elev_bins_onefigure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_elevmet(scenario,dataname,swelist,elevation):\n",
    "    if (scenario == \"historical\"): \n",
    "        elevmet = elevation\n",
    "        cr = 'r'\n",
    "    elif (scenario == \"rcp85\") and (dataname == \"swe_2010_2039\"):\n",
    "        elevmet = elevation - offset\n",
    "        cr = 'g'\n",
    "    elif (scenario == \"rcp45\") and (dataname == \"swe_2010_2039\"):\n",
    "        elevmet = elevation - offset\n",
    "        cr = 'g'\n",
    "    elif (scenario == \"rcp85\") and (dataname == \"swe_2040_2069\"):\n",
    "        elevmet = elevation - offset*2\n",
    "        cr = 'b'\n",
    "    elif (scenario == \"rcp45\") and (dataname == \"swe_2040_2069\"):\n",
    "        elevmet = elevation - offset*2\n",
    "        cr = 'b'\n",
    "    elif (scenario == \"rcp85\") and (dataname == \"swe_2070_2099\"): \n",
    "        elevmet = elevation - offset*3 \n",
    "        cr = 'm'\n",
    "    elif (scenario == \"rcp45\") and (dataname == \"swe_2070_2099\"): \n",
    "        elevmet = elevation - offset*3 \n",
    "        cr = 'm'\n",
    "    else: \n",
    "        print('yay Bernie Sanders')\n",
    "    return(elevmet,cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_swelists(basin,scenario,dataname):  \n",
    "    file = '/raid9/gergel/agg_snowpack/swe_t_p_reg/proc_data/ensavg_%s_%s.npz' %(basin,scenario)\n",
    "    data = np.load(file)\n",
    "    swe = data[dataname]\n",
    "    lats = data['lats']\n",
    "    lons = data['lons']\n",
    "    swe_500 = list()\n",
    "    swe_1000 = list()\n",
    "    swe_1500 = list()\n",
    "    swe_2000 = list()\n",
    "    swe_2500 = list()\n",
    "    swe_3000 = list()\n",
    "    swe_3500 = list() \n",
    "    swe_4000 = list()\n",
    "    count = 0\n",
    "    for lat,lon in zip(lats,lons): \n",
    "        elev = get_elev_for_lat_lon(elev_corr_info,lat,lon) \n",
    "        if elev > 0 and elev <= 750:\n",
    "            swe_500.append(swe[count]) \n",
    "        elif elev > 750 and elev <= 1250:\n",
    "            swe_1000.append(swe[count])\n",
    "        elif elev > 1250 and elev <= 1750:\n",
    "            swe_1500.append(swe[count])\n",
    "        elif elev > 1750 and elev <= 2250:\n",
    "            swe_2000.append(swe[count])\n",
    "        elif elev > 2250 and elev <= 2750:\n",
    "            swe_2500.append(swe[count])\n",
    "        elif elev > 2750 and elev <= 3250:\n",
    "            swe_3000.append(swe[count]) \n",
    "        elif elev > 3250 and elev <= 3750:\n",
    "            swe_3500.append(swe[count])\n",
    "        else: \n",
    "            swe_4000.append(swe[count]) \n",
    "        count += 1\n",
    "    return(swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def swe_stats(swe_array):\n",
    "    meanswe = np.mean(swe_array)\n",
    "    minswe = np.min(swe_array)\n",
    "    maxswe = np.max(swe_array)\n",
    "    swe10 = np.percentile(swe_array,10)\n",
    "    swe90 = np.percentile(swe_array,90) \n",
    "    ## minimum range\n",
    "    xmin = np.arange(minswe,swe10,1)\n",
    "    ## maximum range\n",
    "    xmax = np.arange(swe90,maxswe,1)\n",
    "    ## 10-90 range\n",
    "    xmid = np.arange(swe10,swe90,1)\n",
    "    return(meanswe,minswe,maxswe,swe10,swe90,xmin,xmax,xmid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from snowpack_functions import get_elev_for_lat_lon,import_gridcell_elevation\n",
    "# basins = [\"cascades\",\"california\",\"northernrockies\",\"whites\",\"southernrockies\"]\n",
    "basins = [\"whites\"]\n",
    "scenarios = [\"historical\",\"rcp45\",\"rcp45\",\"rcp45\",\"historical\",\"rcp85\",\"rcp85\",\"rcp85\"] \n",
    "datanames = ['swe','swe_2010_2039','swe_2040_2069','swe_2070_2099','swe','swe_2010_2039','swe_2040_2069','swe_2070_2099']\n",
    "lw = 2.0\n",
    "offset = 60\n",
    "soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt'\n",
    "elev_corr_info  = import_gridcell_elevation(soil_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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RzLgBrtydorlbD/hB+fdZa6T1UZLab7BqZH939pYCzivnJMwDXJCZv4+IicC4iNgTmEz5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\nI8eIvg8TgD1i54sGcQvwNhBdb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jvj+kLA/XnX+4yRm+PfRGJUxbqkkReR9KuCsT6JeysV\nGrI3HTO7BXhQ0skRbQ/BlUg3rTB+4eZCItFL0vu+Gul5vZsuRclrJZ2Xc/1+4CpJkwvGuRZ4RlJr\nhKqCqMpUXJXymYJ2twH3Szq1ZLz98cXeBr2NtF76k7nTpXK9gqTXKtxrZWAKERuuiUQvSIu9EYSZ\nXQj8VlJmjn64/jtJBxeMcRHwG0njagqzFszsW8DKRRNQkEb+hKRClS2LNGRvMmb2ZeD7+OL+jyVt\nZ8I/BjaX9Fjk+K3cGEi0k/S+r0Z6Xu/FzJbAa9gy0/ljRDnM7EvAY7Qs1d/Mvg5sDnxVOR9zZrYA\nXqc2n6Q/FYzVEXXZUdKDNYRbG/3J3DG3uBotKU8XIa/fLcD3JJ1SMcxEojJpsTeC6MrPX0zSLwuu\nF+Xvfxqvb1hU0ht1xttLuiagnSR9L6dNx2R1NUkvFowVZcjedMzsXOCfKjAN7mq7Iy5is2Lex0BG\nn4uAt/I2FxKJXpHe99VIzyuboCj5E0mH51y/HJgq6YiCMc4E/qdNqf5diqIHSLq9oN1k4G1JB5WM\ntxUuXtI2Be/KmTv92QwN/aIWz4lEL0iLvRGGmR0NfEZSpv9c2fXQ5hhgDkk71xRmLZjZlsABFBRG\nm9kYYG1Ja5eMtQvu4bdy7OKnaViEaXBX2+nxNNcJkm6KHL+zMZC5uZBI9Ir0vq9Gel7ZmNnngGfw\nzI1fZ1yfG/gBxYJfrUz1N7MNcJ/URQvqEucEXsJtCn5RMFYlIbAmYWaH4uUc0Zk7/dkMDf2iFs+J\nxEBJi70RhpnNgtevrS3puZzrU4G1sq6HNlEnYE0j1F08AZwu6fKcNjPiC6A9Jd1XMFahgltbMLPx\n+IfLlhFt1wTOoMSTsE+fScCnJO00sEgTiXzS+74a6XnlY2YnAB+VtFvO9ZOAD6lY8OtQfMFY+l5t\nCmF+/C5wuaQLCtodAXxBBYIuoV2UqEvT6E/mTn82Q0O/qMVzIjFQ0mJvBGJmewEbA2tk7UKZ2d7A\nhpLWKBhjDL4gXKe+SHuPma0EXIrXVLyT02ZT3FtoVFEKipUouLUBc9Pg14iY2MLHwD3ADZLOjRy/\nszGwhqTnBxrv/7F3ntGWFdXafl5QELMYMGcyikoOAiI5ZyRnlAwCTZNzbHJocs455wySJOdouJjT\nVRHRy/30/X5U7dvbZtcK5+x1zl6n6xmjh9g1q9Y81d21VlXN+c5Mphd5va9Hnq80kj5MWLO+Y/v5\nHu3TE1ICFnVa8KuzYVjNPYq1DyqS5gauozgvsZKgS7StJOoyaEjaFNiYGpE7kpYFjiEchlb+Hqi6\nec5khkO/1vxcVL1dnA58Hkht5k4Dvigpudkj1O2bWUFmvzXYvo9QM6mobtyVwD8oL7J6M/BroFDQ\nZZDxpKLCpYXQ40tvHLBPfOFXGf+vwEFAZTnrTCaTGS0c1KgPJbFmOQhaHQ4cVjDGW8B+hELrrdlU\n236McLu3S4HN34D9KSg038VuwO4KJRnaxLnAR4A6iqK3AL+i/vfAkcASCvV+M5mBJt/stQxJqwAH\nEMRY/tWjfTVgX0J4wTvao81ahMV8npYlYc8MPEBIjP5TwmZB4BJKiqx2KbTN7IKC7INM3RAUSecD\nP7O9T8XxO6GxW9u+fVjOZjI9yOt9PfJ8FRMFvV4ENrN9d4/298T2jeIBYq8x3kUI9d/N9g1N+ttP\nuvISe+YtRpuOoMsPbd9cMt7pwJ/blpc2lJs6VVBsTfTbClgdWLKtGgCZwSbf7E25XAv8Fdgw0X41\n8CbFt1uXA/8CWpOXAOBQD+dSIFlA3faDwKPADiVjPQ7cBezcTx9HkriZ35VwCv3uCl32AraR9OmK\n478N7B7Hz2tFJpMZaGKO2e6EiId3rFkxBWBPCm634gZhN+DwuDlqBbZ/DpxFuL1L2XR+tiPiYWER\n+wKbRfGbNtG5qdu8aof4PXAPQYm0DmcAnwOWrtkvkxlR8s1eC5E0P3AF4RTqrR7tpbdbamlx0C7F\ntPltv5awqVRkteskdA7bv+m/tyODpNuAa22fVMH2CODDtresOLYIcznR9vnD8zST+U/yel+PPF/l\ndAl6HWv7oh7tUxEOBA+3fVnBGHcD59s+s0l/+0kMu3wZWNz2cwkbAfcC55b9bJIOAL7Utrw0ldRe\nTPT5EvAYMJvt39V4VmG0VSYzHLJAyxSOpMuAp20fnGi/AnjMdjI/QS0tDippD8LCumaBzYmEmkmF\nN3xRoe0Dtr/fZzdHDElzArcSNveFhdC7PgYWs/1CxfEXBi6M4/cUx8lkhkJe7+uR56saZYJeUXHy\nDMKHfc/DTknzAlcR1r2/N+lvP5G0I0FYKynCVvVn6xJ1Wc72k313tkFi2sJPnai9mOhzDDCN7W1q\n9BFwP3CG7XNqO5rJFJA3e1M4kr5COL2czfbve7R3brdmtf2HxBitLA4aFdNepqAYqqSPE3IzkjeA\n0e4jhJfZolU3P4OIpLOB39jeo4LtDwmKdSvWGP9q4CHbRwzDzUzmP8jrfT3yfFVH0rXA/baPTLTf\nCNxm+7iCMS4Bnk0dqg4iMdf6RWBL23cW2F0CPGf7oJLxWpmXppLai4k+HyUoti5k+5Uaz1oAuIxw\nk5jUCshk6pI3exkkHQu8y/a2ifbS2y21tDioQjHULQmLcs+/xFVuAKPdzoTNXh0Fr4FCNQqhRxGD\nl4BNbN9TcfyZgR8RTsp7iuNkMnXJ63098nxVR9IshBuXmR2UOCdvnwO4M7b/JTFG4aHqoCJpTWAP\nCsoQSfoyoYB64c8W88GfBXa0fUsT/jZF3bSF2Gc8MLftNWo+63LgCduH1nQzk0mSN3uZ0lOoivlt\nnyYs5K0qDhqTyx8HDrR9ZcKm9AYw2nU2PxvbvrcJf0cCSQcBn7O9UQXbdQjJ6POlPgZ69JkI/NN2\n3ST2TKYneb2vR56vekg6GXjLdk8hLklnAH+0Pb5gjGOBqW0Xlf0ZKLpyrU+2fV6BXaWwxZiXdiDh\nMLE1eWldaQs9ay8m+kxHiPYp/G7o0W9G4CEKoqkymbrkzV4GAEm7ET7YV0u07wnMaXutgjEOBL5g\nO6XwOZBIWhI4mXAy+XbCZmNgC2DhohAUSesCO1Fj8zNoSPog4cW2rO2nSmynIpzqTrB9acXxZyAc\nHsxj+6fD9TeTyet9PfJ81UPSJwnlY+a2/bMe7Z3Dzm/afj0xxscIh4EL2H61SX/7iaSFgIspFmrr\nHBgv7KB2nRpLwH3AWbbPbsLfppC0E/Bd2yvU6LMxQc3z23VCVyWdAPy7KJoqk6lDv9b8LKfefo4H\n5ooiGr04BlhQ0nwFY0wAlpL0jb571yAOtd9eBX5QYHY+8H5g1ZLhLgEEFIZ8DjK23yCoglUptP5v\nQtmGQ+PNZpXxfwccB7QmfyWTyUy52P4tYc06JNH+a2Ai4dYqNcYfgaNo2bpn+wGC6uiOBTZ/Irz/\nC0MP44ZnV+DAGDHTJiYCs0VRnqqcD3yQesXZIbx/15P01Zr9MplGyTd7YwBJGwBbAwv2OoWStCmw\nCbBIQX7bNsBKtltVL0bS14A7KM67WAo4EZjd9v8WjPUd4ExCGEZrylF005VfsZNLiuZG+xuAO2wf\nW3H89xFCXFax/eiwnM1M8eT1vh55vupTtmZ1KU4mIyK6UgLWsP1Ik/72kyqhhQqF5l8G1rP9o5Lx\nLgOest1z8zyoSPoesAswb420hWWBowkCL5WKs8d+exDCXZPRVJlMVfLNXqabC4FpCYpZvTgX+DDF\np1SnAV+KG6PWYPtZ4AZCId2UzW3Az4DC8gq27yaEKW7VTx9HkriZ3Y1QNLhKQeDxwB6SPlxx/L8D\n+1FQlDiTyWQGhbhm7UtizXKow3YgkFQadqhnu09qjEElhp1eRPA9ZVNaaL6L3YEfRj2ANnEZ8G9g\n7Rp9bgF+A2xa81nHUh5NlcmMKPlmb4wg6bvAqSTy1+Ip1TGEU6qet1uSVgf2Joi1tCZvTdJngGco\nEJmR9HXgNkpq0UmanVBMd6bUTeGgE1/Y9xAKAp9Rwb5UpGAy+3cR5nuc7RuG42tmyiav9/XI8zU0\n4pr1NDDe9vU92t8NPAdsb/vWxBhTA08Be9q+rkl/+0lXzuGCvYTcos1UhILih9i+omS844Cp2iRY\nAyBpUeAcgqJ0pcgdSXMD1wEzukatxRhNtTFB5XuwPrIzrSLf7GX+A4d6OkX5a7cAvwQ2KxjmKuAf\nwHr99a5ZbP8KOImCnArbzwA3E269isZ6HriWgpvCQSe+XHYB9o8hTGXsC2wh6fMVx/9/wDjg8Iq3\nh5lMJjNqdK1ZR/Ras+IB6PjYPnVijH/RwnUv5hweCRxWYNPJ4T5MoU5fEQcC6yjU8m0NDkrbzwKV\nC6bbfgy4l6BcXYdzgY9QP+cvk2mEfLM3hijLX5P0LeBGwq3V3xJjLEwIC505hne0gq68ixVsP56w\n6dSim9P2LwvGamU5ismRdBHwku0DKtgeCHzeFco2RHsRbkAvtH368DzNTKnk9b4eeb6GTlyz7gQu\ntX1qov1+4Azb5xSMcUcc47QG3e0rsZzAS5Tk5Um6GbjZ9vEl4w2pFt1oI2k2QtTLzLb/XLHPlwlC\nN7NFkbKqz+pEU81RJ+cvk+kml17I9ETSmcAfUiF5ks4Hfmp734IxrgYetD2hITcbQdL3ge8BixcI\n0RwCfMr2JiVjHQB80S0rR9GNpC8SahHOHlXpimw/SNgsL5MSKejRZx7gGsLhQeUQl0ymQ17v65Hn\na3hImgu4nrBmvdmjfQFCftdMTpcrKBxjUIlCbtsQSkik3o+dA+OZStIdpiOIunzP9oNN+NsUkk4F\n/mZ7lxp9KtUjnKxP52Dgsl6HC5lMFfJmL9OTrvy1nnWDJH0BeIKQu/frxBizEE44Z3GQZm4FVXLJ\nJH2I8JJa2vbTBWN1bgqXs/1kE/6OBJKOBD5gu1CcJtrWVmSVdDHwgu2kdHkmkyKv9/XI8zV8JF0I\nvGp7v0T75cATtpPlCOpETQwKXXl5h9m+rMDuTOD3tgtTGSRtBGxJSQ3bQUOTai/OZfvnFfuU5j0m\n+rXyYCAzOOTNXiZJWUiepAnAh2xvWTDGycBbtnduyM1GkLQCQVXt66nQCUnbAiuWbWokbUVQOF2y\nTS+zbiR9hLC5Xcz2CyW27ya8BLd1UDCtMv6QQlwyGcjrfV3yfA2froiHOWz/pkd7lXIFXyKse7O3\nad2TtDhwBgXlhboOjL9h+xcFY01NmMcDbF/VhL9NIWk/guhKZX2CoYauSroAeC11uJDJFJE3e5kk\nKqkbVGUD0HX6NbftnzXpbz+JoRN3AZekQieqbmq6FNp2sH1LE/6OBJJ2Ar5re4UKtqsRZLrnioIE\nVcY/BpjW9tbD8zQzpZHX+3rk+eoPZQeeko4HsL19wRhHE9a9yqF9g4CkGwm1VY8psDkI+KztjUvG\nqlTDdtCQ9H7CN9KKqRz/Hn2mi33WtP1wjWd9kYLDhUymiLzZyxQiaWtgVWCpXrdSkn4IfMf2igVj\n7EsIP2iVOmdX6MTMTgvRrEZQofxW0aZG0irAAYSw2Eqbn0FD0rTAi8Dmtu8qsRXwAHCK7fMqjv9R\nQojLwrZfHq6/mSmHvN7XI89Xfyg78JT0ccKaOb/t1xJjtHLd06TyQkmRkq4c7sJ0h2h7K3Cd7ZP6\n7myDxBz/tQkHoZU+hCVtQqi7t0idaJ+YTvF+2ym19EymJ/1a83PphbHL6cDngVSo4knAHJIWKxjj\nKOA7cfPUGuJJ3V2E8gMprgb+BmxQMty1wBtAa4VaYrjOeODImLdRZGuCBPdB8SSzyvh/AiYAyRyX\nTCaTGRTiJucw4PBE+x8I77/kmta17h3ShI9N4VBe6BpCIfWUzRuEEgtVRNrGAXvHDWKbOBP4JLBc\njT7nAR+mfkmFQ4DVJc1as18m0xfyzd4YJt5KHUiIvX/HrZSkdQj1Y+Zzooj6UE6/BoGKQjQd5bWZ\nbb9VMNYCwOWEW86k3SATb+weAk60fUEF+6uAR2z3/BjqYV9J2juT6Sav9/XI89U/uiIeNrV9T4/2\n9xJu/5Jhe21VpZT0KUKKQjJNoyuNYbuyHG5J5wC/tL1Xv31tEkkrEjb9c6Zy/Hv0WY5wEPC1qn1i\nv50JN4IrD8nZzBRJvtnLVOFa4C+kb6Uujf+7VsEYZwKfBpbpo1+N41Af7wxCCGbK5iHgYWDHkrEe\nImyUCu0GmbhR3wU4uOKN3Xhg16hCVmX8fwB7ARPixjKTyWQGlhjxsAeJiId4sLd3bO+5psV1b29a\ntu7F3LHjgYMLbDqF5icoUWi+i72BraK4S5u4AfgjsHGNPjcDvyGEc9bhJGBOSYvU7JfJDJt8szfG\nkTQfcCWJW6kYxnkWxepcKwMHkbghHFQkfZhw6rqE7WcTNl8lbPhms/37grFKFdraQJ0bO0knAW/b\n3qni2FMRblMPtH3l8DzNTAnk9b4eeb76S1yzHgaOsX1xj/apgSeBfWxfkxhjasK6t5/tq5v0t590\niZSsbPvRhE1pofku20OBT9jerN++NomkeQlpHZXrxUqam3CYXqvGrKT1gO0JuaCD9fGdGUiyQEum\nMpIuA5623fMUT9L1wF0pda644N8HnGX77OY87T+Stieoki5bYHMcMLXtbUvGOp7wb2a7Prs5Ykia\nCXiQUEPxjyW2MwAvAPPY/mnF8ZcEJhLU2d4err+ZsU1e7+uR56v/SFoUOIewJr7jwFPS0oRbsDmc\nUJysYjOISNqckLe+WGrzoQqF5qPdhwibx+Th6qAi6RLgedeoF6sh1JiNhwuPAoe7oNZhJtMhb/Yy\nlZH0FeARErdXkmYD7qFYnWt+4ApalrcmaRpCmYWtbd+esPkYIXdjIRcUTO1SaFvA9qtN+DsSSDoB\n+LftHSrY7k3YuH2vxvi3ADfYPnEYbmamAPJ6X488X80g6TrgXttH9WgTcBtwle2TE/07NlfaPqVR\nZ/tIvJV8GtjT9rUFdqWF5qPd9sAytuuInow6GkK92K4+sxZFBfXotzhBQG/WfCCaKSNv9jK1kHQs\n8G4nagJJOg34i+1xBWNcATxetuAPGpLWIOSTJWvHSdoNmNf26iVj7U4o17Bm/z0dGepsWiW9j3Ba\nu6rtH1ccf07gVsLBwBvD9TczdsnrfT3yfDVDVEm8j3Dg+d892r8J3ERY01LlfEptBpEoOHI0QXAk\ndXNZKY0hHq6+AHzf9p1N+NsUGkK92Phd9a6yqKAe/W4CbrF9fE03M1MYebOXqYVKagJ1qXN9K4qb\n9BqjEwLYqry1eOr6AHCq7XMTNh1VtXVsP1AwVkehba0o3NJK4qZ1LttrVLDdHFifUJexaj2ic4Bf\n2U7Ke2cyeb2vR56v5pB0CvCm7Z4leySdB/zc9j4FY5wP/MT2fs142X/i+/F2wq1kz5vLaFdaaD7a\nrQnsTlD67KnyPYh0fSN92/ZLFft8LPapFe0j6WvAHYSDgb8Oxd/MlEHe7GVqI2kcITF4tUT7/sCX\nbSdrz0k6EfhXlRDAQULSggT10WSZBUkbAj8ghHMm/2FI2hjYnPBSGKx/QBWpurmNtu8ihPrsZvuG\niuN/DniKIGn9y+H6mxmb5PW+Hnm+mkPSJwkh/z3LEUj6PEGspaicT6fkzxwOipetIN5K3kxBNEbV\niJC4eaxc5meQkLQrsKDtVWv0GVK0j6Qzgd/b3r2mm5kpiLzZy9RG0nsIH/g9a6FJ+gAhZG95208k\nxvgEIUxjPts/adLffhPzDp603bMIbkyefhw42PYVBeN0FNr2dYvU1yan6uY22i5PKLD7dVevR9RK\ndbbMyJHX+3rk+WoWSfsSDgTXTbQfDkxve4uCMY4E3m/7Bw252QiSzgV+4YJaeVUjQiR9G7iAMJf/\n7K+nzdH1jbS+7fsr9imtx5jo91nCIeo3bP9iKP5mxj55s5cZEpI2ALYmnF694w9f0lbAGgRFrZQ6\n116Ek8vKoh2DgCqUWZC0BHBKtEkmT6ul6mvdVN3cRlsBdwEX2T694vitVWfLjAx5va9Hnq9mUShH\n8DKhHMFjPdo75XwWt/18Yozpo03lcMBBoEo0RldEyNplaQySrgEesD2h7842iKT1gW0JN5hV0xY2\nIdTdW6ROtI+kg4FP295kSM5mxjz9WvNzUfUpjwuBaQkbul6cAXwGWLpgjGOAbyvUp2kNtl8j/Pz7\nFtjcAbxKuPEq4jbgdSB5wjvoxHyKXYDDYmJ9ka2BccB+UbSlyvh/JRTtLa3pl8lkMqON7TeB/UgU\nUrf9F+BQCtY0B4GXw6Nda4i3S6cByVICnlREPllovovxwLiYC9cmLgKmIf2N1IvzgA8DK9Z81hHA\nslHULJNpjHyzNwUi6bvAqSRuryStQljwk0XUhyLaMQhoUpmFnkI10aaTPD1zfLmnxurkOczoFqmv\nTU5UBrvV9nEVbC8GXrR9QMWxpyHM95ZumTpbpnnyel+PPF/NE3Ow0gBUAAAgAElEQVSUnwHG9cpR\n7lrTNrd9d2KM9xCEO9bvlTIxqMRojJeBpW0/nbDpFJHf3/ZVJeOdDPzD9g/77myDlH0jJfosBxxF\nyOmslOoQ+20LrGB7mSE5mxnT5Ju9zJCJH92vkL69uhb4C7BhwTDnAB8Hlu+rcw3jUEh8AsUns88C\nNxBOJovGepJww5csV9ESxgF7xhClMvYAdlAouF5KfFHuDkyIYaOZTCYzsMQP9XHAEXHjN3n724R1\nMLmmxTy1vaJNazbnMRrjIMI7MmXzL2BXQkTIu0uG3A/YSNKX+ubkCBC/kapE+HRzM/BboG5I5mnA\nVyQtWbNfJlOZfLM3hSJpDuBOErdXqlBEXdIKhE3TnHVOskabrlPXDW3fl7D5DOF095u2Xy8Yq1Sh\nrQ1IOh34swvqLHbZHg28xxXrEcWPnYeB421fODxPM2OJvN7XI8/XyNCVo3yx7dMS7Y8Ax9q+KDFG\nJyf6INtXNulvP4kbuOeB7WzfWmB3K3Cd7ZNKxtuHUK5pnf562ixVI3wm6zMPcA3hu+nvNZ61BrAn\nQfymNeUqMs2TBVoywyZK//7Bds8bLEmXAU85rV4p4G7gAttnNOdp/5G0HrADoRRFz8VV0oHAF2wX\n3XB2FNo+anvz/ns6MmhSncW5bP+8xLawZmOizyKEvIZZ2qTOlmmWvN7XI8/XyCFpbuA6wof7mz3a\nFwHOJaxp/5MYYwngZEI4YGuEvCStCuxPOOxMpXLMCdxKQbmGaPc+QiTRKrYfbcLfptAQyiPEVIcX\nbCdzH3v0EaGG8UTb59f3NDNWyZu9zLApu72qqF45L3A1NU+yRpt46vpj4EjblyRsOqUoloshm6mx\nPhztFrf9XBP+jgSS9iPkH65XwXYcQa2sTj2ia4H7bR85dC8zY4m83tcjz9fIIuki4GXb+yfarwXu\ns31UwRi3ANeX3YANEnHzcT9wlu2zCuzOAX7pgnIN0W4LYD3al+Pf+UaqXB5B0pcJ3xbJ76ZEv4UJ\nAnKtKleRaZa82cv0hbLbK0nHAVPZ3q5gjEuA52wf1JCbjSBpMeBsik9mtwJWB5YseklJ2jHatCqH\nsRsF2fFXgJXcQ3Z8MtvCmo2JPrMQPiBmdlCsy0zh5PW+Hnm+RhZJXySEYs5u+7c92mcF7iW8Q3qu\naVVvwAYNSfMBVxLW654HuZpUruHrtn9VMNa7CDXlduslejPISDoI+KztjWv0ORZ4l+1taz7rauBB\nt6xcRaY58mYv0xfKbq+ieuVLhLp8ryTG+Aohf6HWSdYgIOl64J7UyWzMX3gO2MH2LQXjdBTatrB9\nVyPOjgDxBHZdwi1lWaH1wpqNiT6nAG/a3mXYzmZaT17v65Hna+RRKJL+AdvfT7SXrmkKBcv/y/Y+\nDbnZCJIuBZ4tOsiVdCgwg+1NS8ZagVBq4OtuV47/BwnfSEmF0h59Ot9NC9h+tcazZgZ+RDg8+NNQ\n/M2MLfJmL9M3ym6vJI0H5radrDsz1JOs0SaezN5HwW2TKpSiiHZrE1Tc5mlrknXXCex429eX2E5F\nkOA+0BUFCCR9kpD8P7ftnw3X30y7yet9PfJ8jTySPkL42F/E9os92jtrWjLfua1CXjEk8VHCQe7v\nEjYfIszPEg5K1qmxOqI3F9k+vQl/m0LSNsDKtpeq0Wd34Fu216z5rInAP92ychWZZsibvUzfiLdX\nzwI79rq9kjQdYTFf2/aDiTFKbwAHlXgy+1ZqcY0vqfuAs0vyF0oV2tqApOWBI6lQL0hBLnoiIcyp\naj2ifQkhTaW5gZmxTV7v65Hna3SQtDOwqO2VEu2la1oU8vqI7S0bcrMRFNSXp7O9VYHN9sCytpct\nGWtuQmmnmd1D9GZQ0SSF0m1t31axz3sJ301r2H64xrNmAF4gH4hmyJu9TJ8pu72StDGwBUGBsedf\nmio3gINI18nsPLZ/mrApzV+IdosySaGtlUnWcdN6B3C57VMq2N8C3GD7xIrjd3IDV7T9+LCczbSa\nvN7XI8/X6CBpWsJh5sa27+3RXrqmdQl5LWb7hSb97SeSpifkZ/e82Yw20xDeoVvZvqNkvIuAl2wf\n0HdnG0TSasC+hNu6ZITPZH02BTYmHBRU/thWKFcxi+11h+JrZuzQrzU/FznOdOgUUt8o0X4+8H5g\nlYIxjgPmk7RAn31rlJh4fyzQs8REtHkEeADYqWSsewnqXdv008eRJL6UdgX2jTmdZewG7BVzG6qM\n/yZB1rtVBYczmcyUiYOA1x7AkepRSD2uaftRsKY51Go7FDisQVf7TkxvODz+Stm8DexOKERf9l25\nJ7BDPGRtE1cDbwLr1+hzLjA9sGLNZx0NLBpvQjOZYZNv9jL/R9ntlaSlgRMIIXs9awbFG8DNgW/X\nOckabTSpFtBqcWPXy6YjqTx7Kn8h2pXmAbYBSecDP6siKqCKEtxd9u8ihA7vbPumYTmaaS15va9H\nnq/RI25iHgGOco9yPXFNewbYJbWmld0QDioK6ssvApvYvidhU7lWnKSjgPcWhYYOIpIWBC4hvNv/\nUbHP8sAEagrTqKXlKjL9JYdxZhohqm89Y/vgRPttwDW2JybapyYkou9j+5rmPO0/MeRiE0K4SipU\n9RhgWttbl4x1KvA3t1h1sktUoFBWO9pWkuCerM9KhNvUOauGxWTGFnm9r0eer9FFoVzPWcCs7lGu\nR9KKhNu7b6Q+7CWtC+wIzNemj3hJ6wA7A/M6IUAmaSHgIkpqxXWFhn7b9ktN+NsUkq4AHrNd6YZ2\nqMI0XYcHu9q+cUjOZlpP3uxlGkElZRQkfQO4hYKaQZKWJYRFzpG6ARxEujaq+9q+OmHzUcLJbOFL\nqopCWxuQdBjwcdubVbA9FPhEFdtoL0KNqnNtnzk8TzNtJK/39cjzNfoolOu52/bRPdoE3AOcl1rT\n4g3ho8Dhti9r0td+Ev1+mBIBMklXAQ/bPqJkvF0IGgBFqSEDh6QZgYcIG/4/VOwzD3AN4bspmfPf\no9+KhLDfOevcCmbGDnmzl2kMhTIK77bdM+9MoWbQ67b3TrTXEvgYJCqGqu4KLFT2kpK0HzCjW6w6\nqUmy2kvafqaibaEE92R95gWuokT4JjM2yet9PfJ8jT6SZiNs6Ga2/ece7fMS8ruSH/aSFgdOIxyq\nVlIxHgQkLQKcR4EAmSrWiouhoS8BG9i+vwl/m0LSCYT09u1r9LkEeM4FNQt79BFwN3CB7TPqe5pp\nO3mzl2mMrturhW2/3KO9NLxP0lzA9YQXXmskluH/QlWvtX1Sor3SS2qsqE5K2g5Y3vYyFWx3AJay\nvXyN8QtDhzNjl7ze1yPP12AQw/TfsL1ror30w17STcDNtk9oyM1GkHQt8CPbEwpsTgLetl0oaCZp\nPWB7YP6WhbR+nJDDOL/t1yr2KYyaKuhXeniQGbvkzV6mUSSNAxawvWqi/TDgY7Y3LxjjQuAV2/s3\n5GYjSJoTuI2wuP41YVPpJSXp+8DawHfb9DLrRpNktbdxSY2haPsisIXtuyqOP6SXYKb95PW+Hnm+\nBgNJnwKeIxGmX0XMS9LXCBEwyffMICJpFuB+Cm7uJH2CUCtuPts/KRirlSGtAJL2AL7pGkXTJR0H\nTGV7u5rPqn0rmBkb5M1eplHi7dXLwHq2f9Sj/cOx/bu2n0uM8SXgMcIL77dN+ttvJJ0N/Mb2Hon2\nSi8pjRHVSdWoMSRpLUI5hnmcSOTv0edY4F22tx22s5nWkNf7euT5GhzKwvSjmNc0qXSIaHMW4T2z\nZzNeNoOkiYSbux0LbPYCvmZ77ZKxFgdOJ+TAtSmk9b2Eb6A1XbFoeteN4AK2X63xrEpK4JmxR97s\nZRpH0vqEenEL9rqVqhKyp/ZKLH8WeJqgqvaLhE3nJTWbeyizddl1VCeTCm2DTswduB84w/Y5FWwf\noSSRf7I+HyOExi5o+5VhuptpCXm9r0eer8GhK0x/JduP9WgvTIeINp33zJy2f9mkv/1E0gyEm7t5\nUzd3cTP0CrC6E+WMumxvAm61fVzfnW0QSZsAm1Gj1NRQbgRjv9LDg8zYI2/2Mo0Tb68eAw61fXmP\n9tKQPbVbYvkg4HO2U4XmkXQjcLvtYwtsShXa2oCk+YHLCcIEb5XYLgqcQwj1SW6EJ+sznnAbuPpw\nfc20g7ze1yPP12AhaUtgHWDxxIHoOEKo/2oFY9RSMR4UJO1J2KSuVWCzKbAxsGhJusMcwJ2Ed8tf\n+u1rU6iCgnePPpU3wZP1+yjhW2qh1OFBZuyRN3uZEUHSd4FTSaiGVQnZU1CvXDCV/zeoSPogYXFd\nzvaTCZvZCWpZMxW9pMZKkrWky4CnXUFMRdJ1wL22j6o49nSE+f6e7QeH52mmDeT1vh55vgYLldRC\nK0uHiDYdFeNkSsQg0rVpWdP2QwmbqQn1V/eyfW3JeGcCf7A9vu/ONoiCgvfx1Cg1VXUT3KPfboTb\n1HwgOoXQrzV/qn44kxm72L6TsKCnwjAvB/5FON1McQLwLUkL99m9RnGoI3ggMCHezvWyeR64FuiZ\n29dl92NCGGShOlkL2B3YKSbgl7EbMD7e7pZi+x/A3hTMdyaTyQwKMSx/N+CIuPGbvP2fwF4Uv0P+\nSgjzr1Ske1CI0R17AUcW/Gz/AsYBh/ean8nYB9hCQe27TdwGvA5sUaPPucD0wAo1n3U8MI9C8fpM\npjJ5s5epwjhgjyjK8h/EU6ldgIPjKeY7qPLCG2BOBz4LFJUd2AfYTNIXSsbaA9gx5ju0kpifcT5B\nrKXM9kVCDb3dazziAuD9QKtugTOZzBTLDcAfgE0S7RcC7wGKbmNOBmaV9J0++9Y05xPW62SYKnAL\n8EsgqdwN4FDG6WTCAWtriN9A44B9JH2gYp9/EQ4JqmyCu/v9g/Z+S2VGkRzGmamEpDOAP6ZCLBRq\n79yXCtmL+X9PAAfavrI5T/uPpJWBgykQWJG0P/AV2+uXjNX6JOsu4YHSPEyVSJQn+iwFnEhBYfvM\n2CCv9/XI8zWYSJoHuIZEmL6kJQgbmdl7pUNEm7UJB6fzpVIiBpG4Xp9E8c/2LeBGwvz8rWCsDxIi\niZax/VQT/jaFpPOA/7K9d0X7IRVMj6GxjwMH2L5qSM5mWkPO2cuMKJI+TSgh8E3br/donxW4j5Bg\n/d+JMZYEJhLy/1rzEd8lsHJ+alFWxQLqYyXJOuZhLmR7lQq2lTbCk/W5DbjG9sRhuJkZcPJ6X488\nX4OLpIuBF20fkGi/GbjJiSLq8UD0YeBo25c052n/kXQLcGPqZ4s25wM/tV0YFSJpG2Bl20v12c1G\nieGnTxLKTfy6Yp8h5fLnA9Eph7zZy4w4kg4EvmB7w0T7KcCbtncpGONW4DrbJzXkZiN0ndzObPvN\nhE2lAuoxyXq+IoW2QSeG7L4EbGj7vhLbDxA2wsvbfqLi+N8ghP/MFHMnM2OQvN7XI8/X4KKSurKS\nvg7cTkERdUmLAWcRas5VUjEeBLp+tqSaZkxzeIKSzZCkdwPPA9vavq0Jf5tC0uHAR20XhqxO1mdI\nBdPb+i2VqUfe7GVGnK6P9p7qlJI+SVik57b9s8QYrf2Ijye3L9neP9FeqMzWZddRnVw3pdDWBiSt\nC+xIkBYvDDuS9ANgTWCJqupjks4FXq8aFpNpH3m9r0eer8FGoa7s+2z/INF+FvBb20lBL0nXA3e6\noJzPIBJ/tt+nUj2izRHAR2wXiplIWo2QF/6tmN/WCqKuwcvUUFaV9BVCXdrZbP++xrNa+y2VqU7e\n7GVGBUlbERLNl+z10S5pX8Lis17BGK38iO86uZ3D9m8SNisSVNXmTOX3RbsNCQqnPQvWt4EYdvQI\ncFRZ2FE8rX0W2Mn2zRXHrx0Wk2kXeb2vR56vwUaT6sou4iBQNXl7aRF1VSznM2hI+gzhsLNnqke0\n+TDhwPg7DkrWqbEE/Ag4zfa5TfjbFJJ2AJayvXyNPscBU9neruazWvktlalOv9b8UjVOSWdJ+p2k\nZ7t+b3pJt0t6RdJt6lJplLS7pFclvRTjiju/P5ekZ2PbccN1PDNqnEFQp1w60X4U8B1JcxeMsTew\ndcwDbA3xtvJsoOfNXqSjzLZxyXAXANNRrGI20MTbvF2AQyVNW2L7v0ySKJ+64vivE/6+9cyByWRG\nm/x+zHQT89UPJ1FGIW7wTqFAcTJugq4jrJetwUFNcyJBzCxl8xdCmYnDS8YysCtwYIyEaRMnA7NI\nWrxGn4OAdSR9teazOt9Sn6nZLzOFUaX0wtm8U3Z+PHC77ZmAO+P/R9JshJyl2WKfifGEBsI/gM1s\nzwjMKKlIyj4zoHR9tE/o9dEe89n2o7iuUOcjvmjTNKgcDKwST1/fQddLan9J70sNEjdKuwKHSZqm\nEU9HANv3Em7stq1gfh3wF6BnzmeCQ4GVJM0xBPcymabJ78fM5JwIfF3SIon2I4BlY55bin2BLSV9\nru/eNcsRwBIK6pspTgZmU0mZCdsPAj8Gduijf43joEi6O+EbqFJ5M9t/AI4mbITrPOt1QnmoNn5L\nZUaQ0r+Itu8H/jzZb69EKApJ/N+OIt/KwMW2/9dBZv01YD4F+fUPOBSWBjivq0+mfXQ+2jdKtJ8F\nzAAsVzDGocDKqU3ToGL7zwTfkyeTth8lKJPuXDLW7cBPgC376eMosBuwm0qKp092WvveKgN3nQS3\nquBwZsogvx8zk+NQV3ZPQrHxd3xjRXGWgwgbo9QYnZpzrYpqcCirsD/Fhdb/h+qbod2BXSR9vL+e\nNs7lwP8D1qnR51hgQUnz1XzWYeQD0UwJQy2qPoPt38X//h3hwx7g04TimR1+CXymx+//Kv5+poXE\nj/ZdgAN63V7FXLVOyF7PgqHxI/4w2vkRP5FwMlkUprEHsEMUrSliHLCXpA/1zbsRJuamXEko9lpm\n+zDwEEHYpSqVToIzmQEhvx8zlxC+r9ZMtJ8GfEWhHFGKKjeAg8gZwKcoPuy9DPg38L2igWy/ClxM\nhXfLINF1sHmwgnJ1lT5vEW50j0htlBP98oFoppShbvb+j/iXuq8CE5L26/q1WD/HzvQH248ADwA7\nJUxuAP5Ice7aScAcbfszjieT40mc3EabnwHnEEJai8Z6BriZsOlrM/sBG0r6cgXb3YEfVj2trXkS\nnBlgJC3Wvb6Ptj9Nk9+PUyZdYfo985ljqN94wod96h3yBuEGsDC/bdCIh73jKD7s7RwYH1JhM3QA\nsN4Q8tlGFYeSRE9TLcWhw7nAR4EVaj5uIjBrPhBtP429I22X/gK+CDzb9f9fAj4Z//tTBDl6CIvX\n+C67W4D5gE8Sio12fn8d4JTEs1zFp/xr9H8BXyZs6GZItM9LOKV+X8EY6xLi8jXaP0/Nn12EArgb\nFNhMTxBrmbVkrM8CfwI+O9o/1zDnZC/g0oq2xwPH1xh7qvj3ZJ3R/jnzr/79GgvrfX4/5l8Ffzdu\nICgQ92oT8GDJO2Qa4FVCyZpR/3lq/NwC7gG2LLG7Fti5wnh7AJeN9s81hHmYJX4DTF+jzwqEElbv\nqvmstQlq4VON9s+df/XvV7/W/KGekl/HpHytjQjFpju//z1J0yjI1M8I/NihwOgbkuaL19MbdPXJ\ntBTbPwXOB/ZJtP8YuB/4YcEwlwBTkw53GUgc/hXuAhyUUgtzUGY7jHLlsV8Cp9Ky/IweHA0sVDHn\n4EBg3aqntZ50Un5ImfJnJjPK5PdjpsM4YHdJH5m8Ib5DdqX4HfI2YaOTvAEcRLrej/tJen+B6W7A\n+LJ8b0I+2wKS5u+XjyOB7ZeAKwg5nFW5kWqK3pNzOSE0du2a/TJTAKV19hQKSS8KfIyQf7AP4TTm\nMuDzwM+BtRzrwUjaA9iUkJy6g+1b4+/PRQhrmw64yfb2iefZuY5Qa5D0UcJJ9sK2X+7R/mXgUULB\n0N9N3h5tFicoSs0aX26tQdJVwCO2e27o4sbkJWBjB+XK1DgfItQfWtIhtLOVSNqE8O9/EZcsLnGt\n+Kbtyht9SdcB99g+enieZgaBtq/3+f2YKUPSacBfbPcM1Y/vkIdt9xRsiQcADwPH2b6oOU/7j6QL\ngVdt71dgcwrwd9uFgmZ13i2DRMzbfx6Y2yG9o0qfeYGrCbUW/17jWYsS1pFZHNIfMi2nX2t+Lqqe\nGTaSxgEL2F410X4MMI3tbQrGuAm4xfbxDbnZCJJmIoTizOogn9zLZh3C7eZ88YYqNdZ2wHK2l23E\n2REgluN4EtjX9tUltu8lFCBe00G4pcr4sxHCg2Z2UEbNtJi83tcjz1f7iGqrzwHfsv1fPdpnIuS/\nz2L7T4kxFiHkc7XqI17SF4HHga/Z/nXCptJmqOvdso/tVt18S9qH8Ge3bo0+lwLP2E7WLUz0ywei\nY4i82csMDDHB+mVgfQcp8snbC2//os3XgDsIJ1l/bdLffiPpBELkSuo0firgEeBo2xcXjDMN4aW3\nle07GnF2BJC0NHACMLtDXcYi29qntfGk/K+2dx22s5lRJa/39cjz1U4k7Q98xfb6ifaTgLdtpwTP\nkHQtcG/bPuIlHQF8xPYWBTb7Eg7wCjdDCvUnjwPmKHu3DBIxlPVlYGXbj1Xs8xXCd0PyIDnRLx+I\njiHyZi8zUEhan6A6tUCvj/Z4+ze/7dUKxjgL+K3tPZrztP9EVckXgQVtv5KwqRReIWkNQnz/XEW3\ngIOOpFuB62yfVGI3NfAUsJftayuO3Tkpn8uhXlmmpeT1vh55vtpJ/Nh/BVjR9uM92j8BvECI/vhJ\nYoxZgXtp2Ue8pA8TfvbFbT+XsKm0GYohrbcBV9k+uQl/m0LSFsB6wHdqHGweD5A6SC7oVxg6nGkP\nebOXGSji7dWjwGG2L+/RPh3hdm892z9KjPFZglTxnA6iJa1B0nhgHturF9hcRziZParApqPQNtH2\n+f33dGRQqA11G+HDpPCmVtKywDGEUJ9Kp7XxpPzLtjcYtrOZUSOv9/XI89VeJH2fIJ7x3cSB6F6E\nG6tk7TlJpxKiGlr1ES9pR2Ap28nae1U3Q5K+CdxEiAL6W9+dbQiFMhRPA7vZvqFin85B8vy2X6vx\nrMLQ4Ux7yJu9zMARhVZOI4ixvENoRdIGwNaEG7Cef/EkHUKQLd+0UWf7TNzMvkwoDfBAwmZW4D7C\nBui/C8ZaGLgw2v2zCX9HAklnA78pu6mNG9zbgStsn1Jx7A8QTouXt/3EsJ3NjAp5va9Hnq/2Ej/2\nnyWUGripR/t7CWvaag5K1r3GaOVHfExReAH4QSpFIc7PM8C4ss2QpPOAn9vuqQQ+qEhaATgC+LpD\nPcIqffYkHICvVfNZ+UB0DJA3e5mBJAqt3Gr7uB5tUxGStQ+2fUWif0eVcgnbzzbqbJ+RtCGwFcWb\n2VOBv9nepWSsq4GHnFBoawNdN7XfsP2LEttvESSnK5/WSvoBoWTHElXDYjKDRV7v65Hnq91IWgk4\nhLAmvuNjX9JmwIbAYgXvkAOAL9resFFn+4ykNQllJJIpCnEzdDhhc5PcDEn6AvAEBcIvg0g82LwL\nuMj26RX7dA4BVrf9SI1ndQ5El7P95FD8zYw+/VrzW1O3JdMaxgF7xDj9/8CTaqUdGk/63kEM+TuY\nktp0A8oFwHuANQps9gU2UaizVcR4YNcobtNKYijuycBBFWyfAO4ECuW3J+NM4DPA0kNyMJPJZEaW\n64E/ka6hdg7wUWDFgjEmAEtK+kZfPWueK4B/Aj1FaiI3Ar8HNikaKN5qngHs3zfvRoC4gd+V8vqD\n3X3eInw3HBE3i1Wf9TdC7d4Jdfplxib5Zi/TdySdAfzJ9m6J9luAG22fkGifhhCnvqXtO5vztP9I\n+i6TQll7CrHUUB6bCPyPCxTaBp06p4uaJNM9h+3fVBx/FUKB9m/Y/tfwvM2MNHm9r0eer/ajkhpq\nkpYDjiLcWvW83ZK0NUHMpFUHXZIWAi4mvP/+kbCZm1CrcmbbbxaM1RF++Y7t55vwtykkXQS8ZPuA\niva18/1iv3cTQod3tH3LkJzNjCo5jDMzsEj6NGGBSdUV+johRytZZkHSWsBuBNGTVqlSSroRuN32\nsYn2qspjMxDyHOax/dNGnB0BJG0FrE4oGF9WaH0C8EHb3684tgh5kGfZPnvYzmZGlLze1yPP19hA\n0iXA87YP7NEmQhmiy2yfmuj/bkKZnm1t39aos31G0pXAY7YPLbCptBmKwi9L2F6hz242SozseYxQ\nnui3FftUCnHt0W8Vwg3fN/OBaPvIm73MQFOWV1Am3hFfeI8Ax9q+qDlP+4+k2YG7KZDIrqE8tjfh\nlnCdRpwdAbpOF3eyfXOJ7UcIp7WL2H6x4vjzE0KEZoohL5mWkNf7euT5GhtI+jJBvXo227/r0T4X\nIeRzptTtlqTVgH0Ih6qtORCVNCPwEAX146puhiRNS4gC2sz23U342xSSjgLeZ/sHFe1FqJ93vu0z\najxHwP3AGbbPGYKrmVEkb/YyA01Z+J4qlFlQxdp0g4ik0wl1bnoW/u4Kyxhv+/qCcd5HmMdVbD/a\niLMjQJkwwWS2OxM2eyvXGP9y4EnbhwzP08xIktf7euT5GjtIOhqYzvZWifYLgNds75doF/AAcLJb\nVqZHoX7cVLa3LbCptBmStDYhD27elm16pydE+NQ52JwXuIpwkPyOEOCCfgsAl8V++UC0ReTNXmbg\nKQvfU4UyCwq16e6xfXRznvYfVSj8LWl54EgKcjOi3eaEpPbKxVgHjTqnkpLeQzit3cj2fRXHLz0t\nzgweeb2vR56vsUPXx/63bb/Uo/2LlOQwxxy4i2hZmR5JHyPU3V3Q9isJm+mjzaJFm6GWRwHtQvjz\nr3OweSnwjO2Daz7rcuCJovDZzOCRN3uZgacsfE+TyiwsafuZxBizETYJyZDIQUXSfoQwnJ5CLPEl\ndSdwaSo3I9pVugUcdCTNA1xDSeJ9tF0X2BGYr+oGN54WY3v74fqaGRnyel+PPF9jC0m7AgvZXiXR\nXprDLOkqQpmeCQ252QiSdiOs76sV2FSK8mhrFFA82HwJ2JwaAGcAACAASURBVLDGweZXgYepebCZ\nD0TbSd7sZVqBpJUJ0vs91RIlbUcI9Vy2YIzTCCGR45rztP9EIZZXCEIsPUMwu3IzZnZBfbl4CziB\nGsVYB5GYeP+y7ULJbIWajI8Ch9u+rOLYHyfcCC5g+9VhO5tpnLze1yPP19ii62N/A9v392j/COH2\nL3m7JWlm4EeEd8h/N+lvP5E0HeFnX7/Xzx5tKkd5xCige20f1XdnG0TSesD2wPxNH2xKOgH4t+0d\najuaGRXyZi/TCuLt1b3AObbP6tE+DUFVbCvbdyTGKFT3HGSiEMv6FBfJvQD4ie19C8bpFGO92PZp\njTg7AnSFJpWqkElaHDidcBL5dsXx9yCojq05TFczI0Be7+uR52vsUfaxL+mHhPfHSgVjnAz83fYu\nzXnafyRtAGxDOKBLvR8rRXlImpWgzNy2TW/nYPMI25dW7NM52Jzf9ms1njWkfpnRI2/2Mq2hLKlY\n0hrAnoT8tp4J1pIOBL7ghLrnoNIVgrm77esSNl8AnqCkvpxC/aHrKFBoawNVQpO6bG8CbrV9XMWx\n30s4CV/T9sPD8zTTNHm9r0eer7FHWRSDguLkS8DGtu9NjPFJwqFpMkd8EIk/+2PAYakIjmjzY2BC\n2WZI0qnA31q46f0OoUh8sj5vjz57EiJ91q75rD0IkVZr1fc0M9LkzV6mVcSk4mdtH9SjTcCDwEQn\nVMUkfZAQErmM7acadbbPKBTJPZogxPK/CZsJwIdsb1kyVqUwyEGmKzSptBiupDkIeY0z2/5LxfE3\nATYjJL4P1gKX+Q/yel+PPF9jk64ohp4f+5LWAXYi3MikDkT3BWa0vX6jzvaZ+LOfQYjg6LnRiZuh\nM4tsol0rN70AKqnP28P+vYRvotVs/7jGczr9Vrf9yJCczYwYebOXaRUKdYV+TAjf61VXaGHgQgpU\nxSRtA6xke+lGne0zcTN7B3CF7ZMTNpU2QBpCMdZBRNJOwHddoRiupDOB39veveLYUwNPAvvYvmZ4\nnmaaJK/39cjzNXYp+tivcrvVlSO+gu0nGnW2z0i6AbjT9jElNneUbYaiMNqMttfrr5fNEg827yJE\n7lQ92BySUrekTYFNCOI3g7UJyPwHebOXaR2SjgGmtb11ov1qgqrYEYn2dxNO7ba1fVtznvYfSd8E\nbiJsZt9I2FTaAKlmMdZBJIYmvQBsYfuuEtvPAM8QQk9+UXH8ZYDjCKGxPW9TM6NPXu/rkedr7CJp\nduBuEh/7VW63JH0fWAtYok0f8Qqq2/cSfvaeqttl89Nl19n0rmj78Sb8bQpJZwB/sr1bRftOmshu\ntm+o8ZypgaeAvWxfOyRnMyNC3uxlWoekjxJyD1J1hTqqYrPY/lNijNWBvQliLa0poAog6Tzgddt7\nJdqnJSRPb2b77oJxahdjHUQkrQWMB+Yu+7OUdBDwWdsbVxxbwO3Alanb1Mzok9f7euT5GttIOh34\n79THftntVvz4fw7Y0fYtzXnafxRUt98oyreL8/Nnlyhzx03v2oTD08H6yC1Ak8Tovmn79Yp9VgQO\nA+Z0DaVuScsCxxAORFur8D3WyZu9TCtRqCu0oO1VE+0Tgf+xvVOivTS/b1CR9HlCeOHXbf8qYbM2\nMA6Yp2gDpCEUYx004p/lQ8CJti8ose3kbC5t++mK438LuJFwEpwsa5EZPfJ6X488X2MblShPV7nd\nkrQKcABhw/COckeDiqRPETaqc9v+WYlNoTJ33PQ+C+xs+6Ym/G0K1RSji+/Re4DzbJ9Z4zmd9JLL\nXFDnNzO65M1eppUo1M15mURtHUkzEML75rH908QYpfl9g4qkQ4EZbG+aaBehYOrxti8sGKd2MdZB\nRNJCwEWE29x/lNhuS8hHWabG+OcDP3VBWYvM6JHX+3rk+Rr7SDoA+JLtDRLtZbd/Au4HTrd9bnOe\n9p8oMjOL7XUKbPYHvpyany67lYBDCOH/rbm5kvQBwsHmcrafrNino3g+k+23ajyrUp3fzOiRN3uZ\n1iJpfWBbErV1JO1NECD5XsEYVwMP2p7QnKf9R9KHCAv5UqkbKknfBs4nvPSSm1kNoRjrICLpKuAR\n24eX2HVqMm5t+/aKY3fKWnzN9q+H7Wymr+T1vh55vsY+XR/7y7uH0ErZ7V+0WQC4lPARX3iINkh0\n5dutbPvRhE3h/HTZDenGaxCQtBWwOrBk1Xe7pMuAp2wfUvNZFwCv2d6vtqOZxsmbvUxrUXldofcR\nFvNVnZAUljQL4fQymd83qMQbqhVdoCoq6RrggaLNbNk8tgVJMwEPEIQH/lhiWztnUxXLWmRGnrze\n1yPP15SBpB8Aa5IQWom3f18sCvWTdAXwaNkh2qChoDC5AaGQfKrQeuH8dNnNC1xNuPF6R43fQUVB\njO45YIequZeSvkqICprV9h9qPOuLwOOU1PnNjA55s5dpNSqvK1QqKSzpZOAt2zs36myf0SRV0e1s\n35qwKRWriXadeZzV9ttN+DsSSDoBsO3tS+xq52xqUlmLxWy/MGxnM30jr/f1yPM1ZRDfEc8CO9m+\nuUd7aahfnUO0QUJBKfJpYE8nlCK7hGh6zs9ktpcAz7lHjd9BZii5l1Xfoz36HQm83y1W+B6r5M1e\npvUo1BW6zfZxPdo6ksLjbV+f6N8poJpM6B5UJK0G7EfBQl4mVtNldxNwa695bAuSPk5QIl3A9qsl\ntrVzNiX9kHBwsOKwnc30jbze1yPP15SDpJWBgwg5Z+94R1QJ9ZN0IvC/Ze+QQUOTlCK/5kTpnLL5\n6bIrrPE7qHTlXp5p++yKfT5B0DyY3/ZrNZ7VORBd1C1W+B6L5M1epvUoFBG9k/DR3quu0PLABIJ6\nZc8E65jQPZPbV0C1dCGPYjXPA/PZ/knBWIXz2BYkjScI86xewbawJmMP+2kJgjab2L5nWI5m+kZe\n7+uR52vKIb4j7gXO7ZVz1nX7lyyz0PXxP68TgmeDSPzZbweusj2xwOZe4BzbZ5WMdwwwje1t+u5s\ng0iaH7iCGsIrkvYibJLXrvmsnQnlnFqr8D0WyZu9zJhABUVE42J+F3Cx7dMS/dtcQLWzkM+cyieI\nC/ccLhCriXZnAn+wPb7/no4MkqYjnC6uY/uBEttKYa6T9VkH+CFh89yqGo1jlbze1yPP15SFJqks\n9nxHVAn1UxA8m80FCpeDiKRvAjcTNjpvJGzmA66k4B0a7To1fhe2/XIT/jaFpMuBJ11ReKWK5kGi\n35hQ+B5r5M1eZkxQpiwmaW7gOsKC/2ZijFYWUAWQdCnwbCqfoOrCLekzwDPUKMY6iEjaANiaUIux\n8M8yhrn+0/YPK449FSGcZ4LtS4ftbGbY5PW+Hnm+pjyK3hHxQPQ+4KyCCJHOO2QVJxQuBxVJ5wK/\nsL1XgU3hO7TLbhwhvHG1PrvZKF3CK7PZ/n3FPqWaB4l+Y0LheyyRN3uZMYPK6wpdBLxse/9Ee+Vk\n7UEj5hM8SljIe+YTSNoM2IgQT1+kPHYQ8DnbGzXi7AgQN2SPAYfavrzEtlOTsXLOpqTvAGcSRAve\nIQyUGVnyel+PPF9THmU5ZzFC5HLC7VbPUD9JWwDrAou36SNe0ueAp4A5bf8yYfMV4BFKNkMxcuQl\nYD3bP2rC36aQdBwwle3tKtq/i3D4u6vtG2s8Z0wofI8l8mYvM2ZQeV2hLxE2ALPb/m1ijErJ2oNI\nzCd4j+2tEu1TE154ezmhThbtPkiYx2VsP9WIsyOApO8CpxJe3oUKo5L2IWzcKocoSboBuMP2scPz\nNDNc8npfjzxfUybxHTGt7a0T7YU11ro+/nexfVNznvYfSYcCn7S9SYHNscC7y3Ly6kSODBKSPkbY\nqC5o+5WKfVYEDiNslCsXldcYUfgeK+TNXmZMofK6QkcB73NCGrhKOMug0pVPsIgTSliqoE4W7bYh\nFKRdqhFnRwgVKLVOZlc7RCkK2txFCA1uraDNWCCv9/XI8zVlUpZzpgo11uLH/6GEj//WHIhK+hAh\nl3tp208nbCrl5MWbq8eBg21f0YS/TREFzOa2vUZF+0KBn5K+NwG32D6+vqeZfpI3e5kxhcrrCk1P\nWPCLNkS1lasGBUm7EH62lRLtHXWyK22fXDBOp4bftrZva8TZEUDS7MDdVNiQxRCl9aiRn1AkDJQZ\nOfJ6X488X1MuMedsAdurJtoLQ/3iO+Qewsd/oXrloCFpW2ClokPMqjl5kpYATiZECrXm5qpLwOx7\nth+s2KcjYFPrm0hjROF7LJA3e5kxR1koZtwQfdsF0sCSrgAet31oc572n6iE9SIFpQGiOtlNhIX7\nbwVjrQbsSxC9ac0J7uRIOh34s+1xJXa18xO6hIFaLWjTdvJ6X488X1Mu8R3xMrC+7ft7tHdC/ZK1\nSjVJ3bNVB6Jdh5jb2b41YVM5J0/SLcCNtk/ou7MNImkj4PvAQjUONgtDfAv6nQn83vbu9T3N9Iu8\n2cuMOcrCDlRBGljSTMCDBEn+Pzbpb79RKA2wM6EmUs/SAJLOA/7L9t4F44hQluA02+c24uwIIOlT\nBOGduWz/vMS2dn6CpAOBz7vFgjZtJ6/39cjzNWUjaX1gW8KGrle6Q2mo31A//kcbSasSykwk8/Kr\n5uRJ+johUmYm239twt8miPn7jwMH2L6qYp/SEN9Ev88CTxPm+xdD8TczfPJmLzMmiSePVxMW4V51\nhUqlgSWdCPw/2zs26myfifkEjwDH2L4oYfN54ElCoflfFYy1IHAJIQzjH034OxJI2g+Y0fZ6JXYi\nhH1eYPuMimN/kHBSvqxbLGjTZvJ6X488X1M2KlEr7gr1W9v2Q4kxhvTxP9rENf5+Ql5+zzDUOjl5\nks4GfmN7j7472yCSlgJOJIShJvP3J+tzAvBv2zvUfNbBwGdsb1zb0UxfyJu9zJhF0iXAc+5dV6hU\nGljSJwiS/PPZ/kmjzvYZSYsC5xJuJv+ZsDkM+LjtzUrGugJ4zPZh/fd0ZJD0fsLHy8q2HyuxL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VAdZK6aWfBHaw/Z1WhR0wSqmVHwYOsn11QZ8VgG9SEagtaVdgzS4qvb1I+jLw2hqH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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b838c6610d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,14))\n",
    "basin = 'whites'\n",
    "elev_conn = np.ndarray(shape=(8,8),dtype='float') ## [rows: scenarios, columns: elevations]\n",
    "swe_conn = np.ndarray(shape=(8,8),dtype='float')\n",
    "nn = 0.001\n",
    "for scennum in np.arange(4): \n",
    "    ax = fig.add_subplot(1,2,1)\n",
    "    scenario = scenarios[scennum]\n",
    "    dataname = datanames[scennum]\n",
    "    swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000  = make_swelists(basin,scenario,dataname)                                                                   \n",
    "    swees = [swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000]\n",
    "    elevations = [500,1000,1500,2000,2500,3000,3500,4000]\n",
    "    for swenum in np.arange(len(elevations)): \n",
    "        elevation = elevations[swenum]\n",
    "        swe_array = np.asarray(swees[swenum])\n",
    "        if len(swees[swenum]) > 0:\n",
    "            ## y value\n",
    "            elevmet,cr = find_elevmet(scenario,dataname,swe_array,elevation)\n",
    "            meanswe,minswe,maxswe,swe10,swe90,xmin,xmax,xmid = swe_stats(swe_array)\n",
    "\n",
    "            ax.plot(xmin,np.ones(len(xmin))*elevmet,color=cr,linestyle='--',linewidth=lw)\n",
    "            ax.plot(xmax,np.ones(len(xmax))*elevmet,color=cr,linestyle='--',linewidth=lw) \n",
    "            ax.plot(xmid,np.ones(len(xmid))*elevmet,color=cr,linestyle='-',linewidth=lw) \n",
    "            ax.plot(meanswe,elevmet,'o',color=cr) \n",
    "            ax.plot(swe10,elevmet,'s',color=cr)\n",
    "            ax.plot(swe90,elevmet,'s',color=cr)\n",
    "            elev_conn[scennum,swenum] = elevmet\n",
    "            swe_conn[scennum,swenum] = meanswe\n",
    "            ax.set_ylim((500,4000))\n",
    "    for elv in np.arange(len(elevations)): \n",
    "        elevmeansorted = elev_conn[0:4,elv]\n",
    "        swemeansorted = swe_conn[0:4,elv]\n",
    "        if np.sum(elevmeansorted) > 0:\n",
    "            ax.plot(swemeansorted[swemeansorted > nn],elevmeansorted[swemeansorted > nn],'k-')\n",
    "for scennum in np.arange(4,8): \n",
    "    ax1 = fig.add_subplot(1,2,2)\n",
    "    scenario = scenarios[scennum]\n",
    "    dataname = datanames[scennum]\n",
    "    swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000  = make_swelists(basin,scenario,dataname)                                                                   \n",
    "    swees = [swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000]\n",
    "    elevations = [500,1000,1500,2000,2500,3000,3500,4000]\n",
    "    for swenum in np.arange(len(elevations)): \n",
    "        elevation = elevations[swenum]\n",
    "        swe_array = np.asarray(swees[swenum])\n",
    "        if len(swees[swenum]) > 0:\n",
    "            ## y value\n",
    "            elevmet,cr = find_elevmet(scenario,dataname,swe_array,elevation)\n",
    "            meanswe,minswe,maxswe,swe10,swe90,xmin,xmax,xmid = swe_stats(swe_array)\n",
    "\n",
    "            ax1.plot(xmin,np.ones(len(xmin))*elevmet,color=cr,linestyle='--',linewidth=lw)\n",
    "            ax1.plot(xmax,np.ones(len(xmax))*elevmet,color=cr,linestyle='--',linewidth=lw) \n",
    "            ax1.plot(xmid,np.ones(len(xmid))*elevmet,color=cr,linestyle='-',linewidth=lw) \n",
    "            ax1.plot(meanswe,elevmet,'o',color=cr) \n",
    "            ax1.plot(swe10,elevmet,'s',color=cr)\n",
    "            ax1.plot(swe90,elevmet,'s',color=cr)\n",
    "            elev_conn[scennum,swenum] = elevmet\n",
    "            swe_conn[scennum,swenum] = meanswe\n",
    "        ax1.set_ylim((500,4000))\n",
    "    for elv in np.arange(len(elevations)): \n",
    "        elevmeansorted = elev_conn[4:8,elv]\n",
    "        swemeansorted = swe_conn[4:8,elv]\n",
    "        if np.sum(elevmeansorted) > 0: \n",
    "            ax1.plot(swemeansorted[swemeansorted > nn],elevmeansorted[swemeansorted > nn],'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PJUPEI+Rsh5vIKeuXGB6Gpp8pk1uQ01fXI6ewrkLvKZPPkNcIfpgMb3PIGncj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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b838cddd650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,14))\n",
    "basin = 'whites'\n",
    "elev_conn = np.ndarray(shape=(3,8,8),dtype='float') ## [rows: scenarios, columns: elevations]\n",
    "swe_conn = np.ndarray(shape=(3,8,8),dtype='float')\n",
    "nn = 0.01\n",
    "scens = ['rcp45','rcp85']\n",
    "for scenn in scens: \n",
    "    if (scenn == 'rcp45'):\n",
    "        lop = np.arange(4)\n",
    "        ax = fig.add_subplot(1,2,1)\n",
    "        row = 0\n",
    "        col = 4\n",
    "    else: \n",
    "        lop = np.arange(4,8)\n",
    "        ax = fig.add_subplot(1,2,2)\n",
    "        row = 4\n",
    "        col = 8\n",
    "    for scennum in lop: \n",
    "        scenario = scenarios[scennum]\n",
    "        dataname = datanames[scennum]\n",
    "        swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000  = make_swelists(basin,scenario,dataname)                                                                   \n",
    "        swees = [swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000]\n",
    "        elevations = [500,1000,1500,2000,2500,3000,3500,4000]\n",
    "        for swenum in np.arange(len(elevations)): \n",
    "            elevation = elevations[swenum]\n",
    "            swe_array = np.asarray(swees[swenum])\n",
    "            if len(swees[swenum]) > 0:\n",
    "                ## y value\n",
    "                elevmet,cr = find_elevmet(scenario,dataname,swe_array,elevation)\n",
    "                meanswe,minswe,maxswe,swe10,swe90,xmin,xmax,xmid = swe_stats(swe_array)\n",
    "                ax.plot(xmin,np.ones(len(xmin))*elevmet,color=cr,linestyle='--',linewidth=lw)\n",
    "                ax.plot(xmax,np.ones(len(xmax))*elevmet,color=cr,linestyle='--',linewidth=lw) \n",
    "                ax.plot(xmid,np.ones(len(xmid))*elevmet,color=cr,linestyle='-',linewidth=lw) \n",
    "                ax.plot(meanswe,elevmet,'o',color=cr) \n",
    "                ax.plot(swe10,elevmet,'s',color=cr)\n",
    "                ax.plot(swe90,elevmet,'s',color=cr)\n",
    "                elev_conn[0,scennum,swenum] = elevmet\n",
    "                elev_conn[1,scennum,swenum] = elevmet\n",
    "                elev_conn[2,scennum,swenum] = elevmet\n",
    "                swe_conn[0,scennum,swenum] = meanswe\n",
    "                swe_conn[1,scennum,swenum] = swe10\n",
    "                swe_conn[2,scennum,swenum] = swe90\n",
    "                ax.set_ylim((500,4000))\n",
    "        for elv in np.arange(len(elevations)): \n",
    "            elevmeansorted = elev_conn[0,row:col,elv]\n",
    "            swemeansorted = swe_conn[0,row:col,elv]\n",
    "            elevminsorted = elev_conn[1,row:col,elv]\n",
    "            sweminsorted = swe_conn[1,row:col,elv]\n",
    "            elevmaxsorted = elev_conn[2,row:col,elv]\n",
    "            swemaxsorted = swe_conn[2,row:col,elv]\n",
    "            if np.sum(elevmeansorted) > 1:\n",
    "                ax.plot(swemeansorted[swemeansorted > nn],elevmeansorted[swemeansorted > nn],'k-')\n",
    "                ax.plot(sweminsorted[swemeansorted > nn],elevminsorted[swemeansorted > nn],'k-')\n",
    "                ax.plot(swemaxsorted[sweminsorted > nn],elevmaxsorted[sweminsorted > nn],'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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elZVeX5/WFl95dfM++/eBViPD0aieeGdd9tPLPx738fbKIWX1zSmNraH4cCQS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b838ba73190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,14))\n",
    "basin = 'whites'\n",
    "elev_conn = np.ndarray(shape=(3,8,8),dtype='float') ## [rows: scenarios, columns: elevations]\n",
    "swe_conn = np.ndarray(shape=(3,8,8),dtype='float')\n",
    "nn = 0.01\n",
    "scens = ['rcp45','rcp85']\n",
    "for scenn in scens: \n",
    "    if (scenn == 'rcp45'):\n",
    "        lop = np.arange(4)\n",
    "        ax = fig.add_subplot(1,2,1)\n",
    "        row = 0\n",
    "        col = 4\n",
    "    else: \n",
    "        lop = np.arange(4,8)\n",
    "        ax = fig.add_subplot(1,2,2)\n",
    "        row = 4\n",
    "        col = 8\n",
    "    for scennum in lop: \n",
    "        scenario = scenarios[scennum]\n",
    "        dataname = datanames[scennum]\n",
    "        swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000  = make_swelists(basin,scenario,dataname)                                                                   \n",
    "        swees = [swe_500,swe_1000,swe_1500,swe_2000,swe_2500,swe_3000,swe_3500,swe_4000]\n",
    "        elevations = [500,1000,1500,2000,2500,3000,3500,4000]\n",
    "        for swenum in np.arange(len(elevations)): \n",
    "            elevation = elevations[swenum]\n",
    "            swe_array = np.asarray(swees[swenum])\n",
    "            if len(swees[swenum]) > 0:\n",
    "                ## y value\n",
    "                elevmet,cr = find_elevmet(scenario,dataname,swe_array,elevation)\n",
    "                meanswe,minswe,maxswe,swe10,swe90,xmin,xmax,xmid = swe_stats(swe_array)\n",
    "                ax.plot(xmin,np.ones(len(xmin))*elevmet,color=cr,linestyle='--',linewidth=lw)\n",
    "                ax.plot(xmax,np.ones(len(xmax))*elevmet,color=cr,linestyle='--',linewidth=lw) \n",
    "                ax.plot(xmid,np.ones(len(xmid))*elevmet,color=cr,linestyle='-',linewidth=lw) \n",
    "                ax.plot(meanswe,elevmet,'o',color=cr) \n",
    "                ax.plot(swe10,elevmet,'s',color=cr)\n",
    "                ax.plot(swe90,elevmet,'s',color=cr)\n",
    "                elev_conn[0,scennum,swenum] = elevmet\n",
    "                elev_conn[1,scennum,swenum] = elevmet\n",
    "                elev_conn[2,scennum,swenum] = elevmet\n",
    "                swe_conn[0,scennum,swenum] = meanswe\n",
    "                swe_conn[1,scennum,swenum] = swe10\n",
    "                swe_conn[2,scennum,swenum] = swe90\n",
    "                ax.set_ylim((500,4000))\n",
    "        for elv in np.arange(len(elevations)): \n",
    "            elevmeansorted = elev_conn[0,row:col,elv]\n",
    "            swemeansorted = swe_conn[0,row:col,elv]\n",
    "            elevminsorted = elev_conn[1,row:col,elv]\n",
    "            sweminsorted = swe_conn[1,row:col,elv]\n",
    "            elevmaxsorted = elev_conn[2,row:col,elv]\n",
    "            swemaxsorted = swe_conn[2,row:col,elv]\n",
    "            if np.sum(elevmeansorted) > 1:\n",
    "                ax.plot(swemeansorted[swemeansorted > nn],elevmeansorted[swemeansorted > nn],'k-')\n",
    "                ax.plot(sweminsorted[swemeansorted > nn],elevminsorted[swemeansorted > nn],'k-')\n",
    "                ax.plot(swemaxsorted[sweminsorted > nn],elevmaxsorted[sweminsorted > nn],'k-')\n",
    "                ax.fill_betweenx(elevminsorted[swemeansorted > nn],swemeansorted[swemeansorted > nn], \n",
    "                                 sweminsorted[swemeansorted > nn], facecolor='lightsalmon',alpha=0.4)\n",
    "                ax.fill_betweenx(elevminsorted[swemeansorted > nn],swemeansorted[swemeansorted > nn], \n",
    "                                 swemaxsorted[swemeansorted > nn], facecolor='skyblue',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}