crear_distribuciones.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('consultas.csv')\n",
    "plt.title('Histograma de consultas')\n",
    "plt.xlabel('Intervalo de consultas')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.hist(df['Dias'], bins=7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yXQMgEWKAPPH399esWbP0wQcfOH0zT0tLU1xcnHx9fR0hwcfn6oHPK1euONZ78sknJUkTJ07U2bNnHeNnz551HEnIfDbHY489pgIFCmjGjBlO6x48eFCff/55jmv28/OT3W53mkOSpk6d6riwNKfPbsnp9lJTU52uk7Hb7XrnnXccpxuu7cn1qlSpovLly2vlypVOYeHMmTOaM2dOlm21adNGhw4dcjzHJtO2bds0btw4LVu2LMtpKFfVqFFDpUuX1vr167V582anZZMmTdKpU6fUunXrHJ12u5avr68effRRnTlzJsvt7lu2bNHXX3+t0qVLZ7km6HqZp8Suv3j35MmTjtNtrnytc1Ofr6+vVq1apQ8++MBxQXSmzP2tZMmSua4BkDidBORJ0aJF1aVLF82dO1dt27ZVw4YN5eXlpe+//16HDx9Wnz59HKeBihcvLkn67LPPlJSUpM6dOzseOjZ37lw9/vjjaty4sSRp06ZNiouL04svvui4aDTzTpAJEyaoXbt2atq0qS5fvqx169Y5fljl5CLJxx9/XLt27dKzzz6r1q1bq0CBAtq2bZv27dun0NBQJSQk6Ny5c27r0WOPPaY5c+boqaeeUrNmzZSenq4ffvhBR48eVZEiRXT27FmdO3fuhtf12Gw2jRo1Sl27dlWXLl3UsmVLFSxYUN98843THT6ZhgwZot9++01jx47Vxo0bVbVqVcXGxmr9+vXy8fHRqFGj3HYxqZeXl8aMGaMePXropZdeUuPGjXX//ffrt99+065du1SuXDm9/vrrLs392muv6ddff9WsWbO0Y8cOVa9eXcePH9e3336roKAgjR8//pZ39dSoUUPh4eH66quvlJiYqAoVKujUqVPauHGj/Pz8ZLPZXP5a56a+V199VS+//LLat2+vVq1a6Z577tHevXv1888/KyYmRnXr1nWpBoAjMUAevfbaaxoxYoQKFiyoL774QosXL1ZQUJDGjBnjeNqpdPUOlk6dOikpKUkLFy50HM4fOnSoxo8fr/DwcK1cuVJff/21HnjgAU2ePFmDBw922lbPnj01atQohYSEaNmyZfrxxx/VpUsXxxOEb3QB7rWee+45/c///I8KFy6sJUuWaOXKlQoKCtLEiRP1//7f/5OkLEcV8mLgwIHq16+fvLy89Omnn2rDhg0KDw/X7Nmz9dJLL+Voe9HR0frss89Ut25dfffdd1q9erUaNWqkUaNGZVm3SJEiWrx4sbp3767Y2FjNnz9fv/zyi5o0aaLFixc73c3jDg899JCWLl2qNm3a6LffftPChQt17tw59e7dW0uWLMn19TDZfY64uDgtWLBAv//+u5544gktX748y4PushMYGKi5c+eqRYsW2rdvnxYsWKA//vhDjz/+uFasWKEKFSrol19+cTzZN7/qa9q0qWbPnq2oqCht2rRJn3zyiU6fPq2XX35ZM2fO5A4luMxmuPPkN4B8k5iYKLvdnu3dSZMmTdLUqVO1ZMmSbG/BBoA7EfEXsIht27apbt26WW4dPnv2rL744gvdc889Ts8iAYA7HdfEABZRv359hYeHa+rUqfr9998VERGhpKQkbdiwQYmJiRozZkyuLyAFACvjdBJgIWfOnNFHH32k7777TqdPn1ZgYKCioqLUo0cPPfLII54uDwBuK0IMAACwJK6JAQAAlkSIAQAAlkSIAQAAlkSIAQAAlnRX3GJ9/vwl2e0Zni7jhkJCgpSYmPsnZiJv6Ltn0HfPoO+eQd9d4+3tpeDgWz+B/K4IMXZ7htLTzRliMn/1id2eIe4Tu33ou2fQd8+g755B3/Mfp5MAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAlEWIAAIAl+Xi6AKu6fDlA58/b8jyPzSYlJEh2e6AMI+91BQcb8ve/lPeJAAAwOUKMi86ftykqytuNM7pnrr177fL3d8tUAACYGqeTAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJRFiAACAJZkmxKSlpalt27batm2bY2zXrl3q2LGjqlevrpYtW2rJkiUerBAAAJiJKUJMamqqXn31VR08eNAxFhcXpxdffFExMTH64osv1L9/f40cOVLfffed5woFAACm4ePpAg4dOqRBgwbJMAyn8Q0bNigsLEyvvvqqJKlMmTLatm2bVq5cqUaNGnmgUgAAYCYeDzHbt29X7dq1NXDgQFWrVs0xXr9+fVWsWDHL+snJybexOgAAYFYeDzHPPfdctuOlSpVSqVKlHK8TEhK0evVq9evXz6Xt2Gwuve22zecuNpt5azOTzB7Rq9uLvnsGffcM+p7/PB5icuLy5cvq16+fwsLC9Mwzz+T6/SEhQW6vKSHB7VO6hbe3t8LCCnm6DMsIDaVXnkDfPYO+ewZ9zz+mDzEXL15Unz59dOzYMX366acKCAjI9RyJiRdlt2e4tS67PVCSt1vndAe73a74+BRPl2F6NtvVbywJCRd03eVYyEf03TPou2fQd9d5e3vl6ACEqUNMcnKy/v3vf+vvv//Wxx9/rDJlyrg8l7t3ILPukIZh3trMiH55Bn33DPruGfQ9/5g2xGRkZKhv3746ceKE5s+fr3Llynm6JAAAYCKmDTFLly7Vtm3bNH36dAUHBysuLk6SVKBAARUuXNizxQEAAI8zbYhZt26dMjIy1KtXL6fxmJgYzZ8/30NVAQAAszBViDlw4IDj77Nnz/ZgJQAAwOxM8WsHAAAAcosQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALIkQAwAALMk0ISYtLU1t27bVtm3bHGPHjx9X165dVa1aNbVp00Y//PCDBysEAABmYooQk5qaqldffVUHDx50jBmGoZdffllhYWFatmyZ2rVrp759++rkyZMerBQAAJiFj6cLOHTokAYNGiTDMJzGf/75Zx0/flyLFi1SYGCgypUrp61bt2rZsmXq16+fh6oFAABm4fEjMdu3b1ft2rX1+eefO43v3r1blSpVUmBgoGOsRo0a2rVr122uEAAAmJHHj8Q899xz2Y7HxcWpWLFiTmOhoaE6ffq0S9ux2Vx6222bz11sNvPWZiaZPaJXtxd99wz67hn0Pf95PMTcyKVLl+Tr6+s05uvrq7S0tFzPFRIS5K6yHBIS3D6lW3h7eyssrJCny7CM0FB65Qn03TPou2fQ9/xj2hDj5+enc+fOOY2lpaXJ398/13MlJl6U3Z7hpsqustsDJXm7dU53sNvtio9P8XQZpmezXf3GkpBwQdddjoV8RN89g757Bn13nbe3V44OQJg2xBQvXlyHDh1yGouPj89yiimn3L0DmXWHNAzz1mZG9Msz6Ltn0HfPoO/5x+MX9t5IdHS09u3bp8uXLzvGdu7cqejoaA9WBQAAzMK0ISYmJkYlSpTQsGHDdPDgQc2cOVN79uxRhw4dPF0aAAAwAdOGGG9vb02bNk1xcXF68skntWLFCk2dOlUlS5b0dGkAAMAETHVNzIEDB5xely5dWgsWLPBQNQAAwMxMeyQGAADgZggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAkggxAADAknzy8uaEhASlpaXJMAxJUkZGhi5duqRffvlFzz77rFsKBAAAyI5LIWb//v0aPHiwDh8+nO1ym83mthBz6tQpjRgxQjt27FDhwoX1wgsvqGvXrm6ZGwAAWJdLIWbcuHFKSkrSkCFDtGnTJvn6+qpx48basmWLtmzZok8++cRtBQ4YMEAlS5bU8uXLdejQIQ0ePFjh4eFq3ry527YBAACsx6VrYnbv3q1XXnlFXbt2VZs2bXTp0iU999xzmjFjhpo1a6b58+e7pbikpCTt2rVLvXv3VpkyZdSsWTPVr19fW7dudcv8AADAulwKMWlpaSpTpowkqUyZMtq/f79j2ZNPPqldu3a5ozb5+/srICBAy5cv15UrV3TkyBH9+uuvqlixolvmBwAA1uXS6aSSJUvq+PHjqlmzpsqUKaPk5GSdOHFCpUqVkq+vr5KSktxSnJ+fn958802NHDlSn3zyiex2u5588kk9/fTTuZ7LZnNLSfk2n7vYbOatzUwye0Svbi/67hn03TPoe/5zKcS0aNFCEyZMUGBgoFq2bKmyZcvq/fff14svvqg5c+bovvvuc1uBhw8fVuPGjdWtWzcdPHhQI0eO1COPPKLHH388x3OEhAS5rZ5MCQlun9ItvL29FRZWyNNlODl9WnJTrnWrK1eke+81V6/uFqGh9N0T6Ltn0Pf8YzMy74/OhdTUVL322mu6dOmSZs2ape+//159+/ZVWlqavL29NXHiRLVo0SLPxW3dulUDBgzQ5s2b5e/vL0maPn26VqxYoa+//jrH8yQmXpTdnpHneq515kygKlf2duuc7rBvn13FiqV4ugwnZu3V/v1SWNgF5f5fAFxls139hp6QQN9vJ/ruGfTddd7eXjk6AOHSkRg/Pz9NmjRJV65ckSTVr19fK1eu1L59+1S5cmXdf//9rkybxd69e1W6dGlHgJGkSpUqacaMGbmey907kFl3SMMwX21mq+daZuzX3YC+ewZ99wz6nn/y9LC7AgUKOP5+//33uy28ZCpWrJj++usvpaWlydfXV5J05MgRlSpVyq3bAQAA1pPjENO0aVNNnTpVFSpUUJMmTWS7yZVKNptNGzZsyHNxTZo00fjx4zV8+HD17t1bR48e1YwZMzRw4MA8zw0AAKwtxyEmJiZGQUFBjr/fLMS4S6FChTRv3jy988476tChg4oUKaLevXvrmWeeyfdtAwAAc3Ppwt7spKeny8cnT2en8k1i4kWlp7v/wt6oKPNdrLp3rzkv7DVjr/bvl0JDueDudrLZpLCwQoqPp++3E333DPruOh+fnF3Y6/JvsZ45c6Z69uzpeL1z507Vq1dPCxYscHVKAACAHHMpxMyZM0fvv/++46m90tULe1u1aqUxY8ZoyZIl7qoPAAAgWy6d/1m0aJEGDBjgdCSmRIkSGj58uMLCwjRv3jyXnqoLAACQUy4diYmNjVWVKlWyXRYdHa0TJ07kqSgAAIBbcSnEhIeH3/A3Se/YsUP33ntvnooCAAC4FZdOJ/3rX//S+PHjdeXKFTVr1kyhoaE6e/asNm3apLlz52rQoEHurhMAAMCJSyGma9euio2N1fz58zVv3jzHuLe3t7p06aJu3bq5qz4AAIBsufxglyFDhqhPnz767bfflJSUpODgYFWtWlUhISHurA8AACBbeXo6XaFChdSgQQN31QIAAJBjLoWYy5cva/r06dq0aZMuXbqkjAznp+G663cnAQAA3IhLIeadd97R0qVLFRMTo4oVK8rLy+UH/wIAALjEpRCzfv16DRw40OlhdwAAALeTS4dQrly5oqpVq7q7FgAAgBxzKcTUq1dPW7ZscXctAAAAOebS6aQ2bdrorbfe0tmzZxUdHa2AgIAs6zzxxBN5rQ0AAOCGXAoxAwYMkCR9+eWX+vLLL7Mst9lshBgAAJCvXAoxGzdudHcdAAAAueJSiAkPD3d6nZqaKl9fX9lsNrcUBQAAcCsuP7H3yJEjmjRpkn766SclJydryZIlWrp0qcqWLavOnTu7s0YAAIAsXLo76c8//1SHDh20b98+PfbYYzIMQ9LVXwA5atQoffHFF24tEgAA4HouHYkZO3asoqKiNGfOHEnSwoULJUnDhw9XamqqPvnkE7Vv3959VQIAAFzHpSMxu3btUteuXeXj45PlOpg2bdro2LFj7qgNAADghlwKMX5+frp8+XK2y86dOydfX988FQUAAHArLoWYunXratKkSTp9+rRjzGaz6eLFi5ozZ47q1KnjtgIBAACy49I1Ma+99pqeeeYZtWrVShUqVJDNZtOYMWN09OhRGYahiRMnurtOAAAAJy4diSlRooS++uordenSRYZh6P7771dKSoratm2r5cuX67777nN3nQAAAE5cfk5MSEiIBg4c6M5aAAAAcsylELNjx45brlOrVi1XpgYAAMgRl0JM586dZbPZHA+5k5TlVus///wzb5UBAADchEsh5pNPPskylpKSol9++UVfffWVJk+enOfCAAAAbsalEBMTE5PteKNGjRQYGKjp06frww8/zFNhAAAAN+PS3Uk3U7NmTW3fvt3d0wIAADhxe4j59ttvFRQU5O5pAQAAnLh0OumFF17IMpaRkaHTp0/rn3/+0YsvvpjnwgAAAG7GpRBz7V1Jmby8vBQREaFevXrpqaeeynNhAAAAN+NSiJk/f36WsfT0dPn4uPzsPAAAgFxx+ZqYmTNnqmfPno7XO3fuVL169bRgwQK3FAYAAHAzLoWYOXPm6P3331eZMmUcY/fff79atWqlMWPGaMmSJe6qDwAAIFsunf9ZtGiRBgwY4HQkpkSJEho+fLjCwsI0b948Pf30024rEgAA4HouHYmJjY1VlSpVsl0WHR2tEydO5KkoAACAW3EpxISHh2vr1q3ZLtuxY4fuvffePBUFAABwKy6dTvrXv/6l8ePH68qVK2rWrJlCQ0N19uxZbdq0SXPnztWgQYPcXScAAIATl0JM165dFRsbq/nz52vevHmOcW9vb3Xp0kXdunVzV31KS0vT6NGjtWrVKhUoUEAdOnTQwIEDs/zWbAAAcHdx+cEuQ4YMUZ8+fbRr1y6dO3dOwcHBqlq1qkJCQtxZn95++21t27ZNs2fP1sWLFzVw4ECVLFlSHTt2dOt2AACAteTp6XRBQUEqWrSoDMPQQw89pPT0dHfVJUk6d+6cli1bprlz56pq1aqSpO7du2v37t2EGAAA7nIuh5ivvvpKEyZMUFxcnGw2m5YsWaLJkyerQIECmjBhgnx9ffNc3M6dO1WwYEHFxMQ4xq69rRsAANy9XLo7ac2aNRoyZIgefvhhTZw4URkZGZKk5s2ba/PmzZo2bZpbijt+/LjCw8P15ZdfqlWrVmratKmmTp3q2F5u2Gzu/2NG+fE579ReSZ7vy934h77T97vpD313vW854dKRmBkzZqhjx44aMWKE7Ha7Y/ypp57S2bNntXjxYg0YMMCVqZ2kpKTor7/+0qJFizR69GjFxcXpzTffVEBAgLp3757jeUJCgvJcy/USEtw+pVt4e3srLKyQp8twYtZeSVJoqLl6dbeg755B3z2Dvucfl0LM0aNHNWTIkGyXRUdHa/LkyXkqKpOPj4+Sk5M1YcIEhYeHS5JOnjypzz77LFchJjHxouz23B+9uRm7PVCSt1vndAe73a74+BRPl+HErL2SpISEC8rml7Ijn9hsV7+h0/fbi757Bn13nbe3V44OQLgUYkJDQ3X48GHVrVs3y7LDhw8rNDTUlWmzKFq0qPz8/BwBRpIeeOABnTp1KtdzuXsHMusOaRjmq81s9VzLjP26G9B3z6DvnkHf849L18S0adNGkyZN0tq1a5WWliZJstls2rt3r6ZNm6ZWrVq5pbjo6Gilpqbq6NGjjrEjR444hRoAAHB3culIzIABA/Tf//5XAwYMkJfX1RzUuXNnpaSkqGbNmnrllVfcUlzZsmXVqFEjDRs2TCNGjFBcXJxmzpyp3r17u2V+AABgXS6FGF9fX3300Uf68ccf9fPPP+vcuXMqVKiQYmJi1LBhQ7nzabrvvvuuRo4cqWeffVYBAQHq1KmTOnfu7Lb5AQCANbkUYnr06KF///vfqlu3brbXxbhToUKFNG7cuHzdBgAAsB6Xron59ddf3Xq0BQAAILdcCjH169fXihUrdOXKFXfXAwAAkCMunU7y8/PTihUr9PXXX6tcuXIKDAx0Wm6z2fTxxx+7pUAAAIDsuBRiTp8+rerVqzteG9fdAH/9awAAAHfLcYhZv369Hn74YQUHB2v+/Pn5WRMAAMAt5fiamFdeeUXHjh1zGps1a5YSzPyLcQAAwB0rxyHm+lNEdrtdEydO1OnTp91eFAAAwK24dHdSJq59AQAAnpKnEAMAAOAphBgAAGBJeQ4xPLkXAAB4Qq6eE/Pyyy/L19fXaeyll15SgQIFnMZsNps2bNiQ9+oAAABuIMchpn379vlZBwAAQK7kOMSMHj06P+sAAADIFS7sBQAAlkSIAQAAlkSIAQAAlkSIAQAAlkSIAQAAlkSIAQAAlpSrh90ByH+XLwfo/HlzPQk7ONiQv/8lT5cBAE4IMYDJnD9vU1SUt6fLcLJ3r13+/p6uAgCccToJAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYEiEGAABYkqVCTM+ePTV06FBPlwEAAEzAMiFm9erV2rx5s6fLAAAAJmGJEHPu3DmNGzdOVapU8XQpAADAJHw8XUBOjB07Vu3atdOZM2c8XQoAADAJ04eYrVu36pdfftHKlSs1YsQIl+ex2dxXU37M5y42m/lqM1s91zJjbWatyR11Zc5hxs94J6PvnkHf85+pQ0xqaqreeustvfnmm/L393d5npCQIDdWdVVCgtundAs/P28lJBTydBlODMPTFWTPx0eKjzdXryRz9svb21thYe7rVWioe+Y6fVpKSnLLVG5zzz3Svfd6uorsuavvyB36nn9MHWKmTJmiqKgo1a9fP0/zJCZelN2e4aaqrrLbAyV5u3VOd7h40VB0tLli/+7dhiRz1SRJKSlS1aqeriIrM/bLbrcrPj4lz/PYbFe/oSckXHBLWDt7NlCVK5vr3+G+fXb5+OS9V+7k7r4jZ+i767y9vXJ0AMLUIWb16tWKj49X9erVJUlpaWmSpHXr1um3337L1Vzu3oHYIXE3MQz37vPums+M/w7d3St3MnNtdzL6nn9MHWLmz5+v9PR0x+t3331XkjR48GBPlQQAAEzC1CEmPDzc6XVQ0NVDS6VLl/ZEOQAAwEQs8ZwYAACA65n6SMz1xowZ4+kSAACASXAkBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWJKPpwsAYH5+fjadOROY53lsNikhQbLbA2UYea/LMMz3/zB39cqdbDYpJUVKTnZP390pONiQv/8lT5cBiyLEALillBSboqPdGRi83TLL7t0m+4ms/OiVe+zZI1Wt6p6+u9PevXb5+3u6CliV+f6lAQAA5AAhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWBIhBgAAWJLpQ0xsbKz69++vmJgY1a9fX6NHj1ZqaqqnywIAAB7m4+kCbsYwDPXv31/BwcFauHChkpKS9MYbb8jLy0tDhgzxdHkAAMCDTH0k5siRI9q1a5dGjx6tBx98UDVr1lT//v21atUqT5cGAAA8zNQhpmjRovroo48UFhbmNJ6cnOyhigAAgFmYOsQEBwerfv36jtcZGRlasGCBHn74YQ9WBQAAzMDU18Rcb/z48frjjz+0dOnSXL/XZnNvLe6eDwDuRjbbnfv9NPNz3amfzwwsE2LGjx+vjz/+WO+9954iIiJy9d6QkCC315OQ4PYp3cJmwn8tZqzJzMzYLzPWJJmzLjPWZGbe3t4KCyvk6TKcnD4tJSW5Z674eElyz+e75x7p3nvdMtUdwxIhZuTIkfrss880fvx4tWzZMtfvT0y8KLs9w6012e2BkrzdOqc7GIYhyVzfRM1Yk5mZsV9mrEkyZ11mrMnM7Ha74uNTPF2Gk7NnA1W5svm+v+/bZ5ePj7l6lV+8vb1ydADC9CFmypQpWrRokSZOnKhWrVq5PI9huLGofJgPAO5GhmG+76dmqyeTGXvlaaYOMYcPH9a0adPUs2dP1ahRQ3FxcY5lRYsW9WBlAADA00wdYjZu3Ci73a7p06dr+vTpTssOHDjgoaoAAIAZmDrE9OzZUz179vR0GQAAwIRM/ZwYAACAGyHEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAASyLEAAAAS/LxdAEAgLuXn59NZ84EeroMJ4Zhzv/fm7FXwcGG/P0veWz7hBgAgMekpNgUHW2u0LB7t+HpErJlxl7t3WuXv7/ntm+ubgAAAOQQIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFgSIQYAAFiS6UNMamqq3njjDdWsWVP16tXTnDlzPF0SAAAwAR9PF3Ar48aN0969e/Xxxx/r5MmTGjJkiEqWLKlWrVp5ujQAAOBBpg4xKSkpWrJkiWbNmqXKlSurcuXKOnjwoBYuXEiIAQDgLmfq00n79+9Xenq6qlev7hirUaOGdu/erYyMDA9WBgAAPM3UISYuLk4hISHy9fV1jIWFhSk1NVXnzp3zXGEAAMDjTH066dKlS04BRpLjdVpaWo7n8fZ2f1YLCJCuOUBkGmasy4w1SZK/vznrMmO/zFiTZM66zFiTxP6eG2asSTJnXQEBko+P+3/G5vTnts0wDMPtW3eTr7/+Wm+//bZ+/PFHx9jhw4fVpk0bbdu2TYULF/ZccQAAwKNMfTqpePHiSkxMVHp6umMsLi5O/v7+Cg4O9mBlAADA00wdYipWrCgfHx/t2rXLMbZz505VqVJFXl6mLh0AAOQzUyeBgIAAPfHEExoxYoT27NmjDRs2aM6cOXrhhRc8XRoAAPAwU18TI129uHfEiBFav369ChYsqB49eqhr166eLgsAAHiY6UMMAABAdkx9OgkAAOBGCDEAAMCSCDEAAMCSCDEAAMCSCDEekJaWprZt22rbtm2OsePHj6tr166qVq2a2rRpox9++MGDFd6Zsuv722+/rcjISKc/CxYs8GCVd47Y2Fj1799fMTExql+/vkaPHq3U1FRJ7O/56WZ9Z3/PP3/99Zd69Oih6tWrq1GjRvroo48cy9jf84+pf3fSnSg1NVWDBg3SwYMHHWOGYejll19WRESEli1bpg0bNqhv375as2aNSpYs6cFq7xzZ9V26+mssBg0apPbt2zvGChYseLvLu+MYhqH+/fsrODhYCxcuVFJSkt544w15eXnp9ddfZ3/PJzfr+5AhQ9jf80lGRoZ69uypKlWq6IsvvtBff/2lV199VcWLF1fbtm3Z3/MRIeY2OnTokAYNGqTr72r/+eefdfz4cS1atEiBgYEqV66ctm7dqmXLlqlfv34eqvbOcaO+S1dDTI8ePVS0aFEPVHbnOnLkiHbt2qUff/xRYWFhkqT+/ftr7NixatCgAft7PrlZ3zNDDPu7+8XHx6tixYoaMWKEChYsqDJlyuiRRx7Rzp07FRYWxv6ejziddBtt375dtWvX1ueff+40vnv3blWqVEmBgYGOsRo1ajj9ugW47kZ9T05OVmxsrMqUKeOZwu5gRYsW1UcffeT4QZopOTmZ/T0f3azv7O/5p1ixYnr//fdVsGBBGYahnTt3aseOHYqJiWF/z2ccibmNnnvuuWzH4+LiVKxYMaex0NBQnT59+naUdce7Ud8PHz4sm82mGTNmaMuWLSpcuLC6devmdKgdrgkODlb9+vUdrzMyMrRgwQI9/PDD7O/56GZ9Z3+/PZo0aaKTJ0+qcePGatmypUaNGsX+no8IMSZw6dIl+fr6Oo35+voqLS3NQxXdHY4cOSKbzaayZcvq+eef144dO/Q///M/KliwoJo3b+7p8u4o48eP1x9//KGlS5dq3rx57O+3ybV937dvH/v7bTBp0iTFx8drxIgRGj16NN/f8xkhxgT8/Px07tw5p7G0tDT5+/t7pqC7xBNPPKHGjRurcOHCkqQKFSro2LFj+uyzz/im7kbjx4/Xxx9/rPfee08RERHs77fJ9X1/8MEH2d9vgypVqki6ejPB4MGD9dRTT+nSpUtO67C/uw/XxJhA8eLFFR8f7zQWHx+f5RAk3Mtmszm+oWcqW7asYmNjPVPQHWjkyJGaO3euxo8fr5YtW0pif78dsus7+3v+iY+P14YNG5zGypcvrytXrqho0aLs7/mIEGMC0dHR2rdvny5fvuwY27lzp6Kjoz1Y1Z3vgw8+yPIb0ffv36+yZct6pqA7zJQpU7Ro0SJNnDhRjz76qGOc/T1/3ajv7O/558SJE+rbt69TINy7d6+KFCmiGjVqsL/nI0KMCcTExKhEiRIaNmyYDh48qJkzZ2rPnj3q0KGDp0u7ozVu3Fg7duzQ7Nmz9ffff+vTTz/Vl19+qe7du3u6NMs7fPiwpk2bphdffFE1atRQXFyc4w/7e/65Wd/Z3/NPlSpVVLlyZb3xxhs6dOiQNm/erPHjx+ull15if89nNiO7h2cg30VGRuqTTz5R7dq1JV192uN//vMf7d69W6VLl9Ybb7yhOnXqeLjKO8/1fd+wYYMmTZqkY8eOKTw8XAMHDlSLFi08XKX1zZw5UxMmTMh22YEDB9jf88mt+s7+nn9iY2M1cuRIbd26VQEBAXr++efVq1cv2Ww29vd8RIgBAACWxOkkAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAABgSYQYAHcFniYB3HkIMQDyXefOnRUZGamOHTvecJ2BAwcqMjJSQ4cOdeu2T58+rZ49e+qff/5x67wAPI8QA+C28PLy0q5du3T69Oksy1JSUrRp06Z82e5PP/2kzZs358vcADyLEAPgtqhUqZL8/Py0du3aLMs2bdqkgIAAFS9e3AOVAbAqQgyA2yIwMFANGzbMNsSsWbNGLVu2lI+Pj2MsNTVVU6dOVatWrVSlShW1aNFCM2fOVEZGhmOdzp076z//+Y9mzpypRo0aqUqVKurYsaP27NkjSVq+fLmGDRsmSWratKnjVJXdbtfChQv12GOPqWrVqmrUqJHeffddpaam5mcLALgZIQbAbdOmTZssp5SSk5O1ZcsWtW3b1jFmGIZeeuklffTRR3r66ac1Y8YMtWrVSu+//77eeustpznXrVunjRs3avjw4Zo4caLi4+PVr18/2e12NWrUSL1795YkTZkyRX369JEkvfnmmxo9erSaNWum6dOnq1OnTlqwYIH69OnDBcCAhfjcehUAcI9GjRopICBAa9euVdeuXSVJ33zzjUJDQ1WjRg3Helu2bNFPP/2kiRMn6tFHH5Uk1a1bV/7+/vrggw/0wgsv6MEHH5Qkpaena/bs2SpYsKAk6eLFixoyZIj+/PNPRUVF6f7775ckVaxYUaVKldKhQ4e0dOlSDRo0SD179nTMXaxYMb3++uvasmWLGjZseLtaAiAPOBID4Lbx9/dXkyZNnE4prV69Wq1bt5bNZnOMbd++XT4+PmrVqpXT+x9//HHH8kzly5d3BBhJjutqLl26lG0Nme/NDEeZHn30UXl7e2vbtm2ufDQAHkCIAXBbtW7d2nFKKTExUVu3bs0SKJKSkhQSEiJvb2+n8aJFi0qSLly44BgLCAhwWsfL6+q3tWuvnbl+7mvnyuTj46OQkBCnuQGYG6eTANxWDRo0UFBQkNauXavAwECVKlVKUVFRTuvcc889SkxMlN1udwoyZ86ckSSFhIS4vP177rlHkhQXF6fw8HDH+JUrV5SYmJinuQHcXhyJAXBb+fr6qlmzZlq3bp2+/vrrLEdhJCkmJkbp6elZ7mRasWKFJDldP3MrmUdmrp1bunoa61qrV6+W3W7P1dwAPIsjMQBuuzZt2qhXr17y8vLS8OHDsyxv0KCBateureHDhys2NlYVKlTQ9u3bNWvWLLVv317ly5fP8baCg4MlXb2AuEGDBipfvrzat2+vSZMm6dKlS6pVq5b+/PNPTZkyRbVr11b9+vXd9jkB5C9CDIDbrk6dOgoODlaJEiVUrly5LMttNps+/PBDTZo0SfPmzdPZs2dVqlQpvfrqq+rWrVuutlW7dm3VqVNHEyZM0NatWzVz5ky98847Kl26tJYtW6ZZs2apWLFieuGFF9SnT58sR24AmJfN4KEIAADAgvgvBwAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsCRCDAAAsKT/D1SKOwVybTxnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('honorarios.csv')\n",
    "plt.title('Histograma de honorarios')\n",
    "plt.xlabel('Monto')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.hist(df['honorarios_completos'], color='blue', bins=12)   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('duracionterapia.csv')\n",
    "plt.title('Histograma de duracion de terapia')\n",
    "plt.xlabel('Duracion')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.hist(df['Duracion'], color='lightblue', bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-05-06 01:24:53.430\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED _fit distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:53.645\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted arcsine distribution with error=64.089801)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:53.691\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted argus distribution with error=62.865454)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:53.739\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted anglit distribution with error=62.32998)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:53.835\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted bradford distribution with error=62.932701)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:53.921\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted cauchy distribution with error=61.788303)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:53.934\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted alpha distribution with error=62.111864)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.189\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted burr distribution with error=61.929674)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.202\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted cosine distribution with error=62.148175)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.203\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted beta distribution with error=62.007726)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.204\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted betaprime distribution with error=62.066561)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.222\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted chi distribution with error=62.044838)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.257\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted expon distribution with error=63.622521)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.435\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted burr12 distribution with error=62.015872)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.510\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted dgamma distribution with error=59.871448)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.518\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted chi2 distribution with error=62.087242)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.526\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted crystalball distribution with error=62.027005)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.581\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponnorm distribution with error=62.027004)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.670\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted dweibull distribution with error=55.597179)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:54.898\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted erlang distribution with error=62.045715)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.074\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted foldcauchy distribution with error=61.751421)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.141\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted foldnorm distribution with error=61.982514)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.154\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted fisk distribution with error=61.973053)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.327\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted fatiguelife distribution with error=62.033948)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.344\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponpow distribution with error=63.838829)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.552\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted f distribution with error=62.03219)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.574\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genextreme distribution with error=62.091171)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.624\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gamma distribution with error=62.044107)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.962\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genhalflogistic distribution with error=62.456627)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:55.971\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genlogistic distribution with error=61.894445)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.029\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponweib distribution with error=62.064426)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.068\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gengamma distribution with error=62.071126)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.136\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gibrat distribution with error=63.600011)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.407\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted geninvgauss distribution with error=62.047875)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.462\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genpareto distribution with error=63.151879)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.470\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gumbel_l distribution with error=62.062149)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.485\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genexpon distribution with error=62.537116)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.499\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfcauchy distribution with error=63.503818)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.516\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gausshyper distribution with error=62.016157)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.550\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfnorm distribution with error=63.042024)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3865: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = integrate.quad(llc, x0, x1,\n",
      "\u001b[32m2024-05-06 01:24:56.551\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gennorm distribution with error=51.250888)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3860: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = (integrate.quad(llc, x0, mean,\n",
      "\u001b[32m2024-05-06 01:24:56.569\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halflogistic distribution with error=63.161076)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.702\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genhyperbolic distribution with error=55.695319)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.740\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gumbel_r distribution with error=62.479041)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:56.867\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted hypsecant distribution with error=61.95586)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.248\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invgauss distribution with error=62.17736)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.285\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invweibull distribution with error=62.483757)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.287\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invgamma distribution with error=62.120334)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.327\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted johnsonsu distribution with error=61.951097)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.398\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gompertz distribution with error=62.065388)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.404\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED kstwo distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.461\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted laplace distribution with error=61.009824)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted jf_skew_t distribution with error=62.034407)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.616\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy distribution with error=64.180383)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.618\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ksone distribution with error=63.060231)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.649\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted kappa3 distribution with error=62.390166)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.649\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted laplace_asymmetric distribution with error=60.844992)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.677\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfgennorm distribution with error=62.467418)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:57.776\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy_l distribution with error=64.340909)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:58.569\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted logistic distribution with error=61.973006)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:59.137\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loguniform distribution with error=63.688044)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:59.285\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted lognorm distribution with error=62.027014)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:59.464\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loglaplace distribution with error=60.952535)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:24:59.846\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loggamma distribution with error=62.007867)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:00.169\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted johnsonsb distribution with error=62.009664)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:00.254\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED multivariate_normal distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:00.552\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted maxwell distribution with error=62.296335)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:00.897\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted lomax distribution with error=63.626134)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:01.027\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted moyal distribution with error=62.710471)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:01.918\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted nakagami distribution with error=68.506982)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:01.947\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted kstwobign distribution with error=62.58854)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:01.966\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted mielke distribution with error=61.929666)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:02.147\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted pareto distribution with error=64.116097)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:03.220\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted norm distribution with error=62.027005)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:03.517\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ncx2 distribution with error=62.080752)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:03.537\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powerlaw distribution with error=65.388033)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:03.889\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted nct distribution with error=62.032229)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:04.168\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted norminvgauss distribution with error=61.932243)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:04.575\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ncf distribution with error=62.032637)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:05.007\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rayleigh distribution with error=62.469109)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:05.093\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted pearson3 distribution with error=62.008667)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:05.133\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rdist distribution with error=59.519592)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:05.403\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted reciprocal distribution with error=63.688044)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:05.441\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED rv_continuous distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:05.709\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED rv_histogram distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:06.306\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted semicircular distribution with error=62.398258)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:07.105\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rice distribution with error=62.075577)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:07.433\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rel_breitwigner distribution with error=61.471701)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:08.199\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted skewcauchy distribution with error=61.275409)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:08.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powernorm distribution with error=61.996509)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:08.510\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted skewnorm distribution with error=61.992629)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:10.964\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted t distribution with error=62.017079)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:15.646\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted trapezoid distribution with error=63.999525)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:16.299\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted trapz distribution with error=63.999525)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:16.574\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powerlognorm distribution with error=62.006298)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:17.231\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncexpon distribution with error=62.93583)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:17.483\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncpareto distribution with error=64.756395)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:19.209\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted tukeylambda distribution with error=63.599784)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:19.468\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted uniform distribution with error=62.98307)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:19.910\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted vonmises distribution with error=61.918513)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:20.139\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED vonmises_fisher distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:21.577\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted triang distribution with error=61.948511)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:22.027\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncnorm distribution with error=63.070783)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:22.154\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted wald distribution with error=63.599979)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:22.881\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncweibull_min distribution with error=62.76334)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:23.754\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted wrapcauchy distribution with error=62.983101)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:24.370\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted weibull_min distribution with error=62.032562)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:25.164\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted weibull_max distribution with error=69.387034)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:27.342\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED kappa4 distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:27.814\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED levy_stable distribution (taking more than 30 seconds)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\integrate\\_quadpack_py.py:1272: 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",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "\u001b[32m2024-05-06 01:25:34.647\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED recipinvgauss distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:37.556\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED studentized_range distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:25:50.335\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED vonmises_line distribution (taking more than 30 seconds)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\integrate\\_quadpack_py.py:1272: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "from fitter import Fitter\n",
    "\n",
    "df = pd.read_csv('consultas.csv')\n",
    "fitter_llegadas = Fitter(df[\"Dias\"])\n",
    "fitter_llegadas.fit()\n",
    "fitter_llegadas.summary()\n",
    "with open('fitter_llegadas.pkl', 'wb') as f:\n",
    "    pickle.dump(fitter_llegadas, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-05-06 01:26:23.284\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED _fit distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:24.389\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted arcsine distribution with error=0.98441)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:24.527\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted anglit distribution with error=1.112737)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:25.061\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted alpha distribution with error=1.077544)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:25.251\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted argus distribution with error=1.143241)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:25.517\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted bradford distribution with error=0.953571)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:25.945\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted burr distribution with error=1.071858)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:25.962\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted cauchy distribution with error=1.110589)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:26.008\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted beta distribution with error=0.974902)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:26.615\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted betaprime distribution with error=1.074715)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:26.687\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted cosine distribution with error=1.10651)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:27.226\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted chi distribution with error=1.033945)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:27.423\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted dgamma distribution with error=1.084266)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:27.471\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted dweibull distribution with error=1.082331)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:27.532\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted expon distribution with error=1.007443)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:28.088\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted burr12 distribution with error=0.910459)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:28.182\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted chi2 distribution with error=0.906574)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:28.463\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted crystalball distribution with error=1.103418)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:28.911\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted erlang distribution with error=0.891901)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:30.166\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted fatiguelife distribution with error=0.956714)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:30.211\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponnorm distribution with error=1.007442)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:31.050\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted foldcauchy distribution with error=1.030528)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:31.392\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted fisk distribution with error=0.951609)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:31.643\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted f distribution with error=0.858893)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:31.910\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gamma distribution with error=0.932069)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:32.443\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genextreme distribution with error=1.071845)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:32.687\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted foldnorm distribution with error=1.041095)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:33.234\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponweib distribution with error=0.94609)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:33.577\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genhalflogistic distribution with error=1.18503)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:33.669\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gengamma distribution with error=0.961092)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:34.076\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponpow distribution with error=0.832345)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:35.326\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gibrat distribution with error=1.044473)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:35.761\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genlogistic distribution with error=1.083807)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:36.349\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gausshyper distribution with error=0.890442)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3865: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = integrate.quad(llc, x0, x1,\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3865: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  intgrl = integrate.quad(llc, x0, x1,\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3860: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = (integrate.quad(llc, x0, mean,\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3862: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  + integrate.quad(llc, mean, x1,\n",
      "\u001b[32m2024-05-06 01:26:36.349\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gennorm distribution with error=1.105504)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:36.526\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genhyperbolic distribution with error=0.816247)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:36.559\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gumbel_r distribution with error=1.083775)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:37.020\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gumbel_l distribution with error=1.125346)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:37.114\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted geninvgauss distribution with error=0.824183)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:37.458\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halflogistic distribution with error=1.035633)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:37.681\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genpareto distribution with error=1.023318)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:37.681\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfcauchy distribution with error=1.022437)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:38.002\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfnorm distribution with error=1.040952)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:38.447\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invweibull distribution with error=1.071844)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:38.672\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted hypsecant distribution with error=1.104189)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:38.892\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invgauss distribution with error=1.051555)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:38.938\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invgamma distribution with error=1.068065)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:39.845\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genexpon distribution with error=1.015651)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:40.472\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gompertz distribution with error=1.018932)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:40.603\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED kstwo distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:40.719\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ksone distribution with error=1.119445)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:40.916\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfgennorm distribution with error=0.940651)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:41.184\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted laplace distribution with error=1.116465)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:41.637\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted jf_skew_t distribution with error=1.075247)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:42.179\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted johnsonsu distribution with error=1.047402)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:42.316\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy_l distribution with error=1.214976)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:42.395\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted kappa3 distribution with error=1.048558)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:43.018\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy distribution with error=0.993854)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:43.418\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted laplace_asymmetric distribution with error=1.007443)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:43.497\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted lognorm distribution with error=0.985475)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:43.525\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted logistic distribution with error=1.103291)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:44.494\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loglaplace distribution with error=1.110032)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:44.824\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loguniform distribution with error=1.039465)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:45.045\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy_stable distribution with error=1.094587)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:45.265\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loggamma distribution with error=1.103834)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:45.391\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED multivariate_normal distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:45.408\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted moyal distribution with error=1.076156)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:45.953\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted johnsonsb distribution with error=0.994933)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:45.965\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted lomax distribution with error=1.071256)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:46.107\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted maxwell distribution with error=1.093009)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:46.149\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted kstwobign distribution with error=1.080738)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:46.802\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted norm distribution with error=1.103418)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:46.861\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted pareto distribution with error=0.929242)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:48.090\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted nakagami distribution with error=0.812775)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:48.106\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ncx2 distribution with error=0.964738)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:48.341\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powerlaw distribution with error=0.808991)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:48.711\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted mielke distribution with error=nan)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:49.595\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted nct distribution with error=1.072124)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:49.615\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rayleigh distribution with error=1.088283)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:49.771\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ncf distribution with error=1.076454)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:50.477\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted norminvgauss distribution with error=1.051273)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:50.827\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rdist distribution with error=1.191191)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:51.422\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted reciprocal distribution with error=1.039465)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:51.538\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED rv_continuous distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:51.725\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED rv_histogram distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:52.598\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rice distribution with error=1.088283)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:52.754\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted pearson3 distribution with error=0.928318)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:53.515\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted semicircular distribution with error=1.121521)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:53.966\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rel_breitwigner distribution with error=1.049813)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:54.484\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powernorm distribution with error=nan)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:54.513\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted skewcauchy distribution with error=1.023129)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:56.853\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted skewnorm distribution with error=1.040953)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:26:58.157\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted t distribution with error=1.103416)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:00.010\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powerlognorm distribution with error=1.056912)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:00.602\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncexpon distribution with error=1.052969)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:00.901\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted trapezoid distribution with error=1.241578)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:02.555\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncpareto distribution with error=1.204547)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:03.301\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncnorm distribution with error=1.103418)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:03.627\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted trapz distribution with error=1.241578)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:03.758\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted uniform distribution with error=1.114622)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:03.785\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted vonmises distribution with error=2.546147)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:03.979\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED vonmises_fisher distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:05.629\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted tukeylambda distribution with error=1.100463)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:05.755\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted wald distribution with error=1.046022)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:08.050\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncweibull_min distribution with error=0.873105)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:08.812\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted weibull_max distribution with error=1.886768)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:09.091\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED kappa4 distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:09.374\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted wrapcauchy distribution with error=1.102061)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:10.172\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted weibull_min distribution with error=0.823095)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:10.395\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted triang distribution with error=1.039005)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\integrate\\_quadpack_py.py:1272: 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",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "\u001b[32m2024-05-06 01:27:19.941\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED recipinvgauss distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:23.714\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED studentized_range distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:27:34.102\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED vonmises_line distribution (taking more than 30 seconds)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\integrate\\_quadpack_py.py:1272: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('honorarios.csv')\n",
    "fitter_honorarios = Fitter(df['honorarios_completos'])\n",
    "fitter_honorarios.fit()\n",
    "fitter_honorarios.summary()\n",
    "with open('fitter_honorarios.pkl', 'wb') as f:\n",
    "    pickle.dump(fitter_honorarios, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-05-06 01:28:56.671\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED _fit distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:28:58.394\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted anglit distribution with error=0.025963)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:28:59.424\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted bradford distribution with error=0.022784)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:28:59.560\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted argus distribution with error=0.027685)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:28:59.611\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted arcsine distribution with error=0.027056)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:00.698\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted cauchy distribution with error=0.027195)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:01.119\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted beta distribution with error=0.022568)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:01.297\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted burr distribution with error=0.022403)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:01.623\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted cosine distribution with error=0.025951)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:01.906\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted alpha distribution with error=0.025053)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:03.254\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted chi distribution with error=0.022652)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:03.611\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted chi2 distribution with error=0.025133)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:03.688\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted betaprime distribution with error=0.022597)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:03.719\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted dweibull distribution with error=0.023598)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:04.073\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted dgamma distribution with error=0.023239)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:04.075\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted burr12 distribution with error=0.023113)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:05.113\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted crystalball distribution with error=0.02595)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:05.952\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted expon distribution with error=0.022565)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:07.266\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted erlang distribution with error=0.022952)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:08.045\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted fatiguelife distribution with error=0.02333)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:08.346\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponnorm distribution with error=0.022573)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:08.828\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted fisk distribution with error=0.023956)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:09.870\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted f distribution with error=0.022402)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:09.872\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted foldcauchy distribution with error=0.023263)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:10.827\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted foldnorm distribution with error=0.023326)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:12.364\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gamma distribution with error=0.022954)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:12.688\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponpow distribution with error=0.022565)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:14.176\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genextreme distribution with error=0.025176)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:15.171\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genhalflogistic distribution with error=0.02374)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:15.400\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gausshyper distribution with error=0.022172)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3865: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = integrate.quad(llc, x0, x1,\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3860: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  intgrl = (integrate.quad(llc, x0, mean,\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\stats\\_continuous_distns.py:3862: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
      "  the requested tolerance from being achieved.  The error may be \n",
      "  underestimated.\n",
      "  + integrate.quad(llc, mean, x1,\n",
      "\u001b[32m2024-05-06 01:29:16.261\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gengamma distribution with error=0.022897)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:16.357\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genhyperbolic distribution with error=0.022518)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:17.127\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted exponweib distribution with error=0.023641)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:17.663\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gennorm distribution with error=0.024947)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:18.769\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gumbel_l distribution with error=0.026979)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:18.792\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gibrat distribution with error=0.023458)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:19.236\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted geninvgauss distribution with error=0.022558)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:19.378\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genlogistic distribution with error=0.025373)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:19.737\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gumbel_r distribution with error=0.025385)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:20.060\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genexpon distribution with error=0.022566)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:20.327\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfcauchy distribution with error=0.022959)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:21.083\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfnorm distribution with error=0.023232)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:21.163\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halflogistic distribution with error=0.023155)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:21.302\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted genpareto distribution with error=0.023146)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:21.473\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted hypsecant distribution with error=0.026242)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:24.428\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invgamma distribution with error=0.024909)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:25.596\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invweibull distribution with error=0.025176)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:26.360\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted jf_skew_t distribution with error=0.025845)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:26.426\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted invgauss distribution with error=0.024044)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:26.971\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED kstwo distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:27.050\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted halfgennorm distribution with error=0.028764)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:27.461\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted gompertz distribution with error=0.023016)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:28.390\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted johnsonsu distribution with error=0.024071)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:28.986\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted kappa3 distribution with error=0.02478)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:29.033\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted laplace distribution with error=0.027088)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:29.360\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ksone distribution with error=0.025248)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:29.614\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted laplace_asymmetric distribution with error=0.027086)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:30.288\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy_l distribution with error=0.030723)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:30.982\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy distribution with error=0.02312)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:31.046\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted logistic distribution with error=0.026043)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:32.423\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted lognorm distribution with error=0.02407)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:32.502\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted johnsonsb distribution with error=0.02181)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:33.109\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loglaplace distribution with error=0.024478)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:33.390\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted levy_stable distribution with error=0.025744)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:34.278\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loggamma distribution with error=0.025897)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:34.436\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED multivariate_normal distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:34.742\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted maxwell distribution with error=0.025559)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:35.610\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted moyal distribution with error=0.025106)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:36.184\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted kstwobign distribution with error=0.025156)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:36.235\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted loguniform distribution with error=0.032826)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:37.479\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted norm distribution with error=0.02595)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:37.479\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted lomax distribution with error=0.022524)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:37.611\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted nakagami distribution with error=0.023048)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:37.719\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted mielke distribution with error=0.022409)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:39.547\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted pareto distribution with error=0.022565)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:40.500\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powerlaw distribution with error=0.027141)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:40.751\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ncx2 distribution with error=0.026211)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:41.756\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted nct distribution with error=0.025296)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:42.702\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rayleigh distribution with error=0.025355)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:43.407\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted ncf distribution with error=0.022605)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:43.918\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rdist distribution with error=0.029203)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:45.473\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted norminvgauss distribution with error=0.024037)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:47.346\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rel_breitwigner distribution with error=0.02378)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:47.450\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED rv_continuous distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:47.679\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED rv_histogram distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:48.352\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted pearson3 distribution with error=0.022578)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:48.696\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted reciprocal distribution with error=0.032826)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:49.195\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted rice distribution with error=0.025355)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:49.531\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted semicircular distribution with error=0.025906)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:53.013\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powernorm distribution with error=nan)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:53.999\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted skewcauchy distribution with error=0.022947)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:54.717\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted skewnorm distribution with error=0.023231)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:55.151\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted t distribution with error=0.02595)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:55.786\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED kappa4 distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:29:58.974\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted trapezoid distribution with error=0.030731)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:00.154\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncexpon distribution with error=0.023295)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:01.002\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted powerlognorm distribution with error=0.023368)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:04.634\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted tukeylambda distribution with error=0.0249)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:05.241\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncnorm distribution with error=0.025947)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:05.753\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted vonmises distribution with error=2.631881)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:06.095\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED vonmises_fisher distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:06.965\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted uniform distribution with error=0.024947)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:07.526\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted trapz distribution with error=0.030731)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:08.748\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncweibull_min distribution with error=0.023837)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:09.900\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted wald distribution with error=0.023531)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:10.795\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted truncpareto distribution with error=0.022468)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:12.843\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted weibull_max distribution with error=0.05917)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:12.906\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED recipinvgauss distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:13.823\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted weibull_min distribution with error=0.022647)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:14.551\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m337\u001b[0m - \u001b[1mFitted wrapcauchy distribution with error=0.024285)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:19.396\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED studentized_range distribution (taking more than 30 seconds)\u001b[0m\n",
      "\u001b[32m2024-05-06 01:30:25.018\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED triang distribution (taking more than 30 seconds)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\integrate\\_quadpack_py.py:1272: 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",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n",
      "\u001b[32m2024-05-06 01:30:36.362\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mfitter.fitter\u001b[0m:\u001b[36m_fit_single_distribution\u001b[0m:\u001b[36m347\u001b[0m - \u001b[33m\u001b[1mSKIPPED vonmises_line distribution (taking more than 30 seconds)\u001b[0m\n",
      "C:\\Users\\Andres\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\scipy\\integrate\\_quadpack_py.py:1272: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  quad_r = quad(f, low, high, args=args, full_output=self.full_output,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_duracion = pd.read_csv('duracionterapia.csv')\n",
    "fitter_duracion = Fitter(df_duracion['Duracion'])\n",
    "fitter_duracion.fit()\n",
    "fitter_duracion.summary()\n",
    "with open('fitter_duracion.pkl', 'wb') as f:\n",
    "    pickle.dump(fitter_duracion, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'beta': 0.09988947689055722, 'loc': 3.998961573541849, 'scale': 1.6248371198774156e-11}\n",
      "{'a': 0.14349017715809642, 'loc': 8.999999999999998, 'scale': 24.000000000000004}\n",
      "{'a': 0.42361987798205225, 'b': 0.4946976129904108, 'loc': 0.8047291306002482, 'scale': 97.27293102030796}\n"
     ]
    }
   ],
   "source": [
    "best_llegadas = fitter_llegadas.get_best()[\"gennorm\"]\n",
    "best_honorarios = fitter_honorarios.get_best()[\"powerlaw\"]\n",
    "best_duracion = fitter_duracion.get_best()[\"johnsonsb\"]\n",
    "\n",
    "print(best_llegadas)\n",
    "print(best_honorarios)\n",
    "print(best_duracion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import gennorm, powerlaw, johnsonsb\n",
    "\n",
    "def generate_and_plot_distribution(dist_name, params, num_samples=1000, bins=30):\n",
    "    # Generate random samples from the specified distribution\n",
    "    dist = globals()[dist_name]\n",
    "    samples = dist.rvs(size=num_samples, **params)\n",
    "\n",
    "    # Plot histogram with frequency instead of density\n",
    "    plt.hist(samples, bins=bins)\n",
    "    plt.title(f'Histogram of Random Samples from {dist_name.capitalize()} Distribution')\n",
    "    plt.xlabel('Value')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = []\n",
    "while len(samples) < 1000:\n",
    "    r = int(gennorm.rvs(beta= 0.09988947689055722, loc=3.998961573541849, scale=1.6248371198774156e-11))\n",
    "    if r >= 0:\n",
    "        samples.append(r)\n",
    "\n",
    "plt.hist(samples, bins=100)\n",
    "plt.title(f'Histogram of Random Samples from Gennorm Distribution')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wevVqQaVSCadPn5bmmzlzptCoUSNBpVIJy5Ytk9o1f9PHjx8XBEEQ9u/fL6hUKmHMmDFar8mjR48ENzc3oWXLllItNMt6eHhI72saGV+X/GzDU6dOzbRNjxgxQur/rl27pHbNa33r1q0cKi5I9cxJQkKCoFKpBDc3t0yPr3l/3LRpk6BSqYSdO3dqLfvjjz9q1TGrGgjC/7cpHx+fLPvo4uKS6bmbNGkiRERESO3h4eGCk5OT0Lp1ayEtLU0QhKw/A3PqR8bnerdv165dEwQhf5+hgpD39+28KLOH0oKCgrB8+fJsb7lJT0+HIAiIjIzU2kXp6OiII0eOYNGiRTkur9nlP378eK2xHJUrV8bYsWMBQPqWfeDAASQnJ2P48OFaI/Lt7OzQtWvXLB9fqVRm+mZvZ2eHWbNmZdqjY2pqCjs7O6jVarx8+VJrWvXq1bUGvDVu3BjA20Mq736Le++992BpaYknT57kuN5HjhzBy5cvMWDAANjb20vt+vr6mDx5MpRKZaa9C/m1fPlyrdNe69evjyZNmmDYsGGIjY3FxIkT0b17d2n+qlWrYu7cufjmm2+0HkdfXx8uLi4AMg8GVCqVGDBggHRfoVCgZcuWACDV4OTJk4iPj8eAAQNQtWpVaV5HR8dMexquXLmCBw8e4NNPP4WHh4fWtNGjR8Pa2hp//PFHprNC+vTpo/Utx9nZGQDQq1cvrbPGGjZsqNW3nHTu3Bm7du1C165dYWZmhtTUVFy5cgUrVqxA165dMXLkSK3DY7rUz9DQEH379pXu165dWxqH8+6ZKKampqhTpw5iYmIyHWJp3Lgx2rVrJ923sLDA8OHDkZaWlu2hrrS0NPz++++oV69epsMbH330ERo3bozDhw/j9evXAN7ukXnx4oXW4ZeqVaviwIED+O2337KpYN60bdsW5cr9/y22JG0DAHD9+nUsW7ZMuv3888+YMmUK2rdvjydPnqB9+/bSIa69e/ciLS0No0aN0tojY2Jigu+//x7A/9/PNOv27pCDwMBANG/eHNWrV8fFixel9jNnzkCpVEp7kHbu3AkAmDJlCvT1/39Ao0aNGujduzeeP3+Oc+fOaa2Hh4dHrnvI8rMNa95XNf1Xq9W4dOkSunbtCgMDg0z9r169OurVq5fj8+eF5jXWbJtZ0RyyDA8Ph1qtltq/++47nDlzJs97cD7++OM892vAgAF47733pPt2dnbo1KkTnj59ikuXLuX5cfIrP5+hGnl5386LMnsozcfHJ8fd7ba2tjkub25ujg4dOuDPP/+Ep6cnnJ2d4eHhAU9PT9StWzfX579x4wbKlSsn/dG9S9OmGQ/w999/A/j/G9u7GjdujB07dmRqr1q1aqbTaGvVqoVatWohOTkZoaGhuH//Ph49eoTw8HDpePO7f0wAMp1JozmkktXjGxkZ5XpdEM06NWnSJNO0ypUro1atWrh+/Tri4+OzPNyUF++erv/69WscPHgQz549Q6dOnTBz5kwolUqt+atWrYquXbsiLS0N4eHhUl2uX78uvclmHBNkY2OTaberpr+aDy7Nujo4OGTqo7Ozs9ZZcZrj5lnVxdDQUArc9+7dQ/369aVp77//vta8mtfn3Tcq4O1r827fcqNSqTB37lykpaXh77//xvnz53Hq1ClcuXIFR48eRUxMDLZt2waFQqFT/apVq5apfiYmJkhMTESVKlWy7fu7r13GSzIA//8byW4szf3795GQkAC1Wp3ltcySk5OhVqtx8+ZNuLi4oFevXlizZo003sLDwwOtWrWCo6NjbiXMVcbXqKRtAzdu3NCqo56eHszMzFCvXj18/fXX6NWrl9a82fW9Xr16MDc3l+apX78+qlatKgWLmJgY3L59Gz169IC+vj5OnDiB1NRU6Onp4ezZs2jatKn0uoeHh8PIyAhbtmzJ9Dz3798H8LaO7waAjHXISn624WbNmsHQ0BAXLlxA3759ER4ejlevXqF58+b4+++/pTAQFxeHv//+W+sLZEFoxqC9e1g7o08++QR+fn7YsmUL9u/fjxYtWkjbbMa/q5zkpWYami/L72rYsCG2b9+OGzdu6HzWXG7y8xmqkZf37bwos8GoMMybNw8ODg7YtWuXNBZm4cKFcHBwwKxZs3I8Nvr69WsYGRlleUzTzMwMxsbG0oDT2NhYAMjy+g3vHuN/V8YPf+DtH/bq1auxYcMGac+QhYUFnJ2dUb16ddy9exeCIGgtk903rTwfi81A820nu2vgWFlZ4fr160hMTCxQMHo39H7zzTcYNmwY9u7dCzMzM/zwww+Zltm2bRv8/Pzwzz//AHgbfJ2cnFCnTh2EhoZmqktW6685Dq+ZVxMSy5cvn2ned8eJAHmrC4BMZ9Jl9yap6+uTkb6+PpydneHs7IyRI0fiypUrGDlyJEJCQhAYGCh9k89v/bLbrgwMDPLct6y2fc2bf3bfqjWvyb1793LcM6z5+xgzZgxq1qyJbdu2ISwsDKGhoVi2bBlq1aqFadOmZRrPkh8Z/0ZL2jbQtWtXzJ07N0/zavqe3d+slZUVHj58KN338PDAzp078fLlSwQGBkIQBLi5uUFPT08a81GuXDnExcVp7fmOj4+XTnrJTsa93ppAmJu8bsMmJiZo0qQJAgMDkZ6ejgsXLqBcuXJo0qQJgoODsX79evzzzz+4dOkS1Gp1lmPydKHZo5HdGCkAsLa2xs6dO7Fy5UocPXoUf/zxB/744w8YGBjA29sb33//fZ62i6w+P7JjYWGRqU3znpfXi0fqIj+foRp5ed/OCwajHBgYGGDw4MEYPHgwnj59irNnz+LgwYPS4MejR49m+0Zfvnx5JCYm4tWrV1oDMoG331qTkpKkQwuaN8o3b95kOoU6p92qGf33v//FkiVL4ObmhqFDh6JBgwbSB8mXX36Ju3fv5vmxdKX5g9G8+WSk+eDKGBwKwsTEBEuWLEHnzp2xZcsWqFQqrW9xBw4cwLRp02Bra4tp06bB3t4e1apVAwBMmzYNoaGhOj2v5nXNeDYRkPkNQ1OX58+fZ/lYRVGXrIwcORJhYWE4duxYlm8izs7OGDhwIBYvXoyHDx+iadOmRVa/3GR1JeDc6qSpc+fOnTF//vxcn0OhUKB79+7o3r07YmJicO7cORw+fBiHDh3CV199hWPHjmX6m9RVSdkGdPFu37Oqx8uXL7X67eHhgR07diAwMBCBgYGoWLEibG1tpcNjFy9elL7Bv7v3x8TEBOXLl8eJEycKtf/53YY9PDxw9uxZXL9+HUFBQahfvz7Mzc3h7u6O9evX49KlSzh9+jSMjY2lLw8FpdkTpTlUmp0aNWpgzpw5UKvVuHr1Kk6fPo1du3Zh+/btMDMzyzQguaCyen/TvL9rLkaZU/jQ9bIp+fkMLWxldoxRQUVERGDx4sU4fvw4gLe76Hr06IH169ejadOmeP78uXQKYFYj+jW7woODgzNNCw4OhiAI0iE5zVicsLCwTPPm50Nn37590NPTw8qVK+Hh4SGFIkEQcO/ePen/RUmzFy2r9X79+jWuX7+OmjVrFtoeDw1LS0tMnz4dwNuzKN49PXPfvn0AgEWLFsHLy0t6QwRQoLpoXresziLRHB7V0NQlq3nT09MRHBwMExMTVK9ePd/9yA8DAwNERUXleH0azfas2YNRVPXLTcYaApB+7sXJySnLZWrVqgVDQ0OEh4dn2adffvkFK1asQGxsLGJjY7Fs2TLs3r0bwNtvxp999hmWLl0Kb29vJCYmZjpFvCBKyjagi5zezx4+fIioqCitcTbNmjWDgYEBLly4gMuXL8PV1RUKhQJ169aFpaUlLl68iDNnzkClUsHGxkZaztbWFs+ePdMa16lx4sQJ/PTTTzpdkiC/27BmnNS5c+cQEhIiHUJ0cXGBvr4+goKCcObMGTRt2jTPe6xykpaWhu3btwOAdPZVVo4ePYrp06fj9evX0NPTg5OTE3x8fKRDj1m9PgWV09+hZhiBZgdBxi+EgiBke/mE3OTnM7SwMRhlQ6lUYu3atfj555+1jk2mpKQgKioKhoaGUvDQfAt697o53t7eAN5eEffdgawvXryQvslqrij72WefwcDAAKtWrdKa9/bt29IfS14YGRlBrVZnuq6Mn5+ftJs2r9ch0pWXlxfMzMzw22+/ITw8XGpPS0vD7NmzkZSUVGRX0m3bti0+/vhjJCYmSiEJ+P+u9ujoaK35f//9d2nslS51adWqFSpXroxNmzZJ4x8A4O7du9IgUg0XFxfUrFkThw4dwsmTJ7WmLV26FJGRkWjfvn2hB8aMNAOSp0+fnmUQf/DgAX799VdYWVmhefPmAIqufrk5fPiw1uDOqKgorFy5EiYmJmjfvn2WyxgZGaFDhw64c+eOdD0ujcDAQMyfPx8BAQGoUKECypcvj19//RU//fRTpuv1PH36FAC0PrQLqqRsA7ro3Lkz9PX1sWrVKq0PuoSEBMyYMUOaR8PU1BQuLi44duwYbt++rTVerEmTJrh06RLCwsIyDRbu2rUrBEHAzJkztd53//nnH0ybNg1r1qzJ8tB1bvK7DdeuXRvvv/8+tm7divj4eKn/mhNZ9u3bh3/++adQTtPXvDfevn1bGs+anXv37mHr1q3YunWrVrvm/f3d7VUTVt79XNLFpk2btD5TLl26hIMHD6JevXrSmD/N5QBOnz6tNY71t99+y/S3pelbbu8Z+fkMLWw8lJaNKlWq4IsvvsCGDRvQsWNHtGrVCuXKlcPp06dx9+5djBw5UjoEZm1tDeDtdVdevnyJ/v37o0mTJhg0aBA2bNiATp06SWd3HD9+HFFRURg6dKj0LaR69eoYPXo0Fi1ahM6dO+Ojjz5CUlIS/vrrL+kP+t2zW7LTqVMnhISEoE+fPmjfvj0MDAwQGBiI8PBwWFhYICYmJsuNtDCZmppizpw5+O6779C7d2+0bdsWFhYWuHDhAm7dugVXV1cMHTq0yJ7/+++/x7lz53D69Gns27cPHTt2RKdOnfDnn3/Cx8cHn376KUxNTfH3338jKCioQHUpX748Zs6ciW+++QY9evTAJ598AuDthTorV66sNVC9XLlymDt3LoYMGYIRI0bA09MT77//Pq5cuYKQkBDUqVMH48ePL6wyZMvNzQ0TJ07E/Pnz0atXL7i6usLOzg4GBga4d+8eTp06BUNDQ6xbt076gC6q+uVGqVRi4MCBaNeuHUxNTXHkyBFER0dj5syZOQ40nTBhAq5cuYJ58+bh6NGjaNiwIZ4/f45Dhw5BX18fc+bMQbly5WBoaIjRo0dj1qxZ6NixI9q2bQulUomLFy/i77//RufOnXX67cTslJRtQBc1atTAhAkTMHv2bHTt2hVeXl4wMTHBqVOnEBERgU8//TTTmZitWrXCvHnzAEBrgK6bm5t0VmHG8Tne3t44duwY/vrrL9y8eRMtW7aUzkKMi4vD2LFjcxyDkx1dtmEPDw9s3rxZGl/0bv81XyryO77o3RMCUlNTER0djfPnz+Pp06ews7ODr69vjsv37NkTO3bswMKFCxEUFARbW1vExMTg4MGDMDExwbBhw6R5NZ9LkydPRvPmzbXO1soPfX19dO7cGR06dJCeS6lUavXVzs4O9vb2uHLlCvr27YsmTZrg5s2buHDhApycnDId+bCyssKDBw8wbtw4tGjRIsvrheXnM7SwMRjl4D//+Q9q1qwJf39/7N69G2q1GnXr1sXcuXO1TqNv0qQJPv/8c+zZswdbtmzBhx9+CGtra+nKtVu2bMEff/wBfX19NGjQAD/88EOm0yWHDRsGCwsLbNy4EQEBAahYsSK++OILVK5cGbNnz87TBdv69u0rXTXW398fZmZmqFWrFhYvXgwjIyOMGjUKJ0+ezPUYdkF9/PHH+O2337By5UqcPn0aKSkpeP/99zF+/HgMGDAgXwNw88va2hrfffcdZs6ciTlz5qBly5Zo3bo1fvrpJ6xduxZ//PEHlEolatSogR9++AHOzs7o2rUrTp48meMu7Ox4eXnhl19+wbJly7B//34YGxujZ8+ecHR0xHfffac1b+PGjbFz506sWLFCCm82Njb46quvMHToUJ2+Ceti0KBBcHd3x2+//YaLFy/i6tWrUKvVqFatGnr37o0vv/xS6/IDRVm/nHTp0gXW1tbYsmUL4uLi0KBBA8yePTvXD6PKlStjx44dWL16NQ4fPoxNmzahcuXKaNOmDUaOHKl1xlf//v1hYWGBX3/9Ffv370diYiI++OADTJo0KdPVmwtDSdkGdDFgwAB88MEHWL9+PQ4dOgRBEFCnTh0MHz5c6/IYGppgpBlfpKEJSRUqVMj0XqRQKLB06VJs2bIFu3btgr+/P5RKJerWrYtBgwbp/FtaumzDmmBka2ur9cOu7u7uWLduHWxtbbUOyeXFu4PKy5UrB3Nzc2ndevfunevewgoVKmDz5s1YuXIlzp49iwsXLsDU1BQeHh7w8fHROpw5YsQI3L17F2fPnsWDBw90DkZz5szBnj17sGvXLqSlpaF58+YYO3YsVCqV1nyrV6/GokWLcPz4cdy8eRMODg7YuHEjDhw4kCkY/ec//8HkyZNx8OBBxMTEZHsh1fx8hhYmhVDUg04oV7GxsVCr1VmelbZ06VL4+fnB398/y9P5icqawMBADBgwAAMGDMCUKVPk7g4R/ctwjFEJoLn4WcZTVF+8eIHdu3ejQoUKWt9yiYiIqGjwUFoJ0LJlS1SvXh1+fn74+++/oVKp8PLlSxw5cgSxsbGYO3duiRyQSUREVNYwGJUA5cuXx7Zt27Bu3TqcOHEC58+fh4mJCRwcHDBkyJACXWSOiIiI8o5jjIiIiIhEHGNEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRDwrDcCrV4lQq9Pl7ka2KlUqj9jYN3J341+HdZcH6y4P1l0erLtu9PTKwdw891+E0AWDEQC1Oh1paSUzGIk/dA61Oh08f7D4sO7yYN3lwbrLg3UvmXgojYiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiKRvtwdKKtepb9ATFKMTstaKC1gXq5yIfeIiIiIciN7MEpJSYGvry/27dsHAwMDdO/eHd999x0UCgWuXbuGadOm4datW6hbty5+/PFHODg4SMvu27cPS5YsQVRUFFq0aIGZM2eicuWSEShikmKwPGi5Tsv6uPnA3KRkrAcREdG/ieyH0mbNmoVz585h/fr1WLRoEXbs2IHt27cjISEBw4YNg6urK3bt2gVnZ2cMHz4cCQkJAICwsDBMmTIFPj4+2L59O169eoVJkybJvDZERERUmsm6xyguLg4BAQHYsGEDGjZsCAAYPHgwQkNDoa+vDyMjI4wfPx4KhQJTpkzBqVOncPDgQXh7e2Pz5s1o3749unTpAgCYP38+PD09ERERgRo1asi4VkRERFRaybrHKDg4GKampnBzc5Pahg0bBl9fX4SGhsLFxQUKhQIAoFAo0LhxY4SEhAAAQkND4erqKi1XrVo12NjYIDQ0tFjXgYiIiMoOWYNRREQEqlevjt9//x3t2rXDRx99BD8/P6SnpyMqKgpWVlZa81tYWODZs2cAgH/++SfH6URERET5JeuhtISEBDx8+BDbtm2Dr68voqKi8MMPP8DY2BiJiYkwNDTUmt/Q0BApKSkAgKSkpByn55e4Y6rQKMSbzsuKC2f8l4oH6y4P1l0erLs8WPeSSdZgpK+vj9evX2PRokWoXr06AODp06fYunUratasmSnkpKSkQKlUAgCMjIyynG5sbJzvflSqVF7HNchepNoASqWBTssqlQawtDTTarOwMMtmbipKrLs8WHd5sO7yYN1LFlmDUZUqVWBkZCSFIgCoVasWIiMj4ebmhujoaK35o6OjpcNn1tbWWU6vUqVKvvsRG/sGanW6DmuQvaSkVCQlpeq8bHR0PIC33yQsLMwQExMPQSjMHlJOWHd5sO7yYN3lwbrrTk+vXJHs1ABkDkZOTk5ITk7G/fv3UatWLQDAvXv3UL16dTg5OWHt2rUQBAEKhQKCIODy5csYMWKEtGxwcDC8vb0BAJGRkYiMjISTk5NOfSnsjVIQbzovm2FhQSj8PlLuWHd5sO7yYN3lwbqXLLIOvq5duzZat26NSZMm4caNGzh9+jTWrFmDPn36oF27dnj16hVmz56NO3fuYPbs2UhMTET79u0BAH369MGePXvg7++PGzduYPz48WjdujVP1SciIiKdyX6Bx4ULF+L9999Hnz59MGHCBHz++efo378/TE1NsXr1ammvUGhoKNasWQMTExMAgLOzM2bMmAE/Pz/06dMHFSpUgK+vr8xrQ0RERKWZ7D8JYmZmhvnz52c5rWHDhti9e3e2y3p7e0uH0oiIiIgKSvY9RkREREQlBYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISMRgRERERiRiMiIiIiEQMRkREREQiBiMiIiIiEYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISMRgRERERiWQPRocPH4atra3WbfTo0QCAa9euoUePHnByckK3bt1w9epVrWX37dsHLy8vODk5YdSoUXjx4oUcq0BERERlhOzB6M6dO/D09MSZM2ek26xZs5CQkIBhw4bB1dUVu3btgrOzM4YPH46EhAQAQFhYGKZMmQIfHx9s374dr169wqRJk2ReGyIiIirNZA9Gd+/ehUqlQpUqVaSbubk59u/fDyMjI4wfPx516tTBlClTUL58eRw8eBAAsHnzZrRv3x5dunRB/fr1MX/+fJw8eRIREREyrxERERGVViUiGH3wwQeZ2kNDQ+Hi4gKFQgEAUCgUaNy4MUJCQqTprq6u0vzVqlWDjY0NQkNDi6PbREREVAbpy/nkgiDg/v37OHPmDFavXg21Wo127dph9OjRiIqKQt26dbXmt7CwwO3btwEA//zzD6ysrDJNf/bsmU59EfNXoVGIN52XFRfO+C8VD9ZdHqy7PFh3ebDuJZOswejp06dITEyEoaEhlixZgsePH2PWrFlISkqS2t9laGiIlJQUAEBSUlKO0/OjUqXyuq9ENiLVBlAqDXRaVqk0gKWlmVabhYVZNnNTUWLd5cG6y4N1lwfrXrLIGoyqV6+OwMBAVKhQAQqFAg0aNEB6ejr+85//wM3NLVPISUlJgVKpBAAYGRllOd3Y2Djf/YiNfQO1Ol33FclCUlIqkpJSdV42OjoewNtvEhYWZoiJiYcgFGYPKSesuzxYd3mw7vJg3XWnp1euSHZqADIHIwCoWLGi1v06deogOTkZVapUQXR0tNa06Oho6fCZtbV1ltOrVKmiUz8Ke6MUxJvOy2ZYWBAKv4+UO9ZdHqy7PFh3ebDuJYusg69Pnz4Nd3d3JCYmSm3Xr19HxYoV4eLigitXrkAQtxZBEHD58mU4OTkBAJycnBAcHCwtFxkZicjISGk6ERERUX7JGoycnZ1hZGSE77//Hvfu3cPJkycxf/58fPnll2jXrh1evXqF2bNn486dO5g9ezYSExPRvn17AECfPn2wZ88e+Pv748aNGxg/fjxat26NGjVqyLlKREREVIrJGoxMTU2xfv16vHjxAt26dcOUKVPQq1cvfPnllzA1NcXq1asRHBwMb29vhIaGYs2aNTAxMQHwNlTNmDEDfn5+6NOnDypUqABfX185V4eIiIhKOdnHGNWrVw8bNmzIclrDhg2xe/fubJf19vaGt7d3UXWNiIiI/mVkv8AjERERUUnBYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISMRgRERERiRiMiIiIiEQMRkREREQiBiMiIiIiEYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISMRgRERERiRiMiIiIiEQlKhgNGzYMEydOlO5fu3YNPXr0gJOTE7p164arV69qzb9v3z54eXnByckJo0aNwosXL4q7y0RERFSGlJhg9Oeff+LkyZPS/YSEBAwbNgyurq7YtWsXnJ2dMXz4cCQkJAAAwsLCMGXKFPj4+GD79u149eoVJk2aJFf3iYiIqAwoEcEoLi4O8+fPh6Ojo9S2f/9+GBkZYfz48ahTpw6mTJmC8uXL4+DBgwCAzZs3o3379ujSpQvq16+P+fPn4+TJk4iIiJBrNYiIiKiUKxHBaN68eejcuTPq1q0rtYWGhsLFxQUKhQIAoFAo0LhxY4SEhEjTXV1dpfmrVasGGxsbhIaGFmvfiYiIqOyQPRidP38ely5dwsiRI7Xao6KiYGVlpdVmYWGBZ8+eAQD++eefHKcTERER5Ze+nE+enJyMadOm4YcffoBSqdSalpiYCENDQ602Q0NDpKSkAACSkpJynJ5f4o6pQqMQbzovKy6c8V8qHqy7PFh3ebDu8mDdSyZZg9Hy5cvh4OCAli1bZppmZGSUKeSkpKRIASq76cbGxvnuR6VK5fO9TG4i1QZQKg10WlapNIClpZlWm4WFWTZzU1Fi3eXBusuDdZcH616yyBqM/vzzT0RHR8PZ2RkApKDz119/oWPHjoiOjtaaPzo6Wjp8Zm1tneX0KlWq5LsfsbFvoFan67IK2UpKSkVSUqrOy0ZHxwN4+03CwsIMMTHxEITC7CHlhHWXB+suD9ZdHqy77vT0yhXJTg1A5mC0adMmpKWlSfcXLlwIABg3bhwuXryItWvXQhAEKBQKCIKAy5cvY8SIEQAAJycnBAcHw9vbGwAQGRmJyMhIODk56dSXwt4oBfGm87IZFhaEwu8j5Y51lwfrLg/WXR6se8kiazCqXr261v3y5d+mv5o1a8LCwgKLFi3C7Nmz0bt3b2zbtg2JiYlo3749AKBPnz7o378/GjVqBEdHR8yePRutW7dGjRo1in09iIiIqGyQ/ay07JiammL16tXSXqHQ0FCsWbMGJiYmAABnZ2fMmDEDfn5+6NOnDypUqABfX1+Ze01ERESlmax7jDKaO3eu1v2GDRti9+7d2c7v7e0tHUojIiIiKqgSu8eIiIiIqLgxGBERERGJGIyIiIiIRDoFo3379ul8hWkiIiKikkqnYDR+/Hg0b94c06dPR1hYWGH3iYiIiEgWOgWjY8eOYfDgwbhw4QJ69eqFDh06YP369YiKiirs/hEREREVG52CUdWqVfHVV1/h4MGD2LJlC1xdXbF27Vp4enpixIgROHTokNYVrYmIiIhKgwJfx6hx48Zo3LgxevTogfnz5+PEiRM4ceIELC0t8cUXX2Dw4MHQ09MrjL4SERERFakCBaMnT55gz5492LNnDx49eoT3338fY8aMQevWrXHixAn4+fnhzp07mDdvXmH1l4iIiKjI6BSM/P39sWfPHly+fBlGRkZo164dZs+eDVdXV2kelUqF2NhYbNu2jcGIiIiISgWdgtHUqVPh5OSE6dOno0OHDjA1Nc1yPltbW/Tq1atAHSQiIiIqLjoFo3379qFu3bpQq9XS+KGkpCSkpqbCzMxMmq9Lly6F0kkiIiKi4qDTWWkffPABpk2bhp49e0ptly9fRrNmzTBv3jykp6cXWgeJiIiIiotOwWjp0qXYu3cvOnbsKLXZ2dlh3Lhx2LFjB9atW1doHSQiIiIqLjodSvvjjz8wYcIE9O7dW2qrWLEiBg4cCH19ffz6668YNmxYoXWSiIiIqDjotMcoNjYWNWrUyHJa7dq18ezZswJ1ioiIiEgOOgWj2rVr46+//spy2rFjx1CzZs0CdYqIiIhIDjodShswYAAmTpyIuLg4eHl5wcLCAi9evMDx48dx4MAB+Pr6FnY/iYiIiIqcTsGoS5cuePPmDVasWIFDhw5J7ZUqVcLUqVN5mj4RERGVSjr/JMjnn3+Ovn374v79+4iLi4O5uTlq166NcuV0OjpHREREJLsC/VaaQqFA7dq1C6svRERERLLSKRi9ePECs2fPxokTJ5CYmAhBELSmKxQKXLt2rVA6SERERFRcdApGM2bMwPHjx/Hpp5+iatWqPHxGREREZYJOwejUqVOYPHkyfyCWiIiIyhSddvUYGBhke4FHIiIiotJKp2DUtm1b7Nu3r7D7QkRERCQrnQ6l2dnZYcmSJYiIiICTkxOUSqXWdIVCgVGjRhVKB4mIiIiKi86DrwHg4sWLuHjxYqbpDEZERERUGukUjG7cuFHY/SAiIiKSXYHPs4+Pj8fdu3eRkpICtVpdGH0iIiIikoXOwSgwMBA9evSAm5sbPvvsM9y+fRtjx47F3LlzC7N/RERERMVGp2B0/vx5DBkyBEqlEuPGjZOufF2/fn38+uuv2LBhQ6F2koiIiKg46BSMlixZgo8++gibNm3CF198IQWjESNG4Msvv4S/v3+hdpKIiIioOOgUjK5fv45u3boBeHsG2ruaN2+OJ0+eFLxnRERERMVMp2BkZmaGqKioLKdFRkbCzMysQJ0iIiIikoNOweijjz7CTz/9hL///ltqUygUePbsGVatWoXWrVsXVv+IiIiIio1O1zEaO3YsQkND0bNnT1haWgIAxowZg2fPnqFatWoYM2ZMoXaSiIiIqDjoFIwqVKgAf39//P7777hw4QLi4uJgZmaG/v37w9vbG8bGxoXdTyIiIqIip1MwAgBDQ0P07NkTPXv2LMz+EBEREclGp2D0+++/5zpPly5ddHloIiIiItnoFIwmTpyYZbtCoYCenh709PQYjIiIiKjU0SkYHT16NFNbQkICLl26hLVr18LPz6/AHSMiIiIqbjoFo+rVq2fZXq9ePaSmpmLmzJn47bffCtQxIiIiouKm84/IZsfW1hbh4eGF/bBERERERa5Qg1FKSgp27twJCwuLwnxYIiIiomKh06G0Nm3aZPqNtPT0dMTGxiI5ORkTJkwolM4RERERFSedgpGbm1umYAQApqam8PT0xIcffpjnx3r48CFmzJiBy5cvo0KFCujXrx++/PJLAEBERASmTp2KkJAQ2NjYYPLkyWjRooW07Llz5zBnzhxERETAyckJs2fPRo0aNXRZJSIiIiLdgtHcuXML5cnT09MxbNgwODo6Yvfu3Xj48CHGjBkDa2trdOzYEaNGjYJKpUJAQACOHDkCHx8f7N+/HzY2Nnj69ClGjRqFr7/+Gi1btoSfnx9GjhyJvXv3ZhnaiIiIiHKjUzB6+vRpvua3sbHJsj06OhoNGjTA9OnTYWpqig8++ADNmjVDcHAwLC0tERERgW3btsHExAR16tTB+fPnERAQgK+//hr+/v5wcHDA4MGDAQC+vr5o3rw5goKC4O7urstqERER0b9coY0xysn169ezbLeyssKSJUsAAIIg4PLly7h48SKmTZuG0NBQ2NnZwcTERJrfxcUFISEhAIDQ0FC4urpK04yNjWFvb4+QkBAGIyIiItKJTsFoyZIlmDZtGuzt7dGpUydYW1sjNjYWx44dw4EDB/DVV19le62j7LRp0wZPnz6Fp6cnPvnkE8yZMwdWVlZa81hYWODZs2cAgKioqByn51dhH31TiDedlxUXzvgvFQ/WXR6suzxYd3mw7iWTTsFoz5498PT0zDTWqEOHDrCwsMDly5fh4+OTr8dcunQpoqOjMX36dPj6+iIxMRGGhoZa8xgaGiIlJQUAcp2eH5Uqlc/3MrmJVBtAqTTQaVml0gCWlmZabRYWZtnMTUWJdZcH6y4P1l0erHvJolMwOn/+PJYvX57lNA8PD2zbti3fj+no6AgASE5Oxrhx49CtWzckJiZqzZOSkgKlUgkAMDIyyhSCUlJSYG5unu/njo19A7U6Pd/L5SQpKRVJSak6LxsdHQ/g7TcJCwszxMTEQxAKs4eUE9ZdHqy7PFh3ebDuutPTK1ckOzUAHYNRpUqVEBoaqnXqvMb58+dhbW2dp8eJjo5GSEgIvLy8pLa6desiNTUVVapUwb179zLNrzl8Zm1tjejo6EzTGzRokN/VAYBC3ygF8abzshkWFoTC7yPljnWXB+suD9ZdHqx7yaLTla+7d++OlStXYuHChbh8+TIePHiAS5cuYdasWdiwYYN0plhuHj9+DB8fHzx//lxqu3r1KipXrgwXFxeEh4cjKSlJmhYcHAwnJycAgJOTE4KDg6VpiYmJuHbtmjSdiIiIKL902mM0cuRIxMfH45dffsH69esBvD2rzNjYGN999x169+6dp8dxdHSEvb09Jk+ejEmTJuHJkydYsGABRowYATc3N1SrVg2TJk3CyJEjcfz4cYSFhcHX1xcA0K1bN6xfvx5r1qyBp6cn/Pz88N577/GMNCIiItKZQhB034H36tUrhISE4OXLl6hUqRIaNWoEU1PTfD3G8+fPMXPmTJw/fx7Gxsbo168fhg8fDoVCgYcPH2LKlCkIDQ1FzZo1MXnyZK2rap88eRJz5szBs2fP4OzsjJkzZ+p05evY2DdISyvcMUb3E25jeVDW47By4+Pmg1om9QC8PQZtaWmG6Ggegy5OrLs8WHd5sO7yYN11p69fwsYYaZiamkpjfho1aoS0tLR8P4a1tXW2A7lr1qyJzZs3Z7tsq1at0KpVq3w/JxEREVFWdA5Ge/bswaJFixAVFQWFQgF/f38sW7YMBgYGWLRoUaZT6YmIiIhKOp0GX+/fvx8TJkxA06ZNsXjxYqSnvz0M1bZtW5w8eRIrVqwo1E4SERERFQed9hitWrUKvXv3xvTp06FWq6X2bt264cWLF9ixYwe+/fbbwuojERERUbHQaY/R/fv30bZt2yynOTk5aZ1+T0RERFRa6BSMLCwscPfu3Syn3b17FxYWFgXqFBEREZEcdApGHTp0wNKlS3Hw4EHpZzkUCgWuXr2KFStWoF27doXaSSIiIqLioNMYo2+//Ra3bt3Ct99+i3Ll3mar/v37IyEhAa6urvjmm28KtZNERERExUGnYGRoaIh169bh7NmzuHDhAuLi4mBmZgY3Nze0atUKCoWisPtJREREVOR0CkZDhgzBl19+iebNm6N58+aF3SciIiIiWeg0xujy5cvcK0RERERljk7BqGXLlti7dy9SU1MLuz9EREREstHpUJqRkRH27t2LAwcOoE6dOjAxMdGarlAosHHjxkLpIBEREVFx0SkYaX7NXkPI8LPAGe8TERERlQZ5DkaHDh1C06ZNYW5ujk2bNhVln4iIiIhkkecxRt988w0ePHig1bZ27VrExMQUdp+IiIiIZJHnYJTx8JharcbixYvx7NmzQu8UERERkRx0OitNg2OJiIiIqCwpUDAiIiIiKksYjIiIiIhEBQ5GvAI2ERERlRX5uo7RqFGjYGhoqNU2YsQIGBgYaLUpFAocOXKk4L0jIiIiKkZ5DkZdu3Ytyn4QERERyS7PwcjX17co+0FEREQkOw6+JiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISMRgRERERiRiMiIiIiEQMRkREREQiBiMiIiIiEYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZFI9mD0/PlzjB49Gm5ubmjZsiV8fX2RnJwMAIiIiMDAgQPRqFEjdOjQAWfOnNFa9ty5c+jYsSOcnJwwYMAAREREyLEKREREVEbIGowEQcDo0aORmJiILVu24KeffsLx48exZMkSCIKAUaNGwdLSEgEBAejcuTN8fHzw9OlTAMDTp08xatQoeHt7Y+fOnahcuTJGjhwJQRDkXCUiIiIqxfTlfPJ79+4hJCQEZ8+ehaWlJQBg9OjRmDdvHjw8PBAREYFt27bBxMQEderUwfnz5xEQEICvv/4a/v7+cHBwwODBgwEAvr6+aN68OYKCguDu7i7nahEREVEpJeseoypVqmDdunVSKNJ4/fo1QkNDYWdnBxMTE6ndxcUFISEhAIDQ0FC4urpK04yNjWFvby9NJyIiIsovWYORubk5WrZsKd1PT0/H5s2b0bRpU0RFRcHKykprfgsLCzx79gwAcp1ORERElF+yHkrLaMGCBbh27Rp27tyJX375BYaGhlrTDQ0NkZKSAgBITEzMcXp+KRS69TnbxxNvOi8rLpzxXyoerLs8WHd5sO7yYN1LphITjBYsWICNGzfip59+gkqlgpGREeLi4rTmSUlJgVKpBAAYGRllCkEpKSkwNzfP93NXqlRe535nJ1JtAKXSQKdllUoDWFqaabVZWJhlMzcVJdZdHqy7PFh3ebDuJUuJCEYzZ87E1q1bsWDBAnzyyScAAGtra9y5c0drvujoaOnwmbW1NaKjozNNb9CgQb6fPzb2DdTqdB17n7WkpFQkJaXqvGx0dDyAt98kLCzMEBMTD55wV3xYd3mw7vJg3eXBuutOT69ckezUAEpAMFq+fDm2bduGxYsXo127dlK7k5MT1qxZg6SkJGkvUXBwMFxcXKTpwcHB0vyJiYm4du0afHx8dOpHYW+UgnjTedkMCwtC4feRcse6y4N1lwfrLg/WvWSRdfD13bt3sWLFCgwdOhQuLi6IioqSbm5ubqhWrRomTZqE27dvY82aNQgLC0P37t0BAN26dcPly5exZs0a3L59G5MmTcJ7773HU/WJiIhIZ7IGo6NHj0KtVmPlypVo0aKF1k1PTw8rVqxAVFQUvL29sXfvXvj5+cHGxgYA8N5772HZsmUICAhA9+7dERcXBz8/Pyg4io2IiIh0JOuhtGHDhmHYsGHZTq9ZsyY2b96c7fRWrVqhVatWRdE1IiIi+heS/bfSiIiIiEoKBiMiIiIiEYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISMRgRERERiRiMiIiIiEQMRkREREQiBiMiIiIiEYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiISlZhglJKSgo4dOyIwMFBqi4iIwMCBA9GoUSN06NABZ86c0Vrm3Llz6NixI5ycnDBgwABEREQUd7eJiIioDCkRwSg5ORljxozB7du3pTZBEDBq1ChYWloiICAAnTt3ho+PD54+fQoAePr0KUaNGgVvb2/s3LkTlStXxsiRIyEIglyrQURERKWc7MHozp076NmzJx49eqTVfuHCBURERGDGjBmoU6cOhg8fjkaNGiEgIAAA4O/vDwcHBwwePBj16tWDr68vnjx5gqCgIDlWg4iIiMoA2YNRUFAQ3N3dsX37dq320NBQ2NnZwcTERGpzcXFBSEiINN3V1VWaZmxsDHt7e2k6ERERUX7py92Bvn37ZtkeFRUFKysrrTYLCws8e/YsT9PzS6HQabHsH0+86bysuHDGf6l4sO7yYN3lwbrLg3UvmWQPRtlJTEyEoaGhVpuhoSFSUlLyND0/KlUqr3tHsxGpNoBSaaDTskqlASwtzbTaLCzMspmbihLrLg/WXR6suzxY95KlxAYjIyMjxMXFabWlpKRAqVRK0zOGoJSUFJibm+f7uWJj30CtTte5r1lJSkpFUlKqzstGR8cDePtNwsLCDDEx8eC48uLDusuDdZcH6y4P1l13enrlimSnBlCCg5G1tTXu3Lmj1RYdHS0dPrO2tkZ0dHSm6Q0aNNDp+Qp7oxTEm87LZlhYEAq/j5Q71l0erLs8WHd5sO4li+yDr7Pj5OSE8PBwJCUlSW3BwcFwcnKSpgcHB0vTEhMTce3aNWk6ERERUX6V2GDk5uaGatWqYdKkSbh9+zbWrFmDsLAwdO/eHQDQrVs3XL58GWvWrMHt27cxadIkvPfee3B3d5e550RERFRaldhgpKenhxUrViAqKgre3t7Yu3cv/Pz8YGNjAwB47733sGzZMgQEBKB79+6Ii4uDn58fFBzeT0RERDoqUWOMbt68qXW/Zs2a2Lx5c7bzt2rVCq1atSrqbhEREdG/RIndY0RERERU3BiMiIiIiEQMRkREREQiBiMiIiIiEYMRERERkYjBiIiIiEjEYEREREQkYjAiIiIiEjEYEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhGDEREREZGIwYiIiIhIpC93ByizcuWA+wm3AQAKAJFqAyQlpULIw7IWSguYl6tcpP0jIiIqqxiMSqCXKXHYeHkzgLfBSKnMezDycfOBuQmDERERkS54KI2IiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCTiT4KUMe/+zlp+8XfWiIjo347BqIx593fW8ou/s0ZERP92PJRGREREJGIwIiIiIhLxUBoVilfpLxCTFKPTshzbREREJQWDERWKmKQYLA9artOyHNtEREQlBQ+lEREREYkYjIiIiIhEDEZEREREIgYjIiIiIhEHX5OkIFfNThGSCrk3RERExY/BiCQFuWr2F437FXJviIiIih8PpRERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRB18TFbOC/K6cqaExXqck6rQsf5OOiCh3DEZUqpXGH68tyO/KfdG4n85nDo5u6oOY9NJVq3+j0rhNE5UlDEYku4JeP2nNxXU6Lftv+/HaglyOoSC1ys8HvQJApNoASUmpEFCwPWRA6QwK/EFmInmV+mCUnJyMH3/8EYcOHYJSqcTgwYMxePBgubtF+cDrJ5Vt+fmgVwBQKv8fjAqyhwz49+0l0/VLhgJAmtIG+ihf+J0iKmVKfTCaP38+rl69io0bN+Lp06eYMGECbGxs0K5dO7m7RiVcTh8iGfdcZFSQPRm8SnjxKUjoLo2hStf1VQAY5/EdqukVfzDioUMqaUp1MEpISIC/vz/Wrl0Le3t72Nvb4/bt29iyZQuDEeUqpw+RjHsuMirInozSuJfr3/hzMXIdepSLOl2N+0m3s9zec1PQLwq6Hg4vjeH13SCY2xewjApSZ564kXelOhjduHEDaWlpcHZ2ltpcXFywatUqpKeno1w5Xo2AqDDwcGf+lMYgGZcchw0Xf9UpGMn1RaE0jJvL6N0gmNsXsIwKWud/097TgijVwSgqKgqVKlWCoaGh1GZpaYnk5GTExcWhcuXS9WIQUdnAIFnyyXXSR2l8ff9te09LdTBKTEzUCkUApPspKSl5fhw9vcLfs1Te0BgfVHxfp2XNDE3/v6wCMDTUR4pxGvLylUJr2YI877992VzqXiL7XBaWzVD3gjxvvp/737ysAjA1NEXNSu/n6X2m0J5XxmXTkYZ9t/brtGzH+h34/p5H5Q2Noa9f+J+xRfG5raEQBEGXPaclwoEDBzBr1iycPXtWart79y46dOiAwMBAVKxYUb7OERERUalTqgfhWFtbIzY2FmlpaVJbVFQUlEolzM3NZewZERERlUalOhg1aNAA+vr6CAkJkdqCg4Ph6OjIgddERESUb6U6PRgbG6NLly6YPn06wsLCcOTIEfz3v//FgAED5O4aERERlUKleowR8HYA9vTp03Ho0CGYmppiyJAhGDhwoNzdIiIiolKo1AcjIiIiosJSqg+lERERERUmBiMiIiIiEYMRERERkYjBiIiIiEjEYFQCpaSkoGPHjggMDJTaIiIiMHDgQDRq1AgdOnTAmTNnZOxh2ZRV3WfNmgVbW1ut2+bNuv1mEGl7/vw5Ro8eDTc3N7Rs2RK+vr5ITk4GwO29KOVUd27vRefhw4cYMmQInJ2d0bp1a6xb9//fWuP2XrKU6t9KK4uSk5MxduxY3L79/x83FAQBo0aNgkqlQkBAAI4cOQIfHx/s378fNjY2Mva27Miq7sDbn5gZO3YsunbtKrWZmpoWd/fKHEEQMHr0aJibm2PLli14+fIlJk+ejHLlymH8+PHc3otITnWfMGECt/cikp6ejmHDhsHR0RG7d+/Gw4cPMWbMGFhbW6Njx47c3ksYBqMS5M6dOxg7diwyXkHhwoULiIiIwLZt22BiYoI6derg/PnzCAgIwNdffy1Tb8uO7OoOvA1GQ4YMQZUqVWToWdl17949hISE4OzZs7C0tAQAjB49GvPmzYOHhwe39yKSU901wYjbe+GLjo5GgwYNMH36dJiamuKDDz5As2bNEBwcDEtLS27vJQwPpZUgQUFBcHd3x/bt27XaQ0NDYWdnBxMTE6nNxcVF66dQSHfZ1f3169d4/vw5PvjgA3k6VoZVqVIF69atkz6cNV6/fs3tvQjlVHdu70XHysoKS5YsgampKQRBQHBwMC5evAg3Nzdu7yUQ9xiVIH379s2yPSoqClZWVlptFhYWePbsWXF0q8zLru53796FQqHAqlWrcOrUKVSsWBGDBg3SOsxAujE3N0fLli2l++np6di8eTOaNm3K7b0I5VR3bu/Fo02bNnj69Ck8PT3xySefYM6cOdzeSxgGo1IgMTERhoaGWm2GhoZISUmRqUf/Dvfu3YNCoUDt2rXRr18/XLx4EVOnToWpqSnatm0rd/fKlAULFuDatWvYuXMnfvnlF27vxeTduoeHh3N7LwZLly5FdHQ0pk+fDl9fX76/l0AMRqWAkZER4uLitNpSUlKgVCrl6dC/RJcuXeDp6YmKFSsCAOrXr48HDx5g69at/KAoRAsWLMDGjRvx008/QaVScXsvJhnrXq9ePW7vxcDR0RHA2xM+xo0bh27duiExMVFrHm7v8uIYo1LA2toa0dHRWm3R0dGZdr9S4VIoFNKHhEbt2rXx/PlzeTpUBs2cORMbNmzAggUL8MknnwDg9l4csqo7t/eiEx0djSNHjmi11a1bF6mpqahSpQq39xKGwagUcHJyQnh4OJKSkqS24OBgODk5ydirsu/nn3/GwIEDtdpu3LiB2rVry9OhMmb58uXYtm0bFi9ejE8//VRq5/ZetLKrO7f3ovP48WP4+PhohcyrV6+icuXKcHFx4fZewjAYlQJubm6oVq0aJk2ahNu3b2PNmjUICwtD9+7d5e5amebp6YmLFy9i/fr1ePToEX777Tf8/vvvGDx4sNxdK/Xu3r2LFStWYOjQoXBxcUFUVJR04/ZedHKqO7f3ouPo6Ah7e3tMnjwZd+7cwcmTJ7FgwQKMGDGC23sJpBCyungLyc7W1ha//vor3N3dAby9auqUKVMQGhqKmjVrYvLkyfjwww9l7mXZk7HuR44cwdKlS/HgwQNUr14d3333HT7++GOZe1n6rVmzBosWLcpy2s2bN7m9F5Hc6s7tveg8f/4cM2fOxPnz52FsbIx+/fph+PDhUCgU3N5LGAYjIiIiIhEPpRERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCKiEmHQoEFwc3PL8VfFP/vsM3z++ee5PlabNm0wceLEwuweEf1LMBgRUYnQrVs3vHz5EqdOncpyenh4OG7duoUePXoUc8+I6N+EwYiISoS2bduiQoUK2Lt3b5bTd+/eDVNTU+nX4ImIigKDERGVCEZGRujYsSNOnDiB169fa01LTU3Fn3/+iU8//RSJiYn48ccf4enpCQcHB7i5uWHUqFF4/Phxlo8bGBgIW1tbBAYGarX3798f/fv312rz9/fHp59+CgcHB7Ru3RrLli2DWq0u3BUlohKNwYiISoxu3bohOTkZf/31l1b7qVOn8OLFC3Tv3h3Dhw/H2bNnMW7cOKxfvx4+Pj44f/48pk2bVqDnXr16NaZOnYpmzZph1apV+Pzzz7F27VpMnTq1QI9LRKWLvtwdICLSsLe3R4MGDfDHH3+gW7duUvvvv/8OW1tbWFtbw9jYGBMmTICrqysAwN3dHY8ePcL27dt1ft74+HisWLECvXr1wvfffw8AaNGiBSpWrIjvv/8egwYNQr169Qq2ckRUKnCPERGVKN26dUNgYCCeP38OAIiLi8Px48fRvXt3WFtb49dff4WLiwseP36Ms2fPYtOmTbh8+XKOZ7Pl5sqVK0hKSkKbNm2QlpYm3dq0aQMAOHv2bKGsGxGVfNxjREQlymeffYb58+dj//79GDRoEP78808oFAp06tQJALB3714sXrwYkZGRqFixIho0aAClUlmg54yLiwMADBs2LMvp//zzT4Een4hKDwYjIipRKlasCC8vL/zxxx8YNGgQ9uzZg7Zt26JixYq4dOkSJkyYgP79+2PIkCGwtrYGAMyfPx/BwcFZPp5CoQAApKena7W/efMG5cuXBwCYm5sDABYuXIgPPvgg02NYWloW1uoRUQnHQ2lEVOJ069YN4eHhCAoKQmhoKLp37w7g7SGv9PR0fP3111IoUqvVOHfuHIDM4QcATE1NAQDPnj2T2l6+fIm7d+9K952cnGBgYIDnz5/D0dFRuunr62Px4sXZnvFGRGUP9xgRUYnz4YcfwsbGBlOnTsV7772HZs2aAQAaNmwIAJgxY4Z0QcgtW7bgxo0bAICEhAQpCGnY2tqiWrVq8PPzg6mpKRQKBVavXg1jY2NpnkqVKuHLL7/Ezz//jNevX8Pd3R3Pnz/Hzz//DIVCgfr16xfTmhOR3LjHiIhKnHLlyqFr16548OABvL29pcNh7u7u+OGHH3DlyhUMHToUc+fOhY2NDZYvXw4AWR5O09PTw9KlS2FpaYkxY8Zg9uzZ+PTTT/Hxxx9rzfftt99i4sSJOHz4MIYOHYoFCxbAxcUFmzdvhpmZWdGvNBGVCApBEAS5O0FERERUEnCPEREREZGIwYiIiIhIxGBEREREJGIwIiIiIhIxGBERERGJGIyIiIiIRAxGRERERCIGIyIiIiIRgxERERGRiMGIiIiISMRgRERERCRiMCIiIiIS/Q+9+6NFXgVnyQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_and_plot_distribution(\"powerlaw\",{'a': 0.14349017715809642, 'loc': 8.999999999999998, 'scale': 24.000000000000004})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_and_plot_distribution(\"johnsonsb\",{'a': 0.42361987798205225, 'b': 0.4946976129904108, 'loc': 0.8047291306002482, 'scale': 97.27293102030796})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TP6Venv",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}