{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Point Processes\n",
    "\n",
    "**Author: Serge Rey <sjsrey@gmail.com>**\n",
    "\n",
    "## Introduction\n",
    "\n",
    "One philosophy of applying inferential statistics to spatial data is to think in terms of spatial processes and their possible realizations. In this view, an observed map pattern is one of the possible patterns that might have been generated by a hypothesized process. In this notebook, we are going to regard point patterns as the outcome of point processes. There are three major types of point process, which will result in three types of point patterns:\n",
    "\n",
    "* [Random Patterns](#Random-Patterns)\n",
    "* [Clustered Patterns](#Clustered-Patterns)\n",
    "* [Regular Patterns](#Regular-Patterns)\n",
    "\n",
    "We will investigate how to generate these point patterns via simulation (Data Generating Processes (DGP) is the correponding point process), and inspect how these resulting point patterns differ from each other visually. In [Quadrat statistics notebook](Quadrat_statistics.ipynb) and [distance statistics notebook](distance_statistics.ipynb), we will adpot some statistics to infer whether it is a [Complete Spaital Randomness](https://en.wikipedia.org/wiki/Complete_spatial_randomness) (CSR) process.\n",
    "\n",
    "A python file named \"process.py\" contains several point process classes with which we can generate point patterns of different types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pointpats.process import PoissonPointProcess, PoissonClusterPointProcess\n",
    "from pointpats.window import Window, poly_from_bbox\n",
    "from pointpats.pointpattern import PointPattern\n",
    "import libpysal as ps\n",
    "from libpysal.cg import shapely_ext\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "#import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Patterns\n",
    "\n",
    "Random point patterns are the outcome of CSR. CSR has two major characteristics:\n",
    "1. Uniform: each location has equal probability of getting a point (where an event happens)\n",
    "2. Independent: location of event points are independent\n",
    "\n",
    "It usually serves as the null hypothesis in testing whether a point pattern is the outcome of a random process.\n",
    "\n",
    "There are two types of CSR:\n",
    "* $N$-conditioned CSR: $N$  is fixed\n",
    "    * Given the total number of events $N$ occurring within an area $A$, the locations of the $N$ events represent an independent random sample of $N$ locations where each location is equally likely to be chosen as an event.\n",
    "* $\\lambda$-conditioned CSR: $N$ is randomly generated from a Poisson process.\n",
    "    * The number of events occurring within a finite region $A$ is a random variable $\\dot{N}$ following a Poisson distribution with mean $\\lambda|A|$, with $|A|$ denoting area of $A$ and $\\lambda$ denoting the intensity of the point pattern.\n",
    "    * Given the total number of events $\\dot{N}$ occurring within an area $A$, the locations of the $\\dot{N}$ events represent an independent random sample of $\\dot{N}$ locations where each location is equally likely to be chosen as an event."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating CSR\n",
    "We are going to generate several point patterns (200 events) from CSR within Virginia state boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the virginia polygon shapefile\n",
    "import libpysal as ps \n",
    "va = ps.open(ps.examples.get_path(\"virginia.shp\"))\n",
    "polys = [shp for shp in va]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create the exterior polygons for VA from the union of the county shapes\n",
    "state = shapely_ext.cascaded_union(polys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create window from virginia state boundary\n",
    "window = Window(state.parts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Generate a point series from N-conditioned CSR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.process.PoissonPointProcess at 0x119dc9e50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulate a csr process in the same window (200 points, 1 realization)\n",
    "# by specifying \"asPP\" false, we can generate a point series\n",
    "# by specifying \"conditioning\" false, we can simulate a N-conditioned CSR\n",
    "np.random.seed(5)\n",
    "samples = PoissonPointProcess(window, 200, 1, conditioning=False, asPP=False)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.realizations[0] # simulated event points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.pointpattern.PointPattern at 0x119e26490>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a point pattern from the simulated point series\n",
    "pp_csr = PointPattern(samples.realizations[0])\n",
    "pp_csr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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147zCr5RSwET4bX74T4X/Im5rGTAUZ/Va4IJSqhdARL4NfATQwr8A6e3t5eLF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\n0Sww/n/dEDNigH7+RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d1ec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_csr.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Generate a point series from $\\lambda$-conditioned CSR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.process.PoissonPointProcess at 0x11d2bacd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulate a csr process in the same window (200 points, 1 realization)\n",
    "# by specifying \"asPP\" false, we can generate a point series\n",
    "# by specifying \"conditioning\" True, we can simulate a lamda-conditioned CSR\n",
    "np.random.seed(5)\n",
    "samples = PoissonPointProcess(window, 200, 1, conditioning=True, asPP=False)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-79.55664806,  38.25720619],\n",
       "       [-78.5166233 ,  38.30532302],\n",
       "       [-77.21660795,  37.57582983],\n",
       "       [-77.44823305,  36.74925133],\n",
       "       [-79.95384378,  37.55525777],\n",
       "       [-81.36397951,  36.71747455],\n",
       "       [-80.18215183,  37.35242254],\n",
       "       [-78.37289635,  38.26514268],\n",
       "       [-78.78567678,  38.80097021],\n",
       "       [-78.61625258,  38.36290302],\n",
       "       [-81.43369537,  37.00701809],\n",
       "       [-81.95031135,  37.07057506],\n",
       "       [-77.77370645,  36.89065808],\n",
       "       [-78.80401745,  38.72566936],\n",
       "       [-79.32844778,  36.95802405],\n",
       "       [-78.2800756 ,  38.90863979],\n",
       "       [-81.49037579,  36.99262809],\n",
       "       [-76.90806444,  37.72828737],\n",
       "       [-77.17489798,  37.64869473],\n",
       "       [-79.58599474,  37.9904752 ],\n",
       "       [-78.34778262,  37.22853963],\n",
       "       [-76.78862975,  37.8189276 ],\n",
       "       [-78.90167513,  36.90895769],\n",
       "       [-78.31750801,  37.13340462],\n",
       "       [-75.86084614,  37.62727824],\n",
       "       [-75.97937462,  37.17903022],\n",
       "       [-79.0679983 ,  38.62297192],\n",
       "       [-76.96164707,  37.31439593],\n",
       "       [-76.97054563,  37.09732441],\n",
       "       [-78.28781253,  39.27775751],\n",
       "       [-79.51445209,  37.2942029 ],\n",
       "       [-78.75479687,  37.39418456],\n",
       "       [-75.75445326,  37.78241231],\n",
       "       [-79.37160467,  37.43228348],\n",
       "       [-79.64158321,  38.491645  ],\n",
       "       [-79.33464281,  37.14352317],\n",
       "       [-79.90916235,  37.50562343],\n",
       "       [-76.92510237,  36.77946552],\n",
       "       [-76.91730122,  36.62173807],\n",
       "       [-82.06902468,  36.76063464],\n",
       "       [-79.8601599 ,  36.68065846],\n",
       "       [-77.74757811,  39.05919615],\n",
       "       [-79.23362271,  36.56074643],\n",
       "       [-78.10257435,  37.80365574],\n",
       "       [-77.18266793,  37.63777248],\n",
       "       [-77.27402385,  37.57885055],\n",
       "       [-77.68764731,  37.21503901],\n",
       "       [-77.75929316,  36.61739054],\n",
       "       [-77.20460382,  38.67318986],\n",
       "       [-80.52384818,  36.65326387],\n",
       "       [-80.60914236,  36.74210979],\n",
       "       [-76.84214327,  38.07338722],\n",
       "       [-79.88271974,  37.404438  ],\n",
       "       [-76.81805705,  37.34273888],\n",
       "       [-82.02798408,  36.94054096],\n",
       "       [-76.84238532,  37.82794034],\n",
       "       [-78.70526218,  38.83337292],\n",
       "       [-76.71235091,  37.95435389],\n",
       "       [-78.11460871,  36.82540288],\n",
       "       [-78.77656343,  37.18366661],\n",
       "       [-78.1748728 ,  38.89314571],\n",
       "       [-81.63574287,  37.11455891],\n",
       "       [-79.08592614,  38.45792181],\n",
       "       [-78.33817368,  37.64663718],\n",
       "       [-79.22973299,  37.19362583],\n",
       "       [-79.48678905,  37.29187339],\n",
       "       [-78.58205854,  37.40208832],\n",
       "       [-77.36276352,  38.76896337],\n",
       "       [-77.30130598,  37.47745852],\n",
       "       [-79.64493717,  36.72052872],\n",
       "       [-79.10398846,  38.05681891],\n",
       "       [-81.12955413,  37.04549049],\n",
       "       [-77.94793345,  37.59843086],\n",
       "       [-78.60434598,  38.89933921],\n",
       "       [-79.048031  ,  37.77049293],\n",
       "       [-78.25853547,  39.16083334],\n",
       "       [-78.72462602,  37.17396404],\n",
       "       [-77.29314545,  37.9220836 ],\n",
       "       [-77.28147432,  38.79060528],\n",
       "       [-76.66696623,  36.58417386],\n",
       "       [-80.46513532,  36.64153932],\n",
       "       [-78.65541452,  38.74225794],\n",
       "       [-76.805115  ,  36.78532921],\n",
       "       [-79.93532984,  37.08143783],\n",
       "       [-78.29407639,  39.34140688],\n",
       "       [-78.14216323,  38.3665535 ],\n",
       "       [-78.00505977,  36.7069097 ],\n",
       "       [-81.18975884,  36.62150498],\n",
       "       [-80.79290321,  37.05030562],\n",
       "       [-79.5153296 ,  37.75664579],\n",
       "       [-77.87487209,  37.40994143],\n",
       "       [-76.78504902,  38.11807362],\n",
       "       [-80.09560083,  37.35129105],\n",
       "       [-75.85714598,  37.57460507],\n",
       "       [-77.86127482,  38.34868698],\n",
       "       [-82.27493376,  36.99263064],\n",
       "       [-80.83633012,  37.16191786],\n",
       "       [-77.93737278,  36.57794908],\n",
       "       [-78.55997549,  37.60246875],\n",
       "       [-80.74982401,  36.93038256],\n",
       "       [-81.89152421,  37.32986349],\n",
       "       [-76.60039544,  37.49087981],\n",
       "       [-79.89144123,  38.026734  ],\n",
       "       [-77.53568375,  37.64300141],\n",
       "       [-79.36034573,  36.77163754],\n",
       "       [-77.32624847,  36.9372054 ],\n",
       "       [-82.34166665,  36.69837703],\n",
       "       [-77.14780326,  37.60598513],\n",
       "       [-80.24638938,  37.27287164],\n",
       "       [-77.41027396,  37.9199658 ],\n",
       "       [-78.73229552,  37.32763411],\n",
       "       [-79.50446982,  37.98941026],\n",
       "       [-77.34484259,  37.24072148],\n",
       "       [-80.34358018,  36.97299833],\n",
       "       [-78.28959166,  38.70289952],\n",
       "       [-79.23938464,  37.60233533],\n",
       "       [-77.10310375,  38.04796476],\n",
       "       [-78.11943302,  37.5521639 ],\n",
       "       [-82.63246378,  37.05615369],\n",
       "       [-76.6777552 ,  36.99804193],\n",
       "       [-79.31751436,  37.29870784],\n",
       "       [-77.02234401,  37.83495451],\n",
       "       [-77.54221373,  37.3812618 ],\n",
       "       [-75.71630753,  37.92836454],\n",
       "       [-80.21661887,  36.75870423],\n",
       "       [-78.75115924,  38.06910198],\n",
       "       [-76.86872936,  37.50425399],\n",
       "       [-79.21340288,  37.25364651],\n",
       "       [-77.77941742,  39.02429976],\n",
       "       [-77.3124049 ,  38.7084587 ],\n",
       "       [-77.9591418 ,  38.05055288],\n",
       "       [-77.9213301 ,  38.71767437],\n",
       "       [-77.66093307,  36.59501015],\n",
       "       [-77.21170491,  37.13898883],\n",
       "       [-81.91956901,  36.81108808],\n",
       "       [-76.68169985,  36.66281858],\n",
       "       [-77.30664489,  36.92944526],\n",
       "       [-81.85918569,  36.80543902],\n",
       "       [-76.86645645,  37.57131542],\n",
       "       [-80.68521276,  37.18782022],\n",
       "       [-81.41932788,  36.70007358],\n",
       "       [-78.8063798 ,  38.30071805],\n",
       "       [-77.14952496,  37.89347582],\n",
       "       [-79.07890941,  37.5601488 ],\n",
       "       [-83.00313472,  36.75272866],\n",
       "       [-78.68101007,  38.87234553],\n",
       "       [-77.63204774,  37.61550741],\n",
       "       [-78.88306754,  38.05667036],\n",
       "       [-77.45255789,  37.3391586 ],\n",
       "       [-78.47299022,  37.4129918 ],\n",
       "       [-79.02176971,  37.37256883],\n",
       "       [-78.28147935,  37.91147075],\n",
       "       [-79.58518375,  37.78361184],\n",
       "       [-77.77610921,  37.97641435],\n",
       "       [-80.99224637,  36.61994736],\n",
       "       [-80.58914692,  36.8579181 ],\n",
       "       [-77.80923356,  37.58006348],\n",
       "       [-78.86249696,  36.73878708],\n",
       "       [-82.21675753,  36.74153288],\n",
       "       [-83.24670184,  36.69004968],\n",
       "       [-81.28732939,  36.87113389],\n",
       "       [-75.84594392,  37.54487864],\n",
       "       [-79.22235094,  36.87297199],\n",
       "       [-78.48547431,  38.52479965],\n",
       "       [-78.20476584,  38.54799414],\n",
       "       [-77.89852106,  38.1201838 ],\n",
       "       [-81.37405706,  37.04456699],\n",
       "       [-78.02587914,  37.64685616],\n",
       "       [-78.42418842,  36.88536942],\n",
       "       [-76.68842544,  36.74893391],\n",
       "       [-80.81565379,  36.84864385],\n",
       "       [-77.83641513,  37.17473795],\n",
       "       [-77.50393746,  38.0087299 ],\n",
       "       [-82.66226354,  37.12507361],\n",
       "       [-81.50584616,  36.91097209],\n",
       "       [-78.50980015,  38.35200834],\n",
       "       [-77.99991705,  39.2235099 ],\n",
       "       [-77.75281574,  38.65985656],\n",
       "       [-79.18848841,  37.81637356],\n",
       "       [-75.99527387,  36.67900083],\n",
       "       [-78.10679754,  38.48234391],\n",
       "       [-77.72774175,  37.44640927],\n",
       "       [-79.16298721,  38.41845743],\n",
       "       [-80.79353455,  36.6361113 ],\n",
       "       [-79.09248227,  38.09955844],\n",
       "       [-79.43627162,  37.75365148],\n",
       "       [-82.44550608,  36.66466322],\n",
       "       [-78.88007206,  38.4383976 ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.realizations[0] # simulated points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.pointpattern.PointPattern at 0x119d7a310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a point pattern from the simulated point series\n",
    "pp_csr = PointPattern(samples.realizations[0])\n",
    "pp_csr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4XLHnjI9vtw4xODzAc7ves2Ucs4gJv2GkSSAQoK2tjeHhYerr6zOSox8hNue+zzfEY/fe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d2ecc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_csr.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulated point pattern has $194$ events rather than the Possion mean $200$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Generate a point pattern from N-conditioned CSR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.process.PoissonPointProcess at 0x11d3aa790>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulate a csr process in the same window (200 points, 1 realization)\n",
    "# by specifying \"asPP\" True, we can generate a point pattern\n",
    "# by specifying \"conditioning\" false, we can simulate a N-conditioned CSR\n",
    "np.random.seed(5)\n",
    "samples = PoissonPointProcess(window, 200, 1, conditioning=False, asPP=True)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.pointpattern.PointPattern at 0x11d2aec10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_csr = samples.realizations[0] # simulated point pattern\n",
    "pp_csr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P3HOQ0zE7O5usrCyeP3/O8PAwmZmZlJWVYTAYmJqaQq/XR92EEWrNnXgSzCK1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\nAfT8fid5xoHfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d42f410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_csr.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Generate a point pattern of size 200 from a $\\lambda$-conditioned CSR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.process.PoissonPointProcess at 0x11d48f7d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulate a csr process in the same window (200 points, 1 realization)\n",
    "# by specifying \"asPP\" True, we can generate a point pattern\n",
    "# by specifying \"conditioning\" True, we can simulate a lamda-conditioned CSR\n",
    "np.random.seed(5)\n",
    "samples = PoissonPointProcess(window, 200, 1, conditioning=True, asPP=True)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.pointpattern.PointPattern at 0x11d47cb90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_csr = samples.realizations[0] # simulated point pattern\n",
    "pp_csr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RpaWl4AQghGBjY4PNzU2uXbsWPCfgd89LTz22Tk8iG6kHiHXNnUiwWq0xr5N0\nFAHx7+7upr+/f9/f61XhxE+GlLIPuBXi9XeBOyFeXwQ+tvv9FHDj9GYqFMfjcrno6ekhIyODGzdu\nRFxH3+l0Yrfb99V8P4rMzEyampqwWq0YjUYWFxcpKCjAZDLR0NAQnHAOruI/9/EWNmyukCvqi1rA\nLVyys7OxWCxkZmaeyXg6nY4HDx68suKvqnMqzj02m42uri7Kysqi7sNqNBopKSmJqFRAVlYWzc3N\nbG1tYTKZaGxs3Ff+4eAqfsPm4pc/ELrOf6JdLYmmsLCQyclJysvLz2zMV1n8VckGxblma2uLR48e\nUVtbG7XoOxwOLBbLoYBsuOTk5FBfX3+oZnw0ufWvagG3zMxMhBAR7ZWIBQHx39raor+//0zHTiRK\n+BXnFpPJRFdXF62trWHl1x/Fzs4OmZmZh0o4nJZ4lmGOZQZQojduBSgsLGRtbe3YY7xeLyaTCYvF\nErNxAz7/gPjHohx3sqNcPYpzycLCAoODg9y7d4/8/PyoryOlxG63k5aWFkPrfkQ8AqahMoCAqNxE\nkWYTnWaT2Enk5+czODhITU3NoT0QDoeDlZUV1tbWSEtLQ0oZ04YrKSkpwWyfgNsnWauqxgIl/Ipz\nx8TEBDMzM7S3t5+qCJrb7aavrw+v10tDQ0MMLYwvB2MH33hm5JvPjFGlgkaSTRTPlFOfz8fU1BR5\neXlBwZVSsrm5ycrKCna7naKiIlpbW9HpdLx48QKn0xlRuu5JvErir4RfcW6QUjI4OMja2hqdnZ1h\nZeAcx+LiIoWFhVRWVsbczRNPDmYACYg6FTSSbKJ4ppx6vV40Gg0bGxsMDQ2RkZHBxsZGsDpqXl7e\nvkytvLw8NjY2KC0tjcn4AQLivzfgexHFXwm/4lzg8/l49uwZLpeLjo4OdDrdqa7ndDpZW1vj+vXr\nSSn6x7lUDmYAAXy914jb40OrjSwVNJJsonimnOp0OpqamvB6vWxvb7Ozs0NDQ8ORlTrz8vJYXFyM\nufCDX/wPZvtcNPFXwq9IetxuN0+ePEGv19PW1hZxjn4oAmWTTzuBxINwXCp7Ywe9sxsQCEhGEZgM\nNw5xFimnWq2WnJycE2v2ZGdnMzk5GXN3T4CLLv4qq0eR1DgcDh49ekROTg63b9+OiegHcLvdMbtW\nLIm0YFrXlBmPTyIBr0/GtcBasqScajSaoLsnXgTE32q10tfXd6GyfZTwK5IWq9XKu+++y6VLl2hp\naYnpiquG0UtbAAAgAElEQVS4uJitrS08Hs+xxyUi1THS/P/jjk+WVM14kJ+fj9kc3yqiAfHf3t6+\nUOIfVj3+s+bu3bvy6VPVpfFVxmw209vbS0tLCxUVFTG//vLyMqurq1y/fv3IYxJZOC3StMlQxye6\n8Fu8kVLS19dHXV1d3Ftcejweuru7yczM5Pr160np9ol5PX6F4ixZWlri6dOn3L59Oy6ibzQaWV1d\npbGx8djjElmjPlKXSqjjE11jP94IISgtLWVpaSnuY120lb8SfkVSMT09zcDAAO3t7RQWFsb8+haL\nBZPJRHNz84lBwUTXqD8t593+cCgsLGR7exu73R73sS6S+CtXjyIpkFIyMjLC8vIyDx48OFT3JhZ4\nvV4GBgaoqqoKuzlLPHeqngXn3f5wWFhYwOVynapsRyQkq9snElePEn5FwvH5fLx48QKbzcb9+/cj\nqpAZCdPT0wBnJhCKsyGwA7u1tTUuqZ2h8Hg8wTLgySL+ysevODcEPkBer5f29va4if7m5iZbW1tU\nVVXF5fqKxKHT6aioqGBgYIDJyUm2t7fjPmZKSgr3799nZ2eHly9fnju3jxJ+RcJwOp08evSI9PR0\n7t69G7cdtD6fj+npaerq6pJyl67i9JSWlnLjxg3S09OZnJwMlvY4rj/yaQmIv81mO3fir4RfkRC2\nt7d59913KS8vP5NHZa/Xe+T2f8XFICUlhbKyMq5fv055eTlra2u8fPkSo9GIy+WK25gPHjw4d+Kv\nhF9x5mxsbPD48WMaGhrOpCqm1+tNCh+s4mwQQpCXl0dTUxNNTU14PB76+/uZmJiIS6MXrVZ77sRf\nCb/iTFleXqanp4ebN2+emb99c3OTnJwc5eY5JedxF/DQqoP/Oe3Bl19NZmYmU1NTDAwMYDKZ8Hq9\nMRvnvIm/KtKmODNmZ2cZHR3lwYMH5Obmntm4ZrOZkpKSMxvvInIedwGHsvn29etYLBZWVlaYn5+n\nqKiIioqKmNSACoh/d3c3L1684ObNm0n7pKlW/IozYXR0lMnJSTo6Os5U9MEf3E2G1f55XDEHOI+7\ngEPZLIQgJyeHK1eucPXqVba3t1lZWYnZmAHxdzgcvHjxImlX/kr4FXFFSsnLly9ZXV2lo6MjYQHW\nWD7WR0Ng9fnb3x3lF77cde7E/7ztAu6d3WBh006K9mibDQYDNTU1LC0txbRSq1ar5f79+0kt/srV\no4gbXq+XwEa89vZ2UlLO/r/b9vY2LpfrzJ8yDhJt96pk2Xl7FrX4Y8VeF0+KRvD6/So+fbsypM1p\naWkUFBRgNBpjurEvIP49PT1J6fZRK35FXHC5XDx+/Bi9Xs+9e/cSIvrgDyaXlpYm/EOXl56KRgg0\nx6yYD7qCkuUpIWAXkNBa/OG6yvZOsl6fpDw37Viby8vL41LeOZlX/mrFr4g5NpuNrq4uysvLaWpq\nSqgtVquVS5cuJdSG3tkNvvDmIF6fRKsRfO7jLYeEKFQgMp49biOxPRmCupHYEWmLSJ/PF7eFSbKu\n/E9c8QshDEKIHiHESyHEoBDi87uv3xRCdAkhXgghngoh7h9x/keEEKNCiAkhxK/F+gYUycXW1haP\nHj2irq4u4aLvdrvx+Xzo9fqEBlYDAi7xxzw2bIc3E4US+WTwqydLUDcSOwJuqV/9cGNYE5XL5Ypb\nqRDYv/J//vx5Uqz8w5nmnMAHpZTbQggd8K4Q4i+ALwCfl1L+hRDiY8BvAj+290QhhBb4IvATgBF4\nIoT4H1LKoVjehCI5MJlMPHv2jBs3bsSlCXakmM1mcnNzE75qDWcFGuqYZPCrx7LB+mniFZHaEW4f\nYfALf7x7LwfE/8mTJzx//pxbt27FbOXv8/kivtaJwi/901Og6pFu90vufmXvvp4DLIY4/T4wIaWc\nAhBC/DHw04AS/guG0WhkaGiIe/fukZ+fn2hzAP9EVF1dzV/2rp46sApELVrhCPhRx0QiYPEgVpPP\naSffeE6CZrMZt9vNyMgIZWVlJzZ6jxatVsu9e/eiEn+v14vNZmNnZ+fQl91up6WlJSJbwnJs7a7c\ne4F64ItSym4hxK8A3xFC/BZ+l9HDEKdWAPN7fjYCDyKyUJH0jI+PMzs7y8OHD8nMzEy0OYA/m8fn\n85GdnU1bnTfiVevBzBCEwOON/okhHAFPtMgfxXF2hbuKj0W8Il6/H5/PR2ZmJunp6UxPT5OVlUVV\nVVVcngIiEf/Z2VkWFhbY2dnB5XKRnp5ORkYGGRkZZGdnU1ZWhl6v5/HjxxE/YYcl/FJKL3BTCJEL\nvCGEaAU+C/xjKeU3hBA/B3wF+FBEo+9BCPHZ3Wuq0rnnBCklAwMDrK+v09nZicFgSLRJgH91NDs7\nG/wwRLNa3CdUXv8DriRxQdZkJJ4B17NkbywqPz+fhYUFBgYGqKyspLCwMObB2HDE3+FwMDw8zJ07\nd8jMzMRgMIS0Y2ZmhsLCwogbF0WUziml3ATeBj4C/BLwzd23vo7frXOQBWBvSkXl7muhrv0lKeVd\nKeXdoqKiSMxSJACv10tvby/b29s8fPgwaUTf5/MxPj5Oenr6vjINkfaw3RdY1Qp052jz0lkRz4Br\nPAgnwK/VaqmqqqKxsZHV1VWGh4fj0tYxIP4ulytkwHdmZobKykqKiopIS0sLKfpSSqampqirq4t4\n/BNX/EKIIsAtpdwUQqThD9T+Bn6f/vuB7wMfBMZDnP4EaBBC1OIX/NeBz0RspSKpcLvd9PT0kJaW\nxoMHD2JS5yRWzMzMoNFoqKmpOdV1Dj4lQPQ+/otKPAOusSbSGEN6ejpXr15lZWWF4eFhqqqqYt4D\n+riVv9lsprGx8djzTSYTKSkpUcXUwnH1lAH/bdfPrwG+JqV8UwixCfx7IUQK4GDXTSOEKAe+LKX8\nmJTSI4T4h8B3AC3wX6SUgxFbqUga7HY7XV1dlJSU0NzcnBQ5yQHMZjPb29u0tLTExK6DQqUEfz+n\nDbgejA/Ec5dyNDEGIQSlpaXk5OQwMjKCVqsNu1dzuOwV/69+9z0WPRk05ECqZevEIHO0q30IL6un\nD7gV4vV3gTshXl8EPrbn528D347KOkVSYbFY6O7u5vLly1H/h4sXTqeT2dlZGhsbk6IgWzISD2GN\ndhV/cAX+uY+38IU3B2Oachuov5OSknKqGENaWhoNDQ2MjY2RkpJCVlbWqew6iFarRVtSz7/68y68\nch2dVsN/+utXjw0uW61WLBYL5eXlgD8mEAlq564iLNbW1ujt7aW1tZWKiopEm3OI6elpysrKVJet\nI0j0XoaDHFyB/8XA0pEr8mgnrLGxMWw2G1qtltaWllM9nWRmZnL58mXGx8dpamqKOJh6Ej0zm/ik\nQOIvMzFk9vCBY46fnp6mpqYGjUaD1+vlxYsXEY2nhF9xIouLi/T393Pnzp2Y+zljgdvtZmdnhytX\nriTalKQlGco/7OXgCvyjrWU8mVk/tCI/zYSVn59PWloaBoOB6elp7jQ1neqec3JyqKmpYXR0lObm\n5pgmNLTVFaBLEbg8PnQp2mOfSFwuF4uLi3zgA/6pYWhoCJPJFNF4SvgVxzI1NcXk5CTt7e1kZ2ef\nfMIZs729zczMDPn5+UkVZE42ki2dMlR8oLE069CK/DQTVmFhIX19fVy/fp2NjQ1WV1cpLi4+ld35\n+fk4nU7m5uZiutC4U53Hlz9znd/9xlv8Hz//sWPvcW5ujtLSUvR6PUBU5SaU8CtCIqVkaGiI1dVV\nOjs7SUtLS7RJh3C73YyOjlJTU0NBgUqxPI5kKP8QyqaDwfODdp1mwtLpdOTm5mI2m6mtrWVkZISc\nnJygYEZLTk4Oa2trp7pGKJqK9PxkrY5izc6Rx/h8Pqanp3nw4Ef7YMfGxuKzgUvxauHz+Xjx4gV2\nu53Ozs641zGJlo2NDXJzc5Xoh0my7gw+jqMmrHD9/kVFRczMzFBaWkppaSnT09OnLh6o0+lwuQ4X\n2jstNpuNiooKFhcXj0yeWF5eDu7cDfCJT3wi4rGU8Cv24Xa7efr0KTqdjra2tqTOkHG73Qmr8684\nOw5OWOH4/QMTQ156KsNTFrZSjHQ0lbO+vo7JZOI0m0QtFktc/t/Z7fYTn6ynpqaor68/9VjqU6MI\n4nA46O7uJj8/n9bW1qTK0T+Iw+FgZWXlxE0uiSRZumddNE7y+++dGHwSBPC1gT6++tkMmuvqgi6f\naHzjNpuN2dnZuJQct9lsuN3uI/cKbGxs4HQ69+1IjxYl/ArAHyTt6uqiurqahoaGRJtzLD6fj8nJ\nScrLy5M2fTPZ0ifD4bxMVCf5/fdODOAvI+z2Sh6Pr3KnupGSkhKmp6cjXjS43W7Gx8eprq6OeTpn\noPwCQGtra8hjpqenqa2tjcmCTAm/gvX1dZ4+fUpzc3PCu1WFw+rqKikpKXGv+X8aIUy29MmTOE8T\n1UmB6sDEEPj9a/DXW2rY3QhbVlYWTIGMxOUzPT1NXl5ezGNKDoeD/9k9xLcmnPztT7wv5Irf4XCw\nurrKtWvXYjKmEv5XnOXlZV6+fMmtW7dOnep2Vvh8vpivuA5yWiE8alWarKvq8zZRHReo3jsx5KWn\nsmFzcedSFmk7y8zOzlJVVUVdXR2jo6MAYYu/3W6PeeVgn8/HG++84NffWcflyaH7jwf5w7+Tdeje\nAkXbYpVooYT/FWZmZobx8XHa2tri1nwi1vh8PiwWS9z3FBwUwm8+M0ZUsC3UqjSZV9XJlud/WkJN\nDB5PARMTE8zMzFBbW0tzczOjo6N4vd6wnh4D2Tyx3LjlcrkYWnPj9kokIuSkGygz3tnZGbNxlfC/\nooyMjLC4uEhHR0fcV8+xZH5+Ho1GQ1lZWVzH2SuEWq2Grz+dx+OTETVlOSg+ybyqTsY8/1iTkpJC\nfX09fX19lJeXYzAYaGpqYmBggOLi4hM3AKampgbr/8QKp9PJrcosvjZkxe2RISfdhYUF8vLyYhrP\nUsL/iuHz+ejr68NqtdLZ2RnXJtPxwG63U1ZWFveMo71CuLBp54975k7dlCXZV9XnMc9/L+G40VJS\nUiguLmZxcZHa2lr0ej0ZGRlsbGyc6LuPR/6+0+nk1qUcfufjqXRNr/PJhy2HbJ+amjoy4BstSvhf\nITweD729vQA8fPgwqXP0j0Kn0+HxeM5krIAQ9s5u8M1nRv/qf3fF7/VGLt6vwqo6UUTiRispKaGv\nr4+KigpSU1MpLCzEbDafKPypqakxF36Xy4Ver6c2207jvVLqD9gcqMET6xpZSvhfEZxOJz09PWRn\nZ3P9+vWkztE/CrfbjcViiUkecyTEsinLeV9VJyuRuNF0Oh2FhYUsLS1RXV1NXl4es7OzuN3uI4On\ngWbnsa4H5XQ6ycnJwWazhYyzTU9Px6UEuhL+V4CdnR26u7upqKhI6g1PJzE9PU1hYWFCGrqrpizJ\nTaRutLKyMvr7+ykvL99X0ydUkNftdjMyMkJ6enpM0509Hg9bW1tUVFRgt9sPxdp2dnbY2Njgzp1D\nbU9OjRL+C87m5iY9PT00NjZSXV2daHOixmQy4XK5YrJdXXHxiNSNlpqaSn5+PisrK8Gm6vPz8yGF\nX6PRIKUkIyMjpqUaVlZWyMnJwWAwYLPZDgm/z+cD/Dn8sd6oqOrYXmBWV1fp7u7m+vXr51r0wd9x\nqLCwUJVeVhzJneo8fvkD9WE/jZWXl7O6uorH4yE7Oxu3243NZjt0nFarpbGxkfn5+ZjFlzweDysr\nK1RUVODxePB6vYeqhmZlZVFZWcnIyEhMxtyL+hRdUObm5njx4gX379+P+w7Xs+A8xiSShd7ZDb74\n9gS9sxtxPSce1/tq9xx/4yvdfLV7LiZ27EWv15OTk8Pq6ipCiKDfP5S4CyHQarUxW/EvLy+Tm5sb\nXO2HKs7mcDhYWFhQPn5FeIyNjTE/P8/Dhw8T4g+PB3a7HZ1Oh9lsJjc391xmJCWCaDaNxXqjWbTX\n+2r3HP/nG/0A/HDcX//+Mw9iu3O2vLyc4eFhioqKKCkpYXZ2lpcvX5KdnU1hYSE5OTloNBqsVmvM\nPks9U2v8+XtTfPx+I3UQ0r8vpeTFixfU1NTEvME7qBX/hUJKSV9fH0tLS3R0dFwY0Qf/tnqHw4HJ\nZGJ4eBin05lok84FobJd4nHOQfau8KO93l8MLB37cyxIS0ujtLSU/v5+tra2qK+v58aNG+Tk5LC0\ntMSLFy+YmZlhfX09Jp+n3tkN/sZ/6eGPBqz8rd9/Ru/sRkj//vT0NF6vN24FE5XwXxC8Xi9Pnz7F\nZrPR0dER023lyUBRURH19fU0NTWRmprK8vJyok06kli7SU4zViDbRSsIe9/BwXPy0lMjup/ACv+3\nvzvKL3y5i7z01IhtAPho6/7d2S1l8SnTUV5eTlNTE6urqwwPD+NyuSguLubq1au0tLSg0+mCaZen\n5fH4Km6P3DcJHhR+i8XC+Pg4t27dipuLU7l6LgAul4uenh7S09O5c+fOhQ6AOp1OrFYrNTU1iTYl\nJGdZjyecsaLZNHawyNkX3hwMOcZRO2UPrvA3bK6oNq595kEVc+YdvvTDKaSE33tvhp9oKY3L7zM9\nPZ2rV6+yurrKyMgIxcXFlJeXo9frqaiooKKiIibjNOT4K4V6fD8qz2BbnSA/Px/wL+B6e3tpaWmJ\naykVJfznHJvNRnd3N6WlpTQ1NV3oIOj29jYTExPBHZfJyFnW4wl3rGg2jQXO+eLbEyHHOG7SCZVT\nH64NeycTgMElC0BEJTKirYAqhKCkpCS4oau/v5/a2tqYFQQ0m83kyy3+6Ydq+cGUhY+2lnGnOo8f\nzPwouDs+Pk56ejqVlZUxGfMolPCfYywWC93d3dTX11NbW5toc+LK8vJysL5KJMGusy6DfNxGoljb\nEsvaP0fZdtQYx0060Zam2DuZpGg1IOVu1UrQhOkmisUTV2pqKg0NDWxsbDA1NUVWVhZVVVWnKons\ncrmYmZlhzOzm/313GrdP8mRmncbSrH3B3UCAeX19PfgUEA+U8J9T1tbW6O3t5dq1a5SXlyfanLiy\nubnJysoKLS0th3KdjyMRZZCPaw4ea1tiVfvnONuOGuOkSSeap4yDkwn4V/oaoKO+kF/50JUTr3ma\nJy4p/S27Ak/NeXl5ZGdns7CwwMDAAJWVlVH36rVarXi9Xia3tbh9fh+/0+3jT5/M8lqGDMatNjc3\nycjIoLu7m/e///1xc/co4T+HLCwsMDg4yN27d2PeDSgZsdls5OXlRST6kLgyyKFEL162xKL2z0m2\nhRojHgXnDpbCRkq8u77wcET/4DUifQoymUzMz8+Tk5NDbm4uOTk56HQ6qqqqKCgoYGRkBI1GE9Vn\nLvC0cDnTi0aAT/ontW88X6T+ngGv18uLFy+oq6ujvLwcrVYbV3fmicIvhDAA7wD63eP/VEr5r4QQ\nfwIECr/kAptSypshzp8BrIAX8Egp78bI9gvP4uIieXl5+zZ3TE5OMj09TXt7O1lZWQm07uxwOp04\nnU4GBgZobGwM+5E7mcogJ5MtB4nWtlgXnItFMbzTTEhFRUW4XC4WFxfZ2dlhdnaWtLQ0cnJyEEKQ\nmppKbm5uVPdmNpuprKzk/v1yhh39fLV7Dgl4fZIJq4bl5WXy8vK4cuVKVNePlHBW/E7gg1LKbSGE\nDnhXCPEXUsq/HjhACPHbwNYx1/iAlHLtlLa+UiwvL/P8+XOKioq4f/8+Uspgn9COjo6QO/0uKmVl\nZaysrLCyshLRxq2D2SmB/PFEFFhL5pLMyWRbIovhCSGorKwkLS2N2dlZSkpK2N7eZmFhAYBr165F\ntXFQSsn6+nqwX+6nb1fyjd0y3ykawe3KLBYXF8/UZXui8Eu/42t790fd7pcMvC/8DrGfAz4YDwNf\nRSwWCy9fvqS9vZ0nT56wtbXFxMQETqeTjo6OmPXdPE84HA6ysrIiTlUNCEcytDxM5pLMyWxbJEQS\nSzkqoF1QUIBer8dsNpOWlkZ6ejomkynq3eIulwuNRhN03eydaIvZ4k61P4vIZDJRXFwcsUszGsL6\nFAkhtEKIF8Aq8JaUsnvP2+8DVqSU40ecLoHvCSF6hRCfPWaMzwohngohngaaD7yKOJ1Onjx5QktL\nC+vr6/h8Pvr7+5FS0tbW9sqJvtlsZmhoiOzs7KhLSsdiJ6oi/sRi41u4f+uDm8wOjpmZmUl1dTXV\n1dVUVVVRWFgY9aZBp9OJTqfD4XAEXwsUlKvO8JKens69e/cwm8384Ac/CFbljCdhBXellF7gphAi\nF3hDCNEqpRzYffvngT865vROKeWCEKIYeEsIMSKlfCfEGF8CvgRw9+5defD9VwGfz8fTp0+pqKjA\n4XAwOzsLQG5uLi0tLRcyR9/n87G1tYXD4aC0tHTfPbpcLlZWVqiurj5VEDuZ/esKP7HKegr3bx1p\nsL20tJSBgQEqKioiXvkHqn5OT0/T3Ny8772lpSW2t7dxuVxUVFRQXV19JhswI8rqkVJuCiHeBj4C\nDAghUoBPA0d2CpBSLuz+uyqEeAO4jz9YrDhAX18fqampNDY28r3vfQ+z2cy9e/e4fPlyok2LC3a7\nnYmJieAHyePxBBtdWK1WxsbGKCwsjDqgFiCZfNgXldPuUYhV1lO4f+uTJoidnR3S0tKCIqzX68nN\nzWV1dZWysrJQlzyS/Px8tre3D00YgdIMn/rUp6ipqTnTwoPhZPUUAe5d0U8DfgL4jd23PwSMSCmN\nR5ybAWiklNbd7z8MfCE2pl8spqam2NraoqOjg42NDSwWC7m5uTHt+JNMmM1mZmdnuXTpEkVFRWxv\nbzMzM8OlS5fwer1MTU1RV1cXs8qE8fBhn/XmsGQlFqv1WD6VhfO3Pm6C8Pl8DA8Pk5uby+XLl4NP\noaWlpYyNjVFSUhLRqlwIgdVqpapqf2VRq9VKVlZWQhZ24az4y4D/JoTQ4o8JfE1K+ebue69zwM0j\nhCgHviyl/BhQgt81FBjrq1LKv4yV8ReF1dVVJicn6ezsxGQy0dfXx2uvvYbL5WJycvLClWJYWVlh\ncXGRpqam4AYVjUaD1+tlZ2eH+fl5srKy4lKONlYkYnNYuHad9WQUi9V6Ip7KjpogdnZ20Ov1eDwe\npqenqa2tRQhBRkYGaWlpmM3miDZyud1unE7noeqeFoslYRV0w8nq6QNuHfHe3wzx2iLwsd3vp4Ab\npzPxYrO9vc3z58+5d+8ey8vLTExM0NbWRk5ODlJKrFYrCwsLca/dEQuklIyMjJCamkpRUVHIGieL\ni4uYTCauXr26L3vBYDCg0WgYHx+nuLg4Lg3VYymKidoctpeD93MWk1Go32GsVuvxzixyu92YTCbS\n0tLIyso6sqmK1WpFq9VSXl6O0Whkbm4u2MGurKyMubm5iITfYrGEzEhbXFxkY2MDn8935oUV1c7d\nBBKoqnn16lVWVlaCdfQDq2AhBJcuXWJiYuJcCP/m5iY+n4+MjAzGx8e5evXqvv0G8/PzbG5u0tzc\nfGhXokaj4erVq2g0mrg83cRaFM8qYHzUZBXqfuI9GR31OzwvMRQpJUajkczMTGZmZrh27VpI8c/N\nzcXtdjM5OUllZSUrKysYjUYqKyuDm7k2NzfDjj1ZLJZDi6C1tTW2t7cRQtDX18fNm4f2vsYVJfwJ\nwufz0dvbS3FxMWtra+zs7NDZ2XlIEH0+37lJ4VxeXqa0tJSCggLMZjNmsznY2s7r9eJ2u2lqajry\nfuIZ3Iq1KJ5W7MJ5+jhusgp1P/GejE4qzBZpvvxZE9h5m5ubi91uZ2Zmhvr6+kPHpaenU11djU6n\nw2az0djYyMjICHq9nqKiIkpLS4OtE8Nha2trX/vTnZ0d5ubmKCsro7W1NSH9sJXwJ4jBwUF8Pl/w\nsbK9vT2k8Hk8npj1+Yw3Xq83eA/V1dXMzs6yuLhISUkJBoOB0tLShLVMjIcoRuuaCPfp4zihPar0\ncTxX3tH8DpMtFlJaWsrs7CxXr15laGiItbU1CgsL9x3j8XgwGo14vV5cLhc6nY7q6uqgi8dgMITd\ndN1utwMEn3zdbjcTExNUV1czMTFBZmZmQj7f50NRLhgzMzMsLi6i0+koKCjg+vXrR7o3tFotTqcT\nKWXSB3hLSkqCK6HABpiVlRUuXboUFx/mwdrtxwleMrkjwn36OE5oj7qfePrJo/kdJjIWEupJIzs7\nGyEEFouFy5cvMzIyQlZW1r54kxAi2Ns54KLJysrC4/Fgs9nY2dkhIyMjLBu2trb2de4yGo3k5uZS\nUFBAX19fXJutHIcS/jNmbW2Nvr4+NBoNtbW1JxZlys7ORqvV8nb/DMNmb8JF6zgKCgqYm5vD6XSi\n1+vJzMyMW9bCvtrtGgFC4PEev6pMlrIE4a6cA0L7jWdGQk35ibifSMcMda9n4fo57kmjoqICo9FI\na2srpaWlTE1N7cuc02q1FBYWotVqg523hBAUFBSwtraG1+sNW7AtFsu+zYdOpzO4D8BmsyWs5pYS\n/jNkZ2eHH/zgBwDcu3fvUF7vUZhFDn/vT17g8RHTx+XV1VUcDkfYdpzE1pa/Tt9ZPLruW0l6JSAj\n6tKUSCJdOX/zmRGXx8c3nhkT7iqJlFAVN2Pt+gk1kRz3pJGXl8fS0hJms5mysjI2NzdZXl7etzGr\nuLiYkZERysvLg0+rBQUFjI6OotFowsrqCbhy9zZJ8ng8wR6+KSkpCXPjKuE/I9xuN3/5l3+Jw+Hg\nQx/6UETpin3Ldjw+Yvq47HA4MBqNCCHIz88/9cp8eXmZpaUlGhsbz8SPv692++6K3+tNXEmGSFex\n4a6ckyFt9LTsvdejWjlGy8GV/e/90m3yvJu01RUf+1RVWVnJ9PQ0+fn5XL58mcHBQXJycoIr+bS0\ntEM5+4GWiFarNawV//b2Nnq9fl8yg9vtJiUlhZ2dnYS5eUAJ/5kgpeTb3/42FouFT33qUxGXIAiI\nXKAlXSyEbX5+nrKyMlJSUoLBrkhiCDs7O7hcLnJycpidnWVnZ+dQbn48iUXt9kjond0Iulw+fbvy\nxCUITggAACAASURBVNTKWNlw0eoMxfp+Dk6M746t8lqhnfuNjcc+VWVnZ2MwGDCZTJSUlHDp0iWm\npqaCKcXgj1ktLCzsW90XFRWFncNvtVr3pXFKKYPCb7PZlPBfdL797W+zurrK66+/HlXzlIDI/dXA\nPHWZHu5U5+FyuaLu0COlxGKxUFNTQ0pKCiaTiZWVlX0pZ8edu7W1xdjYGODvLJSZmUlzc/OZrPQP\nrqzPonZ77+wGP/+l93B5/bUDv95r5I/+7vGplbGyJZmC0rEg1vdzcCJ5UJMH23a8Xu+JT1WVlZWM\njY2Rn59PUVERm5ubGI3GoOszNzeX6enpqEVao9Hsy/4JZOhpNBol/BcZKSXf+c53WFxc5DOf+UzY\nmQChuFOdx+2qXIaHh3n58iUul4vs7GxqamoiWmVLKVlaWtr3CFpXV8fw8HAwy2gvNpuNra0t7HY7\nNpsNh8OBTqcLBsOEEGe27TxRqYFdU+bdOIKfcFIrY0myBKVjRSzv5+BEUp+rYXTUH0Q9SVgzMjIo\nKipiaGiIxsZGampqGBgYIDc3l6ysLGZnZ9Hr9VE/xRoMBiwWS/DnwGof/J+rvdk+Z40S/jjh8Xh4\n6623WFpa4vXXXz+V6AcQQtDc3Mz29jbp6eksLS0xOTnJ1atXwzrf6XQyOTmJEIKGhobg6waDgcbG\nRkZHR9FqtUFX1PLyMouLixQUFJCVlUVxcTEGgyFhAalE+bvb6grQaUVwxR9OamWybFp6Fdg7kWxs\n+OvqhyP84F/16/V6hoeHqa+vp7a2lqmpKbKzs3E4HKeKWRkMhn01+AOBXfDn90da5TOWKOGPMV6v\nl8HBQb7//e9TUlLCpz/96Zj2xhVCBK9XUVGByWQKVvk7jvHxcSwWCxUVFZSUlBzy56enp9PQ0MDY\n2Bi1tbWYTCZcLhdXr17FYDDEzP7TkCh/953qPP7os+1H+vgDxwReS7ZNS8k+CcXKvq2tLebm5gC/\nsObm5oYVtyoqKiI1NZXx8XGqq6vJy8sL7tg9jftSr9fjcrmCtXjcbndQ+JWr5wKxsrLCkydPWF5e\n5n3vex+3boWsbRczhBDU1NQwPj4eFPRQbG9vY7PZuH79+rHlHzIzM6mvr2d0dJTi4mLq6+vPvHjU\ncSTS3x2JeyKZMnHOchKKRsBjZd/s7Cybm5vU1NTg8XiYm5tjYWGB1NTUfbWhrFYrqamph9w3OTk5\nNDU1MTY2RnFx8aGGKdEQaLfodDpJS0sLCr+UErvdntC+2Ur4Y4DD4WBgYIDFxUXcbjcf+tCHqKmp\nOZOx8/LySE9PZ2hoiIyMjEP+drvdzvj4OJWVlWHV/MnOzub27dsJK61wEufB351MmThnNQlFK+Cx\nsi8Qawr4zQsKCoJF2ebn57l8+TJmszn4RKDVamlpadn3/zw9PZ2rV68yNjaG0+mkpqbm1LvlA+6e\ntLS0YHA30IoxkZ8xJfynQErJ7Owso6OjwTKv9+/fDys7Jpbo9XqqqqqYmpqirKwMn8+HlBKfz8fK\nygqVlZURlZGNx3/IZHc3xJpP36480i10lpzVJBStgMfKvoqKCgYGBlhbWwu6WAJNzi0WCyaTifn5\neRobG8nIyGBiYgKTyXTosxp4QpiYmGBsbCzqPs8B9vr53W43GRkZCXfzgBL+qLFarTx//hytVhss\n4NTW1kZ+fn5C7CkoKMDpdAZLvQa+amtrT9268LQkm887nhy810/fjr6cdiS1iI4iHu6xWNbkj5V9\nWq2W2tpaZmZmSElJITU1FZ1Oh9VqRa/XMz09TWVlJRaLJdjfeWJiguLi4kPuTK1Wy5UrV3j+/Pmp\n0qbBL/xPptaYHdmhQrfD+1tyE755C5TwR4XFYqGrq4umpiZcLhczMzO0t7fHNIgbDeXl5QkdHyLf\nPn/RiNW9RlOL6Chi6R6LR03+WNmXnZ3N9evX973mcDgYGhqisrKSwsJCXr58SWFhIbW1tej1ejY2\nNg6lMIPfdaTX63E6naSmpgaDtJEmOoyuufgnb87i9klSNIKvFBRSolUr/nPH1tYW3d3dtLa2sr6+\njtlsprOzM2kyXxLJUaKQTD7veLB3sovVvSZrLaJoa/InCoPBQGVlJaurqzidTjIyMoKlktPT03G5\nXEeem5KSwvDwcDA+4HQ6///2zj02rvNK7L8zL75E8yVSJEWKlCVakknasi3LWiu2ZFt2E8ewu/Em\ncdxmd9HWRlunwLZFFnGLeoEULXaLZtsAcbEwEgdGC9ubxEm8cJw4sdeKnexa7/dbpCiL4lPDx2g4\n5Dy//nHnXs8MZ8jhcGbuHfL+gIu5z7nnvs453/m+73z09PQsKe5/fGiGcFQRAyIxxdHBm+xpnDMt\nMqBjK/4lMDk5yaFDh+jp6WFoaIhQKMT9999fMgOlFJpMSmGl9T5NJJ2xy8e15jsXUb7qWIppxPMl\nc1NTExMTE0xMTBh5+EFrUrlQGDQajQJaGnWHw0FZWRler3de/v6F2N3VxPf29xGNgdMB929eS2D4\nopH10yxsxZ8lH566ytv/eJYv3L2JK1euUF5ezq5duyzV3NFsFssfv5IUvk46Y/fCQ5uXfa35zEWU\nzzqWYhnxfNcLbdq0KakJpZ5bf6GQiz4619TUFE6nE4/HQ19fH/X19Vl/9/d01vPfHlnHJZ/QWRni\n3o1r+aD/hB3qKQU+PDnA82+eIarg3YFL/PfHmnn6/rstOTCKma1nVrJnn4lCesD5ykVUiGEnC/1s\n8y2z2+02SuYOh4NYLAZoLW0cDkfasZ71St2mpiZA6w3scDgYGxtbUsu9uzbUsiUQYPPmbSiljOad\nZmIr/kWYnp7m7U/OEVVaWuQIMKpqLKv0zW49s1I9+0yUgrErxTqWbGXOxdFxOBwopWhra2NgYIDy\n8nLC4TBdXV3zPPnJyUljYHTQOjku1Vvv7Ow0jEsgEMDj8ZgeKbAV/yJ4vV52dzXx7tURYyAUq344\nq6n1zFIodCnI6sauFIxTKtnInKujIyJEo1Gampq4ceMGExMTuN1uLl++TFdXV5JTF4lE8Pl8VFVV\n0dnZmVMjjsTcVrOzs6aHecBW/ItSVlaGe/oaf76jmrmadv5gU2PRPpylKqxS9OwKjRVKQVbA6sYp\nHYvJnKujo4d69JQnZ86cobOzkytXrszLod/Y2EhdXR0jIyOcPXuW2tpa1q9fn3PGTit03gJb8S9K\nJBLhypUrPPfcc6xZs4YjVyd5+cPLBfecclFYxeqsU8jj8o1dClq55Oro6KEe0FIz33bbbbjdbmKx\nWNoU4y6Xi7a2Npqbm7l+/ToXL16kt7c3J5ltxV8ChMNhPv74Y3bv3m0o/WJ5j7kqrGw9u2wUc67X\nayUv2y4FrVxydXQcDgczMzNGh8va2loikQgiwtzcXEbF7HK5jMyduRIIBJbUHLRQ2Ip/AY4fP05z\nczNtbVq3+2J6j4VUWNkq5lyv10pedinGt22yJ5cQlj7e7tTUFB0dHVRUVOByuWhtbeXq1asLZuac\nnZ1dVmfNkvH4RaQc+Agoi+//E6XUX4jI3wJ6BqNaYEoptT3N8Z8Hvgs4ge8rpf4yX8IXkosXL3L1\n6lU6OzuNQVSK6T0WSmEduTrJ/37/YlaKOdfrtZqXXYrxbZvCsWbNGrq7uxkbG+PcuXM0NzfT2tpK\nU1MT4+PjeL3etGkcQEsBsVzFb3ZTTsjO4w8CDyul/CLiBn4nIr9USn1V30FEvgNMpx4oIk7gZeBR\nYBA4JCJ/p5Q6mx/xC8OpU6f44IMP2LdvH11dXUZFznKUcS4x73znWPnp0UF+fPga4ajW/d8h80eT\nSj1/Ltdre9k2ViX5O2zG7/cTDAaNZGx6ltva2tqkLLWRSITr16/j9XrZunVrTueOxWKEQqHSUPxK\nqwXxxxfd8ckYgFS0tk9fAR5Oc/hO4LJSqj++75vAU4BlFf+xY8f46KOPePLJJ7n11lvnbc9FGZsd\n89bPHwzHjAfnAHZvXsuf7bttQVlyNT62l21jNVK/w7/5ajeVgQlEhMnJSbZu3cott9zCmjVrGBkZ\nYf369SilGBsbY2hoiLq6Onp7e3NO0aKHiazQByirXgQi4hSR48AY8Bul1IGEzQ8Ao0qpS2kOXQ9c\nS1gejK9Ld47nReSwiBweHx/PTvo8c/r0aQ4cOMAjjzySVunnSrqYdzHRz68rfQE8bseiSt8K6K2o\njlydNFsUmxIn9Ts8dt3Pxo0bueuuu+jo6ODSpUuEw2Ha29sZGRkhGAzS39+P1+s1BmNfTl4uq8T3\nIcvKXaVUFNguIrXAz0SkRyl1Or75a8AbyxVEKfUK8ArAjh071CK7553x8XGOHDlCd3c33d3def1v\ns2PeSQm/nA7+6J42njZ5gJBsMLuklC1WabpqszCp3+GDW1tobNSeV0NDgzFa3datW1m3bh1nzpyh\nsrKSrVu3Gj1tlVJEo9GkTlnZYvZwi4ksSXql1JSIfAh8HjgtIi7gS8A9GQ65DrQnLLfF11mO8fFx\n5ubm2LlzZ96LYkuJeRdCiZRqzN1KrYMyUSrGyeoUw3gu9h2sX7+eqakppqamaGlpQSlFa2trUnqF\noaEhhoeHaW1tpbm5eUmpF0rK4xeRRiAcV/oVaBW1fxXfvA84r5QazHD4IaBLRDaiKfxngGeXL3Z+\nmZ2d5cKFC3R3d+fcI28xsol5F1KJmBVzX84HbXZJKRtKwThZncQ6KKdD+PZTPTx734aCnGuh70Ap\nRTAYpLq6GqfTSXt7e9L2YDDI6OgoW7ZsYXh4mFOnTtHR0ZH1CHeBQMBI+GY22Xj8LcBr8RY6DuBH\nSql34tueISXMIyKtaM02H1dKRUTkG8B7aM05X1VKncmf+MsnFotx5MgRurq6jPzbZlEoJWJWKGK5\nhqwUSir5Mk6rOVz0Sb/XaHgQiSleevs0W5qri34ffD4fFRUVGeP4kUgEj8dDdXU11dXVTE1N0dfX\nx5133mmMxKe3DiorKzOm+vp6nE5naXn8SqmTwF0Ztv1pmnVDwOMJy+8C7+YuYmE5d+4cHo+HpqYm\nbt68aaoshfBwzQxF5MOQWb11UD6MUymHi/JhsHbd2oDTIURiWtVeTClTSk5TU1P4/X76+vrYtGmT\nJkssxvT0ND6fj6amJsLhsLF/bW0tFRUVDAwM4PP5WL9+PbW1tQSDQaOJ6OjoKJFIhJaWltJS/CuZ\noaEhRkZGePDBB/n000+TkjOZQSE8XDNDEaUQqskHyzVOhXhGxShB5Mtg3dNRx7ef6uGlt08TU8q0\nDLhr1641cvhEo1GuX7/O+Pg4lZWVzM7O0tDQMC8qUFNTg9/vTwoTJ4aLPR4PPp+PaDRKOBwuWCh5\nqaxaxT8zM8OpU6fYuXMno6Oj+Hy+eTE9M8i3h1vIvOaLUQqhmkJgdlbVYpUg8mmwnr1vA1uaq019\nV8rLy41wz8mTJ6mpqaG3txePx8PZs2dRShmtevTOXYsNoVhRUcHo6KjRoscKbfhhlSr+aDTKwYMH\n6ejoYHh4GKfTSU9Pz4ocO7eQec0zkar4Mp1zJRoEK2RVLVYpL98Gy8ywnlKK8+fPEwwGcbvddHV1\nJWXqdDqdRCIRXC5XkuJfjIqKCmZnZ/H7/ZYJ88AqUvzhcJhz584xMTHB2bNnqaqqoq6ujubmZtat\nW2cZS1wICpXXPB3ZKL5SjmkvRqGzqmZDsUJsK6lENzw8TCAQoLGxkc7OznmDsYTDYZRSuFwuo5I3\nG5xOJ2VlZUxOTlqmDT+sIsV/6tQpRITGxka6urrYu3fvivTwcyGfiiIbxbeSm0BaoV4jF4WcawnM\n6pXv2eDz+RgeHga00E2i0p+bm+PChQvEYjHGxsaYnZ0lEoks6f8rKysZGxujsbExr3Ivh1Wh+Gdm\nZhgfH+fee+/l0KFD7N69u2SUvhU6tiyFbBSfFZRjobCKF7wUhbySS2DZ4Pf7ERHq6+vnefInT54E\noLq6mrq6Onw+H8FgcEn/X1lZycTEBB0dHXmTebmsCsXf19dHa2srx48fp6enJ+0oO1Yk9YN86Ylu\nJgOhgiiUfHlu2Sg+qyjHQlFqXvBKLoFlg1KKSCSStnNVe3s7NTU1Rny+uro66zCPTkVFBV6v147x\nF5O5uTmGhoaoqamhsbFx0Vp4K5H4QYbCsaTmblb2yrJRfKWmHFcyK6EElq5knG1pWSlFRUVF2ubc\nLS0tScu5KG/d47cVfxHp6+sDtMrdfCdfKzSJH6SIEFNq1XplVmUltE4q9RJYulAVkHX4qqqqyhhs\nqRA4nU7C4bClGpCseMV//vx53G43Dz74oJFQyYofazqZEj/IukoP337nTEl7ZfnCKs9vJcXGS7kE\nlintebbhq7q6wl737Ows1dXV+Hw+S4y3Cytc8YdCIa5du8ZTTz1lFLPM/lgzFUkzyZT4QZrdwSVf\nLEdxm/38EinVHrdmk+9rzBSqSrfOjPsbCASMimFb8ReYYDDI/v37aW9vp7m52VhvZkVWJqWVrUyl\n7JXpLFdxW6kislR73JpJIa4xU6gqdZ1Z9zcQCFBfX4/P5yv4ubJlRSn+6elprl27xvDwMAMDA5SX\nl7Nv376kjhNmVmRlUlpmyWSG97NcxV3oe7WUe1KqPW7NpFDXmM4pSl1n1v3VO4bZij+PzM3NMTg4\nyODgINFolLa2Nrq6ugB44IEHKC8vT9q/mBVZqUokk9IqduVa4sDrkVhxWwktV3EX8l7lmm6h1Hrc\nZkshHAMzr9GscwcCAa5du4bb7SYWiy1p8JZCIXo2OiuxY8cOdfjw4QX3iUaj9PX10d/fT0tLC+3t\n7Vzxwe8ujeGZGuDLD+0wNZ6WSYmYHcNNN/C6U+A/PLaFFx7aXDQZzLoHC5375Q8v851fXyCmin9P\nspGv2HIUKixi1edfKPbv38/IyAhVVVXs3buX6urqgpxHRI4opXZks29JevzT09McOnSIuro69uzZ\nQ0VFhfai/uATQuEYLifccUeI6mot4ZIZFjZTsdLsOH26gdeL7XmZdQ8WU2ZW8LjNfj90ChkWMfMa\ni33uI1cn+fEZH5vWlNFVpaWHKJTiXwolpfiVUkxOTnLixAm2bNmSlEbZeFGBSBR++vEp/AMYeTVc\nLhdut3veb7p16X6zycaX6E1YQYmkw4oDrxfLC1tMmZV6e/Z8YtX3d7kU0+M/2H+Dr796kFDEgdvh\n5BvdMTZOTlqiE6mlFf/IyAgDAwPEYjGUUgQCATweDx0dHfNy56e+qM8+utN4sLFYjHA4bGTZS5zX\nf2dnZ9Ou138hs/FwuVxcmozwrV8NEY4p3E7h/3x5K3/z1W6OXfez69YGtreZO8iLjtWUW6IX7nII\nX97RzpcKZIiyUWZW8bjNxmrvST4odKuecDjMxMQEXq8Xr9fLj8/4CEdAIUQUXPLBrQMD9PT05O2c\nuWJJxa+U4tixY0xMTLBt2zZj1Jry8vKMPewWelEdDocx/mWupBqP1N+T58YIRxUxIBxR/ObEVb64\n0cWd7jA3rwzyq4taWtdsShgLbcs2D/hCFEO5ZetZJaWliCpeP/Apbx0dLEhl80pUZoVkpRnBfIev\nwuGwoeS9Xi8zMzPU1tbS0NDA7bffzh+3w69/cJBQOIbbKezuakLNjuXxinLHkor/5s2buFwu9uzZ\ng8uVvYiFfFEXMx5POOt44+Qnhjf55T3b58kSi8UyGg59fm5uDr/fn9HIxGKxnMNV+m8293Qxxb3Q\n9qV4VroXrlc2Kwrb1G6lKTMrYKUK6YXkWG74KlHR37hxw+iY1dDQQE9PD7W1tUn1iQ0NWl+CNz44\nzJP3baWnpYrf/37MGJDdTCyp+CsrK+nt7TVbjCWRjTfpcDjweDxLfuher5dIJILT6UREUEoZ4S/9\nNxqNGgYiGAzi9/szGpl0xiM5bBXlxfc+C1u9/PQW7u6oM4zGqSE/f/LakYyKfSmelX7f3jo6yE+O\nDBKNrqyY8konU56cYhuCbJyNpZb4QqFQkkefqOh7e3vnKfp03L2hlpF1IXZvbTX2HR8fNz3Ob0nF\nDzA2Nr9IpI95mTjpik9EaGlpWVIJId8s1ZtMvA59OfEXtFzhhw4dor6+nkgkQjQaTTuBlgxqw4YN\niyaj00semQzD6QvJYav3T31KRWDE2P5OXyjuoQuhcIzXf3OQmdsqDONRH4/XR2IKl0PorAgxODho\nGI7EkofL5TLu29N3t1nCc7TJnlQj/9bRQX56dLDovWOX0vtd3z9xGZIV/Y0bN5idnaW+vp6Ghgbu\nuOMOampqsmohODs7y9DQEJs2bWJmZoby8vKkEG1/f7+t+NMRDAbp7+83lhOz2jkcDkQkaXI4HASD\nQS5evEhjY6PRUWKhKVG5Zsqal7hPpv4OC/WDSD1eV7a6slZKGS+SLkPqL0BXVxebNm3KeB7QlPlH\nH33E9PQ0ly9fNsJSiZP+n4uVPL7oqOX1ExMZw1Z1XZP86vvxsJbTwZce6KV7XaVxfS0tEf56zRqO\nXvfT0+hhQ1WUsbExY3uq0dGHtHO73fS4XMwNjnJgZL6BSGc0Epf1EpFNflkohJIaPhGyT46WT7IN\n4ySVDJwOvvuHm2n1zOH1epMU/fbt26mpqcnpffL5fJw9e5aOjg78fn9S8023283U1FTO15kvLKn4\nq6qq2LVr15KPm5iYwOfzEQ6HcTgcC06pD3QhQ5C4vNCLsNA2h8NhKCeXy2XIkS8cDgfbtm3D7/cT\nDAa5efMmwWDQmEKhEG63O61BcDqdOJ1OQ6a2cicv/9EWjl2f4d6OGrrqnMzMzBjb72hdw//7lzs5\ncGUyo3fe1tbGE1nKnlgCSTUKicuBQGDBffRBsBczEOmWU9ctx4BYJeadDxYLody9oZb/+y/u5ZP+\nCe7rrEMpxVtHBglHNadge2slN2/eTFtKX2jdUpfLleK/PtTIydE5epo8OCYGOOq9Mu+Yn10IEApr\nzb5DkRj7z17nX93fbgy4kg/HQR9i8erVq8RisSTF7/F4jFaCZlKyPXdtloZSilAolGQM9CkajRKL\nxYxffUq3PnUfh8NhGI1Ew5bohaebT1S2iRXUyzGGiaWqxYzIYvvo15Ot0dDXnxkJ8PwbpwlFNY/y\n1T++i+1tt+RFuS11OXXdUrfHYjF+0R/m5/1axbsD+GJHjEfXJ4coE0veIkK/Dy5Nw9Y6B5tqPyuh\nQ3KJPdEBS9xH/2+9Pks/LnFKdFRSp8T/S/xfEeHs6Cz/8Z2r8forB99/tpc71ldnDLumvl+J29PN\n6+j6a+3atbS3t9PW1gbAb3/7W3w+H0888UTeS6crvueuzdIRkWU3aU1FVw66QdArmHXvO918MBhM\nUrapLZt0hZtYMktVFOmUR+I6PYy1kIIDkgxW4n0REUNm/bpCoVBGo5FY1/Kxt5JgpAaFEIxEeeXn\nH7K3KZh0PdnO64pPD4eJiGFAddn1nun6sr5tIWOuNwZYyNjr96GtzIHL4SEaA6cDNt+ikmTU90uk\nq06bEt8TXTEm/ne6dzSdgtfvg1KKcDictq5Pf64LhUxFhCrgmzvKOD8RZVuDi9h4H8fHSbrnqTKl\nLqcaq9RzKaVobm4mGo3icDior683jl+7di1lZWXEYrG8NM3OlUUVv4iUAx8BZfH9f6KU+ov4tn8H\nvABEgV8opf48zfEDwM34PpFsLZKN9RERQ9Hka/B6XZlm8kRTWzOlmw+FQoRCoSRFohuTVIOR+vGm\nkhoCXGzqGvJz4M0zRKIxXE4nzzx0N131LkPhJRqeTMorcZvL5cLj8eBwONIq6NRJV4x6KaS8vDxt\nCWyhX92I6OxdQaErgIdMPLdVRgHMxuMPAg8rpfwi4gZ+JyK/BCqAp4A7lVJBEZk/UvFnPKSUupEH\neW1WONn2M7Aqe+vref25lTFgjo7d92HlsegXpjSXxx9fdMcnBfwb4C+VUsH4ftbokmZjYzK2orSx\nOlnVpImIU0SOA2PAb5RSB4DbgAdE5ICI/FZE7s1wuALeF5EjIvL8Aud4XkQOi8jh8fHxpV6HjY2N\njU2WZKX4lVJRpdR2oA3YKSI9aKWFemAX8E3gR5I+UPq5+LFfAF4QkQcznOMVpdQOpdQOvTmUjY2N\njU3+WVLbOaXUFPAh8HlgEPip0jgIxIB5I58opa7Hf8eAnwE7lyu0jY2NjU3uLKr4RaRRRGrj8xXA\no8B54OfEK8hF5DbAA9xIObZKRKr1eeAx4HQ+L8DGxsbGZmlk03yiBXhNRJxohuJHSql3RMQDvCoi\np4EQ8CdKKSUircD3lVKPA+uAn8UjQC7gdaXUrwpyJTY2NjY2WZFNq56TwF1p1oeAf55m/RDweHy+\nH7hz+WLa2NjY2OQLS6ZsEJFx4GqBT7OWlNCUxbC6fGB9GW35lo/VZbS6fFA8GTuUUlm1jLGk4i8G\nInLYyr2IrS4fWF9GW77lY3UZrS4fWFPG/KWHtLGxsbEpCWzFb2NjY7PKWM2K/xWzBVgEq8sH1pfR\nlm/5WF1Gq8sHFpRx1cb4bWxsbFYrq9njt7GxsVmV2IrfxsbGZpWxqhS/iGwXkU9E5Hg8E+jO+Pqd\n8XXHReSEiPyhBWV8NJ7h9FT892GLydcgIh+KiF9EvmeGbIvJGN/2oohcFpELIvJPTJLvbxPet4F4\n5ltExCMiP4w/4xMistdi8rlF5LW4fOdE5EUz5FtExn+WsP64iMREZLtV5Itvu0NE/lFEzsTvZXmx\n5cs4jNlKnIBfA1+Izz8O7I/PVwKu+HwLWvppl8VkvAtojc/3ANctJl8V8DngXwPfs+hzvh04gTaa\n3EagD3CaLOt3gJfi8y8AP4zPNwFHAIeF5HsWeDM+XwkMAJ1mypcqY8r6XqDPSvKhZUs4iTaAFUCD\nGe/gqvL40cYGuCU+XwMMASilAkqpSHx9eXw/s8gk4zGlpcMAOANUiEj+BtBdvnwzSqnfAXMmyJRK\nWhnRRox7UykVVEpdAS5jYrbYeBrzrwBvxFfdDvw9GNlspwDTOv6kkU8BVSLiQhuBLwT4TBIPXdRh\n6gAAAuVJREFUSCtjIl8D3iyuRMmkke8x4KRS6gSAUsqrlIoWW67SHeMuN/4MeE9E/idamOt+fYOI\n3Ae8CnQAX08wBMUmo4wJPA0cVfHRz4pMNvKZTSYZ1wOfJOw3GF9nFg8Ao0qpS/HlE8CTIvIG0A7c\nE/89aBH5foJmPIfRPP5/r5SaMEk2nVQZE/kqmrxmkirfbYASkfeARjRH5H8UW6gVp/hF5H2gOc2m\n/ww8gvayviUiXwF+AOwDUNqoYt0isg0tG+kvlVIF8V5zlTF+bDfwV2ieQ0FYjnzFwuoyLiSfUurt\n+PzXSPZUXwW2AYfRclX9A1AQbzBH+XbG5WkF6oCPReR9pSVjtIqM+rH3AQGlVMHSwOconwstJHov\nEAA+EJEjSqkPCiVnWsyOfxU51jbNZ30XBPBl2O/vgR1WkxFtBLSLwG6r3kPgTzE/xp9WRuBF4MWE\n/d4D/sAkGV3AKNC2wD7/ANxuFfmAl9FKw/ryq8BXTHzOGe8h8L+A/2SWbAvcw2eA1xKW/wvwzWLL\nttpi/EPAnvj8w8AlABHZGI9bIiIdwFa0iiszyCRjLfAL4FtKqd+bJBtkkM9iZJLx74BnRKRMRDYC\nXZgXRtkHnFdKDeorRKRStAGLEJFHgYhS6qxV5AM+Rbuf+sBKu9AGZTKLdDIiIg60uLqp8X3Sy/ce\n0Bt/1i6097Toz3jFhXoW4Tngu/EbPgfog79/DviWiITRhpD8t0ops1K9ZpLxG8Bm4CUReSm+7jGl\nVQJaQT5EZACtUtUjIv80Lp8ZiiutjEqpMyLyI7QPLQK8oEyoWIvzDPNDFE1odRMx4Drw9aJL9Rnp\n5HsZ+KGInEErSf1QaeN1mEU6GQEeBK6pAoWglsA8+ZRSkyLy18AhtMryd5VSvyi2YHbKBhsbG5tV\nxmoL9djY2NisemzFb2NjY7PKsBW/jY2NzSrDVvw2NjY2qwxb8dvY2NisMmzFb2NjY7PKsBW/jY2N\nzSrj/wO7pUCS2ZfGkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3aa110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_csr.plot(window=True, hull=True, title='Random Point Pattern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_csr.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustered Patterns\n",
    "\n",
    "Clustered Patterns are more grouped than random patterns. Visually, we can observe more points at short distances. There are two sources of clustering:\n",
    "\n",
    "* Contagion: presence of events at one location affects probability of events at another location (correlated point process)\n",
    "* Heterogeneity: intensity $\\lambda$ varies with location (heterogeneous Poisson point process)\n",
    "\n",
    "We are going to focus on simulating correlated point process in this notebook. One example of correlated point process is Poisson cluster process. Two stages are involved in simulating a Poisson cluster process. First, parent events are simulted from a $\\lambda$-conditioned or $N$-conditioned CSR. Second, $n$ offspring events for each parent event are simulated within a circle of radius $r$ centered on the parent. Offspring events are independently and identically distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Simulate a Poisson cluster process of size 200 with 10 parents and 20 children within 0.5 units of each parent (parent events:  $N$-conditioned CSR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.process.PoissonClusterPointProcess at 0x11d5f3650>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(5)\n",
    "csamples = PoissonClusterPointProcess(window, 200, 10, 0.5, 1, asPP=True, conditioning=False)\n",
    "csamples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'n': 200}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csamples.parameters #number of total events for each realization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 10}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csamples.num_parents #number of parent events for each realization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csamples.children # number of children events centered on each parent event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.pointpattern.PointPattern at 0x11d5f3d90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_pcp = csamples.realizations[0]\n",
    "pp_pcp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8KvB6Crhw8mEqFD/E7XYzMDCAEILXX399T5aIyWSKOGNHq9WGvQFotVpcLhdt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\no9GsMbTi12g0mjWGVvwajUazxvj/Y1B0TClTvhoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5eac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_pcp.plot(window=True, hull=True, title='Clustered Point Pattern') #plot the first realization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is obvious that there are several clusters in the above point pattern."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Simulate a Poisson cluster process of size 200 with 10 parents and 20 children within 0.5 units of each parent (parent events:  $\\lambda$-conditioned CSR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pointpats.process.PoissonClusterPointProcess at 0x11d758350>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(10)\n",
    "csamples = PoissonClusterPointProcess(window, 200, 10, 0.5, 1, asPP=True, conditioning=True)\n",
    "csamples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'n': 260}}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csamples.parameters #number of events for the realization might not be equal to 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 13}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csamples.num_parents #number of parent events for the realization, not equal to 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csamples.children # number of children events centered on each parent event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCkBBQQHT09NhV/T6kVKyvLzMvTXDjvDKk1UFNFfmB4T9peDFVuyfKjmSBR9O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d7637d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_pcp = csamples.realizations[0]\n",
    "pp_pcp.plot(window=True, hull=True, title='Clustered Point Pattern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Simulate a Poisson cluster process of size 200 with 5 parents and 40 children within 0.5 units of each parent (parent events:  $N$-conditioned CSR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CpO/SNBUY2fZ61HlstzG+IIRoEEI0zMzMeCq/QqF4htXVVX7+858zPj7uV6UPDs+fIIMO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d763350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "csamples = PoissonClusterPointProcess(window, 200, 5, 0.5, 1, asPP=True)\n",
    "pp_pcp = csamples.realizations[0]\n",
    "pp_pcp.plot(window=True, hull=True, title='Clustered Point Pattern')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}