make_domain_map_revised.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_map():\n",
    "        from mpl_toolkits.basemap import Basemap\n",
    "        ## set fontsize\n",
    "        fs = 20\n",
    "        ## \n",
    "        m=Basemap(llcrnrlon=-125,llcrnrlat=31,urcrnrlon = -102.5,urcrnrlat=50,projection='cyl',lat_1=33,lat_2=42,lon_0=-110,\n",
    "                resolution='i',fix_aspect=True)\n",
    "        # m.drawcoastlines()\n",
    "        m.drawstates()\n",
    "        m.drawcountries()\n",
    "        m.drawmapboundary()\n",
    "        m.drawparallels(np.arange(-90.,120.,10.),labels=[1,0,0,0],size=fs) # draw parallels\n",
    "        m.drawmeridians(np.arange(0.,420.,10.),labels=[0,0,0,1],size=fs) # draw meridians\n",
    "        m.drawlsmask(land_color='grey',ocean_color='lightblue',lakes=True,resolution='c')\n",
    "        # m.fillcontinents(color='coral',lake_color='lightblue')\n",
    "        return(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "################## INPUTS: BASIN AND MODEL (OR ENSEMBLE AVERAGE OR MEDIAN) ################################################\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import matplotlib.pyplot as plot\n",
    "from snowpack_functions import unpack_netcdf_file_var\n",
    "plt.ion()\n",
    "######################################## SWE GRID CELLS #############################################################\n",
    "direc = '/raid9/gergel/agg_snowpack/goodleap/SWE/'\n",
    "file = 'histmeanmask.nc'\n",
    "lats,lons,boool,datess = unpack_netcdf_file_var(direc,file,\"swe\")\n",
    "# g = boool[(np.array(boool)== 1.0)]\n",
    "boool.shape\n",
    "lats_inc = list()\n",
    "lons_inc = list()\n",
    "for lat in np.arange(len(lats)): \n",
    "    for lon in np.arange(len(lons)): \n",
    "        if boool[0,lat,lon] == 1.0: \n",
    "            lats_inc.append(lats[lat])\n",
    "            lons_inc.append(lons[lon]) \n",
    "lats_inc = np.asarray(lats_inc)\n",
    "lons_inc = np.asarray(lons_inc)\n",
    "## mask out Canada\n",
    "latsinc = lats_inc[lats_inc < 49]\n",
    "lonsinc = lons_inc[lats_inc < 49]\n",
    "## create array for pcolormesh\n",
    "data = np.ones(len(lonsinc))\n",
    "\n",
    "########################################## NO SWE GRID CELLS ######################################################### \n",
    "file = 'histmeanmask_lowlands.nc'\n",
    "lats_low,lons_low,boool_low,datess = unpack_netcdf_file_var(direc,file,\"swe\")\n",
    "\n",
    "## load elevations file\n",
    "data_elevs = np.load('/raid9/gergel/agg_snowpack/sm_summer/elevations.npz')\n",
    "elevations = data_elevs['elevations']\n",
    "\n",
    "#### mask out elevations < 500 m \n",
    "## boool_low = boool_low*elevations\n",
    "\n",
    "lats_inc_low = list()\n",
    "lons_inc_low = list()\n",
    "\n",
    "for lat in np.arange(len(lats_low)):\n",
    "    for lon in np.arange(len(lons_low)):\n",
    "        if boool_low[0,lat,lon] == 1.0:\n",
    "            lats_inc_low.append(lats_low[lat])\n",
    "            lons_inc_low.append(lons_low[lon])\n",
    "lats_inc_low = np.asarray(lats_inc_low)\n",
    "lons_inc_low = np.asarray(lons_inc_low)\n",
    "## mask out Canada\n",
    "latsinc_low = lats_inc_low[lats_inc_low < 49]\n",
    "lonsinc_low = lons_inc_low[lats_inc_low < 49]\n",
    "## create array for pcolormesh\n",
    "data_low = np.ones(len(lonsinc_low))\n",
    "\n",
    "from snowpack_functions import mesh_gridify\n",
    "# lonsmeshlow,latsmeshlow,datameshlow = mesh_gridify(latsinc_low.reshape(len(lonsinc_low),1),\n",
    "                                                   #lonsinc_low.reshape(len(lonsinc_low),1),\n",
    "                                                  #data_low.reshape(len(lonsinc_low),1))\n",
    "# cs = m.pcolormesh(lonsmeshlow,latsmeshlow,datameshlow,shading='flat',cmap='bwr',latlon=True,zorder=1,vmin=1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving figure to '/raid9/gergel/agg_snowpack/maps/historical_domain_map_revised'\n"
     ]
    },
    {
     "data": {
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IFI+uqlLFdAFgl/1mHaw3X3nZdDGQgx5Jm00XAqXB5DiAOT09Pdq8mRodKBKBIxlHDHL8\nWefprqXf0NQDDzZdlOY8z/gkkTjjGGVfczwAoDBkHIHiETgSOMbywC036oNN7+nwj52gWUcerba2\nNtNFylx7Z6e29/aqb8cOtflyU3Kxe2UCjHEcpOTHG0B5McYRKB6v4yBwjGXB5Vdr64cf6je/+oVu\n/c9/Vv+OPu07Y6bmnDxfY8ZPMF28zMw5eb5+/dhDmnv62aaLsru8HvQ9CSAymVXVk321B/YBXOdJ\nA5kvyDgCxSPjSOAYW3tnp46df5aOnX+WqtWq3n79d/rFfT/Wpnc2anh7uw4/7gTNOuJop7N1hxzz\nMd2x5D+0cd1bOvvyq0uZWfVRrhlHHkrhGyZMgkFkHIHiETgSOKYSBIEm73+AJu9/gCRp25YP9Ztf\n/VK3fu3/qL+vX/tMm6E5J52msRMnZbrevF8XEgSBLv7iX+rOb31NL/7yMc05eX6u60MxEmccgUH8\nvm3WuNpYUq+8rm0DyDgCBhA48hyZqeEdnTrmtDN1zGlnSpLefmO1nnzgHr23cYOGDR+uwz52gg46\n6hgnspErnnpCQ4cOsytoLKK7YJRXMzgqdsaxpPsB2WBW1UHIQKJAZByB4jHGkcAxV/tO31/7Tt9f\nUpiNXPnMU1r2tX9RX1+f9p46TXNOmq+9Ju1tuJR7evXF57Tq+Wf1qT/7r6aLggyRcQSAciDjCBSP\njCPPkYUZ3tGpo085XUefcrokad2a1/XUT+/Texv+qGHDh+vQufN00FHHaMjQoUbL+fvXXtHTP39A\nV/63vzFaDmNKnCVgVtWMlPgcgafovuocMo5A8cg4Ejgas8+0Gdpn2gxJ0ratW/TSM0/ptq//q/r6\n+jRpv6mac/J8jZu0T6FlWr/2Tf38tpu18K//TkFgeUc03i0YW+SMY5r9lmbcl6vdhF0c68Ysscmx\nv2ABMo5A8cg4EjhaYXh7h+acPH/XeMI/vPmGnvn5A3pv4x8VKFDbkCGatN80TZ5xgPadMVMdXV2Z\nrXvblg/1+m9XavXKF7V+7Zu66r//rfuzqPJgVxcZR+yS8hrx+rbpYkMBSid1xpHGIyA2AkcCx8RW\n3LIk1+VPrkiTJ46RJPX19Wni4Ufqrddf04pfPaGttZtFVVWNGTdh18yuYydMUqVSabjMgUHi5nff\nkSQN7+jQ9IMP04nnXaDR48bnuk0wq9AxjnG6vrV6gOGhBgB2Q8YRKB6BI4GjE9ra2rTvjJnad8ZM\nzR3w82q1qs3vbNRbq1/Tsw//TJs2/lH9/f27fr+zL3a1WpWCMLNZ2iCR4KIlMo5AhsqeeWSWWKtF\nyjhyzIBMETgSODotCAKNGT9BY8ZP0GEfm1f3M9Vq1f7xiiiE8VlVs3iIselhvcAy+H2bchRdAZEj\nMo5A8QgcCRydkXfX2MFmX7640PU1WnfR211mZBzdRLMPgMGYVRUoHokYAkc7ZN0aTes26jCecSwj\nrjWQWYQBZByB4pFx5DkSZcGkKi3tlnF0fd+kfe0HUqPdFTCHMY4ATCBwTKhZV07j3SuT3iwGfG/X\nNiSYIMFkN1c0tlvGsVHgZVP2pIj1m95GlItNY3CzZlPdADKOAIyg1rFBmR82YA0vxzjmcU01eoCO\nuq4oryUBgCYY4wjABAJHG9jywBilHEzR7izGOGaEhh74jmvAuFJnHHnOAKxV0loHEOMeB4mUcbRp\n/yRpyIj6vbzZUAYApUXGEYAJFdMFAFCMLkmjTRcCpTBJ0ipJ20wXBPBUV1eXRo+mRgdQLAJHwBM9\nkjabLgRKYR9JCyR9W9IHhssC+Kinp0ebN1OjAygWXVVz4OML7JPMMhtl9tXM96VPY3MGTdxSd1bV\nMoqzba26uqZdVonNkHSxpJskfVrSBLPFQZE8O9dt5NUYRwDWIOMIlFV39243YDKOyNokSVdJWiZp\njeGyoECD6hYUj4wjABNK2lwFtOBT5lEKM47r12vItm3S1KmmS2M3F88Jg7MQjpL0OUm3KmyJ/GTt\nZ86J+5oUF88TlEapM44ArEWtg3RqD08rurs1++W3zZalER7wJDELH/LTLulqSWsVZh/3lzTfaIkK\n0Kxeoc5BzqjPAZhA4GgDW18pAPc0OW9oobZc1Cy4xXXDFEmfl7RE0kmShpotjj186+GA3FGfAzCB\nMY6AJxgTg6IcJ+kB04UASoz6HIAJNFfZhNZo5IgWahTlSElPK+y2epocnXE1y/qYuh0Zoz4HYAK1\nDsov6esVXBBjdkPGxDjqr/5Kuvde6corTZcklmsl/V7SPZIWGS5LpspWh8BJ1OcATCBwBFwW4yGW\nFmqHvfKKk5Ox7Kcw27hE0omSDjNbHKA0qM8BmECtYxOXMmPd3Zp9+eK6v1pxy5KCC4MoaKF2xOBr\nP8k4JosmY/mEpD5JN8qxwNGifWgd9olx1OcATCBwBMqk2ayq111HC7WNmjUYRX1AHxzkWPZg36Yw\neERJWHqe+YSMIwATqHVsxY05Hx7vV1qoYdK+ku6TdF6SL8cYy5s5D+sK2I/6HIAJvI4D8ERXV5dG\njx5tuhjw1HmSfidpvemCACVAfQ7ABDKOORs4DrBMY/9mX7449fYM/H6j8ZKZ8zh70PP1r2tX+7TH\n+8F6aY6Npce1T9IPFI5xnJRkAZZuF2AKGUcAJhA4orUBD22FBXj4SNxxbg10iQveSlkFihYHV3dK\n2iHpVNMFMcXjLvLIB2McAZhArYNyaPVA5vIDW0YPnT21P0DRLpb0jKRvSPozhZPlWM+RoByWyvme\nRMYRgAkEjrYz+e62Mj4slbnlv9GDbu3fXevXa8i2bdLUqUWWCnHVex3HvfdKV14Z/XtZzNSaoUDS\nxyS9L+keSfMljSqyABbsg1Jh/xlHxhGACdQ6LuKmHV8Z9lm9bYixXbRQe8qSc3+tpA5JKyR9IOmq\nIlceZR/EDbYt2a9GlLkBzhHU5wBMIHDEnlx8GDCZmbVRnW2mhdojlj3Y90haJmmepE9KmmG2ONFY\ntg+BgajPAZhArVOgMsywmmW5G0204+q+sR0t1DClXdL+CrONIyV1StrHaIkAt1GfAzCBwBHl5UOm\nIEZWhBbqkrP4fG+TdEHt329JekTSnyRNk3S2oTI1RbYxGvaTMdTnAEyg1nFRq/E33d3JbuTc/EuN\nFmo3BUGQbgFJ64OcTJZ0Re3fP5N0q6RLzRWnNYv2HRyS83lDfQ7ABAJHoAwiTN5BC3XJZPR+T5PO\nlPSwpB9KusxwWeqKO6lO3O+WBWPMC5d5fc7MwwAi4CmyDJjxDxHQQg0bzVcYPP5AYXfWLrPFAZxA\nfQ7ABAJHwBNkHGGr+Qonzrld0nZJQyXNknSUwld4WK3ZazyAnBirzznfAa/xFIncNZo9dSBmUs0f\nLdQlU7KJSWbX/kjhxDnPKRz/2Kfw9R3zkyzU5PhOHrCRo8zrc85RABEQOAKeIOMIV4yQdHLtjyR9\nS9IRksYZKxFgF+pzACZQ6wCeIOMIV12osBvr5xW+2iOyorMoTDBSPmmPaU7nRCH1OecvgEEIHA0Z\n2H2z7N00jW1fybrypUULNVw1TtJxkpZIOlbSXLPFiY+6CBkzWp9zHgPe4ikS5cNNrS4yjiXg8bl9\npMIxkD9RGEB+QtK+RksUgcfHa5esZ/0uKqubdLmtvpdRIwL1OQATCBwBGyR5iIj5HTKOJWZyEpgC\nVSSdJ6lH0nclLVKKWVfj7DMyhrBMIfU55z2AQXiKRDm4fmMr4AZNC3WJuX7+x9SlMOP4A4XBYyXJ\nQuLsM8/2b+5sC0hMnAspM7HU5wBMIHAEXBWzyxYZxxLyJNNYz1SF73lcojALObWoFQ/e50yIUyz2\nr6SC63P2OYAaniIBT9BCjbI5RtIhkm6TtJfCLCTgA+pzACYQOKI+V15ebXPZLEPGEWXUKelqSd+X\ntFbSlLxX2CzDSH2ENGJ04aU+B2ACtQ5QFi0eNmihLoFm46LqBTQeBTLnS/qOpOkKM5GTTRUk61lE\ns0bX2j3ZfszqoD4HYAKBI9xh+Y08EzlOGkELNcpslKQvSXpd0s9q/79ACSfOScq2SV/qcTVDmuUr\nPEogs/o8i31Twv0LoD6eIi0w+/LFib+74pYlGZZkAG4EpUMLNcquImlm7c8zkr4p6UJJ+5gslGR3\nls9EsGvT9kdhYYMA9TkAEwgcARvl0HWKjGMJ8AqJyOZKOljS7ZKuKXrlzboP28S28uTFwsAvrdT1\neZb7wtUsNoDYeIqEO8p8cypgPBot1CiERWMrR0qqmi6ES/IIsCx572Gm67Pg/KY+B2ACgSPgCTKO\n8BGBI8qI+hyACdQ6cIcFrby5KWDbaKFGISy7TidLWirpUwozkGgij2Pn4IylxkXYPz21P1E/DwBZ\nIHAEbJLjAwAt1CXQqrt2lPPHs4fMBZLWSfqBpBmSTpA0wmiJHGVRN03jLNgXXeIBDkDxqHcclNlM\nqkVP3MB06kaRcYSv9pH0RYUzrf5I0vuSrpU03GShXFOGOrhE4+SbZhwd3zYA9iJwhDvKctM3VHYy\njiXj8jVgyNzan+9L2iZpk6SHJH2gMDM5zVzRslFUJsyWCZAsyPyZkjrjmOW+83D/A77iKRJu4MaU\nGhlHIFRR2HV1lKTTJI2VdKuk6ZJONVaqDFBPRud40LlbxhEACkLg6LOib5iO36hdR8YRkrgOJV0s\naajCAHKnfRRmcVrZLqlt0HcjK3Lft1pH2jLYdB41K0PRr+zI8vNNdF13nYZs2yZNnZpuQTYcPwDO\n4CkS8AQZRyBUb2zjB5I2SpolafSAn/dLWiXpBYUZnorCbq6HSzop32ICDVGfAzCBwBHwBBlHoLFL\nJK2VdIf2fPfjJEnza39L0mMKM5YoAZsypzFQnwMwgVoHxeO9XkbQQo1YbJkApUBTJF0T4XNrJF2Y\nb1Hyl9WrW2yvz20KDFvNfhqjjD2rV1OfAygcgaMjmr6Cg6m4EQEt1G4aPny4tm/fbroYGGC7pJGm\nC5FEo3tDkntG3q9Xinpfy+p+l3bW7oLHO1KfAzCBWgd2SNJqnffkDyVDxtFNw4cPV39/f/YLtj1T\nZLHBXVlLi/MhXyn2L/U5ABMIHMsqaYup6Xcl8qCSG1qoEQvXYl0rFL7/0Xpxjp/NjQgm70N5Nk6m\n7EJLfQ7ABGqdMrDlBp8H04FslgyXnxZqJGLTGDHDbpA0UdJftvog+yq5vPadyeA4h+Ek1OcATCBw\nxEdMPuwkGb/Cw1kstFAD6YyVdICkYUkXQJ0VTZpsaZb72OJGE+pzACZQ65QVARcGoYUaLUWpNzyu\nP86XtFTSoY0+YHrfuDxRms1dUlt9v9EycpzQh/ocgAkEjhZrOpMqEBMt1O7q7+9XX1+f2traTBfF\naz+XdJTpQgCiPgdgBrUOmsuyBbtM4xUdRAu1m/r6+lStVgkaDatKelPSuaYLArfkNMEO9TkAEwgc\nfdSoew7BXKnRQu2mV155RWPGjDFdDO/tkDTcdCGQjywaNQu+f1KfA2YEQWC6CEZR68AMH4NUw2PE\naKHOULOxSxlnGA466CA99dRTWrlypQ477LBY30V21krqMl2IqHysX9PIY39FXWbCz1GfA2ZUq968\nybcuAkeLPf3005o6dar23nvvfFYQ5YblyQx1PqCF2k2VSkVXX321brjhBnV1dWn69OlmC2TzO/9y\n0i/pJ5I+a7ogeTP1egpbluUQ6nMAJlDrWObxe+7QXpP20fKHHtTq1au1atUq9fX16dxzz9WkSZP2\n+Hxvb69++9vfqq+vT9OmTdO4ceP2+Ex/f7/ee+89rV27VlOmTKn7mUJ4eoPfg6EHb1qoC5DTcWxr\na9M111yjb33rW7rkkkvq1gW5vqzcc3dKOk5SZ6sPmm4cc+UYm95PO5lefwrU5wBMIHC0zIa312rF\nU7/QqLFjdfnll0uS3nnnHd13333q7+/XvHnztGbNGr311lvq6+tTpVLRlClT1NbWplWrVmnLli27\nljWwH3ZHR4fGjx+vJ554Qpdcckl+WUxYixZqtw0fPlxXX321brrpJl133XWmi7M7WwKBnGyQdLHp\nQjQTpYu0S5hIrSXqcwAmUOtY5uLFf7nr3ztfxzFu3DgtXLhQ77zzjp544glNmzZNp556qoYNi/8a\n6vb2dr2m46QEAAAgAElEQVT55psEjh6ihdp9I0aMUHt7u+lieGeIpD5JzGsLW1CfA2YEQaD+/n5V\nKhXTRTGCwNEh48aN0wUXXJBqGePHj9frr7/e8nObNm3SiBEjaNEsEVqoY8or05Gyq3JbW5s++OAD\njRw5Mt53yeIkNlXSCsV8h2OR+9jVdTVblo3naNYT3iRZd+1v6nPADAJHWGv25Yt3/Xtn9jGtyZMn\n66c//al+/etf6+ijj9718/7+fq1YsUIvvPCCent71dHRoe3bt6tarWr79u266KKLNGHChHQrL3l3\nNtvRQl0OZ511lm666SYtXrzYvgfHkl3jayQ9JmmLpFK/EMXm45Vn2fJ8NVXOQTH1OWDGzsDRV5Y9\ndSAz9bIL3d0aKek6SQ/+4Q9aunTpro9Uq1VNnjxZF154oUaNGrXbojZt2qRbb71VBx10kE4++WRv\nW1lyVcADNy3UEZl4iI5x/He+0zHVu6R4h2sk90s6R9J0w+XIXavzj/PDOtTngBkEjvDOEEnnnXde\n5M+PGTNG1157rR555BF961vfUmdnp0455RTtt99++RUSmaOFuhx++MMf6hOf+ITa2hhxl7fPSLpJ\n0vmSItV2BFjx2JKhzmr9BW4H9TlgBoEjrJVV99QsBEGg+fPna/78+dqwYYMefvhh3X///Zo3b55m\nz54dbSGmHw5cMPhBKsNXd9BC3YQj5+bTTz+tsWPHaurUqaaL4oWRkhYqDB7/3HBZCuHbOzqbjfu1\nfLupzwEzgiBQtVo1XQxjqHXKKscHgAkTJujSSy9Vf3+/vvnNb+qQQw6JfwOzpaXZI7RQWyLFOb/X\nXntp+fLl2rBhQ/0xx3GWzbUXyf2S5psuhGl5Twrj8v3AUJmpzwFzfM44MlgNiVUqFZ1wwgm65557\nTBcFEXR1dWn06NGmi4EUDjjgAH3mM5/Rj370I61cudJ0cbzwB0mHmC4EMAj1OWAGXVWBFI488kg9\n88wzeueddzRu3DjTxSmPHFqxaaGuw8EMx+jRo3XttddqyZIlOuSQQ+xq/XNwf7YyRtLbkvaN8mGX\nM2eNlHGbSoD6HDCDwBFI6ZJLLtGyZct08MEH65RTTmn8wbjdZ217UOnuNlumlOu2cUzMCy+8oKee\nekrDhg0zXZR8TJu2588GzGacRm9vrx577DGdlrZ7IJqaIOknkgJJx0qKOKIbzdhy7kW5/9hS1kFs\nrM8BHxA4AimNHTtWixcv1g033KBjjjlGXV1dyRdm6U1akt1li8CmFur169frwQcfVGdnp77whS+Y\nmyE072O6aFG89cZonOiXtOTll3XKKae0fkVOlq9ZaPCqn7I6t/b3m5J+pYiBY4n3xy7NJpbxiaEJ\nhWyqzwGfVCoVAkcgC4cffrieffbZ5lnHsks6OUkBDxppW6i3bt0qSWpvb9/1sx07dmjt2rVas2aN\n1q1bpw8//LDp+wV3zkTW2dmpU089ldlBU6go7Cr+85//XGeddZbp4pTebyQdlsWCTPdcMKnVdvuy\nX6IGm032BxlHwIze3l7dcccdGjp0qOmiGEGt45OcW0bnzJmjm2++2e/A0WJpWqiff/55Pfnkk+rq\n6lJfX5+q1aqCIFAQBBozZowmT56sM844o/5Mn4gu5vV4/PHH65vf/KaOO+44jRo1Kvn6kmYkfXnQ\nlzRF0nJJh5ouiI08Og9sQcYRMKO9vV1nnXWWJk2aZLooueluUqcTOCIzw4cPV19fn+liZCPKO73S\nPiwV/LCVpIW6r69Py5YtU1tbmxYvXsxL51spOJsUBIE+9alP6aabbtJVV13VeJZF397Pl4MjJP1S\n0u8kzTRcFqe4fp4V1TMk5rLJOAJmMMYRyFBXV5fWr19f6pYYV9Vroe7r69Pvf/97rV69Wm+//bZ6\ne3t362ra29urE088UYcffnjRxUVE48eP12WXXabvfe97zYNHxLZdYaD4uqQ1CruqEjTCBmQcATMI\nHFFuBbf2HnXUUVq+fLk+/vGPF7petNbZ2an169fr9ttv1+bNmyWFFeDYsWM1depUzZ07VyNHjjRc\nypzlfT1EyVTnsL7xki6V9L3vfU+dnZ0aOnSoTjvtNE2ZMiXWcvb4t8e2SFoqaUbtz+mShmexYPZv\nY2U5DwuYNIqMI2BGEAS75mvwEbUOMnXggQfqF7/4RfIFmH5wyHL2SQu8/fbbevbZZ/XHP/5RW7du\nVWdnp8444wwmpbFdggfPCZL+4i/+Qv39/Xr//fd19913a+jQobrooos0fHidkCfKOkxfj4bskPRd\nSRdImmy4LJGk7SLt84Q9jiLjCJhBxhHlYvjmX6lUms6q2RIPL6lt2bJFd9xxh7Zs2aJRo0bpyCOP\n1Mc//nFt2LBB27ZtI2gsuUqlojFjxmjhwoV69dVX9Z3vfEednZ2aNWuWjjzySHV0dIQf5FpraJmk\nj8mRoDELSWeDznP9njZaREXGETCDjCOQsVSBY9aadR207GHk7bff1mOPPbbrlRZBEOjEE0/UrFmz\nIn2/v79fjzzyiF599VUtWLBA06dP3+33tFCnYCoTnbLr66xZszRr1iy9//77ev7553X99ddr4cKF\nGjt2bGZFLKMzFQaPh0jqMFyWPVhWb3nLcJaW+hwwg4wj3MeDREN/6O7W05L2krSPwin135f0+9rv\nNkrqmzZN0kfvGGx/801NV/jQOOJv/1abNm3S+++/r56eHs2aNeujjE0G3nvvPT366KPasGGDRo4c\nqVNOOUX77ruvpDBzePfdd+uRRx7RGWecof3331+StH37dvX09GjNmjV6/fXX9e677+5a3syZM/XF\nL36x7rpoofZQrW4YJekUhef0nf/2b1oUpXuqx/XKBEmfVNhd9fOS/HxbV86SZDltOifzKkvExk3q\nc8AMAkfAJmleGzDgc2+++aZ+9u1va5ik4yS9I2mlpCcUTnAxUeHsiPMldSxatNti3unu1suS7pfU\n+/3vq729XV1dXWpvb9ezzz6rvr4+HXzwwTr++OMT3bi3bNmixx9/XGvWrFF7e7uOP/74ulnFjo4O\nXXrppfrwww9177336tFHH5UUdkUcOnSoJk6cqKOOOkr77bdfpNdk0EKdgwTnZqrPpXxYnSBptKQV\nK1ZoNq/oaGqKpOkK642jMlje9yX1Kqx3jpXUmcEyCxEn61328ydqAFvAfqA+B8yoVCoEjkCWpkyZ\nooceekinn3564ev+3e9+p4cfflidnZ26ROFDsiRF6+wZGifpxNofXXPNHr/fvn27nnrqKX3ta1/T\ntddeu1sG8qWXXtLGjRvV2dmpESNGqLOzUyNHjlRHR4eWL1+ul19+WW1tbZozZ47OPPNMVSqVluXp\n7OzUpz/96RhbUB8t1JDCTNr1Tzyhww47LNL557ONChuestAr6UpJzymcrfXPJPFWVCRFfQ6YYdVw\nLAOodXyXw5i/s846S7fffrtuvPFGXXTRRbm/4qFf0suSftHdrdGSrpDUFWcBMffB0KFDdfLJJ2vU\nqFF6+OGHdd555+m1117TQw89pIkTJ2ry5Mnq6enRxo0btWXLFm3dulW9vb2aOXOmrrnmGmM3e29a\nqC0ex9pSo4xGlG2KuK1DJM2dO1f33nuvzj//fL8zSA30S/qRpGGS0o4G/bnC90BOVtjb4fja33dJ\nuijlsp2UdvbXtMtIus4038m63N3d6pEUqTb39BoG8kJXVbiv0UNl0e+UG+CSSy7RW2+9pVtvvbVu\nVmPEiBGaMWOGDjrooNaB5aBy9/T06De/+Y1ee+01bX31VUlhF7yFktqzKX4ks2fP1ssvv6yvf/3r\n2muvvbRw4cJMxz9mjRbqBDJ80MtluQkde+yxuuGGG7Rp0yaNMVoSO70taZOkL6Rczh0KG7E+J2lg\nLThH0npJN9R+foCkkwZ9xkkEKYXpEg9wgAnMqgrjVtyyJL+FG7yRT548WZ///Of3+Hl/f7/WrVun\nV199VXfeeae2b98uSRo2bJj2228/HXTQQdp77713ffaNN97QSy+9pD8uX66qwokqpks6T+mzAXuI\nkempVCq67LLLsi5BbrzJOA5UxAQWRX8ng4xqf3+/dqxdq75//dd46/bE3gq7lqbxoMIb7NkNfn9O\n7e/tCrOPyxW+AqSloo9Lmix40WwpR1aabE/kjCOATDHGEeXiwI2zUqlo8uTJmjx597ekvf/++1q1\napUee+wx/elPf9r183HjxumQQw7RucuX5zMmyMYZ+3JAxjGGol+zUbBly5bpYwrH8+7B8GsGbPCi\npB0KxyJKYZfVsxX2bGhkW+17q2r/3jkzaytDFfaUKLK3hFE+1LetupNnsO1kHAEz6KoKWGLUqFGa\nO3eu5s6dW+yKy/wAM4CXGces2TY+K8Eyf6kwu390dqUonaNrf3baKOlWhYHglDqfv6v2mf0lna+P\nJuWK6g1J58YuJZyT4bVPxhEwg66q8JtLQROvD0iFjKPjWo2TbNV1sLtba9eu1cr779fnL7hAeuGF\nbMtXYuMlXSZpmcJX+ZwtaUTtd7+W9IHCcYxJHaFwvOM0SQtSLKdQaeteG7u6OqRpxpH9CeSGwBGA\nF8g4+m3r1q266667tGjRIl7DkcA4ha/QWKUwgKwqHJ84QWFQmcbJCl//852Uy4E/yDgCZtBVFYAX\nSplxdLFlPWnmPMprM5p85vtf+Yo+Ianjq19tvS40dFDtzxaFYx+zGnf9lsQMt2WUUx0VO+PoYl0J\nWIjAEYAXyDhmwNGJPe5SGOxMNV2QEkn84p0Gkw+tU4PJikwr4lx39LoyiYwjYEalUqGrKuAcHjBi\ncy7j6NMxzut9kZJeUPiAGWWGTxSgwbE+SNJNCsdOHlNgcQrny9jGnLct9qyqrmYhfTlf4AzGOALw\nAhnHGo8ePjZKelLSYtMFwZ4GZdlGS7pO4SQ5UyRNMlIow3J6b2lhCiwfGUfADAJHAF6wOuNo6oGw\n1fvWHPdjSVdIijUVTkm23UVtkkZJGm66IFlpdS41CgpdPgcLKnsm73G0tYtwAe/BBJKqVCqMcUSJ\nleVmjNS8zDjm9TJ7Bx5gtkkKFP+dgijYoHOpV1J7Eeur93+Lz2drGdpnZBwBM3zPODInO+CJrq4u\njR5NGOGLJyUdaroQiO1ESd+W9LLpgsBqXaJRCDCBjCPc1+rF4GVR1u0qSCEZRxeOUZJXX7iwXYP8\nTtJnTRcCsR0gaZqkb0k62HBZChe1a6tN12MZMo5pXwcEeMT3jCOBo0+o+OMr0T6zeoyjlF+3Ug/1\nKuymavHRRhNDFQaN35F0scJxj6Xk8vVuuL7KZIyjreg+DYsROAI24SaRm1wzjrYet7zKZev21vxK\n4esd4K7TFL7b8RZJx0o62mhpIirqurAhsDBcBxQ+xrHIfR5nUqWo3wEyEgQBXVXhOCpMRGB9xlGy\n74HA0WvrFUlXmy4E4qlzru3T3a0vSFoi6SilnJTAhhmEiww4TFy7BWYhS51xbIReKbBApVIh4wig\n/DLLOOZ54+ahILXfKpyVc6jpgiATFUkHKpzs6ETDZYE9mFU1oqj3FO49iIiMI4A9lfAm4kTGEam8\nvXixHr3rLn3hD38wXRRkoVYPndbdrW/UftQm6QhJnTGX4QQbJ7+xVKkzjs2OP+cGDKtU/H4hhd9b\nD3ikp6dHmzdvNl0M5OQDSXfeeacWLlxY3gdKT1UkXaAwaNwiaZnZ4sACPZKozYHi0VUVsAGtiLmL\nlHFsNsaQY2St7ZK+J+mSP/5RXf/0T6aLgyhiZlUm1/5I0q0K3/NY2Os6kkxW0uxzWY63HLhM03VU\ngesvdcbRBO5ziIiuqgA+UuKbRqazqsbZT3EmrDA9uUXUdVvUlapf0o2SzpQ0qdA1ozCDHmovVPie\nxwPVpNuQqclhTJfBBgUEsYxxjIiAEBnjdRxwF4O+EcOujOPg4Kzo86NZtoFzNba7Jc2WNMt0QZCd\nFg0owyTNkfRTSQsyWF6k78X9XNGNKzbVHQWUhYxjTmyb2RvWoasq4DtPbgqRM46NArik+8mT/WtK\nj6SPEXR75xhJtw3+oW3ngC09CBJ6S9I9CgN122yRVFWMSZLSmjbto38vXVrUWu3g2/ZaplKpWNM1\ndMuWLapWq5o7d67pohhD4OgDm2/eNpTH5v2TIetnVbVp3zvUvWmb6QIgf3XOw3cVjm1t+Lko4wib\nNTikPfcduHZaeV1hZvdjpgtSx3qF1/7Uola4aFFRa8pHmvNxzZp8lw9n3H333Tr00EM1c+ZM00Ux\nxuKnSLRElwrEsCvjGGUcUlHvaizj+Zr1/ozwYN/f388U2a7IIDv8jKRfS7o8g+I0FWUymzJeww5g\njCMAEwgcAU9Yn3FEItsUDtaHP34j6TxJo00XBMYwxhEonu8zqkrUOzCBFmojMp1VFVZ4VNIBkoJ/\n+AfTRUFUGdR/l0n6rqTFCt/tmMt6HOqu7SMyjpYpe08aSKKRViJwLB9u9mjAyoyjL9P357CdW/7m\nb/Tb735XX/zDH1IvC5ZpdL7U/u6UdKKkpZIukjSuqHLVwwOzEWQcgeL5/ioOiXoH8AYZxwSKGq8Y\n5TuDvnvbV7+qcyTR/llCEc67IyTtJek+hd2Vj1P4Whb4gYyjxWjALzUCR5STzRWWRS9P94mVGcfB\nfM1exJzZctmyZdpb0vQs1uvTfi5a1jOWDvreFEkLu7vVqzD7uJ+kFyW9Wvt9IGnEoYfqwpdeSvdK\nibjvc+Scyh0Zx5g4N5EBMo7UOzDNtkChxDeXPTKOeez7VsvJa+p/G2SRiWzRqPJ7SQ9ImilpfvK1\nwZSczpFhks6RtEzSYZI+q49u7q+89JJukHSapEMzXi/MIePoAK4dlBCBI+AJJzKO2F3tgWN1d7ce\nUphl+LSYTRMD1M6RaZKurfO7AxU2NNwgaULtj6/elvQTSccrYRBtETKOCdnWWA2nMKsq9Q7gDcY4\nuqm3t1cPSLpGUofhssBNbZKukPQ9hTOx+nTj3yrpOYXdd3slXSzpCYUzEn9W4URDLiLjCBSPWVX9\nun/AtKTdGJGJXRnHuBO+RO1uE3cclKvyLH+dZd8r6RTlFDS6fix8lDBjMkrS6ZJulrQwwfddzdTc\nqnAs6MWSRtR+9klJayTdJulqQ+VKq/CMo6PH3wrsu9JgjCOBI+BNRW404+jJPt4lo7GcH0raqPCV\nC3BInONfRENE7e+DJb0u6SGFQaQPpiscAzpi0M+nKWyM+Y2kwwsuUxbIOGbAl9dBIVMEjoDvbGsN\nzCMz292dfwu1DfvOFhlNinC3mATHWUU8lCZYx7mS/lPSqQq7sGay3phlKMqdkjYo3OZ6LpJ0fe0z\nrl1njHF0VJTrw6JrCLsj40i9A5959iqCui3UzR48G/3Oo32WuRj78H1Jf5J0QFbrrPd/WtyzlbBR\nJ/UyYuiRNFwpgsYoLKkr3lWdCYMGGCLpi5JuUTgGclYRhcoIGccc2NaIDFiIwNGQFbcsKWZFPBja\nL+4xadTdrd6xHvAzoy3Uvp53Kbom3i1pQZ7lGPhzX49PkYrY1xEefNfU/t6hnOoDS86lfoXvsWyl\nIukMhbOtzqz93wVkHIEEUg4jIeNIvQMbeJb5M2W3Fuo0lWfW3dRMHf+sJvPJMiCoLePdd9/V9rvu\n0pQ33ki9LOQs6/2cc9bjUIWzi96lcMKYIvRLWi7pRUnjJX1c0tAW33lf0puS3lJY3rMVZkoH+r2k\nX9R+v4/C19T8QdImScfWfv59SZ+q892BJimcQOcbks6UdGC0zTIq94yj7/UHjWqog8CRwBHwBi3U\nbtixY4eWLVumiy66SPra10wXByV0hKRnFQZVn8l5XT+S9I6k/RW+/uJlha8F2Wm7pJEKM6A7JO18\nJBsmaaKkybWfLVWYETxA0pMKJ44arXDG4bEKu5puUjjRzURJDyrsjru59tlmgaMUZh1PkfQtSe2S\npibb3MJQnwPFI3Ck3nFTlBZpWsmScbWVMUKXyEgZx1a/80le+6HJcjdt2qSbb75ZZ5xxhiZOnJjP\n+mOWCS3EzRAWVcc0WX5F0iKFE8NEtV7SKwqDtMm1v1t5XGGAMzCzObv2Z/CyR9Q+28hshe9ffE5h\nkDdp0O+PGPT/JNnUoQrfl/ptSScoDGSHJVhOERjjCMAEAkeYYXLiFdsekgva9kJaqJNuQ9Hfy0KS\nsan1goza329Iuu8HP9Bll12mcePGFVMm5CeLLqc51g0fKsysNbNd0jJJWxTWHzMkvS1phaSttc9U\nFWb4DlX46pi9FWbrtkhaKWlxhLIMDgIbOSni59JoVxg8Pq4wO3pcAetMInF9Th0Rj6uNychFEATq\n7+83XQyjCBzLpsjxWC7zcPsjt1BHzZqUKdttQYPFc5JOP/305EFjjHUhJ3Hq1xQTJ2VhhMLg7hXV\nH9O3XtLtks5S69lGf63wfYhjJD2tsJvonyRdKHcmmxmoU+HEVF2yt/yJM45ZTMYG+GbwdXDXXcm/\n6zgCRxclndTD9Mlrev2N2FKunIP6Uo6JMdEQktNkKDNffFFr1qzRwQcfvOe68grSmZjKa1dJ+qmk\nhxVmE09T2DXzdUkPSFqocPxhK0fX/gy0Xa0nwEFypazPbZbzpFUoSMpjF+ijcdi+ot5B8UxXuqbX\nb0hhY2Jc2b+WlXPlypU6/fTTi12pZfugNJI2aBTcEDJC0kUKZz19TtKNCruNniCpQ9GCxkYIGvNl\nbIyj4Uw5YFKiwLFk1wyBY9kw6Ul0tu6PnCqZTFqobd1neYmzvVGvvQaf27Ztm8aMGZO+HChWEcFe\nluuo0xOlIumY2p+fSrpH4XjFqqK9CxHFyz3jSJ0D7IGMo73d9wFkrEfh1PSw04wZM/T888+bLgY8\nd5akEyV9SQSNNqM+B2ACGceysnHiEttaMF2YKCjDsjEmxm5HH3207rzzTh13nK3zOKKuPHp5FJG9\nbOKA/NaOjJBxBHLU4Pwn48hzJFB+tQqwZ/Vq9fT0SLMHv0Vtz8/GWW7i7/skwn4ZOXKktm7dqr6+\nPrW1tUmStm7dqvb2Vi9NaLI+jke52dhAuFNW3bxRV6ZjHG2bSM927C9vETgSOAL2yWtW1a4uDRmS\n8pIfGJD4cLNsFIDl9MB+3HHH6frrr9eECRO0ceNG9ff368ADD9T8+fO1cuVK7dixQ3PmzEm1DpQI\nDQTeyjzjyDkEtETgSOBYXtwEymNwkJbwYbGnpyfMOLZaV5LfIRNHHHGEDj/8cL322muaOXOm2tra\ndN999+nb3/62pkyZovXr16ujo2P3V3Y0kyaw8KVxIK24+7iI/Zp3mRrVSUkRAMdmbFZVwAfUSQ0R\nOAKedLlMlHG0uSucjTK42VQqFR144EevZD/vvPN2/fuZZ57R6tWroweOyJ8N10LaOsyGbUAsmWYc\nOf5AJGQcCRxRpKSvK/Dtphb1ITCPjKNJNma44gTOBZR9zpw5uuuuu3T99ddrwYIFmjp1arQvetI4\n4owss3VR1pPHOjw5f2x9SDSacfTk2EdW1PWM4g2qQ2MFjiU9F3gdB+CJrq4ujR492nQxkMKQIUN0\n8cUX64orrtAdd9xhujgADOmSRG0OFIuMIxlHFClq9qakrTSmOZFxRCQdHR0aMWKE6WLAZozRKTXG\nOAIwgcCxQCtuWWK6CPbiISd3mcyqOliZjltWE3zkqL+/X8uXL9fzzz+vY445Jt3C4s4Yi3zYeg3Z\n2HUcu/BeXkvZej0jE2QcqXdgAxsrWFsq/zSzIg76f6yMY5z1ojB33HGHKpWKPve5z2XfCIBiMN4U\nGbBqjCPnLzxB4EjgiCLZEox5KpeMYz1JMxVFPYw4nEmZP3++fvzjHxM0llERE9nEXXbR1wn3iMjI\nOFqOc7lcdk6O88wzqt57r9myGEa9A3iisDGOrt8oLc4I7bXXXmpvb9cbb7yh6dOnmy4OAEOsGuNo\ncZ0JGFPSxgMCRxSPm0x0Gb6LrbCMY1HSTIHu8Pl27LHH6vnnn88ucIz6mpwon0d6ee5zV14bkEXZ\nbN6+DJBxdESra67k52nZBEFAV1XTBYCHXKwoW5XZgW2KlHE0uR2+rjumF198UXPnzjVdDGTJ1Xc5\nwhirMo71ECRF40pjDiQROEoEjkB9JazAS5dxHKiEx6uRTZs2aerUqaaLgaI1GwOcZNwuAaXTyDgC\nxQuCQNVTT5Xmz2/+wRLXq9Q7gCesf48jWurt7VWlUjFdDACGWZ9xBFBKBI5AFhxovS91xtETzz33\nnGbMmGG6GDDB4rrFGh7tIzKOQPEqlYqqVb87q1LvwB4OvybBBUYzjraP47CxTINs375dzz77rD77\n2c+aLgpswSQy2XGg8W8gMo4l4cj5hphKfFwJHJGtNBeLbReabeVJqWHG0dXtdOxBL43+/n5997vf\n1Zlnnqmuri4zhfBgPzsn7azLrh9T18ufAhlHoHhBEJBxNF0AeKqol73HZUs5cpA449hon8T9ORK7\n7bbbNHfuXB144IGmiwKbedSYkpuB++6JJ6Sf/cxYUZpxJuPIuVgf+8VJBI4EjjDF9q6LJVTYGEce\nXjPX09OjI4880nQxgPJy7J5ExtERDpxLiI7AkXoHWUvaHYpgI3exM45x3l2ZtstckuWgWK2uUcYo\nA4WxPuNIXQCUEoEj4AlmVQWAciDjCBSPjCP1DuJolmHK6mXUyE3qWVUd68qFgnFOoEwsP5+dyTha\nvh9z4+t2l1wQBKaLYByBI+CJXGdVzeohgZstEF2rCarKOJPqQB4HJ7lkHG2dtA6wBBlHAkfkgZuN\nlYy+xxEAXOHAPcz6jGMjccbOAxYicASiokJ3WuoxjnEmOUJ5NcryMDmOfXw5HjmdkzZ3SoudcUyy\nH9JkdH059+CVSqVC4Gi6AHBQ1jcEm24wJe76RMYRuSrhNQOL8N7Y3cTOOJa927Itou7TEj9roNwI\nHIEsOFD5M6sqMsVkSWawr+Mp6QN6obOqZrnvSnYc4BfGOBI4At4g4wgA5eDsGEfAYcyqSuAI1FfC\nVr6RYPMAACAASURBVFEyjkAJkOndk4fdWDPJOJY0G1uXh+cIskfGkcARiM7xGwwZR+SGafxRpKTn\nV4nOUzKOjnP43PMdgSMQle8VneMPHWQcAZRK3DrYsTq7mUzHOPqUeQRSYFZVAsfcrbhlieki5Cvq\nDYfuVcaRcURhuN7hCkdfI0PGMSaTx9jB8wtohMAR6cSdehrG7Mo4Mi07AJsVWR85WvcVOqsqAEmM\ncZSodxBH1K6ajnfpLCsyjjDC9m5waeurpJNuMFlHerafWzki4+gAD8/LsmNWVQJHwBuMcQSA6Gx+\nRCTjCBSPjCP1DvJAK5uVyDgCdaStr6jvisc+zyfjyDAGoCkCRwJHpMEEGE5pOsYRyJvvXdgZD44M\nkXG0FNdvqdFVlXqnHEw9kFFBOoWMIwCUQ2FjHMlCArsh4wjAC4xxdFu1Wi1Xa6frPRbqvcbBxe2A\nk8g4AsXjPY7UO0iChyMnkXF0W8PAsV4A5npQBr9wjsZmdFZVj2ezBXxH4FgGVN6IgIyju4IgUH9/\nvyqViumi5KNZHZZF/ZblS94JymEBMo5A8Zgch3oH8AYZR3ftDBwBQOI9joAJpRoukhCBI6Kjdd1p\nZBzdFbmVk2sUruBcTcWKjCNdVuEZMo4W1DsooSy7hdnE8W0i4+gur29WeXQxBRxHxhEoHhlHAkfk\noawPaFHHYVk606J3GceSjUVL3FWVrABQOlZkHHcqWV0LNOJ1I26NNfUOHNAqIBqcabQ0gPIVGUd3\nMcYRMMjCxhcyjoAZBI4A0mv2YJH3jJEReZNxjLNPHWkpzyRwdGRbUSKtGheRmFUZx4EsDLKBrJR2\nZvMYrKx34AjXbwzNMqIlfMAh4+iupuMqeFADcmNrboGMI2DGpk2b9PTTT5suhjEEjoAnvMk4lhBd\nVQEMZG3GESixSqWi/v5+9fb2mi6KMdQ7OVhxy5LsF9oqm2A62xC3i6YNLOlCWpRSZxxLeLwGInBE\nKbh6nVpYbuszjvSEQEmNGTNGJ510kuli5OpnP/tZw98ROMI/eXdDtXQsmZcZR9sbXCKKNJObpecd\nkAiBR1N1M44lHGIB2KRSqTA5jukCoAVuAtnzdJ+WKuPo2TEk4whrMXu2EXUzjjbuexq0gFIhcHSd\nLRVxmtbhMrSSOlB+LzOOJRH73VEOnI/wWJw6n8C0rqZjHNlnQC54jyOBo/vyGlvIjaZ0nM84enxO\n7rpZJW2YifN7j/czMsD5U4imYxxtzfJR18BxTWc49wSBI/Lj8k0iSot4lO2xaJyOVxlHC/Z31uiq\nCucVcV3mXeda0kOm67rrNGTbNmnq1PAHSSd7s2BbAFeQcSRwBLzhfMbRY4xxBDAQ9TlQPDKOBI52\nyLrFL+14Q2S3H6JmLQvY715lHEsmVStno+7sjc45i7LkMKzVOZJ0eXF6a2Sx3hKiPkdLXEOZI+NI\n4OiWrG/icdebthKK0l3G18l1CkALtbuYAhyosXX8XlFq29yzenU29TkNRUBkZBwJHN3iesWe55gT\nEwY/wKQpRwHjQWmhdlumXVVdr0tgls/BhiXbnKg+t23eAd8bAUyIkgDguDRExpHAMRezL1+8698r\nblkS78tJs4pFdK20vQLx+WEmAuczjh7fzJrOqurZvoBlkl6XWVzPUXrDlOX6GLQde9TnabfT4/q1\ntMp2DViAjCOBo/244N1QRDY15TrIOLqLyXGAiDwJgGLV5y7sB4Icc+ImDDhGXuMpEsVpdGMoeoxi\nmhbyKJlfSytV5zOOrVi637PQtHtM0olGgKJFPVc5X0NN9kOu9TlBXLlwPDNDV1UCR8AbZBzdxeQ4\ngBm2XnXU50Dx6KpK4FgennTPyURZ90+L8bGxWqhtOJ/KepwSMNLKSSt1udlwjdeTZVls2q6MlbYH\nCfWO/QYeI1vrkZyQcSRwtIMHF9tuTL4+pNnns/qspRVpaVuoLdrHeUo1xrHVOBXbZluEHbJ4iLfh\nXCph99dCxjgyvg3YDRlHAkc7JJ1JNeoybavobSuPK1I+xCVuobb5XPJE3VZOHuqQlTyzPFmep0nL\nWcLrorQZR2SviMn7PEHGkcDRfnFnu/KRzfvBom43zmUcW+07C/ZpUSqVSrGzqnq0b2GBoupJi+rj\ntJyrz6MqwbFBeZFxJHA0K8vuI1ks11VpW7QHZ9TiZtjyyMhFbTCIsb5ILdRpy092MhfcrDyV9fUU\n596RR1f/rKSZGdslTbaTjCOs40EvGDKOBI65m3354l3/XnHLkmJXbusF62NwYUFLd+wWaluOjS3l\nMKjuexyzDPKBrKQZT1hkPen4uEdjGccsj5HD+98p9FzLTKVSMV0E4wgcAU/QQu0uWjkBDER9DsAE\nAkeb0RJkj7jdtiw8doW3UPuYWc5J3Ywj7JTXeW+iG5iJSTWoKyIp7RhHkxhPD7RErWNSVlOdU6nV\nF3d8YpzvmZTnrKq2b7unyDgCHmpSHxvPOLa6dw7+PfcWoBQIHF2XpDIuulXNphtGUVmwIrc54oQX\nXdddt3sLNRnBj1je0kzgaKmyTDZTj6mGN1u233JkHC1S9ISFLgXlLjbOoylqHRskvUHHXQb2lFUX\n1CIyvymXX0gLNedhLggckdtkJHHvLUVd43k05jTalkbLPPts6cEHk68vqQjbaDzjWI/rXZBdKGPW\nsnj+hFcIHAuUaIbVvMfIZLk8Kher0ULtLsY4IpUy9y4o2/ZERH3uIU/PddiFWsd3RU17XsQ6i2g5\na9ZCnWbMarOMZUZl7lm9evcWam5CziDjiNzErbfi1udZ1/95BMGNlvPLX0oLFkjz5mW7vrjlqMPK\njKOvyn4vzeKaK/s+8giBI+AJWqjdValUCBwB7EJ9DsAEah3fNcqcZT2pA61NzRWwf5xvoc4icxH3\nvM5iTFjc39dBxhGSzFwDZdJo2+vt14E/e+CB3IqUlPP1ObLDsCEUiMARydnyAGJi3VGD5Lhly3Fb\naKF2F4Gjo1x6gHOprDulGR7gOOpzGNEsSPXwOvQRtQ72VMRsdlkqYrrnoirHHLeldC3UUR8aW53P\nRd/sEqzXyslx8hqT6xtXH7yS9ERxafssV7r6HIATCBwNiTzDqo8tqj5ucwFooW6iiMaHFKwMHFEM\n1wKvZhN97fz9wL9dY8nxoD73kI3XjI1lQq6odcqKizk/rj347JxV9eqro7dQW/JwhJBVXVWjZnht\n60Ie5ffIV9bnRl7H04HzhIwjrMCzgncIHNFYlpVAq1boeuuLs/6ipklPOmbRgrGOzrdQm+pSmvR7\nGZaXWVUdkcUxz+M8L6p+tG1ZRS67YM7X5wCcRK0DeIIWandZlXEEYBz1OQATCBzLxpWWXzSXw5i7\nQlqobeq2Ynr9GXJyjGOrc7hEx8cIuuXGV6Lzj4wjABMCWrFDQRBUb3/57ULXefd3lujpn/9EPX9c\nF++La9bs+bNp0xr/zgY7y4ePxD1WjfZhxOVsGj1a1WpVY8eOjbfeOAaWxfQxj7p/TZczgh07dqha\nrWro0KGmi/KRJHXNwH2dY13VLmnr4PUZsnHjRo0fP77xB5JeM632n8ltj1K2Rp/Jch+0KkOL5WyW\nNEnSFQN/mHXwmXAZq1evVk9Pj2bPnp2+DAAiW7p0qRYtWmS6GLnq7u5WtVoN6v2O5iqDzr9msc6/\nZnHzWVV3anVzsTVg3Gln+dKOW3S8lXg3cbelUUUVcTmPzJmjzZs364ILLoi33jgGlsVUxRp3vza7\ndpKcb0V9x7QkZR54ThSxzVmcgylfO1L3IaPRdwfXk2n2UZI6Nyut1rloUePPtDpmWW1PhHPxl5Lq\nPjlZgIwjABOodVxlY5ebwRPguDb7aNEKnrBi2wMPaOvWrfmu01a8czB7ZW/YQXJR6n4b72E77SzP\nL38pBYE0b142y8twmxnjCMAEAkeX2XazLYLNDxuWGz58uNrb200XA/W41MiSNgg3tY1pZm0GLEPG\nEYAJ1DquMNXdKO7DFlmI4kUMOrZt25Z/xtG1Y+1SwOaCuPszj/3vwqsnLBknl7uo22lr+bOW4XaS\ncQRgAoEj4AkyjgBQDmQcAZhAreOiolpnm2UPo2QifWlFNiXm/i0k41gUuiy7ydfuonHP17LvFxsy\n/Y5PYkXGEYAJBI6+s+EGjuw0OY7eZBzL3PUPe8p7bGVRXV/L/l5G2+81LnWXFhlHAGZQ6/guz4ep\nvLNCUbKftj6k5KXJtjufcfTtWNrGtcAmSj2Q52y7tu0PF1hWdwdBID3wQPgnjkb3JGZVBeA4AkcX\n2d5ymxVftjOpmPvHm4xjEqbPMdPrd42L+8vWjGfRbNse28oTERlHACZQ66AYSYLArG7oaZZj80OF\nL2McS3QMkJOsj0PS7qOcD9kxnX20/FiScbRAnj0WbFwvIAJHt5nMyNlccdlQBlMY4+gOn8/Terq7\n8wsAW/0+z670S5dKixZlv3wUJ+lrZqL+PAEyjgBMoNYBPOFsxhEAsBsyjgBMIHB0kcuZijxa+F3e\nH1lr0r2LjCNQoEbXIvVVfK6Ndy+gvGQcDbL1PBx83tUrp61lhzOodSww+/LFdX++4pYlBZckA1lW\nVK49LFiuFBlH186FMp3DNnaNL8v6TK0TjXV3S08+aboUDZFxtAzXLzxB4Ih8UIlaZ4+Mo01ZEJvH\nzKIYRb6yB27iHNmFjKNlXLifNvpdXuW1aZ8gM9Q6Lko6UN+GC9eGMpRZk/3rbMbRxXOG2TaTc2lf\nRXlAY3Kc6Gy6V9VjUfnIOAIwgcARyaS5gUZ9qLbg5uwcxjgiKlqDo6ERADtZdOzJOBYsyavEms0U\nbdG5VEiDCPeb0qDWcZlFrZ+lU8J9ukfGsYTbiALkVe+YPB+5FuAYMo4ATCBwdJmLDzvN+tfn8R43\n7DJ8+HB1dHSYLgZsFHdMTF5ca5UmG7k7146fw8g4OoBrACVEreOTIh9yXJgG2rby5MyZMY6eHRdj\n8u6x0Ky+yStbmefkKYBFvXzIODoqi3OHBhoYROAIeIIxjgBQDmQcAZhAreOTPFqpoiwz7uyvZdRq\n2wqYZr7pGEdT3RHzWLcL2e6kTJxHZZB1poj9jChyzFCScXQU3dutytwjPgLHMnP1oqRSyYXVGUdX\nj7Wr5TahiH1lojGEc+AjvuwLC+5RZBwBmECt4yvGAhU3O2SU5RfwIGLtGEcLHsLgKcYKoQg59IIg\n41gSNvWQoT5EBASOLmv0wJ3kfUNJvpu3VgGFS5WcBcGRNRnHvI+bBfvaKY2OR9z9l/WkDzYtC8Vy\npW43WE4yjsgMM9ojBmod39l4g7alHAMVtZ9yDHqszThmweYGkFZsvAbryaqcrmxvPa6VF/kyeD4U\nknF0+VrNiq/bXQQaeZ1E4FgGebTS23oh21qugZqVMcsbccxjZU3GcaAignAfmJj0pQz7twzbAC+R\ncYQk+5/ZUDrUOoAnSp1xBACPMMYRgAkEjhZYccuS7BaWRRcyl1quTJS1iP0UJWsZdf21zw0/7TT7\nMo6+sv0aSzO+McqYZJvZNFkFzBl4zB94IN/lJ9Cl2gPc7bdntsymiuy26vLQgyxE7bVUxPqAQQgc\nUZ8tD0+2DNp2MagepOl7HF3n+rbEbAQwxvT6TfN9+2GNntofQFI2zyhZ129xJjiEMwgcsSebLuYi\nWh5NtqQWaPiTT6p9ZxnynAGz3vJtOqfgpzzOyajLZJIRZGxXxtEmrc5tl859W8uaZ6M+9RQisK7e\nQUolyIzV3Ya4wU5emcq89muSrn4xj/U2SYxwtITL12cWfN9+RJf3Pc3Rc7HUGUdHjwka4HiWCoGj\ny3xqHXJ1++J2QcxxO4dLSjXC0dVjALNMzpxr8pzlenGDo8epbsbRRMNxkpmYHd3n1rKxnuMYlxaB\nY1nZUEHH7RqWtKxpJvKwUU7bs+2BB7R182bp0ktjFyn2um3fx4guyrF0ZYxm1pI8NCNbzRpQS9y4\nulvGMe8eNiXbd5H4uO0+bSsSI3AEPGHlexwBALFZOcYRQOlR77iiVfauxC2rzshyso244xxbLU8D\nxjj++tecI3AX5y4GanY+lDhrtEfGMQ8l3G+7Kfv2pRVnAjx4g8AR5mXZFc5GllSyQ5XRGEcLtgU5\ny/IYF32+2HJ+7izH0qXSokVGiwK1blytU79NkvRTSS/nVqjkemt/L6/3y6VL8115neW/K2mvFN9H\nTqZNk9asifbZqMclyucyPMaVSkX9/f2ZLS+tLVu2aKnH5zCBYxnZ9oBvSzk816sBs6rado6UQZ7T\npANILkp9V+d3+0v6Yg7FycJqhRnH2fV+GTVQSDrvwMCGkNpnlkqK3Dyys3xFjp/2tS5etCj6tg88\nb5o1eEc5v2gsc1p3k3OGwNEVWUwcU1TFmdV6XKvok3SZyntinwGfH/bQQ2r/05/CrqppuHZcACnZ\necu5DksVMsYxTmBXL7PO9WOHpA3FNDCjDgLHsuOC31PMVueW38ny+znq7e3V1q0ZvsnR4m0FCpG2\nIQhIKJP3OKY9Xznfy4tjiwYIHF2Wd2tQ3LF5UVsnqZCMGDZsWDirKvsfLmMiMMCNWVWzvlZpqGku\nTnfdPF/fglKzvt4BkI3MM44AACMyyTgCQEwEji6zeTC4qy1PA7O4RYyJKnAMwa6MYxy08MI0zjlg\nD1ZkHJvdH2y5bm0pB1ASxusdGNYqMIg6hXmc9VCRG5HrGEeOKRMJmJJ193eOn524vnbjXMYxiwn+\nslomaBRGYgSOvqNStkfOk80kyjgCJlD3IA8lOq+syDg2YzLQL9FxBmxjdb2DjGRZgftUIafJsKZZ\nR04SZRyzeoeWT9gX+aO1HGk53mPCuYwjgFIgcPRBETdFx2/CLZVgm8g4wlpF11GD/8+rZeJLWufb\nsq8d7/pqfcaxSI4ew9KhodkL1DuGrLhliZkVF/U6DBsqBtceDHIuL7OqAkA55J5xTPus4Mp9F0As\nBI6AJ8g4AkA5kHEEYAL1DuKJkhUrw4Q7RZazoHWRcayjqC7WZe/K7SLXeiTYomz7y9HtcWKMo82v\nDAOQCIFjgTLvnpr1g0+arik2P4S5+NCeQznJOMJ5RV2/rtQT8FYuGcdG44CjTEa1dKm0Zk3WJQJg\nGQJH32SVDeTBKhu2z6paVpy/6ZVlZtMk5XZ1W1EaTmQcYS+TdZjNiQa0ROBYBlm/NiKPDJ0tFYTH\nFRYZR4M8PN8y5cuEXkBEu2Uck3YJzXoWTFONSfXu62Vp2AIsQ+CIPXkcXJUZGUcAucji1SatHvS5\nH+3GeMaxUYN13sep3nlRxDuXXeDjNqNwBI4+SBMIxsk+UmlZjYwjMuXa9Z5leWlcM4v9r67rrtOQ\nbdukqVNbf9jj/VQX509xaAAqHQJHwBNkHAGgHHp6etTTwyhHAMUicCybLLtslLFlqIzbFNGwYcPU\n0dGRfAEuzk5rUt4trTZ1z0pybnAOAYl1dXVpyBALH+Hyet5IO04z7XeQHPu7VCysdeAUKoRs5bg/\ne3t7tWXLltyWD2TKhbrFhTJmLa9ufgyFiLWNPatXk3GEvbKawR/WIXAEbJLj2IvUGUeUn01ZTFPY\nB3CAtRlHQGIcaYlR65RNURerq5WBx5VZ6oxjmfaZx+eBNRp1b+WYICqPr2PjYxwH7/OlS6VFi5p/\nptUyAFiPwBHJeHzDLkQOWQ8yjqjLhnGRWXwvyphS6is3ZDmeraTIOMIJXLOlQ60DeIIxjnXkeVNz\nNevv+43e9+3PS5SXtGe5bNulLKvxjGMUebwCJ+/1AGiKwLFAsy9fvOvfK25ZYrAk8BEZRwAoBzKO\nAEyg1kE6jE0qTspWdTKOKDUXs05AQk5kHLPEswZgBQLHsmr1EJX3O+Zs5+FDJhlH1JXFNeDRdYSU\nfDxXcthmMo4DeHg/dw7HpjQS1zpBEFwl6cbaf79QrVa/XeczJ0j6O0nHSeqQ9Kqkb0v692q12l/n\n88dJ+ldJR0haKemL1Wr1uUGf+bKkL9f+++fVavUbdZbzWUlLJf2varX6/ybbQsTmezBalIQtr2Qc\nLVCmVvOirve8xkm5vv/hjhzG53mTceQ6LQfq3tJIFDgGQbCfpH/X/8/enYdHVe/5vv+sCpmBhCGM\nIQFCmARRBAcaB0QR3YpbHEAcGfvZ5977dJ+hn+7b5/Q5nD6nb3efs+/Tu8/t2/1IRLYTk7q9bt22\nup2n7Va3U0ARBHULuBUQIiQQSGrdP0JiEqoqNaxav7XW7/16Hp9IUlXrW2tV/aq+v+9vkI5K6p/k\nNtdLelTScUlbJH0n6TpJ/yBpjqQlCR7z15J2SvonSfMlveg4zlmu6+5PcAhX0n9xHOdB13UTtZ5u\nFk8teniDpuZHY7Z2bSCuAxVHREY67yeqEPkVxIWlLPpyamXFMd3tPSJ+7QGTsm11Nkg6KOkXkv5D\n7z86jjNAUoOkNkmXdlYNHcf5K0kvSbrJcZxbXNfd2u1ud0raK+kC13XbHccplPTR6d//XYIYPpM0\nQdJfSPqrBH93snxusIkfHzAB+RCj4gjAMxYlaXmV5bYxka448noCAivjxNFxnD+RdNnp/+YnudnN\nkoZK+nn3oaau6550HOc/SXpB0k8kdU8ch0n6xHXd9tO3PeU4zg5JwxM8vquOiudfSPq3juP8S5Kq\nZGAFboVVeuryz/BQXiqOARCl95fXzyVK5wbIMysrjgCMi2VyY8dxpkj6W0k/c1339RQ3naeO5O7Z\nBH97VVKLpDmnq4qd3pb0I8dxfuw4TqnjODdIulrSb5Ico0Ud8yfLJP1NJs8DsBEVRwCIhubmZjU1\nNZkOA4Bl0u6uchynQNKDkr6Q9B/7uPmk0z939v7D6WGon0uaKmm8pE9P/2mjOuY9/qLzppIe6zWc\ntbf7Jf1bSbc7jvMPrut+lMZTgV8YytRTPs5BBlXMtCqOWQ6bgkXSeV+bWDjH72HnNr0v+rqemZyX\ndF8z2YjKNUljMZ1IVhyZwwgEXiatzn+RNEPSH7mu29rHbStO/0zWHdb5+8rOX7iu60pa5DjONepI\nPLe5rvvrVAdxXdd1HOfPJD0j6X9KuqqPuADvpPoQC+AKs1QcEXq5vo8C8D4EvBDpOY65ogM0eDj/\nkZFW4nh6m4z/U9JPXdd9O58Bua77tKSnM7j9c47jPCfpSsdxFrqu+0z+ogO6CXJDmKACwBxHBFr3\njhiT7y2/VlrO5PdBk4ftJbKqJoXlfHmps+L4k59Er+KYKxtfD2Fh62iNCOpzjuPpIaoPqGNIae89\nEZOtXNpZUaxI8vfO3x/p6/hp+jN1DG39H47jZL2aavP3Tfr3P77C95/Hjx/Xv/zLvwTjpzr2T/H8\np+nnlezn8OE6/ud/3vEzn8+38zj5Or+9j9v9993i2LZtW3rnI8n9+cnPrvfLn/95+u1Iotslex0n\nun+y43V//yaLN9N2KNXts3hf9OvXL/12NgjXN53rmc+fQY0rYD+/++47Pfroo8F4fSR7nXv1Ouv+\nPs+k3eFnMH8G4PXKz9Q/U3E6RoimuIHjVEg6rI7ELFFS1v33P3Nd9985jvOgpGWSlrmuu6XX4xWo\nI7EslNTfdd1TKQM4M57/oo4EdrXruvd1+/16SXdLWqOObUA2SPrvruv2TnaTPa67dftXOvSHrzVk\nxEhff379ylP6/vvvNXDgwNx//vVfa6Ck7yUN/M//OfXtf/azjtt1v333+3v580//1Jvnl8+fvc9H\nGH92nucE1/G9Sy7Rd999p8WLF3v7OuJndH729f5P932c7HG63z/Z+y1RO/Snf9rz9sni8Op+yW6f\nxfviF7/4hRYvXpzd8YLyM1+fC8muf9TbaQ9+nlDH5tiTA/K6Sfg6z9frLNX7kp/h+RmA1y0/E//8\n2c9+Jtd1Exbi0kkcSyT9ryR/ninpXEmvq6Mi+WvXdR9xHGe5pPWS7nddd3mvx7tc0vOSXnZd9/KU\nB08cT7LEcaSkXepISv9G0j8pw8TxsR1mdvTwdDuOXBYpyOcQsTANTUg1jCzozyPF3MpnLrxQR48e\n1c033+xrSAiRbBc2Sfd26fzeq9ukul2298tiuNWGDRu0fPnynr8M+1DVZLw43169BiNuj6RmSdOl\nQJyThK/zTHn9fkWwcd0Ca+3atUkTxz4HyLuue0IdVbwznE7izlVHgnhftz89KunvJS11HOefXNf9\n3enbF0v67+qoUqauhWbIdd2vHcf5vyX9laQ/PX0MAKcVFRWppKTEdBgAgByVK4uNuAEgR160O2dk\npK7rHnUcZ7WkRyS97DjOZnWMqlgkaaKkR1zXfcTLY572P9SR5E4QiSO8FvLesZMnT+rEiROmwwDs\nFfI2BMHRfPo/APCTF4ljwgTNdd0nHMe5VB17Pi6WVCLpM3Xsu/j/5OmYzaeroJ5WM0Mln8vV2/Kl\nJ0pLeXeLu+jFF6k4InN+748YZAzlT4199vKvc1XVb75Rv9ZWqabGbDwArJJT4ui67n+V9F9T/P03\nkq7N5RhZHHOdJA8nDVqOLwCR4XnFkXkmZuU6nxn5xfLzyKPQ7ePY+/2QS5vE+wkwhiHyNstn73nU\nGvag9qRnEA9zHNGnIFfU8vneC9r7Ouw4n97rdU7Ly8vZxxHBl2snAQKHVicApi9NuPaQt6ut+olG\nwbwE1Q7mOEZAlN5bmX6ZyKaCl81qqZk8frL7ROk6IRh6dV6GouKYyerBvGeiiesaOSSOSC2XL0NR\nbDCCUHnM8thUHCMo1+0xoiZIyVvn8TdskDq3KQhSfEGS7TBFi88hFUeEisXv1aih1QEsQcURAKIh\nFBVHAJFD4oj8oWc4v9Id7nP6d0WXXELF0RTeC+HixciCvhYDsVWu7wXbz2Pnqqo/+UnPiqPf57Kv\nzx/brxMQUSSOSF8Qhmkia1QcESh+Dn0PypYitJ1m5DNxT/S56EMCRcURgAkkjkDYZLnXZtFbb6lE\nkrZvz+wxbReWFeEyWYjCb3Q6AZ7qmuOY63sqyCspIzqoRkcGiWOAdV9tNbQrrEZVUBrBDHrScuN5\n0QAAIABJREFUT0rytN5I458+r84V5zx9ps9VosVxkP/rYqqTwucVRANRcWQINmAdEkek5vUXXj5k\nvJXJPo6SmOGYAV6r4eZH505YqtGIHE9WVQ1qtRLRluWoKQQDiSO8Y2vvY0gawR4VxwDFhTwxXQkP\nk6CMIIB3/KpsenXfDLfWad6zp6PiSKesN+gEAtJC4ghYgoojAEQD+zgCMIFWB7BEyoojvau5s61q\nleg1ZNs5CJKwvKezfY1k+3yCeh5y1DXHMaLPz3ecx2Cggh54JI7Ijp9L6cMTVBwzFLbXbNjiBZA1\nKo4ZsHUaTRAEebVvZIVWJyRYYRW5SrmqKpWi7JiYR5WPFVptv+Z9fbnJcP5Zn4/rB3ruIy0Qq6qG\nBe8BwDMkjkiNBjc3AfoiScXRBza9X/KZ1HohSLEg//y63vleoTfNCs0Z+zgG5fWebSdLLsdL1RYF\n5bygJ65LaJE4ApbIah/HACW+CAgqlQiDiL82mePYB85LMGSTwPMZE2gkjoCfDFZpqDiGnNevEz6Q\no4XrGT0prmkg5jh2j2/DBmn58uzu6/V+krwXvMc5xmkkjoAlsqo4AgAChzmOAEwgcQQsQcUx4Gzu\nwTVVibf5nPstnXOd6wqMQbiePr2WA1FxDJogXH8kls77oq/bcH0DgVYH3mJCemClvapqOr9HBxvP\nj43PGeaZeN0F+JhnzHHM96I9+ZTpMEjaIP+k+92Aa2INEkfAEikrjtn06vHBgXwKck9z0OJBsOXh\ntWx1xTHIbQMQcZa2Osg7GvP0+VSlZY4jkCHaMe9kei459ymdMccxSqN9sk0Mo3QOTMvnOfRq31wY\nQeIYQtOXrun6/8bN6wxGgjDJ+xxHGv2e/Br+yzDjxIK+z6RXwlp9yWQ4IkMXz2B1xbE7C6+9tfiO\nEQi0OoAlelQck22ajOCx4TqFbdhz0OODOT69NvKyqmoQX9epYgpivGGWj/OZzXcNEsRAI3EELMGq\nqgAQDVQcAZhAqxNyDFtFunKe40jvX35wXn/ACn5AWkK3j6MtQ8eRnrAOsQeJIxAYeR6eUXTJJSpp\nbpauuy75sXNB459aNos8IL+SvefCdP7DFGs6gjCM3vTx0+BJxTEEzxM+yPfrgNdZpJA4ApY4+eqr\nHRXH3/3OdCjggzTcTC1IFOYEt5OXMYfx+Xsk0BXHbLZ0svhaGkc1uEMU2lcfkDgClsh5jiMT1vOP\nc5saS/PbgWvWp6QVR84dTOB1Zw0SR8AS7OMYMXxQBw/JKnwSyIpjLq97qj3olO72O7xGjCBxBIIm\nT8N3KiTtkxSXFMvlgWi008P58Q7nEvkWstcYq6qKz6JspDpnfp5HrlloWd7qAPYYLulbSesk/VjS\niO5/ZLNtf3DusuPVF8Ter3OuR35F9fwmai+zmdeXg8BUHKN6jaMqyNcryLGhC4kjYIlySedKmifp\nEUm1kq4wGhE8xwdvtHF9e8qlQyHk55KKYwZyTeqj2skU1ueUr7jDej58ltOINQDh0SypSdJASSsl\nOZLulfS9yaAAABlrbm5WU1OT6TAAWIbuKsAS5er5hp8v6RtJD0uaLWlWXw9Ab5x52V4DFp5AVJh8\n7ZqeF9YNFUcEip9bFDG31ShaHSDIPGwgm0//191wSX8s6QlJn0haoo5tOxAyUfkATfZ69+r5ReU8\nRRHXJiOBmeMYBLQP6bPhOSKvSBwBS/SuOHaKSbpB0i51DF29ctcu1dfXd/wxKL37Yfywy2XeVab3\nzXfCFUapnnu+zws94sgzKo7ISq5Vc5J069HqAGHgwRCpRBXH7urVsWDO1t/+Vh999JFuuOGGMydB\n914kgMYf+cCy8Hbw+9x7uSpvOo+Xx+cXmIpj53PcsEFavrzn72AXrrsVSBwBSySrOHZXJOn222/X\n79au1T2NjfqxpJHdb8AHA6KG6qB38jmfyYvHj1Cnl7GKo1/nMELXKvDSOb+5rlxMOxsZJI6AJfqq\nOHZ3nqSJkrZKqpF0Ze8b0Pjnh9fnlQ/r/OB8QjL6OghMxTGRAC0ihACL6jYnEcd2HIAlyiVVZHD7\nAerYtqNAUoPYtgMAgqK8vFwVFZm06ACQOyqOETJ96Zqkf2vcvM7HSOC5PK2qms6xLpc0TdJGdVQi\nZ6d733TnA9HjCISXl+9f2oK0+VpxTLft7t3+e72Qih8bv/MaTCxfw1kROiSOgCXSmeOYzDBJayT9\nUtIOSbe0tqq4uNir0KIpSAu82P6BnuwLru3nxRTOe85YVdVn+Uouo94W9dWBnM15jeq5CglaHcAS\nmcxxTCQm6ceSdkta/7d/q/mSJqW75YFXi1rY+oFh6/NGT2y3gtMCPcdRCu9rM6xxAz4hcbREsmGs\nDGENCQ8Sp1wqjt3VSVqtjoVzGh99VIsXL1YsxnRp3/DFJj2cJ7M4/3kVmopjGDr9ghBjEGIwzebn\nHiIhaHUAeCHXimN3hZJuk/R+XZ3uuece/fibb3pu29FbOpXJTOdQ8CETPfmYB5vra8bLKl/YXr+5\nxJvuaIRsHhv+VByDdl0yeU0ByAsSR8ASXlUcuzv33HNVX1+vLT/9qcZIWuDx4+M0vhRBsvd1YOvz\nTiE0FcdOdBb0lE0nmR+LA/l5XIQS48sASzRLasrD4/bv318r1VGFbMjTMQAAP2hublZTE60tAH+F\nqLsKQC7yUXHs3hM5T9JZkjZJOlfSBV4fC7CdyaqN6aqD6eMHTOgqjvkWtopmkLa3YAgwMkCrA4RJ\nokY8zYbdyzmOyXRu2/GkpAckLZGUcNMOPoyQSD4WiMj1SxGvVe/k81yGLXHIUeBXVQ2jfG+5ketj\nm1pAJ93zEvH3HDqQOFqu+2qrrLAaMhk20nmpOCYQk3S9pD2S1ku6XNLk3jfKIQE+4/a2f1jlY0EZ\nU8ISsxfnPJMvqSa/KObjccNynQOMimMfsvmMMTWH0Ov7eYn3LHqh1QEs4UfFsbvx6ti24xFJjZIW\nSyrI5AH4wLJbFK+7F69pU+fF7w4KKhspBb7imEuSxvUNLq6N9UgcAUv4VXHsrlDSMknvS1onaZGk\n0clu3PtLNR9Q9jB9rf0aSmb6eUZF2LY1yQMqjlngsyV7vOdwGq0OYAm/K47dnStpoqQtkkZJukqS\nYygWABkI65fERElCWJ9LAr7u4xiEodIRunaBZdk8YWSH7TgAS5RLqjB8/BWSSiTdK+mIwVgAIMzK\ny8tVUWGyRQdgIyqOgCVMVhy7u0wd23ZskTRD0oW9b0AvJ4Iq3ddmop77dOZ0ZbrKqw3vlaAtHBQQ\ngZ/j6JV8XF9LXzOeyaR966vtS/R4CDQSR3RhhdVo832OY4phL1Xq2Lbjl5LuV8e2HSV+xRU1fOjm\nzu/90oJ+zYK2uiTO4OscRxaysVNY2iv4isQRsERQKo6dHHVs2/G5pPskzZM0JZsH4sMtXGy9TrxO\nzetdrQ1x9dZYxTHbc8a8xejKtWOB10aokDgCYZXhRHYTq6qmY5w6qo9bJW1TFtt2IFrC/IXBj0Qk\nzOcnGa+G8VokMKuqdl6XDRuk5cvz9/hc/9z0NZy0r/umc1uulRUC0OoA8IPvFccMPjz6qWPbjg/V\nsW3HdZKq8xIUfMeXiPSZ/OLl9zwk01uWeHEcg88hEHMcU22hlG2lCeHF9bQCiSMQFX18aAe14tjd\nDEkT/uzPtGXLFo38/e+1UKe37ciltxTBFbZrF6TtCcIi6LEHPb4kjFUcqSoFV5AWoUlnURyEEttx\nAJZoltRkOog0lJeXa8WKFSpTx7Ydh00HBAAB09zcrKamMLToAKIk6AUIGNJ9hdXu8rXaarLj+XFs\nW4Sh4tjdperYtmOrpLN/8xtdZDgeAB4IWhUipAtzGK84pvu7EJzLyAjpazkhXj+BFabvkQByELRV\nVbukGII6VNIa19WTTz7Jth0Ijly/1IR4Nc+84TxkJBBzHDNBIhANmVxHrnkkkTgClghbxbHzw8aR\ntEg/bNtxhaSJSW6b7DEQcV4mYiR16cn03HiRZPslBF94Pas4Bvg5wgMZrr7u+XG9bI9T/Q6+CdX3\nSABpStBoB6rimEXDP07Sqr/8S23dulXby8t1/fXXKxZjmnaoRO0D36+VRv0QpedigdBVHDuRCERD\nNpXHdG+PQCNxBCwRiopjHx8qRUVFuv322/XOO+9o3bp1uvHGG1VVVeVPbAD44hcQSSuOQbs+6ez7\nh+jKR/U+BCMCoizw3yMB5KB7xXHPno4e6sce6/n3EDa+s2fPVn19vbZu3aqpU6dqLj2awcX1sEtY\nr3fI4g5txRFm5Ov1nWkSl0vSF9LvK1HDOC/AEuXl5aqoqDAdhmcqKyu1evVqHTlyRA888IBaW1tN\nhwQAvohaew4gHKg4IiPpbJuBYApkD3WOvYeO4+jaa6/VZ599pvXr1+vqq6/WuHSPQc8lTAjjMKsw\nxeqVXEYx5HtBkrVrgzVnPVs2vq5s5cWoIF4vgUDiCFiia05Mqi81IW2YJ0yYoOrqam3evFk7LrhA\nCxculOM4psOCX0L6upUU7tiDKFFinu9hevk8RhKhmLMOIHJodwBLBKri6GWP4+nflZSU6O6779bL\nL7+s9evXa+neveqfbXwAzCW1uRzXkkQ8EhVHAKFD4ghYwrN9vwLusssu08SJE/XgunW6WNI00wEh\nXLxKPDofZ8MGaflybx4TySVaOMPPJDLqFcdUQ6zXru35OrckeQ8Fv/dt9POYMCL63yIBSEpQcYxK\n457gC82oUaO0RtIjknZIukFSge+BhRAf/rkL2znM1/6Nfj/3MJzrXAV1X14gEza8VyOMxBGwhC0V\nx04Fa9dqqaR3167VvZJukjTEcEwAPNa70uh1xTigMq44+lmNDeMCUFFi+ryne/0zXbjO9POCJBJH\nBETj5nVd/8/KrfkRqDmOPpolqU7SVkkzJF1oNhwAyBkVRwAmkDgClrCt4tjdIElrJD0h6WFJSyT1\ny7ZXlF5PBEGKhaICI2jxRAirqiL0wtCG4Qy0O4Alks5xtKShdiT9WB1zHtdJWiSp2mhEiCRL3k8w\noFubnXPFMdv2P5Pbh22+b1hF7dxa9t0kbEgcETjJhq0mG8La/fZIzuaKY3eTJdVI2nz65xVmwwHM\niMqXMj/3pQ1QIpT1HMeoXHf8IGjXNl/b6QTteVqKb5GAJZLOcYz6cJEEX/bKJK2Q9GtJ9913n269\n9VaVlpb2ff+os+m5hl2Uv0QFKEELKs/mOJrYrsHP4wLwFIkjYInIVxyz+LJ5paTf//732vD3f6/5\nt96qSZMm5SMyIDfpfsnuqxMowvsaJhTh5Jo5jjhD1DuBEQi0OwiE7sNQuw89TWcYarL7oqeMVlWN\n8Beu3mokrZa05Z13tGPHDi1atEiO45gOCwin3ttjRElQntfatWres6ejPZ8+vet3fd0HAHIVMx0A\nAH+Ul5eroqLCdBiBVCjp9ttv17Bhw7Ru3TodPnzYdEgAkBTtOQATqDgClkir4mhzr/TatbpIUr2k\nLVu2aPbs2TrvvPNMR4V88nsYp6n3l21DVC1g6768yFAURw9F8TmFCIkjYInIznH0+MNjqKQ1a9bo\n8ccf186dO3XLLbeooKDA02MAUHrv3URfErPdgzVCUrbnEX7eCIFU79lkt030e17HgRTBb5EAEqGH\nOn2xv/5r3Sip8cYbdc8992jx4sUaMWKE6bBgQqZfeBAcmSSmIdPVnoc0fvgsbFW6TN+7YXleEUDi\nCFgirYpjvpZMD0rvYYYfntOnT9fYsWO1ZcsW1dfX69JLL81baLBYqi9A2b53gvB+81KqFSOj9lzT\nEPoRJOns14dw4zpGUohbHaADK6mmx2jFMcQfIAMGDNCqVav09NNP6/7779fSpUtVXFxsOiwEAdXI\n8Atp8skIEqCbkL6Pw4jEEbBEVj3U2QwFCetGz33Eec0112jPnj1av369Fi5cqPHjx/sTF8zy8vWb\n7dDJXL8U9XX/oIwIyFY25zXkzzn0FcdU2I8Q6SBZNILtOABLNDc3q6mpyXQYoTZ+/HitXLlSr732\nmn71q1+ZDgeApWjPAZgQ0e4qAL1FuofaR8XFxbrrrrv02muv6d5779WSJUs0YMAA02EhyuhRz7+Q\nVblozxFqAX5vITVaHcASOc+JieqwkCyfz8UXX6z6+no99NBDmjt3rqZPn+5tXAgf0++NTLanSGdI\neTqPF5aFe0xfG49ZPccxqp9FyA6vA1+ROAKW8LWHOigbjuc5jhEjRmjNmjV69NFHtWPHDi1evJg9\nHxFOfPk6U4ATlNBVHNPpmMh2Hj3CI99zxhN1ZvF68VSIWh0AufCshzrAX6bS5mHsBQUFWrJkid57\n7z01NDTopptu0tChQz17fORRrq8Dv94D+TiOV1/So9YOhOT5sI8jrBPyBa2igsQRsEToeqi9kG6S\n68G2CjNnztT48eO1detWTZs2TXPmzEn7vkDkhXmz7gDGbmV7DsA4Wh3AEp7PiQnIF6ggqays1KpV\nq/TUU0/poYce0pIlS1RYWGg6LKBvvJ87hGREBXMcFfhrBA9kMzKC10VesR0HYIny8nJVVFSYDiPy\nYrGYFi1apPPPP18NDQ368ssvTYcEIGJozwGYQMURsITVPdQGTJw4UTU1Ndq0aZNGjx6tBQsWmA4J\nQETQniMrVOaQIxJHwBLMiUkhTx+gJSUlWr58uV544QWtX79et956q8rKyvJyLCDQwvYlNeDxhq49\nT+d8ZnrOvV6VNdXjBfz1EBq5DjPmOhgXolYHQC7ooU7Bg8VxUpk/f7727dunn//857r88ss1efJk\nTx4XgJ1ozz1kazIShrmiXifvef6stwGJIwJn+tI1CX/fuHmdz5FEi7F9HGmQJUmjR4/WmjVrtGXL\nFn3yySe6/vrrFYsxzRxA5kJXcfRDiLdX8U0YkkUEGq0OYAl6qM3r16+fbrvtNr399ttqaGjQLbfc\nokGDBpkOC0AmAlC1oD1X7h2U6W7TFKUky/RzyeX4+bwedDqkjcQRsISxHuoofvjm6Pzzz1ddXZ22\nbNmiWbNmadasWaZDAhAiVBzT5NXnT6r7pzvXMmifgUGLB6HAOCnAEs3NzWpqajIdBk4bMmSI1qxZ\noy+//FJbt25VPB43HRKAkKA9B2AC3VWAJazuoQ7onMtYLKYbb7xR77//vtatW6elS5eqsrLSdFjw\nkOu6On78uOkw4Acfr3O/fv1UUlKiEydO+HbMVOLxuNlYMjl2pnFmevvOz5i/+Avp7/6ux5+czB4p\nv1pb/TnO3/5tTnd31avK1Xl+//Ivc3rcPvX+rpDv44WE47qu6RgCwXEc97Ed+02HgRRYHCc3e/bs\nUXNzs6ZPn242ENOJWyZzGXyM9dChQ3rkkUc0Z84cnX322b4dF/n1z//8zxowYIDpMBAxLS0tisfj\n6t+/v+lQJEn79u3T6NGjTYeRnd27f/j/urrkv0vn/t3V1fX4W+C+bY8f789x9uzJ/D7jx3fdz9Hp\nc9ftd1238SqW3o+d7DYBduDAAVVVVXnyWHv27JHrugn7OSwtPwD2sbriaFoaFc8hQ4Zo9erVeuSR\nR7Rnzx4tWrSIVVcjoLS0VHfccYfpMBAx33zzjVpbW1VTU2M6FEnShg0bwvs6794mdz6HRL9L5/7d\nJUsog6IzUVq7Nvs9MRPxosP1zjvPfJzev7vzzswfN1lsiY6X6DYBtmHDBt3pUYxrU5wLvkUClgjM\nKnxBHDYakJgKCgq0dOlS/eY3v9H69eu1dOlSqlUAzhCY9jwKcl1RkxU5/ZPPRY6QFhJHwBJUHMPj\noosu0tixY/Xggw/qiiuu0MSJE02HBCAbXlZyujG+SnZfvwOiJiAdzKbxLRKwRCB7qNNpfC1toEeO\nHKk1a9Zo06ZN2r17t66++mrTIQEIiEC25/gB21CFD4lhWphAA1iivLxcFRUVpsNABvr166c77rhD\nJSUlWr9+PatzApBEew7ADCqOgCUC3UMd1N69gPQaz5s3T+PHj9eGDRv0ox/9SLW1tUbjATLW13so\nqG1AQAW6PbdZGKtW2c7n9Pv55WnYNzJD4ojQmL50TcLfs01HepjjaJAHH3C1tbVasWKFNm3apNra\nWl1++eW5xwUgv/L05Zb23HL56NQkEfsB5yIpWh3AEvRQnxaQKmI2SkpKtHz5cj3zzDN64IEHdOut\nt6qwsNB0WOaEsXcf8ADtOeADPmPOQOIIWIIe6jSFILFcuHChdu3apYaGBv34xz/WqFGjTIcEwEeB\nWlUVP8hkOKUXw7f9/rzi+luPb5EIve5DWBm2mhw91DkIYK9jfX29Ro0apU2bNmnq1KmaM2eO6ZD8\nEZDzjzRwrfKG9jzEoji8NChxeCVqz8dDJI6AJQJdcQxBlS+IysvLtXLlSv3yl7/Uxo0btWTJEhUU\nFJgOC0CeBbo9R/psXvBl7drcKrIwgu04AEs0NzerqanJdBjwmOM4uv7663X22Wdr3bp1OnjwoOmQ\nAOQZ7TkAE+iuQqQkW3lVYhgrPdS9ZFvlDGh1dNq0aaqurtaWLVs0a9YsnXfeeaZDgq2yeW8E9H0V\nVLTnFgjgFAmAVgewRCjmxATpgzLR8U3H1IfKykqtXr1ajz32mD7//HMtXrxYsVjEB5YE/JoA+RCK\n9hyJpTM8M912jQ4X+IzEEbAEPdRJROyDNxaL6eabb9a7776rhoYGLV26VBUVFabDAuAh4+15kDr5\nwirZeeN8Ru5zOUr4FglYInQ91EFcNCAIMaRp1qxZqq2t1caNG3XJJZforLPOMh2SN0J0DYB8CV17\njsz40c7la3XXKLTRUXgOeULiCFjCeA910EXtg09SVVWV1qxZoy1btmj37t1atGiR6ZCA1Kg0pCVQ\n7Xmia8b1y00QO04BkTgC1qCH2k4FBQVatmyZXn/9dTU0NGjZsmUqLy83HRaAHNCeo4cgzcmn88dX\ne/fu1ZtvvqnvvvtOW7duzfvxSBxhje4rrtq4wmqgeqgzwYePJ+bOnatx48bp/vvv14IFCzRhwgR/\nDkwVwjyuQeSEtj0HOtEWeWL79u2qqanRvHnzPHvMjz/+OOnfaHUAS9BDjdGjR2vVqlXavHmzdu/e\nrauuusp0SIgiKg55R3seASbeH7wnI2ngwIGqqqry5VgRX6cdQKfy8nJW14SKiop05513ql+/ftqw\nYYNaW1tNhwQgQ7TnAEyg4ghYgh5qdDd//nx98cUXWr9+va699lrV1NTk/6Dp7F8GoE+Bb8+pOnuL\n84mAIHEELMGcGPQ2duxYLV++XJs2bdL48eN12WWXmQ4J+cCXzcihPUefeN8jD2h1AEsEvocaRpSW\nlmrFihV6+umn9eCDD2rp0qUqLCw0HRaAFGjPLRWUymOqVVxNx4a8InEELBHaHmo+jHxxzTXX6NNP\nP1VDQ4MWL16sESNGePPAXD97BWmLgIgx3p7zvkY+8boKrBB+iwSQDXqoPZTsQ83rDzuft1GYNGmS\nRo8erY0bN+rss8/WhRdemPdjAgmxhUhKoWnPuY6ZCfM5CnPsSBuJI2AJ4z3UCIX+/ftr1apVeuKJ\nJ7R582bdfPPNKigoMB0WooJKlScC0553XscNG6QvvzQaihWC8r7pHYdXcQXl+SEptuMALNHc3Kym\npibTYSAEYrGYbrjhBk2ZMkUNDQ367rvvTIcEoBvacwAmBKC7CvDf9KVrEv6+cfM6nyPxT2B6qBEa\nM2bMUE1NjbZs2aILL7xQ55xzjumQQsd1XdMhIIJozyMm20obQ4HhM1odwBKhmROTTJA+IE0f30eD\nBg3SmjVr9Mgjj2j37t264YYbFItlMVjFonPWadeuXSSOybCnZ05C354DnXi/hwqJI2CJ0PdQ+/nh\nwjysHmKxmJYsWaK3335b9957r5YuXaqBAweaDstTbW1t+uCDD7R9+3a1tbXJcRwVFBRo/vz5qq6u\nliQdOXJE27dv18yZM1VaWpry8b7++mu98MILWrlypR/hwzKhbM9pV3OXaYdLvs4119BaIWt1gPyK\n8hBWeqhDKGAfzueff75qa2v10EMPad68eZoyZYrpkHL24Ycf6t1335UkjR07VjfddJPKy8slSU1N\nTfrVr36l5uZmFRQUqLi4WNXV1dqwYYPmz5+vSZMmJX3cJ554QnfccQd7YmYjYK/7IApke05iiEzw\nOgklEkfAEqHsoUbgDB8+XGvWrNHmzZu1e/duXXvttaZDytqTTz6plpYW3X333QlXjq2oqNCyZcvO\n+P2cOXPU0NCgsWPHqri4+Iy/Hzp0SAMHDuxKQNGHdIahB2moegAYa8+9qGqRYPrH63PNNbMe3yIB\nSwSyhzoTmXxhWbs2/Q+4TG4LSVK/fv10++2369VXX9X69eu1bNmyPoduBtGBAwe0YsWKjO9XWFio\n888/X6+++qquvPLKM/7+7rvv6qyzzvIiRCCh0Lfn6CnqyXRUn5eF2I4DsER5ebkqKipMh4EIueSS\nS3TVVVdpw4YN2rNnj+lwfDVz5kx99dVXeuaZZxSPxyVJ7e3teuaZZ/T555+TOCKvaM8BmEDFEbBE\npHqo6b0MjOrqaq1atUobN27U7t27E1bggujkyZNqa2vL+v6xWEwrVqzQm2++qfvuu08FBQVqa2vT\n2WefrauuukqO43gYrUV4b6fF1/a8r2vSvVrG9QMijcQRsETk5zhmO88GOSsqKtLdd9+tX//61/r5\nz3+uZcuWqaioyHRYKT3//PM6//zzc36cOXPmaM6cOR5EZLF0h+nxnu0S6PY86sMuAYsFtNUBgiUK\nq61GquKYSKaLZ/DlxnNXXnmldu/erXvvvVc33nijhg8fbjqkpAYPHqw//OEPpsMAshLo9pw2NVq4\nnuiGxBGwRKB7qDOV76SPD8qs1dXV6c4779TDDz+s2bNna+bMmaZDSujCCy/UunXrdOzYMfXv3990\nOEBGAtWer10rbdggLV9uOhL0xmcZPMbiOIAlmpub1dTUZDoMWKB///5avXq1du3apScUX73SAAAg\nAElEQVSeeMJ0OEldffXV2rp1a9fiNkBY0J4DMCEg3VUA8i1QPdRBwL5weRWLxbRkyRK9/vrrWr9+\nvW6//faEex6aNGbMGE2bNk333XefVq1aZToceLFHoCUC0Z5nMiUA3gjbAkRhihVpoeIIWIIeapgw\nd+5czZ8/X+vXr9fXX39tOpwznHPOOVQcETq05wBMoPwAWCIQPdSw0tixY3XXXXfp4Ycf1rnnnqvZ\ns2ebDqnLs88+qwsuuMB0GOiOKkWfAtGes8AY+sIogsjhWyRgiUCvwpcPfKkJlPLycq1atUqPPfaY\n9u7dq+uvv16xmPlBL99++62uu+4602EgFd7DZzC2jyPDU/3DFlMIIPOf2gB8UV5eroqKCtNhwGKx\nWEw333yzhg8frvvuu08nTpwwHVIgklcgU7TnAEyg4ghYwrqKYyd6YQNnzpw5qq6u1n333adFixap\nurradEhAqFjbniOcC7uFJU70ia5WwBL0UCNIampqdPfdd+tf//Vf9dvf/tZYHPF4nMVxEDq05wBM\noOIIWCLyPdT0aIZOWVmZVq5cqccff1x79+7VDTfc4PvQ0XHjxumtt97SnDlzfD0uUuC93Cdj7TnX\nJr+COK8xWYUz2bHDWBFF2qg4ApaghxpBFIvFdOONN6q6ulrr169XS0uLr8e/9NJL1djY6OsxgVzR\nngMwgYojkIPpS9ekdbvGzevyHEnfIl9xRKhdcMEFGj16tH7+85/r2muvVU1NjS/HLSgo0PDhw/Xx\nxx9r6tSpvhwTyBXtOQATSBwBSwRi3y+vMQwmUqqrq7V8+XI9/PDDmjp1qm/DRxcuXKhNmzaROCI0\nItmeI/jYjsV6tDqAJazroU5n/gUCp7S0VCtWrNATTzyhrVu36qabbsr7vMfCwsK8Pj4ylMl7dO1a\nK+dUWdeeAwgE5jgClmBODMIiFovphhtu0Lhx43Tvvffm/Qvy0aNHVVxcnNdjAF6iPQdgAhVHwBKR\n7KG2sNJgk9mzZ2v06NG6//77dc0112js2LF5Oc57772ncePG5eWxkYFs3sOWvu8j2Z4DCDwqjoAl\n6KFGGI0aNUorV67UCy+8oNdeey0vx9i1a5cuuOCCvDw2kA+05wBMoOII+CDZ6qt+rrZKDzXCqri4\nWCtXrtQTTzyhzZs365ZbbvF03uOAAQO0f/9+VVdXe/aYQD7RngMwgcQRsASr8CHsrr/+er333ntq\naGjQbbfdpv79+3vyuNdcc40ef/xxLV++3JPHg0GJhq5GcDhrJNvzvq5TBK8jEDYRa3UAJBPJHupU\nXyQ6/8aXjUiZOXOmRo0apQceeEALFy7U+PHjc37MyspKFRQU6MCBA6qqqvIgSiC/ItmeAwg8EkfA\nEpHsoYaVRowYoZUrV+rhhx/WV199pUsvvTTnx7z66qv1zDPP6I477vAgQgRKBDuRrGzPLakmA0Fm\nWasD2MvaHmq+WERScXGxVqxYoSeffFKbNm3SkiVLcpr3WFVVpba2NjU1NbHoSFT03uMxQqxtzwEY\nReIIWMLKHmpE3nXXXacPPvhA69at07JlyzRw4MCsH+vKK6/Ur371Ky1btszDCNGniCZ3+UR7flrv\n145XryXmWwIJ0eoABnVfbTXfK6zSQ42oOuecczRq1Cg99NBDWrBggSZMmJDV41RXV+vEiRMeRwej\nIvoFP5LtOUNRgcBjH0fAEuz7hSgbNmyYVq9erddee00vv/xy1o/jOI53QQF5QnsOwAQqjoAlItlD\nDXRTWFio5cuX6+mnn9bDDz+spUuXqqCgwHRY6M7PClIEF8XpRHuOHpK9xiP42odZJI6AJZgTA1tc\nc801amxsVENDg5YuXarKykrTIQGesqY9Tzf59zpBinCnA5ALC1odABI91LDL9OnTNXLkSG3cuFHz\n58/XpEmT0rpfLBZTa2uriouL8xwhfBPBJID2vJd09vQFkDMSR8AS1vRQA6cNHTpUq1ev1saNG/XV\nV1/piiuu6PM+xcXFOnbsGIljvkQwiTPBuvbc1MI5YXmd8r6CT1gcB7BEc3OzmpqaTIcB+KqwsFB3\n3XWX2tra9NBDD6m9vT3pbePxuE6cOEElJ6o693WMwJdr2nMAJljUXQXYzboeaqCbhQsXavv27Vq3\nbp2WLl2qQYMG9fj7iy++qM8++0z19fWqrq42FKVFuidvqapJEUjy8oH2XKlfI7a+bmx93vCN5a0O\nYA/mxMB2Z511lkaOHKnNmzfrsssu05QpU7r+VllZqZEjR2revHkGIwTSQ3sOwAQSR8AS1vRQ91XJ\ngNUGDx6s1atXa9OmTfrqq6+0YMECSdLMmTP11ltvGY4OvundNoSsrbCmPQcQKLQ6gCXooQY69OvX\nT3fccYeee+45PfDAA1q2bJnee+89jRkzxnRodgpZ0hYEtOfd8PoBfEPiCFiCHmqgpwULFmjHjh1q\naGhQQUGBVq1aZTokIC205wBMoNUBLGFNDzW9z8jA5MmTNWLECN1zzz06cuSIBg8ebDokdJfO+9nC\n93zQ2nO2rwHMKCgo8PV4JI6AJeihBhKrrKxUfX29WlpaSBxtFbK50UFrz1tbW02HAFgp1RZT+cA+\njoAl2PcLSG7AgAE6fPiw6TCAtNCeAzAhON1VAPIqaD3UQJAMHDiQL+IIDdpzACbQ6gCWMDYnpq9h\nXyEYFoboq6ys1I4dO0yHAaQlaHMcAdiBoaqAJcrLy1VRUWE6DCCQJkyYoKamJj333HOmQ0Gm1q61\nrgOK9hyACSSOgCUCPSem84ufZV/+EBwFBQW688471dbWpueff950ODApBG1RoNtzAJFF4ghYgh5q\noG8LFy7Uvn37dPLkSdOhAEnRngMwgTmOgCXSmhPTe0n63r3uAe+FB3IVi8U0Z84c3XvvvVq4cKHG\njx9vOiTgDMxxBGACiSNgCWOr8LGBN0Kmvr5eNTU1Wr9+vW6//XYNHDjQdEhAD6yqCsAEhqoClmBO\nDJC+4uJi/ehHP9LWrVtNhwKcgfYcgAl0VwGW8LWHOlkFMZ3fU31EQNTW1mrYsGF65513NHv2bNPh\nAF2oOAIwgVYHsETGcxwT/RuwzLXXXqt77rlHM2bMUFFRkelwkIpFHVDMcQRgAokjEBDTl65J+PvG\nzes8eXx6qIHMxWIxzZ8/X4899phuvfVW0+HAT53JZwCTUNpz9GBRpwnMotUBLOFrD3WmH1x80CHA\nJk6cqFdeecV0GEAXKo4ATCBxBCxBDzWQvcLCQp04cUIlJSWmQ4HfEnVsGe7soj0HYAKtDmAJeqiB\n7F1++eXasGGD7rrrLpWVlZkOB30J8DBTL9Ceo4eIvs4RPGzHAViivLxcFRUVpsMAQqmmpkYLFy7U\nU089ZToUgPYcgBFUHAFL0EMN5GbMmDHMdUSHVBUeH6o/tOcATCBxBAIu2WqrmarYuUMtx45q8swz\n96PzauVWIMr69esn13VNhwEwxxGAEbQ6gCWOHDqgpkMHEyaOSCHV3pbse2md8vJyNTQ0aNKkSbrk\nkktMh4Mg8mF+JRVHACaQOAKWqBxSpaJiVoQEcnHLLbfIdV39+te/VkNDgxYtWqThw4ebDgvJJEvi\nQr7vHRVHACbQ6gCWoOLokUzmNnn1hbSvxwnhF98wcxxHCxYs0JEjR/SLX/xCQ4cO1bXXXqtYjPXm\n4A8qjgBMIHEELEHFMUshr0wgfyorK7VixQq98847uueee3TFFVeovr7edFhIJNvFbAL6nqfiCMAE\nukcBSxw5dEAH9u81HQYQObNnz9aKFSv09ttv6/nnnzcdDizQ3NyspqYm02EAsAzdVYAlqDiGULrV\njhBWTKKmuLhYt912m+677z7t27dPo0ePNh0STEv03vNocS0qjgBMoNUBLJFqjmOmW35Yu30HCRr6\nsGTJEt1///364z/+YxUUFJgOB7nqK/kzhDmOAEwgcQQsQcURyL9YLKZ4PK5vv/1WI0eONB0OwibN\nOdVJK47J7rN2LdsHAcgZiSNgCVZVzTMf9m5DsO3du1e//OUvtWjRIpLGKPPqvZ7D/ak4AjCBxBGw\nhJcVR4a2Aj299dZbamxs1N13362ysjLT4SDiMp7jmM6QWzq9APSBxBGwBBVHn3j55SuXL3Z8CfTF\nsWPH9MQTT6i0tFQrV65kL0ebpDvnOQ/vWyqOAEwgcQQswRxHwDstLS168skndfToUV155ZWqra01\nHRIswqqqAEyg1QEsYbLimOnQ1mwwHBZ+OHHihJ566ikdOXJE8+bNU11dnemQYKHQVxxTLeIDILBI\nHAFLUHGMgGyHv8ETr776qj799FNdeumlmjhxoulwEGR5fn9ScQRgAq0OYAnmOALZi8fj2rFjh9as\nyX/1HOhLXiqO2exTm01nVjZJdV/3oSMN8AWJI2AJKo4RwxclX3300UcaM2aM6TAASWnu4xjkNiLI\nsQFIisQRsAQVRyA78Xhcb731lu6++27ToQCSUlQc85WQ9U5ISfwAK5E4Apag4ghk7siRI9q8ebNm\nz56tkhLePwiG0Mxx9CrBJFEFEnJd19fjhaDVAeCFqFcc01m5lZVXkYlt27bptdde080336yhQ4ea\nDgfoYnRV1VySuLAMpQVCIh6Pq7Cw0LfjkTgClqDiCKTv3XffVWNjo9asWaOCggLT4QA9BL7iSFII\n+OLUqVO+Jo4x344EwKgjhw7owP69psMAQmH8+PGKx+MkjQik5uZmNTU1mQ4DgGFtbW0qKiry7XgB\n7q4C4CUqjhZJ1NtPBSAjgwcP1sCBA/XJJ59oypQppsMBegh8xdErDG0FUmpra2OoKgDvRX2OI+C1\nAwcOsAUHAimtVVVNJFrstwj4qr29XcXFxb4dj8QRsAQVRyB9hw4d0qBBg9S/f3/ToQBnCEXFsXeS\nSNIIeI6hqgDygopj6pVXWXEV3R0/ftzX4T9AJoyuqtrb2rXShg3S8uWmIwGs097eTuIIwHtUHIH0\nVVdX68iRI6bDABIKdMUx28oiFUkgK7GYf2udBrTVAeA1Ko4W4QuYJwoLC3Xs2DGGqyJw0qo4drYD\nJtsD2iIgUkgcAUtQcUyt+zBWhq1CkmbMmKE333xTCxYsMB0K0EOgK44AIot9HAFLsI8jkJnPPvtM\nZWVlpsMAzsA+jgBMoLsKsAQVRyB9L774ogYMGKC5c+eaDgU4Q0YVx76GrK5dm92Q0lSP5yWGuwKB\nQeIIWII5julLtfpqJ4azRltLS4smT55sOgwgoUCtqppvqRJHkkrAVySOgCWoOALpc11Xp06dMh0G\nkFBWcxy9SrLylawFYTEfACmROAKWoOIIpG/fvn360Y9+ZDoMIKHAVBy7J3mZDHklOQRCicQRsAQV\nRyB9/fr183VvLCATnq+qSiIHIA18KgKWYFVVID0ffPCBHMcxHQaQFKuqAjCBiiNgCSqOQN9ef/11\nff7551q+fLnpUICk2MfxNOZFAr6i1QEswRxHb7HyavQ0NTXpk08+0erVq02HAqQUuDmOGzZIdLYA\nkUfiCFiCiiOQ2uOPP67rrrvOdBhAn6g49kLlMX/82q8ToUCrA1iCiiOQ3Mcff6z+/ftrxIgRpkMB\n+hSYiqMf+kpQSGAA35A4Apag4gj09Oijj+ro0aOSpHg8rjvvvNNwREB6QltxzCbJo5oIBEYIWx0A\n2aDiCPR08OBB3XnnnSorKzMdCpARqyqOfSGhBHzDdhyAJSqHVKlqVLXpMIDAuPjii/XMM8+YDgPI\nWHl5uSoqKkyHAcAyVBwBS1BxBHr63e9+pyuuuMJ0GEDGqDh6oHulkqolkBYSR8ASzHEEfnDq1Ckd\nPnyYxXAQSqGf45hJokZSZxbnH90wVBWwxJFDB3Rg/17TYQCBUFhYqLlz56qhoYHKDUKnublZTU1N\npsPwx9q1JC9AAqdOnVIs5m8qF8LuKgDZoOII9HTeeefp8OHD2r59u84//3zT4QBpC23FsVOqRNCv\nJJFkFCHX2tqqgoICX48Z4lYHQCaY4+i/6UvXJPx94+Z1PkeCZEpKSnTo0CHTYQAZYY5jltjMHhFy\n6tQpEkcA+UHFETjTRRddpPvvv990GEBGQl9xTIUkDkhLa2urCgsLfT0mcxwBSzDHETjTrl27NGzY\nMNNhABmxao4jgIROnjzpewdSRLurAPRGxTE4eg9hZeiqOTt27NCMGTNMhwFkJNIVRwBpYagqgLxh\njiNwpkOHDqm2ttZ0GEBGmOOYJfZuRIScOnXK96GqJI6AJag4AmeKx+O+L2cO5IqKIwATQ1X5tAQs\nwRxH4ExVVVV68803TYcBZIQ5jgCoOALIGyqOwJkWLVqkTZs2aceOHZI6KjlVVVUaNWqUampqVFZW\nZjhC4ExUHD3WOWyV4asIkVOnTrE4DoD8YI4jcKZYLKbbbrtNUsew1UOHDumrr77S559/rueff143\n33yzhg8fbjhKoCfmOHqAZBEh19bWpuLiYl+PSeIIWIKKY3B1X2WVFVbNicViqqqqUlVVlSTp+++/\n14MPPqjly5dTeUSgUHEEUFJSotbWVl+PyRxHwBLMcQQyM3DgQI0YMULffPON6VCAHpjjCMB1XcXj\ncV+PSXcVYAkqjkDmjhw5opqaGtNhAD1QcfQQQ1WBtNHqAJZgjiOQuaFDh+qLL75QXV2d6VCALsxx\nhBHsg2k9hqoClqgcUqWqUdWmwwBCZdq0adq2bZvpMIAeysvLVVFRYToMAJYhcQQswRxHIDOu62rb\ntm0aPHiw6VCAHpjjCOPWrqXqaCGGqgKWYI5jOLDCajCcOnVKGzdu1LBhwzR37lzT4QA9MMcRgAlU\nHAFLUHEE0rdx40ZddNFFuvrqq+U4julwgB6oOAIwge4qwBJUHIH0tbe3a+LEiabDABLqqjj2NVSQ\noYQAPETiCFiCVVWBno4fP66tW7cqHo+roKBAAwcOVGVlpQYPHqxTp06ZDg9IilVVYQQdEdYjcQQs\nQcURtnBdV62trWppaen67/jx42ppadGJEye6bvfFF1/oyiuvVHV1tY4fP65vv/1WBw8e1DfffKPL\nLrvM3BMA+sAcRwAm0OoAlqDiiDCKx+M6ceJEV+LXPRFsbW1NeJ9YLKaioiKVlpaqrKxMZWVlGjJk\niMrKylRcXKxYLKavv/5agwcPVnV1xxY1paWlqq2tVW1trZ9PD8hKV8WxdwWIihCAPCJxBCxBxTF8\norbCajwe75EAdv//ZENDHcdRSUlJVwLYv39/DRs2TGVlZSoqKspq4RrXdfXBBx9owYIFuT4lwAgq\njgCkjs8zP9HqAJag4ggvtbe3n5H8df67ra0t4X1isZhKS0u7KoEVFRUaOXKkSktLVVRU5Evc8Xhc\nL730kmbMmKGCggJfjgl4LekcRyqOAPKIxBGwBBVHJHPq1KmklcB4PJ7wPgUFBT2GgnYO+ywtLVVh\nYaHPzyB9b731lqZNm6bhw4ebDgXIGhVHACbQ6gCWoOJoh5MnTyatBCYb0tKvXz+VlZV1JYJVVVVd\n/45aVe7kyZOqqqoyHQaQE1ZVBWACiSNgCSqO4eK6rk40N+v7I9/p6OHD2rVrV4+VQZMlgYWFhV1V\nwLKyMo0YMUJlZWUqKSmJXBKYDcdx1NbW5tvQWCAfqDgCMIFWB7AEFcdwefLn6yTX1YBBgzWgcpBK\nSko0ePBglZaWqqSkRLFYzHSIoXTy5EmSRoQeFUcAJpA4Apag4hgui5b/cY9/N37zuaFIwu3AgQNq\nb29XRUWFGhsbdfDgQX3wwQeqr69XeXm56fCArFBxBGACrQ5gCSqOsInrunrhhRc0ePBglZSUaM+e\nPRoxYoRmz56tgwcP6sMPP1RLS4vKyso0ceJEDR061HTIQNqoOAIwgcQRsAQVR9jk448/1oQJEzR2\n7Ngz/lZVVdW1QE5zc7N27typDz74QAUFBRo3bpzGjBnDfFAEGhVHACbQ6gCWoOIIW8Tjce3du1dX\nXXVVn7ctLy/XueeeK6ljW5IvvvhCr7zyiuLxuEaOHKm6ujqVlNDhgmCh4gjABBJHwBJUHGEL13VV\nXFyc8f0KCwtVX1+v+vp6ua6rr7/+Wu+8845aW1s1YMAATZo0SZWVlXmIGMgMFUcAJtDqAJag4ghb\ntLe3q729PafHcBxHo0aN0qhRoyRJTU1N2rlzp5qamlRUVKS6ujqNHDmS1W1hBBVHACaQOAKWoOII\nW7zxxhuaNWuWp49ZUVGh2bM7Ol1aW1u1e/du7dixQ5JUXV2t8ePHq7Cw0NNjAslQcQRgAq0OYAkq\njuE2femahL9v3LzO50iC7fPPP9fQoUNVUVGRt2MUFxdr6tSpmjp1atd8yjfeeENtbW0aPHiwJk6c\nqP79++ft+AAVRwCO4/h+TBJHwBJUHBF1ra2t+vTTT9NaFMcrsVhMNTU1qqmpkSQdOnRIjY2Nam5u\nVmlpqSZMmKBhw4YZ+YBHdFFxBGACrQ5gCSqOiLr3339ff/RHf2Q0SRsyZIguuugiSVJLS4s+++wz\nNTY2qqCgQLW1taqtrWWrD+SMiiMAE0gcAUtQcUTUnTx5MlDzDMvKynT22WdLktra2vTll192bfUx\nbNgw1dfXq7S01HCUCCMqjgBMoNUBLEHFEVE3depUffzxx5o5c6bpUM7Qr18/1dXVqa6uTq7r6ttv\nv9Xvfvc7nThxQv3799fEiRM1ePBg02EiJKg4AjCBxBGwBBVHRN3QoUP1/vvvmw6jT47jaPjw4Ro+\nfLgk6ejRo9q5c6fee+89FRYWavz48Ro9ejRbfSApKo4ATKDVASxBxTGauq+2ygqr0qBBg3To0CEN\nGTLEdChpGzBggM477zxJHcNtP//8c7300kuSpFGjRqmurk5FRUUmQ0TAUHEEYAKJI2AJKo6wQXV1\ntQ4cOBCqxLG7oqIiTZo0SZMmTVI8Htf+/fv11ltv6eTJk6qsrNTEiRM1cOBA02HCMCqOAEyg1QEs\nQcURNjh69KjKy8tNh+GJWCym6upqVVdXS5IOHz6sTz75REePHlVxcbEmTJigESNGsNWHhag4AjCB\nxBGwBBVH2OD7779XVVWV6TDyYtCgQbrgggskSSdOnNBnn32m7du3d+0lOXbsWKpQlqDiCMAEWh3A\nElQcYYNjx45pwIABpsPIu5KSEk2bNk3Tpk1Te3u7vvrqK7322mtqb2/X0KFDVV9fH5nKK85ExRGA\nCSSOgCWoOMIG8XhcBQUFpsPwVUFBgcaOHauxY8fKdV0dPHhQH374oVpaWlRWVqaJEydq6NChpsOE\nh6g42qelpUWu6xo7vskh8fk8dr6fVz4fv729PW+PnQytDmAJKo7RxwqrcBxHVVVVXcN1m5ubtWvX\nLn3wwQfq16+fxo4dqzFjxliXXEcNFUe7tLa26h//8R81ZswY06H4znEcxeNx02FkLd/J/vz58/P6\n+L2ROAKWoOIIG9TU1JgOIVDKy8t1zjnnSJJOnTqlL774Qq+88opc19WIESNUV1enkhLahbDxteK4\ndm3qfyPviouLNWzYMN1yyy1szQOjSBwBS1BxhA3q6upMhxBYhYWFqq+vV319vVzX1R/+8Ae98847\nam1tVWVlpSZNmmTF/NAooOJon2nTpun111/X5ZdfbjoUWIzEEbAEFUcAnRzH0ciRIzVy5EhJHVt9\nbN++XceOHVN5ebkmTZqkwYMHG44SyTDH0T6zZ8/W+vXrSRxhFK0OYAkqjgCSGTRokC688EJJHSvT\nfvrpp3rvvfdUXFysiRMnatiwYewXGSBUHO0Ti8VUVlamb775RsOHDzcdDixF4ghYgoojgHT0799f\n5513nqSO/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aff747414d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "m = make_map()\n",
    "    \n",
    "# x,y = m(lonsinc,latsinc)\n",
    "from snowpack_functions import mesh_gridify\n",
    "lons_mesh,lats_mesh,data_mesh = mesh_gridify(latsinc.reshape(len(latsinc),1),lonsinc.reshape(len(latsinc),1),\n",
    "                                             data.reshape(len(latsinc),1))\n",
    "\n",
    "#ms = 0.5\n",
    "#m.scatter(x,y,marker='.',color = 'b',s=ms)\n",
    "# m.pcolormesh(x,y,color = 'b')\n",
    "cs = m.pcolormesh(lons_mesh,lats_mesh,data_mesh,shading='flat',cmap='jet_r',latlon=True,zorder=1,vmin=1,vmax=1)\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "### save plot\n",
    "import os\n",
    "direc = '/raid9/gergel/agg_snowpack/maps/'\n",
    "model_spec = 'map'\n",
    "plotname = 'historical_domain_map_revised' \n",
    "savepath = os.path.join(direc,plotname)\n",
    "print (\"saving figure to '%s'\" % savepath)\n",
    "plt.savefig(savepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving figure to '/raid9/gergel/agg_snowpack/maps/historical_domain_map_revised.pdf'\n"
     ]
    },
    {
     "data": {
      "image/png": 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hQc+ePYmOjmbatLu/ZCxevJhx48Zx/PhxMjMzGTBgAHv27KGwsJD69eszffp0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CpiSo6O5jnV57Z2gO50SktL8fDwcHQYNiUnJwdJkggPD9c5RpZlVq5cycyZMwkLC8PHx4d58+Zx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\nyw0dMzMzSy03dMzMzCy1/g96ZYJj/hWQfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aff868d2450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpi = 500\n",
    "\n",
    "'''\n",
    "## Cascades\n",
    "## mask out Olympics \n",
    "latsinc = lats_inc[lonsinc > -123]\n",
    "lonsinc = lons_inc[lonsinc > -123]\n",
    "## California\n",
    "## mask out part of CA\n",
    "mask = (lonsinc > -122) & (latsinc > 40.4)\n",
    "latsinc = latsinc[mask]\n",
    "lonsinc = lonsinc[mask]\n",
    "'''\n",
    "count = 0\n",
    "newlatsinc = list()\n",
    "newlonsinc = list()\n",
    "for el in np.arange(len(latsinc)): \n",
    "    lat = latsinc[el]\n",
    "    lon = lonsinc[el]\n",
    "    \n",
    "    if lon < -122 and lat < 40.7: \n",
    "        count += 1\n",
    "    elif lon < -123:\n",
    "        count += 1\n",
    "    elif lon > -119.75 and lon < -114 and lat > 39 and lat < 43.1:\n",
    "        count += 1\n",
    "    elif lon > -117.6 and lon < -114 and lat > 30 and lat < 39:\n",
    "        count += 1\n",
    "    elif lat > 31.5 and lat < 37 and lon > -114 and lon < -111:\n",
    "        count += 1\n",
    "    ## oregon\n",
    "    elif lat < 46 and lat > 43 and lon > -121 and lon < -117:\n",
    "        count += 1\n",
    "    ## utah\n",
    "    elif lat > 37 and lat < 39 and lon > -114 and lon < -109: \n",
    "        count += 1\n",
    "    elif lat > 39 and lat < 42 and lon > -114 and lat < -112:\n",
    "        count += 1\n",
    "    elif lat < 35 and lon < -117:\n",
    "        count += 1\n",
    "    elif lat > 43 and lat < 44 and lon > -117 and lon < -116: \n",
    "        count += 1\n",
    "    elif lat > 42 and lat < 49 and lon > -108 and lon < -103:\n",
    "        count += 1\n",
    "    elif lat < 33: \n",
    "        count += 1\n",
    "    elif lat < 35.5 and lon > -108.75:\n",
    "        count += 1\n",
    "    elif lat > 45 and lon > -109:\n",
    "        count += 1\n",
    "    elif lat > 48 and lon > -110:\n",
    "        count += 1\n",
    "    elif lat > 35 and lat < 37 and lon < -108 and lon > -111: \n",
    "        count += 1\n",
    "    elif lon > -114 and lon < -113 and lat > 37 and lat < 43: \n",
    "        count += 1\n",
    "    elif lat > 45 and lat < 47 and lon > -118.25 and lon < -117:\n",
    "        count += 1\n",
    "    elif lat > 38.5 and lon > -104.5:\n",
    "        count += 1\n",
    "    else:\n",
    "        newlatsinc.append(latsinc[el])\n",
    "        newlonsinc.append(lonsinc[el]) \n",
    "\n",
    "newlatsinc = np.asarray(newlatsinc)\n",
    "newlonsinc = np.asarray(newlonsinc)\n",
    "\n",
    "\n",
    "## create array for pcolormesh\n",
    "data = np.ones(len(newlonsinc))\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "m = make_map()\n",
    "    \n",
    "# x,y = m(lonsinc,latsinc)\n",
    "from snowpack_functions import mesh_gridify\n",
    "# lons_mesh,lats_mesh,data_mesh = mesh_gridify(newlatsinc.reshape(len(newlatsinc),1),newlonsinc.reshape(len(newlatsinc),1),data.reshape(len(newlatsinc),1))\n",
    "\n",
    "lats_cas = list()\n",
    "lons_cas = list()\n",
    "lats_ca = list()\n",
    "lons_ca = list()\n",
    "lats_nr = list()\n",
    "lons_nr = list()\n",
    "lats_sr = list()\n",
    "lons_sr = list()\n",
    "lats_wh = list()\n",
    "lons_wh = list() \n",
    "## make mountain ranges different colors \n",
    "for lat,lon in zip(newlatsinc,newlonsinc):\n",
    "    if lon > -122.5 and lon < -117 and lat < 41.5 and lat > 34.5:\n",
    "        lats_ca.append(lat)\n",
    "        lons_ca.append(lon)\n",
    "    elif lon > -119.5 and lon < -107.5 and lat < 49.0 and lat > 42.37:\n",
    "        lats_nr.append(lat)\n",
    "        lons_nr.append(lon)\n",
    "    elif lon > -113.0 and lon < -104.0 and lat < 42.37 and lat > 35.0:\n",
    "        lats_sr.append(lat)\n",
    "        lons_sr.append(lon)\n",
    "    elif lon > -111 and lon < -108 and lat < 36 and lat > 32:\n",
    "        lats_wh.append(lat)\n",
    "        lons_wh.append(lon)\n",
    "    else: \n",
    "        lats_cas.append(lat)\n",
    "        lons_cas.append(lon)\n",
    "## convert to arrays\n",
    "lats_ca = np.asarray(lats_ca)\n",
    "lons_ca = np.asarray(lons_ca)\n",
    "lats_cas = np.asarray(lats_cas)\n",
    "lons_cas = np.asarray(lons_cas)\n",
    "lats_nr = np.asarray(lats_nr)\n",
    "lons_nr = np.asarray(lons_nr)\n",
    "lats_sr = np.asarray(lats_sr)\n",
    "lons_sr = np.asarray(lons_sr)\n",
    "lats_wh = np.asarray(lats_wh)\n",
    "lons_wh = np.asarray(lons_wh)\n",
    "\n",
    "## make arrays## create array for pcolormesh\n",
    "data_cas = np.ones(len(np.asarray(lats_cas)))\n",
    "data_ca = np.ones(len(np.asarray(lats_ca)))*2\n",
    "data_nr = np.ones(len(np.asarray(lats_nr)))*3\n",
    "data_sr = np.ones(len(np.asarray(lats_sr)))*4\n",
    "data_wh = np.ones(len(np.asarray(lats_wh)))*5\n",
    "\n",
    "## mesh gridify\n",
    "lons_mesh_ca,lats_mesh_ca,data_mesh_ca = mesh_gridify(lats_ca.reshape(len(lats_ca),1),lons_ca.reshape(len(lons_ca),1),\n",
    "                                             data_ca.reshape(len(lats_ca),1))\n",
    "lons_mesh_cas,lats_mesh_cas,data_mesh_cas = mesh_gridify(lats_cas.reshape(len(lats_cas),1),lons_cas.reshape(len(lons_cas),1),\n",
    "                                             data_cas.reshape(len(lats_cas),1))\n",
    "lons_mesh_nr,lats_mesh_nr,data_mesh_nr = mesh_gridify(lats_nr.reshape(len(lats_nr),1),lons_nr.reshape(len(lons_nr),1),\n",
    "                                             data_nr.reshape(len(lats_nr),1))\n",
    "lons_mesh_sr,lats_mesh_sr,data_mesh_sr = mesh_gridify(lats_sr.reshape(len(lats_sr),1),lons_sr.reshape(len(lons_sr),1),\n",
    "                                             data_sr.reshape(len(lats_sr),1))\n",
    "lons_mesh_wh,lats_mesh_wh,data_mesh_wh = mesh_gridify(lats_wh.reshape(len(lats_wh),1),lons_wh.reshape(len(lons_wh),1),\n",
    "                                             data_wh.reshape(len(lats_wh),1))\n",
    "\n",
    "# cs = m.pcolormesh(lons_mesh,lats_mesh,data_mesh,shading='flat',cmap='jet_r',latlon=True,zorder=1,vmin=1,vmax=1)\n",
    "## plot\n",
    "from matplotlib import colors as c\n",
    "colours = ['maroon','b','m','r','g','blueviolet','chartreuse','aqua','sandybrown','deeppink','cornflowerblue']\n",
    "# cMap = c.ListedColormap(['y','b','m','r','g'])\n",
    "cs = m.pcolormesh(lons_mesh_cas,lats_mesh_cas,data_mesh_cas,shading='flat',cmap=c.ListedColormap(colours[0]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=10)\n",
    "cs = m.pcolormesh(lons_mesh_ca,lats_mesh_ca,data_mesh_ca,shading='flat',cmap=c.ListedColormap(colours[1]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=10)\n",
    "cs = m.pcolormesh(lons_mesh_nr,lats_mesh_nr,data_mesh_nr,shading='flat',cmap=c.ListedColormap(colours[2]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=10)\n",
    "cs = m.pcolormesh(lons_mesh_sr,lats_mesh_sr,data_mesh_sr,shading='flat',cmap=c.ListedColormap(colours[3]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=10)\n",
    "cs = m.pcolormesh(lons_mesh_wh,lats_mesh_wh,data_mesh_wh,shading='flat',cmap=c.ListedColormap(colours[4]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=10)\n",
    "\n",
    "#cs = m.pcolormesh(lonsmeshlow,latsmeshlow,datameshlow,shading='flat',cmap=c.ListedColormap(colours[5]),latlon=True,zorder=1,vmin=1,vmax=1)\n",
    "\n",
    "########################################################################################################################\n",
    "## missouri\n",
    "lats_mo = list()\n",
    "lons_mo = list()\n",
    "latslons_mo = list()\n",
    "## coastal north\n",
    "lats_cn = list()\n",
    "lons_cn = list()\n",
    "## coastal south\n",
    "lats_cs = list()\n",
    "lons_cs = list()\n",
    "## great basin\n",
    "lats_gb = list()\n",
    "lons_gb = list()\n",
    "## NW Interior\n",
    "lats_nw = list()\n",
    "lons_nw = list()\n",
    "latslons_nw = list()\n",
    "## Lower Colorado\n",
    "lats_lc = list()\n",
    "lons_lc = list()\n",
    "\n",
    "## boool_low is the no swe array \n",
    "\n",
    "#### load other masks\n",
    "allmasks = np.load('/raid9/gergel/agg_snowpack/sm_summer/masks.npz')\n",
    "mo = allmasks['mo']\n",
    "gb = allmasks['gb']\n",
    "lc = allmasks['lc']\n",
    "\n",
    "## multiply no swe mask with HUC2 masks for each basin \n",
    "\n",
    "boo_mo = boool_low*mo\n",
    "\n",
    "for lat in np.arange(len(lats_low)):\n",
    "    for lon in np.arange(len(lons_low)):\n",
    "        if boo_mo[0,lat,lon] == 1.0:\n",
    "            lats_mo.append(lats_low[lat])\n",
    "            lons_mo.append(lons_low[lon])\n",
    "            latslons_mo.append([lats_low[lat],lons_low[lon]])\n",
    "\n",
    "boo_gb = boool_low*gb\n",
    "\n",
    "for lat in np.arange(len(lats_low)):\n",
    "    for lon in np.arange(len(lons_low)):\n",
    "        if boo_gb[0,lat,lon] == 1.0:\n",
    "            lats_gb.append(lats_low[lat])\n",
    "            lons_gb.append(lons_low[lon])\n",
    "\n",
    "boo_lc = boool_low*lc \n",
    "\n",
    "for lat in np.arange(len(lats_low)):\n",
    "    for lon in np.arange(len(lons_low)):\n",
    "        if boo_lc[0,lat,lon] == 1.0:\n",
    "            lats_lc.append(lats_low[lat])\n",
    "            lons_lc.append(lons_low[lon])\n",
    "\n",
    "for lat, lon in zip(latsinc_low,lonsinc_low):\n",
    "    if lon > -121.5 and lon < -111 and lat < 49 and lat > 42:\n",
    "        lats_nw.append(lat)\n",
    "        lons_nw.append(lon)\n",
    "        latslons_nw.append([lat,lon])\n",
    "    elif lon > -124.5 and lon < -122.0 and lat < 48 and lat > 41.5:\n",
    "        lats_cn.append(lat)\n",
    "        lons_cn.append(lon)\n",
    "    elif lon > -124 and lon < -118.5 and lat < 41.5 and lat > 33:\n",
    "        lats_cs.append(lat)\n",
    "        lons_cs.append(lon)\n",
    "    '''elif lon > -120 and lon < -104 and lat < 42 and lat > 31.5:\n",
    "        lats_sw.append(lat)\n",
    "        lons_sw.append(lon)'''\n",
    "    '''elif lon > -114 and lon < -106 and lat < 49 and lat > 39:\n",
    "        lats_pl.append(lat)\n",
    "        lons_pl.append(lon)'''\n",
    "\n",
    "## adjust NW Interior lats/lons\n",
    "l3 = [x for x in latslons_nw if x not in latslons_mo]\n",
    "np.asarray(l3)[:,0][0]\n",
    "\n",
    "\n",
    "lats_mo = np.asarray(lats_mo)\n",
    "lons_mo = np.asarray(lons_mo)\n",
    "lats_cn = np.asarray(lats_cn)\n",
    "lons_cn = np.asarray(lons_cn)\n",
    "lats_cs = np.asarray(lats_cs)\n",
    "lons_cs = np.asarray(lons_cs)\n",
    "lats_lc = np.asarray(lats_lc)\n",
    "lons_lc = np.asarray(lons_lc)\n",
    "lats_nw = np.asarray(l3)[:,0]\n",
    "lons_nw = np.asarray(l3)[:,1]\n",
    "'''lats_nw = np.asarray(lats_nw)\n",
    "lons_nw = np.asarray(lons_nw)'''\n",
    "lats_gb = np.asarray(lats_gb)\n",
    "lons_gb = np.asarray(lons_gb)\n",
    "\n",
    "## for Coastal South\n",
    "\n",
    "newcslats = list()\n",
    "newcslons = list()\n",
    "\n",
    "\n",
    "for el in np.arange(len(lats_cs)): \n",
    "    lat = lats_cs[el]\n",
    "    lon = lons_cs[el]\n",
    "    \n",
    "    if lon > -119.5 and lat > 37: \n",
    "        count += 1\n",
    "    elif lon > -121.75 and lat > 40:\n",
    "        count += 1\n",
    "    elif lon > -120.5 and lat > 39: \n",
    "        count += 1\n",
    "    elif lat > 36 and lon > -118:\n",
    "        count += 1\n",
    "    else: \n",
    "        newcslats.append(lats_cs[el])\n",
    "        newcslons.append(lons_cs[el])\n",
    "    \n",
    "lats_cs = np.asarray(newcslats)\n",
    "lons_cs = np.asarray(newcslons)\n",
    "\n",
    "\n",
    "data_mo = np.ones(len(np.asarray(lats_mo)))*6\n",
    "data_cn = np.ones(len(np.asarray(lats_cn)))*7\n",
    "data_cs = np.ones(len(np.asarray(lats_cs)))*8\n",
    "data_lc = np.ones(len(np.asarray(lats_lc)))*9\n",
    "data_nw = np.ones(len(np.asarray(lats_nw)))*10\n",
    "data_gb = np.ones(len(np.asarray(lats_gb)))*11\n",
    "\n",
    "lons_mesh_mo,lats_mesh_mo,data_mesh_mo = mesh_gridify(lats_mo.reshape(len(lats_mo),1),lons_mo.reshape(len(lons_mo),1),\n",
    "                                             data_mo.reshape(len(lats_mo),1))\n",
    "lons_mesh_cn,lats_mesh_cn,data_mesh_cn = mesh_gridify(lats_cn.reshape(len(lats_cn),1),lons_cn.reshape(len(lons_cn),1),\n",
    "                                             data_cn.reshape(len(lats_cn),1))\n",
    "lons_mesh_cs,lats_mesh_cs,data_mesh_cs = mesh_gridify(lats_cs.reshape(len(lats_cs),1),lons_cs.reshape(len(lons_cs),1),\n",
    "                                             data_cs.reshape(len(lats_cs),1))\n",
    "lons_mesh_lc,lats_mesh_lc,data_mesh_lc = mesh_gridify(lats_lc.reshape(len(lats_lc),1),lons_lc.reshape(len(lons_lc),1),\n",
    "                                             data_lc.reshape(len(lats_lc),1))\n",
    "lons_mesh_nw,lats_mesh_nw,data_mesh_nw = mesh_gridify(lats_nw.reshape(len(lats_nw),1),lons_nw.reshape(len(lons_nw),1),\n",
    "                                             data_nw.reshape(len(lats_nw),1))\n",
    "lons_mesh_gb,lats_mesh_gb,data_mesh_gb = mesh_gridify(lats_gb.reshape(len(lats_gb),1),lons_gb.reshape(len(lons_gb),1),\n",
    "                                             data_gb.reshape(len(lats_gb),1))\n",
    "\n",
    "cs = m.pcolormesh(lons_mesh_mo,lats_mesh_mo,data_mesh_mo,shading='flat',cmap=c.ListedColormap(colours[5]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=11)\n",
    "cs = m.pcolormesh(lons_mesh_cn,lats_mesh_cn,data_mesh_cn,shading='flat',cmap=c.ListedColormap(colours[6]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=11)\n",
    "cs = m.pcolormesh(lons_mesh_cs,lats_mesh_cs,data_mesh_cs,shading='flat',cmap=c.ListedColormap(colours[7]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=11)\n",
    "cs = m.pcolormesh(lons_mesh_lc,lats_mesh_lc,data_mesh_lc,shading='flat',cmap=c.ListedColormap(colours[8]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=11)\n",
    "cs = m.pcolormesh(lons_mesh_nw,lats_mesh_nw,data_mesh_nw,shading='flat',cmap=c.ListedColormap(colours[9]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=11)\n",
    "cs = m.pcolormesh(lons_mesh_gb,lats_mesh_gb,data_mesh_gb,shading='flat',cmap=c.ListedColormap(colours[10]),latlon=True,\n",
    "                  zorder=1,vmin=1,vmax=11)\n",
    "\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "# colours = ['maroon','b','m','r','g','blueviolet','chartreuse','aqua','sandybrown','deeppink']\n",
    "\n",
    "l1 = mlines.Line2D([], [], color='maroon', label='Cascades',linewidth=4.0)\n",
    "l2 = mlines.Line2D([], [], color='b', label='Sierra Nevada',linewidth=4.0)\n",
    "l3 = mlines.Line2D([], [], color='m', label='Northern Rockies',linewidth=4.0)\n",
    "l4 = mlines.Line2D([], [], color='r', label='Southern Rockies',linewidth=4.0)\n",
    "l5 = mlines.Line2D([], [], color='g', label='White Mountains',linewidth=4.0)\n",
    "l6 = mlines.Line2D([], [], color='blueviolet', label='Missouri',linewidth=4.0)\n",
    "l7 = mlines.Line2D([], [], color='aqua', label='Coastal South',linewidth=4.0)\n",
    "l8 = mlines.Line2D([], [], color='sandybrown', label='Lower Colorado',linewidth=4.0)\n",
    "l9 = mlines.Line2D([], [], color='deeppink', label='NW Interior',linewidth=4.0)\n",
    "l10 = mlines.Line2D([], [], color='cornflowerblue', label='Great Basin',linewidth=4.0)\n",
    "l11 = mlines.Line2D([], [], color='chartreuse', label='Coastal North',linewidth=4.0)\n",
    "\n",
    "plt.legend(handles=[l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11], bbox_to_anchor=(1.0, 0.5), prop={'size':13}, loc='center left')\n",
    "\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "### save plot\n",
    "import os\n",
    "direc = '/raid9/gergel/agg_snowpack/maps/'\n",
    "model_spec = 'map'\n",
    "# plotname = 'historical_domain_map_revised' \n",
    "plotname = 'historical_domain_map_revised.pdf'\n",
    "savepath = os.path.join(direc,plotname)\n",
    "print (\"saving figure to '%s'\" % savepath)\n",
    "fig.set_size_inches(6, 6)\n",
    "plt.savefig(savepath,format='pdf', bbox_inches='tight', dpi=dpi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved array as /raid9/gergel/agg_snowpack/sm_summer/nwinterior.npz\n"
     ]
    }
   ],
   "source": [
    "\n",
    "nwinterior_mask = np.random.randn(len(lats),len(lons))\n",
    "v = zip(lats_nw,lons_nw)\n",
    "for j in np.arange(len(lats)):\n",
    "    for k in np.arange(len(lons)):\n",
    "        if (lats[j],lons[k]) in v:\n",
    "            nwinterior_mask[j,k] = 1\n",
    "        else:\n",
    "            nwinterior_mask[j,k] = 0\n",
    "\n",
    "cs_mask = np.random.randn(len(lats),len(lons))\n",
    "u = zip(lats_cs,lons_cs)\n",
    "for j in np.arange(len(lats)):\n",
    "    for k in np.arange(len(lons)):\n",
    "        if (lats[j],lons[k]) in u:\n",
    "            cs_mask[j,k] = 1\n",
    "        else:\n",
    "            cs_mask[j,k] = 0\n",
    "\n",
    "\n",
    "filearrayname = '/raid9/gergel/agg_snowpack/sm_summer/nwinterior.npz'\n",
    "np.savez(filearrayname,nwinterior=nwinterior_mask,cs=cs_mask)\n",
    "print(\"saved array as %s\" %filearrayname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "direc_gc = '/raid9/gergel/agg_snowpack/gridcells_is_paper/' \n",
    "\n",
    "if not os.path.exists(direc_gc):\n",
    "    os.makedirs(direc_gc)\n",
    "d = {'lat_cascades': np.asarray(lats_cas),'lon_cascades': np.asarray(lons_cas)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'cascades.csv'))\n",
    "    \n",
    "d = {'lat_california':np.asarray(lats_ca), 'lon_california':np.asarray(lons_ca)} \n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'california.csv'))\n",
    "\n",
    "d = {'lat_northernrockies': np.asarray(lats_nr),'lon_northernrockies': np.asarray(lons_nr)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'northernrockies.csv'))\n",
    "\n",
    "d = {'lat_southernrockies': np.asarray(lats_sr), 'lon_southernrockies': np.asarray(lons_sr)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'southernrockies.csv'))\n",
    "\n",
    "d = {'lat_whites': np.asarray(lats_wh), 'lon_whites': np.asarray(lons_wh)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'whites.csv'))\n",
    "\n",
    "d = {'lat_missouri': np.asarray(lats_mo), 'lon_missouri': np.asarray(lons_mo)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'missouri.csv'))\n",
    "\n",
    "d = {'lat_coastalnorth': np.asarray(lats_cn), 'lon_coastalnorth': np.asarray(lons_cn)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'coastalnorth.csv'))\n",
    "\n",
    "d = {'lat_coastalsouth': np.asarray(lats_cs), 'lon_coastalsouth': np.asarray(lons_cs)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'coastalsouth.csv'))\n",
    "\n",
    "d = {'lat_nwinterior': np.asarray(lats_nw), 'lon_nwinterior': np.asarray(lons_nw)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'nwinterior.csv'))\n",
    "\n",
    "d = {'lat_lowercolorado':np.asarray(lats_lc), 'lon_lowercolorado': np.asarray(lons_lc)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'lowercolorado.csv'))\n",
    "\n",
    "d = {'lat_greatbasin': np.asarray(lats_gb), 'lon_greatbasin': np.asarray(lons_gb)}\n",
    "df_for_csv = pd.DataFrame(data=d)\n",
    "df_for_csv.to_csv(os.path.join(direc_gc,'greatbasin.csv'))\n",
    "         \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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