sapsan/examples/plotting_examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tutorial on the Sapsan's built-in plotting routines.\n",
    "# Depending on the routine, it can rely on matplotlib (mpl), \n",
    "# plotly (px) with pandas, or graphviz (gph). Matplotlib routines\n",
    "# have a custom parameters defined in plot_params(). The routines include:\n",
    "# \n",
    "#   1. line_plot   - familiar matplotlib line plotting (mpl)\n",
    "#   2. slice_plot  - 2D plots (mpl)\n",
    "#   3. pdf_plot    - probability density function (mpl)\n",
    "#   4. cdf_plot    - cumulative distribution function (mpl)\n",
    "#   5. log_plot    - training log plot (px)\n",
    "#   6. model_graph - visualizing ML model layer-by-layer by constructing a graph (gph)\n",
    "# \n",
    "# For all parameters of each routine check Spasan's API at sapsan-wiki.github.io\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "sys.path.append(\"../../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "font.size             14\n",
      "legend.fontsize       14\n",
      "axes.labelsize        20\n",
      "axes.titlesize        24\n",
      "xtick.labelsize       17\n",
      "ytick.labelsize       17\n",
      "axes.linewidth        1\n",
      "patch.linewidth       3\n",
      "lines.linewidth       3\n",
      "xtick.major.width     1.5\n",
      "ytick.major.width     1.5\n",
      "xtick.minor.width     1.25\n",
      "ytick.minor.width     1.25\n",
      "xtick.major.size      7\n",
      "ytick.major.size      7\n",
      "xtick.minor.size      4\n",
      "ytick.minor.size      4\n",
      "xtick.direction       in\n",
      "ytick.direction       in\n",
      "axes.grid             True\n",
      "axes.formatter.limits [-7, 7]\n",
      "grid.linestyle        :\n",
      "grid.color            #999999\n",
      "text.usetex           False\n"
     ]
    }
   ],
   "source": [
    "# List of matplotlib parameters\n",
    "# Default style is 'tableau-colorblind10'\n",
    "\n",
    "from sapsan.utils.plot import plot_params\n",
    "\n",
    "for parameter, value in plot_params().items():\n",
    "    print(f'{parameter:21s} {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(16.375000000000004, 0.5, 'f(x)')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sapsan.utils.plot import line_plot\n",
    "\n",
    "# Simple line plots: we will plot two datasets [x,x2] and [x,x3]\n",
    "# It exists to maintime the same plotting formatting whgen using Sapsan\n",
    "x  = np.linspace(0,1,100)\n",
    "x2 = x**2\n",
    "x3 = np.sin(x)\n",
    "\n",
    "# all matplotlib routines return Axis object\n",
    "ax = line_plot(series    = [[x,x2],\n",
    "                            [x,x3]],\n",
    "               \n",
    "               # other matplotlib types included: plot, semilogx, semilogy, loglog\n",
    "               plot_type = 'semilogx',               \n",
    "               label     = ['x^2', 'sin(x)'],\n",
    "               linestyle = ['-', '--'])\n",
    "\n",
    "# You can alter almost all aspects of the plot through Axes\n",
    "# Let's add axis labels\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:  ['u']\n",
      "Fetures_label: ['None']\n",
      "Loading 'u00000' from file 'data/cnn_krr/t0/u_dim32_fm15.h5'\n",
      "----------\n",
      "Loading 'u00000' from file 'data/cnn_krr/t0/u_dim32_fm15.h5'\n",
      "----------\n",
      "(1, 3, 32, 32, 32) (1, 98304)\n"
     ]
    }
   ],
   "source": [
    "# Majority of the routines can be used with any data,\n",
    "# but for consistency, we will load the 3D cnn example data\n",
    "\n",
    "from sapsan.lib.data import HDF5Dataset, flatten\n",
    "\n",
    "data_loader = HDF5Dataset(path           = \"data/cnn_krr/t{checkpoint:1.0f}/{feature}_dim32_fm15.h5\",\n",
    "                          features       = ['u'],\n",
    "                          target         = ['u'],\n",
    "                          checkpoints    = [0],\n",
    "                          input_size     = [32,32,32],\n",
    "                          train_fraction = 1)\n",
    "\n",
    "x, y = data_loader.load_numpy()\n",
    "y    = flatten(y)\n",
    "\n",
    "# x constains 3 components if velocity 'u', 32^3 each\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sapsan.utils.plot import slice_plot\n",
    "\n",
    "# first 32x32 slice of ux and uy components\n",
    "ux = x[0,0,0]\n",
    "uy = x[0,1,0]\n",
    "\n",
    "# all matplotlib routines return Axis object\n",
    "ax = slice_plot(series = [ux, uy],\n",
    "                label  = ['velocity_x', 'velocity_y'])\n",
    "\n",
    "# colormap ranges will be the same across multiple slices, based on the total min and max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tvj5UPdG3lFdeeQWffvopUlJScOXKFQwdOhR79uzBwIED8dtvv9V5fpMmTZCWlobmzZsDANLS0oxTtpo1a2Yy1a0mzM1nHj16NGJiYvDMM8/A29vbeFdcG7/88gvu3r0LHx8frFixAlOnTq3zHL5JTU1FWFgY32bUCSEE83+/igMnT+Fj6RZ85vjvhP1yZWPIOoyCou1wODbpYNyfeuOGKLTVB7H4DbDdSRm8UN95pkFBQYiIiMBbb72FwYMHw8nJCUOHDsU///yDPXv2QKfToaysDAcPHjSbqvDVV1/FokWLcP/+fdy5cwfLli3DiBEjAFTOJliyZAkSEhJACMHly5dRVFRUrQ9vb+9qATkyMhKenp6YNm2asb/ayMrKwtSpU7FhwwZs2LABixYtws2bN+v1N7EmYki1dyktD4O/+g32JxbhtP3HGGZXGWS1cieQZxbCfnoCFDGfAI8EWUAc2uqLmLTRQCsQXn31Vfz111945ZVXAACNGjXCgQMHsGrVKnh7eyMoKAhr1641e+7HH3+MsLAwhIeHo3v37hgxYgTGjBkDABg5ciTeffddDB06FK6urnjnnXfMzvUbP348zp8/Dzc3N7zzzjvG/aNHj0ZCQgJeffXVOjW89dZbePPNN9G1a1eEhYXho48+wtixY0WVzk6IfHPiJhav/AJr77+H2Y5/wk6iAwCQdiOhmPIPJD2nAArbzbNhC/A+j5YLHp/HZq05m/UdOhAyO3fuxIoVK3D48GFWtQlpHu0Ngf68rtDq8f7Wk2gf/3+Y4HDauF/vFQnZsyuAZlF19iFUbWwgVG2inUcrFvhOYM02FRUV+Pbbb/HGG2/YnLZH4TvQmyOzoBQT1/6K/xX+H7o6qAEAepkDZAMXQdb5jYcT7etGiNrYQkza6NABi5SXl/NtAmtUTbdTKpUYNWqUUdu2bdvg7Oxc7fX666/zbHH9Edo48tmUXLy8dANWF3+Ergo1AEDj1wWyyeeBLm8xDrKA8LSxiZi00aEDFqEVFpgjpKEDvV4vmIQ5O/9Jw/KfN2G3yzfwkVY+tNS3GwXZkC8BheVzxoWkjW2Eqo2WG+cYW7qjfRxb1paYmMi3CQCAH84kY/3P3+CI6zJjkCU934ds2Op6BVlAONq4QEza6BgtiygUliWfEBO2rM1cvgxrcyI5G9t3/YQ9rt/DQaKFQSKDdND/QdLlzSfqVwjauEJM2migZRFbnsZky9rKysp4vf6t3CK89/2viHX+4d8g+/IGIOK5J+6bb21cIiZtNNCyiA0OdxuxZW0ajYa3axeUajDs21h8K18LD2kJAEA66P9YCbIAv9q4Rkza6Bgti1iz4GBQUBBOnTr1RH04OzubzZ1gDiEVU2Qbb29v3q79zrYLmFj6I55SPMzt0X400PkN1vrnUxvXiEkbDbQsUlXtQCwUFxcbcyKMHTu21iQ9YtNmCSkpKbxc94/4DFRc3YP/OhwHABCPlkDMZw8TTrMDX9qsgZi02e5tCg/QcuPihI9UexqdHl/uPIAdLpsAAAalC6SjdwJ2rqxeRyxpBOuDmLTRO1oWqU9J7sWLF2PcuHEm+/r06YNNmzbh6tWrePrpp9G4cWN06tQJf//9t9k+ysvL8e6778LX1xcBAQFYuHChycOrlStXIjQ0FC4uLoiKikJeXh6Ayoxd6enp+Omnn7B582YsWrQIzs7OePvttzFhwgRj0m+gsnRQcHAwzpw5U6OWsLAwHDt2zLh9+/ZtuLm5CX5qGB9lq9edvI5PtV+jsbTygY50yJeAG/vzicVUkttSxKSt4d7RHpwF3GU3+78DIaY/+3zbADGf13qOWCosXL58GUDtFRZGjRqFLVu2GBOTb9myBc8//7zg8z88WvbIGjwo0+Be7DJEKVIBAPqoSZC1rTtpT32wtjZrIiZtDTfQ3r0KpD7Zw6THqc/ImlgqLGzZsqXOCgujR49GVFQUvv76aygUCmzZsgVffvml5X8UK5Ofnw9XV3Z/stfGL3t2Ybb8VwBAoVMQXJ+Zx9m1rK3NmohJW8MNtL5tWO/SYDCYFoBkeA0xVFjYvXt3nRUWQkJC0LJlS/z5558IDAxEbm4u+vXrZ/G1rQ3XRTsfJf1+KVolLINSrocWMri8uq7eq76YYE1t1kZM2hpuoK3jJ319MOh0kNZjGpQtVVgYPXo0tmzZgsDAQLz66quCXIv+ONa8K9q3fTUmySuToeSEvw5/BqkOnwSx3PHVBzFpE89Xggio7wRqMVRYePnllxlpefXVV/H7779j8+bNGDlypOV/DB5IS0uzynXUqSl4NXMZAKBE4gj/wbM5v6a1tPGBmLTRQMsiTzIFSugVFqr6qwsPDw/06tULcrkcXbt2tfTPwAvWmiZ0Z+cMuEsrZ6bci/4McPHl/JpimgJlKWLSRtMkskjVNWyJqgoLhw4dYqxt8uTJaNy4MRYtWlRjGyGlSVSpVJz/pz13NR6dd/WCXGLAZWUHdJx9jNPrVWENbXwhVG20wgLH2NnZ8W0CqzxaYYGptrt372L79u04d+4cx9axR4sWLTi/RtLvy9BNUjm32WPgHM6vV4U1tPGFmLTRoQMWMfeTXKw8XmGhSlttFRa+/vprtGjRApMmTUJISAjPCpiTkZHBaf83Uu9gcOkfAIBUhzAEdujP6fUehWttfCImbYzvaO/fv48tW7YgJSUF9+/fr5bNSSKR4IcffmDdQDEhpukmddG+fXuUlJQYt6v8/eqrr9ZaEXfy5Mmc28Y2Tk5OnPZ//Y+VGPYwM5fyqams5jKoC6618YmYtDEKtIcOHcLw4cNRUlICV1dXNG7cuFobWy3hYgm2/DewZW1cJjUvKS5E96wtgBRQy5ohqNsIzq5lDltO2C4mbYwC7bRp0+Dr64vdu3ejTRv2J/pbi4qKiuqLClhEp9OJyvmWwKY2Qgh0Oh0rfbFBbm4uvLy8OOk7bv9K9HhYlia3/WQEWVBYkQ241MY3YtLGKNAmJydj6dKlog2ydnZ2KC0thV6vR3JyMpRKJSd3aLQ4IzN0Op1xzrEQvphCQ0M56ZcQArebOwEAqcQLHQayl2eWKVxpEwJi0sYo0IaGhpqd5C4WPDw8UFFRYQy2XJXA0Gg01RK22ApcaJPL5fD09GS1z/qQmJjIyTSh+EtH0ZrcBgCo/YcikIcvFa60CQExaWMUaBcvXox3330XI0eOFFXGnCoUCgUCAgJQUFCA0tJSzn622tvb29QDsUdhW5tCoYCnp6cgvpgiIiI46bfsxFcAAB2RIiyGn4eEXGkTAmLSxijQxsbGwsvLCxEREejfvz+aNWtWbQ27RCLBihUrODGSDSQSiTG7FVckJCQgMjKSs/75xJa1JSUlsa6tpOgBWhWeASTASfte6NOMnzmfXGgTCmLSxijQfv3118b3+/fvN9tG6IHWGohlYL4+UG2WEXf0F/SQVM49dmg/nPX+mUL9JgwYBVpbLjXNJra0YOFxqDbLkMVXPgS7TxzRIZq/QEv9Jgxsc0CRJx6d4G9rUG3Myc5KQ8eKyooUCW7RsHPgb2I99ZswsCjXwe3bt/HHH38gNbWyBEdgYCAGDRpUr8TStoi/vz/fJnAG1cac5D9WoOfDvAZu3cey2relUL8JA8aBdtq0aVixYkW1YQSpVIqpU6fiiy++YN04sZGcnCya6SaWQrUxg+h1aH5nNwAgHoGI7DKAlX7rC/WbMGA0dPDll19i+fLlePHFF3H27FkUFBSgoKAAZ8+exfDhw7F8+XIsX76ca1sFj1icXh+oNmbc+vsAfJEPAEgLfhkSnqf7Ub8JA0afgnXr1uG5557D9u3b0bVrV7i6usLV1RVdu3bF1q1b8eyzz+K7777j2lbBI6byx5ZCtTGj4MwGAEAZUaBV//Gs9VtfqN+EAaNAq1arMXDgwBqPDxw4kFFdKlvn0QKJtgbVVjflxffR6sFJAMBZu24I8ON/DJH6TRgwCrTe3t6Ii4ur8XhcXJyo5rRxRWFhId8mcAbVVjfXjm2Bw8O5s7K2r7DS55NC/SYMGAXal19+Gd9//z0+//xzkykVJSUlWLJkCb7//vtac5SaY926dWjRogXs7OzQrl07HDx4sNb2R44cQc+ePeHq6goPDw/ExMTgypUrFl2Ta2x5vjHVVje6K9sAAPcMzujS5yVW+nxSqN8EAmFASUkJ6du3L5FIJEShUJDAwEASGBhIFAoFkUgkpG/fvqSkpIRJV4QQQnbu3ElkMhn55ptvSEJCApk1axZRKpUkLi7ObPuUlBRiZ2dHPvjgA3Lz5k1y9epV8tJLLxFPT09SVlZWrX1ERARjW9jkwYMHvFzXGlBttROfoiaaeW6EzHclZ1aNZsEqdqB+sz7m4g+jO1pHR0fExsbi119/xfjx4xEREYGIiAiMHz8ee/bswZEjRywqSrh06VK8/vrrmDRpEiIiIvDZZ5+hbdu2NS7hvXTpEjQaDT777DOEhoaidevWmDdvHu7du4dbt24xvi7X2PI4NdVWO/GxG6B4OHc2sKdwyqxTvwkDixYsDBs2DMOGDXuiC2o0Gly6dAlTpkwx2R8TE4MdO3aYPadLly5QKpVYt24dJk6cCI1Ggx9++AEtW7Y0m5PSYDCYdYKbmxvc3NyeyP7aENN0E0uh2monJGMfACBX4o4m7fidO/so1G/CwOqT/O7duwedTgcfHx+T/b6+vsjKyjJ7TmBgII4cOYJPP/0U9vb2cHZ2xqFDh3D48GGzafZycnIQHBxc7TV//nykpaWhoKDAODVEpVKhtLQUSUlJyMzMRG5uLhISEqDVaqFSqaDVapGQkIDc3FxkZmYiKSkJpaWlJucXFBQgLS0Nx44dQ2Fhocmx4uJipKSkID09HXl5ebh27Rr0ej1UKhXKy8tx48YNZGdnIzs7Gzdu3EB5eTlUKhX0ej2uXbuGvLw8pKenIyUlBcXFxSZ9FxYWQq1Wc6pJrVajsLAQhw4dsjlNVccOHTr0RJr++usQOiEJABDv1hvFpWW8a6ry09GjR23GT49/9s6ePStITeaQEPJYlUUAwcHBkEqluH79OhQKBYKDg+vMri+RSBj9jM/MzIS/vz9iY2PRt29f4/7Vq1djzpw5ePDgQbVz7t69i+joaAwdOhSjR49GUVER5s6di4qKCpw6dapaysbw8HCzD9e4vqMtLi6Gs7MzZ/3zCdVWM0c2LcIztypXRiY99ytCO/St4wzrQf1mfSIjI5GQkGCyz+zQQXR0NCQSiTHRc9U2G3h6ekIulyM7O9tkf3Z2Nvz8/Myes3r1aiiVSnz55ZfGfZs2bUKzZs1w4sQJ9OnTx6S9VCrlJUF5Tk6OIB3PBlRbzbirK0uJ3yWN0aJdNFtmsQL1mzAwG2g3bNhQ6/aToFQq0alTJxw+fBivvfaacf/BgwfRs2dPs+eUlpZWu2ut2jZzQ84bQqgWwBVUm3nS01PRTpcASIAkzz7wlcrqPsmKUL8JA0ZjtBs3bqz1CV9qaio2btzI+KLTp0/Hzz//jDVr1uD69euYM2cO4uLijA/IZs+ejX79+hnbDxkyBCqVCvPmzUNSUhIuX76McePGwd/fH126dGF8Xa5xcHDg2wTOoNrMc/vUZsgklV/2Xl2tW0qcCdRvwoBRoB03bhzOnDlT4/Fz585h3LhxjC86fPhwrF69GkuXLkW7du2wf/9+7Nmzx/gUMSsry2S8t2/fvvjll1+wf/9+dOjQAQMHDoRCocDBgwfh4uLC+LpcU9PDPFuAajOPe2rls4AsQyO07NifLZNYg/pNGJh9GPY4UqkUmzZtwsiR5ucH/vjjj5g0aRIqKipYN7A+mBuMtgZ6vb7aEIetQLVVx1BeDP3nAVBI9NjnMBjPfriFA+ueDOo368P4YRhQOaXh0SWuJ0+eNFs9tqCgAGvWrEHLli3Zs1SkxMfHi2punyVQbdW5dekgQiV6AICiRZ86WvMD9ZswqDHQ/vrrr1iwYAGAyqlb3333XY2pEN3c3Cwao7VVbPnLhmqrTuHVAwCACiJHeLdn2TSJNajfhEGNgXbChAkYOnQoCCGIiorCwoULMWjQIJM2EokETk5OaN68OeRyixaZ2SSpqakICwvj2wxOoNoegxA0yTkBAPhH0hJd/Xw5sOzJoX4TBjVGRz8/P+O81qNHjyIiIgLe3t5WM0yMcLkYgm+oNlOK7sTBj9wDAGT59mFtnjnbUL8JA0a3odHRwpqETaHwzY2TO9D54Xv3Ds/xagtF+DAKtI8ula0JiUSC2NjYJzZIzBQUFFTL4WArUG2mKNOOAwCSDL7o2SmKC7NYgfpNGDCaR2swGEAIMXnpdDrcunULx44dQ3p6uriS8HJEYGAg3yZwBtX2L6WlxWhZkQgASHPrDLmM3wKMtUH9JgwY3dEeO3asxmP79+/HhAkTsGzZMrZsEi03b94UzXQTS6Ha/uXi+eOIllROdXRt+TRXZrEC9ZsweOKv4qqMWlOnTmXBHHHTqlUrvk3gDKrtX3KuHTa+bx1Vc9FSIUD9JgxY+c3TvHlzXLx4kY2uRE1iYiLfJnAG1VaJVm+AR84FAECWzBcOXkEcWcUO1G/C4IkDrU6nw/bt2+Hp6cmGPaKmpjSPtgDVVsnRxDR0lVUm+S5u0oMrk1iD+k0YMBqjHT9+vNn9BQUFOHfuHO7evUvHaAGUlZXxbQJnUG2VXL98HAMkGgCAfzthDxsA1G9CgVGg/euvv6pNyJZIJGjcuDF69eqFN998EwMGCKdOEl9oNBq+TeAMqu0h6hPGt44te7NvDMtQvwkDRoFWTNUm+cSWV85RbUBafgkiKq4BSiDPvhk8XIS57PZRqN+EgXAnAIqQlJQUvk3gDKoNOJGQgqcUyQAAXUAvLk1iDeo3YcA4E0xFRQXWrVuH33//3XiHGxQUhMGDB+PNN9+Evb09VzaKBrHM6asPVBuQde0Y7B/On/VsN5RLk1iD+k0YMLqjTU9PR/v27fHee+8hLi4OXl5e8PLyQlxcHN577z20b98e6enpXNsqeKrKE9siDV0bIQQOWecAAAZIIAsRxx1tQ/ebUGAUaN99912kpqZi+/btyMjIwPHjx3H8+HFkZGRg27ZtSEtLw7vvvsu1rYKHj8q71qKha1PnlaC9vnLe5j2nUMDejVujWKKh+00oMAq0sbGxeP/99zF8+PBqx15++WVMmTKlwSeUAYD8/Hy+TeCMhq7t5PVURMnVAAASIPz5s1U0dL8JBUaB1sXFpdYnfL6+voIqksgXUqntPlts6Noy4k9A+bBsjWerurPZCYWG7jehwLgK7oYNG1BaWlrtWHFxMdavX4833niDdePEhqurK98mcEZD1kYIgV3GOeO2LKgn1yaxRkP2m5AwO+tg9+7dJtsdOnTAgQMHEB4ejjFjxqBFixYAgKSkJGzcuBHu7u6iegLIFWlpaaLK+m4JDVmbOq8E7fSJgAzIcwiCh5N4lps3ZL8JCbPlxqVSKSQSCaoOPfq+xo4kEuj1em6stBC+yo1TbJOfTl/Hy4d7wVGiRW7YSHiN+JZvkygChnG58aNHj1rFIFtDpVLZ7J19Q9aWkXgajhItAMA9QphlxWuiIftNSJgNtLRGWP2oGlKxRRqyNvmdM8anGbJgccyfraIh+01IiOexnQjIyMjg2wTOaKjaUvOL0UZX+TPwvr0/4NrEWmaxQkP1m9Awe0fbp08fSKVSHDp0CHK5nBZnZIiTkxPfJnBGQ9V2OikLQ+WVa+p1TbtbyyTWaKh+ExpmAy0hxKTYosFgqLNufV0PyxoCCoWCbxM4o6Fqu5t0Ea7ScgBAY5GNzwIN129Cw2ygfbwYY23FGSn/kpubCy8vL77N4ISGqo2oTxvfy0U2Pgs0XL8JjTrHaMvKyvDBBx9g37591rBH1ISGhvJtAmc0RG3FFVqEllYmLilQeANu4ilvXUVD9JsQqTPQOjg44LvvvkN2drY17BE1YioWZykNUduF27no9TD/bKFPF6CO4TMh0hD9JkQYzTro1KkTrl27xrUtoiciIoJvEzijIWpLir8Ad2nlsvPGEb2taBF7NES/CRFGgfarr77C1q1b8f3330On03Ftk2hJSkri2wTOaIjadLdPGd+7tBTfgzCgYfpNiDCqsDB27FhIpVJMnDgR7733Hvz9/eHg4GDSRiKRIC4ujhMjxYJYBubrQ0PTptUb4FdwGVAAD2RuaOQhnsnxj9LQ/CZUGAVad3d3eHh4ICwsjGt7RI1Wq+XbBM5oaNoup+Whh6zyjqnAszMaiXB8Fmh4fhMqjAItnd7FjJKSEr5N4IyGpi0p4RK6SgsBAE5hva1sEXs0NL8JFUZjtBs3bqy15Hhqaio2btzIlk2ixd/fn28TOKOhaXtw66zxvUf409Y0h1Uamt+ECuPE32fOnKnx+Llz5zBu3DjWjBIrycnJfJvAGQ1JGyEEje5dBgCUS+wh8WnFh1ms0JD8JmQYBdq6lteWlJRALmdcudxmEUvKtvrQkLSl3CtGpOE2ACDXtRUgFe9nuyH5TcjU+AlSqVS4cuWKcfvkyZNmp3YVFBRgzZo1aNmyJScGigkx5ce0lIak7e/kVLwor8wMpfBvx5dZrNCQ/CZoSA3Mnz+fSCQSIpFIiFQqNb4392rcuDHZt29fTV2ZZe3ataR58+ZEqVSStm3bkj/++KPOc1auXEkiIiKIUqkkXl5e5O233zbbLiIiwiJb2OL+/fu8XNcaNCRt321cS8h8V0LmuxJNvGWfa6HRkPwmFMzFnxrvaCdMmIChQ4eCEIKoqCgsXLgQgwYNMmkjkUjg5OSE5s2bWzR0sGvXLkyaNAmrVq1C7969sXHjRgwbNgwXL16s8Rvqww8/xJ49e7BkyRK0a9cORUVFuH37NuNrWoPCwkLR1DCylIakzeHu38b3isBuPFjEHg3Jb0Kmxujo5+cHPz8/AJWlbSIiImotOW4JS5cuxeuvv45JkyYBAD777DMcOXIEK1aswA8//FCt/c2bN7F8+XKoVCqTZXft2pn/WWcwGMzOknBzc+PUMY+mlrQ1GpK2kOIrgBRIVwSgqYgKMZqjIflNyDB6GBYdHV0tyBJC8Ndff+GPP/5AUVER4wtqNBpcunQJAwYMMNkfExOD06dPmz1n7969CAkJwZ9//okWLVqgWbNmGDFiBO7cuWO2fU5ODoKDg6u95s+fj7S0NBQUFEClqszKpFKpUFpaiqSkJGRmZiI3NxcJCQnQarVQqVTQarVISEhAbm4uMjMzkZSUhNLSUpPzCwoKkJaWhtLSUhQWFpocKy4uRkpKCtLT05GXl4dr165Br9dDpVKhvLwcN27cQHZ2NrKzs3Hjxg2Ul5dDpVJBr9fj2rVryMvLQ3p6OlJSUlBcXGzSd2FhIdRqNaea1Go1CgsLkZuba3Oaqo7l5OQYNV2Mv4k2UAMA7jVuL1pNVX4qLi62GT89/tmTSqWC1GQWJmMOc+bMIb179zZuGwwG8swzzxjHbgMDA0lycjKj8YuMjAwCgMTGxprs//rrr4mrq6vZcyZOnEjs7OxI586dydGjR8np06dJ7969ScuWLUl5eXm19mFhYeT27dvVXlyP6cTFxXHaP580FG2HYn83js8mHfqGR6vYoaH4TUiYG6NldEe7a9cuREVFGbd37tyJ2NhYLF68GPv374der8f8+fOZdFUvCCGoqKjATz/9hN69e6NHjx7Ytm0bkpOTcejQoWrtpVIpgoKCqr24Hs8RzRPQetBQtBXc/PdXVUBbcSaSeZSG4jehwyjQZmRkmFSc3L17NyIjIzF79mwMHjwYkyZNYrxM19PTE3K5vFp+2+zsbOOY8OP4+flBIpEgPDzcuM/b2xuenp5ITU1ldF1rUPVTwxZpKNrcci8AAPIljaD0EX9uj4biN6HDKNDK5XJUVFQAqLy7jI2NRUxMjPG4j48P7t27x+iCSqUSnTp1wuHDh032Hzx4ED179jR7Ts+ePUEIMUmLlpeXh3v37iEoKIjRda1BSEgI3yZwRkPQVlSuRbj2JgAg07WtKBN9P05D8JsYYBRoW7dujU2bNuH+/ftYv3498vLyMGTIEOPx1NRUeHoyfzo7ffp0/Pzzz1izZg2uX7+OOXPmIC4uDlOmTAEAzJ49G/369TO279evH7p06YI33ngDly5dgkqlwujRoxEaGlrtoRqf5OTk8G0CZzQEbVcS4hEguw8AkDbtzKdJrNEQ/CYGGE1+nTt3Lp599lljMO3Zsyf69Pl3/OrAgQPo0qUL44sOHz4c+fn5WLJkCaZMmYKwsDDs2bPHOOaSlZWFW7duGdtLpVLs27cPU6dORd++faFQKBAdHY0///wTdnZ2jK/LNUqlkm8TOKMhaMtM/DfRd5PW0XyZwyoNwW9igFGg7d+/Py5fvozDhw/Dzc0Nr776qvHY/fv38fTTT2PYsGEWXXjChAmYMGGC2WMbNmyots/Hxwdbtmyx6BrW5vFk6LZEQ9AmSa9cqKCDFG4hUbWdIhoagt/EAOPlXJGRkYiMjKy2v3Hjxli+fDmrRomVrKwseHh48G0GJ9i6NrfGjeFefBOQA9mKpvBXOvFtFivYut/Eoo3RGC2FGWIqFmcptq7tWkYB2korZ7CUebbm2SL2sHW/iQWzgVYqlUIul0Oj0Ri3ZTJZrS+aJhGIj4/n2wTOsHVt/yRcg7e0GADgEmQbD8IA2/ebWDAbHefOnQuJRGIMnlXblNqx5VSRtq7tyOnVxm3vUNsYnwVs329iwWygfXyVF5ervmyJ1NRUmy1gaevaFNkq4OG9hMRPPCuO6sLW/SYWbXSMlkXEkrKtPtiyNp3CES0eLlS4Zx8A2Dfi2SL2sGW/iUlbnQOrFRUV2LRpE/7880/cunULRUVFcHFxQYsWLRATE4ORI0eKaj4bhfI4/2QUYLBcDQDQ+HTg1xiKTVLrHe3Vq1cRERGBCRMmYMeOHbh16xZKS0tx69YtbN++HW+88QZatWqFxMREa9kraAoKCvg2gTNsWVvK9ctwl5YCANzCxFvx1hy27Dcxaasx0BYXF+O5555DdnY2PvnkE9y5cwf37983+Xfx4sXIzMzEs88+K6oa61wRGBjItwmcYcva7AtTjO8dA2zrjtaW/SYmbTUG2vXr1yMtLQ0HDhzArFmzqtVQ9/f3x+zZs7Fv3z7cvn3b7GquhsbNmzf5NoEzbFUbIQQu+VcBAFrIAR/bmUML2K7fAHFpqzHQHjhwAAMGDEDv3r1r7aBv377o378/9u3bx7ZtoqNVq1Z8m8AZtqotNb8EHSSVeTXyXVoCcuHkzmADW/UbIC5tNQbaq1ev1hlkq+jbty+uXr3Klk2ixZbHqm1Vm+pODtrL0wEAuia2s1ChClv1GyAubTUG2vz8fPj6+jLqxMfHB/n5+awZJVZqSlxuC9iqtvSkK7CX6AAA7s1tZ6FCFbbqN0Bc2moMtBUVFVAoFIw6eXS5bkOmrKyMbxM4w1a16TPjjO8dbOxBGGC7fgPEpa3WebRqtRqXL1+us5Pbt2+zZpCYseUvG1vV5pYfB0iBCihh5ymOVUaWYKt+A8SlrdZA+/HHH+Pjjz+usxNCCM2FAFQryW5L2KK2rAdl6Gi4DkiBu66tEShj9gtOTNii36oQk7YaA+369eutaYdNkJKSIqrKnJZgi9oSbt9GP3kWAMDQtCvP1nCDLfqtCjFpqzHQjhkzxpp22ARicXp9sEVteTfPGt83DreN0jWPY4t+q0JM2mhSGRYRU/ljS7FFbfq714zv3UJsb2oXYJt+q0JM2migZREhlT5nG1vU5vZwRVi+xA1w8uLXGI6wRb9VISZtNNCyiC3PJbY1bUXlWkQYKleEpTuG82wNd9ia3x5FTNpooGURqdR2/5y2pi0+6QaCZJX/Ucs82/BsDXfYmt8eRUzaxGOpCHB1deXbBM6wNW25CceN770jnuLREm6xNb89ipi00UDLImlpaXybwBm2pk2WeREAoCUyFMmZLTUXI7bmt0cRkzbGgbawsBCff/45Bg4ciA4dOuDChQsAKsdJli1bhuTkZM6MFAtimm5iKbamzetBZQVVtSIYbTvZ5hxawPb89ihi0sYo0Kanp6NDhw6YO3cu0tPToVKpUFxcWZrZ3d0d3333HVatWsWpoWJATNNNLMWWtD0oKUMk1ACA+26tbErb41BtwoBRoJ0xYwaKiopw5coVHD9+HIQQk+PPP/88jhw5womBYqJFixZ8m8AZtqTt1vVLcJJUrpOXN+tkU9oeh2oTBowC7Z9//on33nsPkZGRZnMahISE4M6dO6wbJzYyMjL4NoEzbElb7iMrwnzCutuUtseh2oQBo0BbVlYGL6+aJ3QXFRWxZpCYcXJy4tsEzrAlbZLMfwAApUSJJi062JS2x6HahAGjQBsZGYkTJ07UeHzPnj3o0MH2cnlaCtP8vWLElrR5FVZm5k9VhkAiU9iUtseh2oQBo0A7depUbN26FUuWLMGDBw8AAAaDAcnJyXj99ddx9uxZvP/++5waKgZyc3P5NoEzbEVbam4+IiSV04JKPSufWtuKNnNQbcKg1ny0VYwePRqpqan43//+h48++ggAEBMTA0IIpFIpPv30Uzz//PNc2ikKQkND+TaBM2xF242r5xH4sHRNo+aV07psRZs5qDZhwCjQAsBHH32E119/Hbt27UJycjIMBgOaN2+OF198ESEhIVzaKBoSExNFNbfPEmxFW6n6ovF908ieAGxHmzmoNmHAONACQEBAAB0iqIWIiAi+TeAMW9GmzKmce1kEB7j4VpausRVt5qDahAGjMdqoqCgsX74c6enpXNsjapKSkvg2gTNsQRshBIGlCQCAdPtQQFL58bcFbTVBtQkDRoFWJpNh2rRpCAoKQq9evfD111/j7t27XNsmOmqbAid2bEFbSvodtJJVzr0s8f030bctaKsJqk0YMAq0Z8+ehVqtxmeffYaKigq89957aNasGfr27Yu1a9fi3r17XNspCrRaLd8mcIYtaEuN/3ehgmvzHsb3tqCtJqg2YcA4qUxAQABmzJiBixcvIjk5GQsXLsT9+/fx9ttvo0mTJoiJieHSTlFQUlLCtwmcYQvaSm+fM74PbP1vakRb0FYTVJswqFeaxJCQEMyePRuXL1/Gd999BwcHBxw+fJht20SHv78/3yZwhi1oa3TvCgAgQ+IDO7d/UyPagraaoNqEQb0C7blz5/DBBx8gICAAb7/9NgBg5MiRFvWxbt06tGjRAnZ2dmjXrh0OHjzI6Lx79+7B398fEolEcOPEtpwqUuzayjU6hOtuAgDuuppWVBC7ttqg2oQB40B76dIlfPjhhwgKCkLPnj2xdu1a9OzZEzt37kROTg5+/vlnxhfdtWsXJk2ahGnTpuHKlSsYPHgwhg0bxijt2bhx49C+fXvG17ImYpnTVx/Eri0x4R94SStTe0qaRZkcE7u22qDahAGjQNu8eXNERUVh1apV6NixI3755Rfk5ORg69ateOGFF2BnZ2fRRZcuXYrXX38dkyZNQkREBD777DO0bdsWK1asqPW8FStWoLS0FNOmTbPoetZCTPkxLUXs2rKvnzS+92tlWrpG7Npqg2oTBowWLERGRmLBggUYNmwYXFxcnuiCGo0Gly5dwpQpU0z2x8TEYMeOHTWe988//2DJkiW4ePFinfPnDAYD1Gp1tf1ubm5wc3Orj9mMCAgI4KxvvhG9tjuVFUHKiBK+LTqbHBK9tlqg2oQBozvaffv2YfTo0U8cZIHKMVadTgcfHx+T/b6+vsjKyjJ7TklJCUaMGIFVq1YxGgDPyclBcHBwtdf8+fORlpaGgoIC47ehSqVCaWkpkpKSkJmZidzcXCQkJECr1UKlUkGr1SIhIQG5ubnIzMxEUlISSktLTc4vKChAWloakpKSUFhYaHKsuLgYKSkpSE9PR15eHq5duwa9Xg+VSoXy8nLcuHED2dnZyM7Oxo0bN1BeXg6VSgW9Xo9r164hLy8P6enpSElJQXFxsUnfhYWFUKvVnGpSq9U2oalp8TUAQLKiOVLTM000xcXFiVITEz/dvHnT5jRV+SkjI0OQmswhIY+XS8C/Rc+qvjGYFkFj8g2TmZkJf39/xMbGom/fvsb9q1evxpw5c4zZwR7ljTfeAAD88MMPAIBjx46hT58+yMrKgq9v9cJ64eHhZh+ucX1Hq1arERQUxFn/fCJmbdl59+C5qgVkEoIzTUajx1urTY6LWVtdUG3WJzIyEgkJCSb7zA4dBAUFQSKRoKysDEql0rhdF3q9vs42np6ekMvlyM7ONtmfnZ0NPz8/s+fExsbizp07+OmnnwDAWEqnadOmmDJlCr788kuT9lKplBcHuLu7W/2a1kLM2pL+OQ4fSeVnxqV592rHxaytLqg2YWA20P7444+QSCTGxLpV22ygVCrRqVMnHD58GK+99ppx/8GDB9GzZ0+z5/z555/QaDTG7YsXL2L8+PGIjY1FWFgYK3axgVqtFtWTUEsQs7aiW/+uCAtp36facTFrqwuqTSAQHtixYweRy+Xk22+/JYmJiWT27NlEqVSSuLg4Qgghs2bNIn379q3x/KNHjxIAJCsry+zxiIgITuymiJOTi/sQMt+V3Jnfgm9TKA0Ac/GH0cOw8ePH4/z58zUev3DhAsaPH884uA8fPhyrV6/G0qVL0a5dO+zfvx979uwxfjtlZWXh1q1bjPsTCmKabmIpYtVWWqFFS03VQoVWZtuIVRsTqDZhYPZh2ONIpVJs2rSpxtVf27Ztw8iRIxmN0VoDc4PR1qC4uBjOzs5Wv641EKu2v69cROe9zwAArrSehfYvza7WRqzamEC1WR9z8adeS3AfJzMzEw4ODmx0JWpycnL4NoEzxKotO+HfhQo+kU+bbSNWbUyg2oRBjQsW9u7di7179xq3165diyNHjlRrV1BQgCNHjqBLly7cWCgilEol3yZwhmi1pVcuVCgnCviGmv+MilYbA6g2YVBjoE1ISDCu1JJIJDh//jwuXbpk0kYikcDJyQlPP/00li1bxq2lIsCW7+rFqM1gIGhakgBIAbV9S4TLzf/HFKM2plBtwqDGoYPZs2ejqKgIRUVFIITghx9+MG5XvQoLC5GVlYX9+/ejZcuW1rRbkNS0ss0WEKO2xDtZaCW5AwAo9+lYYzsxamMK1SYMGOU6MBgMXNthE4ipWJyliFHbjSvH0UpS+dn1DH+qxnZi1MYUqk0YsPIwjFJJfHw83yZwhhi1ld/+d6GCfyvzD8IAcWpjCtUmDBgH2j/++AP9+/eHh4cH5HI5ZDJZtVdDx5aHT8SmjRAC9/uV8yxzZN6QuJpf3g2IT5slUG3CgFGg3bVrF4YOHYrs7GyMGDECBoMBr732GkaMGAEHBwe0bdsWc+fO5dpWwZOamsq3CZwhNm3qe8XogMoM/Pnu7WptKzZtlkC1CQNGgfazzz5DVFQU/vnnHyxYsABA5WqxzZs349q1a8jKykJwcDCnhooBLjOD8Y3YtF2JvwIfaREAwDG4W61txabNEqg2YcAo0CYkJGDEiBGQyWSQyyufn1WV+g0KCsI777yDJUuWcGclhWIhuTfOGN83iezFoyUUCsNA6+joaJwc7ObmBjs7O5OpFT4+Prh9+zY3FoqIgoICvk3gDLFpc8q+CADQQg65f/ta24pNmyVQbcKAUaANCwszWbvbvn17/Pzzz9DpdCgvL8cvv/wiqrISXBEYGMi3CZwhJm33isvRXltZUSHDpTUgt6+1vZi0WQrVJgwYBdoXXngBe/fuRUVFBQDgo48+wrFjx+Dm5gYvLy+cPHkSs2bN4tRQMXDz5k2+TeAMMWm7eP0WWskrf3GRgB51theTNkuh2oQBo+xd5jh58iR2794NmUyGIUOGoE+f6gmV+YKv7F16vd5mp7mJSdv6n7/FuJTKL/7Sl7fBMTKm1vZi0mYpVJv1YVzKhglPPfUUnnqq5tU2DZHExES0bt2abzM4QUzaJA8TyQCAY0jtMw4AcWmzFKpNGNCVYSxSU80zW0As2iq0eoSUXAUAZNkFAfZudZ4jFm31gWoTBmbvaIODgy2uESaRSERZFYFNysrK+DaBM8Si7WKSGt3llZ/D4iZ1j88C4tFWH6g2YWA20EZHR7NWjLEh8WgBSVtDLNpS/olFr4eJZLzbDWJ0jli01QeqTRiYDbQbNmywshm2gbe3N98mcIZYtMnTTgEA9JCgUVg0o3PEoq0+UG3CgI7RskhKSgrfJnCGGLSVa/UIL/0HAJBu3xKwb8ToPDFoqy9UmzBgNOvgxIkTjDp7+umaU9E1BERTY74eiEHb2ZupeEqWDgAobdKd8Xli0FZfqDZhwCjQ9u7dm9GYrVCq4PKFSqUSlfMtQQza0q4eh/zh+GzTtszndYtBW32h2oQBo0B79OjRavv0ej3UajXWrl0Lg8GAzz//nHXjxEZQUBDfJnCGGLTp0y8b37s0Z35HKwZt9YVqEwaMAm10dM0PFcaOHYunnnoKx44dQ9++fVkzTIzk5+fD1dWVbzM4Qeja9AYDAh5cBuRArtwHXs4+jM8VurYngWoTBk/8MEwqlWLEiBH4/vvv2bBH1EiltvtsUejaLqmz0UN2AwCQ78P8bhYQvrYngWoTBqxYmp+fL6qUZVwhlm/X+iB0bdeunIGjpDJHslekZb+shK7tSaDahAGjoYO0tDSz+wsKCnDixAksXbqU5j1A5d9JTFnfLUHo2kpv/Zvo270lsxVhVQhd25NAtQkDRoE2KCioxlkHhBB069YN3333HauGiRGxPAGtD0LWVq7VI7DgAqAEHsjc0cijhUXnC1nbk0K1CQNGgfbHH3+sFmglEgkaN26M5s2bIzIykhPjxIaYpptYipC1XbypRn9FZVq6+3490MjC5eNC1vakUG3CgFGgHTt2LMdm2AYtWlh2JyUmhKwt9dIBPCXRAQAad37N4vOFrO1JodqEgXge24mAjIwMvk3gDCFrc0itnOddAQUaWfggDBC2tieFahMGjBN/nzp1Cj/++CNSUlJw//59PF6YQSKRIC4ujnUDxYSTkxPfJnCGULXdfVCC7tpLgAxQu3ZEmMLR4j6Eqo0NqDZhwCjQLlu2DDNmzIC9vT3CwsLg7u7OtV2iRKFQ8G0CZwhV26Xzf2GI7AEAQBLGLC3i4whVGxtQbcKAUaBdunQpevbsiX379qFRI2YZkRoiubm58PLy4tsMThCstqQ/jW+Du71Yry4Eq40FqDZhwGiMtrS0FKNGjaJBtg5CQ0P5NoEzhKotIK8y/+wtaTMo3OtXflqo2tiAahMGjAJtnz59cPXqVa5tET2JiYl8m8AZQtSWn30HbUhlTtIM7/qn6BSiNrag2oQBo0C7atUqxMbG4osvvkB+fj7XNomWiIgIvk3gDCFqiz+1y/jeufXgevcjRG1sQbUJA0aBtlmzZpg4cSJmzZoFLy8vODk5wdXV1eRFhxWApKQkvk3gDEFqezg+W0Ac0TZqQP27EaI2lqDahAGjh2Fz587FJ598An9/f3Tu3JkG1RoQy8B8fRCatqKiB+hYfhGQADdcotBVoax3X0LTxiZUmzBgFGjXrFmDIUOGYM+ePaylJlu3bh2WLFmCO3fuIDw8HEuWLEFMTIzZtseOHcNXX32FCxcuoKCgAMHBwZg4cSLee+89VmxhC61Wy7cJnCE0barYTegpqayCKm9Tv9kGVQhNG5tQbcKAUdTUaDQYMmQIa0F2165dmDRpEqZNm4YrV65g8ODBGDZsGFQqldn2Z86cQUREBLZv345r167hww8/xMyZM/HVV1+xYg9blJSU8G0CZwhNmzyhcny2gDiiTfQrT9SX0LSxCdUmDCTk8SVeZhg9ejQAYNOmTaxctFu3boiIiMD69euN+7p06YK2bdvihx9+YNTHO++8g7i4OJw+fbrascjISCQkJLBiqyWUlpbC0dHylUliQEjaynPVUK5uD6mE4LjrYES/v+WJ+hOSNrah2qyPufjD6BZ13rx5SEhIwDvvvINLly4hNzcX+fn51V5M0Gg0uHTpEgYMMH14ERMTYzZo1kRhYWGNS/AMBgPUanW1F9fJyZOTkzntn0+EpC3lxM+QSirvD/Qdxz1xf0LSxjZUmzBgFGjDwsJw5coVrFmzBlFRUfD19YWXl1e1FxPu3bsHnU4HHx/Tmk6+vr7Iyspi1MfJkyexbds2TJs2zezxnJwcBAcHV3vNnz8faWlpKCgoMA5TqFQqlJaWIikpCZmZmcjNzUVCQgK0Wi1UKhW0Wi0SEhKQm5uLzMxMJCUlobS01OT8goICpKWlwdXVFYWFhSbHiouLkZKSgvT0dOTl5eHatWvQ6/VQqVQoLy/HjRs3kJ2djezsbNy4cQPl5eVQqVTQ6/W4du0a8vLykJ6ejpSUFBQXF5v0XVhYCLVazakmtVqNwsJC499WCJrskvYBAFL0HujQsWe9NT06VMW3Jrb89Phnz9nZ2eY0VfnJ399fkJrMwWjoYP78+YzKjc+bN6/ONpmZmfD390dsbKxJMcfVq1djzpw5ePDgQa3nX758Gf3798cHH3yAjz76yGyb8PBwHDx4sNp+Nzc3TjOyiyk/pqUIRZsh5wak30YBADbZvYDRszY8cZ9C0cYFVJv1MTd0wGjWwfz581kzwtPTE3K5HNnZ2Sb7s7Oz4efnV+u5Fy5cwMCBAzF16tQagyxQWbSNj1LEAQEBVr+mtRCKtqyzv8D/4Xtp+5Gs9CkUbVxAtQkDq+ejVSqV6NSpEw4fPmyy/+DBg+jZs2eN550+fdp4J8vkzpkPHv2JbWsIQhshcEzYCgC4rG2GXlE1f14sQRDaOIJqEwaM7mgXLlxYZxuJRIKPP/6Y0UWnT5+O1157DVFRUejduzc2btyIuLg4Y8ny2bNn48KFC4iNjQUAHD9+HEOGDMGbb76Jt956C3fv3gUAyGQyQU1aNhgMfJvAGULQRtL/RmNNpe9jHfpihjs7+UiFoI0rqDaBQBggkUhqfEmlUuO/lvDdd9+RkJAQolQqSZs2bcjvv/9uPDZmzBgSGBhosg2g2uvRNo8SERFhkS1s8eDBA16uaw2EoC1322RC5rsS/bxGZNX+Y6z1KwRtXEG1WR9z8YfR0IHBYKj20ul0uHXrFt5//3107twZOTk5FgX4CRMm4NatW6ioqIBKpcKgQf8mbd6wYQPUarXJNiGk2uvRNkJAaPawCe/aDDrY3fwNAHBEG4ah3Tqx1jXv2jiEahMG9R6jlUqlCA4OxhdffIHQ0FD897//ZdMuUSLEJ6Bswbc2TfIxuOgLAABXGvdHkIcza33zrY1LqDZhwMrDsKeffhq///47G12JmpqWENsCfGu7c/xHAEAFkaN5z1dZ7ZtvbVxCtQkDVgLt33//zVoeBDETEhLCtwmcwau2ikI0yTwCADhkaI+hnSNZ7Z76TZyISRujWQcbN240u7+goAAnTpzA7t278eabb7JqmBjJycmBszN7P2mFBJ/aMo+tQxNUAADyQofDTiFjtX/qN3EiJm2MAu3YsWNrPObp6YlZs2Zh7ty5bNkkWpTK+udEFTq8aSME0n9+AgAk6bwQPfA11i9B/SZOxKSNUaC9fft2tX0SiQSNGzeGi4sL60aJFQcHB75N4Ay+tGlTz8G3IhUA8JdLDCZ6u7F+Deo3cSImbYwCbWBg/aqLNjSysrLg4eHBtxmcwJe2O38sRQgALZHCvft/OLkG9Zs4EZO2Gp9glZeX4+2338aqVatq7WDlypWYNGmSqLKdc4WYisVZCh/aDPmpCMz5CwCwh3TH0K4dObkO9Zs4EZO2GgPt2rVrsWHDBgwZMqTWDoYMGYL169cbl882ZOLj4/k2gTP40JZ8YClkqEwuV9z+TTgoGf0AsxjqN3EiJm01Btrt27fjpZdeqnMKRfPmzfHyyy9jy5Yny3JvC7Rs2ZJvEzjD2toMZQ/gl7ITAHBSH4bhg57l7FrUb+JETNpqDLRXr15Fr169GHXSo0cPUU0e5orU1FS+TeAMa2u7cWAZXFAGAMiKHA8XewVn16J+Eydi0lZjoNVoNIynTyiVSlRUVLBmlFjhMqk431hVm6YYvgmVK8ES9E0Q89yTl6upDeo3cSImbTUG2iZNmuDatWuMOrl27RqaNGnCmlGUhk3W4RVoTCpzjZ5vNg6ujnY8W0ShPBk1BtpnnnkGGzdurDMrV05ODjZu3Ij+/fuzbpzY4Lr4I59YTVv5AzheWgMAuK7zQe9hEzm/JPWbOBGTthoD7cyZM1FeXo6+ffvi/PnzZtucP38e/fr1Q3l5OWbMmMGZkWLBlucbW0tb2v5P0ejh3exhv3EI9m7E+TWp38SJmLTVGGhDQkKwfft2pKWloUePHggNDcWLL76IMWPG4MUXX0TLli3Ro0cPqNVqbN26Fc2bN7em3YLk5s2bfJvAGVbRVpYPj/jK5bYXdEF4edQU7q8J6jexIiZtdVbBVavVWLJkCfbv34+MjAzj/iZNmmDo0KH48MMPBZdFx1wVSmug1+shk7Gb8EQoWEPbvU1j4XnrVwDA2sBPMWHsu5xerwrqN3EiVG3m4k+duQ2DgoLw7bff4s6dO3jw4IHx3/T0dKxZs0ZwQZZPEhMT+TaBMzjXduMPY5A9pIlEdMxobq/3CNRv4kRM2ixKIuvi4gJ/f3+aSKYG6iqXLmY41VZegIq9lcMEBQYHHIv4GGG+3I/NVkH9Jk7EpI1m62aRsrIyvk3gDC616Q7+D3Zl2QCAmWXDMWlwNGfXMgf1mzgRkzYaaFlEo9HwbQJncKYt5SjkcT8DAP7UhKPlgHcQwFIZcaZQv4kTMWnjJktHA8Xb25tvEziDE22aYpTvfhf2AIqJHdY2/i+29Q1n/zp1QP0mTsSkjd7RskhKSgrfJnAGF9o0RxbDvqRyJsvcshfw2X+eg4yH2nPUb+JETNrqnN4lRvia3kWxgJsHgS2V1WzPaoNxsd8veK8Pu0UXKRQ+qNf0LgpzbDmDGava7l6FbvtYAJVDBl84TcbkaP6SOFO/iRMxaaOBlkWCgoL4NoEzWNNWkgvdpuGQ6yufGP+3YgwWjBkOqVTCTv/1gPpNnIhJGw20LJKfn8+3CZzBija9FrrtYyAvuQsAmF78Avo8Pwmtm7g9ed9PAPWbOBGTNhpoWUTKw4Mca/HE2giB4cAHkKedBgDsrOgAbdS7+E9X/lcWUr+JEzFpo9O7WMTV1ZVvEzjjibUd/QTSfzYCAC5pm+Fn/5nY/VInFix7cqjfxImYtInnK0EEpKWl8W0CZ9RbGyHAyS+Ak0sBAHf0bpgkm4EN4/ryMpXLHNRv4kRM2uj0Lgp36DUw/P4hpJfXAwCyDS54qWwqVr/3Bto1bcyzcRQKN9DpXRwjpukmlmKxttJ8VGx+zRhkM/WN8Fzp+/h84n8EF2Sp38SJmLTRMVoWadGiBd8mcIZF2m4egva3KbAryQIAXNE1xX/lM7Bu6jC09RdWkAWo38SKmLTRO1oWeTQxuq3BSJumGPq/PoFhy2tQPAyy+ypaY3XIcvw5e7QggyxA/SZWxKSN3tGyiJOTdbNOWZNatREC3PwDmr1ToXyY7rCcyDGl+GUEPvMu1g5sDYmEvwUJddFg/SZyxKSNBloWUSgUfJvAGWa16SqA28ehP70SstSTUD7crdI1wQy8jSnjhmNwK3+r2lkfGpzfbAQxaaOBlkVyc3Ph5eXFtxmcYNRGCJB6GojbAsO1nZDqylFVtem+wQEflT4Pj15v4rfBHWCnEF49J3M0CL/ZIGLSRgMti4SGhvJtAmeEBvoB59eA/P0jJPduAPh3gL+cyLG2rBd2uL6GVW8NQPum7vwZWg9s2m9UmyCggZZFEhMT0bZtW77NYAdtKZD5D5CTAG3qBSjitwMAqkZatUSKI9pw/FbRFmr/IXhjWHscbdsUcpn4nq/alN8eg2oTBrwF2nXr1mHJkiW4c+cOwsPDsWTJEsTExNTYPiEhAZMnT8bZs2fRqFEjjBkzBp9++qmgyg1HRPCX6s9iCAHKC4CSe0BRJpB/G7h/G7h9AiQ7HhJ9hbHp4yNhc0qew48VT6Fn63DM7B+JqCBPq5rONqLym4VQbcKAl0C7a9cuTJo0CatWrULv3r2xceNGDBs2DBcvXjT7DVVUVIT+/fujW7duOH/+PFJTUzF27FhIJBJ8/vnnPCgwT1JSEiIjeU5eTQigKwNKcoHS/MpAmn0NqCgEiu4ChenAgwwg/1aNXTw+P6DIYIfD2nD8STrivEs/jB0UissdAtDEzZFbLVZCEH7jCKpNGPCyBLdbt26IiIjA+vXrjfu6dOmCtm3b4ocffqjWfs2aNZg+fTqys7ONUzpWr16NmTNnIjc3Fw4ODibtLV6CqysHKooqgxQIQAw1vH+4DQIQPPK+cn9+fj7cG7vV0gcq/zXoAG0JAAmg11RevyANUDoDBj3KK8qh0VaA6LQw6LUgei2IrqKynbYCTllnUeLWEhJNKaT6Uki1pZDqSiHXlUKhLYLMUL+idXoiwS29F7KJCxJ0friu98ERSRd0bt0W/Zs3xvCuYVDKhfMLgi3E9FDFUqg262Mu/lj9jlaj0eDSpUuYMmWKyf6YmBjs2LHD7DlnzpxBz549TebNxcTEYPLkyYiLi0O3bt1M2hsMBqjV6mr9uLm5wc3NzXTnnfPAppcATVG99DwKW4+A7B++am1TlFqvvs9rg5BmaIx0fWPcI04oJUrE6/ygNnigxNEfLXzcEe7jinAfVwz2c8PnLbxhp5AhMzPTJoMsAGi1Wr5N4AyqTRhYPdDeu3cPOp0OPj4+Jvt9fX2RlZVl9py7d+/C19e3WnsAZs/JyclBcHBwtf1TpkzBBx98AFdXV6SlpaFt27bIObUR3iwEWS7REwk0kKOMKFBOFCiHHCGyPKh0TZBjcEEJsUMJUaKE2OEBscc9gzPyiRMMkCBB54tc4opCmRsqpI4o1+rwTLgfHEgFmnm4oqmLEt2dpegRGYL8zMq/iUqlQkCAHwoLC5GVcQfu7u64fv06mjRpApVKhZCQEOTk5ECpVMLBwQFZWVmIiIhAfHw8WrZsidTUVOMXWkFBAQIDA3Hz5k20atUKiYmJ8PPzQ1lZGTQaDby9vZGSkmK8blBQEPLz8yGVSk38pFKp0KJFC2RkZMDJyQkKhQK5ubkIDQ1FYmIiIiIikJSUBC8vL2i1WpSUlMDf3x/JycmPaApAYWEhDAYD3N3doVar0bZtWyQmJsLV1dWmNFX56fbt2zAYDDalqcpPubm5yM/PF5wmc1h96CAzMxP+/v6IjY1F3759jftXr16NOXPm4MGDB9XOGTBgAHx9fbFx40bjvpKSEjg7O2P37t144YUXTNqHh4fj4MGD1foxe0ebdg5IOwNc3QF0Hg9IpAAkgERi+h4Pt82+r3xVaLSws7Nj1oe2DHD0ABT2gNwekNsBUgV+v5mPC2mFuJDxAMM7NYdUpoBMJoNcKoVcKoFcVvmvTCpBuVYPL2d7KOVSKGVS478K2b/bDgo5HJWyJ16ZVVpaCkdH2xiTfRyqTZwIVZsghg48PT0hl8uRnZ1tsj87Oxt+fn5mz/H19TXbHoDZc6RSKfN6QgHdKl+9PmDWvhZuqFRoG/lk000GRwVgcNQTm8I6VXcctgjVJk7EpM3qkx6VSiU6deqEw4cPm+w/ePAgevbsafacHj164PTp0ygtLTVp7+TkhHbt2nFqryWIxen1gWoTJ1SbMOBldvn06dPx888/Y82aNbh+/TrmzJmDuLg44wOy2bNno1+/fsb2o0aNgqurK/7zn//g6tWrOHDgAObOnYvJkydXm3HAJ2LKj2kpVJs4odqEAS/zaIcPH478/HwsWbIEU6ZMQVhYGPbs2WP8hsrKysKtW//O83RxccGRI0cwefJkREVFwdXVFePHj8fixYv5ML9GAgIC+DaBM6g2cUK1CQNayoZF0tLSROV8S6DaxAnVZn1oKRsOKSgowLJly1BQUMC3KaxDtYkTqk040EDLEgUFBVixYoVoHG8JVJs4odqEAw20FAqFwjE00FIoFArH0EBLoVAoHEMDLYVCoXCMTU7vcnV1RdOmTa16TYPBgLy8PHh4eEAqta3vL6pNnFBt/JCeno7CwkKTfTYZaCkUCkVICOurgEKhUGwQGmgpFAqFY2igZQghBJ988gmaNm0Ke3t7dO/eHRcvXqz1nGPHjkEikVR7nTp1yqTd6dOnERUVBXt7ewQEBOCLL77gUko16qPt119/xcCBA+Ht7Q1nZ2d07NgRv/zyi0mbDRs2mNWfnp7OmZZ169ahRYsWsLOzQ7t27czmJX6UhIQE9O3bFw4ODvD19cXMmTOh1+tN2vDtnyos0Xbs2DE8//zzaNKkCRwdHdGqVSusXLmyWhsmn09rYIk2tVpt1u5NmzaZtGPiW6tBKIz44osviJOTE9m6dSu5du0aGTt2LHFzcyNZWVk1nnP06FECgKhUKpKVlWV8aTQaYxu1Wk2cnJzIpEmTSHx8PNm4cSOxs7Mj3377rTVkEULqp+29994jn3zyCTl79ixJTk4mX375JZFKpWT37t3GNuvXryd2dnYm2rOysoher+dEx86dO4lMJiPffPMNSUhIILNmzSJKpZLExcWZbV9YWEiaNGlCXnzxRRIXF0d+++034u7uTmbOnGlsIwT/EGK5tk8++YTMmjWLnDx5kty6dYts2LCB2Nvbk+XLlxvbMPl8WgNLtd2+fZsAIAcOHDCxu6yszNiGiW+tCQ20DDAYDMTPz4/Mnz/fuE+n0xEfHx+ycOHCGs+r+iDXFrBmzpxJgoODicFgMO6bPn06CQkJYcf4OqivNnMMGjSIjBw50rhdFWitRdeuXcnYsWNN9nXu3JmMHz/ebPtvv/2WODk5keLiYuO+r7/+mjg5OZHS0lJCCP/+qcJSbeaYNGkS6dGjh3GbyefTGliqrSrQnj17tsY+mfjWmtChAwbcvn0bWVlZGDBggHGfTCZD//79cfr06TrP79atG3x9fREdHY39+/ebHDtz5gz69+9vUmomJiYGKSkpuHv3LnsiauBJtT1KYWGhSQFNoLIYZ3BwMPz9/RETE4MzZ86wYvfjVBX9fFQHUPm3rElHTUU/S0pKEBcXZ2zDp3+A+mkzhzn/ALV/PrnmSbS98sor8PLyQrdu3fDTTz+BPDKBiolvrQkNtAyo+g9lSUFJoLLMzjfffIMdO3Zg79696Ny5M5599ln8+uuvJn2b6xcwX3iSbeqr7XG2bNmCixcv4r///a9xX1hYGH788Ufs3r0b27Ztg7e3N6Kjo+sc/60P9S36Wdffnm//APXT9jgnT57Etm3bMG3aNOM+Jp9PrqmPNmdnZyxduhTbt2/HoUOH8Oyzz2LChAn46quvjG2E4LdH4SXxt1CYP38+FixYUGubLVu21DvnZVhYGMLCwozbXbt2RU5ODj7//PNqBSXZhmttj3Lw4EG8+eabWLduHdq0aWPc3717d3Tv3t243atXL9y+fRtffPEFtm3b9sTXpTDj8uXLeP755zF//nwMHDjQuJ/Pz+eT4OnpienTpxu3O3bsiPLycnz++ed4//33ebSsZhp0oJ08eTJGjBhRaxt/f3/k5uYCqCwIGRISYjxWW0HJmujWrZvJzzNLC08yxVra9u/fj1deeQWrV6/Gf/7znzrbd+vWDUeOHKmznaVwVfSTK/9YQn20VXHhwgUMHDgQU6dOxUcffVTntR7/fHLNk2h7lG7dumHx4sV48OABGjVqJAi/mWD1UWERUvXAaMGCBcZ9er2e+Pn5WfzA6I033iARERHG7ZkzZ1Z7sDJjxgyrPwyrj7adO3cSe3t7smHDBsbX69evHxk0aFC97a2Nrl27knHjxpns69KlS50Pw0pKSoz7Vq9eXe1hGJ/+qcJSbYQQcurUKeLq6mrRZ/Txz6c1qI+2x1m0aBFxdnYmOp2OEMLMt9aEBlqGfPHFF8TZ2Zls376dxMfHk/Hjx1ebAvX666+T119/3bi9fPly8uuvv5IbN26Q+Ph4smDBAiKVSsmaNWuMbaqmD73zzjskISGBbNq0idjb21t9epel2rZs2ULkcjlZunSpyRSbvLw8Y5v58+eTP/74gyQnJ5MrV66Qd999l0gkEvL7779zomPHjh1ELpeTb7/9liQmJpLZs2ebTBOaNWsW6du3r7F9YWEh8fPzIy+99BJRqVRk//79xMPDw+z0Lj79Ux9tx44dI05OTmTKlCkm/snJyTG2YfL5FKK2DRs2kE2bNpH4+Hhy48YNsnLlSuLg4EBmzZplbMPEt9aEBlqGGAwGsmjRItKkSRNiZ2dHunXrRi5cuGDSJjo6mkRHRxu3lyxZQkJDQ4m9vT1xc3Mj3bp1Izt27KjW98mTJ0nnzp2JUqkkTZs2Jf/3f//HtRwT6qMtOjqaAKj2erTN+++/TwIDA4mdnR3x9PQkffr0IbGxsZxq+e6770hISAhRKpWkTZs2JkF9zJgxJDAw0KR9fHw86dOnD7G3tyfe3t5kxowZRKvVmrTh2z9VWKJtzJgxZv3zaBumn09rYIm2n376ibRq1Yo4OjoSZ2dn0r59e7JmzZpq87OZ+NZa0KQyFAqFwjF0eheFQqFwDA20FAqFwjE00FIoFArH0EBLoVAoHEMDLYVCoXAMDbQUCoXCMTTQUhokVcmjN2zYwLcplAYADbQUUfDcc8/B0dERRUVFNbYZNWoUlEol8vLyrGgZhVI3NNBSRMGoUaNQVlZWYwq/0tJS7N27FzExMfDw8LCydRRK7dBASxEFzz33HFxcXKrVJati7969KCkpwahRo6xsGYVSNzTQUkSBg4MDXnzxRcTGxiInJ6fa8V9++QUuLi7o1asXpk+fjjZt2sDZ2Rmurq4YNGgQo6z6vXv3Ru/evavtHzt2LIKCgkz2GQwGfPXVV2jVqhXs7e3h4+ODiRMn4v79+ybt/v77bwwcOBCenp5wcHBAcHAwxo8fb5F2ivihgZYiGkaNGgWdToft27eb7M/Pz8ehQ4fwwgsvICsrC3v27MHQoUOxbNkyzJgxA1evXkV0dDQyMzNZs2XixImYMWMGevbsiRUrVmDcuHHYvHkzBg4cCK1WCwDIycnBgAEDoFarMWvWLKxatQqjRo3CuXPnWLODIhJ4SWVDodQDnU5H/Pz8SPfu3U32r1mzhgAghw4dIuXl5dWyON2+fZvY2dmZ5GWtKvC3fv16477HM5RV8Xj2qJMnTxIAZPPmzSbtDh48aLL/119/JQDIxYsX66mYYivQO1qKaJDJZBgxYgTOnj0LtVpt3P/LL7/Ax8cH/fr1g52dHaTSyo+1Xq9HXl4enJ2dERYWhsuXL7Nix44dO9CoUSP0798f9+7dM746deoEZ2dnHD16FADg5uYGoLIKRdVdLqVhQgMtRVRUPeyqeiiWnp6OkydPYsSIEZDJZDAYDFi+fDlCQ0NhZ2cHT09PeHl5QaVS4cGDB6zYkJSUhAcPHsDb2xteXl4mr+LiYuMYcnR0NF566SUsWLAAnp6eGDZsGNavX4+KigpW7KCIhwZdM4wiPjp16oTw8HBs2bIFc+bMwZYtW0AIMQbgTz/9FB9//DHGjx+PRYsWwd3dHVKpFFOnToXBYKi1b4lEYlKyugq9Xm+ybTAY4O3tjc2bN5vtx8vLy9jfzp07ce7cOezbtw+HDh3C+PHj8eWXX+LcuXNwdnauz5+AIkb4HrugUCxl0aJFBACJi4sj7du3J6GhocZj7dq1I3369Kl2jr+/v8n4q7kx2hdeeIG0a9eu2rlPPfWUyRjtO++8Q2QyWb1qT23evJkAIOvWrbP4XIp4oUMHFNFRdfc6d+5cXLlyxWTurEwmq3ZXumPHDmRkZNTZb/PmzXH9+nVjZWAAiIuLw+nTp03avfLKK9Dr9Vi0aFG1PnQ6HQoKCgAA9+/fr2ZL+/btAYAOHzQw6NABRXQEBwejR48e2Lt3LwCYBNqhQ4di4cKFGDduHHr06IGrV69i8+bNJqXUa2L8+PFYtmwZBg4ciDfeeAM5OTlYs2YNWrVqhcLCQmO76OhoTJw4EZ999hmuXLmCAQMGQKFQICkpCTt27MCKFSswfPhw/PTTT/jmm2/wwgsvoHnz5igqKsK6devg6uqKwYMHs/+HoQgXvm+pKZT6sHr1agKAREVFmewvLy8n06ZNI35+fsTBwYH07NmTnD17ttrULXNDB4QQsmnTJmORwPbt25NDhw6ZLepICCFr164lnTp1Ig4ODsTFxYW0adOGfPjhhyQzM5MQQsjly5fJa6+9RgICAoidnR3x9vYmQ4cOJX///Tfbfw6KwKHFGSkUCoVj6BgthUKhcAwNtBQKhcIxNNBSKBQKx9BAS6FQKBxDAy2FQqFwDA20FAqFwjE00FIoFArH0EBLoVAoHPP/RNDcycWYKCgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sapsan.utils.plot import pdf_plot, cdf_plot\n",
    "\n",
    "# Full 3D 32x32x32 slice of ux and uy components.\n",
    "# However, it will work just fine with any data \n",
    "# dimensionality since the input gets flattened.\n",
    "ux = x[0,0]\n",
    "uy = x[0,1]\n",
    "\n",
    "# Probability density function\n",
    "ax = pdf_plot(series = [ux, uy],\n",
    "              label  = ['velocity_x', 'velocity_y'])\n",
    "\n",
    "# Cumulative distribution function\n",
    "ax = cdf_plot(series = [ux, uy],\n",
    "              label  = ['velocity_x', 'velocity_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=train_loss<br>epoch=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "train_loss",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "train_loss",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1,
          2,
          3,
          4,
          5
         ],
         "xaxis": "x",
         "y": [
          178.3000030517578,
          14.300000190734863,
          12.0600004196167,
          11.869999885559082,
          10.90999984741211
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "variable=valid_loss<br>epoch=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "valid_loss",
         "line": {
          "color": "#EF553B",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "valid_loss",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1,
          2,
          3,
          4,
          5
         ],
         "xaxis": "x",
         "y": [
          172.39999389648438,
          7.824999809265137,
          11.729999542236328,
          8.484999656677246,
          7.611999988555908
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "height": 400,
        "hovermode": "x",
        "legend": {
         "title": {
          "text": "variable"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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    "# Tracking the loss in training and validation is essential.\n",
    "# log_plot() extracts the data from the logs into pandas DataFrame,\n",
    "# and then produces interactive plots with plotly.\n",
    "\n",
    "from sapsan.utils.plot import log_plot\n",
    "\n",
    "class ml_logs:\n",
    "    # Create and cleanup fake training logs\n",
    "    def __init__(self, log_path, valid_log_path, delimiter):        \n",
    "        self.log_path       = log_path\n",
    "        self.valid_log_path = valid_log_path\n",
    "        self.delimiter      = delimiter\n",
    "\n",
    "    def create_logs(self):        \n",
    "        train_log = np.array([[1,1.783e+02],\n",
    "                              [2,1.430e+01],\n",
    "                              [3,1.206e+01],\n",
    "                              [4,1.187e+01],\n",
    "                              [5,1.091e+01]])\n",
    "        \n",
    "        self.write_log(self.log_path, train_log)\n",
    "            \n",
    "        np.random.seed(4)\n",
    "        valid_log      = train_log        \n",
    "        valid_log[:,1]*= np.random.rand(len(train_log))\n",
    "        \n",
    "        self.write_log(self.valid_log_path, valid_log)\n",
    "    \n",
    "    def write_log(self, path, data):\n",
    "        with open(path,'w') as f:\n",
    "            np.savetxt(f, data, fmt=f'%d {self.delimiter} %.3e', \n",
    "                       delimiter=self.delimiter, header=f'epoch {self.delimiter} loss')\n",
    "            \n",
    "    def cleanup(self):\n",
    "        os.remove(self.log_path)\n",
    "        os.remove(self.valid_log_path)\n",
    "\n",
    "\n",
    "log_path       = 'example_train_log.txt'\n",
    "valid_log_path = 'example_valid_log.txt'\n",
    "delimiter      = '\\t'\n",
    "\n",
    "# Let's create pseudo training logs\n",
    "logs = ml_logs(log_path, valid_log_path, delimiter)\n",
    "logs.create_logs()\n",
    "\n",
    "# By default, epochs are assumed to be in column 0, and loss in 1.\n",
    "# Those can be specified via epoch_column, train_column, and valid_column.\n",
    "log = log_plot(show_log       = True,\n",
    "               log_path       = log_path, \n",
    "               valid_log_path = valid_log_path, \n",
    "               delimiter      = delimiter)\n",
    "\n",
    "# cleanup\n",
    "logs.cleanup()"
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: your batch_num=1, hence the data cannot be split into train and valid (perhaps you only loaded 1 checkpoint). Setting valid = test data...\n",
      "\n",
      "Train data shapes:  (1, 3, 32, 32, 32) (1, 98304)\n",
      "Valid data shapes:  (1, 3, 32, 32, 32) (1, 98304)\n"
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    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pkarpov/Sapsan/sapsan/examples/../../sapsan/utils/hiddenlayer/pytorch_builder.py:75: FutureWarning:\n",
      "\n",
      "'torch.onnx._patch_torch._node_getitem' is deprecated in version 1.13 and will be removed in version 1.14. Please Internally use '_node_get' in symbolic_helper instead..\n",
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     "metadata": {},
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   "source": [
    "# Visualize the model structure with model_graph\n",
    "from sapsan.lib.estimator.cnn.cnn3d_estimator import CNN3d, CNN3dConfig\n",
    "from sapsan.lib.data import get_loader_shape\n",
    "from sapsan.utils.plot import model_graph\n",
    "\n",
    "# Convert our raw data to a Torch loader\n",
    "loaders = data_loader.convert_to_torch([x, y])\n",
    "\n",
    "# Initialize the model you want to make a graph of\n",
    "estimator = CNN3d(config = CNN3dConfig(),\n",
    "                  loaders = loaders)\n",
    "\n",
    "# extract shapes of the loaders to initialize the graph\n",
    "shape_x, shape_y = get_loader_shape(loaders)\n",
    "\n",
    "model_graph(model = estimator.model, shape = shape_x)"
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     "text": [
      "/home/pkarpov/Sapsan/sapsan/examples/../../sapsan/utils/hiddenlayer/pytorch_builder.py:75: FutureWarning:\n",
      "\n",
      "'torch.onnx._patch_torch._node_getitem' is deprecated in version 1.13 and will be removed in version 1.14. Please Internally use '_node_get' in symbolic_helper instead..\n",
      "\n"
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    "# To make our graph more consice, we can combine the last 3 layers\n",
    "# via transforms. There are already some defaults which we will copy\n",
    "# from documentation and then add an entry to combine those last layers\n",
    "\n",
    "import sapsan.utils.hiddenlayer as hl\n",
    "\n",
    "transforms = [\n",
    "              hl.transforms.Fold(\"Conv > MaxPool > Relu\", \"ConvPoolRelu\"),\n",
    "              hl.transforms.Fold(\"Conv > MaxPool\", \"ConvPool\"),    \n",
    "              hl.transforms.Prune(\"Shape\"),\n",
    "              hl.transforms.Prune(\"Constant\"),\n",
    "              hl.transforms.Prune(\"Gather\"),\n",
    "              hl.transforms.Prune(\"Unsqueeze\"),\n",
    "              hl.transforms.Prune(\"Concat\"),\n",
    "              hl.transforms.Rename(\"Cast\", to=\"Input\"),\n",
    "              hl.transforms.FoldDuplicates(),\n",
    "              \n",
    "              # combining the last 3 layers\n",
    "              hl.transforms.Fold(\"Linear > Relu > Linear\", \"LRL\")\n",
    "             ]\n",
    "\n",
    "model_graph(model = estimator.model, shape = shape_x, transforms = transforms)"
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