model_fit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "collected-latitude",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import itertools\n",
    "from functionsDiffusion import *\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import matplotlib.ticker as ticker\n",
    "import pandas as pd\n",
    "def ahlgfp():\n",
    "    for i,a in enumerate(ahl):\n",
    "        U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = a)\n",
    "        sim = runModel(1000,0.1,theta,U)\n",
    "        gfp[i] = sim[6,r,c,9000].mean()\n",
    "    ax.plot(ahl, gfp)\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "designed-injury",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mpga(plot):\n",
    "    plot.spines['top'].set_visible(False)\n",
    "    plot.spines['right'].set_visible(False)\n",
    "    plot.spines['left'].set_linewidth(1.5)\n",
    "    plot.spines['bottom'].set_linewidth(1.5)\n",
    "    plot.yaxis.set_tick_params(width = 2)\n",
    "    plot.xaxis.set_tick_params(width = 2)\n",
    "    plot.title.set_size(12)\n",
    "    plot.xaxis.label.set_size(12)\n",
    "    plot.yaxis.label.set_size(12)\n",
    "    plot.tick_params(axis = 'x', labelsize = 15)\n",
    "    plot.tick_params(axis = 'y', labelsize = 15)\n",
    "    \n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "surface-colors",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## THIS THIS THIS THIS THIS ##\n",
    "# theta = {\"x_s\":4,           #luxi production rate  \n",
    "#          \"x_a\":0.0007,      #ahl production rate\n",
    "#         \"x_g\":4, #0.2     #gfp production rate\n",
    "#         \"lambda_a\":2.3, #2.3  #ahl hill coeff\n",
    "#         \"K_a\":20,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.03,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 3,\n",
    "#          \"K_g\": 50,\n",
    "#         \"K_n\":80}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "swedish-barcelona",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = {\"x_s\":50,           #luxi production rate  \n",
    "         \"x_a\":0.001,      #ahl production rate\n",
    "        \"x_g\":1.1, #0.2     #gfp production rate\n",
    "        \"lambda_a\":10, #2.3  #ahl hill coeff\n",
    "        \"K_a\":5,\n",
    "        \"D\":0.03,\n",
    "        \"D_a\":0.03,\n",
    "        \"w\": 0.75,\n",
    "        \"rho_n\": 3,\n",
    "        \"rc\": 0.0006,\n",
    "        \"Dc\": 0.0001,\n",
    "        \"rho\": 0.0005,\n",
    "        \"lambda_n\": 2.0,\n",
    "        \"lambda_g\": 3,\n",
    "         \"K_g\": 45,\n",
    "        \"K_n\":80}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "entitled-absence",
   "metadata": {},
   "outputs": [],
   "source": [
    "#before mistake found\n",
    "# theta = {\"x_s\":6,           #luxi production rate  \n",
    "#          \"x_a\":0.0007,      #ahl production rate\n",
    "#         \"x_g\":1.1, #0.2     #gfp production rate\n",
    "#         \"lambda_a\":2.3, #2.3  #ahl hill coeff\n",
    "#         \"K_a\":20,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.03,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 5,\n",
    "#          \"K_g\": 50,\n",
    "#         \"K_n\":80}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "tutorial-louisville",
   "metadata": {},
   "outputs": [],
   "source": [
    "# theta = {\"x_s\":2,           #luxi production rate  \n",
    "#          \"x_a\":0.0015,      #ahl production rate\n",
    "#         \"x_g\":10, #0.2     #gfp production rate\n",
    "#         \"lambda_a\":2.3, #2.3  #ahl hill coeff\n",
    "#         \"K_a\":50,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.05,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 3,\n",
    "#          \"K_g\": 50,\n",
    "#         \"K_n\":80}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excellent-spring",
   "metadata": {},
   "source": [
    "    # 0 LuxI\n",
    "    # 1 Arabinose\n",
    "    # 2 Nutrients\n",
    "    # 3 Sender\n",
    "    # 4 C6\n",
    "    # 5 Receiver\n",
    "    # 6 GFP\n",
    "    \n",
    "    S = Dc * S_ficks + rc * N * U_grid[3]\n",
    "    R = Dc * R_ficks + rc * N * U_grid[5]\n",
    "    LuxI = x_s * N * hill(U_grid[1], lambda_a, K_a) * U_grid[3]\n",
    "    c6 = D_a * c6_ficks + (x_a * U_grid[0]) - rho * U_grid[4]\n",
    "    arabinose = D * arabinose_ficks\n",
    "    n = D * n_ficks - rho_n * N * (U_grid[3] + U_grid[5])\n",
    "    gfp = x_g * N * hill(U_grid[4], lambda_g, K_g) * U_grid[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "settled-snake",
   "metadata": {},
   "outputs": [],
   "source": [
    "# theta = {\"x_s\":20, \n",
    "#          \"x_a\":0.025,\n",
    "#         \"x_g\": 15,\n",
    "#         \"lambda_a\": 2,\n",
    "#         \"K_a\":50,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.9,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 3,\n",
    "#          \"K_g\": 40,\n",
    "#         \"K_n\":80}\n",
    "\n",
    "## This set works for AND gate far positions\n",
    "# theta = {\"x_s\":10,           #luxi production rate  \n",
    "#          \"x_a\":0.0035,      #ahl production rate\n",
    "#         \"x_g\":0.2, #0.2     #gfp production rate\n",
    "#         \"lambda_a\":2.3, #2.3  #ahl hill coeff\n",
    "#         \"K_a\":10,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.05,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 3,\n",
    "#          \"K_g\": 50,\n",
    "#         \"K_n\":80}\n",
    "\n",
    "\n",
    "\n",
    "#set01\n",
    "# theta = {\"x_s\":2,           #luxi production rate  \n",
    "#          \"x_a\":0.0015,      #ahl production rate\n",
    "#         \"x_g\":0.2, #0.2     #gfp production rate\n",
    "#         \"lambda_a\":2.3, #2.3  #ahl hill coeff\n",
    "#         \"K_a\":10,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.05,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 3,\n",
    "#          \"K_g\": 50,\n",
    "#         \"K_n\":80}\n",
    "\n",
    "# ##BACKUP\n",
    "# theta = {\"x_s\":4,           #luxi production rate  \n",
    "#          \"x_a\":0.0015,      #ahl production rate\n",
    "#         \"x_g\":0.2, #0.2     #gfp production rate\n",
    "#         \"lambda_a\":2.3, #2.3  #ahl hill coeff\n",
    "#         \"K_a\":10,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.05,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 3,\n",
    "#          \"K_g\": 50,\n",
    "#         \"K_n\":80}\n",
    "\n",
    "# theta = {\"x_s\":5, \n",
    "#          \"x_a\":0.01,\n",
    "#         \"x_g\":0.2, #0.2\n",
    "#         \"lambda_a\":3, #2.3\n",
    "#         \"K_a\":20,\n",
    "#         \"D\":0.03,\n",
    "#         \"D_a\":0.02,\n",
    "#         \"w\": 0.75,\n",
    "#         \"rho_n\": 3,\n",
    "#         \"rc\": 0.0006,\n",
    "#         \"Dc\": 0.0001,\n",
    "#         \"rho\": 0.0005,\n",
    "#         \"lambda_n\": 2.0,\n",
    "#         \"lambda_g\": 5,\n",
    "#          \"K_g\": 60,\n",
    "#         \"K_n\":80}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continental-central",
   "metadata": {},
   "source": [
    "#1 ara -> LuxI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "sound-attachment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1589022b0>]"
      ]
     },
     "execution_count": 8,
     "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": [
    "ara = np.linspace(1,100,100)\n",
    "LuxI = hill(ara, theta['K_a'],theta['lambda_a'])\n",
    "plt.plot(ara,LuxI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-diploma",
   "metadata": {},
   "source": [
    "#2 LuxI -> c6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unauthorized-annex",
   "metadata": {},
   "source": [
    "#3 AHL vs GFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "atmospheric-enforcement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1589e52b0>]"
      ]
     },
     "execution_count": 9,
     "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": [
    "ahl = np.linspace(0,200,101)\n",
    "GFP = hill(ahl, theta['K_g'], theta['lambda_g'])\n",
    "plt.plot(ahl,GFP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acoustic-sweden",
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_plots_vertical(sim, title=\"\"):\n",
    "    f, ax = plt.subplots(4, 2, sharex=False, sharey=False, figsize=(3.5, 7))\n",
    "\n",
    "    f.suptitle(title, fontsize=40)\n",
    "    im1 = ax[0, 0].imshow(sim[3], interpolation=\"none\", cmap=cm.viridis, vmin=0, vmax=1)\n",
    "    ax[0, 0].set_title(\"Sender\", fontsize = 10)\n",
    "    divider = make_axes_locatable(ax[0, 0])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    ax[0, 0].axis(\"off\")\n",
    "    ax[0, 0].xaxis.label.set_size(7)\n",
    "    ax[0, 0].yaxis.label.set_size(7)\n",
    "    cb = f.colorbar(im1, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    im2 = ax[0, 1].imshow(sim[5], interpolation=\"none\", cmap=cm.viridis, vmin=0, vmax=1)\n",
    "    ax[0, 1].set_title(\"Receiver\", fontsize = 10)\n",
    "    ax[0, 1].axis(\"off\")\n",
    "    divider = make_axes_locatable(ax[0, 1])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cb = f.colorbar(im2, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    im3 = ax[1, 0].imshow(sim[1], interpolation=\"none\", cmap=cm.viridis, vmin=0)\n",
    "    ax[1, 0].set_title(\"Arabinose (mM)\", fontsize = 10)\n",
    "    ax[1, 0].axis(\"off\")\n",
    "    divider = make_axes_locatable(ax[1, 0])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cb = f.colorbar(im3, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    im4 = ax[1, 1].imshow(sim[3], interpolation=\"none\", cmap=cm.viridis, vmin=0)\n",
    "    ax[1, 1].set_title(\"LuxI\", fontsize = 10)\n",
    "    ax[1, 1].axis(\"off\")\n",
    "    divider = make_axes_locatable(ax[1, 1])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cb = f.colorbar(im4, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    im5 = ax[2, 0].imshow(sim[4], interpolation=\"none\", cmap=cm.viridis, vmin=0)\n",
    "    ax[2, 0].set_title(\"AHL (nM)\", fontsize = 10)\n",
    "    ax[2, 0].axis(\"off\")\n",
    "    divider = make_axes_locatable(ax[2, 0])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cb = f.colorbar(im5, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    im6 = ax[2, 1].imshow(sim[6], interpolation=\"none\", cmap=cm.viridis, vmin=0)\n",
    "    ax[2, 1].set_title(\"GFP\", fontsize = 10)\n",
    "    ax[2, 1].axis(\"off\")\n",
    "    divider = make_axes_locatable(ax[2, 1])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cb = f.colorbar(im6, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    im7 = ax[3, 0].imshow(sim[2], interpolation=\"none\", cmap=cm.viridis, vmin=0, vmax=100)\n",
    "    ax[3, 0].set_title(\"Nutrients\", fontsize = 10)\n",
    "    ax[3, 0].axis(\"off\")\n",
    "    divider = make_axes_locatable(ax[3, 0])\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cb = f.colorbar(im7, cax=cax, shrink=0.8)\n",
    "    cb.ax.tick_params(labelsize=6)\n",
    "\n",
    "    ax[3, 1].axis('off')\n",
    "\n",
    "    return(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "baking-piece",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_cross = [3,10,17,31,38,45,\n",
    "            21,22,23,25,26,27]\n",
    "sender = [24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "controlling-bachelor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.703890085220337\n"
     ]
    }
   ],
   "source": [
    "U = full_setup(sender_seed= sender, receiver_seed=rec_cross, ara = 50)\n",
    "start = time.time()\n",
    "sim = runModel(900,0.1,theta,U)\n",
    "print(time.time() - start)\n",
    "# np.save(\"AND01.npy\",sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "nervous-ideal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x504 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foo = multi_plots_vertical(sim[:,:,:,0]);\n",
    "\n",
    "foo.subplots_adjust(hspace = 0.3, wspace = 0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fitted-correction",
   "metadata": {},
   "outputs": [],
   "source": [
    "# foo.savefig(\"./simulations/sen-rec-t0.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "advance-alliance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'C6')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize = (15,5))\n",
    "\n",
    "n_rows = n_cols = 46\n",
    "\n",
    "first = [17,25,31,23]\n",
    "fr, fc = get_coo_from_pos(first)\n",
    "\n",
    "second = [10,26,38,22]\n",
    "sr, sc = get_coo_from_pos(second)\n",
    "\n",
    "third = [3,27,45,21]\n",
    "tr, tc = get_coo_from_pos(third)\n",
    "\n",
    "ax1.plot(sim[6,fr,fc,:9000].mean(axis=0), color = 'blue')\n",
    "ax1.plot(sim[6,sr,sc,:9000].mean(axis=0), color = 'red')\n",
    "ax1.plot(sim[6,tr,tc,:9000].mean(axis=0), color = 'green')\n",
    "ax1.set_title(\"GFP\")\n",
    "\n",
    "ax2.plot(sim[4,fr,fc,:10000].mean(axis=0), color = 'tab:blue')\n",
    "ax2.plot(sim[4,sr,sc,:10000].mean(axis=0), color = 'tab:orange')\n",
    "ax2.plot(sim[4,tr,tc,:10000].mean(axis=0), color = 'tab:green')\n",
    "ax2.set_title(\"C6\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "varying-antenna",
   "metadata": {},
   "outputs": [],
   "source": [
    "c6 = np.linspace(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "serious-loading",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if 1:\n",
    "    ara = [0,20,40,60,80,100]\n",
    "\n",
    "    f, ax = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(6,3))\n",
    "\n",
    "    for i, a in enumerate(ara):\n",
    "    # im1 = ax.flatten()[0].plot(sim[4,tr,tc,:10000].mean(axis=0), color = 'green')\n",
    "        U = full_setup(sender_seed= sender, receiver_seed=rec_cross, ara = a)\n",
    "        sim = runModel(1200,0.1,theta,U)\n",
    "        im1 = ax.flatten()[i].plot(np.linspace(0,20,12000),sim[6,fr,fc,:].mean(axis=0), color = 'tab:blue')\n",
    "        im1 = ax.flatten()[i].plot(np.linspace(0,20,12000),sim[6,sr,sc,:].mean(axis=0), color = 'tab:orange')\n",
    "        im1 = ax.flatten()[i].plot(np.linspace(0,20,12000),sim[6,tr,tc,:].mean(axis=0), color = 'tab:green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "passive-embassy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.04, 0.5, 'GFP')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    for i,a in enumerate(ara):\n",
    "        mpga(ax.flatten()[i])\n",
    "        ax.flatten()[i].spines['top'].set_visible(False)\n",
    "        ax.flatten()[i].spines['right'].set_visible(False)\n",
    "        ax.flatten()[i].spines['left'].set_linewidth(2)\n",
    "        ax.flatten()[i].spines['bottom'].set_linewidth(2)\n",
    "        ax.flatten()[i].set_title(str(a) + \" mM\", fontsize = 10)\n",
    "        ax.flatten()[i].tick_params(axis = 'x', labelsize = 10)    \n",
    "        ax.flatten()[i].tick_params(axis = 'y', labelsize = 10)\n",
    "        ax.flatten()[i].locator_params(nbins = 6, axis = 'x')\n",
    "        ax.flatten()[i].locator_params(nbins = 6, axis = 'y')\n",
    "    #     ax.flatten()[2].set_xticklabels([\"0\",\"3\",\"6\",\"9\",\"12\",\"15\"]) \n",
    "    #     ax.flatten()[i].xaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "    f.subplots_adjust(hspace = 0.50)\n",
    "    # ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n",
    "    # plt.tick_params(axis = 'y')\n",
    "\n",
    "    f.text(0.5, 0.00, 'Time (hours)', ha='center')\n",
    "    f.text(0.04, 0.5, 'GFP', va='center', rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "secondary-buffer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4buUu5v1yiPaNa/HpvcPp387EFQo0cgocPLRwC9/uTOKOoefxxLU9qtz8Dk8ve3kyUB2QhBWobhmwGGgHHMJyLU21XUtfx/I+ygEmqmp0OeVX61gnAYaJTVRJftl3nEeXbCX+RC4Th3fgL1d3o2ZEQAQxM7GJ3EhMz2XK3Gh+S8zgH3/owYTh7YN1DKdMoU2gOsO5YozBOVK8NfD82D6BFrLAGAObbfHWYjTZ+Q5eu7UfI7o3L/+iwMUEqjMYAoXTWgMXtufRq7sHSmvAUIyvYhJ5ZPEWGteO5LP7LqRbi8BajMbTGGNgMPiAzLxCnl+5mw/XH+K8xrVYNHVYoLUGDDaqyuvf7+XFVXsYcF5D3rljQEAuRlMiTgdsmgO7voLbPoWQsx/XMMbAYPAy3+w4yhPLd5CUmWdaAwFOXqGTx5ZsY/mWI4zp15pnbugdsIvRnIYqxK6CVf+EY7/BeRdB7gmo3fisi/CZMRCRg0Am4AQcqjqwtLhFvpLJYPAmR9PzeGLFdr7ZkUT3FnV56/b+9DOeQgHLscx87v4wmk1xafzl6m7cd1mn4BgoPrIZvv0HHFwHjTrCuA/h/GuhgrL7bADZNgYDVTXFLe15IFVVnxWRGUBDVX2sjDKCfkCqCmEGkEvB6VLm/3qI51fuptDp4qEruzLl4g7BNG+g2g0gb09I5+4PN3I8O5+Xx/XlmmAILXHiEHz/NMQshlqN4dIZMHAihJY69yEwvIlKMQa7gcvcJqStVdVuZZQRND+uaoAxBiUQE5/OP5ZvZ8vhNC7u0oSnr+/FeY2DJ6a9TbUyBks3xzNjSQyNa0fwzh0D6d2mvr9FKpusZPjxFYh6DyQEht4HFz0ENcqVO2CMwQHgBKDAO6r6roikqWoD+7wAJ4qOi117mpCB/uOqJlT6gVGV9Ho8K58Xvt3NwqjDNK4dwd9+fz7X920dHN0MZ1IpoYNFr4VOF898tYtZPx1gSIdGvHFb/8AeKM5OgZ9ehQ3vgTMf+twCl/8V6p/1kpoB41p6kaomiEgzYJWI7HI/ea5xiwwGf+Jwuvho/SFeWrWHnAInky7swINXdjFhpgOclKx8pn+8ifX7U5l4YXv+Our8wO3Gy0mFn2fCr++CIxd6j4NLH4XGnTxajc+Mgaom2NtkEVkKDKb0uEXFr63Sga+qK8GsV1Xlh9gU/vPlb+xOyuSizk144toedGletX3Rz4ZA12v0wVTuX7CZ1OyCwF6sPi0OfnkTNs2Dwhzo9Ue49DFo2tUr1fnEGIhIbSBEVTPt/d8B/6L0uEUGQ8Cy9XAaz63cxc/7jtO2UU3evn0AV/dsHqxdQtUGl0t563/7eGnVHlo3qMmSe4fTq3UAjg8cjYGfZsL2JZZHUK+x1phAs/O9Wq1PxgxEpCOw1D4MAz5W1X+LSGNKiFtURjlBMSBVTah2A8gHUrJ54ZvdfBmTSKPaEdw/ojO3DmlHZFgQ+KFXjCo3gHwsM59HFm9hXWwKf7igJf+5oXdgdeU5HbDna4h6H/avhYg6MGACDL23ImMC5REYA8ieIJB+XIbqYwxikzJ5c+0+lm9JoEZ4KFMu7shdF3eoUuGLi1GljMFXMYn8Y9l2svIdPHldT24e1DZwWnEZibBpLmycC5lHoF4bGDQJBk6Gmg08XZsxBgavUOWNwfaEdN5Ys5eVO45SMzyU24a0Y+olnWhaN4A9TjxDlTAGx7Py+efyHXwZk0jv1vV5cVwfugbCmE5BDuz+CmI+sWYNqxM6XQGDpkCX30Go13rvjTEweIUqaQwKnS6+2XGUeT8fYsPBVOrWCGPC8PZMvLADjWpH+Fs8XxHUxsDpUhZsiOPFb3eTle/gwSu6cPelnfzrLeTIhwPrrHGA31ZAQRbUbQUXjIMB462Zw97HGAODV6hSxuBgSjZLNyewMCqOpIx82jaqyR1Dz+Pmwe0Cq2+5sjgdkBEPx/dCSiwgMPSe4rmC0hioKj/tPc7TX+5k19FMBndoxFOje/kv2mhOqvXmv/tL2LvaMgCR9aDHaMsInHdRhQLJeYDANgYiMhJ4FQgF3lfVZ8vI6/eHhuEkQW8MEtNz+XZHEsu2JLA5Lg0RuKhzEyYMb89l3ZoRGhIg/coVJS8D0g7BiYOQesDanjhg7acfBpfjVN5mPeG+n4uXEFTGwOVS/hd7jNdWx7IpLo3WDWry11HnM6p3C9+ODeSmQdwvVgvg4DrLKwiFOs2h60joNgo6XgrhNX0n0+kErjEQkVBgD3AVEA9EAbeo6s5S8htjEDgEnTHIzCtkW3w6P+1N4ftdyew6mglA9xZ1GdOvNdf1bUXL+n67UctGFfIzIee49caZlQQZCZCZCBlHTn0yE603UHdqNIBGHaBhB2jY3vo07gxNukLtJiUFNAsKYxB3PIfPtx1hUdRh4lJzaN2gJvdc1okbB7TxbqRRVevvf2w3JG6FxC3W9vg+QCE0EtoOhvMutMYAWvXzdQugNALaGAwDnlTVq+3jxwFU9ZlS8htjEDgEpDFwuZS03EJSsvI5kJLN/mPZ7D+WRUxCOruTMlGFsBBhYPuGXN6tGSO6N/PeRDGXywob4MgHZ+GpfUee9WDPz4ICe5ufaT3Ei7a5J049+LNTrH1X4Zl1SCjUbQn1WkK9VlY/dL1WljtiI/vhX7PCkVIDyhioKhl5DuJP5LDzSAY7jmSwLvYY+45lAzCsY2NuHtyWa3q1JCKsEg9dR4H1ty/Ihrx064GffcyKBZSVZE0CSz0AqfuhMPvUdfXbQss+1ue84dB6IITXqNR3Lo6q4lAHhc7Ck9tCVyEOl4MCZwG5jlxyHbnkOfPIdeRS4Czg9x1/X7yYgDYGY4GRqjrFPr4DGKKq04vlKzHWyfyVz7Ms7mMfSVu96RzWjmcmrXBP8lpsol+2reTF9aUGrwW06N8Z6a4SBBOsl98QEUJCrK3gfn1Je6cOtMSazkyx5FfrzVFdZUlfepqI9YAPCUVDQiEkzD4Oc0srOg6HkLAS5Sux3hLu9bb12vLaiNeKJ3slNlF2TibjP7roTLns/4sukuJpJei6SI8hAiJSio7O1Kv7n+BkHeqy9VXy70AREEFDwiA0AkLDUXtLaKSlj+L1afFvVXJa8XMATnXicDkodJ164BeW9CJQDtvu3Fa8myxgYhN5HJFQQj33gmoogxDx3cSqUAkjnBBK+u1K0f9yKkHc8okIofYDIjRE7AeGuF17MmOJJZ+2J+5pJckipxcqIQgh1pNKTn1ETj9GQqwHSUjYyYc8IWFISPgZcpVYbwn94GebrzgtarcoN4+nkJCQUvQq7uo89b+dGOKmx7BQIVTktO8mp1/ldm0Jf8vivxfE/tu76yHMcu8Mq2F/Ii3dQIn1nrbvVuVJuUqU9fTfbHE5I0IjCAsJIzwk3PqEhhMmYYSHWsfu5yJCI6gZVpMaYTWsbWgNaoXVoqL4u2VguomCl4DsJjJ4hIDqJjJ4jIDuJgrDGkC+AkjAGkC+VVV3lJLf/KoCiKKAZJXF6DXw8IRujV4Dj7L06tduIlV1iMh04Bss19JZpRkCg8FgMHgPv88zqAxuzVCPDxx4q2wjs3/rDLZyvVm20av/yvVm2edabkA4vxoMBoPBvxhjYDAYDIbg7iYyGAwGg2cwLQODwWAwGGNgMBgMBmMMDAaDwYAxBgaDwWDgLI2BiDTxtiAGg8Fg8B9lGgMRuVZEjgExIhIvIsN9JJfBYDAYfEh5LYN/Axerakvgj0CJAeQMBoPBENyUZwwcqroLQFV/Bfy0mKjBYDAYvEl5geqaicgjpR2r6kveEctgMBgMvqQ8Y/Aep7cGih8bDAaDoQpgwlEYDAaDoVxvom/d9h/3vjgGg8Fg8AflDSA3ddu/0ZuCGAwGg8F/lGcMTB+SwWAwVAPKMwYdRWSFiHzutr9CRD4XkRW+EDAQEZEGIvKpiOwSkd9EZJid3khEVolIrL1tWIk65ohIjojUdUt7RUTUzAj3DiLysIjsEJHtIrJARGrY6R1E5FcR2Ssii0QkohJ1rBWROBERt7RlIpLlie9gsBCRWSKSLCLbi6WXeI+KxUxbx9tEpH8l6p5g36dXuqVdb6eNPfdv5V3KMwajgReBF4rt/9fer668CqxU1e5AH+A3O30GsFpVuwCr7ePKsBfr746IhAAjgIRKlmkoARFpDTwADFTVXlhrct9sn34OeFlVOwMngMmVrC4NuNCutwHQspLlGc5kDjCyhPTS7tFrgC72ZyrwViXrj+HU7wfgFmBrJcv0KuUZgwZAL1X9n6r+D8sIzMX6QzfzrmiBiYjUBy4BPgBQ1QJVTbNPj8b6+2Bvry/h+gn2m+AqETkoItNF5BER2Swi60WkkVv2hcBN9v5lwE+Aw9PfyXCSMKCmiIQBtYAj9hv8COBTO09pen1SROaKyDoROSQiN4jI8yISIyIrRSTcLftCTj0obgA+89YXqq6o6g9AagmnSrtHRwPz1GI90EBETjPSItLe7g2YIyJ7RGS+iFwpIj/ZLY3BbtnXAYNFJFxE6gCdgS2e+4aepzxj8Cjg3h0UAQzEejDd4yWZAp0OwDFgtv0Af19Eatvnmqtqor1/FGheShm9sB4Cg7BCfuSoaj/gF+BOt3x7gKZ2U/YWrIeIwQuoagJWqzcOSATSVfVboDGQpqpFRjgeaF1KMZ2wDMd1wEfAGlXtDeQCv3fLtxq4RESKWh+LPPx1DKVT2j3aGjjslq80PXfG6hXpbn9uBS4C/gz81S2fAt8BV2MZmoDvVi/PGESoqvsf6EdVPa6qcUDt0i6q4oQB/YG37Ad4NiV0B6k1gaO0Afg1qpqpqseAdOBzOz0GaF8s72dYD4whWG8bBi9gG9zRWMa+FVBbRG6vYDFfq2ohlh5DgZV2enG9OoEfsfRaU1UPnrvkhnOlnHu0NA6oaoyquoAdWF1OSsn3blEL8GZgQSXF9TrlGYPTBkBVdbrbYVOqJ/FAvB2rCazug6LBpqSipqW9TS6ljHy3fZfbsYszZ4UvAp4CVtk/QIN3uBLrRj9mP9A/A4YDx7G6DIr00obSx23yAWw9FeqpGZ0l6XUhMBNY7LmvYDgLSrtHE4C2bvlK0/NZ37uqugHoDTRR1T2VF927lGcMfhWRu4onisjdwAbviBTYqOpR4LCIdLOTrgB22vsrgPH2/nhguQfqOwT8DXizsmUZyiQOGCoitexxgiuA3+wH+hqgyAvEI3rFauU9QxC8MVYxSrtHVwB32l5FQ7G6CRNLKqCCzOD07qOApbzYRA8Dy0TkVmCTnTYAiKSEQbRqxP3AfNvFcD8w0U5/FlgsIpOBQ8A4T1Smqu94ohxD6ajqryLyKdbv3AFsBt61Tz8GLBSRp+30DzxQn2KNURi8gIgswBrbbCIi8cATqvoBpd+jXwGjsDz4cjh1T1cKVf3aE+X4grOKTSQiI4Ce9uEOVf3eq1IZDAaDwaeYQHUGg8FgOLs1kM8WEWkrImtEZKc9k/NBO/1JEUkQkS32Z5TbNY/bs/52i8jVnpTHYDAYDGeHR1sG9uh8S1XdJFYYhY1YYwvjgCxVfaFY/h5YA2iDsdz5vgO6qqrTY0IZDAaDoVw82jJQ1URV3WTvZ2KFaShtgg5Yft0LVTVfVQ9gDd4MLiO/wWAwGLyAR42BOyLSHugHFPnjT7cDQM2SUwHczmrWnx3g6eTHWzIbfIvRa9XE6DU4Kc+19JywY3EsAR5S1QwReQtr4pTa2xeBSZWowvzA/I+Un6XCGL0GBp7WrdFrYFCmXj3eMrADci0B5qvqZwCqmqSqTntm5nuc6go6q1l/qiqq6o2Hj+EscLoUl8vz97PRq3/JyCtk3zHPR842evUvuQVO1u8/XuHrPO1NJFgTcn5T1Zfc0t2j/40BimKMrwBuFpFIEemAFT62Ws5sDlSy8h3cNS+a577Z5W9RDB7kcGoOf3zzZybNiaLQaaKcVBWSM/O4+d1fmDB7A8mZeRW61tPdRBcCdwAxIrLFTvsrcIuI9MVqLh4E7gZQ1R0ishgrnIMDmGY8iQKH+BM5TJkbTWxyFpd3q66hqKoeGw+lMnXeRhwu5e3bBxAe6rWhQ4MP+S0xgylzo0nNLmDmzf1oVrdGha4PqklnRYNRwSRzsLI57gR3zdtIfqGTN27rzyVdzzAGHusGMHr1Hcu3JPCXT7fRqn4NZk0YRMemdUrK5hHdGr36jjW7kpn+8Sbq1gjn/fED6dW6fknZytSrVwaQDcHNiq1H+PMnW2lRrwYL7hpCl+Z1y7/IENCoKi9/F8vM1bEM6dCIt28fQMPa57x6pyFAUFXm/HyQp77YSY9W9Xj/zkG0qF+xFkERxhgYTqKqzFy9l5e/28Pg9o14+44BNDIPjKAnr9DJXz7dxudbj3DjgDb8e0xvIsJM11Cw43C6+L/Pd/Lh+kP8rkdzXrm5L7Uizv2RboyBAYB8h5PHl8Tw2eYEbujfmmdu6E1kWKi/xTJUkmOZ+Uz9MJrNcWk8NrI791zaEcvPwxDMZOQVMv3jzfyw5xh3X9qRx67uTkhI5fRqjIGBE9kF3P3RRjYcSOVPV3Vl+ojO5oFRBYhNymTinChSsvJ5+/b+jOzVsvyLDAHP4dQcJs2J4kBKNs/e0JubB7fzSLkeNQYi0haYh7WuqALvquqr9iLvi7CWhTsIjFPVE7Yr6qtYccRzgAlF4SwMvuFASjaT5kSRkJbLqzf3ZXTfsqKHGIKFn/amcM9HG6kRHsriu4dxQZsG/hbJ4AG2HE5jytwoChwu5k0azPDOTTxWtqc7Dh3An1S1BzAUmGYHo5uBtVZoF6zFwIvWDL4Ga25BF2Aq8JaH5TGUwYYDqYx58yfScwv5eMoQYwiqCIujDzN+1gZaN6jJsmkXGkNQRfhmx1FufvcXakaE8tl9wz1qCMDDLQN7mbhEez9TRIoC1Y3GWnUIYC6wFmv1qNHAPHvVp/Ui0kBEWhZfbs7EN/E8yzYn8Oin22jTqCazJwzivMa1fS6D0atnUVVe/HYPr6/Zy8VdmvDmbf2pWyPc53IYvXoWVWXWTwd5+sud9GnTgPfHD6RJnUiP1+O1MYNigeqauz3gj2J1I0Hpgeo8sfaooQRUlVdXx/LKd7EM7diId24fSP1avn9gGDxLvsPJXz7ZxoqtR7hlcFv+NbqXmUxWBXC6lH99voO5vxziml4teGlcX2pGeMexw1eB6k6eU9UKRzIsinNi3jgqR77DyYwlMSzdnMDYAW34j59dDI1ePcOJ7AKmfhhN1METAeExZPTqGbLzHTywYDOrdyUz9ZKOzBhZeY+hsvC4MSgpUB2QVNT9Y8cpSrbTzypQnaHynMgu4O4PN7LhYCp//l1Xpl1uPIaqAgdTsploOwC8fms//nBBK3+LZPAASRl5TJoTxW+JGTx1fS/uGHqe1+v0tDdRiYHqsALSjQeetbfL3dKni8hCYAiQXny8wFB53D2GZt7Sj+v6mAdGVSD6YCp3zYsGYMFdQxhwXiM/S2TwBLuOZjBpdhRpuYV8MH4Ql3dv5pN6Pb3s5UXAOiAGKAqF+FescYPFQDvgEJZraaptPF4HRmK5lk5U1egyyjexTirIr/uPc/dHGwkR4b07B3jygWFiE/mRz7ce4U+fbKVNg5rMmjCI9k086gBgYhP5iR/2HOO++ZuoHRnKB+MHlRZj6FwpU68mUF0VZunmeB79dBttG9XyhseQMQZ+QFV5c+0+/vvNbga3b8Q7d3glxpAxBn5g4YY4/rZsO12a1WH2xEG0rF/T01WYQHXVDVXlle9ieXV1LMM6Nubt2wcYj6EqQKHTxd+XbmdR9GFG923F82MvMCFDqgAul/LCt7t5c+0+LunalDdu7ecXl2BjDKoYBQ4Xj38Ww5JN8QHhMWTwDBl5hUybv4l1sSncP6Izj1zV1TgAVAHyCp38+ZOtfLEtkVsGt+Nfo3v6zSXYGIMqRGZeIfd+tIkf96bw8JVdeeAK4zFUFUhIy2XS7Cj2Hcvi+bEXMG5g2/IvMgQ8qdkFTJ0XTfShE8y4pjt3X+Jfl2BPL3s5S0SSRWS7W9qTIpIgIlvszyi3c4+LyF4R2S0iV3tSlurG0fQ8bnz7F9bvP85/x17Ag1d2MYagCrDzSAZj3viJI+m5zJ002BiCKkLc8Rz++NbPbEtI5/Vb+3HPpZ38fr962pvoEiALK8RELzvtSSBLVV8olrcHsAAYDLQCvgO6lrXspRmQKpndRzOZMHsDmXkO3ix5VTJvYAaQvcyPsVawubo1wpgzcTDdWvhskSEzgOxFYuLTmThnA4VO5YPxAxnY3mcuwWXq1aMtA1X9AUg9y+yjgYWqmq+qB4C9WIbBUAF+3pfC2Ld/xulSFt091FeGwOBllm6OZ8LsDbRpWJPP7hvuS0Ng8CJrdydz07u/EBkWypJ7h/nSEJSLr0YqpovINrsbqaGdVlpcojMQkQqHsKgOLNucwPhZG2hRrwZLp11Iz1Ye9Un2OkavZ2K5ju7l4UVbGdS+EYvvGeYNF0OvYvRaMp9EH2by3GjaN67N0vuG07lZYBl4XxiDt4BOQF+sAHQv+qDOKo2q8saavTy0aAv92zXk03uG07pBcD0wDGfidCn/XL6D51fu5to+rZgzaRD1/OBiaPAsqsprq2P5y6fbGN6pMYvuHkqzeue2TrE38bo3kaomFe2LyHvAF/bhWcclMoGvTuFwunhixQ7m/xrHdX1a8d8bg9fX3Oj1FHmFTh5YsJlvdyZx9yUdeczLQcm8idHrKRxOF/9YvoMFG+K4oV9rnv3jBQHr6u11Y1BsfYIxQJGn0QrgYxF5CWsAuQuwwdvyBDM5BQ7u/9iKYnjPpZ149OpuQfvAMJwiNbuAKXOj2Hw4jSev7cGECzv4WySDB8gtcHL/gk1891sy913Wib9c3c3vHkNl4elAdQuwFrFpIiLxwBPAZSLSF2sZzIPA3QCqukNEFgM7sVZIm1aWJ1F151hmPlPmRhGTkM5To3tyx7D2/hbJ4AHijucwYfYG4tNyefPW/lzT26xTXBU4npXP5LnRbI1PC5r71cQmCgL2H8ti/OwNHMvM57Vb+nNVj+blX+R9jGtpJXF3MXx//EAGBY5niXEtrQSHjmczftYGEtPzmHlLP67u2cLfIhVhYhMFMxsPpTJlbjQhIiy4ayj92jUs/yJDwLN2dzL3zd9Ew1oRLJw6KOA8Swznxrb4NCbNicLhUj4OsrDixhgEMCu3J/Lgwi20rF+DuZMG+2WdYoPnWRx9mMc/i6Fb87rMmTgoID1LDBVnze5kps3fRKPaEcydNJhOTev4TxhVqOD4RGAOaxuY9eMB7p2/iR6t6rHk3uHGEFQBVJVXv4vl0QB3MTRUnMVRh5kyN5oOTWrz2X3D/WsIdiyF+WPBUVChy3wRm6iRiKwSkVh729BOFxGZaccm2iYi/T0pS7DicilPfbGTf32xk6vOb87HU4bSuE6kv8UyVBKH08Vfl8bw8nd7uKF/az4YP8gvYYpPY+9q2PCef2UIck4a+CVFBn4Yzer6ycC7XLDmP/DJBMjLgIKsCl3u6ZbBHKxVy9yZAaxW1S7AavsY4Bosd9IuwFSsyWnVmrxCJ/cv2MwHPx5gwvD2vHX7AGpGBOccAsMpcgocTP1wIws2HGba5Z148cY+/vU1V4UfX7beHjfNA2eh/2QJYoob+FkTBlEn0k897wXZ8Ml4+N9z0Pc2mPAF1KrYeIVHJVfVH0SkfbHk0VjupgBzgbXAY3b6PLVcDdaLSINicxKqFWk5Bdw1L5qogyf426jzmXJxh4D2STacHSlZ+UyaE8X2hHSevr4Xt/tgYfMyyc+C5ffBzuXQ8wYY/TqEmlnOFcV9zs/0yzvzp9/5cX2JtDhYcCsk74Cr/wND76vweAH4ZgC5udsD/ihQ5BdZWmyiM4xBVZ/JeDg1h/GzNxCfmstrt/Tj2kBbsH7vaoisC209G0ewquv1YEo242dvICkjj3fuGOh/l+Dj+2DhbZCyG656Cobff04PjfKo6no9npXPpLnRxMSn+d/AH/oFFt1ute5u/QS6XHnORfm0TaOqJoBVMSxXtGgKHE4+nDyYIR0b+1ukUzgLYc2/rS6FzlfB7Z/6W6KgYXPcCSbPjUZV+fiuofT3t0tw7CpYMhkkBG7/DDpd7l95gpSiOQRHM/J4+/YB/M6fcwg2zYMvHoEG7eDWRdCkS6WK84UxSCrq/hGRlkCynV7tYxOt2WX5mjeqHcGCu4bQpXkA+ZqnxcGnkyF+A/QfDyOf9XgVVVWv3+1MYvqCTTStG8nciYPp6E/PEpcLfnwJvn8aWvSCmz6Chu29WmVV1evWw9YcApcq86cMZcB5fjLwTgd8+3f49S3oeDncOBtqVl4WXxiDFcB44Fl7u9wtfbqILASGAOnVabxgwYY4/r5sO91b1GX2hADzNd+5AlZMtx4kY2dBrz/6W6KgYf6vh/jHsu30al2fD8YPomldP3qC5Z6AZffB7q+g941w7UyIqOU/eYKY73clMW3+ZprUjfCvgc9OsbyFDq6zxgauegpCPfMY9/RKZydjEwFJWLGJlgGLgXbAIWCcqqaKNdryOpb3UQ4wUVWjyyk/6Ke3qyovfruH19fs5dKuTXnjtv7+80AoTn4WfPO41fxs1c8yBI06lpbbhKNwQ1V5adUeXvt+L5d3a8rrt/antj/1emQLLL4TMo7A1f+GwVMrMj5gwlG4sSgqjr8u3c75Lesya8Ig/7mOJmyCRXdATgr84WXoe2tFSyhTryY2kQ8pcLh4bMk2lm5O4KaBbXl6TC/CQwNk3l/CRlhyF6Tuh4sehsseh7CIsq4wxsCm0OlixpIYlmyK979eVWHTXPjqUajdFG6cA20HVbQUYwyw5H7lu1heXR3LJV2b8qY/X9w2z4cvHoY6zeCmD62XtYpjYhMFAhl5hdzz4UZ+3necP13VlekjOgeG66jLCT+9Yk1WqdPC8k9uf5G/pQoasvId3PvRRtbFpvDQlV148Iou/tNrQQ58+SfY+jF0GgE3vA+1A8ghIYgodLr4+9LtLIo+zI0D2vCfG3r7x8A7CqzWetT70OESGDsbajfxSlXGGPiAI2m5TJwdxb5jWbx4Yx/+OKCNv0WySDsMS++GQz9BzzFW09MDA1HVheSMPCbMjmJ3UibP//ECxg1qW/5F3uL4PqtbKGmH1aq75C8QYiYsngvZ+Q6mfbyJtbuP8cCIzjx8lZ/mEGQehcXj4fB6yw34iic9Nj5QEsYYeJmdRzKYOGcD2flO5kwczEVdvGPVK4QqbF0AX88AdcL1b0Ofm73ic15V2ZucyfhZUZzIKeCD8QO5rFsz/wmzdaHVIgiNsNx/O5+7r3l151imNUlwx5F0/jOmN7cOaecfQQ78AEumQH6mz5w4jDHwIutij3HvR5uoExnGJ/cM4/yW9fwtEmQkwucPQuw30G4YXP9mWYPEhhKIOmiFFQ8PDWHR1GH0blPfP4LkZcBXf4Zti+C8C+GGd6F+gLQ6g5CidUNSMgt4786BXHG+HyYJupyw7kVY+ww06gR3LIXmPX1Stc+MgYgcBDIBJ+BQ1YEi0ghYBLTHWgVtnKqe8JVM3uQTO0xx52Z1mD1xEC3r+3nBelXrDXLlY1Y/5NXPwJB7ICRABrCDhK9jEnlw0RbaNKzJ3ImDadvIT66a8RthySRrPsjlf4OL/2S6hSrBprgTTJ4TZa0bMnUofds28L0QWcnw2V2wfy30Hmd120b6zoXVZ95EtjEYqKopbmnPA6mq+qyIzAAaqupjZZQR8N4JqsrL38Uyc3UsF3Vuwpu396eev6NTZiTCFw/BnpXQdqjVGmjcqbKlVitvIlVl1k8HefrLnfRv15D37xxIw9plelt5B5cTfp5pTSKr2xL++D60G+rpWqqVN9G3O47ywMLNNK9Xg7kTB9O+iR/CxR9YZ80Qz0uHUf+Ffnd4o9s2MFxLSzEGu4HL3GYnr1XVbiVce5qQgfrjyit08qdPtvLltkTGDmjDf8b09m90SpfT8kJY/RS4CuGKf9qtAY+8QVb6lxosei10unhyxQ7m/xrHyJ4teOXmvtQI98Nb+PF9sHwaxP0CPUbDta96a8C/UroNFr2qKu+t288zX+/igjYN+GD8QJr4Oly8owDW/gd+fMV6QbtxrjVT3DsEjDE4AJwAFHhHVd8VkTRVbWCfF+BE0XGxawP+x5Wckcdd86LZlpDOjJHdmXpJR/+6jh7ZDJ8/BIlbLDfDUS94ojXgTrUwBum5hUybv4kf96Zw72Wd+MvvuhES4mO9ulwQ9R6sesIaJL7mOW8P+Fd5Y1DgcPGPZZbr6O8vaMmLN/bxvYFP2glLp8LRGKslMPIZKyCk9wiYeQYXqWqCiDQDVonILveTZQWxC/RYJ9sT0rlrXjTpuYW84+/gVblp1uDThnetSUdjZ1mhigPQUyjQ9XroeDaT5kQRl5rDf8dewI0D/eA6mnoAlk+HQz9awQKvmwn1AiyqbTECXa9pOQXc89FG1u9P5f4RnXn4yq6+NfAulxVX6Lv/sx7+Ny+A7qN8V38p+MwYqGqCvU0WkaXAYEoPYhc0fLktkT9/spWGtcL55J5h9GzlJ88SpwM2zrYMQU4qDJoCI/4ONRv4R54g5+d9KUybvwkFPpo8xPfRZB351tjADy9YrYHrXod+twekUQ8m9iZncte8jSScyOXlm/owpp+Pva+Sf7O8+Q7/Ct1GWfGi6jT1rQyl4BNjICK1gRBVzbT3fwf8i9KD2AU8hU4Xz369iw9+PED/dg14+44B/olZomqFJ/7271ac+vYXWwtctLzA97JUAVSVd37Yz/Mrd9GhSW0+GD/I9wOK+/9nzRs4HmuNDVz9DNRv7VsZqiBfbkvk0U+3UjMilPl3DWFQ+4qtBFYpCvNg3QvW2EBkXbj+LehzS0AZd1+1DJoDS+0+9DDgY1VdKSJRwGIRmYwdxM5H8lSKpIw8ps3fRPShE0wY3p6/jjrfPwPFB3+ENc9YXQiNOlnNzW7XBNQPLJjIzCvkL59sY+WOo/y+d0ueG3uBb2PRpO63ug52LrPCTN/2KXS5ynf1V1EKnS6e+3oX79svbm/eNoAW9X304qYKsd/CyschdR9ccLMVONBLISUqgwlUV0FW7UxixpJt5BQ4efaPvRnd1w9vbAd/srqDDq6DOs0tH/MBE8sLLOdpqpRr6ea4Ezy8aAuHT+Ty+DXdmXyRD5cdzU6B/z0P0R9YXULDH4CLHoJwv81NqTKupYeOZ/PI4q1s9MeL29Ht8O3frHkDjTtbLqOdRvim7pIJmAHkoCYr38HTX+xkYdRhzm9Zj5k39/XtYjROB+z6HNa/ZfU31m5mdR8MnOjPh0bQU+h08dr3e3ljzV5a1KvBgruGMriDj7oPspLhlzcg6gMozIb+d1pxher60QGhiqCqLIo6zL++2EloiDDzln5c56vlZI/tsWYRb1tkjdld8zwMnBTwa02blkE5qCrf7kziX5/v5Eh6Lvdc2omHruxCZJiP3NCykq04Qhveh/Q4q/tgyL3Wg8O/C5UEfctg46FU/r5sB78lZnBDv9Y8ObqnbyYIHt9nGfXNH1oDxT2vt4xA0zOm2PiLoG4Z7D+WxRMrdrAuNoVhHRvz4rg+tGrggxemxG3WErE7llovaIMmW632wAn+GBjzDDyBr39cu49m8szXv7F29zG6Na/Lv8f0YqAvBp0K86zYQVs+tgaH1QnnXQTD7oOuIwMl7EDQGoMjabm8tGoPn26Mp2X9GjxxbQ9G9mrp3UodBbD7S4ieDQf+ByHh1lyBCx+CJp29W3fFCUpjkJ5TyNs/7OP9dfupERbKn37XlTuHtfeu22hhnjXGE/WBtURsRF0YfBcMmxaI4wKBbQxEZCTwKhAKvK+qpS6266sf144j6by5Zh9fxiRSJzKMh67swvjh7b0bzzzrGOxdBbu+hH3fQ2GOFW7ggpusFY0C562xiKAzBnHHc3h33T4WRR0GYPJFHbl/RGfvrUhWmAf718DO5dbSk3npUL8dDLgT+t4O9bxsgMpBVckoyKB+5Bnu0EFlDFKy8pn3yyFm/3iAzHwHY/q15vFR3b3n3Vek1x1LYffXkJ9hjQkMnAx9b/FbS0BVicuMY9uxbcRnxnNv33uLZwlcYyAiocAe4CogHogCblHVnaXk99qP62h6Ht/9lsQn0YfZGp9OncgwJgxvz+SLOng+Bo0jH1JirZmHcT9D3HpI2WOdq9vK8gjq/nvoeJnfWgFOl5MT+Sc4nnucY7nHqBNeh77N+rpnCQpjkJ5TyNo9yXy6MZ51sSmEhwo3DmzLfZd1ok1DD3ez5aTaOl1vDe4f3gDOfKhRH7r/wZr81+lyn+rUpS5S81JJyk4iPiueA+kHTn4OZhykfb32LL52cfHLAt4Y5BY4+XlfCks3J/DNjqMUOpWRPVvwwBVd6NHKw9GB89KtdSLifrFCS8etB0ce1GgA5//BWl+6w6U+8+LLd+ZzNPsoCZkJ7Evfx760fexN28u+tH1kFWYBUCusFmvGraFW+Gm/8YAeQB4M7FXV/QAishAYDZRoDM4VVcWl4HQp+Q4nxzLzScrI50BKNtuPpLMlLo2diRkAdG1ehyev7cGYfm2oXyvcvRDrg546pliaI9+KP56fCQVZ1htDdgpkJFjB4jISLCNwfK/V9QMQWR/aDYE+t6AdL4OWfVE5JTcuK5/adejJ+t1Esw9c6qLAVUCB0+3jOn0/35FPVmEWWQVZZBZmWtuCTDILM8ksyOR47nFSclNIzUvFWSQjcGW7K4sbA79TpFeXKoVOFymZBSRl5hF3PIftR9LZFp/OlsNpOF1Kq/o1ePjKrowb1Ob0CLIn9Qqn6dNdr86CU3rNz7QeDlnJkBEP6QmQftgKLZB5xC5HoEUvdOBk6DwC2l+ChoWflBmX85QeOVOn7rp2uBzkO/MpcBaQ78y3Po78k2lZhVlkFGSQnp9OekE6GfkZZBRkcDz3OEk5SSTlJOFwOU77u7Wq3YoO9TswoPkAujUKuBYnqooqOFVxupTU7AKOZuSRcCKXnYkZxMSnE3UwlXyHi/o1w7ljaHtuGdz2dIeOMvVqpzkL7fs007pX8zOtFrq7XpN3WpFhi2jWE+0/wVozooO7Xl1W8cXvU7sq9zRFT+ovz5FHvjOfPGfeSb3mFOaQXpBOWn4aaflpZORnkJafRlJ2EonZiRzPO37a36thZEM6NejEHzr+ge6NutO7aW861u9IWEjFHu/+bhmMBUaq6hT7+A5giKpOL5avxFgn7yx9nA9PrDitTC22LfFcCfbxrK47LU1OK+vsrpMy8/uDsJAw6obXpW5EXepE1KFxjcY0rdX05LZJzSY0qdmE1nVa06zWaQu4eC020ZqoJfwj5p+n5fW0XrXEPOeiU/s68b9Ow0PCqR9Zn3oR9WhYoyEtaregea3mJ7cta7fkvHrnFX9bLAmvxCbKzE7jmkUXnpY3UPVqbf2v17CQMBpENqBBZAOa1mxKqzqtaFG7Ba3qtKJl7ZZ0rN+RxjXPenZ8QLcMKkWbpufTL+VXBBBbcfYeiJz85tY561hECAsNISIkhPCwECLCQu18YrfyTl3vdnRa+ZxMw/692OVLqOUnHhaBhEZa++G1ILIOEnYqGuLJMovVcdq+UOq5Eq+z0yJCIogIjSAyNJLw0HAiQs7crx1R+6QBiAyNDIy1mN1o3KA1/ZzNOe1bSil6tfUmIoSFCOGhoYSHhRB5Uq9Ffxv36930d5pez/wbu+tVQiMgzNZrRG0ksu5Jd8HSdOOuo+I6LUl/xcsJkzAiQiOoEVbjpF7dPzXDalI/sj71I+tTI7RGwOnSnfCwiNL1itvfHDl5vwlFeg0hPDSUyPAQQk77GxUvx027ZejVKj/E0muRTsMiIbwmElnPuofL0s9Z6NVdF8XTiu7FyLBIaoTWOKXTMEunRQagZlhNn+nU3y2DYcCTqnq1ffw4gKo+U0p+v09iMZwkKMYMDOdEwI8ZGM6JgB5ADsMaQL4CSMAaQL5VVXeUkt/8qgKIouiUlcXoNfDwhG6NXgOPsvTq124iVXWIyHTgGyzX0lmlGQKDwWAweA+/zzOoDG7NUI93qnmrbCOzf+sMtnK9WbbRq//K9WbZ51quWQ3dYDAYDMYYGAwGgyHIu4kMBoPB4BlMy8BgMBgMxhgYDAaDIciNgYiMFJHdIrJXRGZ4sNyDIhIjIltEJLqSZc0SkWQR2e6W1khEVolIrL1t6KFynxSRBFvuLSIy6hzKbSsia0Rkp4jsEJEHPSVzBWTwil7tsj2i22DTq12OX3VbnfVaRtmBc89aQaGC74M1L2Ef0BGIALYCPTxU9kGgiYfKugToD2x3S3semGHvzwCe81C5TwJ/rqS8LYH+9n5drEmBPTwhs7/16kndBpte/a3b6q5Xb+rWU3oN5pbByYinqloAFEU8DShU9QcgtVjyaGCuvT8XuN5D5VYaVU1U1U32fibwG9AaD8h8lhi9ekGvdtn+1G211msZZVcaT+k1mI1Ba+Cw23G8neYJFPhWRDaKyFQPlelOc1VNtPePAs09WPZ0EdlmN0kr1dwXkfZAP+BXvCuzO97UK3hXt0GhV/CLbo1eSycg7tlgNgbe5CJV7Q9cA0wTkUu8VZFabThP+fe+BXQC+gKJwIvnWpCI1AGWAA+paob7OQ/L7Gt8ottA1StUWd0Go14hgO7ZYDYGCUBbt+M2dlqlUdUEe5sMLMVq4nqSJBFpCWBvkz1RqKomqapTVV3Ae5yj3CISjvWjmq+qn3lT5hLwml7B67oNaL3acvlLt0avJRBI92wwG4MooIuIdBCRCOBmYEU515SLiNQWkbpF+8DvgO1lX1VhVgDj7f3xwHJPFFqkeJsxnIPcIiLAB8BvqvqS2ymvyFwCXtEr+ES3AatXuxx/6tbotQQC6p6t7Oi7Pz/AKKyR833A3zxUZkcsT4etwI7KlgsswGr+FWL1k04GGgOrgVjgO6CRh8r9EIgBttk/hJbnUO5FWM3JbcAW+zPKEzL7U6+e1m2w6TUQdFud9epN3XpKryYchcFgMBiCupvIYDAYDB7CGAODwWAwGGNgMBgMBmMMDAaDwYAxBoYgRUQauwX3OuoW7CtLRN70Up0Picid9v5aERnojXpKqXu6iEzyVX2G6ofxJjIEPSLyJJClqi94sY4wYBNWQDCHiKzFCjBWqai2JdQTqqrOEtJrAT+paj9P1mcwFGFaBoYqhYhcJiJf2PtPishcEVknIodE5AYRed4OdbzSnrWJiAwQkf/ZcW2+KTYRqIgRwCZVdbil3SgiG0Rkj4hcbJdVQ0Rm23VsFpHL7fQJIvK6m5xfiMhl9n6WiLwoIluBYSLyrB2OeJuIvACgqjnAQRHx9Gx4gwEwxsBQ9emE9SC/DvgIWKOqvYFc4Pe2QXgNGKuqA4BZwL9LKOdCYGOxtDBVHQw8BDxhp03DCgXTG7gFmCsiNcqRsTbwq6r2wYo4OQboqaoXAE+75YsGLi7/KxsMFSfM3wIYDF7ma1UtFJEYrJj6K+30GKA90A3oBayyZvUTijVLtDgtsR7U7hTFgNlolwXWbNDXAFR1l4gcArqWI6MTK64MQDqQB3xgt3C+cMuXDHQvpyyD4ZwwxsBQ1ckHUFWXiBTqqUEyF9bvX4AdqjqsnHJygeJv+Pn21kn595KD01vi7mXlFY0T2OMRg4ErgLHAdKyWTdE1ueXUYzCcE6abyFDd2Q00FZFhYEV/FJGeJeT7Deh8FuWtA26zy+oKtLPrOAj0FZEQEWlLKdEp7TDE9VX1K+BhoI/b6a54PmiiwQCYloGhmqOqBSIyFpgpIvWx7olXsAKeufM1VlCx8ngTeMvulnIAE1Q1X0R+Ag4AO7EMy6ZSrq8LLLfHGQR4xO3chVjLJBoMHse4lhoMZ4mILAUeVdVYP9TdD3hEVe/wdd2G6oExBgbDWSIi3bCWEvzBD3VfBcSq6kFf122oHhhjYDAYDAYzgGwwGAwGYwwMBoPBgDEGBoPBYMAYA4PBYDBgjIHBYDAYgP8HLMdywycsxCgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x216 with 6 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "unlimited-digest",
   "metadata": {},
   "outputs": [],
   "source": [
    "# f.savefig(\"./simulations/sen-rec-line-plots.pdf\", bbox_inches = 'tight')\n",
    "# /Users/lucarosa/Library/Mobile Documents/com~apple~CloudDocs/THESIS/Upgrade/figures/chapter4/simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promising-quilt",
   "metadata": {},
   "source": [
    "# RECEIVER"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nonprofit-continent",
   "metadata": {},
   "source": [
    "### Line plot AHL VS GFP: 15 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "unique-sound",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 45\n"
     ]
    }
   ],
   "source": [
    "def ahlgfp():\n",
    "    for i,a in enumerate(ahl):\n",
    "        U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = a)\n",
    "        sim = runModel(1000,0.1,theta,U)\n",
    "        gfp[i] = sim[6,r,c,6000].mean()\n",
    "    ax.plot(ahl, gfp)\n",
    "\n",
    "    gfp = x_g * N * hill(U_grid[4], lambda_g, K_g) * U_grid[5]\n",
    "\n",
    "# theta['K_g'] = 25\n",
    "# theta['lambda_g'] = 5\n",
    "\n",
    "print(theta['lambda_g'], theta['K_g'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-ordering",
   "metadata": {},
   "source": [
    "Check Hill function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "forbidden-cleaner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x_s': 50,\n",
       " 'x_a': 0.001,\n",
       " 'x_g': 1.1,\n",
       " 'lambda_a': 10,\n",
       " 'K_a': 5,\n",
       " 'D': 0.03,\n",
       " 'D_a': 0.03,\n",
       " 'w': 0.75,\n",
       " 'rho_n': 3,\n",
       " 'rc': 0.0006,\n",
       " 'Dc': 0.0001,\n",
       " 'rho': 0.0005,\n",
       " 'lambda_n': 2.0,\n",
       " 'lambda_g': 3,\n",
       " 'K_g': 45,\n",
       " 'K_n': 80}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "weekly-princess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GFP rate')"
      ]
     },
     "execution_count": 101,
     "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": [
    "rec_cross = [3,10,17,31,38,45,\n",
    "            21,22,23,25,26,27]\n",
    "r, c = get_coo_from_pos(rec_cross)\n",
    "ahl = np.linspace(0,100,20)\n",
    "gfp = np.zeros(ahl.shape)\n",
    "y = hill(ahl, theta['K_g'], theta['lambda_g'])\n",
    "plt.plot(ahl, y)\n",
    "plt.title('Hill Function')\n",
    "plt.xlabel(\"AHL\")\n",
    "plt.ylabel(\"GFP rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "signal-chester",
   "metadata": {},
   "source": [
    "Run the simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "weekly-database",
   "metadata": {},
   "outputs": [],
   "source": [
    "ahl = np.linspace(0,150,10)\n",
    "gfp = np.zeros(ahl.shape)\n",
    "for i,a in enumerate(ahl):\n",
    "    U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = a)\n",
    "    sim = runModel(1000,0.1,theta,U)\n",
    "    gfp[i] = sim[6,r,c,6000].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "published-partition",
   "metadata": {},
   "source": [
    "Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "conditional-calgary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (3,2))\n",
    "ax.plot(ahl, gfp, marker = 'o')\n",
    "ax.set_xlabel(\"AHL (nM)\")\n",
    "ax.set_ylabel(\"GFP\")\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_linewidth(3)\n",
    "ax.spines['bottom'].set_linewidth(3)\n",
    "ax.tick_params(axis = 'x', labelsize = 12)\n",
    "ax.tick_params(axis = 'y', labelsize = 12)\n",
    "# ax.set_title(r\"$\\lambda_g = 5$\")\n",
    "ax.yaxis.set_ticks(np.arange(0,200,50))\n",
    "ax.xaxis.set_ticks(np.arange(0,151,30))\n",
    "ax.tick_params(axis = 'x', labelsize = 14)\n",
    "ax.tick_params(axis = 'y', labelsize = 14)\n",
    "\n",
    "# f.savefig(\"./simulations/ahl-vs-gfp.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "american-combine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "educated-valuation",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig(\"./simulations/ahl-vs-gfp-line-plot.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "nominated-daisy",
   "metadata": {},
   "outputs": [],
   "source": [
    "!open ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "sustainable-friend",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.949411386672153"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim[6,r,c,6000].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saving-vessel",
   "metadata": {},
   "source": [
    "### Cross images GFP intensity: 15 Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "spare-broadway",
   "metadata": {},
   "outputs": [],
   "source": [
    "ahl = np.linspace(0,100,9, dtype = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "transsexual-sacrifice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ahl = np.array([0,20,40,60,80,100])\n",
    "f, ax = plt.subplots(2,3, figsize = (10,6))\n",
    "im = [0]*6\n",
    "\n",
    "for i, a in enumerate(ahl):\n",
    "    U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = a)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    im[i] = ax.flatten()[i].imshow(sim[6,:,:,-1], interpolation = 'none', vmin = 0, vmax = 270, cmap = cm.viridis)\n",
    "    divider = make_axes_locatable(ax.flatten()[i])\n",
    "    ax.flatten()[i].set_title(str(a) + (' nM'))\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    f.colorbar(im[i], cax=cax, shrink=0.8)\n",
    "    ax.flatten()[i].axis(\"off\")\n",
    "\n",
    "f.text(0.96, 0.5, 'GFP', va='center', rotation=270, fontsize=14)\n",
    "f.subplots_adjust(left=0.025, bottom=0.05, right=0.93, top=0.93)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "signed-experiment",
   "metadata": {},
   "outputs": [],
   "source": [
    "# f.savefig(\"./ahl-vs-gfp-panel-1.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "rotary-nashville",
   "metadata": {},
   "outputs": [],
   "source": [
    "!open {os.getcwd()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "angry-castle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/lucarosa/Documents/PhD/Coding/Python/colony-com'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "legitimate-meeting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "r, c = get_coo_from_pos(rec_cross)\n",
    "ahl = np.linspace(0,100,11, dtype = int)\n",
    "# gfp = np.zeros(ahl.shape\n",
    "\n",
    "df = pd.DataFrame()\n",
    "for i, a in enumerate(ahl):\n",
    "    print(a)\n",
    "    U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = a)\n",
    "    sim = runModel(1200,0.1,theta,U)\n",
    "    df[a] = sim[6,r,c,:-1].mean(axis=0)\n",
    "#     plt.plot(sim[6,r,c,:-1].mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "coordinate-barrier",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.index/600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "nominated-hypothetical",
   "metadata": {},
   "outputs": [],
   "source": [
    "color = ['#a6cee3','#1f78b4','#b2df8a',\n",
    "        '#33a02c','#fb9a99','#e31a1c',\n",
    "        '#fdbf6f','#ff7f00','#cab2d6',\n",
    "        '#6a3d9a','#ffff99','#b15928']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "renewable-peripheral",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0x14e1c6b80>,\n",
       " <matplotlib.axis.XTick at 0x14e1c6d30>,\n",
       " <matplotlib.axis.XTick at 0x14e205fa0>,\n",
       " <matplotlib.axis.XTick at 0x14fa209d0>,\n",
       " <matplotlib.axis.XTick at 0x14f75f220>]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (3,2))\n",
    "df.plot(ax = ax, color = color)\n",
    "ax.legend(title = 'AHL (nM)', frameon = False, bbox_to_anchor=(1.00, 1.1), fontsize = 7)\n",
    "ax.set_xlabel('Time (h)')\n",
    "ax.set_ylabel('GFP')\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_linewidth(3)\n",
    "ax.spines['bottom'].set_linewidth(3)\n",
    "ax.tick_params(axis = 'x', labelsize = 12)\n",
    "ax.tick_params(axis = 'y', labelsize = 12)\n",
    "# ax.set_title(r\"$\\lambda_g = 5$\")\n",
    "# ax.yaxis.set_ticks(np.arange(0,176,25))\n",
    "# ax.xaxis.set_ticks(np.arange(0,101,20))\n",
    "ax.tick_params(axis = 'x', labelsize = 14)\n",
    "ax.tick_params(axis = 'y', labelsize = 14)\n",
    "ax.yaxis.set_ticks(np.arange(0,251,50))\n",
    "ax.xaxis.set_ticks(np.arange(0,21,5))\n",
    "# ax.locator_params(nbins = 5, axis = 'x')\n",
    "# ax.locator_params(nbins = 5, axis = 'y')\n",
    "# ax.set_xticklabels(np.linspace(0,20,5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "floral-population",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig(\"./simulations/receiver-time-vs-gfp.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "neither-newfoundland",
   "metadata": {},
   "outputs": [],
   "source": [
    "!open ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "hispanic-calculation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10., 15., 20.])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,20,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impressive-hands",
   "metadata": {},
   "source": [
    "## AND gate bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "departmental-addition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "print(theta['K_g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "adaptive-diabetes",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x_s': 50,\n",
       " 'x_a': 0.001,\n",
       " 'x_g': 11,\n",
       " 'lambda_a': 10,\n",
       " 'K_a': 5,\n",
       " 'D': 0.03,\n",
       " 'D_a': 0.03,\n",
       " 'w': 0.75,\n",
       " 'rho_n': 3,\n",
       " 'rc': 0.0006,\n",
       " 'Dc': 0.0001,\n",
       " 'rho': 0.0005,\n",
       " 'lambda_n': 2.0,\n",
       " 'lambda_g': 2.2,\n",
       " 'K_g': 35,\n",
       " 'K_n': 80}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bronze-insert",
   "metadata": {},
   "outputs": [],
   "source": [
    "# theta['K_g'] = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "macro-webster",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lucarosa/Documents/PhD/Coding/Python/colony-com/functionsDiffusion.py:21: RuntimeWarning: invalid value encountered in power\n",
      "  h = s**lam / (K**lam + s**lam)\n"
     ]
    }
   ],
   "source": [
    "and_gate = np.zeros(4)\n",
    "senders = [[10,38],10,38,[]]\n",
    "# senders = [[3,45],3,45,[]]\n",
    "output = [24]\n",
    "r, c = get_coo_from_pos(output)\n",
    "\n",
    "df_and = pd.DataFrame()\n",
    "\n",
    "for i, inputs in enumerate(senders):\n",
    "    U = full_setup(sender_seed= inputs, receiver_seed=[17,24,31], ara = 100)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    df_and[i] = sim[6,r,c,:].mean(axis = 0)\n",
    "    and_gate[i] = sim[6,r,c,5000].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "pending-housing",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ax4' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-f8c6ad479b4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# f, (ax1,ax2) = plt.subplots(1,2, figsize =(15,5))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_and\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0max5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"11\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"10\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"01\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"00\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mand_gate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ax4' is not defined"
     ]
    }
   ],
   "source": [
    "# f, (ax1,ax2) = plt.subplots(1,2, figsize =(15,5))\n",
    "df_and.plot(ax = ax4)\n",
    "ax5.bar([\"11\",\"10\",\"01\",\"00\"],and_gate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "spare-conviction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "regional-turner",
   "metadata": {},
   "outputs": [],
   "source": [
    "# f.savefig(\"coop.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "physical-pickup",
   "metadata": {},
   "source": [
    "## LOGIC GATE COMBINATION PANELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dying-explosion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(constrained_layout=True, figsize = (12,8))\n",
    "gs = f.add_gridspec(2, 6)\n",
    "ax = [0]*5\n",
    "ax[0] = f.add_subplot(gs[0, :2])\n",
    "ax[1] = f.add_subplot(gs[0, 2:4])\n",
    "ax[2] = f.add_subplot(gs[0, 4:])\n",
    "ax[3] = f.add_subplot(gs[1, :3])\n",
    "ax[4] = f.add_subplot(gs[1, 3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "later-secretary",
   "metadata": {},
   "source": [
    "## Sender positions: 3 - 45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "peaceful-agriculture",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213.6169748652484\n",
      "213.5930715742558\n",
      "213.61681215387915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(23, 42, 'S')"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create grid structure\n",
    "f = plt.figure(constrained_layout=True, figsize = (12,8))\n",
    "gs = f.add_gridspec(2, 6)\n",
    "ax = [0]*5\n",
    "ax[0] = f.add_subplot(gs[0, :2])\n",
    "ax[1] = f.add_subplot(gs[0, 2:4])\n",
    "ax[2] = f.add_subplot(gs[0, 4:])\n",
    "ax[3] = f.add_subplot(gs[1, :3])\n",
    "ax[4] = f.add_subplot(gs[1, 3:])\n",
    "\n",
    "#cells position\n",
    "rec_pos = [10,17,24,31,38]\n",
    "and_gate = np.zeros(4)\n",
    "senders = [45,3,[3,45]]\n",
    "# senders = [[3,45],3,45,[]]\n",
    "output = [24]\n",
    "r, c = get_coo_from_pos(output)\n",
    "\n",
    "df_and = pd.DataFrame()\n",
    "#run simulations and create dfs\n",
    "for i, inputs in enumerate(senders):\n",
    "    U = full_setup(sender_seed= inputs, receiver_seed=rec_pos, ara = 100)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    ax[i].imshow(sim[6,:,:,-1])\n",
    "    df_and[i+1] = sim[6,r,c,:].mean(axis = 0)\n",
    "    and_gate[i+1] = sim[6,r,c,-1].mean()\n",
    "    [a.axis('off') for a in ax[:3]]\n",
    "    print(sim[6,:,:,-1].max())\n",
    "    \n",
    "#add colorbar\n",
    "im3 = ax[2].get_images()[0]\n",
    "f.colorbar(im3, ax=ax[2])\n",
    "\n",
    "#run simulation w/ no inputs\n",
    "U = full_setup(sender_seed= [], receiver_seed=rec_pos, ara = 100)\n",
    "sim = runModel(900,0.1,theta,U)\n",
    "df_and.insert(loc = 0, column = 'x00', value = sim[6,r,c,:].mean(axis = 0))\n",
    "# and_gate.insert(loc = 0, column = 'x00', value = sim[6,r,c,-1].mean(axis = 0)) \n",
    "# df_and[3] = sim[6,r,c,:].mean(axis = 0)\n",
    "# and_gate[3] = sim[6,r,c,-1].mean()\n",
    "df_and.index = df_and.index/600\n",
    "df_and.plot(ax = ax[3], linewidth = '3', style = [\"-\", \"-\", \"--\",\"-\"])\n",
    "ax[4].bar([\"00\",\"01\",\"10\",\"11\"],and_gate)\n",
    "\n",
    "ax[0].set_title(\"01\", fontsize = 18)\n",
    "ax[1].set_title(\"10\", fontsize = 18)\n",
    "ax[2].set_title(\"11\", fontsize = 18)\n",
    "\n",
    "ax[3].spines['top'].set_visible(True)\n",
    "ax[3].spines['right'].set_visible(True)\n",
    "ax[3].spines['left'].set_linewidth(2)\n",
    "ax[3].spines['bottom'].set_linewidth(2)\n",
    "ax[3].spines['top'].set_linewidth(2)\n",
    "ax[3].spines['right'].set_linewidth(2)\n",
    "ax[3].yaxis.set_tick_params(width = 2)\n",
    "ax[3].xaxis.set_tick_params(width = 2)\n",
    "ax[3].title.set_size(12)\n",
    "ax[3].xaxis.label.set_size(12)\n",
    "ax[3].yaxis.label.set_size(12)\n",
    "ax[3].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[3].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[3].set_yticks(np.arange(0,0.9,0.1))\n",
    "ax[3].set_xlabel('Time (hours)', fontsize = 18),\n",
    "ax[3].set_ylabel('GFP', fontsize = 18)\n",
    "# ax[3].set_ylim(-0.5, 220)\n",
    "\n",
    "ax[4].spines['top'].set_visible(True)\n",
    "ax[4].spines['right'].set_visible(True)\n",
    "ax[4].yaxis.set_tick_params(width = 2)\n",
    "ax[4].xaxis.set_tick_params(width = 2)\n",
    "ax[4].spines['left'].set_linewidth(2)\n",
    "ax[4].spines['bottom'].set_linewidth(2)\n",
    "ax[4].spines['right'].set_linewidth(2)\n",
    "ax[4].spines['top'].set_linewidth(2)\n",
    "ax[4].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[4].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[4].set_yticks(np.arange(0,0.9,0.1))\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='tab:blue', lw=3, label='00'),\n",
    "                   Line2D([0], [0], color = 'tab:orange', lw =3,label='01'),\n",
    "                   Line2D([0], [0], color = 'tab:green', lw =3,label='10'),\n",
    "                   Line2D([0], [0], color = 'tab:red', lw =3,label='11')]\n",
    "ax[3].legend(handles = legend_elements, frameon = False, fontsize = 18)\n",
    "f.text(1,0.75, 'GFP',va = 'center', rotation = 270, fontsize = 18 )\n",
    "f.text(0.54, 0.47, 'Time = 15 hours', fontsize = 14)\n",
    "f.text(0, 0.53, \"B\",fontsize = 25)\n",
    "f.text(0, 0.98, \"A\", fontsize = 25)\n",
    "\n",
    "circle1 = plt.Circle((23,23), 2.5, color = 'r', fill = False)\n",
    "circle2 = plt.Circle((23,23), 2.5, color = 'r', fill = False)\n",
    "circle3 = plt.Circle((23,23), 2.5, color = 'r', fill = False)\n",
    "ax[0].add_patch(circle1)\n",
    "ax[1].add_patch(circle2)\n",
    "ax[2].add_patch(circle3)\n",
    "\n",
    "circle1 = plt.Circle((23,5), 2.5, color = 'hotpink', fill = True)\n",
    "circle2 = plt.Circle((23,5), 2.5, color = 'hotpink', fill = True)\n",
    "ax[1].add_patch(circle1)\n",
    "ax[2].add_patch(circle2)\n",
    "circle1 = plt.Circle((23,41), 2.5, color = 'hotpink', fill = True)\n",
    "circle2 = plt.Circle((23,41), 2.5, color = 'hotpink', fill = True)\n",
    "ax[0].add_patch(circle1)\n",
    "ax[2].add_patch(circle2)\n",
    "\n",
    "ax[1].annotate(\"S\", xy=(23, 6), fontsize=15, ha=\"center\")\n",
    "ax[2].annotate(\"S\", xy=(23, 6), fontsize=15, ha=\"center\")\n",
    "ax[0].annotate(\"S\", xy=(23, 42), fontsize=15, ha=\"center\")\n",
    "ax[2].annotate(\"S\", xy=(23, 42), fontsize=15, ha=\"center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "unauthorized-values",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig('/Users/lucarosa/Library/Mobile Documents/com~apple~CloudDocs/THESIS/Upgrade/figures/logic/3-45-panel-2.pdf', bbox_inches = 'tight')\n",
    "f.savefig('./simulations/3-45-panel-2.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "genuine-temple",
   "metadata": {},
   "outputs": [],
   "source": [
    "!open ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-bosnia",
   "metadata": {},
   "source": [
    "## Sender positions: 10 - 38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "embedded-ending",
   "metadata": {},
   "outputs": [],
   "source": [
    "# theta[\"K_g\"] = 45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "regulated-ending",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x_s': 50,\n",
       " 'x_a': 0.001,\n",
       " 'x_g': 1.1,\n",
       " 'lambda_a': 10,\n",
       " 'K_a': 5,\n",
       " 'D': 0.03,\n",
       " 'D_a': 0.03,\n",
       " 'w': 0.75,\n",
       " 'rho_n': 3,\n",
       " 'rc': 0.0006,\n",
       " 'Dc': 0.0001,\n",
       " 'rho': 0.0005,\n",
       " 'lambda_n': 2.0,\n",
       " 'lambda_g': 3,\n",
       " 'K_g': 45,\n",
       " 'K_n': 80}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "threatened-bishop",
   "metadata": {},
   "outputs": [],
   "source": [
    "# theta[\"K_g\"] = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "ordinary-lambda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213.66645798259245\n",
      "213.666881013196\n",
      "213.66011888091617\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x15daab520>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create grid structure\n",
    "f = plt.figure(constrained_layout=True, figsize = (12,8))\n",
    "gs = f.add_gridspec(2, 6)\n",
    "ax = [0]*5\n",
    "ax[0] = f.add_subplot(gs[0, :2])\n",
    "ax[1] = f.add_subplot(gs[0, 2:4])\n",
    "ax[2] = f.add_subplot(gs[0, 4:])\n",
    "ax[3] = f.add_subplot(gs[1, :3])\n",
    "ax[4] = f.add_subplot(gs[1, 3:])\n",
    "\n",
    "#cells position\n",
    "rec_pos = [17,24,31]\n",
    "and_gate = np.zeros(4)\n",
    "senders = [38,10,[10,38]]\n",
    "# senders = [[3,45],3,45,[]]\n",
    "output = [24]\n",
    "r, c = get_coo_from_pos(output)\n",
    "\n",
    "df_and = pd.DataFrame()\n",
    "#run simulations and create dfs\n",
    "for i, inputs in enumerate(senders):\n",
    "    U = full_setup(sender_seed= inputs, receiver_seed=rec_pos, ara = 100)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    ax[i].imshow(sim[6,:,:,-1])\n",
    "    df_and[i+1] = sim[6,r,c,:].mean(axis = 0)\n",
    "    and_gate[i+1] = sim[6,r,c,-1].mean()\n",
    "    [a.axis('off') for a in ax[:3]]\n",
    "    print(sim[6,:,:,-1].max())\n",
    "    \n",
    "#add colorbar\n",
    "im3 = ax[2].get_images()[0]\n",
    "f.colorbar(im3, ax=ax[2])\n",
    "\n",
    "#run simulation w/ no inputs\n",
    "U = full_setup(sender_seed= [], receiver_seed=rec_pos, ara = 100)\n",
    "sim = runModel(900,0.1,theta,U)\n",
    "df_and.insert(loc = 0, column = 'x00', value = sim[6,r,c,:].mean(axis = 0))\n",
    "# and_gate.insert(loc = 0, column = 'x00', value = sim[6,r,c,-1].mean(axis = 0)) \n",
    "# df_and[3] = sim[6,r,c,:].mean(axis = 0)\n",
    "# and_gate[3] = sim[6,r,c,-1].mean()\n",
    "df_and.index = df_and.index/600\n",
    "df_and.plot(ax = ax[3], linewidth = '3', style = [\"-\", \"-\", \"--\",\"-\"])\n",
    "ax[4].bar([\"00\",\"01\",\"10\",\"11\"],and_gate)\n",
    "\n",
    "\n",
    "ax[0].set_title(\"01\", fontsize = 18)\n",
    "ax[1].set_title(\"10\", fontsize = 18)\n",
    "ax[2].set_title(\"11\", fontsize = 18)\n",
    "\n",
    "ax[3].spines['top'].set_visible(True)\n",
    "ax[3].spines['right'].set_visible(True)\n",
    "ax[3].spines['left'].set_linewidth(2)\n",
    "ax[3].spines['bottom'].set_linewidth(2)\n",
    "ax[3].spines['top'].set_linewidth(2)\n",
    "ax[3].spines['right'].set_linewidth(2)\n",
    "ax[3].yaxis.set_tick_params(width = 2)\n",
    "ax[3].xaxis.set_tick_params(width = 2)\n",
    "ax[3].title.set_size(12)\n",
    "ax[3].xaxis.label.set_size(12)\n",
    "ax[3].yaxis.label.set_size(12)\n",
    "ax[3].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[3].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[3].set_yticks(np.arange(0,0.9,0.1))\n",
    "ax[3].set_xlabel('Time (hours)', fontsize = 18),\n",
    "ax[3].set_ylabel('GFP', fontsize = 18)\n",
    "# ax[3].set_ylim(-0.5, 220)\n",
    "\n",
    "ax[4].spines['top'].set_visible(True)\n",
    "ax[4].spines['right'].set_visible(True)\n",
    "ax[4].yaxis.set_tick_params(width = 2)\n",
    "ax[4].xaxis.set_tick_params(width = 2)\n",
    "ax[4].spines['left'].set_linewidth(2)\n",
    "ax[4].spines['bottom'].set_linewidth(2)\n",
    "ax[4].spines['right'].set_linewidth(2)\n",
    "ax[4].spines['top'].set_linewidth(2)\n",
    "ax[4].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[4].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[4].set_ylim(-0.5, 220)\n",
    "# ax[4].set_yticks(np.arange(0,0.9,0.1))\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='tab:blue', lw=3, label='00'),\n",
    "                   Line2D([0], [0], color = 'tab:orange', lw =3,label='01'),\n",
    "                   Line2D([0], [0], color = 'tab:green', lw =3,label='10'),\n",
    "                   Line2D([0], [0], color = 'tab:red', lw =3,label='11')]\n",
    "ax[3].legend(handles = legend_elements, frameon = False, fontsize = 18)\n",
    "f.text(1,0.75, 'GFP',va = 'center', rotation = 270, fontsize = 18 )\n",
    "f.text(0.54, 0.47, 'Time = 15 hours', fontsize = 14)\n",
    "f.text(0, 0.53, \"B\",fontsize = 25)\n",
    "f.text(0, 0.98, \"A\", fontsize = 25)\n",
    "\n",
    "circle1 = plt.Circle((23,23), 2.6, color = 'r', fill = False)\n",
    "circle2 = plt.Circle((23,23), 2.6, color = 'r', fill = False)\n",
    "circle3 = plt.Circle((23,23), 2.6, color = 'r', fill = False)\n",
    "ax[0].add_patch(circle1)\n",
    "ax[1].add_patch(circle2)\n",
    "ax[2].add_patch(circle3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "historic-bicycle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(23, 36, 'S')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circle1 = plt.Circle((23,11), 2.5, color = 'hotpink', fill = True)\n",
    "circle2 = plt.Circle((23,11), 2.5, color = 'hotpink', fill = True)\n",
    "ax[1].add_patch(circle1)\n",
    "ax[2].add_patch(circle2)\n",
    "circle1 = plt.Circle((23,35), 2.5, color = 'hotpink', fill = True)\n",
    "circle2 = plt.Circle((23,35), 2.5, color = 'hotpink', fill = True)\n",
    "ax[0].add_patch(circle1)\n",
    "ax[2].add_patch(circle2)\n",
    "\n",
    "ax[1].annotate(\"S\", xy=(23, 12), fontsize=15, ha=\"center\")\n",
    "ax[2].annotate(\"S\", xy=(23, 12), fontsize=15, ha=\"center\")\n",
    "ax[0].annotate(\"S\", xy=(23, 36), fontsize=15, ha=\"center\")\n",
    "ax[2].annotate(\"S\", xy=(23, 36), fontsize=15, ha=\"center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "quarterly-kingston",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "informed-documentary",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig('/Users/lucarosa/Library/Mobile Documents/com~apple~CloudDocs/THESIS/Upgrade/figures/logic/10-38-panel-2.pdf', bbox_inches = 'tight')\n",
    "f.savefig('./simulations/10-38-panel-2.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electric-links",
   "metadata": {},
   "source": [
    "## Sender positions: 17 - 31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "mental-profile",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "216.9005266993664\n",
      "216.90052218624558\n",
      "222.43419140281463\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(23, 30, 'S')"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vg5QaXHJgJy49qBPj/53NK7//Ra434mfhiEgptalXl3uP70b9lGQSYmPxWOEf8hNi4cT27Thurzb8u2otT//wE+u2Z0SwWqlIIZoBXOec61LK5wwDHsG/G8UjwADg6lAUEyoKgCIiUqXVTIjn3uO7sW+jBiTFxZX4eYlx/rfA0zrszVGtWvDgV98zb92GcJUpIuVgwJVdD+D8TvuSEFvyj6+xHg+xHg/7NW7AqIvOYfBPk/h2zvzwFSqVgqPiNoJ3zq3e8buZjQDGB+4uB9LzdW0WaIs4rQIqIiJVVu2kRF487ww6NWlUqvCXX2JcLA1qpjDwrFPYv3HDEFcoIuXlMeP+E47hvP33KVX4yy82JoakuDhuOepQLjlw/xBXKJWO8y8EU95bWZhZ43x3zwF2rBD6CXCRmSWYWSugHfBHef7MslIAFBGRKikxNpZBZ59K/Ro1Cj3VszSS4uJ49NTjaVOvbgiqE5FQub3b4XRt3ozEMn7Jk19iXBwXH7A/Z+3bPgSVSWUWoW0gRuNfxGVvM1tmZtcAT5vZDDObDnQHbgNwzs0E3gf+Bb4EbqyIFUBBp4CKiEgVdcMRB5NWI5nYmNB9l5kUF0f/k4/l6tHjyPX5QnZcESmbw1s2p1ublsFTtkMhMS6W3od14e/lK1mySYvDRCNHxFYBvbiA5leK6P8Y8Fj4KioZzQCKiEiVs3+TRnRv27rMp4MVpXZiIlcdfGDIjysipVMzIZ47uh8Rkpm/3cV5PNx34jFFLiIjVZl/FdDy3qKVZgBFRKTK6XNY1xLNCLz237e8MONT5mxeQax5aFmrId2b7MfAI3oX+pzEuFjO7Nied/6azracnFCWLSKlcHbHDiU6vbssr3OPx0PDlBS6pjfl9yXLQlm2VBJlvYavOtAMoIiIVCkt6tQmvU5qsf2e+Ot9ek0YzEnpBzLupHt547jbOavlIXyyqPhr7p2Dk9q3C0W5IlIGHjPO3q/4RV/K8zpPjo/jws4dQ1WyVDLOWblv0UozgCIiUqWc3L4tcZ7iv78c8s94rtvnFB4/9Mpg2xktD+HBLpcU+9zEuFjO3Hdvxk6fWa5aRaRsOjVpRIyn+A/g5XmdA+zdoD61ExPZlJVV5lql8vGv4hm9Aa68NAMoIiJVyv5NGhFTggC4KXs7jZJr79FuJbzmp0HNFOJDsLqoiJReh4b1S/T6K+/rPMfrZa8G9UpbnlQBugawcAqAIiJSpbSoU7tE/Q5Ma8MLM8bz+n/fsT5rS6nHyc7L05YQIhVk/8YNS3T9X3lf54lxsexVP60sJUolV1H7AFYFCoAiIlJlJMfFlXjVvhePvp6UuER6/vAc9V+9lH3fvYEH/niLLTkZJR6vfkpyWUsVkXJokJJSon7lfZ3Hejw0Ta1VnlKlktI1gIVTABQRkSojxuOhpF/K7l+vFbMufolPTrmfGzqeisPxyJ/v0uWDW9mWm1ns8y0wnohEXkmu/4Pyv87BHwIlujhtA1EkLQIjIiJVRnZeHjGl2LcrISaOM1oewhktDwHglVlf02vCYF6Z9TW37H9Wkc91QHaetzzlikgZZXtL/torz+scIDM3t8x1SiWlRWCKpK88RESkysjxetmWXfa9+a7pcCJ1E2ry38bi9/3ymLFk46YyjyUiZbdw/cYyP7c0r/PsvDzmr9tQ5rGkEnMhuEUpBUAREalS5pXww9qajE17tK3N3MzmnO00LGDVwN3FmIflm0u/qISIlN+MlavJys0rtl95X+d5Ph9z1q0vQ4VS2ekawMLpFFAREalSJi1eyr6N6pMYF1dkv/3e78tZLQ/hxPQDaJBUm8Vb1/DstHEkxyZw5d7HFTvOf2vWRvMXwCKV2vQVq/wX4hajvK/zGPNoBjBKRfMqnuWlACgiIlXKt3Pm0+vQg4rt90CXi/h44e/c/PNwNmRvpVFyHQ5v2IH3TriLVrUaFfncjJwcxkz7J1Qli0gpLdm0maUbN9OuftF79JXndZ7n9fHtnPnklOJ6Q6kaHLoGsCgKgCIiUqVsy8nhpwWL6damZZH7hN3Y8XRu7Hh6mcbIzvPyx5LlZS1RRELg3akz+N8xR5BUxGx/eV7nXufjwxn/lrU8qcwcoABYKF0DKCIiVc7w36aQG6Zv7bNy83h2wi/4dP6QSIX6af4iFm/chNfnC/mxs/Py+Hr2PJZs2hzyY0vloI3gC6cAKCIiVc7GzEye//E3skK8fHtOXh6/L1nKH0uKXz1QRMLLAY99M5Fcb+gD4LbsHIb/NiXkx5VKRKuAFkoBUEREqqTv5y3kq9nzQxYCc/O8rNiylQETfgnJ8USk/FZt3cbj304s0YqgJbU9J4e7P/uarLzQHVMqm/KvABrN1xAqAIqISJU15OdJIQmBWbl5LNm8mds++oLMEH7QFJHy+23xUp76/keycvPKdWp2ntfHtuwc+n38JYs2bApdgVI5aQawUAqAIiJSpQ35eRLPTviF7Tk5ZbouMDsvj/H//sdN4z5jW07ZN5kXkfD5eeESbvnoM1Zs3lKmL3wyc3P5Z9Vqer3/EfPXa9uHqOcisw+gmY0yszVm9k++tmfM7D8zm25mH5pZ7UB7SzPLNLOpgdtL4fsPUDStAioiIlXexPmLmLZiFdccchDd27bC51yRKwfmer34nOO/NesY/ttk5qzVRtAild2C9Rvp9f5HnL//vvTo1JG4GA/J8fGF9vf5fGR7vWzKzOL1yX/z3dwFEaxWKlxkZvBeA4YAb+Rr+wa4xzmXZ2ZPAfcAdwUem++c6xyRyoqgACjVg5XzPO5oXgpKJEpsyspmwMRfGfbrZLq3bcXBzZuyd/006iQn4XMOjxkZObks3LCRv5ev5Js581m1dZv/yWZ6nYtUAV6f471pMxkz/V8Oad6MI1s1Z5+G9WlUq2Zw33ivcyzdtJl/Vq7h+3kL+Hf1Wv8DOz4L6LVeTYT/Gj7n3I9m1nK3tq/z3Z0EnB/KMc0sEegVOG5HoBawBZgJfACMcM5lFXUMBUAREYkqGbm5fDZrDp/NmgPmwWNGnMdDns+H1zlwoV9RUEQiy+ccvy1eym+LlwJg5iEuJgbnHLk7to3Qa716C03OTzOz/MvFDnfODS/F868G3st3v5WZ/Y0/sN3nnPupNMUEwubnQHv8f+EWYCX+EHgkcBRwg5md5pwrdMpbAVBERKKazzmyw7RnoIhUDg7I0etc8gtNAFznnOtSliea2f8BecDbgaaVQHPn3HozOwj4yMz2dc5tKeHxEoDxQGvgMWCkc25xvsdbANcAdwLjzewA51x2QcfSIjAiIiIiIhI9HOCs/LcyMrOewOnApc75zzl2zmU759YHfv8TmA/sVYrDXg10AC5yzt2fP/wFjrnYOfcAcAGwN/4wWCDNAEq1FON8NGI7iXjxYmwigU2WWNFliUiIxTkvjdhOPF7y8LCeJLbprU8kqphzNGQbyeTiMLYSzzqSKrosqWAVdamnmZ2Mfxaum3MuI197fWCDc85rZq2BdkBpViY6H5jonPuoqE7OuU/MbCJwHjC0oD56F5SoYgkJBbbPGdiZVqvXctEvv3PIvAXstWIV6+qkkJEYj8fnaLhhC5kJ8Uxvls7nB+zHFwfuT3a+FQT3un3qHsd02QXOqotImHkSC/6yZv/f/Ne8N521kYM/XESLaetptHgz2Y1i8SUanjxH/Mo8NtdJZuk+dZh6cjqzjm6ML9Z/Msz0w/Y8ri+ryOvoRSSMCnutt/vZS+raDA77cAF7T15Fs9kboZaRW8uDOYhfnwcO5u9dn3+Oasofp7Ykq+bO1ULnHhmzxzH1Wo9CEQiAZjYaOAb/tYLLgAfxr/qZAHxj/oWHJjnn+gBHAw+bWS7gA/o450qzJ8l+wMAS9v0auL2wBxUAJeq1863n/sHDab98JWMO68pj557Ov82akNc838XhzpG+eiP7/LmW8ydN4b6xn/LW0Ycz7KTuuwRBEam82k1azUkvzqTWuiwmnduKsfcdwEFdFuJLzHe1g9cxaXo7WkxbzzGvzebsp6Yy8Yq9+OWithVXuIiUWEPfNq6+80/a/7GKKSe14Ktr9mVp+7p0azl7ZyfnSFidx/zfGnLQV4s5fdh0Jp/SkvHX709mrcK3jZDoYr6IrAJ6cQHNrxTSdywwthzDpQKr8jeYWTLQCpjl3C6rHq0Eahd2IAVAiVqxzsulef9wqnceTxx0BtdcfzU5cTv/L59Axs7OZixtVJd5XZvxSdcDaLlmLXd/+DmfPvk8d1x+AZkVUL+IlEySy6V37p8c3n85n/6vE/90b4qL8b/xH5C4eNfOMcaa1rVY07oWk89pRZP/NnLmM9Po9PUyHvEdzjJPagX8BSJSHHOO071zuDx3OhPb7807DxxCVkohX9Cakd0ojundmzG9ezNqrc3k5Fdmcu+Fn/PuvV2ZS/PIFi+R54jUPoCRtB3/ap/5HQp8C9QH8m9omxLoXyAFQIlKyS6Xh3ImkGlxXJ9wKpOOOLhUz1/UoD59rr2CM/6cxshhrzLC24kJMS3DU6yIlFldl8ET2d8x25PGwDEnklWzdDP2K9rX4eUR3Tjs/fkMmPY1j8cfxbSYRmGqVkTKIsb56Jf7K818W+mXcCKJV6eU6vlb6ifx/t1d+Pv4dC7r/ztJuZsZHdux/HsESyVWvkVcKql5+Ld/KInOwNzCHtQqoBJ1Elwej+b8wBJPKg/GdWODlfFCcDM+7dKZS2+5jmtz/6Kbd1FI6xSR8kl1WTyd/S0/xLRiYPxhpQ5/OziP8etFbXk0/mjuzfmJjt7VIa5URMrK43zckfsrNV0O/0s4gaXlmKWf26UhA149ge7eRVyYNzOEVUql5EJwq1y+Ay40s+5m1s3MugGddu9kZvHAmcD3hR1IAVCizg15U1hjNRgS2xUXgm/35jRpxP/Fd6dv7hSa+zaHoEIRKTfnuCvnF36NSefduI4hOeSMmIY8GX8k/5fzE7WdTvwWqQzOy5tFfbedh+O7kWPlP3FtS/0k7k44jtO8c+niXRGCCqXSir4AOAqoiT8I/hC4PVtAv9uAusCrhR1Ip4BK1VVAuGt8Sxz7vbKeU564iW3JO1cPu77zt3v0renZc8WvrR0KXnHspVePJu3bWPr++BfnP3gt3pgY2l4+dc+OFbXmsEi0KuB1nvBVKid+OpNan3oZPfRoEgKreN5ff8IefVM8e76mO8VPKnCoy746i39J5fuXN3LL0r958pGTyexewGq/ep2LhF4Br/W9RuTSo+8s7hxxLvGNa7Fj+ZYnG32+R99kz56Luxyd+EuBQ13x7akMndKdWx+fwG1v9CAjJYGtR+u1HnWi7H8+59xcM7sUSC/g4fzX+70OvOGcW1nYsTQDKFHD43w8Oupj7ul1zi7hL1RGH9uVjIR4LvxhSsiPLSIlV2NrNlcMn8Sge44NbuEQSqN7diV90UYO/H1JyI8tIiXX+7mfee/qLqxtvPu6F+U3o0sz/j40nQte/TPkx5ZKoII3gg8X59wY59zAAm5Z+fqsKir8gQKgRJFDWcnq2jX5eb/wLOfuPB4Gn9Odnl/9pm8FRSrQcV/8x99d01naql5Yjp+bEMvYSw/kjLHTw3J8ESleS7eZpos38s2ZHcI2xgdXHEj3L2aTkJkbtjGk4pgr/y1a6RRQiRpnuvm8ecLRYR3j9w6tcGYcMmvhLmvtikiEOMfJH//DC3d1D+swPx/blquG/kJjt42VVroVB0Wk/M5w8/n2jA54Y/fctD1U1jWqyb+dGnPUN3P5kIZhG0cqSJQFODO7srTPcc69XlC7AqBEhXjnZV/W8WXXfcM7kBnjD9uPY6bNYSxaKl4k0hqzncSsXGbt1zis4+QkxPLHEa3o8sUqPkWbxItEWldW8fjxXcI+zs/HteXI7+YpAEpVMAow9oy2O85VLahdAVCijO08g7k1G1nmanLEvgsL7HpsjVl7tMWZb4+2XFfwWdFz2u0Me3YkHP32PMZakz07Om9xVYtIadiur8l2bhNLO9SlftKe+9vOzt3z9Rtney72VNjrvH7itl3ur+pYi3ZfrNmjBr3ORcIg3+uspsumFtk0238jzTybSvT0bFfy0zgPqbPzs0LKwVl0eGnVnq9z0Gu9iovCUzh7FNLeEegPXA1sDbQdCdxa2IEUACUqtHEbmWt1IjLWkr3r0Xz2hoiMJSK7assmFrcPz7V/u1vcvh6HuDkRGUtEdmrDJhZQGzzhX4RjU/NkErblUdNls9USwj6eRFAlXMSlPJxz4wpqN7NNgV/HO+fWB9qKXA1Ri8BIVEghl81E5h/ubakJJG/L0UIwIhUghRy21o7Ma31rnURqkhORsURkpxRyIvaejhkZdeJIQQvBRJVQ7AEYxR/zNAMoUcEi+Cp1hj/8RdcXSyJVgl52ItHPf5FTZF/tkfwcIRGi/0kLpQAoUWE7cTRiz2uCwqHG1hyyasRBpj6KikTaduKosSUys3I1tuSQobdJkYjbTlzkZt+dI3FzLhnERWY8iZgovAawKKX6a/XOJlWX27mIy0JqcaJbyNqsgpdr3ztuzwVfUjx7nh69zbfnghHALsdt8s9GFrZOg3/2PKaIhJjb9XW2kFrs/1cG3x+11x5d96qxao+2mp49X9NbfQVfGvH93F2PeeFPk1lI6h41iEgY5HudLXC1aM1Gft/QEmzPL1tvrL3nwm4JtmeAy3AFh8jfN7YK/l5/1VayYuPY5OIAvdajSvUJgIuAp4CM3doL/S+gawAlKsyjDi3ZQmxu+FfsajN7LXPbNwj7OCKypznUYb+FyyMyVseFK5hjdSMylojstMkSySaWhiu2hH2s1rPXMr99/QKDplRx1eQaQOfcAufcvc65zHzNHwCF7pekAChRIctimUdtDpq0OOxjHT5xPtMOahb2cURkT8uoicfnY98wh8DYPC8n/DmLv9GXPSIV4S8acujEgrd2CqXDJixgehe9p0cbc6G5VVXOuRzn3JrCHlcAlKjxqbXh1A//CesYbWetJnVTJn8f3Dys44hIwZwZo4/tymXf/hHWcU74cxaLG9ZjkaWGdRwRKdin1oaTPpyJ+cL3KTx1YyYH/raEH07eO2xjSAVyVv5bJWJmLUp7K+xYCoASNX6iGS0XrKPdv6vDM4Bz9HjrT744uyO+GL10RCrKe927cvLkmTRduzEsx/f4fFw3/kfeOOGQsBxfRIo3mzpsrZ3Ikd/OC9sYZ46exm/dW7O9lvb/i0bmK/+tklkALCzlrUD6FCtRI9diGHnTUdzy5HfEZeeF/PhHfj+Ppks28el5+4f82CJScutSU3jpjKN5asS4sOzH2fuzn9mWlMDnh3QM+bFFpITMGHHbkfQc8iupGzOL719KbWetofsXs3mn98EhP7ZUEhG4BtDMRpnZGjP7J19bXTP7xszmBn7WCbSbmQ02s3lmNt3MDizlXzSkgNtHgUpH7tb+dZF1O21mvYcTPD30H6WK8iQkcH/Oj6y1ZF6K6xK8qHv/3/ZcCfDM1L/2aPtkc8GvxdWH5DEo+0v6JxzDbE8aAL6sglcMlcrlG9+YAs/h0Ou86rKEBDzOx6Ccr5kY04KxsR0AmDOw8x59ExrsvigaZK9JLvC4e90+lXa+9Tye8wM3xZ/MKk8KLjs7pLVLeBT2Oge91qsyT2IiV+f+TQvfJh6K74bP/PMW7X7ec8G3U2tP36Pt800Ff2G74ggvz2V/xTuxHZkQ618RVO/pVUNRr/X8Epumu+Y33l7u8eb+3+1/Oue6FPa4mR0NbAPecM51DLQ9DWxwzj1pZncDdZxzd5nZqcBNwKnAIcDzzrlynWpiZscC3wL1nXPr87VfArzlnCtwsk8zgBJdzBgUfygH+FZxad6MkMwO1F6ZwZM53zI6rmMw/IlIxfKZh8fijuTcvP84MW9+SI7ZyreRh3Mm8FzcIazyFLyljIhE1pux+xOHj9tzJ+EJwZYsiVtzeCTnB/72NGJCTMvyFyiVVwRmAJ1zPwIbdms+C3g98PvrwNn52t9wfpOA2mZW6EqdJVSmD7oKgBJ1tloCdyccz1HeJdya+ztJLrfMx2o9ZS03XD2BT2L35pPY9iGsUkTKa7Unhbvij+PyvOn0zJ1KXF7ZT/0+6e8ZPJnzPcPiuvBrTHoIqxSR8si1GB6K70Zdl0n/nAnUcWU/HbTR/M3ceu13zLc6u5wlJFEqNAEwzcym5LtdW4KRGzrnVgZ+XwU0DPzeFFiar9+yQFt57Ji6rr9be32g0BeLAqBEpY2WxO0JJ2I4Xsoez94/ryzVbGDS5hzOevJvLr73Dz68pzMfBk4xE5HKZZmnFrcknEwrt4lPnnyeAxaWbiuYhps2M3jU29z58Rc8FH80P8YUumiaiFSQbIvlgfhjmO+py7Cszzh4/EI8eSWfDYzLzOPEUTO55brv+LFHO16M64pT+It6IdoGYp1zrku+2/DS1OD819qF8zT0eYHjX7ajwcxigAvxLxpToNgwFiRSoTIsnufiD6OLdwU3DZzCGQOn81uPNvx3RCPWp9fYo79nu4/Wk9fQ5dPF7PvDCqaf0IyBH5xAZq34CqheREpqgyXxYFw39j3Bx+BRb7O+ZgpvH3UYP+/djg314/b4lr9WRiYHLlzMBb/+weGz5zH6yEO547ILaHH3zAr6C0SkOHkWw+txnfklJp3bx03mjKHT+fncNkzrns7qljX36O/J9tF85nq6fLmYgz9byLyDGvD0myexsXENGKjwJ2G12swaO+dWBk7x3LEf33Ig/ykmzQJtZeacW2tmE4F7zWw/YA5wHNAZ6F/Y87QITAF0wXh02o91nOYWsB9rSSKPxaSSSSweHGlk0oDtLKYWP1kzvqQVWyzfstB6nVRZWgSmGjHD4xxdWMUpbgH7sB4DllKTLGKJxUdDtlOHbOZRmx+sOd/RnEyL8z9fr/MqS4vAVDNmtHGbON3NpzNrqEcmi0hlG/F4cKSSTTO2spwU/qAx4601ayzfF796rVdZpVkEpsV15V8EZs6DRS8CA2BmLYHx+RaBeQZYn28RmLrOuTvN7DSgLzsXgRnsnCv3MrRm1gb4CmiVr/lr4BznXIGrG2kGUKoHM2ZQnxnmP0W6tsuiuW0jkTy8eNhEAotIZc91xUSkKvGZ8QeN+cMag3PUI4t0thKPlzw8rCOJZdTEp9O/RKq0+Vab5+0gAJJdLq1sC8nk4YAtxLOIVHJ0pVP1tfMUzrAys9HAMfivFVwGPAg8CbxvZtcAi4ELAt0/xx/+5gEZwFWhqME5N9/MOgBHA2nAPOfcn0U9RwFQqqVNlsgmK2Ap+BCsMCYilYQZ60liPUkVXYmIhFGGxTHTdl8DA72nV3cRCIDOuYsLeei4Avo64MYw1ZELfFfS/gqAIiIiIiISXXSmb6EUAEVEREREJGoYkTkFtKpSAJTqIXDBd+cmjeiS3pT9mzSkWe1U4mJicM6xJSub2WvWMX3FKiYuWMSmzAKvmRWRykwLO4hUD4HXeru0ehzashmdmjSiZZ06JMTGAo7tObnMW7ee6StW8eOCxazauq1i65WKobeEQikAStSL8Rhn7NOeCw/oSHJcPImxsXg8uy4AkRQXR8OaKRzcvBnXHtaFKctW8Prkv1mwfmMFVS0iIiIF6d62FZcd1In6KTWIi4kh1rPrYi+JcXHUq5HMAc2acEXXA5izdj2vT/6baStWVVDFEnERWgSmqlIAlKjWqm4d7j/xGNJqJJMUF1ds/8Q4/0vi0ObNOKhZE8ZN/5c3pkwlz6cLyUVERCpS/ZQa3Hvc0bRJq1ui9/T4mBgA9mvckEdPOZ6fFixiyM+/k5GbG+5SpTJQACyU1seVqNW9bSsGn3MaTVNrleiNIj+Px0NCbCxn79eBF887nZoJ2gxeRESkouzXuCEjLzib9g3ql/o9Hfxf8B7dpiWjLjqHRjVTwlChVDouBLcopQAoUenYtq25/ZgjSIyLxVOO/b6S4uJIr53K8+ecRnJ86d9wREREpHz2bdSAx089nuT4OGJjyv7RNSE2ltpJiQw593QapNQo/glSpZkr/y1aKQBK1GlVtw63HXM4ibGhOcM5LiaGhjVTuP+EY0JyPBERESmZ2kmJPHbq8SSWYdavIDEeDykJ8Tx1+onEeMr+BbFUAZoBLJQCoESVGI/xwInHBM/7D5X4mBj2bdSA7m1bhfS4IiIiUrg7ux8V8vf0GI+HtBrJXHZQp5AeVyqRUIQ/BUCRquHMfduTViO5XKd9FiYpLo5bjj4s5G9EIiIisqeu6U3p2LgBcWF4302Mi6NHp4401PWAUct85b9FK60CKlHDgAs771ei00Re++9bXpjxKXM2ryDWPLSs1ZDuTfZj4BG9ixnD6N62FV/NnheiqkVERKQgFx+wX4kWfCnre7oH46x92zN80pRQlSyVSDRfw1demgGUqHFAs8YleqN44q/36TVhMCelH8i4k+7ljeNu56yWh/DJoj+KfW5yfBwXdO4YinJFRESkEI1qprBXg7Ri+5XnPT0uNobT9tl7j30EJUroFNBCaQZQokaX9KYkxhZ/msiQf8Zz3T6n8PihVwbbzmh5CA92uaRE4zSuVZPk+DgycrSPkIiISDh0btoYnyv+E3h539MdjlZ16zB33foy1yqVUJQHuPLSVx4SNfZr3BBPCb7F25S9nUbJtfdotxJeN5id56VdWr3SliciIiIltG+jBiU6q6e87+kxZuxVX+/p0cZCdItWCoASNdJTU0vU78C0NrwwYzyv//cd67O2lHqcuBgPLevWLvXzREREpGTalTCUlfc9PTEujrb6Ujc66RTQQikAStSIK8HpnwAvHn09KXGJ9PzhOeq/ein7vnsDD/zxFltyMkr0/FiPp0TfSoqIiEjZlHQv3/K+pwOkJMSXtUypxLQRfOEUACVqlORaAYD967Vi1sUv8ckp93NDx1NxOB758126fHAr23IzSzSO1xfFawOLiIhUMBeh93RA7+nRSjOAhVIAlKixLSu7xH0TYuI4o+UhDDnqev69aBgjj7mZuZtX8Mqsr4t9bq7Xx8aMkr2piIiISOmtL8X7bHne070+H2u2bS9PqVJZKQAWSgFQosactWVfweuaDidSN6Em/21cVrKxtFqYiIhI2MxYuarMM3OleU/Pys3jvzXryjSOVGIhOP2zJKeAmtneZjY1322Lmd1qZv3NbHm+9lPD/0eXnAKgRI2pK1aRnZdXbL81GZv2aFubuZnNOdtpWMBKYruL9XhYtqn0F5qLiIhIycxavY6sSLynx3iYs1YBMCpFYAbQOTfbOdfZOdcZOAjIAD4MPPzcjsecc5+H7O8KAe0DKFHjxwWL6HXIQcX22+/9vpzV8hBOTD+ABkm1Wbx1Dc9OG0dybAJX7n1ckc/1+nz8tGBRia83FBERkdL7e9mKEvUrz3s6wJKNm1m3veQLxkjVUQGLuBwHzHfOLS7pNiQVRQFQosb67RlMXbGSg5s3K7LfA10u4uOFv3Pzz8PZkL2VRsl1OLxhB9474S5a1WpU5HNzvV4+mD4zlGWLiIjIbnJ9Pj7/dw5nd+xQ5Crf5XlPz8jJ5b2pM0JdulQWoQmAaWY2Jd/94c654YX0vQgYne9+XzO7ApgC9HPObQxJRSGgAChR5bU//qZTk0YkFLF89I0dT+fGjqeX+th5Xi+z165n3roN5SlRRERESmDs9Jmcvu/exFF4ACzrezrA1uxsflm4pKzlSSUXohnAdc65LsWOZRYPnAncE2gaBjyCP4Y+AgwArg5JRSGgawAlqsxdt55PZ/5HVm7x1w2UVq7Px5Pf/Rjy44qIiMie1mdkMvSXP8jMzQ35sbNy83jkmwnkaQuI6BSK6/9KFyBPAf5yzq0GcM6tds55nXM+YARwcLn/phBSAJSoM+r3v1izbRu5Xm/IjpmVm8fzP/6m6wREREQi6Mv/5jJj5eoSLfJWUpm5uYydPpPZWv0zukU2AF5MvtM/zaxxvsfOAf4p2x8RHgqAEnVyfT5u//gL1m7bHpIQmJWbx6t//MV3cxeEoDoREREpjf5f/cDctetLtCpocTJzc/l+7gJem/x3CCqTysqIzDYQAGZWAzgBGJev+Wkzm2Fm04HuwG2h/hvLQwFQotLmrGz6jhvPv6vWlPnUkTyvj8zcXAb9+CvjZvwb4gpFRESkJHK9Xu789Ct+XrC4zJd4+JwjOy+Pd/+ewaAffwtxhVIpRWgG0Dm33TlXzzm3OV/b5c65/Zxz+zvnznTOrQzVnxUKWgRGotbW7Bz+9+lXnLh3W/oeeQgASXFxxT7P6/OR6/Uyd+16nvjuR9bqtE8REZEKlevz8dT3P/Fds/ncddzRxMd4SI6PL9FzM3JyWbd9O498M4FFGzaFt1CpNExbdhVKAVCi3tez5/Hj/EUc1641F3TuSP2UGmTneYmPjSHW48E5R3aeF3DExsTwy8LFfDBtJnPWrq/o0kVERCSfKctWcPGb73NEq+Zc2Hk/WtWtQ1ZeHnExHuI8MTgcuV4vXudIiInl7+UrGTPtH/5eXqkmYCTcHJjW9ymUAqBUC1l5eXw2aw6fzZpDzYQE2tWvS4s6dUiKi8Xrc2zMzGTO2nUs2bhZm7yLiIhUYnk+HxPnL2Li/EUkxcXSNq0erevVoUZ8PD7n2JKVzbx161m4fiO5WuWz+tLHuUIpAEq1szU7m7+WreSvZfo2UEREpCrLzM1jxsrVzFi5uqJLkUomRPsARiUFQBERERERiS4KgIVSABQRERERkehRim0cqiMFQBERERERiS4KgIVSABQRERERkaixYyN4KZgCoIiIiIiIRBet6l4oBUAREREREYkqmgEsnAKgiIiIiIhED4euASyCAqCIiIiIiEQV81V0BZWXAqCIiIiIiEQXzQAWSgFQRERERESiiq4BLJwCoIiIiIiIRA+HVgEtggKgiIiIiIhEFc0AFk4BUEREREREoosCYKEUAEVEREREJGoYmgEsigKgiIiIiIhED+cidg2gmS0CtgJeIM8518XM6gLvAS2BRcAFzrmNESmoBDwVXYCIiIiIiEgomSv/rRS6O+c6O+e6BO7fDXznnGsHfBe4X2koAIqIiIiISFQxX/lv5XAW8Hrg99eBs8v554SUTgEVEREREZHo4QBfSE4BTTOzKfnuD3fODS9gtK/NzAEvBx5v6JxbGXh8FdAwFMWEigKgiIiIiIhEl9BcArgu32mdhTnSObfczBoA35jZf7uU4ZwLhMNKQwFQRERERESiSqQil3NueeDnGjP7EDgYWG1mjZ1zK82sMbAmMtWUjK4BFBERERGR6LJjJdDy3IphZjXMrOaO34ETgX+AT4ArA92uBD4O019ZJpoBFBERERGRqBKhGcCGwIdmBv5c9Y5z7kszmwy8b2bXAIuBCyJSTQkpAIqIiIiISPRwhOoawKKHcW4B0KmA9vXAceGvoGwUAEVEREREJGoYYBHaCL4qUgAUEREREZHoUr59/KKaAqCIiIiIiEQVzQAWTgFQRERERESiR4SuAayqFABFRERERCSKlGwbh+pKAVBERERERKJKpDaCr4oUAEVEREREJLpoBrBQCoAiIiIiIhI9HJhWAS2UAqCIiIiIiEQXzQAWSgFQRERERESii/JfoRQARUREREQkqmgfwMIpAIqIiIiISHRRACyUAqCIiIiIiEQNcw7zKgAWRgFQRERERESii2YAC6UAKCIiIiIi0UUBsFAKgCIiIiIiEj0coH0AC6UAKCIiIiIiUUWrgBZOAVBERERERKKLAmChFABFRERERCSKOAXAIngqugAREREREZGQcfgDYHlvxTCzdDP7wcz+NbOZZnZLoL2/mS03s6mB26nh/pNLQzOAIiIiIiISXSKzCEwe0M8595eZ1QT+NLNvAo8955x7NiJVlJICoIiIiIiIRJVILALjnFsJrAz8vtXMZgFNwz5wOekUUBERERERiS6hOQU0zcym5LtdW9hwZtYSOAD4PdDU18ymm9koM6sT/j+45DQDKCIiIiIi0cMBvpDMAK5zznUprpOZpQBjgVudc1vMbBjwSKCSR4ABwNWhKCgUFABFRERERCSKRG4VUDOLwx/+3nbOjQNwzq3O9/gIYHxEiikhBUAREREREYkuEQiAZmbAK8As59zAfO2NA9cHApwD/BP2YkpBAVBERERERKJLZGYAjwAuB2aY2dRA273AxWbWGf8poIuA6yJRTEkpAIqIiIiISPQI3TWARQ/j3M+AFfDQ52EfvBwUAEVEREREJIo4cJHZCLAqUgAUEREREZHoEqFFYKoiBUAREREREYkeEToFtKpSABQRERERkeji0ymghVEAFBERERGRKBK5fQCrIgVAERERERGJHg7NABZBAVBERERERKKLZgALpQAoIiIiIiLRRQGwUJ6SdjSz/mbmCrllmNlcM3vdzA4PZ8EiIiIiIiKFc/5VQMt7i1JlnQFcne93D1AXaBu4XWFmDznn+pezNhERERERkdJx4LQRfKFKPAOYn3OuUb5bAyABOBL4M9DlQc0EioiIiIhIhdAMYKHKFAB355zzOud+Ac7O13xWKI4tIiIiIiJSKs6V/xalQroIjHNumZmtB+oBKaE8toiIiIiISLGc0zYQRQhpADSzpvjDH8DsUB5bRERERESkRKJ4Bq+8QhIAzSwGOBh4PtC0BngjFMcWEREREREpDacZwEKVKQCa2ap8d3esAhoDbAHeBv7PObep3NWJiIiIiIiUSnRfw1deZZ0BbFhIezKQGnh8cRmPLSIiIiIiUjaOqF7Fs7zKug2E5b8BScABwOvA6cCPZnZ26MoUEREREREpIecr/y1KhWobiCzn3FTnXC/gQ/z7Ar5mZrVCcXwREREREZGScIDzuXLfSsLMTjaz2WY2z8zuDu9fFhohCYC7GRH4mQqcGobji4iIiIiIFMy5iMwABhbCfBE4BdgHuNjM9gnzX1duId0GIiD/tX+twnB8ERERERGRQpV0Bq+cDgbmOecWAJjZu8BZwL+RGLyswhEAm+X7fXsYjh923/jGWEXXICLhpde5SPWg17pI9bOVjV9963s/LQSHSjSzKfnuD3fODc93vymwNN/9ZcAhIRg3rMIRAC/J9/uUQnuJiIiIiIiEmHPu5IquoTILWQA0s0ZAX+DKQNMk4LdQHV9ERERERKQSWQ6k57vfLNBWqYViI3iARPyLvuwwAzjPOe3AKCIiIiIiUWky0M7MWuEPfhex69mQlVKoNoLPBVYB04APgDeccznlKUxERERERKSycs7lmVlf4CsgBhjlnJtZwWUVyzRJJyIiIiIiUj2EYx9AERERERERqYQUAEVERERERKoJBUAREREREZFqQgFQRERERESkmlAAFBERERERqSYUAEVERERERKoJBUAREREREZFqQgFQRERERESkmlAAFBERERERqSYUAEVERERERKoJBUAREREREZFqQgFQRERERESkmlAAFBERERERqSZiK7oAADPrAVwOHASkArOBZ51zo3fr1xu4E0gHZgJ3Oue+261PU2AIcDyQDbwb6JdRgjpc+f8aEREpLeecFddH7xUiItVbSd4rpHiVZQbwdmAbcBtwJvAD8I6Z3bSjg5ldDLwEvAGcgv9NfbyZdczXJw74CmgBXATcAvQAhkfmzxARkTDSe4WIiEg5mXMV/0WmmaU559bt1vYOcJhzrlXg/mzgF+fc1YH7HmAaMM05d1mg7WLgLaCtc25hoO0C/N/s7u2cm1tMHQ6gMvw3ERGpDsz8X+aWcAZQ7xUiItVQad4rpHiVYgZw9zf0gL+BJgBm1hrYC3g/33N8wBj83/DucAoweccbesBHQA5wcmirFhGRSNJ7hYiISPlVigBYiMOAOYHf2wd+/rdbn1lAXTOrn6/fLn2ccznA/HzH2IOZOV3TISJSJem9QkREpBQqZQA0s+OAs4EBgaY6gZ+bduu6cbfH6xTQZ0e/OgW0i4hIFaX3ChERkdKrdAHQzFoC7wAfO+dei8SYzjnTOcUiIlWH3itERETKplIFQDOrC3wBLAYuzffQjm9vU3d7Sp3dHt9YQJ8d/TYW0C4iIlWM3itERETKrtIEQDNLBsYD8cDpu+3FtONajd2vzWgPbHDOrc3Xb5c+ZhYPtGbPa0JERKSK0XuFiIhI+VSKAGhmsfhXaWsHnOycW5P/cefcAvwX+ffI9xxP4P4X+bp+AXQ1sxb52s4EEoAvw1O9iEj1lT13Lt5NmyIylt4rREREyq9SBEBgKHAq8AhQz8wOzXdLCPTpD1xlZveZWXdgFP4PAU/mO84H+L+9HWdmpwb2ehoCvFPcvk4iIlI62//4g0WXXMrSG/viy8qKxJBR+17x2muvkZKSUhFDV1oTJkzAzFi3rqDdP0REpKwqSwA8MfDzeeC33W6NAZxzo4E+QE/839Duj//0n392HMQ5l4t/D6el+PeBGgKMBa6NxB8hIlJdbPnyS5Ze0wvf1q1k/vknqx7sH4lhq+R7hZkVeevZsycXXnghCxYsCMfwYbdy5UouueQS2rdvT0xMDD179tyjz2uvvVbg354VmS8OREQkn9iKLgDAOdeyhP1GACOK6bMM/7LgIiISBhvefIvVjz8Ozr8lXkz9NOr2vDLs41bV94qVK1cGfx8/fjy9e/fepS0pKSl4q4qys7NJS0vj7rvvZvjw4YX2S05OZv78+bu0JSYmhru8MsnJySE+Pr6iy5Ao0PLuzyq6hEpj0ZOnVXQJElBZZgBFRKSSc86xZsAAVj/2WDD8xbdqRcvR75LYoUMFV1d5NWrUKHirXbv2Hm2pqal7nALav39/OnbsyOuvv07Lli2pUaMGV111FTk5OQwdOpT09HTq1avH7bffjs/nCz4vJyeHu+66i2bNmpGcnEzXrl356quvwvr3tWzZksGDB9OzZ0/q1q1baD8z2+XvbtSoUYmOP23aNA455BCSk5Pp0qULf/311y6Pjxs3jv3224+EhATS09N57LHHcIH/f+6o79lnn93lOccccwx9+/bdpU///v25+uqrqV27Npde6l9c9uGHH6ZFixYkJCTQqFEjrrjiihLVLCJSmSkAiohIsVxuLivvvpv1I0YG25I6daLFO28T36xpBVYWvRYtWsTHH3/M+PHjGTduHGPGjOHMM89k8uTJfP3114wcOZIXXniBDz/8MPicq666iokTJ/LOO+/wzz//cOWVV3LGGWcwbdq0Qsd5/PHHSUlJKfL2008/lfvvyczMpEWLFjRr1ozTTz+dv//+u0TPu+eee3jyySf566+/qFevHpdeemkw4P3555/06NGDc889lxkzZvDkk0/yxBNPMGTIkFLXN3DgQNq3b8+UKVN4/PHHGTt2LM8++yxDhw5l7ty5jB8/noMPPrjUxxURqWwqxSmgIiJSeXm3bWf5Lbew/Zdfgm0pxxxD0+cG4qmipy1WBV6vl1dffZXU1FQ6duzIySefzMSJE1m+fDnx8fF06NCBI444gh9++IHzzjuP+fPnM3r0aBYtWkTz5s0B6Nu3L99++y0vv/wyQ4cOLXCcPn36cMEFFxRZS9Om5Qv5e++9N6NGjaJTp05s3bqV559/niOOOIJp06bRrl27Ip/7yCOP0L17dwAeeOABjjzySJYvX06zZs0YOHAg3bp146GHHgJgr732Yu7cuTz11FPcdNNNpaqxW7du3HnnncH7n376KY0bN+bEE08kLi6O5s2b06VLl1L+5SIilY8CoIiIFCpv3TqWXteHrJkzg221e5xPowcfxGL1FhJOzZs3JzV15371DRs2ZK+99trl2rSGDRuyZo1/N4y//voL5xz77LPPLsfJzs7m2GOPLXScunXrFnnqZigcdthhHHbYYcH7hx9+OJ07d+aFF15g8ODBRT53//33D/7epEkTANasWUOzZs2YNWsWp52263VFRx55JA899BBbtmyhVq1aJa5x93DXo0cPnn/+eVq1asVJJ53EySefzJlnnklCQkIhRxARqRr07i0iIgXKWbyYJb2vJXfJkmBb2o03ktb3RsysAiurHuLi4na5b2YFtnm9XgB8Ph9mxuTJk/foV9QCM48//jiPP/54kbV88cUXHHXUUaUpv0gxMTF06dKFuXOL33Uj/9+y4/93+a97LMyOvh6PZ5drAgFyc3P36F+jRo1d7qenpzN79my+++47vv32W/r168dDDz3E77//vkdfEZGqRAFQRET2kDnjH5Zedx3eDRv8DR4PjR58kDoXFn2qoFScAw44AOccq1atCp4yWRKROAV0d845pk+fTqdOncp1nA4dOvBLvlOTAX7++WeaNWtGzZo1Aahfv/4uq65mZWXx33//ccABBxR7/MTERE477TROO+007r77bho1asQvv/zCiSeeWOxzRUQqKwVAKZbP5+P555/n5ZdfZtGiRdSvX58LLriAhx9+eI9vQT///HMeffRRpk2bRkJCAscddxxPP/00rVq1qqDqRaS0tv30E8tuuRWXkQGAJSTQ9LmB1CziNEKpeHvttReXXnopPXv2ZMCAARx44IFs2LCBCRMm0Lp1a84999wCnxeKU0CnTp0KwJYtW/B4PEydOpX4+Pjg6agPPfQQhx56KO3atWPLli0MHjyY6dOnM2zYsHKN269fP7p27Ur//v255JJLmDx5MgMGDNhlRvPYY49l1KhRnHnmmdSvX5/HHnuMvLy8Yo/92muvkZeXxyGHHEJKSgrvvfcecXFxxV6zKCJS2SkASrFuu+02Bg8ezDnnnEO/fv2YNWsWgwcP5u+//+bbb7/F4/EvJjtu3DjOP/98OnXqxDPPPMPmzZsZNGgQRxxxBFOmTAleuyEildemDz9i5f33Q+ADckxqKs2GDSP5wOJnS6Tivfrqqzz22GPceeedLFu2jLp163LwwQeXakawLHafTfv0009p0aIFixYtAmDTpk1ce+21rFq1itTUVA444AB+/PHHcq+qeeCBBzJmzBgefPBBHn/8cRo2bMjdd9+9yxYP99xzD4sWLeKss84iJSWF//u//2PFihXFHrt27do89dRT/O9//yM3N5d99tmHcePG6QtNEanybPfz4qszM3PAHtcKVGczZ85kv/3245xzzmHs2LHB9hdeeIGbb76Zt99+m0suuYTc3FxatmxJbGwsM2fODO5nNXXqVA466CCuueaaIjcIFpGK5Zxj/fARrH3uuWBbbJPGNB85koTWrcM27o7rtJxzVeaiQr1XiEhJaSP4ncqzEXxVfK+ozLQPoBRp9OjROOe49dZbd2nv3bs3ycnJvPXWWwBMnDiRFStW0KtXr102M+7cuTPHHHMM7733XoEX3YtIxXNeL6sfeXSX8Jew9960HP1uWMOfiIiIRJ5OAQ2hyvwtT1m/dZk8eTIej2eP03QSExPp3LkzkydPDvYDdlnme4dDDz2U77//njlz5rDvvvuWqQ4RCQ9fdjYr7riTrV9/HWxLPuQQmg15gZjAIhoiIiISPTQDKEVasWIFaWlpBe571LRpU9atW0dOTk7weoqCVorb0bZ8+fLwFisipeLdvJml1/TaJfzVOvUU0kcMV/gTERGJUpoBlCJlZGQUuultYmJisE9GYLXAgvrm7ycilUPuqlUs7d2b7Lnzgm11r7ySBnfdiXn03aCIiEi0UgAMofJc3FpZJScns2bNmgIfy8rKCvZJTk4GIDs7u8h+IlLxsufOZUnva8lbtSrY1uDOO6l39VUVWJWIiIhEgr7mlSI1adKEdevWFRjsli9fTlpaGvHx8cEtHgo6zXNHW6g3EhaR0suYMoVFl162M/zFxdHkmacV/kRERKoJBUApUteuXfH5fPzxxx+7tGdlZTF16lS6dOkS7Afw22+/7XGMSZMmUatWLfbaa6/wFywihdry9dcsufoafFu2AOBJTqb5yy+ResYZFVyZiIiIRIoCoBTpwgsvxMwYNGjQLu0jRowgIyODSy+9FIBu3brRuHFjRo4cybZt24L9pk2bxoQJE+jRowdxcXGRLF1E8tnw9tssv+VWXE4OADFpaTR/8w1qHH54BVcmIiIikaSN4PPR5r4Fu+mmmxgyZAjnnHMOp556KrNmzWLw4MEcccQRfP/993gCC0aMGTOGCy+8kE6dOtG7d2+2bNnCc889h5nx559/6hRQkQrgnGPtoOdZ//LLwbb4Fi1IHzmC+PT0CqzMrypu7qv3ChEpqcq8RVikaSP4ykMBMB+9qRfM6/UyaNAghg8fzqJFi0hLS+PCCy/k4Ycf3mXTd4Dx48fz6KOPMn36dBISEjjuuON46qmnaNOmTQVVL1J9udxcVj7wIJs//DDYlrj//qS/NIzYunUrsLKdquKbut4rRKSkFAB3UgCsPBQA89GbuohEC19GBstuvZXtP/4UbKvR7WiaPfccnkq0Im9VfFPXe4WIlJQC4E4KgJWHrgEUEYkyeRs2sPjKnruEv9TzziX9xRcrVfgTERGRyNM+gCIiUSRn6VKW9OpF7uIlwbZ61/eh/s03B79BFRERkepLAVBEJEpkzpzJ0uv64F23zt/g8dDogfupc9FFFVuYiIiIVBo6BVREJAps//VXllx+RTD8WUICzQY/H/7wN/tL2LSk+H4iIiJSKSgAiohUcZs/Hc+S6/rgy8gAwFOrFs1HvULN448P67jfTniQze9fAm+cBdvWhHUsERERCQ0FQBGRKmz9q6+x4o47IDcXgNhGjWj59lskH3RQWMd964s+3LZ4HDc0SCNj40L4rF9YxxMREZHQUAAUEamCnM/H6qefYc1TTwXbEtq1peW7o0lo1y6s4w776BKeWvMLANMTExjUtA2cNiBsY4qIiEjoaBEYEZEqxuXksOK++9jyyafBtqSDDiJ96IvEpKaGbVyfN49nxp7DW5mLgm2dXRx9e3wEKQ3CNq5IcbTX2k7l2WsN9N8yv/L+txSprBQARUSqEN/27Sy7+Ra2//JLsC3l+ONo+uyzeBITwzewN48PP+jBW1mLgk1HkMzACz4jOTktfOOKiIhISOkUUBGRKiJv/Xr/Bu/5wl/tCy+k2fPPhzf85WbBmCs5c9b3dMvIBODEmNq8cNF3Cn8iIiJVjGYARUSqgII2eE/r25e0G28I7wbv2Vvh3Utg4Y/EAc+uWcd77btx2XljiImND9+4IiIiEhYKgCIilVzBG7w/QJ2LLgzruBlblpH03uXY8r+CbYmH9eXKEx6BcIZOERERCRudAioiUokVvsF7eMPfmtX/cOkHp/JCxvydjcfeDwp/IiIiVZoCoBTL5/Px3HPP0b59exITE0lPT6dfv35s3759l37vv/8+V111FZ06dSIuLg4zY9GiRRVTtEgU2PzZZxWywfvSpb9xxWcXMy/GMaJ2Kq/VqgmnPgtH/0/hT0REpIpTAJRi3Xbbbdx+++3ss88+vPDCC/To0YPBgwdzxhln4PP5gv2GDh3Ku+++S1JSEm3atKnAikWqvg2vv86Kfv+L+Abvc+d9yZXf9GZ5jP9+jHOkHXgVHNw7rOOKiIhIZOgaQCnSzJkzeeGFFzj33HMZO3ZssL1Vq1bcfPPNvPvuu1xyySUAvPHGGzRp0oTY2Fj69u3L7NmzK6pskSrL+XysGTCADa+MCrbFt21D8xEjiGvcOKxjT5/5Htf/8QhbYvyzfAk+x7MdruaYQ28P67giIiISOZoBlCKNHj0a5xy33nrrLu29e/cmOTmZt956K9jWvHlzYmP1nYJIWbncXFbec88u4S/pwANp+dZbYQ9/k/58mV5/PMIWjz/81fA5hh1wu8KfiIhIlNGn9VDqn1rRFRSu/+YyPW3y5Ml4PB4OPvjgXdoTExPp3LkzkydPDkV1ItWeb/t2lt1yK9t//jnYlnLssTQdOCC8e/wB3/3yFHfMfZPcQPir7XO8dOhD7NvhvLCOKyIiIpGnGUAp0ooVK0hLSyMhIWGPx5o2bcq6devIycmpgMpEokdwg/d84a/2BRfQbHCYN3gHPv7+Hm6f+ya5gcVdGngdrx/zvMKfiIhIlNIMoBQpIyOjwPAH/lnAHX3i47UhtEhZFLjB+403ktb3xvBu8A5s+mUQTy36BF+M/7vA5l4YftIomjY9uJhnioiISFWlABhKZTzNsjJLTk5mzZo1BT6WlZUV7CMipZf1778sufa63TZ4v586F10U3oGdg4lPUXvCEwxOTKBPw/q0IJaXz3iXtPodwju2iIiIVCgFQClSkyZN+Pfff8nOzt5jJnD58uWkpaVp9k+kDLb/9hvL+t6EL7CfpsXH02TAs9Q64YTwDuzzwVf3wu/DAOiSlc1LvjTanf8mqanNwzu2iIiIVDhdAyhF6tq1Kz6fjz/++GOX9qysLKZOnUqXLl0qqDKRqmvzZ5+x5NrrguHPU6sWzV8dFfbwl5ebxfKPegXDHwBtjqXL5Z8r/ImIiFQTCoBSpAsvvBAzY9CgQbu0jxgxgoyMDC699NKKKUykitrwxhu7bvDesCEt3noz7Bu852Rt4X/vHsdlGyexdMd2LfucBRe/C/E1wjq2iIiIVB6V5hRQM2sL3AEcBuwL/OScO2a3PouAFrs9dbVzrtFu/fYBXggcaxMwEnjIOecNR+3RbL/99uPGG29kyJAhnHvuuZx66qnMmjWLwYMH061bt+Am8AA//vgjP/74IwBTpkwBYMiQIdSuXRuA++67L+L1i1QWzjnWDhjA+pGvBNvi27Sh+cjwb/CesW0NN489jd/JgtgYejdqwLv1jqb2WUPBExPWsUVERKRyqTQBEH/oOxWYBMQV0e8d/OFuh132IDCzOsC3wL/AWUAbYAD+2U4lkDIYNGgQLVu2ZPjw4Xz22WekpaVx00038fDDD+Px7JxE/v7773nooYd2ee6AAQOCvysASnXlcnNZed/9bP7442Bb0gEHkD5sKDGBL0jCZfOmRdzw4TlM9+QF205I3ZvUs4aBRyeBiIiIVDeVKQB+6pz7GMDMPgDSCum30jk3qYjj9AGSgHOdc1uAb8ysFtDfzJ4OtEkpxMTE0K9fP/r161dkv/79+9O/f//IFCVSRfi2b2fZrbex/aefgm2R2uB97ZqZXPvZJczz+IJtN9c5gF6nv4Yp/ImIiFRLleYTgHPOV3yvEjkF+Gq3oPcu/lDYLURjiIgUK2/DBhb3vGqX8Fe7x/kR2eB92bLfuGL8RbuEv/9reAy9z3yjSoc/M2trZi+b2XQz85rZhAL6LDIzt9ttVQH99jGz78wsw8xWmNnDZqZzYkVEJKpVxU8B15hZjpltNrMPzGz3awLbA//lb3DOLQEyAo/tYccHhPCUKyLVUc6yZSy++BKyZswItqXdcAONHn4Yiw3vyRfz5n/NFV/3ZlkgysQ4xxMtzuaik18o+olVw47LBWYDc4ro9w7+68B33E7N/2C+ywUc/ssFHgb6Abuexy4iIhJlKtMpoCXxMf5rBJcBHYAHgZ/MbD/n3I5d2OvgX/hldxsDj4mIhFXWv/+y5Lrr8K4NbPBuRqMHHwj/Bu/AjJljuP6Ph9gcYwAk+BwDOlxFt0OLPoW7CtHlAiIiIuVQpWYAnXO3OOdGO+d+cs4NB04CmgBXlfO45pyzkBQpItXa9kmTWHz5FcHwZ/HxNH1+UETCHwsm8umEe9ns8f9zVsPnGNb5tmgKf7pcQEREpJyqVADcnXPuH/ynAR2Yr3kjkFpA9zqBx0REwmLL55+zpPe1u27wPuoVap14YvgH/+8zeLsHd61dwwnbM6jtc7xy6EN0PeCa8I9dOelyARERkQJUtVNAC+ICtx3+Y7c3bzNLB5LZ7c1eRCRUNrzxJqufeAKc/5+j2IYNSR8xnMS99gr/4FNHw8c3gvMSAzyZGc/qU4aQ3uqY8I9dOelyARERkUJU6QBoZh3xh73h+Zq/AO4ws5rOua2BtguBTGBihEsUkSjnnGPtwIGsHzEy2BbfurV/g/cmTcI+/q/f/x+H/jhk5+kcdVoRf8XHpNfZfcKr+nDO3ZLv7k9m9iswFf/lAoPKcVwD/0xgeeoTERGpSJUmAJpZMjtXaWsK1DKz8wP3Pwe6A5cB44EV+IPffcAS4LV8h3oJuBkYZ2ZPAa2B/sBAXdQvIqFU4AbvnTvTbNhQYuuEdxLJ+Xy89MnlDN08ncvq1ubODZuwBvvC5R9CzYZhHbuqcc79Y2a6XEBERIRKFACBBsCY3dp23G8FLA30GQTUBtYDXwL35g92zrmNZnYcMAT4FP8pPs/hD4EiIiHhy8hg2a23sv3HfBu8d+/u3+A9KSm8Y3vzeGbcubyVsRCAt1JrsVeNZpxzyXhI0tmLhdDlAiIiIlSiAOicWwQUtxLncSU81r/AseWtSUSkIHkbNrD0uj677PGXev55NO7fP+x7/OXlZtF/zOl8nLs62HYYSZx08ccKf4XQ5QIiIiI7VZoAKCJSFeQsW8bSa3qRs3hxsC3thutJu+kmzMK7m0xO9lbuev8UvvVtDrad4EnlyQu+ID6hZljHrix0uYCIiEj5KACKiJRQ1qxZLLn22l03eH/gfupcfHHYx87YtoZbx57Ob2QG286Jb8gD548nNi4x7ONXIrpcQEREpBwUAEVESmD7pEksu7FvcI8/i4+nybPPRGSPv82bFnPDh+cw3ZMbbLsiuTX/O+9DzFOlt3MtNV0uICIiUj7V65ODlNoTTzxBjx49aN26NWZGy5Yti+z/+++/c/zxx1OzZk1q1arFySefzNSpUyNSq0i4bPniC5bm3+C9Zk3SR46ISPhbt/ZfrvrwzF3C3021O1fL8CciIiLlpxlAKdK9995L3bp1OfDAA9m0aVORfSdNmsQxxxxD06ZNefjhhwEYMmQIRx11FL/++iv77bdfBCoWCa0Nb77F6scf37nBe4MGpI8YQeLeEdjgfeMiNr17Matr5AIxANzbsBsXnzwk/GOLiIhIVFIAlCLNnz+f1q1bA9CxY0e2bdtWaN+bb76Z+Ph4fvzxR5o2bQrABRdcQIcOHejXrx9ff/11RGoWCQX/Bu/PsX7EiGBbfOvWNB8xnLjA/7/Das0seONs2m5bxbBt8fRp2IB7Wp3DGd0fDf/YIiIiErV0/pAUaUf4K868efOYPHkyPXr0CIY/gKZNm9KjRw++/fZbVq1aFa4yRULK5eay8p57dwl/SZ060eLttyIT/pb/Ca+eAtv8r5n984wvD35Y4U9ERETKTTOAIbTf65X3FMcZV84ovlM5TJ48GYDDDjtsj8cOPfRQRo0axZ9//slpp50W1jpEyqvADd6POYamzw0M+wbvAH/8PZKUbx9mn8zAVg/xKXDxaGq1OjrsY4uIiEj0UwCUkFixYgXALrN/O+xoW758eURrEimtvI0bWdqnD1nTpgfbUs87l8YPPRT2Dd4Bvv/1Ge6Y8zo16qXwWs52WsfWhMvGQtODwj62iIiIVA8KgBISGRkZACQkJOzxWGJi4i59RCqj3OXLWdKrNzkLFwbb6l3fh/o33xz2Dd4BPv3h/7h/8cd4zciJieH+ho1464wPsIYdwj62iIiIVB8KgCEU7tMsK7Pk5GQAsrOz93gsKytrlz4ilU3WnDks7dWbvDVr/A1mNLzv/6h76aURGf/tL67nyTU/QyBopnvhqZNfUfgTERGRkFMAlJBo0qQJUPBpnjvaCjo9VKSiZfz5J0uvvwHfli0AWFwcTZ55mlonnxz2sZ3Px0ufXMbQzTu/PGrn8/Dy6e9Qv8G+YR9fREREqh+tAioh0bVrVwB+++23PR6bNGkSZsZBB+k6Jqlctn7/PUuuviYY/jw1apA+YnhEwp/Pm8dTH5y1S/jr5Ivj1XM/VfgTERGRsFEAlJBo27YtXbp0YcyYMcEFYcC/OMyYMWM49thjadSoUQVWKLKrjWPGsKzvTbjAacsxaWm0ePMNahx6aNjHzs3N4L53T+DtzEXBtsNJYviFX5Oa2jzs44uIiEj1pVNApUhvvvkmixcvBmDt2rXk5OTw6KP+vchatGjB5ZdfHuz7/PPP0717d4466ihuuukmAF544QV8Ph8DBgyIfPEiBXDOsf7l4awdNCjYFte8Oc1HjiC+efjDV272Vm5/7yQmuK3BthNjavNkjy+JS6gR9vFFRESkelMAlCK98sorTJw4cZe2+++/H4Bu3brtEgAPP/xwJkyYwH333cd9992HmXH44YczZswYOnXqFNG6RQrifD5WP/4EG996K9iWsE8Hmg8fTmxaWvgLyNpC7OiLaLRtBdSqCcB58Y25v8d4YmLjwz++iIiIVHsKgFKkCRMmlKr/YYcdxnfffReeYkTKwZeTw4q77mLrF18G25IPPZRmQ14gJiUl/AVsXwdvnYutnMY9wBaPh8aNDuSWc97HPDobX0RERCJDAVBEop5323aW3dSXjN8mBdtqnnIyTZ56Ck98BGbeNi2FN8+B9XMB/8XXj+/fl5ijbg//2CIiIiL56GtnEYlqeevWseSKK3YJf3UuvZSmAwZEJPwtXDSBp949BW8g/GEeOGOwwp+IiIhUCM0AikjUylm6lCXX9CJ3yZJgW/1bb6HedddhgU3Xw+nf/z6iz2/3sTHJyK1Xh//buA07byTse3bYxxYREREpiGYARSQqZc2axaKLL9kZ/jweGj3yMGl9+kQk/E2eOoqrf7uPjR7/WJ+kpLDk3CEKfyIiIlKhNAMoIlFn+6TfWXbjjfi2bwfAEhJoOnAANY87LiLjT5g0gP/NepXsQPir5XO82PVeWnS8KCLji4iIiBRGAVBEosqWL79ixR134HJzAfDUrEn6sKEkd+kSkfE//eE+7l/8Ed5A+EvzOl4++ln2antyRMYXERERKYoCoIhEjY2jR7Pq4UfAOQBiGzQgfcQIEvfeKyLjv/3FDTy55icInGLazAvDTxhBevphERlfREREpDgKgCJS5TnnWPfCENYNHRpsi2/ViuYjRxDXtGn4x/f5eOmTyxi6eUawra3Pw/DT36F+g33DPr6IiIhISSkAikiV5rxeVj30MJvefz/Ylrj//qS//BKxdeqEvwCfj+HjejB0+5xg0/6+OIae8yGptVuEf3wRERGRUtAqoCJSZfmys1l+6627hL8aRx5Ji1dHRSb8eXPho+s5cfYE6nq9ABxOEiMu/FrhT0RERColBUARqZK8W7aw9JpebP3m22BbrTPPIH3YUDw1aoS/gNxMeO9ymP4urXLzGLZqDWfH1OOFi74jOTkt/OOLiIiIlIFOARWRKid39RqW9u5N9pydp13W7dmTBnfegXnC/72Wy9yMvXsJLP452LZPx0t45PTnwBMT9vFFREREykozgCJSpWQvXMjiSy7ZJfw1uOMOGt59V0TC34YN87jq3WOYuuqPnY1H3ApnPK/wJyIiIpWeAqAU6YknnqBHjx60bt0aM6Nly5aF9v3mm2/o06cPXbt2JTExETNjwoQJEatVol/mjBksvuRScpcv9zfExND4ySeod83VERl/1cq/ufKjc/nTk8eNDeszJy4Ojn8ITngouPWDiIiISGWmAChFuvfee/n+++9p06YNdYpZVOPtt99m1KhReL1eOnToEKEKpbrY9vMvLL6yJ96NGwGwxETSh75I7bPPjsj4CxdN4PIvLmdRjH+PwW0eD7MPuRqOvDUi44uIiIiEggKgFGn+/PmsX7+eb775hiZNmhTZ97HHHmPr1q389ddfXHrppRGqUKqDzZ+OZ2mfPriMDABiUlNp8dqrpHTrFpHx//3vI3r+0JdVMf5ZvljneKbNRZxx/NMRGV9EREQkVLQIjBSpdevWJe7bNAIbbkv1s+H111n9xJPB+7GNG9N85AgS2rSJyPiTp47ipr8Hst3jD39JPsegjtdzeNcbIzK+iIiISCgpAIbQrPaV97THDv/NqugSRErFOcfagQNZP2JksC2+bRuajxxJXKNGEalh4qSB9Js1iuxA+Kvlc7zY9V46d7wkIuOLiIiIhJoCoIhUOi4vj5X3P8DmDz8MtiUdeCDpQ18kpnbtiNQwfsL93LfoQ7yB8Fff63j56Gdp1/bkiIwvIiIiEg4KgCJSqfgyM1l+2+1sy7eCbEr37jQdOABPUlJEapg54WHuWfxRcGXPZl4YfsII0tMPi8j4IiIiIuGiABhCOs1SpHy8mzaxtM/1ZE6dGmxLPe9cGj/0EBYbgX+unIOJT7PPhAFcWbc2r6fWoq3Pw/DT36F+g33DP76IiIhImJXpE5WZHQS0BdYBPzrnckNalYhUO7krV7KkV29y5s8PttW79lrq33YrFok99nw++Ooe+P0lDOi3YRN1U5pw3rmjSa3dMvzji4iIiERAqQKgmSUCHwIn5mteaGYnOefmF/I0EZEiZc+bx5JevclbtSrY1vDee6l7xeURGT8vNxPfJzcTP+P9YJu1OZarL3wL4mtEpAYRERGRSCjtDOCdwEnAVOA7YC/gDGAEcGxIKxORaiHj779Z2ud6fJs3+xvi4mjy5BOknnZaRMbPztrM/94/Bc+21Qwg8I/iPmfDucMhNiEiNYiIiIhESmkDYA9gEnCUc84LYGaPAveYWX3n3NpQFygV680332Tx4sUArF27lpycHB599FEAWrRoweWX75yhmT59Op988gkAv/zyS/D5P//8MwA33XQTqampkSxfKrmtEyaw/NbbcFlZAHiSk2n6wmBSjjgiIuNv27qSm8eewWTLhhrJ9E+ry8Ppp+M5YxB4YiJSg4iIiEgklTYAtgbu3RH+At4C7sV/TaACYJR55ZVXmDhx4i5t999/PwDdunXbJQD+9ddfwcd2GDVqVPD3yy67TAFQgjaN+5CV998PXv8/JzF165L+8ssk7dcxIuNv2DCP6z8+n389O/85q9v4QOyM58HjiUgNIiIiIpFW2gCYxJ4hb8f9xPKXI5XNhHxL8RenZ8+e9OzZM2y1SHRwzrF+5EjWDhgYbItr2pTmr4wkvmXLiNSwauXfXPvFlSyMccG2W+t24ZozXo3I+CIiIiIVJZRfc5drmT4za2tmL5vZdDPzmtmEAvqYmd1rZkvNLNPMfjSzzgX028fMvjOzDDNbYWYPm5nO5xKpYM7nY82TT+0S/hLat6fF6HciFv4WLprA5V9cHgx/5hwPNjlB4U9ERESqhbJsA3GFmR2a734i4IC+Znb2bn2dc+6WEh53X+BU/NcYxhXS527gfuAO4D/gduBbM+vonFsFYGZ1gG+Bf4GzgDbAAPxh974S1iIiIeZyclhx7/+xZfz4YFty1640G/oiMTVrRqSGmf+N4/rfHmBjjP/7qljneLLNhZx01P3FPFNEREQkOpQlAJ7IrttA7HB2AW0OKGkA/NQ59zGAmX0ApOV/MLAFxd3AE865IYG234BFQF92hrs++E9VPdc5twX4xsxqAf3N7OlAm4hEkG/7dpbdfAvbA4sDAdQ84QSaPPsMnoTIrLT5x98juWnqIDI8/vCX5HMM6ng9h3e9MSLji4iIiFQGpQ2ArcJSBeCc8xXT5XCgFhDcqMs5t93MPgVOYWcAPAX4areg9y7wFNAN+DRkRYtIsfI2bGDpdX3ImjEj2Fb7ogtpdP/9WExkzsz2zvyIJ6Y8S0a8/+SCWj7H0K730anjRREZX0RERKSyKFUAdM4tDlchJdAe8AJzd2ufBVy4W7/v83dwzi0xs4zAY3sEQDNzu7eJSPnlLFvO0muuIWfxzn860vr2Je3GGzAr12XDJffXG8R8eguDY4wrGjfCYx5e7jaQtm0KOpFBREREJLqVahEYM2tgZvHhKqYYdYBtu21BAbARSM5XVx1gUwHP3xh4TEQiIGv2bBZffPHO8GdGo/4PUr/vjZELfz8Pgk9uAucjPc/L8MwE3jhplMJfFaYFw0RERMqntKuArgTO33HHzJLM7HEzax3asiLLOWfOuQh9IhWJfhmTJ7P4ssvJW+vfJcbi4mg6aBB1LorMKZfO52POZzfDtw/ubGzciXY9v6Zp04MjUoOEzY4Fw2YDcwrps2PBsKeAM4Bt+BcMa7SjQ74Fwxz+BcMeBvoBD4WtchERkUqgtAFw95CUDNwFtAxJNUXbCKQU8O1sHSDDOZeTr19Bu43XCTwmImG05ZtvWHJNL3xbtwLgSUkhfeRIap0UmVm3vNws7n/3RC5e8z2TEwMLzLQ8Cq4cDyn1I1KDhNWnzrl051wPYObuD+6+YJhz7lugB4HVqvN1zb9g2DfOuZfwh7/bAwuHiYiIRKVQ7AMYqZmz/4AYoO1u7e0Dj+Xv1z5/BzNLxx9W8/cTkRDb+N77LL/lVlyO//uYmPpptHjrTWocEplZt6zMjdw2+lg+zl1Njse4qWF9Fu11Alz6ASTqM300KOuCYfiv/z4lX7/CFgxLwr9gmIiISFQK5Ubw4fYrsAX/N7kAmFky/tN7vsjX7wvgJDPLv7HYhUAmMDECdYpUO8451g4dyqoHHwSf//N5XIvmtBw9msT27Yt5dmhs3bKc6987gQlua7DtxMTGNOvxFsQlRqQGqRSKWjCs/W79dvlS0Dm3BMjYrV+QmTktGiYiIlVdWfYBDItAmDs1cLcpUMvMdlxv+LlzLsPMngTuN7ON7NwI3gO8kO9QLwE3A+PM7CmgNdAfGKg9AEVCz3m9rH7sMTa+MzrYlrjvvqQPf5nYevUiUsO6df9x/acX8p9n5+TQVSntuO2cDzBPVfqeS0Kg2AXDApcMaMEwERGplsoSALuYWVbg9x2zbEeaWe2COjvnxpXwuA2AMbu17bjfCv+G70/iD3z3APWAKcAJzrnV+cbbaGbHAUPwn/KzCXgOfwgUkRDy5eSw4o472frVV8G2GocfRtPBLxCTUiMiNSxbNonrvu7NknxXB/erdwg9Tx8ZkfGl+tixWJhmAUVEpCorSwC8JXDLrz/+C+zzs0BbiZbUds4topjrCZ1zDngscCuq37/AsSUZV0TKxrttG8tu7EvG778H22qdeipNnnwCi4/MbjFz533JdT/+j7Ux/n86PM7RP/1Uzjnu6YiML5VScMGw3WYBtWCYiIgIpQ+AV4WlChGpUvLWrmXJtdeRPWtWsK3O5ZfT8J67I3bK5dQZb3PDlCfYGgh/8c7x9F6Xc9zhd0VkfKm08i8YNjtfuxYMExERoZQB0Dn3ergKEZGqIWfxYpb06k3u0qXBtvq330693r0it8H73G/497v/Y2sd/1noNXyOFzrfRtcDronM+FKZ5V8w7FHYZcGw4fn6fQHcYWY1nQuuHKQFw0REJOpVmkVgRKTyy5w5k6XXXod3/Xp/Q0wMjR9+iNrnnRe5IqaPgY/6cIkvj/X4+CC1Fi8d/hgd9j4rcjVIhdGCYSIiIuVT6gBoZo3xX463KnA/EbihgK5LnXO7L+oiIlXU9t9+Y9mNffFlZABgCQk0fe45ah7bPXJF/P4yfHFn8G5fl8rFJ75KWtMukatBKpoWDBMRESmHUgVAM9sb+Ae4D3gq0FwDeBb/gi/5z//KM7Opzrnd92ISkSpmyxdfsPzOuyA3FwBPrVqkvzSM5AMPjMj4zudj3Ge9OfWvsSTtaKzfAbt8HGm1mkSkBqkctGCYiIhI+ZR2tYargA34vyXd3f+A7oHbccBW4OpyVSciFW7DW2+z/PZ+wfAX27AhLd9+K2Lhz5uXw2NjTqP/hj/o1yCNXIBmB8NVn4PCn4iIiEiplPYU0GOBT/Ito53fNOdc8MJ5M3sPfxAUkSrIOcfawYNZP+ylYFt869Y0HzmCuCaRCV652du5d8wpfOn1r8r/U3IS77Q+kCsv+gjiI7PPoIiIiEg0Ke0MYDtgagn7/od/GW4RqWJcXh6rHnhgl/CX2Gl/Wrz9VsTCX0bGOvq+2z0Y/gBOianLJReOV/gTERERKaPSzgDWALbt1rYR2A9YuFv7lkB/EalCfFlZLP/f/9j27XfBthpHH0WzQYPwJCdHpIZNGxdy40fnMt2TF2y7OLE5d5//MZ4YLV4sIiIiUlal/SS1CWicv8E55wNmFtC3EbC5bGWJSEXwbtnCshtuJGPKlGBb6lln0vjRR7G4uIjUsGrVVK774goWeFyw7YbU/elz5psR22ReREREJFqV9tPUDODEEvY9MdBfRKqA3NVrWHzZ5buEv7pXX03jJ56IWPhbtGgiV3x+WTD8mXP8X8NjuP7stxX+REREREKgtJ+oxgLdzOzMojqZ2dlAN+CDMtYlIhGUvXAhiy+5hOw5c4JtDe64g4Z33hGx4DX7vw+54ocbWRnjX+E/1jmeanU+F538QjHPFBEREZGSKu0nu1eA2cD7ZvawmbXI/6CZtTCzR4B3gVnAqNCUKSLhkjnjHxZfcim5y5f7G2JiaPzkE9S7JoK7uMz7joYf9qV2nv+avySf44V9ruOUbv0jV4OIiIhINVCqAOicywZOx7/gy33AAjPbaGaLzWwjsAD4v8DP0wP9RaSS2vbLLyy58kq8G/0rbVpiIulDX6T22WdHrojpY+CdC6idvY2XV61hr1wvI7rex5EH3xS5GkRERESqiVIvp+ecW2BmBwC9gfOBffEvDLMF+An/aZ8jnXNZoSxUREJr82efseLue4IbvMekppL+8kskde4cuSJ+Gwpf3RO827hGY8acORZPg/aRq0FERESkGinTeuqBcPdC4CYiVcyGN95k9eOPB+/HNm5M85EjSGjTJiLjO5+PwR/2YO8Fv3Lyjsb6HeCysXhSm0akBhEREZHqSBtqiVQjzjnWDnqe9S+/HGyLb9OG5iNHENe4cRHPDJ283Cz6jzmdj3NXE1u/HqleL4fVPwAueReS6kSkBhEREZHqSgFQpJpweXms7N+fzR+MDbYlde5M+kvDiKldOyI1ZGZs4I4PTmOi2wZAnhkfNdmLwy76EOIjs8m8iIiISHWmAChSDfiyslje739s++67YFtKt240HfQcnqSkiNSwedMibvzwXKZ5coNt58Q35IHzx0NcYkRqEBEREanuFABFopx382aW3nAjmX/+GWxLPftsGj/ycMQ2eF+18m+u++JKFsS4YFvvmu256ez3tMG7iIiISAQpAIpEsdzVa1jaqxfZc+cG2+r1uob6/fphZhGpYcHC77j2h1tYHbNzvLsbHMmlpwyLyPgiIiIispMCoEiUyl64kKXX9CJ3xYpgW4O77qLeVT0jVsPUf96h7+TH2RwIf7HO8Xir87XBu4iIiEgFUQAUiUKZM2aw9Nrrghu8ExtLk8cfI/XMMyNWw4aZY7lu8mNkBE7xTPY5Bu3fl8MO6hOxGkRERERkV7r4RiTKbPv5FxZf2TMY/iwpifShL0Y0/DH1Hep+0Jt+GzYBUNfnGHXoQwp/IiIiIhVMM4AiUWTzp+NZcc89kJcHQEzt2qS//BJJnTpFpgDn4Jfn4dsHAbhg6zZyk+ty5KnDaNHiqMjUICIiIiKFUgAUiRIb3niD1Y8/Ebwf27gxzV8ZSULr1hEZ3+fNY/vX91Lz952bzNNwPy697AOo2SgiNYiIiIhI0RQARao45xxrnxvE+uHDg20J7dqSPmIEcY0iE7xyczK4f8xpLNq+nFfMqOEctDgSLn4HElMjUoOIiIiIFE8BUKQKc3l5rHzwQTaPHRdsSzrgANKHDSWmdu2I1LB92ypuH3smv5IJCQnc1jCNF2sfTNx5o7TBu4iIiEglowAoUkX5MjNZfns/tv3wQ7At5ZhjaPrcQDxJSRGpYd3aWdww/mJmebzBtma12+I5/zWIjY9IDSIiIiJScgqAIlWQd/Nmll5/A5l//RVsSz33XBo//BAWG5mX9cJFE7j++5tYHrOz7YbU/ehz5luYRwsMi4iIiFRGCoAiVUzu6tUs7dWb7Llzg231evei/u23Y2YRqWH3Dd5jnOOBZidz7vHPRmR8ERERESkbBUCRKiR7wQKW9OpF3oqVwbYGd99FvZ49I1bD978+zZ2z3yDb4w9/ST7Hs/v05uhDbolYDSIiIiJSNgqAIlVE5vTpLL32OrybNvkbYmNp8sTjpJ5xRsRqeP+rm3ls5ff4AuGvrs/x4iEP0nGfHhGrQURERETKTgFQpArY9tPPLLv5ZlxmJgCWnEyz558n5agjI1OAc3i/e5jPF36BL8m/sme6F146/iWaN49QDSIiIiJSbgqAIpXc5k8/ZcU990JeHgAxtWuTPvxlkvbfPzIFeHPhk5uJmfYOz3uMKxs3JDE2mRfPfI96aXtFpgYRERERCQkFQJFKbMPrr7P6iSeD92ObNKb5yFdIaN0qMgVkb4X3r4T53wGQ6nMMT+pAyjkjSK5RPzI1iIiIiEjIKACKVELOOdYOHMj6ESODbQnt2pE+cgRxDRtGpIZ1a//j14+u5Mzl/+1sPOByGpw+CGL0T4eIiIhIVaRPcSKVjMvLY+UDD7J53LhgW9KBB5I+bCgxqakRqWHRoon0+b4vy+PBUyOZ07dnQLe74Zi7IUJbTYiIiIhI6Gm3ZpFKxJeZybK+N+0S/lKOPZbmo16JWPib9s9oLv/hxuAG7w+l1WXdyU9A93sU/kRERESqOM0AilQS3k2bWHrDjWT+9VewLfW8c2n80ENYbGReqt/98iR3z3mLrF32+OtF2iE3RGR8EREREQkvBUCRSiB31SqW9u5N9tx5wbZ6115L/dtuxSIx6+Ycb37Rh2fW/IILhL86PseLBz/Ifvtqjz8RERGRaKEAKFLBshcsYMk1vchbuTLY1vDee6h7xRURGd+bl8Mz487l7czFwVM8070w7LhhtGhxVERqEBEREZHIUAAUqUCZ06ax9Lo+eDdt8jfExdHkiSdIPf20iIyfkbGOuz44kwlua7Ctky+OwWe/T926bSNSg4iIiIhEjgKgSAXZ9uOPLLvlVlxmJgCWnEyzwYNJOfKIiIzvtqzipg9O4Y+YvGDbiTG1eeyCT0hMqhORGkREREQksqrUKqBm1tPMXAG3Pvn6mJnda2ZLzSzTzH40s84VWLbIHjZ9+BFLr78hGP5i6tShxeuvRSz8sWYW9soJXLl2BR7nALgqpR3PXPyDwp+IiIhIFKuqM4DHApn57i/I9/vdwP3AHcB/wO3At2bW0Tm3KnIliuzJOcf6ESNZO3BgsC2uSRPSR44koXWryBSxYCK8dzlkb+Zo4P/Wb4KO53HBSc9HZnwRERERqTBVNQBOds5t273RzBLxB8AnnHNDAm2/AYuAvsB9kSxSJD/n87H68SfY+NZbwbaE9u1JH/4ycQ0aRKSGbX++Sspn/wNf4LTPuBpccMYo2OvEiIwvIiIiIhWrSp0CWgKHA7WA93c0OOe2A58Cp1RUUSK+nByW9+u3S/hLPuQQWrz5RkTCn/P5GPbRJZw79WnWmP+UT2o2hqu/UPgTERERqUaqagCcb2Z5ZjbbzK7L194e8AJzd+s/K/BYgXZcSxiGOkXwbt3K0l692frFl8G2mqecTPqI4cTUrBn28XOzt3Pf6OMZunkGK2Nj6duwPhkN9oFe30LjTmEfX0REREQqj6p2CuhK/Nf3/QHEABcBL5lZsnPuOaAOsM05593teRuBZDOLd87lRLRiqdZyV69h6bXXkj17drCtzuWX0/CeuzFP+L9/2bhhPrd9cgF/2s7/29dOSMV38Tj/DKCIiIiIVCtVKgA6574CvsrX9EXgur/7zKzMK1g45wz8M4HlLFEkKHvBQpb26kXuihXBtvr9bqder15YYMP1cFqw8Dtu/OFWlsXsbDs3vhH3nf8xcXHJYR9fRERERCqfKhUAC/EBcAHQEv9MX4qZxew2C1gHyNDsn0TKHhu8x8TQ+NFHqX3O2REZ/9fJL/K/f4axNcYfNM05bq7XhWtOGxWRmUcRERERqZyi4ZOgy/fzP/ynhrbdrU/7wGMiYbd1wgQWX9kzGP4sKYn0YUMjFv5Gf9mXG2YOY6vHH/6SfI7n9rqcXme8pvAn1YL2jBURESlcNMwAng+sAxbjv0ZwC9ADeBTAzJKBM4DhFVWgVB+bxo5j5QMPgNc/AR1Tpw7pL79E0v77h31sb242T407l9FZSyBwimlDr+OFIx6jw95nhX18kUpIe8aKiIjspkoFQDMbi38BmOn4Z/ouDNxuds75gCwzexK438w2svNN3QO8UDFVS3XgnGP9y8NZO2hQsC2uaVPSR44goVUENnjP3IRnTE+2b/kXaqYA0NEXw+DT36J+g47hH1+kctKesSIiIrupUgEQmA1cDaQDBvwLXOGcezNfnyfxB757gHrAFOAE59zqCNcq1YTzeln92ONsfOedYFtChw40H/4ysfXrh7+ADQvgnQuxdXN4EFgWF0v9Go15pMdHJCXXDf/4IlVPgXvGmtmOPWMVAEVEJGpVqQDonLsXuLeYPg54LHATCStfdjYr7riTrV9/HWxLPvRQmg15gZiUlPAXsOgXeO8yyNwAQDwwtO1lJB3zf3hiqtTLWyQc5ptZPWA+MNA593Kgvag9Yy8s7GBaKVpERKKBPiGKlJF3yxaW3diXjMmTg221Tj2Vxk8+gSc+PryDO8f7X9/C9Hmf8UjmBgwgJgHOepEa+/cI79gilZ/2jBURESmEAqBIGeSuXs3S3teSPWdOsK3ulVfQ4K67wr7SZm72Nh4fdx4f5KyAlGTaZtekpzcRLnoH0g8O69giVYH2jBURESmc1oQXKaXs+fNZdPHFu4S/BnfcQYO77w57+Fu3dhZXjz7aH/4CvqqTRu413yj8iRTtA6Auu+0Zu1sf7RkrIiJRTwFQpBQypkxh0SWXkrdipb8hNpYmTz1JvWuuxgJbL4TLP/+O4cJPezDVcoNtp8bWY9SF3xNXNwIrjYpUbdozVkREBAVAkRLb8uWXLLn6GnybNwNgycmkDxtG6lnh32Pvk+/v5co/HmJNjD9kepyjX71DePLi77XSp0jJ5N8z9ld27hkL7LJn7BcVUp2IiEiE6BpAkRJY/9prrHnqaXD+SYSYtDTSX3qJpI77hnXcvNwsBnx0AW9lLAxu7l7T53imYx+O6No3rGOLVFXaM1ZERKRwCoAiRXBeL6ufeoqNb+zcajK+VSvSRwwnvlmzsI69cf087hh/Mb+TFWxr4zUGHz+M5s2PDOvYIlWc9owVEREphAKgSCF8WVmsuPOuXfb4SzrwQJq9OITYOnXCO/jSP7AxV7IkFYj1v0yP9dTi8fPGUiOlUXjHFqnitGesiIhI4XQNoEgB8jZuZMnV1+wS/mqeeCLNR70S3vDnHPz+Mrx6CrW3rGDg6nUk+HxcX6sjz10yUeFPRERERMpFM4Aiu8lZtoylva8lZ+HCYFsk9vjLy9xE7Ge3wz9jg20dPcl83rU/DTpqc3cRERERKT8FQJF8Mmf8w9I+ffCuX+9vMKPBXXdSr2fPsI67YOH39PvhVvquW8NxOxobd4YL3qBBnRZhHVtEREREqg8FQJGAbRMnsuy223EZGQBYfDxNnn6KWiefHNZxv/rpER6Y9x4ZMcZ99evRbvkqmne6DE5+CuISwzq2iIiIiFQvCoAiwMb332fVQw+D1wuAJzWV9BeHkNylS9jGzM7azDMfX8x7WUvB49/iIRdj3pF9aX7MQ2EbV0RERESqLwVAqdacz8fa5wez/uWXg21xTZqQPmI4CW3ahG3cBQu/544JtzHH4wu2pXvhuSOfZO+9Tg/buCIiIiJSvSkASrXly8pixT33sPWLL4NtifvsQ7OXhhHXoEFYxnQ+Hx//cA+PL/mMzMCsH8DxnlQeOns0tVLTwzKuiIiIiAgoAEo1lbd+PctuuJHMadOCbTWOPoqmA58jJqVGWMbcvm0Vj358MePz1gVP+Yx3jjsbH8sFJwwK6wqjIiIiIiKgACjVUPa8eSy9rg+5y5cH2+pceikN77kbiw3PS8It/5trv7yC6fkO38prPHPUk+zd7rSwjCkiIiIisjtNOUi1sv3XX1l00cU7w5/HQ8N776XR/feFJ/z5vPDTAOyVE7hm/dpg89lxDXn3ou8V/kREREQkojQDKNXG7it9WnIyTQc8S83u3cMz4IYF8GEfWPo7AMdm5NJrayZtOl7M6cc8Ep4xRURERESKoAAoUc/5fKwZMIANr4wKtsU2bEj6sKEk7rNPWMYb990dpE8bw8HbNu98oGkXbjl3ONQL3+qiIiIiIiJFUQCUqObbvp0Vd9/D1m++CbYl7NOB9GHDiGvYMOTjrVv3Hw99fjUT3FYa1anB2Iwt1CIGut0FR94OMXrJiYiIiEjF0adRiVo5y5az7MYbyZ49O9iW0r07TZ99Bk+N0K706Xw+vvzpYZ5Y8AEbAyt8roqNZWSjltx+2ihoemBIxxMRERERKQsFQIlKGZMns+z/27vv8Diqc4/j33dVbMtVlnHDgEMzGEICgQQTbkxLQgnX1JiECzgFcJxcIMkNFxKKaCEkFFPDTS6h905C6MT0fk2zMQYDBmPLVXKRZKzy3j/OSF6vV9qVtKuVdn+f55lH3jNnZt6Zs+Pdd2fOnJNOpqm6urVs6HHHMvzUU7Giooxua/Hitzn/sanM8NWtwzsAHN1vLNO+fxP0K8/o9kREREREOksJoOSd6jvvouq886CxMRSUlDCq8myGHH54RrfT0tfvkgWPsTou8RvR5Jy70zT22HVaRrcnIiIiItJVSgAlb3hDA4svvJDq225vLSuqqGDMlVdQtktmb8H87LOXOOfpk3iFtRtc9ZvcZwynHPQ3BgwcldHtiYiIiIhkghJAyQuN1dV8fvIp1L36amtZ3/HjGXP1VZSMymAy1vgFDc9P50fzbmRx8fpbSTdvgspdfsluX/1x5rYlIiIiIpJhSgCl16t/5x0WnHwyjQsXtZYNOvAARl1wAbF+/TK3oQ+fgn/+hpIV85g6sD/nDKsg5s5xA7bhZwdeR7+yoZnbloiIiIhIFigBlF7L3am58y4WX3AB3tDQWr7JKadQceIJmFk7S6dv9bK5DHz6fJj9YGvZYatreXfIKI6YcBo7jj8yI9sREREREck2JYDSKzXX11NVeQ4rH1yflMUGDWL0RX9g4N57Z2Qba+urufmJk7lu+Rv8pWoJO7XM6DOY2D5nULnbTyCW2SeKioiIiIhkkxJA6XXWzZ/PgpNO3mB8vz7bb8+YKy6ndLPNurz+5qZGHn62kis+foCqIoNYjPMrhnL7wiqKdjoKvnMeDBje5e2IiIiIiHQ3JYDSq6x8+GGqzq6kec2a1rLBhx3GyLPOJNa3b5fX//qbN3DxzOnMijVB0fpbSBuK+7D0B7cyctz3urwNEREREZFcUQIovUJzbS1V51/Ayvvvby2zkhJGnHkGQ448ssv9/d6f+w+uevn30WDu68uHNjs/33Q/Dtv7DxSXdD3BFBERERHJJSWA0uPVz5rFwl/9mnXz57eWlYwZw6aXXUa/L+/YpXXPm/cE17x0Lo831WxQ3qfZOWbQdvzkO1dqTD8RERERyRtKAKXH8qYmVtxwI0umT4e4p3wOOvhgRp59FkUDBnR+5cvnwYw/8Neqp3h8QP8NZh1UPIyT976YUaO/1vn1i4iIiIj0QEoApUf64qOPWHT6b6l/663WslhZGSPPPovBkyZ1fsWf/x+8MB1mPwQ4U4uLeaR/Gc1m7BMbxLTdf8u4bQ7qcvwiIiIiIj2REkDpUVqu+i29/HJ83brW8r477siml1xM6RZbdHydzc289MafuWX2jVww/wPKm5tb541tbOTUohF8dddp7LD94RnZBxERERGRnkoJoPQY9bNmUXXOuax9++31hSUlDPvZVIYdfzxWUtKh9dXVLeORFy7k9gVP8n4sJH23DxrItJqVocLW+8HE/+bozb6eqV0QEREREenRlABKzjWtXMnSyy+n+vY7wL21vM/47Rl94YX0HTeuQ+ubP/857nj1Tzy45iNWx2yDp3reOWgAx4/Zj5I9fwkjv5ypXRARERER6RWUAErOeGMjNffex9Lp02mqrm4tt5ISKjp41a+ubhlPvTKdhz59nJepD4Wx9UND9G12Di3bgmO/eSYlY3bP6H6IiIiIiPQWSgCl27k7qx9/gqXTp7Pu4483mNd/zz0ZecbvKB07NvWKmpth/gssmHk9h698lbrYxmMBbt4E3x8xgUP2PIPBgzfP0B6IiIiIiPROSgCl23hzM2tmPMOya6/dsJ8fUDx6FCNOP52B++3X7qDuTY3raJj/PH3ffxTm/ANWfc6mwPBNR/FJabhaaO5MjA3iqO1/yIRdphIr0ttcRERERASUAEo38IYGVj78MCuuu44vPvhwg3mxAQOo+OlPGXrsMcTKypIuX7dmCa+9ewv/+vhR/rV2IT+uruG4Vatb5xvw72tqeWhwOZOG7cz3djuFkaN2zuYuiYiIiIj0SkoAJWvWzZ9PzT33UHPf/TQtX77BPCstpfzoo6k44XiKy8s3mNfc1Mh7HzzES3Mf5IUVs3iTtTS2XBWMGU/2L1ufAPYrhx0PZ8qXv89Px+yGxWKIiIiIiEhySgAloxqWLGH1E0+w+tHHqHvttY3mx8rKGDJ5MkOnHEfJiBGhcF0tiz96igfev5uZNXN5u7k2PL0TwuU9NrwldGFJKXW7TqFs/KGwxTehqISODRAhIiIiIlKY8jIBNLPxwJXABKAG+F/gHHdvymVc+cgbGqh/513qXnmZNc89T/3MmRsM5dCiePhwyn/4A2IH7MEHy1+mYt5d8OJsqHoHlsxmVXGMq8aMCpWTPMxlXHOMPQZuyT7jjmCn8ZPVr09EREREpBPy7lu0mZUDTwKzgUnAVsAlhNHgzshhaL2eNzXR8NlnrH3vPdbOns3aWbOoe/MtvK4ueX0zqrcbzDtfifHK5muYz1UsnXE1APcvWMTWDQ2tdbdqaGJgUzOri8ItnBVNzu59NuGbo/dg9x1+yCbDd8j+DoqIiIiI5Lm8SwCBqUA/4DB3XwU8YWaDgEoz+2NUJhFvbKR57Vq8vp7m2loaV1TTuHw565YtZt3SKtZVLaRhwQIaFyzCFy/HmprbXV+zwezNjZfHGa+OM2oGrImbu/7K3vulJRskgLFNtufnA0czsGI7dt7mIMZsOkH9+UREREREMiwfE8ADgMcSEr07gIuAicDfs7HRt47ci6Yly1hc3BzSnLi7IC3Jv0sdRjTaBnVrY051zDdaJtk6+jgMbbmhtXV5WGMhzfKoXrJYipugXwOUNnrI2NLUVs2SskbKRnzBHdv24d5xfVjVv+11ljY7mzXGmFOyE4sH7cz84i35pGRL6mP9YQWwAm75AOCVtOMSkdw79bvj2HXs0FyHISIiIinkYwK4HfB0fIG7f2pmddG8jRJAs8R0q+NKq5bB0ibGdmgpj8/NKIum9JfeUEeXbzula1tNf/hkuDFgyDomlq2mb3kDpQNCJrpi2FBW9e9L/+ZmhjQagxtK6bNuIE0NFdR+sSlL132JqnVfYjmlvLnBWr+IJhHprWrqGlJXEhERkZzLxwSwnPDgl0TV0TyJZ06syLEiZ3WpsXBAjJVlxpoyWFPm1JYZ1YNh1SCjdkAMiksobi5hs9rBfLx6FKu8P0vWDWEJ5Sxa1I/mz4dS5YOpyvV+iYiIiIjIRvIxAewwdw+DDXThSmDxr3/HymULWNJYHQrMwGIbDGNgsVDmQGmslJGlw8P2o75utU311DStXr9M6/LRXzPMQt0+RaUMLa0IyxODmFHbVEdtUx2ORds3wDAziLZhZjSXlNCn7yAGDdgEiovBYjQX9WGtwZBYKRXF/YkVlXb2UIhIARo3YmCuQxAREZE05GMCWA0MTlJeHs3Lim0O+UG2Vi0iIiIiIpIR+fiYxTmEvn6tzGwzQve4OTmJSEREREREpAfIxwTwEeC7ZhZ/P9JkoB54JjchiYiIiIiI5F4+JoDXEh4peZ+Z7WdmJwCVwKUaA1BERERERApZ3iWA7l4N7AsUEYZ8OAe4DDg7l3GJiEjPYmbjzewpM6szs4Vmdq6ZFeU6LhERkWzKx4fA4O6zgX1yHYeIiPRMZlYOPAnMBiYBWwGXEH4YPSOHoYmIiGRVXiaAIiIiKUwF+gGHRd0DnjCzQUClmf1RXQZERCRf5d0toCIiImk4AHgsIdG7g5AUTsxNSCIiItln7p0e+zzvdGUgeBER6Tx3t+7cnpktAa5x98qE8lqg0t3/lGQZfUaIiORQd39W5CtdARQRkUJUDtQkKa+O5omIiOQl9QGM09VfFVp+Hc6nXye0T72D9ql30D71bvm0j4XUbt1BxzOzdDwzR8dSktEVQBERKUTVwOAk5eXRPBERkbykBFBERArRHGC7+AIz2wwoi+aJiIjkJSWAGeTulm+X2LVPvYP2qXfQPvUojwDfNbOBcWWTgXrgmdyE1H16cbv1SDqemaXjmTk6lpKMngIqIiIFJxoIfjbwLnARsCVwKTDd3TUQvIiI5C0lgCIiUpDMbDxwFTCB8ETQ/yUMAdGUy7hERESySQmgiIiIiIhIgVAfwDSZ2Xgze8rM6sxsoZmda2ZFaSw32MyuN7NqM1tpZreaWUV3xJwiriPN7CEz+9zM1pjZG2b2gzSW8yTTy90RcypmNqWN+KamWK5HthGAmc1oY5/czCa0sczYNurf0d3xR/FsbWb/Y2Zvm1mTmc1IUsfM7Ldm9pmZ1ZvZs2b21TTXP8nM3jGztWY228wmZ3ofkmyz3X0ys1Fm9iczeys6vz4zsxvNbHQa665so/32z9oOkXY7fZIkrqo019/t7STpfXZ15fwrNGkez2lm9rCZLY/Okb1yE23Plu3PhkKT5vHUe1MAjQOYFgt9RZ4k9BeZBGwFXEJIoFP1FbkL2Bb4KdBM6GvyAPBvWQo3Xb8CPgZ+CSwDDgRuM7Nh7n5limUvAe6Je706OyF22j6EBzm0+ChF/Z7aRgDTgEEJZecCOwOvpVj2v4AX4l4vy2BcHbED4f31MlDSRp3TgDOB3xCewPgr4Ekz29Hd20wwzGxP4F7gGuCkaDu3m1m1uz+euV3YSKp9+hpwKOGWwleAEUAl8GK0T2tSrH8lkJjwvdeVgNOQTjsB3AbE/x+xLtWKc9hOBa0Dn12dOv8KTQeO57GAA48BKX9YLWBZ+2woUOkcT703JXB3TSkm4HTCuFCD4spOBeriy5IsN4Fwon0rruzrUdl+Od6nYUnKbgM+TrGcA7/IdZu0EduUKL4BHVimx7ZRG/GWAiuAP7dTZ2wU//dyHW8UTyzu3/cAMxLm9yUkPGfFlfUHlgLnp1j3Y8DTCWX/BJ7P8T4NAYoTyraN2uW4FOuuBJb1tHaKyj8BLu7EunPSToU+pfPZ1ZXzr9CmdL8LtJxLwI7ROb9XrmPviVM2PxsKcUrz/3C9NzXh7roFNE0HAI+5+6q4sjuAfsDEFMstdvdnWwrc/VXClbcDshFoutw92dWgmUDKW9TyTI9tozbsTxio+vZcB5Iud29OUWUPwlXOu+KWqQX+TjttYGZ9gL3jl4vcAUwws2SDfGdEqn1y9xp3b0wom0v4otgjz7E02qlTctlOktZnV6fOvwKV1neBbJ1L+SZbnw2FKp33nd6b0kIJYHq2I2FgYHf/lPBlbrukS7SxXOS9FMvlygRgbhr1Ks2s0cyWmdnfzGxotgProHlRfO+b2Ykp6va2NjoKWAA8l0bd66N+AIvM7FIz65fl2DprO6AJ+CChPFUbbEW4zSWx/d4j/N+2baYCzAQz24kwyHg659iQ6PxqMLOZZnZYlsPriJ+Y2ToL/WXvMbMtUtTvVe2UZ9L57Ors+VeIOvtdQDpH702RLFEfwPSUEx4Rnqg6mteZ5bbsclQZZGb7AocAP05R9UbCr29LgV0J9+Z/xcy+7rl/dPqiKJ5XgSJCsnStmZW5+2VtLNOb2qgM+Hfgf9y9vcf3fgFcDTwOrAL2Av6b8EV8UpbD7IxyYE2S9081UGZmpe6erJ9Zy7lXk2S5+Pk5Z2Yx4HLCF5mHUlT/kHBb2UxgIHAicK+ZHe7u92U10NQeJPQvWQBsD5wNPGdmX3b3lW0s02vaKQ+l89nV2fOvEHX2u4B0jt6bIlmiBFAws7GE/n8PuvsN7dV19ylxL581s/cIfXkOJjw4JWfc/TFCX6MWj5hZX+AMM7s8D259OJjQ/6Hd2z/dfRHwi7iiGWa2GLjGzL7i7m9lMUZJ7kLCFfaJ7t7QXkV3vyX+tZn9HXgROAvIaQLo7ifHvXzOzF4E3gR+BEzPRUwiIiLSMboFND3VQLJ+KuWs/xU7k8t1m+j2zUeA+cDRnVjFo8AaYJdMxpVB9wBDCQ9GSabHt1Gco4AP3f31Tizb8tTWr2UwnkypBgYkPkqd0AZ17fzC29I+ie1XnjA/p8xsGuEJdse5+ysdXT662nsfsFOSY5RT7v4u8D7tn/+9op3yVDr/v3X2/CtEvenzIh/ovSmSJUoA0zOHhPvNzWwzQn+eZP3H2lwu0la/s24V3VL4D8KTJb/n7nUdXUfcrYjt3ZKYS6ni69Ft1CJ6UMYBdP7hLz25neYQbtndOqE8VRvMAxrYuP22IwznkU5fu6wys8MJQyac6u53dmFVTs9sO0gdW49vpzyWzmdXZ8+/QtTZ7wLSOXpvimSJEsD0PAJ818wGxpVNJow190yK5UZGY2ABYGa7EvqWPZKNQNNlZsXA3cA2wP7uvqST69kfGAC8kcHwMukIwvh389uY32PbKMGhQB86nwAeEf3tie30IqGv4pEtBdGPEwfTThu4+xfAv+KXi0wGXmqnT1q3iAbYvRW40t0v7sJ6DDgceKsH9LPdgJntSPgy1ub7qqe3U55L57OrU+dfgersdwHpHL03RbJEfQDTcy1h8OL7zOwiQnJQCVwa/zhoM/sQeMbdfwLg7i+Z2ePATWb2X6wfZPx5d3+ym/ch0TWEAUNPBirMrCJu3kx3/8LMngJw930BzOwEwoNfniQkVbsQBr99FXi4G2NPyszujWJ5m/Cr4eRoOqml/18va6N4RxESgI0GA0/cJzOrJDw85AXCh+e3CLcg3ufub3dbxOvjKyO81wA2BQaZWUtC+k93rzOzPwBnmlk16wf7jRE34LiZHQv8DdjK3VsS+vMIfRynE/qgHhhNiYOod+s+AVtE8cwB7jSz3eMWX+ru86L1TASeAvZ192eismcIg6bPIfT5PB74BuEhTVmTxj7tDfwH4a6BhYTE7wzgU+CGuPX0mHaS1J9d7r42nfNPgPS/C+xK6HawWVQ00cyGAZ908hb+vJSpzwYJ0jyeem9KkOuBCHvLBIwHnib80reI8IWmKKHOJ8ANCWVDgOsJTw5bRXjYykaDsOdgfz5h/a1bidPYqM4M4gYSBfYlJBXLCbd0fQZcAQzO9f5E8f2e0B+pLmqnN4BjemsbxcU3LDrep7XTljfEvT4KeJ0wgO46wlMlzwX65Cj+sWm81wz4HeHpkvWEYS52TljPlPhl4soPAd4lPP10DnBUrvcpLtZkU3xb7UXCYLzAdcBH0XGojY7FAT1gn3YiJKtLo/djFSHxG91T20lT2p9dKc8/TR06njekOvc1Ze6zQVOHjqfem5pwdyx6Q4iIiIiIiEieUx9AERERERGRAqEEUEREREREpEAoARQRERERESkQSgBFREREREQKhBJAERERERGRAqEEUEREREREpEAoARRJYGY3mFmPHh/FzIab2UozOz6ubC8zczObksPQMsLMLjOzuWZWkutYRERERPKJEkDJe1FSlO40Ntfxpul8woDc1+c6kCy5CBgD/CzXgYiIiIjkk+JcByDSDY5JeP1vwAnAX4DnEuYtBY4HpnZDXJ1iZmOAHwO/dvfGXMeTDe5eZWZ3AKeZ2TX5up8iIiIi3U0JoOQ9d78l/rWZFRMSwJcS58VpyHpgnXci4MDtuQ4kXdGtnEXuvrYDi90M/AiYBNyblcBERERECoxuARVJkKwPYEuZmVVE/15mZqvN7AEzGxnVOcHM3jOztWY2x8wmtbH+yWb2fLR8nZm9YmZHdCDEI4HX3X1JO/vwIzObZWZfmNl8Mzu1jXqHmNkLZlZrZmuif28Ud7TvNyQpnxLN2yuurDIq28HMLjWzBcBaYPdo/kFm9kx0DOvN7FMzu8/Mtk1Y/bNAbbS/IiIiIpIBugIo0jGPAguAs4CtgZOA+83sPsJVxesIyc5JwD1mtq27f9yysJmdD/wuWs+ZQDNwKHC3mf3C3a9ub+NmNgIYB1zRTrWpwIgolhrgP4CLzGyBu98Wt65pwNXAHODcqHgK8ICZnejuf0l5NNp3K1APXEK4YrnIzCYCDwHvAhdG8Y0G9iMcz7ktC7t7k5m9BkzsYhwiIiIiElECKNIxr7r7z1temBnAL4FNgR3dfVVU/jTwFiEpPD0q24WQ/F3o7r+NW+cVZvYAcKGZ3eTuq9vZ/vjo77x26mwObO/uK6Pt/g2YD/wncFtUVg78MVrPN+Li/jMwE7jEzO5y95p2j0b7aoD94vvvmdmJhDsPvp1wBfO8NtYxD9jLzCrcfXkXYhERERERdAuoSEdNT3jd8hCZm1qSKAB3fxtYBWwTV/dowpWwG81sWPxEuCo2EJiQYvubRH9XtFPn+pbkL4qlDng5IZZvA/2BKxLiXkW4ujiAcFWuK6YneXhLS1yHR30xU2lJ+oZ3MRYRERERQVcARTrqo4TX1dHfjxMrRvMq4l5vDxjhlsu2jEix/Za+idZOncQYISRS8bF8Kfo7K0ndlrItU8SSytwkZVcRHupyDeG21OcJt8Pe7u5Lk9Rv2c8ePS6jiIiISG+hBFCkA9y9qY1ZbZVbwr8dOKCd+skSsngtSdLQduq0te5saO//kLrEAndfbma7EYbi+DbwLeAy4BwzO9DdX0pYpGU/kyWHIiIiItJBSgBFus8HwP7Ap+7+XifX0ZIgbtNurdRarhLuADyVMG98Qh0It5wmSzo7fJUwSqJnRBNmthPwBnAGcFBC9a2BKvX/ExEREckM9QEU6T43R39/b2ZFiTOjJ3y2K7pNchbRkApd8ARhiIX/NLOBcTEMJDwsZk1Up8VcYIKZlcXVLSeM05e2qL9jojmEp4UOTahbBOwKPNORbYiIiIhI23QFUKSbuPtrZlYJVAJvmtndwEJgFPA14ECgNI1V3Q2caWaj3H1RJ2OpicYGvBp4JW6MvymEq24nxj9IhtB37xbgaTO7GRgCHE94uujIDmz6r2Y2Bng8WrYfMJnwAJybEupOJDyo5u4OrF9ERERE2qEEUKQbufs5ZvY6YZzAUwgJzhLCuHgnpbmavxJul/whYYy9zsZyjZktAn4DnB0VvwUc6u4PJNS91cxGA78ALiXcHnouYRzDb3RgszcTkszjCE80XQXMBo5w93sT6h4DVAEPdmD9IiIiItIOc9fD9UR6GzO7FvgOMM7dG3IdT6aZ2UhCknmau7c36L2IiIiIdID6AIr0TmcRhnXoUB+8XuQ0YAHw51wHIiIiIpJPdAVQRERERESkQOgKoIiIiIiISIFQAigiIiIiIlIglACKiIiIiIgUCCWAIiIiIiIiBUIJoIiIiIiISIFQAigiIiIiIlIglACKiIiIiIgUiP8HUakPYGNTkHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create grid structure\n",
    "f = plt.figure(constrained_layout=True, figsize = (12,8))\n",
    "gs = f.add_gridspec(2, 6)\n",
    "ax = [0]*5\n",
    "ax[0] = f.add_subplot(gs[0, :2])\n",
    "ax[1] = f.add_subplot(gs[0, 2:4])\n",
    "ax[2] = f.add_subplot(gs[0, 4:])\n",
    "ax[3] = f.add_subplot(gs[1, :3])\n",
    "ax[4] = f.add_subplot(gs[1, 3:])\n",
    "\n",
    "#cells position\n",
    "rec_pos = [24]\n",
    "and_gate = np.zeros(4)\n",
    "senders = [31,17,[17,31]]\n",
    "# senders = [[3,45],3,45,[]]\n",
    "output = [24]\n",
    "r, c = get_coo_from_pos(output)\n",
    "\n",
    "df_and = pd.DataFrame()\n",
    "#run simulations and create dfs\n",
    "for i, inputs in enumerate(senders):\n",
    "    U = full_setup(sender_seed= inputs, receiver_seed=rec_pos, ara = 100)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    ax[i].imshow(sim[6,:,:,-1])\n",
    "    df_and[i+1] = sim[6,r,c,:].mean(axis = 0)\n",
    "    and_gate[i+1] = sim[6,r,c,-1].mean()\n",
    "    [a.axis('off') for a in ax[:3]]\n",
    "    print(sim[6,:,:,-1].max())\n",
    "    \n",
    "#add colorbar\n",
    "im3 = ax[2].get_images()[0]\n",
    "f.colorbar(im3, ax=ax[2])\n",
    "\n",
    "#run simulation w/ no inputs\n",
    "U = full_setup(sender_seed= [], receiver_seed=rec_pos, ara = 100)\n",
    "sim = runModel(900,0.1,theta,U)\n",
    "df_and.insert(loc = 0, column = 'x00', value = sim[6,r,c,:].mean(axis = 0))\n",
    "# and_gate.insert(loc = 0, column = 'x00', value = sim[6,r,c,-1].mean(axis = 0)) \n",
    "# df_and[3] = sim[6,r,c,:].mean(axis = 0)\n",
    "# and_gate[3] = sim[6,r,c,-1].mean()\n",
    "df_and.index = df_and.index/600\n",
    "df_and.plot(ax = ax[3], linewidth = '3', style = [\"-\", \"-\", \"--\",\"-\"])\n",
    "ax[4].bar([\"00\",\"01\",\"10\",\"11\"],and_gate)\n",
    "\n",
    "\n",
    "ax[0].set_title(\"01\", fontsize = 18)\n",
    "ax[1].set_title(\"10\", fontsize = 18)\n",
    "ax[2].set_title(\"11\", fontsize = 18)\n",
    "\n",
    "ax[3].spines['top'].set_visible(True)\n",
    "ax[3].spines['right'].set_visible(True)\n",
    "ax[3].spines['left'].set_linewidth(2)\n",
    "ax[3].spines['bottom'].set_linewidth(2)\n",
    "ax[3].spines['top'].set_linewidth(2)\n",
    "ax[3].spines['right'].set_linewidth(2)\n",
    "ax[3].yaxis.set_tick_params(width = 2)\n",
    "ax[3].xaxis.set_tick_params(width = 2)\n",
    "ax[3].title.set_size(12)\n",
    "ax[3].xaxis.label.set_size(12)\n",
    "ax[3].yaxis.label.set_size(12)\n",
    "ax[3].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[3].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[3].set_yticks(np.arange(0,0.9,0.1))\n",
    "ax[3].set_xlabel('Time (hours)', fontsize = 18),\n",
    "ax[3].set_ylabel('GFP', fontsize = 18)\n",
    "# ax[3].set_ylim(-0.5, 220)\n",
    "\n",
    "ax[4].spines['top'].set_visible(True)\n",
    "ax[4].spines['right'].set_visible(True)\n",
    "ax[4].yaxis.set_tick_params(width = 2)\n",
    "ax[4].xaxis.set_tick_params(width = 2)\n",
    "ax[4].spines['left'].set_linewidth(2)\n",
    "ax[4].spines['bottom'].set_linewidth(2)\n",
    "ax[4].spines['right'].set_linewidth(2)\n",
    "ax[4].spines['top'].set_linewidth(2)\n",
    "ax[4].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[4].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[4].set_ylim(-0.5, 220)\n",
    "# ax[4].set_yticks(np.arange(0,0.9,0.1))\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='tab:blue', lw=3, label='00'),\n",
    "                   Line2D([0], [0], color = 'tab:orange', lw =3,label='01'),\n",
    "                   Line2D([0], [0], color = 'tab:green', lw =3,label='10'),\n",
    "                   Line2D([0], [0], color = 'tab:red', lw =3,label='11')]\n",
    "ax[3].legend(handles = legend_elements, frameon = False, fontsize = 18)\n",
    "f.text(1,0.75, 'GFP',va = 'center', rotation = 270, fontsize = 18 )\n",
    "f.text(0.54, 0.47, 'Time = 15 hours', fontsize = 14)\n",
    "f.text(0, 0.53, \"B\",fontsize = 25)\n",
    "f.text(0, 0.98, \"A\", fontsize = 25)\n",
    "\n",
    "circle1 = plt.Circle((23,23), 2.6, color = 'r', fill = False)\n",
    "circle2 = plt.Circle((23,23), 2.6, color = 'r', fill = False)\n",
    "circle3 = plt.Circle((23,23), 2.6, color = 'r', fill = False)\n",
    "ax[0].add_patch(circle1)\n",
    "ax[1].add_patch(circle2)\n",
    "ax[2].add_patch(circle3)\n",
    "\n",
    "circle1 = plt.Circle((23,17), 2.5, color = 'hotpink', fill = True)\n",
    "circle2 = plt.Circle((23,17), 2.5, color = 'hotpink', fill = True)\n",
    "ax[1].add_patch(circle1)\n",
    "ax[2].add_patch(circle2)\n",
    "circle1 = plt.Circle((23,29), 2.5, color = 'hotpink', fill = True)\n",
    "circle2 = plt.Circle((23,29), 2.5, color = 'hotpink', fill = True)\n",
    "ax[0].add_patch(circle1)\n",
    "ax[2].add_patch(circle2)\n",
    "\n",
    "ax[1].annotate(\"S\", xy=(23, 18), fontsize=15, ha=\"center\")\n",
    "ax[2].annotate(\"S\", xy=(23, 18), fontsize=15, ha=\"center\")\n",
    "ax[0].annotate(\"S\", xy=(23, 30), fontsize=15, ha=\"center\")\n",
    "ax[2].annotate(\"S\", xy=(23, 30), fontsize=15, ha=\"center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "electric-bernard",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig('/Users/lucarosa/Library/Mobile Documents/com~apple~CloudDocs/THESIS/Upgrade/figures/logic/17-31-panel.pdf', bbox_inches = 'tight')\n",
    "f.savefig('./simulations/17-31-panel.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "independent-worthy",
   "metadata": {},
   "source": [
    "## TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "cross-swing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.98, 'A')"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create grid structure\n",
    "f = plt.figure(constrained_layout=True, figsize = (12,8))\n",
    "gs = f.add_gridspec(2, 6)\n",
    "ax = [0]*5\n",
    "ax[0] = f.add_subplot(gs[0, :2])\n",
    "ax[1] = f.add_subplot(gs[0, 2:4])\n",
    "ax[2] = f.add_subplot(gs[0, 4:])\n",
    "ax[3] = f.add_subplot(gs[1, :3])\n",
    "ax[4] = f.add_subplot(gs[1, 3:])\n",
    "\n",
    "#cells position\n",
    "rec_pos = [24]\n",
    "and_gate = np.zeros(4)\n",
    "senders = [[3,45,21,27],[3,45],3]\n",
    "# senders = [[3,45],3,45,[]]\n",
    "output = [24]\n",
    "r, c = get_coo_from_pos(output)\n",
    "\n",
    "df_and = pd.DataFrame()\n",
    "#run simulations and create dfs\n",
    "for i, inputs in enumerate(senders):\n",
    "    U = full_setup(sender_seed= inputs, receiver_seed=rec_pos, ara = 100)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    ax[i].imshow(sim[6,:,:,-1])\n",
    "    df_and[i] = sim[6,r,c,:].mean(axis = 0)\n",
    "    and_gate[i] = sim[6,r,c,-1].mean()\n",
    "    [a.axis('off') for a in ax[:3]]\n",
    "    \n",
    "#add colorbar\n",
    "im3 = ax[2].get_images()[0]\n",
    "f.colorbar(im3, ax=ax[2])\n",
    "\n",
    "#run simulation w/ no inputs\n",
    "U = full_setup(sender_seed= [], receiver_seed=rec_pos, ara = 100)\n",
    "sim = runModel(900,0.1,theta,U)\n",
    "df_and[3] = sim[6,r,c,:].mean(axis = 0)\n",
    "and_gate[3] = sim[6,r,c,-1].mean()\n",
    "df_and.index = df_and.index/600\n",
    "df_and.plot(ax = ax[3], linewidth = '3')\n",
    "ax[4].bar([\"11\",\"10\",\"01\",\"00\"],and_gate)\n",
    "\n",
    "\n",
    "ax[0].set_title(\"11\", fontsize = 18)\n",
    "ax[1].set_title(\"10\", fontsize = 18)\n",
    "ax[2].set_title(\"01\", fontsize = 18)\n",
    "\n",
    "ax[3].spines['top'].set_visible(True)\n",
    "ax[3].spines['right'].set_visible(True)\n",
    "ax[3].spines['left'].set_linewidth(2)\n",
    "ax[3].spines['bottom'].set_linewidth(2)\n",
    "ax[3].spines['top'].set_linewidth(2)\n",
    "ax[3].spines['right'].set_linewidth(2)\n",
    "ax[3].yaxis.set_tick_params(width = 2)\n",
    "ax[3].xaxis.set_tick_params(width = 2)\n",
    "ax[3].title.set_size(12)\n",
    "ax[3].xaxis.label.set_size(12)\n",
    "ax[3].yaxis.label.set_size(12)\n",
    "ax[3].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[3].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[3].set_yticks(np.arange(0,0.9,0.1))\n",
    "ax[3].set_xlabel('Time (hours)', fontsize = 18),\n",
    "ax[3].set_ylabel('GFP', fontsize = 18)\n",
    "\n",
    "ax[4].spines['top'].set_visible(True)\n",
    "ax[4].spines['right'].set_visible(True)\n",
    "ax[4].yaxis.set_tick_params(width = 2)\n",
    "ax[4].xaxis.set_tick_params(width = 2)\n",
    "ax[4].spines['left'].set_linewidth(2)\n",
    "ax[4].spines['bottom'].set_linewidth(2)\n",
    "ax[4].spines['right'].set_linewidth(2)\n",
    "ax[4].spines['top'].set_linewidth(2)\n",
    "ax[4].tick_params(axis = 'x', labelsize = 15)\n",
    "ax[4].tick_params(axis = 'y', labelsize = 15)\n",
    "# ax[4].set_yticks(np.arange(0,0.9,0.1))\n",
    "\n",
    "ax[3].legend([\"11\",\"01\",\"10\",\"00\"], frameon = False, fontsize = 18)\n",
    "f.text(1,0.75, 'GFP',va = 'center', rotation = 270, fontsize = 18 )\n",
    "f.text(0.78, 0.47, 'Time = 15 hours', fontsize = 14)\n",
    "f.text(0, 0.53, \"B\",fontsize = 25)\n",
    "f.text(0, 0.98, \"A\", fontsize = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "characteristic-thing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x158df3430>"
      ]
     },
     "execution_count": 223,
     "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": [
    "U = np.zeros([46,46])\n",
    "r,c = get_coo_from_pos(senders[0])\n",
    "U[r,c] = 100\n",
    "plt.imshow(U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "hairy-morgan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 45, 21, 27]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senders[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adjusted-teaching",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "polish-security",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "substantial-easter",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dependent-covering",
   "metadata": {},
   "source": [
    "## OR gate bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "attractive-french",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 212,
     "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": [
    "logic = np.zeros(4)\n",
    "senders = [17,31,[17,31],[]]\n",
    "output = [24]\n",
    "r, c = get_coo_from_pos(output)\n",
    "\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for i, inputs in enumerate(senders):\n",
    "    U = full_setup(sender_seed= inputs, receiver_seed=[17,24,31], ara = 100)\n",
    "    sim = runModel(900,0.1,theta,U)\n",
    "    \n",
    "    logic[i] = sim[6,r,c,-1].mean()\n",
    "\n",
    "plt.bar([\"10\",\"01\",\"11\",\"00\"],logic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "copyrighted-failing",
   "metadata": {},
   "source": [
    "## 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "occupied-spencer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.229665994644165\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = full_setup(sender_seed= [38], receiver_seed=[17,24,31], ara = 100)\n",
    "start = time.time()\n",
    "sim01 = runModel(1000,0.1,theta,U)\n",
    "print(time.time() - start)\n",
    "# np.save(\"AND01.npy\",sim)\n",
    "multi_plots(sim01[:,:,:,-1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "entitled-island",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x159187610>"
      ]
     },
     "execution_count": 200,
     "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": [
    "plt.imshow(sim01[6,:,:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "dominican-gnome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1613ee160>]"
      ]
     },
     "execution_count": 260,
     "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": [
    "plt.plot(sim01[4,:,23,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "accurate-supplement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1614e5640>]"
      ]
     },
     "execution_count": 263,
     "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": [
    "plt.plot(sim01[6,:,23,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "union-midnight",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = multi_plots(sim01[:,:,:,-1]);\n",
    "# # a.savefig(\"11.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sensitive-robert",
   "metadata": {},
   "source": [
    "## 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "natural-cooperation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4651520252227783\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = full_setup(sender_seed= [10,38], receiver_seed=[17,24,31], ara = 100)\n",
    "start = time.time()\n",
    "sim11 = runModel(1000,0.1,theta,U)\n",
    "print(time.time() - start)\n",
    "multi_plots(sim11[:,:,:,-1]);\n",
    "# np.save(\"AND11.npy\", sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "julian-vietnamese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x150f9ee20>]"
      ]
     },
     "execution_count": 48,
     "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": [
    "plt.plot(sim11[4,:,23,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "romance-external",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x161494370>]"
      ]
     },
     "execution_count": 262,
     "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": [
    "plt.plot(sim11[6,:,23,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "expired-kidney",
   "metadata": {},
   "outputs": [],
   "source": [
    "i01 = [24]\n",
    "fr, fc = get_coo_from_pos(i01)\n",
    "\n",
    "i11 = [24]\n",
    "sr, sc = get_coo_from_pos(i11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "statutory-azerbaijan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15a7e9460>]"
      ]
     },
     "execution_count": 102,
     "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": [
    "plt.plot(sim01[6,fr,fc,:9000].mean(axis=0), color = 'blue')\n",
    "plt.plot(sim11[6,fr,fc,:9000].mean(axis=0), color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "id": "academic-struggle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1590e9610>"
      ]
     },
     "execution_count": 571,
     "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": [
    "foo = np.zeros([46,46])\n",
    "foo[fr,fc] = 4\n",
    "plt.imshow(foo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "prime-multiple",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_g = 1\n",
    "lambda_g = 3\n",
    "K_g = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "political-bridal",
   "metadata": {},
   "outputs": [],
   "source": [
    "gfp = x_g * hill(10, lambda_g, K_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "small-provider",
   "metadata": {},
   "source": [
    "# Receiver simulations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "supreme-inspiration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d90d3d0>]"
      ]
     },
     "execution_count": 58,
     "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": [
    "c6 = np.linspace(0,200)\n",
    "\n",
    "y = hill(c6, 50, 3)\n",
    "\n",
    "plt.plot(c6, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "frequent-apache",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_cross = [3,10,17,31,38,45,\n",
    "            21,22,23,25,26,27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "damaged-chamber",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9812450408935547\n"
     ]
    }
   ],
   "source": [
    "U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = 6)\n",
    "start = time.time()\n",
    "sim = runModel(1000,0.1,theta,U)\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "characteristic-municipality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_plots(sim[:,:,:,-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "educational-subcommittee",
   "metadata": {},
   "source": [
    "## Receiver: AHL VS GFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "matched-complement",
   "metadata": {},
   "outputs": [],
   "source": [
    "r, c = get_coo_from_pos(rec_cross)\n",
    "ahl = np.linspace(0,100,5)\n",
    "gfp = np.zeros(ahl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "declared-extension",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "f, ax = plt.subplots()\n",
    "def ahlgfp():\n",
    "    for i,a in enumerate(ahl):\n",
    "        U = full_setup(sender_seed= [], receiver_seed=rec_cross, ahl = a)\n",
    "        sim = runModel(1000,0.1,theta,U)\n",
    "        gfp[i] = sim[6,r,c,9000].mean()\n",
    "    ax.plot(ahl, gfp)\n",
    "#     print(sim[6,r,c,9000].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hydraulic-winner",
   "metadata": {},
   "source": [
    "## ALL POSITIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "speaking-montreal",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_cross = np.linspace(0,48,49, dtype = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "norman-jewel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2310469150543213\n"
     ]
    }
   ],
   "source": [
    "U = full_setup(sender_seed= [], receiver_seed=rec_cross.tolist(), ahl = 6)\n",
    "start = time.time()\n",
    "sim = runModel(1000,0.1,theta,U)\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cleared-charger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 45.5, 45.5, -0.5)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (3,3))\n",
    "r, c = get_coo_from_pos(rec_cross)\n",
    "foo = np.zeros([46,46])\n",
    "foo[r,c] = 1\n",
    "ax.imshow(foo)\n",
    "ax.axis(\"off\")\n",
    "# plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "deadly-accommodation",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig(\"./full-grid.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "touched-sugar",
   "metadata": {},
   "outputs": [],
   "source": [
    "!open ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "designing-sandwich",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}