{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11453582",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fitter import Fitter\n",
    "from scipy import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a45df375",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from fitter import Fitter, get_common_distributions, get_distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af1f1ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "suv_filename = \"/home/marija/maki/suv_percentilesSLOthenUWM.mat\"\n",
    "flags_filename=\"/home/marija/maki/flags_combined.mat\"\n",
    "normal_range_filename =\"/home/marija/maki/normal_range.mat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a2a1241",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Loading all data\n",
    "\"\"\"\n",
    "suv_dict = io.loadmat(suv_filename)\n",
    "flags_dict = io.loadmat(flags_filename)\n",
    "normal_range_dict = io.loadmat(normal_range_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb8fde87",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    " Extracting only data associated to lungs\n",
    "\"\"\"\n",
    "lung_suv = suv_dict['lung_SUVperc_COMBINED'][0:58,:]\n",
    "flags = flags_dict['flags'][0:58,3]  # 0 == NC, 1 == AE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ba61636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58, 17, 100)\n",
      "(58,)\n"
     ]
    }
   ],
   "source": [
    "print(lung_suv.shape)\n",
    "print(flags.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d7b11d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "NC: 53\n",
      "AE: 5\n"
     ]
    }
   ],
   "source": [
    "print(flags)\n",
    "print(\"NC:\", np.count_nonzero(flags==0))\n",
    "print(\"AE:\", np.count_nonzero(flags==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fb4074a",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 95  # percentile of interest\n",
    "X = np.nanmax(lung_suv[:,:,p], axis=1)  # taking SUV max and ignoring NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2df8bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_NC = X[flags == 0]\n",
    "X_AE = X[flags == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29b7c7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a4f215e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4885eb7210>"
      ]
     },
     "execution_count": 14,
     "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": [
    "\"\"\"\n",
    " Plotting stuff\n",
    "\"\"\"\n",
    "plt.hist(X_NC,density=True,label=\"NC\")\n",
    "plt.hist(X_AE,density=True,label=\"AE\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b64ee380",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting 106 distributions:  14%|▏| 15/106 [00:01<00:04, 18.39WARNING:root:SKIPPED kstwo distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  21%|▏| 22/106 [00:02<00:07, 11.78WARNING:root:SKIPPED rv_continuous distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  25%|▏| 26/106 [00:02<00:07, 11.32WARNING:root:SKIPPED rv_histogram distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  80%|▊| 85/106 [00:09<00:02,  8.72/home/marija/.conda/envs/pytorch/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py:3094: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  t1 = integrate.quad(llc, -np.inf, x)[0]\n",
      "Fitting 106 distributions:  95%|▉| 101/106 [00:24<00:13,  2.6/home/marija/.conda/envs/pytorch/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py:4837: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intg = integrate.quad(f, -xi, np.pi/2, **intg_kwargs)[0]\n",
      "/home/marija/.conda/envs/pytorch/lib/python3.7/site-packages/scipy/integrate/quadpack.py:880: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  **opt)\n",
      "Fitting 106 distributions:  97%|▉| 103/106 [00:29<00:07,  2.5WARNING:root:SKIPPED levy_stable distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  98%|▉| 104/106 [00:31<00:04,  2.3WARNING:root:SKIPPED studentized_range distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  99%|▉| 105/106 [00:32<00:02,  2.1WARNING:root:SKIPPED vonmises distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions: 100%|█| 106/106 [00:34<00:00,  3.0\n"
     ]
    },
    {
     "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>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dgamma</th>\n",
       "      <td>57.256870</td>\n",
       "      <td>425.526074</td>\n",
       "      <td>16.005429</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.096016</td>\n",
       "      <td>0.677014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dweibull</th>\n",
       "      <td>59.582768</td>\n",
       "      <td>414.337885</td>\n",
       "      <td>18.115823</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.098857</td>\n",
       "      <td>0.642004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mielke</th>\n",
       "      <td>61.068543</td>\n",
       "      <td>389.794907</td>\n",
       "      <td>23.391531</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.092708</td>\n",
       "      <td>0.717457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logistic</th>\n",
       "      <td>61.387552</td>\n",
       "      <td>406.729837</td>\n",
       "      <td>15.727087</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.100015</td>\n",
       "      <td>0.627755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burr</th>\n",
       "      <td>61.891249</td>\n",
       "      <td>371.295200</td>\n",
       "      <td>24.100772</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.099767</td>\n",
       "      <td>0.630798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sumsquare_error         aic        bic  kl_div  ks_statistic  \\\n",
       "dgamma          57.256870  425.526074  16.005429     inf      0.096016   \n",
       "dweibull        59.582768  414.337885  18.115823     inf      0.098857   \n",
       "mielke          61.068543  389.794907  23.391531     inf      0.092708   \n",
       "logistic        61.387552  406.729837  15.727087     inf      0.100015   \n",
       "burr            61.891249  371.295200  24.100772     inf      0.099767   \n",
       "\n",
       "          ks_pvalue  \n",
       "dgamma     0.677014  \n",
       "dweibull   0.642004  \n",
       "mielke     0.717457  \n",
       "logistic   0.627755  \n",
       "burr       0.630798  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marija/.conda/envs/pytorch/lib/python3.7/site-packages/scipy/integrate/quadpack.py:880: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  **opt)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_NC)\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c87fdec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting 5 distributions: 100%|█| 5/5 [00:02<00:00,  1.82it/s]\n"
     ]
    },
    {
     "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>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>burr</th>\n",
       "      <td>61.891249</td>\n",
       "      <td>371.295200</td>\n",
       "      <td>24.100772</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.099767</td>\n",
       "      <td>0.630798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm</th>\n",
       "      <td>62.494042</td>\n",
       "      <td>445.292821</td>\n",
       "      <td>16.673888</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.148212</td>\n",
       "      <td>0.176081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>63.467576</td>\n",
       "      <td>352.535206</td>\n",
       "      <td>21.463450</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.134092</td>\n",
       "      <td>0.271326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognorm</th>\n",
       "      <td>63.523706</td>\n",
       "      <td>344.823594</td>\n",
       "      <td>21.510302</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.130770</td>\n",
       "      <td>0.298382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>63.641649</td>\n",
       "      <td>348.827907</td>\n",
       "      <td>25.578906</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.135396</td>\n",
       "      <td>0.261207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sumsquare_error         aic        bic  kl_div  ks_statistic  \\\n",
       "burr           61.891249  371.295200  24.100772     inf      0.099767   \n",
       "norm           62.494042  445.292821  16.673888     inf      0.148212   \n",
       "gamma          63.467576  352.535206  21.463450     inf      0.134092   \n",
       "lognorm        63.523706  344.823594  21.510302     inf      0.130770   \n",
       "beta           63.641649  348.827907  25.578906     inf      0.135396   \n",
       "\n",
       "         ks_pvalue  \n",
       "burr      0.630798  \n",
       "norm      0.176081  \n",
       "gamma     0.271326  \n",
       "lognorm   0.298382  \n",
       "beta      0.261207  "
      ]
     },
     "execution_count": 16,
     "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": [
    "f = Fitter(X_NC, distributions=['gamma',\n",
    "                          'lognorm',\n",
    "                          \"beta\",\n",
    "                          \"burr\",\n",
    "                          \"norm\"])\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6a1a7ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting 10 distributions: 100%|█| 10/10 [00:00<00:00, 29.96it\n"
     ]
    },
    {
     "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>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>norm</th>\n",
       "      <td>62.494042</td>\n",
       "      <td>445.292821</td>\n",
       "      <td>16.673888</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.148212</td>\n",
       "      <td>0.176081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rayleigh</th>\n",
       "      <td>63.176997</td>\n",
       "      <td>inf</td>\n",
       "      <td>17.249947</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.133943</td>\n",
       "      <td>0.272504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>63.467576</td>\n",
       "      <td>352.535206</td>\n",
       "      <td>21.463450</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.134092</td>\n",
       "      <td>0.271326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognorm</th>\n",
       "      <td>63.523706</td>\n",
       "      <td>344.823594</td>\n",
       "      <td>21.510302</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.130770</td>\n",
       "      <td>0.298382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cauchy</th>\n",
       "      <td>70.967112</td>\n",
       "      <td>297.308600</td>\n",
       "      <td>23.412590</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.132246</td>\n",
       "      <td>0.286136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sumsquare_error         aic        bic  kl_div  ks_statistic  \\\n",
       "norm            62.494042  445.292821  16.673888     inf      0.148212   \n",
       "rayleigh        63.176997         inf  17.249947     inf      0.133943   \n",
       "gamma           63.467576  352.535206  21.463450     inf      0.134092   \n",
       "lognorm         63.523706  344.823594  21.510302     inf      0.130770   \n",
       "cauchy          70.967112  297.308600  23.412590     inf      0.132246   \n",
       "\n",
       "          ks_pvalue  \n",
       "norm       0.176081  \n",
       "rayleigh   0.272504  \n",
       "gamma      0.271326  \n",
       "lognorm    0.298382  \n",
       "cauchy     0.286136  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ejrMy0Rlj1fiac9y1qRpzVRdK/ErzMnr0aNasWUPPnj0RQvDqq68SFhbG2LFj+fPPP+nevTudOnWib9+++PmdfeTOqVOnMmnSJHr06IGnpydffPFFI7XiTCqhK4py0SgqckztIITgtdde47XXXjttvUaj4fXXX8fb25vs7Gz69OlTOe1c1WvcvXr1qjx7DwwMZO7cuWd819SpU8/6uSKWmtadK5XQFUVRqrj22mvJy8vDbDbzr3/9i7CwsKYOyWUqoSuKolThynXz5krdFFUURWkhVEJXFEVpIVRCVxRFaSFcmbHIJIRYL4TYKoTYKYR4roYyQggxTQhxQAixTQjRyz3hKoqiKLVx5Qy9HBgspewJxAPDhBD9qpW5GujofE0GPmjIIBVFUZqziRMn8sMPPzR1GHUndOdE0xUdJvXOV/VnXUcBXzrLrgX8hRDhDRuqoiiKcjbClak/nfOJbgRigPeklI9VWz8feEVKudL5+U/gMSnlhmrlJuM4gyc0NDRx5syZgKOD/YUy8HzVWLcfz69cHhdx9qfJGtv24/mEekBGqeNz1fiaY9x1HQNVY66qKdp1oR6vTc3Pz4+YmJha19tsNrRarVtj+Pbbb3nnnXcQQhAbG8uYMWN49dVXsVgsBAYGMmPGDFq1asVLL72Et7c3f//73wHo27cvs2fPJioqim+//ZZp06ah0WiIjY3l448/ZsqUKfj4+LB582YyMzN5/vnnue6667jrrru47rrruOYaxxPNd955J2PHjmX48OEuxXvgwIHKgb4qDBo0aKOUMqmm8i71Q5dS2oB4IYQ/8D8hRHcp5Y4qRURNm9VQz3RgOkBSUpJMTk4GHP0+K943d1VjnVj1Efpbk5smoFpMdD76/8Z2x4+4anzNMe66joGJtT363wTtulCP16a2e/fuysG33rjpWrd8x8Oz5te6bufOnbz55pusWrWK4OBgcnJyEEJwww03IIRgxowZvP/++7zxxhsYjUaMRmNlvBqNBm9vb44ePcqbb77Jb7/9RnR0NDk5Ofj4+KDX68nOzmbNmjXs2bOHkSNHMn78eO655x7++9//Mm7cOPLz80lJSeHbb79Fp3PtESCTyURCwhlTOteqXg8WSSnzhBBLgWFA1YSeBrSp8jkSSK9P3YqiKO60ePFirr/+eoKDgwHHI/vbt2/npptu4sSJE5jNZtq1a+dSHUFBQZV1VLjuuuvQaDR069aNjIwMAAYOHMh9991HZmYmP/74I2PHjnU5mZ+LOmsWQoQAFmcy9wCuBP5Trdg84H4hxEygL5AvpTzR4NEqitIi1HQm7e7hc6WUlcPVVnjggQd46KGHGDlyJEuXLq0cU0Wn02G32yvLlZWV1VpHhYqhcSvKVRg/fjzffPMNM2fO5NNPP22o5tTIlV4u4cASIcQ2IAVYJKWcL4SYIoSY4iyzADgEHAA+Bu51S7SKoijn6IorrmD27NlkZ2cDkJOTQ35+PhEREQCnjZIYHR3Npk2bANi0aROHDx+utY66TJw4kbfeeguA2NjYBmtPTeo8Q5dSbgPOuIgjpfywynsJ3NewoSmKojSc2NhYnnrqKQYOHIhWqyUhIYGpU6dyww03EBERQb9+/SoT99ixY/nyyy+Jj4+nd+/edOrU6bQ6hg8fjl6vJyEhgc8///ys3xsaGkrXrl3Pe3o5V6jBuRRFuWhMmDCBCRMmnLZs1KhRZ5Tz8PDg999/r7WOMWPGnHZ5qHpSrzo0bklJCfv37+fmm28+j8hdox79VxRFcZM//viDLl268MADD9Q5UUZDUGfoiqIobnLllVdy9OjRRvs+dYauKEqjceVBRsXhXPaVSuiKojQKk8lEdna2SuoukFKSnZ2NyWSq13bqkouiKI0iMjKStLQ0Tp06VeP6srKyeiewptIYsZpMJiIjI+u1jUroiqI0Cr1ef9YnMZcuXVqvx9ybUnONVV1yURRFaSFUQlcURWkhVEJXFEVpIVRCVxRFaSFUQlcURWkhVEJXFEVpIVRCVxRFaSFUQlcURWkh6kzoQog2QoglQojdQoidQogHayiTLITIF0Jscb6ecU+4iqIoSm1ceVLUCjwspdwkhPABNgohFkkpd1Urt0JK6Z6ZXxVFUZQ61XmGLqU8IaXc5HxfCOwGItwdmKIoilI/oj4jnwkhooHlQHcpZUGV5cnAHCANSAf+KaXcWcP2k4HJAKGhoYkzZ84EHLN7eHt7n2sbGlXVWLcfz69cHhfh/sHr62P78XxCPSCj1PG5anzNMe66joGqMVfVFO26UI/X5k7F6ppBgwZtlFIm1bTO5YQuhPAGlgH/llL+WG2dL2CXUhYJIYYDb0spO56tvqSkJLlhwwbAMdBNcnKyS3E0taqxRj/+S+Xy1FeuaaKIahb9+C88HGflje2Oq2pV42uOcdd1DFSNuaqmaNeFerw2dypW1wghak3oLvVyEULocZyBf1M9mQNIKQuklEXO9wsAvRAi+DxiVhRFUerJlV4uAvgE2C2lfLOWMmHOcggh+jjrzW7IQBVFUZSzc6WXywBgPLBdCLHFuexJoC2AlPJD4HrgHiGEFSgFxkk1LYmiKEqjqjOhSylXAqKOMu8C7zZUUIqiKEr9qSdFFUVRWgiV0BVFUVoIldAVRVFaCJXQFUVRWgiV0BVFUVoIldAVRVFaCJXQFUVRWgiV0BVFUVoIV54UVS5iUkp2Lv2DgxvX4x8WTkjbaMI7dSEgrHVTh6YoSjUqoSu1sttsLP58Olt/rzbSoRBcfe//0e3ywU0TmKIoNVIJXalRWXER89/6D0e2bUar19P/hluxWSycPLiPQ5tS+O3DafgGtyKyW/emDlVRFCeV0JUzSCmZ+/qLpO3agYevH6P++TQRnbtWrl/82Uds/vVn5r7xb2558XUCwtUEVorSHKiEfoGobfKG2iZ+ONv2dZXpWLSfYaccyfzWf7+JX6vQ08oI2ZprPKJoV3SE//3nOW7595uYvGqevcXV+B6OszKxWtnmMvmGolwoVC8X5TQ6u4UBOWsAuOzmCacl8wpSaPit1RBCotqReyKdlLk/NHaYiqLUQCV05TSJ+ZvxsRXTql0HYpOvqLWcRaNnyOT7Adj863xK8vMaKUJFUWrjyoxFbYQQS4QQu4UQO4UQD9ZQRgghpgkhDgghtgkherknXMWdfCyF9MrfAsDgiXej0WjPWj48pjPte/XGUl5Gys9nzEyoKEojc+UM3Qo8LKXsCvQD7hNCdKtW5mqgo/M1GfigQaNUGkX/3LXopI29XjFEdKn+I65lmxtuBWDLb79QnJfrzvAURalDnQldSnlCSrnJ+b4Q2A1U79YwCvhSOqwF/IUQ4Q0ereI2BacyiSk+iA0NqwMvcXm70PYxdEjqh9Vcznp1LV1RmlS9rqELIaKBBGBdtVURwLEqn9M4M+krzdiW339Bg2S/VweKdDX3WKlN/xtuAWDrogUU5ai5wRWlqQhX53IWQngDy4B/Syl/rLbuF+Bl5/yjCCH+BB6VUm6sVm4yjksyhIaGJs6cOROAoqIivL3rl0SaStVYtx/Pr1weF+Hn1u+t7buqLq8u1AMySs9eb1yEH3arhW1ffoStvAzdZaPQBLSqtT21xXHw17nkHd5PWGI/Ivpc6lJ8dcXqSjtrK+POn8eFerw2dypW1wwaNGijlDKppnUu9UMXQuiBOcA31ZO5UxrQpsrnSCC9eiEp5XRgOkBSUpJMTk4GYOnSpVS8b+6qxlq133Tqrclu/d7avqt63+2qHo6z8sb2s/+IU29NZvvi37GVl5FhaMXstNaQVnt7aosjplUws557nMJD+7jsocfR6nR1xldXrK60s7Yy7vx5XKjHa3OnYj1/rvRyEcAnwG4p5Zu1FJsH3O7s7dIPyJdSnmjAOBU3kVKy+defAdjqe+6P8Ud0jSWwdSTFuTkc2pzSUOEpilIPrlxDHwCMBwYLIbY4X8OFEFOEEFOcZRYAh4ADwMfAve4JV2lox/fs5NSRw3j4+rHfO+ac6xFC0OPKYQBs++PXhgpPUZR6qPOSi/O6uKijjATua6iglMaz+df5APS4Yhj2LWfvd16XbpcPZsW3n5O6dRP5mRk1PmWqKIr7qCdFL2ImWykHUtYihIaeQ64+7/o8fHzp2HcASMn2xb83QISKotSHSugXsU5FB7DbrET3TMAnKLhB6ux5peMXw46li7BZrQ1Sp6IorlEJ/SLWtWgPAN0G1j5mS32ddnN00/oGq1dRlLqphH6RCjRn08qchdHTi5ikfg1WrxCCuMFDAdi1fHGD1asoSt3UeOgXG2FB63mI7iVbAMhoK7l90UT0Gj0ebQqQdk/s5a3484iJbkHdCPeu/wgOXS5NZvk3n3No0wZMEV0p05oauBGKotREJfSLghWd31b0PtvReh1Eg4WO+yMAHasCDnIqywxA1Sf+/7H0DwA6BXTiirZXMCRqCB0DOrr0bd4BgUT1iCd16yY6Fh9g+3n0b1cUxXUqobdgZlmGPnAThsCVaPQFlcvDjkfgWa4jz2jk2bFv4m/yx2wzM+7jlQhdIVpjBslxVrae2sq+3H3sy93HB1s/ICk0Ca13N2xFXajral23ywaRunUTnYv2qYSuKI1EJfQWSaLz3cJM28+YQksAsJWFYsntj7WoK+3S1wEH2O3ZkxfaDKzcylacCTjGS/5oyDWYbWbWnVjHn0f/5NfUX9mQsQHPNhuwlwdTfuoqpByO40HiM8X0vgS90UR4eQZ+ljzy9f5ubrOiKOqmaAsj9Nl4tPkUj4hZlFGCtSSKkqOTKDn8Dyx5fdGbjbQvOYwE9nh3PmtdBq2ByyIvY2r/qfxx/R88kvQIdrM/GmMWHpHfMOHXCWw/tb3GbfUmE536DQCgS9G+hm6moig1UAm9BdF578Sr3dvovPcjrZ4M1Iyh9MgUbMWdqXjYt0PxIXTSRpopol7D5HobvLk99naKDz5C2YnrsFu92Jy5mVsW3MJL616ixFJyxjZdLxsEQOeifeDiqJ6Kopw7ldBbBDuG4EV4tPkKoTVjKYij+NBDdNb0ovqoDRVny3u9O53jd2mx5PWj+OAjTOo+CZ3Q8d2e7xg7bywpJ08flKtNbByFWi/8rIWEl588x+9TFMVVKqFf6IQFU+TXGEP+REpBWcZwyo7fgrRVO/uWEGwuJLIsHRtavHUx9CrXsnXxMXavPkH6gTy87I5yLrGbeCjxIb679js6B3QmrSiNO367g7c3vY3V7nhCVKPRss/5i0NddlEU91M3RS9gpdZSPCK/dFxisZkoPX4LtuIqZ942iDFraG/V0t6ixaN0O1ZAr+9AcrkPACtn768sfi8elAjJUZ2NXavSadstEO+As/ch7xLYhe+u+Y7p26czfdt0ZmyfwdZTW/nPZf8hxDOEPd6dSMzfTEzxAZYHDsCmUYecoriL+t91gSqxlPDA4gfQee/HbvWm9OjfsJeHAeBnE/Qq1yHWmRhtc1xykVJSXr4LAKPVD5/M9WikxNegwaL3pNSjFXn44Kk10cWiY8lXe0BAmy4BdLs0gnY9g9Hqav6DTq/Vc1/8ffQJ68Mjyx4h5WQKN/x8A/8d9F9yDIFkGoJpZc4iuvQIB706NM4OUpSLkEroF6Ayaxn3/XkfGzI2YLf6UHrkLuzmVvjaBAPLdHSyaNE4r53LskxaZ27CP28Lm9sY0FttDNw5s9ZrbSWmYHICuzpeQbEc253Lsd25ePjoSRgShV6CpZbBlHuH9eaHkT/w6PJHSTmZwp2/3YnOdwx78zvRKieLLkX7VEJXFDdSCf2CY+fJlU+yIWMDrTxacWjHeLTlIfQr09GnXIcegZR2/LK30Cn1V3yK0gDY2KY1AHmaYKb2G0yWhz9mjY4lTwxBlpdjPXWKB6b9TtvCDLrmpNLzQArmPYKToX1IDx9AMRGs/vEAk4WRdUYbm401j6QY7BHM9CHT+c/6/zBz70w8ImaRKi5nQI4gquQoJluZGgpAUdykzoQuhPgUuBbIlFKe8cifECIZmAscdi76UUr5fAPGqFRhbPUri44sx1vvzUdDPuK29fsYUWLA3+445w48tYkuB+ZgKs+j1D+Az7tezZqwblyR/zvethJ+iUgmwxRWWZ8hMtJRb4cOLP4pu3L54ReGUpKSQsjChUT9/h6Z2kgOR18DvlEMKtOQUK7h2K4c2nQLPCNGnUbHU/2eop1fO15a9wq21ss5nhpDmzyLGgpAUdzIlV4unwPD6iizQkoZ73ypZO4mev91GIKWoxM63hj4BoUpBm4pMuJv12AoySBh83+J3/kJ+70DeL7PRFbd9wizOl8BegvethLydH5kGF2bRUjo9Xj170/4Cy/QaeUKejw1iUvNC+ix/QM8S07iL7XMm7aFhR9upTi/vMY6bul6C6XHJiFtBg5HOaaY7ewcsldRlIbnyhR0y4UQ0Y0Qi3IWWo9DGMPmAvB0r2fI+Z8XR3YcQIugzbHFdDg0l3RPf97uO4l1Yd1ACPprHJdFuhbtBWC3T2eo5VH9qjTYIWMnnNwBeUcReUfwKz6F70gLWauPk7DrVU4aL+dw9NUc2pLN8R1LSb6pDTGXdj2jfltxJ0qOTiY1/DMu0doJLz+Fn+0k+dqwWr5dUZRzJaQLT/A5E/r8s1xymQOkAenAP6WUO2upZzIwGSA0NDRx5syZABQVFeHt7fpTi02paqzbj+dXLo+L8HPbdxbaCnkx7WVKKKS3eSj9D1xDeT7orCV02/0FAXl7OJQ8hKN9ByC1f/2ODvWAkwXlWH77Buw29FfejPA8fT9XxG0qzcB6cDFtSnYQVnYQo7201nikhOKTRo7simZr+ARyA7sC0MGwiu4dN1MYEkdOYAIWg3/lPsqTWWRumkX0cQN7Y6zEdL0dD+F1WqwZ1b6y6j6tuq9dKePOn8eFerw2dypW1wwaNGijlDKppnUNkdB9AbuUskgIMRx4W0pZ5zirSUlJcsOGDQAsXbqU5OTkOuNoDqrGGv34L5XLU1+5xi3fZ7PbmPLHFNaeWItfdhLX7r8VH6nBs+QkPbe9zwmDgVeTbuGI75njlj8cZ+XXVfsYnL2MY6YIfgofedp6X4rYNvIUbJ8NJ7aevrFfW4hIgIB24N8WfMJAa+SWzzaiQRJIIW9f25qcX1awOcWf/VHXYdca8LafZHjwfwgxHoUOg7lnVyx/2BOxoCPSvJfRxxdTZLIyq7+F0mN/Q1p9K2N9Y/vpfzBW3adV97UrZdz184AL93ht7lSsrhFC1JrQz7uXi5SyoMr7BUKI94UQwVLKrPOtW4Hp26ez9sRaQvK6cO3+WzFKDX55B+ix4yNCb72em3K6YdHW/mOsmGZud5WBuGJEGndoFzJauwp+d4yFjsGbuaU9WGRLJMXemXVTx9dY32r7X9fLp/W/hqD+9zEwI5PIF99iXW4Xirwj+SH7VS7zmkH3A7/zgeEPsqUPX9mG8Jm8igKdF75lxUQWZXO87XRKjt6FtLrvbFpRLibn/ei/ECJMOMdQFUL0cdaZffatFFdsytjEB1s+ILQwmhF7JmOUGlplbiJh14e0ff5pQp944qzJXBblEV6egVnoOejVnvYinWn6d/jd8Bi36JbgIczQYTDc9DU8cpAHLfcz334JGZzZc+Vs9KGt6PLOS4y4KZjWWSnYhZ5lJfewyDqNnbZogkQh/9D9yCrTgwT7Oq7rxxwJQWPMwjNqOkJX8+UURVHqp86ELoT4DlgDdBZCpAkh7hRCTBFCTHEWuR7YIYTYCkwDxklXruMoZ1VqLeVfq/5FSGFbrtv7AAappVXGBiIOzKbd5zPwHzO6zjpsxxyP9R/zasOLhs9YZHiEkdo1WNDylfVKrih/Dcb/D7qOAP359w0PHDGca9+bSLx1NRq7hX1Zbfg97UluK3mWZbYeeIsybg9YAUD7TA26gtZoDNl4tv2YEll43t+vKBc7V3q53FzH+neBdxssIgWAaZumUXzSyujdDyJsOlplbiA4dR4PD7yPNb161bm9kHbszoT+YOBCOusysUgts6wDedd6HekEuyVufWgo/ac/SfBbX7J8ux8YQxiQZeBZn7/j7XmSf+q+p61nLkdLAnjpQDbPdvOn1JTFfNunCO1kpM3HLXEpysVAjbbYDKWcTOGnLb9w7a4p6GwmQjI34Xt0AY9cfg8nvFxLxAPKNkJZMX76Ujp5ZrLE1pMh5ld50vo3tyXzCkKjofNDE7lufBieRcewG/y4qdgT71w/JloeY6V3HwBy8rz4LWMHoeV68jiFR9sZoC12a2yK0pKphN7MlFhKeGHpS4zYOQUPqx/+uftIsK3h0cumkOXhX+f2Oqw8oP2RMUW/A9DWr5DJloeYZHmUVHlmTxh3Chl8Ce8FGTDm7cWu8+ByexhjTqYx3yOZco2Bk2U+FBf7MOvkYdqZbWhNGXi2+Qw0ZY0ap6K0FCqhNzMfbZpOfMpw/MrD8Co6TmLJItp9PoN8Y92XIqLESeYYpnIX80gtCkAIeMzz7yyyJ1F9oovGkuXhy38iW2PL24nU6Ohg6shdaUfZ5xkDwEs5o9lt6crHJ08SabGg9UjDu82nIMxNEq+iXMhUQm9GDuQe4Ni8ckKLO2AsyyUp6yc6zPgAXUBAnduO0KxmvuEpemoOsTq3PRKBCG9Prrbubd3NotPxZlQ7Mor3gLTj4xtLYpGj/3lk0TEmlj/CrsDbeO9ELqFWK8LzKCFtPsJsU0ldUepDJfRmQkrJjK9/pEN2bzS2cnqlz6bTjLfRh7Y663ZGzLykm8E7hnfxEaX8bO3L+rwoADRRXRojdNcIwZcRUeywpiLsVjQB/dHih9FuplPxAXb4X8Hkkud5PF1HoM1GmddxHp83Dput5lEdFUU5k0rozcQPixcSucPReyX28Cxi33uxciTEWuUfZ5bhBW7RLaZc6nnScidv5o/Ey1ZCrs4PEdy6ESKvn4Uh4SzVZKKxlaPxvASAxLytSCk5ICO5t/gFrj3eHm+7nUUF+3lh1tVI85kTUCuKciaV0JuB9OOnOPm9RAgNUUcXkvjC3Rg71jF6wpHVMH0g8ZqDpMlgxpif41vbFXQvdMxKtNO3G8KFgbiawnr/AOZ4FKETESA88LfmYdrrGP7HjJ73iu+hS9oATHbJHMtJ/vvVQELJaeKoFaX5Uwm9iVnNNua+sRINHgRnbaX/nf3wuuSSs2+06Sv4YgQUn2KlLZYR5S+yU0bja8knqvQoNjSnPerfHB3w9ORrP9DpHHOgmvcfp2/Ggcr1S4pH8WbS4+gkfKYr49ZWz9NDHGyqcBXlgqASehNb+NEq7CV+eJRk0jUhj8CxY2stK7DzmO47mHc/2K3Q7z4mWB4nF8cNxviC7Qhgr3dHyrQejdSCc5duNPBDcDcAyuVxhlq8GZK2o3L9ZXG38e++/0JI+DjIxG1Br3GtZk1ThasozZ5K6E1o15LDHN1pRWO3EGz7ibgnnq21rBEz7+mncY/uZxBauPYtGPYSNrSO9bZyuhbuBmCLX8/GCL9BHDP5c9QjCrBRpM3kEm00Yw+nVK4f3vVGHu/9KAD/DvZjtP8M7tb+DKjRJRSlOpXQm0h2WiHLZjoezQ8+MZukN/+N0NTy4yjJ4RvDSwzXrqdAesJtcyBp0mlFYgt3YZBWjpoiyTYEuTv8BrXJLw4Aa/kmSkwBdPXozh17V1AxJNAtseMpP3UFdiF4NCSYK31+5AXdZ2ixNWXYitLsqITeBKxmGwteW4Fd6Ag6tZbsKW1pHdqh5sK5R+CToSRp9nFcBjHWPBU6DDqtiEba6FmwHYDNF9DZeYVjpkiEXxDIUkpKN1JuCiTcvy/7nn4NaXMkbXPWlZhzLsGsETwQGkKi5zI+1L8Flton4lCUi41K6E1g6TsrKCg34VFykmXxC7ltyMM1Fzy5Az4ZCtn72W1vw5jy59gvz+zK2LHoAN62YrL1ARz1aOPm6N1ACDQxjl9ERbbt5FrzMBv9WHaiMzv/73nsZjMgKM8YgSW/B8UaDXeHhdLRuBW+Gg2luU0bv6I0EyqhN7J9f+5l7347wm4h3fgFl11/D96GGqayOroWPhsORSch+jJuMj9T8zjlUpJQsA1wXjtvpl0V66IJb0e+zhc/awErTMfItBdiMfiwqrg32+95GqO1HNBQln4j1qKO5Gk1/C0sjIzj6x37qeBEUzdBUZqcSuiNqCCjkKWzHF3vTPlzWTHEzvWdrj+z4L7f4cvroDzfMVb5rT9QgNeZ5YCo0qOEmLMo0Xiw16vOmf+aLaHRsMkvHoD4/M187a8lqoMRq96TNWIgU7cswNtcAugoTbsNW2kbMvQa7o6IJD9rD3wylChxsknboChNrc7x0IUQnwLXApm1zCkqgLeB4UAJMFFKuamhA63qfOeOdMfck7XNeVlBSPhPmRmLxg/f/J28P3A5jya8gEFrOL3g9h/gf3c7uiX2ut3Rm0Wjrfm7pOTGXEePkE3+8dg09Z9RsK64z6XMue7T3d6d6ZubQitzFq3L0/h7lmCssBOt8yKv3Vie2/oDL8ZdSa7Jl5JjE/GM+oiDxkzubRPNx0cP84PhOW4zP8Fe2facvr+pNNZcqErTaoyfsytn6J8Dw86y/mqgo/M1Gfjg/MNqea4/dYpT5X4YzAXMi/2GoPBoRnQYcXqhDZ/BnL85kvmAB2HEtDOSeVXRpUcINZ+iWOvBdp9YN7fA/WwaHVudPV765G7AjuQHXw37NCXYtUYyO9zI1G2LCC3OBpsXpUfvpLVXa7ZprPwjqiN+Ip9ZhhdIEPubuCWK0jTqTOhSyuVw1ueuRwFfSoe1gL8QonEH3m7muhYV0E7nGFclx+dH9rQp5t6e96Kreka9ahrM/wcg4YpnYcjzZ78eLiV9nWfnG/16YdXo3deARrTVN45SjYnW5SeJKj2KFDDXR7BTW4pdo+d4zM38a+cyogpOIK1+fDTkIwJNgawRZUwKicFHFPO14SU4vLypm6IojU64Mv2nECIamF/LJZf5wCtSypXOz38Cj0kpN9RQdjKOs3hCQ0MTZ86cCUBRURHe3jXcGKzF9uN/TSocF1H/GePPZ/uqsVatpzbCbMVjuY0yYzD+pRt5ZdAXhBta83j442iEBqQkOnUm0Ucc+2Jfx7tJjxh+1pgB7CdSsaYsAqMn+itvQtQwWXSoB2ScY6++qvultnbWVsaVbaurGqvt4DZsO9chfAPRDRzjGJNGguaAFk56grTT6eBs0ofEEdU3jjRzGtNOTqNUljK4zJO3TuzBrjGwM/YxcoKS6tNsl9T3eK3L+R7PZ9PQsbpTS4+1oX7OgwYN2iilrPHAboiE/gvwcrWE/qiUcuPZ6kxKSpIbNjhy/tKlS0lOTq4zjgpNeQ29aqyuXFt+4FgaJp+OGEozWZD8GQc06byZ/CZDooaAlPD707DmXRAaGPU+xNc8hetp3yUlN6d/T7A5m2VBl7LNN67GbR6Os/LG9vpfV4fT90tt7aytjCvbVlc1Vq3dyvi0b/GxFfNryJXs93be7JVwWamgn9kxoXW7A99z+bO34n3ZpWzO3Mz4X+5EaCz0zvXnk7xtCI0exs6A2OtcisFV9T1e6+LOa6sNHas7tfRYG+rnLISoNaE3RC+XNKBq5+dIIL0B6r3gDcl0JHNht7EkYDsHNOnE+MdwRdsrwG6HXx5yJHONHm74vNZkXl2Xor0Em7Mp0nqx07urexvRBGwaHesDegPQL3c9Gul8IlTACk/JMmM5AIdjbmDFv+dQsGABCa0SKE27DSm1pATk8WHcELBb4IdJsOW7pmqKojSqhkjo84DbhUM/IF9KedF3Cg4rKaC3dExOkVOyn62xKwG4K+4uNHY7zL0XNnwKWiOM+xa6jXKpXr3dTP/cdQCsDuh7Tj1bLgS7vTuTq/fH31pAN+cYNRXWe9j53VSOlJJD0SNY9u5ycr79DltxZ8qOj0NKwftFe/kiYRRIO/w0BVI+aaKWKErjqTOhCyG+A9YAnYUQaUKIO4UQU4QQU5xFFgCHgAPAx8C9bov2AqGRdu7MzMVi9ENbks6XXU+hMeQQ7RvNVZGDYM6dsPU70HvBrd9Dp6Eu152UtwkvWwknjKHs9e7kxlY0LSk0rAnoA0C/3BRMttMnjt5qsjPf04xAcrTtUJbN3Mdtu3/DWtCdshOOvv2v521mdh/nXz2/PASr323UNihKY3Oll8vNUspwKaVeShkppfxESvmhlPJD53oppbxPStlBShlX083Qi82EI3sx+3dBYyvnO389upBlAPyt2wS030+AXT+B0RfG/w/aD3S5Xl9LPgn5WwFYETTggn0q1FUHPdtzzBSBh72MS3LXnrF+j9HO8Ht7otVI0ltfRk9tWx7Y+j/sufE81fcpAF44tYr/9XcOZPb7U7DsNce9C0VpgdSTog0sITuNMC/HXJ677BkcDz2E1ngKafZn+NovYP9v4BEAE+ZB2771qvvSnDVosbPbuzMZxlB3hN+8CMGyoMuwoSG2cDehZWc+CRrdI5iR/9cLvR4yW/Ui3LcXT6Z8y41Ro/hn0j8BePbEYn6+/F7HjeclL8Kfz6mkrrRIKqE3IK/yUkYVabDqPTGXpvNzcDCGIMfZ+YT8IvSHl4NXK5j4C7ROqFfd0SWpdCg5jFnoWR1Qv18EF7JcQwCb/XoigOTsFQhpP6NM644BjH60N+XSSm5gVwzhV7F/0j3c2nokD/Z6EInk6bQF/Jr8oGMs+ZX/hYWPOW5MK0oLohJ6Q5GS+4/sptivAxprCZ+F+KL1OoDW4zh+Vvh78SHwjYRJCyG0fk91lhYVMjjL8YthXUBvSnQ1j+vSUqX4J1Kg9aaVOYu4gh01lglp48OXvlaKsFDoG8UqwzB233YPt/sN5d74e7FLO48f+Ylfr/wnaA2w/iOY9wDY1ZjqSsuhEnoDGXVsOwQ5utotNpZQoNPiHfQnABML8jhhC4U7FkJwTL3rXvr5dLxsJaQbw9haS5/zlsyq0bM86FIA+ueuw9+SV2O5PK3kS18rWcJCqWcr1oTezNaJDzHB1o+7e9yNTdp47OAsFgx5FPSesOVr+OEOsJobsTWK4j4qoTeAdvknSNBEYNcaOGXJIMXHi3DTNuzeqXja7STk+3Kj+Rnwr/+gUQc3rmPXiiVYhI4/QgYhxcX5Izvs1Y49Xh3RSytDM//8q296NcUa+NrXSmQnPywGHzZ0uJON//cat2fEcE/Pe7BLO0/s+5r5Qx5z3Jje9RPMvAXMJY3bIEVxg4szOzQgo9XM306cpNg7Emkp4JtgXzqKNHqHfAHAwAItd5b9i0wC6l13SUE+i6Y7utqtCehDvt6/IUO/4CwLuoxCrTeh5kx659X+ILJFwLUPJtC5TyvsWiPbutzB2td+4sb1eu7t4UjqT+7+lP8NeQQ8g+DAIvh6DJTmNV5jFMUNVEI/T1P2ryUvtB9IO9/7Crpr9vO25wss89KhlbDg1IPk4VPveu12GwvffYPivFwiunSr9fH+i4lZa2RRyGAkjv74NfV6qaDVarhiUix9RrQDoWF/xxtZNe8YI747xt9j70EieWbnx3wz6O/gGwFH18AX10JRZuM1SFEamEro52Fw2jZ8AvqA0LBT5BHlsZNvDC8x10+HXQjM+fEUWcPOqe61c2aRunUTHj6+DH/gkYv2Ukt1xz0i2OQXjwbJsMxFlBTUPviXEILe17Tjyknd0GgkaZHJrEiNIPmNdTzZ0fH82yu7PmHGpXdAYAc4uR0+vQpyUxupNYrSsFSWOEdlBw8xpNRIuSmAEms+2oAtzNC/jkVr5nsfX0eZnORzqvvwlo2smfMdCMHwvz+Cb3BIA0Z+4Vsb0IeTxlb42or4+b8vY7Naz1q+c98wRj2UiMlDQ05gN1bqr6Lr1D94KeRvCARv7/6CN5JGI8O6Q84hxzyuJ7c3UmsUpeGohH4O7GVlrHvqE3JC4sFmxhy0mneM72AQNh7zSsKikViLOmIvr//ZeV7GSRa8+wZISf/rbyG6R/36q18M7ELLglbDKNJ6krZrB0u/nFHnNq1j/Lnhqb4Ehpoo8QxjXZtJBL6ymnfN16MTOj7fP5unuw7AGn0pFGU45ilNXdkIrVGUhqMS+jnY/9x/2eVzGQB67xU85TEdgBcs41jh5xhzxJxzab3rLSnI58eXn6GssIB28Yn0G3NTwwXdwhTrvFjQ6iq0Oh1bfpvPtj9/q3Mb32APxj7Rh+i4QKx6T7Z0vYvi73OYsas/3sLEvNSF/F9kFKVdR0B5AXw1GnbMaYTWKErDUAm9ngyr17PmWCR2rQEfsZXJfu9ikVoeNN/LF15t0ejzsZW3wlZcvwmbLeVl/PTq8+SeSCckqh3XPPgYQqN+PGeTYQrjyr/dB8AfM96jXfHhOrcxmHQMv6en82ap4FD7kRzY34EP5kUTXebN0uMruMPLSlbSBLCZHf3UV01TQwUoFwSVMeqhbM8eMtaXU+wdgQ9ZjAt5hULpwSTLo8y1D8AQ6PgT3ZIzgPrsWrvNxi/TXuPE/r34BIcw5vGpGD093dSKlqX7oCH0HX0T0m5n2KlFRJQer3MboXHcLL3mvp4YDIKskHg2el3Pv2aGMfhEIDuyd3Jb+T4OD3zYscGif8GCR8B29mv1itLUVEJ3kS0/n/VPfER62AA00sKwwJfIEp7cYH6WlfY4tB5H0HqkYbd6Ysnv5Xq9VisL3nmdgxvWYfLyZuwTz+MdGOTGlrQ8A266jfirrkEnbVybsZDQ8gyXtouOC+bGf/UluLUHZR4hbOg4hWt/78z9awM5mZ/GbSd/Y+3Qpx1DBaR8DN/dBGWuTamnKE1BJXQXSLudPY++yM5gx7jll/l8TKu2PlxX/gJ7pOPpT33F2XleP5CuTdhss1qY/9Z/2LtmBQYPD0Y//ixBkW3q3lA5jRCCwRPvZo9XRwzSwqgT80nbs9Olbf1CPBn7RB9iLw1HavTs6zSOgMxRvDYnBGNmPlMOfMPMKx5yPoD0B3xyFaZS135hKEpjcymhCyGGCSH2CiEOCCEer2F9shAiXwixxfl6puFDbTonpn3A+pIE7FojMbplxPYywKSFlU9/Cl0eOp+dSKnBktvPpTqtZjPz3nyZAylrMHp6cf3TL9K6U8ubTq6xCI2GP0MGsd+zPUZpZs6/n+HQ5hSXttXptSTf1pWhd8ai18OpkHiOtLqfqT92pv82C//e9w0v9BmLJbgTnNpNr02PQOoqN7dIUerPlRmLtMB7wNVAN+BmIUS3GoqukFLGO1/PN3CcTSb/559ZtbyEEq8w/OUxYnqeQIz7Gox/zfitD1iDEBJrQQ+k1bfOOkvy85j9wpMc2rgek7cPN/zr34THdHZnMy4KdqHlt1ZD2OndBau5nLmvvcjulUtd3r5j71DGPXsJoW29KDcGsL3LPQzbfR0P/aRnwe6FTIqOIaP95Rgs+fDlSFg3Xd0sVZoVV87Q+wAHpJSHpJRmYCbg2gSYF7jStYtZ894fZLZKRCdLGX6LN8c63gJVe58IMwZ/x5mgOad/nXVmHU3lm6ce5sS+PfgEhXDjsy8T2r7+IzAqNZNCw+LgZHqPHIvdZmPBO6+z4rsvsLs4TK5vsAdjHutN0vBohEZwrM0V4PsIL85si37NNm40FvBLmyvAboWFj8Dc+8FSVnfFitIIXEnoEcCxKp/TnMuqu0QIsVUIsVAIUb8Bv5shS8pPbH7yfQ5GjQBpZ+i4EAIGjj2jnN5vC0JXgq20Dfays4+muH/9ar575lEKTmUQFtOJW196k5C20W5qwUVMCC6/dRKDJk5GaDSs/+l7fnr1BcqKi1zaXKPV0Hdke8Y+1hv/IAMlnmHs6fIQYzaP4raZebxsPsD0/hOw6TwcQ/B+cqXjCVNFaWJC1vEnoxDiBuAqKeXfnJ/HA32klA9UKeML2KWURUKI4cDbUsozOmILISYDkwFCQ0MTZ86cCUBRURHe3t7Vi9dq+/G/ehrERfi5vJ1L20sbUfu+oWjmXlZ1eBKbzoOwLqUExXudEeu2tDzm2N4lhwwGaa6noya+xu+TNiu2neuwp+4CIKBDJ6IHX41G59rN0+oxuyrUAzJK670ZcPp+qe27ayvjyrbVnWusdcVQkHaEQ7//jK28DKOvP+2uvAav0HCX67dbJZnbJNn7JCAwlWbRJvU75vbex6n4tryefoi2JSexaj3Z0+XvZIVcUu82nO/xfDb1/b/VlFp6rA31cx40aNBGKWVSTetcSeiXAFOllFc5Pz8BIKV8+SzbpAJJUsqs2sokJSXJDRsc80kvXbqU5OTks7eiiujHf6l8n/rKNS5vV+f2BSeQ39/JwZlpLGv9FGUeIbTv7suw+xIRzgmZq8ba4flpeEZ9jN3qQ/H+xwDdGd8VaM5h6Kk/CTFnodHquPzWSfQaPrKyvnOJ2VUPx1l5Y/uZMbmi6n6p7btrK+PKttWda6yuxJCfeZK5b7zEqdRDCI2GS8beTN/RN6LRal3+nozUAhZ/uo2cTMdkGK0yNmAr+x9zhsOUwCCu2L/CUbD3XTD0BdB7uFz3+R7PZ1Pf/1tNqaXH2lA/ZyFErQndlUsuKUBHIUQ7IYQBGAfMq/YFYcKZoYQQfZz1Zp9zxE1h1zzk+5eQ9uM+Vof8gzKPEILDDFx5V0KtyVcfsBoAS24fqidzjbTRJzeFcce/J8ScRZ7Ol5tfeI3Ea0bVO5kr58evVRi3vPgGSSPGIO12Vn//Dd898wiZqa5fJgmN9uXGZ/vTd1R7hLCRGZpEXsS/mPRzIim/H2Fq58EUVvRXnz4IMlzrNqkoDanOhC6ltAL3A78Bu4HZUsqdQogpQogpzmLXAzuEEFuBacA4Wdepf3NRVgA/3Quzx5O5wcZqj/sp9I3G20fDiIf6oDfWfBZ3svgkOp/djq6KeadP2ty6NJ1xx7+nb94GtNjZ7tONmRE3ENahfsMBKA1Hp9cz8LY7uOFf/8Y7KJiTB/bx9RP/YOmXH2MudW22Iq1WQ9LV0cRcoyO6mx82nYnD7cfQSj5F3xkGXilqz4pW7eDUbkdSXzVNzVmqNCqX/saVUi4AFlRb9mGV9+8C7zZsaO53uWYrfPAY5B8ja58/q813ktW6BwaDZOTDvfH0NdS67ey9sxHCjiX/r66KfpZ8BuSsoUOJY0yRXJ0fi4OTSfdo3SjtUerWtntPJr7+Pqtnf83mX+ez8Ze57Fm9gv433EL35CEuXYYxeAuG/j2RIzuyWfH1DvJpxaGOd9PtyD6ytsxl2qXe3GrfSdCif8Ge+TDq/XOaS1ZR6uvcLrBe4Hwp4mndN9yoWwb5kJ3RiVUFV5EeeSlajeSavycSEOZV6/Zmm5k5+x2j8FlyL8HbWkRi3iZiC3ejxY5F6Njol8Amv3hsmotyFzdrRk9PBk2cTLfLB/PHjPc4eXA/i6a/y8Zf5nLpuPHEJPVzaWC0qO5BRP77MnYuP866H/eS59+JPP9HCN26lT/MHnj3SGPYsXVoP7wUkh+HS+4Dres3whWlvi6ubCMlbJvFn8ZHCBEFlEs9JaaxrD4SQFrbwWiE5Or74mkd43/WahYdWUROWQ4euWH0OnaU2MLf0WJHAru8O7M2oC/Futp/ISjNQ2j7GG558Q32rl3JyplfknP8GPPeeIngNlH0ue4GOl9yWZ1n7Fqthh6D2tCpTxibFh5i259HyQruCTIOza4t/GhfRJcO24j741nYNhtGvA1tejdSC5WLzcWT0DN2wYJ/wpFVhAhYb+/Mr3sSSSzw5mj0UISQXHV3D6Jizz4wlpSSecu+InlzMG0zjGjYhQT2ecWw3j+RXENg47RHaRBCo6FL/8vp2OcStv3xK+vnzSHr2BEWvPM6K2d+RfxV19B90BA8vM8+L6zJS0//6zvTc0g0KT/tZdeaDDJb9QJ6YU/dwmHrH/Rou5GojCGIXrfBFc+Ad6vGaaRy0Wj5Cb0oE5a8BJu+AGkHz2Aezh2LYXc5/ax+pEYnI5AMubM77eNrn+qttKiQ3SuWsHXuLDrlFgFe2NCwxzuGDX4JKpFf4LQ6PQnDRtDjymHsWr6E9XO/J+/kCZZ//SmrZ39DlwGX0z15CK07n328HS8/I8kTepA0sox1P+1i77osskLigXgKsg6y8/Ai4tPn0HrHXMSgR6HP5EZpn3JxaLkJvbwI1n0AK98GcyEILfS+CznwcYJueopOhnakRfZBIyRXTe5B+4Qzk7nVbObQ5hT2rFrGoU0p2CwWAMoMNgwJ0XyePogSdWmlRdHq9MQNHkps8hUc3ryRzb/+zJFtm9mxZBE7liwiILw1Hm3akdu5IwHhNT0w7eAdYOKKSb3oN6acVXO3cGB1Nvl+Hcj368DJshtotWYZsTvfJarnR4zWXMtc+wDsavBT5Ty1vIRuKYWUT2Dlm1Di7ArfaRgMeR6bRyRH/vkE7Ty7kxEUi03aGPmPRNp0+evsurykhNStGzmQspZDm9ZjLnU+vigEkXE9+Fa/lNRWhfw05n3ee3lXEzRQaQwajZYOiX3okNiHnPQ0diz9g13LF5N7Ip3cE+l8un4VIVHt6NTvUjok9SW4TVSNzxd4+RkZentfkm+0snRBCvv/OEGZKYijkWNIs48geN1mbpZLuSt8PtP8xoD96tPHClKUemg5Cb0sHzZ8Cmveh+JMx7LIPjD4aWg/EHPacfZMvpeNPsMoCQrFLK3M8rHyQOcAso6mcnjrJlK3buL47h2nzSIf2j6GLv0vp9MllzE381cOpfxMv/B+tPNrB6iEfjEIbB3J5bdM5NKbxnNk+xaWzplF0bFUTh05zKkjh1k16yt8gkNon9CbqJ4JtI3tgdHz9L/cDCYdQ8dcwpDrJMtXbWDLTzvQFbUhs1UfMumDqfgUI9LXknrXVUROuAXdpbeDxvUnWRUFWkJCzzvmeDpvw2eOiX0BwnvCoKeh4xAQgsLFi9n28hdsj7oRq9ZEvu0Em7XH6ZmTzod3f0tJfl5ldUJoiOjSjZikfnTo3Y+AMEcfcikls1fMBmBc53GN3UqlGdBotbSLT+RIXiGXDhjAkW2bOZCyhkObUijMOsXWRQvYumgBQmgIi+lIZLc4IrvGEtG5W2WCFxrBwMt6M/Cy3mw6sJ3Fc5ZhPBhGmUcIqR4jSGUEPh+mEjntQToleBB+1z/Qtqr90o6iVHWBJnRJP81uJmp/g7c3Om52AkRfBpf+AzpcAUJgLy/n4IuvsH5bDqdad0SWLgCZgclWRsUQSiWAl38A0T17EdWzF1Fx8Xj6njlwzrqT60gtSMVf68/ANgMbq6FKM6XT6ysvyUi7nZOH9nN480aO7tjCif17K18pc38AIQiKaEN4xy6Ed+xEaLsYgttG0Ssmjl6PxdH+6a/oqF9C/2xvgsu6U+gbzW6i2X0MfP7xG63FQdr3a0ubcWPQh9R+415RLsiEHiuOMNPwouOD0EHsGKy9/kaOJpTstKNkrf+Ck9u3cvLgQczCDiag7GDl9oVab06Ywjhuas0XT91MYOvIOsdXmbVnFgD9vfujUw8LKVUIjYbwmM6Ex3Sm/w23UF5SwvG9Ozm+eydpu3dy8uB+stOOkp12lB1LfgdAo9UR3DaKkKh29MguIcsQySyjN89OLmbN4iWE7g3F1+JI7nuJZu9OMD6yjFBrKpFdAmh/dR98E2JdegBKuXhckJlpn601M3P6kF7uT7u2HcldmUXeDy8jK87UKwgAAwZTKDF9e9I+IZbWnboS99q6yiJBEXXP4ZlRnMGSY0vQCR39veuexEK5uBk9PWmf0Jv2CY4HiKxmM5mphzixfy8nD+4j4/BBck8cJ/PwQTIPH+SyKtueetOLPhFt0MQUc0TMJz0zh7DsaHxtCZSbgjlKIEfTYM30k/iWbKCVbzkRsaFEXdETr07t1MBvF7kLMqHrpJXjGUaglEN52wDHtW9fLx9Evp1Sj05IQwQabQg9B3fhkrFd0BnO/QbTD/t/wCZtDI0aih8NO1610vLpDAZad+pC605dKpeVl5Rw6sghso4e4a3vlxFszibAkgvFxZzYt8exHeCYMmU7ZWzHrAeBCaMMRGjDydb6kVPqw95N5YjNO/ApX02ITzmh7fyISGpPSL8Lfp4ZpZ4uyIReqvFgh09XCnU+vHjb5ej27OfIL3s4bLqE8pBANEBYpIHkSfEERZzfgPll1jJm73XeDO0yjuI9xQ3QAuViZ/T0JLJrdyK7dmfpEudCKdn5xACy0446u0ceJ/fEcfIyTpKfeRKDxQKUYScdrOln1FmOlmyLF3tyvRFbvNDMMGKUkhPfLSUwPJCwru1o3bs7fuGh6E0e6my+BbogEzpCsMW7FyOObuXQm0s47hePOdQxYLyfr6Tvjd2JSWzVIAfsvIPzyCnLoVtQN5JCk1i2Z9l516koNRICL/8AvPwDaNu952mrpN1OUW4O+acyKDiVSX5mBmnpB0g/vp+i7Cw0JRKDFaS9AChA2sAOWIHiPEjPgx27gR+dX4UGndaI0eSJh58fPq2C8AoKxMPbB6OXNyZvb0xe3hg9vTF6eWH09MTo6YXB0wudXg0w1lxdkAk9Li+Dm3NKyA25ksPCcVPI38dGnxvjiEkMRWga5szDLu18uetLACbGTlRnNEqTERoNPkHB+AQFQ5czL6WYbWa2pK5iw7af2Je2i/SiQrwKPIjI8cO3xAO91ZGE7dICshSJFYutFEtxKUXF2ZxKd32yD61ej8HDE4OHh+Nfk4fjvckDfeV7EzqjCYPJhN5oQm8yoTMYne+N6A1GdEYTeqMRncGAzmDgQplCoTm7IBP6Pt8Q8mx6kHbaxxjoMSqO1h39GzzhLjm2hCMFR2jt1ZohUUMatG5FaUgGrYE+HQbRp8MgwHEykpq5lYVLPqZIn8GWov3ss5dhterpdCKUttkhBBcG4mH2Q4MXVq0Jm7CBLEfKMpBlSFnu/Hz6vzaLhVJLPqUF9Z/n9qyEYPtn7zkTvBGdQY9Ob0BrMKDTO5K+Vq9Hqzeg0+kq32v1enR6PVqd3rFMp0Or06PR6dDpHf9WXabVatHoHOU0Wq1zudb5uWKZ49+KzxfKyZxLCV0IMQx4G9ACM6SUr1RbL5zrh+Po2j1RSrmpgWOtVK7R8KO3hTS9hj2PDHXX1/DFzi8AGN9tvOqqqFxQNEJD+9AEYsNurJz7UtrtnDyxgf2pSziUtYPUwi1sK8/lKFayhYbIHB8icwJpVRhEQEkgnpZA9NIfIQKw6v2x6r2cZ9FWkGakNFf51wJUeS/NYC9HI8sQmBFYEMIKWJHCBtiwSytS2rDbrdjtFqSUWMrLsJSXNd2Oq4XQaNBqdWh0WjQaLVa7nb0zP0VotX8lfo3G+d5RxrFOg0ajQWi0jDiZjV0I1vn3cVucdWYpIYQWeA8YAqQBKUKIeVLKqs+9Xw10dL76Ah84/3UTO4f8HDeFdmbXf+5GjSmt8n1t2x8rOMbmzM34GHwY03HMuYWpKM2I0GgIj+hDeEQfLq+6wmqmJGsvaenrSc/eS3rBEU6UbOGwuYAMexmZwk4uGjyLjYTn+RFU5IN/qQ8+Zb54WH0w2LzR2b0Rwhs0wVh13lh1XshqQxdUv6BS0YO+opSUNhy/LKwgbciK91gr1wm7GSHLQVoQ0gLSisACVGxrA+cvDMfLjsTuWI4diQRszn/tzl9QdmeXZ4msWCYdZR3rJdJux2o3g+Wv+ItcnLqwQrTz35OekfXarj5cOe3sAxyQUh4CEELMBEZx+kAmo4AvnfOIrhVC+AshwqWUJxo8YgCNGa92jhnvxs2v/8x3Xu3+el/X9jd1vglPvWe9v0NRLhg6A55hcXQKi6NTTestpciCE5TkHyM77yDZBcfIK80irySLPPNRCizF5FtLKLCbKZJWimw2bGWgNRvRl3phKPfE0+yBZ7kHJosHRpsRg9WE3m5CazeilUY00oAQRgQGpDCC0COFAbvGgF3jjV2jw66teUrI+lx5F9X+dUVF0q98yb/ey8r3jl8UnFZWVlvv2LaLbU89vr1+RF03IoQQ1wPDpJR/c34eD/SVUt5fpcx84BUp5Urn5z+Bx6SUG6rVNRmoGAC6M7DX+T4YyDr/5jQKFat7qFjdQ8XqHk0Za5SUssYxIFw5Q6/pl1n13wKulEFKOR2YfsYXCLFBSpnkQixNTsXqHipW91CxukdzjdWVgSDSgKrPx0cC1Z9qcKWMoiiK4kauJPQUoKMQop0QwgCMA+ZVKzMPuF049APy3Xb9XFEURalRnZdcpJRWIcT9wG84bkh/KqXcKYSY4lz/IbAAR5fFAzi6LU6qZxxnXIZpxlSs7qFidQ8Vq3s0y1jrvCmqKIqiXBjUYMqKoigthEroiqIoLYRbE7oQYpgQYq8Q4oAQ4vEa1j8ihNjifO0QQtiEEIHOdalCiO3OdRvOrL3BY/1UCJEphNhRy3ohhJjmbMs2IUSvKuvO2s4miPVWZ4zbhBCrhRA9q6xrbvs1WQiRX+U4eKbKuua2X5vF8SqEaCOEWCKE2C2E2CmEeLCGMs3ieHUx1mZxvLoYa7M5XmskpXTLC8cN1INAe8AAbAW6naX8CGBxlc+pQLC74qvh+y8HegE7alk/HFiIo899P2DdubSzkWLtDwQ4319dEWsz3a/JwPzzPX4aI9ZqZZvseAXCgV7O9z7Avur7prkcry7G2iyOVxdjbTbHa00vd56hVw4ZIKU0AxVDBtTmZuA7N8ZzVlLK5UDOWYpUDm8gpVwL+Ashwql/O90eq5RytZQy1/lxLY7nApqEC/u1Ns1uv1bTZMerlPKEdA5+J6UsBHYDEdWKNYvj1ZVYm8vx6uJ+rU2jH681cWdCjwCOVfmcRi07RwjhCQwD5lRZLIHfhRAbhWPIgKZWW3tcbmcTuRPHmVqF5rZfAS4RQmwVQiwUQlQM9t1s92tzOl6FENFAArCu2qpmd7yeJdaqmsXxWkeszfZ4deeYsC4NB+A0Alglpax6djRASpkuhGgFLBJC7HGeQTWV2tpTn3Y2KiHEIBz/QS6tsri57ddNOMamKBJCDAd+wjFqZ7PdrzST41UI4Y3jl8o/pJQF1VfXsEmTHa91xFpRplkcr3XE2qyPV3eeoddnOIBxVPvzVUqZ7vw3E/gfjj9pmlJt7WmWwx4IIXoAM4BRUsrsiuXNbb9KKQuklEXO9wsAvRAimGa6X52a/HgVQuhxJJ1vpJQ/1lCk2RyvLsTabI7XumJt9seruy7O4zj7PwS046+bBLE1lPPDcd3Sq8oyL8CnyvvVOEZ8dPdNkWhqv3l3DaffZFpfn3Y2cqxtcTy127/a8ua4X8P46wG3PsBR5z5udvu1uRyvzv3zJfDWWco0i+PVxVibxfHqYqzN6nit/nLbJRfp2pABAKOB36WUxVU2DwX+JxzTPumAb6WUv7orVgAhxHc47mAHCyHSgGcBfZVYaxzeoLZ2NnGszwBBwPvOfWiVjpHhmuN+vR64RzimsykFxknH/5bmuF+heRyvA4DxwHYhxBbnsidxJMbmdry6EmtzOV5dibXZHK81UY/+K4qitBDqSVFFUZQWQiV0RVGUFkIldEVRlBZCJXRFUZQWQiV0RVGUFkIldEVRlBZCJXRFUZQW4v8BHWxj55iuzKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_NC, distributions = get_common_distributions())\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c50d9de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting 106 distributions:   8%| | 8/106 [00:01<00:13,  7.51iWARNING:root:SKIPPED kstwo distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  18%|▏| 19/106 [00:01<00:04, 17.84WARNING:root:SKIPPED rv_continuous distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  21%|▏| 22/106 [00:01<00:04, 18.82WARNING:root:SKIPPED rv_histogram distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  96%|▉| 102/106 [00:25<00:09,  2.2/home/marija/.conda/envs/pytorch/lib/python3.7/site-packages/scipy/integrate/quadpack.py:880: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
      "  If increasing the limit yields no improvement it is advised to analyze \n",
      "  the integrand in order to determine the difficulties.  If the position of a \n",
      "  local difficulty can be determined (singularity, discontinuity) one will \n",
      "  probably gain from splitting up the interval and calling the integrator \n",
      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
      "  **opt)\n",
      "Fitting 106 distributions:  97%|▉| 103/106 [00:26<00:05,  1.9WARNING:root:SKIPPED levy_stable distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  98%|▉| 104/106 [00:31<00:05,  2.6WARNING:root:SKIPPED studentized_range distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions:  99%|▉| 105/106 [00:32<00:02,  2.1WARNING:root:SKIPPED vonmises distribution (taking more than 30 seconds)\n",
      "Fitting 106 distributions: 100%|█| 106/106 [00:33<00:00,  3.1\n"
     ]
    },
    {
     "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>sumsquare_error</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>kl_div</th>\n",
       "      <th>ks_statistic</th>\n",
       "      <th>ks_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exponweib</th>\n",
       "      <td>228.925939</td>\n",
       "      <td>440.014005</td>\n",
       "      <td>25.557555</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.403842</td>\n",
       "      <td>0.298813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exponpow</th>\n",
       "      <td>229.805636</td>\n",
       "      <td>394.721026</td>\n",
       "      <td>23.967294</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.388485</td>\n",
       "      <td>0.340837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fisk</th>\n",
       "      <td>231.478144</td>\n",
       "      <td>503.287303</td>\n",
       "      <td>24.003551</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.367344</td>\n",
       "      <td>0.408087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>234.918930</td>\n",
       "      <td>449.229349</td>\n",
       "      <td>24.077327</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.530632</td>\n",
       "      <td>0.076795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powerlognorm</th>\n",
       "      <td>235.582727</td>\n",
       "      <td>876.615438</td>\n",
       "      <td>25.700873</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.761559</td>\n",
       "      <td>0.001558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sumsquare_error         aic        bic  kl_div  ks_statistic  \\\n",
       "exponweib          228.925939  440.014005  25.557555     inf      0.403842   \n",
       "exponpow           229.805636  394.721026  23.967294     inf      0.388485   \n",
       "fisk               231.478144  503.287303  24.003551     inf      0.367344   \n",
       "gamma              234.918930  449.229349  24.077327     inf      0.530632   \n",
       "powerlognorm       235.582727  876.615438  25.700873     inf      0.761559   \n",
       "\n",
       "              ks_pvalue  \n",
       "exponweib      0.298813  \n",
       "exponpow       0.340837  \n",
       "fisk           0.408087  \n",
       "gamma          0.076795  \n",
       "powerlognorm   0.001558  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marija/.conda/envs/pytorch/lib/python3.7/site-packages/scipy/integrate/quadpack.py:880: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  **opt)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = Fitter(X_AE)\n",
    "f.fit()\n",
    "f.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4048b123",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PYTORCH",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}