{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Basic test of the wflow BMI interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import wflow.wflow_bmi as bmi\n",
    "import logging\n",
    "\n",
    "reload(bmi)\n",
    "%pylab inline \n",
    "import datetime\n",
    "from IPython.html.widgets import interact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Startup two models:\n",
    "\n",
    "+ The wflow_sbm model calculates the runoff from each cell (the LA land-atmosphere model)\n",
    "+ the wflow_routing model that uses a kinimatic wave for routing the flow (the RT routing model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR\n",
      "CanopyStorage\n",
      "FirstZoneDepth\n",
      "LowerZoneStorage\n",
      "Snow\n",
      "SnowWater\n",
      "SurfaceRunoff\n",
      "SurfaceRunoffDyn\n",
      "TSoil\n",
      "UStoreDepth\n",
      "WaterLevel\n",
      "WaterLevelDyn\n",
      "PotTransSoil\n",
      "Transpiration\n",
      "ActEvapOpenWater\n",
      "soilevap\n",
      "Interception\n",
      "ActEvap\n",
      "SurfaceRunoffCatchmentMM\n",
      "ExcessWater\n",
      "InfiltExcess\n",
      "ActInfilt\n",
      "zi\n",
      "Transfer\n",
      "CapFlux\n",
      "RunoffCoeff\n",
      "InwaterMM\n",
      "M\n",
      "thetaR\n",
      "thetaS\n",
      "FirstZoneKsatVer\n",
      "PathFrac\n",
      "InfiltCapSoil\n",
      "InfiltCapPath\n",
      "FirstZoneMinCapacity\n",
      "FirstZoneCapacity\n",
      "RootingDepth\n",
      "MaxLeakage\n",
      "CapScale\n",
      "RunoffGeneratingGWPerc\n",
      "rootdistpar\n",
      "CanopyGapFraction\n",
      "MaxCanopyStorage\n",
      "EoverR\n",
      "N\n",
      "N_river\n",
      "cf_soil\n",
      "TTI\n",
      "TT\n",
      "Cfmax\n",
      "WHC\n",
      "w_soil\n",
      "Altitude\n",
      "Bw\n",
      "River\n",
      "DLC\n",
      "IF\n",
      "ERROR\n"
     ]
    }
   ],
   "source": [
    "# This is the LAnd Atmophere (LA) model\n",
    "LA_model = bmi.wflowbmi_csdms()\n",
    "LA_model.initialize('../../examples/wflow_rhine_sbm/wflow_sbm.ini',loglevel=logging.ERROR)\n",
    "\n",
    "# This is the routing (RT) model\n",
    "RT_model = bmi.wflowbmi_csdms()\n",
    "RT_model.initialize('../../examples/wflow_rhine_sbm/wflow_routing.ini',loglevel=logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Now we can investigate some model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86400\n",
      "631152000\n"
     ]
    }
   ],
   "source": [
    "print(LA_model.get_value(\"timestepsecs\"))\n",
    "print LA_model.get_start_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['inputmapstacks:Precipitation',\n",
       " 'inputmapstacks:EvapoTranspiration',\n",
       " 'inputmapstacks:Temperature',\n",
       " 'inputmapstacks:Inflow',\n",
       " 'run:runinfo',\n",
       " 'run:timestepsecs',\n",
       " 'run:reinit',\n",
       " 'run:starttime',\n",
       " 'model:modeltype',\n",
       " 'model:AnnualDischarge',\n",
       " 'model:Alpha',\n",
       " 'model:ModelSnow',\n",
       " 'model:ScalarInput',\n",
       " 'model:InterpolationMethod',\n",
       " 'model:WIMaxScale',\n",
       " 'model:Tslice',\n",
       " 'model:UpdMaxDist',\n",
       " 'model:origTopogLateral',\n",
       " 'model:configfile',\n",
       " 'model:reinit',\n",
       " 'model:fewsrun',\n",
       " 'model:OverWriteInit',\n",
       " 'model:updating',\n",
       " 'model:updateFile',\n",
       " 'model:lateralmethod',\n",
       " 'model:sCatch',\n",
       " 'model:intbl',\n",
       " 'model:timestepsecs',\n",
       " 'model:MaxUpdMult',\n",
       " 'model:MinUpdMult',\n",
       " 'model:UpFrac',\n",
       " 'model:waterdem',\n",
       " 'model:reInfilt',\n",
       " 'model:MassWasting',\n",
       " 'model:wflow_subcatch',\n",
       " 'model:wflow_dem',\n",
       " 'model:wflow_ldd',\n",
       " 'model:wflow_river',\n",
       " 'model:wflow_riverlength',\n",
       " 'model:wflow_riverlength_fact',\n",
       " 'model:wflow_landuse',\n",
       " 'model:wflow_soil',\n",
       " 'model:wflow_gauges',\n",
       " 'model:wflow_inflow',\n",
       " 'model:wflow_riverwidth',\n",
       " 'model:RunoffGenSigmaFunction',\n",
       " 'model:SubCatchFlowOnly',\n",
       " 'model:TemperatureCorrectionMap',\n",
       " 'framework:outputformat',\n",
       " 'framework:debug',\n",
       " 'framework:netcdfinput',\n",
       " 'framework:netcdfoutput',\n",
       " 'framework:netcdfstaticoutput',\n",
       " 'framework:netcdfstaticinput',\n",
       " 'framework:EPSG',\n",
       " 'framework:netcdfstatesinput',\n",
       " 'framework:netcdfstatesoutput',\n",
       " 'framework:netcdf_format',\n",
       " 'framework:netcdf_zlib',\n",
       " 'framework:netcdf_least_significant_digit',\n",
       " 'layout:sizeinmetres',\n",
       " 'outputmaps:self.Inflow',\n",
       " 'outputmaps:self.SurfaceRunoff',\n",
       " 'outputmaps:self.WaterLevel',\n",
       " 'outputmaps:self.FirstZoneDepth',\n",
       " 'outputmaps:self.InwaterMM',\n",
       " 'outputmaps:self.PotenEvap',\n",
       " 'outputmaps:self.Precipitation',\n",
       " 'outputmaps:self.MassBalKinWave',\n",
       " 'summary:self.MaxLeakage',\n",
       " 'summary_sum:self.Precipitation',\n",
       " 'summary_max:self.Precipitation',\n",
       " 'summary_avg:self.Precipitation',\n",
       " 'summary_avg:self.QCatchmentMM',\n",
       " 'outputcsv_0:samplemap',\n",
       " 'outputcsv_0:self.Transfer',\n",
       " 'outputtss_0:samplemap',\n",
       " 'outputtss_0:self.SurfaceRunoff',\n",
       " 'outputtss_0:self.WaterLevel',\n",
       " 'outputtss_1:samplemap',\n",
       " 'outputtss_1:self.ActLeakage',\n",
       " 'outputcsv_1:samplemap',\n",
       " 'outputcsv_1:self.InwaterMM',\n",
       " 'outputcsv_1:self.Precipitation',\n",
       " 'outputcsv_1:self.ActEvap+self.Interception',\n",
       " 'outputcsv_1:self.UStoreDepth + self.FirstZoneDepth + self.CanopyStorage',\n",
       " 'API:CanopyStorage',\n",
       " 'API:FirstZoneDepth',\n",
       " 'API:LowerZoneStorage',\n",
       " 'API:Snow',\n",
       " 'API:SnowWater',\n",
       " 'API:SurfaceRunoff',\n",
       " 'API:SurfaceRunoffDyn',\n",
       " 'API:TSoil',\n",
       " 'API:UStoreDepth',\n",
       " 'API:WaterLevel',\n",
       " 'API:WaterLevelDyn',\n",
       " 'API:PotTransSoil',\n",
       " 'API:Transpiration',\n",
       " 'API:ActEvapOpenWater',\n",
       " 'API:soilevap',\n",
       " 'API:Interception',\n",
       " 'API:ActEvap',\n",
       " 'API:SurfaceRunoffCatchmentMM',\n",
       " 'API:ExcessWater',\n",
       " 'API:InfiltExcess',\n",
       " 'API:ActInfilt',\n",
       " 'API:zi',\n",
       " 'API:Transfer',\n",
       " 'API:CapFlux',\n",
       " 'API:RunoffCoeff',\n",
       " 'API:InwaterMM',\n",
       " 'API:M',\n",
       " 'API:thetaR',\n",
       " 'API:thetaS',\n",
       " 'API:FirstZoneKsatVer',\n",
       " 'API:PathFrac',\n",
       " 'API:InfiltCapSoil',\n",
       " 'API:InfiltCapPath',\n",
       " 'API:FirstZoneMinCapacity',\n",
       " 'API:FirstZoneCapacity',\n",
       " 'API:RootingDepth',\n",
       " 'API:MaxLeakage',\n",
       " 'API:CapScale',\n",
       " 'API:RunoffGeneratingGWPerc',\n",
       " 'API:rootdistpar',\n",
       " 'API:CanopyGapFraction',\n",
       " 'API:MaxCanopyStorage',\n",
       " 'API:EoverR',\n",
       " 'API:N',\n",
       " 'API:N_river',\n",
       " 'API:cf_soil',\n",
       " 'API:TTI',\n",
       " 'API:TT',\n",
       " 'API:Cfmax',\n",
       " 'API:WHC',\n",
       " 'API:w_soil',\n",
       " 'API:Altitude',\n",
       " 'API:Bw',\n",
       " 'API:River',\n",
       " 'API:DLC',\n",
       " 'API:IF',\n",
       " 'API:timestepsecs',\n",
       " 'outputcsv_2:samplemap',\n",
       " 'outputtss_2:samplemap']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA_model.get_attribute_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA_model.get_attribute_value(\"run:reinit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA_model.set_attribute_value(\"run:reinit\",'1')\n",
    "LA_model.get_attribute_value(\"run:reinit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2f4a93710>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ut2kacW4qD7BHllUxSSORoB6Jc4tjmAzQfZu13hTOrsJLyWc4r19Bx6FBnCQ+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\ne15ugOam3JSb8p6XG6C5KTflprzn5f8DSARi2kATyTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2f4cb2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(LA_model.get_value(\"Altitude\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:21,428 - wflow_bmi_logging - ERROR - get_value: Slope not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "2015-12-18 09:39:21,428 - wflow_bmi_logging - ERROR - get_value: Slope not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "2015-12-18 09:39:21,428 - wflow_bmi_logging - ERROR - get_value: Slope not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "2015-12-18 09:39:21,428 - wflow_bmi_logging - ERROR - get_value: Slope not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "ERROR:wflow_bmi_logging:get_value: Slope not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Image data can not convert to float",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-cd6181db3e22>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLA_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Slope\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/jaap/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, **kwargs)\u001b[0m\n\u001b[0;32m   2959\u001b[0m                         \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morigin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2960\u001b[0m                         \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2961\u001b[1;33m                         imlim=imlim, resample=resample, url=url, **kwargs)\n\u001b[0m\u001b[0;32m   2962\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2963\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jaap/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   4642\u001b[0m                        filterrad=filterrad, resample=resample, **kwargs)\n\u001b[0;32m   4643\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4644\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4645\u001b[0m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4646\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jaap/anaconda/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    432\u001b[0m         if (self._A.dtype != np.uint8 and\n\u001b[0;32m    433\u001b[0m             not np.can_cast(self._A.dtype, np.float)):\n\u001b[1;32m--> 434\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Image data can not convert to float\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m         if (self._A.ndim not in (2, 3) or\n",
      "\u001b[1;31mTypeError\u001b[0m: Image data can not convert to float"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAEACAYAAAC+rrMfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2f4ac0a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(LA_model.get_value(\"Slope\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2f49bbdd0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z20Ql7OiC0HwkHiiMqIYn7ws+hrRySJkJXfWg+TklyQCEI9DVB9v3QkUBXHMh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RjI+5pEcprK1HWsSChl3NxaJdep0+kBYcPsOyaUbZdgsoigOt1LtOGFev1xS/bFVcBuYFcdxjVE\n96EjC2GWVlcGBbhBpt4Gc6uOIPtJCgHSW/PKg7BipnZGPt9rQwdUjqT4xF89xbveUWfbtg28lPFj\na2jGx+f5wQ9q7NqVodlME5HpLCKhqTTR4oUoQ2PSiK0Jp0bGRrwX9QI6iQSsYshTHyeSW1B2b+RO\nRp5QhggEVq9DQzc1dkrKm+DcEt08kcrePOfWLulurN5GO/9GvZ4skVQERN6Jnlt7zZQJD5PD4Beh\nWQarA/o65fONuvmIDZesh8tXRpq3JaifTNIoLiCZKGJnPcK1EBuoQwpyYYGBxDh+1qFY6sQP4ySD\nOumuEvV6gmDAJXQtOb1pWlXCPEDUnkR39Mk2HpMViBdyMoCNKViXgF3ATN2kik36uGaImFYAbtoY\nqRAsH9IN8GqCQzgJ8x2heAzxUOxSAnADGLbhzSn4bwh1v5Xlo+2cQln0WnKmGL3uaepg95jX50NZ\n0KFuGKZgKWZHta8hYviqgYHMXDEqS4kcUlVxnQYeDmHOh0AJqA7EUsKIbiDYkE5nzREsRDpjHA5h\nwpEwzoNWBGDVDEXMbEquC44PXoOKb/Hpv7mUTRsfZ9s2XtL4sTU0b37zNaxYcYjXvGYHc3MNfD+G\n7yu4qvGlbi9qTFTmU2U5Z8zfhhFT3d4GRfGZErJo9bgaurTroUBkZFQxT38UyaPtd01DK2FQe1It\nQEgd+v5TRGGLprV1R461HdMmwoTaPZx2I6RiTxo6EB3PcmDgaqgewZ7djRsL4KrNEPYSTNp4TVdu\n3XXmVn0Vmcc9wCIIBy2qX+jAen2D1LuKdHdPQAB7D13Cd+d+GjoNMc2HzqFZhi86zmhxIfWNOcLx\nWFRkrf2p70IMx3UIy3XehidzEOSihesnwK6LbOUlwIeAfBct9pqVgNB0oHDTYqubiD5LHFiehnwa\nZiahlgdniUk6WpC25VxOhdCISdmD9vRz7AjCUEsSBpKZmXIERA6h5SnXEaPpIgS7BWb6uJZp55Uy\nKfcCkIOZmIQlMQRfspvgNsAOINUBP2/JFPGsqHDyLuArwFYL9qdhuixFp6EPM+3paEuuS9GFNUi5\n2ERdwlLSQoxsDQtiSViTFIN/wlzDkZpgY9SQcpuXPn5sDQ3A8uWLufPOOp4XcNttB/j4x3Wnh3O3\nE12IyjRTgExTz1NENfbKO+827z1FtL2o16IN6JQJrA131ABpSjqGbP1KwnORFatNmDHv06efIiqV\nVZxmC2IQy0QejRInNLukorIKEGuVnWatIBLp0kdqvKpYFrpfC1YPrMpw4aXz/OHP/Vcy/p9RSnbw\n4LFX8dFP/66UdB1FDICH2Golkhl7Hu6MsTZ3hF94zef4zI738sz8RgEwv2vCizdAaUsH9ckEjRUW\nQc2WW/GsuewzCPFMxz6ELNYBvMlc8ihyzEngLTG4xpLbdCmwswrTdUkVd3RAMYSmJzyYvDnfrAUp\nG4544JXBK5lFjCxq3xaphmoIjTL8TAyWx+F/+fLdOVtYszPGE3Ns6LMldOmyZK9qIOxfiIDdBFI3\ndBrznlDYuqE+V/WYjUfhhxJW3dwJF6TN1LPg+0243Y8w21+Ky/mXAtgZgxUWLOyCyTSc8cTYUJOK\n9/YqlBKGtVyWa25lO9s83Rhwo5neJ4DlIewuwFQ7t2uOcxMmL278RGSdAHbt2scPfnC49ftDD5X5\nznc05IBzU9E1IiS+i6gpsRqUDqI09hxRyOUQ9VBSRlUPEYCraeN2RX4jFdfCjHqIuiooD0aLJzWk\n0UA6SZRfPUOE17STCJuIIdLr1NfV0LQLmFjRd67YAG4WKhkI18EwXPK6x7lk407iB+okAp+aF6eR\ndYmtbOIM+4QlC6/oUgkzfPt7NzIz1QdpSCyu86aNd9AzOENzIEZtKEWmUMIJfY4eWMUPbnutTOpN\niG2vIRKZRVs8iE5k8e1AwGTN2vQTZdaXtD1Ggw8JxlMWct/hLkh6khr3bCim5bbUfUnx1mIGgG1K\nSrlotHtyFlyehv/WDV/14H4bTtpCx7+4AV2OeCq7QoOxWFIPVUayPxYRHSkeiOiVbbyaTebRnTDX\n1kPEcXERA+FWwQkEH7qzDle6UjPlA5YnHJtaTMIn6vC4DWftCFS+xRVZ0acCeNiBBRY4NalZmjOC\naW5cNhRlb6iD7AGVuiHhKWZo5mJ/DC4yG8RRM+UXhOLJ1DDvHQcy3HDDKO94h8Pb3vbcvJqf+KwT\nwCWXbOKSSza1fl+16gHCcBQI2LkzztRUe1ZG/1XxVZsoiK4iM18B3HblfIj4KoqZKDDoEoVD3UTh\nkIYxGttX2o6p20u+7fhKt1VDV0D81QlhkGaGjSjftGjGdCbBy4mWSiIUN/z0hKRizxHrsmCgA/p6\nIR+DwQvA7aXLnmfzyL34Sx1WbzxIoxDjr7/4AfnYDKxb+jS/+j//CifwCOM2zQUxKgNprjj6EPPd\n3fg5h9iSJquvOUiuWWD//GbuPHMj78l9ksHaOMVch4RAccQLedRcUtmRy16EpLLHEMdSE3Zas6qV\nHbvN7VmP0PAvBD5age15WjHEVSnojQuTNV8y+jJ6L5GFHfPB9UXyoNsGKy1V2tsrkEzAatsA0hZc\nnYBHAvnJhVA3dUpqr7WN2CTieXmh9NFehOw1B821rTfXXEMyP8O+1HDZMYjHJIyqurDFgsviMGQJ\nbmJbcJcF+3zR9dUQ2HLlkhZY8t1dFlxmi4DVUaBYM/MoJs89bkdULRUy1ync4nLpRgkMxmBFTAz9\nXsTbLCCiXV0JEcWq1iU8I8Y992ygp+cZ3vY2XvT4ifFonmvcdNPnuftu3eEdajUH39edXr0J5ZLo\n7FeOfXsBooK3GpYlELBZ+22bdCn9RE/HaTu2oncqI1Ew7/eREEvZXK75PY0pDQZGYbAbVm8SnOSx\nhyGbhou3yCFcZBIHU/DZO6BZl4yLDy0uzk9dDNddLot2N7iBx7ZrHuNT/9+7qZDir7/863zum78s\nX3sScfNnkAW00uAUvZB9Z4nP3nwrIwMnqVop8l4Xv3ngkxy/c4XY7isQz+Qpcwk3II7j1xFMQAWk\nNMvumtuutCEXMS6rzGsT5jwWIloBr0Uwg987CY82ovu0tMtg60Xwp4iwLG1XXIe1DqyKyaO9IoQd\nJbiniCy2Ybg5Bass+BLwDmB7E+5uGAA6DSknesw64oijq/2sOkMYtKJIfDPwemRj+CxwXxUqBVqt\ncWxbwp3ftSQsvK8Bu1UiTxnnTVrKja4lntYmInbFMqR84C9C2K+58bicQGcaknG5j+eUpig+qCe+\nCOLT8MYU9GXhcyH8jCXPcr95y5ZQDn1mFkqHUQWvm28+wuc/fwnJZBI76gZwznguj+YVYWgOHz5G\nPh/NjA98YD+PP65ymyqRoBmbTsRIqIFR3o2GXppl0krwAlH61EaewiLOfYCar1RvaAGRkdEdV7fH\nChFyqU2RLwKOw8YeuGSbPPR8UXbkZRmZr5po8JvgzUNHAPsPwfanaG1fm7bCBZfLIi7CT7/xG1y8\neSdf++xbCWybyUsGmd7YLxDPUwhNaNJc3ilz/C6wR3yWx4+SWFgn6LfxXZeT3lLqxYRc6gJkJzyM\n2Nm1iF1daS77HiLMXZuveeayVUhQQxPPnEM3ogngIotxHDhVgJLGWR3SS2kY6PRgXx2CSaIWxYb0\nmDRVywGQPSvyDHldyD68oxNe1yHfcRfwbCh4Sk8IU7aAsBAligKiqNo3YXkmhO50xIRIh9AZABMw\n3QmFGAQqTma84HgTFuZg2IG5PByYlZtNRjwYOzCbhg+rbFgWE7zsesRoTiLV3Z8qw9OK88VkPjuB\nAOdNDfdT5nrPmn875btc4L02jDrwmCM6xt2WwIQDwNFA+n4lM0KcHKtAMAthk56eElu3TvPpT69j\nyZJF/HPjFRE6PddYtWr5Ob//9m9P85nPjHHXXV1EaWcNbVQpT8l6mkJuL2nIEGWYIKqXUuC1QFQV\n3d7ETkMZxVPU20kR8Xm0U6YygE2YdeVSNr/xGJsv+Fu+krmV2lM5+dMggnucRQwIMUj1y+EGLNjW\nIbyQGvLmI0Ac3vqzf0/nmnkeHHs1+w9vkktSob0GMrGSSEgzwTmJrKDgcGR8tUzuHiIcPE3LcRCi\nm7n8CXObR30BFG9x5bM28EweHpmG65ZAtxtJ7SjWrhT7DsTwPIMwhucapuAzZQ5UF/7MHOAFkE5D\ntU8Mb+s+OqavUt48JsW7NPxNwmgcdobwdACLbFhoSfilbW31MTUQ3GXWF05L4MqirNoylUohVGuC\nD5UtkdzA0DAcVwyeb8tPgKTZE3a0P2ER9WoHApNkWGNLecYJJEWetuSZnUCk5dbEINYBT7vQLMiN\n933wdePMSyq+NwUXdooURzwtWbqKL0bmGUt6byet6HwSoVSmzycl3Ot0YXkORm2olRgZmeDGGy06\nOrTN0AsbrwhD88PjlltezcGDt3PXXSCTVEW81ZVcbN7ZLhakga2uBFU+UpFYDbM05u0lCox1dkqt\niAxlKmvIZaqlSSJgRTv3Jw+dS8gNx1m+9iiXJh/jqcxmZid7ZS4uJFq4GXPKs0ByAC4YkMNOIsao\nAqThta/+R56Y3Mq9j98gp5pDFum0Od2LOBen7jWvazXEAnOps+ZHExcWEhm6yCKbDuWn0xMPZ84V\nFz/hwYgtTNPHq7AgkHOHiAHQMP8uNt85JbeCjlBwmNCW3d6yISjLxc0HAjKn42BlwTJZnTBpcgFV\nk06GVvGpVZXF72dFeKoRwqOheAhpX7JXritaOzaCnTRD2GCJ4Hg9jFLnTVfU66pAoO5OO6iDLHTb\nGBgXqb/KxKLwq2w2HzsG2YZweULkOrtswUoOE1WgdMgz5UHg9XHJkrkJyWr5umma7/MrJryLQU+v\nbEJJT/gy8yE8lhTBszCQ60w6wn6eDOCULfesgXhJ3TbEsmxac4K33Vrjve99DS92vCINDUAy6dLV\nJYBvqeTgeWoIFDxVL6fdMKgbPk+E7yg5TrGbtswOEM0mPaaCdPqehPm8EgyVB6PDB47Bd+HhymZO\nZVbxB6/7EJ/t+hV2PXgpHJU1VCsk8WuuxOvqNKmTVgBOQNxukFhcJxyEipWmeiQlhX8riDiIMfP+\nfchiiSFG5ixRBJlEcJizyOcVQ28ixmIa8XQONGDShItzeaALnoiL3gw1+KUE2L3Q6BX8pkxkq3uR\nRbcYwSJOykdYArzGgq8mJBRwTZan1gnhGQibEGShmAWrKTu1lRaj59oQpMBrJ2zGwI5L2OUjC88O\nhDvzNFJqcEw3FEMhsGzIBPBbDvQ6JnQJJe3smb/XACtDqx4XBKOhKYRBz5KK7owtmE4ihD2W2Shi\nYiRjNixORdowriNFo7OhXHeH8Wa6kZbgjyD9vq8Bfj4ODLWBv0lDFDch0hjwRaDpG76R8bzjg8Yg\nNeTYg0nxHovqyRuqSD7EKsbJdpf4jQ/M8cvvevFGBl4hGM0/NyYmJhkfnwFC3vOeXezcqZMvhcQN\nM5h+pnpmRFs8RGS79jBLU8gVxM2II+CGbj3KDFbZA81C+UQoqALO7SUCxg25ZB3xn97A8PAob7ny\nS6zpPYhft/FzLn99z6+z79SFUQlVQCRbfFD+vfGtX+Mtv/ZlGm6cL33rl3hyx1ZmpvvEO5lDGKf9\nSHhVJqoNPUIkhuQjoVA/EXauWITWzEwiE/nYUZieNfdwArESHUAVnGHYGpfm8r3m5R8g2Ihefg0J\nC9eYa9CC9z7gIpPhUc2uo0CzKZjLKLCrCYkEBEVozEuFtGV6uFuWpHubIQQlMUhON7zagtl5eLYo\nWbENfTDjwpgKvNfkOSU6jGKfJd5R4ImhujkOD9siN2ERQUPK4VyKGJTOUGCRA7YJMU2jv4YDtrH2\nviVESnWgjTPBnDleP8Jx2WGutxNR1LvbPBMVCxgholrNALcQAex14H5PuEeq4mWZjTU04X5sVAy3\nr1J+euCQrq4Gn/nMM1xzzWp6e1UF4V8er3iM5p8bg4MDlEoVPvGJ7Zw9q1XM0CoVIcUAACAASURB\nVFJfw0ZuauWH/qYZJt2qym2/K3cGIhxGCxbbw6cK57J21cVWMp56RZqDtKB/MdhDNA7HOeEv46FH\nruVozyrR7E3ZbF25nc5VMzw2cSXew8mW4BxxuYxbb/wC3f2zfOs7N+H7Dnv3bmGm3ieZ8x4kVCoi\nXoraTZ3UARHlSCPJGSIISys4VOhv0BzH6pZd3M+bN04iizULgQ0nTIihUNlCc6ynkAW4Vt7aUqpT\njTLNsp0JxNDYtrzHr8litVICHD9lw5ThzdALYRFIQKZDMlqHfZF+CH3xYFwkbCra4lGM+sISHojD\nZI5W9s7PQ7kMc73gORIi5fxIYlP3ScV7FxIxHmqWeDOlEAp1U1BpCbhKIN6QGkOt2la8uxnKe/sQ\nYD1ExNunzXftQTYNi6hJqvYi1AYYk0DZg8OBGMdtNhxNwCEzX0MtOEbOo6nQgOKMDW6++Umuvtoj\nlXK59tqN9PR081LHK9bQADQaTUZHfep1wzc4h9CndH8T37bAYF1d7cUs7elC3/xNCxVztPK56Qx0\ndMH0WfCUyasGqgmZLPT1QYdjJIJDESnyAhhcA7mBVh3m9pNXRi02lsGtmS+wZtEzVDMJdv/gcvyz\nTrSrDkL36jnw4Nkda9jzyMWES6xI5ng1ovN71IqK+FS1QqNH7S6j4ZTPuaFVH2IA1O7GgGSPcDiq\nJSK5C5NVC5swY0uTNq8JQRxuckSftmi+W9d2BVlAo0TUor1ITVIcwTwKFoSenHBPAKt8WJCBbBzq\nOWlfG56W52nlTEImlDYroSPXU0ZKG3IO2B2Qr5tHGJPf60BlFup5yXa5xsONOQLQehj9F8RghIih\n0X7ip4n2k2IItaph7iqLPC5Aa2hFOQidmg6RPIQqlBwg8vJ8xOAMEVXHWER7pk4zGwGwD3uCDY3Y\nMGALszhsylwrNU2pRhLCTpMD8STstC2uvtrjN37j1byc4xVtaNatW8VXv7qKn/3Zv+Xuu30qlRyR\n96LZon5ke9XUtIZOGtq05zjVm0kQbUPd8v8YMDQkmrsPPwrloiwQyzUZHR8WD8Gll8B6sKoBViGE\nEgRFW6qElYg8joREvYiRuAC+vOcX+en5b/DWRbdxqriK2WKPSGUCZOHz33wXb73xC7zrHf+HD979\n14QDFtTBqfmkt5WxvhNSrySp9yckmzJH5KlMEwG8MwivQu2s8neGze05gWjvdppblbCE2RpmOdcI\nV2SHbrlBvXAgBQlHel2fQEoTFiEVxhmE/3MCU3ZmQZ8DjUDIbS3WdglmJ+EbFfippbC0U1LK0wh+\n4tehWIYdMVjjwFBWPIwyEnY5adM6NoSTCblegAst+f8pjBhXHLxRwX/SvTDUJZ0Dwqakt2NxeT7a\nEOPZULyUpWaKzYaQMOzkFqN8WMojNGysE8l5JoEbrAgOLCHPSZvWdQCvRnCas0T5BZ2OaucXIdea\ntCT0uxtYFUgo6rtQjsEpVwD8dEqmdR2oxbDCgM6+Iqn0y28WXtGGRsef/dlr+OM/fohPflIfvG4R\nIS1xJE2httjEibbXHM7VevGJyhgMcWzzBli5AoIErNoGBIZfgbjWCaAclzVXgI7XzZJcUcZvuszO\n9BBUjMLZLHB728l7tLycB45fx+yiPv7od36Tj3/6v7DnkS2yMGPwm7/7xwxeM8bjo5cRrkSyFsDS\nzcd4/5qPkxku8e0dP8udd98olziMLOhZIuhqBtmVy0Q65zYS71+GpJ5PEFUWW0A8C5nlQnUPJuW6\n6UOMwoz5AkNYPG6J0doEvAHpH32GqKPMCsTolIDvIt6cY8NJSwSzanUjtzAgxz3jSLV13Zfq67HF\ncCSEUQ8YhxMJsE05c4DUOnXbAsJONmBBHPK2LO6d5hwqyigHyEOiC/w4HJiAyX5Y44pWcNo8l7OI\nBKjdgHRStF5SSGuXC3ol7GogoRu2fOY0kTDVEJGReMDci/VI5u5+xMj0EklULDHzqYhoB4fIJrHe\n/O1JxHu8LpTp+QwwWYAzeeNJJWF9FpYl4WEiyo3nk83O8ZlPHebaazfyco9XLBj8w2Pnzn3s3Hny\nnNcOHizwiU+0S0AoBqMV2e1MYf09QFahSf2kPHj9KkiNQLpDJs8OonBE+YHtKhe9kFxZwU03CfI2\n1VqKsO5IQaDWaAaB8WxsAUvH5Ou6h+a4+oL76G9MYBcDvIpDxqowW+lhrLKQqpNizbKDhFWLBnFq\nYYpKPk3cbRAfrFNKZzhwZgN0Qv67PRTv6pRL+1XEiGgpQA6xpUOIARhFMIJRorKtVjebUJqr5Spi\nA9JpIR3WCkRb9AikUrDFgauIun9ptGWSHS2nMURA1GIgxqDmCQ+nHjNN58qwKS2LfKYJm3IwXpD0\ncy4OwxWRRZiIG8IeUC+JR+XEoOxDZ1NS19UAaZmbBepCtguackPcRUA3eKamaMiFJUnoz4jBWAcU\nmvCtOsTSsMEWA2kZg1dzxLgNIhk8xZ+UGaFSRJreTyDY1SKE+KiVJskmxGcERO7JisbOjoYwgrtt\nCT2HgH01Ccd7HOhNwuG8tFjxbOkPTlPe25cAv0OeZ1kSHV0DNe7/x2k2b17zT9bP8xn/IcHgHx5b\nt25i69ZN57y2b98zTE3tAAL27g155pksUaikxjDOP63udoC6TOiVi2H9CphP4XY0SF1VwcqFBK5N\nsxmnfiZlCFFEso01qO1NR/16NKvoIq74ZmAYYt0N3LRP7Wya0DSPn6t0883jN/GWC7/E4oFTZBaV\nedWW+/l///xPeeLebQwlx9iybhf0WNRrcWaO9XLPPW8EH7bc/DiLLzyB43vQgPTqEo7tgWdR9Dvw\ny65c7iBiMAbM+diI4RglAqBV5BCkbsh1wMmIBELSgZ6m6OjWPKgWgCYMJQTv2IvstJebYx0hqkGd\nQlLJ6z3pCjnjG7lOSzImrithj5uBii1GouYY5b+aGOseSyQXVGbIDqRSmjpUhXrgOHDd1j309opg\nWbMZcO+9q5ib7xeMhwAxMB6tyn+vAfNp8apO12EskOLEXhvWu5KG70NqsOZ92B8KFjJCxM2bRjJT\nwwHYTkSgrBLpoalsKIEYh4amGM2mZ3nQb6gUVgqKMSESTrswNSee32wS5gYgHxgMKC6SEF4exquR\nd9QEfJ+FI2d53RvP0tW17Pkspxc8/sN4ND9qfOQjX+Wv/qpIGPrMzMSM2Jbq9KoOjCuYS7IkhWer\nhuGGS1vRV3ZLnoE3ncEOhZxWeKybyb9cJDCOsnKniNptaIw9gngPRxEXvg+4ALKvniezrsDU9xcS\nHHIkHBqTj3R05ImnGyzbeJTf/p9/yEe//bvs/e4W8RS6EGOhSqKLgGmwbmlgrfMJ/jgFszD49lF6\nbx0nDG1O/9/l1J9Jgw3hMgQbWoBgFl8E/6xLWDObVRIpeuwn6jl0AKhVpG7KTsM6w1WZLcL0FDg5\nuLxLwNjHkQV4vSX3YTcSZf2cuUd3+1CvwOKk7M5TDSBnaC6hUdCwDYcykMxXtwPzc0b31uTIkza5\ndIlsQumvPvLhNMlkyO23l7noorUAlEolbr75IZ56agBIEQQpZmZSeJ7K1sXBisOyXnBjkiK3Peju\ngLVpuDSUSvFvA7t9SanbCeOBIFmkdUjR6dGmzJ9lJqQrIyS9siE1ps19zvsigxEoHyApxmypC70+\n7Jow7OkEOClppNc4LPIYYRZYCpmElKz4dUgPQrkm2TssMxElM/rmG3dwxzeeuzr7R41XfK3TyzGm\np2eYnZ2nWq3z9rc/zv79WqT5Q8JWiTjcsBVWZmHYhQUpeV4psM8GuNub0IQ1/2k/oWex//9sEXzj\nCAJ+FhBDsg1ZVE8gO0sDMSKjaB0bdtbHHvYI3h0Q7o4THnNaeMaH3/Q/uGjJE5xgOZ/w/x/G9wxT\nO5SU73gKMQYLkIlryHDuL1axrvBpfjkL+8CteTj9HtYFAUNvPkWir0aIRRCzW/wOywsJx2xOfXQl\n9aeScs454BfM9RyClnRlKpD1/GwIiSkhkfUkYYUtdP9nHcFp8qFUn/fFYJUjntNdiMFdjADnD5py\nj76YpJ99G06WoVQV0NXqkfAyLEuldKZHChk9m5amkNXDB3/jUd73viX/5HlblsXixQtIJsUtC4KA\nM2fGqNXEaygUqrz97XEOHRqR529Z4q3ZjoRVXhlyWeh1wQpEqfC1KXjSFtKdg3hWM0TqH4qH1ErQ\nLEoDvbABgSMkvqAg3lc8BR0Z6eh5BGH0KsG0My7fV69CtWoerqmls+vQNST9u0tGhdJejPTf9uRz\nYcKkuOvmcylwXd78Mzu54/bLXtCa+efu6X/40OlHjb6+Xs5OzvDx78ww3n8JrEtL+tm35Xl0I55C\nzoalvUKBLyIQhAkngrJNYy4BDoweGSG+qkbneyaJDzUpnuqmNp6Oegk9TST5exgJRRKIETLlBkHB\ngURIarDEG676Oss2n8DyQ7DgxO7l7D+4idlML6fOLCWYtGWxlxBvRnFvW76v67XTBJcG1KxUq27J\n8128ORdre8jsFQO4z3iEhxHR8UXALFgFsLIBfbeMk8/0UBrvgNchfz9GxBZeAhRsuSd9Ifg5KNUh\n34TRnNw/ZSf7QMkVzkkcAaZTCCYVQ4zSyiSMhVByJKUczoGXNMWHhuComcMgIeGV3y6/mea//PbD\n3HrrMKtX/+hwwLZtRkaiYsF6vc5HPrKbT35yjHvvXSRcHd+DZqcYUCuAWofUL4U+jIZidPPI7/WG\n1A01bCnGrPgiveDEZOGHGajDe9+7ky1bbEZHA/74TzdRKWeg4Up6/EwgHKKkJfcKpI4rqIt+cqto\nFyCEYBAqOVP3ZML/QDDHDRtO88EPSrrq9jsTjE2l+fV3TqPY5JIlL72lynON84ambZyeyPN3Z14l\niP8iolo8FSXTVGMTWdRaz6fGJobM/zTMPttPvF4jvTKPPR2QXFIm3GJRP24wm7NE2fMJJJMwgCzY\nBYh3E0Kuu8SV6QcYGj1L4mwdKx1gbfXZfsflHNm3JnK4FOOJIR6HT2RohsBaHBKWHIIzsUiV30SG\nYcMiP9YjDeQfRQzhxbQ0ZJxlHiv++zPUiilKTxvAe4qoD9EQYuC0mL3bgkxG8IgZxEuLB5JmTlrS\nsK3qiIegWbUR4Hgo9zWHgKlZVxrZzdegMSeSna1um3Ncd90zDA8HRE2SIMLT4F3vWsratctf1FxI\nJBLcfPNllMs/YHh4D7Vak3vuyZDPm5y23WUIjoGEi0kHjjWgbIMXkE2UuOG6vWQyivcJwebhh5cS\nj4ds2yYx8DvfOcC2bRs4fXqMs+NPU6k4kQNNwH2PL2G00kurCK2uNGTJjF5//Rmmp9Ps3btAJl7N\nZ/PmY1xwwTSREXK44IKA97znWgByXY9y5uwM73nPy8uVea5xPnRqG/c9upv/dOcgZGGeHMVkDnrB\nmg8I91rCdNUyJiXDGVwYJcdqv55ZIrmafuh9xzh2p0/hO70Ed9n4DZfAtqO2T/1Idmed+fk+pJsV\n1l5xgPe/+eP81ac+yP6dm4gN1an+lkt4V4LwPkc+u5oWgGvFQ+yTPsGsTehZEu/3Id5SkSjTcZQW\ntYMliGF9BJEmyCChnSlydFZ5LP+dg8ye7WfmgUEJ97TquhvBHx4jqmWqmXMqIBjPmVDEqGq2AKda\npW06hdBtzmG7byq5QzhdgoEk2C7MN6B4CsI8kCIeTzEwMM7f/32aa665+GV7/s81pqamufbaJzhw\nICU3KLESLs7BKV8KL7ttGCvQkyuSyQSMjBT5ylcGGB5ecM5xPvCBB+nsDPjIR659Xt/7znd+nzvu\nGGF+3gaq9Pc3SCa1q4bDl740zZNPzvInfzKC3PAY//k/j/PBD17/sl7/8xnnMZrnOSqVCuOT0wB8\n9Isn+cyhV8FwSPwtJfy74vj3JmRxKDNWJRJGiKQS1hNlDTRUWADOAo/+9eMs2nyCQrmTsd8fobSj\nUxbZjPnMAmSBjgI3wZuu/iavC7/Dn/3+7zC2YiFrrn6aVX2H+Oaf3UJzIi6fXYAYkBwwAPG1NXpu\nGCdf6KR+IEPwRFzqiLJEvYQ0uzWDGLq1iLDSk0iKeyWCxeTlX6fLY8WnDzC7Z5Dpbw1GjdI2I0Z1\nnzmmModNWQ1bkE31MLAyhPstOBaId+O6cv5Nc6+uxBglTyj0uRiUJ0T2IciJTAPHgAabN+f58pdH\nWLp0IalU6uV49D9yiKE5yoED3UBCsI+cDUEomSmvAV6C//2xh7jllhXEYi5DQwO47rlBw+TkFLZt\n09fX+7y+d3x8kj//8yf42Mc2AAN89rM7uPrqpa2/Dw31U6vVmZsrtF7r7u6gu7vrZbjqFzbOG5oX\nMXY+eYCnj80aOc2AL4+muXtmqyzEIuJJ1JBwJ4fs/koj302kL55DjEEMUp0VcoPzNIjT6IjhdHuk\nMlWchk9QdSjvz1La1wUr4Rdv+Tu6Z2bYc/vFPPrslXjLYiy/6VmG15/msfdfg7/MFX5NB2JIKtBx\n8RyZq4oUYl3Udybwyy5h05HUtOqlKycxhhgD3RyzSDiXR8BtpRx1gzUU0tE3T2pbidiSJmHZUOgH\nADvEmhRy2Ow3+inv7Ij02K9FjM6jSHg1hgDFx0Mh4qm4nIcYvhpCla/4gnPUNEOiEhszkE5x6ZWz\n3P+tC1pA7r/FqNVq3HXXk/zFX+S5774RqfOKDwAul2/dx6/88jxgcdVVI6xc+U/B55cy9u9/ll27\nxgCHG25YycKFQy/r8V+ucR4MfhFj65b1bN0S/R6//xG8xG7ujV0UCT4FyAKyEJxGPRj9ezsBrQbV\nIE01kYYcxK6r4fR6BuiTzyXXVOnqnWfdxQcZKIzz7D1refDBa2EtpFeWKHo5Du9ZQ+CYql/t5VOD\n5NoK8YV1/HmH8pkOWdwWYuhUjqc0L3IOszb0DEjqM05E0WiazxTMj1nHYdMi/91ubMsn1tGUzOkm\nuU5rgpb0ZWZ9kcRwHVwIu61W723fdigd7STotWG1JWULZzm3M8wohgNpm2SfLwV/gWNCpryIrWe7\nIaN6M/92I5lMcuONV3D33d/kvvssEcPygTBkxbIC73jH1f9q371x42o2blz9r3b8f4tx3tA8z/G2\na68kee927q0goVIFYYZqmX+diMq/hKhToVYxKMC/GFgEzSeSNHclKaoOVhkWv+E4l7//Ad4w8T0+\n+ZEPsvOxywR76Yfsa+exClD8bhfxoTrBvClXCCGIW2S25anPJyl+p1vOJ49oreRNB85e4MwxOHxc\n9Fn6t4DfD4V4pI5RJ1IXVeULpaBUYe5bfeR39GBv9PBdl/CsDdsRzGYWhj54it53niG0LcGffLBT\nPsFFDsf/PEMwFhfQezGC6RxHaoIsjAgTtFqcrEmK8ZlzRJcmrEBHH11DPsODJSzrn904/9VHX1+c\n5cvriGUeBUL6+5o/4lPnx/nQ6QWMr397Ozf//mVRP5zlSFgCYnRMmQA/g8zBM5zbHCGJGKGNSCp0\n0nzWYDRvetMdvPmmr/OHv/a7jI4tor4oIZjNLDgXeCy/7DDLNh5mPBikRA4/tAmxqFkpCt/spfFM\niqBpS9hSAB7fD0/sk++wkcrdprGGCWDRRli4Wrww1TCZ5dwEjmaz5oAeyLy+QO/7xjn70BKajyeM\neJRcp5P1sLsD+f4Oeb3zqll6PjDJ8TvW0twdlxR23lz/uG/UUU35hyqhDgPvQrCd40iTtIoHIw7v\nvf5hfu9X1jEw0PfvYmzm5uYpldrTypBOJ5+XXssrfZwPnV6uoSzOPmQh9SGeAkTF3h5Rt0KVHoao\nMC4WwrOWLLa2Jgw3veUr9C6e5u+++25OTy2mOfkszE/BaQsqNv5TKSaOpqlesYlqLU3TihHGLZyh\nJskbioRrbYJpWxanlmPFmiYdqrIXKr5VhXoTxg8KiLloYxT2dRG1TtEWIpa51uuhvjzJ7OcH8U+4\ngutsQYxQGfy6KxSXGVpp68Lj3QQ4DLxpjLl8H5X5rKSz+5AsFEgXSC8UFm0fgj0NItivg7QnWRaD\nU9DhBgwOvjzdE1/M6O7u+ncBWn/Sx3lD80KGhkarwRryoTMkXOBGjRBcpPyk3yNM2ZCzsRMBmZ4C\ntVqKZiNxbjcXI7xGH2y6eDfz5W4eeehq0xV3EqaOCxHMaJkU5hZQeHYh1D05xmA3zqYugqyFX3Sw\nF/uQDghmYnBsFCZU9Vu1c2wiCVMPypOmeNDUcA30Q393FAqqfXLlHJ3NTeiB0n2dcj+05mkpYmzm\nidpV9clbGrMJSrs76H/3GGU7R6VixK1ziLh6iBi05ZgG82CPBMTXVmnOJvATruk0AK9btYtt6//t\nAODz4+Ub5w3NCxkW4qWMgLugjtXn49XSBBUnkta0QqzBJk6XBVUbJ+vTOThNeE8/zT0Jqf+5ANNq\nto5bLjJwUZVaPUX+2TQcyJtaF5W6U4WkKpw+BqdP0RLwWr0K311P4VgcsjWci2vYmzyC/Rn4h6fh\n2SpiaBSV1n7KqlkcQHUaTj4IuFDeBNV1kMtEXQpU4rMH3I46djrA64/LKZwk4vHMIZ5MiHhui8xp\nphBMRomJ40SCV71EWsQXWi2OjzXkk1pZJAgc/CFXQtBd8OvvDfmpG14aTf78+PcZ5w3NCxla+rQP\nspeWiF9Ypeh41L7RQXDWkbTwNiAJXcPTxBN1quUME3ctxtsek1AAZGEWgbFnGeRb/MH7dnLb13+F\nh786BM9+R4r3PMUBkkSiXBCpkvtw7Bk4fVzebwX4twUEbgjNAEpG8qCl7NQuc6EotcqJmizO7D5w\n87D4eskqKU/HyEo28hnYHpo2JETtwWeI2gw5iNHpRQovFyPgeYaoCUQXUZdEDwnZnkA8o4vAv9Al\n//1+wilbsK9xotbR58dP5DhvaF7IcBEjUQHH9oi5DZy4J8xQI6TvdPn0d07Q2JNi/mQnXsPF2xEn\nbFiycD3gabjp6q9w0Y3fp8c6zAWLxvkHy6Lc6IGmFgVpf2+bc2UroEWz1yb2qq9ZzhKSJmobkybq\nwqBtI00hXat5URNZ/TGpi+loir7wPiJn6OtymLDbCFBpczgFuUOiVrfqAR0396oB3l0xxu5dQmZj\nke7egLmH+8W5WoMYpAKiHrcQ8XrusAhwor8r4/q8ofmJHecNzQsZLrK7nwI7FsKsRfOpJOHiUGQi\ne0KcngZd6XkmxzJUduUirogDLAjJLCtyhfMIPatmyPcvpdYc4s4jHqNHszCn8Yo2A9PmZ6pirYLo\n+q+qJnlEylpe22c17OrCCJwQqQdaRBZDvaZARLmPHoTCUphJSqR1DLFlg0hGaaN5TVX29CuNo0Ua\nIfNVgf0Q7LMphl24P+cR31Cn05uh+EQ3gWOTXF8h2Vel4HYTYEdOnCOf35w4xJWrJiABS4Z+PIlq\n58ePHucNzQsZKhFcBpLgn41ReyALr2niWnViqSaJvhJUIPStSGh7HVAPya7Ks+qNz/CGzJ38w4G3\ns+PJyyX7VAC274fT4+aL8kTFgwGRa6EdFdRodJoTwrw+b34sxLjo+wfbLiAkMkyaKktFnxstwO07\nYWsSRoekUVkO4QGdQLyWK4g6VapEsNq1MIRcCNcYjs0+874UzD3dR8/AFIO3juJNxWnMJkivKNF5\n6RzF3+8Ust5q4FXmdI7D9b3j/On7r3kpT+38+DEY5w3NCxkeEhIsQ3Z2kCZfd8fofcMoA5vGSDbr\nPPPgZioT2aiyugzWdU0u3fwwN+W+wR+d/jBjpUWy42tL2tIsYmCyRJ6GUc5vdRHTlM5cdGCiGpeI\n+KKqgNoSVusJUoirUELcjk7znmmitpFpqd3Z9QiMbIGedeLVaKp+ChEnz5qPa/hkiIzWtT7W1U2C\nbyXhsClV6CFqpXUaYlc3GPmvRygEHRSmuzn15Ar8WVcMkm0u/ygCmudezIM6P37cxnlD8zzH397x\nEH+zpztao9oltwpc5pFeWiQYdzn66DKqJzMEE8KMZSEwDGHO5pCzju9ZZW4d/AIPd17JU90Xks/2\nCuAarINHT8De04gHojUM6pWUiDwS7fwGkbxo0PbT3m7XRqxCmQgEVgNUIBLBUbZrSb6jCZw9ArNj\nImq9eht42SiqU2ylA5GUOAnMQXjGgTkLDhmuEIj3U4K+G8eJZRuc/t8rYBiaozGa03H8mAPvaMIj\njmgkm15uvzN0P2+7dPilPrrz48dgnDc0z3PsOzDL9qPDkknphMpohl5vmgu37GLiol78qRjTT3cw\n+3R/q1d17+Jpll94mGx3iYPOOuIFj1ymxPx8F9VShiDviLcwA/T2w+oA5poSvvhpJKwpETEFFRyO\nEYmpgxgeDbPU0LQ1CWthMu0tY7TPhsaDChZbtDpEVGagMgExF+wL5WMZBKTtQQxJDgl3ejEdDSzC\nbzqivmeUAp1Bn47lc7hBk+qeDIXHu6PIzgcWhNLR8pQxTsYmXr48xab1P9k1PueHjPOG5jlGPp/n\n+CnBTabO5CF/Ei5YAd0BzhnoSc6z/sp9hKUNnNi+ivzenqgjSx90rZnl4mt3sCWxi7sO/jSZRonr\n/Xv47YOfYGa+B8qWMHB3YeCWQdiUgInvG0OTJPJqdFWqwVDEFKKaAQVKFCxWwSX1fvQ9epz2FjKq\noJ4kEpYJaOX0LUuMyiCCOanqoH68C/H09gHfMq+ZzpT2Ro+eX5xg6qPDFO7rFgfrOELS60OEsE64\n8tVZJJJrz+ifHz/x47yheY7x6JPPcuvXVot4fs8t8IYYhCF2X50rV/+AbKHK1x74BbxHHILAdI6c\nAi6Rz58orODOYz/LFSsf4R2r/waLkJlqH9QgvrECKYv6oawsujPIYl1ki6g1BWTlLkQMwxQtvgsg\nK1FxFuXGmKZRraHEFo+oK6d6NBpC2cjKVk9GWzOox2RDGIeKIRomgTsQW1Qjypir1EQcEfA6Zl4v\nQHDAId/opOHG5e9JZOZNE0WGn0ZS3ANIHdT58Yoa5w3Nc4ymE5B/XaeA3DV7lQAAIABJREFUtUUg\nExBfWubqvvuYf6aXvUdXUK8lZP2XiByHOWAz+Mstpuw+Pr77Q3Qtn2JL/y42JfeR2zpL6ZFFNEYT\n8gRWmM8UEO3boIEYERCXJ0RilvYaG5WsVKUpVdrqMAfS/lRJJIbRFLZqbiqOYyEXoAQ+1SxtyL+5\nYVi6DtZkIkfpCsQwTgN9YF0e4i6u4AdxglJMiiy/RqvrQ5CxKBczuG+pkRyB2vaMnPq0OVVt06v/\nVwLzeY/mFTPOG5rnGCuGevnA3APcZm1gqq8Pei2cRsBI7DS7a1sZnV0cqckp238YGPGJrW1ABzSm\n4uyPredy52HKZNjuXEa1M47XdAimzO2PE+nuTmm4oykrXW1+2/81+6RhlUVEvIsT1TQlEOOUQUCV\neWQFJ5GVrQI0OvS7zfeODMLi1dAxbPRxzUc3ASMQs+sQhjRrCcIZR/pja8r7alp6VWHWpn5/muQV\nZVjtCyM4T9Rb3kPCMKNR3FIBPD9eMeP843yOsWHVSv7XQD8P/+U4U70JSPuEpxJ4S2IEcdMdoYI4\nDCB4w4UhLApIDJeJVXzCpsvidce4yHuS2niKb/tvYma2Hz8Tk7V/AlmcC4hYsDZEHkmKqEygZt6Q\nIurprFhL64NtI0nUVbOfyCipd+O3/R62/RhsZkE3LOmNIjYtHDXZdndFA06GND+dxKuaVixphNR4\nOVFL3YpF8M0EDS/AGghxFnpghQSWQ2jZ0SWdBKYgHa+wZtkpujoSL/LJnR8/buO8oXm+40u7oVEi\nfN915P0c9TMJMRLdyHrtRYyFbcGpGLEOn76FEywbPMEv2p/n77/3Lu47+Bq8wCWYsmCpFUU+SaR/\nzyJEc9iFqEpTQ6NOIsumhDvX/K1KZCQ8opaHPhLzmZa0JM3PLFHxkH5GQWIt5vThiTNwMgbrL5Rz\n60TCom8BPVBdkcVS/aequQchImX6EFHUZqrBvf+bIvamGqkPzGFbAdU/7aS5Iyl/Px4dY8WmUe78\ncNe/qxzE+fHyjvOG5keMTCbDJ15b50/STb7ZvASrComgzqYrnyTZUWPPjksi7ZnuEIYCqDiUxrrY\n0DjA9cM/4LM7fpV9XEhjSVzwnpOIEPcy4FKkoLCJ4B5zGbjhKth+AMZmiIyJpqbbwVyIeDUaAjXM\na1miVg0diH5FFqylEMwQ9RNvr38aNO+tALPgF2H2JOwrwHFHBMVrrqjyWatgLEmYQTwYFf3S0qsa\nUjO1GYngHgaK4NXihBOdUA3xg9j/396ZR8lx1ff+c6u6q7fp6enZ902jkTTaF0tgWZbkDUMMtjGO\nicNzWPICMTgkeXDIwgNDTk62BycvbI89EMCGGGywg8HGtmR5kSXL2kf7LJq1Z3qmZ7qn96q674/b\nrR4by8EGzUij/pyjo+rqUvWtW1Vf3fv7/e7vV0gdmjMmv2PdHj7+B26qqlrR9bxXrcilTlFo/hsc\nDgdXXbGKSHoPWl8PDzvXc/LUctw1SdxtSa7Sd2AKBz3ZDsJlFTiqUmRjbtaY+1juOULIruGlxAam\nZoJqIDFEIWwlS6EKwQjKNpF0gr8OHCcoJOXOr3Uit50PvMuPUHIx/ueKJFkoo0/+2GzuB3y5fctQ\nw4i8naaUgvAkKayQFJBNwkQYJvy538yAloVRAWVN4A2cq9Z6LtdOGWp0F6DgJd8GHs8MaILkoz41\n0HIBy1GiOwC3btzN+96qcdWml9dIL3LpUxSa35C3X7URv/sAg32nOHqwi0S1l7bW01yz4jEywoAZ\nCDrC+AMRkrqXDcYenHaGx8PXk651Ik7ayMFc2oNW1MsZjsO+GLyzCoReiB2Jkqu6mDdy5KdHs5cX\naKgRSzkF43F+qmWiRCW/2CoXzisdIKtQqyIzKKGxKNh8hlBertxS9HP2m3IKtVGmwE7C+Fmoc6n8\nviEJjSWqaiOo0UkZanSzHyUofwSulSnsA4LkN3L5bjajRkNlQA/84TXw9rds/B3dsSIXE0WheR1c\nvW4VP2sKc923hjka7aBvcBH/7vkg6PDON/+QD7T9PxrEEBjwhfBf8tjht8Ix8N4eweHQycqc1bgX\n5XXpOQGTL8DKu+CIT02rSlGhM95mlAE3jSq4lI+Bya8ByBdytoHFqMCVGOoNNlGhuVBwW4MSKRNl\nXMrP3SqAAyhBCuQ+5xd12hQSy+QqJRJEjZZicDgCYhx8FmxbD1FvrgAcOQ8aSmTqgUdgarCicGon\najp1Bcpd/jzFNBALmKLQvA40TcPpcCCqpMrPIgRWwoTv7qLa9wLjIxZf+bcb4O1X0p9YjjXhAFuS\nfCyAZTkK7+kx1EChqwPMKjjhVrpRD4RT8HAvDCcolFgoo+DeKs21Jl92QVBYu5Q35JookcrnCgU1\n+mnJ/YgDFRkXRUXHDc06l5b7bmbWuR3AYXKFpTkXUSw9IG1IzMAje8H0Q2U1LG1Udqi8J93MbVcL\n0KCxbITPvaePLz/iYefZNUovAxT0sMiCoyg0rxeBMvgatqqzrGuwOYjRqBH3BDnQvAX7pw7wSDX7\nMASmZRRMIbUoE4kLMEohU6rMJwCRKBwbhb4ImF4KXqN8FG++2hu5z57cvn4KkcL5UU8ZiBr1WWqg\ne0C6wM6n8syvAo9QcIPnRUWiBClvYD6LGp7M5D47UBeXc5dbDhiIopapO2GqUX2VQWldPqF7CDDA\nF0hx9aZFSHmaLX07VHOWwNK2xt/y5hS5WCkKzRuhxEaUWOCQ6D5ou9tFItBGeFIgNpUjDp5EatVQ\nUlWok5TPjVuJstHkI/zTKC2ZRi2mPDaCGsHkI3ynOJedGweFhZACNQzwouYdpYBbveh6JVgVIHLR\nxVKoWk72OIVKbfkUExkKUcc5Yy8x1ErJfDmEfKHufPxNfrV4JneuvEhNQ3wCV3iYVUsncGdtRocC\nnAq1qp8Yz52mWjXrjluu5I7fwe0ocvHzmrNiIcS3hBAhIcThWfvuFUIMCiH25/68ddZ3fy2EOCWE\nOC6EuOFCNnzekMCIA4cvi6Mjib9ihk9U/hODyRa+95MbsP58N/KubXBTuxKUCgphLVlUHaTDKG3Y\nj3IL53NVOXXUS5sPpklRqIOST3AVQo1C8vVrZyeEmQA9AyVbQG8FOwT2r4CnIHsQrH4K/vUQhaxb\noyi7TX9uO5Y77whKmAIoe05trh3lqJFTCUpBkqhAGydM91Iz8Uvu+3s/O/5zJZ+4e0DpU79qHjMU\nZnNFLhtes4CcEGIL6tH4rpRyZW7fp4GYlPLzrzi2C/gByrzXAPwK6JRS2q847pIsIPfFR3by42iA\nbMrmwP0hkr/fhuOWOtx2hobBEFuqd5KyTB44fgOp6UXYxww148hH1c4qPHCulG4dSkNOAruTcHIU\npkYpxLLkRwxB1FsaolB7dylKnUKoUYcOdIBoAT0MVlzVr8ZCjV7yyxA0VMPyOYMNlO0mb0jOx+Fs\nQgX2DOb+bQ1KFWdy7RnKtTOfSGslasgyhKsuxdp/WsqnFwvWtDZz/OSwuvacGcjrcbJm5WIMw/ht\nb0uRi4g3XEBOSrlLCNH6aud8lX03A/dJKbNAnxDiNKomwO7X19yLk9MZ2NGyRlVMXHICoQdwRm1q\nGkZZnjhCMuGln2as+mZkr6OwbjFnW21bchqZFfQdWqRc3FnUux1ATacSCZiavSQ6QWFRZITC1CXv\n2s4ng6lF2VNQ29IAsyd3HoNCkqx8hu98OQIfSuXy66bSFIJ78re3atY58ksZcpn49LKczSdXCgaP\natfScszbKujbWMq3B/u5s7eHm68ulki53HmjNpp7hBB3oTKp/C8p5RTqaZ8tKoOokc0ljWma7O8+\nyYBwgU9CmYb2h0uQMQ0xNoMrkKHTf5yTY8voD7WTDvvU7CPnGdaDJlWLx6jvGERkJbahYZY4iZwu\nJ3naq6ZW1wElAjQrt3LbQ6EWUy6Vp94GIh/P71LfSz/IZrDTs1qcq697rkJdPgNfvipc/pYLlNDo\nqJFKLnewcIHuANuZi7vJe6LgnB1H6KCVgZUfnqk4n5KlKbzvcuD60zIWVR/jaH01PzoQov7wflZ1\nduFyFdcuXa68EaH5CvDZ3PbfAZ8DPnCeY191jnTvvfee2962bRvbtm17A82YG+LxOH9yVOdA6Zsg\nbiHaMjjr0ph9bmZGSzmyez1HWM9HV/4Li2tP8A/7PgVpgfBJaJZ4N8S5tutR9j50JW49yR1//D0i\nlPHUN26g54XF4FMrA2RJEOmOqox2lKGmO3l38jJwV4PTjQqlzWECmQhkXqCwNCFfcgEKnimBmvro\nFEqvxFECU4qahuXq+DpQ+pMBMvvAPgXCPetcHnWcBYgM6PkyD146Ph6n4/2TlNBNBROk/S5mtpTw\n1f4h7p2sprHukv9/p8gsduzYwY4dO36jY1+30Egp86XpEUJ8A3g493EIVS4sT2Nu368xW2guGSps\ntOosDmcG87QXRyCDs32G1Bnlbtax0U/2wrcfhm3XsP3tz6GN9PPUPc08vvUaYukaNM3mvjOVmE4H\n69fvYev2J7E1jXr/MA9lbqfb2QG7AqgpUi3qbc4ZgzNDkE6jpjD1QEyl2HT7wXgzJHvAGqfg5s7/\n8aOEK4sa7UxSSC/RAXoHGB5Vo9s+C2YU4k6wbZATsKYGPrxaVdi0dLA0VSPbBVowhSOQwjDSOB0W\nqU4dF8Ns5llSuEnjoo9Whmjn1WfbRS5lXjlI+MxnPnPeY1+30Agh6qSU+ciPW1E+FFBren8ghPg8\nasq0GFXT8NLHBCICw5MlUB1hIunBqMogDJuUVgIVJs8+3kL72RFue++zyOVTJM+EGQ058b3FS3io\nFjvpgYlh4uEhaF9Hr3MR0alSpKYRtqqZGi9TUyBzisKUJp9nZgTMURX6j4GaGpmAB2QZ4AJnPTjL\nKWTfcqHsL/n4mzSYKTCngQA4K8GuUzE21jDIFBDBuSSD7z0BLASWXUFFS4Kl1+9hX2YdCZws5hTX\nyCc57FxJ0Bem1DPNlKOMw/oK/KSYwc8hVvIO62GcWpY4Pp4pvZqvHNpL3Yk4SzzVXL/pujm8eUUu\nBl5TaIQQ9wFbgUohxADwaWCbEGINakzfC3wQQErZLYT4EdCNegvuviTdS6+GA0gLtIjAmLEoKY1S\n4p3GsnWSeoDO+sNkn0ozZdSw/E8t7MgQj+2o5XSkmtKbnbgqJ8hOe5CDQ0h5ghlW0r1/pXJ1axQW\nV06cBvO4KnHidVFbMUpzxRlM4kzhJo1GNuVgvF8isy6clRYOd4Lki04wysEReEXD89G+ucTjdqnK\n/YsHtICK6rUGwBpGWx9AC+iUXGnQ9LdZDDIIS1ImI7TpfYxmKvBqCdY79rFFPEkzZ3CSRaIxRAM9\ntFPLKEEiJKSPNtnDlCwjIwwigQCPb02ziElG9p7Cd6iS9UuWFW02lxGv6d6+ID94ibm3o9Eo1+8e\nYF/NUqyUjiNi0vHmIwT9E0RHg4web+afN32UKk+IJ2eu41+HPgZP61AmYP8xPP/5JJseqyLTWkFG\nGNhhnaNfW0cmbIANQgKWRKZAHn0RTuyD694H7R5u334fH77uX4lSyov6BsZENeGBav7ry7diRRyU\nXzeGr6WfvhsmkWkd0JGz4vglOsjc52wGaAOtFZBgPQ/6BOg2eMD90FpcmzzUGqOs016ijV4qCNMr\n23jAvp0rtL3cwk8x0fmeeA8f51/YyVae4Sqk1JjBxzaxgxvlL1gv9yE0yTf5AE9wLWkM2ugFIEQt\ndq+PbzsXU1NZia7rOJ1Oilz6vGH3dhGVj+YbLU7+fvcL/DB4JVarRv+xxQxZbQQCEdas34PHneSh\nQ7fzaP9NgK48NWcArZX07bfx0hNO5CIH0i9wmCY1dw6AJnGSpUTO4CfGib9JMd49Bl5dxcbF4Mmf\nX0/3nhVYTgd/c/NneE/V/UxmKuj80AnK7QhawGLUVcmzj68maXtJYZDCQwIvSdNDKuUhm3WiCRvr\nY0eQR06A7EctS5hBf28t2h31SFujbeUIHiOLT4sjkMTxoWNiC502rZebxCM8P3kV++wNBCrD9NKG\nhoUzYXJwagNaSRqfJ05TbBhjWPDdznczaZTTxABTlOEizbU8SQIvP6/9PT7Qq+Gd6uaGcJaPbPu9\neb3HRS48RaF5DbpPnuY7+wchA6fDNcpb7ASjJcV614usLDlE0B/m0ehNPBe5mpFQg4pdy5dVKvdg\n13iIplHOnijocQsRtxBI9KxF2vIR9wZI1mbRbq/CudKk5IoJYuNBJkbLmYhUQhr8zyap6RrD6tJZ\nHjxCu+ihxIwTtqvwXZGifmwMV3+GbNhB1m+QKTPINjhI+wxiPg/dnyhjetiFhYMUBiYOqjdPUb6+\nG9N20uQeQNcsNGwCTONjBjcpSoiTEWpEcsi9BmyLCiaQCAwylDqilPvG2WQ8x0i2ludnruSOxI+Z\nsCs5llqGhU6lO0wfrexiC40Mssmzm7PLmkkLF93HDb668xHes2E7Pp9vPm93kQtIUWjOw7FTZ7j/\n8CD/HNymgm9ziZ2cIZN1m/ay3HmYquQ4M6ESfjF+E+PT1UqIJjhXUqSiNkxrZw/ClAghMVNOUsKF\nHdHRsha+TBw9abM/tZ5Mo4G2KYu2NI7hmkLzW7Q09LJs4gT6SxLvdIIhu55QsIIWcwDnS8NoI1OU\nWSN0uNNsHztNdXqGrFvHzmjYTg3T5yDh8jDuDbD/PWuZpAIbDaREjjgo80aokBN4w0mStS5cIosn\nm8IpMzjJoguLiB4kYExTlwjRZRwl43RQwQR1jKBh02ScRTdM3sbPeShxC4f0ldzhfwCnlmHcrETH\notXu40xiCwOuFt5i/pI7k/eRLHNxXCzl9NIO9vptag8+y6aO1dRW17zWbSlyiVIUmlcgpSSZTPKV\nvYN8oXybCj9B4gxmcdgWfhHlTsf3+cXATXxj4CNgg0wJ3OEUMgN4BBmvgUSwouIgf7bxc7hlEodt\nMUUZQ3oDWZy4SNNKH65ohvf96vtMTQURo2Cd8DKaCKDXW9x0xSP8Y8fH8T5g8uL7l3NwdSdG1OTG\n2JM8+lkYeFqgkQb2k/RIYve4iP6FhzQu0riIO/3EnaXE8bKIHjo4g1umWGSexv9Ult7FzUSrfSz/\n2XGefvtGGh2jLJ7qLZRV0SBa4qOmfIBFA0PEa/wEKqcolTHarTME9QiaZlHHCEEZodl7lkrHJJYP\nNIdNgzGMmySl6SjZHh8N9SMsTZxixdkTjFxRzkuutQSYYllDNw82SJy7dd5WFJoFSVFoXkEqleKD\nD+3ncbNL1R1KSpzL47wz+GOudu5Cx6LGF+Lmth+zreEJJIKU7SZlurFMnZmMn2/3/gmTxyvIZgyy\nlsGbIy/hPZFgyFuLa22aTvskI6KOPtHCgK+Ft23/KausgzitLC9NrOcHv3wvGzc/T83SIfY61rPy\nk0cJ/2Mf0/sGQMIvLIvRP78K+7M1BJhiJQc59udTxL+awfqpiU2cNIJl7xqn/a4KdtevJ8gUPuL4\nk3FK+7No4zaVTWESfVM8+Dmb6W8eZkBk2Z+r6qKhTE2WlkRzD9PUmSX9fhf+N8XZMvoCrr1p9q/a\nwNTSIFvkMyyPn8QynGjTNp7+DMtXdGO403TTxZH4Sr776F00LB/i8IouPrLm81iGxioO0ckp0hS9\nTwudotDkeGTXHp4aSJJNwOOxLkJ2FZSA0ZTh1sqfYKSyPDl+vRrBaMptnHeemTWC7KiBPeFAd1ts\nb3sc8aseah49RGzHUU4lJjHGs0w5JbGGBANyjEkxw5SYJsMATTyN+B81yDVB6ksH2XDNbmpbBql0\nhwkOjHDo6xkmn8hS3WBTcZOHEb0Dc2sTRpuHchmlyUoz/NE20meVh2mYeiQ2nlAvM58KEQ10M0gU\ngwzuTIaxiI19FtLPJUkvduH9aC2nWEWQSSoYwEsSr0zgmjGxbI2428nhn2QZ/Ld+zAcdHI5GYQCi\ndUfQa3oJyx4OZqaocx9ArKjhpa2rWX7qBMMN9STKvbhlilWZQzzt3cwTwe2MlVRRyyg2GmEqmaQc\nUawWt6C5rIVmbHyc57p7wILvnpE8aGxVhtwo4ILy8glWtB+iMT3MM0NXM9DXzsZ0fyE8xYa01NnV\nsIiZuB8iGj4twS2BB1jl30m5dgjZJ+knQGA6hi4SuDJZwrjop4IkggD96GhM7ihD9iVxcZKNjJHp\ndWKYp3CG+jG7oWS5i5ouSf0KDc9yD7FqgYmJjsWMLKHi1hLKhSAWKSN0+nqci/oxn4gSuX8M58Qk\nSSCRizIepYREhZtk1sDlLqHuLxsIswUPZzHw4SVG0I4QiMbRLJspl4tTIfAcHMPZazKdAbsJ6qe7\nqZtWfRkFAseHSR4WnCpZwuqRYSqvnaR9pofaZ0dxj6YIG5W4PGmuyj6H6dCYFOX00kYGg3qGGU3Z\nHD9Zy9LO5ee5Y0UuVS6LOJpYLIZpWr+2/6kDx7lt8k3K2BsFh8fE3ZFUq6sbYd3yF/mTxi/yv3f/\nC6HJWm6L7+e7d25+2TnGx8Pc9K0QB9e1kq4yoNcJUXjfxq9xXecv8WkzeLMJlh8/hd8RJdwVIEKQ\nB3gXMfys5BBZnFjvfxbxUB8OTAQ2mRkQmkbtep0b7xOE6yqJfM1E+2qEzg9lefIt1xFqrMGtJ/E7\nYiyJnMSfnaGvZxObrvgZD+67m4bOF9ExGVOGJmwEFg7SuBmnkghBYpSSwEscL9XuELVuZeQtJUqQ\nCAGmKWEGLwlqD0wQ3B9Dm7DI3gqyQoXpSF2FTrzwcThzH/gMyfUrIXRPE3YI2j45gLcK+CtgO0wY\n5fx7+Z3s0a8gTCWVhLmDH5KOu2HvVdy57S8u7ANR5ILwWnE0l4XQ/PGXdrE3UaM8RwGUsJgQw0Ov\no0ktmIjDitJD3PPWz6sofieccbbzUOZm+g928tHQC9z95g5aml6ebtI0Tc70neWTe0M80LoJSmzo\n06lwTXB70/3cW/9JPM9YzHS4CbeUE3JXEaeEF9hIlACVhEnixnV2gvap46ySh6g0J9j3cYtofTWB\nTy2C1lKqnGHiYYORXQ60v36WK97upfoKB/YiQXydk55n/o6G2k24XT6am9s5O9BLMhUHQL7KOqP8\nvtl/d499m+qrdtJLOxNUkMKNhk0JM5QwQ0NymLZYH61T/TiOqsXkmTad+GIXAov4kEZ2wsYeznDk\nLyWTWYPWNbD1mgzaclSir2nIdjgYf0uQ54Mb6Xc0k8bFNAHW7FzM1iXbqaut/7X2Frn4uewC9r70\nw52cyKdqMOCX0U4GRZ3K/xKjkHpXB12zuGX5A9QaI2QNnadLriLZ58dKOgjZNZzJLMVOG9R7xK+J\nDKi6T0s62rl7cpqrw7sgJlWYfxrK+hs5NPQxyqp/QrimjDF3FWkMvCRZzGnGqWSYBhJ4KWvW8Db2\nUT2UYPzfbBatlji2JmDxKDNihghB3D0Z1u86i6VJSjptnEtSGGVpNOHC72thSWehHlJLc/vr7jcz\ndQujOzsI4qYEJ1YuZaiOiq9Jk6VXZhiy0mgakAa7T2CPCgRSZcqZeJENU9/Hf085SbsUoyyB3RZi\nbEUZE6XlDE83EA2UcmP8cdI+N72ONmYooZoxgq6qosgsUC5ZoTnZ08uh/lH1wQZSGUib4PbytX11\nHLI6ISCh3oZKDeJQlp1iZdkBta48JzSaw6axsZ9SLUpfqo2HR28hPunHijshI3CmsmyP7Wdxi/c1\n27N941q2v8r+cHgzj3dbTB2DFB50THQSeFpO0lQ/gDedhD4duz9KRWwAbzTJ2HCAqtv9lFwpMWSU\nMhFBHBOU/zxKx84+6ILJRg07IJlKBzn+wnbqgr+9W3jNqitRtU/eOAeff5qj3RPUve04pZ1uXLbG\nxGSaMyUtDK+r5yCr6TEX4Qmn2C/WcoA12FEHy47HqCqp+K2vocjFySU5dZqenuYLT+zl//g3qB0x\nED9/EZ7rQV5zm0pnsAjYYMOSrLKbDGksdXfzsbf8Q6GOvYSsdPLpic9waqgLY9wiYEWhOauMD1GN\n0nCch1ZnWLFk8W976S/j6898lpYrnmB96CWCP4pz9CcSBp3UrA1w7DuLOFm2GI+VYKl5kmo7RNX/\nncazM6MyR7wZCEKiwku382pWvfOnF1VazJHhIaYevpmKrT3ElnoYoY6e3HTsIKvZLTdRSRg9ZpPK\neKkczvIfTWspDwb/+5MXuWhZcDaaD9//NBPNw1y36r/UDgscsRhaIkm2pAophYrO9QKGhKyALPRN\nt/O93vepZQIRIAYyKwhRQ9rn4mrtEF/Z7HtZ6hRd02hpqMftdv9WbX4lT/7i/WwUP8KzL4leaxNt\nF+ytXM8LJVezpOk4E3oF7YP9bD66B0fIRG+w0HSpljJEgCzscnyA1uv+lsamVoS4ePK9ZLNZhgf7\nOTr89/g2d9NHK1FK2cd6DtqrGDCbcWDyyedOcG1jO4bToLWxsVhr+xJnQdho9ux/mr7YrwCo7hLE\nPc38tPdd574X2AgkdlqntG0CM2GQ6PG/7BxhKugva4CMA+KCZeEz/NmyIRD9oENzhYeuztVzcj3S\nJZlq9jHuLKdpcpjuttWcbuoipbl4iXV4SCJKIdnhRdTZOGpN3K4U/tQMldFxqsOTWCOVNDW3zUl7\nXw9Op5OWtg7Gp97LwWd3cmazyQh1JPGw4XAvfzqVAuDatqUsammZ59YWmQsuGaHpi56l703TTA8F\nkZpgNF3D05ltpDJuZFZXWd8sIAtl3jCWreMNpdk+1nNugOI3JG2lz0NcB12wsjXDh961bV6ux9Y2\n8VLIg6k7GExPEpmModdJ3EaSacrI4qSvtIUjpV257L5x/MSoCEzSUtOPO3gEOT0vTf+N2bB2KwO7\nInTPHKPsqJ9mK80KK8jtW6+Z76YVmWMuaqHJZrNMTEawbZtBM8B/xO7i2BNrsBw6/pVT+LsmyIQr\nsSNuZEpX+Z1SMNVTiV6dYoPrBN9/95vQtIuvqPP1Wz/0ss9P/+KSY5Z7AAAETElEQVRDBCsOE2/0\nsFQ7wQBN9MVbicRqsNAoJUqaEpIESOEjaM1gG/7znP3iwaM7aOiNcmfrtdTW1M13c4rME/MiNDt2\n7PiNEpL3nh3kD59OMm16MTrbEJm0qmmWgbjbT1L3YR3RYTgnJG7OVSuxXS4sww2bX+MH5pnZ/bB0\n3b0cOvh1bi/9EoOBGgZFI037S/hw41aAc9Eu+W2BzaLVF7/x9MoVV9MVXUllRdV5j/lNn4eFzkLu\nh4taaNKpND0/7sVxVyNGY4D4eC12uQYm2IaOLTVYZKpp06RQpZBiQEJFq8rgxWMgfTVm90N1dS2t\nbbdx6kA9Gc1Jq/DQEFxER+vrj4e5mCgtLaW0tPQ1j1nIL9jrYSH3w0U9dVL/gdtoLotkykNkpFIt\nKw5yrrQRpUCbBL9QuWDI7de42K/u1+jo6KKjo2u+m1GkyO+ci/tVdGjw/qsYdQThFKrMUQhoR4nQ\npIBpJ9RLFZxXKlQgniP3xzN/TS9SpEiBeYmjmdMfLFKkyJxx0QTsFSlS5PLj4vP7FilSZMFRFJoi\nRYpccIpCU6RIkQvOnAuNEOJGIcRxIcQpIcQn5vr35xMhRJ8Q4pAQYr8QYk9uX7kQ4nEhxEkhxGNC\niLL5bufvGiHEt4QQISHE4Vn7znvdQoi/zj0fx4UQN8xPq3/3nKcf7hVCDOaeif1CiLfO+m7B9MOc\nCo0QQge+CNwIdAF/IIRYNpdtmGcksE1KuVZKuTG376+Ax6WUncATuc8LjW+j7vlsXvW6hRBdwB2o\n5+NG4MtCiIUy8n61fpDA53PPxFop5aOw8Pphrhu+ETgtpeyTUmaB+4Gb57gN880r3X/vAL6T2/4O\ncMvcNufCI6XchUpuMZvzXffNwH1SyqyUsg84jXpuLnnO0w/w688ELLB+mGuhaUCl/s4zmNt3uSCB\nXwkhXhRC/M/cvhopZSi3HSJXsu4y4HzXXY96LvJcDs/IPUKIg0KIb86aQi6ofphrobncg3Y2SynX\nAm8FPiyE2DL7y1xGsMuuj36D617IffIVoA1Yg1oy/LnXOPaS7Ye5FpohoGnW5yZertoLGinlSO7v\nceBB1FA4JISoBRBC1AFj89fCOeV81/3KZ6Qxt29BIqUckzmAb1CYHi2ofphroXkRWCyEaBVCGChj\n18/muA3zghDCK4Tw57Z9wA3AYdT1/1HusD8CHpqfFs4557vunwHvFkIYQog2YDGwZx7aNyfkRDbP\nrahnAhZYP8zpokoppSmE+AjwS0AHvimlPDaXbZhHaoAHc7l9HcD3pZSPCSFeBH4khPgA0Af8/vw1\n8cIghLgP2ApUCiEGgE8B/8irXLeUslsI8SOgGzCBu+e8ENgF4lX64dPANiHEGtS0qBf4ICy8fiiu\ndSpSpMgF55L1yxcpUuTSoSg0RYoUueAUhaZIkSIXnKLQFClS5IJTFJoiRYpccIpCU6RIkQtOUWiK\nFClywfn/NK0lPpyfB8wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2f668e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(LA_model.get_value(\"FirstZoneDepth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:23,632 - wflow_bmi_logging - ERROR - get_value: TopoId not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "2015-12-18 09:39:23,632 - wflow_bmi_logging - ERROR - get_value: TopoId not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "2015-12-18 09:39:23,632 - wflow_bmi_logging - ERROR - get_value: TopoId not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "2015-12-18 09:39:23,632 - wflow_bmi_logging - ERROR - get_value: TopoId not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n",
      "ERROR:wflow_bmi_logging:get_value: TopoId not in list of output values ['CanopyStorage', 'FirstZoneDepth', 'LowerZoneStorage', 'Snow', 'SnowWater', 'SurfaceRunoff', 'SurfaceRunoffDyn', 'TSoil', 'UStoreDepth', 'WaterLevel', 'WaterLevelDyn', 'InwaterMM', 'M', 'thetaR', 'thetaS', 'FirstZoneKsatVer', 'PathFrac', 'InfiltCapSoil', 'InfiltCapPath', 'FirstZoneMinCapacity', 'FirstZoneCapacity', 'RootingDepth', 'MaxLeakage', 'CapScale', 'RunoffGeneratingGWPerc', 'rootdistpar', 'CanopyGapFraction', 'MaxCanopyStorage', 'EoverR', 'N', 'N_river', 'cf_soil', 'TTI', 'TT', 'Cfmax', 'WHC', 'w_soil', 'Altitude', 'Bw', 'River', 'DLC', 'IF', 'timestepsecs']\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-93d59840db86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mLA_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"TopoId\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m2147483648\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "a =LA_model.get_value(\"TopoId\")\n",
    "a[a==-2147483648] = 0\n",
    "imshow(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2f48a9f50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KWVGf4ZNK3JzdKZOVyfGqzwNDUyrXp15vtpfTLIfNt8/v/FW+4p4ebrfztB/lKvWTp6K2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\n0vn/AQIujnfMbRH1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2f4ad4290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(LA_model.get_value(\"River\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Start and end times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_end = RT_model.get_end_time()\n",
    "t_start = RT_model.get_start_time()\n",
    "t = RT_model.get_current_time()\n",
    "\n",
    "(t_end  - t_start)/(86400)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Now start the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:31,844 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:31,844 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:31,844 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:31,844 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,075 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,075 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,075 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,075 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-01-02 01:00:00\n",
      "1990-01-03 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:32,314 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,314 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,314 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,314 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-04 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:32,548 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,548 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,548 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,548 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-05 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:32,778 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,778 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,778 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:32,778 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-06 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:33,023 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,023 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,023 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,023 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-07 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:33,265 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,265 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,265 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,265 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-08 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:33,505 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,505 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,505 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,505 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-09 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:33,745 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,745 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,745 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,745 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-10 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:33,979 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,979 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,979 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:33,979 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-11 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:34,220 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,220 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,220 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,220 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-12 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:34,462 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,462 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,462 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,462 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-13 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:34,690 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,690 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,690 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,690 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-14 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:34,929 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,929 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,929 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:34,929 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-15 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:35,169 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,169 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,169 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,169 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-16 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:35,456 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,456 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,456 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,456 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-17 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:35,703 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,703 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,703 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,703 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-18 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:35,940 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,940 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,940 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:35,940 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-19 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:36,175 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,175 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,175 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,175 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-20 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:36,413 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,413 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,413 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,413 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-21 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:36,650 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,650 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,650 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,650 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-22 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:36,919 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,919 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,919 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:36,919 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-23 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:37,157 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,157 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,157 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,157 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-24 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:37,402 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,402 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,402 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,402 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-25 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:37,646 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,646 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,646 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,646 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-26 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:37,893 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,893 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,893 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:37,893 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-27 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:38,134 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,134 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,134 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,134 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-28 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:38,368 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,368 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,368 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,368 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-29 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:38,602 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,602 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,602 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,602 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-30 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:38,838 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,838 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,838 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:38,838 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-01-31 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:39,068 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,068 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,068 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,068 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-01 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:39,300 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,300 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,300 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,300 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-02 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:39,531 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,531 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,531 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,531 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-03 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:39,771 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,771 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,771 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:39,771 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-04 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:40,007 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,007 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,007 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,007 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-05 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:40,236 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,236 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,236 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,236 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-06 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:40,466 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,466 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,466 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,466 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-07 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:40,698 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,698 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,698 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,698 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-08 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:40,931 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,931 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,931 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:40,931 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-09 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:41,160 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,160 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,160 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,160 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-10 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:41,386 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,386 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,386 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,386 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-11 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:41,632 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,632 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,632 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,632 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-12 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:41,870 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,870 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,870 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:41,870 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-13 01:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2015-12-18 09:39:42,112 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:42,112 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:42,112 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "2015-12-18 09:39:42,112 - wflow_bmi_logging - ERROR - get_value: SurfaceRunoff not in list of output values []\n",
      "ERROR:wflow_bmi_logging:get_value: SurfaceRunoff not in list of output values []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1990-02-14 01:00:00"
     ]
    }
   ],
   "source": [
    "t_end = RT_model.get_end_time()\n",
    "t = RT_model.get_start_time()\n",
    "\n",
    "res = []\n",
    "resq = []\n",
    "\n",
    "# Loop in time and put output of SBM in seperate routing module - 1way link\n",
    "while t < t_end:\n",
    "    LA_model.update()\n",
    "    # Now set the output from the LA model (specific Q) as input to the RT model\n",
    "    thevar = LA_model.get_value(\"InwaterMM\")\n",
    "    RT_model.set_value(\"IW\",thevar) # The IW is set in the wflow_routing.ini var as a forcing\n",
    "    RT_model.update()\n",
    "    resq.append(RT_model.get_value(\"SurfaceRunoff\"))\n",
    "    res.append(thevar)\n",
    "    t = RT_model.get_current_time()\n",
    "    \n",
    "    print datetime.datetime.fromtimestamp(t)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "LA_model.finalize()\n",
    "RT_model.finalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Define function to view the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def browse_res(digits):\n",
    "    n = len(digits)\n",
    "    def view_image(i):\n",
    "        plt.imshow(log(digits[i]+1))\n",
    "        plt.title('Step: %d' % i)\n",
    "        plt.colorbar()\n",
    "        plt.show()\n",
    "    interact(view_image, i=(0,n-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "browse_res(res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    " browse_res(resq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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