csv_to_shp.ipynb

csv_to_shp.ipynb

{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-08-16T13:10:44.473509",
          "end_time": "2016-08-16T13:10:44.477722"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import gpxpy.gpx\nimport os\nimport pandas as pd\nimport shapefile\nimport urllib",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-08-16T13:10:45.233536",
          "end_time": "2016-08-16T13:10:45.437060"
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        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getPRJwkt(epsg):\n   \"\"\"\n   Grab an WKT version of an EPSG code\n   usage getPRJwkt(4326)\n\n   This makes use of links like http://spatialreference.org/ref/epsg/4326/prettywkt/\n   \"\"\"\n   \n   import urllib\n   f=urllib.request.urlopen(\"http://spatialreference.org/ref/epsg/{0}/prettywkt/\".format(epsg))\n   return (f.read())\nprint(getPRJwkt(4326))",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "b'GEOGCS[\"WGS 84\",\\n    DATUM[\"WGS_1984\",\\n        SPHEROID[\"WGS 84\",6378137,298.257223563,\\n            AUTHORITY[\"EPSG\",\"7030\"]],\\n        AUTHORITY[\"EPSG\",\"6326\"]],\\n    PRIMEM[\"Greenwich\",0,\\n        AUTHORITY[\"EPSG\",\"8901\"]],\\n    UNIT[\"degree\",0.01745329251994328,\\n        AUTHORITY[\"EPSG\",\"9122\"]],\\n    AUTHORITY[\"EPSG\",\"4326\"]]'\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-08-16T13:10:46.048926",
          "end_time": "2016-08-16T13:10:46.054202"
        },
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def get_spp_color(spp):\n    if spp == 'T':\n        return \"blue\"\n    if spp == \"P\":\n        return \"yellow\"\n    if spp == \"E\":\n        return \"magenta\"\n    if spp == \"G\":\n        return \"orange\"\n    return \"white\"",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-08-16T13:10:47.745059",
          "end_time": "2016-08-16T13:10:47.750526"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "coords = pd.read_csv(\"coords.txt\", sep=\"\\t\")",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-08-16T13:10:50.544924",
          "end_time": "2016-08-16T13:10:50.563723"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "coords.head()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P-AL-5-13</td>\n      <td>33.17237</td>\n      <td>-86.17225</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>T-AR-2-7</td>\n      <td>34.63728</td>\n      <td>-93.15251</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P-GA-4-8</td>\n      <td>31.14648</td>\n      <td>-82.21552</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>P-GA-2-12</td>\n      <td>31.17607</td>\n      <td>-83.53644</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>E-GA-1-4</td>\n      <td>31.13760</td>\n      <td>-82.21436</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        name       lat       lon\n0  P-AL-5-13  33.17237 -86.17225\n1   T-AR-2-7  34.63728 -93.15251\n2   P-GA-4-8  31.14648 -82.21552\n3  P-GA-2-12  31.17607 -83.53644\n4   E-GA-1-4  31.13760 -82.21436"
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-08-16T13:11:00.128888",
          "end_time": "2016-08-16T13:11:00.466218"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "w = shapefile.Writer(shapefile.POINT)\nw.field(\"name\")\nw.field(\"species\")\nw.field(\"color\")\nrecords = []\nshpfile = \"sample_shapefile\"\n\ndef record(row):\n    w.point(row['lon'], row['lat'])\n    spp = row['name'][0]\n    w.record(row['name'], spp, get_spp_color(spp))\n    \ncoords.apply(record, axis=1)\nw.save(shpfile)\n\nprj = open(\"%s.prj\" % shpfile, \"w\")\nepsg = getPRJwkt(4326)\nprj.write(epsg.decode(\"utf-8\"))\nprj.close()",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
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      "display_name": "Python 3",
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        "description": "csv_to_shp.ipynb",
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