bottom_up_exposure_modelling.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"logo_gfz.jpg\" alt=\"GFZ-Potsdam\" style=\"width: 200px;\"/>\n",
    "\n",
    "# Risk-oriented, bottom-up modelling of building portfolios with faceted taxonomies \n",
    "### Implementation of fuzzy scoring to assign building vulnerability classes starting from faceted taxonomy descriptions. \n",
    "#### Massimiliano Pittore, Michael Haas, Konstantinos G. Megalooikonomou and Vitor Silva\n",
    "\n",
    " Copyright © GFZ-Potsdam 2018\n",
    " \n",
    "#### Disclaimer: \n",
    " This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n",
    "\n",
    "The present code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### introduction\n",
    "\n",
    "The following notebook implements the methodology described in the companion paper \"Risk-oriented, bottom-up modelling of building portfolios with faceted taxonomies\" Pittore et al., 2018.\n",
    "\n",
    "The methodology aims at classifying an unknown building based on its taxonomic description according to the GEM taxonomy v2.0 (Brzev et al., 2013).\n",
    "\n",
    "**NOTE**: Besides Python2 the notebook requires several packages which can be installed using the file 'requirements.txt'\n",
    "          \n",
    "          pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations on triangular fuzzy numbers (TFN)\n",
    "\n",
    "A triangular fuzzy number (TFN, see, e.g., Dhutta et al.,2011) is hereby defined as an array of three real numbers $[n_1,n_2,n_3]$; the first describes the value with the highest membership, i.e. the mode of the distribution. The second and third numbers represent the support of the fuzzy number, i.e. the interval outside which its membership is zero ($n_1 \\in [n_2,n_3]$). The membership function will be then linearly increasing from 0 at $n_2$ to 1 at $n_1$, and then will linearly decrease from 1 at $n_1$ to 0 at $n_3$.\n",
    "\n",
    "In the following a few basic operations with TFNs are defined.\n",
    "\n",
    "See e.g., Dhutta et al. (2011),Fuzzy Arithmetic with and without using\n",
    "α-cut method: A Comparative Study.\n",
    "International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) \n",
    "99 Volume 2, Issue 1, March 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def fuzzy_sum(a,b,ac=1,bc=1):\n",
    "    '''\n",
    "    Weighted sum of two triangular fuzzy numbers. implements:\n",
    "    sum = ac*a + bc*b\n",
    "    'ac' and 'bc' are the (real-valued) multiplicative coefficient \n",
    "    of the two summands 'a' and 'b'. \n",
    "    '''\n",
    "    #a,b: triangular fuzzy numbers\n",
    "    #ac,bc: coefficients (multiplicative)\n",
    "    # equivalent to d = ac*a + bc*b\n",
    "    d = [ac*a[0]+bc*b[0],ac*a[1]+bc*b[1],ac*a[2]+bc*b[2]]\n",
    "    return d\n",
    "\n",
    "def defuzzify(a,method='mode'):\n",
    "    '''\n",
    "    turn a triangular fuzzy number into a crisp number (i.e., a single real value). \n",
    "    Mode may take the following values: 'mode','mean','median', \n",
    "    indicating the respective statistical descriptors considering the TFN as \n",
    "    a piecewise linear probability density.\n",
    "    '''\n",
    "    r = 0\n",
    "    if (method=='mode'):\n",
    "        r = a[0]\n",
    "    elif (method=='mean'):\n",
    "        r = sum(a)/3.0\n",
    "    elif (method=='median'):\n",
    "        if (a[0]>=(a[1]+a[2])/2.0):\n",
    "            r = a[1]+np.sqrt((a[2]-a[1])*(a[0]-a[1])/2.0)\n",
    "        else:\n",
    "            r = a[2]-np.sqrt((a[2]-a[1])*(a[2]-a[0])/2.0)\n",
    "    else:\n",
    "        raise NumError('unrecognized method: {}'.format(method))\n",
    "    return r\n",
    "\n",
    "\n",
    "def fuz_greater(a,b):\n",
    "    '''\n",
    "    Compute the degree of membership of the relation \"a > b\"\n",
    "    between two triangular fuzzy numbers.\n",
    "    after:\n",
    "    D. Dorohonceanu and A. Marin (2002) Simple Method for Comparing Fuzzy Numbers, and\n",
    "    A. S. Dadgostar, A Decentralized Reactive Fuzzy Scheduling System for \n",
    "    Cellular Manufacturing Systems, \n",
    "    Ph.D. Thesis, University of South Wales, Australia, 1996.\n",
    "    \n",
    "    NOTE: crisp inputs are not supported\n",
    "    '''\n",
    "    dsum=dsuml=0\n",
    "    for p in np.arange(0,1,0.2):\n",
    "        i_a = [a[1]+p*(a[0]-a[1]),a[2]-p*(a[2]-a[0])]\n",
    "        i_b = [b[1]+p*(b[0]-b[1]),b[2]-p*(b[2]-b[0])]\n",
    "        d = (i_a[1]-i_b[0]) / (i_b[1]-i_b[0]+i_a[1]-i_a[0])\n",
    "        if d>1: d=1\n",
    "        if d<0: d=0\n",
    "        dl = (i_b[1]-i_b[0])*(i_a[1]-i_a[0])\n",
    "        dsum = dsum+d*dl\n",
    "        dsuml = dsuml+dl\n",
    "        assert abs(dsuml)>1e-8,'error, crisp numbers are not supported'\n",
    "    return dsum/dsuml\n",
    "\n",
    "def fuz_argmax(fvec,mThresh=0.5):\n",
    "    '''\n",
    "    implement argmax of a list of fuzzy numbers 'fvec'.\n",
    "    'mThresh' is the threshold on the degree of membership of \n",
    "    the 'greater than' relation in order to consider the number actually greater.\n",
    "    '''\n",
    "    ind1=0\n",
    "    ind2=1\n",
    "    for step in range(1,len(fvec)):\n",
    "        if fuz_greater(fvec[ind1],fvec[ind2])<mThresh:\n",
    "            ind1 = ind2\n",
    "        ind2 = ind2+1\n",
    "    return ind1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to draw triangular fuzzy numbers\n",
    "\n",
    "Auxiliary functions to plot one or more TFNs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import figure, show, cm\n",
    "from matplotlib.patches import Polygon\n",
    "\n",
    "def plot_fnum(fnumlist):\n",
    "    '''\n",
    "    Plot a list of triangular fuzzy numbers as triangles (polygons)\n",
    "    '''\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "\n",
    "    for fnum in fnumlist:\n",
    "        print fnum\n",
    "        poly = get_tripoly(fnum)\n",
    "        p = Polygon(poly,closed=True,color='red',alpha=0.2,fill=False)\n",
    "        ax.add_patch(p)\n",
    "\n",
    "    ax.set_xlim(-1,1)\n",
    "    plt.show()\n",
    "\n",
    "def get_tripoly(f_num):\n",
    "    '''\n",
    "    auxiliary. Turns a triangular fuzzy number into a polygon \n",
    "    '''\n",
    "    poly = [[f_num[1],0],[f_num[0],1],[f_num[2],0]]\n",
    "    return poly\n",
    "\n",
    "def plot_v_fnum(ax,fnum, xx, width,alpha):\n",
    "    '''\n",
    "    plots a triangular fuzzy number 'fnum' on a predefined axix 'ax' at a x-coord 'xx'\n",
    "    as a rectangular patch with gradient fill, opacity 'alpha' and width 'width'. \n",
    "    Also overlay three defuzzified estimates. It needs the auxiliary \n",
    "    function 'defuzzify'.\n",
    "    '''\n",
    "    Xup = [[1, 1], [.0, .0]]\n",
    "    Xdown = [[.0, .0], [1, 1]]\n",
    "    ax.imshow(Xdown, interpolation='quadric', cmap=cm.Blues,vmin=0,vmax=1,\n",
    "                      extent=(xx-width, xx+width, fnum[0],fnum[2]), alpha=alpha)\n",
    "    ims = ax.imshow(Xup, interpolation='quadric', cmap=cm.Blues,vmin=0,vmax=1,\n",
    "                      extent=(xx-width, xx+width, fnum[1],fnum[0]), alpha=alpha)\n",
    "    mode=defuzzify(fnum,'mode')\n",
    "    median=defuzzify(fnum,'median')\n",
    "    mean=defuzzify(fnum,'mean')\n",
    "    hl1 = ax.hlines(y=mode, xmin=xx-width, xmax=xx+width,alpha=alpha)\n",
    "    hl2 = ax.hlines(y=median, xmin=xx-width, xmax=xx+width,alpha=alpha,linestyle='dashed')\n",
    "    hl3  = ax.hlines(y=mean, xmin=xx-width, xmax=xx+width,alpha=alpha,linestyle='dotted')\n",
    "    return ims\n",
    "\n",
    "\n",
    "def plot_fnum_vec(fnum_list,fnum_lab,figfilename='',alpha=0.5,rot=0,ha ='center',loc='upper right'):\n",
    "    '''\n",
    "    Plots a list of triangular fuzzy nubers 'fnum_list', with respective\n",
    "    labels 'fnum_lab'. 'alpha' is the level of opacity, 'rot' the rotation of the x labels,\n",
    "    'ha' is the location of the x-labels with respect to the theoretic position, \n",
    "    'loc' is the location of the legend. \n",
    "    if 'figfilename' is defined then is used as path and filename to save the figure.\n",
    "    Needs the auxiliary function plot_v_fnum.\n",
    "    '''\n",
    "    fig = figure()\n",
    "    bar_width = 0.5\n",
    "\n",
    "    n_groups= len(fnum_list)\n",
    "    ax = fig.add_subplot(111, xlim=[bar_width/2,n_groups+bar_width], ylim=[-1,1],\n",
    "                         autoscale_on=False)\n",
    "\n",
    "    ax.set_xticks(np.arange(n_groups) + 1)\n",
    "    ax.set_xticklabels(fnum_lab,rotation=rot,ha=ha)\n",
    "    \n",
    "    ims = 0\n",
    "    for i,fnum in enumerate(fnum_list):\n",
    "        ims = plot_v_fnum(ax,fnum,i+1,0.25,alpha)\n",
    "    \n",
    "    ax.set_aspect('auto')\n",
    "    ax.legend(('mode','median','mean'),loc=loc)\n",
    "    cbar = fig.colorbar(ims,orientation='vertical')\n",
    "    plt.ylabel('compatibility score')\n",
    "    plt.xlabel('compatibility level (fuzzy)')\n",
    "    if (figfilename):\n",
    "        plt.tight_layout()\n",
    "        print 'saving fig {}'.format(figfilename)\n",
    "        plt.savefig(figfilename,dpi=300)    \n",
    "    plt.show()\n",
    "    show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fuzzy compatibility levels\n",
    "\n",
    "Let us define two sets of compatibility levels expressed as triagular fuzzy numbers (TFN).\n",
    "Both are composed by 7 compatibility levels: \n",
    "- highly compatible\n",
    "- moderately compatible\n",
    "- slightly compatible\n",
    "- neutral\n",
    "- slightly incompatible\n",
    "- moderately incompatible\n",
    "- highly incompatible\n",
    "\n",
    "these are used to encode into a fuzzy representation the degree of compatibility of an attribute value with respect to a reference class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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soBHs06s7e/XoVnhgkfh2dqWl1O3szvj//ifS2AX/cnLW44nIqcANNJfevTbj8QHAXcBe4ZgrVPWRfMfynnor3GuSAZaTiXbhCdfKAatpQS+PpzDF2AoRSQA3AScB9cAyEZmnqumNp68E/qSqN4vIIcDDwIH55HqjnoFlSqN7lwYMa7/YLWayS9e0jKn7i4MnAsWd5GOBNapaByAic4HJQLpRTwI9w/t7kdHuLhveqGcgCImEW+ZYgG67dS63Gi1QhYZG12q8G+apm6XO5xKUrYOsp9IoMqWxH5Ae691AYOjTmQ0sFJHLgd2BkwsJ9UY9A3GxoBfw6orneWt18XPoA/f8nLOmnF68Tg7WeAc3G3c0ZHmbRHyGTTWwfnP2lMZP1q3gk3UmOS/nAXeo6n+IyNHAPcDwfE/wRj0TsfTU7S4Og4aOYNDQEYUHFuDY44410CbArMa7ZTPsCll85MM71UHtPjlSGvc5FkY3n2v/9fQfs416BxiYtt2f1uGVS4CJAKq6VES6ikgfVd1EDrxRz0AwLBPglsNviq2nbtgM20FP3ePJwTJgsIjUAhuBcwk883TqCEIud4UTpbvlM+jgjXorxNRTr17UwTIB4EvveuKlmFCtqjaKyAxgIc0pja+LyGxgmao+BPwIuEVEfkAwaTq9kFxv1DOwjKlX86XBRU8d3OvG5KlyijzJVfVRYGjGvplp918Hjm+LTG/UM3Ax+8UUw5fmXDcmDD11Eymease1pArwRr01pqV3XcTmtdn2TbWLg1vVyPGOuicKvkpjFiIsk50O/DtBDifATap6e6n0SYhkrSBXblybuw2qNDpmQE3j/JYZOWaiPI6xYYt7VRrLar3SlslOJMi9PE9EhmUZOldVjwxvJTPogVKQSIjJzfYiHrXFSkwtWDSz61L7b9YxdZsWe6btsPPc3MWqnV2ltahrCwP67BHpFifl9tSjLJOFGOccLWPqltnurv3IUwy9YhMpgRwXY+pum+78pNrZ7dq1i5EjR7J8+XJuv/12hg0bxqRJk5gzZw6XXHIJp59+Ovfeey8TJ05k0aJFTJ06lVWrVtG7d++mFnUHHHAATz31FKeeeipjx45t6maU3qJu4cKFnHXWWUyZMoU99yy+8Fyp8eGX1kRZJgvwVREZB6wGfqiq2dumGCCm4RfDD9yx706Q0uhenRW7DlGW9YDb/9RUa7pUN6Nit9tKse3sLrzwwsprUdcG/ERp+5gP3Kequ0TkWwRlKE/KNnDWrFlN99vb1su2R6kR1pEcE9xssedik4xiRGV+h4vdbivFtrMDN1rU5er1WjTunZhlN+oFl8mq6pa0zVuBX+USlm7U24uIUJMj/KIU/gyjjGk7YjdTaoSbqzfVzSqNVTxTWqidnSst6jKdvNmzZ5vIddFTL3eaR9MyWRHpQrBMdn76ABHZN21zMvBaKRUSAUlI1lsix/5cY6yjLw5NkwJB/NriZlmuxUwnG5Wa9Kq8adLoTJs2jfnz57Nw4UKSySQ7duxgyZIl1NfX52xR5ykdZfXUIy6TvVxEzgR2AZuBb5RSp8BTt7nW2Sa/ZJeWKuCa/mgcxkLVMPxiqLGVTtaeeml+wZUWi3Z21d6irv6jz8qtQit8O7sMNn7wMes2bik8MAKjDulPl87FXzdff3MjN/zubtauWtHqsUFDR3DQsCNa7X9z5UtZx3/74nOZ+g/Fl96tf/8j/nuRTYu9SeMO48D+fYqW8+lnO1ny3BoDjeDQg/ajdv+9i5ajqny2M3fd+T26dvYVG0tIqdvZXfDbpZHG3vt/ji76eFEpd0zdOUTC0IlLCAw+5AgGH9LaeOci1/jjjjvaRCVX0wftuij5xUeeKDhmK/BGvRUi5JwoLSdmP0nNlpTaTUralQlwNPvFW/WqxcVIkTfqrbBsPG21iMm9JQ6KYR10EymBJLtfD5ZaeaqVYqffIpRJuR74EsHXaA9gH1XNGxf0Rj0DEcwWH1lexd8I4+ODw/j5Gytfatc2Z9iEXyxzwi2tnt1EqYmYEgjzuEX7T/K0MiknAfXAMhGZp6pNK+pV9Ydp42cAIwvJ9UY9AxFxcvHRwYe0/CyL3bbAuZi6Ylil0cEWex7n2PhxUdkvUcukpDgP+FkhoZGMuogcDxysqneIyD5Ad1V9K5LaFYaAc/XUBfdid5bhFyuzbjl5a/nrwdv06qX/3rsX8/SoZVIQkYHAAcDjhYQWNOoiMhMYQ9Cd4w6gM0FH6+MKPbcScdJTx8VuTIaVDE3DLw7G1PO8TwMH1lZUXnalUVtbW+IjxPbZnQv8OUredhRPfQowCngBQFXrRaRHcfo5TLii1C1SE6XpS4206bFmMj/vzHFqWlDdQZtu2sTaityilBdfyfylnXuZUtcuNXTbrbOdYp6iyXU93rjyOTaufL7Q0wuWSUnjXOA7UXSKYtR3qqqKiAKISLzFgWNGsPPUzXJoWhQZy/xb6IhS4PH2YRt+scIwzdJylWve9ynbY9nHO/d2e3Ia9f0PGcP+h4xp2n5x/i3ZhjWVSQE2Ehju81ofQ4YBe6lqpJVOUYz6/SJyM7CXiFwKXAxk1bAasGw8bYl7vx4sjYydd+2kp+7g5K3HhmJsRcQyKQBfA+ZGlVvQqKvqdSLyZWArQVz9Z6r6WJtfQaUgho2nrcQYXmgsLw1mcWfLmLprGTl4Y+zJjao+SmBX0/fNzNhuU0nJvEZdRGqA/1HVLwHVa8jTCDJN3Fp85OKCKKjumLqlq+7irwePDe9u3VFuFVqR16iHPw+SIrKnqn4cl1LlxMkmGYBZ9MX0pblnZVwsE2C3Rsu997uj069Xt3Kr0IooMfVtwMsi8hiwPbVTVS8vmVZlxLJHqRVOhl8Ms18sqerwi4Pvt8ctWwHRjPqD4a1j4OREqWX4xQYX7YtpRo6L4Rcn3/WOjWOnJRBtovSusCvRkHDXKlXdVVq1yoc0GdD0fOEoOeEtpYCWKKXRk5Os2S/pufpRESfLAbv4y6ij4+JpGWVF6XiCZs9vE5whA0Rkuqo+WVrVykRTO9BC+d2FPk3bT9u50ruO0trwtccS2lpPq5RGb9Xdw7Vf0BAt/PJr4BRVXQUgIkOAPwKjS6lYuRDc84otdbIq4isCPfboaiLLDsPSu4YG1MU4f6NRJ8LgV6SNLI8NUYx655RBB1DV1SJSvWuVxTB+bRh/cW3xkSq89MKzvLX65aJl9eo0lSEHfNVAK/dywk3rzjt4oUmAmzGImHjvk8/LrUIrohj150TkVoIiXgAXAM+VTqXyYpunboOLvx4g6I86aOiIouV88Wi7H30u5oS7mGbpsWH/vSozpfH/AN8FUimMTwG/KZlGZcbFn5OSWuWardZTRr2uFnO7mfO6Ut1OVVB610qWe16xz37xRCGKUe8E/KeqXg9Nq0x3K6lWZcW99EEIfz20pV5X5lj3XlJJcDEn3MVfDx4bHDQVkYz6IuBkgkVIAN0ICtAcWyqlyomLBb1sm2E7tojJEsOCXmYoNFZxQS/3NIoX12wFRDPqXVU1ZdBR1W0iUlS7D9exmyd1r/aLg99BUxxce2RYpdFEjMcQF8+nKEZ9u4gcqaovAIjIaKCoxnxu4174JWiG7ZZO4Ka37lqLPbBcfGS4ytVIjovfgTgp9vWLyKnADTSX3r02y5hzgJlAEnhJVaflkxnFqH8feEBE6glew74E9X2rEjfDL0JCEjayzOoB4+QZbbck345k0iYp3HSi1Hv9Jry/bWe7nysiCeAm4CSgHlgmIvNUdWXamMHAT4BjVHWriPQpJDdKmYBlYeeNVM3fqi4TAO79pBIMPXXD17Z21cu8tXpFq/0HDsme6rh21Yqs4wf03GGYp24ixs2+qaaG2PvqFuy3Z1EL8MYCa1S1DkBE5gKTgfQeh5cC/1dVtwKo6qZCQqOUCTgbeFRVXxGRK4EjReTnqXBMtRE4oI59UR0Nvxw0dAQHtSFPPdf4Lx59pIk+iqU368MvccqpVIo8K/sB69O2NxAY+nSGAIjI3whCNLNV9a/5hEYJv1ylqg+IyPEEPxOuA34LfDGi4pWFiIOeul05YNOX5tyPB3WycYeTnrqZo567UXZHIAZb0QkYDJxA0KT6SRE5LOW553pCIRrDv18BblHV/yciPy9aVUdxckWpYBZTt/wWvrUqKBGQCrWsXbWiXducYuOpg5tpf2YxdWOrbiJN3TpXYifH+bTu5WdZ98qzhZ79DoGhTtE/3JfOBmCpqiaBt0VkNXAw8HwuoVGM+jth4+kvA9eKyG6EJR+qlnzf07Y4Joa1X1z01AcNG2G6bYKDcQU7T92yxruRoI5u03Psrz18LLWHN0dSnp6bdRH+MmCwiNQCG4FzgfMyxvx3uO+ucJL0YGBtPp2iGPVzgFOB61T1IxHZD/hxhOdVJlIgpl6GL7HpRKkRtskvpgEYIzlWuFk50sFrX0VSzGkZtgudQbCYM5XS+LqIzAaWqepDqvpXETlFRF4FGoAfqeqWfHKjZL98SlrnI1XdSHBVqUqC8Eu5tWhJUPvFKKXR8LW5FqaybbFnZ64azcIvJmKahNmEXyyEVC7FpDQCqOqjNGcWpvbNzNj+J+CfosqM4ql3MMS57BfbxUdVnqfunKfuaPil6T8DOR2Y/Xq61lPAG/XWGBorwyIB1LjoqRs23DDDsbiCAo1GHSmswjhgeYFw8MoeJw6+/Ch56t8D7ikUx6kmXPPUMS3oZYV7qZ/gZnnaqvbUHcw2ihMHT4FInnpfguWrLwC3A3/VKv4kpek/d7CMqVtaYudi6uBkPMAupdFETJMwG3kOfgdixMVzIMpE6ZUichVwCnARcJOI3E8wU/tmqRUsB659TJbZL5aReTezX9zDyewXtXmvqte9i4aDNj1aTF1VVUTeBd4lSKvpBfxZRB5T1X8upYLxk6MZRRkRwzx1S8zKAZtISYUU3OsyZFVP3Tqm7t6UcuWxqcjsl1IQJab+j8DXgU3ArcCPVXVXWGFsDVBlRt05mx7WfnFsotSyNZ7hG+5gRqOTK0q9p25D3x7uNYGL4qnvDXw1VUkshaomReT00qhVPsTBVD3BsvG0j6nHirpZ+0WN6uS49nbHjYvnQBSjPijToIvI3ap6oaq+XiK9yoprH5Nl+MU0pu5Y+AXcNDJOZr9Ytf7r4K66gzY9klEfnr4RNp4eXRp1XMG9T8q9euqGKY2mb7d7RsZuotRETCjLe+oWVJRRF5ErgH8BuolIqsyjADuB38egW/lw7IOy7HxkhmmHKBeD6na42PnIzKh3dE/dNWNBHqOuqtcA14jINap6RYw6lR3XPibblEa7kIlVnN/F8IttSrh74ZekmVEvXkYlU2me+rCwV94DItKq4HW1dj5yEdOURhcLejl4Ylji4kQpPqZuwofb3evsmS+m/k8E/fF+neUxBSaURCNPa8RNr9huorS6rbqLE6WBp168vI5t0uELPbqUW4VW5Au/XBr+/VIpFRCRU4EbaK4nfG3G412APxBMzm4Cvqaq60qpk2s4WU9dxDDNsrpxdaLUQi8fUy/uHIhg/6YD/07QAQngJlW9PZ/MfOGXvO3dVfXBfI9HIVzAdBNB79N6ghoz88KwT4pLgM2qerCIfA34FUGHkI6DpQE1rf1iJchIjqO46Klr0sfULSjmHIho/wDmqurlUeXmC7+ckecxJa1xRhGMBdak8uBFZC4wGUh/UZOBVNH4PxO8CR0KAcSxPHVwM0/dRVz0ZpPY6OViVcw4KfIUiGL/oI2nSL7wy0Vt1bAd9APWp21vIHihWceE7Z8+EpG9VXVzKRQSkUjuh4g0nRS57tspZbmi1AYnG3c4iOKup24TfjFQpoIp0rGJYv8Avioi44DVwA9VdUOWMU3kC79MU9V7ROSH2R5X1esL61wScr6Ls2bNaro/fvx4xo8f32bhCYFETdQPSiLcLx5BHMw0sdPJ8no1/77fUPfmq3TbvTsAn326DYD9BhxEr9592fLhewD06t0XgHVrV7Jt6+ZW4z+84ELGXHu1iU52KY0mYkJZRhOlhkqgiB5ZAAAfnUlEQVQ12KTzI0BNxrKOxYsXs3jxYpsDZBwrG28sX8obLy61OMR84L6w3ta3gLsIwjU5yRd+2SP828NCsxy8AwxM2+4f7ktnAzAAqA9Xs/bM5aWnG/WqwtArtrzcuPbrAeDM879jImfi8cMLD4qI2USpYagjqWryC8L0V6lV5cgsTREynbzZs2dbHIktn2ZPaew9dDS9hzYvvP/rnf+ZbVhB+5fRnOhWgjnFvOQLv9wc/rV59dlZBgwWkVqCZtbnAudljFkATAeeAc4GHi+hPk5iWWfFyqxbhl9cLIpkiZnhs0xp9OEXE/p0LyqlsaD9E5F9VfXdcHMy8FohoVFK7w4C/hM4miBE+HfgB6q6tk3qZyGMkc8AFtKc0vO6iMwGlqnqQ8BtwN0isgb4kI6W+UIQfnHRK652Y2yFqymN6pqnXoEU49dEtH+Xi8iZwC5gM/CNQnKjFPS6D/i/wJRw+1zgj8AX2/wqsqCqjwJDM/bNTLv/OXCOxbEqFsM6K3ahebsWe9V+bTCLqZtICUhq0uepG1BsnnoE+/cvBDW4IhPFqO+uqnenbd8jIj9uy0E8xWFbT90I01Wujr02Y1xsZ5dM2mTl2NZ4rzxcOy0hf/bL3uHdR0Tkp8Bcgvf9a8DDMejmSSFilqduhYurXF3FIswRCLIRA+FEabV66jGqVFFGHXie4O1JqX1Z2mMKdKjKjeXGxS+PiDR/O9K/Kdm2yf2Yi6/NDpuVm6EkG0GEeeo+pl40Ln5182W/HBinIp7c2Ja5tSs30MJTzxSb7zCtxrpXusDyZHVxojSpSROjbtkMG2xeY5xOwsefNcR3sIjkC79MUNXHc9WAsaj94omIZUMKQzEu5s4H0jItQ7Z92R4vNK59uOjNupvSmBKY71uR72dgvLH53nt0jvFo0cgXfjmRICc8Ww0Yq9ovngiYrii1QnAy+0XS/s/2SKFnluIHtZNNMpJq0pHJtm9qeoCpkNzcj+f+Dtjj2mkJ+cMvqbSaq1X1rfTHRMSHZmLGtS+Pbe68uVV3Cgcd9aD0rosxdQffq3y4dl5CtJTGvwCZnY/+TNU3n3YIcTDtz3RFqYkYZ7Ga4DTNUzeaKLWOqVsQp0YJ185LCrSzA4YDe2bE1XsCXUutmKcZyzIBlr51jYNVGi0ngq0o3psNY/2GBrTRLKXRQJkmWTaXvzgdIBcdknye+lDgdGAvWsbVPyFoc+eJDXGwIYV7K0qz1HEqTpYFJrZY0/63QR2MqQcCDWTEaGgryqir6jxgnogco6p/j1EnTxZcmyg1LehlIsVelhV2Bb1sxAA0JpM0Ohh+sZAWZ7bR1h0VlNKYxnIR+S5BKKYp7KKqF5dMK08LRGiZbddWy9UiA8zOEFt56rZ56lIweRGyJzCWJqExj9AoB0wbY7/4yMBTt1otS/iLJotBTv/65k9mDInRAdq7W2WlNKa4m6C90kTgauAC4PVSKuVpjSDtt8el+I6LmMXUrc/BKOJyJTCW4q3KaYyj2MMSOZ2N6p6nrnkuW5rjftaxvkxAQQar6tkiMllV7xKR+4CnSq2YpyWufXksPXXLiS0H+3PblQmwXFHa6GBM3WouuIJi6iJyKnADzaV3r80xbirwADBGVV/IJzOKUU+19vhIRA4D3gW+EFlrjwkOzpO656mnr7xtq3EoWezFQmykoEObsOp8ZOup5/hVE/Xlh+PiTGksZq5LRBLATQTt6eqBZSIyT1VXZozrDlwOROqPF8Wo/15EegFXEfTL6x7e98REkNXhlqtuWU+9JK5Ve0RKzo3iMDF8tqaqMZl0LqaOau6XGfUw2oaxBhT5LRkLrFHVOgARmUvQ3Whlxrh/A34J/HMUoQWNuqreGt5dAgyKqq3HEBFEHFvkIYZFxiwXlDqWzw+W4RfbMgFtjalnm1g2tenYvEaNMf6ybWdR2S/9gPVp2xsIDH0TIjIK6K+qj4iIjVEXkd7ALOA4gvf9KeDfVPXDaHp7LHBtRalpQS9To24lyEgOtlkrVrhYJkCtYuoxzpTu1bV02S8SeCjXE/Robtpd6HlRwi9zgSeBqeH2BcCfgJPbqKPHBcyMnhg2yXBwRakha1etAGDQ0BFFbR878iAznRrNqjRaF/SqLE89lxPxyrKneWXZ04We/g4wMG27f7gvRQ+CVPLFoYHfl2Dt0Jn5JkujGPX9VPXf0rZ/LiJfi/A8jyVS0rm8NmOa/WK2CtRu5a2lWRg0JDDOqQ+v3duGqFVM3cF66nEuPsr1PTn8qGM5/Khjm7b/9Nvrsw1bBgwWkVpgI0H/5/NSD6rqVtKSUkTkCeCHqro8n05RjPpCETkXuD/cPgv4a4TneQxJeaDB/5npAFGXZ9h+2Z1r3IHhylvDmNCbq17irdUvt9p/4JDDGTT0CDIj1WtzjO/WeConHjXERKek2vQoNc1+UbUxyBqfp17MOaCqjSIyA1hIc0rj6yIyG1imqg9lPgWj8MulwPeBe8LtBLBdRC4L9NKeUV+Ep320/hTzLZnJ95nbGk8Xe5S6OFE6aOiIplBKazKNmOYcP9Iw/JJMhp2P8l3/I7wJ5gW9TCZKY/TUi/yiqOqjBHW20vfNzDF2QhSZUbJfekTSzlNSHMtoBKp8otQQB6vTkkyGBjSfbhH0dnGi1K8ojYCIjAAOSB/v29l1bMQwpdG09ouDpXddxMnsF2y87Divods/b4zxaNGIktJ4OzACeBVIzawovp1dfBjOkFqaKrsyAUaIm+EXF0mqklS3JkrtYurxmfWeXSP5xbESRaOjVfXQkmviqShs29nZ4WKeuotYdT4ytZ+qlRd+ie9QkYli1P8uIoeq6msl18ZTOQiIWUzdzru2k+Xi6WpH0mpS0nKVq5G8eCdK3fueRDHqfyAw7O8CnxMGA1TVPnnWU1G46am7p5OLqLOeulsXmkK4+G2LYtRvAy4EXqY5pu6JEQFaOsW5guzZqnNk5rAb6uRg9otr9WhUlZ7du7bJ+IlkN5Y1NVYF1FIxdffy1F3rm1oIF32IKEb9A1WdX3JNPDkREWpafXlyfZvam8PeRkQMvWLL/HkzUSaICC8+/2zWxURtJfmViYwbfbCBVgBGk5KW3ZgUDOZuY/bUHfvCEb2d3X3AAoLwC+BTGjs6wa8Ht7xisLvQWIZx8i8+is7IUca1XxwLv9jF+Q2UicinxVVpLAlRjHo3AmN+Sto+n9LocS9+bVkO2ESKu5it3nQx/BLjRGn33SqwR6mqXhSHIp4KQ0Cs+k7biDHNfql2q65J91ZvBitKLZQqXkRUXPNrINrio/7AjQT11CGop/6PqrqhlIp53MYy/GK6otRq8ta0HDCt56yjYt/JLhDroFds9evBcvK2EHZT13ZECb/cAdwHnB1uTwv3fblUSnkqAcP4tYmUALsLjY2YQJa0X2arrCcbkkYTpdYxdddKFxTGPVc9yoVmH1W9Q1UbwtudwD4l1stTAUiYAVPszUWdbMsB292sSHnFxd4srbqVTrFmvxT5uYnIqSKyUkRWi8hPsjx+mYisEJHlIvKkiAwrpFMUT/1DEZkG/DHcPg/wrew6OLYFvWzEgJu58+75cuHiI8c89YrMUy/muSIJ4CbgJKAeWCYi81Q1vfH0vap6czj+DOA/gEn55EYx6hcTxNT/gyDC9zTgJ089zi3JF8OCXpa4mGZplj5onafuWEZOIXbsKiqxfiywRlXrAERkLjAZaDLqqrotbXx3IiwAjZL9Ugec2VZtPdWPe/bTrsiY6cWhDbLS51NLa5rEueyXSoyp79Glppin9wPWp21vIDD0LRCR7wA/BDoDBRtlFIypi8hdIrJX2navsByvp0NjFLsWMQ+/WNxsSxdEv0nG38zHrNCkUfza0lMPQ0LF3uItE1D6eSNV/Y2qDgZ+AlxVaHyU8MsIVf0o7QBbRGRUETp6qgDLUIdtoomLpQvc00mtygQ46KnHmdKY6xN5funfeGHp3wo9/R1gYNp2/3BfLv4E/K6Q0ChGPSEivVR1C4CI7B3xeZ4qx3J5vxVOTpQ6V04hlWliIMfSqldgTD3XZzLmmOMZc8zxTdu3zflVtmHLgMEiUgtsBM4lSERJky+DVfWNcPN0YHUhnaIY518TlN59INw+G/hFhOd5qhzXJiUtM3Ls2uK5WbrAxdWbjRUYUy/mM1HVRhGZASwkCIXfpqqvi8hsYJmqPgTMEJGTgZ3AFmB6IblRJkr/ICLP0Ryg/6pvmOEBw+X91Z7S6OLkLTb22LacetK5csCFKPbir6qPAkMz9s1Mu//9tsqMFEYJjbg35J4mBPc8dbCs/eJeSMjSVV/96nJ2NjRw0LAjAHhz5UsAbd4eftB+ZjppEucqRxbi8wb3Wkz42LinfRhnY9ggdp66iZRQlmshIeCgQ47g850NTQZw0NDAWLd520wju8YdcYZfuhWX0lgSvFH3tBPbpfQWCC7WeHczJAQ2k5y24RdFLTx1AxlRcesMCCibUReRXgQpOrXA28A5qvpxlnGNwEsE71+dqv5DnHp68uBapEMgkbCpm2eZhujigqjVry1n1SvLW+0fNHREU4glnTdXvsTaVSta7U+edBKnjTvMRKdkUmlMFh/OiLdKo3tmvZye+k+B/1HVX4WFbK4I92WyXVWPjFc1TxRczL+284qrW6chh4xkwKDhWR/LFr7I1b3p8CH9zHSqxDx1B216WY36ZODE8P5dwGKyG3UH3zaP4N4HIxhOShqSMOomYhl+SapNhrlpmYCkTfaLD7+Ujy+o6nsAqvquiHwhx7jdRORZoAG4VlXnxaahJz8OfqPdC79AjdGFxqwqJqExjrPseARUlWSFhV8cPAVKa9RF5DGgb/ougq/SlVmG5/okalV1o4gcCDwuIitU9a1sA2fNmtV0f/z48YwfP749ansi4tpEKWIZvzYRA7gZfhGRWHt5RiFp1gy7tYzFixezePHiomVnYnmhtaKkRl1Vc3ZHEpH3RKSvqr4nIvsC7+eQsTH8+5aILAZGAQWNuqfEGMZfLE8LK6/Ysq6Ni78eksmkiadumT5oZdSzeeqZTt7s2bOLPg7gpKtezvDLfOAbwLUES19bhVXC6pCfqupOEekDHBuO9ziAa99nAcMepXbU1Lhn1MG56Itdk4wY1wM592uV8hr1a4H7ReRioA44B0BERgOXqeq3gEOAm8O0xgRwTUZXEE9Zce8LXeOgV2wXUzcREyCpSKg7JH1M3YSyGXVV3QycnGX/88C3wvt/B1rnUXncwL2MRkdj6kYXGkOrHvQXNRNnQilj6iXDQavuV5R62o1z32exNHyGnrpR+MV6Us69gl6VVybARbxR91QNghhOlJqIAdwMCZlZY9M8daOJ0hjz1Hf5gl4eT2lxsXiWi566i75sEiNPPcZXt1vn4j5bETkVuIHmeurXZjz+A+CbwC7gA+BiVV3fSlAa3qh7qgq79EETMSCGaZYOrpa1RJNGE6Vxrigt4osiIgngJuAkoB5YJiLzMpJBXgBGq+oOEfk28O8EHZJy4o26p3ow7DJkGX+pqbEpz+riQhdLklZVGl38GZKdscAaVa0DEJG5BOVTmoy6qi5JG78UuKCQUG/UPVWDZe0XS/vZySr8Uu2eutqkI1ZKOzugH5AeStlAYOhzcQnwSCGh3qh72oUgdLKxVaa41qNUcDOmvv7NV1n5yvNFy6n5/Euc8SWbrONkMllxKY25PpJn/vdJnnn6KcPjyDRgNM1FEHPijbqnXbgaCTBbUeqgpy5G8wUAAw4azhcGDClazvAh/Q20CbBqhh1r6d0cfPG4E/jicSc0bd/462uyDXsHGJi23T/c14Kw8fQVwAmquqvQsb1R91QRdgW9qj1P3cVrshrVU4/TU9/VWNSxlgGDRaQW2EgwAXpe+gARGQX8Dpioqh9GEeqNuqdqsGyGbWc/xc6oW8bUjXrMmtd4N4mpGygTkd2KiEGqaqOIzAAW0pzS+LqIzAaWqepDwK+APYAHJPhyF+z+5o26p3oQw4lSEykBZhOlxp66c8WoKnCitNi3UFUfBYZm7JuZdj9npdtceKPuqSrMVl2aFvRyr/aLiJi8V6blgDVZcZ66cxdGvFH3VBl22S92uBhTr0mIc+EXVZuFQxWU0lgSvFH3VBUu5qk7WftFxOS9stSpIgt6OWjVvVH3VA2WE6VmVt0wzm85T5owaf1nG3xYsXwZ2z7dwbDDjgRg5SsvALR5e/8JEwy1qjy8UfdUEZlxYqW1K5W5L7Wd7t3ZGiuzzkeGVj2REJM6OZY6DT9iNB9t/TT0tIWhw48EmjNihg4fBVB420yjwjQUl9JYErxR91QPrdL0shmczH2SY78dLjaeTiQSDoZf0ic5228s4wy/dOnkXvzFG3VPVWHX+ciwTICDMXUrT920HLBZTN1AmYhY9421wBt1T1Xh4kkWeMQpvVIWJxXySf+bTmZYyHK1bHChsQgLWZU6Bnjlped4ftnSVvuHHDqyKbSSzqpXl7P6tRdb7T/uuHGcdsJhZnpVGt6oe6oKuxWltqGONMlZ7ucLEzU/ZpuRY9MlynLy9tDDR7Nf7bCsj2ULqQw5dCRDDh3Zan//vr3slCqAey6EN+qeKkKwNTJW2LXYM7zQ1CSoaXTLUw/CLwZyihcRGQd/GHqj7qku3Kv94l45YAhKFyQNmndY1qNRs9ov7mWkxIk36p6qwsWYulXJXNtywDUkOxVv/GyNupFBjtGmO9h32ht1TxVhVHkwEGUY6nAwzt+pU4JksviLjVVmDwS2uMJsOl1q3HMivFH3VBV2K0ptxIC7pQtqampyv85sCTlZsI6pV1z4pcjPREROBW6gufTutRmPjwsfHwF8TVUfLCTTG3VPVeFk+MW10gVAp041Jl6x1SQw2Bn1OF31Yn7RiUgCuAk4CagHlonIPFVdmTasDpgO/CiqXG/UPVWETeVBcHOi1LIlbOeaGhPjZ1UCAQC1Cr9UTJXGscAaVa0DEJG5wGSgyair6rrwscgvyht1T9Vg2/jBsHa5ld0zjqlbmD7bmLqaGORYk1+K+0j6AevTtjcQGPqi8EbdU1U4GFJ3ssZ75841JmEhU08dqbiJ0lzv4P8+tYSn//ZkjJo04426p6qo7pi6jRgIwi8Wv2osjbpVj9I4XfXGHCmNRx93Ikcfd2LT9nW//Hm2Ye8AA9O2+4f7isIbdU91kauUSnvkGGEVErJMs+zcucakbK5V/1UgTFQ3EFO8iMh0Lm791jJgsIjUAhuBc4Hz8oyP9IF5o+6pKgQxMciW/r7ZjwfjxUcm4RfrPHUTqx5rmcZ2P1VVG0VkBrCQ5pTG10VkNrBMVR8SkTHAfwF7AaeLyCxVPTyfXG/UPVWFXfTFcKLUxZh6pxoSSbfCL0FKo4Gc4kVEpth3UFUfBYZm7JuZdv85YEBbZHqj7qkexM3u7nYq2Wa/SKNBlUbjCmom2S8GelQy3qh7qgsH89TNLjSWK0prErQh9Tm3HNMVpdhY5BjDLw7Oy3uj7qkuRCS6YchsTVoq3LPpQUEvA6Nu66nb5KnHSdIX9PJ4YqAtdiYGT8tBZ46ahI2nblv7BZvslxivCzW+oJfHU1rcO8XcpKZGSKiLMfXKwsXvmzfqnqpBwM38QQepSSRMPFrTxtNWxV/irP3i4NfEG3VPVWFm0i0nSrPKatlUuqUhyh7st1wtW1NjZNQtPXWbtUfxpqk7ePH3Rt1TXTh2jokInXLqlK0Jda5tWxKJhMmSfEtPHai8+IuDeKPuqSocs+nOkkgIahBTt661U3E23cEvnDfqnirDwbPMQRKJNqR+5sGifkwKNUp/ifPC4GKPa2/UPdWFt+mRSIhNjRzL8Eub1hjkI0ZD28k4+8cCb9Q9VYV7p5ibJERMbJ/t0qMKDL84iDfqHk8HRMQmb8PF+vUxNyl1Dsu2JW1CRM4SkVdEpFFEjswz7lQRWSkiq0XkJ3Hq6Om4OHiumiIiJjfLN8qkQUbMSMR/OZ9fwL6JSBcRmSsia0Tk7yIyMJucdMpm1IGXgSnAklwD0rptTwSGA+eJyLB41MvP4sWLy61CK7xO0VjioE5xv09RDPaTTy4pPC7my9/aVStiPV4hJOIt63Oj2bdLgM2qejBwA/CrQjqVzair6ipVXUP+a31Tt21V3QWkum2XHReNldcpGkuWLC63Cq2I36gXvj25ZHHBMXF3iHpr9ct2B7SgGKsezb5NBu4K7/8ZOKmQSuX01KOQrdt2vzLp4vF4MrCu0VhpaMRbDqLYt6YxqtoIfCQie+fTqaQTpSLyGNA3fRfBa/xXVV1QymN7PNloigV7nMPFJfeF6Bx/SmPhA2rYwbtcN+AJ4Mgcjx0NPJq2/VPgJznGRr1o+pu/+Zu/qYHtersNx3u3PfYNeAT4Yni/Bni/kF6upDTmuvpE7ratFmuePR6PJyKqekCRIqLYtwXAdOAZ4Gzg8UJCy5nS+A8isp7gavWQiDwS7t9PRB4CUjGkVLftV4G5qvp6uXT2eDweK3LZNxGZLSKnh8NuA/qIyBrg+wTefF6kEnNDPR6Px5Md17NfPB4z/EI2T0fAG/UsiMiJInJHnsffKvD8mSLydXvNWhyjKB3joJCOceLyQjaPxxJv1HOTLy7lSsyq0nWME2cXsnk8lnijnpt82TRRMm3iyMYpVsc4cEUPv5DN0yFwJaXRCURkKdAF6AH0EpEXwod+AhxFkFIEsF/aY/+rqt8TkcOAuwk80/2Az0Xk++H2Saq6pdw6Why/WB1V9bG49PB4OiI++yULInIiMF1VL87x+FpVHZTn+T8D3lbVP7iqYxwU0jFmXY4GZqnqqeH2TwkWoFxbXs1ARL4DXErgAJymqu86ossHwBeAZar6rTLrsgnoAzwH3ArcHO7/mao+FLduLuM99fZRKKTgQsjBBR1cIvJCtrhR1d8Avym3HlBRuoyKU5dKwhv19lHo540LP39c0MEZVLVRRFILPRLAbX4hm6ca8eEXj8fjqSJ89ovH4/FUEd6oezweTxXhjbrH4/FUEd6oezweTxXhjbrH4/FUEd6oezweTxXhjbonFkRkcnpVxLARwITw/lvZmumKyGUiMi28f4eIfDW8f0tKlohc0Q5dPmnv62irTBHpKiKLJWyMKiL/LiIvi0jJVrKKSGcRWRJWpvR0MHyeuicWwhK8D6nqX7I8thYYo6qbCzx/gao+mLH/E1Xt0UZdtqpqz7Y8p70yw6XuNap6Y7j9EdBLS3ziichVwJuqel8pj+NxD38l7wCIyNdF5CURWS4id4X7akVkkYi8KCKPiUj/cP8dIvIbEfm7iLwR1kS/TUReE5Hb02R+IiLXi8gr4fN7h/u/KSLPhsd6IPRUjwHOBH4lIi+IyIHpnjdBSYOfiMgKEVkqIoNCWTNF5IdZXs8TInKkiFwDdAtl3h16//+YNu7nIpK3kJmI/CjU90URmRnuuyY0xqkxTXpkG1+AC4B54XPnAd2B50XknIz3oMnbD1/H8vB1bQjf/8vS9q0NP7uLROQ/0p7/TRH5dbg5Lzy2p6NRbEdtf3P7BhwKrCTwDgH2Cv/OB6aF9y8C/iu8fwdwX3j/TOBj4NBw+zlgRHg/CZwb3r8KuDG83yvt2P8GfDdN7lfTHmvaBt4Cfhrev5DAIweYCfwwy/gngCPD+1vTZNYCz4f3BXgjXZ+0cVvDv18Gbk4bvwA4HhgJLE4b/ypBmd6s48PtT7IcpzNQn+3YOd6TrRlj9wReAkam7esELAFOA/YIX2NN+Nj/AsPD+wkidJ73t+q7eU+9+pkAPKBh6V9V/Sjcfwzwx/D+3cBxac9ZEP59GXhXVV8Lt18FDgjvJ4H7w/v3pD1/hIg8KSIrgPMJugxFYW74948EzcjbjKrWAZtE5AjgFOAFzV/y+BTgyxKUBn4BGAocrKovAvuIyL4iMgLYrKrv5BqfR34f4KM8jxfiHuDXoT4p5gCPq+rDqrodWAScLiJDgU6q+iqAqiYJyj/vUcTxPRWIL+jVcckX0/08/JtMu5/azvWdScm7AzhTVV8RkenAie3Qpy3x5sxqlLcS/PLYF7i99fBWz71GVW/J8tgDBLXp9wX+FGF8Np0/A7rmOX4DYQg0nEjt0qSYyCxgnaaVbxaRbwADVPU7aTJuA/6F4NdYZuvA3YAdeY7vqUK8p179PA6cncouEZFe4f6naS49Ow14Ksfzc5XwTQBnhfcvSHt+d+BdEelMy5juJ0C+ycmvhX/PBf6eZ1wmO0WkJm37v4FTgTHAX3M8J/Wa/gpcnPJmRWR/EdknfOz+UJepBAY+1/g+GTKbCH8V1YhIl7Td6ePeDvWEoLVe51DuGcDJQPr8wGjgnwg+q/RjPAsMIPgs/5g2fm9gk6o25ngPPFWK99SrHFV9TUR+ASwRkQZgOXAxcDlwh4j8iKAZwkWpp2SKyHF/OzA2zLJ4j2ajfBXwLPA+8AxB9yMIwiu3hBOXZ2WR20tEXiLwLLPVOc+lx++Bl0XkeVW9UFV3icgTwBZVzeXxK4CqPiZBauTfA0eZTwiM5gfh+9YD2KCq7xUYv4ncvy4WEsTpH8+i+y3APBFZTnDB2Bbu/wGwP7BMRJRg/mMg0At4Ijz2c9rcuOJ+4AhV/ThN9peA/5dDJ08V41MaPe1C2pFKGAcS5GY/D5ylqm86oM8o4PuqOr2Ex1gAXK+qT6Tt+wtB+8A3SnVcj5v48IunvTjnDYjIIcAa4DEXDDqAqi4n8K7NO1GJyJ4isgrYnmHQOxNkM3mD3gHxnrrH4/FUEd5T93g8nirCG3WPx+OpIrxR93g8nirCG3WPx+OpIrxR93g8nirCG3WPx+OpIv5/+OSlY0Tz+/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cf8b90990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cf69ef190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "#more uncertain\n",
    "mf=collections.OrderedDict()\n",
    "mf['+++'] = [1,0,1]  # highly compatible\n",
    "mf['++'] = [0.6,-0.3,1]  # moderately compatible\n",
    "mf['+'] = [0.3,-0.6,0.9] # slightly compatible\n",
    "mf['0'] = [0,-1,1]  # neutral\n",
    "mf['-'] =  [-0.3,-0.9,0.6] # slightly incompatible\n",
    "mf['--'] = [-0.6,-1,0.3]  # moderately incompatible\n",
    "mf['---'] = [-1,-1,0]   # highly incompatible\n",
    "\n",
    "#more sharp\n",
    "mf2=collections.OrderedDict()\n",
    "mf2['+++'] = [1,0.5,1]  # highly compatible\n",
    "mf2['++'] = [0.6,0.3,0.9]  # moderately compatible\n",
    "mf2['+'] = [0.3,0,0.6] # slightly compatible\n",
    "mf2['0'] = [0,-0.5,0.5]  # neutral\n",
    "mf2['-'] =  [-0.3,-0.6,0] # slightly incompatible\n",
    "mf2['--'] = [-0.6,-0.9,-0.3]  # moderately incompatible\n",
    "mf2['---'] = [-1,-1,-0.5]   # highly incompatible\n",
    "\n",
    "#plot the two fuzzy compatibility level sets\n",
    "plot_fnum_vec(mf.values(),mf.keys())\n",
    "plot_fnum_vec(mf2.values(),mf2.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fuzzy Compatibility Scores\n",
    "\n",
    "This section provides the functions to evaluate the fuzzy scores for a single building or a list of buildings against one or more building classes. \n",
    "The functions accept as input the class definition schema, the attribute weights and the description of the fuzzy levels.\n",
    "\n",
    "* * *\n",
    "\n",
    "For each building a score $S$ will be assigned, defined as:\n",
    "\n",
    "$$ S(b) = \\sum_i w_i ( \\sum_k \\delta_{ik}(b) s_k) $$\n",
    "\n",
    "where\n",
    "\n",
    "$$   \n",
    "\\delta_{ik}(b) =\\left\\{\n",
    "  \\begin{array}{@{}ll@{}}\n",
    "    1, & \\text{if}\\ v_k \\in B(b) \\\\\n",
    "    0, & \\text{otherwise}\n",
    "  \\end{array}\\right.\n",
    "$$\n",
    "\n",
    "\n",
    "The score is based on the building type dictionary defined below, for each building tye\n",
    "\n",
    "The score $S$ is then subject to the constraints $C_i$, aggregated such that:\n",
    "\n",
    "$$ C = \\prod_i \\delta_{i}(b)$$\n",
    "\n",
    "where\n",
    "\n",
    "$$   \n",
    "\\delta_{i}(b) =\\left\\{\n",
    "  \\begin{array}{@{}ll@{}}\n",
    "    1, & \\text{if}\\ C_i \\ \\text{is satisfied}\\ \\\\\n",
    "    0, & \\text{otherwise}\n",
    "  \\end{array}\\right.\n",
    "$$\n",
    "\n",
    "---\n",
    "\n",
    "In this case, a single constraint is defined for the height numerical attribute. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that computes the score of a building for a building type\n",
    "def compute_single_bscore_fuzzy(bdg,btype,wgsScheme, bmodScheme,fuzcompScheme, bDefuz=False, defuzMode='mode'):\n",
    "    '''\n",
    "    Computes the fuzzy score of a single building 'bdg' against a reference\n",
    "    building type 'btype', according to a specific weighting scheme\n",
    "    'wgs_scheme' and a building definition scheme 'bmod_scheme'. Also needs the definition \n",
    "    of a set of fuzzy compatbility levels 'fuzcompScheme'. If the booleab 'bDefuz' is True \n",
    "    then a crisp value is returned as score according to the defuzzifcation mode 'defuzMode'.\n",
    "    The default compatibility value for attribute values not included in the\n",
    "    class definition schema is '0' (neutral).\n",
    "    The height is automtically considered as a constraint. If the constraint is not \n",
    "    verified, the final score is set to '---' (highly incompatible).\n",
    "    '''\n",
    "    #compute the compatibility score. Default comp value is '0', i.e. neutral\n",
    "    s=[0,0,0]\n",
    "    for k in wgsScheme.keys():\n",
    "        a = fuzcompScheme[bmodScheme[btype][k].get(bdg[k],'0')]\n",
    "        s = fuzzy_sum(s,a,1,wgsScheme[k])\n",
    "    \n",
    "    #check the additional attribute, in this case the height\n",
    "    ch1 = True\n",
    "    if (bdg['height_1'] != 'NO_DATA'):\n",
    "        cs1 = ((bdg['height_1'] >= bmodScheme[btype]['height_1']['H_MIN']) & \n",
    "               (bdg['height_1'] <= bmodScheme[btype]['height_1']['H_MAX']))\n",
    "    \n",
    "    #apply the constraint. \n",
    "    #if the constraint is not fulfilled the resulting\n",
    "    #score is set to 'highly incompatible'\n",
    "    if (cs1 == False):\n",
    "        s= fuzcompScheme['---']\n",
    "\n",
    "    if bDefuz:\n",
    "        score = defuzzify(s,defuzMode)\n",
    "        return score\n",
    "    else:\n",
    "        return s\n",
    "\n",
    "def compute_bscore_fuzzy(bdg, wgsScheme, bmodScheme,fuzcompScheme,bDefuz=False, defuzMode='mode'):\n",
    "    '''\n",
    "    Computes the scores for the building 'bdg', against all building \n",
    "    types defined in the class definition scheme 'bmodScheme'.\n",
    "    Also needs the definition of a set of fuzzy compatbility levels 'fuzcompScheme'. \n",
    "    If the booleab 'bDefuz' is True then a crisp value is returned as score \n",
    "    according to the defuzzifcation mode 'defuzMode'.\n",
    "    The default compatibility value for attribute values not included in the\n",
    "    class definition schema is '0' (neutral).\n",
    "    The height is automtically considered as a constraint. If the constraint is not \n",
    "    verified, the final score is set to '---' (highly incompatible).\n",
    "    '''\n",
    "    res={}\n",
    "    for bt in bmodScheme.keys():\n",
    "        res[bt]=compute_single_bscore_fuzzy(bdg,bt, wgsScheme, bmodScheme,fuzcompScheme,bDefuz,defuzMode) \n",
    "        res['object_id']=bdg['object_id']\n",
    "    return res\n",
    "\n",
    "def compute_expo_model_fuzzy(bdgs_set, wgsScheme, bmodScheme, fuzcompScheme, bFilterScore = True,bFilterScoreThreshold = 0, bDefuz=False, defuzMode='mode'):\n",
    "    '''\n",
    "    Applies the fuzzy scoring defined by the components 'wgs_schema','bmod_schema'\n",
    "    to the buildings set 'bdgs_set'. \n",
    "    Returns the final exposure model and separately the scores for all\n",
    "    buildings in 'bdgs_set'.\n",
    "    if 'bFilterScore' is set to True, the 'not Valid' buildings (i.e., those whose fuzzy \n",
    "    score is smaller than 'bFilterScoreThreshold') are associated \n",
    "    to the 'OTH' building type.\n",
    "    \n",
    "    Returns the input buildings set with the respective assigned class, \n",
    "    and an additional dataframe with all the scores of the buildings with respect\n",
    "    to the classes defined by the class definition scheme.\n",
    "    '''\n",
    "    #compute the score for each building in the dataframee\n",
    "    scores = pd.DataFrame([compute_bscore_fuzzy(b, wgsScheme, bmodScheme,fuzcompScheme,False, defuzMode) for i,b in bdgs_set.iterrows()])\n",
    "    \n",
    "    # assign to each building the argmax of the score and \n",
    "    # set a flag to 'true' for the buildings whose best score is >= threshold\n",
    "    if (bDefuz):\n",
    "        scores['btype']=scores[bmodScheme.keys()].idxmax(1)\n",
    "        scores['valid'] = scores.apply(lambda df:df[df['btype']]>bFilterScoreThreshold, 1)\n",
    "    else:\n",
    "        scores['btype'] = [bmodScheme.keys()[fuz_argmax(x.tolist())] \n",
    "             for ind,x in scores[bmodScheme.keys()].iterrows()]\n",
    "        scores['valid'] = scores.apply(lambda df:defuzzify(df[df['btype']])>bFilterScoreThreshold, 1)\n",
    "\n",
    "    #select only the valid ones ?\n",
    "    if (bFilterScore):\n",
    "        #the building type 'OTH' groups all the entries whose assignment was invalid\n",
    "        for it, bd in scores[scores['valid']==False].iterrows():\n",
    "            scores.set_value(it,'btype','OTH')\n",
    "            bd['btype']='OTH'\n",
    "\n",
    "    expo_df = pd.merge(bdgs_set,scores[['object_id','btype']], how='inner', on='object_id')\n",
    "    return expo_df,scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the class definition schema\n",
    "\n",
    "Functions to provide a synthetic graphical representation of a class definition schema in form of a gridded matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_bmod_matrix(bmodScheme,wgsScheme,fuzcompScheme):\n",
    "    '''\n",
    "    Auxiliary funtion. \n",
    "    Creates a matrix with as many rows\n",
    "    as the defined building types, and as many columns as\n",
    "    the number of the attribute values explicitly rated in terms\n",
    "    of compatibility value. The compatibility values \n",
    "    are defuzzified using the 'mode' to be colormapped accordingly.\n",
    "    '''\n",
    "    bmod_mat = pd.DataFrame(columns=['btype','attribute_type','attribute_val','score'])\n",
    "    for bt in bmodScheme.keys():\n",
    "        for k in wgsScheme.keys():\n",
    "            for av in bmodScheme[bt][k]:\n",
    "                a = fuzcompScheme[bmodScheme[bt][k][av]]\n",
    "                s= defuzzify(a,'mode')\n",
    "                bmod_mat = bmod_mat.append({'btype':bt, \n",
    "                                            'attribute_type':k,\n",
    "                                'attribute_val':'{} : {}'.format(k,av),\n",
    "                                                               'score':s},ignore_index=True)\n",
    "            \n",
    "    bmod_mat['score'] = pd.to_numeric(bmod_mat['score'])\n",
    "    return bmod_mat\n",
    "\n",
    "def plot_bclass_schema(bmodScheme, wgsScheme,fuzcompScheme,figfilename=''):\n",
    "    '''\n",
    "    Plots the class definition scheme 'bmodScheme' in form of a matrix with as many rows\n",
    "    as the defined building types, and as many columns as\n",
    "    the number of the attribute values explicitly rated in terms\n",
    "    of compatibility value. The defuzzified compatibility value (as mode)\n",
    "    is shown with a colormap.\n",
    "    Uses the auxiliary function 'create_bmod_matrix'.\n",
    "    '''\n",
    "    bmod_mat = create_bmod_matrix(bmodScheme,wgsScheme,fuzcompScheme)\n",
    "    bmod_mat_piv = bmod_mat.pivot('btype','attribute_val','score')\n",
    "    f, ax = plt.subplots(figsize=(18, 8))\n",
    "    sns.heatmap(bmod_mat_piv,vmin=-1, vmax=1,\n",
    "                cmap=sns.color_palette(\"coolwarm\", 7),linecolor='gray',\n",
    "                    linewidth=0.1,xticklabels=1,ax=ax)\n",
    "    if (figfilename):\n",
    "        print 'saving fig {}'.format(figfilename)\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figfilename,dpi=300)\n",
    "        plt.show()\n",
    "    show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Serialization of the class definition schemas\n",
    "\n",
    "In order to make the procedure more friendly, the class definition schema is serialized using simple text-based formats. This is exemplified using both the JSON (www.json.org) and the YAML (www.yaml.org) formats, which allow for a textual description that can be manually modified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import json\n",
    "import os\n",
    "\n",
    "def exportClassDict(classdict,filename, output_format = 'json'):\n",
    "    '''\n",
    "    Exports a class definition schema (in form of a dictionary) into a text file. \n",
    "    possible extensions are '.json' and '.yml' / '.yaml'\n",
    "    '''\n",
    "    pre, ext = os.path.splitext(filename)\n",
    "    if (output_format == 'json'):\n",
    "        output = json.dumps(classdict)\n",
    "        filename=pre+'.json'\n",
    "    elif (output_format == 'yaml') | (output_format == 'yml'):\n",
    "        output = yaml.dump(classdict, default_flow_style=False, explicit_start=True)\n",
    "        filename=pre+'.yml'\n",
    "    else:\n",
    "        raise NameError('unrecognized output_format: {}'.format(output_format))\n",
    "    outf = open(filename,'w')\n",
    "    outf.write(output)\n",
    "    outf.close()\n",
    "\n",
    "def importClassDict(filename):\n",
    "    '''\n",
    "    import a class definition schema into a dictionary. \n",
    "    possible extensions are '.json' and '.yml' / '.yaml'\n",
    "    '''\n",
    "    pre, ext = os.path.splitext(filename)\n",
    "    inf = open(filename,'r')\n",
    "\n",
    "    if ((ext == '.yml') | (ext == '.yaml')):\n",
    "        cdict = yaml.load(inf.read())\n",
    "    elif (ext == '.json'):\n",
    "        cdict = json.loads(inf.read(), object_pairs_hook=OrderedDict)\n",
    "    else:\n",
    "        inf.close()\n",
    "        raise NameError('Unrecognized extension: {}'.format(ext))\n",
    "    inf.close()\n",
    "    return cdict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define EMS-98 class definition schema\n",
    "\n",
    "We define here the class definition schema that implements the vulnerability classes described in the EMS-98 original formulation (Grünthal et al.,1998).\n",
    "\n",
    "| building type | description | GEM tax string         \n",
    "| :- |:-------------: | :---------------------------------------------------:\n",
    "|MUR1|rubble stone|MUR+STRUB+MOM/LN/FW+FW1+FWCN\n",
    "|ADO|adobe|MUR+ADO+MOM/LN/FW+FW1+FWCN\n",
    "|MUR2|simple stone|MUR+ST99+MOL/LN/FW+FW1+FWCN\n",
    "|MUR3|massive stone|MUR+ST99+MOL/LN/FW+FW1+FWCN\n",
    "|MUR4|unreinforced masonry with manifactured stone units|MUR+ST99+MOCL/LN/FW+FW1+FWCN\n",
    "|MUR5|unreinforced masonry with RC floors|MUR+MO+MOC/LN/FC\n",
    "|MR|reinforced or confined masonry|M99/FC+FC2+FWCP\n",
    "|RC1|reinforced concrete frame without earthquake-resistant design (ERD)|CR+CIP/LN/FC+FC2+FWCP\n",
    "|RC2|reinforced concrete frame with moderate earthquake-resistant design (ERD)|CR+CIP/LFM/FC+FC2+FWCP\n",
    "|RC3|reinforced concrete frame with high level of earthquake-resistant design (ERD)|CR+CIP/LFINF+DUC/FC+FC2+FWCP\n",
    "|RC4|reinforced concrete walls without ERD|CR+CIP/LWAL+DNO/FC+FC2+FWCP\n",
    "|RC5|reinforced concrete walls with moderate level of ERD|CR+CIP/LWAL/FC+FC2+FWCP\n",
    "|RC6|reinforced concrete walls with high level of ERD|CR+CIP/LWAL+DUC/FC+FC2+FWCP\n",
    "|STEEL|steel structures|S/FME\n",
    "|WOOD|timber structures|W/FW\n",
    "\n",
    "\n",
    "\n",
    "In order the class assignment completely reproducible, we define contextually also the set of weights for the attribute types, and the compatibility levels in terms of TFNs.\n",
    "\n",
    "The definition of this class definition schema is completely independent from a particular set of observations\n",
    "\n",
    "The class definition schema is then assigned a set of basic metadata and serialized into a simple text file in a format that can be later parsed back. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of values: 1.0\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cf695cd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define the weights \n",
    "wgs_ems98 = collections.OrderedDict()\n",
    "\n",
    "wgs_ems98['mat_type'] = 0.35\n",
    "wgs_ems98['mat_tech'] = 0.1\n",
    "wgs_ems98['mat_prop'] = 0.05\n",
    "wgs_ems98['llrs'] = 0.2\n",
    "wgs_ems98['llrs_duct'] = 0.1\n",
    "wgs_ems98['floor_mat'] = 0.1\n",
    "wgs_ems98['floor_type'] = 0.05\n",
    "wgs_ems98['floor_conn'] = 0.05\n",
    "\n",
    "# check if sum of values is \"1\"\n",
    "print \"sum of values:\", sum(wgs_ems98.values())\n",
    "\n",
    "#define the classes\n",
    "bmod_schema_ems98 = {\n",
    "'ADO': \n",
    " {'floor_mat': {'FW': '+', 'FE': '+', 'FN': '+++'}, \n",
    "  'floor_conn': {'FWCN': '++'}, \n",
    "  'mat_prop': {'MON': '-', 'MOM': '+++', 'MOL': '++', 'MOC': '+', 'MOCL': '+'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++'}, \n",
    "  'llrs_duct': {}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 1}, \n",
    "  'floor_type': {'FW4': '+', 'FE99': '++', 'FW99': '+', 'FW3': '+', 'FW1': '++', 'FW2': '+'}, \n",
    "  'mat_type': {'M99': '+', 'E99': '++', 'MUR': '++', 'EU': '+++', 'ER': '++',\n",
    "              'SRC': '--', 'CR': '---', 'CU': '--','C99': '--','MR': '--'}, \n",
    "  'mat_tech': {'ETR': '+++', 'ET99': '+++', 'ST99': '+', 'MUN99': '+', 'ETC': '+++', \n",
    "               'ADO': '+++','ETO': '+'}\n",
    " }, \n",
    " 'MUR1': \n",
    " {'floor_mat': {'FW': '+++'},\n",
    "  'floor_conn': {'FWCN': '+++'}, \n",
    "  'mat_prop': {'MON': '+', 'MOM': '+++', 'MOL': '+', 'MOC': '+', 'MOCL': '+'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++'}, \n",
    "  'llrs_duct': {'DNO': '+'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 5}, \n",
    "  'floor_type': {'FW4': '+', 'FW99': '+', 'FW3': '+', 'FW1': '+++', 'FW2': '+'}, \n",
    "  'mat_type': {'M99': '+', 'MUR': '+++','MR': '-','SRC': '--', 'CR': '---', 'C99': '--',\n",
    "              'SRC': '--', 'CR': '---', 'C99': '--', 'CU': '-'}, \n",
    "  'mat_tech': {'STDRE': '++', 'ST99': '+', 'STRUB': '+++', 'MUN99': '+'}\n",
    " }, \n",
    " 'MUR2': \n",
    " {'floor_mat': {'FW': '+++'}, \n",
    "  'floor_conn': {'FWCN': '+++'}, \n",
    "  'mat_prop': {'MON': '+', 'MOM': '++', 'MOL': '+++', 'MOC': '+', 'MOCL': '++'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++'}, \n",
    "  'llrs_duct': {'DNO': '+', 'DUC': '--'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 5}, \n",
    "  'floor_type': {'FW4': '+', 'FW99': '+', 'FW3': '+', 'FW1': '+++', 'FW2': '+'}, \n",
    "  'mat_type': {'M99': '+', 'MUR': '+++','SRC': '--', 'CR': '---', 'C99': '--',\n",
    "              'SRC': '--', 'CU': '-','MR': '--'}, \n",
    "  'mat_tech': {'STDRE': '++', 'ST99': '+++', 'STRUB': '++', 'MUN99': '+'}\n",
    " }, \n",
    " 'MUR3': \n",
    " {'floor_mat': {'FW': '+++'}, \n",
    "  'floor_conn': {'FWCN': '+++'}, \n",
    "  'mat_prop': {'MON': '+', 'MOM': '++', 'MOL': '+++', 'MOC': '+', 'MOCL': '+'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++'}, \n",
    "  'llrs_duct': {'DNO': '+', 'DUC': '--'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 5}, \n",
    "  'floor_type': {'FW4': '+', 'FW99': '+', 'FW3': '+', 'FW1': '+++', 'FW2': '+'}, \n",
    "  'mat_type': {'M99': '+', 'MUR': '+++','MR': '--','SRC': '--', 'CR': '---', 'C99': '--'}, \n",
    "  'mat_tech': {'STDRE': '++', 'ST99': '+++', 'STRUB': '++', 'MUN99': '+'}\n",
    " },\n",
    " 'MUR4': \n",
    " {'floor_mat': {'FW': '+++'}, \n",
    "  'floor_conn': {'FWCN': '+++'}, \n",
    "  'mat_prop': {'MON': '+', 'MOM': '+', 'MOL': '+', 'MOC': '++', 'MOCL': '+++'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++'}, \n",
    "  'llrs_duct': {'DNO': '+', 'DUC': '--'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 5}, \n",
    "  'floor_type': {'FW4': '+', 'FW99': '+', 'FW3': '+', 'FW1': '+++', 'FW2': '+'}, \n",
    "  'mat_type': {'M99': '+', 'MUR': '+++','UC': '++','SRC': '--', 'CR': '---',\n",
    "               'C99': '--','MR': '--'}, \n",
    "  'mat_tech': {'ST99': '+', 'CLBRS': '++', 'CBH': '+', 'CBS': '+', 'CLBLH': '++', \n",
    "               'MUN99': '+', 'STDRE': '+', 'CLBRH': '++', 'CB99': '+', 'STRUB': '+','MO': '+','CL99': '++'}\n",
    " }, \n",
    " 'MUR5': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {}, \n",
    "  'mat_prop': {'MON': '+', 'MOM': '+', 'MOL': '+', 'MOC': '+++', 'MOCL': '+++'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++'}, \n",
    "  'llrs_duct': {'DNO': '+'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 6}, \n",
    "  'floor_type': {'FC1': '++', 'FC2': '++', 'FC3': '++', 'FC4': '++', 'FC99': '++'}, \n",
    "  'mat_type': {'M99': '+', 'MUR': '+++','SRC': '--', 'CR': '---', \n",
    "               'MR': '--','C99': '--'}, \n",
    "  'mat_tech': {'CLBRS': '++', 'CBH': '+', 'CLBLH': '++','CBS': '+', \n",
    "               'CLBRH': '++', 'CB99': '+', 'MO': '++','CL99': '++'}\n",
    " }, \n",
    " 'MR':\n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {}, \n",
    "  'mat_prop': {'MON': '+', 'MOM': '+', 'MOL': '+', 'MOC': '+', 'MOCL': '+++'}, \n",
    "  'llrs': {'LN': '++', 'LWAL': '++', 'LFINF': '+'}, \n",
    "  'llrs_duct': {'DNO': '+'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 6}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'MCF': '+++', 'MR': '+++','SRC': '-', 'CR': '-', 'C99': '-'}, \n",
    "  'mat_tech': {'STDRE': '++', 'ST99': '++', 'STRUB': '++', 'MUN99': '+'}\n",
    " }, \n",
    " 'RC1': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'MOC': '++', 'MOCL': '++'}, \n",
    "  'llrs': {'LN': '+++', 'LWAL': '++', 'LFINF': '+'}, \n",
    "  'llrs_duct': {'DNO': '+'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 12}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'SRC': '++', 'CR': '+++', 'C99': '++','CU': '-',\n",
    "              'M99': '--', 'MUR': '---','MR': '-'}, \n",
    "  'mat_tech': {'CIP': '+++'}\n",
    " }, \n",
    " 'RC2': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'MOC': '++', 'MOCL': '++'}, \n",
    "  'llrs': {'LFM': '+++', 'LWAL': '+', 'LFINF': '+'}, \n",
    "  'llrs_duct': {}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 12}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'SRC': '++', 'CR': '+++', 'C99': '++','CU': '-',\n",
    "              'M99': '--', 'MUR': '---','MR': '-'}, \n",
    "  'mat_tech': {'CIP': '+++'}\n",
    " }, \n",
    " 'RC3': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'MOC': '++', 'MOCL': '++'}, \n",
    "  'llrs': {'LFM': '++', 'LWAL': '+', 'LFINF': '+++'}, \n",
    "  'llrs_duct': {'DNO': '-', 'DUC': '+++'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 12}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'SRC': '++', 'CR': '+++', 'C99': '++', 'CU': '-',\n",
    "              'M99': '--', 'MUR': '---','MR': '-'}, \n",
    "  'mat_tech': {'CIP': '+++'}\n",
    " },\n",
    " 'RC4': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'MOC': '++', 'MOCL': '++'}, \n",
    "  'llrs': {'LFM': '+', 'LWAL': '+++', 'LFINF': '+'}, \n",
    "  'llrs_duct': {'DNO': '-', 'DUC': '+++'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 12}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'SRC': '++', 'CR': '+++', 'C99': '++', 'CU': '-', 'S': '--',\n",
    "              'M99': '--', 'MR': '-','MCF': '--','MUR': '--'}, \n",
    "  'mat_tech': {'CIP': '+++'}\n",
    " }, \n",
    " 'RC5': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'MOC': '++', 'MOCL': '++'}, \n",
    "  'llrs': {'LFM': '+', 'LWAL': '+++', 'LFINF': '+'}, \n",
    "  'llrs_duct': {'DNO': '-', 'DUC': '+++'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 12}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'SRC': '++', 'CR': '+++', 'C99': '++','CU': '-',\n",
    "              'M99': '--', 'MR': '-','MCF': '--','MUR': '--'}, \n",
    "  'mat_tech': {'CIP': '+++'}\n",
    " }, \n",
    " 'RC6': \n",
    " {'floor_mat': {'FC': '+++'}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'MOC': '++', 'MOCL': '++'}, \n",
    "  'llrs': {'LFM': '+', 'LWAL': '+++', 'LFINF': '+'}, \n",
    "  'llrs_duct': {'DUC': '+++'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 24}, \n",
    "  'floor_type': {'FC1': '+', 'FC2': '+++', 'FC3': '+', 'FC4': '+', 'FC99': '+'}, \n",
    "  'mat_type': {'SRC': '++', 'CR': '+++', 'C99': '++', 'CU': '-',\n",
    "              'M99': '--', 'MR': '-','MCF': '--','MUR': '--'}, \n",
    "  'mat_tech': {'CIP': '+++'}\n",
    " }, \n",
    " 'WOOD': \n",
    " {'floor_mat': {'FW': '+++'}, \n",
    "  'floor_conn': {}, \n",
    "  'mat_prop': {}, \n",
    "  'llrs': {}, \n",
    "  'llrs_duct': {'DUC': '+'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 5}, \n",
    "  'floor_type': {'FW4': '+++', 'FW99': '+++', 'FW3': '+++', 'FW1': '+++', 'FW2': '+++'}, \n",
    "  'mat_type': {'W': '+++','SRC': '--', 'CR': '---', 'C99': '--',\n",
    "              'M99': '-', 'MUR': '-','MR': '--'}, \n",
    "  'mat_tech': {}\n",
    " }, \n",
    "'STEEL': \n",
    " {'floor_mat': {}, \n",
    "  'floor_conn': {'FWCP': '+++'}, \n",
    "  'mat_prop': {'SC99': '++', 'BOL': '++', 'RIV': '++', 'WEL': '++'}, \n",
    "  'llrs': {'LFM': '++', 'LWAL': '+', 'LFINF': '+++'}, \n",
    "  'llrs_duct': {'DUC': '++','DNO': '-'}, \n",
    "  'height_1': {'H_MIN': 1, 'H_MAX': 24}, \n",
    "  'floor_type': {'FC99': '+'}, \n",
    "  'mat_type': {'S': '+++','CR': '--','SRC': '--','C99': '--','CU': '--',\n",
    "               'M99': '--','MUR': '--','MR': '--'}, \n",
    "  'mat_tech': {'SO': '++', 'SL': '+', 'SR': '+', 'S99': '++'}\n",
    " }\n",
    "}\n",
    "\n",
    "#fuzzy compatibility values\n",
    "fuzComp_ems98=collections.OrderedDict()\n",
    "fuzComp_ems98['---']= [-1, -1, 0] \n",
    "fuzComp_ems98['--']= [-0.6, -1, 0.3]\n",
    "fuzComp_ems98['-']= [-0.3, -0.9, 0.6]\n",
    "fuzComp_ems98['0']= [0, -1, 1]\n",
    "fuzComp_ems98['+']= [0.3, -0.6, 0.9]\n",
    "fuzComp_ems98['++']= [0.6, -0.3, 1]\n",
    "fuzComp_ems98['+++']= [1, 0, 1]\n",
    "\n",
    "#set the metadata to serialize the class definition schema\n",
    "bmod_ems98_meta = collections.OrderedDict()\n",
    "bmod_ems98_meta['name'] = 'bmod_ems98'\n",
    "bmod_ems98_meta['description'] = 'definition of EMS-98 building types, using fuzzy compatibility formulation'\n",
    "bmod_ems98_meta['version'] = '1.0'\n",
    "bmod_ems98_meta['classes'] = bmod_schema_ems98.keys()\n",
    "bmod_ems98_meta['weights'] = wgs_ems98\n",
    "bmod_ems98_meta['fuzzy_values'] = fuzComp_ems98\n",
    "bmod_ems98_meta['definition']= bmod_schema_ems98\n",
    "\n",
    "#NOTE: we use ordered dicts but the order is not guaranteed\n",
    "#      when loading the structure from a json / yaml file\n",
    "\n",
    "#save file ? \n",
    "#outf = \"path/filename.yml\"\n",
    "#exportClassDict(bmod_ems98_meta,outf,output_format='json')\n",
    "\n",
    "#plot schema\n",
    "plot_bclass_schema(bmod_schema_ems98,wgs_ems98,fuzComp_ems98)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data from file\n",
    "\n",
    "In order to create a risk-oriented exposure model we load a raw dataset from an external .csv file.\n",
    "\n",
    "The building data refers to a mostly residential biuilding portfolio randomly selected in a location between Rittershofen and Soultz-sous-fôrets, in Alsace (France) where a geothermal plant is located.\n",
    "\n",
    "The empty entries are replaced by a 'NO_DATA' value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>object_id</th>\n",
       "      <th>mat_type</th>\n",
       "      <th>mat_tech</th>\n",
       "      <th>mat_prop</th>\n",
       "      <th>llrs</th>\n",
       "      <th>llrs_duct</th>\n",
       "      <th>height</th>\n",
       "      <th>height_1</th>\n",
       "      <th>...</th>\n",
       "      <th>roof_shape</th>\n",
       "      <th>roofcovmat</th>\n",
       "      <th>roofsysmat</th>\n",
       "      <th>roofsystyp</th>\n",
       "      <th>roof_conn</th>\n",
       "      <th>floor_mat</th>\n",
       "      <th>floor_type</th>\n",
       "      <th>floor_conn</th>\n",
       "      <th>position</th>\n",
       "      <th>plan_shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.921125</td>\n",
       "      <td>48.914632</td>\n",
       "      <td>21069</td>\n",
       "      <td>MCF</td>\n",
       "      <td>RW</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>RSH4</td>\n",
       "      <td>RMT1</td>\n",
       "      <td>RSM99</td>\n",
       "      <td>RWO1</td>\n",
       "      <td>RTDP</td>\n",
       "      <td>FW</td>\n",
       "      <td>FW1</td>\n",
       "      <td>FWCN</td>\n",
       "      <td>BP1</td>\n",
       "      <td>PLFR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.922771</td>\n",
       "      <td>48.914305</td>\n",
       "      <td>21123</td>\n",
       "      <td>MUR</td>\n",
       "      <td>ST99</td>\n",
       "      <td>MOC</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>RSH4</td>\n",
       "      <td>RMT1</td>\n",
       "      <td>RWO</td>\n",
       "      <td>RWO1</td>\n",
       "      <td>RTDP</td>\n",
       "      <td>FC</td>\n",
       "      <td>FT99</td>\n",
       "      <td>FWCP</td>\n",
       "      <td>BPD</td>\n",
       "      <td>PLFR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.922857</td>\n",
       "      <td>48.913089</td>\n",
       "      <td>21164</td>\n",
       "      <td>MUR</td>\n",
       "      <td>MUN99</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>RSH2</td>\n",
       "      <td>RMT1</td>\n",
       "      <td>RWO</td>\n",
       "      <td>RWO1</td>\n",
       "      <td>RTDP</td>\n",
       "      <td>FC</td>\n",
       "      <td>FC99</td>\n",
       "      <td>FWC99</td>\n",
       "      <td>BPD</td>\n",
       "      <td>PLFR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.923595</td>\n",
       "      <td>48.913941</td>\n",
       "      <td>21193</td>\n",
       "      <td>MUR</td>\n",
       "      <td>MUN99</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>RSH4</td>\n",
       "      <td>RMT1</td>\n",
       "      <td>RWO</td>\n",
       "      <td>RWO1</td>\n",
       "      <td>RTDP</td>\n",
       "      <td>FW</td>\n",
       "      <td>FW1</td>\n",
       "      <td>FWCP</td>\n",
       "      <td>BP99</td>\n",
       "      <td>PLF99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.948804</td>\n",
       "      <td>48.922147</td>\n",
       "      <td>21210</td>\n",
       "      <td>MUR</td>\n",
       "      <td>MO</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>RSH4</td>\n",
       "      <td>RMT1</td>\n",
       "      <td>RWO</td>\n",
       "      <td>RWO1</td>\n",
       "      <td>RTDP</td>\n",
       "      <td>FC</td>\n",
       "      <td>FC1</td>\n",
       "      <td>FWC99</td>\n",
       "      <td>BPD</td>\n",
       "      <td>PLFR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lon        lat  object_id mat_type mat_tech mat_prop llrs llrs_duct  \\\n",
       "0  7.921125  48.914632      21069      MCF       RW     MO99   LO      DU99   \n",
       "1  7.922771  48.914305      21123      MUR     ST99      MOC   LO      DU99   \n",
       "2  7.922857  48.913089      21164      MUR    MUN99     MO99   LO      DU99   \n",
       "3  7.923595  48.913941      21193      MUR    MUN99     MO99   LO      DU99   \n",
       "4  7.948804  48.922147      21210      MUR       MO     MO99   LO      DU99   \n",
       "\n",
       "  height  height_1    ...     roof_shape roofcovmat roofsysmat roofsystyp  \\\n",
       "0      H       1.0    ...           RSH4       RMT1      RSM99       RWO1   \n",
       "1      H       2.0    ...           RSH4       RMT1        RWO       RWO1   \n",
       "2      H       1.0    ...           RSH2       RMT1        RWO       RWO1   \n",
       "3      H       2.0    ...           RSH4       RMT1        RWO       RWO1   \n",
       "4      H       2.0    ...           RSH4       RMT1        RWO       RWO1   \n",
       "\n",
       "  roof_conn floor_mat floor_type floor_conn position plan_shape  \n",
       "0      RTDP        FW        FW1       FWCN      BP1       PLFR  \n",
       "1      RTDP        FC       FT99       FWCP      BPD       PLFR  \n",
       "2      RTDP        FC       FC99      FWC99      BPD       PLFR  \n",
       "3      RTDP        FW        FW1       FWCP     BP99      PLF99  \n",
       "4      RTDP        FC        FC1      FWC99      BPD       PLFR  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infile_destress = 'RRVS_survey_Alsace_Destress_21032018.csv'\n",
    "bdg_df_destress = pd.read_csv(infile_destress).fillna('NO_DATA')\n",
    "\n",
    "#print the first rows to peek at the data\n",
    "bdg_df_destress.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration\n",
    "\n",
    "We can group the observations (rows) by the unique combinations of a set of atructural attributes, in order to analyse the clusters which naturally emerge from the data.  \n",
    "\n",
    "The column 'object_id' refers in this case to the number of occurrences of the considered combination of values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mat_type</th>\n",
       "      <th>mat_tech</th>\n",
       "      <th>mat_prop</th>\n",
       "      <th>llrs</th>\n",
       "      <th>llrs_duct</th>\n",
       "      <th>floor_type</th>\n",
       "      <th>floor_mat</th>\n",
       "      <th>object_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MUR</td>\n",
       "      <td>MUN99</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>FW1</td>\n",
       "      <td>FW</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>MUR</td>\n",
       "      <td>MUN99</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>FT99</td>\n",
       "      <td>FN</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>MR</td>\n",
       "      <td>RW</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LH</td>\n",
       "      <td>DU99</td>\n",
       "      <td>FW1</td>\n",
       "      <td>FW</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MAT99</td>\n",
       "      <td>MATT99</td>\n",
       "      <td>MO99</td>\n",
       "      <td>L99</td>\n",
       "      <td>DU99</td>\n",
       "      <td>FT99</td>\n",
       "      <td>F99</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>MUR</td>\n",
       "      <td>MUN99</td>\n",
       "      <td>MO99</td>\n",
       "      <td>LO</td>\n",
       "      <td>DU99</td>\n",
       "      <td>FC1</td>\n",
       "      <td>FC</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mat_type mat_tech mat_prop llrs llrs_duct floor_type floor_mat  object_id\n",
       "71      MUR    MUN99     MO99   LO      DU99        FW1        FW         77\n",
       "70      MUR    MUN99     MO99   LO      DU99       FT99        FN         66\n",
       "32       MR       RW     MO99   LH      DU99        FW1        FW         65\n",
       "26    MAT99   MATT99     MO99  L99      DU99       FT99       F99         61\n",
       "66      MUR    MUN99     MO99   LO      DU99        FC1        FC         40"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_list = bdg_df_destress.groupby(['mat_type','mat_tech','mat_prop','llrs','llrs_duct','floor_type','floor_mat'])['object_id'].count().reset_index().sort_values('object_id',ascending=False)\n",
    "\n",
    "#only the first 5 rows are displayed\n",
    "comb_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DESTRESS BUILDING TYPES\n",
    "\n",
    "The following representative building typologies have been defined following the explorative analysis of the collected data as described above. \n",
    "\n",
    "**NOTE** The prefix 'D_' to the building types has been added in order to avoid confusion with the classes defined within the EMS-98 schema.\n",
    "\n",
    "\n",
    "| building type | description | GEM tax string         \n",
    "| :- |:-------------: | :---------------------------------------------------:\n",
    "|D_MUR1|unreinforced masonry|MUR/FN \n",
    "|D_MUR2|unreinforced masonry|MUR/FC\n",
    "|D_MUR3|unreinforced masonry|MUR/FW\n",
    "|D_MR1|reinforced masonry|MUR/FN \n",
    "|D_MR2|reinforced masonry|MR/FC\n",
    "|D_MR3|reinforced masonry|MR/FW\n",
    "|D_RC1|reinforced concrete|CR+CIP/LFINF/FC+FC2+FWCP\n",
    "|D_W1|timber structure|W/FW\n",
    "\n",
    "These typologies have been converted into a class definition schema.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of values: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cf6925e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "wgs_destress = collections.OrderedDict()\n",
    "\n",
    "wgs_destress['mat_type'] = 0.35\n",
    "wgs_destress['mat_tech'] = 0.2\n",
    "wgs_destress['mat_prop'] = 0.05\n",
    "wgs_destress['llrs'] = 0.2\n",
    "wgs_destress['llrs_duct'] = 0.1\n",
    "wgs_destress['floor_mat'] = 0.1\n",
    "\n",
    "# check if sum of values is \"1\"\n",
    "print \"sum of values:\", sum(wgs_destress.values())\n",
    "\n",
    "bmod_destress={}\n",
    "\n",
    "bname = 'D_MUR1'\n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'MUR':'+++','M99':'++','CR':'---','C99':'--'}\n",
    "bmod_destress[bname]['mat_tech']={'MUN99':'+++','CLBRS':'+++','CLBRH':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LO':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={}\n",
    "bmod_destress[bname]['floor_mat']={'FN':'+++'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':1,'H_MAX':1}\n",
    "\n",
    "bname = 'D_MUR2'\n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'MUR':'+++','M99':'++','CR':'---','C99':'--'}\n",
    "bmod_destress[bname]['mat_tech']={'MUN99':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LO':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={}\n",
    "bmod_destress[bname]['floor_mat']={'FC':'+++','FW':'---'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':2,'H_MAX':2}\n",
    "\n",
    "bname = 'D_MUR3' \n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'MUR':'+++','M99':'++','CR':'---','C99':'--'}\n",
    "bmod_destress[bname]['mat_tech']={'MUN99':'+++','CLBRS':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LH':'+++','LO':'++'}\n",
    "bmod_destress[bname]['llrs_duct']={'DNO':'+++'}\n",
    "bmod_destress[bname]['floor_mat']={'FW':'+++'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':2,'H_MAX':2}\n",
    "\n",
    "bname = 'D_MR1' \n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'MR':'+++','M99':'++','CR':'--','C99':'--'}\n",
    "bmod_destress[bname]['mat_tech']={'FW':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LH':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={}\n",
    "bmod_destress[bname]['floor_mat']={'FN':'+++'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':1,'H_MAX':1}\n",
    "\n",
    "bname = 'D_MR3' \n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'MR':'+++','M99':'++','CR':'--','C99':'--'}\n",
    "bmod_destress[bname]['mat_tech']={'FW':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LH':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={}\n",
    "bmod_destress[bname]['floor_mat']={'FW':'+++'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':2,'H_MAX':2}\n",
    "\n",
    "bname = 'D_MR2' \n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'MR':'+++','M99':'++','CR':'--','C99':'--'}\n",
    "bmod_destress[bname]['mat_tech']={'FW':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LH':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={}\n",
    "bmod_destress[bname]['floor_mat']={'FC':'+++','FW':'--'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':2,'H_MAX':2}\n",
    "\n",
    "bname = 'D_RC1' \n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'CR':'+++','C99':'++'}\n",
    "bmod_destress[bname]['mat_tech']={'CIP':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MOC':'+++','MON':'+++','MO99':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LFINF':'+++','LO':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={'DUC':'+++'}\n",
    "bmod_destress[bname]['floor_mat']={'FC':'+++','FW':'--'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':1,'H_MAX':5}\n",
    "\n",
    "bname = 'D_W1' \n",
    "bmod_destress[bname]={}\n",
    "bmod_destress[bname]['mat_type']= {'W':'+++','CR':'---','C99':'--','MR':'---','M99':'--',}\n",
    "bmod_destress[bname]['mat_tech']={'WLI':'+++'}\n",
    "bmod_destress[bname]['mat_prop']={'MON':'+++'}\n",
    "bmod_destress[bname]['llrs']={'LO':'+++'}\n",
    "bmod_destress[bname]['llrs_duct']={}\n",
    "bmod_destress[bname]['floor_mat']={'FN':'+++'}\n",
    "bmod_destress[bname]['height_1']={'H_MIN':1,'H_MAX':1}\n",
    "\n",
    "#serialize bmod class\n",
    "bmod_destress_meta = collections.OrderedDict()\n",
    "bmod_destress_meta['name'] = 'bmod_destress'\n",
    "bmod_destress_meta['description'] = 'definition of DESTRESS building types, using fuzzy compatibility formulation'\n",
    "bmod_destress_meta['version'] = '1.0'\n",
    "bmod_destress_meta['classes'] = bmod_destress.keys()\n",
    "bmod_destress_meta['weights'] = wgs_destress\n",
    "bmod_destress_meta['fuzzy_values'] = mf\n",
    "bmod_destress_meta['definition']= bmod_destress\n",
    "\n",
    "#save file ? \n",
    "#outf = \"path/filename.yml\"\n",
    "#exportClassDict(bmod_destress_meta,outf,output_format='json')\n",
    "\n",
    "#let us plot the schema\n",
    "plot_bclass_schema(bmod_destress,wgs_destress,mf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test on an individual building\n",
    "\n",
    "We can test the fuzzy scoring procedure on a single building from the 'Alsace' dataset loaded from file. \n",
    "\n",
    "In the following cell the first building of the dataset will be selected, and its structural fetures listed. \n",
    "\n",
    "Then we will evaluate the set of compatibility scores of the considered buildings with respect to the two class definition schemas (EMS-98 and DESTRESS). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lon               7.92277\n",
      "lat               48.9143\n",
      "object_id           21123\n",
      "mat_type              MUR\n",
      "mat_tech             ST99\n",
      "mat_prop              MOC\n",
      "llrs                   LO\n",
      "llrs_duct            DU99\n",
      "height                  H\n",
      "height_1                2\n",
      "yr_built             YAPP\n",
      "occupy                RES\n",
      "occupy_dt            RES1\n",
      "str_irreg            IRRE\n",
      "str_irreg_dt        IRP99\n",
      "str_irreg_type        IRN\n",
      "roof_shape           RSH4\n",
      "roofcovmat           RMT1\n",
      "roofsysmat            RWO\n",
      "roofsystyp           RWO1\n",
      "roof_conn            RTDP\n",
      "floor_mat              FC\n",
      "floor_type           FT99\n",
      "floor_conn           FWCP\n",
      "position              BPD\n",
      "plan_shape           PLFR\n",
      "Name: 1, dtype: object\n"
     ]
    }
   ],
   "source": [
    "bdg = bdg_df_destress.loc[1]\n",
    "#print the observed feastures of the considered building \n",
    "print bdg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0dde50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0dddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#compute the scores of the considered building with respect to the EMS-98 class definition schema, and plot them\n",
    "s_ems98 = [compute_single_bscore_fuzzy(bdg,bt,wgs_ems98, bmod_schema_ems98, mf) for bt in bmod_schema_ems98.keys()]\n",
    "plot_fnum_vec(s_ems98,bmod_schema_ems98.keys(),'',rot=70,loc='upper left')\n",
    "\n",
    "#compute the scores of the considered building with respect to the DESTRESS class definition schema, and plot them\n",
    "s_destress = [compute_single_bscore_fuzzy(bdg,bt,wgs_destress, bmod_destress, mf) for bt in bmod_destress.keys()]\n",
    "plot_fnum_vec(s_destress,bmod_destress.keys(),'',rot=70,loc='upper left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Compute the exposure model according to the DESTRESS class definition schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cf5d5b610>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cf67b3d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_destress, cl_destress_scores = compute_expo_model_fuzzy(bdg_df_destress,wgs_destress,\n",
    "                                                   bmod_destress,mf,bFilterScore = True,bFilterScoreThreshold = 0.0)\n",
    "\n",
    "cl_destress['btype'].value_counts().plot(kind='bar',rot=70,color='brown')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the exposure model according to the EMS-98 class definition schema\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cf5bbac10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cf67b3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_ems98, cl_ems98_scores = compute_expo_model_fuzzy(bdg_df_destress,wgs_ems98,\n",
    "                                                   bmod_schema_ems98,mf,bFilterScore = True,bFilterScoreThreshold = 0.0)\n",
    "\n",
    "cl_ems98['btype'].value_counts().plot(kind='bar',rot=70,color='brown')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}