working nbviewer.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "source": [
      "## Comparison between extents using SMMR mask and using default Data"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "In looking to move to the F17 pole hole, I found that the daily extents are currently computed after slapping on a SMMR (N07) mask.  (This is the largest mask for SMMR data).\n",
      "\n",
      "This is coded into the `generate_extent_series` function and **only** affects daily csv files. The code chooses to use the SMMR/N07 Mask and mentions that any areas for F13 would be bad, but extents will be fine.\n",
      "\n",
      "What I'm seeing is that there are a number of days where the openwater grid cells creep into the SMMR mask effectively raising the extent value for those days.\n",
      "\n",
      "There are 47 days where this occurs in the NRT data with the max difference of 0.016 M km^2\n"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "    Below is an image showing the problem area for a single day: **2014-08-19**\n",
      "        white = ice\n",
      "        blue = open water\n",
      "        brown = land\n",
      "        light_gray = ssmr mask\n",
      "        dark_gray = ssmi mask\n",
      "        red = open water in the smmr mask"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "        img = np.copy(data)\n",
      "        img[ data < 15 * 2.5 ] = blue\n",
      "        img[ data > 251] = brown\n",
      "        img[ (data > 15 * 2.5 ) & (data <= 250)] = white\n",
      "        img[smmr_mask != 0] = light_gray\n",
      "        img[ssmi_mask != 0 ] = dark_gray\n",
      "        img[(smmr_mask != 0) & (data < (15 * 2.5))] = red\n",
      "        \n",
      "        imshow(img, cmap=categories, norm=norm)\n",
      "\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.image.AxesImage at 0x1122f4f50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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416zXYd3WWh2I4uC9rUkwcFw868ZoM+CHP1THVTPGcOedd1a4l47I6iqs7Y/I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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1124ac550>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "import os"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.rcParams['figure.figsize'] = (8.0, 8.0)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "base = '/Users/savoie/projects/sii_migration/pm488'\n",
      "smmr_mask = 'baseline'\n",
      "data_only = 'builtinmask'"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "current_file = os.path.join(base, smmr_mask, 'NH_seaice_extent_nrt.csv')\n",
      "correct_file = os.path.join(base, data_only, 'NH_seaice_extent_nrt.csv')"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "current_data = pd.read_csv(current_file, header = None, skiprows=2, names=[\"year\", \"mm\", \"dd\", \"extent\", \"missing\", \"source\"])\n",
      "correct_data = pd.read_csv(correct_file, header = None, skiprows=2, names=[\"year\", \"mm\", \"dd\", \"extent\", \"missing\", \"source\"])"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "source": [
      "You can see that there are a number of days where this happens in 2014, but I have looked and there are also days in the final data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot((current_data.extent - correct_data.extent))\n",
      "plt.title('extent differences with and without N07 Mask  (in Mkm^2)')\n",
      "plt.show\n",
      "print(min(current_data.extent - correct_data.extent), max(current_data.extent - correct_data.extent))"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(0.0, 0.016000000000000902)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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WuF1m2bJlSEpKQkpKCioqKhzfX7JkCQwGA8aNG+d2vbVr1yIsLAzHjh1TuPlE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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112304f50>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "        print len(current_data[current_data.values != correct_data.values])\n",
      "        print correct_data[current_data.values != correct_data.values][0:5]"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "47\n",
        "     year  mm  dd  extent  missing  \\\n",
        "229  2014   8  18   5.892        0   \n",
        "230  2014   8  19   5.902        0   \n",
        "231  2014   8  20   5.818        0   \n",
        "232  2014   8  21   5.744        0   \n",
        "233  2014   8  22   5.773        0   \n",
        "\n",
        "                                                source  \n",
        "229   ftp://sidads.colorado.edu/pub/DATASETS/nsidc0...  \n",
        "230   ftp://sidads.colorado.edu/pub/DATASETS/nsidc0...  \n",
        "231   ftp://sidads.colorado.edu/pub/DATASETS/nsidc0...  \n",
        "232   ftp://sidads.colorado.edu/pub/DATASETS/nsidc0...  \n",
        "233   ftp://sidads.colorado.edu/pub/DATASETS/nsidc0...  \n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "    sample_filename = '/projects/DATASETS/nsidc0081_nrt_nasateam_seaice/north/nt_20140819_f17_nrt_n.bin'\n",
      "    smmr_mask_file = '/projects/NRTSI-G/30yr/dev/nrtsig/ancillary/gsfc_pole_hole.N07'\n",
      "    ssmi_mask_file = '/projects/NRTSI-G/30yr/dev/nrtsig/ancillary/gsfc_pole_hole.N13'\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def read_icefile(filename):\n",
      "    with open(filename, 'rb') as fp:\n",
      "        _ = fp.read(300)\n",
      "        data = np.fromfile(fp, dtype=np.uint8).reshape((448, 304))\n",
      "    return data\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "        black_col = '#000000' # 0\n",
      "        red_col = '#FF0000'   # 1\n",
      "        white_col = '#FFFFFF' # 2\n",
      "        blue_col = '#011892'  # 3\n",
      "        brown_col = '#9d5717' # 4\n",
      "        light_gray_col = '#b7b7b7' #5\n",
      "        dark_gray_col = '#484848' #6\n",
      "#        dark_gray_col = '#00FF48' #6\n",
      "        black = 0\n",
      "        red=1\n",
      "        white=2\n",
      "        blue=3\n",
      "        brown=4\n",
      "        light_gray=5\n",
      "        dark_gray=6\n",
      "\n",
      "        colors = [ black_col, red_col, white_col, blue_col, brown_col, light_gray_col, dark_gray_col]\n",
      "        bounds = range(0, 8)\n",
      "        \n",
      "        categories = mpl.colors.ListedColormap(colors, name='categories')\n",
      "        norm = mpl.colors.BoundaryNorm(bounds, categories.N)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "        data = read_icefile(sample_filename)\n",
      "        smmr_mask = read_icefile(smmr_mask_file)\n",
      "        ssmi_mask = read_icefile(ssmi_mask_file)\n",
      "\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "outputs": []
    }
   ]
  }
 ]
}