Binning_a_seismic_survey.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Seismic acquisition fiddling"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook is to accompany the blog post published on January 8, 2015: <a href=\"http://www.agilegeoscience.com/blog/2015/1/8/it-goes-in-the-bin\">It goes in the bin</a> at <a href=\"www.agilegeoscience.com\">Agile Geoscience</a>. \n",
      "\n",
      "The idea is to replicate what we've done so far but with 3 enhancements:\n",
      "\n",
      "- With a Survey object to hold the various features of a survey.\n",
      "- With more GeoPandas stuff, and less fussing with (x,y)'s directly.\n",
      "- Making bins and assigning midpoints to them."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll start with the usual prelims..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from shapely.geometry import Point, LineString\n",
      "import geopandas as gpd\n",
      "import pandas as pd\n",
      "from fiona.crs import from_epsg\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Survey object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Survey:\n",
      "    \"\"\"\n",
      "    A seismic survey.\n",
      "    \"\"\"\n",
      "\n",
      "    def __init__(self, params):\n",
      "        \n",
      "        # Assign the variables from the parameter dict,\n",
      "        # using dict.items() for Python 3 compatibility.\n",
      "        for k, v in params.items(): \n",
      "            setattr(self, k, v)\n",
      "          \n",
      "        # These are just a convenience; we could use the\n",
      "        # tuples directly, or make objects with attrs.\n",
      "        self.xmi = self.corner[0]\n",
      "        self.ymi = self.corner[1]\n",
      "        \n",
      "        self.x = self.size[0]\n",
      "        self.y = self.size[1]\n",
      "        \n",
      "        self.SL = self.line_spacing[0]\n",
      "        self.RL = self.line_spacing[1]\n",
      "        \n",
      "        self.si = self.point_spacing[0]\n",
      "        self.ri = self.point_spacing[1]\n",
      "        \n",
      "        self.shiftx = -self.si/2.\n",
      "        self.shifty = -self.ri/2.\n",
      "           \n",
      "    @property\n",
      "    def lines(self):\n",
      "        \"\"\"\n",
      "        Returns number of (src, rcvr) lines.\n",
      "        \"\"\"\n",
      "        slines = int(self.x/self.SL) + 1\n",
      "        rlines = int(self.y/self.RL) + 1\n",
      "        return slines, rlines\n",
      "\n",
      "    @property\n",
      "    def points_per_line(self):\n",
      "        \"\"\"\n",
      "        Returns number of (src, rcvr) points per line.\n",
      "        \"\"\"\n",
      "        spoints = int(self.y/self.si) + 2\n",
      "        rpoints = int(self.x/self.ri) + 2\n",
      "        return spoints, rpoints\n",
      "    \n",
      "    @property\n",
      "    def src(self):\n",
      "        s = [Point(self.xmi+line*self.SL, self.ymi+s*self.si)\n",
      "             for line in range(self.lines[0])\n",
      "             for s in range(self.points_per_line[0])\n",
      "             ]\n",
      "        S = gpd.GeoSeries(s)\n",
      "        S.crs = from_epsg(26911)\n",
      "        return S\n",
      "\n",
      "    @property\n",
      "    def rcvr(self):\n",
      "        r = [Point(self.xmi + r*self.ri + self.shiftx, self.ymi + line*self.RL - self.shifty)\n",
      "             for line in range(self.lines[1])\n",
      "             for r in range(self.points_per_line[1])\n",
      "             ]\n",
      "        R = gpd.GeoSeries(r)\n",
      "        R.crs = from_epsg(self.epsg)\n",
      "        return R\n",
      "    \n",
      "    @property\n",
      "    def layout(self):\n",
      "        \"\"\"\n",
      "        Provide a GeoDataFrame of all points,\n",
      "        labelled as columns and in hierarchical index.\n",
      "        \"\"\"\n",
      "        # Feels like there might be a better way to do this...\n",
      "        sgdf = gpd.GeoDataFrame({'geometry': self.src, 'station': 'src'})\n",
      "        rgdf = gpd.GeoDataFrame({'geometry': self.rcvr, 'station': 'rcvr'})\n",
      "\n",
      "        # Concatenate with a hierarchical index\n",
      "        layout = pd.concat([sgdf,rgdf], keys=['sources','receivers'])\n",
      "        layout.crs = from_epsg(self.epsg)\n",
      "\n",
      "        return layout"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Perhaps s and r should be objects too. I think you might want to have survey.receivers.x for the list of x locations, for example."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Instantiate and plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "params = {'corner': (5750000,4710000),\n",
      "          'size': (3000,1800),\n",
      "          'line_spacing': (600,600),\n",
      "          'point_spacing': (100,100),\n",
      "          'epsg': 26911 # http://spatialreference.org/ref/epsg/26911/\n",
      "          }\n",
      "\n",
      "survey = Survey(params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = survey.src\n",
      "r = survey.rcvr\n",
      "r[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0    POINT (5749950 4710050)\n",
        "1    POINT (5750050 4710050)\n",
        "2    POINT (5750150 4710050)\n",
        "3    POINT (5750250 4710050)\n",
        "4    POINT (5750350 4710050)\n",
        "5    POINT (5750450 4710050)\n",
        "6    POINT (5750550 4710050)\n",
        "7    POINT (5750650 4710050)\n",
        "8    POINT (5750750 4710050)\n",
        "9    POINT (5750850 4710050)\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "layout = survey.layout\n",
      "layout[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>geometry</th>\n",
        "      <th>station</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">sources</th>\n",
        "      <th>0</th>\n",
        "      <td> POINT (5750000 4710000)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> POINT (5750000 4710100)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> POINT (5750000 4710200)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> POINT (5750000 4710300)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> POINT (5750000 4710400)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> POINT (5750000 4710500)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> POINT (5750000 4710600)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> POINT (5750000 4710700)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> POINT (5750000 4710800)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> POINT (5750000 4710900)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
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       "metadata": {},
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       "prompt_number": 5,
       "text": [
        "                          geometry station\n",
        "sources 0  POINT (5750000 4710000)     src\n",
        "        1  POINT (5750000 4710100)     src\n",
        "        2  POINT (5750000 4710200)     src\n",
        "        3  POINT (5750000 4710300)     src\n",
        "        4  POINT (5750000 4710400)     src\n",
        "        5  POINT (5750000 4710500)     src\n",
        "        6  POINT (5750000 4710600)     src\n",
        "        7  POINT (5750000 4710700)     src\n",
        "        8  POINT (5750000 4710800)     src\n",
        "        9  POINT (5750000 4710900)     src"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With a hierarchical index you can do cool things, e.g. show the last five sources:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "layout.ix['sources'][-5:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>geometry</th>\n",
        "      <th>station</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>115</th>\n",
        "      <td> POINT (5753000 4711500)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>116</th>\n",
        "      <td> POINT (5753000 4711600)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117</th>\n",
        "      <td> POINT (5753000 4711700)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
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        "      <td> src</td>\n",
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        "    <tr>\n",
        "      <th>119</th>\n",
        "      <td> POINT (5753000 4711900)</td>\n",
        "      <td> src</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
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       "prompt_number": 6,
       "text": [
        "                    geometry station\n",
        "115  POINT (5753000 4711500)     src\n",
        "116  POINT (5753000 4711600)     src\n",
        "117  POINT (5753000 4711700)     src\n",
        "118  POINT (5753000 4711800)     src\n",
        "119  POINT (5753000 4711900)     src"
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     "prompt_number": 6
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "layout.crs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "{'init': 'epsg:26911', 'no_defs': True}"
       ]
      }
     ],
     "prompt_number": 7
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = layout.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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n6SFJ16V5myLiZJo+CWxK0+cCJ7reewI4b537nyj3789LW/r2g/v356Yck9T8B2qtxenr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\nFMzMrOKiYGZmFRcFMzOr/H+hIhHEujNTNgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112039650>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Export GeoDataFrames to GIS shapefile."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# gdf.to_file('src_and_rcvr.shp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Midpoint calculations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We need midpoints. There is a midpoint between every source-receiver pair.\n",
      "\n",
      "Hopefully it's not too inelegant to get to the midpoints now that we're using this layout object thing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "midpoint_list = [LineString([r, s]).interpolate(0.5, normalized=True)\n",
      "                  for r in layout.ix['receivers'].geometry\n",
      "                  for s in layout.ix['sources'].geometry\n",
      "                  ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As well as knowing the (x,y) of the midpoints, we'd also like to record the distance from each *s* to each live *r* (each *r* in the live patch). This is easy enough to compute:\n",
      "\n",
      "    Point(x1, y1).distance(Point(x2, y2))\n",
      " \n",
      "Then we can make a list of all the offsets when we count the midpoints into the bins. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "offsets = [r.distance(s)\n",
      "           for r in layout.ix['receivers'].geometry\n",
      "           for s in layout.ix['sources'].geometry\n",
      "           ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "azimuths = [(180.0/np.pi) * np.arctan((r.x - s.x)/(r.y - s.y))\n",
      "            for r in layout.ix['receivers'].geometry\n",
      "            for s in layout.ix['sources'].geometry\n",
      "            ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "offsetx = np.array(offsets)*np.cos(np.array(azimuths)*np.pi/180.)\n",
      "offsety = np.array(offsets)*np.sin(np.array(azimuths)*np.pi/180.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make a Geoseries of the midpoints, offsets and azimths:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "midpoints = gpd.GeoDataFrame({\n",
      "                   'geometry' : midpoint_list,\n",
      "                   'offset' : offsets,\n",
      "                   'azimuth': azimuths,\n",
      "                   'offsetx' : offsetx,\n",
      "                   'offsety' : offsety\n",
      "                   })\n",
      "midpoints[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>azimuth</th>\n",
        "      <th>geometry</th>\n",
        "      <th>offset</th>\n",
        "      <th>offsetx</th>\n",
        "      <th>offsety</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>-45.000000</td>\n",
        "      <td> POINT (5749975 4710025)</td>\n",
        "      <td>  70.710678</td>\n",
        "      <td>  50</td>\n",
        "      <td>-50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 45.000000</td>\n",
        "      <td> POINT (5749975 4710075)</td>\n",
        "      <td>  70.710678</td>\n",
        "      <td>  50</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18.434949</td>\n",
        "      <td> POINT (5749975 4710125)</td>\n",
        "      <td> 158.113883</td>\n",
        "      <td> 150</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 11.309932</td>\n",
        "      <td> POINT (5749975 4710175)</td>\n",
        "      <td> 254.950976</td>\n",
        "      <td> 250</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  8.130102</td>\n",
        "      <td> POINT (5749975 4710225)</td>\n",
        "      <td> 353.553391</td>\n",
        "      <td> 350</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "     azimuth                 geometry      offset  offsetx  offsety\n",
        "0 -45.000000  POINT (5749975 4710025)   70.710678       50      -50\n",
        "1  45.000000  POINT (5749975 4710075)   70.710678       50       50\n",
        "2  18.434949  POINT (5749975 4710125)  158.113883      150       50\n",
        "3  11.309932  POINT (5749975 4710175)  254.950976      250       50\n",
        "4   8.130102  POINT (5749975 4710225)  353.553391      350       50"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = midpoints.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Am1qkfwN4k1XtFEVRlJ6iK5pTsH//fr78pU+E0uI0Bs+XaeO7PHr0aKy/FMIa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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112050a50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Save to a shapefile if desired. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#midpt.to_file('CMPs.shp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Spider plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "midpoints[:5].offsetx # Easy!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0     50\n",
        "1     50\n",
        "2    150\n",
        "3    250\n",
        "4    350\n",
        "Name: offsetx, dtype: float64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "midpoints.ix[3].geometry.x # Less easy :("
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "5749975.0"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We need lists (or arrays) to pass into the [matplotlib quiver plot](http://matplotlib.org/examples/pylab_examples/quiver_demo.html). This takes four main parameters: *x, y, u,* and *v*, where *x, y* will be our coordinates, and *u, v* will be the offset vector for that midpoint.\n",
      "\n",
      "We can get at the GeoDataFrame's attributes easily, but I can't see how to get at the coordinates in the geometry GeoSeries (seems like a user error \u2014\u00a0it feels like it should be really easy) so I am resorting to this: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [m.geometry.x for i, m in midpoints.iterrows()]\n",
      "y = [m.geometry.y for i, m in midpoints.iterrows()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "plt.quiver(x, y, midpoints.offsetx, midpoints.offsety, units='xy', width=0.5, scale=1/0.025, pivot='mid', headlength=0)\n",
      "plt.axis('equal')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FrTX7Ge7d5P4e4Mc9rk1jsRgr7LnvED45RFALnyickE2lUuygzwlAbrAFeOHPDZbhcJi1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//858JBq1RXI9ctedrb7Tb29/fleKPRQCaTgdPplLGIxWIy5v1+H4VCAcFgEK+//rpkYZXZ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5cvEbjR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\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gFhcsZeTs7Iy7yOl0OggEAqRhCvDCAZQigLFYDIZhwOFwcEmLx+MxI8tUe7NFh8fj4bZ5\no9FAPB6Hz+cjNaR1XSe1sIHztpBlGZ1OhxuNZ8Vt1OIDeCEVJyr6kiQJZ2dn3NQuZi7j8XhIsnvx\n2+MR/1AoZBJuXirRaDQyiei8PrKyssKN1rP3QSm3SJJk9mWKqLJ+TO3+WaYrFiz8+WERagsW/sS4\nGOmiSCL7DXX8YlEbVQTHiAYVzWTEr1KpkBFqZojCi2bKsmzKX1Fb5tlsFoZhYHt7m7vl3e12oes6\nptMpaQLCiDgVqYxGo6hWqzg6OuIqPgyHQ4TDYUynUzKXMJlMYjqdkhbRm5ub6HQ6ps35LBRFQSgU\nwmQyIUkNS7OIx+PcVAiPx4NKpYJcLkemB7AFAfXOWZR0NBpxI+W6rkPXdZLIAuffC5OB4y0uWGEk\nVXDV7/fh8/lQr9eFbcEiszxin06noaqqmdfLA9v5oOo6gsGgaZnNW8ylUimcnp6iXq9zU8iAF+pG\nVJpOvV7HcDg05dxm4XA4sL29DcMwyF0H1oeOjo64kXT2Pnq9HrmYYwsXqmicjQW1Wo37HN9kPJk3\nJn2Tcc2Chb9lWITawncSzEVtFuPx2JwMqEp0NjmLiCpLceCRxF6vZ8q8PXjwgHuOp0+fAgDu3r3L\nvc9qtQpd15HL5biqKZPJBJVKBYZhCGWZhsMhdF0nI9TtdhuVSoVUlQBgus3xsLS0hFwuB1VVuVFC\nn89nqjlQee/D4RCDwYBU6GDmHOy/s9A0DYeHhzg5OSF1ejVNw87ODpnz2+l0UKvVUKlUuOdgBHR7\ne5sk7eFwGPl8HrqucyPUjJzOI5FbW1tcExEAuHnzJnK5HDRN427/2+12OJ1OHB0dYWNjg3uOtbU1\nbG1tke88mUxC13UzT38WDocDnU4HhUKB1LIOBoPY2dkxNZZnweTEarUaSfDK5TKePXsmXHSenJzg\n2bNn3DQHJsvXarXIb6vdbpt67bzI7traGnZ2dsyF0izY3+TzeTL/mfUBKl0jFAqhUqlA13XS3bLV\namE8HpPfd6lUMhetvN8wPXld18nxYmtrCwDw+eefc48zlaednR3u2HhRz/6bEH8e2LfCTHlEsKTi\nLHzbYBFqC/9riBwCAToCwv6dGSXMolKpoFQqIZvNcqM90+kUz549w3A4xP3797nXyOVy6Ha7ePDg\nATcqOx6PcXp6auqM8sCk6qiJ3Ov1olQqodFokDnUnU4Hu7u7ZA51OBzG9vY2kskkdyKPRqM4OjqC\nruvcqn9FUdBqtbC9vU0W+7FJlHKTazQaKBaLKBQKZL5tu93Gp59+SqZS6LqOk5MT7O/vcyfyfr+P\nk5MTNJtNUo5L0zS89957ZJpDPB7H4eEhKpUKty2YpN3u7i5JatLpND744AO89NJL3KjswsICSqUS\nqtUqt1BJkiQkEgl8/PHHZA5qKBTCo0eP4PP5uN83W/ycnJyQut+apuH3v/892cc0TcPp6alpzT0L\npt6RzWbJa9hsNnz44YcYjUbc6yiKYvYRqg94PB7cu3ePjPwuLCzg7t27ZpHwLJiF+unpKbnQWl9f\nxwcffED2IfZdKIrCXTA6HA6Uy2Xs7++TCxSfz4d33nmHJLusnx8dHXFJ5GQywdnZGQ4PD8k+NBwO\n8eWXX+Lk5ITbns1mEycnJygWi2TBYK1Ww+HhIfk+EokEtre30Ww2uf2I6VAfHR2R44XX68WzZ8/I\n1JWVlRXs7++j3+9zF1o2mw3NZhOlUolcdPb7fdNci4der4dyuYxisUg6fe7s7KDX6+Grr77inuPw\n8BD9fp+UCOx0Oua4SI1JLFBCjXssr7/dblsa0RZMWIT6O4BisYinT5+ahg+zuKhDTTlYMYOPR48e\ncY+3Wi08efIEW1tbJBEtFAr46quvyBw9u92Oe/fumTqgs4hEItje3ka5XCZVPgKBAP7jP/6DLJxZ\nWFjAm2++iVgsxp3IbTYbstksvvzyS1LZotPp4K233iIXBrVaDXfv3sXh4SGXRI7HY7TbbXz88cfk\nBDkYDPDOO++QkZp2u40vv/wSuVyO/A0AvPvuu+Rxv9+PX/3qV0gkElwSGQgEcHR0hP39e1Dn7QAA\nIABJREFUfZKIbmxs4L//+7/J9k6lUvjNb36Dl19+mRsldDgccLlceP/998mq/Egkgjt37pDRzm63\niw8//BCdToeM1rtcLrzzzjtkrmyv18O9e/fINIhWq4WdnR2T2MzCMAy4XC589tln5DUmkwlu375t\npiLMwm63Q1EU/OEPfyCLslZXV/HJJ5/g5Zdf5h5fWFjAu+++izfeeINbgKlpGr7//e/jzp07JFFN\np9P45JNPcPPmTW4/tNvt+OKLL0iTm+l0inA4jN///vfCxfMnn3wCwzC474zt7jx69Igkoqqq4vPP\nPycLH9vtNt555x1Mp1NuP5xOp1BVFe+//z652xQKhXD79m2uZB5wTlQ/+ugj2O12LklUFAUbGxt4\n88038b3vfY97jtXVVbz11lu4desW2Q9zuZyQ+EejUfz6178miWq328W7776LwWDAJXCDwQDVahWf\nfvqp8Pt9++23ybaq1+v47LPPUCwWSVWVUqmEzz77DAcHB9xzjEYjvP322+bu2iy8Xi8ePXpEOj4y\nHerf/OY3uHbtGvcaKysr+M1vfoN4PM6dA9xuN8rlMr766itS5UOSJKFLJwA8efIEpVKJ1HJ//vw5\nHj9+TEbrc7kcnj59ii+//JJ7vNls4unTp3j06BH3nVzUoebVllj4y8PSof4OgEmf/V90qEOhEGKx\nGOLxOKlysLCwAKfTCbfbfek3TFs6mUyi0+lwyVW9Xsf6+rpZvDWLarVqFv6Mx2OuDvXe3h5ee+01\nM7o7i+PjY1y7dg0nJyeIxWKXJjDmJBaPx00iNotOp4P19XVSOs9utyMYDCIUCiEQCFxqC1mWUa1W\nsbS0RA76wDnZpHR82aLD5/Mhk8lcusZ0OkWpVMLKygoCgQB38qlUKohEImi1WtyoE0uh8Hq9sNls\n3HM8ePAAPp8P/X6fq998fHwMp9OJk5MTUwXgIsbjMR49eoSVlRXY7XYuCWQFauw7nsVkMjGjkMlk\n8lI0keV6h0IhvPHGG9zvlxkT/eAHPyDttMvlMqlDLUkSGo0GGo0Gfvazn3EnakYo/H4/1whnMBjg\nyZMnWFhYwNLSEvfbe/78OdrtNgKBADclo1AooFqtolarkTrUd+7cgdvtRiAQ4LbnwcEBGo0GnE4n\nlpaWLrVXr9cz8+/T6fSla0iShMPDQ4xGI7z22mvc59A0DcfHx9jc3OSOKZqmYW9vD4lEAm+88cal\nvwfOv89isYh//dd/JeXP+v0+PB4Pt72ZQU06nSZ1v09PT2EYBjweD3dBV6/XMZlM0Gq1kMlkLr13\n5krJ1Eh452Da4sViEevr65faotVqodPpwOfzwW63c/thNps1+x8vLYRJ1hmGgXA4fCmowfLVU6kU\nNjY2SBLo8XiwvLzMfacul8s0J+LpUMuyDEmSsLS0hEQiQbqzptNpGIbBTSPr9Xqw2+1wOBzweDyX\n2tswDORyOdy8eRNHR0fcoEg2m8Xm5iZ2d3e599Fut9Hr9RCLxVCpVLjt2W63sbq6irOzM+5cNRqN\nEIvFYLfbzblvti28Xi8WFxcxnU65ufOsf1K7lA6HA7FYzNKh/jPD0qG28GcDpavJJlVKVkxVVXg8\nHlMWjIdIJAJJkkh76HA4jJWVFbz00ktcsizLMm7evIlQKERaSC8uLiIcDmNzc5MbKWcmEOl0mtwS\nZ1rYlGxTvV5HIpEgHSHZpLC2toYnT55wf3N8fIw33ngDuVyOG6k5PT3F4uIiAoEANzohyzKcTidW\nV1fJXPDRaISbN28iGAxy79Vms2F5eRmZTIZMt/D5fPinf/onMoIXj8exvr6OjY0N7jkMw8D6+rop\nWcdDvV7Hz3/+czJ6zHSRbTYb6QI3mUxw69Ytsr23trbwb//2b7h37x73OCPCLpeLjOBtbW3hpz/9\nKambXC6X8aMf/QitVov7LKqqIhgMkhMocL6b9NOf/pT8NoPBIK5fvw6n08md6DVNg6qquHHjBpmO\nIUkSfvazn2E0GpH3kUgkEAwGue09nU5RrVbxk5/8RBh9+4d/+AfSybPZbMLj8SAUCpG63l999RX+\n5V/+hUzv2t/fx8svvwxJkri1DKqqwuFwYGlpiewjvV4PP//5z8mCWYfDgeXlZVy9epWbbqEoChYX\nF7G5uUmmAvl8Pvzd3/0dMpkMqUO9urqKlZUVLplmko2vv/46OeYwWciL0nWzx5kMIdXeJycn+OEP\nf0hGl7PZrHkNaleMaVlT3wUjgW63mzvuMa+BjY0N7q6DJEm4evUqgsEgmbqSyWQQDofx93//99yF\nr8fjwbVr15DJZMh3Fg6HTSMpHpxOJzRNg9frJbXBLyqviGBpWX93YBFqC99KzBtkqONsAKUGOebi\nKMsyWWjHIphUCgKLBlDWzXa7Hevr6/B6veQWLisYu3btGvccPp/PjD7wItij0QgLCwvweDzks7rd\nbqiqauo8z6LRaJiTG5XTCJwrNlDay7u7u2bEizfZK4oCSZKQTCbJgkCXy4VAIECSyF6vh0wmg/X1\nda5Mm6ZpWFhYgNfrJSM1TqfTjJbyiJHP50M4HMbS0hJ3AmRqE6urq+RCq1arYXFxEV6vl3RrdDgc\nWFxcJOXmTk5OsLm5SRKW3d1dBAIBjMdjMk+bRd8oQ5RGo4H19XXk83nuOXRdRyKRgKqq3PfO+k4m\nkyGVLWRZRiaTQTAY5Kahseh4IBAgtcMjkQheeeUV0lyG9Y1QKMRdrHW7XaRSKWQyGXKxJkkSAoEA\nuaBkBbuSJHG/T9Z+6+vrJBEtl8vkghQ4j9o6nU5EIhFue7OdKqYpz4Pb7YbP58PKygo3Jc/hcCCZ\nTCIYDJK7lExBh9pVSyaT8Pv9WF5eJiUZWf+jyC5717FYjIzKsjGDUpBhYzx1DwySJP3R84gFC38M\nLEJtwcKfGEy3FqANDOZFLxhJdrvdXHKmaZo5uc4j/lSUJRQKmVEWikSy3QTqORYWFiBJEuLxOLcq\nX9d1bGxswO/3C93RAJA57ZPJxLSq55GByWSC8XgsJOW1Wg12ux3T6ZRLiFutlqmbzCOArPDR6/WS\ni4vBYIBAIABVVblmJC6XCw6HAzabjSwuY+Yboh0Dl8uFZDLJLZjq9XpIpVKw2WxkLYPH44HH4yFJ\nnizLppEO751Np1MoigKfz0ea8bCtdEqNpNfrmbbOlJrDYDAwUxV46Pf7CIfDmEwm3AUhS1HQNI00\nh2EpMXa7nbu48Hg8CAaDWFlZ4b738XhspmRRfYT1Ib/fT+bWM4tsSnllXl++qC3Oe2dsRwvgp4wA\n801X2JjFswS3YMGCRagtWPirxTwtWIpEMDACCYBLRC+6lVFEFTgnFbycd+A82l+v19Hr9chI5DyD\nmsXFRdTrdW6+Ors++1ve4gM4T5UYjUamCsYsWP5rOp3myopNp1MEAgEMBgNuGgRwnm/caDRMh8pZ\ndDodRKNRM/WDB+bYR12jXq8jFAqh1WpxFyBMCjEajZLFw0xSUtM0coeFReSpyK7T6US1WiXTt4LB\nILrdLhwOB/cc/X4fsVgMkUiEjNZ7vV5zMcUD+54ikQgpJ8fURK5cuUJeg/2O1xZerxf9fh+NRoOb\nQqNpminFSS0u2PdGLXLYt1Kr1eZGTKnFBYNop8mCBQt/XliE2oKFGcyTORoOh3N/0+l0SJ1q4IXp\nClVRP5lMUK/XSRUF4IWTHEWsu90uN9o6e58iDIdDtFotcvuV2SXPIwKtVkuYayjSoWZkvlwuc4m9\n3W7HaDQydbd5WFlZQblcRigU4p5jaWnJNEXhqZEwC/Z6vU6qZzidTvT7fQQCAS65cjqd6PV6UFWV\njB4HAgHk83kymu9yudDtdhEOh5HL5S4dZwsPke5xOBzGyckJKe/ncDjMd8I7B0v5GA6H5DVWV1fR\narXMArNZxONxjEYjVKtVbG5ucs8RDAZRKBTIFITxeGwW1vIWa5IkmaYpFNltNBoYj8fk+2ARcpG7\n4HA4RKVSIXPWPR4Pms3m3D4yGo2Edtuj0Yj8vtl7pBRRLt4ntfhgYwGV/8x0pqkx6yJE4xZgGcNY\n+G7CItQW/mL4JnqdbJKlwAZ9qsiIaZyKrkVpizKcnZ0J/15RFNIcgaHdbqPVapFRV1VVhXJ4iqJg\nOBySz8mg6zoZ4QsGg6jX66QsGfBCLYSa4Gw2m3ASHg6HpkwgBWaEQ7XpaDTC4eEhuRXNTGxE28yd\nTgeVSoVMG5EkCblcTlgN3+/3hYV6wWAQuVyOPB4Oh1Eqlci2YMotpVKJXFykUikcHx+TKR9ra2sY\nDAZwuVzc/PxgMAi/3492u00Sf4fDgZOTE1y5coWMULNoKZVvOxgMcHp6StYIMIUNnsoIA9N6p8iq\n2+1GPp8ndx3cbjcajQb5bTJUKhUy0h4Oh7Gzs0Mu5th3S+X/Ay+ILmVJP5lMUKvVyIJC4JzYzxuz\narUa+f32ej3hIgqAeQ9UTvpwOEStViO/PZvNZhrMiFCpVMh3xnB8fCw8TuX3A+fvZDwek+M4i9LP\nm0e63a7wnbBrWbDwTWER6u8IdF0nIwvA+eAiEqHvdDrCqAKTBBOhUqkI0wwY6aEgSRIKhYJw23Iy\nmWB3d5fcEne73Tg6OiKLpYBzErmzs0OSyFgshr29PTJXVlEU9Ho90o4beOHeR7WHLMvY2toi0yCY\nlbFogmTufxQR7ff7yGazQhJ5enqKfr9PRsYkScLW1hZJrOx2Ox4/fiwsEHI6nXj06BH5XsPhMPb3\n90lCoqoqGo0G+T6AcyL6+PFjMuLudDpJEweGs7Mz4ffrcDhweHhIPutwOMTe3h6pHAC86EdUxHQw\nGODg4IAkooqi4P79+0ilUkKFjocPH5KkxufzYXd3l9w9kSTJdDGksLCwgL29PfIcLpcL+/v7wlQh\nSZJwcHBAjkl2ux37+/vkc4zHYzx+/Jgr4cYwGAyEJLHf7+Pg4IBMXTEMA9vb28Lv2+Fw4PHjx2Qf\ncTqd2NraIokscD42Hh4ekm3h8/lwcHBA9gGXy4WDgwPhjpTNZsPh4SFJiAOBALa2tsh+Op1OcXx8\nLIy0NxoNdDod8vvu9XrY29sjc8GBc4t2Kh0JeGFyQ41rsixjb2+PvAfgPIJeLBaF32etVhOS8sFg\nQPoMMBSLReGCr9VqCefEXq8nDIiwXbV5CwMLfxlYhPo7gNFoBE3TyOI04Jy0iCK3brcbiqII5ZDY\nVjEFlg9JkRqPxwOHwyHclqQKuhjG4zFkWSYHwn6/bxZMUZhOp/B4PCSJ7Ha7wjxXtkVMRRnZfTCd\nUh6cTic8Hg/ZVpIkIRQKCdvKbrebtsQ8sIioiIjG43FTi5e6D1VVyUlhPB5/Ldeah16vB5/PRy4O\nOp0OFEUhFwZs4SMyWdB1XShhNRwO4XA4yNQV4Dy6K5r8nE4nZFkmib/NZoOmaUIi6nK50Gq1yAmQ\nqRxQEzlTmxCl6rBUDOqddDodqKpKEkDDMNDpdITR5W63C03TyH42GAxMIybRfYokBN1uN+x2O/lO\nVFU1XUsp2Gw2TCYTckxxuVxQVZUkNayYj9oFugjqGix3X9RHSqUS0uk02RbdbleoTMRIsKi9G40G\n4vE4uSPANL2pfijLMiKRiPDbU1UVkiSREX+n0yn8vtk5RP2Q5aJT4xrTw6Z2JQAIc96B8101TdNI\n0j4ej9FsNoWLObajQC2ker2e2aY8sEi8aPHBCoPn1ctY+MvAItTfAWiaJox+MASDQeGg7nK5yMEU\neKG5KkI8HhcSTY/HQ1ays0Io0cLA5/NhdXWVJDUOhwOZTEY4IDPnQIrUeDweLC0tCR3cFhcXhRF9\nJldHEdVGo4HV1VXyHlhhmii63G63sbS0RBJNXddx48YNYZTb4/FgZWWFnMiHwyE2NjaEkZybN28K\nCYff70ckEiEHfVVVsb6+Ti4eZFnG2tqacHEhSRJisRj53g3DQDqdFu7i2Gw2ISFhKQ5UezNDINGW\n+Gg0wtramlAicGlpiST+o9EIN27cEBan6bqOl156iUw9cbvdSKVSJPFn5hwiQtLr9bC+vk6mPtls\nNmQyGWF0jcqvZiiVSkilUmRbjUYjhEIhYfS4Uqng2rVrwiLPK1eukO90Op2a5hsUOp0Obt68Se54\nKYqCzc1NYR9ZXFyEzWYTpjm8/PLL5PeraRoWFxeFRJUpBlH3ORgMsLq6KlyAixRogPMxKZ1Ok79h\n0pLUYo2Ne5RzK/vNysoKubjo9/tYXV0VLsBjsRipugKcj1miv1dVVbjgBM7VVES598wkjYJIOWb2\nPKJ3YuEvB4tQW/jWQJIkOJ1O4STLIiDUYMowb7CjnLwY2AQlgugemK4sRWqYVBY1oBuGgWg0KtzK\ni8fjCAQCJNFkEmuiFJp5pgOTyQTJZJI8B4uYUqkrF69DkRpd14WT8Gg0gs1mE0Zq2I6CKE97eXlZ\nmOvNTIuoc6iqikwmQ7Y3czYTkRq28KWuUa1WceXKFZKoapqG4XAoJGe1Wg2dTodsz2q1Opc4AbRi\nCnBORCORCElmG40GlpeXhQt4p9MJp9NJfuOj0QjpdJpcuDKtYlEficVi0DSNPAfTMKcWKLIsC0kP\n8KKIUxT5jUajwvZk/V20sHW5XGSwQdd1+Hw+MqcdOH/WaDRKjjmyLAvHg9FoRKqhMMxz7AuFQsIF\nvkh/mmHemOV0Os25hIKqqvD5fELCa8HC/xYWobZggcC8qnxRVMDr9Zpb0jywwZ4i/iythVIOYLDb\n7cKIPjDfAEGki8zc5qhoZafTQTgcFkZlWXoNtQ0cDAZhs9nI/E/WjqL3cVFzmIdGo4FAICDcuej3\n+0IziEajYToR8tDpdDAYDIQLrU6ng0AgQC5QBoMB/H6/UInB6XQKiYDdbkcoFBLmZno8HqFiRCgU\nEi7E2G4Xtf2vaRpkWZ5L2rvdLrmwZWkn1DlYqtG83TmHw0GS4n6/L4wUjsdjKIoiTA9g6UrUfU6n\n07kL23mRSNHOIft7RiRF51BVlSS0jKiKJB3nRWYtgmrhbxkWobZg4a8c84i/iFBPJhMEAgEyksjS\nVqjjXq9XGA0Fzkkiy5/ngUVbqXNIkoRarSbcUWi1WggGg8I0hn6/Ty4cDMPAdDoV1hk4HA5TiYYH\nRVHgdDqFUXCfzydULGGpBVTElFk2iyK/7FkpxZNqtQq73U4SI0mS0Gg04HQ6ybao1+vmbhEPrABa\nFI1kCxhRERyrZ6CuEQ6HhfmjbrcbzWaTbC+W4iMqQPb7/cL0F3YNioiqqorpdCqMdLtcLuFziAg9\nYDn+WbDwbYBFqC1Y+DNgOp1C13WSkLDooChqNU8yb16hVL/fn6sHO09TVtd11Go1kiywAiQRGRiN\nRqjVaiSp8Xg8Qvm/i+ehwIovqS3nRCIBXdfJ4yxfUZS6YrPZzMItHhiZFkXpdF2H2+0WyhDm83ky\n6trtdjEej4VpDqwwl3r3TDGCwnQ6hc1mg67rwoLBZrNJkkSWfiMi1IFAAA6HgySLzK6eyl/2+/3o\ndrtCMjkajYQLMV3XzV0DHr7JYk7XdQwGA/I5nE7nXAUkYH5f/CYa0KI6g3nXYItNUX7+cDi0zGMs\nWCBgEWoL3zqIRP/n6YIyG2oR5pmZUAWPFzFPy5ppN1OTPTNumaeVKirqGg6HqFar5ATncDjQ6/Xm\nTsIi0s2stqnnYJFdEakZDodCkjmdTnFyciJMb9F1XRiVbTabwvSB8XiM09NTIcHTdV1owGGz2XB0\ndEQ+C1MAEUUTp9OpUAuYaVlTaTyyLMPhcAiLJ2OxmGmVTl2j2WwKZccURRG2lcPhQL1eJ3cdFEVB\nNpsVpjLUajVh5HcymaBcLgv7M5M/ozAcDlEqlchxIxQKIZvNzjVAEi1uJUnCeDwWjk2dTkcYoZ7X\nRzqdjhmR58EwDGEkHjgfk0Rt2Ww2Ua/XhedoNBpzSXuhUJhr3CIqDgbmLy6Gw6Hw2wH+b/OIBQt/\nDCxCbQHANxtg5g1g8yKq38SopFwuC7U9a7UacrkcOQEqioJSqSQcsFutltCYxe124/T0VEi8J5OJ\nMEqoqqpw8nI6nSgUCqTiCTBf2snlcgk1jRkhoYoBgfMJUvROhsMhstms8N0/f/6czI8GziNr2WyW\nPM6kFEX30W63hdeIRqNCSUeHw4FCoUCmQQDnfUBEaqLRKCqVCkmMDMPA6empMKd9Op0KFzDD4RBn\nZ2ckUXU4HNja2hKSXaYNTkXbFUVBPp+fa8YjilR6vV4UCgXyG1dVFaVSSVhgJiL9wIvFhWjX4PT0\nVFisahgGBoMBuUgajUYolUrCQrdSqTQ3uvz06VPyWSaTCQ4PD4XjSa1WE8r/SZIk1DSWJGkuSZ1M\nJnPz4guFwtwUMVHOumEYUBSFJOXj8RjlcllYZzDPPEbXdZRKJXJcYxr+Ip+AYrE411ym0+nMtXqf\nF1j5JsEZS0P6uwOLUH8HwJy8RKv6ZrMpJByj0Qinp6fCSbbX6wkHbYfDgWw2S5Jzm82GdrstHCx7\nvZ4wH9fn80HXdSGhKJVKQuIEiKPUbNAX3QdzLqOgqip2dnbI46PRCJVKRThgHx8fC+9T0zRks1mh\nXFelUhEO2Ha7XTgp+Hw+NBoNYX5zo9EQyoqFw2FhW02nU9RqNSFxYlbZFBRFQS6XI7+9yWSCZrMp\nnODOzs6E72MwGODs7IxczDGra9HiIxaLYXd3lzzucrlQq9WEC6lqtYpUKiW8RqfTIUkkKxYUkd39\n/X3hO2XvnVqATKdTVCoV4SJIlmVhWzBTFlEhaTabFX7fbEyiwOzkRRHqQqEgLAoMBoOmljoPzBVV\nVMzXbDaF4w1LaaLIrGEYyOfzwj4iyzL29/fJ48yaXDQHZLNZ4eKi3W4LXQ5VVcXZ2ZlwXKvX68I+\nxLTHqe+bpemI5gCmT05hMpmYyikUCoWCcMxqNpvCa4zHYxSLRWEfqdVqwjm32+3i6OjIIuXfEliE\n+jsApsErmiD9fr9QBs5msyGZTAqjE8FgUBh9U1V1ripFIpEQDtjz1Bz6/T6SySQ5AU6nU1y7dk24\neIhGo8JIpMPhQDKZFEbPrl+/Ljze6/Vw/fp18riqqohGo8L2Xl5eFh7XdR2vvvoqGQ2aTCaIxWJz\nt+5Fcl7MvICaAA3DQCaTEbZ3p9MRTm6yLCOVSgnJ7quvviqcWMbjMdbX18lvR1EUodEOcC5DOE82\nbHl5Wdie8XhcGMFjusgUdF3H8vKykNhnMhnhLk6lUkEsFpsrISjaur9y5Yowp12WZeFYwN6pCOPx\nGFevXhX+Zl57rqysCPObS6USfvjDH5LHe70eFhcXhQuYeZrz1WoVCwsLJKkZj8dCuUUAc5VbmOQd\nRWYlScJLL70k7EPT6RSbm5vCa0QiEWFqytramrCter0ebt26RfZDpustilDb7XbhYq5arWJjY4Ns\ni+l0iqWlJWFb2Gw24VgwGo2E9wCcf5uitvD7/cKFmKqqSCQSwrSoeb4MLpcL6XRa2Acs/OVgEWoL\nf1LYbLa5Feei/M9gMCgkX16vF5FIhCQ1sizDbrcLJ3NFUebqpUYiEeEkOs8dkG1nU6Sm1WphY2ND\naJHucrmEkQev1yu8BwC4evWq8Dni8bgwysKIEzXJSpI0dyHmcDiE7+Ob6PSqqiokHEynl2qvbreL\neDwu3MWx2WzC6BuTxBOpUmQyGeHWPZMIpKBpGtxut7A9Y7GYcFehWCwKHQg1TYPL5RLexzy30XK5\njKWlJSGpCYVCQlIu0vwGzhdi8Xhc+Kx+v18YXbbb7cLntNlsiMfjwvcejUaFfUSSJGQyGWGEOpFI\nCL9vh8MhdN1rNBpzFx+SJAlJIgDheFGr1bC5uSlcuGqaJowez5PuG41Gc3cPReM7O+7xeMhnZQ6d\n8+YREWEWvSsGi8RamIUlGmnhbxLzBkxJkoTEH4BwwJ5nLMPIiij/0+v1Cgdth8OByWRiWk3Pgv3t\nvAWOKAWHWaiXSiXuMzFCJIrWz8snn06nWFxcRKfTISdJn88nfI5gMGiazPDgcrmETorAObmal9u5\nuLhIPisjK6JFkKjoETgnw/F4HGdnZ+RuT7/fF7bnYDCYew/pdBqVSoWMoH0Tu23masfDdDrFwsKC\nMNUnEAgIt/673S6CwaAwyufxeISpEg6Hw3Qg5LV7t9s1U2REkcJ5hc4sP5kXsWQqIqKduXn6zXa7\nHYqikN+3YRjw+XzCcS0cDguvw3Y4RXUdwWBQ2Efm6WnPI/yARVQt/PXCilBbsPAtxjxLWbfbLYzm\nAGLiD0CYpsMmT5HcXDAYFE6kLpdrborNvIKpXq8nJDWNRgOj0Ygkkr1eD71eT0g0O52OkOD1ej0U\nCgWyvSeTCWRZFkbw5i3k4vE42u22MCIaCASEiz2/3w+Px0NGf5lyy7xdmnka0k6nk8z/VBQFtVpN\n+E7n5S4Ph0MMBgMhETUMQ7jroCiKUFqPaXqLiN480h4MBuHz+cjtf5fLBYfDMXdrX4R5RFWSpLnf\nlmW6YsHCnxcWobZg4VuKeYUm85QHgPnKK/Oc7JrNplBDGjgnsyJSw3R6KWiahmKxKEwxGI/HQlJj\nGIbQoGY6nWI8HgsXD36/X5gTubq6CpvNJjQBcTgcwmg9k1gTYTQaCYl9s9kUvlem6U2RyHq9jul0\nOrcGQPTODcOAYRjkO+n3+5BlWVi34XK5hO90aWlJaKQDnBNRUY61x+MREkmPx4PT01MhYR0MBsLv\nt9FozE0Pq1arwmg9MF9DulKpzFVj+iba9BYsWPjzwCLUFv7mME8jdTgczp245mlZNxoNoSyTYRjI\nZrPC81Sr1blEdd4E2u12hYRknknDvDxY4P+1d6Yxkl3Xff+fWruW7up1pmc4M+SQM6QoggEV2lIQ\nL2ScSKaySNaXRDag0LIjxEEgGVHgRTIQEwmCKAGsxPkQfXAkR5IdAkYCKxKiOJITETAiSIwdUhzu\nywyHs3VPb9Xd1dXVtd18qFdkazR9/nf69TSHM/8fMJjuetVV7513373/e+655/C7seaOAAAgAElE\nQVRUWoC/jDusiOd52NrttuvN7Pf7uHTpkluIZGlpyQ35WF9fd+3RbDaxvLzsehMvXrzoivZms0m9\n+RsbG64tut2uu3FsenoaW1tbO7bz0dFRrK2tuZuYh4VZduLo0aNu6slSqYS1tTV38jDMvLIT7XYb\nFy5ccEMMWGaWtbU193gmk0Gj0XD7hE6n44bpZLNZOvldWVmhm9zYM9Lr9dzz2Nracp+RjY0NV9R3\nu12cPn3a/YzFxcVUxWOAQd/H0tGpeIx4JyJBLfaMmFzWrIDCxsaG22Gvr6+7m74A0HR0CwsLNIc0\nyy/qpToCBgOC5xkzM1rsAfBjNycnJ91NjcDAQ+15MzudDi0eMz8/797bs2fPuuewtrbm2tPMqPfN\n87gCg1AKNkgvLy/THNHed8RMLtbX12mOaK/99no9N9c1MPBQe6J+cnIS6+vrO3qYi8UiTe/HRBHL\npw0MxK5nb6+6IDDwHg893TuxvLzshr/kcjn3PIchI15/wNKFFgoFvPrqqzseBwZ9knce7Hir1aLt\n+/XXX3dt4ZVxBwa22tzcdNvvMKzJg63ALC8v01zW3jMyrCbqwcYJtqoGgE60AOWQFj+MBPVNwtLS\nktshN5tNt4Pp9/s4d+6cK2oWFhZcz8HGxgYVHKwTW1pacgfZ0dFRN2E/MEiV5XXY+XzeHQAzmQzO\nnDnj2mJlZcUNl6hWq24eamAgOLzB59y5c9Rzxux99uxZ18PXarVozC8Ts3Nzc+5AfujQIZw5c2bH\n42aGy5cvu9fKBtBSqeR+B8Ar812+fNmdKHW7Xdp+z58/73qgx8bG3JWLbDaLtbU111t54cIFN5Ri\nYmKCTgjr9brrBX/ttdfc57BSqdCJVL1ed1dgRkdH3Wek1+vRyfMbb7xBs6bEhFJ4n/HCCy/g9ttv\n3/F4qVSiBTzm5+fdZ2Rzc9Ntm+VyGadPn3b7pKWlJdfehULBtfewMBGbPHtts9Vq0Xt24cIFt22d\nP3+eZqBh/d6ZM2fcvSNmRicoMdVZvfPodrtuektgcK2ek2l9fd1dVeh0OtSpIvYPCeqbhKmpKbqU\n7GWeyGQydElyZmbGFRzVapV6J8rlsvsZxWLRXZbf2Nig4QWVSsU9j0Kh4A5uvV4Phw8fdkXk7Oys\nu5EphOBm8AAGG9A8j9Hs7Kw7eLHNVMBgA5s3cAy9gDsxrDLnDbLVatUdWFZXV93wghACarWae60z\nMzN0VYFtvpycnKRZELx0dblczt0sOPwOb0VgY2PD/YxhNgpvclGpVOgqDtugViwW3Xs6PT3tnkO7\n3ab9xdjYmOtpb7VauPPOO3c8ns1m6T0dHx93xVm73XbvqZmhXC67omZmZsa1t1fUZUipVKIeaG/i\n22633ywpvxO1Wo16fr3UfGaG2dlZ9ztYXnvA3+QMDCZ8LNuOdw7dbpdusBwdHXUnlWwFsdvt0o2g\n2WzW7dd6vZ5rb2CQ49yz5+joqLsalc/nqb3F/iFBLd4kl8vRFGtMULDMAbVazd2INDMz4wrRSqWC\n48ePu99x++23u550tlEpm82iUCi4gmJsbMz1HDQaDVfUxKSbY2K52WzS4hmTk5OuvcfHx92JVj6f\nx8mTJ912ceedd7qihondYYpCT9QcOXLEHSCXl5dpRpSRkRHXk5jNZt37Ua/XaTaGiYkJukzsDYDd\nbhfHjx93PWN33nknFe2sXRw6dMgVNSyGutvtUtE+Pj7ues86nY57nZubm/QZmJiYcCeM9XodJ06c\n2PF4CAEzMzO0WI83Ac/n87j77rvd8zx27Jgrdmu1mjv5GOYO9+7JgQMH3Iltu92mG00BP4yBOUQa\njYYrMoGBSPRsUalU6DjiFagBBmOA1+dMT0+76QFzuRwV1N5EDYhLEcjGW/HOQoJa3FCw+DwArngb\n4mWMAEA7/QMHDtBz8YR/rVajg8Jdd93liprp6Wn3HMbHx2kM37Fjx9wQmWKx6H5Gq9WKSt3HYuNZ\njt/x8fGoAhw7USqVoiYX3neMj4+7g2ClUqGeyFqt5ooalh4wl8thdXWVinIv9OSNN96g51mv111R\nY2Y0rphNrlmBmtHRUbddDFeSPFvMzMy4z0hMurnR0VF3gsKekfX1dWqLarVK9114fdby8rJbCRQY\n9IueSCyXy26f1ev1cOLECVe0T01NuRMp1ucBfmEvYG+E6l6NI0JcCxLUQlwHisWi2/EP88ayTYls\ngGLL7mZGhaY3wHW7XUxOTrqDbEzRFS8EJ5fLoVqtupMLlo7Oq0o5pF6vu8J/dXXVFT2rq6v0O1jI\n09bWliu+NjY2MDs7617ryMiI27bGxsaooD527JgrysfGxtxUcb1ejy6793o99562Wi13xaDT6WBx\ncZF6VT1becVngEG7KRaLqUJ9SqUSDYM4ePCg2/bYysfU1BS9p2mFai6Xw8jICF29Y5MHIW5VJKiF\nuIlhAm9zc9P1VI6NjWFhYcEVzI1Gwx3sV1ZWaNz71taW+55Go+EKgmw2S1NtVatVVySyEIZqtUpX\nBAqFguv5mpiYcDeOVatVLCwsuKImJv8484bOzc25k7WY8BY2udja2qL7DDwhOiwnzyYXzN7ePctk\nMtRTWSqV3HCj4UZSj4sXL7r2zOVyNCc8u6dCiLcXCWohdgETVq1Wi2ZaYJkr6vW6K562trboQM4G\naZb6DOA5Yev1OvW0MxHI8noDcGPWi8ViVFELFsvtiXomuIfn6C3dr6ysRC01swqD3nmOjo7S+5rN\nZt1JUrVadcMgJiYmokqTe+2iXC675zA2NvZmJcOduHz5siva2TkCoBk62CbPYrFI2+/W1hb1MLPn\njD3rLM90r9ejGZJYn9RoNKhNYyZ8QtyMSFCLG4p+v+8OHCEEOnjFFF1hMDHM0qdtbm7SgYUN5J1O\nh4rI06dPu5+xuLhIxT+7FibOWJo3M6PeNXbP5ufnXQ91p9Oh37G6uuoKuFar5S7LmxmtPFmv113v\ncqVSiWp/nugOIbhhPIcOHaKbnVi4RrPZdIVqLpej7XtlZcX1QDcaDdo2WbtgVThLpRJee+019zPY\ns95ut2klRCZ2m82m69Hf2Nig94y1vTfeeMPNFLK0tERji1mf1Gg0qPBnFSFZelaAV41kfVaz2aTO\nCBWPEXuNBPVNQqfTcZdGe70e7cRYWVrWobfbbSpq2OA1NzfndnRmRosonD592g11CCHQ/KBeFThg\n0CF78aHj4+NRxTE8FhcX3XjFfr8f5dXyxG69XnevAwD1al24cMG1d6VSwfPPP+9+RkyuYI9SqUTt\nzcRbo9GgG55YCE2z2aTeY3atTJBcunTJ/Y5MJkNzATNRc+7cOZqyMUbUeG18amqKPmcx4s0Tkfl8\nnrZv1q+99NJL7spGCMEt/ALAzT0ODCatzDt84cIF+hnehHB8fJyeB3tGFhcXaao41q+xypNLS0vu\nhI8VfgEGY4A3mWs2m1E5oj2azSadgMTkg/cIIbj3lO0hEPuLBPVNQkx8HVv+ZwNLJpNxPTGFQoGK\ngY2NDXcgj6m2yLxrZua+J5PJUE8NW5bvdDquqOn1enSCwjrTbrfrioFMJkPvOxM14+PjVDAzhhu7\nvOPMnmw5vNVquaEUMZvk2P1ot9tUfMVUyPRE4MjISNTE1CPmGWH2ZCkyNzc33U1uIQT6HYCfbaHf\n71N7Mw91t9t172uhUKArLOwc+v2+e99jSo+zEJvV1VW6AZnZm23ADCHQZ4TZe3Nz07VXTDw589a3\n223qVGH3jJHJZKgt2EbTZrPpOjyGewA82D3d3NxMlfFH7C8S1DcJtVrNHbyy2SzdZMQ69HK5TNPR\nsWImR48epRkhvI4shEDP8/Dhw64oqdVqdHmVxcsePHjQFUZbW1tRhUY8ZmZm3M602+26abKAwfK/\n1y6KxSK9ZyzF4OHDh92BOITgehEBnoHgjjvucD1GmUyGDoDsO1hxmeF72Hd4k4fLly/j3nvvdT+D\nZW6Znp52l7NDCPQ5HcYn78SJEyfcHNJmRr35IyMj7nesrq7SDDTM3uVy2bX34uKiWzwG4Pa+7bbb\n3O/odDpuLneA5ydn/R7A2y/b8LqxsUH7JNZuZmdnXbHbarVooRFWoKZQKLj3JIRA7+ltt93mnmep\nVKIpFZmt2DhUKBSooGbnUC6X3bHIzOh5iv1DglrsK8wzXC6X3U7GzNx8xADPNwzALd0MgApVln2g\n2+3SwYllvpicnHSFUyaToYMsE+0xIjJtoYbNzU3ccccd7mcwQVKr1dyBZX19nX5GqVRyBV6lUkmV\ngg0YePyZx5S1TebNbzQaND6UefCYx7Tf77ttq9/vU7E7Njbmhsjk83l6rcxWx44dc8Mt8vk8fc5Y\nujlWNTKXy9HPmJqacvu+mH4tpjKl94z0+/0oAecR0yelLf7F+pthISgPNkEpFAq0b1TRFXGtSFAL\ncRViwko8wVyr1ejAwLwshULB9R5nMpmoVQUW98smOWzJcXJy0o0VZJMPgHtqWJGbsbEx+hnDst47\n0ev13EHYzKit2LJ6u91OvZehWq26YQybm5tUiLJJ1NjYGD0PJji63a4rmLPZLG2b7Dv6/b4rNDud\nDp0cs9Uo1m42NjaoaGeFctjfA9xbPzMz497X6elpOslnVWgrlYor/Fm7A+KKrgjxTkQtW4h3MMxj\nysqsx4TQxCwpeoMsS48G8M1nLP4/5j3r6+uusGfxz2yDEMDjnycnJ2lML1t1YEvJrBAJwL31q6ur\n7nfExLF2u13Xm9lqtWimEPYd7XbbbVsjIyO0XbC2ybzc1Wo1qkCTJ3YrlQqdaDHhHxPTLoS4fkhQ\nC3GdYBvYYoquMJg4Y2KC7UIH+KYXtuM+k8nQjY9sM9Tq6ioViWxycfToUerl9oSRmdElcVZiOpfL\n0U2JbGNvvV53r6Pb7dKNYYuLi66QbDabrkDrdrtURFYqFfeeTE9P08kD8y4zz+7o6Cid5LDncGFh\ngXpe2T1jWUAAnlXCi2kHeGYigPcHafssIW5lJKjFTQfbcb+1tUW9QUz0xHhMWY7oU6dOucfn5ubo\nebBBmA30MZ5GZqu0YRAAH6jr9ToVNTEpGT3m5+dT5zCv1+vusnqxWKT2YiJzZGTEFfbj4+PU3iwc\nicXn5/P5qBSCHntR/Ojs2bOpcxqzZ3kvCpXEhFUxDzSzN+srer0etTfrs1544QV6HmlTYPZ6Peoo\nYOFIQrwdSFCLaGLyXaatiHfp0iXqEX3uuefc4y+//LJ7PKZiWEzRFQYbRJlHdWtri3pE2T1hA1O3\n26W2YIPsK6+84h4HQHO+Mi/3xsYGvda08bjtdpt6wdlnsMmcmdF7ErMi4HmHC4UCFYnM23nx4kXa\nfpmAY/erVqvRyRx7RjKZTFQstwfrk5hABEBz47PiMgsLC/S+s/OIqY7JvPXsnmezWTpZY6En7BnZ\n3Nyk94zl5GZjRKvVoveM3Y+1tTU6mVPxmFsLCeqbBCYAQwh0GZgtS168eJEOkswjyrxWa2trVNQw\nWIedz+fp4MO8KAsLCzSfNhMc7Bzq9Tq9FjbIsnuez+fpeTLYoMI2ZAE89pi1b4B7GplwajQa9FrS\nhtBkMhl639kgzMpxA3Gb+TzW19fpZI7dE2arfD5P+4uYkCZ2z9hkjV1Hq9Wi2UiYvdk5drtd+hnM\nWcEEYAiBvof1BTHPCLM3E8MLCwvU3qy/YLYsFovUy836rFarRb+H2YKNuY1Gg04uYvpGsT9IUN8k\nxHjW2A5vlrbpyJEjdCBn58G8G7VajQqftKm2zIyeJ0tRNTMz43amZkaX7tn9qNVq1F7sPNk59Pt9\naq+0RXBiQj7YebK0Y8P3eLCY32q1SgdIlk+bpe7rdrs0GwmzVS6Xo4I45jM8ZmZmqLhim1mZrdrt\ndup7NjExQe8Ze0bYd5TLZSpm09qbFaMCeH/B2p6Zpbb36OgotTc7D5b3vlQqpbYn85K32+2oa/Vg\nafcAnp6VjblsUznAx1yxf0hQiz2FebXYIDs7O0s7iLvvvts9fvLkSfd4LpejAo51YgD3OjFPI/OA\nVKtVOpCzQYFt6pqenqYDA8shzY6bGY4dO+a+hw1exWIxynPrwexdqVSot4dNHmLSn6XNqsIKpgB8\nUslEZswgHRM771Gv12lIR9rrBPg9iSmOxO777bffnup4LpejKeuYoGZ9Vr/fTx0eA/DnKCbzkMfU\n1BSNJz9x4oR7/J577nGPF4tFam/WbmLGCHFrIUEtbjpiEvKnLYjCqn0BwP333+8ef9e73uUeZ94L\ngF8H89SYWeosCEz09Pt9OviwidbMzAwVNSyvN6vgNjExkdrzFVPGOmZjo8fa2hoVsyy0Kqakd0wR\nJo+YSnLsnjCP6vT0NG1/bNIZswKTNvsFO8dsNkvPg/VJzPMLcGcD67Puvvtu2u8x4c/aDXvOARVd\nETcmEtRCXCfYAMnEW6FQoJ4aJjhiRDkbiJl3jS3xxpQFZ574ra0tGoLA4hFZxoj19XXqBWfHmXdu\nc3OTikh2Pw4cOEAFGvP4s/tRKpVovC07zjY+rqys0MkDi8PudDpReaQ9YqocsokrK6HOnhE2+QD4\nqkHMxIHZImZTogqzCHF19GQIcYMSQkid3o9t+gL4xhmWoYMJ2X6/T6+DCZbR0dGopWIPJpzK5TL1\nQLNlYObtLxaL9J6wfMLsfgF8Mypbtu/1enTVIMZ77FGtVumEMSbONWZTrEdMDnTmoT537px7nD0j\nMRvL2OpHTEgUs5UQYvdIUAtxFdgAWq/X6eDE0vexdHNzc3M0ywEbiJlwioGJTOaxymQydEc9EzWX\nL1+my+os/V9MVT52T5moYcvZhUIh9SbPQqFAzzPtpq4QAvVis7bFniEzi8pV7cEmewDPFBKTt56t\nPKQNFYrJdc0mz2xFoN/v0/R9rM86c+ZM6oJUKg4jblYkqMW+wgbIlZUVKq7Y0j07DnCPEhOqrOQx\nwAeOmGImzCvLzpOlcavX63QAZB5RlrovhEA/g4nhmLhh5h1mwmltbY0KYvYZMbHJ7L7HeLlj0oJ5\nMJG5sbFBBRy7VpaJpFwup85lHRPykTZF5uLiYuriMcxDHUKgfVJMyJJHNpulwp15uVutFp08pF01\ni6n4yOzNbNnpdOhn7IUzQtxaSFDfJOxFHmo2sMzPz9MBjnVkrDNdWlqi3hx2HSwuc2trK3XVMXYO\nIQQ6wLHPWF9fp506G3xiBo20uYBj8lizz4jZRMcENRN47Pj6+joVZ2nFQrFYpJOHmHzazBZsksSu\nY25ujm6CY9/B7F0ul2mfExOOwcQu+w422YsJm0qbc77ZbNKJVNqNpjG58WOug9k7pjAROwdmc3ZP\n2XiYyWToc8aug50DwMcidp7KQ/3OggpqM/uSmc2b2altr02a2bfN7GUz+5aZjW879hkze8XMXjSz\nD2x7/UEzO5Uc+929v5RbG7azOuY9LGayVqvRgZx9BotBzWaz1EvCvCwxcYJsYsCEFVt2N7OovMce\n5XI5dW5atjmt1WrR2OKYohQeZpY6djOmOEzMRjuPYrGYOi0eu6e9Xo++h3mXY2LSmThj7b9QKFAB\nx4gJK2ErAux4TF5k9gzEpNBkk/y0KTRHRkZS25thZrR9sz6rUqlQe7P+hG2urFar9DxZ7D2j3+/T\nfium6mnafo0965VKJfUYIPaPGA/17wN45IrXfhPAt0MIdwP4X8nvMLN3A/h7AN6d/M1/sLeevi8A\n+OUQwkkAJ83sys8UKWCdXEyaITZoxHRibHBigvvYsWNUGLHUTixHabfbpbmT2YarkZERatO0eXqL\nxSL1GLF7xgbIkZERKjhYcQKWiiuEQPNQs2wkY2Nj9FrTZjCoVqvU68oGWXaOABflMUWHWNth7Zvd\n05i0kDG5wz1WVlbotcbkFo9JSefBRPvBgwfpJJ7Zk+XOHx8fj8qH7cFWFHq9Hm17MRk80k4e2P2Y\nnJykE0LWP7MUmplMBvfdd5/7Hmbvqamp1JMLNqbGjNtKIXjjQJ+eEMKfAbhybeRDAL6c/PxlAD+X\n/PxhAI+HEDohhNcBvArgfWZ2CMBoCOHJ5H1f2fY3QrxJNptNvQGIDRqVSoV26jF5kRms02eptA4c\nOEAnKGwgZ5kW+v0+fQ8THDG5rtkkiR0fHx+ngywTFMyTMzY2RttW2olBq9WiwilGqMakN/Ng9j5w\n4ABdrmaTNWbvmMkcu6fsfgBcGMVkI2GTtbTPSKlUSl3EJiZtXtrMLbOzs3RSydI+srSR2WyWnge7\njpj7ofR/Yq/ZbYs6GEIY7rKYBzAcRQ4D2L5z4jyA267y+oXkdSHekcR4BZi4YoK6VqvR72EDDzse\nUxGPeVHYBKbf79MBLiZPb4wIZJ/hMTo6GhVi4MFEZD6fT23PkZGRqJAND3adpVKJXitr36zthRDo\nPWOfMTY2ltpLGBMKx8IxmBOA3dMQAm0XbCLGhKqZ0c+IuacxZdSFuBVJXTszhBDMbM+SWz722GNv\n/vzwww/j4Ycf3quPFuKWg3lhYko3s+XTmOVVFoIQM7lgMJG4H7H1TBR1u10qalghnSNHjtD7yryE\nzLN76NAhKiLTxoJns9moypIemUwGIQT3vrDzYKtNrG0C/BlgZa7NLHVpcSEE54knnsATTzxxXT7b\nYgYZM7sDwDdCCPcnv78I4OEQwlwSzvGdEMK7zOw3ASCE8LnkfX8C4LcBnE3ec2/y+s8DeCiE8CtX\nfE9Q4nkhxI1Kt9t1hU2r1XJFdavVQjabdUMIlpeX3VCHZ599Fvfdd58rIp9++mk88MADOx6/ePGi\n69HsdDpoNBru0nq9Xne9v5ubm9QzK4QQbyfJ5tI9WVbZbcjH1wE8mvz8KICvbXv9o2ZWMLPjAE4C\neDKEMAdgzczel2xS/Ni2vxFCiHcEzEvIPNQjIyM0HpfFDd91113U2842XLHwgHw+T+NUWSiFxLQQ\n4laCeqjN7HEADwGYxiBe+p8B+G8A/gjAMQCvA/i7IYR68v7PAvglAF0AvxpC+J/J6w8C+E8ASgC+\nGUL41FW+Sx5qIYQQQghx3dlLD3VUyMd+IUEthBBCCCH2gxsh5EMIIYQQQggBCWohhBBCCCFSIUEt\nhBBCCCFECiSohRBCCCGESIEEtRBCCCGEECmQoBZCCCGEECIFEtRCCCGEEEKkQIJaCCGEEEKIFEhQ\nCyGEEEIIkQIJaiGEEEIIIVIgQS2EEEIIIUQKJKiFEEIIIYRIgQS1EEIIIYQQKZCgFkIIIYQQIgUS\n1EIIIYQQQqRAgloIIYQQQogUSFALIYQQQgiRAglqIYQQQgghUiBBLYQQQgghRAokqIUQQgghhEiB\nBLUQQgghhBApkKAWQgghhBAiBRLUQgghhBBCpECCWgghhBBCiBRIUAshhBBCCJECCWohhBBCCCFS\nIEEthBBCCCFECiSohRBCCCGESIEEtRBCCCGEECmQoBZCCCGEECIFEtRCCCGEEEKkQIJaCCGEEEKI\nFEhQCyGEEEIIkQIJaiGEEEIIIVIgQS2EEEIIIUQKJKiFEEIIIYRIgQS1EEIIIYQQKZCgFkIIIYQQ\nIgUS1EIIIYQQQqRAgloIIYQQQogUSFALIYQQQgiRAglqIYQQQgghUiBBLYQQQgghRAokqIUQQggh\nhEiBBLUQQgghhBApkKAWQgghhBAiBRLUQgghhBBCpECC+ibmiSeeeLtP4aZC9tw7ZMu9RfbcW2TP\nvUO23FtkzxsXCeqbGD14e4vsuXfIlnuL7Lm3yJ57h2y5t8ieNy4S1EIIIYQQQqRAgloIIYQQQogU\nWAjh7T6HNzGzG+dkhBBCCCHETU0Iwfbic24oQS2EEEIIIcQ7DYV8CCGEEEIIkQIJaiGEEEIIIVIg\nQS2EEEIIIUQK9lVQm9nrZvaMmT1lZk8mr02a2bfN7GUz+5aZjW97/2fM7BUze9HMPrCf53ojYmZf\nMrN5Mzu17bVrtp+ZPWhmp5Jjv7vf13GjsIM9HzOz80kbfcrMPrjtmOy5A2Z21My+Y2bPmdmzZvap\n5HW1z13g2FPtcxeY2YiZfd/Mnjaz583sXyWvq31eI44t1TZTYGbZxG7fSH5X20zBVex5/dtnCGHf\n/gE4A2Dyitf+DYBfT37+DQCfS35+N4CnAeQB3AHgVQCZ/TzfG+0fgJ8C8B4Ap3Zpv+Em1CcBvDf5\n+ZsAHnm7r+0GsudvA/j0Vd4re/q2nAXwQPJzFcBLAO5V+9xze6p97t6m5eT/HIDvAfhJtc89taXa\nZjqbfhrAHwL4evK72ube2vO6t8+3I+TjyvQkHwLw5eTnLwP4ueTnDwN4PITQCSG8jsFFvndfzvAG\nJYTwZwBWrnj5Wuz3PjM7BGA0hPBk8r6vbPubW4od7An8aBsFZE+XEMJcCOHp5OcGgBcA3Aa1z13h\n2BNQ+9wVIYRm8mMBQBaDZ1/tcxfsYEtAbXNXmNkRAH8TwH/EWzZU29wlO9jTcJ3b534L6gDgT83s\nz83sE8lrB0MI88nP8wAOJj8fBnB+29+ex1sDiniLa7Xfla9fgOx6JZ80sx+Y2Re3LbPJnpGY2R0Y\neP6/D7XP1Gyz5/eSl9Q+d4GZZczsaQza4XdCCM9B7XNX7GBLQG1zt/xbAL8GoL/tNbXN3XM1ewZc\n5/a534L6J0II7wHwQQD/2Mx+avvBMPCre4mxlTTbIcJ+gvMFAMcBPADgEoDfeXtP552FmVUB/FcA\nvxpCWN9+TO3z2kns+V8wsGcDap+7JoTQDyE8AOAIgJ82s792xXG1z0iuYsuHoba5K8zsbwO4HEJ4\nClf3oKptXgOOPa97+9xXQR1CuJT8vwDgjzEI4Zg3s1kASFzsl5O3XwBwdNufH0leEz/MtdjvfPL6\nkStel10TQgiXQwIGy0XDMCPZk2BmeQzE9FdDCF9LXlb73CXb7PkHQ3uqfaYnhLAK4L8DeBBqn6nY\nZssfU9vcNX8VwIfM7AyAxwH8jJl9FWqbu+Vq9vzKfrTPfRPUZlY2s9Hk5/HO0G8AAARnSURBVAqA\nDwA4BeDrAB5N3vYogOFA/HUAHzWzgpkdB3ASgwBx8cNck/1CCHMA1szsfWZmAD627W9ueZKOa8hH\nMGijgOzpklz7FwE8H0L4d9sOqX3ugp3sqfa5O8xserjEa2YlAO8H8BTUPq+ZnWw5FH8JapuRhBA+\nG0I4GkI4DuCjAP53COFjUNvcFTvY8+/vS9/p7Vjcy38YuNqfTv49C+AzyeuTAP4UwMsAvgVgfNvf\nfBaDAPEXAfzsfp3rjfoPg9nWRQBtAOcAfHw39sPAM3MqOfbv3+7ruoHs+UsYbDx4BsAPkofnoOwZ\nZcufxCBe7WkMhMpTAB5R+9xTe35Q7XPX9rwfwP9L7PkMgF9LXlf73Dtbqm2mt+1DeCsrhdpmens+\nvM2eX73e7XOYGkQIIYQQQgixC1QpUQghhBBCiBRIUAshhBBCCJECCWohhBBCCCFSIEEthBBCCCFE\nCiSohRBCCCHEnmJmj5nZeTN7Kvn3yFXec8+240+Z2aqZfeqK9/xTM+ub2WTye8HMft/MnjGzp83s\nocjz+Zdm9pKZPW9mn9ybq3yL3F5/oBBCCCGEuHVIqmU+GkL4+LaXA4DPhxA+v9PfhRBeAvCe5DMy\nGBRP+eNtn3sUg1znZ7f92ScA9EMIf8nMZgD8DzP78eCkrTOzjwO4LYRwT/L7zDVeIkUeaiGEEEII\nkYadxOxVy6nvwN8A8FoI4dy21z4P4NeveN+9AL4DvFl5uw7gxwDAzD5gZt81s78wsz9KCgkCwK8A\n+Odvnuzg7/YUCWohhBBCCJGGnYTzJ83sB2b2xWGFTYePAvjPb36g2YcBnA8hPHPF+36AQXnxbFLd\n8EEAR8xsGsBvAfjrIYQHAfwFgE8nf3MXBhUR/6+ZfdPMTlzb5XEU8iGEEEIIIa4ZM/segCKAKoBJ\nM3sqOfQbAL6At7zC/wLA7wD45R0+pwDg7yR/BzMrY1DB8P3b35b8/yUMvNR/jkEoyHcxqCz7VwC8\nG8B3B9XCUUiOITnHzRDCj5vZR5LP+OndXvdVr0GVEoUQQgghxG5JNgb+4hUx1NuP3wHgGyGE+3c4\n/mEA/yiE8Ejy+/0YlF5vJm85gkF89XtDCJev+Nv/g4FQPwHgF0IIv3CVz38BwCMhhLM2UNsrIQTm\nMb8mFPIhhBBCCCHS8CMhH2Z2aNuvHwFwyvn7nwfw+PCXEMKpEMLBEMLxEMJxAOcB/OUQwmUzKw1j\no83s/QA6IYQXAXwfwE+Y2V3JsYqZnUw+8msAfib5+SEAL+3qKh0U8iGEEEIIIdIQ8KMbE/+1mT2Q\nvH4GwD8EADM7DOD3Qgh/K/m9gsGGxE+Qzx9yEMCfmFkfA6H9MWCw0dDMfhHA42ZWTN77WwBeAfA5\nAH9oZv8EwDqAf7DL69wRhXwIIYQQQgiRAoV8CCGEEEIIkQIJaiGEEEIIIVIgQS2EEEIIIUQKJKiF\nEEIIIYRIgQS1EEIIIYQQKZCgFkIIIYQQIgUS1EIIIYQQQqTg/wNRRHFwI+BZMgAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12ebeb4d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Bins"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The bins are a new geometry, related to but separate from the survey itself, and the midpoints. We will model them as a GeoDataFrame of polygons. The steps are:\n",
      "\n",
      "1. Compute the bin centre locations with our usual list comprehension trick.\n",
      "1. Buffer the centres with a square.\n",
      "1. Gather the buffered polygons into a GeoDataFrame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Factor to shift the bins relative to source and receiver points\n",
      "jig = survey.si / 4.\n",
      "bin_centres = gpd.GeoSeries([Point(survey.xmi + 0.5*r*survey.ri - jig, survey.ymi + 0.5*s*survey.si + jig)\n",
      "                             for r in range(2*(survey.points_per_line[1]-1))\n",
      "                             for s in range(2*(survey.points_per_line[0]-1))\n",
      "                            ])\n",
      "\n",
      "# Buffers are diamond shaped so we have to scale and rotate them.\n",
      "scale_factor = np.sin(np.pi/4.)/2.\n",
      "bin_polys = bin_centres.buffer(scale_factor*survey.ri, 1).rotate(-45)\n",
      "bins = gpd.GeoDataFrame(geometry=bin_polys)\n",
      "\n",
      "bins[:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>geometry</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> POLYGON ((5750000 4709999.999999999, 5749950 4...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> POLYGON ((5750000 4710049.999999999, 5749950 4...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> POLYGON ((5750000 4710100, 5749950 4710100, 57...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "                                            geometry\n",
        "0  POLYGON ((5750000 4709999.999999999, 5749950 4...\n",
        "1  POLYGON ((5750000 4710049.999999999, 5749950 4...\n",
        "2  POLYGON ((5750000 4710100, 5749950 4710100, 57..."
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Suspect there's a super easy way to get all midpoints in a bin poly, without stepping over all bins.\n",
      "\n",
      "**WARNING** This step is very slow for more than a few thousand midpoints. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a copy because I'm going to drop points as I\n",
      "# assign them to polys, to speed up subsequent search.\n",
      "midpts = midpoints.copy()\n",
      "\n",
      "offsets, azimuths = [], [] # To hold complete list.\n",
      "\n",
      "# Loop over bin polygons with index i.\n",
      "for i, bin_i in bins.iterrows():\n",
      "    \n",
      "    o, a = [], [] # To hold list for this bin only.\n",
      "    \n",
      "    # Now loop over all midpoints with index j.\n",
      "    for j, midpt_j in midpts.iterrows():\n",
      "        if bin_i.geometry.contains(midpt_j.geometry):\n",
      "            # Then it's a hit! Add it to the lists,\n",
      "            # and drop it so we have less hunting.\n",
      "            o.append(midpt_j.offset)\n",
      "            a.append(midpt_j.azimuth)\n",
      "            midpts = midpts.drop([j])\n",
      "            \n",
      "    # Add the bin_i lists to the master list\n",
      "    # and go around the outer loop again.\n",
      "    offsets.append(o)\n",
      "    azimuths.append(a)\n",
      "    \n",
      "# Add everything to the dataframe.    \n",
      "bins['offsets'] = gpd.GeoSeries(offsets)\n",
      "bins['azimuths'] = gpd.GeoSeries(azimuths)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>geometry</th>\n",
        "      <th>offsets</th>\n",
        "      <th>azimuths</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> POLYGON ((5750000 4709999.999999999, 5749950 4...</td>\n",
        "      <td>                [70.7106781187]</td>\n",
        "      <td>                         [-45.0]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> POLYGON ((5750000 4710049.999999999, 5749950 4...</td>\n",
        "      <td>                [70.7106781187]</td>\n",
        "      <td>                          [45.0]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> POLYGON ((5750000 4710100, 5749950 4710100, 57...</td>\n",
        "      <td>                [158.113883008]</td>\n",
        "      <td>                 [18.4349488229]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> POLYGON ((5750000 4710150, 5749950 4710150, 57...</td>\n",
        "      <td>                 [254.95097568]</td>\n",
        "      <td>                  [11.309932474]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> POLYGON ((5750000 4710200, 5749949.999999999 4...</td>\n",
        "      <td>                [353.553390593]</td>\n",
        "      <td>                 [8.13010235416]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> POLYGON ((5750000 4710250, 5749950 4710250, 57...</td>\n",
        "      <td>                [452.769256907]</td>\n",
        "      <td>                 [6.34019174591]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> POLYGON ((5750000 4710300, 5749950 4710300, 57...</td>\n",
        "      <td>  [552.268050859, 651.92024052]</td>\n",
        "      <td>   [5.19442890773, -4.398705355]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> POLYGON ((5750000 4710349.999999999, 5749950 4...</td>\n",
        "      <td>  [651.92024052, 552.268050859]</td>\n",
        "      <td>   [4.398705355, -5.19442890773]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> POLYGON ((5750000 4710400, 5749950 4710400, 57...</td>\n",
        "      <td> [751.664818919, 452.769256907]</td>\n",
        "      <td> [3.81407483429, -6.34019174591]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> POLYGON ((5750000 4710450, 5749950 4710450, 57...</td>\n",
        "      <td> [851.469318296, 353.553390593]</td>\n",
        "      <td> [3.36646066343, -8.13010235416]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "                                            geometry  \\\n",
        "0  POLYGON ((5750000 4709999.999999999, 5749950 4...   \n",
        "1  POLYGON ((5750000 4710049.999999999, 5749950 4...   \n",
        "2  POLYGON ((5750000 4710100, 5749950 4710100, 57...   \n",
        "3  POLYGON ((5750000 4710150, 5749950 4710150, 57...   \n",
        "4  POLYGON ((5750000 4710200, 5749949.999999999 4...   \n",
        "5  POLYGON ((5750000 4710250, 5749950 4710250, 57...   \n",
        "6  POLYGON ((5750000 4710300, 5749950 4710300, 57...   \n",
        "7  POLYGON ((5750000 4710349.999999999, 5749950 4...   \n",
        "8  POLYGON ((5750000 4710400, 5749950 4710400, 57...   \n",
        "9  POLYGON ((5750000 4710450, 5749950 4710450, 57...   \n",
        "\n",
        "                          offsets                         azimuths  \n",
        "0                 [70.7106781187]                          [-45.0]  \n",
        "1                 [70.7106781187]                           [45.0]  \n",
        "2                 [158.113883008]                  [18.4349488229]  \n",
        "3                  [254.95097568]                   [11.309932474]  \n",
        "4                 [353.553390593]                  [8.13010235416]  \n",
        "5                 [452.769256907]                  [6.34019174591]  \n",
        "6   [552.268050859, 651.92024052]    [5.19442890773, -4.398705355]  \n",
        "7   [651.92024052, 552.268050859]    [4.398705355, -5.19442890773]  \n",
        "8  [751.664818919, 452.769256907]  [3.81407483429, -6.34019174591]  \n",
        "9  [851.469318296, 353.553390593]  [3.36646066343, -8.13010235416]  "
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can compute the fold from the length of the list of offsets in each bin. We use a mini-function, called a [lambda](http://www.diveintopython.net/power_of_introspection/lambda_functions.html), to do this. This piece of code applies a lambda to each row in the GeoDataFrame. Essentially it says:\n",
      "\n",
      "    set each row in the 'fold' column in my `bins` GeoDataFrame to the length of the offsets list for that row. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins['fold'] = bins.apply(lambda row: len(row.offsets), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can use the GeoDataFrame's built-in `plot()` method to plot these:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = bins.plot(column=\"fold\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x104b00950>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can use a similar trick to compute the minimum offset, but with an added test for there being valid data in the bin:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins['min_offset'] = bins.apply(lambda row: min(row.offsets) if row.fold > 0 else None, axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = bins.plot(column=\"min_offset\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JeinwN8CHI+IX9ee6dEzLfv41RT+fomPHNCKej4gzgZOAN0s6t/F8J45lop9j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\nWcWDgpmZVTwomJlZ5f8D0qvUje+jh80AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x130dc9d50>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}