PM-531-test-max-ice-composition.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:70ff9dc95791ff376675f94d4312d6c0713bbd45fe95894c19c5f40508be2cb1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "source": [
      "# Visualization of monthly maximum observed ice compared with proposed LOCI-mask\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import os\n",
      "pylab.rcParams['figure.figsize'] = (8.0, 8.0)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ddir = '/Users/savoie/projects/maximum_observed_seaice/source/'\n",
      "month = 1\n",
      "data_fn = os.path.join(ddir, \"maximum_observed_ice_{month:02}.uint16.bin\")\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# mask info\n",
      "the_clim_dir = '/sharehome/savoie/projects/nsidc0081/ancillary/'\n",
      "clim_fn = os.path.join(the_clim_dir,\"icelakeclim.nsidc0622.N{month:02}_v1.byte\")"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for m in range(1,13):\n",
      "    data = np.fromfile(data_fn.format(month=m), dtype=np.uint16).reshape((448, 304))\n",
      "    mask = np.fromfile(clim_fn.format(month=m), dtype=np.uint8).reshape((448, 304))\n",
      "    ice_not_mask = (data > 0) & (mask == 0)\n",
      "    showdata = np.copy(data) * 0.\n",
      "    showdata[ice_not_mask] = data[ice_not_mask]\n",
      "    plt.figure()\n",
      "    imshow(showdata > 0 , cmap=\"Blues\")\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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k95mNi+CbL2nJh5lvr8xVdefW+mZTVVJxLZ/4H1sRWjqXb5JNCCHAzFwE1tTF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\nKLCEEDIQ/x/LD5vPeYx2SQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111632b90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Sdm7LSDDxNp+vt5a9C8FnMHjrOAa3CCGk925a3hdqmPD6/NKmgJE9+ntLMnav\n8tkKvprKHuX9I31yIWhm7frrlPyuzBwghHh6cxH4R+00DsZPHkhlp21SybwP1Pyq5mrwFqdZyd7H\nG90Adj6f2mWVZnZeW6t/TXElhHh6s2CbigXyoFNzUGtWkH1q18FMI27bbpvMP1s38tsLrb+m/4Xg\nXxNCiNHr2G7vGshr+lNz7TzYVU53isUEceJrwzqyXrA6mVgbm3vHIgRCyG4ymmYvplcmYNZ/wAJK\nJnJxGGGJ2KegLSlAVb23PFzfC8HWSAghXenNRVA3MDBF9vezR/OYyxrxY2aMun093ndqPRGA2Sot\nQgjpSm8Ca6lX/rE/nyxbX25aZ1DGfNpo0daV0kZ/KoNVhJBV0GserE9/MrG1nqy+rWAX8v4C9f0B\nmoSU4koIWQW9dtNKLQnzqa4+8T92qKob511znYLLIM8GIISQtnQNcvUmsNfd8d5skiuAaQZB9MHG\nRixd1kxRJYSsilEJrE/UB/JqqyYLtmt3quRjxdygFyGE1DGafrAlYgS/1Ilq3tDAUuaA7zdLCCGb\noudmLzrz+J+3L5zNImiTE+uLBTx0DxBCNslgKrliXmzdupp8sHEsjAXRfLUYIYQsymgqubwV6nu3\nzvOvNomrF1KbjMCcVkJIX/Rmwb78tVN7ndX61zVdaXleiGBmoCEhhKyCUQW54sDBkv+0TbmrYc1h\n2N2KEDIEerVgrdtVnOhaKhBoW1hAlwAhZF2MxoLNZ1il0Su+ostPEDDqLFqKKyFkaPQW5KoPVs0O\nLyztk7+nuBJChkevU2WBPPrvp8ECqGk/eFg8nuJKCBkaPbYrzANZQN5dC0BRXFNLwTR1oEuTbUII\n2RS9CWwsebXcVT/BIPpavZXqJyKwBJYQMkR6DHLtZyWxcby2ia3Hl9bGsdmEEDI0ek3T8sUA9tO3\nKowugmoMd9rfGnJTYAkhm2A07Qr/8pXZ66aR2hr3LwayWKlFCNkko8mDjZ20SuI6zw1AcSWEDJlB\njIwxSmsxd8C8PrCEELJuRmPB2ohsq+Kqs1zNDcBMAULI2OhNYPOUqyqAdXJ8VBy3HfcnhJAx0KuL\nIA44BOqbvfjZXYQQ0gejabh9eqo4c77KfY3i6Rtvl8pjCSFkDPQ+9NB8q+cuHkytWHMPsM8AIWTM\n9N7sBcA0Pcv6w5p7wFK3OG6bEDJGeg1yWTDLrNiYTWDblhkjQwghfdGri8BPfgWax8P0EIsjhJCl\n6LFd4WFmzuqwAAAgAElEQVTWatAmF/iZXCa4DHQRQsZIr81erGtWbO5Ses1KLkJI34ymkgvAZAKs\nvVa3ffY1/bCEkLHR+1TZthNj3bHMKiCE9MJoLFibKttVXF+6dMiAFyFkFPTarnCRY6ogGANehJDh\n03slVxfMgmVlFyFkDAxiqmxbOEGWkM0SG+OTbvRYyRXft0vDYjYBIZuDvZiXo/eJBl4su2QUMJuA\nELJpRpNFYOO5PXUjY0oiWvUtWNvyCCFkaXr1wc6zVkvTDdq6EgghpG96cxFcf+f7WrkDLGPAp2aJ\nyNQXyxJaQsimGI2LwPca8AIZ31sLQ2vIbYK7t1eNkaEDnhAyVHrPg/XiaW6D0sDDKKT+OEIIGSK9\nC6zx0qXDaavCumBXhEEuQsiQ6c0He90d751apuZL9c23bbs7ZvIzpWbZBFpCCNkEo/HBAnlllrUu\nTL7Z2X0BZK4Cy5tl4QEhZIgMwkVgwSrrsGWUAl5nLxzgzPn9TFRZ3UUIGSK9uggMS7Wy4gMb1W0u\ngDoB9e4BjvcmhKybri6CXsd2+1lbvhVh8rcmv2sJ/8uB470JIUNjED5Yy2/1QS77/Mz5fZy7eIBr\nl4/miidzYgkhQ6LXSq5qsixmRNVbrn4oon1eB6u6CCHrZOUuAhG5DcAvA/ibABTAf1TVQxE5A+DX\nAXwzgBcA/KCqfnVyzIMAfhTAXwM4UNXH4nljUGvelAIT3muXLUULM35Z5sUSQoZEGx/sKwB+UlWf\nEZHXA/hfIvJJAD8C4JOq+rCI3A/gAQAPiMjtAN4D4HYAtwB4XETeqqqn/qSVtZmnXEXMhXDt8lFm\n6Z67eEB3ACFk8Mz1warql1T1mcnrPwfwR6iE810AHpns9giAd09e3wfgUVV9RVVfAPA8gPOlc5uo\nnrt4MPW3xuICE1IfAKvcBrOTDZgTSwgZEp2CXCLyZgB3AjgGcJOqXpl8dAXATZPXbwLwojvsRVSC\nnHH2wv7UQjURVdUsJcsHwWyfyh9b74ulm4AQMhRaB7km7oEnAfycqn5MRL6iqm9wn19T1TMicgTg\nkqr+6mT7hwH8tqr+pttXX/7aaZbrCuSi6bd594Htc+Z8vaXKYBchZB2sJQ9WRF4D4DcA/Iqqfmyy\n+YqIvFFVvyQiNwP48mT7FwDc5g6/dbIt4xu/6QLu/7EfAADcfc+9uPuee6eCWxUbHNm1s23zxspw\nhhAhZFU89eQTeOrJJxY+fq4FK5X5+AiAE1X9Sbf94cm2D4nIAwBuUFULcn0Eld/1FgCPA3iLugtZ\nmlYVvMoF1cphT46Tn9XStSzI5dO3wlpZ0UUIWRtdLdg2AvvdAJ4C8Aeo0rQA4EEAlwF8FMA3YTZN\n66dRpWl9HcBPqOonwjn15a9VSQXmg41ugGip+pJZS9eyuVxeaOkeIISsi5V301LV31PVPVW9Q1Xv\nnPz5HVW9pqrfp6pvVdW3m7hOjvl5VX2Lqn5bFFfDrNFKNA+K02V9Q20LbvnyWn8eO84HzJhRQAjp\nk94qucyCNXeAKqYNtw0f4LJGMHVBr8l5M7dDtIwJIWQZRtMP1voP2NTYKvVqdh8gpWuZuJ4cH04t\n1egOMB9uaSw4IYRskt4E1ooKTFjjo79hghkt7RvvOpgZ6e33ZTYBIaRvep0qa8Lqx8OU/Ka+D4Gv\n5jKrtrQvswkIIX3Te8Nti/r7zAHvGrBUrWitGl6cvRVMcSWErJqVp2mtAwtyWYpVU2qVWbomrr7F\noYkurVZCyCYYWZALrnlL6j8Q3QTmb02pW+XAF1B2MRBCSB8MYuihBbp8BkBsBmOGaTS4zb0Qm8IQ\nQkjf9DaT6+yF/UwMU8ns4dR1cO7iwbQ1oQimfWGr9z7FK1nBrOIihAyF3nywf/FXp9NHfyAVHFig\ny7/3JbJAKi7w5bT2mU08IISQVTMaH+zeXnqcj6O6S+8BC3ilXwh1rwkhZAj0mgfr/aZ+VHep+Qsh\nhIyN3lwEf/lKskYtkOXHwIik3NdSa8LCOZmmRQhZK6NxEZhg+kAWgGk6lnXZ6tJTgOJKCBkSvRYa\n+MqtUuAKSOO5Y0ltOB+tV0LI2hlVJVd0CcRWhfZ5nfjaNoorIWQTjMZFYFgmQalRts8aaCoioLgS\nQoZIbxbs9Xe+DwCmDbJ9SlZdp6y6gYccE0MI2QRrmSq7Dvz47ZgxUBJXf4xR10OWEEKGQG8C68XR\nyl99A5e2x9M9QAgZKr35YE1cfXcsoF1RAcWVEDIGehPYVP662PEUV0LI0Om12Yvv59rFcrXXhBCy\nSUYT5PK+VroFCCHbSG8ugi6NsSmuhJAx0uPImK77U1wJIeOix4kGzQ1cmONKCBk7vfaDrcO7BMxy\n5TBDQsjY6M2CjUSL1Y+HsX6xhBAyJnoX2Jh65btoWQmtKn2whJDx0Vse7HV3vNe/n/xMY7j9Z5Yr\nSwghfTKaPFhg1i2gmpq/+O5YFFdCyBjptR+sH3xY/YEbF8OgFiFk3PRmwV67XO7fyvQsQsi20OtU\n2RL0txJChspoRsaoKs6c359xBVBcCSHbwmBmchFCyLbRYy8CNnAhZKj44aNkcXq1YCmuhAyX09Oy\nG4+0p/dKLkLIcFHVLD/9pUuHWaOmmLNOciiwhBAAyMrUS5/54aQJPoU2QYElZEcpCeq8tM0+0jrH\nTO9ZBISQzWPiWlmm6Y+nCkTz8X8ZBldoQAhZH95qbTsLr26/XcwEGlWzF0LI5vDiauXolV81ZQn4\nUvX4mWcXxXUR6CIgZIcwV4A1Wdrbq9wA5g6wzwBMf9ZBcZ0PXQSE7AB+MohqJZ7XLh9lvljvDqBr\noExXFwEFlpAdwYtoldcq09zW6nNMRZiugTL0wRJCinhRNKG03NZqWyW2da6BXRfXRaAPlpAtx/cV\nMFeBBbLOXUypWqenOn1fB8W1GxRYQrYcEZm6Aey1n9rsKeXCXrt8xHzYBaHAErLl+MwA4+yF/bmF\nBN4lQMN1MSiwhGwxKUsgugqqz+uEM/pbOcppMSiwhGwpeWpWElpzE3TNFKD/tTtM0yJkS7F0rL09\nmZkccu7iAa4+fVgb1LICBJLDPFhCyBRfOFDafub8rBVrFu6Ndx1QZAMUWEJIEe8yqGv44t0DnPA8\ny2imyhJCNkOa4HwAr6cnx4dFC9XEdV4vAjIfVnIRsgNEV0HT9ALbjxOfl4cWLCFbzPwxMLOFBpZd\nQPfA8lBgCdlyvPU6L5eVlutqocASsoV4SzTfdtDoGjA4qns10AdLyJZR5xbIO2iVBdRnGpDloQVL\nyAAxCzRlALSzKOMww9SKULIOWs3noP91VTAPlpCBEtsHWtMVG+3SFLwyot/VxHcerOQqw4bbhGwB\n9qjuxbCySlP1VRuhNCG2ZtptDaoe7K6thC4CQgZIUzTfRLKthWnNtNsEt8hqocASMlDqHtNTOWvz\n8b7fqx0zT5StwXZTty3SHvpgCRk4lfWZi13dRALfitBE1VwKPnDVJKAm7OxFMAubvRCyhcxWXNWn\nWvmR23t7gqtPH2bVWTbYsOn/PoNcZSiwhGw5PhWrDV5wS+/rjrl2mQIbYTctQracruWs8wYbkvVB\ngSVkhDQ9ws8bZtiGrgUOpAwFlpCRUhLZthkGbbC8W7I49MESMlLqfLF1GQZd8H0LODomQR8sITtC\nky/Wb1/UZSAiM1kIpBu0YAkZMeYrjSySOVA6h4dZBbRgCSGITbaPFnIXWEcuZh0sDgWWkBHT5vF/\n0YCXP3epgTeZD10EhIyYpqKDtlVbTUTXwq5XeNFFQMiOUSeeqoob71puOkGpRJe0hwJLyEiZN3ob\nSE277XF/FUUIpD10ERAyYkqdtuqwx/1FMgo8u+wmoIuAkB3BXABdGm/7n4tfd6nDdwoKLCEjxlwA\ni2Jds7pYpMwoaA9dBISMEO9/nTeKOxIHIQLthyF6dtFVQBcBITuAL5O1abN1+3kRTBMPDrI/ixha\nXfy/uwotWEJGiM9/ndfcpVQ2CzSPjenCLlmytGAJ2RF80KrJUCp9tqqWhgAt2SYosIRsOTEH1qzf\nVY7xZmZBGQosISOjrsCgrohAVaflspbWteomLswsKNMosCJynYgci8gzIvKHIvLQZPsZEfmkiDwn\nIo+JyA3umAdF5PMi8jkRefua10/ITtHUZNv6DtRxcny0dFpX89rWctpRMzfIJSKvU9WXReTVAH4P\nwE8A+McAXlLVh0XkfgBvUNUHROR2AB8B8F0AbgHwOIC3quppOCeDXIR0ZN402VIwyweylq3gmsfQ\nJ9Ha358IFg7KrTzIpaovT16+FsBrACiAdwF4ZLL9EQDvnry+D8CjqvqKqr4A4HkA59suhhBST90E\ng6aer3t7eSHBrkT761BVnJ5WTco3MdRxrsCKyJ6IPAPgCoDHVPUygJtU9cpklysAbpq8fhOAF93h\nL6KyZAkhS2K+VE90DZgVWYnq4fQYkfz4tk1fujSHGbIfNvqtUzPx9V63jQV7qqp3ALgVwAUR+Y7w\nuaKyamtPsdwSCSHNfV/zlC3LELAuWi9dOpyxcktiXUeXdK6h+mHrrP91/1J4ddsdVfXPRORTAL4f\nwBUReaOqfklEbgbw5cluXwBwmzvs1sm2GT7w/oemr+++517cfc+93VZOyI5R8q/mFunhZD/g3MW8\nD2xpbleTP7ZUTtt2jWcv7A/OFdH0C8XyeEtrfurJJ/DUk08sfN3GIJeInAPwdVX9qohcD+ATAD4I\n4F4AJ6r6IRF5AMANIch1HinI9RYNF2GQi5D2ROs1TiqwyiwLMNn+8X2Xyi0fsKobrFg6xkQ5Dkzs\nk7q/Px/8qwov5q951UGumwH8dxH5fQCXUflgfxuVyL5NRJ4D8L2T91DVZwF8FMCzAD4O4MejuBJC\nuhEfb7015nNaU+DmYLrfmfP7U2vWtzYsNeBedgZX8vcOR1yBJKB5T4b8s3Wtmb0ICBk4PkBjlldp\n3lZTWlap2XbTNnt9cnzYqRnMkFO1vCXu773LetmLgJAtJIleeh+HGXoLMqZt+dfeSo2flc7XZabX\nkDMJPCaui/iZu0CBJWQE1IlBSsk6ylwJpQ5X6XG4ffpV18qvumj9EJh1h6zPNWBQYAkZKN4aLOWw\neovVUrUqP+thtn9+vtT/te24ma5uxKH5YD3+F88m1kmBJWTAVClEB9lrL56qvgRUcPXpw2kObLQk\nU7DncCLQeY7rLkyc9a6VTbgyWufBkv6xfxzb/p+AJGJ+qxeFk+MjJ7SVlWnvrz492/jFB8tEMJ1s\n4D83q3YMftRF2ITf1UOBHRlMets97FFWxOe6YqY81gRYRLC3J8W8VwvumADHx/9lu20NtdDAs0kX\nBgWWkIETLc8qoIXMEqtEIxdLa/TihddEtySu/nrLrXepw7cK5sGODF+5Q7af09Pqsd37U+P3HxuZ\nmPDGx3+zdK001B6Xu0ykbcOQc2GXpWseLC3YkUFx3R1iJkCcYmB+1MovXx7DHYsP8gBZynON+5HV\nwCwCQgZKHM390qXD6eN79brar8oGkOkvX59BEAU1VmWZz7VrfmwTYyk22AQUWEIGjH9gMZ+q9RUw\n6/WlS4fT5tE+gGWP6jFdK/YgML31KWDLCu2qJs2aWI9VtCmwhAwY/+ieN8/GNFPAfibfanWsVSp5\nkbZUrDi/y/eNNUt52dldqxBZn9s7xvHgDHIRMgIs2HX16cMs6GUWrP1UxXS/lMaFiUglcSrN77L9\nmjIMFqFUttsW79bYVHlrE2z2QsgWsrcnmbiaz9UEMW0zn2w+FiVqUhRQv9/8RtxHte9L2AysRa3P\n+ItgTFBgCRkJSTwl22aBqjxftiyEy/pWfbWXbx7T5riuQluaozU2FwHTtAgZOF5o8p6myPymhjV7\nufGug6zQwHyrtj2dH5mP1x9TXg9cpVgag91G/MxStvXMK/2etbTnXmJQ0AdLyIAppVbFevqzFw6y\nxtjm8zS/rb02n6zPQjCB9D5as5CbtMGu4fdpM1bGE0fdtL3vMflgKbCEDJSSyBjmb7UULRMdswpN\ntGJvARNXHwSLkxHaEKu12s7tqjtXidJ6lgmYrQIGuQjZIpr6BfhWhgCm/lDvq6wE6TBLvbJHfNvu\nA2flJt2pf6o1+F5l1VcKxuV/SowtVYsCS8hAMUGb56MsPTqnPrGpu1Z8pPddt0yYSy0OU+FByl6w\n4+J6N2Fdjklk6SIgZASk+Vnl4FMurkl0vX/Wn8cHqeI1Sudu4/u04xd1FXTF3CSbdBnQRUDIiDFf\nZkxn8pZjnVXrexX486UihLwHgffvzpuj1SawZGvclOAtkvq1aWjBEjIA6loONglbfFQuVWd5a7bJ\nsozH+u1dIvdNgbl1YWlqbWeMLQOzCAgZEWmeVm5FthG0LpH7OgFt2r+NyJfow0dq97fuLAO6CAgZ\nCd7a8y0H15HnuYi4LrqWJjfGurD7G1oAjAJLSE/ECqy+SVkLyyfzxy5em2RI1V4UWEJ6pO/EeWBW\nWMfWUGXIUGAJ6ZlF9CwGxRa/9nLugPnn3s7ZXG1hkIuQnrAUKisA2GSkfpkgVlf68Iuu68mAQw8J\nGQHRAu0qcouIa2wSsylXgIndpkR2SL5tCiwhGyS2HtyUyPXdiWpTl+37PiN0ERCyZkqNo4HFxKCr\ne2CTroB5rNtVsAlxZaEBIQNiniCaGLSpqW9zLu8CiNcYAusU2dhCcR3QB0vIQGhjbabP2tX6lwQ0\n7jNEVpX1UEebv5s+oAVLyIqJftZ5DM1vuGo21Z9gEznFtGAJ6ZFFxWRbxdXYhCE3pAoug4UGhKyI\nPjpJjYFNFRwMceosBZaQJbGuVotarkP0Ha6aTRnoQ2v2QhcBIUuwjNW67b7XvhjSAwQtWEKWwERy\n0Udgiut6GIq7gAJLyJL02ZpvLGx6lEz1VLGRyzVCFwEhc/BNWeo+75rjuSu+V49I+b7rBjkug7ls\nzl7Y77WjF/NgCWnA+1hLeZZjLl3tg7r+C+sKTq26uot5sISsGD+O5Mz5/WlZaxdx3XVhNeru3cbM\nRNZh3W4SCiwhNZQe/W1+lg0b7DrritRT+vsRAa5dPsqENroZhizCFFhCCsyzThed0Eq6Y0FEE9pz\nFw+CGA/X3cgsAkICTeK6SDTcgmRkOURk4gc/nNle+k6G8IuNAkuIo41fdbEZWkssastIlW+LPdbX\nuRLa7rtJKLCETKgTV28hzUvZiviJrSShqtPMgWWLAtbdCnEZKLCEOEqWa3zEPz3t9sjPUdiJKIaV\nyC5XFGAzuPwvwiG4BwAKLCGt8MI7pKF6Y8U/AVSZGctZseab9b/HhvBLjQJLCLo9ZjJotTj2y6n0\n97dssYEXVPYiIGRgdEm9Ys/XxdnbKz8BrPrJYAitC5kHSwhSICv+h7T/9H3/R9026iq3lnmsLxeG\nLHy6lUCBJWRC6f823QHrY5U+0qFOk6CLgBA0+2CtQUmfXZlIPUMVV4DdtAhp/R9URBYaCbPKbk6k\nTHNhyOos5a7dtCiwhGC9ARGrpacFPH66CixdBGTn2YSflXmzuwkFlhAsnnbV1jc7hKR3snkosIQs\nQRvrdyhJ72TzUGAJWZI2wTG6CHYTCizZaSyDoGsa1iL7k92DAktGQ+wjuopHb7Muuwa6WIBA2kCB\nJaMhiWGyPJdNrzKhXOQxvm1gjD7Y3YUCS0aFf9I2gUuCu5iIWbu8dVqkAywyIhuAhQZk0NRNdo1V\nVfa+6gta7xutO9+6YUXXdtC10IDNXkhvpABTVeVU1w+gbspA6b3f7Me72GvztxKyCegiIL1hfs/T\nUwteHUwf1/2fLueLY0L84TaeZFVrZ+krmQddBKR3VtkHwLsILOvAUqRW/W+9S/MXugi2A7oIyKhY\nJrhUaoZt1nC8xjqgq4HMgy4C0juLCFVptpM9ti/iXiBkHVBgSW/EoJYJZBv/plm+JqJNgktIX1Bg\nSW/E5H4vmiaU5rssCWW0UC1H1vtgWW1F+oQCS3olluindKs8xzXmt0br1FvDtm+pOqvOqqW1S9YB\nswhI71gWgX/Mt58nx4fTaH0c61IXxbcJAlefTi4DHwxrOm5d/x+YRbAdcKIBGR1mcaoqbryrEtcb\n7zqY5rT6HFkgD2aVLE9Vxelpcjf4fgN2nOGPt/Ndu3xUdEvQyiVdoQVLBoO3ZAHg5PiwdhihWZsm\niPNyab11POu7zS3XvT1L/5q/bxusAIItC8cPLVgyWkx/vNV5cnxYazWm8tr96bHRykxBskOI1KeE\n+WPM+i1111pmtAzZPVhoQAaFLx6oK22NKVmqSUhVUcwcsJ4Hfh+7jrkmTo6PpqJedx5CukCBJYOh\nlMtaekQ336y5CEwoKyu0vsDg6tOHrrlMbp16n62J+qrcZ2Zl03+7e9BFQAaF+T/rMAFMY16qR//o\ng/UBLfOlmnCb0JZGxVy7fDS9/qoEkTO5dhcGucggqQtaxf4DsZGLCbQqphkJqpgRXyClbu3tyUR0\n9zNf6cnx4Uxfg0Vgitb2wCAX2QrqYkL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       "text": [
        "<matplotlib.figure.Figure at 0x11146a150>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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yLTivwvOKPacHoh2LRZi1zUXQF7d8UYv7vfpx3XfLghldAdU5qmPJaiWEmEan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\n6AIIXtlyQ5YokF7IwNVj/tiPWRU9NBc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       "text": [
        "<matplotlib.figure.Figure at 0x117ad4c50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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QYkj0OLbbJ7q2p8Fy0CtmEtS2c4tVPVqFEEOi13aF/Fgf4fHdszpqxXaFQggx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\na0ICK4QQa0ICK4QQa+L/A7dKk/OuEEZsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113175f10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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LQINbIuk8Vp+mRbcAIWRJzO4ioGVKCNlVsqvk6lMeSwghS2D2sd0etWiLH7Yc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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115f55c90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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m75f0V1JKj5rZ2yX9tKRvlHSvpE9K+pqU0kU450ofbFxX38BVqUVh7p8FANiU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\n2PkKYYUIAgt7JRerKRcaxBJ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w90r63fD+RdVfyhxIkj5pZr9mZn9vse2elNLtxevbku7Z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\n600AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115f67710>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11144d090>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ErgdTZQEgCDO5ACAIAQsAQQhYAAhCwAJAEAIWAIIQsAAQhIAFgCAELAAE+X+F\nvMwscmSrdAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1148f0790>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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rtGnk8XlPLNBnjGAL8TCNo704css30Z6m1AjOa8Nxb9rYvpVvRjNu56t8tj0/\noaN/9PfVXPlztvXE5sdJmQB9Q8AWku/dWv2/KSFMW0CwCs0fheHrmjOm7o5MSnlXxPACi2bhQtsm\nKR60bafXaUK+H9sXApNQIihodOOSNLQMdJXyEJvl6/gx39um8omxWCpoC1eXr+aKJYXYkuVLa4E+\nImCXrEuj1vxY4qKHccFYXTfc0RD7WPNJs7ZeXh/1+hLhaHgfAg2Fd6laNFAKJYKCZukWWEVHwbiP\n4Pl9jvb8uyOvzSezfILPv4WPYr3ly0O1qv+On4QDuo6VXEswrYF/lpVMi94rYJ5FBXHUGY8n1kzj\nhjb+/L65S76LVqxNx9fu+xFYfQrvWx/Y692pvLHe5l3JRYmgsFl6S2f5I7foRQazPN2ksyXktVfv\nDvBAbU7s2Jw+58bls4Og9RVb/lySBrd7MPflVN7AOJQIChhtsj/8aL1UqMxammhbQRZLALm4eMC/\nh3ch+OlhxvW4xg6LfDGCP+ciNqUBloURbCE+oltECabUTk6TnnZasMc2LA9D5yPW4ztNr6+PZo/v\n7A7KDb45Ttv3zhcj5N8X6ANGsAsWJ5COGq6r+kg8S803jmKbzbRt8PWtD4yOQKXmD088i4Okocmt\nWGKoyg7DdVvattAXTHIt2CJ6XRe5LeE0bRtvj/u++X09CPNtCv2++b62PoLNv0/ccDxuwi1VexzE\nwOXMAlglJrlWaJb2p1lHpcsKED9l9izfN94U66F5uPqm4b7JuI9UPXB9tNv2PeOGMPHx+d4HQB8Q\nsAsyz4RW1wLisMfjI8q4JWE8Bbk0fI4t31Fr2h+YfCIsjlzjajIWHKDrCNgFaMKl6ffcBE3oNX9c\n8vJI/PgfHzfLc0vDrVuShtq/WNWFrmOSa0F80uYwp13pMx/BXr+0P3jd8fxjzSKFw7VX5fvrxk8I\nRzkDA7AMBOwCNFv9+deT7re+jfJed803cMlrqPOKE1v5puUesnQXoIsI2AWIEzHjfsk3YfY7ntBQ\nUusE1WH5z80n5eKeBk3P8ZG/DbBQBOyCeIj4L3ubvodrvvor35zbb8vvU+I42gJ3nT8doJ8I2AIm\nnW677/K/EfEjeiwRLPMMsfnZFICuYKHBgkyacFm3HaDGLTjId8eKiwj8/+v0c8DmYaHBimzK4Knt\nlNr5xtneQhVrscAmokSAueTbFErD2woe39kb6iLYpJY1IEfALsCkJbLrVH+Vxm8ZmG/eks/0A5uI\ngF2A/EzT6rDNAAAH5UlEQVSrPrnjIbMuky9xXwEXX288iaHUrLryPQiATUMNdkHyTVOk/rdl5dpC\nVGr2DvDrfcTupdf8zATApmAEu0CxXWidygKRn3HANfvCaqh7YPgxRp8qNhIBW8i6jV5d25li204B\nE9u3fC8CRrHYNPTBYm75rlnxTLD5ubry3lgmvdBn8/bBErA4lLaFFfnpx+MervFMBus6usf6I2BR\n3LjNxfNA9ev8MRIjWPTbvAFLDRZzG1d39eCNexH4vrDNfZZ5pMBqEbA4lHEjUQ/T2LaV386+rdgU\nBCwObVLITtoHllEsNgUBiyOZNpLNrXOPMJBjkgsLEU9E6GKY+okL6SJAnzHJhZUws9bRrK9s8+W0\nx3f2qMFiYzCCxcK0tW/lo1XatdBnbLiNlcr/YOdhyxkNsEkoEWBhJm2wHeux3icLrDsCFgs1KTe9\nHut7FgDrjhosFq6to0Aa3jf22kW6CdA/dBFg5cZ1FPjG222bdgPriIBFMfkA1csDtz6w17qXAbBu\nZgpYM3uTmX3TzH6n/nrLzJ4xs++Z2dNmdku47yNm9n0ze8HM3lvqwNE/zRLa9d2QHIhmHcF+UtLz\nkrxw+rCkZ1JK75L01fprmdm9kj4s6V5JD0r6nJkxSsZQhwFLZbEppoafmd0l6e9L+hVJPuz4gKRz\n9eVzkj5YX35I0pMppddTSlckvShpe5EHjH6Kp/tm9IpNMcvo8t9J+ueSDsJ1J1NKV+vLVyWdrC/f\nIemlcL+XJN151IPE+mD0ik0ycSWXmf0DSa+mlL5pZmfa7pNSSmY2qeeq9bbHH3t0cPnU6TM6dbr1\n6dFTvmxWGl5kwOgVfXLh/LO6cP7ZQz9+Yh+smf0bST8v6Q1Jb5H0VyX9tqSfkXQmpfSKmd0u6Wsp\npb9lZg9LUkrpifrx/13SL6eULmXPSx/sBpi0JwHQRwvtg00pfTqldHdK6e2SPiLpf6SUfl7SlyR9\ntL7bRyV9sb78JUkfMbM3m9nbJb1T0uV5XwTWg6/cIlyxqebd7MWHJE9IesrMPi7piqQPSVJK6Xkz\ne0pVx8Ebkj6RVrFUDAA6gKWyADAjlsoCQEcQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQ\nCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAEL\nAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQsABQCAELAIUQ\nsABQCAELAIUQsABQCAELAIUQsABQCAELAIWsLGAvnH92Vd96YdbhNUi8jq7hdXTLUV4HAXsE6/Aa\nJF5H1/A6uqWXAQsA646ABYBCLKW0/G9qtvxvCgALkFKyWe+7koAFgE1AiQAACiFgAaCQpQesmT1o\nZi+Y2ffN7FPL/v5HYWZXzOxbZvZNM7tcX7dlZs+Y2ffM7Gkzu2XVx5kzs181s6tm9u1w3djjNrNH\n6vfnBTN772qOetiY1/Comb1Uvx/fNLP3h9s69xokyczuNrOvmdkfmtl3zGyvvr5v78e419Gr98TM\n3mJml8zsufp1PFpfv5j3I6W0tP8kvUnSi5LukXSzpOck/eQyj+GIx/+/JW1l131W0r+oL39K0hOr\nPs6W436PpHdL+va045Z0b/2+3Fy/Ty9KOtbR1/DLkv5Zy307+RrqY7tN0n315bdJ+iNJP9nD92Pc\n6+jje/IT9f9vknRR0s6i3o9lj2C3Jb2YUrqSUnpd0q9LemjJx3BU+QziBySdqy+fk/TB5R7OdCml\n35P0p9nV4477IUlPppReTyldUfUPaHsZxznJmNcgjb4fUkdfgySllF5JKT1XX/6RpO9KulP9ez/G\nvQ6pf+/JX9QX36wqOJMW9H4sO2DvlPSD8PVLat6UPkiSvmJm3zCzf1RfdzKldLW+fFXSydUc2tzG\nHfcdqt4X1/X3aNfM/sDMPh8+xvXiNZjZPapG5ZfU4/cjvI6L9VW9ek/M7JiZPafq5/50SumyFvR+\nLDtg+94T9rdTSu+W9H5J/9jM3hNvTNVniN69xhmOu6uv6T9Keruk+yT9iaR/O+G+nXoNZvY2Sb8l\n6ZMppT+Pt/Xp/ahfx2+qeh0/Ug/fk5TSQUrpPkl3Sdoxs5/Kbj/0+7HsgH1Z0t3h67s1/Neg01JK\nf1L//zVJ/1XVR4OrZnabJJnZ7ZJeXd0RzmXccefv0V31dZ2TUno11ST9ipqPap1+DWZ2s6pw/bWU\n0hfrq3v3foTX8V/8dfT1PZGklNKfSfqapPdpQe/HsgP2G5LeaWb3mNmbJX1Y0peWfAyHYmY/YWZ/\npb78lyW9V9K3VR3/R+u7fVTSF9ufoXPGHfeXJH3EzN5sZm+X9E5Jl1dwfFPV//DdP1T1fkgdfg1m\nZpI+L+n5lNK/Dzf16v0Y9zr69p6Y2QkvY5jZWyX9PVX15MW8HyuYsXu/qhnHFyU9ssrZwzmP++2q\nZg+fk/QdP3ZJW5K+Iul7kp6WdMuqj7Xl2J+U9MeS/q+qGvgvTDpuSZ+u358XJL1v1cc/5jV8TNJ/\nlvQtSX9Q/wKc7PJrqI/rZyUd1P+Ovln/92AP34+21/H+vr0nkn5a0u/Xx/ttSb9YX7+Q94OlsgBQ\nCCu5AKAQAhYACiFgAaAQAhYACiFgAaAQAhYACiFgAaAQAhYACvn/A0806sMm3yIAAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1116552d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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AoSPA4tzaTQrLe5F5YIzNXlw87l/HIBpXYBFcMTYEWGxF25QlD7LN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\nkQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11313bbd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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AgJFAYAEARgKBBQAYCQQWAGAkEFgAgJFAYAEARgKBBQAYCQQWAGAkEFgAgJFA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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d540e90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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0EJEXIRPXXw0hfGr18lURuTWE8A0ReQWAb65e/zqA283ur1q9VuD5P7oCAPju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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114946050>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.unique(showdata)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "array([  0.00000000e+00,   1.00000000e+00,   2.00000000e+00,\n",
        "         3.00000000e+00,   4.00000000e+00,   5.00000000e+00,\n",
        "         6.00000000e+00,   7.00000000e+00,   8.00000000e+00,\n",
        "         9.00000000e+00,   1.00000000e+01,   1.10000000e+01,\n",
        "         1.20000000e+01,   1.30000000e+01,   1.40000000e+01,\n",
        "         1.50000000e+01,   1.60000000e+01,   1.70000000e+01,\n",
        "         1.80000000e+01,   1.90000000e+01,   2.00000000e+01,\n",
        "         2.10000000e+01,   2.20000000e+01,   2.30000000e+01,\n",
        "         2.40000000e+01,   2.50000000e+01,   2.60000000e+01,\n",
        "         2.70000000e+01,   2.80000000e+01,   2.90000000e+01,\n",
        "         3.00000000e+01,   3.10000000e+01,   3.20000000e+01,\n",
        "         3.30000000e+01,   3.40000000e+01,   3.50000000e+01,\n",
        "         3.60000000e+01,   3.70000000e+01,   3.80000000e+01,\n",
        "         3.90000000e+01,   4.00000000e+01,   4.10000000e+01,\n",
        "         4.20000000e+01,   4.30000000e+01,   4.40000000e+01,\n",
        "         4.50000000e+01,   4.60000000e+01,   4.70000000e+01,\n",
        "         4.80000000e+01,   4.90000000e+01,   5.00000000e+01,\n",
        "         5.10000000e+01,   5.20000000e+01,   5.30000000e+01,\n",
        "         5.40000000e+01,   5.50000000e+01,   5.60000000e+01,\n",
        "         5.70000000e+01,   5.80000000e+01,   5.90000000e+01,\n",
        "         6.00000000e+01,   6.10000000e+01,   6.20000000e+01,\n",
        "         6.30000000e+01,   6.40000000e+01,   6.50000000e+01,\n",
        "         6.60000000e+01,   6.70000000e+01,   6.80000000e+01,\n",
        "         6.90000000e+01,   7.00000000e+01,   7.10000000e+01,\n",
        "         7.20000000e+01,   7.30000000e+01,   7.40000000e+01,\n",
        "         7.50000000e+01,   7.60000000e+01,   7.80000000e+01,\n",
        "         7.90000000e+01,   8.00000000e+01,   8.10000000e+01,\n",
        "         8.20000000e+01,   8.30000000e+01,   8.40000000e+01,\n",
        "         8.50000000e+01,   8.60000000e+01,   8.70000000e+01,\n",
        "         8.80000000e+01,   8.90000000e+01,   9.00000000e+01,\n",
        "         9.10000000e+01,   9.20000000e+01,   9.30000000e+01,\n",
        "         9.40000000e+01,   9.50000000e+01,   9.60000000e+01,\n",
        "         9.80000000e+01,   9.90000000e+01,   1.00000000e+02,\n",
        "         1.01000000e+02,   1.02000000e+02,   1.03000000e+02,\n",
        "         1.04000000e+02,   1.05000000e+02,   1.06000000e+02,\n",
        "         1.07000000e+02,   1.08000000e+02,   1.09000000e+02,\n",
        "         1.10000000e+02,   1.11000000e+02,   1.12000000e+02,\n",
        "         1.13000000e+02,   1.14000000e+02,   1.15000000e+02,\n",
        "         1.16000000e+02,   1.17000000e+02,   1.18000000e+02,\n",
        "         1.19000000e+02,   1.20000000e+02,   1.21000000e+02,\n",
        "         1.22000000e+02,   1.23000000e+02,   1.24000000e+02,\n",
        "         1.25000000e+02,   1.26000000e+02,   1.28000000e+02,\n",
        "         1.29000000e+02,   1.30000000e+02,   1.31000000e+02,\n",
        "         1.32000000e+02,   1.33000000e+02,   1.34000000e+02,\n",
        "         1.35000000e+02,   1.36000000e+02,   1.37000000e+02,\n",
        "         1.38000000e+02,   1.39000000e+02,   1.40000000e+02,\n",
        "         1.41000000e+02,   1.43000000e+02,   1.44000000e+02,\n",
        "         1.45000000e+02,   1.46000000e+02,   1.47000000e+02,\n",
        "         1.48000000e+02,   1.49000000e+02,   1.50000000e+02,\n",
        "         1.52000000e+02,   1.54000000e+02,   1.57000000e+02,\n",
        "         1.58000000e+02,   1.61000000e+02,   1.62000000e+02,\n",
        "         1.63000000e+02,   1.65000000e+02,   1.66000000e+02,\n",
        "         1.68000000e+02,   1.72000000e+02,   1.73000000e+02,\n",
        "         1.74000000e+02,   1.75000000e+02,   1.76000000e+02,\n",
        "         1.78000000e+02,   1.79000000e+02,   1.80000000e+02,\n",
        "         1.81000000e+02,   1.82000000e+02,   1.85000000e+02,\n",
        "         1.86000000e+02,   1.87000000e+02,   1.88000000e+02,\n",
        "         1.90000000e+02,   1.92000000e+02,   1.94000000e+02,\n",
        "         1.95000000e+02,   1.98000000e+02,   1.99000000e+02,\n",
        "         2.00000000e+02,   2.03000000e+02,   2.05000000e+02,\n",
        "         2.06000000e+02,   2.08000000e+02,   2.09000000e+02,\n",
        "         2.10000000e+02,   2.12000000e+02,   2.13000000e+02,\n",
        "         2.18000000e+02,   2.21000000e+02,   2.23000000e+02,\n",
        "         2.24000000e+02,   2.25000000e+02,   2.26000000e+02,\n",
        "         2.29000000e+02,   2.32000000e+02,   2.33000000e+02,\n",
        "         2.34000000e+02,   2.35000000e+02,   2.36000000e+02,\n",
        "         2.37000000e+02,   2.38000000e+02,   2.39000000e+02,\n",
        "         2.41000000e+02,   2.43000000e+02,   2.44000000e+02,\n",
        "         2.45000000e+02,   2.46000000e+02,   2.47000000e+02,\n",
        "         2.48000000e+02,   2.50000000e+02,   2.53000000e+02,\n",
        "         2.54000000e+02,   2.55000000e+02,   2.56000000e+02,\n",
        "         2.57000000e+02,   2.59000000e+02,   2.60000000e+02,\n",
        "         2.61000000e+02,   2.63000000e+02,   2.64000000e+02,\n",
        "         2.66000000e+02,   2.67000000e+02,   2.69000000e+02,\n",
        "         2.70000000e+02,   2.71000000e+02,   2.72000000e+02,\n",
        "         2.73000000e+02,   2.76000000e+02,   2.77000000e+02,\n",
        "         2.78000000e+02,   2.82000000e+02,   2.83000000e+02,\n",
        "         2.84000000e+02,   2.85000000e+02,   2.86000000e+02,\n",
        "         2.87000000e+02,   2.88000000e+02,   2.89000000e+02,\n",
        "         2.90000000e+02,   2.91000000e+02,   2.93000000e+02,\n",
        "         2.94000000e+02,   2.95000000e+02,   2.96000000e+02,\n",
        "         2.97000000e+02,   3.02000000e+02,   3.03000000e+02,\n",
        "         3.05000000e+02,   3.08000000e+02,   3.10000000e+02,\n",
        "         3.11000000e+02,   3.12000000e+02,   3.15000000e+02,\n",
        "         3.16000000e+02,   3.17000000e+02,   3.18000000e+02,\n",
        "         3.19000000e+02,   3.20000000e+02,   3.21000000e+02,\n",
        "         3.22000000e+02,   3.24000000e+02,   3.25000000e+02,\n",
        "         3.27000000e+02,   3.28000000e+02,   3.30000000e+02,\n",
        "         3.31000000e+02,   3.32000000e+02,   3.33000000e+02,\n",
        "         3.35000000e+02,   3.37000000e+02,   3.40000000e+02,\n",
        "         3.43000000e+02,   3.45000000e+02,   3.46000000e+02,\n",
        "         3.48000000e+02,   3.49000000e+02,   3.50000000e+02,\n",
        "         3.53000000e+02,   3.54000000e+02,   3.55000000e+02,\n",
        "         3.56000000e+02,   3.60000000e+02,   3.62000000e+02,\n",
        "         3.64000000e+02,   3.67000000e+02,   3.68000000e+02,\n",
        "         3.70000000e+02,   3.71000000e+02,   3.73000000e+02,\n",
        "         3.74000000e+02,   3.75000000e+02,   3.78000000e+02,\n",
        "         3.79000000e+02,   3.81000000e+02,   3.83000000e+02,\n",
        "         3.84000000e+02,   3.85000000e+02,   3.86000000e+02,\n",
        "         3.87000000e+02,   3.88000000e+02,   3.89000000e+02,\n",
        "         3.91000000e+02,   3.92000000e+02,   3.93000000e+02,\n",
        "         3.95000000e+02,   3.96000000e+02,   3.97000000e+02,\n",
        "         3.98000000e+02,   4.01000000e+02,   4.03000000e+02,\n",
        "         4.05000000e+02,   4.06000000e+02,   4.07000000e+02,\n",
        "         4.08000000e+02,   4.09000000e+02,   4.10000000e+02,\n",
        "         4.11000000e+02,   4.12000000e+02,   4.13000000e+02,\n",
        "         4.14000000e+02,   4.15000000e+02,   4.16000000e+02,\n",
        "         4.17000000e+02,   4.18000000e+02,   4.19000000e+02,\n",
        "         4.23000000e+02,   4.26000000e+02,   4.27000000e+02,\n",
        "         4.28000000e+02,   4.29000000e+02,   4.31000000e+02,\n",
        "         4.32000000e+02,   4.33000000e+02,   4.35000000e+02,\n",
        "         4.37000000e+02,   4.39000000e+02,   4.40000000e+02,\n",
        "         4.41000000e+02,   4.42000000e+02,   4.43000000e+02,\n",
        "         4.44000000e+02,   4.45000000e+02,   4.46000000e+02,\n",
        "         4.49000000e+02,   4.51000000e+02,   4.52000000e+02,\n",
        "         4.53000000e+02,   4.54000000e+02,   4.56000000e+02,\n",
        "         4.57000000e+02,   4.58000000e+02,   4.59000000e+02,\n",
        "         4.60000000e+02,   4.62000000e+02,   4.64000000e+02,\n",
        "         4.65000000e+02,   4.67000000e+02,   4.68000000e+02,\n",
        "         4.69000000e+02,   4.70000000e+02,   4.71000000e+02,\n",
        "         4.73000000e+02,   4.74000000e+02,   4.75000000e+02,\n",
        "         4.79000000e+02,   4.80000000e+02,   4.81000000e+02,\n",
        "         4.82000000e+02,   4.84000000e+02,   4.86000000e+02,\n",
        "         4.87000000e+02,   4.88000000e+02,   4.89000000e+02,\n",
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        "         5.02000000e+02,   5.03000000e+02,   5.04000000e+02,\n",
        "         5.05000000e+02,   5.06000000e+02,   5.07000000e+02,\n",
        "         5.08000000e+02,   5.10000000e+02,   5.12000000e+02,\n",
        "         5.13000000e+02,   5.15000000e+02,   5.16000000e+02,\n",
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        "         5.26000000e+02,   5.27000000e+02,   5.31000000e+02,\n",
        "         5.32000000e+02,   5.34000000e+02,   5.36000000e+02,\n",
        "         5.39000000e+02,   5.40000000e+02,   5.41000000e+02,\n",
        "         5.42000000e+02,   5.43000000e+02,   5.45000000e+02,\n",
        "         5.46000000e+02,   5.47000000e+02,   5.48000000e+02,\n",
        "         5.50000000e+02,   5.52000000e+02,   5.53000000e+02,\n",
        "         5.55000000e+02,   5.56000000e+02,   5.57000000e+02,\n",
        "         5.59000000e+02,   5.60000000e+02,   5.61000000e+02,\n",
        "         5.62000000e+02,   5.63000000e+02,   5.64000000e+02,\n",
        "         5.65000000e+02,   5.68000000e+02,   5.69000000e+02,\n",
        "         5.70000000e+02,   5.71000000e+02,   5.72000000e+02,\n",
        "         5.74000000e+02,   5.75000000e+02,   5.77000000e+02,\n",
        "         5.78000000e+02,   5.79000000e+02,   5.80000000e+02,\n",
        "         5.81000000e+02,   5.84000000e+02,   5.85000000e+02,\n",
        "         5.87000000e+02,   5.88000000e+02,   5.89000000e+02,\n",
        "         5.90000000e+02,   5.91000000e+02,   5.92000000e+02,\n",
        "         5.93000000e+02,   5.95000000e+02,   5.96000000e+02,\n",
        "         5.97000000e+02,   5.98000000e+02,   5.99000000e+02,\n",
        "         6.00000000e+02,   6.01000000e+02,   6.02000000e+02,\n",
        "         6.07000000e+02,   6.10000000e+02,   6.13000000e+02,\n",
        "         6.15000000e+02,   6.16000000e+02,   6.17000000e+02,\n",
        "         6.18000000e+02,   6.19000000e+02,   6.20000000e+02,\n",
        "         6.21000000e+02,   6.22000000e+02,   6.25000000e+02,\n",
        "         6.27000000e+02,   6.28000000e+02,   6.31000000e+02,\n",
        "         6.32000000e+02,   6.33000000e+02,   6.34000000e+02,\n",
        "         6.35000000e+02,   6.36000000e+02,   6.39000000e+02,\n",
        "         6.40000000e+02,   6.41000000e+02,   6.42000000e+02,\n",
        "         6.43000000e+02,   6.45000000e+02,   6.46000000e+02,\n",
        "         6.49000000e+02,   6.52000000e+02,   6.53000000e+02,\n",
        "         6.55000000e+02,   6.56000000e+02,   6.57000000e+02,\n",
        "         6.58000000e+02,   6.59000000e+02,   6.60000000e+02,\n",
        "         6.61000000e+02,   6.62000000e+02,   6.63000000e+02,\n",
        "         6.64000000e+02,   6.66000000e+02,   6.68000000e+02,\n",
        "         6.69000000e+02,   6.70000000e+02,   6.73000000e+02,\n",
        "         6.74000000e+02,   6.75000000e+02,   6.76000000e+02,\n",
        "         6.77000000e+02,   6.78000000e+02,   6.79000000e+02,\n",
        "         6.80000000e+02,   6.81000000e+02,   6.83000000e+02,\n",
        "         6.84000000e+02,   6.85000000e+02,   6.86000000e+02,\n",
        "         6.87000000e+02,   6.89000000e+02,   6.90000000e+02,\n",
        "         6.91000000e+02,   6.94000000e+02,   6.95000000e+02,\n",
        "         6.96000000e+02,   6.97000000e+02,   6.98000000e+02,\n",
        "         6.99000000e+02,   7.00000000e+02,   7.01000000e+02,\n",
        "         7.02000000e+02,   7.03000000e+02,   7.06000000e+02,\n",
        "         7.08000000e+02,   7.11000000e+02,   7.12000000e+02,\n",
        "         7.13000000e+02,   7.15000000e+02,   7.17000000e+02,\n",
        "         7.18000000e+02,   7.19000000e+02,   7.20000000e+02,\n",
        "         7.21000000e+02,   7.22000000e+02,   7.23000000e+02,\n",
        "         7.24000000e+02,   7.26000000e+02,   7.27000000e+02,\n",
        "         7.29000000e+02,   7.30000000e+02,   7.33000000e+02,\n",
        "         7.34000000e+02,   7.35000000e+02,   7.36000000e+02,\n",
        "         7.37000000e+02,   7.39000000e+02,   7.40000000e+02,\n",
        "         7.42000000e+02,   7.44000000e+02,   7.45000000e+02,\n",
        "         7.50000000e+02,   7.52000000e+02,   7.53000000e+02,\n",
        "         7.55000000e+02,   7.57000000e+02,   7.58000000e+02,\n",
        "         7.61000000e+02,   7.62000000e+02,   7.64000000e+02,\n",
        "         7.65000000e+02,   7.66000000e+02,   7.67000000e+02,\n",
        "         7.68000000e+02,   7.70000000e+02,   7.71000000e+02,\n",
        "         7.72000000e+02,   7.74000000e+02,   7.75000000e+02,\n",
        "         7.76000000e+02,   7.77000000e+02,   7.78000000e+02,\n",
        "         7.79000000e+02,   7.80000000e+02,   7.81000000e+02,\n",
        "         7.84000000e+02,   7.85000000e+02,   7.86000000e+02,\n",
        "         7.87000000e+02,   7.88000000e+02,   7.89000000e+02,\n",
        "         7.93000000e+02,   7.94000000e+02,   7.95000000e+02,\n",
        "         7.97000000e+02,   7.99000000e+02,   8.00000000e+02,\n",
        "         8.02000000e+02,   8.04000000e+02,   8.05000000e+02,\n",
        "         8.06000000e+02,   8.07000000e+02,   8.08000000e+02,\n",
        "         8.09000000e+02,   8.10000000e+02,   8.12000000e+02,\n",
        "         8.15000000e+02,   8.16000000e+02,   8.19000000e+02,\n",
        "         8.20000000e+02,   8.21000000e+02,   8.22000000e+02,\n",
        "         8.24000000e+02,   8.25000000e+02,   8.26000000e+02,\n",
        "         8.27000000e+02,   8.28000000e+02,   8.29000000e+02,\n",
        "         8.30000000e+02,   8.31000000e+02,   8.32000000e+02,\n",
        "         8.34000000e+02,   8.35000000e+02,   8.36000000e+02,\n",
        "         8.37000000e+02,   8.41000000e+02,   8.44000000e+02,\n",
        "         8.45000000e+02,   8.46000000e+02,   8.52000000e+02,\n",
        "         8.54000000e+02,   8.57000000e+02,   8.64000000e+02,\n",
        "         8.65000000e+02,   8.67000000e+02,   8.68000000e+02,\n",
        "         8.70000000e+02,   8.71000000e+02,   8.73000000e+02,\n",
        "         8.74000000e+02,   8.75000000e+02,   8.76000000e+02,\n",
        "         8.77000000e+02,   8.78000000e+02,   8.79000000e+02,\n",
        "         8.81000000e+02,   8.82000000e+02,   8.84000000e+02,\n",
        "         8.85000000e+02,   8.86000000e+02,   8.89000000e+02,\n",
        "         8.90000000e+02,   8.91000000e+02,   8.92000000e+02,\n",
        "         8.93000000e+02,   8.95000000e+02,   8.97000000e+02,\n",
        "         8.99000000e+02,   9.00000000e+02,   9.04000000e+02,\n",
        "         9.05000000e+02,   9.06000000e+02,   9.08000000e+02,\n",
        "         9.10000000e+02,   9.11000000e+02,   9.12000000e+02,\n",
        "         9.13000000e+02,   9.14000000e+02,   9.16000000e+02,\n",
        "         9.18000000e+02,   9.21000000e+02,   9.22000000e+02,\n",
        "         9.25000000e+02,   9.26000000e+02,   9.28000000e+02,\n",
        "         9.30000000e+02,   9.31000000e+02,   9.32000000e+02,\n",
        "         9.37000000e+02,   9.38000000e+02,   9.41000000e+02,\n",
        "         9.43000000e+02,   9.46000000e+02,   9.48000000e+02,\n",
        "         9.49000000e+02,   9.55000000e+02,   9.59000000e+02,\n",
        "         9.60000000e+02,   9.64000000e+02,   9.67000000e+02,\n",
        "         9.68000000e+02,   9.72000000e+02,   9.74000000e+02,\n",
        "         9.75000000e+02,   9.88000000e+02,   9.93000000e+02,\n",
        "         9.96000000e+02,   9.97000000e+02,   1.00000000e+03,\n",
        "         1.00100000e+03,   1.00500000e+03,   1.00600000e+03,\n",
        "         1.00700000e+03,   1.00800000e+03])"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for m in range(1,13):\n",
      "\n",
      "    plt.figure()\n",
      "    imshow(mask, cmap=\"Blues\")\n"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "    month = 1\n",
      "    mask = np.fromfile(clim_fn.format(month=m), dtype=np.uint8).reshape((448, 304))\n",
      "    data = np.fromfile(data_fn.format(month=m), dtype=np.uint16).reshape((448, 304))    "
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "    ice_not_mask = (data > 0) & (mask == 0)\n",
      "    ice_not_mask\n",
      "    "
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       ..., \n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False]], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "    imshow(data > 100 ) \n",
      "\n",
      "    "
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.image.AxesImage at 0x1131a2ed0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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k4m4FFjif/PgYJ1UtC3OFl68DMJmzAF07xcbgWwih38/h0rBpTdhTN9AWrViz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\nVLHd02QeIVsZQmBNbCEtHa6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       "text": [
        "<matplotlib.figure.Figure at 0x1127564d0>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplt as mpl\n",
      "    fig = plt.figure(figsize=(8,3))\n",
      "    ax2 = fig.add_axes()\n",
      "    \n",
      "# The second example illustrates the use of a ListedColormap, a\n",
      "# BoundaryNorm, and extended ends to show the \"over\" and \"under\"\n",
      "# value colors.\n",
      "cmap = mpl.colors.ListedColormap(['r', 'g', 'b', 'c'])\n",
      "cmap.set_over('0.25')\n",
      "cmap.set_under('0.75')\n",
      "\n",
      "# If a ListedColormap is used, the length of the bounds array must be\n",
      "# one greater than the length of the color list.  The bounds must be\n",
      "# monotonically increasing.\n",
      "bounds = [1, 2, 4, 7, 8]\n",
      "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
      "cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,\n",
      "                                     norm=norm,\n",
      "                                     # to use 'extend', you must\n",
      "                                     # specify two extra boundaries:\n",
      "                                     boundaries=[0]+bounds+[13],\n",
      "                                     extend='both',\n",
      "                                     ticks=bounds, # optional\n",
      "                                     spacing='proportional',\n",
      "                                     orientation='horizontal')\n",
      "cb2.set_label('Discrete intervals, some other units')\n"
     ],
     "language": "python",
     "outputs": [
      {
       "ename": "AttributeError",
       "evalue": "'NoneType' object has no attribute 'set_xticks'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-79-ae1a62a7a096>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m                                  \u001b[0mticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# optional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m                                  \u001b[0mspacing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'proportional'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m                                  orientation='horizontal')\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0mcb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Discrete intervals, some other units'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/savoie/.virtual_envs/ipython/lib/python2.7/site-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, cmap, norm, alpha, values, boundaries, orientation, ticklocation, extend, spacing, ticks, format, drawedges, filled, extendfrac, extendrect, label)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[0;31m#: The axes that this colorbar lives in.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_patch_ax\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    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcmap\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    280\u001b[0m             \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/savoie/.virtual_envs/ipython/lib/python2.7/site-packages/matplotlib/colorbar.pyc\u001b[0m in \u001b[0;36m_patch_ax\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    332\u001b[0m         \u001b[0;31m# bind some methods to the axes to warn users\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# against using those methods.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_set_ticks_on_axis_warn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_yticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_set_ticks_on_axis_warn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'set_xticks'"
       ]
      },
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1148f0a50>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "outputs": []
    }
   ]
  }
 ]
}