Rusty.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:3f69eb8642e311940249f4d9da10e2e384a47b1e80494e33b4e94bf3428d952d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import netCDF4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url='http://comt.sura.org/thredds/dodsC/comt_1_archive_full/estuarine_hypoxia/VIMS_EFDC/2004_DO3d/Output/EFDC_ele2d_20040101to_20041225.nc'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = netCDF4.Dataset(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncv = nc.variables"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncv.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[u'Water_Level', u'Time', u'X', u'Y', u'Latitude', u'Longtitude', u'LIJ']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lij=ncv['LIJ'][:,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shape(lij)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(3, 8932)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lij[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([ 1, 88,  2], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lij[:,-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([8932,   87,  240], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lon = ncv['Longtitude'][:,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat = ncv['Latitude'][:,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lon = ma.masked_where(lon==-999.,lon)\n",
      "lat = ma.masked_where(lat==-999.,lat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zt= ncv['Water_Level'][:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zeta = zeros_like(lon)\n",
      "zeta = ma.masked_where(lon==-999.,zeta)\n",
      "zeta[lij[2,:]-1,lij[1,:]-1]=zt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pcolor(lon,lat,zeta)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.collections.PolyCollection at 0x3813110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GhYzEQNK2h5BlzN313Qll0ryPNj2Iah/OS9QZUcgy0u0IQbs9odMSSMMpCr0K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\nhBBCCCEGlf8P/R1NLhiD/8gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3863250>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nco = netCDF4.Dataset('efdc.nc','r+')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncov = nco.variables"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncov['lon'][:,:]=lon\n",
      "ncov['lat'][:,:]=lat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncov['zeta'][0,:,:]=zeta\n",
      "r = shape(ncov['do'][0,:,:,:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nco.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc2=netCDF4.Dataset('efdc.nc')\n",
      "nc2v = nc2.variables\n",
      "lon = nc2v['lon'][:,:]\n",
      "lat = nc2v['lat'][:,:]\n",
      "zeta = nc2v['zeta'][0,:,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pcolor(lon,lat,zeta)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.collections.PolyCollection at 0x56e46d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GhYzEQNK2h5BlzN313Qll0ryPNj2Iah/OS9QZUcgy0u0IQbs9odMSSMMpCr0K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\nhBBCCCEGlf8P/R1NLhiD/8gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3873110>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url='http://comt.sura.org/thredds/dodsC/comt_1_archive_full/estuarine_hypoxia/VIMS_EFDC/2004_DO3d/Output/EFDC_DO3d_20040101_to_20040131.nc'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = netCDF4.Dataset(url)\n",
      "ncv = nc.variables"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "[u'DO', u'Time', u'X', u'Y', u'Latitude', u'Longtitude', u'LIJ']"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(ncv['Time'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "6"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dox = zeros(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doxt = ncv['DO'][:,:,0]\n",
      "print shape(doxt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(20, 8932)\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dox[:,lij[2,:]-1,lij[1,:]-1] = doxt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pcolor(lon,lat,dox[0,:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "<matplotlib.collections.PolyCollection at 0x583a3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3MLSznh4WuYFjzAa+DMwZYV7rxoxFwQiCPeto2yPW2/2l1smWKMy621+P2Wbn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\neNPBmvF4WxRO06UiR3QnvY1WADbS7t7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       "text": [
        "<matplotlib.figure.Figure at 0x61b2ad0>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}