phobson · December 16, 2015 17:08
diff --git a/gistfile1.ipynb b/gistfile1.ipynb
 {
 "metadata": {
  "name": "netCDF_stuff"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Reading Climate Projections in netCDF4 format\nSource of data: \nftp://gdo-dcp.ucllnl.org/pub/dcp/subset/2013042521460Ac001_HlwaoL/bcca/\n\n### netCDF/Python reference:\nRead/write netCDF files: http://www.unidata.ucar.edu/software/netcdf/examples/programs/\n\nPlotting with matplotlib: http://www-pord.ucsd.edu/~cjiang/python.html"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Setup up the Notebook Environment"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%pylab inline\n%cd C:/Users/workshop/Desktop/netcdf/",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Setup the python environment and read the data"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import netCDF4\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import ImageGrid\nimport matplotlib.dates as mdates\n\nprojections = netCDF4.Dataset('Extraction_pr.nc')\nextent = [\n    projections.variables['lon'][0], \n    projections.variables['lon'][-1],\n    projections.variables['lat'][0], \n    projections.variables['lat'][-1]\n]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Plot the first timestamp(?) of the three projections"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure(figsize(12, 5))\ngrid = ImageGrid(fig, 111, nrows_ncols=(1, 3),\n                axes_pad=0.1, add_all=True, \n                label_mode=\"L\")\nfor n, ax in enumerate(grid):\n    ax.imshow(rain.variables['pr'][n][0][:,::-1], interpolation='none', extent=extent)\n    ax.set_xlabel('Longitude')\n    ax.set_title('Projection %d' % (n+1,))\n    if n == 0:\n        ax.set_ylabel('Latitude')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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7Bpgj9e7dF6LFkB9upUo6zKx8eaE6vO6668o8dv31FU/uhQsXqm3btkpMTDxs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\n+P9W99ai+2+33wAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 60
    }
   ],
   "metadata": {}
  }
 ]
 }
	{
	"metadata": {
	"name": "netCDF_stuff"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": "# Reading Climate Projections in netCDF4 format\nSource of data: \nftp://gdo-dcp.ucllnl.org/pub/dcp/subset/2013042521460Ac001_HlwaoL/bcca/\n\n### netCDF/Python reference:\nRead/write netCDF files: http://www.unidata.ucar.edu/software/netcdf/examples/programs/\n\nPlotting with matplotlib: http://www-pord.ucsd.edu/~cjiang/python.html"
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": "### Setup up the Notebook Environment"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "%pylab inline\n%cd C:/Users/workshop/Desktop/netcdf/",
	"language": "python",
	"metadata": {},
	"outputs": []
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": "### Setup the python environment and read the data"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "import netCDF4\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import ImageGrid\nimport matplotlib.dates as mdates\n\nprojections = netCDF4.Dataset('Extraction_pr.nc')\nextent = [\n projections.variables['lon'][0], \n projections.variables['lon'][-1],\n projections.variables['lat'][0], \n projections.variables['lat'][-1]\n]",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 65
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": "### Plot the first timestamp(?) of the three projections"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "fig = plt.figure(figsize(12, 5))\ngrid = ImageGrid(fig, 111, nrows_ncols=(1, 3),\n axes_pad=0.1, add_all=True, \n label_mode=\"L\")\nfor n, ax in enumerate(grid):\n ax.imshow(rain.variables['pr'][n][0][:,::-1], interpolation='none', extent=extent)\n ax.set_xlabel('Longitude')\n ax.set_title('Projection %d' % (n+1,))\n if n == 0:\n ax.set_ylabel('Latitude')",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "display_data",
	"png": 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	}
	],
	"prompt_number": 60
	}
	],
	"metadata": {}
	}
	]
	}