{
 "metadata": {
  "name": "UGrid"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# see also: U groups\n",
      "# https://groups.google.com/forum/#!forum/ugrid-interoperability\n",
      "# https://groups.google.com/forum/#!topic/ugrid-interoperability/IIFRDFsKgJM\n",
      "filename = '/Users/fedorbaart/Documents/checkouts/cases_unstruc/e00_unstruc/f03_advection/c020_Waalgrof/input/WAALGROF_net.nc'\n",
      "filename = '/Users/fedorbaart/Downloads/wa01/r07e_net.nc'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import netCDF4\n",
      "import numpy as np\n",
      "import ogr\n",
      "import osr\n",
      "from openearthtools.io.dflowfm import UGrid\n",
      "import vtk\n",
      "import matplotlib.collections"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid = UGrid.fromfile(filename)\n",
      "cellcoords = [xy[:,:2] for xy in grid.cellcoords()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1)\n",
      "ax.plot(grid.netnodex ,grid.netnodey,'k.')\n",
      "cells = matplotlib.collections.PolyCollection(cellcoords)\n",
      "ax.add_collection(cells)\n",
      "ax.autoscale()\n",
      "#ax.set_ylim(4185000, 4190000)\n",
      "#ax.set_xlim(560000, 562000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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dXtNZU05OjlP3amtr6zFJoT333nsvBoOhQwzO/s+RAZLj354n6dUIvf766zz8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\nBAKBQKAY/w+VNjfsl9C/GAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matplotlib.patches.Path(cellcoords[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Path([[  588382.34579639  4146837.08261736]\n",
        " [  588179.10402848  4146946.61715301]\n",
        " [  587962.15356973  4146530.43471492]\n",
        " [  588167.9561072   4146418.70843193]], None)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid = UGrid.fromfile(filename)\n",
      "# add the coordinate of the orgin\n",
      "\n",
      "# The coordinates are in some local system... Let's shift to RD\n",
      "rd = osr.SpatialReference()\n",
      "rd.ImportFromEPSG(28992)\n",
      "wgs = osr.SpatialReference()\n",
      "wgs.ImportFromEPSG(4326)\n",
      "transform = osr.CoordinateTransformation(wgs,rd)\n",
      "\n",
      "lat, lon = (51.802458, 5.35)\n",
      "\n",
      "x,y,z = transform.TransformPoint(lon,lat)\n",
      "x0, y0 = grid.netnodex[np.argmin(grid.netnodey)]  , grid.netnodey[np.argmin(grid.netnodey)] \n",
      "xt, yt = x-x0, y-y0\n",
      "print xt, yt, x0, y0, x,y, np.argmin(grid.netnodey)\n",
      "\n",
      "grid.netnodex += xt\n",
      "grid.netnodey += yt\n",
      "# transform to latlon\n",
      "transform = osr.CoordinateTransformation(rd, wgs)\n",
      "lonlats = np.array(transform.TransformPoints(np.c_[grid.netnodex, grid.netnodey]))\n",
      "grid.netnodex, grid.netnodey = lonlats[:,:2].T\n",
      "\n",
      "grid.cellcoords()\n",
      "with open('grid.json', 'w') as f:\n",
      "    f.write(grid.export())\n",
      "#grid.export(drivername='GeoJSON', filename='gridogr.json')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-8-013a60e56258>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcellcoords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'grid.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;31m#grid.export(drivername='GeoJSON', filename='gridogr.json')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/fedorbaart/Documents/checkouts/openearthtools/python/OpenEarthTools/openearthtools/io/dflowfm/__init__.pyc\u001b[0m in \u001b[0;36mexport\u001b[0;34m(self, drivername, filename, epsg)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0mdatasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSyncToDisk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdrivername\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Memory'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0mfeaturestr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\",\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetFeature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExportToJson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetFeatureCount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m'{ \"type\": \"FeatureCollection\",\\n\"features\": [\\n%s\\n] }'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfeaturestr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mdatasource\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/osgeo/ogr.pyc\u001b[0m in \u001b[0;36mExportToJson\u001b[0;34m(self, as_object, options)\u001b[0m\n\u001b[1;32m   2870\u001b[0m                 \u001b[0moptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2871\u001b[0m             \u001b[0mgeom_json_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExportToJson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2872\u001b[0;31m             \u001b[0mgeom_json_object\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimplejson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom_json_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2873\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2874\u001b[0m             \u001b[0mgeom_json_object\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/fedorbaart/.virtualenvs/main/lib/python2.7/site-packages/simplejson/__init__.pyc\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mparse_constant\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mobject_pairs_hook\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             and not use_decimal and not kw):\n\u001b[0;32m--> 385\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/fedorbaart/.virtualenvs/main/lib/python2.7/site-packages/simplejson/decoder.pyc\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m         \"\"\"\n\u001b[0;32m--> 402\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    403\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/fedorbaart/.virtualenvs/main/lib/python2.7/site-packages/simplejson/decoder.pyc\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    416\u001b[0m         \"\"\"\n\u001b[1;32m    417\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No JSON object could be decoded\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-373826.25987 -3720574.19302 526260.259795 4144332.78163 152433.999924 423758.588615 63898\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#triang = matplotlib.tri.Triangulation(grid.netnodex, grid.netnodey)\n",
      "grid.netnodex.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "(77521,)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1)\n",
      "ax.add_collection(matplotlib.collections.PolyCollection([xyz[:,:2] for xyz in grid.cellcoords()]))\n",
      "ax.autoscale()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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gIWWfGS2kt4/8ZIFnhwrse2WSUzpTFCPDeMlwWmeSq964mpFixFfvf4HegTz3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\nRK5CYII=\n"
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(ogr.GetDriverCount()):\n",
      "    driver = ogr.GetDriver(i)\n",
      "    print(driver.GetName())\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ESRI Shapefile\n",
        "MapInfo File\n",
        "UK .NTF\n",
        "SDTS\n",
        "TIGER\n",
        "S57\n",
        "DGN\n",
        "VRT\n",
        "REC\n",
        "Memory\n",
        "BNA\n",
        "CSV\n",
        "GML\n",
        "GPX\n",
        "KML\n",
        "GeoJSON\n",
        "GMT\n",
        "PCIDSK\n",
        "XPlane\n",
        "AVCBin\n",
        "AVCE00\n",
        "DXF\n",
        "Geoconcept\n",
        "GeoRSS\n",
        "GPSTrackMaker\n",
        "VFK\n",
        "PGDump\n",
        "GPSBabel\n",
        "SUA\n",
        "OpenAir\n",
        "PDS\n",
        "WFS\n",
        "HTF\n",
        "AeronavFAA\n",
        "EDIGEO\n",
        "GFT\n",
        "SVG\n",
        "CouchDB\n",
        "Idrisi\n",
        "ARCGEN\n",
        "SEGUKOOA\n",
        "SEGY\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}