Centerville_preABM_xh_230402.ipynb

{
 "cells": [
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "1304ba2b",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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U+8u+fr3oWAb6Vft/iSkWy2v/n9aRJ9/EFIvF9O1PL7+3Ttu+r3/5uQIA4PXweidROLuHa/romycXHQUAAK89/rcvF+Tq5001P3+1fWT/s6nP3nMc+Oe+8p+s6VH5H3ry+VVdfbXdAwAw1sZmJeqXv+cd21bfyrvW8qvr/ZhintUYv+21zmfyf7c3wJzbhz9962ivt01nf9LevovZn/+lL15vf86+XNtxfdtvzrbz2v9n/1bhUB+/o3ecP3fG5Tt/vf765uEsfQIAMGbGI4l6uKYbXzxyHCjphiP5ePJNTB+53pdUvhG+Pujhmj76c8lx4JHWCp22ftX+Xz7S2kPHu1/ccP08yKMvPtKNsruvu53kpa/tR1r75K5+CGir9GdPPdQna3qka7qbX9BbnZN++tY+7vqkbnQTpLe0kL+ra5IefXHXStoK1vnXvl7Twh9HGxcAAONmbLbzeltXT/Rt7IZKD9f0t58W9Nm/7et+ecg57wW3O7S/n75V7M8l6eEv+kXSW//8h354KEnXdPfvBSvJ6JwzWsu63/xMV2UlfzfK0qP//kXSW1Jg248Gtuj2SGuffKt3mp/pqn7V/v+148rcV/Nza4PP6veR1v7riRY+vyr9cUFrX/+gR1880tondl8f39XaJ28F9IGL9PPPP190CABegcuXL190CPAYjyTq47v6UzcRuqoPMlKpLP2/X36V9Iu1ypK570iWrupPX19T6YtHKh0+0WfvnbU6KKsPOm2994GyKqmbIv3/Tn83eqs07/1Jdz8ujbYalfmgW6t0dS4rlR3J1xnb7quJ6iSPKukfP32mq+/9ol8eWuO5/3lvDq7+x11dK7trp976ZE13Hz2y+8nq/l8dq1n4l8JftABwPsZjO+8lshIvyy//HbDC460peomu/furalnqJJiSe5yj6SRcklTSP17BoxkAAHidjEcS9XBNf+te1J/oH/b23f9+5y3p397RNUkq33AUjT/R3+waqeycexXK2jpzt3Mm3f7u94quf/rbyDVRr7Ztz9zoHb3zsSSVdMNRaP/kv+waKceq2JNvbsi5IVn6c3/xPgAAk2Q8tvNkFVG7qo66W3wLupFZ06PyoHOkd/79mqRHVsF5mOSp448f6KOPpUcPH2ntk5jWfkdTo7V9Tdc+lh75JFJ94+3qbEe+pYX/k9Xaw5LPuK/p7n/YKdRP39rF7la9lr6J6Ua5pBvffNCtowIAYNKMx0rUx3d1/+trrp9/cNTsXP28qR+c70tWIbXjnLc+Keh+ZkCbI3tLC3/9QXc/7h259vV918/hedu+prt/X9NHZ2qjV7guSXrvMzX/bt195z7HLlzXE31rF8Vn/9P63NXP7Rhcq3sAAEyWN05PT0/Pq7MXL16cV1fjqfOwTDmKxrvHHHfsYaJdunTpokMAgIkwNtt5E6G7nee3NfmRPiCBAgDg3IzHdt7EeEsLf72vrPewZ/sSAAC8emznAWOG7TwAOB+sRAEAAIRAEgUAABACSRQAAEAIJFEARtLYiioa7bwyqp5cdETnqFlyjD2qUjP4PddrqzGg0baqtwaf297NDJnzhkqB/Xnec71K8o3spKpMXz+eOO1XZrcdfM6tqtqept2/P545HHE++trrO2d4HNYYg8bhF6vPXLm+8wn7swAXkigAQzW2okpvOI/UlJufkItHs6ToUkH5HVOmaco0K9KSIwmIZe3jjtdOXpKh4qfxgEbbqt5KKHelYn+mruLTtOuC3t7NKLEiFQ/sNg8WtO+a84ZK0bQKq5VuvxWlHYlFXFlvXGZFeUnG5nX1R9ZW9aucan3HW2rtyTF+61VejHTPaGwllFNR9c5YlFPCkeA0tqJKP+28b8o8KOrIOYcjzIf3O3H/Po4Wh5olRedzmt3pxaGVhCcOd6yV1YLSzmSsWVJ06aj3vezMKjcfkJRi7JFEARiiocMNSTLsC0ddxaQk1bT/Y8BFbow0Hhek1Yqysc6RuLI7eRW+81nlsD6h0lJBxuYdpaYCGj051P5eXpXbnVQmotTNvGq1Q7vNtg5rNXcbUynd2ZRy39uX6+ahCnK2IcVvV5Tf2A5MbhtbaRWSRd1xJEAd7d115WTI6Iv1WEcyNPN20Fiq2t4wVPwypUhnLF8WZXTjsH5/8jc771tjWV6VCo8bI86HaxQqLR3JSPYfd/fjjaOt6nee79IbR/OBcnvOsUjxT4sy9vZ1aM9p47Hnu41dVzFZ0GHfyhomAQ/bBDBY81AFSUouaG5KkiKaMwxpr3/NYvy0dfxUMowZ9+G3Z2TstdSS5E1HOolK3SdR6ZpKqWz6HO+2aa3+zN50txGZnpVqx2orLh0fSckFuSOb0UyyptZzSd4ErllSesNQ8SDVF7OaJSVWZlU5mNH23r77vect1TSr5aCE0O/9qTktJHN2HNaK2EBD56OjreqttI4261p+VvNZNRvEXlG76V6Di9821em6bc/pnGssKZXNlOt8v/+96tFxW4oN+M4xlliJAjBYZ7vqXufia62SSIYW3h/3i0ZE01ek2rOW+/Dzlmo60rF3xeekqm3vqstIOqtXnW22Gc0k7QuzQ/v4qJdYTM92/7vHShS8n+utwiz7rI5Zfed3sj5bfHafKigdUEdkJR4znmTO0h9Hr8/DDcl41+9TvZi8247Wapn/SpoU19yqXCuEje9zqnWSou6KmrtWzDmW1rOadGVactWiDd6qa++uK7eX1/KgpBljiyQKwJk0thLK7Snggjx+4h/mpY20u27mu4Lvue0f91VLFnU95vu23yfsQui0CnJeiK3VvtrKuqsG6sGKY+0lNqe8Cko7an7au9vyjezkUPt7/jVajS2rriobEHPrWU1SXhVHXdXsSiK4XmkoazXJf1sxaD4knVS1viLXVptX/LapypWcEnYClFbFkfxLVi3foeZMd02Uaywbaa3rTq/ObLWg9IDi8sRKLaDGDJOAJArAyLoF5smi6rcn5LIRy8rcyauw1FmZWJduFvtrhzp1TMbcGVahIkrd6xQoS2nHnV6RxbLqm1JuvtPvoeZ28o7PxpU1K8pvpLurJutatuvVPJH9uN9bkXFqlpTecNdVecVvmzJN5ypVXNc3DdVWHoQoprYLyPfyqtzzS4aC5sMqetegOjP7bsTtd+vdBKj+7rbn7jlDxQPHWKZSuuMdy2rFVTQfv11RXgVte5PG7g0FdS3UEkPvJsR4IokCMBJXAuV7AXzNeW59d90e77oDr6yUrDqgaecF3V7tCb3FGbuuYtJdrB9ZLDv6zWqmb+vMfQdeeVFWLdW0M4bg5K7xuCDXVt18TjXVlJv3v/W/G9f0rPu/+7YVLd44ugmU6b91GDgfJ4fa35NqK4nu95PekLSR7m63tXe3+1a3Iot3+uZ00Fj8WVurwawieG0ccofeBCKJAjBUezdj31IetIIwBqZSKjsfB2CPs72b6XvekHXH3pw7EXjuk1gFaZaG1tr0PwfJkwz5PdPJvmNvzrU11/JJrCzWKpPjdVCUYd+Faa3GWNtr3oTKVQf19owMb32YnVD27uizEygVVfdLoIbNh/e7MU1VViWtVjyrZANMzWmhU3QfMJb4h3np6bHnjkDn/PnPByYXSRSAwU6qWu/W4gQXGI+ryPsLMvZyetCpierc5eapLxpUYN3Ht57JXaBs1WL1HlfQV8BsJwXdRx4EFGMPfUTBQJ3aLMd2l/370C2en0ppebWm3FfV7uMZql/lVHPUzHWf3xSUgI8wH0MjXVxWfi+nddezttaV664OWitGhaVS31i6iWnsuopyt9HYSjsSU/9aNd95x0TgEQcABmr/uH/GW8nHzFRK5Z2WoktRu2jbUPGg3FebY93ZteyfJJxUlZnf10L3c1Y9k6JpRbsPjfRsc8Wyqm9mlJiPKuf3viJK3auo5WjD2Ky76nkkDX9EwRCRxbLqyigRjXaP5XdMVyF6/HZdxVsJJaJWpK6aOfuORSnXe7+juzU8wnwMZbVRiiYUXQloI5aVuWMVhHe45yyi1L26dCu4je58dL+XgHnHRHjj9PT09Lw6e/HixXl1BUysS5cuXXQIADAR2M4DAAAIgSQKAAAgBJIoAACAEEiiAAAAQiCJAgAACIEkCgAAIASSKAAAgBBIogAAAEIgiQIAAAiBJAoAACAEkigAAIAQSKIAAABCePOiAwDwcv38888XHQKAV+Dy5csXHQI8SKKAMcNftABwPtjOAwAACIEkCgAAIASSKAAAgBBIogAAAEIgiQIAAAiBJAoAACAEkigAAIAQSKIAAABCIIkCAAAIgSQKAAAgBJIoAACAEEiiAAAAQiCJAjCChkrRqKKd11bjogN6dZql3jijGVVPBr3feZU0cEZOqso4z79VVXvIOZldzxmj9Os5p9QMEcew8Y/wu9DYigbG0d7N+IzDZ8zD4hhlLN6YvLG+jDnti8lvzjCuSKIADNFW9VZaBeehjfR4JlLNkqJLRyoemDJNU+bOrHLz7otq+/hIShZVN+1zTFOmmVU8qM2TqjLzOc3u9M6vXMkp4bzoN0uKOs85KEorCXfyMazfk6oySwXlO23s5FVYclzQR41j4PgbKkXTKqxWem3I/bvQ2Ioq/dQR50FRR0u95COyWHbEb73qm4akvJYXI6PFMcpYPN9reqP/8O+eU3drqn6VU83vLYwtkigAg50can9PkvKq2BdFQ5I2DgevvryGGo8LMjbvKDVlH4hdVzFZ0KEjmWk9q0lXphUZsc32j/uqJYu6Husdi39alLG3r8MTSWqr+l1BWq0o2zlnKqXlVanwuDfDw/rt6yeWVWW1ptz3jRHjGGH8zUMVlFfldi9ljN+uKL+xbScWDR1uSPmbqV6cPmNxOalqfaWm/E4veRkWxyhj6WmotHQkI9nf9bA5bXyfU835vXjm1Km9u66cDOvPBibGmxcdAIB/cVMplc1U//HkjGbOP5pXKn7bVNnn+NFxW4pFJLV1/FQyjNFHHlksy1wcdEZLrT0pf9O9lhW/bcrs/nT2fs8ex/DxWys3C57vfUYzyZpazyVNxZU1TZ8WgtirN6sVlZ0J0ZA4RhlLt/1baR1t1rX8rOZZJRo2p/7vxz/MS98dq614L/lqlpRYmVXlYEbbe/ujBIYxwUoUgJG1dzOKzudUk6Hil6mRV2NeV+3ddeX2HNtMdsJTW0mMXg/l1+6P+6ppVtNTkk6OdSRDM2+7a43cNVHD+428vyBjL6cHnVWjk6q2N6T8h4Ebje44Rhh/ZHpW2mup5TrLiu3oOKgiyVqdMt71SVaaD5TbM1T8NDhGvzhGHYu1OlTUHd/PDZtT6/3ZaZ/PuuagodJSwbWShslBEgVgZK1nnX/L+29pjA27mDixUpOxed1RI3OsI0nGZt1Rz3Ok9FkSqWZJCc/2lVRTbv5Qc6a7JqqbSI3S71RKZbv+KBqNKjq/r4UDs7cVNVIcQ8Yfm1NeBaUdNVDt3W13vZyLXU+X9E9kGo8L0upyb9tu1DhGGctJVesrCk72X8Z3KamxZdWIBc4zxhpJFICRxW+bMs26iklJG+nBdyq9zmJZ+8Ja10It0SucnkqpbJoqOxKCyOKy8ipo23s3nZ9mSdGlgozNuueia6h44EgAplK6s2motvLAuqCP0K+1StjScrdAelmt+YA7KQPjGDJ+xZU1K8pvpLurN+tatn4f+rRVvZVQbi+vyj2/RMaunxqwUhYcx7CxWNuEctZVef3e79LuO73hrhHDZCGJAjBQ95b07gUsojnDKp8N3sIZFxGlbuaHFNHPaMY3ifBwXOzLA7aluj1Pzw45w9lvQw/6VpXiyu74xH6mOPzGb9U9dVZvyovy2fZyJFBBdy7aRepzI63gBHwPQWOxb4ZwbtWlN2TdVTpwpck5p9Z/+/6O2/WAjccFSQWlO9uB8znVVFNu3ucRFRhLJFEABupezLsXsLYOa9a2nm+9yGurreqtIRe/ZsnnIjygdsb5OftW+b7EZWpOC53CbDrGCLAAAAI9SURBVGc0x0e94v2w/Z4ljlHG7/ccpL5kyE6gVFR9wKMfXOM7axzDxmKvMjkfo1BZlbRa6T3CYOicRjR9Rao9c1eANR4Xunf0WSuzjtdBUYYMFQ/85hfjiCQKwGCx6/Z2Tedf3Anl9iR5bjF//VkrbLWVdUeSYBUNd+tx7Jog53ZPY8uq+QmcC8ezhvzrZqxVlsKS+zlI6ys1GcactQ02tN+4rm8a7jbs2LU6Z8U+QhxDx28nfL16OM/7khpbdgLlu4XXE/x4gRHiGDqWEYzwXcY/Lcpwbls3S0pvDC+Ex+R44/T09PS8Onvx4sV5dQVMrEuXLr2CVjvbM/aPyeEXyddVezejxErvZvj+bS/7gZOdH71z0SwpuqTuNlZjK+r7oEdJ7iTAXlkJ3e+Q2EeN46zjd71vPwTT94GTrnit36d9I3hL8WWMxamxFVVaFZmu+qXhc+r9XgYmbidVZeb3tXBQDq7FwlghiQLGzKtJogAAXmznAQAAhEASBQAAEAJJFAAAQAgkUQAAACGQRAEAAIRAEgUAABACSRQAAEAIJFEAAAAhkEQBAACEQBIFAAAQAkkUAABACCRRAAAAIbx5np399ttv59kdMJH4cwaMp8uXL190CPB44/T09PSigwAAAHjdsJ0HAAAQAkkUAABACCRRAAAAIZBEAQAAhEASBQAAEAJJFAAAQAgkUQAAACGQRAEAAIRAEgUAABACSRQAAEAIJFEAAAAhkEQBAACEQBIFAAAQAkkUAABACP8DrNbz51Ovf28AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "ff41b0f9",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5929b41",
   "metadata": {},
   "source": [
    "In anylogic, x in [0,1288] y in [0,805]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c6f713f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "599904a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_epn_xh_mo = pd.read_excel(r'C:\\Users\\Xu Han\\Anylogic_xh\\Centerville_with variable center_2021_07_15\\Centerville_with variable center_2021_03_23\\database\\Electnode.xlsx',sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7a082644",
   "metadata": {},
   "outputs": [
    {
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       "      node_num           x           y type  critical_ds  serve_industry  \\\n",
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  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a1434504",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_epn_xh_mo['Lon']=-97.51386932905343+centerville_epn_xh_mo['x']/1288*(97.51386932905343-97.39974502348663)\n",
    "centerville_epn_xh_mo['Lat']=35.265823927324504-centerville_epn_xh_mo['y']/805*(35.265823927324504-35.19587226475656)"
   ]
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  {
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   "execution_count": 67,
   "id": "c8e5e04d",
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   "cell_type": "code",
   "execution_count": 68,
   "id": "10e522bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_epn_xh_mo['coordinates']=centerville_epn_xh_mo[['Lat','Lon']].values.tolist()"
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  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "15f063d9",
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       "   node_num           x           y type  critical_ds  serve_industry  \\\n",
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     "execution_count": 69,
     "metadata": {},
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b323f5b5",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.474636</td>\n",
       "      <td>35.196781</td>\n",
       "      <td>POINT (35.19678086313577 -97.47463623269599)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>494.749267</td>\n",
       "      <td>793.524939</td>\n",
       "      <td>TS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.470032</td>\n",
       "      <td>35.196869</td>\n",
       "      <td>POINT (35.19686940709705 -97.47003166091196)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_num           x           y type  critical_ds  serve_industry  \\\n",
       "0         1  529.393880   25.229716  EPP            0               0   \n",
       "1         2  530.412839   67.007043   TS            0               0   \n",
       "2         3  444.820267   67.007043   DS            0               0   \n",
       "3         4  442.782349  794.543898   DS            0               0   \n",
       "4         5  494.749267  793.524939   TS            0               0   \n",
       "\n",
       "   serve_retail  demand  government  network        Lon        Lat  \\\n",
       "0             0       0           0        1 -97.466962  35.263632   \n",
       "1             0       0           0        1 -97.466872  35.260001   \n",
       "2             0       0           0        1 -97.474456  35.260001   \n",
       "3             0       0           0        1 -97.474636  35.196781   \n",
       "4             0       0           0        1 -97.470032  35.196869   \n",
       "\n",
       "                                     coordinates  \n",
       "0  POINT (35.263631553899096 -97.46696194638928)  \n",
       "1   POINT (35.26000125148678 -97.46687166066802)  \n",
       "2   POINT (35.26000125148678 -97.47445566125347)  \n",
       "3   POINT (35.19678086313577 -97.47463623269599)  \n",
       "4   POINT (35.19686940709705 -97.47003166091196)  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import Point\n",
    "centerville_epn_xh_mo['coordinates']=centerville_epn_xh_mo['coordinates'].apply(Point)\n",
    "centerville_epn_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a56c6185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gpd_centerville_epn_xh_mo=gpd.GeoDataFrame(centerville_epn_xh_mo,crs='epsg:4326', geometry='coordinates')\n",
    "gpd_centerville_epn_xh_mo=gpd.GeoDataFrame(centerville_epn_xh_mo, crs='epsg:4326',geometry='coordinates')\n",
    "type(gpd_centerville_epn_xh_mo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c04bf122",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gpd_centerville_epn_xh_mo = gpd.GeoDataFrame(centerville_epn_xh_mo, crs='epsg:4326', geometry=gpd.points_from_xy(centerville_epn_xh_mo.Lon, centerville_epn_xh_mo.Lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "227f6f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>node_num</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>type</th>\n",
       "      <th>critical_ds</th>\n",
       "      <th>serve_industry</th>\n",
       "      <th>serve_retail</th>\n",
       "      <th>demand</th>\n",
       "      <th>government</th>\n",
       "      <th>network</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "      <th>coordinates</th>\n",
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       "      <td>POINT (35.26000 -97.46687)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>444.820267</td>\n",
       "      <td>67.007043</td>\n",
       "      <td>DS</td>\n",
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       "      <td>-97.474456</td>\n",
       "      <td>35.260001</td>\n",
       "      <td>POINT (35.26000 -97.47446)</td>\n",
       "    </tr>\n",
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       "      <td>POINT (35.19678 -97.47464)</td>\n",
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       "      <td>5</td>\n",
       "      <td>494.749267</td>\n",
       "      <td>793.524939</td>\n",
       "      <td>TS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.470032</td>\n",
       "      <td>35.196869</td>\n",
       "      <td>POINT (35.19687 -97.47003)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_num           x           y type  critical_ds  serve_industry  \\\n",
       "0         1  529.393880   25.229716  EPP            0               0   \n",
       "1         2  530.412839   67.007043   TS            0               0   \n",
       "2         3  444.820267   67.007043   DS            0               0   \n",
       "3         4  442.782349  794.543898   DS            0               0   \n",
       "4         5  494.749267  793.524939   TS            0               0   \n",
       "\n",
       "   serve_retail  demand  government  network        Lon        Lat  \\\n",
       "0             0       0           0        1 -97.466962  35.263632   \n",
       "1             0       0           0        1 -97.466872  35.260001   \n",
       "2             0       0           0        1 -97.474456  35.260001   \n",
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       "\n",
       "                  coordinates  \n",
       "0  POINT (35.26363 -97.46696)  \n",
       "1  POINT (35.26000 -97.46687)  \n",
       "2  POINT (35.26000 -97.47446)  \n",
       "3  POINT (35.19678 -97.47464)  \n",
       "4  POINT (35.19687 -97.47003)  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpd_centerville_epn_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "11381716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gpd_centerville_epn_xh_mo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "725efe27",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd_centerville_epn_xh_mo.to_file(\"gpd_centerville_epn_xh_mo.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6ba6af7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyincore import GeoUtil\n",
    "\n",
    "GeoUtil.add_guid(\"gpd_centerville_epn_xh_mo.shp\", \"gpd_centerville_epn_node_xh_mo_r1.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "701c7467",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyincore import Dataset\n",
    "dataset_centerville_epn_node_xh_mo_r1 = Dataset.from_file(\"gpd_centerville_epn_node_xh_mo_r1.shp\", data_type=\"incore:epnNodeVer1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "59525586",
   "metadata": {},
   "outputs": [
    {
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       "      <td>494.749267</td>\n",
       "      <td>793.524939</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   node_num           x           y type  critical_d  serve_indu  serve_reta  \\\n",
       "0         1  529.393880   25.229716  EPP           0           0           0   \n",
       "1         2  530.412839   67.007043   TS           0           0           0   \n",
       "2         3  444.820267   67.007043   DS           0           0           0   \n",
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       "4         5  494.749267  793.524939   TS           0           0           0   \n",
       "\n",
       "   demand  government  network        Lon        Lat  \\\n",
       "0       0           0        1 -97.466962  35.263632   \n",
       "1       0           0        1 -97.466872  35.260001   \n",
       "2       0           0        1 -97.474456  35.260001   \n",
       "3       0           0        1 -97.474636  35.196781   \n",
       "4       0           0        1 -97.470032  35.196869   \n",
       "\n",
       "                                   guid                    geometry  \n",
       "0  ee3b6c78-7c86-4611-aaf7-9846ccedde89  POINT (35.26363 -97.46696)  \n",
       "1  62629c59-e584-48f3-a727-647a8e803ed3  POINT (35.26000 -97.46687)  \n",
       "2  754648f3-2109-4538-a126-fd551386c871  POINT (35.26000 -97.47446)  \n",
       "3  c39c20e2-aae3-4f65-9d12-dd448c7a4065  POINT (35.19678 -97.47464)  \n",
       "4  c0d14ae4-f25a-4618-aa8d-538bc92a9c00  POINT (35.19687 -97.47003)  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpd_centerville_epn_node_xh_mo_r1 = dataset_centerville_epn_node_xh_mo_r1.get_dataframe_from_shapefile()\n",
    "gpd_centerville_epn_node_xh_mo_r1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "315411eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_epn_graph_xh_mo = pd.read_excel(r'C:\\Users\\Xu Han\\Anylogic_xh\\Centerville_with variable center_2021_07_15\\Centerville_with variable center_2021_03_23\\database\\Electline.xlsx',sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e4530022",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   line_num  from_db  to_db type     length\n",
       "0         1        0     32   DL  39.683726\n",
       "1         2       32     33   DL  39.683726\n",
       "2         3       33     34   DL  39.683726\n",
       "3         4       34     35   DL  39.683726\n",
       "4         5       35     36   DL  39.683726"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_epn_graph_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0d23863d",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_epn_graph_xh_mo.to_csv('centerville_epn_graph_xh_mo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8bc279d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyincore import NetworkDataset, NetworkUtil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "214fb1e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xu Han\\AppData\\Local\\Temp\\ipykernel_25360\\3253929297.py:6: FionaDeprecationWarning: This function will be removed in version 2.0. Please use CRS.from_epsg() instead.\n",
      "  NetworkUtil.build_link_by_node(node_filename, graph_filename, id_field, out_filename)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create output link shapefile\n",
    "id_field = 'node_num'\n",
    "out_filename = 'centerville_epn_links_xh_mo.shp'\n",
    "node_filename='gpd_centerville_epn_xh_mo_r1.shp'\n",
    "graph_filename='centerville_epn_graph_xh_mo.csv'\n",
    "NetworkUtil.build_link_by_node(node_filename, graph_filename, id_field, out_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1b4bf31a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection successful to IN-CORE services. pyIncore version detected: 1.9.0\n"
     ]
    }
   ],
   "source": [
    "from pyincore import IncoreClient\n",
    "client = IncoreClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1291245f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xu Han\\Learning_python_230115\\files\n"
     ]
    }
   ],
   "source": [
    "from pyincore import DataService\n",
    "datasvc = DataService(client)\n",
    "import os\n",
    "file_dir = os.path.join(os.getcwd(), 'files')\n",
    "print(file_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "91688c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "network dataset is created with id 6429a9beea308903b2b995df\n",
      "attching files to created dataset\n"
     ]
    }
   ],
   "source": [
    "dataset_prop = {\n",
    "  \"title\": \"Centerville epn network\",\n",
    "  \"dataType\": \"incore:epnNetwork\",\n",
    "  \"format\": \"shp-network\",\n",
    "  \"networkDataset\": {\n",
    "    \"link\": {\n",
    "      \"dataType\": \"ergo:powerLineTopo\",\n",
    "      \"fileName\": \"centerville_epn_links_xh_mo.shp\"\n",
    "    },\n",
    "    \"node\": {\n",
    "      \"dataType\": \"incore:epf\",\n",
    "      \"fileName\": \"gpd_centerville_epn_node_xh_mo_r1.shp\"\n",
    "    },\n",
    "    \"graph\": {\n",
    "      \"dataType\": \"incore:networkGraph\",\n",
    "      \"fileName\": \"centerville_epn_graph_xh_mo.csv\"\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\n",
    "response = datasvc.create_dataset(dataset_prop)\n",
    "\n",
    "if 'id' not in response:\n",
    "    print(\"Failed to create a dataset in the data service\")\n",
    "else:\n",
    "    dataset_id = response['id']\n",
    "\n",
    "    print('network dataset is created with id ' + dataset_id)\n",
    "    print('attching files to created dataset')\n",
    "\n",
    "    file_dir = os.path.join(os.getcwd(), 'files')\n",
    "  # file_dir = r'C:\\Users\\Xu Han\\Learning_python_230115'\n",
    "\n",
    "\n",
    "    files = ['centerville_epn_links_xh_mo.shp',\n",
    "             'centerville_epn_links_xh_mo.dbf',\n",
    "             'centerville_epn_links_xh_mo.shx',\n",
    "             'centerville_epn_links_xh_mo.prj',\n",
    "             'gpd_centerville_epn_node_xh_mo_r1.shp',\n",
    "             'gpd_centerville_epn_node_xh_mo_r1.dbf',\n",
    "             'gpd_centerville_epn_node_xh_mo_r1.shx',\n",
    "             'gpd_centerville_epn_node_xh_mo_r1.cpg',\n",
    "             'centerville_epn_graph_xh_mo.csv']\n",
    "    linkname = dataset_prop[\"networkDataset\"][\"link\"][\"fileName\"]\n",
    "    nodename = dataset_prop[\"networkDataset\"][\"node\"][\"fileName\"]\n",
    "    graphname = dataset_prop[\"networkDataset\"][\"graph\"][\"fileName\"]\n",
    "\n",
    "    response = datasvc.add_files_to_network_dataset(dataset_id, files, nodename, linkname, graphname)\n",
    "    dataset_electricity_network_centerville_xh_mo = Dataset.from_data_service(response[\"id\"], data_service=datasvc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c9b6aaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyincore_viz.geoutil import GeoUtil as geoviz\n",
    "from pyincore_viz.plotutil import PlotUtil as plotviz\n",
    "from pyincore import DataService\n",
    "data_service = DataService(client)\n",
    "#epf_id = \"6429a1a6ea308903b2b995d5\"     # electric power poles and substation\n",
    "#epn_network_dataset = Dataset.from_data_service(\"6429a1a6ea308903b2b995d5\", data_service)\n",
    "#epn_network = NetworkDataset.from_dataset(epn_network_dataset)\n",
    "#epf_dataset = epn_network.nodes\n",
    "#epf_df = epf_dataset.get_dataframe_from_shapefile()\n",
    "dataset_try=Dataset.from_file(\"gpd_centerville_epn_node_xh_mo_r1.shp\", data_type=\"incore:epnNodeVer1\")\n",
    "#geoviz.plot_map( dataset_electricity_network_centerville_xh_mo, column=None, category=False)\n",
    "geoviz.plot_map( dataset_try, column=None, category=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "cb451f5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already exists locally. Reading from local cached zip.\n",
      "Unzipped folder found in the local cache. Reading from it...\n"
     ]
    }
   ],
   "source": [
    "epf_id = \"6429a9beea308903b2b995df\"     # electric power poles and substation\n",
    "epf_dataset = Dataset.from_data_service(epf_id, data_service)\n",
    "\n",
    "epf_df = epf_dataset.get_dataframe_from_shapefile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "da35e01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1448</td>\n",
       "      <td>dbf6a9b0-2d9a-462c-9bc2-f73f396cf269</td>\n",
       "      <td>1449</td>\n",
       "      <td>1450</td>\n",
       "      <td>LINESTRING (35.21395 -97.45533, 35.21395 -97.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>1449</td>\n",
       "      <td>827bd1f3-82c7-4a17-be2e-6266d7050e7b</td>\n",
       "      <td>1450</td>\n",
       "      <td>1451</td>\n",
       "      <td>LINESTRING (35.21395 -97.45567, 35.21396 -97.4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1450 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     linkid                                  guid from_node to_node  \\\n",
       "0         0  8ac86db4-cb78-4020-a908-fcde823a5123         1       0   \n",
       "1         1  180c17bd-8069-4cb7-8ec5-09099037868d         2      32   \n",
       "2         2  72e493c8-b5d5-46fb-861d-21213553c807         3      33   \n",
       "3         3  0bc1ba85-6e9c-49b4-a53e-d26a7497ce3c         4      34   \n",
       "4         4  c3c02e6a-b835-4fc2-9096-d772e2012ec0         5      35   \n",
       "...     ...                                   ...       ...     ...   \n",
       "1445   1445  46bc1d6e-1df8-4b2e-a966-93e55943da8b      1446    1447   \n",
       "1446   1446  2ca671ad-095c-44c8-9482-c6b345ef6e11      1447    1448   \n",
       "1447   1447  dee738d5-3e5b-4608-83b1-879dd7b90bd6      1448    1449   \n",
       "1448   1448  dbf6a9b0-2d9a-462c-9bc2-f73f396cf269      1449    1450   \n",
       "1449   1449  827bd1f3-82c7-4a17-be2e-6266d7050e7b      1450    1451   \n",
       "\n",
       "                                               geometry  \n",
       "0     LINESTRING (35.26363 -97.46696, 35.26363 -97.4...  \n",
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       "3     LINESTRING (35.19678 -97.47464, 35.26307 -97.4...  \n",
       "4     LINESTRING (35.19687 -97.47003, 35.26279 -97.4...  \n",
       "...                                                 ...  \n",
       "1445  LINESTRING (35.21395 -97.45433, 35.21395 -97.4...  \n",
       "1446  LINESTRING (35.21395 -97.45467, 35.21395 -97.4...  \n",
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       "1448  LINESTRING (35.21395 -97.45533, 35.21395 -97.4...  \n",
       "1449  LINESTRING (35.21395 -97.45567, 35.21396 -97.4...  \n",
       "\n",
       "[1450 rows x 5 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259c26bc",
   "metadata": {},
   "source": [
    "**Electric Power Facility Damage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e0364d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a fragility curve set with three limit state\n",
    "TSDS_tornado_fragility_curve_LS_0 = {\n",
    "    \"description\": \"Slight\",\n",
    "    \"rules\": [\n",
    "        {\n",
    "            \"condition\": [\n",
    "                \"wind > 0\"\n",
    "            ],\n",
    "            \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(33.4))/(0.2))\"\n",
    "        }\n",
    "    ],\n",
    "    \"returnType\": {\n",
    "        \"type\": \"Limit State\",\n",
    "        \"unit\": \"\",\n",
    "        \"description\": \"LS_0\"\n",
    "    }\n",
    "}\n",
    "TSDS_tornado_fragility_curve_LS_1 = {\n",
    "    \"description\": \"Moderate\",\n",
    "    \"rules\": [\n",
    "        {\n",
    "            \"condition\": [\n",
    "                \"wind > 0\"\n",
    "            ],\n",
    "            \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(39.5))/(0.2))\"\n",
    "        }\n",
    "    ],\n",
    "    \"returnType\": {\n",
    "        \"type\": \"Limit State\",\n",
    "        \"unit\": \"\",\n",
    "        \"description\": \"LS_1\"\n",
    "    }\n",
    "}\n",
    "\n",
    "TSDS_tornado_fragility_curve_LS_2 = {\n",
    "    \"description\": \"Extensive\",\n",
    "    \"rules\": [\n",
    "        {\n",
    "            \"condition\": [\n",
    "                \"wind > 0\"\n",
    "            ],\n",
    "            \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(48.6))/(0.2))\"\n",
    "        }\n",
    "    ],\n",
    "    \"returnType\": {\n",
    "        \"type\": \"Limit State\",\n",
    "        \"unit\": \"\",\n",
    "        \"description\": \"LS_2\"\n",
    "    }\n",
    "}\n",
    "\n",
    "TSDS_tornado_fragility_curve_LS_3 = {\n",
    "    \"description\": \"Complete\",\n",
    "    \"rules\": [\n",
    "        {\n",
    "            \"condition\": [\n",
    "                \"wind > 0\"\n",
    "            ],\n",
    "            \"expression\": \"scipy.stats.norm.cdf((math.log(wind) - math.log(60.8))/(0.2))\"\n",
    "        }\n",
    "    ],\n",
    "    \"returnType\": {\n",
    "        \"type\": \"Limit State\",\n",
    "        \"unit\": \"\",\n",
    "        \"description\": \"LS_3\"\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4f49de3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# place three curves into a set with extra metadata\n",
    "TSDS_metadata = {\n",
    "    \"id\":\"TSDS_tornado_fragility_curve_set\",\n",
    "    \"description\": \"TSDS fragilities\",\n",
    "    \"demandTypes\": [\"wind\"],\n",
    "    \"demandUnits\": [\"mph\"],\n",
    "    \"resultType\":\"Limit State\",\n",
    "    \"hazardType\":\"tornado\",\n",
    "    \"inventoryType\":\"electric_facility\",\n",
    "    \"fragilityCurves\":[\n",
    "        TSDS_tornado_fragility_curve_LS_0,\n",
    "        TSDS_tornado_fragility_curve_LS_1,\n",
    "        TSDS_tornado_fragility_curve_LS_2,\n",
    "        TSDS_tornado_fragility_curve_LS_3\n",
    "    ],\n",
    "    \"curveParameters\": [\n",
    "        {\n",
    "            \"name\": \"wind\",\n",
    "            \"unit\": \"mph\",\n",
    "            \"description\": \"wind value from hazard service\",\n",
    "            \"fullName\": \"wind\",\n",
    "        },        \n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "9edf1d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyincore.models.fragilitycurveset.FragilityCurveSet at 0x14870da9550>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct the fragility curve object to use\n",
    "from pyincore import FragilityCurveSet\n",
    "tornado_fragility_curve_set_TSDS = FragilityCurveSet(TSDS_metadata)\n",
    "tornado_fragility_curve_set_TSDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f8642010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a fragility curve set with three limit state\n",
    "Tow_tornado_fragility_curve_LS_0 = {\n",
    "    \"description\": \"Transmission Towers\",\n",
    "    \"rules\": [\n",
    "        {\n",
    "            \"condition\": [\n",
    "                \"wind > 0\"\n",
    "            ],\n",
    "            \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(60.8))/(0.12))\"\n",
    "        }\n",
    "    ],\n",
    "    \"returnType\": {\n",
    "        \"type\": \"Limit State\",\n",
    "        \"unit\": \"\",\n",
    "        \"description\": \"LS_0\"\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "da09ffb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# place three curves into a set with extra metadata\n",
    "Tow_metadata = {\n",
    "    \"id\":\"Tow_tornado_fragility_curve_set\",\n",
    "    \"description\": \"Tow fragilities\",\n",
    "    \"demandTypes\": [\"wind\"],\n",
    "    \"demandUnits\": [\"mph\"],\n",
    "    \"resultType\":\"Limit State\",\n",
    "    \"hazardType\":\"tornado\",\n",
    "    \"inventoryType\":\"electric_facility\",\n",
    "    \"fragilityCurves\":[\n",
    "        Tow_tornado_fragility_curve_LS_0,\n",
    "        ],\n",
    "    \"curveParameters\": [\n",
    "        {\n",
    "            \"name\": \"wind\",\n",
    "            \"unit\": \"mph\",\n",
    "            \"description\": \"wind value from hazard service\",\n",
    "            \"fullName\": \"wind\",\n",
    "        },        \n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "17a6e578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyincore.models.fragilitycurveset.FragilityCurveSet at 0x14870da20d0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_fragility_curve_set_Tow = FragilityCurveSet(Tow_metadata)\n",
    "tornado_fragility_curve_set_Tow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "cbeaf1dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a fragility curve set with three limit state\n",
    "Pol_tornado_fragility_curve_LS_0 = {\n",
    "    \"description\": \"Distribution Poles\",\n",
    "    \"rules\": [\n",
    "        {\n",
    "            \"condition\": [\n",
    "                \"wind > 0\"\n",
    "            ],\n",
    "            \"expression\": \"scipy.stats.norm.cdf((math.log(wind) - 3.95)/(0.149))\"\n",
    "        }\n",
    "    ],\n",
    "    \"returnType\": {\n",
    "        \"type\": \"Limit State\",\n",
    "        \"unit\": \"\",\n",
    "        \"description\": \"LS_0\"\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "2f345568",
   "metadata": {},
   "outputs": [],
   "source": [
    "# place three curves into a set with extra metadata\n",
    "Pol_metadata = {\n",
    "    \"id\":\"Pol_tornado_fragility_curve_set\",\n",
    "    \"description\": \"Pol fragilities\",\n",
    "    \"demandTypes\": [\"wind\"],\n",
    "    \"demandUnits\": [\"mph\"],\n",
    "    \"resultType\":\"Limit State\",\n",
    "    \"hazardType\":\"tornado\",\n",
    "    \"inventoryType\":\"electric_facility\",\n",
    "    \"fragilityCurves\":[\n",
    "        Pol_tornado_fragility_curve_LS_0,\n",
    "        ],\n",
    "    \"curveParameters\": [\n",
    "        {\n",
    "            \"name\": \"wind\",\n",
    "            \"unit\": \"mph\",\n",
    "            \"description\": \"wind value from hazard service\",\n",
    "            \"fullName\": \"wind\",\n",
    "        },        \n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5f0fec0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyincore.models.fragilitycurveset.FragilityCurveSet at 0x14870d0e190>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_fragility_curve_set_Pol = FragilityCurveSet(Pol_metadata)\n",
    "tornado_fragility_curve_set_Pol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1df9357b",
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities = tornado_fragility_curve_set_Pol.calculate_limit_state(hazard_values={\"wind\": 100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "526855b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LS_0': 0.999994515, 'LS_1': 0.0, 'LS_2': 0.0}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "025fbea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyincore import Mapping, MappingSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ae7c4866",
   "metadata": {},
   "outputs": [],
   "source": [
    "                # for new format rule matching {\"AND/OR\":[]}\n",
    "                # {\"AND\": [xx, \"OR\": [yy, yy], \"AND\": {\"OR\":[\"zz\", \"zz\"]]}\n",
    "\"\"\"    \n",
    "tornado_entry_TSDS = {\"Non-Retrofit Fragility ID Code\": tornado_fragility_curve_set_TSDS}\n",
    "rules_TSDS = {\"OR\":[\"java.lang.String type EQUALS DL\",\"java.lang.String type EQUALS TL\"]}\n",
    "mapping_TSDS = Mapping(tornado_entry_TSDS, rules_TSDS)\n",
    "\"\"\"\n",
    "\n",
    "tornado_entry_TS = {\"Non-Retrofit Fragility ID Code\": tornado_fragility_curve_set_TSDS}\n",
    "rules_TS = [[\"java.lang.String type EQUALS TS\"]]\n",
    "mapping_TS = Mapping(tornado_entry_TS, rules_TS)\n",
    "\n",
    "tornado_entry_DS = {\"Non-Retrofit Fragility ID Code\": tornado_fragility_curve_set_TSDS}\n",
    "rules_DS = [[\"java.lang.String type EQUALS DS\"]]\n",
    "mapping_DS = Mapping(tornado_entry_DS, rules_DS)\n",
    "\n",
    "tornado_entry_Tow = {\"Non-Retrofit Fragility ID Code\": tornado_fragility_curve_set_Tow}\n",
    "rules_Tow = [[\"java.lang.String type EQUALS Tow\"]]\n",
    "mapping_Tow = Mapping(tornado_entry_Tow, rules_Tow)\n",
    "\n",
    "tornado_entry_Pol = {\"Non-Retrofit Fragility ID Code\": tornado_fragility_curve_set_Pol}\n",
    "rules_Pol = [[\"java.lang.String type EQUALS Pol\"]]\n",
    "mapping_Pol = Mapping(tornado_entry_Tow, rules_Pol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d3c12f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "tornado_centerville_epn_mapping_set = {\n",
    "    'id': 'local placeholder',\n",
    "    'name': 'tornado centerville epn mapping object creation',\n",
    "    'hazardType': 'tornado',\n",
    "    'inventoryType': 'electric_facility',\n",
    "    'mappings': [\n",
    "        mapping_TS,\n",
    "        mapping_DS,\n",
    "        mapping_Tow,\n",
    "        mapping_Pol\n",
    "    ],\n",
    "    'mappingType': 'fragility'\n",
    "}\n",
    "tornado_centerville_epn_mapping_set_r = MappingSet(tornado_centerville_epn_mapping_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "91294ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyincore.models.mappingset.MappingSet at 0x14870c4d0a0>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_centerville_epn_mapping_set_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d70239cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyincore.analyses.epfdamage import EpfDamage\n",
    "\n",
    "tornado_centerville_epn_dmg_xh = EpfDamage(client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "063f074b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter username: xuhan\n",
      "Enter password: ········\n",
      "Dataset already exists locally. Reading from local cached zip.\n",
      "Unzipped folder found in the local cache. Reading from it...\n"
     ]
    }
   ],
   "source": [
    "epn_network_dataset_temp = Dataset.from_data_service(\"6429a9beea308903b2b995df\", data_service)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c9e4aa6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "epn_network = NetworkDataset.from_dataset(epn_network_dataset_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "abd8f33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "epf_dataset = epn_network.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "3a46ebeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyincore.dataset.Dataset"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(epf_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "44043a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_num</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>type</th>\n",
       "      <th>critical_d</th>\n",
       "      <th>serve_indu</th>\n",
       "      <th>serve_reta</th>\n",
       "      <th>demand</th>\n",
       "      <th>government</th>\n",
       "      <th>network</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "      <th>guid</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>529.393880</td>\n",
       "      <td>25.229716</td>\n",
       "      <td>EPP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.466962</td>\n",
       "      <td>35.263632</td>\n",
       "      <td>ee3b6c78-7c86-4611-aaf7-9846ccedde89</td>\n",
       "      <td>POINT (35.264 -97.467)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>530.412839</td>\n",
       "      <td>67.007043</td>\n",
       "      <td>TS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.466872</td>\n",
       "      <td>35.260001</td>\n",
       "      <td>62629c59-e584-48f3-a727-647a8e803ed3</td>\n",
       "      <td>POINT (35.260 -97.467)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>444.820267</td>\n",
       "      <td>67.007043</td>\n",
       "      <td>DS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.474456</td>\n",
       "      <td>35.260001</td>\n",
       "      <td>754648f3-2109-4538-a126-fd551386c871</td>\n",
       "      <td>POINT (35.260 -97.474)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>442.782349</td>\n",
       "      <td>794.543898</td>\n",
       "      <td>DS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.474636</td>\n",
       "      <td>35.196781</td>\n",
       "      <td>c39c20e2-aae3-4f65-9d12-dd448c7a4065</td>\n",
       "      <td>POINT (35.197 -97.475)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>494.749267</td>\n",
       "      <td>793.524939</td>\n",
       "      <td>TS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.470032</td>\n",
       "      <td>35.196869</td>\n",
       "      <td>c0d14ae4-f25a-4618-aa8d-538bc92a9c00</td>\n",
       "      <td>POINT (35.197 -97.470)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>1448</td>\n",
       "      <td>664.398992</td>\n",
       "      <td>596.935609</td>\n",
       "      <td>Pol</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-97.455000</td>\n",
       "      <td>35.213952</td>\n",
       "      <td>4e33c878-572b-44b3-9073-c594bd91ac34</td>\n",
       "      <td>POINT (35.214 -97.455)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>1449</td>\n",
       "      <td>660.630239</td>\n",
       "      <td>596.921650</td>\n",
       "      <td>Pol</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-97.455334</td>\n",
       "      <td>35.213954</td>\n",
       "      <td>d5ba82c0-767b-4fa7-a1d1-61a094d673e5</td>\n",
       "      <td>POINT (35.214 -97.455)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>1450</td>\n",
       "      <td>656.861486</td>\n",
       "      <td>596.907692</td>\n",
       "      <td>Pol</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-97.455668</td>\n",
       "      <td>35.213955</td>\n",
       "      <td>62bd4f5f-124b-49a9-9f29-a70f05373edb</td>\n",
       "      <td>POINT (35.214 -97.456)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1451</td>\n",
       "      <td>653.092732</td>\n",
       "      <td>596.893734</td>\n",
       "      <td>Pol</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-97.456002</td>\n",
       "      <td>35.213956</td>\n",
       "      <td>f0754895-7ebd-4cdb-a5e8-a4a527f4adf7</td>\n",
       "      <td>POINT (35.214 -97.456)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1452</td>\n",
       "      <td>649.323979</td>\n",
       "      <td>596.879775</td>\n",
       "      <td>Pol</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-97.456335</td>\n",
       "      <td>35.213957</td>\n",
       "      <td>f446149d-3e11-4876-8e47-c19361d718be</td>\n",
       "      <td>POINT (35.214 -97.456)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1452 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      node_num           x           y type  critical_d  serve_indu  \\\n",
       "0            1  529.393880   25.229716  EPP           0           0   \n",
       "1            2  530.412839   67.007043   TS           0           0   \n",
       "2            3  444.820267   67.007043   DS           0           0   \n",
       "3            4  442.782349  794.543898   DS           0           0   \n",
       "4            5  494.749267  793.524939   TS           0           0   \n",
       "...        ...         ...         ...  ...         ...         ...   \n",
       "1447      1448  664.398992  596.935609  Pol           0           0   \n",
       "1448      1449  660.630239  596.921650  Pol           0           0   \n",
       "1449      1450  656.861486  596.907692  Pol           0           0   \n",
       "1450      1451  653.092732  596.893734  Pol           0           0   \n",
       "1451      1452  649.323979  596.879775  Pol           0           0   \n",
       "\n",
       "      serve_reta  demand  government  network        Lon        Lat  \\\n",
       "0              0       0           0        1 -97.466962  35.263632   \n",
       "1              0       0           0        1 -97.466872  35.260001   \n",
       "2              0       0           0        1 -97.474456  35.260001   \n",
       "3              0       0           0        1 -97.474636  35.196781   \n",
       "4              0       0           0        1 -97.470032  35.196869   \n",
       "...          ...     ...         ...      ...        ...        ...   \n",
       "1447           0       0           0        0 -97.455000  35.213952   \n",
       "1448           0       0           0        0 -97.455334  35.213954   \n",
       "1449           0       0           0        0 -97.455668  35.213955   \n",
       "1450           0       0           0        0 -97.456002  35.213956   \n",
       "1451           0       0           0        0 -97.456335  35.213957   \n",
       "\n",
       "                                      guid                geometry  \n",
       "0     ee3b6c78-7c86-4611-aaf7-9846ccedde89  POINT (35.264 -97.467)  \n",
       "1     62629c59-e584-48f3-a727-647a8e803ed3  POINT (35.260 -97.467)  \n",
       "2     754648f3-2109-4538-a126-fd551386c871  POINT (35.260 -97.474)  \n",
       "3     c39c20e2-aae3-4f65-9d12-dd448c7a4065  POINT (35.197 -97.475)  \n",
       "4     c0d14ae4-f25a-4618-aa8d-538bc92a9c00  POINT (35.197 -97.470)  \n",
       "...                                    ...                     ...  \n",
       "1447  4e33c878-572b-44b3-9073-c594bd91ac34  POINT (35.214 -97.455)  \n",
       "1448  d5ba82c0-767b-4fa7-a1d1-61a094d673e5  POINT (35.214 -97.455)  \n",
       "1449  62bd4f5f-124b-49a9-9f29-a70f05373edb  POINT (35.214 -97.456)  \n",
       "1450  f0754895-7ebd-4cdb-a5e8-a4a527f4adf7  POINT (35.214 -97.456)  \n",
       "1451  f446149d-3e11-4876-8e47-c19361d718be  POINT (35.214 -97.456)  \n",
       "\n",
       "[1452 rows x 14 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epf_dataset.get_dataframe_from_shapefile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "fd39ae23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_centerville_epn_dmg_xh.set_input_dataset('epfs', epf_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "33cc99f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_centerville_epn_dmg_xh.set_input_dataset('dfr3_mapping_set', tornado_centerville_epn_mapping_set_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "80c1e067",
   "metadata": {},
   "outputs": [],
   "source": [
    "hazard_type = \"tornado\"\n",
    "hazard_id = \"5c6323a0c11bb380daa9cbc1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "853846e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_centerville_epn_dmg_xh.set_parameter(\"hazard_type\", hazard_type)\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"hazard_id\", hazard_id)\n",
    "result_name = \"tornado_centerville_epn_dmg_result\"\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"result_name\", result_name)\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"num_cpu\", 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "adfb8e91",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'pga'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py\", line 246, in _process_worker\n    r = call_item.fn(*call_item.args, **call_item.kwargs)\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py\", line 205, in _process_chunk\n    return [fn(*args) for args in chunk]\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py\", line 205, in <listcomp>\n    return [fn(*args) for args in chunk]\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\analyses\\epfdamage\\epfdamage.py\", line 129, in epf_damage_analysis_bulk_input\n    fragility_set = self.fragilitysvc.match_inventory(self.get_input_dataset(\"dfr3_mapping_set\"), epfs,\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\dfr3service.py\", line 203, in match_inventory\n    curve = m.entry[entry_key]\nKeyError: 'pga'\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[158], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Run Analysis\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mtornado_centerville_epn_dmg_xh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\baseanalysis.py:293\u001b[0m, in \u001b[0;36mBaseAnalysis.run_analysis\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    290\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError reading parameter: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m result[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    291\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\analyses\\epfdamage\\epfdamage.py:71\u001b[0m, in \u001b[0;36mEpfDamage.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     68\u001b[0m     inventory_args\u001b[38;5;241m.\u001b[39mappend(inventory_list[count:count \u001b[38;5;241m+\u001b[39m avg_bulk_input_size])\n\u001b[0;32m     69\u001b[0m     count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m avg_bulk_input_size\n\u001b[1;32m---> 71\u001b[0m (ds_results, damage_results) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepf_damage_concurrent_future\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepf_damage_analysis_bulk_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     72\u001b[0m \u001b[43m                                                                 \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[43m                                                                 \u001b[49m\u001b[43minventory_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrepeat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhazard_type\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     74\u001b[0m \u001b[43m                                                                 \u001b[49m\u001b[43mrepeat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhazard_dataset_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_result_csv_data(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m\"\u001b[39m, ds_results, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult_name\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m     77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_result_json_data(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m, damage_results,\n\u001b[0;32m     78\u001b[0m                           name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult_name\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_additional_info\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\analyses\\epfdamage\\epfdamage.py:98\u001b[0m, in \u001b[0;36mEpfDamage.epf_damage_concurrent_future\u001b[1;34m(self, function_name, num_workers, *args)\u001b[0m\n\u001b[0;32m     96\u001b[0m output_dmg \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mProcessPoolExecutor(max_workers\u001b[38;5;241m=\u001b[39mnum_workers) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[1;32m---> 98\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ret1, ret2 \u001b[38;5;129;01min\u001b[39;00m executor\u001b[38;5;241m.\u001b[39mmap(function_name, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m     99\u001b[0m         output_ds\u001b[38;5;241m.\u001b[39mextend(ret1)\n\u001b[0;32m    100\u001b[0m         output_dmg\u001b[38;5;241m.\u001b[39mextend(ret2)\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py:562\u001b[0m, in \u001b[0;36m_chain_from_iterable_of_lists\u001b[1;34m(iterable)\u001b[0m\n\u001b[0;32m    556\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_chain_from_iterable_of_lists\u001b[39m(iterable):\n\u001b[0;32m    557\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    558\u001b[0m \u001b[38;5;124;03m    Specialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[0;32m    559\u001b[0m \u001b[38;5;124;03m    Each item in *iterable* should be a list.  This function is\u001b[39;00m\n\u001b[0;32m    560\u001b[0m \u001b[38;5;124;03m    careful not to keep references to yielded objects.\u001b[39;00m\n\u001b[0;32m    561\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 562\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[0;32m    563\u001b[0m         element\u001b[38;5;241m.\u001b[39mreverse()\n\u001b[0;32m    564\u001b[0m         \u001b[38;5;28;01mwhile\u001b[39;00m element:\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\_base.py:609\u001b[0m, in \u001b[0;36mExecutor.map.<locals>.result_iterator\u001b[1;34m()\u001b[0m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[0;32m    607\u001b[0m     \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 609\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    610\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    611\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m fs\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mresult(end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\_base.py:446\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    444\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[0;32m    445\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m--> 446\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    448\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\_base.py:391\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 391\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[0;32m    392\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    393\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m    394\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'pga'"
     ]
    }
   ],
   "source": [
    "# Run Analysis\n",
    "tornado_centerville_epn_dmg_xh.run_analysis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c994ee",
   "metadata": {},
   "source": [
    "It seems that the creation of mapping is problematic. Redo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "1ef63eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyincore import FragilityService\n",
    "fragility_services = FragilityService(client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "43143442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '642a50f1094d5f0e78ecf6e1',\n",
       " 'description': 'TSDS fragilities',\n",
       " 'authors': ['HAZUS'],\n",
       " 'paperReference': None,\n",
       " 'resultUnit': None,\n",
       " 'resultType': 'Limit State',\n",
       " 'hazardType': 'tornado',\n",
       " 'inventoryType': 'electric_facility',\n",
       " 'creator': 'xuhan',\n",
       " 'curveParameters': [{'name': 'wind',\n",
       "   'unit': 'mph',\n",
       "   'description': 'wind value from hazard service',\n",
       "   'fullName': 'wind',\n",
       "   'expression': None}],\n",
       " 'spaces': ['xuhan'],\n",
       " 'demandTypes': ['wind'],\n",
       " 'demandUnits': ['mph'],\n",
       " 'fragilityCurves': [{'description': 'Slight',\n",
       "   'rules': [{'condition': ['wind > 0'],\n",
       "     'expression': 'scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(33.4))/(0.2))'}],\n",
       "   'returnType': {'type': 'Limit State', 'unit': '', 'description': 'LS_0'},\n",
       "   'curveParameters': None},\n",
       "  {'description': 'Moderate',\n",
       "   'rules': [{'condition': ['wind > 0'],\n",
       "     'expression': 'scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(39.5))/(0.2))'}],\n",
       "   'returnType': {'type': 'Limit State', 'unit': '', 'description': 'LS_1'},\n",
       "   'curveParameters': None},\n",
       "  {'description': 'Extensive',\n",
       "   'rules': [{'condition': ['wind > 0'],\n",
       "     'expression': 'scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(48.6))/(0.2))'}],\n",
       "   'returnType': {'type': 'Limit State', 'unit': '', 'description': 'LS_2'},\n",
       "   'curveParameters': None},\n",
       "  {'description': 'Complete',\n",
       "   'rules': [{'condition': ['wind > 0'],\n",
       "     'expression': 'scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(60.8))/(0.2))'}],\n",
       "   'returnType': {'type': 'Limit State', 'unit': '', 'description': 'LS_3'},\n",
       "   'curveParameters': None}]}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_fragility_data_TSDS = {\n",
    "    \"description\": \"TSDS fragilities\",\n",
    "    \"authors\": [\n",
    "        \"HAZUS\"\n",
    "    ],\n",
    "    \"resultType\": \"Limit State\",\n",
    "    \"hazardType\": \"tornado\",\n",
    "    \"inventoryType\": \"electric_facility\",\n",
    "    \"creator\": \"xuhan\",\n",
    "    \"spaces\": [\n",
    "        \"coe\"\n",
    "    ],\n",
    "    \"demandTypes\": [\n",
    "        \"wind\"\n",
    "    ],\n",
    "    \"demandUnits\": [\n",
    "        \"mph\"\n",
    "    ],\n",
    "    \"fragilityCurves\": [\n",
    "        {\n",
    "            \"description\": \"Slight\",\n",
    "            \"rules\": [\n",
    "                {\n",
    "                    \"condition\": [\n",
    "                        \"wind > 0\"\n",
    "                    ],\n",
    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(33.4))/(0.2))\"\n",
    "                }\n",
    "            ],\n",
    "            \"returnType\": {\n",
    "                \"type\": \"Limit State\",\n",
    "                \"unit\": \"\",\n",
    "                \"description\": \"LS_0\"\n",
    "            },\n",
    "        },\n",
    "        {\n",
    "            \"description\": \"Moderate\",\n",
    "            \"rules\": [\n",
    "                {\n",
    "                    \"condition\": [\n",
    "                        \"wind > 0\"\n",
    "                    ],\n",
    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(39.5))/(0.2))\"\n",
    "                }\n",
    "            ],\n",
    "            \"returnType\": {\n",
    "                \"type\": \"Limit State\",\n",
    "                \"unit\": \"\",\n",
    "                \"description\": \"LS_1\"\n",
    "            },\n",
    "        },\n",
    "        {\n",
    "            \"description\": \"Extensive\",\n",
    "            \"rules\": [\n",
    "                {\n",
    "                    \"condition\": [\n",
    "                        \"wind > 0\"\n",
    "                    ],\n",
    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(48.6))/(0.2))\"\n",
    "                }\n",
    "            ],\n",
    "            \"returnType\": {\n",
    "                \"type\": \"Limit State\",\n",
    "                \"unit\": \"\",\n",
    "                \"description\": \"LS_2\"\n",
    "            },\n",
    "        },\n",
    "        {\n",
    "            \"description\": \"Complete\",\n",
    "            \"rules\": [\n",
    "                {\n",
    "                    \"condition\": [\n",
    "                        \"wind > 0\"\n",
    "                    ],\n",
    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(60.8))/(0.2))\"\n",
    "                }\n",
    "            ],\n",
    "            \"returnType\": {\n",
    "                \"type\": \"Limit State\",\n",
    "                \"unit\": \"\",\n",
    "                \"description\": \"LS_3\"\n",
    "            },\n",
    "        }\n",
    "    ],\n",
    "    \"curveParameters\": [\n",
    "        {\n",
    "            \"name\": \"wind\",\n",
    "            \"unit\": \"mph\",\n",
    "            \"description\": \"wind value from hazard service\",\n",
    "            \"fullName\": \"wind\",\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "created_tornado_fragility_TSDS = fragility_services.create_dfr3_set(tornado_fragility_data_TSDS)\n",
    "created_tornado_fragility_TSDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "fbd990ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '642a5379bd05ec5a1877051f',\n",
       " 'description': 'Tower fragilities',\n",
       " 'authors': ['HAZUS'],\n",
       " 'paperReference': None,\n",
       " 'resultUnit': None,\n",
       " 'resultType': 'Limit State',\n",
       " 'hazardType': 'tornado',\n",
       " 'inventoryType': 'electric_facility',\n",
       " 'creator': 'xuhan',\n",
       " 'curveParameters': [{'name': 'wind',\n",
       "   'unit': 'mph',\n",
       "   'description': 'wind value from hazard service',\n",
       "   'fullName': 'wind',\n",
       "   'expression': None}],\n",
       " 'spaces': ['xuhan'],\n",
       " 'demandTypes': ['wind'],\n",
       " 'demandUnits': ['mph'],\n",
       " 'fragilityCurves': [{'description': 'Complete',\n",
       "   'rules': [{'condition': ['wind > 0'],\n",
       "     'expression': 'scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(60.8))/(0.12))'}],\n",
       "   'returnType': {'type': 'Limit State', 'unit': '', 'description': 'LS_0'},\n",
       "   'curveParameters': None}]}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_fragility_data_Tow = {\n",
    "    \"description\": \"Tower fragilities\",\n",
    "    \"authors\": [\n",
    "        \"HAZUS\"\n",
    "    ],\n",
    "    \"resultType\": \"Limit State\",\n",
    "    \"hazardType\": \"tornado\",\n",
    "    \"inventoryType\": \"electric_facility\",\n",
    "    \"creator\": \"xuhan\",\n",
    "    \"spaces\": [\n",
    "        \"coe\"\n",
    "    ],\n",
    "    \"demandTypes\": [\n",
    "        \"wind\"\n",
    "    ],\n",
    "    \"demandUnits\": [\n",
    "        \"mph\"\n",
    "    ],\n",
    "    \"fragilityCurves\": [\n",
    "        {\n",
    "            \"description\": \"Complete\",\n",
    "            \"rules\": [\n",
    "                {\n",
    "                    \"condition\": [\n",
    "                        \"wind > 0\"\n",
    "                    ],\n",
    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind*0.44704) - math.log(60.8))/(0.12))\"\n",
    "                }\n",
    "            ],\n",
    "            \"returnType\": {\n",
    "                \"type\": \"Limit State\",\n",
    "                \"unit\": \"\",\n",
    "                \"description\": \"LS_0\"\n",
    "            },\n",
    "        }\n",
    "        \n",
    "        \n",
    "    ],\n",
    "    \"curveParameters\": [\n",
    "        {\n",
    "            \"name\": \"wind\",\n",
    "            \"unit\": \"mph\",\n",
    "            \"description\": \"wind value from hazard service\",\n",
    "            \"fullName\": \"wind\",\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "created_tornado_fragility_Tow = fragility_services.create_dfr3_set(tornado_fragility_data_Tow)\n",
    "created_tornado_fragility_Tow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1518592f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '642a54a98d2bc33e7ff4055b',\n",
       " 'description': 'Pole fragilities',\n",
       " 'authors': ['HAZUS'],\n",
       " 'paperReference': None,\n",
       " 'resultUnit': None,\n",
       " 'resultType': 'Limit State',\n",
       " 'hazardType': 'tornado',\n",
       " 'inventoryType': 'electric_facility',\n",
       " 'creator': 'xuhan',\n",
       " 'curveParameters': [{'name': 'wind',\n",
       "   'unit': 'mph',\n",
       "   'description': 'wind value from hazard service',\n",
       "   'fullName': 'wind',\n",
       "   'expression': None}],\n",
       " 'spaces': ['xuhan'],\n",
       " 'demandTypes': ['wind'],\n",
       " 'demandUnits': ['mph'],\n",
       " 'fragilityCurves': [{'description': 'Complete',\n",
       "   'rules': [{'condition': ['wind > 0'],\n",
       "     'expression': 'scipy.stats.norm.cdf((math.log(wind) - 3.95)/(0.149))'}],\n",
       "   'returnType': {'type': 'Limit State', 'unit': '', 'description': 'LS_0'},\n",
       "   'curveParameters': None}]}"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_fragility_data_Pol = {\n",
    "    \"description\": \"Pole fragilities\",\n",
    "    \"authors\": [\n",
    "        \"HAZUS\"\n",
    "    ],\n",
    "    \"resultType\": \"Limit State\",\n",
    "    \"hazardType\": \"tornado\",\n",
    "    \"inventoryType\": \"electric_facility\",\n",
    "    \"creator\": \"xuhan\",\n",
    "    \"spaces\": [\n",
    "        \"coe\"\n",
    "    ],\n",
    "    \"demandTypes\": [\n",
    "        \"wind\"\n",
    "    ],\n",
    "    \"demandUnits\": [\n",
    "        \"mph\"\n",
    "    ],\n",
    "    \"fragilityCurves\": [\n",
    "        {\n",
    "            \"description\": \"Complete\",\n",
    "            \"rules\": [\n",
    "                {\n",
    "                    \"condition\": [\n",
    "                        \"wind > 0\"\n",
    "                    ],\n",
    "                    \"expression\": \"scipy.stats.norm.cdf((math.log(wind) - 3.95)/(0.149))\"\n",
    "                }\n",
    "            ],\n",
    "            \"returnType\": {\n",
    "                \"type\": \"Limit State\",\n",
    "                \"unit\": \"\",\n",
    "                \"description\": \"LS_0\"\n",
    "            },\n",
    "        }\n",
    "        \n",
    "        \n",
    "    ],\n",
    "    \"curveParameters\": [\n",
    "        {\n",
    "            \"name\": \"wind\",\n",
    "            \"unit\": \"mph\",\n",
    "            \"description\": \"wind value from hazard service\",\n",
    "            \"fullName\": \"wind\",\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "created_tornado_fragility_Pol = fragility_services.create_dfr3_set(tornado_fragility_data_Pol)\n",
    "created_tornado_fragility_Pol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "c43278ec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '642a58eebd05ec5a18770520',\n",
       " 'name': 'Tornado fragilities mapping for epn, unit: mph',\n",
       " 'hazardType': 'tornado',\n",
       " 'inventoryType': 'electric_facility',\n",
       " 'mappings': [{'legacyEntry': {},\n",
       "   'entry': {'Non-Retrofit Fragility ID Code': '642a50f1094d5f0e78ecf6e1'},\n",
       "   'rules': [['java.lang.String type EQUALS DL']]},\n",
       "  {'legacyEntry': {},\n",
       "   'entry': {'Non-Retrofit Fragility ID Code': '642a50f1094d5f0e78ecf6e1'},\n",
       "   'rules': [['java.lang.String type EQUALS TL']]},\n",
       "  {'legacyEntry': {},\n",
       "   'entry': {'Non-Retrofit Fragility ID Code': '642a5379bd05ec5a1877051f'},\n",
       "   'rules': [['java.lang.String type EQUALS Tow']]},\n",
       "  {'legacyEntry': {},\n",
       "   'entry': {'Non-Retrofit Fragility ID Code': '642a54a98d2bc33e7ff4055b'},\n",
       "   'rules': [['java.lang.String type EQUALS Pol']]}],\n",
       " 'creator': 'xuhan',\n",
       " 'spaces': ['xuhan'],\n",
       " 'mappingType': 'fragility'}"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_centerville_epn_mapping_data = {\n",
    "  \"name\": \"Tornado fragilities mapping for epn, unit: mph\",\n",
    "  \"hazardType\": \"tornado\",\n",
    "  \"inventoryType\": \"electric_facility\",\n",
    "  \"mappingType\": \"fragility\",\n",
    "  \"mappings\": [\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"Non-Retrofit Fragility ID Code\": \"642a50f1094d5f0e78ecf6e1\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                 \"java.lang.String type EQUALS DL\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"Non-Retrofit Fragility ID Code\": \"642a50f1094d5f0e78ecf6e1\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                 \"java.lang.String type EQUALS TL\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"Non-Retrofit Fragility ID Code\": \"642a5379bd05ec5a1877051f\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                  \"java.lang.String type EQUALS Tow\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "               \"Non-Retrofit Fragility ID Code\": \"642a54a98d2bc33e7ff4055b\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                  \"java.lang.String type EQUALS Pol\"\n",
    "              ]\n",
    "          ]\n",
    "      }\n",
    "  ]\n",
    "}\n",
    "created_mapping_tornado_centerville_epn = fragility_services.create_mapping(tornado_centerville_epn_mapping_data)\n",
    "created_mapping_tornado_centerville_epn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "43800457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '642a60cbaa50e751003761fb',\n",
       " 'name': 'Tornado fragilities mapping for epn, unit: mph',\n",
       " 'hazardType': 'tornado',\n",
       " 'inventoryType': 'electric_facility',\n",
       " 'mappings': [{'legacyEntry': {},\n",
       "   'entry': {'pga': '642a50f1094d5f0e78ecf6e1'},\n",
       "   'rules': [['java.lang.String type EQUALS DL']]},\n",
       "  {'legacyEntry': {},\n",
       "   'entry': {'pga': '642a50f1094d5f0e78ecf6e1'},\n",
       "   'rules': [['java.lang.String type EQUALS TL']]},\n",
       "  {'legacyEntry': {},\n",
       "   'entry': {'pga': '642a5379bd05ec5a1877051f'},\n",
       "   'rules': [['java.lang.String type EQUALS Tow']]},\n",
       "  {'legacyEntry': {},\n",
       "   'entry': {'pga': '642a54a98d2bc33e7ff4055b'},\n",
       "   'rules': [['java.lang.String type EQUALS Pol']]}],\n",
       " 'creator': 'xuhan',\n",
       " 'spaces': ['xuhan'],\n",
       " 'mappingType': 'fragility'}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tornado_centerville_epn_mapping_data = {\n",
    "  \"name\": \"Tornado fragilities mapping for epn, unit: mph\",\n",
    "  \"hazardType\": \"tornado\",\n",
    "  \"inventoryType\": \"electric_facility\",\n",
    "  \"mappingType\": \"fragility\",\n",
    "  \"mappings\": [\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"pga\": \"642a50f1094d5f0e78ecf6e1\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                 \"java.lang.String type EQUALS DL\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"pga\": \"642a50f1094d5f0e78ecf6e1\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                 \"java.lang.String type EQUALS TL\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"pga\": \"642a5379bd05ec5a1877051f\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                  \"java.lang.String type EQUALS Tow\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "               \"pga\": \"642a54a98d2bc33e7ff4055b\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                  \"java.lang.String type EQUALS Pol\"\n",
    "              ]\n",
    "          ]\n",
    "      }\n",
    "  ]\n",
    "}\n",
    "created_mapping_tornado_centerville_epn = fragility_services.create_mapping(tornado_centerville_epn_mapping_data)\n",
    "created_mapping_tornado_centerville_epn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "46c1be9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'642a60cbaa50e751003761fb'"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "created_mapping_tornado_centerville_epn['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "52aecd3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping_id = created_mapping_tornado_centerville_epn['id']\n",
    "fragility_service = FragilityService(client)\n",
    "mapping_set_tornado_centerville_epn = MappingSet(fragility_service.get_mapping(mapping_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "6d564396",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already exists locally. Reading from local cached zip.\n",
      "Unzipped folder found in the local cache. Reading from it...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyincore.analyses.epfdamage import EpfDamage\n",
    "tornado_centerville_epn_dmg_xh = EpfDamage(client)\n",
    "\n",
    "epn_network_dataset_temp = Dataset.from_data_service(\"6429a9beea308903b2b995df\", data_service)\n",
    "epn_network = NetworkDataset.from_dataset(epn_network_dataset_temp)\n",
    "epf_dataset = epn_network.nodes\n",
    "tornado_centerville_epn_dmg_xh.set_input_dataset('epfs', epf_dataset)\n",
    "#tornado_centerville_epn_dmg_xh.set_input_dataset('epfs', dataset_centerville_epn_node_xh_mo_r1)\n",
    "tornado_centerville_epn_dmg_xh.set_input_dataset('dfr3_mapping_set', mapping_set_tornado_centerville_epn)\n",
    "hazard_type = \"tornado\"\n",
    "hazard_id = \"5c6323a0c11bb380daa9cbc1\"\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"hazard_type\", hazard_type)\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"hazard_id\", hazard_id)\n",
    "result_name = \"tornado_centerville_epn_dmg_result\"\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"result_name\", result_name)\n",
    "tornado_centerville_epn_dmg_xh.set_parameter(\"num_cpu\", 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "b6e63f46",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "electric_facility tornado damage analysis do not support 1 limit state",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py\", line 246, in _process_worker\n    r = call_item.fn(*call_item.args, **call_item.kwargs)\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py\", line 205, in _process_chunk\n    return [fn(*args) for args in chunk]\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py\", line 205, in <listcomp>\n    return [fn(*args) for args in chunk]\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\analyses\\epfdamage\\epfdamage.py\", line 249, in epf_damage_analysis_bulk_input\n    dmg_interval = selected_fragility_set.calculate_damage_interval(\n  File \"C:\\Users\\Xu Han\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\models\\fragilitycurveset.py\", line 154, in calculate_damage_interval\n    raise ValueError(inventory_type + \" \" + hazard_type + \" damage analysis do not support \" +\nValueError: electric_facility tornado damage analysis do not support 1 limit state\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[179], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Run Analysis\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mtornado_centerville_epn_dmg_xh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\baseanalysis.py:293\u001b[0m, in \u001b[0;36mBaseAnalysis.run_analysis\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    290\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError reading parameter: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m result[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    291\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\analyses\\epfdamage\\epfdamage.py:71\u001b[0m, in \u001b[0;36mEpfDamage.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     68\u001b[0m     inventory_args\u001b[38;5;241m.\u001b[39mappend(inventory_list[count:count \u001b[38;5;241m+\u001b[39m avg_bulk_input_size])\n\u001b[0;32m     69\u001b[0m     count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m avg_bulk_input_size\n\u001b[1;32m---> 71\u001b[0m (ds_results, damage_results) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepf_damage_concurrent_future\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepf_damage_analysis_bulk_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     72\u001b[0m \u001b[43m                                                                 \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[43m                                                                 \u001b[49m\u001b[43minventory_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrepeat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhazard_type\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     74\u001b[0m \u001b[43m                                                                 \u001b[49m\u001b[43mrepeat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhazard_dataset_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_result_csv_data(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m\"\u001b[39m, ds_results, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult_name\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m     77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_result_json_data(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m, damage_results,\n\u001b[0;32m     78\u001b[0m                           name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult_name\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_additional_info\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\site-packages\\pyincore\\analyses\\epfdamage\\epfdamage.py:98\u001b[0m, in \u001b[0;36mEpfDamage.epf_damage_concurrent_future\u001b[1;34m(self, function_name, num_workers, *args)\u001b[0m\n\u001b[0;32m     96\u001b[0m output_dmg \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mProcessPoolExecutor(max_workers\u001b[38;5;241m=\u001b[39mnum_workers) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[1;32m---> 98\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ret1, ret2 \u001b[38;5;129;01min\u001b[39;00m executor\u001b[38;5;241m.\u001b[39mmap(function_name, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m     99\u001b[0m         output_ds\u001b[38;5;241m.\u001b[39mextend(ret1)\n\u001b[0;32m    100\u001b[0m         output_dmg\u001b[38;5;241m.\u001b[39mextend(ret2)\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\process.py:562\u001b[0m, in \u001b[0;36m_chain_from_iterable_of_lists\u001b[1;34m(iterable)\u001b[0m\n\u001b[0;32m    556\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_chain_from_iterable_of_lists\u001b[39m(iterable):\n\u001b[0;32m    557\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    558\u001b[0m \u001b[38;5;124;03m    Specialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[0;32m    559\u001b[0m \u001b[38;5;124;03m    Each item in *iterable* should be a list.  This function is\u001b[39;00m\n\u001b[0;32m    560\u001b[0m \u001b[38;5;124;03m    careful not to keep references to yielded objects.\u001b[39;00m\n\u001b[0;32m    561\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 562\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[0;32m    563\u001b[0m         element\u001b[38;5;241m.\u001b[39mreverse()\n\u001b[0;32m    564\u001b[0m         \u001b[38;5;28;01mwhile\u001b[39;00m element:\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\_base.py:609\u001b[0m, in \u001b[0;36mExecutor.map.<locals>.result_iterator\u001b[1;34m()\u001b[0m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[0;32m    607\u001b[0m     \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 609\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    610\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    611\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m fs\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mresult(end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\_base.py:446\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    444\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[0;32m    445\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m--> 446\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    448\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n",
      "File \u001b[1;32m~\\.conda\\envs\\pyincoreEnv\\lib\\concurrent\\futures\\_base.py:391\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m    390\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 391\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[0;32m    392\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    393\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m    394\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: electric_facility tornado damage analysis do not support 1 limit state"
     ]
    }
   ],
   "source": [
    "# Run Analysis\n",
    "tornado_centerville_epn_dmg_xh.run_analysis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a902be7f",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "def calculate_damage_interval(self, damage, hazard_type=\"earthquake\", inventory_type: str = \"building\"):\n",
    "       \n",
    "        Args:\n",
    "            damage (list): A list of limit states.\n",
    "            hazard_type (str): A string describing the hazard being evaluated.\n",
    "            inventory_type (str): A string describing the type of element being evaluated.\n",
    "        Returns:\n",
    "            list: LS-to-DS mapping\n",
    "        \n",
    "        # Organize conceptually per LS-to-DS mapping , then by event, then by structure and by count\n",
    "        # This may help keep track of scientific requirements also.\n",
    "\n",
    "        ls_ds_dspatcher = {\n",
    "            # 1 LS to 4 DS\n",
    "            (\"hurricane\", \"building\", 1): FragilityCurveSet._1ls_to_4ds,\n",
    "            (\"hurricane\", \"electric_facility\", 1): FragilityCurveSet._1ls_to_4ds,\n",
    "            # 1 LS to 5 DS\n",
    "            (\"hurricane\", \"road\", 1): FragilityCurveSet._1ls_to_5ds,\n",
    "            (\"hurricane\", \"bridge\", 1): FragilityCurveSet._1ls_to_5ds,\n",
    "            # 3 LS to 4 DS\n",
    "            (\"earthquake\", \"building\", 3): FragilityCurveSet._3ls_to_4ds,\n",
    "            (\"tornado\", \"building\", 3): FragilityCurveSet._3ls_to_4ds,\n",
    "            (\"hurricane\", \"building\", 3): FragilityCurveSet._3ls_to_4ds,\n",
    "            (\"flood\", \"building\", 3): FragilityCurveSet._3ls_to_4ds,\n",
    "            (\"tsunami\", \"building\", 3): FragilityCurveSet._3ls_to_4ds,\n",
    "            # 4 LS to 5 DS\n",
    "            (\"earthquake\", \"bridge\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"earthquake\", \"pipeline\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"earthquake\", \"road\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"earthquake\", \"water_facility\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"earthquake\", \"electric_facility\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tornado\", \"bridge\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tornado\", \"electric_facility\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"flood\", \"bridge\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tsunami\", \"bridge\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tsunami\", \"pipeline\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tsunami\", \"road\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tsunami\", \"water_facility\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"tsunami\", \"electric_facility\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "            (\"hurricane\", \"bridge\", 4): FragilityCurveSet._4ls_to_5ds,\n",
    "        }\n",
    "\n",
    "        if not (hazard_type, inventory_type, len(self.fragility_curves)) in ls_ds_dspatcher.keys():\n",
    "            raise ValueError(inventory_type + \" \" + hazard_type + \" damage analysis do not support \" +\n",
    "                             str(len(self.fragility_curves)) + \" limit state\")\n",
    "\n",
    "        return ls_ds_dspatcher[(hazard_type, inventory_type, len(self.fragility_curves))](damage)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df8a7b5",
   "metadata": {},
   "source": [
    "**comment: The fragilitycurveset.py needs to be modified!**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "6709c55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#try if what I think is true\n",
    "trial_dict=ls_ds_dspatcher = {\n",
    "            # 1 LS to 4 DS\n",
    "            (\"hurricane\", \"building\", 1): [2, 4, 6],\n",
    "            (\"hurricane\", \"electric_facility\", 1): [2, 4, 6],\n",
    "            # 1 LS to 5 DS\n",
    "            (\"hurricane\", \"road\", 1): [2, 4, 6],\n",
    "            (\"hurricane\", \"bridge\", 1): [2, 4, 6],\n",
    "            # 3 LS to 4 DS\n",
    "            (\"earthquake\", \"building\", 3): [2, 4, 6],\n",
    "            (\"tornado\", \"building\", 3): [2, 4, 6],\n",
    "            (\"hurricane\", \"building\", 3): [2, 4, 6],\n",
    "            (\"flood\", \"building\", 3): [2, 4, 6],\n",
    "            (\"tsunami\", \"building\", 3): [2, 4, 6],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "6edc9b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"hurricane\", \"building\", 1) in trial_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "78fc844e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"hurricane\", \"building\", 2) in trial_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69666afb",
   "metadata": {},
   "source": [
    "## try waternetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "6b34b63b",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_waternode_xh_mo = pd.read_excel(r'C:\\Users\\Xu Han\\Anylogic_xh\\Centerville_with variable center_2021_07_15\\Centerville_with variable center_2021_03_23\\database\\Waternodes.xlsx',sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "2941309c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>ennode</th>\n",
       "      <th>tank1_dep</th>\n",
       "      <th>tank2_dep</th>\n",
       "      <th>ps2_dep</th>\n",
       "      <th>ps3_dep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>tank</td>\n",
       "      <td>113.398160</td>\n",
       "      <td>260.122059</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>20</td>\n",
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       "      <td>3</td>\n",
       "      <td>junction</td>\n",
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       "      <td>110.353383</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>pump</td>\n",
       "      <td>359.122308</td>\n",
       "      <td>431.811803</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>junction</td>\n",
       "      <td>368.690746</td>\n",
       "      <td>158.053436</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      type           x           y  ennode  tank1_dep  tank2_dep  \\\n",
       "0   1      tank  113.398160  260.122059       0          0          0   \n",
       "1   2  junction  233.472461  320.062037       0         80          0   \n",
       "2   3  junction  310.131252  110.353383       0         70         10   \n",
       "3   4      pump  359.122308  431.811803      15          0          0   \n",
       "4   5  junction  368.690746  158.053436       0         70         10   \n",
       "\n",
       "   ps2_dep  ps3_dep  \n",
       "0        0        0  \n",
       "1        0       20  \n",
       "2        0       20  \n",
       "3        0        0  \n",
       "4        0       20  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_waternode_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "b09b372d",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_waternode_xh_mo['Lon']=-97.51386932905343+centerville_waternode_xh_mo['x']/1288*(97.51386932905343-97.39974502348663)\n",
    "centerville_waternode_xh_mo['Lat']=35.265823927324504-centerville_waternode_xh_mo['y']/805*(35.265823927324504-35.19587226475656)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "6c9238e5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Lat</th>\n",
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       "      <th>0</th>\n",
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       "      <td>431.811803</td>\n",
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       "      <td>-97.482049</td>\n",
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      ],
      "text/plain": [
       "   id      type           x           y  ennode  tank1_dep  tank2_dep  \\\n",
       "0   1      tank  113.398160  260.122059       0          0          0   \n",
       "1   2  junction  233.472461  320.062037       0         80          0   \n",
       "2   3  junction  310.131252  110.353383       0         70         10   \n",
       "3   4      pump  359.122308  431.811803      15          0          0   \n",
       "4   5  junction  368.690746  158.053436       0         70         10   \n",
       "\n",
       "   ps2_dep  ps3_dep        Lon        Lat  \n",
       "0        0        0 -97.503822  35.243220  \n",
       "1        0       20 -97.493182  35.238012  \n",
       "2        0       20 -97.486390  35.256235  \n",
       "3        0        0 -97.482049  35.228301  \n",
       "4        0       20 -97.481201  35.252090  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_waternode_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "8ff4f646",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_waternode_xh_mo['coordinates']=centerville_waternode_xh_mo[['Lat','Lon']].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "3ab37d9b",
   "metadata": {},
   "outputs": [
    {
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       "1   2  junction  233.472461  320.062037       0         80          0   \n",
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       "4        0       20 -97.481201  35.252090   \n",
       "\n",
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       "1     [35.2380116644251, -97.4931823085294]  \n",
       "2   [35.25623460727628, -97.48638989291506]  \n",
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       "4  [35.252089640827904, -97.48120118048499]  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_waternode_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "d51d16b9",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   id      type           x           y  ennode  tank1_dep  tank2_dep  \\\n",
       "0   1      tank  113.398160  260.122059       0          0          0   \n",
       "1   2  junction  233.472461  320.062037       0         80          0   \n",
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       "\n",
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     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_waternode_xh_mo['coordinates']=centerville_waternode_xh_mo['coordinates'].apply(Point)\n",
    "centerville_waternode_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "802ffa95",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd_centerville_waternode_xh_mo=gpd.GeoDataFrame(centerville_waternode_xh_mo, crs='epsg:4326',geometry='coordinates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "69906dd9",
   "metadata": {},
   "outputs": [
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       "   id      type           x           y  ennode  tank1_dep  tank2_dep  \\\n",
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       "1   2  junction  233.472461  320.062037       0         80          0   \n",
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   "source": [
    "gpd_centerville_waternode_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "6c169b9e",
   "metadata": {},
   "outputs": [
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       "      <td>POINT (35.22830 -97.48205)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>junction</td>\n",
       "      <td>368.690746</td>\n",
       "      <td>158.053436</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>-97.481201</td>\n",
       "      <td>35.252090</td>\n",
       "      <td>2800b8d4-4f61-4da6-921e-8abd5449e4bd</td>\n",
       "      <td>POINT (35.25209 -97.48120)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      type           x           y  ennode  tank1_dep  tank2_dep  \\\n",
       "0   1      tank  113.398160  260.122059       0          0          0   \n",
       "1   2  junction  233.472461  320.062037       0         80          0   \n",
       "2   3  junction  310.131252  110.353383       0         70         10   \n",
       "3   4      pump  359.122308  431.811803      15          0          0   \n",
       "4   5  junction  368.690746  158.053436       0         70         10   \n",
       "\n",
       "   ps2_dep  ps3_dep        Lon        Lat  \\\n",
       "0        0        0 -97.503822  35.243220   \n",
       "1        0       20 -97.493182  35.238012   \n",
       "2        0       20 -97.486390  35.256235   \n",
       "3        0        0 -97.482049  35.228301   \n",
       "4        0       20 -97.481201  35.252090   \n",
       "\n",
       "                                   guid                    geometry  \n",
       "0  f2437881-294e-4c04-aef4-77609afb99ce  POINT (35.24322 -97.50382)  \n",
       "1  1fafd9ed-4b2c-41da-a98b-5c8dd9ff55fd  POINT (35.23801 -97.49318)  \n",
       "2  10266208-9204-442b-b9e8-72d99997bac4  POINT (35.25623 -97.48639)  \n",
       "3  9dac2492-353d-4f3f-8e59-ce8b547abb96  POINT (35.22830 -97.48205)  \n",
       "4  2800b8d4-4f61-4da6-921e-8abd5449e4bd  POINT (35.25209 -97.48120)  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpd_centerville_waternode_xh_mo.to_file(\"gpd_centerville_waternode_xh_mo.shp\")\n",
    "GeoUtil.add_guid(\"gpd_centerville_waternode_xh_mo.shp\", \"gpd_centerville_waternode_xh_mo_r1.shp\")\n",
    "dataset_centerville_waternode_xh_mo_r1 = Dataset.from_file(\"gpd_centerville_waternode_xh_mo_r1.shp\", data_type=\"incore:waterNodeVer1\")\n",
    "gpd_centerville_waternode_xh_mo_r1 = dataset_centerville_waternode_xh_mo_r1.get_dataframe_from_shapefile()\n",
    "gpd_centerville_waternode_xh_mo_r1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246d3293",
   "metadata": {},
   "source": [
    "**comment: I don't think Mo did fragility analysis on waternode in centerville. The status of the waternode is contingent upon the status of associated electric nodes**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62149698",
   "metadata": {},
   "source": [
    "## Try do fragility analysis on household"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "e78cb24d",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_household_xh_mo = pd.read_excel(r'C:\\Users\\Xu Han\\Anylogic_xh\\Centerville_with variable center_2021_07_15\\Centerville_with variable center_2021_03_23\\database\\Household.xlsx',sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "9859f970",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>household_id</th>\n",
       "      <th>building_id</th>\n",
       "      <th>zone</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "      <th>sector</th>\n",
       "      <th>number_of_people</th>\n",
       "      <th>tot_students</th>\n",
       "      <th>elementry_student</th>\n",
       "      <th>...</th>\n",
       "      <th>high_student</th>\n",
       "      <th>high_school</th>\n",
       "      <th>university_student</th>\n",
       "      <th>income_level</th>\n",
       "      <th>homeowner</th>\n",
       "      <th>close_friends_location</th>\n",
       "      <th>archetype</th>\n",
       "      <th>socialvulnerability</th>\n",
       "      <th>elecnode</th>\n",
       "      <th>waternode</th>\n",
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       "  </thead>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>...</td>\n",
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       "      <td>IN</td>\n",
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       "      <td>0.058614</td>\n",
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       "      <td>7867</td>\n",
       "      <td>7867</td>\n",
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       "      <td>359.868325</td>\n",
       "      <td>461.032532</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>IN</td>\n",
       "      <td>T5</td>\n",
       "      <td>0.223498</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7868</td>\n",
       "      <td>7868</td>\n",
       "      <td>Z1</td>\n",
       "      <td>333.673081</td>\n",
       "      <td>580.486955</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>15129</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>1</td>\n",
       "      <td>IN</td>\n",
       "      <td>T5</td>\n",
       "      <td>0.527567</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7869</td>\n",
       "      <td>7869</td>\n",
       "      <td>Z1</td>\n",
       "      <td>318.819355</td>\n",
       "      <td>487.800259</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HI</td>\n",
       "      <td>1</td>\n",
       "      <td>FC</td>\n",
       "      <td>T5</td>\n",
       "      <td>0.104863</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>7870</td>\n",
       "      <td>Z1</td>\n",
       "      <td>243.104216</td>\n",
       "      <td>454.661296</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HI</td>\n",
       "      <td>0</td>\n",
       "      <td>NC</td>\n",
       "      <td>T5</td>\n",
       "      <td>0.033507</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   household_id  building_id zone           x           y  appr_bldg  \\\n",
       "0          7866         7866   Z1  271.174420  335.842046     358524   \n",
       "1          7867         7867   Z1  359.868325  461.032532     358524   \n",
       "2          7868         7868   Z1  333.673081  580.486955     358524   \n",
       "3          7869         7869   Z1  318.819355  487.800259     358524   \n",
       "4          7870         7870   Z1  243.104216  454.661296     358524   \n",
       "\n",
       "          sector  number_of_people  tot_students  elementry_student  ...  \\\n",
       "0  single family                 4             2                  0  ...   \n",
       "1  single family                 2             0                  0  ...   \n",
       "2  single family                 4             2                  0  ...   \n",
       "3  single family                 3             1                  1  ...   \n",
       "4  single family                 1             0                  0  ...   \n",
       "\n",
       "   high_student  high_school  university_student  income_level  homeowner  \\\n",
       "0             0            0                   0            HI          0   \n",
       "1             0            0                   0            MI          0   \n",
       "2             1        15129                   0            MI          1   \n",
       "3             0            0                   0            HI          1   \n",
       "4             0            0                   0            HI          0   \n",
       "\n",
       "   close_friends_location archetype  socialvulnerability elecnode waternode  \n",
       "0                      IN        T5             0.058614       11         1  \n",
       "1                      IN        T5             0.223498       11         1  \n",
       "2                      IN        T5             0.527567       11         1  \n",
       "3                      FC        T5             0.104863       11         1  \n",
       "4                      NC        T5             0.033507       11         1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_household_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "da8ef53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_household_xh_mo['Lon']=-97.51386932905343+centerville_household_xh_mo['x']/1288*(97.51386932905343-97.39974502348663)\n",
    "centerville_household_xh_mo['Lat']=35.265823927324504-centerville_household_xh_mo['y']/805*(35.265823927324504-35.19587226475656)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "cf824f2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_household_xh_mo['archetype']=centerville_household_xh_mo['archetype'].str.replace('T', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "ad0b71a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>7868</td>\n",
       "      <td>Z1</td>\n",
       "      <td>333.673081</td>\n",
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       "      <td>0</td>\n",
       "      <td>MI</td>\n",
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       "      <td>IN</td>\n",
       "      <td>5</td>\n",
       "      <td>0.527567</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.484304</td>\n",
       "      <td>35.215382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>7869</td>\n",
       "      <td>Z1</td>\n",
       "      <td>318.819355</td>\n",
       "      <td>487.800259</td>\n",
       "      <td>358524</td>\n",
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       "      <td>-97.485620</td>\n",
       "      <td>35.223436</td>\n",
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       "      <th>4</th>\n",
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       "      <td>7870</td>\n",
       "      <td>Z1</td>\n",
       "      <td>243.104216</td>\n",
       "      <td>454.661296</td>\n",
       "      <td>358524</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>HI</td>\n",
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       "      <td>0.033507</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.492329</td>\n",
       "      <td>35.226315</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   household_id  building_id zone           x           y  appr_bldg  \\\n",
       "0          7866         7866   Z1  271.174420  335.842046     358524   \n",
       "1          7867         7867   Z1  359.868325  461.032532     358524   \n",
       "2          7868         7868   Z1  333.673081  580.486955     358524   \n",
       "3          7869         7869   Z1  318.819355  487.800259     358524   \n",
       "4          7870         7870   Z1  243.104216  454.661296     358524   \n",
       "\n",
       "          sector  number_of_people  tot_students  elementry_student  ...  \\\n",
       "0  single family                 4             2                  0  ...   \n",
       "1  single family                 2             0                  0  ...   \n",
       "2  single family                 4             2                  0  ...   \n",
       "3  single family                 3             1                  1  ...   \n",
       "4  single family                 1             0                  0  ...   \n",
       "\n",
       "   university_student  income_level  homeowner  close_friends_location  \\\n",
       "0                   0            HI          0                      IN   \n",
       "1                   0            MI          0                      IN   \n",
       "2                   0            MI          1                      IN   \n",
       "3                   0            HI          1                      FC   \n",
       "4                   0            HI          0                      NC   \n",
       "\n",
       "   archetype  socialvulnerability elecnode  waternode        Lon        Lat  \n",
       "0          5             0.058614       11          1 -97.489842  35.236640  \n",
       "1          5             0.223498       11          1 -97.481983  35.225762  \n",
       "2          5             0.527567       11          1 -97.484304  35.215382  \n",
       "3          5             0.104863       11          1 -97.485620  35.223436  \n",
       "4          5             0.033507       11          1 -97.492329  35.226315  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_household_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "affe94a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_household_xh_mo['archetype'] = centerville_household_xh_mo['archetype'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "e60b9bf8",
   "metadata": {},
   "outputs": [
    {
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       "   household_id  building_id zone           x           y  appr_bldg  \\\n",
       "0          7866         7866   Z1  271.174420  335.842046     358524   \n",
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       "\n",
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       "0  single family                 4             2                  0  ...   \n",
       "1  single family                 2             0                  0  ...   \n",
       "2  single family                 4             2                  0  ...   \n",
       "3  single family                 3             1                  1  ...   \n",
       "4  single family                 1             0                  0  ...   \n",
       "\n",
       "   university_student  income_level  homeowner  close_friends_location  \\\n",
       "0                   0            HI          0                      IN   \n",
       "1                   0            MI          0                      IN   \n",
       "2                   0            MI          1                      IN   \n",
       "3                   0            HI          1                      FC   \n",
       "4                   0            HI          0                      NC   \n",
       "\n",
       "   archetype  socialvulnerability elecnode  waternode        Lon        Lat  \n",
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       "4          5             0.033507       11          1 -97.492329  35.226315  \n",
       "\n",
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     },
     "execution_count": 213,
     "metadata": {},
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    }
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   "source": [
    "centerville_household_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "7ce4afd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "centerville_household_xh_mo['coordinates']=centerville_household_xh_mo[['Lat','Lon']].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "151f264e",
   "metadata": {},
   "outputs": [
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       "   household_id  building_id zone           x           y  appr_bldg  \\\n",
       "0          7866         7866   Z1  271.174420  335.842046     358524   \n",
       "1          7867         7867   Z1  359.868325  461.032532     358524   \n",
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       "\n",
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       "0  single family                 4             2                  0  ...   \n",
       "1  single family                 2             0                  0  ...   \n",
       "2  single family                 4             2                  0  ...   \n",
       "3  single family                 3             1                  1  ...   \n",
       "4  single family                 1             0                  0  ...   \n",
       "\n",
       "   income_level  homeowner  close_friends_location  archetype  \\\n",
       "0            HI          0                      IN          5   \n",
       "1            MI          0                      IN          5   \n",
       "2            MI          1                      IN          5   \n",
       "3            HI          1                      FC          5   \n",
       "4            HI          0                      NC          5   \n",
       "\n",
       "   socialvulnerability  elecnode waternode        Lon        Lat  \\\n",
       "0             0.058614        11         1 -97.489842  35.236640   \n",
       "1             0.223498        11         1 -97.481983  35.225762   \n",
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       "4             0.033507        11         1 -97.492329  35.226315   \n",
       "\n",
       "                                    coordinates  \n",
       "0   POINT (35.23664043728982 -97.4898416952258)  \n",
       "1  POINT (35.22576182532288 -97.48198289840538)  \n",
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       "3   POINT (35.2234358041964 -97.48562007637877)  \n",
       "4    POINT (35.2263154632742 -97.4923288788738)  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_household_xh_mo['coordinates']=centerville_household_xh_mo['coordinates'].apply(Point)\n",
    "centerville_household_xh_mo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "486ef052",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd_centerville_household_xh_mo=gpd.GeoDataFrame(centerville_household_xh_mo, crs='epsg:4326',geometry='coordinates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "6807cfa8",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <td>7867</td>\n",
       "      <td>7867</td>\n",
       "      <td>Z1</td>\n",
       "      <td>359.868325</td>\n",
       "      <td>461.032532</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>IN</td>\n",
       "      <td>5</td>\n",
       "      <td>0.223498</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.481983</td>\n",
       "      <td>35.225762</td>\n",
       "      <td>b97fa0b5-5ad7-4417-95ab-f63c2b9580cc</td>\n",
       "      <td>POINT (35.22576 -97.48198)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7868</td>\n",
       "      <td>7868</td>\n",
       "      <td>Z1</td>\n",
       "      <td>333.673081</td>\n",
       "      <td>580.486955</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>IN</td>\n",
       "      <td>5</td>\n",
       "      <td>0.527567</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.484304</td>\n",
       "      <td>35.215382</td>\n",
       "      <td>5cdc2ed3-b775-4e58-84f6-9b68a50afbe0</td>\n",
       "      <td>POINT (35.21538 -97.48430)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7869</td>\n",
       "      <td>7869</td>\n",
       "      <td>Z1</td>\n",
       "      <td>318.819355</td>\n",
       "      <td>487.800259</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>FC</td>\n",
       "      <td>5</td>\n",
       "      <td>0.104863</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.485620</td>\n",
       "      <td>35.223436</td>\n",
       "      <td>6f9a002e-a304-4dcf-92f1-a2ca02621f92</td>\n",
       "      <td>POINT (35.22344 -97.48562)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7870</td>\n",
       "      <td>7870</td>\n",
       "      <td>Z1</td>\n",
       "      <td>243.104216</td>\n",
       "      <td>454.661296</td>\n",
       "      <td>358524</td>\n",
       "      <td>single family</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NC</td>\n",
       "      <td>5</td>\n",
       "      <td>0.033507</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-97.492329</td>\n",
       "      <td>35.226315</td>\n",
       "      <td>be886cea-31ec-4bcf-8243-c9c4326de1f7</td>\n",
       "      <td>POINT (35.22632 -97.49233)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   household_  building_i zone           x           y  appr_bldg  \\\n",
       "0        7866        7866   Z1  271.174420  335.842046     358524   \n",
       "1        7867        7867   Z1  359.868325  461.032532     358524   \n",
       "2        7868        7868   Z1  333.673081  580.486955     358524   \n",
       "3        7869        7869   Z1  318.819355  487.800259     358524   \n",
       "4        7870        7870   Z1  243.104216  454.661296     358524   \n",
       "\n",
       "          sector  number_of_  tot_studen  elementry_  ...  homeowner  \\\n",
       "0  single family           4           2           0  ...          0   \n",
       "1  single family           2           0           0  ...          0   \n",
       "2  single family           4           2           0  ...          1   \n",
       "3  single family           3           1           1  ...          1   \n",
       "4  single family           1           0           0  ...          0   \n",
       "\n",
       "   close_frie  archetype  socialvuln  elecnode  waternode        Lon  \\\n",
       "0          IN          5    0.058614        11          1 -97.489842   \n",
       "1          IN          5    0.223498        11          1 -97.481983   \n",
       "2          IN          5    0.527567        11          1 -97.484304   \n",
       "3          FC          5    0.104863        11          1 -97.485620   \n",
       "4          NC          5    0.033507        11          1 -97.492329   \n",
       "\n",
       "         Lat                                  guid                    geometry  \n",
       "0  35.236640  02eaf8cc-a894-4019-9b4b-84bd35de7d59  POINT (35.23664 -97.48984)  \n",
       "1  35.225762  b97fa0b5-5ad7-4417-95ab-f63c2b9580cc  POINT (35.22576 -97.48198)  \n",
       "2  35.215382  5cdc2ed3-b775-4e58-84f6-9b68a50afbe0  POINT (35.21538 -97.48430)  \n",
       "3  35.223436  6f9a002e-a304-4dcf-92f1-a2ca02621f92  POINT (35.22344 -97.48562)  \n",
       "4  35.226315  be886cea-31ec-4bcf-8243-c9c4326de1f7  POINT (35.22632 -97.49233)  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpd_centerville_household_xh_mo.to_file(\"gpd_centerville_household_xh_mo.shp\")\n",
    "GeoUtil.add_guid(\"gpd_centerville_household_xh_mo.shp\", \"gpd_centerville_household_xh_mo_r1.shp\")\n",
    "dataset_centerville_household_xh_mo_r1 = Dataset.from_file(\"gpd_centerville_household_xh_mo_r1.shp\", data_type=\"ergo:buildingInventoryVer7\")\n",
    "gpd_centerville_household_xh_mo_r1 = dataset_centerville_household_xh_mo_r1.get_dataframe_from_shapefile()\n",
    "gpd_centerville_household_xh_mo_r1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "c0964483",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter username: xuhan\n",
      "Enter password: ········\n",
      "Dataset already exists locally. Reading from local cached zip.\n",
      "Unzipped folder found in the local cache. Reading from it...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use tornado from IN-CORE Hazard Services\n",
    "hazard_id = \"5c6323a0c11bb380daa9cbc1\"\n",
    "\n",
    "# visualize the tornado using pyincore-viz package\n",
    "from pyincore_viz.geoutil import GeoUtil as viz\n",
    "viz.plot_tornado(hazard_id, client, basemap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "a36a40ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "hazard_type = \"tornado\"\n",
    "result_name = \"centerville_tornado_household_dmg_result\"\n",
    "tornado_mapping_id = \"5d096430b9219c065bf7033f\" # 19 archetype tornado mapping, this takes into account the conversion from miles per hour to m/s\n",
    "from pyincore import DataService\n",
    "from pyincore.analyses.buildingdamage import BuildingDamage\n",
    "from pyincore import FragilityService,MappingSet\n",
    "fragility_services = FragilityService(client)\n",
    "# create fragility mapping object with id\n",
    "tornado_mapping_set = MappingSet(fragility_services.get_mapping(tornado_mapping_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "b0f05314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerville_tornado_household_dmg = BuildingDamage(client)\n",
    "centerville_tornado_household_dmg.set_input_dataset(\"buildings\", dataset_centerville_household_xh_mo_r1)\n",
    "\n",
    "centerville_tornado_household_dmg.set_input_dataset('dfr3_mapping_set', tornado_mapping_set)\n",
    "\n",
    "centerville_tornado_household_dmg.set_parameter(\"result_name\", result_name)\n",
    "centerville_tornado_household_dmg.set_parameter(\"hazard_type\", hazard_type)\n",
    "centerville_tornado_household_dmg.set_parameter(\"hazard_id\", hazard_id)\n",
    "centerville_tornado_household_dmg.set_parameter(\"num_cpu\", 1)\n",
    "centerville_tornado_household_dmg.run_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "e80649d2",
   "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>guid</th>\n",
       "      <th>LS_0</th>\n",
       "      <th>LS_1</th>\n",
       "      <th>LS_2</th>\n",
       "      <th>DS_0</th>\n",
       "      <th>DS_1</th>\n",
       "      <th>DS_2</th>\n",
       "      <th>DS_3</th>\n",
       "      <th>haz_expose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02eaf8cc-a894-4019-9b4b-84bd35de7d59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b97fa0b5-5ad7-4417-95ab-f63c2b9580cc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5cdc2ed3-b775-4e58-84f6-9b68a50afbe0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6f9a002e-a304-4dcf-92f1-a2ca02621f92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>be886cea-31ec-4bcf-8243-c9c4326de1f7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   guid  LS_0  LS_1  LS_2  DS_0  DS_1  DS_2  \\\n",
       "0  02eaf8cc-a894-4019-9b4b-84bd35de7d59   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "1  b97fa0b5-5ad7-4417-95ab-f63c2b9580cc   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "2  5cdc2ed3-b775-4e58-84f6-9b68a50afbe0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "3  6f9a002e-a304-4dcf-92f1-a2ca02621f92   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "4  be886cea-31ec-4bcf-8243-c9c4326de1f7   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "\n",
       "   DS_3 haz_expose  \n",
       "0   0.0         no  \n",
       "1   0.0         no  \n",
       "2   0.0         no  \n",
       "3   0.0         no  \n",
       "4   0.0         no  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more viz will be in next secsion\n",
    "centerville_tornado_household_dmg_table = centerville_tornado_household_dmg.get_output_dataset(\"ds_result\").get_dataframe_from_csv()\n",
    "centerville_tornado_household_dmg_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "4f69c778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.70310720291354"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "3.898-math.log(0.44704)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6e6811",
   "metadata": {},
   "outputs": [],
   "source": [
    "tornado_centerville_household_mapping_data = {\n",
    "  \"name\": \"Tornado fragilities mapping for household, unit: mph\",\n",
    "  \"hazardType\": \"tornado\",\n",
    "  \"inventoryType\": \"electric_facility\",\n",
    "  \"mappingType\": \"fragility\",\n",
    "  \"mappings\": [\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"pga\": \"642a50f1094d5f0e78ecf6e1\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                 \"java.lang.String type EQUALS DL\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"pga\": \"642a50f1094d5f0e78ecf6e1\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                 \"java.lang.String type EQUALS TL\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "              \"pga\": \"642a5379bd05ec5a1877051f\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                  \"java.lang.String type EQUALS Tow\"\n",
    "              ]\n",
    "          ]\n",
    "      },\n",
    "      {\n",
    "          \"entry\": {\n",
    "               \"pga\": \"642a54a98d2bc33e7ff4055b\",\n",
    "              \n",
    "          },\n",
    "          \"rules\": [\n",
    "              [\n",
    "                  \"java.lang.String type EQUALS Pol\"\n",
    "              ]\n",
    "          ]\n",
    "      }\n",
    "  ]\n",
    "}\n",
    "created_mapping_tornado_centerville_epn = fragility_services.create_mapping(tornado_centerville_epn_mapping_data)\n",
    "created_mapping_tornado_centerville_epn"
   ]
  }
 ],
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