RutgerK/Presample_test

## Presample_test
{
 "metadata": {
  "name": "Pyresample_test"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pyresample import kd_tree, geometry",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "np.__version__",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": "'1.7.0'"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',{'a': '6378144.0', 'b': '6356759.0','lat_0': '50.00', 'lat_ts': '50.00','lon_0': '8.00', 'proj': 'stere'},800, 800,[-1370912.72, -909968.64,1029087.28, 1490031.36])\n\ndata = np.fromfunction(lambda y, x: y*x, (50, 10))\nlons = np.fromfunction(lambda y, x: 3 + x, (50, 10))\nlats = np.fromfunction(lambda y, x: 75 - y, (50, 10))\n\nswath_def = geometry.SwathDefinition(lons=lons, lats=lats)\nresult = kd_tree.resample_nearest(swath_def, data,area_def, radius_of_influence=50000, epsilon=0.5)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.imshow(result)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "<matplotlib.image.AxesImage at 0x4117550>"
      },
      {
       "output_type": "display_data",
       "png": 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padm+pOVr//q790hC2cjQooA13sA6rwRPbwj9HXiZcxskLzPFGQAMHQDDS9Vc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\nIAiCGREDQRAAEQNBEMz8f8A3JboLDlX/AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "result.min(),result.max()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "(0.0, 297.0)"
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "Pyresample_test"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "from pyresample import kd_tree, geometry",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "np.__version__",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "pyout",
	"prompt_number": 2,
	"text": "'1.7.0'"
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',{'a': '6378144.0', 'b': '6356759.0','lat_0': '50.00', 'lat_ts': '50.00','lon_0': '8.00', 'proj': 'stere'},800, 800,[-1370912.72, -909968.64,1029087.28, 1490031.36])\n\ndata = np.fromfunction(lambda y, x: y*x, (50, 10))\nlons = np.fromfunction(lambda y, x: 3 + x, (50, 10))\nlats = np.fromfunction(lambda y, x: 75 - y, (50, 10))\n\nswath_def = geometry.SwathDefinition(lons=lons, lats=lats)\nresult = kd_tree.resample_nearest(swath_def, data,area_def, radius_of_influence=50000, epsilon=0.5)",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "plt.imshow(result)",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "pyout",
	"prompt_number": 4,
	"text": "<matplotlib.image.AxesImage at 0x4117550>"
	},
	{
	"output_type": "display_data",
	"png": 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padm+pOVr//q790hC2cjQooA13sA6rwRPbwj9HXiZcxskLzPFGQAMHQDDS9Vc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\nIAiCGREDQRAAEQNBEMz8f8A3JboLDlX/AAAAAElFTkSuQmCC\n"
	}
	],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "result.min(),result.max()",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "pyout",
	"prompt_number": 5,
	"text": "(0.0, 297.0)"
	}
	],
	"prompt_number": 5
	}
	],
	"metadata": {}
	}
	]
	}