LinearityAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook builds off of BasinStreamflow_PhysRadIndexing\n",
    "Goal is to assess which basins have a roughly linear relatoinship (i.e., phys+rad+residual=full). Then for those, plot contributions of PHYS and RAD to the FULL change. \n",
    "<br><br>\n",
    "**Approach**: <br>\n",
    "1) Estimate the residual for each basin as the Root Mean Squared Error (RMSE) between the change in FULL from CTRL and the change in PHYS+RAD from CTRL (as a fractional change, i.e., dFull = (FULL-CTRL)/CTRL) over all thirty years assessed. <br>\n",
    "2) If a basin is to be considered \"linear\", the sum of PHYS+RAD must be larger than the residual. Otherwise, the basin is ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basins are defined as follows: \n",
    "<font color=green>\n",
    "% Key: <br>\n",
    "   1.......Yukon <br>\n",
    "   2.......Mackenzie<br>\n",
    "   3.......Columbia<br>\n",
    "   4.......Nelson<br>\n",
    "   5.......Mississippi<br>\n",
    "   6.......St-Lawrence<br>\n",
    "   7.......Rhine<br>\n",
    "   8.......Danube<br>\n",
    "   9.......Dniepr<br>\n",
    "   10......Volga<br>\n",
    "   11......Ob<br>\n",
    "   12......Yenisei<br>\n",
    "   13......Lena<br>\n",
    "   14......Amur<br>\n",
    "   15......Orinoco<br>\n",
    "   16......Parana<br>\n",
    "   17......Amazonas<br>\n",
    "   18......Niger<br>\n",
    "   19......Congo<br>\n",
    "   20......Nile<br>\n",
    "   21......Zambezi<br>\n",
    "   22......Tigris & Euphrates<br>\n",
    "   23......Indus<br>\n",
    "   24......Ganges & Brahmaputra<br>\n",
    "   25......Mekong<br>\n",
    "   26......Huang-he<br>\n",
    "   27......Yangtze<br>\n",
    "   28......Murray & Darling<br>\n",
    "   29......Don<br>\n",
    "   30......Fraser<br>\n",
    "   31......Tocantins<br>\n",
    "   32......Vuoski & Neva<br>\n",
    "    </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:26:03.362049Z",
     "start_time": "2019-02-22T19:26:01.541538Z"
    }
   },
   "outputs": [],
   "source": [
    "import scipy as scipy\n",
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.patches as patches\n",
    "import cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.util\n",
    "import xarray as xr\n",
    "import pickle \n",
    "import os\n",
    "import copy\n",
    "from netCDF4 import Dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=purple> \n",
    "    Set up river-relevant constant arrays <br> <br> \n",
    "    Read in data and define arrays for basic use \n",
    "</font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:26:04.908694Z",
     "start_time": "2019-02-22T19:26:04.903238Z"
    }
   },
   "outputs": [],
   "source": [
    "#Define list of basin names based on key (used in plotting)\n",
    "basinNames = ['Yukon','Mackenzie','Columbia','Nelson','Mississippi','St-Lawrence','Rhine','Danube','Dniepr','Volga','Ob','Yenisei',\\\n",
    "'Lena','Amur','Orinoco','Parana','Amazonas','Niger','Congo','Nile','Zambezi','Tigris & Euphrates','Indus',\\\n",
    "'Ganges & Brahmaputra','Mekong','Huang-he','Yangtze','Murray & Darling','Don','Fraser','Tocantins','Vuoski & Neva']\n",
    "\n",
    "#Define time array for plotting too\n",
    "days = np.arange(365)+1\n",
    "\n",
    "#Beginning julian days for each month\n",
    "startMon = [0, 31,59, 90,120,151,181,212,243,273,304,334]\n",
    "months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:26:20.274857Z",
     "start_time": "2019-02-22T19:26:20.218685Z"
    }
   },
   "outputs": [],
   "source": [
    "#Get dimensions from a single file (same will be used for all files)\n",
    "file1 = 'fldare1161.nc'\n",
    "dat   = Dataset(file1)\n",
    "lat   = dat.variables['lat'][:] \n",
    "lon   = dat.variables['lon'][:] \n",
    "\n",
    "#Read in bsin ID identifiers from Matlab file \n",
    "basinData = sio.loadmat('BasinMask.mat')\n",
    "basinMask = basinData['BasinMask']\n",
    "\n",
    "#Read in CaMa gridcell areas\n",
    "matData = sio.loadmat('CaMa_gridAreas.mat')\n",
    "area    = matData['area']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:26:22.687321Z",
     "start_time": "2019-02-22T19:26:22.651626Z"
    }
   },
   "outputs": [],
   "source": [
    "## Read in daily streamflow data from CaMa ##\n",
    "\n",
    "#Read indices for subsetted data \n",
    "lonFile = open(\"lonIndices_subsetKey.txt\",\"r\")\n",
    "latFile = open(\"latIndices_subsetKey.txt\",\"r\")\n",
    "\n",
    "lonSubset=[]\n",
    "for line in lonFile.readlines():\n",
    "    lonSubset.append(float(line))  \n",
    "\n",
    "latSubset=[]\n",
    "for line in latFile.readlines():\n",
    "    latSubset.append(float(line))\n",
    "\n",
    "#Set filepaths for daily data\n",
    "ctrlFile = 'Control_outflow_subset.nc'\n",
    "fullFile = 'Full_outflow_subset.nc'\n",
    "physFile = 'Physiology_outflow_subset.nc'\n",
    "radFile  = 'Radiation_outflow_subset.nc'\n",
    "\n",
    "#Read in outflow data  \n",
    "fCtrl = xr.open_dataset(ctrlFile,decode_times=False)\n",
    "fFull = xr.open_dataset(fullFile,decode_times=False)\n",
    "fPhys = xr.open_dataset(physFile,decode_times=False)\n",
    "fRad  = xr.open_dataset(radFile,decode_times=False)\n",
    "\n",
    "outflwCtrl = fCtrl.outflow\n",
    "outflwFull = fFull.outflow\n",
    "outflwPhys = fPhys.outflow\n",
    "outflwRad  = fRad.outflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:26:24.760910Z",
     "start_time": "2019-02-22T19:26:24.694763Z"
    }
   },
   "outputs": [],
   "source": [
    "## Read in monthly CESM data ##\n",
    "#fileBase = '/gdata/pritchard2/mdfowler/Flooding-physiology/RelatedCESMvariables/'\n",
    "\n",
    "# Read in rainfall and soil liquid \n",
    "fCtrl = xr.open_dataset('cesm1_0_6.1850_4xco2_fulgb.1deg.002.clm2.MonthlyConcat_Meg.nc',decode_times=False)\n",
    "# fFull = xr.open_dataset((fileBase + 'cesm1_0_6.1850_4xco2_fulgb.1deg.002.clm2.MonthlyConcat_Meg.nc'),decode_times=False)\n",
    "# fPhys = xr.open_dataset((fileBase + 'cesm1_0_6.1850_4xco2_fixgb.1deg.001.clm2.MonthlyConcat_Meg.nc'),decode_times=False)\n",
    "# fRad  = xr.open_dataset((fileBase + 'cesm1_0_6.1850_4xco2_fdbgb.1deg.002.clm2.MonthlyConcat_Meg.nc'),decode_times=False)\n",
    "\n",
    "#CESM lat and lon\n",
    "cesmLon = fCtrl.lon.values\n",
    "cesmLat = fCtrl.lat.values\n",
    "\n",
    "#Read in basin mask at CESM resolution \n",
    "basinData = sio.loadmat('BasinMask_CESMres.mat')\n",
    "basinMask_CESM = basinData['basinMaskNew']\n",
    "\n",
    "#Gridcell area at CESM resolution \n",
    "cesmArea = fCtrl.area.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For each basin, determine changes in mean/max/min streamflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:26:58.418432Z",
     "start_time": "2019-02-22T19:26:30.451116Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "281  points used in the Yukon\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "252  points used in the Mackenzie\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "96  points used in the Columbia\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "66  points used in the Nelson\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "264  points used in the Mississippi\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "128  points used in the St-Lawrence\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "34  points used in the Rhine\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "100  points used in the Danube\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "50  points used in the Dniepr\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "243  points used in the Volga\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "318  points used in the Ob\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "357  points used in the Yenisei\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "477  points used in the Lena\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "246  points used in the Amur\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "165  points used in the Orinoco\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "323  points used in the Parana\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "1105  points used in the Amazonas\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "185  points used in the Niger\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "705  points used in the Congo\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "265  points used in the Nile\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "233  points used in the Zambezi\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "7  points used in the Tigris & Euphrates\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "177  points used in the Indus\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "361  points used in the Ganges & Brahmaputra\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "147  points used in the Mekong\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "111  points used in the Huang-he\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "463  points used in the Yangtze\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "65  points used in the Murray & Darling\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "39  points used in the Don\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "45  points used in the Fraser\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "136  points used in the Tocantins\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "55  points used in the Vuoski & Neva\n",
      "Basin isolated\n",
      "Area weighting completed\n",
      "Annual cycle computed... Done with basin\n",
      "**** DONE COMPUTING BASIN STATS ****\n"
     ]
    }
   ],
   "source": [
    "#Define empty arrays to store basin-specific data in \n",
    "annCtrl = np.full([len(basinNames),30,365],np.nan)\n",
    "annFull = np.full([len(basinNames),30,365],np.nan)\n",
    "annPhys = np.full([len(basinNames),30,365],np.nan)\n",
    "annRad  = np.full([len(basinNames),30,365],np.nan)\n",
    "avgCtrl = np.full([len(basinNames),365],np.nan)\n",
    "avgFull = np.full([len(basinNames),365],np.nan)\n",
    "avgPhys = np.full([len(basinNames),365],np.nan)\n",
    "avgRad = np.full([len(basinNames),365],np.nan)\n",
    "avgLat = np.full([len(basinNames)],np.nan)\n",
    "avgLon = np.full([len(basinNames)],np.nan)\n",
    "basinArea_CaMa = np.full([len(basinNames)],np.nan)\n",
    "\n",
    "## ---- Loop over basins ---- ##\n",
    "\n",
    "for iBasin in range(len(basinNames)):\n",
    "    basinChoice = iBasin+1\n",
    "    \n",
    "    #Define empty arrays and variable to count number of obs in basin\n",
    "    iSubset  = []\n",
    "    gridArea = []\n",
    "    basinObs = 0\n",
    "    \n",
    "    #Loop over every location in the subset of daily data \n",
    "    for iLoc in range(len(lonSubset)): \n",
    "        ilon = int(lonSubset[iLoc])\n",
    "        ilat = int(latSubset[iLoc])\n",
    "\n",
    "        if (basinMask[ilon,ilat]==basinChoice):\n",
    "            iSubset = np.append(iSubset,iLoc)               #Save iLoc (index of subset) if in basin\n",
    "            gridArea = np.append(gridArea,area[ilon,ilat])  #Save area of each valid gridcell   \n",
    "            basinObs = basinObs+1\n",
    "\n",
    "    iSubset=np.asarray(iSubset,dtype=int)  \n",
    "    print(basinObs, ' points used in the', basinNames[basinChoice-1])\n",
    "\n",
    "    #Isolate just the data in specified basin for further analysis \n",
    "    basinLon  = np.take(lonSubset,iSubset)\n",
    "    basinLat  = np.take(latSubset,iSubset)\n",
    "    basinCtrl = np.take(outflwCtrl.values,iSubset,axis=0)\n",
    "    basinFull = np.take(outflwFull.values,iSubset,axis=0)\n",
    "    basinPhys = np.take(outflwPhys.values,iSubset,axis=0)\n",
    "    basinRad  = np.take(outflwRad.values,iSubset,axis=0)\n",
    "    avgLat[iBasin] = np.nanmean(lat[np.asarray(basinLat,dtype=int)])\n",
    "    avgLon[iBasin] = np.nanmean(lon[np.asarray(basinLon,dtype=int)])\n",
    "    basinArea_CaMa[iBasin] = np.nansum(gridArea*1e6) #Convert from km^2 to m^2 \n",
    "    print('Basin isolated')\n",
    "\n",
    "    #-------------------------------------------------------------------------------\n",
    "\n",
    "    ## Create annual climatology ##\n",
    "\n",
    "    # Area weighted averages \n",
    "    wgtAvgCtrl = np.full([30*365],np.nan)\n",
    "    wgtAvgFull = np.full([30*365],np.nan)\n",
    "    wgtAvgPhys = np.full([30*365],np.nan)\n",
    "    wgtAvgRad  = np.full([30*365],np.nan)\n",
    "\n",
    "    for iDay in range(30*365): \n",
    "        wgtAvgCtrl[iDay] = np.nansum(gridArea*basinCtrl[:,iDay])/np.nansum(gridArea)\n",
    "        wgtAvgFull[iDay] = np.nansum(gridArea*basinFull[:,iDay])/np.nansum(gridArea)\n",
    "        wgtAvgPhys[iDay] = np.nansum(gridArea*basinPhys[:,iDay])/np.nansum(gridArea)\n",
    "        wgtAvgRad[iDay]  = np.nansum(gridArea*basinRad[:,iDay])/np.nansum(gridArea)\n",
    "    print('Area weighting completed')\n",
    "\n",
    "    # Split into 30 years to get at composite seasonal cycle (dimensions are [30 x 365])\n",
    "    annCtrl[iBasin,:,:] = np.split(wgtAvgCtrl,30)\n",
    "    annFull[iBasin,:,:] = np.split(wgtAvgFull,30)\n",
    "    annPhys[iBasin,:,:] = np.split(wgtAvgPhys,30)\n",
    "    annRad[iBasin,:,:]  = np.split(wgtAvgRad,30)\n",
    "\n",
    "    # Average over years ()\n",
    "    avgCtrl[iBasin,:] = np.nanmean(annCtrl[iBasin,:,:],axis=0)\n",
    "    avgFull[iBasin,:] = np.nanmean(annFull[iBasin,:,:],axis=0)\n",
    "    avgPhys[iBasin,:] = np.nanmean(annPhys[iBasin,:,:],axis=0) \n",
    "    avgRad[iBasin,:]  = np.nanmean(annRad[iBasin,:,:], axis=0)\n",
    "\n",
    "    print('Annual cycle computed... Done with basin')\n",
    "\n",
    "print('**** DONE COMPUTING BASIN STATS ****')\n",
    "#Only takes about a minute to run \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assuming that every year, dPhys+dRad+residual = dFull, what is the internal variability of that residual? \n",
    "* Use that to determine which basins are \"linear\" or not - if dPhys+dRad not bigger than that residual, don't assess that basin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:27:37.020612Z",
     "start_time": "2019-02-22T19:27:36.442354Z"
    }
   },
   "outputs": [],
   "source": [
    "# For each basin, use all years to arrive at a measure of the residual \n",
    "\n",
    "#Define 30-year average mean, max, and min \n",
    "annMeanCtrl = np.nanmean(annCtrl,axis=2)\n",
    "annMeanFull = np.nanmean(annFull,axis=2)\n",
    "annMeanPhys = np.nanmean(annPhys,axis=2)\n",
    "annMeanRad  = np.nanmean(annRad, axis=2)\n",
    "\n",
    "# annMaxCtrl  = np.nanmax(annCtrl, axis=2)\n",
    "# annMaxFull  = np.nanmax(annFull, axis=2)\n",
    "# annMaxPhys  = np.nanmax(annPhys, axis=2)\n",
    "# annMaxRad   = np.nanmax(annRad,  axis=2)\n",
    "\n",
    "# annMinCtrl = np.nanmin(annCtrl, axis=2)\n",
    "# annMinFull = np.nanmin(annFull, axis=2)\n",
    "# annMinPhys = np.nanmin(annPhys, axis=2)\n",
    "# annMinRad  = np.nanmin(annRad,  axis=2)\n",
    "\n",
    "# --- Updated: use percentiles to define high and low flows each year --- #\n",
    "annMaxCtrl = np.nanpercentile(annCtrl,95,axis=2)\n",
    "annMaxFull = np.nanpercentile(annFull,95,axis=2)\n",
    "annMaxPhys = np.nanpercentile(annPhys,95,axis=2)\n",
    "annMaxRad  = np.nanpercentile(annRad, 95,axis=2)\n",
    "\n",
    "annMinCtrl = np.nanpercentile(annCtrl, 5,axis=2)\n",
    "annMinFull = np.nanpercentile(annFull, 5,axis=2)\n",
    "annMinPhys = np.nanpercentile(annPhys, 5,axis=2)\n",
    "annMinRad  = np.nanpercentile(annRad,  5,axis=2)\n",
    "\n",
    "#Take fractional difference from CTRL for each case (FULL,PHYS,RAD)\n",
    "diffMeanFull_ann = (annMeanFull-annMeanCtrl)/annMeanCtrl\n",
    "diffMeanPhys_ann = (annMeanPhys-annMeanCtrl)/annMeanCtrl\n",
    "diffMeanRad_ann  = (annMeanRad-annMeanCtrl)/annMeanCtrl\n",
    "diffMeanSum_ann  = diffMeanPhys_ann+diffMeanRad_ann \n",
    "\n",
    "diffMaxFull_ann  = (annMaxFull-annMaxCtrl)/annMaxCtrl\n",
    "diffMaxPhys_ann  = (annMaxPhys-annMaxCtrl)/annMaxCtrl\n",
    "diffMaxRad_ann   = (annMaxRad-annMaxCtrl)/annMaxCtrl\n",
    "diffMaxSum_ann   = diffMaxPhys_ann+diffMaxRad_ann\n",
    "\n",
    "diffMinFull_ann = (annMinFull-annMinCtrl)/annMinCtrl\n",
    "diffMinPhys_ann = (annMinPhys-annMinCtrl)/annMinCtrl\n",
    "diffMinRad_ann  = (annMinRad-annMinCtrl)/annMinCtrl\n",
    "diffMinSum_ann  = diffMinPhys_ann+diffMinRad_ann\n",
    "\n",
    "#Compute RMSE for every basin from yearly data \n",
    "rmseMean_ann = np.full([len(basinNames)],np.nan)\n",
    "rmseMax_ann  = np.full([len(basinNames)],np.nan)\n",
    "rmseMin_ann  = np.full([len(basinNames)],np.nan)\n",
    "\n",
    "for iBasin in range(len(basinNames)): \n",
    "    rmseMean_ann[iBasin] = np.sqrt(np.mean((diffMeanSum_ann[iBasin,:]-diffMeanFull_ann[iBasin,:])**2))\n",
    "    rmseMax_ann[iBasin]  = np.sqrt(np.mean((diffMaxSum_ann[iBasin,:]-diffMaxFull_ann[iBasin,:])**2))\n",
    "    rmseMin_ann[iBasin]  = np.sqrt(np.mean((diffMinSum_ann[iBasin,:]-diffMinFull_ann[iBasin,:])**2))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-17T17:24:24.457928Z",
     "start_time": "2018-09-17T17:24:24.450393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 30)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(annMeanCtrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map of basin-level residuals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:27:57.933309Z",
     "start_time": "2019-02-22T19:27:40.317635Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x1817d51b00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/meganfowler/anaconda3/lib/python3.6/site-packages/cartopy/io/__init__.py:260: DownloadWarning: Downloading: http://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_boundary_lines_land.zip\n",
      "  warnings.warn('Downloading: {}'.format(url), DownloadWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Map of RMSE between sum and full for each case \n",
    "\n",
    "#Need to make lon/lat arrays with useful information \n",
    "# -- How about for the entire basin, use the avg pctDiff for each variable? \n",
    "globalDiffMean = np.full([len(lat),len(lon)],np.nan)\n",
    "globalDiffMax  = np.full([len(lat),len(lon)],np.nan)\n",
    "globalDiffMin  = np.full([len(lat),len(lon)],np.nan)\n",
    "globalDiffTime = np.full([len(lat),len(lon)],np.nan)\n",
    "\n",
    "for iBasin in range(len(basinNames)):\n",
    "    basinChoice=iBasin+1\n",
    "    for ilon in range(len(lon)):\n",
    "        for ilat in range(len(lat)):\n",
    "            if (basinMask[ilon,ilat]==basinChoice):\n",
    "                globalDiffMean[ilat,ilon] = rmseMean_ann[iBasin]\n",
    "                globalDiffMax[ilat,ilon]  = rmseMax_ann[iBasin]\n",
    "                globalDiffMin[ilat,ilon]  = rmseMin_ann[iBasin]\n",
    "                \n",
    "\n",
    "#Set up figure window \n",
    "## --- Set up figure window --- \n",
    "fig, ax_array = plt.subplots(1,3,subplot_kw={'projection':ccrs.Robinson()})\n",
    "ax1,ax2,ax3 = ax_array.flatten()\n",
    "fig.set_size_inches(19,14)\n",
    "\n",
    "#Plot linearity for mean streamflow \n",
    "ax1.set_global()\n",
    "contour_plot = ax1.pcolormesh(lon,lat,globalDiffMean,hatch='/',cmap='viridis',vmin=0,vmax=1,transform=ccrs.PlateCarree())\n",
    "#Other figure options \n",
    "ax1.set_title('Mean Streamflow')\n",
    "ax1.title.set_fontsize(16)\n",
    "ax1.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax1.gridlines()\n",
    "ax1.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "#Plot linearity for max streamflow\n",
    "ax2.set_global()\n",
    "contour_plot = ax2.pcolormesh(lon,lat,globalDiffMax,cmap='viridis',vmin=0,vmax=1,transform=ccrs.PlateCarree())\n",
    "#Colorbar \n",
    "ax_position = ax2.get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0-0.1, ax_position.y0+0.24, ax_position.width+0.2, 0.02])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',cax=cbar_ax)\n",
    "cbar.set_label('Residual between dFull and dRad+dPhys',fontsize=18)\n",
    "cbar.ax.tick_params(labelsize=16)\n",
    "#Other figure options \n",
    "ax2.set_title('Max Streamflow')\n",
    "ax2.title.set_fontsize(16)\n",
    "ax2.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax2.gridlines()\n",
    "ax2.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "#Plot linearity for min streamflow\n",
    "ax3.set_global()\n",
    "contour_plot = ax3.pcolormesh(lon,lat,globalDiffMin,cmap='viridis',vmin=0,vmax=1,transform=ccrs.PlateCarree())\n",
    "#Other figure options \n",
    "ax3.set_title('Min Streamflow')\n",
    "ax3.title.set_fontsize(16)\n",
    "ax3.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax3.gridlines()\n",
    "ax3.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make a table with information on actual changes in each category and RMSE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:09.577869Z",
     "start_time": "2019-02-22T19:28:09.565780Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- MEAN STREAMFLOW ------------------------------- \n",
      "               Basin        CTRL     dFull(%)     dPhys(%)     dRad(%)    RMSE\n",
      "-------------------------------------------------------------------------------\n",
      " 0                Yukon    4011.96      33.68       2.66      29.81       0.17\n",
      "\n",
      " 1            Mackenzie    3700.77      29.09      10.69      19.66       0.18\n",
      "\n",
      " 2             Columbia    3144.51      14.65       3.87      23.02       0.43\n",
      "\n",
      " 3               Nelson    1175.45      15.06       8.82      18.65       0.26\n",
      "\n",
      " 4          Mississippi    2752.70      41.25      13.55      29.83       0.43\n",
      "\n",
      " 5          St-Lawrence    3426.80       7.05      -5.77      14.86       0.21\n",
      "\n",
      " 6                Rhine    1677.32      -0.81       8.91      -5.89       0.38\n",
      "\n",
      " 7               Danube    2290.04     -16.58      13.04     -22.88       0.41\n",
      "\n",
      " 8               Dniepr    1901.25      -4.89       9.56       9.26       0.55\n",
      "\n",
      " 9                Volga    3353.25      32.00      26.70      36.54       0.55\n",
      "\n",
      "10                   Ob    3129.92       2.90       2.26      -5.95       0.28\n",
      "\n",
      "11              Yenisei    3694.71      26.55       8.41      19.91       0.20\n",
      "\n",
      "12                 Lena    4262.81      35.38       4.18      26.60       0.24\n",
      "\n",
      "13                 Amur    3688.36      57.62      26.79      26.47       0.37\n",
      "\n",
      "14              Orinoco    8033.61     -12.24       2.94      -9.77       0.22\n",
      "\n",
      "15               Parana    4118.13      49.37      70.42      -4.22       0.51\n",
      "\n",
      "16             Amazonas    8584.14      14.45      31.74     -12.47       0.14\n",
      "\n",
      "17                Niger    2604.15      19.46      50.63     -43.34       0.26\n",
      "\n",
      "18                Congo    7722.23      63.93      42.69       9.04       0.28\n",
      "\n",
      "19                 Nile    5523.38     120.02      47.85      56.14       0.44\n",
      "\n",
      "20              Zambezi    4101.12      32.39      48.97     -10.82       0.55\n",
      "\n",
      "21   Tigris & Euphrates     958.72     -53.87      -4.32     -42.38       0.40\n",
      "\n",
      "22                Indus    2897.65      -5.23      -3.27      -2.95       0.60\n",
      "\n",
      "23 Ganges & Brahmaputra    5499.62      30.01       7.27      16.72       0.22\n",
      "\n",
      "24               Mekong    3200.94      89.40      77.07      29.96       0.52\n",
      "\n",
      "25             Huang-he    4207.62      32.31      12.93      12.78       0.41\n",
      "\n",
      "26              Yangtze    5290.31      44.78      34.46       3.17       0.28\n",
      "\n",
      "27     Murray & Darling    1430.79     105.39     197.73     -27.12       3.47\n",
      "\n",
      "28                  Don    2512.12      16.32      20.10      24.47       0.56\n",
      "\n",
      "29               Fraser    2576.83      18.58       4.75      21.08       0.37\n",
      "\n",
      "30            Tocantins    5144.31      -1.22      11.97       2.73       0.59\n",
      "\n",
      "31        Vuoski & Neva    1039.55      22.35      -5.01      24.12       0.22\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('------------------------------- MEAN STREAMFLOW ------------------------------- ')\n",
    "print('               Basin        CTRL     dFull(%)     dPhys(%)     dRad(%)    RMSE')\n",
    "print('-------------------------------------------------------------------------------')\n",
    "for iB in range(len(basinNames)):\n",
    "    print('%2i %20s %10.2f %10.2f %10.2f %10.2f %10.2f' % (iB, basinNames[iB], np.nanmean(annMeanCtrl[iB,:]), \n",
    "                                       np.nanmean(diffMeanFull_ann[iB,:])*100,\n",
    "                                       np.nanmean(diffMeanPhys_ann[iB,:])*100, \n",
    "                                       np.nanmean(diffMeanRad_ann[iB,:])*100,\n",
    "                                       rmseMean_ann[iB]))\n",
    "    print()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:10.331228Z",
     "start_time": "2019-02-22T19:28:10.318678Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- MEAN STREAMFLOW ------------------------------- \n",
      "               Basin        CTRL     dFull(%)     dPhys(%)     dRad(%)    RMSE\n",
      "-------------------------------------------------------------------------------\n",
      " 0                Yukon    7496.18       0.32       5.24      -3.64       0.23\n",
      "\n",
      " 1            Mackenzie    6251.49       5.67      11.93      -1.87       0.28\n",
      "\n",
      " 2             Columbia    8725.18      -3.53      10.70       4.70       0.64\n",
      "\n",
      " 3               Nelson    1628.75      -0.42       5.09       0.63       0.16\n",
      "\n",
      " 4          Mississippi    3822.31      58.57      23.57      49.11       0.61\n",
      "\n",
      " 5          St-Lawrence    3775.37       9.34      -4.71      14.27       0.25\n",
      "\n",
      " 6                Rhine    3005.07      16.87      21.09       8.50       0.61\n",
      "\n",
      " 7               Danube    2885.41     -13.76      18.04     -21.47       0.55\n",
      "\n",
      " 8               Dniepr    2654.52      -3.81      16.31      16.79       0.78\n",
      "\n",
      " 9                Volga    6502.04      11.41      22.36      11.91       0.57\n",
      "\n",
      "10                   Ob    5691.13     -10.65       1.17     -18.74       0.32\n",
      "\n",
      "11              Yenisei    7223.18      16.73      -0.13      14.22       0.32\n",
      "\n",
      "12                 Lena    8749.14      21.72      -2.86      22.94       0.34\n",
      "\n",
      "13                 Amur    5176.19      77.75      29.83      40.37       0.62\n",
      "\n",
      "14              Orinoco   15938.49      18.51      22.36       3.48       0.24\n",
      "\n",
      "15               Parana    6428.74      61.88      85.30      -3.39       0.72\n",
      "\n",
      "16             Amazonas   12878.61      24.57      35.33      -5.93       0.18\n",
      "\n",
      "17                Niger    4665.74      24.43      56.11     -40.38       0.37\n",
      "\n",
      "18                Congo   13601.92      52.92      27.01      14.53       0.40\n",
      "\n",
      "19                 Nile    6083.53     119.93      53.50      55.40       0.41\n",
      "\n",
      "20              Zambezi    8013.36      53.63      71.40      -6.18       0.79\n",
      "\n",
      "21   Tigris & Euphrates    1296.36     -55.51      -2.07     -46.36       0.50\n",
      "\n",
      "22                Indus    5929.52      -7.09      -2.15      -5.94       0.59\n",
      "\n",
      "23 Ganges & Brahmaputra   11961.83      37.53      11.79      22.61       0.33\n",
      "\n",
      "24               Mekong    5928.97     141.76     111.98      66.02       1.21\n",
      "\n",
      "25             Huang-he    6357.90      52.56      23.50      20.37       0.77\n",
      "\n",
      "26              Yangtze    9842.45      46.04      34.14       5.66       0.40\n",
      "\n",
      "27     Murray & Darling    2877.77     149.66     312.39     -22.06       6.00\n",
      "\n",
      "28                  Don    4574.01       4.68      17.47       7.88       0.64\n",
      "\n",
      "29               Fraser    8037.87     -12.24      18.26     -12.88       0.62\n",
      "\n",
      "30            Tocantins   15096.85      11.62      15.99       5.43       0.55\n",
      "\n",
      "31        Vuoski & Neva    1173.58      28.35      -4.03      28.46       0.27\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('------------------------------- MEAN STREAMFLOW ------------------------------- ')\n",
    "print('               Basin        CTRL     dFull(%)     dPhys(%)     dRad(%)    RMSE')\n",
    "print('-------------------------------------------------------------------------------')\n",
    "for iB in range(len(basinNames)):\n",
    "    print('%2i %20s %10.2f %10.2f %10.2f %10.2f %10.2f' % (iB, basinNames[iB], np.nanmean(annMaxCtrl[iB,:]), \n",
    "                                       np.nanmean(diffMaxFull_ann[iB,:])*100,\n",
    "                                       np.nanmean(diffMaxPhys_ann[iB,:])*100, \n",
    "                                       np.nanmean(diffMaxRad_ann[iB,:])*100,\n",
    "                                       rmseMax_ann[iB]))\n",
    "    print()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:15.554991Z",
     "start_time": "2019-02-22T19:28:15.542283Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- MEAN STREAMFLOW ------------------------------- \n",
      "               Basin        CTRL     dFull(%)     dPhys(%)     dRad(%)    RMSE\n",
      "-------------------------------------------------------------------------------\n",
      " 0                Yukon    1118.12     223.74       7.73     206.82       0.82\n",
      "\n",
      " 1            Mackenzie    1959.35      80.97      24.02      67.06       0.38\n",
      "\n",
      " 2             Columbia     719.92      -1.83      -0.36       3.85       0.38\n",
      "\n",
      " 3               Nelson     710.94      57.94      28.81      67.47       0.64\n",
      "\n",
      " 4          Mississippi    1636.28      24.25       4.11       8.81       0.44\n",
      "\n",
      " 5          St-Lawrence    3103.41       5.60      -6.71      14.97       0.20\n",
      "\n",
      " 6                Rhine     699.42     -13.15      12.49      -9.39       0.81\n",
      "\n",
      " 7               Danube    1608.07     -20.42      12.03     -24.43       0.37\n",
      "\n",
      " 8               Dniepr    1113.61       0.10      10.27      15.56       0.75\n",
      "\n",
      " 9                Volga    1502.64      52.55      39.72      73.51       0.95\n",
      "\n",
      "10                   Ob     857.89      97.17      22.66      75.39       0.74\n",
      "\n",
      "11              Yenisei    1643.60      56.88      26.13      33.07       0.27\n",
      "\n",
      "12                 Lena    1301.99      29.28      11.43      19.04       0.21\n",
      "\n",
      "13                 Amur    1920.18      92.45      54.71      37.82       0.49\n",
      "\n",
      "14              Orinoco    1077.24     -35.79     -33.07      -9.61       0.51\n",
      "\n",
      "15               Parana    2430.49      59.57      73.05      -1.61       0.68\n",
      "\n",
      "16             Amazonas    2847.29     -10.23      47.39     -25.18       0.58\n",
      "\n",
      "17                Niger    1298.61      16.12      51.42     -46.66       0.31\n",
      "\n",
      "18                Congo    2841.73      94.06      96.08      -1.18       0.39\n",
      "\n",
      "19                 Nile    5091.86     121.02      47.89      57.17       0.51\n",
      "\n",
      "20              Zambezi    1569.89      12.28      38.98     -14.78       0.49\n",
      "\n",
      "21   Tigris & Euphrates     724.03     -57.94      -4.44     -44.44       0.38\n",
      "\n",
      "22                Indus     440.80      38.56      -2.82      31.98       0.90\n",
      "\n",
      "23 Ganges & Brahmaputra     732.20      69.48      18.28      32.27       0.84\n",
      "\n",
      "24               Mekong    1211.87      51.63      53.15      10.78       0.54\n",
      "\n",
      "25             Huang-he    2754.64      13.29       2.86       7.48       0.19\n",
      "\n",
      "26              Yangtze    2104.30      43.15      44.61      -2.53       0.37\n",
      "\n",
      "27     Murray & Darling     716.70      55.45     103.34     -26.28       1.24\n",
      "\n",
      "28                  Don    1364.88       4.85      19.82      22.71       0.64\n",
      "\n",
      "29               Fraser     604.16      -0.19       6.07      12.45       0.53\n",
      "\n",
      "30            Tocantins     239.66      -6.02      56.71      -7.20       1.42\n",
      "\n",
      "31        Vuoski & Neva     889.14      18.41      -5.68      21.77       0.20\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('------------------------------- MEAN STREAMFLOW ------------------------------- ')\n",
    "print('               Basin        CTRL     dFull(%)     dPhys(%)     dRad(%)    RMSE')\n",
    "print('-------------------------------------------------------------------------------')\n",
    "for iB in range(len(basinNames)):\n",
    "    print('%2i %20s %10.2f %10.2f %10.2f %10.2f %10.2f' % (iB, basinNames[iB], np.nanmean(annMinCtrl[iB,:]), \n",
    "                                       np.nanmean(diffMinFull_ann[iB,:])*100,\n",
    "                                       np.nanmean(diffMinPhys_ann[iB,:])*100, \n",
    "                                       np.nanmean(diffMinRad_ann[iB,:])*100,\n",
    "                                       rmseMin_ann[iB]))\n",
    "    print()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining basins as linear or non-linear "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-05T19:33:43.702122Z",
     "start_time": "2018-09-05T19:33:43.695186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 30)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(diffMeanPhys_ann)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:20.092052Z",
     "start_time": "2019-02-22T19:28:20.083739Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basins where mean is valid: (array([ 0,  1,  3,  4,  9, 11, 12, 13, 15, 16, 18, 19, 21, 23, 24, 26]),)\n",
      "Basins where max is valid:  (array([ 4, 13, 14, 15, 16, 18, 19, 23, 24, 26]),)\n",
      "Basins where min is valid:  (array([ 0,  1,  3,  9, 10, 11, 12, 13, 15, 18, 19, 21, 24, 26]),)\n",
      "Number of basins with at least one metric valid:  (1, 18)\n"
     ]
    }
   ],
   "source": [
    "# Define basins to consider as valid if change in sum is greater than the residual (RMSE) \n",
    "\n",
    "#Average over all 30 years in a basin\n",
    "avgDiffMeanPhys = np.nanmean(diffMeanPhys_ann,axis=1)\n",
    "avgDiffMeanRad  = np.nanmean(diffMeanRad_ann,axis=1)\n",
    "avgDiffMeanSum  = avgDiffMeanPhys + avgDiffMeanRad\n",
    "\n",
    "avgDiffMaxPhys  = np.nanmean(diffMaxPhys_ann,axis=1)\n",
    "avgDiffMaxRad   = np.nanmean(diffMaxRad_ann,axis=1)\n",
    "avgDiffMaxSum   = avgDiffMaxPhys + avgDiffMaxRad\n",
    "\n",
    "avgDiffMinPhys  = np.nanmean(diffMinPhys_ann,axis=1)\n",
    "avgDiffMinRad   = np.nanmean(diffMinRad_ann,axis=1)\n",
    "avgDiffMinSum   = avgDiffMinPhys + avgDiffMinRad\n",
    "\n",
    "allValidMean = np.where( np.abs(avgDiffMeanSum)>rmseMean_ann )\n",
    "allValidMax  = np.where( np.abs(avgDiffMaxSum)>rmseMax_ann ) \n",
    "allValidMin  = np.where( np.abs(avgDiffMinSum)>rmseMin_ann )\n",
    "\n",
    "allValid     = np.where( (np.abs(avgDiffMeanSum)>rmseMean_ann) | \n",
    "                         (np.abs(avgDiffMaxSum)>rmseMax_ann) |\n",
    "                         (np.abs(avgDiffMinSum)>rmseMin_ann))\n",
    "\n",
    "\n",
    "print('Basins where mean is valid:',allValidMean)\n",
    "print('Basins where max is valid: ',allValidMax)\n",
    "print('Basins where min is valid: ',allValidMin)\n",
    "print('Number of basins with at least one metric valid: ',np.shape(allValid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:21.100241Z",
     "start_time": "2019-02-22T19:28:21.095780Z"
    }
   },
   "outputs": [],
   "source": [
    "#Compute PHYS or RAD contribution (as dPhys or dRad /dFull) only for basins where linearity roughly holds \n",
    "\n",
    "meanFull = np.nanmean(diffMeanFull_ann,axis=1)\n",
    "maxFull  = np.nanmean(diffMaxFull_ann, axis=1)\n",
    "minFull  = np.nanmean(diffMinFull_ann, axis=1)\n",
    "\n",
    "physMeanInd = avgDiffMeanPhys/meanFull\n",
    "physMaxInd  = avgDiffMaxPhys/maxFull\n",
    "physMinInd  = avgDiffMinPhys/minFull\n",
    "\n",
    "radMeanInd  = avgDiffMeanRad/meanFull\n",
    "radMaxInd   = avgDiffMaxRad/maxFull\n",
    "radMinInd   = avgDiffMinRad/minFull \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting adventures \n",
    "* Bars to represent change in PHYS/RAD and a star for the FULL change? (Baird's idea) \n",
    "* Circles of varying diameters to represnt changes? (Mike's idea)\n",
    "* Bar plots (Chris) \n",
    "* Just the usual maps? (Chris) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:26.156804Z",
     "start_time": "2019-02-22T19:28:25.805069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Set up figure window \n",
    "## --- Set up figure window --- \n",
    "fig = plt.figure() \n",
    "ax1 = fig.add_subplot(131,projection=ccrs.PlateCarree())\n",
    "ax2 = fig.add_subplot(132,projection=ccrs.PlateCarree())\n",
    "ax3 = fig.add_subplot(133,projection=ccrs.PlateCarree())\n",
    "\n",
    "# fig, ax_array = plt.subplots(1,3,subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "# ax1,ax2,ax3 = ax_array.flatten()\n",
    "fig.set_size_inches(20,12)\n",
    "ax1.set_extent([-180,160,-65,90], crs=ccrs.PlateCarree())\n",
    "ax2.set_extent([-180,160,-65,90], crs=ccrs.PlateCarree())\n",
    "ax3.set_extent([-180,160,-65,90], crs=ccrs.PlateCarree())\n",
    "# ax1.set_global()\n",
    "# ax2.set_global()\n",
    "# ax3.set_global()\n",
    "\n",
    "#Set up backgrounds\n",
    "#ax1.set_title('Change in Mean Streamflow')\n",
    "ax1.set_title('a',{'fontweight' :'bold','fontsize':16},loc='left') \n",
    "ax1.title.set_fontsize(16)\n",
    "ax1.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax1.gridlines()\n",
    "ax1.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "#ax2.set_title('Change in Max Streamflow')\n",
    "ax2.set_title('b',{'fontweight' :'bold','fontsize':16},loc='left') \n",
    "ax2.title.set_fontsize(16)\n",
    "ax2.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax2.gridlines()\n",
    "ax2.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "#ax3.set_title('Change in Min Streamflow')\n",
    "ax3.set_title('c',{'fontweight' :'bold','fontsize':16},loc='left') \n",
    "ax3.title.set_fontsize(16)\n",
    "ax3.coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "ax3.gridlines()\n",
    "ax3.add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "\n",
    "for iBasin in range(np.size(allValidMean)):\n",
    "    #Index of basin \n",
    "    basinChoice = allValidMean[0][iBasin]\n",
    "    #Define fractional increase from CTRL in experiments \n",
    "    diffFull  = np.nanmean(diffMeanFull_ann[basinChoice,:])   \n",
    "    ax1.plot(avgLon[basinChoice],avgLat[basinChoice],color='Red',linestyle='none',markeredgecolor='black',marker='o',markersize=(diffFull*20),alpha=0.85)\n",
    "    if diffFull<0:\n",
    "        ax1.plot(avgLon[basinChoice],avgLat[basinChoice],color='Blue',linestyle='none',markeredgecolor='black',marker='o',markersize=(abs(diffFull)*20),alpha=0.85)\n",
    "\n",
    "\n",
    "for iBasin in range(np.size(allValidMax)):\n",
    "    #Index of basin \n",
    "    basinChoice = allValidMax[0][iBasin]\n",
    "    #Define fractional increase from CTRL in experiments \n",
    "    diffFull  = np.nanmean(diffMaxFull_ann[basinChoice,:])   \n",
    "    ax2.plot(avgLon[basinChoice],avgLat[basinChoice],color='Red',linestyle='none',markeredgecolor='black',marker='o',markersize=(diffFull*20),alpha=0.85)\n",
    "    if diffFull<0:\n",
    "        ax2.plot(avgLon[basinChoice],avgLat[basinChoice],color='Blue',linestyle='none',markeredgecolor='black',marker='o',markersize=(abs(diffFull)*20),alpha=0.85)\n",
    "    \n",
    "    #Add reference circle \n",
    "#     rect = patches.Rectangle((-175,-65),90,70,linewidth=2, facecolor='white',edgecolor='black', transform=ccrs.PlateCarree())    #SE US\n",
    "#     ax2.add_patch(rect)\n",
    "    ax2.text(0.02,0.05,'   ', va='bottom', ha='left',\n",
    "        rotation='horizontal', rotation_mode='anchor',\n",
    "        transform=ax2.transAxes,fontsize=65,bbox=dict(facecolor='white',edgecolor='white', alpha=0.75))\n",
    "    \n",
    "    ax2.plot(-165,0,color='red',linestyle='none',markeredgewidth=2,marker='o',markersize=(1*20), zorder=5)\n",
    "    ax2.plot(-165,-25,color='red',linestyle='none',markeredgewidth=2,marker='o',markersize=(0.5*20),zorder=5)\n",
    "    ax2.plot(-165,-45,color='blue',linestyle='none',markeredgewidth=2,marker='o',markersize=(0.5*20),zorder=5)\n",
    "\n",
    "\n",
    "for iBasin in range(np.size(allValidMin)):\n",
    "    #Index of basin \n",
    "    basinChoice = allValidMin[0][iBasin]\n",
    "    #Define fractional increase from CTRL in experiments \n",
    "    diffFull  = np.nanmean(diffMinFull_ann[basinChoice,:])   \n",
    "    ax3.plot(avgLon[basinChoice],avgLat[basinChoice],color='Red',linestyle='none',markeredgecolor='black',marker='o',markersize=(diffFull*20),alpha=0.85)\n",
    "    if diffFull<0:\n",
    "        ax3.plot(avgLon[basinChoice],avgLat[basinChoice],color='Blue',linestyle='none',markeredgecolor='black',marker='o',markersize=(abs(diffFull)*20),alpha=0.85)\n",
    "\n",
    "        \n",
    "    \n",
    "fig.subplots_adjust(wspace=0.1, hspace=0.2)\n",
    "\n",
    "#fig.savefig('MapCircles_StreamflowChange_V2.eps',bbox_inches = 'tight')  #Save figure as pdf\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=purple> \n",
    "    Bottom panels <br> \n",
    "</font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:37.907447Z",
     "start_time": "2019-02-22T19:28:37.901487Z"
    }
   },
   "outputs": [],
   "source": [
    "#Define percent changes for mean streamflows \n",
    "meanFull = np.nanmean(diffMeanFull_ann,axis=1)*100\n",
    "meanPhys = np.nanmean(diffMeanPhys_ann,axis=1)*100\n",
    "meanRad  = np.nanmean(diffMeanRad_ann,axis=1)*100\n",
    "meanSum  = meanPhys+meanRad\n",
    "\n",
    "#Define percent changes for max streamflows \n",
    "maxFull = np.nanmean(diffMaxFull_ann,axis=1)*100\n",
    "maxPhys = np.nanmean(diffMaxPhys_ann,axis=1)*100\n",
    "maxRad  = np.nanmean(diffMaxRad_ann,axis=1)*100\n",
    "maxSum  = maxPhys+maxRad\n",
    "\n",
    "#Define percent changes for min streamflows \n",
    "minFull = np.nanmean(diffMinFull_ann,axis=1)*100\n",
    "minPhys = np.nanmean(diffMinPhys_ann,axis=1)*100\n",
    "minRad  = np.nanmean(diffMinRad_ann,axis=1)*100\n",
    "minSum  = minPhys+minRad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T19:28:44.512237Z",
     "start_time": "2019-02-22T19:28:44.011545Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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DDzyQCy64gIkTJ25d13YTq6uuuoqbb765wOgk1YL+mmvNj5K0c/pL/m/av4mXnP4Sho8fvnXdgF0GmO+lGmBRVUX7Allx8/3AUWSFyB8ANwAP7kzHKaXJEfE34GzgK8BSYAqwG1mhs7Wk7Z0R8VngdOBKYABZgXRl3uTVbBuNWuo/gYfzPj4dEfOAjwAfA5rJbmr1W+CjO/NepP5q1qxZ7W6bOHHidsUESaon5kdJqg+v+K9XtLvNfC/1bxZVVXUppZnAzHa2bSIrdE6psDnK2v5P+bqSbSe3s/4Sshthbes04or8xz+Vtf0CWZG33Lcr9d3O/n4K/LSz7SVJkiRJktT/OKeqalZE7FJh3UhgAjA/pfS33o9K9eSW428pOgRJqnnmWkmqT+Z/SUWzqKpa9taIeDAiJkfEGfmcpw8AjWSX7UtVtd8T+xUdgiTVPHOtJNUn87+kollUVS1bDDxJdqOpbwIfBhYCb04p3VFkYKoPr37o1UWHIEk1z1wrSfXJ/C+paM6pqpqVUvo9cELRcUiSJEmSJKm2OFJVkiRJkiRJkrrAkaqSVCUnn3wyk8dMLjqMnjW26AAkaXt9MteaKyWpKp44d9vPixf3wfwvqa44UlWSqmSfffYpOgRJqnnmWkmqT+Z/SUWzqCpJVXLxxRcXHYIk1TxzrSTVJ/O/pKJZVJUkSZIkSZKkLrCoKkmSJEmSJEldYFFVkqpk7FjvVCJJ1WaulaT6ZP6XVLRIKRUdgyTgkEMOSfPnzy86DPWgloUtRYegKnl2zoKiQ+iW0jvm1ruIWJBSOqToOMD8v7PMtbWvv+bcajOnd4/5v3bUQ/7vav4zL0iVVSv3O1JVkqrksHsPKzoESap55lpJqk/mf0lFs6gqSVUybNWwokOQpJpnrpWk+mT+l1Q0i6qSJEmSJEmS1AUWVSWpStY1ris6BEmqeeZaSapP5n9JRbOoKklVcvvRtxcdgiTVPHOtJNUn87+kollUlaQqGbN4TNEhSFLNM9dKUn0y/0sqmkVVSaqSMUs80VPHNq9ZwbJvH8PmNSuKDkXqt8y1qibztNR31Wr+37RyE4+e/SibVm4qOhRJO2BRVf1eRNwdEUuLjkOSumrN/TNZ94fZrLn/6qJDkSRVYJ6W1NtW3LqCv//q76y4zT/mSH2dRVVVTUQcEREpX77RTps9I2JD3ubuXg5RkgqTUmL13TMAWH3PDFJKBUckSSplnpbU21JKLPvBMgCWXbvMvCP1cRZV1RvWAf8eEY0Vtp0CBOC1Dao59xx6T9EhqA9b//gctqxbBcCWtStZ//h91dvX0rk8e/UEWlpamDBhAnPnzq3avqTeZq5VtfRmnu4Kc7qUqcX8//wDz7N59WYANq/e3Om8Y16QimFRVb3hx8DuwPgK2/4D+BmwvlcjkqSCrb77EtKGNQCkjWu2jobqaSt/Ppll3zyKtQ/ewMKFC7nxxhs56qijmDx5clX2J0m1orfydFeY06XatuzaZWxZtwWALeu2dCrvmBek4gwsOgDVhYXA/yMroN7YtjIiXg8cBFwAHFX+oog4BPgMcCgwFFgKXAN8JaXU4cjWiHgxcFu+33emlO7M1w8HpgAnAHsBfwVuAT6XUlpR8vpTge/mcY0FPgS8FHgC+GJKabuJtSJiAPBp4PS83yXAF4EDgcnA/imlpR3FrNpz+JzDueX4W4oOQ33A8u+Mp/V3Zb8LAwZD2yVdKdG6aBZPfjS2a7LLq05gxOk/7fZ+1y+dy+q7ppE2rt26bsuWLaxdu5Zp06Zx7LHHMm7cuG73L/UF5lr1hKLydFeY06Xt9ff8/9h5j7Hq3lXbrYuBAW1X/Cd2mHfMC1KxHKmq3vJd4OiIeGnJuonAMrLi53Yi4m3A/wKjgenAOcCvgIuA6zraUUSMAv4P2A84vKSgulu+/kPAbOCjwP/kz++LiKEVuptKNkXBfwOfALYAMyPijWXtvpHH9hjwceAnwLeA4zqKVVJ92O3tUxmw+0gY2LRt5eYN2zcqfT6oiQG778dub5+6U/tdfc+lpE2tFbetW7eOSy+9dKf6l6RaUVSe7gpzulRb9jl7Hwa/ZDDRuK1omjaVzaG6g7xjXpCKZVFVveX7ZPOmvg8gInYBTga+Vz7qNCKagKuAecCrU0pfSin9d0rpVGAS8K6IOKLSTiLiNWTF1wT8c0rpwZLNnwBeCZydUjo9pfStlNIZwEeAA/Lt5RqB16WUvppSupRs5OoG4OySfR4EfJCsUPvmlNLXU0qfA44AXtvRQYmIMyNifkTMX758eUdNJfVjg/c+iL0/tYjmV51ADG7usG0Mbqb5VePZ+1MPM3jvg3ZqvxuXL9k2yqrMli1beOSRR3aqf0mqFUXl6a4wp0u1ZZeX78KBNx3IboftRkNTx6WZ9vKOeUEqlkVV9Yr80vpbgFPzVe8EdiMrnpZ7C9kl9N8FhkXE8LaFbP5VgKPLXxQRbwbuJZsm4I0VLrd/B7AcuLxs/X8Dz+bby30rpbT1z4Mppb+QXdr/ypI2baNRL00pbSlp+1uyQmu7UkqXp5QOSSkdMmLEiI6aqh9aPHpx0SGoD2loHMLwU29g2PjpMLDSffuAgY0MGz+d4e+/nobGITu9z0EjRkNU/qpvaGhg9OjRO70PqWjmWvWUIvJ0V5jTpe3VQv4fsMsAXvbll/EP5/0DMTgqN+og75gXpGJZVFVv+i7wyoj4F7JL/+9PKS2q0O7A/PEqsiJo6fKHfNteZa/Zi6zg+jhwVOn8qCX2BxaXj4zNny8GXlbhNY9XWLcCeHFZv+R9lOv/3/TqtsVj/Pj1QoNfOpZo5z/rMbCRwfu29Ni+hh5+LlF6KWuJpqYmzjnnnB7bl1QUc616Wm/m6a4wp0vbq6X833xAMzGoclG1o7xjXpCKZVFVvWk28BeyGzcdSeVRqgBt3yYfJxu1WmmZXvaa5/L+Dwbe04Mxb95BjOU/S1sdffsLBlRLbPjTfNi8MX8WxKBmtqaRzRvZ8OT8HttX46hxDH3TpGwf+SiGhoYGmpubmTRpkjcuUE0w16qn9Wae7gpzurS9Wsr/axet3TafakA0xbb/ZXaQd8wLUrEGFh2A6kdKaXNEXAP8J9AKXN9O07aJX9aklO7oZPcbyaYUuAH474gYlFL6Vlmbx4ExETGwdLRqRAwkuyFWpVGpnfHH/HFMhT7GdLNP1YCm9ZX/aqz6tv6xOaSNrdnNBnbdi93fcQl/+/G5bF69jLSxlfWPz2Hov3yox/Y37Ngp7HLgsay+51IObHiEV77ylZx77rmeZKtmmGvV03o7T3eFOV3appby/+oHVpPWJ6IxGLTHIF56/kv587Q/s+HZhh3mHfOCVByLqupt3ya70dPjKaVV7bSZDSwDPhURN6SUnivdmN/kamBKaXXp+pTSxog4CfgB8M28sFp6u8OfAJ8GTs/jaHMGMIJsbtXuuBX4EnBuRMxum1c1Iv4ReGs3+5RUo9Y/MQ8aBtD8qvHscfKVNDQOoWnMW3juuomsfehmNjwxr8f32ThqHI2jxjH/3B7vWpJqThF5uivM6VLtWfu7tdAAww4fxsjPjWTALgMYOm4oj5w9plN5x7wgFcOiqnpVSulJ4MIdtFkTEe8jK4IujoirgEeBYcABZCNS3wHcXeG1myJiAtnI1UvyUaltUwV8Ffg3soLrWOAB4LXAaWRzn361m+/p4Yi4HDgTuCMifkxWpD0r30cLUPmWjKppK3dbWXQI6oMG7XUgux19AbuOm7h1XdvNUZ6fexVrf3NzgdFJ/Y+5Vj3NPC31D7WU/5v2b+Ilp7+E4eOHb103YJcB5h2pj7Ooqj4ppTQ7Il4HfAp4L1mR8m/AY8DFwEMdvHZzRJxCVlidFhGDU0pfSimtiog3AlOAE4D/AP5KNmp1cvnI1y76MPAUWYF2GlmR9kPA68mKqq070bf6qXsPu7foENQH7fmBWe1u23XcxO3+Ey9px8y16mnmaal/qKX8/4r/ekW728w7Ut9lUVVVk1K6m07exCmltGuFdb8jK6ju6LVHVFi3BTg1X0rXLycrgH54B33OBGZ2YX+byYq1U0rX58XdtcCKjvan2nTwbw7moVe3W/+XJPUAc60k1Sfzv6SiNRQdgFQL8nley9cdDBwL3JUXXVVnRj05qugQJKnmmWslqT6Z/yUVzZGqUs94fz4P7CxgOdncr2eS3ZTrc0UGJkmSJEmSpJ5lUVXqGQvJbp51DrAHsBq4C5iSUnqgyMAkSZIkSZLUsyyqSj0gpXQ/8Nai41DfMvsts4sOQZJqnrlWkuqT+V9S0ZxTVZKqZNjKYUWHIEk1z1wrSfXJ/C+paI5UlaQqecOv38DkyZOLDkPVMLboACS1MdfWAXOupArqIv+b/6Q+zZGqkiRJkiRJktQFFlUlSZIkSZIkqQssqkpSlRx33HFFhyBJNc9cK0n1yfwvqWgWVSWpSlpaWooOQZJqnrlWkuqT+V9S0SyqSlKVTJkypegQJKnmmWslqT6Z/yUVzaKqJEmSJEmSJHXBwKIDkKRa1rKwc5clPTtnQZUjyTxxbq/sRpJ6VWdzbZtq51xzrSRJUu1zpKokVckzez1TdAiSVPPMtZJUn0aPHl10CJLqnEVVSaqS+19/f9EhSFLNM9dKUn2aMGFC0SFIqnMWVSWpSl5//+uLDkGSap65VpLq03XXXVd0CJLqnEVVSaqSl/z1JUWHIEk1z1wrSfVpyZIlRYcgqc5ZVJUkSZIkSZKkLrCoKknaasWKFRxzzDGsWLGi6FAkqSaYVyVJkmqTRVW9QETcHRFLi45D6u9uOf6WokPospkzZzJ79myuvvrqokORpE7p67nWvCpJ1TF58uSiQ5BU5zpdVI2Ipoj4cETcFRHLI2JjRKyMiF9HxFci4oBqBtpfRMSoiPhBRDwVEWsi4g8R8V8RsWsX+7kwIlLZ8veIWBQRX4iIPar1HupZRHw0Ik4tOg7Vhv2e2K/oELokpcSMGTMAmDFjBimlgiOSpB3ry7nWvCpJ1bNgwYKiQ5BU5zpVVI2IlwELgW/mr5kBnAl8FngImAg8HBH/UKU4+4WIGAL8AngHcC1wHnBb/nx4N7v9HHBKvnwa+D3wGeDOiHCkcc/7KHBq0UGoNrz6oVcXHcJW65fO5dmrJ9DS0sKECROYO3fuC9rMmTOHVatWAbBy5Uruu+++3g5TkrqsL+fab3/72+ZVSaqS2267regQJNW5gTtqEBG7ALOAlwPvTCn9uEKbJrICYr3/+f2NwCuA/0wpfbltZUR8Eohu9vnzlNL8kuffiIgfkRVqXw080N4LI2IA0JhSWtvNfWsHImJoSml10XFIHVn588msvmsaaVMrC1PiwQcf5JZbbmHSpElMmTJla7tLLrmENWvWALBmzRpmzJjBoYceWlTYktSvVMq1P/zhD9m8eTNgXpUkSao1nRnpeDpwAPC1SgVVgJTSupTSl1JKT7Wti4h9ImJ6RDwYEX+LiHX5peufzIt9lLQ9Nb+8/U0RMSkiHouI9RGxJCLeX76/iBgQEZ+NiCfyfh+KiHeXXDI/qqz93hFxWUQ8GREb8kvzL4+IPcva7RERM/L9r4uIFRGxICI+3onjBNuKytsVUFNKm1NKmzrZR2e0HecNbStKjuGb82PzGLAOOCnffnRE3BARj0dEaz51w+0RcXh7O8k/w+vyz29NRMyOiNFlbdr2e1REfC7/TFojYl5EjMvbHB4R9+V9PB0Rn62wr07H1zbna0S8LCJ+GhGr8qkRfpyPqq4U3xHt9VPyPAH7AYeXTbswKt++NH/Na/NjsYpspDYRMTSflmFeRDyb//4+GhFfjojm9o6xVG3rl87N/pO/cS3kl51u2bKFtWvXctFFFxERW5dZs2ZtvTQ1pcSsWbO22x4RjB8/vsi3I0l90vqlc/n77V98Qa5tK6iCeVWSJKnW7HCkKvCu/PE7Xez7YOCdwI+Bx4BBwLHAl4GXAR+o8JqpwC7AfwPrgQ8BMyPi0ZTS/5a0+wbwQeCXwDRgBPAt4I/lHUbESOBXwGDgyjyWV+R9HxkRh6SUVuXNfwgclu//N0AzWUH5COBrnXjP95Bdnv/xiPhpSmlRJ16zI7tFRNvUAbsBhwP/AdwHVOp/GtmxvgI5JLTRAAAgAElEQVT4O7A4X38qsAdwDfBn4B/ICuZ3RsSRKaU5Zf0MAe4F5pJNO7A/cC7w04h4VUppc1n7LwMDgEvJjvX5wOy8KH4lcDnZlAgnARdFxB9TSt8veX134vslcD/wn8ArgQ8D4yLitSmlZyocmx05hWxqi2eBL5asX17y80jgLrLflZuBtrly2+K9GfgBsInss/oE8Frgrd2IR/3cvNfNKzoEVt9zKWlTa7vbGxoa2LJlCwAbNmzYblvp86amJvbaay+mTp1anUAlqZv6Sq4lbdlhO/OqJPWck08+uegQJNW5zhRVXwX8PaW0XcEyH226e1nbNSmltv+93wO8LG0/I/8lEfE94PSIuDCl9HTZ6xuB16WUNuT7uAl4HDgb+N983UFkBdXZwNtSys5gI+KHwIMV4v86WZHxtSmlP5fE/0OyguF5wIURsRvwJuCylNLZOzoo7RiV76uZrBh4VA8UVu+osO6nwHvLjm2bXcjea/kl/2eklNaUroiIbwMPkxUly4uWw8lGJ3+1pP1y4KvAm8mOf6kBwLiSz25RHudNwD+llH6dr78SeAI4CygtqnYnvktTSh8taX8v8CPgQrLfkS5JKX0/Ir4A/LWs4Ftq/zzW8j8yPA7sm1LaWLLumxHxeeCCiHh9Sun+8s4i4kyy+YkZOXJkV0NWH7dy2MqiQ2Dj8iVbR01Vsttuu7F+/XrWrm1/lpDm5maOP/54rrzySoYMGVKNMCWp2/pMru3CLFjmVUnaefvss0/RIUiqc525/P9FZCMeyx1INoKvdDmrbWNKqbWt6BcRg/NL64eTFeMagEMq9PmttqJc3sdfgCVkoxDbHJc/XtpWUM3b/payQl9eKD0OuAVYFxHD2xZgKfAocHTevJVsdOwbomz6gM6IiL3IRjA+Tja69Xng7oj4x7J2S/LiX2edBbwlX95FNpLyWOCmiBhcof1lleZQLS1YRsSuEfFiYDMwD3hDhX62AP9Vtu6u/PGVvNBlpZ8d24qgc9sKqnkcG8hGl27XRzfig2x0bGkfPyYbmXtiO+17wnPAd8tXppQ2tBVUI2JgROye/561FcUrvoeU0uUppUNSSoeMGDGiakGrGG/9RfEDlAeNGA3t3NOuoaGBY445hunTp9PY2FixTWNjI9OnT+f666/3P/6S+qS+nmvLmVclqWdcfPHFRYcgqc515uzv72SF1XJ/ZFuxb1L5xrywdEFELCGb23MFWeH1e3mT8lGukBUky60AXlzyfP/8cXGFtuXrxpC9x9N4YQF4eb59L9ha7Pso2cjcP0bEwxHx9Yg4qsJ+Kvk0sDfZKMalZKNenwd+GRGvgaxYmMfflVu/3p9SuiNfbk4pfQz4HNnl5BMrtF9SqZOIeHlEXB8RfwNWk13ivhx4G5U/i6dSSuvK1q3IH19c3piyzy6l9Lf8xxdMyQD8rbyPbsS3sp1L/H8P7BUR1fpfymMVpj4AICI+HBEPkRXnnyOL/+58c6X3IFXd0MPPJQY2VdzW1NTEOeecw9ixYzssqra0tFQzREnq9zrKteXMq5IkSbWhM0XV3wEvioj9S1emlNa0FfuABRVedzHweWAh2RygbyMrwH6yg31XLFax/Y2fop02Hb3u+2wrAJcv72trnFL6Ntkl/Gfkcb8LuCMiru/Evo4AlqaUnsz7+hNwJFlh9a6IOITskvSgwkjHLmobkfumCtteMEo1L+beCxxDNufpu8iKsm8hG31a6Zi291nQxfYd9bMz8bV3jV15246uxevM9BflKl4jHREfA74JPE02X/DbyeI/NW/SueErUg9rHDWOoW+aRAxq3jqKqqGhgebmZiZNmsS4ceOYP38+GzdmM1dEBM3NzURk/5Q2btzI/PnzC4tfkvqDSrm2lHlVkiSp9nSm0HNT/nh6F/s+Bbg3pXRySunqlNLP8wJspakEuqJt5OOYCtvK1z1KVlQbXDLas3wpvQEWKaWnU0rfSSmdArwUuA54d0S8bgdxbQH2iYhBJX09wbbC6h3Ap4DLU0qPdPK9tqdtH0M72f4oYB/gvJTShfmI19vzz6MvXHfWnfh2j4iXVFh/ALCsZDqB5/LHPSq03b/Cus5PiLa9U8imlDg2//35WR7/X7vZn2rA0pFLiw4BgGHHTmHPs+6k+TUn0dLSwkknncSdd97JlClTAJgzZw6tra00NTUxcuRIrr32Wvbdd1+amppobW1lzpzyKY0lqe/oi7l2992zC1QGDx7MfvvtZ16VpCoYO3Zs0SFIqnOdKap+B/gD2R3t39FOm/ZGLm63Pr8k+7wuRfhCt+aP50ZsGwqQz1263aRaKaUVwM+Ad0bEuBcEnRmR/9wcEc1lr98MPJQ/rVSUK/UzoAn4QlkffyQbrbgb2SXvN+ygn85omzO00gjhStpGi5Z/HkfT/nylvam78X2qrP07yArrPylZ3TYdwpvL2k4gK+SWe54df9aVbCYryG59DxExsDxG1ZeHXv3Qjhv1ksZR4xj+/uuYP38+1113HePGbUuJ8+bNY8CAAYwfP56HH36YE088kUWLFnHCCScwYMAA5s0r/s7aktSevphrhw0bxoABA3jHO95hXpWkKjn++OOLDkFSndvh5c8ppdaIeDtwG/CjiLgbuB14hmyu1QOAd5MVlf5U8tKbgA9ExA1kozT3IpsDdAU7IaX0cERcTnbH9Dsi4sfACLIbOj0AtLD9aMMPkc1hem9EXJO3aQBeBowHriG7W/xo4J68v9+Rzft5YP76P/LCu8+X+zLZDaQ+ERFvIDteK8luyPUeYBHZvJo/jIhDU0qV5oSt5NiIOCD/+UXAG4EJwJ954Y2k2nMf2ec1Pb8J15+B15CNrvwt8I/tvrJ3dCe+Z8mK5fuQzVv6SuDDZCNDL2xrlFJaHBF3kP0uBvBg3vc7yEYyD9q+W+YCp0XE58nmZ90C3Fp6I6123AR8Cfh5RPyI7LP6d2DjDt+9atZh9x7GvYd15b50xTjwwAO54IILmDhx2zTNQ4YM4YYbbuCqq67i5ptvLjA6SepYX8y15lVJqr7LL7+cM888s+gwJNWxTs0pmVJ6PCJayIqi7wLOJxt5uYasMPUd4MqyQuHHyG44dBJZ8fJPwOXAr9l2R/Tu+jDwFNkNqKaR3aDqQ8DryYqqrSWx/ymP/ZN5HO8lu3HWn8hGvd6YN/0TcBXZ5fonAo3AX4ArgK+klCrOpVmyn9UR8Uaym3a9m2zE6mayYupk4FtkxcF7gF9ExL+0zb+6AxeV/Lwpj+m/gYtSSss68XpSSisj4q3AV4GPkH3uC8jmuT2Ngouq3YxvDdmcsjPICtoB/A9wfkrp6bK2pwBfJytun0JWID8SuIxsDt1SnyEbqXoWMCzvd/98fx35Wt72NLJ5YZ8hG5X8XbLfAdWhYauGFR1Cp8yaNavdbRMnTtyuKCBJfU1fzLXmVUmqvqefLv9vnyT1rk7fqCel1Ep2I55vdrL9WuDj+VIuytrOBGa2088RFdZtBqbky7ZOI04hu5HQirL2z3YQS1ubFezk1AT5Mfp8vlTya6C5nW3lfV1IyYjLTrSfSTvHMN/+ENmNoMrNYdvNlNraHtFOH0vp2mdX8aZiKaVTK+yz0/GVvOZxskJ5h1JKzwD/VmHTERXaLgP+tZ1+RnWwj81kI1W/VGFzV26uJkmSJEmSpD6uX96RPCJ2qbDuYLLL7+/KC1ySVKh1jeuKDkGSap65VpLq06677lp0CJLqXKdHqvYx74+I9wGzgOVk87qeCWwAPldkYJLU5vajby86BEmqeeZaSapP559/ftEhSKpz/XKkKrCQbL7Wc8imIzgVuAs4NKX0QIFxSdJWYxaPKToESap55lpJqk9333130SFIqnP9sqiaUro/pfTWlNJeKaVBKaU9UkonpJQWFB2bqi+ldERH85tKfcWYJf5HX5KqzVwrSfXpnnvuKToESXWuXxZVJUmSJEmSJKko/XVOVUnqFxaM7eQA+rHVjUOSalmnc20bc64kSZJ2kiNVJalKzjjjjKJDkKSaZ66VpPpk/pdUNIuqkiRJkiRJktQFFlUlqUquuOKKokOQpJpnrpWk+mT+l1Q0i6qSJEmSJEmS1AUWVSVJkiRJkiSpCyyqSlKVHH744UWHIEk1z1wrSfXJ/C+paAOLDkBS/7TfpUVH0DcMP7Sl3W0LjljQi5FIUn064ogjig5BkvqF/nz+Xumc23NtSUVzpKokVcn06dOLDkGSap65VpLqk/lfUtEsqkpSlTz//PNFhyBJNc9cK0n1yfwvqWgWVSVJkiRJkiSpCyyqSlKV7L333kWHIEk1z1wrSfXJ/C+paBZVJalKzjzzzKJDkKSaZ66VpPpk/pdUNIuqklQlt956a9EhSFLNM9dKUn0y/0sqmkVVSQCsWLGCY445hhUrVhQdSs1YuHBh0SFIUs0z10qqZ/V8Dm/+l1Q0i6q5iBgVESkiLuyP/dcKj1NxZs6cyezZs7n66quLDkWSJElSJ3gOL0nFqdmial6Y6+wyquh4uysiBkbExyPiDxHRGhFPRcRPIuKfu9jPqE4cp5dW630ULSKGRcSFEXFE0bEUIaXEjBkzAJgxYwYppYIjkiRJktQRz+ElqVgDiw6gik4pe34ocCZwOTCnbNtyYC2wC7CpSvE8UaX+Pw98CpgNzABeDLwlX/6vG/39ArimnW3PdSfAfmIYMDn/+e4C4yjEnDlzWLVqFQArV67kvvvu49BDD63Ydu7cuVx66aU8fd8SBo0YzdDDz6Vx1LjeDLff+NjHPlZ0CJJU88y1kupVZ8/ha/X83fwvqWg1W1RNKX2/9HlEDCQrqv6qfFuJdT0dR0QMTSmtTtmfDXu8f+D9wO+Bt6WUtuTrpkZEYzf7W9LB8ekX2o550XH0J5dccglr1qwBYM2aNcyYMaPiCdnkyZOZNm0ara2tpJTY+JcHaf3dLQx90ySGHTult8Pu85566inGjBlTdBiSVNPMtZLqVWfO4Wv5/N38L6loNXv5f1e1N5dnRDRHxMUR8XRErI2IuRFxVETMjIhU1vbuiFgaES+LiJsi4jng7zvo/30RcX9ErIyINRHxeERcGxEjOhl6AhpKCqrZypTWd/EQdFpH857ml9BvN6VC27GKiBERcU1ErMjf650R8doO9nNcRPw6Itblx/9reXG8tE1Hx7whIj4TEfdGxDMRsSEinoyIyyLixSV9HAH8MX86uWS6g6Vl+3p3RNwXEavz34V5EfGuCnG/PSLuiYhn8ykZnoyIH0XE6B0f3eobP348EbHdMmvWrK2XC6WUmDVr1gvaHHrooUybNo21a9duu7QobSFtXMvqu6axfuncAt9V33T99dcXHYIk1TxzraR60N1z+C9+8Ys1e/5u/pdUNIuqO/ZD4DxgLvBx4D7gx0B7xcBdgXvILvP/DHBhex1HxHuBq8lGsH4O+ChwLTAG2LOT8V0GjImIT3Wy/Y40RcTwCsuwHuj7f4C9yY7JJcAhwL0R8aoKbd8GXAX8nOz4/waYBHyiQtv2jvlgss/sEeBrwDlk0xucBtwdEYPzdr/P9wHZZ3tKvny0bQcR8QXgemA18FmyKRfWAj+MiLNK2h0O3ALsBnwJOBu4gmxahld0dHB6y9SpUxk5ciRNTU1b123YsGG7NqXPm5qa2G+//Rg6dCitra0V+0yb1rH6nkurE7AkSZJU57pzDt/c3MyWLduNvdnK83dJ2nk1e/l/T4iIt5EV976TUjqjZP1dwKx2XvZi4IsppQs6sYt3khXp3pRSKp1r9bOdjG8QsD+wHvhSRGxIKV3cmdd24LR8KfcwUKn42RVPAP+aT4VARPwI+DUwDTimrO1BwEEppaV5228DvwU+Akwta9veMV8P7J1SKq0Efjsi/g/4DnAicGNK6a8R8ROyOWkfqjB1xFiyYu2XUkqfLtn0X/nrvhQR1+RTDown+2PF0SmlZSVtP1/pgETEmWTTUjBy5MhKTXrcQQcdxKJFi5g4cSK33XYba9eubbdtc3Mzxx9/PFdeeSWHHXZY+5Pfpy1sevaRKkUsSZIk1bfunMMvXryYBx98sHIjz98laac5UrVjx+eP2xUqU0o/Ixvd2J5pnex/FdAMvD0iouvhcRnwb2QjPi8DpkfEJ0sbRMSn80vZX9bJPn/KthtdlS6ndyO+cl9NJVW5lNICspGjb46IXcva/qStoJq3TcAvgZdUaAsVjnnKtAJExICIGBYRw4G78iZv6GTc7yGbZuHq8hG8ZKNShwL/lLddlT/+a/lUBZWklC5PKR2SUjpkxIjOzviw84YMGcINN9zA9OnTaWysPP1uY2Mj06dP5/rrr2fIkCGMHj2ahoZ2UkY0MHBEn5jdoE857rjjig5BkmqeuVZSvejqOfwBBxxQ0+fv5n9JRbOo2rH9gS3AoxW2LW7nNctTSis72f9UstGbPwGWR8TNEXF6RAzd0QvzuUhPA76SUvodcBbZZeZfjojSka4HA39h25yhO/LnlNIdFZaemHCnUiF6ETAA2K9s/eMV2q7IH19ctr7dYx4RJ0XEPKAV+BuwvKTv3TsTNHAgEMAf8teXLlfmbfbKH78BPAB8C3guIn4WEed0YY7cXjV27NgOT8haWlq2Pj/33HO3u9yoVAxsYuhh51Qlxv6s9PhJkqrDXCup3nT2HL7Wz9/N/5KKZlG1Y22jR9u55rmi9q/DKJNSegT4f8DbyeZW3Y+sMPqHiHj5Dl5+RP54b95XAj4AfBe4KCIuioh/ILvE/arU7nXb3dJRX12dUqK9Ebqbu/Caisc8It4J3JA/PZds5PFb2DbVQGd//4PsPR9D5VG8bwHuAEgprQBeBxwJfJ1sFOsMYElE/NMLei7Y/Pnz2bhxIwARQXNzM22Dpjdu3Mj8+fO3th03bhyTJk2iubl521+8o4EY1MzQN02icdS4Xo+/r5sypX/fUVWS+gNzraR609lz+Fo/fzf/SyqaRdWO/ZHsGL2ywrYxPbGDlNL6lNLPUkrnp5QOISuw7gN8bAcvbZtxfFRJX4nsMv1ryOZlvQd4jrLpC3rAc/njHhW2dTTNwIHtrNtMNmK3p51CdhOwI1NKl6WUbksp3dHOvjoqFD9CVlh9sp1RvHeklJ7e2lFKm1NKd6eUPpNSOpTspma7Ap2ZZ7dXzZkzh9bWVpqamhg5ciTXXnst++67L01NTbS2tjJnzpzt2k+ZMoU777yTk046icH7ttD8mpPY86w7GXasJzSSJElSb+jKObzn75JUPRZVO3Zr/nhe6cr8BlaVCoRdks/JWW5h/lipYFlqNllh9YKIGNa2MqW0BTiDbMqClwOzuzAdQafkN2R6BnhT6Vyw+bytJ3bw0k+UtR8LvBm4M6X0fE/GmNtMVizd+nue779ScbNt/5WO+/fyx6kRMaB8Y0TsWfJzpc/0D2TTD+zoM+118+bNY8CAAYwfP56HH36YE088kUWLFnHCCScwYMAA5s2b94LXjBs3juuuu46XnD+f4e+/rt//hVuSJEnqT7p6Du/5uyRVR1cv1a43PyMrXp6RF8vuIJtn9UzgIbL5SnfG7RGxiuwS/j8Bw4BTyQqB3+vgdaSU/hARk8nuKv+HiLiSbK7QfYB/B14K/B9wakT8LqU0vZMxjY6I97az7Y6U0jP5z98AvgD8PCJ+ku/3g8DvyC5/r2Q/YHZE3ALsDZxNVmz8eCdj66qbgH8F7oqIa4BBZEXf5vKGKaUVEfEocHJEPAb8FViTUro1pfTr/FhPAR6MiB8CT+XvoQV4GzA47+qKiHgpcDvZiNhdgHeTTQNwTZXeZ7cdeOCBXHDBBUycOHHrurYJ8K+66ipuvvnmAqPr/0aP7t+T/0tSf2CulVRvPIfPmP8lFc2iagdSSiki/hX4IjABOJasmPoO4MNUnhagKy4DTiKbC3UPshsxPQB8JKX0y07E94WIWAh8lOxGVc1kxb7bgROAJ4E7ga9FxHMppe92Iqa2OULb29ZWVP0KsBvZJfZHkN1w6jSyImN7RdVjyKYimEJWbJwLfDyl9FAn4uqylNL1+U2/zgOmkd2o6lbgU2y76VWp95DNfzqV7Fg+kbcnpXRRRCwAziE73kOAZWRF5HNL+vgeWWH8/cAI4O9kx+ZdKaU+d3Yza9asdrdNnDhxuxM1dd2ECROKDkGSap65VlK98Rw+Y/6XVLTo2fsX1Y+I+C0wKKV0QNGx9HURMRN4f0qpvZtSCTjkkENS6Y2h+rr9Li06gr5h+KHt33V00uJJnuxJfVRELMjnMi9cf8v/fc11111nrpXUafWc//vz+Xulc27PtSV1VrVyv3Oq7kBE7FJh3duBVwG/6P2IJPUXS5YsKToESap55lpJqk/mf0lF8/L/HftcRLwW+CWwCngNMJHs8vGvFBmYJEmSJEmSpN5nUXXH5gBvJLuZ0m7Ac8DNwGdTSn8uMjBJkiRJkiRJvc+i6g6klH4G/KzoOPqzlNKpZDdvkurK5MmTiw5BkmqeuVaS6pP5X1LRnFNVkqpkwYIFRYcgSTXPXCtJ9cn8L6lojlSV1C1PnFt0BH1F+ydzU6ZMoaXlhXcqlST1nNtuu81cK0md0L/P3194zu25tqSiOVJVkiRJkiRJkrrAoqokSZIkSZIkdYFFVUmqkpNPPrnoECSp5plrJak+mf8lFc2iqiRVyT777FN0CJJU88y1klSfzP+SimZRVZKq5OKLLy46BEmqeeZaSapP5n9JRRtYdACSVMtaFnpH0h15ds4L7+aqntG/7/IrdV495Np6ypXmLkmdVSv5v55yfCnzvfo7R6pKkiRJkiRJUhdYVJWkKlk6cmnRIUhSzTPXSlJ9Mv9LKppFVUmqkode/VDRIUhSzTPXSlJ9Mv9LKppFVUmqksPuPazoECSp5plrJak+mf8lFc2iqiRVybBVw4oOQZJqnrlWkuqT+V9S0SyqSpIkSZIkSVIXWFSVpCpZ17iu6BCkqlqxYgXHHHMMK1asKDoU1TFzbX0zD0n1y/wvvwNUNIuqUgci4u6IWFq2bmZEpIJCUj9y+9G3Fx2CVFUzZ85k9uzZXH311UWHojpmrq1v5iGpfpn/5XeAimZRVXUjIo6IiJQvp7fTJkXEbb0dm2rTmMVjig5BqpqUEjNmzABgxowZpOTfmlQMc239Mg9J9c38X9/8DlBfYFFV9WpKROzSiXZHA35bq1vGLPFXR8VYv3Quz149gZaWFiZMmMDcuXN7fB9z5sxh1apVAKxcuZL77ruvx/chdYa5tnZ0NXeZh6T6Zv7vv3riXNXvAPUFFlVVj+YD+wAf3VHDlNKGlNL66ockST1j5c8ns+ybR7H2wRtYuHAhN954I0cddRSTJ0/u0f1ccsklrFmzBoA1a9ZsHSkgSd3RndxlHpKk/qenzlX9DlBfYFFV9ehGYAHwyYh4cUcNK82p2kHbvSPisoh4MiI2RMRTEXF5ROzZAzFL0g6tXzqX1XdNI21cC/klUFu2bGHt2rVMmzat2yNWx48fT0Rst8yaNWvrZVYpJWbNmvWCNuPHj++x9yapdnUmd5mHJKn/6+65qt8B6qssqqoeJeCTwG7AZ3qiw4gYSTYC9l3AD4CzgO8BJwP/GxG79cR+1L/cc+g9RYegOrP6nktJm1orblu3bh2XXnppt/qdOnUqI0eOpKmpaeu6DRs2bNem9HlTUxP77bcfU6dO7db+pK4w1/Z/ncld5iFJ5cz//U93z1X9DlBfZVFVdSmldCfwC+DDEbFfD3T5dWAQ8NqU0qdSSleklD4JvAnYHziv0osi4syImB8R85cvX94DYUiqZxuXL9n6V/9yW7Zs4ZFHHulWvwcddBCLFi3ihBNOoLm5ucO2zc3NjB8/nocffpiDDjqoW/uTVF86k7vMQ5LU/3X3XNXvAPVVFlVVzz4JDAY+vzOd5KNQjwNuAdZFxPC2BVgKPEp2w6sXSCldnlI6JKV0yIgRI3YmDPVBh885vOgQVGcGjRgNUfmrvaGhgdGjR3e77yFDhnDDDTcwffp0GhsbK7ZpbGxk+vTpXH/99QwZMqTb+5K6wlzb/3U2d5mHJJUy//c/O3Ou6neA+iKLqqpbKaUHgOuA90TEwTvR1Riyf0unAcsrLGOAvXYuWknasaGHn0sMbKq4rampiXPOOWen9zF27NgOT2RbWlp2eh+S6ktXc5d5SJL6p544V/U7QH2JRVXVuwuATcBXdqKPyB+/D7ylneV9O9G/JHVK46hxDH3TJGJQ89ZRAA0NDTQ3NzNp0iTGjRu30/uYP38+GzduBCAiaG5uJiJLgxs3bmT+/Pk7vQ9J9aWrucs8JEn9U0+cq/odoL7EoqrqWkrpj8BlwDERcWQ3u3mU7OZXg1NKd7Sz/G+PBa1+Y/HoxUWHoDo07Ngp7HnWnTS/5iRaWlo46aSTuPPOO5kyZUqP9D9nzhxaW1tpampi5MiRXHvttey77740NTXR2trKnDlzemQ/UmeZa2tDV3KXeUgSmP/7q509V/U7QH3JwKIDkPqAL8D/Z+/u46yu6/z/P17DxYyDBhZ4gTmCF1ysZeWY0lZiGWqtSu0WP6n8ZphupWkqXe3XlnBbsxLRdiuj3KDWRNPdUsnVwkXH3cCA1NRdvEqyrxcoNQIzMALz/v1xzuBwODMwF2c+c8553G+3czvO5/P+vD+vo/A8H1/zueDj9PJs1ZTS+oj4BfDXETElpbS88/rI/dpsdErJJ1FVmTUTPdBTNmrHTaF23BRWXtj/c69YsYIhQ4Ywffp0rrvuOkaMGMG0adOYNWsWt9xyCytWrOj/nUrdMGsrx55mlzkkCcz/ctaXY1W/AzSYeKaqql5K6SXgm8Bb+zDNp4BngXsj4gcRcV5EfCYi5gNPAuf1Q6kqMyfdVfT5ZFJZmzx5MgsWLNjpAQAdDw5YsGABkyZNyrhCVRuztvqYQ5LA/K9WfgdoMPFMVSnnKuDTwIG92Til9ExENAJfAKYDHwW2AHCyG4cAACAASURBVM8AtwE39VOdKiN1bcVvwi6VsyVLlnS5btasWcyaNWsAq5HM2mpkDkkC879a+R2gwcSmqqpGSmkZrz5UqnBdKzC2yPITiiw7CziryPKXgM/lX5IkSZIkSapQXv4vSSXSPLI56xIkqeKZtZJUncx/SVmzqSpJJXLv8fdmXYIkVTyzVpKqk/kvKWs2VSWpRI568KisS5CkimfWSlJ1Mv8lZc2mqiSVyLg/jMu6BEmqeGatJFUn819S1myqSpIkSZIkSVIPDM26AEmqZKuOXpV1CYPf0VkXIKncVUXWmpWStIuKyX8zXipLnqkqSSVy8cUXZ12CJFU8s1aSqpP5LylrNlUlqUSeffbZrEuQpIpn1kpSdTL/JWXNpqoklcjixYuzLkGSKp5ZK0nVyfyXlDWbqpIkSZIkSZLUAzZVJUmSJEmSJKkHbKpKUomceuqpWZcgSRXPrJWk6mT+S8ra0KwLkKRKdW6cC6uzriJ7LzWt6tV2ay/s50IkVaQssra3udbBfJOkvjnkGhj9To+1B6tVR/fte1IqF56pKkklcvptp2ddgiRVPLNWkqqT+S8pazZVJUmSJEmSJKkHbKpKkiRJkiRJUg/YVJWkEnl+/+ezLkGSKp5ZK0nVyfyXlDWbqpJUIvcfe3/WJUhSxTNrJak6mf+SsmZTVZJK5Nj7j826BAHr16/nlFNOYf369VmXIqkEKj1rzTBJKq7S87/cbGvexhPnP8G25m1ZlyINGJuqklQiB7xwQNYlCFi4cCF33nknixYtyroUSSVQ6VlrhklScZWe/+Vm/W3r2fDrDay/3V8CqnrYVNWgFBH1EfHZiGiKiD9FxNaIeCEifhERZ0XE0KxrlDT4pZSYP38+APPnzyellHFFkrTnzDBJUjlIKbHuJ+sAWHf9Or+vVDVsqmrQiYjDgd8C84EtwNeAc4GrgGHAD4HLMytQUkm1Pb2clxbNpLGxkZkzZ7J8+fJez9XU1MTLL78MQHNzM/fdd19/lSlJPdbTfDPDJEnlYNNvN7Ht5dxl/1tf3Mq0adP6dAwvlQubqhpUImIv4HbgUOBvUkrTUkrfTCn9MKX09ZTSNOBY4Oks65T2xK2n3Zp1CWWn+Y45rPv2ibQ+cCOrV6/mpptu4sQTT2TOnDm9mu/qq6+mpaUFgJaWlh1nfEmqHOWStb3JNzNMkrpWLvlfDf7w1T+Q2vJnpyZYunRpn47hpXJhU1WDzSeAicC8lNK/FRuQUvpNSuk7nZdFxPsj4r8iYlP+9V8RMb1w24h4OiKWRcSkiFgSERsj4uWIuDkidrkpT0QcFRF3RURLRKyPiEURMToiUkQsLBg7NCK+EBGPRsSW/Ph/j4g39unfiMrWIWsPybqEstL29HI23n0laWsr5C8Zam9vp7W1lSuvvHK3v+2ePn06EbHTa8mSJTsuP0opsWTJkl3GTJ++S1RIKiPlkLV7km9mmCT1TDnkfyV68qInWd24eqdX29q2Xca1trZy2WWX+X2limZTVYPNB/PvC/Z0g4j4NPDvwGuBrwL/kP/nn0XEuUU2OQhYBvwB+BzwE+CvgR8VzHsE0AS8DfgWMAcYA9zRRSnXA1cAf8zPey3wLuDXEfGWPf08qhxveuhNWZdQVjbecw1p2+ai67Zs2cI111zT7faXX345DQ0N1NXV7Vj2yiuv7DSm8891dXUccsghXH65dxORylk5ZO2e5JsZJkk9Uw75X4nGnj+W4QcMJ2pjj8bX1NT4faWKZVNVg80bgI0ppaf2ZHBE7At8A3gSOC6ldEVK6evAccBTwLyIGFWw2eHABSmlT6WUrk0pfQr4LjAtIiZ1GvePwGuAU1JKX0op/XNK6X35eQvrmAbMAG4CTk4p/VNK6f8CxwN15Jqyxeo/NyJWRsTKF198cU8+slSxtr742I4zuAq1t7fz+OOPd7v9kUceyaOPPsrpp59OfX19t2Pr6+uZPn06jzzyCEceeWSva5akPbEn+WaGSZLKwV6H7cXkmycz8viR1NTtvqU0cuRIv69UsWyqarB5DbChB+OnASOAb6WUdmyX/+d/AvYG3lOwzbMppZsKlt2dfz8cICKGAO8D7k8p/VfB2HlF6vhA/v0fU6dHHaaUHiJ3j9h3RMSYwo1SSgtSSseklI4ZM2aX1VJVGTZmAkTxr6WamhomTJiw2zlGjBjBjTfeyLx586itrS06pra2lnnz5rF48WJGjBjRp5olaU/sab6ZYZKkcjBkryEcesWhHHTRQdDNCasRwSmnnOL3lSqWTVUNNhuAfXowfnz+/ZEi6x7Ovx9asLzYWbDr8++vy7+PIdesXVNkbLFl44F24H+6qWN8kXWqYCveuiLrEsrKPlMvJIbWFV1XV1fHBRdcsMdzHX300d02JBobG3tVo6TBpxyytqf5ZoZJ0u6VQ/5XuvpJ9d3eBqC2trZHx/BSubGpqsHmYeA1EVHYCO3Knt3IZWfb92C+7uYtdv1eb+pQhWse1Zx1CWWldtwU9nn3bGJY/Y4zumpqaqivr2f27NlMmTJlj+dauXIlW7duBXK/Ia+vryci99d069atrFy5sv8/gKRMlEPW9jTfzDBJ2r1yyP9K1/poa/H/O85797vf3aNjeKnc2FTVYHNL/v0Tezj+yfx7sRu0/EX+fY/uz1pgHdACTCyyblKRZU+S+/s0uZs6ft+LOlTGTv7lyVmXUHZGvXcu+523lPo3z6CxsZEZM2awdOlS5s6d26N5mpqa2Lx5M3V1dTQ0NHD99ddz8MEHU1dXx+bNm2lqairRJ5A00Mola3uSb2aYJO1eueR/Jdv4242ktkTUBkNfN5QRbx5BDA1qanKtppEjR2ZcoVRaNlU12PyA3OX1syNierEBEdEYEZ/O//hLcs3Pz0TEPp3G7AN8BtiUH9MjKaXtwB3AsRHx9oLVlxTZ5Gf59y9Fx6kkuTreAJwO3JdS8klU0h6oHTeF0R+7gZUrV3LDDTf06rfbK1asYMiQITse5PL+979/xwNghgwZwooVXi4maeDtab6ZYZKkctD6cCvUwKipozjy50cy8bqJHLXsKD74wQ/6faWqYFNVg0pKqRU4ldxZnT+LiDsjYnZEfDwiPh8RdwC/ARry45uBz5N7wNSK/JjPAyvyyy5JKb3cy3IuBTYC/xERX4uI8yLiF7x6j9bOD6T6JXATcAZwR0ScHxFfBZqALYA3kpEG0OTJk1mwYMFOD3LpeADMggULmDSp2AnnkjQ4mGGSpHJQN76OhksbGP+18QzZawiQe4iV31eqFkOzLkAqlFJ6IiLeAvwt8DfA/wX2Bv4ErAQ+Bvyk0/jvRMRzwOeAOfnFDwIfSCn9jF5KKa2JiOOBK4ELyTVHbwfOI3dLgc0Fm3wEWA2cBcwjdwbtPcCXU0q/620dKl9PNzyddQlVa8mSJV2umzVrFrNmzRrAaiSVUiVmrRkmSbtXiflfbg7/1uFdrvP7StXApqoGpfwZq/Pzrz0Z/+/Av+/BuHFdLF9GkYdNpZQeAN7TeVlEdDxy9w8FY7cBX8+/JB5600NZlyBJFc+slaTqZP5LypqX/0vdiIi9Cn4OcrcbgF7cq1XV5fh7j8+6BEmqeGatJFUn819S1jxTVereAxFxN/A7YARwGvBO4MaU0qpMK9OgN+rlUVmXIEkVz6yVpOpk/kvKmk1VqXs/J9dIPZPc35ffA1/GS/wlSZIkSZKqlk1VqRsppc/z6uX+Uo9sqd2SdQmSVPHMWkmqTua/pKx5T1VJKpG7Tror6xIkqeKZtZJUncx/SVnzTFVJKpF5G+ZxwgknZF1G9o7OugBJlSyTrDXXJClTay+EZcs81paULc9UlaQSueeee7IuQZIqnlkrSdXJ/JeUNZuqkiRJkiRJktQDNlUlSZIkSZIkqQdsqkpSiZxzzjlZlyBJFc+slaTqZP5LyppNVUmSJEmSJEnqAZuqklQi3//+97MuQZIqnlkrSdXJ/JeUtaFZFyBJg8Uh1/TvfLOAxtWN/TvpAFt19KqsS5AkoOuMHqxZa35KkiRVNs9UlSRJkiRJkqQesKkqSSWyZsKarEuQpIpn1kpSdZo6dWrWJUiqcjZVJalE1kz0f/QlqdTMWkmqTieccELWJUiqcjZVJalETrrrpKxLkKSKZ9ZKUnWaN29e1iVIqnI2VSWpROra6rIuQZIqnlkrSdVp06ZNWZcgqcrZVJUkSZIkSZKkHrCpKkkl0jyyud/n3Na8jSfOf4Jtzdv6fW5JKkelyNpizF9JGlwOPPDArEuQVOVsqkpSidx7/L39Puf629az4dcbWH/7+n6fW5LKUSmythjzV5IGl3PPPTfrEiRVOZuq2iEi9o2ILRGRIuKjWdcjlbujHjyqX+dLKbHuJ+sAWHf9OlJK/Tq/JJWj/s7aYsxfSRp8brvttqxLkFTlbKqqs48Aw4HfA2dnXItU9sb9YVy/zrfpt5vYvnE7ANs3bqflgZYd61p+18Lvv/R7/ucj/8Pvv/R7Wn7X0tU0klRR+jtriymWv+auJGVr9erVWZcgqcoNzboADSpnA/8J/By4OiIOSyk9mXFNkvLWXb+O9i3tALRvaeeF619g77fszbPXPssLP36B1JYgweY1m2m+t5n9z9yfsZ8cm3HVklT+CvN37VfX8srzr5i7kiRJVcwzVQVARBwNvBlYBFwPbAU+XmRcioiFEfHuiPh1RLRGxB8j4gv59ftGxHURsS6/7vaIGFswx9iImBcRD0TEn/O3HHg0Ir4QEUOK7K+r18KCsZ+IiNURsTkiXo6IuyLiHd18hrdFxD0R0RIRL0XEDyJi74KxkyLiOxHxSERszH+mVRFxTpF5XxsR8yPiyfxnWp8f+7k9/e8gdXjyoidZ3bh6p9eG+zZAxxWnCTY0bWB142qe//7zpC1pp3VpS2Ldj9d55pQk9dCe5G/b023mriRJUpWzqaoOZwMtwC0ppfXAEuBjEVHsz8hbgJ8Cy4BLgMeBKyLiQmApsC/wFeBa4BTgRwXbHwX8NXA3cCnwReAZ4ArgOwVjzyzyWpxf90LHoIj4OvB9cs3gvwPmAX8B/GdEvK/IZ3gzcDvwG+Bi4Jf5fwdXFYw7ATg+P/ZzwJfz+1gQEV8qGPtT4HzgDuAzwGXA/fk5VIXunHZnr7cde/5Yhh8wnKiNHcvStp3v4Vf4c6H2tvYd9wCUpErVl6wtZk/ytyvmriQNnIsvvjjrEiRVOS//FxFRB8wEbk4pdZxesQj4AHAyuSZhZ28E3pZSWpHf/jpgLTAf+OeU0gWd5ga4KCImppTW5BffAxyadn7Kw9UR8WPgExHxlZTScwAppX8tqLUR+B6wHJiTXzaRXMPzv4B3p5ReyS//AfAo8J38rQy2d5rqKOAvU0rL8z9/LyJeA3w8Ii5OKW3KL/9xSunaghrmk2sIfzEirkwpbY2IkcC7ge+mlM7f9d9ycRFxLnAuQENDw55upjIxqnkULxzwwu4HFrHXYXsx+ebJrJ27lg1NG3ZcdtojCdqeaevV/iWpXPQla4vpU/6au5I0YJ599lkmTpyYdRmSqphnqgpyZ43uS66R2mEJsA6YVWT8rzsaqgD5Jub9QADfKhjblH8/otP4zR0N1YgYnr9sfjRwJ7k/k8cUKzIiDgZuA14EpqeUtuRXTc/v+xsdDdX8fp4FFgKHkDu7tvAzLC9Ydje5XzSM6zTHjmv4IqIuIl4HvBa4C3gNMCm/ejPQBhwXETu2352U0oKU0jEppWPGjBmzp5upTBz3m+P6tP2QvYZw6BWHctBFBxHDo/igyL+6WFfbUNunGiRpsOtr1hazR/lbjLkrSQNm8eLFux8kSSXkmaqC3GXvLwJ/jIjDOy3/JfChiBidUnqp0/Kniszx5/z777tY/rqOBRExlNwl//8HOJxdW0L7Fk4eEfuQuwR/BDAtpdT52rrx+fdHitT1cP79UGDlbj7D+iK17k3uVgYzgIOLbLMv5BrLEfFZ4Brg9xHxKLkm7c9SSkuLbCftsfpJ9cSwIL2y6+WnURvQTtF1NbU17Ddzv4EoUZIqUnf5W4y5K0mSVD08U7XKRcR44F3AGOAxcvdH7Xh9BBgOfLRgs+10oeAS+5121emfrwL+AVhN7mFY7wOmAV/Ir9/pz2X+4VU3AkcCM1JKhc3THpxCskOXn6Fgvp+Qu+fqL8j9+3hvvtb5hbXmbxMwDjiH3Gf7IPCriPBXqOqT1kdbX72fX0DUxat/ShPs89Z9qKmreXVZQE1dDfuduR8j3jgii5IlqSJ0m7/5ZR3v5q4kSVJ18UxVfZzc/xKcAzQXWf9VcmeyXt2P+zwTuDeldEbnhQVnyXZ2Dblm5qdTSsWeRvFk/v3ITv/c4S/y78XOTO1WRIwCTiV3X9VPFqx7T7Ft8veC/QHwg3wz+MfAzIiYl1L6TU9rUHl78KgH+2Wejb/dSGpLRG0w7LXDeP0lr+ePV/6RrX/eSmpLDNl7CEdcewTrfrKOtmfaqD24lv0+7P/YS6oO/ZW1xewuf4ftP4xh+w4zdyUpA6eeemrWJUiqcjZVq1hE1ABnAb9LKf2gizFHAl+JiLf2Y1NwOwVnl0bECOCiIvv/LHAecE1K6btdzHcr8HXgcxFxR0ppa37bA8k1jdcCv+1lnRSp9UDgEwXL6gFSSq0dy1JK2yPiIXIPAXttL/avMrf2kLX9Mk/rw61QA6OmjqLh7xsYstcQ9pmyD2vnrqV5aTMtD7cw4o0jGP+18bufTJIqTH9lbTG7y98YEkz610m7n0iS1O8aGxuzLkFSlfPy/+p2Ern7hN7SzZiOdWf3435vBt4ZETdGxDkRcSnwO2BD50ER8QZgHvA8sDoiPlrwehtASmkN8E3gHcC9EfHZiPgyuYdn7U3uDNfuLvcvKqW0kdwDqT4aEd+LiE9ExD8AD7LrvWMnAM9FxMKImB0RZ0fElcCX82ObUNU5/bbT+2WeuvF1NFzawPivjWfIXkOAVx+i0nBpA3Xj6vplP5JUjvora4sxfyVp8Jo7d27WJUiqcp6pWt06GqX/1tWAlNLDEfEYcEZE7HImaS9dDGwk9/Cn6cAzwALgN8CvOo0bTa7xfwCwqMg8i4Bf5+v8QkQ8AXwauAJ4BVgBfDil1JeG5kfz850GfIzcvWb/L7AV+GGncc8A/0Lu/rTvB2qB/wd8H/h65zNYpZ46/Ftd3RkDRk8fzejpowewGkmqHuavJEmSumJTtYqllD60h+Mmdvqx6EOhUkpnkbuVQOHyZYXb5BuMn8u/CkWncbtsu5s6v0+uibm7cV19hoXAwoJlL1FwqX8nCzuNW0+R2xdIkiRJkiSp8nj5vySVyPP7P591CZJU8cxaSapOEyZMyLoESVXOpqoklcj9x96fdQmSVPHMWkmqTjNnzsy6BElVzqaqJJXIsfcfm3UJklTxzFpJqk433HBD1iVIqnI2VSWpRA544YCsS5CkimfWSlJ1euyxx7IuQVKVs6kqSZIkSZIkST0wNOsCJGmwWHth/843dy6sOnpV/04qSVWqq4w2ayVJkpQFz1SVpBKZM2dO1iVIUsUzayWpOpn/krJmU1WSSmTVKs+ckqRSM2slqTqZ/5KyZlNVkkrk9ttvz7oESap4Zq0kVSfzX1LWbKpKkiRJkiRJUg/YVJUkSZIkSZKkHhiadQGSVM4OuabrdUe+dQWNqxsHrhj1mU8Ql8pD5+w1aweWOSkpax3fAeZ/75jjUv/xTFVJKpHmUc1ZlyBJFc+slaTqZP5LyppNVUkqkZN/eXLWJUhSxTNrJak6mf+SsmZTVZIkSZIkSZJ6wKaqJEmSJEmSJPWATVVJKpGnG57OugRJqnhmrSRVJ/NfUtZsqkpSiTz0poeyLkGSKp5ZK0nVyfyXlDWbqpJUIsffe3zWJZSNbc3beOL8J9jWvC3rUiSVGbM2xxyVVG2qOf/NfGlwsKkqSSUy6uVRWZdQNtbftp4Nv97A+tvXZ12KpDJj1uaYo5KqTTXnv5kvDQ42VZWZiDghIlLBa1NErIqICyNiSNY1Siq9lBLrfrIOgHXXryOllHFFklRezFFJqh5mvjR4DM26AAm4AfgFEMBY4CzgauBI4NzsypL6ZkvtlqxLKAubfruJ7Ru3A7B943ZaHmhh77fs3e/7afldC+t+so4tf9hCXUMd+314P0a8cUS/70fSwDJrS5OjZqakwa5a8783md8502e+eSYXXnghU6ZMGYhypYpmU1WDweqU0r92/BAR3wX+B/hERHw5pfRCXybPn/Fam1Jq7WOdUo/cddJdWZdQFtZdv472Le0AtG9p54XrX+j3puqz1z7LCz9+gdSWIMHmNZtpvreZ/c/cn7GfHNuv+5I0sMza/s/R7jKTBf1VtST1TbXmf08zvzDTb3rsJm699VZmz57N3LlzB6psqSLZVNWgk1LaEBG/Bv4GODTfFL0EOBE4BNgLeApYBFyZUtresW1EnAX8EJgGvI3cWa8NwDnAwog4CTgbeCtwINAG3A/8Y0rpns51RMQyYBzwl8A84BRgOHAf8JmU0mOdxu4DfCG/38OAfYBngJuBy2zoVqeJayayZuKarMsYVJ686ElevvflnZbF0ICOq5YSbGjawOrG1TuNGTl1JIdddViv9tnyu5bcgeSWTpdGJUhbEut+vI6Rbx/p2VdSGau2rC11ju4uM5fPWu7ZTZIGhWrI/75mfrFMb29vp7W1lSuvvJL3vve9ZrrUB95TVYNORARweP7Hl4CjgL8G7gYuBb5IrmF5BfCdLqa5EjgD+D5wIdDxbXsW8FrgR8BngPnAZGBpRLyzyDwjgHuB7cDfAd8GTgB+XnDP14OATwArgX8ALgZWA58H/n0PP7oqzMTHJmZdwqAz9vyxDD9gOFEbO5albTvfB6rzz1EbDD9wOGPP6/3ZpOt+si73m/ki2tvad9yTSlJ5qrasLXWO7i4zr7nmml5ULUn9rxryv6+Z312mb9myxUyX+sgzVTUY1EfEaHL3VD2QXLPzTcDylNLjEfFH4NC08x24r46IH5O7RcBXUkrPFcy5F/CWImeInpNSaum8ICKuBR4BvgQ0FYwfDXwzpfSNTuNfBL4BvAe4M7/4KeDglNLWTtt+OyL+Abg0Io5NKd1f+MEj4lzy941taGjY9d+MVGH2OmwvJt88mbVz17KhacOOS5eKqamrYeTxI2n4+waG7NX759Zt+cOWV3+bXyhB2zNtvZ5bkgZaqXN0d5n5+OOP96JqSVJv9DXzu8v09vZ2M13qI89U1WAwF3gRWAc8CMwCbgXeD5BS2tzRUI2I4RHx2nwT9k5yf4aPKTLnd4tdct+5oRoRe0fE68idhboCOK7IPO3AtwqW3Z1/P6LTvK90NFQjYmhE7Juv8Vf5IcXmJqW0IKV0TErpmDFjxhQbIlWcIXsN4dArDuWgiw4ihkfRMTE8OOiigxj/tfF9aqgC1DXU5X5lU3RHUNtQ26f5JWmglTJHd5eZEyZM6EXFkqTe6kvmd5fpNTU1ZrrURzZVNRgsIHcv0veQuw/qmJTS9I4HVOWblJdGxGPAFmA9uSbsj/Pb71tkzseKLCMiDouIxRHxZ2AjudsLvAi8r4t5nk0pFT5Wcn3+/XUFc386Ih4id5/WP+XnXdZNjapw97zznt0PqmL1k+qJYV0cGA4L6ifX98t+9vvwfjtdMtVZTW0N+83cr1/2Iykb1Zy1pcjR3WXmBRdc0OM5JakUqi3/e5P53WV6XV2dmS71kU1VDQaPp5R+lVJamlJanlL6U8H6q8jdp3Q18HFyDdBp5B4MBcX/HO9ylmpE7E3u/qinANcAHwROzs91N8V/h7e9yLIdU3aa+2Jy91t9Dvhb4K/y857VTY1SVWt9tPXVe0AFRF3s+FuVtiVaH+2f57uNeOMI9j9zf2rqal79Wxu5S6T2O3M/H1IlqWyVIkd3l5k+0ESSstGbzC+W6TU1NdTX1zN79mwzXeojGz0qB2cC96aUzkgpLUop3ZFS+hWwoYfznAiMBS5KKX0lpXRLSumu/Fx97aqcCTwNvDel9IOU0i/y877Qx3lVxqY2Tc26hEFt4283ktpS7ob6Bwxn/FfHM3z/3I34U1ti02839du+xn5yLEdcewT7TtuX+sn17DttX4649gjGfrL3D8CSNDhUc9aWKkfNTEnloNryv7eZX5jpM2bMYOnSpcydO3eAP4FUeXxQlcrBdgrOIo2IEcBFvZiHInOdRBf3PO3h3Imdz14dCnyxj/NKFav14VaogVFTR+24of4+U/Zh7dy1NC9tpuXhlt1P0gMj3jiC8V8b369zSlKWSpmjZqYkDS59yfzOmX7D0TcMVMlSxbOpqnJwM/C3EXEjuQc/7U/uYVbru91qV/cBzwPzImIc8EfgzeTOMv0d8MY+1vg14I6I+DfgNcCHga19mFOqaHXj6zjgEwcwevroHcs6bsT/0s9fonlpc4bVSdLgZ45KUvUw86XBx6aqysHF5B4qNQOYDjxD7uFWvyHXZN0jKaXmiDgZ+AbwGXJ//leRu0fr2fStqfpNcmepnk3ufq3PAzcCPwQe7cO8KmNrJqzJuoRB7fBvHd7lutHTR+90wChJXanmrDVHJVWzast/M18afGyqKjMppWUUfzhU4bhW4HP5V6EoGLsQWNjNXA+Re1BVoSZefahUx9gTupjj6SL73U7uTNWv7a5GVY81E6vrQE+SsmDWSlJ1Mv8lZc0HVUlSiZx010lZlyBJFc+slaTqZP5LyppNVUkqkbq2uqxLkKSKZ9ZKUnUy/yVlzaaqJEmSJEmSJPWATVVJKpHmkT6BU5JKzayVpOpk/kvKmk1VSSqRe4+/N+sSJKnimbWSVJ3Mf0lZG5p1AZJUztZe2PW62277CqeddtrAFSNJVaJz9pq1klRdOr4DzH9JWfNMVUkqkdWrV2ddgiRVPLNWkqqT+S8pazZVJUmSJEmSJKkHbKpKkiRJkiRJUg/YVJWkErn44OJcRAAAIABJREFU4ouzLkGSKp5ZK0nVyfyXlDWbqpJUIs8++2zWJUhSxTNrJak6mf+SsmZTVZJKZPHixVmXIEkVz6yVpOpk/kvK2tCsC5AkSZL6onF1Y8nmfqlp1U4/r72wZLuSJElSGfFMVUmSJEmSJEnqAZuqklQip556atYlSFLFe/CoB7MuQZKUAY+1JWXNpqoklUhjY+kuR5Uk5aw9ZG3WJUiSMuCxtqSs2VSVpBKZO3du1iVIUsU7/bbTsy5BkpQBj7UlZc2mqiRJkiRJkiT1gE1VSZIkCdjWvI0nzn+Cbc3bsi5FkiRJg5xNVZWdiFgYESnrOqTdmTBhQtYlSFLFe37/5/ttrvW3rWfDrzew/vb1/TanJKk0PNaWlDWbqiq5iHhNRHw5IlZHxMaIaI2IRyPimxGxf9b1SaUyc+bMrEuQpIp3/7H398s8KSXW/WQdAOuuX0dK/v5WkgYzj7UlZc2mqkoqIiYADwJzgaeALwKfBZYDFwKPRMTbejjtOcBe/VmnVAo33HBD1iVIUsU79v5j+2WeTb/dxPaN2wHYvnE7LQ+07LS+7enlvLRoJo2NjcycOZPly5f3y34lSb3jsbakrNlUVclERD1wG3AQcFpK6YMppW+nlBaklGYBfwkMBX6+uzNWI2dvgJTS1pTSllLXL/XVY489lnUJklTxDnjhgH6ZZ93162jf0g5A+5Z2Xrj+hR3rmu+Yw7pvn0jrAzeyevVqbrrpJk488UTmzJnTL/uWJPWcx9qSsmZTVaV0NjABmJ9SWlK4MqW0Evg7YAzwuY7lEXFCRKSIOCsizouIR4EtwOz8+l3uqdqxLCJGRsR3I2JdRGyJiP+KiOMK9x0RIyLiaxHxZES0RcTzEfGjiDikyNiIiHMiYkVEbMq/fhcRlxWMGx0R346IZyLilfz7tyPidb36tydJkkriyYueZHXj6p1eG+7bAB1HFwk2NG1gdeNq/vDZYMOdl5G2tkL+lgDt7e20trZy5ZVXesaqJElSlbKpqlL6YP79+92MWQhsBf6myLrPkrtdwGLgM8CKPdjnncDrgcuArwFvAH4REft0DIiIoflxXwRWAxcBNwAzgBUR8fqCOX8MLCD3v1r/SK4BfHenz0dEjAT+G/hUfu7PAv+R//m+zvuXJEnZGnv+WIYfMJyojR3L0rad76Fa+HMxW7Zs4Zprrun3+iRJkjT4Dc26AFW0NwAbU0pPdDUgpdQaEWuAN0TE3imlTZ1WNwCTUkrrerDP1SmlT3f8kD/L9Sbgw8D38os/Drwd+GZK6fOdxv4KuJ1cM/bM/LIZwEeAfwU+llJq7zS+8y8lPg8cAZyXUvpOpzEPAP+cX//lwmIj4lzgXICGhoYefEyVAy8LlaTSu/W0W3u8zV6H7cXkmyezdu5aNjRt2HHZf1ERO85QLdTe3s7jjz/e4/1LkvrOY21JWfNMVZXSa4CX92Bcx5iRBct/1MOGKsD8gp/vzr8f0WnZB4B2cs3THfK3KHgAmN6pYfqR/Pvszg3V/PjOP38AeJHcGa2dfQ94Kb9+F/n7yx6TUjpmzJgxXX4oladVq1ZlXYIkVbxD1u5y5549MmSvIRx6xaEcdNFBxPAoOiaGB8Ne3whR/JC5pqaGCRMm9Gr/kqS+8VhbUtZsqqqUNpBrrO5Ox5jCBmxv7jz+VOcfUkrr8//Y+b6m44FnU0p/LrL9I8A+wOj8z0cAz6WUXigytrPxwJqU0raC/W8D1gCH7ln5qiS333571iVIUsV700Nv6tP29ZPqiWFdNFWHBXu//VPE0Lqi6+vq6rjgggv6tH9JUu94rC0pazZVVUoPA6+JiMO7GhAR9cBE4OmCS/8BWnu6w5TS9q521cU/707w6mMrJElShWl9tPXV+6cGRF3sOFJI2xJsa2Ofd88mhtXvOGO1pqaG+vp6Zs+ezZQpUzKqXJIkSVmyqapS+rf8+ye6GfN/gOGdxg6EJ4GxETGqyLq/IHeG7Uv5n9fkx+6/mzmfAibmH4K1Q/7nCRScQStJkgaHjb/dSGpLRG0w/IDhjP/qeIbvn3uIVWpLtD3VxKj3zmW/85ZS/+YZNDY2MmPGDJYuXcrcuXOzLl+SJEkZsamqUvoB8ARwUUScUrgyIo4md1/TF4FvDmBdPyP3Z/+LBfW8F3gLcGun+6Ven3//RsGDqYiIzme8/gwYw64N5HPyy/+9f0pXOTnjjDOyLkGSKt6Kt67o0/atD7dCDYyaOorJP53MqHeNYvLNkxl5/EiogVfW5uavHTeF0R+7gZUrV3LDDTd4hqokZcxjbUlZG7r7IVLvpJRaIuJ04D+AJRFxC7AM2AYcC5wJbALen1J6fgBLWwh8DPhCRIwD7gUOBz4NvAD8XcfAlNJPI+JGcmfUHhERtwJ/Jnf26cnAG/JDvwF8CPh2vln8W3IN2rPJne36jVJ/KA0+Y8eOzboESap4zaOa+7R93fg6DvjEAYyePnrHso6HWL3085dY99NJfS1RklQCHmtLyppnqqqkUkr/AxwFzCXXiPwGcA3wDuCfgCNTSv89wDVtJdcQvYJcc/dq4KPAT4HjUkrPFGzyYeB8oA74e+BKYFp+fMecLwNvB74HvA/4Vv79WuAdKaWNJfxIGqSuuuqqrEuQpIp38i9P7tP2h3/r8J0aqp2Nnj6a/f52SZ/mlySVhsfakrLmmaoquXzD8bL8a0/GL6Obh0mllM4Cztrdsk7rdpkrpdQCfCn/2l097cC386/uxr1I7mzXT+9uTkmSJEmSJJUvz1SVJEmSJEmSpB6wqSpJJXL00UdnXYIkVbynG57OugRJUgY81paUNZuqklQip512WtYlSFLFe+hND2VdgiQpAx5rS8qaTVVJKpEFCxZkXYIkVbzj7z0+6xIkSRnwWFtS1myqSlKJPPfcc1mXIEkVb9TLo7IuQZKUAY+1JWVtaNYFSJIkSX2x6uhVpZvcW/ZJkiSpCM9UlaQS2XvvvbMuQZIqnlkrSdXJ/JeUNZuqklQil1xySdYlSFLFM2slqTqZ/5KyZlNVkkpk2bJlWZcgSRXPrJWk6mT+S8qaTVVJKpF77rkn6xIkqeKZtZJUncx/SVmzqSpJkiRJkiRJPWBTVZIkSZIkSZJ6YGjWBUhSpbrnnffQuLox6zK69VLTqqxL2K21F2ZdgaTBLOusLYcc7cxMlVQpss7/gTTYv2v8blG18kxVSZIkSZIkSeoBm6qSVCJTm6ZmXYIkVTyzVpKqk/kvKWs2VSVJkiRJkiSpB2yqSpIkSZIkSVIP2FSVpBJZM2FN1iVIUsUzayWpOpn/krJmU1WSSmTNRA/0JKnUzFpJqk7mv6Ss2VSVpBI56a6Tsi6hoq1fv55TTjmF9evXZ12KpAyZtf3HXJVUTsz/wWF7i98dql42VSWpROra6rIuoaItXLiQO++8k0WLFmVdiqQMmbX9x1yVVE7M/8Gh5X6/O1S9bKqqrEXEvhGxJSJSRHw063okDYyUEvPnzwdg/vz5pJQyrkiSypu5KknqqZQSG5f53aHqZVNV5e4jwHDg98DZGdci7aR5ZHPWJZS1tqeX89KimTQ2NjJz5kyWL1++Y11TUxMvv/wyAM3Nzdx3331ZlSkpY2btnukuU8FclVR+zP/sbfrva9m+8XkAnnvuOb73ve9lXJE0sGyqqtydDfwncDUwNSIOy7ieLkXEPlnXoIF17/H3Zl1C2Wq+Yw7rvn0irQ/cyOrVq7nppps48cQTmTNnDgBXX301LS0tALS0tOw4u0pS9TFrd293mQrmqqTyY/5nq/mOOfz55s9A+3YAtm/fzvnnn7/Td4tU6WyqqmxFxNHAm4FFwPXAVuDjRcaliFgYEe+OiF9HRGtE/DEivpBfv29EXBcR6/Lrbo+IsQVzLIyIotcydMzf6edx+WVfiYj/LyJWRcRm4J/67cOrLBz14FFZl1CW2p5ezsa7ryRtbYX8JUTt7e20trZy2WWXEREsWbJkx+VFKSWWLFlCROz0mj59epYfQ9IAMWu7tyeZaq5KKkfm/8B68QfT+cNnY8drw52XQdq+05jt27fv9N3id4cqnU1VlbOzgRbglpTSemAJ8LGIKPbn+i3AT4FlwCXA48AVEXEhsBTYF/gKcC1wCvCjfqjv/cB3gf8ALgDu6Ic5VUbG/WFc1iWUpY33XEPatrnbMa+88kqXP9fV1XHIIYdw+eWXl6Q+SYOLWdu9PclUMFcllR/zf2CN/KvLGbJvAwzdsweE+d2hajA06wKk3oiIOmAmcHNKqSW/eBHwAeBkdm1gvhF4W0ppRX7764C1wHzgn1NKF3SaG+CiiJiYUlrThzKPBI5KKf1PN5/jXOBcgIaGhj7sSqocW198bMfZVD1VX1/PaaedxnXXXceIESP6uTJJKj+7y9SI6PbBIuaqJAlg+IFHcuAXH+VPN8yi9cGf7va7Zfr06X53qOJ5pqrK1V+TO7t0UadlS4B1wKwi43/d0VAFSCm9AtwPBPCtgrFN+fcj+ljjku4aqvk6FqSUjkkpHTNmzJg+7k6qDMPGTICiJ5znDtBqaoqvq62tZd68eSxevNiDN0nK6y5Ta2pqaGxspLa2tuh6c1WS1FlN7QhGn3Ujw17f2O24xsZGvztUFWyqqlydDbwI/DEiDo+Iw4FxwC+B0yNidMH4p4rM8ef8+++7WP66Ptb4WB+3V5m7c9qdWZdQlvaZeiHRxWVFtbW11NV1va6xsfsDPEmVx6ztXneZWldXxyc/+clum6rmqqTByvzPzt5v/2S36z/1qU8NUCVStmyqquxExHjgXcAYco3Lxzu9PgIMBz5asNl2upBS6mpddB7WRS3d3UKjtZt1qgKjmkdlXUJZqh03hX3ePZsYVr/j7Kqamhrq6+t517veteMy1Yigvr6+45YdbN26lZUrV2ZWt6RsmLXd6y5TZ8+eTVtbG1u3bgXMVUnlxfzP0LY2qCn+v8JDhw6lra1tgAuSsmFTVeXo4+QanucAHyryWkPuTNb+9CeAiHhtwfJD+3k/qiDH/ea4rEsoW6PeO5f9zltK/Ztn0NjYyIwZM1i6dCkjR45k8+bN1NXV0dDQwPXXX8/BBx9MXV0dmzdvpqmpafeTS6ooZu3udZWpc+fOpampyVyVVJbM/+y0PdkE7dtgyHAYVs9hhx1GfX09w4cPZ9u2bX53qGr4oCqVlYioAc4CfpdS+kEXY44EvhIRb00p/aafdt1xKf97gJs6Lb+kn+aXVKB23BRqx01h5YWvLvvwhz/MkCFDdrrx/bRp05g1axa33HILK1as6HpCSapixTIVYMWKFeaqJKlH2taugJoh1B/1AV57xnU88fkRtLS0+N2hquOZqio3JwEHA7d0M6ZjXX+erXoDubNVF0TE30fEeRFxO/CmftyHpN2YPHkyCxYs2OnG9yNGjODGG29kwYIFTJo0KeMKJam8mKuSpJ4atv9kXjtjAaM/tpiaWr87VL08U1XlpqNR+m9dDUgpPRwRjwFnRMRF/bHTlNKGiHgfcBXwd8CmfA0f5dUHW0k7efCoB7MuoeIsWbKky3WzZs1i1qxZA1iNpMHArO0bc1VSuTL/s7Pf3/rdIYFNVZWZlNKH9nDcxE4/RhdjziJ3K4HC5cuKbZNSWgG8vchUUTDu6a72qeqy9pC1WZcgSRXPrJWk6mT+S8qal/9LUomcftvpWZcgSRXPrJWk6mT+S8qaTVVJkiRJkiRJ6gGbqpIkSZIkSZLUAzZVJalEnt//+axLkKSKZ9ZKUnUy/yVlzaaqJJXI/cfen3UJklTxzFpJqk7mv6SsDc26AEmqVLPXzGbmzJlZl9G9o7MuQJL6JvOsNUclKROZ5/9A8rtGGpQ8U1WSSuSxxx7LugRJqnhmrSRVJ/NfUtZsqkqSJEmSJElSD9hUlSRJkiRJkqQesKkqSSUyZ86crEuQpIpn1kpSdTL/JWXNpqoklciqVauyLkGSKp5ZK0nVyfyXlLWhWRcgSZXq9ttv59w4d5flLzUN/gPAtRdmXYEk7Znbb7+dxsbGrMuQJA2wro61u1KKY3CPmaXq5pmqkiRJkiRJktQDNlUlSZIkSZIkqQdsqkpSiax464qsS5CkinfGGWdkXYIkKQMea0vKmk1VSSqR5lHNWZcgSRVv7NixWZcgScqAx9qSsmZTVZJK5ORfnpx1CZJU8a666qqsS5AkZcBjbUlZs6kqSZIkSZIkST1gU1WStMfWr1/PKaecwvr167MuRZIkSRpw21vWs+7aU9je4vGwVO1sqkpSiTzd8HTWJfS7hQsXcuedd7Jo0aKsS5EkAI4++uisS5AkZSCrY+2W+xey5X/vpOV+j4elamdTVRUhIk6IiBQRs7OuRerw0JseyrqEfpVSYv78+QDMnz+flFLGFUkSnHbaaVmXIEnKQBbH2iklNi7LHQ9vvMfjYana2VSVpBI5/t7jsy6hx9qeXs5Li2bS2NjIzJkzWb58+Y51TU1NvPzyywA0Nzdz3333ZVWmJO2wYMGCrEuQJGUgi2PttqeaaN+SOx7e/vJzTJo0aZdjZknVw6aqJJXIqJdHZV1CjzTfMYd13z6R1gduZPXq1dx0002ceOKJzJkzB4Crr76alpYWAFpaWnactSpJWXruueeyLkGSlIEsjrU3Lrua1LYp90PazmOPPbbLMbOk6mFTVVUlImoj4u8i4pGI2BIRzRFxW0S8pWBcx+0EzoqIj+fHt0XE2oj4fJF5T4qIGyPiqYjYnJ/3roiYOnCfTuq9tqeXs/HuK0lbWyF/GVN7ezutra1cdtllRARLlizZcYlTSoklS5YQETu9pk+fnuXHkCRJkvrFiz+Yzh8+Gzu9Nj9y2y7jCo+ZPR6WqsfQrAuQBkpEDAP+A/hL4MfAPwMjgXOA/4qI41NKKws2+ySwP3Ad0Ax8FPh6RPwxpfSTTuPOAl4L/Aj4I3AQ8AlgaUS8K6XUVLIPpkFrS+2WrEvYYxvvuYa0bXO3Y1555ZUuf66rq2P//ffn8ssvL0l9ktSVvffeO+sSJEkZKPWx9si/upxX/t8DbN+4Drbl99W+rdtt6uvrPR6WqohNVVWT84ETgFNSSnd2LIyI7wAPA1fm13fWAPxFSqk5P/ZfgLXAZ4DOTdVzUkotnTeMiGuBR4AvAUWbqhFxLnAuQENDQy8/lgaru066K+sS9tjWFx/bcYZqT9XX13Paaadx3XXXMWLEiH6uTJK6d8kll2RdgiQpA6U+1h5+4JEc+MVH+dMNs9j86O2kV1p3u83EiRM58sgjS1qXpMHDy/9VTT4K/C+wKiJGd7yA4cAvgXdExF4F2/ywo6EKkFJqBZYDR3Qe1LmhGhF7R8TrgO3ACuC4rgpKKS1IKR2TUjpmzJgxffx4GmwmrpmYdQl7bNiYCRDFvxIigpqa4utqa2uZN28eixcvtqEqKRPLli3LugRJUgYG4li7pnYEo8+6kVHT58HQ2u7H1tQwadKkktckafCwqapqMhmYBLxY5DULGAKMLtjmqSLzrAde13lBRBwWEYsj4s/ARuCl/LzvA/btx8+gMjLxsfJpqu4z9UJiaF3RdbW1tdTVdb2usbGxlKVJUrfuueeerEuQJGVgII+1h7/+aGI3TdW6ujouuOCCAapI0mBgU1XVJIDfAdO6eb1YsM323U4asTdwL3AKcA3wQeDk/Hx35/crDWq146awz7tnE8Pqd5yxWlNTQ319Pe9617t2PKAqIqivryci98d669atrFxZeCtiSZIkqXK88sxK2L61y/XDhw9n9uzZTJkyZQCrkpQ1m6qqJo8DY4C7U0q/6uLVm7udnwiMBS5KKX0lpXRLSumulNKvAK+HVtkY9d657HfeUurfPIPGxkZmzJjB0qVLGTlyJJs3b6auro6Ghgauv/56Dj74YOrq6ti8eTNNTT6HTZIkSZWr7ckm0tbNMKyOIfsewshTr4Bhr55oMHXqVObOnZtxlZIGmk1VVZMfAQcAFxdbGRH793LejrNZdzojNSJOopv7qary3fPO8rsktXbcFEZ/7AZWrlzJDTfcwJQpU1ixYgVDhgxh+vTpPPLII7z//e/n0Ucf5fTTT2fIkCGsWLEi67IlVbFzzjkn6xIkSRkYyGPttrUroGYI9W+YzoFffISR7/kCr//qOj70oQ8xZMgQnnzyyQGrRdLgMTTrAqR+dmJEFLv540vkLs2fBnwzIt5N7tL8DUADubNNtwDv6sU+7wOeB+ZFxDjgj8CbgTPJ3W7gjb2YUxo0Jk+ezKWXXsqsWbN2LBsxYgQ33ngj//Iv/8Itt9ySYXWSJElSaQ3bfzIjT7qUvae8ejxcU+vxsFTtbKqq0pySfxVak1K6NiL+Cvg0uYZnx/UZzwL3A4t6s8OUUnNEnAx8A/gMub9Xq8g9pOpsbKpWralNU7n1tFuzLqPPlixZ0uW6WbNm7dRslaSB9v3vf585c+ZkXYYkaYAN5LH2fn/r8bCkXdlUVUVIKS1jDx4IlVLaBnwr/+rVfCmls4CzCpY9RPFmblPhWEmSJEmSJJU376kqSZIkSZIkST1gU1WSSmTNhDVZlyBJFW/q1KlZlyBJyoDH2pKyZlNVkkpkzUQP9CSp1E444YSsS5AkZcBjbUlZs6kqSSVy0l0nZV2CJFW8efPmZV2CJCkDHmtLyppNVUkqkbq2uqxLkKSKt2nTpqxLkCRlwGNtSVkbmnUBklTJVh29ateFRw98HZIkSVKlKXqs3RWPwSX1M89UlaQSOfDAA7MuQZIqnlkrSdXJ/JeUNZuqklQi5557btYlSFLFM2slqTqZ/5KyZlNVkkrktttuy7oESap4Zq0kVSfzX1LWbKpKUomsXr066xIkqeKZtZJUncx/SVmzqSpJkiRJkiRJPWBTVZIkSZIkSZJ6YGjWBUhSpbpz2p3cuvrWHm3zUtOqElXTvbUXZrJbSeqziy++OOsSJEkZ6M2xdl9kdZzeFY/fpex5pqoklcio5lFZlyBJFe/ZZ5/NugRJUgY81paUNZuqklQix/3muKxLkKSKt3jx4qxLkCRlwGNtSVmzqSpJkiRJkiRJPWBTVZIkSZIkSZJ6wKaqJJXIg0c9mHUJklTxTj311KxLkCRlwGNtSVmzqSpJJbL2kLVZl9Ct7S3rWXftKWxvWZ91KZLUa42NjVmXIEnKwGA/1u5vHrtLg49NVVWtiFgeEf9b4n1MiogUEV8s5X40OJ1+2+lZl9CtlvsXsuV/76Tl/kVZlyJJvTZ37tysS5AkZWCwH2v3N4/dpcHHpqoGXETcHBHbI+IdXax/R379zQNdm1QtUkpsXDYfgI33zCellHFFkiRJkorx2F0anGyqKgufAl4CFkbEiM4rIqIe+GF+/SdLXMfxwFEl3scaYC/gmyXej9Qj/z97dx4fVXXHffzzSwIJIUhQ9rKVIosgIEGhtSwKglSRWhVBEDCP4tIKinG3BdQHV0CttZZFcAWsCwJWQaNBfCRQCIsFAWVzAWVREAhZSM7zx0xiEiYhgUxuMvN9v17zinPvued+B/Vw5pd7z83ctozcjIMA5BzcTdu2bRk6dCipqakeJxMRERERkYIKzd0P72fPcxeRkJCg+buIx1RUlQrnnNsL3Aj8Bni8yO7HgFbAjc65fUHOkeWcywryOZxzLsM5lxPM80jl9H2D772OUKxDKU/hMg/73rgctmzZwuuvv06fPn0YP368t+FERMqgdevWXkcQEREPVOa5dnk7lPIULuuI782xo2R+mUxaWprm7yIeU1FVPOGcmw+8DNxsZn0BzKw38GfgJf9+/NtrmNnfzGyjmWWY2Y9mNt/Mzi7Yp5ld7F+/dIiZjTazL8ws08y2m9ntRTMEWlPVzDqa2Vtmtst/7G4zSzazfkXalTaT1lQNYyvPW+l1BAD2zhjE17dZodfRDQuPa5ebm0t6ejoPPvggZsagQYM8SCsiUjZDhw71OoKIiHigssy1y1vAufvGdyHALf8F5+89evTwIK1IeFNRVbw0BvgOeMHMfgW84H8/Jq+BmUUDHwL3AcuA24Angc7AcjPrFKDf24C78BVt7wD2A1PM7E8lhTGzBsDHQHfgX/iWKXgKOACce4qZJAydt/I8ryMAUPuSSUTWaQZRMb9szD1W4jGxsbFMmjQpyMlERE7dnDlzvI4gIiIeqCxz7fIWcO6ec+IbLGvVqhXEVCISSJTXASR8OecOmNn/ARYDa4G6QH/n3MECzcbhK3L2dc59nLfRzJ4HNuBbLuDiIl03Ato75w77274IfA3cCrxVQqRewOnAIOfcghLanUymgMxsNDAaoFmzZqU5RKqQhj809DoCANUbtafRPRv5cU4iRzcuwmWln/CYNm3a0L59+wpIJyJyarZs2eJ1BBER8UBlmWuXt5OZuwPs2bMnyMlEpChdqSqecs4tAabhK6hO878vaDjwOfC5mdXNe+H7bzcZuMDMiv5yYEZeQdV/jkPAKuDME8TJK+ZeYmYl/ZrvZDIF5Jyb5pzr6pzrWq9evdIcInJSIqJrUnfUPOIHTYao6JLbRkTQtm3bCkomIiIiIiIFlWXuDr75u9YYF6l4ulJVKoPl+K7WXF5wo5kZ0AaIBPaWcHydIvu3BWizHzjjBDmWAPP8WUaZ2X/ztjnnNp9iJpFKoXqTLlhUNO5YZrFtYmJiGDNmTLH7RUREREQk+EozdwfN30W8oqKqVGbmf60GSnrQ08Ei73NK6K9YzjkHDDGzR/Ddvt8DuBv4q5nd4pybfgqZJAwtGFjSKhLeyPpmFeRkF7u/evXqJCUl0b179wpMJSJy8vTEYxGR8FQZ59rlrfDc3SAistCzEcyMGjVqaP4u4hEVVaXScs7lmtlWfEsDJPuLnhVx3nXAOuAxMzsD+C/wKDDdq0xSNTXf2ZydzXd6HaOQzK3LcNlHoVoMkXENiDv/Zg4ufhA7dhTnHL169WLixIlexxQRKbXVq1eTkJDgdQwREalglXGuXd6Kzt23vVX7AAAgAElEQVTrXP4UP/77JnIP7wWXS9OmTZk3b54KqiIe0ZqqUtm9BDQH/hxop5k1KK8Tmdnp/tv78znn9uN7yNVpZhZZ0Zmkauu0vpPXEY6TuXMFREQS22EQje7ZQO2+d9Pk4T1cddVVREZGsnXrVq8jioiUyaJFi7yOICIiHqiMc+3yVnTuHtvxjzR+YCuxna4kMjKSqKgoFVRFPKQrVaWyewLoA/zdzPoDKcBhoBlwEb61UgeU07lGA6PN7B3gK3zLCFwI9AJecs7lLStQkZlEylW1Bu2o3e8B4ron5m+LiK7JvHnzeOGFF3jzzTc9TCciIiIiInmKm7vXHTWP8bU0dxfxmoqqUqk55zLNrB9wKzAMeNC/axe+B1vNLsfTfQi0By4DGgLH8D306nbgHx5lEilX9W98t9h9iYmJJCYmFrtfREREREQqjubuIpWbiqriOefcbEooRDrnsoEp/ldJ/bxPMQ+jcs4NCbCte5H3q4BrTxi4bJk2FZdJQt+Kc1d4HUFEJOQNGXLcX/EiIhIGNNcWEa9pTVURkSA5EH/A6wgiIiGvcePGXkcQEREPaK4tIl5TUVVEJEj6f9Df6wgiIiFvypQSbxoREZEQpbm2iHhNRVURERERERERERGRMlBRVURERERERERERKQMVFQVEQmSHc12eB1BRCTkdenSxesIIiLiAc21RcRrUV4HEBEJVbOum1X2g1QbEBEpk4EDB3odQUREPHBSc+1ToXm6iBShK1VFRIJk2rRpXkcQEQl5GmtFRMKTxn8R8ZqKqiIiQbJ7926vI4iIhDyNtSIi4Unjv4h4TUVVERERERERERERkTJQUVVEJEji4uK8jiAiEvI01oqIhCeN/yLiNRVVRUSC5I477vA6gohIyNNYKyISnjT+i4jXVFQVEQmSlJQUryOIiIQ8jbUiIuFJ47+IeC3K6wAiIqFq6dKl9O7d2+sYIiIhTWOtiEh4Wrp0KXecFryrVfctWx20vsPFzrFeJxAJLl2pKiIiIiIiIiIiIlIGKqqKiIiIiIiIiIiIlIGKqiIiQXLDDTd4HUFEJORprBURCU9Leyz1OoKIhDkVVUVERERERERERETKQEVVEZEgmT59utcRRERCnsZaEZHw1GtZL68jiEiYU1FVREREREREREREpAxUVBURERERERERkXKRc2Q/e56/mP3793sdRSSoVFQVKQdm1tvMnJmN8jqLVB69eumWJBGRYNNYKyISnja33ux1BCnGkZWzydi0mBdffNHrKCJBpaKqSABmdpqZ/dXM0szskJmlm9lGM3vCzBp4nU+qht69e3sdQUQk5GmsFREJT5vbqKhaGTnnOJQyFYCpU6finPM4kUjwqKgqUoSZtQbWAROBbcA9wG1AKjAW2GBmv/UuoVQVkydP9jqCiEjI01grIhKe+i3p53UECSBz2zJy0n8EYNeuXVx00UWkpqZ6nEokOFRUFSnAzGKBhcCvgIHOuSudc/9wzk1zziUCvwOigHd0xaqcyOHDh72OICIS8jTWioiEp5jMGK8jSAA/zhsN2UcByM3NJTk5mT59+jB+/HiPk4mUPxVVRQr7P0BrYKpz7t2iO51zq4D7gHrAnYE6MLNbzWyLmWX4f94a1MQiIiIiIiIiFWzvjEF8fZsVeh3bc/yyDOnp6Tz44IOYGWbGoEGDPEgrUv5UVBUp7Er/z+kltJkNZANXBNh3K77lAl4B7gUOAM+YmX4tF4YaNWrkdQQRkZCnsVZEJDwdqH3A6whhr/Ylk4is0wyiSnfVcEREBM2bN2fSpElBTiZSMVRUFSmsA3DIOfdVcQ2cc+nAZqCFmcUV2d0a6Oace9A5NxX4PfBf4AEza1K0LzMbbWarzGzV3r17y+9TSKUwevRoryOIiIQ8jbUiIuHpk56feB0h7FVv1J5G92wktsNlWPXYE7avXbs2GzZsoH379hWQTiT4VFQVKew04GAp2uW1qV1k+6vOuW/z3jjnsoCp+NZhHVi0E/9arV2dc13r1at3kpGlslq4cKHXEUREQp7GWhGR8NRxXUevIwgQEV2TuqPmET9oMljxJSYz4+KLL6ZmzZoVmE4kuFRUFSnsZ3yF1RPJa1O0APtFgLYb/T9bnmwoqZrS0tK8jiAiEvI01oqIhKcWX7fwOoIUUL1JF6hW/DIA0dHRjBkzpgITiQSfiqoihf0POM3MWhXXwMxigTbADudc0UcOu0CHlGM+ERERERERkUol65tVmAv0ddjnwgsvpHv37hWYSCT4VFQVKewt/8/rS2gzAqheoG1BZwXY1s7/c9sp5BIRERERERGplDK3LsNlH4VqMUTUakj1X/+e6tWrExHhKzvVrl105TyRqk9FVZHCZgBfAbeb2cVFd5pZF+ARYC/wRIDjhxV8IJWZVQduB3KARUFJLJXWuHHjvI4gIhLyNNaKiISnxRct9jqCFJC5cwVERBLbYRCNH/iKhmOX8eOPP3LllVcSGRnJihUrvI4oUu5UVBUpwDl3BLgM+B5418xeN7NbzGy0mc0AlgO5wB+dc98H6GILsMLM/mpmtwGfAucCk5xz31TQx5BKYteuXV5HEBEJeRprRUTCU/yBeK8jSAHVGrTj9MHTqDtyLhHRvodR1axZk3nz5jFt2jTatm3rcUKR8qeiqkgRzrkvgI7ARKA18DjwNPB74O9Ae+fcZ8Uc/nfgMXxLBDwG1AFuc879Ldi5pfKZO3eu1xFEREKexloRkfDU7b/dvI4gBdS/8V3iuicG3JeYmMi7775bwYlEgi/K6wAilZFz7iDwoP9VmvYpFH4g1TNBiCUiIiIiIiIiIpWArlQVERERERERERERKQMVVUVEguTSSy/1OoKISMjTWCsiEp7WdVzndQQRCXMqqoqIBElCQoLXEUREQp7GWhGR8LSz+U6vI4hImFNRVUQkSCZOnOh1BBGRkKexVkQkPF228DKvI4hImFNRVURERERERERERKQMorwOICIiIiIiIiJSVqu7rA5e512C17WIhAZdqSoiEiStW7f2OoKISMjTWCsiEp40/ouI11RUFREJkqFDh3odQUQk5GmsFREJTxr/RcRrKqqKiATJnDlzvI4gIhLyNNaKiIQnjf8i4jUVVUVEgmTLli1eRxARCXkaa0VEwpPGfxHxmoqqIiIiIiIiIiIiImUQ5XUAEZFQlpCW4HWEEu1bFsQnplYSO8d6nUBEREREgqGyz7XzhMOcO1g0l5fKTFeqiogEyYKBC7yOICIS8saPH+91BBER8YDm2iLiNRVVRUSCpPnO5l5HEBEJeatX6+ofEZFwpLm2iHhNRVURkSDptL6T1xFERELeokWLvI4gIiIe0FxbRLymoqqIiIiIiIiIiIhIGaioKiIiIiIiIiIiIlIGKqqKiATJinNXeB1BRCTkDRkyxOsIIiLiAc21RcRrKqqKiATJgfgDXkc4JTlH9rPn+YvJObLf6ygiIsVq3Lix1xFERMQDVX2uXRLNw0WqBhVVJeyZ2bdm9qHXOST09P+gv9cRTsmRlbPJ2LSYIytf9DqKiEixpkyZ4nUEERHxQFWfa5dE83CRqkFFVan0zOzfZubMrHMJbczMtpvZATOrUZH5REKRc45DKVMBOLR0Ks45jxOJiIiIiIQ+zcNFqg4VVaUqmOn/eV0JbS4AWgBznXNHg55IJMRlbltGbsZBAHLTD5C57VOPE5Vd5o5U9r04lISEBIYOHUpqaqrXkUREREREShQK8/DyoLm8VAUqqkpVsAT4BhhmZtWLaZNXcJ1ZzH6RCrej2Q6vI5y0QylP4bKOAOCyj+T/tryqOPDeePb8ow/pa+eRlpbG66+/Tp8+fRg/frzX0USknHXp0sXrCCIi4oGqPNcuSVWfh5cHzeWlqojyOoDIiTjncs1sNvBX4DLgjYL7zew04E/A/5xz/y2w/UbgJqAtkAmsACY65z4rzXnN7M/AWKAZsBN4CsgGpgM9nHOf+ts1AcYBfYDmQAywFZgNTHHO5ZzM55aqb32n9V5HKJW9MwZx9H8LCm+MrA55txo5x9GN7/L1bVaoSY0Ol1Hv+ncqKGXpZe5I5dBHT+Ky0/O35ebmkp6ezpNPPsmAAQPo3r27hwlFpDwNHDjQ6wgiIuKBqjLXLkmozcPLg+byUpXoSlWpKmYBjsBLAAwBYilwlaqZTQaeBzKAe4GpwNlAipn1O9HJzOx+4FngoP/4l4H78BVpi+oM/BH40N/mHuA74HHgmVJ9OglJPT/p6XWEUql9ySQi6zSDqJhfNuZkFW5U8H21GCLrNKf2JZMqJmAZHVr6NO5Y4FVAMjIyePrppys4kYgE07Rp07yOIKVkZnrpVSEvCQ9VZa5dklCbh5cHzeWlKtGVqlIlOOe2m9nHQH8za+yc21Vg93VAFvAKgJmdhe/K0U+Avs65bP/2mcBG4J9mdqZzLjfQucysHvA3YC3we+dcpn/7LGBTgEOSgd+4wiuIP2VmrwE3mtlE59yeYs41GhgN0KxZs9L8UUgVEn8w3usIpVK9UXsa3bORH+ckcnTjIlxWerFtrXosNdoP5PQhM4mIrlmBKUsve++WX367X0Rubi5ffvllBScSkWDavXu31xFERMQDVWWuXZJQm4eXB83lpSrRlapSlcwEIoFr8zaYWVugO7DAObfPv/mP/p+P5RVUAZxz3wIvAS2BjiWcpx9QHXgur6DqP/47YE7Rxs65o3kFVTOrbmanm1ldfGvBRgIJxZ3IOTfNOdfVOde1Xr16JUQSCa6I6JrUHTWP+EGTISo6cKOoaOIHTabuyLmVeiJXrV5rsMB/vUVERNC6desKTiQiIiIiElgozcPLg+byUpXoSlWpSt4CDuC7MvUx/7ZE/88XCrT7tf/nhgB9/M//syW+K1EDyTt+c4B9x20zs2r4lgi4FvgNUPSeozrFnEdCXEZ0htcRyqx6ky5YVDTuWOZx+ywqmupNi/0dQaVRq9dYjv5vQaF1mPLExMQwZswYD1KJSLDExcV5HUFOgivmKiSRk6Xb/sNPVZxrlyQU5uHlQXN5qUp0papUGc65DOA1oI2Z/c7M8q5a/RbfVaF5TnVGVdbjnwYmAv8FRgF/AC7Ct74q6P+zsLWk35ITN6pksr5ZBTl5F3gbVi2W/P8lcrLJ+nqVV9FKLbpFd2pdmOTL7v8td0REBLGxsSQlJWlhe5EQc8cdd3gdQUREPFAV59olCYV5eHnQXF6qEhV7pKrJexjVdcAAoCHwonMup0Cbrf6f7QMcf5b/57YSzrHd/7NNgH2Btg0HPnLOXeOce8k5955z7kPgUAnnkDDQZnOg/1wqt8yty3DZR/2L4DfjjGtfJbJOU4iKwWUfJXPbMq8jlkr8gInU/3MysZ0Hk5CQwODBg0lOTmbixIleRxORcpaSkuJ1BBER8UBVnGuXJFTm4eVBc3mpKnT7v1Qpzrk0M1sLXA00BRwwq0izd4BJwJ1mtsQ5dwzAzH4FjMRXUF1fwmkW43vw1S1m9lKBB1U1BoYGaJ9DkatbzawWcFsZP56EmDZb2rC5TaBVJCqvzJ0rICKS2A6D8hfBj2lzET/OSSR9/Ztk7VzhdcRSi27RnegW3Vk11uskIhJMS5cupXfv3l7HEBGRClYV59olCaV5eHnQXF6qAl2pKlXRTKAW0B9Y6pzbWnCnc24jMAXoDSw1s7Fm9ldgJVADuMU5l1tc5865vcDDQGfgUzO73czuB1YAm/KaFTjkTeACM5tjZjf4z7Ue+OnUP6pIxarWoB2nD55WaBH8vMXzTx88jaj6bT1OKCIiIiISejQPF6l6dKWqVEWvAk8AMRR+QFU+59wdZrYFuBnfQ62ygFRgonPu/53oBM65h8zsIDAGeBTYie/q1xigK3C0QPOxwM/AlcDlwNfAP/E9CGvxSXw+Ec/Uv/HdYvfFdU8krntisftFREREROTkaB4uUvWoqCpVjnPuJ3xXnJ6o3b+Af5WiXZNitj8DPFNwm5n90/+P3xRodwQY538VpceQhrGlPZZ6HUFEJOTdcMMNXkcQEREPaK4tIl7T7f8iAZjZcUVb/5qsw4C1/iUCREREREREREQkDKmoKhJYHzNbb2YT/OukTgLWAbHAvR5nkyqi17JeXkcQEQl506dP9zqCiIh4QHNtEfGabv8XCWwLsB0YDZwBZAD/BSY55z7yMpiIiIiIiIiIiHhLRVWRAJxzW4BBXucQEREREREREZHKR7f/i4gEyebWm72OICIS8nr10u2fIiLhSHNtEfGarlQVEQmS14a+5nWEE+vidQARkVPTu3dvryOIiIgHqsRcO4/m3CIhSVeqiogEyeTJk72OICIS8jTWioiEJ43/IuI1FVVFRILk8OHDXkcQEQl5GmtFRMKTxn8R8ZqKqiIiIiIiIiIiIiJloKKqiEiQNGrUyOsIIiIhT2OtiEh40vgvIl5TUVVEJEhGjx7tdQQRkZCnsVZEJDxp/BcRr6moKiISJAsXLvQ6gohIyNNYKyISnjT+i4jXorwOICISqtLS0pjwqwnl2ue+ZavLtb+KsnOs1wlEJFQFY6wNpKqOv8Wp7ONy86eD029l/9wiUnppaWkMHDjQ6xgiEsZ0paqIiIiIiIiIiIhIGaioKiIiIiIiIiIiIlIGKqqKiATJ4osWex1BRCTkaawVEQlP48aN8zqCiIQ5FVVFRIIk/kC81xFEREKexloRkfC0a9curyOISJhTUVVEJEi6/beb1xFEREKexloRkfA0d+5cryOISJhTUVVERMpdzpH97Hn+YnKO7Pc6iohISNI4KyIiIuItFVWlzMwsxcx2BLH/HWaWEsT+nZnNDlb/Ac4X1M8jUhkdWTmbjE2LObLyRa+jiIiEJI2zcjJmz55NXFyc1zFERERCgoqqYcrMevuLi87Mni2mTX0zy/K3SangiCJV3rqO67yO4AnnHIdSpgJwaOlUnHMeJxKRUBaOY63G2cpn1KhRmBlmRlRUFM2aNePmm2/mp59+Cth+ypQpREZGcv/99x+3LyUlJb+viIgITjvtNDp27MjYsWPZvn17sD+KSJVx6aWXeh1BRMKciqqSAVxjZtEB9l0LGHCsyPZ+QJsgZmrjP0ew1ABuCGL/RQX780gltbP5Tq8jeCJz2zJyMw4CkHN4P3ueu4iEhASGDh1Kamqqx+lEJNSE41hbcJzNTT9A5rZPS3/sjlT2vThU43IQ9O3bl927d7Njxw5mzJjBwoULueWWWwK2nTlzJvfccw+zZ88mJycnYJsNGzawa9cu0tLSGD9+PGlpaZx99tksXbo0mB9DpMpISEjwOoKIhDkVVeVtoA4wKMC+64D/AJkFNzrnspxzmQHalwvnXKZzLiuI/Wc457KD1X+A8wX180jlddnCy7yO4IlDKU/hso743hw7SuaXyaSlpfH666/Tp08fxo8f721AEQkp4TjWFhxnXfaR/KtWT+TAe+PZ848+pK+dp3E5CKKjo2nYsCFNmjShX79+XH311SxZsuS4dsuXL2ffvn1MmDCBGjVq8N577wXsr379+jRs2JBWrVpxxRVXkJKSwjnnnENiYmKxhViAn3/+mZtvvplGjRoRExNDu3btmDdvXqE2ycnJdOjQgZo1a3LBBRcUugJ269atDBo0iIYNG1KzZk26dOnCokWLCh3fokULHn74YW688UZOO+00mjRpwhNPPFGozZYtW+jVqxcxMTG0adOG//znP8TFxTF79uz8Nt999x1DhgyhTp061KlTh0suuYQvv/yy2M8mUtDEiRO9jiAiYU5FVUkD1uEroOYzs/OA9sCsogcEWlPVzNqb2b/N7DszyzSz783sYzO7pECbGDObYGabzSzdzA6Y2edm9kSRvo5bg9TMfmdm7/n7zfCf5z9m1r1Am9PNbKqZbfW32W9mq83sziJ9Hbemat42M+trZqn+fN+b2dNmVrNI2wn+9u3N7Bl/u6NmtsLM+gT489KaqhKy9s4YxNe3WaHX0Y3vQoBbUXNzc0lPT+fBBx+kR48eHqQVEal6TjjOOsfRje8e12bvjMK/L8/ckcqhj57EZafnH5s3Lj/55JO6YrWcbdu2jffff59q1aodt2/GjBkMGTKEatWqMXz4cGbMmFGqPiMjI7n99tvZtm0ba9asCdjGOceAAQNYunQps2bNYuPGjUyZMoXq1avnt8nMzOSRRx7hhRdeYPny5Rw4cICbbropf//hw4cZMGAAH3zwAevWreOKK67gT3/6E5s2bSp0rqlTp3L22WeTlpbG3XffzV133cXy5csB339bl19+OVFRUaSmpjJ79mwmTpxIZuYv12Wkp6dzwQUXEBMTw9KlS1m+fDmNGjWib9++pKenl+rPRERExEtRXgeQSmEWMMXMmjjnvvVvSwT2AIuKP8zHzM4APvK/fR7YCdQFugLdgHf9+/7h7/clYCoQCZwJXHiC/tsAHwDfA08DPwANgfOBTkDet4B/Az2Bf+ErFMcCbYHeQOFfnQfWBbgSmO7PeAEwBuhgZhc553KLtH8JyAEeA2oBNwLvm9kA59yHpTifSJVX+5JJZH23lpxDe+BYhm9jzokvzK5Vq1aQk4mIhIZSjbMF31eLITKuAbUvmVSoyaGlT+OOHQ14joyMDJ5++mm6d+8ecL+Uzvvvv09cXBw5OTlkZPj+XU2ZMqVQm8OHD/P666/z8ccfAzBixAgmTZrE999/T8OGDU94jrPOOgvwFW27du163P4PP/yQ5cuXs2HDBtq1awdAy5YtC7U5duwY//jHP2jTxreaV1JSEtdddx25ublERETQqVMnOnXqlN/+/vvvZ+HChbzxxhs88MAD+dv79evHX/7yFwBuvfVWnnnmGZKTk/ntb3/LBx98wObNm1myZAm/+tWvAF8R9vzzz88/fu7cuTjnmDVrFmYGwL/+9S/q16/PokWLGDx48An/PERERLykoqoAvAI8DowAJplZDWAIMMM5dyxvklOC84H6wNXOuddLaHc58J5zbmQZ8/XHVyAd6pxbGaiBmdXGV5z9p3PuL2XsP8/ZwOXOufn+98+Z2dP4CquDgblF2h8DeuTd2m9mLwCbgL8D7UpzQjMbDYwGaNas2UnGlsrq+wbfex0h6Ko3ak+jezby45xEjm5chMsq3ZUle/bsCXIyEQkXoT7WlmWcteqx1Gg/kNOHzCQiutCNNmTv3RLwLgLwXVWoW65PXc+ePZk2bRpHjx5l+vTpbN26lTFjxhRqM3fuXJo0aZJfEG3ZsiXnnnsuL774InffffcJz5H3ULLi5udr1qyhUaNG+QXVQKKjo/MLqgCNGzcmOzubAwcOcPrpp3PkyBEmTpzIokWL2L17N9nZ2WRkZNCxY8dC/RR937hx4/y/3zdt2kTjxo3zC6oA5557LhERv9wouXr1arZv337cL1rT09PZunVrSX8MIgC0bt3a6wgiEuZ0+7/gnNsPLABG+Tf9CagNvFDKLg76fw4ws9NO0K69mXUoY8S8/geZWUwxbY7iW/u1m5m1KGP/eTYXKKjmedT/8/IA7acWXCvVf5Xvq0BbMytVUdU5N80519U517VevXonFVoqr5XnBfwdQMiJiK5J3VHziB80GaICPfOuSPuICE2CRaTchMNYW6pxNiqa+EGTqTty7nEFVYBq9VqDBZ76a1wuH7GxsbRq1Yqzzz6bZ555hvT0dB566KFCbWbMmMHmzZuJiorKfy1fvpyZM2eW6hwbN24Ejr/6NI8rpnBeUFRU4etq8gq0ubm+m7KSkpL497//zUMPPcTSpUtZu3Yt5513HllZha+QLrq0gZnl9+GcK7bwmyc3N5fOnTuzdu3aQq8tW7Zw4403nvBziAwdOtTrCCIS5lRUlTyzgDPN7Pf4btFf6ZzbWJoDnXNL8d0KPwrYZ2b/z8wmmtlZRZrehu+hWJ/71z2dYWaDzIqZ4f9iLvAhcB/wo5l9ZGZ3m1nzAhmy/P13ALab2QYz+3ugNU5L8EWAz7YbOAAEmrke1x7I+zMLPNOVsHLeyvO8jlChqjfpgpWiqBoTE3PclTsiIicrnMbaksZZi4qmetPin4Rdq9dYLCrw76Y1LgfH+PHjeeyxx9i1axcAGzZsYMWKFSxZsqRQEXHFihXs2LGDTz75pMT+cnJyeOqpp/jNb35D586dA7bp0qULu3fv5osvAk1TS+fTTz9lxIgRXHHFFXTs2JEmTZqU+crRdu3a8d133+V/doBVq1blF13zsn711VfUrVuXVq1aFXqdfvrpJ51fwsecOXO8jiAiYU5FVcmzGPgOGI9vLdHSXqUKgP+W/rOBB4D9wB3AejP7S4E27wAtgGvxrcHaB5gPpJhZ9aJ9Fjgu0zl3Eb71WR/Bt47pg8AmM7u8QLvn/f3fgO8BXFcCH5pZ0dv2iz1VMduL+zV7oPYnXCtBwkfDH068NlooyfpmFeRk+98ZRBx/JUxsbCxJSUlat09Eyk04jbVFx1mrFkv+1CMnm6yvVxV7bHSL7tS6MMl3jP/32RERERqXg6h37960b9+ehx9+GPBdpXrOOefQt29fOnTokP8699xz6dOnz3EPrNqzZw/ff/89X331FW+99Ra9e/dmzZo1vPDCC0RGRgY8Z58+fejWrRtXXHEFixcvZvv27XzwwQfMn1/0ZqzitW7dmrfffpu0tDQ+//xzhg8fnr9GbGlddNFFtGnThpEjR7Ju3TpSU1MZN24cUVFR+VewDhs2jAYNGjBo0CCWLl3K9u3b+eSTT7jjjju0HIWUypYtW7yOICJhTkVVAcA5l4PvatO+QAbHrx9amj7+55x73Dl3GdAE2Ao8agXu/XHO/eice8U5dwO+qzkfB3oAgwJ2Wrj/lc65h/wF1lbAEeDhIm12O+dmOOeu9WeYA1xtZueW4iMUvbIWM2uEbymEbaVpzy9rqQZqLxLSMrcuw2Uf9T0kpU4z6o76NxG1GuR/eW/atCnJyclMnDjR46QiIlVT0XH2jNgZCeYAACAASURBVGtfJbJOU4iKwWUfJXPbshKPjx8wkfp/Tia282ASEhIYPHiwxuUgGzduHDNnzmTnzp288sorXHnllQHbXXXVVbzxxhscPHgwf1v79u1p1KgR55xzDn/7298455xzWL9+PT179iz2fBEREbz33nucf/75DB8+nHbt2jF27Njjbt0vyZQpU6hfvz49evRgwIABdO/enR49epT+Q/tzvP3222RmZnLeeecxcuRI7r//fsyMmBjfFdOxsbF88skntGzZkquuuoq2bdsycuRIfvrpJ+rUqVOm84mIiHhBD6qSgp4HsoBtzrmDJ2qcx8xOBw445/Lv53HOHTCz7cCZQIyZZQG1nHMHCrRxZrbG/7bYe3zMrK5zbl+Rzd8Ce/OOM7NYf5/5T29wzuWY2XpgaEn9F9DGzP5YZF3VvCcGBPr1/u1m9laBB1U1Aa7Btzbryd9zJVJFZe5cARGRxHYYlP+QlJg2F/HjnEQyP3+TqKgoXQklInIKShpn09e/SdbOFSfsI7pFd6JbdGfV2AoIHEZmz54dcPs111zDNddcA8DevXuLPT4xMZHExETAd4VradZGLU58fDzTp09n+vTpx+0bNWoUo0aNKrSt6PmaN2/Ohx9+WKhNUlJSofc7duw4ru+UlJRC71u3bl1oSYN169aRnZ1Nq1at8rc1aNCAWbNmnegjiYiIVEoqqko+59zXwISTOHQEvgLj28BXQDbQC+gPvO6cO2pm8cBuM1sArAH2AL8GbgZ+AhaW0P8DZtYPWARsx3ef20CgLb4rXQFaA0v9Gf7n77Odv//tQMmXbvh8DrxiZtOBL/Etg3AlsBSYF6B9FLDMzOYAtYCbgBqAFiUTABYMXOB1hApVrUE7avd7gLjuifnb8h6uMr7WC7z55psephORUBVOY21J4+zh1BdIX6dxViqPt99+m5o1a3LmmWeyY8cOxo0bR6dOnejSpYvX0SREjB8/3usIIhLmVFSV8pACnANcCjTCt+bpdiAJeNbfJh14Ct86qn2BOGA3sAB4xDm3i+LN9/c7GGgAHMVX9LwByHtU6jf41oG9APgjEI1vjdjpwGMFr2AtQRowDvi/+AqkP/vz31fwKtwCRvjb3QPEA+uBUc65D0pxLgkDzXc2Z2fznV7HqDD1b3y32H0Fr8ARESlP4TTWljTOxnVPLFRsFfHaoUOHuPvuu/nmm2+oU6cOvXv3ZurUqRRYGUzklKxevZqEhOIf0CciEmwqqoYp51wKpXyoknMursj73kXerwVGnqCPLODeUp6vRZH3KfgKtyUdsx+4vZT9F/u5nXMfAh8Wt7+II865W4FbT3C+FqXsT0JMp/WdwuaLvoiIVzTWilROI0aMYMSIEV7HkBC2aNEiFVVFxFN6UJWIiIiIiIiIiIhIGaioKiIiIiIiIiIiIlIGKqqKiATJinNP/BRmERE5NRprRUTC05AhQ7yOICJhTmuqilDyOqsB2k4AJgQtjISMA/EHvI4gIhLyNNaKiISnxo0bex1BRMKcrlQVEQmS/h/09zqCiEjI01grIhKepkyZ4nUEEQlzulJVRCSIVndZXb4ddinf7kREQkG5j7WBaPytUDvHep1AREREpGS6UlVERERERERERESkDFRUFREJki5ddFmTiEiwaawVEQlPGv9FxGsqqoqIBMnAgQO9jiAiEvI01oqIhCeN/yLiNRVVRUSCZNq0aV5HEBEJeRprRUTCk8Z/EfGaiqoiIkGye/duryOIiIQ8jbUiIuFJ47+IeE1FVREREREREREREZEyiPI6gIhIqMqIziAhLaFMx+xbtjpIaYq3c2yFn1JEpNzExcV5HUGCoKx/f5bW6i4V//esiATHycy1y4sXc/aqRN8vJFzoSlURkSBZ0m+J1xFERELeHXfc4XUEERHxgObaIuI1FVVFRIKkzeY2XkcQEQl5KSkpXkcQEREPaK4tIl5TUVVEJEjabNFET0Qk2JYuXep1BBER8YDm2iLiNRVVRURERERERERERMpARVURERERERERERGRMlBRVUQkSJb20C2pIiLBdsMNN3gdQaTKS0lJwczYt2+f11FESk1zbRHxmoqqIiJVTM6R/ex5/mJyjuz3OoqIiIgAo0aNwswwM6pVq0bLli1JSkriyJEjAOzYsQMzY9WqVccde+mllzJq1CgAhg8fTufOncnKyirUJjk5mWrVqvHZZ58BMH/+fH77298SHx9PXFwcbdu25frrrw/uhxQRz+l7gEjloqKqhDwzm21mzuscEn56LesVlH6PrJxNxqbFHFn5YlD6FxGpSqZPn+51BBEA+vbty+7du9m2bRsPP/wwzz33HElJSWXq49lnn2X//v1MnDgxf9vPP/9MYmIid955J7/73e9ITk7mqquuYuDAgaSmprJmzRqeeOIJnNN0V8JLsObalZm+B4hULiqqiufMrLeZOf8r4K/Y/fsWVXQ2kcrGOcehlKkAHFo6VV+gREREKono6GgaNmxI06ZNueaaaxg2bBjz588vUx/x8fHMmjWLxx9/nJUrVwJw++23U6dOHSZMmADAwoUL6datG/fddx9t27blzDPPZODAgcycObPEvrOysrjvvvto3rw50dHRtGzZkmeeeaZQm3Xr1tGtWzdiY2Pp2rUraWlp+fv279/P0KFDadKkCTVq1KB9+/bMmjWr0PG9e/fmlltu4b777qNu3brUr1+fpKQkcnNz89v88MMPXHbZZdSoUYPmzZsza9YsOnTokP/5AA4ePMjo0aOpX78+tWrVolevXgGv8hUJJ/oeIFL5qKgqlc1EM6vhdQiRyipz2zJyMw4CkJt+gMxtn558XztS2ffiUBISEhg6dCipqanlFVNERCTs1ahRg+zs7DIf17dvX26++WZGjBjBG2+8wauvvsrLL79M9erVAWjYsCGbNm1i3bp1Zep35MiRvPTSS0yZMoUvvviCmTNnEh8fX6jNvffey6OPPkpaWhpnnHEGw4YNyy/cZGRk0KVLFxYtWsSGDRsYO3YsN954I8nJyYX6ePXVV4mKiuKzzz7j2Wef5amnnmLevHmFcuzcuZOPPvqId955h1deeYWdO3fm73fOcckll/Ddd9+xaNEi1qxZQ8+ePbnwwgvZvXt3mT6zSCgpz+8BwaLvFxJuorwOIFLAKqArcBvwiMdZRE7Z5taby73PQylP4bJ867O57CMcSplKzG96lLmfA++N59BHT+KOHSXNOdauXcuCBQtISkoqdMuhiEhl16tX+N3+KZXfypUree211+jTp0+h7T179iQiovB1LRkZGQwfPrzQtscee4wlS5Zw9dVX8+ijj3L22Wfn77v11ltZtmwZnTt3pkmTJnTr1o2+ffsyfPhw4uLiAub58ssvmTt3Lu+99x4XX3wxAC1btjyu3UMPPcQFF1wAwN/+9jd+//vf891339GkSRN+9atfceedd+a3HT16NB999BFz5swp9DnPOussHnzwQQBat27N9OnTSU5OZujQoWzevJnFixezfPlyunfvDsDs2bNp0aJF/vEff/wxa9euZe/evdSoUSM/18KFC3n55Ze56667An5GCT/BmGtXZuX1PSBY9P1CwpGKqlKZvA4YcLeZTXPOlbj6tpl1Be4HegC1gB3AS8BjzrljJzi2KTAR6AM0BA4CXwH/cs69WKBdTeABYDDQBPgJWAL81Tm3s0C73sDHwHX+z5AEtAK+B/7hnHu8VH8CElI2tzm1id7eGYM4+r8FhTdGVoe8W32c4+jGd/n6NivUpEaHy6h3/TvF9pu5I9U34clOz9+Wm5tLeno6Tz75JAMGDMj/oiMiUtn17t3b6wgiALz//vvExcVx7NgxsrOzGTRoEH//+98LtXnttdfo0KFDoW2jR48+rq8aNWqQlJTEmDFjuOOOOwrtq1mzJu+++y5bt27l448/JjU1lXvvvZdHHnmElStX0qBBg+P6W7NmDREREfkF0+J07Ngx/58bN24MwJ49e2jSpAk5OTk8+uijzJs3j++++47MzEyysrKO+3+wYB95/ezZsweATZs2ERERQdeuXfP3N23aNP9cAKtXryY9PZ169eoV6icjI4OtW7eWmF/Cy6nOtSuzYH0PCBZ9v5Bwpdv/pTJxwN1AbXzF0mKZ2R+A/we0BiYDY4DlwIPAnBMcGwV8AFwFzAVuAR4FtuAr0BZstxi4B0gDbvf3PRhYYWZNAnR/E/A3f7s7gN3AY2Z2TUmZJDT1W9LvlI6vfckkIus0g6iYXzbmFH4acKH31WKIrNOc2pdMKrHfQ0ufxh07GnBfRkYGTz/99MlGFhGpcJMnT/Y6ggjguwp17dq1bN68mYyMDN566y3q169fqE2TJk1o1apVoVdsbGzA/qKiooiIiDjuytY8v/nNb7j++uuZMWMGaWlp7Nq1i3/+858B25Z27cVq1arl/7OZr1iTtx7qk08+yeTJk7nzzjtJTk5m7dq1/PGPfyQrK6vYPvL6yeujNDlyc3Np0KABa9euLfTatGkTDz30UKk+h4SHU51rV2bB+h4QLPp+IeFKV6pKpeKcSzazD4BbzOzpgleD5jGzGOAFYAVwYYGrUv9lZuuAKWbW2zmXUsxpzgLaAHef4ArS64DzgSecc/n3GZnZh8AifEsUXFvkmGbAWc65A/62LwA7gVuB1wJ8ltHAaIBmzZqVEEWqopjMmBM3KkH1Ru1pdM9GfpyTyNGNi3BZ6cW2teqx1Gg/kNOHzCQiumaJ/Wbv3fLLb7mLyM3N5csvvzyl3CIiFenw4cNeRxABIDY2llatWnly7hYtWhAbG1vs/w9dunQhNzeXjz/+OP/2/7L69NNPGThwINde65v+OufYsmXLceuylqRdu3bk5uayevVqunXrBsC3337Lrl27CmX94YcfiIiICLhEgUieU51rV2bB+h4QLPp+IeFKV6pKZXQ3UB0o7lfRFwENgFlAvJnVzXsB//G3KenXlgf9Py8ws/oltLscyKXI+q7OuXeBtcAgMyv6/9CsvIKqv206kAqcGegEzrlpzrmuzrmuRW9xEgGIiK5J3VHziB80GaKiAzeKiiZ+0GTqjpxbqolUtXqt4bj/dP3ni4igdevWpxJZREREgmjChAncddddpKSksH37dtasWUNiYiKHDx/msssuC3jMmWeeyeDBg7n++ut588032b59O8uWLePll18u9Xlbt25NcnIyn376KZs2beIvf/kL27dvL1P2Nm3a0L9/f2666SZSU1NZu3Yt1113HbGxsflXxvbt25fzzz+fQYMG8d5777F9+3aWL1/O+PHjWbZsWZnOJ1KVBeN7QLDo+4WEKxVVpdJxzq3Bd/v8MDPrGKBJO//PF4C9RV6b/PuOX0zql/53Av8XX+F1t5mtNrPHzezcIk1/Dexyzv0UoJsN+NZxrVtk+7YAbfcDZxSXR0LXgdoHTtyolKo36YIVM5myqGiqN00odV+1eo3FogL/Zj8mJoYxY8acVEYRES80atTI6wgiFapXr15s376dkSNH0q5dO/r378+OHTtYsGABPXv2LPa4l156iWuuuYYxY8bQtm1bRo0axcGDB4ttX9QDDzzAeeedx4ABA+jZsyc1a9Zk2LBhZc4/e/ZsmjRpQu/evbnssssYNmwY9evXJybGNzcxM/7zn/9w4YUXcsMNN9CmTRsGDx7M5s2bC629KlKec+3KrDy/BwSLvl9IuLLSrq8jEiwFHvJ0p3PuSf+2X+MrkH7knBtgZg541zl3qZndjW8N1DvxXTEayC7n3EZ/X7OBkc65Qqt4m1lL4BJ866j2BeoAjzvn7vbv3wTUdM41DZD5FWAY0MA5t6fgg6qcc7OLtA14/qK6du3qVq1aVVITqWIS0so+wdm3bHXA7Yc+fY4D7yThso8ChlWr4f9nh1WrQfygydT6/c2lPs8vT+fMAJdLREQEMTExejqnhA0zW+2c63rilsGn8V/CRd6ViHDitTVP5u/Q0ljdJfDfs+Kdffv20bhxY+bMmcMVV1xx0v2U9r8vjf+hI1jjRGkUN2cPhvL+HhAs+n4hlVmwxn5dqSqVknNuO/BP4GIzK/qY0rwFWY445z4s5rWxFOfY5pz7u3NuMNAY+AS4q8CSAFuBxmYWaKGos4CfgX0n8/kkPHRcF+hC65OTuXWZb/JULYbIOs0449pXiazTFKJicNlHydxWttvh4gdMpP6fk4ntPJiEhAQGDx5McnKyJjwiUuUsXLjQ6wgiUgYfffQR77zzDtu2bSM1NZWrr76aunXrnvRarxK+ynOuXZmV9/eAYNH3CwlHelCVVGYP43tY1GNFti8G9gD3mNk859yPBXeaWQ0gyjl3KFCnZlYbSHfOZedtc85lmNkXQE98V6zuAeYDfwDu8b/yjh8AnAO84pzLPbWPKKGsxdctWN9pfbn0lblzBUREEtthUP4i9DFtLuLHOYmkr3+TrJ0rytxndIvuRLfozqqx5RJRRMQTaWlpDBw40OsYIlJK2dnZPPDAA2zbto3Y2Fi6devGJ598Qs2a3q0HKVVTec61K7NgfA8IFn2/kHCjoqpUWs65fWb2BEUeWOWcO2JmI/AVPTeb2QvAV0A80Bb4E76HTKUU0/UFwDQzexPYDBwGEoDrgRXOuc3+drOBkcDdZtYC35WsrYBbgB+A+8rjc4qURrUG7ajd7wHiuifmb8tbvP5w6gukr3vTw3QiIiIipdO/f3/69+/vdQyRKkPfA0QqLxVVpbKbgq+IWegpFM65xf4HS90DDAfqAT/hu2V/ClDSryzXAW8BvfGtixoJfA1MAiYXOEe2mfUHHgCuxlesPQD8G3jAOffNqX88kdKpf+O7xe6L655YaJIlIiIiIiKhQd8DRCovFVXFc865FCDgQ5ycc+n41jsNtO9/+AqqJ+p/FDCqwPvtwE2lzHYEuNf/KqldCsV/hkLnl/Cx+KLFXkcQEQl548aN8zqCiIh4QHNtEfGaHlQlIhIk8QcCPeNMRETK065du7yOICIiHtBcW0S8pqKqiEiQdPtvN68jiIiEvLlz53odQUREPKC5toh4TUVVERERERERERERkTJQUVVERERERERERESkDFRUFREJknUd13kdQUQk5F166aVeRxAREQ9ori0iXovyOoCISKh66/K3yn5Ql/LPISISyhISEryOIEGwustqryOISCV3UnPt8qI5u4igK1VFRIJm4sSJXkcQEQl5GmtFRMKTxn8R8ZqKqiIiIiIiIiIiIiJloKKqiIiIiIiIiIiISBmoqCoiEiStW7f2OoKISMjTWCsiEp40/ouI11RUFREJkqFDh3odQUQk5GmsFREJTxr/RcRrUV4HEBEJVTc/fzMrz1t50sfvW+bdk493jvXs1CIiZTJnzhx9sRYRCUOnOtcuLS/n5FWBvjdIONOVqiIiQdLwh4ZeRxARCXlbtmzxOoKIiHhAc20R8ZqKqiIiIiIiIiIiIiJloKKqiIiIiIiIiIiISBmoqCoiEiQLBi7wOoKISMgbP3681xFERMQDmmuLiNdUVBURCZLmO5t7HUFEJOStXq0HiIj3Ro0ahZlhZlSrVo2WLVuSlJTEkSNHANixYwdmxqpVq4479tJLL2XUqFEADB8+nM6dO5OVlVWoTXJyMtWqVeOzzz4DYP78+fz2t78lPj6euLg42rZty/XXX3/S+SdMmJCfPyIigsaNGzNs2DC++eabgO3feustIiMjGTZs2HH78j5r3isuLo42bdpw/fXXs379+pPOKFKU5toi4jUVVUVEgqTT+k5eRxARCXmLFi3yOoIIAH379mX37t1s27aNhx9+mOeee46kpKQy9fHss8+yf/9+Jk6cmL/t559/JjExkTvvvJPf/e53JCcnc9VVVzFw4EBSU1NZs2YNTzzxBM65YvtNSUmhRYsWJZ67TZs27N69m2+//ZZ58+bx+eefM3jw4IBtZ8yYwV133cX8+fP56aefArZ5//332b17N59//jlTp05lz549JCQkMHfu3BP/QYiUgubaIuI1FVVFRASAnCP72fP8xeQc2e91FBERkSonOjqahg0b0rRpU6655hqGDRvG/Pnzy9RHfHw8s2bN4vHHH2flypUA3H777dSpU4cJEyYAsHDhQrp168Z9991H27ZtOfPMMxk4cCAzZ848pfxRUVE0bNiQxo0b06NHD2644QZSU1P5+eefC7X79ttv+fjjj0lKSqJ79+68+uqrAfs744wzaNiwIb/+9a/5wx/+wIIFC7jqqqu46aabOHDgwCllFZHg0XcCkdJTUVUqDTPbYWYpXuc4ETObYGbOzFp4nUWkPB1ZOZuMTYs5svJFr6OIiIhUeTVq1CA7O7vMx/Xt25ebb76ZESNG8MYbb/Dqq6/y8ssvU716dQAaNmzIpk2bWLduXXlHzvf999/n3+IfGRlZaN+sWbPo168fZ5xxBtdeey0zZswodb9JSUkcPHiQDz/8sLwji0g50XcCkdJTUTWMmVlvf3HQmdmzxbSpb2ZZ/jYpFRxRpEpbce4KryOUmnOOQylTATi0dGqJtxCKiFQmQ4YM8TqCyHFWrlzJa6+9Rp8+fQpt79mzJ3FxcYVe77///nHHP/bYYwBcffXVPPTQQ5x99tn5+2699Va6detG586dadq0KVdeeSXPP/88hw8fPqXMX3zxBXFxccTGxtKoUSNSUlL485//TM2aNfPbOOeYNWsW1157LQBXXnklX375ZanXNj7rrLMA2LZt2yllFYGqNdeuKvSdQKRsVFQVgAzgGjOLDrDvWsCAYxUbqVJ7GKgB7PQ6iFRuB+Krzq1tmduWkZtxEICcg7tp27YtQ4cOJTU11eNkIv+/vXuPk6I68z/++Y7A4ICixrsGCRLBkGgEdUlMBJWouCrmZnRzI8QbSZQksJs1qz/AbDRm8RY3ahAVowbwEg2K0USNihdQJINRNuAlaLxxUxCF4Xp+f1QN9PT0zHQz013d1d/369WvZqpOVz+nunm6+ulTp8xat/feeycdgiVk9uzZnH766QwcOLAsPrMeeOABunfvTteuXfnMZz7DkUceydVXX92kze9+9zvq6+ub3I488shm29p+++0ZO3YstbW1jBkzpsm6bt26MXPmTF5++WXGjRvHTjvtxPnnn0///v1ZsmQJAK+//nqTwu2wYcOaLTvnnHOabHf//fenvr6eZ599lp///OcMGDCAiy++uEmbhx9+mPfee4+TTjoJgO7du3PKKafkPVq1sUAjKa/2Zq2ppGPtSpH5nWDzmpWse/WJltsuns3ym8snB5slwUVVA7gb2BkYnmPdd4D7gXUljaiMhRA2hhAagn+2szYc9+fjkg4hb6sfvZKwLh7hEjaxaNEibr/9do455hjGjRuXbHBmZq24/PLLkw7BEjBu3DiOOeYYpk+fzrx588riM+vII4+kvr6ehQsX0tDQwO9//3t23333Jm323Xdf+vTp0+RWV1eXc3udOnWipqaGmprcX9n2339/zjjjDCZPnsy8efN46623uPbaa4Hox4bMwu3kyZObLbvooouabK9Lly706dOH/v3789Of/pSDDjqI73//+03aTJ48mZUrV9KtWzc6depEp06dmDZtGlOnTmXNmjVt7qMFCxYA0Lt37zbbmrWlko61K8XqR68krP8QgLDhwy2jVrOt/OM4lv76GNbUl08ONkuCi6oGMA+YT1RA3ULS4UB/4KbsB0g6VtJ0Sa9KWitppaQ/SRqc6wkk9ZF0k6Q34ukE3pL0B0kDWwtM0sckLYzbH5SxvIekSyW9LGmdpGWSpkrqnfX4EfHUBUdLGivplbj9Iknfzmo7JWM6hGa3jHY551TNNyazpC2bPJzXf6gmt7Uv3tus3ebNm1mzZg0XXXQRkhg+PNfvLmZmZqU1e/ZsJk6cyJo1a7aMfGz8zJo4cWJio6Xq6uro06cP++23H507dy7pc/fq1Yu6urotUwB06tSpSeF2n332abYsu+Cb7cILL+S2227bcmr/u+++yz333MPNN9/cpDg7f/58amtrufPOO9uMc+LEifTo0YOhQ4e2v9Nm1i45vxMsmAmNY4dCYO2Cmc3aLPnV51n9yETChjVb2pZDDjZLQqekA7CycRNwuaR9QwhvxMtGAkuB+3K0HwHsAvwWeAPYBzgDeFjSUSGEWY0NJR0KPAx0Bm4AXogfOxj4LJBzEiZJA4hGyb4HfCaE8Fq8vAfwFNATuBF4EdgL+B4wR9KhjW0zXEx0yv5viEbdjgKmSHo5hPBk3OY3QPas+R8B/ieOoUXbGJNZInr868Wsf7OeTauXwsaGaOHm1mf4qKura3YKoJmZWRKuuuoq1q5dm3NdQ0MDV111FYMGDSpxVKUzfvx41qxZwwknnMB+++3HypUr+dWvfsUHH3zAySef3GHP07t3b04++WQuvPBC7r//fm655RZ22GEHvv71rze7eNWXvvQlJk+ezLe+9a0ty1asWME777zD2rVr+fvf/861117LH//4R2655RZ69OjRYXGa2bbJ+Z1g0/qmjTL/7tyV7brvgWp3IGys3hxslslFVWt0K/BL4FvAxZK2B04DJocQNuaY9+jMEMKHmQskXUdUTDwfmBUvEzAFqAUODyE8n/GQSyTlHC0t6QvAXUQF2JNCCCsyVl8E9AYGhRDmZzxmCvA3YAJR0TdTLXBYCGF93PZO4FXgB8CTACGEp4GnM7bXhajIugk4JVec7YwJSWcBZwH07NmzjaewSrO45+KkQ8ipy1792es/F/Du1JGsXXAfYX3bp+v17duX/v37lyA6M7PCDBgwIOkQrMQWLVrU4sVTNm/ezEsvvVTiiEpr8ODBXHPNNXz7299myZIl7LjjjvTv358ZM2bknJ+1PcaMGcMRRxzBU089xQ033MAXv/jFZgVVgK9+9atcd911LFq0iC5dugBw/PHHA9H8sPvuuy+f//znmTt3LgcffHCHxmjVq1yPtStFId8J9kWMqwAAIABJREFU1KWO7fufxC6n3cCSq4/cOpo1SzXkYLNMLqoaACGEFZJmEBX+Lga+BPQgGnWZq/2Wgqqk7kRFy03AHCDzZ6lPE00hcF1WQbVxO5uzl0n6Rvy8fwROCyGszVgn4OvA48CbknbNeOiHwGzg2BwhX9NYUI2f901Ji4CP5+pf7Abgc8DpccE1p3bERAhhEjAJ4NBDD/UcrSnz/MHN3vJlo6a2G7uOmM7qJ6/jvbt/CBtbnja5pqaGfv36lTA6M7P8NV4wx6rHAQccQH19PZs3NzuMpKamhgMOOKDkMU2ZMqXV9b169WqxEHzffblOCoMRI0YwYsSIZsuPOuoojjrqqILiGzJkCIsXL25x/fjx4xk/fnyz5Z/97Ge3xP388y0f1xx99NFN+udLD1gplPOxdqXI6ztBp1p2Gn4ZOxwRXdyu824HsOHNemj+VT6xHGyWFM+papluAj4u6XNEp/4/E0JYkKuhpP0lTZP0HrAaWA4sA04guuhVo8ai5V/zjGEg0ZQCDwNfyiyoxnYjOiX/2Pj5sm9fAPbIsd1XcyxbEW+rGUnjgG8A40II09uIeVtjspQ78vGOHS1SDF32HYA61bbapmvXrpx33nklisjMrDCTJk1KOgQrsdGjR9O1a9ec6/yZZVY9KuFYu1K09p1AnWrp8tGtl0LZYfBo1Mk52Aw8UtWaehB4ExgHHEU072gz8cjUx4FuwJVEp7evBjYTnfp/dGbz+D7fn6tfAjbEz388MDP76eP7h4BL89wmRKNoc2k2r4GkrwPjgVtCCD/LY9vbGpOl3E6rdko6hDat/+dc2LShxfVdunRh7NixnhfJzMrW22+/nXQIVmKDBg1i7NixTJw4kYaGBjZv3kxNTQ1du3b1Z5ZZFamEY+1K0fQ7gVDn7Qkb1gIBNm1g/etzqe15GAC1vQaxw9Fjo4tVbWyA4Bxs1ctFVdsihLBJ0m+JCqNrgWktND0G2BsYGUK4KXOFpP/Oarswvj8kzzDeB04GHgB+L+nUEMIfMtYvA1YCO4YQsi8q1W7xKN0bgCeILryVj6LGZFZM616ZFR0wxRPPdz9iFKsevAhtXEsIgcGDBzNhwoSkwzQzM2tiwoQJDBs2jKuuuoqXXnqJj3/844wePdpf5s3MtkH2d4Kdv3gl7909mk2rlxI2rGXdq7PY4XNbx1ztNGwC2x84jNWPXcWBNc7BVr1cVLVs1wHrgVdDCKtaaNM46rPJKE9JxwL/ktV2PtHFq0ZK+nUI4cWsxyhkTboUQng/3tb9wB2STg8h3BWv2yzpNuD7kr4SQrgzOzhJu4cQlubV26aP2x+4B3gDOCVzDtbWFDMmq2wNtQ1Jh9Cmda/NgZrtqPvkcHY57QZqaruxw+d/wGefG8ldd93FK6+8knSIZmat6t69e9IhWEIGDRrkL/BmVawSjrUrRa7vBF37foF3p45kzfN3sf61Oc0eU9trELW9BjF3dAIBm5UJF1WtiRDC60SnvrfmCeAd4DJJvYiKkJ8Gvkk0FcCnMrYXJH2HaI7UZyTdALwA7AQMJhqRenWOOD6QdDxwLzBN0jcy5jb9L+AI4HZJtxNdCGo9sB/RnK7PEV1wq1C/I5ob9VpgWHT9qSYx3drKY4sVk1WwPx37p6RDaFPnPQ6kx7EX0H3QyC3Lamq7MX36dG688UbuuuuuBKMzM2vbmDFjkg7BzMwSUAnH2pWipe8Eu46Yzgezb2TNfH8nMMvFRVUrWAhhpaTjgF8C5xK9j54jKh5+l4yiatz+WUmHARcCpwLnEF3Y6hngyVaeZ42kE4lGj94mqXMI4dYQwipJRwBj4u0NBzYSFXefACZvY9caLyZ1QQvrWyyqFjEmq2B9F/ZlYd+FbTdM0O5nZ09bvNXIkSMZOXJki+vNzMrBo48+ypAhQ5IOw8zMSqwSjrUrRWvfCboPGtmk2GpmW7moWsVCCI+S40JNLbTtnvX380QXkso2ixwjMkMIC4FvtPEcvXIsWwscl2P5GuBn8a21bU4BprSwbkhbz9/C48aTYzRvvjFZ9ei7yAd6ZmbF9thjj7moamZWhXysbWZJq0k6ADMzMzMzMzMzM7NK4qKqmZmZmZmZmZmZWQFcVDUzK5LHPv9Y0iGYmaXemWeemXQIZmaWAB9rm1nSXFQ1MzMzMzMzMzMzK4CLqmZmRTJ41uCkQzAzS73rr78+6RDMzCwBPtY2s6R1SjoAM7M0e27Ac9v+4AEdF4eZmZmZWdq061g7Xz4mN7MWeKSqmZmZmZmZmZmZWQFcVDUzK5LBg31KkplZsTnXmplVJ+d/M0uai6pmZkUyZMiQpEMwM0s951ozs+rk/G9mSXNR1cysSC677LKkQzAzSz3nWjOz6uT8b2ZJc1HVzKxIPvjgg6RDMDNLPedaM7Pq5PxvZklzUdXMzMzMzMzMzMysAJ2SDsDMLK1W9ljJwHkDt/y9fNZzCUaz7V4bnXQEZmYt22uvvZIOwczMEpB9rJ2tUo+9zSpZtX139EhVM7MiefzIx5MOwcws9c4666ykQzAzswT4WNvMkuaiqplZkRw0/6CkQzAzS71777036RDMzCwBPtY2s6S5qGpmViS9Xu+VdAhmZqk3b968pEMwY8SIEUhqdquvr2fIkCH84Ac/aPaYKVOm0L179y1/jx8/nk9+8pMtPkdL2zGrVj7WNrOkeU5VMzMzMzOzdho6dCi33HJLk2W77rprQtGYmZlZsbmoamZmZmZm1k61tbXsueeeSYdhZmZmJeLT/83MiuTBLzyYdAgdZsWKFRx//PGsWLEi6VDMzJr48Y9/nHQIliB/PplVrzQda5tVkzR9druoalVFUldJQdJ1Scdi6bfTyp2SDqHDTJkyhQcffJCbb7456VDMzJp46623kg7BElROn08PPPAA3bt333IbNmxY0iGZpVqajrXNqkk5fXa3l4uqVpYk3Slpk6TPtbD+c/H6O0sdm1m+/uXZf0k6hA4RQuCKK64A4IorriCEkHBEZmZbTZs2LekQLCHl9vl05JFHUl9fv+U2efLkROMxS7u0HGubVZNy++xuLxdVrVyNApYDUyR1y1whqQ64KV5/TgKxmVWFdYtns/zm0+nXrx/vvPMOACtXruSJJ55IODIzMzOYNWsWq1atAsrj86muro4+ffpsue2zzz4A7LjjjlvizLRy5Up69OhR6jDNzMw6XON3x4EDB3L66acze/bsnO3K7bO7vVxUtbIUQlgGnA3sD/wya/WlQB/g7BDC8lLHZlYNVv5xHEt/fQxr6qezaNEiNm3aBMAHH3yw5ZdFMzOzJF155ZV8+OGHAHz44Ydl+/nUt29f5s2b12w0zrx58+jbt29CUZmZmXWMzO+O8+bN4/bbb+eYY45h3LhxzdpWymd3vlxUtbIVQrgHuAUYJWkogKQhwPeB38brkfSLeJ7UZpdblfSOpAfaei5J/yJpmaTnJe2dsXyApBmS3pXUIOkFST+SVJP1+Gnx+l0kXR9va62kxyUNbNeOsIo1/6D5SYdQsGWTh/P6D8X7D15E2LAGcpyOce+99yKpyW348OEJRGtmBieeeGLSIVgJDB8+vNlnz8yZM7cUKkMIzJw5syw/n0aNGsWrr77Kueeey/z581m4cCFXXHEFU6dOZezYsU3aNjQ0NJlCoL6+nkWLFm1Zv3z58mbrPa+wVatKPNY2S5t1i2ez+pGJTb47bt68mTVr1vDzn/+8Yj+789Up6QDM2nAecBRwo6TPADcCb8bLO4SkE4HpwDPAKSGEVfHyzwIPA2uBXwPLgFOAy4FPAt/N2lQN8BDwGjAe2B34MTBTUu8QwpqOitkqw2v7vZZ0CAXr8a8Xs3bhQ7Ch5bfrxo0bt/y7a9eu7LHHHlx88cWlCM/MrJmBA/3bZTW4+OKLqa+vZ+nSpTQ0NACwfv36Jm0y/y6nz6fevXvz+OOPc8EFF3DsscfS0NBAv379uOOOOzjhhBOatH3llVc45JBDmiwbOHAgc+fOBWD69OlMnz69yfoxY8YwceLE4nbCrAxV4rG2Wdqsfuwqwsa1OdeFEKirq2Pz5s0V99mdL49UtbIWQlhJVLz8KFAPfAz4bmPhs70knQHcA8wEjs/a7tXAdsARIYQLQwi/AoYCM4CRko7I2lxn4NEQwhdDCL8OIYwDvgfsAXy1hec/S9JcSXOXLVvWEV2yMnLyvScnHULBuuzVn867H5BX27q6OoYPH86LL75I//79ixyZmVluEyZMSDoEK4H+/fuzYMECTj75ZOrq6lptm8Tn05QpU7jvvvtaXH/YYYfx4IMPsmTJElatWsWcOXM45ZRTmrQZP348IYRmt8aC6qOPPppzvQuqVq0q8VjbLG02LFuU8+xGiEas9u3bt2w/uzuCi6pW9kIIfwImAbsCk+K/203S/wOuJxqFeloIYV3Gup7AAODOEML/ZcSyGbgk/vOLOTabPSHII/H9x3PFEEKYFEI4NIRw6G677bZtHTHrYJ137wdq/eOhtraWyy67jGnTptGtW7dW25qZmXWEbt26MX36dC677DJqa2tztvHnk5mZWel03u2AFr871tTU0K9fv1R/druoapXi6az79vo3YAJwdQhhdFwszfSx+P7FHI99Ib7vnbV8A/BG1rIV8f1HtjVQs1LbYfBo1Klrq21qa2t9yq2ZmSViwIABrX4x8+eTmZlZabT23bFr166cd140c2NaP7tdVLU0yD3WPNLSvMFPEc19erqkg3Os1zbEsTlkX9a1fduzCvfOHu8kHcI2qe01iB2OHgvbdW6xzYYNG7acjmhmlqQDDshvyhJLj7lz57JhwwYAJFFXV4cUHWr588mselTqsbZZmjR+d1Tnui0jVmtqaqirq2Ps2LEMGjQISO9nt4uqlgbvxve7ZC6UtCMtjxBdDAwG3gcekZT9s8gr8X2uyTw+Ed+/WnCkVlWeOfyZpEPYZjsNm0Dt/oOBrR96v/jFL+jZsyddu3Zl7dq1zJo1K+Eozczg9NNPTzoEK7FZs2axdu1aunbtSs+ePbntttv46Ec/6s8nsypTycfaZmmy07AJ7P79h6n79KkMHDiQU089lYcffrjJvPdp/ex2UdXSYFF8PzRr+ZjWHhRCeI2osLoCeEjS4Rnr/gnMA74sacsQGEU/pfxn/Ofd7YzbUu7wZw5vu1EZ27j8FajZjlNPPZWlS5fyk5/8ZMtFQrbbbjvmzJmTdIhmZkydOjXpEKzE5syZw3bbbbflghannHKKP5/MqlClH2ubpUltr0Hs+u2pzJ07l6lTp24ZodoorZ/dLqpaGtwP/AO4VNIlkkZJmgZ8DVjV2gNDCG8QFVbfAf4s6bMZq88FNgNPSbpI0rnAn4kuUHVjCOHJIvTFUmTPJXsmHUK7dN7jQHY5dVKTCcMbLxIyadIk+vXrl3CEZmawaNGithtZqhx44IFMmuTPJ7NqV+nH2mbVJK2f3S3NN2lWMUIIGySdCFwFjAbWERVahwD1eTz+bUlDgIeBByWdEEKYFUJ4StIRRBe0Og/YHniZaATslcXoi1k52f3smS2uGzlyJCNHjixhNGZmZpGZM/35ZGZmVknS+tntoqpVhBDCFGBKK+sXAF/IsWrPrHYN5LhoVAhhCfDJHMvnASflEd9pLSzP+XxmZmZmZmZmZla5fPq/mVmRzDhpRtIhmJml3rhx45IOwczMEuBjbTNLmouqZmZFst9r+yUdgplZ6j333HNJh2BmZgnwsbaZJc1FVTOzIjn4+YOTDsHMLPXuu+++pEMwM7ME+FjbzJLmoqqZmZmZmZmZmZlZAVxUNTMzMzMzMzMzMytAp6QDMDNLq9NOO41xfTMuoDIguVjMzNLqtNNOSzoEMzNLQLNj7Ww+9jazIvNIVTOzItl7772TDsHMLPWca83MqpPzv5klzUVVM7Miufzyy5MOwcws9Zxrzcyqk/O/mSXNp/+bmZmZmVlJSUo6BDMzM7N28UhVMzMzMzMzMzMzswK4qGpmViQDBnh2fDOzYnOuNTOrTs7/ZpY0n/5vZlYkJ510UtIhmJmlnnNt5QghJB2CmaWI87+ZJc0jVc3MimTSpElJh2BmlnrOtWZm1cn538yS5qKqmVmRvP3220mHYGaWes61ZmbVyfnfzJLmoqqZmZmZmZmZmZlZAVxUNTMrku7duycdgplZ6jnXmplVJ+d/M0uai6pmZkUyZsyYpEMwM0s951ozs+rk/G9mSXNR1cysSB599NGkQzAzSz3nWjOz6uT8b2ZJc1HVzKxIHnvssaRDMDNLPedaM7Pq5PxvZklzUdXMzMzMzMzMzMysAC6qmpmZmZmZmZmZmRXARVUzsyI588wzkw7BzCz1nGvNzKqT87+ZJc1FVTMzMzMzMzMzM7MCuKhqZlYk119/fdIhmJmlnnOtmVl1cv43s6S5qGpmZmZmZmZmZmZWABdVzczMzMzMzMzMzAqgEELSMZgZIGkZ8FrScbRgV2B50kFUIO+3wnmfbRvvt8L1DSHskHQQUPL8n8b3Shr7BO5XJUljnyC9/arG/J/W19L9qhxp7BO4X5WkKLm/U0dv0My2TQhht6RjaImkuSGEQ5OOo9J4vxXO+2zbeL8VTtLcpGNoVMr8n8b3Shr7BO5XJUljnyDd/Uo6hkalyv9pfi3dr8qQxj6B+1VJipX7ffq/mZmZmZmZmZmZWQFcVDUzMzMzMzMzMzMrgIuqZpaPSUkHUKG83wrnfbZtvN8KV637LI39TmOfwP2qJGnsE7hfaZLWPrtflSONfQL3q5IUpU++UJWZmZmZmZmZmZlZATxS1czMzMzMzMzMzKwALqqamZmZmZmZmZmZFcBFVTNrkaTFkkKO2yk52p4p6e+S1klaKOmcJGJOmqSPSrpT0ipJ70v6vaSeScdVDiQNaeH9tDKr3c6SJktaLulDSQ9J+lRScZeSpH0lXS3paUlr4v3TK0e7rpL+R9LbktbG7Y/M0a5G0vnx/+UGSfMlfbkUfSmlAvZbrvdfkPTprHap229py+eVnmvTkA/Tmq/SmE8kfUXSXZJei1+DhZIukbRDVru83m/5vqbFlk+/JPVq5bXaqRz71ZHSlvvB+T+puDM5/zv/J5knyy33u6hqZm15EPhM1u2xzAaSzgR+A9wFHA/cAVwjaVRpQ02WpDrgEaAf8G3gm8DHgb9I6pZkbGXmPJq+n4Y2rpAkYAbR++hc4MtAZ6J9uG/pQy25PsCpwHvArFba3QCcCfw/4ETgbeDB7IM54GfAeOB/gWHAbOAOSSd0bNiJy3e/AUyheU5blNUmrfstFfk8Zbm2kvNhWvNVGvPJWGAT8FOi99O1wCjgz5JqoOD3W76vabG12a8Ml9D8tVqd1aZc+tXRUpH7wfkf5/9ic/6vjPxfXrk/hOCbb775lvMGLAZubaNNJ2ApcHPW8huB5UDnpPtRwv01Ok7wfTKWfQzYCPw46fiSvgFDgAAMbaXN8LjNURnLegDvAr9Kug8l2Ec1Gf8+I94XvbLaHBwv/07Gsk7AQmBGxrLdgXXAhKzHPww8n3RfS73f4nUB+O82tpXK/ZamfJ6GXJuGfJjWfJXGfALslmPZt+I+HF3I+y3f17SM+tUr/vuMNrZVNv3q4H2Umtwfx+T87/yfaL/idc7/rbymZdKnkuV+j1Q1s/b6DLAbcGvW8luAjwCfK3lEyTkZmB1CeLlxQQjhH8CTRB9W1raTgbdCCH9pXBBCWAXcSxXswxDC5jyanQxsAKZnPG4jMA04TlJtvPg4oAvN/2/eCnxK0sfaH3F5yHO/5atq9lsOlZLPqyXXlnU+TGu+SmM+CSEsy7H42fh+n/g+3/dbvq9p0eXZr3yVTb8SUCm5H5z/nf+LyPm/MvJ/ueV+F1XNrC0nxXPKrJM0W83nYOof37+QtfzF+P4TxQ2vrPSn+X6AaF9U035oy22SNklaIel3ajoPVmv7sKek7qUJsaz1B/4RQliTtfxFooO3Phnt1gEv52gH1fueHBXnszWSHpH0+az1ad5vacnnacq1ac+Hac9XlZxPBsf3/xff5/t+y/c1TUp2vxpdImmjonk4Z+SYK7Dc+9Ueacn94Pzv/F8+nP+btks6TyaW+11UNbPW3Es0r8pxwNeBBuBuSd/IaLNLfP9e1mPfzVpfDXah+X6AaF/sXOJYytEq4DKi02mOJppnaCjwtKTd4zat7UPwfoS299EuGfcrQ3weSyvtqsmtwPeI3ndnEY3AeUTSkIw2ad1vacrnaci11ZIP05yvKjafSNoHuAh4KIQwNyOWfN5v+b6mJddCv9YRzRV6NnAU0Vx8nwKeknRgxsPLtl/tlKbcD87/zv/lwfk/d7ty6lPJcn+nbYzbzCqMpKHAn/No+lgIYQhACOHcrG3cTTTB9iVsPZVB8X32B0a1yrUflGNZ1Qkh/BX4a8aixyQ9DjxDNFn/BUT7yvuwdfnuI+/LLCGEb2b8OUvSH4h+mf9vtp7eWPb7zfkcKPPXqC1VlA9Tm68qNZ/EI47+QDQH5XcyV1HBr1VL/QohvA1kXsV+lqQHiEYh/RfQWFwsy35lcu7foqxfp7Y4/1dGTmmN83+b7UqmHHK/i6pm1eMp4MA2W0H20PctQgibJN0BXCpprzhZZf6K83ZG88Zfdd6lerxH7l+zdib3L2BVL4QwT9Ii4LB40bu0vA/B+xGifdQzx/KdM9Y33u8sSVm/kme3q1ohhNWSZgLfzVhcCfut2vN5KnNtSvNh1eSrSsgnkroSXeG5NzA4hPBGxup832/5vqYl00a/mgkh/FPSE2z9vwZl2K8cqj33g/O/838Zcv4vyz41U6zc76KqWZWI5wn5ewdsKvvX7MZ5YfrT9ECscZ6YBR3wnJXiRbbOS5XpE1TXfihU5i+ELwLH5mjzCeD1EMIHJYuqfL0IfFFSXdb8P58A1rN17qYXgVpgf5rO51SN/zdbk/0LddnvN+fzVOfatOXDastXZZtPJHUG7gIOJ7rq+N+ymuT7fsv3NS2JPPrV4kNp/lqVTb9yce4HnP+d/8uX838J82Q55X7PqWpmeZPUCfgqUXJ9J178NLCcaJ6mTN8g+mXnydJFmLgZwCBJvRsXSOoFHBGvsyySDgUOAObEi2YA+0ganNFmR+AkvA8bzQA6E/1fBLb83/wa8KcQwrp48QNEBwO5/m++EF8tt6rF761/Zev7D6pkv1V4Pk9lrk1pPqyafFXO+URSDXAbcAwwPIQwO0ezfN9v+b6mRZdnv3I9ridRvsh8rcqmX8VU4bkfnP+d/8uQ839p82S55X6PVDWznCSdDgwH7gf+CewBfB8YCJze2C6EsEHShcA1kt4EHiKadH0kcG4IYX2pY0/Q9cAPgD9IuoDoV7CfEe2/3yQZWDmQdBvwD2AesBI4BDgfeBO4Om42g+jg/lZJ/050usn5RL8q/rLUMSdB0lfifw6M74dJWgYsCyE8FkKolzQduDL+lfYfwCjgY2QcuIUQlkq6Ajhf0mqi/f41ov+fw0vUnZJpa79JGgv0Bf4CvAXsRzRp/Z6kfL+lMJ9XfK5NSz5Ma75KYT75NdEXxp8DH0oalLHujfiUybzeb/m+piXSZr8kXUY0kOhpYBnR63Y+sBm4uLFxmfWrQ6Qw94Pzv/N/kTn/V0T+L6/cH0LwzTfffGt2AwYBjwBLgA1EV6p8CDiuhfZnA4uIrrT3EvC9pPuQ0H7rSXQqwvvAauAeoFfScZXDLf4gez5+L20gOgCeBOyV1W4X4Eai0RFrgIeBg5OOv4T7KbRwezSjzfbA5cA7RFfynQMMybGt7YguePBa/H/zeeArSfcxif1G9Gv7k0SjcTYAK4gOIg9P+35LYz6v9FyblnyY1nyVtnwCLG6lT+MLfb/l+5qWQ7+ICoPPEhUJNsYx/w7oW6796sD9k7rcH8fp/F8Gt7byZNzG+d/5P5E+UcLcr3gjZmZmZmZmZmZmZpYHz6lqZmZmZmZmZmZmVgAXVc3MzMzMzMzMzMwK4KKqmZmZmZmZmZmZWQFcVDUzMzMzMzMzMzMrgIuqZmZmZmZmZmZmZgVwUdXMzMzMzMzMzMysAC6qmpmZmZlVCEmLJT2adBxJkPQVSfMlrZUUJA1JOiYzMzOrXi6qmpmZmVlRSBoSF78ybx9Iek7SaEnbJR1jMcT9Hi9pp6RjSQtJBwBTgVXAD4BvAv+XaFDbQNIBkq6R9HdJH8YF4kWSJkk6LG6zOMf/m5ZuQ+LHZC9fJ+llSVdK+kiOOMbH7Q4t8S4wMzNLjU5JB2BmZmZmqTcVuB8QsDcwArgS6A+clVxYRTMEGAdMAVYmGkl6DCH67vLDEMK8hGPZJpK+C1wLNBD9n6gHNgIHAF8GzpTUH/gh0D3joQcCPwXuBn6ftdnMwnI9cFn8752BY4HRwFBJA0II6zu0Q2ZmZlXORVUzMzMzK7Z5IYRbG/+QdC1RMegMSReGEJa09wniUa+1IYQ17d2WlaU94/t322ooqTOwXQihobgh5U/SUGASsAA4LoTwVtb684FzAUII92StG0JUVH0+8/9RDm9mrb9a0t3AKcBJwF3t7YeZmZlt5dP/zczMzKykQgjvA08TjVzt3bhcUg9Jl8anLa+TtEzSVEm9Mx8vaUR86vJQSRdKeoVo9N+pGW0OkXSHpCXxtv4Zb2v/rG0NlfQnSSslNUh6XtI52TE3zmUqqZ+kmZJWS1ol6U5Je2a0m0I0ShXgHxmnY49va79I6iPpJklvSFov6S1Jf5A0MEfbVuOI2+wt6TJJ9ZLei/u3QNJPsqdeyNinR0saK+mVeL8tkvTtHM+/XbzvX8vYb1/LOK28V1b7vSRdK+n1jL5NkrR7HvslABOy9unieF3j8/WXdLmkN4jeC4MyHn+GpHmKTrVfFb/en8v1PJKmxPvgaUlr4tfiJ/H6nSXdIGlpvO4+SXu3FX/sUqL3+9eyC6oAIYSNIYQrQggL8txevh6K7z/ewds1MzNcMaDMAAAIrklEQVSreh6pamZmZmYlJUlAn/jP5fGyHsBTQE/gRuBFYC/ge8AcSYeGEF7L2tREoDNwPfA+sDDe1olEo/I+BCYDLxONdDwO+CTwStzuLOA6YDbw87j9F4BrJe0fQvj3rOfbB3iU6DTsfwcOBs4GdiQ61RrgN/HfXwR+1Ng/4Pk29smhwMNxf24AXgB2AQYDnwWeKzAOgIOAL8XtXom3PQz4BVEx++wcoVwMbB/3Yx0wCpgi6eUQwpMZ7f4XOAf4C9HrsBtwDfCPHH3rSVRE7xL37RWi138UcFT82q5qZfd8M+5H5j79IKvNbcBaotPfA/B2/NyXAv8BPEM02nMHoikn/iJpeAjh/qztHEI0qnMS8FuiQv0vJDUA3wYWA+Pj+M+L2wxtJXYkfQwYAMwqQtG0LY0/IrQ5wtfMzMwK46KqmZmZmRVbnaRdiUbq7UV0mvPBwOwQwktxm4uICn2DQgjzGx8Yj/z8G9FIxRFZ290eOCTzlH9JdcBNRBc0OiSE8GZG+4sk1cTt9gJ+BUwLIfxbRptrJF0F/FjSdSGEVzLW9SEaaXh7xvNtBr4nqV8I4e8hhKclPU9UALwnhLC4rZ0TF5mnALXA4SGEzALsJY0xFxJHvPgxoHcIIWQ89kpJtxBNvTA+hPB21rZrgcMa59+UdCfwKtHFoZ6Ml/UnKqg+CJwQQtgcL7+DaF7PbFcTFXQPCSG8kRHzHUQF7R8RFSpzCiHcKqkPre/TlcDQEMLGjO33JSo6PwkcndGnyUSn4V8TF883ZWznU8BnQghz4rY3AK8BVwD/G0I4L2P7AD+S1DeEsLCl+IkK+ZB733SkzvH/M4CdiH5E+D5RAfqeFh9lZmZm28Sn/5uZmZlZsU0AlgFLgfnASGAG0VyPjUXFrwOPA29K2rXxRjR6dDZNR2A2ujbHHKrHAbsCl2UVVAFoLAACXyEqIN6Q+Xzxc95LdJx8TNbD38osZMYeie/7sO0+TXTRrpuyCqrZMRcURwhhbWNBVVIXSbvE/XuQqH+5rvx+TeYFjeJ9uIimp4+fGN9flRlbCOFv8ba3iEcgn0j0ejdk7efFRKOIc722hboys6AaG05UyP9lVp/eIipi70c0MjXT040F1bjteqJRriIqwmeaFd+3dWr9jvH9+220a69jif6fLQNeIhpN/AJRsXlpkZ/bzMys6nikqpmZmZkV2yTgDqLTsj8EFoUQMk9H3g34CFuLQrlkFxYhKvZlayxw/bWNmA6M7x9qpc0eWX+/mqPNivj+I208X2vyjbmgOCR1Av4T+BZRsVVZj9m5gG3vl/H3x+L7XKMzFxJNMdCoL1EB97vxLZdcz1moXO+FxjhfzLHuhfi+NzC3jVjei++zpzZoXN7Wa99YTN2hjXbtNQe4gOh17gn8GNgXWN/ag8zMzGzbuKhqZmZmZsX2UgihteJlY7HvIaIL+uQre5Rq5rZCjnW52n2LeP7NHLILbJtytmq6vW2Rb8yFxnE50VQL04nmjF0KbCCa3/NScp+11tK21cK/29LY9lbg5hbarC1gey1p7b1QiBb3bdY0AYU8T2MBN3tUbEdbnvn/TNLdRFNn3CWpfwihI/azmZmZxVxUNTMzM7OkLSOaE3PHNoqv+WgcPXkI8OdW2jXO5bq8A54zW77F0UaZMXekbwKPhxBOy1wYz0/aHo0jNvvSvPDcN+vvl4n2R5ci7Oe2NM6H2z/j340+Ed93xCjZVoUQ/iHpr8ARWXPeFvt535V0AdGF335EdBEyMzMz6yCeU9XMzMzMEhXPy3kbcLikr+RqI2n3PDf3J6Krw4+JL0aVvZ3GUYW3E13dfoKk7XO06yGpNs/nzNZ4Zfpd8mw/n+gU9ZHxRaCyY9nWUbCbyBpFKakbUYGtPe6N70dnXkRL0qeI5rTdIoSwArgf+JKkQdkbUmS3dsbTkhlEBd1/l9Q54zn3Ar5DdAGqfKdcaK+fxPfTJO2ZvVLSdpJ+KOkT2eva6RaiwvFYSTu21djMzMzy55GqZmZmZlYO/gs4Arhd0u1EF6daTzSX5wnAc8CItjYSQlgj6bvAncAL8ZXeXyaat/U4olPi/xBCeEPSKGAy8H+SbiEqsu1GdAX4U4hGMy7ehr7Mju8vlXQb0AC8EEJ4IVfjEEKQ9B3gYeCZ+IrzLxBdwX0w8ABw9TbEcSdwtqTpRFMr7EF0kbAVrT6qDSGEFyVNAs4CHopPM9+N6ErzfwUG0nS07ijgCeBxSb+N29QQzWc6HPgtML49MbUQ50JJ/wP8R/zc04nmNT0L6A58vZVT+js6lj9LOgu4FlgoaSpQD2wkmu/2y8D+wCc7+Hk3SroEuB4YDfwsq8lIScfneOhzIYQ/dmQsZmZmaeOiqpmZmZklLoSwStIRwBjgVKJi20bgDaKC3OQCtjVD0ueAnxJdHGkHYEm8nb9ltLtJ0iJgLHA2URFzOdHp+BcC72xjX56U9BPgHKJiVidgAlvn1sz1mGclHRY/76nxY5cTXXn+yW2Jg+hCRavZuj//SXTRsGdp/QJd+fge8BbR/p1ItM9GAYcTFVW3zN8ZQvinpIFEozWHA98gKjT/k2jU6+3tjKVFIYSfSHo5jvcXRIX6OcC/hRBmFet5W4jlBklPAD8EjiGaz7eGqJj/CHBqCGFBEZ76ZqL31Y8l/SqEsCpj3agWHvMbwEVVMzOzViiEQqd8MjMzMzMza07SvcDRRPPjlmQUqJmZmVkSPKeqmZmZmZkVpIV5aA8ChgGPuKBqZmZmaeeRqmZmZmZmVhBJ5xCdvj4TWAb0I5qrtAY4IoRQqgtAmZmZmSXCRVUzMzMzMyuIpMOJLnr0aWAXorlbnwAmhBCeSzI2MzMzs1JwUdXMzMzMzMzMzMysAJ5T1czMzMzMzMzMzKwALqqamZmZmZmZmZmZFcBFVTMzMzMzMzMzM7MCuKhqZmZmZmZmZmZmVgAXVc3MzMzMzMzMzMwK8P8BvU5Ulm+ozV8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can we combine all three into one single plot? \n",
    "\n",
    "## --- Set up figure window --- \n",
    "fig, ax_array = plt.subplots(1,3)\n",
    "ax1,ax2,ax3 = ax_array.flatten()\n",
    "fig.set_size_inches(20,12)\n",
    "\n",
    "fig.subplots_adjust(wspace=0.1, hspace=0.2)\n",
    "\n",
    "#Set up axes \n",
    "# ax1.set_title('Change in Mean Streamflow',fontsize=18)\n",
    "# ax2.set_title('Change in Max Streamflow',fontsize=18)\n",
    "# ax3.set_title('Change in Min Streamflow',fontsize=18)\n",
    "ax1.set_title('d',{'fontweight' :'bold','fontsize':16},loc='left') \n",
    "ax2.set_title('e',{'fontweight' :'bold','fontsize':16},loc='left') \n",
    "ax3.set_title('f',{'fontweight' :'bold','fontsize':16},loc='left') \n",
    "\n",
    "ax1.set_xlim(-75,150)\n",
    "ax2.set_xlim(-75,250)\n",
    "ax3.set_xlim(-75,250)\n",
    "ax2.set_xlabel('Percent change from CTRL',fontsize=18)\n",
    "ax1.tick_params(axis='x',labelsize=16)\n",
    "ax2.tick_params(axis='x',labelsize=16)\n",
    "ax3.tick_params(axis='x',labelsize=16)\n",
    "\n",
    "\n",
    "ax1.set_yticks(np.arange(np.size(allValid)))\n",
    "ax2.set_yticks(np.arange(np.size(allValid)))\n",
    "ax3.set_yticks(np.arange(np.size(allValid)))\n",
    "ax1.set_yticklabels(np.array(basinNames)[allValid],fontsize=18)\n",
    "ax2.get_yaxis().set_visible(False)\n",
    "ax3.get_yaxis().set_visible(False)\n",
    "ax1.set_ylim(-1,np.size(allValid))\n",
    "ax2.set_ylim(-1,np.size(allValid))\n",
    "ax3.set_ylim(-1,np.size(allValid))\n",
    "\n",
    "ax1.axvline(linewidth=1,color='gray',linestyle='dashed')\n",
    "ax2.axvline(linewidth=1,color='gray',linestyle='dashed')\n",
    "ax3.axvline(linewidth=1,color='gray',linestyle='dashed')\n",
    "\n",
    "\n",
    "#Work on first subplot: MEAN STREAMFLOW \n",
    "for iBasin in range(np.size(allValidMean)):\n",
    "    #Index of basin \n",
    "    basinChoice = np.where(allValid == allValidMean[0][iBasin])\n",
    "    basinChoice = np.array(basinChoice)[1,0]\n",
    "        \n",
    "    ax1.plot(meanFull[allValid[0][basinChoice]],basinChoice, color='black', marker='*',markersize=15)\n",
    "    ax1.plot(meanSum[allValid[0][basinChoice]],basinChoice,color='black',marker='.',markersize=15)\n",
    "    \n",
    "    if ((meanPhys[allValid[0][basinChoice]]<0) & (meanRad[allValid[0][basinChoice]]>0)): \n",
    "        ax1.barh(basinChoice,meanPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax1.barh(basinChoice,meanRad[allValid[0][basinChoice]],left=0,color='DodgerBlue')\n",
    "    elif ((meanPhys[allValid[0][basinChoice]]>0) & (meanRad[allValid[0][basinChoice]]<0)): \n",
    "        ax1.barh(basinChoice,meanPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax1.barh(basinChoice,meanRad[allValid[0][basinChoice]],left=0,color='DodgerBlue')\n",
    "    else: \n",
    "        ax1.barh(basinChoice,meanPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax1.barh(basinChoice,meanRad[allValid[0][basinChoice]],left=meanPhys[allValid[0][basinChoice]],color='DodgerBlue')\n",
    "\n",
    "\n",
    "#Work on second subplot: MAX STREAMFLOW \n",
    "for iBasin in range(np.size(allValidMax)): \n",
    "    #Index of basin \n",
    "    basinChoice = np.where(allValid == allValidMax[0][iBasin])\n",
    "    basinChoice = np.array(basinChoice)[1,0]\n",
    "\n",
    "    ax2.plot(maxFull[allValid[0][basinChoice]],basinChoice, color='black', marker='*',markersize=15)\n",
    "    ax2.plot(maxSum[allValid[0][basinChoice]],basinChoice,color='black',marker='.',markersize=15)\n",
    "\n",
    "    if ((maxPhys[allValid[0][basinChoice]]<0) & (maxRad[allValid[0][basinChoice]]>0)): \n",
    "        ax2.barh(basinChoice,maxPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax2.barh(basinChoice,maxRad[allValid[0][basinChoice]],left=0,color='DodgerBlue')\n",
    "    elif ((maxPhys[allValid[0][basinChoice]]>0) & (maxRad[allValid[0][basinChoice]]<0)): \n",
    "        ax2.barh(basinChoice,maxPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax2.barh(basinChoice,maxRad[allValid[0][basinChoice]],left=0,color='DodgerBlue')\n",
    "    else: \n",
    "        ax2.barh(basinChoice,maxPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax2.barh(basinChoice,maxRad[allValid[0][basinChoice]],left=maxPhys[allValid[0][basinChoice]],color='DodgerBlue')\n",
    "#Plot legend in second plot at bottom \n",
    "ax2.plot(120,0, color='black', marker='*',markersize=15)\n",
    "ax2.plot(120,1,color='black', marker='.',markersize=15)\n",
    "ax2.barh(2,20,left=110,color='LimeGreen')\n",
    "ax2.barh(3,20,left=110,color='DodgerBlue')\n",
    "rect = patches.Rectangle((100,-0.5),142,4.5,linewidth=3,edgecolor='black',facecolor='none')\n",
    "ax2.add_patch(rect)\n",
    "\n",
    "ax2.text(0.69,0.05, 'FULL',va='bottom', ha='left',\n",
    "    rotation='horizontal', rotation_mode='anchor',\n",
    "    transform=ax2.transAxes,fontsize=14,bbox=dict(facecolor='white',edgecolor='white', alpha=0.75))\n",
    "ax2.text(0.69,0.10, 'PHYS+RAD ',va='bottom', ha='left',\n",
    "    rotation='horizontal', rotation_mode='anchor',\n",
    "    transform=ax2.transAxes,fontsize=14,bbox=dict(facecolor='white',edgecolor='white', alpha=0.75))\n",
    "ax2.text(0.69,0.15, 'PHYS change',va='bottom', ha='left',\n",
    "    rotation='horizontal', rotation_mode='anchor',\n",
    "    transform=ax2.transAxes,fontsize=14,bbox=dict(facecolor='white',edgecolor='white', alpha=0.75))\n",
    "ax2.text(0.69,0.20, 'RAD change',va='bottom', ha='left',\n",
    "    rotation='horizontal', rotation_mode='anchor',\n",
    "    transform=ax2.transAxes,fontsize=14,bbox=dict(facecolor='white',edgecolor='white', alpha=0.75))\n",
    "\n",
    "\n",
    "    \n",
    "#Work on third subplot: MIN STREAMFLOW \n",
    "for iBasin in range(np.size(allValidMin)): \n",
    "    #Index of basin \n",
    "    basinChoice = np.where(allValid == allValidMin[0][iBasin])\n",
    "    basinChoice = np.array(basinChoice)[1,0]\n",
    "\n",
    "    ax3.plot(minFull[allValid[0][basinChoice]],basinChoice, color='black', marker='*',markersize=15)\n",
    "    ax3.plot(minSum[allValid[0][basinChoice]],basinChoice,color='black',marker='.',markersize=15)\n",
    "    \n",
    "    if ((minPhys[allValid[0][basinChoice]]<0) & (minRad[allValid[0][basinChoice]]>0)): \n",
    "        ax3.barh(basinChoice,minPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax3.barh(basinChoice,minRad[allValid[0][basinChoice]],left=0,color='DodgerBlue')\n",
    "    elif ((minPhys[allValid[0][basinChoice]]>0) & (minRad[allValid[0][basinChoice]]<0)): \n",
    "        ax3.barh(basinChoice,minPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax3.barh(basinChoice,minRad[allValid[0][basinChoice]],left=0,color='DodgerBlue')\n",
    "    else: \n",
    "        ax3.barh(basinChoice,minPhys[allValid[0][basinChoice]],left=0,color='LimeGreen')\n",
    "        ax3.barh(basinChoice,minRad[allValid[0][basinChoice]],left=minPhys[allValid[0][basinChoice]],color='DodgerBlue')\n",
    "\n",
    "\n",
    "#fig.savefig('BarChart_StreamflowChange.eps',bbox_inches = 'tight')  #Save figure as pdf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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