jump_distribution_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eyring_model import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = 'Helvetica'\n",
    "plt.rcParams['font.size'] = 8\n",
    "plt.rc('text.latex', preamble=r'\\usepackage[cm]{sfmath}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 300\n",
    "multi = True\n",
    "dH_barrier = 3.5\n",
    "dS_barrier = -9/T\n",
    "dH_sigma = dH_barrier/3\n",
    "dS_sigma = -dS_barrier/3\n",
    "n_paths = 2000 # infinite limit, approximately corresponds to unit area = 0.1 um^2\n",
    "\n",
    "barrier_dist = 'equal'\n",
    "barrier_params = {'mu' : np.array([dH_barrier, dS_barrier])}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Equal number of jumps, equal jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'mu' : 200}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = np.ones(n_paths)*n_jump_params['mu']\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "jump_length_params = {'mu' : 2}\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "\n",
    "\n",
    "dG0 = model.calculate_effective_barrier()\n",
    "\n",
    "h, b, bar = plt.hist(n_jumps, bins=25)\n",
    "plt.text(n_jump_params['mu']+0.5, h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('number of jumps', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xlim(190, 210)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normally distributed number of jumps, equal jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 200**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'mu' : 200,\n",
    "                'sigma' : 10}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.normal(loc=n_jump_params['mu'], scale=n_jump_params['sigma'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "jump_length_params = {'mu' : 2}\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "\n",
    "h, b, bar = plt.hist(n_jumps, bins=25)\n",
    "plt.text(b[-11], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('number of jumps', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xlim(160, 250)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 2000 jumps, std = 100**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'mu' : 2000,\n",
    "                'sigma' : 100}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.normal(loc=n_jump_params['mu'], scale=n_jump_params['sigma'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "jump_length_params = {'mu' : 2}\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "\n",
    "h, b, bar = plt.hist(n_jumps, bins=25)\n",
    "plt.text(b[-11], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('number of jumps', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exponentially distributed number of jumps, equal jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 200**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_paths = 30_000\n",
    "\n",
    "n_jump_params = {'beta' : 200}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.exponential(n_jump_params['beta'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "jump_length_params = {'mu' : 2}\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n] // 1 + 1), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "\n",
    "h, b, bar = plt.hist(n_jumps, bins=25)\n",
    "plt.text(b[-15], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('number of jumps', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paths with low permeability:\n",
      "2721\n",
      "2097\n",
      "1754\n",
      "1733\n",
      "1723\n",
      "\n",
      "Paths with high permeability:\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "model.calculate_permeability()\n",
    "sorted_paths = np.array(model.paths)[model.permeabilities.argsort()]\n",
    "print('Paths with low permeability:')\n",
    "for p in sorted_paths[:5]:\n",
    "    print(p.n_jumps)\n",
    "\n",
    "print()\n",
    "\n",
    "print('Paths with high permeability:')\n",
    "for p in sorted_paths[-5:]:\n",
    "    print(p.n_jumps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 2000**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_paths = 30_000\n",
    "\n",
    "n_jump_params = {'beta' : 2000}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.exponential(n_jump_params['beta'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "jump_length_params = {'mu' : 2}\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n] // 1 + 1), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "\n",
    "h, b, bar = plt.hist(n_jumps, bins=25)\n",
    "plt.text(b[-15], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('number of jumps', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Equal jump lengths, equal number of jumps, equal barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_paths = 2000\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "jump_length_params = {'mu' : 2}\n",
    "\n",
    "n_jump_params = {'mu' : 200}\n",
    "n_jumps = np.ones(n_paths)*n_jump_params['mu']\n",
    "\n",
    "jump_lengths = np.zeros((n_paths, n_jump_params['mu']))\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    jump_lengths[n,:] = model.paths[n].jump_lengths\n",
    "\n",
    "h, b, bar = plt.hist(jump_lengths.flatten(), bins=25)\n",
    "plt.text(jump_lengths.mean()+0.05, h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.3f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('jump lengths', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normally distributed jump lengths, equal number of jumps, equal barriers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jump_length_dist = 'normal'\n",
    "jump_length_params = {'mu' : 2, 'sigma' : 0.5}\n",
    "\n",
    "n_jump_params = {'mu' : 200}\n",
    "n_jumps = np.ones(n_paths)*n_jump_params['mu']\n",
    "\n",
    "jump_lengths = np.zeros((n_paths, n_jump_params['mu']))\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    jump_lengths[n,:] = model.paths[n].jump_lengths\n",
    "\n",
    "h, b, bar = plt.hist(jump_lengths.flatten(), bins=25)\n",
    "plt.text(b[-12], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('jump lengths', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 20**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jump_length_dist = 'normal'\n",
    "jump_length_params = {'mu' : 20, 'sigma' : 5}\n",
    "\n",
    "n_jump_params = {'mu' : 200}\n",
    "n_jumps = np.ones(n_paths)*n_jump_params['mu']\n",
    "\n",
    "jump_lengths = np.zeros((n_paths, n_jump_params['mu']))\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    jump_lengths[n,:] = model.paths[n].jump_lengths\n",
    "\n",
    "h, b, bar = plt.hist(jump_lengths.flatten(), bins=25)\n",
    "plt.text(b[-12], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('jump lengths', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exponentially distributed jump lengths, equal number of jumps, equal barriers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jump_length_dist = 'exponential'\n",
    "jump_length_params = {'beta' : 2}\n",
    "\n",
    "n_jump_params = {'mu' : 200}\n",
    "n_jumps = np.ones(n_paths)*n_jump_params['mu']\n",
    "\n",
    "jump_lengths = np.zeros((n_paths, n_jump_params['mu']))\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['beta'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    jump_lengths[n,:] = model.paths[n].jump_lengths\n",
    "\n",
    "h, b, bar = plt.hist(jump_lengths.flatten(), bins=25)\n",
    "plt.text(b[-15], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('jump lengths', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paths with low permeability:\n",
      "mean, std, min, max\n",
      "2.231782144943108 2.605415003422494 2.565262681989671e-05 21.431528101564556\n",
      "2.1393339515369507 2.23016464863961 3.149439171730708e-05 13.32179454528887\n",
      "1.8926645937982005 1.960077254234184 3.623531924852667e-05 11.89789028748801\n",
      "2.1885816715461597 2.1636706752354713 3.342754574669469e-05 11.654278004045537\n",
      "2.036056551908291 1.8648388614327072 4.176857401765302e-05 9.890707056874454\n",
      "\n",
      "Paths with high permeability:\n",
      "mean, std, min, max\n",
      "2.2810142332104313 2.201463413719766 0.05269937272048161 10.017104857252827\n",
      "2.3121020998809243 2.0375292321601224 0.04229173874104909 9.903622933899001\n",
      "2.173661136008926 1.917047945091458 0.07060299362358834 10.21941252447797\n",
      "2.2459425506861965 1.9295108107800811 0.0726088166448597 9.757770648958074\n",
      "2.0810694754685146 2.02038064307872 0.08908727362289456 11.713937650698877\n"
     ]
    }
   ],
   "source": [
    "model.calculate_permeability()\n",
    "sorted_paths = np.array(model.paths)[model.permeabilities.argsort()]\n",
    "print('Paths with low permeability:')\n",
    "print('mean, std, min, max')\n",
    "for p in sorted_paths[:5]:\n",
    "    print(p.jump_lengths.mean(), p.jump_lengths.std(),  p.jump_lengths.min(), p.jump_lengths.max())\n",
    "\n",
    "print()\n",
    "\n",
    "print('Paths with high permeability:')\n",
    "print('mean, std, min, max')\n",
    "for p in sorted_paths[-5:]:\n",
    "    print(p.jump_lengths.mean(), p.jump_lengths.std(),  p.jump_lengths.min(), p.jump_lengths.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean = 20**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ft/ypmFIBAIDVs7ZTKoRhqIuLiyJQPSZJEqVpOtarlc9eHMfx3OUAAADSCoaqw8NDBUEwsT6Koqljnu5eTszZtq0kSeYuf8j19bWurq7GFgAAsJ5WKlSFYajd3d2xXqNcr9dTt9vV+fm5er2ePM9bQA3HHR8fa2dnp1hevXq16CoBAIAXslKhqt1uy3XdifVBEOj8/FyO48iyLAVBoDAMlabpg/vb3d190fKjoyONRqNi+fDhw4PbAwCA1bUyoSpNU8VxPPXBo7Zty7KssdfSzaW//O+7AStJElmWNXf5Q7a2trS9vT22AACA9bQyoSofv3T76e53y3L50+Bt2y4C1+1B5Xl5rVabuxwAAEBawVB1t3coSRJVq1WFYVi89n1fjUaj2DYIgmIOrDRNdXh4ODb9wrzlAAAAnyy6AtLN/FS379grlUqybXtsvqr7LrfZtq1utyvf9+V5nmzbVrPZHAs9jUZDaZoWlw5d1x27g3DecgAAgKWa/HPdMfknAACrZ20n/wQAAFhGhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgwFKFqjAMVa/XJ9Z7nqdSqTS23N3u5ORElUpF5XJZzWZzYh8vXQ4AADbbUoQq3/dVLpfl+76SJJm6TavVUpZlxRJFUVHW6XTUbrcVRZH6/b56vZ583/9o5QAAAKUsy7JFVyLX6XQUBIGGw+HYes/ztL+/r1arNfV95XJZ3W5XtVpNkjQYDFStVpU37aXLn+rq6ko7OzsajUba3t5+1nuf4vUvvzayn+++/NzIfgAAWAdP/f1eip6qeSRJojRNi8AjSY7jSJLiOH7xcgAAAGmFQlUURVPHNN13udC2bSVJ8uLlD7m+vtbV1dXYAgAA1tPKhKper6dut6vz83P1ej15nrfoKj3q+PhYOzs7xfLq1atFVwkAALyQlQhVQRDo/PxcjuPIsiwFQaAwDJWm6YPv293dXWj50dGRRqNRsXz48OHB7QEAwOr6ZNEVeArbtqe+TpKk+DtNU1mWVWyTJIksy3rx8odsbW1pa2vrOU0FAAAraiV6qu6OXRoMBpJuwpVt27Isa2zQeF5eq9VevBwAAEBagVCVJImq1arCMCxe+76vRqNR9BQFQVDMcZWmqQ4PD8emX3jpcgAAgKW4/NfpdMbu6CuVSrJtW8PhULZtq9vtyvd9eZ4n27bVbDbHQk2j0VCapsUs667rKgiCj1YOAACwVJN/rjsm/wQAYPVszOSfAAAAy4BQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAxYqlAVhqHq9frE+jRN5XmeyuWyKpWKfN8fK/c8T6VSaWy5u5+TkxNVKhWVy2U1m82Jz5i3HAAAbLalCFW+76tcLsv3fSVJMlF+eHioer2u8/NztdtthWEoz/PGtmm1WsqyrFiiKCrKOp2O2u22oihSv99Xr9cbC2bzlgMAAJSyLMsWXYlcp9NREAQaDofFuiRJ1Gw2x0JSHMeq1+u6vLyUZVnyPE/7+/tqtVpT91sul9XtdlWr1SRJg8FA1WpVedPnLX+qq6sr7ezsaDQaaXt7+1nvfYrXv/zayH6++/JzI/sBAGAdPPX3eyl6qh5i27ba7fbYur29PUlSr9d79P1JkihN0yIQSZLjOJJuwtm85QAAANIKhCrpJljdloepPFxJUhRFU8c8TbucmO8zSZK5yx9yfX2tq6ursQUAAKynlQhVdwVBoFarJcuyinW9Xk/dblfn5+fq9XoTY64W4fj4WDs7O8Xy6tWrRVcJAAC8kLlC1dXVlX7zm98Ur3/3u99pf39ff/u3f6vf/va3c1duGs/z5DiOgiAo1gVBoPPzczmOI8uyFASBwjBUmqYP7mt3d/dFy4+OjjQajYrlw4cPD24PAABW1yezvvH8/FzValWlUkk//elPNRqNinFHlmUpiiLFcay/+qu/MlZZz/Nk2/ZYoJImLw/mr5MkKf5O03SsZytJElmWNXf5Q7a2trS1tfWcJgIAgBU1c09Vs9lUmqY6OjqSJJ2dnUm6uTPu4uJCOzs7E+FnHp7nqV6vT93n3bFNg8FA0k24sm1blmWNDSrPy2u12tzlAAAA0hyhqtfrqV6v6xe/+IWk/x0o/uMf/1jSTeB4//69kUrmUyYcHBwoTdNikW4CVbVaVRiGxWvf99VoNIqepCAIijmw0jTV4eHh2PQL85YDAADMfPkvTdOxy26DwWBsFnPbtvXrX//6SfvqdDpjd+yVSiXZtq3hcKgkSRSGocIwnJhwM4oi1Wo1dbtd+b5fXB5sNptjoafRaChN06J+ruuO9XjNWw4AADDz5J97e3sqlUp6//69zs/PValUFIahfvrTn0qS/vqv/1rn5+f6n//5H6MVXmVM/gkAwOp58ck/G42G+v2+/uZv/qYIWLVaTaPRSP/0T/+kb775hjFHAABgY8x8+S8PVaenp5JuLuFtb2/r22+/Vbvdnpj2AAAAYJ3NHKokqd1uTzxCxrZt9ft9ffbZZ3NVDAAAYJXMfPnvhz/8oX7/+99PrN/Z2dFnn32m09NTffrpp3NVDgAAYFU8q6fqv/7rv4q/Ly8vNRgMVC6XJ7a7uLhQt9t99Nl4AAAA6+JZoernP/+5vv32W0k30x48NFdTlmVL8fw9AACAj+FZoSoIAqVpqizLdHBwoEajMTY31W2WZent27dGKgkAALDsnhWqboekWq2mf/zHfyxmUAcAANhkM9/995//+Z8m6wEAALDS5ppS4erqSnEc6+Li4t5tfv7zn8/zEQAAACth5lD17bffam9vT9LNoPRpSqUSoQoAAGyEmUPV4eGhsixTu93W3t6eLMsyWC0AAIDVMnOoGgwGajabOjw8NFkfAACAlTTzjOq2batSqZisCwAAwMqaOVT9/d//vd69e2eyLgAAACtr5lAVBIGyLNPPfvYz/eEPfzBZJwAAgJUzc6j69NNPNRqNdHZ2Jtu29ed//ucTyyefzDVjAwAAwMqYOfX8+Mc/VqlU0meffWayPgAAACtp5lDV7XZN1gMAAGClzXz5DwAAAP9r5p6q3/zmN49u4ziOXr9+PetHAAAArIyZQ5XruiqVShOPqCmVSsXfrVZLx8fHs9cOAABgRcwcqqIomrq+3+/ryy+/1P7+vo6OjmauGAAAwCqZOVS9ffv23vWNRkN7e3s6PT3Vv/zLv8xcOQAAgFXxIgPVLcuS67r6j//4j5fYPQAAwNJ50bv/BoPBS+4eAABgabxIqLq6ulKn05Ft2y+xewAAgKUz85iqTz/99N6yJEmUZZn+9V//ddbdAwAArJSZQ9Xr16/Hpk+47c2bN/I8T4eHhzNXDAAAYJUYn1IBAABgE/GYGgAAAAPmDlW/+93v9MUXX2h/f1/7+/v64osv9Nvf/namfYVhqHq9PrXs5ORElUpF5XJZzWZz6coBAMBmmytUffHFF6rX6+p2uxoOhxoOh+p2u3JdVz/72c+evB/f91Uul+X7vpIkmSjvdDpqt9uKokj9fl+9Xk++7y9NOQAAQCm7+/C+Jzo9PVWz2ZTrujo9PdXOzo4kaTQaqdVq6auvvlKn09E//MM/PHmfnU5HQRBoOByOrS+Xy+p2u6rVapJu5r+qVqvFcwcXXf5UV1dX2tnZ0Wg00vb29rPe+xSvf/m1kf189+XnRvYDAMA6eOrv98yham9vT6VSSe/fv5+pfJppoSpJElUqlakPbo6iSLZtL7Q8D1pPQagCAGD1PPX3e+a7/waDwYOXwGq1mv793/991t0Xpl0OlCTbtu8tW4ZySbq+vtb19XXx+urq6sHtAQDA6po5VD0WKpIk2fgZ1Y+Pj/WrX/1q0dV4Nnq8AAB4vpkHqn/22WcKw1D//d//PVH27bffKgxDOY4zV+Ues7u7u9TlR0dHGo1GxfLhw4cHtwcAAKtr5p6qr776SnEcy3EceZ6nvb09SdL79+8VhqFKpZJOT0/nrmDe25WmqSzLKtYnSSLLshZe/pCtrS1tbW09s8UAAGAVzdxTtbOzo8FgoJ/85Cc6OztTq9VSq9VSt9vVZ599pl6vZ2Qwtm3bsixLcRwX6waDgaSbcVuLLgcAAJDm6KmSbp7xF0WRRqNRMb7Ktu1iegVTgiCQ7/tyHEe7u7s6PDxUq9VamnIAAIC5QlVuZ2dHn332maTZ7nDrdDpjs5SXSiXZtl1MrdBoNJSmaTHbuuu6CoKg2H7R5QAAADPPUyVJv/71r9VoNHR0dKRf/OIXkqSDgwN98803+uqrr/R3f/d3xiq6DlZlnipTuPsPALAOnvr7PfOYqm+//Vae5+ny8nLsLr+DgwNZliXXdfX73/9+1t0DAACslJlDle/7qlQqury81E9+8pNiveu6Gg6Hev36Nc/HAwAAG2PmUNXr9VSr1e4dlO553sQz/AAAANbVzKHqsYkv0zSdddcAAAArZ64Z1c/OzvSHP/xhomw0Guns7KyYEBQAAGDdzTylQhAE+su//EvZti3f98dmVO90OhqNRmq328YqCgAAsMzmeqByr9fT4eGhvvzyS5VKJUlSlmWybVtnZ2d6/fq1qXoCAAAstbkm/3QcR/1+X+fn58WjWxzH0Zs3b4xUDgAAYFUYmVH9zZs3BCkAALDRZh6oDgAAgP9FqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAANWJlSFYahSqTR16XQ6kiTP8ybK6vX62H5OTk5UqVRULpfVbDYnPmfecgAAsJlWJlS5rqvhcDi29Pt9WZalWq1WbNdqtZRlWbFEUVSUdTodtdttRVGkfr+vXq8n3/eNlQMAgM1VyrIsW3QlZlWv1+V5nhqNhqSbnqr9/X21Wq2p25fLZXW73SKEDQYDVatV5Ydg3vLHXF1daWdnR6PRSNvb27M3/B6vf/m18X3O47svP190FQAAmNtTf79XpqfqrjAMdXFxUQSqxyRJojRNx3q1HMeRJMVxPHc5AADYbCsbqg4PDxUEwcT6KIqmjnlKkmTqfmzbVpIkc5dPc319raurq7EFAACsp5UMVWEYand3d6zXKNfr9dTtdnV+fq5eryfP8xZQwxvHx8fa2dkpllevXi2sLgAA4GWtZKhqt9tyXXdifRAEOj8/l+M4sixLQRAoDEOlafrg/nZ3d1+k/OjoSKPRqFg+fPjw4H4AAMDqWrlQlaap4jiemCpBurkUZ1nW2Gvp5tJf/vfdgJUkiSzLmrt8mq2tLW1vb48tAABgPa1cqMrHL+3t7d1blhsMBpJuwlUeuG4PKs/La7Xa3OUAAGCzrWyouts7lCSJqtWqwjAsXvu+r0ajUWwbBIF83y/u5Ds8PBybfmHecgAAsLk+WXQFnuu+y222bavb7cr3fXmeJ9u21Ww2x0JPo9FQmqbFpUPXdcfuIJy3HAAAbK6Vnvxz1TD5JwAAq2ftJ/8EAABYJoQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYMBKhSrP81QqlcaWer0+ts3JyYkqlYrK5bKazebEPl66HAAAbKaVClWS1Gq1lGVZsURRVJR1Oh21221FUaR+v69eryff9z9aOQAA2FylLMuyRVfiqTzP0/7+vlqt1tTycrmsbrerWq0mSRoMBqpWq8qb+NLlj7m6utLOzo5Go5G2t7dnPAr3e/3Lr43vcx7fffn5oqsAAMDcnvr7vXI9VfdJkkRpmhaBR5Icx5EkxXH84uUAAGCzrVyoiqJo6pimJEmmbm/btpIkefHyaa6vr3V1dTW2AACA9bRyoarX66nb7er8/Fy9Xk+e5y26Svc6Pj7Wzs5Osbx69WrRVQIAAC9kpUJVEAQ6Pz+X4ziyLEtBECgMQ6Vp+uD7dnd3F1J+dHSk0WhULB8+fHhwPwAAYHV9sugKPIdt21NfJ0lS/J2mqSzLKrZJkkSWZb14+TRbW1va2tqapalrwdTAeQa8AwBWwUr1VN0duzQYDCTdhCvbtmVZ1tig8by8Vqu9eDkAANhsKxOqkiRRtVpVGIbFa9/31Wg0ip6iIAjk+35xp97h4eHY9AsvXQ4AADbXylz+s21b3W5Xvu/L8zzZtq1mszkWahqNhtI0LWZZd11XQRB8tHIAALC5Vmryz1W3aZN/msKYKgDAIm3c5J8AAACLRKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABqxUqErTVJ7nqVwuq1KpyPf9sXLP81QqlcaWer0+ts3JyYkqlYrK5bKazebEZ8xbDgAANtNKharDw0PV63Wdn5+r3W4rDEN5nje2TavVUpZlxRJFUVHW6XTUbrcVRZH6/b56vd5YMJu3HAAAbK5SlmXZoivxFEmSqNlsjoWkOI5Vr9d1eXkpy7LkeZ729/fVarWm7qNcLqvb7apWq0mSBoOBqtWq8kMwb/ljrq6utLOzo9FopO3t7dkOxANe//Jr4/tcBt99+fmiqwAA2GBP/f1emZ4q27bVbrfH1u3t7UmSer3eo+9PkkRpmhaBSJIcx5F0E87mLZ/m+vpaV1dXYwsAAFhPKxOqpJtgdVsepvJwJUlRFE0d85Qkyb37TJJk7vJpjo+PtbOzUyyvXr26v3EAAGClrVSouisIArVaLVmWVazr9Xrqdrs6Pz9Xr9ebGHP1MR0dHWk0GhXLhw8fFlYXAADwsj5ZdAVm5XmeHMdREATFuiAItLu7W4SsIAhUr9eVpumD+9rd3X2R8q2tLW1tbT34XgAAsB5WMlR5nifbtscClTR5eTB/nSRJ8XeapmM9W0mSyLKsucvxckwNwGfAOwDgJa3c5T/P81Sv1ycClTQ5bmowGEi6CVe2bcuyrLFB5Xl5rVabuxwAAGy2lQpV+ZQJBwcHStO0WKSbQFWtVhWGYfHa9301Go2xy4G+7xd38h0eHo5NvzBvOQAA2Fwrc/kvSRKFYagwDCcm3IyiSLVaTd1uV77vF5cHm83mWOhpNBpK07SYZd113bEer3nLAQDA5lqZyT/XAZN/LhZjqgAAs1i7yT8BAACWGaEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABjwyaIrAHwsr3/5tZH9fPfl50b2AwBYL/RUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABjAjOrAMzEzOwBgGnqqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwADu/numk5MTtdttXVxc6ODgQO12e9FVworiLkIAWC/0VD1Dp9NRu91WFEXq9/vq9XryfX/R1QIAAEuglGVZtuhKrIpyuaxut6tarSZJGgwGqlareuohvLq60s7Ojkajkba3t43Xz1TPBzYTPV4AMN1Tf7+5/PdESZIoTdMiUEmS4ziSpDiOx9bnrq+vdX19XbwejUaSbk7OS/jT9f/3IvvFZvg//q+ukf38v7/6GyP7AYBlkf9uP9aJQqh6oiRJpq63bfvesuPjY/3qV7+aWP/q1SujdQOWyc7/vegaAMDL+P7777Wzs3NvOaHqBR0dHemf//mfi9d/+tOfdHFxoR/+8IcqlUpGP+vq6kqvXr3Shw8fXuTS4rLa1HZLtJ22b1bbN7XdEm1fhrZnWabvv/9eP/rRjx7cjlBlwO7u7tT1W1tb2traGltnWdaL1mV7e3vj/qWTNrfdEm2n7ZtlU9st0fZFt/2hHqocd/89kW3bkqQ0TcfWJ0ny4kEJAAAsP0LVE9m2LcuyFMdxsW4wGEjS1EHqAABgsxCqniEIAvm+X9wJeHh4qFartehqSbq51Phv//ZvE5cb192mtlui7bR9s9q+qe2WaPsqtZ15qp4pn1FdklzXVRAEC64RAABYBoQqAAAAA7j8BwAAYAChCgAAwABCFQAAgAGEqhV3cnKiSqWicrmsZrO56Oq8GM/zVCqVxpZ6vT62zTodizAMJ9qXe6ydq34c7mv7On8H0jSV53kql8uqVCryfX9im3U974+1fZ3PuyQ1m02Vy+WZz+u6tn1lz3uGldVutzPbtrPhcJgNh8PMcZys1WotulovwnXdB9u2Lsei1WpllmVltm1ntm1PlD/WzlU+Do+1fZ2/A67rZu12O7u8vMyiKMps285c1y3K1/m8P9b2dT7vtVqtqOvl5WVWq9U25rw/1vZVPe+EqhVmWVYWRVHxut/vZ+uak13XzYIguLd83Y5F/h+Mux5r5zoch/vavq7fgeFwmNVqtbF1URRlkrLLy8ssy9b3vD+l7et63rPs5rt+97VlWcXrdT3vWfZ421f1vC++BpjJcDic+gWSNPZFWxcP/Qu2jsdiWrB4rJ3rchxmCVWr3vbhcDj2+vLy8snndZ3bnmXrfd7vcl23CJnrft7vut32/PUqnnfGVK2oJEmmrrdt+96yVRdF0dTr55tyLB5r5yYch3X9DuTPFs31ej1J0t7e3tqf94fanlvX855L01QnJyeK47gYU7bu5z03re25VTzvhCqsjF6vp263q/Pzc/V6PXmet+gq4SPblO9AEARqtVob+bD2aW1f5/Mex7HK5bJ831ej0dioZ8k+1vZVPO+fLLoCMG93d3fRVTAuCALt7u4W/6ENgkD1el1pmj74vnU8FtM81s51OA6b8h3wPE+O4zzpEVjrdt6ntX3dz3utVlOWZUqSRM1mU/V6XVEUPfiedTnvD7V9Vc87PVUrKu8yv/sFS5JkLf/v1rbtsXbl7U+SZGOOxWPtXPfjsAnfAc/zZNv2WKjYlPM+re3SZpx36aZd3W5XcRwrDMONOe/SZNvzdat43glVKyr/wsVxXKwbDAaStJbdx3evk+dttW17Y47FY+1c9+Ow7t8Bz/NUr9fvDRXrfN7va7u0vuc9TdMHe13W+bw/1nZphc/7QofJYy635+m4vLxcmnk6TBsOh5llWVm32y1e27adNRqNYpt1Oxb33QH3WDvX4Tjcd+fjOn8H8judLi8vx5bcOp/3h9q+zuc9b0u32y3a7Lru2Hd/Xc/7Y21f5fNOqFpxQRAUkyUuwxfqpURRlDmOk0nKbNueeqvtOhyLdrudSRpb7gaMx9q5qsfhsbav63cgvz182nL79vB1PO9Pafu6nvcsu2m/67qZZVmZZVmZ67pjYTrL1vO8Z9njbV/V817Ksiz7eP1iAAAA64kxVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAC+F5nkql0qKrMRPP81QulxddjUet8jEGVhGhCgDWQBzHqlarxTPQAHx8nyy6AgA2U7fbXXQV1kqapgQqYMHoqQIAADCAUAVgIe6O96nX66pUKhPbxXGsUqmkMAzH3lupVJSmaTG+qVKpqNlsSpIGg4Hq9brK5bLK5bJ835/47DAMNRgMJt6fpunMbWo2m6pUKiqVSqpUKjo5OZna7rydvu8X29936S5JkqItlUqlaEu9Xi+OSb1el+d5kqRqtTox3itN07G63fdZnU5H1WpVpVJJ5XJZ9XpdSZLMfDyATUOoArCSLi4uVK1WJUlHR0eyLEudTkf1el1v376Vbds6OjrS7u6uTk5OxgJOGIZqt9uqVqtKkkQHBwfF+6vV6rODVZqmKpfL6nQ6chxHrVZLtm3L9/2ijndVq1XFcaxms6lGo6HBYDDx2YPBQJVKRb1eTwcHB6rVakUb4zguAk8QBGo0GsXfdy+tVqtV9Xo9NZtNua479bOazWYRShuNhvb29opxWvMETWCjZACwAK7rZrf/E1Sr1TLbtie2i6Iok5R1u92J97bb7WLd5eVlJmli2+FwmEnKarVasS7frtVqjX1WEART10+ru2VZE/Xp9/tj27Xb7UxSFgTBxLau607d9nbdbdvOLMvKLi8vx9ppWdbEfrvd7kQdnvNZd49RfjwsyxrbDsD96KkCsLLy3hlJsixLjuNIklzXLdbbti3Lsibea1mWgiAYW5f3MHU6nWfVIwxDNRqN4vNv18+yLL17927iPXc/e29vT9JND5x000uVJEnRC3e73kdHR8+q32Oflev1emOX+1qtli4vL8eOJ4D7EaoArBXbtifW7e7uTqzLg8VdjuM863JXHkI6nY5KpdLEkqbp1HFJd+t5N/jl77kb1O5b95Bpx+SudrutNE1VqVRUqVTkeZ46nQ6X/oBnYEoFADDAdV198cUXU8um9ZQ95m4v0m3TQuK8Go2GDg4OFMexoihSHMcKw1DNZlNRFKlWqxn/TGDdEKoALLWXmnup1+vd+3nPCUG3e4GmXSYLw3CmEJT3pA0Gg4lAc1/dZ5X3ptm2Ldd1i3bkA9p931e/3zf6mcA64vIfgKWRJMnY5aY0TXV8fPwin5Wm6cSUB51Op7gb8Dlc11UYhorjeGy97/vyPG+mEOQ4jmzb1vHx8cQxuTtG6raHerjukyRJEZ7u1sGyrBfpGQPWET1VAJaC53mK41hv3rwpBqCHYVjc2m+aZVnyfV9RFMlxHA0GA8VxLNu2Hwwt05yeniqOY9XrddVqNTmOoziONRgM5DjO2ID652i326rX63rz5k0R9M7OzrS3t6ckSSYGsEs3g9KTJHnWZzqOI8dxilDpOI6SJFEcx8UcVwAeR08VgKXQaDQUBEExr1Q+h1O321Wr1Xr24OzH1Gq14pJWHiYajYb6/f6zx0BZlqXz83M1Gg0lSaKTk5OiR2mey2a1Wk3D4VB7e3s6OztTr9fT0dHRRI9Svq3ruorj+NmhUJK++eabsfrHcay9vT1FUcTdf8ATlbIsyxZdCQCbJ5/VfBH/CSqVSnJdd+mfPzgYDKZOCRGGoTzPYwA5sGToqQKAJfX27Vu9fft2Yv3x8bEsyyJQAUuGMVUAPrp8vM4sUw1skvxSX/5svzRN9e7dOw0GA7Xb7UVXD8AdhCoAH1V+6Uq6mbEb92u1WrIsS+12W81mU5ZlFeOc6KUClg9jqgB8VHkv1d7envHB5wCwSIQqAAAAAxioDgAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGDA/w974yKysW3+JQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jump_length_dist = 'exponential'\n",
    "jump_length_params = {'beta' : 20}\n",
    "\n",
    "n_jump_params = {'mu' : 200}\n",
    "n_jumps = np.ones(n_paths)*n_jump_params['mu']\n",
    "\n",
    "jump_lengths = np.zeros((n_paths, n_jump_params['mu']))\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['beta'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    jump_lengths[n,:] = model.paths[n].jump_lengths\n",
    "\n",
    "h, b, bar = plt.hist(jump_lengths.flatten(), bins=25)\n",
    "plt.text(b[-15], h.max()*0.9, f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "plt.xlabel('jump lengths', fontsize=14)\n",
    "plt.ylabel('counts', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normally distributed number of jumps, normally distributed jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'mu' : 200,\n",
    "                'sigma' : 10}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.normal(loc=n_jump_params['mu'], scale=n_jump_params['sigma'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'normal'\n",
    "jump_length_params = {'mu' : 2, 'sigma' : 0.5}\n",
    "\n",
    "jump_lengths = []\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    [jump_lengths.append(lam) for lam in model.paths[n].jump_lengths]\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(14,6))\n",
    "plt.suptitle(f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "\n",
    "h, b, bar = ax[0].hist(n_jumps, bins=25)\n",
    "ax[0].set_xlabel('number of jumps', fontsize=14)\n",
    "ax[0].set_ylabel('counts of paths', fontsize=14)\n",
    "ax[0].set_xlim(160, 250)\n",
    "\n",
    "h, b, bar = ax[1].hist(jump_lengths, bins=25)\n",
    "ax[1].set_xlabel('jump lengths', fontsize=14)\n",
    "ax[1].set_ylabel('counts of jumps', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normally distributed number of jumps, exponentially distributed jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gBABwBWRZFtvb25FlWSRJEsViMSIiOp1OzM7ODrZL0zSyLBtb+0levnwZBwcHQzcA4GZxyhgAwBXQ7XajVCpFtVqNiIhmsxmtVmsitaysrMSnn346kdcGAC6HGUIAAFdAmqYxMzMzuJ8kSXS73Zibmxta66c/g2hc7SdZXl6O/f39we27774bY08BgKtAIAQAcAWUSqWhGUHtdjtKpdJQe7fbjSzLxtp+klu3bg2uKObKYgBwMzllDADgki0sLAwWdM6yLJrNZiRJEvV6fbB2UL1ejyRJIkmSqNVqUSwWY2ZmZnDZ+DRNx9IOAOTTVK/X6026iMt0cHAQ09PTsb+/79suALjCfGZfHd4LALgezvKZ7ZQxAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZ96ddAHA9XHv6ZcjP/fbZz8dYyUA3BTn+Ww5zOcMAJyNGUIAAAAAOSMQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQM64yBlwKVygDAAC4OswQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDMCIQAAAICcEQgBAAAA5IxACAAAACBnBEIAAAAAOSMQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjEAIAOCSdbvd2NzcjFqtduJjd+7cic3NzUHb2tpaFIvFKBaL0Wq1xt4OAOTPu5MuAAAgb2q1WmRZFoVC4dhjKysrMT8/P7jf7XajXq9Hp9OJbrcbxWJx8PM42gGAfDJDCADgkjUajSiXy8fasyyLdrs99NjGxkZUKpWIiEiSJNI0jXa7PbZ2ACCfBEIAAFdErVaLer0+1NbpdGJ2dnZwP03TyLJsbO0nefnyZRwcHAzdAICbRSAEAHAFtNvt6Ha7J55GdtlWVlZienp6cLt79+6kSwIAxswaQgAAV8DKykpkWRblcnkwc2d3dzfm5uZiZ2dnsF2WZZEkydjaT7K8vBw///nPB/cPDg6EQgBwwwiEAACugGazOfh5cXExyuVyVCqVyLJscBpZt9uNLMuiVCqNrf0kt27dilu3bl1kdwGACRMIAQBcsoWFhcGCzlmWDYVBR6VpGrVaLYrFYszMzESj0RhrOwCQT1O9Xq836SIu08HBQUxPT8f+/n7cvn170uXAtXLv6ZcTed1vn/10Iq8LTJbP7KvjIt+LcX22+KwAgLN9ZltUGgAAACBnBEIAAAAAOSMQAgAAAMiZKxUI1Wq1KJfLMTc3F61Wa9C+trYWxWIxisXiW7UDAAAAcLorc5WxVqsVs7OzsbW1Fe12O4rFYuzt7UVERL1ej06nE91uN4rF4uDnk9oBAAAAeL0rEwjNz8/H/Px8REQUCoVI0zSyLIvt7e2oVCoREZEkSaRpGu12+9T2QqEwsT4AAAAAXAdX5pSxJEkiSZKIiOh2u7G7uxuFQiE6nU7Mzs4OtusHRae1H/Xy5cs4ODgYugEAAADk2ZUJhA578uRJNJvNsexrZWUlpqenB7e7d++OZb8AAAAA19WVC4QWFxdjcXExSqVSRETMzc0NrQ2UZVkkSXJq+1HLy8uxv78/uH333XcX3gcAAACAq+xKBUJHw6CIiFKpNLiCWLfbjSzLolQqndp+1K1bt+L27dtDNwAAAIA8uzKLSq+trcXGxsbQ5eObzWYUCoWo1WpRLBZjZmYmGo1GRLxaM+ikdgAAAABeb6rX6/UmXcRlOjg4iOnp6djf3zdbCM7o3tMvJ/K63z776UReF5gsn9lXx0W+F+P6bPFZAQBn+8y+UqeMAQAAAHDxBEIAAAAAOSMQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDPvTroA4HLde/rlpEsAAABgwswQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjMvOA1fevadfjvzcb5/9dIyVAAAA3AxmCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBABwybrdbmxubkatVhtqr9VqUS6XY25uLlqt1qB9bW0tisViFIvFC2kHAPLn3UkXAACQN7VaLbIsi0KhMGhrtVoxOzsbW1tb0W63o1gsxt7eXkRE1Ov16HQ60e12o1gsDn4eRzsAkE8CIQCAS9ZoNGJ1dTV+//vfD9rm5+djfn4+IiIKhUKkaRpZlsX29nZUKpWIiEiSJNI0jXa7Pbb2w6EUAJAfThkDALgCkiSJJEki4tUpZbu7u1EoFKLT6cTs7Oxgu35QNK72k7x8+TIODg6GbgDAzSIQAgC4Yp48eRLNZnNir7+yshLT09OD2927dydWCwBwMQRCAABXyOLiYiwuLkapVIqIiLm5uaG1frIsiyRJxtZ+kuXl5djf3x/cvvvuuzH3EgCYNIEQAMAVcTQMiogolUqDK4J1u93IsixKpdLY2k9y69atuH379tANALhZLCoNAHDJFhYWot1uR8SrmTrNZjPW1tZiY2Nj6HLwzWYzCoVC1Gq1KBaLMTMzE41GIyJerQE0jnYAIJ+mer1eb9JFXKaDg4OYnp6O/f1933aRS/eefjnpEi7Vt89+OukSgBH5zL46LvK9GNfnkr/3AHC2z2ynjAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZ96ddAEAF+ne0y9Hfu63z346xkoAAACuDjOEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADnz7qQLAM7u3tMvJ10CAAAA15gZQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOTMu5MuAPLs3tMvJ10CAAAAOWSGEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAABvcHBwEL/5zW8G97/66qt4+PBh/MVf/EX89re/nWBlAACjeXfSBQAAXGXffPNNFIvFmJqair/8y7+M/f39KJVKERGRJElsbW1Fq9WKH//4xxOuFADg7Z1rhpBvywCAm25xcTG63W4sLy9HRMTGxkZERLTb7djd3Y3p6emo1+uTLBEA4MxGDoS++eabuHfvXjx58iQiYvBt2c7OTvzjP/5jVCqV+N3vfvfW++t2u7G5uRm1Wm2obWpqKorFYhSLxVhYWBg8tra2NmhvtVqjdgMA4LW2t7ejXC7HL37xi4iI2Nrairm5uXjvvfciIqJUKsWLFy8mWCEAwNmNfMpY/9uy1dXViBj+tuy9996LmZmZqNfrbz19ularRZZlUSgUhtoLhULs7OwMtXW73ajX69HpdKLb7UaxWIxOpzNqVwAATtXtdiNN08H9drsd5XJ5cD9N0/j1r389idIAAEY28gyhcX9b1mg0hgZXr7OxsRGVSiUiXp27n6ZptNvtE7d9+fJlHBwcDN0AAN5WoVCI7e3tiHg1QzrLsqExS7vdHgqMAACug5EDoZO+LesvsBjx6tuybrd7ruIiYjDoKhaLsbm5GRERnU4nZmdnh14ry7ITn7+yshLT09OD2927d89dEwCQH9VqNXZ2duLP//zPY35+PqampqJUKsX+/n781V/9VTx//nxoDAQAcB2MHAhdxrdlSZLE48ePY2trK54/fx5Pnjw5c8i0vLwc+/v7g9t33313rpoAgHypVqvx5MmT2Nrair29vWg0GnH79u3IsiwajUYUCgWLSgMA187IgdBlfVvWaDQi4lU4FPFqxtDc3NzQmkFZlg0eP+rWrVtx+/btoRsAwFk0Go34/vvv4/vvv4+PPvooIl7NUN7Z2YkXL14YXwAA1865AqGL/ras1WoN1gbqnxJWKBSiVCoNrizW7XYjyzJTtQGASzU9PR0PHjwY6bknXV014vSrqF50OwCQPyNfZSzi1bdl/Rk8ff1vy846QFpYWBgKf5rNZszPzw9OE9vd3Y3nz58PXqNWq0WxWIyZmZljNQAAjNPBwUGsrKzE2tra4PT1JEnigw8+iGfPnp15htBJV1c97SqqF90OAOTTuQKhk4z6bVmz2TzWliTJie0Rr2YoVavVM78OAMBZ7O/vx/z8fGRZFvfv349Hjx5FxKv1Ej/77LNotVqxvb19plCo0WjE6upq/P73vx+0nXYV1e3t7QttPxxK9b18+TJevnw5uO8qrQBw85wrEDo4OIhWqxW7u7unbtM/zx4A4Dqq1WrR6XSiXq/H3/zN3ww9tra2Fh9//HE8ffo0/sf/+B/nep3TrqJ60e0nBUIrKyvx6aefnqs/AMDVNnIg9PXXX8f8/HxERPR6vRO3mZqaEggBANdaq9WKYrF4LAyKeDVjeXNzM7a2tiZQ2cVZXl6On//854P7BwcHcffu3QlWBACM28iB0JMnT6LX60Wj0RgEQwAAN83u7m6Uy+VTHy8UCrGzs3Pu15mbmxvaT/8qqhfdfpJbt27FrVu3zt0nAODqGjkQarfbsbCwEE+ePBlnPQAAV8qjR49ie3v71Mfb7fZYvhwrlUqDK7QevopqlmUX2g4A5NPIgVCapvHw4cNx1gIAcOV88sknUSwW45NPPom//du/HXrsl7/8ZTx//vzMM4ROurrqaVdRveh2ACCfpnqnLQD0BouLi9Fut+PFixfjrulCHRwcxPT0dOzv75/5ErEwbveefjnpEniNb5/9dNIlQK5dlc/s3/zmN/HZZ5/F8+fPI0mSwWyg7e3t6Ha7kSTJsZk2U1NT8fnnn0+i3Atxke/FuD4L/c0GgLN9Zo88Q2h1dTWKxWJ8+OGHUa/X44c//OGouwIAuLI+//zzaLVaERGxt7d3bAHpvb29aDabQ203LRACAG6etw6E3nnnnZiamhpq6/V6g2nOJ5mamor/+I//OF+FAAATtLGxMekSAADG7q0Doffff/9YIAQAAADA9fPWgdBps4AAAG66v/u7vzt2qthhU1NT8T//5/+8xIoAAM5n5DWEZmdnY3NzM3784x+f+Pj6+nr88pe/jP/9v//3yMUBAEza06dP45e//GX0er1IkiRmZmYmXRIAwLmdKRD6p3/6p8HPe3t70W63486dO8e2293djWazGZ1O59wFAgBM0traWqRpGv/wD/8Q9+/fn3Q5AABjcaZA6KOPPoqvv/46Il5NjV5aWjp1216vFwsLC+erDgDgClhcXBQGAQA3ypkCoXq9Ht1uN3q9Xjx+/Diq1WqUy+UTt02SJB49ejSWIgEAJmV+ft6sZwDgxjlTIHQ44CmVSvHxxx/He++9N+6aAACujGfPnkWpVIo/+7M/i5/97GeTLgcAYCxGXlT6H/7hH177+LfffhtZlsVPfvKTUV8CAGDiCoVCzM/PR6VSiYiINE2PbTM1NeVCGhN27+mXY9nPt89+Opb9AMBVN3Ig1HdwcHCsbXd3N549exbr6+vxn//5n+d9CQCAifn444+j1WpFxKtwCADgJhg5ENrf349SqRTtdvvUbR48eDDq7gEAroRWqxXFYjFarVZMT09PuhwAgLEYORCq1Wqxs7MT1Wo1fv/738evf/3rWFpaipmZmWg0GjEzMxMvXrwYZ60AAJdud3c3Pv74Y2EQAHCjjBwItVqtKJfL8dlnn0VExMzMTPzZn/1Z/OQnP4lqtRppmsbvfve7+PGPfzy2YgEALtujR498yQUA3DjvjPrELMuGzqOfn5+PLMsi4tUl5x8/fhzPnj07f4UAABP0q1/9KnZ2duLv//7vJ10KAMDYjBwIpWka3W536P7Ozs7g/tzcXGxvb5+rOACASZufn4/d3d2oVqvxgx/84MTbu++e+zodAACXauTRy4MHD2JjYyM+/vjj+NM//dOYm5uLtbW1weOmVgMAN8F7770XU1NTky4DAGCsRg6EVldXY25uLorFYuzu7sb7778ftVot/uIv/iLu378fm5ubsbCwMM5aAQAuXbPZnHQJAABjN3IgdP/+/dje3h6sE5SmaXz22Wfx8ccfR0REoVCI9fX18VQJAAAAwNic64T3QqEQGxsbg/vVajWq1Wrs7++7NCsAcCP85je/eeM2hUIh7t27d/HFAACMyYWsgCgMAgBuikqlElNTU9Hr9YbaD68rtLS0FCsrK5ddGgDAyM4VCB0cHMTKykqsra0NrjiWJEl88MEH8ezZs7h9+/Y4agQAmJitra0T23d2duLZs2fx8OHDWF5evuSqAADOZ+RAaH9/P+bn5yPLsrh//348evQoIiLa7XZ89tln0Wq1Ynt7WygEAFxr/THOSe3VajXm5+djfX09/vqv//qSKwMAGN07oz6xVqtFp9OJZ8+exb/+67/GxsZGbGxsxL/+67/GZ599Fv/6r/8aT58+HWetAABXSpIkUalU4vPPP590KQAAZzJyINRqtaJYLMbf/M3fHHusWq1GqVQ6dYo1AMBN0m63J10CAMCZjBwI7e7uxvz8/KmPFwqF2N3dHXX3AABX3sHBQaytrUWappMuBQDgTEZeQ+jRo0exvb196uPtdvu1gREAwHXwox/96NTHsiyLXq8Xn3zyySVWBABwfiMHQp988kkUi8X45JNP4m//9m+HHvvlL38Zz58/j52dnXMXCAAwSffu3Ru6xPxh9+/fj4WFhXjy5MklVwUAcD4jB0LffPNNlEqlqNfr0Wg0BrOBtre3o9vtRpIksbKyMvScqakpiy4CANeKNREBgJto5EDo888/j1arFRERe3t7xwZLe3t70Ww2h9oEQgAAAACTN/Ki0hsbG/H999+f6faf//mf46wdAGDs3nnnnXj33XeH7v/gBz9469vs7Kw1hQCAK2/kGUIAADfR+++/P7Rm0NH7b9JqtaJer8fjx4/jvffeu4AKAQDOTyAEAHDI0VPej95/kyzL4k/+5E9ie3tbIAQAXFkCIQCAMUrTNJrNZhQKhUmXAgBwKoEQAMCYvf/++5MuAQDgtUZeVBoAAACA60kgBAAAAJAzbx0Izc7Oxn//7/99cP8HP/hB/Pa3v72QogAAAAC4OG+9htD3338fn332WaRpGtPT09Hr9eIf//EfY3p6+rXP+8lPfnLuIgEAAAAYn7cOhJaXl+Pp06dRqVQiImJqaipWV1djdXX1xO17vV5MTU3Ff/7nf46nUgCASzA7OxuffPJJ/PVf/3VEvJoVvbm5GT/72c8mXBkAwPi8dSC0tLQUCwsLkWVZ9Hq9+LM/+7NYWlqKcrl8kfUBAFwqs6IBgDw402Xn79+/H/fv34+IiEqlEh9++GG89957F1EXAMBEmBUNAOTBmQKhwzY2NsZZBwDAlWBWNACQByMHQn1fffVVNBqNyLIsIiLSNI0PP/zQefYAwLVlVjQAcNOdKxD64IMPYnNzM3q9XiRJEhEROzs7sbm5GQsLC/H555+Po0YAgIkxKxoAuIneGfWJ6+vr0Ww24/3334+9vb3Y3d2N3d3d2Nvbi48++iiazWb8/d///ThrBQCYmK+++io++OCDePjwYTx8+DA++OCD+O1vfzvpsgAARjJyINRoNKJYLMbGxsbQVTemp6ej0WjEgwcP4rPPPhtLkQAAk/TBBx9EuVyOZrMZnU4nOp1ONJvNwelkAADXzcinjLXb7ajVaqc+XiqV4pe//OWouweYuHtPvxz5ud8+++kYKwEmqT8rulKpxPr6+uCLsP39/VhaWopf/epXUS6X47/+1/864UoBAN7eyDOE0jQdLCR9kizLIk3TUXcPAHAlXPas6NXV1SgWi1EsFmNtbW3Qvra2NmhvtVpjbwcA8mXkGUIPHjyIzc3N+Od//uf40z/906HHvv7668HC0gB5ZHYR3ByXOSu63W7H1tZW7OzsRETE3NxcPH78OCIi6vV6dDqd6Ha7USwWBz+Pox0AyJ+RA6Ff/epX0Wq1olAoxMLCQszPz0dExIsXL2JzczOmpqZifX19bIUCAEzCZc6K7l+19fD9JElibW0tKpXKoC1N02i327G9vT2W9kKhMPS6L1++jJcvXw7uHxwcjKV/AMDVMXIgND09He12O6rVamxsbAxdkrVQKMT6+nrcvn17LEUCAEzKZc6KTtM0FhcXY25uLiIims1mRER0Op2YnZ0d2i7LsrG1Hw2EVlZW4tNPPx1LnwCAq2nkQCgi4v79+7G1tRX7+/uDb87SNB06vx4A4Dq7zFnR3W43VlZWYmtrK7IsiydPngxOH7tMy8vL8fOf/3xw/+DgIO7evXvpdQAAF+dcgVDf9PR0PHjwYBy7AgC4Ui5zVvTGxkbMz89HmqaRpmnMzMxEq9WKubm5oWAoy7JIkmRs7UfdunUrbt26NZY+AQBX01gCIQCAm+yyZkWnaRqNRmNwf3t7exAO1ev1iHg1iyjLsiiVSpFl2VjaAYD8EQgBALyli54VXSqVolQqRbFYjIhXVxbrL1hdq9WiWCzGzMzMIDRK03Qs7QBA/kz1er3epIu4TAcHBzE9PR37+/sWvWbiznNpcm4ul52HV3xmXx0X+V5ctc9Cf4MBuM7O8pn9ziXVBAAAAMAVca5A6ODgIH7zm98M7n/11Vfx8OHD+Iu/+Iv47W9/e+7iAAAAABi/kdcQ+uabb6JYLMbU1FT85V/+Zezv7w8WJUySJLa2tqLVasWPf/zjsRULAAAAwPmNPENocXExut1uLC8vR0QMLsHabrdjd3c3pqenB1exAAC4zsyKBgBumpFnCG1vb0e5XI5f/OIXERGxtbUVc3Nz8d5770XEq6tkPH/+fCxFAgBMilnRAMBNNPIMoW63O7gMasSrmUH9wVHEq8uadrvdcxUHADBpZkUDADfRyIFQoVCI7e3tiHj1zVmWZVEulwePt9vtocAIAOA6eptZ0S9evJhghQAAZzdyIFStVmNnZyf+/M//PObn52NqaipKpVLs7+/HX/3VX8Xz58+HZgwBAFxHZkUDADfRyGsI9QOh9fX1iIhYW1uL27dvx9dffx2NRiMKhYLp0wDAtWdWNABwE408QygiotFoxPfffx/ff/99fPTRRxHx6luynZ2d2N7ejtu3b4+lSACASTErGgC4iUYOhGZnZ+N3v/vdsfbp6el48OBBrK+vx49+9KNzFQcAMGnVajWePHkSW1tbsbe3F41GI27fvh1ZlkWj0YgHDx6YFQ0AXDtnOmXsn/7pnwY/7+3tRbvdjjt37hzbbnd3N5rNZmRZdu4CAQAmrdFoRKPRGGrrz4p+8ODBhKoCABjdmQKhjz76KL7++uuIiJiamoqlpaVTt+31erGwsHC+6gAAJmx2djY2Nzfjxz/+8VD74VnRq6ur8S//8i8TqhAA4OzOFAjV6/XodrvR6/Xi8ePHUa1WhxZVPCxJknj06NFYigQAuExmRQMAN92ZAqHDAU+pVIqPP/443nvvvXHXBAAwUWZFAwA33ciXnf+Hf/iHcdYBAHBlmBUNANx0IwdCEREHBwfRarVid3f31G36l6MHALguzIoGAG66kQOhr7/+Oubn5yPi1VTpk0xNTQmEAIBrzaxoAOAmGjkQevLkSfR6vWg0GjE/Px9JkoyxLACAq8OsaADgphk5EGq327G4uBhPnjwZZz0AAFeKWdEAwE00ciCUpmnMzc2NsxYAgCvHrGgA4CYaORB6//3344svvohf/OIX46wHAOBKMSsaALiJ3hn1ifV6PXq9Xnz44Yfxf/7P/xlnTQAAV4ZZ0QDATTRyIPSjH/0o9vf3Y2NjI9I0jR/84AfHbu++e66r2gMATFx/VjQAwE0ycmLz3nvvxdTUVDx48GCc9QAAXCn1ej3m5+fjww8/jHq9Hj/84Q8nXRIAwLmNHAg1m81x1hHdbjdarVa8ePEi6vX6oH1tbS0ajUZEvBqQlUql17YDAIzTj370o4iI2NjYOHX8MzU1Ff/xH/9xmWUBAJzLlTmnq1arRZZlUSgUBm3dbjfq9Xp0Op3odrtRLBYHP5/UDgAwbmZFAwA30ciB0G9+85s3blMoFOLevXtvtb9GoxGrq6vx+9//ftC2sbERlUolIiKSJIk0TaPdbsf29vaJ7YfDpL6XL1/Gy5cvB/cPDg7eqh4AgIjxz4oGALgKRg6EKpVKTE1NRa/XG2qfmpoa/Ly0tBQrKysjF9fpdGJ2dnZwP03TyLLs1PaTAqGVlZX49NNPR64BAAAA4KYZORDa2to6sX1nZyeePXsWDx8+jOXl5ZELG5fl5eX4+c9/Prh/cHAQd+/enWBFAMB1Mu5Z0QAAV8HIgdCjR49Oba9WqzE/Px/r6+vx13/91yMXNzc3Fzs7O4P7WZZFkiSntp/k1q1bcevWrZFrAADy7TJmRQMAXLYLWVQ6SZKoVCrx+eefnysQKpVKgyuOdbvdyLIsSqVSZFl2YjsAwLhdl1nRAABncaFXGWu322+97cLCwmD7LMui2WxGmqZRq9WiWCzGzMzM4DLzp7UDAIzbZcyKBgC4bFO9o/Ofx+Dg4CDu3bsXs7Oz8S//8i/j3v25HBwcxPT0dOzv78ft27cnXQ45d+/pl5MugSvo22c/nXQJcCVcl8/sp0+fxvPnz+PFixeTLuXCXOR7cdU+C/0NBuA6O8tn9sgzhH70ox+d+liWZdHr9eKTTz4ZdfcAANfGWWZFAwBcBSMHQvfu3RtaTPGw+/fvx8LCQjx58mTkwgAArrqDg4NYW1uLNE0nXQoAwJmM/bLzAAA3iVnRAMBNdKGLSgMAXHdmRQMAN9G5A6GvvvoqGo1GZFkWEa+uAPbhhx/Gz372s3MXBwAwaWZFAwA30bkCoQ8++CA2Nzej1+tFkiQREbGzsxObm5uxsLAQn3/++ThqBAAAAGCM3hn1ievr69FsNuP999+Pvb292N3djd3d3djb24uPPvooms1m/P3f//04awUAmJivvvoqPvjgg3j48GE8fPgwPvjgg/jtb3876bIAAEYyciDUaDSiWCzGxsZGTE9PD9qnp6ej0WjEgwcP4rPPPhtLkQAAk/TBBx9EuVyOZrMZnU4nOp1ONJvNqFQq8eGHH066PACAMxs5EGq321EqlU59vFQqRbvdHnX3AABXglnRAMBNNHIglKbpYCHpk2RZFmmajrp7AIArwaxoAOAmGnlR6QcPHsTm5mb88z//c/zpn/7p0GNff/31YGFpuOnuPf1y0iUAcIHa7XbUarVTHy+VSvHLX/7yEisCADi/kQOhX/3qV9FqtaJQKMTCwkLMz89HRMSLFy9ic3MzpqamYn19fWyFAgBMglnRAMBNNHIgND09He12O6rVamxsbMTGxsbgsUKhEOvr63H79u2xFAkAMClmRQMAN9HIgVBExP3792Nrayv29/cH35ylaTp0fj0AwHVmVjQAcBONvKj0YdPT0/HgwYN48OBBTE1NjWOXAABXQn9W9E9+8pPY2NiIpaWlWFpaimazGQ8ePIjt7e2xzorudruxsLAQ5XI5FhcXB+1ra2tRLBajWCxGq9UaezsAkC/nmiH061//OqrVaiwvL8cvfvGLiIj46KOP4vnz5/GrX/0qfvazn42lSACASbrMWdGPHj2K9fX1KBQKg7Zutxv1ej06nU50u90oFouDn8fRDgDkz8gzhL7++utYWFiIvb29oQHL48ePI0mSqFQq8bvf/W4sRQIAXAUXPSu61WrFzMxM7O7uxurq6iB82tjYiEqlEhERSZJEmqbRbrfH1n7Uy5cv4+DgYOgGANwsIwdCtVot5ubmYm9vL37yk58M2iuVSnQ6nbh3795rL9EKAHBd/PrXv47Z2dn4u7/7u0HbRx99FLOzs/Hb3/52bK+TZVlsb29HlmWRJEkUi8WIiOh0OjE7OzvYrn/ls3G1H7WyshLT09OD2927d8fWRwDgahg5ENre3o5SqXTqVOmFhQVTkAGAa+8yZ0V3u90olUpRrVajWq3G/Pz8RNb5WV5ejv39/cHtu+++u/QaAICLNXIgNDMz89rHu93uqLsGALgyLnNWdJqmQ2OsJEmi2+3G3Nzc0Bdt/RlE42o/6tatW3H79u2hGwBws4y8qPSDBw9iY2Mjnj59Gj/84Q+HHtvf34+NjY14+PDhuQuEy3Dv6ZeTLgGAK2p7ezs++OCD186KHtdl50ul0lC41G63Y319PXZ3d6Ner0fEqy/dsiyLUqkUWZaNpR0AyJ+RA6F6vR5/8id/EmmaRq1Wi/n5+YiIePHiRaytrcX+/n40Go2xFQoAMAmXOSs6SZKo1+uDtYPq9XokSRJJkkStVotisRgzMzODMVZ/HHbedgAgf6Z6vV5v1Ce32+148uRJfP3114MrbfR6vUjTNBqNRjx69GhshY7LwcFBTE9Px/7+vunPDJghxFXy7bOfTroEuBKuymf2wsJCfPXVV9Fut0+cFX3//v14+PBh/K//9b8mVOHFu8j34qZ+BvtbDsAknOUze+QZQhERhUIhdnZ24ptvvhlcsrRQKMT9+/fPs1sAgCvDrGgA4CY6VyDUd//+fSEQAHAjpWka29vb8eTJk3j27NmxWdEbGxtx7969yRYJAHBGYwmEAABuMrOiAYCbRiAEAPCWzIoGAG6KdyZdAAAAAACXSyAEAAAAkDMCIQAAAICcEQgBAAAA5IxACAAAACBnBEIAAAAAOSMQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDMCIQAAAICcEQgBAAAA5IxACAAAACBnBEIAAAAAOSMQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAcubdSRcAwLB7T78c+bnfPvvpGCsBAABuKjOEAAAAAHJGIAQAAACQMwIhAIArptvtxp07d2Jzc3PQtra2FsViMYrFYrRarbG3AwD5Yg0hAIArZmVlJebn5wf3u91u1Ov16HQ60e12o1gsDn4eRzsAkD8CIQCAKyTLsmi321EulwdtGxsbUalUIiIiSZJI0zTa7XZsb2+Ppb1QKAzV8PLly3j58uXg/sHBwYX2GQC4fE4ZAwC4Qmq1WtTr9aG2TqcTs7Ozg/tpmkaWZWNrP2plZSWmp6cHt7t3746ziwDAFSAQAgC4ItrtdnS73WMzdi7b8vJy7O/vD27ffffdROsBAMbPKWMAAFfEyspKZFkW5XJ5MHNnd3c35ubmYmdnZ7BdlmWRJMnY2o+6detW3Lp16wJ6CABcFQIhAIArotlsDn5eXFyMcrkclUolsiwbnEbW7XYjy7IolUpjawcA8kcgBABwxaVpGrVaLYrFYszMzESj0RhrOwCQP1O9Xq836SIu08HBQUxPT8f+/n7cvn170uVwRdx7+uWkS4Cx+PbZTyddAoyNz+yr4yLfi5v6GezvMQCTcJbPbItKAwAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAABy5t1JFwAAADfNvadfjmU/Ll8PwEUxQwgAAAAgZ658INTtdmNqaiqKxWIUi8VYWFgYPLa2tjZob7VaE6wSAAAA4Pq4FqeMFQqF2NnZGWrrdrtRr9ej0+lEt9uNYrEYnU5nQhUCAAAAXB9XfobQaTY2NqJSqURERJIkkaZptNvtY9u9fPkyDg4Ohm4AAAAAeXYtZghlWRblcjl2d3djeXk5KpVKdDqdmJ2dHWyTpmlkWRaFQmHouSsrK/Hpp59edslMwLgWbwQAAICb7srPEEqSJB4/fhxbW1vx/PnzePLkSXS73bd+/vLycuzv7w9u33333cUVCwAAAHANXIsZQo1GIyJehUMRr2YMzc3NDa0rlGXZ4PHDbt26Fbdu3bqMMgEAAACuhSs/Q6jVag3WBsqyLCJeLTJdKpUGVxbrdruRZVmUSqWJ1QkAAABwXVz5GULz8/OD08R2d3fj+fPnEfFqzaBarRbFYjFmZmYGs4gAAAAAeL0rHwglSRLNZvPEx6rValSr1UuuCAAAAOB6u/KnjAEAAAAwXgIhAAAAgJwRCAEAAADkjEAIAAAAIGcEQgAAAAA5IxACAAAAyJkrf9l5AN7evadfjvzcb5/9dIyVAAAAV5kZQgAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDMCIQAAAICcEQgBAAAA5IxACAAAACBnBEIAAAAAOSMQAgAAAMgZgRAAAABAzgiEAAAAAHJGIAQAAACQMwIhAAAAgJx5d9IFAAAAJ7v39Mux7OfbZz8dy34AuDnMEAIAAADIGYEQAMAVUqvVolwux9zcXLRarUH72tpaFIvFKBaLF9IOAOSLU8YAAK6IVqsVs7OzsbW1Fe12O4rFYuzt7UVERL1ej06nE91uN4rF4uDncbQDAPkjEAIAuCLm5+djfn4+IiIKhUKkaRpZlsX29nZUKpWIiEiSJNI0jXa7Pbb2QqEwVMfLly/j5cuXg/sHBwcX3ncA4HI5ZQwA4IpIkiSSJImIiG63G7u7u1EoFKLT6cTs7Oxgu35QNK72o1ZWVmJ6enpwu3v37gX0FgCYJDOEuFLGdSUNALjunjx5Es1mcyKvvby8HD//+c8H9w8ODoRCAHDDmCEEAHDFLC4uxuLiYpRKpYiImJubG1rrJ8uySJJkbO1H3bp1K27fvj10AwBuFoEQAMAVcjQMiogolUqDK4J1u93IsixKpdLY2gGA/HHKGADAFbG2thYbGxtDl4NvNptRKBSiVqtFsViMmZmZaDQaEfFqDaBxtAMA+TPV6/V6ky7iMh0cHMT09HTs7++b/nwFWUMIJufbZz+ddAkwxGf21XGR74XP/svhbzxAPpzlM9spYwAAAAA5IxACAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADnz7qQLAOBquPf0y5Gf++2zn46xEgAA4KKZIQQAAACQMwIhAAAAgJxxyhgA5zbq6WZONQMAgMkQCAEAwA13nnXiDhPkA9wcThkDAAAAyBmBEAAAAEDOCIQAAAAAckYgBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDPvTroAbp57T7+cdAkAAADAa5ghBAAAAJAzZggBAABvZVwzwb999tOx7AeA0ZkhBAAAAJAzAiEAAACAnBEIAQAAAOSMQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM68O+kCAACAfLn39Mux7OfbZz8dy34A8sgMIQAAAICcMUPoBhvXNy8AAADAzWKGEAAAAEDOmCEEwMRcx5mM1qsAAOAmMEMIAAAAIGfMEAIAAK4lVysDGJ0ZQgAAAAA5IxACAAAAyBmBEAAAAEDOWEMIAADINWsRAXkkELoE1/GyygAAAMDNda0DobW1tWg0GhERUa/Xo1QqTbgiAICry9gJLpaZRsB1cm0DoW63G/V6PTqdTnS73SgWi9HpdCZdFgDAlWTsBNeHYAm4DNc2ENrY2IhKpRIREUmSRJqm0W63o1AoDG338uXLePny5eD+/v5+REQcHBxcWq3fv/x/L+21ALhYl/n5kXf9Y93r9SZcyc3wtmOniMsdPxknwcX5L/+f5qRLGPL/+/TPJ10C3HhnGT9d20Co0+nE7Ozs4H6appFl2bFBzcrKSnz66afHnn/37t0LrxGAm2f6/zvpCvLn97//fUxPT0+6jGvvbcdOEcZPwMXwGQqX59///d/fOH66toHQ21peXo6f//zng/vdbjd++MMfxv/9v//3xg0uDw4O4u7du/Hdd9/F7du3J13OWOnb9XNT+xWhb9eVvl0/+/v78V/+y3+JmZmZSZeSO0fHT99//33s7u7G7OxsTE1Nje11burv7lXleF8ux/tyOd6Xy/G+XGc53r1eL/793/89/viP//iN+722gdDc3Fzs7OwM7mdZFkmSHNvu1q1bcevWrWPt09PTN/YX9/bt2/p2Dd3Uvt3UfkXo23Wlb9fPO++8M+kSboS3HTtFnDx+Om3bcbipv7tXleN9uRzvy+V4Xy7H+3K97fF+28kv13aEVSqVotVqRcSrWT9ZlrlSBgDAKYydAIDDru0MoTRNo1arRbFYjJmZmcElVAEAOM7YCQA47NoGQhER1Wo1qtXqmZ5z69at+G//7b+deBrZdadv19NN7dtN7VeEvl1X+nb93NR+TdIoY6eL5D2+XI735XK8L5fjfbkc78t1Ucd7qudargAAAAC5cm3XEAIAAABgNAIhAAAAgJwRCAEAAADkjEAIAAAAIGduXCDU7XZjc3MzarXasfaFhYUol8uxuLg4aF9bW4tisRjFYjFardZll3smJ/VtdXU15ubmBrc7d+7E5uZmRFz/vkW86l+/D2tra4P2m9C3tbW1KJfLx/pwnfoWEVGr1aJcLsfc3Nxb9eO69O+0fr3u/bwO/Yo4vW9nfS+votf1bWFhIYrF4uBvZMTN6FvEq9/Lw3//I65/37rdbkxNTQ36sLCwMNj+OvWN1/NeXp7X/T/FeNyEMcJ1ctLx9nt+sW7CWPE6Oev4aCS9G6ZarfZKpVJvaWlpqL1QKPR2dnaG2vb29nppmh77+ao6rW99e3t7vVKpNPj5uvdtZ2dn0J9er9dL07S3t7d3I/rW6XR6hUKh1+ud/l5dh75tbW316vV6r9d79X5FxGvfo+vSv9P61eud/H5el371eqf37azv5VV0Wh86nU6v0Wj0er1Xfeh/9N2EvvUtLS31SqVSr9ls9nq9m9G3vb29wd/Jw65T33g97+XlOu3/Kcbnuo8RrpvTjrff84txE8aK18lZx0ejunEzhBqNRpTL5aG2VqsVMzMzsbu7G6urq5FlWUREbGxsRKVSiYiIJEkiTdNot9uXXvPbOqlvhy0sLES9Xo+Im9G3JEmO3U+S5Eb0bXNzM0qlUkT8oV/tdvva9W1+fj6q1WpERBQKhUjTNLIsO7Uf16V/p/Ur4uT387r0K+L0vp31vbyKTutDmqaD9larFYVCISJuxvsWEZFlWbTb7aHfy5vSt5Ncp77xet5LbprrPka4bt70byPG6yaMFa+Ts46PRnXjAqGTZFkW29vbkWVZJEkSxWIxIiI6nU7Mzs4Otruog3wZ+v/T9f+hcxP6lqZpLC4uDk6HW19fj4ib0beT7O7uXru+9cOsiFfTF3d3d6NQKJzaj+vSv9P6dZrr0q+I0/t21vfyKnrT+9afVttsNiPiZrxvEa+mE/e/DOi7KX3Lsmxwam3/dLjr1Ddez3t5+U76f4qL5ff88vk9vxg3Yax4nZx1fDSqd89b6HXQ7XajVCoNErZms3njzm1sNBo37hzZbrcbKysrsbW1FVmWxZMnT2JnZ2fSZY1FpVKJYrEYy8vLsb29fSNS9CdPngz+oX2T3NR+RZzet5vQ55P6sLOzE61WK8rlcnQ6nQlVdn6H+9Zut6Pb7UahULgRn2uH+5YkSTx+/DgajUZ0u924f//+YGYlcHan/T91dEY2XGd+zy/HTRgrXidvMz4a9Xc8FzOE0jSNmZmZwf0kSaLb7cbc3NzQPwr6M4iuo42NjXj8+PHg/k3o28bGRszPz0eaplEqlWJmZiZardaN6FuaprG+vh5PnjwZnNKSpum17dvi4mIsLi4O/rF2Wj+uW/+O9us0161fEaf37W3fy6vsde9bv+26/i052reVlZXBt0SNRiNqtVqsra3diL5FvPqyI+IPpxBnWXYt+8bJvJeX76T/p7hYfs8vn9/zi3UTxorXyduOj0Y2ttWIrpB6vX7qYm693h8WJ+50OtduAayjfev1hhdJ7bsJfdva2hpaMCtJkl6n07kRfTtsa2trsHj2dexbtVo9tmD7af24Tv07qV99R9/P69SvXu/0vp3lvbyqTupDs9kctPX/Xl7HvyWv+53sP95fVPom9G1ra2vQ1ul0ekmSDH6+Tn3jdN7Ly3Xa/1OM13UfI1w3J/37we/5xbkJY8Xr5Czjo1HduFPGFhYWBqffZFkWzWYzkiSJer0+WDuoXq8Pzsmr1WpRLBZjZmZmkLRdVSf1rf9zmqZD26Zpeu37ViqVolQqDb1v/X5e975FxGARvCRJBm3X7X1bW1uLjY2NoVNVms1mFAqFE/txXfr3un6d9H5el35FnN637e3tM72XV9FpfatUKrG4uBhZlg29ZxHX52/J634nT3ITfifn5+fjyZMng/Pmnz9/HhHXq2+8nvfycp32/xTjc93HCNfNScfb7/nFOeu4n/M56/hoVFO9Xq933mIBAAAAuD5ysYYQAAAAAH8gEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDMCIQAAgLewsLAQU1NTky5jJAsLC3Hnzp1Jl/FG1/kYw3UjEALOZWpqKhYWFiZdxpmtrq7GnTt3XjswMiABALg4rVYrisVitNvtSZcCufTupAsAuGxZlkWtVos0TWNxcXHS5QAA10Sz2Zx0CTdKt9sVBsEECYSA3MmyLCIi6vV6VCqVU7cz6AMAAG4qp4wBuZUkyaRLAACukaOnk5fL5Zibmzu2XavViqmpqdjc3Bx67tzcXHS73cF6PnNzc4PZyu12O8rl8uCU9lqtduy1Nzc3o91uH3t+t9sduU+Li4sxNzcXU1NTMTc3F6urqyf2u9/PWq022P60072yLBv0ZW5ubtCXcrk8OCblcnmw7ECxWDx2Gn+32x2q7bTXWltbi2KxGFNTU3Hnzp0ol8uDL/+A1xMIwTXyth/Glzk46Tu83ZsGJ2878CgWi4O6pqam3jiluL/t4eOytrZ2bL/lcnlwnF63RtB1GvQZDAHA1be7uxvFYjEiIpaXlyNJklhbW4tyuRyPHj2KNE1jeXk5ZmZmYnV1dWiMtLm5GY1GI4rFYmRZFo8fPx48vz9mOotutxt37tyJtbW1KBQKsbS0FGmaRq1WG9R4VLFYjFarFYuLi1GtVqPdbh977Xa7HXNzc7G9vR2PHz+OUqk06GOr1RqaqV2tVgc/H52ZXSwWY3t7OxYXF6NSqZz4WouLi4OxVbVajfn5+cG6ROcJySA3esC1UalUemma9gqFQq9QKPTq9XqvWq32IqIXEb29vb1er9frlUqlXpqmx56/tbXVi4hes9kc2meSJL00TXuVSqVXr9d7hUKhFxG9UqnUS5KkV61We/V6vZemaS8ievV6ffD8iBi0FwqFXrVaHTw/SZJBTb1er7e3t9dLkqQXEb1KpdJbWlrqlUqlwXNP62upVOrV6/WhfR3V6XSO7btfR6VSGWy3s7PTW1pa6kVEb2lpaehYnHS8D/+ZvOzjevjYHD6uaZoOHYv+70B/u/7zjh5/AOB8zjs2iIheo9EYtO3t7Q3GcYe37XQ6g7FAX3+7paWlodeq1+sntp9Ue5Ikx+rZ2dkZ2q7RaBwbl/S3PTymOrzt4drTNH3tGPDwfpvN5rEazvJaR49R/3gkSfLaMR7wikAIrpG3/YCcxODk8Id7r/eHwUm1Wj32WucZeJymH4Ic3Xd/P1tbW8eOw+G2k1yXQZ/BEABcjnGMDY7qf+FzVJIkx8YGhwOdw/ohzJtqP7zN0XHa0dc+/GVdv/ZOpzO03c7OztB4p3//6Liw1/vDGOZtA6E3vVa/D0mSHNsWeDtOGYNrqF6vD92fn5+PiFfTkEfVn7Ib8WptnUKhEBExtOhymqYnrruTJEksLS0NtfWnHW9sbAzaNjc3o1qtDvZ9+LWTJIkvvvji2L6P9vUk3W43Wq1WVCqVY/teX1+PiIhGo/HG/VyE8x7Xo/3vH9ejp8Jtb28PnSK2tLQUe3t7r100GwC4GtI0PdY2MzNzrK0/5juqUCic6RSp/phhbW0tpqamjt263e6Jp54frfPo+KX/nKPjsdPaXuekY3JUo9GIbrcbc3NzMTc3FwsLC7G2tuZ0MXhLAiG4ht70YXxRrxMx+uBkXAOPk/Sf9/Dhw2OPJUkSSZJcqfV0xj3oMxgCAEZRqVSi2Wyeejur1305edJY57yq1Wrs7e1Fs9mMUqkU7XY7FhcX486dO9Fqtcb+enDTuOw8cKkqlUp88MEHJz7mql+jqVar8fjx42i1WrG1tRWtVis2NzdjcXExtra2olQqTbpEAMiVN10IY1Tb29unvt5ZxlGHv5w6aTbx5ubmSAFO/8usdrt9bPxxWu2j6n+ZmKZpVCqVQT/6i0/XarXY2dkZ62vCTWOGEOTIJAcnRwceR28Ro39z1N/3ixcvjj3W7Xaj2+2+1UyjUU3yuHa73cHrVyqVaDQa0el0BgOg064KBwCMR5ZlQ7Nyu91urKysXMhrdbvdY1dnXVtbG1x17CwqlUpsbm4em0lTq9ViYWFhpACnUChEmqaxsrJy7Ji8bhmAUZY9yLJsEPwcrSFJkguZkQQ3jUAIbqhJD05WV1cjy7KhNXQuYuAR8Ye1efqXaT+674g4dVbSWU36uB4d9BkMAcDkLCwsRETE/fv3o1arDS7Zftpp3+eVJEnUarUol8uD/y4uLkaapm+17uJh6+vrkSRJlMvlwf6KxWKsrq5GoVAYGsOdRf9U9vv37w8uC3///v3Bl3OHZzL1f67X68fWR3yTQqEQhUJhcEn7/njyzp070e12B5ejB07nlDG4gRYWFqLVasX9+/cHH+abm5sxPz9/IedTFwqFqNVqsbW1FYVCIdrtdrRarWODk/X19Wi1WlEul6NUKkWhUIhWqxXtdvtcA4/+vovFYhSLxahUKpGm6WDfh2chncdlH9f+oO91x/XwYCjLsigUCpFlWbRaLYMhALhg1Wo1ut1uNBqNQZCyuLgY1Wo1VlZWzryQ8puUSqVYXl6OWq0Wa2trMTMzE9VqNer1+plPvU+SJL755puo1WrRarWGxhhHLxZy1ho7nU4sLi7GxsZGpGkay8vLg3Hf0W37Xxge/SLxbTx//nyo/iRJYn5+Pmq1mlPm4W1M+jJnwNs77XKl/cuWH74MZ71e76Vp2ouIXqFQ6NXr9d7e3l5vaWlp6NKcr7sE6kmXUU3TdOgSqEtLS72tra3e1tZWr1Qq9ZIk6aVpeuplTPf29nrVanVQW5qmJ16a9LS6Xmdvb69XqVSG+n34mPSNetn5Xu/yjmtE9CqVSm9nZ+fYcd3b2zvW78PHtH+Z2jf1DwA4m1HGJ+PSHxtcdTs7O8fGKr3eHy4xb3wCV8dUr9frXX4MBXD1LSwsxObmZkziz+TU1NTgyh8AwNVgbPBmd+7ciTRNjy3oXCwWI8uy2Nvbm1BlwFFOGQMAAHiD/inZror6ev1T2srlciwsLES3240vvvgi2u12NBqNSZcHHCIQAjiBQR8A0Le5uTlYQPo86+vkwdLSUiRJEo1GIxYXFwfr+mxtbVnXB64YgRDAEQZ9AMBhhUIhGo1GzM/Pj32h6Ld1nVb6qFar57pYCHA5rCEEcER/dtAkB30AAAAXSSAEAAAAkDPvTLoAAAAAAC6XQAgAAAAgZwRCAAAAADkjEAIAAADIGYEQAAAAQM4IhAAAAAByRiAEAAAAkDMCIQAAAICc+f8DF6JXuidi2RoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'mu' : 200,\n",
    "                'sigma' : 10}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.normal(loc=n_jump_params['mu'], scale=n_jump_params['sigma'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'exponential'\n",
    "jump_length_params = {'beta' : 2}\n",
    "\n",
    "jump_lengths = []\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n]), lam=jump_length_params['beta'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    [jump_lengths.append(lam) for lam in model.paths[n].jump_lengths]\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(14,6))\n",
    "plt.suptitle(f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "\n",
    "h, b, bar = ax[0].hist(n_jumps, bins=25)\n",
    "ax[0].set_xlabel('number of jumps', fontsize=14)\n",
    "ax[0].set_ylabel('counts of paths', fontsize=14)\n",
    "ax[0].set_xlim(160, 250)\n",
    "\n",
    "h, b, bar = ax[1].hist(jump_lengths, bins=25)\n",
    "ax[1].set_xlabel('jump lengths', fontsize=14)\n",
    "ax[1].set_ylabel('counts of jumps', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exponentially distributed number of jumps, normally distributed jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'beta' : 200}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.exponential(n_jump_params['beta'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'normal'\n",
    "jump_length_params = {'mu' : 2, 'sigma' : 0.5}\n",
    "\n",
    "jump_lengths = []\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n] // 1 + 1), lam=jump_length_params['mu'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    [jump_lengths.append(lam) for lam in model.paths[n].jump_lengths]\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(12,4))\n",
    "plt.suptitle(f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "\n",
    "h, b, bar = ax[0].hist(n_jumps, bins=25)\n",
    "ax[0].set_xlabel('number of jumps', fontsize=14)\n",
    "ax[0].set_ylabel('counts of paths', fontsize=14)\n",
    "\n",
    "h, b, bar = ax[1].hist(jump_lengths, bins=25)\n",
    "ax[1].set_xlabel('jump lengths', fontsize=14)\n",
    "ax[1].set_ylabel('counts of jumps', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exponentially distributed number of jumps, exponentially distributed jump lengths, equal barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_jump_params = {'beta' : 200}\n",
    "rng = np.random.default_rng()\n",
    "n_jumps = rng.exponential(n_jump_params['beta'], size=n_paths)\n",
    "\n",
    "jump_length_dist = 'exponential'\n",
    "jump_length_params = {'beta' : 2}\n",
    "\n",
    "jump_lengths = []\n",
    "\n",
    "model = EyringModel(T=T)\n",
    "for n in range(n_paths):\n",
    "    model.add_Path(n_jumps=int(n_jumps[n] // 1 + 1), lam=jump_length_params['beta'])\n",
    "    model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "    model.paths[n].generate_jump_distribution(dist=jump_length_dist, dist_params=jump_length_params)\n",
    "    [jump_lengths.append(lam) for lam in model.paths[n].jump_lengths]\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(12,4))\n",
    "plt.suptitle(f'$\\Delta G_{{eff}}^{{\\ddag}}$ = {model.calculate_effective_barrier():.4f} kcal/mol', fontsize=14)\n",
    "\n",
    "h, b, bar = ax[0].hist(n_jumps, bins=25)\n",
    "ax[0].set_xlabel('number of jumps', fontsize=14)\n",
    "ax[0].set_ylabel('counts of paths', fontsize=14)\n",
    "\n",
    "h, b, bar = ax[1].hist(jump_lengths, bins=25)\n",
    "ax[1].set_xlabel('jump lengths', fontsize=14)\n",
    "ax[1].set_ylabel('counts of jumps', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normal distributions sweeping through variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [01:27,  1.14it/s]\n"
     ]
    }
   ],
   "source": [
    "n_paths = 5000\n",
    "delta = 400 # membrane thickness\n",
    "\n",
    "jump_length_dist = 'equal'\n",
    "n_jump_mu = 100\n",
    "\n",
    "num_sigs = np.linspace(5, 200, 100)\n",
    "num_dG = np.zeros((len(num_sigs)))\n",
    "models1 = []\n",
    "\n",
    "for i,ns in tqdm(enumerate(num_sigs)):\n",
    "\n",
    "    rng = np.random.default_rng()\n",
    "    n_jumps = rng.normal(loc=n_jump_mu, scale=ns, size=n_paths)\n",
    "    idx = np.where(n_jumps <= 1)[0]\n",
    "    while len(idx) > 0:\n",
    "        n_jumps[idx] = rng.normal(loc=n_jump_mu, scale=ns, size=len(idx))\n",
    "        idx = np.where(n_jumps <= 1)[0]\n",
    "\n",
    "    model = EyringModel(T=T)\n",
    "    for n in range(n_paths):\n",
    "        model.add_Path(n_jumps=round(n_jumps[n]), lam=delta/n_jumps[n])\n",
    "        model.paths[n].generate_membrane_barriers(dist=barrier_dist, multi=multi, dist_params=barrier_params)\n",
    "\n",
    "    num_dG[i] = model.calculate_effective_barrier()\n",
    "    models1.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EJEkoFou4ePHihrF8kiRhYmICc3NziMVimvtOTU1BkiQEAgFks1kzp0FERETkSqYCvFQqpfua0QAvmUxCFEWEQqENr6XTaUQiEd19Dx48iLm5Oc19iYiIiAadqSJ2e/fuVX9GR0fh8XjUn6mpKUPHyGazEARhw/OiKKJUKmm+BgDFYhF+vx+VSgUzMzMQRbHl+6yurqJWqzX8EBEREbmZpSrFx44dQzQaRTgcxokTJ3D//fdvqInXqWQy2bL8iiiKuHTpEkRRhM/nQzgcbnm8dDoNr9er/oyPj1tqH21ut9ZkXCgv44XFd3GhvIxba7YMQSUiInIUS3Xw5ufnsby8jJMnT+Ib3/gGRkZGWqZv2ymVSpAkCaFQCMViUXMbSZIQjUYxPT0NAMjlcigWi7qBZSqVwvHjx9XHtVqNQd6AeunyNZw+dwXXqjfV53Z6t+LUoV14aPfOPraMiIjIXpYCPGUC7r59+7CwsIC/+qu/QqlUMn28dDoNURQhCIKaeq1UKmowBwCBQADlcll97PP5IEmS7jGHh4cxPDxsuk3kDi9dvoZjz5TQ3F93vXoTx54p4alHQwzyiIjINSwFeFNTU3jwwQdx5swZCIKAq1evYmJiwvTxcrmc+v+JRAKCIGyYRRuNRpFMJtXHpVIJc3Nzpt+T3O/WmozT565sCO4AQAbgAXD63BUIu3Zgy5CxSUJEREROZnmpsmq1Cq/Xq6ZKjZYsicfjam+fKIoNwZ2WYDCIhYUF+Hw+ZDIZdexdJpOBz+ezchrkcm8sVRrSss1kANeqN/HGUgUHgmO9axgREVGX2FboGFgf3+b04sEsdDx4Xlh8F999brHtdv/wjT342p7Pd79BREREJhmNYyzNon3++ecxNrbe41GtVnH33Xfjxz/+sZVDEtnuzu1bbd2OiIjI6SwFeCdPnlQnQ3i9XoiiiBMnTtjSMCK77J/wY6d3K/RG13mwPpt2/4S/l80iIiLqGksBnizL8Hq96mOfz4fR0VHLjSKy05YhD04d2gUAG4I85fGpQ7s4wYKIiFzD0iSLw4cPY9++fThy5Ah8Ph+y2Szi8bhdbSOy5NaajDeWKnj/xk3cuX0rfvTNEB5/sbEO3g7WwSMiIheyPMmiWCwin88DAARBwOHDh21pWLdwksVg0Ctq/NhXv4DRbcNq0Ld/ws+eOyIi2jSMxjG2zqIFgMXFRezZs8fOQ9qKAZ776RU1VsI4FjUmIqLNqiezaI8dO4YbN26oj48ePcoULfVVu6LGwHpRY65BS0REbmYpwDt8+DDuv/9+PP300/D7/RgdHcVbb71lV9uIOtZJUWMiIiK3sjTJIhqNAgAefPBBLCwsODo1S4Ph/Rv6wZ2Z7YiIiDYjUwHeyZMn4fF8OjB97969mJqaUgO+dDptT+uImjTPjG2eJMGixkRERCYDvH379jU8jkQitjSGqBW9mbH1ZU6UosbXqzc1x+F5sF4ahUWNiYjIzWyfRet0nEW7OXUyM1bZFkDD9pxFS0REm11XZ9EePXoUqVQKb7/9tubrzz//PI4cOYLFxUUzhydq0OnM2Id278RTj4aww9uYht3h3crgjoiIBoKpFO2ZM2fw5ptv4sSJEyiVSuryZCsrKxgdHUUikcDZs2dtbSgNrk5mxh4IjgFYD/KEXTtajtcjIiJyK9OzaPfu3Yv5+XkAQLVaRaVSwcTEhG0NI1KYnRm7ZcijBnxERESDxFKZFIXX64XX67XjUEQbcGYsERFRZywVOrZCkiTk83kkk0nN10ZHR9U1bvX2b7cNuYMyM1YvuerB+mxazowlIiJa17cAL5lMIpvNar6WTqfbll4xsg25w5YhD04d2gUAG4I85fGpQ7s4vo6IiOgPbA3wnn76acPbZrNZCIKw4XlRFFEqlTRf62QbxerqKmq1WsMPbT6cGUtERGScLWPwFFevXgUA/PSnP8XDDz9s6hjJZBKZTAbFYtHSNop0Oo3Tp0+bags5S6uZse1WuCAiIhoktgZ4+/btw+TkJERRhCiKCIVCiEQihgsKl0olSJKEUCikG7wZ2aZeKpXC8ePH1ce1Wg3j4+PGTogcR2tmrJEVLlqxEhwysCQiIieyNcC7dOkS5ufnsbS0BAAoFovI5XJ46qmnDO2fTqchiiIEQYAoigCASqWC6enpjrapNzw8jOHhYSunRQ6mt8LF9epNHHum1DZ9ayU4tBpYEhERdYutS5XNzc2hWq2iXC4bCupmZmawvLyMTCaz4bVEIgFBEBCLxXT3N7JNMy5V5h631mTcl3lFtwiysu7sa8n7NXvVOln+zM59iYiIzOrqUmV6pqamcPDgQVy8eBFHjhzB5OQkjhw5orltPB5HNptFPp9HPB5ve+xgMAhJkuxsLm1ynaxw0azT5c/s2peIiKgXOkrR/vCHP8T3v/999XEqlUI6nW7YZu/evbh06RKq1WrL4se5XK7lezWXUCmXy223ocFidoULwNzyZ3bsS0RE1AuGe/BOnjyJ5eVlpFIpLC0t4eTJk5BlGalUakPpkaNHj2JychLAelBI1A1WVriwEhxa2ZeIiKgXDAd4TzzxBGRZRjqdxsTEBJ544gkA65MetHLASo06rZ43IjtYWeHCSnDIpdOIiMjpOhqDl0qlWj5WCIKAbDaLn/70p7BxDgdRAysrXFgJDrl0GhEROV1HAZ4ypm5xcVGdxaHl8OHDePnll+H1enHmzBnrrSTSYXaFCyvBIZdOIyIipzNVJmXfvn3I5XK4++67AayPs1teXoYgCB0VNu4HlklxJ7MFh7tVB09vxQ0iIiIrjMYxpgK8SCSCS5cuqY8feOABCIKAixcvwuPx4OzZs+Za3QMM8KiZ3StZFK5cZwFkIiLqCqNxjC0rWQQCAfzN3/wNgPXePaLNRGv5M7P7Wl1Zg4iIyA6mCh1HIhG8+uqr6mOOsyO3ubUm40J5GS8svosL5WXcWpM1n2vehwWQiYjICUz14GUyGYTDYczNzeHLX/6y+vwrr7zCWbO06WmNrfPdfhsAQProE/W55rQrCyATEZFTmArwvF4vXn75ZTzwwAMYHR1FIBCAKIoQRRHnz5+3u41EPaOXYq0P7BTNaVcWQCYiIqcwPQYvEAjg6tWrOH/+PEqlEqLRKCYnJ1suT0bkZK1SrFpkrJdFOX3uCoRdO1gAmYiIHMPyJIuDBw/i4MGDdrSFqK/apVi11KddlQLI16s3NYNED9br87EAMhERdZupSRatNK9LS7RZWEmdvn/jJgsgExGRY9gW4NVqNZw8eRJ+P3snaHOykjpV9jW7sgYREZGdLKdo3377bTzxxBOYm5vDwYMHOYuWNq12KVYtWmnXh3bv3BQrWVgp8ExERM5mOsB7++23ceLECTz//POIxWK4dOkS9u7diy1bttjZPqKeUVKsx54pwQO0DfJapV2tFE/uhU6WaGMgSES0+ZhK0R45cgTBYBB+vx9Xr17F2bNnsXfv3o6OIUkS8vk8ksmk5mujo6PI5/Oa+yaTSQiCgGAwiGKxaOYUiDTppVh9t9+m1sJTbNa0q1IKpnlCiVL25aXL1xq2vS/zCh6Zex3ffW4Rj8y9jvsyrzRsQ0REzmOqB69QKODEiRNIpVKm13NNJpMQRRGhUGjDa+l0GpFIRHO/YrGIsbExFAoFlEolhMNhrKyswOfzmWoHUTO9FCuATd+T1W61jfqyL4Ur17nsGhHRJmWqB69SqSAQCCAUCuHYsWN4++23Oz5GNpuFIAgbnhdFEaVSSfM1YH2ZtOnpaQBAKBRSiyzrWV1dRa1Wa/ghakdJsX5tz+dxIDiGLUMezec2G6OrbbxeXuaya0REm5jpWbRTU1O4evUqDh48iGg0iiNHjmBxcdFyg5LJJDKZjO7rPp9P7a2TJAmVSkWzF1CRTqfh9XrVn/HxccttJLJLu/Vt7Wa0FMwF8QPDy64REZHzWJ5FG4vFEIvF8Pzzz+Ov//qvLR2rVCpBkiSEQiFDY+umpqaQy+VabpNKpXD8+HH1ca1WY5BHjtDJRAe7GC8FY6x3ksuuERE5k+UAT3H48GEcPnzY0lq06XQaoihCEAQ17VqpVNSUbL1EIoFEIoFoNNrymMPDwxgeHjbdJqJu0Fvzttvj24yutnEgOIYnX73a9nhcdo2IyJlsC/AUVpYtq++NSyQSEAQBsVhsw3ZKcNcqNUvkVJ1MdLB7nF+rUjD1ZV++GBjjsmtERJuY7UuVGRWPx5HNZpHP5xGPx9tuHwwGIUkSZmdnMT8/j3g8jmAwiGAwiFKp1IMWE9nD6ESHbo1vM7LaBpddIyLa3DzygC09UavV4PV6Ua1WTZd4IbLihcV38d3nFttu95cH/gRf2b2za+VYjBQwtmOcIAslExHZx2gcwwCPqEeUQOcXV3+LJ18tG96v2xMv2rESoPVjIgkRkZsxwNPBAI/6QSvQMUoJpTZbYWG9iSSb9XyIiJzAaBxj+yQLImqkF+gYpTfxohupT7uO2c+JJETtcNgADQIGeERd1CrQ6UT9xIsDwbGupD7tPGYnE0kOBMdMtZfIDA4boEHRt1m0RIOgXaCjeGDXZw0d7/0bN9UewebjKjX0Xrp8reN22nVMZWWOnxvcnoWSqZe68bdD5FQM8Ii6yGgA86ef/WND292xbdj2NWLbpVONHvOly9dwX+YVPDL3Ov7xwq8NvXe3CyX3eim4bnLTufSDXfc50WbBFC1RFxkNYA4E7sDzpXfbFhaGB7anPu1Ip3Y6ztBooWTO4F3npnPpFw4boEHDAI+oi4wuDfbF4JihFSY++N2qofftJPVpdFu97TodZ2i0ULKVoKZbS8H1Y3B+v5a1cxur9znRZsMULVEXdbIihJEVJoz2CHaS+rR6TKPjDBX156PHylipbqXi6lPQ331uEY/MvY77Mq90ddwW04r26cbfDpGTsQePqMuUwK25N2qHRm/UQ7t3Qti1Q7eXyGiPYCdrxFo9ptEeD6Mrc1gtsdKNVFy/etGYVrRPN/52iJyMAR5RD7QL3OptGfJs+LKuTw1+Y9+/w98Xf9UyldtJ2lDpZWyXHq4/Zn17PrhhLG38ld07DQUhVoMau1Nx/azpx7Sifczc50SbGQM8oh7RCtyM0BqL5rv9NgCA9NEn6nNaPYJGddLLqNWeIQ+glyXU6hlpNZbNalBjdyrOaMD5d4Vf4Uv33GHruDymFe3VyX1OtNkxwCNyML3UYPWjTyAD+F70Xtx9xzZbBvwb6WXUa0+r4A5o7BnRmzzx2Fe/gNFtw3jrNzcMtVcvqLE7FWc04Hzy1at48tWrLSeCdDpJwylpRTet/NBJbzrRZsYAj8ihjKQGn7v4Dl5L3m/bl1OrXkYjs2Wbe/Kae0b0AsRr1Zv41rNvGmpju6DG7lRcp71jeuPyzMwKdkJa0Y0lWsz2phNtJpxFS+RQnYxFc0J7gPXg7rGvfgH/8I09+MnUF/Fa8n41CLBj2TajQY2RGclGKb1oRkMordmtVmYF23kuneLKDxux4DRtFuzBI3Iopw2wN/o+d2wfxtf2fH7D852WU9HSyVgpu1JxrXrR9NQH3/sn/JYnaXR6LnakVPs5ucSp3NibSe7VtwBPkiQUi0VcvHgRmUxmw2sTExOYm5tDLBbbsO/s7Cyy2SwAIJPJIBqN9qTNRL3ktAH2VttjJRD9zpfvMTWBwa5UnN7g/Hbev3HTtlInRs/FriCEJVoaseA0bTZ9C/CSySREUUQoFNrwWjqdRiQS0dxPkiRkMhmUy2VIkoRwOIxyudzt5hL1nFMG2NvVHiuB6L2f/WM1iNDrner2RID6XrRfXP0tnny1/efOndu39rQn1s4gxGk9yP3Uj95MN01sof7oW4CXzWYxMzOD5eXlhudFUUSpVIIgCJr7zc/Pq716Pp8PgUAApVJJM1AEgNXVVayuflqnq1ar2XQGRN1ldoC91hcDgK6mKo2MjWsXILaiBId6vVP/9T/txP/5l2tdT50pvWj7J/yG1g7eP+E3PEbSak+s3UGIU3qQnRDo9Lo3k6lgsoPjxuAlk0lkMhkUi0XN18vlMsbGPv0DCgQCuj2BwHpv4OnTp7vSVqJu67Rul9GaeWa/LKzUETMzlq0+UGo1Azf7z0sb9u3murOdBLu96om1q16fct7Xq7+Hf9tnsPLhx33rQXZKoLNZe2FpsDkqwCuVSpAkCaFQSDfA61QqlcLx48fVx7VaDePj47Ycm6gXjA6w1/tiqA/sFFa+LKxMXuhkLFt9oASg4xm4VlJnRgILo8Fur0qd2FGvT+u8tfSiREurQOfoMyVba0C206veTE5sITs5KsBLp9MQRRGCIEAURQBApVLB9PS0uk0wGMTCwoL6WBRF+Hw+3WMODw9jeHi4a20m6oV2A+w7LUFi9cvCyuQFrQBx5cOP8fiL+oHShfKyqRm43V53tlWw29wD+KNvhjTP8bGvfgHef/MZvLD4bsfBipkl4/TOR++8tXR75Yd2gQ4A/F3xLfW5bvfqOa0XdlAmtpA1jgrwcrmc+v+JRAKCIGyYRRuNRtVZt5IkQRRFzqKlgWemBEk/vyy0AsQHd+v3ClpNfbXbvz4t+fiLv+yoB0XrXNqt1tEqsDUarHS6ZFyr87n/33+27T8Q/Ntuw2P/5T9gx0j3e8w6vZ+7nb50Wi/sIExsIev6FuDF43GUSiUA671w9cGdFqXnLhAIIJlMIhwOw+/3q+VSiAaZlQ98p3xZtOoVtJr6arW/0bQkYCwobtUD+O1n38RTj4bwtT2fx0uXr+Hbz5oba9XpknHtzud/X3i77flXPvwEO0a2dvUfA0qg/fMOCyj3In3Zi3VsnTKxhdyhbwFeu4CuOXCrL4UyPT3dkLYlGnRWPvA3w5eF2Rm47VJnnaQl6+kFxUbHULXqMWsXrJhZMq6d//ur3xrarv68O5ndamTbTgJtLUYnkXSiud3Crh1dXcfWyH3u33Ybrtdu4kJ5uSvnyHIs7uGoFC0RmWMmAOp1HT0rzM7ABTamzoykY9vRC4qNjqFq12PWqqewkyXjVj762FC9vv/31gdttwHal6sxOrO7eVuzgbaWVpNIOtGtGbxmZ2crKh9+gu+dXbSlPU6ZpVzPaQGn09rTCQZ4RC7QaQDUq4Xq7aSXItOrg6eVOrPaS9QuKDaa7v515SND22kdr5Ml4/77lyZa1uszyki5Gq3UspGZsP/Of7vpQLsVpT0/+ubehnGPRr6g7SpV0hwcGBlz2clMcytjD51YjsVpAafT2tMpjyzLA7VScq1Wg9frRbVaxcjISL+bQ2SrbtfBcwKzK1lY7SVSjtTqi+9CeRmPzL3e9liPffULePzFX7bd7idTX9zQg2f0PZR9lfMGjPV8Nqs/b2HXDtyXeUU38FACwdeS9wNAy217pTld3e7ev7UmGz7HVoFipyVnmu+r5p7myocfW2qP1WN2uydL7+/TyN9dNzitPfWMxjHswSNyEb2yHYD1lSycQm8yRqtJGp2WkdFiZDC90XIa/+3A3Xj6tSVTY606Ldlhdh1dRSflapTU8v/6xRJWPvrY1uDuLw/8Cca2DePvi79S38uI5rGI7Xqo7ChV0sk/JtrNzr5QXtYNxFq1x0jvodFj2tGT1SpANFIW529/9q/4/SdrPZnF7ZZ6hAzwiFxGL9AZ5LpZZsrIAJ2XBjFaTuMzfzRkeqyVmZId9YH/zy9fwz9e+HXbc//LA3+Cr+zeaapcjZHeyU59ZfdOHAiO4c92/LHlyRhaX9CdzuA1M9GmVZv0gkYzpVOsDkWoP6Ydqdx2ZYN+cfW3hmZx2zX2sB231CMc6ncDiIi6rdNSMJ4//Pzg63+Or+/9PA4Exwz/S13pMdvhbZyIscO7dUORZK3ttChfpi/9Ifgw+h71lMD/Kwa/FJWAqv68+zHj2oP1L/T6HsnXkvfjJ1NfxHe+HDR1zPovaGA9ALkv8woemXvdUPALmJ9o04rWfdpp6RQlILOj9/SObcNte9ZOn7uCWy2mbOu151r1Jr717Jt4ZO51QxOB6jX/PdjNLfUI2YNHRK7XaWBitbaZ0eXc6rdrNS5Kq9fJ7JJxVlZlMFuuxiy9HkklWN0/4bc0iaRVD1WrNjVfn/r041u/+Z2JlqzTuk87+X3ZMRSh/pjwwNL6xna1R+t9u5kmdUs9QgZ4ROR6RuuL2blSg9Hl3KyMtTKzZJyVVRnMlKuxol2gbbU9d2wbxvfz/9JRcAc0Xh870qGtgupOfl9ml/RrbotyzA9+Z2z5O73SNFZ6M9vpZprUzD+CnFhOhSlaInI95UsS+PQLTGElHWunXqaFzKR42+1rF/+22/B3R/bgJ1NfxGvJ+9v2ouq1p9WvUEn7tuuhatZ8fexIhxopWWT092XHvVF/zE57qJpTp71IYXbjPdp9XgAbg3wlxf/d5xbxyNzruC/zStdSyEaxB4+IBkIvlpqyotdpIbMpXq19P7ixamhixXe+fA9u2zKkORNWedcffP3PO/5daJ3Lyocf49vPbiwPY6aHSmvCiV3pR6P3n5Hfl9l7Q6/3utOUfHPqtBcpzG69h9HPCyfWE1QwwCOigWElqOk2K2PjzDKT4tXa99aa3LLsi9L27wl/ii1DHs2ZsFYDba1zeWqo9Rf0hfKyoWMrE07qmUk/1s8cNXP/aZ1jfWrwjm3D2DGyFb+pGQvI2gXVZlLg9alTK2M2v/UXQTx38R2sfPix5b8Hs+nTdp8XTi+nwgCPiAaKlaCmm6yMjeu3Ttveq0C73ftYCaqNpga/8+Ug7v3s9q4VB9YqbK4EF+2CKiNBtdk6iu/fuGl6icEd3q34Hw/8Gf7jXV7Lfw9Wa/i1+rxwejkVBnhERA7h9DRyK522vVeBdqv3sRJUG00Nfumef9uV89RLDVb/sGKN9/bbNqxeY7b3sD5Q/sXV3xoqa6Jcn04CxOZrbvXvodvpU6eXU+FSZUREDuPEGXlGbca2m+nlUZY0a9f7Z2QJsU4ZWU7tsyPD+J+Te/DB71Zt/T2YPW8z6/Lq7WvkXOxacq6VTpcNtAuXKiMi2qScmkY2YjO23UzKuJ8pdSOpweu1VQx5PPjans/b+t5mz1vrvnhwt7Fr3m7soda+vUif9mPcbCcY4BER0cAzE5jalVLvtIeq36lBu87b7D8GjPS4Gj13ZWk6Mz2cTh8327cUrSRJKBaLuHjxIjKZjPp8MpmEKIoQRRGpVAqxWGzDvjMzMzh79iwAIJFIYHp62vD7MkVLRER2spKWNpMe7ldqsFk/0vF64+qUd1XG1Rm9Rgor69tancjRKcenaJVALhQKqc+JoohgMIhMJgNJkjA6Oorm+LNUKqFQKGBhYQEAEAwGMTk5CZ/P18vmExERAbDWE2VmEkC3U4NGAze98+5W4NdJWZJOS7RYmXjh1PJLfQvwstksZmZmsLz8aR2iQCCg9sYVi8WG4E/RHMj5fL6Wwd3q6ipWVz8tZlmr1aw1nIiIyCIrNdS6mRq02hvVzd4so+PqlLVxH/vqLnz7WWMlWpTX//Zn/4rff7LW8ZKFThx76silysLhMOLxOHK53IbXAoEAEokEgsEggsEg5ubmWh4rnU7D6/WqP+Pj491qNhERkSGdTALQYmW5OT16S681L0HWrf3bMTqu7slXr+KRudfx+ItXMP2fJzpaVq/y4Sf43tnWy43dWpNxobyMFxbfxYXyMm6tObMYiSMnWSwsLKBYLEIQBJTLjfV2JElCOp1GoVCAKIqYmppS07VaUqkUjh8/rj6u1WoM8oiIqK/smChhZ2rQ6qoMvVjVwczauLP/vIQffXMvRrcN4+eXr+EfL/y6o/2b07a9Hm9nhSN78AAgGo0CWE/V1pufn0ckEkEgEEA0GoXf79+wTb3h4WGMjIw0/BAREfWTXWsPK6nBr+35PA4Ex0wHT1Z7FK3ub4Qyrs7oGSrB5uMv/hL7J/z4SocBmLL/6XNXcGtN7noPpd0cFeDl83mUSuuLQ0uSBFEUEQgEGrYJBAK4dOmS+vjSpUsbtiEiInKydsGKB+s9Q72qoWa1R7EXpVuUsYcAOgrymtfG7SQEVvZ/vbzcsocS+DQQdIq+BXjxeBzZbBb5fB7xeBwAEIvFkM1mIQgCwuEwcrmcGrwFg0FIkoRoNIpoNIpwOIxwOIxMJsMAj4iINpVWwUo/aqhZ7VG0q0eyHb2xh+3Ur40LGA8QFRfED7reQ2m3vo3B05pAAazPrtVSPxavvm4eERHRZuSktYetll7p5aoOvVobt5GxkLBf685qceQkCyIiokHglBpqVkuv9HpVB2Xs4f4JP54vvdtRYFl/za9Xf4/HX/wlVj78uOX+B4JjePLVq23bZbWH0k6OGoNHREQ0aOyaKGGV1dIr3Sjd0o7ZVLdyzb8eugs/+Prutvt/MTDmqDGTRvRtqbJ+4VJlRERE+qyuRNGvJcy6XaBZmUULaPdQdiuIbWY0jmGAR0RERJteLwJTJ9TBY4CngwEeERERmdWPHsp6RuMYTrIgIiIiMsiJ685q4SQLIiIiIpcZuB48JSNdq9X63BIiIiKizijxS7sRdgMX4N24cQMAMD4+3ueWEBEREZlz48YNeL1e3dcHbpLF2toa3nvvPWzfvh0ej7FBkbVaDePj43jnnXcGdmIGr8E6XgdeA4DXQMHrwGug4HXo3TWQZRk3btzA5z73OQwN6Y+0G7gevKGhIdx1112m9h0ZGRnYG1fBa7CO14HXAOA1UPA68BooeB16cw1a9dwpOMmCiIiIyGUY4BERERG5DAM8A4aHh3Hq1CkMDw/3uyl9w2uwjteB1wDgNVDwOvAaKHgdnHcNBm6SBREREZHbsQePiIiIyGUY4BERERG5DAM8IiIiIpdhgEdERETkMgzw2pidnUU4HEY4HEaxWOx3c3ommUxCEAQEg0H1vCVJgsfjUa9HPB7vcyu7q9X5Dsp9MTMzg2AwqP6Mjo4in88PxL0gSRLy+TySyWTD83q/ezfeE3rXYNA+H7Suw6B9Pmhdg0H9fNC6/wEHfjbIpGtlZUUOBAIb/t/tCoWCnMlkZFmW5YWFBRmAvLKyIq+srMihUKjPresdvfMd1PtiZWVFjkaj6v+7/V6Ynp6Wo9GofOLECfU5vd+9W+8JrWswiJ8PevfCIH0+aF2DeoPy+dDq/nfaZwN78FqYn59HLBYDAPh8PgQCAZRKpT63qvsikQimp6cBAKFQCIFAAKIo9rlVzjGo90U8Hkcmk+l3M3omm81CEISG5/R+9269J7SuwSB+PmhdBz2DdC/UG5TPB73734mfDQzwWiiXyxgbG1MfD8IHGbB+E/p8PgDr3fKVSgWhUAgAIIoiBEFAOBxGPp/vYyt7Q+t8B/G+UD6QlPsAGLx7AdD/3Q/SPcHPh0/x82HdIH0+6N3/Tvxs+KOevAttWlNTU8jlcgDWb+zJyUlks1lIkoSJiQlEo1H1ZncbvfMdRNlstmEczaDdC6SNnw/8fAAG9/Oh/v53IvbgtRAMBlEul9XHoii67gZtJZFIIJFINHxoZbNZAFCvg9v/Zap1voN4X8zPz2NycrLhuUG7FwD9z4RBvCf4+cDPB8Ugfj403/9O/GxggNdCNBptmCEmiuLA/AtN68O7WCyqXfHKH2t9l7zb6J3voN0XkiRBkqSGD6VBuxcUer/7Qbsn+PnAzwfFIH4+aN3/TvxsYIq2hUAggGQyiXA4DL/fr/6LxO1mZ2cxPz/fMJ07l8shEolgampKHXdw/vz5Pray+/TOd9DuC1EUEQgEGp4bhHshHo83fEnlcjnd371b7wmtazCInw9a12HQPh+0roHy/4P0+aB3/4dCIcd9NnhkWZZ79m5ERERE1HVM0RIRERG5DAM8IiIiIpdhgEdERETkMgzwiIiIiFyGAR4RERGRyzDAIyIiInIZBnhELlcsFhEOhzE6OorR0VHMzMyor9X/vxUzMzOWjmV0f7PvUywWG5ZS0ju20W3tes9um52dte133EoikdiwEH2pVOr7+RMNMgZ4RC4mSRIEQcDc3BxWVlawtLSEQqGgLv7tliKs7USjUczNzbXcRrkWRra16z3dYn5+HoVCoeG5UCg0MOdP5EQM8IhcrFKpwOfzqZXmfT4fstksAoEA4vE4RFFEIpEAACSTSbWXT3lO6YVJJBIIBoMNPTKJRAKjo6MIh8O4ePGi+nyr48TjcbVHSW//ZnrbzczMIBgMIhgMIplMAgAEQWioMB8MBiGKIkqlEmZnZ3XbV38t6rfVeo9W16Sechwj27faplgsqu1Urkc+n0epVIIgCBAEAaOjo0gmk0gmkwgGgw29aRcvXlR7cOuP2+rc6n9P9bT2icfjkCRpw3mJoojZ2VlL7c/n85iZmVHbX3+ceDyOYDCIcDjc8Dsnoj+QicjVTpw4Ift8PjkWi8nZbFZeWVlRXwsEArIsy/LKyoocjUYbni+Xy/LCwoIMQC6Xy7Isy3I0GpULhYKczWblUCik7uvz+eRMJtPyOD6fTz2O3v7N9LYrFApyKBRSzyUajcrZbFbOZrPy9PS0LMuyXC6X1fMrFApyLBbTbV/9tVC2LRQK6nOyLMuhUEguFAq616SZchwj27faplAoqOcky7I8PT0t53I5dZ+VlRV5ZWVFBiBns1lZlmU5FovJuVxOzmazss/na7hOra5f8++p+Xy0rocsy7LP59uw/cLCgnod7W5/NpuVY7GY+j71xyeidezBI3K5TCaDpaUlJBIJLCwsYGJiQk3RKpSevXw+j2QyCVEUIUkSgPVUm9IDGAqFIEkSCoWC2pvi8/kwOTnZ9jiRSEQ9jt7+zfS2KxQKaq+RIAgQRRGFQgGTk5OYn58HsJ5yre/xadc+rfeOxWLq4yNHjqhpSK1r0oqR7Ts9JrCeBvb5fGovrXJ99u3bh0ql0rANsN57VigUdK8f0Ph7Mno9zDLbfmUB92QyiUqlMjBDDYg6wQCPyMWKxSJmZ2fh8/kQjUaRzWYxNzeHdDrdsJ2SLqtUKjhy5Aii0aj6mt/vN/x+rY6jfEnbYWxsrCFYKZfLyOVy8Pl8iEQiKJVKyOfzmJ6eNtw+vfeppwRdnVwTo9sbPaYS+Ggxeo31rl+7Y+hdj07Y0f5AIIClpSXs27cP2Wx2wwQPImKAR+RqkUgEyWQSpVJJfe7ixYuIRCIN2xWLRcRiMUxPT8Pn8+HSpUstjysIgtprIkmS2mtm9Dh6+xvdTglWlQAjHA6r55hIJJBMJhEIBDYEDJ2cZ/17A8DZs2f7Ekj4/X61nZIkbeh9badYLKrXKZvNIh6Pt7x+esxeD7vbLwgCZmZmkE6nEYvFMDc3xzF4RBoY4BG5mM/nQy6Xw9TUlDqxQJIk9Yva5/MhHo8jFouhVCqpg+enp6cxNTWle9zp6WlEIhGMjo7i4MGDSKVSCAQCho+jt7/R7UKhEJLJpDr4PhqNIhQKAQBisdiGgf2KVu1TroUiGo2qkx6CwSCi0WhDirJXQqEQIpEIgsEgpqamkMlkNK+VlkAggFQqhYMHDyIYDCIQCGB6errl9dNj9npYab/yvgcPHsTo6CgCgQBOnDiB6elpFItF9b5Qeh+J6FMeWZblfjeCiIjcpVQqIZ1OWwq+ZmdnUS6XkclkbGwZ0WBgDx4REdlOFMV+N4FooLEHj4iIbKXUFMxkMm1Tv60oY+/snKBDNCgY4BERERG5DFO0RERERC7DAI+IiIjIZRjgEREREbkMAzwiIiIil2GAR0REROQyDPCIiIiIXIYBHhEREZHLMMAjIiIicpn/D9qWG8vPJqNuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 725x266.25 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_sigs = np.linspace(5, 200, 100)\n",
    "fig, ax = plt.subplots(1,1, figsize=(7.25,2.6625))\n",
    "plt.scatter(num_sigs, num_dG)\n",
    "plt.ylabel('$\\Delta G_{eff}^{\\ddag}$ (kcal/mol)')\n",
    "plt.xlabel('Standard deviation in number of jumps')\n",
    "plt.savefig('figs/jump_number_variance.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_deltas = np.zeros((len(models1), n_paths))\n",
    "all_njumps = np.zeros((len(models1), n_paths))\n",
    "all_lengths = []\n",
    "permeabilities = np.zeros(len(models1))\n",
    "for i,m in enumerate(models1):\n",
    "    all_deltas[i,:] = np.array(m.deltas) / 10\n",
    "    permeabilities[i] = m.calculate_permeability()\n",
    "    for j,p in enumerate(m.paths):\n",
    "        [all_lengths.append(l) for l in p.jump_lengths]\n",
    "        all_njumps[i,j] = len(p.jump_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Standard deviation in number of jumps')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5gldeecWSHaTOoFxiD9qYmlOP3mBiJrM/hLSkil0pLRo6FiNjtHr/r83+AGzFAqJ66BpeuHDhAo4dOwZRFAEA2WwWo6OjCAQCHMOlhpy0PHerpymbWdfBymMx2mvU839tdsaDFcWKzKQr6B45ckT9fmZmBtlsFtPT0zh27BhmZmYwNzdn+g5SZ7EzNaceJ1yCmvUhZOWxNDNGq/X/2ooPDTM/1MxmeEx3eHgY0WgUx44dw/T0tK6AOz4+DkEQIAgCstmsrjbxeByhUAg+n6/icS3bJFI45RJUCUz33X4zAOBnb1/WnbZm5bHYMR5v1YfG3u1DeC2+C89H78IPHrwDz0fvwmvxXS2fcu6SZVnz/26pVGp68oMkSRAEAYVCoeJ7LW2y2Szy+TwOHjyIfD4PQRAwPz8PAA23Wet4+vr6sLCwwEkdbcSsQirKzRug+iWoXWPNZty1t/pYrEytu74k497kqYa9aTMXATWbnliia3ghlUqhWCwiFAph165dhnZOmUwBAG63G16vF/l8Hn6/v2GbQCCAQCAAAPD7/fB6vRBFEdPT0w23SZ3BzDe/Ey5BzUpbs+pYlA+4xX9dwlPhzwOu5ckuej/s6n1QOn0M1my6gu6BAwcAAGfOnMGhQ4ewadMmhMNhbNmyRfM2CoUCBgY+HudRAmd5gNTSRpIkFItF+P1+nDhxomF7AFhcXMTi4sezo0qlkub9ptazIq/W6pt79YJNs3UKVm47tG3Q1GOp9wGnZ1xeywelEz4A7WKo9sKOHTuwY8cOAMDx48chiiJ8Ph8efvhhU3eunmg0inQ6res5Y2NjOHz4sEV7RFayspCKVTf3GgWbZu7aWz2TzqwPOD3bcVJ2i5WaLu0YjUYxNjaGkZERfPe730UikcCpU6dqtvf5fBXjraIowu1262ozOjqK0dFRBINBzdsEgEQigYWFBfXr0qXWzb8mfVqZV1tOa50GLcn+Ru/aW1060ay8WSPbUT4Av3THp6uWnLSyToZdTCvtODw8XDH8UEswGEQymQSwPEQgiqIaPLW0UQJu+dCBlm0CQE9PD3p6nL9SAa3W6rxaQHvvUmuv/KnI5zW9bvldeztKJ5qVN2t2/m0rCqNbwZJ6usrQQzVerxfxeByCIMDj8SCVSqm/8/l8yOVyNduMj49jYmKiIiUsnU7D7/fX3CZ1hlbn1eq5TNYabCBDdw6sHaUTzfqAM/OD0s46GVbTPLxw6tQp7N69G3v27MHFixcBLBe9MSIWiyGXyyGTyVT0SAuFgjosUK1NLBbD/Pw8CoWC+qX0eGttkzpDK/Nq9V4maw02772/qDsH1o4ev1kfcGZtpxV1MqykOegmk0m8+uqreOWVV3Ds2DE18BLZoZVFc/SOJ+sJNnrrFNjR4zfrA86s7ThlPN8smocXyks2HjlyBIcOHVJrMBDZoVZaUf+GdfjyHZ9G34034PqSbHrg1du71Dt1Vs9dezvKZJqVN2vWdpwwnm8mzT3deDyOl156Sf35yJEjvIwn25VP7fzje7bAs+EGFN//CD/+xcWaqxkAzd311tu7NNIrb3TXvrydHT1+I5XCqp1fM6rLtXo832y6pgGvdPHiRV0TI5yG04Cdq9FU31o3VqpNe232rrfRaapW3m23606+linXWvalmanb7TBNWE8sMRx0A4EARFGEy+VCIBCA3+/H2NiYoR1uFQZdZ2r0JlbehLXG+crfhJnzVzQH53Irg8T8+x/iT5/TX9vArDoR1Vi5ba30fPiZ8TpAa+tk1GJZ7YVyHo8H09PTAIB8Po+pqSmjmyJSaUkN6rvxBk03Vt4ozBnKaa0V9GO/P4yfvjWra5qqlaUsW10m087l1rVME3bCh5AWhoNuKBTC1atXsXHjRvj9fhaXoaZpfRMf3Pu7mrb3uvie7pzWekF//G9n8KP/tAP9G3oc/8a2g93Lrde74dhOEycMB10lpzYcDlcsTElklNY3cVHjku6rbzVVp9z1vr4k41s/rR/0v/1X/+DoEoN2akVWQbXefbtNnDBceyGbzeLChQtcmJJMo/XN6dlwg6b8T629K+Wu9w9PvYMrpc7JB7WakawCI1kk9Z7TjhMnDPd0lYUplcUpiZql9U082HejpvzPu7wDmnNaT56bxfey72h6/XbJB7Wa3pxhI0MAVlZqa5WmlmDfuXMnnnnmGZw9e9bEXaJupWcGk5b8T605rcByb0irdskHtZqenGEjldGsrNTWSoZ7uul0Gvl8HqdPn+bClNSU8rvOD+78HXw/+0tNM5i0zOTSctf79cJcwyXMFa1cutuJtGYV6M1ysLJSWyNWZ0EYDrrK0MIDDzxg2s5Q96l2+ei+aR0AQPrgI/WxWqlZWtKmGgVnPb2gTlo2xiyNzq+RIQArK7XVY0cWhK6ga8bClESKWnedFz74CDKAx4NbsWXTBlN6G/WCs9Ze0OPBzzrqLriT1Du/RoYA9FZqM2N9NbuyIHSN6aZSqYYrQxBpoeXy8YXTl3Df7TfXrUVghkZjyQAw2NuDx3bdatk+dDIjWQ5WVmqrxs4sCNsXpqTOpnU8zOy7zs2Mw2mphvWtP/w9DisYZKQympWV2qqxMwuibRemJOfRMx5m9qoCzY7DmbEabbtMQ7Vbow81GcCDOzfjZ29frjhveocNmpkWbWcWRFNVxsrNzMxgcnISxWIRoVAIu3btMmOzlmLBG/PoLXzyemEODx1/o+F2n4/eVfeNZHbBFaOBs52moZpJz/nSetO0/LzZdV6b/Xu0pcpYPWfOnKm7TppTMOiaQ0/Vr/KUoGbL9Rl5XSvYVWnLano/cIwExPLXuPjeB/h+9pcNz5sdVxDN/j3qiSVNL8Gu2LNnj/p9OwRcMo+R5VTMKMbthGVc2nEaajUnz83i3uQpPHT8DXz9hbN1C8Ir7Y0sA68MAdx3+8144fQ/ajpvWgu8N8PO5aBMC7qcGNG9jI6HNXvX2QmzkZwQ+JulN4Ca8UFj5nlrZlWQcmZkQWih60ba7t27IQgCQqEQAoFARTfa5eINg27VzHIqje4617u0dMIyLk4I/M0wMlvMjDv9Zp03s8d8m82C0EJX0I1EIsjlcjh48CDy+Tz6+/sRCAQQCoUgSZJpO0XtpdnFEmvddW70hrJjkcZGnBD4m2EkgJoRMM04b1ZNZrC6OLyu4YVoNIpjx45henoaS0tLyGazCIfDePPNN2HB/ThqE1aMh2m55G3lsuwKs5YZbxUjAdSMgNnseWvnsfSmxnR37NiBaDSKiYkJXLhwwax9ojZk5niYnjeUXeNwtTgh8DfDSAA144Om2fPWzmPphgveEK1k1niY3kteO8bh6jFjYkWrGBmiMTJxoZpmzls7j6XrCrpf+9rX0N/fj9HR0apTf1988UVMTEwgkUhw6Z4uZcZ4mJE3VKsXaWx14DfKaAA164PG6Hlr57F0XUH32LFjOHPmDA4ePIgzZ87A7XYDAObn59VgfOLECSv2k7pIu76hWh34jTIaQM36oDFy3pxwE9WopmakLSwsoFgsYnh42Mx9sg1npDlTo9lBAODZsA5vJIK44ROmpZp3vXarHaHcbAWq99DtnAnY8mnA7YJB17lqvaHKdUNtA6rPKTUvGHQ1YtB1tmpvqHLtVtuAzFPeK9+0oQdwAe/9ZrFlPXQ9sYTZC+RYe7cPYdfv/hvcNTaF4vsfrvp9rRlT1Nnq9W7bYUzd0IBYqVQyez+Iqsq9O1814CqcnI9J5jNaaMdJDAXdLVu2YM+ePXjppZfM3h+iCu2cj0nmaudZaOUMBd1isYgDBw7ghRdewMDAAB599FG89dZbmp8/Pj4OQRAgCAKy2ayuNpIkYXJyEvF4vOIxl8ulto9EIkYOixyoXdPHyHxWzEIzq0KZHobHdIPBIO68806cOHECo6OjagCenJzE5z9fey16SZKQTCZRKBQgSRIEQUChUNDcJh6PQxRF+P3+iuf4/X7kcjmjh0MO1c75mGQus696WpX5YKin++KLL2L37t0YHh5GPp9HLpdDsVjEiRMnGi7TMzExgXA4DABwu93wer3I5/Oa26RSKYRCISO7jcXFRZRKpYovcrZ2r21A5jHzqqeVY8OGgm4mk0E8Hsfc3ByefvppdaUIv9+P8fHxus8tFAoYGPj4DqPX64UoirrbrCSKIkKhEARBwOTkZNU2Y2Nj6OvrU782b95cd5tknJmXba0uakPOYFZFt1aPDRsaXnC5XBgZGal4bP/+/Thx4gQeeOABU3ZMD7fbjX379iGVSkGSJAwPDyMYDKrTlBWJRAJPPPGE+nOpVGLgtYAVl23tWtuAzGNWoR07l1uvRlfQ/e53v4tUKoWZmRlks1nIsgyXywVZltHf369pGz6fr2LsVRTFVcFRS5uVUqkUAKjtqo379vT0oKenR9N+kjFWFZYG2re2AZnHjEI7rc6I0RV0Dxw4gAMHDmDfvn2YmJgw9ILBYBDJZBLA8g0zURQRDAZ1tymXzWbh8Xjg9/vVYYiVAZesZ2TpFyK9mr3qaXVGjKHhBaMBF1gen43H4xAEAR6PR+2hAh/3cOu1iUQi6k01URSRTqcRCAQQjUYhSRKKxSKmpqYM7x/pp0zJ/MWFf2npZRt1j2auelqdEaOr9sL+/ftx/PhxpFIpTE9Pq48rwwztVtaRtRea16g+QjU/ePAOfOmOT1u4V0T1mV2hzLLaC7FYDL29vQgGg6su37kacPf5+duzePS5fOOGK3AiA7VaK1f70NXT3bdvX9Xgyp5u9/n525fx2PNnoCerRrlsey2+i2O65Ahm1RC2rKebSCRq/o493e5x8twsHn3ujK7naEnpabci2tT+WpERoyvoHjlypOqYrqLderqkn5KhoFejyzanFKMmspppY7rUHd4Q53TdNHvsC7finls31e21WpnbS+Q0uoKuMgttx44deOqpp5DJZAAsp3E9/PDD5u8dOcrJc7M49OLfaW4/1Lcej4c+WzXYKkMJVxZ+i2//1T8wt5e6hqE83f3792N+fh6jo6MAlmeD5fN5/MVf/IWpO0fOUas3Wk+t8Vs9aWbM7aVOYyjoZrNZzM3NqT8/8MAD2Lp1q2k7Rc5Sb6ZZNWtcwA8fqj4kYCR4AyxSTp3DUJWx4eFhXL16Vf1ZSZegztSoQMhKP3xoB/7j7asDrt7gXY65vdQpdPV0n3rqKQDLU3m3bNmi1kPIZrN108movWntZbpvWocjX7mt5k0vvcEbYJFy6jy6gq7Smw2FQhWFxIPBIHw+n7l7Ro6htZf5o4f8uGfrppq/1ztEwCLl1Il0Bd1oNKp+f/HiRUiSVPG706dPm7Zj5BxaC4Tc1eBGl94hAjumZHYbTkBpPUM30h555BFkMhnMzMwgGAxienoasVjM7H0jhzCreHSj4A0Ang3r8M37fg+DvQwIZuMEFGcwdCNtYmICFy5cwIEDB5BMJnH69OmGy+lQezNjyZxG6525AHzny7fhyzs+jbt9Awy4JmrlmmBUSVfBG4XH40GxWMSLL74ISZLwJ3/yJ9i6dSveeecdK/bRMix4o58Zl6fscdnr+pKMe5Onat7EZCGi5llW8EYRjUaxZ88eHDt2DKFQCBcuXMDw8LChnaX2YkaBEK53Zq9WrwlGlQwF3WQyiYWFBfT19SGdTiObzVas7kDUCNc7s0+r1wSjSoaCLgCk02lkMhmcOHECLpeLPV0ih2r1mmBUydCNtEQigYmJCXWtsocffhjf+MY3TN0xIjKHkjVSa/DGheUxdU5AsYehoDs5OYlXX31V7d1OT09zeIHIoRpljQCcgGInQ0FXlmWUSiV1tYiFhQX09/ebumNEZB4zUv7IHIbGdGOxGHbt2oWFhQU89dRTOHbsGA4dOmT2vhGRiZg14gyG8nQBYGpqCplMBps2bcLIyAh27Nhh9r5Zjnm6RGQGPbHE0PACsDykcOutt8LtdiOXy+GZZ54xuikioq5haHhhz5496Ovrw86dO83eHyKijmYo6BYKBVy4cMHsfSEi6niGhhfC4TB+/OMfo1Qqmb0/REQdzfCMtGg0iiNHjgBYTiFzuVxtV/CGiMhuhoLu8ePHMT8/z3XRiIh0MjS8IAgC1qwxnPhARNS1DA8vuN1udWFKxSuvvNL0DhERdTLDpR2JiEg/Q0F3amoKf/Znf2b2vhARdTxDA7OnT5/Gu+++a/hFx8fHIQgCBEFANpvV1UaSJExOTiIej+veJhFRqxnq6cqyDL/fj0AgALfbrT5+4sSJhs+VJAnJZBKFQgGSJEEQBBQKBc1t4vE4RFGE3+/XtU0iIicwFHQTiQQSiYShF5yYmEA4HAawfDPO6/Uin89XBNF6bVKpFI4ePYq5uTld2yQicgJDwws7duxALpfDkSNHsGPHDrhcLs1VxgqFAgYGPl4by+v1rlq+XUsbI+0XFxdRKpUqvmi160syXi/M4Sdnf4XXC3O4vmSoEB0RVWG4p5vL5TAzMwNgebme3bt34zvf+Y6pO2e2sbExHD58uNW74Wi1lkf/5h98Dv0beliHlahJhoLu5OQk3nnnHezevRvA8nI9AwMDmoKuz+dDLpdTfxZFsWJcWGsbI+0TiQSeeOIJ9edSqYTNmzc33OducfLcLB55No+V/drZhWt49LkzFY8N9a3Hk/dv44oDRDrZvlxPMBhUswskSYIoiqsmWWhpY6R9T08Pent7K75o2fUlGYdfPr8q4NZyZeEaHnk2j5PnZi3dL6JOY8pyPalUSvNyPV6vF/F4HIIgwOPxVCxoqfRY67WJRCLqKsSiKCKdTtdtT9q8OVOsGFJoRMbyooaHXz6P0LZBDjUQadT0cj0DAwMIBoNcrqfN/eTsr/D1F84aeu7z0btwt2+gcUOiDqUnlujq6ZZKJcTjcUxPTyMQCCCZTHZ9sOoUn9q4vnGjGn59VXsPmajb6RrTjUQimJubw6FDh1AoFLBv3z6r9otsJnymH54NNxh6bjMBm6jb6OrpZjIZSJKE3t5ejIyMwOfzWbVfZCMlTaz4/oe6nucCMNi3nD5GRNroCrput1sdTqiXwkXto1aaWCPKbbMn79/Gm2hEOugaXlBSxKgzaEkT82xYh//x0A4M9VUOIQz2rcfTX/UzT5dIJ1093fn5eaxdu1b9WZZlrF27Vl0j7fr166bvIFlHS5pY8f2PsOmTPXgtvgtvzhQ5I42oSbqC7tLSklX7QS2gNevg11evYe0aF9PCiEzAhc66mNasA2YnEJmHQbeL3TnswVDfetQaJHBhucYCsxOIzMOg28XWrnHhyfu3AcCqwMvsBCJrMOh2ub3bh/D0V/0YZHYCkS0ML8FOnWPv9iGEtg2uyk4AgNcLc8xYIDIRgy4BwKrshFrFzFlDl6g5HF6gVZRZaitzeFlDl6h5DLpUod4sNeWxwy+f57ppRAYx6FKFRrPUZCwv3/PmTNG+nSLqIAy6VEHPLDUi0o9BlypwlhqRtZi90OWuL8kVqWLCZ/ox1LceVxauVR3XZQ1douYw6HaxWmlhf/j5IYz/7QxcQEXg5Sw1ouZxeKFL1UsLG//bGcR+f5iz1IgswJ5uF2qUFuYC8NO3ZvE3B76A3LvznJFGZCIG3S6kNS0s9+48a+gSmYzDC12IaWFErcOg24WYFkbUOhxe6HArU8LuHPaoxcuZFkZkPwbdDlavUtiT92/DI8/mmRZGZDMOL3SoRpXCALB4OVELsKfbgbSkhB1++Txei++qWrycPVwi6zDodiA9lcLu9g0wLYzIRhxe6EBMCSNyLgbdDsSUMCLnYtDtQEpKWK2RWReWsxiYEkZkPwbdDrR2jQtP3r8NAFYFXqaEEbVWS4Lu+Pg4BEGAIAjIZrO62lR7XJIkuFwu9fFIJGLLcTjZ3u1DTAkjciCXLMu2rjAoSRIEQUChUKj4Xkubet+PjIwgl8vp2pdSqYS+vj4sLCygt7fXzMN0jGoz0tjDJTKXnlhie8rYxMQEwuEwAMDtdsPr9SKfz8Pv9zdsMz09XfVxr9dr92E43spge9/tNwNYTie7svBbFN//EJ5P9mCwl4GYyE62B91CoYCBgY/zQr1eL0RRrAi6tdrUelz5NxQKoVgsIpFIqMG53OLiIhYXF9WfS6WS2YfnCNWm/7pvWgcAkD74aFV7ZWowhxyIrNcRN9Lcbjf27duHTCaDqakpRKNRSJK0qt3Y2Bj6+vrUr82bN9u/sxarNf1X+uCjqgEXWJ4o8cizeZw8N2vHLhJ1NduDrs/nqxjDFUURbrdbU5t6z02lUgCg/iyK4qrXTiQSWFhYUL8uXbpk0lE5Q73pv1ocfvk8ri/ZOsRP1HVsD7rBYLAi60AURQSDQU1taj2ezWaRzy8XcVGCbflwhaKnpwe9vb0VX52k0fTfesqnBhORdWwf0/V6vYjH4xAEAR6PR+2hAss93FwuV7NNrccDgYA6pFAsFjE1NWX3YTmCGdN6OTWYyFq2p4w5SaeljL1emMNDx99oahvPR+9iARwinfTEko64kUbLGk3/rYdTg4nswaDbQepN/9WCU4OJrMeg22FqTf9137ROzdVdaYhTg4lswyLmHWjv9qGqK0IAnJFG1GoMuh1q7RpX1RtivElG1FocXiAishGDLhGRjRh0iYhsxKBLRGQjBl0iIhsxe6GDcJUIIudj0O0Q1QqXszg5kfNweKED1CpcfoXFyYkch0G3TV1fkvF6YQ5/mf8nfOMvz1UtXK48xuLkRM7B4YU2VG0ooZby4uScjUbUegy6bUYZStDbb2VxciJn4PBCG2lmDbRPbVzfuBERWY493TZiZA00F4BBFicncgz2dNuIkSECGSxOTuQkDLptxMgQwR/fs4V5ukQOwqDbRubfX4TeDmto26A1O0NEhnBMt02cPDeLP33ujOabaBzLJXIm9nTbgN6sBaUzzLFcIudhT7cNvCHO6cpaGGTNBSLHYtB1KKViWOb8FZyYvqTpOX9092fwxe1DrC5G5GAMug5QXpJx04YenL5YxP/8Pxch/fYjXdv54vYhTvUlcjgG3SYpAbN8WfNPfbIHcAG/Ll1r+JjRAFuON82I2geDrg4rA+w/Sb/FT85eRvH9D1u9a7xpRtQmGHQ10lPZy07um9bhyFdu400zojbBoKuB0cpedvjRQ37cs3VTq3eDiDRi0G2gmcpeVlLGce/ijTOitsLJEQ0YqexlNU5+IGpf7Ok24MTi35z8QNS+GHQbcErx70/2rMX+wGYEtw1y8gNRG2vJ8ML4+DgEQYAgCMhms7ra6H28WXcOezDU17rA675xHR4PbsVbT+7BN+//PdztG2DAJWpjLlmWbb1HJEkSBEFAoVCo+F5LG73fN1IqldDX14eFhQX09vbWbGdX9oL7xnX4L//uM7hzeADv/WYRn9q4nr1aojagNZYALRhemJiYQDgcBgC43W54vV7k83n4/f6Gbaanp3U9Xr5NAFhcXMTi4qL6c6lU0rTPe7cP4emv+mvm6Xo2rMOXPn8zbum/SfeMNLjAAEvURWwPuoVCAQMDH6c5eb1eiKJYESBrtdH7+MqgOzY2hsOHDxva773bhxDaNrhqyu9gL4MlEWnXVTfSEokEnnjiCfXnUqmEzZs3a37+2jUuFpQhoqbYfiPN5/NVjLeKogi3262pjd7HV+rp6UFvb2/FFxGRrWSbFQoF2ev1yrIsy/Pz8+r3WtrofbyRhYUFGYC8sLDQ3EERUVfTE0tsH17wer2Ix+MQBAEejwepVEr9nc/nQy6Xq9lG7+NERE5je8qYk+hJ8yAiqsXRKWNOonzeaE0dIyKqRokhWvqwXR10r169CgC6MhiIiGq5evUq+vr66rbp6uGFpaUlXL58GRs3boTL9XGerZJKdunSpa4cduj24wd4Dnj8+o5flmVcvXoVN998M9asqZ8U1tU93TVr1uCWW26p+ftuTyvr9uMHeA54/NqPv1EPV8F6ukRENmLQJSKyEYNuFT09PXjyySfR09PT6l1piW4/foDngMdv3fF39Y00IiK7sadLRGQjBl0iIhsx6BIR2YhBdwWr1lpzMkmS4HK51OOORCLq7zr1fEiShMnJScTj8YrH7V6Dr1WqHX+3/R3E43GEQiH4fD5712G0rthZ+ykvC6mnRGS7m5+fl/1+f9XHO/V8xGIxORgMygcPHlQfq3W8nXgeah1/t/wdZDIZOZlMyrIsy7lcTgYgz8/P2/I3wJ5umVprs3WrTj4fqVQKoVCo4rFax9uJ56Ha8dfSiccfCAQQi8UAAH6/X13iy46/AQbdMrXWWusGoigiFApBEARMTk4C6L7zoXcNvk7ULX8HbrdbXV1GkiQUi0X4/X5b/ga6uvYCLXO73di3bx9SqRQkScLw8DCCwWCrd4ts1q1/B9FoFOl02rbXY0+3jNa11jqRstqGcryiKHbd+Wh2Db5O0G1/B6OjoxgdHVU/XGz5G2hyPLqjGF1rrd1lMhk5l8vJsrx8Dtxut/p9J5+PZDJZcSPJ7DX4nG7l8Xfb30EsFlOPV2HH3wCHF8p061prgUAA0WhUHduampoC0NnnIxKJqDdCRFFEOp3uqjX4qh1/N/0djI+PY2JioiL1K51Ow+/3W/43wNoLREQ24pguEZGNGHSJiGzEoEtEZCMGXSIiGzHoEhHZiEGXiMhGDLptJpvNQhAE9Pf3o7+/H0ePHlV/V/59M44ePdrUtrQ+3+jrZLPZirKDtbatta1Zr2m18fFx0/6P6xkdHV1VDCefz7f8+DsFg24bkSQJoVAIx48fx/z8PGZmZpDJZNTCJJ2QtK5FMBjE8ePH67ZRzoWWtma9ZqeYmJhAJpOpeMzv93fN8VuNQbeNFItFtawcsDw/PpVKwev1IhKJQBRFjI6OAlgu0Kz0hpXHlN7K6OgofD5fRc9ldHQU/f39EAQBp0+fVh+vt51IJKL2vGo9f6Va7Y4ePQqfzwefz6cW1g6FQhUzhnw+H0RRRD6fx/j4eM39Kz8X5W2rvUa9c1JO2Y6W9vXaZLNZdT+V8zE5OYl8Po9QKIRQKIT+/n7E43HE43H4fL6KXufp06fVK53y7dY7tvL/p3LVnhOJRCBJ0qrjEkUR4+PjTe3/5OQkjh49qu5/+XYikQh8Pl9HFUmvyfAEYmqJgwcPym63Ww6Hw3IqlZLn5+fV35XPDQ8GgxWPFwoFtVhzoVCQZVmWg8GgnMlk5FQqpRavnp+fl91ut5xMJutux+12q9up9fyVarXLZDKy3+9XjyUYDMqpVEpOpVJyLBaTZbly7nsmk5HD4XDN/Ss/F0rbTCZTMV/e7/ertQaqnZOVlO1oaV+vTSaTUY9Jlpfn/6fT6VWFtAHIqVRKlmVZDofDcjqdllOplOx2uyvOU73zt/L/aeXxVDsfsiyrNRdWHpNyHs3e/1QqJYfDYfV1yrffidjTbTPJZBIzMzMYHR1FLpfD8PCwOrygUHrAynIsoihCkiQAHxdsVr6XJAmZTEbtdSjl/RptJxAIqNup9fyVarXLZDJq7yoUCkEURWQyGezbtw8TExMAlocLyntGjfav2msrRagBYP/+/eoldLVzUo+W9nq3CSwPYSh1Xr1er3p+du7ciWKxWNEGWO5lZjKZmucPqPx/0no+jDK6/8FgENlsFvF4HMViseOHyRh020g2m8X4+DjcbjeCwSBSqRSOHz+OsbGxinbKpV6xWMT+/fsraqJ6PB7Nr1dvO2aW9hsYGKgIIIVCAel0Gm63G4FAAPl8HpOTk2qlfy37V+t1yimBUM850dpe6zaVYFSN1nNc6/w12kat86GHGfvv9XoxMzODnTt36lrRol0x6LaRQCCAeDxesUzI6dOnEQgEKtpls1mEw2HEYjG43W5MT0/X3W4oFFJ7F5Ikqb1Lrdup9Xyt7ZQPEOVNLwiCeoyjo6OIx+Pwer2r3sR6jrP8tQHgxIkTLXlzezwedT+VxSH1yGaz6nlKpVKIRCJ1z18tRs+H2fsfCoVw9OhRjI2NIRwO4/jx4x0/psug20bcbjfS6TSi0ah680iSpIrC05FIBOFwGPl8Xr1BEovFEI1Ga243FoshEAigv78fIyMjSCQS8Hq9mrdT6/la25WX0+vv70cwGITf7wcAhMPhVTdvFPX2TzkXimAwqN7Y8vl8CAaDFZfXdvH7/QgEAvD5fIhGo0gmk1XPVTVerxeJRAIjIyPw+Xzwer2IxWJ1z18tRs9HM/uvvO7IyAj6+/vh9Xpx8OBBxGIxZLNZ9e/CzlUcWoGlHYmooXw+j7GxsaYC4vj4OAqFApLJpIl71n7Y0yWihtp9IUonYU+XiOpScp6TyWTDYYt6lLHcTllfzSgGXSIiG3F4gYjIRgy6REQ2YtAlIrIRgy4RkY0YdImIbMSgS0RkIwZdIiIbMegSEdmIQZeIyEb/D/e9kZRad72MAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 355x355 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_sigs = np.linspace(5, 200, 100)\n",
    "fig, ax = plt.subplots(1,1, figsize=(3.55,3.55))\n",
    "plt.scatter(num_sigs, permeabilities)\n",
    "plt.ylabel('Permeability ($L/m^2/h$)')\n",
    "plt.xlabel('Standard deviation in number of jumps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_deltas.flatten(), bins=50)\n",
    "plt.xlabel('membrane thickness (nm)')\n",
    "plt.ylabel('counts');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_njumps.flatten(), bins=50)\n",
    "plt.xlabel('number of jumps')\n",
    "plt.ylabel('counts');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_lengths, bins=50)\n",
    "plt.xlabel('jump lengths')\n",
    "plt.ylabel('counts');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>permeability</th>\n",
       "      <th>percent permeability</th>\n",
       "      <th>path effective barrier</th>\n",
       "      <th>jump number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.011979</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>12.5</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.006311</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>12.5</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.006496</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>12.5</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.023724</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>12.5</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.029790</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>12.5</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   path  permeability  percent permeability  path effective barrier  \\\n",
       "0     1      0.011979              0.000134                    12.5   \n",
       "1     2      0.006311              0.000071                    12.5   \n",
       "2     3      0.006496              0.000073                    12.5   \n",
       "3     4      0.023724              0.000266                    12.5   \n",
       "4     5      0.029790              0.000334                    12.5   \n",
       "\n",
       "   jump number  \n",
       "0          241  \n",
       "1          332  \n",
       "2          327  \n",
       "3          171  \n",
       "4          153  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = models1[-1]\n",
    "m.calculate_permeability()\n",
    "\n",
    "df1 = pd.DataFrame()\n",
    "df1['path'] = np.arange(n_paths)+1\n",
    "df1['permeability'] = m.permeabilities\n",
    "df1['percent permeability'] = m.permeabilities / m.permeabilities.sum() * 100\n",
    "df1['path effective barrier'] = [p.calculate_effective_barrier() for p in m.paths]\n",
    "df1['jump number'] = [p.n_jumps for p in m.paths]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Permeability ($L/m^2/h$)')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 355x355 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(3.55, 3.55))\n",
    "ax.scatter(df1['jump number'], df1['permeability'])\n",
    "ax.set_xlabel('Number of jumps')\n",
    "ax.set_ylabel('Permeability ($L/m^2/h$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 355x355 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(3.55, 3.55))\n",
    "ax.scatter(df1['jump number'], df1['percent permeability'])\n",
    "ax.set_xlabel('Number of jumps')\n",
    "ax.set_ylabel('Percentage of permeability (\\%)')\n",
    "fig.savefig('figs/percent_perm_vs_n_jumps_high_variance.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>permeability</th>\n",
       "      <th>percent permeability</th>\n",
       "      <th>path effective barrier</th>\n",
       "      <th>jump number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.068183</td>\n",
       "      <td>0.019451</td>\n",
       "      <td>12.5</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.057209</td>\n",
       "      <td>0.016321</td>\n",
       "      <td>12.5</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.068113</td>\n",
       "      <td>0.019432</td>\n",
       "      <td>12.5</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.083628</td>\n",
       "      <td>0.023858</td>\n",
       "      <td>12.5</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.059670</td>\n",
       "      <td>0.017023</td>\n",
       "      <td>12.5</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   path  permeability  percent permeability  path effective barrier  \\\n",
       "0     1      0.068183              0.019451                    12.5   \n",
       "1     2      0.057209              0.016321                    12.5   \n",
       "2     3      0.068113              0.019432                    12.5   \n",
       "3     4      0.083628              0.023858                    12.5   \n",
       "4     5      0.059670              0.017023                    12.5   \n",
       "\n",
       "   jump number  \n",
       "0          101  \n",
       "1          110  \n",
       "2          101  \n",
       "3           91  \n",
       "4          108  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = models1[0]\n",
    "m.calculate_permeability()\n",
    "\n",
    "df1 = pd.DataFrame()\n",
    "df1['path'] = np.arange(n_paths)+1\n",
    "df1['permeability'] = m.permeabilities\n",
    "df1['percent permeability'] = m.permeabilities / m.permeabilities.sum() * 100\n",
    "df1['path effective barrier'] = [p.calculate_effective_barrier() for p in m.paths]\n",
    "df1['jump number'] = [p.n_jumps for p in m.paths]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Permeability ($L/m^2/h$)')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 355x355 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(3.55, 3.55))\n",
    "ax.scatter(df1['jump number'], df1['permeability'])\n",
    "ax.set_xlabel('Number of jumps')\n",
    "ax.set_ylabel('Permeability ($L/m^2/h$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 355x355 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(3.55, 3.55))\n",
    "ax.scatter(df1['jump number'], df1['percent permeability'])\n",
    "ax.set_xlabel('Number of jumps')\n",
    "ax.set_ylabel('Percentage of permeability (\\%)')\n",
    "fig.savefig('figs/percent_perm_vs_n_jumps_low_variance.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mpipy",
   "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": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}