Calculating affected population.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculation of affected population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The impacts of a typoon event are most strongly felt through the impacts on the community - the extent of building damage, the costs of repaiir and recovery and the number of people that require alternative accomodation while their houses are repaired. Here, we demonstrate the process to calculate the number of people affected by the impacts of a typhoon. \n",
    "\n",
    "It uses the fragility information on the different building types and the population information as described in the exposure database for the project. From these, we use monte carlo simulation to determine the probable damage state of all building types for a given typhoon event, and then from this we can evaluate the number of people affected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fragility curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fragility curves describe the probablity of a selected building type existing in a given damage state. For the GMMA RAP, the damage states were defined as slight, moderate, extensive and complete, and were based on the definitions from HAZUS-MH (Vickery _et al._, 2006). \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code block will plot the damage curve for a concrete wall building with wood or steel frame structure for the upper storey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import lognorm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plotcurve(windspeed):\n",
    "    \n",
    "    x = np.arange(0, 361., 1.)\n",
    "    \n",
    "    v_mu = 321.\n",
    "    v_sd = 0.52\n",
    "    v_sc = 1.0\n",
    "    \n",
    "    s_mu = 139.\n",
    "    s_sd = 0.34\n",
    "    s_sc = 1.0\n",
    "    \n",
    "    m_mu = 162.\n",
    "    m_sd = 0.30\n",
    "    m_sc = 1.0\n",
    "    \n",
    "    e_mu = 218.\n",
    "    e_sd = 0.29\n",
    "    e_sc = 1.0\n",
    "    \n",
    "    c_mu = 263.\n",
    "    c_sd = 0.31\n",
    "    c_sc = 1.0\n",
    "    \n",
    "    vcdf = v_sc * lognorm.cdf(x, v_sd, scale=v_mu)\n",
    "    \n",
    "    spdf = s_sc * lognorm.pdf(x, s_sd, scale=s_mu)\n",
    "    mpdf = m_sc * lognorm.pdf(x, m_sd, scale=m_mu)\n",
    "    epdf = e_sc * lognorm.pdf(x, e_sd, scale=e_mu)\n",
    "    cpdf = c_sc * lognorm.pdf(x, c_sd, scale=c_mu)\n",
    "    \n",
    "    spdf[0] = 0.\n",
    "    mpdf[0] = 0.\n",
    "    epdf[0] = 0.\n",
    "    cpdf[0] = 0.\n",
    "    \n",
    "    scdf = s_sc * lognorm.cdf(x, s_sd, scale=s_mu)\n",
    "    mcdf = m_sc * lognorm.cdf(x, m_sd, scale=m_mu)\n",
    "    ecdf = e_sc * lognorm.cdf(x, e_sd, scale=e_mu)\n",
    "    ccdf = c_sc * lognorm.cdf(x, c_sd, scale=c_mu)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Set up the figure canvas:\n",
    "    fig, ax1 = plt.subplots(1,1, figsize=(10,6))\n",
    "    \"\"\"\n",
    "    ax2.fill_between(x, 0, vcdf, facecolor='b', alpha=0.25)\n",
    "    ax2.set_xlim((0,360))\n",
    "    ax2.set_ylim((0,1))\n",
    "    ax2.set_ylabel('Damage ratio')\n",
    "    ax2.axvline(windspeed, color='k',linestyle=':')\n",
    "    \n",
    "    ypos = v_sc * lognorm.cdf(windspeed, v_sd, scale=v_mu)\n",
    "    if ypos < 0.95:\n",
    "        ax2.text(windspeed+2, ypos+0.05, \"%.3f\"%ypos, fontsize='x-small',\n",
    "                 color='b', ha='left', va='center')\n",
    "    else:\n",
    "        ax2.text(windspeed+2, ypos-0.05, \"%.3f\"%ypos, fontsize='x-small',\n",
    "                 color='b', ha='left', va='center')\n",
    "    ax2.grid(True)\n",
    "    ax2.set_title(\"Building damage ratio - %s\"%buildingtype)\n",
    "    \"\"\"\n",
    "    ax1.fill_between(x, 0, ccdf, facecolor='k', alpha=0.25)\n",
    "    ax1.plot(x, ccdf, 'k', linewidth=1.)\n",
    "    ax1.fill_between(x, ccdf, ecdf, facecolor='r', alpha=0.25)\n",
    "    ax1.plot(x, ecdf, 'r', linewidth=1.)\n",
    "    ax1.fill_between(x, ecdf, mcdf, facecolor='b', alpha=0.25)\n",
    "    ax1.plot(x, mcdf, 'b', linewidth=1.)\n",
    "    ax1.fill_between(x, mcdf, scdf, facecolor='g', alpha=0.25)\n",
    "    ax1.plot(x, scdf, 'g', linewidth=1.)\n",
    "    \n",
    "    bbox=dict(facecolor='white', alpha=0.5)\n",
    "    cypos = c_sc * lognorm.cdf(windspeed, c_sd, scale=c_mu) \n",
    "    ax1.plot([windspeed, windspeed], [0,cypos], color='k', label=\"Complete: %.3f\"%cypos)\n",
    "    if cypos > 0.05:\n",
    "        ax1.text(windspeed+2, cypos/2., \"%.3f\"%cypos, fontsize='x-small',\n",
    "                 color='k', ha='left', va='center', bbox=bbox)\n",
    "    eypos = e_sc * lognorm.cdf(windspeed, e_sd, scale=e_mu) \n",
    "    ax1.plot([windspeed, windspeed], [cypos,eypos], color='r', \n",
    "             label=\"Extensive: %.3f\"%eypos)\n",
    "    \n",
    "    if (eypos - cypos) > 0.05:\n",
    "        ax1.text(windspeed+2, (cypos+eypos)/2., \"%.3f\"%(eypos - cypos), \n",
    "                 fontsize='x-small',color='r', ha='left', va='center', bbox=bbox)\n",
    "        \n",
    "    mypos = m_sc * lognorm.cdf(windspeed, m_sd, scale=m_mu) \n",
    "    ax1.plot([windspeed, windspeed], [eypos,mypos], color='b', \n",
    "             label=\"Moderate: %.3f\"%mypos)\n",
    "    if (mypos - eypos) > 0.05:\n",
    "        ax1.text(windspeed+2, (mypos+eypos)/2., \"%.3f\"%(mypos - eypos), \n",
    "                 fontsize='x-small',color='b', ha='left', va='center', bbox=bbox)\n",
    "        \n",
    "    sypos = s_sc * lognorm.cdf(windspeed, s_sd, scale=s_mu) \n",
    "    ax1.plot([windspeed, windspeed], [mypos,sypos], color='g', \n",
    "             label=\"Slight: %.3f\"%sypos)\n",
    "    if (sypos - mypos) > 0.05:\n",
    "        ax1.text(windspeed+2, (sypos+mypos)/2., \"%.3f\"%(sypos - mypos), \n",
    "                 fontsize='x-small',color='g', ha='left', va='center', bbox=bbox)\n",
    "    \n",
    "    ax1.plot([windspeed, windspeed], [sypos,1.0], color='orange', \n",
    "             label=\"No damage: %.3f\"%(1.-sypos))\n",
    "    if sypos<0.95:\n",
    "        ax1.text(windspeed+2, (sypos+1.)/2., \"%.3f\"%(1.-sypos), \n",
    "                 fontsize='x-small',color='orange', ha='left', va='center', bbox=bbox)\n",
    "    ax1.set_xlim((0,360))\n",
    "    ax1.set_ylim((0,1))\n",
    "    ax1.set_ylabel(r\"$ P(D \\geq ds) $\")\n",
    "    ax1.set_xlabel(\"Wind speed (km/h)\")\n",
    "    ax1.axvline(windspeed, color='k',linestyle=':')\n",
    "    ax1.grid(True)\n",
    "    \n",
    "    l = ax1.legend(loc=2)\n",
    "    for t in l.get_texts():\n",
    "        t.set_fontsize('medium')\n",
    "        \n",
    "    ax1.set_title(\"Probability of damage states for %d km/h\"%windspeed)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to plot it up. We examine the case when the incident wind speed is 180 km/h. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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V4eHhXrq6uvkdahzMIYOgHgtFUSSnMsfwRe6L/nGFcU5JFUl2H+s+mvAkPLIu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\nRDoLAADcv4WffWq/6qrMDLwHCwsKh3QeekTo/Mgzy08/Y0BLrU7azS3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XN6OfgYGYlpHh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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda9f5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcurve(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colour-coded numbers adjacent to the vertical line indicate the probability of this type of building being in the given damage state. At 180 km/h, there is a 22.4% probability of no damage to buildings of this type. There is a 13.9% probability of only slight damage, 38.3% probability of moderate damage, 14.4% probability of extensive damage and finally, just over 11% probability that the building type will be completely destroyed. A building type can be in any one of these states, the probability of it being in that state is defined by these curves. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In any one land use polygon, there are a range of building types. Each building type has its own suite of fragility curves, dependent on the structural characteristics of that type. The complete table of building fragility curves is included at the bottom of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exposure data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In GMMA RAP, the exposure database is based around land use polygons. Each land use polygon is attributed with floor areas of different building types, land use classifications and population per unit floor area (people per m$^2$). For determining the affected population, the number of people in each building type is proportional to the floor area of that building type in the land use polygon. And the total population of a land use polygon is the total floor area multiplied by the population per unit floor area. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FID,Shape,NAME,L5_USE,L4_USE,AREA_SQM,OID1,FOOTPR_PC,FLAREA_SUM,STOR_MAX,STOR_MIN,STOR_RANGE,STOR_MED,STOR_MAJ,STOR_MNR,STOR_MEAN,STOR_STD,W1,W2,W3,N,CHB,URA,URM,RM1,RM2,MWS,CWS,C1,C2,C4,PC1,PC2,S1,S2,S3,S4,FLAREA_L1,FLAREA_L2,FLAREA_M\tFLAREA_H,FLAREA_V,FLAREA_E,,FLAREA_S,POP_SQM_FL,POP_EST,ERA_CONST,AGGPOLY_ID,W1_L_1,W2_L_1,W3_L_1,N_L_1,CHB_L_1,URA_L_1,URM_L_1,RM1_L_1,RM2_L_1,MWS_L_1,CWS_L_1,C1_L_1,C2_L_1,C4_L_1,PC1_L_1,PC2_L_1,S1_L_1,S2_L_1,S3_L_1,S4_L_1,W1_L_2,W2_L_2,W3_L_2,N_L_2,CHB_L_2,URA_L_2,URM_L_2,RM1_L_2,RM2_L_2,MWS_L_2,CWS_L_2,C1_L_2,C2_L_2,C4_L_2,PC1_L_2,PC2_L_2,S1_L_2,S2_L_2,S3_L_2,S4_L_2,W1_M,W2_M,W3_M,N_M,CHB_M,URA_M,URM_M,RM1_M,RM2_M,MWS_M,CWS_M,C1_M,C2_M,C4_M,PC1_M,PC2_M,S1_M,S2_M,S3_M,S4_M,W1_H,W2_H,W3_H,N_H,CHB_H,URA_H,URM_H,RM1_H,RM2_H,MWS_H,CWS_H,C1_H,C2_H,C4_H,PC1_H,PC2_H,S1_H,S2_H,S3_H,S4_H,W1_V,W2_V,W3_V,N_V,CHB_V,URA_V,URM_V,RM1_V,RM2_V,MWS_V,CWS_V,C1_V,C2_V,C4_V,PC1_V,PC2_V,S1_V,S2_V,S3_V,S4_V,W1_E,W2_E,W3_E,N_E,CHB_E,URA_E,URM_E,RM1_E,RM2_E,MWS_E,CWS_E,C1_E,C2_E,C4_E,PC1_E,PC2_E,S1_E,S2_E,S3_E,S4_E,W1_S,W2_S,W3_S,N_S,CHB_S,URA_S,URM_S,RM1_S,RM2_S,MWS_S,CWS_S,C1_S,C2_S,C4_S,PC1_S,PC2_S,S1_S,S2_S,S3_S,S4_S,Shape_Leng,REMARKS,Shape_Le_1,Shape_Area,vmax\n",
    "80,'Polygon','','Residential','Formal Settlements',113864.493237,23634,0.182146,25761,6,1,5,1,1,6,1.24209,0.696785,1977.259794,0,0,891.923103,3577.120938,25.529151,14.864097,0,0,2333.560185,9060.968052,7435.526235,0,0,0,0,38.847588,0,17.826612,0,17852,3120,4787,0,0,0,0,0.021107,543.743568,1972-1992,'MUNTINLUPA - POBLACION',2525.1,0,0,1139,4568.2,32.6,19,0,0,0,0,9495.7,0,0,0,0,49.6,0,22.8,0,243.1,0,0,109.7,439.9,3.1,1.8,0,0,286.9,1114.2,914.3,0,0,0,0,4.8,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,4762.1,0,0,0,0,24.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1868.979849,'' ,1868.979849,113864.493237,35.9053\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fragility curve table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table contains the parameters for the full suite of fragility curves used in GMMA RAP. The scale parameter is used to "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| TYPE    | Vulnerability |    SD    |   Scale  | Slight |  SD  |   Scale  | Moderate |  SD  |   Scale  | Extensive |  SD  |   Scale  | Complete |  SD  |   Scale  |\n",
    "|---------|:-------------:|:--------:|:--------:|:------:|:----:|:--------:|:--------:|:----:|:--------:|:---------:|:----:|:--------:|:--------:|:----:|:--------:|\n",
    "| W1_L_1  |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_L_1  |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_L_1  |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_L_1   |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_L_1 |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_L_1 |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_L_1 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_L_1 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_L_1 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_L_1 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_L_1 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_L_1  |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| C2_L_1  |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| C4_L_1  |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| PC1_L_1 |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| PC2_L_1 |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| S1_L_1  |         379.1 |     0.61 |        1 |    154 | 0.35 |        1 |   180.45 |  0.4 |        1 |    202.75 | 0.43 |        1 |    315.1 | 0.46 |        1 |\n",
    "| S2_L_1  |         379.1 |     0.61 |        1 |    154 | 0.35 |        1 |   180.45 |  0.4 |        1 |    202.75 | 0.43 |        1 |    315.1 | 0.46 |        1 |\n",
    "| S3_L_1  |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_L_1  |         379.1 |     0.61 |        1 |    154 | 0.35 |        1 |   180.45 |  0.4 |        1 |    202.75 | 0.43 |        1 |    315.1 | 0.46 |        1 |\n",
    "| W1_L_2  |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_L_2  |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_L_2  |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_L_2   |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_L_2 |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_L_2 |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_L_2 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_L_2 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_L_2 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_L_2 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_L_2 |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_L_2  |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| C2_L_2  |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| C4_L_2  |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| PC1_L_2 |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| PC2_L_2 |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| S1_L_2  |         379.1 |     0.61 |        1 |    154 | 0.35 |        1 |   180.45 |  0.4 |        1 |    202.75 | 0.43 |        1 |    315.1 | 0.46 |        1 |\n",
    "| S2_L_2  |         379.1 |     0.61 |        1 |    154 | 0.35 |        1 |   180.45 |  0.4 |        1 |    202.75 | 0.43 |        1 |    315.1 | 0.46 |        1 |\n",
    "| S3_L_2  |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_L_2  |         379.1 |     0.61 |        1 |    154 | 0.35 |        1 |   180.45 |  0.4 |        1 |    202.75 | 0.43 |        1 |    315.1 | 0.46 |        1 |\n",
    "| W1_M    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_M    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_M    |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_M     |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_M   |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_M   |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_M   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_M   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_M   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_M   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_M   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_M    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C2_M    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C4_M    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC1_M   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC2_M   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| S1_M    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S2_M    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S3_M    |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_M    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| W1_H    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_H    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_H    |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_H     |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_H   |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_H   |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_H   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_H   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_H   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_H   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_H   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_H    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C2_H    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C4_H    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC1_H   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC2_H   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| S1_H    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S2_H    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S3_H    |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_H    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| W1_V    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_V    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_V    |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_V     |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_V   |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_V   |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_V   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_V   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_V   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_V   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_V   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_V    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C2_V    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C4_V    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC1_V   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC2_V   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| S1_V    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S2_V    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S3_V    |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_V    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| W1_E    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_E    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_E    |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_E     |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_E   |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_E   |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_E   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_E   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_E   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_E   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_E   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_E    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C2_E    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C4_E    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC1_E   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC2_E   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| S1_E    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S2_E    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S3_E    |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_E    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| W1_S    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W2_S    |           176 |     0.09 |        1 |    143 | 0.16 |        1 |      163 | 0.17 |        1 |       200 | 0.26 |        1 |      259 | 0.28 |        1 |\n",
    "| W3_S    |         95.31 |     0.25 |        1 |     62 | 0.16 |        1 |       82 | 0.17 |        1 |       120 | 0.26 |        1 |      180 | 0.28 |        1 |\n",
    "| N_S     |           136 |      0.2 |        1 |    103 | 0.16 |        1 |      123 | 0.17 |        1 |       162 | 0.26 |        1 |      219 | 0.28 |        1 |\n",
    "| CHB_S   |           455 |     0.59 |        1 |    164 | 0.15 |        1 |      188 | 0.19 |        1 |       230 | 0.22 |        1 |      324 | 0.17 |        1 |\n",
    "| URA_S   |       416.409 | 0.523497 |        1 |    132 | 0.17 |        1 |      208 | 0.16 |        1 |       240 | 0.14 |        1 |      301 | 0.17 |        1 |\n",
    "| URM_S   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM1_S   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| RM2_S   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| MWS_S   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| CWS_S   |           321 |     0.52 |        1 |    139 | 0.34 |        1 |      162 |  0.3 |        1 |       218 | 0.29 |        1 |      263 | 0.31 |        1 |\n",
    "| C1_S    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C2_S    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| C4_S    |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC1_S   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| PC2_S   |           221 |     0.33 | 0.133333 |    144 | 0.17 | 0.133333 |      171 | 0.17 | 0.133333 |       218 | 0.22 | 0.133333 |      270 | 0.17 | 0.133333 |\n",
    "| S1_S    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S2_S    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |\n",
    "| S3_S    |           387 |     0.37 |        1 |    231 | 0.24 |        1 |      270 | 0.24 |        1 |       322 | 0.24 |        1 |      478 | 0.36 |        1 |\n",
    "| S4_S    |           180 |     0.24 | 0.133333 |    140 | 0.15 | 0.133333 |      152 | 0.15 | 0.133333 |       188 | 0.21 | 0.133333 |      205 | 0.17 | 0.133333 |"
   ]
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   "source": []
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