Work for greenland hack week stuff.

greenland services hack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "import transformers\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "topdir = '/projects/greenland_today/integration/data/nrt_melt_data/all_input_data'\n",
    "fn = os.path.join(topdir, 'greenland_melt_20120901_daily_EASE2_N25km.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  0  1  2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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L0kZJU0rbrJa0W9JOSYsa+yuYWb1VDIaIeCoizo+I84G3A78B7qaoX9kXEfOATWkZSV3A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108cf2210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.fromfile(fn, dtype=np.int16)\n",
    "a = a.reshape(720, 720)\n",
    "plt.imshow(a)\n",
    "print(np.unique(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "# set ease projection\n",
    "ease2_proj = '+proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from transformers.srs import SpatialReference\n",
    "import transformers.raster as raster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs \n"
     ]
    }
   ],
   "source": [
    "s_srs = SpatialReference.from_unspecified(ease2_proj)\n",
    "print(s_srs.to_proj4())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs \n"
     ]
    }
   ],
   "source": [
    "t_srs = SpatialReference.from_epsg(3413)\n",
    "print (t_srs.to_proj4())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "# -3400000. 900000. \n",
    "top_left = (-700000.,-600000.)\n",
    "lower_right = (top_left[0] + 2000. * 800, top_left[1] - 2000. * 1400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900000.0, -3400000.0)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "pix_size = (2000., 2000.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "#logger.setLevel(logging.ERROR)\n",
    "\n",
    "big_pix = raster.warp(a, (-9000000., 9000000.), 25000., s_srs,\n",
    "                      dest_srs=t_srs,\n",
    "                      dest_top_left = top_left,\n",
    "                      dest_bottom_right=lower_right,\n",
    "                      dest_pixel_size=pix_size[0])\n",
    "std_pix = raster.warp(a, (-9000000., 9000000.), 25000., s_srs,\n",
    "                      dest_srs=t_srs,\n",
    "                      dest_top_left = top_left,\n",
    "                      dest_bottom_right=lower_right,\n",
    "                      dest_pixel_size=2000.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-700000.0, -600000.0)\n",
      "(1400, 800)\n"
     ]
    },
    {
     "data": {
      "image/png": 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WdNMmpGfN7FJJylbbn8ye35V0MPe6K7RYEt3NHuefr1hhvC73ttliePOV/4Wr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089e1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print big_pix[1]\n",
    "print big_pix[0].shape\n",
    "with mpl.rc_context(rc={'figure.figsize': (10,9), 'axes.grid':False},):\n",
    "    plt.imshow(big_pix[0], interpolation='nearest')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-700000.0, -600000.0)\n",
      "(1400, 800)\n"
     ]
    },
    {
     "data": {
      "image/png": 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UjCnPUFPrmAzw4dtfSYiKiNep1QbmTPza6jJszTFTaZq9Zypdn4SdtHhgu9Ul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\nSnIhNTMr6X9h5JwS1AGopQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d30210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print std_pix[1]\n",
    "print std_pix[0].shape\n",
    "with mpl.rc_context(rc={'figure.figsize': (10,9), 'axes.grid':False},):\n",
    "    plt.imshow(std_pix[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "# play with pixel areas\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(900000.0, -3400000.0)\n"
     ]
    }
   ],
   "source": [
    "dims = big_pix[0].shape\n",
    "\n",
    "top_left\n",
    "lower_right = (top_left[0] + pix_size[0] * dims[1], top_left[1] - pix_size[1] * dims[0])\n",
    "print (lower_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "xbounds = -700000. + np.arange(big_pix[0].shape[1]+1) * pix_size[0]\n",
    "ybounds = -600000. - np.arange(big_pix[0].shape[0]+1) * pix_size[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1400, 800)\n"
     ]
    }
   ],
   "source": [
    "print dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-700000.0, -600000.0)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-700000.0, -600000.0), (-698000.0, -600000.0), (-698000.0, -602000.0), (-700000.0, -602000.0), (-700000.0, -600000.0)]\n"
     ]
    }
   ],
   "source": [
    "firstcel =  [(xbounds[0], ybounds[0]),(xbounds[1], ybounds[0]),(xbounds[1], ybounds[1]),(xbounds[0], ybounds[1]),(xbounds[0], ybounds[0])]\n",
    "print firstcel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-700000.0, -698000.0, -698000.0, -700000.0, -700000.0), (-600000.0, -600000.0, -602000.0, -602000.0, -600000.0)]\n"
     ]
    }
   ],
   "source": [
    "xy = zip(*firstcel)\n",
    "print(xy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "Create projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "import pyproj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "nps = pyproj.Proj(t_srs.to_proj4())\n",
    "eaa = pyproj.Proj(s_srs.to_proj4())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "project cells from Northen Polar Stereo back into Equal Area Azimuthal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((-945203.9512959841, -943758.3074976095, -945205.1470853135, -946650.7666760008, -945203.9512959841), (72707.99625353748, 71254.47930259316, 69799.76470783883, 71253.28351324749, 72707.99625353748))\n"
     ]
    }
   ],
   "source": [
    "eaa_xy = pyproj.transform(nps, eaa, xy[0], xy[1])\n",
    "print eaa_xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-945203.9512959841, 72707.99625353748), (-943758.3074976095, 71254.47930259316), (-945205.1470853135, 69799.76470783883), (-946650.7666760008, 71253.28351324749), (-945203.9512959841, 72707.99625353748)]\n"
     ]
    }
   ],
   "source": [
    "points = zip(*eaa_xy)\n",
    "print(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "poly = {\"type\": \"Polygon\", \"coordinates\": [zip(*eaa_xy)]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "apoly = poly.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pix_size[0] * pix_size[1]) / (1000 * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.205969798665469"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(poly).area /(1000. * 1000.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x108d4b650>]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fH3po2BGlrkTu/nLF1G+/wW23WbfFXXfZRVdPKG5v1K1rH1Lq17eq5eWXvWpJJV6puN2a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\nzjkXNf8PKsBp1ONQHaAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114912350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(zip(*points)[0], zip(*points)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "Compute grid area for each pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1400, 800)\n",
      "(1400,)\n"
     ]
    }
   ],
   "source": [
    "print big_pix[0].shape\n",
    "print(big_pix[0][:,20]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "rows = big_pix[0].shape[0]\n",
    "columns = big_pix[0].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "xbounds = -700000. + np.arange(big_pix[0].shape[1]+1) * pix_size[0]\n",
    "ybounds = -600000. - np.arange(big_pix[0].shape[0]+1) * pix_size[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1400, 800)\n"
     ]
    }
   ],
   "source": [
    "dims =  big_pix[0].shape\n",
    "print(dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "xbounds represents each boundary of each grid cell starting with the outtermost left, and including the outtermost right.\n",
    "That means that there are Xdim+1 boundaries for Xdim gridcells.\n",
    "\n",
    "ybounds shoulnd't have to be explained at this point.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-700000., -698000., -696000., -694000., -692000., -690000.,\n",
       "       -688000., -686000., -684000., -682000., -680000., -678000.,\n",
       "       -676000., -674000., -672000., -670000., -668000., -666000.,\n",
       "       -664000., -662000., -660000., -658000., -656000., -654000.,\n",
       "       -652000., -650000., -648000., -646000., -644000., -642000.,\n",
       "       -640000., -638000., -636000., -634000., -632000., -630000.,\n",
       "       -628000., -626000., -624000., -622000., -620000., -618000.,\n",
       "       -616000., -614000., -612000., -610000., -608000., -606000.,\n",
       "       -604000., -602000., -600000., -598000., -596000., -594000.,\n",
       "       -592000., -590000., -588000., -586000., -584000., -582000.,\n",
       "       -580000., -578000., -576000., -574000., -572000., -570000.,\n",
       "       -568000., -566000., -564000., -562000., -560000., -558000.,\n",
       "       -556000., -554000., -552000., -550000., -548000., -546000.,\n",
       "       -544000., -542000., -540000., -538000., -536000., -534000.,\n",
       "       -532000., -530000., -528000., -526000., -524000., -522000.,\n",
       "       -520000., -518000., -516000., -514000., -512000., -510000.,\n",
       "       -508000., -506000., -504000., -502000., -500000., -498000.,\n",
       "       -496000., -494000., -492000., -490000., -488000., -486000.,\n",
       "       -484000., -482000., -480000., -478000., -476000., -474000.,\n",
       "       -472000., -470000., -468000., -466000., -464000., -462000.,\n",
       "       -460000., -458000., -456000., -454000., -452000., -450000.,\n",
       "       -448000., -446000., -444000., -442000., -440000., -438000.,\n",
       "       -436000., -434000., -432000., -430000., -428000., -426000.,\n",
       "       -424000., -422000., -420000., -418000., -416000., -414000.,\n",
       "       -412000., -410000., -408000., -406000., -404000., -402000.,\n",
       "       -400000., -398000., -396000., -394000., -392000., -390000.,\n",
       "       -388000., -386000., -384000., -382000., -380000., -378000.,\n",
       "       -376000., -374000., -372000., -370000., -368000., -366000.,\n",
       "       -364000., -362000., -360000., -358000., -356000., -354000.,\n",
       "       -352000., -350000., -348000., -346000., -344000., -342000.,\n",
       "       -340000., -338000., -336000., -334000., -332000., -330000.,\n",
       "       -328000., -326000., -324000., -322000., -320000., -318000.,\n",
       "       -316000., -314000., -312000., -310000., -308000., -306000.,\n",
       "       -304000., -302000., -300000., -298000., -296000., -294000.,\n",
       "       -292000., -290000., -288000., -286000., -284000., -282000.,\n",
       "       -280000., -278000., -276000., -274000., -272000., -270000.,\n",
       "       -268000., -266000., -264000., -262000., -260000., -258000.,\n",
       "       -256000., -254000., -252000., -250000., -248000., -246000.,\n",
       "       -244000., -242000., -240000., -238000., -236000., -234000.,\n",
       "       -232000., -230000., -228000., -226000., -224000., -222000.,\n",
       "       -220000., -218000., -216000., -214000., -212000., -210000.,\n",
       "       -208000., -206000., -204000., -202000., -200000., -198000.,\n",
       "       -196000., -194000., -192000., -190000., -188000., -186000.,\n",
       "       -184000., -182000., -180000., -178000., -176000., -174000.,\n",
       "       -172000., -170000., -168000., -166000., -164000., -162000.,\n",
       "       -160000., -158000., -156000., -154000., -152000., -150000.,\n",
       "       -148000., -146000., -144000., -142000., -140000., -138000.,\n",
       "       -136000., -134000., -132000., -130000., -128000., -126000.,\n",
       "       -124000., -122000., -120000., -118000., -116000., -114000.,\n",
       "       -112000., -110000., -108000., -106000., -104000., -102000.,\n",
       "       -100000.,  -98000.,  -96000.,  -94000.,  -92000.,  -90000.,\n",
       "        -88000.,  -86000.,  -84000.,  -82000.,  -80000.,  -78000.,\n",
       "        -76000.,  -74000.,  -72000.,  -70000.,  -68000.,  -66000.,\n",
       "        -64000.,  -62000.,  -60000.,  -58000.,  -56000.,  -54000.,\n",
       "        -52000.,  -50000.,  -48000.,  -46000.,  -44000.,  -42000.,\n",
       "        -40000.,  -38000.,  -36000.,  -34000.,  -32000.,  -30000.,\n",
       "        -28000.,  -26000.,  -24000.,  -22000.,  -20000.,  -18000.,\n",
       "        -16000.,  -14000.,  -12000.,  -10000.,   -8000.,   -6000.,\n",
       "         -4000.,   -2000.,       0.,    2000.,    4000.,    6000.,\n",
       "          8000.,   10000.,   12000.,   14000.,   16000.,   18000.,\n",
       "         20000.,   22000.,   24000.,   26000.,   28000.,   30000.,\n",
       "         32000.,   34000.,   36000.,   38000.,   40000.,   42000.,\n",
       "         44000.,   46000.,   48000.,   50000.,   52000.,   54000.,\n",
       "         56000.,   58000.,   60000.,   62000.,   64000.,   66000.,\n",
       "         68000.,   70000.,   72000.,   74000.,   76000.,   78000.,\n",
       "         80000.,   82000.,   84000.,   86000.,   88000.,   90000.,\n",
       "         92000.,   94000.,   96000.,   98000.,  100000.,  102000.,\n",
       "        104000.,  106000.,  108000.,  110000.,  112000.,  114000.,\n",
       "        116000.,  118000.,  120000.,  122000.,  124000.,  126000.,\n",
       "        128000.,  130000.,  132000.,  134000.,  136000.,  138000.,\n",
       "        140000.,  142000.,  144000.,  146000.,  148000.,  150000.,\n",
       "        152000.,  154000.,  156000.,  158000.,  160000.,  162000.,\n",
       "        164000.,  166000.,  168000.,  170000.,  172000.,  174000.,\n",
       "        176000.,  178000.,  180000.,  182000.,  184000.,  186000.,\n",
       "        188000.,  190000.,  192000.,  194000.,  196000.,  198000.,\n",
       "        200000.,  202000.,  204000.,  206000.,  208000.,  210000.,\n",
       "        212000.,  214000.,  216000.,  218000.,  220000.,  222000.,\n",
       "        224000.,  226000.,  228000.,  230000.,  232000.,  234000.,\n",
       "        236000.,  238000.,  240000.,  242000.,  244000.,  246000.,\n",
       "        248000.,  250000.,  252000.,  254000.,  256000.,  258000.,\n",
       "        260000.,  262000.,  264000.,  266000.,  268000.,  270000.,\n",
       "        272000.,  274000.,  276000.,  278000.,  280000.,  282000.,\n",
       "        284000.,  286000.,  288000.,  290000.,  292000.,  294000.,\n",
       "        296000.,  298000.,  300000.,  302000.,  304000.,  306000.,\n",
       "        308000.,  310000.,  312000.,  314000.,  316000.,  318000.,\n",
       "        320000.,  322000.,  324000.,  326000.,  328000.,  330000.,\n",
       "        332000.,  334000.,  336000.,  338000.,  340000.,  342000.,\n",
       "        344000.,  346000.,  348000.,  350000.,  352000.,  354000.,\n",
       "        356000.,  358000.,  360000.,  362000.,  364000.,  366000.,\n",
       "        368000.,  370000.,  372000.,  374000.,  376000.,  378000.,\n",
       "        380000.,  382000.,  384000.,  386000.,  388000.,  390000.,\n",
       "        392000.,  394000.,  396000.,  398000.,  400000.,  402000.,\n",
       "        404000.,  406000.,  408000.,  410000.,  412000.,  414000.,\n",
       "        416000.,  418000.,  420000.,  422000.,  424000.,  426000.,\n",
       "        428000.,  430000.,  432000.,  434000.,  436000.,  438000.,\n",
       "        440000.,  442000.,  444000.,  446000.,  448000.,  450000.,\n",
       "        452000.,  454000.,  456000.,  458000.,  460000.,  462000.,\n",
       "        464000.,  466000.,  468000.,  470000.,  472000.,  474000.,\n",
       "        476000.,  478000.,  480000.,  482000.,  484000.,  486000.,\n",
       "        488000.,  490000.,  492000.,  494000.,  496000.,  498000.,\n",
       "        500000.,  502000.,  504000.,  506000.,  508000.,  510000.,\n",
       "        512000.,  514000.,  516000.,  518000.,  520000.,  522000.,\n",
       "        524000.,  526000.,  528000.,  530000.,  532000.,  534000.,\n",
       "        536000.,  538000.,  540000.,  542000.,  544000.,  546000.,\n",
       "        548000.,  550000.,  552000.,  554000.,  556000.,  558000.,\n",
       "        560000.,  562000.,  564000.,  566000.,  568000.,  570000.,\n",
       "        572000.,  574000.,  576000.,  578000.,  580000.,  582000.,\n",
       "        584000.,  586000.,  588000.,  590000.,  592000.,  594000.,\n",
       "        596000.,  598000.,  600000.,  602000.,  604000.,  606000.,\n",
       "        608000.,  610000.,  612000.,  614000.,  616000.,  618000.,\n",
       "        620000.,  622000.,  624000.,  626000.,  628000.,  630000.,\n",
       "        632000.,  634000.,  636000.,  638000.,  640000.,  642000.,\n",
       "        644000.,  646000.,  648000.,  650000.,  652000.,  654000.,\n",
       "        656000.,  658000.,  660000.,  662000.,  664000.,  666000.,\n",
       "        668000.,  670000.,  672000.,  674000.,  676000.,  678000.,\n",
       "        680000.,  682000.,  684000.,  686000.,  688000.,  690000.,\n",
       "        692000.,  694000.,  696000.,  698000.,  700000.,  702000.,\n",
       "        704000.,  706000.,  708000.,  710000.,  712000.,  714000.,\n",
       "        716000.,  718000.,  720000.,  722000.,  724000.,  726000.,\n",
       "        728000.,  730000.,  732000.,  734000.,  736000.,  738000.,\n",
       "        740000.,  742000.,  744000.,  746000.,  748000.,  750000.,\n",
       "        752000.,  754000.,  756000.,  758000.,  760000.,  762000.,\n",
       "        764000.,  766000.,  768000.,  770000.,  772000.,  774000.,\n",
       "        776000.,  778000.,  780000.,  782000.,  784000.,  786000.,\n",
       "        788000.,  790000.,  792000.,  794000.,  796000.,  798000.,\n",
       "        800000.,  802000.,  804000.,  806000.,  808000.,  810000.,\n",
       "        812000.,  814000.,  816000.,  818000.,  820000.,  822000.,\n",
       "        824000.,  826000.,  828000.,  830000.,  832000.,  834000.,\n",
       "        836000.,  838000.,  840000.,  842000.,  844000.,  846000.,\n",
       "        848000.,  850000.,  852000.,  854000.,  856000.,  858000.,\n",
       "        860000.,  862000.,  864000.,  866000.,  868000.,  870000.,\n",
       "        872000.,  874000.,  876000.,  878000.,  880000.,  882000.,\n",
       "        884000.,  886000.,  888000.,  890000.,  892000.,  894000.,\n",
       "        896000.,  898000.,  900000.])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xbounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "801"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(xbounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "remember how to reshape things\n",
    "( I want a 3 x 4 final array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,4])\n",
    "y = np.array([5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4]])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(np.expand_dims(x, axis=0))\n",
    "np.repeat(np.expand_dims(x, axis=0), 3, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5]\n",
      " [6]\n",
      " [7]]\n",
      "[[5 5 5 5]\n",
      " [6 6 6 6]\n",
      " [7 7 7 7]]\n"
     ]
    }
   ],
   "source": [
    "y = np.array([5,6,7])\n",
    "print(np.expand_dims(y, axis=1))\n",
    "print (np.repeat(np.expand_dims(y, axis=1), 4, axis=1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]]\n",
      "[[5 5 5 5]\n",
      " [6 6 6 6]\n",
      " [7 7 7 7]]\n"
     ]
    }
   ],
   "source": [
    "xbroadcast = np.repeat(np.expand_dims(x, axis=0), 3, axis=0)\n",
    "ybroadcast = np.repeat(np.expand_dims(y, axis=1), 4, axis=1)\n",
    "print xbroadcast\n",
    "print ybroadcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1400, 800)\n"
     ]
    }
   ],
   "source": [
    "print dims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "I want to broadcast the xbounds along the y dim 57 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(801,)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xbounds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-700000. -698000. -696000. ...,  896000.  898000.  900000.]\n",
      " [-700000. -698000. -696000. ...,  896000.  898000.  900000.]\n",
      " [-700000. -698000. -696000. ...,  896000.  898000.  900000.]\n",
      " ..., \n",
      " [-700000. -698000. -696000. ...,  896000.  898000.  900000.]\n",
      " [-700000. -698000. -696000. ...,  896000.  898000.  900000.]\n",
      " [-700000. -698000. -696000. ...,  896000.  898000.  900000.]]\n",
      "(1401, 801)\n"
     ]
    }
   ],
   "source": [
    "xbroadcast = np.repeat(np.expand_dims(xbounds, axis=0), ybounds.shape, axis=0)\n",
    "print (xbroadcast)\n",
    "print xbroadcast.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -600000.  -600000.  -600000. ...,  -600000.  -600000.  -600000.]\n",
      " [ -602000.  -602000.  -602000. ...,  -602000.  -602000.  -602000.]\n",
      " [ -604000.  -604000.  -604000. ...,  -604000.  -604000.  -604000.]\n",
      " ..., \n",
      " [-3396000. -3396000. -3396000. ..., -3396000. -3396000. -3396000.]\n",
      " [-3398000. -3398000. -3398000. ..., -3398000. -3398000. -3398000.]\n",
      " [-3400000. -3400000. -3400000. ..., -3400000. -3400000. -3400000.]]\n",
      "(1401, 801)\n"
     ]
    }
   ],
   "source": [
    "ybroadcast = np.repeat(np.expand_dims(ybounds, axis=1), xbounds.shape, axis=1)\n",
    "print ybroadcast\n",
    "print ybroadcast.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11e0caa50>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jYIo3iEjam+Icb0WF8QDTI2I2xa/mByPivArj51BcaaA8JTiF4grASMd/HHhO0pxy+UKK\nH4y7K76G57DztGJErx/Fac1HgdXlX0QuLJ9L1X0PbSze0O3pRvFTvpHi3emiIdZZQnF+1UNxTn0B\nxZuIB9j95Zsryu1tAP6A4p1zP8W74YFLSKdW2MZXKM7zVlJcAru05Y3MiB5Dy/wT2XnVYqTj/7Tc\n90qKy4eLqu4fmEfxJnUVxVFxasXx+wD/R/HXjFTc/w3svPx2C8UVicqv3VA3fyBiKYyHUwuzxhxk\nS8FBthQcZEvBQbYUHGRLwUG2FBxkS+H/AYAH4BPk1d5dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141a5290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(xbroadcast, interpolation='nearest', cmap=\"Accent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x114c87650>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j385b/ZGvAZ4F1g/njyxEhKz8kYUIUyUvqddGqjuvI0Xsi4c451pSfr62pm0/YAXwDOn5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\ni5ZAQhYtgYQsWgIJWbQEErJoCf4fsedt8zfPvxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141a5190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ybroadcast, interpolation='nearest', cmap=\"Accent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "allx = xbroadcast.ravel()\n",
    "ally = ybroadcast.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "stride = 591"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vO0/Sycb+C9eiVNLdkl7SeO9+SZcdcN0jhGsYU3fbZvETwRK6bbP4cfIktGm4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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d1c2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with mpl.rc_context(rc={'figure.figsize': (10,9), 'axes.grid':False},):\n",
    "    plt.scatter( allx[::stride], ally[::stride])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "reproject all of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "eaa_allxy = pyproj.transform(nps, eaa, allx, ally)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HxBAYCg2F0hlyD225H6ybaD91T+as4V+HXvPYD9bQXA1V/4gYOkOhoVA6Qx9C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\nkoShUJIkSRgKJUmSBPz/moecUqxV6XAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11066c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with mpl.rc_context(rc={'figure.figsize': (10,9), 'axes.grid':False},):\n",
    "    plt.scatter(eaa_allxy[0][::stride], eaa_allxy[1][::stride])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "Reshape the points back into grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1401,)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ybounds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "eaa_allx = eaa_allxy[0].reshape(xbroadcast.shape)\n",
    "eaa_ally = eaa_allxy[1].reshape(xbroadcast.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TA9ARJNV6pDyoWhU21XmkS1pzEAWG2s4SxDr4wiYFdDJsMgvyD/M1WOMcCq9j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\naGeIntu5ndu5VbQzRM/t3M7t3Cra/wd6MDlTIQmg2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a2a410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with mpl.rc_context(rc={'figure.figsize': (10,9), 'axes.grid':False},):\n",
    "    plt.imshow(eaa_allx, cmap='Accent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bSD/+nB+HXdtWIZLsB+pCpLR/rlJLC+nHfnIOtP/GpvPbeqADUFxIv0/hTedp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\nZzlLgZwhepaznOUsBfL/ARsgBgTsKJMNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113bcf290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with mpl.rc_context(rc={'figure.figsize': (10,9), 'axes.grid':False},):\n",
    "    plt.imshow(eaa_ally, cmap='Accent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [
    "Now I need to figure out how to compute area of each polygon in the Equal Area Azimuthal projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1401, 801)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eaa_ally.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-945203.95129598, -943758.30749761],\n",
       "       [-946650.766676  , -945205.14708531]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eaa_allx[0:2, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 72707.99625354,  71254.47930259],\n",
       "       [ 71253.28351325,  69799.76470784]])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eaa_ally[0:2, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0)\n",
      "[(-945203.95129598409, 72707.99625353748), (-943758.30749760952, 71254.479302593158), (-945205.14708531345, 69799.764707838825), (-946650.76667600079, 71253.283513247487), (-945203.95129598409, 72707.99625353748)]\n",
      "(0, 1)\n",
      "[(-946650.76667600079, 71253.283513247487), (-945205.14708531345, 69799.764707838825), (-946651.93983657006, 68345.07092522092), (-948097.53518351028, 69798.591547252538), (-946650.76667600079, 71253.283513247487)]\n",
      "(1, 0)\n",
      "[(-943758.30749760952, 71254.479302593158), (-942312.61335283332, 69800.934322432193), (-943759.47711219464, 68346.217911052867), (-945205.14708531345, 69799.764707838825), (-943758.30749760952, 71254.479302593158)]\n",
      "(1, 1)\n",
      "[(-945205.14708531345, 69799.764707838825), (-943759.47711219464, 68346.217911052867), (-945206.29407116526, 66891.522349651888), (-946651.93983657006, 68345.07092522092), (-945205.14708531345, 69799.764707838825)]\n",
      "(2, 0)\n",
      "[(-942312.61335283332, 69800.934322432193), (-940866.86897130159, 68347.361424499584), (-942313.75686627883, 66892.643234334697), (-943759.47711219464, 68346.217911052867), (-942312.61335283332, 69800.934322432193)]\n",
      "(2, 1)\n",
      "[(-943759.47711219464, 68346.217911052867), (-942313.75686627883, 66892.643234334697), (-943760.59799687995, 65437.945931987153), (-945206.29407116526, 66891.522349651888), (-943759.47711219464, 68346.217911052867)]\n"
     ]
    }
   ],
   "source": [
    "# naive way is create a polygon around each grid cell center. \n",
    "for x in range(3):\n",
    "    for y in range(2):\n",
    "        print (x, y)\n",
    "        polygon = [(eaa_allx[y,x], eaa_ally[y,x]),\n",
    "                   (eaa_allx[y,x+1], eaa_ally[y,x+1]),\n",
    "                   (eaa_allx[y+1,x+1], eaa_ally[y+1,x+1]),\n",
    "                   (eaa_allx[y+1,x], eaa_ally[y+1,x]),\n",
    "                   (eaa_allx[y,x], eaa_ally[y,x])]\n",
    "\n",
    "        print(polygon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "# naive way:\n",
    "area = np.zeros_like(big_pix[0], dtype=np.float)\n",
    "for x in range(area.shape[1]):\n",
    "    for y in range(area.shape[0]):\n",
    "        # print (x, y)\n",
    "        polygon = [(eaa_allx[y,x], eaa_ally[y,x]),\n",
    "                   (eaa_allx[y,x+1], eaa_ally[y,x+1]),\n",
    "                   (eaa_allx[y+1,x+1], eaa_ally[y+1,x+1]),\n",
    "                   (eaa_allx[y+1,x], eaa_ally[y+1,x]),\n",
    "                   (eaa_allx[y,x], eaa_ally[y,x])]\n",
    "\n",
    "        poly = {\"type\": \"Polygon\", \"coordinates\": [polygon]}\n",
    "        pix_area= (shape(poly).area /(1000. * 1000.))\n",
    "        area[y,x] = pix_area\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "plt.imshow(area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "print np.max(area), np.min(area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "area.tofile('moge_area_by_shapely.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}