OCNC_STEPS_Tutorial_Examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Second-order reaction, well-mixed simulation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "example script: ex1_reac.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model checking:\n",
      "Warnings:\n",
      "SC in cyt is only ever present on the RHS of reactions.\n",
      "Model checking:\n",
      "Warnings:\n",
      "SC in cyt is only ever present on the RHS of reactions.\n"
     ]
    },
    {
     "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": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Example 1: Second-order reaction, well-mixed simulation \n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Import the new steps interface\n",
    "import steps.interface\n",
    "\n",
    "# Import biochemical model module\n",
    "from steps.model import *\n",
    "\n",
    "# Create model container\n",
    "mdl = Model()\n",
    "\n",
    "# Create reaction manager\n",
    "r = ReactionManager()\n",
    "\n",
    "# Using this model, declare species and reactions\n",
    "with mdl:\n",
    "    # Create chemical species\n",
    "    SA, SB, SC  = Species.Create()\n",
    "\n",
    "    # Create reaction set container\n",
    "    vsys = VolumeSystem.Create()\n",
    "\n",
    "    with vsys:\n",
    "        # Create reaction\n",
    "        # SA + SB - > SC with rate 200 /uM.s\n",
    "        SA + SB >r[1]> SC\n",
    "        r[1].K = 200e6\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Import geometry module\n",
    "from steps.geom import *\n",
    "\n",
    "# Create well-mixed geometry container\n",
    "wmgeom = Geometry()\n",
    "\n",
    "# Using this geometry, declare compartments\n",
    "with wmgeom:\n",
    "    # Create cytosol compartment, associate it with vsys, give volume in m^3\n",
    "    cyt = Compartment.Create(vsys, vol=1e-18)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Import random number generator module\n",
    "from steps.rng import *\n",
    "\n",
    "# Create random number generator, with buffer size as 256 and starting seed 899\n",
    "rng = RNG('mt19937', 256, 899)\n",
    "\n",
    "# Could use time to get random seed\n",
    "# import time\n",
    "# rng = RNG('mt19937', 256, int(time.time()))\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Import simulation and data saving modules\n",
    "from steps.sim import *\n",
    "from steps.saving import *\n",
    "\n",
    "# Create well-mixed stochastic simulation object\n",
    "sim_wm = Simulation('Wmdirect', mdl, wmgeom, rng)\n",
    "\n",
    "# Create a result selector associated to sim_wm\n",
    "rs = ResultSelector(sim_wm)\n",
    "\n",
    "# Specify which values should be saved, here all species counts in compartment \"cyt\"\n",
    "counts_wm = rs.cyt.ALL(Species).Count\n",
    "\n",
    "# Save these values every 0.001s\n",
    "sim_wm.toSave(counts_wm, dt=0.001)\n",
    "\n",
    "# Signalize the start of a new run\n",
    "sim_wm.newRun()\n",
    "\n",
    "# Initialize the number of 'SA' molecules to 10\n",
    "sim_wm.cyt.SA.Count = 10\n",
    "\n",
    "# Or you can set the concentration (M), as for 'SB'\n",
    "sim_wm.cyt.SB.Conc = 3.32e-08\n",
    "\n",
    "# Run simulation until 0.5s, data is recorded automatically\n",
    "sim_wm.run(0.5)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Create well-mixed deterministic solver\n",
    "sim_rk4 = Simulation('Wmrk4', mdl, wmgeom, rng)\n",
    "\n",
    "# Set the integration time-step (s)\n",
    "sim_rk4.setRk4DT(0.00001)\n",
    "\n",
    "# Declare values to be saved for the deterministic solver\n",
    "rs = ResultSelector(sim_rk4)\n",
    "counts_rk4 = rs.cyt.ALL(Species).Count\n",
    "sim_rk4.toSave(counts_rk4, dt=0.001)\n",
    "\n",
    "# Repeat the simulation process for the deterministic solver\n",
    "sim_rk4.newRun()\n",
    "sim_rk4.cyt.SA.Count = 10\n",
    "sim_rk4.cyt.SB.Conc = 3.32e-08\n",
    "sim_rk4.run(0.5)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Use Matlpotlib functions to plot data from both simulations\n",
    "# Note that we access data from run 0\n",
    "plt.plot(counts_wm.time[0], counts_wm.data[0])\n",
    "plt.plot(counts_rk4.time[0], counts_rk4.data[0], color='black')\n",
    "\n",
    "plt.ylabel('Number of molecules')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.legend(counts_wm.labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Surface reactions: IP3 receptor model \n",
    "\n",
    "### 1. Biochemical model and geometry\n",
    "example script: ex2_ip3model.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# Example 2: Surface reactions: IP3 receptor model \n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "import steps.interface\n",
    "\n",
    "from steps.model import *\n",
    "from steps.geom import *\n",
    "\n",
    "###############################################################################\n",
    "\n",
    "def getModel():\n",
    "\n",
    "    # Create model container object\n",
    "    mdl = Model()\n",
    "    r = ReactionManager()\n",
    "    with mdl:\n",
    "        # Chemical species\n",
    "        Ca, IP3 = Species.Create() # Calcium and IP3\n",
    "        \n",
    "        # Receptor state objects\n",
    "        R     = Species.Create() # IP3 receptor in 'naive' state\n",
    "        RIP3  = Species.Create() # bound IP3 \n",
    "        Ropen = Species.Create() # bound IP3 and Ca (open)\n",
    "        RCa   = Species.Create() # 1 bound Ca to inactivation site\n",
    "        R2Ca  = Species.Create() # 2 bound Ca to inactivation sites\n",
    "        R3Ca  = Species.Create() # 3 bound Ca to inactivation sites\n",
    "        R4Ca  = Species.Create() # 4 bound Ca to inactivation sites\n",
    "        \n",
    "        surfsys = SurfaceSystem.Create()\n",
    "\n",
    "        with surfsys:\n",
    "            # The binding reactions:\n",
    "            R.s + IP3.o   <r[1]> RIP3.s\n",
    "            RIP3.s + Ca.o <r[2]> Ropen.s\n",
    "            R.s + Ca.o    <r[3]> RCa.s\n",
    "            RCa.s + Ca.o  <r[4]> R2Ca.s\n",
    "            R2Ca.s + Ca.o <r[5]> R3Ca.s\n",
    "            R3Ca.s + Ca.o <r[6]> R4Ca.s\n",
    "\n",
    "            # The reaction constants\n",
    "            r[1].K = 1000000000.0, 25800.0\n",
    "            r[2].K = 8000000000.0, 2000.0\n",
    "            r[3].K = 8889000.0, 5.0\n",
    "            r[4].K = 20000000.0, 10.0\n",
    "            r[5].K = 40000000.0, 15.0\n",
    "            r[6].K = 60000000.0, 20.0\n",
    "\n",
    "            # Ca ions passing through open IP3R channel\n",
    "            Ca.i + Ropen.s <r[1]> Ropen.s + Ca.o\n",
    "            # Corresponds to Ca input ~ 20000/ms for open receptor\n",
    "            r[1].K = 8000000.0, 8000000.0\n",
    "\n",
    "    # return model container object\n",
    "    return mdl\n",
    "\n",
    "###############################################################################\n",
    "\n",
    "def getGeom(mdl):\n",
    "\n",
    "    geom = Geometry()\n",
    "    with geom:\n",
    "        # Create cytosol compartment\n",
    "        cyt = Compartment.Create()\n",
    "        # Assign volume to cytosol \n",
    "        cyt.Vol = 1e-19\n",
    "        \n",
    "        # Create ER compartment\n",
    "        ER = Compartment.Create()\n",
    "        # Assign volume to ER \n",
    "        ER.Vol = 2e-20\n",
    "        \n",
    "        # Create the ER membrane and associate it with a surface system\n",
    "        ERmemb = Patch.Create(ER, cyt, mdl.surfsys)\n",
    "\n",
    "    # return geometry container object\n",
    "    return geom\n",
    "\n",
    "###############################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Simulation\n",
    "example script: ex2_sim.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model checking:\n",
      "No errors were found\n",
      "Ran 100 sim iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AtfdK1m+//Vbdddddpu2CggKllFJ9+/ZVubm5pv1nz55VShlWKU+bNk3t37/fdN7rr7+ulFLqzTffVHfeeadSSqkHHnhAPffcc0oppX7++WcFmNqra6u0tFRFRUWpvLw8pZRSgPrqq6+UUkplZmaqPn36qDNnzqiKigo1efJkdf/99zfS31w/hw8fVgsXLlSVlZVKKaXuu+8+9fHHH6szZ86oyMhI0/Xz5s1TmzdvVkop5eHhoZRSqqqqSp0/f14pZVhdHRkZqWpra9XJkydVVFSU6dr626+88oq6/fbblVJKHTlyRPXp00eVlZWpDz/8UPXv318VFBSosrIyFR4erlJTUxu9jtauZNXZJDVN4urqyuTJk9m5cyexsbGkpKSQkZFhimrJzMxk9OjR9hXZDnJzc02jKjBM8ubk5ODp6UlaWhpubm4opUwuHkdHxw61Anbfvn0WdRn5+vq2+HmOGDGCP/zhDzz++OMsXLiQ2NjYJs/7+uuveeedd0x3QYcPH2bkyJEALFq0CIBx48bx/fffA7Bp0ybT8wULFuDn52dq6/XXX+eHH34AIC0tjePHj9OzZ08cHR25+uqrAUNuoenTp1O3hmbx4sUN8hLV0Vw/69atIyEhwXTnWVZWRmBgIL169SIiIoIdO3YwcOBAkpKSGmUsVUrxpz/9iU2bNuHg4EBGRkaDldpNsWXLFh544AEAhgwZQt++fU16Z82aZVr3MWzYME6fPk2fPn1abO9iaAOvaURdfLurq6spDUF2djYODv/z6JWVldGjRw97SWw3fn5+DBo0yLQdHBxMfHw8kZGRVFVVERQUxLlz5/D396eqqgonJyeqqqoaGH17Yusf10GDBrFnzx5WrFjBk08+yaxZs3j66acbnHPy5EleeeUVdu3ahZ+fH7fddlsDN1/dnIs5KYE3bNjA2rVr2b59O+7u7kyfPt3UlpubW5O1AdqCUopbb72Vv/3tb42OXXfddXz99dcMGTKEq666qtHnvnTpUnJzc0lISMDZ2Zl+/fq16Na8GNZIm6x98JpGFBQUmBYY1V8wZM5kZGdhzpw5ODn9b3xT92NWVVWFg4MD/fr149SpU4Ahtt/LywsfHx/Onz9vJ8X2JTMzE3d3d2666SYeffRR9uzZAzRcQVxYWIiHhwc+Pj7k5OSwcuXKi7Y7depUPv/8cwBWrlzJuXPnAEN6YT8/P9zd3UlKSmLHjh1NXj9hwgQ2btzI2bNnqaqqajaTaHP9zJo1i2+//da0jiM/P5/Tp08DcNVVV7Fs2TK++OILrrvuukZtnj9/nsDAQJydnYmLizNdV/89uZDY2FiWLl0KwLFjx0hNTW1TnQRz0SN4TSPqG/jw8HCOHDmCu7s7Tk5OFBUV4eTk1KlH780xePBg4uLi6NevH35+fqZc5oWFhXh7e+Pm5kZmZia+vr4UFBSQkZHR5CRtV+TgwYM8+uijODg44OzszL///W8A7r77bubNm0doaChxcXGMGTOGIUOG0KdPH7OKsDzzzDNcf/31REVFMXnyZFNI67x583j77bcZOnQogwcPZuLEiU1eHxISwrPPPsukSZNadDM118+wYcP4y1/+wty5c6mtrcXZ2Zk333yTvn374ufnx9ChQzl8+DAxMTGN2rzxxhu57LLLGDFiBNHR0QwZMgSAnj17MmXKFIYPH878+fO5//77Tdf85je/4b777mPEiBE4OTnx0UcfWbUamXSkEVl0dLSqq9OosR87d+4kKioKDw8PampqeO+997j88supqamhtLSU8+fPM3DgwEZpBDo7Sinef/99brzxRnr06MH69euZMWMGBw8epHfv3vj6+rJy5Up69+5NTk4Ojo6OzJkzxybajhw5wtChQ23Sl6bj0tT3QEQSlFLRTZ2vXTSaRpSUlODu7g4YfIHBwcEEBQWZJiLrj/C7EiLCddddZ7o78ff3Jz8/3+SicXR0ZMGCBQQFBTFnzhxcXFx0eTlNh0YbeE2T1J9Quvzyy0235tnZ2aaEXl2R+gu16vzwVVVVphh8ESE0NBRHR0fCwsLIyMiwl1SN5qJc1MCLyBQR8TA+v0lEXhWRvtaXpuko1Df2Pj4+3cZVUOdrb44+ffqQlpZmMz0dyZ2qsT1t+fzNGcH/GygVkVHAH4ATwCet7knTJZg7d67FVpR2Fpr7x3JxcaGyspLc3FwOHjxoVQ1ubm6cPXtWG/luilKKs2fPNkh5bQ7mRNFUK6WUiFwBLFFKvS8id7ZJpaZDkp2dTXV1NWFhYZSXl7f6S9SVCQgIID09vdnjjo6OHDlypEHZQmsQFhZGenp6owLnmu6Dm5sbYWFhrbrGHANfJCJPADcBU0XEAXBugz5NB+Xw4cN4enoSFhbGuXPnuuQEalvp16+fKfNkU0ydOhURIS4uzqqLoJydnenfv79V2tZ0Xcxx0SwGKoA7lVLZQBjwslVVaWyGUora2lpTyb2CgoIGy8W7Oz4+PlxyySXNHq8z6N15EZSm49KigRcRR+ALpdSrSqnNAEqpVKWU9sF3EfLy8qhfC1eP4Btjzqg8JCREFw3RdDhaNPBKqRqgVkR8WjqvKURksIjsq/coFJHftVWoxjocO3asQU6WiooK7YNvA7169TItd9doOgrm+OCLgYMisgYoqduplHqwpYuUUkeB0WC6E8gAfmizUo3Fyc3Npby8vEumHbA1Tk5OTS56ys/Px9fXt0GiNo3GVphj4L83PtrDLOCEUup0O9vRWIi8vDwOHDhgKtTg6Ohol+o4XZmcnBz27dtHeXk506dPN6WC1WhsxUUNvFLqYxFxAeru448qpVobE3Yd8EVTB0TkbuBuoFHtTI312LdvH9OnTzeNLL29vfUkYTvp06cPcXFx1NTUMH36dPbv38/s2bPJzs4mOztbG3iNzTFnJet04DjwJvAWcExEpprbgfHH4XKgyTyeSql3lFLRSqno+pN9GutRWFhoyg5Zh4+PD5mZmaYi2prWM3DgQGbMmEF0dDT//e9/6dWrF46OjgQEBLSrkLRG01bMcdH8A5hr9KkjIoMwjMbHmdnHfGCPUqrlUicaq6OUoqioiPj4+Eahf76+viQmJlo1N3V3wdfXl9mzZ5t+LF1dXU059TUaW2LOzI9znXEHUEodo3ULna6nGfeMxrakpKSQkJDAsGHDGk2sent7k5qaqkMkLYSXl5eeWNXYHXNG8LtF5D3gM+P2jYBZSduNScrmAPe0TZ7GkhQVFTF69OgmFzI5OjpSUVGhDbwVUUqRkpJCz5499fussQnmDDHuAw4DDxofh4F7zWlcKVWilOqplNKzdx2A0tJSU573pujduzfOzjoLhTXw9PSkuLiYpKQkjh492uCYzimvsRbmGPh7jStZFxkf/8Rg9DWdjIqKihYzQU6bNs2GaroXvXr1MrnAiouLGxz75JNPKCsrs5MyTVfGHAN/axP7brOwDo2NaGnZfVBQkA2VdC8CAgLYvHkzQ4cOxdHR0TRqr6ioIDw8nG3bttlZoaYr0qwPXkSuB24A+ovIT/UOeQP51ham0XQlPDw88Pb2xt/fn/DwcNLS0ujfvz+5ubn07duX6upq1q9fT2BgIMOHD7e3XE0XoaVJ1m1AFhCAIVSyjiLggDVFaayDtVLZai6OiLB48WLAsKBvy5Yt9O/fnzNnzhAZGWmqlBUXF2dnpZquRLMuGqXUaaXUBqXUJOAo4INh9J6plNKzQhpNK3F0dAQa5q05f/483t7epnNcXFyoqKiwiz5N18Oclax3AjuBRcA1wA4RucPawjSaroyfnx/5+QZPZ/07q6CgIJ2VUmMxzImDfwwYo5Q6CyAiPTG4bz6wpjCNZamsrGyQmkBjXwYPHsyhQ4ca7Q8KCiIpKYk+ffrYQZWmq2FOFM1ZDH73OoqM+zSdiNLSUp1npgPh6elJbm5uoxXFdfHyGo0lMGdIlwzEi8gyQAFXAAdE5GEApdSrVtSnsRAlJSXawHcw/Pz8CAwMbLDvwonw4uJiqqur8fX1RSmFUkqnQNCYjTkG/oTxUccy418vy8vRWAs9gu94TJgwwTTxWh8Roba2FgcHB5KSksjJyWHBggXEx8dTVVVFbGysHdRqOiPm5IN/DkBE3JVSpdaXpLEGJSUlBAQE2FuGph7NpYWom4ANCAigsLCQvn37cuDAAaqrqxERCgsLG0TeaDTNYU4UzSQROQwkGbdHichbVlemsSjaRdN56N+/PykpKabtqKgoUlNTmThxIjExMezatcuO6jSdCXOcea8Bl2KcWFVK7QfMLvih6RhUVVW1mIdG03Hw9vamsLCQwsJCvLy8EBEWLlyIk5MTLi4uODg4UFtba2+Zmk6AWbM1Sqm0C3bp4p2dDKWUvSW0iezsbAYPHtxp9bcVPz8/9u/f32QZy+DgYHJydP0czcUxx8CnichkQImIs4g8Ahyxsi6NBoAtW7Zw7NgxTp06ZW8pNmXo0KHs3bu3UZQNGGq/pqam2kGVprNhVrpg4H6gN5ABjDZuXxQR8RWRb0UkSUSOiMikNivVmM2xY8e6zGrIuiyLCQkJdlZiW9zd3Vm4cGGT+YM8PT0pKSmxgypNZ+OiBl4plaeUulEpFaSUClRK3VS3qtUM/gWsUkoNAUahR/42ISUlhczMTNN2VVVVh17FWltba3LBpKamUlhYaDq2bds2ZsyYwZ49e5q9tqv6oyMiIlo8XveelZaWcvasXnuoaYw5UTQfi4hvvW0/EblomgIR8cEwGfs+gFKqUilV0HapGnNxdnbm3Llzpu3s7GyCg4Ptpufs2bN8/vnnTR6rK/S9ZMkSamtrWbBgAYsWLaKmpoaysjIOHjzIPffcQ0JCAufOnWPRokWkpqZSU1PD+++/z4ABA/jTn/50UQ2FhYUsWrSIRx55pEu4e/z9/U2fcUJCAklJSXZWpOmImOOiGVnfMCulzgFjzLiuP5ALfCgie0XkPWON1gaIyN0isltEdufm5pqrW9MM5eXljZa/Z2VlERISYidF8MUXX/Doo4822p+Zmcn06dO5/vrr+etf/8o333yDk5MTtbW1PPXUU+zevZthw4YRGxvLnj17ePfddzl48CDTp08nNjaWjz76iP/3//4fH374IVVVVc32r5TizjvvxMPDg5ycHJ588klrvlybEBkZyb59+6iqqqKqqorKykrAUHf3wpKAmm5M3fLn5h7AfsCv3rY/cNCM66KBamCCcftfwPMtXTNu3DilaR/Hjh1Tqampav369aZ969ats7mO0tJSdejQIaWUUrNnz1aAysrKanDOzz//rC699FKllFLXXnutcnNzU1999ZXKzs5WI0eOVCEhIeqhhx5StbW1KigoSPn7+6vdu3erDz74QL399tuqpqZGKaVUbGys+uGHH0ztlpeXN+jn008/VWPGjFFlZWUqLi5OxcbGWvGV247k5GS1dOlSlZuba/q8k5KSGrwXmq4PsFs1Y1PNGcH/A9guIs+LyPMYMkn+3Yzr0oF0pVS8cftbYKyZvzuaNpKZmUlISIjd84r//ve/Z8qUKZw4cYL4+HgmTZrUaKI0OTmZAQMGAPDcc88xY8YMrr76aoKCgti1axcPP/wwN998MyLCuHHjiIqKYty4cdx+++3cc889ppwsd9xxBx98YPAa5ubm0rNnTxYtWsTx48cB+OCDD3jyySdxc3MjPDy8UQRKYmJip/TjR0ZGMnv27AYrlOsSmKluFlaqaRpzJlk/wZALPsf4WKSU+tSM67IxhFgONu6aBRxuh1aNGdTU1ODk5ESvXr3Izc2lsrKy2SXx1mL16tWsXLmSK664ggULFjB16lSmTp3aaKL0+PHjDBw4EDCEBa5YscKUm8XFxYVHHnmEcePGAfDggw/yj3/8g6a45ppr2LZtG0ePHuWjjz7isssuIyoqihtvvJGsrCz27NnD/PnzAejduzdZWVnU1BiWcpSWlhITE8OKFSus8l5Ym7owyrof9KqqKoKDg/WkqwYwL9kYGNwyJUqpD0Wkl4j0V0qdNOO6B4ClIuICpAC3t1Wo5uIopUxhdYGBgZw8edL0D29LnnjiCd58800mTJhAZGQkDz/8MH5+fixdurTBecnJySbDezEuvfTSZo95enry+OOP88gjj3DkyBGWLl3K+PHjWbZsGXfddReXXXaZaV7C1dUVf39/srOz6d27N6tXr6ayspKvvvqKhQsXtv1F25mePXuajHr//v05evSozj2kMSuK5hngceAJ4y5n4DNzGldK7VNKRSulRiqlrlSGCVqNlaifhMrHx4dTp05x7NgxIiMjbabh/PnzHDt2jLlz59KrVy82b97MLbfcwtixY1scwbeXBx54gEOHDuHl5UVMTAwODg4899xzrFixwlQLtY76C4W+++47nnjiCX7++WfKy8stosUe9OzZkzNnzuDo6Ii3tzdFRUUXv0jT5THHB38VcDlQAqCUykSnCu6Q5Obmmm7ZRYTw8HDmzJlj0/zh27dvZ/z48aa8N6NGjcLNzY2IiAiKioqoi5SqrKwkPT2dfv36WaRfNzc3Pv30U1577TXTXcyVV17Jq6++yty5cxucGx4eTlpaGpWVlSxfvpz77ruPMWPGsHLlSotosQd+fn4cP35cj9o1DTDnP7/SOFOrAJoKddR0DM6cOUOvXr1M2+PGjbN5cYgtW7ZwySWXNNpfN1G6fft2AE6dOkVYWJhFE6BdcsklTJs2rUGfv//97xv1UTfRun79eoYOHUpISAiLFy/mm2++sZgWW+Pk5ERubi5BQUGArgylMWDOf//XIvIfwFdEfg2sBd61rixNW6ioqMDNzc2mfdbW1jZYebp58+YmDTzAVVddxVdffQU0jKCxNX369CEtLY3ly5dz+eWXAzB//nzWrl1LbW0tGzdu5K677rKLtvbQs2dP/Pz8AMMcTFdJV6FpOy0aeDHc636FIcTxO2Aw8LRS6g0baNN0cJRS3HHHHURHR1NeXk5FRQUJCQlMmtR0yqHFixezfPlyioqKLOp/by11I/jVq1ebJm/Dw8Px9fXl4MGDvP/++7z//vvs2LHDLvrayvz58013bEFBQTrjpKblKBqllBKRFUqpEcAaG2nStAF7xD3/5S9/4fDhwwwePJiXXnqJPn36MGjQoGarDQUEBDB16lR+/PFHjh07ZjcD36dPH7Zv345SilGjRpn2z549mxUrVvDzzz/zzDPP8PTTT/PLL7/YRWNbqP++9+jRo1NPGmssgzlhkntEZLxSSpeR6cAUFBTg6+trs/5KS0t55ZVXOHz4MDU1NQwfPpzQ0FA++eSTFq+76aabuOOOO+jRowdr1thnzBAeHk5OTg433XRTgzmK2bNnc/fddzNkyBD+/Oc/M2DAAPbv38+oUaO44YYbuOyyy7j++uvtolmjaQvmGPgJwI0ichpDJI1gGNyPtKoyTavIzs5uMne4tVi9ejXR0dH07t0bgPj4ePr169coD86FXH311YSHhxMdHW23DJeBgYE4Ozszb968BvtnzJhBfn4+ixYtwtnZmUsvvZTNmzczdOhQli1bxtq1awkJCWH69Ol20d1a6hfv1nRPzPnkLwUigZnAZcBC419NB6GiooJTp04RGhpqsz6///57rr76atP20KFDL2rcARwdHZk4caJd0xc7ODhw1113NTLwfn5+PPbYY6ZR+sSJE9mxYwf79+9nwIABvPfee/z+97+3h+Q2UT/jZHFxMTt37iQtLY2srCxKS0vtrE5jC6Qj5ayIjo5Wu3fvtreMTkVNTQ1r1qwhNjbWZkW1KysrCQ4OJjEx0a5ZKq3N4cOHufzyy3nooYc4dOgQS5Yswd/fn9TUVFO0SkcmOzubU6dOMWDAAHbu3MmUKVM4c+YM1dXVpKWlNVofoOmciEiCUiq6qWP63q0Tc/bsWVavXk1MTIzVjXt5ebkpKuO7774zxY93ZYYMGUJeXh7//e9/mThxIs7OzkycOJEtW7Zw/vx5Vq1aZW+JLRIUFIS/vz8pKSnMmTMHHx8fBg4cyNChQwkICECn5+76aAPfidm/fz+XXnop/v7+Fm87LS2tQWTOv//9bwYNGsSrr77Kgw8+yKuvvmrxPjsaDg4OxMTEsGbNGiZOnAjAtGnT2LhxI6+//jpXXXVVh07qJSIMGjSImJiYRgnnRo4cyYEDB+ykTGMrWmXgRcTylkTTJuryPddlX7R028OHD+evf/2rad+6deu4++67efPNN3nnnXeYMGGCxfvtiEycOBEfHx8GDzYkRZ06dSpr1qzh3//+N9HR0bzzzjt2Vtg2nJycCAkJYf369Rw5oitpdlmaSxQPTMFQQzURQyTNGuAEkAZMau669jx0wQ/zycjIUImJiVZpOy0tTfn7+6u+ffuqL7/8UlVVVSlvb2915swZq/TXkdm4caO6/vrrTdtlZWXK1dVVzZkzR+3bt0/17t1bVVZW2lFh+9m4caMqKCiwtwxNG6GNBT/+CfwKuAtYDjynlIoErgBesdovjsYsTpw4YbUskUePHmXEiBF89dVXPProo+zYsYPw8PAGeW66C1OnTm1QT9bNzY1rr72WP/7xj4waNYqRI0cyZ84c4uPjW2ilYzNp0iRTjiBN16IlA++slDqolNoO5CqltgAopfYAF4+H01ic+ul2KysrcXV1tUo/SUlJDB48mAkTJjB48GDuv/9+Zs6caZW+OiOffvqp6f1YtmwZixcv5le/+pWdVbUdZ2dnevXqRUFBgb2laCxMSwa+/rEnLjhmuRSAGrOoqqoyGXhrGncwjODrfM5//OMfOXDgADNmzLBaf50ZZ2dn7r33XkpKSkhPT290vLOUAuzXrx+nT5+2twyNhWnJwD8lIu4ASqkf63aKSCTQ8nr0/517SkQOisg+EdEB7u2gsLCQ0tJSamtryc7OtmqVpqNHjzJkyBAAZs6cyXPPPcesWbOs1l9nR0SYPHlyIzfHgQMHGDVqVKeoj+rv79+hI4I0baNZA6+U+gnwEJFoEfGtt/+EUsqcott1zFBKjVbNBOJrzKOwsJB+/fqRn59vEwNfN4IXEZ5++mm8vHSNl5aYPHky27Zta7Bv6dKlHDp0qFOEI9YVSdF0LZo18CJyF4YImjeAJBG53GaqNI0oLCxk0KBBZGdnU1JSgqenp1X6KS0tJScnx2KVlroLU6ZMaWDglVJ8/fXXxMbGdppKUZ6enrrUXxejJRfN74AopdQkYDKN/fDmoIBfRCRBRO5u6gQRuVtEdovIbr2yrnkKCwuJiIggLy/Pqv0cP36ciIgIq8TXd2Wio6M5dOgQn332GXPnzuXrr7/G1dWVxx9/vMOveK1D++G7Hi0Z+EqlVC6AUioFaMus3iVKqbHAfOB+EZl64QlKqXeUoTB3dHcMwzOX6upqXFxcqKqqsqrxre+e0ZhPjx49GD58OE888QTR0dHceOONLF68mBkzZpCQkNCg6lVHpVevXmRnZ9tbhsaCtJTSL0xEXm9uWyn14MUaV0plGP+eEZEfgBhgU1vFagw5Yfr06WO19uPj4xk9erTV2u/KvP/++wQGBhIYGMjVV19NREQE7u7uTJ48mbVr17Jo0SJ7S2wREcHZ2ZmKigpcXV1RSnHo0CEGDhxo81KQGsvQ0gj+USCh3uPC7RYREQ8R8ap7DswFDrVXcHenf//+phzsliIrK4uUlBSABmXsNK1j+PDhppz848aNM2WcnD9/vskP/+KLL7J8+XK7abwYUVFRJCYmUllZyS+//IKjoyO7dulaP52VZkfwSqmP29l2EPCDcXbeCfhcKdU5nJEdmOHDh1u8zQceeIDs7Gy+/PJLsrKyiI7WAU+WZP78+bzyyitUVVXx6quvMmvWLBYsWGBvWU0SEBDA7t27Wbt2LdOmTcPDw4MtW7ZQVFSkI6k6Ic0aeBH5L4ZJ0iZRSrUYVWP0249q6RyNeVRXV1utQMbp06eJi4vD3d2dv/3tb8yZM0dPsFqYQYMG4eLiwpIlS/Dw8GDdunUdutJSVFQU/v7+phTU0dHRxMfHM23aNDsr07SWlqyGzjfTQSgsLGy2kHVbOXHiBMeOHWPVqlXcfvvtBAUF8dhjj/HBBx9YtB+Nwbc9f/58nnrqKZ577jn+85//cODAgQ4713HhHI+bmxs1NTVNnmvNnEia9tOSi2ajLYVomscaBv7Pf/4zu3btIjMzkyNHjuDr68sbb7yh/e9WYt68ebz11ltce+21pKSksHbt2g5r4JvCx8enUWH33NxctmzZog18B6Zj3iNqGmBpA19SUsLKlSvZsWOHaYWsr68vJ0+etGld1+7EnDlz+PjjjwkPD2f27NmsXbu20TlKKeLi4uyg7uJERERw4sSJBvuOHj1q00LvmtajDXwn4Ny5c/j4+FisveXLlzNhwgR69erVoNKP9r1bDzc3N2655RYAZsyYwc6dO0lPT6e6upoff/wRpRQrV65k5syZlJSU2FltY/z8/Bpkm1RKUV5ejre3NxUVFfYTpmkRbeA7AZaeZP3qq69YvHixxdrTtA5fX19+/etf89e//pVXXnmFq666iqVLl/Liiy/i4OBgClntaIgIlZWVAOTk5BAYGIi/vz/5+fl2VqZpjotaDREZhCEGvm/985VSOkG4DSgvL7foIpO0tDTWr1/Pu+++a7E2Na3n0UcfZfDgwTg4OPDDDz9w6623EhAQwNy5c0lJSWHEiBH2ltiIMWPGEB8fT1VVFS4uLsTExHDu3Dny8vK6fAH2zoo5w8JvgLeBd4Gmp9I1VuP06dP07dvXYu397ne/46GHHrJKoW6N+QQEBPDCCy/Qq1cvrrzySl566SWCgoLYsGFDI193R8HPz4/Y2NgG+/z9/Tl27JidFGkuhjkGvlop9W+rK9E0SWZmpsXij1evXs2BAwdYunSpRdrTtI9777230fO0tDSOHj1qL0mtxtnZmerqanvL0DSDOT74/4rIb0QkRET86x5WV6YBsOiCmBUrVnDvvffqvCIdmMjIyA47gm8tJSUlHXLCuDthzgj+VuPfR+vtU0CE5eVo6lPn67QUGRkZXHLJJRZrT2N5upKB37lzJ9XV1UREROhYeTtx0aGhUqp/Ew9t3G1AXl4eAQEBFmsvPT3d4onKNJalX79+pKammtwen3zyCY899liHHgm7urpy5MgRtm3bRnl5OQAVFRWICHPmzOH06dOUlZXZWWX35KIGXkTcReRJEXnHuD1QRBZaX5rG0gY+IyODsLAwi7WnsTxubm4EBgaSlpZGRkYGDz/8MCdOnGDgwIFMmDCBjz9ubw5AyzN06FB69OjBiBEj2Lp1KwkJCezdu5dRowypqGJiYti9W5dktgfmOHc/BCoxVHUCyAD+YjVF3ZyamhoSEgzZmPPz8y0W7VJTU0NOTo4OZ+sEREZGkpyczO9+9zvuvfdevvvuO9auXcsjjzzCM888Q21trb0lNsDPz49+/frh5eXFrFmz6N27NzU1NaZ0yZ6eniilTHchZWVllJaW2lNyt8EcAx9pLLJdBaCUKgV0hV4rcebMGQ4ePAgYJlgttbo0JyeHnj17Nli52tlQSvHzzz/bW4bViYyM5OabbyY9PZ0//elPAAwbNoxrr70WPz+/DpvOoI7g4GCmTJnSYN/48ePZsWMHAFu3bmXv3r32kNbtMMfAV4pID4ypg0UkEtBrk61ERkYGwcHBJl+mpUhPT+/07pns7GzS09NRqtks1l2CxYsX86c//YktW7bg7u7e4Ngdd9zB+++/bydlbadHjx74+/uza9cuAgICtE/eRphj4J8BVgF9RGQpsA54zKqqujGFhYUMHz6ckydPWjRfeEZGRqefYE1OTmbEiBEUFRXZW4pVmTt3Lg8++GCTd2833HADK1asaFA7dfny5bz66qu2lNgmRo8ezfnz5xk1ahS9evXizJkz9pbU5TEnimYNsAi4DfgCiFZKbTC3AxFxFJG9ItL1760tRGhoKImJiSYfpiXoCiP4iooKwsPDycnJsbcUu9GzZ0/uuOMOnn32WcDgtnryySf529/+RlVVlX3FXQQRYfbs2YgIgwcP7lQLujor5g4RpwGzgBlA7EXOvZCHgCOtvKZbUllZiYuLCw4ODlRVVdGrVy+Ltd3RQySrq6vZtm0b27Zta3D7rpTi6NGjZGRk4O3tTWBgYKtHfkopcnNzycjIsLRsu/Dkk0/y3XffcfjwYTZt2kRZWRkDBgzgl19+sbc0s3Fzc6OioqLDTRh3NcwJk3wLuBc4iKFo9j0i8qY5jYtIGLAAeK89IrsL2dnZBAcHA4aizd0lRLKmpoa1a9cydOhQRowYwaZNm1BKUVlZyerVq3FyciIrK4uoqChcXV1NGQ3NNdinT5/myJEjJCUlce7cOWu+FJvg7+/PX/7yF2bMmMFvfvMbHnroIW655RY+++wzUlJSOHKkc4ynhg0bxqFDh+wto2ujlGrxASQBUm/bAThyseuM534LjAOmAz83c87dwG5gd3h4uOrObNmyRZWUlFil7WnTpql169ZZpe32UFtbq9asWaPy8vJM+06dOqV++ukntWbNGlVUVNTomnXr1qnMzEz1wQcfqIKCgov2sWXLFlVWVqYqKirU+vXrLarfniQmJqrHH39cFRcXq7y8POXu7q68vLzUiBEjVG1trb3lmcWqVas6jdaOCrBbNWODzXHRJAPh9bb7GPe1iHEx1BmlVMJFfmDeUUpFK6WiLemS6GxUVFRQVlbWKGrCUnTUEfzOnTsZNGgQPXv2NO3r27cvc+bMYfbs2Xh6eja6xsnJib1793LdddexZ8+eJtvNz88nMzMTMLy3bm5uuLi44OTk1KFXhbaGYcOG8eKLL+Lh4UHPnj1ZtWoVp0+fpqKigm3bttlbnlkMHjyYw4cP21tGl8UcA+8FHBGRDSKyATgMeIvITyLyUwvXTQEuF5FTwJfATBH5rL2Cuyrx8fFMmDDBIm2VlZWZ3BhgiKe3dxRNUxOASimKi4sJDw9vdKylhGhhYWEMHDiQHj164OzsTHFxcaNzDh8+3KSrYvjw4V02vW1sbCx+fn7cc889vP322/aWYxb9+vUjKytLL3yyEuYY+KeB+RjCJZ8B/s+47x/GR5MopZ5QSoUppfoB1wHrlVI3tVtxF6S6upqamhq8vLws0t7vfvc7Xn75ZdP2K6+8wtixY/Hw8LBI+62lsrKyyRqk5eXlbdIUERHBwIEDAUPoXd3CsPrUlZErKipqcBfg4+NDYWFhg3Pj4uJYt25dl6lMdNttt/Hf//63QShlR2bKlCls3brVtF1To8tOWApzwiQ3YvDDexkfR5RSG+se1hbYHTh37pxFI2a2bNnC6tWrAdi8eTOvvvoqX3zxhcXaby3nzp1rZFTBMsXEPT09G43gi4uL8fDwICwsjJ07dzYoJO7g4NAgcqOqqgoHBwemTZvGgQMH2qWlo+Dv78/dd9/NY491juUqPXr0IDw8nKNHj1JUVMSXX35JamqqvWV1CcyJovkVsBO4FvgVEC8i17SmE6XUBqWUTlDWDJbMOVNQUMDp06fZs2cPxcXFPPfcc/zjH/+gT58+Fmm/LeTn5ze5+rSoqMgidy1BQUENYuOPHz/OoEGDiIiI4MCBA6bIpKY4ceIEkZGRODk5damQvaeffpoNGzawYcMGe0sxi8GDB5OamsqmTZu49tprOXbsWIMi35q2YY6L5s/AeKXUrUqpW4AY4CnryupeWNLA79q1i3HjxjF+/Hg+/vhj9u/fzzXXtOr32CKUlpaaco/k5+c3uWjLEiN4gCFDhpCUlAQY/Po5OTn4+/vj7OzM3LlzWyxYfuHcRFM/RJ0RT09PXn/9de6+++5OM6kcGxvLJZdcgouLC9OmTdP5aiyAOQbeQSlVf2XJWTOv05iJJQtr79ixg4kTJzJ79mz++Mc/csMNN+Dq6mqRtltDUlKSaVRdWVlJjx49GpV2u9A/3lZcXFzw8PAgMTGRrVu3mtLUAkRFRTU6X0Sora2lpqYGEUHEkDvP19eX8+fPt1tPR+HKK68kJiaGJ554wt5SzMLNzQ0fHx8AU1K8jr46t6NjjqFeJSKrReQ2EbkNWA6ssK4sTVupb+CLi4u544477KIjPz8fLy8vlFKICN7e3o1yyFgyW2Z0dDQODg4EBwdfNCWyl5cXxcXFZGVlNRi9h4aGkpmZSX5+PsuXL+err77q9PVG33jjDZYtW8a8efP473//a285rWLo0KGdZtFWR+WiJfuUUo+KyCKgrtbbO0qpH6wrS9MWlFLs2LGDd999l6CgID7//PMGo1lbUV5ejqurK15eXqaVo97e3hQWFlo0v86FDB061KzzfHx8OH/+PBkZGYwcOdK0v1evXhw9epSsrCzmzp1LcnIyubm5nTqHvp+fH4mJiSxfvpz77ruP9PR0qqqqKC4uNqUi7qgEBwc3GSGllDKFFdfdfWmaxpyarCilvge+t7KWbkl1dXWLPuLWkJKSgru7uylq5Prrr7dIu62lbpITIC0tDUdHR7y9vcnKyrKLngvx8fEhJyeH0tLSBgvLHB0dKSgoIDAwEGdnZ0JCQkhJSenUBh4M/vjFixczduxYFi5cyLBhw9i6dStXXXWV2T+K9sLPz6/RHNWuXbs4e/Ysubm5BAYG2lFdx0f70u1MQUEBvr6+Fmlr7969jBkzxiJttRWlFOnp6QQGBtKrVy+OHz+On58f3t7eHca/XTeCb4rg4GDGjh3b7HmdeRJ24MCBHD16lB9++IGHH36Y5557zt6SLsrw4cMb5KvJz8+ntraWadOmcfLkSTsq6xxoA29nLBlB0xEMfN3oXURwcHCgpKQEf39/XFxcGkyYVVVV2a26lIuLC3l5eU1G8EyYMME0KS0iDQx6fn4+69evt5lOa/Lb3/6WDRs2mHzcOTk5HTJM1M3NjaqqKpO2EydOMGzYMDw9PTtNdJA9adbAi8g649+XbCen+1EX0mcJ7G3glVKkpKQQERFh2hccHNzk67NUiGRbycvLM2ttQF3EDUBiYmKXierw9PTk5ptvZunSpdTW1jJx4kS+/fZbe8tqkgEDBpjSS9T/3tR9NmlpaR3yx6kj0NIIPkREJmPIJzNGRMbWf9hKYFclPz+fFStW4OXlRY8ePSzSpr0N/I4dOxg9enSDia85c+Y0GQJqbwMfEBBg1urhnj17mhZqVVRU2CXk1FosXryYr776ivXr13Pq1Ck2buyYC9PDw8M5ffo0NTU1DaqchYaGEhcXR1JSko6Zb4aWZveexrCgKQy4sB6YAmZaS1RXpy7a5WKLcFpDdna2qeKRrTh37pwpKiY1NRUXF5dGq0abKztYVFRk1+Rnl112mVklEUNCQkhPT6e0tJQ+ffqYMlR2BcaNG0dtbS2PPPIIN998c4c18CJCeHg427dvb/Cd6devH+Xl5YwaNYq4uDiKi4stsq6iK9HsN1wp9a1Saj7wd6XUjAse2ri3g7qRtqWMe/02bRU2lpeXx7Jly8jNzQUMC5vqJiebw9nZ2ZQE7OzZs6ZFLfbA3Hq3PXv2pKKiguTkZAYMGICrq6vFC6LbCxHhuuuu48iRI7zyyiukpaWRl5dnb1lNMmTIEA4fPky/fv1M+1xdXU1hwJMmTSIhocXM5N0Sc+LgnxeRy4Gpxl0blFK6vmo7OH/+/EWNYWvZs2ePTdwz69atM7krrrvuOnbs2MHw4cMJCAi46I9LVFQU+/fvN+m05A+ctXBwcGiQxtnPz49z5851+tDJOu6++26Cg4MJDAxk8uTJbN68mauuusreshohItx66624uLg0edzNzY3q6mrTwjqNAXOSjf0NQ13Vw8bHQyLyV2sL05jPd999x2uvvWb1f8za2lpEhFmzZjF//nzc3NyoqanhwIEDDB8+/KLX+/n5cf78eRITExk2bJhVtVoLX19fCgoKqKqq4ocffiAlJcXektpF3759eeCBBwCYNm1ah3XTABed/wgODm6UIrm2trZBbYTuhjn3qQuAOUqpD5RSHwDzAJ0Zso3U1taa7R4wh/j4eO6//35WrVrFlClTLNZuU2RlZREaGmoKgQRDFsCysrJmR1YXMnToUA4dOkRQUJA1pVqNuhH8qVOnGDduHAUFBZw4caLZ8zuqy6MpZs+ezU8//WSKFKqpqeHXv/4169ats7My8xg4cCDHjx83bR88eJD169d3moya1sBcS+Nb77n9HKddgOYyK7aVp556iueee45x48ZZrM3mOHXqVAMfKBiqK82bN8/sNsLCwrjsssssrMx2uLm5UVFRQXp6OmFhYYwZM4bjx483GaZXWlrKmjVr7KCybURHR9O/f38++eQTlFI8+OCDbN68mQceeKBT5ORxcXGhoqKCgoICDhw4gIODA7Nnz8bFxaVT6LcG5hj4vwF7ReQjEfkYSABeuNhFIuImIjtFZL+IJIpIx182ZwPy8vIsVtxj8+bNHD9+nNtvv90i7V2M5rJetjZhmD0nVy1F3Z2YiDBq1Cj279/f6JzTp0/bQVn7+H//7//x/PPPc/XVV7N9+3bi4+MJCQnhvffes7c0s5gwYQIpKSk4OzubMon269ePU6dOcfbsWb744gvOnDlzkVa6DuZUdPoCmIghF813wCSl1FdmtF0BzFRKjQJGA/NEZGI7tHYJ8vLyCAgIaPV1NTU17Ny507RdVVXFww8/zLPPPmu2e6Q92HPlaUejuLi4QXqJkJCQJsv9ZWVlERIS0qkW4UyZMoUZM2YwdOhQtm3bho+PDy+//DLPP/98p1jk5e3tzdixYxvk2AkPDyc1NZWEhASuueYaDh482GXKM14Ms1w0SqkspdRPxodZhR6Vgbpaas7GR+dN5NFOtm/fTklJSZsN5a5du5g/f75p6fyLL76Iv78/t9xyi6WlNsnp06fp27evTfrq6PTo0YMBAwY02Ofs7NxoMk8phZ+fX6erTPThhx/ywgsvmO7Wxo4dS2RkJD/99JOdlbUNBwcHysvL6d27N87OzkydOpU9e/bYW5ZNsGouGhFxFJF9wBlgjVIq3pr9dWTOnz/fYATeWlJSUsjPzyc5OZnU1FT+9a9/8f7779ssJKzO56yBmTNnNnIz1bkBlFKcPn3aVMzE39+fs2fP2kmp5bj33nt5++23G+wrLCzk9ddft5Oi1jFjxgxT5JazszN+fn4cOXKEZcuWNele6ypY1cArpWqUUqMxrIaNEZFGsXQicreI7BaR3XWLZroirq6u9OjRo83ZCOsy5+3YsYMVK1Ywf/58qxrcnJycBlpramosVpyjs9NUFFRYWBgZGRkcOnSIzMxMVqxYQUREhCnVQWfn6quv5sCBA7z66qumgu7vv/8+Dz30UIN6uB2VHj16NBgMjR49moKCAhYuXEhRUREZGRkcP36cDRs2sHnzZjsqtSwtGnjjCDypvZ0opQqAOAwhlhcee0cpFa2UirbU5GNHRESIjo5ucwGOlJQUhgwZwo4dO1i9ejWXXnqphRU2ZNOmTaZiCyUlJXh4eFi1v86Og4MDVVVV5OTkMGnSJBYvXkyvXr3o0aMHpaWlpvOys7MbbHcWXF1defvttzl69Ci33XYb69ev5/XXX2fgwIH88ssv9pbXahwdHZk0aRKOjo5MnjyZzMxM3NzciI2NJTAwkOTkZHtLtAgtGnilVA1wVERaneBERHqJiK/xeQ9gDtDuH4vOSN1I2MnJiZ49e7apjZMnT3L99dezefNm4uLimDt3riUlNqCmpobAwEAKCgrIyspqlCFS0zSDBg1i4sSGcQQXutASEhIaxGp3Jq666ir+85//8O6773L55ZcTHBzMI488YhrRd1YcHBwYP348ffr0wdHRkUGDBnHixAk2bNjA8uXL7S2vXZjjovEDEkVknYj8VPcw47oQIE5EDgC7MPjgu2WKA0sU1T558iRXX301SUlJREZGWrWSzZkzZwgMDGTKlClkZWVx4sQJi4V2dmX69evXYrKr6upq3N3dO71PfuHChTz00EM899xzXHrppaxZs4ba2touE34oIkydOpUJEybg7+/fqfPOm5MM5Km2NKyUOgDYt/pEB6G9We6qqqrIzMxk4MCBjBkzhlmzZllQXWOysrKIiIjA0dGRsWPHWjxvTnfD0dGRmpoaUlJSiIyMJDk5udPnTHnhhf8thfH39yc2Npbdu3eze/duRowYYUdllqEuhffgwYM5evRop/0fMCfZ2EYR6QsMVEqtFRF3QM+2tYL2Gvi0tDSCg4NxcXHhueeeM9U7tRYFBQVdYjFSRyEiIoINGzZQWVnJvHnzOHv2LOfOnbNYoRd789vf/pbc3FxuuukmbrnlFuLj422yNsMW+Pv7d+pc8xc18CLya+BuwB+IBHoDbwPWHUZ2Idqb+zwlJYX+/fsDtCotQHvozKPLjkZYWBh+fn5kZmYiIvTv358TJ050GQN///33A4a5ptWrVxMREcHgwYNxcXHh/vvvZ+HCzp26qi5FdHvdrPbAHBfN/UAMEA+glDouIrqUeSto7wj+5MmTNpnkPHv2LJ6enhZNhqYx4OHhwcCBAwFDRsrMzEyGDh2Ku7u7nZVZDhHh+++/5+TJk5w4cYKsrCzuuusuDh8+3Kl/zAYNGsTx48c7pevJnP/kCqWUaYmeiDjRjVektoWKiop23bLWH8Fbk+3bt7N169ZGCcU0lmfOnDls3ry5UYx8R07Xaw4ODg5ERkYyd+5cbr31Vq6++moeeeSRTp3sq1evXo0mkFNSUli7dq0pIurkyZNkZWXZQ16LmGPgN4rIn4AeIjIH+Ab4r3VldT3a4/JITk62+gi+sLCQoKAgZs6cqQ28DXBzc2PatGkkJiaa9pWXl3Po0CE7qrI8L7zwAklJSYSEhPDSSy+1eaGfPRERnJycTLl4KioqOHnyJLNmzSIzM5OjR49y4sQJU2HwjoQ5Bv6PQC5wELgHWAE8aU1Rmv9RXV1NXFwcsbGxVu3n5MmTNrlL0PyPutTDdUYvIyMDT0/PTmkEm8PX15dt27axfft2vvvuO6666io+++wzOtuq9QEDBpjy/h86dMhUXD42NpYzZ84wY8YM011KbW1th1nMZk42yVrgY+B54DngY9WVvoEdnG3bthEeHm71Ytpnz55t8yIsTdsJCQkx3dpnZGQQERHRZWq+1mfAgAFs2rSJSZMm8e233xIbG9upUjiEhoZy4sQJampqyM/PN/2vODg4EBsbi6OjIz169KCsrIxdu3aRkJDA8uXL7Z5J1JySfQuAE8DrwBIgWUTmW1tYZ6S2tpYvvvjCNAKzxO/gTz/9xOWXX97udlqiTqeOnLE9gwYNMt3aV1dX4+/vT1FRkZ1VWQc3Nzcef/xxfvzxRxYuXMiVV15JTU2NvWWZhYgwefJkfv7552YXGfbv35/k5GSKioqIjY1l9OjRDVxw9sAcF80/gBlKqelKqWnADOCf1pXVOdm1axfBwcGm28+VK1cSFxfXquLSZ8+e5fvvvwcMhnfZsmVWMfB1vkMwJBbTo3f74OzsjKurK1lZWYgI3t7eFBYW2luW1fn73/9OSUlJp8pj4+fnx8KFCxk5cmSTx4ODg9myZYsp31Tv3r3JzMy0q8vNHANfpJSqn3knBeiaQ4x2UFhYSGVlJePGjeP06dPU1NTg7u7O+PHjTZVlzOHvf/87N9xwA2fPnmX//v2Ul5czZozlFwQfP36ckydPUlxcTEJCgllFszXWYeLEiWzfvp3AwMBuY+AdHBz4zW9+Y0pB3FlG8o6Ojs3e6YoICxcubJDWY8iQIWzZssVur69ZAy8ii0RkEbBbRFaIyG0iciuGCJpdNlPYSdi7dy/jx4/H29uboqIiUlNTCQ8PN+UEN4eCggLee+89pkyZwkcffcQLL7zAgw8+aBXXSXV1NdOmTePbb781ZdXT2AcR4f/+7/8YNGgQnp6eJhfNhQVEuhrXXXcdW7Zs4dVXX8Xf37/TJ/YC6NOnT4Ptvn37MmTIEFauXGmXilgtjeAvMz7cgBxgGjAdQ0RND6sr60TURULUX+lWZ+Bbw1tvvcWCBQt4/vnn+fvf/86WLVtMqwQtSWlpKe7u7vTo0YNbbrmlUy9C6Sq4ubnh7OyMo6OjaWLu66+/Jjk5merqalM9gK6Eh4cHN998M2+88QavvfYat912G/HxXa8mUK9evYiNjWXHjh0N9hcWFrJy5UrWrVtHXl6eVfpu1jmslLJNJecuwL59+xg9erRp28/Pj7S0tFb53pVSvPvuu3z33XeMGTOG0NBQbrvtNqusdKwfEqlXrXZMioqKGDRoEHl5eaSkpODu7o6ItLhG4fDhw6aqRZ2Fl156ib/+9a+4u7sTEBDAwoULufPOO3Fzc2P8+PEsWLDA3hItgo+PD25ubmRmZhIaGkpubi579uxh9uzZploC1sCcXDT9gQeAfvXPV0pZN7SjE1FaWtqgCHP//v1bXXd13759ODo6MmbMGESELVu2WG0Ze05OTqczBN2N1NRU+vbtS1BQEGAYAKxatYqwsLAmBw4HDx7kwIEDREZG4urqamu5baa+1ssuu4y9e/fy4osvUlBQwB/+8Af+7//+r8tEd0VHRxMXF8f58+dJTU1lzpw5pgGWtVyk5gwxfwTex+B77zzl4e2Ir69vA4NvDt999x2LFi0yfZmtVUFJKdXpU9V2By78ERYRxo0bZ1pkU5/8/HzOnz9vqkzUmReshYWFsWTJEpRSDBw4kISEBKKjo+0tyyKICDNmzODAgQPMmjXLJnfP5vRQrpR6XSkVp5TaWPewurJOgqVCoL7//nuuvvpqi7TVErp4dsfH2dmZqqqqRj/CgYGBTRYLOXDgADExMYSGhpKZmWkrmVZFRLjpppv47LPP7C3FoogIo0aNapX7tj2YY+D/JSLPiMgkERlb97jYRSLSR0TiROSwiCSKyEMW0NvhKCsrMxUHaCtJSUkUFhYyfvx4C6lqnrqiE5qOi5eXF15eXk0ec3BwaBByVxdp4+LigqurKxUVFTbRaAtuvPFGvvjiiy65stdWmPMzMgK4GZjJ/1w0yrjdEtXAH5RSe0TEC0gQkTVKqcNtVtsBOX/+fLuLY2zevNk02WJNlFJUV1fbbPSgaRst3WGFh4eTmppqcsPs37+/0cKbOhdcSUkJZ86c6bQum4EDBzJv3jymT5/ODz/8QEhICLW1tezevZvU1FQWLlzYKXO02xJz/tOvBSLqpww2B6VUFpBlfF4kIkcwFAvpcga+tf72C4mPjycmJsYyglqgrtaqpmPT0qrivn37smnTJnJycigpKSEgIKBBmKuPjw+FhYX4+Piwf/9+zp0712kNPMBHH33En/70J2699VZ++eUX3njjDV599VX8/Pz45ZdfeOedd+wtsUNjjoE/BPgCba6oKyL9MNRnbRTkKiJ3Y6gYZfWEWtbg/Pnz7da9c+dO7r33Xgspap6srKxO+R5r/oeTkxM9evQgMjKyyULoYWFhpKSkMGrUKEpLSwkICKCwsBBvb287qG0/IsKzzz5Lnz59OHbsGG+++Saff/45I0eOZPz48Tz22GP06dOHkydPEhkZaZV1I50Zc3wCvkCSiKwWkZ/qHuZ2ICKewHfA75RSjdZgK6XeUUpFK6Wim/rCdnRKS0vb5YMvLi7mxIkTzea3sCQFBQXtvtvQ2J9JkyY1adwBgoKCKC8vZ/Xq1QwePJioqCi7J7xqL66urtx2223ceuutuLm5MXnyZLy8vFi2bBmlpaUkJSURHBzMCy+80CUXSrUHc0bwz7S1cRFxxmDclyqlvm9rOx2d9oQc7tmzhxEjRtikSLFSSi9s6gZMmjSJY8eOERYWhohQXFzc6UNj77nnHl5++WXeeust0+sYPHgwS5YsMZ3Tu3dv7r33XnbtMmRSWbBgAcOHD+f555/vUqURW8NFDXxbQyLF8Cm8DxxRSr3alja6Mvv37+fLL7/E09PTJv53Tfdi0KBBDZ4fOnSoU9YUrSMyMpJPP/2Uq666qtlzbrjhBpYuXcqdd95JREQE1dXVZGZmMmPGDLZv394tBzfm5IMvEpFC46NcRGpExJx0d1MwRt+IyD7j4//arbgLUF5ezg033MCaNWt4+umnmTBhgtX77OwjOE3b6du3L9nZ2Z0+hPKmm25qcQGgiPDNN9+QkZHBP//5Tz7++GM+//xzHBwc+PTTT22otONgTkUnL6WUt1LKG0OSsauBt8y4botSSpRSI5VSo42PFRbQ3CH44YcfSE9Pb9MS42effZahQ4eyc+dO3n33XZvk2+jME22a9jNx4sRGya66Ih4eHvz888/s3r3b5KL6xz/+wZNPPklxcTFgCDboLrH10paVmCKyVyll8STl0dHRavfu3ZZu1uIUFhZy5MgRCgoKCA4ONiX4N4fa2lp69erFnj176Nu3rxVVGkhKSsLBwQEnJyccHR1t0qemYxIfH09kZCQVFRVkZWV1mRQA5vDAAw/w3XffMWHCBNauXUvv3r158cUX6dWrF9HR0Z0qf8+FiEiCUqrJD9OcZGOL6m06ANFA9/j5a4akpCSGDh2Kl5dXq1MVJCYm4uvrazNDm5WVRVVVFb6+vq0qPKLpekRHR7Ns2TICAgJwcHBosEhvz549REVFdWpD1xJvvPEGd955J9u3b+ejjz5i3bp1vPbaa+Tm5hIREcEPP/zQJRcAmjPrcFm9x6UYqjldYU1RHY0zZ86QnZ1t2i4qKsLb2xsRafXEzaZNm5g2bZqlJTaLUoqAgABOnz7dbSMJNAYcHR2ZP38+sbGxTJgwgZ07d5qOnTp1itTUVDuqsz6jR4/mvvvuw8fHh0WLFrFhwwb27dtHRUUFv/nNb0wFtX/3u9/Rt29fLr/8ctLT0+0tu12Y44O/vd7j10qpF5RSbV701BlJTEw0FUYuLi5uV6bHjRs32szAl5eX4+bmxogRI6ioqNCTrBp69OiBiODs7EyPHj1ME69eXl5kZGTYWZ3tcXZ25ttvvyU5OZkFCxYQExNDVVUVy5cvZ/z48cTExFBWVmZvmW2m2XsSEXm6heuUUup5K+jpcNTU1JhyuIDBPTNkyJA2taWUYuPGjbz00kuWlNiIxMRElFK4uLjQp08fnJ2dWbx4sVX71HQ+goKCOHPmjCnHfGepi2ppvL29Wb16NU8//TQ33XQTN910EwDDhw9n27ZtfPPNN9xyyy12Vtk2WhrBlzTxALgTeNzKujoMKSkpRERE4OzsTGVlZbtWgx47dgxXV9cWq/JYgjNnzpCTk8OJEycIDQ0FaHUBEk3XJygoiJycHIqLi/H09DR9x7sjzs7O/O1vfzMZ9zruvfdeU2HwzkizBl4p9Y+6B/AOhhDJ24EvgQgb6bM7p0+fpm/fvvTp04ejR4+2y4/9ySefsHDhQqu7SpRSTJ48maqqKl1MW9MsXl5eFBUVkZ+fT8+ePenTp08jn/PRo0cpKSlppoWuz4IFC0hLS2PPnj32ltImWvTBi4i/iPwFOIDBnTNWKfV4d/HBl5WV4erqiogQFhbGpk2b2uyeycvL4+233+bxx61781OXDrhHjx5cfrmuqqhpnrqBxtmzZ+nZsydhYWGcOHHCdLy4uJhTp051i/j55nBycuK5555j3rx5LF26tMExpZSpQHpHpVkDLyIvA7swRM2MUEo9q5Q6ZzNlHYBDhw4xfPhwwPBBjxkzpkFq1tbw97//ncWLF1s9PDI7O5vg4GCr9qHpWpw/fx5vb2+cnZ3p3bs3R44cobq6mq1btzJ9+nT8/f275QRsHXfccQerV6/m0UcfZdu2bQDs2rWLkSNHmrLA/uc//+Hdd9+1p8ymqavReeEDQ3GPMgwGvrDeowgobO669jzGjRunOgq1tbXql19+sUhbFRUVyt/fX506dcoi7bXEjh07VHFxsdX70XQNdu3apX788ccG+3bs2KHi4uJUTk6OUsrwv7B8+XJVXl5uD4kdhi+++EKNHDlS/fOf/1SBgYHqvffeU6Ghoerf//636tmzpwoICFDnzp1TtbW1qqqqyma6gN2qGZvabBSNUqr7ZeapR0ZGBr1797ZIWxs2bGDQoEE2WdxUUlJitYLdmq5HUFAQKSkpDfZdmBtJRJg2bRpxcXGmgIPOXESkrSxevJj333+fDz74gB07dtC/f388PT257rrreP/999m0aRNPPvkk+/bt49ChQ1x55ZX8/e9/t2uRnW5txFviyJEjbfa3X8h3331nk4LaGk1rCQwMNEVatYSHhwcxMTG4u7tz/PjxRiu4lVJNFgTvSogIy5YtY9euXaYfuF/96lf88ssv3H777Tz99NO8++67zJo1i8TERIKCghg/fjzbt2+3m+autzbXAhQUFODt7W2R9KI1NTX8+OOPNvmQLVEAXNO9cHV15ZJLLjHrXH9/f/z9/cnPzyc3N5fAwECT/37Lli1kZ2ezaNGiLh25dWEUnYgwZ84cACIiIsjNzTUl9XvppZcYP348v/rVrxg1ahSff/65zRP+6RF8E+zbt4/Ro0e3+jqlFOvXrzctjlqwYAFTpkwhNDSUiAjrR5Za0q2k0TTHoEGDOHbsGJmZmezYsYP169fTt29fLrnkkk5fPaq9XGjAr7nmGpKTk/H39+cPf/hDo/M3bdrEpk2brHb3o0fwF3DmzBnc3NxanXQpMzOTm266iU2bNvHll18SFhbGsWPHWLJkic2iWrKyspg4caJN+tJ0X9zc3CgvL2ffvn3Mnz+/wbqOgwcPMmLECKqrq/XiOiOurq4sWbKEUaNG8eWXX3LdddcBsHLlSm6//XYiIyMZNWoUb7110SzsrabbG/hjx47h5OREREQEtbW17N69m3nz5rW6nWeffZYhQ4Zw991389prrzFs2DDuvPNOLr30Uiuobpqqqir9T6WxCeHh4fj4+DRatBcREcH69espLCzkiiuu6JZVlJrC29ubL774gptvvpklS5Ywffp03nvvPb755htiY2Ot1m+b8sGb1bDIB8BC4IxSarg519gjH/yaNWuora1l6tSpbN26ldGjRxMQENCqNs6dO0dERARJSUn07NmTyMhI8vLyOH78uFkTWJZAKUVcXBwzZ860SX8aTUucOnWKiooKBg8ebG8pHYrq6mqWL19OQkICI0aM4Nprr213m+3KB98OPgKWAJ9YsY92UZcZctSoUSxbtozZs2e32rgDfPjhhyxYsICgoCAA/vCHP7Bx40abGXcwTAz7+fnZrD+NpiX69u3LmjVrtIG/ACcnJ6644gquuMI2GdetNoIHEJF+wM8dbQRfVVUFwN69exk8eLCp6EFbqK2tZeDAgSxdurSB/7umpsZm0QTHjx8nKSmJKVOmtHmlrUZjafbu3Uvv3r3tGgfeHbDXCN4sRORu4G4w+PVswaZNm3B0dKSysrJdxh0MEyV+fn6NFofYyriXl5eTnp5ukyRmGk1riIqKYtu2bdrA2xG7G3il1DsYslUSHR1tvdsJI3l5eXh7ezN+/HiLtLdkyRJ++9vf2sS4NpVnJjk5mWHDhmnjrulwuLi4UFtbS3V1taliVE1NDbNnz9bfVxvRraa4Kysr2bVrF2PGWKZe+PHjx0lISLBJMY3KykqWL1/eaH9OTo4eIWk6LEOHDmXbtm2kp6czc+ZMIiIiSE5ObnReaWmpHdR1fbqNgT9y5Ajbtm0jJibGIsV1KyoquOuuu3jwwQdtsnq0biFEefn/6p3X1NTg4OCgR0OaDktISAjV1dWmUMCIiAhSUlIoLCxk69atgCEC7Oeff2bTpk0dPv1uZ8NqBl5EvgC2A4NFJF1E7rRWX+aQnp7O9OnT6dmzZ7vbUkpxxx130KtXL/70pz9ZQN3FycvLIzo6mlOnTplqRq5cudImCcw0mvYwc+ZM05yUiDBo0CD27NmDiFBQUEBubi5DhgxhyJAh7Nq1y85quxZW88Erpa63VtvmcujQIVPoYlvCH5vjk08+4eDBg8THx9tsIUd+fj5Tpkxh8+bNFBYWMmnSJDw8PHBxcbFJ/xqNpejfvz/9+/entLSUvXv3opQiOjoaNzc39u/fj1KKiooKysvL21weU2PA7pOs1iIrK4vCwkJSU1NxdHRkxowZFmk3LS2NRx55hLVr19o0sVdtbS1OTk5UV1dTWFiowyE1nR53d3dKS0sREdzc3ADo3bs3GRkZJCUlWfT/trvSJX3wxcXF7N+/n0mTJjFjxgz8/PzaPNJVSvHSSy/x8ssvU1VVxfXXX8/DDz/MqFGjLKzaPHr37m2xSWKNxt6EhoY2WKA3ePBgNmzYQO/evamtrW2UlljTOqy60Km1WGKhU0lJCRs3bmTOnDntzstSUlLCbbfdRlpaGsXFxbi4uBAcHMzPP/9s0xwbtbW1bNq0ienTp9usT43GFtTZn/qBAllZWYSEhHDs2DHc3NwICQmhoqICT09Pe8ns0LS00KnLjeDj4+OZPXt2u417WloaU6ZMwdPTkw0bNhAXF8f48eP59NNPrWbck5KSmgwhO3funE5DoOmSiEijKLCQkBDAEHFz9OhR1qxZw86dO1ts5+zZs/z888+sXbuWyspKALZu3cr58+etI7yT0KUMfFVVFQ4ODu2eeKytreW6665j0aJFfPDBB7i5udGrVy/+85//WCQKpzkyMzOpra1lzZo11NTUmPbn5eXRq1cvq/Wr0XREnJyc8PLyMoVY1v+fAEPIcGpqKgB79uxh3rx5jB8/np07d3L+/Hmqq6uJj4+3ue6ORJcy8EeOHGHYsGHtbufDDz+kpqaGJ5980mYx5nW3qoMGDWL8+PH88ssvppjgnJwci0YBaTSdhYkTJ+Ll5cXAgQNJTk7m6NGjrFq1ioqKCtavX8/p06eJj4/H398fJycnU+qRDRs2MGnSJHr37t3kXXF3oUsZ+DNnzrR7VWdSUhJPPPEE//73v23qZ8/PzzfdHfj6+jJ8+HCOHj2KUorKykodDqnp1oSFhZGYmEhOTg6XXHIJq1atYvz48cTGxuLq6tqgAltMTAzDhw/HxcWFYcOGUVxczPLlyxssEgRaXFRVWlpKdXW1tV6OzegyYZI1NTVt9rsrpcjKymLPnj3cc889vPLKKzaPVElLS2uQbC0sLIw1a9bg5ubWLSvYazT1ERFiYmIIDQ3FwcGhQbrdC8truri4EBkZabpu9OjRhIWFceTIEcaMGUNZWRnbtm2jrKyMYcOGNVlOc9u2bXh4eDBp0iSrvi5r02VG8IWFha3ODJmQkMCiRYvw9fVlzJgx/OMf/+Bf//oXt9xyi5VUNk9+fn6DiVQRITg4mJ07d2oDr9FgGPS09a46ICCAvLw8wJBNdvLkySxcuJDz58+zd+/eBueWlZXh4uJCVVUVxcXF7dZtT7qMgT937pxZq95qampYsmQJI0aM4Morr2TatGkkJyeTk5NDXFwc11xzjfXFNsOF/v6oqCiGDx+uy55pNBbAx8eHffv20bdvX9MixTFjxuDu7k5CQoLpvH379jF69GhiYmJM0TtKKQ4fPmwX3e2hy1gOcyoabdmyhdjYWL755hvefvttTp8+zUMPPWSXCBWllMnHV1hYiIeHR6NzHB0diYqKsrU0jaZLEhUVxeHDhxtVmRo8eDAlJSUUFxdTXl5OSUkJ3t7euLm54e3tzZkzZzh69Cj79u3rdFkvu4yBLywsxMvLq8G+vXv3kpOTQ21tLTfffDO33HILd911F3FxcUyZMsWuI+PExERT+t8DBw4wYsQIu2nRaLoDHh4eXH/99U1GxsXExLBr1y6T+6aOMWPGkJCQwOnTp1mwYAEHDx60peR202UmWZVSODg4UFhYyO7du/nyyy/58ccfcXV1Zdq0aaSlpXH48GFTzgt7UlhYSG5uLpGRkZw+fZry8nLc3d3tLUuj6fI0F/bs5uaGl5cXPXv2bPC/6OjoyLBhw/Dw8MDHx4fCwkKUUp0mRXeXGcGDoSjG5MmTeeqpp/Dz8+P48eMsWbKE3Nxcfvzxxw5h3MGw2vaSSy4hKiqKTZs2MWTIEHtL0mi6PdHR0U0GNPTt29e0DiUyMpJ9+/YBhkibpKQkwDBo279/P7m5uTbTaw5dJhdNXFwcO3fuZOPGjSxfvrzD/sJmZWWRk5NjCu0qKirC09Ozw+rVaDQNSUxM5OjRo4wZM4Zjx44xd+5cfvnlF8aMGWP6/w4NDWXIkCEcP36c0tJSoqKirDbA7NBFty1BVVUVJSUlvPzyy+zcubNDG8sDBw4wZ84c0/aF8wYajaZjExUVxeDBg3FycsLR0ZEVK1YQERFBYGCgaaFlRkYGGzduZODAgQQFBbFu3TqmTJli8/z2VnXRiMg8ETkqIski8kdr9VNQUMCaNWv49a9/3eSihY5CUVERvr6+OuxRo+nk1JX9DA8PJzAwsJGbtXfv3syaNYvw8HD8/f259NJL2bZtW6PVs+np6SQkJHDmzBnr6LRKq4CIOAJvAnOAdGCXiPyklLJ4MOmRI0dYs2YNmzdvtnTTFiUnJ8eUKU+j0XQNxo8ff9FznJyciImJIS4ujvLycqZPn46DgwNJSUnExMRYrXiQNV00MUCyUioFQES+BK4ALG7gf/zxR6655hqrZnpsipKSEsrKyppNBJafn8+RI0fo3bs3/fr148yZM4wdO9amGjUaTccgICCAWbNmUV5ezoYNG3BzczOV3rQW1jTwvYG0etvpwIQLTxKRu4G7jZvFInK0jf0FPP/883ltvNaaBABal3l0RE2gdbUWrat1tFdX3+YO2H2SVSn1DvBOe9sRkd3NzSTbE63LfDqiJtC6WovW1Tqsqcuas30ZQJ9622HGfRqNRqOxAdY08LuAgSLSX0RcgOuAn6zYn0aj0WjqYTUXjVKqWkR+C6wGHIEPlFKJ1uoPC7h5rITWZT4dURNoXa1F62odVtPVoVayajQajcZy6BU3Go1G00XRBl6j0Wi6KB3SwF8sxYGIuIrIV8bj8SLSr96xJ4z7j4rIpea2aU1dIjJHRBJE5KDx78x612wwtrnP+Gh11fB26OonImX1+n673jXjjHqTReR1aUOCn3bourGepn0iUisio43HbPF+TRWRPSJSLSLXXHDsVhE5bnzcWm9/u96vtmoSkdEisl1EEkXkgIgsrnfsIxE5We+9Gt0aTe3RZTxWU6/vn+rt72/8vJONn3+rK8q34/2accF3q1xErjQes8X79bCIHDZ+VutEpG+9Y5b/bimlOtQDw4TsCSACcAH2A8MuOOc3wNvG59cBXxmfDzOe7wr0N7bjaE6bVtY1Bgg1Ph8OZNS7ZgMQbaf3qx9wqJl2dwITAQFWAvNtpeuCc0YAJ2z8fvUDRgKfANfU2+8PpBj/+hmf+7X3/WqnpkHAQOPzUCAL8DVuf1T/XFu+V8Zjxc20+zVwnfH528B9ttR1weeZD7jb8P2aUa+/+/jf/6JVvlsdcQRvSnGglKoE6lIc1OcK4GPj82+BWcZftSuAL5VSFUqpk0CysT1z2rSaLqXUXqVUpnF/ItBDRFxb2b/FdTXXoIiEAN5KqR3K8A37BLjSTrquN15rKS6qSyl1Sil1AKi94NpLgTVKqXyl1DlgDTDPAu9XmzUppY4ppY4bn2cCZwBL1aBsz3vVJMbPdyaGzxsMn/+VdtJ1DbBSKWWpOnzm6Iqr198ODOuDwErfrY5o4JtKcdC7uXOUUtXAeaBnC9ea06Y1ddXnamCPUqqi3r4PjbeET7X21t4CuvqLyF4R2SgisfXOT79Im9bWVcdi4IsL9ln7/Wrtte19vyzx/UREYjCMHE/U2/2C0R3wzzYMKtqry01EdovIjjo3CIbPt8D4ebelTUvoquM6Gn+3bPl+3YlhRN7Ste36bnVEA99lEZEo4CXgnnq7b1RKjQBijY+bbSgpCwhXSo0BHgY+FxFvG/bfIiIyAShVSh2qt9ue71eHxTjS+xS4XSlVN2p9AhgCjMdw6/+4jWX1VYYl+DcAr4lIpI37bxbj+zUCwzqdOmz2fonITUA08LK1+oCOaeDNSXFgOkdEnAAf4GwL11oibUJ7dCEiYcAPwC1KKdMISymVYfxbBHyO4TbPJrqMrqyzxv4TMIz8BhnPD6t3vc3fLyONRlg2er9ae2173692fT+NP8rLgT8rpXbU7VdKZSkDFcCH2Pa9qv9ZpWCYOxmD4fP1NX7erW7TErqM/Ar4QSlVVU+vTd4vEZkN/Bm4vN6dvHW+W22dULDWA8Pq2hQMk6R1ExVRF5xzPw0n5742Po+i4SRrCoaJj4u2aWVdvsbzFzXRZoDxuTMGv+S9NtTVC3A0Po8wfnH8VdMTO/9nK13GbQejnghbv1/1zv2IxpOsJzFMgvkZn7f7/WqnJhdgHfC7Js4NMf4V4DXgRRu+V36Aq/F5AHAc44Qj8A0NJ1l/Yytd9fbvAGbY+v3C8CN3AuPEuNW/W615AbZ6AP8HHDO+EX827vt/GH7xANyMX5Jk44uvbwT+bLzuKPVmm5tq01a6gCeBEmBfvUcg4AEkAAcwTL7+C6PBtZGuq4397gP2AJfVazMaOGRscwnGVc82/BynAzsuaM9W79d4DL7OEgwjzsR6195h1JuMwR1ikferrZqAm4CqC75bo43H1gMHjbo+Azxt9V4Bk4197zf+vbNemxHGzzvZ+Pm72vgz7Idh8OBwQZu2eL/WAjn1PqufrPnd0qkKNBqNpovSEX3wGo1Go7EA2sBrNBpNF0UbeI1Go+miaAOv0Wg0XRRt4DUajaaLog28plMjIj3rZf/LFpEM4/NiEXnLSn3+TkRusWB7X4rIQEu1p9HUocMkNV0GEXkWQwbDV6zYhxOGNQNj1f/yqbS3zWnATUqpX1uiPY2mDj2C13RJRGS6iPxsfP6siHwsIptF5LSILBKRvxtzbK8SEWfjeeOMSdcSRGS1MV/JhczEkCyu2njNg/Xye39p3OchIh+IyE5jIrcrjPsdReQVETlkPP8BY5ubgdn1lu9rNBZBG3hNdyESg3G+HMMqxThlSFpWBiwwGvk3MCxrHwd8ALzQRDtTMKymreOPwBil1EjgXuO+PwPrlVIxGPJ/vywiHsDdGFZRjjaevxRAGZKDJQOjLPdyNRpD7gSNpjuwUilVJSIHMeQnWmXcfxCD0R2MoRjLGmMGYkcM2TYvJAQ4Um/7ALBURH4EfjTumwtcLiKPGLfdgHBgNobcO9UASqn8eu2cwVCwo/6Ph0bTLrSB13QXKsAwWhaRKvW/yadaDP8HgiFfyaSLtFOGwWDXsQCYClwG/FlERhjbulopdbT+hRdJXe9mbFujsRjaRaPRGDgK9BKRSQAi4mzM338hR4ABxnMcgD5KqTgMucN9AE8MOcYfqCtGIiJjjNeuAe6p87WLiH+9dgdhSCil0VgMbeA1GkAZSqxdA7wkIvsxZPqb3MSpKzGM2MHgxvnM6PbZC7yulCoAnseQzviAiCQatwHeA1KN+/djKISBiAQBZUqpbCu8NE03RodJajStRER+AB5TxlqoFmjv90ChUup9S7Sn0dShR/AaTev5I4bJVktRwP+Kj2s0FkOP4DUajaaLokfwGo1G00XRBl6j0Wi6KNrAazQaTRdFG3iNRqPpomgDr9FoNF2U/w9MgcivXvapUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Example 2: Surface reactions: IP3 receptor model \n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "import steps.interface\n",
    "\n",
    "from steps.rng import *\n",
    "from steps.sim import *\n",
    "from steps.saving import *\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy  as np\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Simulation control variables\n",
    "NITER = 100\n",
    "T_END = 0.201\n",
    "DT = 0.001\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Import model\n",
    "mdl = getModel()\n",
    "\n",
    "# Import geometry \n",
    "geom = getGeom(mdl)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Create random number generator\n",
    "r = RNG('mt19937', 512, 654)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Create well-mixed solver object\n",
    "sim = Simulation('Wmdirect', mdl, geom, r)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "rs = ResultSelector(sim)\n",
    "\n",
    "results = rs.cyt.Ca.Conc * 1e6 << rs.ERmemb.Ropen.Count\n",
    "\n",
    "sim.toSave(results, dt=DT)\n",
    "\n",
    "# Run the simulation\n",
    "for i in range(NITER):\n",
    "    \n",
    "    # Start a new run\n",
    "    sim.newRun()\n",
    "    \n",
    "    # Set initial conditions\n",
    "    sim.cyt.Ca.Conc = 3.30657e-08\n",
    "    sim.cyt.IP3.Conc = 2.5e-06\n",
    "    sim.ER.Ca.Conc = 0.00015\n",
    "    sim.ERmemb.R.Count = 16\n",
    "    \n",
    "    # Clamp ER calcium to fixed concentration\n",
    "    sim.ER.Ca.Clamped = True\n",
    "\n",
    "    # Run the simulation\n",
    "    sim.run(T_END)\n",
    "\n",
    "print(f'Ran {NITER} sim iterations')\n",
    "\n",
    "# Numpy array manipulation\n",
    "mean_res = np.mean(results.data, 0)\n",
    "std_res = np.std(results.data, 0)\n",
    "res_std1 = mean_res + std_res\n",
    "res_std2 = mean_res - std_res\n",
    "\n",
    "tpnts = results.time[0]\n",
    "\n",
    "# Plot Ca concentration\n",
    "plt.plot(tpnts, mean_res[:, 0], color = 'black', linewidth = 1.0, label = 'mean')\n",
    "plt.plot(tpnts, res_std1[:, 0], color = 'gray', linewidth = 0.5, label = 'standard deviation')\n",
    "plt.plot(tpnts, res_std2[:, 0], color = 'gray', linewidth = 0.5)\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Cytosolic Ca concentration ($\\mu$M)')\n",
    "plt.title(f'{NITER} iterations')\n",
    "plt.ylim(0)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot open IP3 receptors\n",
    "plt.plot(tpnts, mean_res[:, 1], color = 'black', linewidth = 1.0, label = 'mean')\n",
    "plt.plot(tpnts, res_std1[:, 1], color = 'gray', linewidth = 0.5, label = 'standard deviation')\n",
    "plt.plot(tpnts, res_std2[:, 1], color = 'gray', linewidth = 0.5)\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Number of open IP3 receptors')\n",
    "plt.title(f'{NITER} iterations')\n",
    "plt.ylim(0)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the simulation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: Unbounded diffusion \n",
    "example script: ex3_diff.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading mesh...\n",
      "Mesh Loaded\n",
      "Finding tetrahedron samples...\n",
      "Tetrahedron samples found\n",
      "Model checking:\n",
      "No errors were found\n",
      "Running iteration 0\n",
      "Running iteration 1\n",
      "Running iteration 2\n",
      "Running iteration 3\n",
      "Running iteration 4\n",
      "Running iteration 5\n",
      "Running iteration 6\n",
      "Running iteration 7\n",
      "Running iteration 8\n",
      "Running iteration 9\n"
     ]
    }
   ],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# Example 3: Unbounded diffusion \n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "import steps.interface\n",
    "\n",
    "from steps.model import *\n",
    "from steps.geom import *\n",
    "from steps.sim import *\n",
    "from steps.saving import *\n",
    "from steps.rng import *\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "########################################################################\n",
    "\n",
    "# The Abaqus file to import\n",
    "MESHFILE = 'meshes/sp_10r_3875.msh'\n",
    "# The number of iterations to run \n",
    "NITER = 10\n",
    "# The data collection time increment (s)\n",
    "DT = 0.001\n",
    "# The simulation endtime (s)\n",
    "INT = 0.1\n",
    "\n",
    "# The number of molecules to be injected into the centre\n",
    "NINJECT = 10000\n",
    "\n",
    "# The number of tetrahedral elements to sample data from. \n",
    "SAMPLE = 2000\n",
    "\n",
    "# The diffusion constant for our diffusing species (m^2/s)\n",
    "DCST= 20.0e-12\n",
    "\n",
    "########################################################################\n",
    "\n",
    "def gen_model():         \n",
    "    mdl = Model()\n",
    "    with mdl:\n",
    "        SA = Species.Create()\n",
    "        vsys = VolumeSystem.Create()\n",
    "        with vsys:\n",
    "            Diffusion(SA, DCST)\n",
    "    return mdl\n",
    "\n",
    "########################################################################\n",
    "\n",
    "def gen_geom(meshPath, mdl):\n",
    "\n",
    "    print(\"Loading mesh...\")\n",
    "    mesh = TetMesh.LoadGmsh(meshPath, 1e-6)\n",
    "    print(\"Mesh Loaded\")\n",
    "\n",
    "    with mesh:\n",
    "        # Create a compartment containing all tetrahedron\n",
    "        cyto = Compartment.Create(mesh.tets, mdl.vsys)\n",
    "\n",
    "        print(\"Finding tetrahedron samples...\")\n",
    "        # List to hold tetrahedrons\n",
    "        tets = TetList()\n",
    "\n",
    "        # Fetch the central tetrahedron\n",
    "        ctet = mesh.tets[0, 0, 0]\n",
    "        tets.append(ctet)\n",
    "        tets += ctet.neighbs\n",
    "\n",
    "        # Find the maximum and minimum coordinates of the mesh\n",
    "        bmin = mesh.bbox.min\n",
    "        bmax = mesh.bbox.max\n",
    "\n",
    "        # Run a loop until we have stored all tet indices we require\n",
    "        while len(tets) < SAMPLE:\n",
    "            # Pick a random position within the bounding box\n",
    "            pos = bmin + (bmax - bmin) * np.random.random(3)\n",
    "            if pos in mesh.tets:\n",
    "                tets.append(mesh.tets[pos])\n",
    "\n",
    "        # Find the radial distance of the tetrahedrons to mesh center:\n",
    "        tetrads = np.array([np.linalg.norm(tet.center - ctet.center)*1e6 for tet in tets])\n",
    "\n",
    "        print(\"Tetrahedron samples found\")\n",
    "\n",
    "    return mesh, tets, tetrads\n",
    "\n",
    "########################################################################\n",
    "\n",
    "model = gen_model()\n",
    "mesh, tets, tetrads = gen_geom(MESHFILE, model)\n",
    "\n",
    "rng = RNG('mt19937', 512, 2903)\n",
    "\n",
    "sim = Simulation('Tetexact', model, mesh, rng)\n",
    "\n",
    "rs = ResultSelector(sim)\n",
    "\n",
    "AConc = rs.TETS(tets).SA.Conc\n",
    "\n",
    "sim.toSave(AConc, dt=DT)\n",
    "\n",
    "for i in range(NITER):\n",
    "    sim.newRun()\n",
    "    print(\"Running iteration\", i)\n",
    "    # Inject all molecules into the central tet:\n",
    "    sim.TET(0, 0, 0).SA.Count = NINJECT\n",
    "    sim.run(INT)\n",
    "\n",
    "########################################################################\n",
    "\n",
    "def plotres(tidx, tpnts, res_mean, tetrads):\n",
    "    if tidx >= len(tpnts):\n",
    "        raise ValueError('Time index is out of range.')\n",
    "    t = tpnts[tidx]\n",
    "\n",
    "    plt.scatter(tetrads, res_mean[tidx,:], s=2)\n",
    "    plt.xlabel('Radial distance of tetrahedron ($\\mu$m)')            \n",
    "    plt.ylabel('Concentration in tetrahedron ($\\mu$M)')\n",
    "    plt.title(f'Unbounded diffusion. Time: {t}s')\n",
    "\n",
    "    plotAnalytical(t)\n",
    "\n",
    "    plt.xlim(0.0, max(tetrads))\n",
    "    plt.ylim(0.0)\n",
    "    plt.show()\n",
    "\n",
    "########################################################################\n",
    "\n",
    "def plotAnalytical(t):     \n",
    "    segs = 100     \n",
    "    anlytconc = np.zeros((segs))     \n",
    "    radialds = np.zeros((segs))     \n",
    "    maxrad = 0.0     \n",
    "    for i in tetrads:         \n",
    "        if (i > maxrad): maxrad = i     \n",
    "    maxrad *= 1e-6     \n",
    "    intervals = maxrad/segs     \n",
    "    rad = 0.0     \n",
    "    for i in range((segs)):         \n",
    "        # Find the conc from analytical solution, and convert to mol/L         \n",
    "        anlytconc[i]=1.0e3*(1/6.022e23)* \\\n",
    "                ((NINJECT/((4*np.pi*DCST*t) ** 1.5))* \\\n",
    "                (np.exp((-1.0*(rad*rad))/(4*DCST*t))))         \n",
    "        radialds[i] = rad*1e6         \n",
    "        rad += intervals     \n",
    "    plt.plot(radialds, anlytconc, color = 'red')\n",
    "\n",
    "########################################################################\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the simulation and plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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"
    },
    {
     "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": [
    "tpnts = AConc.time[0]\n",
    "res_mean = np.mean(AConc.data, axis=0)*1e6\n",
    "\n",
    "plotres(10, tpnts, res_mean, tetrads)\n",
    "plotres(100, tpnts, res_mean, tetrads)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: Hodgkin-Huxley Action Potential propagation model\n",
    "example script: ex4_HH.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found  4 I_inject vertices\n",
      "Found  2 triangles on bottom face\n",
      "Model checking:\n",
      "No errors were found\n"
     ]
    }
   ],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #  \n",
    "\n",
    "# Example 4: Hodgkin-Huxley Action Potential propagation model\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #  \n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # IMPORTS # # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "import steps.interface\n",
    "\n",
    "from steps.model import *\n",
    "from steps.geom import *\n",
    "from steps.sim import *\n",
    "from steps.saving import *\n",
    "from steps.rng import *\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #  \n",
    "# # # # # # # # # # # # # # # # # # PARAMETERS  # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# # # # # # # # # # # # # # # # # # CHANNELS  # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# Potassium conductance = 0.036 S/cm2\n",
    "# Sodium conductance = 0.120 S/cm2\n",
    "\n",
    "# Potassium single-channel conductance\n",
    "K_G = 20.0e-12 # Siemens\n",
    "\n",
    "# Potassium channel density\n",
    "K_ro = 18.0e12 # per square meter\n",
    "\n",
    "# Potassium reversal potential\n",
    "K_rev = -77e-3 # volts\n",
    "\n",
    "# Sodium single-channel conductance\n",
    "Na_G = 20.0e-12 # Siemens\n",
    "\n",
    "# Sodium channel density\n",
    "Na_ro = 60.0e12 # per square meter\n",
    "\n",
    "# Sodium reversal potential\n",
    "Na_rev = 50e-3 # volts\n",
    "\n",
    "# Leak single-channel conductance\n",
    "L_G = 0.3e-12 # Siemens\n",
    "\n",
    "# Leak density\n",
    "L_ro = 10.0e12 # per square meter\n",
    "\n",
    "# Leak reveral potential\n",
    "leak_rev = -54.4e-3 # volts\n",
    "\n",
    "\n",
    "# A table of potassium channel population factors: \n",
    "# n0, n1, n2, n3, n4\n",
    "K_facs = [ 0.21768, 0.40513, 0.28093, 0.08647, 0.00979 ]\n",
    "\n",
    "# A table of sodium channel population factors\n",
    "# m0h0, m1h0, m2h0, m3h0, m0h1, m1h1, m2h1, m3h1:\n",
    "Na_facs = [[0.34412, 0.05733, 0.00327, 6.0e-05],\n",
    "           [0.50558, 0.08504, 0.00449, 0.00010]]\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # RATE FUNCTION # # # # # # # # # # # # # # # #\n",
    "\n",
    "def HHRateFunction(celsius, A, B, C, D, F, H, V, abs_tol=1e-13):\n",
    "    # Temperature dependence\n",
    "    thi = 1e3 * math.pow(3.0, ((celsius-6.3)/10.0))\n",
    "    \n",
    "    num = A + B * V * 1e3\n",
    "    denom = C + H * math.exp((V * 1e3 + D) / F)\n",
    "    if math.isclose(num, 0, abs_tol=abs_tol) and math.isclose(denom, 0, abs_tol=abs_tol):\n",
    "        return thi * F * B / (H * math.exp((V * 1e3 + D) / F))\n",
    "    else:\n",
    "        return thi * num / denom\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # MESH  # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "meshfile_ab = 'meshes/axon_cube_L1000um_D443nm_equiv0.5_19087tets.inp'\n",
    "\n",
    "# # # # # # # # # # # # # # # SIMULATION CONTROLS # # # # # # # # # # # # # # # #\n",
    "\n",
    "# Temperature for gating kinetics\n",
    "celsius = 20.0\n",
    "\n",
    "# Current injection\n",
    "Iclamp = 50.0e-12 #\tamps\n",
    "\n",
    "# Voltage range for gating kinetics in Volts\n",
    "Vrange = [-100.0e-3, 50e-3, 1e-4]\n",
    "\n",
    "# The simulation dt\n",
    "DT_sim = 1.0e-4 # seconds\n",
    "\n",
    "# The time until which the simulation should be run\n",
    "ENDT = 4.0e-3\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # BIOCHEMICAL MODEL # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "mdl = Model()\n",
    "\n",
    "r = ReactionManager()\n",
    "\n",
    "with mdl:\n",
    "    ssys = SurfaceSystem.Create()\n",
    "\n",
    "    #  Potassium channel\n",
    "    Ko, Kc = SubUnitState.Create()\n",
    "    KSU = SubUnit.Create([Ko, Kc])\n",
    "    VGKC = Channel.Create([KSU]*4)\n",
    "\n",
    "    # Sodium channel\n",
    "    Na_mo, Na_mc, Na_hi, Na_ha = SubUnitState.Create()\n",
    "    NamSU, NahSU = SubUnit.Create(\n",
    "        [Na_mo, Na_mc],\n",
    "        [Na_hi, Na_ha]\n",
    "    )\n",
    "    VGNaC = Channel.Create([NamSU, NamSU, NamSU, NahSU])\n",
    "\n",
    "    # Leak channel\n",
    "    lsus = SubUnitState.Create()\n",
    "    Leak = Channel.Create([lsus])\n",
    "\n",
    "    # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "    # Hodgkin-Huxley gating kinetics\n",
    "\n",
    "    _a_n = VDepRate(lambda V: HHRateFunction(celsius, -0.55, -0.01, -1, 55, -10, 1, V), vrange=Vrange)\n",
    "    _b_n = VDepRate(lambda V: HHRateFunction(celsius, 1, 0, 0, 65, 80, 8, V), vrange=Vrange)\n",
    "    \n",
    "    _a_m = VDepRate(lambda V: HHRateFunction(celsius, -4, -0.1, -1, 40, -10, 1, V), vrange=Vrange)\n",
    "    _b_m = VDepRate(lambda V: HHRateFunction(celsius, 1, 0, 0, 65, 18, 0.25, V), vrange=Vrange)\n",
    "    \n",
    "    _a_h = VDepRate(lambda V: HHRateFunction(celsius, 1, 0, 0, 65, 20, 1 / 0.07, V), vrange=Vrange)\n",
    "    _b_h = VDepRate(lambda V: HHRateFunction(celsius, 1, 0, 1, 35, -10, 1, V), vrange=Vrange)\n",
    "\n",
    "    with ssys:\n",
    "\n",
    "        with VGKC[...]:\n",
    "            Kc.s <r[1]> Ko.s\n",
    "            r[1].K = _a_n, _b_n\n",
    "\n",
    "        with VGNaC[...]:\n",
    "            Na_hi.s <r[1]> Na_ha.s\n",
    "            r[1].K = _a_h, _b_h\n",
    "            \n",
    "            Na_mc.s <r[1]> Na_mo.s\n",
    "            r[1].K = _a_m, _b_m\n",
    "\n",
    "        # Create ohmic current objects\n",
    "        VGKC_I = OhmicCurr.Create(VGKC[Ko, Ko, Ko, Ko], K_G, K_rev)\n",
    "        VGNaC_I = OhmicCurr.Create(VGNaC[Na_mo, Na_mo, Na_mo, Na_ha], Na_G, Na_rev)\n",
    "        Leak_I = OhmicCurr.Create(Leak[lsus], L_G, leak_rev)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # TETRAHEDRAL MESH  # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "mesh = TetMesh.LoadAbaqus(meshfile_ab, scale=1e-6)\n",
    "\n",
    "# # # # # # # # # # # # # # # MESH MANIPULATION # # # # # # # # # # # # # # # # #\n",
    "\n",
    "facetris = TriList([tri for tri in mesh.tris if tri.center.z == mesh.bbox.min.z])\n",
    "injverts = facetris.verts\n",
    "\n",
    "print(\"Found \", len(injverts), \"I_inject vertices\")\n",
    "print(\"Found \", len(facetris), \"triangles on bottom face\")\n",
    "\n",
    "memb_tris = mesh.surface - facetris\n",
    "\n",
    "# The points along (z) axis at which to record potential\n",
    "pot_pos = np.arange(mesh.bbox.min.z, mesh.bbox.max.z, 10e-6)\n",
    "pot_tet = [mesh.tets[(0, 0, z)] for z in pot_pos]\n",
    "\n",
    "# # # # # # # # # # # # # # # GEOMETRY OBJECTS  # # # # # # # # # # # # # # # # #\n",
    "\n",
    "with mesh:\n",
    "    # Create cytosol compartment\n",
    "    cyto = Compartment.Create(mesh.tets)\n",
    "\n",
    "    # Create the patch and associate with surface system ssys\n",
    "    patch = Patch.Create(memb_tris, cyto, None, ssys)\n",
    "\n",
    "    # Create the membrane across which the potential will be solved\n",
    "    membrane = Membrane.Create([patch], opt_method = 1)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # SIMULATION  # # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "rng = RNG('mt19937', 512, 1234)\n",
    "\n",
    "sim = Simulation('Tetexact', mdl, mesh, rng, True)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "rs = ResultSelector(sim)\n",
    "\n",
    "NaCurrs = rs.TRIS(memb_tris).VGNaC_I.I\n",
    "KCurrs = rs.TRIS(memb_tris).VGKC_I.I\n",
    "CellPot = rs.TETS(pot_tet).V\n",
    "\n",
    "NaCurrs.metaData['trizpos'] = [tri.center.z for tri in memb_tris]\n",
    "KCurrs.metaData['trizpos'] = [tri.center.z for tri in memb_tris]\n",
    "NaCurrs.metaData['triarea'] = [tri.Area for tri in memb_tris]\n",
    "KCurrs.metaData['triarea'] = [tri.Area for tri in memb_tris]\n",
    "CellPot.metaData['tetzpos'] = pot_pos\n",
    "\n",
    "sim.toSave(NaCurrs, KCurrs, CellPot, dt=DT_sim)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "sim.newRun()\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# Inject channels\n",
    "surfarea = sim.patch.Area\n",
    "\n",
    "for state in VGNaC:\n",
    "    prop = Na_facs[state.Count(Na_ha)][state.Count(Na_mo)]\n",
    "    sim.patch.VGNaC[state].Count = Na_ro * surfarea * prop\n",
    "\n",
    "for state in VGKC:\n",
    "    prop = K_facs[state.Count(Ko)]\n",
    "    sim.patch.VGKC[state].Count = K_ro * surfarea * prop\n",
    "\n",
    "sim.patch.Leak[lsus].Count = L_ro * surfarea\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# Set some simulation variables:\n",
    "\n",
    "# Set dt for membrane potential calculation to 0.01ms\n",
    "sim.EfieldDT = 1.0e-5\n",
    "\n",
    "# Initialize potential to -65mV\n",
    "sim.membrane.Potential = -65e-3\n",
    "\n",
    "# Set capacitance of the membrane to 1 uF/cm^2 = 0.01 F/m^2\n",
    "sim.membrane.Capac = 1.0e-2\n",
    "\n",
    "# Set resistivity of the conduction volume to 100 ohm.cm = 1 ohm.meter\n",
    "sim.membrane.VolRes = 1.0\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# Set the current clamp\n",
    "\n",
    "sim.VERTS(injverts).IClamp = Iclamp/len(injverts)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# Run the simulation\n",
    "sim.run(ENDT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xyAb+DMwA9orIByJyk4jEBkda+LFgh7GisruFfc5Fv1TDzdWeQzdwNkLplsDPN4CRkiOpn/YcusmakjWMThtNr6jAT3XqhXAdGAellFsptUAp9S2gH/A8MA84KCKvBUtgOPHZzqMM75PIwPT4gPaTHh9NlM2ijUN3KN0Cbkfg5xuaSemvPYduUOOoYfuJ7UzNCoIxB9Ji0oi1xWrP4VwopRzALmA3UA2MCKQoABG5UkT2ikiBiDwc6P66S1lNE+sPVQTcawCwWISc5FiKtXHoOkfWGI85U4LTX3J/7Tl0gw1HN+BWbs7ve35Q+hMR+sb3jeiFcB0aBxHpJyI/FpFNwMfe8+cqpSYEUpSIWIEnMUJoRwI3i8jIQPbZXb7YfQylAj+k1Ex2SixFJ+uD0lePpHAtpA6GBP+nOGmX5FxoqIAmPU/UFdaUriHWFsu43uOC1md2YmSHs3a0zmEVsALIwAhpHaaU+h+lVDCWek4BCpRSB7xey78xhrRClgU7j5KbGseILP9VpeqInJRYiiu159AllDKMQ7CGlOBUOKv2HrrEmtI1TMicQJS1e7XYO0Pf+L4U1xZHbJLLjjyHh4EBSqkfK6U2BkuQl2yg9YqhIu++kKSq3snKghNcObqP3wr7nIuclDhO1DpocLiD0l+PorwA6suDMxndTPIA41HPO3SaY3XHOFB1gPOzgjOk1ExOYg61zlqqHdVB7TdUOOsiOKXUlwAiMhD4PjCg9flKqbmBFtcRInIPcA9Abm6umVL4fNdRnG7F1WOygtZndrIRNFZcWc+QjOB4Kz2GI6uNRzM8h8p2lw5pOmDtUWOx4rSsIN4vTs/OmhSdFNS+Q4Fz5lYC3geeAz4CgpVPqRgjQqqZHO++FpRSTwNPA0yaNMlUv++T7aXkpMQyNid4f0A5KYZxKDrZoI1DZzm8CuLSIX1o8PqMSwN7vB5W6gKrS1aTGpNKXkpeUPttbRxGpoX0lGdA8MU4NCql/hZwJaezHsjzei3FwE3AN4KswScq6x2syD/BXRcODNqQEhgT0qDXOnSJwyuh/wWBTbZ3JiI6nLULKKVYU7qGqX2mYpHgJpFuWQgXoRFLvhiHx0XkV8DnQFPzTqXUpkCJUkq5ROR7wALACjyvlArJaimf7zyGy6O4ZkzfoPabkRiD3Sp6UrqzVBYaQzvT7g9+3zqctdPsr9zPiYYTQQthbU2vqF4kRiVG7EI4X4zDGOBW4BJODSsp7+uAoZT6FPg0kH34g4+3l5KbGsfo7OAmqLVahL7Jsdpz6CzN8w39Lwh+3yn94dByI1oqmF5LGLO61LhfwZ5vaCYnISdiw1l9MQ5fAwZ5Q0o1rThZ52BlwQnuuWhQUIeUmslOjqVYr3XoHIdXQnTSqdrOwSS5Pzhqob4C4tOC338YsqZ0Df179ScrIXjBHq3JTshmf9V+U/o2G18G8XYAyQHWEZZ8vusobk9wo5Rak5OiPYdOc3iVEaVk8X8hpnPSOnW35pw43U7WH11vmtcA3roONcV4IrC2mS/GIRnYIyILROTD5i3AusKCj7eVMiAtjlF9zal5lJ0cx/GaJhqdeq2DT9SWwYl95gwpgU7d3UmaU3QHe31Da3ISc3B4HJxoOGGaBrPwZVjpVwFXEYbUNDpZvb+cuy80Z0gJToWzllY1BjzZX4/gyCrjsf90c/pv8Ry0cfCFlhTdWYFP0X02WoezZsRlmKbDDM5qHERElMGyc50TGGmhzer95bg8ipnDgpSbpx1OhbPWa+PgC4dXgT0OsoKXn+c0ohMhNlV7Dj6yumR10FJ0n43mcNaimiLOyzjPNB1m0NGw0hIR+b6InLb8WESiROQSEXkJuD2w8kKX5fkniI+yMiE3xTQNzZ6Dzs7qI4dXQs5ksAUvP08b9FoHn6hx1LDjxI6gpeg+G5Fc16Ej43Al4AbeEJESEdklIgeBfOBm4DGl1ItB0BiSfJlfxvmD04iyBXdhTmv69IrBahE9Ke0LDZVwdId5Q0rN6LUOPrH+6Pqgpug+G9HWaDJiMyJyIVxHuZUagX8A/xARO5AONCilKoOkLWQ5XF7H4fJ67pw+0FQdNquFPr1i9EI4XziyBlDmTUY3k9If9n4KHo9RIU7TLmak6D4bkZq629diP06lVKk2DAbL843IhQvz0k1W0hzOqtc6nJOCL4z5hhzzJjcBo66D2wG1R83VEeKsLlkd9BTdZyM7IVsPK2l8Y3l+GdnJsSExCZyt1zqcG6UgfwEMvBjsMeZqSdbZWc9FaW0ph6oPcUGWyV6el5zEHI7VHcPpdpotJaho49BJXG4PqwrKuWhoumkhrK3JSYnjWHUjDlfkLdLxmRP7jC/jvMvMVqKNgw80p8wwe76hmeyEbBSK0rpSs6UEFW0cOsnWokpqmlxclGdeCGtrcpJj8Sg4WtVotpTQZd8C4zHvcnN1ACR7M9HrSemzsqpkFb1jezMkeYjZUoDIjVjqaJ1DDUaCvTaHAKWUMi/42ESW7TuBReCCwebPN0Crug6V9eSmxZmsJkTJ/xwyRp36YjYTeyzEZ+hw1rPg9rhZU7qGi3MuDgnPHKBfovF3U1SjjQMASildQaYdlueXMa5fMklxdrOlAMawEkBRRQMMNllMKNJYZWRiveD7Zis5RUp/Pax0FvZU7KGqqSpkhpQAesf2xmaxRVzEks/DSiKSISK5zVsgRYUqFXUOthZWcmGIDCkBZCXHYBEo1BFL7bN/MXhckHeF2UpOkZyrjcNZMDtFd3tYLVb6xvfVxuFMRGSuiOQDB4FlwCFgfoB1hSTzd5TiUXDlqD5mS2nBbrXQNzmWIxXaOLTLvs8hJtn8ENbWJOdCVRF4dMLEM1lVsophKcNIjw2NYdtmshOyI25YyRfP4X+BacA+pdRAYDawJqCqQpSPt5YyqHc8I7JCa8StX0ochdo4tMXjgYKFMGQ2WH3JMRkkkvuDxwk1kRX9ci7qnfVsPr6ZC/qGRghra3ISI6/ojy/GwamUKgcsImJRSi0BJgVYV8hxvKaRtQfLuWZs35CZKGsmNzWOIxV6rUMbSjdDXVloDSmB4TmAHlo6gw3HNuDyuJjWN3SGlJrJTsimsqmSOmed2VKChi/GoVJEEoAvgddE5HEgcj4hL/O3H8Wj4Nqx5hT26Yh+qbGcqG2iwaGHKU4jfyEgMORSs5Wcjq7r0C6rS1YTbY1mQsYEs6W0oXV21kjBF+MwD2gAfgR8BuwHrg2kqFDk420lDMtMJC8ztIaUAPqlGhFLelL6DPI/N+YaQq0kZ3NIrfYcTmNVySomZEwgxmbyKvZ26JfgDWeNoLUO5zQOSqk6pZRbKeVSSr2klPqbd5gpYiipbGD9oZNcE4JeAxjDSoCed2hNbRkUbwqNhW9nYouGxCxtHFpRUlvCgaoDzMieYbaUdmkp+hNB2Vk7WgS3Qik1o53FcBG3CO7T7cbE4TXj+pqspH2aPQcdsdSK/YsAFRopM9ojOVcvhGvFiuIVAMzICU3jkBSdRLw9PqI8h44Wwc3wPobeOEqQ+WhbKaP69gqJRHvtkRYfRVyUVRuH1uR/DgmZ0Ges2UraJzkXCtearSJkWF60nOyEbAb26n4afKfTSVFREY2N/k0p86dhf8JqsbJ7926/thsMYmJiyMnJwW73ffHuOeP7ROQVpdSt59rXU9lfVsvWwkoeunK42VLOioh4w1l1xBIAbhcULILh14RuzYTk/rDjPUNrKIXZmoDD7WDt0bXMHTzXL5GARUVFJCYmMmDAAL9GFsZXx+NwOxiSEho5n3xFKUV5eTlFRUUMHOi78fXlX86o1i9ExAZM7KS+sOWFlQeJslq4YWKO2VI6pF+qXuvQQvEGaKyEvBCLUmpNci4oN1RHzhj22dhwbAMNrgYuyrnIL+01NjaSlpbm95DzKGsUTo8TpdpLORe6iAhpaWmd9qTOahxE5Cfe+YaxIlLt3WqAY8AH3ZMbHlTWO3h3YzHzxveld2K02XI6JDc1jsKT9WH3hxsQ8j8HscKgWWYrOTt6rUMLK4pXEGWJYnIf/61iD8RaJLvFjkd5cCmX39sONF35PM5qHJRS/+edb/izUqqXd0tUSqUppX7SHaHhwuvrjtDgdHPXheaWA/WFfqmx1DvclNc5zJZiPvmfQ+40iE02W8nZSdF1HZpZUbyCyX0mE2uLNVtKhzRXpYuUoj++hLL+RESyReQCEbmoeQuGODNxuDy8tOoQM4akM7xP6Adm5eqIJYPqEji6PXSjlJrplQNIxEcsFdUUcbDqYMiGsLbGbjEmcx2ec/8Au/POO8nIyGD06NFnPUcpxQMPPMCQIUMYO3YsmzZt8ptWf+BL4r0/ACuBnwM/9m7/HWBdpvPp9lKOVTdx14zQ9xqg1UK4SDcOBV8Yj6G4vqE1tijo1TfiPYfmENYLcy40Wcm56YzncMcdd/DZZ591eM78+fPJz88nPz+fp59+mvvuu88vOv2FLxPS1wPDlFJzlFLXere5gRYWDKoanPzl8700uU5PO6GU4tkVBxjUO56Lh4ZOeu6O6JeijQNgGIfEvpAx0mwl5yZZ13VYXrycfon96N+rv9lSzolFLNgsNp88h4suuojU1NQOz/nggw+47bbbEBGmTZtGZWUlpaWnJ2M8dOgQw4cP54477mDo0KHccsstfPHFF0yfPp28vDzWrVsHwLJlyxg/fjzjx4/nvPPOo6ampusX6sWXGLoDgB1o6nZvIcbiPcf42+ICvsw/wVPfnEBWUiw1jU5+9eFOdhRX87vrR2OxhFaSvbMRG2UlPSE6ssNZ3S44sBRGXAshlhyxXZJz4dAKs1WYRqOrkXWl67g+7/qA9fHrj3ayq6Tab+01uhsZkhHDozdkd7ut4uJi+vU7VZ0wJyeH4uJisrJOz8RQUFDA22+/zfPPP8/kyZN5/fXXWbFiBR9++CG///3vef/993nkkUd48sknmT59OrW1tcTEdD8FiS/GoR7YIiKLaGUglFIPdLt3k7n+vBxibFb+++2tXPvECn546VD+9eV+ik828MDsPG6aHF41jXJTI7yuQ8kmo/Lb4NlmK/GN5FyoKQGXwxhmijDWlq6l0d3IzJyZZkvxGUHwKE9Q+xw4cCBjxowBYNSoUcyePRsRYcyYMRw6dAiA6dOn8+CDD3LLLbfwla98hZyc7ofe+2IcPvRufkNE/oyRvM+BkcjvW0qpSu+xnwB3AW7gAaXUAn/2fSZXjckiLzOBe17ZyM/f30FOSixvfed8Jg3o2CUMRXJT41h/6KTZMsyjYBGIBQbNNFuJb6QOBOUxhpbSw2thlT9YWrSUOFsck/oErgLAr64dde6TOsGxumOcaDiBUqrb4bLZ2dkUFha2vC4qKiI7u61HEh19KozeYrG0vLZYLLhcRljtww8/zNVXX82nn37K9OnTWbBgAcOHd2/h7jmNg1LqJRGJBXKVUnu71dspFgI/UUq5ROSPwE+Ah0RkJHATxsK7vsAXIjJUKRXQXNRDMhL54P7pfLq9lDljskiMCY360J2lX2ocH24twen2YLeG6MrgQLJ/EfSdAHFhYthTBxmPFQcizjgopfiy8EumZ09vmegNB1ompT3ObuueO3cuf//737nppptYu3YtSUlJbYaUfGX//v2MGTOGMWPGsH79evbs2dNt4+BLtNK1wBaMdN2IyHgR6ZYnoZT6XKmWlSRrgGYfaB7wb6VUk1LqIFAATOlOX76SGGPn65Nzw9YwgGEcPMrIIhtx1FdA8Uaj6lu4kDrYeKzYb64OE9hVsYvjDce5OOdis6V0ipZwVnfHk9I333wz559/Pnv37iUnJ4fnnnsOgH/+85/885//BGDOnDkMGjSIIUOG8O1vf5t//OMfXdb12GOPMXr0aMaOHYvdbueqq67qclvN+DKs9D8YX9BLAZRSW0RkULd7PsWdwJve59mcXoK0yLuvDSJyD3APQG5ueM0NBIpTEUsN9E8LzSSBAePAUmOIJlzmGwDi0yEq0fAcIoxlhcsQJCxCWFvT2nPoiDfeeKPd/ffee2/LcxHhySef7LCdAQMGsGPHjpbXL774YrvHnnjiiQ7b6Qq+lgmtOmPfOWdkROQLEdnRzjav1Tk/A1zAa52TDUqpp5VSk5RSk3r3Do9w00CTm2YYh8MVEVeozxhSik6C7DBK+yUCaYMi0jgsLVzKuN7jSI0JkyFAL756Dj0BXzyHnSLyDcAqInnAA8Cqc71JKdVh1jMRuQO4BpitTiUEKgb6tTotx7tP4wN9esUQZbVEXsSSUlCwGAZdHH4ZTlMHQckWs1UElWN1x9hdsZsfTPiB2VI6jYhgt9rP6Tn0BHzxHL6PMUHcBLwOVAHduqsiciXw/4C5SqnW32QfAjeJSLSIDATygHXd6SuSsFqEnNRYDp+IMONQtscICQ21WtG+kDrIiFaKkHw9AMuKlgGEVQhra6IsUT4thAt3fPmZdbVS6mfAz5p3iMjXgLe70e/fgWhgoTccbI1S6l6l1E4ReQvYhTHcdH+gI5V6GgPS4jkcaZ5DwSLjMZwmo5tJHWyk7q48AmmDzVYTFJYVLSM7IZvByeF5vXarnVpHrdkyAo4vnkN7GVi7lZVVKTVEKdVPKTXeu93b6tjvlFKDlVLDlFLzu9NPJJKbGsfh8rrISt1dsBDSh0FSaNfcaJfW4awRQIOrgbWla7k45+KApNUOBlGWKFweV9AXwwWbjmpIXwXMAbJF5G+tDvXC+FWvCUEGpMVR73BzotYR8jUo/EJTLRxaCdPuPfe5oUiztxAhxmFd6Tqa3E1c3C+8Qlhb0xyx5HA7iLF1P01FqNKR51ACbAAagY2ttg+BKwIvTdMVmkNYD5dHSMTSgaXgcYZ+FtazEd8bohKgPDLWOiwrWmasis4M3KroQNMcsXS2SenCwkJmzZrFyJEjGTVqFI8//ni754V6yu6zeg5Kqa3AVhF5HRBgqPfQXqVU5MyehRn9m8NZy+vDMgVIp8n/3FgrkHu+2Uq6hogxtBQBnoNSimVFy7ig7wVhtSr6TOzWjsNZbTYbjz76KBMmTKCmpoaJEydy2WWXMXLk6ZmCW6fsXrt2Lffddx9r164NuH5f8WXO4QIgH3gS+AewLxKK/YQrOSlxWCRCPAelIH8hDJ4F1vBd2W4Yh57vOew9uZfj9cf9VivaLGxiwyKWs3oOWVlZTJgwAYDExERGjBhBcXHbiPyekLL7L8DlzXmVRGQo8AYQRquNIocom4W+ybGREbF0bIcRwjo0zEc5UwfBno+NcNZwNnLnYFmhEcIa1FXR8x82KgP6EekzBvu0u31aCHfo0CE2b97M1KlT2xwL9ZTdvngO9tYJ95RS+zDqO2hClP5pcRwqjwDjsM+bsHdIiJcEPRdpg8HjgqrCc58bxnxZ9CVj0seQHptutpRuE2WNosndcYmb2tpavvrVr/LYY4/Rq1fXSw03p+y2WCznTNn9t7/9jcrKSmy27i8G9aWFDSLyLPCq9/UtGBPVmhClf1o887eXnvvEcCd/IWSNh8RMs5V0j+Zw1vIDp573ME40nGD7ie18d/x3g9vxVX8ISLPRdceoddTiUR4s0vY3ttPp5Ktf/WpLfYX2CPWU3b54DvdhLEp7wLvt8u7ThCgD0uI4We+kqqEHxw3UV0DRuvCNUmpNas8PZ11RvAKFCrssrGcj2hqNQrVbT1opxV133cWIESN48MEHz9rG3Llzefnll1FKsWbNGr+k7H7ooYeYPHkye/bs6VI7rfGlnkOTiPwdWISRcG+vUqrnrx0PY3JTjXDWI+X1jMlJMllNgNi/2MjCGu7zDQAJGWCP79GT0l8WfUlGbAbDU7v3azZUiLYav96b3E1E205fT7Ry5UpeeeUVxowZw/jx4wH4/e9/z5w5c1rSdd97773MmTOHTz/9lCFDhhAXF8cLL7zQZT2PPfYYS5YsaRl6CkrKbhG5GvgnRsU2AQaKyHf06uXQZUC6Ec56qLyu5xqHvZ9CXBr0Pc9sJd2nh4ezOt1OVpWs4qqBV4XtqugzaQ7FbW/eYcaMGWfNUBBOKbt9mXN4FJillCoAEJHBwCeANg4hSm6qYRx6bHbWxirY8wmc902wWM1W4x/SBsGxnWarCAjrjq6jzlnXY4aUAKwWKzaL7ZyT0uGML3MONc2GwcsBoPtBtJqAERdlIyMxmkMneuhah53/AVcjjP+G2Ur8R+ogOHkI3D0vM82SwiXE2mKZljXNbCl+Jdoa3aONg6/RSp8CbwEK+BqwXkS+AqCUei+A+jRdpH9aXM9d67Dldeg93KgX3VNI9YazVh7uUdlZlVIsKVzCBX0v6HF5iKKt0VQ2VaKU6jHDZa3xxXOIAY4BFwMzgTIgFrgWo1iPJgTpnxbfM1dJn8iHwrWG19CT/kH29k7Unthnrg4/s6t8F8frj3NJ7iVmS/E70dZoPMqDy9PzvD3wLVrpW8EQovEv/VPjeKe6iQaHm9ioHjIuD4bXIFYY+3WzlfiX3t7UZWV7YFj3I01ChUVHFmEVKxdlh3fKjPZoHbFk74Er233xHDRhSP90bzhrTxpa8rhh67+Nim+JfcxW419ikiAxC8r2nvvcMGJJ4RImZE4gOSbZbCl+J8p29oilnoA2Dj2U/qnN2Vl70NDSgaVGLqWeNBHdmt7DDM+hh1BYXUhBZQGz+s0yW0pAaE7Ad6ZxaGxsZMqUKYwbN45Ro0bxq1/9qt33NzU18fWvf50hQ4YwderUllQYoYI2Dj2UAd66Dod6knHY+gbEJPeoYZfT6D0cyvYZ2WZ7AIsLFwP0WOMgIkTbotsk4IuOjmbx4sVs3bqVLVu28Nlnn7FmzZo273/uuedISUmhoKCAH/3oRzz00EPBku4T5zQOIpIpIs+JyHzv65EiclfgpWm6Q1KcnfSEaPYd6yG1bh11xtqGUdeDrYdWuEsfCs46qCoyW4lfWFK4hKEpQ8lJDMPyrT7SXjiriJCQkAAYOZacTme70UwffPABt99+OwA33HADixYtarN4bunSpVx88cXMmzePQYMG8fDDD/Paa68xZcoUxowZw/79xqr6t99+m9GjRzNu3Dguusg/8zu+hLK+CLwA/Mz7eh/wJvCcXxRoAsbIvr3YXVpttgz/sG8BOOth9FfNVhI4miOWyvZCcr+Ozw1xTjaeZPPxzXx7zLdN1fHHdX9kT4V/h+qGpw7noSnGr/xoazSVnkrcHjfWVgsy3W43EydOpKCggPvvv/+cKbttNhtJSUmUl5eTnn561tqtW7eye/duUlNTGTRoEHfffTfr1q3j8ccf54knnuCxxx7jN7/5DQsWLCA7O5vKykq/XKcvw0rpSqm3MPIqoZRyAW6/9K4JKCOyEsk/VovD1QMKoe98DxIyof8FZisJHC3GIfznHZYWLsWjPD0yhLU1rSOWWmO1WtmyZQtFRUWsW7futBQYnWXy5MlkZWURHR3N4MGDufxyI9nkmSm777jjDp555hncbv98PfviOdSJSBrGAjhEZBpQ5ZfeNQFlZFYvHG4P+8tqGZHV9XzyptNYDfs+h0nf6jnpMtojPg3i0uFE+EcsLT6ymL7xfRmROsJUHc2/8ANF6xxLcfa4NseTk5OZNWsWn332GaNHjz7tWHPK7pycHFwuF1VVVaSlpbVpw5eU3f/85z9Zu3Ytn3zyCRMnTmTjxo3tttUZfPEcHgQ+BAaLyErgZeD73epVExRG9TUMwq6SMB9a2jsf3E0wqv28+D2K3sPDPpy13lnPqpJVXJJ7SY9cOdyaKEsUInKa51BWVtYytNPQ0MDChQvbra0wd+5cXnrpJQDeeecdLrmk65/X/v37mTp1Kr/5zW/o3bv3aXUiuoovi+A2icjFwDCMrKx7lVI9uFBAz2FgegIxdgu7SqsJ65H6He9CUj/ImWy2ksDTe6hxvUqF7QrwlSUrcXgcPX5ICYzJ5yhr1GkRS6Wlpdx+++243W48Hg833ngj11xjJJP45S9/yaRJk5g7dy533XUXt956K0OGDCE1NZV///vfXdbx4x//mPz8fJRSzJ49m3HjxnX72nytJTcFGOA9f4KIoJR6udu9awKK1SIM6xPmk9L1FUbthmn3giUCIq97DzeyztYeC9uFfouOLCI5OpnzMnpAOnUfiLZG0+BqaHk9duxYNm/e3O65v/nNb1qex8TE8Pbbb3fY9syZM5k5c2bL66VLl7Z77L33/J/izpd6Dq8Ag4EtnJqIVhjDS5oQZ2RWIvN3HA3f5GB7PgaPs2dHKbWm9zDjsWxPWBoHp9vJl4VfMrv/bGyW7tcxDgdirDFUN1Xj8rh61DX7ciWTgJHqbNUrNCHNyKxevLGukNKqRvomx5otp/PseA9SBhq1oiOBloilfTBopqlSusL6Y+upcdYwO3e22VKCRqzN+HfV5GrCFtVzjIMvfvoOIPx+wmgAY60DhOmkdMNJOLQcRs4L2/H3TpOQCdFJYRvOuvjI4pCo3RDM37LNqcgb3A3nONM8uvJ5+LTOAdglIgtE5MPmrdM9aUxhWJ9eiMCucJx32LfAqHEw4lqzlQQPEW+OpfCLWPIoD4uPLGZG9gxTazfExMRQXl4eNANhs9iwWWw0uhqD0l9nUUpRXl5OTEzn7okvPtD/dEmRJiRIiLbRPzUuPCeld39kZCrtSUV9fKH3MCN8N8zYVraNsoYy06OUcnJyKCoqoqysLGh9VjRWcNRzlJq40CySGRMTQ05O59KY+BLKuqzLijQhwci+vdgZbsNKjnooWATn3RIZUUqt6T0cNr8CdeXGwrgw4bNDnxFtjWZmzkxTddjtdgYOHBjUPp/Y/ATP7XyONd9Y02Mq3vmSeG+aiKwXkVoRcYiIW0T88k0jIv8lIkpE0r2vRUT+JiIFIrJNRCLsJ2NgGJnVi8Pl9dQ0htHylANLwNUAwyOw2GDmSOOxdIupMjqD2+NmwaEFXJRzEQlRCWbLCTojUkfgVm7yT+abLcVv+PKT7O/AzUA+RnnQu4Enu9uxiPQDLgeOtNp9FZDn3e4BnupuP5pTk9J7joamy9suuz82CuAMmGG2kuCTMxnEAkdWm63EZzYc28CJhhNcOeBKs6WYwvBUI8psz8nwDCRoD5/8daVUAWBVSrmVUi8A/vgL+Cvw//DmbPIyD3hZGawBkkUkyw99RTQjs5KAMIpYcrtg33wYeiX0wPKL5yQ6EfqMhSNtawCEKvMPzifOFseFOReaLcUUshOySYxKZE95ZBmHehGJAraIyJ9E5Ec+vu+siMg8oFgptfWMQ9lA66QgRd59mm6Q2SuatPgotheHSb7EI6uMMNZIHFJqpv8FULQeXI5zn2syTreTL458wazcWS0x/5GGiDA8dbjf04ObiS9f8rd6z/seUAf0g3On6hGRL0RkRzvbPOCnwC+7I1xE7hGRDSKyIZhRCeGIiHBebgobD580W4pv7P4YbDEwJHIWUrUhdxq4GsNi3mF16Wqqmqq4akAPrdDnI8NTh7Pv5D7cnp5R0aBD4yAiVuD3SqlGpVS1UurXSqkHvcNMHaKUulQpNfrMDTgADAS2isghIAfYJCJ9gGIM49NMjndfe+0/rZSapJSa1Lt3b58uNpKZNCCFgyfqOFEb4sXQlTIqvg2+BKLizVZjHrnnG4+HV5mrwwfmH5xPr6heXNC3B9fa8IERqSNodDdyqPqQ2VL8QofGQSnlBvp7h5X8glJqu1IqQyk1QCk1AGPoaIJS6ihGavDbvFFL04AqpVSpv/qOZCb1TwEIfe+heCNUFxmroiOZhAxIGxLyk9KNrkYWH1nMZf0vwx6J80OtaJ6U3l2x22Ql/sGXYaUDwEoR+YWIPNi8BUjPp97+CoBngO8GqJ+IY3R2ElFWS+gbh53/AYvdmIyOdHLPNyalPaFbyW9p0VLqXfVcOVDfrwFJA4iyRPWYSWlfVkjv924WINHfArzeQ/NzBdzv7z40EGO3MiYniQ2HKsyWcnaUgl0fGkNKsclmqzGf/hcYi+HKdkPmKLPVtMv7+e/TJ74PkzMjoNbGObBb7OSl5PWYSWlfVkj/GkBEehkvVRgFy2taM2lACs+vOEij002MPQTLbZZshqojMPNhs5WEBq3nHULQOBytO8qqklXcM/YerD25fGsnGJ0+mo/2f4TT48RuCe9hNl9WSE8Ske3ANmC7iGwVkYmBl6bxN5P6p+J0K7YVhWhI6673wWKDYZEd9dJCygAjt1SIzjt8uP9DFIp5QyJ8fqgVU7OmUu+qZ3vZdrOldBtf5hyeB77bagL5fuCFgKrSBISJ3knpDYdDcGhJKdj1gVHDIC7VbDWhgYjhPRxebXw+IYRSivcL3mdyn8n0S+x37jdECFP6TMEiFlaXhqZB7wy+GAe3Ump58wul1ArAFThJmkCRGh/FoN7xbDwUgpPSR7fByUM6SulM+l8ANSVQeeTc5waRjcc2UlhTyHVDrjNbSkiRFJ3EqLRRrC7pwcZBRCZ4E98tE5F/ichMEblYRP4BLA2aQo1fmdw/lQ2HT+LxhNYvUXa+D2KFYVebrSS0aM4tdWCpqTLO5P2C94m3x3Np7qVmSwk5pmVNY8eJHdQ4wnt6tiPP4VHvNg4YCvwKo7bDCGB8oIVpAsPEASlUNTjZX1ZrtpRTNA8pDbworFJUB4Xew6FXDhQsNFtJC3XOOj4//DlXDriSOHuc2XJCjgv6XoBbuVl3dJ3ZUrrFWaOVlFKzgilEExwmtcw7nCQv0++RyV2jdCtU7IfpD5itJPQQgbxLYfu7Rp4lm9/Wo3aZzw5+RoOrQQ8pnYVxvccRa4tldcnqsK6l7Uu0UrKIPCAif/HWWvibiPwtGOI0/mdgejxp8VGsPVButpRT7HjXiFIaMddsJaFJ3uXgqIHCtWYrQSnFm3vfJC8lj3G9x5ktJySxW+1M7jOZNaXhk1W3PXyZkP4UGABsBza22jRhiIgwc1gGi/ccx+EKgZW3Hg/seA8Gz9ZRSmdj4EXGqvH8z81WwvYT29ldsZuvD/06ImK2nJDl/KzzOVx9mOLadlPDhQW+GIcYb7K9F5RSLzVvAVemCRhzxvShutHFyv0nzJYCReuMXEqjz5noN3KJTjSilgq+MFsJb+59kzhbHNcMjuB06j5wfl9jAWM4Ry35YhxeEZFvi0iWiKQ2bwFXpgkYM/LSSYy28em2EMhpuONdIz338DlmKwlt8i6D47ugqsg0CZWNlXx28DOuHXwt8fYIzpjrA4OSBpERm9HjjYMD+DOwmlNDShsCKUoTWKJtVi4bmcnnu47hdJs4tOR2GYn2hl5h/DrWnJ28y43HfPOilt4veB+Hx8HXh33dNA3hgogwre801pSuwekOo9rtrfDFOPwXMMS7QnqgdxsUaGGawHLVmCyqGpys2m/ixPSh5VBXpoeUfCF9KCTlmmYcPMrDm3vfZELGBPJS8kzREG5c3v9yqh3VrCxZabaULuGLcSgA6gMtRBNcLsxLJyHaxvztJg4t7XgXohJP/SrWnB0RY2jp4DJwBb9g06qSVRTVFnHT8JuC3ne4ckH2BSRHJ/PJgU/MltIlfDEOdRj1o/+lQ1l7DjF2K7NHZLBg51FzhpZcTbD7Qxh+Ndgjs+5wp8m7DBy1cGhF0Lt+dderpMem6xXRncBusXPFgCtYUriEWkcILTr1EV+Mw/vA74BV6FDWHsWcMVmcrHey9oAJifj2fgqNVTD2xuD3Ha4MmgkxSbD1jaB2u79yPytLVnLTsJsivtpbZ7lm0DU0uZtYdGSR2VI6zTmNgzds9S1gjQ5l7VlcPLQ38VFWPtleEvzON78GvbKNLzyNb9hjYezXjYJI9cEz6K/seoVoazQ3DtOGvLOM6z2O7ITssBxa8mWF9LXAFuAz7+vxIvJhgHVpgkCM3coVo/rw0dZS6pqCmGi3ugT2L4JxN4EuEtM5JtwG7ibY9lZQuqtorOCj/R9x7eBrSYlJCUqfPQkR4epBV7P26FrK6svMltMpfBlW+h9gClAJoJTaAuhopR7CLdNyqW1y8eHWIHoPW/8NygPjbwlenz2FPmOg7wTY9FJQajy8tfctHB4Ht464NeB99VSuHnQ1HuVh/sH5ZkvpFL4YB6dS6szSYSGQd0HjDybkpjC8TyKvrjmMCkZBGaVgy2tGEZu0wYHvrycy8XZjQVxRYJcbOdwO/r3n38zInsGgZP17sKsMShrEyLSRfHzgY7OldApfjMNOEfkGYBWRPBF5AmNyWtMDEBFumZrLzpLq4JQPLVoP5QXaa+gOo78K9njDewgg8w/Op7yxnFtHaq+hu8wbPI/dFbtZf3S92VJ8xhfj8H1gFNAEvAFUAz8MoKagsfPETu794l7qnHVmSzGV687LJi7KyqtrDge+s82vgj0ORl0X+L56KtGJMPorRsLCpsAUlFFK8dru1xicNJjzs84PSB+RxFfyvkLv2N48ueXJ4HjofsCXaKV6pdTPlFKTlVKTvM8bgyEu0Dg9TlYWr+SpLU+ZLcVUEmPszBufzUfbSqiqD+BSf0edkS5j5DydLqO7TLgdnHVG1FcA2Fq2ld0Vu/nGiG/o7Kt+IMYWw91j7mbjsY1hk8q7ozKhH3a0BVNkoBifMZ6v5n2VV3e/yr6T+8yWYyq3TM2l0enhvc0BTOy2/W1oqjYibjTdI2cSDLgQvvxzQLyH13e/TqI9kWsG6eyr/uKGoTfQJ74Pf9/y97DwHjryHM4HcoDlwCOcKhvavPUIfjjhhyRGJfLbNb/FoyJ3nn10dhLj+yXz9JcHOFnn8H8HSsG6ZyFztDEZrekeInDZr6H+BKx6wq9NH68/zsLDC7ku7zpdBtSPRFmjuGfsPWwr28by4uVmyzknHRmHPsBPgdHA48BlwAml1DKl1LJgiAsGyTHJPDjxQTYf38wHBR+YLcdUfjNvFOW1Dn701hY8Hj//sjmyBo5thynfNr7YNN0neyKMvA5W/R1qjvmt2bf2voVbubl52M1+a1NjcN2Q68hOyA6LuYezGgellFsp9ZlS6nZgGkYCvqUi8r2gqQsS84bMY3zv8fxl41+oaDQhlUSIMDYnmV9cO5Kle8v4x9IC/za+7mkj9cOYr/m33Uhn9i+NRXHL/uiX5hxuB2/ve5sLcy6kX69+fmlTcwq7xc594+5jV/mukA9t7XBCWkSiReQrwKvA/cDfgP8EQ1gwsYiFX5z/C+qd9Tz05UO4PW6zJZnGN6fmMm98X/6ycB+rCvxUKa661Eiyd96tEKWLxPiVtMEw8Vuw8UU40X2DvuDQAioaK/jG8G90X5umXa4dfC2j0kbx141/DelIyY4mpF/GKPAzAfi1N1rpf5VS4VsUtQOGpgzl59N+zprSNTyx2b9juOGEiPD768cwqHcCP3hzC1UNfohe2vgieNww6c7ut6Vpy8UPGeHBH9xvFFDqIkopXtn1CgOTBraUudT4H4tY+OnUn1LWUMbT2542W85Z6chz+CaQB/wAWCUi1d6tRkSqgyMvuFyfdz1fG/o1ntvxHF8cNr9er1nER9v4643jKa9t4s8L9nSvMZcDNr4AQy7VK6IDRUJvuOYvULimW8NLa0rXsLtiN3eMugOL+LIEStNVxvYey7zB83h518scqjpktpx26WjOwaKUSvRuvVptiUqpXsEUGUwenvIwY9PH8rMVP+NA5QGz5ZjGmJwk7rhgIK+tPcLGwye73tCOd6D2GEz9jv/Eadoy9kYY9w0jtPVg1yJhXtjxAumx6Tp8NUj8cOIPibZG86f1fzJbSrvonwdnEGWN4tGZjxJji+GBJQ9Q7eiRTpJPPHj5UPr0iuFn/9netYJAHg+seAwyRhmegyawzPmz4Z29d0+nU3rvKt/F6tLVfHPEN4myRgVIoKY16bHp3DfuPpYXL+e13YFZzNgdTDMOIvJ9EdkjIjtF5E+t9v9ERApEZK+IXGGGtj7xffjrzL9SXFMc0RPUCdE2fj13FHuO1vDs8oOdb2DffDixF2b8SIevBoPoBLjheWPtwxs3QaPvP2xe3PEi8fZ4XbMhyHxzxDe5pN8l/HHdH0OuIJApxkFEZgHzgHFKqVEYi+wQkZHATRi5nK4E/iEipiT8n5A5gZ9M/Qkrilfw9y1/N0NCSHD5qD5cMSqTvyzcy6LdnYilVwqW/wWS+8Oo6wMnUHM6WePgq89B8UZ49StGtb1zUFRTxILDC7hx6I0kRum0JsHEarHyh4v+wOj00Tz85cNsL9tutqQWzPIc7gP+oJRqAlBKHffunwf8WynVpJQ6iLG2YopJGrlx2I3cMPQGnt3+LB/t/8gsGabzpxvGMSKrF/e9uonFe3w0EIdWQPEGmP4AWG2BFag5nZFz4WsvQckWeOV6aKjs8PQXdryARSzcMkJnyjWDWFssT1zyBGmxaXxv8ffYW7HXbEmAecZhKHChiKwVkWUiMtm7PxsobHVekXdfG0TkHhHZICIbysoCV2Hpp1N+ypQ+U/jlyl+yvCj0l7wHgqRYO6/cOZVhfRK595VNLNlz/NxvWvFXiM+A8d8MvEBNW0ZcA19/BUq3wTOXGIaiHfad3Mc7+e9wQ94NZMZnBlejpoW02DSeuvQpbGLjtvm3sbRwqdmSAmccROQLEdnRzjYPsAGpGCuvfwy8JZ1M/aiUetqbJXZS7969A3AFBnarncdnPU5eSh4PLn2QLce3BKyvUCYpzs6rd01laJ8Evv3yBp758sDZU2wUbzLKgE67D+wxwRWqOcWwq+C298HZAM9eCiv/ZgQJeFFK8ad1fyLBnsD94+83T6cGgIFJA3n96tcZkDSABxY/wEs7XzI1xUbAjINS6lKl1Oh2tg8wPIL3lME6jMpy6UAx0HrNfo53n6kkRCXw1KVPkRmfyXcXfTdk3L5gkxRn57W7pzF7RAa/+3Q3d720nvLaptNPUgoW/hLi0mDy3eYI1ZxiwAy4byUMvQIW/gKevwIOLAWlWHxkMWuPruV7532P5Jhks5VqgMz4TF688kUu7X8pj2x4hG8t+BY7y3eaokXMsEwici/QVyn1SxEZCiwCcoGRwOsY8wx9vfvzlFIdhgtNmjRJbdjQhZKJjjqjnvGkO32KpimuLea2+bfhcDt45vJnGJ46vPN99gCUUry65jD/+8lukmLtPHjZUL42MQeb1QL7PofXvwZX/Rmm3mO2VE0zzeVZF/8Oakpoyj2feXENxMam8va1b2Oz6HmhUMKjPLyz7x2e3PIkFY0VzB08lztG3UFeSp5f+xGRjUqpSe0eM8k4RAHPA+MBB/DfSqnF3mM/A+4EXMAPlVLnrMrdZeOw+VUj5cC8f8B5vk3GFVYXctfnd1HnrOPpy55mVPqozvfbQ9hVUs3P39/OpiOVDEyP58FLB3PNyq8h7ib47lqw6Xj5kMPZCJte5l8bH+Pv8VaeqYFpY283yrb2yjJbneYMahw1PLv9WV7d9SoOj4NRaaO4bsh1zMieQXZCdrcLMYWccfA3XTYOHg+8OAeO74bvrYeEDJ/eVlxbzF0L7qKqqYonZz/JhMwJne+7h6CUYtHu4/x5wV7GnviIP9ufZu3kvzDxym8ZnoQm5NhbsZebP7mZmb3y+MvJOji0HBDoPx1GXw8j5vr8b0ETHCoaK/j0wKe8X/A+e08aw9qpMaktaTgu7d+1RabaOHRE2T7453QYNgdu9L1g+9G6o3z7829TXFvM/1zwP8wdPLdr/fcQ3E11OP46nkPOFK6q+yX9UuO4aXIuX52QQ58kPSkdKjS5m7j5k5upaKjgvXnvkRqTCuX7jSp9O96FE96KiKmDIXeaUXGu93BIH2rMI+nFjKaTfzKfzcc3s7VsK9vKtnHdkOu4a8xdXWpLG4dz8eWfYfFv4abXYfjVPr+tqqmKB5c+yLqj67hr9F08MOGByE1YtuBnsPrveG7/lEUNQ3jmywOsO1SBRWD6kHSmD0lnfL9kxmQnER+tx7fN4pH1j/DSrpd4cvaTXJRz0ekHlYJjO6HgCyhcaxRoamiVhiMmCXrlQK++xhBUQh9IzISETMNwxKVBbKpxnh5SDBpKqS4PL2njcC7cTnh6JtSXw3dXQ2yKz291epz8fu3veWffO0zPns5vp/+W9Nj0rmsJRw4sg5fnwuRvw9WPtOw+dKKO9zYV8eHWEg6V1wPGD8+sXjH0S42jX2ocA9Li6J8Wz4C0eLKSY0iJi8JqCY1fpy63h4p6B+W1DirqHNQ2uWhyeWh0uqlvclHb5KK2yU29w0Wj002D04Pb40EQELCIYPE+CuBRCo8Cl8dDo9NDg8ONw+3BIkaqdKsINqtgtQg2i2AR47lFBGnVns1qwW4VoqwWesXaSYmLIjU+iqQ4O8mxdpJi7STE2IiPshFrt2Lxfp7rStdx9+d3c+OwG/n5tJ+f+wNQCioPG3UiTuyDigNQXQLVxcZjXRlwlu8PWwxE9zJSetjjISoObNFgjQJrNFgsIM2bFSw2Y7GkNQqPNQaXNRqXROO22HFhw4UVh8dCk7LicCsanW4cLjeNLkWjstHgttHgseBWgsujcHmEJg84PNDoFuqd0OAW6p2KGreVaqeNWpeVJkssLlscdnsUNqvxuVstFlr/BYrQch9sFvGeZ2l5tFsFi8W4xyKCBUWUuIjGTbTFRZQFoqwQZVVE4cIubuMRD3ZxYxc3VgGrxYLVAlZR2PBgFQ+iPFg8bgQ3VuXChhsrbqI9jUR5Gohy1xM98HySx17Zpb9xbRx8oXgTPHc59D8fvvkeWO0+v1UpxVt73+JP6/9EYlQiv5vxO6ZnT++ennCh4SQ8Nd0o4nPPMuNLoB3Ka5vYVlTF1qJKDpfXU1hRz5GKeo7XnB4Ka7UIqfFR9IqxERtlJc5uI9puwe79QrRZLd5/wKf+Idutp75IbRZBRPAohduj8CiFUuD2KNxK4XJ7cLkVDu+jy+PB6VY4XB4cbg9NLjdVDU4q65zUNJ27NkKU1UJctJVYu5UYuxWrRVBKoTC+W5XXIHiUavmCsVqEWLvxHrtNWvQ1a3Z5FC638hoTY19ze26P8drh9tDkdFPT5KKjf8IiGH3F1uLM/CtWYhjQ9HMSouJIiLaRGGMjMcZ++mO08dnHR9uItlkQpGU0qVmf26NwOR1IQzmW2uN46k+g6iuw1FcgjmqsTdVYnTXYXPVYXfXY3A1YlBOrx4nV40Ap44tPlBuUwqJcWJULOy5iaCIGB1YJ3neTQ6KNzRKNkyhcYsMjVjxYUQigjBuAB4tXt0W5seHCplzYcGJXTuwY1xBMVmfdxvnf6VoNGm0cfGXLG/D+vUbFsrlPdHp8dd/JfTz05UMUVBZw68hb+cGEHxBtje6+rlDmnTth1wdw9xfQ97xOv73e4eJIRT2HTtRzvKaRspomymqaqGl00eA0fpU7XMYXuNNtfIF7Wn2BGl9UHlwehcdrADwe44epVYwvY4vF+MVtGA8LdptgP+OXX5TNQrTNSpTNQpL313dynJ20+CjSEqJJjY8iIdpGjN04Lz7aRny0lWibKam/WnB7FNUNTirqHVQ1OKmqd1LZ4DA8miYXdU0uqpsa+KLyV9R6ShjFTxFnX2q9x2oaXdQ0Oqlz+D+5ZLTNQrTNQpTN2uq5xftZW4jxGtRYu5W4KCuxUcbzaJuVGJsQbVXEWN3EiJsYq4c4qyLWCjE2RVyUnfgYG3E2Icbiwq6ciMdpODLKc/rmcYFyGwWn3E6jrKqryVgc6KgDRy001YCr0dicjeBxnjq/GZFT3o6l2duJMn5INntEzd6RLcp4tNiNc0UMD8kWjUdsuMWGx2LHLVbcyvB43M0/aLDgRvAoC8piQ4kVj1iM92DDgRUH0TRIDPUqmv69Exma2bWcWB0ZBz3425rxN0PFfmMOIm0IzPhhp94+NGUob1z9Bo9ueJRXdr3C6pLV/OHCPzAsdVhg9JrNppeNScxLft4lwwAQF2VjeJ9eDO/TY0uEBBSrRUiJjyIlvv0xfqUUDy9/mOqKgzw26zEuyb2k3fNcbg+1LcbCMMwNDjdNLjfK6/kA3mEvS8sQmN0q2K0W75e7jTi78SUfbbN0O8yyp2IhPGolaM/hTDweePcu2Pmekd1yzA1damZF8Qp+sfIXVDVV8d3x3+X2kbdj78RQVchTsAhevxEGXAi3vKOT64Uoz25/lsc3Pc4D5z3At8d+22w5mhCjI88hHAxYcLFY4Lp/GDHf//kO7D3nGrx2mZE9g/fmvsfMfjN5fNPj3PDRDawtXetnsSZxdDu8dbsR4njjy9owhCgv7XyJxzc9zpyBc7h7jE5loukc2ji0hz0Wbv439BljfAkeWNalZlJiUvjLzL/w5OwnaXI3cffnd/PjZT+mpLbEz4KDSGUhvHYjxPSCW942HjUhx7Pbn+WRDY9wef/L+e2M3+ohHk2n0cbhbMT0MqKWUgfBGzcb9Qm6yEU5F/H+vPe5b9x9LClcwtz35/LE5ieod9b7UXAQOLbTiOhy1MI33jLi3TUhhVKKf2z5R4vH8MeL/ojd0oOGMzVBQxuHjohLNVIeJ2XDy9cZSfq6SIwthu+O/y4fXfcRs3Nn8/S2p7nqvat4dvuz1Dhq/CY5YBxYBs9fCSj41qfQZ7TZijRn0ORu4ucrf85TW59i7uC5/H7G73VCPU2X0RPSvtBwEt681chBc/FDMPMn3U4jsLVsK09teYqVJStJsCdw47AbuWnYTWQlhGDys63/hg++Z0RwffMdSMoxW5HmDE40nOCHS37I1rKt3D/+fr4z9jt6KElzTvQ6B3/gcsDHPzTSHg+72lgHEZ/W7WZ3l+/muR3PsfDwQgAu6XcJNw+/mUl9JpmfisPtgi9+Bav/bkQlff1ViE02V5OmDSuLV/KrVb+iqqmK3834HZcPuNxsSZowQRsHf6EUrHnKKGYTnw7X/wsGXeyXpktqS3hz75u8m/8uVU1V5CTkMHfIXOYNnkffBBPG9usr4N27jYpuU+6BK37fqVXjmsBT7ajmkfWP8J+C/zAwaSB/uPAPjEwbabYsTRihjYO/Kd0K79wF5QVGtbNZPzXmJ/xAo6uRhYcX8kHBB6w9aoS+TsiYwJyBc7hswGVGFs1AcnQHrH8Wtr0FboeRK2niHYHtU9Mp3B43H+7/kCc2P0FFYwV3jLqD+8bf1/NX42v8jjYOgcBRB1/8GtY/Y2ShnPUzmPgtv8b8l9SW8NH+j5h/cD77q/ZjFSsTMycyq98sZuXOIjsh2299cXgVLP0/OPilkTRt9A1GDWg98RwyKKVYVbKKRzc+Sv7JfMamj+WnU38a0QWnNN1DG4dAcmwnzH/ImKxOGQjTH4Bx3wC7/2oYKKXYd3IfCw4tYEnhEgoqCwAYljKMmf1mMrPfTEakjsBq6WSeH4/H0L38UTi4DOIz4ILvGbml/OQJabqPy+Ni4eGFvLjzRXaV7yInIYcfTPwBV/S/Qk86a7qFNg6BRinY+6mRk6lks5HnfsrdMP6bASm9eLj6MEuOLGFJ4RK2lG3Bozwk2hMZnzGeiZkTGZ8xnpFpI4m1xbbfwMnDRgTSlteMlMzxGUYeqYnfOmtWVU1wcXvcbD+xncVHFvPZoc8orStlQK8B3DbqNuYNnkeUVddL0HQfbRyChVJwYCms+KvxS1wskHc5jL0RBl/SqToRvnKy8SQrilew8dhGNh3fxMGqgwDYxMbQ1KGMSB1BXkoeQ229yDu6h+Q986F4IyAw8CI475sw4lpjVbjGVGocNawuWc3y4uUsL1pOeWM5NouNqX2mcuOwG5nZb6b5EWyaHoU2DmZQvh82vwJbXofaY4ahyJkMAy82Si/2nQAJvf3fbUM5249vZVvhl2w7tpG9dcVUqlNph3srIS+uL4P7TmZA7zEMTBrIgF4DSI9N10MUQcTpdnK4+jD7Tu5ja9lWtpZtZW/FXlzKRa+oXkzvO51ZubOYkT2DxKiupWPWaM6FNg5m4nEbv9QLvoD8hVC6xcgxD5CYZaTnSBkIybnGuom4NMPDiEr0VtGKM0JILXYjKaDbaeSidzVCQ6VRxrG+3Mh5dPIQnDxozIM0VQOgopMo6z+V/Mw88hPTyHdUkH8yn4NVB2l0N7bIjLfH079Xf7ITskmNSSUtJo30uHT6xvclKz6LPvF9iLPrISdfUUpR0VjB0bqjHK07SnFtMcW1xRTVFnGk+giFNYW4lVFDIdYWy+j00YzvPZ4Z2TMY23usXtmsCQraOIQSjjojFLZ4IxzbZXyZVxyE2qPdbzuxL6T0h8xRkDUe+o6H3iPajaDyKA/H6o5xsPogh6sPc7j6MIeqDlFaV0p5YzlVTVVt3hNri20xHEnRSSRFJ9ErqhcJUQn0iupFvD2eOFscsbZYYu2xxFhjiLZGE22LJsoShd1ix261YxUrFrFgFStWi9V4FGvIeC5KKVzKhcvjwuF24HA7aHQ34nQ7aXI34fA4qHPWUeuopcZRw8mmk5Q3lFPeUM7xhuMcqzvG8frjODyO09qNt8eTk5BDv8R+DEwayKDkQQxJHsKQ5CHaGGhMQRuHcMDtNBae1Zcb3kBzhSpHnXHM4zI2a5RRbcoWY3gYzVuvbL9GSDk9Tk7Un6C0rpSSuhKO1R2jvLGcisYKKhoqqHJUUdVURXVTNbXOWtTZagl3gmajYbPYvKUpheb/jP8Fi1hObVja7G9+X/NzoKUdoEWnUgqnx4nT48TlceF0O3EpV8vrzhJvjze8rdh0+sT3ITM+k8y4TLLis1q2pOikkDGAGg3oSnDhgdUOiZnGFgLYLXayErLISshiAhM6PNejPNQ766l11tLgamjZmlxNNLiNR6fHicPjwOl24lEe3Mrd8uj2uHEpF26Pu+W1Bw/NP1wUyluXWeFRxn4PnpbnzfubtXiU57T3tG6ntdFp9mRsYmt5tFlOfx5tjSbKGkWUNcp4bonCbrUTb48nMSqRRHsiyTHJZ48M02jCFG0cNN3GIhYSohJIiEowW4pGo/ETOi5Oo9FoNG3QxkGj0Wg0bdDGQaPRaDRt0MZBo9FoNG3QxkGj0Wg0bdDGQaPRaDRt0MZBo9FoNG3QxkGj0Wg0begR6TNEpAw43MW3pwMn/CgnXIjE647Ea4bIvO5IvGbo/HX3V0q1mx66RxiH7iAiG86WW6QnE4nXHYnXDJF53ZF4zeDf69bDShqNRqNpgzYOGo1Go2mDNg7wtNkCTCISrzsSrxki87oj8ZrBj9cd8XMOGo1Go2mL9hw0Go1G0wZtHDQajUbThog2DiJypYjsFZECEXnYbD3+QkT6icgSEdklIjtF5Afe/akislBE8r2PKd79IiJ/834O20Sk49JvIYyIWEVks4h87H09UETWeq/tTRGJ8u6P9r4u8B4fYKrwbiAiySLyjojsEZHdInJ+hNzrH3n/vneIyBsiEtPT7reIPC8ix0VkR6t9nb63InK79/x8Ebndl74j1jiIiBV4ErgKGAncLCIjzVXlN1zAfymlRgLTgPu91/YwsEgplQcs8r4G4zPI8273AE8FX7Lf+AGwu9XrPwJ/VUoNAU4Cd3n33wWc9O7/q/e8cOVx4DOl1HBgHMb19+h7LSLZwAPAJKXUaMAK3ETPu98vAleesa9T91ZEUoFfAVOBKcCvmg1KhyilInIDzgcWtHr9E+AnZusK0LV+AFwG7AWyvPuygL3e5/8Cbm51fst54bQBOd5/LJcAHwOCsVrUduY9BxYA53uf27znidnX0IVrTgIOnqk9Au51NlAIpHrv38fAFT3xfgMDgB1dvbfAzcC/Wu0/7byzbRHrOXDqj6uZIu++HoXXfT4PWAtkKqVKvYeOApne5z3ls3gM+H+Ax/s6DahUSrm8r1tfV8s1e49Xec8PNwYCZcAL3uG0Z0Uknh5+r5VSxcAjwBGgFOP+baTn32/o/L3t0j2PZOPQ4xGRBOBd4IdKqerWx5TxE6LHxDGLyDXAcaXURrO1BBkbMAF4Sil1HlDHqWEGoOfdawDvsMg8DOPYF4in7fBLjyeQ9zaSjUMx0K/V6xzvvh6BiNgxDMNrSqn3vLuPiUiW93gWcNy7vyd8FtOBuSJyCPg3xtDS40CyiNi857S+rpZr9h5PAsqDKdhPFAFFSqm13tfvYBiLnnyvAS4FDiqlypRSTuA9jL+Bnn6/ofP3tkv3PJKNw3ogzxvdEIUxmfWhyZr8gogI8BywWyn1l1aHPgSaIxVux5iLaN5/mzfaYRpQ1cptDQuUUj9RSuUopQZg3MvFSqlbgCXADd7Tzrzm5s/iBu/5YffrWil1FCgUkWHeXbOBXfTge+3lCDBNROK8f+/N192j77eXzt7bBcDlIpLi9bgu9+7rGLMnW0ye6JkD7AP2Az8zW48fr2sGhqu5Ddji3eZgjLEuAvKBL4BU7/mCEbm1H9iOEQFi+nV04/pnAh97nw8C1gEFwNtAtHd/jPd1gff4ILN1d+N6xwMbvPf7fSAlEu418GtgD7ADeAWI7mn3G3gDY07FieEl3tWVewvc6b32AuBbvvSt02doNBqNpg2RPKyk0Wg0mrOgjYNGo9Fo2qCNg0aj0WjaoI2DRqPRaNqgjYNGo9Fo2qCNgyaiEJHrRWTLGZtHRK7yQ9u/EZFLO3H+dSLyy+72623reyJypz/a0mhAV4LTRDgicg9wCzBLKeU51/l+7nsVMFcpdcIPbcUBK5WRQkOj6Tbac9BELCIyFPglcGt7hkFEvi0i60Vkq4i86/0CRkQ+EJHbvM+/IyKveZ+/KCI3eJ//QYx6GttE5JGz9N3UbBhav9f7utb7OFNElnn7POBt9xYRWSci20VkMIBSqh44JCJT/PspaSIV27lP0Wh6Ht7cU69j1L04cpbT3lNKPeM9/7cYq1OfwMiVv1JEDgL/hVEzo3XbacD1wHCllBKR5Hbang5s8lHuOGAEUAEcAJ5VSk0Ro4jT94Efes/bAFyIsQJYo+kW2nPQRCr/C+xUSr3ZwTmjRWS5iGzHGHoaBaCUOobhcSzBMC4VZ7yvCmgEnhORrwD17bSdhZFq2xfWK6VKlVJNGKkRPvfu346R67+Z4xgZSjWabqONgybiEJGZwFeB752x/wXvBPWn3l0vAt9TSo3ByOMT0+r0MRhZPdt8GSujXsAUjAyp1wCftSOj4Yz2XHj/PYqIBYhqdayp1XNPq9ceTvf+Y7ztajTdRhsHTUThzUr5AnCbUqqm9TGl1LeUUuOVUnO8uxKBUu8Q1C2t2piCUZLxPOC/RWTgGX0kAElKqU+BH2EMC53JbmBIq9eHgIne53MBexcubyhGEjqNptto46CJNO4FMoCnzghn/Xo75/4Co4LeSozsn4hINPAMcKdSqgRjzuF5b9roZhKBj0VkG7ACeLCdtr8Ezmv1vmeAi0VkK0Z5y7ouXNt0YGEX3qfRtEGHsmo0JiEijwMfKaW+8ENb5wEPKqVu7b4yjUZ7DhqNmfweiPNTW+kYno5G4xe056DRaDSaNmjPQaPRaDRt0MZBo9FoNG3QxkGj0Wg0bdDGQaPRaDRt0MZBo9FoNG34/yGOZKYUoRvQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "NBINS = 100\n",
    "\n",
    "def getTIdx(saver, t):\n",
    "    return min(enumerate(saver.time[0]), key=lambda v: abs(v[1] - t))[0]\n",
    "\n",
    "def plotPotential(t):\n",
    "    tidx = getTIdx(CellPot, t)\n",
    "    plt.plot(\n",
    "        CellPot.metaData['tetzpos'] * 1e6, \n",
    "        CellPot.data[0, tidx, :] * 1e3, \n",
    "        label=f'{CellPot.time[0, tidx]*1e3} ms'\n",
    "    )\n",
    "\n",
    "def plotCurrents(t):\n",
    "    tidx = getTIdx(NaCurrs, t)\n",
    "    for results, currName in zip([NaCurrs, KCurrs], ['Na', 'K']):\n",
    "        data = results.data[0, tidx, :] * 1e12\n",
    "        pos = results.metaData['trizpos'] * 1e6\n",
    "        areas = results.metaData['triarea'] * 1e12\n",
    "        bins = np.histogram_bin_edges(pos, NBINS)\n",
    "        dig = np.digitize(pos, bins)\n",
    "        # Ignore empty bins\n",
    "        with np.errstate(invalid='ignore'):\n",
    "            meanData = np.bincount(dig, weights=data) / np.bincount(dig, weights=areas)\n",
    "            meanPos  = np.bincount(dig, weights=pos) / np.bincount(dig)\n",
    "        plt.plot(meanPos, meanData, label=f'{currName} {results.time[0, tidx]*1e3} ms')\n",
    "\n",
    "plotPotential(1e-3)\n",
    "plotPotential(2e-3)\n",
    "plotPotential(3e-3)\n",
    "plt.xlabel('Z-axis (um)')\n",
    "plt.ylabel('Membrane potential (mV)')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "    \n",
    "plotCurrents(1e-3)\n",
    "plotCurrents(2e-3)\n",
    "plotCurrents(3e-3)\n",
    "plt.xlabel('Z-axis (um)')\n",
    "plt.ylabel('Current  (pA/um^2)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}