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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Laying out a seismic survey"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook goes with a blog post \u2014\u00a0[Laying out a seismic survey](http://agilegeoscience.com/journal/2014/12/17/laying-out-a-seismic-survey.html) \u2014 on Wednesday 17 December 2014. \n",
      "\n",
      "We start with the usual preliminaries..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from shapely.geometry import Point\n",
      "from geopandas import GeoDataFrame\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Set up a basic geometry"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next we can define the survey parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# initial conditions\n",
      "xmi = 575000   # leftmost corner of grid (m)\n",
      "ymi= 4710000   # bottommost corner of grid (m)\n",
      "SL = 600       # shot line interval (m)\n",
      "RL = 600       # receiver line interval (m)\n",
      "si = 100       # source point interval (m)\n",
      "ri = 100       # receiver interval (m)\n",
      "x = 3000       # width of survey (m)\n",
      "y = 1800       # height of survey (m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some easy geometry: calculate the number of source and receiver lines we can fit into the shape we have defined, given the spacing we want:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Number of receiver lines and source lines\n",
      "rlines = int(y/RL) + 1\n",
      "slines = int(x/SL) + 1\n",
      "\n",
      "# Put recevier lines East-West, and shot lines North South\n",
      "rperline = int(x/ri) + 2\n",
      "sperline = int(y/si) + 2\n",
      "\n",
      "# offset the receivers relative to the sources\n",
      "shiftx = -si/2.\n",
      "shifty = -ri/2."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a double list comprehension. It's a bit sticky. First, to illustrate the point, see how this one-liner makes a simple list:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[x**2 for x in range(10)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now check this out:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[x**y for x in range(4) for y in range(4)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[1, 0, 0, 0, 1, 1, 1, 1, 1, 2, 4, 8, 1, 3, 9, 27]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now hopefully it's not too hard to see what we're doing here..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Find x,y coordinates of recs and shots\n",
      "rcvrx = [xmi + rcvr*ri + shiftx for line in range(rlines) for rcvr in range(rperline)]\n",
      "rcvry = [ymi + line*RL - shifty for line in range(rlines) for rcvr in range(rperline)]\n",
      "\n",
      "srcx = [xmi + line*SL for line in range(slines) for src in range(sperline)]\n",
      "srcy = [ymi + src*si for line in range(slines) for src in range(sperline)] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_geoms(xcoords, ycoords, color = 'none', size = 50, alpha = 0.5):\n",
      "    \"\"\"\n",
      "    A helper function to make it a bit easier to plot multiple things.\n",
      "    \"\"\"\n",
      "    plot = plt.scatter(xcoords,\n",
      "                       ycoords, \n",
      "                       c=color, \n",
      "                       s=size,\n",
      "                       marker='o',\n",
      "                       alpha=alpha,\n",
      "                       edgecolor='none'\n",
      "                       )\n",
      "    return plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(15,10))\n",
      "r = plot_geoms(rcvrx, rcvry, 'b')\n",
      "s = plot_geoms(srcx, srcy, 'r')\n",
      "plt.axis('equal')\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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t0ZTtc2TvXrv7ItGUrV18+WVehtWKUfFWTPrMS4jX12f9mYSfb2P/c+Ve7SIv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       "text": [
        "<matplotlib.figure.Figure at 0x10bcdb0d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Making a more robust data container"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Zip into x,y coordinate pairs.\n",
      "rcvrxy = zip(rcvrx, rcvry)\n",
      "srcxy = zip(srcx, srcy)\n",
      "\n",
      "# Create a list of Point objects.\n",
      "rcvrs = [Point(x,y) for x,y in rcvrxy]\n",
      "srcs = [Point(x,y) for x,y in srcxy]\n",
      "\n",
      "# Put lists into a GeoDataFrame.\n",
      "receivers = GeoDataFrame({'geometry': rcvrs})\n",
      "sources = GeoDataFrame({'geometry': srcs})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you want to, save the `GeoDataFrame`s as shapefiles:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sources.to_file('sources.shp')\n",
      "receivers.to_file('receivers.shp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Computing the midpoints"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We're going to need Shapely's `LineString` geometry \u2014\u00a0essentially we ask for the 50% (0.5) mark along a line interpolated between two `Point` objects. The `interpolate` method returns another `Point`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from shapely.geometry import LineString"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another nested list comp gets the job done. Python is awesome!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "midpoints = [LineString([r, s]).interpolate(0.5, normalized=True) for r in rcvrs for s in srcs]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We won't get into this too much right now, but let's just check we got what we wanted. We can throw the midpoints into a GeoPandas `GeoSeries` object, essentially a 1D GeoDataFrame \u2014\u00a0a bit like a 1D labelled array \u2014\u00a0then use its built-in `plot` method."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from geopandas import GeoSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = GeoSeries(midpoints)\n",
      "m.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10ee96c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aG9SKbXfVdis7izzi0tjWTZJrbH/HSa7pRHsO21n0Q5O+3YuIG8qAWq0WuYk7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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10da80950>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Voil\u00e0!"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}