code/notebooks/try_out_things.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#import seaborn as sns\n",
    "#from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import shapiro, pointbiserialr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(''))), 'datasets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "'''stress = []\n",
    "# Max value for n_components\n",
    "max_range = 21\n",
    "for dim in range(1, max_range):\n",
    "    # Set up the MDS object\n",
    "    mds = MDS(n_components=dim, dissimilarity='precomputed', random_state=0)\n",
    "    # Apply MDS\n",
    "    pts = mds.fit_transform(gower_data_v2)\n",
    "    # Retrieve the stress value\n",
    "    stress.append(mds.stress_)\n",
    "# Plot stress vs. n_components    \n",
    "plt.plot(range(1, max_range), stress)\n",
    "plt.xticks(range(1, max_range, 2))\n",
    "plt.xlabel('n_components')\n",
    "plt.ylabel('stress')\n",
    "plt.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "llamados_v2= pd.read_excel(os.path.join(dataset_dir, 'xlsx/llamados_v2.xlsx'), parse_dates=['llamado_fecha_hora'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnas_pocos_si = []\n",
    "for i in llamados_v4:\n",
    "    if (llamados_v4[i]== 'SI').sum() < 191:\n",
    "        columnas_pocos_si.append(i)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "vs = list(llamados_v4.loc[:, llamados_v4.columns.str.startswith('vs')].columns)\n",
    "ofv = list(llamados_v4.loc[:, llamados_v4.columns.str.startswith('ofv')].columns)\n",
    "\n",
    "for i in vs:\n",
    "    if i in columnas_pocos_si:\n",
    "        llamados_v4.drop(i, axis=1, inplace=True)\n",
    "\n",
    "for i in ofv:\n",
    "    if i in columnas_pocos_si:\n",
    "        llamados_v4.drop(i, axis=1, inplace=True)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['llamado_fecha_hora', 'llamado_provincia', 'llamante_edad',\n",
       "       'llamante_genero', 'llamante_vinculo', 'caso_judicializado',\n",
       "       'hecho_lugar', 'victima_a_resguardo', 'victima_edad', 'victima_genero',\n",
       "       'victima_nacionalidad', 'victima_vinculo_agresor',\n",
       "       'victima_discapacidad', 'victima_convive_agresor',\n",
       "       'vs_violacion_via_vaginal', 'vs_violacion_via_anal',\n",
       "       'vs_violacion_via_oral', 'vs_tentativa_violacion',\n",
       "       'vs_tocamiento_sexual', 'vs_intento_tocamiento', 'vs_grooming',\n",
       "       'vs_exhibicionismo', 'vs_explotacion_sexual',\n",
       "       'vs_obligacion_sacarse_fotos_pornograficas', 'vs_acoso_sexual',\n",
       "       'vs_iniciacion_sexual_forzada_inducida',\n",
       "       'vs_otra_forma_violencia_sexual', 'vs_no_sabe_no_contesta',\n",
       "       'ofv_sentimiento_amenaza', 'ofv_amenaza_explicita',\n",
       "       'ofv_violencia_fisica', 'ofv_enganio_seduccion', 'ofv_grooming',\n",
       "       'ofv_otra_forma_violencia', 'ofv_no_sabe_no_contesta',\n",
       "       'agresor_fam_no_fam', 'genero_agresor', 'agresor_conocido_no_conocido',\n",
       "       'tipo_vinculo_llamante', 'vs_explotacion_sexual_group',\n",
       "       'vs_violacion_group', 'vs_tentativa_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llamados_v4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['llamado_fecha_hora', 'llamado_provincia', 'llamante_edad',\n",
       "       'llamante_genero', 'llamante_vinculo', 'caso_judicializado',\n",
       "       'hecho_lugar', 'victima_a_resguardo', 'victima_edad', 'victima_genero',\n",
       "       'victima_nacionalidad', 'victima_vinculo_agresor',\n",
       "       'victima_discapacidad', 'victima_convive_agresor',\n",
       "       'vs_violacion_via_vaginal', 'vs_violacion_via_anal',\n",
       "       'vs_violacion_via_oral', 'vs_tentativa_violacion',\n",
       "       'vs_tocamiento_sexual', 'vs_intento_tocamiento', 'vs_grooming',\n",
       "       'vs_exhibicionismo', 'vs_explotacion_sexual',\n",
       "       'vs_obligacion_sacarse_fotos_pornograficas', 'vs_acoso_sexual',\n",
       "       'vs_iniciacion_sexual_forzada_inducida',\n",
       "       'vs_otra_forma_violencia_sexual', 'vs_no_sabe_no_contesta',\n",
       "       'ofv_sentimiento_amenaza', 'ofv_amenaza_explicita',\n",
       "       'ofv_violencia_fisica', 'ofv_enganio_seduccion', 'ofv_grooming',\n",
       "       'ofv_otra_forma_violencia', 'ofv_no_sabe_no_contesta',\n",
       "       'agresor_fam_no_fam', 'genero_agresor', 'agresor_conocido_no_conocido',\n",
       "       'tipo_vinculo_llamante', 'vs_explotacion_sexual_group',\n",
       "       'vs_violacion_group', 'vs_tentativa_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llamados_v5= pd.read_excel('/home/vcolombo/Documents/Vic/linea_137_llamados_vs/datasets/xlsx/llamados_v5.xlsx')\n",
    "llamados_v5.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = '/home/vcolombo/Documents/Vic/linea_137_llamados_vs/images'\n",
    "image_name= 'test_1.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/vcolombo/Documents/Vic/linea_137_llamados_vs/images/test_1.png'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(image_path, image_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vcolombo/.local/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
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FRclwJ87OzoqJiZGfn1+u/nchNTVV7u7uOnDgQK67AOWfREREKCIiQg8//LBatmxpdZx79m/82pKbvr/+X92Rk5OT3N3dVbZsWdWpUydXXZyCnOXixYv65JNPbD9QDQ4OVrdu3dJtBWU1CvMH2IULF/TZZ59p/vz5+vXXX62Oc88CAgK0fv16ValSRQUKFLAV5idOnFDlypV1+fJlqyPet4SEBA0fPlxLliyx7Vfu4+OjTp066Z133slVe0ueOnVKkmz7oeVUt/+nRbq1d93evXtte9n9G+T0H3rc7uDBg4qOjlZycrLdeE6+QivNp59+qo4dO8rNzc1uPDk5WV9++WWO3mu/WbNmMsZo0aJFtm+QLly4oM6dO8vZ2Vlr1qyxOCH+ybvvvqvff/9dc+fOVZ48ufelcXJrqTxz5kyNGjVKAwYM0DvvvKP9+/erdOnSWrBggRYuXKitW7daHfGe3amQvP3fu5xcSN5Namqq+vTpozJlymjo0KFWx7kv/fv3V9myZdPtR/vBBx/o2LFjmjp1qjXB8I8qVqyoTz75RDVr1rQ6CrJIbvraktsFBQXp3Llzunr1qq2HuHjxovLlyydPT0+dPXtWpUuX1tatW3Ps/+n//PNPrVy58o7//5s8ebJFqe7d9OnT1atXL7m7u//jxbKenp6qWLGinnzyyWxKl7W2b9+uVq1aycvLSzVq1JAkRUZGKj4+XqtWrXqwLmiwZicY/Jt4enqa33//3fb28ePHjTHG/PTTT6ZQoUJWRstyqamp5uzZsyY2NjbX7NFujDE3btwwI0aMMF5eXsbZ2dk4OzsbLy8v89///tckJydbHe+e/P3V32//3MyNFi5ceMfXRbh27ZpZuHChBYnuzd/3vTx+/LipXLmy3d7laW/nlj3MnZ2d7T5X05w/fz7HrzFfvnzmt99+Sze+d+/eXPEq8Hdy7do1qyNkqdatW5sCBQqYokWLmsaNG5vnnnvO7pYbfPTRR6ZIkSLm7bffNh4eHrZ/K+bPn2/q1atncbr7U6FCBdtrlNz+7+C+fftM4cKFLUx2/+Lj4+1u586dMxs2bDBPPvmk2bRpk9XxHOrw4cMmICDA6hj3rVixYubnn39ONx4ZGZkr9hjOzVauXGmefvpps2/fPqujONSCBQvM6tWrbe8PGTLEeHt7m5CQEPPHH39YmMwxcsvXFmOMee2112z7RN9uxowZ5vXXX8/+QFlo8eLFpl69eubYsWO2saNHj5oGDRqYL7/80pw6dcrUqlXLtG3b1sKU927Tpk0mX758plKlSiZPnjymatWqxsfHx3h7e5v69etbHe+elCpVypw/f9729t1uRYsWNS4uLmbw4MEWp743lSpVMi+//LLd68XdvHnT9OrVy1SqVMnCZOlxhfkD4OjRo/rtt99UrVo1BQUFac2aNZowYYKuXbum1q1b64033sjRV4A2a9ZM1atX11tvvaUCBQrot99+U8mSJdWpUyelpqZq+fLlVkfEP+jTp49WrFihsWPHKiQkRNKtXzkcPXq0WrdurZkzZ1qcMPP+foX57b/9kBs5Ozsrf/78WrBggdq2bWsbj42NVbFixXLMlXZvvfWW3NzcbFe2tGzZUq6urpo9e7Z8fX119epV7dy5U8OGDdOUKVNUu3ZtixPfP2dnZ8XGxsrX19du/Ndff1X9+vUVFxdnUbL7V6hQIa1evVpPPfWU3fjOnTvVsmXLHL2226WkpOjdd9/VrFmzFBsbq99//12lS5fWyJEjVapUKfXo0cPqiPesW7dudz0+f/78bEriOMHBwXr33XfVunVru38r9u/fr3r16un8+fNWR7xnHh4eOnz4sEqWLGm3tqNHj6py5cq6du2a1RGz3LZt2xQeHq7IyEirozjMd999p7CwMJ07d87qKPfF3d1d+/fvT7fv9bFjx1SpUiVdv37domT3b+zYsXc9PmrUqGxK4hgFCxbU1atXdfPmTbm6uqbb8jC3/Pterlw5zZw5Uw0aNFBERIQaNmyoqVOnavXq1cqTJ0+u27okt3xtkW5tKbdy5UpVr17dbnzPnj1q1aqV/vzzT4uS3b8yZcroq6++UtWqVe3Gf/nlF7Vt21YnTpzQDz/8oLZt2+rMmTPWhLwPTzzxhJo2baoxY8bYvnfx8/PTiy++qCZNmqhPnz5WR3S4jRs36oUXXsiRfxc9PDy0d+9e23acaY4cOaKqVas+UN975t7fnc0hvv76a3Xo0EHOzs5ycnLS7Nmz1bt3b9WrV09eXl4aPXq08uTJo2HDhlkd9Z5NnDhRDRs21M8//6zk5GQNHTpUBw4cUFxcnHbu3Gl1vPtWv379f/yBhpOTkzZv3pxNibLe4sWL9eWXX6pp06a2scqVKyswMFDPP/98jizMpVv7ent7e0u69WuGmzdvTvfil7lhS480Y8aM0UsvvaR9+/Zp9OjRVse5J71791bnzp118uRJffjhh4qIiNDWrVtVuHBhOTk5KW/evGrYsKHGjx+v/v3765dffrE68j177LHH5OTkJCcnJzVs2NBuu4uUlBRFRUWpSZMmFia8fy1atFCvXr30ySef6IknnpAk7d69W6+88kqO/ru3ZMkShYSEqESJEpKkd955RwsXLtTEiRP18ssv2+ZVqlRJU6dOzdGFeW4oxP9JVFSU7YU+b+fm5qYrV65YkCjrBAUFae/eveleIHndunW5dl96f39/HTlyxOoYWSI8PNzufWOMzpw5ozVr1igsLMyiVFmnbNmyWrdunfr162c3vnbt2hx/gcPXX39t9/6NGzcUFRWlPHnyqEyZMjm+MP+3bJdz6tQp2w90vvnmG7Vr1069evVSrVq1VK9ePWvD3Yfc/rVFurUFYNr/A2/n5eWVo38QLklnzpzRzZs3043fvHlTMTExkqRixYrp0qVL2R0tSxw6dEhffPGFJClPnjy6du2aPD09NXbsWD377LP/isL86aef1ogRI6yOcU+qVaumQ4cOpSvMDx06pCpVqliU6s4ozC32zjvvaOjQoXr77be1YMECvfLKKxo3bpwGDBggSZo9e7amTJmSowvzSpUq6ffff9cHH3ygAgUK6PLly2rTpo369u2rokWLWh3vvv39J7e3u3TpkhYvXqykpKTsC+QAbm5uKlWqVLrxoKAgubq6Zn+gLPL3b/h69+5t935Of9Glv+vcubOeeuopPffcc9q/f78+++wzqyNlmp+fn9avX6/x48dLulUce3p6SpKKFCmiP//8UyVLllRQUFCOL0Rat24tSdq7d69CQ0Nt65QkV1dXlSpVyu63BXKi6dOnKywsTCEhIcqbN6+kW9/Mt2rVStOmTbM43b1zd3dXnTp19O2336pKlSpauHChZs+erYYNG+qVV16xzatSpYoOHz5sYVJkRG4ulcPDw9W3b19dv35dxhj9+OOP+uKLLzRu3DjNnTvX6nj35bfffrN7P63wGT9+/F2/d8tJ/v5DYWdnZ/n6+mrSpEnq3r27RamyTnh4uPr166dz587ZXpx88+bNmjRpUo4vZO/0A/3ExER17do13Qsu5kS5pVT9J56enrpw4YJKlCihDRs22Ipmd3f3B+oqyczK7V9bpNz9A7n69eurd+/emjt3ru0H/r/88ov69Olj+1q6b98+BQUFWRnznuXPn9+2b3nRokV1/PhxVaxYUZJy/A870mzbtk3vv/++3YtiDhkyxPbb0x4eHnr99f/X3p3H1Zi//wN/naKUFtlCFC22LAmNfY3ECI1hyBLGNmSNMvaQmBGy72SMvUHDWCNL9bFEoUJExj6laKHt/P7o1/k6lbVTd/fd6/l4eHyc+z4z8/JJnfu+7vf7uiYKGfGbTZgwARMnTkR0dLRizkVISAjWrFkDLy8vpes3wQfwCtkPhrL7Reb0lsrMzJSrq6sr9XqLiYmRa2lpCRWvwNLS0uSdOnVS9DAvKdLT0+UrVqyQV6pUSW5ubi7fvXu30JEKZP78+fIBAwbI3717pzj27t07uZOTk3zevHkCJqMv9WEf7EePHskbN24st7KykoeEhIi6D3abNm0U/XcdHBzkQ4YMkQcFBckHDhxY7Hqgfavt27dLpu/1x2Ye3L17V37kyBH5kSNH5Pfu3SviVIUjODhY8XewTJkyil6mH/aJvn37tuh7tVtZWcmbNGmS55e1tbW8VatW8iFDhsgDAgKEjlkgmzZtkhsZGcn37NkjL1u2rHz37t3yhQsXKn4vdn/88Yfc3NxcMQPCyMhIvnnzZqFjFVju2RY5v1q2bCmPjIwUOh59obVr18qNjIwUX79atWqJavbK1woPD5ebmJgIHUMloqOj5TNnzpT/9NNPimvQY8eOyW/duiVwMtUZOHCg3NraWj5ixAi5tra2ogfx4cOH5ZaWlgKn+zZZWVnyR48eyVNSUoSOUqi2bNki19LSks+ZM0d+7tw5+blz5+SzZ8+Wa2tryzdu3Ch0vAJ59uyZ3NbWVi6TyeQaGhpyDQ0NuZqamrxLly7y58+fy+VyuTwgIEB+4sQJgZN+m169eim+RlOnTpWbm5vLFy5cKLe2tpZ37txZ4HQFt3PnTnmpUqXk/fr1k69cuVK+cuVKeb9+/eSlS5eW79q1S+h4BZb7uiz3r5xrt+JQo2APc4F9ro+y2PoL56dSpUoICgqChYWF0FGKxK5duzBnzhykpqZi1qxZGDVqlFIrBbFwdHRUen369GloamoqtsmEhYUhLS0NnTt3llx/PinK/bMmJSUFTk5OOHPmDJKTk0X7M+bEiRNITk6Go6MjoqKi4ODggOjoaFSsWBH79+9H+/bthY5IH/Dy8oKpqSn69esndJQiER8fj/Lly6Np06aYPHkyBg0apPQ57+HhgVOnTuHChQtCR/1mM2bMwLp169CwYUNFW50rV64gPDwczs7OiIiIwJkzZ+Dn54devXoJnPbb7dq1C/PmzcP9+/cBZG9lnj9/vqjb6eSWkpKCpKQkxeeE2D169Ejpdc4KyTJlygiUqPC8fPlSsauqTp06kvkafujVq1fQ0tJS2m0lRRcvXkTPnj3x+vVroaMUSGBgIOzt7dG6dWucP38ekZGRMDU1hZeXF65evSqZGVYJCQmYNWsWHj9+jLFjxypa5c2dOxcaGhqYOXOmwAm/XlZWFsqUKYPbt29L/v593bp1WLRoEZ4+fQoAqFmzJubNm4chQ4YInEw1oqKicPfuXQDZnw25W2CI1YMHD5CUlIRGjRohOTkZU6dOVdSbvL298+wIFJt69eph1KhRmDx5stJxb29vbNq0SbHqXKxyX599itBfSxbMBaauro7nz58rhrnp6ekhLCxMsT1GCgXzyZMnQ1NTU9FCQaqOHz8Od3d3xMTEwNXVFVOmTEHZsmWFjvXNPjfI7UMloYet2M2fPx/Tpk2Dtra20vG5c+fi/PnzOHv2rEDJVC8uLg7ly5cX9bBkAwODL84vpsFZd+/eRZ8+fTBq1ChMnDgxT4/M3Ly9vYsoWeE6fPgwhg4dihkzZsDDwwPz58/HnTt34Ovri7///htdunQROuI3GzlyJIyNjTF79myl4wsXLsSjR4+wadMmzJ07F0ePHsXVq1cFSqk6Uisqd+rUCX5+fihXrpzS8Tdv3qB3794ICAgQJhh9kTdv3mDcuHHYvXs3srKyAGTfW/Tv3x9r1qzJtz8vFQ8+Pj5Kr+X/v2XQzp070b59e/z5558CJVONli1b4scff8SUKVOUHhRfvnwZjo6Ooh6oWBJYWlpiy5YtinYJUpORkYE///wTdnZ2MDQ0lOwDubS0NMTExMDMzEyUC/hKKk1NTdy+fVuSA6/FhgVzgampqUFfX19RGElISICenh7U1NQAZF88vXnzRtQFcxcXF/j6+sLCwgJNmzbNU0QWe0Hk8uXLcHNzQ0hICMaMGYOZM2eiYsWKQsciIhHbsWPHF79XbH1C3759C2dnZxw8eBAdO3b86PtkMpmkinUXLlyAh4cHwsLCkJSUBGtra8yZMwddu3YVOlqB6Ovr49q1a/le1Ddt2hSJiYmIiopC8+bNRTtcSspy7z7K8fLlSxgZGSE9PV2gZN/Gx8cHo0aNQpkyZfIUJHPT0dGBpaUlvvvuuyJKp3r9+/fH9evXsWrVKrRs2RIAEBwcjIkTJ8LKygp79uwROGHBHThwAPv27UNsbKyiZ22O0NBQgVIVXO7ewTk7IDp16oQZM2ZAV1dXoGSqoaOjo+iR/GHB/OHDh6hbt66kCj4JCQnYsmWLYtWnpaUlhg8fLuoHVv7+/li6dCnWrVuHBg0aCB2nUGhrayMyMlLwFayFISUlBS4uLor7ibt378LU1BQuLi4wMjKCu7u7wAnpU8zNzTFt2rQ889XWr1+PZcuW4d69ewIlU52nT5/i4sWLePnypeKBf44JEyYIlCovPmYSWElYmXvr1i1YW1sDgGJLUA4xrwDN0aJFC2hpaWHMmDGoVavWR1eEFKdv/G/16tUrpS2/OTsjSBzu37+PFStWKA0PmTRpkqgH27x79w6rVq3C2bNn8/3AFevNtNiK4F9DV1cXBw8eBABJ7Wz4nLZt2+LUqVNCx1C5MmXKICgoKE/BPCgoSNH6Imd7t1i9ePECrq6uOHPmDF6+fInca03EuKjhw4FKEREReP78ueJ1ZmYmjh8/DiMjIyGiFcjy5cvh5OSEMmXKYPny5Z987/v37/Hy5UtMnjwZv/32WxElVK2///4bJ06cQJs2bRTH7OzssGnTJkVrCDHz8fHBzJkz4ezsjMOHD2PYsGG4f/8+rly5gnHjxgkdr0BiYmLyPZ6RkYGXL1+KvmBerlw5PHv2LM+DgevXr4vyZ8vHXL16FXZ2dtDS0lK0JfP29saiRYtw8uRJxT2w2AwZMgQpKSlo3LgxNDQ0oKWlpXReTLsbP8bGxgbXr1+XZMF8xowZCAsLw7lz55Q+C2xtbTFv3jzRF8zV1NQ+WUcS43XZh6ZOnYoJEybgxo0baNWqFQDg0qVL2L59O1auXClwuoLbvn07Ro8eDQ0NDVSoUEHpaymTyYpV3YwFc4F9SVFE7N/wUi+IGBsbQyaT4dChQx99T3H7xv9aycnJip0CH275HTJkCFatWpWnzQcVPydOnICDgwOsrKzQunVrANkfvPXr14e/v79oW0KMGDECJ0+eRN++fWFjYyOJh3Cf8u7duzwr7PT09ARKU7gOHDiAvn37Ch2DPsPFxQVjxozBtWvX0Lx5cwDZPcw3b96MX3/9FUD2zx8rKysBUxaMs7MzYmNjMXv2bFStWlUSP2esrKwgk8kgk8nQqVOnPOe1tLSwatUqAZIVzIdFyI8VJD906tQpDBw4ULQF8woVKuS7ilVfXx8GBgYCJFKttWvXYuPGjRgwYAC2b9+O6dOnw9TUFHPmzJFEwS4/t2/fhrW1tejv/3766Se4ublh//79kMlkyMrKwqVLl+Dq6iqZ/tBAdutRBwcHbNq0SdHyIiMjAz///DMmTZqE8+fPC5zw26xYsULoCIXul19+wdSpU/Hvv//muwu+UaNGAiUruEOHDmHv3r1o0aKF0jWLpaWlYhaLmP31119Kr9PT03H9+nXs2LED8+fPFyiV6owdOxZVqlTBsmXLsG/fPgDZfc337t0r6nlAOWbPno05c+ZgxowZis4axZZg40bps+7cuSOfPn26vEqVKkJHoRJu1KhRclNTU/mxY8fkiYmJ8sTERPnRo0flZmZm8jFjxggdj76AlZWV3M3NLc9xNzc3eZMmTQRIpBp6enryixcvCh2jUCUlJcnHjRsnr1SpklxNTS3PL7FKT0+X37x5U37nzh2l44cOHZI3atRIrqGhIVAy1cuZ9P6xX2L3xx9/yFu0aCE3MDCQGxgYyFu0aCHftWuX4nxKSoo8NTVVwIQFo6OjI79+/brQMVTq4cOH8piYGLlMJpNfuXJF/vDhQ8Wvp0+fyjMyMoSOWCRSUlLkK1asEDrGN9uwYYPc1tZW/uzZM8WxZ8+eybt27Spfv369gMlUQ0tLS/7w4UO5XC6XV6pUSX7jxg25XC6X3717V16+fHkhoxWaGzduSOJz4f379/Kff/5ZXqpUKblMJpOXLl1arqamJh80aJCkfr6UKVNGHhkZmef47du35VpaWgIkoi8lk8ny/FJTU1P8r5hpaWnJ79+/L5fLs69hcn5/48YNuZ6enpDRCtWuXbvkDg4OQsegzyhfvrw8Ojpa6BhfhCvMi5mUlBTs3bsXW7duRXBwMJo1a/bZoWjFkaOjI7Zv3w49PT04Ojp+8r1+fn5FlKpwDBkyBL169UK3bt1EPeTzUw4ePIgDBw6gQ4cOimPdu3eHlpYW+vXrh3Xr1gkXroAyMzOxfPnyj/bHlMoKpsjISMUT6g8NHz5c1KtIjIyMRL9t+XOmT5+Os2fPYt26dRg8eDDWrFmDJ0+eYMOGDaIdpnzr1i18//33ePz4MQCgV69eWLduHfr164dbt25h5MiROHr0qMApVUfqK2GcnJzg5OT00fO5t3KLTY0aNfK0YRG7nC3oudtYSYmHh8cnz8+ZMwdaWlqYOHFiESVSjSZNmiitGLx37x6MjY1hbGwMAIiNjYWmpiZevXqVp/+p2FSpUgXx8fEwMTGBsbExQkJC0LhxY8TExEjue1JqNDQ0sGnTJsyZMwc3b95EUlISmjRpAgsLC6GjqZSenh5iY2NRt25dpeOPHz8W5fXp06dP4e3tjTlz5uTZwZiYmIiFCxfC1dUVhoaGAiVUnS/ZhSRWzZo1w9GjR+Hi4gLg/9rgbt68WTHvQopatGiBUaNGCR2DPmPEiBHYv3+/KFoDsWBeTISEhGDz5s3Yv38/jI2NERkZibNnz6Jt27ZCR/smHw4yFfPAky9hbm4OT09PDBo0CB06dICDgwMcHBwk1Z8vJSUl3wujypUrIyUlRYBEqjN//nxs3rwZU6dOxaxZszBz5kw8fPgQhw4dwpw5c4SOpzKVKlXCjRs38tyo3LhxI8+wNzFZtmwZ3NzcsH79ekn2IASyBy/5+vqiQ4cOGDZsGNq2bQtzc3OYmJhg165dnyxUFldubm4wNzfH6tWrsXv3buzevRuRkZEYMWIEjh8/LvoCa275bZ/s27cvLC0tsXfvXowYMUKAVKqTkJCAAwcO4MGDB3B1dUX58uURGhoKQ0NDSXwWrlixAu7u7tiwYQNq1qwpdByVu3fv3kfnQIj5czC/B1UxMTEoVaoUzMzMRPtn6927t9ARikynTp1w5MgRNGnSBMOGDcPkyZNx4MABXL169bMLckhYHh4ecHV1RY0aNVCjRg3F8dTUVPz222+i/f7LrX///hgxYgR+//13pV7D06ZNw4ABAwRO9/W8vb3x5s2bfNv96evr4+3bt/D29saSJUsESKdaUr1vAABPT0/Y29sjIiICGRkZWLlyJSIiIhAUFITAwECh4xWK1NRU+Pj4iPa6s3z58rh79y4qVqwIAwODT7b/E/uCvsWLF+P777/H8ePH0bBhQ5QuXVrpvLe3t0DJ8pLJ+XheUMuWLcPWrVuRmJiIAQMGYNCgQWjcuDFKly6NsLAw1K9fX+iI9IX+/fdfHDlyBIcPH0ZgYCAsLS3Rq1cvRd9oMevcuTMqVKgAX19fxeC21NRUDB06FPHx8Th9+rTACb+dmZkZfHx80KNHD+jq6uLGjRuKYyEhIR8d4io2Hh4eWL58Odzd3ZUu6JcsWYIpU6Zg9uzZAif8Nq9evUK/fv1w/vx5aGtr5/nAFfsFBQDo6OggIiICxsbGqF69Ovz8/GBjY4OYmBg0bNgQSUlJQkf8apUrV8bJkydhZWWFxMREGBgYYMeOHRg8eLDQ0YrUgwcP0KhRI1F+DXOEh4fD1tYW+vr6ePjwIe7cuQNTU1PMmjULsbGx8PX1FTpigRkYGCAlJQUZGRmS+zmzadMmjB07FhUrVkSVKlXyDF4S6+Dkj3nz5g2cnZ3Rp0+fEvfzRoyysrKQlZWl6A29Z88eBAUFwcLCQjEwTGw+HLibn6ioKAwYMED0PczV1dXx7NmzPIsy4uLiULlyZdH/+XKkpaVh2rRpWL9+PTIyMgAApUuXxtixY+Hl5QVNTU2BE36dBg0aYP369UqDhD8UFBSEkSNH4vbt20WcrPBERETku8vYwcFBoESqcf/+fXh5eSEsLAxJSUmwtraGm5sbGjZsKHS0AstdUJbL5Xj79i20tbXxxx9/iPJrt2PHDvz000/Q1NTEjh07PvneL5mDWJwtXLgQc+bMQZ06dWBoaJjn2jMgIEDAdMpYMBdYqVKl4ObmBg8PD6irqyuOS71g/ubNG+zatQtbtmzB1atXhY6jcm/fvsU///yDw4cP459//oGuri569uyJsWPHwtLSUuh4X+3WrVuws7PD+/fv0bhxYwBAWFgYypQpgxMnTojyz5SjbNmyiIyMhLGxMapWrYqjR4/C2toaDx48QJMmTZCYmCh0RJWQy+VYsWIFli1bhqdPnwIAqlWrhmnTpmHChAmiHWJna2uL2NhYjBgxIs8HLiD+Cwoge+jQqlWr0L59e9ja2sLKygq///47fHx8sHTpUvz7779CR/xqampqeP78ueJGWldXF6GhoZLbqv0pqampmDFjBv755x/cuXNH6DjfzNbWFtbW1li6dCl0dXURFhYGU1NTBAUFYeDAgXj48KHQEQtMyjcuJiYm+OWXX+Dm5iZ0lCJz8+ZN9OzZUxJ/N0l81NTUIJPJ8m0pk3NcJpOJvqCspqaGFy9eoFKlSkrHAwIC0L9/f7x69UqgZIUjJSVFMUzRzMwM2traAif6Nh/eF+UnNjYW9erVQ3JychEnU70HDx6gT58+uHnzptL3ZM69hNi/B6Vs+/btSvd8ampqqFSpEr777jtJDLyWOgMDAyxfvhzOzs5CR/kstmQR2IIFC7Bt2zbs3LkTAwYMwODBg9GgQQOhYxWas2fPYuvWrfDz84O+vj769OkjdKRCoauri379+qFfv37IzMzEuXPncOTIEQQHB4uyuNygQQPcu3cPu3btQlRUFABgwIABcHJyEn3rhOrVq+PZs2cwNjaGmZkZTp48CWtra1y5ckV0q0I+RSaTYfLkyZg8eTLevn0LAKLsrZhbUFAQgoODFQ9ypGjYsGEICwtD+/bt4e7ujp49e2L16tVIT08vVlvWvoZMJsPbt29RpkwZRXEgNTUVb968UXpffluCxehzK2HE7MqVK9iwYUOe40ZGRnj+/LkAiVRPzAXxz3n9+jV+/PFHoWMUqcTERMk8DM8pvn6MFAo+CQkJuHz5cr4tg4YMGSJQqm8n5b7JwP993slkMtSuXVvp72dmZiaSkpIwZswYARMWDm1tbUms3NXS0sLDhw8/WjB/+PCh6O/9ckycOBG1atXCmTNnUKtWLVy+fBlxcXGYOnUqfv/9d6HjFUhsbOwnz3/s6ysWYii0FobQ0FDMmTMHf//9t9BRCkRTUxOtW7cWOsYXYcFcYDNmzMCMGTMQGBiIrVu34rvvvoO5uTnkcjlev34tdDyVePLkCbZv345t27YhISEBr1+/xp9//ol+/fqJdlXrlwgMDERycjJatmyJzp07o3PnzkJHKhBtbW2MHDlS6Bgq16dPH5w5cwbfffcdXFxcMGjQIGzZsgWxsbGYPHmy0PEKhRQK5Tnq1q2L1NRUoWMUqg//Htra2iIqKgrXrl2Dubk5GjVqJGCybyeXy1G7dm2l102aNFF6LYUVdjmWL18u2ZUwmpqaeR50AMDdu3fzrCwUs8zMTBw6dAiRkZEAAEtLSzg4OCjtDhSjH3/8ESdPnpRkAcvHx0fptVwux7Nnz7Bz507Y29sLlEq1pD5Q2N/fH05OTkhKSoKenl6ebdtiLJhLuW8ykD3zQS6XY/jw4Zg/f77SLCsNDQ3UrFlTUkMH3717h1WrVn10DoTY2lp999132LlzJ9q1a5fveV9fX9jY2BRxqsIRHByMgIAAVKxYEWpqalBTU0ObNm2wePFiTJgwAdevXxc64jerWbOmJB+mfq6lFZDdwaFKlSooX758ESRSvRMnTuDUqVPQ0NDAzz//DFNTU0RFRcHd3R3+/v6ws7MTOmKBTZw4EatWrcpznVYcsSVLMfP27Vv8+eef2Lp1K65duwYbGxv07dsXU6ZMETraVzt48CC2bNmC8+fPw97eHoMGDYK9vT3Kli0rqXYzS5YsQVJSEhYsWAAg+4bM3t4eJ0+eBJDdq/fMmTOiW1l+5MiRL36vGPuEfUxISIiiP2bPnj2FjlMgTZo0+eKHUmK7oM9x8uRJzJ8/H4sWLcp3aIiYVygHBwcjLi4O33//veKYr68v5s6di+TkZPTu3RurVq0S5U6ILx041L59+0JOQgX1888/Iy4uDvv27UP58uURHh4OdXV19O7dG+3atcOKFSuEjlhg0dHR6N69O548eYI6deoAAO7cuYMaNWrg6NGjMDMzEzjht1u8eDG8vb3Ro0ePfH+GTpgwQaBkBVerVi2l1zkPqjp16oQZM2ZI6uFxbn/++Sf27t2Lw4cPCx2lQGrXro3u3bvD09NTtC0uSqrAwEC0bt1a0X9eqpycnHDy5En07ds339aAc+fOFSjZtzl79iy6dOmCSZMmYdq0aTA0NAQAvHjxAkuXLsXKlStx8uRJdOrUSeCkBWdgYIDQ0FDUqlULZmZm2Lx5Mzp27Ij79++jYcOGSElJETriNwsLC1N6nfMw1dvbG4sWLRLt0ORPtbT6kEwmQ+PGjeHr6yuq7g1btmzByJEjUb58ebx+/RoVKlSAt7c3XFxc0L9/f0ycOBH16tUTOmaB9enTBwEBAahQoQIsLS3zXHv6+fkJlCwvFsyLsVu3bmHLli3YtWsXXr58KXScr5bTn93d3V3ppkRq/dlzBmj0798fALB//34MHToUp06dQr169TBkyBBoa2tj3759Aif9Ompqakqv8/twkkKPt/Pnz6NVq1Z5LugzMjIQFBT00RUWYvDh6rJ3795h7dq1qF+/vmJlT0hICG7fvo1ffvkFixcvFipmgeT8Pc19gyKFFcr29vbo0KGDorfwzZs3YW1tDWdnZ9SvXx9Lly7F6NGjMW/ePGGD0md9yYqYHGLbNZCYmIi+ffvi6tWrePv2LapVq4bnz5+jZcuWOHbsGMqWLSt0xALr3r075HI5du3apVixFBcXh0GDBkFNTQ1Hjx4VOOG3y11U/pBMJsODBw+KMA2pihQGCgPZ/ZRv3rwJU1NToaPQVwoNDUXp0qUVbUoOHz6Mbdu2oX79+pg3b54oB7bmR19fH8eOHRNNe4EvsWHDBkycOBHp6emKnR2JiYkoXbo0li9fjrFjxwodUSXatm2LqVOnonfv3hg4cCBev36NWbNmYePGjbh27Rpu3boldESVO3r0KH777TecO3dO6Cjf5NGjR599T1ZWFl68eIHffvsNL1++xIULF4ogmWo0atQIgwcPxrRp03Dw4EH8+OOPaNGiBfbt24fq1asLHU9lhg0b9snz27ZtK6Ikn8eCucACAgIwfvx4hISE5FkJmZiYiJYtW8LHxwe2trYCJfx2o0ePxt69e2FpaYnBgwejf//+MDAwkFzB3MDAAEFBQYqnfcOGDUNmZiZ8fX0BZBclf/zxRzx+/FjImAVy+vRpuLm5wdPTU1FsDQ4OxqxZs+Dp6YkuXboInPDbqaur49mzZ4rhgzni4uJQuXJlURdcP/Tzzz+jatWqip0QOebOnYvHjx9j69atAiUrmM+tVBbzCuWqVavC398fzZo1AwDMnDkTgYGBuHjxIoDsh3Nz585FRESEkDHpC3yuzzAg/oc8Fy9eRHh4OJKSkmBtbS3K65aPKVu2LEJCQvL0pw0LC0Pr1q1FX5SUmi9ZOZezZbtLly6i302Wm1QGCgPZX8uffvoJ/fr1EzoKfaXmzZvD3d0dP/zwAx48eID69evD0dERV65cQY8ePSSx+wgA6tevjz179ojuYffnPHnyBPv27UN0dLSijV7fvn0lVbQ7ceIEkpOT4ejoiOjoaHz//fe4e/cuKlSogL1790piFX1u0dHRaNy4sSSGtn6OGP+sZcuWxe3bt1GzZk3I5XJoamri7NmzknogJzbS3iMlAitWrMDIkSPzbRugr6+PMWPGYM2aNaK88dywYQNWrFiBffv2YevWrZg0aRLs7Owgl8vz9HcTs4yMDKWWCMHBwZg0aZLidbVq1fDff/8JkEx1Jk2ahPXr16NNmzaKY3Z2dtDW1saoUaMUPV3FKKdIlVtcXJwkVkbm2L9/P65evZrn+KBBg9CsWTPRFszFXBD/nNevXyu2wgLZDwc+7LvbvHlzUT+IK0n8/Pzg6uqKadOmKT10XLZsGZYuXarUv12s2rRpo/QZISWampqKYckfSkpKkswqybS0NMTExMDMzEz0LRQ+7Jn8MVlZWbh37x42b94MV1dXeHh4FEEy1ZPiQOEPWwL26NED06ZNQ0RERL4tg6TUElBq7t69CysrKwDZ16Dt27fHn3/+iUuXLuGnn36STMF82bJlcHNzw/r16yXVn97IyEiys5xyfNgL2tzcHFFRUYiPj8/zc1WMcs+WyZnhMW/ePFhYWAiUqmjVqlULQUFBQsf4KqmpqYr2YzKZDJqamqhatarAqUo2cV8RS0BYWBiWLFny0fNdu3YV9ZRmLS0tDB06FEOHDsW9e/ewbds2XL16Fa1bt0aPHj3Qt29f0fbQymFmZobz58/D1NQUsbGxuHv3rlIbj3///RcVKlQQMGHB3b9/H+XKlctzXF9fHw8fPizyPKqQ8/dOJpPB2dlZ6aFHZmYmwsPD0apVK6HiqZyWlhYuXbqU5yLp0qVLKFOmjECpCu78+fOfPC/mljqGhoaIiYlBjRo1kJaWhtDQUKU2O2/fvs1TPKDiydPTEz4+PujevbviWKNGjVCjRg3Mnj0b165dEzDdt8nZRfU5YhzKl9v333+PUaNGYcuWLYphZ//73/8wZswY0RfsUlJS4OLigh07dgDILnKZmprCxcUFRkZGcHd3Fzjh1/uarbx///03fvnlF9EWzHMXHaUwULh37955juX39RHzjpyS4MMFUqdPn1bMY6lRo4boFxJ9qFmzZnj37h1MTU2hra2d57osPj5eoGT0LcQ6KDK3cuXK5duuskaNGtizZ49AqYqWuro6GjduLHSMr7Z582bo6OgAyF6YuX37dlSsWFHpPWKeL5PjwIED2LdvH2JjY5GWlqZ0rjjNVmPBXGAvXrz4ZMGjVKlSePXqVREmKjwWFhbw9PTEwoULcfToUWzZsgUDBgzA+/fvhY5WIOPGjcP48eNx4cIFhISEoGXLlkrtZgICAkS/erB58+aYMmUKdu7cqTT8Zdq0aaKdlJ6zAk0ul0NXVxdaWlqKcxoaGmjRogVGjhwpVDyVmzRpEsaOHYvQ0FClgs/WrVsxe/ZsgdN9uw4dOuQ59uEFophvprt37w53d3csWbIEhw4dgra2Ntq2bas4Hx4eLuphgyXJzZs38+0VXatWLdG21Jk4ceJHz8lkMiQnJyMjI0MSBXMfHx8MHToULVu2VFyzZWRkwMHBAStXrhQ4XcHMmDEDYWFhOHfuHLp166Y4bmtri3nz5omyYP412rRpo2h7JUZDhw4VOoLKSWkX6qe8ePECrq6uOHPmDF6+fJlnTpCYr1+A7ELywoULYWtri8DAQKxbtw4AEBMTo7R7TuwGDBiAJ0+ewNPTM9+hn1R8vXv3DqtWrcLZs2fx8uXLPD97ilPR7mudPXtW6XXOw1Rzc3PR7yKTMmNjY2zatEnxukqVKti5c6fSe2QymegL5j4+Ppg5cyacnZ1x+PBhDBs2DPfv38eVK1cwbtw4oeMp4XeLwIyMjHDr1i2Ym5vnez48PFxy2zDU1NTQs2dP9OzZU5TDTHMbOXIk1NXV4e/vj3bt2uWZhv706dPPDjYo7rZs2QJHR0cYGxujRo0aAIDHjx/DwsIChw4dEjbcN8pZgVazZk24urpKqv1Kftzd3WFqaoqVK1cqtmnXq1cP27ZtE3Vv0NevXyu9zpkCP3v2bCxatEigVKqxYMECODo6on379tDR0cGOHTuU2j9s3boVXbt2FTCh6kRHR+P+/fto164dtLS0PtoqSazq1auHxYsXY/PmzYqvYVpaGhYvXizaafe5v/dyPHv2DPPnz8fWrVtFPd/iQ+XKlcPhw4dx7949REVFAcj+mn7s2k1MDh06hL1796JFixZK33OWlpa4f/++gMmKRrly5eDn5yd0jAJJSEjA5cuX8y34iPWB1edmPLVq1Qrr169XeogsNs7OzoiNjcXs2bNRtWpVSX3mAdm7H5ycnHDo0CHMnDlT8fPywIEDktrBGRQUhODgYFGuZC3pRowYgZMnT6Jv376wsbGRxPfgnDlz4O7urmhZ+fr1a9HuNiqJxLpz/2utXbsWGzduxIABA7B9+3ZMnz4dpqammDNnTrHblcOhnwJzcXHBuXPncOXKlTxtEVJTU2FjY4OOHTvCx8dHoIRE2eRyOU6dOqVULLC1tZXExQVJT2BgIKZMmSLKVhe5JSYmQkdHB+rq6krH4+PjoaOjI+oeynFxcejfvz8CAgIgk8lw7949mJqaYvjw4TAwMMCyZcuEjqgSly9fRs+ePSGXyxWDwcLDwyGTyeDv7y/anTofevv2LZYsWYKVK1fC0tISixcvRseOHYWORZ+hra2NW7duwdTUFLq6uggLC4OpqSnCwsLQrl07JCYmCh2RPsHf3x9OTk5ISkqCnp6e0jWZTCYrdjeeX8rBwQEdO3b8aA9lHx8fnD17Fn/99VcRJ1MdXV1dXLhwQdHnu6R49+4d1NXVJdNSztraGmvXrkWLFi2EjkJfSV9fH8eOHZPUQEV1dXU8e/YMlStXBgDo6enhxo0bMDU1FTgZ0f/R1tZGZGQkTExMULlyZZw6dQqNGzfGvXv30KJFC8TFxQkdUYErzAU2a9Ys+Pn5oXbt2hg/fjzq1KkDAIiKisKaNWuQmZmJmTNnCpySPkVNTe2zRWOZTIaMjIwiSqRa6enp0NLSwo0bN9C1a1fJrGjNIfUtsbmlpaXluwrN2NhYoESFw9DQEHfu3BE6hkp8bICdFPosTp48GaVKlUJsbKzSSuv+/ftjypQpkimY29jY4MGDB9i1a5fioWP//v0xcOBA0e9uSU9Px6pVq+Dp6YkKFSpg27Zt6Nu3r9CxCmzKlClYsGABypYtiylTpnzyvd7e3kWUSvWaNWuGo0ePwsXFBcD/tbTavHmzYkAtFV9Tp07F8OHD4enpqRgUJgVSn/EEZPfyluK6tcuXL6Np06Z5HvLnkMlk+Ouvv0S9u/FDXl5emDp1KhYtWpTvYNrcOySo+DAyMoKurq7QMVQq988UKf6MAUre/bvUVKlSBfHx8TAxMYGxsTFCQkLQuHFjxMTEFLu/syyYC8zQ0BBBQUEYO3YsZsyYofgLIpPJYGdnhzVr1kiqz5sUfWp1S3BwMHx8fETdj7F06dIwNjaW7AeP1LfE5rh37x6GDx+eZ1p4TusLsX59w8PDlV7nTIH38vIqcau2xOjkyZM4ceIEqlevrnTcwsICjx49EihV4ShbtixGjRoldAyVkcvl8PX1xZw5c5CRkQFPT0+MGDHio0USsbl+/TrS09MVv/8YsX9meHp6wt7eHhEREcjIyMDKlSsRERGBoKAgBAYGCh2PPuPJkyeYMGGCpIrlQMmY8bRixQq4u7tjw4YNqFmzptBxVKZly5afXOGakJCAAQMGSKZgnjP7oXPnzkrHxX59nZmZieXLl390KJ9Yd698aNmyZXBzc8P69ethYmIidBz6CiXl/l2qOnXqhCNHjqBJkyYYNmwYJk+ejAMHDuDq1atwdHQUOp4SFsyLARMTExw7dgyvX79GdHQ05HI5LCws2G9KJHr16pXn2J07d+Du7q7YKuvh4SFAMtWZOXMmfv31V+zcuVMSq1o/dPHixRKxJdbZ2RmlSpXC33//LakLCysrK8hksjxPo1u0aIGtW7cKlIq+VHJycr6Fnvj4eGhqagqQqHD4+vp+8rwY+ww3atQIDx48gIuLCyZNmgRtbW0kJyfneZ9YV9d9ODAr9/AsKWnTpg1u3LgBLy8vNGzYECdPnoS1tTWCg4PRsGFDoePRZ9jZ2eHq1auS225fEmY89e/fHykpKTAzM4O2tnaeBwRiLUh+yQrX4raCsCCk+vkwf/58bN68GVOnTsWsWbMwc+ZMPHz4EIcOHcKcOXOEjqcSzZo1w7t372BqaiqZ70GZTIa3b9+iTJkyioc2SUlJePPmjdL7xHptlqOk3L9L1caNGxULSseNG4cKFSogKCgIDg4OGD16tMDplLGHORW6x48fQyaTKVYQXr58GX/++Sfq168vqdV2QPaAz7lz52LHjh2ws7PD4sWL0aBBA6FjFViTJk0QHR2N9PR0mJiY5GkhIOYp4vXr18euXbvQpEkToaMUqrJly+LatWuoW7eu0FFUKvcq5Jwp8LlnQlDx1L17dzRt2hQLFiyArq4uwsPDYWJigp9++glZWVk4cOCA0BFVIvcD8PT0dKSkpEBDQwPa2tqivClTU1NT/D6/B3BiX133KW/evEFAQADq1q0ruZ+pVPwdOXJE8ftXr17Bw8MDw4YNy7cdhIODQ1HHU4mSMONpx44dnzw/dOjQIkqiWmpqanj+/LlihfmHsxGA7N0D1apVk8RnQ3p6Orp164b169fDwsJC6DgqZWZmBh8fH/To0QO6urq4ceOG4lhISAj+/PNPoSMWmK2tLWJjYzFixAgYGhrmuZYR4/dg7laxOddiuV+L/fuvpNy/S1FISAj8/f2RlpaGzp07K3bpFFdcYU6FbuDAgRg1ahQGDx6M58+fo0uXLrC0tMSuXbvw/PlzSTylTkxMhKenJ1atWgUrKyucOXMGbdu2FTqWyvTu3VvoCIVGqltic6tfvz7+++8/oWOoHLdQitvSpUvRuXNnXL16FWlpaZg+fTpu376N+Ph4XLp0Seh4KvP69es8x+7du4exY8di2rRpAiQqOKmuqstPv3790K5dO4wfPx6pqalo1qwZHj58CLlcjj179uCHH34QOuJXefPmjWJ1We5VZ7mJfRWaFOV3TZbfTkYxF0VKwownMRbjSFnp0qXztAaUiufPnyt2Geno6CgGQH///feYPXu2kNFUJigoCMHBwWjcuLHQUVSmpFyblZT7dyB7UHLulkhivTY7cOAA+vfvDy0tLZQuXRre3t5YsmQJXF1dhY72UVxhToXOwMAAISEhqFOnDnx8fLB3715cunQJJ0+exJgxY/DgwQOhIxbI0qVLsWTJElSpUgWenp75tmih4svAwAApKSnIyMiQzHa8/AQEBGDWrFnw9PSU1FCiCRMmwNzcHBMmTFA6vnr1akRHR2PFihXCBKMvlpiYiNWrVyMsLAxJSUmwtrbGuHHjRL/d/ktcvXoVgwYNUgwCpeKpSpUqOHHiBBo3bow///wTc+fORVhYGHbs2IGNGzd+ssd5caSurq7oMfyxweVSWYVG4vXo0SOMHTsWJ06cyHfGU61atQROqDpSKoioqakhICBA0cKxVatW2Ldvn2Kn8X///YcuXbpI5mfL5MmToampCS8vL6GjqFSdOnXg6+uL7777Dm3atMH3338Pd3d37N27Fy4uLnj58qXQEQvM2toaa9euRYsWLYSOQl9J6vfvKSkpmD59Ovbt24e4uLg858X687Np06Zo3rw51qxZA3V1dSxevBi//fZbsf56sWBOhU5HRwe3bt1CzZo14eDggNatW8PNzQ2xsbGoU6cOUlNThY5YIGpqatDS0oKtre0nh535+fkVYarCce3aNURGRgIALC0tJbENSqpbYnPLaZ+QuzAi9qKIkZERjhw5gqZNmyodDw0NhYODA/7991+BktHnSHkr85e6ceMG2rVr99lVviQsLS0t3L17FzVq1MCQIUNQrVo1eHl5ITY2FvXr10dSUpLQEb9KYGAgWrdujVKlSn12sGf79u2LKBV9jYCAAIwfPx4hISF5CquJiYlo1aoV1q9fL4ndjlKd8ZScnAw3NzfJFURyHsLlV2LIOS7m687cXFxc4OvrCwsLCzRt2jRP20pvb2+BkhWMu7s79PT08Ouvv2Lv3r0YNGgQatasidjYWEyePFkSDwhOnjyJ+fPnY9GiRZJaTFQSSP3+fdy4cTh79iwWLFiAwYMHY82aNXjy5Ak2bNgALy8vODk5CR3xm+jo6ODGjRuK+SRpaWkoW7Ysnjx5omjjVdywJQsVOktLS6xfvx49evTAqVOnsGDBAgDZ/b4rVKggcLqCGzJkiGQGKOZ4//690sC9ly9f4qeffsK5c+dQrlw5ANlT7jt27Ig9e/agUqVKAiUtOLF/oH4pqW7Ri4uLg76+fp7jenp6kmxBIyVS3sqc24c9h4HsB1XPnj3D6tWr0bp1a4FS0ZeqUaMGgoODUb58eRw/fhx79uwBkF3IE+O8hJwieEZGBgIDAzF8+HDF6k8ShxUrVmDkyJH5FnT09fUxevRoeHt7S6JgbmBggObNmwsdQ+WmT5+Os2fPYt26dfkWRMQqJiZG6AhF6tatW7C2tgYA3L17V+mcmO8PP/w72L9/f5iYmCAoKAgWFhbo2bOngMlUJ6d3cufOnZWOS+2hjhRJ/f7d398fvr6+6NChA4YNG4a2bdvC3NwcJiYm2LVrl2gL5ikpKUrXLRoaGihTpgySkpKKbcGcK8yp0J07dw59+vTBmzdvMHToUGzduhUA8OuvvyIqKkoSK6+lZvHixTAyMsKQIUMAZF8oPXjwAL6+vqhXrx4AICIiAkOHDoW5uTl2794tZFyVkdKW2JKiQYMGGDNmDMaPH690fNWqVVi3bh0iIiIESkZfQqpbmXP7cEAmkH0TXalSJXTq1AnLli0rEe1nxGzt2rWYOHEidHR0YGJigtDQUKipqWHVqlXw8/MT9QNJXV1d3Lx5U/I9QKXGxMQEx48fV1yT5RYVFYWuXbsiNja2iJPRlzI2NlYURPT09BAaGgpzc3Ps3LkTu3fvxrFjx4SOSCXY+fPn0apVK5Qqpby+MiMjA0FBQWjXrp1AyVSHO6ykQYr37zo6OoiIiICxsTGqV68OPz8/2NjYICYmBg0bNhTdzsYcampqWLhwIXR0dBTH3NzcMG3aNFSsWFFxLHerVSFxhTkVug4dOuC///7DmzdvlLZRjho1Ctra2gImo4/p168f+vbti0ePHmH27Nk4fvw4Tp8+rXRjVr9+faxZswZdu3YVMGnBSXVLbH4uXLiADRs24MGDB9i/fz+MjIywc+dO1KpVC23atBE63jeZMmUKxo8fj1evXqFTp04AgDNnzmDZsmXsXy4CGRkZ2Lp1K06fPi2prcy5ZWVlCR2BCuCXX36BjY0NHj9+jC5duigegJiammLhwoUCpyuYTp06ITAwkAVzkXnx4kWe9gEfKlWqFF69elWEiehrxcfHw9TUFEB2cSenh2ubNm0wduxYIaPRN8ppAyiFHTsdO3ZUzLr4UGJiIjp27CiJ+yMWxMVL6vfvpqamiImJgbGxMerWrYt9+/bBxsYG/v7+it3+YmRsbIxNmzYpHatSpQp27typeC2TyVgwp5JHXV0dGRkZuHjxIoDsQSK8OSu+zMzMEBwcrPhhlZWVle+NWenSpUVfCJLqltjcDh48iMGDB8PJyQmhoaF4//49gOwLX09PT9GuZBo+fDjev3+PRYsWKdo91axZE+vWrVPskKDi61NbmaUoLS0NMTExMDMzy7NqSwqkVCzIrVmzZmjWrJnSsR49egiURnXs7e3h7u6Omzdv5vvQysHBQaBk9ClGRka4deuWog9obuHh4dy5UsxJtSBS0mRlZWHhwoVYtmyZYtWnrq4upk6dipkzZ+bZYSYWOW1JcouLi8vzOSF2KSkpiI2NzbNKuVGjRgIlos+R+v37sGHDEBYWhvbt28Pd3R09e/bE6tWrkZ6eLurFRA8fPhQ6wldjSxYqdMnJyYqBKDnFVXV1dQwZMgSrVq3iKnMR6NWrFxISErB7925Uq1YNAPDkyRM4OTnBwMAAf/31l8AJv11J2RLbpEkTTJ48GUOGDIGuri7CwsJgamqK69evw97eHs+fPxc6YoG9evUKWlpaStu8iIqDlJQUjB8/Hr6+vgCyHw6YmprCxcUFRkZGcHd3Fzjht5NqsWDKlClYsGABypYtiylTpnzyvWK+efnU14c9XIsvFxcXnDt3DleuXMnTRz81NRU2Njbo2LEjfHx8BEpIn7N8+XKoq6tjwoQJOH36NHr27Am5XK4oiEycOFHoiPQFZsyYgS1btmD+/PmKmSQXL17EvHnzMHLkSCxatEjghF/H0dERAHD48GF069ZNaaZVZmYmwsPDUadOHRw/flyoiCrz6tUrDBs2DP/880++58X2+ZfztfsSYm+JW1Lu33M8evQI165dg7m5OR/kFDHpLW+iYmfKlCkIDAyEv7+/0oXEhAkTMHXqVKxbt07ghPQ5q1evhoODA2rWrIkaNWoAAB4/fowGDRrgjz/+EDhdwZSULbF37tzJt9+gvr4+EhISij5QIRDz8NmSavjw4Vi5ciV0dXWVjuc8aM2ZeSF2M2bMQHh4OM6dO6cYMgUAtra2mDdvnqgL5jNnzsSWLVvg5eWVp1jw7t070RULcly/fh3p6emK30uV2HeJlVSzZs2Cn58fateujfHjx6NOnToAsnuXr1mzBpmZmZg5c6bAKelTJk+erPi9ra0tIiMjFUUfFkTEY8eOHdi8ebPSbpxGjRrByMgIv/zyi+g+A/X19QFkrzDX1dWFlpaW4pyGhgZatGiBkSNHChVPpSZNmoSEhAT873//Q4cOHfDXX3/hxYsXikUAYpPztSsJSsr9ew4TExOYmJjg33//xahRo7Bx40ahI5UYXGFOha5ixYo4cOAAOnTooHT87Nmz6NevH3ssioRcLsfp06cRFRUFAKhXrx5sbW0FTlVwjRo1wqpVq9C+fXvY2trCysoKv//+O3x8fLB06VJFiwGxMzU1xcaNG2Fra6u0wtzX1xdeXl6iHY5Zq1atfLeM5njw4EERpqGvpa6unm+PzP/++w9VqlRBRkaGQMlUy8TEBHv37kWLFi2Uvv+io6NhbW2NN2/eCB3xm1WrVg3r16/P07rj8OHD+OWXX/DkyROBktHnpKenQ0tLCzdu3ECDBg2EjkNf6dGjRxg7dixOnDiBnNs5mUwGOzs7rFmzBrVq1RI4IZH0lSlTBuHh4ahdu7bS8Tt37sDKygqpqakCJSuY+fPnw9XVVXLtVz5UtWpVHD58GDY2NtDT08PVq1dRu3ZtHDlyBEuXLlW0kqXip6Tcv+cWFhYGa2tr0e1+EDOuMKdCl5KSAkNDwzzHK1eujJSUFAES0ZcKCAjA+PHjERISAj09PXTp0gVdunQBkN372tLSEuvXr0fbtm0FTvrtpNojLLeRI0di4sSJ2Lp1K2QyGZ4+fYrg4GC4urpi9uzZQsf7ZpMmTVJ6nZ6ejuvXr+P48eOYNm2aMKHos968eQO5XA65XI63b98qtRTIzMzEsWPH8hTRxezVq1f5/nmSk5M/+cBHDOLj41G3bt08x+vWratY8SNWw4cP/+x7ZDIZtmzZUgRpVK906dIwNjbmjZdImZiY4NixY3j9+jWio6Mhl8thYWEBAwMDoaPRJwQHByMuLg7ff/+94pivry/mzp2L5ORk9O7dG6tWrVJqhSEWTZo0+eLPtNDQ0EJOUzQaN26M1atX52l/tHr1ajRu3FigVAU3d+5coSMUuuTkZMW1mYGBAV69eoXatWujYcOGkvj7mZGRgXPnzuH+/fsYOHAgdHV18fTpU+jp6Ym+fWVJuX8n4bFgToWuZcuWmDt3Lnx9fRVFkdTUVMyfPx8tW7YUOB19yooVKzBy5Ejo6enlOaevr4/Ro0fD29tb1AXzkrIl1t3dHVlZWejcuTNSUlLQrl07aGpqwtXVFS4uLkLH+2Yf6/G5Zs0aXL16tYjT0JcqV64cZDIZZDJZnlVZQHYRcv78+QIkKxzNmjXD0aNHFd9rOQWFzZs3i/5zUKrFAgDYvn07TExM0KRJE0h1Q+bMmTPx66+/YufOnShfvrzQcegbGBgYoHnz5kLHoC/k4eGBDh06KArmN2/exIgRI+Ds7Ix69erht99+Q7Vq1TBv3jxhg36D3r17Cx2hyC1duhQ9evTA6dOnFZ/nwcHBePz4saj7KL948QKurq44c+YMXr58meczUAoPWuvUqYM7d+6gZs2aaNy4MTZs2ICaNWti/fr1oh+a/OjRI3Tr1g2xsbF4//49unTpAl1dXSxZsgTv37/H+vXrhY5YICXl/p2Ex5YsVOhu3boFOzs7vH//XnHzHBYWhjJlyuDEiROwtLQUOCF9jImJCY4fP4569erlez4qKgpdu3ZFbGxsESejb5WWlobo6GgkJSWhfv36ol9h8DEPHjyAlZWVqFtdSFlgYCDkcjk6deqEgwcPKhXqNDQ0YGJiohgwLAUXL16Evb09Bg0ahO3bt2P06NGIiIhAUFAQAgMD0bRpU6EjfrPAwED06NEDxsbG+RYLxPxAddy4cdi9ezdMTEwwbNgwDBo0SHJF5SZNmiA6Ohrp6ekwMTHJs/1eCqvsiIqTqlWrwt/fH82aNQOQ/dAqMDBQ0f5h//79mDt3rmhb5ZUUDx48ULQFfPr0KdauXYvIyEgA2W0rf/nlF1Ffx9jb2yM2Nhbjx49H1apV8+wc6NWrl0DJVOePP/5ARkYGnJ2dce3aNXTr1g3x8fHQ0NDA9u3b0b9/f6EjfrPevXtDV1cXW7ZsQYUKFRStAM+dO4eRI0fi3r17QkekbyDFlizv3r1DWlqa0rH8FmsKhQVzKhIpKSnYtWuXUv9rJycnpUEiVPyUKVMGt27dgrm5eb7no6Oj0bBhQ1H255PyltgPfUlLAQCSGa6YY+nSpVi7di0ePnwodBT6hEePHqFGjRpQU1MTOkqhu3//Pry8vBAWFoakpCRYW1vDzc0NDRs2FDpagT19+hRr1qxR+owXe7Egx/v37+Hn54etW7ciKCgIPXr0wIgRI9C1a1fRt9MB8NmdHCVhWz5RUSpTpgzu3buHGjVqAMgeUmdvb68Y0vrw4UM0bNgQb9++FTKmSiQkJODAgQO4f/8+pk2bhvLlyyM0NBSGhoYwMjISOl6B5J7B0r9/f/j4+OTbhlSMdHV1ceHCBVhZWQkdpcikpKQgKioKxsbGqFixotBxCqRChQoICgpCnTp1lGbnPHz4EPXr1xdtW1yp3787Ojp+8nxCQgICAwNFXzBPSUnB9OnTsW/fPsTFxeU5X5z+fGzJQkVCW1tbMhO1SxIjI6NPFszDw8NFu2VNyltiPyTVlgIeHh6YOnUq2rRpo1S0ksvleP78OV69eoW1a9cKmJC+hImJCRISEnD58mW8fPkSWVlZSueHDBkiUDLVMzMzw6ZNm4SOUWCHDx9Gy5YtlXqyV6tWDYsWLRIwVeHR1NTEgAEDMGDAADx69Ajbt2/HL7/8goyMDNy+fVv0u3RYECcqWoaGhoiJiUGNGjWQlpaG0NBQpQdXb9++RenSpQVMqBrh4eGwtbWFvr4+Hj58iJEjR6J8+fLw8/NDbGwsfH19hY5YILmvqf/55x8kJycLlEb1atSoIan7hvx4eHjA1dUV2traALLrFdbW1khNTYWHhwfmzJkjcMJvl5WVlW/R8d9//4Wurq4AiVRD6vfv+vr6nz0vhXujadOm4ezZs1i3bh0GDx6MNWvW4MmTJ9iwYQO8vLyEjqeEK8ypSNy7dw9nz57NtyAi5g8jqXNxccG5c+dw5coVpaF8QHYfehsbG3Ts2DFP71oxKClbYqXaUiBnZc/atWuVCuZqamqoVKkSOnTokO8gQipe/P394eTkhKSkJOjp6Sl9LWUymeiHRqqpqX12FbJMJkNGRkYRJSq4ffv2YdasWfjnn39gZmaG8PDwT75fSr0kHz9+jG3btmH79u1IS0tDVFSU6AvmOa5du6ZoJ2BpaYkmTZoInIhImsaOHYuwsDAsWbIEhw4dwo4dO/D06VNoaGgAAHbt2oUVK1bgypUrAictGFtbW1hbW2Pp0qVKK1yDgoIwcOBA0e8AVFNTw/PnzxUPjz/8M0rByZMnsWzZMkVfbynKvUsgR1xcHCpXrlysVrl+rf79+0NfXx8bN26Erq4uwsPDUalSJfTq1QvGxsbYtm2b0BG/SUm5f5c6Y2Nj+Pr6okOHDtDT01P0n9+5cyd2795drOY/sGBOhW7Tpk0YO3YsKlasiCpVquQpiLA/ZvH14sULWFtbQ11dHePHj0edOnUAZPcuX7NmDTIzMxVbK8WmJG2JlWJLgdw3KiROtWvXRvfu3eHp6alY4SMlhw8f/ui54OBg+Pj4ICsrC+/evSvCVAV3/PhxTJ48GZGRkYqHAvldTspkMlHfcALKPz8vXryI77//HsOGDUO3bt0k0Uro5cuX+Omnn3Du3DmUK1cOQPaW344dO2LPnj2oVKmSsAGJJOa///6Do6MjLl68CB0dHezYsQN9+vRRnO/cuTNatGgh+l07+vr6CA0NhZmZmVIx+dGjR6hTp47oPvdyU1dXx/PnzxU/I3OKkrVq1RI4mWoYGBggJSUFGRkZ0NbWzrPrQewLGoDse4kXL17k+ZwLCAhA//798erVK4GSFdy///4LOzs7yOVy3Lt3D82aNcO9e/dQsWJFnD9/XrT3TyXp/l3KdHR0EBERAWNjY1SvXh1+fn6wsbFBTEwMGjZsiKSkJKEjKrAlCxW6hQsXYtGiRXBzcxM6Cn0lQ0NDBAUFYezYsZgxY4aiICKTyWBnZ4c1a9aIslgOlJwtsYB0WwqItdhP/+fJkyeYMGGCJIvlQP5Dse7cuQN3d3fF6noPDw8BkhVMt27dFDs4YmJiBE5TeH755Rfs2bMHNWrUwPDhw7F7927R9zXNzcXFBW/fvsXt27cVA74jIiIwdOhQTJgwAbt37xY4IZG05BSsEhMToaOjA3V1daXz+/fvF+112Yc0NTXzHbx+9+5dSTyIk8vlcHZ2VvRKfvfuHcaMGZNncLKfn58Q8QpsxYoVQkcoNAYGBpDJZJDJZKhdu7bS/URmZiaSkpIwZswYARMWXPXq1REWFoY9e/YgPDwcSUlJGDFihOhnyJWk+3cpMzU1RUxMDIyNjVG3bl3s27cPNjY28Pf3VyzeKC5YMKdC9/r1a/z4449Cx6BvZGJigmPHjuH169eIjo6GXC6HhYUFDAwMhI5WIN27d4e7u7tiS6y2tjbatm2rOB8eHg4zMzMBExaOD1eDin3lZ+6L3PxIYQWMlNnZ2eHq1auS2cL8KU+fPsXcuXOxY8cO2NnZ4caNG2jQoIHQsb5ZzhZtExOTj75HjAOhP7R+/XoYGxvD1NQUgYGBCAwMzPd9Yi2IANm7BU6fPq0olgNA/fr1sWbNGnTt2lXAZETS9rFetVJomwcADg4O8PDwwL59+wBkL3KIjY2Fm5sbfvjhB4HTFdzQoUOVXg8aNEigJIUj959PSlasWAG5XI7hw4dj/vz5St+LGhoaqFmzJlq2bClgQtUoVaqU5P5eltT7d6kZNmwYwsLC0L59e7i7u6Nnz55YvXo10tPT4e3tLXQ8JSyYU6H78ccfcfLkSdE/qS3pDAwM0Lx5c6FjqMyCBQvg6OiI9u3bK7bE5vSPBICtW7dKpliQX0uB1atXi76lQO6LXBKfHj16YNq0aYiIiEDDhg3zrApxcHAQKJnqJCYmwtPTE6tWrYKVlRXOnDmjdHEvRe/fv8fq1avx22+/4fnz50LH+WZDhgyR/E6WrKysfFdjlS5dOs/MGSKiL7Vs2TL07dsXlStXRmpqKtq3b4/nz5+jZcuWom83A0C0PaC/xbt375CWlqZ0TE9PT6A0BZfzMKBWrVpo1aqVZFYkHzly5IvfK9br65J0/y5lkydPVvze1tYWUVFRuHbtGszNzYvd7CP2MKdCt3jxYnh7e6NHjx75FkQmTJggUDIifHRLbHx8PHR0dJQ+hMUod0sBJycnSbQUYA9zafjUAxsp9L9eunQplixZgipVqsDT0zPfFi1i9f79e8ybNw+nTp2ChoYGpk+fjt69e2Pbtm2YOXOmYvYF27EVb7169UJCQgJ2796NatWqAchuleTk5AQDAwP89ddfAickIjG7ePGioiWEtbU1bG1thY5EXyA5ORlubm7Yt28f4uLi8pwX+/VZblJ4KJD7mjq/+TI5iwDE/vWT+v17SfXvv//Cw8MDGzduFDqKAgvmVOg+NfxEJpPhwYMHRZiGqGRRU1ODsbExmjRp8smVkmJrKfCxyfZExYmamhq0tLRga2ub56L+Q2L7/gMANzc3bNiwAba2tggKCsKrV68wbNgwhISE4Ndff8WPP/74yT8zFQ+PHz+Gg4MDbt++rRii9fjxYzRo0ABHjhxB9erVBU5IRERFbdy4cTh79iwWLFiAwYMHY82aNXjy5Ak2bNgALy8vODk5CR2xwFJSUjB9+nRJPhQ4ffo03Nzc4OnpqWgvExwcjFmzZsHT0xNdunQROCFRXmFhYbC2ti5W33tsyUKFTsoDwYiKO6m2FOCzXhIDqX7/AdmD6Xx9feHg4IBbt26hUaNGyMjIQFhYmGT/zFJUo0YNhIaG4vTp04iKigIA1KtXj6tAieir+fj4fPF7ucO4ePP394evry86dOiAYcOGoW3btjA3N4eJiQl27doliYL5tGnTcPbsWaxbty7fhwJiNmnSJKxfvx5t2rRRHLOzs4O2tjZGjRqFyMhIAdMRiQdXmFORSUtLQ0xMDMzMzFCqFJ/VEBGVVN27d8fu3bsVPei9vLwwZswYxWT0uLg4tG3bFhEREQKmpE/R0NBATEwMjIyMAABaWlq4fPkyGjZsKHAy+hIBAQEYP348QkJC8mw7T0xMRKtWrbB+/XrJ99snItXJvav41atXSElJUXy2JyQkQFtbG5UrV+YO42JOR0cHERERMDY2RvXq1eHn5wcbGxvExMSgYcOGSEpKEjpigRkbGyseCujp6SE0NBTm5ubYuXMndu/ejWPHjgkd8ZtpaWnhypUreYbLh4eH47vvvhP9UHaSpuK4wly8095INFJSUjBixAhoa2vD0tISsbGxAAAXFxfRP70lIqKvd+LECbx//17x2tPTE/Hx8YrXGRkZuHPnjhDR6AtlZmYq9YgsVaoUdHR0BExEX2PFihUYOXJkvj1a9fX1MXr0aHh7ewuQjIjEKiYmRvFr0aJFsLKyQmRkJOLj4xEfH4/IyEhYW1tjwYIFQkelzzA1NVXsEq9bty727dsHIHvlec4DELGLj4+HqakpgOx+5TnXoW3atMH58+eFjFZgzZs3x5QpU/DixQvFsRcvXmDatGmwsbERMBmRuHCZLxW6GTNmICwsDOfOnUO3bt0Ux21tbTFv3jy4u7sLmI6IiIpa7s1t3OwmPnK5HM7OztDU1ASQPTBrzJgxKFu2rNL7xNifvSQICwvDkiVLPnq+a9eu+P3334swERFJyezZs3HgwAHUqVNHcaxOnTpYvnw5+vbtK4mWHlI2bNgwhIWFoX379nB3d0fPnj2xevVqpKenS+Zhas5DAWNjY8VDARsbG0k8FNi6dSv69OkDY2NjpfkkFhYWOHTokLDhqMRydHT85PmEhISiCfIVWDCnQnfo0CHs3bsXLVq0UOpramlpifv37wuYjIiIiL7F0KFDlV4PGjRIoCT0LV68eIHSpUt/9HypUqXw6tWrIkxERFLy7NkzZGRk5DmemZmptOqViqfJkycrfm9ra4vIyEhFy5JGjRoJmEx1PvZQIC0tDcuXLxc6XoGYm5sjPDwcp06dyjOfhHNmSCg5rTg/dX7IkCFFlObLsGBOhe7Vq1eoXLlynuPJycn8gU1EVALJZLI8P//5eSAu27ZtEzoCFYCRkRFu3boFc3PzfM+Hh4ejatWqRZyKiKSic+fOGD16NDZv3gxra2sAwLVr1zB27FgOFRahmjVrombNmkLHUKncDwWioqJw7do1WFhYSGIei0wmQ9euXdG1a1ehoxABEOe9A3uYU6Fr1qwZjh49qnidUxTZvHkzWrZsKVQsIiISSE47D0dHRzg6OiraeeS8Hj58uNARiSSte/fumD17Nt69e5fnXGpqKubOnYvvv/9egGREJAVbt25FlSpV0KxZM2hqakJTUxM2NjYwNDTE5s2bhY5HHxEcHIy///5b6Zivry9q1aqFypUrY9SoUUozaMQoICAA9evXx5s3b5SOm5iYoHPnzvjpp59w4cIFgdIRUXEik7NxKBWyixcvwt7eHoMGDcL27dsxevRoREREICgoCIGBgWjatKnQEYmIqAgNGzbsi94nxpUIRGLw4sULWFtbQ11dHePHj1f0GY6KisKaNWuQmZmJ0NBQGBoaCpyUiMTs3r17iIyMBJA9PLJ27doCJ6JPsbe3R4cOHeDm5gYAuHnzJqytreHs7Ix69erht99+w+jRozFv3jxhgxaAg4MDOnbsqLTC/EM+Pj44e/Ys/vrrryJORkTFDQvmVCTu378PLy8vhIWFISkpCdbW1nBzc5PEdiciIiIisXn06BHGjh2LEydOKAbvymQy2NnZYc2aNahVq5bACYmIqChVrVoV/v7+aNasGQBg5syZCAwMxMWLFwEA+/fvx9y5cxERESFkzAIxMTHB8ePHUa9evXzPR0VFoWvXroiNjS3iZERU3LCHORUJMzMzbNq0SegYRERERITsosGxY8fw+vVrREdHQy6Xw8LCAgYGBkJHIyIiAbx+/VppZ1FgYCDs7e0Vr5s3b47Hjx8LEU1lOPSaiL4UC+ZU6D73dNbY2LiIkhARERHRhwwMDNC8eXOhYxARkcAMDQ0RExODGjVqIC0tDaGhoZg/f77i/Nu3bz9ZbBaDkjb0+t27d0hLS1M6pqenJ1AaInFhwZwKXc2aNRWDPvOTmZlZhGmIiIiIiIiI6EPdu3eHu7s7lixZgkOHDkFbWxtt27ZVnA8PD4eZmZmACQsuZ+h1t27dUKZMGaVzUhl6nZKSgunTp2Pfvn2Ii4vLc571F6Ivw4I5Fbrr168rvU5PT8f169fh7e2NRYsWCZSKiIiIiIiIiABgwYIFcHR0RPv27aGjo4MdO3ZAQ0NDcX7r1q3o2rWrgAkLbtasWfDz80Pt2rU/OvR65syZAqcsmGnTpuHs2bNYt24dBg8ejDVr1uDJkyfYsGEDvLy8hI5HJBoc+kmCOXr0KH777TecO3dO6ChERERERESkIgkJCdiyZQsiIyMBAJaWlhg+fDj09fUFTkafk5iYCB0dHairqysdj4+Ph46OjlIRXYykPvTa2NgYvr6+6NChA/T09BAaGgpzc3Ps3LkTu3fvxrFjx4SOSCQKLJiTYKKjo9G4cWMkJycLHYWIiIiIiIhU4OrVq7Czs4OWlhZsbGwAAFeuXEFqaipOnjwJa2trgRMSQbJDr3V0dBAREQFjY2NUr14dfn5+sLGxQUxMDBo2bIikpCShIxKJAluyUKF78+aN0mu5XI5nz55h3rx5sLCwECgVERERERERqdrkyZPh4OCATZs2oVSp7JJDRkYGfv75Z0yaNAnnz58XOCGRdIdem5qaIiYmBsbGxqhbty727dsHGxsb+Pv7o1y5ckLHIxINrjCnQqemppZn6KdcLkeNGjWwZ88etGzZUqBkREREREREpEpaWlq4fv066tatq3Q8IiICzZo1Q0pKikDJiKRv+fLlUFdXx4QJE3D69Gn07NkTcrkc6enp8Pb2xsSJE4WOSCQKXGFOhe7s2bNKr9XU1FCpUiWYm5srVhwQERERERGR+Onp6SE2NjZPwfzx48fQ1dUVKBVRyTB58mTF721tbREVFYVr167B3NwcjRo1EjAZkbhwhTkRERERERERqcSECRPw119/4ffff0erVq0AAJcuXcK0adPwww8/YMWKFcIGJJKo9PR0dOvWDevXr2f7W6IC4vJeKnRxcXGoUKECgOxVBZs2bUJqaip69uyJdu3aCZyOiIiIiIiIVOX333+HTCbDkCFDkJGRAQAoXbo0xo4dCy8vL4HTEUlX6dKlER4eLnQMIkngCnMqNDdv3kTPnj3x+PFjWFhYYM+ePejWrRuSk5OhpqaG5ORkHDhwAL179xY6KhEREREREalQSkoK7t+/DwAwMzODtra2wImIpG/y5MnQ1NTkwymiAmLBnAqNvb09SpUqBXd3d+zcuRN///037OzssGnTJgCAi4sLrl27hpCQEIGTEhERERERERGJm4uLC3x9fWFhYYGmTZuibNmySue9vb0FSkYkLiyYU6GpWLEiAgIC0KhRIyQlJUFPTw9XrlxB06ZNAQBRUVFo0aIFEhIShA1KREREREREKtGnTx/IZLI8x2UyGcqUKQNzc3MMHDgQderUESAdkbR17Njxo+dkMhkCAgKKMA2ReLFgToVGTU0Nz58/R+XKlQEAurq6CAsLg6mpKQDgxYsXqFatGjIzM4WMSURERERERCri7OyMQ4cOoVy5corFUqGhoUhISEDXrl0RFhaGhw8f4syZM2jdurXAaYmkIzMzE5cuXULDhg1hYGAgdBwiUVMTOgBJW+6VBfmtNCAiIiIiIiJpqFKlCgYOHIgHDx7g4MGDOHjwIO7fv49BgwbBzMwMkZGRGDp0KNzc3ISOSiQp6urq6Nq1K3fxE6kAV5hToVFTU4O9vT00NTUBAP7+/ujUqZOih9b79+9x/PhxrjAnIiIiIiKSiEqVKuHSpUuoXbu20vG7d++iVatW+O+//3Dz5k20bduWhT0iFWvWrBmWLFmCzp07Cx2FSNRKCR2ApGvo0KFKrwcNGpTnPUOGDCmqOERERERERFTIMjIyEBUVladgHhUVpVgsVaZMGe4+JioECxcuhKurKxYsWJDv0E89PT2BkhGJCwvmVGi2bdsmdAQiIiIiIiIqQoMHD8aIESPw66+/onnz5gCAK1euwNPTU7FgKjAwEJaWlkLGJJKk7t27AwAcHByUHkrJ5XLIZDLu8Cf6QmzJQkREREREREQqkZmZCS8vL6xevRovXrwAABgaGsLFxQVubm5QV1dHbGws1NTUUL16dYHTEklLYGDgJ8+3b9++iJIQiRsL5kRERERERESkcm/evAHANhBERCQuakIHICIiIiIiIiLp0dPTY7GcqIhduHABgwYNQqtWrfDkyRMAwM6dO3Hx4kWBkxGJBwvmRERERERERKQyBw4cQL9+/dCiRQtYW1sr/SKiwnPw4EHY2dlBS0sLoaGheP/+PQAgMTERnp6eAqcjEg8WzImIiIiIiIhIJXx8fDBs2DAYGhri+vXrsLGxQYUKFfDgwQPY29sLHY9I0hYuXIj169dj06ZNKF26tOJ469atERoaKmAyInFhwZyIiIiIiIiIVGLt2rXYuHEjVq1aBQ0NDUyfPh2nTp3ChAkTkJiYKHQ8Ikm7c+cO2rVrl+e4vr4+EhISij4QkUixYE5EREREREREKhEbG4tWrVoBALS0tPD27VsAwODBg7F7924hoxFJXpUqVRAdHZ3n+MWLF2FqaipAIiJxYsGciIiIiIiIiFSiSpUqiI+PBwAYGxsjJCQEABATEwO5XC5kNCLJGzlyJCZOnIj//e9/kMlkePr0KXbt2gVXV1eMHTtW6HhEolFK6ABEREREREREJA2dOnXCkSNH0KRJEwwbNgyTJ0/GgQMHcPXqVTg6Ogodj0jS3N3dkZWVhc6dOyMlJQXt2rWDpqYmXF1d4eLiInQ8ItGQyfmIl4iIiIiIiIhUICsrC1lZWShVKnt93p49exAUFAQLCwuMHj0aGhoaAickkr60tDRER0cjKSkJ9evXh46OjtCRiESFBXMiIiIiIiIiIiIiIrAlCxEREREREREVQHh4OBo0aAA1NTWEh4d/8r2NGjUqolREJYOjoyO2b98OPT29z7Y98vPzK6JUROLGgjkRERERERERfTMrKys8f/4clStXhpWVFWQyWb4DPmUyGTIzMwVISCRd+vr6kMlkit8TUcGxJQsRERERERERfbNHjx7B2NgYMpkMjx49+uR7TUxMiigVUcnh4eEBV1dXaGtrCx2FSBJYMCciIiIiIiIilTh//jxatWqlGPqZIyMjA0FBQWjXrp1AyYikS11dHc+ePUPlypWFjkIkCSyYExEREREREZFKfKxwFxcXh8qVK7MlC1EhUFNTU7RFIqKCUxM6ABERERERERFJg1wuV/RT/lBcXBzKli0rQCKikiG/7zsi+jYc+klEREREREREBeLo6Aggu2jn7OwMTU1NxbnMzEyEh4ejVatWQsUjkrzatWt/tmgeHx9fRGmIxI0FcyIiIiIiIiIqEH19fQDZK8x1dXWhpaWlOKehoYEWLVpg5MiRQsUjkrz58+crvg+JqGDYw5yIiIiIiIiIVGL+/PlwdXVl+xWiIsQe5kSqxYI5ERERERERERGRSH1s2C4RfRsO/SQiIiIiIiIilXjx4gUGDx6MatWqoVSpUlBXV1f6RUSqx7WwRKrFHuZEREREREREpBLOzs6IjY3F7NmzUbVq1c8OISSigsvKyhI6ApGksCULEREREREREamErq4uLly4ACsrK6GjEBERfRO2ZCEiIiIiIiIilahRowbbQxARkaixYE5EREREREREKrFixQq4u7vj4cOHQkchIiL6JmzJQkREREREREQqYWBggJSUFGRkZEBbWxulS5dWOh8fHy9QMiIioi/DoZ9EREREREREpBIrVqwQOgIREVGBcIU5ERERERERERERERG4wpyIiIiIiIiICsG7d++QlpamdExPT0+gNERERF+GQz+JiIiIiIiISCWSk5Mxfvx4VK5cGWXLloWBgYHSLyIiouKOBXMiIiIiIiIiUonp06cjICAA69atg6amJjZv3oz58+ejWrVq8PX1FToeERHRZ7GHORERERERERGphLGxMXx9fdGhQwfo6ekhNDQU5ubm2LlzJ3bv3o1jx44JHZGIiOiTuMKciIiIiIiIiFQiPj4epqamALL7lcfHxwMA2rRpg/PnzwsZjYiI6IuwYE5EREREREREKmFqaoqYmBgAQN26dbFv3z4AgL+/P8qVKydgMiIioi/DlixEREREREREpBLLly+Huro6JkyYgNOnT6Nnz56Qy+VIT0+Ht7c3Jk6cKHREIiKiT2LBnIiIiIiIiIgKxcOHDxV9zBs1aiR0HCIios9iwZyIiIiIiIiIiIiICOxhTkREREREREQFFBwcjL///lvpmK+vL2rVqoXKlStj1KhReP/+vUDpiIiIvhwL5kRERERERERUIB4eHrh9+7bi9c2bNzFixAjY2trC3d0d/v7+WLx4sYAJiYiIvgxbshARERERERFRgVStWhX+/v5o1qwZAGDmzJkIDAzExYsXAQD79+/H3LlzERERIWRMIiKiz+IKcyIiIiIiIiIqkNevX8PQ0FDxOjAwEPb29orXzZs3x+PHj4WIRkRE9FVYMCciIiIiIiKiAjE0NERMTAwAIC0tDaGhoWjRooXi/Nu3b1G6dGmh4hEREX0xFsyJiIiIiIiIqEC6d+8Od3d3XLhwATNmzIC2tjbatm2rOB8eHg4zMzMBExIREX2ZUkIHICIiIiIiIiJxW7BgARwdHdG+fXvo6Ohgx44d0NDQUJzfunUrunbtKmBCIiKiL8Ohn0RERERERESkEomJidDR0YG6urrS8fj4eOjo6CgV0YmIiIojFsyJiIiIiIiIiIiIiMAe5kREREREREREREREAFgwJyIiIiIiIiIiIiICwII5EREREREREREREREAFsyJiIiIiAiAs7MzevfurbJ/3/bt21GuXDmV/fuIiIiIiIoCh34SERERERESExMhl8tVVuROTU3F27dvUblyZZX8+4iIiIiIigIL5kREREREIpaWlgYNDQ2hYxARERERSQJbshARERERFSMdOnTA+PHjMX78eOjr66NixYqYPXs2cta51KxZEwsWLMCQIUOgp6eHUaNGAQAOHjwIS0tLaGpqombNmli2bJni3/nrr7/iu+++y/Pfaty4MTw8PADkbcnSoUMHTJgwAdOnT0f58uVRpUoVzJs3T+mfT0hIwOjRo2FoaIgyZcqgQYMG+PvvvwHkbcly//599OrVC4aGhtDR0UHz5s1x+vRpVfxfRkRERESkMiyYExEREREVMzt27ECpUqVw+fJlrFy5Et7e3ti8ebPi/O+//47GjRvj+vXrmD17Nq5du4Z+/frhp59+ws2bNzFv3jzMnj0b27dvBwA4OTnh8uXLuH//vuLfcfv2bYSHh2PgwIGfzFG2bFn873//w9KlS+Hh4YFTp04BALKysmBvb49Lly7hjz/+QEREBLy8vKCurp7vvyspKQndu3fHmTNncP36dXTr1g09e/ZEbGysCv4fIyIiIiJSDbZkISIiIiIqRjp06ICXL1/i9u3bkMlkAAB3d3ccOXIEERERqFmzJpo0aYK//vpL8c84OTnh1atXOHnypOLY9OnTcfToUdy+fRsAYGVlhR9++AGzZ88GkL3qPCAgACEhIQCyV5gnJCTg0KFDihyZmZm4cOGC4t9pY2ODTp06wcvLCydPnoS9vT0iIyNRu3btPH+O7du3Y9KkSUhISPjon7VBgwYYM2YMxo8f/23/ZxERERERqRhXmBMRERERFTMtWrRQFMsBoGXLlrh37x4yMzMBAM2aNVN6f2RkJFq3bq10rHXr1kr/jJOTE/78808AgFwux+7du+Hk5PTJHI0aNVJ6XbVqVbx8+RIAcOPGDVSvXj3fYnl+kpKS4Orqinr16qFcuXLQ0dFBZGQkV5gTERERUbFSSugARERERET0dcqWLfvV/8yAAQPg5uaG0NBQpKam4vHjx+jfv/8n/5nSpUsrvZbJZMjKygIAaGlpfdV/39XVFadOncLvv/8Oc3NzaGlpoW/fvkhLS/u6PwgRERERUSFiwZyIiIiIqJj53//+p/Q6JCQEFhYWH+0PXq9ePVy6dEnp2KVLl1C7dm3FP1O9enW0b98eu3btQmpqKrp06YLKlSt/c8ZGjRrh33//xd27d79olfmlS5fg7OyMPn36AMhecf7w4cNv/u8TERERERUGtmQhIiIiIipmYmNjMWXKFNy5cwe7d+/GqlWrMHHixI++f+rUqThz5gwWLFiAu3fvYseOHVi9ejVcXV2V3ufk5IQ9e/Zg//79n23H8jnt27dHu3bt8MMPP+DUqVOIiYnBP//8g+PHj+f7fgsLC/j5+eHGjRsICwvDwIEDFavViYiIiIiKCxbMiYiIiIiKmSFDhiA1NRU2NjYYN24cJk6ciFGjRn30/dbW1ti3bx/27NmDBg0aYM6cOfDw8ICzs7PS+/r27Yu4uDikpKSgd+/eBc558OBBNG/eHAMGDED9+vUxffp0Rc/03Ly9vWFgYIBWrVqhZ8+esLOzg7W1dYEzEBERERGpkkwul8uFDkFERERERNk6dOgAKysrrFixQugoREREREQlDleYExERERERERERERGBBXMiIiIiIiIiIiIiIgBsyUJEREREREREREREBIArzImIiIiIiIiIiIiIALBgTkREREREREREREQEgAVzIiIiIiIiIiIiIiIALJgTEREREREREREREQFgwZyIiIiIiIiIiIiICAAL5kREREREREREREREAFgwJyIiIiIiIiIiIiICwII5EREREREREREREREAFsyJiIiIiIiIiIiIiAAA/w9qmJBkplLuuwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_provincia = sns.catplot(x=\"llamado_provincia\", kind=\"count\", data=llamados_v4, height= 5, aspect = 3, order=llamados_v4.llamado_provincia.value_counts().index)\n",
    "plot_provincia.set(xlabel='provincia', ylabel='cantidad de llamados')\n",
    "plt.title(\"Cantidad de llamados por provincia\") \n",
    "plt.xticks(rotation=90)\n",
    "plt.show\n",
    "plt.savefig(os.path.join(image_path, image_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnas_agrupar_1 = ['vs_explotacion_sexual', 'vs_explotacion_sexual_comercial', 'vs_explotacion_sexual_viajes_turismo','vs_sospecha_trata_personas_fines_sexuales']\n",
    "columnas_agrupar_2 = ['vs_violacion_via_vaginal', 'vs_violacion_via_anal', 'vs_violacion_via_oral']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['vs_explotacion_sexual',\n",
       " 'vs_explotacion_sexual_comercial',\n",
       " 'vs_explotacion_sexual_viajes_turismo',\n",
       " 'vs_sospecha_trata_personas_fines_sexuales',\n",
       " 'vs_violacion_via_vaginal',\n",
       " 'vs_violacion_via_anal',\n",
       " 'vs_violacion_via_oral']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = columnas_agrupar_1 + columnas_agrupar_2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "llamados_v2.drop(columnas_agrupar_1 + columnas_agrupar_2,\n",
    "  axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['llamado_fecha_hora', 'llamado_provincia', 'llamante_edad',\n",
       "       'llamante_genero', 'llamante_vinculo', 'caso_judicializado',\n",
       "       'hecho_lugar', 'victima_a_resguardo', 'victima_edad', 'victima_genero',\n",
       "       'victima_nacionalidad', 'victima_vinculo_agresor',\n",
       "       'victima_discapacidad', 'victima_convive_agresor',\n",
       "       'vs_tentativa_violacion', 'vs_tocamiento_sexual',\n",
       "       'vs_intento_tocamiento', 'vs_Intento_violación_tercera_persona',\n",
       "       'vs_grooming', 'vs_exhibicionismo',\n",
       "       'vs_amenazas_verbales_contenido_sexual',\n",
       "       'vs_existencia_facilitador_corrupcion_nnya',\n",
       "       'vs_obligacion_sacarse_fotos_pornograficas',\n",
       "       'vs_eyaculacion_partes_cuerpo', 'vs_acoso_sexual',\n",
       "       'vs_iniciacion_sexual_forzada_inducida',\n",
       "       'vs_otra_forma_violencia_sexual', 'vs_no_sabe_no_contesta',\n",
       "       'ofv_sentimiento_amenaza', 'ofv_amenaza_explicita',\n",
       "       'ofv_violencia_fisica', 'ofv_intento_ahorcar', 'ofv_intento_quemar',\n",
       "       'ofv_intento_ahogar', 'ofv_amenaza_muerte',\n",
       "       'ofv_uso_sustancias_psicoactivas', 'ofv_intento_privacion_libertad',\n",
       "       'ofv_privacion_libertad', 'ofv_uso_arma_blanca', 'ofv_uso_arma_fuego',\n",
       "       'ofv_enganio_seduccion', 'ofv_intento_matar',\n",
       "       'ofv_uso_animal_victimizar', 'ofv_grooming', 'ofv_otra_forma_violencia',\n",
       "       'ofv_no_sabe_no_contesta'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llamados_v2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "llamados_v2= pd.read_excel('/home/vcolombo/Documents/Vic/linea_137_llamados_vs/datasets/xlsx/llamados_v2.xlsx')\n",
    "# completo_victima_edad\n",
    "completo_victima_edad = llamados_v2[~(llamados_v2['victima_edad'].isnull())]\n",
    "\n",
    "# mapping SI/NO in \"victima_convive_agresor\" labels to 'y_convive'\n",
    "y_convive = []\n",
    "for value in completo_victima_edad['victima_convive_agresor']:\n",
    "    if value == 'SI':\n",
    "        y_convive.append('SI')\n",
    "    elif value == 'NO':\n",
    "        y_convive.append('NO')\n",
    "    else:\n",
    "        y_convive.append('NS/NC')\n",
    "\n",
    "\n",
    "cols = [col for col in completo_victima_edad.columns if col not in ['llamante_edad']]\n",
    "llamados_2 = completo_victima_edad[cols]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17263"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_convive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17263"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(llamados_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(llamados_2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probar get dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#llamados_v2= pd.read_excel(os.path.join(dataset_dir, 'xlsx/llamados_v2.xlsx'), parse_dates=['llamado_fecha_hora'])\n",
    "llamados_v5= pd.read_excel(os.path.join(dataset_dir, 'xlsx/llamados_v5.xlsx'), parse_dates=['llamado_fecha_hora'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<DatetimeArray>\n",
      "['2016-11-19 12:46:38', '2016-11-19 13:46:24', '2016-11-19 18:20:08',\n",
      " '2016-11-20 00:05:55', '2016-11-20 14:39:21', '2016-11-21 11:23:48',\n",
      " '2016-11-21 11:49:28', '2016-11-21 13:23:59', '2016-11-21 14:44:42',\n",
      " '2016-11-21 15:58:48',\n",
      " ...\n",
      " '2021-07-31 07:15:00', '2021-07-31 08:15:00', '2021-07-31 12:30:00',\n",
      " '2021-07-31 15:45:00', '2021-07-31 15:35:00', '2021-07-31 16:10:00',\n",
      " '2021-07-31 17:10:00', '2021-07-31 21:20:00', '2021-07-31 19:00:00',\n",
      " '2021-07-31 20:30:00']\n",
      "Length: 16542, dtype: datetime64[ns]\n",
      "['Misiones' 'CABA' 'Tucumán' 'Santa Fe' 'Buenos Aires' 'San Juan' 'NS/NC'\n",
      " 'Jujuy' 'Mendoza' 'Salta' 'Río Negro' 'Chubut' 'Córdoba'\n",
      " 'Santiago del Estero' 'Tierra del Fuego' 'Santa Cruz' 'Corrientes'\n",
      " 'Entre Ríos' 'San Luis' 'Neuquén' 'La Pampa' 'Catamarca' 'La Rioja'\n",
      " 'Chaco' 'Formosa']\n",
      "[30. nan 47. 51. 45. 40. 50. 31. 35. 27. 63. 29. 22. 73. 44. 60. 13. 25.\n",
      " 26. 38. 28. 39. 53. 36. 32. 61. 54. 33. 65. 19. 79. 37. 34. 55. 70. 21.\n",
      " 72. 85. 83. 49. 48. 64. 66. 78. 67. 62. 41. 68. 17. 69. 71. 23. 24. 18.\n",
      " 57. 43. 46. 42. 56. 14. 15. 20. 12. 59. 16. 74. 52. 58. 77. 99. 11. 75.\n",
      " 80.  3. 76. 82.  6. 10.  9.]\n",
      "['Femenino' 'Masculino' 'NS/NC' 'Transgénero']\n",
      "['Hospital' 'Madre' 'Víctima' 'Vecina/o' 'Padre' 'Agresor/a' 'Familiar'\n",
      " 'Otro conocido' 'Abuela/o' 'Otra Institución' 'NS/NC' 'Comisaría'\n",
      " 'Escuela' 'Hermana/o' 'Defensoría' 'Otra institución']\n",
      "['NS/NC' 'NO' 'SI']\n",
      "['Vivienda de la Víctima' 'Ámbito educativo' 'Descampado' 'NS/NC' 'Otro'\n",
      " 'Vivienda del Agresor' 'Vivienda de un familiar' 'Calle' 'Redes Sociales'\n",
      " 'Automóvil' 'Subterráneo/Tren/Colectivo' 'Comercio'\n",
      " 'Albergue transitorio' 'Plaza' 'Taxi' 'Residencia turística'\n",
      " 'Obra en construcción']\n",
      "['SI' 'NO']\n",
      "[12. 11. 47. 17. 50. 10.  3.  9.  5. nan 13. 35. 14.  6. 27.  4. 16. 30.\n",
      "  7.  2. 40.  8. 18. 26. 15. 28. 39. 34. 36. 41. 61. 19. 32. 24.  1. 70.\n",
      " 21. 25. 43. 23. 20. 37. 78. 62. 55.  0. 33. 72. 44. 60. 31. 53. 38. 54.\n",
      " 22. 29. 45. 74. 56. 51. 46. 71. 48. 52. 58. 49. 42. 66. 63. 67. 65. 57.\n",
      " 64. 77. 76. 59. 75. 99. 69. 68. 73. 82. 88. 80. 79. 81. 85. 87.]\n",
      "['Femenino' 'Masculino' 'NS/NC' 'Transgénero']\n",
      "['Argentina' 'NS/NC' 'Paraguaya' 'Peruana' 'Otra' 'Boliviana' 'Uruguaya'\n",
      " 'Brasileña' 'Chilena']\n",
      "['Tío' 'Padre' 'Madre' 'Conocido no familiar (Amigo, vecino, entre otros)'\n",
      " 'NS/NC' 'Desconocido' 'Madrastra' 'Otro pariente' 'Padrastro' 'Hermano'\n",
      " 'Abuelo' 'Ex pareja de la víctima' 'Abuela' 'Pareja de la víctima'\n",
      " 'Hermana']\n",
      "['SI' 'NO' 'NS/NC']\n",
      "['SI' 'NO' 'NS/NC']\n",
      "['SI' 'NO']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['SI' 'NO']\n",
      "['SI' 'NO']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['NO' 'SI']\n",
      "['Familiar' 'No Familiar' 'NS/NC' 'Pareja/Ex']\n",
      "['Masculino' 'Femenino' 'No especificado' 'NS/NC']\n",
      "['Agresor conocido por víctima' 'NS/NC' 'Agresor no conocido por víctima']\n",
      "['Institución' 'Conocido (fam/no fam)' 'Víctima' 'Agresor/a' 'NS/NC']\n",
      "[1 0]\n",
      "['mediodía' 'tarde' 'noche' 'mañana' 'madrugada']\n",
      "['Verano' 'Otoño' 'Invierno' 'Primavera']\n",
      "['NO' 'SI']\n",
      "['SI' 'NO']\n",
      "['NO' 'SI']\n"
     ]
    }
   ],
   "source": [
    "for column in llamados_v5:\n",
    "    print(llamados_v5[column].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# todas las columnas si/no\n",
    "vs = list(llamados_v5.loc[:, llamados_v5.columns.str.startswith('vs')].columns)\n",
    "ofv = list(llamados_v5.loc[:, llamados_v5.columns.str.startswith('ofv')].columns)\n",
    "otras = ['victima_a_resguardo']\n",
    "\n",
    "si_no = vs+ofv+otras\n",
    "si_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SI' 'NO']\n",
      "['NS/NC' 'NO' 'SI']\n",
      "['SI' 'NO' 'NS/NC']\n",
      "['SI' 'NO' 'NS/NC']\n"
     ]
    }
   ],
   "source": [
    "print(llamados_v5.victima_a_resguardo.unique())\n",
    "print(llamados_v5.caso_judicializado.unique())\n",
    "print(llamados_v5.victima_discapacidad.unique())\n",
    "print(llamados_v5.victima_convive_agresor.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gm/4r6lhs5j5p12g3_ml4zjfy500000gq/T/ipykernel_95924/3220565958.py:6: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  llamados_corr.loc[:, i] = llamados_corr[i].replace({'NO': 0})\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>llamado_fecha_hora</th>\n",
       "      <th>llamado_provincia</th>\n",
       "      <th>llamante_edad</th>\n",
       "      <th>llamante_genero</th>\n",
       "      <th>llamante_vinculo</th>\n",
       "      <th>caso_judicializado</th>\n",
       "      <th>hecho_lugar</th>\n",
       "      <th>victima_a_resguardo</th>\n",
       "      <th>victima_edad</th>\n",
       "      <th>victima_genero</th>\n",
       "      <th>...</th>\n",
       "      <th>agresor_fam_no_fam</th>\n",
       "      <th>genero_agresor</th>\n",
       "      <th>agresor_conocido_no_conocido</th>\n",
       "      <th>tipo_vinculo_llamante</th>\n",
       "      <th>fin_de_semana</th>\n",
       "      <th>momento_dia</th>\n",
       "      <th>estacion_del_año</th>\n",
       "      <th>vs_explotacion_sexual_group</th>\n",
       "      <th>vs_violacion_group</th>\n",
       "      <th>vs_tentativa_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-19 12:46:38</td>\n",
       "      <td>Misiones</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Hospital</td>\n",
       "      <td>NS/NC</td>\n",
       "      <td>Vivienda de la Víctima</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Institución</td>\n",
       "      <td>1</td>\n",
       "      <td>mediodía</td>\n",
       "      <td>Verano</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-19 13:46:24</td>\n",
       "      <td>CABA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Madre</td>\n",
       "      <td>NS/NC</td>\n",
       "      <td>Vivienda de la Víctima</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Conocido (fam/no fam)</td>\n",
       "      <td>1</td>\n",
       "      <td>mediodía</td>\n",
       "      <td>Verano</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-19 18:20:08</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Víctima</td>\n",
       "      <td>NS/NC</td>\n",
       "      <td>Vivienda de la Víctima</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Víctima</td>\n",
       "      <td>1</td>\n",
       "      <td>tarde</td>\n",
       "      <td>Verano</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-20 00:05:55</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Vecina/o</td>\n",
       "      <td>NS/NC</td>\n",
       "      <td>Vivienda de la Víctima</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Conocido (fam/no fam)</td>\n",
       "      <td>1</td>\n",
       "      <td>noche</td>\n",
       "      <td>Verano</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-20 00:05:55</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Vecina/o</td>\n",
       "      <td>NS/NC</td>\n",
       "      <td>Vivienda de la Víctima</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Conocido (fam/no fam)</td>\n",
       "      <td>1</td>\n",
       "      <td>noche</td>\n",
       "      <td>Verano</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19138</th>\n",
       "      <td>2021-07-31 17:10:00</td>\n",
       "      <td>CABA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Comisaría</td>\n",
       "      <td>SI</td>\n",
       "      <td>Vivienda de un familiar</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>No especificado</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Institución</td>\n",
       "      <td>1</td>\n",
       "      <td>tarde</td>\n",
       "      <td>Invierno</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19139</th>\n",
       "      <td>2021-07-31 21:20:00</td>\n",
       "      <td>CABA</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Víctima</td>\n",
       "      <td>NO</td>\n",
       "      <td>Redes Sociales</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Pareja/Ex</td>\n",
       "      <td>No especificado</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Víctima</td>\n",
       "      <td>1</td>\n",
       "      <td>noche</td>\n",
       "      <td>Invierno</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19140</th>\n",
       "      <td>2021-07-31 19:00:00</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Madre</td>\n",
       "      <td>NO</td>\n",
       "      <td>Redes Sociales</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>...</td>\n",
       "      <td>No Familiar</td>\n",
       "      <td>No especificado</td>\n",
       "      <td>Agresor no conocido por víctima</td>\n",
       "      <td>Conocido (fam/no fam)</td>\n",
       "      <td>1</td>\n",
       "      <td>tarde</td>\n",
       "      <td>Invierno</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19141</th>\n",
       "      <td>2021-07-31 20:30:00</td>\n",
       "      <td>CABA</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Comisaría</td>\n",
       "      <td>SI</td>\n",
       "      <td>Automóvil</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>...</td>\n",
       "      <td>No Familiar</td>\n",
       "      <td>No especificado</td>\n",
       "      <td>Agresor no conocido por víctima</td>\n",
       "      <td>Institución</td>\n",
       "      <td>1</td>\n",
       "      <td>noche</td>\n",
       "      <td>Invierno</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19142</th>\n",
       "      <td>2021-07-31 20:30:00</td>\n",
       "      <td>CABA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>Comisaría</td>\n",
       "      <td>SI</td>\n",
       "      <td>Vivienda de la Víctima</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Femenino</td>\n",
       "      <td>...</td>\n",
       "      <td>Familiar</td>\n",
       "      <td>No especificado</td>\n",
       "      <td>Agresor conocido por víctima</td>\n",
       "      <td>Institución</td>\n",
       "      <td>1</td>\n",
       "      <td>noche</td>\n",
       "      <td>Invierno</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19143 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       llamado_fecha_hora llamado_provincia  llamante_edad llamante_genero  \\\n",
       "0     2016-11-19 12:46:38          Misiones           30.0        Femenino   \n",
       "1     2016-11-19 13:46:24              CABA            NaN        Femenino   \n",
       "2     2016-11-19 18:20:08           Tucumán           47.0        Femenino   \n",
       "3     2016-11-20 00:05:55          Santa Fe            NaN       Masculino   \n",
       "4     2016-11-20 00:05:55          Santa Fe            NaN       Masculino   \n",
       "...                   ...               ...            ...             ...   \n",
       "19138 2021-07-31 17:10:00              CABA            NaN        Femenino   \n",
       "19139 2021-07-31 21:20:00              CABA           23.0        Femenino   \n",
       "19140 2021-07-31 19:00:00          Santa Fe           56.0        Femenino   \n",
       "19141 2021-07-31 20:30:00              CABA           40.0        Femenino   \n",
       "19142 2021-07-31 20:30:00              CABA            NaN        Femenino   \n",
       "\n",
       "      llamante_vinculo caso_judicializado              hecho_lugar  \\\n",
       "0             Hospital              NS/NC   Vivienda de la Víctima   \n",
       "1                Madre              NS/NC   Vivienda de la Víctima   \n",
       "2              Víctima              NS/NC   Vivienda de la Víctima   \n",
       "3             Vecina/o              NS/NC   Vivienda de la Víctima   \n",
       "4             Vecina/o              NS/NC   Vivienda de la Víctima   \n",
       "...                ...                ...                      ...   \n",
       "19138        Comisaría                 SI  Vivienda de un familiar   \n",
       "19139          Víctima                 NO           Redes Sociales   \n",
       "19140            Madre                 NO           Redes Sociales   \n",
       "19141        Comisaría                 SI                Automóvil   \n",
       "19142        Comisaría                 SI   Vivienda de la Víctima   \n",
       "\n",
       "      victima_a_resguardo  victima_edad victima_genero  ...  \\\n",
       "0                       1          12.0       Femenino  ...   \n",
       "1                       1          11.0      Masculino  ...   \n",
       "2                       1          47.0       Femenino  ...   \n",
       "3                       0          11.0       Femenino  ...   \n",
       "4                       0          12.0       Femenino  ...   \n",
       "...                   ...           ...            ...  ...   \n",
       "19138                   1           8.0       Femenino  ...   \n",
       "19139                   1          23.0       Femenino  ...   \n",
       "19140                   1          13.0      Masculino  ...   \n",
       "19141                   1          15.0      Masculino  ...   \n",
       "19142                   1           5.0       Femenino  ...   \n",
       "\n",
       "      agresor_fam_no_fam   genero_agresor     agresor_conocido_no_conocido  \\\n",
       "0               Familiar        Masculino     Agresor conocido por víctima   \n",
       "1               Familiar        Masculino     Agresor conocido por víctima   \n",
       "2               Familiar        Masculino     Agresor conocido por víctima   \n",
       "3               Familiar         Femenino     Agresor conocido por víctima   \n",
       "4               Familiar         Femenino     Agresor conocido por víctima   \n",
       "...                  ...              ...                              ...   \n",
       "19138           Familiar  No especificado     Agresor conocido por víctima   \n",
       "19139          Pareja/Ex  No especificado     Agresor conocido por víctima   \n",
       "19140        No Familiar  No especificado  Agresor no conocido por víctima   \n",
       "19141        No Familiar  No especificado  Agresor no conocido por víctima   \n",
       "19142           Familiar  No especificado     Agresor conocido por víctima   \n",
       "\n",
       "       tipo_vinculo_llamante fin_de_semana momento_dia estacion_del_año  \\\n",
       "0                Institución             1    mediodía           Verano   \n",
       "1      Conocido (fam/no fam)             1    mediodía           Verano   \n",
       "2                    Víctima             1       tarde           Verano   \n",
       "3      Conocido (fam/no fam)             1       noche           Verano   \n",
       "4      Conocido (fam/no fam)             1       noche           Verano   \n",
       "...                      ...           ...         ...              ...   \n",
       "19138            Institución             1       tarde         Invierno   \n",
       "19139                Víctima             1       noche         Invierno   \n",
       "19140  Conocido (fam/no fam)             1       tarde         Invierno   \n",
       "19141            Institución             1       noche         Invierno   \n",
       "19142            Institución             1       noche         Invierno   \n",
       "\n",
       "      vs_explotacion_sexual_group vs_violacion_group vs_tentativa_group  \n",
       "0                               0                  1                  0  \n",
       "1                               0                  0                  0  \n",
       "2                               0                  0                  1  \n",
       "3                               1                  1                  0  \n",
       "4                               1                  1                  0  \n",
       "...                           ...                ...                ...  \n",
       "19138                           0                  0                  0  \n",
       "19139                           0                  0                  0  \n",
       "19140                           0                  0                  0  \n",
       "19141                           0                  0                  0  \n",
       "19142                           0                  1                  0  \n",
       "\n",
       "[19143 rows x 40 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# en todas las que tienen solo SI/NO reemplazo SI x 1 y NO x 0\n",
    "llamados_corr = llamados_v5.copy(deep=True)\n",
    "\n",
    "for i in si_no:\n",
    "    llamados_corr.loc[:, i] = llamados_corr[i].replace({'SI': 1})\n",
    "    llamados_corr.loc[:, i] = llamados_corr[i].replace({'NO': 0})\n",
    "\n",
    "llamados_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['llamado_fecha_hora', 'llamado_provincia', 'llamante_edad',\n",
       "       'llamante_genero', 'llamante_vinculo', 'caso_judicializado',\n",
       "       'hecho_lugar', 'victima_a_resguardo', 'victima_edad', 'victima_genero',\n",
       "       'victima_nacionalidad', 'victima_vinculo_agresor',\n",
       "       'victima_discapacidad', 'victima_convive_agresor',\n",
       "       'vs_tocamiento_sexual', 'vs_intento_tocamiento', 'vs_grooming',\n",
       "       'vs_exhibicionismo', 'vs_obligacion_sacarse_fotos_pornograficas',\n",
       "       'vs_acoso_sexual', 'vs_iniciacion_sexual_forzada_inducida',\n",
       "       'vs_otra_forma_violencia_sexual', 'vs_no_sabe_no_contesta',\n",
       "       'ofv_sentimiento_amenaza', 'ofv_amenaza_explicita',\n",
       "       'ofv_violencia_fisica', 'ofv_enganio_seduccion', 'ofv_grooming',\n",
       "       'ofv_otra_forma_violencia', 'ofv_no_sabe_no_contesta',\n",
       "       'agresor_fam_no_fam', 'genero_agresor', 'agresor_conocido_no_conocido',\n",
       "       'tipo_vinculo_llamante', 'fin_de_semana', 'momento_dia',\n",
       "       'estacion_del_año', 'vs_explotacion_sexual_group', 'vs_violacion_group',\n",
       "       'vs_tentativa_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llamados_v5.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>llamado_fecha_hora</th>\n",
       "      <th>llamante_edad</th>\n",
       "      <th>victima_edad</th>\n",
       "      <th>dummy_Buenos Aires</th>\n",
       "      <th>dummy_CABA</th>\n",
       "      <th>dummy_Catamarca</th>\n",
       "      <th>dummy_Chaco</th>\n",
       "      <th>dummy_Chubut</th>\n",
       "      <th>dummy_Corrientes</th>\n",
       "      <th>dummy_Córdoba</th>\n",
       "      <th>...</th>\n",
       "      <th>dummy_NO</th>\n",
       "      <th>dummy_SI</th>\n",
       "      <th>dummy_NO</th>\n",
       "      <th>dummy_SI</th>\n",
       "      <th>dummy_NO</th>\n",
       "      <th>dummy_SI</th>\n",
       "      <th>dummy_NO</th>\n",
       "      <th>dummy_SI</th>\n",
       "      <th>dummy_NO</th>\n",
       "      <th>dummy_SI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-19 12:46:38</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-19 13:46:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-19 18:20:08</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-20 00:05:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-20 00:05:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   llamado_fecha_hora  llamante_edad  victima_edad  dummy_Buenos Aires  \\\n",
       "0 2016-11-19 12:46:38           30.0          12.0                   0   \n",
       "1 2016-11-19 13:46:24            NaN          11.0                   0   \n",
       "2 2016-11-19 18:20:08           47.0          47.0                   0   \n",
       "3 2016-11-20 00:05:55            NaN          11.0                   0   \n",
       "4 2016-11-20 00:05:55            NaN          12.0                   0   \n",
       "\n",
       "   dummy_CABA  dummy_Catamarca  dummy_Chaco  dummy_Chubut  dummy_Corrientes  \\\n",
       "0           0                0            0             0                 0   \n",
       "1           1                0            0             0                 0   \n",
       "2           0                0            0             0                 0   \n",
       "3           0                0            0             0                 0   \n",
       "4           0                0            0             0                 0   \n",
       "\n",
       "   dummy_Córdoba  ...  dummy_NO  dummy_SI  dummy_NO  dummy_SI  dummy_NO  \\\n",
       "0              0  ...         1         0         1         0         1   \n",
       "1              0  ...         1         0         1         0         1   \n",
       "2              0  ...         1         0         1         0         1   \n",
       "3              0  ...         1         0         1         0         1   \n",
       "4              0  ...         1         0         1         0         1   \n",
       "\n",
       "   dummy_SI  dummy_NO  dummy_SI  dummy_NO  dummy_SI  \n",
       "0         0         1         0         1         0  \n",
       "1         0         0         1         1         0  \n",
       "2         0         1         0         1         0  \n",
       "3         0         1         0         1         0  \n",
       "4         0         1         0         1         0  \n",
       "\n",
       "[5 rows x 182 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llamados_dummies = pd.get_dummies(llamados_v2,prefix='dummy', dtype=int)\n",
    "llamados_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- correlacion entre numerica y binaria: point biserial -> edad v, edad ll, fecha_hora contra victima convive si/no\n",
    "- correlacion entre binaria y binaria: phi coefficient -> violencia (todas), fin de semana,  resguardo contra convive\n",
    "- correlacion entre categorica y categorica: chi2 -> provincia, ll genero, ll vinculo, caso judic, lugar, v genero, v nacionalidad, vic vinculo agr, vic discapacidad contra convive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "llamados_v2= pd.read_excel(os.path.join(dataset_dir, 'xlsx/llamados_v2.xlsx'), parse_dates=['llamado_fecha_hora'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fecha hora to timestamp\n",
    "\n",
    "llamados_v2['llamado_fecha_hora'] = llamados_v2['llamado_fecha_hora'].apply(lambda x: x.timestamp())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "completo_edades = llamados_v2[~(llamados_v2['victima_edad'].isnull()) & ~(llamados_v2['llamante_edad'].isnull()) & ~(llamados_v2['victima_convive_agresor'] == 'NS/NC')]\n",
    "\n",
    "\n",
    "del llamados_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gm/4r6lhs5j5p12g3_ml4zjfy500000gq/T/ipykernel_12585/2823055894.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  completo_edades['victima_convive_agresor'] = completo_edades['victima_convive_agresor'].replace({'NO': 0, 'SI': 1})\n"
     ]
    }
   ],
   "source": [
    "# Map target to binary values\n",
    "completo_edades['victima_convive_agresor'] = completo_edades['victima_convive_agresor'].map({'SI': 1, 'NO': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk Test: Statistics=0.886, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gm/4r6lhs5j5p12g3_ml4zjfy500000gq/T/ipykernel_12585/1608486622.py:2: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 8936.\n",
      "  stat, p = shapiro(completo_edades['victima_edad'])\n"
     ]
    }
   ],
   "source": [
    "# Check normality of continuous variable\n",
    "stat, p = shapiro(completo_edades['victima_edad'])\n",
    "print('Shapiro-Wilk Test: Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk Test: Statistics=0.974, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gm/4r6lhs5j5p12g3_ml4zjfy500000gq/T/ipykernel_12585/3329373257.py:2: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 8936.\n",
      "  stat, p = shapiro(completo_edades['llamante_edad'])\n"
     ]
    }
   ],
   "source": [
    "# Check normality of continuous variable\n",
    "stat, p = shapiro(completo_edades['llamante_edad'])\n",
    "print('Shapiro-Wilk Test: Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual inspection\n",
    "sns.histplot(completo_edades['victima_edad'], kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual inspection\n",
    "sns.histplot(completo_edades['llamante_edad'], kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# point biserial correlation\n",
    "from scipy.stats import  pointbiserialr\n",
    "\n",
    "numerical_variables = ['llamante_edad','victima_edad']\n",
    "\n",
    "# Calculate Point-Biserial correlation for numerical and binary categorical variables\n",
    "point_biserial_results = {}\n",
    "for variable in numerical_variables:\n",
    "        correlation, p = pointbiserialr(completo_edades[variable], completo_edades['victima_convive_agresor'])\n",
    "        point_biserial_results[variable] = {'Correlation': correlation, 'p-value': p}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             llamante_edad  victima_edad\n",
      "Correlation         0.0377       -0.2005\n",
      "p-value             0.0004        0.0000\n"
     ]
    }
   ],
   "source": [
    "# Convert results to DataFrame\n",
    "results_df = pd.DataFrame(point_biserial_results)\n",
    "print(results_df.round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['vs_amenazas_verbales_contenido_sexual', 'vs_existencia_facilitador_corrupcion_nnya', 'vs_eyaculacion_partes_cuerpo', 'ofv_amenaza_muerte', 'ofv_uso_sustancias_psicoactivas', 'ofv_intento_privacion_libertad', 'ofv_privacion_libertad', 'ofv_uso_animal_victimizar', 'ofv_uso_arma_group', 'ofv_intento_violencia_fisica_group']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "#from scripts.herramientas import seteo_agrupador,tipo_vinculo_llamante,conocido_no_conocido,genero_agresor,fam_nofam,tipo_hecho_lugar,provincias_red\n",
    "\n",
    "\n",
    "dataset_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(''))), 'datasets')\n",
    "\n",
    "llamados = pd.read_excel(os.path.join(dataset_dir, 'xlsx/llamados_v4.xlsx'))\n",
    "\n",
    "\n",
    "\n",
    "columnas_pocos_si = []\n",
    "for i in llamados:\n",
    "    if (llamados[i]== 'SI').sum() < 191:\n",
    "        columnas_pocos_si.append(i)\n",
    "    \n",
    "vs = list(llamados.loc[:, llamados.columns.str.startswith('vs')].columns)\n",
    "ofv = list(llamados.loc[:, llamados.columns.str.startswith('ofv')].columns)\n",
    "borradas = []\n",
    "for i in vs:\n",
    "    if i in columnas_pocos_si:\n",
    "        borradas.append(i)\n",
    "\n",
    "for i in ofv:\n",
    "    if i in columnas_pocos_si:\n",
    "        borradas.append(i)\n",
    "\n",
    "print(borradas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19143, 48)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llamados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9977537481063574"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(191*100)/19143"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}