Capacitance and distributions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import math\n",
    "import seaborn as sns\n",
    "from scipy import stats as st\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_ox = 300/1e3\n",
    "eps = 3.4\n",
    "eps0 = 8.85/1e18\n",
    "Cq =  4/1e22\n",
    "r = 0.001\n",
    "dens_arr = np.arange(0.001, 40, 0.001)\n",
    "C_norm = np.zeros((len(dens_arr)))\n",
    "C_hard = np.zeros((len(dens_arr)))\n",
    "\n",
    "for i in range(0, len(dens_arr)):\n",
    "    \n",
    "    C_norm[i] = eps*eps0/t_ox*1e9\n",
    "    C_hard[i] = (Cq + 1/(math.pi*2*eps*eps0)*math.log(1/(r*dens_arr[i]*math.pi)*math.sinh(2*math.pi*t_ox*dens_arr[i])))**(-1)*dens_arr[i]*1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (3,3))\n",
    "plt.semilogy(dens_arr, C_norm, label = 'Usual')\n",
    "plt.semilogy(dens_arr, C_hard, label = 'With linear density')\n",
    "plt.xlabel('Linear density, um⁻\\u00b9')\n",
    "plt.ylabel('C, nF/um\\u00b2')\n",
    "# plt.ticklabel_format(style='sci', axis='y', scilimits=(0,1))\n",
    "plt.legend()\n",
    "plt.savefig('Capacitance graph normal.png', format = 'png', bbox_inches = 'tight', dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(6.474944), array(22.46617471))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BA261m5eXx/3338/bb79NcnIyeXl5XH311TRq1IikpCRWrVrl8rjs7GwAatSoUWb9QIl3o0+ZMoW2bduSmJhIo0aN6NSpEwBxcXHUrVuX1NRUt86jPMpMDMaYLGAB8HcRiRaRXsDNwEznsiIyHPgHMMAYk+xUz25gG/CMiESIyGCgAzDfVuQD4F4RaSsidYH/AaZ7fGZKqSolLCyMrl27snz5cofty5cvp2fPniUe16tXL3755ReHbbt376ZZs2ZutfvCCy/Qr18/evToQVFREQUFBef25eXlUVhY6PK49u3bA5R4WWxx4ujcuXOJ5aZNm8by5ct54403zusVbdq0ifz8fJo2berWeZSHuze4PQhEAilYl5w+YIzZISJ9RCTTrtzzQD1go4hk2l72g3B3AIlYVzm9BAwxxpwAMMYsAf4JrAT2217PeH5qSqmq5uGHH2b69Om899577Nq1i/Hjx3PkyBHuv/9+ACZPnsyll17qcMxDDz3Ehg0beOGFF/j111+ZN28e//73v0sc07e3c+dOZs+ezQsvvABA69atCQkJ4a233mLNmjWsWLGixCuFLr/8cq6//nrGjRvH9OnT2bNnD7/++itz585lwIAB54aIunXrxg033MC4ceP4+OOPSU5O5vvvv2fs2LE88MADTJs2jX79+jnUnZaWxogRI5g2bZpPljdx6z4GY0w6cIuL7WuwJo2L3zcvo559QFIp+ycBk9yJSSlV/QwdOpS0tDSef/55jh49Svv27Vm8ePG5b/+pqann9Q4uv/xyFi1axFNPPcVzzz1HfHw8zz33HA8++GCpbRljGDNmDK+++io1a1oj4JGRkcycOZOxY8eSkZHB3/72t1LnEBYsWMDrr7/OpEmTGDt2LGFhYefujbA/bt68eTz77LM8/vjjHDlyhHr16pGUlMTGjRvp2LGjQ51nz55l8ODBPPnkk6X2lCqizPsYKju9jyHA6H0MfhfI9zFUd8YYhg0bRuvWrZk4cWKZ5T29j0Ef1KOUAvSDOhCsW7eOOXPm0KFDBxYtWgTAzJkzXV4VVRGaGJRSKkD07t2boqIin7ejq6sqpZRyoIlBKaWUA00MSimlHGhiUEop5UATg1JKKQeaGJRSSjnQxKCUUsqBJgallFIONDEopZRyoIlBKaWUA00MSimlHOhaSUopizdXvnWHh6vjTp06lZdffpmjR4/Srl07XnvtNfr06VNi+YkTJ/Lss886bCvrOdGzZ8/miSeeICsri1GjRjFp0u9PAzh8+DC9evXiu+++K/XJcfa2bdvGyy+/zOrVq0lNTaVRo0b06tWLCRMm0KFDh3PlduzYwUsvvcSKFStITU3loosuYujQoUycOLHEJ7z5gvYYlFIBY86cOYwfP56nnnqKrVu30rNnT6677royH9PZunVrjh49eu71448/llg2NTWV0aNH869//YulS5cya9YsPv/883P7x44dy9NPP+12Upg+fTqJiYmEh4czZ84cdu/ezYwZMwB4/fXXz5WbNWsWXbp0oWbNmixcuJCff/6ZF198kenTp3PLLec9DsentMeglAoYkyZNYtSoUfzpT38C4I033mDJkiW8+eabvPhiyT2ekJAQGjZs6FYbycnJ1K5dm6FDhwLQt29fdu3axaBBg5g/fz4ZGRncc889btW1fv16Ro8ezcsvv8xDDz10bnt8fDxXXnkl6enpAKxdu5ZRo0YxdepUxowZc65cixYtCAsLY8iQIaxdu7bEp8V5m/YYlFIBIS8vj82bNzNw4ECH7QMHDuTbb78t9djk5GQaN25M8+bNueOOO0hOTi6xbMuWLcnOzmbr1q2kp6ezceNGOnToQEZGBo899hhvv/2224/TfOSRR+jevbtDUrAXExMDwPjx40lKSnJICsX69u0LwPbt291q0xs0MSilAkJqaiqFhYXnDeGUNV/QvXt3pk+fzpdffsm7777LsWPH6NmzJ2lpaS7L161blxkzZjBixAi6devGiBEjuOaaa5gwYQKjR48mNTWVxMRE2rRpw1tvveWyDoA9e/awfv16xo0bV+p5bd++nS1btpT4DOqcnBwAwsLCOHjwIElJSbRt25aOHTuyYMGCUuv2lA4lKaUCivO3dWNMqd/gr7vuOof3PXr0oEWLFsyYMYOHH37Y5TGDBw9m8ODB596vXbuWDRs28Morr9C6dWtmzJhBu3bt6NChA7169XL5BLUtW7YAlPpMaPtyXbt2LXV/p06dCAkJ4bXXXqNTp06kpKTQtWtXrr32WqKiokpto7y0x6CUCgj169cnODj4vN5BSkqK2xPBADVq1KBdu3bs2bPHrfJ5eXncf//9vP322yQnJ5OXl8fVV19No0aNSEpKYtWqVS6Py87OPtdeWfUDJV51NGXKFNq2bUtiYiKNGjWiU6dOAMTFxVG3bl1SU1PdOo/y0MSglAoIYWFhdO3aleXLlztsX758OT179nS7ntzcXH7++WcaNWrkVvkXXniBfv360aNHD4qKiigoKDi3Ly8vj8LCQpfHtW/fHoDVq1e73F+cODp37lxiuWnTprF8+XLeeOON83pFmzZtIj8/n6ZNm7p1HuWhQ0lKqYDx8MMPc9ddd9GtWzd69erFW2+9xZEjR7j//vsBmDx5MpMnT+bnn38+d8yjjz7KjTfeSHx8PCkpKTz33HNkZWUxcuTIMtvbuXMns2fPZuvWrYB12WtISAhvvfUW7dq1Y8WKFTz99NMuj7388su5/vrrGTduHDk5OfTq1QsRYcuWLbz77rs888wz9O7dm27dunHDDTcwbtw4CgoK6NatG6mpqcyYMYN3332XadOm0a9fP4e609LSGDFiBNOmTXN7Irw8NDEopQLG0KFDSUtL4/nnn+fo0aO0b9+exYsX06xZM8CaoP7ll18cjjl06BB33nknqampxMbG0qNHDzZs2HDumJIYYxgzZgyvvvoqNWvWBKzhnpkzZzJ27FgyMjL429/+VuocwoIFC3j99deZNGkSY8eOJSwsjBYtWnDDDTc4HDdv3jyeffZZHn/8cY4cOUK9evVISkpi48aNdOzY0aHOs2fPMnjwYJ588sly9ZTKQ4wxPqn4QklMTDSbNm3ydxjKXd68u9bDO2eru127dtGmTRt/h6E8YIxh2LBhtG7dmokTJ5ZZvrS/axHZbIxxmdV0jkEppQLEunXrmDNnDosWLaJTp0506tSp1Lu4PaVDSUopFSB69+5NUVGRz9vRHoNSSikHmhiUUko5cCsxiEiMiCwUkSwR2S8iw0oo11dEVopIhojsc9oXLyKZTi8jIo/Y9ieJSJHT/rKvJ1NKKeVV7s4xTAHygAZAJ+ALEdlujNnhVC4LeB/4CHjKfocx5gBw7hZAEWkO/ArMtyt2xBjTpFxnoJRSyqvK7DGISDRwG/C0MSbTGLMW+BS4y7msMeZ7Y8xMoOSlC383AvjGGLOvfCErpSoq0C9TV2WryN+xO0NJrYBCY8xuu23bgXYet2oZAcxw2hYnIsdFZK+IvGpLSkopLwoNDT23YqequnJycggNDfXoWHcSQw0gw2lbBlDToxYBEemDNSz1id3mn7GGqRoB/YCuwKTzjwYRGSMim0Rk04kTJzwNQ6lqKS4ujsOHD5Odna09hyrIGEN2djaHDx8mLi7OozrcmWPIBGo5basFnPGoRctIYL4xJrN4gzHmGFC8bOJeEZkAfAHc53ywMeYd4B2w7nyuQBxKVTu1aln/nY8cOUJ+fr6fo1G+EBoaSoMGDc79XZeXO4lhNxAiIi2NMcXr1HYEnCee3SIikcAfgMFlFDWA91eHUkpRq1Ytjz80VNVX5lCSMSYLWAD8XUSiRaQXcDMw07msiASJSAQQar2VCBEJcyo2GDgFrHQ6Nsl2SauISFPgJeC/Hp2VUkopj7l7g9uDQCSQgnUp6gPGmB0i0kdEMu3KXQnkAIuBeNvvy5zqGgl8YM4f3OwCrMe65PVb4CfgL+U4F6WUUl7g1n0Mxph04BYX29dgd2+CMWYVZQz/GGOuKWH7JEqYbFZKKXXh6JIYSimlHGhiUEop5UATg1JKKQeaGJRSSjnQB/WowOXNx4SCPipUKRvtMSillHKgiUEppZQDTQxKKaUcaGJQSinlQBODUkopB5oYlFJKOdDEoJRSyoEmBqWUUg40MSillHKgiUEppZQDTQxKKaUcaGJQSinlQBODUkopB5oYlFJKOdDEoJRSyoEmBqWUUg40MSillHKgiUEppZQDTQxKKaUcaGJQSinlQBODUkopB5oYlFJKOdDEoJRSyoFbiUFEYkRkoYhkich+ERlWQrm+IrJSRDJEZJ+L/ftEJEdEMm2vZU77HxKRY7bj3xeRcI/OSimllMfc7TFMAfKABsBw4E0RaeeiXBbwPvBYKXXdaIypYXsNLN4oItcATwD9gQSgBfCsm/EppZTykjITg4hEA7cBTxtjMo0xa4FPgbucyxpjvjfGzASSPYhlJDDNGLPDGHMSeA4Y5UE9SimlKsCdHkMroNAYs9tu23bAVY/BHbNF5ISILBORjnbb29nqtW+jgYjU87AdpZRSHnAnMdQAMpy2ZQA1PWhvONYwUTNgJbBUROqU0E7x7+e1IyJjRGSTiGw6ceKEB2EopZQqiTuJIROo5bStFnCmvI0ZY9YZY3KMMdnGmBeBU0CfEtop/v28dowx7xhjEo0xibGxseUNQymlVCncSQy7gRARaWm3rSOwwwvtG0Bsv++w1WvfxnFjTJoX2lFKKeWmMhODMSYLWAD8XUSiRaQXcDMw07msiASJSAQQar2VCBEJs+2LF5FeIhJm2/4YUB9YZzv8A+BeEWkrInWB/wGme+EclVJKlYO7l6s+CEQCKcBHwAPGmB0i0kdEMu3KXQnkAIuBeNvvxfcq1ATeBE4Ch4FrgeuKewTGmCXAP7HmHvbbXs94fmpKKaU8EeJOIWNMOnCLi+1rsCaNi9+v4vehIeeyO4AOZbQzCZjkTkxKKaV8Q5fEUEop5UATg6p6jIGibDB5/o5EqYDk1lCSUpVeQRrkbIfcHVBwAiiwtksEhDWDiHYQcRkE6fJbSpVFE4MKbIVn4MwyyNlivQ9rBtE9ILg2mHwoPAVn90DGL3BmCUQnWftF/+krVRL936ECV852yFgIphCie0N0TyshODMG8g/Ama/gzGLI3Q517oAQXW1FKVc0MajAY4qsXkLWNxDaDOrcBiH1Sy4vYvUk6t0LOT9ZyST1Dag7DMJbXbi4lQoQOvmsAospglNzrKQQ1d36sC8tKTiLbA+x4yC4HqTPtHodSikHmhhU4DBFkLEAcn+EmtdC7Zs9mysIrgP1RkNYUzg115qwVkqdo0NJKnCc/sKaZK7RH2pcWbG6giKh7ihInwYn50DMKFj5ojeitPR90nt1KXWBaY9BBYbsTZC9HqJ7QY1+3qkzKAxiRkJIXTg5GwpOeqdepQKcJgZV+eUfhoxPIewSqHmdNZnsLUFRUPcuwFjJweR7r26lApQmBlW5FeXAyQ8hKBrq3A7ig3+yIfWtuguOwOnF3q9fqQCjiUFVbqc/h8IM69LS4Bpll/dUxKXWvRDZ30HuL75rR6kAoIlBVV65P0POVqiRZF1B5Gs1B0BIQ8iYb621pFQ1pYlBVU5FuZCxCEIaWInhQpBQqDPESgqnl1yYNpWqhDQxqMrp9JdQdAZq33Zh1zUKvcgaUsrZBGeTL1y7SlUimhhU5ZN/GHI2WpemhjW58O3X7AfBdeH0p9Y6TEpVM5oYVOViDGR8bl2F5K37FcpLwqzLYgtSrPsnlKpmNDGoyiX3R8jfDzUHQlCE/+KIaGct0Jf5lTXfoVQ1oolBVR4m33pmQkhDiOzq31hEoNb1UJRlLdinVDWiiUFVHlnrrQfr1LrBNzeylVdYU4joAJlrrbiUqiYqwf8+pYCis5D5jfV8hPCL/R3N72peY/08s9y/cSh1AWliUJVD9now2dbKqZVJSF2IvgJytkFBqr+jUeqC0MSg/K8oFzLXQPilF+YO5/KK7g2EQOYqf0ei1AWhiUH5X9a3YHKgZiXrLRQLrgnR3Wy9hnR/R6OUz2liUP5VlAtZayG8DYQ29nc0JYvuAwRpr0FVC5oYlH9lfw8m17rbuDILrtFjO6cAACAASURBVAVRXa1F/fQKJVXFaWJQ/mMKrGGksIsrd2+hWI2rrJ+Zq/0bh1I+polB+U/Odig6XfHnN18owXUgsjNkb7ZufFOqinIrMYhIjIgsFJEsEdkvIsNKKNdXRFaKSIaI7HPaFyciH4nIEdv+dSLS3W5/kogUiUim3Wtkhc5OVV6mCLLWQEgj65GdgSK6F1AAWd/5OxKlfMbdHsMUIA9oAAwH3hSRdi7KZQHvA4+52FcD2Ah0BWKAGcAXImL/WK4jxpgadq8ZbsanAs3Z3dYiddG9vfsMZ18LbQDhLa0nvZkCf0ejlE+UmRhEJBq4DXjaGJNpjFkLfArc5VzWGPO9MWYmcN5C9saYZGPMJGPMUWNMoTHmHSAMaF3hs1CBJ2sNBNWGyA7+jqT8ontbz4rI+cHfkSjlE+70GFoBhcaY3XbbtgOuegxuE5FOWInhV7vNcSJyXET2isirtqSkqpr8o5C3F6J7ggT7O5ryC7sEQuIga521TLhSVYw7iaEGkOG0LQOo6WmjIlILmAk8a4wprvtnoBPQCOiHNeQ0qYTjx4jIJhHZdOLECU/DUP6StQEIhahEf0fiGRFrrqHgKOTpU95U1eNOYsgEajltqwWc8aRBEYkEPgM2GGNeLN5ujDlmjNlpjCkyxuwFJgBDXNVhjHnHGJNojEmMjY31JAzlL0U51h3EkR0hKNLf0XguspP1MKGsb/0diVJe505i2A2EiEhLu20dgR3lbUxEwoFFwGHgvjKKGyCAZiWVW7I3A/kQ3cPfkVSMhEJkIpz9WW94U1VOmYnBGJMFLAD+LiLRItILuBlrKMiBiASJSAQQar2VCBEJs+0LBT4BcoARxpgip2OTRCReLE2Bl4D/VvD8VGViiiB7g/VktNCL/B1NxUV1s35mb/RvHEp5mbuXqz4IRAIpwEfAA8aYHSLSR0Qy7cpdifXBvxiIt/2+zLavJzAIGAicsrtXoY9tfxdgPdYlr98CPwF/8fjMVOVzdCkUpgd+b6FYSF3r+RHZm8AU+jsapbwmxJ1Cxph04BYX29dgTU4Xv19FCcM/xpjVJe2z7Z9ECZPNqorYPRmCalrPU64qorrDyQ8gdydEXubvaJTyCl0SQ10YWQfgyJfWlUji1veRwBDeyloqI1vvhFZVhyYGdWEk/8f6GRmgl6iWRIKsuYa8ZCjQS6dV1aCJQfleUSH89j40HGCNy1c1kV2BIJ2EVlWGJgble8eWQ/YBuGS0vyPxjeCa1mNJc7bqJLSqEjQxKN/77T0Irw+Nb/J3JL4TlWgtxX32F39HolSFaWJQvpWbAof+C81HQnC4v6PxnfCW1hVX2Zv9HYlSFaaJQflW8gxreeqL7/V3JL4lwdZDfM7+AoUerRajVKWhiUH5jjGQPA1ie0HtNv6OxveiugBF1lpQSgUwTQzKd9K+g9O/QIt7/B3JhRESB6HxkLNJl+NWAU0Tg/Kd5BkQHAnxLhfJrZqiulr3M6R97+9IlPKYJgblG4W5sP9jaDIYQp1Xba/CIi6zVl5Nft/fkSjlMU0MyjcOfw75p6DFSH9HcmEFRUBEe9j3ERRk+zsapTyiiUH5RvIMiLwIGvT3dyQXXmRXKDgDBxf4OxKlPKKJQXlfznE4+iUk/BGCAvCZzhUVlgBR8bBvtr8jUcojmhiU9+3/yFoaoroNIxWTIEgYbi0FknPc39EoVW6aGJT37Z0BMYlQu62/I/GfhOFWcjwwx9+RKFVumhiUd536CU5ug+Yj/B2Jf9VpB3U66nCSCkiaGJR37ZttLQ/RbKi/I/G/hOHW/Qyn9/g7EqXKRROD8h5TZM0vNBwAEXH+jsb/Eu4EBPZ/6O9IlCoXTQzKe058C1n7rW/KCqKaQIMkqxelS2SoAKKJQXnP/g+tJTCa3OzvSCqPhOFwZg+kb/J3JEq5TROD8o6ifDgw10oKoTX9HU3l0fQ2CAqDvbP8HYlSbtPEoLzj6DI4mwbNhvk7ksolrA40HgQHPoaiAn9Ho5RbNDEo79j3IYTFQKNr/B1J5ZMw3HqS3bEV/o5EKbdoYlAVl58JhxZB/B8gOMzf0VQ+F10PobX1ngYVMDQxqIo7/CkUZkOCDiO5FBxhJc1DC3XFVRUQNDGoits/ByIbQ2xvf0dSeSUMh4JMOPyZvyNRqkyaGFTF5J2Co0sg/nZr8TjlWmwfiGxkJVGlKjm3/ieLSIyILBSRLBHZLyIuxwxEpK+IrBSRDBHZ52J/gm1/toj8LCJXO+1/SESO2Y5/X0TCPTordeEc+hSK8qDZ7f6OpHILCoamQ+DIYsg/7e9olCqVu1/xpgB5QANgOPCmiLRzUS4LeB94rIR6PgK2AvWAvwGfiEgsgIhcAzwB9AcSgBbAs27Gp/zlwFzr2QP1uvs7ksqv2VAoOguHdDhJVW5lJgYRiQZuA542xmQaY9YCnwJ3OZc1xnxvjJkJJLuopxXQBXjGGJNjjJkP/GirG2AkMM0Ys8MYcxJ4Dhjl2WmpCyLvJBxbZvUWRPwdTeVX/wprmQxdiltVcu70GFoBhcaY3XbbtgOuegylaQckG2POlFBPO9t7+30NRKReOdtRF8rBRdYdz/E6jOQWCbL+rI4useZmlKqk3EkMNYAMp20ZQHnXPSirHuf9xb+f146IjBGRTSKy6cSJE+UMQ3nNgbkQ3dx6KI9yT/ztVjI9tMjfkShVIncSQyZQy2lbLeCMi7IVqcd5f/Hv57VjjHnHGJNojEmMjY0tZxjKK86mwbGvdBipvOp1g+gE2D/X35EoVSJ3EsNuIEREWtpt6wjsKGdbO4AWImLfA7CvZ4ftvf2+48aYtHK2oy6EgwvBFOgwUnmJWH9mx5ZbyVWpSqjMxGCMyQIWAH8XkWgR6QXcDMx0LisiQSISAYRabyVCRMJs9ewGtgHP2LYPBjoA822HfwDcKyJtRaQu8D/A9AqfofKNA3OhxiVQt7O/Iwk8zYZaSfXgQn9HopRL7l6u+iAQCaRgXXL6gDFmh4j0EZFMu3JXAjnAYiDe9vsyu/13AInASeAlYIgx5gSAMWYJ8E9gJbDf9nrGw/NSvpR7Ao5/rcNInqrb2UqqenWSqqRC3ClkjEkHbnGxfQ3WpHHx+1VAiZ8Uxph9QFIp+ycBk9yJSfnRwQVgCnUYyVMiVlLd+ZKVZCN0nkxVLrqGgSq/A3OhVmuo08HfkQSu+KHWM7IPzi+7rFIXmCYGVT45xyFllW1tJB1G8lidy6DWpbp2kqqUNDGo8jk43/qmq8NIFSNi9RpSVkPOUX9Ho5QDTQyqfA7MhdptoU57f0cS+JrdDhg4oMNJqnLRxKDcl3MUUr7R3oK31G4Ltdvr1Umq0tHEoNx34BPAaGLwpmZD4cRayD7k70iUOkcTg3LfgbnWpGntNv6OpOqIH2r9PPCJf+NQyo4mBuWe7MPWN1vtLXhXrZbWDW96dZKqRDQxKPccmGf91MTgffG3Q9oGyNrv70iUAjQxKHcdmAt1O0GtVv6OpOopfixqcfJVys80MaiyZR2A1PXaW/CVGi2sZ1roUtyqktDEoMqmw0i+12wopG+EzPOeiqvUBaeJQZXtwFyI6Qo1L/Z3JFVX/B+snzqcpCoBTQyqdJn7IO177S34WnQzqNddh5NUpaCJQZXugO2DqvgbrfKdZkPh5BY486u/I1HVnCYGVboDc63nFNdo7u9Iqr6mQ6yfB7TXoPxLE4Mq2ZnfIH2zDiNdKNFNoX5PHU5SfqeJQZVMh5EuvPjb4dR2OP2LvyNR1Zhbj/ZU1dSBuVD/CoiO93ck1Uf8ENjyELm75/Jp8tMsWwa7d8OhQxAcDLVrw2WXwRVXwODBEKtPBVU+oD0G5drp3XBymw4jXWD7UhqzJ6M3v349l6FDYeFCa/sVV8Dll0NMDCxeDPfdB40awZAhsG2bf2NWVY8mBuXauWGkIf6No5rIzYWnnoKWLWHKZ7fTvslPbPp6Jykp8M03MHs2fPghLFsGx47B9u3w8MPw1VfQuTPcfTekp/v7LFRVoYlBubZ/DsT2gqgm/o6kytu2Dbp0gRdfhD/+ESZMHgIIXevNJTj4/PIi0KED/POfsG8fPP44zJoFbdrA119f6OhVVaRzDOp8p3ZAxk+QONl6v/JF/8ZThS1YAHfdBXXrwpIlcM01AA0h+Sqr13bZM1YmKEGdOvDSS3DnndZrwADr/aOPlnqYUqXSHoM634E5IEG/X1evfGLSJLjtNmsyedOm4qRg0+x2OL0LMna4VVfHjvD999acw4QJ8Ne/QlGRb+JWVZ/2GJQjY2D/xxDXFyIb+DuagPXq8t2l7l85ry6fvRtLxz5nuPXxY3z0o4Eff98fVdSRPxHE96um0uOWqW61WaMGfPQRNG4Mr74KWVnw7rvac1Dlp4lBOTq5Fc7sgTYT/B1JlbXqkzp89m4sna46zfAnjrmcR8gOqs+h0O60Ovullazd/HQPCoJXXoGoKHjhBahZ0+qZaHJQ5aFDScrR/jkgIdD0Vn9HUiVtWVmTT9+Jo+OVZ0pMCsV2h19HTOE+OPVDudoQgeeeg7/8BV57zfpdqfLQxKB+VzyM1GgghMf4O5oqZ++OCD7+VwNatM9m+ITSkwLAnvABFBHs0fOgRazhpJEj4ZlnYI4+UlqVgyYG9bvUDZB9AJrd4e9Iqpz04yG8P/Ei6sQWcPfEI4SEmTKPyQ2K4UDoFdbVSabs8s6CguDtt6FXL+s+hy1bPIlcVUduJQYRiRGRhSKSJSL7RWRYCeVERP5PRNJsr3+KWKObItJHRDKdXkZEbrPtHyUihU77k7x2pqpsB+ZAUDg0udnfkVQpBfnwwQuNKMgXRj9/mOha7l8utDv8Wsj8zZr78UB4OMyfD/Xrw803Q2qqR9WoasbdHsMUIA9oAAwH3hSRdi7KjQFuAToCHYBBwH0Axpg1xpgaxS/bvkxgid3x6+3LGGNWeXJSygNFhdY304uuh9Ba/o6mSvni/foc+DmSoQ8fJ65JfrmO/S38amvOpwJLcTdoYC2tcfw43HOPR50PVc2UmRhEJBq4DXjaGJNpjFkLfArc5aL4SOAVY8whY8xh4BVgVAlVjwQ+McZkeRS58q4TayDnqA4jedlP66NZPT+GXjedotOVmeU+PjeoLjS82ppnqMAneteu1p3Sn30Gkyd7XI2qJtzpMbQCCo0x9hdmbwdc9Rja2faVWk5EooAhwAynXZ1FJFVEdovI0yKil9NeKPvnQEg0NL7B35FUGZmngpk7qQGNL8nl5jEnPK+o2VDI2gfpmyoUz/jxMGiQdVf0D+W70ElVM+4khhpAhtO2DKCmG2UzgBrF8wx2bgNSgdV2274B2gNxtv13Ao+5CkhExojIJhHZdOJEBf7DKUtRPhz8BBrfaCUHVWHGwCf/jiMnO4jhE465NdlcoiY3Q1BohZ/sJgL/+Y+1jMY990BBQYWqU1WYO4khE3AedK4FnHGjbC0g05jz+sAjgQ/stxtjko0xe40xRcaYH4G/Y/UqzmOMeccYk2iMSYzVBekr7tjXcDZVh5G8aOuqmvywtibXjkijYUJexSoLqwsNB1pPdqvgBEH9+jBlCmzeDC+/XLGwVNXlTmLYDYSISEu7bR0BV4u47LDtK7GciDQFkoAPymjXAHq/5oVw4GMIrQ2NrvV3JFXC8fQo5k+Oo1mbHPoOOemdSpsNtS4lTvuuwlUNGWK9Jk6EnTsrHpqqesocwzfGZInIAuDvIjIa6ATcDPR0UfwD4GERWYz1wf4I8IZTmbuAb40xv9lvFJHrgC3GmOMicinwNDCvvCekyqnwLBxcCE1ugeBwf0dTJTzyVn/ycoK445FjBJVxE5vbGt8EQWFWr6F+D4+rKV7D6bI/BPPlsgSuuy2Pv7x60OM4HxrQyuNYVOXl7uWqDwKRQArwEfCAMWZH8b0JduXeBj7DWg7sJ+AL2zZ7Izh/0hmgP/CDiGQBi4EFwD/cPRHloaNLIT9Dh5G8ZMWWZsz+qj39hqbTIL58l6aWKszWozswF0zFl02tWbeQWx5I4cDPkWz4srYXAlRViVtX/Rhj0rHuT3DevgZrwrn4vQEm2F4l1XVpCdsfBR51Jx7lRfs/hvB60LC/vyMJeLl5wTzw2rVcfNFJ+t/hg8epNbsDDn8KJ9ZC3JUVrq5LvzN8t6Q2X/ynPh16Z1KjTqEXglRVgS6JUZ0VZFsfNE1vs656URXyfx9dwZ5DMUwdv5SwcB/cRdbkJgiOgn0feqU6Ebj1zymczQ7ii/fre6VOVTVoYqjOjnwBBVk6jOQFB1Nq8n8f9+APV+1i4OV7fdNISLQ1F3RgHhRW8Eonm4bN8rjq1pN8t6Q2+3ZGeKVOFfg0MVRn+z+GiIYQW/FhieruqfeSKCoSXr7Pxw9dThgOeenW3JCXDBieRq16BSyYHKdPfVOAJobqK/80HP4C4v+A9y6dqZ6+39WIWV+15+E/fE+zhqd921ijARBeH/bN9lqVEVGGQfee4NCvEWxe4eq+VVXdaGKorg59CkVndRipgoyBh9/sT1zdLJ64c73vGwwKhfjbrbmhfFf3mHqmS78zNG2Vy+L/1CcvV28fqu40MVRX+z+GqPgKXROv4JPVl7Lup6Y8f/dqakV7Z9y/TAnDoDAHDi3yWpVBQXDTmBNkpIayen5dr9WrApMmhurorG2MutlQEP0n4KncvGAefzeJy1qkcM91F3BVuvpXQHQzr12dVOziDjm075nJijkxnE7X4cXqTD8VqqODn4ApsBKD8ti/FySy92hdJj2wguDgC/iQAwmCZsPg2HLITfFq1YNGn6AgX1jyQT2v1qsCiy5rXR3tnQm12kDdLv6OpFJZn5zmdtkz2RE8P6sHPTvsJLruZtYnO+7vwTtejOxf529KGAY7X7SWyGj9Z6+1FNckn143nmLtp3W46tZTNIi/QMNjqlLRHkN1k7nXunO2+V3WHU7KI/NW9OFMdhR/umVJ2YV9oU57qNPBq1cnFRswPI2wcKO9hmpME0N1s3eW9TNhuH/jCGBnsiP4eNmVXNn5R1rHH/FfIAnDIG0DZCaXXbYcatQu4qrbTrL9m5oc2qMLK1ZHmhiqE2Ng30yIuwqi4/0dTcCa+1UfMnMiuefG5f4NpNmd1k8vT0IDXHXbSSJrFrJ4ui6VUR1pYqhO0r6HM3usYSTlkTPZEcxZfiVXdf6RVv7sLYCV3GP7WMNJFXyAj7PI6CL6D03n543RJP+kS2VUN5oYqpO9MyE4Apq6fDCecsOc5VdavYWblvk7FEvCcDj9M5zc5vWqe990ipoxBSz+T31v5x1VyWliqC4K86wntTW+yVrbX5Xb6axI5nzVh6QuP9Cy6VF/h2OJH2LdDb13pterDoswDBiWRvKPUezeHOX1+lXlpYmhuji6BM6m6TBSBcz9qg9ZlWFuwV54PWh8I+yfDUVefDCQTY/rMohpkM/i6dprqE40MVQXe2dCeCw0usbfkQSkc72Frj9wSWXpLRRrPsq60e2I9y+dDQmFgXelcXB3BD+uq1H2AapK0MRQHeSdgsOfWQvm6QN5PDJn+ZVWb2FQJeotFLvoWoiIg73TfVJ91/6niW2cx7JZMbosdzWhiaE6ODDPWklVh5E8cjrT6i307bq98vUWwEr2zYZbyf+s+3dvuys4GK4elsaR5Ah2rI/2ev2q8tHEUB3snQm1WkNMor8jCUgff3Ul2bkRlWtuwVmLUdYcw76PfFJ9l35nqH9RHstm19O5hmpAE0NVd+Y3OLEGEnQJDE+czoxk7ld96Je4nYubHPN3OCWr2wHqdvbZcFJwMAwYls7hXyPYsUF7DVWdLqJXVa180fp5ehkgcOwMnHjRryEFoo+WX0XO2TDursy9hWItRsHm8XDqJ2stJS/r0v80yz6MYdnMerTrkaXfM6ow7TFUZaYQcjZDeCsI1nsXyisjM4p5X/WmX9cfuLhxJe4tFGt2J0gI7J3hk+qDg2HAnekc+jWCnd9pr6Eq08RQlZ3dA0VnIErnFjzx0bIryckLkN4CQEQsNB5kzSkVFfikia79TxPTMI9ls3SuoSrTxFCVZW+EoGgIv9TfkQScU2ei+GRFb/olbqdF4+P+Dsd9LUZB7nHrCX0+EBwCV9+ZzsHdEezaqL2GqkoTQ1VVeBrO/gKRXUH0MY3l9fHyq8jJC+OeG7/ydyjlc9H11o2Mye/7rInLB5wmpkE+y2bGaK+hitLJ56oqZytQBFFd/R2Jz5TniWvlYfUWetH/8u00v8j/vYVXl+8uV/k+QTfS+eAHvLd0HdlBsV6PJzgE+t+ZzrzXGrB0KVx7rdebUH6mPYaqyBjI3gRhCRDi/Q+Gqu6jZVZv4e5BAdZbsPkx4naCKaBd7kKftXH5gAzqxuXz7LNeX/FbVQJuJQYRiRGRhSKSJSL7RWRYCeVERP5PRNJsr3+K/H5Rm4gYWx2Zttd77h6ryuHEGihMg0iddC6vU2ei+OTr3lx9+bZK0VvwxKmQ5hwM7Ub7nHlgfLOGRUgo9L8jnQ0bYFklWYFceY+7PYYpQB7QABgOvCki7VyUGwPcAnQEOgCDgPucynQ0xtSwvUaX81jljl/fAwmHCO9fy17Vfbg0idy80IDtLRT7MeJ26hQdoGn+dz5ro9s1GTRtivYaqqAyE4OIRAO3AU8bYzKNMWuBTwFXC++MBF4xxhwyxhwGXgFGuRlLRY5VxfJOwcF5ENkRgsL8HU1AOXkmmvkrezGg2zYSLkrxdzgV8mv4QHKkDpflzvFZGyGh8NRTsH49fBXYeVQ5cafH0AooNMbYz4BtB1z1GNrZ9pVW7hsROSYiC0QkoZzHqrLs/wgKc3UYyQMfLk3ibF4ooyrjCqrlVCjh7Iq4mUvOfkVkUbrP2rn7bmjSRHsNVY07iaEGkOG0LQOo6UbZDKCG3VzBVUACcClwBPhcRELcPPYcERkjIptEZNOJEyfcOIVq5Nf3oE5HCG3s70gCyskz0cz/uhdXd9tKQqOq8W/KmoTOp60PJ6HDw+HJJ2HdOvj6a581oy4wdxJDJlDLaVst4IwbZWsBmcZY3yWMMd8YY/KMMaeA8UBzoI07x9ozxrxjjEk0xiTGxupVN+ekbYKTW+Die3XBvHL6cEkSefkhjArwuQV76SGXcDikC+1z5/r06/y990LjxjBxovYaqgp3EsNuIEREWtpt6wjscFF2h21fWeWKGaD4E6y8xypnu9+AkGhoPsLfkQSU9NM1mL+yavUWiv0YOZSYwn00zt/oszbCw+GJJ2DtWli50mfNqAuozBvcjDFZIrIA+LuIjAY6ATcDPV0U/wB4WEQWY33oPwK8AWC7iikU+BGIBJ4HDgO7yjpWuSE3BfZ/DBePhjBdMK88Plxq9RburqR3Ofc48I7HxwaRT0FEOP3SnmNP/o1siB/jxch+N3o0vPiiNdfQr59PmlAXkLuXqz6I9WGeAnwEPGCM2SEifUQk067c28BnWB/+PwFf2LaBdanrHOA0kIw11zDIGJPvxrGqLL++C0V50OrP/o4koFi9hZ4M6L6VZg2rVm8BoIhQUgraExP8K2EuR3+9IyLC6jV88w2sWuWzZtQF4taSGMaYdKx7DJy3r8GaNC5+b4AJtpdz2a+B1qW0UeKxqgxF+bDnTWg4AGq3Kbu8Omf2kiTy80O4uwpciVSSY4WdaBSymQYhP/i0nT/96fdeQ1KST5tSPqZLYlQFBxdCzmFoNc7fkQSU9IwaLFjVk4E9thDfMNXf4fjMWVOHk0UtiAv5gWCT57N2IiLg8cetHsPq1T5rRl0Amhiqgt1vQHRza2VN5bZZS/raeguVc27Bm44VdCZMsml59kuftjNmDDRsaPUaVODSxBDoTm6DE2uh1VgI0uW13ZWWUZOFq6/gmh5baNqg6vYWimUUNSOnqC6dcmb5tJ3ISKvXsHIlrFnj06aUD2liCHS/vAHBUXDxPf6OJKDMWpJEQUFwlZ5bcCQcK+hMo4IfaJDv27mG++6DBg201xDINDEEstxU2Dcbmt8FYXX9HU3ASMuoycJVPbmmxxaaNPDNMx0qo5TCdpyVaDrnzPRpO5GRMGECrFhh3dugAo8mhkD223tQdFYvUS2nDxb3o7AwiFE3VP25BXtFhLEz4lZanf2SqCLfXpp7//0QF6e9hkCliSFQFRXAnqnQoC/U0eW13XU8vTaLVl/B9b02VqveQrFtEcMJooCOObN92k5UFDz2mLXq6rff+rQp5QOaGALV4U8h+yC0+ou/Iwko//l8AEC1uBLJlVMhzfktrD+dcj4k1GT5tK0HHoDYWO01BCJNDIHIGNj1L4huBo1v9Hc0AePQ8Xp8sfZybr5yPQ3rnfJ3OH6zKepeIkwG7XLm+7Sd6Gir17BsmfYaAo0mhkCUsgpS10ObCXqJajm8//kAQkIKGXF99V4f+mhoFw6HdKFLznTEFPi0rQcftOYann7ap80oL9PEEIh+egEiGuolquWw90gDlm7owpC+66hfx3drBgWKTVH3UrvoMC3PLvVpO9HR8Le/Wc9q0Ke8BQ5NDIEmdQMcXwFtHoXgCH9HEzDe++9AIsPz+ON1ui40QHJYP9KDm5OY857PH6Jw330QH289BlSf1xAYNDEEmp9egLAYuOQ+f0cSMH45cBErN3fkjgHfULtGtr/DqRwkiM2R99KgYCdN8zf4tKnwcOshPhs3wqJFPm1KeYlbq6uqSuLkNjjyOXR4DkJrlF1eAfDeomupGZXNHQO+8XcolcquiJvomfUal2e/y8GwKzyq49Xlu8suBBQ2grimzXjgIUiO3O9yauyhAa08ikF5n/YYAsmOf0BoLb2hrRx++i2edT+0Zfi1K6kRlevvcCqVQglnS9QomuWvo2H+Np+2FRwM141M4/j+cLasdPW4eFWZaGIIFBm74MAnVlIIq+PvaALGO4uupW7NM/yh/zp/h1IpbY8cRrbUpUfWZJ+3dVnvTJpcksuSD+pRkF92YGQPJQAAGZ1JREFUeeU/mhgCxc6XIDgSWv/V35EEjA0/tWLTrlaMuGEFkeG+ew5BIMuXaLZE3UPz/DU+X1wvKAiuvyeV9GNhfPelPn62MtPEEAgyk63F8i65DyJi/R1NQCgsEqbMG8RFsancmrTe3+FUatsihpEjdeiR7fteQ+uu2bS4LJtlH9bjbI74vD3lGU0MgWDn/4EEW5eoKrd8+W0ivx2+iAdvW0xoSKG/w6nU8oNqsDnqHlrkrfZ5r0EEBt2bypn0EFbO+//27jw8qupu4Pj3NzNZJjsJSQiBsAlhExCQRVlEEfTFBRWsIvAim2i1reIu2qeob+tKK0oLKFBtFUuVTUAFFRUBISACYZVFCBDIQkLWWc/7x4RK0iyT5WYmyfk8z30y3Dl3zm/Czfzm3HuWaEPr0mpOJwZ/V5gGR5dA+8kQ0tLX0TQIRbZAFqy4gW7tjzOsj7EfdI3FruB7SloNbxleV9uuxfQamsfGZc3IydQdI/2RTgz+bt9LoFzQ9XFfR9JgLF0/mMycSB4a+wmir1Z4xWEKY6d1Eu3tG2nh+NHw+kZNzsDlhnVLYgyvS6s+nRj82YWDcPhv0GEKhLXzdTQNQnZuGP9cN4yhV+yhR8fjvg6nQfnBOoECiWFQwauGD1GOSXAyZHQOKesjSPspyNC6tOrTicGf7XzU0xPp8tm+jqTBeGf1CGzOAO6/Y42vQ2lwHKYwvg/9Na0d22hrN34w4PC7swkJd7NqfqyeKsPP6MTgr9I3eEY5d38GrPG+jqZBOHKqBau+6c/oIVtJapHp63AapD3BY8kxJTGo4FVEGXvT3hrmZsSELH76MYR9W0MNrUurHn3nxx+5XbBzJoS2heTf+jqaBkEpeP2ftxFqLWbqrcbOGNqQDTixoMoy58yX0ylwDaNOzyTT1bXSsluTptcqnqtG5fDdyihWLYzF/iQEBtbq5bQ6olsM/ujoIsjZDVe8rGdQ9dL6bb344VAHZty+Vk+UV0tZrmTy3fEkWTYhGLteg9kCt8zIICMtkD//2dCqtGrQicHfOC7A7lkQOwhaj/F1NA1CQXEQby67mc5tTnLz4G2+DqcREH52DCbIlEeCZafhtXXtV0D3gfnMng0nTxpeneYFnRj8Teofofgc9H4d3dfSO0tWDyczJ5JHxi3HbNJ3MevCBXcbsl3taWXZSgD5htc3+v5zuFwwc6bhVWle0InBn+QfhwNzoO14iLnS19E0CMdPx7F0wxBuGvQ93Tuc8HU4jcpxxzAEN20CjO+hFN3CyTPPwLJlnjWiNd/yKjGISLSILBeRAhH5WUTGVVBOROQlEckq2V4W8XztFZFOIrJSRDJEJFtEPhOR5EuOnSQiLhHJv2S7pk7eZUOx6wkQE/T6o68jaRCUgjkfjCYkyMaM29f5OpxGx6aiOO3sS6xlP+GmU4bX9+ijcNll8NBDYLMZXp1WCW9bDG8BdiAeuAf4q4h0K6fcdGA00BPoAdwEXFxqLApYBSSXvM42YGWZ47copcIu2TZW4700bBnfwYl/QZfHIKSVr6NpEDZs78X2/Z2YNvozoiOMv9zRFJ1y9sOmwmgb8CXgNrSu4GCYOxcOHYLXXjO0Kq0KVSYGEQkF7gCeVUrlK6U24fmAn1BO8f8FXlNKpSmlTgGvAZMAlFLblFLvKKWylVIOYA6QLCJ6TLxyw46HwdpST33hpYwcK3PeH02XticYPVTPnmoUN4H87BhKmOkc8Wbj55264Qa4/XaYPRsOHjS8Oq0C3rQYOgEupdSla/j9CJTXYuhW8lxV5QCGAOlKqaxL9l0hIpkickhEnhWRpjHO4tA8yN4OPf8PLHqgjzcemjuC/KJgnr73X1jMxn6TbeqyXMnkuJJICviWQPIMr+/NN8FqhalTwa3/a33Cm8QQBuSW2ZcLlLc+X9myuUDYxfsMF4lIKzyXpx65ZPc3QHcgDk8L5W7gsfICEpHpIpIiIikZGRlevAU/lnsAdj0GCTdAu4m+jqZBWP5tJz78qiv33ryeDonpvg6nCRCOOq5HcNMucANgbM+vhASYMwc2bYJ58wytSquAN9/I84GIMvsioNyvDmXLRgD5Sv0yE4qIxAKfA/OUUh9c3K+UOnrJcXtEZDaexPBfd2KVUguABQB9+/ZtuP0T3Q7YMgHMITBgke6e6oXsC8E88JeR9LosnQk3fOXrcJoMm4ripPMq2gZ8Q7TpENnu5KoPqqY563+5KKFaQnLfRGY+ZuVs5HGiW1RvoN3D13eq6/CaFG9aDIcAi4h0vGRfTyC1nLKpJc+VW05EmuFJCquUUi9WUa8CGvcn5d4XIDsF+s0Ha4Kvo2kQHp43nMxcK4sfX4PFoq8z1Kczzj7ku+NoF/glFooMrUsE7vzdWUTgX3Pi9SR79azKxKCUKgA+BmaLSKiIXA3cCrxXTvF3gUdEJFFEWgIzgSUAIhIBfAZ8p5R6suyBInKjiMSXPO4MPMt/91pqPDK/h9QXoe0ESNIjnL2xdmsH3v38cp68eyu9Ljvn63CaIBNH7COxUEy7gC8Mr61ZnJObpmZw6IdQNn+i14iuT952V30AsALngA+A+5VSqSIyWEQu7Sc4H1gN7AH2AmtK9gHcBlwJ3FtmrEJSyfPXAbtFpABYiycZ/V8t3pv/chZ4LiFZE6HvXF9H0yBk5VqZ/voNdGubwazx3/k6nCarUMWR5hxIc8tBmpv3G17fwFG5dO5bwMr5saT/rGfYqy+iGngbrW/fviolJcXXYVTP9gc8C/Bc9yXEX2NMHV/55yC5LUezqi5UhlLw1Lz/ZfPuLix8ei7JbYwfbKVVxk23wA8JMWWxJOZT8szGLjmbd97MK9PbEBHj5HdvnMQSWPVnlr7HUDUR2aGU6lvec3pKjPp2eh0c/it0fti4pNDIrPh6IN/8cDkzbl+nk4JfMPGT40YENyPznjB83YbwZi7uevQsp48Gs2ZRc0Pr0jx0YqhPtizYOhkiu0HPqu69a+BZfOcvH95Cv24Huet64+fs0bxjU1Ecc1xLa8c2+hca36e0a/8CBt16nq8/bsb+7SGG19fU6cRQX5SCbfeBPQuu+odeZ8ELBUVBPDNvImHWIp6dvBSTnjnVr2S4upEadBsDCt+ijf1bw+u7aWomCe1svP9SAtlnm8bYV1/RiaG+/LQATn7kWb+5WS9fR+P3lIIXF/+KUxkxPD/jH8REGj/iVqsu4cvw35Np7siNFx4l3HXa0NoCgxSTnjuNywlLZrfEYW/cvdl9SSeG+pC2ElIe8Ixu7lLuYG6tjA8+H8rGnT24/461XNHpaNUHaD7hFCufRLyBCQc3XfgNZlVsaH2xiQ7ueSKdtMPBfDQ3To9vMIhODEbL+A6+uwua9YFBy8Bk9nVEfm/Lns7M+/corumzm7tHfO3rcLQq5Fja8Wn4y8Q79zIy70nPpJAG6jawgOvvyWLbZ5FsWaPHNxhBJwYj5aTC1zdDSGu4Zg0EhPk6Ir935FQLnp0/ng6tzjDr3qV6lpAG4mjQcL4NfZRk2zoGFr5heH0jx2fR+coCPn4rjoM79M3ouqYTg1EKTsLGG8AUBMM+g+BYX0fk97IvhPH43MlYg+y88tAiQoLtvg5Jq4Yd1insCR7DgMK/0qV4haF1mcww8ekzxLexs+T5BE4f04Pf6pJODEawZXuSgj0Xhq2DsHa+jsjvFRQH8ehfppCVG85LDy4mLrrshL6a3xPhy7DfcyJgANfnPUNbm7GXAYND3Ux7/hRBVsXbzySSk6l7KtUVnRjqmrMIvrkF8n6CoSt1DyQv2BwWnnxzEodPtuTF+9+la7uTvg5JqyG3BLI64k0yLcncfOEhWtm/N7S+qFgn0144RVGBmYWzEim4oD/S6oJOsXXJ7fTcaM7YDIM+hPhhvo7I7zldJv7w9jh2HOjIs1Pe5+oexs+/o9WdAScWlLv/BEMID0rn9pwp7LONJV9VPXvw1qTpNYohsYONSc+d5u3nWrLg6VbMeCmtRq+j/UKn17qilGcOpFOroM8bkDTW1xH5PafTxB8WjmPjjh785s6V3Dhwp69D0uqIkxD22cbgUKF0CfqIMJOxU5kk9ylk0qwznDoSxNuzEsnXS4DXik4MdcHtgJQH4chC6PY0JD/o64j8ntNp4rmF4/kipRe/HvMJd40wfuSsVr8chJFqH4tDWeka+BERpp8Nra/bwAImPHWG4/uDGTUKLlwwtLpGTSeG2ipKhy+ug8PzoPNM6PGCryPyeza7hWf+NvE/LYV7btjo65A0g9hVBKm2uyhWkXQJXE4z00+G1tdzSD7jn0hn82a49lpo6Cv/+opODLWRuRU+7eNZhe2qf0LvV/XynFXIzQ/ht6/fx7e7uvPIuOW6pdAEOAhln+1OClQsyYErSTDvwMh1o68YlseKFZCaCkOGwEndl6HadGKoqZ8WwIYhnnEKI7ZA23G+jsjvHTsTyX1/epADx1vxwox3GXOtXnCnqXBiZZ/tTrLdHWkbuJH2ARsQjJuue9Qo+OwzOH0a+veHbdsMq6pR0r2SqstVDCkPwZG3IWEkXPU+BEX7Oiq/99UPSfzq+dEU24U/P7KAXp2O+TokrZ65CeCQ/WZaWzbRKmAbVsnksGMUdhVR53XNWX8IgPteCeSd5xK5erCZu2aepfew6k/G2BQX/dEthuooOAnrh3iSQrdnYOganRSqoBS8srQ/wx+7m5iIIuY/NVcnhSZNOOkczCH7KEJNGfQIeo8ok3GTJCa0s/O7uSdISi7mH39MYPXC5richlXXaOgWg7fOboRNd3paDIM/hta31X0dfrocZ01l5lqZ/tqNLN+UzJgh+1n0+Fr2puu7gRpkuTpT4I6jU+AndAlazhlnb1LUeJxS9/MehUW5mPGnNFb8NY6vlkVzZI+VCU+dISZBZ4iK6BZDVRz5sGc2fDnc0zoYuc2YpNDIrN58Gd0nT+WTrZfx6owv+NfvVxAeouc+0n5RrKLZa7ubM85eJFh2MiH7VsNGSlsCYMxvzjFx1mnOnQzktfvbsH19uJ62uwK6xVARZ6FnbeZ9L4EtA5LuhP4LIaDur4c2Jlm5Vh6bP4zFn/akR/uzfPbyh/TscM7XYWl+yk0Axx3Xke3qRKJ1K2NzJ7I3+Ha+C32EQlPdTzzZa0g+rTsW8/7LCXzwSgI7v4xg7G/PEt1Ctx4upVsMZblscHAurOoAPzzqmetoxBbPFBc6KVTI6RLmrexNx4n38e7nl/PUuM1sm/d3nRQ0r1xwt+a96FVst06lS/Fq7s0ewZWFCzArW53XFZPg5NevneT2B89yfJ+Vl6e1ZcMHzbDbdFfzi3SL4SKXHY4uhtQXoDAN4oZ4kkHcEF9H5teUgs9T2vH4/GHsPhrPsF7HeeOh9XRvl+nr0LQGxilWNoU9xh7rnQzJf4lBBa/Rs+gfbA+Zzt7gsbgkqM7qMplg0C25dBtQwPK34li7OJbNn0TxP/dm0vvaPExN/CuzTgxuJxx7D/bOhoLjEDMABiyG+Ov0YLVKKAXrvu/A7Peu5vv9ibSJz2HZ7z/mjiEH9a9Nq5VccxtWR86jlX0rAwvncm3+8/Qv/Bsp1insDR6D3RReZ3U1i3My+Q+n+elHK6sWxPL+ywl8tSyaa3+VTa+heZib6IKLohr43Ze+ffuqlJSU6h/odsHPS2HvHyDvMET3gR7Pe9Zl9tUnWwPolVRks/DhV114c0UfdhxKoE18Dk/fs4VJI3cTGFD1ko5bjmbVQ5RaQ1TR7Kqt7N/Tv/Atkhzf48DK/uBb+NE6jkxL5zqt3+2GHzaGs+GDaM7+HERMgp1r7jjPohfjiWiEV5FFZIdSqm95zzXdFsPRxbBtGkT1gCErIPEW3UKoxIET0by9pheLPu3B+TwrnZMyWThzLRNH7PEqIWhaTaUF9ictsD9xjr30LH6frsUr6FH8IWct3TgQdBMHg0ZRYI6vdT0mE/S5No8rrskjdUsoXyyN5qM34/l0Mdx9N0ybBlde2TQ+Jppui8FZBKfXerqeip9cUPSzFsOBE9Es+7oLy77uzJ6jcVjMLm4bdIgHbt3J0J4navQHolsMWm1ZKKK5eR+xlgOEmdJRCvLciZx3tyPH1Z5C1ZytSffVuh6l4MTBYPJ2JbF0KRQWQocOMGYMjB0LvXs37CRRWYuh6SYGf+TjxHA+L5iNu5LYsLMtG3a05VBaDCKKq7unMXboAcYO3U9CTEGt6tCJQatLwXKe5ub9NDMfIczk6QFnc4eR627DBXciee5EilUzoOaf4AOnvEpuLixb5tm++AJcLmjdGq6/HoYPh+uug7i4OnpT9aTWl5JEJBp4BxgBZAJPKaXeL6ecAH8Cppbsegd4QpVkHxHpVbKvC7AfmKKU2uXNsVrdcjhNpB5vTsrBBLYfTGD7gQR+PBqH220iNNjO0J4n+PXondwx+ACJsXrVE80/FatmpDmvIs15FQHk08x8jCjzUZqZjxBnSQXAoazkuRModMdS4I6lUMVSrKKoTm/9yEiYOtWzZWXBypWwdi0sXw6LFnnKdOkC/fp5Ljf16wc9ekBQ3XWkqlfe3mN4C7AD8UAvYI2I/KiUSi1TbjowGuiJZ17d9cBR4G8iEgisBP4MzAPuA1aKSEellL2yY2v+9po2p0s4kxXGyXMRHD4VzcGT0RxK8/w8nBaNzeH5748KK6Jvcjqzxm9meO9j9O9yWt830BocB2Gcc13OOdflgMIq2YSbThFuOkWY6SzNLMcQ8XzPdCkLxSoKm4qi2B1JsYqiWEVhV+HYVSgugqiolRETA5MnezaXC3buhA0bYPNmWLcO/v53TzmTCdq39ySMi1vbtp6WRqtW/p00qryUJCKhwHmgu1LqUMm+94BTSqkny5TdDCxRSi0o+fcUYJpSaoCIjAAWA60uaUGcAKYrpT6t7NjK4msKl5LcbigoDiS/KIC8wiDPz6JAcvODyLpgJeuClczckJKfVk5nhZOWEU76+VDc7l++FVnMLton5JDcOpvOSVn07phO3+R0OrQ8X2/XSvWlJM1XBCdWySLUlEGIKZNgOU+w5BIsuZik9MhntzJjV6E4CCU9uDvFpihsEkGxRGAzhWOTCM9mCschITgJxiFWnASRmRXGkQORnDkWzLkTgZw9EUjGqUBcztJ/ZPHxniTRooUn2TRvXnqLjISwMAgN9fy8uAUG1tHvo5aXkjoBrotJocSPwNByynYree7Sct0ueW53mUtDu0v2f1rFsXVu1y5YssRzg6m6m9td8+McDrDbf9lK/Tt3Bg6nCbvTjN1p9jx2mP/zzb4yARYXMRFFxEQUkdg8j+7tMmgVm0er5hdIbJ5Ph5bnad8yhwCLbgloTZPCQqGKp9AVT+mlIBSB5BNkyiVQ8gikkADJJ1AKCJACEot3YpFiLNj+K4GUS4Au4OpswY0Ft7LgxozTFYDdGYAExVJsD6DYZqGw2EKRzfO4qNhCcUYAzrMWHG4z55SJdLcJt/JsSgluZQJMmMwmTCYTcQMmMOKewXX+u/ImMYQBuWX25QLljTIpWzYXCCu5f1DV61R4bNn7DCIyHc+lJ4B8ETlY8jiynDoqU53y3pStqkxVzzfHcw+n2hxOSM/2bKnHa/IKtVbd372/1Veb16vJsd4eU1flKnu+xuedn/DTc89ZspV1pgavVYE3F0YyvsafeW0qLKWUqnQDrgAKy+ybCawup2wu0O+Sf/cB8koePwysLVN+NTCzqmO93YAFRpX3pmxVZbx4PqU68fvTVt3fvb/VV5vXq8mx3h5TV+Uqe74hn3dGnAv1WV9tX8uozzxvbssfAiwi0vGSfT2BsjeeKdnXs4JyqUCPktbDRT3KPF/Rsd5abWB5b8pWVaa68TUk9f3e6rq+2rxeTY719pi6KqfPPf+sr7avZchnnlfjGERkKZ6eQlPx9EpaC1ylyvRKEpEZwG+B4fzSs2iuUupir6TDwOt4ehpNAx4DOiql7JUd680baQxEJEVVcDNI04yizzutLG878j4AWIFzwAfA/UqpVBEZLCKXdnKfjycj7QH2AmtK9qE8XVJHAxOBHGAyMLpkf6XHNiELfB2A1iTp804rpcGPfNY0TdPqlp9MEqRpmqb5C50YNE3TtFJ0YmgARORFEflWRP4tIiG+jkdr/EQkUkS2iUi+iHT3dTxa/dKJwc+V/FF2UEoNBjbguWmvaUYrBEYB//Z1IFr904nB/w0G1pU8XgcM8mEsWhOhlHIopTJ8HYfmGzox1BMReVBEUkTEJiJLyjwXLSLLRaRARH4WkXGXPN2MX4aw5wLR9RSy1gjU4rzTmrCmu7Rn/TsNvACMxDMm5FKVTWt+Hs/8JpT8zK6fcLVGoqbnndaE6RZDPVFKfayUWgGUmne6ZFrzO4BnlVL5SqlNwCpgQkmRTXj+qCn5+V09haw1ArU477QmTCcG36toWvNuAEqpPcDPIvItnsSwqP5D1BqhSs87ABFZi2fVxoUiMql+w9N8SV9K8r0qpzVXSj1VrxFpTYE3593/1GtEmt/QLQbfywciyuyLAPJ8EIvWdOjzTquQTgy+V51pzTWtrujzTquQTgz1REQsIhIMmAGziASLiEUpVQB8DMwWkVARuRq4FXjPl/FqjYM+77Sa0Imh/swCioAngfElj2eVPFfutOa+CFJrdPR5p1WbnnZb0zRNK0W3GDRN07RSdGLQNE3TStGJQdM0TStFJwZN0zStFJ0YNE3TtFJ0YtA0TdNK0YlB0zRNK0UnBk3TNK0UnRg0TdO0UnRi0DRN00r5f4lOSGMdUzyhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path1 = r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\For count\\Results 1 regime.csv'\n",
    "path2 = r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\For count\\Results 2 regime.csv'\n",
    "df1 = pd.read_csv(path1, engine = 'python', sep = ',')\n",
    "df2 = pd.read_csv(path2, engine = 'python', sep = ',')\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "ax.hist(np.array(df1['Length'], dtype = 'float'), density = True, \n",
    "        bins = np.logspace(np.log10(1e0),np.log10(5 * 1e1), 15), alpha = 0.5, label = '0.6 % $CO_{2}$')\n",
    "ax.hist(np.array(df2['Length'], dtype = 'float'), density = True, \n",
    "        bins = np.logspace(np.log10(1e0),np.log10(5 * 1e1), 15), alpha = 0.5, label = '0.5 % $CO_{2}$')\n",
    "params = st.lognorm.fit(df1['Length'], floc = 0)\n",
    "ax.plot(np.arange(0.6, 50, 0.1), st.lognorm.pdf(np.arange(0.6, 50, 0.1), *params), color = 'blue')\n",
    "params = st.lognorm.fit(df2['Length'], floc = 0)\n",
    "ax.plot(np.arange(0.6, 50, 0.1), st.lognorm.pdf(np.arange(0.6, 50, 0.1), *params), color = 'orange')\n",
    "ax.set_xscale('log')\n",
    "ax.tick_params(axis = 'both', labelsize = 12)\n",
    "ax.legend(fontsize = 14)\n",
    "ax.set_title('SWCNT length distribution', fontsize = 14)\n",
    "fig.savefig('Regime 1.png', format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "st.lognorm(*params).stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d15fddbe48>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(-20,20,0.01),st.norm.pdf(np.arange(-20,20,0.01), loc = 14.51916542, scale = 37784**0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0557573350570057e-05"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(st.norm.pdf(np.arange(-1000,1000,0.01), loc = 14.51916542, scale = 37784))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024328735203443693"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(st.lognorm.pdf(np.arange(1, 20, 0.001), 0.5251087985615228, -0.5503096691861848, 10.57440936245917)).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regimes = {'Regime_A': '0 (1)', 'Regime_B': '0 (2)', 'Regime_C': '0.02', 'Regime_D': '0.03', 'Regime_E':  '0.04', 'Regime_F': '0.035'}\n",
    "path = r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\B3919'\n",
    "\n",
    "for file in os.listdir(path):\n",
    "    \n",
    "    if 'Regime' in file and '.csv' in file:\n",
    "        \n",
    "        make_distplot(path + '\\\\' + file, file.split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_distplot(path, regime):\n",
    "\n",
    "    df = pd.read_csv(path, engine = 'python', sep = ',')\n",
    "    fig, ax = plt.subplots(figsize = (6,6))\n",
    "    mmin = df['Length'][df['Length'] > 0].min()\n",
    "    mmax = df['Length'][df['Length'] > 0].max()\n",
    "    ax.hist(np.array(df['Length'], dtype = 'float'), color = 'b', density = True, \n",
    "            bins = np.logspace(np.log10(mmin),np.log10(mmax), int(np.log10(mmax/mmin)*10) + 1))\n",
    "    params = st.lognorm.fit(df['Length'][df['Length'] > 0], floc = 0)\n",
    "    ax.plot(np.arange(mmin/3, mmax*3, 0.1), st.lognorm.pdf(np.arange(mmin/3, mmax*3, 0.1), *params), c = 'red')\n",
    "    ax.set_xscale('log')\n",
    "    ax.tick_params(axis = 'both', labelsize = 12)\n",
    "    ax.set_title('{} \\n $G.Mean$ $=$ {} $\\mu$$m,$ $SD$ $=$ {} $\\mu$$m$ \\n {} $counts$'.format(regime, 2, int(np.round(st.lognorm(*params).stats()[0], 0)), \n",
    "                                                                                 int(np.round((st.lognorm(*params).stats()[1] ** 0.5), 0)), \n",
    "                                                                                 len(df['Length'])), fontsize = 14)\n",
    "    ax.set_xlabel('$Length,$ $\\mu$$m$', fontsize = 14)\n",
    "    fig.savefig(path.replace('.csv', '.png'), format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "    plt.close(fig)\n",
    "    st.lognorm(*params).stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7157873909448121"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(st.lognorm.cdf(np.arange(1, 20, 0.1), 0.8131917668542937, 0.859659107337317, 4.1219996596238175))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       8.08104388e-02, 4.22651428e-01, 6.73251379e-01, 7.79007969e-01,\n",
       "       7.90989286e-01, 7.53432577e-01, 6.93397540e-01, 6.25844015e-01,\n",
       "       5.58568011e-01, 4.95354160e-01, 4.37801846e-01, 3.86350066e-01,\n",
       "       3.40845523e-01, 3.00854832e-01, 2.65833942e-01, 2.35217909e-01,\n",
       "       2.08466324e-01, 1.85084177e-01, 1.64629341e-01, 1.46712976e-01,\n",
       "       1.30996380e-01, 1.17186286e-01, 1.05029675e-01, 9.43086805e-02,\n",
       "       8.48358575e-02, 7.64499410e-02, 6.90121140e-02, 6.24027688e-02,\n",
       "       5.65187211e-02, 5.12708255e-02, 4.65819418e-02, 4.23852047e-02,\n",
       "       3.86225523e-02, 3.52434760e-02, 3.22039570e-02, 2.94655617e-02,\n",
       "       2.69946716e-02, 2.47618265e-02, 2.27411631e-02, 2.09099360e-02,\n",
       "       1.92481067e-02, 1.77379908e-02, 1.63639554e-02, 1.51121584e-02,\n",
       "       1.39703231e-02, 1.29275443e-02, 1.19741201e-02, 1.11014057e-02,\n",
       "       1.03016875e-02, 9.56807216e-03, 8.89439117e-03, 8.27511687e-03,\n",
       "       7.70528921e-03, 7.18045169e-03, 6.69659503e-03, 6.25010785e-03,\n",
       "       5.83773315e-03, 5.45653007e-03, 5.10384008e-03, 4.77725715e-03,\n",
       "       4.47460132e-03, 4.19389537e-03, 3.93334397e-03, 3.69131534e-03,\n",
       "       3.46632476e-03, 3.25702005e-03, 3.06216847e-03, 2.88064516e-03,\n",
       "       2.71142270e-03, 2.55356182e-03, 2.40620311e-03, 2.26855947e-03,\n",
       "       2.13990948e-03, 2.01959131e-03, 1.90699736e-03, 1.80156929e-03,\n",
       "       1.70279369e-03, 1.61019805e-03, 1.52334721e-03, 1.44184008e-03,\n",
       "       1.36530672e-03, 1.29340567e-03, 1.22582153e-03, 1.16226280e-03,\n",
       "       1.10245984e-03, 1.04616310e-03, 9.93141465e-04, 9.43180742e-04,\n",
       "       8.96082317e-04, 8.51661895e-04, 8.09748365e-04, 7.70182767e-04,\n",
       "       7.32817337e-04, 6.97514644e-04, 6.64146792e-04, 6.32594692e-04,\n",
       "       6.02747397e-04, 5.74501489e-04, 5.47760514e-04, 5.22434471e-04,\n",
       "       4.98439333e-04, 4.75696615e-04, 4.54132966e-04, 4.33679807e-04,\n",
       "       4.14272984e-04, 3.95852455e-04, 3.78362004e-04, 3.61748969e-04,\n",
       "       3.45963994e-04, 3.30960807e-04, 3.16696001e-04, 3.03128843e-04,\n",
       "       2.90221093e-04, 2.77936835e-04, 2.66242323e-04, 2.55105834e-04,\n",
       "       2.44497539e-04, 2.34389376e-04, 2.24754936e-04, 2.15569355e-04,\n",
       "       2.06809216e-04, 1.98452457e-04, 1.90478281e-04, 1.82867081e-04,\n",
       "       1.75600364e-04, 1.68660680e-04, 1.62031557e-04, 1.55697445e-04,\n",
       "       1.49643653e-04, 1.43856302e-04, 1.38322275e-04, 1.33029168e-04,\n",
       "       1.27965252e-04, 1.23119427e-04, 1.18481192e-04, 1.14040602e-04,\n",
       "       1.09788241e-04, 1.05715192e-04, 1.01813000e-04, 9.80736570e-05,\n",
       "       9.44895665e-05, 9.10535256e-05, 8.77587011e-05, 8.45986085e-05,\n",
       "       8.15670927e-05, 7.86583099e-05, 7.58667100e-05, 7.31870202e-05,\n",
       "       7.06142307e-05, 6.81435791e-05, 6.57705381e-05, 6.34908020e-05,\n",
       "       6.13002753e-05, 5.91950611e-05, 5.71714508e-05, 5.52259139e-05,\n",
       "       5.33550888e-05, 5.15557740e-05])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.lognorm.pdf(np.arange(1, 20, 0.1), 0.8131917668542937, 4.1219996596238175, 0.859659107337317)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-3-27371451289f>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-3-27371451289f>\"\u001b[1;36m, line \u001b[1;32m8\u001b[0m\n\u001b[1;33m    dist = st.lognorm\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "def get_best_distribution(data):\n",
    "    \n",
    "    dist_results = []\n",
    "    params = {}\n",
    "    for dist_name in dist_names:\n",
    "        \n",
    "    dist = st.lognorm\n",
    "    param = dist.fit(data)\n",
    "\n",
    "    params['lognorm'] = param\n",
    "        # Applying the Kolmogorov-Smirnov test\n",
    "    D, p = st.kstest(data, dist_name, args=param)\n",
    "    print(\"p value for \"+dist_name+\" = \"+str(p))\n",
    "        dist_results.append((dist_name, p))\n",
    "\n",
    "    # select the best fitted distribution\n",
    "    best_dist, best_p = (max(dist_results, key=lambda item: item[1]))\n",
    "    # store the name of the best fit and its p value\n",
    "\n",
    "    print(\"Best fitting distribution: \"+str(best_dist))\n",
    "    print(\"Best p value: \"+ str(best_p))\n",
    "    print(\"Parameters for the best fit: \"+ str(params[best_dist]))\n",
    "\n",
    "    return best_p, params[best_dist]\n",
    "\n",
    "get_best_distribution(df['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5251087985615228, -0.5503096691861848, 10.57440936245917)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.lognorm.fit(df['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(10.64700816), array(143.11651331))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\For count\\Results 3 regime.csv'\n",
    "df = pd.read_csv(path, engine = 'python', sep = ',')\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "ax.hist(np.array(df['Length'], dtype = 'float'), color = 'b', density = True, \n",
    "        bins = np.logspace(np.log10(1e0),np.log10(4 * 1e1), 17))\n",
    "params = st.lognorm.fit(df['Length'])\n",
    "ax.plot(np.arange(1, 40, 0.1), st.lognorm.pdf(np.arange(1, 40, 0.1), *params), c = 'red')\n",
    "ax.set_xscale('log')\n",
    "fig.suptitle('0.035 $CO_{}$ \\n Mean = {} um, SD = {} um'.format(2, np.round(st.lognorm(*params).stats()[0], 1), \n",
    "                                                                np.round((st.lognorm(*params).stats()[1] ** 0.5), 1)))\n",
    "ax.set_xlabel('Length, um')\n",
    "fig.savefig('Regime 3.png', format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "st.lognorm(*params).stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9629968970766822, 0.9685380887820001, 6.087407701452393)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.lognorm.fit(df['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(5.24447752), array(23.03192261))"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\For count\\Results 4 regime.csv'\n",
    "df = pd.read_csv(path, engine = 'python', sep = ',')\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "ax.hist(np.array(df['Length'], dtype = 'float'), color = 'b', density = True)\n",
    "params = st.lognorm.fit(df['Length'])\n",
    "ax.plot(np.arange(1, 40, 0.1), st.lognorm.pdf(np.arange(1, 40, 0.1), *params), c = 'red')\n",
    "# ax.set_xscale('log')\n",
    "fig.suptitle('Regime 4')\n",
    "ax.set_xlabel('Length, um')\n",
    "fig.savefig('Regime 4.png', format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "st.lognorm(*params).stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8887184071032839, 0.8689325923575961, 2.947981986095825)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.lognorm.fit(df['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(8.69333113), array(57.47549054))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = r'C:\\Users\\kpebe\\OneDrive\\Рабочий стол\\For count\\Results 5 regime.csv'\n",
    "df = pd.read_csv(path, engine = 'python', sep = ',')\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "ax.hist(np.array(df['Length'], dtype = 'float'), color = 'b', density = True, \n",
    "        bins = np.logspace(np.log10(1e0),np.log10(4 * 1e1), 17))\n",
    "params = st.lognorm.fit(df['Length'])\n",
    "ax.plot(np.arange(1, 40, 0.1), st.lognorm.pdf(np.arange(1, 40, 0.1), *params), c = 'red')\n",
    "ax.set_xscale('log')\n",
    "fig.suptitle('0.025 $CO_{}$ \\n Mean = {} um, SD = {} um'.format(2, np.round(st.lognorm(*params).stats()[0], 1), \n",
    "                                                                np.round((st.lognorm(*params).stats()[1] ** 0.5), 1)))\n",
    "ax.set_xlabel('Length, um')\n",
    "fig.savefig('Regime 5.png', format = 'png', bbox_inches = 'tight', dpi = 300)\n",
    "st.lognorm(*params).stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7771700528139625, 0.3688568660616337, 6.1546213368361125)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.lognorm.fit(df['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EI42jy3Qo0CWh1Dd1sLZiNhm+5PzqLi7OZU5epgJdpiU5fyskKQ0MB9h9vCvpLlccz8xYv6hId4zKtCjQJWHsbO7CH3QJ/4Sii6muLOLwmT7a+4a8LkUSjAJdEsbYMEQiP0M0HJqoS6ZLgS4Jo76xgyUleczJy/S6lKhaW1FIhs8U6DJlCnRJCM65c08oSnbZGT5WLyjUOLpMmQJdEkJjWz/tfUNJfUJ0vOrKIt5s7mTIH/S6FEkgCnRJCGPDD6kU6IP+IHtOdntdiiQQBbokhPqmDgqy0lk+N9/rUmJi7B+uuqPtHlciiUSBLglhbEKuVJnfpGxWNhVFOeemOhAJhwJd4l73wDD7T/ck/fXnE1VXFlHf2IFz7uI7i6BAlwSwo6kT51Jn/HxMdWURp7sHOd551utSJEEo0CXubWvqwAwuX1jodSkxpQdeyFQp0CXu1Td2sLKsgILsDK9LialL5hWQl+lToEvYFOgS1wJBx46mzpQbbgFI96VxxaLZCnQJmwJd4trBlh56Bv0pGegA1YuK2Huym75Bv9elSAJQoEtcOzchV4pd4TJmfWURQQdvHuv0uhRJAAp0iWu1R9opyc+isjjX61I8sW5REWY6MSrhUaBLXKs92sGGqiLMUuOGookKczJYMbeAOgW6hCGsQDezjWa238wazOzBENs/Y2a7zGyHmb1sZqsiX6qkmuOdZzneeZYrR5+zmarWVxaxramDYFA3GMmFXTTQzcwHPALcBqwC7gsR2I855y5zzl0BfAP4dsQrlZRTe2RkHpNUD/TqyiJ6Bvw0tPZ6XYrEuXB66BuABufcYefcEPA4cOf4HZxz46eEywPUlZAZe+NoOwVZ6Vw6f5bXpXhKTzCScIUT6OXAsXHLzaPr3sbM/tjMDjHSQ/9sqAOZ2f1mVmdmda2trdOpV1JI7ZF21lcW4UuRCbkms7g4l+K8TAW6XFQ4gR7qt+m8Hrhz7hHn3FLgy8BfhDqQc+5R51yNc66mtLR0apVKSunoG+JgSy8bqlJ7uAXAzFg/OlGXyIWEE+jNwMJxyxXAiQvs/zjwgZkUJVJ7VOPn41VXFnHkTB9tvYNelyJxLJxArwWWm1mVmWUC9wKbxu9gZsvHLd4OHIxciZKKao+2k+lLY21Fak3INZmxcfRtTbrBSCZ30UB3zvmBB4Bngb3Ak8653Wb2sJndMbrbA2a228x2AF8APh61iiUlvHG0g8sXFpKd4fO6lLhwWXkhGT7TsItcUHo4OznnNgObJ6x7aNzrz0W4Lklh/UN+dh/v4v4bl3hdStzIzvCxpryQbQp0uQDdKSpxZ3tTJ/6g40qdEH2b6kVFvNncyZA/6HUpEqcU6BJ33jjSjlnqPaHoYqorixj0B9l9osvrUiROKdAl7tQebefSebOYlWIPtLgY3WAkF6NAl7gy6A9Q39jB1UuKvS4l7sydlc3COTlsa1KgS2gKdIkr25s6GfQHuWapAj2U6kUjNxg5p9k15HwKdIkrrx5qI83QHaKTqK4s4nT3IMfaz3pdisQhBbrEldcOtbGmvJDCHI2fhzL2Xy5bD5/xuBKJRwp0iRtnhwJsP9bBNRo/n9TS0nxKC7J49VCb16VIHFKgS9yob+xgOOA0fn4BZsY1S4p59VCbxtHlPAp0iRuvHjpDepppQq6LuHZpMa09gxzSAy9kAgW6xI2th9tYW1FIXlZYM1KkrGuXlgBo2EXOo0CXuNA76Gdnc9e5sJLJLZyTQ/nsHF5tUKDL2ynQJS7UHmknENT4eTjMjGuXFvPakTY9OFreRoEucWHr4TYyfWmavyVM1y4rprN/mL2nui++s6QMBbrEhZcPnmHdotma/zxM1ywZGZraqnF0GUeBLp5r7Rlkz8lublyh58yGa15hNktK8nRiVN5GgS6ee7mhFYAblyvQp+KapcW8friN4YDmR5cRCnTx3IsHzlCcl8nqBbO8LiWhXLeshL6hADuO6TmjMkKBLp4KBh0vHTzD9ctLSEszr8tJKNctLSHN4MUDrV6XInFCgS6e2nuqmzO9g9yg4ZYpK8zNYN2iIl5QoMsoBbp46sUDI7MG3rhcNxRNxztWlLLreBdtvYNelyJxIKxAN7ONZrbfzBrM7MEQ279gZnvMbKeZbTGzysiXKsnopYOtXDKvgLmzsr0uJSG9Y0UpzsHLDZpOV8IIdDPzAY8AtwGrgPvMbNWE3bYDNc65tcBTwDciXagkn/4hP3VHO3S54gxcVl7InLxMXtivYRcJr4e+AWhwzh12zg0BjwN3jt/BOfc751z/6OJrQEVky5RktPVQG0OBoC5XnIG0NOP6ZSW8eLBV0wBIWIFeDhwbt9w8um4ynwSeCbXBzO43szozq2ttVY8i1W3Z10Jepo8rq3S7/0y8Y0UpZ3qH2HNS0wCkunACPdS1ZCG7Amb2MaAG+Gao7c65R51zNc65mtJS9cpSmXOO5/a2cOOKUrLSdbv/TNywYuSEsq52kXACvRlYOG65AjgxcSczuwX4c+AO55xOucsF7T7RzanuAd55yVyvS0l4cwuyWb1glsbRJaxArwWWm1mVmWUC9wKbxu9gZuuA7zMS5i2RL1OSzW/3nsYMblagR8TNK+dS19hOR9+Q16WIhy4a6M45P/AA8CywF3jSObfbzB42sztGd/smkA/8xMx2mNmmSQ4nAsCWvS2sWzibkvwsr0tJCreuKiPo4Ll96k+lsrCe9eWc2wxsnrDuoXGvb4lwXZLETncPsOt4F196z0qvS0kal5UXMrcgi9/uPc2HqnWRWarSnaISc1v2jvQib7m0zONKkkdamnHLqjJeONDKwHDA63LEIwp0ibkte09TUZTDirJ8r0tJKreuKqN/KKCHXqQwBbrEVM/AMC81nOHdq+ZhptkVI+napcXkZfr4zZ7TXpciHlGgS0w9t6+FIX+Q29fO87qUpJOV7uMdK0vZsve07hpNUQp0ianNu05SNiuLdQt1d2g03LqqjJaeQXYe7/K6FPGAAl1ipm/Qz/P7W7ltzXw9zCJKbl45l/Q045ldJ70uRTygQJeYeW5fC4P+ILet0XBLtMzOzeT65SU8vfMkzmnYJdUo0CVmnnnrJKUFWdQsnuN1KUntfWsXcLzzLNv1rNGUo0CXmOgf8vO7fa1sXD0Pn4Zbourdq8vI9KXx9Jsadkk1CnSJid/sPs3Z4QDvWzvf61KS3qzsDG5cUcrmXSd1tUuKUaBLTPxs+3HKZ+dwpYZbYuL9l8/nVPcAdY0dXpciMaRAl6hr6R7g5YOtfGDdAl3dEiO3XFpGdkYaT+88b6ZrSWIKdIm6TW+eIOjgrnWaNCpW8rLSedclZTy98yRD/qDX5UiMKNAl6n6+/ThrKwpZNldzt8TSh6sraO8b0pS6KUSBLlG1/1QPu090c9e6Cz2GVqLhhuUlzC3I4qn6Zq9LkRhRoEtU/fsbTWT60rjzCgV6rKX70rhrfTm/299Ca4+eCpkKFOgSNWeHAvx0WzMb18xjTl6m1+WkpLurKwgEHb/YftzrUiQGFOgSNb/adZKeAT8fvWqR16WkrGVzC7hi4Wx+Un9MUwGkAAW6RM1jrzeypDSPq6p07bmX7qlZyIHTvdTrmvSkp0CXqNh3qpttTZ18dMMiPcjCY3desYCCrHT+dWuj16VIlCnQJSp+tLWRzPQ0PrRe1557LS8rnQ/XVLB510lauge8LkeiKKxAN7ONZrbfzBrM7MEQ2280s21m5jezD0e+TEkk7X1DPFXfzF1XlFOkk6Fx4b9dsxh/0PHvbxzzuhSJoosGupn5gEeA24BVwH1mtmrCbk3AJ4DHIl2gJJ4fv9bIoD/Ip26o8roUGVVVksc7VpTy49cbGQ7oztFkFU4PfQPQ4Jw77JwbAh4H7hy/g3PuqHNuJ6BvSoobGA7wL1sbuWllKcvLCrwuR8b5+LWVtPQM8qudmlY3WYUT6OXA+P9Oax5dN2Vmdr+Z1ZlZXWtr63QOIXHulzuOc6Z3kE9dv8TrUmSCm1bMZfncfP7h+UOaVjdJhRPooS5RmNa3wTn3qHOuxjlXU1paOp1DSBzzB4L8/fOHWFM+i+uWFXtdjkyQlmb84U1L2X+6R/O7JKlwAr0ZWDhuuQLQnJxynp9vP05jWz+ff9cKXaoYp95/+QIqinJ45PkG3WiUhMIJ9FpguZlVmVkmcC+wKbplSaLxB4J893cNrCmfxbsunet1OTKJDF8a/+PGJWxv6mTr4Tavy5EIu2igO+f8wAPAs8Be4Enn3G4ze9jM7gAwsyvNrBm4G/i+me2OZtESf36m3nnCuLtmIWWzsvjWbw6ol55k0sPZyTm3Gdg8Yd1D417XMjIUIyno7FCAv/3PA6ytKFTvPAFkZ/j43LtW8Gc/38V/7jnNu1fP87okiRDdKSoz9uiLhznZNcBf3L5KvfMEcU9NBUtK8vjms/vx67r0pKFAlxk52XWW771wiNsvm88GTcKVMNJ9aXzpPSs52NLLT7fpARjJQoEuM/KNX+8n4BwP3naJ16XIFG1cM4/qyiK+8ev9dPYPeV2ORIACXabt1UNn+Pn243zq+ioWzsn1uhyZIjPj4TtX09E/xDef3e91ORIBCnSZlv4hPw/+dBeLi3P5k3cu97ocmabVCwr5+LWLeeyNJnYc6/S6HJkhBbpMy7d+c4Cm9n6+/qG15GT6vC5HZuALt66gND+LLz+1k4HhgNflyAwo0GXKXjzQyj+/coSPXb2Iq5foFv9EV5CdwV9/eC37T/fwrd9o6CWRKdBlSlq6B/jCkztYVprPn7934izKkqhuXjmXj129iB+8fIRXD53xuhyZJgW6hM0fCPL5J3bQO+jnkd9br6GWJPNn772UquI8Pvf4Dk7ryUYJSYEuYXHO8dCm3bx6qI2v3rmGFZrrPOnkZqbzDx+rpm/Qz2f+rZ5Bv8bTE40CXcLyg5eO8NjrTfzhTUu5u2bhxX9AEtLKeQX8zd2Xs72pkz//+Vua6yXBKNDlop6qb+b/PrOX2y+bz5fevdLrciTK3nvZfD73ruU8Vd/M15/Z53U5MgVhTc4lqeuJ2iYe/Nkurl9WwrfuuZy0NM3Vkgo+f8ty2vuG+P6Lh5mVk8Ef37zM65IkDAp0mdS/bj3KQ7/czTtWlPL9368mO0MnQVOFmfG/71hN76Cfbz67n54BP1/euFKTr8U5Bbqcxx8I8rVf7eWHrx7llkvn8t2PrleYp6C0NONv7r6c3Ewf33vhEG29g3ztrjVkpeu7EK8U6PI2JzrP8vkndvDGkXY+eX0Vf/beS/FpmCVl+dKMr31gDSX5WfzdloMcaOnlH35vPQtm53hdmoSgk6ICjFyW+NP6Zm77u5fYfbyLb99zOX/5vlUKc8HM+J+3ruB7H6vmUEsv7/3OS/xyx3FdAROH1EMX9pzo5qtP72Hr4TbWLZrNt++5gqqSPK/Lkjizcc08VpTl88WfvMnnHt/Bph0n+Mv3rWKxvitxw7z6V7ampsbV1dV58t4yYt+pbr77XANP7zxJYU4Gf7pxJfdduUhXssgFBYKO//fKEb71mwMMB4Lct2ERf3zzMuYVZntdWkows3rnXE3IbQr01HJ2KMCWfaf50dZGXj/STm6mj09eX8WnblhCYU6G1+VJAmnpGeA7Ww7y+BvHgJHr1z9+bSXrFxXpapgoUqCnuNaeQbYebuO3e07z272n6R8KUFGUw+9fXclHrlzI7NxMr0uUBHasvZ8fvnqUJ2qP0Tvop6Ioh/dfvoBbV5WxtryQdJ9O1UXSjAPdzDYCfwf4gB84574+YXsW8K9ANdAGfMQ5d/RCx1SgR0ffoJ/9p3vYfaKbPSe62NbYyf7TPQAU5Wawcc183r92PlctKdYJT4monoFhfv3WKf5j50leaThDIOgoyErnqiVzWLeoiNULZrFqwSzmFmhoZiZmFOhm5gMOALcCzUAtcJ9zbs+4ff4IWOuc+4yZ3Qvc5Zz7yIWOq0APj3OOs8MB+gYD9A366R300943RFvfIG29Q5zpHeJU11ma2vtpaj/Lmd7Bcz9bmJPB2opCrllazHVLS1i9YJZ6SxITHX1DvHLoDK80tLH10BmOtvWf21aUm8GiOblUzMll0Zxc5s3Kpigvk+K8TIpyM5mTl0lelvN8ficAAAY7SURBVI/czHR1OkKYaaBfA/wv59x7Rpe/AuCc+6tx+zw7us9WM0sHTgGl7gIHn7tklbv7q/92btlx/q6hfjrkujB+NlQhoasLs46Qx3Nh7DNyUmkoEMQfCOIPOoYDDn8gyHAgOPI6GMQfcAz5g/QN+Qle4CPK8BlzC7JZNCeXyuJcFs7JZdncfFYvmEX57ByNZUpc6Do7zJ4T3ew+0cWh1j6aO/o51t5Pc8dZ/Bf4gmemp5GT4SM300dOpo9MXxq+NCM9zUb/Hl322bn1ZoYBY1/9kaVxy/Zf68b/Nfa7MvYbYyG2RcqHqyu4blnJtH72QoEezmWL5cCxccvNwFWT7eOc85tZF1AMvG2mfDO7H7gfIHf+0nNDAee2hyw+xLoQe4bz/3eoDyXs95xmHaGO70szMnxp5Gamk+4b+VJmpo/8ne4zMsb+9qWRn5VOXlY6+Vk+8kZfzxntzRTnZzErO12hLXGvMCeDa5YWc83Stz/hKhB0dPYP0d437k//EP2DAc4OB+gfCnB2yH/u9XAgSCDo8AfdyN+Bkb8H/YG3rR8z1sca6/Q5918drbEOmDv3P6G3ReM0400rSyN/UMIL9FBpMbGJ4eyDc+5R4FEYGXL57RfeEcbbi0iy8qUZxflZFOdneV1KUghnQLUZGD8BdgVwYrJ9RodcCoH2SBQoIiLhCSfQa4HlZlZlZpnAvcCmCftsAj4++vrDwHMXGj8XEZHIu+iQy+iY+APAs4xctvjPzrndZvYwUOec2wT8E/AjM2tgpGd+bzSLFhGR84U1l4tzbjOwecK6h8a9HgDujmxpIiIyFbooWUQkSSjQRUSShAJdRCRJKNBFRJKEZ7Mtmlkr0MjINetd4zaNX55sWwkT7kKdgYnvMd39Jtsean24bR7/OlJtDre94eyrNk++firLidjmqX7GE5fjuc2R+l5PXI5Umyudc6FvNXXOefoHeHSy5cm2MXK5ZFTef7r7TbY91Ppw2zzhdUTaHG571eaZtXkqy4nY5ql+xonU5kh9r2PR5ol/4mHI5T8usHyhbdF6/+nuN9n2UOvDbbOX7Q1nX7V58vVTWU7ENk/1M564HM9tjtT3euJyNNr8Np4NucyEmdW5SWYbS1Zqc2pQm1NDtNocDz306XjU6wI8oDanBrU5NUSlzQnZQxcRkfMlag9dREQmUKCLiCQJBbqISJJIikA3szwz+xcz+0cz+z2v64kFM1tiZv9kZk95XUusmNkHRj/jX5rZu72uJxbM7FIz+56ZPWVmf+h1PbEw+vtcb2bv87qWWDCzm8zspdHP+aaZHCtuA93M/tnMWszsrQnrN5rZfjNrMLMHR1d/EHjKOfdp4I6YFxshU2mzc+6wc+6T3lQaOVNs8y9GP+NPAB/xoNyImGKb9zrnPgPcAyTkpX1T/F0G+DLwZGyrjKwpttkBvUA2I09/m75o3K0Uobu+bgTWA2+NW+cDDgFLgEzgTWAV8BXgitF9HvO69li0edz2p7yu24M2fwtY73XtsWozI52UV4GPel17tNsL3MLIA3I+AbzP69pj1Oa00e1lwI9n8r5x20N3zr3I+c8l3QA0uJHe6RDwOHAnI/+qVYzuE7dtupgptjkpTKXNNuKvgWecc9tiXWukTPVzds5tcs5dCyTkcOIU23szcDXwUeDTZpaQv89TabNzLji6vQOY0dOyw3piURwpB46NW24GrgK+A3zXzG4nBrfXxljINptZMfB/gHVm9hXn3F95Ul10TPY5/wkjPbhCM1vmnPueF8VFyWSf802MDClmMeGpYQkuZHudcw8AmNkngDPjwi4ZTPYZfxB4DzAb+O5M3iDRAt1CrHPOuT7gD2JdTIxM1uY24DOxLiZGJmvzdxj5xzsZTdbm54HnY1tKTIRs77kXzv0wdqXEzGSf8c+An0XiDRLtP2eagYXjliuAEx7VEitqs9qcjFKtvRCDNidaoNcCy82syswyGTl5ssnjmqJNbVabk1GqtRdi0WavzwZf4CzxvwMngWFG/mX75Oj69wIHGDlb/Ode16k2q81qs9obL23W5FwiIkki0YZcRERkEgp0EZEkoUAXEUkSCnQRkSShQBcRSRIKdBGRJKFAFxFJEgp0EZEkoUAXEUkS/x9+G1Nxoo1TtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(st.lognorm.pdf(np.arange(0.001,100,0.001), s = 1))\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 2.0, 3.0]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = ['1', '2', '3']\n",
    "list(map(float, a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}