Custom_Agent_Template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "17331f63",
   "metadata": {},
   "source": [
    "## Basic Case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4ee870e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1266a20a0>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Load configuration file\n",
    "import json\n",
    "with open('data_files/config_1hrad.json') as f:\n",
    "    config = json.load(f)\n",
    "    \n",
    "# 2. Import SIMOC\n",
    "from agent_model import AgentModel\n",
    "\n",
    "# 3. Initialize model\n",
    "model = AgentModel.from_config(config, data_collection=True)\n",
    "\n",
    "# 4. Step the model forward\n",
    "model.step_to(n_steps=601)\n",
    "\n",
    "# 5. Get data from model\n",
    "data = model.get_data(debug=True)\n",
    "\n",
    "# 6. Inspect data\n",
    "import matplotlib.pyplot as plt\n",
    "radish_growth = data['radish']['growth']['current_growth']\n",
    "plt.plot([i for i in range(len(radish_growth))], radish_growth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4d86a4",
   "metadata": {},
   "source": [
    "## Custom Agent\n",
    "\n",
    "Define an example agent, mushrooms.\n",
    "- Consumes 10g o2 and 10g inedible_biomass per hour (sigmoid)\n",
    "- Grow 20g per hour (normal)\n",
    "- Lifetime of 30 days\n",
    "- At end of lifetime, 90% edible (mushroom) 10% waste (mushroom_waste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4b3c68e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I specify 2 new currencies:\n",
    "currency_desc = {\n",
    "    'food': {\n",
    "        # The edible product of the mushroom plant\n",
    "        'mushroom': {\n",
    "            'label': 'Mushroom'\n",
    "            # Here I could also specify nutrition data\n",
    "        }\n",
    "    },\n",
    "    'nutrients': {\n",
    "        # The inedible portion of the mushroom. This is a separate \n",
    "        # currency from inedible_biomass, because mushrooms *consume*\n",
    "        # inedible_biomass.\n",
    "        'mushroom_waste': {\n",
    "            'label': 'Mushroom Waste'\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "agent_desc = {\n",
    "    # Add a capacity to store these currencies in the appropriate agent\n",
    "    'storage': {\n",
    "        'food_storage': {\n",
    "            'data': {\n",
    "                'characteristics': [\n",
    "                    {\n",
    "                        'type': 'capacity_mushroom',\n",
    "                        'value': 1000,\n",
    "                        'unit': 'kg'\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        },\n",
    "        'nutrient_storage': {\n",
    "            'data': {\n",
    "                'characteristics': [\n",
    "                    {\n",
    "                        'type': 'capacity_mushroom_waste',\n",
    "                        'value': 1000,\n",
    "                        'unit': 'kg'\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    # Agents under 'plants' have lifecycle, growth & reproduction\n",
    "    'plants': {\n",
    "        'mushroom': {\n",
    "            'data': {\n",
    "                'input': [\n",
    "                    {\n",
    "                        # Matches a currency\n",
    "                        'type': 'o2',\n",
    "                        # Average value over life of plant\n",
    "                        'value': 0.01,\n",
    "                        # If amount in storage is less than demand at any step:\n",
    "                        # - if required == 'desired', increment deprive (see below),\n",
    "                        #   don't increment age, but process other currencies as normal\n",
    "                        # - if required == 'mandatory', exit step function with no side-effects\n",
    "                        # - if not required, ignore and continue as normal\n",
    "                        'required': 'desired',\n",
    "                        'flow_rate': {\n",
    "                            'unit': 'kg',\n",
    "                            'time': 'hour'\n",
    "                        },\n",
    "                        'growth': {\n",
    "                            'lifetime': {\n",
    "                                'type': 'sigmoid'  # Value is 'reshaped' over lifetime\n",
    "                            }\n",
    "                        },\n",
    "                        # If demand is unfulfilled for longer than this, agent dies.\n",
    "                        'deprive':  {\n",
    "                            'value': 72,\n",
    "                            'unit': 'hour'\n",
    "                        }\n",
    "                    },\n",
    "                    {\n",
    "                        'type': 'inedible_biomass',\n",
    "                        'value': 0.01,\n",
    "                        'required': 'desired',\n",
    "                        'flow_rate': {\n",
    "                            'unit': 'kg',\n",
    "                            'time': 'hour',\n",
    "                        },\n",
    "                        'growth': {\n",
    "                            'lifetime': {\n",
    "                                'type': 'sigmoid'\n",
    "                            }\n",
    "                        },\n",
    "                        'deprive': {\n",
    "                            'value': 72,\n",
    "                            'unit': 'hour'\n",
    "                        }\n",
    "                    },\n",
    "                    {\n",
    "                        # This field and output:mushroom/mushroom_waste comprise \n",
    "                        # 'harvest': when grown == True (aka age == lifetime), consume \n",
    "                        # internal biomass and convert to food or waste (as outputs).\n",
    "                        # Current_growth is kg of accumulated biomass, and 'weighted'\n",
    "                        # multiplies that value by the input value (in this case 1).\n",
    "                        'type': 'biomass',\n",
    "                        'value': 1,\n",
    "                        'weighted': 'current_growth',\n",
    "                        'flow_rate': {\n",
    "                            'unit': 'kg',\n",
    "                            'time': 'hour',\n",
    "                        },\n",
    "                        'criteria': {\n",
    "                            'name': 'grown',\n",
    "                            'limit': '=',\n",
    "                            'value': True\n",
    "                        }\n",
    "                    }\n",
    "                ],\n",
    "                'output': [\n",
    "                    {\n",
    "                        # Plants 'store' their accumulated biomass internally. See \n",
    "                        # the 'capacity_biomass' characteristic, and the connection\n",
    "                        # {from: self.biomass, to: self.biomass}.\n",
    "                        'type': 'biomass',\n",
    "                        'value': 0.02,\n",
    "                        'flow_rate': {\n",
    "                            'unit': 'kg',\n",
    "                            'time': 'hour'\n",
    "                        },\n",
    "                        'growth': {\n",
    "                            'lifetime': {\n",
    "                                'type': 'norm'\n",
    "                            }\n",
    "                        }\n",
    "                    },\n",
    "                    {\n",
    "                        'type': 'mushroom',\n",
    "                        'value': 0.9,\n",
    "                        'weighted': 'current_growth',\n",
    "                        'flow_rate': {\n",
    "                            'unit': 'kg',\n",
    "                            'time': 'hour'\n",
    "                        },\n",
    "                        'criteria': {\n",
    "                            'name': 'grown',\n",
    "                            'limit': '=',\n",
    "                            'value': True\n",
    "                        }\n",
    "                    },\n",
    "                    {\n",
    "                        'type': 'mushroom_waste',\n",
    "                        'value': 0.1,\n",
    "                        'weighted': 'current_growth',\n",
    "                        'flow_rate': {\n",
    "                            'unit': 'kg',\n",
    "                            'time': 'hour'\n",
    "                        },\n",
    "                        'criteria': {\n",
    "                            'name': 'grown',\n",
    "                            'limit': '=',\n",
    "                            'value': True\n",
    "                        }\n",
    "                    }\n",
    "                ],\n",
    "                'characteristics': [\n",
    "                    {\n",
    "                        'type': 'lifetime',\n",
    "                        'value': 720,\n",
    "                        'unit': 'hour'\n",
    "                    },\n",
    "                    {\n",
    "                        'type': 'growth_criteria',\n",
    "                        'value': 'out_biomass'\n",
    "                    },\n",
    "                    {\n",
    "                        'type': 'capacity_biomass',\n",
    "                        'value': 100,\n",
    "                        'unit': 'kg'\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "agent_conn = [\n",
    "    # Specify where to get/put currencies.\n",
    "    # Note that 'greenhouse' and 'habitat' are special connections\n",
    "    # which will be updated at initialization to match the selected\n",
    "    # habitat and greenhouse agents (e.g. 'crew_habitat_small')\n",
    "    {\n",
    "        'from': 'greenhouse.o2',\n",
    "        'to': 'mushroom.o2'\n",
    "    },\n",
    "    {\n",
    "        'from': 'nutrient_storage.inedible_biomass',\n",
    "        'to': 'mushroom.inedible_biomass'\n",
    "    },\n",
    "    {\n",
    "        'from': 'mushroom.biomass',\n",
    "        'to': 'mushroom.biomass'\n",
    "    },\n",
    "    {\n",
    "        'from': 'mushroom.mushroom',\n",
    "        'to': 'food_storage.mushroom',\n",
    "    },\n",
    "    {\n",
    "        'from': 'mushroom.mushroom_waste',\n",
    "        'to': 'nutrient_storage.mushroom_waste'\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b1df293",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('data_files/config_1hrad.json') as f:\n",
    "    config = json.load(f)\n",
    "    \n",
    "# Add 10 square meters of mushrooms to the simulation\n",
    "config['agents']['mushroom'] = {'amount': 10}\n",
    "# Start with 200 kg of inedible_biomass in storage\n",
    "config['agents']['nutrient_storage']['inedible_biomass'] = 200\n",
    "\n",
    "from agent_model import AgentModel\n",
    "model = AgentModel.from_config(config,\n",
    "                               data_collection=True,\n",
    "                               currency_desc=currency_desc,\n",
    "                               agent_desc=agent_desc,\n",
    "                               connections=agent_conn)\n",
    "model.step_to(n_steps=721)  # One past the agent's lifetime\n",
    "data = model.get_data(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab74601",
   "metadata": {},
   "source": [
    "## Inspect a group\n",
    "Plot all elements for one group with an agent.\n",
    "\n",
    "Available groups vary by agent based on function, and may include: growth, storage, flows, deprive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "93a1656d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DBgn7IaMNZ1UVvE1N0GRny35v3JcuiT37arQhd9TPdfaskCKgoI1YeIcDLcuWI2fOHKT26pnQHmZDURG4jAzkPerrFErUpPb9++E6dy4gRU8HeL0JqUzUHiVX8KvTqtp7KQYhzz4T9Lj4paHSSeu8IMzGzH96AbgUYVKQvzdLzWAkXP4y4AuuAFUDLOPKlWBOJwqeCw6y1Q5InefOwVq6TyhB5wtYEpGTzbtcaFm2PKjHXNiWupUymMeDlqWfIWvaNDGXNRE9v6ZNm4SJbgsCSimpOOGNdzhg2rQJuXPntt44iuk1yrbhrKyE68IF5D7wAICAoFrBueW1WGAtLUXO3LnCF4W/N1nm++q+dg2Oigpk+6pGKBmVYi4XLHv3InvWTPH6ILcHzLpvH5jbjeyZMxVVKvEajbAdPYrsmXcqumlyXboE16VLyJ5xp6Lz3FpSgrQhQ6DrJkyYk3OsrSUl4DIzkTlhvPCAnDb0eiAlBVm+66uc88eqLwU4DlnTpgkPyPie8advZE2dKvu98adNpA0dKrsTxt+GtnMn2eesv41ELihk2rABXqMRBc8/n7CqUYCQvmT0dwoV5ANIXIpFc1ERtAUFyL3/PuGBBM1Vsu4/APP27e2uVzmpgl+lQ4uBmNstlOiaPh1p/fuHbMd3WFV6sw2LhNmYBb7yZgBae7NUPFHD5i8DAUGJOq9HzMGdMAHpg28XHxe+JNW9yzUsWgwuJQX5Tz3Z+mACcrLNW7bA29QkTKgLoHZutnnHTnjq64NuuLgETHgzLP0MaYNuQ8bYMa3bUfH9MW/fAd5iae3dgHo9v8YNGwCdDjnzhFESNVIULLv3gLndyPH1UisdDTHvFBa2yJnTOhoh9z20HjoE3mJBzpw5io+hefduaLKzkTnujoAc7PjbspSUAF4vsmfOVHTT5F+pLvvOGbLPc6/FCtuRI8iePk3R8bHo9ciaOFFRCoZFX4rMsWOhzc3x7Uf8r8daWor0ESOg69RR9n5Y9aVIGzwYum5dFeUdp/TujdR+fWV3KvnbSOvbT3avrVVfitQBA5DSpUtC8mMZY2guLELaoEHInDghYXMsAKBlxUowh0PojU1QpSBAqFVs2bkL+U8+CY3vfOYS8L0IAI1//Svqf/97VdtUQ8zgl+O4TziOa+A47mTAY7/nOO40x3HHOY5bxXFcfkL3UiWcirme/tmYBYHpAeJ21Ovt81p8Cybccw90nTu3biMBPb/NRYugycpC3vyHgx7nNBqA41QLsCx79sB99Wpwry+geqkzr8UiHLv774euQwfxcfELUMXhJENhEVL79UPW1CnBv1D5gmJYvBi6Ht3F3EUAQmqFisNw4kS3J59qcxOk1vtjXL0aKT16tNb2BVTJY2c8D9OmTciaNhU6X7UCNSanmbduha5zZ6F8HQI+f3J7q3bsRGrfvkgdMEB4QMFkQsuOneDS05E1ZYqiQJ/xPKx79iJrxnRwKSmKen4te/ZAW1CAjFGjFAULluK9wnHq08c3IUdOusIBwONB1vQZrQF9nO24rlyB+9JlZE2bJvv67rl+Hc7KSqHHVubkTq/ZDPuxY0KPrczPC2+zwX74sPha5ARXzO2Gbf9+ZE1XoY1pU2VXYuKdTtgOHRKOaYIWsbIfPQpnZaWYHpCo7fgntYudQgnqiQUg1lQueOZp8bFEzFXyLyzVwTepvT2RsjcLAdwb8tg2ACMYY6MAVANQVuH6RtGqV+JKLNE1Y0ab36nZ22dat1ZYMOG54B7F1p4ndV6Pp7ERps2bkffYY9CGKySv06mWMmIoLIKua1ex18tP7VJnxtVrwNtsbW9QtOpMgPKznzgJ+7FjYtWKQGpeUJznzsG2fz8KFjzdGtDBd9xUnIBh8K3olvfw/KDH1Sp15q6vh7W0VKjtG3C81Pjc2MvL4amtE5aV9pMZ8PjxdjssxcXImXt36/4qCDK9ZjOsBw8i+667xJsL2QEEYzDv2oWsadOgycgQb1TlBOWOU5XwNDYKvbWA7DQU5vXCurdYCKK1WtnBAu9wwHbgILL8ebo6eee5mK5wx1jZlT/8E6qypk+T38Y+oRxX1rRpssv62Q4cALxeZE2bKvvzYj14UEht8b8WObnLx46Bt9mQPW2a7CoB/jay/G0oyKEWjkdiSmUaCouEyjoPPQjAN4k5AQGpZc/eoE4hLlGT2h0OtHy2DDlz5iCle/fWXySg59dQtCg4f7kdiRn8Msb2AmgOeWwrY8x/lu0H0CsB+6Y6TqNOKQ9HZSXshw8LwU5AECJS6SRijKG5qAjpw4eLPU5+rcN36nzYI+UvB25PjQuL8/x5WEtLUfDM0635y+I21Ct1Ji42MWoUMkaODNmOusNJhkXCynF5jz7S9pcqTn4UUziefCLocWGioEq5uDYbTGvXBU10E7ejUs68cdUqgOfFKg8iFdITTOs3gEtLQ/Zdc8THlE4OtRQXg9ntQpUHf5sajez6ytbiYsDtDrr5kxtAOCsr4amrQ85ds1sf1OlkHUPL7t0Ax4kTujiZNw3248fhbWkRg2i5E41tBw+COZ1Cvi/kl6q0FJcga5IwYY6TeY5ZfWXS/CuFymqjRA9tfj7Shw2VfWNh0euFQH7MGNltWPWl4NLTkTFunOz8VUtJCaDVInPSJNmdSha9HtBqkTVpkm/UT0EO9YQJqpdiBAB3fQNMW7ci/7HHWucmJKjn11BYKEzMvlu4dqld99zPtGGjkL8cMvKq9oiouLDUw/Ohzc1VpU01qdEP/SUAmyL9kuO4VzmOK+M4rqyxsVGFzSmg0uIGzb7ZmGKJrhDiSavwJLIdOADX2dbZmEFUzMONmr/sI3zxqBDAFS3yBXBPtv2liqXObPv2wXXhQnDVCnE76g0neQwGmDZsED7gOTltfq/W5EevxQrj6tXIue/eoBQOAKpOFDT5V3Rb8FTbX6ow0YPxPFqWr0Dm5MlI7dMn6HdiYCKzF5t5PDBt2YLs2bODRi+Uluozb90GbX4+MsePD/6FVt7NgHnnLiElwLfypL8tOe+hecdOIWANSIORmxtr2b0bGaNHi+eX3B5Oy549gFaL7OnTW9uRcZwse4vBpae3psbImPDmunQJ7itXkDXdNzlMRs1y5nbDum+/0EPJcbJupoQljfXImjrVV9NcZuBaWirkHaemiteWeK9j1pISZE6YAE1amtjjGu8Mf6u+FBmjR0ObkyO7Y8SqL0XGqFHQ5ubKHr2y6EuROWYMNFlZCen5bfnsM8DrDZljoX7wK3YKPb2gtZKMgpz7SIQOtUKkDboNmZMmBv9S5RHRluXLWxeWaocUBb8cx/0EgAdAUaTnMMY+ZIyNZ4yN7xyQs3ozqFGn1NvS0qZEVxv+meAKA5LmwkJo8/NbF0wIoObCE2L+cmhqRSAVVsdrzcG9D7qOHdv8Xs0i282FRdB26ICce0MzdtQdTvJ/wDtE+oCrFGib1q0Fb7WK5c0CqVnqzLBkKVJvGxgcmPm3o1P+/tj274e7pgb5TzzR5netPQ8yJ74cOABvU1Pr7GU/BZ8V3uWCZfduZN89p/VLKWB/422Tud2+ygyzQlJX5B1b886dyBg7NujzxMkIyj2NjXCcPInsWTNbH5SZ22rZs1eY1OW/PsrtXSzei6xJkwIm5MQ/4VIsceZLT5NT+cN+4gR4iwVZvmAeMtrwL2ksVmiQMfFOzDueOtXXRvzBivvqVbguXBDybAFZpRg9BgMcJ08GtxHnOeJtaYHjxImA4xH/6FVQDjX8nyEVA0WXC4bPliLrzhlI7dtXfFxuqkg04TqFlOTcR2I/Wg7nqcqwHWpqjogyrxeGxa0LS7VHsoNfjuNeAvAggOfY56QwnBoT3lpWrGhboqvNdpT39oWbjRnEn3uowoe9uah1ieFI1AiwxBzcSMdOpSLb/pV48p96EprU1Da/V2s4iXm9aFm8BJkTJ4o1N9tsS41JXIzBsGgR0oYNbZP+AkAY0VDhPLCfOAnHyZMoePqZtiMNgBDEKHx/WpYvhzYvDzlz7277S4UVFEwbNkKTnd2as+qjpNSZtbQUvMWCXF997TbtxnncbWVl4E2mNvnucoZ93bW1cFZWtm1Lxo2qZe9eAAjuQZaRU+qur4ezsjIoiJZz0+S6eFEI9O4MmFOh1chINShBSp8+raMMuvgDemuJHtBokDV5EoCAjoc4XpOYM+wLGOVMmvMvSuHvxZbThr8smNgrL6N30bZ/P8AYsv1Bp4zrgtXXRuDxiLsXPCCHGoCqc3oAwLR1G7yN19Eh9PtK5Z7fiJ1CKi3OE8hQWOjLX36o7S9VHBG17NoFT21d9A61m0xW8Mtx3L0Avg9gPmPMpu4uJZDCYfVIJbrabkf5SRRuNmYgYblZreKJTv7VZAqeeSZ8/rKfwlqyYg7uyJHIGDUq7HM4rU6Vnt+WJYsBjQYFgTVqA6k0nGTZs8e3cly0GyEVJnGVlcF55qwwYzZMUKrWhDfDksXC5ISQiW6t21HW8+sxGGDeth25D88Pe0OnpIIC73LBvG0bcu6+u23bCnKJzdu2CaW/pkxp+0sZX7bmHTvBpaW19t75cFodwPNxDT+bdwpLrWbPDpk4KuNG1bJ7N3TduiFt8ODWB2Xkq4tLIwfegMg4Tq0lzlpvyOM9z3mXC9YDB8RAD5BXs9yiL0HGyJHBi70AcV3frXo90gbdhhRfnWHxuhDH67GWlkLXvTtSfalpctJSrPpS6Lp2FVepk5OiZ9HrocnNRfqIEa1txHldsOr10OTkiPMx5Ex4C8qhhvLrUyhDYSFS+vZp7fH3Ubvnt3Vi9vPB21F5boq7wZ+/HLIoV5vtqZMaquveXVxYqj2SUupsMYB9AAZzHFfDcdzLAP4OIAfANo7jyjmO+1eC91MVSnt+I5boarMdZSdR62oyd4lr0YffjvIPoaGoCFx6esT8ZXFbCmvJWktLI+fg+smc9BAocCWeoJmsAdQaTjIU+laOC+15C6TCBax50SJo8vLERRvabkP5hDev0QjTho3Ie/DBsLnLABTnzBvXrAFzu8OmPAjtyy8RaC0uBm82R00RijcgZB4PLDt2Inv27PAjCHEOwzPGYN65A1lTpojL24ptyTgnLTt3ILV/f6QNCMnTj/NGlXc6YSnRI3v2rKCbKzkr41n27BEmht12W2s7MiYaW4qLkdqvH1J7925tJ86edvuRI2B2e3DwEue1WRieP6moDd7hgK2sDFlTp7VtQ+Lr8S9pnDVtaut7FOfEO+bxwLpvn1Cxwt9GnJOzhaWES5E1eXJrGlCcvbaMMaFmclAb8fWmtuZQTxE/35yKE9HsJytgLy8PX6JLxcV+ok7MVjntoWXpZ4DHEzEHV60RUefZs7Dt2x+0sFR7JKXawzOMse6MsRTGWC/G2MeMsdsYY70ZY2N8/331RuysYgpn3ovBzt1zoj5PaW+facNGeFtaUPDc89GfqLDuatBqMvn50Z+scKa/oWiRkIN7330Rn8OpMOGtdSWeKEG2CsNJYScohKH0AuZuaIB523bkP/ooNBkZ4behQs+vcc0aobh6hJEGQNlED8YYWpYvR8bo0RFzwJT0/Jo2bIC2oEBcOSuIzJtR26FD8La0tC6W0abd+D5/ztOn4amtQ3a4myVNfNcmr8kE68FDYW+84s1Fth04IFSzmD07+Bdx1hLnnU5Y9+0TVpoLHKGIMyjh7XbYDhxoXYrYL86gw1JcLFQCCJjYE+9kNev+AwDPtw6tI/7ru63sMJjT2TrpTsZ+OE6eBG8yITtgxCDeYEVsI9xrkXhz4jp/Hp66uuDXoo2vCofrwkV4autCjml852ybHGrAl5aldgWftp1C/lJnamR7Ru0UUliiMVCk/OWw21PY82tYtAhcamqbqkTtTfuqOpxg4uxYOSWFJAY7ABQV6486GzOE0hW3xNVkogWK4rbkz6R11dREzcFt3YaynvmglXgmTIj4PDWGk1rLjoWpWhFIYaDd8tky4W796QgpHFBe6owxBsOSpUgfPQrpw4ZF3o6CCW/2o+VwnT0X/YIoMz3Ba7HCvHMXcu69p035PKD1Cz7e89e0dSu4jIygYfPgduP7wvZXZmgTZCL+4WfL3mLA4wkq6SaKcyTAvGuXUD5r0qTgfYrzc2I7eAjMbg+Tcx3fTa3t4EEwlwtZM4KD33hHuqwlemTecUfQEG+8iwNZ9SXC8PyogF65OHttrXo9uJSU4Goh4o2etONi0esBjgtOv4mzM8dSEqUNycfDl7sc1Isd301JYM1kUZydK637EXAzoNKEN7FE1/yHwpfokjFJMBKxUyjcxGyZ1VbCMW3z5S8/H7lDTY2eX6/ZjJbVa9osLNUeJVXwq6SUR9QSXSGUnETRZmO2oZU/0UlcTWb8eKQH5vlFoCTAMiz25eA+HblXEYDinGz70fLglXgiUNob6191L+e+e8NWrQjelvxAm7ndaFkqlKBL7dcv8hMVljqzHTgI1/nzKHg6fI1nkYIJby3Ll0OTmYncaD3//jz2OD835i1bwBwO5M2PkqsMxPV+M56Heft2ZN95Z5Qe9/i+bM07dwilxDp1avvLOIMQy86d0HbsiIzRbfPn4xkJYIzBsnsPsqZOaZMrzcU5d8GyZ49QmqxNEB3fTa1lbzG4jAxkTggpLRdHD7K7vgHOqqrgAAuIa1Jl2OF5BPTaSjzGVr0eGePHBZ1H8aaUWEv3IX34cHHVQiD+iXdWvV5YFjmojfiuTxa9XkhH6dUzuI048tWtej1S+vZBaq/W5QHiHVWylpYideDA4NQ2FSoSAQEVfCKkN8qZJBhOzE4hFUudGQqLwuYvB29P+YiocdVqsGiT2tuRpAp+5ebixirR1YZ4EsUfKBiKiiLPxgzBKZjwJq4mI6HXF4DsAIu329GyfAVy7r5bnOwRidIJC60zWR+M/kSFw0n+smORLo6B5ARefuYdO+BpbIxZJ1HxcVuyRMgpvq9t70PQdmT2/HotFpg2bULuAw+EnWgRtA0Z9aSNa9YgtW/f8JUwAFlVJOxHj8LbeD1yygMQ14RTd10dnKcqw6c8oPULVcp5wlwuX7m0mWEnqcYTlDtPnxYWyQjTGx1PDydjDJY9e5A1eTI06elt2pF67BljsOwNLnHmF89iGf6ewdAVOMUV8CTsT7jheQBxpYO4GxrgrK4OSjUA4rsueC0W2MvL2+xHXG2YTLAfP972ZiCOGxze5YLt4KG2xyOONpjLJaxuGPpa4ri2tC5pHDJpVOYiKEH75/GIJboiVvBRa85IjE4htbYjdYlhJR01gNBhYFi0CBmjRyNj5AhZbdxISRb8ysvFjTQbM+J2xJMovu24Gxpg2rIl4mzMNhRMdDIUFvqWGI6ev+wnt9SZacMG8EajtJIn/p55GUF260zWx2IHWAqGkwJX3UuPULUiiIJA21C0CCk9erTNfQylYKKgu6EB5u2+nOLQoKXNduTlzJvWbwCz26XlgMXZe+OquQrbwYPCUskRevvjHeoGAPPWreBSUtoM4Qe1G0eQad6xEwAif9500nt5rIcOgbdYIrcVxzC0eadvkYwwrzOez4nrwgW4r1wJrhPsbyeOhRRcFy8Ki1LcOaPN7+KpX2wtKYa2c6fg6hV+EnO1rSVCjeDQgJGLY2KmtdRXnqxNwCg9v1Jc0jikQkg8k2mt+/cDXm+YIFx6h5D9yFFhAmHENmLvh628HMy3pHGQOEb97IeFHOrQ16LGymuW3buFEl1RJ2YrT5uT0ikkdyGUUFKXGFbSUQMIlURcFy9K71C7yZIq+JWz7DDj+YizMSOR82ULBOR3hlnIIOx2ZE50CspfDpMjGZaMUmdCoLgoZg6un5KcbPHYRVieOYiC4STbgYORV90LQ26g7aiuhu3QIeQ/83T0EnRQNuHNuGJFzJzi1u3EXxYJEIYR0wYPRrqEz0+8eZ3GtWsAIGLKgyiO85fxPEybtyBrxgxos7OjtBlHkLl1K1IHDkTagAFhfx9PzVbLjp3g0tORFa78GnzHUOL5YNm1GxmjRoVPxYhjGFQscRauVrhWen6rtbhtiTORxBsj5vXCqi9F9rTpEUoDSpsrYdXrkdq3b9DwvPj3gKRjbNWXQtuxY5sgPJ4KJFbfksYZY8eEtCH9e8ZaoocmKwsZo0cHtxFHip5Vrwd0OmRODJ6LEk++ulVf2roscmAbGumpMRbfksah3ylqrPDWXOgr0RVuJETcjvJSpv5OoejVj5RPeGvNX5awxLDCNAtDURG0HTsi5557ZP39jZZUwa+cmobWaMvkRiLjzpC5XDAsXSLMxoyW3xlAboK/mL/8VJglbKNsK94Li/3IESEH9/nnJQWKcnOyg45dpJmsAZQMJxmKioRV90JXEYtEZs+vYfFiYcZspLJggWSOADCvF4bPliFr6hRp55yMnghHZSUcJ08i/4knJJ4D0gNKxhiMa9Ygc9IkpPTsGfW58Yxc2A8fhqe+PmzZtOA2pQWZnuZm2MrKoqZQSK37yhiDedcuZE2bFjEXWeqiJ+76BmFVtwhf9PHkpVp270ba7beHLc0YT8+gZc9epPbv3ybgBKQHSI6KCniNxoj5jZyEuRK8b3i+TQ8lIDkoYTwvLEfsW9I4bBtSAld9KbImTGibFyp1PxgTljSePLltZ0ccAY9Vr0fmmDFBS4cHtSHl5kavR8aYMW1vKuO4kbTqS4WJjKHlArXxp0wFcp49C9t+CSW6FPbIihOzb78dGaFLpgeI5yYrEnGJYQkjr0q+F11XrsCyZw8KFjwVdVJ7e5JUwa+chG5D0SLhbibMbMxI5JxE4mzMeBLFZRSP9+cvS5msFbyt+C8shqIiaHJzY+fg+sjNyY732MkdTnLX1sK8Ywfyn3widoqAf1syen69FgtMa9Yi9777gianRNyGzImClj174KmrQ36siYj+7cgohdOybDm41NQ4zgHpN1n2o+VwX7qMvIcfjv1knU7yDYhx40ZwGRlRe3+ENqUFmebt2wGeR+68eZGfJDFAdFZWCjm6UYrHcxJLPln27AaAyMGvxJXxvGYzbEeOREwRkXo95O122A4dQnaYlAcAkutMW4qLAY5rkxMqkpCrLQ7xh+bIIvD6Ef08dVZVwdvUFHY/pF6DXDU1cF26FDYIFwO0GK/FdfEi3LW1yA73WiS+N56mJjhOnQp7QyF14p3HYICjoiLC8ZB2DfM0NsJ5+nSEmxJlE96ai4okleiSs8JfIPuRI3CePo2C52OMHiqcmyIuMTxxorQlhpWk6C1aDGi1yI+0sFQ7lFTBrzisLvEDIrVEVxsyggRDYRFS+vRB1owIF/4w5JQ68+cvRyt5En5b8Q0puesbYNq6TcjBDblDj7gNmVUyDEWL4jt2MoeTDEs/AwDkL5AWLALyAu3WZaClLQ0pt0ScYfES6Lp0kbwKT7zvD+9wwLhuHXLuuSd2HWm/OG6yjGvWCEFqtKDSR+rkUObxwLx5C3Jmz4p53kr9TJi3bkNKr15IGzIkSlvS6q36y6WFy61tbUvaiJBl5y6k9OiBtNvDT+yR+jmx6vVC2bVI+ySxd9F64ACYyxU5iJY44c1a0raqQVA7Eo5P6xD/pDa/a01ZiH6ehivH1baNWPvhzxkO04bEzpzWNqL0YsdqozRkKeFAEgNo2759CFwWOXg/pF3DxCWNIx1TmQGp12yGcc1aaSW6FCyXDgDNhYVCp9CD0TsElJY6E5cYljhqLXd7vN2OlhUrkDP3bqR07Rrvbt40SRX8xjusLs7GjPNuJt4gwT8bs+DZZ6LOxmwjzrXupSwxHEm8pc5ali4FvN6oiya0EWehfwBwnDoF+5EjcR07OcNJvMuFlmXLkD17dlCZn5jiDLQZYzAsXoz0ESOkv0cyejxcV67AWlKC/CeflL4KT5w58+YtW8CbzdJSN3ykBpS80wnTpk3ImXt322HYcO1KPH+t+w/AazAg9/7oKQ9imzECIK/RCOv+/ciZNy96L4/EyYTmnTuRMXZs9FEbCbmxvN0uLEhx112RJwpK/JxYdu+BJi+vTT6p2I7EdDPr3r1CbmukoWBd7KDVazTCfuwYsmdEKekk4fhEHOIHAgLG6K/HotcLqSBduoTdB6GNWEFnKXTduiE1XK64VlpairWkBCl9+iC1T582v+PiuMEJXEo4qA2JnT2hyyIH74cOYCzm+R9tP6DVyC7FaFy1SijRJaFTiFPQQ+qurxcWLXr88didQgpzfuNeYlhmqTPjunXgTab4Rq3bgaQKfuPJPRNnY86dG7NEVxtxnkT+2Zj5jz0W12binehkk5O/7BdHqTNhNZnPJOfg+snJyW5e5Dt2MWayBpFx8TJv3gxvczM6SOyN9Ys30LYdOADXuXMxy5u12Ua8kyuXLgU0mrhW4Yl3NaiWZcuR0rcPMifGnuzYug2pvZY7wZtMyH/kEWkN66R9VkwbNkCTnS1tFEFCEGXetQvweJAbrWQaAie8RT627tpaOCsroy+nDWkln6z79gsz5mfPivwkCV++jOdhKS5G9owZkW+iJAT2Qqm0vUKptAijbFKud9Z9+4UV2aLUM+U0mqifx9Yh/jA9lICkUme8zQZ72eHwPaWApEnRzOsVliMOXNI4sA0Jiy0wMXc5fAqIlOuTsKRx8FLCYfcjys2AuCzylFhtRN8Piz5CDjV8nyEZPb/CpHZfia4Rw2M+X0mPrNgpJGFittT0mnDkLDEsp9SZ0KG2CGmDByNj3Li49/NmSq7gN45cXHE2ZpzBjrAd6SdRXLMxQ7cT54S35sKiiKvJSNuWtA+hactWeK9HX00mrDhvGjwGA0z+5Znz8iRvRs7Fq7moCKn9+wevjiRFnHfvhqJF0OblSZ9QB8Rd6ox3udCyYiWyZ8+K78YujlJnzgsXYCsrkz7RLWAbUto3rl4DXdeubWaNRyLlBoF3uWDetg05c+e2qTEbtk0JQaZ56zbounaNXRZPwgxy885dAIDsWD05EiYPWXbtgiYrC1lRV0KM/eXrOHkS3qam2CXhED2wd124APfVq5HzfeFP74nxukqKfSuyRTneMXK1W4f4I0yY47iY1UNsZWVgbnfkAFpCubRwyxEHkXATbzsqlBaLtEqhlFrOzjNn4GlsjBjISxkRcp0/D8+1a5HzsCWkxjirq+G9fj3Kfsgr+WjV6+G6dElyKdN4V/jz410uGJZ+huyZM5Hau3fM50vNuQ9HzhLDckqd2cvK4Kyqip2/3A4lVfArNfdMnI05eHDU2ZiRxFPqLJ7ZmG3EUWTfVXNVyF9+8klJX+xttyV9SMlQVITUvn0jX6QiiDcn27hyJZjTGf9qMnEGpPYTJ+A4dhwFzz4b9wc8np5f97VrMO/cibwnHpc8oU7YRnwjAOYtW+A1GGKv6BZmOwAk3QS1LF8O6HTSe2bFbcTOY/dcvw5LSQny5s+PWQZOJGGGv7W4GLzFIinlAUDMIJO3WmEtKUHO3LkxU3KkTPa07NyB1AEDkNa/f/S2YuTGMp6HZfduZE2fDi7qcuOxc1stu/cAGk30NAMJX6qWPXsBtF2UIrid6KNPQlUDvdC7GKW3K1ZqTdShdX8bMaqHWPV6cGlpyIzQGybluhB2SePANiT0uIq5yxFuEqW14csZDpNnC0gbEQq7LHJQG7HPEWuJr42IvdjyJv4aCoug7dQJuffEnjsgbEfeegHmLVvgbWqS/n0ld7l3uUsMy5if0ly0CJq8vJj5y+1RUgW/Unt+xdmYz8Uf7ACQnM8V92zMEPH0/BoWL/KtJiNvNqbUXEz7yQrYy8uFYxdP/jIQV062sDzzYmROmID0wfEdu3iHkwxFi6DJzETeo4/EtR0AcV3ADEuXAjwvuc5z6zbiy8c2LFkqTBCcGm8vtrT3h7lcMK5eg5zZs6Dr3DnubcTKHTSuXw94vch7REKVBx8ptW9NGzZCW1CArMlSe5OjfyYse/eCuVzRV4nztxUjCPGaTLAePIScu2JUoACE3Ngox9BRUQFPY2PstiQs1mPZvVsoXRVlQqOUnFBr8V6kDbotasm6WKNPrrNnhd7FSL2t4v5EztWONcQvilE9xKLXI3P8+Mg3sRJ6ba2lpUgfNiz6xD3EOK4lJcgYMzpyvWoJq5Fa9fq2SwkHkhA0hVsWOZC4ZHS04FevR+ptAyMvCiFjBUrX5cuw7N2LgqeeinojGETmYj+GwiKk9usXufc7hNxSZ3KXGI6359d97RrM27YJ+cuRyi62Y0kV/EodVhdnY0pYYjgcqSdRvLMx25BYE1XMX54zJ/IFLOa2pAVYhqIicJmZyIuzxw+Ivx6o++pVWWuIxzOc5GluhmnjRuQ98nD0BQ9ibCtWoM1cLrQsW47sO+8MW+M0+jakT3hzVFXDfvgwChYsiPvmROr7Y961G96mprgmugVuI9axMq5eg/SRI5E2cKD0hmNMDuVtNph37ULOPfOkL/wSozKFaetWaDt2jNj7FyTGqJRlb7FQUeGuOTGbinU+WHbtAjQaZIVbSCKwnRgjWO6GBjhOnYqa8gDEDux5qxXWQ2XImqFsJUOLr2cw4hC/X5RcbWe1f4g/ehvRqoe4r12D6+y5qCNfsYI9YUnjYxF7WwHEnHjnz12OmDaBwBS9CO+NwxF2KeGgNmJcFyIuixwoxvnPOxywlZVFfS3QxL/Cm5wSXXJKndlPnIT92DFh9FDqdVdGeoWiJYbjnAvT2lkTx6T2diSpgl9xKCzKByRoNqbcuxmJS1fGPRszhNSJTuISwwqWHZQytO4xGGDasAF58x+KO38ZiC8nu3V5ZhnHLo7e2JblK4S0lDgmoAWSWhbJtHUbvNevy0p/iWfCW8vSJULd3cfimCAobkdaOk/L8uXQdesWddJR5G1EH81wnD4N5+nTcfX6Cu1GP3/Nu3aB2e3Ie+CB+NqM8mVt2bMXOXPmSErNiHXuW3buhLZjR2SMjl0BJFbpO/Ou3ULFiFg1pHXRUzGse32pClHKrgEIqGEcoZ39+wG3O/yqbgFivYfW4mKhhzLMQhvB7UROrRGH52P0zkWrHhK1tFjgPgARe/VsBw4AHk/0iXsxOlnE3GUFbdj8SwlH+yzHuC5EWhY5aD9inP+2ssNgLlfsYypxGW1AuOFtWbkSufPmIqVrmIockcjIxTUUFQmjh3FcdzlxpC2ORbkULDEcz1wY3uVCy2fLkD1rlqT85fYouYJfCcMz8czGjLWdaDlBznPn4p6N2YaEBP94lxiORMpkk5ZlQv6y7JInEnOyxeWZn3laei9dAKnDSczrhWHJYmROnoy0226LezsAJOdRGRYvFlIRZASMkkcArFahluV990paPKPtdmLnpbpra4USao89Jj0fN2gb0fPYjavXACkp0vNyfWIF1aaNm6Dr0iWuGcvRhuGtej2YzSapBrHYFsKfJ8zlgmXvXmTPniXtmEbJfXTX1QkVI2bPir1Pmui5jZY9e6Dr1g1pMVK2YuVIWvYWQ5OZicw7xkZvJ0qpM95uF3oGpXx+tNrIgXhJSdShdVGU3mOrXg9t506R6yf79gGIfANuKSkRjknIksbBbUTvcbWWlECbl4f0YcMitxHjmmvVl4JLSUFm1JXIovdQRloWOUiM66RVr4+5H5BQ/SKQcd168CaTjPQA6SsfAgpGDyV2nARSssSw1LJ3gK/6UTz5y+1QUgW/sbr1452NKXc7gC89ICUlrtmYoaT0xsa9xHAksSabeDxCoDhpEtIGRbnoRyG159ewaLHv2D0paztSh5Msu3cLaSlq3AhFeZ8cVVVCKsLTT8efJw1I7vk1rlsP3mqVvKJbm+1ImBzTsmIlACAvzrJ94jaiBKnM44Fx/XrkzJoZf/Ae5QbBazLBuncvcu+7L84625ErU5i3boUmNxdZk6J84Ye0BYQ/J62HDoG3WJAjIeUBiB6Um3f5KkbEWr0OiPo54V0uWPWlyJ45M/Z1JVpgzxgse/cK5bxi5VxGGda2HTok9AxKCH4jrSIoBtAxUh6AyOXS/EsaZ0+dFvW4cBoNwHERe/WsJXpkTpoUY0Ji5B5XxhgspXrhuEa5YYp1zbXq9cgYNy5qTdpYPYZRayb72/DvY6SbydLS2PsRx1LN/pr3aUOHIuOOO2I+P3hf/UuRSwtK/Z1C8Y4exjs3RfESw2Iuc+zj11zky1+Od95IO5JUwW+sD6k4GzPeEl0RthPpJJI9GzN0OxImvMW7xHD0bUWf+KIofxmQlJPttVjlLc8cQOpwkqGoCLpu3WSnpQCQlGJhKFoELi0N+TJSEYRtSBsBMCxZgrQhQ5AxZoy87cR4f5jXi5aVK5E1dWp8C4EEihJQWvV6eK9fl5lPHvkGwbxtO5jbjdwH5PQmtz2HmMsF885dyJk9W/LIRLSbJMuOneDS05E1ZbK0HYtyPlh27RYWPAi3aELoPkW5jtnLysDbbLFTHhC99KPr7Fl46uok1VWO9h5aSkqE6goTYlfniXTdFIfWY0yYAxBxwpvjVCW8LS2S2wjXq+e6fBnuK1diV8uJcm1xVlfD2xilLJiENtwNDXBWVcWeoBUl6PQ0N0evmRy6H2E++57GRkn7EU/anO3QITirq9FBzqT2OHJxhU6hJcicEv/oodSUOT+lSwxLnaskVj967jlZnTXtxed3z+WI0SMrzsZUeDcT6yQSZ2MqDLJj1USVs8Rw5G1FL3XWXOjLX5bSoxSBlJxs45rV4K1WZavJSLioOM+dg7V0n7K0FMTu4fGaTDCuW4fcBx+QvgRw6DYkTHizl5cLFUyeflr2CECs98eq18NTVye/Rx7Rb7JaVq+GNj8/ejmsSKKUJTNt3IiU3r2RPnJkXE1yuvCfP+uBA+DNZuRILJ0EIOIXKmMM5l27kDVtmuQ5CJFGhHirFbb9+5Eze5akcyDahDfLnj3g0tKQNVlCQB6tHX/ecIx8XwBR30NrcQkyJ0yQViIwQmqNtaQEXGpq9KF1n0i9x2LOsIR64JxGE7YNS0kJACA7VtUK/+cx7Gvx5y7HaCPKTZfNt5Rw1ElmCLhJCvO5jbosctB+RE4lsJZGL7UmimPCtFhPXUaJrnhKnZl37oSnrk7e91Uc6wXwNpvyJYYlpj0YCovkVz9qR5Iq+I3W8yvOxlTjbibaMJ9vNmb66FHxz8YMEas3Vo385dZtRZ6F7zx7Frb9CvOXETsn27+aTPrIkRGXUpW0HQklnFpTK+SnpYgi9PAAgHH1ajC7XfaEOsB3XseahLZkCTRZWcpGAGL0/LYsWy4soiKlHFcEXIQAx2s0wrJjJ3IffFB6SaLAdjXhS515mppg3b8fufffH/9NQYR6uuatW6HJzIyrznWk0QhnZSU8dXVxjT5Eyo217tsH5nZLS3kAoi7EYN69G5mTJkoKyKPlhFr27EXa4MGSFlvhIqQ9uK9ehevChei1hoP2J/zxsehLhPJkUm4yIlT6sOr1SBs6FLpOnWTvh7VEj5RevZASY3VM//dU2FQZvbTc5WjXXEuJHtoOHZA2ZIikNsKlAVj1+th5x4ieSmDR66EtKED60Mh1l4PaiJGL666rg3n79rjrqYskTmgHhCBb16O79M9cgFg594GM69crXmJYSoqep6nJl7/8iKzqR+1JUgW/0YJScTamCncz/t6+cAGJfzZm3KufhRMlj9G/xHD2nXeGXdM9/m1FnvDWXFQU92oyYcXI2bLt3w/X+fPyFgQJ0HpRCX/x8losMK5ahdz775OdWhG6vXBBknAjtFgoSzM89rKaEWm0AGMRe+Y9BgNMmzYj7+H50GRFzruLJVpJI8/16zDv2oW8Rx6RFZyKNOF75Uybt4C5XMh7OL4qD35chEUuTFu2AF5v3BPogPBBJvN6Yd6+A9mzZsa3mEyE0Qjzjp3CIhKzZ0lvK0KQaN61C5qcHGml1xBwHQt5P5wXLsB96XLMEmdiOxE6HbwWC2xHjkRd1S1IhJs8f4kzyZNFw9xgSSlPFijcaAtvtcJ29CiyJdZxDbdkPHO5YNu/H1nTo+cM+/8eaPt5FHKXD0vKXY50g+PPXY60lHCQCD2G/prJmbFqJgNRRz6spfvi248YubitJbqUVfCJlTbnqK6G7cABFDzzjOzJv8J2or8exhgMhUVCSpuSJYYl9Py2LFsO5nYr/g5uD5Iq+I1Un691NqaKdzMRvmyVzMYMFS0Hzr/EsKIc3KBthZ/w5jWbhQoCCvOXgdg5W82FRdAWFCD3vjiW/g0nxnCScc0a8DKKhEcivE9hekX27RPK0igN5mMcN+PKVWAul+yJblK2Y1y9GvB4kP/E48q2EaE3zLh6NVJvG4j0ETJvEiLcKJo2bkTqbQOjz8yPRNM27cFWdhheg0FylQe/SF+o5p07hbJkcXy2wl0XGM/DsmcvsmdMj69CSpjrmGXPHgBA9sxZ0tqIkG5mLS0VahdLSXmA7+aL59scc2tJMXQ9ukvKYwZ8owAhx0dMV5AYQIcrl2Y9eBBwu6UH0GFWibMfOwbeZpPURqSeOltZmeTcZTGgDL3BqaqCt6lJWhsRqhu5zp6Fp6EhZtoEEHkyrbikcayUB0irwsA7nUKJrtmzFc1LEBqLEWQv8s3lkFHvHPC9NxE6TgLZy8rgrK6WvyiXf3sxen79+ctZU6fEV2O9nUrK4Df0pBVnY6p4NxNuRSnFszFDtxElB07uEsMRtxUhxUK1/GUgak624uWZA0QbTmKMwbBosZBaMSp2TVVJIpRFMixaLKQJ3Huvsvaj9Jgznodh6VJkjBsnaxXBsNsJ0zvTsmw5MsaNQ5rEACSiMMfKdfEi7EePIv+RR+TnK4cJCN3XrsFedhh5Dzwgq91wnwnz1q3g0tLizksON0/AXVsrlCWLN40kzHXBceIEvNevxz38Gu46ZtmzB6m3DZQcPLQu6BASLBYXQ5OdLXkCZrjqBszthnXffmRPmy79PQxzfKx6PXSdO0u/CQpTLs2qLwWXni69ekCYSXOWEj2g1UrLpY7QU2ct0UvOXY7U49q6HLGEXuwIJRAt8bQRYaW5WEsaB+9H7Drk5s2b4W1uRgclNe91sYNsr8kkdAo98IC8spJ+ETpOAqm1xHCsUmfmHTvhuXbtc13eLFBSBb/hLhYJu5sJ02OidDZmGxGGiBUtMRxJmMl1jOdhKCoShu3l9sgFiJaT3bJ0CQCos5pMlOEk24EDcJ07pygHN1S4Hh53ba0QzD/xhOIboWgXLWvpPrgvX0aBwl5fIPL7Yy8rg+vSJVXyo4VjFdy+ce1aQKNBrswVFwGEDXhMmzYDgPyRhJA2Gc/DvG0bsmZMjz+9JMyCEuadvrJkcVYbCZcba969W0ifkBGUB55XXotVGFKXmPLgbwNAUKeDUOKsGFnTpsWxop7vGAW0Yz92DLzFEld97NCUBeb1wqovFfZFYgAdbsKbVa8XJt1JvDkPt0qcsBzxGGhzcmL/PceF7Rm0luqRMe4OafnYEW5MLHo90m6/HSldYi/+EClf3aovReqAATEXHRH2wz+ZNvS1lEqru4zAtKzoE7NTBwxApoQJiRG3E2PlQwAwrlolzOVQOqoXYWKln6pLDMcodWYoKkJKjx7InjVL2XbaiaQKfsN16/tnY6p9NxN6YVNlNmboNiJMbBGXGH5UZumscNsK08tl1ZfCdemSOr2+QMScbN7hQMuy5cqWZw4QbTjJUFQEbX4+cu9XmFoRSKdrc0E2LFkKACh4WvmNULSbBsMSX+9yPJUHIonQw2xYtgya7GzkqpDKEy6gNK5eg6wpUxR9bsINdZs2bED68OFI7ddPlTbtx47B09CA3DhTHoS22o5GWHbuQOqAAUjr3z++tsLkxlp27UbGHWPjrygSki5iLdUDbjdy4vkCDJNu5qyuhqe+XnLKAxBw/Q5ox1JSIvSUSi0DB1+wFvCaHBUV8BqN8S0wE3LtFSfdSSlx5hcyj8JfFiyeNkJTqtz19XCeOSttsQ8goBJHaxu83Q572WHpo4ZhRi14p9O3LLLUHOq21xb/ksaSeo4ROXXCz378OBwnTihOD4i12A/jeTQvWoSMsWOVzeUAoq4kCATkL6swqV08F8KV8Kuqhu3gQRQ8KzN/uR1KquA33BCPoWhRQu5mQnMX1ZiN2UaYHh5Pc7OwxPDD8yX1HkgWpnaoobAQ2k6dkKtGYIXIM49NGzbC29Ki7g1KmOEkd20tzDt2qpJaESh0whvvcqFl+XJkz54tqVckpghBqfvaNVh27kL+44+plmYTuh2v0Qjzlq3IfehB5T0PaBtQ2srK4K6tjXs54zZCgmrXpUtwnDwpa6KbX2jakXnrNiAlRd61JOTa5DWZYD14SOby3cE9m+7aWjhPn5ZVhjA0XcSyZw80ubnIGBt9NbY2bQAh7QglzrIkVmgAIA5rBwXjxSXIGD06vuXUQ46PVa8HOC6uEpdcyKibpTT2ksZt2gjdj9J9AGPxpaqF9AyKpcUkB4xtK9/YysrA4sldDjOHwn7kCJjDIS1dAQh7DROXVpZ6PGJMmG4uLBQq3jz8iLT2IohV6sxaUgL3pcvqzLcJMynSL2iJ4V69FG+K47iIcyP8+ct5jyub09GeJFXwGzo8I87GTMTdTEDPr2qzMUOEy2NsWb5CWGJYxWF7YVu+ySa+ddNdly/DsncvCp56Utns/qBttP2SFJZnLkTaoNuQKXW1LInbCu35VbM3NoguOND2552pcreOyBPRWj5bBjCmWppNuPfHuG49mNOJAgW1fYO2ERJQGlevgSYrCzl3362s3ZBSfaZNmwAAufcpyLcOCF4YYzBv3YqsKZPjC8T8+xeyoIRlb7EwGUzGAivCUuStr9W8ezcAiau6hQrIwWY8LyyzPH1afCUNw/SUWfbuQdqwoZKG1f1Cq414mprgqKiQXOKstR1tSO+xHunDhsU/qdATGECXQte1K1LjSJ0LvX6LyxHH0VvYdj/00HbsiLTBg6X/PdBmP4Sc4TiqgoS2odcDKSnImjBBWhthrmHW0lIgJQWZCtrw81y/DvOmzb5J7fIr3gCI2fPbXFgIbedOyJ07V9l2EH5ipV8ilhgOl+PvNZlgXLsWuQ8qzF9uZ5Iq+A0tJ5TIu5nAC5taszHbCLlLYx4PDIsXI3PyZNlLDEfeVvAFzrB4iS9/WYUcXHEbbXNX7eXlcJ6qFOovq3jsQoeTeKcTLcuWIfuu2UjpKXMWcKRthQRehqJFwmIqCvLOgoTp8WBuN1qWLUPWjOmq9AoAaPP+CBPdliF92LCYdTylb6M12OJtNpg3b0bOvfco7lXmQuqymjZsQMa4cYp63gOHzx2nTsF99Spy5H7hhQw5WnbuhLZjR3mTLkNKPll27UZK3z5IjTN9AggetXBUnIK38Xpc+b5A254yr8kE+9FyZM+QnvIAoM01SFz8IJ50BV87/mPjNZthLy+Pf2JwQI8c83ph3bcvrpxhYT+0QTdPVn3s5Yij7kc85ckC9kF4DQE3Jnq9UO9Yag3cMOXSLPpSZI4dKz33Pcy13+pvQ+oCTVFKmbYsW6Zaia5IedKAMKJk3VuMggVPq9MpFGZipV9zYRFS+/dXd4nhMJMwW1auBLPb1R21bgeSKvgNLCckzsZM1N1MwESR5sIiVWZjhgrtOTDv2uXLX1a/Bh8XMNkkOH9Zes9N7G20zV01FC2CJjsbeUomO4UTMpxk2rQJXoMhIR/wwEDbXlEhLKby7DOqTUYUezwCe/t27oKnsREFT6vTuwy0fX8cJyvgrKpSZyEQcRutAaV5xw7wNpvs2r5BAoJqR3U1nGfOKs/r1rbWVzZv3QZoNMiZM0dWU4FDx8zlEnpYZ8+SNSLl/6zC42ld1W3WbHk3jwGjFpY9e4T0gHhX2AsJSqylpYDXi+yZ8QW/oT2/1pISYfGDOPMqA29GbQcOAF6vtOWIA9vQtPauOyoqwBuN0of4xf1oHX1yVp+Bp7ERWVJq84buh7+Nqip4m5slpzz49wGA2Ea89Y6D2vBdFzzXr8NZWam8jdOnZbXRphqN2w3DkqXImjpVeTUaIGyetJ9h0SJAp0P+UyqNhEWY8GY/fhyO48dR8KyKk9rhO58Cvn/FWvR33KFeB0c7kZTBL7y8OBszUXcz/uED97VrMG/frs5szNBthAwRGwqLoOuhbInhaNsCAHg8rfnLak108wu5eHkaG2HasgV5jz2qaHGGcEKHkwyLFguzgKWUGIpXQKBtWLQIXEYG8h55RL32NcE9fYAw0U3Xo3vcAUZUIe9Py7Jl4NLTZS0RGm0b/nPauGo1Unr2lFayKYbAKhKmjRuF6hEKJ+gFTsAyb92KzAkTZNe6Dvzytx46BN5iQc5d8gJpMTeW5+Nf1S10vwJWsbPs2YOMUaPifo2hufyWvcVC3nCcvdqBw9qM52Ep0cfXyxnYjr+HvaQEmsxMZEostyYKuPa25gzHF/wG3lhYfUsaxxtAQ9fayRJXeTK/kM+0VS+jNz2kBKJ1n7QljcPuhzekDTmBfMiwvXnHDnjq61WbmB1phVDeakXLylXIveeeuNJ5ooqwuJSai3IFCq3uYi0uFqoF3QKLWoRKquBXHJ5xu4XZmAm8m/GfRIYlS9SbjRkqoMi+88wZIX/56WcULTEcbVuAcHES85el1rOUKPTiZfjsM8DtRsEzCTh2AR/yoLtoNdNSfPy9M96WFpjWb0DeQw/JyguN2L5/GM7XE+W8cAG2fftR8NRTquayB74/vNUK0/r1yL33XlUnVvonvLnr62Hdtw95D89Xp2fDF6wwxmDauAlZkydJWoI2Kt/xcFSfgevCBeTMU5DjFxCEWHbsBJeRIXs4M3A54dZV3eR9Vv05pZ7r1+E4cQLZs+JLeRDbAFqD1mIZecNA0DXIefq0sAhDnPm+/nb8102rvhSZkybFPUQtLPrjC6D1vpzhOEcQA28srPoSpA26TVJJr2j7kTZoUFyjcWK5NL41gNZ27hTXoi+haQDWEj20+flIHxZ9OeLgNoJr58ppI1LNYkNhEVJ69lStIyBSqTPjunXgzWaVc3B1bUqPCUsMb0rMEsMhPc3NRUWq5S+3N0kV/PpPWsuePXBfuqyo0HVMWq1QokvF2ZihhB4M4URVbYnhSNvyfYHZDhxITP4yEBQAMLcbLUuWImvGjLhLPUkRWIrOULRIuItWWlEgEl8eVcvKVcLkMJXvokPrqLYsWSoMvamdyx7w/pg2bwZvs6k2vCfyBanGtWsBxtRJeUDrULfj5Em4L19G7gMPqNImAJg3C5Pncu6W/wXROmTrhnnXLmRNmyo95zK0LX8w4nbJW9UtkG/UwrK3GADkVbIIyAl1VFbC23gdWXGUOPML7Pm1FAs9pZIrAQS245vw5rp0Ce4rV+JOeQAg9sh5LRbYy4/JWkzIf2PhX4443pQHoDWlirfbYT98JP7e54D98OcMZ0+NL3c5cESVMQZLafw98q0rzfG+JY1LkSVlWeSg/WhbhcFRVQVbWZnQsaFWR0CYmtyMMRiKipA+bBgyxo5RZztoO6ERCMhfTkD8Elg6z3Xxorr5y+1MUgW//pPWdvAgdJ07y5+cIgGn1cJaUiLM6k9UkK0Rcg69RqM6q8lE47uwNP/304TkLwMBAQDvhXn7diFnNVHDLb4vr4QsbR1CGAUQFlPJGDcO6RJnYksWMOzIOxxoWb0aOXPvhq5zZ1U3E5gz37JsOVIHDIir5JW0bQi5ucbVa5Bxxx1I7dtXnYZ9E95MGzYCKSmKq0cArV+2pk2bkTF2rLL8d38vcsUpeOrqkDNbRokzP9/5YD92TNaqboH8oxaWPXug69oVaUOGyGsDEFI6in1BdLyT1BCcPmEtKUHa0KHyznGd1pc2oSSAFs5T24EDgMcjbyVN341F63LEMnqxfdU4bGWHJS9pHKkNx6lKeFta4m8j4H1xVp8Rbm7iPR4BvbbOM77853gD+TCT9wyFReDS05H/+GPxtRVFuOWcbQcOwnnmrOoTs0MXlxImtfsW5VIjfznc9sRJ7YtVzV9ub5Iq+A28E81/eoH83hAptFowp1OYjanWrP4Q/p6QluXLhdVkEtiT7e/lsh06lJD8ZWEb/hxKL5oLi5DSu3fcK1JJ35YwnNSybLlqs4Aj0mpgP3xYyJ1KQPpLYHF308ZN4I1GVSe6idvxvT/OqmrYy8uR/8QTqvf+c1oNmM0G17lzqvX6Cu3qwNxumDZtQvb06dDm5Slv1PeZcF+9ihwZC1sE7Z9v+NlaUiKsxDZ7lvy2fOeDZccOQKtV9hnS6cA7nLDq9ci+806Zk+ZaAxvLnr1IHzFCXsqJL6j3Gk2wHT0a34ISATiN0PNr1ZcipVcvpMi4wfJPNrbq9eAyM2X19vknzVlLSsClpUkuLRZ2P0pLwaWkyMqP9+fDi3nHcX5fBQaDYt5x3JP/Am6Q/HnHcQa/obm4XqMRxnXrkPfQg/Ev7hJNuCDbvzjSA/LrhocTOjfFvGOnqvnL4bfnTUz+cjuTVMGvWOosJQUFTz2V0E35LwgFzz2n6mzMIL5tNBcWIWPMGMWryUQjBlgcl5j8ZSCg96sC9sOHUfBM4laT4bRaMKcLhiVLkDllsrpLW7fZlg68zSYsCJKI0QZN68XYsGQJUgcOROZEabUx4+J7L0zr1wMpKYlJE/EFlFxqqrIavCE4rRa82QxPfb2ihS2C2tS1nptqjCJxWi2Yy4WMsWNlT5wDIL5P5p27kDlWxqpuIftkP3oUvMUiK9/X3wYAeJqbYD92DNl3ygvG/cfbWloq9LZOl98O73bDtn9//OXJ/Hw9ZBa9HlkTJshbRMaX4mMpibO0WNB+aHzLM+uRMW6cvE4JX8+vVa8XetPjvTEJSAOw6vWSlyMOEtDzay2VvixyoNBc3JYVK8EcjoSs3gq0BtnC4kg7kP/kE7JTlSIKmYBmKCz05S/L+yzG5E87S0D+cnuTVMEvx3HC7PR581QfEm5Dq01sHil8PRiAUN4sQXeCIt+2EpW/DLReVEwbN6o+VNWGVgtraSk8dXUJr1/ovyiruSBIUPu+oMB+wjdxb8GCxEzc870/vM2GnDlzlAVoEbchHKvsOXepOinQHxBy6enIuUulaii+9zV9+HCk9lKhNrRvH3NkLGwRyH9d8DY1KUp5EPZJA95iAZeSgiy5lVB8+2PdWwzwfFxLGgfvi69He88eoUKD3NxKrQ5wu8HbbPLSBCB8Fjz19XBfuiwv5QHCTbH76lW4zp2Tl/Lga8Nz7Rqc1dXxV4oQ29CCN5tgKy9Htow2/Nc33mqFraxMXhqJ7xrG223CssgycpcRUAqPeb0wLF6MjPHjkC4jVSeqkJrcrYsjqVjz3idwboqjqhq2Q4cSusSwfxJmIvKX25sElAVo3/r8+yOk3XZbwreT/8Tj4DSahOWRAq0XDG3nTshVMtNcAk2m0KOQ0DvBgGoc+Y88os7QdAT+nkBd9+6qL23dhk7nWxBE5ZXj/HwXwtYyaombuOeX/0RiJlb6X0u+mqXg0Bq4Z8+epVrZPH8qkNKUh9b2tGCAvCWNA9sJ6JFWGvz6X2PmxImyj5t/fxwVFcIM/pEjZe6Lr50TJ5A9e7bsG0lxiF2rlR/Q64ReegCyA2hoNfA0NACA/BQOrRb2EyeENmQG4dBqYN23H3C75QXh/snQBw+COZ3ybgZ8AaXtUFl8yyIHCJoQuXcv3FeuoMsb34l/X2Jtx78MMO9N6OJIQPCEN0NRkbAo12OJ6xTidFrYysrgbbyO7r/5TUI6UdqLmMEvx3GfAHgQQANjbITvsQ4AlgLoB+AigKcYY4bE7aZ61KgZKkWi0yoAiD0qBU8tSPhszOzZs9Hnk4+RmaD8ZSA4JzvhdQX9aSlPP52Y0nABsmfORMaY0Ujp2jUh7fuDAtfZc8h74nF1e0wDt+N7f1J69FB3VaEAmeMnIGfePNm9aRH5Ah61Uh4AQNelM7iUFOTeq6xecGuDOqQOGIDUfv2UteO7LgiruilrS7xpUDDMGthLlTV9uvxeq8B25JQ4C2knY/Ro2WX6/L3ruh7dZa2cB7TeWOi6dkWq3A4ZrRbgeWg7dJA1GdG/H55r18Clp8sqX+kPBu3Hj8vPO/Zdg616PaDTIXOCjKXsAyb+tixZCl2XLqpMbA3Hnydt2pi4xZEAiEG2P3854UsMa7TwNl5PSP5yeyPlW38hgL8D+DTgsR8C2MEYe4fjuB/6fv6B+rtHoknp3QvaggLk34BAW5OWJm8oKh7+L6VEDFWF4LRacCkpCSsNF6jjF19KaPuBwUQiJrqJtFroenRHh5deTFgee9akiciaJOOLL4a0/v2R0reP/CH3MLKmT8cgfYlqNxuZY8aoEvT7e8Bkr+oWyB/8ysz3DWwDgKJaq+LKdZBXLSK0Hdk9tmg9xtlyc4bR+rmVnXcc2MaUKbI/k/42MifKzF2GEAwytxsZEydKX444zD7wZjMyx4+HNjv+UQb/e+I8dxbWkhJ0eu1biZvU7itfaSgsFOZYJGJxJLT2/LasTOyiXEHbAxKTv9zOxAx+GWN7OY7rF/LwwwBm+f79XwC7QcHvDZczaxay9SWJm1B3g3Ech07f+iay70xQMn+ArBnTkT1zZkLyVm843wUrfeRIZIxI4KRHjsNt27a15rx9juQ9/DBy589XdRiP4zhVe9l7f/AvVdrxBx/ZCtMnAECTnYXU2wYitU8f2W1wGg3gO+5yc1uB1nSFlL59FO2PfxRAdpoAIOaXKrpZ8d9YKAjCoWsNoJW2oeh46IQ8arl5x0G9+gpSQAAIC0sleFI7p9XCfuQIHBUV6PqznyYuPUCrBXM4YEjwolwinQ7QaBKSv9zeyB3v7coYq/P9+xqAiOO5HMe9CuBVAOij5IJFwrpVAl+/zt/4xg3ZTpdvf/uGbOdG0PjyyhOyEl6IRE20uBFu5fy1QFlTp6LPwv8gc4Lyih/d/u//xFW3FNHpkD5sqLIhW1/AmS1jMYhA2dOnw11bi/QRI2S3waWkABwnP2cYvs8SxylKJfOnX8gOOoPaUNAT7stXlxtAB6XGyB1h9LXhbbyO3IceUr6CY4xt2Y8dgyY7G/kqlmQMxWm1sFVUAG43unzn9YRtxy996FCkD749IfnL7Y3iZEfGGOM4jkX5/YcAPgSA8ePHR3weIUSe9NtvR79lnyn6Mie3DkWVGULEXbIqgoyRIxXXbdZ16ghwHHLmKsvjTB86FN1//nNFbRQ8vUDIGVZQRi5r6hRocnMU3RBoMjN9SxrLn0/A6bRC3rGSco9arcK8YyFw1eTlIV1myc7AADqhq7cGbCvv0UdVm0Ablk4LuN0JX5TLr/sv3kr4NtoLucFvPcdx3RljdRzHdQfQoOZOEULikyFzBj0hN0K/RUWK20jt0weDSvWJnfATx74oSr0AkP/444qXIO/6f/8H8N7YT4wia+o0aDt1VDQyos3KQuaE8fJHIgNzl2VPiBTCmfQRI5A+apS8NiRvy1e+MlE17338vfIJX5QrCckNftcCeBHAO77/r1FtjwghhJAw2kPg256oUV+6649+qLiN3h/8C1olaQYaDQpeeEHRwjbavFyk9OmDTl/7asJTnLRZ2UgfMhRpMit9SMXptDdkUa5kxDEWPROB47jFECa3dQJQD+DnAFYD+AxAHwCXIJQ6a461sfHjx7OysjJle0wIIYQQcpM4z1+AtiA/4TdjluISuK/VoeDJJxO6nVsZx3GHGWNt6u9JqfYQqV9/juK9IoQQQgj5HEkbkNgeX79sJfWsSVS3VqkAQgghhBBCoqDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0lAU/HIc9x2O4yo4jjvJcdxijuPS1doxQgghhBBC1CY7+OU4rieA1wCMZ4yNAKAF8LRaO0YIIYQQQojalKY96ABkcBynA5AJoFb5LhFCCCGEEJIYsoNfxthVAH8AcBlAHQAjY2yrWjtGCCGEEEKI2pSkPRQAeBhAfwA9AGRxHPd8mOe9ynFcGcdxZY2NjfL3lBBCCCGEEIWUpD3cDeACY6yRMeYGsBLA1NAnMcY+ZIyNZ4yN79y5s4LNEUIIIYQQooyS4PcygMkcx2VyHMcBmAOgUp3dIoQQQgghRH1Kcn4PAFgO4AiAE762PlRpvwghhBBCCFGdTskfM8Z+DuDnKu0LIYQQQgghCaUo+CWEEEIIIZ8PbrcbNTU1cDgcN3tXVJWeno5evXohJSVF0vMp+CWEEEIISQI1NTXIyclBv379IEzX+vxjjKGpqQk1NTXo37+/pL9RusgFIYQQQgj5HHA4HOjYseMtE/gCAMdx6NixY1y92RT8EkIIIYQkiVsp8PWL9zVR8EsIIYQQQpIGBb+EEEIIIaRd+dOf/oRhw4Zh1KhRmDNnDi5duqRa2xT8EkIIIYSQdmXs2LEoKyvD8ePH8cQTT+D73/++am1T8EsIIYQQQm6ITz/9FKNGjcLo0aPxwgsv4OLFi7jrrrvEHt7Lly8DAGbPno3MzEwAwOTJk1FTU6PaPlCpM0IIIYSQJPOLdRU4VWtStc1hPXLx84eGR/x9RUUFfv3rX6O0tBSdOnVCc3MzXnzxRfG/Tz75BK+99hpWr14d9Hcff/wx7rvvPtX2k4JfQgghhBCScDt37sSTTz6JTp06AQA6dOiAffv2YeXKlQCAF154oU16Q2FhIcrKyrBnzx7V9oOCX0IIIYSQJBOth7a92L59O37zm99gz549SEtLU61dyvklhBBCCCEJd9ddd2HZsmVoamoCADQ3N2Pq1KlYsmQJAKCoqAgzZswAABw9ehRf+cpXsHbtWnTp0kXV/aCeX0IIIYQQknDDhw/HT37yE8ycORNarRZjx47F3/72N3zxi1/E73//e3Tu3Bn/+c9/AADf+973YLFY8OSTTwIA+vTpg7Vr16qyHxxjTJWGpBg/fjwrKyu7YdsjhBBCCCGCyspKDB069GbvRkKEe20cxx1mjI0PfS6lPRBCCCGEkKRBwS8hhBBCCEkaFPwSQgghhJCkQcEvIYQQQghJGhT8EkIIIYSQpEHBLyGEEEIISRoU/BJCCCGEkJvipZdewvLlyyP+3mQyoVevXvjmN7+p2jYp+CWEEEIIIe3ST3/6U9x5552qtknBLyGEEEIIuSE+/fRTjBo1CqNHj8YLL7wAANi7dy+mTp2KAQMGBPUCHz58GPX19Zg3b56q+0DLGxNCCCGEJJtNPwSunVC3zW4jgfveifjriooK/PrXv0ZpaSk6deqE5uZmvPHGG6irq0NJSQlOnz6N+fPn44knngDP8/jud7+LwsJCbN++XdXdpJ5fQgghhBCScDt37sSTTz6JTp06AQA6dOgAAHjkkUeg0WgwbNgw1NfXAwD+8Y9/4P7770evXr1U3w/q+SWEEEIISTZRemhvtLS0NPHfjDEAwL59+1BcXIx//OMfsFgscLlcyM7OxjvvKN9vCn4JIYQQQkjC3XXXXXj00UfxxhtvoGPHjmhubo743KKiIvHfCxcuRFlZmSqBL0DBLyGEEEIIuQGGDx+On/zkJ5g5cya0Wi3Gjh17U/aD83cv3wjjx49nZWVlN2x7hBBCCCFEUFlZiaFDh97s3UiIcK+N47jDjLHxoc+lCW+EEEIIISRpUPBLCCGEEEKSBgW/hBBCCCEkaVDwSwghhBBCkgYFv4QQQgghJGlQ8EsIIYQQQpIGBb+EEEIIIeSGuHjxIkaMGNHm8e9973sYMmQIRo0ahUcffRQtLS0J2wcKfgkhhBBCyE01d+5cnDx5EsePH8ftt9+Ot99+O2HbouCXEEIIIYTcMF6vF6+88gqGDx+OefPmwW63Y968edDphIWHJ0+ejJqamoRtX9HyxhzH5QP4N4ARABiALzHG9qmwX4QQQgghJEF+d/B3ON18WtU2h3QYgh9M/EHM5505cwaLFy/GRx99hKeeegorVqzA888/L/7+k08+wYIFC1Tdt0CKgl8AfwWwmTH2BMdxqQAyVdgnQgghhBByi+rfvz/GjBkDABg3bhwuXrwo/u43v/kNdDodnnvuuYRtX3bwy3FcHoA7AbwEAIwxFwCXOrtFCCGEEEISRUoPbaKkpaWJ/9ZqtbDb7QCAhQsXYv369dixYwc4jkvY9pXk/PYH0AjgPxzHHeU47t8cx2WFPonjuFc5jivjOK6ssbFRweYIIYQQQsitaPPmzXj33Xexdu1aZGYmNpFASfCrA3AHgH8yxsYCsAL4YeiTGGMfMsbGM8bGd+7cWcHmCCGEEELIreib3/wmzGYz5s6dizFjxuCrX/1qwralJOe3BkANY+yA7+flCBP8EkIIIYQQAgD9+vXDyZMnxZ/ffPNNAMBbb711w/ZBds8vY+wagCscxw32PTQHwClV9ooQQgghhJAEUFrt4VsAinyVHs4D+KLyXSKEEEIIISQxFAW/jLFyAOPV2RVCCCGEEEISi1Z4I4QQQgghSYOCX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEHJDXLx4ESNGjGjz+Pe+9z0MGTIEo0aNwqOPPoqWlpaE7QMFv4QQQggh5KaaO3cuTp48iePHj+P222/H22+/nbBtUfBLCCGEEEJuGK/Xi1deeQXDhw/HvHnzYLfbMW/ePOh0QhGyyZMno6amBgDgcDjwxS9+ESNHjsTYsWOxa9cuxdtXWueXEEIIIYR8zlz77W/hrDytaptpQ4eg249/HPN5Z86cweLFi/HRRx/hqaeewooVK/D888+Lv//kk0+wYMECAMD7778PjuNw4sQJnD59GvPmzUN1dTXS09Nl7yf1/BJCCCGEkBumf//+GDNmDABg3LhxuHjxovi73/zmN9DpdHjuuecAACUlJWJgPGTIEPTt2xfV1dWKtk89v4QQQgghSUZKD22ipKWlif/WarWw2+0AgIULF2L9+vXYsWMHOI5L2Pap55cQQgghhNxUmzdvxrvvvou1a9ciMzNTfHzGjBkoKioCAFRXV+Py5csYPHiwom1R8EsIIYQQQm6qb37zmzCbzZg7dy7GjBmDr371qwCAr3/96+B5HiNHjsSCBQuwcOHCoJ5jOTjGmBr7LMn48eNZWVnZDdseIYQQQggRVFZWYujQoTd7NxIi3GvjOO4wY2x86HOp55cQQgghhCQNCn4JIYQQQkjSoOCXEEIIISRJ3Mh01xsl3tdEwS8hhBBCSBJIT09HU1PTLRUAM8bQ1NQU16IXVOeXEEIIISQJ9OrVCzU1NWhsbLzZu6Kq9PR09OrVS/LzKfglhBBCCEkCKSkp6N+//83ejZuO0h4IIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSoOCXEEIIIYQkDQp+CSGEEEJI0qDglxBCCCGEJA0KfgkhhBBCSNKg4JcQQgghhCQNCn4JIYQQQkjSUBz8chyn5TjuKMdx69XYIUIIIYQQQhJFjZ7fbwOoVKEdQgghhBBCEkpR8MtxXC8ADwD4tzq7QwghhBBCSOIo7fn9C4DvA+AjPYHjuFc5jivjOK6ssbFR4eYIIYQQQgiRT3bwy3HcgwAaGGOHoz2PMfYhY2w8Y2x8586d5W6OEEIIIYQQxZT0/E4DMJ/juIsAlgC4i+O4QlX2ihBCCCGEkASQHfwyxn7EGOvFGOsH4GkAOxljz6u2Z4QQQgghhKiM6vwSQgghhJCkoVOjEcbYbgC71WiLEEIIIYSQRKGeX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEJI0KPglhBBCCCFJg4JfQgghhBCSNCj4JYQQQgghSYOCX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEJI0KPglhBBCCCFJg4JfQgghhBCSNCj4JYQQQgghSYOCX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEJI0KPglhBBCCCFJg4JfQgghhBCSNCj4JYQQQgghSYOCX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEJI0KPglhBBCCCFJg4JfQgghhBCSNCj4JYQQQgghSYOCX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEJI0KPglhBBCCCFJg4JfQgghhBCSNCj4JYQQQgghSYOCX0IIIYQQkjQo+CWEEEIIIUmDgl9CCCGEEJI0KPglhBBCCCFJg4JfQgghhBCSNCj4JYQQQgghSYOCX0IIIYQQkjRkB78cx/XmOG4Xx3GnOI6r4Dju22ruGCGEEEIIIWrTKfhbD4DvMsaOcByXA+Awx3HbGGOnVNo3QgghhBBCVCW755cxVscYO+L7txlAJYCeau0YIYQQQgghalMl55fjuH4AxgI4oEZ7hBBCCCGEJILi4JfjuGwAKwC8zhgzhfn9qxzHlXEcV9bY2Kh0c4QQQgghhMimKPjlOC4FQuBbxBhbGe45jLEPGWPjGWPjO3furGRzhBBCCCGEKKKk2gMH4GMAlYyxP6m3S4QQQgghhCSGkp7faQBeAHAXx3Hlvv/uV2m/CCGEEEIIUZ3sUmeMsRIAnIr7QgghhBBCbhHVhmpct1/H1B5Tb/auBFFS55cQQgghhJAg51rO4ZOTn2Dzhc3okNEB6x9djzRt2s3eLREFv4QQQgghRBGj0wiHx4EWZwte2vwSLG4LUjWp+MPMP7SrwBeg4JcQQgghhMhUb63H0qqlWFq1FGaXGem6dOSk5mDJg0uQqctE58z2V+mLgl9CCCGEECKZy+uCwWHAz0p/htLaUvHx2b1no1NGJzwz5Bn0ze17E/cwOgp+CSGEEEJITM2OZuy5sgcfHP8AVy1XAQApmhT8eNKPMarzKNxecPtN3kNpKPglhBBCCCERtTha8Puy32Prxa1weB0AgNGdR+NLI76Eu/rcdZP3Ln4U/BJCCCGEJLEWRwsa7A1tem5tbhu+vPXLOHH9hPjYS8Nfwp297sSEbhNu9G6qhoJfQgghhJAkY3KZ8N+K/2Joh6F4v/x9nG05iwxdBriAJRy8zAun14me2T3x2tjXMK3nNOSl5d3EvVYHBb+EEEIIIUnC7rGjyd6Eb+z4Bs4bz4uP39fvvrCVGSZ2m4iZvWfeyF1MOAp+CSGEEEJuYU6vE+vOrcOwjsPwVulbqGyuhIbT4J93/xMZugx0yujUrqszqI2CX0IIIYSQWwxjDB7eg98d+h02XdgEk8sk/m5it4l4esjTmN5z+k3cw5uHgl9CCCGEkFuEm3djf+1+fFb1GXbX7BYfXzB4AUZ3Ho3eOb0xpsuYm7Z/7QEFv4QQQgghn3Ne3ot/HvsnNpzfgBpLDQCgS2YXPHn7k3h5xMtI0abc5D1sPyj4JYQQQgj5HPLwHvzp8J+wt2Yvemb3FFdbm9pjKu7vfz/mD5wPjuNitJJ8KPglhBBCCPkc4RmPpVVLse7cOpy4fgJDOwxFtaEaL494GU8Nfgrds7pT0BsFBb+EEEIIIe2IxWVBdmp2m8cZY/jT4T9hRfUKmN1mAMCLw17EmxPevNG7+LlGwS8hhBBCSDvg4T349f5fY8WZFRjaYShStalBv3d5XahsrgQAzB84H48NegxjOo+5CXv6+UbBLyHkpnp7UyUGds7GU+N7S3q+w+3F33eexYtT+6FzTpqibdcYbLjSbMeUgR2jPs/t5XGpyYbburTtiZGqyeLE+etWTOjXQXYb7YGXZ+AZQ4pWo3rbtS12pKdo0SErNfaTJeJ5BgZAq4k9BMwYAwAaLv6ccnv5hJyXNwLPePz96N+x/vx61FnrcE+/e2B2mcVz0i9Tl4mXR7yM1+54DRru8/la2wM6cgEYYzDa3OLPFbVG2FweAMDpaybsrW4EIFygf7f5NJosTvG5RrtbPEmbLE5cuG4FAFidHiw/XAOH2wueZ/ik5AKKzwjtrDpag+8sLYfD7cWhi82Y9+c92HSiTmxzV1UDztQLwxplF5vx/q6z4HkWtE2/c40WGKwuAMDlJhv+tuMM1pRfFfep9Nx1fGnhIXx/+THxef/bfwn3/mUvLjUJ++r28lh99Kr4WlYdrcGG4637w/MMFqdHPFYHLzRjR2U9PF4eAODy8Pjf/kuoM9rBGMOSg5fxH/0F8e89Xh4NZgfONlhw71/24g9bqsTfHbrYLB7fsw1m/Hr9KRjtbhisLry+5ChWHa2B28vjrbUVePQfelh9+9Fic+GDPefw39KLcHl48Vj8ZNUJ1JscYvsNZge8vmPXYHLgbINwXC0h789C/QWUnrsOAFh/vBY/WnlcbJcxhqOXDTh2pUVs1+3lseJwDRYfvAyH2yu2+cetVXhrbQVqDDZxm28sLceB801weXj8av0p/GD5cfH9cbi92FPdiNPXWuswWp0e7DvXBMYYGGPYdboBDWbhNZ28asTWimsAAIPVhT9urcKft1XD7BDOCZvLg/+WXkSz770uPXcd7+86i6pr5qBz5lStSdz+mvKr2Hm6XtwnxhguN9kQ6rrFiX8Xn8f647VBF2b/ebD5ZB02n7wmHouVR2pQdrFZfE3/23cR132fnZNXjfhgz3n8fE0F6ox2AMLn61yjpc12/YoOXMbfd53F2xsrw/7eyzPxvQhkdrjFbfh997NjeOHjAzjv216D2YH3dpyBy8PDaHNj1dEa1Bnt+P2WKsz78x5U15vbtBvNmvKrqKg1AgC+89kxLPhgn/h5C903/2drR2U97vrDbvG9Ntrc+HrRYfGc9TM53PjDlioY7W58Z2k5vrTwEADhvfef+4wxvLv5tHjNkcJod4v7eOSyAQfON6HR7MQrn5bhfKMFry8tx0N/KwHPMzRZnOI5tOpoDT47dKVNew63Fy4PD7eXx5aKa+Ln0G9HZT2mvbMT1fVmzP+7Hq9+WgbGGLadqsfaY7VtvvxDNZgcUZ/z9aIjePJfpeJzrE6PeD6G+saiI1jw4f6g9moMNvGaKdcHe87hT9uq2zzu5RkaTI42x8Roc+OeP+/F6qNX2/wNYwzHrrS0ec3HrrTg2Y/2i5/5QA0mB8oDrluR8DyDx8ujxebC/vNNMNrduG5x4qO958XPd+i+nG0wi99F1y1OLNRfgJdneG/HmbD7Dwjn6NUWO4w2Nz7cew67qhqi7hdjDLUtrZ/djSfqcM0onOOfHbqCX68/hXONFoz/9Xb8u/h80N96vLz42fK70mwLe40Ix+nxisfa7HBjV1VD2M9wKJ4Xrp92V+t2WmwunLxqFH+uuF6BD459gBlLZuBLW76Ej058hDprHZ4f+jzemf4u3pn6N3w470N8MPcDvHr7O/j7Xf/Ch/M+xMS853H0cgsA4Rr6zqbTQa8nMDa53GTD1Zbg6x4A2F1e/GlrVdD+2F1ecX/tLi+O17SEfW1bKq6hcP8l8Wf/eeB/TwDhPd5T3RgUs7Qn1PPrY3d58ebyY9hy8hoWvzoZhy42493NVZg1uDO+PH0Anv/4AADgk5fG4/vLT+C6xYnzjRb887lxWH+iDm8uO4YF43vjmYl98Ng/9fDyDOu+NR2vLynH6WtmVNQaYXd5scT35fDD+4bgnU2nAQgnSfGZ67C5vPj+iuO4rUs2tlRcwx+2ViM3XYc/PDkar/7vMAAgM1WLl6b2w6/WV+IT/QW8/+wdcHm9+M7SYxjWPRe/e3wUnv/4gHgx2niiDo/f0QtfKzqCLjlpaLK4sLf6Op4a3wvv7TwLAHjjs2P4+MXxeG1JOfZWN+Ke4V0xsmce/rBVuFj37TgdvTtk4iv/K0NFrQkbX5uBD/eex/98J//4vgX4y9Nj8ONVJ7G3uhHLy/Iwb3g3/N4X3PbrlIXxfQvw8sIyHL1iQH5mKhrNTpy+ZsbwHrlweXl8e0k5AODDF8bhtSVH4XDzaPTdRByvMWJ1eS3WH6vDjtPCRfL/Vp/Ej+4bgqc+2IeLvgBt5ZEa/Oj+oXjl0zKYHcLF9eMXJ2D54Sv40coTeGp8bzw7qQ8e/YfwRbjhtRl4bfFRnGmwoLLOBKvTI74/P31wGH61/hQAoCAzFd+dNxjfWVqOtcdqAQALxvfG/z04FF/532GUnmsCABTuv4Q/LxiD7ywtR2WdCSlaDVYeqcGvHx2JP22twsUmG/aeacSEfh2wyRccDu2eg/ljeuKFjw+gotYEjgNen3M7np/cB4//sxQXm2z445OjceKqEQtLL2J4j1y8OW8wvugLdD5+cTx+se4Uagw2MADrjtXir0+PxQ9WHMepOhP0Z69j2m2d8PO1FQCA93acwduPjURuegq+/GkZAGDrd+7EG5+V4+RVIYh5bGxP/OqREfj+8uPYcKIO7zw2EiN65uE7S8sxqGs2Dl4wiMHrrjsa8dvHRuAv28/g45ILuHNQZ2yvrAcALH5lMn686oR4I/jaXbdhw4k6nGu0ovjMdfzfA8Pwo5UnkJWqhYdn+PJ/y/DcpL748aoTyEzVYs/3ZqPqmhkLSy+gV0Em3rxnMC432fCvPeeQouWw8uhV3D2sKzrnpOFvO88iO02Le4Z3wx+3VsPj5fHspD5YdOAyehZkYPaQLvjHrnOwOD14YFR3/HXBGKwur8WBC0IQ9LM1FVj4xQl4Z9NprDxyFQVZqVh84DJO1ZkwoFMWalrs4Bnw1+1n8NSE3lh/rBajeufjiTt6Yeupa3B7GR4Y2R2NZif2nb+OqQM7weRw4/Wl5eiYlYoJ/TqIN3f/3H0Oz0zsg6OXDZgztCu656XjyX/tQ2aqFr9+ZCR+se4ULjfb8HHJBfzw3iF4b+cZbDxxDRtPXEPP/Ayk6TR4a/5w7KpqwH/0F1F8phHHaoQvsFVHa/CzNRWwOD1Y8bWpON9oxT92n8O/Sy5gzpAuMNhc+Nqs2zD9tk64arAjPVWDRQcuw+Xh8aXp/dFic+Ohv5XA7vbir0+PwVtrK8AATB3YEdtO1WP/uSaYfYHEwtKLeHfLaTjcPH7+0DC8vek03F4eR6+0wGB14ZlJfTBlQEfM/3sJ+nbMwvAeufjL9jP41cPD0bdjFnp3yMSB8034644zqDM6MO/PewEIAdT/rT6JogOXheuj0wOby4veHTKxpvwqLE4Pvj1nEMb2KUDZxWY89cE+/ObRkXh0bE9oOA51RjtOXzOjT4dMFO6/hM2+m8TSc02Ydlsn/GTVCawur8XiVybD4fFiRI88dM5Jw7ZT9dh4Qnhu2SUDJvTrgPd3ncXvt1She146dr05CxanB1mpOmyrrEeKhsM9w7vhisGGLy48hHcfH4Xxvl59h9uLepMDnbLTsOF4HX63WbjWPzmuF5weL3686iTSU7RwebzYf74ZL0/vjzfm3g6724tGsxN/2laNqnoz/rK9GhwH/HT1SYzomYeiL0/C//Zfws/WVOCfz92B4T3y0CE7FRVXjfj52gqcvmZG4f5LYAxYfuQKvnXXIDxxRy98pfAwKmpNWPrqZDSYnejXMQvrjtUiTafBl6b3R1aaDocuNuNLCw9Bp+HQNTcdp6+Zcc/wrtBqOGw8cQ3NNhdStRoM75GLucO6guM4fH/5cSw7XIPRvfKw9CtT8JNVJ7Cloh7VDRYsOnAZHbJSce+IbjjbYMEft1ZhQOdsvDS1Hx78WwmMdjf6dMjE5WYbMlK0+PUjI3DN5MD80T3Qu0MmHG4vFnywD/eN7A63h8dfdpzB5m/PQI3Bjq8XHcG9w7vhsTt64mdrT8Lh5rHscA2Mdjfe3VyF3h0ycc/wbig9dx3fXHQUzVYXnp3UBz97cBjWltfi+yuOY+btnbHwixPg4Rn+uLUaqVoOb8wbjIX6C/jf/kv48AvjUdfiwNeLDuPhMT3xyowBeOGTA7jUZEOqToOfPjAUz0/uC5eXx6/XV2JzxTWM71uA3zw6EhyAr/zvMA5ebEbnnDT8ZcEY9C7IxMPvl8Bgc+PNxywob9mGA3VCXKHhtKgzNyLdehfm9XwWrwwbgyf+tQ9n6s3Y+O0ZeG/HWaw4UoMvTOmLId1y8eNVJwAA//7CePE67vby+OmDw/D3nWfwh63V+Mn9QzGwSxa+tLAMnbLTsPPNmchO1eH9XWdxrMYIk92NgxebsbTsCra/MROXmoTzuFtuOj55aQKe/Wg/zjRY8K/nx+HeEd1w5LIB72w6jWHdc7Gw9CIAoEd+OqYO7IRvLzmKLRX1SNFy+OF9Q/HEuF54/J+lONtgwS8fHo4vTOmH9oaLdVetpvHjx7OysrIbtj2p6ox2vPKpENjlZ6TAEND769c1Nw0tNjecvl7A+aN7YO2xWky/rRMOXmiGK8xdsV+vggzUGIQ7r/tGdMOe6kbYXF6M7p2P4T1ysch3kf/fyxPx+pJyWF0euDw8ehUIFwYA6N8pCx2yUnHksgGzbu+MXVWRe3I6Zadi+VenYtupevxu82l4eIY+HTKx4bXpuNRkw7cWH8WF61YM7Z6Lr9w5AN9ddgyZKVqYnR70zM8Q7xKn39YJp6+Z4PTwGNo9FwcvBPeWfGlafwztnoO31lbAzTO4PDzu6JOPI7470ruGdMGlJisuN9swvEdeUM/Df780EX/cWoXjNUak6TTomZ+BOqMDdrcX6Ska3D+iO1b6eg3+74Gh+Mfuc2i2uvDEuF7omZ+Bv+44g6xULZweHotemYxmqwvfX34MJocHGSlaPD2xN/6jv4ixffJxosYIT5S7T/8FGADmDuuK4jONcLh5jOqVh/6dsrCmvBbDe+SiotaE1+YMgpfn8Y/d55CbngKj3Y3fPjoSnbJT8b3lx2FyuMEY8K/nx2Fo9xx8c9FRnPDdWf/oviF423fD86Vp/XH+ugW7qxrRp0Mm6ox2/OHJ0dhd1YhVR6+iIFNoO3C3O2Slir06vQoyYLS5xUBk2VenwMszfHPREVy3CM8JfS9+MX843lx2DAcuNCNNp0GaTgOTo7VH5B/P3YEz9Rb8eXs1OmaloilCb1d2mg5LXp2MHZUN+PP26qDj5z9vjtW0wOxr+z9fnIC15bVYdfQqNBwwoV8HHLjQjE7ZaXB5vHjn8VHITNXi1f8dhsvDIy9DeO1j++Sj/EoLuuSk4brFhWHdc3GxyYo0nRbvPzsW726pwrErLdBpORRkpsLq9AS9HgCY1L8DzjZY0GR14Y4++ZjQvwM+2HMeg7vmoKrejGHdc/HEuF745fpTmDusK3ZU1oPjOLEnrmtuGupNQqD/9ITe4s1RVqoWVpc36DgN6JyFy002eHiGVJ0GAzploareDP8l9sUpfeFlDIX7L4PjAMaANJ0GD4/pgc/KasR9TtFyGNg5G5eabLi9Ww6OXWlBTroOZocHI3rmwuHmUWOwwe0VRgR4BgzsnIXrFlfQaJBfTppOPE/6dMjE1RY7ehVk4FKTDVoNB8YYOI5D97x09MzPwPEaI+xhesU6ZafiusWFId1yYHF6xGuaX3qKBg4373tuGq5bnBjRM1e8qYqkU3YaOmWn4vQ1M74ycwCWldWg2eoSX3Poa0lP1cJgdWHe8K44fMkgvj8AkJ+ZApvTK16PU7Ua3Hl7JxyrMaJzdhryMlKw73xTcJvpOvz92Tvw45XCTVejxYluuemYPKAjFh28jFStJqjn0H9+AsLnigPEm3L/dfrCdSuarS7xPOmel44GsxPPTOyN03VmlF0yhD0WHAekaDRweXlM7NcBB0N6qHPTdXB6ePF7CGg9F7Ua4T0MfV8GdM7C+ca2PZVajXCeD+ueixem9MUHe87hcrMNUjrqvjClL3jfuZydpmvTswoI70WLzY2fPTgMiw9expkGYXSlc04ajHa3OKI2vm9B0PHISNHilw8Px+6qRmwIGAmNJPD9+MG9Q7DpZB2O1xjxzMTeWFteix75GZjYvwOKDlzGoC7C58p/frw0tR9sLo/4+XtwVHes9412ds9LR11ATyYgHP93nxiNJYcuY3eV0FGUlabDyiNXMXdYV+ypakSHrFT06ZiJ8ssteH3uIKw8chXnm6+hW3YHNKIEqZ23QKMT3g/elQ93y0T01t6DBiMTP6ex9O+UhcvNNnh5hg5ZqZjQrwBbKuoxY1AnFJ+5HvZvJvbrgDF98vHh3tae8afG98LywzXo1zELLi/f5tzxH9/v3zsYv15fKV4XxvctQLPVhZoWOyb0K4D+bBPemHs7jtcYsb2yHl1y0tBocYIxIFWnwVsPDcczE3vflHQijuMOM8bGt3k82YPfo5cNePV/h2F3efHeM2PQISsNX1p4COk6DTZ/5068t/0MFh28jP+9PBF5Gan48coTmDe8K16e3h//238Jv95QiTSdBhtfm4GfrBZ6Pj98YRwyU3V4felRPDS6B3543xC8vfE0zjZY8O8Xx+PCdSve33UWr989CAM7Z+OzsivonJOGu4Z0RYPJge8uO4bzjVZsfG0GDl5sxh+3VuFXj4zA8B65+MXaU1hadgXj+hbg3SdG4cVPDqLF5sbW79yJdcdq8eft1fjTU2Nw/8juAIDyKy14d/NpvH737ZjYX+iVsDg9+Lj4Ah4Y1R23dcnG4UsGvL70KIZ0y8W/nh+H93acEXqVXpoAm8uLby4+imNXWvD1WQMxunc+vrnoCIZ0y8Wqr0+FTqvBhetWfPezcnTISsNHXxiHpYeuYE15Lf76zBikaDT43vJj2F7ZgJen98fjd/TC+esWPDiqB5weL97ZdBrrjtViyatC8PazNSfx7KQ+mD+6Bz7ddwmNZie+O+92NJidWHroCl6c0g95mSnYcLwOP151Ai9P74/X5gwCIAxPvrX2FO4Z3hVPju+NFYdr8NM1J5GRosXGb8/AT1adxI7T9fjkpQlI1Wrw+tJyPDKmB747bzB+u7ES5xot+PjFCThTb8Hfdp7Bm/cMxsDO2fjbzjP4644zmHV7Z3zy0gRwHIfiM434ztJyTBnYCX97ZiwA4GqLHT9YLvTcvzV/OABhyOy9HWeQlabD12fdhpNXjVh3rBbfvnsQUrQavLv5ND4qvoDX7x6E1+++HYwxLD54BW+tq8DXZw3Eggm98bXCI3B7eaz8+lQs1F/ExyUX8NEXxiM3IwU/WHEcswd3wddmDQQAXDM68P0VxzGsey6+d89g/GFrFXZU1qPoy5PROScNbi+PtzeexqqjNVj6lSmoMdjw+pJyfGFKP7x5z2AAwLZT9fjO0nLMGtwZ37tnMB5+X48WmxvLvzoF/y6+gIfH9MB9vvNr5ZEa/GDFcUwe0BHfu2cw/qO/iLfmD8eF61Z8Z2k5XpraDy9O7QeeZ/hg73l0zErFI2N74huLjmB7ZT0+/dJEzBjUGQCgP3sd/y4+j988OhIbjtfhNxsrMahLNtZ8cxr0Z5vwjUVHkKoVPmt9OmbC5HDj+X8fQIPJibXfmoYGkxN/2FqFn9w/FAt9KR9/e2Ysqust+G/pRfzgviHokJWKv24/gz9vr8Z9I7rh78/eAa2Gw9sbK/HB3vPo0yETv3l0BF74+CAA4OCP5+DP26uxYEIfjOiRi68VCe/Fv54fh99vqcLHJRfw60dGIDcjBd9echTDuufi7cdG4huLjqDGYMdfFoxBRa0J9wzvinF9O8Dh9uKZj/ajY1Yqvn/vELz830O40mzHrMGdcanJhgGdsvD24yPB88CDfyuG0e7GD+8bikfH9sTWimt4ZGxPGGwuPPheCQZ2zsZPHhiKJYcu4425g7HzdD1+sOIEJvbvgOm3dcKftlXj5w8Nw/zRPbC07ArmDOmK7vnpeOC9YrRY3fjWnNuw71wTvjJzIHQaDgs+3A8vz/Du46NgdXnwi3Wn8NtHR+LwJQM6ZqfilRkD8FnZFTw/uS+OXDLgiwsPYVSvPLwwuS9+se4U/vbsWNQ029A5Jx2zBnfGvX/Zi4tNNtw3oht2VTWgU3YavjpzID4qPo/v3TMYpeeaMKx7Lp6f3BdGuxsVtUZMHdgJm0/W4auFR/B/DwxFVpoOWyuu4ScPDMPuqgbMHtIFOek6TPrtDjAW/IX+2NieqGmxIyNFiy9N74/1x2rxyp0DcHvXHGw4XodvLDqCgswULJjQBzxj2FvdiDfnDcZvNlaKoxNLXp2M841WsWdt6sCO+POCMXj5v4dwqcmGl6b2w6aT1/D63YNw1WAXb2aHdMvB6WtmjOqVh9oWB3LTdVgwoTfWH6/D12cNxOwhXfCbDZXiaNm7j4/CR8XncbnZhsIvT8KT/9qHSf07YGj3XNQY7Hjn8ZEoyEzFnD/uxsUmG34xfzh+sa4CPBMClh75GfjH7nP49pxB2HeuCYO75eAbs29DbYsdD/6tBADwq0dG4KerTyI3XYdv3TUIZxssuNpixyt3DsCG47X44rT+uNRkxVcLjwAACjJT8N4zY/Ef/UUcON+Edd+ajkf/UYqZt3fGQ6N7YPXRq/jZQ8Pw7uYqrDhSg1SdBtMGdhS+b94rRm56Cl6/exA8PMNnZVfwzuOjhFQvX6fJXxaMwetLywEAbz00DDqtBr9YV4Etr9+Jwv2XkZWmxZPjeuORf+jRbHVBq+EwdWBHFJ+5juw0HWYO7owNx+vw2pxBmHl7J7y19hSemtAb80f3QNnFZvBM6Lxwerz43aYqfKK/gPQUDbZ9ZyZ6d8jE7qoGfPezY/DwDNveuBP/2HVO7MF8aWo/IaWtxogh3XLwrbsG4RuLhOOy5fU78Yt1FSg914R3HhuJpyf2Ac8zfFxyQexcenl6f/z0wWE4edUodi69Oe92TB5mxLaLO7C4ajG8nnRodXYw8NC5boex5l50Te+FN+aOwPeWHxfPvz9urcKhiwa8fvcgjOqVh28uEq4rn748ET9ZdRLFZ65jzTen4ZrRgV+tP4VvzL4Nswd3xns7zuBvu86if6csFH15El799DCqrpmx9lvTUHXNjB+tPAGby4t7h3fDbV2y0WB24HePj8L+8814felR1JucWPzKZOw7dx3v7TyLr80aiGcm9MFXCg+jss6E7DQdfvvYSCzUX8CfF4xBdpoOby47hl1VjXj8jl7441OjwRjDwtKLeHvjabw0rR++cucAvL60HPUmB9Z9azrSdFrFMVu8IgW/Yj7hjfhv3LhxrD1ZeeQKG/STjWzG73ayqmsm8XGT3cWaLU7xZ4+Xj9jGuQaz+LdeL8+uNFuD/o7nI/9tNNG2eeB8E7tudjDGGLO7PKzeZJf0d9F4vTzzRvhbp9vLdlReY063lzHGWKPZwUx2l+S2eZ5nhy40MZfHG/H3ckh5rVcNNnbpulXcTuCx8sbx/pxvtDCb0xP0mMvjjXjM4lHbYmuzH4Hb4nle9vsaSeB+u8O8LxaHW9ymwepkp2qNEduqMdiY3eWJ+PtwPF6e1bbYoj5nd1VD0HNO1RrZiZqWoOe4PV5mcbjj2jbP8+zghaagffZ6eVa0/xK73CScK/vOXWebT9bFbKeupfV8On6lhbXYhM/FVYONHbzQFPHv/E7Xmdgft5xmdpenzftwrsHMjl9pCf1zxpjw/oSeezzPsxWHr7C6FjvjeV7cl1CNZge7amh77DccrxVfM8/zzGB1tnlOoN1VDazeKLz+cOfQpetWtuTgJeb18uya0R7XOXK5yRr1s1l+2cC2BLw//v2IZtOJWnbxuqXN40cvG9iM3+1knx26zBgTXvtHe8+xI5eaxeeY7K42n3+e59lvN55i/y4+z5xuL9tacY25PF5md3mYw932tVocbvbSJwfYJyXnGc/z7ERNC9tReU3c/3Cf8asGG/vLtmpmd3lYZZ2Rnak3i7/zX49DrTh8hf195xnG8zzbWnEt6LssnH/uPivuE2OMNVmc4ndauG2YHW721tqTQZ9Fc8D1ItDF6xZ2z5/3sP/tu8gYY2zLyTq27thVxphw/BpMjjZ/c+B8E3tjabn4HVdc3ciaLU7m9nhZU4zXEqjkTCPTn2kMesxgdQad+5tP1rE3PytnVqebWZ1u9v6uM+xcg3CMq66ZWNlF4TPs9fKsss7Y5pw8etnAfrWuIuga1GJzsPXHatlnp5ezUf8dxUYsHMHu+PQO9rWtr7Ff7fsVMzvNzGR3sbc3VrKyi8I5Vn7ZIH723B4v059pFI9nizX43Iv2nVNx1SheM90eb9D33dkGM/vZ6hNBj/k1W5zia2VMOO/8r9Xu8rA/ba0K+rwF7sve6oY2nw2j3SX+vdcb/n2+UQCUsTDxaFL2/PI8w7tbqvCvPecweUAH/PO5cShQcXYxIYQQQpLHuZZzKG8ox+8O/Q798/qjurkaE7pNwEsjXsKAvAHoltXtZu9iUorU85t0E94sTg9eX3IU2ysb8OykPvjF/OGf29IohBBCCLkx9tfth/6qHmO6jIEmoFiWzWPDL/f9EjaPMPdBy2kxu89svDX1LeSm5t6s3SVRJFXwe6XZhi//twxnG4UZiC9M7kv1HAkhhBASVp2lDqebT+OX+3+J63bfZLKKts/LTsnG10d/HQ8MeAB9cvvc2J0kcUua4PfA+SZ8tfAwvDzDf784EdMHdbrZu0QIIYSQdqi8oRxrz63FqjOr4GFCFYZhHYfhV9N+BQ/ftipD75zeyEnNudG7SWRKiuB3w/E6fHvJUfTpmImPX5yA/p2ybvYuEUIIIaQdsbltqDJU4e0Db4tLCKdr0/HwgIfx8oiX0TtX2iqUpP275YPfEzVGvPFZOcb0zsfHL01AXkbKzd4lQgghhLQTZwxnUFpbivfL34fdI9S67ZbVDT+d/FMMLhiMrlldb/IeErXd8sGv0e5G/05Z+NcL4yjwJYQQQggAoKKpAu8ffR/FV4vFx2b2mok3xr+BAXkDbuKekUS75YPf6YM6YeNrM6DR0MQ2QgghJFkZHAY02huhgQZf3vplNDmE1f5yUnLwtTFfw4yeM9Avr9/N3UlyQ9zywS8ACnwJIYSQJNVga8AnJz/BmrNrYHFboOW08DIvhnccjp9P+Tn65PZBVgrNBUomSRH8EkIIIeTWZHFZ0GBrwE/1P0W/vH7omN4x6Pf6Wj2qDdXgwOHBAQ+ic0ZnPDTwIQwqGHST9pjcbBT8EkIIIeRz55r1GjZe2IiFJxfC4DQAAKoN1W2el6HLwNsz3saoTqOoBi8BQMEvIYQQQto5xhh4xsPFu/Crfb9Cp8xOKK4pxtmWswCA6T2n4wvDvoApPabc5D0lnwcU/BJCCCGkXfLyXuyv248159Zg95Xd6JndUwx407Xp+N7472F8t/EY1nHYzd1R8rlCwS8hhBBCbpqiyiI4PI6w5cWKrxZjWfUyAECH9A7I0GXgp5N/igcGPAAOHDJTMm/07pJbAAW/hBBCCLmhzrecx7ZL2/CPY/8Az/iozx3XdRweGvAQHrntEWg12hu0h+RWpij45TjuXgB/BaAF8G/G2Duq7BUhhBBCbiklV0vQZG9Cg60B7x19T3z83n734rWxr8HsNrf5mxRNCm7Lvw0cRyVLiXpkB78cx2kBvA9gLoAaAIc4jlvLGDul1s4RQggh5PNt5+Wd+MuRv+CC8YL4WPes7nho4EN4ddSrSNOm3cS9I8lISc/vRABnGWPnAYDjuCUAHgbQroLf4v1/wb9OLbzZu0HaYDd7BwghhNwA1RoeDg64zcvhTVcqvAAmW1qQWl8IlBaquzFG3y3tTl5P4IVVN3svgigJfnsCuBLwcw2ASaFP4jjuVQCvAkCfPje+vp4uNQvZuowbvl1CPrdodLEdojeFfH7N5rT4XkovdOZSbtAW6fPSrmR3udl70EbCJ7wxxj4E8CEAjB8//obfkk254xVMueOVG71ZQgghhBDSDmkU/O1VAL0Dfu7le4wQQgghhJB2SUnwewjAII7j+nMclwrgaQBr1dktQgghhBBC1Cc77YEx5uE47psAtkAodfYJY6xCtT0jhBBCCCFEZYpyfhljGwFsVGlfCCGEEEIISSglaQ+EEEIIIYR8rlDwSwghhBBCkgYFv4QQQgghJGlQ8EsIIYQQQpIGBb+EEEIIISRpUPBLCCGEEEKSBgW/hBBCCCEkaVDwSwghhBBCkgYFv4QQQgghJGlwjLEbtzGOawRw6YZtsFUnANdvwnZvZXRM1UXHU310TNVHx1RddDzVR8dUXZ/349mXMdY59MEbGvzeLBzHlTHGxt/s/biV0DFVFx1P9dExVR8dU3XR8VQfHVN13arHk9IeCCGEEEJI0qDglxBCCCGEJI1kCX4/vNk7cAuiY6ouOp7qo2OqPjqm6qLjqT46puq6JY9nUuT8EkIIIYQQAiRPzy8hhBBCCCG3fvDLcdy9HMdVcRx3luO4H97s/fm84DjuE47jGjiOOxnwWAeO47ZxHHfG9/8C3+Mcx3Hv+Y7xcY7j7rh5e94+cRzXm+O4XRzHneI4roLjuG/7HqdjKgPHcekcxx3kOO6Y73j+wvd4f47jDviO21KO41J9j6f5fj7r+32/m/oC2jGO47Qcxx3lOG6972c6pjJxHHeR47gTHMeVcxxX5nuMPvMKcByXz3Hcco7jTnMcV8lx3BQ6pvJxHDfYd376/zNxHPf6rX5Mb+ngl+M4LYD3AdwHYBiAZziOG3Zz9+pzYyGAe0Me+yGAHYyxQQB2+H4GhOM7yPffqwD+eYP28fPEA+C7jLFhACYD+IbvXKRjKo8TwF2MsdEAxgC4l+O4yQB+B+DPjLHbABgAvOx7/ssADL7H/+x7Hgnv2wAqA36mY6rMbMbYmIByUfSZV+avADYzxoYAGA3hXKVjKhNjrMp3fo4BMA6ADcAq3OrHlDF2y/4HYAqALQE//wjAj272fn1e/gPQD8DJgJ+rAHT3/bs7gCrfvz8A8Ey459F/EY/tGgBz6ZiqciwzARwBMAlCMXad73Hx8w9gC4Apvn/rfM/jbva+t7f/APSC8EV3F4D1ADg6poqO50UAnUIeo8+8/OOZB+BC6HlGx1S14zsPgD4Zjukt3fMLoCeAKwE/1/geI/J0ZYzV+f59DUBX37/pOMfBNzw8FsAB0DGVzTc8Xw6gAcA2AOcAtDDGPL6nBB4z8Xj6fm8E0PGG7vDnw18AfB8A7/u5I+iYKsEAbOU47jDHca/6HqPPvHz9ATQC+I8vNeffHMdlgY6pWp4GsNj371v6mN7qwS9JECbc8lGpkDhxHJcNYAWA1xljpsDf0TGND2PMy4Shul4AJgIYcnP36PON47gHATQwxg7f7H25hUxnjN0BYaj4GxzH3Rn4S/rMx00H4A4A/2SMjQVgRetwPAA6pnL5cvnnA1gW+rtb8Zje6sHvVQC9A37u5XuMyFPPcVx3APD9v8H3OB1nCTiOS4EQ+BYxxlb6HqZjqhBjrAXALghD8vkcx+l8vwo8ZuLx9P0+D0DTjd3Tdm8agPkcx10EsARC6sNfQcdUNsbYVd//GyDkUU4EfeaVqAFQwxg74Pt5OYRgmI6pcvcBOMIYq/f9fEsf01s9+D0EYJBvtnIqhC79tTd5nz7P1gJ40ffvFyHkrfof/4JvFuhkAMaA4RICYYYsgI8BVDLG/hTwKzqmMnAc15njuHzfvzMg5E9XQgiCn/A9LfR4+o/zEwB2+noziA9j7EeMsV6MsX4QrpU7GWPPgY6pLBzHZXEcl+P/N4R8ypOgz7xsjLFrAK5wHDfY99AcAKdAx1QNz6A15QG41Y/pzU46TvR/AO4HUA0hH/AnN3t/Pi//QfgQ1AFwQ7jbfhlCPt8OAGcAbAfQwfdcDkJVjXMATgAYf7P3v739B2A6hGGj4wDKff/dT8dU9vEcBeCo73ieBPAz3+MDABwEcBbC8F2a7/F0389nfb8fcLNfQ3v+D8AsAOvpmCo6hgMAHPP9V+H//qHPvOLjOgZAme+zvxpAAR1Txcc0C8KoTV7AY7f0MaUV3gghhBBCSNK41dMeCCGEEEIIEVHwSwghhBBCkgYFv4QQQgghJGlQ8EsIIYQQQpIGBb+EEEIIISRpUPBLCCGEEEKSBgW/hBBCCCEkaVDwSwghhBBCksb/A69pLCTjZz/PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# A helper function to plot all elements in a group.\n",
    "def plot_group(group, exclude=[], i=None, j=None):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    length = len(next(iter(group.values())))\n",
    "    i = i if i else 0\n",
    "    j = j if j else length-1\n",
    "    steps = [i for i in range(j-i)]\n",
    "    for currency, values in group.items():\n",
    "        if sum(values) == 0 or currency in exclude:\n",
    "            continue\n",
    "        plt.plot(steps, values[i:j], label=currency)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "# Uncomment lines below to see the results:\n",
    "\n",
    "plot_group(data['crew_habitat_small']['storage'], exclude=['n2', 'o2'])\n",
    "# plot_group(data['mushroom']['flows'], j=719)\n",
    "# plot_group(data['mushroom']['growth'], exclude=['agent_step_num'])\n",
    "# print(data.keys())\n",
    "# print(data['mushroom'].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "519dac64",
   "metadata": {},
   "source": [
    "## Inspect a Currency\n",
    "Plot all flows of a particular currency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b7b1bcc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = model.get_data(debug=True)\n",
    "def plot_currency(data, currency, exclude=[], i=None, j=None):\n",
    "    \"\"\"Plot all agents who use a particular currency\"\"\"\n",
    "    \n",
    "    # Iterate through data and extract all records\n",
    "    flows = {}\n",
    "    length = None\n",
    "    for agent_name, agent_data in data.items():\n",
    "        if 'flow_records' not in agent_data:\n",
    "            continue\n",
    "        for currency_name, currency_data in agent_data['flow_records'].items():\n",
    "            if currency_name != currency:\n",
    "                continue\n",
    "            flow_records = {}\n",
    "            if not length:\n",
    "                length = len(currency_data)\n",
    "            for n, step in enumerate(currency_data):\n",
    "                # Steps can include records for multiple currencies, \n",
    "                # or multiple records for the same currency\n",
    "                at_least_one = False\n",
    "                for record in step:\n",
    "                    if agent_name not in flows:\n",
    "                        flows[agent_name] = [] + [0] * n\n",
    "                    if not at_least_one:\n",
    "                        at_least_one = True\n",
    "                        flows[agent_name].append(-record['amount'])\n",
    "                    else:\n",
    "                        flows[agent_name][-1] -= record['amount']\n",
    "                if not at_least_one and agent_name in flows:\n",
    "                    flows[agent_name].append(0)\n",
    "    \n",
    "    if not length:\n",
    "        print(\"No flow records for\", currency)\n",
    "        return\n",
    "    \n",
    "    # Plot data\n",
    "    plt.figure(figsize=(12,6))\n",
    "    i = i if i else 0\n",
    "    j = j if j else length\n",
    "    steps = [x for x in range(j-i)]\n",
    "    for agent_name, agent_data in flows.items():\n",
    "        if agent_name in exclude:\n",
    "            continue\n",
    "        pad_zeros = len(steps) - len(agent_data)\n",
    "        if pad_zeros > 0:\n",
    "            agent_data += [0] * pad_zeros\n",
    "        plt.plot(steps, agent_data[i:j], label=agent_name)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n",
    "            \n",
    "plot_currency(data, 'o2')\n",
    "# plot_currency(data, 'potable', exclude=['multifiltration_purifier_post_treatment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f50487c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}