Seismic_acquisition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seismic acquisition fiddling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survey object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "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",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = survey.src\n",
    "r = survey.rcvr\n",
    "r[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
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       "      <td>POINT (5750000 4710200)</td>\n",
       "      <td>src</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (5750000 4710300)</td>\n",
       "      <td>src</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (5750000 4710400)</td>\n",
       "      <td>src</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POINT (5750000 4710500)</td>\n",
       "      <td>src</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POINT (5750000 4710600)</td>\n",
       "      <td>src</td>\n",
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       "    <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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      ],
      "text/plain": [
       "                          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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layout = survey.layout\n",
    "layout[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a hierarchical index you can do cool things, e.g. show the last five sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>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",
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       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>POINT (5753000 4711800)</td>\n",
       "      <td>src</td>\n",
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       "    <tr>\n",
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       "      <td>POINT (5753000 4711900)</td>\n",
       "      <td>src</td>\n",
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      "text/plain": [
       "                    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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layout.ix['sources'][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:26911', 'no_defs': True}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layout.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pop/hWUmHAndFxBslXQwQEZem/W8DLomIe3uOkf2Q1DaNXe7HeeSnK7k4j+ZlMU9B0v7A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\nmZmVXCmYmVnJlYKZmZVcKZiZWen/ARSr8iLgVzNQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102f52950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = layout.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export GeoDataFrames to GIS shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# gdf.to_file('src_and_rcvr.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "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",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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",
    "                  ]"
   ]
  },
  {
   "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",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "offsets = [r.distance(s)\n",
    "           for r in layout.ix['receivers'].geometry\n",
    "           for s in layout.ix['sources'].geometry\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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",
    "            ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a Geoseries of the midpoints, offsets and azimths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>"
      ],
      "text/plain": [
       "     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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midpoints = gpd.GeoDataFrame({\n",
    "                   'geometry' : midpoint_list,\n",
    "                   'offset' : offsets,\n",
    "                   'azimuth': azimuths,\n",
    "                   'offsetx' : offsetx,\n",
    "                   'offsety' : offsety\n",
    "                   })\n",
    "midpoints[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L2i/paUmbRq284ziO0y+jegoGbDSzvy7t+wDwqJl9TNJdwf6ApMsZxDUvB94A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\n0mnvAV42s3WSlgF/JOlKqxk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105811a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = midpoints.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to a shapefile if desired. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#midpt.to_file('CMPs.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spider plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     50\n",
       "1     50\n",
       "2    150\n",
       "3    250\n",
       "4    350\n",
       "Name: offsetx, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midpoints[:5].offsetx # Easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5749975.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midpoints.ix[3].geometry.x # Less easy :("
   ]
  },
  {
   "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 — it feels like it should be really easy) so I am resorting to this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = [m.geometry.x for i, m in midpoints.iterrows()]\n",
    "y = [m.geometry.y for i, m in midpoints.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oJ8NUj/E008U/QqSgfkQQzQzVnNnbUl5T7NoAHi0ZniT2/4z8/KaCHe3RBp8o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\nNxExhrpKvFQpH7dnkFLKlxvX+DA6AF2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9S9rWzsfRGNLf0ZjMzWBh7aTij2t8GK2LaiY5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\nW0SeGPyG7JjjqOBdQCe3twfK5+0epoHR2kRJOjFim5AbxoT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WnzW0jra5s4UQ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Ak/C/wpAkZ5n3tdv4mYN/4E8XavWnofAoluzfw/kZLywNI9SM804qr+YwT43vay2piYAv4n4NuI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\nQgghhBBiDCSohRBCCCGEGIP/Dw9gH8YUMaF1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1f50d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "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",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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 ((5750050 4710000, 5750000 4710000, 57...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((5750050.000000001 4710050, 5750000 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((5750050.000000001 4710100, 5749999.9...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  POLYGON ((5750050 4710000, 5750000 4710000, 57...\n",
       "1  POLYGON ((5750050.000000001 4710050, 5750000 4...\n",
       "2  POLYGON ((5750050.000000001 4710100, 5749999.9..."
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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] - 3)\n",
    "                             for s in range(2*survey.points_per_line[0] - 2)\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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122322890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = bins.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New spatial join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you to Jake Wasserman and Kelsey Jordahl for this code snippet, and many pointers. \n",
    "\n",
    "This takes about 20 seconds to run on my iMac, compared to something close to 30 minutes for the old nested loops. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reindexed = bins.reset_index().rename(columns={'index':'bins_index'})\n",
    "joined = gpd.tools.sjoin(reindexed, midpoints)\n",
    "bin_stats = joined.groupby('bins_index')['offset']\\\n",
    "                  .agg({'fold': len, 'min_offset': np.min})\n",
    "bins = gpd.GeoDataFrame(bins.join(bin_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>bins_index</th>\n",
       "      <th>geometry</th>\n",
       "      <th>azimuth</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>0</td>\n",
       "      <td>POLYGON ((5750050 4710000, 5750000 4710000, 57...</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>70.710678</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((5750050.000000001 4710050, 5750000 4...</td>\n",
       "      <td>-45.000000</td>\n",
       "      <td>70.710678</td>\n",
       "      <td>50</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((5750050.000000001 4710100, 5749999.9...</td>\n",
       "      <td>-18.434949</td>\n",
       "      <td>158.113883</td>\n",
       "      <td>150</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((5750050 4710150, 5750000 4710150, 57...</td>\n",
       "      <td>-11.309932</td>\n",
       "      <td>254.950976</td>\n",
       "      <td>250</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((5750050 4710200, 5750000 4710200, 57...</td>\n",
       "      <td>-8.130102</td>\n",
       "      <td>353.553391</td>\n",
       "      <td>350</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((5750050 4710250, 5750000 4710250, 57...</td>\n",
       "      <td>-6.340192</td>\n",
       "      <td>452.769257</td>\n",
       "      <td>450</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((5750050 4710300, 5750000 4710300, 57...</td>\n",
       "      <td>4.398705</td>\n",
       "      <td>651.920241</td>\n",
       "      <td>650</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((5750050 4710300, 5750000 4710300, 57...</td>\n",
       "      <td>-5.194429</td>\n",
       "      <td>552.268051</td>\n",
       "      <td>550</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((5750050 4710350, 5750000 4710350, 57...</td>\n",
       "      <td>5.194429</td>\n",
       "      <td>552.268051</td>\n",
       "      <td>550</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((5750050 4710350, 5750000 4710350, 57...</td>\n",
       "      <td>-4.398705</td>\n",
       "      <td>651.920241</td>\n",
       "      <td>650</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bins_index                                           geometry    azimuth  \\\n",
       "0           0  POLYGON ((5750050 4710000, 5750000 4710000, 57...  45.000000   \n",
       "1           1  POLYGON ((5750050.000000001 4710050, 5750000 4... -45.000000   \n",
       "2           2  POLYGON ((5750050.000000001 4710100, 5749999.9... -18.434949   \n",
       "3           3  POLYGON ((5750050 4710150, 5750000 4710150, 57... -11.309932   \n",
       "4           4  POLYGON ((5750050 4710200, 5750000 4710200, 57...  -8.130102   \n",
       "5           5  POLYGON ((5750050 4710250, 5750000 4710250, 57...  -6.340192   \n",
       "6           6  POLYGON ((5750050 4710300, 5750000 4710300, 57...   4.398705   \n",
       "7           6  POLYGON ((5750050 4710300, 5750000 4710300, 57...  -5.194429   \n",
       "8           7  POLYGON ((5750050 4710350, 5750000 4710350, 57...   5.194429   \n",
       "9           7  POLYGON ((5750050 4710350, 5750000 4710350, 57...  -4.398705   \n",
       "\n",
       "       offset  offsetx  offsety  \n",
       "0   70.710678       50       50  \n",
       "1   70.710678       50      -50  \n",
       "2  158.113883      150      -50  \n",
       "3  254.950976      250      -50  \n",
       "4  353.553391      350      -50  \n",
       "5  452.769257      450      -50  \n",
       "6  651.920241      650       50  \n",
       "7  552.268051      550      -50  \n",
       "8  552.268051      550       50  \n",
       "9  651.920241      650      -50  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>min_offset</th>\n",
       "      <th>fold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((5750050 4710000, 5750000 4710000, 57...</td>\n",
       "      <td>70.710678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((5750050.000000001 4710050, 5750000 4...</td>\n",
       "      <td>70.710678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((5750050.000000001 4710100, 5749999.9...</td>\n",
       "      <td>158.113883</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((5750050 4710150, 5750000 4710150, 57...</td>\n",
       "      <td>254.950976</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((5750050 4710200, 5750000 4710200, 57...</td>\n",
       "      <td>353.553391</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POLYGON ((5750050 4710250, 5750000 4710250, 57...</td>\n",
       "      <td>452.769257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POLYGON ((5750050 4710300, 5750000 4710300, 57...</td>\n",
       "      <td>552.268051</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POLYGON ((5750050 4710350, 5750000 4710350, 57...</td>\n",
       "      <td>552.268051</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>POLYGON ((5750050.000000001 4710400, 5750000 4...</td>\n",
       "      <td>452.769257</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>POLYGON ((5750050.000000001 4710450, 5749999.9...</td>\n",
       "      <td>353.553391</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  min_offset  fold\n",
       "0  POLYGON ((5750050 4710000, 5750000 4710000, 57...   70.710678     1\n",
       "1  POLYGON ((5750050.000000001 4710050, 5750000 4...   70.710678     1\n",
       "2  POLYGON ((5750050.000000001 4710100, 5749999.9...  158.113883     1\n",
       "3  POLYGON ((5750050 4710150, 5750000 4710150, 57...  254.950976     1\n",
       "4  POLYGON ((5750050 4710200, 5750000 4710200, 57...  353.553391     1\n",
       "5  POLYGON ((5750050 4710250, 5750000 4710250, 57...  452.769257     1\n",
       "6  POLYGON ((5750050 4710300, 5750000 4710300, 57...  552.268051     2\n",
       "7  POLYGON ((5750050 4710350, 5750000 4710350, 57...  552.268051     2\n",
       "8  POLYGON ((5750050.000000001 4710400, 5750000 4...  452.769257     2\n",
       "9  POLYGON ((5750050.000000001 4710450, 5749999.9...  353.553391     2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z5ZwUzMws55pCG1xTMBteVZyjXFOohG2FdcbV65vHZY5ZvZ4TCuujR9Q8LnNMvbgfdYuH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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bbf350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = bins.plot(column=\"fold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/STq9+WZUv296d7Nbh3ZN0t8Aa4ATgJ8An5xsc2ZJehHwJeCqiPh1870+HdPU\nzv9B1c6n6NkxjYhnI+IEYDXweklvaL3fi2OZaec6enYsJf0HYG9EPEj+X9y9OJ5D2rngx3NRO4WI\n+En670+Br1ANB+2RdDhA+qmzN318N3B0Y/PVad1i2Zd27UrrV7fWL3h7I2JvJFQ/M2eG2CbaTknP\np+oQvhgRm9Pq3h3TRjv/bqadfT2mEfFL4B+Ak+nhscy089U9PJb/Djhf0g+BjcAbJX2R/h3PXDtv\nWpTjWVoI6foHeCHV2BbAIcA3gLOpCjzXpPXrGSzwrKDqGX9AKpwsUPuOYbDQvE/tohoiOZWqZ1+o\nwlO7nUc0Xv8pcMuk25n2exPwl631vTqmQ9rZm2MKHEZ1AyjAwcA/Amf28FjO1c7D+3IsM20+A7it\nj383h7Rzwf9ujj3AkGBrUqO3A98Grk3rV1Ld3fxdqruiD21s8yGqgsljwB8uYNs2Av8H+H/Aj4FL\nR2kX1b/gHknvXbcI7Xwn1UntYeAhYDPV2Oik2/k6qnHQ7cCD6c85fTumc7Tz3D4dU+B44FupjQ8D\nHxz1/5sFPpZztbM3xzLT5jOYvaqnV8ez1c51jXZ+caGPp29eMzOz2n75kBszM1sY7hTMzKzmTsHM\nzGruFMzMrOZOwcxsiMwkdOdkPvPKxvsPSvql0gSLjc98QNKzklam5RWSPq9qktDtks7o2J6PSno8\nTTz4vvGknFX6OE4zsyUj3YV9SURc2lgdwKci4lNzbRcRj1NNk4KkA6huEPtKY79HA2cBP2ps9m7g\n2Yh4lapHGt8u6ZQYckmopEuBoyLilWn5d/Yx4rz8S8HMbNZcJ+R9edTlHwA/iIgfN9Z9Cri69blj\ngXugnuXhF8CrASSdLeleSQ9I2qTq6ZYAlwN/Xje22m6s3CmYmc2a6+Sfm4RuLhdRPXq42qF0AbAr\nIh5ufe4hqqksnidpDdVNZqslHQZ8GDgzIk4GHqCaQhvg5cBFkv5J0hZJr9i3ePPz8JGZLXuS7gNe\nQPVcjZWSHkxvXUM1Cd3Mv84/QjUJ3WVz7GcFcF7abuZZ9h+iGjqqP5b++zmqXwvfpBpWupfqDvvX\nUk1bca+qxx2vSO+R2vivEXGKpDenfbx+1NzZDL6j2cyskoq972jVFJrvH0M1D9Hxc7x/AfAfI+Kc\ntHw81fQZM8+amJmQ7jURsbe17TeoOptXAG+PiLdn9v8o1dxFP1LVYzwZEfP9ctknHj4yM5s1MHyU\nZk2d8WZCXWqiAAAAx0lEQVSqeYTm8jaqOcoAiIhHImJVRKyJiDVUM5eeFBF7JR08UyuQdBbwdEQ8\nRjWB3WmSXp7eO0TS2rTLzcAb0+szqJ4WOFYePjIzm5V7lsLHJZ2Q1v8QeA+ApCOB/xYRb0rLh1AV\nmd/N3Jr7XgXcIelZqs7iYqiKx5LeAWyU9IL02Q8D3wM2ADdL+lPg18C7Rsw5Jw8fmZlZzcNHZmZW\nc6dgZmY1dwpmZlZzp2BmZjV3CmZmVnOnYGZmNXcKZmZWc6dgZma1/w83gvWCs5dfCAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1227c0c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = bins.plot(column=\"min_offset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}