33e5ff+16t/sA1pWv836S2fNEQD0K/47/m5ABrEQikbjhi0LRt/JMDepfbUSHG5iSEcfuco3keIfXiJVpx4zESLGt6IdYRnywqg1FgWmZvjWuXzoxldgIAx/sqw7QzByo2S8MVHx1f55+maiFPKqNjLi+vdcv+TAMBLABbaXT3QwW09AAtlvI/jyaOIFVQuy+BhYIcAA7vEHXmbln2p//4Yw/2J/f/bm1jccozsB+tL+afovKpXOyXe6/dmEukUY9L27xsfWIluxfDUnjRQAxGEWBU+4ULWWO+hCMuKGwWjhPz8z2wphtEFMyYjla3xF4BUjNAQiLhcRxvh0XFiV+jrYMbEMH41NCn30FGJccTWy4wdmJuLNefFYmuw9gN1a67l3+9Y1fs+fmPSRFJLH2qrXcMPUGl+NeLnyZWc/P8m6StlZE1Xu8Gy8JCDKAlUgkEjd8UVRHVnwEUzRuLzAvN4E95S1YBtf5+EpfJ5Ru1FY+DOLmJipZGISEkIPVrYxLjvZZFhtm0HHO9Aw+PRhgGbGpBxqKIMPLGx9Hxq8QQdHBdzSZSkNHr18tdGBAQtwQSCdiew9Y5xrY9h4T0WF6DPphbkeiU0Ug6cH4Ki7SaJcka4qbAPZYy0D29Zlzn3Ha5xjMlraViie2DGzP6DNxen9vFRPTYpiW6fqzLj7SyGVzs3h3b2VoHIk76qDkK1E/7m6xaNolQqK+/V8jvlyhNYM6LcP3z/4pGbH0W1SO1ntZV+kvtQVCDuzCXGxY0qfbA9iShk7GpYS+/hWE0eHsMfHOAWzDEfGY4lpCXNlRyfe++N6Q7S+e/yKRhkj0Oj0A6dHp/HjxjxkfP97t9YczZQMga671wlJGHEpkACuRSCQu6O03s/FIAyumpvlUA+UNc3MTae/tp3ikNzglX4neeFrKh0HcIOYsDHkAe6i6nWlZvmVfbVw4O4P2nn42FnvO2o2I+kLRUsGfANYuI/5QExlxQ0cvKX600IEgSYg7hJrB3tPVSluPybMDsY0o0UN5uF6wAQlge9tcBrCXvjtQ67oww4s6RFsGdpRJD9t6TOwoa+bcGekeP+tuWJRHj8nCRweCoGwYTPHngArTPPTuNITB3BtFWUXHyP7uC2vaSIoO80uWPzVDfGYFVEasqqKVS/oM/45PmwFNx2hta6Who4/xo8DAycbsnAQKq9vpMVk9GBqtAaybGtjz3jzP5fY5aXNcbv/ZqT9ze+0Vr63ghYIXPE8wKkks8kojp5AiA1iJRCJxwfaSZjr7zJw5RTv5sI15ViOnXSN19Ty8RvSmzDtt5JMaTM5CYQ7V1aT9ub2grcdEeVMX032UD9sIioy4Zr94zJjt3/EzLhfB0Qglj2aLSku3iaRo/zKwcZFGFAWaA5lZs9WvRqc4bW7r7h9ePgziphGg2/37MSASYotZ/I7C3b8Pr558tcvt38n/jv15n7lPOHzrDKNOQrzlaCNmi8rpwzitz8yOY3xqNO/srgzSzBw4uk4sfqQPs1g06xpQzSNWNhyqbmdqRqxfi5fjU6Mx6hV7FjcgtFWJ95G/AWz6dEClungPMDoMnGzk5yTQb1E5aJVx01gM+nCIH+P1Ob4373tu981Ln8ez5z5LbuxQ34Lm3mZ+t+N3w18ge74MYEOMDGAlEonEBV8U1RFm0LFkYrLm5x6XEk1ClJFdI6mDVVU48pmQohr8y7x5xNbXNEQyqUJrGwp/A9gwg45zZ2Tw2cFaevsD5KZcsx+M0b7XoNkYt1xk5QreGdE0Wrr6UFVI8tMpW68TBkiatnYajC1zGuX899TWYxrewAlEH1jwuKASkAC213oT7aEG9ieLf+Jy+51z7uTi8RcD8FHJR0LZEJEw6jKwG47UEx2mZ+4wZmmKonDZnGy2lTRR1dIdpNkhTJmOfQHjzxheLps+XbSJ2ff6CC6nUlTTbs+k+opRr2NcSjTFdQEMYK3yX9Jn+nd86jQA2ssPAIyqDGz+GPG3ts9m5NRQLDwerFJgRwZLfi+dcCn7b93PN2Z9w+M1FmQs4MMrPuSKSVf4N8msedBeBW0hUCNIABnASiQSiUvWFdZxyvhkosJ8b0syHIqiMHdMwsiciGsLoK0CJp2j2bycyJoHii5kMuKDVaJOcLqfEmKAC2dlChnxkQC5EdskfP7UoMGAjLhoZG7EzdbAMzHa/4WMxKiwAGdgrYGnLRC14r2E2LsMbFtPP6o6wtpyR2z1qoMC2OLmYvtzg879Z4RZFYsnP930U7EhMmHUZWA3HGng1AnJhBmGfx9fkp8FiJrZoFF7QNRQTzhz+LEAs66Ciq/tfU59pbypi26T2d7T1R/Gp8TY+6sGhFoReJI2ze2Q323/HV9VfOV6Z+JYUPSY6w+j1ymMSYzSfo5+khEXQVpsOHsrrH97blrodJo6yX8h3/7aoDPw4KIHfbrWo6c+yqRE9+ZQbsmeLx6rZBY2VMgAViKRSAZxtL6DkoZOzp6mvXzYxrzcRI7UdfifMTr8sXicrLGBk43wGFEnFSIn4kPV7SRFh5HmZ2sYgNMmphAXYeDD/QFYJVdV4QKa4WcGxIYGMuKmTvEeSvZTQgyQEBWEDGx4/BC1QFt3P3HeSIi9yMDGRRowW1Q6evtHMlNn3ASw5e3lXh0eaYh03jDKMrC1bT2UNXZxynjvlCZjU6KZlR3PJwU1AZ6ZA2WbxeO4Zd6Nn3G5eDz0gV+XK6wRWfdpfmZgQWQ0y5u6MJkDZCJXWyBad7lRBhxrOcYLB1/grs/v4r2j73Gw8aDzAEMYJI4lrPUYuUlRXi1eBAtFUZidkyCMnMwmsRDhooXO4L6vu27aRUyYb1JoRVHQuQiFhl0Ey5gFil7KiEPI6HnHSiQSySjh80O1gLbtcwYzL0/I9fY49rvzhaJPRJY0NkO7SQ1mzEIhIdagr6KvHKlrZ0q6fzVoNsIMOs6ZkcFnBQGQEbdXi9YOadNHdp7xNhnx236foqlTZG8To/3v4ygysAEMYDsbBrKoDmidgQW0lRG7CWD/tvdv/p1vlGVgbe28bJ9H3nDO9HR2l7dQ19YTqGk5U7EdYrMgPse78YljRa1s4Yd+Xe5QdTs6BSal+18XOiE1hn6LSnlTl9/n8EjtgSHy4dbeVmo6a2jra3MyGHto40Nc+8G1Q8+RMpnErjLGjxIHYkfmjInnWH0n7dVHhFHeoBY6dV11vHToJadt/n5XPLbksSHbZr8wm9ZeD27hYVFCri6diEOGDGAlEolkEGsP1TE1I5acAMqqZufEoyj41w+2o058cU45X/uJOZKzUGQHG4oCe51BqKpKcV0HE9JGfmN14exM2nsDICNuOCweUyaP7Dx6o3BWLfpYtOXxA1sGNmkEEuKEKCPNnYGUEDcOqX9VVZX2nn7vamCNkWCIHLYGFoITwBY2FQJwdu7ZHg+/fur1zhtGWQZ2V3kLYXodM3yQ6p8zQyyarT1UF6hpOVPxtVhM84WpF8LxrX65ERfWtDEuJZoI49CaS2+x1ZQGREZs6hGtZQYZOF3x3hWsXL2SbdXbXB5W0+mcNVeTJ5JlrmJCSoT2cxwhs3MSAKg4sk9sGCQhPuuNszS71oyUGey/df+Q7Q989YDnA7PmCQmxliULXtJl6sJkCUE7q1GEDGAlEonEgZauPnaWNXP2tPThB4+A2AgjU9Jj2eVPHezhNYAKk123D9CMHKuRU5BlxA0dfbT19DNBA2fM0yZYZcRauxHXaxTAAkwfmYzYXgMbNbIa2IBLiAcFsF19ZswWlbhIL+vMo5Kg2/2CT1wQA1gbTyx/wuPhU5Km2J8XNRWNugzsrrJmZmbHEW7wPlibnB5DXnIUnx4Mgoy4vRZaygc+i7xl6oWgWuDwJz5fsrCmnal+msfZsLn6HgtEL9iGIuG0PCiAresSCwo/+PIHLg9buXolzT0Dfz/NkXmEKyZmRAfQbMpPZueIv7em41bps5sesDbWXrV2xNf8/YrfO73eVLXJ8wHZ88XnQ9Mxz+MCwOKXF3PX2ruCft3RhAxgJRKJxIEvi+oxW1TOCmD9q425uYnsKW/GYvFxBffwJxCX41//UV9IngCRiUE3cjpqvembmDbyADZgbsQNh0VrFS0k3COUETd29BEdph9Rxigxykhnnzlwjs1djUNb6PSIQNOrDCyIOlgvMrCa9oJ1EcBurxn4e9C7cEa1Ud/ey783lthff+PTb4jfc09rSGT5g+nrt6BU7uB/wn3rm6ooCudMT2dzcSPtPQHOAtk+e3J8zMBmzIL4XJ9lxJ29/ZQ1djEtw38DJxDvxZSYsMBkYEfgQPzsgWftz8t12QBMNgSxntlLEqLCGJcSjaX+CESliO8hD8SGjez3BRBl9FFxZTNyClEd7JbqLSG57mhBBrASiUTiwGeHakmJCSffKmEKJHNzE2jr6edYgw+r9KZuOPqFMG8aQX2oVyiKuHEMcgBbXCd+HlpkYGFARrzhsIYy4obDwlhEi9+B3gjTLvZbRtzc1TciB2IQN4wALYFwIlZVawZ2kANxtzBbivU2gI1KHGjH44LASIitbXTCB26Q3z4y/EJDa5eJy/+6iV98MGCeMzZurMjAqhboC33Wq2LH+7ymf4Rzj/8e/r1yIFj3gpXTM+gzW1h/2HeJrk9UbAedETLzhx/riKLA1AuEqqHP+yCyqFb8XvxtoePI+JQY3z7bvaW2AAwRkDQegF5zrz37OhzPFgwEsIUmoTLKsVRoP0cNmJ0TT0xHqUsDJ0e+O++7vgefLpibNpfx8eOdtq14bYX7A1KnirIGWQcbEmQAK5FIJFa6+8x8UVjHyunp6HQBDg4RTsQAu8pavD/oyGdg6hR1k8EgZxHUFwa1bu9ofQdRYXoy4rSpzTptYgrxkUZt3YgbjmgjH7Yx4zIR1Bz93OdDmzr7RlT/CgPy44AYOZm6oL/HZQ9YwAcJcXJoTJzC45x6UL5/7H0Anj77abeH/eXLYqpbe3hm1QIMpjEA7K3fyzGsGe5Q18FazKR+9RDFahb1F78gnF7X/9brw+fnJZIUHcbag7WBmyNA9V5hlmP047NgygVg7hULfl5i6z89khY6NsanRgcoA3tAtM+xvie/+8V3/aoJPdgaRpsaTUx7qcYT1Ib8nARyLBV0x7nvs70kawl3zLpDk+tFGCJ485I3nbY19rhfMENvgKw5spVOiJABrEQikVhZV1hHV5+Zi/Mzg3K98SnRxEca2X3cByOngreEpGrs6YGbmCM5C8RjEFeZj9Z3Mj41WrNFBKNex7kz0ll7sJYekwYS2d520cR+mMyAT4xbLmRyBe/4fGhzlxYBrAj+AmLkZMuaRg2SEHdrKyGOCTeg1ynaB7Bu6l9Pyz7N5faO3n5e2VbOBbMyOXNqOg8veNK+76jabT1vi3Zz9IfCD4ntOs7flGtJmXcJzLoadj4Pvd5lDPU6hTOmpPFFUT39gWoVA1a3XT9LJfKWiNZNtpZjXlBY00ZsuIHshMjhBw/D+NRoGjv7tK8try1wqn/dVOm+VtNVj9P1x9cDUNLYRZUxB6XxiLbz04i56TpSlTaOKwPfx2aLmVnPD7wf7p17r6bX1CtDSwI8miVlzROLLOb/bkOlUCADWIlEIrHy/t4qUmPDWTzOu56II0WnU5gzJsH7DGxvh2ifM/1SsfobDLLnA0pQZcRH6zqYqJF82MYFs6wyYi3ciO0OxFM8j/MFvRGm+udG3NjRR9IIDJzAUUIcgAxsp/Vn7jYD662EOEkEfm7qRxVFIS7CELQA1h3rCuto7+3n5lPyALh01kCmvldv/b+GOgO7/w2adElUZZwp2o8svEMoAA695/Upzp6WRmu3iR1lfjipe0N7LXTW+99rWW+ESWcL0zsva44PVbcxNXNk7btsjEsRn2ElDRpmYTvqxM9kmPrX5Ihk/m/5//Hcec+RGe28IHv3urvpM/dR3tRFa2QuNB7Vbn4aMj1CLFYd6B743Og19wb0moqikB2T7bRt3ovz3B8wZqFQl1TvC+i8HBm2R+1/CTKAlUgkEkTW5IuiOi6clYk+CPJhG/NyEzlc126/mffI4U+gvxtmXhn4idmIiBNytSA5EXf19VPZ0q1Z/auN0yamkBQdxps7Naj3arBmLLSUEIP4vfa1Q+EHPh2mRQ2srYdscyBqYG1Z08EBrLUGNi7Cy8WYyCRRP+ohexkfabSfVxMGBbAee0Na+bSghpSYMOZbe6sa9AO3Wm222y4f6k01x9SDWvw5n5nnMzXLao4zZhHEZftkerRsciphep29b7bm1Fpbm4zErG7KBSLg80JBoqoqhdXtmtS/AuQli7pMTXvB1h4Qj9YMbHlbucthv1v+O84Zew5xYXF8etWnbLreOUs7/6X5VDZ3Y4ofC21V0B/YwNAfwtvKANjUOCDn7jM7L7AZ9V4ufvnAM+c+M2RbY7cbKfGYU8RjeWANlVRVZfXh1XT3d1PbNfTvrbW3FbMlQAZ8oxQZwEokEgnw0f5qevstQZMP25ibm4Cqwr7jXtzQ7nsNYrMg99TAT8yRnIVQuSMozqm2mrHxGgewRr2Oqxfk8NmhWqpbu0d2sobDoOghcawmc7MzbrkwZvn6n14f0mMy09VnHt01sHYJsXMAa3Ow9d7EyWoC5aGVTnykMaAZ2Ls/v9vj8H6zhfVF9Zw1Nd1pIeyyPFGnt7G2zHreFu3m6CulG1FMnXxkmsc0W7sYRRGtZ4o/F0ZxXhATbuCUCcmB6wdbYw1gB7WL8YmJZ4m/1aKPhh1a2dJNe2+/JvWvALlJ1gC2UcsA1upAnCZ+Jg9ueHDIkJcueImFGc6uzXFhQ4PyfouKIXk8oEJzmXZz1Ipm4eC9ri7KXvphqz8HOC3rNCYlaFjGYSUrJotfLf2V07YVr6/gV1t/NXRwXKb4HghgAKuqKnd9fhePbXmMx79+nJWrV9r3fXTsI947+h5LX13KY1seC9gcRiMygJVIJBLg1a/LmZAabTdWChZzchNQFNhVPowMr+W4MHCaeyPogvzRPWaRuJEPQq2ULVsxNmXkrpKDuWlxHhZV5eVtrrMWXtN0DBJywTCyoHEIOh0s+AYc3+q1JM0WcI40gI0w6okw6gIjIbYFsNGDJcT9RBr1hBm8fD9HWgNYD3WwcQEOYPfU7wEg1ug6yCmsaae9t58lE53/r3cvvAGADS3WXr+hlBCXb8ai6PnaMmUggAWYcJZQeFTs8PpUK6elUdLQaW99pSk1ByB+zLAtVDwSmShqYb3oB2s3cNIoAxth1JMeF06ZphnYAojNhOhkjrUeY3/Dfqfdr170Kvmprh2b8+LyhmyLybIGgM0lQ/aFnKYS+sKTaDZHsvd4CwC/3T5gNHbL9Fs0kXq74pIJlwzZ9mrRq64H554K5VuF23oA2FG7gw2VGwD4otzZkOyBDQ/w0MaHAHi72L82bCcqMoCVaEpHbz8Pv7Of03/7BXe9vIu6dt9bQgSc2gJ4/hL44zz46nejoh+fJLQU1rSxq7yF6xflBuwL0R1xEUYmpcWwe7gAdveL4nHuzYGf1GBsPRiDUAdbZs1W5CVHa37uMUlRnDkljVe+Lh9Zv9PmUkhy74w5IubeKFozfP0Pr4Y3doiAM3GENbC2cwRGQtwgsmDhzrWkbd0m7x2IwSED69mJWPM+sC5qYFMGGVLZ2GmtB7XJh+3zcmjDg6IPbQa2fBt10ZPpVSKY4tjvNPcUQIEy96ZAgzlzmmjFEhA34toDfvU6HcKU86HuoPi79UBhjWiZNGWEPWAdyUuK1l5CbM1IX/rOpU67piVNY0ay+2z1v8/5N6mRqfbXYSmfkzrGWsc/zM8mJDSXoEsWbW1c1VnPSZsT5Am5IfdU8RkXoFriLVUD2d3m3gDVm5+AaBLAKopynqIoRYqiFCuKMkTPoCjKDxRFOagoyj5FUT5XFCXPYZ9ZUZQ91n/euwdIRh39Zgt3vrSTl7eVMzEthnWH6rj+H1vp7NWwHmmkNB6FZy+AukMQnw3rfgmfPhzqWUn84EhtOz979wCrnv2aR949YF+h9YeXtpYRptdxxbwc7SaoqnDofVj9DXj5OrFY0uG6Z+LcMYnsPt7i3pyhvxd2vQATzoTEoavoASd5kriJD0IdbHlTJ8nRYcSEB8ak6val42jo6OP17cf9P0lTCSQGKICNTIQ518PeV6F1+HpdrTKwIIycApaBjUoaohxo6zF5Lx+GgUychwysphJiiwV620QbnUE8csojLg/ZUdZMelz4EBfbcH24/fnWuKTQZWDNJqjcyQHdNMalRBNhdHBdjUwQ9aalG70+XXZCJNMz4/hcaxmxqUfUmvtr4OTIlPPFY5HnLOyhmnZyk6I0/ewZkxSlnYTYbIL6IreSaleOw46kR6ez7pp19tfhqZ+Rmp4DxmjxmTbaaCrFkDyeyekxbC91/pvfdP0mTXq/euKDy730IrCV9GgsI+4z99Fl6uKf+70vKflvYsQBrKIoeuAvwPnAdOB6RVGmDxq2G1igqupsYDXg2GysW1XVOdZ/Q3P2khOGl78uZ8ORBn59+SyeWbWQf9+6gGMNnfxuTVGopyZQVXj/u+L5Nz6FW96Dhd+ErX+Bkg2hnZvEJ1bvrOCCP27gtR3HqW/v5Y0dFVz6l0388PW99ro6b6lr7+H1HRVcMS9bkyAAEDcaq2+D124SN4PNJbDuV/CHfNj53BCp0by8BFq6TBxz51a591Vor4ZT79Jmfr6i00H2gqBlYHOTA3djsmRCMgvyEvnrl0f9y8J2N4vsWaAysABLvy8eNz417NCmTlsAO3Izk8QoY4AysI1D6l9BmDgNNnBq72uno8+NHNXLDGxrt0kbp86+dkB1mYGdnz7f5SG7yppZkJc0RMnh+LrMEBm6DGzNPujvZlPfRNeZxrwlQkJs9n7h+expaewoa6K5U8PFj6ajoJqFgdxISRovHMOHqYMtrG5jqobZVxBGTjVtPdq072osBnMfpM9kb/3eIbuNOt8/Axp7G8Vn2WiTEPf3QVsFJI1jwdgkdpY1c+NHN9l3xxi19UhwRV5cHr893bk3ssvPlZRJoryhfKtm1z7Weoz5L81n8cuLfTquprOGwqZCzeYxmtEiA7sIKFZV9Ziqqn3Aq4CTrkFV1S9UVbUtQW0FNExzSEYDHb39PPXZYU4dn8y1C0XT9iUTU7hxcS4vbS3juJYSGn85vAZKN8BZPxUf2IoC5/wC4nLg858HrH5Boi1fFNZx3+q9LB6XzOYHz+LDe5fx9UNncdcZE3h7dwVX/HUzVS3em/T8a0MJ/WYL/7N8gnaT/OD7UPA2nPUI/OAg3LUN7t4ueqq+/1344HtON4i2utvd5S1Dz2U2wabfQ+YckYENFWMWCeVCT1tAL1PW2EVeUuACWEVR+O7Zk6hu7fGvFtaWqQhUBhZEfe3cG0XWfRhpX7M9gA33OM4bhIQ4EBnYpiE9YEFkYG0tdPot/Wyp2sKSV5Zw6iunMuv5WWyoGLSwGB4Pim7YGth+i0pXnwYBg80p2BrA2m5ex8WPc1lq0NLVR2VLN7NyPLfd6SM8dBlYqzHSurZsJqTG0NLTQntfO/etv49fbf0VRYnZog62wfuF57Onp2NR4YsiDbOw9dbra+X0PeV8IY124/7cYzJT0tDJ1Ext6l9t2JyINbkHshk4pc/gg6PO2cEJ8RO4beZtXp3mh/N/aH9+7pvnChOi0ZaBbSkXjuOJ41g4NpF2Uyv7HIJ2nRKcCsjzx53v9HrVJ6uGDlIUax2sNhnYtr62IfJwb1m5eiVXv3+1JvMY7WjxDsgGHLVYFdZt7vgG4NhVOkJRlB2KomxVFOUydwcpivIt67gd9fWuZXiS0PH27kqau0z86NwpTl/sd50xEZ2i8K8Nx0I4Oytb/yIMIeatGthmjIRl34eKr6Fsc8imJvGOps4+7lu9lynpsfzr1gX2jGlshJH7zp3KS99YTE1rD5f/dROHqocPtMoaO3luUymXzclmXIpGNZeH3hf1qkt/AMt+CDqrRC9lEtz8jti28zl47UboExnXCakxxEYYXBs5bf+XMA1a8aD4ogwVOQsB1at2FP7S12+hurWb3ADUvzqydGIKSyem8ORnh2no8LF9hC1TEcgMLMDp94POCJ/82OOwps4+FEVkHkdKYrSRlkBkYDsbBrKnDrR1m4izSoj/ue+ffOuzbznt/87n32F7jUPWX6cTMuJhMrCANjLiQQHshyWixYwrR1cQBk7AsFm8NktY6DKwtQWYjTGUW5LJSlRZ9toylryyhE9KP+HVole5ufgFMa5qj9ennJkVT1psOGu1bKfTcARQIHmiNuebcj5Y+qF4rcvdR2o7sKgwTeMMrM2JuEwLGXHtAdAZUZMmDjEUeueyd1yaNLniiklX2J+bLCbMCXlioWw0+YE0We8Zk8ZZFQ0B+Fzyg111u+gyufhd5p4iVAPtI/8b2F+/f/hBw7CrdteIzzHaCaqJk6IoNwELgN85bM5TVXUBcAPwe0VRXKZBVFX9h6qqC1RVXZCamupqiCREqKrKi1tKmZkdx7zcBKd9mfGRnDczg7d3V2ojofGXukIo+QoW3A76QfUt+TeIGqddz4dmbhKv+fO6Ypo6+/j9dXOca7esLJmYwht3noqCwjVPb2FzcYPbc1ksKo+8W4BBr/DA+VO1mWB/H6x5SBiPnPGToft1OpGVvfBJOPIpPH8xdNSj0ynMGZMwNAPbWgFf/K/IvE4+T5s5+ku2VTLpg0Opr1Q0d2FRCWgGFkQW9tFLZtBjMvPLDw76drA9AztW83k5EZ8Ny+8XssdD7muxmrr6SIg0atK7ONFaA2uxaKxGcSch7uknNkLP1uqtHG4+7PLQ29fcjkV1uLGOSh62BhYCE8AebBTvleKWYpfDi6wB7DQ3WbwHFj4AwNMJ7aHLwNYepC12Iio6oqKHzqHb3CtqIqv3eH1KnU7hrGnpfHW4YWTmaI40HBZKBGOky91bqrZw2TuX0Wfuw6JamPX8LJa/ttz9+XIWiveOmzrYQ1YDJ+0zsGIxThMjp5oDkDqFbpx/xgnhCT6dpqfPuVRmTu37YO6FjpqRzlA7mgeULjmJkcRMety+65ULXwnRpAS3r7l96MaxS8VjyVcjPv+3137b7b748Hium3LdsOe49ZNbRzyP0Y4WAWwlMMbhdY51mxOKopwNPARcoqqqfclbVdVK6+Mx4EtgrgZzkgSRgqo2Dtd2cOPiPJeyqusWjqGtp581BSH8cNz3qnB+nHfL0H1hUTDrKjj4bmiby0s8UtXSzUtby7hqfo7HNgdTM+J4+64lZCVEcuuzX/PO7iEfRwD8bf1R1h+u54HzppIeF6HNJHe/AC1lcPaj4KnB+sJvwLX/gdqD8O+V0HiUebmJFNW00WEzPTN1wxu3iTqwC54IbfYVhMFL6lShVggQtnYTeQGsgbUxMS2Gu86YyDt7qnhz5/BmSXaaSyAmHcICmyUG4JTvQMZseO9ut4ZOzZ0mEjWq3U6ICsOiCmmvZlgsImM6KIBVVZX2HhONbOGbn36TteWuM2MAGysdTIUikwba8rggkAGsLcheMWaFy+GFNW0kRBlJi3Ut5z537Ln256ZQBLCqCrUHqAwXzq4Pb/+my2HHMqf5lIEFUQfb0dvPtmPuFxd8oqHIo3z4W599i6OtR9lUuYn8F0TbmKaeJt47+h73f3W/k3MrIJQwk84VC4cu6nsLq9uJNOrtGVOtSIwyEhNu0CaArS2A9Bk8uuVRp80vnP+CT6dxZSrVD6NLRtxUIhZSYtKG3FfOTNHA2MsHnjn3GafXBY0FvHzoZWo7HbKtmfkQkQDHvvT7OsfbjtPa6/kedON1G3nolIfsr1+64CW/r3eio0UAux2YpCjKOEVRwoDrACc3YUVR5gJ/RwSvdQ7bExVFCbc+TwFOA3xcDpeEmo/2V6PXKZw3I8Pl/lPGJ5MZH8FH+6uDPDMrqiqC03GnQ7Tr1gfMvg76e+Dwp8Gdm8RrXtxaRr/Fwr1nDd+4PDM+kte/fSrz8xL53mt7uO+NvdS1iZZOvf1mnvy0iN+tKeLi/CxuOVUjV1+LBbb8VZgdTTx7+PFTL4BVHwiX03+s4KLeDzGqfaKdTmsFvHSVME269M+QrGF97kjIWQjHtwVMamarEwukiZMj95w5icXjknj4nQPDtzGy0VQa2PpXRwxhcPVzog76tZugt33IkMbOXpK1CmCtwZ+mMuKeFlHLNuizt8dkwWRW6VWGr5u863MH87LIRI/Zy0AGsNuqtwFw95y7XQ4vrGlnakas21ZcqVED6rG+3tbg+y60V0NPC4WWXNJT3Jdi3WxoFrWyFu+zqadNTCHCqONzLWTEFgs0FLsNYB0llvd+ca/Tvoc2PsTHJR8PkaMDQkbc0yL6LA+isKaNyRmxmigZHFEUhdykKMoa3Rj0eUtXE7RXQfoMPi752GlXdoynqr2hlDV20de41Glbn6KMLiOn5hK7V4kmhmwjYE7qnCHb/vfr/+WH639IS08Lmyo3iQWS8ctFAOvHfMvbyrng7QtY+upSt2Mcpd/TkoS52dQkz+qx6o7qkP/8AsWIA1hVVfuBu4E1wCHgdVVVCxRF+bmiKDZX4d8BMcAbg9rlTAN2KIqyF/gCeFxVVRnAnkCoqspH+6s5dXyy2yyATqewcno66w/X062FsYav1BaIeorpHkyucxZCdBoUemmbLgkqff0WXt9+nLOmpZOT6F1wEx9p5PnbF/Ht5RN4a3clpz6+jrOfXM+CX67lj+uKuXJeDk9ek69d39eS9aIGZtG3vM+W5iyAb66DrDlM2vEoO8LvZNpbK+H3s0Wt6RX/gBmXazM/LRi7TNzU1468RscVZY1dRBr1pMaM3JDIG/Q6hT/dMJfU2HBue247B6u8MKhqLgm8fNiR5Alw5b+geh+8eoO9bto+nU6TJj1gARKiNAz+bNiypYMysLYs747W14ccsjJvJbtv3u20ranHmtmLTPRYPxrIANaGY0scGxaLSlFNu0d1CMDYOJH97McC7tyWA4XVBOgttYmu1P9zO6xNNQkjJx96g0YY9SydmMraQ3Ujv2FuqxDXT3UdwP5t79+8Ok1P/6A+9BPOBH0YFDkHgKqqcqi6TfP6Vxt5yVF2dYnfWH93appzk4+/r/w7YXrf/v7LmrqwdDn/bPt0htGXgbV+zposA3/Lt2Q9G/SpGPVGtlw/1KCpra+Ne7+4l2+v/bZwTh+/Qrx3G12XGHiivtv9gtKEeLGA/YP5P7Bve+XCV9h98270ytBSKhuznp/FOW+ewxuH3/B5PicCmtTAqqr6kaqqk1VVnaCq6q+s2x5RVfU96/OzVVVNH9wuR1XVzaqqzlJVNd/6+G8t5iMJHodrOyht7OK8ma6zrzbOmZ5Bj8nCRg81iQHj0PuAAlMvcj9GpxMZseK1ov+cZFTx6cEaGjv7uOkU37Kl4QY9D54/lc9/sJzvrJjAhNRoLpqdyX/uWMz/XZOPUa+hDcDOZ4W8cbqP7oGJY0VLp5vfZnPkCorNGbD0e8K5ePY12s1PC8YtE48a1Pm4oqyxi9ykKO0WFbwgLTaCl76xmAiDnmv/voVNnj6jTD3QVhV4A6fBTDkfLvubaMn03EVO/YSbuvo0a/9kC/5aAhLAOps4tbm5xl/P+itPrngSg85gzzIALH9tOcdajgkpe7f7bLnN1djd+X3CFsAO6gPrKmCoau2mq8/MpHTP7T1umSFagfQqSvDrYOsL6Qf2R3857NBORYF639pxrJyeRmVLt93Mym/qrfXQbjKwGyq9a3u38D8LnTeExwglVtFHTlmy+vZemrtMmrfQsZGbHEVFU/fIasutAWxn8ninzUuylvh8qvLGTtIM+UxMGDDI6o3L9GnBIqBYLGIu1s/Z73/5ffuunSWhMXOKCYvh10t/7bRNr+g51irMpkwWE4w/Q+zwQ0b8nbXfcbvv9YtfZ/+t+4kPH1hI0+v0GHQGewC7Mm+l2+N31gbOeDGUBNXESXLyseGIuJE6Y2qax3GLxycRG25gXaGGLoXecuwLyJ4HMZ7nyJQLxYp42abgzEviNe/tqSI9LpxlE91IwIdhbEo0PzxnCn+/eQH/e8VsTvPzPG7pbRfmILOuBqMf9bSKAhPOZN/cR7mx4146lv4EEjWSNmtJXBYkTQhY3+Typs6gyYcdyU2OGqibfuZr9zWxLWWAGjwJsSP514q66bpD8O+zoeEIqqrS3KldABvYDKzz31xbjwl0Aw7Q+an5vHD+CyzLWWbf9tez/+p0zLtH3xV1Zn0dQlbtgthwA4qiVQa2DcJiQG+gvG2g5ZKrrIfNZXY4N3Nb9rZXUYLvRNx4lK7IxCGbZyYPrSm8MCfL5wDWdh+w9uAIv+cb3AewbX2+tfFqGfwznnyeUGQ1HLFvOmRzj9bYwMlGXlI0fWYLNW0jWByvPQBRyZz6/mX2TUkRQ529vaG0sYu85CimJw9kc9ckJI0eCXF7tTCVShzHuvJ1fFUhFkzTjNPZWdZMV5/3PYq1ZHDrnuKWYvtngVk1i4A7IQ+OfuHTeWs6a+jqd52h33/rfo8ZdkVR+OKaL/jNst8QZQj+d2cokQGsZERsPtrI+JRoshNcOwXaMOp1LB6fzKZi9+YbAaGnTbimjl8x/Ni8JaAzBCy7JPGPjt5+vjxcz/kzM9FpXJ+kGUc+FV+4My4b0WlOHZ+C2aKyvVQjI5RAMG6ZaDnlwghlJKiqSnlTYHvAesJWN71oXBI/fGMvf/z8yFAppE1iF+wMrA173XQH/HslncWb6LeomgWwcVrKb210WjPagyXE3f1ET/it/fWL57/I3DRnD8eUyBR+d/pA04J+S7+QEINbwz2dTiEuwqhdBtYqH3bM+sWEDc2yljQIafdwAaztPXXMaMDS1TLyOfpC0zF+kzTUDfpPZ/2J/bfudwqIGg16+mp9q+hKi41gzpgE1haOsB9sQ5FQszjUTbf3tXPx2xdz2iunOQ39+ZKfezzVAxsecN4wxdrX8/CAjLjQ2nJt2jDyb3/JSRT3R5U+9CcfQm2BcLd34KHFD7kZ7JnyJhHATk4cWCD4HU3QctzDUUHEoVXZoaZD9s3pMfGYzGrw7yOtuMpy2kob+szW/tkTzhTlRD4o+TZXuW7h+MU13gXCKZEpGPVGtt24jW/OGmrMVtjk20LUiYIMYCV+YzJb2Hqs0ets1mkTkylv6tKmobe3lG0SLq7eBLDhMaIWtmR9wKcl8Z4vCuvo67dwwazMUE/FPQXvCGfaMYtHdJr5eYmE6XVsPRqaL2ivGHc69LVD9d7hx/pAfXsvPSZLSDKwNuIjjTx32yKumJvNk58d5idvH3AOYm0Su2DWwA4mZwHc8RlEJRP96hWs1O3QrAbWXj/a1afJ+QC3NbCFTQfRGUTQ98y5z7iVjZ83bqB91JHmI0JCDB5lxPGRRo0ysC32APbxrx/3OLS0oZMIo470WM8KjKqOKgBejI+joSHIiqSmY3xmdF54+uyqz0iJFN/h6691/u5b2bEdXzl7Whp7j7fYTfP8ouHIkOzr9prtlLaVDhl62cTLABgTO2bIPhDBwfE2h8AsPgcyZjnVwRbWtJMVH0F81Mh7KbvCFsBWNPt572MxQ90hipJynTb7at4E0N5joqmzj7zkaG6efrPzzs464X4fauytysY5tdBKjxX90j8NQEeL9Yfr+faLOznv919x+3Pb+cyFisBTJtRepzv1IqEQ8SER4k7ia/u7FBfoESaRz14ITy+F9+4VRmeDUBkqUz/WeoyHNz7M1uqh5mUnMjKAlfjNnuMtdPWZOW3i0BVdV9gC3S3BvDk/9iUYIiBnkXfjxy0X7QM83BxJgssnBTWkxIQzP2+o9G1UYOqBI5+JLy6de0MFb4gM0zMnN4HNozmAHWurg9V2oceWnbDd7IWKMIOO/7smn++smMArX5fz2zVFAztbj4vPk+gQ9yJPGg/f+IyuxKn82fgnxnVpY6oVbtATadRrLyE2RIp2ZQ789cg99ucLMxYOPsqJa6dcC8CW6i0DGdhhnIg1M3EaZOD0y9N+6XJoaWMXeUnRw6pErp0q/i/bIiOorx9hptIXTD3QWkG3MhAQpEamkhHt3r+iSVF9ciIGOHt6OgDrRpKFbTwKyROdNn33i+8OGfbcec+hKAqfXvkpb13yFj+c/0OXp7vg7QucN0y5QLipd4rP2UPVbQGTDwNkWRVqFU1+BodNJdDfzY3NA+VNY2LHMCNlhs+nsknd85KihkhiW3S60ZGFbS4Rarj4MXx5/Ev75nBDGGdNTWPtoVr6zdo44feYzHzv1d3c+szX7CpvJicxkqKadr75wg4efHMf5kF1y59d9ZnL89gzsOOWQVisT4agn5W5Pqed+iL4+zJY82PRtSAmA/a/IQLZg+86DV01YxXLspcNOcW7R9/lm5+6bpt1oiIDWInfbC5uRFGE7NEbJqXFkBITzqajQTRyKvkKck/xvi5x/HJAFWYpkpBjtqhsKm5gxZRUzdsbaMbxrcIxc/K5w4/1giUTkjlQ1UpTp4ZZMC2JSYPUaaK2XENsAWzWMOUIwUBRFO47dwrXL8rlb18eHehh3XpcZHA0NJlq62sb6pbqDVFJ7Fz6dyrVZGZtvmdAqjtC4iON2rbR6Woa0kLH1k/VW+5beJ/9eY/RGggPk4HVxIjKRQDrWKPrSGljJ2NThlcPxIYNGAVVN/rQf3ikNJfAoOzM2PixQ4bdOv3WQceV+nSZKemxZCdEstbfdjp9XdBRA0lD5zaYxHCxmJEZk0mEIYLxCePdjnVsvcPk80RrpyOf0tdv4Wh9R8AMnEA4NKfFhlPR7GcAW3sAC9CrDmTPnz/veb9OVT6oVdljSx6z79sTHg4t5S6PCypNxyB+DOgNTp8VmdGZnDMjg+YuEzvLRp5k6DGZuf257byzp4rvnjWJTQ+eyb9uXcj6+1bwnRUTeHX7cR57v8DpGHcLPn0W6/e1IRwmrRRGYcMs/vxr/7+Y9fwsuvud3xdJEUlkRlsVZ9X74JnzxILdTW/CtzfATavh3t2QORtW3w5H19mPjQ+P57en/xZ33Ln2To9zOpGQAazEb3aWNzM5LdZr2Y2iKCwen8T2kiDV9/W0CsOTXB9c+rIXiAxL+ckltThROVDZSkuXiWWTNDZd0pKj60BnhLzThh/rBWdMSUNVYf3hIGZnfGXSSlEH2+OboYonKq03d8PV0wcLRVF47JIZzMiK48dv7aeho1dkJ+JdSxX95bRXTuPaD67169ia/hi+Y/oeht5W+OD7wx/gBQlRGmUvbXQ1DHEgvvK9K+3PHz310WFP4di2ZuH6b4snw7TSCVQGNtIw9P1ptqiUN3Yxdpj6VwCjbuD78u+mIC6UNh2jfdDCy3ljzxsy7EcLf8QlEwZazv3PhvsGskteoCiibd7G4gb/2ubZZfoDdebba4ZKmf985p+HBKyOrtWDueGjGwZeZM6B2Ew4/DHHGjowmdWAZmBBKEsqWvyUENcWUBrmLF917CnsC/YMbLJ4rzq+H+/JSLUa1YWYphKXPgPfyf8Op09OJcygY03ByOX3P3u3gM1HG3nymny+v3KyvSuBQa/j/vOmcsfScbywpYwP91U7HXf7zNuHnKvL5PC7nXohdNaLPu4uWPrqUp458Ax/2PUHl/v/evZf+fSqT8Ui3as3gjESbv/Eub98bIYIaJMnwVvfgvaBn0dMWAzPnPuMy3NvrDx5kjMygJX4hcWisru8mXl5CT4dNy83karWHmpag9CqpnInoMIYz/I0Jwxh4svt+NeBmpXEB2xtlzR3DdaSo+tE7Wu45/YZ3jIrO57U2HA+PzSKA9gp54Ol32nld6RUtXQTF2EgNiIwdWj+EGbQ8dS1c2jrNvH7tYehtQIS/A9gZz0/i1nPz2JHzQ6KmopY/tpyQNQoNfc0U9NZw+tFr3vdR7O5s49CNZe+ZffBoffg2Mhl3XFaBX82uhqH1L86YqthHI4h5iQeJMRxkdqaODV0D2S3XQWw1a3d9JktjE0ePoB1JKcniO/1xqOsyky3v4wNi+XqyVe7HPqTxT+xP9/ceoSDjb6ZOZ09LZ0ek4Uvi/z4DHMRwK4/PvR9vXzM8iHbUqNS2X/rfj658hO3N/CAaJs3+Tw4spbDx4W6IlA9YG3kJEaNKAPbmzDgSj8vbZ7f8yhv6iQ5OoyYcAMAC9IXOA8YDRnY5hJIGk9Bw0D2c2XeSox6IzHhBk6flMqH+6uGyHt9YfXOCl7bcZy7z5jIFfNyXI554PypzM6J52fvFdDeM/B58v353yc5wvkz7fY1t5P/Qj695l6YdI5IhOxfPeScqqrS2tvKUzufcju39Kh00Uro7TuFI/M1L4p+4IMJj4WrnxWfU5894rRrYcZC1l+73mU21uxjWcBoRQawEr84Wt9Be08/c3N9q0ucl5sAwK7yINSYHt8OKCKr6gtjFkL1HujvHXaoJLB8dbieGVlxpMSEDz84FHTUQ81+mHCGZqfU6RTOnJLG+sP1mDSq89GcnEWincnhTzQ7ZWVLN9mJo68NwOT0WG5cnMubXx8VJifxucMf5AK7yQdw25rbuOr9q+wOlgCnv3Y6K1ev5Bdbf0FVZxU1ncMblTR19RGm1xG29B4xr08fdupv6Q+aZS9tDApgK9oHZLMJLT9E72Xd+D1zB2pmzeCViZO3CwEusVhEvVl4HI3dnmvSSxtE9sXXALbI0OLv7Hyn6SiHwweyeBuu3eDWOCva6Nv/YzCnTkgmLTacN3dV+n6wgwOtjXDDwOd/bmwu/7vsfz2eIjsmm4UZC4f8P5498OzAi9nXgKkTDn1AmEE3rHv0SMlJjKSqpduvoKu/5gDXRA/cj1w60cde4w6UNXY5GeVlxmSy+mKHQCvUAWxXkwjIEsex+sjAvJ5c8aT9+VXzs6lt67W3cfSVopp2Hn5nP6eOT+b7K133GgbRPeMXl86koaOXP69zNkxy5RBsUS3Ud9VDRBxMvYiKg6vZWC7GqapKeVs55e3uf77fmv0tdt20S5g3bfmTcMo+91eQM9/9fyZtGpx6F+x7VXTccCApIsnZCMrK/37t+e/nREEGsBK/sAWg83wMYGdkxRNm0LFLg/qFYan4WvxxR/goDcpZBOY+UXsgCRndfWZ2lTezdDTLh21GRhoGsABnTkujvad/xO109hxv4cE39/E/L+7gxS2l9PVrFBDrDWKV+cinPpu8OGHuhz0vw+u3cF3FrzgzfHTa/d971iTy9NYgJt71ar0ndtbuZN6L3mdNznvzPFauXskT25+gqKnI7ThbD1jFGAkrHoSafXD0c5/n50iC5gFsk1MP2PPfOt/+PMkw0dURLnEMtoqj4oeVEJvMKt2mEbw3+zpEnWRE/LC9R8uahJtyno8O2scj+mjVst7YEzZnVyveLhyAa2dTT+h1CpfPzebLojoaO3xcCG4uhfA4u1mXqqr8Y98/7LufXvk0F42/yKtTDTYpenLnk3z/C6vUPvdUSMhjfOV7TEmPxaAP7O1wTmIUJrNKXbuP6rPuFqo7q5w2XT7xcr/nUdY4tFVZlHHg9SdtvtWna451AcOSmMfqw0MzmABnTk0nMcrIanf9uj3Q2dvPnf/ZSUy4kT9cP2dYb438MQlcPjebF7aUOb2X3S3+vH/0fQDaZl7G+Wmx3PnFvQCsPrKaC9++kIvedv3effOSN7ln7j0Y9UYo2wJrH4Ppl8Kibw3/n1r2I7FIuP43w48FPjr2kVfjRjsygJX4xa6yFuIjjYz3cdUyzKBjVnZ84DOwFouoP8jxQT5sY4zVsbhCyohDyd6KFkxmlcXj/GvWHhTKt4IxGjLyNT3t0okphBt0fLzfv3YBFovKE2uKuOwvm/hwfzWFNe389N0Cbnlmm391aa6Ycr7IrpVtGn6sKzrq4fmL4J07oXI38007uK/mR7DJdV1QKEmOCeeaSeKGpSXMvWvrYHrNvTx34DlWfbLKr+s+f/B5rnr/Kj489qFTBtdGU2cfibYesLOuFu6Um//s17VsaJqB7e8VWUwXEmKjJYP4SN/a//xowY8A2B4TN6wLMYywn22vNWiNiOf2NUNr3hypbO7GoFNIj/POLNCWFZndpVDS2On/HH2gyCHzc3rW2R5GCu5bMGCc1W/xvefzFfNy6LeovLe3avjBjjSViDZV1gDhF1t/4bTbXbscVzyx/Ikh29aWrxVPFAU1/zqm9+7hlJTAt44ZaKXj47VqC+hxCJbuX3i/2+BpOHr7zVS1dtvrX23Ehw/Uef9eafHr3JphXWjZhfuFjzCDjkvnZPPpwVrhTeAlqqry47f2U9rQyZ+un0vaMC2vbNx1xgR6+s08u6nUafurF746ZOxf9/6VWc/P4qI9A/2rzRYzBxoOeLyGvSdvRz2svg0S8+CSP3lnGBgeA6fcKRaUvWhv125qH/6cJwAygJX4xa7yZubmJgzbMsAV83ITOFDZRm9/AHX4jUeEDGWMl+1zHInNEHK8419zuLadTcUNTvUPo4r+PijdJKQjJ0ldgw2by6CvWf6gcnyb6MupN2h62uhwAyunp/PBviq/ZMRPrT3Mn78o5poFOWz58Vl8+aMV/N/V+WwraeLhdzx/kXrN5PMgLAb2ve77sX1d8NIVomXVZU/T9u2dnNLzJ46mnSNqeQpH3wrxRXni7+v9Mu8yV809zSx4aQH/t/P/XO4/Lct7068HNzzIvBfnMe/FeU59EZs6+0iKttZRGsJg0TeFO3TjUa/PPZj4SCNdfWZtsvVdVgVB1NBFqPjG+4mL8O3vxmbm9Jtohf4u9+oETQLYnlbx6GDiNDN5psuhVS3dZCZEeO2U/sMFot1Ll85MWTACWIuFx8IGMn9/ONP1e9KRW2bcYn8+XADviikZsczMjuMtX2XEzc4GPm8cfsP+fHDd4XAsyVrCt/O/PWR7izV73zThCnSonGve4Nsc/cDfXrB1FVu5ImegB7q7umVvqGjuRlWHKgXiwuLstd2Vel1oe8FaM7CNxgHZeE7MUNXLzafmYTJbeGFzqden/s+2ct7bW8UPz5nCqRO8fy9NTIvlvBkZPL+l1GkB2FMbo+begSTNXzb9jLeOvDX8hSxmeOsO8bl5zQtDDOQ8svCbQrmw+U9Omx0XJxx5cseTLrefSMgAVuIzHb39FNd3MHeMf4HFvNxE+swWDlZp52A6hEprY2h/MrBAX+Z8moo2cc5TX3Hjv7ax5PF1vLPbj3qeQHJ8O/xpHjx3AfzrLPj76S4bW5+o7CxrZmJaDAlRvmVpgkZvO9QeEAZOAeDyudk0d5n46rBvdT4f76/mT+tE8PqbK2cTE25AURSunJ/D3WdM5M1dFWwq1qDlSlgUTLtE9KEz+SCLU1V4/15RO3zN8zDneqpae+gljKJTfwfps+CjH0Fvx8jnqCFp5jrM6PjPQe9qK1e8vsLl9ovHX8z+W/fz9Mqn2XbDNnbctIOvrv2Kvbfs5aULXvJ4TpPFRP4L+cx6fhY1nTU0d5lIinaoD8+/HlBg32s+/M+cSYjSIPiz0WWVXVvb6NR3DbyX23uE2ZIvhOkHPgvW9bpXJ9gD2JHIc10EsFdMvsLl0MqWbrLivXfPtklgiyN0lDQEIYDtrGd/uPVnbQkLuFzWxhVzc9hf2UpBVat3B1jMogYzcSyAk3kWwDuXvuPzHO6acxcT4p0NcJa9toyipiJ2dkSx3TKZGbXvCtVWAPG3F+wHlc4GVhEGL1sCuqDc7kA8VOru5Egdyl6wTaUQk8Gf9w/Ixh1b/diYkBrDOdPTeX5LGZ29wysE9lW08PP3D7J8cip3LndhiDQMq5aMpb2nnw/2+agoAP557F2P+8fHW9201/8Wjn0JF/wOMmb5dpHIBPH5f/BdKiuP8/T6ozyweh+vbernm5MeJ0znfB/1bMGzfikrRhMygJX4TFFNO6oKM7L8s52flSNuCA4EMoCtLQBD5JBm6N7QYzLzckUySeZ6Hj0zjedvX8S0jDi+99qe0RPE1hyAFy8DnV6s1F32N+FW9+x5win1BMdiUdlZ1syCvFGcfa3cKWrkcgMTwJ4+OZXEKCNv7vL+91lU084P39jLnDEJ/PzSmUOkZnedMZGcxEh++0nhyAxubMy+Rkgtiz70/pgtfxZN2M982N4719ZCJzM5Hi58AtoqYfu/Rj4/LWmtoCcilcL6Hgo8fHa9feRtZj0/yylT+viyx9l7y14eW/IYj502cDMWZYwiXB9OYkQiOkVHfmo+/zznn7x7mecbHoBbP76VBlMRSY5tzOKzRS/rva/6beYUZ89eatCHuMsagFglxGe+caZ9V1tPP3E+Ok5fMO4C+/N+k/sFjkBlYLNjsl0OrWzuJjvRv/ZPe2uHl/yNmNaBgCTf8hevD1uRMndEl71yXg6RRj3Pe5sla6sS/hNWB+IHvnrAaXdCRIJf83j5wpdZNWOV07ar3r+Kn+26lRf7VxLZXqqpo7orIox6Uv3oBdvb4V8ZiSts2f7cJM+lX++6qT0NCs0lFCRlU9pWat+0KNO1ku7byyfQ2m3i7+s9K04aO3r59os7SY0N56lr5/ilHFw0LokJqdG8/LVrE6Zrp/jXCu28sefx8oUvQ9EnsP5xEYTOu2X4A11gnncrmPt44W//y+MfF7KuqI7/bCvjyffA0rxyyHgZwEr+6yisETdvU/y0nc9OiCQhykhBpZersv5QewDSpooAz0ee+uwwa5pEu4FV41tZPjmVF+9YxKJxSTz41j6O1oc4M9TfC2+sEhbqt30iCv3n3AC3fSwyYW/cdsLLiY81dNDabWLeaA5gy7fhl8u1lxj1Oq6an8OagloqW4a/6Wnp6uObL+wgOtzA0zfNJ8I49L0fYdTz7eUT2FvRypZjnp1VvWLc6ZCQB9v+7t34o+uERHj6pbDsh/bNVdb/X3ZiJOSeAuOWw9a/jS4n8NbjhCePxahXeHeP+4WsRzY7tzO4M/9OLhx/ITpFxxWTrnDqu+iKUzJPYXz8eD698lO+O++77Lhph8txVZ1VkPVnDve/7LwYMfta0cuxwvVxw2FTPGiagY1Kprxt4MbvuXNepK/fQlykbxJix+xTWK/7zKXWAezYuLFEG6NZkjW0p7jJbKGmrYccP/sXV3T41qLGL6wBbGRPxrDBiyO/XzHQ6sNxQcZb4qOMXD4vm3f3VNHc6cWCiL2FzlgAqjsH+m9eM/kan69vI8oYZZdtO9JhbmBv7HKISYevvfwMGwE+94I197POMrBY9scz/jii65c1dREVpiclZqiqKS5sICHxcPErI7rOiGg6Rk2Md/LeubmJXDoni6e/OuZWydDbb+bul3fT0NnH0zfNJynaP0WXoihcvyiX3eUtHKoe+J28cuErPHvuszy0+CEuHn+x1+eLC4sjTBfG75b/jujm46KXa8ZsuOgp7+peB2G2qNy9tocdlsl8I+orNj1wBtsfOpsDj57Ls7ctJDZiaLi3cvVKJ1f4Ew0ZwEp8pqimnZhwg72mw1cURWFWdjwHvJUV+UPtQUhzX5/gjiO17fx7YwmT8q03KtaC+HCDnj9fPxejXsej7xVok73yly1/FjW+l/wZ4gZqY0idIrJXFV/D7hdDNz8N2FEq6kfmj+YA9vg24XIdmRCwS6w6TWQint1Y4nGc2aJyzyu7qW7t5umb5pER715mdtX8HBKjjPxnqwbtEnR6YeF/fJs1oPdAU4lYXEmdCpf+1elLuqKlmzC9jhSbHHbJvdBRA4U+ZHYDTUs5hsQxnDI+2W2P3l6zc8B93ZTr+M6c7/h1ucyYTO6YdQfh+nCXLRtsHOh43zlonnweKHrRgsEPNAn+bNhrYJO576sBU6Cc6CkAfvX8HRcv/ib+5uHrR+sAtrStlE6T6xvkmtYeLOqARNRbDFaH3K6OACqRrKwu+wyA7oiaIQ60ntBHDwQSLx30LG93x62njqW338Kr272QpQ5qoXO8feCYh0952K/rO+Iqgz4xMwnm3yYMcEZQO+4NPveCbTpKYdjA38gZuSNzuy9v7CI3KcqlCdRdc+8a0bk1wdQN7dXoYjOHH2vlx+dPI9Ko586Xdg6REveYzNz1n91sOdbIb66cZVf/+cuV83Iw6BQnJd7MlJksyFiAoij8etmvvfY2ePvSt1l79VpoOAIvXArGCLj2JTD6d1/92PsFfHyghs7p15PWd5zszkMAGPQ6zpiSxi2nDJVNt/S28Le9f/PreqMBGcBKfKawup0pGbF+O+GBaKdTVNOuXVsPRzrqRb/GdN8D2D+uKybCqOe7Fy4UMiYHR7e0uAh+sHIyG440sN7HukTN6G0XLq2Tz4dJLpwkZ18LeafB578QRjknKLvLW0iI8t3lOmioKlTu8M8kzAeyEyK5aHYm/9lWTk2r+zrT33xSyIYjDfz80pnMz/Ps2hxh1HPpnGw+O1irTQuPOTeKlhdf/tq9bLW3HV69QTy/7j/CNdGByuZushIiBqRdE86AuGzYG8JMgCMWs5A3Jozh7GnpHGvo5NggJUZDdwMLXhrIxl835ToeOuUhTS6fEpnCAwsf4LnznmPtVWuH7H+n+B3WlK4RL6KSRBa7yL8evbbgr0WL94YtAxuZxMHGgUxje49QiPhq4gTw/HnPA3A4zOjWbCY2woCiQJsGAazZ2kt0cB2lDSf1gA/834TrAdD3qt5lJ0fAY/UDJkW+tvqxcaDRP/O3KRmxLJmQzPObS4c3bmwqAZ0B4nJo7XVe4B7J/YYNV67EE9ONsOB20BnF4nAA8bkXbM1+Ta9f2tjp9vdvM3ECyFFC1HfdmoFXY9Lsm74///seD8mIj+D3183hcG07N/xrG8V17aiqyp7jLVz99BbWHqrlF5fO4PK5vrc/G0xidBinT07l/b1VWNz8Dv901p9cbnckLTKVtLAEEg99BP88Eyz9cMu7wnnYD9YU1PDCljLuWDqO5ZfeDvowOOAsA795xk12EzxHBi+6nkjIAFbiE6qqUljTxlQ/5cM2ZmbHYTKrHK4NgJ13XYF4TJ/u02FljZ18uK+KGxfnkhwTDpn5wiXVgRsX55EVH8FfvwjsSq1bdj4nbqxOv8/1fkWBM38qas92vRDUqWnJgapWZmXHa3LTEhCaS8XvIWtkNWLe8MOVUzCrKr/80LXU8PXtx/nHV8e4+ZQ8rl+U69U5r5qfQ5/Zwgf7fTekGEJ4DKz4sTCfOPTe0P0WM7x5B9QXwdXPQtL4IUMqWwbVEOr0YjGm+HPocJ3tDCodtWAxQfwYzpombq4GZ2HPeN05OzLcjZev3DT9JuanzyctKs3l/h+t/xE/3fRTOvo6RBa2rkAY4vhIgpYZ2M4GiEhwcum+d+69tFld3X01cQKINg4salncOBHrdApxESNsB9TTCsYo9jaLTMZFE1z3b7TJ+7N9zMBOSxRZ6Obsz4LWSgdgjA8ZWIBERfzuPi752G0Weji+s2IiNW09vD5cFra5FOLHgN7AkeYj9s2XTrjUr+sOZmbKTCYlTnK+pGE9xKbD3Jtg14sB9ZDISYz0qResRcMA1mJROd7czdhk94vC224QKpoKtZfu/hA4EVtb6DgGsLfPHN4B+4wpafztpvkcrevg7Ce/Ytojn3DZXzZR0dzFP29ZwM2njtVsipfkZ1HV2sNON60gjToj+27Zx8brNro9x2+PHYRfZ8G73xHKuW99IdRcftDabeInb+1nRlYc9583VSjCJp0DB95yKiUL14ez9YatPLTYeVF1Tema0CoKR4AMYCU+Ud3aQ1tP/8gD2CyrkVMg6mBrbQGs65YH7nhucykGnY7bl1ot/DPzRS1Z98AHVZhBx7dOH8/XpU1sL3XfxiEgWMyiLnDsMsiZ735c3qmQuwQ2/xHMo7T9jwd6+80crm1nZvbI5D4BxZaZz9S2/6srcpOjuGvFRD7YV82LW8uc9q3eWcGDb+1j2aQUfnax9ws2M7LiGJcSzZqCWq/Gq6rquQZuwTdE/c579zg7Yff3iuD18Cdw/m9gwoCJT5+5j8ZukaGrcuXiOvNKUM1Q5J8UVlNsrpzxY8hJjGJyeoyTCsM8qOb850t+TpTRv0zXcCiK4jY4fqf4HU595VS7ORbFQ7O1wxGndQY2Kpm2vgGZ7M3Tb7ZnRn01cQJnJ+KOdvcLMCPuZ9vTChHx3PrJrQC8Wji05yMMGJD5KiGOikm3Pz/eFDi1jOPNaffxm8n1MYA9J3pgwamlt8WvOZw2MZkFeYn89cujnrOwDi10bltzm32zFvJhG0+f/bTT64+r/sUjmx7hf+OtP5eNT7k4ShtyEsU1vJER/89n/8OlVR/YX796kev3n7fUtPXQ128h10MG3vEzKyQBrFVC/vgR313Uz52RwRc/WsFPL5rOjYvz+PXls/jq/jNYOT19+IN9YOX0dCKMOt7b4/6zR1EUYsNc3yP/dc4PmL/oHlF6c+Nq+MZnkODdwrMr/rzuCE1dffz2qtmEGawh3cwrRQnOoB7tBp2B66ZeNyS4PkHjVxnASnyjqEZkTKdm+udAbCM3KYrYcENg6mBrD0J0mr11gzf09Vt4Z3clK2ekDzSjtwUn1fucxl67MJfYCAMvDQomAs6xL4U768JvDD92yT1i7OE1AZ+W1hyu6cBkVu2LHKOS6j1CcpbmW5bfX+4+cyJnTEnlp+8c4KG39/PunkrufnkXP3pjL6eMT+bvN8/3qTWGoiicPS2NrUcb6RimBcGR5iO8eeRN8l/Ip7G7kX31+zBZBgUGegPtl/+NLr0B/r1S9KLb+Rz8YwUUvAUrf07X3BvZXrOdYy3HqGiv4FuffYsVr69gb10BrTEv8rXpp9y3/j5mPT+LO9bcwQG9ihqfK4LfUGNzcU0YA8CSCSnsLGu2l0DMeXGOfeiTK57k8kmXB3Q6t8+8nTvG/o3OY99letLQdgsv1G0TxjSl7rMA7tDrFGIjDNqZOEUlc9orA3VhEYYI2nrEey7eRxOnwbxZ8oHbfVoFsDbqulwrASpbukmJCXNpmuaJuJgs+/NABrDrKwbasMSpc32uO35wwlX25/5mYBVF4btnT6K6tYdXtnlQBTSX0h6fwyXvXGLf9OpFr46odcxgUiNTh2R03y5+m5dL3oe5NwrlUnNgvtt96QW7uWozpQxIy2ck+14S5UiZrYWOlyZez+//94iu5xdNJXRGxFPT7Z/qJjU2nG8sHcdPL5rODYtz/aqxH47ocANnTU3no/3VHqXgOmXg+/imaTex6+ZdbL1hK8vyb4MzH4KVj8GklX4ZNtkoa+zkuc2lXD0/hxmO90uTzwNjNBx40+Vxg3vD+uPKPBoY2beH5L+OQ1YH4snpI8vA6nQK07PiOFAZAAOL2gM+17+uK6ylucvEVfMd6iTsAexe0ZrCSmSYnivmZvPK9uM82tlHop+udj6z52Uhx5t8/vBjJ50DsZkiiJjmWvo2WrEtaszMHtkiSUCp3iskP4bg1ArpdQpP3zyfX314iJe3lfOfbeVEh+m558yJ3HPmpIGVVx84a1o6/9xQwobD9Zw/a8A0Y1v1Np458AybqzYPOWZwb1OdosOiWrht5m08e+BZSIvmW5YYPi34C6VhRuIjYcacs9lc/C8odt0W56aPr8MYD00m+KRUrMBvq9nG9R/dAEkQ372Pz3taCPezhYYm2ALYePH5cMr4ZJ7bXMq+ihZeL3vcPuz6qdezMm9ou4JAoOtPx9Lbyovnv4RepzoF0b/b8TsuyDuFlNKNYnndx5uk+EjjyOpHbXQ1iaC/T7QCmWKVzY4kAwtwVfYKVld+yZPH3uK2ZUN7RIKWAay48b9rjmuTm8qWbp/lwwBKVCIrO7v4LDqK8gAGsHWtAwGjr9lXAEPSOP5YW8+96an8ZMNPWH2Jfy1Wlk5M4bSJyTy19giXzske+r3Z3QLdzXwdbqCkZsC0LlLvn6mNOxRF4ZdLf8m7R4e2qprVtI59OgPKpw/DtdobIWb72QvWXyM4R8qbxOLDcDXQCfpIWszdPHPwBb6/0E2pUqBoLuFAcjYwunqAD+b8WRl8uL+anWXNLBrn3nNiz8170Ck6eynUcA70vvLXL46iUxR+eM4U5x1hUSI4LvwILnwKdEPvD26bcRvPFjzLipwVms4pmMgMrMQnimrayU6ItBt9jISZ2fEcqm6j36yhkZPFDPWFPgewq3dWkBYbzrKJDlnb6BSIy3EycrJx3aJc+votvBWsvrA9rVD4Acy6SrjVDYfeAHNvFhLCUDYl94MDla3ERhj8utkKCqoq3hNBkA87Em7Q8/NLZ7LzpytZ873T2fHwSn54zhS/gleABXmJxEcaWVcoVrt7zb28U/wOd3x6h8vg1RU2WfGzB561b/uHroNSq3Nmqw42tx72a342WvU6Fry2jFcLX+WZA8/Q0N0wovP5RctxsXgULhbuFo9LQlFgU3EdH5cOSJx/vOjHQZtSY2cfseEGwgw69Do9Y+PGOu0/o2s3q2JU4XLpI/GRRlo0CWAbaI8cWIiy9cBtt2Zg/amBBfjhrG/bn5vclElonYH91uxvuRxW2dLts3wYgLBYPosWn3FHmzRwBHdDR/vAd5RfBk4JeYRZNYZFzUV+z0NRFB65aAYdvf08+ZmLzwSrfNQQm+W0OTwAi4R9/Rb6yr7ncl/XkntELX/JV5pf19tesEVNzj/nkbQQslHW2IVBp5DpwaEe4P5J19mf9/R7V6urGU0lvBM5sLAx+DNttLB8cipheh2fFnju0avX6QPm41HT2sNbuyu4ZsGYAdWgI1MvEmamla7bqf1gwQ/Yf+t+r0ynRisygJX4hM2BWAumZ8bR22+htFHD1eemY9Df41MA295jYv3hei7Ozxoqw8yYNVBT68C0zDhm58Tz9u4g9dA6vEb8v2b58EU29yZAhf1vBGxageBAVRszsuJGr4FTW6WQRgY5gLURH2lkSkYskWG+9zh2xKDXcer4ZDYeK2fW87NY8NICfrrppxrNUnt+te1XPLXzKc54/QxKWj23FdKc1gq7fBiEG+XUjDg+Lnvfvu3O/DuD+p5t7nJWf7x/+fvMSZ3jNGZnZATHD7sw1hqGhKgRBn8gFnq6GvlL/0CdtU0G2dZjIkyvI9zPxZeYuIEgp6nHtRdBnAYBbE/4wHedq9+tqqpUt/SQObh+2xt0Oh62OotXtgduUeapwy/bn/u1KBiTzikaWSlMyYjlpsW5/GdbGXuPtzjvtDrQWmJSnTYnRXh2VfeHw7Xt9HZluNzXu2CVqEn88Eeir7rGeNML9q7PnbP9ceEjVyOVNXWRkxg5bKnJjOxT7c/9rXn2C3M/tJTzgXng7/k/F/4neNf3gdgII0smJvPZodqQGSD9e+MxLCp86/ShpoiAyMDqDKOrFZ3GyABW4jV9/RaO1neM2MDJxtRMcZ7CGg1lxLZg04faxPWH6zGZVc6d4eILLX0GNBwWZjSDuHh2Fgcq2yh100BbUwo/EDVtOQu9PyYxT4wveCtw89IYk9nCoeo2Zp0QBk5zQjoNLZiY00ZHxk+GHZcSmcLUpKn21wadgenJ/tf/5sTkMC1pGimRKeRGLMDcncvm677m+qnXe3X8Je9cwq+3/drv6/tM63GIdzbamJrbQaVBOH0/cuojmsj8fKGps4+kQTLMXy/9NT+c/0OnbRcUP8+euj0+nTs+0khL1whbu/R1gLmPQ+ahcsC2bpO11Y2fAb9DDdehpkMuh9gysH7fYPa0sk/vWR3U0dtPt8lMRrx/WcKZOhFQtui2YdJSiWTF0VxM15foXwCr06FPyCXaerv4/tH3hznAMz84ZwppsRH88I299JgcDJ2sDrQvVQ70PN52wzan9i5asbeiBYAnT3tmyL5f7nyCjUu+BQ1FojWYxnjTC1avDCxOrshZoYn0tLyxi1wPDsQ2EpMGXJrfO+r74pfftFUIp3crBp2BuLDRW0a0cno6ZY1dHK4Nvty5x2Tm1a+Pc8GsTPeu4pEJMHapDGAlEoCj9R30W1TNMrAT02LQ6xQKqzVspVNbAIoOUqcOP9bKpwW1JEWHMT8vcejO9BnCCbV+qHTqgtmibvDD/dV+T9crTD2inciUC1zWMnhkxhWil5yjM+wo5mh9B339ltHvQKzoIcM3l+vRxsbKjTxffq/b/VdOupKN121k+43bWXPlGt64+A123byLvbfsZffNu3ntotfYf+t+PrvqM/bdso8fL/oxt06/lZwYUSd67ZRr2XfLPj66/CN+tOBHvHzBy6y9ai37b93Px1d+zOsXv84X13zBDP33iG36PrHhkfxk8U/Yf+t+p387x1zrcn6vFL4yxP03IKiqkBA7ZGC7TF2sbbvf/vrqyVcHfh6DaO4aGsCOiRvDqpmr2H+rc/uNmz++mcKmQq/PHR8ZRmu3Z3OvYbH2gN3VM9Tpuq2n32/5MAA6HS9YFw7/uf+fLofERxoxmVW6TX68R1QVelrRhYl+xb9a+iuXw2rbxMJmWqx/JkPpYeJzzpi0ieoW7bN9jj0ee8ruIM+LAMYliWPJscbXj2x+ZERzio808purZlNc18FTjlLi5lKITuXrul32TYFy8t5f0Up8pJGzJyzgxfOda10/K/uMOw/9gxsmzmDj7n9A+TZNrz1cL9guUxdVnQMOt1dMumLE11RVldLGTsZ6ISFPjBooo/rT7iDKS5tKcFzC2XGja+nraGHlNOFu/NlBzzLiQPDpwVrae/u5fuEYzwOnXAiNR6B+ZGU8oxUZwEq8xuZAPG2EDsQ2wg16xqdEa5uBrTsIyRO9qxNFZJW/KKzj7Glp6F05sdmkyC5kxNkJkczLTeCDfQEOYEu+EtmM/2fvvOPbKs/+fR3J8pD3tuPEK3G2swcJCZCEGaDsPQItpYXS9i20hVK6218HLXS8pW+BUsLeI+wZSELIXs524tiJ97blbVnn98ejYdnaOpKd8FyfD0g65zlHj6N17ue+7+93cgBiTFOtaov7Xtd2TiHCJuo1bczoXXmlZjekTQSD9pmBcLGxeiN3fHyH07apqVP55KpPePuyt3nnsnf45eJfkhiVSHREtN26xKAzOKkrAmTFZqEoCtdPuZ4fzv8hay5bw+rzV/PAaQ+gKArjEsaxatoqitOLyYwdbmngrYcwsvAsNpWf4M6cs4fte3TPo4H8+f7R0wZ9JruAE8A5rziEmiKU8Ah5DaW5o49ko3sBuT03O6unX/eOb9ltcIg4BVUe19nEpmjHv839Cx2Z/vbufhKig9OQnBQhgss9DXtc7k8Mxs+2rxPUAQ5bVWDd9eLZ/DwzEgJ7DyREORZNQyHkNLgEtNecGriuQFIeh3QivDBbzEEvHJ05MZ3rF+by6Poy1tnsqFqOQXJ+UOf1lT2VbcwYK3zGJ6VMcjmmZMDEHZnpDLx2m5OVXrB484L987Y/Oz0eEzfG5Th/aO3qx9Rj9vn1v5y4oJ/Tb1qO8WGsY356XXAtMqEmIyGaWeOS+HC/b1Z0WvL6jkqyE6M5rTDV88DJK8XtoVMzCysDWInPHKhtx6BXKEgLcBXXBZOzEzigaQbWPwXiLceaMfWaOWeq634YUsaDPkqc1wUXzRjDgZp2yhpCWEZy6B2IjIeCpf4fm5gDuYtOmgD2cJ2JyAidR7P1Ead+v98iYaOJn6z/Cd/66FtO2+JrHuLFi14kw5hBXkIeuQmB+9IZdAbmZM7xeXxVixcV17HzidVFcseAkc3XO2dDHtn9CNvrtgc6Vd+wKxA7VrvtvqZ9mZxrHF6GGA6au/pIiXWfxVQUhY2LHrQ/NlvMbKnZ4tO5E2MM9A1YAsteWlE7G/lmtmPBYnB5eHtPf3AZWMAYI3ojF2Uvcrk/qAC2Ryih/77xSwA6+lx/v9cHmYGNNDoC2PJm7RX5/7T1T/b7URE6MuIDXGwZEliWt5cHPikrP7twKpMy4/n+Czupbu2G5nLMSflejwuWnv4BDtWZmDFWZL9jImKG2YoM5gW1DV7/Nli0KfH25gXb0O3wl45Q9G4DbH+osC6O+JqBvyPB8ft2pCVM1VvNx3giaRRXXrng3GmZ7Klso6YtfJ65DaZe1pU2csmsHO/2N4ljhVbHKVpGLANYic8cqjUxISMegx9+k96YnBVPVWs37T0aqET0dogypAzfg4v1pQ0Y9AqnT3CzkqWPgIzJLjOwIL7AAD45EJhvmVdUFY5+Kmx8AlVjnHwR1O8LmbedlhyqNTEhPc4vT9Ow0muC1uPCQuckZFf9Lt4uc/bOXJh0LdWtfW4zAqHEYlGpbu0hJ9lDABsRJX6EK7dhNBgpWVXC18Y7fCJvef8WilcXh05Mo9XZA/bxEocd0KyIn7FrqBhNGOjuG6Cn30JKrOfvhPjcxU6Pv/HhN+zK0Z5IMgYR/FnZ1rDTfv+tS537JkUGNsi+Pqut0pc1X2K2DC93tgewXQH8Db0imLw6bR4AC7MXuhxm+8xkBpiBZZA1VGmj9pmcKL2Y1yUdMYxLMQbu95icxw+aHVlILdRpYyL1PHLDHPoHVL7/7GbU9kp+qYbod3QQJVVtDFhUZoxNsm97buVzbsf/ISVReFF/8VdNnt/mBevO+/dAk6OnOy0m3eUYf6lo8s1Cx0bWoKD5sjWh9bS203KMA5Ha+7aGElsZcciu/1zw1u5qBiwql8/J8e2ASSuhcht0NHgfe5IxSq8SJaORgzUmzQScbEyxCjnZypODot76xe9HdmzDkUbm5CZjjPRQzpY53W0AOzbZyOSseD45GKIykpZjImAqPCvwc0w8X9yWfqjJlELJ4TrtVK5Dgq0X2g+RsNHC3sa93PTeTfbHfznzL+y4cQffniksSXYdbw37nBo7e+kbsHj30Rw7H2p2gdUy5ZeLfjlsyG82/Ub7CYJTBrbH3MPfdvzNvmvuuGwO15vo6A2yX9RPmq0CS54ysABEJ3Bpv/N32yVvXOL1/LbgrzWQ4M9Ke5fjgik/Md9pn6nHTEJMkDb0MUn2u65UqbXIwBoMRuIN8W7LGevae4kx6ImLCvBvGfQ3hKIH9sNy8Z1/Wls2ecHYkiXlcWub4zd68CJOMBSmx/GnK2fQUHkERbWwvs/xO/rRlR9p8hxD2VYuAvF5gzQvchNy+etZf3V7zMC0y+HT32hirWP3gnWRge3q76Kuy/FvoJWq+XGr08O4ZB/fA8l5XGFyVB0cbT2qyTw80lxuvzs3c27on08DJmTEkZdq5JMD4Ssjfn1nFdNzEpiY6eN10qQLABVKPwjpvEYCGcBKfKK1q4/a9h7NA9jJWaLX8WCNBuVT9dYgM9O34KKpo5d91e0sLUrzPDBzmvDTcrOCtXxyBlvLW4K3nXBF2WfitnBZ4OdImyBKoQ+/r8mUQkVbdz81bT2+fzGPBPX7xe1JloE91HxoWA/k2XlnY9AbmJ6TRIROGZFMYpX1Is57ADtX2EhZF5IMegMlq0rIS8izD3n58MtBK6S6pO0ERERDbDpnvXSWffMflv6BmWOTUFXYX619+acnmjtEAOupB9bGb9IWca/JoShc3l5OaYtnb9ikYII/G73u/03aezTIwMYk841O8Xftbhju1a1FAPtszTpM/e4XV+tNvWQmRAUeaAzKwFa1aqtmr6oqZlUsrFT3JpAbiAesjeQ8Bv+F7qyLAmFlcTbfnC4uRTP1jqySLXusNdvKmylMjyU1zvn8S8cudZ9pX/FTnskq4N/v3g7t1S7H+Eq0QU9GfBSVLcMzsEtfcG4T+ueKfwb1XDYqmrvISoj23XotKZfDBsfns6YzxDofqoq5xbEI9fi52iyQhBpFUTh7SiZfHG2iMwyLmKV1Jkqq2rhs9ljvg21kzYCEHDj0nvexJxkygJX4xEFrhlTr7Fh2YjQJ0RH28wdF3T7RK5roW//exqNCJfP0CT4EsOAIkIewYkoGAxbVIUahJWWfQcJYSB0f3Hkmni9Wj3vDL/nuK6V1tvfYCAhI+Er9ATAYISnP+9hRgkW1cOVbV9ofR+gi2HPzHrsYU7RBz5TsBHaOQAa2qtUawHoqIQbIEaWcVG512vz2Zc7l0PdvuJ+aDo0vtlpPQOJY9jXvp7PfEWRcUHABU61iYwe0WIDzA0cG1nsAS1Yx1zTWMie12L7p8jWXeywlTtAggH24VQSVsQbnvrtesyh/DrYHlugkrmkWgdQJ04lhu7UIYL1R194TcP8r4JSBrezzrT/ZV57e71DXrTIHaKFjIzoRYpL5NFkEWDsGKQVrwbUTxMX/gV7HwopNOE5LLBaV7cdbmJ833Fs2Uh/J4+c+PkzBG+Dcty7jj9H9/G+cAV6+BczBWUyNTY7hRPPwDGyfxfm8RclFw8YEQkVTp38LGEm5fKvV8RkYKvinOZ0NvBfp+D6K0AVZnRFGVkzJoM9sYX1p6Lycbby2swq9TuFrM/0Q9lIUkYU9+in0h69XNxzIAFbiE7YMqVYKxDYURWFydoJ2AWzGFJ+tZr440kh8dIRTL4xLMqc7zu+CWeOSSTYa+PSgxn0QlgEo+1yUDwdbSjTxPBjog2OfazK1UHDIGsCO+gxs+mT/7YxGCFOfiQe3OoR8fr341+y8aeewjNHs3CT2VLa6tXYIFdXWANaTCjEASbkQmwFVwwWb1l691unxua+ei6lPQ2G4thN0JIzh2revtW8qWVWCThGiOCmxkWHPwLZ0+hfAGoDVk77utNmTj25Q/aNWKizitX31a686bTf1iGAlPkgVYmKSSLKWlD+xd7iQlvCZFf22ftPTRkWE9/k1mHoDViAGIDoJvbV325Lyuqal6OsqHeWu9WqSz/2PbknOJ70tNJk4fWsFdZHO89PC+3QoRxs6aO3qZ16+C8u8QSzIWuB2X3FEDaXv/SCoeYxLMVLZ6pyBfbfs3aDO6YmKpi7/SsjjsljUp70vsTt6Gw5zf7qXRMIoZX5+CgnREXwc4jJii0XlzZ1VLC1KI91fMbaJF0B/lyYl8KMJTa7CFEU5X1GUQ4qiHFEU5T4X+6MURXnRun+zoij5g/b9xLr9kKIo52kxH4n2HKozkWQ0BK5i6IEpWfEcqjVhCebiWVVFgOlj/6uqqqwvbWTx+FTX9jmDiU2DuEy3Aaxep7BsUgZrD9VrGwDU7IaeVhgfRPmwjdxFEJUwqstIDteaiI3Uey8nHUnqD5xU/a+Ln1/MMweesT++rMi1IMescUl09g1wpD68Gfqqlm7ioyO8l5MqCoydJ8QohpAWk8aP5v3Iaduq91dpJupUY6pkEeX2x+PiHWrEiqIwNTuBA1pagflAkz8BrN0KrITLJjhe/xcPvUhjt+usQTAiTqqq2nsvATKMGU77bQGlFiXE0YNe46GZd51OIT4qIsAMbCslUZ7/bVVV1SQDO7nPkXVzJ+wTCJtrHYrddWqQGVgQVSeDhADdvXcCoqWcs3McAUyu8jUiFO2zcFtt/a/5wzOwg1HwfE1wefM63lv3y4CrPcYmx1DT2oN5QASJPeYe7l1/r9OYwsTCgM49lK4+M/WmXv8WMHQ6IhO9eIxqSPsg4aqTDYNex7LJGaw9qPH13xA2HWuiuq2Hy2b7KN40mIKlEBk3qq//AiHoAFZRFD3wT+ACYCpwnaIoQ6/wvgG0qKo6AXgY+KP12KnAtcA04HzgEev5JKOMA1YBJ61EBQYzOTuBjl6zvZwwINqrRbDnYwB7vLmLqtZulngrH7aROc2tlQ7AWZMzaO3qZ09lq2/n8wVbtrTgjODPFREpMrlHPxXB/ijkUJ2JiSF6j2lCZxN01J0U/a/tfe0sf2m507Z3LnMvpW+zlNhb5VvppFZUtXqx0BnMmNnQdMRlGfzN027mta+9Zn9c2lLKX3f8NfgJ9vdwbqrzot1QRd0p2fEcrDXZL0bDQUtnH3qd4lsQGJMs2ipqS/j16b92ujBe9tIyGrqGtz7ERUWg1ykBBX8vH36Zez6/B4AFurhhmbR2awY2aBGn6CSvfZmJRkPAJcQ/yRC/DTaRs6F09Jrp6hsIXIEYIDqJJ2sclTvlTdpVDkxImgBAhKrSQJLdviVgkvOEoKCVf+z8R3DnG0yzswjXvv2LeeKLcu3Ob2XLsSbS4qLI9xLMXTP5GgDev8K9bsSPj73Kua+eS/+A/++vsclGzBaVOpOwYfqowlmw6qPkM3jjkjf8Pq8rjvtpoWMnKXArNX/pGdT/+q+z/xW259WKs6dk0tTZx64T2vkFD+X1HVXERUVwrjvLR09ERMH45UIHRSM7qNGAFhnYBcARVVXLVFXtA14AhsocXgKstt5/BVihiKvUS4AXVFXtVVX1GHDEer6TlgFzH/WtjXR3t9LX005fTzv9PSb6ezvo7+3AbP1voLeTgd5OLH1dqH3dqH3d0N8Tvv/8CGIsFpXDdSa74JLW2IShguojs4nr+BjAbjkmLnYWejOCtpE5DeoPwoDrEq/Tx4vzbNCyD6LiS0ibCHEZ3sf6wvhl0F4FjZ4FXEYCVVU5VGti0mguH26wrhKfBAHsEyVPOPkJfnjFhx69XQvS4og26Ngf5l7OqtYe3wPYrGJAdVsJUZRcxGPnPmZ//MTeJ/jv3v8GNT9Lm3Nv5b/P+fcwRdop2Qn0mS0ca9RWhMcTzV19JBsNvtuiZE2HWrEAN/TC+PmDzw/LViuKQmKMgdZu/3v9NlRtsN9PNwz/PGuZgQX4T/H3AHj6wNPDhiTFRAbdA3vjlBtdDqm3Bh+ZCcFkYJ2zyAfqh/fyBoKqqhxpFf6dW8tPoIvPItoQZG4gOR8sjn/L10pfcz/WH1RV2N8N4typmfz+vYPsOK5dQGCxqGw40sjpE1K9LpKek3cOJatKyInL4aGzHvI49ovjaz3ud4VNDfhEcxe9A7387IufOe3PKrpAs4XciiZbAOvnAkays86DK6sqrWhocagcL8lZErLnCRVnTkonQqfw0f7Q2Ol09w3w3t5aLpie5bsQ11AmrQRTjVDzP0XQokYjBxj8rVsJDJVys49RVdWsKEobkGrdvmnIsQHkx0cPj7xxP492aiNXraiqfYV56K3TNtX2WLVvH/rVN3ibAqCLQDHEEh8Zz4z0GczKmMXCrIVMSJ4wbB4nWrro6hvQXIHYhq3n8WCtiXOnBbC6BI7sqI/BxfaKFhKiI5iQ7qNgUMY0GOiF5qOQPtxYPDUuimljElh/pJHvrtBAeMFigRObYcrFwZ/Lhk3JuGwtpE/U7rwa0NjRR0tX/yjvf7UFsKO7hLihq4H/7P2P/fGm6zcNE9IZil6nMDkrgX3VYc7AtnQx30s/mp0sqwhR7R7Ida0WOidjDtmx2XbVzIe2P8SVE68kPjKw99XMdy6331dQWDxm8bAxNiGn/TXtFIXp/dvS2eeTArGdrGKx+t7fjWKI4aWLXuLqt68G4LGSx2jva+eB0x5wOiQxxkBbt/8XrVUdVfb7P0wb/jrZPL+DFnGyCiDl6kQG9J2yd/jD0j84DRF/Q2AB7MQBhYrISBKjEl0OqWsXtjdBtdUMEnECqGzTZgGp2+yoZupR4shMTXI/2FeswnX3jr+KPx59Ofjz2ehsQO3vRFwSis/Zn6+eyQV/Xc89L+3mne8t8Wxz5yMHa000dvSxtMg/b9Vz8s7hhYtecOqBH8x3193Dw7qHWTp2qc/KyTYv2MqWbtTowwyoA/Z9325tFyWfGmGz0MlL8T8De/7xTt6PE8cdbT3KpJTh1z7B0m3uZlX3fs3PG04Sog0sLEzhkwN13HfBZM3P/9GBOjp6zVzmq/erK4rOBUUnfgdy5mg3uRHkpJH6UhTlduB2gNzc8JU2+MukcUuYtf4A03ISSIuNQkXFtr5qu+d4POj/KkP2DT9u6H7n4zyMGbxNVYUthKkO5l9LQ08TO+t38n65KJWZkDSBy4su5/Kiy+0XvaFSILYRGxVBXqoxuAxs3X6h1hvj28XwtooW5uWn+J7BsGV2a0tcBrAAS4rSeGLDMTp7zcQG6gtoo/GwKInOXRTceQaTUiBW0Y+uhYXf0u68GnDYKuAUqkUSTajfL2wv4gNcZAkDFtXC8pcdpcPzMud5DV5tTB2TwNu7q1FVNSxl3Kaeftp7zL5nYBNyICZFBLBuiNRH8uGVH/K9T7/H2hMiM7L4+cXMSJvBf8//r1/KpvuanDO9O2/a6XLc+PQ4IvUie33JrPCsvzZ19pHsS/+rjczpoFrEezhnLlNSnRf6Xjz04rAANiHGQGuXfxlYVVU53HIYgGm9vaTFD7d7sIk4BZ2BtVrQpPS7n2NijIGaNv9bU+q6mzmsV8WipRsarBnYjGAysJHx4qLSSlVba+DnGkR5e7n9fp2aHJwHrI3kfABuMBaKHjCgrrOOzNjM4M7bfIyfpjkqod657B0Sog08eNUMrn9sM79/9yC/uXR6cM8BrC8VFSk+tw0NYmKy5wXfH3wmRJ3evuxt0mPSMRo8/3tnJ0WjKFDZ0kVPjLPH6nfiJgnVZ40ob+okyWgg0ejn5y0pj//X0GQPYK9860qXCs3B8t6xU6Mv8+wpmfzqrf2UN3aSn+bnYoEXXt9RyZjEaE4r8LFi0BWxqTBuIRx6F5bdr93kRhAtAtgqYHC391jrNldjKhVFiQASgSYfjwVAVdVHgUcB5s2bNzqb+IDFsy7i9tcNLJwzkduWayOBrjm7noM37oDxV0KamGNNRw2fV37Oa6Wv8aetf+JPW//EwuyF/PmMP3OwxoSihFYddrJVyClg6vb57P/a0tnHkfoO/5rh0yeBLsJRquyCpRPS+ffnZWw+1sTyyUH+qJ+wFibknhbceYZSuAxKXoGBftBrr/IYKLbXfuKoDmCtAk6jtEdXVVVmPjXTadsjZz/i8/HTxiTw3ObjVLZ0M06LC14v+GyhY0NRRCax1vtF1N+X/52Htj9kLyHe07iHN468wdWTrvbpqbr6u5wyLr9c+LNhpcM2DHod4zPiOKyFkrqPtHT2MSHDD7spuxXYAciZC8D2G7cz95m5bg9JijHQ4mcA+2GFQ7zp6eo6WDT8gsteQhxsD6x1sTKy14QxwvX7NSHALPKRvhbwsj5gz8AG0wOr00F0It+MKeSx7jLK+t4HfHuPeuLuz+62368eSAxewAkgcRygoLQ5+mAf+OIBp7L9gGgp5614xwW/LSBePD6Nbywp4D8bjnH21EzOnOhf5nQo60sbmZgZR1ai/wsOvioiX/T6RczJmMPqC1bzRdUXxBpimZUxa9i4qAg9mfHR7Gs8wKaqPzrvHL982PhgON7spwKxjaQ8wnGFEGF2VEg8f+HzYXjG0GALYD8+UMdtS7UR4AKxULautJHbzyj0PeHijkkXwEc/F9ZwSeET6QoVWvTAbgWKFEUpUBQlEiHKtGbImDXAKuv9K4FPVdF0swa41qpSXAAUAdqaoYWZuKgIxqXEcCCMFzN+YyuBHNRLlh2XzbWTr+XFi17k8XMfpyCxgM01m1n64lIer7qcsWn9wWcVPTAlO4FjTZ109QXQZ2HuExlLH/tft1dYlQjzfCxdBNEEnzbR3kfminn5yURF6LTxAzu+CYxpkKLdFyEg+mD7TC7VXEeS0noTyUYDaXFBXAyGElUVF/9usu+jgVvev8Xp8adXfUpMhO+KzlOzHaWw4cBnC53BZBWLags3veiDuXvu3czPmm9//JtNv+Gdsnc40X6C9ZXr6ep3rfi6q34XC59zlL7e1mXhismeg4oJGXEcaQifgnNLl58Z2KQ80EdBwyH7pkh9JFdNvMr+eFf9LidvWH/Lby2qhR9+/kP7YwOA0UUA29NPhE4hJtiezMhYsajY3UqXuYsuc5c9+2sjMcZAe3e/34rUuj7Rz+zp81Pf3kuMQU98sL+L0UlcpYjPXpe5TRP17MFl3PUk++cB6o6ISEgc6yS4tKlmk0c/YZ8YJOBz+pjTnaokfnTeJCZkxPHjV3YHZenU3tPPlmPNnOFn+fBgdty4g2dWPuN9XP0OytvK+fbH3+am925yOy4x7QCbeh8YvmP8ioDn6IqKpi5y/RVwAruI013pGi+iDyGi2yG+Nj0t+Ez7SDEuxcikzHjN7XTW7K5mwKJyeSDqw0OZtFLcHnYvTnYyEXQAq6qqGbgL+AA4ALykquo+RVF+rSjK16zD/gOkKopyBLgbuM967D7gJWA/8D7wHVUd1AxwkjIpMyG4bGKoSZ8kypZciKEoisLC7IW8ecmb/HPFP+3bW9N+xk83/JS+geAMvN0xOSsBVYXDdQFcBDaVCnGJDN8C2G0VLRj0CjPHJfn3PJnT3ArIAEQb9CwoSNFGyOn4JpF91TrbV3CGeO3L/BeeCCVHGzoZ72s/8kjQ1SRKutNGV++wjeLVxeyo32F/vPG6jaQb/btYm5yVgE6BfWHyNK1qEQHsWH8C2OyZoqyz8bD3sTCsJ/K+9fex8vWV3PnJnfxy4y+d9h1uOcxN79407KLzpkjvJeMT0uOobOmmuy/0P18Wi0pLVz8p/vTA6iMgdYJTAAvw04U/td+/6b2b+Mu2v9gfJ/mp4PvrL389fKOrALbbbPVoDfK7TVFEFran1b6puqPaaUhijIG+AQvd/f69Lp1msbjx73P+7XZMndUDNui/IyaJ7F7xfLrYw/bSZK2oU5P9V6B1R3I+tFaQE+e4mB4s2hUI79Rvtd8vbXUWGIw26Hn46lk0dfTxy7fc//Z6Y+3BevoGLJw/PfD2D4PewIy0GfzstJ8N8zYeysVvOLQruvq77IsSHX0d9Fv6eb30daqjhr+3tjT22ysktKB/wEJVa7dX1WWXxGVARDSZvT32TeVt5ZrNzcZP97r/jJ1snD01g63lLUEttgzl9Z2VTM9J0EZfIa1I/A6cInY6mvjAqqr6rqqqE1VVHa+q6u+s236uquoa6/0eVVWvUlV1gqqqC1RVLRt07O+sx01SVfWU+FednBXPscZOes2jNBY3xEDKeI/lsIqicMbYM9h87U4GuvIBWHN0DXOfmctLh17SfEq27M/BQLI/df4pEG+vaGZ6TqL/qoyZ06G9ErrdKyMumZBGaX0HtW09bsd4paNerEoHUT6sqioDlgHK2sp4vORxrlhzBS8ffpnPGnezN2c6R49+5P0kYaSsoWN0B7C2gGkUBrBtvc7CSx9f+XFAokUxkXoK0+PYH6YAtrK1m0i9zr+su13IybderAxjBk+e/6TLfe+Vv0fx6mIe2/MYf9n2F65YcwW7GnY5jflmD6Qk5nt9nqLMOFQVjoYhC9ve08+ARfUvAwti4bLROYDV6/TcOetO++On9j9lv59kzcD66s/9aqnjov6q5BnijpsMbNACTjZikqG7hRcvehFg2AJrYkwAfraqyp9jxW9DltF9wFPf3kNmMB6wNqKToLvV/vC4hl6w9za1UK8maVNCDEKZtqWcty5zWEkFmzG+r9uxGPXnM/88bH/x2ETuWj6B13dW8f7ewHxX399bS0Z8FHNy/ai6coGiKFw96WomJk/k6QuGq167YuFzC5nxlPg8LHp+ET9Y+wN+vvHnw8Z9t7WTmCkXi7Jyjahq6WbAogb2+isKJOWiH5QhfWSX7y0pvmI++XNWds6eksmAReWzw9qoEZfWmdhb1c7ls4drCQTMxPOhfD30juIkm49o90mR2JmUFc+AReVIffhKyvwmc6rHbKKNIw0ddFV8m9/PfpcrJ14JiFK84tXFbKzeqNl0xibHEBupD0zIqW4v6Az2fl5P9JoH2F3Z5l/5sI1Ma3mLh383m8LhhiNBZGGPW/tfx/kfwF76xqUUry5mxlMzmPX0LC554xL+tuNvHG45zK+//DXf/fS7XGdo5VJDI8Wri3ly75OUtZV5P3EIaevqp7Gjj/EZ2gofaIrNesiH91g42VyzmSUvOGwH3rr0raBEVaZmJ7A/TErE1a09ZCdF+9fXk1oEEdEehZyGMjdzLn9d9ld+v/T3Lvf/feffeXLfk07bYiJieHHlC3y3rkaUTXrB1o8aju/85k4RpKUGEsC2VEC/s6jRHTPvYEbaDPvjo61CVCYhxoCqgqnXe7n2znqHwNVds+7i50mzxQOXGdh+4qM1akexBn+p0eJ5hv4mBRTA9ndTZRDz87QQVG/qJT2Y/lcbMUlOWeRjjcFdWL511BFcXm7qoD0ijWR/BXzckVwAHXUYzI6Fggiddq1FszNmu9z+nWUTmJ6TwE9f30tjh38Z6s5eM58dauC8aVnB9xAOYmb6TO+DBrG/SSy0f175+bB9F6XM4JstTdq6DgAVgXrA2kjK5QyT45rsvXJtc0wdfY7vyz+d8SdNzz0SzBybRFpcFB/t16aM+LWdVeh1Cl+bNUaT8wGijHigD45+qt05RwgZwIaAKdniR29UlxFnTBPea32evQttCsRTs5P5xaJfsPZqR+nptz76FsWri4f1HQWCTqcwOTshsN7h+v3i4swHUaK9VW30mS3MzUvxOrarv4stNVtYe3wtLx58kd/VrmV/pMFjADs5K560uEg2lDa4HeOV45vERXq2bz+Qqqry4NYHKV5dzNG2o94PGMRftv+FS964hOLVxRSvLuaESRsfQn842ih+xArTRnkGNiLaKmQyOvj8xOfc9uFt9sdXT7yafB+yhZ6YnB1PdVuP3eoklFS1dPmuQGxDHyE+6x6qR1yxIncFFxVe5NPYFy96kS03bGFqVAqKpd8nsYv81Fj0OiUsAaxNWMnvDGzaREB16QP97IXP2u9f+ualdPV3kWQtUfalHO7m92623x+fNF6U3EfGgWF4hrK9x2wPLIPGmoG1lcu/Wvoqnf2O3zR7AOtPSd8gD1hPCt5aZ2C/P1sIL71/4pWgTveHLY6y+WhVRZeQpZ2quFWJmJYKChILAMeCR0B4uf6wYdDreOjqWZh6zNz/WolfWd+391TT3T/ApVr0EA5CURRuK77N+0Ar17x9jcvtp2dewO/belASciDvdK2mB0BFk/j39dsD1kZSHomtJ5yu+2o7a7WYGgB1XY5A7/z88zU770ih0ymsmJzB54cb6DMH1xtusai8sbOKM4rStNUGGbdQfG+eAmXEMoANAfmpsURG6EZ3AJs5DVCh/qDHYQdrTEQbdPYVvLSYNPbcvIdrJjm+jK9YcwXf/fS7QYs5TM6K50BNu/8lSXX7fPbm3FYuyn/nesjArjm6huLVxSx8biHf+PAbfG/t9/jt5t/yQtkarsnJpvjg/1K8upiHtz88rHxTp1NYPD6NDUeaAi+tOrEJxswRohleeOnQS8x4aoZT6Z+Ndy57hx037aBkVQnrrlnHZ1d/RsmqEl5Z+Tyze/sp1A+/OFv52kpWvLQirIHsUetF/3h/VFXDTWOp6B3RsLwrGKo7qrnr07uctv30tJ+6Ge07EzPE4ltpXei/u6pau/0PYEF81m2evH6y86advHnpm25VRZePW87UVOt3SZv1M5Do3bYtMkJHXqqR0vrQ/7s1d4pgzK8eWIB0qz/hkD5YG4+e86j9/sLnFpJozZK2dnvWPRj6PXd23tkigDW6XiRs7+7XOIBtRTfIiqa5x1HyGEgGtq/TUT3jLvDr6DXT2TcQnAKxDXsGVpRSbml/MqjT3T7jdvt9HRCTqmHgliyCVlrK+cuZol/6wW0PBny6/qYj9vs3TrnR49iJmfHcc+5EPtxfxxu7XJpVuOSFrScoyohjTm5SoNN0y7dnfpufnfYzPrjiAyJ1fn4erUzTzYAjn8DsG8GN0nmgVDR1EW3QBe5VnJQL3S0kDLq0O+eVc+gxB9EiNYhL37zUfj8c1m3h4OypmZh6zGwtb/Y+2AObypqoaevhsjkalg+DWAQuOg8Of+CTGOJoZnRcjZ1iROh1TEiPs2cvRyU2y5l6z2XEh+ramZgZj35Q6Y2iKDxw2gPsuMkhGvPZic+Y+dRMfrD2BwEHblOyEzD1mKn2p3+0uwXaq3zuf91W0UJ+qpH0IV/oXf1dvH/sfYpXF/PTDb4FAk/sfYIlLyzhvFfOo73PUWazZEIajR29gQlS9XVBzW7IXeh2SI+5hyf2PkHx6mJ+s+k3TvuevuBpdt60k5JVJeQm5Nov1JOjk0mNEWV2k9Kn85RxOm+2mClZVcLbl73tdI767npWvraSy968jP6B0Gfiyho7MegVxvlqpzISNFkD2FHA+8fe57xXz7M/zk/Ip2RVidNFfKDYvJ4Deu/6QZ/ZQr2p1z8FYhvpk8FU47EX3R0RuggKEwvtCztD//vb8r85Brda7UJ8KCEGKMqIC1MJsSihTInz84I5dbwQcGt0HcAuGuPsOX33tnPQRdV4DP56B3rt/X1OdDUJFXUXtHX3B+8Ba2NQ+e2MdDGP7XXb7bsDCWAb2o97HVNvtdDJ1CKAjU4Cixmtciy9Vu/a36aJbF5CuoZVI/YMbLnT983HFR8HdLo5HzqC1nvm3eN1/G1LC5mbl8zP39znk7/vnspWdh5v5Zr540ISIEXpo7h60tWMiRvDj+f/2O/jOw/+noUVm8WDWTdoPDsRwOalxAb+t1uViCM7nEtitQpgT0WWTEgjKkIXdBnxK9sriY+O4NypQVoyumLS+dDdDJUntemLDGBDxeSseA7WhkcMJSCS8sEQ6xBAcsPBGhOT3XhzGnQGSlaVcP9Chynyx8c/ZsZTMzjU7PoiyRO20usD/ojI+CHgpKoq2ytamJfvnBl4YMMDLHxuIT9a9yP7tkxjJv86+1/svnk3JatK2H7jdnbctIOnkk/nig7nH87qzmpOf/507l9/Py09LZxeJC7cAuqDrd4BFjPkLnK5W1VV5j87n4e3P+y03XZBPitjlm89SYVniaCsrZK8hDy+vO7LYX5+R1qPMOeZOWyrDa3lztH6DvJSY4nQj9KvI3OvKLcfBQJO75a9yy82/sL+OEof5VakKBBykmKIMehDXj1S29aDqvrhATsYW7WFl+qRoGmrFLc++uVNyIijoqkr6NIxbwScgY2IErZcbjKwILxhB2PM/xeNHe5FhX618VdOj7+47gtxp7PRZf8rhEDEqbcdBvr5+WlCGOeT45/YdwcSwJ6/6T4A/nfm992OqWsXQaImJcQxSQCsSHfd/+kv/9j5DwCW90TRqsYyNj044SInjCkQlQAt5YyLd3wufvDZD/w6jaqqw37DfPnd0usU/nLVTMwDKj9+ZY9XgbH//fQIiTEGrpkf+taPyydezoNnPMiGazdwWeIUr+OfOO8JJiYMMKPmZZh+uRDI0piKps7gLJRsc2qpICkqyb6536LtwnZ+lOvvipORmEg9Syak8fGBuoCTOaaeft7dW8PFM8f4LzbqC+NXCN2YQ+9qf+4wMkqvGE9+JmXFU9feS6ufRvBhQ6eDjMlCAMkNDaZemjr7mJyV4PFU102+jt037+byosvt265860qKVxcPK7H1xCTr8/gV+Nf7HsCWNXbS3NlnF3Dqt/Rz58d38ubRN+1j/nTGn9hz8x4+vupjluQssa8yR+ojMegMzB63lF82NFBy4Ws8cd4TTj6Bb5W9xRkvnsEP1q8iL8PMF4EEsMe/FLdj5ztt7h3o5VsffWtYtuPOWXey5+Y9Phut2yk8S9yWCUGJuMg4Tss+jZJVJXx0pbNC8a0f3Erx6mL7yr7WlDV2Upg2igWcmo+BahlRAadKUyXFq4u5d/29dFktPvIT8tl24zZ7Zl0LdDqFiZlxIS+FrWwVf4NfFjo2MmylsIGVEftM2wmRHYvyTdG5KCMes0W1952FipauPqINOmIiA7iwSZvkMYCN1Efy68UOOxxF18dDB77pJLZi46VDL/FWmUMw6PNrPich0vpb0dXsMoDtNQ/Q028hQSsRpxhrcNbTxqSUSWQYM0iMTLTvFnY9omzZXxKN7jMf9SaRgdKkhDg6CYBsHAsSgdoxDf6O7mutFhY6WikQg1CmTc6DlmNOfq3+UtdVxxN7n7A/Xj5uuc/H5qfF8tMLp7C+tJEHP3T/Xt5W3syH++u49fR84rXK+HvAoDNwfsH5JEYlctfZf/c4dknOEuZnzeeuiNeJtPTAUu/ZZ3+xWFSON3cFZqFjI8kawLYeZ2nOUvtmLa4FNtdstt+/JOesoM83mjh/ehaVLd3sON4a0PHvltTQ02/hqrkalw/biE6A8ctg7+tgCe2CayiRAWyImGK1hQmXJUVAZE4XAaybVSJbIOkuAzsYnaLjV4t/xftXOBskL3lhCcWri6nv8i4rHhcVQW6KkQM1flw81+0VFwDx2V6Hbrf2v87LT6a9r505T89hfdV6+/6tN2zlgoILPJfbZFmViGtLmJ81ny03bGH9Neu5d/699iEHmg/QnPoAm1uep9fsZ4/B8c2iRNLaP9bV38VvN/2Wec/Mc1LYPCfvHPbcvIc7Zt4RWHlQxlSITYeyz4btyorN4pmVzzhl1gHmPTOPb330Lf+fywPmAQsVTZ2jvP/VZqEzcgHsNz/8ptPjKSlTnKwstKQoM55DtaEthbV5wAaUgU0cJwSCAuyD9ZnWEz5nXyF8SsRNHX3+Z19tpE+E5qPgoTXgsqLLmJLiyCC19tew6PlFfO/T73Hac6fxf7v/jwNNB4a1L6RED6ps6WpyGcCaesT3oWYZWGvwZ7Ohae1p5Vj7MftunU4hPirCPxViK/0uBKhs1FszsBkJ2mVglUGLvesqdgV0quvfud5+XzXVCgudYAIYVyTni4oUIDHKsVjgT7vJ0AyeU+m+D9ywMJcbFubyr8+O8vSmimH7O3vN3PdaCTlJMdx+RqFf59aCKIP4XkuyxgY6FHbfvJsdN+7g69O/zq8W/wpObGFl55u8oTvX5xYof6g39dJrtpAbjAewMRUMRmg97qTIPdSuKhAGCxDOzT0z6PONJi4ozibGoOfVHZUBHf/ytkrGp8cya1ySthMbzIxrhC1kxRehe44QIwPYEFGcI77YS6rCY0kREGNmiT6y1uE/AOBQUZ7kQwBrIycuh5JVJfxw3g+dtq94eQUfln+Iqc9zcDo5K54D/mRg6/aLQNyHIG5bRTOJxgg+rXme0593VvsrWVVCdIQPFyNpk0DROykRJ0UncePUG4eZ3utSPmLes7N59sCzNPc002320rNjsYiehHELqe+qZ2vtVhY+t5AXD73oNGzrDVt56KyHguvp0emg4EwRwLpYwJiZPpPrJl9HyaoSXrjoBfv2jdUbKV5dzKaaTYE/9yBOtHTTP6CO7gxsk1W1dQR6YMtayyheXUxlh+OH8Ptzvs9LF2vvxWxjUmY8jR29tHSGrnqkulVksLISAwgAFEUs8oQ6gG2r9Et1Ot/6Hj4Whgys3/2vNlIniBaFVs99ng+eOVyYZ+2JtXT2d/LPXf/k6revdtrnVP7Z3w39nRDr2kIH0FbECez90H2WPvY0OFssJRoNAQWwkzLdl/TWtfcQbdARH6VBJnlIEA7ww423BHSqwY4Ahq56GpRkshM11hZIzhd2TBYL3531XfvmwerP3ujqD87rVlEUfvm1aayYnMHP3tjLnz84ZC/d7+g1c+ezOyhr6OCPV8zAGKmdzY+vxEfGc3nR5fz99P/HmuY+Pq5rR3fkUwx6Az+Y+wMy2mrhhRtoj87ml91XhqTtwK5AHEwGXlFEFra1grtmO0QDL3nzkqDmNvS9Mjv3rKDON9qIi4rg/OlZvL27mp5+/6opjtSb2FbRwlXzQtO3bWfSSrEQvOdF72NHKTKADRHJsZGMTY5hz6gOYK0/0NU7Xe4+UGMiPT6K1AAkvFdNW8XG65w9+e75/B4WP7+YrbVb3R43JTuB8sZO30qoLBZRQpzpowJxRQtj87bwj13/sG+7a9Zd7LnZd09JDNGiF9JF6fXiMYvZc/Menlv5nNP2P2z5A2e+eCYLnl3Attpt7n+8Gw7S09vOj9U6Vry8gq9/8HWn3bdOu5VdN+3yLdD2hfHLoLPeayAwLXXasMz6Nz/8JsteWsa+Ju9ewp44aRSI48f4XEqqBa+Xvs6/dv1r2IXChKQJflk3BEJRpngtDodQibiqtYuM+CiiIgLs78mYEoYA9oRfAWxcVATp8VEcawhtANvc2UdyoBnYlPHitsmz9UleQh4/mvcjj2MG88W1g1bxu5rErYsMrC2Q1E7EyVZC3Oq0uam7yX4/MSawADbBjQgViOxWZkK0NheY1gzs0L8hGO6d/2Ni+xrpjUp3EmDUhOQCGOiFjlouLLzQvnlwObk3Bi/mfichsOyjQa/j/26ay1Vzx/K/a49wxp/W8s2ntnHWg2tZX9rA/7usmCVF7l/DUGKrSJs98WIKbvmAdGMGPHsFPLoMnr4cHlsGegObFj5CuxrrkyCVv1Q02Txgg8zAJ+VCa4VHT2R/Gewb/WZPnE8JiJONy+fk0N5j5pMD3qsPB7N6YwWREbrQlQ/biDTClK+BqdZtFeZoRwawIWTG2ERKKkdxAJsxFfSRbgPYQ3XtPpUPuyM+Mp5nVj7jZLkD8PUPvu7Wc3RKdjwW1ceL57bj0NfhU/lNU0cv9ck/5DiOzNXG6zbyrZnf8v8iJHOaWy9YRVEoTi+mZFUJY7p+OGz/rR/cysLnFnLWi2dx1yd38c0Pv8nD2x8WPqzvX8P8/HG81+osrHVe/nnsuXkPd8+7G72WMvsF1rIdF2XEQ7Fl1p9d6fCLbOxu5Nq3rw3KC7jM6gE7flR7wJZCWviyrxXtFfx84895ZPcjw/a9fPHLIX9+hxJxKAPY7sDKh21kTIGuRugIwm/ZE92tQhzIjxJigILUWMrDkYH11wPWRqo1gG327t1587SbvY7Jjs1m9827MRoGXSR7CGDb7SXEWvXAJolbawb24sKLAdjb6Fhg9CeANVvE/M7p9ZwRq2vvCdyaZCiDMrCD+4+DIU0XQwRmBuKyNDmfEzYl4uZjxEU6vrf/tPVPPqsR2/r4AaKtHr6BYNDrePCqmTx563ym5yRS0dTJ3LxkXv72Yq5d4N3+Kiwk58O31sG5vxPXWx31sOB2uP0zEnOLAahs0T6APdYk1P0DsiobTFKuy4qNIy1HXAz2Dd2g0CM1eXzA5xnNLB6fRnZiNM9tcV3h6Iq27n5e3VHJ12aOCShx5Ddf+wfc+MpJu4AgA9gQUpyTxPHmrtEr5BQRJYJYFwGsecDC4bqOoAJYEKWoD5z2ACWrSvhmsXMf38rXVlLSUOK0zdY7fKDGhzJiWxCZ4TmAPdR8iLNenYeidwgPvHnpm4GvKGZNF9kZLzYeZxcupOvQH/nvOc8O29fU08TnlZ+zqWaTk5jFUNZevZY/n/nn0JSSJI0TJYU+BLA2ZqTPoGRVCc+sfMZp+xVrruCh7Q/5rU54tL6TtLhIEo2hF9kICFW1BrDhUSDu6u/iotcvGrb98qLLuXf+vb4pTAdJVkI08VERIbXSqWrpDsxCx0aGtUczVEJOdg9Y/1bB89OMHGsMrjzSG80dQQSwsekQGe81A2ujqOdBxvf+hnvmuhaZuXX6rcPtm+wB7PDsV3uoMrDW7+KeAVGaPtgj2Z8AdvbToirpoyjPl0YNpl5t+l9BqPqiQE8r5+Wf53W4L2QjFjoNid61IfxmkJUO4CT69+4x31RNB2dgr5lwWdBTOmtSBo+vmseHPziTf980z6PP+4hgiIbFd8E3PoA7NsD5v4e4DMaliO/AE83af2eUNXSQm2IMXt0/OQ962qC7lZcuciQALltzGUdbffseGYqKI+MXkzpy2hKhRK9TuGlRHl8cafLtehZ4aesJuvoGuGVxfmgnZ0Mf/vJ6LZEBbAiZMVb0we6tGsVCTmNmQ/XuYSUE5VY7CG8KxP7wvTnfG1ZWfP2711O8uph+Sz99A32MSzYSG6n3zUPXZqGT4V6yfsAywJVvXWl/nBSVzMbrNlKYGISwQ+Z05+d3w+kT0hiwqLS1ZfLpVZ/y+LmP+3T635z+G9ZevZY9N+8hLSbEJVCFZ0H5Bo+iLq6YmT6Tzddvdtr2373/Zc7Tc/zKxpY1dlA4mrOvnQ3Q2wYh/pEtbSnlx+t+zMLnhvv/vnnpm/xq8a+4ceqNLo7UHkVRmJgVz6EQZWAtFpXqtp7AFIhtpFs/86EqI7ZZ6CT6l8UpSIujsaMXU09o/JP7zBZMvebARZwURWRhfcjAAqTFpNHTlcIt029h3TXr+NfZ/2Lz9ZspWVXCx1d+zLWTrh1+UKenDKzGPbDRVhEha//oLxb9YtgQEcD6KabnBU0zsDqd+Du6W51U7f1l7fG1gKh8KjSL8xjTQlCGmDhO+AlbA9isWEeW96OKj9wcJFBVlcMth/mftf9j3xaTNkn7OZ4kZCVEo9cpocnANnZSoMVvq9ULltbjTE6Z7LTrzSNvujjAM3/c8kfu/uxuAL7b3ErkKfz6X78glxiDnic2HPM6trtvgH+vK2NRYSrTcxK9jpfIADakTB8j3oR7qlpHdiKeGDNbXKA3lzlttikQ+yPg5AvxkfHsvnk3N0xxNu2e8/Qc5j4zl2PtZUzKivcxA7tXrAZHDf+S7h3o5e87/s6sp2fZtxkHprL+2nXB93LYA1jP/Z9zcpOJNujYcKSRdGM6C7MXUrKqxP7fnpv38MR5T7DumnWUXPYh28qPUzLx21w64VLSYtJC28Bvo/AsIbhS6b/Xq9FgpGRVCf9c8U+n7VesuYLi1cXUdXo38j7a0Mn4jFEs4BQGBeJnDzzL5Wsu571j7w3b9+V1Xwa32BIgEzPjKK0zBexj54nGzl76zJbgSojjs0TpZagC2FZrBtbfEuI0UUpr6z/TGls1T3KgGVgQAayPGdgko4HWLhF0JkcnsyRnib1cODM20/V3lC89sFoFsHqDyChbM7CJUYlcVCgqGH6/+fdiW0wk7d39fr2X/5/Ofeayo9dMZ98AmVplYEFkkntanf49j7T4nt1SVZXvrf0eAKdln0ZzrShbTM4IQRltRKSoTLAGsEM50T68NcjGC4de4Io1V9gff1zV4JODwKlKhF5HdmI0lS3afl8MWFTKm7oYn67Bb+ugAHbo531amn/9y6Y+E88ceMZeQj6vpzfki8MjSZIxkivnjuXNXdVes+zPbKqgsaOXH5wz8n7zJwsygA0hiUYDeanG0d0HO2aWuB1SRnygph29TrFbQ2iJTtFx34L72HT9Jk7LPs1p36VvXkp06mYO1LR7v+Co2+sIJgdhtpiZ98w8Hit5zPncWb8MduqC+CyISYG6Eo/Dog165uenuPWDVRSF+VnzSY5OhhObiFKBcae5HBsy8peI1XQ/yoiHcsbYM4Z5xwKc/crZ3Pr+rTT3NLs8rqWzj+bOvtGdgW20KhCHIIC1qBbeKXuHP2z5g8v9z1/4vFOPWTiZmBlPS1c/DR3ae//aLHTGBKOOqiii/SFkGdjjoI9yWQbrCbsScWNo+mCbrQFswCXEIISc2k6A2ftrmxhjoLXbzxaYriZAcfSnDqK920ykXkdUhIaXHjHJTu0ci8csBuC5g0JMLzHGQN+AhZ5+z32th5odnqJToz0IOLVbPWC1ysCC+LcapEIMsP6Ee7HDoVR3Vtvvp8ek09UkKgiycvK0mN1wBlnpgLOFkq2M2xXvH3MWA4xNGCcy0F9hxiUbOa5xCXF1azd9ZgsFWqj727xgra/34Mqrt8ve9utUi59f7PR4cl8fpIZ/gTac3LlsPDod/NmDZ3FjRy//+LSUpUVpLChIcTtO4sxX+5sjDBTnJLL7ROtIT8M9GVPFhVrVDqfNB2tMjE+PJdqgoWjQEGINsTx27mO8damzemFJz39RC37IvGfmu7fd6esSWYRBAeyAZYA/bPmDvY/JxgXjrqfz6A+Yl6/RF4OiiD5YLxlYgCUT0jhc10Fdu/sfdQCOb4KIGMieoc0cfSUmWWThy9YGdZqs2CxKVpWw9mrn82yr28aZL57J9e9cP8w7zi7gNKozsKXidUnQthTv7s/uZuZTM7lv/X1O2xePWcz2G7ez/pr1TE8bvjgTLiZmiiqF0hD0wVa1BuEBO5iMyaIHNhQKiq0nRJbJz4vrvJQQB7AdGgSwqeNBtbjNoA0myRhJT7/FPyuIrkbhY+1CcK69p5+EmAhtq0tiEp0UfNOHiALZypW99cHWdNbY7xfGuBc/qjeJwF/TDGx00jAV4od2/s7nwy0WR3B+7eRr6WutoVWNZWxGiC6Gk/OhxVEW+d7ljuqRy9dcTqsbReXeAedFE2NyQShmd1KRn2bUvGKjzPr9o0kAa0wR709rld5gwbbPTnzmtWzc46ljUh197Kco2YkxfGNJAW/uqnaZzFBVld+8vZ/u/gF+cbFvjhoSgQxgQ8zcvGSq23qobtW+x0ET9AbImQMnnPsZD9S02wWVQk1+Yj4lq0r467K/Om3vs/Sy+PnFFK8u5t0yIQ5R3lbOM/uf4fEtD9KlwEDmVD478RnFq4uZ9fQsnj3gLJj04kUvkq+7CktfprbCDpnFIoAd8NxbdfoEsZLvLgtr5/gmGDtPvB7hpvAsUULcE3yvdlpMGiWrSvj+nO87bS9pLGHuM3M51HzInlk/arUbGZ8+ijOwTVYFYg2zBN/55Dsuf/Q/vepT/n3Ov4nUR5JkUyYdIUJppVOtVQCbPkWIi5hqNZjVENoq/S4fBoiJ1JOdGE35aM/Agk9lxLbgr90fG5quJpflw7bzaCbgZGNIBjYjJsNpt+1v8JZJ3lC1wX5fcZE9tmFbjMxM0DoD61kU0BNm1fE7VJBYgNJRS7MuJXQL0Mn5Vn0AscA1tHf3+YPPDzukrbdtmO2azqaK/RUmLzWWps4+e3+4FhxrEK9LoVa/rSmFw9rMbNj6WQMihK05o4m7lhVRmB7L3S/tGpbMeGZTBW/uquauZUVMyAifVd+pgAxgQ8x8a9Zva7nrMspRQe5pULML+sRFV2tXH9VtPWELYG2syF3Bp1d9SpR++IXBvevvpXh1MRe/cTF/3PpH/nb0FRbmj2PW1gf47qffdXE2KFlVwtTUqWwrb2F8emxwF31DGTMbzD1eVVCnZieQEhvJBk8BbG8H1JaI12EkKDwL1AGo+MLrUF+5rfi2YYJdAFe+dSUznprB66Wvc6TeRKRex9jkIH3qQknjYc16dAYsA/yn5D+sq1w3bN/bl709LHM0kqTHRZFkNIQkgK1q6SY+OiL4QCbDKihS71lMLSD89IAdTH5qLMdCZKXT3GntgQ1UxAn8stLxNXvpRKeHALbHrF3/q42YZKfy28Gfo35Lv+Nv6PL8N7x46EUAvt/c6rC2cUF9u8gipsdrnIG1/g33zr/fvtlTP+lgbLZBCZHiNzuyu4HOyBAKANoyp62i11ZRFCddiw8rPuS5A8/Rbe5mZ/1OajpqqOtyoYmQGj57stFKfqrIklZoqF5e1thJfFQEaXEaXfN4EX5r7PayQO+CF5p7Hd9FpzgxkXr+cd1sOnrMXPPvL9lc1kRzZx9/+7iUn6/Zx/LJGdy1XH4W/EUGsCFmclY8sZF6tpUHvroacnIXg8UMVdsBOFAjLlqDtdAJhHRjOttu3EZOyz/Js9zm9/G/WPQLtt6wlS03bAGE4un2ihbm5WlcSpUzR9wOKb0eik6nsHh8Kl8caXTf01u1TQSQ4e5/tTF2gSiTDaIP1hXxkfGUrCrhh/OG++H+fOPP+aThn+SnGdHrRqkHWX+P8L/TwELnN1/+hllPz+KvO/7qtP1vy/7Gjht3kJcQol61AFEUhYkZ8SGx0qlq7Q7emxAcSsQNB4M/12D6e6CjLvAANi02ZBnYxo4+FAWSg7GdspUE+pCBTTLaspf+BLANEOs6eGrr7tc+gI1OcspexkfG27Owv930W7//htk9vQ51YxfUm3qINuhIiNbQgsIq4oSqkj/ofTfYbsQT928QQe8Dpz2AqqokmBsZMGZqN7+hDPKCtTE11VH+eKT1CL/f8ntuff9Wbn7vZs599Vwn8SaAF6pqvzIZOE/kW4XftPSPPtbYSUF6rHal+imFoirF2jf/7mXOdknLXlrm9RQvHHzBfv/OaV9nWlvdV2oBY9qYRJ76xgK6+we45tFNzPnNRzz88WEunjGGR26YM3qvg0YxMoANMRF6HXPykkd3BnbcfEARZaw4FIinhjkDO5iZY5OoPDGFbTdsoyCxgPPzz/c4/pEVj/DhFR9y5cQriY6Itpc0HWnooK27n3n5GvdZpBSKi5xqzwEsiD7YuvZejja4CQaObwIU6+swAhiiIW+R5gGsjVXTVrHjxuH/TvWsoz3lQQ40HfDLeidsNJeJXsEgLrL6Bvp4bM9jvHT4pWH7frHoFyzPXY5hJMrGfaAoM47DIVAirmrt0SaAjUsXmT6thZzaq8RtACXEAIVpsbR09XvN+AVCU0cvKcbI4L0dUyf4lYFt9edv6WyA2AyXu0zd/doGfuAoIXbxPn2t9DVHAOvBj33we1yP6jGArWvvJSM+Wts+XmOKWETubWdJzhL75o5+/xaQzBYzjaZe0tQWdKHwgLWRYhXeGfQeWjZueBAztGTYxo7J32FaX98prUDrK7a+eS0XvcoaOinUov/VRoq1b771OADjEsZx6YRLfT68q7+L32129HTfnrVU3PmKvf5z81L4+O4z+ctVM7l/5WReu3Mxf79udki1Zk5lZAAbBubnp3CozuRfGVY4iUkWYk4VouTzQE07qbGRpGupsugnM8Yl0trVT13bAGsuXcODZz5ot54BiFBV1iQtZt0161h79VqWjl1KdtzwH2xb5lszAScbiiLKiL1kYMHRB7uh1E2ZzfFNkDnN40VTyCk8S2Sy2mu8Dg0Eg97Anpv3sLJgpdP2Tiq4+u2ruWLNFZgt2no1Bk1T8ArELx16ib/v/Puw7XfOvJMriq5wccToYWJmPKYeM3Xt2ikRq6pKZUtX8P2vNtKnaJ+BtV6kBZOBBUJSRtzU0UeqFmWBqeOhyXVP22CSYsRz+fzbNWCG7maIdV0OL0ScQlBCbOmHfkcJ5iUTLrHft7WOtHgIwr+s+dJ+32jxHMDWm3q07X8FR8l1l1joLow8F4Br3r7G66FtvQ6XA7PFzPHKE0QqAxhTcrSd42BiksQiRaNj4dEfezpDUxlExglF/684MZF6shKiKddIyKmnf4Dqtm5tPGBt2BYsBlVtTEjynj01W8zsa9rn5G+eEp2C3pa5/wplYG3ERxu4Yu5Ybj9jPHNyT20Bq1AjA9gwMC8/GVWFHRWjuIw4bxFUboUBMwdqTEzJTgiPD6kbZo5NAmB3ZavTdkVReHfF43xyvIqCnIUkRyeTFuO+12dbeTNpcZHkp4agz3LMHNF/1+9ZYXhcipG8VCMbjjQN3zlgFv/uI9X/aqPwLHF77POQPYWiKPzxjD+y5+Y9zM9YMmz/7Kdnj64g1nZxFsSP7D93/XPYtp8s+Al3zLpjRD9fvhAKIae27n5MPWZyUzT6PGZMhoZD2ioR29R5bWWSfmLzgj3WqH35dWNHL6mxGgRPKeOhvVKouXsg0YfspRNd1kW6uOEBrKqqtHeb7VldzbAJLg0qIx4X71h8KG8/TGSEjpZO939DU7fju3lif79LCyAb9dYMrKbEWBdYrQFsXLTj0qy9z7O43mArk/Pyz6OuWvSlJmWGwAN2MGlF0HjEadMzK5/xetgDCx8Qi4OpE8RCsIS8VKNmJcTlTZ2oKhRo4QFrw55xdyx6Lc9d7jTkvWPv8cTeJ9haK+yf+i39zH56Nte+fa3TuOaeZvH6KzpIkSrUksCRAWwYmD0umUi9jo1H/W90Dxt5i6GvA3Pldg7XmZiSPbJqaJOy4omM0LFnSAALMK6jiRSLRSgBe2FbRQtz85JDEyzkzBFlX7We/WBBZGE3lTVhHhjiRVi/D/o6Rq7/1UZmsbiIClEZ8WAUReG6vF9iOvD/hu2b/fRsp4zCiNJ4BBJyINL/C4HG7kbK2spclgBeP+V6LWYXcmxWOloGsDa/w3FaBbDpk6G33VH2qwUt5aAzQMKYgA4fl2JEUaBcQ1EWG02dfaRpURljE0/xYqUTHxWBoviRge1sELcuMrA9/Rb6BiyhUSEGpwD24vEX2+8faTtCijGSFg9B+DMHhgReHjOwvWSEKgPbLQLYM3NW2Hfd+v6tHg8d7CNtNBhpqxcVBCmhDmBTJziqVKzMTJ/Jv87+l8fDrpl8jfhulf2vdgrSYqnQKIA9ZlX317SE2JgiPhODSsbHxY9jYrJDH+LH637Mw9sf5usffB2A0pbSYaex03QEknIhYuSq/CQnPzKADQMxkXrmFySz7vAoDmALlwEKbXvfp9dsYXLWyPW/Ahj0OqZmJ7C70kUwU7cXUCBjisdzVLd2c7y5y64ErTljrEJOPvbBdvSah/895Vbl37xFGk/OT3Q6KDxTBLCh8NUcQllDB6Bj/VXbh+1b8sIS7v7sbs17L/2mqTTgi6xlLy3jkjccZYyFiWIF+8nzn9RiZmEhLS6KlNhITb1gTzQLCx3tMrDW74B6DcuIWyvExZULH1NfiIrQMyYxRrML0sE0mnpJ1UJN3Zb5cGONYUOnU0iMMWgSwNpsQhJiQtADC05KxBE6x3P8dMNPSY6NpLnT/d+wv0koWT857jKxwY0KcWevmY5es/YZWKMtAysywRdOPNO+y199gO4msZijSwhxeW7aRDHfLmd9j8E9vEOZkDQB+ruFyvdXrP/RE3mpsTR29GHSwEpHUw9YG4ri0kqnvK3c5fB/7/632/L3C/IvEP7qX8HyYYm2yAA2TJxRlM6hOhO1bZ7LTUcMYwrkzEE5+ilA2C10XDFjbCJ7q9oYsAwJZGpLxAVYlOcejy+PiouBxeNDZCeQMEb0AfnQB7uoMBVFceEHe2yd+GFIHBuaOfpD4VlgqnHqawoVZQ2dpMVFkmSM5Inznhi2/6OKj7jn83tCPg+3qKrIEgTwI/vIrkeGbXv54pcpWVXC3My5WswubBRlxHG4fjRnYG1KxBoKObWUB1w+bKMgLZZjGvW02ejpH8DUa9bGGsOFCI87kmIMvos4ddgC2OEiTjYvWc0zsLZg04OPapIxwqcy6BxbgUyU69+/epPoB9e+B9a5hDg7wbcAud8y/HWx2HyRQ91falvcaxyeaXOlPA/w3/P+a+2jVIW/tgRwtB1UaPCdcaS+g+zEaGKjNF4oShk/TLn878uH6zsA/O+u/3V7mj8t/YPIwGqg7i/5aiMD2DBx5iSxIr3ucMMIz8QD41eQ1LyHNH0XEzI0FAAIkJljk+jqGxhewli7B7K8lw9vPNpESmxk6OyAFAVy5ooeVi8kx0YyfUyis5DTgFl4rxac6f7AcGLrgz26NuRPVdbYQaFVZGJ+1nxKVpXw92XOP4YfVXzEW0ffCvlcXNLZAL1tfmcJ+i39/Gu3cwndk+c/SaReQw/iMDIxM54jdR2aZcOPN3eREhtJnFYXV7GpItunZQa2pRySg7M1yks1ap6BtXnApsVpEDzFJIuWAS8ZWBBKxD7b6NgzsMMXDR0Z2NCXEINztYMas59mNwHswWbHeyepv0eIC+ldvz/r2sUCtOYZ2KhE0RNozcDqdAqR5nyPh6iqypyn59gfFyUX0We2ENldT48+HgwaCaW5wxbANg0PYFdNW8Xm6zdzedHlFKcV89vTf0vJqhKSopMc42UG1k6e1Qv2mAZKxIfrTBRlhuCaJ6VQZM7Njs/R6Tmn860Z3/L5FFcUXSEqXPq7RPuHRBIEMoANE5My48lMiOLz0RzATliBDgtXpRwhMmLk3xoLCsSqtJMFUVezuMAcM9vjsaqq8uXRRhYVpqILpb9W3iKRxTC5MGkfwtKiNHYcb3GU49XsFv17BWeEbn7+kJwvfqSOfBzypypr6KRwiMjEstxlYoV+EPdvuJ+7Prkr/OXEtqyCn1mCwReUAP859z8nXdZ1MBMz4zD1mqnRqHLkRHOXdtlXG+mTtcvA9rSJQCjIDGx+aiytXf2+ix/5QGOHyP6lahHAgsuSQFckGiP9KyHWR7rsIbWdI2QiTj2tTpuzYh0ZyNgoxa2I01VvXSVOExFDdG+H2/JhCGEGVqcTCwpdDjGpBdH32+//ccsfhx2yrW6b/f4VRVfw3MrnON7cRTot9MW4tjHSlKQ88Vq7yMCC6Mf91eJf8dyFzzmpQtuFn2x92BLyrCKTwVrpDFhUjtR3MDEUCYhUm5VOhdPmu2bfRVJUktfDLy+6nJ8s/IlDNd5LC5hE4o2Rj1K+IiiKwlkTM/j8cAM9/QMjPR2XWMbMpZU4ztMP70scCcYmx5CdGM2WY4MC2Jpd4tZLAFvR1EV1Ww+LxqeGboIAeaeL2+Nfeh4HrJiSidmiOhYxbIq/+UtDNLkAmHiBmFev9gqqNtq6+mnq7BsWwALMy5rHlhu2OG37vPJzZjw1g6oODYV6vOFnlqCjr4Pi1c5VAd+f830WZC/QemZhpUhjIacTLV3a9b/ayJiinRJxi/XiLMgA1nZBqkVJoI2mDhGAaWKjA9YA9pjXYYkxBtp8DcQ7G0VG3IVoXnu3UBjX3Ac2Mg50EcMysDlxDhuZLV0P09bdP6wd5WiroyRyftZ8sYDhScDJloH1scTXL4wpdhEngKIMx2/XYJGpstYyilcX83rp6/Zti8YsIjoimsN1JjKVltD3v4LoEU8pdBvAuqV+vwh+AxDHO1UxRkaQmRAVtJXOieYues0WuwCfprhQIraxKNuzhseds+7kV4t/RZQ+yuHbnT5J6xlKvmLIADaMrJyRTUevmfXu/EBHmIrWPt43z2Nax0av1jDhQFEUFhSksOVYsyMDV71T3GbP9HjsRnv/a4gD2OyZYDDaPXQ9MWtcEmlxkXy835qtPbYOMqa5tJwYMSZdAAN9YO2FDgVHrfYihW586mIiYthw7Qb0irOIzvmvns8vN/4yZPNyorEUIqJ99gLd07DH6fGi7EXcVnxbKGYWVmwXQloIOZkHLFS1dJObonFpY/pkoeTddiL4cwVpoWPDJqCilTUGODKwaVrY6IDIqLRVev2uT/JLxKneZfkwhLCEWFFEGfEgEScbf1jqUOi1qAP2PlwbD2590H7fGGH0HsCaeomK0GkfhINQIh4kiDR+yALfjjqhtfDusXcBeKvM0V5hUUXz7sFaExlKKzGpYdJUSCtyWULskfr9wnde4kRBWixHG4L7nrUtNNos0DTFpgfhQiPj16f/2uOh357xbceDhoNC3X8kfe8lpwQygA0ji8enkmQ08G5JzUhPxSV7Klt517IQg7kTjn4y0tMBRBlxvanXkcmo3ilWAm19T2744kgjmQlR2irxuUJvgHELfApg9TqF5ZMzWHuonv7eLji+CQpGUfYVIHeRKKE79F7InqLMJvPvwacuMSqRzTdsZkqKc5nRq6Wv0m3uDtnc7DQdEaIVOu9fkWaLmW997NwHdNfsu0I1s7CSEhtJWlykJhnYmrYezBY1NBlY0KYPVqMANhRWOo3WDGxavIYZWFSvVjpJRhHAWoaK6bmis8GlAjGEUMQJxHeWCxGnvARHL3NM3mPDrHS21DqqPe6Zd48oQ/ZwYV3X3kNmQnRobNliUoYEsHH0Npxtf7zq/VU09zS77KdfPGYxAIdq2shQWtEnZGs/P1ekTRQZObOPGXpzr1gczJQB7FCKMuI5Wh+c3kBpvQiAQ9IDa0wRn+2GQ8N2RUdE843p33B52M9O+5nz56X+gOx/lWiCDGDDiEGv49ypmXy0v25UlhHvqWxju64YNSYZ9r420tMBYKG1D9ZeRly102v5cP+AhXWlDZw1MSM0FxpDyTtdWPt4UMG0cfaUTEw9Zko3vwfmbphwTujn5w/6CJh4Hhx+HyyheY8ea+wgQqd47YWM0kfx2yW/HbZ9wbMLuPj1i+kxh7BKoOmIT/2vO+p2MPtp5/djyaoSZqTPCNXMwk5RRjyH64PPwJ6wKRAnh6AHFrTpg20pF8FQkNmBaIOe7IRoTYWcmjp6iTHoMUZqlP3zUBI4mMQYAxYVTL1m7+fsbHSpQAzQ3mMmxqAPjb5CTLLL799Yg2ORLMJYzsHGMhq7G1l7fC0vH37ZScU3KzYLutscPbUuqGvvIUMLH15XGJ17YCdkxGFuOttpyJkvnsn75e87bXv83MdJjBLv15raaiIxh16B2EbGVOGF7qtyfeNhUAdkBtYFRVa9gbr23oDPcbjORE5SjHYieUNJn+wygAXHIspgkqKSOHPsIJFKy4B4D8j+V4kGyAA2zFwyK4eOXjPv760d6akMo6SyjYljUlCmXgoH3/YpIAs149PjSI2NZOPRRuioh/ZKrwHstvIWTD1mlk8Jg5AFQN5iQHV4unpgSVEaURE6OkreEaXH+e4980aMSReIXqwTW7yPDYCyhk5yU4wY9N6/fiYmT2TjdRu5dtK1TtvL28upaK9wc1SQDPSLQMaH/tf71t/n9Pi1r42OhR8tmZgZx5E6U9BCWidaNLbQsWFMgbhMbTKwrRVBZ19t5KXGalpC3NTZp13/K/gVwALDym+HoariO9pNCXFbV7/2HrA2YpKHiTgBFCQWOD1+6eh/WfbSMr639nv8+ktH2eMvFv1C3PGhhDgzFP2vIEqIu5vtvdzRBj2F6cNLQUtbnEt2bYtlXX1m+lqrxcZwBbCZ08Vt3V7fxtfttx43LTTzOYmxOT+UBmFbdriuI7QOEumT3OoNLMhewObrN3PjlBsBeOzcx1h/7XoyYzMdg1rKwdwj+18lmiAD2DCzqDCV/FQjz24O0cV3gAxYVPZWtzEjJxHm3Sq+ZHa/ONLTQlEUzpiYzueHGxiosva/eglgPz1YR6Rex5IJIfJ/HcrYBRAZD0c+8jrUGBnBkvGpjGtch1p4FhhCdDEUDONXgM4Ah94NyeldKRB7Ij4ynp+e9tNh29ccXaPltBy0lIusQpr7AHZPwx7OeOEMajod7QDrrllHUfKpZw1RlBlPZ98AVa3BlW4fb+4iQqeQnRiC97xWSsQaeMDayE+LDVqUZTCNHb3aWOjYMKaIbLMXL9gkowiavXrB9ppgoNd9CXFPf2jKh0FkTd0suG64doP9/vYm1wrrV068EiwWoQrvUcSpl/RQZmAH+kQ/t5Up2QnE1/0/j4fFRIie8tK6DjKw/hvEhSmATZ0A+ijfA9j6feK3JQB/7VOdoozg9AYGLCpHGzqYGIr+Vxvpk4W9nMl1AsZoMHLvgnspWVXCadmnDR9gy96mywysJHiCCmAVRUlRFOUjRVFKrbfDGhMVRZmlKMqXiqLsUxRlj6Io1wza96SiKMcURdll/W9WMPM5GdDpFK5bkMvW8hYO1Wqj7KkFRxs66OoboHhskhAmypkL257QRtkzSJZNzqClq5+6gxsBBbI8l2d+crCe08anam/k7Y6ISBi/DEo/8unf64pcE9lqA9UZo8T/dSjRCaI398Bbmr/+AxaVY02dLjML3ihZVUJ2rKO366n9T7F632otpyewqWq6ucjqH+jnhndvoKXXccG89uq1JEd77ss+WdFKyOl4czdjkmKI8CHz7jc2JWKLJfBzWAag9XjQHrA28lONNHf2+S6A5IXGjj7StMzAgk9WOrYMbGu3lz5HmwdsnLsS4n7tBZxsuBFxAtFPPzvdByur3nZAdRvAdvaa6eg1hzYDC059sJOz4qlu1vG9Wfe4POSqiVfZ7x+yCjgB4cvA6iMgYzLU+pGBTZ8ktCMkTqTFRZJsNNj7WP3leHMXfWZLaPpfbdgypw0BVrs0SAViiXYEeyVxH/CJqqpFwCfWx0PpAm5WVXUacD7wV0VRkgbt/5GqqrOs/+0Kcj4nBVfNG0eMQc8jnx0Z6anY2VYuLsbn5lkvwhd8CxoPwcF3RnBWgjOK0tAp0Ff2pSg9ik5wO7a0zkRZQycrJoepfNhG0bnQXgV1+7wOPcuyBYuq8HrHKC6jmnY5tBxzqD5rRHVrN31mC4UBimt9eOWHTo//vO3PFK8upr2vXYvpCZo8B7D/2PWPYdvSYsKU7R8BbCv6wQo5HWvsID9Uomrpk6G/C9qOB36OthMiA5aijT9lXqr4W49rlIVt7OglVSsFYhup470GsElGEWx4DcRtAaw7FeJuc2jUe0EEsL3tMOC6T/eCgvPcHppptJY49rSJWzcBbJ3VQicrMUQZ2Bih9zC4D3Zqtvitmxx7rstDrpx4pf3+/pp2xupbxYNwBbAAmcW+Z2BrS2T5sBsURaEoI54jAZYQH6gRv4GTs0IZwNr0Blz3wXqldi8k5nq8hpNIfCXYAPYSwJYCWQ1cOnSAqqqHVVUttd6vBuqBUeQbEn5SYiO5eXEea3ZXc0QDcRQt2FbRTGpsJPlW/0KmXyEu5D7/w4hnYZOMkcwbl0BG2x4Yt9Dj2DW7q9EpsLI4TCqMNoqsYkyH3/c8TlUxHnyVQ9EzeP5Af9B9hSFjykWi1Gvvq5qe1mYTEEgG1saem/dQmFjotO3050/n7s/upq23Laj5ASIDG5vuVszlv3v/6/T4oyu9l46fzCQZI0mPj+JwEBlYVVU51tAZ8MKFV7RQIm6yLih6KB33B5sC+jEN+mAtFpXmzj7tFIhtpBQKKx2ze+EYewbWWwmxPYB1/fPe2t1nL0fWnOgkcdvj+vN/7eRrMTRf63LfA6c9IO7YSpDdKNzbxHUy40OcgR3kBTs5WwQjxxr6WH/Nevv2e+beQ8mqEqamOsSQdle2MiWuU8zfoLFVlScyp4nXvqPe87j2auioFdVdEpdMyIzjcF1gSsT7qtvQ65TQeMDaiE0X769AM7C1eyD71BE4lIwswQawmaqq2prAaoFMT4MVRVkARAKDm25+Zy0tflhRFLdLm4qi3K4oyjZFUbY1NDQEOe2R5/alhcQY9PzhvQOjIojZVt7CvPxkh2qvPgLO/LFYMd39wshODrgu34SRbmqT3Pe/qqrKm7uqOX1CWuj6lNwRnyV6Yfe+6jngr90DTaV0T7qUqtZudhxvDdsU/SImGSacDfteD64scwg2C51g7I0UReGNS94Ytv2jio9Y8sISttQEKT7VdMStgNNbR99yerx83HKhXnqKMzEzLihxkXpTL519A371PvuFFkrEjdYAVqP+PJtdUEVj8AFsW3c/AxZV+wxsSiGoFmhxr8lgC2B9z8C6rn5p7eq3n0tzbEGnCyEnEN8ZGcpS0nG2Lbtl2i0sybEK6XkJYOtNIgObEbISYlsG1hHAZiVEk2Q0sK+qnaToJEpWlVCyqoRbpt/idGif2cK+6nbyI9shPsyLt1lWIaeaPZ7HVW0Xt2PmhHY+JzFFGXG0dffbLbP8YV91O0UZcUQb9N4HB4qieFQi9khvBzQd9doCJpH4itcAVlGUjxVF2eviv0sGj1NFFOb2yl1RlGzgaeBWVVVtV8Q/ASYD84EU4F53x6uq+qiqqvNUVZ2Xnn7yJ3BT46L4n7OL+PhAPR/sG1lF4npTD8ebu5iXl+K8o/hqGDsfPvwpdDa5PjhMnB17DIA1zePcjtlW0cLx5i6+NnNMuKblzMxrhEl7bYn7MbueB52BomU3EBmh463d1eGbn79Mv0KURZ/YpNkpyxo7iI+OCLqXT1EU9ty8h7vn3j1s3xfV3tWgPdJY6tJC5/xXz+f+Dfc7bfvdkt8F91wnCUUZ8ZTWdfjmBeoCLRYuPBKTJC7cg83ARiW4zSD6PaVIPVkJ0ZoIOTV0iOxfmtYLc7ZyaQ9lxNEGPdEGnfcA1paBc1FCbB6wYOox28uRNccWdHpQzk82RpLafSOvXPwKFxVexM6bdnLPvHuI0EU4H+s2AysC2MyEUIk42XpgHb+1iqIwc2wSu060ejz0cJ2JPrNF9MCGs3wYIHsWoEDVNs/jqnaALgKyisMxq5MSm5BTIO0a+6vbmTomDKW56ZPEdY6/iZe6fYAqM7ASzfAawKqqeraqqtNd/PcmUGcNTG0BqssaEkVREoB3gJ+qqrpp0LlrVEEv8F9ggRZ/1MnC108vYNqYBO57rcTukTgSbLf1v+YP+eHW6eDiv0FPO7x+e8h8QX0hvmE7zfo0Vu8bYMDNRfSTG8tJiI7gwhlhXoG2Me1yUXa7+3nX+3tNsOtZmHYp8cmZLJ+Uwdt7ajAPaJfh1JRJF0BEDJS8rNkphQJxnCb+vIqicOv0W4dtf2LvE/x0w0+p7QxgYai7BboanTKwnxz/hAXPLqCqo8pp6PYbtxMXGULFx1HExMx4uvsDVyI+1hjiABaCVyJuOiKyrxp6R+elGjXxgrUHT5oHsL5b6bR2eckKmWpE8O9CoKe9R/SmJoUsA5skbj0FsLEGWrr6mZQyid8v/b0jcLXhQwmxMVIfOo/N6ERAccrAAszJTeZwvYn2HvcLCLYAN76/MfwZ2OgE4et6YrPncVXbRbnxaFTeHyVMsZaM76v2rxWmwdRLvamXaWOC86/2iaxiUenQdsK/42qtGXqZgZVoRLAlxGuAVdb7q4A3hw5QFCUSeB14SlXVV4bsswW/CqJ/1kclgFODCL2Of14/B4tF5dYnt9pLlMLN1vIWoiJ0THf15Zc5DVY+CEc+hnd/qGk5qc+oKhzfRE/2AqraelxmrCtbunh/by3XzB+HMTJM6sNDMabAlIth5zOuFTF3PiOERhZ8C4DL5+TQ2NHL2kOjtCQ+Kg6mfg1KXoE+bRZYyho6Ga9xEPOvs/81bNuao2s455Vz/C8nbnTugyxrK+N/1v4P3WbnwG3z9ZuJ1Ieon28UEqyQU1lDB1EROsYkhrA3L2MKNBwO/DvKFsBqSEGaNl6w9db+S83LV40pEJXo3UonJtJ7BtZU59a+xRb8hqwH1p6BbXU7JNkYSUunhyDchwxsZkK0JotvLtHpxXN3OVc7zclLQlVhl4d2k10nWkmJ0aPvrAt/BhZg3Hyo3O7+s2cZgOpdsnzYC6lxUYxJjGZvlX+ihLaAd1o4MrDZs8RtzW7/jqvZLaoMEkaoQk5yyhFsAPsH4BxFUUqBs62PURRlnqIoj1vHXA2cAdziwi7nWUVRSoASIA34bZDzOenIT4vl3zfNo6qlm2v+vYmDtRqqqfrIxqONzM1LJjLCzdth3q1w+veFrc5r3xSZxHDSdBTaq8iasYLCtFj+99Mjw0oZH/roMHqdwq2nF7g5SZhY8j8iSN3ymPP2XhOs/wvkLYGx8wBYPjmDjPgont8ShHJqqJl7i/h79r0e9Kk6e83Utvdo3ge5JGcJf1j6B5f7vvHhN/xTKLYK+XQljuXvO/7OJW9cMmxIyaoSjAZjQHM9WbFZMwQq5HSssZOCtFh0uhBd/IPIwJq7obXc/2P7u0VGQSMBJxt5qbE0dvRh8pA984V6kzWA1ToDqyiQUuA9A2s0eBdxMtVAvGsZjFZr8DvSJcSt3f3uy+C7W8AQCxGu/43r23u1//cfijHFScQJYNa4JBQFtle4/9s2lTWxPFeHog6EPwMLQlyxt004F7iido/Yn3d6eOd1EjItJ9HvDOy+avEbF5YS4sxpoOi99zwPpXaPyL6GagFI8pUjqABWVdUmVVVXqKpaZC01brZu36aq6m3W+8+oqmoYZJVjt8tRVXW5qqrF1pLkG1VVHR2SvGFm0fhUnrltAaYeM5f87xf89ePDdPW5tgPQmnpTDwdrTSwp8mIDcvavYMXPYd9r8H9LQuIR6paytQDoxi/jeyuK2F/TztObHKIjG4808vrOKm49PZ8xSWFUX3RF9kyYfBFseMj5ovCTXwuRk3N+bf8Cj9DruGb+OD47VB9waWbIyV0EaRNh+5NBn8pWRhqMArE7Liy8kM3Xuy5hO/3503nv2Hu+naipFHQR/OnoyzxW8tiw3X9f9vdgpnnSkhhjIDMhitIAM7C2ADakBKNEbPuspmpjoWPDpupeEWQfbF17D3FREaHxtvbBSicxxuBDD6z77F9bly2ADZUKsbV6yGMJcSQDFtV9KW53i9vsK0CdqSd0HrA2jKnQ2ei0KT7awKTMeLcB7InmLipbujkz29riMxIZ2LHW7q/jbvQSjq0TtwVLXe+X2Jk2JoGyxk46e32/BiypbCM3xUhCdBj8dQ0xog/Wnwxsf4/wAJb9rxINCYGjvCQQ5ual8N73l3L2lEz++nEpZz74GU9+cYxec2j7Tr84In4sl07wIlyiKLD0Hlj1Nuij4MUb4YnzHD9MoeToWkjKhZRCLpk1hjMnpvPbd/bz4tbjfLivljuf20FBWizfX6Ft9iRgLviT6IV99moo3wBr/x9seRROuxPGOlsIXD1vHCrw4lY/+0nChaKILGzlFp88bj1RZg9gQxPIGA1GJ6uJwfx43Y8pXl1M8epiTH3OQdixtmM0dTdR0lDCbdXvUZw3hlePDM843zr9VpblLgvJ3E8GJmbGczgAJeL+AQvHm7tCH8CmTxK3gfTBNnr2/g0Um+9tsAFsg6mXjFCJB6UUQutxMLsvr03yFsBaBoSIk7sS4u4++3lCgt4AkfFuVYgBu3CcW4VXDwGsqqrWEuIQZ2Bj04cFsACnT0hjS3mzy4XtjUfF+DkpViukkcjApo6HhLFw9BPX+4+tg7RJIxNcn2RMH5OIqjp8Xb2hqirbj7cwJzcptBMbTPZM/wLYmt1g6XcsdEgkGiAD2FFEenwU/7xhDq/esYiCtFh++dZ+lj34Gc9vOU5/iIR+1pc2kmw0+N47kX863LERLnoYWk/A6ovhyYvcr7wGy4AZytdD4TJQFBRF4e/XzWbG2CTufbWE25/eTrIxkidvWTByva9DScyB618UYkBPXgif/xFmXCOyr0MYl2Jk2aQMnt1UQU//yIlkeWTmdULM6ct/BnWasoYOFAXyU0MXyCRFJ7nNxNpY/Pxi3jr6Fre8fwvFq4v52htf46yXzuL6d69ns8V9EYgrxeOvEpMyhRKxv6Jjx5u7MFvUkGTenYhOhMRxgS20NFkD2BRtM7B51gxssH2w9aae0JWv2qx0Wt23MiQZDbR4EnHqbAR1wG2A0tIZ4hJisPaPNrvdnRYn/v0aO9x43na3uPV+bu8209NvCX0GNjYdOodrYZ41KZ0+s4Uvjw53A1h3uJH0+CjG6FrFhpEIEhUFis6Go58NXwjp64KKjVBwRvjndRIyPUdUE+yt8q2MuLKlmwZTL3Py3FcPaE72TOHpa/JRKNEm8DVOBrAS7ZAB7Chkbl4KL95+Gs98YyGZidH85LUSVvzlc97YWaWpZ6zForKhtJHFE9L8603TR8C8r8P3dsL5fxCeYE+cB89cAbUa63BVbhU9mOMdma/EGAMvfWsRz962kP+smsf7/7OU3NRR1pOYtwi+uwOueQa++Slc/qhLdU6A288opKmzj5e3jdIsrDEF5twEe16Ctirv491Q1tDJmMSY0PrUITKxn139mccx92+4n+112306o78IQAAANo9JREFUX7Q+mle/9qoGMzu5mZ6TSK/ZQmm9f50eh2tF1tYmBBVSsmcKsRh/qdsvqjyitJ2jMTKCjPgoyoP0gq1r7yUjPkTBkw9WOknGSHr6LXT3uVlk67BeyLoJnlq7+1EUUQ4bMozJw/pHB+NbAOtGwCnUHrA24jJEED7gnGldUJBCjEHPpwedg9vuvgHWHqrn3KmZKLbXIM51H3LIKToX+kxw/Evn7Uc/gf4umHLRyMzrJCMzIYq0uEj2+BjA7jguSsvn5IY5gAXfv2srt0Byvnh/SyQaIQPYUYqiKCwpSuO1Oxbz31vmkxATwf+8uIu7ntvpvRfJR3ZVtlJv6uXsKQF+qRii4bQ74Pu7RHaxagc8tgw2/1u7/tiDb4ty3PHLnTbrdQqnT0hjxZRMoiJCGxAFjE2VOGeux2ELC1KYnZvEo+vLRq+lzqK7RJZm0yMBn6KssSNk5cNDSY1JpWRVCdtu3EZ6TOC+nhOSJrD1xq1MTJ6o4exOTvzNDNg4WGsSCRqrx2FIyZ4lFHV7/BTDqz8AGdNCMqX81NigSohVVaXeFMLyVR+sdFJjRfmt2yysLRPjpoS4rauPhGgD+lCKeBnThin4DsZeQmwKIIC12hhlhSMDiyqqdwYRFaFn2eR03ttbS5/Z8Rvx2aF6uvoGWFmc7dHGKCwUnClEsIbaru17HWJShIChxCuKojA7N9mjaNdgdh5vxRipZ3JWGL5fbWTPEtdlvnjEqyqc2CrLhyWaIwPYUY6iKCybnMGa7yzhvgsm88G+Wq7410aqNRD9+WBvLRE6heWTg1yxjYwVKsV3bROB5ns/hre+N2wV2W9UVYhFFZ7lEOk4BVEUhW+fOZ4Tzd28vjPwDGdISc6D6VfAtv+KXjc/sVhUjtR3hCeIGUSUPoq/Lfub38ddOfFKnjz/SV646IUQzOrkpDAtlthIvd8B7OE6E/mpscREhmGhacwscVvrh0KmuU+UENtEoDQmP83IsSBKiNt7RPlqyDKwsWmif9SDlU6yNYBtdmdDY/KegQ1p+TAIASQPAWyyMRK9TnHdA6uqXgJYEfSGvAfWlqFy8R179bxxNHf28dH+Ovu2pzdVkJ0YzcKCFPEajGSPaVQcTL8c9r7mcCow1cH+NVB8lajckvjEgvwUKpq6qG/3bq24vaKFGWMTidCH8XI+0ii+ayu+9DqU1uOiQkOWD0s0RgawJwk6nQhynrltIXVtPVz+yEaONgQu2qyqKh/sq2XxhDQStRLWiE2F616AM34EO56Cl27yKAzilbq90FohspinOOdMyWTm2EQe+uiw+zK9keas+2CgV4hS+UlVazc9/RaKwlFGOoTi9GJKVpVw89SbfRr/2dfe5BeLfsHczLlE6UN8wXoSodMpTB2TQImfAeyhWlN4yofB/9I2EMGrxSzsIUJAXmosDaZev1RFB9NgL18N0XtRUSC10GMGNsXnDKwbG52u/tAJONkwpnrsgdXpFFJiI12XEPd1wkCf1wxsyBYRbMRaA1gXfbBLi9LJSzXyj09LGbCobCtvZuPRJm5elC+CF1PNyAg4DWbe16G/Ezb+Qzz+4m/is7XwWyM7r5OM+QUpAGwpd/9+BuGvvLe6jdMKU8MxLWdyF0HVdmFB5oljn4vbfJmBl2iLDGBPMk4rTOWlby/CbLFw4+ObOdEcWGnavup2ypu6OH+axiu2igLLH4ALHoRD78Kr3wg8E7v3NVB0MGmltnMcheh0CvevnEJNWw//2eDZ0mLESB0P82+DHatFyaUfHLH2TRZlhD+AtXHHzDvIT8gHYM2la7hm0jVO+++LHs+zLf2kJheOwOxODqbnJLK/pp0Bd16aQ+jpH6C8qZNJWWHwJwSRwYof459CZt1+cRuqDGxqcErE9e02D9gQBk8pngPYZKOXDGxHrSgTjXBtkyMysCGy0LFhTBV6CR4WTdPiolwHsDb7HWOKy+Pq23tIiI4IfRWBPQPbMGyXXqdw3/mTOVhr4o5ntvP9F3YxJjGamxfliQEjnYEFyJkjKnXWPwRvfR82/wvmrtLcnupUZ9qYBGIMerYe8xzAbjzahKrCUm82iKEgb7FQFq7yoiVxdK1oLUifHJ55Sb4yyAD2JGRKdgJPfX0hnb1mbvzPZp/KTIby4tYTREXouHBGiFZsF94O5/0/OLBGlBNb/OztHDDDrueEMERc4D2MJxMLC1M5d2om/1x7NGjRl5Bxxo8hKh7evtuv17TUar8yYQQD2LjION667C123LiDgsQCfrLgJ2y6fhMlq0ooWVXCDe0mZiSPEiumUUpxTiI9/Rafqz+O1HdgUYWCcdgYMwtqdvk+vn4f6CIgNTSvfX6azQs2sM+0TUAopOWrNiudAdf6CvYMrNsS4jqP2b+2rr4wlBBbg0+PQk6RNLgqIbYFsB5KiEOuQAzWHlhcZmABzp+exfdXFPHJwXpUVeXRm+cJb+ABsyg7HukMLMCFD8G4hcI7fMLZ4jpA4hcGvY45eUls9hLAbjjSSFxUBDPGJoVnYoMZtxBQoPwL92MsFpGBLTxLJDckEg2RAexJytQxCTz59QU0mHq5+YktdqN4X+jpH+CNXVVcMD1Lu/JhVyz6Dpz1E9j1LHz0M/+EnY58JFb1Z98UuvmNQn51yTQi9Ao/emU3Fh+zXGElNlUoTx/fCFv+7fNhpXUdpMdHhT4L4wMGq8iJXqcn1mAVlVJVoaYtV4k9UmwVciqp9K2M2OZlOCncAiONpY4+PG/U7RPBq5vsYbDkWTOwgfbB2jOwoQygUgpFqacbK53EGAOK4qkHtgbi3WsptHaHqYQYXPqo2kiLi3It4uQtgDX1hCeAjYqHiGi3OgOKovCDcyay71fnsf7e5XZhNRHwqiOfgQVhRXTL23B/NdzwstDIkPjNGUXpHKw1UeVG70RVVdYdbuC0wlQM4ex/tWFMERn30g/cj6ndLfrSB7lISCRaIQPYk5g5uck8etM8jjZ08I3VW33unXx52wlMPWauW5Ab4hkCZ94LC78NX/4vbHjY9+O+/KdYTZ54XujmNgrJTozhFxdPY2t5C3/9pHSkp+OamdfBxPPho194Lx+yUlrfMaLlw15prxYWEOmTRnomo5rC9DiMkXp2V7b6NH5PZRtxUREUpoXxInbMbED1rQ9WVaF6p/WY0BAXFUFaXBQVjQGWEJt6MUbqiYsKoQiO3UrnmMvdep1CsjGSZnc9sB11bhWILRaVtu5+EkO9eBVrLaP0okTc1Nk73I7OWwDbFkIf3sEoiuiD9RCEA0Qb9M6Kzu014tbNaxB2FEUGrkGyYopYEPr0QJ3L/fuq26ls6eacqSNoTTPxAnENYHI9R/avAUUPE84J77wkXwlkAHuSs6Qojb9dO5vtx1u489nt9HuxYekfsPB/n5cxJzeJBQWu+300RVHgvN9D8dXwya9EWZE3TmyB8vXCumWkLAFGkCvm5HDl3LH8/ZNS3t5TPdLTGY6iwCWPiIzLCzeI4M8DqioUiEeyfNgrDQfFrczAekSvU5iTm8y2ct8sHvZUtjI9J8E/n+lgGTdf3Ppi8dBWCZ0NIpMQQvJTjZQHmIGtaesmOzHE2T8frHSSjQZaOl1U+gyYRQY2McflcaYeM6pK+DKwHgPYKHr6LXQOXez1EMAOWFTqTL1kJ4UhAwuiZcZNCbFb2ivFrZvXQHLyMT49loK0WD7c7zo4fLekBr1O4dypI7hoMekCcXv4veH7VBX2vwEFZ4jKLYlEY2QAewqwsjib3146nbWHGvjxK3s8lp6u3lhOVWs3311ehBKungSdDi59RKzCvf0D2P+m+7EWC3z4gPD0m3tLeOY3ylAUhd9dNp25ecn84MVdvL+3dqSnNJzYVLj2eVGm+eRF0Obe/qeuvZeOXvPozsA2Hha3MoD1yrz8ZA7UttPe47ltoc9s4UCNiZnh7s+KSYaMqb5ZPFTvELdjQhzApgXuBVvV2sOYpBiNZzSEuAyIjPOqROyyhNhUIzyiE8e6PM6mXBwWGx3wGsCCCy9YDwFsvamHAYsa+tfARmyGSxEnj9i+fxPHaT8fyYigKArnTsvky6NN1JucdU4sFpW39lSzeHyq3eJqRMicBsn5UPLK8H3VO8X3ybRLwz0ryVcEGcCeItywMI8fnTeJ13dW8eu39w8vkQKONnTw8EeHWTYpnbMmhVkYSW+Aq58SZtav3gZHP3U9butjcGIznPNr4Sv3FSUqQs9/b51PcU4i33luB09sOObyNR1RsqbDTa+LDNZ/zoVK1+XEDgGn8HrA+kXDQaGiGjsCao4nGfPzU1BV4T/oiYO17fQNWEZGYCT3NFHJYfHSVlG1A3QG8V4OIfmpRmrbewKyyKpp7WZMYoiDJ0WBlALPXrDGSNc2Ou3W4CnBdQDb2i0WOkIewNqCTw9WOmnWMuBhSsTdzaL31DD839nmuR7y18BGIBnYtkqIiHFbAi05Oblm3jjMFpWXt1U6bf/8cAMnmru5et4IL1goCsy5WVTMNR5x3rf1cTDEwrTLRmZuklMeGcCeQtx51ni+saSAJzeW8+1ntjspRla1dvPN1duIMuj57WXF4cu+DibSCNe/IARTnr0KvnzE+QLzwFvwwf2iv3LmdeGf3ygjIdrAU99YyPLJGfz67f1886ntVLYElsUJGeMWCMEORQdPnAefPwhm54vD0jqrhc4IeMD6jBRw8pnZuUnodQrbvHgU7j7RCsCMsYlhmNUQcheLnua6fZ7HVW0XWYSI0PY32oScKpr9KyPuNQ9Qb+oNT/bPi5WO2wxsm6181U0Aa8/AhjhTpDdAdKLXHlhwFcC2iAUsF1S3iuxXWDOwnY3+Kfe3V4p/f6n0ekpRmB7HosJUnvqynK4+YUeoqir/+uwoGfFRnD99FPQ8z7pRLAJu/JtjW+txkZWdeY34TEokIUAGsKcQiqLwwIVT+NlFU/n4QD1nPLiWe17azb2v7OG8h9fR0NHLozfNJSdcP8SuiEmGW98V5cQf/AT+uRDe/ZEIaF+8USiIXv6oKDuWEBcVwaM3zeWBC6fwxZFGzn7oc37/3gEaXClpjhTZM+Fbn8PkC2Htb8VruvNZuyVHaX0HyUYDqSNZ6uQJVRW+tlLAySeMkRFMH5PAFi8WD5vKmslOjGZs8gh83+SeJm4rNrofY+6Fyq3CzzDE2Lxgy/0UcqprE5/zMeHov0wphJYKt77dybEiAzusEqTthLh103/ZZs3AhlTx3oYx1WMAm24tIR5mpdPdKtRzXWDLwIavBzYD1AGPdkDDaKuU/a+nKPecO5G69l7+/onIcL6yvZIt5c18/+yikVEfHkp8Jiz4Jux8Biq3iYWXd38sFrWX3jPSs5OcwoyCd79ESxRF4RtLCnjv+0tZNimDzw7V8+H+Ws6alM5bdy1hXn4YhJu8EZME1z0PV/5XlGzuel5kwM68F1a9JVfshqAoCrctLeTje87kvGlZPLaujCV//JSfvbF39GRkjSlw9WpRUhwVB2/eCX+bBZv/zfG6Rooy4kcm6+8LnQ3Q0yozsH6waHwaO4+3uu2DVVWVTWVNLCpMHZnXPWkcJBfA0U/cj6ncBuYeyF8a8unkWb1g/RVysllohCcDOx4s/Y6AdAipsZH0D6iYeocEuG1V4js7ynWLQJM1WAzLApYxDbrcK/imxEaiKC56YDsbHT20Q6hp6yE+KoKE6DAJCtq8YN1Y6bikrcptBlxycjMvP4Vr5o3j/z4/yg2Pb+L+10tYWCC2jRrO+JF4/z1zuajEOvwenP0L+Z6UhJQQ6vJLRpKJmfH8/brQWUMEjaLA9MvFfxKfyEmK4W/XzuYHZ0/k/z4/ygtbj/P8luNcMiuHO84aPzpUfscvh8JlcOQTWP9neO/H/J1EtmZdCz3TIDphpGc4HLsC8cSRncdJxPLJGfzf50dZf7iRC2dkD9tfWt9BU2cfp40fQfXJiecJ1fO+LtG+MJTy9SJLEIYMbEK0qECo8DOArWkLZwA7SIk4pWDY7mRrCXBLZ59zMNdW6VE8qLmzD71OCU8AaEx1KPK6IEKvIzU2apgoDl2NkOm6D7qqtTt82VcQ9nEgxLEyp3ofb+4TNkZuepAlJz+/uXQ68dERfHygjotnjuEXF00jYjRkX20YU+DmN+G9e6GlXHjFL/z2SM9Kcoozij4BEonEF/LTYvnDFTNY9+Nl3LQoj3dKqjnn4c/52Rt7vSrDhgVFgaKz4evv03z1m+wdyOP82n/D32aIMqPRJkZVt1/cZkwb2XmcRMzJTSIxxsCnB11nidaXiizYosIRDmDNPSJQdcXRtZA1w23pqNbkpRr9LiG2l6+G2kYHvFrppFgzqMP6YNsqPWZamjr7SDYawmOlZEz1KOIEkJUYRU3bkAC2s9GtgFtNW3f4+l8BEqwBrBd7MjumakCV2a5TmMgIHQ9cNJXPfrSMh66eRWKoBdECIaUQbngZ7toKp90h+7ElIUcGsBLJSUp2Ygy/uHgaX9y7nFWL8nlmcwXnPPS5V3GdcLJHP5VV/fexd+UbkDYJ3vyOKDPycpEZVur2itLDuBE0hD/JiNDrOHNiOp8dqsfswnv6g321TM6KZ1yKi8xnuMg7XahgHnp3+D5TnVA7n7QybNMRVjr+lhD3kBobSbRBH6JZDSI+CwxGaD7mcrfNrmOYEnF7JSS4779s7uy1B78hx5giemA9LJJlJcRQOziAHegXLQRGNwFsaw/Z4VIgBkcG1tcA1m6hI3tgJRLJVwcZwEokJzmpcVH88mvTeOPO04kx6Ln20U28tNV1H1u4OVAjLHTGFS+BW9+DlX+G8g3w6Jmi73k0ULdX2KjIFWO/WFmcTVNnH+tKnT0rG0y9bC1v5txpI6yQGREFk1fCvtehf0jG7eDbgApTLgrbdPJTY6lu66Gn33crnerWMGb/FMWqROzaSifFaMvADqry6OsUCr4esn/NnX1hDGBTRda9332mOzsxmtr2Qe8HmwesiwxsT/8ATZ195ISzhDgiSvTBmnwNYG0q0KOoJ1IikUhCjAxgJZJThJnjknjzriUsGp/Kj1/dw9ObKkZ6ShysbScnKUYokOp0Qq3w6+8LBdgnLxTqvyOJZUDMwU3/m8Q9yydnkBobyUtbnXsOX91RiarCxS56Y8PO7Buhp80asA5i5zNCtCvDhx5DjchLFdno482+lxFXtXaHR4HYRkqB2xLi5FhRtjjYno1WmwKx++CpqbOP1NjQ2hTZsQkxeVAizkqMprWr3+HJ29nofOwgbKXGYc3AAiSM8T0Da+v59ZAFl0gkklMNGcBKJKcQiTEGHl81j7OnZPCzN/by5q6qEZ3PgZp2JmcNUSfNmQu3vAOKHp66xJFBGAmajoqMTabsf/WXyAgdV80bx4f7azlSLzLtfWYLz2yqYGFBCkWZrlVpw0r+GSKruOFhh6/miS1QvQPmfSOsWXeHlY5vZcQWi8rx5i67h2xYSCkUIiyW4VniuKgIIvU6mgeXENuCXReiT/Yh4czA2rKoHgJYWz+xPQtrUy12kYGtDqcK9GAScvwoIa4UHrauhMokEonkFEUGsBLJKUZUhJ5/3jCHBfkp/OiVPew83jIi8+g1D3C0oZMp2S6Uh9OK4OY3hELs89eKUsSRoG6vuJUZ2IC4/YxCYgx6frlmPwMWlcfWl1HZ0s0dZ40f6akJdDo4637xOm9/Qii2vn8fxGbArOvCOpX8NBGIlvkYwNa299Bnttgzt2EhZTwM9EHr8WG7FEUhOdbgnIFtsfbL2gSghmAesNDa1R/eEmLwnIFNEAGsTeHZkYEdHsDabIzC7p0enw3tPi4+ehHRkkgkklMRGcBKJKcgURF6/u+muWQmRPGdZ3fQ1h1+deLSug4GLCqTs91k4jKmwFX/hbp98N6Pwzs5G3V7QRcB6ZNG5vlPclJiI/nphVPZcKSRcx76nD9/eIgLi7M5a9IoEsSafgWMXwHv/ggeWQhV2+GCP7r1LQ0ViTEGshKiOVxr8ml8RZMoNc5LCWMG1lZS7aa0PyU2isaOQR6qzccgKhFikl2Ob+kS3zupcWEOYDsa3A7JsmZg6+wZWGuw6yIDe7ypC71OCa+NDogS4u4W6O/2Prb5GCTnh3xKEolEMpqQAaxEcoqSEhvJP66bQ52pl5+/uTfsz3/QeqHuMgNro+gcWPID0ZN44K0wzWwQdfsgbaIQTpEExPULc/njFcWkxUXxjdML+PNVM0d6Ss7odHD1apj/TZHZuvyxEfOfnpQVb/9ceON4s8jUhjUDmzFZ3Nbvc7k7LS6Sho4hGdiUfLel2DbLHZuHbMixKYl3urZ3AkcAa7fSsWVgXQThx5u7GJMUjSHcnpu2flZvZcQWC7RWyABWIpF85YgY6QlIJJLQMWtcEt9fUcRDHx3mvGlZrCwOn7DOgZp2oiJ09t4/t5x5Hxz5GNZ8D3IXQ2wYvUNr90LeovA93ynKNfNzuWZ+7khPwz1R8bDyTyM9CyZnxfPl0Sb6Byxeg6Lypi4MeiU8HrA2ouIhKU8s7LggPT6KsoZBJdDNZZDtfsGiqVNka1PDVUIcGSesgDrcB7DGyAgSYwwOK52uRohOAv1wb83jzV3hzYDbSBgjbturIdVDOb6pRpR8e+hBlkgkklMRmYGVSE5x7jxrPFOzE/j1W/vp7DWH7XkP1LQzKSsevc6LUE5EJFz6f0It9pNfhWdyIEr02iulgJMkbEzKiqdvwOKTkNPxpi7GJhuJCHf2L3Ma1O13uSs9PoqGjl5UVYUBs+iVTfYs4ASQEq4SYkURWVgPASyIPlinDKyL8mEQAeyIeBkPDmA9YetBlhlYiUTyFUMGsBLJKU6EXsdvLp1ObXsP//j0SFieU1VVSqramJ6T6NsBmVPhtDtgx1NQuS20k7NRvUvcZs8Kz/NJvvJMzhLl9L6UEVc0d5I7EsFT5jRoOjLcOxdIj4uiz2yhvccsFn8sZq8KxED4RJxACHR11HkcMiYpmqoWa39pV5NLASdTTz/NnX0j8xrYSojbhotpOdFSLm5lACuRSL5iyABWIvkKMDcvmavnjeXx9WVUNIVe8beiqQtTj5kZvgawAGfdB/FZQtBJVUM3ORvVO8XtmFmhfy6JBBifEYtep3Cwtt3jOItF5Wh9J4XpI1C+mjEV1AFoPDRsV3q86BVvMPVCw2GxMbXI7anC3gMLPmVgc1OMnGjuEplkNxlYm19vWHuQbUQaIS7TEaC6o/mYsCPz4MMrkUgkpyIygJVIviL88NxJROgVHvrocMifa3dlKwAzxib5flBUPCz7qVCJPfh2SOblRPUOYf/hRkFVItGaqAg9hWmxHPKSga1q7aa7f4CJI+Glayupd1FGnB4nAtjGjl5osCoV24SfXNDc2UdijCG8IkhxmR5FnAByU2Mx9ZqFSnJXk0O9eBAnrAHsiGRgQZRmN5d7HtNSLix0XPTvSiQSyamMDGAlkq8IGQnRfP30AtbsrmZ/tecMULCUVLYRFaGjKDPOvwNnXidUgT/5DVgGQjM5G9W7YMzs0D6HRDKESVnxHKjxHMAerhP7J/r7+dGClPGgj3J4JA/CKQNbfxDisjwuADV19oVPwMlGXKYISgfcW4flWYPSikaTGOsiA2uzMRqRHlgQpdneMrAt0kJHIpF8NZEBrETyFeJbZ4wnPiqCBz84GNLn2VPZxtQxCf5nXvQRsPwBUb64+/nQTA6ET2TbCRnASsJOcU4iVa3dNA32Ux3C4boOACZkjEAGVh8BWdMdJfaDSIsbXEJ8wGP2FaChvZe0+DBbVMWli9tO916wuday4NraKlEuHZc5bExZQydpcZEkxoxQdjM5H9qrwOzmfaKq0HgE0tyXcEskEsmpSlABrKIoKYqifKQoSqn11uVSrKIoA4qi7LL+t2bQ9gJFUTYrinJEUZQXFUUJ81KtRPLVItFo4FtnjmftoQZ2n2gNyXMMWFT2Vrcx05/y4cFM+ZoQVlr3oFA6DQX2/tc5oTm/ROKGWeOSANjl4fNXWmciKyF65IKnnHniMzLk8yfKgRUaTN3QcAjSp3g8Tb2ph4ywB7DWYNSDkJOtLLi19oTzMYMorTcxPn0EMuA2kgsAFVoqXO831UJvG6RNCuu0JBKJZDQQbAb2PuATVVWLgE+sj13RrarqLOt/Xxu0/Y/Aw6qqTgBagG8EOR+JROKFmxflkRAdwSOfhUaR+GhDB119AxT7I+A0GEWBM34kyuf2va7p3OxU7wAUyJ4RmvNLJG4oHpuIXqd4DGAP1Zn8L7/XkrHzob/L0edqRadTSIuLwtJcIfZ7ycDWm3rJiA+jjy0MCmDdZ2CjDXoyE6LoaKoSG+KznParqsqR+o6RfQ1s6s7uyohtIlvpMoCVSCRfPYINYC8BVlvvrwYu9fVARVEUYDnwSiDHSySSwIiPNnDL6QV8sK+O0jrvdh7+sut4KwAzrZmmgJi0EtInw4aHwGLRZF5OVG4T548agRJNyVcaY2QEkzLj3QawPf0DHKo1+W5BFQrGzhW3Liyt0uKiSGix9sdmz3R7io5eM119A2QkhDkDG2stIfZipZObYqS/tUY8GJKBbTD10t5jZsKIZmDzxa3N63UoDTKAlUgkX12CDWAzVVW1/gJQCwyvwxFEK4qyTVGUTYqiXGrdlgq0qqpqq1GqBHLcPZGiKLdbz7GtocH9yqpEIvHOrYvzMUbqeeSzo5qfe2t5M8lGA+ODsQDR6WDJ3VC/Hw6/r93kQIhDndgMuadpe16JxEdm5Sax60QrFstwu6j9Ne2YLWrgJfhakFwglHldBLDp8VFkd+wTQk8Z09yeor5d+MiGv4Q4Q9x6CWAL0+IYMFkvX4ZkYI/Uix7kopFQgbYRmw7RiY5AdSgNh8R+F+XPEolEcqrjNYBVFOVjRVH2uvjvksHjVFVVAXfmjXmqqs4Drgf+qijKeH8nqqrqo6qqzlNVdV56erq/h0skkkEkx0Zy/YJc1uyu5rhVbVMrtpY3My8/BVFkEQTTr4CkPFj/Z219Yev3Q2875C7S7pwSiR/My0vG1GNmf81wNXBbb/qsYCoYgkVRYNxCqNgwbFd6XBQFvQdF+X2Ee9mKBpMQHwp7CbEhBqISvXrBTsqKJ76/CUtUgjhmEKX1NhGtEczAKorw5K0fbmcEiAA2bZIYJ5FIJF8xvAawqqqerarqdBf/vQnUKYqSDWC9dfmLoapqlfW2DPgMmA00AUmKokRYh40FqoL+iyQSiU9884xC9IrCv9dpl4WtN/VQ3tTF/HwNvFX1EbDkf4Qv7LF1wZ/PxvFN4jZPBrCSkWHJBGHbsr60cdi+ncdbyUyIIisxzIHfUMYvF/2XzWVOmzNjdUyylKF6EUCrtwWw4S4hBpGF9eIFOykrngyllZ6o4Qvi+6rbSImNDH/2eCgZU0QAO3QBz2KB2hKhFi2RSCRfQYItIV4DrLLeXwW8OXSAoijJiqJEWe+nAacD+60Z27XAlZ6Ol0gkoSEzIZor5ubw8vZK6k09mpxze3kLAPPyUzQ5HzOvFyVyGx7W5nwAFRshIQcSx2l3TonEDzISopmcFc+GI87tMBaLysajjSwqTB2hmQ2icJm4PbrWafM0y0GMSi9tWZ5L8O0B7EgEgfFZ0F7jcYgtgG3VD/+u2lPZxvScxOCrSIIlYyr0tEF7tfP2lmNCgTh71ohMSyKRSEaaYAPYPwDnKIpSCpxtfYyiKPMURXncOmYKsE1RlN2IgPUPqqraamLuBe5WFOUIoif2P0HORyKR+MG3zhiPecDCExvKNTnf1vIWog06po/RSIDGEA2n3Qlla136UvqNqooMbO5psvROMqIsLUpj67EW2nv67dsO1LbT2NHHkqJR0CaTOh4Sc6H0Q6fNRR3bMKs6TiTO83h4vamHyAjdyFgBJeQID1UPpMVFkaVrpdbi/F3V0z9AaX0HxTkJoZyhb2Rae4yHlhHbbcBmhXU6EolEMloIKoBVVbVJVdUVqqoWWUuNm63bt6mqepv1/kZVVYtVVZ1pvf3PoOPLVFVdoKrqBFVVr1JV1b2zu0Qi0Zz8tFhWFmfz7KYKpwvpQNl4tJE5uclERgS7NjaIeV8XPW3rHwr+XE1HwFQNeacHfy6JJAhWFmfTN2Dh/ZJa+7aP9tehKCK4HXEUBaZdAkc+hs4msU1VGVP7KTvUIqq6PQemDe29pMdFjUwWMzFHZC0tA+7HqCoZSitl3c59rvtr2hmwqBTnJIV2jr6QOQ1QRBvFYKp3ChEtLz68EolEcqqi4VWmRCI5Gfn2meMx9Zp5+suKoM5T197DwVoTS7XOHkUnwILb4MBb0Fga3LmOfCJuxy8Pfl4SSRDMGpdEQVosL207AYjy4Td2VnFaQSqZCSPc/2pjxjVgMcOeF8Xj2j1EtxxizcBiqls9tx3Um3pJH6ke0oQcUAc8KxH3tBGp9nGo00hbl2PxbsuxZgDm5CaFeJI+EJ0ImdPh+JfO2ys2wpjZHkW0JBKJ5FRGBrASyVec6TmJnDkxnf9+cYyefg8ZCy+sOyz6+c6YGILs0cI7ICIKvvhrcOc5+imkFEJKgSbTkkgCRVEUbl6Ux7aKFj4/3MDbJTWUN3Vx7YJR1JudVQy5i8XnrtcE6/6MGhnHh8rp1LR1ezy0rr2HzJEQcAJIHCtu2zyUEZtE5rtOTWZbRbN98xdHGinKiCNjtCwi5J4GJ7bCgNVxsLsVanZB4ZkjOSuJRCIZUWQAK5FIuOOs8TR29PGyNRsUCOtKG0mLi2JKVgh6x+LSYfZNsPtFzxelnjD3Qvl6mX2VjBquX5hLQVosdz27g5+8uoep2QlcNGPMSE/LmXN+BZ0N8M+FcGANyunfJzYpneo29xlYVVWpau0mJ8kYxokOIsFqKd9e6X5Mm9hXr8vgiyOiRLqnf4Bt5S2cPmEUlHDbyFsM/Z1QZfXkPbYOVAsUyABWIpF8dZEBrEQiYWFBCnNyk/j3ujLMAxa/jx+wqGwobeCMojR0uhD1vC3+rrhw+/KfgR1//Evo74LxK7Sdl0QSIFERep68dT5z85NZWJjKozfPRR+qz0+gjFsAVz4hlH0X3QVL7yE7MZrqVvcZ2Lbufrr6BshJjnE7JqQkWgPYNk8B7HEAxhVM5L29NVgsKh8fqKO7f4Bzp2WGYZI+MuFs0e+67w3xeO+rYEwTPr0SiUTyFUUGsBKJBEVRuOOsCVS2dPP2Hs/2E67YfKyJlq5+VkwJ4YVfch4UXwnbn4SuZq/Dh3HgLYiIkaV3klFFXmosT966gCdumc/Y5BHKWHpj2mXwzU/hvN+BTk92Ygw1HnpgK1tEcJuTNEJluNFJYIj1XK3RVgm6CJbMmU5NWw8fH6hj9cZyxiRGs7BgFNgY2YhOgInnwp4XoG4fHHxHfA/qI0Z6ZhKJRDJiyABWIpEAsGJyBhMz4/jXZ0cRNs2+815JLdEGHcsmh9j+4/T/EeV0Wx717zjLgAhgi86ByNiQTE0i+aowJimaelOP22oNW3Z2xEqIFcWqROwhA9t6AhLGcOGMsRSmx3L709vZWt7Cd5ZPGH1Z8KU/FL2v/1oMugiRCZdIJJKvMDKAlUgkAOh0Ct8+czyH6kx8erDe5+MGLCrv76tl+eQMjJEhzgpkToWJF8Dm/4PeDt+PO75JKJJOuzRkU5NIvipkJ8ZgUaHO5Nr5rsoawI4ZqQwsiD5YbxnYxHFE6HU8dvM8zp+Wxd3nTOT6Bbnhm6OvjJkFVzwORefBdc9B0igS+pJIJJIRQAawEonEzsUzxzA2OYaHPjqMxeJbFnZ9aQMNpt7wic8svQe6W2DTv3w/ZvfzoqSw6LzQzUsi+Yow1trbeqK5y+X+6tZuog06UmJH0OYlKRdaPViDWQNYgPHpcfzfTXP53oqikfGt9YXiK+GGl6QInUQikSADWIlEMgiDXsePzpvEvup2Xtvpm9rvs5uPkxYXydmh7H8dzLj5MPki2PAwmDz4PNroaRPCJ8VXQFRc6OcnkZzi5KeKMvyKpk6X+6tauxmTFDOywWBKIXQ1ic//UAbM0F7lsNuRSCQSyUmFDGAlEokTF88Yw8xxSTz4wUG6+swex1a1dvPpwXqunDuOyIgwfp2c82sY6IO1v/U+dtfzQn147q2hn5dE8hVgTFI0Br3CsUbXGdjKlm5ykkZIgdhG6nhx23R0+D5TNagDshRXIpFITlJkACuRSJzQ6RR+duEU6tp7+cuHhz2OfWTtEXQK3LQoL0yzs5I6HhbcDjuehoqN7sf198AXf4Nxp0HOnPDNTyI5hYnQ6xiXYnSZgVVVlWMNnRSmjbBYWoo1gG0uG76v6Yi4TZ0QvvlIJBKJRDNkACuRSIYxLz+Fm07L44kvjvHl0SaXY8oaOnhp2wmunjduZLIty+4X1jqvfxt6Ta7HbH1MZFuW/SS8c5NITnHyU2M51jg8gG3s6MPUa6ZgpAPY5Hxx6zKAtWZlZQArkUgkJyUygJVIJC75ycrJ5KfG8t3ndw4Ta+kfsHDvq3uIMej5/oqikZlgVBxc+n/Qehxeu130tQ2m8Qh8+juYeD4USO9XiURL8lNjqWjqGma5VdYg1MEL0ke43zzSKJSIXZUQN5ZCZDzEhalvXyKRSCSaIgNYiUTiEmNkBI/dPJf+AQvXPbaJvVVCDKWnf4AfvbybreUt/OqSaWQkjKBVRt4iuOBPcOhdeOUWh2BLcxk8eyUYYuDCh4QvpEQi0YyCNCPd/QPUtTtb6diysiNeQgxCyKnZRQDbdES0IcjvBYlEIjkpCbFpo0QiOZmZkBHP099YwDef2sbF/7uBGTmJVLX20NjRy4/Om8Rls0eBiufC28HSDx8+AGXrIH0i1OwGgxFueAUSc0Z6hhLJKceEjHgADtWZyEp0LGKVNXYSqdcxZqRFnADSJ8PuF8BiAd2g9fqmUhi7YOTmJZFIJJKgkBlYiUTikRljk3jv+2fw3WUTiI2KYGFhCs/dtpDvLBtF/WOLvgPfXAtTLgZ9FMz7Onx7g7DckUgkmjM1OwGAfdXONjUHatopyoxDrxsF2c2sYugzQcsxx7buVtF2kDl1xKYlkUgkkuCQGViJROKVlNhI7j530khPwzNjZsGl/xzpWUgkXwkSjQbGJsewv7rdvk1VVfZWtXHetKwRnNkgsmeI29oSh61ObYl138yRmZNEIpFIgkZmYCUSiUQikfjN1OwEpwC2uq2Hlq5+puUkjuCsBpE+BRQ91O5xbKvZLW6zZAArkUgkJysygJVIJBKJROI3M8clUdbYSVOHEHLafaIVgOljEkZwVoMwREPmNDixxbGtartQJ45LH7l5SSQSiSQoZAArkUgkEonEbxaPTwVgo9UreuPRRmIj9UwfLRlYgMIz4cRm6OsUYk7H1kH+kpGelUQikUiCQAawEolEIpFI/KY4J5H46AjWHW5AVVXWHW5kYWEqBv0ourQoPAsG+qD8C1FK3NUofaElEonkJEeKOEkkEolEIvGbCL2Oc6Zm8t7eWlYWZ3O8uYvvLh9F6uQAeUsgJhl2PQNxWaAzwKQLRnpWEolEIgmCUbRMKpFIJBKJ5GTilsX5dPSaufXJraTFRbKyOHukp+SMIRrm3gr734Qt/4biq8CYMtKzkkgkEkkQyAysRCKRSCSSgJgxNolfXzKNN3dVc8+5E4mNGoWXFWf+GNpOQF8XnPvbkZ6NRCKRSIJEUVV1pOfgN/PmzVO3bds20tOQSCQSiUQikUgkEkkIUBRlu6qq84ZulyXEEolEIpFIJBKJRCI5KZABrEQikUgkEolEIpFITgpkACuRSCQSiUQikUgkkpMCGcBKJBKJRCKRSCQSieSkQAawEolEIpFIJBKJRCI5KZABrEQikUgkEolEIpFITgpkACuRSCQSiUQikUgkkpOCoAJYRVFSFEX5SFGUUuttsosxyxRF2TXovx5FUS617ntSUZRjg/bNCmY+EolEIpFIJBKJRCI5dQk2A3sf8ImqqkXAJ9bHTqiqulZV1Vmqqs4ClgNdwIeDhvzItl9V1V1BzkcikUgkEolEIpFIJKcowQawlwCrrfdXA5d6GX8l8J6qql1BPq9EIpFIJBKJRCKRSL5iBBvAZqqqWmO9Xwtkehl/LfD8kG2/UxRlj6IoDyuKEuXuQEVRblcUZZuiKNsaGhqCmLJEIpFIJBKJRCKRSE5GvAawiqJ8rCjKXhf/XTJ4nKqqKqB6OE82UAx8MGjzT4DJwHwgBbjX3fGqqj6qquo8VVXnpaene5u2RCKRSCQSiUQikUhOMSK8DVBV9Wx3+xRFqVMUJVtV1RprgFrv4VRXA6+rqto/6Ny27G2voij/BX7o47wlEolEIpFIJBKJRPIVI9gS4jXAKuv9VcCbHsZex5DyYWvQi6IoCqJ/dm+Q85FIJBKJRCKRSCQSySlKsAHsH4BzFEUpBc62PkZRlHmKojxuG6QoSj4wDvh8yPHPKopSApQAacBvg5yPRCKRSCQSiUQikUhOUbyWEHtCVdUmYIWL7duA2wY9LgdyXIxbHszzSyQSiUQikUgkEonkq0OwGViJRCKRSCQSiUQikUjCggxgJRKJRCKRSCQSiURyUiADWIlEIpH8//buJkSvswzj+P8iY9BWsGqh2iTYgEEZBKmU0loQabtIVUwVFQtKkEI3VusHaNSFWwXxY1GEUKsBS2uJLQ0ajFoL3YVqK5g0BoeozaSpqa31axNDbxfvKQwx6Rje4X3eZ87/t5n3PHNIrsVNJtec55wjSZLUBQusJEmSJKkLFlhJkiRJUhcssJIkSZKkLlhgJUmSJEldsMBKkiRJkrpggZUkSZIkdcECK0mSJEnqggVWkiRJktQFC6wkSZIkqQsWWEmSJElSFyywkiRJkqQuWGAlSZIkSV2wwEqSJEmSumCBlSRJkiR1wQIrSZIkSeqCBVaSJEmS1AULrCRJkiSpCxZYSZIkSVIXLLCSJEmSpC5YYCVJkiRJXbDASpIkSZK6YIGVJEmSJHXBAitJkiRJ6oIFVpIkSZLUBQusJEmSJKkLFlhJkiRJUhcssJIkSZKkLlhgJUmSJEldmKrAJvlwksNJXkxy1cuctz3J0SRLSXatWN+a5OCw/qMkG6fJI0mSJElav6a9AnsI+CDw6PlOSLIBuBO4CVgEbkmyOHz768C3qurNwN+AW6fMI0mSJElap6YqsFV1pKqOrnLa1cBSVR2rqtPAfcCOJAGuB/YO5+0Bbp4mjyRJkiRp/ZrFPbCbgOMrjpeHtdcDL1TVmbPWJUmSJEn6HwurnZDkl8AbzvGtr1TVQ2sf6bw5bgNuGw7/lWS1K78tXQr8tXUICWdR88E51DxwDjUvnEXNgx7m8E3nWly1wFbVjVP+xSeALSuONw9rzwGXJFkYrsK+tH6+HLuB3VNmmYkkv66q8z7USpoVZ1HzwDnUPHAONS+cRc2DnudwFluIHwO2DU8c3gh8FNhXVQU8AnxoOG8nMLMrupIkSZKkvkz7Gp0PJFkGrgV+muTAsH55kv0Aw9XV24EDwBHg/qo6PPwRXwQ+l2SJyT2x35smjyRJkiRp/Vp1C/HLqaoHgQfPsf408J4Vx/uB/ec47xiTpxSvN11sddYoOIuaB86h5oFzqHnhLGoedDuHmezklSRJkiRpvs3iHlhJkiRJkqZmgV1jSbYnOZpkKcmu1nk0Pkm2JHkkyZNJDie5o3UmjVeSDUmeSPKT1lk0XkkuSbI3ye+THElybetMGp8knx1+Lh9Kcm+SV7bOpHFIcneSU0kOrVh7XZJfJPnD8PW1LTNeCAvsGkqyAbgTuAlYBG5Jstg2lUboDPD5qloErgE+6RyqoTuYPMBPauk7wM+q6q3A23EmNWNJNgGfBq6qqrcBG5i8mUOahR8A289a2wU8XFXbgIeH4y5YYNfW1cBSVR2rqtPAfcCOxpk0MlV1sqoeHz7/k8l/1Da1TaUxSrIZeC9wV+ssGq8krwHexfCmg6o6XVUvNA2lsVoAXpVkAbgIeLpxHo1EVT0KPH/W8g5gz/B5D3DzLDNNwwK7tjYBx1ccL2NxUENJrgCuBA42jqJx+jbwBeDFxjk0bluBZ4HvD9vZ70pycetQGpeqOgF8A3gKOAn8vap+3jaVRu6yqjo5fH4GuKxlmAthgZXWqSSvBn4MfKaq/tE6j8YlyfuAU1X1m9ZZNHoLwDuA71bVlcC/6WirnNaH4f7CHUx+oXI5cHGSj7VNJU3U5LU03byaxgK7tk4AW1Ycbx7WpJlK8gom5fWeqnqgdR6N0nXA+5P8icntFNcn+WHbSBqpZWC5ql7aibKXSaGVZulG4I9V9WxV/Qd4AHhn40wat78keSPA8PVU4zz/Nwvs2noM2JZka5KNTG7O39c4k0YmSZjc63Wkqr7ZOo/Gqaq+VFWbq+oKJv8W/qqqvNqgmauqZ4DjSd4yLN0APNkwksbpKeCaJBcNP6dvwIeJqa19wM7h807goYZZLshC6wDrSVWdSXI7cIDJ0+XurqrDjWNpfK4DPg78Lslvh7UvV9X+dpEkqalPAfcMv1w+BnyicR6NTFUdTLIXeJzJ2wKeAHa3TaWxSHIv8G7g0iTLwFeBrwH3J7kV+DPwkXYJL0wmW54lSZIkSZpvbiGWJEmSJHXBAitJkiRJ6oIFVpIkSZLUBQusJEmSJKkLFlhJkiRJUhcssJIkSZKkLlhgJUmSJEldsMBKkiRJkrrwX1s9PZO8hCASAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Simulator(net) as sim:\n", + " sim.run(sim_t)\n", + "\n", + "# we'll break up the output into multiple plots, just for\n", + "# display purposes\n", + "t_per_plot = 10\n", + "for i in range(sim_t // t_per_plot):\n", + " plot_slice = (sim.trange() >= t_per_plot * i) & (\n", + " sim.trange() < t_per_plot * (i + 1)\n", + " )\n", + "\n", + " plt.figure(figsize=(16, 6))\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_stim][plot_slice], label=\"input\")\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_ideal][plot_slice], label=\"ideal\")\n", + " plt.plot(sim.trange()[plot_slice], sim.data[p_out][plot_slice], label=\"output\")\n", + " if i * t_per_plot < sim_t * 0.8:\n", + " plt.title(\"Learning ON\")\n", + " else:\n", + " plt.title(\"Learning OFF\")\n", + " plt.ylim([-1, 1])\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the network is successfully learning to compute a delay.\n", + "We could use these same principles to train a network\n", + "to compute any time-varying function of some input signal,\n", + "and an LMU will always provide\n", + "an optimal representation of that input signal.\n", + "\n", + "See these other examples for some other applications of LMUs:\n", + "\n", + "- [State of the art performance on the psMNIST task using LMUs in NengoDL](\n", + "https://www.nengo.ai/nengo-dl/examples/lmu.html)\n", + "\n", + "\n", + "As well as [the original paper][paper] for more information on LMUs.\n", + "\n", + "[paper]:\n", + "https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/networks/basal-ganglia.html b/examples/networks/basal-ganglia.html new file mode 100644 index 0000000000..ebc95f95e1 --- /dev/null +++ b/examples/networks/basal-ganglia.html @@ -0,0 +1,713 @@ + + + + + + + + The basal ganglia — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

The basal ganglia

+

The basal ganglia according to Stewart 2010 is an action selector that chooses whatever action has the best “salience” or “goodness”. Its really interesting behaviour manifests itself when it interacts with the thalamus and other components of the brain, but in this example we will only show the basal ganglia’s basic behaviour. It will choose between three actions that we’ll pretend are “eating”, “sleeping” and “playing”.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+

Step 1: Create the Network

+

Here we create the basal ganglia and the action input node.

+
+
[2]:
+
+
+
+model = nengo.Network(label="Basal Ganglia")
+with model:
+    basal_ganglia = nengo.networks.BasalGanglia(dimensions=3)
+
+
+class ActionIterator:
+    def __init__(self, dimensions):
+        self.actions = np.ones(dimensions) * 0.1
+
+    def step(self, t):
+        # one action at time dominates
+        dominate = int(t % 3)
+        self.actions[:] = 0.1
+        self.actions[dominate] = 0.8
+        return self.actions
+
+
+action_iterator = ActionIterator(dimensions=3)
+
+with model:
+    actions = nengo.Node(action_iterator.step, label="actions")
+
+
+
+
+
+

Step 2: Connect the Network

+

Connect the input to the basal ganglia and connect the probes

+
+
[3]:
+
+
+
+with model:
+    nengo.Connection(actions, basal_ganglia.input, synapse=None)
+    selected_action = nengo.Probe(basal_ganglia.output, synapse=0.01)
+    input_actions = nengo.Probe(actions, synapse=0.01)
+
+
+
+
+
+

Step 3: Simulate the Network and Plot the Results

+
+
[4]:
+
+
+
+with nengo.Simulator(model) as sim:
+    # This will take a while
+    sim.run(6)
+
+
+
+
+
[5]:
+
+
+
+plt.figure()
+plt.subplot(2, 1, 1)
+plt.plot(sim.trange(), sim.data[input_actions].argmax(axis=1))
+plt.ylim(-0.1, 2.1)
+plt.xlabel("time [s]")
+plt.title("Index of actual max value")
+plt.subplot(2, 1, 2)
+plt.plot(sim.trange(), sim.data[selected_action].argmax(axis=1))
+plt.ylim(-0.1, 2.1)
+plt.xlabel("time [s]")
+plt.title("Basal ganglia selected max value")
+plt.tight_layout()
+
+
+
+
+
+
+
+../../_images/examples_networks_basal-ganglia_8_0.png +
+
+

As expected, the maximum index is found at 0, then 1, then 2 or “eating”, “sleeping”, then “playing”. Note that if you zoom in enough on the basal ganglia values, you’ll be able to see a bit of a delay between finding max values. If you read the aforementioned paper, you’ll see that this is expected and matches previous experiments.

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+ + +
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+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/networks/basal-ganglia.ipynb b/examples/networks/basal-ganglia.ipynb new file mode 100644 index 0000000000..45c0643818 --- /dev/null +++ b/examples/networks/basal-ganglia.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The basal ganglia\n", + "\n", + "The basal ganglia\n", + "according to [Stewart 2010](\n", + "http://compneuro.uwaterloo.ca/files/publications/stewart.2010.pdf)\n", + "is an action selector\n", + "that chooses whatever action has the best \"salience\" or \"goodness\".\n", + "Its really interesting behaviour manifests itself\n", + "when it interacts with the thalamus and other components of the brain,\n", + "but in this example we will only show the basal ganglia's basic behaviour.\n", + "It will choose between three actions\n", + "that we'll pretend are \"eating\", \"sleeping\" and \"playing\"." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.141750Z", + "iopub.status.busy": "2020-11-25T16:50:41.140864Z", + "iopub.status.idle": "2020-11-25T16:50:41.635135Z", + "shell.execute_reply": "2020-11-25T16:50:41.635584Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the Network\n", + "\n", + "Here we create the basal ganglia and the action input node." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.644344Z", + "iopub.status.busy": "2020-11-25T16:50:41.642908Z", + "iopub.status.idle": "2020-11-25T16:50:41.819549Z", + "shell.execute_reply": "2020-11-25T16:50:41.820069Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Basal Ganglia\")\n", + "with model:\n", + " basal_ganglia = nengo.networks.BasalGanglia(dimensions=3)\n", + "\n", + "\n", + "class ActionIterator:\n", + " def __init__(self, dimensions):\n", + " self.actions = np.ones(dimensions) * 0.1\n", + "\n", + " def step(self, t):\n", + " # one action at time dominates\n", + " dominate = int(t % 3)\n", + " self.actions[:] = 0.1\n", + " self.actions[dominate] = 0.8\n", + " return self.actions\n", + "\n", + "\n", + "action_iterator = ActionIterator(dimensions=3)\n", + "\n", + "with model:\n", + " actions = nengo.Node(action_iterator.step, label=\"actions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Connect the Network\n", + "\n", + "Connect the input to the basal ganglia and connect the probes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.826012Z", + "iopub.status.busy": "2020-11-25T16:50:41.825510Z", + "iopub.status.idle": "2020-11-25T16:50:41.828561Z", + "shell.execute_reply": "2020-11-25T16:50:41.828956Z" + } + }, + "outputs": [], + "source": [ + "with model:\n", + " nengo.Connection(actions, basal_ganglia.input, synapse=None)\n", + " selected_action = nengo.Probe(basal_ganglia.output, synapse=0.01)\n", + " input_actions = nengo.Probe(actions, synapse=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Simulate the Network and Plot the Results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:41.836860Z", + "iopub.status.busy": "2020-11-25T16:50:41.836007Z", + "iopub.status.idle": "2020-11-25T16:50:45.393115Z", + "shell.execute_reply": "2020-11-25T16:50:45.392223Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " # This will take a while\n", + " sim.run(6)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:45.415990Z", + "iopub.status.busy": "2020-11-25T16:50:45.408677Z", + "iopub.status.idle": "2020-11-25T16:50:45.692484Z", + "shell.execute_reply": "2020-11-25T16:50:45.692010Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[input_actions].argmax(axis=1))\n", + "plt.ylim(-0.1, 2.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.title(\"Index of actual max value\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[selected_action].argmax(axis=1))\n", + "plt.ylim(-0.1, 2.1)\n", + "plt.xlabel(\"time [s]\")\n", + "plt.title(\"Basal ganglia selected max value\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the maximum index\n", + "is found at 0, then 1, then 2\n", + "or \"eating\", \"sleeping\", then \"playing\".\n", + "Note that if you zoom in enough on the basal ganglia values,\n", + "you'll be able to see a bit of a delay between finding max values.\n", + "If you read the aforementioned paper,\n", + "you'll see that this is expected and matches previous experiments." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/networks/basal_ganglia.html b/examples/networks/basal_ganglia.html new file mode 100644 index 0000000000..72e949ae65 --- /dev/null +++ b/examples/networks/basal_ganglia.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/networks/ensemble-array.html b/examples/networks/ensemble-array.html new file mode 100644 index 0000000000..90885a057e --- /dev/null +++ b/examples/networks/ensemble-array.html @@ -0,0 +1,693 @@ + + + + + + + + Ensemble array — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Ensemble array

+

An ensemble array is a group of ensembles that each represent a part of the overall signal.

+

Ensemble arrays are similar to normal ensembles, but expose a slightly different interface. Additionally, in an ensemble array, the components of the overall signal are not related. As a result, network arrays cannot be used to compute nonlinear functions that mix the dimensions they represent.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+
+import nengo
+
+
+
+
+
[2]:
+
+
+
+model = nengo.Network(label="Ensemble Array")
+with model:
+    # Make an input node
+    sin = nengo.Node(output=lambda t: [np.cos(t), np.sin(t)])
+
+    # Make ensembles to connect
+    A = nengo.networks.EnsembleArray(100, n_ensembles=2)
+    B = nengo.Ensemble(100, dimensions=2)
+    C = nengo.networks.EnsembleArray(100, n_ensembles=2)
+
+    # Connect the model elements, just feedforward
+    nengo.Connection(sin, A.input)
+    nengo.Connection(A.output, B)
+    nengo.Connection(B, C.input)
+
+    # Setup the probes for plotting
+    sin_probe = nengo.Probe(sin)
+    A_probe = nengo.Probe(A.output, synapse=0.02)
+    B_probe = nengo.Probe(B, synapse=0.02)
+    C_probe = nengo.Probe(C.output, synapse=0.02)
+
+
+
+
+
[3]:
+
+
+
+with nengo.Simulator(model) as sim:
+    sim.run(10)
+
+
+
+
+
[4]:
+
+
+
+# Plot the results
+plt.figure()
+plt.plot(sim.trange(), sim.data[sin_probe])
+plt.plot(sim.trange(), sim.data[A_probe])
+plt.plot(sim.trange(), sim.data[B_probe])
+plt.plot(sim.trange(), sim.data[C_probe])
+
+
+
+
+
[4]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f55d3f33940>,
+ <matplotlib.lines.Line2D at 0x7f55d1ef46d8>]
+
+
+
+
+
+
+../../_images/examples_networks_ensemble-array_4_1.png +
+
+

These plots demonstrate that the ensemble array works very similarly to a standard N-dimensional population. However, this is not true when it comes to computing functions. Ensemble arrays cannot be used to compute nonlinear functions that mix the dimensions they represent.

+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/networks/ensemble-array.ipynb b/examples/networks/ensemble-array.ipynb new file mode 100644 index 0000000000..727fed3e2e --- /dev/null +++ b/examples/networks/ensemble-array.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ensemble array\n", + "\n", + "An ensemble array is a group of ensembles\n", + "that each represent a part of the overall signal.\n", + "\n", + "Ensemble arrays are similar to normal ensembles,\n", + "but expose a slightly different interface.\n", + "Additionally, in an ensemble array,\n", + "the components of the overall signal are not related.\n", + "As a result, network arrays cannot be used\n", + "to compute nonlinear functions that mix the dimensions they represent." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:47.036723Z", + "iopub.status.busy": "2020-11-25T16:50:47.034583Z", + "iopub.status.idle": "2020-11-25T16:50:47.528328Z", + "shell.execute_reply": "2020-11-25T16:50:47.527819Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:47.546842Z", + "iopub.status.busy": "2020-11-25T16:50:47.546314Z", + "iopub.status.idle": "2020-11-25T16:50:47.549499Z", + "shell.execute_reply": "2020-11-25T16:50:47.549895Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Ensemble Array\")\n", + "with model:\n", + " # Make an input node\n", + " sin = nengo.Node(output=lambda t: [np.cos(t), np.sin(t)])\n", + "\n", + " # Make ensembles to connect\n", + " A = nengo.networks.EnsembleArray(100, n_ensembles=2)\n", + " B = nengo.Ensemble(100, dimensions=2)\n", + " C = nengo.networks.EnsembleArray(100, n_ensembles=2)\n", + "\n", + " # Connect the model elements, just feedforward\n", + " nengo.Connection(sin, A.input)\n", + " nengo.Connection(A.output, B)\n", + " nengo.Connection(B, C.input)\n", + "\n", + " # Setup the probes for plotting\n", + " sin_probe = nengo.Probe(sin)\n", + " A_probe = nengo.Probe(A.output, synapse=0.02)\n", + " B_probe = nengo.Probe(B, synapse=0.02)\n", + " C_probe = nengo.Probe(C.output, synapse=0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:47.555746Z", + "iopub.status.busy": "2020-11-25T16:50:47.554945Z", + "iopub.status.idle": "2020-11-25T16:50:50.730511Z", + "shell.execute_reply": "2020-11-25T16:50:50.730951Z" + } + }, + "outputs": [], + "source": [ + "with nengo.Simulator(model) as sim:\n", + " sim.run(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:50.737109Z", + "iopub.status.busy": "2020-11-25T16:50:50.736321Z", + "iopub.status.idle": "2020-11-25T16:50:50.940064Z", + "shell.execute_reply": "2020-11-25T16:50:50.939434Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[sin_probe])\n", + "plt.plot(sim.trange(), sim.data[A_probe])\n", + "plt.plot(sim.trange(), sim.data[B_probe])\n", + "plt.plot(sim.trange(), sim.data[C_probe])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots demonstrate that the ensemble array\n", + "works very similarly to a standard N-dimensional population.\n", + "However, this is not true when it comes to computing functions.\n", + "Ensemble arrays cannot be used\n", + "to compute nonlinear functions that mix the dimensions they represent." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/networks/ensemble_array.html b/examples/networks/ensemble_array.html new file mode 100644 index 0000000000..252cace726 --- /dev/null +++ b/examples/networks/ensemble_array.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/networks/integrator-network.html b/examples/networks/integrator-network.html new file mode 100644 index 0000000000..86ef998b0a --- /dev/null +++ b/examples/networks/integrator-network.html @@ -0,0 +1,730 @@ + + + + + + + + Integrator — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Integrator

+

This demo implements a one-dimensional neural integrator.

+

This is the first example of a recurrent network in the demos. It shows how neurons can be used to implement stable dynamics. Such dynamics are important for memory, noise cleanup, statistical inference, and many other dynamic transformations.

+

When you run this demo, it will automatically put in some step functions on the input, so you can see that the output is integrating (i.e. summing over time) the input. You can also input your own values. Note that since the integrator constantly sums its input, it will saturate quickly if you leave the input non-zero. This makes it clear that neurons have a finite range of representation. Such saturation effects can be exploited to perform useful computations (e.g. soft normalization).

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.processes import Piecewise
+
+
+
+
+

Step 1: Create the neural populations

+

Our model consists of one recurrently connected ensemble, and an input population.

+
+
[2]:
+
+
+
+tau = 0.1
+
+integrator = nengo.networks.Integrator(tau, n_neurons=100, dimensions=1)
+
+
+
+
+
+

Step 2: Create input for the model

+

We will use a piecewise step function as input, so we can see the effects of recurrence.

+
+
[3]:
+
+
+
+with integrator:
+    input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))
+
+
+
+
+
+

Step 3: Connect the network elements

+
+
[4]:
+
+
+
+# Connect the input
+with integrator:
+    nengo.Connection(input, integrator.input, synapse=tau)
+
+
+
+
+
+

Step 4: Probe outputs

+

Anything that is probed will collect the data it produces over time, allowing us to analyze and visualize it later.

+
+
[5]:
+
+
+
+with integrator:
+    input_probe = nengo.Probe(input)
+    integrator_probe = nengo.Probe(integrator.ensemble, synapse=0.01)  # 10ms filter
+
+
+
+
+
+

Step 5: Run the model

+
+
[6]:
+
+
+
+# Create our simulator
+with nengo.Simulator(integrator) as sim:
+    # Run it for 6 seconds
+    sim.run(6)
+
+
+
+
+
+

Step 6: Plot the results

+
+
[7]:
+
+
+
+# Plot the decoded output of the ensemble
+plt.figure()
+plt.plot(sim.trange(), sim.data[integrator_probe], label="A output")
+plt.plot(sim.trange(), sim.data[input_probe], "k", label="Input")
+plt.legend(loc="best")
+
+
+
+
+
[7]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f95758cd668>
+
+
+
+
+
+
+../../_images/examples_networks_integrator-network_13_1.png +
+
+

The graph shows the response to the input by the integrator. Because it is implemented in neurons, it will not be perfect (i.e. there will be drift). Running several times will give a sense of the kinds of drift you might expect. Drift can be reduced by increasing the number of neurons.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/networks/integrator-network.ipynb b/examples/networks/integrator-network.ipynb new file mode 100644 index 0000000000..78ab3b0616 --- /dev/null +++ b/examples/networks/integrator-network.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Integrator\n", + "\n", + "This demo implements a one-dimensional neural integrator.\n", + "\n", + "This is the first example of a recurrent network in the demos.\n", + "It shows how neurons can be used to implement stable dynamics.\n", + "Such dynamics are important for memory, noise cleanup,\n", + "statistical inference, and many other dynamic transformations.\n", + "\n", + "When you run this demo, it will automatically\n", + "put in some step functions on the input,\n", + "so you can see that the output is integrating\n", + "(i.e. summing over time) the input.\n", + "You can also input your own values.\n", + "Note that since the integrator constantly sums its input,\n", + "it will saturate quickly if you leave the input non-zero.\n", + "This makes it clear that neurons have a finite range of representation.\n", + "Such saturation effects can be exploited\n", + "to perform useful computations (e.g. soft normalization)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.105687Z", + "iopub.status.busy": "2020-11-25T16:50:52.104804Z", + "iopub.status.idle": "2020-11-25T16:50:52.598006Z", + "shell.execute_reply": "2020-11-25T16:50:52.597511Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import Piecewise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Create the neural populations\n", + "\n", + "Our model consists of one recurrently connected ensemble,\n", + "and an input population." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.605371Z", + "iopub.status.busy": "2020-11-25T16:50:52.604866Z", + "iopub.status.idle": "2020-11-25T16:50:52.608053Z", + "shell.execute_reply": "2020-11-25T16:50:52.608452Z" + } + }, + "outputs": [], + "source": [ + "tau = 0.1\n", + "\n", + "integrator = nengo.networks.Integrator(tau, n_neurons=100, dimensions=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create input for the model\n", + "\n", + "We will use a piecewise step function as input,\n", + "so we can see the effects of recurrence." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.614140Z", + "iopub.status.busy": "2020-11-25T16:50:52.612656Z", + "iopub.status.idle": "2020-11-25T16:50:52.614776Z", + "shell.execute_reply": "2020-11-25T16:50:52.615188Z" + } + }, + "outputs": [], + "source": [ + "with integrator:\n", + " input = nengo.Node(Piecewise({0: 0, 0.2: 1, 1: 0, 2: -2, 3: 0, 4: 1, 5: 0}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Connect the network elements" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.620954Z", + "iopub.status.busy": "2020-11-25T16:50:52.619108Z", + "iopub.status.idle": "2020-11-25T16:50:52.621486Z", + "shell.execute_reply": "2020-11-25T16:50:52.621898Z" + } + }, + "outputs": [], + "source": [ + "# Connect the input\n", + "with integrator:\n", + " nengo.Connection(input, integrator.input, synapse=tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Probe outputs\n", + "\n", + "Anything that is probed will collect the data it produces over time,\n", + "allowing us to analyze and visualize it later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.627541Z", + "iopub.status.busy": "2020-11-25T16:50:52.626065Z", + "iopub.status.idle": "2020-11-25T16:50:52.628176Z", + "shell.execute_reply": "2020-11-25T16:50:52.628578Z" + } + }, + "outputs": [], + "source": [ + "with integrator:\n", + " input_probe = nengo.Probe(input)\n", + " integrator_probe = nengo.Probe(integrator.ensemble, synapse=0.01) # 10ms filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:52.633661Z", + "iopub.status.busy": "2020-11-25T16:50:52.632900Z", + "iopub.status.idle": "2020-11-25T16:50:53.414720Z", + "shell.execute_reply": "2020-11-25T16:50:53.415146Z" + } + }, + "outputs": [], + "source": [ + "# Create our simulator\n", + "with nengo.Simulator(integrator) as sim:\n", + " # Run it for 6 seconds\n", + " sim.run(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:53.421312Z", + "iopub.status.busy": "2020-11-25T16:50:53.420138Z", + "iopub.status.idle": "2020-11-25T16:50:53.633156Z", + "shell.execute_reply": "2020-11-25T16:50:53.632721Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decoded output of the ensemble\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[integrator_probe], label=\"A output\")\n", + "plt.plot(sim.trange(), sim.data[input_probe], \"k\", label=\"Input\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph shows the response to the input by the integrator.\n", + "Because it is implemented in neurons,\n", + "it will not be perfect (i.e. there will be drift).\n", + "Running several times will give a sense of\n", + "the kinds of drift you might expect.\n", + "Drift can be reduced by increasing the number of neurons." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/networks/integrator_network.html b/examples/networks/integrator_network.html new file mode 100644 index 0000000000..b2b47460c1 --- /dev/null +++ b/examples/networks/integrator_network.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/quirks/config.html b/examples/quirks/config.html new file mode 100644 index 0000000000..e56a3dafce --- /dev/null +++ b/examples/quirks/config.html @@ -0,0 +1,803 @@ + + + + + + + + Context matters, membership doesn’t — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Context matters, membership doesn’t

+

When you create a Nengo object and leave the parameters as their default values, we determine the default values dynamically based on Config objects. Every Network has an associated Config object, and you can make new Config objects for additional flexibility.

+

Nengo keeps track of the current context. The following example works based on the context.

+
+
[1]:
+
+
+
+import nengo
+
+
+
+
+
[2]:
+
+
+
+with nengo.Network() as model:
+    model.config[nengo.Ensemble].radius = 2.0
+    subnet = nengo.Network()
+
+    with subnet:
+        a = nengo.Ensemble(10, 1)
+        print(a.radius)
+
+
+
+
+
+
+
+
+2.0
+
+
+

The radius of a is 2.0 because the current context includes both subnet and model. subnet does not change the default value of the radius, but model does, so it uses the model default of 2.0.

+

Here’s a similar example.

+
+
[3]:
+
+
+
+with nengo.Network() as model:
+    model.config[nengo.Ensemble].radius = 2.0
+    subnet = nengo.Network()
+
+with subnet:
+    a = nengo.Ensemble(10, 1)
+    print(a.radius)
+
+
+
+
+
+
+
+
+1.0
+
+
+

While this example looks nearly identical, the difference is that a is created in the context of subnet only; model is not part of the config context. Because of that, when a is created, Nengo sees that no default is set in subnet and uses the global default value of 1.0.

+

This may seem counterintuitive since subnet is a member of model; it’s stored as a sub-network of the model network. However, Nengo objects are not aware of their parents. This allows subnet to be used the same way whether it’s the top-level network or whether it’s nested twenty layers deep, but it also means that we can’t set defaults based on network membership.

+
+

Context details

+

The configuration context is stored in the nengo.Config class as nengo.Config.context. context is a thread-local list. We add new contexts to the end of that list at the start of with blocks, and pop contexts off of that list at the end of with blocks.

+
+
[4]:
+
+
+
+# No context
+len(nengo.Config.context)
+
+
+
+
+
[4]:
+
+
+
+
+0
+
+
+
+
[5]:
+
+
+
+# Model context
+with model:
+    print(len(nengo.Config.context))
+    print(nengo.Config.context[0] is model.config)
+
+
+
+
+
+
+
+
+1
+True
+
+
+
+
[6]:
+
+
+
+# Subnet, Model context
+with model:
+    with subnet:
+        print(len(nengo.Config.context))
+        print(nengo.Config.context[0] is model.config)
+        print(nengo.Config.context[1] is subnet.config)
+
+
+
+
+
+
+
+
+2
+True
+True
+
+
+

If you are not sure what context you’re in, but you want to know what the defaults are in the current context, use nengo.Config.all_defaults. You can optionally pass in the type that you’re interested in.

+
+
[7]:
+
+
+
+with model:
+    with subnet:
+        print(nengo.Config.all_defaults(nengo.Ensemble))
+
+
+
+
+
+
+
+
+Current defaults for Ensemble:
+  bias: None
+  encoders: ScatteredHypersphere(surface=True)
+  eval_points: ScatteredHypersphere()
+  gain: None
+  intercepts: Uniform(low=-1.0, high=0.9)
+  label: None
+  max_rates: Uniform(low=200, high=400)
+  n_eval_points: None
+  neuron_type: LIF()
+  noise: None
+  normalize_encoders: True
+  radius: 2.0
+  seed: None
+
+
+
+
[8]:
+
+
+
+with subnet:
+    print(nengo.Config.all_defaults(nengo.Ensemble))
+
+
+
+
+
+
+
+
+Current defaults for Ensemble:
+  bias: None
+  encoders: ScatteredHypersphere(surface=True)
+  eval_points: ScatteredHypersphere()
+  gain: None
+  intercepts: Uniform(low=-1.0, high=0.9)
+  label: None
+  max_rates: Uniform(low=200, high=400)
+  n_eval_points: None
+  neuron_type: LIF()
+  noise: None
+  normalize_encoders: True
+  radius: 1.0
+  seed: None
+
+
+

Note above that the radius changes despite the fact that both situations occur in the context of subnet! Configuration outside matters if no default has been set in the immediate context.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/quirks/config.ipynb b/examples/quirks/config.ipynb new file mode 100644 index 0000000000..36b3077cac --- /dev/null +++ b/examples/quirks/config.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context matters, membership doesn't\n", + "\n", + "When you create a Nengo object\n", + "and leave the parameters as their default values,\n", + "we determine the default values dynamically\n", + "based on `Config` objects.\n", + "Every `Network` has an associated `Config` object,\n", + "and you can make new `Config` objects for additional flexibility.\n", + "\n", + "Nengo keeps track of the current context.\n", + "The following example works based on the context." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:54.960374Z", + "iopub.status.busy": "2020-11-25T16:50:54.959462Z", + "iopub.status.idle": "2020-11-25T16:50:55.274081Z", + "shell.execute_reply": "2020-11-25T16:50:55.274520Z" + } + }, + "outputs": [], + "source": [ + "import nengo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.280607Z", + "iopub.status.busy": "2020-11-25T16:50:55.280025Z", + "iopub.status.idle": "2020-11-25T16:50:55.284376Z", + "shell.execute_reply": "2020-11-25T16:50:55.283674Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0\n" + ] + } + ], + "source": [ + "with nengo.Network() as model:\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " subnet = nengo.Network()\n", + "\n", + " with subnet:\n", + " a = nengo.Ensemble(10, 1)\n", + " print(a.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The radius of `a` is `2.0`\n", + "because the current context includes both\n", + "`subnet` and `model`.\n", + "`subnet` does not change the default value of the radius,\n", + "but `model` does, so it uses the `model` default of `2.0`.\n", + "\n", + "Here's a similar example." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.291687Z", + "iopub.status.busy": "2020-11-25T16:50:55.290070Z", + "iopub.status.idle": "2020-11-25T16:50:55.293362Z", + "shell.execute_reply": "2020-11-25T16:50:55.292916Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "with nengo.Network() as model:\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " subnet = nengo.Network()\n", + "\n", + "with subnet:\n", + " a = nengo.Ensemble(10, 1)\n", + " print(a.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this example looks nearly identical,\n", + "the difference is that `a` is created\n", + "in the context of `subnet` only;\n", + "`model` is not part of the config context.\n", + "Because of that, when `a` is created,\n", + "Nengo sees that no default is set in `subnet`\n", + "and uses the global default value of `1.0`.\n", + "\n", + "This may seem counterintuitive\n", + "since `subnet` is a member of `model`;\n", + "it's stored as a sub-network of the `model` network.\n", + "However, Nengo objects are not aware of their parents.\n", + "This allows `subnet` to be used the same way\n", + "whether it's the top-level network\n", + "or whether it's nested twenty layers deep,\n", + "but it also means that we can't set defaults\n", + "based on network membership." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Context details\n", + "\n", + "The configuration context is stored in the\n", + "`nengo.Config` class as `nengo.Config.context`.\n", + "`context` is a thread-local list.\n", + "We add new contexts to the end of that list\n", + "at the start of `with` blocks,\n", + "and pop contexts off of that list\n", + "at the end of `with` blocks." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.302961Z", + "iopub.status.busy": "2020-11-25T16:50:55.301762Z", + "iopub.status.idle": "2020-11-25T16:50:55.305358Z", + "shell.execute_reply": "2020-11-25T16:50:55.305766Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# No context\n", + "len(nengo.Config.context)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.311374Z", + "iopub.status.busy": "2020-11-25T16:50:55.309874Z", + "iopub.status.idle": "2020-11-25T16:50:55.313043Z", + "shell.execute_reply": "2020-11-25T16:50:55.312609Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "True\n" + ] + } + ], + "source": [ + "# Model context\n", + "with model:\n", + " print(len(nengo.Config.context))\n", + " print(nengo.Config.context[0] is model.config)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.319110Z", + "iopub.status.busy": "2020-11-25T16:50:55.317617Z", + "iopub.status.idle": "2020-11-25T16:50:55.320773Z", + "shell.execute_reply": "2020-11-25T16:50:55.320352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "# Subnet, Model context\n", + "with model:\n", + " with subnet:\n", + " print(len(nengo.Config.context))\n", + " print(nengo.Config.context[0] is model.config)\n", + " print(nengo.Config.context[1] is subnet.config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are not sure what context you're in,\n", + "but you want to know what the defaults are\n", + "in the current context,\n", + "use `nengo.Config.all_defaults`.\n", + "You can optionally pass in the type that you're interested in." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.326818Z", + "iopub.status.busy": "2020-11-25T16:50:55.325287Z", + "iopub.status.idle": "2020-11-25T16:50:55.328512Z", + "shell.execute_reply": "2020-11-25T16:50:55.328091Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIF()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 2.0\n", + " seed: None\n" + ] + } + ], + "source": [ + "with model:\n", + " with subnet:\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:55.333738Z", + "iopub.status.busy": "2020-11-25T16:50:55.332323Z", + "iopub.status.idle": "2020-11-25T16:50:55.335436Z", + "shell.execute_reply": "2020-11-25T16:50:55.334997Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIF()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 1.0\n", + " seed: None\n" + ] + } + ], + "source": [ + "with subnet:\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note above that the radius changes\n", + "despite the fact that both situations occur\n", + "in the context of `subnet`!\n", + "Configuration outside matters if no default\n", + "has been set in the immediate context." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/config.html b/examples/usage/config.html new file mode 100644 index 0000000000..a5fb6c1251 --- /dev/null +++ b/examples/usage/config.html @@ -0,0 +1,1294 @@ + + + + + + + + The config system — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

The config system

+

Nengo objects have many parameters that can be modified. Some of these parameters are critical characteristics of that object, and others are hints or suggestions that a backend can use or ignore.

+

Nengo’s config system is designed to make setting large numbers of parameters easy, and to allow backends to introduce additional parameters without changing core Nengo objects.

+
+
[1]:
+
+
+
+import nengo
+import nengo.params
+from nengo.utils.ipython import hide_input
+
+hide_input()
+
+
+
+
+
[1]:
+
+
+
+Show Input + +
+
+
+

Setting default parameters

+

The config system aids in setting many parameters with a hierarchy of defaults. When you create a Nengo object, any parameters not specified will be given the value nengo.Default. This value tells Nengo to use the default value with the highest precedence in the hierarchy. Every Network has an associated config object, on which defaults can be set. The network hierarchy is traversed from most to least specific and the first network with a default set for that particular +parameter is used. For example:

+
with nengo.Network() as net:
+    with nengo.Network() as subnet:
+        with nengo.Network() as subsubnet:
+            ens = nengo.Ensemble(10, 1)
+
+
+

When filling in defaults for ens, the hierarchy looks like

+
└── net                <- least specific
+    └── subnet
+        └── subsubnet  <- most specific
+
+
+

so defaults set in subsubnet will take precedence over those in subnet, which take precedence over those in net.

+

If no default has been set in the network hierarchy, then the parameter default is used. These defaults are specified when the Nengo objects are created. We can investigate these defaults by printing the class attributes associated with them.

+
+
[2]:
+
+
+
+# Get all info about the radius
+print(nengo.Ensemble.radius)
+# Just get the default
+print(nengo.Ensemble.radius.default)
+
+
+
+
+
+
+
+
+NumberParam('radius', default=1.0, optional=False, readonly=False)
+1.0
+
+
+

We can inspect which defaults have been overridden in a particular config object by printing it.

+
+
[3]:
+
+
+
+model = nengo.Network()
+print(model.config)
+
+
+
+
+
+
+
+
+No parameters configured for Connection.
+No parameters configured for Ensemble.
+No parameters configured for Node.
+No parameters configured for Probe.
+
+
+

To configure a parameter (i.e., change its network-local default), set it as shown below.

+
+
[4]:
+
+
+
+model.config[nengo.Ensemble].radius = 1.5
+print(model.config[nengo.Ensemble])
+
+
+
+
+
+
+
+
+Parameters configured for Ensemble:
+  radius: 1.5
+
+
+

Within this network, the default radius will be 1.5.

+
+
[5]:
+
+
+
+with nengo.Network():
+    ens = nengo.Ensemble(10, 1)
+print("Normal network: ens.radius = %s" % ens.radius)
+
+with model:
+    ens = nengo.Ensemble(10, 1)
+print("Configured network: ens.radius = %s" % ens.radius)
+
+
+
+
+
+
+
+
+Normal network: ens.radius = 1.0
+Configured network: ens.radius = 1.5
+
+
+

Note that if a radius is explicitly passed in, it will always be used.

+
+
[6]:
+
+
+
+with nengo.Network():
+    ens = nengo.Ensemble(10, 1, radius=2.0)
+print("Normal network: ens.radius = %s" % ens.radius)
+
+with model:
+    ens = nengo.Ensemble(10, 1, radius=2.0)
+print("Configured network: ens.radius = %s" % ens.radius)
+
+
+
+
+
+
+
+
+Normal network: ens.radius = 2.0
+Configured network: ens.radius = 2.0
+
+
+

When networks are nested within one another, the most specific network configuration is used. For example, if you create an Ensemble without specifying a radius, it will first check the network that the Ensemble is a part of; if that network has not configured a default, then it will check the network that that network is part of, and so on.

+
+
[7]:
+
+
+
+with model:
+
+    with nengo.Network() as subnet:
+        subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
+
+        with nengo.Network() as subsubnet:
+            subsubnet.config[nengo.Ensemble].radius = 2.0
+            print("Creating e1 in subsubnet")
+            e1 = nengo.Ensemble(10, 1)
+            # Uses subsubnet.config value for radius
+            print("  radius =", e1.radius)
+            # Uses subnet.config value for neuron_type
+            print("  neuron_type =", e1.neuron_type)
+
+        print("Creating e2 in subnet")
+        e2 = nengo.Ensemble(10, 1)
+        # Uses model.config value for radius
+        print("  radius =", e2.radius)
+        # Uses subnet.config value for neuron_type
+        print("  neuron_type =", e2.neuron_type)
+
+    print("Creating e3 in model")
+    e3 = nengo.Ensemble(10, 1)
+    # Uses model.config value for radius
+    print("  radius =", e3.radius)
+    # Uses nengo.Ensemble default for neuron_type
+    print("  neuron_type =", e3.neuron_type)
+
+
+
+
+
+
+
+
+Creating e1 in subsubnet
+  radius = 2.0
+  neuron_type = LIFRate()
+Creating e2 in subnet
+  radius = 1.5
+  neuron_type = LIFRate()
+Creating e3 in model
+  radius = 1.5
+  neuron_type = LIF()
+
+
+

Note that each config object only knows about the defaults set on itself.

+
+
[8]:
+
+
+
+with model:
+    with nengo.Network() as subnet:
+        subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
+        with nengo.Network() as subsubnet:
+            subsubnet.config[nengo.Ensemble].radius = 2.0
+print("subsubnet:")
+print(subsubnet.config[nengo.Ensemble])
+print("\nsubnet:")
+print(subnet.config[nengo.Ensemble])
+print("\nmodel:")
+print(model.config[nengo.Ensemble])
+
+
+
+
+
+
+
+
+subsubnet:
+Parameters configured for Ensemble:
+  radius: 2.0
+
+subnet:
+Parameters configured for Ensemble:
+  neuron_type: LIFRate()
+
+model:
+Parameters configured for Ensemble:
+  radius: 1.5
+
+
+

If you want a more global picture of the defaults in the current context, you can query the Config class itself (all config objects are instances of Config).

+

To query all parameters, print Config.all_defaults(). You may pass a Nengo object class to this function to filter the results. For example, to get all defaults set for Ensemble, use Config.all_defaults(nengo.Ensemble).

+
+
[9]:
+
+
+
+with model:
+    print("In 'model' context:")
+    print(nengo.Config.all_defaults())
+
+    with nengo.Network() as subnet:
+        subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
+        subnet.config[nengo.Ensemble].radius = 3.0
+        print("In 'subnet' context:")
+        print(nengo.Config.all_defaults(nengo.Ensemble))
+
+        with nengo.Network() as subsubnet:
+            subsubnet.config[nengo.Ensemble].neuron_type = nengo.Direct()
+            subsubnet.config[nengo.Ensemble].radius = 2.0
+            print("In 'subsubnet' context:")
+            print(nengo.Config.all_defaults(nengo.Ensemble))
+
+
+
+
+
+
+
+
+In 'model' context:
+Current defaults for Ensemble:
+  bias: None
+  encoders: ScatteredHypersphere(surface=True)
+  eval_points: ScatteredHypersphere()
+  gain: None
+  intercepts: Uniform(low=-1.0, high=0.9)
+  label: None
+  max_rates: Uniform(low=200, high=400)
+  n_eval_points: None
+  neuron_type: LIF()
+  noise: None
+  normalize_encoders: True
+  radius: 1.5
+  seed: None
+Current defaults for Probe:
+  attr: None
+  label: None
+  sample_every: None
+  seed: None
+  solver: ConnectionDefault
+  synapse: None
+Current defaults for Connection:
+  eval_points: None
+  function_info: None
+  label: None
+  learning_rule_type: None
+  scale_eval_points: True
+  seed: None
+  solver: LstsqL2()
+  synapse: Lowpass(tau=0.005)
+  transform: None
+Current defaults for Node:
+  label: None
+  output: None
+  seed: None
+  size_in: None
+  size_out: None
+In 'subnet' context:
+Current defaults for Ensemble:
+  bias: None
+  encoders: ScatteredHypersphere(surface=True)
+  eval_points: ScatteredHypersphere()
+  gain: None
+  intercepts: Uniform(low=-1.0, high=0.9)
+  label: None
+  max_rates: Uniform(low=200, high=400)
+  n_eval_points: None
+  neuron_type: LIFRate()
+  noise: None
+  normalize_encoders: True
+  radius: 3.0
+  seed: None
+In 'subsubnet' context:
+Current defaults for Ensemble:
+  bias: None
+  encoders: ScatteredHypersphere(surface=True)
+  eval_points: ScatteredHypersphere()
+  gain: None
+  intercepts: Uniform(low=-1.0, high=0.9)
+  label: None
+  max_rates: Uniform(low=200, high=400)
+  n_eval_points: None
+  neuron_type: Direct()
+  noise: None
+  normalize_encoders: True
+  radius: 2.0
+  seed: None
+
+
+

The default value for a particular parameter can be queried from the global context with the nengo.Config.default function.

+
+
[10]:
+
+
+
+help(nengo.Config.default)
+
+
+
+
+
+
+
+
+Help on function default in module nengo.config:
+
+default(nengo_cls, param)
+    Look up the current default value for a parameter.
+
+    The default is found by going through the config stack, from most
+    specific to least specific. The network that an object is in
+    is the most specific; the top-level network is the least specific.
+    If no default is found there, then the parameter's default value
+    is returned.
+
+
+
+
+
[11]:
+
+
+
+def print_defaults():
+    def_radius = nengo.Config.default(nengo.Ensemble, "radius")
+    def_type = nengo.Config.default(nengo.Ensemble, "neuron_type")
+    print("  default radius: %s" % def_radius)
+    print("  default neuron_type: %s" % def_type)
+
+
+with model:
+    with nengo.Network() as subnet:
+        subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
+        with nengo.Network() as subsubnet:
+            subsubnet.config[nengo.Ensemble].radius = 2.0
+            print("subsubnet:")
+            print_defaults()
+        print("\nsubnet:")
+        print_defaults()
+    print("\nmodel:")
+    print_defaults()
+
+
+
+
+
+
+
+
+subsubnet:
+  default radius: 2.0
+  default neuron_type: LIFRate()
+
+subnet:
+  default radius: 1.5
+  default neuron_type: LIFRate()
+
+model:
+  default radius: 1.5
+  default neuron_type: LIF()
+
+
+
+

Defaults are filled in immediately

+

One important feature about the defaults hierarchy is that defaults are filled in immediately. When you create a Nengo object, the attributes are filled in with the current defaults that are set. Changing the defaults after object creation will not update objects already created.

+
+
[12]:
+
+
+
+with model:
+    e1 = nengo.Ensemble(10, 1)
+    print("e1.radius =", e1.radius)
+    print("Changing default radius to 2.0")
+    model.config[nengo.Ensemble].radius = 2.0
+    e2 = nengo.Ensemble(10, 1)
+    print("e1.radius =", e1.radius)
+    print("e2.radius =", e2.radius)
+
+
+
+
+
+
+
+
+e1.radius = 1.5
+Changing default radius to 2.0
+e1.radius = 1.5
+e2.radius = 2.0
+
+
+
+
+

Resetting to default

+

If you ever wish to reset a value back to the default, you can remove it from the config object you modified.

+
+
[13]:
+
+
+
+with model:
+    e1 = nengo.Ensemble(10, 1)
+    print("e1.radius =", e1.radius)
+    print("Resetting radius back to default")
+    del model.config[nengo.Ensemble].radius
+    print("\n" + str(model.config[nengo.Ensemble]) + "\n")
+    e2 = nengo.Ensemble(10, 1)
+    print("e2.radius =", e2.radius)
+
+
+
+
+
+
+
+
+e1.radius = 2.0
+Resetting radius back to default
+
+No parameters configured for Ensemble.
+
+e2.radius = 1.0
+
+
+
+
+

Making new configs

+

Typically, several Nengo objects will share a set of parameters, but won’t make sense to encapsulate in a network. One method of having those objects share parameters is to use dictionary unpacking.

+
+
[14]:
+
+
+
+with nengo.Network():
+    hippocampus_args = {"radius": 1.5, "neuron_type": nengo.LIFRate()}
+    e1 = nengo.Ensemble(100, 2, **hippocampus_args)
+    e2 = nengo.Ensemble(150, 3, **hippocampus_args)
+    e3 = nengo.Ensemble(200, 4, **hippocampus_args)
+print(e1.radius, e2.radius, e3.radius)
+
+
+
+
+
+
+
+
+1.5 1.5 1.5
+
+
+

An alternative method that can be very useful for large networks and for more readable models is to create a new config object to encapsulate those parameter settings.

+
+
[15]:
+
+
+
+in_hippocampus = nengo.Config(nengo.Ensemble)
+in_hippocampus[nengo.Ensemble].radius = 1.5
+in_hippocampus[nengo.Ensemble].neuron_type = nengo.LIFRate()
+
+with nengo.Network():
+    with in_hippocampus:
+        e1 = nengo.Ensemble(100, 2)
+        e2 = nengo.Ensemble(150, 3)
+        e3 = nengo.Ensemble(200, 4)
+print(e1.radius, e2.radius, e3.radius)
+
+
+
+
+
+
+
+
+1.5 1.5 1.5
+
+
+
+
+
+

Advanced: adding new parameters

+

This section is targeted to those implementing new backends or large libraries of networks (like, for example, nengo.SPA).

+

Often, you want to associate some kind of metadata with a Nengo object, or a type of Nengo objects. For example, in backends that communicate with specific hardware, it can be helpful to mark certain nodes as being time-dependent, or to assign certain ensembles to a particular portion of the hardware memory.

+

Python allows us to make new attributes on Nengo objects. However, we highly discourage this activity, because a Nengo object should be a backend-agnostic part of a model. The parameters pre-defined on Nengo objects make up the parameters that all backends should deal with in some way.

+

For this reason, we raise a warning when creating a new attribute.

+
+
[16]:
+
+
+
+with nengo.Network():
+    ens = nengo.Ensemble(10, 1)
+    ens.memory_location = 0x1000
+
+
+
+
+
+
+
+
+/home/travis/build/nengo/nengo/nengo/base.py:106: SyntaxWarning: Creating new attribute 'memory_location' on '<Ensemble (unlabeled) at 0x7f4427818b38>'. Did you mean to change an existing attribute?
+  SyntaxWarning,
+
+
+

So how should backends associate arbitrary information with Nengo objects? The config system!

+

We saw above that we can create new config objects by specifying which Nengo objects they can configure. We can also create new parameters on those config objects.

+
+
[17]:
+
+
+
+my_config = nengo.Config(nengo.Ensemble)
+# memory_location must be a positive integer
+my_config[nengo.Ensemble].set_param(
+    "memory_location",
+    nengo.params.IntParam("memory_location", default=None, optional=True, low=0),
+)
+
+
+
+

Now, we can set that parameter for the nengo.Ensemble class as a whole, or with individual instances.

+
+
[18]:
+
+
+
+# Make the network (this code is backend-agnostic)
+with nengo.Network():
+    e1 = nengo.Ensemble(10, 1)
+    e2 = nengo.Ensemble(10, 1)
+
+# Set backend-specific parameters
+my_config[nengo.Ensemble].memory_location = 0x1000  # Set Ensemble default
+my_config[e2].memory_location = 0x2000  # Set value for e2
+
+print("e1 will be stored at 0x%x" % my_config[e1].memory_location)
+print("e2 will be stored at 0x%x" % my_config[e2].memory_location)
+
+
+
+
+
+
+
+
+e1 will be stored at 0x1000
+e2 will be stored at 0x2000
+
+
+

Parameter types for the most common Python objects are available in nengo.params, as well as other types that Nengo uses frequently, but it is possible to implement your own in order to do additional processing like validation. See the nengo.params source for examples.

+
+
[19]:
+
+
+
+print([cls for cls in dir(nengo.params) if cls.endswith("Param")])
+
+
+
+
+
+
+
+
+['BoolParam', 'DictParam', 'EnumParam', 'FunctionParam', 'IntParam', 'NdarrayParam', 'NumberParam', 'ObsoleteParam', 'ShapeParam', 'StringParam', 'TupleParam']
+
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/usage/config.ipynb b/examples/usage/config.ipynb new file mode 100644 index 0000000000..27d2196f31 --- /dev/null +++ b/examples/usage/config.ipynb @@ -0,0 +1,1067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The `config` system\n", + "\n", + "Nengo objects have many parameters\n", + "that can be modified.\n", + "Some of these parameters\n", + "are critical characteristics of that object,\n", + "and others are hints or suggestions\n", + "that a backend can use or ignore.\n", + "\n", + "Nengo's `config` system is designed\n", + "to make setting large numbers of parameters easy,\n", + "and to allow backends\n", + "to introduce additional parameters\n", + "without changing core Nengo objects." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:56.655264Z", + "iopub.status.busy": "2020-11-25T16:50:56.654376Z", + "iopub.status.idle": "2020-11-25T16:50:56.981942Z", + "shell.execute_reply": "2020-11-25T16:50:56.982346Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nengo\n", + "import nengo.params\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting default parameters\n", + "\n", + "The `config` system aids in\n", + "setting many parameters\n", + "with a **hierarchy of defaults**.\n", + "When you create a Nengo object,\n", + "any parameters not specified\n", + "will be given the value `nengo.Default`.\n", + "This value tells Nengo\n", + "to use the default value\n", + "with the highest precedence\n", + "in the hierarchy.\n", + "Every `Network` has an associated\n", + "`config` object,\n", + "on which defaults can be set.\n", + "The network hierarchy is traversed\n", + "from most to least specific\n", + "and the first network with a default\n", + "set for that particular parameter\n", + "is used. For example:\n", + "\n", + " with nengo.Network() as net:\n", + " with nengo.Network() as subnet:\n", + " with nengo.Network() as subsubnet:\n", + " ens = nengo.Ensemble(10, 1)\n", + "\n", + "When filling in defaults for `ens`,\n", + "the hierarchy looks like\n", + "\n", + " └── net <- least specific\n", + " └── subnet\n", + " └── subsubnet <- most specific\n", + "\n", + "so defaults set in `subsubnet`\n", + "will take precedence over those in `subnet`,\n", + "which take precedence over those in `net`.\n", + "\n", + "If no default has been set in the\n", + "network hierarchy,\n", + "then the parameter default\n", + "is used.\n", + "These defaults are specified\n", + "when the Nengo objects are created.\n", + "We can investigate these defaults\n", + "by printing the class attributes\n", + "associated with them." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:56.986750Z", + "iopub.status.busy": "2020-11-25T16:50:56.986224Z", + "iopub.status.idle": "2020-11-25T16:50:56.989977Z", + "shell.execute_reply": "2020-11-25T16:50:56.990412Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumberParam('radius', default=1.0, optional=False, readonly=False)\n", + "1.0\n" + ] + } + ], + "source": [ + "# Get all info about the radius\n", + "print(nengo.Ensemble.radius)\n", + "# Just get the default\n", + "print(nengo.Ensemble.radius.default)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can inspect which defaults\n", + "have been overridden in a\n", + "particular `config` object\n", + "by printing it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:56.996244Z", + "iopub.status.busy": "2020-11-25T16:50:56.994685Z", + "iopub.status.idle": "2020-11-25T16:50:56.997941Z", + "shell.execute_reply": "2020-11-25T16:50:56.997507Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No parameters configured for Connection.\n", + "No parameters configured for Ensemble.\n", + "No parameters configured for Node.\n", + "No parameters configured for Probe.\n" + ] + } + ], + "source": [ + "model = nengo.Network()\n", + "print(model.config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To configure a parameter\n", + "(i.e., change its network-local default),\n", + "set it as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.004191Z", + "iopub.status.busy": "2020-11-25T16:50:57.002355Z", + "iopub.status.idle": "2020-11-25T16:50:57.005804Z", + "shell.execute_reply": "2020-11-25T16:50:57.005358Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters configured for Ensemble:\n", + " radius: 1.5\n" + ] + } + ], + "source": [ + "model.config[nengo.Ensemble].radius = 1.5\n", + "print(model.config[nengo.Ensemble])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this network, the default radius will be 1.5." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.013382Z", + "iopub.status.busy": "2020-11-25T16:50:57.011825Z", + "iopub.status.idle": "2020-11-25T16:50:57.015026Z", + "shell.execute_reply": "2020-11-25T16:50:57.014596Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normal network: ens.radius = 1.0\n", + "Configured network: ens.radius = 1.5\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1)\n", + "print(\"Normal network: ens.radius = %s\" % ens.radius)\n", + "\n", + "with model:\n", + " ens = nengo.Ensemble(10, 1)\n", + "print(\"Configured network: ens.radius = %s\" % ens.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if a radius is explicitly passed in,\n", + "it will always be used." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.022488Z", + "iopub.status.busy": "2020-11-25T16:50:57.020901Z", + "iopub.status.idle": "2020-11-25T16:50:57.024173Z", + "shell.execute_reply": "2020-11-25T16:50:57.023740Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normal network: ens.radius = 2.0\n", + "Configured network: ens.radius = 2.0\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1, radius=2.0)\n", + "print(\"Normal network: ens.radius = %s\" % ens.radius)\n", + "\n", + "with model:\n", + " ens = nengo.Ensemble(10, 1, radius=2.0)\n", + "print(\"Configured network: ens.radius = %s\" % ens.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When networks are nested within one another,\n", + "the most specific network configuration is used.\n", + "For example, if you create an Ensemble\n", + "without specifying a radius,\n", + "it will first check the network\n", + "that the Ensemble is a part of;\n", + "if that network has not configured a default,\n", + "then it will check the network\n", + "that that network is part of,\n", + "and so on." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.036480Z", + "iopub.status.busy": "2020-11-25T16:50:57.034896Z", + "iopub.status.idle": "2020-11-25T16:50:57.038117Z", + "shell.execute_reply": "2020-11-25T16:50:57.037696Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating e1 in subsubnet\n", + " radius = 2.0\n", + " neuron_type = LIFRate()\n", + "Creating e2 in subnet\n", + " radius = 1.5\n", + " neuron_type = LIFRate()\n", + "Creating e3 in model\n", + " radius = 1.5\n", + " neuron_type = LIF()\n" + ] + } + ], + "source": [ + "with model:\n", + "\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + "\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"Creating e1 in subsubnet\")\n", + " e1 = nengo.Ensemble(10, 1)\n", + " # Uses subsubnet.config value for radius\n", + " print(\" radius =\", e1.radius)\n", + " # Uses subnet.config value for neuron_type\n", + " print(\" neuron_type =\", e1.neuron_type)\n", + "\n", + " print(\"Creating e2 in subnet\")\n", + " e2 = nengo.Ensemble(10, 1)\n", + " # Uses model.config value for radius\n", + " print(\" radius =\", e2.radius)\n", + " # Uses subnet.config value for neuron_type\n", + " print(\" neuron_type =\", e2.neuron_type)\n", + "\n", + " print(\"Creating e3 in model\")\n", + " e3 = nengo.Ensemble(10, 1)\n", + " # Uses model.config value for radius\n", + " print(\" radius =\", e3.radius)\n", + " # Uses nengo.Ensemble default for neuron_type\n", + " print(\" neuron_type =\", e3.neuron_type)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that each `config` object\n", + "only knows about the defaults set on itself." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.047055Z", + "iopub.status.busy": "2020-11-25T16:50:57.045500Z", + "iopub.status.idle": "2020-11-25T16:50:57.048742Z", + "shell.execute_reply": "2020-11-25T16:50:57.048294Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subsubnet:\n", + "Parameters configured for Ensemble:\n", + " radius: 2.0\n", + "\n", + "subnet:\n", + "Parameters configured for Ensemble:\n", + " neuron_type: LIFRate()\n", + "\n", + "model:\n", + "Parameters configured for Ensemble:\n", + " radius: 1.5\n" + ] + } + ], + "source": [ + "with model:\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + "print(\"subsubnet:\")\n", + "print(subsubnet.config[nengo.Ensemble])\n", + "print(\"\\nsubnet:\")\n", + "print(subnet.config[nengo.Ensemble])\n", + "print(\"\\nmodel:\")\n", + "print(model.config[nengo.Ensemble])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want a more global picture of the defaults\n", + "in the *current context*, you can query the `Config`\n", + "class itself (all `config` objects are instances of `Config`).\n", + "\n", + "To query all parameters, print `Config.all_defaults()`.\n", + "You may pass a Nengo object class to this function\n", + "to filter the results.\n", + "For example, to get all defaults set for `Ensemble`,\n", + "use `Config.all_defaults(nengo.Ensemble)`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.059045Z", + "iopub.status.busy": "2020-11-25T16:50:57.057506Z", + "iopub.status.idle": "2020-11-25T16:50:57.060784Z", + "shell.execute_reply": "2020-11-25T16:50:57.060350Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In 'model' context:\n", + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIF()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 1.5\n", + " seed: None\n", + "Current defaults for Probe:\n", + " attr: None\n", + " label: None\n", + " sample_every: None\n", + " seed: None\n", + " solver: ConnectionDefault\n", + " synapse: None\n", + "Current defaults for Connection:\n", + " eval_points: None\n", + " function_info: None\n", + " label: None\n", + " learning_rule_type: None\n", + " scale_eval_points: True\n", + " seed: None\n", + " solver: LstsqL2()\n", + " synapse: Lowpass(tau=0.005)\n", + " transform: None\n", + "Current defaults for Node:\n", + " label: None\n", + " output: None\n", + " seed: None\n", + " size_in: None\n", + " size_out: None\n", + "In 'subnet' context:\n", + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: LIFRate()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 3.0\n", + " seed: None\n", + "In 'subsubnet' context:\n", + "Current defaults for Ensemble:\n", + " bias: None\n", + " encoders: ScatteredHypersphere(surface=True)\n", + " eval_points: ScatteredHypersphere()\n", + " gain: None\n", + " intercepts: Uniform(low=-1.0, high=0.9)\n", + " label: None\n", + " max_rates: Uniform(low=200, high=400)\n", + " n_eval_points: None\n", + " neuron_type: Direct()\n", + " noise: None\n", + " normalize_encoders: True\n", + " radius: 2.0\n", + " seed: None\n" + ] + } + ], + "source": [ + "with model:\n", + " print(\"In 'model' context:\")\n", + " print(nengo.Config.all_defaults())\n", + "\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " subnet.config[nengo.Ensemble].radius = 3.0\n", + " print(\"In 'subnet' context:\")\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))\n", + "\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].neuron_type = nengo.Direct()\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"In 'subsubnet' context:\")\n", + " print(nengo.Config.all_defaults(nengo.Ensemble))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default value for a particular parameter\n", + "can be queried from the global context\n", + "with the `nengo.Config.default` function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.066272Z", + "iopub.status.busy": "2020-11-25T16:50:57.064776Z", + "iopub.status.idle": "2020-11-25T16:50:57.067991Z", + "shell.execute_reply": "2020-11-25T16:50:57.067538Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function default in module nengo.config:\n", + "\n", + "default(nengo_cls, param)\n", + " Look up the current default value for a parameter.\n", + " \n", + " The default is found by going through the config stack, from most\n", + " specific to least specific. The network that an object is in\n", + " is the most specific; the top-level network is the least specific.\n", + " If no default is found there, then the parameter's default value\n", + " is returned.\n", + "\n" + ] + } + ], + "source": [ + "help(nengo.Config.default)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.077229Z", + "iopub.status.busy": "2020-11-25T16:50:57.075692Z", + "iopub.status.idle": "2020-11-25T16:50:57.078881Z", + "shell.execute_reply": "2020-11-25T16:50:57.078454Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subsubnet:\n", + " default radius: 2.0\n", + " default neuron_type: LIFRate()\n", + "\n", + "subnet:\n", + " default radius: 1.5\n", + " default neuron_type: LIFRate()\n", + "\n", + "model:\n", + " default radius: 1.5\n", + " default neuron_type: LIF()\n" + ] + } + ], + "source": [ + "def print_defaults():\n", + " def_radius = nengo.Config.default(nengo.Ensemble, \"radius\")\n", + " def_type = nengo.Config.default(nengo.Ensemble, \"neuron_type\")\n", + " print(\" default radius: %s\" % def_radius)\n", + " print(\" default neuron_type: %s\" % def_type)\n", + "\n", + "\n", + "with model:\n", + " with nengo.Network() as subnet:\n", + " subnet.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " with nengo.Network() as subsubnet:\n", + " subsubnet.config[nengo.Ensemble].radius = 2.0\n", + " print(\"subsubnet:\")\n", + " print_defaults()\n", + " print(\"\\nsubnet:\")\n", + " print_defaults()\n", + " print(\"\\nmodel:\")\n", + " print_defaults()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Defaults are filled in immediately\n", + "\n", + "One important feature about the defaults hierarchy\n", + "is that defaults are filled in **immediately**.\n", + "When you create a Nengo object,\n", + "the attributes are filled in with the **current**\n", + "defaults that are set.\n", + "Changing the defaults after object creation\n", + "will not update objects already created." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.085793Z", + "iopub.status.busy": "2020-11-25T16:50:57.083367Z", + "iopub.status.idle": "2020-11-25T16:50:57.088084Z", + "shell.execute_reply": "2020-11-25T16:50:57.088499Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e1.radius = 1.5\n", + "Changing default radius to 2.0\n", + "e1.radius = 1.5\n", + "e2.radius = 2.0\n" + ] + } + ], + "source": [ + "with model:\n", + " e1 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"Changing default radius to 2.0\")\n", + " model.config[nengo.Ensemble].radius = 2.0\n", + " e2 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"e2.radius =\", e2.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Resetting to default\n", + "\n", + "If you ever wish to reset a value\n", + "back to the default,\n", + "you can remove it from the `config` object you modified." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.096565Z", + "iopub.status.busy": "2020-11-25T16:50:57.095132Z", + "iopub.status.idle": "2020-11-25T16:50:57.098387Z", + "shell.execute_reply": "2020-11-25T16:50:57.097955Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e1.radius = 2.0\n", + "Resetting radius back to default\n", + "\n", + "No parameters configured for Ensemble.\n", + "\n", + "e2.radius = 1.0\n" + ] + } + ], + "source": [ + "with model:\n", + " e1 = nengo.Ensemble(10, 1)\n", + " print(\"e1.radius =\", e1.radius)\n", + " print(\"Resetting radius back to default\")\n", + " del model.config[nengo.Ensemble].radius\n", + " print(\"\\n\" + str(model.config[nengo.Ensemble]) + \"\\n\")\n", + " e2 = nengo.Ensemble(10, 1)\n", + " print(\"e2.radius =\", e2.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Making new `config`s\n", + "\n", + "Typically, several Nengo objects\n", + "will share a set of parameters,\n", + "but won't make sense to encapsulate in a network.\n", + "One method of having those objects share parameters\n", + "is to use dictionary unpacking." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.106435Z", + "iopub.status.busy": "2020-11-25T16:50:57.105040Z", + "iopub.status.idle": "2020-11-25T16:50:57.108291Z", + "shell.execute_reply": "2020-11-25T16:50:57.107864Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 1.5 1.5\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " hippocampus_args = {\"radius\": 1.5, \"neuron_type\": nengo.LIFRate()}\n", + " e1 = nengo.Ensemble(100, 2, **hippocampus_args)\n", + " e2 = nengo.Ensemble(150, 3, **hippocampus_args)\n", + " e3 = nengo.Ensemble(200, 4, **hippocampus_args)\n", + "print(e1.radius, e2.radius, e3.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An alternative method that can be very useful\n", + "for large networks and for more readable models\n", + "is to create a new `config` object\n", + "to encapsulate those parameter settings." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.117222Z", + "iopub.status.busy": "2020-11-25T16:50:57.115711Z", + "iopub.status.idle": "2020-11-25T16:50:57.118916Z", + "shell.execute_reply": "2020-11-25T16:50:57.118493Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 1.5 1.5\n" + ] + } + ], + "source": [ + "in_hippocampus = nengo.Config(nengo.Ensemble)\n", + "in_hippocampus[nengo.Ensemble].radius = 1.5\n", + "in_hippocampus[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + "\n", + "with nengo.Network():\n", + " with in_hippocampus:\n", + " e1 = nengo.Ensemble(100, 2)\n", + " e2 = nengo.Ensemble(150, 3)\n", + " e3 = nengo.Ensemble(200, 4)\n", + "print(e1.radius, e2.radius, e3.radius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced: adding new parameters\n", + "\n", + "This section is targeted to those\n", + "implementing new backends\n", + "or large libraries of networks\n", + "(like, for example, `nengo.SPA`).\n", + "\n", + "Often, you want to associate some kind of\n", + "metadata with a Nengo object,\n", + "or a type of Nengo objects.\n", + "For example, in backends\n", + "that communicate with specific hardware,\n", + "it can be helpful to mark certain nodes\n", + "as being time-dependent,\n", + "or to assign certain ensembles\n", + "to a particular portion of the hardware memory.\n", + "\n", + "Python allows us to make new attributes\n", + "on Nengo objects.\n", + "However, we highly discourage this activity,\n", + "because a Nengo object should be\n", + "a backend-agnostic part of a model.\n", + "The parameters pre-defined on Nengo objects\n", + "make up the parameters that all backends\n", + "should deal with in some way.\n", + "\n", + "For this reason, we raise a warning\n", + "when creating a new attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.124825Z", + "iopub.status.busy": "2020-11-25T16:50:57.123649Z", + "iopub.status.idle": "2020-11-25T16:50:57.126471Z", + "shell.execute_reply": "2020-11-25T16:50:57.126848Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/travis/build/nengo/nengo/nengo/base.py:106: SyntaxWarning: Creating new attribute 'memory_location' on ''. Did you mean to change an existing attribute?\n", + " SyntaxWarning,\n" + ] + } + ], + "source": [ + "with nengo.Network():\n", + " ens = nengo.Ensemble(10, 1)\n", + " ens.memory_location = 0x1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So how should backends associate arbitrary information\n", + "with Nengo objects?\n", + "The `config` system!\n", + "\n", + "We saw above that we can create new `config` objects\n", + "by specifying which Nengo objects they can configure.\n", + "We can also create new parameters\n", + "on those `config` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.132209Z", + "iopub.status.busy": "2020-11-25T16:50:57.130715Z", + "iopub.status.idle": "2020-11-25T16:50:57.132878Z", + "shell.execute_reply": "2020-11-25T16:50:57.133278Z" + } + }, + "outputs": [], + "source": [ + "my_config = nengo.Config(nengo.Ensemble)\n", + "# memory_location must be a positive integer\n", + "my_config[nengo.Ensemble].set_param(\n", + " \"memory_location\",\n", + " nengo.params.IntParam(\"memory_location\", default=None, optional=True, low=0),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can set that parameter\n", + "for the `nengo.Ensemble` class as a whole,\n", + "or with individual instances." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.141063Z", + "iopub.status.busy": "2020-11-25T16:50:57.139492Z", + "iopub.status.idle": "2020-11-25T16:50:57.142761Z", + "shell.execute_reply": "2020-11-25T16:50:57.142328Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e1 will be stored at 0x1000\n", + "e2 will be stored at 0x2000\n" + ] + } + ], + "source": [ + "# Make the network (this code is backend-agnostic)\n", + "with nengo.Network():\n", + " e1 = nengo.Ensemble(10, 1)\n", + " e2 = nengo.Ensemble(10, 1)\n", + "\n", + "# Set backend-specific parameters\n", + "my_config[nengo.Ensemble].memory_location = 0x1000 # Set Ensemble default\n", + "my_config[e2].memory_location = 0x2000 # Set value for e2\n", + "\n", + "print(\"e1 will be stored at 0x%x\" % my_config[e1].memory_location)\n", + "print(\"e2 will be stored at 0x%x\" % my_config[e2].memory_location)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Parameter` types for the most common Python objects\n", + "are available in `nengo.params`,\n", + "as well as other types that Nengo uses frequently,\n", + "but it is possible to implement your own\n", + "in order to do additional processing\n", + "like validation.\n", + "See the `nengo.params` source\n", + "for examples." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:57.148200Z", + "iopub.status.busy": "2020-11-25T16:50:57.146836Z", + "iopub.status.idle": "2020-11-25T16:50:57.151547Z", + "shell.execute_reply": "2020-11-25T16:50:57.151119Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['BoolParam', 'DictParam', 'EnumParam', 'FunctionParam', 'IntParam', 'NdarrayParam', 'NumberParam', 'ObsoleteParam', 'ShapeParam', 'StringParam', 'TupleParam']\n" + ] + } + ], + "source": [ + "print([cls for cls in dir(nengo.params) if cls.endswith(\"Param\")])" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/delay-node.html b/examples/usage/delay-node.html new file mode 100644 index 0000000000..4e3d8c2113 --- /dev/null +++ b/examples/usage/delay-node.html @@ -0,0 +1,701 @@ + + + + + + + + Delaying a connection with a node — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Delaying a connection with a node

+

Nodes allow for all sorts of advanced behavior that is typically done by modifying the code of a neural simulator. In Nengo, the Node object allows us to run custom code.

+

In this example, we will implement an n-timestep delayed connection by using a node.

+
+
[1]:
+
+
+
+%matplotlib inline
+import numpy as np
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.processes import WhiteSignal
+
+
+
+
+
[2]:
+
+
+
+model = nengo.Network(label="Delayed connection")
+with model:
+    # We'll use white noise as input
+    inp = nengo.Node(WhiteSignal(2, high=5), size_out=1)
+    A = nengo.Ensemble(40, dimensions=1)
+    nengo.Connection(inp, A)
+
+
+# We'll make a simple object to implement the delayed connection
+class Delay:
+    def __init__(self, dimensions, timesteps=50):
+        self.history = np.zeros((timesteps, dimensions))
+
+    def step(self, t, x):
+        self.history = np.roll(self.history, -1)
+        self.history[-1] = x
+        return self.history[0]
+
+
+dt = 0.001
+delay = Delay(1, timesteps=int(0.2 / 0.001))
+
+with model:
+    delaynode = nengo.Node(delay.step, size_in=1, size_out=1)
+    nengo.Connection(A, delaynode)
+
+    # Send the delayed output through an ensemble
+    B = nengo.Ensemble(40, dimensions=1)
+    nengo.Connection(delaynode, B)
+
+    # Probe the input at the delayed output
+    A_probe = nengo.Probe(A, synapse=0.01)
+    B_probe = nengo.Probe(B, synapse=0.01)
+
+
+
+
+
[3]:
+
+
+
+# Run for 2 seconds
+with nengo.Simulator(model) as sim:
+    sim.run(2)
+
+
+
+
+
[4]:
+
+
+
+# Plot the results
+plt.figure()
+plt.subplot(2, 1, 1)
+plt.plot(sim.trange(), sim.data[A_probe], lw=2)
+plt.title("Input")
+plt.subplot(2, 1, 2)
+plt.plot(sim.trange(), sim.data[B_probe], lw=2)
+plt.axvline(0.2, c="k")
+plt.title("Delayed output")
+plt.tight_layout()
+
+
+
+
+
+
+
+../../_images/examples_usage_delay-node_4_0.png +
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/usage/delay-node.ipynb b/examples/usage/delay-node.ipynb new file mode 100644 index 0000000000..5362f28449 --- /dev/null +++ b/examples/usage/delay-node.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Delaying a connection with a node\n", + "\n", + "Nodes allow for all sorts of advanced behavior\n", + "that is typically done by modifying the code of a neural simulator.\n", + "In Nengo, the `Node` object allows us to run custom code.\n", + "\n", + "In this example, we will implement\n", + "an `n`-timestep delayed connection by using a node." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:58.845063Z", + "iopub.status.busy": "2020-11-25T16:50:58.844186Z", + "iopub.status.idle": "2020-11-25T16:50:59.336196Z", + "shell.execute_reply": "2020-11-25T16:50:59.335253Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.processes import WhiteSignal" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:59.349624Z", + "iopub.status.busy": "2020-11-25T16:50:59.349069Z", + "iopub.status.idle": "2020-11-25T16:50:59.352654Z", + "shell.execute_reply": "2020-11-25T16:50:59.352199Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"Delayed connection\")\n", + "with model:\n", + " # We'll use white noise as input\n", + " inp = nengo.Node(WhiteSignal(2, high=5), size_out=1)\n", + " A = nengo.Ensemble(40, dimensions=1)\n", + " nengo.Connection(inp, A)\n", + "\n", + "\n", + "# We'll make a simple object to implement the delayed connection\n", + "class Delay:\n", + " def __init__(self, dimensions, timesteps=50):\n", + " self.history = np.zeros((timesteps, dimensions))\n", + "\n", + " def step(self, t, x):\n", + " self.history = np.roll(self.history, -1)\n", + " self.history[-1] = x\n", + " return self.history[0]\n", + "\n", + "\n", + "dt = 0.001\n", + "delay = Delay(1, timesteps=int(0.2 / 0.001))\n", + "\n", + "with model:\n", + " delaynode = nengo.Node(delay.step, size_in=1, size_out=1)\n", + " nengo.Connection(A, delaynode)\n", + "\n", + " # Send the delayed output through an ensemble\n", + " B = nengo.Ensemble(40, dimensions=1)\n", + " nengo.Connection(delaynode, B)\n", + "\n", + " # Probe the input at the delayed output\n", + " A_probe = nengo.Probe(A, synapse=0.01)\n", + " B_probe = nengo.Probe(B, synapse=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:59.357985Z", + "iopub.status.busy": "2020-11-25T16:50:59.357182Z", + "iopub.status.idle": "2020-11-25T16:50:59.830021Z", + "shell.execute_reply": "2020-11-25T16:50:59.829183Z" + } + }, + "outputs": [], + "source": [ + "# Run for 2 seconds\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:50:59.851476Z", + "iopub.status.busy": "2020-11-25T16:50:59.845061Z", + "iopub.status.idle": "2020-11-25T16:51:00.215978Z", + "shell.execute_reply": "2020-11-25T16:51:00.216408Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure()\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(sim.trange(), sim.data[A_probe], lw=2)\n", + "plt.title(\"Input\")\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(sim.trange(), sim.data[B_probe], lw=2)\n", + "plt.axvline(0.2, c=\"k\")\n", + "plt.title(\"Delayed output\")\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/delay_node.html b/examples/usage/delay_node.html new file mode 100644 index 0000000000..2bc89c8194 --- /dev/null +++ b/examples/usage/delay_node.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/usage/network-design-advanced.html b/examples/usage/network-design-advanced.html new file mode 100644 index 0000000000..528e953bea --- /dev/null +++ b/examples/usage/network-design-advanced.html @@ -0,0 +1,963 @@ + + + + + + + + Additional tips and tricks for designing networks — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Additional tips and tricks for designing networks

+

This tutorial assumes that you have read the network_design tutorial, and have designed a network or two. Here, we will give a few advanced tips and tricks for designing networks that can be reused flexibly. In particular, these tips will use the config system, so we will also assume that you have gone over the config tutorial.

+

Briefly, the general principles covered in this tutorial are

+
    +

  1. Accept **kwargs to pass through network arguments

    2. +

  2. Accept a config argument for groups of parameters

    3. +
+

We will demonstrate these principles using the two examples from the network_design tutorial.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.dists import Choice
+from nengo.processes import Piecewise
+from nengo.utils.ipython import hide_input
+
+
+def test_integrators(net):
+    with net:
+        piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})
+        piecewise_inp = nengo.Node(piecewise)
+        nengo.Connection(piecewise_inp, net.pre_integrator.input)
+        input_probe = nengo.Probe(piecewise_inp)
+        pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)
+        post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)
+    with nengo.Simulator(net) as sim:
+        sim.run(6)
+    plt.figure()
+    plt.plot(sim.trange(), sim.data[input_probe], color="k")
+    plt.plot(sim.trange(), sim.data[pre_probe], color="b")
+    plt.plot(sim.trange(), sim.data[post_probe], color="g")
+
+
+hide_input()
+
+
+
+
+
[1]:
+
+
+
+Show Input + +
+
+
+

1. Accept a **kwargs argument

+

The standard nengo.Network accepts a number of arguments, including the widely used seed and label arguments. Sometimes it is helpful to be able to set these on your custom networks too. While there is nothing wrong with explicitly passing these arguments along, it is less typing to use the Python **kwargs construct. This special argument allows a function to accept any number of keyword arguments which we can then pass into the Network constructor.

+
+
[2]:
+
+
+
+def Integrator(n_neurons, dimensions, tau=0.1, **kwargs):
+    with nengo.Network(**kwargs) as net:
+        net.input = nengo.Node(size_in=dimensions)
+        net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)
+        nengo.Connection(net.ensemble, net.ensemble, synapse=tau)
+        nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)
+    return net
+
+
+net = nengo.Network(label="Two integrators")
+with net:
+    # Make both integrators use LIFRate neurons
+    net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
+    net.pre_integrator = Integrator(50, 1, label="pre")
+    net.post_integrator = Integrator(50, 1, label="post")
+    nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)
+test_integrators(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design-advanced_3_0.png +
+
+
+
[3]:
+
+
+
+print("pre integrator label:", net.pre_integrator.label)
+print("post integrator label:", net.post_integrator.label)
+
+
+
+
+
+
+
+
+pre integrator label: pre
+post integrator label: post
+
+
+
+
+

2. Accept a config argument for groups of parameters

+

Often, you will not want to use the network-level defaults for all of your objects. Some objects need certain things overwritten, while others need other values overwritten. Again, it is possible to deal with this issue by adding more and more parameters, but this quickly gets out of hand. Instead, add a small number of arguments that optionally accept a config object, which allows for setting multiple parameters at once.

+

In the coupled integrator network example, we make two connections. We have to be careful changing the defaults for those connections, as they are wildly different; one is a recurrent connection from an ensemble to itself, while the other is a connection from a node to an ensemble. We will accept a config object for the recurrent connection to make this easier.

+
+
[4]:
+
+
+
+def ConfigurableIntegrator(n_neurons, dimensions, recurrent_config=None, **kwargs):
+    net = nengo.Network(**kwargs)
+    if recurrent_config is None:
+        recurrent_config = nengo.Config(nengo.Connection)
+        recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)
+    with net:
+        net.input = nengo.Node(size_in=dimensions)
+        net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)
+        with recurrent_config:
+            nengo.Connection(net.ensemble, net.ensemble)
+            tau = nengo.Config.default(nengo.Connection, "synapse").tau
+        nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)
+    return net
+
+
+net = nengo.Network(label="Two integrators")
+with net:
+    # Make both integrators use LIFRate neurons
+    net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()
+    net.pre_integrator = ConfigurableIntegrator(50, 1)
+    # Give the post_integrator a shorter tau (should make integration fail)
+    recurrent_config = nengo.Config(nengo.Connection)
+    recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.01)
+    net.post_integrator = ConfigurableIntegrator(
+        50, 1, recurrent_config=recurrent_config
+    )
+    nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)
+test_integrators(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design-advanced_6_0.png +
+
+
+
+

Longer example: double integrator network

+

Recall in the previous tutorial that we created a model that released a lever 0.6 to 1.0 seconds after pressing a lever. Let’s use the above principles, and the config system in general, to improve the code constructing this model.

+
+
[5]:
+
+
+
+def controlled_integrator(n_neurons, dimensions, recurrent_config=None, **kwargs):
+    net = nengo.Network(**kwargs)
+    if recurrent_config is None:
+        recurrent_config = nengo.Config(nengo.Connection)
+        recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)
+    with net:
+        net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)
+        with recurrent_config:
+            nengo.Connection(
+                net.ensemble,
+                net.ensemble[:dimensions],
+                function=lambda x: x[:-1] * (1.0 - x[-1]),
+            )
+    return net
+
+
+def medial_pfc(
+    coupling_strength,
+    n_neurons_per_integrator=200,
+    recurrent_config=None,
+    tau=0.1,
+    **kwargs
+):
+    net = nengo.Network(**kwargs)
+    with net:
+        recurrent_config = nengo.Config(nengo.Connection)
+        recurrent_config[nengo.Connection].synapse = nengo.Lowpass(tau)
+        net.pre = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)
+        net.post = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)
+        nengo.Connection(
+            net.pre.ensemble[0], net.post.ensemble[0], transform=coupling_strength
+        )
+    return net
+
+
+def motor_cortex(
+    command_threshold, n_neurons_per_command=30, ens_config=None, **kwargs
+):
+    net = nengo.Network(**kwargs)
+    if ens_config is None:
+        ens_config = nengo.Config(nengo.Ensemble)
+        ens_config[nengo.Ensemble].encoders = Choice([[1]])
+        ens_config[nengo.Ensemble].intercepts = Choice([command_threshold])
+    with net:
+        with ens_config:
+            net.press = nengo.Ensemble(n_neurons_per_command, dimensions=1)
+            net.release = nengo.Ensemble(n_neurons_per_command, dimensions=1)
+    return net
+
+
+def double_integrator(
+    mpfc_coupling_strength,
+    command_threshold,
+    press_to_pre_gain=3,
+    press_to_post_control=-6,
+    recurrent_tau=0.1,
+    **kwargs
+):
+    net = nengo.Network(**kwargs)
+    with net:
+        net.mpfc = medial_pfc(mpfc_coupling_strength)
+        net.motor = motor_cortex(command_threshold)
+        nengo.Connection(
+            net.motor.press,
+            net.mpfc.pre.ensemble[0],
+            transform=recurrent_tau * press_to_pre_gain,
+        )
+        nengo.Connection(
+            net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control
+        )
+        nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)
+    return net
+
+
+def test_doubleintegrator(net):
+    # Provide input and probe outside of network construction,
+    # for more flexibility
+    with net:
+        nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)
+        pr_press = nengo.Probe(net.motor.press, synapse=0.01)
+        pr_release = nengo.Probe(net.motor.release, synapse=0.01)
+        pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)
+        pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)
+    with nengo.Simulator(net) as sim:
+        sim.run(1.4)
+    t = sim.trange()
+    plt.figure()
+    plt.subplot(2, 1, 1)
+    plt.plot(t, sim.data[pr_press], c="b", label="Press")
+    plt.plot(t, sim.data[pr_release], c="g", label="Release")
+    plt.axvspan(0, 0.2, color="b", alpha=0.3)
+    plt.axvspan(0.8, 1.2, color="g", alpha=0.3)
+    plt.xlim(right=1.4)
+    plt.legend(loc="best")
+    plt.subplot(2, 1, 2)
+    plt.plot(t, sim.data[pr_pre_int], label="Pre Integrator")
+    plt.plot(t, sim.data[pr_post_int], label="Post Integrator")
+    plt.xlim(right=1.4)
+    plt.legend(loc="best")
+
+
+for coupling_strength in (0.11, 0.16, 0.21):
+    # Try the same network with LIFRate neurons
+    with nengo.Config(nengo.Ensemble) as cfg:
+        cfg[nengo.Ensemble].neuron_type = nengo.LIFRate()
+        net = double_integrator(
+            mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0
+        )
+    test_doubleintegrator(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design-advanced_8_0.png +
+
+
+
+
+
+../../_images/examples_usage_network-design-advanced_8_1.png +
+
+
+
+
+
+../../_images/examples_usage_network-design-advanced_8_2.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/usage/network-design-advanced.ipynb b/examples/usage/network-design-advanced.ipynb new file mode 100644 index 0000000000..35e937d632 --- /dev/null +++ b/examples/usage/network-design-advanced.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Additional tips and tricks for designing networks\n", + "\n", + "This tutorial assumes that you have read\n", + "the `network_design` tutorial,\n", + "and have designed a network or two.\n", + "Here, we will give a few advanced tips and tricks\n", + "for designing networks that can be reused flexibly.\n", + "In particular, these tips will use the\n", + "`config` system, so we will also assume that\n", + "you have gone over the `config` tutorial.\n", + "\n", + "Briefly, the general principles covered\n", + "in this tutorial are\n", + "\n", + "0. Accept `**kwargs` to pass through network arguments\n", + "0. Accept a config argument for groups of parameters\n", + "\n", + "We will demonstrate these principles\n", + "using the two examples from the `network_design` tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:14.598778Z", + "iopub.status.busy": "2020-11-25T16:51:14.594056Z", + "iopub.status.idle": "2020-11-25T16:51:15.093338Z", + "shell.execute_reply": "2020-11-25T16:51:15.093757Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "\n", + "def test_integrators(net):\n", + " with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, net.pre_integrator.input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + " plt.figure()\n", + " plt.plot(sim.trange(), sim.data[input_probe], color=\"k\")\n", + " plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\")\n", + " plt.plot(sim.trange(), sim.data[post_probe], color=\"g\")\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Accept a `**kwargs` argument\n", + "\n", + "The standard `nengo.Network` accepts a number of arguments,\n", + "including the widely used `seed` and `label` arguments.\n", + "Sometimes it is helpful to be able to\n", + "set these on your custom networks too.\n", + "While there is nothing wrong\n", + "with explicitly passing these arguments along,\n", + "it is less typing to use the Python `**kwargs` construct.\n", + "This special argument allows a function\n", + "to accept any number of keyword arguments\n", + "which we can then pass into the `Network` constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:15.110886Z", + "iopub.status.busy": "2020-11-25T16:51:15.106529Z", + "iopub.status.idle": "2020-11-25T16:51:16.283314Z", + "shell.execute_reply": "2020-11-25T16:51:16.283766Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def Integrator(n_neurons, dimensions, tau=0.1, **kwargs):\n", + " with nengo.Network(**kwargs) as net:\n", + " net.input = nengo.Node(size_in=dimensions)\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " nengo.Connection(net.ensemble, net.ensemble, synapse=tau)\n", + " nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)\n", + " return net\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " # Make both integrators use LIFRate neurons\n", + " net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net.pre_integrator = Integrator(50, 1, label=\"pre\")\n", + " net.post_integrator = Integrator(50, 1, label=\"post\")\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:16.288096Z", + "iopub.status.busy": "2020-11-25T16:51:16.287536Z", + "iopub.status.idle": "2020-11-25T16:51:16.292129Z", + "shell.execute_reply": "2020-11-25T16:51:16.291550Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pre integrator label: pre\n", + "post integrator label: post\n" + ] + } + ], + "source": [ + "print(\"pre integrator label:\", net.pre_integrator.label)\n", + "print(\"post integrator label:\", net.post_integrator.label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Accept a config argument for groups of parameters\n", + "\n", + "Often, you will not want to use the\n", + "network-level defaults for all of your objects.\n", + "Some objects need certain things overwritten,\n", + "while others need other values overwritten.\n", + "Again, it is possible to deal with this issue\n", + "by adding more and more parameters,\n", + "but this quickly gets out of hand.\n", + "Instead, add a small number of arguments\n", + "that optionally accept a `config` object,\n", + "which allows for setting multiple parameters at once.\n", + "\n", + "In the coupled integrator network example,\n", + "we make two connections.\n", + "We have to be careful changing the defaults\n", + "for those connections, as they are wildly different;\n", + "one is a recurrent connection from an ensemble to itself,\n", + "while the other is a connection from a node to an ensemble.\n", + "We will accept a `config` object for the recurrent connection\n", + "to make this easier." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:16.309167Z", + "iopub.status.busy": "2020-11-25T16:51:16.299592Z", + "iopub.status.idle": "2020-11-25T16:51:17.420431Z", + "shell.execute_reply": "2020-11-25T16:51:17.419984Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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9GkpL3bJli3vctq2ub6jbokWuOdVLXiSCOUBvEcnDJYCzgXOjC4jIQOBxYLCqrola3w4oVdVtIpIDHInrSPZVPDWCJUvc2PwvvnBNHWPHNs2IGRFJurO22vTv7zqXTXAkS2dxSQlcdpm7vmX06JrbvW5GTUtzN39s375xTbwVFTuTwtat7v5gu1vKy3d9XVHh+jG8FnciUNVyERkOTMMNH52gqgtE5A6gQFUnA38FWgL/ifzHVA0T7Qc8LiKVuKucx1QbbeSLxv4IZsyA3/3OVdtefRWGDPE+tiphSwRPPeXOotrYnQBMAwwf7i4ce/vt5LiGJT3d9ScErU/BkxZrVZ0KTK227rao54Nqed8s4AAvYvBSYxLB4sXuwJ+X58bXN3F/dqgSQVWH8uLF7l5LJhiCXiOYPdt1/o4a5WoEsQR9H4LC7jUUQ0MTQWkp/P73rhPorbeaPglAuBJBVdus9RMERzL8bY0Z40bv/PGPdZdNhv3xk91iwgPXXutGvbz5po2Fb4y8PFett0Rg6quoyHUSjxwZvGaWZGQ1ghgaUiN48kkYP94NDY2+JUNTC1ONID3djYKwRBAcQe8sHj/edZzWde1JMlxzEwSWCGKo74+gaprI44939/RPpDAlAnB3Nv30Uzcywpjd2b7d3atr8GC7XYRXLBHEUJ9EsGqVmxO4c2fXYeXlhWL1EbZEcPrp7mKdt9/2OxJTJag1gv/8x43RHzGi7rJB3YegsUQQQ12JYONGN3HMhg1umGiHDgkLbYewJYLBg93EOI8+6nckBoLblKIKDz3kxvCfeGJD3hfM/QkKSwQNtHUrnHaau+T85Zddk4aJX7NmrtN9yhT3b2tMLLNmwZw57m8lzY5enrF/yhhqqxFUVMB558H777tO4kR2DlcXthoBuPvIt2gB997rdyQGgtmscscdbsjoRRf5HUm4WCKIIVYiUHV3EX35ZXfbiPPO8ym4iDAmgvbtXef7s8+6uQ2Mf4L4tzV7tutDGjmy4bdtCeL+BIldRxBD9USg6iaSefxxuOUWN/lFfVRUVrBp+yY2b9/MlvItlJaVsqVsC1vKt+x4rL6utKy05vZY5Ydt4bE9HuOZB54hIy2DzLRM95ieudvX9V0aWj76u+L5nutvaM1jj2Vyxx3ult3GVBk92tUGrrqq9jJbyrZQUlrC2i1r3WPpWjgUPuRDxnw0hmbpzXYsmWmZu7zOSMtARBCENEnb8byux8aUjSbsfB29rbb1+7Tbh6wMb++nYYkghupnD3/5C4wdq1w6YjWnXPE/XlxQSPHmYkpKS9yyxf3Bbdy2kR+3/cjGre5xc9nmRn1/dkY2zTOa0zyzOS0yW+x43jyjObktcmme2ZzFHywmr0UeR+x7BGWVZZRXlu98rKj5elv5Nsoryxu8lFUmdjynILQY2YlnVnVl1T+70bdLV7q27kq31t3o2so979qqK62yWiU0rlQTtOsIPvsM3npLueX/X8VnxYv5dum3fLfxO77b+B0rf1y547G0rLTmm0+B93iP92a8V3NbElp09SL65nh7+1FLBLUR+Oi7j7h14mt8uOoTMm77gvFppYz/167F2mW3I6dFDh1adKBddjt6tu1J62ataZ3lllZZrWjZrOUuB/MWmS1iPm+e2ZzsjGzSpO4Wu1aXtuKEy0/gwV8/2ET/AI6qUqmVjUoi0cmkXuUqyli7ZS3fFBfx3IpCPl++nC/WzWT91vU14mqd1XpHYtiRJFpFnkeSRe4eufX6tzTB9OO2H5n+zXQ+/O5DJs74FLllIfds/5F7ntpZpnPLznRv050DOx3Iyb1PpuMeHenQvAMdWnRwv8vmHdh/n/255fpbuO2229hesX2XpayibOfzyjJUFUV3/N1XPa/rsb5lK3XXyQyiTzoVrXM9QJdW3t9+1BJBFFXlyzVfMr/dfFb+biVH/+toKM8iJ/cQzv3Z5ezboTd57fLo3qY7uS1yad+8PZnpmb7Emqg+AhEhXdJJT0sni8Td3rH7PLjnHpg/H/buU8r3m76n8MdCin4sco+bitzyYxHTv5nOqp9W1fiRZaZl0qVVlx3Jonvr7vyi5y84Lu84mmc2T9i+JKtE1wjKKspYXLKYj777iBn/m8GUpVPYWr6VrLTmbCs5jMP3voALTupH35y+5LXLo2urrvVqIkkrTSODDLIzssnOiGOS8BCzRADM/2E+T8x9gpcXvUzRpiLIgezvW5I961H6chofvdsycLe4DWNncbQbboCHH3btwv/5Twt6te9Fr/a9ai1fUVnBD5t/2JEsijZFJYwfi5i3eh6vLX6N+z+5nxaZLRi09yB+ve+v+WXPX7J3u70D1QwSBE35t1VaVsqSkiUsKlnEouJFLCpZxMLihSxbt2xHU2SXVl24dOClnLXfWdx77eHM+rAZb69ws9k1Rph/K15I6UQwb/U8bn7nZqZ9M41m6c04pfcpnLrvqUx+cDKfvH0Aa9aczyOfBvM+52FPBO3bu075O+90tYLabjNcJT0tnS6turhqcy03/ttWvo33V7zP61+/zutfv87kJZMB6LhHR47Y6wh+3u3nHNrlUAbsOYB2zdt5u0MpaP2W9TsO9guLF7rnJYv4dsO3O5o70iSNXu170S+nH0P6DGG/jvtxxF5HkNc2DxFh7lyYMtn9HTQ2CZi6pWQiqKis4O4P7+aOD+6gTXYb7ht0H5cMvIQOLdwlwm+XvU1p6WF07hzsKSbDnAgArr8e/v53d7/5V16J//OyMrI4qddJnNTrJB7+1cMsLF7Ixys/ZtbKWcxaOYtXF7+6o2xe2zwO7nzwLkvHPTrGH0SSqG9nsaqy6qdV7kAfObuvOvj/sPmHHeWyM7Lp06EPP+v2My4ecDH9cvrRL7cfvdv33m3zzh13uDmAr7mm8fsS9pMmL3iSCERkMPAQboayJ1R1TLXtWcBTwCHAWuAsVV0R2XYLcClQAVyrqtO8iKk2ZRVlXPDKBbyw4AXOO+A8/nHyP2ib3XaXMqqwZcthnHqqm9M0iFKhKaNdOzcp+ahRburPgQO9+2wRYb+O+7Ffx/0YdsgwAEpKS/hi1RfMXTWXuavnMnfVXF5a9NKO93Rt1ZWBnQdyYMcD6d2hN73b96ZX+1503KNj6P8/1m1ZxzfrvmHZumVuWb9sR/POj9t+3FGubXZb+uX045Tep9Avt9+OA36PNj1IT0tv0HfOmwevveaaB23muqYVdyIQkXTgEeAEoBCYIyKTq005eSmwXlV7icjZwL3AWSLSHzfH8X5AF+AdEdlXVSvijSsWVeWSyZfwwoIXuG/QfYw8cmTMcps2daKiohO/+EVTROGNVDnLGTHCXcB3++3uoNCUclrkcMI+J3DCPifsWLdx60bmrZ63Izl8/v3nvLn0TSqi/kRbZ7Wme5vudNyjIx336Ehui1yy0rPqPeKkvqNN6hqxUqmVu5aPsS36c2rbVqmVFA0u4q3Wb3Hw4wezYsOKGiO3urXuxr4d9uWCAy/YcbDvl9OPPVvu6VlSvP12lwCuvTa+zwl7kvaCFzWCw4BlqrocQESeB4YA0YlgCHB75Pkk4B/i/neGAM+r6jbgfyKyLPJ5n3gQVw19Lr6KpXn/plXB5Tz0z6N4qMbXuAPrqlU3A3DssU0RhTdEhKlTp7Jq1Sq/Q4lbhw4dePjhh2nWrFmNbW3auBmo/vxnKCiA/PzExtYmuw3H9jyWY3vu/GMoqyjj243fsnTtUpatW8bSdUsp/LGQb0u+5bPFn7EtYxuVEhnBpNUuDKp6Hmu9VisTtT56HYCo7FK26nWs9zT0NUBZaRmZLTPp3KozP+v2sx2d9b3a9yKvbV6Tj7r67DOX+O+80zUNxWvSpEksXrw4/g8KgIceeoiuHs+A5UUi6AqsjHpdCFSfeXZHmchk9xuBDpH1s6u9N+YeisgwYBhA90bMBamqlFR+T9qiX7Ft+h8pqXF3jZ0/gszMUvLy/kufPsG9o9zpp5/OrFmzkv6Pe+PGjRQWFjJixAj6V81ZWc0118CDD7ozxDfeSGx8sWSmZ8YcxXT//fcz8p6R9O3bl/T0hjWDBE1HOnLTTTdxwbkX+PL9f/6zu4q4Prearsvvf/975s+fn/S/lSrbtm3z/DOTprNYVccB4wDy8/Mb3CYiIqyd+CrlleW+jf330rhx4/wOwRMvvvgiZ5111m7LtG7t7i9zyy1u8pqgT3A/Z84cWtr8iY32wQcwfTrcfz+08uAC8meffTb+Dwk5Ly67LAL2inrdLbIuZhkRyQDa4DqN6/Nez4hIKJJAGNXV3zF8uDtDHDUqQQE1Qir02TQ1VfjTn6BLl93fU8h4y4tEMAfoLSJ5ItIM1/k7uVqZycDQyPMzgHfV/WomA2eLSJaI5AG9gc88iMmETMuWcOONMG2auye9Cae33oKPP3ZNQ83t4u+EiTsRqGo5MByYBiwCXlTVBSJyh4j8JlJsPNAh0hn8B+DmyHsXAC/iOpbfAq5uqhFDJpgaMqLjqqugY8dg1wrARqk0lqq7wWPPnnDppX5Hk1o86SNQ1anA1Grrbot6vhX4fS3vvRu424s4TPKqT7PKHnvATTe52098+CEcfXQCAmsAaxqKzxtvwOefw/jxbsY6kzh2a0bjq4aePV9xBXTqFOxagdUIGk7VjQrbe2+4wJ+BSinNEoEJhPqeTbdo4UYPvfeemzI0SKxG0HhTpsDcua6jONPGcyScJQKTdIYNc6NKRo1yZ5ImuVXVBvLyrDbgF0sExleNaUZp3tzVCmbOhHffbYKg4mRNQw0zdarrG7DagH8sEZhAaGizymWXQbduwaoVWNNQw6m6m8r17AkXXuh3NKnLEoHxVWPPnrOz4dZb3Zjz6dM9DipOViOovzffhDlz3HUDVhvwjyUCEwiNOZu+5BJXK7g7IIOPrUbQMKquRme1Af9ZIjBJKyvLzVcwcybMnl13eRMsU6a4O8pabcB/lgiMr+JtRrn8cjeBzV//6lFAHrCmobpFXzdgtQH/WSIwgdDYZpWWLd2tJ155Bb7+2uOgGsiahuqv6ipiqw0EgyUC4ysvzp6vucbdkuD++z0IyANWI9i9qr6Bffax6waCwhKBSXqdOsHFF8PEibB6tX9xWI2gfl55xc1BPWoUZCTNjCjhZonABEK8B9EbboCyMvj73z0KyDSJ8nJ3h9G+feHcc/2OxlSxRGB85VUzSq9ecPrp8H//Bxs2ePKRjWZNQ7WbMAEWLoS77oIkn80zVCwRmEDwolnlT3+CH390Bxk/WNPQ7hUXu1uDHH00/O53fkdjolkiML7y8ux5wAB3kdlDD/k7gshqBLHdcANs2gSPPgr2TxQscSUCEWkvItNFZGnksV2MMgNE5BMRWSAi80XkrKhtT4rI/0RkXmQZEE88xtx9t7sp3fXXJ/4eRFYjqN0778DTT7vpRvfbz+9oTHXx1ghuBmaoam9gRuR1daXAhaq6HzAY+JuItI3aPlJVB0SWeXHGY5KUVwfRqklrpk6Fl17y5CNNnLZscRMK9erlmu9M8MSbCIYAEyPPJwKnVS+gql+r6tLI8++BNUBunN9rQqIpmlFGjIBDDoHhw2H9es8/vk7WNLSru+6Cb76Bxx6zCemDKt5E0ElVV0WerwY67a6wiBwGNAO+iVp9d6TJaKyIZO3mvcNEpEBECoqLi+MM2wSNl80qGRnwxBNQUgJ//KNnH1snaxqq6auv4L77YOhQOP54v6MxtakzEYjIOyLyVYxlSHQ5db+CWn8JItIZeBq4WFUrI6tvAfoChwLtgZtqe7+qjlPVfFXNz821CkVYNNXZ84ABrj16wgR3c7NEshqBo+qahNq0Cc5V3ya2Oq/rU9VBtW0TkR9EpLOqrooc6NfUUq41MAX4k6ruuE9kVG1im4j8C0jg+ZsJu1GjXBK49FL48kto6vMHqxHs6pln3HwR48dDTo7f0ZjdibdpaDIwNPJ8KPBa9QIi0gx4BXhKVSdV29Y58ii4/oWv4ozHJKmmOIhmZbmRKuvXu6GLJnGqriDOz4eLLvI7GlOXeBPBGOAEEVkKDIq8RkTyReSJSJkzgWOAi2IME31GRL4EvgRyAJ8uBTJ+aepmlAMPdHMWPP00fPppk37VDtY0BC+8ACtWwG23QZpdrRR4cd3ySVXXAjW6gFS1ALgs8vzfwL9ref9x8Xy/MfVx663w5JNuNNGsWU13YLKmIUcVxoxx1wuccorf0Zj6sFxtAqEpD6KtWsE997gawbPPNtnXmIg333SjhW680WoDycL+m4yvEtWMcuGFrr365puhtLRpvqMqmaV609ADD0DXrnDOOX5HYurLEoEJhKZuVklLgwcfhKIi92iaxhdfwLvvumY4m3kseVgiML5K5Nnz0UfDb3/r2q/9nMAmzB54wDXFDRvmdySmISwRmJRy772wbZsbzeK1VO8sXrkSnn8eLrvMXURmkoclAhMIiTqI9u4NV1/tLnJasCAhX5kyqmaHGzHC3zhMw1kiML7yo2P1L39xzRc31XpDk8ZR1ZTtKN64ER5/HM48E3r08Dsa01CWCEwgJLJZpUMHd23BlCnw3nsJ+9pQe/xxN+nMyJF+R2IawxKB8ZVfZ9DXXAN77eXGuldW1l2+vlKxRrB9u5sV7vjjYeBAv6MxjWGJwKSk5s3hzjuhoABeftmbz0zVzuLnnoPvv7faQDKzRGACwY+D6PnnQ58+MHq0t7WCVKLqrsvYbz848US/ozGNZYnA+MrPppT0dHer6q++gkmT6i5fl1TsLH73XZg/393YL8V2PVQsEZhA8KtZ5cwzoV8/uP12qKjwJYSk9uCD0LEjnHuu35GYeFgiML7y+ww6Pd0lgUWL3K2T4+X3/iTSokUwdaq7LiM72+9oTDwsEZiUd8YZro17zBjX5t1YqdZZ/Le/ucl/rrzS70hMvOJKBCLSXkSmi8jSyGO7WspVRE1KMzlqfZ6IfCoiy0TkhchsZiYF+XkQTUuD669301nadQX1U1wMTz3l7upqU4gnv3hrBDcDM1S1NzAj8jqWLao6ILL8Jmr9vcBYVe0FrAcujTMek2SC0pRy3nnugDZ2bOM/I5U6ix97DLZuheuu8zsS44V4E8EQYGLk+UTcvMP1Epmn+DigarxGg95vjJeys10TxxtvwNdf+x1NsG3dCv/4B/zqV9C/v9/RGC/Emwg6qeqqyPPVQKdaymWLSIGIzBaR0yLrOgAbVLU88roQ6FrbF4nIsMhnFBQXF8cZtgmKqjPoILSvX3UVNGu28+ZpjZEKNYLnnoM1a9yQURMOdSYCEXlHRL6KsQyJLqful1zbr7mHquYD5wJ/E5F9Ghqoqo5T1XxVzc+1RknTBDp1csMg//UvWL++4e8PQjJralUXkB1wgLulhAmHOhOBqg5S1f1jLK8BP4hIZ4DI45paPqMo8rgceB8YCKwF2opIRqRYN6Ao7j0ySSkoB9HrrnNTWf7zn35HEkwzZrgL8K6/3i4gC5N4m4YmA0Mjz4cCr1UvICLtRCQr8jwHOBJYGKlBvAecsbv3m3ALWlPKQQfBL38JDz8MZWUNe28qdBY/9JC7gMzmIw6XeBPBGOAEEVkKDIq8RkTyReSJSJl+QIGI/Bd34B+jqgsj224C/iAiy3B9BuPjjMeYuF1/PRQWwuTJdZdNJUuXus70K6+0C8jCJqPuIrVT1bVAjZZCVS0ALos8nwUcUMv7lwOHxRODSW5B6iyucvLJsOeerlP09NMb9t4w1wj+/nfXmX7FFX5HYrxmVxYbU016urvaeMoUN9lKfQUpmXlt/XrXiX7OOS5JmnCxRGACIWgH0bPOcuPlX3/d70iCYexY2LzZhoyGlSUC46ugNqUccQR07dqwG9GFtbN43Tp3X6EzzoADD/Q7GtMULBEYE0NamqsVvPUWrF3rdzT+euAB+OknN3eDCSdLBMZXQewsrnLhhW4+3meeqf97wlYjKClxncRnngn77+93NKapWCIwphYHHQSHHALjx9fv9tRBTGbxquobsNpAuFkiMGY3Lr3UTcU4Z47fkSTe5s3w6KPw29+6WdxMeFkiML4KctMQuNtTt23rJq2pS9g6i6dNc8NGr77a70hMU7NEYMxutG4N114Lr7ziagapZOpUaNMGjjnG70hMU7NEYHwV9BoBwIgR0KoV/OUvdZcNU41g9mw4+mjIiOv+AyYZWCIwpg7t28Ott7p7D02fXnu5ICezhqqocPcWsr6B1GCJwJh6uO462Htvd0O68vI6iye9FSvc0Nm+ff2OxCSCJQLjq2RoGgJ3t80HHoAFC+D++2OXCVNn8eLF7tESQWqwRGBMPZ12mrsb6ahRsHBhncWTWlUi6NPH3zhMYlg3kPFVstQIqjzyCLz/PlxyCXz8sbtTabTG1gjKy91N7rZudU0y5eW7LmVlNdftbltFhVuqntf2WNu2mTMhNxc6dIj/38wEnyUCYxqgUyd3y4XzznNNRDfdtHNbVTIrLYVJk1ytoaQEiovd/Yq2bNl5sK++VFb6tEO4ZJaR4R6jn9ssZKkjrkQgIu2BF4CewArgTFVdX63ML4GxUav6Amer6qsi8iRwLLAxsu0iVZ0XT0zGNLVzzoGXX4Y//xkGDXK3oaiimsmxx0JBgTug5ubuPLNu1871NexuycpyS0ZG/ZfMzJ3Pow/ktT1GP09Ls7mHTfw1gpuBGao6RkRujry+KbqAqr4HDIAdiWMZ8HZUkZGqOinOOEySSramIXAHznHj4NNP3R1K338funVz28rLT6WgACZMgKFD3YHWmKCL9890CDAx8nwicFod5c8A3lTV0ji/1xhftW8PL74Ia9a4uQsWL3bJrLx8MLm57s6llgRMsoj3T7WTqq6KPF8NdKqj/NnAc9XW3S0i80VkrIhk1fZGERkmIgUiUlBcXBxHyCZIkrFGUOXnP3edqtu3w1FHwaZNbams7MvBB9fsRDYmyOpMBCLyjoh8FWMZEl1O3S+51l+ziHTGTWI/LWr1Lbg+g0OB9lRrVqr2+eNUNV9V83Nzc+sK25iEGDAAPvgANm6Ezz8/joqK3jb23iSdOvsIVHVQbdtE5AcR6ayqqyIH+jW7+agzgVdUtSzqs6tqE9tE5F/AH+sZtzGB0acPnHsuPPXUsYDdlsEkn3ibhiYDQyPPhwKv7absOVRrFookD8S1D5wGfBVnPCbJJHPTULQ/Rp3CWI3AJJt4E8EY4AQRWQoMirxGRPJF5ImqQiLSE9gL+KDa+58RkS+BL4Ec4K444zHGFwccAM2abQGgf3+fgzGmgeIaPqqqa4HjY6wvAC6Ler0C6Bqj3HHxfL9JfmG5Nw/A2WeP4aWXVpOb+7jfoRjTIDbAzQRCsjcNAbRqtZasLLskxiQfSwTGGJPiLBEYX4Wls7hKmJq6TOqwRGCMR8KSzEzqsURgfBW2M+iw7Y9JDZYITCCE4Ww6DPtgUpMlAmOMSXGWCIyvrLPYGP9ZIjDGI2FJZib1WCIwvgrbGXTY9sekBksEJhDCcDYdhn0wqckSgTHGpDhLBMZXYWtKCdv+mNRgicAEQhiaVcKwDyY1WSIwvgrbGXTY9sekBksEJhDCcDYdhn0wqSmuRCAivxeRBSJSKSL5uyk3WESWiMgyEbk5an2eiHwaWf+CiDSLJx5jjDENF2+N4Cvgd8DM2gqISDrwCPAroD9wjohUTeZ3LzBWVXsB64FL44zHJJmwNaWEbX9Maoh3qspFUOcf/2HAMlVdHin7PDBERBYBxwHnRspNBG4HHo0nJpOchg8fzq233up3GHH5/vvvad68ud9hGNNgcSWCeuoKrIx6XQgcDnQANqhqedT6GvMaVxGRYcAwgO7duzdNpCbh+vbty+WXX8769ev9DiVu/fv358gjj/Q7DGMarM5EICLvAHvG2PQnVX3N+5BiU9VxwDiA/Px865ULiezsbMaNG+d3GMaktDoTgaoOivM7ioC9ol53i6xbC7QVkYxIraBqvTHGmARKxPDROUDvyAihZsDZwGR1Y+3eA86IlBsKJKyGYYwxxol3+OhvRaQQ+DkwRUSmRdZ3EZGpAJGz/eHANGAR8KKqLoh8xE3AH0RkGa7PYHw88RhjjGk4ScaLYPLz87WgoMDvMIwxJqmIyOeqWuOaL7uy2BhjUpwlAmOMSXGWCIwxJsVZIjDGmBSXlJ3FIlIMfNvIt+cAJR6G46ew7EtY9gNsX4IqLPsS7370UNXc6iuTMhHEQ0QKYvWaJ6Ow7EtY9gNsX4IqLPvSVPthTUPGGJPiLBEYY0yKS8VEEKY7nIVlX8KyH2D7ElRh2Zcm2Y+U6yMwxhizq1SsERhjjIliicAYY1JcyiQCERksIktEZJmI3Ox3PPEQkQkiskZEvvI7lniIyF4i8p6ILBSRBSIywu+YGktEskXkMxH5b2RfRvsdUzxEJF1EvhCRN/yOJR4iskJEvhSReSKS1HeqFJG2IjJJRBaLyCIR+blnn50KfQQikg58DZyAmxJzDnCOqi70NbBGEpFjgJ+Ap1R1f7/jaSwR6Qx0VtW5ItIK+Bw4LRn/X8RN3L2Hqv4kIpnAR8AIVZ3tc2iNIiJ/APKB1qp6qt/xNJaIrADyVTXpLyYTkYnAh6r6RGRulxaqusGLz06VGsFhwDJVXa6q24HngSE+x9RoqjoTWOd3HPFS1VWqOjfyfBNuvopa560OMnV+irzMjCxJeZYlIt2AU4An/I7FOCLSBjiGyJwtqrrdqyQAqZMIugIro14XkqQHnLASkZ7AQOBTn0NptEhzyjxgDTBdVZN1X/4G3AhU+hyHFxR4W0Q+F5FhfgcThzygGPhXpMnuCRHZw6sPT5VEYAJMRFoCLwHXqeqPfsfTWKpaoaoDcPNvHyYiSddsJyKnAmtU9XO/Y/HIUap6MPAr4OpIs2oyygAOBh5V1YHAZsCzvs5USQRFwF5Rr7tF1hmfRdrTXwKeUdWX/Y7HC5Eq+3vAYJ9DaYwjgd9E2tafB44TkX/7G1LjqWpR5HEN8AqumTgZFQKFUbXMSbjE4IlUSQRzgN4ikhfpZDkbmOxzTCkv0sE6Hlikqg/6HU88RCRXRNpGnjfHDUxY7GtQjaCqt6hqN1XtifudvKuq5/scVqOIyB6RQQhEmlFOBJJypJ2qrgZWikifyKrjAc8GVWR49UFBpqrlIjIcmAakAxNUdYHPYTWaiDwH/ALIEZFCYJSqjvc3qkY5ErgA+DLStg5wq6pO9S+kRusMTIyMUEsDXlTVpB56GQKdgFfc+QYZwLOq+pa/IcXlGuCZyMnscuBirz44JYaPGmOMqV2qNA0ZY4yphSUCY4xJcZYIjDEmxVkiMMaYFGeJwBhjUpwlAmOMSXGWCIwxJsX9P6JsSGPIDsVzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def ConfigurableIntegrator(n_neurons, dimensions, recurrent_config=None, **kwargs):\n", + " net = nengo.Network(**kwargs)\n", + " if recurrent_config is None:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)\n", + " with net:\n", + " net.input = nengo.Node(size_in=dimensions)\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " with recurrent_config:\n", + " nengo.Connection(net.ensemble, net.ensemble)\n", + " tau = nengo.Config.default(nengo.Connection, \"synapse\").tau\n", + " nengo.Connection(net.input, net.ensemble, synapse=None, transform=tau)\n", + " return net\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " # Make both integrators use LIFRate neurons\n", + " net.config[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net.pre_integrator = ConfigurableIntegrator(50, 1)\n", + " # Give the post_integrator a shorter tau (should make integration fail)\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.01)\n", + " net.post_integrator = ConfigurableIntegrator(\n", + " 50, 1, recurrent_config=recurrent_config\n", + " )\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Longer example: double integrator network\n", + "\n", + "Recall in the previous tutorial that\n", + "we created a model\n", + "that released a lever 0.6 to 1.0 seconds\n", + "after pressing a lever.\n", + "Let's use the above principles,\n", + "and the `config` system in general,\n", + "to improve the code constructing this model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:17.448485Z", + "iopub.status.busy": "2020-11-25T16:51:17.433384Z", + "iopub.status.idle": "2020-11-25T16:51:20.132378Z", + "shell.execute_reply": "2020-11-25T16:51:20.133210Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def controlled_integrator(n_neurons, dimensions, recurrent_config=None, **kwargs):\n", + " net = nengo.Network(**kwargs)\n", + " if recurrent_config is None:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(0.1)\n", + " with net:\n", + " net.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)\n", + " with recurrent_config:\n", + " nengo.Connection(\n", + " net.ensemble,\n", + " net.ensemble[:dimensions],\n", + " function=lambda x: x[:-1] * (1.0 - x[-1]),\n", + " )\n", + " return net\n", + "\n", + "\n", + "def medial_pfc(\n", + " coupling_strength,\n", + " n_neurons_per_integrator=200,\n", + " recurrent_config=None,\n", + " tau=0.1,\n", + " **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " with net:\n", + " recurrent_config = nengo.Config(nengo.Connection)\n", + " recurrent_config[nengo.Connection].synapse = nengo.Lowpass(tau)\n", + " net.pre = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)\n", + " net.post = controlled_integrator(n_neurons_per_integrator, 1, recurrent_config)\n", + " nengo.Connection(\n", + " net.pre.ensemble[0], net.post.ensemble[0], transform=coupling_strength\n", + " )\n", + " return net\n", + "\n", + "\n", + "def motor_cortex(\n", + " command_threshold, n_neurons_per_command=30, ens_config=None, **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " if ens_config is None:\n", + " ens_config = nengo.Config(nengo.Ensemble)\n", + " ens_config[nengo.Ensemble].encoders = Choice([[1]])\n", + " ens_config[nengo.Ensemble].intercepts = Choice([command_threshold])\n", + " with net:\n", + " with ens_config:\n", + " net.press = nengo.Ensemble(n_neurons_per_command, dimensions=1)\n", + " net.release = nengo.Ensemble(n_neurons_per_command, dimensions=1)\n", + " return net\n", + "\n", + "\n", + "def double_integrator(\n", + " mpfc_coupling_strength,\n", + " command_threshold,\n", + " press_to_pre_gain=3,\n", + " press_to_post_control=-6,\n", + " recurrent_tau=0.1,\n", + " **kwargs\n", + "):\n", + " net = nengo.Network(**kwargs)\n", + " with net:\n", + " net.mpfc = medial_pfc(mpfc_coupling_strength)\n", + " net.motor = motor_cortex(command_threshold)\n", + " nengo.Connection(\n", + " net.motor.press,\n", + " net.mpfc.pre.ensemble[0],\n", + " transform=recurrent_tau * press_to_pre_gain,\n", + " )\n", + " nengo.Connection(\n", + " net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control\n", + " )\n", + " nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)\n", + " return net\n", + "\n", + "\n", + "def test_doubleintegrator(net):\n", + " # Provide input and probe outside of network construction,\n", + " # for more flexibility\n", + " with net:\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)\n", + " pr_press = nengo.Probe(net.motor.press, synapse=0.01)\n", + " pr_release = nengo.Probe(net.motor.release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "for coupling_strength in (0.11, 0.16, 0.21):\n", + " # Try the same network with LIFRate neurons\n", + " with nengo.Config(nengo.Ensemble) as cfg:\n", + " cfg[nengo.Ensemble].neuron_type = nengo.LIFRate()\n", + " net = double_integrator(\n", + " mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0\n", + " )\n", + " test_doubleintegrator(net)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/network-design.html b/examples/usage/network-design.html new file mode 100644 index 0000000000..309be546be --- /dev/null +++ b/examples/usage/network-design.html @@ -0,0 +1,1222 @@ + + + + + + + + Designing networks — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Designing networks

+

Networks are how Nengo encapsulates functionally complementary objects. Networks can contain ensembles, nodes, connection, even other networks. Models are often made much more readable my grouping objects into networks. Tools for visualizing networks will often use the network structure to produce cleaner images.

+

Here, we will go over some general principles of network design. In the first section, we will go over these principles with a simple example. In the second section, we will apply these principles to a more complicated example.

+

Briefly, the general principles we will go over in this tutorial are

+
    +

  1. Group related objects in networks with with

    2. +

  2. Reuse networks by defining functions

    3. +

  3. Parameterize functions for more flexible reuse

    4. +
+

We will demonstrate these principles with a network consisting of two connected integrators as an example.

+
+
[1]:
+
+
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.dists import Choice
+from nengo.processes import Piecewise
+from nengo.utils.ipython import hide_input
+
+
+def test_integrators(net):
+    with net:
+        piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})
+        piecewise_inp = nengo.Node(piecewise)
+        nengo.Connection(piecewise_inp, net.pre_integrator.input)
+        input_probe = nengo.Probe(piecewise_inp)
+        pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)
+        post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)
+    with nengo.Simulator(net) as sim:
+        sim.run(6)
+    plt.figure()
+    plt.plot(sim.trange(), sim.data[input_probe], color="k")
+    plt.plot(sim.trange(), sim.data[pre_probe], color="b")
+    plt.plot(sim.trange(), sim.data[post_probe], color="g")
+
+
+hide_input()
+
+
+
+
+
[1]:
+
+
+
+Show Input + +
+
+ +
+

2. Reuse networks by defining functions

+

Often, the same network is constructed more than once, resulting in duplicated code. We can reduce this code duplication by defining a function that creates the network and returns it.

+

Even if you only use the network once, it can be helpful to define all of your subnetworks in functions to keep your code cleaner. Determining when a network should be defined in a function is informed by experience designing large networks and personal preference.

+
+

Store useful objects on the network

+

A corollary to putting networks in functions is that any objects that you might want access to outside of that function should be stored on the network. Functions that create networks should only return that network. While all objects in that network are stored somewhere (e.g., ensembles are stored in a net.ensembles list), that list can be large and difficult to search. Therefore, we recommend storing objects on the network itself to make them accessible outside of the function.

+
+
[5]:
+
+
+
+# Example: two connected integrators. Now with functions!
+def Integrator1():
+    with nengo.Network() as integrator:
+        integrator.input = nengo.Node(size_in=1)
+        integrator.ensemble = nengo.Ensemble(100, dimensions=1)
+        nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=0.1)
+        nengo.Connection(
+            integrator.input, integrator.ensemble, synapse=None, transform=0.1
+        )
+    return integrator
+
+
+net = nengo.Network(label="Two integrators")
+with net:
+    net.pre_integrator = Integrator1()
+    net.post_integrator = Integrator1()
+    nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)
+
+
+
+
+
[6]:
+
+
+
+# This function does the same as the previous example
+test_integrators(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_9_0.png +
+
+
+
+
+

3. Parameterize functions for more flexible reuse

+

Now that your network is created in a function, you can add parameters to your function to change how your network is constructed. This could be a huge change, allowing you to compare two different approaches to some component of your model, or it could something straightforward like changing the number of neurons in the ensembles in the network.

+

Unlike other languages, Python allows you to add two different types of parameters: positional and keyword arguments. Positional arguments must be passed to your function.

+
>>> def my_network(arg1, arg2):
+...     return "%s %s" % (arg1, arg2)
+>>> my_network()
+TypeError: my_network() takes exactly 2 arguments (0 given)
+>>> my_network(0, 0)
+'0 0'
+
+
+

Keyword arguments are assigned a default value, and so do not have to be passed to your function.

+
>>> def my_network(arg1=0, arg2=0):
+...     return "%s %s" % (arg1, arg2)
+>>> my_network()
+'0 0'
+>>> my_network(1)
+'1 0'
+
+
+

Order does not matter if you explicitly label the argument in your function call.

+
>>> my_network(arg2=2, arg1=1)
+'1 2'
+>>> my_network(arg2=2)
+'0 2'
+
+
+
+
[7]:
+
+
+
+# Example: two connected integrators. Functions have parameters!
+def Integrator2(n_neurons, dimensions, tau=0.1):
+    with nengo.Network() as integrator:
+        integrator.input = nengo.Node(size_in=dimensions)
+        integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)
+        nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=tau)
+        nengo.Connection(
+            integrator.input, integrator.ensemble, synapse=None, transform=tau
+        )
+    return integrator
+
+
+
+
+
[8]:
+
+
+
+# Try 50 neuron ensembles
+net = nengo.Network(label="Two integrators")
+with net:
+    net.pre_integrator = Integrator2(50, 1)
+    net.post_integrator = Integrator2(50, 1)
+    nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)
+test_integrators(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_12_0.png +
+
+
+
[9]:
+
+
+
+# Try shorter tau (spoiler: it fails to integrate well)
+net = nengo.Network(label="Two integrators")
+with net:
+    net.pre_integrator = Integrator2(50, 1, tau=0.01)
+    net.post_integrator = Integrator2(50, 1, tau=0.01)
+    nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)
+test_integrators(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_13_0.png +
+
+
+
+

Longer example: double integrator network

+

While the coupled integrator model above seems like a toy example, it is in fact a critical component of many models that capture neurophysiological and behavioral data. Let’s looks at a more complicated network that uses coupled integrators, and show how the network design principles discussed above improve model organization. We will look at a simplified version of the network described in Bekolay et al., 2014.

+

This network uses two coupled integrators in the context of a simple reaction time task. The subject presses down a lever, and should release that lever between 0.6 and 1.0 seconds later. In order to time the interval, the lever press starts the coupled integrators, which are tuned such that the second integrator should reach a high point by around 0.6 seconds. The second integrator effects the lever release.

+

We’ll start with a completely disorganized network. Even if you’ve read the paper, the code is quite hard to follow.

+
+
[10]:
+
+
+
+with nengo.Network() as net:
+    tau = 0.1
+
+    pre_integrator = nengo.Ensemble(200, dimensions=2)
+    nengo.Connection(
+        pre_integrator,
+        pre_integrator[0],
+        function=lambda x: x[0] * (1.0 - x[1]),
+        synapse=tau,
+    )
+    post_integrator = nengo.Ensemble(200, dimensions=2)
+    nengo.Connection(
+        post_integrator,
+        post_integrator[0],
+        function=lambda x: x[0] * (1.0 - x[1]),
+        synapse=tau,
+    )
+    nengo.Connection(pre_integrator[0], post_integrator[0], transform=0.16)
+    press = nengo.Ensemble(
+        30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])
+    )
+    release = nengo.Ensemble(
+        30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])
+    )
+
+    nengo.Connection(press, pre_integrator[0], transform=tau * 3)
+    nengo.Connection(press, post_integrator[1], transform=-1 * 6)
+
+    nengo.Connection(post_integrator[0], release)
+    nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)
+
+    pr_press = nengo.Probe(press, synapse=0.01)
+    pr_release = nengo.Probe(release, synapse=0.01)
+    pr_pre_int = nengo.Probe(pre_integrator[0], synapse=0.01)
+    pr_post_int = nengo.Probe(post_integrator[0], synapse=0.01)
+
+
+def test_doubleintegrator(net):
+    with nengo.Simulator(net) as sim:
+        sim.run(1.4)
+    t = sim.trange()
+    plt.figure()
+    plt.subplot(2, 1, 1)
+    plt.plot(t, sim.data[pr_press], c="b", label="Press")
+    plt.plot(t, sim.data[pr_release], c="g", label="Release")
+    plt.axvspan(0, 0.2, color="b", alpha=0.3)
+    plt.axvspan(0.8, 1.2, color="g", alpha=0.3)
+    plt.xlim(right=1.4)
+    plt.legend(loc="best")
+    plt.subplot(2, 1, 2)
+    plt.plot(t, sim.data[pr_pre_int], label="Pre Integrator")
+    plt.plot(t, sim.data[pr_post_int], label="Post Integrator")
+    plt.xlim(right=1.4)
+    plt.legend(loc="best")
+
+
+test_doubleintegrator(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_15_0.png +
+
+

Recall that the goal is to release the lever 0.6-1.0 seconds after the press. The blue “Press” line in the upper plot represents the activity of neurons in motor cortex which effect the lever press. If we assume that the press occurs at 0.2 seconds, then the release window is between 0.8 and 1.2 seconds (shown in green). This will occur if the “Post Integrator” (see lower plot) is sufficiently high during this window. If you rerun the cell above several times, you can see that it usually +achieves this, but not all of the time.

+

Looking at the code, it’s difficult to tell how we achieve this result. It’s also difficult to see what parameters needed tweaking. Rather than just showing one parameter set that works, we should investigate reasonable ranges for each parameter. It would be easy to think that there is only one parameter, tau, in the model as presented, but this is not the case.

+

Let’s start with the first network design principle: group objects into networks with with.

+
+
[11]:
+
+
+
+with nengo.Network() as net:
+    tau = 0.1
+
+    with nengo.Network() as mpfc:
+        with nengo.Network() as pre_integrator:
+            pre_ensemble = nengo.Ensemble(200, dimensions=2)
+            nengo.Connection(
+                pre_ensemble,
+                pre_ensemble[0],
+                function=lambda x: x[0] * (1.0 - x[1]),
+                synapse=tau,
+            )
+        with nengo.Network() as post_integrator:
+            post_ensemble = nengo.Ensemble(200, dimensions=2)
+            nengo.Connection(
+                post_ensemble,
+                post_ensemble[0],
+                function=lambda x: x[0] * (1.0 - x[1]),
+                synapse=tau,
+            )
+        nengo.Connection(pre_ensemble[0], post_ensemble[0], transform=0.16)
+
+    with nengo.Network() as motor:
+        press = nengo.Ensemble(
+            30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])
+        )
+        release = nengo.Ensemble(
+            30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])
+        )
+
+    nengo.Connection(press, pre_ensemble[0], transform=tau * 3)
+    nengo.Connection(press, post_ensemble[1], transform=-1 * 6)
+
+    nengo.Connection(post_ensemble[0], release)
+    nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)
+
+    pr_press = nengo.Probe(press, synapse=0.01)
+    pr_release = nengo.Probe(release, synapse=0.01)
+    pr_pre_int = nengo.Probe(pre_ensemble[0], synapse=0.01)
+    pr_post_int = nengo.Probe(post_ensemble[0], synapse=0.01)
+
+test_doubleintegrator(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_17_0.png +
+
+

This exhibits some of the hypotheses of this model; specifically, that the medial prefrontal cortex contains these integrators, and that they can be involved in control of motor actions.

+

Let’s do both of the remaining principles by making some parameterized functions to get a sense of all of the knobs that be be tweaked in this model.

+
+
[12]:
+
+
+
+def controlled_integrator(n_neurons, dimensions, tau=0.1):
+    with nengo.Network() as integrator:
+        integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)
+        nengo.Connection(
+            integrator.ensemble,
+            integrator.ensemble[:dimensions],
+            function=lambda x: x[:-1] * (1.0 - x[-1]),
+            synapse=tau,
+        )
+    return integrator
+
+
+def medial_pfc(coupling_strength, n_neurons_per_integrator=200, tau=0.1):
+    with nengo.Network() as medial_pfc:
+        medial_pfc.pre = controlled_integrator(n_neurons_per_integrator, 1, tau)
+        medial_pfc.post = controlled_integrator(n_neurons_per_integrator, 1, tau)
+        nengo.Connection(
+            medial_pfc.pre.ensemble[0],
+            medial_pfc.post.ensemble[0],
+            transform=coupling_strength,
+        )
+    return medial_pfc
+
+
+def motor_cortex(command_threshold, n_neurons_per_command=30):
+    with nengo.Network() as motor_cortex:
+        ens_args = {
+            "dimensions": 1,
+            "encoders": Choice([[1]]),
+            "intercepts": Choice([command_threshold]),
+        }
+        motor_cortex.press = nengo.Ensemble(n_neurons_per_command, **ens_args)
+        motor_cortex.release = nengo.Ensemble(n_neurons_per_command, **ens_args)
+    return motor_cortex
+
+
+def double_integrator(
+    mpfc_coupling_strength,
+    command_threshold,
+    press_to_pre_gain=3,
+    press_to_post_control=-6,
+    recurrent_tau=0.1,
+    seed=None,
+):
+    with nengo.Network(seed=seed) as net:
+        net.mpfc = medial_pfc(mpfc_coupling_strength)
+        net.motor = motor_cortex(command_threshold)
+        nengo.Connection(
+            net.motor.press,
+            net.mpfc.pre.ensemble[0],
+            transform=recurrent_tau * press_to_pre_gain,
+        )
+        nengo.Connection(
+            net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control
+        )
+        nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)
+    return net
+
+
+def test_doubleintegrator_wprobes(net):
+    # Provide input and probe outside of network construction,
+    # for more flexibility
+    with net:
+        nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)
+        pr_press = nengo.Probe(net.motor.press, synapse=0.01)
+        pr_release = nengo.Probe(net.motor.release, synapse=0.01)
+        pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)
+        pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)
+    with nengo.Simulator(net) as sim:
+        sim.run(1.4)
+    t = sim.trange()
+    plt.figure()
+    plt.subplot(2, 1, 1)
+    plt.plot(t, sim.data[pr_press], c="b", label="Press")
+    plt.plot(t, sim.data[pr_release], c="g", label="Release")
+    plt.axvspan(0, 0.2, color="b", alpha=0.3)
+    plt.axvspan(0.8, 1.2, color="g", alpha=0.3)
+    plt.xlim(right=1.4)
+    plt.legend(loc="best")
+    plt.subplot(2, 1, 2)
+    plt.plot(t, sim.data[pr_pre_int], label="Pre Integrator")
+    plt.plot(t, sim.data[pr_post_int], label="Post Integrator")
+    plt.xlim(right=1.4)
+    plt.legend(loc="best")
+
+
+net = double_integrator(mpfc_coupling_strength=0.16, command_threshold=0.85)
+test_doubleintegrator_wprobes(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_19_0.png +
+
+

While this does add some complexity and length, the effort is worth it, as we can now do simple parameters sweeps to investigate the effects of changing various parameters.

+
+
[13]:
+
+
+
+for coupling_strength in (0.11, 0.16, 0.21):
+    net = double_integrator(
+        mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0
+    )
+    test_doubleintegrator_wprobes(net)
+
+
+
+
+
+
+
+../../_images/examples_usage_network-design_21_0.png +
+
+
+
+
+
+../../_images/examples_usage_network-design_21_1.png +
+
+
+
+
+
+../../_images/examples_usage_network-design_21_2.png +
+
+

The coupling_strength changes how quickly the “Post Integrator” rises, which causes the release to occur at different times. Of the values tested, only a coupling strength of 0.16 effects the release during the release window (for this particular network seed).

+

We will looks at some more techniques for cleaning up this network code and making it more flexible in “Additional tips and tricks for designing networks”.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/usage/network-design.ipynb b/examples/usage/network-design.ipynb new file mode 100644 index 0000000000..67444a7e18 --- /dev/null +++ b/examples/usage/network-design.ipynb @@ -0,0 +1,996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Designing networks\n", + "\n", + "`Networks` are how Nengo encapsulates\n", + "functionally complementary objects.\n", + "Networks can contain\n", + "ensembles, nodes, connection, even other networks.\n", + "Models are often made much more readable\n", + "my grouping objects into networks.\n", + "Tools for visualizing networks\n", + "will often use the network structure\n", + "to produce cleaner images.\n", + "\n", + "Here, we will go over some general principles\n", + "of network design. In the first section,\n", + "we will go over these principles with a simple example.\n", + "In the second section, we will apply\n", + "these principles to a more complicated example.\n", + "\n", + "Briefly, the general principles we will\n", + "go over in this tutorial are\n", + "\n", + "0. Group related objects in networks with `with`\n", + "0. Reuse networks by defining functions\n", + "0. Parameterize functions for more flexible reuse\n", + "\n", + "We will demonstrate these principles\n", + "with a network consisting\n", + "of two connected integrators\n", + "as an example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:01.433967Z", + "iopub.status.busy": "2020-11-25T16:51:01.433109Z", + "iopub.status.idle": "2020-11-25T16:51:01.939708Z", + "shell.execute_reply": "2020-11-25T16:51:01.940154Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Show Input\n", + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.processes import Piecewise\n", + "from nengo.utils.ipython import hide_input\n", + "\n", + "\n", + "def test_integrators(net):\n", + " with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, net.pre_integrator.input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(net.pre_integrator.ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(net.post_integrator.ensemble, synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + " plt.figure()\n", + " plt.plot(sim.trange(), sim.data[input_probe], color=\"k\")\n", + " plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\")\n", + " plt.plot(sim.trange(), sim.data[post_probe], color=\"g\")\n", + "\n", + "\n", + "hide_input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Group related objects in networks with `with`\n", + "\n", + "All Nengo objects must be created\n", + "in the context of some network.\n", + "We use the `with` keyword to denote\n", + "the network context in which\n", + "a Nengo object is created.\n", + "\n", + "These `with` statements can be nested\n", + "to group objects in appropriately named networks." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:01.953677Z", + "iopub.status.busy": "2020-11-25T16:51:01.952320Z", + "iopub.status.idle": "2020-11-25T16:51:01.954503Z", + "shell.execute_reply": "2020-11-25T16:51:01.954918Z" + } + }, + "outputs": [], + "source": [ + "# Example: two connected integrators\n", + "# (See integrator example network for more details)\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " with nengo.Network() as pre_integrator:\n", + " pre_input = nengo.Node(size_in=1)\n", + " pre_ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(pre_ensemble, pre_ensemble, synapse=0.1)\n", + " nengo.Connection(pre_input, pre_ensemble, synapse=None, transform=0.1)\n", + " with nengo.Network() as post_integrator:\n", + " post_input = nengo.Node(size_in=1)\n", + " post_ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(post_ensemble, post_ensemble, synapse=0.1)\n", + " nengo.Connection(post_input, post_ensemble, synapse=None, transform=0.1)\n", + " nengo.Connection(pre_ensemble, post_input)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:01.966524Z", + "iopub.status.busy": "2020-11-25T16:51:01.962510Z", + "iopub.status.idle": "2020-11-25T16:51:03.397887Z", + "shell.execute_reply": "2020-11-25T16:51:03.397430Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Test the integrators by giving piecewise input and plotting\n", + "with net:\n", + " piecewise = Piecewise({0: 0, 0.2: 0.5, 1: 0, 2: -1, 3: 0, 4: 1, 5: 0})\n", + " piecewise_inp = nengo.Node(piecewise)\n", + " nengo.Connection(piecewise_inp, pre_input)\n", + " input_probe = nengo.Probe(piecewise_inp)\n", + " pre_probe = nengo.Probe(pre_ensemble, synapse=0.01)\n", + " post_probe = nengo.Probe(post_ensemble, synapse=0.01)\n", + "with nengo.Simulator(net) as sim:\n", + " sim.run(6)\n", + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[input_probe], color=\"k\", label=\"input\")\n", + "plt.plot(sim.trange(), sim.data[pre_probe], color=\"b\", label=\"pre integrator\")\n", + "plt.plot(sim.trange(), sim.data[post_probe], color=\"g\", label=\"post integrator\")\n", + "plt.legend(loc=\"best\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `with` statements\n", + "are not just cosmetic;\n", + "Nengo objects are stored\n", + "in the network that they're constructed in." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:03.401866Z", + "iopub.status.busy": "2020-11-25T16:51:03.401372Z", + "iopub.status.idle": "2020-11-25T16:51:03.404425Z", + "shell.execute_reply": "2020-11-25T16:51:03.404818Z" + } + }, + "outputs": [], + "source": [ + "assert pre_input in pre_integrator\n", + "assert pre_input not in net\n", + "assert pre_integrator in net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Reuse networks by defining functions\n", + "\n", + "Often, the same network is constructed\n", + "more than once, resulting in duplicated code.\n", + "We can reduce this code duplication\n", + "by defining a function that\n", + "creates the network and returns it.\n", + "\n", + "Even if you only use the network once,\n", + "it can be helpful to define all of\n", + "your subnetworks in functions\n", + "to keep your code cleaner.\n", + "Determining when a network\n", + "should be defined in a function\n", + "is informed by experience\n", + "designing large networks\n", + "and personal preference.\n", + "\n", + "### Store useful objects on the network\n", + "\n", + "A corollary to putting networks in functions\n", + "is that any objects that you might want\n", + "access to outside of that function\n", + "should be stored on the network.\n", + "Functions that create networks\n", + "should only return that network.\n", + "While all objects in that network\n", + "are stored somewhere\n", + "(e.g., ensembles are stored in a `net.ensembles` list),\n", + "that list can be large and difficult to search.\n", + "Therefore, we recommend storing objects\n", + "on the network itself\n", + "to make them accessible outside of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:03.415550Z", + "iopub.status.busy": "2020-11-25T16:51:03.412406Z", + "iopub.status.idle": "2020-11-25T16:51:03.418288Z", + "shell.execute_reply": "2020-11-25T16:51:03.418690Z" + } + }, + "outputs": [], + "source": [ + "# Example: two connected integrators. Now with functions!\n", + "def Integrator1():\n", + " with nengo.Network() as integrator:\n", + " integrator.input = nengo.Node(size_in=1)\n", + " integrator.ensemble = nengo.Ensemble(100, dimensions=1)\n", + " nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=0.1)\n", + " nengo.Connection(\n", + " integrator.input, integrator.ensemble, synapse=None, transform=0.1\n", + " )\n", + " return integrator\n", + "\n", + "\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator1()\n", + " net.post_integrator = Integrator1()\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:03.426287Z", + "iopub.status.busy": "2020-11-25T16:51:03.425532Z", + "iopub.status.idle": "2020-11-25T16:51:04.753958Z", + "shell.execute_reply": "2020-11-25T16:51:04.753503Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# This function does the same as the previous example\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Parameterize functions for more flexible reuse\n", + "\n", + "Now that your network is created in a function,\n", + "you can add parameters to your function\n", + "to change how your network is constructed.\n", + "This could be a huge change,\n", + "allowing you to compare two different approaches\n", + "to some component of your model,\n", + "or it could something straightforward\n", + "like changing the number of neurons\n", + "in the ensembles in the network.\n", + "\n", + "Unlike other languages, Python allows you\n", + "to add two different types of parameters:\n", + "**positional** and **keyword** arguments.\n", + "Positional arguments must be passed\n", + "to your function.\n", + "\n", + "```python\n", + ">>> def my_network(arg1, arg2):\n", + "... return \"%s %s\" % (arg1, arg2)\n", + ">>> my_network()\n", + "TypeError: my_network() takes exactly 2 arguments (0 given)\n", + ">>> my_network(0, 0)\n", + "'0 0'\n", + "```\n", + "\n", + "Keyword arguments are assigned a default value,\n", + "and so do not have to be passed to your function.\n", + "\n", + "```python\n", + ">>> def my_network(arg1=0, arg2=0):\n", + "... return \"%s %s\" % (arg1, arg2)\n", + ">>> my_network()\n", + "'0 0'\n", + ">>> my_network(1)\n", + "'1 0'\n", + "```\n", + "\n", + "Order does not matter if you explicitly\n", + "label the argument in your function call.\n", + "\n", + "```python\n", + ">>> my_network(arg2=2, arg1=1)\n", + "'1 2'\n", + ">>> my_network(arg2=2)\n", + "'0 2'\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:04.759315Z", + "iopub.status.busy": "2020-11-25T16:51:04.758795Z", + "iopub.status.idle": "2020-11-25T16:51:04.762327Z", + "shell.execute_reply": "2020-11-25T16:51:04.761898Z" + } + }, + "outputs": [], + "source": [ + "# Example: two connected integrators. Functions have parameters!\n", + "def Integrator2(n_neurons, dimensions, tau=0.1):\n", + " with nengo.Network() as integrator:\n", + " integrator.input = nengo.Node(size_in=dimensions)\n", + " integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions)\n", + " nengo.Connection(integrator.ensemble, integrator.ensemble, synapse=tau)\n", + " nengo.Connection(\n", + " integrator.input, integrator.ensemble, synapse=None, transform=tau\n", + " )\n", + " return integrator" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:04.776047Z", + "iopub.status.busy": "2020-11-25T16:51:04.769777Z", + "iopub.status.idle": "2020-11-25T16:51:06.032163Z", + "shell.execute_reply": "2020-11-25T16:51:06.031695Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try 50 neuron ensembles\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator2(50, 1)\n", + " net.post_integrator = Integrator2(50, 1)\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:06.046560Z", + "iopub.status.busy": "2020-11-25T16:51:06.039875Z", + "iopub.status.idle": "2020-11-25T16:51:07.322939Z", + "shell.execute_reply": "2020-11-25T16:51:07.322488Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try shorter tau (spoiler: it fails to integrate well)\n", + "net = nengo.Network(label=\"Two integrators\")\n", + "with net:\n", + " net.pre_integrator = Integrator2(50, 1, tau=0.01)\n", + " net.post_integrator = Integrator2(50, 1, tau=0.01)\n", + " nengo.Connection(net.pre_integrator.ensemble, net.post_integrator.input)\n", + "test_integrators(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Longer example: double integrator network\n", + "\n", + "While the coupled integrator model above\n", + "seems like a toy example,\n", + "it is in fact a critical component\n", + "of many models that capture\n", + "neurophysiological and behavioral data.\n", + "Let's looks at a more complicated network\n", + "that uses coupled integrators,\n", + "and show how the network design principles\n", + "discussed above improve model organization.\n", + "We will look at a simplified version\n", + "of the network described in\n", + "[Bekolay et al., 2014](\n", + "http://compneuro.uwaterloo.ca/files/publications/bekolay.2014a.pdf).\n", + "\n", + "This network uses two coupled integrators\n", + "in the context of a simple reaction time task.\n", + "The subject presses down a lever,\n", + "and should release that lever\n", + "between 0.6 and 1.0 seconds later.\n", + "In order to time the interval,\n", + "the lever press starts the coupled integrators,\n", + "which are tuned such that\n", + "the second integrator should reach\n", + "a high point by around 0.6 seconds.\n", + "The second integrator\n", + "effects the lever release.\n", + "\n", + "We'll start with a completely disorganized network.\n", + "Even if you've read the paper,\n", + "the code is quite hard to follow." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:07.345944Z", + "iopub.status.busy": "2020-11-25T16:51:07.341259Z", + "iopub.status.idle": "2020-11-25T16:51:08.285722Z", + "shell.execute_reply": "2020-11-25T16:51:08.285269Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Network() as net:\n", + " tau = 0.1\n", + "\n", + " pre_integrator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " pre_integrator,\n", + " pre_integrator[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " post_integrator = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " post_integrator,\n", + " post_integrator[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " nengo.Connection(pre_integrator[0], post_integrator[0], transform=0.16)\n", + " press = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + " release = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + "\n", + " nengo.Connection(press, pre_integrator[0], transform=tau * 3)\n", + " nengo.Connection(press, post_integrator[1], transform=-1 * 6)\n", + "\n", + " nengo.Connection(post_integrator[0], release)\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)\n", + "\n", + " pr_press = nengo.Probe(press, synapse=0.01)\n", + " pr_release = nengo.Probe(release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(pre_integrator[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(post_integrator[0], synapse=0.01)\n", + "\n", + "\n", + "def test_doubleintegrator(net):\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "test_doubleintegrator(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the goal is to release the lever\n", + "0.6-1.0 seconds after the press.\n", + "The blue \"Press\" line in the upper plot\n", + "represents the activity of neurons\n", + "in motor cortex which effect the lever press.\n", + "If we assume that the press occurs at 0.2 seconds,\n", + "then the release window is between 0.8 and 1.2 seconds\n", + "(shown in green).\n", + "This will occur if the \"Post Integrator\" (see lower plot)\n", + "is sufficiently high during this window.\n", + "If you rerun the cell above several times,\n", + "you can see that it usually achieves this,\n", + "but not all of the time.\n", + "\n", + "Looking at the code, it's difficult to tell\n", + "how we achieve this result.\n", + "It's also difficult to see what parameters\n", + "needed tweaking.\n", + "Rather than just showing one parameter set that works,\n", + "we should investigate reasonable ranges for\n", + "each parameter.\n", + "It would be easy to think that there is only\n", + "one parameter, `tau`, in the model as presented,\n", + "but this is not the case.\n", + "\n", + "Let's start with the first network design principle:\n", + "group objects into networks with `with`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:08.298563Z", + "iopub.status.busy": "2020-11-25T16:51:08.293408Z", + "iopub.status.idle": "2020-11-25T16:51:09.273316Z", + "shell.execute_reply": "2020-11-25T16:51:09.272746Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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RP4/8adyO7R3L+bTzuLq42lCRwho4RQ0/f9SLI3Taqhq+wt7J0mWx4dQGAB7r8hg+7j40qdPExqoU1sAiwxdCDBZC/CeEOCqEmGrm+HghxGUhxB7D3/3Wl1oyjljDV4avsFcKj73vEd7DhkoU1qbMJh0hhCswCxgAJADbhRBLpZQHi2RdIqV8uAo0lkm+4TtCDd/HR3vNj/2jUNgbufpc47aHq4cNlSisjSU1/M7AUSnlcSllDvAdMLxqZZUPR6rh5696pWr4CnsjT+aRmp3KgcsHbC1FUUVY0mkbCpwptJ8AmFve5lYhRC/gMPC4lPKMmTxVgiMZvqrhK+yVJQeWsDhusXF/WLNhNlSjqAqsNUrnd2CxlDJbCPEA8CXQt2gmIcREYCJAeHi4lS7tWIbv7q5FzFQ1fIW9sfXsVpP9mPCYKrmOS54LgbpA3KVtZ+7Wd6/PoUOHbKqhNLy8vAgLC8Pd3XqfkyWGfxZoVGg/zJBmREqZVGh3PvCWuYKklPOAeQDR0dFWm2uanq41lbg5yCDTgAC4csXWKhSKAq5kXikWCbOqxt0H6gJpGNSQWgG1EEJUyTUsIUefQz3feja7fmlIKUlKSiIhIYGIiAirlWtJG/524AYhRIQQwgMYDSwtnEEI0bDQ7jCgWh+bjjLLNp+gIEhMLDufQlEd6PP0jPttHEeTj5qkF+68tSbu0t3mZm/vCCEIDAwkKyvLquWW+QiXUuqEEA8DKwFXYIGU8oAQ4hVgh5RyKfCIEGIYoAOSgfFWVVkGjhI4LZ/gYLh82dYqFAoNXZ7ObHqAV0CVXVOZfdlUxWdk0W82KeUyYFmRtBcLbU8DpllXmuWkpztWDT84GOLibK1CodAwZ/jvD3qfEP8QG6hRVCUO0updOmlpUM8+m+LMomr4CnvCnOGH+Tt33I9A30Batm4JedCyZUu+/PJLfPKH0DkxThFaITOzYLijIxAcDMnJoNfbWolCYd7wnT1Qmre3N2v/XUtcXBweHh7MnTvX5LhOZ76Zy9FxCsPX6bThjo5CcLAWDz8pqey8CkVVsvfiXn489KNx383FjQlRE2pUoLSePXty9OhR1q9fT8+ePRk2bBitWrVCr9fz9NNP06lTJ9q1a8enn34KwPnz5+nVqxeRkZG0adOGv//+G71ez/jx42nTpg1t27bl/ffft/G7Mo9TPMZ1OnB1oPszOFh7vXzZsZqiFM7H9HXTTfZ1eTqGN6++ifTTnvJm/z7rfnnbttPzxjuZFuXV6XQsX76cwYMHA7Br1y7i4uKIiIhg3rx51K5dm+3bt5OdnU1MTAwDBw7k559/ZtCgQTz//PPo9XoyMjLYs2cPZ8+eJc7QOXfVTmdWqhq+Dcg3/EuXbKtDoaipZGZm0rdbX6KjowkPD2fChAkAdO7c2TjufdWqVSxatIjIyEi6dOlCUlISR44coVOnTnzxxRfExsayf/9+/P39uf766zl+/DhTpkxhxYoV1LLTBbadpobvKJOuABoaZi2cP29bHQqFrbG0Jm5t8tvwi0688i00vltKyccff8ygQYOKnb9x40b+/PNPxo8fzxNPPME999zD3r17WblyJXPnzuX7779nwYIFVf4+yosD2WTJOJrh5y98cqbaog0pFGXTK7wXQ5oOsbUMu2HQoEHMmTOHvn374u7uzuHDhwkNDSUxMZGwsDD+7//+j+zsbHbt2sVNN92Eh4cHt956K82bN+euu+6ytXyzOJBNmkdKbbSLIxm+vz/Urg0JCbZWolAUMKTpEFrXa21rGXbD/fffz8mTJ+nQoQNSSoKDg/n1119Zv349b7/9Nu7u7vj5+bFo0SLOnj3LvffeS15eHgBvvPGGjdWbx4Fs0jz5o6ccqdMWtFq+MnyFrantWZuUbC2Sn6ebp43VVB8JiQnk6HNM0m688UZuvPFG476LiwszZsxgxowZJvnGjRvHuHHjipW5a9euKtFqTRy+0zbf8B2p0xaU4SvsA3fXgi+Op2vNMfyaitMYviM16QA0aqTa8BW2R59XMPuvJtXwayoOb/i5hoB+jlbDb9wYLl4sWIBdobAFhWfZqhq+8+Pwhu9Ii58Uplkz7fXIEdvqUNRMNpzawH9J/5kYvpeblw0VKaoDB2sIKY6jG/7hwxAZaVMpihpGRm4G7/77LgDuLu4MbjqY3uG9VZNODcDha/jXrmmv/v621VFemjbVXg8ftq0ORc0jLSfNuJ2bl0stj1pqOGYNwWlq+I60AApoehs1AjteUlPhpBTuqAXnj4xpjsLhkSMiIvjqq68ICAgoMX9sbCx+fn489dRT1SeyClA1fBsSGQm7d9tahaKmoZfK8AuHR65bty6zZs2ytaRqweENPy0NXFzA29vWSspPx44QH1/w0FIoqoM8mWeyX5NCIZujW7dunD17FoBjx44xePBgOnbsSM+ePYmPjy+Wv6Q8v//+O126dCEqKor+/ftz8eJFADZs2EBkZCSRkZFERUVxzfCFf/vtt42hl1966aVqea8O/2jPygJPT830HY3oaC00xO7d0KuXrdUoagpFm3SKBhCrTqatnsb+S/utWmbbem15o79loQ30ej1r1qwxRsucOHEic+fO5YYbbmDr1q1MnjyZtWvXmpxTUp4ePXqwZcsWhBDMnz+ft956i3fffZd33nmHWbNmERMTQ1paGl5eXqxatYojR46wbds2pJQMGzaMjRs30quKjcDhDd/RAqcVpksXEAI2blSGr6g+8pt0+kX04/y187Sv397Giqqf/PDIF89fpGXLlgwYMIC0tDT++ecfRo0aZcyXnZ1tcl5peRISErjjjjs4f/48OTk5xjDLMTExPPHEE4wdO5ZbbrmFsLAwVq1axapVq4iKijKWe+TIEWX4ZeHIhh8UBB06wKpV8MILtlajqCnkG36PRj3oGNLRplosrYlbm/w2fD/hx6BBg5g1axbjx48nICCAPXv2lHheXl5eiXmmTJnCE088wbBhw1i/fj2xsbEATJ06laFDh7Js2TJiYmJYuXIlUkqmTZvGAw88UDVvsAQcsCHEFEeLlFmUgQPh338hMdHWShQ1hfwmHRfh8F//SuPj48NHH33Eu+++i4+PDxEREfzwww+AFg9/7969Jvlr1apVYp6UlBRCQ0MB+PLLL43nHDt2jLZt2/Lss8/SqVMn4uPjGTRoEAsWLCDNMMzw7NmzXKqGFZEc/j/uaMsbFuWOO7T3sGSJrZUoagr5nbYujtjxVQVERUXRrl07Fi9ezDfffMPnn39O+/btad26Nb/99lux/CXliY2NZdSoUXTs2JGgoCBj/g8++IA2bdrQrl073N3dGTJkCAMHDuTOO++kW7dutG3blttuu83YmVuVCClllV/EHNHR0XLHjh0VPn/OHC3i5JtvwunT4CijqhISYNIk07SoKMjOhrg4x+x8VliPOdvnEFYrrEqvsffiXqavm86MvjNoU69NlV7LHCHZITRp1qTar1uUHH2OTTusLeHQoUO0bNnSJE0IsVNKGV2R8hzeXvR6x67hAzz7rDYBa9EiWytR1ATyF+mo6cMxayIOb/iO3Gmbz6hR0KMHPPoo/PefrdUonJ38TltXoQy/pmGR4QshBgsh/hNCHBVCTDVz3FMIscRwfKsQorHVlZaAMxi+q6tWu/fy0oZn/v67Nj5foagK7MHwbdWU7EhUxWdUplUKIVyBWcAAIAHYLoRYKqU8WCjbBOCKlLKpEGI08CZwh9XVGkhNzyYlMxffdMjKA+EJmfqyz7MHsvIgLad4enAoLFsNY8fCsNugRQvo3x9at4bQUKhbV5tN7O2tPRgQINDG8Zf2p3AsUlJ1+EozN4gVSTXcgLnZ7mSmV7/pZ7noSElOxb92bYQNb1KZJ9DbqW9IKUlOTsLd3csYL8walNlpK4ToBsRKKQcZ9qcZBL1RKM9KQ55/hRBuwAUgWJZSeGU6bXvHvsRG8UqFzlUoFAY+joek5tV+2Tp1comNTaBp0yw1SKEE8vLg6FEvYmPDuHKl6OpOFe+0taQxJBQovBhfAtClpDxSSp0QIgUIBExGlwshJgITAcLDwyuiF4DRnQaSvNQfL0P8nNAQrQbsCFxNge7dLMur00PKVbh6FTIyQZerrfCVv8pX/uO08KsEyH9VOBynrp7Ey7XqFyLxpDY33JeL4ECVX8scexK0P1tyNTuF7o2621ZEGTz3XPG0p5+ueHnV2votpZwHzAOthl/RciYNjYHTMYRV7ei1KiEhFybZ9z2msCHVMSyzgBPVdB37JCE1gUmdHO/LWBnDt+QH1VmgUaH9MEOa2TyGJp3aQFLFZSkUCoXC2lhi+NuBG4QQEUIID2A0sLRInqXAOMP2bcDa0trvFQqFQlH9WDTTVghxE/AB4AoskFK+LoR4BdghpVwqhPACvgKigGRgtJTyeBllXgZOVUJ7EEX6CBwER9TtiJrBMXU7omZwTN2OqBmguZSyQks+2Sy0QmURQuyoaE+1LXFE3Y6oGRxTtyNqBsfU7YiaoXK61aAohUKhqCEow1coFIoagiMb/jxbC6ggjqjbETWDY+p2RM3gmLodUTNUQrfDtuErFAqFonw4cg1foVAoFOVAGb5CoVDUEOze8O05NHNJWKD5CSHEQSHEPiHEGiHEdbbQWZSydBfKd6sQQgohbD6kzRLNQojbDZ/3ASHEt9Wt0RwW3CPhQoh1QojdhvvkJlvoLKJpgRDikhAiroTjQgjxkeE97RNCdKhujWY0laV5rEHrfiHEP0KI9tWt0Rxl6S6Ur5MQQieEuM2igqWUdvuHNtHrGHA94AHsBVoVyTMZmGvYHg0scQDNfQAfw/YkW2u2VLchnz+wEdgCRNu7ZuAGYDdQx7BfzxE+a7SOuUmG7VbASTvQ3QvoAMSVcPwmYDla5O6uwFYH0Ny90L0xxB40W6K70H20FlgG3GZJufZew+8MHJVSHpdS5gDfAcOL5BkO5C8R/yPQT9gyyLYFmqWU66SUGYbdLWjxiWyNJZ81wKto6x1kVae4ErBE8/8Bs6SUVwCklJeqWaM5LNEtgVqG7drAuWrUZxYp5Ua0mfQlMRxYJDW2AAFCiIbVo848ZWmWUv6Tf29gP99FSz5rgCnAT4DF97S9G7650MyhJeWRUuqA/NDMtsISzYWZgFYrsjVl6jb8RG8kpfyzOoWVgiWfdTOgmRBisxBiixBicLWpKxlLdMcCdwkhEtBqcFOqR1qlKO+9b2/Yy3exTIQQocBIYE55znPwxQEdGyHEXUA00NvWWspCCOECvAeMt7GU8uKG1qxzI1rtbaMQoq2U8qotRVnAGGChlPJdwyJEXwkh2kgp82wtzBkRQvRBM/wettZiIR8Az0op88rToGHvhl+e0MwJdhKa2RLNCCH6A88DvaWU2dWkrTTK0u0PtAHWG26wBsBSIcQwKWXFli6rPJZ81glo7bK5wAkhxGG0B8D26pFoFkt0TwAGA0htJTkvtGBf9tAkVRIW3fv2hhCiHTAfGCKldJSw7tHAd4bvYhBwkxBCJ6X8tdSzbN05UUbHhRtwHIigoHOrdZE8D2Haafu9A2iOQuu0u8HWn3F5dBfJvx7bd9pa8lkPBr40bAehNTkEOoDu5cB4w3ZLtDZ8YQf3SWNK7gAdimmn7TZb67VAczhwFOhua53l0V0k30Is7LS16xq+1JZLfBhYSUFo5gOFQzMDn6P93D2KITSz7RRbrPltwA/4wfCEPi2lHGYz0Vis266wUPNKYKAQ4iCgB56WNq7FWaj7SeAzIcTjaB2446Xh220rhBCL0ZrGggx9Cy8B7gBSyrlofQ03oRloBnCvbZQWYIHmF9H6/GYbvos6aQcRNC3QXbFybXwPKRQKhaKasPdROgqFQqGwEsrwFQqFooagDF+hUChqCDbrtA0KCpKNGze21eUVCoXCIdm5c2eilDK4IufazPAbN27Mjh22Gr6tUCgUjokQ4lRFz1VNOgqFQlFDUIavUCgU9oqUcPGA1YpThq9QKBS2JvEIpCeBPheOroaDv2npu7+GOd3h2FrIyYC931XqMnY901ahUCjsntRzIFxg72JoNhgykiC8O7i4QMJOyE2HiF6Ql6el5ZOnh8TDcOBX2DCzeLnj/oC/pmvbv02B1IRKS1WGr1AoFOXlQhzsWgRntsD5vQXpq2O115hHodvDML+vtn/n9/Dt7dp23xegQbuC/ZL48uaCbSuYPdgwtEJ0dLRUo3QUCoXdcG4PJGyHZU/BbV9Am1vg4FL48V545jjM6grXzoGbN+gybSZTvJy6s6LxflQNX6FQOCe/PQxJR+GepeDmobWPu7rD/h8hojfkZsDvj0KjLlr+ws0qP96r/eUzM7xgu6rNvvezsOFN7VdA4x7QYRxkX4PP+1e66DJr+EKIBcDNwCUpZRszxwXwIVqUvAy0qH67yrqwquErFIoqQ58LrwZp2wNfA1cPWP4MDHwdVj0PwS3gcnz1ahq/DIKba9teAfDvx7DtM6gVorXlj1kC13XTjudkgJtX8TZ/BMLVtcI1fEsMvxeQhrZWpTnDvwlt+bWbgC7Ah1LKLmVdWBm+QqGoEBfioE5j8PSDv9+Fv9+H+1dD+iWtszTrKrzdpOp1NB0A3nVg//em6UPe1vQ1GwhHVkP8HxB5JzTqbJXLCiGqzvANF2gM/FGC4X8KrJdSLjbs/wfcKKU8X1qZyvAVCkW5uHZRGw3zTlNtP7QjnN1Zddcb8pb2qwDg4R3wicFjO9yjddg+tK2gxp6VAroc8KtQxINyURnDt0YbfkkLF5dq+AqFooai14GLK+SvxarLgbxcyEqF91pqhjrkLa2tXJcDf70ITfvDz/eblmNNsy/axDN5C9RrCeHdwK8e+DeAFy5D2gXwrQfREwrMHsCrtvW0VCHV2mkrhJgITAQIDw8vI7dCoXB40g2Li/kGFqS9Gqh1RA77SNtfOBQSthUc3/Wl9leYfZWbcIRnLchOhZAOcN8KQEDSEfj6Vuj1FHS6H078DZcOQueJBQ+jhu0KynDzgACDb4VEVk6PjbCG4Vu8cLGUch4wD7QmHStc2yHIy5PopWRfwlXahwXg5qomOCtqCG9fr73Gpmivep32uutL6PKgNoO0sNlXBt96ENYJaodC18kwuyvosuDBTVCvFax8Hro+CG6eWv76reHJQrX6iJ7anxNjDcNfCjwshPgOrdM2paz2+5pG7O8HWPSvFuBuRGQIH4yOsrEihcLKSAmzukCPx7QOSoAz2wuO/zwRTmyEa4WsYU43y8tvNQIO/qqNXBn9jTbz9No5eCwOfAJB6sHT3/ScR3ZrcWgatNX2h5iZzVrDKNPwHXHhYnsj3+wBft1zThm+wrn44wnQZ0Pif/DrJM14UxJgxdSCPPuWlL/c/M7RRl3h9iJNPI/HQdolqNWw5PNrhWh/CiNlGr6UckwZxyXwkNUU1QAOX7xGs/r+ZWdUKByBHZ+b7i+5y/Jzmw6Au34s2I//E76/BzqOh6HvwrCPzZ/n4lq62SvMombaVjGfbzpRLO345XRl+Ar7R5ettYHnj0A5v09rHjm3G9bPhHa3w+l/LS8v6m4YNAO2ztWaYZr0hdphpnlaDIUXk6z3HhQmKMOvQvaeucqrfxw07k+6sQlz1h/jrZXxDG7TwIbKFIpSyMvTwg4sGAwX98Okf823tx9ZWXo5rUdC5F1wfg90f0Qb5QLQ+xmrS1ZYhjL8KmJd/CXuXVjQabX6iV40rO3NnPXHOH45HSklIn/ol0JhS3IyYM83EN5Vm75/fF1B1Ecou3O1SV+4ZT789ye0HAbpl6Fuk4KwADdUPgaMwjoow7cyWbl6thxPMjH7D0dH0rSe1oTTo2kQm44mcjo5g+sCfW0lU1FTuXYBrpzUzF1Krdnmp/s1s7aUG6dpwynP79WafEbM0cbZd7hHO+4dUBXKFVZAGb4V2ZdwlWGfbDZJW/tkb64P9jPuT+nbVBm+ovrJydBWUlr2tDZb9P418P04y+Ks3/SONtO1TmOt/T60A9w4VRtTf/WUNgtV4RAow7ci6/+7bLJ/4o2bijXbhAR4A3D+ala16VLUUA4u1Uz+fx/CjCIjWub3K/v8O3/QojcWHt8e2qFg29UNAqshSJnCaijDtxIHzqXw3l+HTdLMtdHXr+WFEHD2qu0WUFA4EWtegVP/wn3Ltf3j62H3NxDeBf58UksrGqbAHNf1gFObtJmqd/9SfBKTwilQhm8FdPo8hn60ySRt3t0dzeb1cHMh2M+T8ynK8BWVJD1JCw8MkJsFlw7AouHaftGQvUVpPRIO/KKt3vTCBS0t8ahWY1eDCZwWZfiVZOvxJO6Yt8W4f3t0GPd0a0yb0JKj5zUM8OacatJRVIZNH8Dqlwr2X69f9jmNumprsAKMWgj9XtQMP5+gptZUqLBDlOFXktf+PGSy/+at7cocbimlZNPRRHacTCa6cd2qlKdwdHTZcHaX1paesBM+HwBRd1nWTAPwwN9aGOEbBkCtUC3cQdtR2rG611edboVdogy/EqRl6/B0M418acnY+sMXrwHw/Y4zyvAVpbPmFfj3E6jfBi7GaWmlmX2/l2DNy+AbrI2HD25hGuJ3yJtVq1dh1yjDryA7TiZz29yCaeVz7+pAWB0fi87t16I+f+4/r8IkK4pz6RDk6bRX4QJxP2np+WZfEl0mFUSD7PmENltWl1kwu1WhQBl+hdl+8orJ/uA2lgdyemdUe/7cf56LKaodv8YjJWReAZ+68PMD5V/o487vIemotqBIYVxcwEPN81CYogy/AmTk6HhzRcHCCd8/UI643oC3hysAa+IvcTUjhwAfVQurEWRegX0/aItZn/5Xiwj5umHSUoO2cGG/ZeWM+hJajyiUMMjKQhXOimpTqADHL6eb7HeOqHg7/MJ/TlZSjcJh+OMJWP40zOutdZ6+XmiGallmf/sirR0fipi9QmE5qoZfTj7fdMIkAuYX4ztVqrwPVh/hsf7NKitLYY+c3qJFnWzSV1sQ5MDPlp/rW09bms+/0HDLZkO09n2FooIowy8HC4qYPUCfFvUqVFaAjztXM3IBVORMZ+L8PvjlQfCqVb5Y8Td/oJl5ncbgVw8CmxZvg3fzAFTzn6LiqCadcvDG8oIx9+0bBXD4tSEVLmv78/25I1pb+73vuxsqrU1hI9KT4LIhpMb+H+HTntqMV0vN3tMwQS/6Xuj8f9p4+YbtVYerokpQNXwLuZKeQ65eAnBrhzDevb19pcpzd3XB30v7+E8kqvj4DkXyCa0mnpEMbxsmLwU1g8TDpZ4GwDMn4Ns7IGEbTFwP9VprIYYVimpAGb6FFA6M9sYtba1S5viYxsw3LIF4+Vo2209eoY6PO+5uLnRSE7Lsk79ehM0fgncdbdRNPiWZfZvbtHjx+ePoferC/X+Z5lFj5RXVhDL8Mth9+gqPfreH08kZAPRvWR8PN+u0hIXV8eGrCZ25+/NtdJ6xxuTYyZlDrXINhZXZ/KH2mnnF/PGQDnDvcpjVSQtlMPJTLYxw4bDCCoWNUIZfBiNn/2PcjgoPYM5d1v3illSTT8vW4edZtf+ejBwd3u6uZOvy8HRzUU1KRZFSW/BDCLhyCn4YV/Y53R8Gdy94zMIx9QpFNaIMvxSOXrpm3H6s/w3c2z0CdyuHQ/Byd2X+PdHcv2iHSfrOU1f4fvsZ/L3ciL9wjchGAQT7e5KWrePpgc25lqWj/SurAFj9RG+S03NM5gP8feQyF1KyGGXoGC7Kh6uP8P7qw0wd0oKZy+N5/qaW9GwWxH8XrjE8MtSq7zGfd1f9R7P6/vyvfUiVlG8VslLhw/bg5gnXzlt2zqN7wcMfko9DWHTV6lMoKoGQUtrkwtHR0XLHjh1lZ6xm8vIkGbl6XIWg5YsrAOjXoh6fV3K8fVk0nlqONUVLwMfDlft7RCCE4MM1R4zpJ2cOJStXz9FLafh7uTF63hbOG8I6BPl5kpiWTf1anlxMzQa0mcOdGtch7mwqLRr64+7qQl6exMVFoNPn8cvus4yICjU+/PYnpJCYls3xxHQm9IgAtF8P7q4uJg/I/Pf4w4Pd6NS4rrHMkpBScvlaNvVqeVX6s7GIC/vhh3sh6UjZeQe8Aq6e2gzZxjFVr02hMCCE2CmlrFDNQtXwizBzRTzzNh43SYtpGlTl131qYDPeWWXBKI9SyMjR89Hao8XSJ3+zk2X7L5g9JzFNM/l8swe4/dOCIYX9WtRjVHQYD369iwa1vLiQqj0ojl1OZ1LvJryz6j++2nLKmD81M5cgf0+m/xpH65BadG8SyO97z/PHIz2MeUbN/ZeH+jRh1rpj3N8jguYN/BkWWVDr/2PveerX8uKuz7cCsPKxXjRvYLoCU0pGLpm5eur5eyKENiHO19ONH3cmMLpTI44npjM8MoQWDWoBcCk1i5UHL3JXl/CCpqvkE3A5HhaPLogyaQmRd0HMo5blNXAiMZ08KQn09SgzlMaxy2mE1fHG083V7PFNRxJp16g2tbzckVKSlq3D38u9XHpsQVaunmX7zzMyKlQ1H9oIVcMvxJnkDHq+tc4kbUibBrx7e3t8PKr22Zit0/PNltPEX0jl+x0WLCxtB/S8IYi/jyRatcxgf08uX8s2SevTPJj+rerTJNiP8ymZPL5kr8nxHx7sxqi55sa9S46OzSUptC9d3tqEC3lMbq3n8RExuH7YpszhkNNyJ/C02xLqijQyhs5G5mZxTXoy8HcPurW+HoFg7t0dSbiSwdWMXG7+eBM/PNiNtqG18XLXzPrPfef5fe85VhwoeODOu7sj7646zHNDW1LHx53MHD2nkzPwcHNhUOsGtJi+gn4t6jFrbAdjOWnZOnw9XLmclk3n19fQOaIuD/Vpyn8XUpmxLJ4Xhrbk5nYhNKht+mvoZGI6yRk5PPfzfjpcV4cZI4uPMDt2OY39CSmMiCq9Ke+vgxdZuvccH4+JIiNHR5cZa/jgjkj6tbRg8RXgtT8OMn/TCb68rzO9mwUb01Mycqnl7VbmQyBXn4eLELiW8qvQEi5fyyY5PadYJaKyHLucRj1/zyp/+Famhq8MvxAdXv2L5PQc4/6i+zrTq9CNWV1M+3kfi7edAeDTuzvywFc7AfB0c2H22A5M+LL45zahRwSfbzrBmM6NjOfWdDqIw/zsGcuv+u58qvsfH7jPorlLyQ/T43kNGJHzKn95Ps23un58qL8VT3IYFRnM13tSynXt/L6R8rLw3k6M/2K7cf/OLuFEhgXwzE/7eGFoSzpH1GXYJ5vNnuvh6sKQtg14ckBzzqdkosuTjJ2/1SRPvxb1uJymGV7ClUw2T+3L4Pc3ci1bR/yrg8nM0fPd9jNEBPnw066zfHpXR1xcBIv+PcmLvx0wllP4ITtnbAdSs3KJCPLDzVXQIbyOMQTJw32aMiwyhE1HEnnFMEv9ozFR3Ny2IZ/9fZza3u5M/Xk/k29swjODWxjLz8rV4+7qgquLYOepZIL9vOj1tlYZW/NkbxZsOsH0m1sZH4j5SCmZs+EY/VvWp0FtL7rNWMNbt7VnaLuCaLbRr60mMS2b+FcHsyLuAsMjQ0p92Hy37TQNanvRNrQ28ReuMXb+Vv5+pg8hAd4mD5/GU/8kIsiXXyfHFHuAxZ1NoXkDf5MmzhxdHrq8PIsrk3FnU2gc5Iu/l7sy/MpyLSuXtrGrjPujOzVi5q3tSjmj6shvb8+/QRKuZHAiMZ2eNxQ8fJLTc5i97ig3GzpAIxsFANr6uskZOfx34Rr/HEtizvpjPNrvBh4f0IwfdpzhzRXxJKYVPNReHtaa1/48aJxUlt9s89OkbiRcyeTR7/YA4O3uSmaunpf+14rx3Rvz+aYTvPbnIUIDvMnI0XHFECYinzGdw1m87XQVfkpls99zAv7C8rWDW2YtIJNq6i9wEEZEhpCSmcu6/y6bpNf19TCpHJWHD0dH8vvec6w+dKnEPE3r+XH0UlqZZYXU9iI3T9KzaRBnr2ay9USy2XyP9G3K4wOasf7wZe4t9EAF7Vdliwb+dG8SxNdbThHTNJCD51MZ2+U6Pvv7eLFgiYV5amAzohvXJTTA26R1IDTAm7q+HoQGeHMiMZ3/LhYMAPnm/i7ENA1i+Ceb2JuQwgtDW+Lp5sLcDcfp1SyIu7pehz5P0iTYjz1nrjJ2/la6Nwnkn2NJAJx682Zl+BVFSsmppAzGf7GNk0naWPvIRgH8+pDjd8Tp8ySLt53mto5hZmtCUoKLi+DZH/exZMcZVjzWk6bBfiYLs2w7kcyUxbtY8Wgv6viW3PZ8MTWLS6nZ/O8TbTH3kzOHErv0AAv/OclHY6J4ZPFuQPuy5z9EPrsnmv9btIMgP08e6tOEJsF+9GoWbGxaaxdWm30JWs165WO9GPTBRgDGd29sjDJ6XaAPbslH0OHK7X270/z0tySf2Esbl5O0cjlFacyp/QSnElPxIZtzMpAVeZ0t/3AVChuhDL+C6PR5NH1+uUnaH1N6EFbHu0bFqNfp8zh8MY1WIbUqXVb8hVRydHm0CwtASokuTxp/xl7LysXPs+Cnbq4+j2d+3McDva83dq7ms/fMVUICvFlz6CIRQb50uT6QcQu2MaRNA0Z3DjeO+PnsnmgGfG+INhrzGGz+oFR9J6JfJGLHK9rO1DNku/ly8FwqZ65kciU9h5eWHuCFoS3ZcjzJpAbq5iJ4745IZvx5yNhxXZj8h1BogDdnrxb8qmgbWpsAH/cS+zrmjO3Ags0nii2oM6RNA25q25A6Ph7GzuvSsEanv8IxUIZfQc6nZNLtjbXG/c/HRVvcAaWwLZk5euJPniEqbSMsnWLZSfevhbCOkJUCF+LMDqe8fC2bYH9PQGtaEwKuZuRSv9DQ0OX7z5ObJ+kQHsDXW04zpE0D2obWJkefh5e7KykZuSRczeDnXWd57qaWCOCP/efp0TSI5PRs/jqoPUjeXBHP7ukDWLD5BB+vPUr3JoFMG9KSP/adY+qQFgghkFIyYvY/DGnTgJnL4xkRGcLv+86jzyv43na8rg7f3N+FFtNXMKh1fT69O9rYQXpLVCg/7z5rzHty5lCW7j1n/MXVo2kQd3YJp3ezYHJ0eQgBv+05x+A2DUjL1tHv3Q3MGNkWPy832ofVpvfb62lW34/DF02bW54Z3JwDZ1P5c/95o6aRUaG88GvpSzN6uLmQo8szSZt/TzTfbD1lbEZqHOjDkwObM8WguTBF3x/AxF7X89uesyYjz54e1Jy3V/4HQB0fdwa1bsAdnRoZJ1aG1PaifaMAlseZH81W1fh4uHJ31+v4tMgIwXyGtmtI65Ba/LQzgbVP9alawxdCDAY+BFyB+VLKmUWOjwfeBvI/+U+klPNLK9PWhr/79BUmf7PLOB69lpcb+2LVykF2TZ5eizG/8jkY+BosGg5Sbz5vcAu462f4aiQk/mdmlSjbIqUkW6c9IDJz9Hy89giT+zQtdXZ1tk6Pp5sr+jxJnpRsP5FMi4a1qOPjjhCCS6lZ+Hm5FesETMvWcTE1iyA/T2p7F4wgOXAuheuD/IwrsJkjOT2HuoWa8pbvP0/DAG9AeyD+d+EaLy09wLf3d6FTRF0e/W43D/ZuQruwAECbAAgQFV6Hc1czuZSazePf7+FqRg5LHuhGVKMA/th3Hon2Hdx56gpPDmwOwJGL18jKzaNtmBZR9Kstp5j+axy/TO5OnpTU8/eiUV0fpJQcvpjGlMW7+PK+zjSsrel7fMkeTiSm0zqkFi/9rzWnktJZ+M9JXhnextjZuvPUFc6nZHJzO60vbNfpKzy+ZA9v3NKWjYcTmbvhGICxWfLmdg05cjGNWt5unLuaReuQWozpEo67iwutQmoRfyGV/y5c478L1/hf+xDC6/rQ8611TL+5Fa/+cZBXR7RhTKdGXLqWTT1/T+ZvOsHM5fHMvasjg9s0YOpP+/hu+xl2vtCfYZ9s5uzVTH6a1I3IRnWMmqt0lI4QwhU4DAwAEoDtwBgp5cFCecYD0VLKhy29sC0N/8C5FIZ+tMm4P/euDrRsWIvrAlVIWrsi9TxkX4M614GLOyx7EnYssOzcu36Cpv2rVp8C0H4p55usJeQZfp2UNunOHIUfktVB4Yl/On0eb6/6j//reT1Bfp5WvcaBc6m0CdUeateycknJzCWsjg8931rLmeRM1j7Zm+uD/YznVPXEq87AUSnlccPFvgOGAwdLPcuOmfzNLpN9ZfZ2hJSw6T0tbPDiO8p//rOnwDvA6rIUJVMes4fyG30+QohqM/v86+XP8nZzdWHakJZVco18swfw93I3juMfERnKx2uPEuhrvQeMJYYfChQe2J0AdDGT71YhRC+0XwOPSymLDQYXQkwEJgKEh4eXX20liTubwrYTyZwyjMbJx5pPbEUFuHYRTm2Gg79B5FhY80rZ54R1guGz4cAvsH6GFstm8r/K7BVOw+P9m3F/z+tNmuEqi7Wmj/4OLJZSZgshHgC+BPoWzSSlnAfMA61Jx0rXtggpJTd/XNCM8/Sg5vRoGsSyuPP4lNKGqbAyaZfAzQs8/CAnDf54DOJ+Kjh+8NeyyxjwKnR7CFxcISRKS7v5PQgwHyhOoXBEXFyEVc0eLDP8s0Dhb1IYBZ2zAEgpkwrtzgfeqrw06zJilunsxHr+nrRvFEB7w4QlRRWSlwdZVyH+T1hqcTePKX4N4PEDsGshdBinmT1As4EweSsEN7eWWoXCabHE8LcDNwghItCMfjRwZ+EMQoiGUsr8WLLDgEPYGXsTCqbGj+nciFs6hNlQTQ3i446QVDygm1l8gyHdMKOz28PaqlJrX4XOE6HjvdpCIp3uL35evRbF0xQKRTHKNHwppU4I8TCwEm1Y5gIp5QEhxCvADinlUuARIcQwQAckA+OrUHO5Gf/FNuP21uf6mYypVliJT3tp4YInrIK938GvD2rt7JaaPcBTRyDlDPjW0xYRkRJaj4TAJlWnW6GoQTj9xCspJRHTlhn31dKBVuDUP7BrEURP0GrXn/UtWNPV1QP0ZcRY8aoN966A83sgojfE/QjdpoCLdReXUSicERUPvxS+31EwWOjTuzvaUIkT8cUQ7XXv4uLHzJl921Fac03U3XDlJIR2BP/6UL+VdrycseUVCkXFcHrDf/YnbW3Rj8dEMah1AxurcTAuxEFGIvgEglcA/DVdM2tL8KqthTAAuOUzbV1YKDB5hUJR7Ti14ReONxJWp3yTQ2ok+lzY8KbWVOMTCHPNRAw98ItlZU1YrQ23DOtUYPYKhcKmOK3hSykZ8qEWTtfD1YWo8Do2VmRnbP5QG+rYvtBs1kO/w8a3tb+KcNM72jj7vs9r+32mVV6nwirk5uaSkJBAVlbpq3wp7AcvLy/CwsJwd7e/iVd2xz0Lthkj+q1/+kbbirFH/npRe/X0h/RLkHZZm+1aGr71tLyBN0DTftB1Mnj4gszT2u5rq6Gu9kpCQgL+/v40btxYrSfrAEgpSUpKIiEhgYiICKuV65SGfy0r1xh//IcHuxESoJpzAMjNhOXPQEiHgrTvxpR9nocfBDWDe5dps2SVYTgcWVlZyuwdCCEEgYGBXL58uezM5cApDf+LzSeN250a17WdEFuSeUWLLBl1DywaBpfjtZo4aEMqLaXPC9D76arRqKhWlNk7FlXx/3JKw/euxoh6dsnV0/BBW23bkkBk+TQfCnd8rdXgczO1P9/AqtGoUCiqHaeb6XIxNYvXl2mRHer4WDfwkF1y6RBkXtW2s1K1tvjFd5Z6Cu6+MO538DSEZb3bMPKm0wRt8pMQ4OGjzF5hVVxdXYmMjKRNmzaMGjWKjIyMsk8qAT8/vzLzfPDBB5W6hqWcPHmSb7/9tsqvYw2czvBXFFqibN1TN9pOSHWQmwmzu8JXI7QhlTMbwTtN4eL+ks9pfhM8fw4iesHYH6DNrdps19gUrSNWoagivL292bNnD3FxcXh4eDB37lyT4zqdzqrXs6bhl6atIoZv7fdqKU7XpJNXKFSEwy9ErtdB8nEINizSHb8M8nTQapi2P+9G7fXcbpgRar6Mpv3h6GoI7wa3LYBaIQXHwrtof4oaxcu/H+DguVSrltnKsIygpfTs2ZN9+/axfv16pk+fTp06dYiPj+fQoUNMnTqV9evXk52dzUMPPcQDDzxQYjnr168nNjaWoKAg4uLi6NixI19//TUff/wx586do0+fPgQFBbFu3TpWrVrFSy+9RHZ2Nk2aNOGLL77Az8+PZcuW8cQTT+Dr60tMTAzHjx/njz/+IDY2lmPHjnH8+HHCw8N54403uPvuu0lPTwfgk08+oXv37kydOpVDhw4RGRnJuHHjmDRpEpMmTWLHjh24ubnx3nvv0adPHxYuXMjPP/9MWloaer2eDRs2VPpzLy9OZ/gnErV/xo8PdrOxEiuw8S1tItTDOyDohoIRNYNmaOu6FkafXfz8WMNM1zPboH5rbQilQmFjdDody5cvZ/DgwQDs2rWLuLg4IiIimDdvHrVr12b79u1kZ2cTExPDwIEDSx2auHv3bg4cOEBISAgxMTFs3ryZRx55hPfee49169YRFBREYmIir732GqtXr8bX15c333yT9957j2eeeYYHHniAjRs3EhERwZgxpqPWDh48yKZNm/D29iYjI4O//voLLy8vjhw5wpgxY9ixYwczZ87knXfe4Y8//gDg3XffRQjB/v37iY+PZ+DAgRw+fNj4Xvft20fdurYZTOJ0hr/o31P4e7kR7cijczKvaqEJzhqWYvysL9xaaE34omZfmPyx8k8dKUhr1LlKZCock/LUxK1JZmYmkZGRgFbDnzBhAv/88w+dO3c2GvqqVavYt28fP/74IwApKSkcOXKkVMPv3LkzYWHaHJDIyEhOnjxJjx49TPJs2bKFgwcPEhOjzR7PycmhW7duxMfHc/311xvLHzNmDPPmzTOeN2zYMLy9tWHdubm5PPzww+zZswdXV1ejiRdl06ZNTJkyBYAWLVpw3XXXGfMOGDDAZmYPTmb4+ZE/HXpW7dUz8EEbGPymVjMHyE6Fb283n7/FzdD3BZg/AHKuwVOHtUlQbmrZRoV9kd+GXxRf34JfnlJKPv74YwYNGmRxuZ6eBfe6q6ur2fZxKSUDBgxg8WLTgH/m9JSk7f3336d+/frs3buXvLw8vLzKH2a9cHm2wKk6bTNy9ADENHHA0SVH/oK/39OiSQKseBayU0o9BYCbP4B6LeHpIzD1jDbCRpm9wkEZNGgQc+bMITc3F4DDhw8b28zLi7+/P9euXQOga9eubN68maNHtfUZ0tPTOXz4MM2bN+f48eOcPHkSgCVLlpRYXkpKCg0bNsTFxYWvvvoKvV5f7Dqg/Xr55ptvjPpPnz5N8+b2sSKbU9XwDxg6ooL9Hczwzu6Cb26zLG/H8dDmNqhzHQQUWgje3Vv7UygcmPvvv5+TJ0/SoUMHpJQEBwfz66+/VqisiRMnMnjwYEJCQli3bh0LFy5kzJgxZGdr/V2vvfYazZo1Y/bs2QwePBhfX186depUYnmTJ0/m1ltvZdGiRcb8AO3atcPV1ZX27dszfvx4Jk+ezKRJk2jbti1ubm4sXLjQ5FeILXGqBVCe/mEvKw9c4N9p/fD1dKBn2b+zSm6XD2quxYv3DtCGUrr7qoVCFOXm0KFDtGzZ0tYy7JK0tDT8/PyQUvLQQw9xww038Pjjj9taFmD+/6YWQEELhbz+8GU6XlfHccx+4zuwdW7BOq6FmXYWpF7rvFUoFFXGZ599xpdffklOTg5RUVGlDgN1dBzEGcvm8rVsLl/LpnezYFtLsZy1rxZPm/AXeNYCz7JnEioUisrz+OOP202NvqpxGsO/dE2L8+0wkTHz9Kb745dBYzMLjigUCoWVcJrG4PgLWi/5dYEOMLko7TK8YhiL2/J/8NB2ZfYKhaLKcRrDP3opDQ83F5rVd4CmkPyhl6CFH84PnaBQKBRViNMYfsKVDOr5ezpGzO+ctILtoBtsp0OhUNQonMLw9yVcZdn+CzQJdoDaPRQY/sQN4FLDY/cragyVDY9cWlTKkydP0qZNmzLLmDFjRrmuWVH27NnDsmXLquVa5cEpDH/biWQApg5pYWMlFpJ2SXv19LetDoWiGikrPHJZWCPuvDUNv7QQxxUx/OoImewUo3RSM3MRAprXdxAD/fNJ7dUrwKYyFDWU5VPhQilrJlSEBm1hyEyLs+eHR05OTua+++7j+PHj+Pj4MG/ePNq1a8eGDRt49NFHAW2pv40bNxYLQ1zSUMqFCxeydOlSMjIyOHbsGCNHjuStt95i6tSpxgBurVu35ptvvuHrr7/mo48+Iicnhy5dujB79mxcXV35/PPPefPNNwkICKB9+/Z4enryySefMH78eLy8vNi9ezcxMTGMHj2aRx99lKysLLy9vfniiy+IiIjgxRdfJDMzk02bNjFt2jQGDBhg9n0WDcFcNNaPtXEKw0/JzMXf0w0XFztuv792QRuKmZcLSGjST60opaiRFA6P/NJLLxEVFcWvv/7K2rVrueeee9izZw/vvPMOs2bNIiYmhrS0NLy8vIqFIS6NPXv2sHv3bjw9PWnevDlTpkxh5syZfPLJJ8aAaYcOHWLJkiVs3rwZd3d3Jk+ezDfffEP//v159dVX2bVrF/7+/vTt25f27dsby05ISOCff/7B1dWV1NRU/v77b9zc3Fi9ejXPPfccP/30E6+88go7duzgk08+AWDKlClm3yeYhmCuapzC8PckpFCvVvkj11lE4lHY+DYM/wRcCy2ZmJsJWSkgXMGv0GQvKUGXDe6F9Kx8Hv79xLTc9qOrRq9CURblqIlbE3Phkbt06cJPP/0EQN++fUlKSiI1NZWYmBieeOIJxo4dyy233GIMf2wp/fr1o3ZtbZZ6q1atOHXqFI0aNTLJs2bNGnbu3GmMn5OZmUm9evXYtm0bvXv3NoYxHjVqlEko5FGjRuHqqvW9paSkMG7cOI4cOYIQwhj0rSibNm0y+z7BNARzVePwhi+l5OjFa9zW0cIbIk+vmXJeLri4g6uZjyArFdx9QLjAz/drK0p1/j8I7Qi6LHi9gWn+8O4w6HXwqwefdNba5psPgZ1flKyjxVDL36RC4QSUFB7ZHFOnTmXo0KEsW7aMmJgYVq5cWa5rWRoyedy4cbzxxhsm6WUFaysc4nj69On06dOHX375hZMnT3LjjTeWS2fR8qoah++0TcnMJT1HT1gdn7Iz5+XBB23h1UDNtL8aYXo8+QQsnaKtDftqILxSRzN7gPn94OWA4mYPcPof+KwPvN8actMh7ULpZl+/jVp9SqHANJTw+vXrCQoKolatWhw7doy2bdvy7LPP0qlTJ+Lj44uFIa4I7u7uxlp4v379+PHHH7l0SRtEkZyczKlTp+jUqRMbNmzgypUr6HQ6Y83cHCkpKYSGasuLLly40JheWsjkwu+zurHI8IUQg4UQ/wkhjgohppo57imEWGI4vlUI0djqSs2Rdhk+H8T14hzNG5TSYSsl7P5aM/DUswXpJ/+G2Nra34pp8FEk7FpU5bIZ+FrVX0OhcABiY2PZuXMn7dq1Y+rUqXz55ZeAtgB5mzZtaNeuHe7u7gwZMsQkDPH7779foetNnDiRdu3aMXbsWFq1asVrr73GwIEDadeuHQMGDOD8+fOEhoby3HPP0blzZ2JiYmjcuLGxeagozzzzDNOmTSMqKsrkV0SfPn04ePAgkZGRLFmypMT3Wd2UGR5ZCOEKHAYGAAnAdmCMlPJgoTyTgXZSygeFEKOBkVLKO0ort7LhkZOPbsNv8XA89Bms17en64vr8HIvNKY95SzE/wHLn6nwNcqNizsENYNLB4ofa30LBDaBrpPBx4GXX1Q4JCo8cvnID5ms0+kYOXIk9913HyNHjqx2HbYIj9wZOCqlPG642HfAcOBgoTzDgVjD9o/AJ0IIIaso2P6Jo4eI+HqAcb+by0E8z20FFzet3X1+v4oXfl0MnNpcPD2oGUzeCp/3h7M7oekAiL5Xe7C0HlmwrKBvEMzuBpcOwrOnYMfn2kpWI2arBUoUCgchNjaW1atXk5WVxcCBAxkxYoStJVkFSww/FDhTaD8B6FJSHimlTgiRAgQCiYUzCSEmAhMBwsPDqRBSEvF1V5MkT5ELXwwpXznDZ0PmFVjzsmbWABPXQ0iU1sQDWgTL9Mvwwzi46W1t4ZF7foMrp6BBKbP67l+tjeLxDoCeT2p/CoXCYXjnnXdsLaFKqNZROlLKecA80Jp0KlSIEMxq8Cpjz8/kbd0dvO6+oOxzHt4Bh5bCmlcK0tqP1sIadH8Y1s3QRuCERGnHJqzWauP5pt660Nqynv6lmz1oHbKqU1ZhZ0gpHSPWlALQ/l/WxhLDPwsUHsAaZkgzlydBCOEG1AaSrKLQDINvvY9hX7Sle5NA2G/G8O/+RRsfv2iYth90g1bLjnlcW+S76E3fp8jygo1KXtdSoXBEvLy8SEpKIjAwUJm+AyClJCkpCS8v684vssTwtwM3CCEi0Ix9NHBnkTxLgXHAv8BtwNqqar8HaBLsx8Zn+mg7YW9pHbMdxsGuL7Vww036aseePgb6QhMh1FqwihpKWFgYCQkJXL5sZjlNhV3i5eVV7glnZWHRIuZCiJuADwBXYIGU8nUhxCvADinlUiGEF/AVEAUkA6PzO3lLoioWMUfK4rV3hUKhcCKqfBFzKeUyYFmRtBcLbWcBoyoiwKoos1coFIoSUW0cCoVCUUNQhq9QKBQ1BIva8KvkwkJcBk5VooggiozzdwAcUTMo3dWJI2oGx9TtiJoBmkspK7T4h82iZUopg8vOVTJCiB0V7biwFY6oGZTu6sQRNYNj6nZEzaDprui5qklHoVAoagjK8BUKhaKG4MiGP8/WAiqAI2oGpbs6cUTN4Ji6HVEzVEK3zTptFQqFQlG9OHINX6FQKBTlQBm+QqFQ1BDs3vDtdnnFUrBA8xNCiINCiH1CiDVCiOtsobMoZekulO9WIYQUQth8SJslmoUQtxs+7wNCiG+rW6M5LLhHwoUQ64QQuw33yU220FlE0wIhxCUhRFwJx4UQ4iPDe9onhOhQ3RrNaCpL81iD1v1CiH+EEO2rW6M5ytJdKF8nIYROCHGbRQVLKe32Dy1Y2zHgesAD2Au0KpJnMjDXsD0aWOIAmvsAPobtSbbWbKluQz5/YCOwBYi2d83ADcBuoI5hv54jfNZoHXOTDNutgJN2oLsX0AGIK+H4TcByQABdga0OoLl7oXtjiD1otkR3oftoLVqcs9ssKdfea/jG5RWllDlA/vKKhRkO5K8I/CPQT9g24HeZmqWU66SUGYbdLWhrDNgaSz5rgFeBN4Gs6hRXApZo/j9glpTyCoCU8lI1azSHJbolUMuwXRs4V436zCKl3IgWDbckhgOLpMYWIEAI0bB61JmnLM1Syn/y7w3s57toyWcNMAX4CbD4nrZ3wze3vGJoSXmklDogf3lFW2GJ5sJMQKsV2ZoydRt+ojeSUv5ZncJKwZLPuhnQTAixWQixRQgxuNrUlYwlumOBu4QQCWg1uCnVI61SlPfetzfs5btYJkKIUGAkMKc859kstIIChBB3AdFAb1trKQshhAvwHjDexlLKixtas86NaLW3jUKItlLKq7YUZQFjgIVSyneFEN2Ar4QQbaSUebYW5owIIfqgGX4PW2uxkA+AZ6WUeeVp0LB3w7e75RUtwBLNCCH6A88DvaWU2dWkrTTK0u0PtAHWG26wBsBSIcQwKaWVV7KxGEs+6wS0dtlc4IQQ4jDaA2B79Ug0iyW6JwCDAaSU/xoWGQqiHD/fbYBF9769IYRoB8wHhkgpbekd5SEa+M7wXQwCbhJC6KSUv5Z6lq07J8rouHADjgMRFHRutS6S5yFMO22/dwDNUWiddjfY+jMuj+4i+ddj+05bSz7rwcCXhu0gtCaHQAfQvRwYb9huidaGL+zgPmlMyR2gQzHttN1ma70WaA4HjgLdba2zPLqL5FuIhZ22dl3Dl1LqhBAPAyspWF7xQOHlFYHP0X7uHsWwvKLtFFus+W3AD/jB8IQ+LaUcZjPRWKzbrrBQ80pgoBDiIKAHnpY2rsVZqPtJ4DMhxONoHbjjpeHbbSuEEIvRmsaCDH0LLwHuAFLKuWh9DTehGWgGcK9tlBZggeYX0fr8Zhu+izppBxE0LdBdsXJtfA8pFAqFopqw91E6CoVCobASyvAVCoWihqAMX6FQKGoIyvAVCoWihqAMX6FQKGoIyvAVCoWihqAMX6FQKGoI/w9kYEQY2RvCzQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with nengo.Network() as net:\n", + " tau = 0.1\n", + "\n", + " with nengo.Network() as mpfc:\n", + " with nengo.Network() as pre_integrator:\n", + " pre_ensemble = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " pre_ensemble,\n", + " pre_ensemble[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " with nengo.Network() as post_integrator:\n", + " post_ensemble = nengo.Ensemble(200, dimensions=2)\n", + " nengo.Connection(\n", + " post_ensemble,\n", + " post_ensemble[0],\n", + " function=lambda x: x[0] * (1.0 - x[1]),\n", + " synapse=tau,\n", + " )\n", + " nengo.Connection(pre_ensemble[0], post_ensemble[0], transform=0.16)\n", + "\n", + " with nengo.Network() as motor:\n", + " press = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + " release = nengo.Ensemble(\n", + " 30, dimensions=1, encoders=Choice([[1]]), intercepts=Choice([0.85])\n", + " )\n", + "\n", + " nengo.Connection(press, pre_ensemble[0], transform=tau * 3)\n", + " nengo.Connection(press, post_ensemble[1], transform=-1 * 6)\n", + "\n", + " nengo.Connection(post_ensemble[0], release)\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), press)\n", + "\n", + " pr_press = nengo.Probe(press, synapse=0.01)\n", + " pr_release = nengo.Probe(release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(pre_ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(post_ensemble[0], synapse=0.01)\n", + "\n", + "test_doubleintegrator(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This exhibits some of the hypotheses of this model;\n", + "specifically, that the medial prefrontal cortex\n", + "contains these integrators,\n", + "and that they can be involved\n", + "in control of motor actions.\n", + "\n", + "Let's do both of the remaining principles\n", + "by making some parameterized functions\n", + "to get a sense of all of the knobs\n", + "that be be tweaked in this model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:09.298866Z", + "iopub.status.busy": "2020-11-25T16:51:09.293621Z", + "iopub.status.idle": "2020-11-25T16:51:10.310287Z", + "shell.execute_reply": "2020-11-25T16:51:10.309847Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def controlled_integrator(n_neurons, dimensions, tau=0.1):\n", + " with nengo.Network() as integrator:\n", + " integrator.ensemble = nengo.Ensemble(n_neurons, dimensions=dimensions + 1)\n", + " nengo.Connection(\n", + " integrator.ensemble,\n", + " integrator.ensemble[:dimensions],\n", + " function=lambda x: x[:-1] * (1.0 - x[-1]),\n", + " synapse=tau,\n", + " )\n", + " return integrator\n", + "\n", + "\n", + "def medial_pfc(coupling_strength, n_neurons_per_integrator=200, tau=0.1):\n", + " with nengo.Network() as medial_pfc:\n", + " medial_pfc.pre = controlled_integrator(n_neurons_per_integrator, 1, tau)\n", + " medial_pfc.post = controlled_integrator(n_neurons_per_integrator, 1, tau)\n", + " nengo.Connection(\n", + " medial_pfc.pre.ensemble[0],\n", + " medial_pfc.post.ensemble[0],\n", + " transform=coupling_strength,\n", + " )\n", + " return medial_pfc\n", + "\n", + "\n", + "def motor_cortex(command_threshold, n_neurons_per_command=30):\n", + " with nengo.Network() as motor_cortex:\n", + " ens_args = {\n", + " \"dimensions\": 1,\n", + " \"encoders\": Choice([[1]]),\n", + " \"intercepts\": Choice([command_threshold]),\n", + " }\n", + " motor_cortex.press = nengo.Ensemble(n_neurons_per_command, **ens_args)\n", + " motor_cortex.release = nengo.Ensemble(n_neurons_per_command, **ens_args)\n", + " return motor_cortex\n", + "\n", + "\n", + "def double_integrator(\n", + " mpfc_coupling_strength,\n", + " command_threshold,\n", + " press_to_pre_gain=3,\n", + " press_to_post_control=-6,\n", + " recurrent_tau=0.1,\n", + " seed=None,\n", + "):\n", + " with nengo.Network(seed=seed) as net:\n", + " net.mpfc = medial_pfc(mpfc_coupling_strength)\n", + " net.motor = motor_cortex(command_threshold)\n", + " nengo.Connection(\n", + " net.motor.press,\n", + " net.mpfc.pre.ensemble[0],\n", + " transform=recurrent_tau * press_to_pre_gain,\n", + " )\n", + " nengo.Connection(\n", + " net.motor.press, net.mpfc.post.ensemble[1], transform=press_to_post_control\n", + " )\n", + " nengo.Connection(net.mpfc.post.ensemble[0], net.motor.release)\n", + " return net\n", + "\n", + "\n", + "def test_doubleintegrator_wprobes(net):\n", + " # Provide input and probe outside of network construction,\n", + " # for more flexibility\n", + " with net:\n", + " nengo.Connection(nengo.Node(lambda t: 1 if t < 0.2 else 0), net.motor.press)\n", + " pr_press = nengo.Probe(net.motor.press, synapse=0.01)\n", + " pr_release = nengo.Probe(net.motor.release, synapse=0.01)\n", + " pr_pre_int = nengo.Probe(net.mpfc.pre.ensemble[0], synapse=0.01)\n", + " pr_post_int = nengo.Probe(net.mpfc.post.ensemble[0], synapse=0.01)\n", + " with nengo.Simulator(net) as sim:\n", + " sim.run(1.4)\n", + " t = sim.trange()\n", + " plt.figure()\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(t, sim.data[pr_press], c=\"b\", label=\"Press\")\n", + " plt.plot(t, sim.data[pr_release], c=\"g\", label=\"Release\")\n", + " plt.axvspan(0, 0.2, color=\"b\", alpha=0.3)\n", + " plt.axvspan(0.8, 1.2, color=\"g\", alpha=0.3)\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(t, sim.data[pr_pre_int], label=\"Pre Integrator\")\n", + " plt.plot(t, sim.data[pr_post_int], label=\"Post Integrator\")\n", + " plt.xlim(right=1.4)\n", + " plt.legend(loc=\"best\")\n", + "\n", + "\n", + "net = double_integrator(mpfc_coupling_strength=0.16, command_threshold=0.85)\n", + "test_doubleintegrator_wprobes(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this does add some complexity and length,\n", + "the effort is worth it,\n", + "as we can now do simple parameters sweeps\n", + "to investigate the effects\n", + "of changing various parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:10.325996Z", + "iopub.status.busy": "2020-11-25T16:51:10.323149Z", + "iopub.status.idle": "2020-11-25T16:51:13.128838Z", + "shell.execute_reply": "2020-11-25T16:51:13.129272Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for coupling_strength in (0.11, 0.16, 0.21):\n", + " net = double_integrator(\n", + " mpfc_coupling_strength=coupling_strength, command_threshold=0.85, seed=0\n", + " )\n", + " test_doubleintegrator_wprobes(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `coupling_strength` changes how quickly\n", + "the \"Post Integrator\" rises,\n", + "which causes the release to occur\n", + "at different times.\n", + "Of the values tested,\n", + "only a coupling strength of 0.16\n", + "effects the release during the release window\n", + "(for this particular network seed).\n", + "\n", + "We will looks at some more techniques\n", + "for cleaning up this network code\n", + "and making it more flexible\n", + "in \"Additional tips and tricks for designing networks\"." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/network_design.html b/examples/usage/network_design.html new file mode 100644 index 0000000000..ad7dbe5652 --- /dev/null +++ b/examples/usage/network_design.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/usage/network_design_advanced.html b/examples/usage/network_design_advanced.html new file mode 100644 index 0000000000..606f9f59a1 --- /dev/null +++ b/examples/usage/network_design_advanced.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/usage/rectified-linear.html b/examples/usage/rectified-linear.html new file mode 100644 index 0000000000..90ea73fb61 --- /dev/null +++ b/examples/usage/rectified-linear.html @@ -0,0 +1,746 @@ + + + + + + + + Adding new objects to Nengo — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + +
+

Adding new objects to Nengo

+

It is possible to add new objects to the Nengo reference simulator. This involves several steps and the creation of several objects. In this example, we’ll go through these steps in order to add a new neuron type to Nengo: a rectified linear neuron.

+

The RectifiedLinear class is what you will use in model scripts to denote that a particular ensemble should be simulated using a rectified linear neuron instead of one of the existing neuron types (e.g., LIF).

+

Normally, these kinds of frontend classes exist in a file in the root nengo directory, like nengo/neurons.py or nengo/synapses.py. Look at these files for examples of how to make your own. In this case, because we’re making a neuron type, we’ll use nengo.neurons.LIF as an example of how to make RectifiedLinear.

+
+
[1]:
+
+
+
+%matplotlib inline
+import numpy as np
+import matplotlib.pyplot as plt
+
+import nengo
+from nengo.utils.ensemble import tuning_curves
+
+
+# Neuron types must subclass `nengo.neurons.NeuronType`
+class RectifiedLinear(nengo.neurons.NeuronType):
+    """A rectified linear neuron model."""
+
+    # We don't need any additional parameters here;
+    # gain and bias are sufficient. But, if we wanted
+    # more parameters, we could accept them by creating
+    # an __init__ method.
+
+    def gain_bias(self, max_rates, intercepts):
+        """Return gain and bias given maximum firing rate and x-intercept."""
+        gain = max_rates / (1 - intercepts)
+        bias = -intercepts * gain
+        return gain, bias
+
+    def step(self, dt, J, output):
+        """Compute rates in Hz for input current (incl. bias)"""
+        output[...] = np.maximum(0.0, J)
+
+
+
+

You can use RectifiedLinear like any other neuron type without making modifications to the reference simulator. However, other objects, including more complicated neuron types, may require changes to the reference simulator.

+
+

Tuning curves

+

We can build a small network just to see the tuning curves.

+
+
[2]:
+
+
+
+model = nengo.Network()
+with model:
+    encoders = np.tile([[1], [-1]], (4, 1))
+    intercepts = np.linspace(-0.8, 0.8, 8)
+    intercepts *= encoders[:, 0]
+    A = nengo.Ensemble(
+        8,
+        dimensions=1,
+        intercepts=intercepts,
+        neuron_type=RectifiedLinear(),
+        max_rates=nengo.dists.Uniform(80, 100),
+        encoders=encoders,
+    )
+with nengo.Simulator(model) as sim:
+    eval_points, activities = tuning_curves(A, sim)
+plt.figure()
+plt.plot(eval_points, activities, lw=2)
+plt.xlabel("Input signal")
+plt.ylabel("Firing rate (Hz)")
+
+
+
+
+
[2]:
+
+
+
+
+Text(0, 0.5, 'Firing rate (Hz)')
+
+
+
+
+
+
+../../_images/examples_usage_rectified-linear_3_1.png +
+
+
+
+

2D Representation example

+

Below is the same model as is made in the 2d_representation example, except now using RectifiedLinear neurons insated of nengo.LIF.

+
+
[3]:
+
+
+
+model = nengo.Network(label="2D Representation", seed=10)
+with model:
+    neurons = nengo.Ensemble(100, dimensions=2, neuron_type=RectifiedLinear())
+    sin = nengo.Node(output=np.sin)
+    cos = nengo.Node(output=np.cos)
+    nengo.Connection(sin, neurons[0])
+    nengo.Connection(cos, neurons[1])
+    sin_probe = nengo.Probe(sin, "output")
+    cos_probe = nengo.Probe(cos, "output")
+    neurons_probe = nengo.Probe(neurons, "decoded_output", synapse=0.01)
+with nengo.Simulator(model) as sim:
+    sim.run(5)
+
+
+
+
+
[4]:
+
+
+
+plt.figure()
+plt.plot(sim.trange(), sim.data[neurons_probe], label="Decoded output")
+plt.plot(sim.trange(), sim.data[sin_probe], "r", label="Sine")
+plt.plot(sim.trange(), sim.data[cos_probe], "k", label="Cosine")
+plt.legend()
+
+
+
+
+
[4]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f89031df710>
+
+
+
+
+
+
+../../_images/examples_usage_rectified-linear_6_1.png +
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/usage/rectified-linear.ipynb b/examples/usage/rectified-linear.ipynb new file mode 100644 index 0000000000..2c527c5e80 --- /dev/null +++ b/examples/usage/rectified-linear.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding new objects to Nengo\n", + "\n", + "It is possible to add new objects\n", + "to the Nengo reference simulator.\n", + "This involves several steps and the creation\n", + "of several objects.\n", + "In this example, we'll go through these steps\n", + "in order to add a new neuron type to Nengo:\n", + "a rectified linear neuron.\n", + "\n", + "The `RectifiedLinear` class is what you will use\n", + "in model scripts to denote that a particular ensemble\n", + "should be simulated using a rectified linear neuron\n", + "instead of one of the existing neuron types (e.g., `LIF`).\n", + "\n", + "Normally, these kinds of frontend classes exist\n", + "in a file in the root `nengo` directory,\n", + "like `nengo/neurons.py` or `nengo/synapses.py`.\n", + "Look at these files for examples of how to make your own.\n", + "In this case, because we're making a neuron type,\n", + "we'll use `nengo.neurons.LIF` as an example\n", + "of how to make `RectifiedLinear`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:21.402613Z", + "iopub.status.busy": "2020-11-25T16:51:21.401749Z", + "iopub.status.idle": "2020-11-25T16:51:21.900434Z", + "shell.execute_reply": "2020-11-25T16:51:21.899908Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nengo\n", + "from nengo.utils.ensemble import tuning_curves\n", + "\n", + "\n", + "# Neuron types must subclass `nengo.neurons.NeuronType`\n", + "class RectifiedLinear(nengo.neurons.NeuronType):\n", + " \"\"\"A rectified linear neuron model.\"\"\"\n", + "\n", + " # We don't need any additional parameters here;\n", + " # gain and bias are sufficient. But, if we wanted\n", + " # more parameters, we could accept them by creating\n", + " # an __init__ method.\n", + "\n", + " def gain_bias(self, max_rates, intercepts):\n", + " \"\"\"Return gain and bias given maximum firing rate and x-intercept.\"\"\"\n", + " gain = max_rates / (1 - intercepts)\n", + " bias = -intercepts * gain\n", + " return gain, bias\n", + "\n", + " def step(self, dt, J, output):\n", + " \"\"\"Compute rates in Hz for input current (incl. bias)\"\"\"\n", + " output[...] = np.maximum(0.0, J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use `RectifiedLinear` like any other neuron type\n", + "without making modifications to the reference simulator.\n", + "However, other objects, including more complicated neuron types,\n", + "may require changes to the reference simulator.\n", + "\n", + "## Tuning curves\n", + "\n", + "We can build a small network just to see the tuning curves." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:21.910143Z", + "iopub.status.busy": "2020-11-25T16:51:21.909328Z", + "iopub.status.idle": "2020-11-25T16:51:22.100888Z", + "shell.execute_reply": "2020-11-25T16:51:22.100418Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Firing rate (Hz)')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " encoders = np.tile([[1], [-1]], (4, 1))\n", + " intercepts = np.linspace(-0.8, 0.8, 8)\n", + " intercepts *= encoders[:, 0]\n", + " A = nengo.Ensemble(\n", + " 8,\n", + " dimensions=1,\n", + " intercepts=intercepts,\n", + " neuron_type=RectifiedLinear(),\n", + " max_rates=nengo.dists.Uniform(80, 100),\n", + " encoders=encoders,\n", + " )\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(A, sim)\n", + "plt.figure()\n", + "plt.plot(eval_points, activities, lw=2)\n", + "plt.xlabel(\"Input signal\")\n", + "plt.ylabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2D Representation example\n", + "\n", + "Below is the same model as is made in the 2d_representation example,\n", + "except now using `RectifiedLinear` neurons insated of `nengo.LIF`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:22.112905Z", + "iopub.status.busy": "2020-11-25T16:51:22.112112Z", + "iopub.status.idle": "2020-11-25T16:51:22.606898Z", + "shell.execute_reply": "2020-11-25T16:51:22.607360Z" + } + }, + "outputs": [], + "source": [ + "model = nengo.Network(label=\"2D Representation\", seed=10)\n", + "with model:\n", + " neurons = nengo.Ensemble(100, dimensions=2, neuron_type=RectifiedLinear())\n", + " sin = nengo.Node(output=np.sin)\n", + " cos = nengo.Node(output=np.cos)\n", + " nengo.Connection(sin, neurons[0])\n", + " nengo.Connection(cos, neurons[1])\n", + " sin_probe = nengo.Probe(sin, \"output\")\n", + " cos_probe = nengo.Probe(cos, \"output\")\n", + " neurons_probe = nengo.Probe(neurons, \"decoded_output\", synapse=0.01)\n", + "with nengo.Simulator(model) as sim:\n", + " sim.run(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:22.613628Z", + "iopub.status.busy": "2020-11-25T16:51:22.612517Z", + "iopub.status.idle": "2020-11-25T16:51:22.799865Z", + "shell.execute_reply": "2020-11-25T16:51:22.799413Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(sim.trange(), sim.data[neurons_probe], label=\"Decoded output\")\n", + "plt.plot(sim.trange(), sim.data[sin_probe], \"r\", label=\"Sine\")\n", + "plt.plot(sim.trange(), sim.data[cos_probe], \"k\", label=\"Cosine\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/rectified_linear.html b/examples/usage/rectified_linear.html new file mode 100644 index 0000000000..9b2a497d07 --- /dev/null +++ b/examples/usage/rectified_linear.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/examples/usage/tuning-curves.html b/examples/usage/tuning-curves.html new file mode 100644 index 0000000000..abc1e8c938 --- /dev/null +++ b/examples/usage/tuning-curves.html @@ -0,0 +1,899 @@ + + + + + + + + Tuning curves — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Note

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+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

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Tuning curves

+

One of the most common ways to tweak models and debug failures is to look at the tuning curves of the neurons in an ensemble. The tuning curve tells us how each neuron responds to an incoming input signal.

+
+

1-dimensional ensembles

+

The tuning curve is easiest to interpret in the one-dimensional case. Since the input is a single scalar, we use that value as the x-axis, and the neuron response as the y-axis.

+
+
[1]:
+
+
+
+%matplotlib inline
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+import nengo
+from nengo.dists import Choice
+from nengo.utils.ensemble import response_curves, tuning_curves
+
+
+
+
+
[2]:
+
+
+
+model = nengo.Network()
+with model:
+    ens_1d = nengo.Ensemble(15, dimensions=1)
+with nengo.Simulator(model) as sim:
+    eval_points, activities = tuning_curves(ens_1d, sim)
+
+plt.figure()
+plt.plot(eval_points, activities)
+# We could have alternatively shortened this to
+# plt.plot(*tuning_curves(ens_1d, sim))
+plt.ylabel("Firing rate (Hz)")
+plt.xlabel("Input scalar, x")
+
+
+
+
+
[2]:
+
+
+
+
+Text(0.5, 0, 'Input scalar, x')
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_3_1.png +
+
+

Each coloured line represents the response on one neuron. As you can see, the neurons cover the space pretty well, but there is no clear pattern to their responses.

+

If there is some biological or functional reason to impose some pattern to their responses, we can do so by changing the parameters of the ensemble.

+
+
[3]:
+
+
+
+ens_1d.intercepts = Choice([-0.2])  # All neurons have x-intercept -0.2
+with nengo.Simulator(model) as sim:
+    plt.figure()
+    plt.plot(*tuning_curves(ens_1d, sim))
+
+plt.ylabel("Firing rate (Hz)")
+plt.xlabel("Input scalar, x")
+
+
+
+
+
[3]:
+
+
+
+
+Text(0.5, 0, 'Input scalar, x')
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_5_1.png +
+
+

Now, there is a clear pattern to the tuning curve. However, note that some neurons start firing at -0.2, while others stop firing at 0.2. This is because the input signal, x, is multiplied by a neuron’s encoder when it is converted to input current.

+

We could further constrain the tuning curves by changing the encoders of the ensemble.

+
+
[4]:
+
+
+
+ens_1d.encoders = Choice([[1]])  # All neurons have encoder [1]
+with nengo.Simulator(model) as sim:
+    plt.figure()
+    plt.plot(*tuning_curves(ens_1d, sim))
+
+plt.ylabel("Firing rate (Hz)")
+plt.xlabel("Input scalar, x")
+
+
+
+
+
[4]:
+
+
+
+
+Text(0.5, 0, 'Input scalar, x')
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_7_1.png +
+
+

This gives us an ensemble of neurons that respond very predictably to input. In some cases, this is important to the proper functioning of a model, or to matching what we know about the physiology of a brain area or neuron type.

+
+
+

2-dimensional ensembles

+

In a two-dimensional ensemble, the input is represented by two scalar values, meaning that we will need three axes to represent its tuning curve; two for input dimensions, and one for the neural activity.

+

Fortunately, we are able to plot data in 3D.

+

If there is a clear pattern to the tuning curves, then visualizing them all is (sort of) possible.

+
+
[5]:
+
+
+
+model = nengo.Network()
+with model:
+    ens_2d = nengo.Ensemble(15, dimensions=2, encoders=Choice([[1, 1]]))
+with nengo.Simulator(model) as sim:
+    eval_points, activities = tuning_curves(ens_2d, sim)
+
+plt.figure(figsize=(8, 8))
+ax = plt.subplot(111, projection="3d")
+ax.set_title("Tuning curve of all neurons")
+for i in range(ens_2d.n_neurons):
+    ax.plot_surface(
+        eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn
+    )
+ax.set_xlabel("$x_1$")
+ax.set_ylabel("$x_2$")
+ax.set_zlabel("Firing rate (Hz)")
+
+
+
+
+
[5]:
+
+
+
+
+Text(0.5, 0, 'Firing rate (Hz)')
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_10_1.png +
+
+

But in most cases, for 2D ensembles, we have to look at each neuron’s tuning curve separately.

+
+
[6]:
+
+
+
+ens_2d.encoders = nengo.Default
+with nengo.Simulator(model) as sim:
+    eval_points, activities = tuning_curves(ens_2d, sim)
+
+plt.figure(figsize=(12, 12))
+for i in range(4):
+    ax = plt.subplot(2, 2, i + 1, projection=Axes3D.name)
+    ax.set_title("Neuron %d" % i)
+    ax.plot_surface(
+        eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn
+    )
+    ax.set_xlabel("$x_1$")
+    ax.set_ylabel("$x_2$")
+    ax.set_zlabel("Firing rate (Hz)")
+
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_12_0.png +
+
+
+
+

N-dimensional ensembles

+

The tuning_curve function accepts ensembles of any dimensionality, and will always return eval_points and activities. However, for ensembles of dimensionality greater than 2, these are large arrays and it becomes nearly impossible to visualize them.

+

There are two main approaches to investigating the tuning curves of ensembles of arbitrary dimensionality.

+
+

Clamp some axes

+

In many cases, we only care about the neural sensitivity to one or two dimensions. We can investigate those dimensions specifically by only varying those dimensions, and keeping the rest constant.

+

To do this, we will use the inputs argument to the tuning_curves function, which allows us to define the input signals that will drive the neurons to determine their activity. In other words, we are specifying the eval_point parameter to generate the activities.

+
+
[7]:
+
+
+
+model = nengo.Network()
+with model:
+    ens_3d = nengo.Ensemble(15, dimensions=3)
+
+inputs = np.zeros((50, 3))
+# Vary the first dimension
+inputs[:, 0] = np.linspace(-ens_3d.radius, ens_3d.radius, 50)
+inputs[:, 1] = 0.5  # Clamp the second dimension
+inputs[:, 2] = 0.5  # Clamp the third dimension
+
+with nengo.Simulator(model) as sim:
+    eval_points, activities = tuning_curves(ens_3d, sim, inputs=inputs)
+
+assert eval_points is inputs  # The inputs will be returned as eval_points
+
+plt.figure()
+plt.plot(inputs.T[0], activities)
+plt.ylabel("Firing rate (Hz)")
+plt.xlabel("$x_0$")
+
+
+
+
+
[7]:
+
+
+
+
+Text(0.5, 0, '$x_0$')
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_15_1.png +
+
+

Notice that these tuning curves are much more broad than those in the 1-dimensional case. This is because some neurons are not very sensitive to the dimension that are varying, and are instead sensitive to one or two of the other dimensions. If we wanted these neurons to be more sharply tuned to this dimension, we could change their encoders to be more tuned to this dimension.

+
+
+

Response curves

+

If all else fails, we can still get some information about the tuning properties of the neurons in the ensemble using the response curve. The response curve is similar to the tuning curve, but instead of looking at the neural response to a particular input stimulus, we are instead looking at its response to (relative) injected current. This is analogous to the tuning curves with the inputs aligned to the preferred directions of each neuron.

+
+
[8]:
+
+
+
+model = nengo.Network()
+with model:
+    ens_5d = nengo.Ensemble(15, dimensions=5)
+with nengo.Simulator(model) as sim:
+    plt.figure()
+    plt.plot(*response_curves(ens_5d, sim))
+
+plt.ylabel("Firing rate (Hz)")
+plt.xlabel("x along preferred direction")
+
+
+
+
+
[8]:
+
+
+
+
+Text(0.5, 0, 'x along preferred direction')
+
+
+
+
+
+
+../../_images/examples_usage_tuning-curves_18_1.png +
+
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/examples/usage/tuning-curves.ipynb b/examples/usage/tuning-curves.ipynb new file mode 100644 index 0000000000..d158fc3ece --- /dev/null +++ b/examples/usage/tuning-curves.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tuning curves\n", + "\n", + "One of the most common ways\n", + "to tweak models\n", + "and debug failures\n", + "is to look at the **tuning curves**\n", + "of the neurons in an ensemble.\n", + "The tuning curve tells us\n", + "how each neuron responds\n", + "to an incoming input signal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-dimensional ensembles\n", + "\n", + "The tuning curve is easiest\n", + "to interpret in the one-dimensional case.\n", + "Since the input is a single scalar,\n", + "we use that value as the x-axis,\n", + "and the neuron response as the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.072197Z", + "iopub.status.busy": "2020-11-25T16:51:24.071310Z", + "iopub.status.idle": "2020-11-25T16:51:24.568207Z", + "shell.execute_reply": "2020-11-25T16:51:24.567647Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "import nengo\n", + "from nengo.dists import Choice\n", + "from nengo.utils.ensemble import response_curves, tuning_curves" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.575636Z", + "iopub.status.busy": "2020-11-25T16:51:24.574831Z", + "iopub.status.idle": "2020-11-25T16:51:24.786325Z", + "shell.execute_reply": "2020-11-25T16:51:24.786764Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Input scalar, x')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_1d = nengo.Ensemble(15, dimensions=1)\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_1d, sim)\n", + "\n", + "plt.figure()\n", + "plt.plot(eval_points, activities)\n", + "# We could have alternatively shortened this to\n", + "# plt.plot(*tuning_curves(ens_1d, sim))\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each coloured line represents the response on one neuron.\n", + "As you can see, the neurons cover the space pretty well,\n", + "but there is no clear pattern to their responses.\n", + "\n", + "If there is some biological or functional reason\n", + "to impose some pattern to their responses,\n", + "we can do so by changing the parameters\n", + "of the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.793268Z", + "iopub.status.busy": "2020-11-25T16:51:24.792367Z", + "iopub.status.idle": "2020-11-25T16:51:24.964251Z", + "shell.execute_reply": "2020-11-25T16:51:24.964673Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Input scalar, x')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens_1d.intercepts = Choice([-0.2]) # All neurons have x-intercept -0.2\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens_1d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, there is a clear pattern to the tuning curve.\n", + "However, note that some neurons start firing at\n", + "-0.2, while others stop firing at 0.2.\n", + "This is because the input signal, `x`,\n", + "is multiplied by a neuron's *encoder*\n", + "when it is converted to input current.\n", + "\n", + "We could further constrain the tuning curves\n", + "by changing the encoders of the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:24.970856Z", + "iopub.status.busy": "2020-11-25T16:51:24.969990Z", + "iopub.status.idle": "2020-11-25T16:51:25.144028Z", + "shell.execute_reply": "2020-11-25T16:51:25.144436Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Input scalar, x')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens_1d.encoders = Choice([[1]]) # All neurons have encoder [1]\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*tuning_curves(ens_1d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"Input scalar, x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us an ensemble of neurons\n", + "that respond very predictably to input.\n", + "In some cases, this is important to the\n", + "proper functioning of a model,\n", + "or to matching what we know about\n", + "the physiology of a brain area or neuron type." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2-dimensional ensembles\n", + "\n", + "In a two-dimensional ensemble,\n", + "the input is represented by two scalar values,\n", + "meaning that we will need three axes\n", + "to represent its tuning curve;\n", + "two for input dimensions, and one for the neural activity.\n", + "\n", + "Fortunately, we are able to plot data in 3D.\n", + "\n", + "If there is a clear pattern to the tuning curves,\n", + "then visualizing them all is (sort of) possible." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:25.153023Z", + "iopub.status.busy": "2020-11-25T16:51:25.152164Z", + "iopub.status.idle": "2020-11-25T16:51:28.096268Z", + "shell.execute_reply": "2020-11-25T16:51:28.096699Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Firing rate (Hz)')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_2d = nengo.Ensemble(15, dimensions=2, encoders=Choice([[1, 1]]))\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_2d, sim)\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "ax = plt.subplot(111, projection=\"3d\")\n", + "ax.set_title(\"Tuning curve of all neurons\")\n", + "for i in range(ens_2d.n_neurons):\n", + " ax.plot_surface(\n", + " eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn\n", + " )\n", + "ax.set_xlabel(\"$x_1$\")\n", + "ax.set_ylabel(\"$x_2$\")\n", + "ax.set_zlabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But in most cases, for 2D ensembles,\n", + "we have to look at each neuron's tuning curve separately." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:28.104268Z", + "iopub.status.busy": "2020-11-25T16:51:28.103479Z", + "iopub.status.idle": "2020-11-25T16:51:29.731048Z", + "shell.execute_reply": "2020-11-25T16:51:29.731465Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ens_2d.encoders = nengo.Default\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_2d, sim)\n", + "\n", + "plt.figure(figsize=(12, 12))\n", + "for i in range(4):\n", + " ax = plt.subplot(2, 2, i + 1, projection=Axes3D.name)\n", + " ax.set_title(\"Neuron %d\" % i)\n", + " ax.plot_surface(\n", + " eval_points.T[0], eval_points.T[1], activities.T[i], cmap=plt.cm.autumn\n", + " )\n", + " ax.set_xlabel(\"$x_1$\")\n", + " ax.set_ylabel(\"$x_2$\")\n", + " ax.set_zlabel(\"Firing rate (Hz)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## N-dimensional ensembles\n", + "\n", + "The `tuning_curve` function accepts\n", + "ensembles of any dimensionality,\n", + "and will always return `eval_points` and `activities`.\n", + "However, for ensembles of dimensionality\n", + "greater than 2, these are large arrays\n", + "and it becomes nearly impossible\n", + "to visualize them.\n", + "\n", + "There are two main approaches\n", + "to investigating the tuning curves\n", + "of ensembles of arbitrary dimensionality." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clamp some axes\n", + "\n", + "In many cases, we only care about\n", + "the neural sensitivity to one or two dimensions.\n", + "We can investigate those dimensions specifically\n", + "by only varying those dimensions,\n", + "and keeping the rest constant.\n", + "\n", + "To do this, we will use the `inputs` argument\n", + "to the `tuning_curves` function,\n", + "which allows us to define\n", + "the input signals that\n", + "will drive the neurons\n", + "to determine their activity.\n", + "In other words, we are specifying\n", + "the `eval_point` parameter\n", + "to generate the `activities`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:29.740532Z", + "iopub.status.busy": "2020-11-25T16:51:29.739761Z", + "iopub.status.idle": "2020-11-25T16:51:29.913121Z", + "shell.execute_reply": "2020-11-25T16:51:29.913529Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$x_0$')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rhEdgXFSN6Zdq5DERxJ43nPCMoT+TCorCQYTb46ZEX8LaprWsbV7L2qa1tFi9nVOjwqMYnzCeCYkTmJA4gYLYAkJkQ6fXUZXVzpL2Dpa0m1ja3tFdAjssMpzDY7ytPKZHq1AdgiuuXU4nLZXlNJRsp75kB42lO7oTwZJMhjYji+S8fJLzhpOUl09salqvdvsKsnfcJgdtn2zHXmYgcnwC0afkBrxnka8IisJBjBCCWlMtxc3FFDcVs7ZpLdWmagAiQyK9M4mkiUxMnEhhXCGh8qGRvPYIwTazjcVtJha3m1ih78DqEYRIMEGj5PAYNUfGqhmriTzo8hFCCEytLTSU7KB+53YaSrbTXFHWvQeAKk5LSt5wkoYNJznPGwYaSm0ghgL2SgOtH23HY3ERc1oeyolDoxdabwmKwiFGi6WF4qZi1jStobipmFJ9KQAR8gjGJIxhUuIkJiVNokhbNGREwub2sMZo5vc2E7+3m9hksiKA6BA5M2PUHNXZFDB1CIaaXE4nzRVl1O/cRsPO7dTv3EZHu3cnvpDQMBJz80geVkDKsAKShuWjjh0atfBDESEEHUvrMcyvQB4TTtyFIwhLGfxN7A6UoCgc4rTZ2ljbtJbipmJWN65mZ/tOBIIIeQRjE8YyKckrEqPiRg0ZkWh1uFjSbuK3Nu+t0eFtqTAsMpyj4zQcHatharRyUO53bTHoqdu5jfod3ltTRWl3SwhNfCIp+QVeEcgvID4zG3nIwRGyGOx4bC7avyzBuklHxMg4Ys/OR6Y4OP/tg6IQZBcMdgNrmtawpnENqxtXs6N9B+Atgx2fMJ7JyZOZnDSZEbEjhkQfJyEE2802fm8z8WubiRWGDuwegUIm47AYFUfHqjk6TkOWYuDLB4XHQ1t9HXU7tlC/Yxt1O7Z2rwmQh4SQkJNHSv4IUvNHkJxfgComdsBtDALOJjOt72/D1WYl6vhsVIenHtRJ+aAoBNknepueNU1rWNW4ilUNqygzlAGgDlUzIWkCU5KmMDV5KrnRuUPih2J2u1muN/NLq5Ff2oxUWr1VTbmKcI6J0zArzjuL8Mcqa28oqJS67Vup3e4VAluHCQCFWkPK8JGk5BeQOnwkiTl5hIQNvXDXwYZ1exttH29HCpURd0EB4TnRgTZprwghsG3egunnn4mcMB7VEUf06TxBUQhyQOisOlY3rmZlw0pWNqyktqMWAK1Cy5TkKd0iMVTWSVRY7CxqM7Ko1cgyvXcWoZTLODxG3S0SfW0P7rBaqN+xjdrtW6nbvoXG0p24nF4RiklOJbVgJCnDR5A6vJCY5JQhIaqHCt78QR2GeRWEJiuJu6RwULa5Fm431nXrMP38M8aff8ZV34A7Wkb8RdeQeMNNfTpnUBSC9Iu6jjpWNqxkRcMKVjaspM3mTYJmajKZmjyVaSnTmJw0GXXY4NqAfE+Y3W7+aO9gYatXJOrs3lj+aLWCY+M0HBsXxWi1AtlenLfFaKBu+xZqt22hbvsWmivKEcKDJJORkJVL2oiRpBYUkjp8JJFR0QP4lwU5EITLQ/vXpViKm1CMiiPmnOHIwgZPqFS4XFhWrcK44CdMixbh1umQQkNRHnYYYnYWldFfkJJyNnl5/+zT+YOiEMRnCCEo0Zd0i8TqxtVYXVbkkpxR2lFMS5nGtORpFMUXDfreTV25iIWtRn7SGVljNCOAxLAQjonTcJw2igkyF7pOEajduonWWm+5b0hoGEnD8kkbMYrUgkJS8gsOqv0BDmbcHQ5aP9iGo9KIelYGmlkZg2J1snA6Ma9chWnBj5h+Xohbr0eKjER1xOFojj0WxczDqG5+g8qql4mMzKVo1AuoVMP7dK2gKATxG063kw0tG1jesJwV9SvY3LoZj/CgClUxOWkyh6UexrSUaaSr0wNt6n5pdbiYV13PD3WNrHTKsMlDCHE6yKotY3hdGTMjJEbkDSNtxCgSc4cREjq4RS/I7jgbzeje3YLb5CT27PyAN7MTTifmFSsx/jifjoWLcBsMyCIjUR11FOoTjkc1cyayiAhstga2bLkVvWE1yclnMTz/QeTyvu/6GBSFIAOGwW5gVeMqltUvY1ndsu524RnqDKanTGdG6gwmJU0aNNuYdrS3UbN1E7VbNlGzdRPtDXUAhKjUmCcdTnn2SNYooml0CyRgokbJ8VoNs+OjyYkcfPHnIHvHVqan9b2tSOFytJeMJCwtMOFO4XZjWb0G4/z5mH76CXd7OzKlEtWso9EcfzzKGTOQhf/53dLpfmXrtjvweOwMH/4oyUmn9duGoCgECQhCCCqNlV6BqF/WHWoKlYUyIXECM1JnMCN1BjlROQOWgLUYDdRu3UT15o1Ub9lIe703iR6miCRtRCHphaNJH1lEfFZ2d5sIIby70C3QGVmgM7CpwwpAfmQEs+OjODE+itEqRTCJPIixbGyh7dMdhMQp0F4xasATykIIrOvXY5w7D+OCH3G36JAUCtRHHYVm9okoZ87cRQgAPB4nZeVPUV39BirVCEYVvoBSmeMTe4KiEGRQ4HA7WNu8lqW1S/mj/o/uldbJymRmpM5gZupMpiRP8ekswm6xULvNKwI1mzfQUl0JeBvGpRWMJH3UGNJHFpGQndPrXkE1NgcLdAbmtRhYoe/AA6SGh3KCNoo58dFMiVYedK03hjKmP+ow/FBOWKYG7SUjkUUOXNjPtnMnxh/mYpw7F2ddHVJYGKojjkAzZzaqI45ApthzHsru0LF5843o9atITb2QYXn3Ivfhbo1BUQgyKGnoaOCP+j9YUruEFQ0rsLgshMpCmZQ0iZmpMzk87XAyNBkHdE6X00nDzm1Ub95A1eYNNJbuRHg8hISGkTJ8BBmjxpBeWERizjCfrBRudbj4udXAfJ2B39tM2DwCbWgIJ8ZHMSc+isOi1cENhQKE8AgMCyrp+L2WiMI44s4bjhTq/wojZ10dhrnzMP7wA/adO0EuRzltGpqT5qA+5hjkqn23zTAYN7Bp099xOvWMKHiMpKRTfW5jUBSCDHqcbidrm9eyuHYxS+qWUGGoALxlr0ekHcERaUcwLnHcbhVNwuOhpbqSqk3rqdq4jrrtW3E57EgyGUm5w8gYNZaMUWNIyS/w+0Ixs8vNwjYjc1sMLGw1YnF7iA6Rc5xWw8nx0RwRqw5uSzpACJeH9i9LsKxrRjk1mehTcv1aYeQ2GjH++CPG777H0umTFOPGoTlpDpoTTiAkLq5X56mr/5QdOx4iPDyB0UUvo1aP9Iu9QVEIMuSoMdWwpHYJi+sWs6phFU6PE3Womump05mumkBaayTN23ZQvXkDVqMBgLi0DDKKxpAxaizpI0cRHqkMmP1Wt4ff20z80KLnp1YDRpeHqBA5xwcFwu94HG5aP9iGfWc7muMyUR+V7pd8j3A46FiyBMN339Px668Ih4Ow7GyiTj0FzUknEZaW1nubPXZ27HyE+vpPiI2ZwahRzxEa6r8NkIKiEGRIYzC18cuyr9i2dhmOskbUJq8zdSoklHnpjJl4JOMmHz1ou4faPR4Wt5n4vkXPj7pdBeLUhBgOjwmGmHyFx+6m9d0t2CsMxJwxDOWkJJ+eXwiBbctWDN98g/GHH3Dr9chjY9HMmUPUKScTMWrUAQuQ3d7Exk3XYzSuIzPzWnJz/oEk+TfMFRSFIEMKIQS66koq1hdTtXEtddu34na5CAkNI3VEIRF5KZRH61lsW8MO/U4AsqOyOTL9SI5KP4rR2tGDtpFfl0B816JnQadAxITIOSkhmlMTopkWrQomqfuIx+ZC9/YWHDVGYs8ZTuTYBJ+d29XSguH7HzB8/TX2khJvwnjW0USdeiqqww5D6uOaFZNpGxs2/g2Xy8iIEU+SmHCiz2zeF0FRCDLosXaYqNq4jsoNa6ncsBZz534C2owsssaMJ3P0OFILRhIatmsFRn1HPb/V/MavNb+ypnENLuEiNiKWo9KP4uiMo5mSPIVwH1Zt+BK7xxti+qbZO4OwuD0khIVwSkI0pyXEMEETGSxz7SUei5OWtzbjrDcTe34BkUX9nzUKhwPTb79h+PIrOpYuBbcbxZgxRJ1+GpoTT0QeFdWv8+t0v7B5y82EhGgYM/p1v+UP9kRAREGSpHTgPSAREMBrQojnJUl6CLgKaOk89B4hxLzOz9wNXAm4gZuEEAv2dY2gKAxdhMdDU0UZFevXULFuDY2lJQjhIUKpImP0OLLHjCdrzHhUsb1L0AGYHCaW1i3ll+pfWFK3BLPTTGRIJDNSZ3B0xtEcnnb4oO3PZHF7WNhq5Nvmdha2GrF7BBkRYZyRGMPpiTEMVwZ3VtsbbrMT3RubcDZbiLtwBIqRvf/O7Al7aSn6L77E8N13uNvaCElIIOrUU4k6/TTCc/q/TkAIQU3tO5SUPIZaPZIxo18jPHxgd3YLlCgkA8lCiLWSJKmBYuA04BygQwjx1F+OHwl8DEwGUoCFQL4Qwr23awRFYWhh6+igcuNaKtcXU7G+GItBD5JEUu4wssZMIHvsBJLyhvlkb2GH28GqxlUsql7Er9W/0mprJUQWwtTkqRybeSxHph9JbMTg3LvA5HIzX2fgq8Z2Freb8ACFqgjOSIzltIToIbm7nL9wmxy0vLEJV6sN7SUjicjvW3LW3WHGOG8uhi+/wrphA4SEoD7qKKLPOhPljBlIct+EIz0eFztLHqWu7gPitcdSWPhMv9pV9JVBET6SJOlb4EXgMPYsCncDCCEe73y+AHhICLF8b+cMisLgRghBa00V5evWUL52FfU7tntnAyo1WWPGkz1uIlljxhOp6d80fH94hIeNLRtZWLWQhdULqeuoQybJmJA4gWMyjmFWxiwSlYNzD94Wh5Nvm/V81dTOWqMFCZgereKspBhOio9GHTI4cycDgdvkoOW1jbj1duIuKyQiN/qAPt+1N4H+s88wzJ2LsFgIy8sl+syziDrl5F6XkfYWl8vEps030ta2hMyMq8nNvQNJCkwFWsBFQZKkLGAxMAr4B3AZYATWALcJIdolSXoRWCGE+KDzM28C84UQX+ztvEFRGHw4HXZqtmykfK1XCEw6b5QwPiuHnHGTyBk/kaS8fJ/MBvqCEIId7Tv4uepnFlUt6t5QaGz8WI7LOo5jM48lSenbihVfUWGx82VTO180tVFpdaCQSZygjeKspFiOiFETcghVMHksTlpe2+idIVwxivDs3g8s3B0dGH/4gfbPPsO+dRtSRASa2bOJPvssFGPH+iWPY7c3s37D5ZjNpQwf/gipKef6/BoHQkBFQZIkFfA78G8hxFeSJCUCOrx5hkfxhpiu6K0oSJJ0NXA1QEZGxoSqqqoDtsmos1KxQUd8hhptuoqwiINzH9aBwqxvp3ztasqKV1K1aT0uu53Q8AgyisaSM34i2eMmDtpy0XJDOT9X/szPVT93b0s6On40x2Uex3GZxw3KjYSEEBQbLXze2MZ3zXraXW7iw0I4MzGGc5NiGaE6uFt4e2wuWt7YhLPBjPbyQiLyehcysm7Zgv6TTzD8MBdhtRJeUEDMueegOekk5Gr/5ZoslirWr78Mh1NH0aiXiIub6bdr9ZaAiYIkSaHAD8ACIcQze3g/C/hBCDFqIMNHO1Y2svDtrZ1GQHRCJPEZ6l1u4Qfpht2+QAiBrqaKsjUrKSteSWOptyxUHRdP7sTJ5IyfTPrIoiG31WSloZKfq7wCsa1tGwBj4sdwQtYJHJt57KAMMTk8Hha1Gvm8sZ2fWg24hHfDoHOTYjk9MYbY0IPre+xxuNG9vRlHlZG4i0buN6nssdkwzv+R9k8+xrZhI5JCgWbObGLOPbdPawoOFJNpC+s3XIEQbsaMeZMozRi/Xq+3BCrRLAHvAm1CiFt6vJ4shGjofHwrMEUIcZ4kSYXAR/yZaF4EDPNXotlssNNSbdrl1tFu734/Kl5BQqaa+AwN8ZlBofC43dRt30LpmpWUrVmBobkJgKTcYeROmELOhMnEZ2YfNCWU1cZqfqr6iR8rfmRH+w4kJMYljOOEbK9AaBWDb+ajc7j4prmdTxva2NRhJVSSOE6r4bykWI6K1Qz58JJwedC9txV7STux5+57HYKjqor2Tz7F8NVXuA0GwnJyiDnvPKJOOxW5RjMg9ra3r2DDxmsICVEzbuy7KJW5A3Ld3hAoUZgBLAE2AZ7Ol+8BzgfG4g0fVQLX9BCJe4ErABdwixBi/r6u4eucgsXooKXGREuVVySaq410tPUQigQFCZkaEjLVJGRqDvrQk9Nmo3LDWkpXL6d83RpsHSbkoaFkjBpD3sSp5EyYjCpmcFbw+JIKQwU/Vv7IgooFlBnKkEkyJidNZnb2bGZlzkITNjBO5kDY0mHls4Y2vmhqp9XpIikslHOTYzk/OZYsxeBct7EvhFvQ9tE2rFtavSuVJ++e9xEeD+alS2n74APMi5d4K4iOOYaY884jcsrkAR2wNLcsYMuWW4iIyGDc2HeIiBhcYciAJ5r9xUAkmq0mB83VJlqqjDRX/WVGIUFMkrJbJBKy1GjTVIQMQCdGf2HtMFFevIqSVcup2rAWl9NBhFJFzvhJ5E6aStaY8Yf0tpOl7aXMr5zP/Ir51JhqCJWFMjN1JifmnMgRaUegCBlc/zYOj4efW418VN/Gr21GPHirly5IjmVOfDQK+eDvvyQ8gvYvdmJZ20zUSTmoZ6Tu8r7bZMLw9de0f/gRjqoq5PFaYs45l+hzziE00XermntLff1nbNt+LxrNGMaOed2vPYz6SlAUfExX6Km5ykRzp1hYjQ4AZDKJuDQV8ZlqErM0JGZpiElWIhvEU/eOtlZKVi+ndNUyarZuRng8qOK05E2cyrDJ00gbMQqZj+q0DxaEEGzWbWZexTwWVC6gxdpCZEgkx2Qew5ycOUxJmjLoWm002B182tDGxw1tVNkcRIXIOSsxhotS4gZ1clo/r5yOxXVojs1EM+vPVur28graP3gf/TffIiwWFGPHEnPRRWiOOxYpQPms2toP2LHzQWJjZzK66KWArEHoDUFR8DNCCDra7V6BqPxTKBxWFwAhYTLiM7wikdApFOq4iIDG3w3NTZSsWkbJymXU7/QmVWNT0sibPI1hk6aRmDvsoMkP+Bu3x82apjXMq5jHz5U/Y3KaiFfEc0L2CZyUcxIjYkcMqn9LjxAs03fwUUMbPzTrcQjBRE0kF6doOTkhmshBNHvoWF6P/tsylNO87a8BLMuX0/ruu5h/X4wUGopmzhxiLroIxajCgNpaU/MOO0seRaudRdGo/0MmG7xhuqAoBADhERharDRVGmmuNNJUaURX04Hb5U2vKNShf4pEtoaETA0RSv/uCNXeWM/O5UspWbWMpnLvrmfxmdnkTzmMYVMOIy4t3a/XPxSwu+0srl3MD2U/sLhuMS6Pi5yoHE7OPZmTck4adGsgWh0uvmhq44P6VkosdjQhMs5KjOXSVG3AW2tYt7bS+v5WIgpiiTknF9O8ubS9+x72nTuRx8URc/75xJx/ns8XmfWF6uq3KCn9N/HaYxk16gVkssFdeRcUhUGC2+Whta6jWySaKoy0N1q6349OjCQx2zuTSMqJIjZVibyfo7YuIdi54g+aK70LtZLy8hk2eTr5Uw4jOmlwJcAOJgx2AwsqFzC3fC5rm9ciITEleQqn5J7CrIxZPt12tL8IIVhpMPN+fSs/tOixewRTo5RclqpldnzUgO/94Kg10fLqRuRxYcjkq9F//CHutjbChw8n9tJL0cyZvduexoGiqvp1Skv/Q3z8CYwqfA6ZbOC2++wrQVEYxNitLpqrvALRVOEVi678REio7M/cRHYUSTlRqGL2/0PQNzWyY/kSdi5f2i0EycOGkz91BvlTD0OjHfjk26FOjbGGH8p/4Luy76jtqEURouDYzGM5NfdUJiZNRBagdgd7otXh4pPGNt6r01FlcxAfFsIFyXFclBJH+gD0XXK122j+v2I8VjPmRf/CY2xBdcQRxF5+GZFTpgyqUFxl5SuUlf+XhIQ5FI58ekgIAgRFYUghhMDUavPOJMqNNFYYaKkx4XF5/59UMeGdAuGdTcSnq5GHyjDqWti5fAk7li+hsawECArBYEQIwbrmdXxX9h0LKhfQ4ewgVZXKqXmncmruqaSoUgJtYjceIfitzcS79Tp+1hkBOF4bxRWpWmbEqPzinM2r19P+eS3CHYpl2dOoj55E3OWXE56X5/Nr9ZeKihcpr3iWxMRTGDniv8hkQ6c8PSgKQxy300NLralbJBrLDd3rJySZQC7TY+sow+NqIC41nBEzJjN82oygEAxybC4bi6oX8U3pN6xsWAnA5OTJnJZ3GrMyZg2q8tZam4P36nR80NBKm9NNfmQEV6RpOTsxBmU/m/IJITAvXUrrq28gwmcgj8sjNLaMuCtOITRhcH6Hq6rfoLT0cZKSTmPkiCf9vlOarwmKwkGE3WKmZOUytixZQUNJG5I8mTBlFkLEITzekZsqNpzknCiScqNIzo0mLlWJbBBVlATZnfqOer4t+5ZvS7+lrqMOdaia2TmzOXPYmYyIGxFo87qxuT1826znzboWNpqsqOUyzkuO5cq0+ANeFCfcbkwLFqB7/Q3s27ahmH4NIQkTiDo1E/W0jP2fIEDU1X/K9u33kJAwm1GFzw05QYCgKAx5XA4H5etWs33p75SvW43b6SQ6MZmCGUdQMP1w4tIyumcTjWUGGsuNNJbpMRs6cxPhchKzNCTnevMSSTkawiOHRuzzUMMjPBQ3FfNVyVf8XPUzdredEbEjOHPYmZyYc+KgWT0thGCt0cKbdTq+b9bjEoLjtRquSotnevS+Q0sehwPD19/Q+uabOKurCcvORnPKDdir1KiPSifq+KyB+0MOkKamH9i85Rbi4g5ndNErg77KaG8ERWEIIjweardtZuuSX9m54g8cVguRUdEUTD+cghlHkJSbv88fXtfaiYYyPY1lRhrK9LTWdiAEIEFciork3CiS87wzCnVsYNdNBNkdg93AvIp5fLnzS3a07yBCHsFxWcdxVv5ZjI33T4vnvtBkd/JOnY5363W0Od0UqiK4Ki2e0xNjCO9RteSxWtF//jmtb7yJq7mZiMJC4q6+mrBhk9C9uYWIvGjiLi1EGqQLPXW6X9m46VqiNOMYO/Zt5PLAhfecdXVIkZGExPRttXRQFIYQuupKti79jW1Lf6OjVUdohIL8KYdRMOMIMkaN7tc+BA6bi6ZKI41lBhpK9TSWG3Havf0GVTHhnSIRTXJeNHEpykH74zzUEEKwtW0rX+38irkVczE7zeRF53FW/lmcnHvyoJk9WN0evm5q57XaFrabbWhDQ7g8VcslMZHIvvyC1rfewt3aSuTEicRddy3K6dPxmJw0/d9apDA5idePRTZIZ7Dt7atYv+EylMo8xo/7kJCQwGzr6u7ooPW112l75x2izz2XpHvv6dN5gqIwyDHr29n+x+9sWfwLLZXlSDIZ2WMnMGLGkeROnEJouH8WEXncHlrrzDSUGWgo09NQasCs9yawwxQh3TOJ5LxoEjM1yEODeYlAY3FamF8xn893fs6W1i1EyCM4Put4zh5+NqO1owfF7EEIwZL2Dl6pbOAXg4Vwh4Pjl//OJe31jL/oAiInTfIe5/LQ8vomnA0dJPx9LKFJygBbvmeMxo2sXXcx4eFJTBj/MWFhA98EUrhc6L/8ipYXXsDd2ormlJNJuPVWQpP7ts4oKAqDEJfDQVnxKrYuXkTF+mKEx0NS7jBGHn40w6fNJDIqesBt6iqHbSjVU1/qnU10La6Th8hIzNaQMiyalLxoEnM0B3WH2KHA1tatfL7zc+aVz8PislAQW8C5w89ldvbsgC6Mc3eYaf/gfVrffodyhYqvL7qSH3MLcCJxojaK6zISmBSlpP2bUswrGoi9oIDI0fEBs3dfmM3lFK89B7lcyYQJnxIRPvAr0juW/kHzE09gLylBMWE8mttvxpybTERIBPGRfft367MoSJIUAZwEzMS7x4EV2AzMFUJs6ZM1PqSvorC5zsCri8v571mjiRjAjqZCCBpLd7Ll94VsX7YYu9mMKjaOkTOPYuThswZlmwmryUFDmYH6Ej0NpXpaqk0IAZJMIj5dRUp+DKnDoknOiwomrwOE2WlmbvlcPtnxCSXtJahD1ZySdwrnDj+X7KjsAbPDYzbT9tFHtL35Fm69HtWRR6K9/noURaNotjt5q07HO3U69C4342WhXLTGwAkjk4g9MWfAbDwQHA4da9acjcttZuKEz4mMzPTLddweN3q7nlZbK222NtqsbbTaWrGVl5Pz7m+kbGqkLS6Mb47T8FuOFZvHO5v/W9HfuHn8zX26Zp9EQZKkh/EKwm9AMdAMRAD5wFGdj28TQmzsk1U+oK+isLyslfNfX8F/zijivMn+L30z69vZuuRXtvy2kNbaakLCwhk2eRojj5jV7zzBQOOwuWgs94pEfYmepkqjd2GdBNo0FSnDokkdFkPKsGgiVEGRGEiEEKxvWc/H2z/m56qfcXlcTEmewvnDz+fI9CP91rXVY7XS/tHHtL7xBu72dpSHzyT+hhtQjB6927Fmt5sPtjbwSk0zDQoZwyMjuCEzgdMSYggdRDkst9vG2nUX0tGxnfHjPzrgHdOcbiettlbvzdp563T6f32st+vxCE/3ZxV2wZl/eJi9WuAMlVh8TCIls4YRpdYSGxFLnCKO2IhYCmILGB47vE9/X19FYY4QYu4+TpoAZAghAha/6asoCCGY88JSnG4PP916uF/isG6Xi/J1q9ny20LK165GeDwk5xcw6shjGT5tJuGRg6fvTX9wOdw0VRqpL9FTt1NPU7kBl9P7BY9LVXpnEvnRpAyLRqEamuV7QxGdVcfXJV/z+c7PaTA3kKJM4dyCczlz2JlEhfd+k/t94XE40H/6GbpXX8Wt06GcPh3tjTcQOW7c3j9jddH0/FqcMlh5bjb/a2plu9lGWkQo16UncH5yXMC7tArhYdPmG2hp+Ymiov+REH884F1s+Fcnv7d7k8O0x3MrQhTdjj0uIm4XJx8XHkPi4u1EvPY5tBuIOuMMEv5xq18a/vUrpyBJ0kxgWc9tMSVJGi+EWOtbMw+c/uQUviiu4fbPN/LuFZM5It938cy2+jo2//oTW35fhMWgRxkdw8gjZlF4xCziUgdfeMjXuF0emiuN1JXoqd/ZTkOZAZfDKxKxKUpS82NIHe6dTQRnEv7H5XHxe83vfLj9Q1Y3riZCHsGcnDmcX3B+n0eZwu3G8O136F58EWd9PZGTJhF/y81ETpiw788JQdvH27FubiX+2tGEZ2gQQrCw1ciL1c2sNJiJDZVzbXoCl6dqUfdzpXRv6HL0Oquu26HvrP2a2va1CMVIrJK629l3ODv2eA51qPpPx97p7OMUcbs8jo2IJS4ibq+5HuumTTT+61/YNmxEMWYMiffdi6KoyG9/d39FwQKsBs4WQjR3vrZWCDHe55YeIH0VhS31Bm77bAPNRhuFqVG8f+WUftnhtNsoWbmMTb/8RO22zUgyGbkTJjPqqOPIHjvhkN6gxu3y0Fxlom5nu1ckSjtnEhLEpapI6xSJlGHRwZyEn9nZvpOPtn3E3PK52Nw2JiVN4qIRF3FE2hG9Ci0JITD9/DMtz7+Ao6yMiMJC4m+9FeVh03s12zavbqT9yxI0J2ShOXL3AdJKfQfPVzXxS5uJqBA5f0vT8re0eGJCD6ygweF20Gr1OnqdVbeb0++611l1mJ3mPZ5DFRJGgjJtVye/h/tYRSzh8r53a3Xr9TQ//Qz6zz9HrtWScPttRJ1yCpKfu9L2VxTWAfcD/wWuFEIskyRpnRBi73PEAaKvorCmso1rP1iL3uLA5REsuOVwhicdeN1xS1UFGxf9yLYlv2G3mIlOSqbo6OMpPGIWyujBtwXfYMDt8tBUaaRuRzt1O9tpLDPidnmQJIjPUJNWEEva8BiS8qIIDTt0xdSfGOwGvir5io+3f0yDuYF0dToXjriQ0/NO3+tI1rxiBc1PP4Nt0ybCsrOJv/lm1Mcf1+vQq7PFQvML6wjLUKO9smifa2A2mCw8V9nEfJ0BpVzG5ala/pYaS4jo6Hb03Q5/D85/b6EbTZgGrULb7dB7Po5TxCG3V9BY9i+yEo5g/JhX/dq+Qng8GL7+huannsJtNBJ78cVob7geuUrlt2v2pL+isFYIMV6SpGHAp8BbwBVDeaYA0Gy0ce0Hxayt1pMbr2TuTTN7VYnktNvYsXwpGxfOp6FkB/LQUPKnHEbRrONJGzFqUNSJDyVcTjdN5UZqd7ZTt6OdpnIjHo9AJpdIyokidXgM6QUxJGRr+r23RJBdcXlcLKxeyPtb32djy0bUoWrOzD+T8wvO7+7Watuxk+ann8K8eAkhycnE33ADUaeeghTS+9G7cHlo/t963AY7ibeMR67xjqyFEJicpt2ce9etqqOZUmMTJnsbkseIxO6+KjIkEq1Cu0dn3/W6VuFN0IbJ957TMpm2sKb4XJTKXCaM/9iv22jadu6k8eFHsBYXoxg3jqSHHiRieN9CeX2l3zOFrlmBJEkqvKJwhhAi4EXq/V2n4HJ7OOXFpWxtMDEiWc1rF08kPXbPXwZdTRUbF/7I1iW/YDebiUlJY8wxJzDy8KNRqAfHitKDAYfNRUOZgbrt7dTuaKelxgQCQsPlpORHkzY8hrSCWOJSlUEB9iEbWjbw/tb3WVi1EIDTomZyzmIP0rxfkanVaK+5hpiLLuzVxjZdlTctlhZ0Vh01q7bTUFmFZaQcfUQHOpsOncXr+B0ex26fD5WF7uLow0Jj2GELZ4s1HHlINCcmZfK3rDyGqRN9sh7D4Whj9epTEXiYNPFrwsP905nVYzbT8r+XaHv3XeRqNQl33E7U6af7PVS0J3y+eE2SpAwhRHW/Lesnvli8VtbSwaynfycsREZEiIxnzhnLMSMTAXC7nJSsWs6Gn+ZRu20z8pAQhk05jDHHnEjqiMKgUxoAbGYndTvaqe0UCX2TdzGdQh1KWkEs6SNiSB8RiyomsFtHHizUN5ZQ/NR9ZPy4EUnAhsNTyL7xDqYXHIfZZabF2kKr1evwux9bW3YZ4evt+j2eOyY8hjhFHPGK+F3utQot8Yr4bhHQhGn2+Nsqs9h4trKJr5raCZfJuCw1jr9nJBAf1vdclMfjYv2GyzAYipkw/lM0mt3LaH1Bx+LFNDz0EK76BqLOOpOE227rc98iX9DXktT/gz3M1zoRQty0n4umA+8BiZ3neU0I8bwkSbF4w1BZQCVwjhCiXfJ+C54HZgMW4LL9VTj5akXz5W+vYn2NnuQoBVsbjFwzNYUZts1s/mUBFoOeqIRExhw7m8IjjyFS45tyviB9w9Rm8wrE9jZqtrd371IXnRhJ+ohY0gpiSBseQ5gi4BPZIYFHeGiztdFiaqLy56+pXPgtbZjpGJVJSXoI5fY6nB4nEhJiD+4gTBbmHdVH/unYu0M2RCP7Woc2Io78a2cS5qN2LaUWG8/1EIe/pWn5e0bCASekAUpKHqO65k1GjHiClOSzfGJfT1xtbTQ99jjGH34gLCeH5Ecf2W+V1kDQV1G4tMfTh4EHe74vhHh3PxdNBpKFEGslSVLjXQB3GnAZ0CaE+I8kSXcBMUKIOyVJmg3ciFcUpgDPCyH2WRbkK1H4o1THha+v4P4Jofy0pYmV9jiyLRVcnapn6vEnkjV6XECmeEH2jRCCtnozNdvaqNnWTn1JOy6HB0kmkZSjIX1ELOkjY0nI1CAbRAujBgKnx9kdp+8a1eusOpotzd7XrC3oLN7krPvPavNu1GHq7pG8w+2gyliF3q5HE6bhxOwTOT3vdNI16ahD1Xsc1Qsh0L29BXu5gcQbxxKa6Pu+RiVmG89UNvJNsx6VXMZ1GQlcnRaPqpelrI2N37Fl662kpV3M8PyHfGqbEALDt9/S/J8ncJvNaK++mrhrrkYWNjjW6vQ7fOSLaiNJkr4FXuy8HSmEaOgUjt+EEMMlSXq18/HHncfv6Dpub+f0hSg4bFa2/L6I7z7+HJW1lQi1hsbRp/JRg4rseBWvXzKRbO3gbNQVZFfcTg8N5QavSGxt685HhEeGkFYQQ8bIONJHxqKOHbqhpi5n32xppsXaskeH32xppt3WvtvIXkIiJiLGO6KP1BLriiCyeAfKrVVow+PIOf0iMo6YTXxkPBEhu/4bCSFYWreUtza/xZqmNUSFR3FBwQVcUHAB0RHRu9nZVX4afWouqmn+3WJ0W4eVJysama8zEBsq58aMRC5L1aLYR2GCybSNNcVnoVEXMW7c+z7dW9lRW0vjAw9iXrYMxdixJD/6COHDhvns/L7AF6LQr3UJkiRlAYuBUUC1ECK683UJaBdCREuS9APwHyHE0s73FgF3/nXFtCRJVwNXA2RkZEyoqqrqk03tDXWsXzCXzb8txGG1EJaUyVxXLvdcfx6Hj0xhWamO6z9ai9sjeOH8cRw5fHBuCxhk71g7HNRub6dmaxvVW9u6O8DGJEV6BaIwltRh0YQMgtLXLmffYmmh2drsve9y8tZmdBbv6L7N1rbbZ2WSrLvqJiEywRujj4zvDud0vRaniCNUForbaET3v//R9uFHyMLD0f79OmIuvrjXo9j1zet5a/Nb/FrzK4oQBWcOO5NLCy8lSeltFuc22Gl8tpjQZBXxV+27/NSXrDWaeaK8kd/bTSSFhfKPrETOT47brX2G06ln1erTEMLJpEnfEh6m9cn1hcdD+8cf0/z0M0hA/G3/IOb88wdllCGgotBZsfQ78G8hxFeSJOm7RKHz/XYhRExvRaEnfZ0pbFvyK/NefBqZPIT8qYcx7oSTic3OY8YTvzEqVcM7l08GoKbNwlXvrWFnk4k7Tyjg6sNzgsnlIYoQgrYGc7dA1O/U43Z5kIfISMmPJrMwjozCWKITI336f+z2uGmzte3i6LtG+F2P9zayl0kytBF/Ovi93cdGxPZu8ZnHg+Hrr2l++hnc7e1En3028Tff1Oc2CmX6Mt7a/BbzyueBBKflncYVhZej+MqEvUxP4s3jCdEO/EY0y9o7+E9FA6sMZnIV4dydk8yc+CgkSUIIN+s3XEl7+0omjP+YqKixPrmmo6aGhnvvw7JqFcoZM0h+5GFCU/w7Q+oPfc0pmPgz0RyJN/kLIAFCCLHfOkxJkkKBH4AFQohnOl/rDgsFKnxk1rez4ef5jDn2xF0Wmb2wqIRnft7Jwn8cTl6CdzGbxeHijs83MndTA6ePS+U/ZxYRPgDL74P4F6fDTX2JnpotbVRvbe1uEa6Oi/AKxKg4UvOj99oeXAiB0WGkydLU7eB7Ovlmi1cEdDbdLs3OwBvG6aq+SYhM2MXJJygSDtjZ9wbrxo00/uvf2DZuRDFunLeNQmGhT85d31HPW5vf4quSr5ipH8cddZfhnhVF5rH+qeTpDUIIfm418q+yBnZabIzXRHJfTgqJba9QWfUyBQWPkZpybv+v03N2IEkk3HUn0WedNegHjwHZT6EzNPQu3qTyLT1e/y/Q2iPRHCuE+KckSXOAG/gz0fyCEGLyvq7h6/0UWjvsTH18EZdMy+L+k0Z2vy6E4MVfSnn6551Mzorl1YsnEKMcHAmjIL7BqLNSvaWVqi1t1G5v8yas5RCe7saTbkSfWEtzSC3N1j+dv91t3+080eHR3c69p8NPiOx83pm8DZENTHWUq7WV5meewfDlV8jjtSTefjuaU07xi9NqaqnH9H/bqZLXcVvmUxybfRxXjb6K/Jh8n1+rt7iF4LPGNv5b0Ui93clYUcyN2hbmjL6r3+d21NZ6ZwcrV6KcPp3kfz06qGcHPenrTEElhNhzB6heHCNJ0gxgCbAJ6Boq3QOsBD4DMoAqvCWpbZ0i8iJwAt5ZyeX768Dqj012rvugmFUVbay4Zxahf0lUfbehnts/30BqtIK3LpsUTEAPMbrKL5ssTTSbvY69ydLUPbLvunXYzCSbcklvH0GGfiSxVm+s3KLQY0pqhMwOorJCSNB4R/eJkYndI/3+9MHxJcLtRv/ZZzQ/8yweq5XYSy5B+/fr/NpGofWjbVi3tBJ+TQ4f6T7n4+0fY3aaOS7zOK4dcy3DYgKXbDVaW3hk1Qt87ZmNFQXnJ8dyZ3YyCeEHnmAWQmD48kuaHnscJImEO/9J9NlnD/rZQU/6KgqLgPXAt0CxEMLc+XoO3v0UzgFeF0J84Q+je4M/RGHRtiaufHcNr108geMKd99lqbiqjaveK8YjBK9eNIEpOb5vaxvkwLG5bDRbmndz8k2Wpu7XdBYdLuHa5XNdcfvukXynk+/5ONIahW6HjeotrdRub8fl9BASKiNtRCyZo+LIKoobVIvnrFu20PjQw9g2bSJy6lSSHrif8Bz/bmRj3ayj9YNtaI7PRHOUd48Sg93Ae1vf48NtHwZUHITwsGHDlbTrV5A/9mte06l5q1ZHqEzihowErk1P6HW7bldbGw0PPEDHwkVETplCyuOPDZnZQU/6s/PabOBC4DAgBnABO4C5wJtCiEbfm9t7/CEKLreHqY//wviMaF67ZI//ZlS1mrn8ndXUtFl48qzRnD4uzac2BPmTnrH7JnPTLo6+pwAY7IbdPqsMVXY7+y5H3/N5X0I5Lqebup16qja1UrlJh6nVBkBcmoqsojiyirQkZmkGrOKmJ+6ODlqef4H2Dz9EHhtL4p13ojlpjt9HsB6Lk8Zni5Grwki4YSzSXxxsoMWhuvpNSkofY3j+w6SlXQRApdXOo2X1zG0xkBQWyt05yZydFINsH/9WHb//Tv299+ExGIi/9VZiL7t0UFYW9YbgHs0HyGPztvHW0gpW3jOLONWewwEGi5NrPyhmeXkrtxwzjJtnDRtS08fBgEd4umvuGy2NNJn/dPQ9RcDmtu3yOQmJ2IhYEpWJ3Q6+p9NPVHqfK0P9G94TQtDeYKFyk46qza00lBkQHoFCHUpmkZbs0VrSR8QSGu7fwgQhBKYFC2j692O4dDpizj+P+FtuQa4ZmJ5cbZ/twLK+hYQbxhKWsvfwlN6m7xYHq8vKidkn8vexfydT459tLgGMxk2sKT4brfYoika9tNtvdKW+g4dK61lnslCkUvDosFSmRu/6N3gsFpqefBL9J58Snp9Pyn//S8TwwOVJfEFQFA6QHY0mjn9uMfefNJIrZ+x9j1uHy8M9X2/ii+JaLpiSwaOnjkJ+iK2c3RsujwudVUejubHbwXeP8Dsft1hadgvnhMhCuh18z/suR58YmYg2UkuoDxcb+Qqb2Un1llYqN+qo2tKGw+pCHiIjdXgM2WO0ZBVpUcX4NufgrK+n8eFH6Pj9dyJGjiTp4Yf8ujnLX7GV6tG9sQn1UelEHZ/Vq8/obXre3vI2H237CKfHyal5p3Lt6GtJViX71DaXq4NVq0/B43EwZfIPhIZG7/E4jxB806zn32X11NmdnJoQzf25KaRFhGHdvIX622/HUVVF7OWXE3/LzYNmVXJ/CIpCHzjlxaU43YL5N8/c53FCCP67YAcv/VbGCYVJPHfe2F614B7KOD1OWiwt3Q6+2/F3Pbc0orPuXooZIY/Yxbl3Pe45uo+NiEUmDc0peU/cbg8NpQYqN+qo2KjD2GIFvHtGZI/Rkj1GS1yqqs+zS+F20/7xJ7Q88wxCCOJvvonYiy9GGsANnYTbQ9Pz6xAuD0m3TkAKPbD/N51Vxxub3uCzHZ8BcFb+WVxVdBXxkb7ZCXHLlttobPqO8eM/IiZ60n6Pt7g9/K+6if9VNyMJwX/XLqXwrVcJ0WpJeeIJlFP2WQw5pAiKQh94f3kl93+7hbk3zaAwZf9N8N5aWsEjP2xlSnYsr10ykSjF4BvJ9gaXx0WLpaU7nNNobtzlcZOlCZ1Vt9tCq8iQSJKUSd3OvvtxD+e/t+6XBztCCNobLVRsaKFyo47GCiMIUMdGkDVGS84YLSnDopH1MtlpLymh4f4HsK5fj3LGDJIeeoiwtFQ//xW7Y1pah+GHcuIuHomisO8FFw0dDby68VW+Kf2GMHkYF424iMtHXY467MA3vuo+Z8NXbN12B9nZN5OTvc/enbtRXd/IpjvuJKd4FWvGTSLmkUeZk5dxUH13fbGieQYwTAjxtiRJ8YBKCFHhYzsPGH+Kgt7iYPK/F3HBlAweOqV3i3y+XV/H7Z9vIDdexXtXTCZBM3gqUsC7urbV1up19F23Lodv8T7f0wi/p8NPUiZ5nX7krvf9+QEfaliMDio36ajYoKNmWxtup4dwZQhZRVpyxsSTPnLPeQiPw0Hrq6+he+015EoliffcjebkkwPirNwmB41PrSEsU4P2ct+0ka82VvPi+heZXzGfqPAoriq6ivMKzjvgMl+brZ4VK09ErR7J+HEfHNAOauYVK6m/4w7cBgPmG2/mn2OmscVsZ0a0isfy08hXDq7fdF/p7yY7DwITgeFCiHxJklKAz4UQh/ne1APDn6IAcP2Ha1lWpmPlPccQFtK7UdySkhaueb+YWGUY7185ZcDWMggh0Nv13c6+wdzQ7ei7RvnNlubdYviKEMWfo/vIJJKUSbsIQJIyCVVo38McQfaN0+6mZmsb5Z2zCLvFhTxURvqIWHLHxZM1WkuEMhTr5i003H039pISNKecTOJddxESGxswu9s+34llfTOJt4wnNN63u5Rtbd3K82ufZ1n9MpKUSVw/9npOzjm51/tIr19/GQbjWqZMno9C0bvKQOFyoXvpJXQvv0JYVhapzz5DREEBbiF4v76Vx8sbsLg9XJMez62ZiSiHeFeD/orCemAcsLbHDmwbhRCBW8Peib9F4dcdzVz+9mpeuWgCJ4zafc3C3thYq+eyt1cjAe9fOYWRKf2vArE4Ld1Ovtvpd953Of2/Vul0JW27nHuyMnkXx5+kTDpkQzqDka48RPn6FirWt9DRbkeSQXyEieiN80ny1JD1wB2ojzwyoHbaq420vLQB1eFpRM/eeyFGf1nZsJLnip9jc+tm8qLzuHXCrcxMnbnP72td3Sds33Evw/MfIS3twl5dx9XSQt0/bsOyejVRZ5xB0n33IovcVeh0Dhf/Lq/n44Y2UsJDeTgvlZM6+ykNRforCquEEJN77NWsBJYfCqLgcnuY/p9fGJ0WxRuX7j9R1ZPylg4ufGMlFoeb966YzJj06L0e6/a4abG27OLwu25dr/11NysJiXhFfHc4J1mZvKvzVyYdNEnbQxEhBLW/bmDzmz/SKM/AEukdlCTlaMgZl0DuuHg0AWg2JzyC5pe8+y0n3T4RWbh/23UIIfi56meeX/s81aZqpiRP4faJt1MQW7DbsV1hI416FOPGvY/Ui+++edUq6m67DU+HmeSHHiTq1FP3efxqg5m7dtawpcPGETFqHs9PIydycKxiPxD6Kwq3A8OAY4HHgSuAj4UQL/ja0APF36IA8Pj8bbyxpIIVd88iXn1g//k1bRYueGMF7RYT/zo7jdgo865Ov6Nhr2EddZh6l9F9sqrT6Xc+TlAkECofmsnsIPtGOBy0vPwyra+9TkhcHEmPPIxz+CTK17VQtq4ZXY23s0x8hprc8fHkjksgOtF/G833pGufhJhz8lGOTxyQa4J33+fPdn7GKxtewWA3cHLuydw47sbudt1CCNZvuByDoZgpk+ehUKTv83xCCNrefJPmZ58jLCOD1OefIyK/d2sPXB7Bu/U6nqhowO4R3JKZyPUZCYQNoYVsvkg0Hwsch7dD6gIhxM++NbFvDIQolDabOOaZxdw3ZwR/m7l7qwCP8KCz6rqdfIO5gfqO+m7nX9dRT4fTtMtnQqSQ7gqdZGXyLqP8FGWKN44f5r8eNUEGL7adO6n/553Yt28n6rTTSLz7LuRRu1a/GVqslK1rpnxdC00VRgDiUpXkjk8gd3wCscn+yWN5rC4an1pDiFZB/LWjAxI6MTqMvLHpDT7Y+gEyScYlIy/hyqIrMbTMZdv2u3dZtbw33EYj9XffQ8eiRahPOIHkf/0LuerA/82a7E7uL63ju2Y9wyLDeWp4OlOih8bvtr8zhSeEEHfu77VAMBCiYHfbOf3VuVjcOm48Po5GS2O306/vqKfR0ojLs/sov8vZJyuT0YTG8/kKM03tCv5zykxOGVXgs5bIQQ4OhMdD27vv0fLMM8g0GpIffRT10Uft93OmNlv3DKKhzAACYlO8ApE3PoHYFN8JhP77MjqW1ZNwwzjCUgPr/Oo66nh+7fPMr5hPjjKaG+PaiIka01lttPcRu23bNmpvvgVnfT2J/7yDmIsv7re4LWw1ctfOGmptTi5MjuW+3JQ+7Rc9kPRXFHbbYOdgSjSbHCbqO+q7R/h/vW+1te5yvEySEa+I9zp8VTIpypTux10isKdRfpvZwcVvrmRnk4n/O3/8ASWugxzcOOvrqb/7HiwrV6I6+miSH32kTxvfmPV2ytY1U1r8p0DEJCvJm5DAsIkJxCT1XSCcTWaanl+LclISMacPnq0lNzRvYP2Gy4mXDHxlL+L6SQ8yLmHPOwcbfphLw333IY+KIvXZZ4kc368dhnfB7HbzdEUTr9Y2ExMSwqPDUjktIXrQJqL72iX1OuDvQA5Q1uMtNfCHEGLfc7QBoK+isKxuGc8UP0N9Rz2mv4R2wmRh3Q4+ReV1+NFhiTz4VR2njCzksVNn9LnFgsHq5LK3V7Gx1sD/nT+O2UW+XdYfZGghhMD4ww80PvIowu0m6Z67iTrzTJ84Eq9AtFC2tpn6Uj0IiEtVkTfRKxBRB1hGqnt3C/ZyA0n/nIRcOXhyWfX1n7Nt+104Ys/kqZJimi3NnJh1IrdOuLW7bYZwu2l59lla33gTxYQJpD3/HCFa32zB+Ve2dFi5fXsN60wWjovT8MTwNJLDB19bjL6KQhTezqiPAz13pDAJIXbfKDYA9FUU1jev5/VNr3c7/hRVCilK7/3eKnauem8NW+oM/HHX0f360XbYXVz61io21Oh56cLxe2zPHeTgx2000vjQwxjnzUMxbhwpT/yHsIwMv1yro91O2dpmSoubaCz35iDiM9SdApGIOnbfC7LsVUZaXt6A5rhMNEf7x8a+YLc3s3zFsajVhYwf9wFWl423t7zN25vfBuDKUVdySfqZtN55L+alS4k+/zyS7r4byc+9i9xC8HpNC09UNBAqk3gwN5ULkmMH1azBJ20uJElKALq/PUKIat+Y13cGIqfQxedrarjji418f8MMitL23/ZiXxhtTi5+cxVb6w28dvFEjipI8JGVQYYClnXrqL/9DpyNjcTfeANxV101YD2LTG02SoubKV3TRHOVd5acnBfFsImJ5I5PIFKzq8MUQqB7fRPOZgtJd0xC5ueOrwfC5i230tz8I1OnzCMy8s/1Eg0dDTxT/AybV8/n7q8ktEZB8v0PEHPuOQNqX4XFzm07alim72BmjIqnhqeTqRgc5av7EoX91lBJknSyJEklQAXwO1AJzPephUOAY0YkIpPgp63930JCExHKe5dPZniSmms+KGZJSYsPLAwy2BFuN7pXX6PqoosByPrwA7TXXjugTezUsRGMOzaDs++exEWPTmXKKTnYLS4Wf7KTd+76g+9eWM+2ZQ04rN7iCXuJHnu5Ac1R6YNKENral9PU9B2ZmVfvIggAyapkHnAez9MfhhLhEDxwPtwT9ytVxqoBtTE7MpwvxubyZH4a64wWjly1gzdqW/AM8n5zvUk0bwCOBhYKIcZJknQUcJEQ4sqBMHBfDORMAeC815bTZnbw061H+OR87WYH57++ggqdmbcvn8T0XP/EOYMEHmdTM/V33ollxQo0s08k6eGHkasHT7+o1roOSlY3UbKmCaPOhjxURlZRHAktFhJkkHrHJKRetnrxNx6Pk5WrTsLjsTF1ygLk8j/DX0IIWt94g5annyGiqIikF57h87ZfeGnDSzjcDi4rvIy/Ff2NyNCBWdfRRZ3NwT931LKozci0aCXPFWQEdNbQ3+qjNUKIiZ3iME4I4ZEkaYMQYow/jD0QBloU3v6jgoe/38qvtx/ps55GrR12znttBbXtVt69YjKTswPXzyaIfzD99hsNd9+Dx2Yj6b57iTrjjEEVX+6JEIKmCiM7VzVRsrIBm9VNWJiMvEmJDJucROqw6IDsKteTqqrXKC17gtGjXyNeO6v7deFw0PDwwxi+/ArN7BNJfuwxZBFewdBZdTyz5hm+L/+eZGUyd0++m6My9l/y60uEEHzS2Mb9JXUI4KG8FC5KjgvId6G/orAQOA1vwlkLNAOThBDTfWznATPQolDbbmHGE79y94kFXHNErs/O22yycd5rK2gy2Pjgb1MYlxHjs3MHCRzC5aLluedofeNNwgsKSH3mab/vlewrhFvQ8Owamq1uWlLVlG/Q4bK7UcWEkz85kfwpScTtY5c1f2GzNbBi5XHExExjzOjXul936/XU3nQzllWr0P79OrQ33LDHrTLXNq3l0RWPUqov5aj0o7h78t0+39xnf9TaHNy6vZol7R0cFavmmYL0Aa9Q6q8oKAEr3vzDhUAU8KEQonWfHxwABloUAOa8sISIUDlfXudbTWwy2jj7leUYbU4+v2YawxIHT2ghyIHjbGqi7rbbsK4pJvq8c0m8+25k4YMjydgbutpZxF00AsUoLU67m4qNLexY0UTNtjaER6BNVzF8ShLDJiWijBqYv23T5hvR6RYxdcqC7lYWjspKaq65Fmd9Pcn//hdRp5yyz3M4PU7e3/o+L69/GUmSuH7s9Vww4oIB3c3PIwTv1rfySGk9YTKJfw9L5czEmAGbNfRZFCRvI/KFQogDnmdJkvQWcBLQLIQY1fnaQ8BVQFdm9R4hxLzO9+4GrgTcwE1CiAX7u0YgROGFRSU8u3AnK++ZRYLat73Vq1stnPnKMkJkEl9cN53U6IFveBak/5iXLaPu9jvw2GwkP/wwUSefFGiTDgjh9ND41BpkmjAS/j5mN0dlMTooWd3EzlWNNFeZkCRIHxlLwdRkssdoCQnzT0K6tW0p69dfSk72LWRn3wh0NrS78SaQJNL+9yKREyb0+nx1HXU8vvJxfq/9nfyYfO6fej9jE8b6xfa9UWGxc/P2alYZzJySEM2T+WlED8Bq6P7OFBYBZwghDAd40cOBDuC9v4hChxDiqb8cOxL4GJgMpAALgXwhhHtf1wiEKGxvNHLCc0t47PQiLpji+5rtbQ1Gznl1OfHqcD6/ZhpxqqEzujzUER4PupdfRvfi/wjLySHthecJz/VdmHGgMC2pwzC3HO3fRhGRt+9QZluDmZ0rG9mxspGOdjthihDyJiQwfGoSybm+ay3t8dhZuWoOQriZMvlH5PJwDD/Mpf7uuwlLTyf9lZf7tM5DCMEvNb/w+MrHabY0c+7wc7llwi0oQwdmHxTwrmt4qbqZJyoaSAgL5YURGcyI8W+koF8lqXgd+yZJkt6UJOmFrtv+PiSEWAz0dpHbqcAnQgh7545upXgFYtAxPFFNZlykT0pT98SIZA1vXjqJunYrl7+zmg67a/8fChJwXO3t1Fx9Dbr/exHNSSeR/dmnQ1IQPHYXpt+qCc+L3q8gAMQmK5l6Wi6X/Hs6p94yluwxWnaubuLrp9bywQMrWD23AmOrtd92VVe/hcVSwfD8h5DLw2l9623qb7+dyDFjyPr4oz4v/JMkiVkZs/j2tG+5cMSFfLrjU0795lQW1y7ut829RS5J3JiZyA/j81HIZJy9voxHSuuxezz7/7Af6I0ofAXcDywGinvc+soNkiRtlCTpLUmSur51qUBNj2NqO18bdEiSxHEjE1lW2orJ5vTLNSZnx/K/C8azpd7INe+vwe7a54QpSICxbdtG5VlnY1m5kqSHHiLlySeQKQdupOlLOpbW4zG7iDo+64A+J8kk0gpiOeaykVz+xGHMumwE6tgIVn1fwfv3Lefb59axY2UjTseBf5dttnoqKl8kPv44YmNm0vT4f2h+8knUJ5xA+ptv7NZFti8oQ5XcOflO3p/9PuowNdcvup5//v5PWq0Dlzodq4nkp0n5XJQSx0s1zcwpLmGn2bb/D/qYXq9o7tPJJSkL+KFH+CgR0AECeBRIFkJcIUnSi8AKIcQHnce9CcwXQnyxh3NeDVwNkJGRMaGqamAXpACsqWzjrFeW83/nj+PkMSl+u84XxbXc/vkG5hQl88L545AHuBQwyO4Yvv+BhvvvRx4VRdr/vYBidMD7RPYZj8NN439WEZahQXtZ7/Yl3x/GVis7VjSyfXkDRp2NsAg5eRMTGTE9mcTs3u36t2XrbTQ3z2PKuHm0P/wixnnziLn4YhLvvmuPFUb9xel28ubmN3lt42tEhkZyx8Q7OCX3lAEtHf2xxcA/dlRjcXt4JC+Vi1N8W7rqkzYXfbxwFj1EYW/vdSaZEUI83vneAuAhIcTyfZ0/EDkFALdHMOWxhUzNiePFC8bv/wP94LXFZTw2bzuXTMvk4VN8s0F6kP4jXC6a//sUbe++S+TEiaQ+96zfmqwNFKY/6jB8X078dWMIz+z/FrI9ER5BfYmebcsbKFvbjMvhISYpkhGHpTB8StJu7TW6bTJtZdXqU8iIu5SwZ8qwrFxJwh23E3vFFX7/LZTpy3ho2UOsb1nPjNQZPDjtwe5NfQaCJruTm7ZV83u7iZPjo3m6IB2Nj/aG7m9OwWdIktSzIPh0YHPn4++A8yRJCpckKRvvTm+rBtK2A0EukzhmRCK/7Wjxe2jn6sNzuWpmNu8tr+KtPyr9eq0gvcPV1kb1lX+j7d13ibnoIjLefmvIC4Jwe+hYXEdYlsbnggDe8FLq8BhveOnJGRx1UQFhihCWfVnKu3f9wY+vbqJqcysez66D1NKyJwnr0CC7fxWW4mJSnnyCuCuvHJDBUW50Lu+e+C53Tb6L4qZizvj2DL4u+Rp/DqR7khgeysdjcrg3J5l5Oj3HrN7BOqPF79f120xBkqSPgSPxLnhrAh7sfD4Wb/ioErhGCNHQefy9eLf6dAG3CCH2218pUDMFgF+3N3P5O6t5+/JJHDXcvw3tPB7B3z9cy4Ktjbx60YRgZ9UAYtu6lZobbsCtayXp4YeJPv20QJvkE8zFTbR/vpO4ywpRFAzcqvrW+g62LWtgx4pGbB1OVDHhFExPZuRhKTgpZuOiS0h6OQH0DtJeeAHVjMMGzLae1BhruH/Z/RQ3FTMzdSYPTnuQROXAbUe62mDm2i2VNDmc3JuTwjXp8cj6IYz9LUn9Hq8T74kBWAO8KoQY+ExIJ4EUBZvTzYRHf+aUsak8fkaR369ndbg577Xl7Gzq4LNrpvW7U2uQA8f400/U33kX8uho0v7v/1CM8k3cPdAIj6DpubVIMki4eXxAQpRul4eKDTq2/VFP9TZv0WJUXBlpq38hsb2SrNdfQTEmsJ11PMLDx9s/5vm1zxMihXDHpDs4Le+0Afv3ane6+Mf2GubrDBwTp+H5ggziwvq2pqG/4aNyvGWpr3fejIAJyO98fkgSESrnyIIEft7atNuU1x8owuS8fulEYpVhXPHuaur0/S/zC9I7hBDoXnmVuptuJiI/n+zPPj1oBAHAtr0NV7MF9ZHpActZyUNk5E1I4OSbxnLxo9PIL9RhrY9lS/ZVLJ/xOOsrVBh1gf3OyyQZF464kC9P/pL82HweWPYAN/5yIzqrbkCuHxMawlujsvj3sFQWt5l4oqLBL9fpzUxhtRBi0p5ekyRpixAiYL+OQM4UAL5dX8fNn6zny+umMSFzYKbcOxpNnPXyMlJjFHx+7TTUEYNnF6yDEY/dTsN992P8/ns0J59M8r8eHVLtKvaHEIKWlzfgNjlIun0SkjzwhQwda1ZQddUVeCJDCX/oI0pK3FRt0iGAjBGxFB6eSlZRHDJ54Lq2eoSHj7Z9xHNrnyMyJJIHpz3IrMxZ+/+gj9hsspAeEUZUH1c/93emoJIkqXtlSOfjrk5Yjj5ZdJBwVEECoXKJn7Y0Ddg1hyepeemi8ZQ0d3DDR+twuQOzwOVQwKXTUX3JpRi//574W272rj84iAQBwFFhxFFtQn142uAQhD/+oObKq/GoPSS9/ij5xxYy5++jueSx6Uyak01rvZn5r2zivXuWsfL7ckxtgYleyyQZF428iM9O+owkZRK3/HYL9y29jw5Hx4Bcf5Q6ss+CsD96Iwq3AUslSfpVkqTfgCXA7Z2N8t71i1VDBE1EKFNz4li4beBEAWDmsHj+ddooft/ZwsPfbx3Qax8q2LZvp+Lsc7Dt2EHq8897N8M5CMuBTb/XIFOGopw4cEnTvdryyy/UXHsdzngXrkcmED/yz8Z2qpgIJp+UzSX/nsbs64qIS1OzZl4l79+7jHkvb6R6aytiAMK4fyUnOocPZ3/I1aOv5vvy7znzuzNZ3bh6wO3wJfuVGiHEPEmShgEFnS/t6JFcfs5fhg0VZg7T8ti87TQZbSRqfNsgb1+cPzmDSp2ZVxeXMzJFw/mTB8/euUOdjqV/UHfTTcjUajI//ABF4cGTP+iJo8GMbUc7muMzkUIDu6uaaeFCam+5FSk3Bt1V9UyacO8ej5PJZWSPiSd7TDxGnZUtS+vZ9kc9FRt0RMUrGHVEKgXTkolQDlxYNVQeyo3jbmRm6kzuXXovVy64kstGXcaNY28kVD70wru9DcpNAAqBMcA5kiRd4j+ThhZdu6WtKB/4TuL/PKGAw/PjeeDbzRRXtQ/49Q9G9F9+Rc211xKank7WZ58etIIA3lmCFC5HNdV/q/J7g/Hnn6m95VbCRw6j8doWEnNORa0eud/PabQKpp2Wy6WPHcaxV4wkUhPGH1+U8s5df7DovW00VxkHwPo/GZswls9P/pyz8s/i7c1vc9H8iwZ8C1Bf0Js9mt8HngJmAJM6b3tMUByKjEjWEKUIZVnpwIuCXCbxwnljSY5ScN0HxTQZA1YdPOQRQtDyfy/ScO+9KCdPJvPDDwhNDHxIxV+4Wq1YN7SgnJKMTOH/Vs17w/jTT9Td+g8UhYXY78rFEwE52f84oHPIQ2XkT07ijDsmcO59kyiYmkRpcTOfP76GL55Yw85VjbhdA5N7iwyN5IFpD/Dckc9Ra6rl7O/P5pvSbwZswZsv6E310TZgpBiEf1Wgq4+6uPq9NWxrNLLkn0cH5PrbG42c8dIyCpLUfHz1VMJ9tBT+UEE4nTQ8+BCGr74i6rTTSH70EaTQoTftPxDavynFvLqR5DsnIdcEJnluXPATdbfdhmLUKBJefIQVm04mJeVcCoY/3O9z260uti9rYNNvtRharCg0YRTOTGHUzFSU0QPz9zaaG7l7yd2saVrDiVknct+0+9CE+X61eF/ob/XRZiC4hHYfTM+No6bNSk2b/5eg74mCJA3/PWsMa6v1PPRdMPF8ILg7Oqi59joMX32F9u9/J/nxxw56QfDYXFiKm4gcmxBYQfjHP1AUFZH+xuvUtH4IQFbmNT45f7gihDGz0rnw4amcdOMYEjK8ien37lnGT29sprH8gLaH6RNJyiTeOO4Nbhp3Ez9V/cTZ353N+ub1fr9uf+nNvFELbJUkaRVg73pRCLHvPe8OIabnefMKy8taSY+NDIgNc0Yns7k+l5d/K6MoNcovGwAdbLh0Oqqvuhr7zp0k/+tRos86K9AmDQiW4iaE04Nq2sDuTdxFtyCMHk3666/jCjVT3/ApyUmnExHh2/yGJJPILIwjszAOfbOFzb/Xse2PekrWNJOYrWH00Wnkjk9A7qc1D3KZnKtGX8Xk5MncufhOLvvxMm4afxOXFV6GTArcOot90Zvw0RF7el0I8btfLDoABkv4SAjBpH8vZOaweJ49d2zA7HB7BFe8s5plZTo+uXrqgC2oG4o4auuovvIKXM0tpD3/HKrDDw+0SQOCEIKmZ4uRwkNIvH7sgF+/4/ffqbn+Bu8M4fXXkauUlJQ8Rk3tO0ybuhCFwv+DGYfNxfbljWz8tQZDsxVlVBijjkyjcGYKCtWeu7X6ApPDxIPLHuTnqp85PO1w/n3Yv4mOiPbb9fZFwFpn+5vBIgoAN3y0ltWVbay4e1ZA69kNFien/G8pFoebeTfNJF59cC228gW2nTup+dtVeOx20l95mchx4wJt0oBhK9Oje30TMWflD/jaBPPKVdRcfTXheXlkvPM2crUah6OVP5YdQULCCRSOfGr/J/EhwiOo2tLKxl9rqdnahjxUxvCpSYw5Op3YZP9skiSE4OPtH/PUmqeIU8Tx38P/O+D7QkMfcwqSJC3tvDdJkmTscTNJkjSwtV5DgOm5WpqMdsp15oDaERUZyqsXT8BodXLrp+txB2BBz2DGsm4dVRdfAkKQ+f57h5QgAJhXNiApQogcM7Ctvq0bNlB73XWEpqeR/sbryNXePYira97C47GRlXndgNoD3tBSVpGWU24ay/kPTGH41CR2rGjk44dX8sOLG6jZ3ubzqiFJkrhgxAW8f+L7yCU5l/94Oe9ueXdQVSftVRSEEDM679VCCE2Pm1oIMThS6IOI6blxACwrG/jS1L9SkKTh4VMKWVqq46VfSwNtzqChY8lSqq+4Enl0FJkff0REfn6gTRpQ3EYH1s2tKCckDuhiNduOHVRffQ1yrZaMt94iJMa7C6/Tqae29n0SEmajVAZ2P+vYFCVHXVjApY9NZ/LJ2TRXGfnuufV8+q/VbF/e4POS1kJtIZ+d/BlHph/JU2ue4qZfb8Jg93/yuzfsM9MhSZJckqTtA2XMUCYzLpKUqAiWlw1Mx8T9ce6kdE4dm8KzC3cGZGHdYMM4bx41f/87YZmZZH34IWFpaYE2acAxr24Ej0A5deASzPbyCqqvuBKZQkHGW28RmvDn3iM1Ne/gdpvJzrp+wOzZHwp1GJPmZHPJY9M5+pIChBAsencb79+7jLULqrBbfLcvuyZMwzNHPsNdk+9iad1Szp97Pjvadvjs/H1ln6IghHADO3o2xAuyZyRJYmpuHCvK2waklXZv7Pn36UVkxSm5+ZN1tHbY9/+hgxT9l19Sd9vtKMaMJvP994b8Lml9QbgF5lUNhA+LJlSrGJBrOuvqqL7iCgAy3nqLsLTU7vdcLhM1te8QH38cKtXwAbHnQAgJlTNiegrn3T+Zk28cQ0yykuVfl/HuPctY+kWJzxrxSZLEhSMu5O3j38bmsnHRvIv4ofwHn5y7r/SmJioG2CJJ0iJJkr7ruvnbsKHI9FwtbWYHO5pMgTYFAFV4CC9eMJ52i5NbP9swKMRqoGn/5BMa7r0P5WGHkfHGG92x7EMN2/Y23AYHqikDM0twtbZSdcUVeCwWMt58g/Cc7F3er6l9D5fLRNYgmiXsCUmSyCiM49RbxnHOPZPIKtKy8ZdaPrhvOT+/vQVdrW+6oo5NGMtnJ39GobaQu5fczX9W/Qenx3ezkgOhN+sU7ve7FQcJ03rkFUYkD460y8gUDQ+cNJL7vtnMK4vL+PuReYE2acBoe+99mh57DNWRR5L6/HMHXdvrA6FjRT1yTRgRI+L8fi2P2UzNtdfhamom4+23iCgo2OV9l8tMTc3bxMUdhUY9yu/2+Ir4DDXHXVnI1NNy2Lioli1/1LNzZROZo+IYf3wmyXlR/ao81Cq0vH7c6zxb/Czvb32fra1befqIp4mPjPfhX7F/9jtTEEL8vqfbQBg31EiNVpAVFzlo8gpdXDglgzlFyTz9005WV7YF2pwBofXNN2l67DHUxx5L2gvPH9KC4NRZsZfoUU5O8vueCcLppPbWW7Ft2ULqM8/ssbqrru5DnM52srNu8Kst/kITp2DGOcO49LHpTDklm6ZKI18/vZav/ruWio26frXwDpWF8s9J/+TJw59ke9t2zvnhnAFfBR0sSfUx03K1rCxvG1Sb30iSxONnFpEareCmj9fRbj6490bSvfwyzf99Cs3s2aQ+8zRSmP8WJA0FzCsbQCahnOzfbjVCCBoefAjz4iUkPfQg6qOP2u0Yj8dJTe27xMRMJypqrF/t8TcRylAmzvYmpQ8/Lx+z3s68lzbyyb9WsWNFA55++IATs0/kw9kfoghRcMWCK/i65GsfWr5v9jVTuBCCJakHyrTcOEx2F1vqB5duaiJC+d8F49F12Lnvm82Dqi7aVwghaH7+eVqef4GoU08h5b9PHvR9jPaHcLoxr2lCURjn9z5HLS+84O0hdf31xJxzzh6PaW75Ebu9kYz0K/xqy0ASGian6Mg0Lnx0Ksdc7m35vfCdbXz44Ao2L67D7eybOAyLGcbHcz5mfOJ4Hlj2AE+ufhKXx+VL0/fIvkShW5okSfrS75YcJEzLGTzrFf5KUVoUtxyTz9xNDXy7vj7Q5vicluefp/XlV4g660ySH3sMSR7sFmvZqENYXX4vQ23/5BNaX36F6LPPQnvD3pPHNTXvolBkERe3x+45Qxq5XMbwKUmcd99kZl9XRIQylN8/2sH79y1j/cJqnHb3AZ8zKjyKV455hQtHXMj7W9/n+kXX+309w75EoWfwMedATyxJ0luSJDVLkrS5x2uxkiT9LElSSed9TOfrkiRJL0iSVCpJ0kZJksYf6PUGC/HqcPITVSwbZHmFLq45PIfxGdHc/+1m6vXWQJvjM1peeonWV14l+uyzSH7kkaAgdNKxooGQeAXhOVF+u4Zp4UIaH3kU1ZFHkvTgg3tNthqMGzAa15GedgnSIG0G5wskmUT2mHjOumsip9w0lqiESP74opT37l3GmvmVOKwHONr3wHXDr+OugruoKK/gpg9vYu6vc6msrPSL/fuqPhJ7edxb3gFeBN7r8dpdwCIhxH8kSbqr8/mdwInAsM7bFODlzvshyfRcLZ+srsbh8hAWMri+/CFyGc+cM5bZLyzhji828P4VU5DJhvbew7rXX0f3wv8RdeqpJD38MJJscP2bBwpnkxlnjYmoOTl+68dl3biRuttuJ6JolDd/E7J3l1JT8w5yuYrk5DP9YstgQ5Ik0kfGkj4yloZSPWvmV7Hy23LW/VRFwWEJpI1T4nTbsFgsmM3mvd47HH/mAKczHYDV1atpam3iiizfh+H2JQpjOhPKEqDokVyWALG/vIIQYrEkSVl/eflU4MjOx+8Cv+EVhVOB9zo38lkhSVK0JEnJQoiGA/ljBgtTc+J4Z1kl62v0TM4efJ1Ks7RK7p0zgnu/3sx7yyu57LDs/X9okNL6zju0PP0MmjlzSH7s30FB6IFlfQtIEDnWPyWNzoYGav5+PSHx8aS//DKyyL23jbfbm2hunkda2sWEhKj8Yk+gcDgcmM3mXRz5npy72WXGnigRpk9h40I36xe5sEbWYVXWIWQu5HI5kZGRKJVKIiMjiY2N3eV5171FsvBw8cPkDfdPefleRUEI4Y/5d2IPR98IdLVpTAVqehxX2/nabqIgSdLVwNUAGRmDc6H11JxYJMm7v8JgFAWACyZnsHBrE4/P386MYfHkJQy9H2rbRx/R/J8nUB93HClP/CcYMuqB8Ags65oJHxaDXO376iuP2UzNdX9H2Gykv/M2IbH7/p7X1n2IEG7SUi/2uS2+psvJ78nB7+m5y7XncFBISEi3M1cqlWi1WpQjvY4dazgNG120lGYS5cqm8PAUJhyf1evW3e+lvkeozD9FFAHbnFUIISRJOuCwlBDiNeA18LbO9rlhPiA6MozCFA3LynTcfMywQJuzRyRJ4okzR3P8c4v5x2fr+fK66YT6aaMRf9D++ec0PfIoqqOOIvWp/+4zbHEo4qg24tbb0RyX6fNzC4+HujvvxL5zJ+mvvkJ43r5HrG63nbq6j9FqjyYy0vf27A+n07lPp/7X15zOPa8kDgkJ2WXUHh8fv4vT/+uoPiwsbN9hu+Ogta6D1XMr2bCwlq1LGhgzK52xs9IJj9y3ww+T+6/MeqB/SU1dYSFJkpKB5s7X64D0Hseldb42ZJmWE8e7y6qwOd1EDGBHygMhQRPBv08v4u8fruXFX0q59dih0TXU8N13ND7wIMqZM0l9/rlDfh3CnrCsa0YKlaEo9H2fp5Znn6Vj4SIS770X1cyZ+z2+qel7nM420tMu9cn13W43Vqt1N6e+Nydvt++575dcLt/FiWu12l2e/9XZ79fJ94G4VBUnXD3KKw4/VLBmbiUbf6ll7DHpjD46nXDFwA92BvqK3wGXAv/pvP+2x+s3SJL0Cd4Es2Go5hO6GJcRw+tLKtjRaGJMenSgzdkrs4uSOX1cKi/+WsrRBQmD2lYA02+/UX/3PUROnkza/72ALCgIuyFcHiwbdUSMjEMW7tsBif7rb2h9/Q2izzuXmIsu3L8tQlBT+w5KZT4xMdP3eozdbqejo2Ovo/i/Ovs9IUnSLg49JSVlj8696xYeHh7QDbF6Epeq4oRrimipMbH6hwpWfV/BhkU1jD02g9FHpREWMXCu2m9XkiTpY7xJZa0kSbXAg3jF4DNJkq4EqoCuFS7zgNlAKWABLveXXQNFUaq3BHBTnWHQO9qHTilkeVkrd365ke9vnDFow0iW4mLqbr6FiIIC0v73P2QREYE2aVBi29GOsLqIHJew/4MPAEtxMQ0PPEDktKkk3XvvPh2qy+XCYrHQ3PIHHR3bUEZey7Jly/bq6N3uPdfwR0RE7BKTz8zM3MWx97xFREQgG+KFBvHpamZfN5rmKiOrf6hg5bflbPylhgknZFF4eAohAxB1CG7H6SeEEIx79GeOH5nEE2eNDrQ5++XnrU1c9d4a7jh+ONcfNfia5tl27KDqoosJiYsj86MP95vYPJRp/XAb9nI9yfdMQfKRwNtra6k8+2wklZqwZ5/BIpN1O/SuEX7Pm83mbS09YsTvREU3smrlmXg8IcjlclQqFZGRkahUqr06+K6RfcghnitqLDew8rtyare3o4oJZ+LsLAqmJyPv5//rvrbjPLT/xf2IJEkUpUaxqW5w7Ka0P44dmcjsoiSeX1TCiaOSyIkfPNVIjpoaqv/2N2SRkWS89WZQEPaBx+bCuq0V5aSk/QqCx+PpDtX81bH3fG7R65n89TcoO8z8PG06HV98sct5FApFtyNPSkrqfqxQmDF1fEhM9Hlcf/0tqFQqv8TlD2aScqI49ZZx1G5vY8W35fz24Q7W/lTNlJOzGTYxEckPa4yCouBHilKjeG1x+aBONvfkoZMLWVKi456vN/HxVVMHxY/X1dJC9RVXgsNJxodvE5qSEmiTBjUdG5swu6y40gUtO3fu5uR73u8tNi+TyVAqld6RfGQko3/7naj2dkw33cSxh03vdvpdI375XkqBS0ofp8MsMXLkdURE+L9l98FMWkEsZw6PoWpTKyu+Lefnt7bSWG7k8PN8XxwSFAU/UpQahcsj2N5oYuwgzyuAtxrpntkjuPurTXy+ppZzJqXv/0N+xG00Un3V1bh0OjLffmu/pY8HK06nc49O/a8j+o6ODm/YJgL47o9dzhEaGtodromJiSE9PX0X597zcURERPeAoP2TT2hcuxbt9dcz4u/X9dpmt9tOff3nxMcfT0REUMh9gSRJZI3WkjkqjtLiZmJTlH65TlAU/MioHsnmoSAKAOdOTOfrdXX8e942jipIIF4dmH0IPA4HtdffgL2sjPSXXkIxdmxA7PAXXQukejr3vTn8vZVUhoeHdzv0hIQEslIzEMV6oocnET8pYxdHH9aHKi3r+vU0/vsxlEccjvb6vx/QZ3W6n3G5DKSmnHfA1w2ybySZxLBJifs/sI8ERcGPpMUoiIkMZVOtHhj4RTt9QSaTePyMIk58bgkPf7+FFy8Y+N6EQgga7rsPy+rVpPz3SVQzZwy4DX3hryP6fT3u2c+mJ13xeZVKRXJycrfT/+u9Uqkk9C9twU2LazG4Kkg8cWK/92F26XTU3nwLoYmJpD7xxAG3D6lv+IKI8BRiYqb1y44gA09QFPyIJEmMSo1iU93g2lthf+TGq7jh6Dye+XknZ4xv4ugC/41K9oTu/17E+N33xN98E1Ennzyg1/4rLpdrl/DMX289X9/biD4iIgKVSoVKpequne96/ldH359qG8u6ZkLT1f0WBOFyUfeP23Dr9WR98jHy6OgD+rzN1kBb21Kysq4/qLuhHqwERcHPjE6L4tXfh06yuYtrj8jlh4313Pf1Zn76Rxyq8IH5qui//gbdSy8RdcYZxF17rV+u0VV1szfn3vNmte65vXhX6EalUnVX3HQ9/6uzH4iySmeTGWeDmaiTD7jL/W40P/MsllWrSP7P40SMGHHAn29s/AYQJCed0W9bggw8QVHwM13J5m0NRsZlxATanF4TFiLj8TNGc9Yry3j6px08eHKh369pXrGChvvvJ3LaVJIffuiAqp96rord381sNu9x57muZKxKpepeKKVWq/fo8P8augk0lnUtIIPI0f3riGr8cQFtb71FzAXnE33aaQf8eSEE9Q1fEB09OSB9joL0n6Ao+JmuZPPmOsOQEgWACZkxXDA5g/eWV3H+5AzyE9V+u5a9tJTaG28iLCuTtOef795G0+1298rRd3R07LGRWc/ySrVa3R2n39MtPDwwSfX+IjwCy/pmwvP61xHVUVtLw333ETFmNIl33dWncxgMxVitlWRl9b5SKcjgIigKfiY1WkGsMoyNtUNjEdtfue244Xy/oZ5Hf9jKe1dM9snahZ6jepPJhLGhgeqXX8FSOJKQY47hj2++6Xb0e6ulj4iIQK1Wo1KpSEtL26ujVygUQ771wf5w1Ji8HVGPz+rzOYTTSf1ttwOQ+vTTfW4y2NDwJXJ5JAnxJ/bZliCBJSgKfubPZPPQFIVYZRj/ODafh77fysJtzRw7cu9JZ4/Hs0ts3mQy7XLf8/FuPehzc5DLZKhaWlCpVMTExJCRkbGbk+8K5xzq7Q96YtvWBjJQFPR9pXfL//6HdcMGUp95mrC0tD6dw+220NQ8j4T4EwkJ8U8NfRD/E/xlDQCjU6N4uVQ35JLNXZw7MZX3llfw0DcbSfCkYrfs2envLVbfVX2jVqtJT0/fxbk7PvkU96JF5Dz8EPEnnDAoVlEPNWzbWwnPikLWxzbL5hUraH31NaLOPAPN7Nl9tqO5eQFudwfJyWf1+RxBAk9QFAaAUalRuD2CrQ1Gxg+ivILD4djFsf/VyXfdW61W8twafnIO55FPFlMU0ogkSbvF6rvCOV2vdT3eW1K27b33aPr6a7Q33ED8icFwQ19wtdtwNlqImtO3LVVd7e3U3/FPwrKySLr33n7Z0tD4JYqIDKKjJ/XrPEECS1AUBoCitD+TzQMhCna7fTcnv6fHe6qr7+piqVariYuLIysrq/u5aYWRDQ2Z/Pfqs8hKiu1XrN68fDlNTzyJ6phZaA+gfUKQXbFtbwMgog+hIyEEDXffg1uvJ/21V/e5x/L+sFpraW9fTk72LcHZ3hAnKAoDQEpUBHE+SDZ3Ofuezr3nrev1Pa2WDQkJ6R69JyYmkpeX1+3su15Xq9UoFIq9/qgfzzBz3LOL+d/SOp4+p+87ejlqa6m79R+EZWeR8p8DXy0b5E+s29oI0SoIjT9wh97+wYd0/PYbiffc06f1CD1paPwKkEhOPrNf5wkSeIKiMAB0JZs37yXZ7HA49uroe9725ezVajVJSUkMGzZsF2ff5fB7NjnrK1laJVfMyOaV38u4eFpmn/o5eSwWaq+/AeHxkP6//yFXBROSfcVjd2Mv06OaduAN52zbt9P85JOojjiCmIsv6pcdQnhoaPiSmJhpweZ3BwFBUfAjLper29mPUBhpKa/mxwU2rBbzLs6+a0OSnsjl8m6n3jWy7+nou24DvaXgDUfn8eXaWh76bgtfXTcd2QH0cxdCUH/vvdhLSkh/9RXCMoOLm/qDvbQd3IKIEQcWOvLY7dTdfjvy6GiSH3+s398fvX4VNlstuTn/6Nd5ggwOgqLQB7raJHQ5daPRuNuo3mg07lZjPzkEVq6sRa1SodFo0Gq1ZGdn7zKi12g0qNVqn4zs/YEqPIQ7Tyjg9s838M36Os4Y3/vyxdbX38A0/0cSbr+tVxu+B9k31m1tSBFywrM0B/Q53Yv/w1FaRvrrr/lkw6L6hi+Qy1XExx/X73MFCTxBUfgLPZO0PZ39Xx2/x+PZ7bNKpRK1Wo1GoyE1NXWXEb2NMM5+az13nzyGyw7rW6XIYOGMcam8v6KKJ37czuyi5F6V2ZqXL6fl2WfRzJ5N7JVXDoCVBzfCI7BtbyMiP+aAtty0btpE65tvEnXmGT4RZperg+bmH0lKOgW5vH+N+IIMDg5JUWhra6O0tHQXZ991v6eKnLCwsO4RfGZmZvfjLgHoGuXvbQcq8IZO1KoSNtcPrY6pe0Imk7jnxALOfW0FH6yo4m8z992EzaXTUXfHPwnLySH5X48OyhnQUMNZ14Gnw0nEiN7vaOZxOKi/+25CEhL63Mbir7S2LcHjsZKUeKpPzhck8BySotDY2Mi8efOQJKnbucfHx5OTk7OLo++690VPnO6VzUO03cVfmZITx4w8LS//Vsb5kzNQ7qWLqvB4qP/nP/GYTGS89Wa/yh6D/Il1WytIEJHf+xLn7rDRa68iV/umj5VOt5CQkGiioib45HxBAk9AREGSpErABLgBlxBioiRJscCnQBZQCZwjhGj3x/Xz8vK47bbbUCqVA9oXZ3RqFIt3tmB1uFGEDb2VzX/lH8flc8ZLy3hnWSXXH7XnrTJbX3sd87LlJD36CBH5vt9P9lDFtr2NsEwNcmXvurVaN22i9Y03iDrjDFSHH+4TGzweFzrdb2i1RyKTHZLjy4OSQBaIHyWEGCuEmNj5/C5gkRBiGLCo87lfCAsLQ61WD3ijtFGpUXgEbG04OGYL4zNiOLoggdcWl2O07d6h1LJmDS0vvIBmzhyizwq2PvAVboMdZ7251wvWPA4HDffcQ0h8PIl33ekzOwzGdbhcerTaY3x2ziCBZzCtGjoVeLfz8bvAaYEzxT90rWw+WEJIAP84Nh+D1cmbSyp2ed3V3k7dbbcTmp5G0gHujRBk31g7VzErelmKqvvfS9hLSkl+5GHkmgOrVNrneXULkaQw4mKDlWQHE4ESBQH8JElSsSRJV3e+liiEaOh83AgM7B6QA0CSJgKtKnzIbc+5L0alRnFCYRJvLa2g3exdXCeEoOGuu3G3tZH6zDPIVaoAW3lwYdvWhjw2gpCE/ednrJs2e8NGp5+O6ogjfGqHTreImJgphIQE/38PJgIlCjOEEOOBE4HrJUnaJcgpvK02d2+3CUiSdLUkSWskSVrT0tIyAKb6DkmSKErVsKlOH2hTfMqtx+bT4XDx2pJyANreeZeO338n4c47URT6f8e2QwmPw42tVI+iIHa/sy/hdHrDRnFxPg0bAZjN5VgsFWi1s3x63iCBJyCiIISo67xvBr4GJgNNkiQlA3TeN+/ls68JISYKISbGx/dv68FAUJQWTWlzBxaHa/8HDxGGJ6k5ZUwK7/xRSe3qDTQ//TTqY48h5sILAm3aQYe9TA8uT69WMbd//An2khKSHrgfeVSUT+3QtS4CID4oCgcdAy4KkiQpJUlSdz0GjgM2A98Bl3Yedinw7UDbNhAUdSabtxwE6xV6cvOsYdhdbp57dR4hcXEk/+tfwTyCH7Btb0MKkxOevW8n72pro+XFF1FOn45qlu8dt65lESrVyGCvo4OQQMwUEoGlkiRtAFYBc4UQPwL/AY6VJKkEOKbz+UHHuIxoANZW+aXaNmDkxKuYozDxXcwIpLt8PzIN4s3V2La1ETEsGilk3z/dlhdewGM2k3jP3T4XZ4ejDb2hGK32aJ+eN8jgYMBFQQhRLoQY03krFEL8u/P1ViHELCHEMCHEMUKItoG2bSDQqsLJjItkbfXBJQq2HTs54/uXcMvkvGs/6GoEBgXOejNuo2O/q5ht27ej/+xzYi64gPC8Pa8f6Q+trb8BnmDo6CBlMJWkHjJMyIihuEq/x60rhyLC5aLh3ntJDXVz7rhkPlldTbNx986vQfqHbad3IBExfO+rmIUQND32OHKNhvgbrveLHTrdL4SHJaJWj/LL+YMElqAoBIDxmTHoOuzUtFkDbYpPaHv3XWybN5N0/31ccfRwnG7B/M2NgTbroMNerickMRK5Omyvx5gW/IRl1Srib77JLyE8j8dOa9titNqjkaSg+zgYCf6vBoCuLTkPhhCSvaKClhf+D9Uxs1CfcAJ5CWqGJ6qZu6lh/x8O0muE24Oj0kh4zt4dvcdmo/nJJwnPzyf67LP9Ykd7+0rcbnOwFPUgJigKAWB4khplmJziIZ5sFh4PDfffjxQeTtIDD3QnNGcXJbO6so2mYAjJZzhqOxBODxG50Xs9pu3tt3HW15N4zz1IIf7pRdSiW4RMpiAmZppfzh8k8ARFIQDIZRLjMmKGvCi0f/IJ1jXFJN55J6EJCd2vzxmdhBAwPzhb8Bn2cj0AYXspRXU2NqJ77XXUxx2HcuoUv9gghECnW0Rc7Azk8gi/XCNI4AmKQoAYnxnD9kYjZvvQXMTmrKuj5amnUR52GFFnnL7Le3kJagqSgiEkX2IvNxCaFLnXrqjNTz0NbjcJ/7zDbzZ0dGzDbm8INsA7yAmKQoAYnxGNR8CGGn2gTekTLS+9hBCCpIcf3mMd/OyiZNZUtdNoCIaQ+otwdeUTovf4vm3HTow//EDsFZcTltb77VEPlBbdIkBCqz3Sb9cIEniCohAgxnUmm4diCEk4HJh+XojmuOMIS0vd4zGzi5K9IaTNwdlCf3HUefMJe0syG+fOBbmc2Esv3eP7vkKnW0iUZixhYVq/XidIYAmKQoCIUoSSn6iieAhWIJmXL8djNKI+4fi9HpOXoPKGkDYGRaG/2Mv0wJ7zCUIIjD/+iHLqVEJier8L24FiszdiMm0Oho4OAYKiEEDGZ8SwrlqPxzO0FrEZf1yATK1Gedhh+zxuTjCE5BO8+QTlHvMJtq1bcVZXoznxBL/a0Nw8HwBtfLAU9WAnKAoBZHxmDAark3JdR6BN6TXC4cC0aBHqo49GFrb3RVQAs0cnAzAvmHDuM8LlwVG19/UJpvnzISTEL03vum0Qgvr6T9FoxqBSDvPbdYIMDoKiEEAmZA69vEJ36KgXI9PceBUjkjXBKqR+4Kg17TWfIITAOP9HlNOm+TV0ZDCuxWwuISXlXL9dI8jgISgKASRHqyQ6MpS1VfpAm9JrjPN/RKZWo5o+vVfHzylKoriqnXr9wdHSY6CxlxtA2nM+wbZ5M866OjQn+Dd0VF/3KXK5ksSEk/x6nSCDg6AoBBBJkhifETNkks3doaNZs5D2EzrqYnaRN4QU7IXUN/aVTzDO/xFCQ1Ef47/QkdNppKl5LomJJxMSovTbdYIMHoKiEGAmZMZQ2tyB3uIItCn7pWPZMjwm0z6rjv5KTryKkcka5m6s96NlByf7yid4q47mo5o+3a97VzQ1fYfHYyM15Ty/XSPI4CIoCgGmqzneuiGwiM3UWXXU29BRF3NGJ7O2Wh8MIR0g+8on2DZswFXf0KvcTl8RQlBX/wlqVSEaTZHfrhNkcBEUhQAzJj0KuUwa9DuxefoQOuqiK4QUrEI6MOxl3nzCnrbeNM7/ESk0FPXR/tv9zGTaREfHtmCC+RAjKAoBJjIshBHJ6kFfgWTuDB31pR4+W6ukMCVYhXSg2Mv1hCYpkUXumk8QHg/GBQtQzpiBXKPx2/Xr6j5GJlOQlHSK364RZPARFIVBwISMGDbU6HG5PYE2Za+Y5v+ITKNBOa1vLZNnFyWzrlpPTZvFx5YdnAiXB3uVaY+hI+v6DbgaG9HMPtFv13e5Omhq/oHExDmEhKj9dp0gg4+gKAwCxmfGYHa4+f/27j+2qvqM4/j700qr0jGLioBIhQTnSMxQG6NzQ3BO0T+AbY7VxAw3f+9HlmxLhvGPGbfFbf+YbGqQGOecRnFsxpoJTAW2aMDJjIpokKITqUoZ+KOItnDvsz/O994dyr1tb3vOvaft80qanvs9v54+5/Q+93zPueds291d61BKyvf20r1u3ZC6jgoWfmEqDfV1XHv/ZvZ09yQc4ejT+3Y3HMqXvAneR2tWo4YGmubPT239u3c/Ti53wE8wj0FeFDKg+CS2jHYhffzss1HXUQVXHfV1ysRjuWdpK2/tPcCSuzey630/YuhP4fsJjTMO7x6yfJ7uNWsZP/fL1Dc1pbb+zndWMn78aUyYMCe1dbhs8qKQAdOaj2HSZxp5YecHtQ6lpO41a4fVdVQw97QTeeCac9i7v4clyzeyY8/Iub1HtZU7n/DJCy9wqKuLCQvS6zrq7t5Kd/cWTp7aVvK26G50y1xRkLRA0jZJHZKW1TqeapDE2S3ZfBLbcK46KuXslok8fN159ObyLFm+kVc6P0wgytGleD6hxKM3P1q9BjU20jRvXmrr73xnJXV1jUyevDi1dbjsylRRkFQP3AlcCswGrpA0u7ZRVcfZLc3s3Hcgc/3tHz/zLPn9+xO9C+fsqRN45PrzOHpcPVes2MTz/9mX2LJHg96dhfMJh59ktlyOj/6+lqa5c6lvSufbxbncAd577zEmTbqUcePS+1Kcyy6ZZee2zZLOA24xs0vC65sAzOy2UtO3trba5s2bK17Pzh99iXxH13BCTYcZZO1ovbB7pNCNkDf4ONdIHjFOucSXP1IVMn3Ef+ZBQ51G/vx6mF6f0srzqC5HvvcYsJTW4RKxX1M499bHhjSvpH+bWWupcUcNK6rknQy8HXu9CzjsKeSSrgOuA5g+ffqQVmIHD9GbwZqQvYqQvgZ6yeXrsDH4t5fT38e0/PF19ExugHx6+bKcvCCMAJ9YOvtA1orCgMxsBbACoiOFoSyj5a5NicbknHOjRabOKQCdwCmx19NCm3POuSrIWlF4HpglaYakBqANaK9xTM45N2ZkqvvIzA5J+gGwFqgH7jWzrTUOyznnxoxMFQUAM3sCeKLWcTjn3FiUte4j55xzNeRFwTnnXJEXBeecc0VeFJxzzhVl6jYXlZK0B3hriLOfAPw3wXCSktW4ILuxeVyV8bgqMxrjajGzE0uNGNFFYTgkbS53749aympckN3YPK7KeFyVGWtxefeRc865Ii8KzjnnisZyUVhR6wDKyGpckN3YPK7KeFyVGVNxjdlzCs455440lo8UnHPO9eFFwTnnXNGoLgqSvilpq6S8pLKXbklaIGmbpA5Jy2LtMyQ9F9pXhtt5JxHXRElPStoefjeXmGa+pBdjP59KWhzG3Sfpzdi4OdWKK0yXi627PdZey3zNkbQxbO+XJX0rNi7RfJXbX2LjG8Pf3xHycWps3E2hfZukS4YTxxDi+rGkV0N+npbUEhtXcptWKa6rJO2Jrf+a2LilYbtvl7S0ynHdHovpdUkfxMalma97JXVJeqXMeEn6XYj7ZUlnxcYNP19mNmp/gM8DnwM2AK1lpqkHdgAzgQbgJWB2GPcI0BaGlwM3JhTXb4FlYXgZ8JsBpp8I7AOODa/vAy5PIV+DigvYX6a9ZvkCTgNmheGpwLvAcUnnq7/9JTbN94DlYbgNWBmGZ4fpG4EZYTn1VYxrfmwfurEQV3/btEpxXQXcUWLeicAb4XdzGG6uVlx9pv8h0a38U81XWPZc4CzglTLjLwNWEz2/91zguSTzNaqPFMzsNTPbNsBk5wAdZvaGmfUCDwOLJAm4EFgVpvsjsDih0BaF5Q12uZcDq83sQELrL6fSuIpqnS8ze93Mtofhd4AuoOQ3Noep5P7ST7yrgK+E/CwCHjazHjN7E+gIy6tKXGa2PrYPbSJ6smHaBpOvci4BnjSzfWb2PvAksKBGcV0BPJTQuvtlZv8k+hBYziLgfotsAo6TNIWE8jWqi8IgnQy8HXu9K7QdD3xgZof6tCfhJDN7Nwy/B5w0wPRtHLlD/iocOt4uqbHKcR0tabOkTYUuLTKUL0nnEH362xFrTipf5faXktOEfHxIlJ/BzJtmXHFXE33aLCi1TasZ1zfC9lklqfBI3kzkK3SzzQDWxZrTytdglIs9kXxl7iE7lZL0FDC5xKibzeyxasdT0F9c8RdmZpLKXhccPgGcQfQ0uoKbiN4cG4iuVf4ZcGsV42oxs05JM4F1krYQvfENWcL5+hOw1MzyoXnI+RqNJF0JtAIXxJqP2KZmtqP0EhL3OPCQmfVIup7oKOvCKq17MNqAVWaWi7XVMl+pGvFFwcwuGuYiOoFTYq+nhba9RIdlR4VPe4X2YcclabekKWb2bngT6+pnUUuAR83sYGzZhU/NPZL+APy0mnGZWWf4/YakDcCZwF+ocb4kTQD+RvSBYFNs2UPOVwnl9pdS0+ySdBTwWaL9aTDzphkXki4iKrQXmFlPob3MNk3iTW7AuMxsb+zlPUTnkArzzusz74YEYhpUXDFtwPfjDSnmazDKxZ5Ivrz7CJ4HZim6cqaBaAdot+jMzXqi/nyApUBSRx7tYXmDWe4RfZnhjbHQj78YKHmVQhpxSWoudL9IOgE4H3i11vkK2+5Ror7WVX3GJZmvkvtLP/FeDqwL+WkH2hRdnTQDmAX8axixVBSXpDOBu4GFZtYVay+5TasY15TYy4XAa2F4LXBxiK8ZuJjDj5hTjSvEdjrRSduNsbY08zUY7cC3w1VI5wIfhg8+yeQrrTPoWfgBvkbUr9YD7AbWhvapwBOx6S4DXieq9DfH2mcS/dN2AH8GGhOK63jgaWA78BQwMbS3AvfEpjuVqPrX9Zl/HbCF6M3tAaCpWnEBXwzrfin8vjoL+QKuBA4CL8Z+5qSRr1L7C1F31MIwfHT4+ztCPmbG5r05zLcNuDTh/X2guJ4K/weF/LQPtE2rFNdtwNaw/vXA6bF5vxvy2AF8p5pxhde3AL/uM1/a+XqI6Oq5g0TvX1cDNwA3hPEC7gxxbyF2ZWUS+fLbXDjnnCvy7iPnnHNFXhScc84VeVFwzjlX5EXBOedckRcF55xzRV4UnHPOFXlRcC4hktZL+moY/qWk39c6JucqNeJvc+FchvwcuFXSJKLbHiyscTzOVcy/vOZcgiT9A2gC5plZt6TxwF1AL7DBzB6saYDODcC7j5xLiKQzgClAr5l1h+avE91h81r8yMGNAF4UnEtAuKnbg0QPQNkvqfBwk2n8/x73uVLzOpclXhScGyZJxwJ/BX5iZq8BvyA6vwDRDc0KTzjz/zeXeX5OwbkUhXMKdwCfAs/4OQWXdV4UnHPOFfnhrHPOuSIvCs4554q8KDjnnCvyouCcc67Ii4JzzrkiLwrOOeeKvCg455wr8qLgnHOuyIuCc865ov8BnpTSE1SKbnIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_3d = nengo.Ensemble(15, dimensions=3)\n", + "\n", + "inputs = np.zeros((50, 3))\n", + "# Vary the first dimension\n", + "inputs[:, 0] = np.linspace(-ens_3d.radius, ens_3d.radius, 50)\n", + "inputs[:, 1] = 0.5 # Clamp the second dimension\n", + "inputs[:, 2] = 0.5 # Clamp the third dimension\n", + "\n", + "with nengo.Simulator(model) as sim:\n", + " eval_points, activities = tuning_curves(ens_3d, sim, inputs=inputs)\n", + "\n", + "assert eval_points is inputs # The inputs will be returned as eval_points\n", + "\n", + "plt.figure()\n", + "plt.plot(inputs.T[0], activities)\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"$x_0$\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that these tuning curves\n", + "are much more broad than those\n", + "in the 1-dimensional case.\n", + "This is because some neurons are not\n", + "very sensitive to\n", + "the dimension that are varying,\n", + "and are instead sensitive\n", + "to one or two of the other dimensions.\n", + "If we wanted these neurons\n", + "to be more sharply tuned\n", + "to this dimension,\n", + "we could change their encoders\n", + "to be more tuned to this dimension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Response curves\n", + "\n", + "If all else fails,\n", + "we can still get some information\n", + "about the tuning properties\n", + "of the neurons in the ensemble\n", + "using the **response curve**.\n", + "The response curve is similar to the tuning curve,\n", + "but instead of looking at the neural response\n", + "to a particular input stimulus,\n", + "we are instead looking at its response\n", + "to (relative) injected current.\n", + "This is analogous to the tuning curves\n", + "with the inputs aligned to the\n", + "preferred directions of each neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-25T16:51:29.921051Z", + "iopub.status.busy": "2020-11-25T16:51:29.920279Z", + "iopub.status.idle": "2020-11-25T16:51:30.097695Z", + "shell.execute_reply": "2020-11-25T16:51:30.098114Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'x along preferred direction')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model = nengo.Network()\n", + "with model:\n", + " ens_5d = nengo.Ensemble(15, dimensions=5)\n", + "with nengo.Simulator(model) as sim:\n", + " plt.figure()\n", + " plt.plot(*response_curves(ens_5d, sim))\n", + "\n", + "plt.ylabel(\"Firing rate (Hz)\")\n", + "plt.xlabel(\"x along preferred direction\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usage/tuning_curves.html b/examples/usage/tuning_curves.html new file mode 100644 index 0000000000..d435bafd18 --- /dev/null +++ b/examples/usage/tuning_curves.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/frontend-api.html b/frontend-api.html new file mode 100644 index 0000000000..9bc9eee1dd --- /dev/null +++ b/frontend-api.html @@ -0,0 +1,5373 @@ + + + + + + + + Nengo frontend API — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Nengo frontend API

+
+

Nengo Objects

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + +

nengo.Network

A network contains ensembles, nodes, connections, and other networks.

nengo.Ensemble

A group of neurons that collectively represent a vector.

nengo.ensemble.Neurons

An interface for making connections directly to an ensemble’s neurons.

nengo.Node

Provide non-neural inputs to Nengo objects and process outputs.

nengo.Connection

Connects two objects together.

nengo.connection.LearningRule

An interface for making connections to a learning rule.

nengo.Probe

A probe is an object that collects data from the simulation.

+
+
+class nengo.Network(label=None, seed=None, add_to_container=None)[source]
+

A network contains ensembles, nodes, connections, and other networks.

+

A network is primarily used for grouping together related +objects and connections for visualization purposes. +However, you can also use networks as a nice way to reuse +network creation code.

+

To group together related objects that you do not need to reuse, +you can create a new Network and add objects in a with block. +For example:

+
network = nengo.Network()
+with network:
+    with nengo.Network(label="Vision"):
+        v1 = nengo.Ensemble(n_neurons=100, dimensions=2)
+    with nengo.Network(label="Motor"):
+        sma = nengo.Ensemble(n_neurons=100, dimensions=2)
+    nengo.Connection(v1, sma)
+
+
+

To reuse a group of related objects, you can create a new subclass +of Network, and add objects in the __init__ method. +For example:

+
class OcularDominance(nengo.Network):
+    def __init__(self):
+        self.column = nengo.Ensemble(n_neurons=100, dimensions=2)
+
+network = nengo.Network()
+with network:
+    left_eye = OcularDominance()
+    right_eye = OcularDominance()
+    nengo.Connection(left_eye.column, right_eye.column)
+
+
+
+
Parameters
+
+
labelstr, optional

Name of the network.

+
+
seedint, optional

Random number seed that will be fed to the random number generator. +Setting the seed makes the network’s build process deterministic.

+
+
add_to_containerbool, optional

Determines if this network will be added to the current container. +If None, this network will be added to the network at the top of the +Network.context stack unless the stack is empty.

+
+
+
+
Attributes
+
+
connectionslist

Connection instances in this network.

+
+
ensembleslist

Ensemble instances in this network.

+
+
labelstr

Name of this network.

+
+
networkslist

Network instances in this network.

+
+
nodeslist

Node instances in this network.

+
+
probeslist

Probe instances in this network.

+
+
seedint

Random seed used by this network.

+
+
+
+
+
+
+static add(obj)[source]
+

Add the passed object to Network.context.

+
+ +
+
+static default_config()[source]
+

Constructs a Config object for setting defaults.

+
+ +
+
+property all_objects
+

(list) All objects in this network and its subnetworks.

+
+ +
+
+property all_ensembles
+

(list) All ensembles in this network and its subnetworks.

+
+ +
+
+property all_nodes
+

(list) All nodes in this network and its subnetworks.

+
+ +
+
+property all_networks
+

(list) All networks in this network and its subnetworks.

+
+ +
+
+property all_connections
+

(list) All connections in this network and its subnetworks.

+
+ +
+
+property all_probes
+

(list) All probes in this network and its subnetworks.

+
+ +
+
+property config
+

(Config) Configuration for this network.

+
+ +
+
+property n_neurons
+

(int) Number of neurons in this network, including subnetworks.

+
+ +
+ +
+
+class nengo.Ensemble(n_neurons, dimensions, radius=Default, encoders=Default, intercepts=Default, max_rates=Default, eval_points=Default, n_eval_points=Default, neuron_type=Default, gain=Default, bias=Default, noise=Default, normalize_encoders=Default, label=Default, seed=Default)[source]
+

A group of neurons that collectively represent a vector.

+
+
Parameters
+
+
n_neuronsint

The number of neurons.

+
+
dimensionsint

The number of representational dimensions.

+
+
radiusint, optional

The representational radius of the ensemble.

+
+
encodersDistribution or (n_neurons, dimensions) array_like, optional

The encoders used to transform from representational space +to neuron space. Each row is a neuron’s encoder; each column is a +representational dimension.

+
+
interceptsDistribution or (n_neurons,) array_like, optional

The point along each neuron’s encoder where its activity is zero. If +e is the neuron’s encoder, then the activity will be zero when +dot(x, e) <= c, where c is the given intercept.

+
+
max_ratesDistribution or (n_neurons,) array_like, optional

The activity of each neuron when the input signal x is magnitude 1 +and aligned with that neuron’s encoder e; +i.e., when dot(x, e) = 1.

+
+
eval_pointsDistribution or (n_eval_points, dims) array_like, optional

The evaluation points used for decoder solving, spanning the interval +(-radius, radius) in each dimension, or a distribution from which +to choose evaluation points.

+
+
n_eval_pointsint, optional

The number of evaluation points to be drawn from the eval_points +distribution. If None, then a heuristic is used to determine +the number of evaluation points.

+
+
neuron_typeNeuronType, optional

The model that simulates all neurons in the ensemble +(see NeuronType).

+
+
gainDistribution or (n_neurons,) array_like

The gains associated with each neuron in the ensemble. If None, then +the gain will be solved for using max_rates and intercepts.

+
+
biasDistribution or (n_neurons,) array_like

The biases associated with each neuron in the ensemble. If None, then +the gain will be solved for using max_rates and intercepts.

+
+
noiseProcess, optional

Random noise injected directly into each neuron in the ensemble +as current. A sample is drawn for each individual neuron on +every simulation step.

+
+
normalize_encodersbool, optional

Indicates whether the encoders should be normalized.

+
+
labelstr, optional

A name for the ensemble. Used for debugging and visualization.

+
+
seedint, optional

The seed used for random number generation.

+
+
+
+
Attributes
+
+
biasDistribution or (n_neurons,) array_like or None

The biases associated with each neuron in the ensemble.

+
+
dimensionsint

The number of representational dimensions.

+
+
encodersDistribution or (n_neurons, dimensions) array_like

The encoders, used to transform from representational space +to neuron space. Each row is a neuron’s encoder, each column is a +representational dimension.

+
+
eval_pointsDistribution or (n_eval_points, dims) array_like

The evaluation points used for decoder solving, spanning the interval +(-radius, radius) in each dimension, or a distribution from which +to choose evaluation points.

+
+
gainDistribution or (n_neurons,) array_like or None

The gains associated with each neuron in the ensemble.

+
+
interceptsDistribution or (n_neurons) array_like or None

The point along each neuron’s encoder where its activity is zero. If +e is the neuron’s encoder, then the activity will be zero when +dot(x, e) <= c, where c is the given intercept.

+
+
labelstr or None

A name for the ensemble. Used for debugging and visualization.

+
+
max_ratesDistribution or (n_neurons,) array_like or None

The activity of each neuron when dot(x, e) = 1, +where e is the neuron’s encoder.

+
+
n_eval_pointsint or None

The number of evaluation points to be drawn from the eval_points +distribution. If None, then a heuristic is used to determine +the number of evaluation points.

+
+
n_neuronsint or None

The number of neurons.

+
+
neuron_typeNeuronType

The model that simulates all neurons in the ensemble +(see nengo.neurons).

+
+
noiseProcess or None

Random noise injected directly into each neuron in the ensemble +as current. A sample is drawn for each individual neuron on +every simulation step.

+
+
radiusint

The representational radius of the ensemble.

+
+
seedint or None

The seed used for random number generation.

+
+
+
+
+
+
+property neurons
+

A direct interface to the neurons in the ensemble.

+
+ +
+
+property size_in
+

The dimensionality of the ensemble.

+
+ +
+
+property size_out
+

The dimensionality of the ensemble.

+
+ +
+ +
+
+class nengo.ensemble.Neurons(ensemble)[source]
+

An interface for making connections directly to an ensemble’s neurons.

+

This should only ever be accessed through the neurons attribute of an +ensemble, as a way to signal to Connection that the connection +should be made directly to the neurons rather than to the ensemble’s +decoded value, e.g.:

+
with nengo.Network():
+    a = nengo.Ensemble(10, 1)
+    b = nengo.Ensemble(10, 1)
+    nengo.Connection(a.neurons, b.neurons)
+
+
+
+
+property ensemble
+

(Ensemble) The ensemble these neurons are part of.

+
+ +
+
+property probeable
+

(tuple) Signals that can be probed in the neuron population.

+
+ +
+
+property size_in
+

(int) The number of neurons in the population.

+
+ +
+
+property size_out
+

(int) The number of neurons in the population.

+
+ +
+ +
+
+class nengo.Node(output=Default, size_in=Default, size_out=Default, label=Default, seed=Default)[source]
+

Provide non-neural inputs to Nengo objects and process outputs.

+

Nodes can accept input, and perform arbitrary computations +for the purpose of controlling a Nengo simulation. +Nodes are typically not part of a brain model per se, +but serve to summarize the assumptions being made +about sensory data or other environment variables +that cannot be generated by a brain model alone.

+

Nodes can also be used to test models by providing specific input signals +to parts of the model, and can simplify the input/output interface of a +Network when used as a relay to/from its internal +ensembles (see EnsembleArray for an example).

+
+
Parameters
+
+
outputcallable, array_like, or None

Function that transforms the Node inputs into outputs, +a constant output value, or None to transmit signals unchanged.

+
+
size_inint, optional

The number of dimensions of the input data parameter.

+
+
size_outint, optional

The size of the output signal. If None, it will be determined +based on the values of output and size_in.

+
+
labelstr, optional

A name for the node. Used for debugging and visualization.

+
+
seedint, optional

The seed used for random number generation. +Note: no aspects of the node are random, so currently setting +this seed has no effect.

+
+
+
+
Attributes
+
+
labelstr

The name of the node.

+
+
outputcallable, array_like, or None

The given output.

+
+
size_inint

The number of dimensions for incoming connection.

+
+
size_outint

The number of output dimensions.

+
+
+
+
+
+ +
+
+class nengo.Connection(pre, post, synapse=Default, function=Default, transform=Default, solver=Default, learning_rule_type=Default, eval_points=Default, scale_eval_points=Default, label=Default, seed=Default)[source]
+

Connects two objects together.

+

The connection between the two object is unidirectional, +transmitting information from the first argument, pre, +to the second argument, post.

+

Almost any Nengo object can act as the pre or post side of a connection. +Additionally, you can use Python slice syntax to access only some of the +dimensions of the pre or post object.

+

For example, if node has size_out=2 and ensemble has +size_in=1:

+
with nengo.Network() as net:
+    node = nengo.Node(np.zeros(2))
+    ensemble = nengo.Ensemble(10, 1)
+
+
+

We could not create the following connection:

+
with net:
+    nengo.Connection(node, ensemble)
+
+
+

But, we could create either of these two connections:

+
with net:
+    nengo.Connection(node[0], ensemble)
+    nengo.Connection(node[1], ensemble)
+
+
+
+
Parameters
+
+
preEnsemble or Neurons or Node

The source Nengo object for the connection.

+
+
postEnsemble or Neurons or Node or LearningRule

The destination object for the connection.

+
+
synapseSynapse or None, optional

Synapse model to use for filtering (see Synapse). +If None, no synapse will be used and information will be transmitted +without any delay (if supported by the backend—some backends may +introduce a single time step delay).

+

Note that at least one connection must have a synapse that is not +None if components are connected in a cycle. Furthermore, a synaptic +filter with a zero time constant is different from a None synapse +as a synaptic filter will always add a delay of at least one time step.

+
+
functioncallable or (n_eval_points, size_mid) array_like, optional

Function to compute across the connection. Note that pre must be +an ensemble to apply a function across the connection. +If an array is passed, the function is implicitly defined by the +points in the array and the provided eval_points, which have a +one-to-one correspondence.

+
+
transform(size_out, size_mid) array_like, optional

Linear transform mapping the pre output to the post input. +This transform is in terms of the sliced size; if either pre +or post is a slice, the transform must be shaped according to +the sliced dimensionality. Additionally, the function is applied +before the transform, so if a function is computed across the +connection, the transform must be of shape (size_out, size_mid).

+
+
solverSolver, optional

Solver instance to compute decoders or weights +(see Solver). If solver.weights is True, a full +connection weight matrix is computed instead of decoders.

+
+
learning_rule_typeLearningRuleType or iterable of LearningRuleType, optional

Modifies the decoders or connection weights during simulation.

+
+
eval_points(n_eval_points, size_in) array_like or int, optional

Points at which to evaluate function when computing decoders, +spanning the interval (-pre.radius, pre.radius) in each dimension. +If None, will use the eval_points associated with pre.

+
+
scale_eval_pointsbool, optional

Indicates whether the evaluation points should be scaled +by the radius of the pre Ensemble.

+
+
labelstr, optional

A descriptive label for the connection.

+
+
seedint, optional

The seed used for random number generation.

+
+
+
+
Attributes
+
+
functioncallable

The given function.

+
+
function_sizeint

The output dimensionality of the given function. If no function is +specified, function_size will be 0.

+
+
labelstr

A human-readable connection label for debugging and visualization. +If not overridden, incorporates the labels of the pre and post objects.

+
+
learning_rule_typeinstance or list or dict of LearningRuleType, optional

The learning rule types.

+
+
postEnsemble or Neurons or Node or Probe or ObjView

The given post object.

+
+
post_objEnsemble or Neurons or Node or Probe

The underlying post object, even if post is an ObjView.

+
+
post_sliceslice or list or None

The slice associated with post if it is an ObjView, or None.

+
+
preEnsemble or Neurons or Node or ObjView

The given pre object.

+
+
pre_objEnsemble or Neurons or Node

The underlying pre object, even if post is an ObjView.

+
+
pre_sliceslice or list or None

The slice associated with pre if it is an ObjView, or None.

+
+
seedint

The seed used for random number generation.

+
+
solverSolver

The Solver instance that will be used to compute decoders or weights +(see nengo.solvers).

+
+
synapseSynapse

The Synapse model used for filtering across the connection +(see nengo.synapses).

+
+
transform(size_out, size_mid) array_like

Linear transform mapping the pre function output to the post input.

+
+
+
+
+
+
+property learning_rule
+

(LearningRule or iterable) Connectable learning rule object(s).

+
+ +
+
+property size_in
+

(int) The number of output dimensions of the pre object.

+

Also the input size of the function, if one is specified.

+
+ +
+
+property size_mid
+

(int) The number of output dimensions of the function, if specified.

+

If the function is not specified, then size_in == size_mid.

+
+ +
+
+property size_out
+

(int) The number of input dimensions of the post object.

+

Also the number of output dimensions of the transform.

+
+ +
+ +
+
+class nengo.connection.LearningRule(connection, learning_rule_type)[source]
+

An interface for making connections to a learning rule.

+

Connections to a learning rule are to allow elements of the network to +affect the learning rule. For example, learning rules that use error +information can obtain that information through a connection.

+

Learning rule objects should only ever be accessed through the +learning_rule attribute of a connection.

+
+
+property connection
+

(Connection) The connection modified by the learning rule.

+
+ +
+
+property modifies
+

(str) The variable modified by the learning rule.

+
+ +
+
+property probeable
+

(tuple) Signals that can be probed in the learning rule.

+
+ +
+
+property size_out
+

(int) Cannot connect from learning rules, so always 0.

+
+ +
+ +
+
+class nengo.Probe(target, attr=None, sample_every=Default, synapse=Default, solver=Default, label=Default, seed=Default)[source]
+

A probe is an object that collects data from the simulation.

+

This is to be used in any situation where you wish to gather simulation +data (spike data, represented values, neuron voltages, etc.) for analysis.

+

Probes do not directly affect the simulation.

+

All Nengo objects can be probed (except Probes themselves). +Each object has different attributes that can be probed. +To see what is probeable for each object, print its +probeable attribute.

+
with nengo.Network():
+    ens = nengo.Ensemble(10, 1)
+print(ens.probeable)
+
+
+
('decoded_output', 'input', 'scaled_encoders')
+
+
+
+
Parameters
+
+
targetEnsemble, Neurons, Node, or Connection

The object to probe.

+
+
attrstr, optional

The signal to probe. Refer to the target’s probeable list for +details. If None, the first element in the probeable list +will be used.

+
+
sample_everyfloat, optional

Sampling period in seconds. If None, the dt of the simluation +will be used.

+
+
synapseSynapse, optional

A synaptic model to filter the probed signal.

+
+
solverSolver, optional

Solver to compute decoders +for probes that require them.

+
+
labelstr, optional

A name for the probe. Used for debugging and visualization.

+
+
seedint, optional

The seed used for random number generation.

+
+
+
+
Attributes
+
+
attrstr or None

The signal that will be probed. If None, the first element of the +target’s probeable list will be used.

+
+
sample_everyfloat or None

Sampling period in seconds. If None, the dt of the simluation +will be used.

+
+
solverSolver or None

Solver to compute decoders. Only used for probes +of an ensemble’s decoded output.

+
+
synapseSynapse or None

A synaptic model to filter the probed signal.

+
+
targetEnsemble, Neurons, Node, or Connection

The object to probe.

+
+
+
+
+
+
+property obj
+

(Nengo object) The underlying Nengo object target.

+
+ +
+
+property size_in
+

(int) Dimensionality of the probed signal.

+
+ +
+
+property size_out
+

(int) Cannot connect from probes, so always 0.

+
+ +
+
+property slice
+

(slice) The slice associated with the Nengo object target.

+
+ +
+ +
+
+

Distributions

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.dists.Distribution

A base class for probability distributions.

nengo.dists.get_samples

Convenience function to sample a distribution or return samples.

nengo.dists.PDF

An arbitrary distribution from a PDF.

nengo.dists.Uniform

A uniform distribution.

nengo.dists.Gaussian

A Gaussian distribution.

nengo.dists.Exponential

An exponential distribution (optionally with high values clipped).

nengo.dists.UniformHypersphere

Uniform distribution on or in an n-dimensional unit hypersphere.

nengo.dists.QuasirandomSequence

Sequence for quasi Monte Carlo sampling the [0, 1]-cube.

nengo.dists.ScatteredHypersphere

Quasirandom distribution over the hypersphere or hyperball.

nengo.dists.Choice

Discrete distribution across a set of possible values.

nengo.dists.Samples

A set of samples.

nengo.dists.SqrtBeta

Distribution of the square root of a Beta distributed random variable.

nengo.dists.SubvectorLength

Distribution of the length of a subvectors of a unit vector.

nengo.dists.CosineSimilarity

Distribution of the cosine of the angle between two random vectors.

+
+
+class nengo.dists.Distribution[source]
+

A base class for probability distributions.

+

The only thing that a probabilities distribution need to define is a +Distribution.sample method. This base class ensures that all +distributions accept the same arguments for the sample function.

+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.DistributionParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A Distribution.

+
+ +
+
+class nengo.dists.DistOrArrayParam(name, default=Unconfigurable, sample_shape=None, sample_dtype=<class 'numpy.float64'>, optional=False, readonly=None)[source]
+

Can be a Distribution or samples from a distribution.

+
+ +
+
+nengo.dists.get_samples(dist_or_samples, n, d=None, rng=numpy.random)[source]
+

Convenience function to sample a distribution or return samples.

+

Use this function in situations where you accept an argument that could +be a distribution, or could be an array_like of samples.

+
+
Parameters
+
+
dist_or_samplesDistribution or (n, d) array_like

Source of the samples to be returned.

+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return.

+
+
rngRandomState, optional

Random number generator.

+
+
+
+
Returns
+
+
samples(n, d) array_like
+
+
+
+

Examples

+
from nengo.dists import get_samples
+
+rng = np.random.RandomState(seed=0)
+
+def mean(values, n=100):
+    samples = get_samples(values, n=n, rng=rng)
+    print("%.4f" % np.mean(samples))
+
+mean([1, 2, 3, 4])
+mean(nengo.dists.Gaussian(0, 1))
+
+
+
2.5000
+0.0598
+
+
+
+ +
+
+class nengo.dists.PDF(x, p)[source]
+

An arbitrary distribution from a PDF.

+
+
Parameters
+
+
xvector_like (n,)

Values of the points to sample from (interpolated).

+
+
pvector_like (n,)

Probabilities of the x points.

+
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.Uniform(low, high, integer=False)[source]
+

A uniform distribution.

+

It’s equally likely to get any scalar between low and high.

+

Note that the order of low and high doesn’t matter; +if low < high this will still work, and low will still +be a closed interval while high is open.

+
+
Parameters
+
+
lowNumber

The closed lower bound of the uniform distribution; samples >= low

+
+
highNumber

The open upper bound of the uniform distribution; samples < high

+
+
integerboolean, optional

If true, sample from a uniform distribution of integers. In this case, +low and high should be integers.

+
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.Gaussian(mean, std)[source]
+

A Gaussian distribution.

+

This represents a bell-curve centred at mean and with +spread represented by the standard deviation, std.

+
+
Parameters
+
+
meanNumber

The mean of the Gaussian.

+
+
stdNumber

The standard deviation of the Gaussian.

+
+
+
+
Raises
+
+
ValidationError if std is <= 0
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.Exponential(scale, shift=0.0, high=inf)[source]
+

An exponential distribution (optionally with high values clipped).

+

If high is left to its default value of infinity, this is a standard +exponential distribution. If high is set, then any sampled values at +or above high will be clipped so they are slightly below high. +This is useful for thresholding.

+

The probability distribution function (PDF) is given by:

+
       |  0                                 if x < shift
+p(x) = | 1/scale * exp(-(x - shift)/scale)  if x >= shift and x < high
+       |  n                                 if x == high - eps
+       |  0                                 if x >= high
+
+
+

where n is such that the PDF integrates to one, and eps is an +infinitesimally small number such that samples of x are strictly less +than high (in practice, eps depends on floating point precision).

+
+
Parameters
+
+
scalefloat

The scale parameter (inverse of the rate parameter lambda). Larger +values make the distribution narrower (sharper peak).

+
+
shiftfloat, optional

Amount to shift the distribution by. There will be no values smaller +than this shift when sampling from the distribution.

+
+
highfloat, optional

All values larger than or equal to this value will be clipped to +slightly less than this value.

+
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.UniformHypersphere(surface=False, min_magnitude=0)[source]
+

Uniform distribution on or in an n-dimensional unit hypersphere.

+

Sample points are uniformly distributed across the volume (default) or +surface of an n-dimensional unit hypersphere.

+
+
Parameters
+
+
surfacebool, optional

Whether sample points should be distributed uniformly +over the surface of the hyperphere (True), +or within the hypersphere (False).

+
+
min_magnitudeNumber, optional

Lower bound on the returned vector magnitudes (such that they are in +the range [min_magnitude, 1]). Must be in the range [0, 1). +Ignored if surface is True.

+
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.QuasirandomSequence[source]
+

Sequence for quasi Monte Carlo sampling the [0, 1]-cube.

+

This is similar to np.random.uniform(0, 1, size=(num, d)), but with the +additional property that each d-dimensional point is uniformly scattered.

+

While the sequence is defined deterministically, we introduce two stochastic +elements to encourage heterogeneity in models using these sequences. +First, we offset the start of the sequence by a random number between 0 and 1 +to ensure we don’t oversample points aligned to the step size. +Second, we shuffle the resulting sequence before returning to ensure we don’t +introduce correlations between parameters sampled from this distribution.

+

This is based on the tutorial and code from 1.

+
+

See also

+
+
ScatteredHypersphere
+
+
+

References

+
+
1
+

Martin Roberts. “The Unreasonable Effectiveness of Quasirandom Sequences.” +http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/

+
+
+

Examples

+
rd = nengo.dists.QuasirandomSequence().sample(10000, 2)
+plt.scatter(*rd.T, c=np.arange(len(rd)), cmap='Blues', s=7)
+
+
+
+
+sample(self, n, d=1, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.ScatteredHypersphere(surface=False, min_magnitude=0, base=QuasirandomSequence(), method='sct-approx')[source]
+

Quasirandom distribution over the hypersphere or hyperball.

+

Applies a spherical transform to the given quasirandom sequence +(by default QuasirandomSequence) to obtain uniformly scattered samples.

+

This distribution has the nice mathematical property that the discrepancy +between the empirical distribution and \(n\) samples is +\(\widetilde{\mathcal{O}} (1 / n)\) as opposed to +\(\mathcal{O} (1 / \sqrt{n})\) for the Monte Carlo method [1]. +This means that the number of samples is effectively squared, making this +useful as a means for sampling eval_points and encoders.

+
+
Parameters
+
+
surfacebool, optional

Whether sample points should be distributed uniformly +over the surface of the hyperphere (True), +or within the hypersphere (False).

+
+
min_magnitudeNumber, optional

Lower bound on the returned vector magnitudes (such that they are in +the range [min_magnitude, 1]). Must be in the range [0, 1). +Ignored if surface is True.

+
+
baseDistribution, optional

The base distribution from which to sample quasirandom numbers.

+
+
method{“sct-approx”, “sct”, “tfww”}

Method to use for mapping points to the hypersphere.

+
    +

  • “sct-approx”: Same as “sct”, but uses lookup table to approximate the +beta distribution, making it faster with almost exactly the same result.

    • +

  • “sct”: Use the exact Spherical Coordinate Transform +(section 1.5.2 of [1]).

    • +

  • “tfww”: Use the Tashiro-Fang-Wang-Wong method (section 4.3 of [1]). +Faster than “sct” and “sct-approx”, with the same level of uniformity +for larger numbers of samples (n >= 4000, approximately).

    • +
+
+
+
+
+
+

See also

+
+
UniformHypersphere
+
QuasirandomSequence
+
+
+

Notes

+

The QuasirandomSequence distribution is mostly deterministic. +Nondeterminism comes from a random d-dimensional rotation.

+

References

+
+
1(1,2,3)
+

K.-T. Fang and Y. Wang, Number-Theoretic Methods in Statistics. +Chapman & Hall, 1994.

+
+
+

Examples

+

Plot points sampled from the surface of the sphere in 3 dimensions:

+
from mpl_toolkits.mplot3d import Axes3D
+
+points = nengo.dists.ScatteredHypersphere(surface=True).sample(1000, d=3)
+
+ax = plt.subplot(111, projection="3d")
+ax.scatter(*points.T, s=5)
+
+
+

Plot points sampled from the volume of the sphere in 2 dimensions (i.e. circle):

+
points = nengo.dists.ScatteredHypersphere(surface=False).sample(1000, d=2)
+plt.scatter(*points.T, s=5)
+
+
+
+
+classmethod spherical_transform_sct(samples, approx=False)[source]
+

Map samples from the [0, 1]-cube onto the hypersphere.

+

Uses the SCT method described in section 1.5.3 of Fang and Wang (1994).

+
+ +
+
+static spherical_transform_tfww(c_samples)[source]
+

Map samples from the [0, 1]-cube onto the hypersphere surface.

+

Uses the TFWW method described in section 4.3 of Fang and Wang (1994).

+
+ +
+
+static random_orthogonal(d, rng=numpy.random)[source]
+

Returns a random orthogonal matrix.

+
+ +
+
+sample(self, n, d=1, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.Choice(options, weights=None)[source]
+

Discrete distribution across a set of possible values.

+

The same as Numpy random’s choice, +except can take vector or matrix values for the choices.

+
+
Parameters
+
+
options(N, …) array_like

The options (choices) to choose between. The choice is always done +along the first axis, so if options is a matrix, the options are +the rows of that matrix.

+
+
weights(N,) array_like, optional

Weights controlling the probability of selecting each option. Will +automatically be normalized. If None, weights be uniformly distributed.

+
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.Samples(samples)[source]
+

A set of samples.

+

This class is a subclass of Distribution so that it can be used in any +situation that calls for a Distribution. However, the call to +Distribution.sample must match the dimensions of the samples or +a ValidationError will be raised.

+
+
Parameters
+
+
samples(n, d) array_like
+
n and d must match what is eventually passed to

Distribution.sample.

+
+
+
+
+
+
+
+
+sample(self, n, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.SqrtBeta(n, m=1)[source]
+

Distribution of the square root of a Beta distributed random variable.

+

Given n + m dimensional random unit vectors, the length of subvectors +with m elements will be distributed according to this distribution.

+
+
Parameters
+
+
n: int

Number of subvectors.

+
+
m: int, optional

Length of each subvector.

+
+
+
+
+
+

See also

+
+
nengo.dists.SubvectorLength
+
+
+
+
+sample(self, num, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+
+cdf(x)[source]
+

Cumulative distribution function.

+
+

Note

+

Requires SciPy.

+
+
+
Parameters
+
+
xarray_like

Evaluation points in [0, 1].

+
+
+
+
Returns
+
+
cdfarray_like

Probability that X <= x.

+
+
+
+
+
+ +
+
+pdf(x)[source]
+

Probability distribution function.

+
+

Note

+

Requires SciPy.

+
+
+
Parameters
+
+
xarray_like

Evaluation points in [0, 1].

+
+
+
+
Returns
+
+
pdfarray_like

Probability density at x.

+
+
+
+
+
+ +
+
+ppf(y)[source]
+

Percent point function (inverse cumulative distribution).

+
+

Note

+

Requires SciPy.

+
+
+
Parameters
+
+
yarray_like

Cumulative probabilities in [0, 1].

+
+
+
+
Returns
+
+
ppfarray_like

Evaluation points x in [0, 1] such that P(X <= x) = y.

+
+
+
+
+
+ +
+ +
+
+class nengo.dists.SubvectorLength(dimensions, subdimensions=1)[source]
+

Distribution of the length of a subvectors of a unit vector.

+
+
Parameters
+
+
dimensionsint

Dimensionality of the complete unit vector.

+
+
subdimensionsint, optional

Dimensionality of the subvector.

+
+
+
+
+
+

See also

+
+
nengo.dists.SqrtBeta
+
+
+
+ +
+
+class nengo.dists.CosineSimilarity(dimensions)[source]
+

Distribution of the cosine of the angle between two random vectors.

+

The “cosine similarity” is the cosine of the angle between two vectors, +which is equal to the dot product of the vectors, divided by the L2-norms +of the individual vectors. When these vectors are unit length, this is then +simply the distribution of their dot product.

+

This is also equivalent to the distribution of a single coefficient from a +unit vector (a single dimension of UniformHypersphere(surface=True)). +Furthermore, CosineSimilarity(d+2) is equivalent to the distribution of +a single coordinate from points uniformly sampled from the d-dimensional +unit ball (a single dimension of +UniformHypersphere(surface=False).sample(n, d)). These relationships +have been detailed in [Voelker2017].

+

This can be used to calculate an intercept c = ppf(1 - p) such that +dot(u, v) >= c with probability p, for random unit vectors u +and v. In other words, a neuron with intercept ppf(1 - p) will +fire with probability p for a random unit length input.

+
+
Voelker2017
+

Aaron R. Voelker, Jan Gosmann, and Terrence C. Stewart. +Efficiently sampling vectors and coordinates from the n-sphere and +n-ball. Technical Report, Centre for Theoretical Neuroscience, +Waterloo, ON, 2017

+
+
+
+
Parameters
+
+
dimensions: int

Dimensionality of the complete unit vector.

+
+
+
+
+
+

See also

+
+
nengo.dists.SqrtBeta
+
+
+
+
+sample(self, num, d=None, rng=numpy.random)[source]
+

Samples the distribution.

+
+
Parameters
+
+
nint

Number samples to take.

+
+
dint or None, optional

The number of dimensions to return. If this is an int, the return +value will be of shape (n, d). If None, the return +value will be of shape (n,).

+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
samples(n,) or (n, d) array_like

Samples as a 1d or 2d array depending on d. The second +dimension enumerates the dimensions of the process.

+
+
+
+
+
+ +
+
+cdf(x)[source]
+

Cumulative distribution function.

+
+

Note

+

Requires SciPy.

+
+
+
Parameters
+
+
xarray_like

Evaluation points in [0, 1].

+
+
+
+
Returns
+
+
cdfarray_like

Probability that X <= x.

+
+
+
+
+
+ +
+
+pdf(x)[source]
+

Probability distribution function.

+
+

Note

+

Requires SciPy.

+
+
+
Parameters
+
+
xarray_like

Evaluation points in [0, 1].

+
+
+
+
Returns
+
+
pdfarray_like

Probability density at x.

+
+
+
+
+
+ +
+
+ppf(y)[source]
+

Percent point function (inverse cumulative distribution).

+
+

Note

+

Requires SciPy.

+
+
+
Parameters
+
+
yarray_like

Cumulative probabilities in [0, 1].

+
+
+
+
Returns
+
+
ppfarray_like

Evaluation points x in [0, 1] such that P(X <= x) = y.

+
+
+
+
+
+ +
+ +
+
+

Learning rule types

+ ++++ + + + + + + + + + + + + + + + + + + + + +

nengo.learning_rules.LearningRuleType

Base class for all learning rule objects.

nengo.PES

Prescribed Error Sensitivity learning rule.

nengo.RLS

Recursive least-squares rule for online decoder optimization.

nengo.BCM

Bienenstock-Cooper-Munroe learning rule.

nengo.Oja

Oja learning rule.

nengo.Voja

Vector Oja learning rule.

+
+
+class nengo.learning_rules.LearningRuleTypeSizeInParam(name, default=Unconfigurable, low=None, high=None, low_open=False, high_open=False, optional=False, readonly=None)[source]
+
+ +
+
+class nengo.learning_rules.LearningRuleType(learning_rate=Default<1e-06>, size_in=0)[source]
+

Base class for all learning rule objects.

+

To use a learning rule, pass it as a learning_rule_type keyword +argument to the Connection on which you want to do learning.

+

Each learning rule exposes two important pieces of metadata that the +builder uses to determine what information should be stored.

+

The size_in is the dimensionality of the incoming error signal. It +can either take an integer or one of the following string values:

+
    +

  • 'pre': vector error signal in pre-object space

    • +

  • 'post': vector error signal in post-object space

    • +

  • 'mid': vector error signal in the conn.size_mid space

    • +

  • 'pre_state': vector error signal in pre-synaptic ensemble space

    • +

  • 'post_state': vector error signal in post-synaptic ensemble space

    • +
+

The difference between 'post_state' and 'post' is that with the +former, if a Neurons object is passed, it will use the dimensionality +of the corresponding Ensemble, whereas the latter simply uses the +post object size_in. Similarly with 'pre_state' and 'pre'.

+

The modifies attribute denotes the signal targeted by the rule. +Options are:

+
    +

  • 'encoders'

    • +

  • 'decoders'

    • +

  • 'weights'

    • +
+
+
Parameters
+
+
learning_ratefloat, optional

A scalar indicating the rate at which modifies will be adjusted.

+
+
size_inint, str, optional

Dimensionality of the error signal (see above).

+
+
+
+
Attributes
+
+
learning_ratefloat

A scalar indicating the rate at which modifies will be adjusted.

+
+
size_inint, str

Dimensionality of the error signal.

+
+
modifiesstr

The signal targeted by the learning rule.

+
+
+
+
+
+ +
+
+class nengo.PES(learning_rate=Default<0.0001>, pre_synapse=Default<Lowpass(tau=0.005)>)[source]
+

Prescribed Error Sensitivity learning rule.

+

Modifies a connection’s decoders to minimize an error signal provided +through a connection to the connection’s learning rule.

+
+
Parameters
+
+
learning_ratefloat, optional

A scalar indicating the rate at which weights will be adjusted.

+
+
pre_synapseSynapse, optional

Synapse model used to filter the pre-synaptic activities.

+
+
+
+
Attributes
+
+
learning_ratefloat

A scalar indicating the rate at which weights will be adjusted.

+
+
pre_synapseSynapse

Synapse model used to filter the pre-synaptic activities.

+
+
+
+
+
+ +
+
+class nengo.RLS(learning_rate=Default<0.001>, pre_synapse=Default<Lowpass(tau=0.005)>)[source]
+

Recursive least-squares rule for online decoder optimization.

+

This implements an online version of the standard least-squares solvers used +to learn connection weights offline (e.g. nengo.solvers.LstsqL2). It can be +applied in the same scenarios as PES, to minimize an error signal.

+

The cost of RLS is \(\mathcal{O}(n^2)\) extra time and memory. If possible, +it is more efficient to do the learning offline using e.g. LstsqL2.

+
+
Parameters
+
+
learning_ratefloat, optional

Effective learning rate. This is better understood as +\(\frac{1}{\alpha}\), where \(\alpha\) is an L2-regularization +term. A large learning rate means little regularization, which implies +quick over-fitting. A small learning rate means large regularization, +which translates to slower learning 2.

+
+
pre_synapseSynapse, optional

Synapse model applied to the pre-synaptic neural activities.

+
+
+
+
+
+

See also

+
+
nengo.PES
+
nengo.solvers.LstsqL2
+
+
+

Notes

+

RLS works by maintaining the inverse neural correlation matrix, +\(P = \Gamma^{-1}\), where \(\Gamma = A^T A + \alpha I\) are the +regularized correlations, \(A\) is a matrix of (possibly filtered) +neural activities, and \(\alpha\) is an L2-regularization term +controlled by the learning_rate. \(P\) is used to project the +error signal and update the weights each time-step.

+

References

+
+
2
+

Sussillo, D., & Abbott, L. F. (2009). Generating coherent patterns +of activity from chaotic neural networks. Neuron, 63(4), 544-557.

+
+
+

Examples

+

Below, we compare PES against RLS, learning a feed-forward +communication channel (identity function) online, starting with 100 spiking +LIF neurons with decoders (weights) set to zero. A faster learning rate for +PES results in over-fitting to the most recent online example, while a +slower learning rate does not learn quickly enough. This is a general problem +with greedy optimization. RLS performs better since it is L2-optimal.

+
from nengo.learning_rules import PES, RLS
+
+tau = 0.005
+learning_rules = (
+    PES(learning_rate=1e-3, pre_synapse=tau),
+    RLS(learning_rate=1e-3, pre_synapse=tau),
+)
+
+with nengo.Network() as model:
+    u = nengo.Node(output=lambda t: np.sin(2 * np.pi * t))
+    probes = []
+    for lr in learning_rules:
+        e = nengo.Node(size_in=1, output=lambda t, e: e if t < 1 else 0)
+        x = nengo.Ensemble(100, 1, seed=0)
+        y = nengo.Node(size_in=1)
+
+        nengo.Connection(u, e, synapse=None, transform=-1)
+        nengo.Connection(u, x, synapse=None)
+        conn = nengo.Connection(
+            x, y, synapse=None, learning_rule_type=lr, function=lambda x: 0
+        )
+        nengo.Connection(y, e, synapse=None)
+        nengo.Connection(e, conn.learning_rule, synapse=tau)
+        probes.append(nengo.Probe(y, synapse=tau))
+    probes.append(nengo.Probe(u, synapse=tau))
+
+with nengo.Simulator(model) as sim:
+    sim.run(2.0)
+
+plt.plot(sim.trange(), sim.data[probes[0]], label=str(learning_rules[0]))
+plt.plot(sim.trange(), sim.data[probes[1]], label=str(learning_rules[1]))
+plt.plot(sim.trange(), sim.data[probes[2]], label="Ideal", linestyle="--")
+plt.vlines([1], -1, 1, label="Training -> Testing")
+plt.ylim(-2, 2)
+plt.legend(loc="upper right")
+plt.xlabel("Time (s)")
+
+
+
+ +
+
+class nengo.BCM(learning_rate=Default<1e-09>, pre_synapse=Default<Lowpass(tau=0.005)>, post_synapse=Default<None>, theta_synapse=Default<Lowpass(tau=1.0)>)[source]
+

Bienenstock-Cooper-Munroe learning rule.

+

Modifies connection weights as a function of the presynaptic activity +and the difference between the postsynaptic activity and the average +postsynaptic activity.

+
+
Parameters
+
+
learning_ratefloat, optional

A scalar indicating the rate at which weights will be adjusted.

+
+
pre_synapseSynapse, optional

Synapse model used to filter the pre-synaptic activities.

+
+
post_synapseSynapse, optional

Synapse model used to filter the post-synaptic activities. +If None, post_synapse will be the same as pre_synapse.

+
+
theta_synapseSynapse, optional

Synapse model used to filter the theta signal.

+
+
+
+
+

Notes

+

The BCM rule is dependent on pre and post neural activities, +not decoded values, and so is not affected by changes in the +size of pre and post ensembles. However, if you are decoding from +the post ensemble, the BCM rule will have an increased effect on +larger post ensembles because more connection weights are changing. +In these cases, it may be advantageous to scale the learning rate +on the BCM rule by 1 / post.n_neurons.

+
+
Attributes
+
+
learning_ratefloat

A scalar indicating the rate at which weights will be adjusted.

+
+
post_synapseSynapse

Synapse model used to filter the post-synaptic activities.

+
+
pre_synapseSynapse

Synapse model used to filter the pre-synaptic activities.

+
+
theta_synapseSynapse

Synapse model used to filter the theta signal.

+
+
+
+
+
+ +
+
+class nengo.Oja(learning_rate=Default<1e-06>, pre_synapse=Default<Lowpass(tau=0.005)>, post_synapse=Default<None>, beta=Default<1.0>)[source]
+

Oja learning rule.

+

Modifies connection weights according to the Hebbian Oja rule, which +augments typically Hebbian coactivity with a “forgetting” term that is +proportional to the weight of the connection and the square of the +postsynaptic activity.

+
+
Parameters
+
+
learning_ratefloat, optional

A scalar indicating the rate at which weights will be adjusted.

+
+
pre_synapseSynapse, optional

Synapse model used to filter the pre-synaptic activities.

+
+
post_synapseSynapse, optional

Synapse model used to filter the post-synaptic activities. +If None, post_synapse will be the same as pre_synapse.

+
+
betafloat, optional

A scalar weight on the forgetting term.

+
+
+
+
+

Notes

+

The Oja rule is dependent on pre and post neural activities, +not decoded values, and so is not affected by changes in the +size of pre and post ensembles. However, if you are decoding from +the post ensemble, the Oja rule will have an increased effect on +larger post ensembles because more connection weights are changing. +In these cases, it may be advantageous to scale the learning rate +on the Oja rule by 1 / post.n_neurons.

+
+
Attributes
+
+
betafloat

A scalar weight on the forgetting term.

+
+
learning_ratefloat

A scalar indicating the rate at which weights will be adjusted.

+
+
post_synapseSynapse

Synapse model used to filter the post-synaptic activities.

+
+
pre_synapseSynapse

Synapse model used to filter the pre-synaptic activities.

+
+
+
+
+
+ +
+
+class nengo.Voja(learning_rate=Default<0.01>, post_synapse=Default<Lowpass(tau=0.005)>)[source]
+

Vector Oja learning rule.

+

Modifies an ensemble’s encoders to be selective to its inputs.

+

A connection to the learning rule will provide a scalar weight for the +learning rate, minus 1. For instance, 0 is normal learning, -1 is no +learning, and less than -1 causes anti-learning or “forgetting”.

+
+
Parameters
+
+
post_taufloat, optional

Filter constant on activities of neurons in post population.

+
+
learning_ratefloat, optional

A scalar indicating the rate at which encoders will be adjusted.

+
+
post_synapseSynapse, optional

Synapse model used to filter the post-synaptic activities.

+
+
+
+
Attributes
+
+
learning_ratefloat

A scalar indicating the rate at which encoders will be adjusted.

+
+
post_synapseSynapse

Synapse model used to filter the post-synaptic activities.

+
+
+
+
+
+ +
+
+class nengo.learning_rules.LearningRuleTypeParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+
+ +
+
+

Neuron types

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.neurons.settled_firingrate

Compute firing rates (in Hz) for given vector input, x.

nengo.neurons.NeuronType

Base class for Nengo neuron models.

nengo.Direct

Signifies that an ensemble should simulate in direct mode.

nengo.RectifiedLinear

A rectified linear neuron model.

nengo.SpikingRectifiedLinear

A rectified integrate and fire neuron model.

nengo.Sigmoid

A non-spiking neuron model whose response curve is a sigmoid.

nengo.Tanh

A non-spiking neuron model whose response curve is a hyperbolic tangent.

nengo.LIFRate

Non-spiking version of the leaky integrate-and-fire (LIF) neuron model.

nengo.LIF

Spiking version of the leaky integrate-and-fire (LIF) neuron model.

nengo.AdaptiveLIFRate

Adaptive non-spiking version of the LIF neuron model.

nengo.AdaptiveLIF

Adaptive spiking version of the LIF neuron model.

nengo.Izhikevich

Izhikevich neuron model.

nengo.neurons.RatesToSpikesNeuronType

Base class for neuron types that turn rate types into spiking ones.

nengo.RegularSpiking

Turn a rate neuron type into a spiking one with regular inter-spike intervals.

nengo.StochasticSpiking

Turn a rate neuron type into a spiking one using stochastic rounding.

nengo.PoissonSpiking

Turn a rate neuron type into a spiking one with Poisson spiking statistics.

+
+
+nengo.neurons.settled_firingrate(step, J, state, dt=0.001, settle_time=0.1, sim_time=1.0)[source]
+

Compute firing rates (in Hz) for given vector input, x.

+

Unlike the default naive implementation, this approach takes into +account some characteristics of spiking neurons. We start +by simulating the neurons for a short amount of time, to let any +initial transients settle. Then, we run the neurons for a second +and find the average (which should approximate the firing rate).

+
+
Parameters
+
+
stepfunction

the step function of the neuron type

+
+
Jndarray

a vector of currents to generate firing rates from

+
+
statedict of ndarrays

additional state needed by the step function

+
+
+
+
+
+ +
+
+class nengo.neurons.NeuronType(initial_state=None)[source]
+

Base class for Nengo neuron models.

+
+
Parameters
+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
Attributes
+
+
state{str: Distribution}

State variables held by the neuron type during simulation. +Values in the dict indicate their initial values, or how +to obtain those initial values. These elements can also be +probed in the neuron population.

+
+
negativebool

Whether the neurons can emit negative outputs (i.e. negative spikes or rates).

+
+
+
+
+
+
+current(x, gain, bias)[source]
+

Compute current injected in each neuron given input, gain and bias.

+

Note that x is assumed to be already projected onto the encoders +associated with the neurons and normalized to radius 1, so the maximum +expected current for a neuron occurs when input for that neuron is 1.

+
+
Parameters
+
+
x(n_samples,) or (n_samples, n_neurons) array_like

Scalar inputs for which to calculate current.

+
+
gain(n_neurons,) array_like

Gains associated with each neuron.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
Returns
+
+
current(n_samples, n_neurons)

Current to be injected in each neuron.

+
+
+
+
+
+ +
+
+gain_bias(max_rates, intercepts)[source]
+

Compute the gain and bias needed to satisfy max_rates, intercepts.

+

This takes the neurons, approximates their response function, and then +uses that approximation to find the gain and bias value that will give +the requested intercepts and max_rates.

+

Note that this default implementation is very slow! Whenever possible, +subclasses should override this with a neuron-specific implementation.

+
+
Parameters
+
+
max_rates(n_neurons,) array_like

Maximum firing rates of neurons.

+
+
intercepts(n_neurons,) array_like

X-intercepts of neurons.

+
+
+
+
Returns
+
+
gain(n_neurons,) array_like

Gain associated with each neuron. Sometimes denoted alpha.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Compute the max_rates and intercepts given gain and bias.

+

Note that this default implementation is very slow! Whenever possible, +subclasses should override this with a neuron-specific implementation.

+
+
Parameters
+
+
gain(n_neurons,) array_like

Gain associated with each neuron. Sometimes denoted alpha.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
Returns
+
+
max_rates(n_neurons,) array_like

Maximum firing rates of neurons.

+
+
intercepts(n_neurons,) array_like

X-intercepts of neurons.

+
+
+
+
+
+ +
+
+rates(x, gain, bias)[source]
+

Compute firing rates (in Hz) for given input x.

+

This default implementation takes the naive approach of running the +step function for a second. This should suffice for most rate-based +neuron types; for spiking neurons it will likely fail (those models +should override this function).

+

Note that x is assumed to be already projected onto the encoders +associated with the neurons and normalized to radius 1, so the maximum +expected rate for a neuron occurs when input for that neuron is 1.

+
+
Parameters
+
+
x(n_samples,) or (n_samples, n_neurons) array_like

Scalar inputs for which to calculate rates.

+
+
gain(n_neurons,) array_like

Gains associated with each neuron.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
Returns
+
+
rates(n_samples, n_neurons) ndarray

The firing rates at each given value of x.

+
+
+
+
+
+ +
+
+step(dt, J, output, **state)[source]
+

Implements the differential equation for this neuron type.

+

At a minimum, NeuronType subclasses must implement this method. +That implementation should modify the output parameter rather +than returning anything, for efficiency reasons.

+
+
Parameters
+
+
dtfloat

Simulation timestep.

+
+
J(n_neurons,) array_like

Input currents associated with each neuron.

+
+
output(n_neurons,) array_like

Output activity associated with each neuron (e.g., spikes or firing rates).

+
+
state{str: array_like}

State variables associated with the population.

+
+
+
+
+
+ +
+ +
+
+class nengo.neurons.NeuronTypeParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+
+ +
+
+class nengo.Direct(initial_state=None)[source]
+

Signifies that an ensemble should simulate in direct mode.

+

In direct mode, the ensemble represents and transforms signals perfectly, +rather than through a neural approximation. Note that direct mode ensembles +with recurrent connections can easily diverge; most other neuron types will +instead saturate at a certain high firing rate.

+
+
+gain_bias(max_rates, intercepts)[source]
+

Always returns None, None.

+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Always returns None, None.

+
+ +
+
+rates(x, gain, bias)[source]
+

Always returns x.

+
+ +
+
+step(dt, J, output)[source]
+

Raises an error if called.

+

Rather than calling this function, the simulator will detect that +the ensemble is in direct mode, and bypass the neural approximation.

+
+ +
+ +
+
+class nengo.RectifiedLinear(amplitude=1, initial_state=None)[source]
+

A rectified linear neuron model.

+

Each neuron is modeled as a rectified line. That is, the neuron’s activity +scales linearly with current, unless it passes below zero, at which point +the neural activity will stay at zero.

+
+
Parameters
+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+gain_bias(max_rates, intercepts)[source]
+

Determine gain and bias by shifting and scaling the lines.

+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Compute the inverse of gain_bias.

+
+ +
+
+step(dt, J, output)[source]
+

Implement the rectification nonlinearity.

+
+ +
+ +
+
+class nengo.SpikingRectifiedLinear(amplitude=1, initial_state=None)[source]
+

A rectified integrate and fire neuron model.

+

Each neuron is modeled as a rectified line. That is, the neuron’s activity +scales linearly with current, unless the current is less than zero, at +which point the neural activity will stay at zero. This is a spiking +version of the RectifiedLinear neuron model.

+
+
Parameters
+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+rates(x, gain, bias)[source]
+

Use RectifiedLinear to determine rates.

+
+ +
+
+step(dt, J, output, voltage)[source]
+

Implement the integrate and fire nonlinearity.

+
+ +
+ +
+
+class nengo.Sigmoid(tau_ref=0.0025, initial_state=None)[source]
+

A non-spiking neuron model whose response curve is a sigmoid.

+

Since the tuning curves are strictly positive, the intercepts +correspond to the inflection point of each sigmoid. That is, +f(intercept) = 0.5 where f is the pure sigmoid function.

+
+
Parameters
+
+
tau_reffloat

The neuron refractory period, in seconds. The maximum firing rate of the +neurons is 1 / tau_ref. Must be positive (i.e. tau_ref > 0).

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+gain_bias(max_rates, intercepts)[source]
+

Analytically determine gain, bias.

+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Compute the inverse of gain_bias.

+
+ +
+
+step(dt, J, output)[source]
+

Implement the sigmoid nonlinearity.

+
+ +
+ +
+
+class nengo.Tanh(tau_ref=0.0025, initial_state=None)[source]
+

A non-spiking neuron model whose response curve is a hyperbolic tangent.

+
+
Parameters
+
+
tau_reffloat

The neuron refractory period, in seconds. The maximum firing rate of the +neurons is 1 / tau_ref. Must be positive (i.e. tau_ref > 0).

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+gain_bias(max_rates, intercepts)[source]
+

Analytically determine gain, bias.

+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Compute the inverse of gain_bias.

+
+ +
+
+step(dt, J, output)[source]
+

Implement the tanh nonlinearity.

+
+ +
+ +
+
+class nengo.LIFRate(tau_rc=0.02, tau_ref=0.002, amplitude=1, initial_state=None)[source]
+

Non-spiking version of the leaky integrate-and-fire (LIF) neuron model.

+
+
Parameters
+
+
tau_rcfloat

Membrane RC time constant, in seconds. Affects how quickly the membrane +voltage decays to zero in the absence of input (larger = slower decay).

+
+
tau_reffloat

Absolute refractory period, in seconds. This is how long the +membrane voltage is held at zero after a spike.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+gain_bias(max_rates, intercepts)[source]
+

Analytically determine gain, bias.

+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Compute the inverse of gain_bias.

+
+ +
+
+rates(x, gain, bias)[source]
+

Always use LIFRate to determine rates.

+
+ +
+
+step(dt, J, output)[source]
+

Implement the LIFRate nonlinearity.

+
+ +
+ +
+
+class nengo.LIF(tau_rc=0.02, tau_ref=0.002, min_voltage=0, amplitude=1, initial_state=None)[source]
+

Spiking version of the leaky integrate-and-fire (LIF) neuron model.

+
+
Parameters
+
+
tau_rcfloat

Membrane RC time constant, in seconds. Affects how quickly the membrane +voltage decays to zero in the absence of input (larger = slower decay).

+
+
tau_reffloat

Absolute refractory period, in seconds. This is how long the +membrane voltage is held at zero after a spike.

+
+
min_voltagefloat

Minimum value for the membrane voltage. If -np.inf, the voltage +is never clipped.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+step(dt, J, output, voltage, refractory_time)[source]
+

Implement the LIFRate nonlinearity.

+
+ +
+ +
+
+class nengo.AdaptiveLIFRate(tau_n=1, inc_n=0.01, tau_rc=0.02, tau_ref=0.002, amplitude=1, initial_state=None)[source]
+

Adaptive non-spiking version of the LIF neuron model.

+

Works as the LIF model, except with adapation state n, which is +subtracted from the input current. Its dynamics are:

+
tau_n dn/dt = -n
+
+
+

where n is incremented by inc_n when the neuron spikes.

+
+
Parameters
+
+
tau_nfloat

Adaptation time constant. Affects how quickly the adaptation state +decays to zero in the absence of spikes (larger = slower decay).

+
+
inc_nfloat

Adaptation increment. How much the adaptation state is increased after +each spike.

+
+
tau_rcfloat

Membrane RC time constant, in seconds. Affects how quickly the membrane +voltage decays to zero in the absence of input (larger = slower decay).

+
+
tau_reffloat

Absolute refractory period, in seconds. This is how long the +membrane voltage is held at zero after a spike.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+

References

+
+
1
+

Camera, Giancarlo La, et al. “Minimal models of adapted neuronal +response to in Vivo-Like input currents.” Neural computation +16.10 (2004): 2101-2124.

+
+
+
+
+step(dt, J, output, adaptation)[source]
+

Implement the AdaptiveLIFRate nonlinearity.

+
+ +
+ +
+
+class nengo.AdaptiveLIF(tau_n=1, inc_n=0.01, tau_rc=0.02, tau_ref=0.002, min_voltage=0, amplitude=1, initial_state=None)[source]
+

Adaptive spiking version of the LIF neuron model.

+

Works as the LIF model, except with adapation state n, which is +subtracted from the input current. Its dynamics are:

+
tau_n dn/dt = -n
+
+
+

where n is incremented by inc_n when the neuron spikes.

+
+
Parameters
+
+
tau_nfloat

Adaptation time constant. Affects how quickly the adaptation state +decays to zero in the absence of spikes (larger = slower decay).

+
+
inc_nfloat

Adaptation increment. How much the adaptation state is increased after +each spike.

+
+
tau_rcfloat

Membrane RC time constant, in seconds. Affects how quickly the membrane +voltage decays to zero in the absence of input (larger = slower decay).

+
+
tau_reffloat

Absolute refractory period, in seconds. This is how long the +membrane voltage is held at zero after a spike.

+
+
min_voltagefloat

Minimum value for the membrane voltage. If -np.inf, the voltage +is never clipped.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+

References

+
+
1
+

Camera, Giancarlo La, et al. “Minimal models of adapted neuronal +response to in Vivo-Like input currents.” Neural computation +16.10 (2004): 2101-2124.

+
+
+
+
+step(dt, J, output, voltage, refractory_time, adaptation)[source]
+

Implement the AdaptiveLIF nonlinearity.

+
+ +
+ +
+
+class nengo.Izhikevich(tau_recovery=0.02, coupling=0.2, reset_voltage=- 65.0, reset_recovery=8.0, initial_state=None)[source]
+

Izhikevich neuron model.

+

This implementation is based on the original paper [1]; +however, we rename some variables for clarity. +What was originally ‘v’ we term ‘voltage’, which represents the membrane +potential of each neuron. What was originally ‘u’ we term ‘recovery’, +which represents membrane recovery, “which accounts for the activation +of K+ ionic currents and inactivation of Na+ ionic currents.” +The ‘a’, ‘b’, ‘c’, and ‘d’ parameters are also renamed +(see the parameters below).

+

We use default values that correspond to regular spiking (‘RS’) neurons. +For other classes of neurons, set the parameters as follows.

+
    +

  • Intrinsically bursting (IB): reset_voltage=-55, reset_recovery=4

    • +

  • Chattering (CH): reset_voltage=-50, reset_recovery=2

    • +

  • Fast spiking (FS): tau_recovery=0.1

    • +

  • Low-threshold spiking (LTS): coupling=0.25

    • +

  • Resonator (RZ): tau_recovery=0.1, coupling=0.26

    • +
+
+
Parameters
+
+
tau_recoveryfloat, optional

(Originally ‘a’) Time scale of the recovery variable.

+
+
couplingfloat, optional

(Originally ‘b’) How sensitive recovery is to subthreshold +fluctuations of voltage.

+
+
reset_voltagefloat, optional

(Originally ‘c’) The voltage to reset to after a spike, in millivolts.

+
+
reset_recoveryfloat, optional

(Originally ‘d’) The recovery value to reset to after a spike.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+

References

+
+
1
+

E. M. Izhikevich, “Simple model of spiking neurons.” +IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1569-1572. +(http://www.izhikevich.org/publications/spikes.pdf)

+
+
+
+
+rates(x, gain, bias)[source]
+

Estimates steady-state firing rate given gain and bias.

+
+ +
+
+step(dt, J, output, voltage, recovery)[source]
+

Implement the Izhikevich nonlinearity.

+
+ +
+ +
+
+class nengo.neurons.RatesToSpikesNeuronType(base_type, amplitude=1.0, initial_state=None)[source]
+

Base class for neuron types that turn rate types into spiking ones.

+
+
+gain_bias(max_rates, intercepts)[source]
+

Compute the gain and bias needed to satisfy max_rates, intercepts.

+

This takes the neurons, approximates their response function, and then +uses that approximation to find the gain and bias value that will give +the requested intercepts and max_rates.

+

Note that this default implementation is very slow! Whenever possible, +subclasses should override this with a neuron-specific implementation.

+
+
Parameters
+
+
max_rates(n_neurons,) array_like

Maximum firing rates of neurons.

+
+
intercepts(n_neurons,) array_like

X-intercepts of neurons.

+
+
+
+
Returns
+
+
gain(n_neurons,) array_like

Gain associated with each neuron. Sometimes denoted alpha.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
+
+ +
+
+max_rates_intercepts(gain, bias)[source]
+

Compute the max_rates and intercepts given gain and bias.

+

Note that this default implementation is very slow! Whenever possible, +subclasses should override this with a neuron-specific implementation.

+
+
Parameters
+
+
gain(n_neurons,) array_like

Gain associated with each neuron. Sometimes denoted alpha.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
Returns
+
+
max_rates(n_neurons,) array_like

Maximum firing rates of neurons.

+
+
intercepts(n_neurons,) array_like

X-intercepts of neurons.

+
+
+
+
+
+ +
+
+rates(x, gain, bias)[source]
+

Compute firing rates (in Hz) for given input x.

+

This default implementation takes the naive approach of running the +step function for a second. This should suffice for most rate-based +neuron types; for spiking neurons it will likely fail (those models +should override this function).

+

Note that x is assumed to be already projected onto the encoders +associated with the neurons and normalized to radius 1, so the maximum +expected rate for a neuron occurs when input for that neuron is 1.

+
+
Parameters
+
+
x(n_samples,) or (n_samples, n_neurons) array_like

Scalar inputs for which to calculate rates.

+
+
gain(n_neurons,) array_like

Gains associated with each neuron.

+
+
bias(n_neurons,) array_like

Bias current associated with each neuron.

+
+
+
+
Returns
+
+
rates(n_samples, n_neurons) ndarray

The firing rates at each given value of x.

+
+
+
+
+
+ +
+
+step(dt, J, output, **state)[source]
+

Implements the differential equation for this neuron type.

+

At a minimum, NeuronType subclasses must implement this method. +That implementation should modify the output parameter rather +than returning anything, for efficiency reasons.

+
+
Parameters
+
+
dtfloat

Simulation timestep.

+
+
J(n_neurons,) array_like

Input currents associated with each neuron.

+
+
output(n_neurons,) array_like

Output activity associated with each neuron (e.g., spikes or firing rates).

+
+
state{str: array_like}

State variables associated with the population.

+
+
+
+
+
+ +
+ +
+
+class nengo.RegularSpiking(base_type, amplitude=1.0, initial_state=None)[source]
+

Turn a rate neuron type into a spiking one with regular inter-spike intervals.

+

Spikes at regular intervals based on the rates of the base neuron type. [1]

+
+
Parameters
+
+
base_typeNeuronType

A rate-based neuron type to convert to a regularly spiking neuron.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+

References

+
+
1
+

Voelker, A. R., Rasmussen, D., & Eliasmith, C. (2020). A Spike in +Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation +Functions. arXiv preprint arXiv:2002.03553. (https://arxiv.org/abs/2002.03553)

+
+
+
+
+step(dt, J, output, voltage)[source]
+

Implements the differential equation for this neuron type.

+

At a minimum, NeuronType subclasses must implement this method. +That implementation should modify the output parameter rather +than returning anything, for efficiency reasons.

+
+
Parameters
+
+
dtfloat

Simulation timestep.

+
+
J(n_neurons,) array_like

Input currents associated with each neuron.

+
+
output(n_neurons,) array_like

Output activity associated with each neuron (e.g., spikes or firing rates).

+
+
state{str: array_like}

State variables associated with the population.

+
+
+
+
+
+ +
+ +
+
+class nengo.StochasticSpiking(base_type, amplitude=1.0, initial_state=None)[source]
+

Turn a rate neuron type into a spiking one using stochastic rounding.

+

The expected number of spikes per timestep e = dt * r is determined by the +base type firing rate r and the timestep dt. Given the fractional part f +and integer part q of e, the number of generated spikes is q with +probability 1 - f and q + 1 with probability f. For e much less than +one, this is very similar to Poisson statistics.

+
+
Parameters
+
+
base_typeNeuronType

A rate-based neuron type to convert to a Poisson spiking neuron.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+step(dt, J, output, rng, **base_state)[source]
+

Implements the differential equation for this neuron type.

+

At a minimum, NeuronType subclasses must implement this method. +That implementation should modify the output parameter rather +than returning anything, for efficiency reasons.

+
+
Parameters
+
+
dtfloat

Simulation timestep.

+
+
J(n_neurons,) array_like

Input currents associated with each neuron.

+
+
output(n_neurons,) array_like

Output activity associated with each neuron (e.g., spikes or firing rates).

+
+
state{str: array_like}

State variables associated with the population.

+
+
+
+
+
+ +
+ +
+
+class nengo.PoissonSpiking(base_type, amplitude=1.0, initial_state=None)[source]
+

Turn a rate neuron type into a spiking one with Poisson spiking statistics.

+

Spikes with Poisson probability based on the rates of the base neuron type.

+
+
Parameters
+
+
base_typeNeuronType

A rate-based neuron type to convert to a Poisson spiking neuron.

+
+
amplitudefloat

Scaling factor on the neuron output. Corresponds to the relative +amplitude of the output spikes of the neuron.

+
+
initial_state{str: Distribution or array_like}

Mapping from state variables names to their desired initial value. +These values will override the defaults set in the class’s state attribute.

+
+
+
+
+
+
+step(dt, J, output, rng, **base_state)[source]
+

Implements the differential equation for this neuron type.

+

At a minimum, NeuronType subclasses must implement this method. +That implementation should modify the output parameter rather +than returning anything, for efficiency reasons.

+
+
Parameters
+
+
dtfloat

Simulation timestep.

+
+
J(n_neurons,) array_like

Input currents associated with each neuron.

+
+
output(n_neurons,) array_like

Output activity associated with each neuron (e.g., spikes or firing rates).

+
+
state{str: array_like}

State variables associated with the population.

+
+
+
+
+
+ +
+ +
+
+

Processes

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + +

nengo.Process

A general system with input, output, and state.

nengo.processes.WhiteNoise

Full-spectrum white noise process.

nengo.processes.FilteredNoise

Filtered white noise process.

nengo.processes.BrownNoise

Brown noise process (aka Brownian noise, red noise, Wiener process).

nengo.processes.WhiteSignal

An ideal low-pass filtered white noise process.

nengo.processes.PresentInput

Present a series of inputs, each for the same fixed length of time.

nengo.processes.Piecewise

A piecewise function with different options for interpolation.

+
+
+class nengo.processes.WhiteNoise(dist=Gaussian(mean=0, std=1), scale=True, **kwargs)[source]
+

Full-spectrum white noise process.

+
+
Parameters
+
+
distDistribution, optional

The distribution from which to draw samples.

+
+
scalebool, optional

Whether to scale the white noise for integration. Integrating white +noise requires using a time constant of sqrt(dt) instead of dt +on the noise term [1], to ensure the magnitude of the integrated +noise does not change with dt.

+
+
seedint, optional

Random number seed. Ensures noise will be the same each run.

+
+
+
+
+

References

+
+
1
+

Gillespie, D.T. (1996) Exact numerical simulation of the Ornstein- +Uhlenbeck process and its integral. Phys. Rev. E 54, pp. 2084-91.

+
+
+
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+ +
+
+class nengo.processes.FilteredNoise(synapse=Lowpass(tau=0.005), dist=Gaussian(mean=0, std=1), scale=True, **kwargs)[source]
+

Filtered white noise process.

+

This process takes white noise and filters it using the provided synapse.

+
+
Parameters
+
+
synapseSynapse, optional

The synapse to use to filter the noise.

+
+
distDistribution, optional

The distribution used to generate the white noise.

+
+
scalebool, optional

Whether to scale the white noise for integration, making the output +signal invariant to dt.

+
+
seedint, optional

Random number seed. Ensures noise will be the same each run.

+
+
+
+
+
+
+make_state(shape_in, shape_out, dt, dtype=None)[source]
+

Get a dictionary of signals to represent the state of this process.

+

The builder uses this to allocate memory for the process state, so +that the state can be represented as part of the whole simulator state.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
dtypenumpy.dtype

The data type requested by the builder. If None, then this +function is free to choose the best type for the signals involved.

+
+
+
+
Returns
+
+
initial_state{string: numpy.ndarray}

A dictionary mapping keys to arrays containing the initial state +values. The keys will be used to identify the signals in +Process.make_step.

+
+
+
+
+
+ +
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+ +
+
+class nengo.processes.BrownNoise(dist=Gaussian(mean=0, std=1), **kwargs)[source]
+

Brown noise process (aka Brownian noise, red noise, Wiener process).

+

This process is the integral of white noise.

+
+
Parameters
+
+
distDistribution, optional

The distribution used to generate the white noise.

+
+
seedint, optional

Random number seed. Ensures noise will be the same each run.

+
+
+
+
+
+ +
+
+class nengo.processes.WhiteSignal(period, high, rms=0.5, y0=None, **kwargs)[source]
+

An ideal low-pass filtered white noise process.

+

This signal is created in the frequency domain, and designed to have +exactly equal power at all frequencies below the cut-off frequency, +and no power above the cut-off.

+

The signal is naturally periodic, so it can be used beyond its period +while still being continuous with continuous derivatives.

+
+
Parameters
+
+
periodfloat

A white noise signal with this period will be generated. +Samples will repeat after this duration.

+
+
highfloat

The cut-off frequency of the low-pass filter, in Hz. +Must not exceed the Nyquist frequency for the simulation +timestep, which is 0.5 / dt.

+
+
rmsfloat, optional

The root mean square power of the filtered signal

+
+
y0float, optional

Align the phase of each output dimension to begin at the value +that is closest (in absolute value) to y0.

+
+
seedint, optional

Random number seed. Ensures noise will be the same each run.

+
+
+
+
+
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+ +
+
+class nengo.processes.PresentInput(inputs, presentation_time, **kwargs)[source]
+

Present a series of inputs, each for the same fixed length of time.

+
+
Parameters
+
+
inputsarray_like

Inputs to present, where each row is an input. Rows will be flattened.

+
+
presentation_timefloat

Show each input for this amount of time (in seconds).

+
+
+
+
+
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+ +
+
+class nengo.processes.PiecewiseDataParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

Piecewise-specific validation for the data dictionary.

+

In the Piecewise data dict, the keys are points in time (float) and +values are numerical constants or callables of the same dimensionality.

+
+
+hashvalue(instance)[source]
+

Returns a hashable value (hash can be called on the output).

+
+ +
+ +
+
+class nengo.processes.Piecewise(data, interpolation='zero', **kwargs)[source]
+

A piecewise function with different options for interpolation.

+

Given an input dictionary of {0: 0, 0.5: -1, 0.75: 0.5, 1: 0}, +this process will emit the numerical values (0, -1, 0.5, 0) +starting at the corresponding time points (0, 0.5, 0.75, 1).

+

The keys in the input dictionary must be times (float or int). +The values in the dictionary can be floats, lists of floats, +or numpy arrays. All lists or numpy arrays must be of the same length, +as the output shape of the process will be determined by the shape +of the values.

+

Interpolation on the data points using scipy.interpolate is also +supported. The default interpolation is ‘zero’, which creates a +piecewise function whose values change at the specified time points. +So the above example would be shortcut for:

+
def function(t):
+    if t < 0.5:
+        return 0
+    elif t < 0.75:
+        return -1
+    elif t < 1:
+        return 0.5
+    else:
+        return 0
+
+
+

For times before the first specified time, an array of zeros (of +the correct length) will be emitted. +This means that the above can be simplified to:

+
from nengo.processes import Piecewise
+
+Piecewise({0.5: -1, 0.75: 0.5, 1: 0})
+
+
+
+
Parameters
+
+
datadict

A dictionary mapping times to the values that should be emitted +at those times. Times must be numbers (ints or floats), while values +can be numbers, lists of numbers, numpy arrays of numbers, +or callables that return any of those options.

+
+
interpolationstr, optional

One of ‘linear’, ‘nearest’, ‘slinear’, ‘quadratic’, ‘cubic’, or ‘zero’. +Specifies how to interpolate between times with specified value. +‘zero’ creates a plain piecewise function whose values begin at +corresponding time points, while all other options interpolate +as described in scipy.interpolate.

+
+
+
+
+

Examples

+
from nengo.processes import Piecewise
+process = Piecewise({0.5: 1, 0.75: -1, 1: 0})
+with nengo.Network() as model:
+    u = nengo.Node(process, size_out=process.default_size_out)
+    up = nengo.Probe(u)
+with nengo.Simulator(model, progress_bar=False) as sim:
+    sim.run(1.5)
+f = sim.data[up]
+t = sim.trange()
+print(f[t == 0.2])
+print(f[t == 0.58])
+
+
+
[[ 0.]]
+[[ 1.]]
+
+
+
+
Attributes
+
+
datadict

A dictionary mapping times to the values that should be emitted +at those times. Times are numbers (ints or floats), while values +can be numbers, lists of numbers, numpy arrays of numbers, +or callables that return any of those options.

+
+
interpolationstr

One of ‘linear’, ‘nearest’, ‘slinear’, ‘quadratic’, ‘cubic’, or ‘zero’. +Specifies how to interpolate between times with specified value. +‘zero’ creates a plain piecewise function whose values change at +corresponding time points, while all other options interpolate +as described in scipy.interpolate.

+
+
+
+
+
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+ +
+
+class nengo.Process(default_size_in=0, default_size_out=1, default_dt=0.001, seed=None)[source]
+

A general system with input, output, and state.

+

For more details on how to use processes and make +custom process subclasses, see Processes and how to use them.

+
+
Parameters
+
+
default_size_inint

Sets the default size in for nodes using this process.

+
+
default_size_outint

Sets the default size out for nodes running this process. Also, +if d is not specified in run or run_steps, +this will be used.

+
+
default_dtfloat

If dt is not specified in run, run_steps, +ntrange, or trange, this will be used.

+
+
seedint, optional

Random number seed. Ensures random factors will be the same each run.

+
+
+
+
Attributes
+
+
default_dtfloat

If dt is not specified in run, run_steps, +ntrange, or trange, this will be used.

+
+
default_size_inint

The default size in for nodes using this process.

+
+
default_size_outint

The default size out for nodes running this process. Also, if d is +not specified in run or run_steps, +this will be used.

+
+
seedint or None

Random number seed. Ensures random factors will be the same each run.

+
+
+
+
+
+
+apply(self, x, d=None, dt=None, rng=numpy.random, copy=True, **kwargs)[source]
+

Run process on a given input.

+

Keyword arguments that do not appear in the parameter list below +will be passed to the make_step function of this process.

+
+
Parameters
+
+
xndarray

The input signal given to the process.

+
+
dint, optional

Output dimensionality. If None, default_size_out will be used.

+
+
dtfloat, optional

Simulation timestep. If None, default_dt will be used.

+
+
rngnumpy.random.RandomState

Random number generator used for stochstic processes.

+
+
copybool, optional

If True, a new output array will be created for output. +If False, the input signal x will be overwritten.

+
+
+
+
+
+ +
+
+get_rng(rng)[source]
+

Get a properly seeded independent RNG for the process step.

+
+
Parameters
+
+
rngnumpy.random.RandomState

The parent random number generator to use if the seed is not set.

+
+
+
+
+
+ +
+
+make_state(shape_in, shape_out, dt, dtype=None)[source]
+

Get a dictionary of signals to represent the state of this process.

+

The builder uses this to allocate memory for the process state, so +that the state can be represented as part of the whole simulator state.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
dtypenumpy.dtype

The data type requested by the builder. If None, then this +function is free to choose the best type for the signals involved.

+
+
+
+
Returns
+
+
initial_state{string: numpy.ndarray}

A dictionary mapping keys to arrays containing the initial state +values. The keys will be used to identify the signals in +Process.make_step.

+
+
+
+
+
+ +
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+
+run(self, t, d=None, dt=None, rng=numpy.random, **kwargs)[source]
+

Run process without input for given length of time.

+

Keyword arguments that do not appear in the parameter list below +will be passed to the make_step function of this process.

+
+
Parameters
+
+
tfloat

The length of time to run.

+
+
dint, optional

Output dimensionality. If None, default_size_out will be used.

+
+
dtfloat, optional

Simulation timestep. If None, default_dt will be used.

+
+
rngnumpy.random.RandomState

Random number generator used for stochstic processes.

+
+
+
+
+
+ +
+
+run_steps(self, n_steps, d=None, dt=None, rng=numpy.random, **kwargs)[source]
+

Run process without input for given number of steps.

+

Keyword arguments that do not appear in the parameter list below +will be passed to the make_step function of this process.

+
+
Parameters
+
+
n_stepsint

The number of steps to run.

+
+
dint, optional

Output dimensionality. If None, default_size_out will be used.

+
+
dtfloat, optional

Simulation timestep. If None, default_dt will be used.

+
+
rngnumpy.random.RandomState

Random number generator used for stochstic processes.

+
+
+
+
+
+ +
+
+ntrange(n_steps, dt=None)[source]
+

Create time points corresponding to a given number of steps.

+
+
Parameters
+
+
n_stepsint

The given number of steps.

+
+
dtfloat, optional

Simulation timestep. If None, default_dt will be used.

+
+
+
+
+
+ +
+
+trange(t, dt=None)[source]
+

Create time points corresponding to a given length of time.

+
+
Parameters
+
+
tfloat

The given length of time.

+
+
dtfloat, optional

Simulation timestep. If None, default_dt will be used.

+
+
+
+
+
+ +
+ +
+
+

Solvers

+

Classes concerned with solving for decoders or full weight matrices.

+
Inheritance diagram of nengo.solvers
+ + + + + + + + + + + + + + + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.solvers.Solver

Decoder or weight solver.

nengo.solvers.Lstsq

Unregularized least-squares solver.

nengo.solvers.LstsqNoise

Least-squares solver with additive Gaussian white noise.

nengo.solvers.LstsqMultNoise

Least-squares solver with multiplicative white noise.

nengo.solvers.LstsqL2

Least-squares solver with L2 regularization.

nengo.solvers.LstsqL2nz

Least-squares solver with L2 regularization on non-zero components.

nengo.solvers.LstsqL1

Least-squares solver with L1 and L2 regularization (elastic net).

nengo.solvers.LstsqDrop

Find sparser decoders/weights by dropping small values.

nengo.solvers.Nnls

Non-negative least-squares solver without regularization.

nengo.solvers.NnlsL2

Non-negative least-squares solver with L2 regularization.

nengo.solvers.NnlsL2nz

Non-negative least-squares with L2 regularization on nonzero components.

nengo.solvers.NoSolver

Manually pass in weights, bypassing the decoder solver.

+
+
+class nengo.solvers.Solver(weights=False)[source]
+

Decoder or weight solver.

+

A solver can have the weights parameter equal to True or False.

+

Weight solvers are used to form neuron-to-neuron weight matrices. +They can be compositional or non-compositional. Non-compositional +solvers must operate on the whole neuron-to-neuron weight matrix +(i.e., each target is a separate postsynaptic current, without the bias +term), while compositional solvers operate in the decoded state-space +(i.e., each target is a dimension in state-space). Compositional solvers +then combine the returned X with the transform and/or encoders to +generate the full weight matrix.

+

For a solver to be compositional, the following property must be true:

+
X = solver(A, Y)  if and only if  L(X) = solver(A, L(Y))
+
+
+

where L is some arbitrary linear operator (i.e., the transform and/or +encoders for the postsynaptic population). This property can then be +leveraged by the backend for efficiency. See the solver’s +compositional class attribute to determine if it is compositional.

+

Non-weight solvers always operate in the decoded state-space regardless of +whether they are compositional or non-compositional.

+
+ +
+
+class nengo.solvers.SolverParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A parameter in which the value is a Solver instance.

+
+ +
+
+class nengo.solvers.Lstsq(weights=False, rcond=0.01)[source]
+

Unregularized least-squares solver.

+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
rcondfloat, optional

Cut-off ratio for small singular values (see numpy.linalg.lstsq).

+
+
+
+
Attributes
+
+
rcondfloat

Cut-off ratio for small singular values (see numpy.linalg.lstsq).

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.LstsqNoise(weights=False, noise=0.1, solver=Cholesky())[source]
+

Least-squares solver with additive Gaussian white noise.

+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
noisefloat, optional

Amount of noise, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver, optional

Subsolver to use for solving the least squares problem.

+
+
+
+
Attributes
+
+
noisefloat

Amount of noise, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver

Subsolver to use for solving the least squares problem.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.LstsqMultNoise(weights=False, noise=0.1, solver=Cholesky())[source]
+

Least-squares solver with multiplicative white noise.

+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
noisefloat, optional

Amount of noise, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver, optional

Subsolver to use for solving the least squares problem.

+
+
+
+
Attributes
+
+
noisefloat

Amount of noise, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver

Subsolver to use for solving the least squares problem.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.LstsqL2(weights=False, reg=0.1, solver=Cholesky())[source]
+

Least-squares solver with L2 regularization.

+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
regfloat, optional

Amount of regularization, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver, optional

Subsolver to use for solving the least squares problem.

+
+
+
+
Attributes
+
+
regfloat

Amount of regularization, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver

Subsolver to use for solving the least squares problem.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.LstsqL2nz(weights=False, reg=0.1, solver=Cholesky())[source]
+

Least-squares solver with L2 regularization on non-zero components.

+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
regfloat, optional

Amount of regularization, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver, optional

Subsolver to use for solving the least squares problem.

+
+
+
+
Attributes
+
+
regfloat

Amount of regularization, as a fraction of the neuron activity.

+
+
solverLeastSquaresSolver

Subsolver to use for solving the least squares problem.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.LstsqL1(weights=False, l1=0.0001, l2=1e-06, max_iter=1000)[source]
+

Least-squares solver with L1 and L2 regularization (elastic net).

+

This method is well suited for creating sparse decoders or weight matrices.

+
+

Note

+

Requires scikit-learn.

+
+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
l1float, optional

Amount of L1 regularization.

+
+
l2float, optional

Amount of L2 regularization.

+
+
max_iterint, optional

Maximum number of iterations for the underlying elastic net.

+
+
+
+
Attributes
+
+
l1float

Amount of L1 regularization.

+
+
l2float

Amount of L2 regularization.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
max_iterint

Maximum number of iterations for the underlying elastic net.

+
+
+
+
+
+ +
+
+class nengo.solvers.LstsqDrop(weights=False, drop=0.25, solver1=LstsqL2(reg=0.001), solver2=LstsqL2())[source]
+

Find sparser decoders/weights by dropping small values.

+

This solver first solves for coefficients (decoders/weights) with +L2 regularization, drops those nearest to zero, and retrains remaining.

+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
dropfloat, optional

Fraction of decoders or weights to set to zero.

+
+
solver1Solver, optional

Solver for finding the initial decoders.

+
+
solver2Solver, optional

Used for re-solving for the decoders after dropout.

+
+
+
+
Attributes
+
+
dropfloat

Fraction of decoders or weights to set to zero.

+
+
solver1Solver

Solver for finding the initial decoders.

+
+
solver2Solver

Used for re-solving for the decoders after dropout.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.Nnls(weights=False)[source]
+

Non-negative least-squares solver without regularization.

+

Similar to Lstsq, except the output values are non-negative.

+

If solving for non-negative weights, it is important that the +intercepts of the post-population are also non-negative, since neurons with +negative intercepts will never be silent, affecting output accuracy.

+
+

Note

+

Requires +SciPy.

+
+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
+
+
Attributes
+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.NnlsL2(weights=False, reg=0.1)[source]
+

Non-negative least-squares solver with L2 regularization.

+

Similar to LstsqL2, except the output values are non-negative.

+

If solving for non-negative weights, it is important that the +intercepts of the post-population are also non-negative, since neurons with +negative intercepts will never be silent, affecting output accuracy.

+
+

Note

+

Requires +SciPy.

+
+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
regfloat, optional

Amount of regularization, as a fraction of the neuron activity.

+
+
+
+
Attributes
+
+
regfloat

Amount of regularization, as a fraction of the neuron activity.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.NnlsL2nz(weights=False, reg=0.1)[source]
+

Non-negative least-squares with L2 regularization on nonzero components.

+

Similar to LstsqL2nz, except the output values are non-negative.

+

If solving for non-negative weights, it is important that the +intercepts of the post-population are also non-negative, since neurons with +negative intercepts will never be silent, affecting output accuracy.

+
+

Note

+

Requires +SciPy.

+
+
+
Parameters
+
+
weightsbool, optional

If False, solve for decoders. If True, solve for weights.

+
+
regfloat, optional

Amount of regularization, as a fraction of the neuron activity.

+
+
+
+
Attributes
+
+
regfloat

Amount of regularization, as a fraction of the neuron activity.

+
+
weightsbool

If False, solve for decoders. If True, solve for weights.

+
+
+
+
+
+ +
+
+class nengo.solvers.NoSolver(values=None, weights=False)[source]
+

Manually pass in weights, bypassing the decoder solver.

+
+
Parameters
+
+
values(n_neurons, size_out) array_like, optional

The array of decoders to use. +size_out is the dimensionality of the decoded signal (determined +by the connection function). +If None, which is the default, the solver will return an +appropriately sized array of zeros.

+
+
weightsbool, optional

If False, connection will use factored weights (decoders from this +solver, transform, and encoders). +If True, connection will use a full weight matrix (created by +linearly combining decoder, transform, and encoders).

+
+
+
+
Attributes
+
+
values(n_neurons, size_out) array_like, optional

The array of decoders to use. +size_out is the dimensionality of the decoded signal (determined +by the connection function). +If None, which is the default, the solver will return an +appropriately sized array of zeros.

+
+
weightsbool, optional

If False, connection will use factored weights (decoders from this +solver, transform, and encoders). +If True, connection will use a full weight matrix (created by +linearly combining decoder, transform, and encoders).

+
+
+
+
+
+ +
+

Solver methods

+

These solvers are to be passed as arguments to Solver objects.

+

For example:

+
from nengo.solvers import LstsqL2
+from nengo.utils.least_squares_solvers import SVD
+
+with nengo.Network():
+    ens_a = nengo.Ensemble(10, 1)
+    ens_b = nengo.Ensemble(10, 1)
+    nengo.Connection(ens_a, ens_b, solver=LstsqL2(solver=SVD()))
+
+
+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.utils.least_squares_solvers.format_system

Extract data from A/Y matrices.

nengo.utils.least_squares_solvers.rmses

Returns the root-mean-squared error (RMSE) of the solution X.

nengo.utils.least_squares_solvers.LeastSquaresSolver

Linear least squares system solver.

nengo.utils.least_squares_solvers.Cholesky

Solve a least-squares system using the Cholesky decomposition.

nengo.utils.least_squares_solvers.ConjgradScipy

Solve a least-squares system using Scipy’s conjugate gradient.

nengo.utils.least_squares_solvers.LSMRScipy

Solve a least-squares system using Scipy’s LSMR.

nengo.utils.least_squares_solvers.Conjgrad

Solve a least-squares system using conjugate gradient.

nengo.utils.least_squares_solvers.BlockConjgrad

Solve a multiple-RHS least-squares system using block conj.

nengo.utils.least_squares_solvers.SVD

Solve a least-squares system using full SVD.

nengo.utils.least_squares_solvers.RandomizedSVD

Solve a least-squares system using a randomized (partial) SVD.

nengo.utils.least_squares_solvers.LeastSquaresSolverParam

A parameter where the value is a LeastSquaresSolver.

+
+
+nengo.utils.least_squares_solvers.format_system(A, Y)[source]
+

Extract data from A/Y matrices.

+
+ +
+
+nengo.utils.least_squares_solvers.rmses(A, X, Y)[source]
+

Returns the root-mean-squared error (RMSE) of the solution X.

+
+ +
+
+class nengo.utils.least_squares_solvers.LeastSquaresSolver[source]
+

Linear least squares system solver.

+
+ +
+
+class nengo.utils.least_squares_solvers.Cholesky(transpose=None)[source]
+

Solve a least-squares system using the Cholesky decomposition.

+
+ +
+
+class nengo.utils.least_squares_solvers.ConjgradScipy(tol=0.0001, atol=1e-08)[source]
+

Solve a least-squares system using Scipy’s conjugate gradient.

+
+
Parameters
+
+
tolfloat

Relative tolerance of the CG solver (see [1] for details).

+
+
atolfloat

Absolute tolerance of the CG solver (see [1] for details).

+
+
+
+
+

References

+
+
1(1,2)
+

scipy.sparse.linalg.cg documentation, +https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.cg.html

+
+
+
+ +
+
+class nengo.utils.least_squares_solvers.LSMRScipy(tol=0.0001)[source]
+

Solve a least-squares system using Scipy’s LSMR.

+
+ +
+
+class nengo.utils.least_squares_solvers.Conjgrad(tol=0.01, maxiters=None, X0=None)[source]
+

Solve a least-squares system using conjugate gradient.

+
+ +
+
+class nengo.utils.least_squares_solvers.BlockConjgrad(tol=0.01, X0=None)[source]
+

Solve a multiple-RHS least-squares system using block conj. gradient.

+
+ +
+
+class nengo.utils.least_squares_solvers.SVD[source]
+

Solve a least-squares system using full SVD.

+
+ +
+
+class nengo.utils.least_squares_solvers.RandomizedSVD(n_components=60, n_oversamples=10, n_iter=0)[source]
+

Solve a least-squares system using a randomized (partial) SVD.

+

Useful for solving large matrices quickly, but non-optimally.

+
+
Parameters
+
+
n_componentsint, optional

The number of SVD components to compute. A small survey of activity +matrices suggests that the first 60 components capture almost all +the variance.

+
+
n_oversamplesint, optional

The number of additional samples on the range of A.

+
+
n_iterint, optional

The number of power iterations to perform (can help with noisy data).

+
+
+
+
+
+

See also

+
+
sklearn.utils.extmath.randomized_svd

Function used by this class

+
+
+
+
+ +
+
+class nengo.utils.least_squares_solvers.LeastSquaresSolverParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+

A parameter where the value is a LeastSquaresSolver.

+
+ +
+
+
+

Synapse models

+ ++++ + + + + + + + + + + + + + + + + + +

nengo.synapses.Synapse

Abstract base class for synapse models.

nengo.LinearFilter

General linear time-invariant (LTI) system synapse.

nengo.Lowpass

Standard first-order lowpass filter synapse.

nengo.Alpha

Alpha-function filter synapse.

nengo.synapses.Triangle

Triangular finite impulse response (FIR) synapse.

+
+
+class nengo.synapses.Synapse(default_size_in=1, default_size_out=None, default_dt=0.001, seed=None)[source]
+

Abstract base class for synapse models.

+

Conceptually, a synapse model emulates a biological synapse, taking in +input in the form of released neurotransmitter and opening ion channels +to allow more or less current to flow into the neuron.

+

In Nengo, the implementation of a synapse is as a specific case of a +Process in which the input and output shapes are the same. +The input is the current across the synapse, and the output is the current +that will be induced in the postsynaptic neuron.

+

Synapses also contain the Synapse.filt and Synapse.filtfilt methods, +which make it easy to use Nengo’s synapse models outside of Nengo +simulations.

+
+
Parameters
+
+
default_size_inint, optional

The size_in used if not specified.

+
+
default_size_outint

The size_out used if not specified. +If None, will be the same as default_size_in.

+
+
default_dtfloat

The simulation timestep used if not specified.

+
+
seedint, optional

Random number seed. Ensures random factors will be the same each run.

+
+
+
+
Attributes
+
+
default_dtfloat

The simulation timestep used if not specified.

+
+
default_size_inint

The size_in used if not specified.

+
+
default_size_outint

The size_out used if not specified.

+
+
seedint, optional

Random number seed. Ensures random factors will be the same each run.

+
+
+
+
+
+
+make_state(shape_in, shape_out, dt, dtype=None, y0=None)[source]
+

Get a dictionary of signals to represent the state of this process.

+

The builder uses this to allocate memory for the process state, so +that the state can be represented as part of the whole simulator state.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
dtypenumpy.dtype

The data type requested by the builder. If None, then this +function is free to choose the best type for the signals involved.

+
+
+
+
Returns
+
+
initial_state{string: numpy.ndarray}

A dictionary mapping keys to arrays containing the initial state +values. The keys will be used to identify the signals in +Process.make_step.

+
+
+
+
+
+ +
+
+filt(x, dt=None, axis=0, y0=0, copy=True, filtfilt=False)[source]
+

Filter x with this synapse model.

+
+
Parameters
+
+
xarray_like

The signal to filter.

+
+
dtfloat, optional

The timestep of the input signal. +If None, default_dt will be used.

+
+
axisint, optional

The axis along which to filter.

+
+
y0array_like, optional

The starting state of the filter output. Must be zero for +unstable linear systems.

+
+
copybool, optional

Whether to copy the input data, or simply work in-place.

+
+
filtfiltbool, optional

If True, runs the process forward then backward on the signal, +for zero-phase filtering (like Matlab’s filtfilt).

+
+
+
+
+
+ +
+
+filtfilt(x, **kwargs)[source]
+

Zero-phase filtering of x using this filter.

+

Equivalent to filt(x, filtfilt=True, **kwargs).

+
+ +
+ +
+
+class nengo.LinearFilter(num, den, analog=True, method='zoh', **kwargs)[source]
+

General linear time-invariant (LTI) system synapse.

+

This class can be used to implement any linear filter, given the +filter’s transfer function. [1]

+
+
Parameters
+
+
numarray_like

Numerator coefficients of transfer function.

+
+
denarray_like

Denominator coefficients of transfer function.

+
+
analogboolean, optional

Whether the synapse coefficients are analog (i.e. continuous-time), +or discrete. Analog coefficients will be converted to discrete for +simulation using the simulator dt.

+
+
methodstring

The method to use for discretization (if analog is True). See +scipy.signal.cont2discrete for information about the options.

+
+

New in version 3.0.0.

+
+
+
+
+
+

References

+
+
1
+

https://en.wikipedia.org/wiki/Filter_%28signal_processing%29

+
+
+
+
Attributes
+
+
analogboolean

Whether the synapse coefficients are analog (i.e. continuous-time), +or discrete. Analog coefficients will be converted to discrete for +simulation using the simulator dt.

+
+
denndarray

Denominator coefficients of transfer function.

+
+
numndarray

Numerator coefficients of transfer function.

+
+
methodstring

The method to use for discretization (if analog is True). See +scipy.signal.cont2discrete for information about the options.

+
+
+
+
+
+
+combine(obj)[source]
+

Combine in series with another LinearFilter.

+
+ +
+
+evaluate(frequencies)[source]
+

Evaluate the transfer function at the given frequencies.

+

Examples

+

Using the evaluate function to make a Bode plot:

+
import matplotlib.pyplot as plt
+
+synapse = nengo.synapses.LinearFilter([1], [0.02, 1])
+f = np.logspace(-1, 3, 100)
+y = synapse.evaluate(f)
+plt.subplot(211); plt.semilogx(f, 20*np.log10(np.abs(y)))
+plt.xlabel('frequency [Hz]'); plt.ylabel('magnitude [dB]')
+plt.subplot(212); plt.semilogx(f, np.angle(y))
+plt.xlabel('frequency [Hz]'); plt.ylabel('phase [radians]')
+
+
+
+ +
+
+make_state(shape_in, shape_out, dt, dtype=None, y0=0)[source]
+

Get a dictionary of signals to represent the state of this process.

+

The builder uses this to allocate memory for the process state, so +that the state can be represented as part of the whole simulator state.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
dtypenumpy.dtype

The data type requested by the builder. If None, then this +function is free to choose the best type for the signals involved.

+
+
+
+
Returns
+
+
initial_state{string: numpy.ndarray}

A dictionary mapping keys to arrays containing the initial state +values. The keys will be used to identify the signals in +Process.make_step.

+
+
+
+
+
+ +
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Returns a Step instance that implements the linear filter.

+
+ +
+
+class Step(A, B, C, D, X)[source]
+

Abstract base class for LTI filtering step functions.

+
+ +
+
+class NoX(A, B, C, D, X)[source]
+

Step for system with no state, only passthrough matrix (D).

+
+ +
+
+class OneX(A, B, C, D, X)[source]
+

Step for systems with one state element and no passthrough (D).

+
+ +
+
+class OneXScalar(A, B, C, D, X)[source]
+

Step for systems with one state element, no passthrough, and a size-1 input.

+

Using the builtin float math improves performance.

+
+ +
+
+class NoD(A, B, C, D, X)[source]
+

Step for systems with no passthrough matrix (D).

+

Implements:

+
x[t] = A x[t-1] + B u[t]
+y[t] = C x[t]
+
+
+

Note how the input has been advanced one step as compared with the +General system below, to remove the unnecessary delay.

+
+ +
+
+class General(A, B, C, D, X)[source]
+

Step for any LTI system with at least one state element (X).

+

Implements:

+
x[t+1] = A x[t] + B u[t]
+y[t] = C x[t] + D u[t]
+
+
+

Use NoX for systems with no state elements.

+
+ +
+ +
+
+class nengo.Lowpass(tau, **kwargs)[source]
+

Standard first-order lowpass filter synapse.

+

The impulse-response function is given by:

+
f(t) = (1 / tau) * exp(-t / tau)
+
+
+
+
Parameters
+
+
taufloat

The time constant of the filter in seconds.

+
+
+
+
Attributes
+
+
taufloat

The time constant of the filter in seconds.

+
+
+
+
+
+ +
+
+class nengo.Alpha(tau, **kwargs)[source]
+

Alpha-function filter synapse.

+

The impulse-response function is given by:

+
alpha(t) = (t / tau**2) * exp(-t / tau)
+
+
+

and was found by [1] to be a good basic model for synapses.

+
+
Parameters
+
+
taufloat

The time constant of the filter in seconds.

+
+
+
+
+

References

+
+
1
+

Mainen, Z.F. and Sejnowski, T.J. (1995). Reliability of spike timing +in neocortical neurons. Science (New York, NY), 268(5216):1503-6.

+
+
+
+
Attributes
+
+
taufloat

The time constant of the filter in seconds.

+
+
+
+
+
+ +
+
+class nengo.synapses.Triangle(t, **kwargs)[source]
+

Triangular finite impulse response (FIR) synapse.

+

This synapse has a triangular and finite impulse response. The length of +the triangle is t seconds; thus the digital filter will have +t / dt + 1 taps.

+
+
Parameters
+
+
tfloat

Length of the triangle, in seconds.

+
+
+
+
Attributes
+
+
tfloat

Length of the triangle, in seconds.

+
+
+
+
+
+
+make_state(shape_in, shape_out, dt, dtype=None, y0=0)[source]
+

Get a dictionary of signals to represent the state of this process.

+

The builder uses this to allocate memory for the process state, so +that the state can be represented as part of the whole simulator state.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
dtypenumpy.dtype

The data type requested by the builder. If None, then this +function is free to choose the best type for the signals involved.

+
+
+
+
Returns
+
+
initial_state{string: numpy.ndarray}

A dictionary mapping keys to arrays containing the initial state +values. The keys will be used to identify the signals in +Process.make_step.

+
+
+
+
+
+ +
+
+make_step(shape_in, shape_out, dt, rng, state)[source]
+

Create function that advances the process forward one time step.

+

This must be implemented by all custom processes. The parameters below +indicate what information is provided by the builder.

+
+
Parameters
+
+
shape_intuple

The shape of the input signal.

+
+
shape_outtuple

The shape of the output signal.

+
+
dtfloat

The simulation timestep.

+
+
rngnumpy.random.RandomState

A random number generator.

+
+
state{string: numpy.ndarray}

A dictionary mapping keys to signals, where the signals fully +represent the state of the process. The signals are initialized +by Process.make_state.

+
+

New in version 3.0.0.

+
+
+
+
+
+
+ +
+ +
+
+class nengo.synapses.SynapseParam(name, default=Unconfigurable, optional=True, readonly=None)[source]
+
+ +
+
+

Transforms

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + +

nengo.transforms.Transform

A base class for connection transforms.

nengo.Dense

A dense matrix transformation between an input and output signal.

nengo.transforms.SparseMatrix

Represents a sparse matrix.

nengo.Sparse

A sparse matrix transformation between an input and output signal.

nengo.Convolution

An N-dimensional convolutional transform.

nengo.transforms.ChannelShape

Represents shape information with variable channel position.

nengo.transforms.NoTransform

Directly pass the signal through without any transform operations.

+
+
+class nengo.transforms.Transform[source]
+

A base class for connection transforms.

+
+

New in version 3.0.0.

+
+
+
+sample(self, rng=numpy.random)[source]
+

Returns concrete weights to implement the specified transform.

+
+
Parameters
+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
array_like

Transform weights

+
+
+
+
+
+ +
+
+property size_in
+

Expected size of input to transform.

+
+ +
+
+property size_out
+

Expected size of output from transform.

+
+ +
+ +
+
+class nengo.transforms.ChannelShapeParam(name, default=Unconfigurable, length=None, low=0, optional=False, readonly=None)[source]
+

A parameter where the value must be a shape with channels.

+
+

New in version 3.0.0.

+
+
+ +
+
+class nengo.Dense(shape, init=1.0)[source]
+

A dense matrix transformation between an input and output signal.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shapetuple of int

The shape of the dense matrix: (size_out, size_in).

+
+
initDistribution or array_like, optional

A Distribution used to initialize the transform matrix, or a concrete +instantiation for the matrix. If the matrix is square we also allow a +scalar (equivalent to np.eye(n) * init) or a vector (equivalent to +np.diag(init)) to represent the matrix more compactly.

+
+
+
+
+
+
+sample(self, rng=numpy.random)[source]
+

Returns concrete weights to implement the specified transform.

+
+
Parameters
+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
array_like

Transform weights

+
+
+
+
+
+ +
+
+property init_shape
+

The shape of the initial value.

+
+ +
+
+property size_in
+

Expected size of input to transform.

+
+ +
+
+property size_out
+

Expected size of output from transform.

+
+ +
+ +
+
+class nengo.transforms.SparseInitParam(name, default=Unconfigurable, optional=False, readonly=None)[source]
+
+ +
+
+class nengo.transforms.SparseMatrix(indices, data, shape)[source]
+

Represents a sparse matrix.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
indicesarray_like of int

An Nx2 array of integers indicating the (row,col) coordinates for the +N non-zero elements in the matrix.

+
+
dataarray_like or Distribution

An Nx1 array defining the value of the nonzero elements in the matrix +(corresponding to indices), or a Distribution that will be +used to initialize the nonzero elements.

+
+
shapetuple of int

Shape of the full matrix.

+
+
+
+
+
+
+allocate()[source]
+

Return a scipy.sparse.csr_matrix or dense matrix equivalent.

+

We mark this data as readonly to be consistent with how other +data associated with signals are allocated. If this allocated +data is to be modified, it should be copied first.

+
+ +
+
+sample(self, rng=numpy.random)[source]
+

Convert Distribution data to fixed array.

+
+
Parameters
+
+
rngnumpy.random.RandomState

Random number generator that will be used when +sampling distribution.

+
+
+
+
Returns
+
+
matrixSparseMatrix

A new SparseMatrix instance with Distribution converted to +array if self.data is a Distribution, otherwise simply +returns self.

+
+
+
+
+
+ +
+
+toarray()[source]
+

Return the dense matrix equivalent of this matrix.

+
+ +
+ +
+
+class nengo.Sparse(shape, indices=None, init=1.0)[source]
+

A sparse matrix transformation between an input and output signal.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shapetuple of int

The full shape of the sparse matrix: (size_out, size_in).

+
+
indicesarray_like of int

An Nx2 array of integers indicating the (row,col) coordinates for the +N non-zero elements in the matrix.

+
+
initDistribution or array_like, optional

A Distribution used to initialize the transform matrix, or a concrete +instantiation for the matrix. If the matrix is square we also allow a +scalar (equivalent to np.eye(n) * init) or a vector (equivalent to +np.diag(init)) to represent the matrix more compactly.

+
+
+
+
+
+
+sample(self, rng=numpy.random)[source]
+

Returns concrete weights to implement the specified transform.

+
+
Parameters
+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
array_like

Transform weights

+
+
+
+
+
+ +
+
+property size_in
+

Expected size of input to transform.

+
+ +
+
+property size_out
+

Expected size of output from transform.

+
+ +
+ +
+
+class nengo.Convolution(n_filters, input_shape, kernel_size=3, 3, strides=1, 1, padding='valid', channels_last=True, init=Uniform(low=- 1, high=1))[source]
+

An N-dimensional convolutional transform.

+

The dimensionality of the convolution is determined by the input shape.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
n_filtersint

The number of convolutional filters to apply

+
+
input_shapetuple of int or ChannelShape

Shape of the input signal to the convolution; e.g., +(height, width, channels) for a 2D convolution with +channels_last=True.

+
+
kernel_sizetuple of int, optional

Size of the convolutional kernels (1 element for a 1D convolution, +2 for a 2D convolution, etc.).

+
+
stridestuple of int, optional

Stride of the convolution (1 element for a 1D convolution, 2 for +a 2D convolution, etc.).

+
+
padding"same" or "valid", optional

Padding method for input signal. “Valid” means no padding, and +convolution will only be applied to the fully-overlapping areas of the +input signal (meaning the output will be smaller). “Same” means that +the input signal is zero-padded so that the output is the same shape +as the input.

+
+
channels_lastbool, optional

If True (default), the channels are the last dimension in the input +signal (e.g., a 28x28 image with 3 channels would have shape +(28, 28, 3)). False means that channels are the first +dimension (e.g., (3, 28, 28)).

+
+
initDistribution or ndarray, optional

A predefined kernel with shape +kernel_size + (input_channels, n_filters), or a Distribution +that will be used to initialize the kernel.

+
+
+
+
+

Notes

+

As is typical in neural networks, this is technically correlation rather +than convolution (because the kernel is not flipped).

+
+
+sample(self, rng=numpy.random)[source]
+

Returns concrete weights to implement the specified transform.

+
+
Parameters
+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Returns
+
+
array_like

Transform weights

+
+
+
+
+
+ +
+
+property kernel_shape
+

Full shape of kernel.

+
+ +
+
+property size_in
+

Expected size of input to transform.

+
+ +
+
+property size_out
+

Expected size of output from transform.

+
+ +
+
+property dimensions
+

Dimensionality of convolution.

+
+ +
+
+property output_shape
+

Output shape after applying convolution to input.

+
+ +
+ +
+
+class nengo.transforms.ChannelShape(shape, channels_last=True)[source]
+

Represents shape information with variable channel position.

+
+

New in version 3.0.0.

+
+
+
Parameters
+
+
shapetuple of int

Signal shape

+
+
channels_lastbool, optional

If True (default), the last item in shape represents the channels, +and the rest are spatial dimensions. Otherwise, the first item in +shape is the channel dimension.

+
+
+
+
+
+
+property spatial_shape
+

The spatial part of the shape (omitting channels).

+
+ +
+
+property size
+

The total number of elements in the represented signal.

+
+ +
+
+property n_channels
+

The number of channels in the represented signal.

+
+ +
+
+property dimensions
+

The spatial dimensionality of the represented signal.

+
+ +
+ +
+
+class nengo.transforms.NoTransform(size_in)[source]
+

Directly pass the signal through without any transform operations.

+
+

New in version 3.1.0.

+
+
+
Parameters
+
+
size_inint

Dimensionality of transform input and output.

+
+
+
+
+
+
+sample(self, rng=numpy.random)[source]
+

Returns concrete weights to implement the specified transform.

+
+
Parameters
+
+
rngnumpy.random.RandomState, optional

Random number generator state.

+
+
+
+
Raises
+
+
TypeError

There is nothing to sample for NoTransform, so it is an error +if this is called.

+
+
+
+
+
+ +
+
+property size_in
+

Expected size of input to transform.

+
+ +
+
+property size_out
+

Expected size of output from transform.

+
+ +
+ +
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/frontend_api.html b/frontend_api.html new file mode 100644 index 0000000000..d90e5a3aba --- /dev/null +++ b/frontend_api.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000000..3dcdb5649f --- /dev/null +++ b/genindex.html @@ -0,0 +1,2229 @@ + + + + + + + + + Index — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + +

Index

+ +
+ A + | B + | C + | D + | E + | F + | G + | H + | I + | K + | L + | M + | N + | O + | P + | Q + | R + | S + | T + | U + | V + | W + | Z + +
+

A

+ + + +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

H

+ + + +
+ +

I

+ + + +
+ +

K

+ + + +
+ +

L

+ + + +
+ +

M

+ + + +
+ +

N

+ + + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

Q

+ + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + + +
+ +

V

+ + + +
+ +

W

+ + + +
+ +

Z

+ + + +
+ + + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/getting-started.html b/getting-started.html new file mode 100644 index 0000000000..55bcfa425e --- /dev/null +++ b/getting-started.html @@ -0,0 +1,605 @@ + + + + + + + + Getting started — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Getting started

+
+

Installation

+

To install Nengo, we recommend using pip.

+
pip install nengo
+
+
+

pip will do its best to install +all of Nengo’s requirements when it installs Nengo. +However, if anything goes wrong during this process, +you can install Nengo’s requirements manually +before doing pip install nengo again.

+
+

Installing NumPy

+

Nengo’s only required dependency is NumPy, +and we recommend that you install it first. +The best way to install NumPy depends +on several factors, such as your operating system. +Briefly, what we have found to work best +on each operating system is:

+ +

For more options, see +SciPy.org’s installation page. +For our recommended options, read on.

+
+

Anaconda

+

If you’re new to Python and just want to get up and running, +Anaconda is the best way to get started. +Anaconda provides an all-in-one solution +that will install Python, NumPy, +and other optional Nengo dependencies. +It works on all operating systems (Windows, Mac, Linux) +and does not require administrator privileges. +It includes GUI tools, +as well as a robust command line tool, conda, +for managing your Python installation.

+
+
+

Package managers

+

If you are comfortable with the command line, +operating systems other than Windows +have a package manager that can install Python and NumPy.

+
    +

  • Mac OS X: Homebrew has excellent Python support. +After installing Homebrew, brew install python and pip install numpy.

    • +

  • Linux: Linux distributions come with a package manager +capable of installing Python and NumPy. +In Debian, Ubuntu, and other distributions with apt use: +sudo apt-get install python-numpy. +In Fedora and others distributions with yum use: +sudo yum install python-numpy. +For other package managers, +try searching the package list for numpy.

    • +
+
+
+

From source

+

If speed is an issue +and you know your way around a terminal, +installing NumPy from source +is flexible and performant. +See the detailed instructions +here.

+
+
+
+

Installing other packages

+

While NumPy is the only hard dependency, +some optional Nengo features require other packages. +These can be installed either through +Anaconda, a package manager, or through +Python’s own package manager, pip.

+
    +

  • Additional decoder solvers and other speedups +require SciPy and scikit-learn.

    • +

  • Running the test suite requires +pytest, Matplotlib, and Jupyter.

    • +

  • Building the documentation requires +Sphinx, NumPyDoc and nengo_sphinx_theme.

    • +
+

These additional dependencies can be installed +through pip when installing Nengo.

+
pip install nengo[optional]  # Additional solvers and speedups
+pip install nengo[docs]  # For building docs
+pip install nengo[tests]  # For running the test suite
+pip install nengo[all]  # All of the above
+
+
+
+
+
+

Usage

+

Everything in a Nengo model is contained within a +nengo.Network. To create a new Network:

+
import nengo
+net = nengo.Network()
+
+
+
+

Creating Nengo objects

+

A Nengo object is a part of your model that represents information. +When creating a new object, you must place it within a with +block in order to inform Nengo which network your object +should be placed in.

+

There are two objects that make up a basic Nengo model. +A nengo.Ensemble is a group of neurons that represents +information in the form of real valued numbers.

+
with net:
+    my_ensemble = nengo.Ensemble(n_neurons=40, dimensions=1)
+
+
+

In this case, my_ensemble is made up of +40 neurons (by default, Nengo uses leaky integrate-and-fire neurons) +and it is representing a one dimensional signal. +In other words, this ensemble represents a single number.

+

In order to provide input to this ensemble +(to emulate some signal that exists in nature, for example) +we create a nengo.Node.

+
with net:
+    my_node = nengo.Node(output=0.5)
+
+
+

In this case, my_node emits the number 0.5.

+

In most cases, however, we want more dynamic information. +We can make a nengo.Node using a function as output +instead of a number.

+
with net:
+    sin_node = nengo.Node(output=np.sin)
+
+
+

This node will represent a sine wave.

+
+
+

Connecting Nengo objects

+

We can connect nodes to ensembles +in order to represent that information +in the activity a group of neurons.

+
with net:
+    nengo.Connection(my_node, my_ensemble)
+
+
+

This connects my_node to my_ensemble, +meaning that my_ensemble will now represent +0.5 in its population of 40 neurons.

+

Ensembles can also be connected to other models. +When the dimensionality of the objects being +connected are different, we can use Python’s +slice syntax to route information from +one node or ensemble to another. +For example:

+
with net:
+    two_d_ensemble = nengo.Ensemble(n_neurons=80, dimensions=2)
+    nengo.Connection(sin_node, two_d_ensemble[0])
+    nengo.Connection(my_ensemble, two_d_ensemble[1])
+
+
+

This creates a new ensemble that represents +two real-valued signals. +By connecting sin_node to two_d_ensemble, +its first dimension now represents a sine wave. +Its second dimensions now represents the same +value as my_ensemble.

+

When creating connections, +we can specify a function that +will be computed across the connection.

+
with net:
+    square = nengo.Ensemble(n_neurons=40, dimensions=1)
+    nengo.Connection(my_ensemble, square, function=np.square)
+
+
+

Functions can be computed over multiple dimensions, as well.

+
def product(x):
+    return x[0] * x[1]
+
+with net:
+    product_ensemble = nengo.Ensemble(n_neurons=40, dimensions=1)
+    nengo.Connection(two_d_ensemble, product_ensemble, function=product)
+
+
+
+
+

Probing Nengo objects

+

Once you have defined the objects in your model +and how they’re connected, +you can decide what data you want to collect +by probing those objects.

+

If we wanted to collect data from +our 2D Ensemble and the Product of those two dimensions:

+
with net:
+    two_d_probe = nengo.Probe(two_d_ensemble, synapse=0.01)
+    product_probe = nengo.Probe(product_ensemble, synapse=0.01)
+
+
+

The argument synapse defines the time constant +on a causal low-pass filter, +which approximates a simple synapse model. +The output of ensembles of spiking neurons +can be very noisy, so a filter is recommended.

+
+
+

Running an experiment

+

Once a model has been constructed and we have probed +certain objects, we can run it to collect data.

+

To run a model, we must first build a simulator +based on the model we’ve defined.

+
sim = nengo.Simulator(net)
+
+
+

We can then run that simulator. +For example, to run our model for five seconds:

+
sim.run(5.0)
+
+
+

Once a simulation has been run at least once +(it can be run for additional time if desired) +the data collected can be accessed +for analysis or visualization.

+
print(sim.data[product_probe][-10:])
+
+
+

For more details on these objects, +see the API documentation.

+
+
+
+

Next steps

+ +
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/getting_started.html b/getting_started.html new file mode 100644 index 0000000000..797fff2ff0 --- /dev/null +++ b/getting_started.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/history.html b/history.html new file mode 100644 index 0000000000..07a3e74137 --- /dev/null +++ b/history.html @@ -0,0 +1,522 @@ + + + + + + + + Nengo history — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Nengo history

+

Some form of Nengo has existed since 2003. +From then until now, +students and researchers have used Nengo to create models +which help us understand the brain. +Nengo has evolved with that understanding.

+

We’re currently in what could be called the fifth generation +of Nengo development. Many of the design decisions of this generation +are a result of lessons learnt from the first four generations.

+

Summary

+

Generation 1: NESim (Matlab) -> Nemo (Matlab)

+

Generation 2: NEO (Java) -> Nengo (Java) -> Nengo GUI (Piccolo2D)

+

Generation 3: Nengo scripting layer (Jython) -> Nengo Theano backend (Python)

+

Generation 4: Nengo API -> Nengo reference implementation (Python, Jython, Theano, PyNN)

+

Generation 5: Nengo 2.0

+
+

Generation 1

+

When Chris Eliasmith and Charles H. Anderson released the book +Neural Engineering [Eliasmith2003], +they described a framework for creating models of spiking neurons +called the Neural Engineering Framework. +With the book, they provided a set of Matlab scripts +to facilitate the use of these methods in theoretical neuroscience research.

+

NESim (Neural Engineering Simulator) was that set of Matlab scripts, +along with a basic graphical user interface that allowed users +to set the parameters of simulations. +Its primary developers were Chris Eliasmith and Bryan Tripp.

+

NESim offered fast simulations by leveraging the computational power +of Matlab. And while the GUI may not have been pretty, +it enabled researchers to quickly test out ideas before +making a full model. The use of Matlab also meant that researchers could +do their simulation and analysis in the same Nemo.

+

However, there were some downsides to NESim. It was difficult +to communicate with other simulation environments. +The GUI, while functional, was not very eye-catching or dynamic. +The object model of Matlab is limited. +And, even though NESim is open source software, +Matlab itself is not, meaning that NESim +was not accessible to everyone.

+
+
Eliasmith2003
+

Eliasmith, Chris, and Charles H. Anderson. Neural engineering: +Computation, representation, and dynamics in neurobiological systems. +MIT Press, 2003.

+
+
+
+
+

Generation 2

+

The desire for a more robust object hierarchy +and to interact with other simulation environments +resulted in the creation of NEO (Neural Engineering Objects) +in 2007 by Bryan Tripp. NEO was later renamed to Nengo, +also a short form of Neural Engineering Objects.

+

Nengo was created in Java, which encourages the use +of deeply nested object hierarchies. +This hierarchy of objects allowed Nengo to be flexible; +it could implement the same types of simulations as +NESim did, but it could also have those simulated objects +communicate with other types of objects. +Also, despite a common misconception about the speed of Java, +simulations in Nengo maintained the speed of the Matlab NESim. +Java, like Matlab, is multi-platform, meaning it would work +on any modern operating system, but unlike Matlab, +is available at no cost to the end user. +Java is common enough that, for most, installing Nengo +is quite easy.

+

Nengo was a robust neural simulator, but required modelers +to be proficient in Java. To overcome this, +in the summer of 2008, a graphical user interface +was created to make model creation and simulation +easy for anyone.

+

Check out a demo video of this interface.

+

The GUI leveraged the Java graphical toolkit Piccolo2D.java, +which makes it easy to make zoomable interfaces that +can play well with the Java Swing ecosystem. +The new GUI made it easy for beginners to become familiar +with the methods of the NEF, and gradually transition +to writing models outside of the GUI.

+

While Nengo and its GUI introduced many people +to the NEF, its deeply nested object hierarchy +proved difficult for many people to use productively. +While the GUI provided easy access for beginners, +the transition to being an expert user was difficult. +Additionally, while Java is cross platform and free to download, +it is not open source (though an open source version exists). +And while Java can simulate many networks quickly, +efforts to leverage non-standard computing devices +like GPUs were difficult to implement +in a cross-platform manner.

+

While new versions of Nengo followed, +this version remains in use to this day. +nengo.ca +contains documentation for this version, +now referred to as Nengo 1.4.

+
+
+

Generation 3

+

Shortly after the GUI was released, +Terry Stewart began making it possible +to make simulations using Nengo +through a simple Python scripting interface. +The interface was originally known as nef.py +due to the first implementation’s file name, +but it quickly became the preferred way +for modelers to create models due to its simplicity, +and therefore was incorporated with the rest of Nengo. +While the scripting interface used Python syntax, +it was still able to operate within the Java ecosystem +thanks for a Java implementation of Python called Jython.

+

Although this boost in productivity allowed for the creation of Spaun, +the simulation speed was still much to be desired. Because of that, +a project that had the same interface as the nef.py scripts, +but with an implementation that used Theano.

+

However, there were concerns that the Jython +and the Theano backed implementations would soon +diverge, fracturing the population of people building +nengo models.

+
+
+

Generation 4

+

In order to deal with the fracturing of the Nengo community, +the decision was made to standardize the API that +had been evolving since the introduction of nef.py. +Because the name “Nengo” was now well known, +the name stuck, and the API was called the Nengo API.

+

Through a grueling weekend of meetings, +the CNRG tentatively decided on an API +that any software claiming to be “Nengo” +would have to implement. In addition to the API, +the CNRG would produce a reference implementation +in Python with as few dependencies as possible, +change the Jython version to conform to the new API, +and in Theano to continue the work making a fast backend.

+

Although these three implementations may choose to +implement their own specific capabilities, +since they all conform to the Nengo API, +they can all run the vast majority of models +that a modeler would want to run.

+
+
+

Generation 5

+

Issues with the implementation of Theano led to +a new effort to create an OpenCL-backed version of Nengo. +With so many possible backends, the Nengo API +switched focus to being able to provide a consistent +front-end experience while being able to run models +on different backends. The Nengo API, and a NumPy-backed +reference simulator, matured into what we have now released as +Nengo 2.0.

+

Since standardizing on the scripting frontend of Nengo 2.0, +new backends have begun development, for the BlueGene, Neurogrid, +SpiNNaker and other hardware.

+

We hope that, in this generation, +we have made all the right compromises such that +we can build large models with concise, expressive code, +and that we can create backends that can build and simulate +those models much more quickly than before. +Further, by making this API available, +we hope to be able to interact even further with +the rest of the neuroscience packages written in Python.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/improving-performance.html b/improving-performance.html new file mode 100644 index 0000000000..535d235768 --- /dev/null +++ b/improving-performance.html @@ -0,0 +1,513 @@ + + + + + + + + Improving performance — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Improving performance

+

Once you start creating larger models, Nengo might become a little bit slow. +This section walks you through different things that might help you to get the +best performance out of Nengo.

+
+

In a nutshell

+
+

To improve build time

+
    +

  1. Set a seed on the top level model to enable decoder caching:

    2. +
+
net = nengo.Network(seed=1)
+
+
+
    +

  1. Disable the operator graph optimizer:

    2. +
+
with nengo.Simulator(net, optimize=False) as sim:
+    ...
+
+
+
    +

  1. Reduce the number of neurons in very large ensembles, or consider using the +RandomizedSVD solver.

    2. +
+
+
+

To improve run time

+
    +

  1. Enable the operator graph optimizer +(and install SciPy):

    2. +
+
with nengo.Simulator(net, optimize=True) as sim:
+    ...
+
+
+
    +

  1. Consider switching to the nengo_ocl +backend if you have a powerful GPU.

    2. +
+
+
+

To lower peak memory consumption

+
    +

  1. Disable the operator graph optimizer:

    2. +
+
with nengo.Simulator(net, optimize=False) as sim:
+    ...
+
+
+
    +

  1. Reduce the number of neurons in very large ensembles. +Consider replacing them with +multiple smaller ensembles (EnsembleArray is useful for that).

    2. +

  2. Reduce the number of probes or their sampling intervals +(with the sample_every argument).

    3. +
+
+
+
+

Build and run time performance

+

The two main determiners of how long your model takes to run are the +build time and the run time. Build time is the time required to +translate the model description into the actual neural network that gets +simulated. A build happens when you create the simulator with +nengo.Simulator(net). Run time is how long it takes to simulate this +network for the desired amount of simulation time. A run happens when you +call sim.run.

+

Some of the techniques described below +will influence one of these variables, while others will +reduce one variable at the cost of increasing another. +While the run time is usually the most important variable, +sometimes the memory consumption can be the main problem.

+

Getting the best performance for your model depends on your model +and your computing environment.

+
+
+

Decoder caching

+

Influences build time.

+

A significant amount of build time is spent on finding the NEF +decoders. Because of that, it is possible to cache the decoders. The first +build of a model will still take about the same time (technically a bit longer +because the computed decoders will be stored), but all subsequent builds of the +same model can load the stored decoders and will be faster.

+

To enable the decoder caching, set a seed on the network like so:

+
with nengo.Network(seed=1) as net:
+    ...
+
+
+

There are a few configuration options for more +advanced control of the cache. The most important might be the possibility to +control the path where the cache files are stored. On high performance +clusters, certain file systems might provide better performance.

+
+
+

Operator graph optimizer

+

Influences build time, run time, and memory consumption.

+

By default, Nengo optimizes its internal data structures +(the “operator graph”) to access memory in a linear manner. +However, this can increase build time significantly +and in some cases it can be better to turn this +optimization off to speed up the build at the cost of slowing the run. +To turn the optimizer off, +set the simulator’s optimize argument to False:

+
with nengo.Simulator(net, optimize=False) as sim:
+    ...
+
+
+

Another situation where it is helpful to disable the optimizer is when the peak +memory usage is too high. The optimizer can use up to three times as much +memory as would be required without the optimizer. Note that limiting the +number of optimization passes does not noticeably reduce memory consumption.

+
+
+

SciPy

+

Influences run time.

+

To gain the maximum performance gain from the operator graph optimizer, +install SciPy. +When the operator graph optimizer is deactivated, +installing SciPy has no effect on performance.

+
+
+

nengo_ocl

+

Improves run time.

+

If you have a powerful GPU, you have the option to switch to the nengo_ocl backend. It will utilize your GPU, +which is optimized for the sorts of calculations done by Nengo. +Build times with nengo_ocl are usually comparable to Nengo, +but run times can be significantly faster.

+
+
+

Model structure

+

Influences build time, run time, and memory consumption.

+

Some aspects of the model structure, apart from the size of the model, +influence performance. Ensembles with many neurons will take a long +time to build and consume a lot of memory. Sometimes it is +feasible to split large ensembles into multiple smaller ensembles (the +EnsembleArray is helpful for that). Alternatively, using the +RandomizedSVD decoder solver can reduce the build time.

+

However, be aware that many small ensembles will take longer to simulate if the +operator graph optimizer is deactivated.

+
+
+

Limiting probed data

+

Influences memory consumption.

+

All data that gets probed in the model has to be stored in memory. +Depending on how long the simulation runs and how many things are probed, +this might consume a significant amount of memory. By reducing the number +of probed objects, the memory consumption can be reduced. An alternative +is to not record a value for every time step. Probes accept a +sample_every= argument to reduce the number of recorded samples.

+

Note that in most cases, +probing data does not noticeably affect run time.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/improving_performance.html b/improving_performance.html new file mode 100644 index 0000000000..7b45e1d659 --- /dev/null +++ b/improving_performance.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/index.html b/index.html new file mode 100644 index 0000000000..e37692e006 --- /dev/null +++ b/index.html @@ -0,0 +1,420 @@ + + + + + + + + Nengo — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Nengo

+

Nengo is a Python library for building and simulating +large-scale neural models. +Nengo can create sophisticated +spiking and non-spiking neural simulations +with sensible defaults in a few lines of code:

+
import nengo
+import numpy as np
+import matplotlib.pyplot as plt
+
+with nengo.Network() as net:
+    sin_input = nengo.Node(output=np.sin)
+    # A population of 100 neurons representing a sine wave
+    sin_ens = nengo.Ensemble(n_neurons=100, dimensions=1)
+    nengo.Connection(sin_input, sin_ens)
+    # A population of 100 neurons representing the square of the sine wave
+    sin_squared = nengo.Ensemble(n_neurons=100, dimensions=1)
+    nengo.Connection(sin_ens, sin_squared, function=np.square)
+    # View the decoded output of sin_squared
+    squared_probe = nengo.Probe(sin_squared, synapse=0.01)
+
+with nengo.Simulator(net) as sim:
+    sim.run(5.0)
+plt.plot(sim.trange(), sim.data[squared_probe])
+
+
+

Yet, Nengo is highly extensible and flexible. +You can define your own neuron types and learning rules, +get input directly from hardware, +build and run deep neural networks, +drive robots, and even simulate your model +on a completely different neural simulator +or neuromorphic hardware.

+
+ +
+ +
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/license.html b/license.html new file mode 100644 index 0000000000..db97d18978 --- /dev/null +++ b/license.html @@ -0,0 +1,503 @@ + + + + + + + + Nengo license — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Nengo license

+

Copyright (c) 2013-2020 Applied Brain Research

+

Nengo is made available under a proprietary license +that permits using, copying, sharing, and making derivative works from +Nengo and its source code for any non-commercial purpose, +as long as the above copyright notice and this permission notice +are included in all copies or substantial portions of the software.

+

If you would like to use Nengo commercially, +licenses can be purchased from Applied Brain Research. +Please contact info@appliedbrainresearch.com for more information.

+

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.

+
+

Licensed code

+

Nengo imports or vendorizes several open source libraries.

+ +

Nengo also includes code modified from other libraries.

+
+

The file nengo/builder/operator.py contains code with the following +license:

+

Copyright (c) 2014, James Bergstra +All rights reserved.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met:

+
    +

  1. Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer.

    2. +

  2. Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the +distribution.

    3. +
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +“AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+
+

The file nengo/utils/magic.py contains code with the +following license:

+

Copyright (c) 2013, Graham Dumpleton. All rights reserved.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met:

+
    +

  • Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer.

    • +

  • Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution.

    • +
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +“AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+
+

The file nengo/utils/graphs.py contains code with the following +license:

+

Theano is copyright (c) 2008-2013 Theano Development Team.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met:

+
    +

  • Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer.

    • +

  • Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution.

    • +

  • Neither the name of Theano nor the names of its contributors may be +used to endorse or promote products derived from this software +without specific prior written permission.

    • +
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ‘’AS IS’’ AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+
+

The file nengo/utils/filter_design.py contains code with the +following license:

+

Copyright (c) 2001, 2002 Enthought, Inc. +All rights reserved.

+

Copyright (c) 2003-2012 SciPy Developers. +All rights reserved.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met:

+
    +

  1. Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer.

    2. +

  2. Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the +distribution.

    3. +

  3. Neither the name of Enthought nor the names of the SciPy Developers +may be used to endorse or promote products derived from this +software without specific prior written permission.

    4. +
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS +BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, +OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +THE POSSIBILITY OF SUCH DAMAGE.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/nengorc.html b/nengorc.html new file mode 100644 index 0000000000..ce3b1fb311 --- /dev/null +++ b/nengorc.html @@ -0,0 +1,487 @@ + + + + + + + + Nengo configuration — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Nengo configuration

+

Certain features of Nengo can be configured globally through RC settings.

+

RC settings can be manipulated either through the nengo.rc object, +or through RC configuration files.

+
+

The nengo.rc object

+

The nengo.rc object gives programmatic access to +globally configured features of Nengo.

+
+
+nengo.rc = <nengo.rc._RC object>
+

Allows reading from and writing to Nengo RC settings.

+

This object is a configparser.ConfigParser, which means that +values can be accessed and manipulated like a dictionary:

+
oldsize = nengo.rc["decoder_cache"]["size"]
+nengo.rc["decoder_cache"]["size"] = "2 GB"
+
+
+

All values are stored as strings. If you want to store or retrieve a +specific datatype, you should coerce it appropriately (e.g., with int()). +Booleans are more flexible, so you should use the getboolean method +to access boolean values.

+
simple = nengo.rc["exceptions"].getboolean("simplified")
+
+
+

In addition to the normal configparser.ConfigParser methods, +this object also has a reload_rc method to reset nengo.rc +to default settings:

+
nengo.rc.reload_rc()  # Reads defaults from configuration files
+nengo.rc.reload_rc(filenames=[])  # Ignores configuration files
+
+
+
+ +
+
+

Configuration files

+

nengo.rc is initialized with configuration settings read +from the following files with precedence to those listed first:

+
    +

  1. nengorc in the current directory. This is intended to allow for project +specific settings without hard coding them in the model script.

    2. +

  2. An operating system specific file in the user’s home directory.

    +
      +

    • Windows: %userprofile%\.nengo\nengorc

      • +

    • Other (OS X, Linux): ~/.config/nengo/nengorc

      • +
    +
    3. +

  3. INSTALL/nengo-data/nengorc (where INSTALL is the +installation directory of the Nengo package).

    4. +
+

The RC file is divided into sections by lines containing the section name +in brackets, i.e. [section]. A setting is set by giving the name followed +by a : or = and the value. All lines starting with # or ; are +comments.

+

For example, to set the size of the decoder cache to 512 MB, +add the following to a configuration file:

+
[decoder_cache]
+size = 512 MB
+
+
+
+
+

Configuration options

+

All of the configuration options are listed in the example +configuration file, which is included with Nengo +and copied below.

+

Commented lines show the default values for each setting.

+
[precision]
+
+# Default bit precision for all NumPy arrays made by Nengo (e.g., bits: 64
+# would use float64/int64 dtypes). Must be one of '16', '32', or '64'. (string)
+#bits: 64
+
+# --- Settings for the decoder cache
+[decoder_cache]
+
+# Enable or disable the cache. (bool)
+#enabled = True
+
+# Path where the cached decoders will be stored. (str)
+#path = ~/.cache/nengo/decoders  # Linux/Mac OS X default
+
+# Set the cache to readonly. In readonly mode cached decoders will be
+# loaded, but no newly calculated decoders will be written to the cache.
+# (bool)
+#readonly = False
+
+# Set the maximum cache size. Whenever the cache exceeds this limit, cached
+# decoders will be deleted, beginning with the oldest, until the limit
+# is met again. Please specify the unit (e.g., 512 MB). (str)
+#size = 512 MB
+
+
+# --- Settings for error messages due to exceptions
+[exceptions]
+
+# Use simplified exceptions. In most cases, simplified exceptions will be
+# easier to read and more useful for model development. However,
+# developers of Nengo internals or tools extending Nengo
+# may benefit from full exception tracebacks.
+#simplified = True
+
+
+# --- Settings for the progress bar used when running the simulator
+[progress]
+
+# Set the progress bar to use. The default of 'auto' or will display
+# a progress bar when the estimated simulation run time exceeds one second.
+# Setting this to 'none' will disable to progress bar. Any other string
+# will be interpreted as a module and class name
+# (e.g., 'nengo.utils.progress.ProgressBar'), which Nengo will attempt
+# to load. (str)
+#progress_bar = auto
+
+
+# --- Settings for the Nengo core simulator
+[nengo.Simulator]
+
+# Fail misconfigured operators immediately. If True, operators are tested
+# when they are created in build functions, rather than when they are
+# initialized by the simulator. Failing fast is useful when creating and
+# debugging new operators, but adds some time to the build.
+#fail_fast = False
+
+
+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/networks.html b/networks.html new file mode 100644 index 0000000000..7676e136d1 --- /dev/null +++ b/networks.html @@ -0,0 +1,1108 @@ + + + + + + + + Reusable networks — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Reusable networks

+

Networks are an abstraction of a grouping of Nengo objects +(i.e., Node, Ensemble, Connection, and Network instances, +though usually not Probe instances.) +Like most abstractions, this helps with code-reuse and maintainability. +You’ll find the documentation +for the reusable networks included with Nengo below.

+

You may also want to build your own reusable networks. +Doing so can help encapsulate parts of your model, +making your code easier to understand, +easier to re-use, and easier to share. +The following examples will +help you build your own reusable networks:

+
+ +
+

You may also find the config system documentation useful.

+
+

Built-in networks

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.networks.EnsembleArray

An array of ensembles.

nengo.networks.BasalGanglia

Winner take all network, typically used for action selection.

nengo.networks.Thalamus

Inhibits non-selected actions.

nengo.networks.AssociativeMemory

Associative memory network.

nengo.networks.CircularConvolution

Compute the circular convolution of two vectors.

nengo.networks.Integrator

An ensemble that accumulates input and maintains state.

nengo.networks.Oscillator

A two-dimensional ensemble with interacting recurrent connections.

nengo.networks.Product

Computes the element-wise product of two equally sized vectors.

nengo.networks.InputGatedMemory

Stores a given vector in memory, with input controlled by a gate.

+
+
+class nengo.networks.EnsembleArray(n_neurons, n_ensembles, ens_dimensions=1, label=None, seed=None, add_to_container=None, **ens_kwargs)[source]
+

An array of ensembles.

+

This acts, in some ways, like a single high-dimensional ensemble, +but actually consists of many sub-ensembles, each one representing +a separate dimension. This tends to be much faster to create +and can be more accurate than having one huge high-dimensional ensemble. +However, since the neurons represent different dimensions separately, +we cannot compute nonlinear interactions between those dimensions.

+

Note that in addition to the parameters below, parameters affecting +all of the sub-ensembles can be passed to the ensemble array. +For example:

+
ea = nengo.networks.EnsembleArray(20, 2, radius=1.5)
+
+
+

creates an ensemble array with 2 sub-ensembles, each with 20 neurons, +and a radius of 1.5.

+
+
Parameters
+
+
n_neuronsint

The number of neurons in each sub-ensemble.

+
+
n_ensemblesint

The number of sub-ensembles to create.

+
+
ens_dimensionsint, optional

The dimensionality of each sub-ensemble.

+
+
labelstr, optional

A name to assign this EnsembleArray. +Used for visualization and debugging.

+
+
seedint, optional

Random number seed that will be used in the build step.

+
+
add_to_containerbool, optional

Determines if this network will be added to the current container. +If None, this network will be added to the network at the top of the +Network.context stack unless the stack is empty.

+
+
+
+
Attributes
+
+
dimensions_per_ensembleint

The dimensionality of each sub-ensemble.

+
+
ea_ensembleslist

The sub-ensembles in the ensemble array.

+
+
inputNode

A node that provides input to all of the ensembles in the array.

+
+
n_ensemblesint

The number of sub-ensembles to create.

+
+
n_neurons_per_ensembleint

The number of neurons in each sub-ensemble.

+
+
neuron_inputNode or None

A node that provides input to all the neurons in the ensemble array. +None unless created in add_neuron_input.

+
+
neuron_outputNode or None

A node that gathers neural output from all the neurons in the ensemble +array. None unless created in add_neuron_output.

+
+
outputNode

A node that gathers decoded output from all of the ensembles +in the array.

+
+
+
+
+
+
+property dimensions
+

(int) Dimensionality of the ensemble array.

+
+ +
+
+add_neuron_input()[source]
+

Adds a node that provides input to the neurons of all ensembles.

+

Direct neuron input is useful for inhibiting the activity of all +neurons in the ensemble array.

+

This node is accessible through the ‘neuron_input’ attribute +of this ensemble array.

+
+ +
+
+add_neuron_output()[source]
+

Adds a node that collects the neural output of all ensembles.

+

Direct neuron output is useful for plotting the spike raster of +all neurons in the ensemble array.

+

This node is accessible through the ‘neuron_output’ attribute +of this ensemble array.

+
+ +
+
+add_output(name, function, synapse=None, **conn_kwargs)[source]
+

Adds a node that collects the decoded output of all ensembles.

+

By default, this is called once in __init__ with function=None. +However, this can be called multiple times with different functions, +similar to the way in which an ensemble can be connected to many +downstream ensembles with different functions.

+

Note that in addition to the parameters below, parameters affecting +all of the connections from the sub-ensembles to the new node +can be passed to this function. For example:

+
ea.add_output("lstsq_output", None, solver=nengo.solvers.Lstsq())
+
+
+

creates a new output at ea.lstsq_output with the decoders +of each connection solved for with the Lstsq solver.

+
+
Parameters
+
+
namestr

The name of the output. This will also be the name of the attribute +set on the ensemble array.

+
+
functioncallable or iterable of callables

The function to compute across the connection from sub-ensembles +to the new output node. If function is an iterable, it must be +an iterable consisting of one function for each sub-ensemble.

+
+
synapseSynapse, optional

The synapse model with which to filter the connections from +sub-ensembles to the new output node. This is kept separate from +the other conn_kwargs because this defaults to None rather +than the default synapse model. In almost all cases the synapse +should stay as None, and synaptic filtering should be performed in +the connection from the output node.

+
+
+
+
+
+ +
+ +
+
+class nengo.networks.BasalGanglia(dimensions, n_neurons_per_ensemble=100, output_weight=- 3.0, input_bias=0.0, ampa_config=None, gaba_config=None, **kwargs)[source]
+

Winner take all network, typically used for action selection.

+

The basal ganglia network outputs approximately 0 at the dimension with +the largest value, and is negative elsewhere.

+

While the basal ganglia is primarily defined by its winner-take-all +function, it is also organized to match the organization of the human +basal ganglia. It consists of five ensembles:

+
    +

  • Striatal D1 dopamine-receptor neurons (strD1)

    • +

  • Striatal D2 dopamine-receptor neurons (strD2)

    • +

  • Subthalamic nucleus (stn)

    • +

  • Globus pallidus internus / substantia nigra reticulata (gpi)

    • +

  • Globus pallidus externus (gpe)

    • +
+

Interconnections between these areas are also based on known +neuroanatomical connections. See [1] for more details, and [2] for +the original non-spiking basal ganglia model by +Gurney, Prescott & Redgrave that this model is based on.

+
+

Note

+

The default Solver for the basal ganglia is NnlsL2nz, which +requires SciPy. If SciPy is not installed, the global default +solver will be used instead.

+
+
+
Parameters
+
+
dimensionsint

Number of dimensions (i.e., actions).

+
+
n_neurons_per_ensembleint, optional

Number of neurons in each ensemble in the network.

+
+
output_weightfloat, optional

A scaling factor on the output of the basal ganglia +(specifically on the connection out of the GPi).

+
+
input_biasfloat, optional

An amount by which to bias all dimensions of the input node. +Biasing the input node is important for ensuring that all input +dimensions are positive and easily comparable.

+
+
ampa_configconfig, optional

Configuration for connections corresponding to biological connections +to AMPA receptors (i.e., connections from STN to to GPi and GPe). +If None, a default configuration using a 2 ms lowpass synapse +will be used.

+
+
gaba_configconfig, optional

Configuration for connections corresponding to biological connections +to GABA receptors (i.e., connections from StrD1 to GPi, StrD2 to GPe, +and GPe to GPi and STN). If None, a default configuration using an +8 ms lowpass synapse will be used.

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
+

References

+
+
1
+

Stewart, T. C., Choo, X., & Eliasmith, C. (2010). +Dynamic behaviour of a spiking model of action selection in the +basal ganglia. In Proceedings of the 10th international conference on +cognitive modeling (pp. 235-40).

+
+
2
+

Gurney, K., Prescott, T., & Redgrave, P. (2001). +A computational model of action selection in the basal +ganglia. Biological Cybernetics 84, 401-423.

+
+
+
+
Attributes
+
+
bias_inputNode or None

If input_bias is non-zero, this node will be created to bias +all of the dimensions of the input signal.

+
+
gpeEnsembleArray

Globus pallidus externus ensembles.

+
+
gpiEnsembleArray

Globus pallidus internus ensembles.

+
+
inputNode

Accepts the input signal.

+
+
outputNode

Provides the output signal.

+
+
stnEnsembleArray

Subthalamic nucleus ensembles.

+
+
strD1EnsembleArray

Striatal D1 ensembles.

+
+
strD2EnsembleArray

Striatal D2 ensembles.

+
+
+
+
+
+ +
+
+class nengo.networks.Thalamus(dimensions, n_neurons_per_ensemble=50, mutual_inhib=1.0, threshold=0.0, **kwargs)[source]
+

Inhibits non-selected actions.

+

The thalamus is intended to work in tandem with a basal ganglia network. +It converts basal ganglia output into a signal with (approximately) 1 for +the selected action and 0 elsewhere.

+

In order to suppress low responses and strengthen high responses, +a constant bias is added to each dimension (i.e., action), and dimensions +mutually inhibit each other. Additionally, the ensemble representing +each dimension is created with positive encoders and can be assigned +positive x-intercepts to threshold low responses.

+
+
Parameters
+
+
dimensionsint

Number of dimensions (i.e., actions).

+
+
n_neurons_per_ensembleint, optional

Number of neurons in each ensemble in the network.

+
+
mutual_inhibfloat, optional

Strength of the mutual inhibition between actions.

+
+
thresholdfloat, optional

The threshold below which values will not be represented.

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
Attributes
+
+
actionsEnsembleArray

Each ensemble represents one dimension (action).

+
+
biasNode

The constant bias injected in each actions ensemble.

+
+
inputNode

Input to the actions ensembles.

+
+
outputNode

Output from the actions ensembles.

+
+
+
+
+
+ +
+
+class nengo.networks.AssociativeMemory(input_vectors, output_vectors=None, n_neurons=50, threshold=0.3, input_scales=1.0, inhibitable=False, label=None, seed=None, add_to_container=None)[source]
+

Associative memory network.

+
+
Parameters
+
+
input_vectors: array_like

The list of vectors to be compared against.

+
+
output_vectors: array_like, optional

The list of vectors to be produced for each match. If None, the +associative memory will be autoassociative (cleanup memory).

+
+
n_neurons: int, optional

The number of neurons for each of the ensemble (where each ensemble +represents each item in the input_vectors list).

+
+
threshold: float, optional

The association activation threshold.

+
+
input_scales: float or array_like, optional

Scaling factor to apply on each of the input vectors. Note that it +is possible to scale each vector independently.

+
+
inhibitable: bool, optional

Flag to indicate if the entire associative memory module is +inhibitable (entire thing can be shut off). The input gain into +the inhibitory connection is 1.5.

+
+
labelstr, optional

A name for the ensemble. Used for debugging and visualization.

+
+
seedint, optional

The seed used for random number generation.

+
+
add_to_containerbool, optional

Determines if the network will be added to the current container. +If None, will be true if currently within a Network.

+
+
+
+
+
+
+property am_ens_config
+

(Config) Defaults for associative memory ensemble creation.

+
+ +
+
+property default_ens_config
+

(Config) Defaults for other ensemble creation.

+
+ +
+
+property thresh_ens_config
+

(Config) Defaults for threshold ensemble creation.

+
+ +
+
+add_input_mapping(name, input_vectors, input_scales=1.0)[source]
+

Adds a set of input vectors to the associative memory network.

+

Creates a transform with the given input vectors between the +a named input node and associative memory element input to enable the +inputs to be mapped onto ensembles of the Associative Memory.

+
+
Parameters
+
+
name: str

Name to use for the input node. This name will be used as the name +of the attribute for the associative memory network.

+
+
input_vectors: array_like

The list of vectors to be compared against.

+
+
input_scales: float or array_like, optional

Scaling factor to apply on each of the input vectors. Note that it +is possible to scale each vector independently.

+
+
+
+
+
+ +
+
+add_output_mapping(name, output_vectors)[source]
+

Adds another output to the associative memory network.

+

Creates a transform with the given output vectors between the +associative memory element output and a named output node to enable the +selection of output vectors by the associative memory.

+
+
Parameters
+
+
name: str

Name to use for the output node. This name will be used as +the name of the attribute for the associative memory network.

+
+
output_vectors: array_like

The list of vectors to be produced for each match.

+
+
+
+
+
+ +
+
+add_default_output_vector(output_vector, output_name='output', n_neurons=50, min_activation_value=0.5)[source]
+

Adds a default output vector to the associative memory network.

+

The default output vector is chosen if the input matches none of +the given input vectors.

+
+
Parameters
+
+
output_vector: array_like

The vector to be produced if the input value matches none of +the vectors in the input vector list.

+
+
output_name: str, optional

The name of the input to which the default output vector +should be applied.

+
+
n_neurons: int, optional

Number of neurons to use for the default output vector ensemble.

+
+
min_activation_value: float, optional

Minimum activation value (i.e. threshold) to use to disable +the default output vector.

+
+
+
+
+
+ +
+
+add_wta_network(inhibit_scale=1.5, inhibit_synapse=0.005)[source]
+

Add a winner-take-all (WTA) network to associative memory output.

+
+
Parameters
+
+
inhibit_scale: float, optional

Mutual inhibition scaling factor.

+
+
inhibit_synapse: float, optional

Mutual inhibition synapse time constant.

+
+
+
+
+
+ +
+
+add_threshold_to_outputs(n_neurons=50, inhibit_scale=10)[source]
+

Adds a thresholded output to the associative memory.

+
+
Parameters
+
+
n_neurons: int, optional

Number of neurons to use for the default output vector ensemble.

+
+
inhibit_scale: float, optional

Mutual inhibition scaling factor.

+
+
+
+
+
+ +
+ +
+
+class nengo.networks.CircularConvolution(n_neurons, dimensions, invert_a=False, invert_b=False, input_magnitude=1.0, **kwargs)[source]
+

Compute the circular convolution of two vectors.

+

The circular convolution \(c\) of vectors \(a\) and \(b\) +is given by

+
+\[c[i] = \sum_j a[j] b[i - j]\]
+

where negative indices on \(b\) wrap around to the end of the vector.

+

This computation can also be done in the Fourier domain,

+
+\[c = DFT^{-1} ( DFT(a) DFT(b) )\]
+

where \(DFT\) is the Discrete Fourier Transform operator, and +\(DFT^{-1}\) is its inverse. This network uses this method.

+
+
Parameters
+
+
n_neuronsint

Number of neurons to use in each product computation

+
+
dimensionsint

The number of dimensions of the input and output vectors.

+
+
invert_a, invert_bbool, optional

Whether to reverse the order of elements in either +the first input (invert_a) or the second input (invert_b). +Flipping the second input will make the network perform circular +correlation instead of circular convolution.

+
+
input_magnitudefloat, optional

The expected magnitude of the vectors to be convolved. +This value is used to determine the radius of the ensembles +computing the element-wise product.

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
+

Notes

+

The network maps the input vectors \(a\) and \(b\) of length N into +the Fourier domain and aligns them for complex multiplication. +Letting \(F = DFT(a)\) and \(G = DFT(b)\), this is given by:

+
[ F[i].real ]     [ G[i].real ]     [ w[i] ]
+[ F[i].imag ]  *  [ G[i].imag ]  =  [ x[i] ]
+[ F[i].real ]     [ G[i].imag ]     [ y[i] ]
+[ F[i].imag ]     [ G[i].real ]     [ z[i] ]
+
+
+

where \(i\) only ranges over the lower half of the spectrum, since +the upper half of the spectrum is the flipped complex conjugate of +the lower half, and therefore redundant. The input transforms are +used to perform the DFT on the inputs and align them correctly for +complex multiplication.

+

The complex product \(H = F * G\) is then

+
+\[H[i] = (w[i] - x[i]) + (y[i] + z[i]) I\]
+

where \(I = \sqrt{-1}\). We can perform this addition along with the +inverse DFT \(c = DFT^{-1}(H)\) in a single output transform, finding +only the real part of \(c\) since the imaginary part +is analytically zero.

+

Examples

+

A basic example computing the circular convolution of two 10-dimensional +vectors represented by ensemble arrays:

+
from nengo.networks import CircularConvolution, EnsembleArray
+
+with nengo.Network():
+    A = EnsembleArray(50, n_ensembles=10)
+    B = EnsembleArray(50, n_ensembles=10)
+    C = EnsembleArray(50, n_ensembles=10)
+    cconv = CircularConvolution(50, dimensions=10)
+    nengo.Connection(A.output, cconv.input_a)
+    nengo.Connection(B.output, cconv.input_b)
+    nengo.Connection(cconv.output, C.input)
+
+
+
+
Attributes
+
+
input_aNode

The first vector to be convolved.

+
+
input_bNode

The second vector to be convolved.

+
+
productNetwork

Network created with Product to do the element-wise product +of the \(DFT\) components.

+
+
outputNode

The resulting convolved vector.

+
+
+
+
+
+ +
+
+class nengo.networks.Integrator(recurrent_tau, n_neurons, dimensions, **kwargs)[source]
+

An ensemble that accumulates input and maintains state.

+

This is accomplished through scaling the input signal and recurrently +connecting an ensemble to itself to maintain state.

+
+
Parameters
+
+
recurrent_taufloat

Time constant on the recurrent connection.

+
+
n_neuronsint

Number of neurons in the recurrently connected ensemble.

+
+
dimensionsint

Dimensionality of the input signal and ensemble.

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
Attributes
+
+
ensembleEnsemble

The recurrently connected ensemble.

+
+
inputNode

Provides the input signal.

+
+
+
+
+
+ +
+
+class nengo.networks.Oscillator(recurrent_tau, frequency, n_neurons, **kwargs)[source]
+

A two-dimensional ensemble with interacting recurrent connections.

+

The ensemble connects to itself in a manner similar to the integrator; +however, here the two dimensions interact with each other to implement +a cyclic oscillator.

+
+
Parameters
+
+
recurrent_taufloat

Time constant on the recurrent connection.

+
+
frequencyfloat

Desired frequency, in radians per second, of the cyclic oscillation.

+
+
n_neuronsint

Number of neurons in the recurrently connected ensemble.

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
Attributes
+
+
ensembleEnsemble

The recurrently connected oscillatory ensemble.

+
+
inputNode

Provides the input signal.

+
+
+
+
+
+ +
+
+class nengo.networks.Product(n_neurons, dimensions, input_magnitude=1.0, **kwargs)[source]
+

Computes the element-wise product of two equally sized vectors.

+

The network used to calculate the product is described in +Gosmann, 2015. A simpler version of this network can be found in the +Multiplication example.

+

Note that this network is optimized under the assumption that both input +values (or both values for each input dimensions of the input vectors) are +uniformly and independently distributed. Visualized in a joint 2D space, +this would give a square of equal probabilities for pairs of input values. +This assumption is violated with non-uniform input value distributions +(for example, if the input values follow a Gaussian or cosine similarity +distribution). In that case, no square of equal probabilities is obtained, +but a probability landscape with circular equi-probability lines. To obtain +the optimal network accuracy, scale the input_magnitude by a factor of +1 / sqrt(2).

+
+
Parameters
+
+
n_neuronsint

Number of neurons per dimension in the vector.

+
+

Note

+

These neurons will be distributed evenly across two +ensembles. If an odd number of neurons is specified, the +extra neuron will not be used.

+
+
+
dimensionsint

Number of dimensions in each of the vectors to be multiplied.

+
+
input_magnitudefloat, optional

The expected magnitude of the vectors to be multiplied. +This value is used to determine the radius of the ensembles +computing the element-wise product.

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
Attributes
+
+
input_aNode

The first vector to be multiplied.

+
+
input_bNode

The second vector to be multiplied.

+
+
outputNode

The resulting product.

+
+
sq1EnsembleArray

Represents the first squared term. See Gosmann, 2015 for details.

+
+
sq2EnsembleArray

Represents the second squared term. See Gosmann, 2015 for details.

+
+
+
+
+
+ +
+
+class nengo.networks.InputGatedMemory(n_neurons, dimensions, feedback=1.0, difference_gain=1.0, recurrent_synapse=0.1, difference_synapse=None, **kwargs)[source]
+

Stores a given vector in memory, with input controlled by a gate.

+
+
Parameters
+
+
n_neuronsint

Number of neurons per dimension in the vector.

+
+
dimensionsint

Dimensionality of the vector.

+
+
feedbackfloat, optional

Strength of the recurrent connection from the memory to itself.

+
+
difference_gainfloat, optional

Strength of the connection from the difference ensembles to the +memory ensembles.

+
+
recurrent_synapsefloat, optional
+
difference_synapseSynapse

If None, …

+
+
**kwargs

Keyword arguments passed through to nengo.Network +like ‘label’ and ‘seed’.

+
+
+
+
Attributes
+
+
diffEnsembleArray

Represents the difference between the desired vector and +the current vector represented by mem.

+
+
gateNode

With input of 0, the network is not gated, and mem will be updated +to minimize diff. With input greater than 0, the network will be +increasingly gated such that mem will retain its current value, +and diff will be inhibited.

+
+
inputNode

The desired vector.

+
+
memEnsembleArray

Integrative population that stores the vector.

+
+
outputNode

The vector currently represented by mem.

+
+
resetNode

With positive input, the mem population will be inhibited, +effectively wiping out the vector currently being remembered.

+
+
+
+
+
+ +
+
+ + +
+
+
+
+ +
+
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+
+ + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000000..030624db47 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ 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topics","Nengo backend API","Release history","Citation","Setting parameters with Configs","Connections in depth","Contributing to Nengo","Converting from Nengo 1.4 to Nengo 2.0","Examples","Improving function approximation by adjusting tuning curves","Inhibitory gating of ensembles","Using the Izhikevich neuron model","Matrix multiplication","The NEF algorithm","NEF summary","Processes and how to use them","2-dimensional representation","Addition","Combining","Communication channel","Many neurons","Multiplication","A single neuron","Squaring the input","Two neurons","Controlled integrator","Controlled integrator 2","Controlled oscillator","Integrator","The Lorenz chaotic attractor","A simple harmonic oscillator","Learning new associations","Learning a communication channel","Learning to compute a product","Learning to square the input","Unsupervised learning","Legendre Memory Units in Nengo","The basal ganglia","Ensemble array","Integrator","Context matters, membership doesn\u2019t","The config system","Delaying a connection with a node","Designing networks","Additional tips and tricks for designing networks","Adding new objects to Nengo","Tuning curves","Nengo frontend API","Getting started","Nengo history","Improving performance","Nengo","Nengo license","Nengo configuration","Reusable networks","Project information","Semantic Pointer Architecture","User guide","Utilities"],titleterms:{"default":41,"final":33,"function":[7,8,9,19,43,58],"new":[31,41,45],Adding:45,LTS:11,The:[1,4,9,13,29,37,41,53],Using:[11,19],accept:44,add:[19,22,27,30,32],adding:41,addit:[17,44],adjust:9,advanc:[0,8,41],algorithm:13,anaconda:48,api:[1,4,47],appli:15,approach:9,approxim:9,april:2,architectur:56,argument:44,arrai:[7,38],associ:31,attractor:29,axes:46,backend:1,bar:58,basal:37,basic:1,bcm:35,big:7,bonu:21,build:[1,6,8,31,50],builder:[1,58],built:54,burst:11,cach:[1,50,58],caveat:11,chang:[7,15,31,32],channel:[19,32,35],chaotic:29,chatter:11,citat:3,clamp:46,code:52,collect:19,combin:18,common:7,commun:[19,32,35],compon:10,comput:[8,33],config:[4,41,44],configur:[31,53],connect:[1,5,7,8,10,16,18,20,21,22,24,28,37,39,42,48,58],consumpt:50,context:40,contribut:6,control:[25,26,27],convert:7,creat:[10,16,17,18,19,20,21,22,23,24,25,26,27,28,30,32,33,34,35,37,39,48],curv:[9,45,46],custom:15,data:[19,31,50],decemb:2,decod:[1,5,14,32,50],default_dt:15,defin:[25,26,43],delai:42,depth:5,design:[43,44,58],detail:40,develop:6,differ:[10,19],dimension:[16,46],direct:5,distribut:[9,47],document:6,doe:[32,35],doesn:40,doubl:[43,44],dynam:[8,14,25,26],element:[18,20,21,22,24,28,39],encod:[14,31],ensembl:[1,5,7,8,10,16,38,46,58],examin:[31,33],exampl:[8,31,43,44,45],except:1,experi:48,fast:11,februari:2,file:53,fill:41,filter:58,filterednois:15,flexibl:43,from:[7,48],frontend:47,ganglia:37,gate:10,gener:[32,49],get:[6,48],graph:[50,58],group:[43,44],guid:57,harmon:30,help:6,histori:[2,49],hood:8,how:[6,15,32],immedi:41,improv:[9,50],individu:6,inform:55,inhibitori:10,initi:[13,33],input:[7,10,15,16,17,18,19,20,21,22,23,24,25,26,27,28,30,34,39],instal:[6,48],instead:7,integr:[25,26,28,39,43,44],intercept:9,interfac:15,intrins:11,introduct:13,introductori:8,investig:9,ipython:58,izhikevich:11,januari:2,juli:2,june:2,kwarg:44,learn:[1,8,31,32,33,34,35,47],legendr:36,length:15,licens:52,limit:50,lock:58,longer:[43,44],lorenz:29,low:11,lower:50,magic:58,make:[7,21,41],manag:48,mani:20,march:2,matplotlib:58,matrix:12,matter:40,membership:40,memori:[36,50],method:47,model:[7,10,11,16,17,18,19,20,21,22,23,24,26,27,28,30,31,32,33,34,39,47,50],more:43,multipl:[12,21],naiv:9,nco:58,nef:[13,14],nengo:[1,6,7,36,45,47,48,49,51,52,53],nengo_ocl:50,network:[1,7,8,10,16,18,19,20,22,24,25,26,27,28,37,39,43,44,54,58],neural:[18,20,28,39],neuron:[1,5,11,20,22,24,47],next:48,node:[1,5,7,8,42],novemb:2,number:15,numpi:[48,58],nutshel:50,object:[7,43,45,47,48,53],octob:2,oja:35,onli:7,oper:[1,50],optim:[1,50],option:[6,53],origin:7,oscil:[27,30],other:[6,48],output:[10,16,17,18,20,21,23,24,28,31,33,34,39],own:6,packag:48,paramet:[4,41,44],parameter:43,passthrough:5,path:58,peak:50,perform:50,plot:[6,13,18,19,20,21,22,24,26,27,28,30,31,37,39],pointer:56,popul:[18,20,28,39],pre_synaps:32,presentinput:15,preset:4,principl:14,probe:[1,7,10,16,17,18,19,20,21,22,23,24,27,28,30,34,39,48,50],process:[1,8,15,47],product:33,progress:58,project:55,provid:[10,16,17,21,22,23,30,34],quirk:4,refer:1,relat:43,releas:2,repres:8,represent:[14,16,45],reset:41,reson:11,respons:46,result:[6,13,18,19,20,21,22,24,26,27,28,30,37,39],reus:43,reusabl:54,rule:[1,47],run:[6,10,13,15,16,17,18,19,20,21,22,23,24,26,27,28,30,31,34,39,48,50],run_step:15,scipi:50,semant:56,separ:7,septemb:2,set:[4,41],signal:[1,25,26],simpl:[30,35],simul:[7,13,31,37,58],singl:22,solver:47,some:[31,46],sourc:48,spike:11,squar:[23,34],start:48,stdlib:58,step:[10,13,15,16,17,18,19,20,21,22,23,24,25,26,27,28,30,31,32,34,35,37,39,48],store:43,string:7,structur:50,subnetwork:21,summari:14,synaps:47,system:[4,8,41],takeawai:9,termin:7,test:[6,58],them:15,thi:32,thing:7,thread:58,threshold:11,time:[15,50],tip:44,togeth:35,topic:0,transform:[1,14,47],trick:44,tune:[9,45,46],tutori:8,two:24,type:47,under:8,unit:[6,36],unreleas:2,unsupervis:35,usag:48,use:15,useful:43,user:57,util:58,valu:8,weight:32,what:[32,35],whitenois:15,whitesign:15,without:32,work:32,write:6,your:6}}) \ No newline at end of file diff --git a/spa.html b/spa.html new file mode 100644 index 0000000000..5f07013838 --- /dev/null +++ b/spa.html @@ -0,0 +1,358 @@ + + + + + + + + Semantic Pointer Architecture — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Semantic Pointer Architecture

+
+

Deprecated since version 3.0: Use the NengoSPA project instead.

+
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/user-guide.html b/user-guide.html new file mode 100644 index 0000000000..ab7563dbee --- /dev/null +++ b/user-guide.html @@ -0,0 +1,397 @@ + + + + + + + + User guide — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + + + + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/user_guide.html b/user_guide.html new file mode 100644 index 0000000000..ea8c42291d --- /dev/null +++ b/user_guide.html @@ -0,0 +1,12 @@ + + + This page has moved + + + + + diff --git a/utils.html b/utils.html new file mode 100644 index 0000000000..615220eefe --- /dev/null +++ b/utils.html @@ -0,0 +1,2860 @@ + + + + + + + + Utilities — Nengo 3.1.1.dev0 docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ + + +
+
+
+
+ + +
+

Note

+

+ This documentation is for a development version. + + Click here for the latest stable release (v3.1.0). + +

+
+ + +
+

Utilities

+

Nengo provides various utility functions in the nengo.utils module. +In general, anything in this module +should not be considered part of the core API. +This functionality may move or change +with less notice than we provide for the core API, +and it is not as thoroughly tested.

+

These utilities are mainly designed for internal use within Nengo, +but we expose them as they may be useful to some users.

+
+

Builder

+

Helper functions for backends generating their own Builder system.

+ ++++ + + + + + + + + + + + + + + + + + + + + +

nengo.utils.builder.full_transform

Compute the full transform matrix for a Dense connection transform.

nengo.utils.builder.default_n_eval_points

A heuristic to determine an appropriate number of evaluation points.

nengo.utils.builder.objs_and_connections

Given a Network, returns all (ensembles + nodes, connections).

nengo.utils.builder.generate_graphviz

Moved to nengo_extras.graphviz.

nengo.utils.builder.remove_passthrough_nodes

Returns a version of the model without passthrough Nodes.

nengo.utils.builder.find_all_io

Build up a list of all inputs and outputs for each object.

+
+
+nengo.utils.builder.full_transform(conn, slice_pre=True, slice_post=True, allow_scalars=True)[source]
+

Compute the full transform matrix for a Dense connection transform.

+
+
Parameters
+
+
connConnection

The connection for which to compute the full transform.

+
+
slice_preboolean, optional (True)

Whether to compute the pre slice as part of the transform.

+
+
slice_postboolean, optional (True)

Whether to compute the post slice as part of the transform.

+
+
allow_scalarsboolean, optional (True)

If true (default), will not make scalars into full transforms when +not using slicing, since these work fine in the reference builder. +If false, these scalars will be turned into scaled identity matrices.

+
+
+
+
+
+ +
+
+nengo.utils.builder.default_n_eval_points(n_neurons, dimensions)[source]
+

A heuristic to determine an appropriate number of evaluation points.

+

This is used by builders to generate a sufficiently large sample +from a vector space in order to solve for accurate decoders.

+
+
Parameters
+
+
n_neuronsint

The number of neurons in the ensemble that will be sampled. +For a connection, this would be the number of neurons in the +pre ensemble.

+
+
dimensionsint

The number of dimensions in the ensemble that will be sampled. +For a connection, this would be the number of dimensions in the +pre ensemble.

+
+
+
+
+
+ +
+
+nengo.utils.builder.objs_and_connections(network)[source]
+

Given a Network, returns all (ensembles + nodes, connections).

+
+ +
+
+nengo.utils.builder.generate_graphviz(*args, **kwargs)[source]
+

Moved to nengo_extras.graphviz.

+
+ +
+
+nengo.utils.builder.remove_passthrough_nodes(objs, connections, create_connection_fn=None)[source]
+

Returns a version of the model without passthrough Nodes.

+

For some backends (such as SpiNNaker), it is useful to remove Nodes that +have ‘None’ as their output. These nodes simply sum their inputs and +use that as their output. These nodes are defined purely for organizational +purposes and should not affect the behaviour of the model. For example, +the ‘input’ and ‘output’ Nodes in an EnsembleArray, which are just meant to +aggregate data.

+

Note that removing passthrough nodes can simplify a model and may be useful +for other backends as well. For example, an EnsembleArray connected to +another EnsembleArray with an identity matrix as the transform +should collapse down to D Connections between the corresponding Ensembles +inside the EnsembleArrays.

+
+
Parameters
+
+
objslist of Nodes and Ensembles

All the objects in the model

+
+
connectionslist of Connections

All the Connections in the model

+
+
Returns the objs and connections of the resulting model. The passthrough
+
Nodes will be removed, and the Connections that interact with those Nodes
+
will be replaced with equivalent Connections that don’t interact with those
+
Nodes.
+
+
+
+
+ +
+
+nengo.utils.builder.find_all_io(connections)[source]
+

Build up a list of all inputs and outputs for each object.

+
+ +
+
+

Cache

+

Utilities to convert to and from bytes.

+

Used by nengo.rc in order to present file sizes to users in +human-readable formats.

+

This code adapted from +https://web.archive.org/web/20200817051754/http://code.activestate.com/recipes/578019-bytes-to-human-human-to-bytes-converter/?in=user-4178764 +under the MIT License.

+ ++++ + + + + + + + + + + + +

nengo.utils.cache.bytes2human

Convert from a size in bytes to a human readable string.

nengo.utils.cache.human2bytes

Convert from a human readable string to a size in bytes.

nengo.utils.cache.byte_align

Returns the int larger than size aligned to alginment bytes.

+
+
+nengo.utils.cache.bytes2human(n, fmt='%(value).1f %(symbol)s')[source]
+

Convert from a size in bytes to a human readable string.

+

Examples

+
from nengo.utils.cache import bytes2human
+
+print(bytes2human(10000))
+print(bytes2human(100001221))
+
+
+
9.8 KB
+95.4 MB
+
+
+
+ +
+
+nengo.utils.cache.human2bytes(s)[source]
+

Convert from a human readable string to a size in bytes.

+

Examples

+
from nengo.utils.cache import human2bytes
+
+print(human2bytes('1 MB'))
+print(human2bytes('1 GB'))
+
+
+
1048576
+1073741824
+
+
+
+ +
+
+nengo.utils.cache.byte_align(size, alignment)[source]
+

Returns the int larger than size aligned to alginment bytes.

+
+ +
+
+

Connection

+ ++++ + + + + + +

nengo.utils.connection.eval_point_decoding

Get the targets and actual decoded values for a set of eval points.

+
+
+nengo.utils.connection.eval_point_decoding(conn, sim, eval_points=None)[source]
+

Get the targets and actual decoded values for a set of eval points.

+

This function evaluates the static decoding (i.e. using the neuron type’s +rates function) of a connection for a given set of evaluation points.

+
+
Parameters
+
+
connConnection

The Connection to evaluate the decoding of.

+
+
simSimulator

A Nengo simulator storing the built connection.

+
+
eval_pointsarray_like (N, E) (optional)

An N x E array of evaluation points to evaluate the decoding for, where +N is the number of points and E is the dimensionality of the input +ensemble (i.e. conn.size_in). If None (default), use the connection’s +training evaluation points.

+
+
+
+
Returns
+
+
eval_pointsndarray (N, E)

A shallow copy of the evaluation points used. E is the dimensionality +of the connection input ensemble (i.e. conn.size_in).

+
+
targetsndarray (N, D)

The target function value at each evaluation point.

+
+
decodedndarray (N, D)

The decoded function value at each evaluation point.

+
+
+
+
+
+ +
+
+

Ensemble

+ ++++ + + + + + + + + + + + +

nengo.utils.ensemble.tuning_curves

Calculates the tuning curves of an ensemble.

nengo.utils.ensemble.response_curves

Calculates the response curves of an ensemble.

nengo.utils.ensemble.sorted_neurons

Sort neurons in an ensemble by encoder and intercept.

+
+
+nengo.utils.ensemble.tuning_curves(ens, sim, inputs=None)[source]
+

Calculates the tuning curves of an ensemble.

+

That is the neuron responses in dependence of the vector represented by the +ensemble.

+

For 1-dimensional ensembles, the unpacked return value of this function +can be passed directly to matplotlib.pyplot.plot.

+
+
Parameters
+
+
ensnengo.Ensemble

Ensemble to calculate the tuning curves of.

+
+
simnengo.Simulator

Simulator providing information about the built ensemble. (An unbuilt +ensemble does not have tuning curves assigned to it.)

+
+
inputssequence of ndarray, optional

The inputs at which the tuning curves will be evaluated. For each of +the D ensemble dimensions one array of dimensionality D is needed. +The output of numpy.meshgrid() with indexing='ij' is in the +right format.

+
+
+
+
Returns
+
+
inputssequence of ndarray

The passed or auto-generated inputs.

+
+
activitiesndarray

The activities of the individual neurons given the inputs. +For ensembles with 1 dimension, the rows correspond to the inputs +and the columns to individual neurons. +For ensembles with > 1 dimension, the last dimension enumerates the +neurons, the remaining dimensions map to inputs.

+
+
+
+
+
+

See also

+
+
response_curves
+
+
+
+ +
+
+nengo.utils.ensemble.response_curves(ens, sim, inputs=None)[source]
+

Calculates the response curves of an ensemble.

+

That is the neuron responses in dependence of an already encoded value. +This corresponds to the tuning curves along the neuron’s preferred +directions.

+
+
Parameters
+
+
ensnengo.Ensemble

Ensemble to calculate the response curves of.

+
+
simnengo.Simulator

Simulator providing information about the built ensemble. (An unbuilt +ensemble does not have response curves assigned to it.)

+
+
inputs1d array, optional

The inputs between -1 and 1 at which the neuron responses will be +evaluated. They are assumed to be along each neuron’s preferred +direction.

+
+
+
+
Returns
+
+
inputs1d array

The passed or auto-generated inputs.

+
+
activities2d array

The activities of the individual neurons given the inputs. The rows +map to inputs and the columns to the neurons in the ensemble.

+
+
+
+
+
+

See also

+
+
tuning_curves
+
+
+
+ +
+
+nengo.utils.ensemble.sorted_neurons(ensemble, sim, iterations=100, seed=None)[source]
+

Sort neurons in an ensemble by encoder and intercept.

+
+
Parameters
+
+
ensembleEnsemble

The population of neurons to be sorted.

+
+
simSimulator

Simulator providing information about the built ensemble.

+
+
iterations: int

The number of times to iterate during the sort.

+
+
seed: float

A random number seed.

+
+
+
+
Returns
+
+
indices: ndarray

An array with sorted indices into the neurons in the ensemble

+
+
+
+
+

Notes

+

The algorithm is for each encoder in the initial set, randomly +pick another encoder and check to see if swapping those two +encoders would reduce the average difference between the +encoders and their neighbours. Difference is measured as the +dot product. Each encoder has four neighbours (N, S, E, W), +except for the ones on the edges which have fewer (no wrapping). +This algorithm is repeated iterations times, so a total of +iterations*N swaps are considered.

+

Examples

+

You can use this to generate an array of sorted indices for plotting. This +can be done after collecting the data. E.g.

+
from nengo.utils.ensemble import sorted_neurons
+from nengo.utils.matplotlib import rasterplot
+
+with nengo.Network() as net:
+    ens = nengo.Ensemble(10, 1)
+    ens_p = nengo.Probe(ens.neurons)
+
+with nengo.Simulator(net) as sim:
+    sim.run(0.1)
+
+    indices = sorted_neurons(ens, sim)
+    rasterplot(sim.trange(), sim.data[ens_p][:, indices])
+
+
+
+ +
+
+

Filter design

+

Functions for filter design.

+
+

These are borrowed from SciPy and used under their license:

+

Copyright (c) 2001, 2002 Enthought, Inc. +All rights reserved.

+

Copyright (c) 2003-2012 SciPy Developers. +All rights reserved.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met:

+
+
    +

  1. Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer.

    2. +

  2. Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution.

    3. +

  3. Neither the name of Enthought nor the names of the SciPy Developers +may be used to endorse or promote products derived from this software +without specific prior written permission.

    4. +
+
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS +BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, +OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +THE POSSIBILITY OF SUCH DAMAGE.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.utils.filter_design.BadCoefficients

nengo.utils.filter_design.tf2zpk

Return zero, pole, gain (z,p,k) representation from a numerator, denominator representation of a linear filter.

nengo.utils.filter_design.zpk2tf

Return polynomial transfer function representation from zeros and poles

nengo.utils.filter_design.normalize

Normalize polynomial representation of a transfer function.

nengo.utils.filter_design.tf2ss

Transfer function to state-space representation.

nengo.utils.filter_design.abcd_normalize

Check state-space matrices and ensure they are rank-2.

nengo.utils.filter_design.ss2tf

State-space to transfer function.

nengo.utils.filter_design.zpk2ss

Zero-pole-gain representation to state-space representation

nengo.utils.filter_design.ss2zpk

State-space representation to zero-pole-gain representation.

nengo.utils.filter_design.cont2discrete

Transform a continuous to a discrete state-space system.

+
+
+exception nengo.utils.filter_design.BadCoefficients[source]
+
+ +
+
+nengo.utils.filter_design.tf2zpk(b, a)[source]
+

Return zero, pole, gain (z,p,k) representation from a numerator, +denominator representation of a linear filter.

+
+
Parameters
+
+
bndarray

Numerator polynomial.

+
+
andarray

Denominator polynomial.

+
+
+
+
Returns
+
+
zndarray

Zeros of the transfer function.

+
+
pndarray

Poles of the transfer function.

+
+
kfloat

System gain.

+
+
+
+
+

Notes

+

If some values of b are too close to 0, they are removed. In that case, +a BadCoefficients warning is emitted.

+
+ +
+
+nengo.utils.filter_design.zpk2tf(z, p, k)[source]
+

Return polynomial transfer function representation from zeros +and poles

+
+
Parameters
+
+
zndarray

Zeros of the transfer function.

+
+
pndarray

Poles of the transfer function.

+
+
kfloat

System gain.

+
+
+
+
Returns
+
+
bndarray

Numerator polynomial.

+
+
andarray

Denominator polynomial.

+
+
+
+
+
+ +
+
+nengo.utils.filter_design.normalize(b, a)[source]
+

Normalize polynomial representation of a transfer function.

+

If values of b are too close to 0, they are removed. In that case, a +BadCoefficients warning is emitted.

+
+ +
+
+nengo.utils.filter_design.tf2ss(num, den)[source]
+

Transfer function to state-space representation.

+
+
Parameters
+
+
num, denarray_like

Sequences representing the numerator and denominator polynomials. +The denominator needs to be at least as long as the numerator.

+
+
+
+
Returns
+
+
A, B, C, Dndarray

State space representation of the system.

+
+
+
+
+
+ +
+
+nengo.utils.filter_design.abcd_normalize(A=None, B=None, C=None, D=None)[source]
+

Check state-space matrices and ensure they are rank-2.

+

If enough information on the system is provided, that is, enough +properly-shaped arrays are passed to the function, the missing ones +are built from this information, ensuring the correct number of +rows and columns. Otherwise a ValueError is raised.

+
+
Parameters
+
+
A, B, C, Darray_like, optional

State-space matrices. All of them are None (missing) by default.

+
+
+
+
Returns
+
+
A, B, C, Darray

Properly shaped state-space matrices.

+
+
+
+
Raises
+
+
ValueError

If not enough information on the system was provided.

+
+
+
+
+
+ +
+
+nengo.utils.filter_design.ss2tf(A, B, C, D, input=0)[source]
+

State-space to transfer function.

+
+
Parameters
+
+
A, B, C, Dndarray

State-space representation of linear system.

+
+
inputint, optional

For multiple-input systems, the input to use.

+
+
+
+
Returns
+
+
num2-D ndarray

Numerator(s) of the resulting transfer function(s). num has one row +for each of the system’s outputs. Each row is a sequence representation +of the numerator polynomial.

+
+
den1-D ndarray

Denominator of the resulting transfer function(s). den is a sequence +representation of the denominator polynomial.

+
+
+
+
+
+ +
+
+nengo.utils.filter_design.zpk2ss(z, p, k)[source]
+

Zero-pole-gain representation to state-space representation

+
+
Parameters
+
+
z, psequence

Zeros and poles.

+
+
kfloat

System gain.

+
+
+
+
Returns
+
+
A, B, C, Dndarray

State-space matrices.

+
+
+
+
+
+ +
+
+nengo.utils.filter_design.ss2zpk(A, B, C, D, input=0)[source]
+

State-space representation to zero-pole-gain representation.

+
+
Parameters
+
+
A, B, C, Dndarray

State-space representation of linear system.

+
+
inputint, optional

For multiple-input systems, the input to use.

+
+
+
+
Returns
+
+
z, psequence

Zeros and poles.

+
+
kfloat

System gain.

+
+
+
+
+
+ +
+
+nengo.utils.filter_design.cont2discrete(sys, dt, method='zoh', alpha=None)[source]
+

Transform a continuous to a discrete state-space system.

+
+
Parameters
+
+
sysa tuple describing the system.

The following gives the number of elements in the tuple and +the interpretation:

+
+
    +

  • 2: (num, den)

    • +

  • 3: (zeros, poles, gain)

    • +

  • 4: (A, B, C, D)

    • +
+
+
+
dtfloat

The discretization time step.

+
+
method{“gbt”, “bilinear”, “euler”, “backward_diff”, “zoh”}

Which method to use:

+
+
    +

  • gbt: generalized bilinear transformation

    • +

  • bilinear: Tustin’s approximation (“gbt” with alpha=0.5)

    • +

  • euler: Euler (or forward differencing) method (“gbt” with alpha=0)

    • +

  • backward_diff: Backwards differencing (“gbt” with alpha=1.0)

    • +

  • zoh: zero-order hold (default)

    • +
+
+
+
alphafloat within [0, 1]

The generalized bilinear transformation weighting parameter, which +should only be specified with method=”gbt”, and is ignored otherwise

+
+
+
+
Returns
+
+
sysdtuple containing the discrete system

Based on the input type, the output will be of the form

+
    +

  • (num, den, dt) for transfer function input

    • +

  • (zeros, poles, gain, dt) for zeros-poles-gain input

    • +

  • (A, B, C, D, dt) for state-space system input

    • +
+
+
+
+
+

Notes

+

By default, the routine uses a Zero-Order Hold (zoh) method to perform +the transformation. Alternatively, a generalized bilinear transformation +may be used, which includes the common Tustin’s bilinear approximation, +an Euler’s method technique, or a backwards differencing technique.

+

The Zero-Order Hold (zoh) method is based on [1], the generalized bilinear +approximation is based on [2] and [3].

+

References

+
+
1
+

https://en.wikipedia.org/wiki/Discretization +#Discretization_of_linear_state_space_models

+
+
2
+

http://techteach.no/publications/discretetime_signals_systems/discrete.pdf

+
+
3
+

G. Zhang, X. Chen, and T. Chen, Digital redesign via the generalized +bilinear transformation, Int. J. Control, vol. 82, no. 4, pp. 741-754, 2009.

+
+
+
+ +
+
+

Functions

+ ++++ + + + + + +

nengo.utils.functions.function_name

Returns the name of a function.

+
+
+nengo.utils.functions.function_name(func)[source]
+

Returns the name of a function.

+

Unlike accessing func.__name__, this function is robust to the +different types of objects that can be considered a function in Nengo.

+
+
Parameters
+
+
funccallable or array_like

Object used as function argument.

+
+
+
+
Returns
+
+
str

Name of function object.

+
+
+
+
+
+ +
+
+

Graphs

+

Simple graph manipulation algorithms.

+

Nengo models are essentially graphs where ensembles, nodes, and networks +are graph vertices, and connections are edges. We make use of this fact +in some places in the code; this file contains functions to help with that.

+
+

toposort and reverse_edges are adapted from Theano (theano/gof/sched.py). +This modified code is included under the terms of their license:

+

Theano is copyright (c) 2008–2013 Theano Development Team.

+

Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met:

+
    +

  • Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer.

    • +

  • Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution.

    • +

  • Neither the name of Theano nor the names of its contributors may be +used to endorse or promote products derived from this software without +specific prior written permission.

    • +
+

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ‘’AS IS’’ AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+ ++++ + + + + + + + + + + + + + + + + + +

nengo.utils.graphs.BidirectionalDAG

Directed acyclic graph supporting bidirectional traversal.

nengo.utils.graphs.toposort

Topological sort algorithm by Kahn [1].

nengo.utils.graphs.transitive_closure

Constructs the transitive closure of a directed acyclic graph (DAG).

nengo.utils.graphs.reverse_edges

Reverses direction of dependence dict.

nengo.utils.graphs.add_edges

Add new edges to graph.

+
+
+class nengo.utils.graphs.BidirectionalDAG(forward)[source]
+

Directed acyclic graph supporting bidirectional traversal.

+
+
Parameters
+
+
forwarddict

Forward edges for each vertex in the form +{1: {2, 3}, 2: {3}, 3: set()}.

+
+
+
+
Attributes
+
+
forwarddict

Maps vertices to edges in forward direction.

+
+
backwarddict

Maps vertices to edges in backward direction.

+
+
+
+
+
+
+merge(vertices, merged_vertex)[source]
+

Merges vertices in the graph.

+
+
Parameters
+
+
verticesset

Vertices that are being merged.

+
+
merged_vertex

The vertex that replaces vertices.

+
+
+
+
+
+ +
+ +
+
+nengo.utils.graphs.toposort(edges)[source]
+

Topological sort algorithm by Kahn [1].

+

Complexity is O(nodes + vertices).

+
+
Parameters
+
+
edgesdict

Dict of the form {a: {b, c}} where b and c depend on a

+
+
+
+
Returns
+
+
An ordered list of nodes that satisfy the dependencies of edges
+
+
+
+

Notes

+

Closely follows the wikipedia page [2].

+

References

+
+
1(1,2)
+

Kahn, Arthur B. (1962), “Topological sorting of large networks”, +Communications of the ACM

+
+
2
+

https://en.wikipedia.org/wiki/Toposort#Algorithms

+
+
+

Examples

+
from nengo.utils.graphs import toposort
+
+print(toposort({1: {2, 3}, 2: {3}, 3: set()}))
+
+
+
[1, 2, 3]
+
+
+
+ +
+
+nengo.utils.graphs.transitive_closure(edges, topo_sorted=None)[source]
+

Constructs the transitive closure of a directed acyclic graph (DAG).

+

The complexity is O(nodes + vertices).

+
+
Parameters
+
+
edgesdict

Dict of the form {a: {b, c}} where b and c depend on a. +Must not contain cycles.

+
+
topo_sortedsequence, optional

The topological sorting of the vertices. If not passed in, the +algorithm will do a topological sort.

+
+
+
+
Returns
+
+
The transitive closure using the same data structure as edges: a dict
+
of the form {a: {b, c}} where b and c are nodes that either
+
directly or indirectly depend on a.
+
+
+
+
+ +
+
+nengo.utils.graphs.reverse_edges(edges)[source]
+

Reverses direction of dependence dict.

+
+
Parameters
+
+
edgesdict

Dict of the form {a: {b, c}, b: set(), c: set()} where b and c depend +on a.

+
+
+
+
Returns
+
+
Dict of the form {a: set(), b: {a}, c: {a}} where b and c depend on a.
+
+
+
+
+ +
+
+nengo.utils.graphs.add_edges(edges, new_edge_iter)[source]
+

Add new edges to graph.

+
+ +
+
+

IPython

+

Functions for easy interactions with IPython and IPython notebooks.

+ ++++ + + + + + + + + + + + + + + + + + +

nengo.utils.ipython.check_ipy_version

Check that ipython version is >= min_version.

nengo.utils.ipython.hide_input

Hide the input of the Jupyter notebook input block this is executed in.

nengo.utils.ipython.load_notebook

Load notebook from file.

nengo.utils.ipython.export_py

Convert notebook to Python script.

nengo.utils.ipython.iter_cells

Iterate over cells of a notebok.

+
+
+nengo.utils.ipython.check_ipy_version(min_version)[source]
+

Check that ipython version is >= min_version.

+
+ +
+
+nengo.utils.ipython.hide_input()[source]
+

Hide the input of the Jupyter notebook input block this is executed in.

+

Returns a link to toggle the visibility of the input block.

+
+ +
+
+nengo.utils.ipython.load_notebook(nb_path)[source]
+

Load notebook from file.

+
+ +
+
+nengo.utils.ipython.export_py(nb, dest_path=None)[source]
+

Convert notebook to Python script.

+

Optionally saves script to dest_path.

+
+ +
+
+nengo.utils.ipython.iter_cells(nb, cell_type='code')[source]
+

Iterate over cells of a notebok.

+
+ +
+
+

Lock

+ ++++ + + + + + +

nengo.utils.lock.FileLock

Lock access to a file (for multithreading).

+
+
+class nengo.utils.lock.FileLock(filename, timeout=10.0, poll=0.1)[source]
+

Lock access to a file (for multithreading).

+
+ +
+
+

Magic

+
+
+nengo.utils.magic.decorator(wrapper)[source]
+

Decorate decorators.

+

This imposes a particular style of writing descriptors. +The descriptor must accept four positional arguments:

+
    +

  • wrapped: the function being wrapped

    • +

  • instance: the instance that is bound to the function in the case of +bound functions (None in the case of plain functions)

    • +

  • args: the positional arguments passed to the wrapped function

    • +

  • kwargs: the keyword arguments passed to the wrapped function

    • +
+

Examples

+

Decorating a normal function (i.e., instance will always be None):

+
from nengo.utils.magic import decorator
+
+@decorator
+def my_decorator(wrapped, instance, args, kwargs):
+    return wrapped(*args, **kwargs)
+
+@my_decorator
+def f():
+    return 1
+
+
+

Decorating a bound function:

+
@decorator
+def my_decorator(wrapped, instance, args, kwargs):
+    return wrapped(*args, **kwargs)
+
+class MyClass:
+    @my_decorator
+    def f(self):
+        return self.num + 1
+
+
+
+ +
+
+

Matplotlib

+ ++++ + + + + + + + + + + + + + + + + + +

nengo.utils.matplotlib.get_color_cycle

Get matplotlib colour cycle.

nengo.utils.matplotlib.set_color_cycle

Set matplotlib colour cycle.

nengo.utils.matplotlib.axis_size

Get axis width and height in pixels.

nengo.utils.matplotlib.implot

Image plot of general data (like imshow but with non-pixel axes).

nengo.utils.matplotlib.rasterplot

Generate a raster plot of the provided spike data.

+
+
+nengo.utils.matplotlib.get_color_cycle()[source]
+

Get matplotlib colour cycle.

+
+ +
+
+nengo.utils.matplotlib.set_color_cycle(colors, ax=None)[source]
+

Set matplotlib colour cycle.

+
+ +
+
+nengo.utils.matplotlib.axis_size(ax=None)[source]
+

Get axis width and height in pixels.

+

Based on a StackOverflow response: +https://stackoverflow.com/questions/19306510/determine-matplotlib-axis-size-in-pixels

+
+
Parameters
+
+
axaxis object

The axes to determine the size of. Defaults to current axes.

+
+
+
+
Returns
+
+
widthfloat

Width of axes in pixels.

+
+
heightfloat

Height of axes in pixels.

+
+
+
+
+
+ +
+
+nengo.utils.matplotlib.implot(plt_, x, y, Z, ax=None, colorbar=True, **kwargs)[source]
+

Image plot of general data (like imshow but with non-pixel axes).

+
+
Parameters
+
+
plt_plot object

Plot object, typically matplotlib.pyplot.

+
+
x(M,) array_like

Vector of x-axis points, must be linear (equally spaced).

+
+
y(N,) array_like

Vector of y-axis points, must be linear (equally spaced).

+
+
Z(M, N) array_like

Matrix of data to be displayed, the value at each (x, y) point.

+
+
axaxis object (optional)

A specific axis to plot on (defaults to plt.gca()).

+
+
colorbar: boolean (optional)

Whether to plot a colorbar.

+
+
**kwargs

Additional arguments for ax.imshow.

+
+
+
+
+
+ +
+
+nengo.utils.matplotlib.rasterplot(time, spikes, ax=None, use_eventplot=False, **kwargs)[source]
+

Generate a raster plot of the provided spike data.

+
+
Parameters
+
+
timearray

Time data from the simulation

+
+
spikesarray

The spike data with columns for each neuron and 1s indicating spikes

+
+
axmatplotlib.axes.Axes, optional

The figure axes to plot into. If None, we will use current axes.

+
+
use_eventplotboolean, optional

Whether to use the new Matplotlib eventplot routine. It is slower +and makes larger image files, so we do not use it by default.

+
+
+
+
Returns
+
+
axmatplotlib.axes.Axes

The axes that were plotted into

+
+
+
+
+

Examples

+
from nengo.utils.matplotlib import rasterplot
+
+with nengo.Network() as net:
+    a = nengo.Ensemble(20, 1)
+    p = nengo.Probe(a.neurons)
+
+with nengo.Simulator(net) as sim:
+    sim.run(1)
+
+rasterplot(sim.trange(), sim.data[p])
+
+
+
+ +
+
+

NCO

+

Implementation of the Nengo cache object (NCO) protocol.

+

Nengo cache objects store a Numpy array and some associated, picklable Python +object in a single, uncompressed file. These files are not platform independent +as they are optimized for fast reading and writing, and cached data is not +supposed to be shared across platforms.

+

The protocol version 0 is as follows:

+
    +
  • +
    A header consisting of:
      +

    • 3 bytes with the magic string ‘NCO’

      • +

    • 1 unsigned byte indicating the protocol version

      • +

    • unsigned long int denoting the start of the Python object data

      • +

    • unsigned long int denoting the end of the Python object data

      • +

    • unsigned long int denoting the start of the array data

      • +

    • unsigned long int denoting the end of the array data

      • +
    +
    +
    +
    • +

  • Potentially some padding bytes.

    • +

  • The Python object data pickled by the (c)pickle module using the highest +available protocol.

    • +

  • Potentially some padding bytes.

    • +

  • The array data in NPY format.

    • +
+

Files will be written with padding to have both the Python object data and the +array data an alignment of 16 bytes.

+

The Numpy NPY format is documented here: +https://numpy.org/devdocs/reference/generated/numpy.lib.format.html

+

As of legacy version 1 of the cache, multiple NCO files will be concatenated +into one file. The start and end of each subfile will be stored in a cache +index, but can also be recovered from reading the headers of the NCO files in +order as each one gives the start of the next header (corresponding to the +end of the array data).

+ ++++ + + + + + + + + + + + +

nengo.utils.nco.Subfile

A file-like object for limiting reads to a subrange of a file.

nengo.utils.nco.write

Writes a Nengo cache object.

nengo.utils.nco.read

Reads a Nengo cache object.

+
+
+class nengo.utils.nco.Subfile(fileobj, start, end)[source]
+

A file-like object for limiting reads to a subrange of a file.

+

This class only supports reading and seeking. Writing is not supported.

+
+
Parameters
+
+
fileobjfile-like object

Complete files.

+
+
startint

Offset of the first readable position in the file.

+
+
endint

Offset of the last readable position + 1 in the file.

+
+
+
+
+
+ +
+
+nengo.utils.nco.write(fileobj, metadata, array)[source]
+

Writes a Nengo cache object.

+
+
Parameters
+
+
fileobjfile-like object

File object to write the data to.

+
+
metadataobject

Python object with metadata (will be pickled).

+
+
arrayndarray

Numpy array with the actual data to store.

+
+
+
+
+
+ +
+
+nengo.utils.nco.read(fileobj)[source]
+

Reads a Nengo cache object.

+
+
Parameters
+
+
fileobjfile-like object

The file object to read from.

+
+
+
+
Returns
+
+
metadata, array

Returns a tuple with the Python object containing the metadata as first +element and the array with the actual data as second element.

+
+
+
+
+
+ +
+
+

Network

+ ++++ + + + + + + + + +

nengo.utils.network.with_self

Wraps a method with with network:.

nengo.utils.network.activate_direct_mode

Activates direct mode for a network.

+
+
+nengo.utils.network.with_self(method, network, args, kwargs)[source]
+

Wraps a method with with network:.

+

This makes it easy to add methods to a network that create new +Nengo objects. Instead of writing with self at the top of the method +and indenting everything over, you can instead use this decorator.

+

Examples

+

The two methods in the following class do the same thing:

+
from nengo.utils.network import with_self
+
+class MyNetwork(nengo.Network):
+    def add_one_1(self):
+        with self:
+            node = nengo.Node(output=1)
+
+    @with_self
+    def add_one_2(self):
+        node = nengo.Node(output=1)
+
+
+
+ +
+
+nengo.utils.network.activate_direct_mode(network)[source]
+

Activates direct mode for a network.

+

This sets the neuron type of all ensembles to a nengo.Direct +instance unless:

+
    +

  • there is a connection to or from the ensemble’s neurons

    • +

  • there is a probe on an ensemble’s neurons

    • +

  • the ensemble has a connection with a learning rule attached.

    • +
+
+
Parameters
+
+
networkNetwork

Network to activate direct mode for.

+
+
+
+
+
+ +
+
+

Numpy

+

Extra functions to extend the capabilities of Numpy.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.utils.numpy.is_spmatrix

Check if obj is a sparse matrix.

nengo.utils.numpy.is_integer

Check if obj is an integer type.

nengo.utils.numpy.is_iterable

Check if obj is an iterable.

nengo.utils.numpy.is_number

Check if obj is a numeric type.

nengo.utils.numpy.is_array

Check if obj is a numpy array.

nengo.utils.numpy.is_array_like

Check if obj is an array like object.

nengo.utils.numpy.compare

Return -1/0/1 if a is less/equal/greater than b.

nengo.utils.numpy.as_shape

Return a tuple if x is iterable or (x,) if x is integer.

nengo.utils.numpy.broadcast_shape

Pad a shape with ones following standard Numpy broadcasting.

nengo.utils.numpy.array

Create numpy array with some extra configuration.

nengo.utils.numpy.array_hash

Simple fast array hash function.

nengo.utils.numpy.array_offset

Get offset of array data from base data in bytes.

nengo.utils.numpy.norm

Compute the Euclidean norm.

nengo.utils.numpy.meshgrid_nd

Multidimensional meshgrid.

nengo.utils.numpy.rms

Compute the root-mean-square amplitude.

nengo.utils.numpy.rmse

Compute the root-mean-square error.

nengo.utils.numpy.nrmse

Compute the root-mean-square (RMS) error normalized by the RMS of b.

+
+
+nengo.utils.numpy.is_spmatrix(obj)[source]
+

Check if obj is a sparse matrix.

+
+ +
+
+nengo.utils.numpy.is_integer(obj)[source]
+

Check if obj is an integer type.

+
+ +
+
+nengo.utils.numpy.is_iterable(obj)[source]
+

Check if obj is an iterable.

+
+ +
+
+nengo.utils.numpy.is_number(obj, check_complex=False)[source]
+

Check if obj is a numeric type.

+
+ +
+
+nengo.utils.numpy.is_array(obj)[source]
+

Check if obj is a numpy array.

+
+ +
+
+nengo.utils.numpy.is_array_like(obj)[source]
+

Check if obj is an array like object.

+
+ +
+
+nengo.utils.numpy.compare(a, b)[source]
+

Return -1/0/1 if a is less/equal/greater than b.

+
+ +
+
+nengo.utils.numpy.as_shape(x, min_dim=0)[source]
+

Return a tuple if x is iterable or (x,) if x is integer.

+
+ +
+
+nengo.utils.numpy.broadcast_shape(shape, length)[source]
+

Pad a shape with ones following standard Numpy broadcasting.

+
+ +
+
+nengo.utils.numpy.array(x, dims=None, min_dims=0, readonly=False, **kwargs)[source]
+

Create numpy array with some extra configuration.

+

This is a wrapper around np.array.

+

Unlike np.array, the additional single-dimensional indices added by +dims or min_dims will appear at the end of the shape (for example, +array([1, 2, 3], dims=4).shape == (3, 1, 1, 1)).

+
+
Parameters
+
+
dimsint or None

If not None, force the output array to have exactly this many indices. +If the input has more than this number of indices, this throws an error.

+
+
min_dimsint

Force the output array to have at least this many indices +(ignored if dims is not None).

+
+
readonlybool

Make the output array read-only.

+
+
**kwargs

Additional keyword arguments to pass to np.array.

+
+
+
+
+
+ +
+
+nengo.utils.numpy.array_hash(a, n=100)[source]
+

Simple fast array hash function.

+

For arrays with size larger than n, pick n elements at random +to hash. This strategy should work well for dense arrays, but for +sparse arrays (those with few non-zero elements) it is more likely to +give hash collisions.

+

For sparse matrices, we apply the same logic on the underlying elements +(data, indices) of the sparse matrix.

+
+ +
+
+nengo.utils.numpy.array_offset(x)[source]
+

Get offset of array data from base data in bytes.

+
+ +
+
+nengo.utils.numpy.norm(x, axis=None, keepdims=False)[source]
+

Compute the Euclidean norm.

+
+
Parameters
+
+
xarray_like

Array to compute the norm over.

+
+
axisNone or int or tuple of ints, optional

Axis or axes to sum across. None sums all axes. See np.sum.

+
+
keepdimsbool, optional

If True, the reduced axes are left in the result. See np.sum in +newer versions of Numpy (>= 1.7).

+
+
+
+
+
+ +
+
+nengo.utils.numpy.meshgrid_nd(*args)[source]
+

Multidimensional meshgrid.

+
+ +
+
+nengo.utils.numpy.rms(x, axis=None, keepdims=False)[source]
+

Compute the root-mean-square amplitude.

+
+
Parameters
+
+
xarray_like

Array to compute RMS amplitude over.

+
+
axisNone or int or tuple of ints, optional

Axis or axes to sum across. None sums all axes. See np.sum.

+
+
keepdimsbool, optional

If True, the reduced axes are left in the result. See np.sum in +newer versions of Numpy (>= 1.7).

+
+
+
+
+
+ +
+
+nengo.utils.numpy.rmse(x, y, axis=None, keepdims=False)[source]
+

Compute the root-mean-square error.

+

Equivalent to rms(x - y, axis=axis, keepdims=keepdims).

+
+
Parameters
+
+
x, yarray_like

Arrays to compute RMS amplitude over.

+
+
axisNone or int or tuple of ints, optional

Axis or axes to sum across. None sums all axes. See np.sum.

+
+
keepdimsbool, optional

If True, the reduced axes are left in the result. See np.sum in +newer versions of Numpy (>= 1.7).

+
+
+
+
+
+ +
+
+nengo.utils.numpy.nrmse(a, b, axis=None, keepdims=False)[source]
+

Compute the root-mean-square (RMS) error normalized by the RMS of b.

+

Equivalent to rms(a - b, **kwargs) / rms(b, **kwargs)

+
+
Parameters
+
+
a, barray_like

Arrays to compute RMS error over, normalized by the rms amplitude of b.

+
+
axisNone or int or tuple of ints, optional

Axis or axes to sum across. None sums all axes. See np.sum.

+
+
keepdimsbool, optional

If True, the reduced axes are left in the result. See np.sum in +newer versions of Numpy (>= 1.7).

+
+
+
+
+
+ +
+
+

Paths

+ ++++ + + +
+
+
+

Progress bars

+

Utilities for progress tracking and display to the user.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.utils.progress.MemoryLeakWarning

nengo.utils.progress.timestamp2timedelta

Convert timestamp to timedelta.

nengo.utils.progress.Progress

Stores and tracks information about the progress of some process.

nengo.utils.progress.ProgressBar

Visualizes the progress of a process.

nengo.utils.progress.NoProgressBar

A progress bar that does not display anything.

nengo.utils.progress.TerminalProgressBar

A progress bar that is displayed as ASCII output on stdout.

nengo.utils.progress.VdomProgressBar

A progress bar using a virtual DOM representation.

nengo.utils.progress.HtmlProgressBar

A progress bar using a HTML representation.

nengo.utils.progress.VdomOrHtmlProgressBar

Progress bar using the VDOM or HTML progress bar.

nengo.utils.progress.IPython5ProgressBar

ProgressBar for IPython>=5 environments.

nengo.utils.progress.WriteProgressToFile

Writes progress to a file.

nengo.utils.progress.AutoProgressBar

Suppresses the progress bar unless the ETA exceeds a threshold.

nengo.utils.progress.ProgressTracker

Tracks the progress of some process with a progress bar.

nengo.utils.progress.get_default_progressbar

The default progress bar to use depending on the execution environment.

nengo.utils.progress.to_progressbar

Converts to a ProgressBar instance.

+
+
+exception nengo.utils.progress.MemoryLeakWarning[source]
+
+ +
+
+nengo.utils.progress.timestamp2timedelta(timestamp)[source]
+

Convert timestamp to timedelta.

+
+ +
+
+class nengo.utils.progress.Progress(name_during='', name_after=None, max_steps=None)[source]
+

Stores and tracks information about the progress of some process.

+

This class is to be used as part of a with statement. Use step() to +update the progress.

+
+
Parameters
+
+
max_stepsint

The total number of calculation steps of the process.

+
+
name_duringstr, optional

Short description of the task to be used while it is running.

+
+
name_afterstr, optional

Short description of the task to be used after it has +finished. Defaults to name_during.

+
+
+
+
+

Examples

+
from nengo.utils.progress import Progress
+
+max_steps = 10
+with Progress(max_steps=max_steps) as progress:
+    for i in range(max_steps):
+        # do something
+        progress.step()
+
+
+
+
Attributes
+
+
max_stepsint, optional

The total number of calculation steps of the process, if known.

+
+
name_afterstr

Name of the task to be used after it has finished.

+
+
name_duringstr

Name of the task to be used while it is running.

+
+
stepsint

Number of completed steps.

+
+
successbool or None

Whether the process finished successfully. None if the process +did not finish yet.

+
+
time_endfloat

Time stamp of the time the process was finished or aborted.

+
+
time_startfloat

Time stamp of the time the process was started.

+
+
+
+
+
+
+property progress
+

The current progress as a number from 0 to 1 (inclusive).

+
+
Returns
+
+
float
+
+
+
+
+ +
+
+elapsed_seconds()[source]
+

The number of seconds passed since entering the with statement.

+
+
Returns
+
+
float
+
+
+
+
+ +
+
+eta()[source]
+

The estimated number of seconds until the process is finished.

+

Stands for estimated time of arrival (ETA). +If no estimate is available -1 will be returned.

+
+
Returns
+
+
float
+
+
+
+
+ +
+
+step(n=1)[source]
+

Advances the progress.

+
+
Parameters
+
+
nint

Number of steps to advance the progress by.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.ProgressBar[source]
+

Visualizes the progress of a process.

+

This is an abstract base class that progress bar classes some inherit from. +Progress bars should visually displaying the progress in some way.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+
+close()[source]
+

Closes the progress bar.

+

Indicates that not further updates will be made.

+
+ +
+ +
+
+class nengo.utils.progress.NoProgressBar[source]
+

A progress bar that does not display anything.

+

Helpful in headless situations or when using Nengo as a library.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.TerminalProgressBar[source]
+

A progress bar that is displayed as ASCII output on stdout.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+
+close()[source]
+

Closes the progress bar.

+

Indicates that not further updates will be made.

+
+ +
+ +
+
+class nengo.utils.progress.VdomProgressBar[source]
+

A progress bar using a virtual DOM representation.

+

This HTML representation can be used in Jupyter lab (>=0.32) environments.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.HtmlProgressBar[source]
+

A progress bar using a HTML representation.

+

This HTML representation can be used in Jupyter notebook environments +and is provided by the _repr_html_ method that will be automatically +used by IPython interpreters.

+

If the kernel frontend does not support HTML (e.g., in Jupyter qtconsole), +a warning message will be issued as the ASCII representation.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.VdomOrHtmlProgressBar[source]
+

Progress bar using the VDOM or HTML progress bar.

+

This progress bar will transmit both representations as part of a MIME +bundle and it is up to the Jupyter client to pick the preferred version. +Usually this will be the VDOM if supported, and the HMTL version where VDOM +is not supported.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.IPython5ProgressBar[source]
+

ProgressBar for IPython>=5 environments.

+

Provides a VDOM/HTML representation, except for in a pure terminal IPython +(i.e. not an IPython kernel that was connected to via ZMQ), where a +ASCII progress bar will be used.

+

Note that some Jupyter environments (like qtconsole) will try to use the +VDOM/HTML version, but do not support HTML and will show a warning instead +of an actual progress bar.

+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.WriteProgressToFile(filename)[source]
+

Writes progress to a file.

+

This is useful for remotely and intermittently monitoring progress. +Note that this file will be overwritten on each update of the progress!

+
+
Parameters
+
+
filenamestr

Path to the file to write the progress to.

+
+
+
+
+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+ +
+
+class nengo.utils.progress.AutoProgressBar(delegate, min_eta=1.0)[source]
+

Suppresses the progress bar unless the ETA exceeds a threshold.

+
+
Parameters
+
+
delegateProgressBar

The actual progress bar to display, if ETA is high enough.

+
+
min_etafloat, optional

The minimum ETA threshold for displaying the progress bar.

+
+
+
+
+
+
+update(progress)[source]
+

Updates the displayed progress.

+
+
Parameters
+
+
progressProgress

The progress information to display.

+
+
+
+
+
+ +
+
+close()[source]
+

Closes the progress bar.

+

Indicates that not further updates will be made.

+
+ +
+ +
+
+class nengo.utils.progress.ProgressTracker(progress_bar, total_progress, update_interval=0.1)[source]
+

Tracks the progress of some process with a progress bar.

+
+
Parameters
+
+
progress_barProgressBar or bool or None

The progress bar to display the progress (or True to use the default +progress bar, False/None to disable progress bar).

+
+
total_progressint

Maximum number of steps of the process.

+
+
update_intervalfloat, optional

Time to wait (in seconds) between updates to progress bar display.

+
+
+
+
+
+
+next_stage(name_during='', name_after=None, max_steps=None)[source]
+

Begin tracking progress of a new stage.

+
+
Parameters
+
+
max_stepsint, optional

The total number of calculation steps of the process.

+
+
name_duringstr, optional

Short description of the task to be used while it is running.

+
+
name_afterstr, optional

Short description of the task to be used after it has +finished. Defaults to name_during.

+
+
+
+
+
+ +
+
+update_loop()[source]
+

Update the progress bar display (will run in a separate thread).

+
+ +
+ +
+
+nengo.utils.progress.get_default_progressbar()[source]
+

The default progress bar to use depending on the execution environment.

+
+
Returns
+
+
ProgressBar
+
+
+
+
+ +
+
+nengo.utils.progress.to_progressbar(progress_bar)[source]
+

Converts to a ProgressBar instance.

+
+
Parameters
+
+
progress_barNone, bool, or ProgressBar

Object to be converted to a ProgressBar.

+
+
+
+
Returns
+
+
ProgressBar

Return progress_bar if it is already a progress bar, the default +progress bar if progress_bar is True, and NoProgressBar if +it is None or False.

+
+
+
+
+
+ +
+
+

Simulator

+ ++++ + + + + + + + + +

nengo.utils.simulator.operator_dependency_graph

Sort operators in a directed graph based on read/write dependencies.

nengo.utils.simulator.validate_ops

Validate operator reads/writes.

+
+
+nengo.utils.simulator.operator_dependency_graph(operators)[source]
+

Sort operators in a directed graph based on read/write dependencies.

+
+ +
+
+nengo.utils.simulator.validate_ops(sets, ups, incs)[source]
+

Validate operator reads/writes.

+
+ +
+
+

stdlib

+

Functions that extend the Python Standard Library.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

nengo.utils.stdlib.WeakKeyDefaultDict

WeakKeyDictionary that allows to define a default.

nengo.utils.stdlib.WeakKeyIDDictionary

WeakKeyDictionary that uses object ID to hash.

nengo.utils.stdlib.WeakSet

Uses weak references to store the items in the set.

nengo.utils.stdlib.FrozenOrderedSet

A set that preserves insertion order and is hashable.

nengo.utils.stdlib.OrderedSet

A set that preserves insertion order and is mutable.

nengo.utils.stdlib.CheckedCall

nengo.utils.stdlib.checked_call

Calls func and checks that invocation was successful.

nengo.utils.stdlib.execfile

Execute a Python script in the (mandatory) globals namespace.

nengo.utils.stdlib.groupby

Group objects based on a key.

nengo.utils.stdlib.Timer

A context manager for timing a block of code.

+
+
+class nengo.utils.stdlib.WeakKeyDefaultDict(default_factory, items=None, **kwargs)[source]
+

WeakKeyDictionary that allows to define a default.

+
+ +
+
+class nengo.utils.stdlib.WeakKeyIDDictionary(*args, **kwargs)[source]
+

WeakKeyDictionary that uses object ID to hash.

+

This ignores the __eq__ and __hash__ functions on objects, +so that objects are only considered equal if one is the other.

+
+
+get(k, default=None)[source]
+

Return item from dictionary.

+
+ +
+
+keys()[source]
+

Return dictionary keys.

+
+ +
+
+items()[source]
+

Return dictionary key, value pairs.

+
+ +
+
+update(in_dict)[source]
+

Update with items from other dictionary.

+
+ +
+ +
+
+class nengo.utils.stdlib.WeakSet(items=None)[source]
+

Uses weak references to store the items in the set.

+
+
+add(key)[source]
+

Add an element.

+
+ +
+
+discard(key)[source]
+

Remove an element. Do not raise an exception if absent.

+
+ +
+ +
+
+class nengo.utils.stdlib.FrozenOrderedSet(data=None)[source]
+

A set that preserves insertion order and is hashable.

+
+ +
+
+class nengo.utils.stdlib.OrderedSet(data=None)[source]
+

A set that preserves insertion order and is mutable.

+
+
+add(elem)[source]
+

Add an element.

+
+ +
+
+discard(elem)[source]
+

Remove an element. Do not raise an exception if absent.

+
+ +
+ +
+
+class nengo.utils.stdlib.CheckedCall(value, invoked)[source]
+

Create new instance of CheckedCall(value, invoked)

+
+
+property invoked
+

Alias for field number 1

+
+ +
+
+property value
+

Alias for field number 0

+
+ +
+ +
+
+nengo.utils.stdlib.checked_call(func, *args, **kwargs)[source]
+

Calls func and checks that invocation was successful.

+

The namedtuple (value=func(*args, **kwargs), invoked=True) is returned +if the call is successful. If an exception occurs inside of func, then +that exception will be raised. Otherwise, if the exception occurs as a +result of invocation, then (value=None, invoked=False) is returned.

+

Assumes that func is callable.

+
+ +
+
+nengo.utils.stdlib.execfile(path, globals, locals=None)[source]
+

Execute a Python script in the (mandatory) globals namespace.

+

This is similar to the Python 2 builtin execfile, but it +also works on Python 3, and globals is mandatory. +This is because getting the calling frame’s globals would +be non-trivial, and it makes sense to be explicit about +the namespace being modified.

+

If locals is not specified, it will have the same value +as globals, as in the execfile builtin.

+
+ +
+
+nengo.utils.stdlib.groupby(objects, key, hashable=None, force_list=True)[source]
+

Group objects based on a key.

+

Unlike itertools.groupby, this function does not require the input +to be sorted.

+
+
Parameters
+
+
objectsIterable

The objects to be grouped.

+
+
keycallable

The key function by which to group the objects. If +key(obj1) == key(obj2) then obj1 and obj2 are in the same group, +otherwise they are not.

+
+
hashableboolean (optional)

Whether to use the key’s hash to determine equality. By default, this +will be determined by calling key on the first item in objects, and +if it is hashable, the hash will be used. Using a hash is faster, but +not possible for all keys.

+
+
force_listboolean (optional)

Whether to force the returned key_groups iterator, as well as the +group iterator in each (key, group) pair, to be lists.

+
+
+
+
Returns
+
+
keygroupsiterable

An iterable of (key, group) pairs, where key is the key used for +grouping, and group is an iterable of the items in the group. The +nature of the iterables depends on the value of force_list.

+
+
+
+
+
+ +
+
+class nengo.utils.stdlib.Timer[source]
+

A context manager for timing a block of code.

+

Examples

+
import time
+from nengo.utils.stdlib import Timer
+
+with Timer() as t:
+   time.sleep(1)
+assert t.duration >= 1
+
+
+
+
Attributes
+
+
durationfloat

The difference between the start and end time (in seconds). +Usually this is what you care about.

+
+
startfloat

The time at which the timer started (in seconds).

+
+
endfloat

The time at which the timer ended (in seconds).

+
+
+
+
+
+ +
+
+

Testing

+ ++++ + + + + + + + + +

nengo.utils.testing.signals_allclose

Ensure all signal elements are within tolerances.

nengo.utils.testing.ThreadedAssertion

Performs assertions in parallel.

+
+
+nengo.utils.testing.signals_allclose(t, targets, signals, atol=1e-08, rtol=1e-05, buf=0, delay=0, plt=None, labels=None, individual_results=False, allclose=<function allclose>)[source]
+

Ensure all signal elements are within tolerances.

+

Allows for delay, removing the beginning of the signal, and plotting.

+
+
Parameters
+
+
tarray_like (T,)

Simulation time for the points in target and signals.

+
+
targetsarray_like (T, 1) or (T, N)

Reference signal or signals for error comparison.

+
+
signalsarray_like (T, N)

Signals to be tested against the target signals.

+
+
atol, rtolfloat

Absolute and relative tolerances.

+
+
buffloat

Length of time (in seconds) to remove from the beginnings of signals.

+
+
delayfloat

Amount of delay (in seconds) to account for when doing comparisons.

+
+
pltmatplotlib.pyplot or mock

Pyplot interface for plotting the results, unless it’s mocked out.

+
+
labelslist of string, length N

Labels of each signal to use when plotting.

+
+
individual_resultsbool

If True, returns a separate allclose result for each signal.

+
+
allclosecallable

Function to compare two arrays for similarity.

+
+
+
+
+
+ +
+
+class nengo.utils.testing.ThreadedAssertion(n_threads)[source]
+

Performs assertions in parallel.

+

Starts a number of threads, waits for each thread to execute some +initialization code, and then executes assertions in each thread.

+

To use this class, create a derived class that implements init_thread +to start each thread running and assert_thread to check that the thread +has run successfully. finish_thread can be used for any cleanup/shutdown +of the thread.

+
+
+class AssertionWorker(parent, barriers, n)[source]
+

This constructor should always be called with keyword arguments. Arguments are:

+

group should be None; reserved for future extension when a ThreadGroup +class is implemented.

+

target is the callable object to be invoked by the run() +method. Defaults to None, meaning nothing is called.

+

name is the thread name. By default, a unique name is constructed of +the form “Thread-N” where N is a small decimal number.

+

args is the argument tuple for the target invocation. Defaults to ().

+

kwargs is a dictionary of keyword arguments for the target +invocation. Defaults to {}.

+

If a subclass overrides the constructor, it must make sure to invoke +the base class constructor (Thread.__init__()) before doing anything +else to the thread.

+
+
+run()[source]
+

Method representing the thread’s activity.

+

You may override this method in a subclass. The standard run() method +invokes the callable object passed to the object’s constructor as the +target argument, if any, with sequential and keyword arguments taken +from the args and kwargs arguments, respectively.

+
+ +
+ +
+ +
+
+

Threading

+ ++++ + + + + + +

nengo.utils.threading.ThreadLocalStack

+
+
+class nengo.utils.threading.ThreadLocalStack(maxsize=None)[source]
+
+ +
+
+ + +
+
+
+
+ +
+
+
+ + + + + + + + \ No newline at end of file