FLUXNET_SubDailyComparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:00:33.329658Z",
     "start_time": "2021-03-02T17:00:27.805573Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "\n",
    "# Plotting utils \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.ticker as ticker \n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.animation as animation\n",
    "import matplotlib as matplotlib\n",
    "import cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.util\n",
    "\n",
    "# Analysis\n",
    "import os\n",
    "import numpy as np \n",
    "import xarray as xr\n",
    "import datetime\n",
    "from   datetime import date, timedelta\n",
    "import pandas as pd \n",
    "#import regionmask\n",
    "import pickle\n",
    "import scipy.stats as stats\n",
    "import glob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:00:34.739467Z",
     "start_time": "2021-03-02T17:00:34.737143Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('once')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in FLUXNET data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:03:51.311574Z",
     "start_time": "2021-03-02T17:03:16.298251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available site characteristics (metadata) include: \n",
      "\n",
      " ['SITE_ID' 'SITE_NAME' 'FLUXNET2015' 'FLUXNET-CH4' 'LOCATION_LAT'\n",
      " 'LOCATION_LONG' 'LOCATION_ELEV' 'IGBP' 'MAT' 'MAP'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# --------------------------------------\n",
    "# Define data directory: \n",
    "# --------------------------------------\n",
    "fluxnetDir   = '/Users/mdfowler/Documents/Analysis/Coupling_initial/data/FLUXNET2015/'\n",
    "\n",
    "# --------------------------------------\n",
    "# Get general site info\n",
    "# --------------------------------------\n",
    "siteInfoFile = fluxnetDir+'SiteList.csv'\n",
    "# Now read that CSV file into a pandas dataframe \n",
    "siteInfoDF = pd.read_csv(siteInfoFile)\n",
    "\n",
    "# Print out information available:\n",
    "print('Available site characteristics (metadata) include: \\n\\n', siteInfoDF.columns.values, '\\n')\n",
    "\n",
    "# --------------------------------------\n",
    "# And read in metadata for good measure\n",
    "# --------------------------------------\n",
    "metadataFile = fluxnetDir+'FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.xlsx'\n",
    "\n",
    "# Read in that file \n",
    "metadataDF = pd.read_excel(metadataFile, engine='openpyxl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:05:30.325117Z",
     "start_time": "2021-03-02T17:05:30.238840Z"
    }
   },
   "outputs": [],
   "source": [
    "# --------------------------------------\n",
    "# Get a list of all fluxnet data files\n",
    "# --------------------------------------\n",
    "dataFiles = glob.glob(fluxnetDir + 'FLX_*/*SUBSET_H?*')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:05:31.081778Z",
     "start_time": "2021-03-02T17:05:31.073955Z"
    }
   },
   "outputs": [],
   "source": [
    "# ------------------------------------------\n",
    "# Define a function to read in FLUXNET data \n",
    "# ------------------------------------------\n",
    "\n",
    "# --------------------------------------------------------\n",
    "# Function to read requested variables from FLUXNET file. \n",
    "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
    "# \n",
    "# Inputs:  fileName --> Full path to FLUXNET data file \n",
    "#          varNames --> An array of variable names to be \n",
    "#                       retrieved from said data file. \n",
    "#                       NOTE: If you wish to retrieve *all* \n",
    "#                       variables, pass the string 'ALL'. \n",
    "#          Note: varInfo found here\n",
    "#                https://fluxnet.org/data/fluxnet2015-dataset/subset-data-product/\n",
    "#  \n",
    "# Outputs: fluxnetID --> ID string used to identify station\n",
    "#          fluxnetDS --> An x-array dataset containing the \n",
    "#                        requested variables.\n",
    "#                        Missing values will be set to NaN. \n",
    "# \n",
    "# --------------------------------------------------------\n",
    "\n",
    "def readFLUXNET_var(fileName, varNames): \n",
    "    # Get ID of station \n",
    "    startID   = fileName.find('FLX_')\n",
    "    fluxnetID = fileName[startID+4:startID+10]\n",
    "    \n",
    "    # print('Reading in site - ', fluxnetID)\n",
    "    \n",
    "    # Read in CSV file containing data \n",
    "    dataDF = pd.read_csv(fileName)\n",
    "    \n",
    "    # Return ALL variables from dataDF if requested\n",
    "    if varNames=='ALL':\n",
    "        fluxnetDF = dataDF\n",
    "        \n",
    "        # Set any value that's missing to NaN (not -999)\n",
    "        fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
    "\n",
    "        \n",
    "        # If time has been requested, reformat to pandas make index\n",
    "        fluxnetDF['TIMESTAMP_START'] = pd.to_datetime(fluxnetDF['TIMESTAMP_START'].values, format='%Y%m%d%H%M')\n",
    "        fluxnetDF = fluxnetDF.set_index(['TIMESTAMP_START'])\n",
    "        \n",
    "        # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
    "        fluxnetDS = fluxnetDF.to_xarray()\n",
    "            \n",
    "    # Reduce returned DF to contain only variables of interest  \n",
    "    else:\n",
    "        \n",
    "        # Check that requested variables are available in specific file\n",
    "        errCount = 0                       # Initialize flag for error \n",
    "        colNames = dataDF.columns.values   # Available variables in file \n",
    "        \n",
    "        for iVar in range(len(varNames)):  # Check each variable individually\n",
    "            if (varNames[iVar] in colNames)==False:\n",
    "                # Turn on print statement for more verbose output\n",
    "                # print('** ERROR: %13s   not contained in file for %8s  **' %(varNames[iVar], fluxnetID))\n",
    "                \n",
    "                # If any variable is not conatined in file, return a NaN \n",
    "                fluxnetDS = -999\n",
    "                errCount  = errCount+1\n",
    "                \n",
    "        # If all the variables *are* available, isolate those in DF and return that\n",
    "        if errCount == 0: \n",
    "            fluxnetDF = dataDF[varNames]\n",
    "            \n",
    "            # Set any value that's missing to NaN (not -999)\n",
    "            fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
    "            \n",
    "            # If time has been requested, reformat to pandas make index\n",
    "            if ('TIMESTAMP_START' in varNames)==True:\n",
    "                fluxnetDF['TIMESTAMP_START'] = pd.to_datetime(fluxnetDF['TIMESTAMP_START'].values, format='%Y%m%d%H%M')\n",
    "                fluxnetDF = fluxnetDF.set_index(['TIMESTAMP_START'])\n",
    "        \n",
    "            # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
    "            fluxnetDS = fluxnetDF.to_xarray()\n",
    "    \n",
    "    return(fluxnetID, fluxnetDS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:08:53.270618Z",
     "start_time": "2021-03-02T17:05:32.948621Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define emtpy arrays to save stuff into \n",
    "startTime_fluxnet = np.full([len(dataFiles)], np.nan)\n",
    "endTime_fluxnet   = np.full([len(dataFiles)], np.nan)\n",
    "nMonths_fluxnet   = np.full([len(dataFiles)], np.nan)\n",
    "# allStationID      = np.empty([len(dataFiles)]).astype(str)\n",
    "\n",
    "# Variables I want returned:\n",
    "varNames = ['TIMESTAMP_START','H_F_MDS','LE_F_MDS','WS_F','P_F','PA_F','TA_F']\n",
    "\n",
    "# Loop over each station (data file)\n",
    "for iStation in range(len(dataFiles)): \n",
    "    \n",
    "    # Read in data \n",
    "    # ----------------------------------------------------------\n",
    "    fluxnetID,fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
    "#     allStationID[iStation] = fluxnetID\n",
    "    \n",
    "    # Save lat and lon for this station \n",
    "    # ----------------------------------------------------------\n",
    "    indStation  = np.where(fluxnetID==siteInfoDF['SITE_ID'])\n",
    "    lat_fluxnet = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
    "    lon_fluxnet = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
    "    \n",
    "    # Check that there was data saved for this particular site: \n",
    "    # ----------------------------------------------------------\n",
    "    if (type(fluxnetDS)==int):\n",
    "        print('No data for station: %8s' % fluxnetID)\n",
    "    \n",
    "    elif ( (np.all(np.isnan(fluxnetDS['H_F_MDS']))==True) | (np.all(np.isnan(fluxnetDS['WS_F']))==True) | (np.all(np.isnan(fluxnetDS['LE_F_MDS']))==True) ):\n",
    "        print('No data for station (all Nan): %8s' % fluxnetID)\n",
    "        \n",
    "    # If data is present: \n",
    "    # ----------------------------------------------------------\n",
    "    else:        \n",
    "        # Only consider where data is actually present for selected vars\n",
    "        iReal      = np.where((np.isfinite(fluxnetDS['LE_F_MDS'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['H_F_MDS'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['WS_F'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['P_F'])==True) &\n",
    "                              (np.isfinite(fluxnetDS['PA_F'])==True) &   \n",
    "                              (np.isfinite(fluxnetDS['TA_F'])==True))[0]\n",
    "        fluxnetDS  = fluxnetDS.isel(TIMESTAMP_START=iReal)\n",
    "#         # Upsample to 3-hourly?\n",
    "#         fluxnetDS = fluxnetDS.resample(TIMESTAMP_START='3H').mean()\n",
    "        \n",
    "        # Check how long data is available for and isolate JJA \n",
    "        # -----------------------------------------------------------\n",
    "        startTime_fluxnet[iStation]  = fluxnetDS['TIMESTAMP_START'].values[0]\n",
    "        endTime_fluxnet[iStation]    = fluxnetDS['TIMESTAMP_START'].values[-1]\n",
    "        \n",
    "        dateRange = pd.date_range(start=startTime_fluxnet[iStation],end=endTime_fluxnet[iStation],freq='M')\n",
    "        nMonths_fluxnet[iStation] = len(dateRange)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:10:05.021205Z",
     "start_time": "2021-03-02T17:10:05.010039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations with at least 5 years of data:  114\n",
      "\n",
      "Looking at every station available...\n",
      "Earliest start of data:     1992-01-01 00:00:00\n",
      "Latest end of data:         2014-12-31 23:30:00\n",
      "\n",
      "Of stations with at least 5 years of data...\n",
      "Earliest start of data:     1992-01-01 00:00:00\n",
      "Latest end of data:         2014-12-31 23:30:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# Check how many/which stations have enough years of data to be used, then\n",
    "#    print some useful information on all that and \n",
    "\n",
    "iMinStart = np.where(startTime_fluxnet==np.nanmin(startTime_fluxnet))[0]\n",
    "iMaxEnd   = np.where(endTime_fluxnet==np.nanmax(endTime_fluxnet))[0]\n",
    "\n",
    "limYears = 5 \n",
    "iStation_limYears = np.where(nMonths_fluxnet>=limYears*12)[0]\n",
    "\n",
    "print('Number of stations with at least 5 years of data: ', len(iStation_limYears))\n",
    "print()\n",
    "\n",
    "print('Looking at every station available...')\n",
    "print('Earliest start of data:    ', pd.DatetimeIndex(startTime_fluxnet[iMinStart])[0] )\n",
    "print('Latest end of data:        ', pd.DatetimeIndex(endTime_fluxnet[iMaxEnd])[0] )\n",
    "\n",
    "print()\n",
    "del iMinStart\n",
    "del iMaxEnd\n",
    "\n",
    "startTime_fluxnet_sel = startTime_fluxnet[iStation_limYears]\n",
    "endTime_fluxnet_sel   = endTime_fluxnet[iStation_limYears]\n",
    "\n",
    "iMinStart = np.where(startTime_fluxnet_sel==np.nanmin(startTime_fluxnet_sel))[0]\n",
    "iMaxEnd   = np.where(endTime_fluxnet_sel==np.nanmax(endTime_fluxnet_sel))[0]\n",
    "print('Of stations with at least 5 years of data...')\n",
    "print('Earliest start of data:    ', pd.DatetimeIndex(startTime_fluxnet_sel[iMinStart])[0] )\n",
    "print('Latest end of data:        ', pd.DatetimeIndex(endTime_fluxnet_sel[iMaxEnd])[0] )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:10:06.035980Z",
     "start_time": "2021-03-02T17:10:06.029396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max number of days:  403248\n"
     ]
    }
   ],
   "source": [
    "# So let's filter stations to use just the ones that have at least 5 years of data \n",
    "#    (or whatever the limYears threshold is set to above...)\n",
    "dataFiles_filter = np.squeeze(dataFiles)[iStation_limYears]\n",
    "\n",
    "# Let's also figure out just how many days we'll need to create empty arrays for \n",
    "dayRange = pd.date_range(start=pd.DatetimeIndex(startTime_fluxnet_sel[iMinStart])[0],\n",
    "                         end=pd.DatetimeIndex(endTime_fluxnet_sel[iMaxEnd])[0],\n",
    "                         freq='30min')\n",
    "\n",
    "# dayRange = pd.date_range(start=pd.DatetimeIndex(startTime_fluxnet_sel[iMinStart])[0],\n",
    "#                          end=pd.DatetimeIndex(endTime_fluxnet_sel[iMaxEnd])[0],\n",
    "#                          freq='3H')\n",
    "nDays = len(dayRange)\n",
    "\n",
    "print('Max number of days: ', nDays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:12:51.573204Z",
     "start_time": "2021-03-02T17:10:07.019055Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define emtpy arrays to save stuff into \n",
    "lat_fluxnet     = np.full([len(dataFiles_filter)], np.nan)\n",
    "lon_fluxnet     = np.full([len(dataFiles_filter)], np.nan)\n",
    "stationID       = np.empty([len(dataFiles_filter)]).astype(str)\n",
    "fluxnet_SH      = np.full([len(dataFiles_filter), nDays], np.nan)    # H_F_MDS  [W/m2]\n",
    "fluxnet_LH      = np.full([len(dataFiles_filter), nDays], np.nan)    # LE_F_MDS [W/m2] \n",
    "fluxnet_spd     = np.full([len(dataFiles_filter), nDays], np.nan)    # WS_F     [m/s]\n",
    "fluxnet_precip  = np.full([len(dataFiles_filter), nDays], np.nan)    # P_F      [mm/day - summed from half/hourly for daily data]\n",
    "fluxnet_pressr  = np.full([len(dataFiles_filter), nDays], np.nan)    # PA_F     [kPa] -- seriously? kPa? \n",
    "fluxnet_temp    = np.full([len(dataFiles_filter), nDays], np.nan)    # TA_F     [deg C]\n",
    "\n",
    "# Variables I want returned:\n",
    "varNames = ['TIMESTAMP_START','H_F_MDS','LE_F_MDS','WS_F','P_F','PA_F','TA_F']\n",
    "\n",
    "# Loop over each station (data file)\n",
    "for iStation in range(len(dataFiles_filter)):\n",
    "    \n",
    "    # Read in data \n",
    "    # ----------------------------------------------------------\n",
    "    fluxnetID,fluxnetDS = readFLUXNET_var(dataFiles_filter[iStation], varNames)\n",
    "    \n",
    "    # Save lat and lon for this station \n",
    "    # ----------------------------------------------------------\n",
    "    indStation            = np.where(fluxnetID==siteInfoDF['SITE_ID'])\n",
    "    lat_fluxnet[iStation] = siteInfoDF['LOCATION_LAT'].values[indStation]\n",
    "    lon_fluxnet[iStation] = siteInfoDF['LOCATION_LONG'].values[indStation]\n",
    "    stationID[iStation]   = fluxnetID\n",
    "    \n",
    "    # Check that there was data saved for this particular site: \n",
    "    # ----------------------------------------------------------\n",
    "    if (type(fluxnetDS)==int):\n",
    "        print('No data for station: %8s' % fluxnetID)\n",
    "    \n",
    "    elif ( (np.all(np.isnan(fluxnetDS['H_F_MDS']))==True) | (np.all(np.isnan(fluxnetDS['WS_F']))==True) | (np.all(np.isnan(fluxnetDS['LE_F_MDS']))==True) ):\n",
    "        print('No data for station: %8s' % fluxnetID)\n",
    "        \n",
    "    # If data is present: \n",
    "    # ----------------------------------------------------------\n",
    "    else:        \n",
    "        # Only consider where data is actually present for selected vars\n",
    "        iReal      = np.where((np.isfinite(fluxnetDS['LE_F_MDS'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['H_F_MDS'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['WS_F'])==True) & \n",
    "                              (np.isfinite(fluxnetDS['P_F'])==True) &\n",
    "                              (np.isfinite(fluxnetDS['PA_F'])==True) &   \n",
    "                              (np.isfinite(fluxnetDS['TA_F'])==True))[0]\n",
    "        fluxnetDS  = fluxnetDS.isel(TIMESTAMP_START=iReal)\n",
    " \n",
    "        \n",
    "        # Check where to save data in time arrays \n",
    "        # -----------------------------------------------------------\n",
    "        startTime_fluxnet = pd.DatetimeIndex(fluxnetDS['TIMESTAMP_START'].values)[0]\n",
    "        endTime_fluxnet   = pd.DatetimeIndex(fluxnetDS['TIMESTAMP_START'].values)[-1]\n",
    "        \n",
    "        iTime_start = int(np.where(startTime_fluxnet==dayRange)[0])\n",
    "        iTime_end   = int(np.where(endTime_fluxnet==dayRange)[0])\n",
    "        \n",
    "#         # Upsample to 3-hourly?\n",
    "#         fluxnetDS=fluxnetDS.resample(TIMESTAMP_START='3H').mean()\n",
    "        \n",
    "        # If any 30-minute period is missing, fill it in with NaN's but keep a row present for it \n",
    "        fluxnetDS = fluxnetDS.reindex({'TIMESTAMP_START': dayRange[iTime_start:(iTime_end+1)]})\n",
    "       \n",
    "        # Save timeseries of each variable out to arrays \n",
    "        # -----------------------------------------------------------\n",
    "        fluxnet_SH[iStation,iTime_start:(iTime_end+1)]     = fluxnetDS['H_F_MDS'].values\n",
    "        fluxnet_LH[iStation,iTime_start:(iTime_end+1)]     = fluxnetDS['LE_F_MDS'].values\n",
    "        fluxnet_spd[iStation,iTime_start:(iTime_end+1)]    = fluxnetDS['WS_F'].values\n",
    "        fluxnet_precip[iStation,iTime_start:(iTime_end+1)] = fluxnetDS['P_F'].values\n",
    "        fluxnet_pressr[iStation,iTime_start:(iTime_end+1)] = fluxnetDS['PA_F'].values\n",
    "        fluxnet_temp[iStation,iTime_start:(iTime_end+1)]   = fluxnetDS['TA_F'].values\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Uncomment below to average up to three hourly**<br>\n",
    "The issue with this is that everything's in local time. The model only outputs every three hours in *UTC* time, so the hours wouldn't match up with the three hourly averaged FLUXNET data if averaging were constant. So keep it in hourly for now, then get just the hours that match later. \n",
    "\n",
    "**UPDATE**: Upsample to *hourly*, not thirty minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:18:52.518052Z",
     "start_time": "2021-03-02T17:17:02.268870Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "<frozen importlib._bootstrap>:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to get 1-hour means (sum for precip)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    }
   ],
   "source": [
    "# Upsample to hourly \n",
    "fluxnetSH_DS = xr.DataArray(fluxnet_SH, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "fluxnetLH_DS = xr.DataArray(fluxnet_LH, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "fluxnetSpd_DS = xr.DataArray(fluxnet_spd, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "fluxnetPrec_DS = xr.DataArray(fluxnet_precip, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "fluxnetTemp_DS = xr.DataArray(fluxnet_temp, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "\n",
    "print('Starting to get 1-hour means (sum for precip)')\n",
    "fluxnet_SH     = fluxnetSH_DS.resample(time='1H').mean()\n",
    "fluxnet_LH     = fluxnetLH_DS.resample(time='1H').mean()\n",
    "fluxnet_spd    = fluxnetSpd_DS.resample(time='1H').mean()\n",
    "fluxnet_precip = fluxnetPrec_DS.resample(time='1H').sum()\n",
    "fluxnet_temp   = fluxnetTemp_DS.resample(time='1H').mean()\n",
    "\n",
    "# Fill in NaN's for precip anywhere there should be one (based on whether or not SH has data)\n",
    "#   If not, xarray is filling these with zeros and it's throwing things off later. \n",
    "# fluxnet_precip[np.where(np.isnan(fluxnet_SH)==True)[0]] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:32:16.785035Z",
     "start_time": "2021-03-02T17:32:16.781495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1992-01-01T00:00:00.000000000', '1992-01-01T00:30:00.000000000',\n",
       "       '1992-01-01T01:00:00.000000000', '1992-01-01T01:30:00.000000000'],\n",
       "      dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxnetSH_DS['time'].values[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:33:53.362340Z",
     "start_time": "2021-03-02T17:33:53.351619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 6)&gt;\n",
       "array([  4.7,   nan, -34.1,   nan, -94.2,   nan])\n",
       "Coordinates:\n",
       "    stationID  &lt;U32 &#x27;US-Ha1&#x27;\n",
       "  * time       (time) datetime64[ns] 1992-01-01 ... 1992-01-01T02:30:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-05b2579e-29ec-487e-a79d-8957043da8ec' class='xr-array-in' type='checkbox' checked><label for='section-05b2579e-29ec-487e-a79d-8957043da8ec' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.7 nan -34.1 nan -94.2 nan</span></div><div class='xr-array-data'><pre>array([  4.7,   nan, -34.1,   nan, -94.2,   nan])</pre></div></div></li><li class='xr-section-item'><input id='section-3755dc4c-0e5b-45f9-98be-c0b6ceb42a12' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3755dc4c-0e5b-45f9-98be-c0b6ceb42a12' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>stationID</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U32</div><div class='xr-var-preview xr-preview'>&#x27;US-Ha1&#x27;</div><input id='attrs-6043b2f7-0e54-455c-ac16-803fae93a161' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6043b2f7-0e54-455c-ac16-803fae93a161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a34c8248-a61c-44fa-ad85-dddcd2a50b0a' class='xr-var-data-in' type='checkbox'><label for='data-a34c8248-a61c-44fa-ad85-dddcd2a50b0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;US-Ha1&#x27;, dtype=&#x27;&lt;U32&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1992-01-01 ... 1992-01-01T02:30:00</div><input id='attrs-efa47ef1-e3ad-4605-a5f7-e06ba0f2670d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efa47ef1-e3ad-4605-a5f7-e06ba0f2670d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a00c0df8-f5b2-4e47-9a41-2dd232a7d8f5' class='xr-var-data-in' type='checkbox'><label for='data-a00c0df8-f5b2-4e47-9a41-2dd232a7d8f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1992-01-01T00:30:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T01:00:00.000000000&#x27;, &#x27;1992-01-01T01:30:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T02:00:00.000000000&#x27;, &#x27;1992-01-01T02:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49ff3e7c-94c4-4445-887d-545cda615fd0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-49ff3e7c-94c4-4445-887d-545cda615fd0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 6)>\n",
       "array([  4.7,   nan, -34.1,   nan, -94.2,   nan])\n",
       "Coordinates:\n",
       "    stationID  <U32 'US-Ha1'\n",
       "  * time       (time) datetime64[ns] 1992-01-01 ... 1992-01-01T02:30:00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxnetSH_DS[-1,0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:32:30.495397Z",
     "start_time": "2021-03-02T17:32:30.491668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1992-01-01T00:00:00.000000000', '1992-01-01T01:00:00.000000000',\n",
       "       '1992-01-01T02:00:00.000000000', '1992-01-01T03:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxnet_SH['time'].values[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:33:47.962624Z",
     "start_time": "2021-03-02T17:33:47.952788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 4)&gt;\n",
       "array([  4.7, -34.1, -94.2, -89.1])\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1992-01-01 ... 1992-01-01T03:00:00\n",
       "    stationID  &lt;U32 &#x27;US-Ha1&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a95fb310-1277-4689-a7a8-ac28de5a3b11' class='xr-array-in' type='checkbox' checked><label for='section-a95fb310-1277-4689-a7a8-ac28de5a3b11' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.7 -34.1 -94.2 -89.1</span></div><div class='xr-array-data'><pre>array([  4.7, -34.1, -94.2, -89.1])</pre></div></div></li><li class='xr-section-item'><input id='section-dd59b110-7e71-437a-a8c2-a94a01bea4e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd59b110-7e71-437a-a8c2-a94a01bea4e7' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1992-01-01 ... 1992-01-01T03:00:00</div><input id='attrs-98e8cb47-e6a7-47ab-97b5-036ae2739524' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98e8cb47-e6a7-47ab-97b5-036ae2739524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6ecb2f5-7f19-4409-a41c-7e396256de1c' class='xr-var-data-in' type='checkbox'><label for='data-b6ecb2f5-7f19-4409-a41c-7e396256de1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1992-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T02:00:00.000000000&#x27;, &#x27;1992-01-01T03:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stationID</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U32</div><div class='xr-var-preview xr-preview'>&#x27;US-Ha1&#x27;</div><input id='attrs-048d8827-22ce-4fdf-a830-5daee36e6080' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-048d8827-22ce-4fdf-a830-5daee36e6080' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cd8fdba-60c6-496b-a15f-f3ace09f3d03' class='xr-var-data-in' type='checkbox'><label for='data-3cd8fdba-60c6-496b-a15f-f3ace09f3d03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;US-Ha1&#x27;, dtype=&#x27;&lt;U32&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-087d7d78-4146-498b-85cd-b08ac683c656' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-087d7d78-4146-498b-85cd-b08ac683c656' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 4)>\n",
       "array([  4.7, -34.1, -94.2, -89.1])\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1992-01-01 ... 1992-01-01T03:00:00\n",
       "    stationID  <U32 'US-Ha1'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxnet_SH[-1,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:34:16.881347Z",
     "start_time": "2021-03-02T17:34:16.391172Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Fill in NaN's for precip anywhere there should be one (based on whether or not SH has data)\n",
    "#   If not, xarray is filling these with zeros and it's throwing things off later. \n",
    "\n",
    "for iStation in range(len(stationID)):\n",
    "    indNan=np.where(np.isnan(fluxnet_SH[iStation,:])==True)[0]\n",
    "    fluxnet_precip[iStation,indNan] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:34:20.456603Z",
     "start_time": "2021-03-02T17:34:20.450715Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "201624"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set day range to 3-hour intervals too \n",
    "dayRange = pd.date_range(start=pd.DatetimeIndex(startTime_fluxnet_sel[iMinStart])[0],\n",
    "                         end=pd.DatetimeIndex(endTime_fluxnet_sel[iMaxEnd])[0],\n",
    "                         freq='1H')\n",
    "nDays = len(dayRange)\n",
    "len(dayRange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:34:24.091180Z",
     "start_time": "2021-03-02T17:34:22.513107Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of days before deletes:  201624\n",
      "Number of days after deletes:  201480\n"
     ]
    }
   ],
   "source": [
    "# Remove from record if it's Feb. 29th (i.e., skip leap years )\n",
    "# -------------------------------------------------------------\n",
    "\n",
    "# Indices to skip \n",
    "iSkips = np.where((dayRange.month==2) & (dayRange.day==29))[0]\n",
    "print('Number of days before deletes: ', len(dayRange))\n",
    "\n",
    "dayRange       = np.delete(dayRange,iSkips)\n",
    "fluxnet_SH     = np.delete(fluxnet_SH,iSkips,axis=1)\n",
    "fluxnet_LH     = np.delete(fluxnet_LH,iSkips,axis=1)\n",
    "fluxnet_spd    = np.delete(fluxnet_spd,iSkips,axis=1)\n",
    "fluxnet_precip = np.delete(fluxnet_precip,iSkips,axis=1)\n",
    "fluxnet_pressr = np.delete(fluxnet_pressr,iSkips,axis=1)\n",
    "fluxnet_temp   = np.delete(fluxnet_temp,iSkips,axis=1)\n",
    "\n",
    "print('Number of days after deletes: ', len(dayRange))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Save station lat/lon to pickle file \n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/FLUXNET_lat.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(lat_fluxnet, f)\n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/FLUXNET_lon.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(lon_fluxnet, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Also save out station ID\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/FLUXNET_stationID.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(stationID, f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in model data (3-hourly) at same sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:39:26.735655Z",
     "start_time": "2021-03-02T17:39:26.725779Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "dataPath = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/3hr_IslaSims/processed_3hourCESMatFLUXNETstations.nc'\n",
    "modelDS  = xr.open_dataset(dataPath, decode_times=True)\n",
    "#modelDS['time'] = modelDS.indexes['time'].to_datetimeindex()\n",
    "\n",
    "# FLUXNET hourly means are attributed to the hour *at which they start* \n",
    "# CESM means are attributed to the hour at which they *end*. So subtract three hours from CESM data to match up. \n",
    "modelDS['time'] = modelDS['time'] - pd.Timedelta(hours=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:39:35.458829Z",
     "start_time": "2021-03-02T17:39:35.441626Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Slightly refined which stations to use above, so filter for consistency here \n",
    "listMatch = []\n",
    "\n",
    "for iStation in range(len(stationID)): \n",
    "    iList = np.where(modelDS.station.values==stationID[iStation])[0]\n",
    "    \n",
    "    if len(iList)>0:\n",
    "        listMatch.append(iList[0])\n",
    "        \n",
    "modelDS = modelDS.isel(station=listMatch)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:40:57.423179Z",
     "start_time": "2021-03-02T17:40:17.391660Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# FLUXNET obs are in local time. So read in their time zone and convert model to it (currently UTC)\n",
    "fileName = '/Users/mdfowler/Documents/Analysis/Coupling_initial/data/FLUXNET2015/FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.xlsx'\n",
    "metaDS   = pd.read_excel(fileName, engine='openpyxl')  \n",
    "\n",
    "# Get fluxnet UTC time offset\n",
    "utc_offset = np.empty([len(stationID)]).astype(str)\n",
    "\n",
    "for iStation in range(len(stationID)):\n",
    "    selStation = stationID[iStation]\n",
    "    iLoc=np.where((metaDS['SITE_ID']==selStation) & (metaDS['VARIABLE_GROUP']=='GRP_UTC_OFFSET'))[0]\n",
    "    utc_offset[iStation] = str((metaDS['DATAVALUE'].values[iLoc])[0])\n",
    "\n",
    "# Apply offset to each station in the model \n",
    "modelTimes_pd = pd.DatetimeIndex(modelDS.time.values, tz='UTC')\n",
    "\n",
    "modelTimes_local = np.empty([len(stationID),len(modelTimes_pd)])\n",
    "for iStation in range(len(stationID)):\n",
    "    modelTimes_local[iStation,:] = modelTimes_pd+pd.DateOffset(hours=float(utc_offset[iStation]))\n",
    "    \n",
    "    # Remove leap years again, since shifiting in time can cause issue\n",
    "    iLeap = np.where((pd.DatetimeIndex(modelTimes_local[iStation,:]).month==2) & (pd.DatetimeIndex(modelTimes_local[iStation,:]).day==29))\n",
    "    modelTimes_local[iStation,iLeap] = np.nan\n",
    "\n",
    "modelTimes_local = pd.DatetimeIndex(modelTimes_local)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2000-01-01 00:00:00+00:00', '2000-01-01 03:00:00+00:00',\n",
      "               '2000-01-01 06:00:00+00:00', '2000-01-01 09:00:00+00:00',\n",
      "               '2000-01-01 12:00:00+00:00', '2000-01-01 15:00:00+00:00',\n",
      "               '2000-01-01 18:00:00+00:00', '2000-01-01 21:00:00+00:00',\n",
      "               '2000-01-02 00:00:00+00:00', '2000-01-02 03:00:00+00:00'],\n",
      "              dtype='datetime64[ns, UTC]', freq=None)\n",
      "DatetimeIndex(['2000-01-01 01:00:00', '2000-01-01 04:00:00',\n",
      "               '2000-01-01 07:00:00', '2000-01-01 10:00:00',\n",
      "               '2000-01-01 13:00:00', '2000-01-01 16:00:00',\n",
      "               '2000-01-01 19:00:00', '2000-01-01 22:00:00',\n",
      "               '2000-01-02 01:00:00', '2000-01-02 04:00:00'],\n",
      "              dtype='datetime64[ns]', freq=None)\n"
     ]
    }
   ],
   "source": [
    "print(modelTimes_pd[0:10])\n",
    "print(modelTimes_local[1,0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:05.840895Z",
     "start_time": "2021-03-02T17:41:05.833668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  3.  6.  9. 12. 15. 18. 21. nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan]\n",
      "[ 0.  3.  6.  9. 12. 15. 18. 21.]\n"
     ]
    }
   ],
   "source": [
    "modelHours  = np.unique(modelTimes_local[14,:].hour)\n",
    "print(modelHours)\n",
    "modelHours  = modelHours[np.isfinite(modelHours)==True]\n",
    "print(modelHours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:12.907449Z",
     "start_time": "2021-03-02T17:41:07.737997Z"
    }
   },
   "outputs": [],
   "source": [
    "# Match up fluxnet hourly obs to the hours that are in the model per station \n",
    "fluxnet_time_match = np.full([len(stationID), int(len(dayRange)/3)], np.nan)\n",
    "\n",
    "fluxnet_SH_match = np.full([len(stationID), int(len(dayRange)/3)], np.nan)\n",
    "fluxnet_LH_match = np.full([len(stationID), int(len(dayRange)/3)], np.nan)\n",
    "\n",
    "\n",
    "for iStation in range(len(stationID)):\n",
    "    modelHours  = np.unique(modelTimes_local[iStation,:].hour)\n",
    "    modelHours  = modelHours[np.isfinite(modelHours)==True]\n",
    "    \n",
    "    # Get indices that match *all* of those hours\n",
    "    matchInds = []\n",
    "    for iHour in range(len(modelHours)):\n",
    "        imatchHours = np.where(dayRange.hour==modelHours[iHour])[0]\n",
    "\n",
    "        matchInds.append(imatchHours)\n",
    "\n",
    "    matchInds = np.reshape(matchInds,[np.shape(matchInds)[0]*np.shape(matchInds)[1]])\n",
    "    matchInds = np.sort(matchInds)\n",
    "    \n",
    "    # Now select just those indices in fluxnet data \n",
    "    fluxnet_time_match[iStation,:] = dayRange[matchInds]\n",
    "    fluxnet_SH_match[iStation,:]   = fluxnet_SH[iStation,matchInds]\n",
    "    fluxnet_LH_match[iStation,:]   = fluxnet_LH[iStation,matchInds]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:14.741022Z",
     "start_time": "2021-03-02T17:41:14.667915Z"
    }
   },
   "outputs": [],
   "source": [
    "# Precip saved in m/s for model data. Ideally, let's just work in mm/day. \n",
    "#   Multiply by number of seconds in 3 hours (dT) and by 1000 to convert to mm\n",
    "modelPrecip_mm = modelDS.model_precip.values*(3.0*60.0*60.0)*1000.0\n",
    "modelDS['modelPrecip_mm'] = (('station','time'), modelPrecip_mm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify days via precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:19.959950Z",
     "start_time": "2021-03-02T17:41:18.752328Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    }
   ],
   "source": [
    "# Get daily rain totals for model/fluxnet sites\n",
    "fluxnetPrec_DS = xr.DataArray(fluxnet_precip, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "rainDaily_fluxnet = fluxnetPrec_DS.resample(time='1D').sum()\n",
    "\n",
    "rainDaily_model = modelDS['modelPrecip_mm'].resample(time='1D').sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:22.687775Z",
     "start_time": "2021-03-02T17:41:21.566764Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    }
   ],
   "source": [
    "#Fill in NaN's for precip anywhere there should be one (based on whether or not SH has data)\n",
    "#   If not, xarray is filling these with zeros and it's throwing things off later. \n",
    "fluxnetSH_DS = xr.DataArray(fluxnet_SH, coords=[stationID, dayRange], dims=['stationID','time'])\n",
    "shDaily_fluxnet = fluxnetSH_DS.resample(time='1D').mean()\n",
    "\n",
    "for iStation in range(len(stationID)):\n",
    "    indNan=np.where(np.isnan(shDaily_fluxnet[iStation,:])==True)[0]\n",
    "    rainDaily_fluxnet[iStation,indNan] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:25.130686Z",
     "start_time": "2021-03-02T17:41:24.912921Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "## Step 1: What should the threshold be for a \"wet\" day? Should it vary by station? \n",
    "# Could try 3 mm, in line with Burns et al...  \n",
    "\n",
    "minWet = 3\n",
    "\n",
    "pctWetDays_obs   = np.full([len(stationID)], np.nan)\n",
    "pctWetDays_model = np.full([len(stationID)], np.nan)\n",
    "for iStation in range(len(stationID)):\n",
    "    pctWetDays_obs[iStation]   = len(np.where(rainDaily_fluxnet[iStation,:]>=minWet)[0])/len(np.where(np.isfinite(rainDaily_fluxnet[iStation,:])==True)[0])\n",
    "    pctWetDays_model[iStation] = len(np.where(rainDaily_model[iStation,:]>=minWet)[0])/len(np.where(np.isfinite(rainDaily_model[iStation,:])==True)[0])\n",
    "    \n",
    "    # Just to double check that the order is consistent between model and obs \n",
    "    if (modelDS.station.values[iStation]!=stationID[iStation]):\n",
    "        print(modelDS.station.values[iStation], '  ', stationID[iStation])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/metPy/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FLUXNET: Percent days classified as wet by station')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEICAYAAACpqsStAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXp0lEQVR4nO3debgcVZ3G8e8LRNawhFwwrFcBkUWIEDZxlFUgoGR0HEXRMIDREUdRFAPjKIgj6CDEGRQGBGFYXViFYRMFREBNMELYRCCGkJAEMARwGZbf/HHOhaLpvrfv2nXC+3mefrq2rv5VddXbVae67lVEYGZmZVqm0wWYmdnAOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmELcRJelGSYd2ug4ASSFp42Ga94clXVfp31nSA5KekTRJ0tWSJg9w3rVZh4MlabakPTpdR18k3S1pl07X0UxRIZ4/8L/kHaHnsY6k7rxDLtcw/S6S5jaZz0s7gaRvSbq2Yfw0SVdW5hGSvtMwzS2SDsrdB0l6oaGuntqq/S821P/hPpY3JD2bp31U0kmSlh3QyhsGks6W9LVO11FHEXF+RLyrMuirwCkRsUpEXBYR+0TEOZ2qbyRIOkbSeTWoo19fFM2264jYIiJuHPLihkBRIZ69O+8IPY95g5zfvwEbSfonAEk7AZOBT1SmeRb4qKTuXuZzW0Ndq0TEvGo/MKeh/vPbqG/r/NrdgQ8BH+vPwjV+sVnHbAjc3ekibOlTYogPqYj4M3AocGIO6bOAqRFRPYJfDJwNfGWk6+sREfcBvwC2BJC0n6SZkhZLulXSVj3T5iOPL0q6E3hW0nKS3p6nWyzpkcpZxPKSTpQ0R9ICSadJWjGP20XSXElHSFooaX7ly24K8GHgyHym8JNmdUvaU9J9kp6SdAqgyriNJP1M0hOSHpd0vqTV87gvSLq4YV7/JWla7j5I0kOSnpb0cKuzGknLSjpa0oN52hmS1m8y3b6SfitpSV4/x1TGrSDpvFznYkm/kbR2b3Xk4bfk7geBNwI/yetqeTU0iUg6WNK9kv4k6VpJG7azDpssx/aSbst1zpd0iqTX5XGSdHL+LJ+SdKekLZvMY1dJd1X6fyrp15X+WyRNyt3rSLpY0qK8/J/Ow/cGjgY+kJf5d61qBraTdE9e9u9LWiHPY5akd1fed1TeTsY3qXmspCvzcj8p6ReSlpF0LrBBZd0fmaf/kaTH8nq4WdIWeXjT7VqVo/n8+U2TNC8/pklaPo9ruc8Mm4go5gHMBvZoMrwbCGC5huG7AHObTH8jcGjDsP8GHs/j1DgP4PXAEmDTPPwW4KDcfRBwS3/rB74LfLeX6QPYOHdvDjwGHAJsAywEdgCWJZ05zAaWr7zPTGB9YEXSRvw0cAAwClgTGJ+nnQZcAYwBRgM/AY6vLPvzpKaAUcBE4M/AGnn82cDXeql/bF5n/5Bf/9k8v0Pz+I2BPYHlgS7gZmBaHjeOdAa0eu5fLi/ztsDKDZ/FOGCLFjV8AbgL2JQUflsDazZZv7sAbyEd2GwFLAAm5XEfz+tlpby+twVW7a2Oxm2iyWd/Y2U9TAL+AGyWl/NLwK3trMMmy7stsGOeTzdwL3B4HrcXMANYPa+LzYBxTeaxAvCX/N7Lkba7eXn7WDGPWzOvqxnAl4HXkb6oHgL2yvM5BjivjX1iFmlbHQP8krxNAUcCP6hMuz9wV4v5HA+cltfRKODvyPtx47rPww7Oy7M8aR+YWRl3Ng3bdXUepP3hdmAt0nZ7K3BcO/vMsOTicM14WIpNK/IZ0pHxYuCyPLybwYf4gXkeH2s1D+CbPRsVrw7x5yt1LQYebFH/q76EelneIO3AfwIeBL6Wd5xTezaayrT3A++svM/BlXFHAZc2mb9IQblRZdhOwMOVZf9Ldb2SgnTHVht7w/w/Ctze8H5zG9d9Zfwk4LeV/qt7Pg9gP+Ce3L1yXsfvA1bsYx3eD+zfy/rduMW4acDJuftg0o66VcM0LeugfyF+NXBIZdwypB1/w/6uwybLcXjPZw/sBvyeFPLL9PG6XwDvzdNeB/wQ2BvYFbgzT7MDMKfhdUcB38/dx9BeiH+i0j+RvO8A65AOPlbN/T8Gjmwxn68Clzf7PBvXfZPxq+dtYbVW2zWvDPEHgYmVcXsBs9vZZ4bjUWJzyqSIWD0/JvUx7fOkb8NGo4DnenokrQmcSNpxv9pzSt/EN4C9JG3dZNztlbpWj4iN+qitXdtExBoRsVFEfCkiXiTt3EfkU8fFkhaTjmTWqbzukUr3+qQNr1EX6ehyRmU+1+ThPZ6IiOcr/X8GVmmz9nWqdUTaol/ql7SWpIuULtouAc4jHf31OIf05Up+PjfP51ngA6TrFvMlXSXpzS1qaLXsryBpB0k/z80CT+V599RyLnAtcFE+ff6mpFH9rKM3GwLfrnwGT5LCel36WIdNluNNuVnhsbxOv96zHBHxM+AU4DvAAkmnS1q1xaxuIgXSO3L3jcA78+OmSt3rNGyHRwNr93P5q8vzR/J2HOl61y+B9+V9ch+g1XWk/yCdzVyn1Lw1tdWbKTWxnaDUxLaEFNDwym2vN+vkOl9VczaYfabfSgzx/pgDjJX00gqUJNLGV/0QpgHXRMRnSaf0JzabWUQ8kac9bpjqbdcjwL83fGmsFBEXVqaJhumbfak8Tjpq2KIyn9UiXUhtR/Qxfj4pRIGX1n21Pfr4PI+tImJVUlBX23svA7bK7bb7UdmBI+LaiNiT1IRxH3BGixpaLXujC0jNSutHxGqkU3Pl93ouIo6NiM2Bt+VaPtrPOnrzCPDxhs9zxYi4lb7XYaNTcx2b5HV6NJV1GhH/GRHbAlsAbyI1NzXTGOI38eoQf4R01late3RETOx5uzaXv7o8G5Cabnr0fJG/n/TjgUebzSAino6IIyLijcC7gc9J2r1FHR8iNc3sAaxGOpOHl9dTX3XPI2VIq5pH1NIW4ssrXYRaIV8cmQv8CviGpFXyxYcvkI7QbweQNJHULvu5PI9/ASZJ2rXFe5xE2pE3G8bl6MsZwCfy0aMkrax0YW50i+nPB/aQ9I9KFznXlDQ+H9WfAZwsaS0ASetK2qvNOhaQ2kFbuQrYQtJ7lX4l82nStYUeo8nNY5LWpSFQIuKvpFPoC4BfR8ScXOPakt4jaWXgb3keL7So4XvAcZI2yetqq3zm1Wg08GRE/FXS9qQdnfx+u0p6i9LPO5eQzuJe6GcdvTkNOKpycW01Se/P4/pah82WYwnwTD4r+OfKcmyXt5lRpGa0v/ZS762k6wjbk9b93aTg2oF0oAPwa2CJ0kX0FfMR7paStsvjFwDdkvrKmcMkrSdpDOlL5weVcZeRrgF9BvifVjNQutC/cf6SW5KXq2fZGrfT0aTP6wnSmejXG2bX13Z9IfAlSV2SxpKuCXTsp5RLW4g/Qzqy7HnsRjrdXYt0qvUo6ad6E/POOpq0A306Ip4EiIiFwBHAGcq/0qiKiCWktvExDaN20qt/J75d4+urlH4Jclp/FzIippN+angKqb38D6Q22FbTzyG1NR5BOlWfSbrAB/DF/Prb86nlT0k7bzvOBDbPp9KXNXnfx0lHUCeQdphNSKfHPY4l7aBPkcLqkibvcQ7pguO5lWHL5GWZl5fnncAnW9R4Eqk99zrSzn0m6eJco0+SmtKeJu2UP6yMez3py2QJ6ULhTaSdtj91tBQRl5Ka6i7Kn8EsUtNBO+uw0edJX0BPk76gq4G4ah72J9KZ6BO0Put8FrgDuDsi/i8Pvg34Y95HiIgXSEe944GHSWd23yMd3QL8KD8/IemOXmq+gPT5PJQfL/1GOyL+AlwMvIHm20ePTUjb7jO5zu/Gy7/rPp4UuoslfZ70ZfBHUh7cQz6gq+h1u871TQfuJF00v6Na80jruXprVkuSNiA1D7w+f4Haa4ykLwNviogD+5z4Ncg3glht5dPwzwEXOcBfm3ITyyHARzpdS10tbc0ptpTI7cxLSNcrvtLhcqwDJH2MdPH06oi4ua/pX6vcnGJmVjAfiZuZFWxE28THjh0b3d3dI/mWZmbFmzFjxuMR0dVs3IiGeHd3N9OnTx/JtzQzK56kP7Ya5+YUM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OC+U/R1lj31KsG/NrZJ+w7hJWYWV35SNzMrGAOcTOzgjnEzcwK5hA3MyuYQ9zMrGAOcTOzgjnEzcwK5hA3MyuYb/YZZoO5YcfMrC8+EjczK5hD3MysYA5xM7OCOcTNzArmEDczK1ifIS5pfUk/l3SvpLslfSYPHyPpekkP5Oc1hr9cMzOraudI/HngiIjYDNgROEzS5sBU4IaI2AS4IfebmdkI6jPEI2J+RNyRu58G7gXWBfYHzsmTnQNMGqYazcyshX61iUvqBt4K/ApYOyLmQwp6YK0hr87MzHrVdohLWgW4GDg8Ipb043VTJE2XNH3RokUDqdHMzFpoK8QljSIF+PkRcUkevEDSuDx+HLCw2Wsj4vSImBARE7q6uoaiZjMzy9r5dYqAM4F7I+KkyqgrgMm5ezJw+dCXZ2ZmvWnnD2DtDHwEuEvSzDzsaOAE4IeSDgHmAO8flgrNzKylPkM8Im4B1GL07kNbjpmZ9Yfv2DQzK5hD3MysYA5xM7OCOcTNzArmf8+2lBrMv4WbfcK+Q1iJmQ0nH4mbmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRWszxCXdJakhZJmVYYdI+lRSTPzY+LwlmlmZs20cyR+NrB3k+EnR8T4/PjfoS3LzMza0WeIR8TNwJMjUIuZmfXTYNrEPyXpztzcssaQVWRmZm1bboCvOxU4Doj8/C3g4GYTSpoCTAHYYIMNBvh2g9M99aqOvK+Z2XAb0JF4RCyIiBci4kXgDGD7XqY9PSImRMSErq6ugdZpZmZNDCjEJY2r9P49MKvVtGZmNnz6bE6RdCGwCzBW0lzgK8AuksaTmlNmAx8fvhLNzKyVPkM8Ig5oMvjMYajFzMz6yXdsmpkVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBXOIm5kVzCFuZlYwh7iZWcH6DHFJZ0laKGlWZdgYSddLeiA/rzG8ZZqZWTPtHImfDezdMGwqcENEbALckPvNzGyE9RniEXEz8GTD4P2Bc3L3OcCkoS3LzMzaMdA28bUjYj5Afl6r1YSSpkiaLmn6okWLBvh2ZmbWzLBf2IyI0yNiQkRM6OrqGu63MzN7TRloiC+QNA4gPy8cupLMzKxdAw3xK4DJuXsycPnQlGNmZv3Rzk8MLwRuAzaVNFfSIcAJwJ6SHgD2zP1mZjbClutrgog4oMWo3Ye4FjMz6yffsWlmVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRWszz+AVRfdU6/qdAlmZrXjI3Ezs4I5xM3MCuYQNzMrmEPczKxgDnEzs4I5xM3MCuYQNzMrmEPczKxgDnEzs4IVc8emjZzB3h07+4R9h6gSM+uLj8TNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYIP6e+KSZgNPAy8Az0fEhKEoyszM2jMU/xRi14h4fAjmY2Zm/eTmFDOzgg02xAO4TtIMSVOaTSBpiqTpkqYvWrRokG9nZmZVgw3xnSNiG2Af4DBJ72icICJOj4gJETGhq6trkG9nZmZVgwrxiJiXnxcClwLbD0VRZmbWngGHuKSVJY3u6QbeBcwaqsLMzKxvg/l1ytrApZJ65nNBRFwzJFWZmVlbBhziEfEQsPUQ1mJmZv3knxiamRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRVsKP5RstkrdE+9asCvnX3CvkNYidnSz0fiZmYFc4ibmRXMIW5mVjCHuJlZwRziZmYFc4ibmRXMIW5mVjCHuJlZwXyzjy01fJORvRb5SNzMrGAOcTOzgjnEzcwK5hA3MyuYQ9zMrGAOcTOzgjnEzcwK5hA3MyuYb/YxY3A3CoFvFnotqOs24iNxM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzAo2qBCXtLek+yX9QdLUoSrKzMzaM+AQl7Qs8B1gH2Bz4ABJmw9VYWZm1rfBHIlvD/whIh6KiP8DLgL2H5qyzMysHYO5Y3Nd4JFK/1xgh8aJJE0BpuTeZyTdXxk9Fnh8EDUMt7rXB/WvsV/16RvDWElrg16HI1D3UvU5d0hHa2xjG+mtvg1bvWgwIa4mw+JVAyJOB05vOgNpekRMGEQNw6ru9UH9a6x7feAah0Ld64P61zjQ+gbTnDIXWL/Svx4wbxDzMzOzfhpMiP8G2ETSGyS9DvggcMXQlGVmZu0YcHNKRDwv6VPAtcCywFkRcXc/Z9O0maVG6l4f1L/GutcHrnEo1L0+qH+NA6pPEa9qxjYzs0L4jk0zs4I5xM3MCtaREK/j7fqSzpK0UNKsyrAxkq6X9EB+XqOD9a0v6eeS7pV0t6TP1LDGFST9WtLvco3H1q3GXM+ykn4r6cqa1jdb0l2SZkqaXtMaV5f0Y0n35W1yp7rUKGnTvO56HkskHV6X+ip1fjbvJ7MkXZj3n37XOOIhXuPb9c8G9m4YNhW4ISI2AW7I/Z3yPHBERGwG7AgcltdbnWr8G7BbRGwNjAf2lrQj9aoR4DPAvZX+utUHsGtEjK/8brhuNX4buCYi3gxsTVqftagxIu7P6248sC3wZ+DSutQHIGld4NPAhIjYkvTjkA8OqMaIGNEHsBNwbaX/KOCoka6jRW3dwKxK//3AuNw9Dri/0zVWarsc2LOuNQIrAXeQ7uKtTY2k+xluAHYDrqzj5wzMBsY2DKtNjcCqwMPkH0bUscZKTe8Cflm3+nj5jvcxpF8JXplr7XeNnWhOaXa7/rodqKMda0fEfID8vFaH6wFAUjfwVuBX1KzG3FQxE1gIXB8RdatxGnAk8GJlWJ3qg3Tn83WSZuQ/WwH1qvGNwCLg+7lZ6nuSVq5ZjT0+CFyYu2tTX0Q8CpwIzAHmA09FxHUDqbETId7W7frWnKRVgIuBwyNiSafraRQRL0Q6jV0P2F7Slh0u6SWS9gMWRsSMTtfSh50jYhtSk+Nhkt7R6YIaLAdsA5waEW8FnqXzzTuvkm9CfA/wo07X0ii3de8PvAFYB1hZ0oEDmVcnQryk2/UXSBoHkJ8XdrIYSaNIAX5+RFySB9eqxh4RsRi4kXSdoS417gy8R9Js0l/d3E3SeTWqD4CImJefF5LacrenXjXOBebmsyyAH5NCvU41QvoSvCMiFuT+OtW3B/BwRCyKiOeAS4C3DaTGToR4SbfrXwFMzt2TSe3QHSFJwJnAvRFxUmVUnWrskrR67l6RtKHeR01qjIijImK9iOgmbXc/i4gD61IfgKSVJY3u6Sa1k86iRjVGxGPAI5I2zYN2B+6hRjVmB/ByUwrUq745wI6SVsr79u6ki8P9r7FDjfoTgd8DDwL/2qmLCw01XUhqm3qOdKRxCLAm6SLYA/l5TAfrezup2elOYGZ+TKxZjVsBv801zgK+nIfXpsZKrbvw8oXN2tRHam/+XX7c3bN/1KnGXM94YHr+rC8D1qhTjaQL608Aq1WG1aa+XM+xpIOcWcC5wPIDqdG33ZuZFcx3bJqZFcwhbmZWMIe4mVnBHOJmZgVziJuZFcwhbmZWMIe4mVnB/h8Ob5GoykUMuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pctWetDays_obs*100,20)\n",
    "plt.title('FLUXNET: Percent days classified as wet by station')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model: Percent days classified as wet by station')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pctWetDays_model*100,20)\n",
    "plt.title('Model: Percent days classified as wet by station')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:30.094482Z",
     "start_time": "2021-03-02T17:41:30.089013Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Function to send back which classification each day falls into, \n",
    "based on precipitation amount, similar to Burns et al. (2015; 2018).\n",
    "This version of the function assumes a single column run/input, \n",
    "in that only one dimension exists for precip. \n",
    "\n",
    "Inputs: \n",
    "   time:   An array of times to worry about. \n",
    "           Should be a pandas datetime.\n",
    "   precip: Daily precipitation (summed). Units can be in mm or m, \n",
    "           but need to be consistent with the threshold the user\n",
    "           defines (i.e., no unit conversions done here).\n",
    "   wetDef: User-defined threshold for defining days as wet vs. dry. \n",
    "Outputs:\n",
    "    classificationArr: Array with one fewer length than time, where\n",
    "                       the classification falls into four categories:\n",
    "                       1 :: wWet   (wet day following a wet day)\n",
    "                       2 :: wDry   (dry day following a wet day)\n",
    "                       3 :: dWet   (wet day following a dry day)\n",
    "                       4 :: dDry   (dry day following a dry day)\n",
    "                       \n",
    "                       Difference in length due to the first day not having a \n",
    "                       \"before\" day to reference. So time[0] on the \n",
    "                       classification array corresponds to time[1]. \n",
    "\n",
    "'''\n",
    "\n",
    "def classifyPrectDays_SCAM(time,precip,wetDef): \n",
    "    \n",
    "    # Define empty arrays of the appropriate length\n",
    "    nTimes               = len(time)\n",
    "    classificationArr    = np.empty([len(time)]).astype('str')\n",
    "    \n",
    "    for iDay in range(nTimes-1): \n",
    "        \n",
    "        # Wet day...\n",
    "        if precip[iDay]>=wetDef:\n",
    "            if precip[iDay+1]>=wetDef:\n",
    "                classificationArr[iDay+1] = 'wWet'  # Wet day following wet \n",
    "            elif precip[iDay+1]<wetDef : \n",
    "                classificationArr[iDay+1] = 'wDry' # Dry day following wet \n",
    "        # Dry day...\n",
    "        elif precip[iDay]<wetDef:\n",
    "            if precip[iDay+1]>=wetDef:\n",
    "                classificationArr[iDay+1] = 'dWet'  # Wet day following dry \n",
    "            elif precip[iDay+1]<wetDef : \n",
    "                classificationArr[iDay+1] = 'dDry' # Dry day following dry \n",
    "        # If missing...\n",
    "        else: \n",
    "            classificationArr[iDay+1] = 'nan'\n",
    "\n",
    "    return(classificationArr[1::])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Get classification of all days... \n",
    "# nDays_obs   = len(rainDaily_fluxnet.time.values)\n",
    "# nDays_model = len(rainDaily_model.time.values)\n",
    "\n",
    "# dayClass_obs   = np.empty([len(stationID), nDays_obs]).astype(str)\n",
    "# dayClass_model = np.empty([len(stationID), nDays_model]).astype(str)\n",
    "\n",
    "# for iStation in range(len(stationID)):\n",
    "#     dayClass_obs[iStation,1::]   = classifyPrectDays_SCAM(rainDaily_fluxnet.time.values, rainDaily_fluxnet[iStation,:], minWet)\n",
    "#     dayClass_model[iStation,1::] = classifyPrectDays_SCAM(rainDaily_model.time.values, rainDaily_model[iStation,:], minWet)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Fill in first spot with 'nan' (since it's missing anyway)\n",
    "# dayClass_obs[:,0] = np.nan\n",
    "# dayClass_model[:,0] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype='<U32')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayClass_obs[10,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save out to pickle file? \n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailyPrecipClassification.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(dayClass_obs, f)\n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailyPrecipClassification.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(dayClass_model, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:34.297532Z",
     "start_time": "2021-03-02T17:41:34.155163Z"
    }
   },
   "outputs": [],
   "source": [
    "# Read in from pickle file: \n",
    "fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailyPrecipClassification.p'\n",
    "with open(fileName, \"rb\") as f:\n",
    "    dayClass_obs = pickle.load(f)\n",
    "\n",
    "fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailyPrecipClassification.p'\n",
    "with open(fileName, \"rb\") as f:\n",
    "    dayClass_model = pickle.load(f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['nan', 'nan', 'nan', ..., 'nan', 'nan', 'nan'], dtype='<U32')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayClass_obs[10,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:38.516020Z",
     "start_time": "2021-03-02T17:41:38.383502Z"
    }
   },
   "outputs": [],
   "source": [
    "## Remove leap year days (Feb 29th)\n",
    "obsTime_daily   = pd.DatetimeIndex(rainDaily_fluxnet.time.values)\n",
    "modelTime_daily =pd.DatetimeIndex(rainDaily_model.time.values)\n",
    "\n",
    "# Where is it Feb 29th?\n",
    "iSkips          = np.where((obsTime_daily.month==2) & (obsTime_daily.day==29))[0]\n",
    "dayClass_obsSel = np.delete(dayClass_obs,iSkips,axis=1)\n",
    "\n",
    "iSkipsModel       = np.where((modelTime_daily.month==2) & (modelTime_daily.day==29))[0]\n",
    "dayClass_modelSel = np.delete(dayClass_model,iSkipsModel,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:40.141603Z",
     "start_time": "2021-03-02T17:41:40.138374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 5476)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(dayClass_modelSel)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at flux biases based on day classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:41:41.916288Z",
     "start_time": "2021-03-02T17:41:41.907169Z"
    }
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Function to split up an input array\n",
    "into day/hour.\n",
    "\n",
    "Inputs:  Time should be a pandas datetime array. \n",
    "         Var should be a 2d array, location x time.\n",
    "         MODIFIED: just 1d array [time]\n",
    "Outputs: Output will be station by days by hours.\n",
    "\n",
    "'''\n",
    "\n",
    "def var_separateTime(time,var):\n",
    "    \n",
    "    #nLoc = int(np.shape(var)[0])\n",
    "    \n",
    "    # Get arrays of unique years/months/days/hours \n",
    "    startDay = datetime.date(int(time.year[0]), int(time.month[0]), int(time.day[0]))\n",
    "    var_separate = np.full([1,8], np.nan)  # Initialize array to hold daily data \n",
    "    \n",
    "    # Counter for number of days to split things into\n",
    "    dayCheck = 0 \n",
    "    while np.isfinite(dayCheck)==True:\n",
    "        iDay    = np.where((time.year==startDay.year)  & \n",
    "                           (time.month==startDay.month) &\n",
    "                           (time.day==startDay.day))[0]\n",
    "        \n",
    "        if len(iDay)>=1:\n",
    "            timeDay = time[iDay]\n",
    "            varDay  = var[iDay]\n",
    "\n",
    "            # Initialize array to hold 3-hourly values\n",
    "            hourlyVars = np.full([8],np.nan)\n",
    "            hourValues = np.unique(time.hour)\n",
    "            hourValues  = hourValues[np.isfinite(hourValues)==True]\n",
    "\n",
    "            for iHr in range(len(hourValues)):\n",
    "                selHour = np.where(timeDay.hour==hourValues[iHr])[0]\n",
    "\n",
    "                # Check if not already in hourly form\n",
    "#                 if len(selHour)>1: \n",
    "#                     varHour = np.nanmean(varDay[:, selHour])\n",
    "                if len(selHour)<1:\n",
    "                    varHour = np.nan\n",
    "                else:\n",
    "                    varHour = varDay[selHour]   \n",
    "                    \n",
    "                # Fill in var_separateTime by day/hour\n",
    "                hourlyVars[iHr] = np.squeeze(varHour)\n",
    "\n",
    "            # Add to a daily array \n",
    "            if dayCheck==0:\n",
    "                var_separate[0, :] = hourlyVars\n",
    "            else:\n",
    "                #np.concatenate((var_separate, hourlyVars[:,None]),axis=0)\n",
    "                var_separate = np.append(var_separate, np.reshape(hourlyVars,[1,8]), axis=0)\n",
    "            \n",
    "            # Set next day \n",
    "            startDay = startDay+datetime.timedelta(days=1)\n",
    "            dayCheck = dayCheck+1\n",
    "        elif ((startDay.month==2) & (startDay.day==29)):\n",
    "            # Set next day \n",
    "            startDay = startDay+datetime.timedelta(days=1)\n",
    "            #dayCheck = dayCheck+1\n",
    "            #print('Skipping a leap year...')\n",
    "        else: \n",
    "            dayCheck = np.nan\n",
    "    \n",
    "    return(var_separate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T19:50:50.828759Z",
     "start_time": "2021-03-02T17:47:31.998906Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with station 1\n",
      "Done with station 2\n",
      "Done with station 3\n",
      "Done with station 4\n",
      "Done with station 5\n",
      "Done with station 6\n",
      "Done with station 7\n",
      "Done with station 8\n",
      "Done with station 9\n",
      "Done with station 10\n",
      "Done with station 11\n",
      "Done with station 12\n",
      "Done with station 13\n",
      "Done with station 14\n",
      "Done with station 15\n",
      "Done with station 16\n",
      "Done with station 17\n",
      "Done with station 18\n",
      "Done with station 19\n",
      "Done with station 20\n",
      "Done with station 21\n",
      "Done with station 22\n",
      "Done with station 23\n",
      "Done with station 24\n",
      "Done with station 25\n",
      "Done with station 26\n",
      "Done with station 27\n",
      "Done with station 28\n",
      "Done with station 29\n",
      "Done with station 30\n",
      "Done with station 31\n",
      "Done with station 32\n",
      "Done with station 33\n",
      "Done with station 34\n",
      "Done with station 35\n",
      "Done with station 36\n",
      "Done with station 37\n",
      "Done with station 38\n",
      "Done with station 39\n",
      "Done with station 40\n",
      "Done with station 41\n",
      "Done with station 42\n",
      "Done with station 43\n",
      "Done with station 44\n",
      "Done with station 45\n",
      "Done with station 46\n",
      "Done with station 47\n",
      "Done with station 48\n",
      "Done with station 49\n",
      "Done with station 50\n",
      "Done with station 51\n",
      "Done with station 52\n",
      "Done with station 53\n",
      "Done with station 54\n",
      "Done with station 55\n",
      "Done with station 56\n",
      "Done with station 57\n",
      "Done with station 58\n",
      "Done with station 59\n",
      "Done with station 60\n",
      "Done with station 61\n",
      "Done with station 62\n",
      "Done with station 63\n",
      "Done with station 64\n",
      "Done with station 65\n",
      "Done with station 66\n",
      "Done with station 67\n",
      "Done with station 68\n",
      "Done with station 69\n",
      "Done with station 70\n",
      "Done with station 71\n",
      "Done with station 72\n",
      "Done with station 73\n",
      "Done with station 74\n",
      "Done with station 75\n",
      "Done with station 76\n",
      "Done with station 77\n",
      "Done with station 78\n",
      "Done with station 79\n",
      "Done with station 80\n",
      "Done with station 81\n",
      "Done with station 82\n",
      "Done with station 83\n",
      "Done with station 84\n",
      "Done with station 85\n",
      "Done with station 86\n",
      "Done with station 87\n",
      "Done with station 88\n",
      "Done with station 89\n",
      "Done with station 90\n",
      "Done with station 91\n",
      "Done with station 92\n",
      "Done with station 93\n",
      "Done with station 94\n",
      "Done with station 95\n",
      "Done with station 96\n",
      "Done with station 97\n",
      "Done with station 98\n",
      "Done with station 99\n",
      "Done with station 100\n",
      "Done with station 101\n",
      "Done with station 102\n",
      "Done with station 103\n",
      "Done with station 104\n",
      "Done with station 105\n",
      "Done with station 106\n",
      "Done with station 107\n",
      "Done with station 108\n",
      "Done with station 109\n",
      "Done with station 110\n",
      "Done with station 111\n",
      "Done with station 112\n",
      "Done with station 113\n",
      "Done with station 114\n"
     ]
    }
   ],
   "source": [
    "# modelDaily_SH = np.full([len(stationID), int(np.ceil(len(modelTimes_local[10,:])/8)), 8], np.nan)\n",
    "# modelDaily_LH = np.full([len(stationID), int(np.ceil(len(modelTimes_local[10,:])/8)), 8], np.nan)\n",
    "\n",
    "# for iStation in range(len(stationID)):\n",
    "\n",
    "#     # Get model data separated by day/hour \n",
    "#     modelDaily_SH[iStation,:,:] = var_separateTime(modelTimes_local[iStation,:], modelDS.model_SH.values[iStation,:])\n",
    "    \n",
    "#     modelDaily_LH[iStation,:,:] = var_separateTime(modelTimes_local[iStation,:], modelDS.model_LH.values[iStation,:])\n",
    "\n",
    "#     print('Done with station %i' % (iStation+1))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:16.220764Z",
     "start_time": "2021-03-02T20:31:16.153658Z"
    }
   },
   "outputs": [],
   "source": [
    "# meanSH_model = np.nanmean(modelDaily_SH,axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:32:01.682623Z",
     "start_time": "2021-03-02T20:32:01.674578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  7., 10., 13., 16., 19., 22., nan, nan, nan, nan])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(modelTimes_local[22,:].hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:19.762379Z",
     "start_time": "2021-03-02T20:31:19.602105Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd14a34c160>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.unique(modelTimes_local[3,:].hour)[0:8], meanSH_model[3,:],label=stationID[3])\n",
    "plt.plot(np.unique(modelTimes_local[10,:].hour)[0:8], meanSH_model[10,:],label=stationID[10])\n",
    "plt.plot(np.unique(modelTimes_local[53,:].hour)[0:8], meanSH_model[53,:],label=stationID[53])\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:46.599598Z",
     "start_time": "2021-03-02T20:31:46.531825Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# # Save out to pickle file? \n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailySH.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(modelDaily_SH, f)\n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailyLH.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(modelDaily_LH, f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/metPy/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with station 1\n",
      "Done with station 2\n",
      "Done with station 3\n",
      "Done with station 4\n",
      "Done with station 5\n",
      "Done with station 6\n",
      "Done with station 7\n",
      "Done with station 8\n",
      "Done with station 9\n",
      "Done with station 10\n",
      "Done with station 11\n",
      "Done with station 12\n",
      "Done with station 13\n",
      "Done with station 14\n",
      "Done with station 15\n",
      "Done with station 16\n",
      "Done with station 17\n",
      "Done with station 18\n",
      "Done with station 19\n",
      "Done with station 20\n",
      "Done with station 21\n",
      "Done with station 22\n",
      "Done with station 23\n",
      "Done with station 24\n",
      "Done with station 25\n",
      "Done with station 26\n",
      "Done with station 27\n",
      "Done with station 28\n",
      "Done with station 29\n",
      "Done with station 30\n",
      "Done with station 31\n",
      "Done with station 32\n",
      "Done with station 33\n",
      "Done with station 34\n",
      "Done with station 35\n",
      "Done with station 36\n",
      "Done with station 37\n",
      "Done with station 38\n",
      "Done with station 39\n",
      "Done with station 40\n",
      "Done with station 41\n",
      "Done with station 42\n",
      "Done with station 43\n",
      "Done with station 44\n",
      "Done with station 45\n",
      "Done with station 46\n",
      "Done with station 47\n",
      "Done with station 48\n",
      "Done with station 49\n",
      "Done with station 50\n",
      "Done with station 51\n",
      "Done with station 52\n",
      "Done with station 53\n",
      "Done with station 54\n",
      "Done with station 55\n",
      "Done with station 56\n",
      "Done with station 57\n",
      "Done with station 58\n",
      "Done with station 59\n",
      "Done with station 60\n",
      "Done with station 61\n",
      "Done with station 62\n",
      "Done with station 63\n",
      "Done with station 64\n",
      "Done with station 65\n",
      "Done with station 66\n",
      "Done with station 67\n",
      "Done with station 68\n",
      "Done with station 69\n",
      "Done with station 70\n",
      "Done with station 71\n",
      "Done with station 72\n",
      "Done with station 73\n",
      "Done with station 74\n",
      "Done with station 75\n",
      "Done with station 76\n",
      "Done with station 77\n",
      "Done with station 78\n",
      "Done with station 79\n",
      "Done with station 80\n",
      "Done with station 81\n",
      "Done with station 82\n",
      "Done with station 83\n",
      "Done with station 84\n",
      "Done with station 85\n",
      "Done with station 86\n",
      "Done with station 87\n",
      "Done with station 88\n",
      "Done with station 89\n",
      "Done with station 90\n",
      "Done with station 91\n",
      "Done with station 92\n",
      "Done with station 93\n",
      "Done with station 94\n",
      "Done with station 95\n",
      "Done with station 96\n",
      "Done with station 97\n",
      "Done with station 98\n",
      "Done with station 99\n",
      "Done with station 100\n",
      "Done with station 101\n",
      "Done with station 102\n",
      "Done with station 103\n",
      "Done with station 104\n",
      "Done with station 105\n",
      "Done with station 106\n",
      "Done with station 107\n",
      "Done with station 108\n",
      "Done with station 109\n",
      "Done with station 110\n",
      "Done with station 111\n",
      "Done with station 112\n",
      "Done with station 113\n",
      "Done with station 114\n"
     ]
    }
   ],
   "source": [
    "# fluxnetDaily_SH = np.full([len(stationID), int(np.ceil(len(fluxnet_time_match[10,:])/8)), 8], np.nan)\n",
    "# fluxnetDaily_LH = np.full([len(stationID), int(np.ceil(len(fluxnet_time_match[10,:])/8)), 8], np.nan)\n",
    "\n",
    "# fluxnet_time_match = pd.DatetimeIndex(fluxnet_time_match)\n",
    "\n",
    "# for iStation in range(len(stationID)):\n",
    "\n",
    "#     # Get fluxnet data separated by day/hour \n",
    "#     fluxnetDaily_SH[iStation,:,:] = var_separateTime(fluxnet_time_match[iStation,:], fluxnet_SH_match[iStation,:])\n",
    "    \n",
    "#     fluxnetDaily_LH[iStation,:,:] = var_separateTime(fluxnet_time_match[iStation,:], fluxnet_LH_match[iStation,:])\n",
    "\n",
    "#     print('Done with station %i' % (iStation+1))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/metPy/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# # Save out to pickle file? \n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailySH.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(fluxnetDaily_SH, f)\n",
    "\n",
    "# fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailyLH.p'\n",
    "# with open(fileName, \"wb\") as f:\n",
    "#     pickle.dump(fluxnetDaily_LH, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:46:47.228230Z",
     "start_time": "2021-03-02T17:46:47.072837Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "<ipython-input-64-75b3e932f0a6>:4: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailySH.p'>\n",
      "  fluxnetDaily_SH = pickle.load( open( fileName, \"rb\" ) )\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "<ipython-input-64-75b3e932f0a6>:7: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailyLH.p'>\n",
      "  fluxnetDaily_LH = pickle.load( open( fileName, \"rb\" ) )\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "<ipython-input-64-75b3e932f0a6>:11: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailySH.p'>\n",
      "  modelDaily_SH = pickle.load( open( fileName, \"rb\") )\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "<ipython-input-64-75b3e932f0a6>:14: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailyLH.p'>\n",
      "  modelDaily_LH = pickle.load( open( fileName, \"rb\") )\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    }
   ],
   "source": [
    "# # Save out to pickle file? \n",
    "\n",
    "fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailySH.p'\n",
    "fluxnetDaily_SH = pickle.load( open( fileName, \"rb\" ) )\n",
    "\n",
    "fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/fluxnet_dailyLH.p'\n",
    "fluxnetDaily_LH = pickle.load( open( fileName, \"rb\" ) )\n",
    "\n",
    "\n",
    "fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailySH.p'\n",
    "modelDaily_SH = pickle.load( open( fileName, \"rb\") )\n",
    "\n",
    "fileName = '/Users/mdfowler/Documents/Analysis/CLUBB_initial/data/model_dailyLH.p'\n",
    "modelDaily_LH = pickle.load( open( fileName, \"rb\") )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:46:50.316252Z",
     "start_time": "2021-03-02T17:46:50.312647Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions for model data:\n",
      "(114, 5476, 8)\n",
      "(114, 5476)\n",
      "\n",
      "Dimensions for fluxnet data:\n",
      "(114, 8395, 8)\n",
      "(114, 8395)\n"
     ]
    }
   ],
   "source": [
    "# Check that the dimensions all work out\n",
    "print('Dimensions for model data:')\n",
    "print(np.shape(modelDaily_SH))\n",
    "print(np.shape(dayClass_modelSel))\n",
    "\n",
    "print('\\nDimensions for fluxnet data:')\n",
    "print(np.shape(fluxnetDaily_SH))\n",
    "print(np.shape(dayClass_obsSel))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:29.078469Z",
     "start_time": "2021-03-02T20:31:28.812878Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "<ipython-input-76-e758a30aa155>:3: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  fluxnetDaily_EF = fluxnetDaily_LH/(fluxnetDaily_SH+fluxnetDaily_LH)\n",
      "<ipython-input-76-e758a30aa155>:3: RuntimeWarning: invalid value encountered in true_divide\n",
      "  fluxnetDaily_EF = fluxnetDaily_LH/(fluxnetDaily_SH+fluxnetDaily_LH)\n"
     ]
    }
   ],
   "source": [
    "# Compute EF ahead of time...\n",
    "modelDaily_EF   = modelDaily_LH/(modelDaily_SH+modelDaily_LH)\n",
    "fluxnetDaily_EF = fluxnetDaily_LH/(fluxnetDaily_SH+fluxnetDaily_LH)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:31.174879Z",
     "start_time": "2021-03-02T20:31:30.372197Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
     ]
    }
   ],
   "source": [
    "# Now get means for each rain/dry day classification\n",
    "classCodes = ['wWet','wDry','dWet','dDry']\n",
    "\n",
    "modelMean_SHbyClass = np.full([len(classCodes), len(stationID), 8],np.nan)\n",
    "obsMean_SHbyClass   = np.full([len(classCodes), len(stationID), 8],np.nan)\n",
    "modelMean_LHbyClass = np.full([len(classCodes), len(stationID), 8],np.nan)\n",
    "obsMean_LHbyClass   = np.full([len(classCodes), len(stationID), 8],np.nan)\n",
    "modelMean_EFbyClass = np.full([len(classCodes), len(stationID), 8],np.nan)\n",
    "obsMean_EFbyClass   = np.full([len(classCodes), len(stationID), 8],np.nan)\n",
    "\n",
    "\n",
    "for iClass in range(len(classCodes)):\n",
    "    selClass = classCodes[iClass]\n",
    "    \n",
    "    for iStation in range(len(stationID)): \n",
    "        iModelDays = np.where(dayClass_modelSel[iStation,:]==selClass)[0]\n",
    "        iObsDays   = np.where(dayClass_obsSel[iStation,:]==selClass)[0]\n",
    "        \n",
    "        # Average over those days \n",
    "        modelMean_SHbyClass[iClass,iStation,:] = np.nanmean(modelDaily_SH[iStation,iModelDays,:],axis=0)\n",
    "        obsMean_SHbyClass[iClass,iStation,:]   = np.nanmean(fluxnetDaily_SH[iStation,iObsDays,:],axis=0)\n",
    "        \n",
    "        modelMean_LHbyClass[iClass,iStation,:] = np.nanmean(modelDaily_LH[iStation,iModelDays,:],axis=0)\n",
    "        obsMean_LHbyClass[iClass,iStation,:]   = np.nanmean(fluxnetDaily_LH[iStation,iObsDays,:],axis=0)\n",
    "        \n",
    "        modelMean_EFbyClass[iClass,iStation,:] = np.nanmean(modelDaily_EF[iStation,iModelDays,:],axis=0)\n",
    "        obsMean_EFbyClass[iClass,iStation,:]   = np.nanmean(fluxnetDaily_EF[iStation,iObsDays,:],axis=0)\n",
    "\n",
    "    if (iStation % 10)==0:\n",
    "        print('Done with station %i' % iStation)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T17:46:57.008533Z",
     "start_time": "2021-03-02T17:46:57.005386Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 114, 8)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(modelMean_SHbyClass)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:33.446028Z",
     "start_time": "2021-03-02T20:31:33.326300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd0dc307250>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test plot...\n",
    "plt.plot(np.unique(modelTimes_local[1,:].hour)[0:8], modelMean_EFbyClass[0,1,:],'*-')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:34.362440Z",
     "start_time": "2021-03-02T20:31:34.092120Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# What does mean bias look like, averaged across stations? \n",
    "\n",
    "absBias_SHbyClass = modelMean_SHbyClass - obsMean_SHbyClass\n",
    "absBias_LHbyClass = modelMean_LHbyClass - obsMean_LHbyClass\n",
    "absBias_EFbyClass = modelMean_EFbyClass - obsMean_EFbyClass\n",
    "\n",
    "absBias_SHbyClass_24hours = np.full([4, len(stationID), 24], np.nan)\n",
    "absBias_LHbyClass_24hours = np.full([4, len(stationID), 24], np.nan)\n",
    "absBias_EFbyClass_24hours = np.full([4, len(stationID), 24], np.nan)\n",
    "model_SHbyClass_24hours   = np.full([4, len(stationID), 24], np.nan)\n",
    "model_LHbyClass_24hours   = np.full([4, len(stationID), 24], np.nan)\n",
    "model_EFbyClass_24hours   = np.full([4, len(stationID), 24], np.nan)\n",
    "obs_SHbyClass_24hours     = np.full([4, len(stationID), 24], np.nan)\n",
    "obs_LHbyClass_24hours     = np.full([4, len(stationID), 24], np.nan)\n",
    "obs_EFbyClass_24hours     = np.full([4, len(stationID), 24], np.nan)\n",
    "\n",
    "for iStation in range(len(stationID)):\n",
    "    iPlace = np.unique(modelTimes_local[iStation,:].hour)[0:8].astype(int)\n",
    "    \n",
    "    absBias_SHbyClass_24hours[:, iStation, iPlace] = absBias_SHbyClass[:,iStation,:]\n",
    "    absBias_LHbyClass_24hours[:, iStation, iPlace] = absBias_LHbyClass[:,iStation,:]\n",
    "    absBias_EFbyClass_24hours[:, iStation, iPlace] = absBias_EFbyClass[:,iStation,:]\n",
    "    \n",
    "    model_SHbyClass_24hours[:,iStation,iPlace] = modelMean_SHbyClass[:,iStation,:]\n",
    "    model_LHbyClass_24hours[:,iStation,iPlace] = modelMean_LHbyClass[:,iStation,:]\n",
    "    model_EFbyClass_24hours[:,iStation,iPlace] = modelMean_EFbyClass[:,iStation,:]\n",
    "    \n",
    "    obs_SHbyClass_24hours[:,iStation,iPlace] = obsMean_SHbyClass[:,iStation,:]\n",
    "    obs_LHbyClass_24hours[:,iStation,iPlace] = obsMean_LHbyClass[:,iStation,:]\n",
    "    obs_EFbyClass_24hours[:,iStation,iPlace] = obsMean_EFbyClass[:,iStation,:]\n",
    "    \n",
    "    \n",
    "    #plt.plot(np.unique(modelTimes_local[iStation,:].hour)[0:8], modelMean_EFbyClass[0,iStation,:],'*-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:35.371951Z",
     "start_time": "2021-03-02T20:31:35.367270Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 114, 24)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(model_LHbyClass_24hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:31:36.149584Z",
     "start_time": "2021-03-02T20:31:35.754482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30.0, 250.0)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,4, figsize=(16, 4))\n",
    "axs = axs.ravel()\n",
    "\n",
    "axs[0].plot(np.arange(0,24,1), np.nanmean(obs_SHbyClass_24hours[0,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[0].plot(np.arange(0,24,1), np.nanmean(model_SHbyClass_24hours[0,:,:], axis=0),'r', label='CESM2')\n",
    "axs[0].set_title('Mean SH:\\n'+classCodes[0]+' Days',fontsize=14)\n",
    "axs[0].legend()\n",
    "axs[0].set_xlabel('Hour (local)')\n",
    "axs[0].set_ylabel('SHFLX [W/m2]')\n",
    "\n",
    "axs[1].plot(np.arange(0,24,1), np.nanmean(obs_SHbyClass_24hours[1,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[1].plot(np.arange(0,24,1), np.nanmean(model_SHbyClass_24hours[1,:,:], axis=0),'r', label='CESM2')\n",
    "axs[1].set_title('Mean SH:\\n'+classCodes[1]+' Days',fontsize=14)\n",
    "axs[1].legend()\n",
    "axs[1].set_xlabel('Hour (local)')\n",
    "\n",
    "axs[2].plot(np.arange(0,24,1), np.nanmean(obs_SHbyClass_24hours[2,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[2].plot(np.arange(0,24,1), np.nanmean(model_SHbyClass_24hours[2,:,:], axis=0),'r', label='CESM2')\n",
    "axs[2].set_title('Mean SH:\\n'+classCodes[2]+' Days',fontsize=14)\n",
    "axs[2].legend()\n",
    "axs[2].set_xlabel('Hour (local)')\n",
    "\n",
    "axs[3].plot(np.arange(0,24,1), np.nanmean(obs_SHbyClass_24hours[3,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[3].plot(np.arange(0,24,1), np.nanmean(model_SHbyClass_24hours[3,:,:], axis=0),'r', label='CESM2')\n",
    "axs[3].set_title('Mean SH:\\n'+classCodes[3]+' Days',fontsize=14)\n",
    "axs[3].legend()\n",
    "axs[3].set_xlabel('Hour (local)')\n",
    "\n",
    "# Set y-axis limits\n",
    "axs[0].set_ylim([-30,250])\n",
    "axs[1].set_ylim([-30,250])\n",
    "axs[2].set_ylim([-30,250])\n",
    "axs[3].set_ylim([-30,250])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:32:48.066547Z",
     "start_time": "2021-03-02T20:32:47.639368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 200.0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,4, figsize=(16, 4))\n",
    "axs = axs.ravel()\n",
    "\n",
    "axs[0].plot(np.arange(0,24,1), np.nanmean(obs_LHbyClass_24hours[0,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[0].plot(np.arange(0,24,1), np.nanmean(model_LHbyClass_24hours[0,:,:], axis=0),'r', label='CESM2')\n",
    "axs[0].set_title('Mean LH:\\n'+classCodes[0]+' Days',fontsize=14)\n",
    "axs[0].legend()\n",
    "axs[0].set_xlabel('Hour (local)')\n",
    "axs[0].set_ylabel('LHFLX [W/m2]')\n",
    "\n",
    "axs[1].plot(np.arange(0,24,1), np.nanmean(obs_LHbyClass_24hours[1,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[1].plot(np.arange(0,24,1), np.nanmean(model_LHbyClass_24hours[1,:,:], axis=0),'r', label='CESM2')\n",
    "axs[1].set_title('Mean LH:\\n'+classCodes[1]+' Days',fontsize=14)\n",
    "axs[1].legend()\n",
    "axs[1].set_xlabel('Hour (local)')\n",
    "\n",
    "axs[2].plot(np.arange(0,24,1), np.nanmean(obs_LHbyClass_24hours[2,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[2].plot(np.arange(0,24,1), np.nanmean(model_LHbyClass_24hours[2,:,:], axis=0),'r', label='CESM2')\n",
    "axs[2].set_title('Mean LH:\\n'+classCodes[2]+' Days',fontsize=14)\n",
    "axs[2].legend()\n",
    "axs[2].set_xlabel('Hour (local)')\n",
    "\n",
    "axs[3].plot(np.arange(0,24,1), np.nanmean(obs_LHbyClass_24hours[3,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[3].plot(np.arange(0,24,1), np.nanmean(model_LHbyClass_24hours[3,:,:], axis=0),'r', label='CESM2')\n",
    "axs[3].set_title('Mean SLH:\\n'+classCodes[3]+' Days',fontsize=14)\n",
    "axs[3].legend()\n",
    "axs[3].set_xlabel('Hour (local)')\n",
    "\n",
    "# Set y-axis limits\n",
    "axs[0].set_ylim([-10,200])\n",
    "axs[1].set_ylim([-10,200])\n",
    "axs[2].set_ylim([-10,200])\n",
    "axs[3].set_ylim([-10,200])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:32:53.059799Z",
     "start_time": "2021-03-02T20:32:52.634249Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-10.0, 10.0)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA74AAAEoCAYAAAB/3i12AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABwNElEQVR4nO3dd5gT5fYH8O/ZAktZRJDeUeQCFhDsYhcBewcLKCJ6rSher12uV0W5qNgpili4gv4EK3pREHuhwwIigkjvCy592T2/P86EzYZkN8kmmUny/TxPniSTyeTdbM7MnDnvvCOqCiIiIiIiIqJUleF2A4iIiIiIiIjiiYkvERERERERpTQmvkRERERERJTSmPgSERERERFRSmPiS0RERERERCmNiS8RERERERGlNCa+RERERERElNKY+KYhERktIioirwZ5bbDz2idutC2QiCxz2hN4e9J5vXmI1z9wuelEUWOMEnkbY5TI2xijFEyW2w0g16wAcIWI3KGq2wFARLIAXANguast29+jAF4JmLYt4HlXAHP8nu+Ka4uI4o8xSuRtjFEib2OMUims+KavuQAWA7jcb9o5sCCaGjiziFwnIgtEZJeI/CYid4pIht/rd4nIXBHZLiKrRORVEanp9/q1IrJNRM4QkTxnvq9EpEUYbS1Q1bUBt8CVwaaA17eE/1UQeRJjlMjbGKNE3sYYpVKY+Ka31wD08XveB8DrANR/JhG5AcATAB4G0AbAAAD/BHCz32zFAPoDaAfgSgDHAHgh4PMqA7jP+ZzjAdQEMCwWf0goTleQgfH8DKI4YowSeRtjlMjbGKNUQlV5S7MbgNEAPgFwIICdAFoBqA9gN4Cmvtf95l8O4JqAZfQHsKCMz+jqLC/DeX4tbCXT2m+eqwDs8c0TYjnLnOVsC7id67ze3FnujoDXOzuv/wrgVre/c954i+TGGOWNN2/fGKO88ebtG2OUt2A3nuObxlQ1X0QmwI5KbQEwVVWXi8i+eUSkDoAmAIaLiP+5B1kAxG++02FHuNoAOABAJoBKsJXMame23aq6yG8ZqwFkw46GbS6jqc/Ajtj5WxPw/EoAeX7PVzl/49/KWC6RpzFGibyNMUrkbYxR8sfEl0YBeAN25OjhIK/7usPfBOCHYAsQkWYAPgUw0lnGJgBHAXgHtkLw2RvwVl83k/K63G9S1d/LmWdlGPMQJSPGKJG3MUaJvI0xSgCY+BIwGdYF4yAAHwS+qKrrRGQVgINV9c0Qy+gEC/o7VbUIAETk3Pg0lyjtMEaJvI0xSuRtjFECwMQ37amqisgRAERVd4eYbSCAF0RkC4CJsC4bRwFopKqDYCPmZQDoLyLjARwHOy8iVnJFpH7AtJ2qurW8N4rIrwBeVNUXY9geooRhjBJ5G2OUyNsYo+TDUZ0Jqlqgqn+V8fqrsHMjroFdP+xbAP0A/OG8PhfAHQDuArAAQF8Ad8ewiQ/DznPwv70U5ntbw47wESUtxiiRtzFGibyNMUqAHflwuw1EREREREREccOKLxEREREREaU0Jr5ERERERESU0pj4EhERERERUUpj4ktEREREREQpjYkvERERUZhE5BMRGe12O4goNMYpBcPElyAia0TkgYBpD4mIisglAdPHiMg3YS53tIh8EsZ8A53PUhHZKyKbReQHEblPRKpH9tcQpSYRae4XJyoi20RkkYi86lyfMF6fO9XvM/c464vPReRqEZF4fS5RMhCRJ0VkccC0xk68fBww/SxnesswlnuqM2+Zlyhxa71AlGzc2JaJyLV+n1kkIltEZLqIPC4idePxmVQ2Jr4EAF8BOC1g2qkAloeY/lUc2rAIQAMATQB0BvAGgBsBzApyQW+idNYVFiuHA7gTQF0AM0SkR6g3iEiGiGRW4DNfdz6zJYDzAfwIYDiACRVcLlGymwLgEBFp7DftNNj28+SA+DgVwHJVXRqHdkS8XiBKQ1Fty0SkUgU+c4fzmY0BHAtgqPPZeSLSpgLLpSgw8U1BItJNRApEJMt53so52vSK3zyPi8gXztOvAJwgIpWd1yoDOB7AIPglviJyKICGsA09RKStiHzqfNZ6EXnHl6SKyEAAvQGc43e069Qymr1XVdeq6hpVna+qw5021ALwlF8buorItyKS71SG/+e/4hCRKSLyYsD3UUNEdojIxc7zi0VkrojsdJbxtYjUC/8bJqq4KOLUZ5MTK3+o6kRVPR/AewCGiUhN533XOpWf7iKSB2APgBNFpDDwQJLzGXPLae4O5zNXquo0Vf0XgIsAXACgl9+y7nJia7uIrHKqTr42VRORv0Tk0oDPP8tpVz3n+cMi8qeI7BaRtSLyZphfKVHMiUhVsd5L20RknYjcHzDLdwAKUfog8WkA3gRQAOCogOm+7WclEXlKRFY68TJNRM52XmuOkgPMG5z1wuhymhrOeqG2s51e6Wz/5ovIdX5/ay8R2eTbF/CbPkZEPnIeNxGRD51t5w4R+ZXJNbktjDj1CXdbpiJyi4iMF5HtAAaJyO8icnfA5/q220chNPXbv12kqm/D9m+3ABjmt6yjRWSSiGx0tpXficjxfq+PkoBelGIHtZeLyF3O85NF5Cfne9gqIj+LyGHhfIfpgolvavoWQA6ATs7zUwFsROkN86kApjqPvwJQBXYkCgCOA7AJtuE+xC8pPA3ATgA/iUgDAN8AyANwDIAzAVQH8JGIZAAYAuBdAF/CjnQ1APBDJH+Eqq4BMAbAhc4yAaAa7GjZMc7fsBXAx1JyNG4kgCsDNtw9AWxz5qsPYCysotwGwMkA3oqkXUQxEmmclmUIgANgceiTA+BBWM+JtgBmAViC0hv3DOf5a5E2XlUnAZgHwP90iGIA/QG0A3AlLE5fcObfDuAdAH0CFtUHwCequk7s1Iq7AdwMoBWAcwH8EmnbiGJoCICzYL/zMwB0gG03AACqugP2Gw1MfKcC+No3Xey0naNRktC+DuAUWJwcDtsmfSwiRwJYgZK4agfbft4RZdv91ws5AGbC4qodgOcADBeRM5zX34PtF17gW4CIHABLDHzriJcBVHX+rnaweN8SRduIYqnMOC1LiG0ZADwCYCIsPl+ExUCw7ddsVZ0ZSWNVdRss6T1ZROo4k3Nh+6OdYdvO2QAmSsnpDiMBdHX2v33OAlAfwFvOQfQPYQfjjoTt0z8HoCiStqU8VeUtBW8AfgZwn/N4DCyAd8I2oFXhVID85l8B4BHn8UAAbzmPfwBwhfN4LIAvncePApgc8JkHAlAAxzjPR8N2aMtr60AAeSFeu8lZZt0Qr1eDBfVJzvPKsOShR8B3McR5fJSzvGZu/4944y2SOAXQ3PntdgqynBzntXuc59c6zzsGzHc3gIV+z7sB2A2gdhltnArgxRCvjQWwoIz3dnWWn+E87wRgL4BGzvMDnb/3XOf5XbDTHrLd/t/wxhvsYO5uAFcFTNsCYLTftH8D+MN53Nx5TxUA/QB85kzv6sRkYwAHww4SNQ34vA8AvOw8PtWZ/6By2hj2eiHE+8cCeNXv+YsAPvd7/ncAawFkOc/nwtlX4I03L9wiiNOwt2VO3LwQME99WO+O45znmQBWAbi1jLZdC2BbiNd864RjQrwuANYAuNpvWh6Ae/2ejwPwf87jWs7yTnH7f+LlGyu+qWsqbMMJ2FHlz2BHpU8FcCIseP0rKf7n+fqOVvuW45t+KpxuWgA6wo5UbfPdYMkzYBv1WPENOGBrAZGDReS/IrJERP4CsA52hLopAKjqbtgRsz7O/G1hR85GOcuZA6tC54nI+yLyd7+jbUSJNhWRxWkopeLEsRd2xNjfGwBaisgJzvM+AD5Q1U2RNbvU5+77TBE5XUS+cLpSFgAYD6ASbIcBqjoddmS9t/OWKwHkw/5uwCpOOQD+EJHXROSywG6XRAl0MOz3+6NvglqlZl7AfF8BaO50UT4NwC+quhMW3yc5lZjTACxW1ZWwA7ACYEHANvQcxHf7mSkiDzinI2xyPvNiONtPx0gAZ0nJOct9ALyhqnud588BeFBEfhSRx0SkYwzbSxSNcOO0LKW2ZY7p/k9UdS2AT1BS9e0KoDbsoHU0AuOzrogMF5HfRGQr7FSJutg/Pq9z5q8F653xmtO+zbCC0//ETkO8S0SaRNm2lMXEN3VNhZ3T1xbWfWIGSpLYUwH8oKqFfvNPAXCciBwI6x4x1Zn+NYBTxc6jrYeSbloZAD4F0D7g1gq2YoiVtgD+gnW9BoCPAdSBdd88FtadZS9spefzKoAzRKQpgOsB/KiqCwBAVYsAdHFuc53XFzvdy4gSbSoii9NQ2jr3/oPm7HZ+7/uo6gYAHwHoIyK1YQNsRNzNOeBzlwKAiDSDrRMWArgMdnDMt4MQGJ++8wr7wI7IFzntWwGgNSy+/wLwNGyAnmoVaCNRtMId6fUHWMXpVPidnqCqv8F2Xjuh9MCQGbCd3aNRevvZBvt3payIwPXC3QAGAPgPrDtoe1iVeV98quocWHfoa51zAzuh5MAxVPU1AC1gXbUPBfCD2JgeRG6JxYjM+7ZlfrYHme9VAFeISFVYrI5X1fwKfKYCWOY8fwO2TrgTwAmw+FyJ0tvPtwA0E5GTAFwF6+E4yfeiql4H2zf+BrZ9/803dgAZJr6p61tYt997AHzn7FhORckO9dSA+b9y5h8AYIOqLnGmfwcb/e4q2Hmy05zpM2Hn9/ypqr8H3AqcefbAuoJExTmP4UrYiqXY2VFvA+AJVf1SVRfCkoUs//ep6nxYF9IbAFwNv42287qq6o9qgxocDWA1gCuibSdRBUQap6HcDTvf/csw5h0J4HJYcrkuzPfsx9mYHgbg/5xJnWAb6Dud+PoNNhheoLcBNBKRW2GVr9f9X1TVXar6qareCYvPdrDqN1Gi/Q6na6NvgnMQptRgMaq6C1ZtOg2le0wBdvD4AtiBIF+PqVmwnfX6Qbafq5x59jj3FRk1PXC9cBKAj1X1LVWdDTvn/9Ag7xsJ66LZF8D3qrrI/0W1gYFGqOrlAB6GdekmcktYcRpKkG1ZWT6HHZS9CcB5CNi/DJdzzv9NAL52DkgDFp8vONu/+bCDZv7n8/qquuNhSXepA8d+88xR1adU9VTYuqg3aJ+s8mehZKSq20RkJizxu9eZ/CPsckEtYDva/vP/KSJ/ALgddnK8b/p2Zzm3A/jWr7vTS7DEcpyIPAVgAyxBvhzAACf5XQagm4i0hlVst5ZRvcpyBp4S2HkKJwK4H8BmAPc58+TDjm7dICIrADSCHbneu//iMBI2cEAh7BwIAICIHAcb6ON/sJ3+Ds53siBEu4jiJtI4ddR2YqUKgL/BzsHrBuAaVd0axsd+AYvHRwA8qarFYbynqvOZWbANcXenbR/CElkAWAw7mNpfRMbDdkL6By5IVbeKyHuwau43qrrvGqgicq3zGT/DDrRdAYvhxYHLIYo3Jz5fA/CUiGyAHSR9GMGT0a9giWZllB7I8WsAg533THWW+5uIjAEwWkQGwA4k14Id7FqqquMB/AmrBp0jdj3gnU73zVDCWS/8BqtWnQTblt4GW8/MCljWOwCecZZxk/8LIvIc7NSE3wDUgHX35PaTXBNhnIazLSvrs4pEZBTsqierAEwOo4kiJVdTOAB2QPefzuPz/eb7DcDVIvIzbPyawSg5AOZvJCwBzwaw7yoJItICdkD7I6dtLQEcAeCVIMtIX26fZMxb/G4AnkTAoBewDe92BBk8BtblUQFcH2I5dwdMbwU7QpYPG6BmEWwE10rO63VgXTAKnPefGqKdA53XFTZQVT5s5/9+ALkB854OO7l/l3N/NmwH+dqA+ao6nzsqYHob2EZ7Haxr2u8oY+AP3niL9y3cOEXJIDa+2w7YhvI1AEcGLPNahBhQw3n9YdjgOs3DaN9Uv8/cAxvo5nMA1wCQgHlvh21wd8J2CC533tc8YL6Tnem9AqZf6MT+FufvnwZn4CveeHPjBtsBfdPZzqwH8BDsdJ7RAfN1dn7T3wZM/5szPS9geraz7VvqF1cfwW9AOuez1jixOjpE+yJZLxwIqxYVOH/LYNgozVODLHeUM1+1gOkvwA5E7YId8B4LZ7A63nhz6xZOnEa4LVMAl4b4rGbO6w+H0a5r/T6zGNYDYyaAJxAwaCtsJOafne3nEqddeQAGBswnzutTAqbXc+J7FWz/drkT4xws0u8mzpdFlFJEpCEs6E9R1e/dbg+Rl4hdK/gQVT3Lpc+/AsBwAA3VLgdDRB4iIp8BWKmqN7jdFiIvEZFjAXwPoKWqLnfh86vAktvbVDXagbXSFrs6U0oRkWxY95XHAcxi0ktUwrkmZ0fYtXsvd+Hzq8IqVPcDGMmkl8hbnJFiz4QNAMlBH4kczhUGmgB4DMCERCe9IpIBq+reCasKv5fIz08Vrg5uJSKjRGS9iOT5TavlXA5jsXN/YIj3dhWRRSLyu4jcG2weSksnws6NOhZ2DjJVAGM05XwI6/41SlU/deHz74FdUmwz7NqnVEGMUYqxmbCRa+9X1bzyZqayMT5TSk/YKX21YdecT7SmsPOXewK4TlWDnf9L5XC1q7OInAzrj/+mqh7mTBsMYLOqPukE+oGq+s+A92XCzmE5CzbU9zQAPdW5ZA0RxQZjlMjbGKNE3sX4JPIWVyu+qvoN7Mi/vwtg17KCc39hkLceA+B3VV3qHPEY67yPiGKIMUrkbYxRIu9ifBJ5ixev41tPVdcAgHNfN8g8jQCs8Hu+0plGRPHHGCXyNsYokXcxPolckqyDW0mQaUH7bItIPzgXV69WrVrHv/3tb/FsFxFQWAjMnQs0bQrUqRPee5YuBXbuBNq1i2/bAMyYMWOjqobZsKgxRomixBgl8rYExGjY8Ql4JEbXrAFWrwYaNAAaNkz85xP5CRWjXkx814lIA1VdIyINYNfjCrQSNrKaT2PYCd/7UdURAEYAQKdOnXT69Omxbi9RaStXAk2aAA8+CNwQ5vhaPXsCM2cCCfh9isifFVwEY5QojhijRN5WwRiNaXwCHonRu+4Cnn0WuOQS4IUXEv/5RH5CxagXuzp/BKC387g3bBTSQNMAtBKRFiJSCUAP531E7tu71+6zs8N/T1ZWyfu8jzFK5G2MUSLvSs34zM+3+82BpzQTeYfblzN6B8CPAFqLyEoRuR7AkwDOEpHFsNHsnnTmbSgiEwFAVfcCuBXA/wAsBPCuqs53428g2k9hod1nRdChwqOJL2OUyNsYo0TelVbxycSXkoCrXZ1VtWeIl84IMu9qAN39nk8EMDFOTSOKXgpVfBmjRN7GGCXyrrSKTya+lAS8eI5vQhUWFmLlypXYtWuX201JSjk5OWjcuDGyI0nyUl0KVXyJysL1Z8Vw/UnxxhitGMZoBJj4RoUxWjGRxmjaJ74rV65Ebm4umjdvDpFgg+hRKKqKTZs2YeXKlWjRooXbzfGOFKr4EpWF68/ocf1JicAYjR5jNEJMfKPCGI1eNDHqxcGtEmrXrl2oXbs2f2xREBHUrl2bR6kCseJLaYLrz+hx/UmJwBiNHmM0Qr7ENz8fKC52ty1JhDEavWhiNO0TXwD8sVUAv7sgWPGlNMJ1QPT43VEi8HcWPX53YSosBLZvB2rXBlSBrVvdblFS4e8sepF+d0x8PSAzMxPt27ffd1u2bBmmTp2Kc889t9R8o0ePxq233lpq2qmnnorp06ejoKAABx98MBYvXgzAzhk4/PDD8fPPPwOwH8aAAQP2vW/IkCEYOHAgAGDgwIFo1KhRqTaMGzdu3+Pq1aujdevWaN++PXr16hXHbyJFsOJLlDBcfxJ5G2M0DfiqvQcfbPfs7pxU0ilG0/4cXy+oUqUKZs+eXWrasmXLIlpGbm4uBg0ahFtuuQWTJk3CkCFDcMIJJ+DYY48FAFSuXBnjx4/Hfffdh4MOOmi/99955524++67S0274oorANiPesiQIejUqVNEbUpbrPgSJQzXn0TexhhNA/6J7y+/WOLrS4LJ89IpRlnxTSGXX345MjIyMHjwYAwbNgyDBg3a91pWVhb69euHZ5991sUWpglfxTfSxLe4mOfFELmE608ib2OMepgv8W3Z0u5Z8U1LyRCjrPh6wM6dO9G+fXsAQIsWLTBhwoSolzV06FC0adMGI0aMQK1atUq9dsstt+CII47APffcs9/7nn32Wbz99tsAgAMPPBBfffVV1G1Ie9F2dQas6lupUuzbRJSiuP4k8jbGaBpgV+eklk4xysTXT//+/fcr9VdU+/btMXTo0DLnCdbFIJhQJ3D7T//888/RoEED5OXl7TdfjRo10KtXLzz//POoUqVKqdeCdTGgKEXT1dk3LxNfSlJcf3L9Sd7GGGWMxg0T35hgjMY/RtnVOYnUrl0b+b6Vi2Pz5s37+sqvXr0azz//PH755RdMnDgRc+fO3W8Z/fv3x2uvvYbt27cnpM1pqaIVXyKKOa4/ibyNMZrEArs6b9rkXlsoblIhRlnx9VPeERG3HX300bj11luxdu1a1K9fH9OnT8fu3bvRpEkTAHa05P7770fjxo3xzDPP4JZbbsE333xT6khMrVq1cPnll+O1115Dnz593PpTUlu0g1v5v5coyXD9SeRtjFGKG18yVLcukJvLim+UGKPxx4qvh02ePBmNGzfed1u6dCmee+45dO/eHe3bt0f//v3xzjvvICMjA1988QWWL1+O66+/HgBw3nnn4cADD8Sbb76533IHDBiAjRs3lpr27LPP7jeUOUWJFV8i13H9SeRtjNEUkp8PVK1qp2rVqsXEN0WkYoyKqsZlwV7UqVMnnT59eqlpCxcuRJs2bVxqUWrgdxjg1VeBG24AVqwAGjcO7z0jRgA33gisWgU0bBjX5onIDFX15HUbgsUoeRdjv+KCfYeMUYoVxmjFMUbD0KcPMGkSsHIlcNRRth/zySeJbUOSYoxWXCQxyoovUayx4ktERETpIj8fOPBAe8yKL3kYE1+iWOM5vkRERJQumPhSkmDiSxRrvoovE18iIiJKdf6Jb+3aTHzJs5j4EsWaL3llV2ciIiJKdVu27F/xTaMxhCh5MPElijVWfImIiChdBHZ1LioCCgrcbRNREEx8iWKtIoNb+d5LRERE5HV791qS65/4AuzuTJ7kycRXRFqLyGy/218i0j9gnlNFZKvfPA+71NwKW7t2LXr06IGDDz4Ybdu2Rffu3fHbb7+hSpUqpa5p5bsW1qhRo3D44YfjiCOOwGGHHYYPP/wQAHDttdeiatWqKPA7ynbHHXdARLBx40asWLECp512Gtq0aYN27drhueeec+XvTXl79wKZmYDfBbvLlWQV33SLUfI2rkP3xxglL2GMlpZS8blli90z8U1q6RKjEZSkEkdVFwFoDwAikglgFYAJQWb9VlXPTWDTYk5VcdFFF6F3794YO3YsAGD27NlYt24dDj74YMyePbvU/CtXrsTjjz+OmTNn4oADDsC2bduwYcOGfa8fcsgh+PDDD3H11VejuLgYX331FRo1agQAyMrKwtNPP42jjjoKBQUF6NixI8466yy0bds2YX9vWigsjKzaC5R0i06SxDedYpS8jevQ4Bij5BWM0f2lVHzm59s9E9+klU4x6smKb4AzACxR1T/dbkg8fPXVV8jOzsZNN920b1r79u3RpEmToPOvX78eubm5qF69OgCgevXqaNGixb7Xe/bsiXHjxgEApk6dihNPPBFZThLWoEEDHHXUUQCA3NxctGnTBqtWrYrL35XW9u6N7PxeIOkqvgFSOkbJ27gODQtjlFzDGC1XcscnE9+kl04xmgyJbw8A74R47XgRmSMin4lIu0Q2Klby8vLQsWPHoK8tWbKkVPeCb7/9FkceeSTq1auHFi1a4LrrrsPHH39c6j2tWrXChg0bkJ+fj3feeQc9evQIuuxly5Zh1qxZOPbYY2P+N6W9aCq+yZ34pnSMkrdxHRoWxii5hjFaruSOTya+SS+dYtSTXZ19RKQSgPMB3Bfk5ZkAmqnqNhHpDuADAK2CLKMfgH4A0LRp07I/sH9/IKCcX2Ht2wNDh0b11mDdCwDg888/x7Rp0zB58mTceeedmDFjBgYOHLjv9Ysvvhhjx47Fzz//jOHDh+/3/m3btuGSSy7B0KFDUaNGjajaRmVIo4pvwmOUvMtj60+A61CAMUp+GKOeE4v4dJbjXowGJr6++02bEtuOVMAYjTuvV3y7AZipqusCX1DVv1R1m/N4IoBsETkoyHwjVLWTqnaqU6dO/FscoXbt2mHGjBkRvUdEcMwxx+C+++7D2LFj8f7775d6vUePHnjooYdw1llnISOj9L+4sLAQl1xyCa666ipcfPHFFW4/BVFYmDaJL9IgRsnbuA4tF2OUXMUYLVOF49N53b0Y9SW+NWvafU4OULUqK75JJJ1i1NMVXwA9EaL7h4jUB7BOVVVEjoEl8RU7vFSBIyLROv3003H//fdj5MiRuOGGGwAA06ZNw44dO4LOv3r1aqxdu3Zf//jZs2ejWbNmpeZp2rQpHn/8cZx55pmlpqsqrr/+erRp0wZ33XVXHP4aAmDJa/p0dU5sjJJ3ubD+BLgODQNjlAxj1IuSPz4DK76AdXdm4hs5xmjceTbxFZGqAM4CcKPftJsAQFWHAbgUwN9FZC+AnQB6qKq60daKEBFMmDAB/fv3x5NPPomcnBw0b94cQ4cO3dev3qdPnz644IILcPfdd2P16tXIyclBnTp1MGzYsP2We+ONN+437fvvv8dbb72Fww8/fN9yn3jiCXTv3j1ef156SpOKb7rEKHkb16GhMUbJCxijwaVMfObnW5U3J6dkGhPfpJJOMSpejKF46dSpk06fPr3UtIULF6JNmzYutSg18DsM0KOHnaPx66/hv2fhQqBtW2DsWOCKK+LWNAAQkRmq2imuHxKlYDFK3sXYr7hg3yFjlGKFMVpxjNFy9O0LTJwIrF5dMu200+xA/rffJq4dSYoxWnGRxKjXz/ElSj5pUvElIiKiNJefX7qbM8CKL3kWE1+iWKvI5YwKC2PfHiIiIqJ4CJb41q7NxJc8iYkvUayl0eWMiIiIKI2VVfFNo9MpKTkw8YWNMEbR4XcXREUqvkx8KclwHRA9fneUCPydRY/fXRhCJb579gAhRgWm0vg7i16k313aJ745OTnYtGkTf3RRUFVs2rQJOf4j+VF0FV/f/Ex8KYlw/Rk9rj8pERij0WOMhilU4guwu3MYGKPRiyZGPXs5o0Rp3LgxVq5ciQ0bNrjdlKSUk5ODxo0bu90MbyksLD2sfzhY8aUkxPVnxXD9SfHGGK0Yxmg5ioqAv/4qO/Ft0iTx7UoijNGKiTRG0z7xzc7ORosWLdxuBqWSvXvZ1ZnSAtefRN7GGKW42rLF7lnxjRpjNLHSvqszUczxckZERESU6vLz7Z6JLyUJJr5EscaKLxEREaU6Jr6UZJj4EsVaNBXfzEy7Z+JLREREyaC8rs6bNiW0OUTlYeJLFGvRVHxFLPll4ktERETJIFTFt0oVoHJlVnzJc5j4EsVaNBVfwJJlJr5ERESUDEIlviJW9WXiSx7DxJco1goLI6/4Akx8iYiIKHmESnwBJr7kSUx8iWJt797oK76FhbFvDxEREVGs5edbl+YqVfZ/jYkveRATX6JYY1dnIiIiSnX5+UDNmsFfY+JLHsTElyjWohncCmDiS0RERMkjPz94N2cAqF2biS95DhNfoliLtuKbnc3El4iIiJJDWYkvK77kQUx8iWKNFV8iIiJKdeUlvjt32o3IIzyb+IrIMhGZJyKzRWR6kNdFRJ4Xkd9FZK6IHOVGO4lKUU2bc3wZo0Texhgl8q6UiM/yEl/fPEQeEUVZKqFOU9WNIV7rBqCVczsWwCvOPZF7iovtPn0qvoxRIm9jjBJ5V3LHZziJ7+bNQMOGiWsTURk8W/ENwwUA3lTzE4CaItLA7UZRmvNdjigNKr5hYIwSeRtjlMi7vB2fxcXA1q3hJb5EHuHlxFcBTBKRGSLSL8jrjQCs8Hu+0plG5B5f4poeFV/GKJG3MUaJvCu543PrVju9i4kvJREvd3U+UVVXi0hdAF+IyK+q+o3f6xLkPRo4wVmZ9AOApk2bxqelRD7pVfFljBJ5G2OUyLtiEp+ASzHqO3eXiS8lEc9WfFV1tXO/HsAEAMcEzLISQBO/540BrA6ynBGq2klVO9WpUydezSUyvsQ3DSq+jFEib2OMEnlXrOLTWUbiYzTcxHfTpsS0hygMnkx8RaSaiOT6HgPoAiAvYLaPAPRyRr07DsBWVV2T4KYSleZLXKOt+PoSZ49jjBJ5G2OUyLtSIj7LS3yrV7f9GlZ8yUO82tW5HoAJIgJYG/+rqp+LyE0AoKrDAEwE0B3A7wB2ALjOpbYSlUifrs6MUSJvY4wSeVfyx2d5ia+IVX2Z+JKHeDLxVdWlAI4MMn2Y32MFcEsi20VUrooObrV7d2zbEyeMUSJvY4wSeVdKxGd5iS/AxJc8x5NdnYmSVvpUfImIiChdMfGlJMTElyiW0utyRkRERJSO8vPtIH/VqqHnYeJLHsPElyiWKlLxzc5m4ktERETel59v1V4JdtUlR+3aTHzJU5j4EsUSK75ERESU6rZsKbubM8CKL3kOE1+iWNqyxe6rVYv8vUx8iYiIKBn4Kr5lqVUL2LYN2LMnMW0iKgcTX6JYmj/f7tu0ify9THyJiIgoGYSb+PrmJfIAJr5EsZSXB9SpA9SrF/l7mfgSERFRMsjPB2rWLHseX+LL7s7kEUx8iWJp3jzgsMOiey8TXyIiIkoGkVR8mfiSRzDxJYqV4mLr6nz44dG9n4kvEREReV1xcfiDWwFMfMkzmPgSxcqyZcD27RWr+Pouh0RERETkRQUFlvwy8aUkw8SXKFbmzbN7VnyJiIgoVfkGqwo38d20Kb7tIQoTE1+iWMnLs/t27aJ7PxNfIqLoFBfbZVOIKP7CTXxr1AAyMljxJc9g4ksUK/PmAc2bA7m50b2fiS8RUXRefBFo1gzYscPtlhClvnAT34wMm4eJL3kEE1+iWMnLi76bM1CS+KrGrk1EROng889t5/qXX9xuCVHqCzfxBay7MxNf8ggmvkSxsGcPsGhR9ANbAUB2tt0XF8emTVTaqFF2I6LUUlwM/PijPf7uO3fbQpQOmPhSkmLiSxQLixZZtbaiFV+A3Z3jYc0a4JZbgEGD3G4JEcXaggV2aRUA+PZbV5tClBaY+FKSYuJLFAu+EZ0rUvFl4hs/Tz0F7NoFLFkC7NzpdmvIbbxsWGr5/nu779IF+OEHrkOJ4i0/H8jMBKpXL3/e2rWZ+JJnMPElioW8PEtcW7eOfhkVTXwXLbJufuwqXdrq1cCwYUDjxnb+9K+/ut0ictMXX9hIo/Pnu90SipUffgDq1gV697aRnefOdbtFRKktP9+qvSLlz8uKL3mIJxNfEWkiIl+JyEIRmS8idwSZ51QR2Sois53bw260lQiAVXxbtwYqVYp+GRVNfEeOBM46K/rPj0BSxeiTT9p3+sor9pwJT3obMcKq/yNGuN2SuEqqGK2o778HTjgB6NzZnvM8X/K4pI9PX+Ibjlq1gK1b2RODPCHL7QaEsBfAAFWdKSK5AGaIyBequiBgvm9V9VwX2kdUWl4ecNxxFVtGRRPfpUuBFi3s8gHxlxwxumqVJTi9ewNnn20DiDHxTV9btwIff2wx8tZb1gU+J8ftVsVLcsRoRa1bZ6cw3HQT0KSJXdLo22+B2293u2VEZUnu+Iw08QXsPPyDDopbk4jC4cmKr6quUdWZzuMCAAsBNHK3VRW0ahWwe7fbraB4KCgAli2r2Pm9QEniG+35h0uWAC1bVqwNYUqaGB00CCgqAh580JLeQw9l4pvOxo+39fC//207buPHu92iuEmaGK0o3/m9J55o9yedZBVfXhaOPCzp4zOaxJfdnckDPJn4+hOR5gA6APg5yMvHi8gcEflMRNoltmUR2LoVaNsWuPVWt1tC8ZCXZ/cVGdEZqFjFV9UqvgcfXLE2RMGzMbpihXX/vvZaq4QDQLt2NgIspacxYyxG7r3XfhOvvup2ixLCszEaCz/8AFSuDBx1lD3v3BlYu9YOBBIlgaSMTya+lKQ8nfiKSHUA7wPor6p/Bbw8E0AzVT0SwAsAPgixjH4iMl1Epm/YsCGu7Q1pzBjgr7+A118Hfv/dnTZQ/PgS31hVfKNJfDdutEFdElTx9fF0jA4aZAN9PfBAybR27ewAwY4dsfscSg6rVwNTpgBXXWVdna+/Hvjqq5RfJ3s6RmPh+++BTp0s+QWs4gvwPF9KCrGIT2c5iY1RJr6UpDyb+IpINmxlMEZV9+uPpqp/qeo25/FEANkist/JA6o6QlU7qWqnOnXqxL3d+1EFhg+3LpbZ2cBjjyW+DRRf8+YB1aoBzZtXbDkVSXx91Y0EJr6ejtHly62a16dP6f9Lu3Yc2TldjR1r//urrrLn115rCfCoUa42K548HaOxsHMnMGNGSTdnAGjTxna0eT1f8rhYxafzeuJiVNXO12XiS0nIk4mviAiA1wAsVNVnQsxT35kPInIM7G/ZlLhWhmnaNLu0wp13AjffbAOq/Pab262iWMrLs4SqooNKVSTxXbrU7hPU1dnzMTpokN37V3sB+z8BPM83Hb39tlUGDz3UnjdqBHTvbj1xUnC0Uc/HaCxMn25jIvgnvhkZ9pyJL3lYUsdnQYGNnVGzZnjz+xLfTe43ncirozqfCOAaAPNEZLYz7X4ATQFAVYcBuBTA30VkL4CdAHqoenA0ixEjrBp45ZV2dPqVV2xglbfecrtlFAuqVvG94IKKLysWiW9Fq87h826M/vkn8Npr1pW1adPSrx1yCEd2TkcLFwKzZgFDh5ae3rcv8MknwMSJwPnnu9K0OPJujMbKDz/Y/QknlJ7eubON3r1uHVCvXuLbRfH16KN2GsvAgW63pCKSNz63bLH7cCu+vgSZFV/yAE8mvqr6HYAyr4qtqi8CeDExLYrSX38B77xjSW+NGna79Vbg6aetEvW3v7ndQqqo9evt/NqKDmwFWEIGRJ/4NmgAVK1a8XaEwdMx+sQTgAhw//37v5aVZddbZuKbXsaMsUrgFVeUnt69O1C/vnWLT7HE19MxGivff28V/MBLpPhfz/eSSxLfLoqfpUst8RUB+vUDGjZ0u0VRSer4zM+3+3AT38xMS36Z+JIHeLKrc8r4739tEJ1+/Uqm/eMfQJUqtuKm5Ddvnt1XdGAroOLn+LoworPnLFtm52z27WvX9AymXTsmvulE1dbFZ55pSa6/7Gw71/fTT+2Sc5Q8VK3i69/N2eeoo2w7ywGuUs/gwXYQq6jIetBR4kWa+ALW3ZmJL3kAE9948Q1q1b69nVfmU6eOVX3HjuVlVVJBrC5lBFS8q3OCR3T2pMcft52i++4LPU+7dsAffwDbtyeuXeSeH3+0/7dvUKtA119v3SbfeCOx7aKKWbTIzhkMlvhWqgQceyzP8001q1bZOfnXXw+ce67tY+3a5Xar0g8TX0piTHzjZcYMYPZsq/ZKQG+Wu++2837/9S9XmkYxNG+eHcyoW7fiy4o28d21y3YI0j3xzcsDRo+2mGvcOPR8vgGuFi5MSLPIZWPGWPXvoouCv37IIcBpp9l54cXFiW0bRc93fm+wxBewyxrNmmUD8VBqGDLEKr333APcfjuwYQMwbpzbrUo/THwpiTHxjZcRI+x8yyuv3P+1gw6ylfZ775VUDCk55eXFptoLRJ/4LltmPQzStavz9u3Agw8CHTsC1asD995b9vy+xJc9LlJfYaHtGJ9/PpCbG3q+vn2t18RXXyWubVQx339vO9O+UboDde5sBzJ++imx7UoVmzcD111nV6XwgvXrrcJ79dVAixbAGWfYpauee862f5Q40SS+tWsz8SVPYOIbDwUFdk5Zjx7AAQcEn2fAANtJZ9U3eRUX27misTi/F4g+8fWN6JxuFV9VO3jUpo11cb7sMvt/NGpU9vsOPti6QvI839T3v/9Zd9hQ3Zx9Lr7YduJefTUx7aKK+/57G8051GXkjjvOXuN5vpErLLT16ejRNi5JFGbNmoUVK1bErk1Dh1rvJt9pLCJWQJg1y34LlDj5+RZbZR1MDMSKL3kEE994eOcdq0L5D2oVqFYtoH9/4P/+D5gzJ2FNoxhatsz+z7Gu+BYWRva+dEx858+3I/6XX26x9O23dp3WcEb4zMqyEdWZ+Ka+MWOs0nD22WXPl5NjlaTx43mtyWSwcaOd4xuqmzNgV1Fo357n+UZK1cYhmTIFOPVUYNIkO3UrAjt27MBll12GCy64ADG5+k5+PvDii5aMt25dMv2aa2y04Oefr/hnUPjy8+17D3XQKZhatex9PJ2EXMbENx5GjACOOAI45piy57vzTqsIs+qbnGI5ojMQfcV3yRLrVp8O16vcutXi5sgj7Rz6l16ynbKTTopsOW3bMvFNdQUFwIcf2sGRSpXKn79vX2DPHjuAQt724492X1biC9h64aef7P9K4Xn+eduHufde4IMP7ADCU09FtIhHHnkES5YswTPPPAMJHOMkGi+8YPH8wAOlp1erZnE7fjwQy+oylS0/P7JuzoAlvqq2DSdyERPfWJsxw27BBrUKdOCBthM/YYJ116Hk4kt8feeMVlRFujq3bFn+7y3Z/f67nc/33HM2qudvvwE332zXCIxUu3ZWsd+2LebNJI/44ANg587yuzn7HHEEcPTR1t2Z5wx62/ff26Wo/K+YEEznzvYb4PY1PJ99Btx1lw0E9/jjdmD+5putZ9pvv4W1iGnTpuGZZ57BjTfeiFNPPbXibSoosHX+eedZjAa65RaLV17aKHGiTXwBdncm1zHxjbWRI20E0XB3tvr3ty4jAwfGsVEUF3l5QPPmkZ3nUpaKJr6prmVLOxdz2jQb5OSgg6JfFkd2Tn1jxlh8nnBC+O/p29fi+pdf4tYsioHvvy+5Vm9ZfD1B2N25fPPnA1dcYcnlW2+VdGO94w7rMfGf/5S7iD179qBPnz5o0KABnoqwShzSsGGWLAVWe32aNwcuuMCq1Dt3xuYzqWxMfCmJMfGNpYIC29m64gpLZsNxwAF2hPWjj4AbbgCWL49rEymG5s2L3fm9QHSJr6olvukwonNGhh3V79ix4svyJb4p3t15ypQp6NmzJ5YtW+Z2UxJr3Trgiy9sVP1IekL07GndJ4cPj1/bqGJ277aDX+V1cwaA+vXtclUc4KpsGzZYRbVaNeDjj+3ep359oE8fu871qlVlLmbQoEHIy8vD8OHDcUCogT0jsXMn8PTTwJln2nWZQ7n9djs3/7//rfhnUvmY+FISY+IbS2PHWtfJsga1CuYf/7AV95tvAq1a2eM1a8J//8qV7LKZaLt3W9evWJ3fC0SX+K5bB+zYkR4V31g6+GCgcuWUTnxffvlldOnSBWPHjsXxxx+PGREOUOOKnTvt8kPnnGOjcy9ZEt1yxo2zQVTC7Xnjk5sLXHst8Prr1r2SvGfWLFv/hpP4Atbd+bvvOKhOKLt3W9fmNWvsnPhg10D/xz/s+3v22ZCLycvLw+OPP46rrroK55xzTmza9tprto178MGy5zvlFKtUP/88T1NIBCa+lMSY+MbSiBGWCB13XGTvy8mxnazffwd69wZeftl2zO+5J/gIo3/9ZRuoW26xo9lNmthK5fTTgcGDbZTodF/5x/vvX7TIEtRYVnyzs+0+ksQ3HUd0joXMTBvZOQWv5VtYWIibb74Zt9xyC7p164affvoJlSpVwimnnIKJEye63bz9FRcDX39t523Xr2+XgZs7F7ppU8SD6uzz9ttAhw42iFmknnnGutT37w888UR0n0/x47t0Tbhd2E86ybajixbFr03JStUO1H//vV26KNSAnC1aWE82X7fjAHv37kWfPn1Qs2ZNDB06NDZt27PH9mdOOgk4+eSy5/Vd2mjuXOCbb2Lz+RScasUSX46aTy5j4hsrM2cC06eHN6hVKE2aWPL866/ApZcCQ4bYBueRR+yI9b//bUeva9UCLrzQuh61aWNHYfv3txXKP/9pl3Bo2NCS6HfesUs/pANV4OefgRtvtC7khx1m5ypFenkgAH/99VfZM+Tl2b3bFV9f4psOXZ1jrV27lKv4btq0CWeffTZeeeUV/POf/8QHH3yAY489Fj/99BMOPfRQnH/++RgxYkRiGlNcbJf7ys8H1q+3bpLLlllPifnzLVYffNAO2px6KvDuu8BFF+GPV1/F+UccgWG7d6Pwtdcw8+OPI/vcr7+2rrDXXhtduytVsorxVVfZeYUPPsgDiV7y/ff2m6lfP7z5O3e2e57nW9qmTXaa1Ztv2hgjV1xR9vz33mvx/NJL+700dOhQTJs2DS+88AIOqsjYC/7eestGan7ggfD2qa680vaN2FMjvrZvt32USBNf3/ys+JLbVDVtbh07dtS4ufVW1Zwc1c2bY7fM+fNVL71U1Xa7VEVUO3VSvf9+1alTVXfv3v89q1apvv66ao8eqrVr2/syMlS7dVN97z3VXbsiasLu3bu1uLg4Nn9PONauVf3kE9WBA1WvvNLuP/lEdd260O9Zv1716adV27Wzv7dqVdWrrlI97DB73rSp6nPPqW7bVuZHL1++XIcMGaJHH320Nm3aVIuKikLPfO+9qllZwf8H0dq40dr7/PPhv2fgQPtd7NwZ9lsATFcPxGOwW1xjNNDjj9v3XVCQuM+Mo/nz52vLli21cuXK+tZbb+33ekFBgXbr1k0B6P3331/xuF68WPXVV1Ufe0z19ttVe/ZUPeMM1cMPV61fXzUzs2TdFeqWkaF69tmqY8boqt9+0z59+mhGRoYecMAB+o9LL9VCQIcAevLJJ+unn35afpuLi1VPPFG1YUPVHTsq9vft3avat6+1s39/W3aCMEZDKC5WrVtX9Zpr4vueCP3rX//SoUOHxm35MZWXp3rDDba/AuiOq64K/7d9zjm2X+G3LV28eLHm5OToBRdcELt9hcJC1YMPVu3YMbK4u/deW6f88Uds2lGGtI3R5cttnThiROTvzc1VveOOmDeJKJhQMZrlatadSiZOBM46K/KjYGVp2xZ47z3ruvz773YeS3lHUxs2tErHtdcCRUVWif7gAzuqe9lldkT06qttsIojj9z3NlXFypUrMWfOnFK3xYsX48EHH8Sjjz4au78LsG5MGzda5XT6dLtNm2bnKwN2hLdxY6tYq1NtadoU2rEj1jVtijnZ2ahRuTKOX7jQBgbbu9cGvxgxwo5c16hh7/vsM+DJJ21kykcfBW67Dbj1VqB2bQDAmjVr8N5772HcuHH44YcfAABHHXUUbr31VhQWFqJy5crB25+XZ11lw7k+aLiirfg2amTd5Skyvm6wCxaUf83titq40c5VE7FBukRKP87OBurUCTpK7cyZM1FYWIjGjRujfv36yAxy+aZPP/0UPXv2RNWqVTF16lQcF+R0i+rVq+Ojjz7C3//+dzzxxBP4888/MWrUKFQK9zdcXGxx+uGHtk7x7yZeowZQt67dWra00z3q1rWeF9nZFifZ2SW3SpXsdswx2Fq1KgYPHoxn+/ZFUVER7rjjDjzwwAOoXbs2Cnv0wG0TJmD04sU455xzcNhhh+Gee+5Bjx49kO07NcDf559bRfCVV8of8bc8mZm2PqlaFRg61M6lf+WVktFuA+3ZA0yZYhXt666L3WjvVGLpUus9EO75vYDFV+fOoSu+CxYAL75oY3TUqWOnrxxxhN0ffrj9nkP9zx0ff/wx6tatizvuuCOCP6Zi9uzZg/Hjx2PChAm44IIL0LNnz9DXzC0utn2U554DvvwSyMnBhq5dccO8edixfj3+ByCsfmr33Wddj197Dbj9dhQXF6Nv376oXLkyXn755dhcsxew9cuSJXZ93kiWefPNNvr0yy9bN2mKvfx8u49mX7dWregrvlu22O+hXTu7jFk0lzH0GlW7zviffwJdu4Y/KC5VCBPfWFiyxDbId94Zn+UfeWSpJDVsmZl2Xcqjj7ak78svbdCWYcNsEIijjsK3rVrh5T/+wOxFi7Bh61bsBVAIoHHz5mh75JHIycnBiBEj8PDDDyMrK7KfyxdPPIHdr72GIxo1QuMqVZCxaZN1r9q4cf/BuFq1sp2To4+2lVqHDiiuWhVLZs/GsgkTsOPrr1F94UI0/+ADHKwKXye3olq1kHn77ZbIB15PVwTo3t1uP/xg5wsOHAgdPBgLjj8eT2/ditEzZkBVcfjhh+Oxxx7DFVdcgUMOOaT8P27ePOD44yP6PsoVbeLL83uj4z+yczwT30WLbCTq7dvLn7d6daBevZIksl49LJo8Gd8sWYJlAFZmZGBPgwY4qFkzNG7cGI0bN0ZRURGef/55dOjQAR988AGaVK1qG9PFi+22bh3QrBnQqhWyDjkEI55+Gi1atMADDzyA1atX491338UBBxwAEUFGRgZEpGQHdvduFH35JYrHj0fmp58iY906aGYmtnfsiM133YUNnTphV926KPZLntV3oApAVlYWqlWrtu9WvXp1VK1aFZmZmdizZw+GDx+ORx99FBs3bsSVV16Jxx57DC1atNj3/uyHHgLGjcPs66/HmFatMHjwYPTq1QsPPPAA7rrrLtx2220lBwKKi61bZMuWtj6IBRFLeqtVAwYNsuT39ddLYrWgwA6uTZhgiYXvFIkxY+y5c4CNYiTS83t9OncG3n/fDqw2bmwHhT/5BHjhBWDyZBvo7uKLbaCnOXNsB9v3O65WzdYVHTrY6MJnnLHfTn9WVhb2RnoZuiitWbIEHw4ejHnjxqHh1q3okZmJXe++i8/++U+c0L07ajZpYgeiatSwA08rVlhiv3gx0KgR9j76KJ7ctAkDX3wR9evXx4j+/cNPWE880RLfIUOAm27CiFGj8PXXX+PVV19Fw4YNS+bbu9cOWjdpYqcxROrTTy1JuuCCyN7XpIn9H0eOtFPE/EenDpdq9KerJYPVq61YUL++bWt8t3C/K1/iG02SFk3iq2qjdQ8YYNsy33LOPNOSxS5d7OB/MvnjDxuH4s03ragF2IHgbt3sygLnnWcHXCkumPjGwhdf2H2XLu62oyyZmcDZZ9tt82ZbkYwahc7jxqFzsPmXLQOWLUNxZiZeLSrCF59+im4RbISKJ05E5wcfRJEq1i5dijnZ2ajatCkadeiA6s2a2Q5h7dpA69Z2PUZnJbpmzRp89tlnmPjcc/jyyy+xdetWAECVKlXQoUMHdOrUCSf87W9ot2sXHrznHjS6+GK89PTT5TfohBOsUjV/PhbfcAMOnTwZowAMqlsXuPJK1LvjDrseYDj++suO0EU6end5fDvTkZyTvGSJrfwpci1bWqU8nuf5qtogdFlZlgxlZlqCFtjpd88eu6TI+vW2cV+3zg5q/PgjemzciJ6+5RUXA6tWYcv69VgxYwZ+LyzEsuJifN2sGU7IzERm+/aldywyMmwn3W9AEQFwf7166HvooZg4dSreqlMHNYGgtwMAZALYCeBzAB8C+LSoCPm//FKha93m5OQgMzMT27dvx2mnnYb//Oc/6BjsMlXt2gEXXIDMl15Crz//xDXXXIOJEydi8ODBGDduXOkK2/jxNuLvm2/GtieGiA1yVa2ane+7c6etRz/4wA4m7tljlcLLLrPRcXftsvODTz4ZmDQp+XbKvOz77y2ZCzzIWR7f9Xw//rjkPNVlyywJfuIJu35znTol82/fbuuFefPsNneubTOHD7eYOuaYku3p0UcjKysLhYHr7cJCGyl51SpLNrZvt9/Grl32G/I93rXL5s3KKt0rwu+5rluH/O+/R+GcOai3fTtucj6iODMT0qIFtm7dij2rVqFyqPP3jzsOePRRTG/WDL379sWCBQtw3XXX4ZlnnkHNSBOY++4DzjkHm158EfcMHIgzzjgDfXwHmlTtAMMDD1jPh0MOsYQ7Eqp2MOK008qttAd1++3WU+7ll2006kjdfbf9r15+ObrP97o5c4C//33/6b6DrvXq2brr8ceD//2JrPguWmRV/ClTLObefddi6X//s9u779p8hx1msXjGGVY48Y/lilC1bfHcubbtPfPMkv20SG3dCvzf/9n2yTcA26mnWqy0amWvjRtn+6nVqtlBn549La+Idnu2fr2tg6IpnKUw8T86n+o6deqk06dPj/2CL74YmDHDNqTJdqRw9mxre2GhHaUtLCx1K1q2DJlvvIG59erhiEWLbKejPO+/j+IePTBn7178/uKLqNKsGYYNG7ZvRNmuXbvipptuQvfu3SEi+OWXXzBx4kR8+umnmDVrFgCgUaNG6Nq1K0444QR06tQJbdu23a/i/Pe//x2jRo3C0qVL0SjMncvdu3fj0EMPxd9q18bn118Peeed0lWEK6+0Hdi6dUMv5McfSxLp888P63PDUlRkK9V//Qt4+OHy59+xw1aQ//53+Zd78CMiM1S1UwVaGjdxi9FQOnSwI9+ffRaf5Y8daxuvl16yDXg0iops47tsmR1w8bvXP/8Eli+H1K5tG89WrYBDDy153KKFVbO2bbODJIsX2xFm537PggXQv/7CripVsCsnx26VK2NnTg52Vq6MXZUrY22zZljdpg2yc3NRpUoV5OTkICcnB1WqVEGlSpWQ4ewc+VeNfI8LCwuxfft2bN++Hdu2bdv3ePv27di5cye6d++Orl27ll1x+vln23EfMsSO+jsKCgqQ6+tOXFRkOz8ZGbaTEq9ucEOHlvTsadHCEt2LLrLeH/6fOXWqrRtq17YDo+H0IvHDGA3hsMOsqhdpvPoG4/H1NDr5ZDvt5cILw9+RLSy03+KkSbbTPW2a7RjXrImvs7KwOTsbFx1zjFWVV62ymC1v/6pSJTv4lpVVevsbUD3eI4JfVbE4OxvVOnXCkT17osEZZ9jvytkpXrlyJW699VZM/PBDnHDYYXju3//Gkc2bA5UrY1eLFvjXv/6FwYMHo0GDBhg5ciS6desW2Xfoowq0b49Na9eiWUEB5s2fb700Jk+2AbCmT7cDE+3b28G+JUsi65W0eLGtw15+OXiCFk77LrzQ/kczZkR2kGTSJEugbrnFquRlSNoY3bu35ODq2rX7P16+3HrIDRhg69xAr79uPWoi/b8CwOWXW+L9669l7yvv3Gk9bJ56yk5ZefJJ4IYbSq9jVe2glC8J/vZbOwgJ2AGtjh2tqNKxo93KGwxv1y477WHu3NK3DRtK5mnWzH4bffuGl/jv3GltGzvW9hd37bLfdq9edsphs2al5y8qsqR47FhLhDdvtoMFV15pp+i1bo0tW7Zg2bJl+PPPP/fd33DDDWjTpk3JctatK+nyv3On9Xp85hkrNHnF3r32/4xjzhQyRoOd+Ou7AbjY7/GBZc0b6xuArgAWAfgdwL1BXhcAzzuvzwVwVHnLjMsJ/4WFqjVq2CAocZTQAaYCvHXmmboH0MK2bVVXrCh75tGjVTMy9NfatbV5zZq602/QpT///FMfeughbdCggQLQhg0baq1atRSAZmZmaufOnXXQoEE6Z86csP7epUuXamZmpt51111h/y0vvviiAtBJkyaVTPzjD9VBg0oGw8rMtAF3RowIPqjWiBE239KlYX9uWIqLbbkPPRTe/PPn2/xjxkT0MYjBoBzxiE+N96AcwVx1lWqTJvFZ9pYtNshTx442UBJF7/TTVRs0CD043+jRFgvvvx//tkybpjpnTvmD7kybZgMB1aunOnt2RB/BGA0iP9/+x//+d3Tvf/JJ1RtvjPh/EdKmTarjxqlef72uy8nRrZmZNrBb1662P/DII7atmDjRPnPJEht8ctMmG3itrMETi4tV9+xR3b5dzzv5ZD2yXTsdNmyYFoQxEN/48eO1YcOGmpGRoXfccYdOmTJF27ZtqwC0T58+umXLlor/7f/9ryqg64YPV50+XfXMM3XfQJKjR9v67tdfbdqwYZEt+5VX7H2LFkXfvnXrVOvUUW3fPvwBKDdssPV1mzZhDYyXsjFaXGyDtYYaaPPpp+21aAZyve02e+9BB9lAiHfdpfrGGxYfvv/T55/bwGaA6tVX24Cn4di2TXXKFNUhQ2ygxdatbeBPX7+qBg1U27ZVPeQQ2+bXq6das6YNhho4EGNOjg0k26eP6tChqpMn27bllFN03wCqN92kumDB/u3YsUN1wgRrQ/XqNn/t2qo336z600/hD9a2e7fqJ5/orLZtdU9GhiqgkzIztRugAiicW9WqVfXjjz+296xdqzpggGqVKjbI2zXX2CCeNWrYYKx33WXrUTcVFto+bkaG6nHH2fcap/2jUDFaXlDODPY43jdY77olAFoCqARgDoC2AfN0B/CZs2I4DsDP5S03LhvsH36wr3HcuNgvW1V37dqlDz/8sF544YWuJb/Tpk3TMwDdnZOj2qhR6B2Hl15SBXR3585aMytL7wgxet+ePXt0/PjxeuGFF2qvXr103LhxujnK0bCvueYarVq1qm7YsKHceXfs2KENGjTQzp07h/4u5861kSFbttR9o86ecoqNCr18uc1z222q1aqVveMSraws1fvuC2/ejz6yNv70U0QfUdENdrziU+MVo2V54gn7Drdujf2y77jDNry//BL7ZaebL7+0/9Pw4fu/tnu3avPmkY8AmwgLFqg2bqx6wAGq330X9tsYo0EsWGC/gXfecefzy9CtWzc9+uij47LszZs3R7zt37Jli95yyy0qIgpAGzdurJ999lnsGlVYaNvImjVLEpmhQ0sfmCoutkT44osjW/all1rMVDSWP/zQ2hbO9rS4WPWCC1QrVVKdNSusxad0jO7da9+HiCVx/h56yKZHs/+zbp3tS/XpY+vrypV1X7KZna3aqpU9bt3aks2K+usv1W++UX32WdVevey32LOnau/eqv36WYI/YID9Rv79b9V337UDNmUlYrNmWft9bT/rLNWPPw6e7Pbtqzppkh3EitLxxx+vp/ztb/p269a6pWpVVUD/atBA/xwwQDcuXWrrhmAJr/+Bo7VrrS0iFqvDh7tzMP7PP+2qC4D9vnz72Yccovryy6rbt5f9/hUr7EoSl10WVoEo2sR3VrDH8b4BOB7A//ye3wfgvoB5hgPo6fd8EYAGZS03Lhts3+VkNm6M+aK/+eYbbd26tQLQq666qlT1NJGKi4u1bdu2es2RR1rim5ur+r//lZ5p0CD7OZ13nj47aJAC0Ly8vLi3bcGCBSoi+lAYQfD0008rAP3666/LX3BxsSX4Dz9cUgkGVI8+2nayjz02Bq0PIidH9R//CG/eoUOtTevXR/QRMdhgxyU+NV4xWhbfzlGEBw/KNWuWbYBuuim2y01XxcWqxxxjG8rCwtKvOQfc9PPP3WlbeZYtsx26KlVUw0w+Ui5Gi4tth/LZZ23HNxp5eRrPg8wVcd5552mHDh3cbsZ+fvzxR3300UdjU+UN9PbbqgceaJXtUAcO+/a1gz6BMRtKUZElDL17x6aN119v6+HyDjoNH26/raefDnvRKRejgbZvt3VuTo7qjz+WTL/1VjvgEQuFhdZz7Z13rOBw3nl2MDrCy266Yv16u5Rfw4Yl+4e1a9ulwiqY7Ia0e7d9V8cfb59Xvbol86ES3kAzZ6p27mzvPfJIuyxqorz/vv1ucnNLeinu3WsHG44+WvcdQHvkkZJ92h07bLt+551Wrfev3scx8f0VQAcAHQEsdB4f5buV9d6K3ABcCuBVv+fXAHgxYJ5PAJzk93wygE5lLTcuO9Unnmj/tBjKz8/Xfv36KQBt3ry5fu6BHbqnnnpKAejSb75RPeIIq0yOGmU7NPfdZz+lnj21ePdubd26tR5//PEJa9vFF1+sNWvW1K1lVO0KCgr0oIMO0rPOOiu6D1m0yJJ7X4DG61p01atbkIfj9ttt/giPjMdggx2X+NR4xWhZfv/d/p+vvRa7ZRYV2YapTp3YXtc73U2YoPt17d++3bonnnyy96q9/tautW6X2dmqY8eWO3tKxOi2bVYJuflm1RYtSnZagOiqRfPm2Xvfey/y98bZRRddpIcffrjbzfCed9+1/9kPP4Q3/6xZNv8bb8Tm8//6y357LVva42AWLrTE4cwzI/pdpkSMlmfdOvvuDjrItpWqdnpQixaxWX4q2LNH9YMPLNkN9wBPLEybZlXsmjXLT3j9FRfbwcOmTS3Wevcuv9JaETt2qP7977qvcOT7HQW26euvVc89V/d1Ne/ced+1xrVyZYvP//zHemWGua0PFaPlDVm3FsAzAIb4PX7auQU56z1mgp3trFHMAxHpJyLTRWT6Bv+T1AMVFNhofpEMmrF1q1025Kyzwn9PGVQV//d//4c2bdrg1VdfxYABA5CXl4ezzz47JsuviKuvvhoZGRkYNWmSDSJw2mk2wEHnzjYIwQ03AG+9he9+/hmLFi3CDTfckLC23X///diyZQteeeWVkPM8//zz2LhxI/79739H9yGHHmoDd/zyiw0c8NRTUba2HL5BTsKxdClw8MFuDKgWs/gEIojReGjePPYjO7/+ug2A9p//xPa63unu/PPt2suDBtkIm4ANQLN2rY1A6uWBBevVswGvjj0W+Oc/bcCR+HInRjdsAJ591kYirV3bLssxerQNSvXSS8CNNzqfEvRjyub7n3vw/xx0VGeyUXZFbICfcEyeXPK+WMjNtVF0//gDuOuu/V/fs8cGDqpaFXjjjUSP4uz97WjdurZPXFxsl9rZuNFGdeZ2rUR2to3AfNZZ0Y/4HI1Onew3m59vv/FDDw3vfSI2yNivv9qI0m++aYNHRjr6ejh8l4p85RUbYf2772yfNVibTj7ZRt1fsMCuirBtm20vJk60Qb6++MJytMMPr/g2IFg27PYNbnR1Liy080q6dAnrSIKq2lEeICbdBZYvX67nn3++AtAOHTrojBkzKrzMWOvSpYs2bdpUi4qK7CjXddfZ33/XXfuOwFxzzTVao0YN3bZtW0LbdvbZZ2vdunV1R5BBKfLz87VmzZp67rnnJrRNUTnoIDs6Fo42bVQvuijij0Cqd9GKVIcONphZLGzYoFqrlh2t9HIFMlm99Zatcz76yAYPq1VLtVs3t1sVvu3bwxoUL2lj1DegUZs2tl344ovS3RYffdRej6YyMnu2vXf8+MjfG2c9e/bUQw45xO1meNMxx1gPmHB062bnd8bavffab+fDD0tPv+cemx54HmsYkjZGo/Hdd1Z1O/54216ecUZsl0/u+ewz247WqBFVHIT02mvWk6Ju3f1PjUyQUDFaXmDe4/f4soDXnijrvRW5wa4vvBRAC5Sc9N8uYJ5zUPqk/1/KW265K4N//cu+kmCl+GBuvtkGOQp31MAQPv/8c61evbpWqVJFhwwZooWJ7C4RgTFjxigAnTJlik0oLraRKp0d/M2bN2tOTo7e5MJ5jV9//bUC0BdffHG/1x5++GEFoDNnzkx4uyLWoIGdI1KeoiLrBjJgQMQfEYMNdlziU+OxwQ7H1VfbQa9Y6NvXRoicNy82y6PSCgvtHPvjjrPz7wFVDx4krKikjdHiYhvAJJTHHrP/WTTbzJkzNdokJd569eqlzZs3d7sZ3vTgg3b+YXmjye7ebftTN98c+zbs3m3nNNapU3KlhsmTbXyWfv2iWmTSxmi0/u//SkZJvvTS2C+f3LNsmY1iDdjBoIrkIMXFNlAYYN2Twx2VOw5CxWh5/Tp6+D2+L+C1ruW8N2qquhfArQD+Bzu3+F1VnS8iN4mI79rtE2Erjd8BjAQQ5UUy/Vx/vV1XKtRF4AN98YVdgDrai0s7jjjiCHTt2hXz58/HgAED9rterVdceOGFyM3NxZtvvmkTROw6bk63gzFjxmDXrl0J7ebs07lzZ5x44okYPHhwqS5nmzZtwrPPPotLLrkEHTp0SHi7IlapknW7L8/atXZNuGDdRuLMtfiMl3bt7NqbW7dWbDk//QS8+qpd5/Www2LTNiotKwu45x77rgcNAi691K7VSKW4FqMiQNOmZb9uDYx82b73JLY7aliysrKwN9xTVNJNly7WVXbKlLLnmzYN2L49dt2c/VWqBLz9tq3jb7gB2LTJrqXaqpVd39QFSbcdveQS4Omn7XGtWq41g+KgWTM7hbFfP2DwYIvZdesiX05xsV3/+aGHgGuusW7y9erFvr0VFSwb1pKjTbOCPQ72PBluYR0Fu+gi625a3qhyf/xhRzSGDi1/mSnk+uuv1+rVq+/Xlbm4uFiPOOIIPeqoo1xqmerEiRMVgL7++uv7pv3zn/9UEUnICNMx0aOH/f7KO+L2zTf2+4ti4DPE4PqD8bq5UvH1XRYq3AFYgikstMGLGjVSDeNam1QBO3faNRgzMoJfRzEFpGyM+kb/D+M6qfuZNk33dXP3mH79+mn9+vXdboY37dljI7neeGPZ8/3rX1ZR3LQpfm3xXYO2VSsboHP69KgXlbIxWpbiYhvUlD2aUtfrr1tvwoYNVb//Pvz3FRaqXnutxddtt8Xncp8RChWj5R061RCPgz1PDTfdZCfwjx9f9nxffGH3XbrEv00e0rt3b2zbtg3jA76fadOmYe7cua5Ue326du2K9u3bY9CgQSgqKsLatWvx/PPPo2fPnmjXrp1r7YrIZZfZ72/q1LLnW7rU7lu2jHuTUp7vt7FgQfTLGDYMmD0beO45oHr1mDSLQsjJscr6Sy8Bbdq43RqKBCu+6Sc7Gzj9dBvgqqz/++TJQIcO8a0m9u9vA3MuXgw89hjQsWP8PisViQDXXcceTans2mttcM6cHOCUU2yAqfIGvtq1ywbMGj0aGDjQ9oM8uJ72Ka9lR4rIXyJSAOAI57Hv+eEJaF/inXmmdR8dNqzs+SZNAho1Av72t8S0yyNOPPFEtGjRAm+88Uap6SNHjkTVqlVx5ZVXutQyQERw//3347fffsP48ePx5JNPYs+ePXjkkUdca1PEunUDqlUD3n237PmWLrUVS7NmiWlXKmve3Eb1rMjIzh9+CBxxBHDxxTFrFpXh3HPtICUlF9/OkG+E5kh4fFRnJr5l6NIFWLYMWLIk+Ovbt9vOdjy6OfvLyADGjgVGjbIRYolof+3bAzNmAH372sjRrVtbUWb69P3nLSiw7fGECZbwPvKIJ9fR/spMfFU1U1VrqGquqmY5j33PsxPVyITKyLAjHN98E7oCVFRkRye7dPH8PzjWMjIy0KtXL0yZMgUrVqwAABQUFOCdd97BFVdcgRo1arjavosvvhitW7fGgw8+iGHDhqF37944NNxh3r2gShW7BMiECWVf1mjJEqBJkwqfX06wmG/TpmKJb0EB0KBB2q0PiCKSwhVfXs6oDL6ecZMmBX/9u++AwsL4J76AXaLnuutsPBciCq5mTbsM0bJldinPL74Ajj7aYnTSJFsfb9pkxcKpU+2ySLff7nKjw+O9LYgXXHutJRShqr4zZti1s2J0/d5k06tXL6gq3n77bQDA2LFjsX37dle7OftkZmbi3nvvxW+//Ybi4mI89NBDbjcpcuF0d166lN2cY6lt24onvuziTFS2FK34Zmdns+JbloMPBlq0CJ34TpliXaJPOimx7SKistWvDzzxBLB8uQ18tXAhcPbZNqhk587AnDl2aug117jd0rAx8Q2mTh0bLfTNN60LTiDfyvvMMxPbLo9o2bIlOnfujDfeeAOqipEjR6Jdu3Y47rjj3G4aAOCqq65CmzZtcNttt6F58+ZuNydyvu7O770Xep6lS10Z0TlltWsHrFoFbNkS3fu3bQNyc2PaJKKUk8IVXya+ZRCxqu+UKVbZDTR5MnD88bbdIyLvqVED+Mc/gD/+AF57Ddi5066G8dlnwPnnu926iHhvC+IVN91kQ9+PG7f/a198YUc76tRJfLs8onfv3li0aBFGjhyJadOm4YYbboB45Eh8dnY25s+fjyFDhrjdlOhUqWLnTIwfH7y787ZtNtQ8K76xU9EBrljxJSpfilZ8s7KyUFRUBI0moU8XXbrYevKnn0pP37wZmDnTBsAiIm+rXBno08f2ldasscHikgwT31BOOsl2hgO7OxcUAD/8kLbdnH0uvfRS5OTk4Pbbb0flypVxjce6OYiIZxLxqFx+eejuzn/8YfdMfGOnoonvtm1MfInKk8IVXwAoKipyuSUedvrp9r8L7O48dar9bxNxfi8RxUZGRtL20PDeFsQrRKzqO22andPr8/XXVoVLs8sYBTrggANw0UUXYffu3bjkkktQixc0j62yujv7LmXErs6x07Ch3a9fH/l79+yx7nvs6kxUthSu+AJgd+ey1KwJHHvs/onv5Mm2rTvmGFeaRUTphYlvWa65xi5zMnx4ybRJk6wr6oknutcuj7jxxhuRmZmJW265xe2mpJ6yujvzGr6xV6kSkJVlPToi5XsPK75EZUvxii9Hdi5Hly5WTNi8uWTalCnAySfzCgVElBDe24J4yQEHAD17AmPG2Pm+gJ3fe8op1s89zZ1yyinYvHkzTjjhBLebkpp8ozt//XXp6UuW2G/zwAPdaVcqErGKbTSJ77Ztds/El6hsrPimt7PPtgMYkyfb81WrgF9/ZTdnIkoYJr7luekmYMcO4O23gRUrbCWd5uf3+nP7ur0pLVR3Z9+ljDy4A5jUcnNLkthI+N7Drs5EZUvRim92djYAJr7lOvpoO2jr6+48ZYrdc2ArIkoQ721BvKZTJ7sNG1aysk7z83spQapWDd7dmZcyio9oK77s6kwUHlZ801tWllV3J00qqfzWrg0ceaTbLSOiNMHENxw33QTk5QFPPQU0aFAyAixRvF12GbBhQ0l356IiG9WZ5/fGXkW7OrPiS1S2FK34MvGNQJcuwPLlwKJFlviedpon/6dElJq4tglHjx528ebFi62bswePOFOK6tbNKr++7s6rV9sowkx8Y48VX6L4YsWXfD3mXn4ZWLmS5/cSUUIx8Q1HtWpAr172mOf3UiJVrQqcd15Jd2deyih+qlfn4FZE8ZTiFV+O6hyGFi2AQw6x08cAJr5ElFDe24J41d13A1deaUkIUSL5ujt/842N6Ayw4hsP7OpMFF+s+BJgVd/CQqBJE0uCiYgShIlvuJo1s8saHXCA2y2hdOPf3XnpUiAz03YYKLaiHdWZXZ2JwpOiFV+O6hwhX3fn00/35IEMIkpd3tuCEFFpvtGd338f+O03Owjj7GhRDFWk4iti/yciCo0VXwIs4T3ySOCqq9xuCRGlmSy3GxBIRP4D4DwAewAsAXCdqm4JMt8yAAUAigDsVdVOCWwmUWJddhnw7rvAJ58AJ57oalNSNkZzc6373e7dQOXK4b9v2zar9npwh5zSj6fjM0Urvkx8I5SbC8ye7XYrXOPpGCVKcd7bggBfADhMVY8A8BuA+8qY9zRVbc+VAaW87t2torhzpxfO703NGPWdoxtp1beggN2cyUu8G5+s+BIBXo5RohTnucRXVSepqm/r8ROAxm62h8gTfN2dAddHdE7ZGPUlr5Emvtu2cWAr8gxPxycrvkTejlGiFOe9LUhpfQB8FuI1BTBJRGaISL8EtonIHZddZvfeupRR6sSoL3mNdIArVnzJu7wVnyle8eXljCgK3opRohTnyjm+IvIlgPpBXnpAVT905nkAwF4AY0Is5kRVXS0idQF8ISK/quo3QT6rH4B+ANC0adOYtJ/IFRddBLzxRkIuqZWWMRptV2ffOb5ECZLI+HSWFZsYTdGKL0d1pkBJG6NEKc6VxFdVzyzrdRHpDeBcAGeoBt9Cqupq5369iEwAcAyA/VYIqjoCwAgA6NSpUxRbWyKPyMwEevVKyEelZYxWJPGtH2z/hig+EhmfzjyxidEUr/gy8SWfpI1RohTnuUOnItIVwD8BnK+qO0LMU01Ecn2PAXQBkJe4VhKlr5SNUQ5uRSnA0/HpS3xTrOLLxJci4ekYJUpx3tuCAC8CyIV165gtIsMAQEQaishEZ556AL4TkTkAfgHwqap+7k5zidJOasZoRSq+HNyKvMO78emr1rLiS+nNuzFKlOI8dx1fVT0kxPTVALo7j5cCODKR7SIik7IxWpFRnVnxJY/wdHyy4kvk7RglSnHe24IQEbkhmlGdVZn4EoUrxSu+HNWZiMjbmPgSEQFA5cpAdnZkFd+dO22HnF2dicrHii8REbnIe1sQIiK35OZGlvj65mXFl6h8KVrx5eWMiIiSAxNfIiKfSBNfX7doJr5E5WPFl4iIXOS9LQgRkVuiTXzZ1ZmofCla8WXiS0SUHJj4EhH5VK/Ors5E8cKKLxERuch7WxAiIrfk5kY2qjMrvkThY8WXiIhcxMSXiMiH5/gSxU+KV3x5OSMiIm/z3haEiMgtHNWZKH5StOLLUZ2JiJIDE18iIh8ObkUUPyla8c1w2sTEl4jI27y3BSEicosv8Q13x5wVX6LwpWjFV0SQlZXFxJeIyOOY+BIR+VSvDuzdC+zeHd7827YBWVlApUrxbRdRKkjRii8AJr5EREnAm1sQIiI3+Loshzuy87Zt9h4PVqGIPCdFK74AE18iomTAxJeIyMeX+IZ7nm9BAbs5E4UrxSu+HNWZiMjbvLkFISJyQ6SJr6/iS0TlS+GKb3Z2Niu+REQex8SXiMgnmsSXFV+i8KR4xZeJLxGRt3lzC0JE5AZ2dSaKnxSu+DLxJSLyPia+REQ+viSWXZ2JYo8VXyIicpHntiAiMlBEVonIbOfWPcR8XUVkkYj8LiL3JrqdROkqpWM00lGdWfElj/F0fLLiS+TtGCVKcVluNyCEZ1V1SKgXRSQTwEsAzgKwEsA0EflIVRckqoFEaS41Y5Tn+FJq8GZ8pnjFl6M6UwS8GaNEKc6bW5DyHQPgd1Vdqqp7AIwFcIHLbSKiEskZoxzVmdKDO/HJii9RuJJzG0rkcV5NfG8VkbkiMkpEDgzyeiMAK/yer3SmEVFipGaMVqpkt3AS36IiYMcOVnzJi7wZnylc8eXljChC3oxRohTnyhZERL4UkbwgtwsAvALgYADtAawB8HSwRQSZFnRLKiL9RGS6iEzfsGFDrP4EopSW1jGamxte4rt9e8n8RAmUyPh0Pi82McqKL6WJpI1RohTnyjm+qnpmOPOJyEgAnwR5aSWAJn7PGwNYHeKzRgAYAQCdOnWK4jAzUfpJ6xitXj28xNc3Dyu+lGCJjE/n82ITo7Go+DLxpSSQtDFKlOI812dIRBr4Pb0IQF6Q2aYBaCUiLUSkEoAeAD5KRPuI0l3Kx2hubnijOvvmYeJLHuLp+GTFl8jbMUqU4rw4qvNgEWkP69KxDMCNACAiDQG8qqrdVXWviNwK4H8AMgGMUtX5LrWXKN2kdoyG29XZl/iyqzN5i3fjs6IVX4+e3wtY4rtnzx63m0HJwbsxSpTiPJf4quo1IaavBtDd7/lEABMT1S4iMikfo7m5wNat5c/Hrs7kQZ6Oz4pWfD1a7QUs8d3uO++fqAyejlGiFOfdw6dERG5gxZcoPlK44stRnYmIvM+7WxEiIjdEmviy4ksUnhSv+DLxJSLyNia+RET+OKozUXykcMWXiS8Rkfd5dytCROQG36jO5e2cs6szUWRY8SUiIhcx8SUi8pebC+zdC+zeXfZ8vopvtWrxbxNRKmDFl4iIXOTdrQgRkRt8Fdzyujtv2wbk5ABZnhscn8ibUrziW1hY6HYziIioDEx8iYj8RZL4spszUfhSuOLLUZ2JiLzPu1sRIiI3hJv4FhRwYCuiSKR4xZeJLxGRtzHxJSLy50tmfYNXhcKKL1FkUrjiy8SXiMj7vLsVISJyQyRdnVnxJQofK75EROQiJr5ERP7Y1ZkoPljxJSIiF3l3K0JE5AYObkUUHyle8eWozkRE3sbEl4jIHyu+RPHBii8REbnIu1sRIiI3+JJZnuNLFFspXPHNzs6GqqI4mr+NiIgSgokvEZG/SpXsxlGdiWIrxSu+AFj1JSLyMO9uRYiI3JKbW3bFd88eu7HiSxS+FK74MvElIvI+Jr5ERIHKS3x91WBWfInCx4ovERG5yLtbESIit4Sb+LLiSxQ+VnyJiMhFTHyJiAKVl/j6XmPiSxS+NKj48pJGRETeleV2AwKJyDgArZ2nNQFsUdX2QeZbBqAAQBGAvaraKUFNJEpraRGjublAfn7o19nVmTzK0/GZwhXf7OxsAKz4Uvk8HaNEKc5zia+qXuF7LCJPA9haxuynqerG+LeKiHzSIkarVwdWrAj9Oiu+5FGejs80qPgy8aXyeDpGiVKc5xJfHxERAJcDON3tthDR/lI6RnmOLyU5T8ZnCld8mfhSpDwZo0QpzruHT4HOANap6uIQryuASSIyQ0T6JbBdRGRSN0Y5qjMlP+/FJyu+RP68F6NEKc6Viq+IfAmgfpCXHlDVD53HPQG8U8ZiTlTV1SJSF8AXIvKrqn4T5LP6AegHAE2bNq1gy4nSQ9rHqC/xVQ1eZWJXZ3JRIuPT+bzYxCgrvpQmkjZGiVKcK4mvqp5Z1usikgXgYgAdy1jGaud+vYhMAHAMgP1WCKo6AsAIAOjUqVMUh5mJ0k/ax2huLlBUBOzaBVSpsv/rrPiSixIZn848sYnRNKj4clRnApI4RolSnFe3ImcC+FVVVwZ7UUSqiUiu7zGALgDyEtg+onSX2jHqS2hDdXfets2qT8GSYiL3eTM+U7jiy1GdKULejFGiFOfVxLcHArp/iEhDEZnoPK0H4DsRmQPgFwCfqurnCW4jUTpL7Rj1dWH2VXYDFRQA1ap5ugJFac278SmS0hVfJr4UJu/GKFEK8+Sozqp6bZBpqwF0dx4vBXBkgptFRI6Uj9FwKr7s5kwe5en4FEnJii8TX4qEp2OUKIV59/ApEZFbykt8Cwo4sBVRNDIyWPElIiJXeHcrQkTkFlZ8ieKDFV8iInIJE18iokDhJL6s+BJFLsUrvhzVmYjIu7y7FSEicgu7OhPFByu+RETkEia+RESByhvVmV2diaKTohVfXs6IiMj7vLsVISJyC7s6E8UHK75EROQSJr5ERIGys4HKldnVmSjWUrTiy8SXiMj7vLsVISJyU25u8MRXlV2diaLFii8REbmEiS8RUTChEt+dO20nnBVfosix4ktERC7x7laEiMhNoRJf34BXrPgSRS7FK768nBERkXcx8SUiCqZ69eCjOvumseJLFLkUrfhyVGciIu/z7laEiMhNoSq+vmlMfIkil+IVXya+RETexcSXiCgYdnUmir0Urfgy8SUi8j7vbkWIiNxUXuLLii9R5FjxJSIilzDxJSIKhl2diWKPFV8iInKJd7ciRERu8iW+gTvp7OpMFL0Ur/hyVGciIu9i4ktEFEz16razvWtX6ems+BJFL0UrvhkZGcjIyGDFl4jIw7y7FSEicpOvohvY3ZkVX6LopWjFF7CqLxNfIiLvciXxFZHLRGS+iBSLSKeA1+4Tkd9FZJGInB3i/bVE5AsRWezcH5iYlhOlB8Yoyk58s7KASpUS3yYiR9LGaIpWfAEmvlRa0sYoUQpzayuSB+BiAN/4TxSRtgB6AGgHoCuAl0UkM8j77wUwWVVbAZjsPCei2GGMhkp8Cwqsm7PHq0+U8pIzRlnxpfSRnDFKlMJcSXxVdaGqLgry0gUAxqrqblX9A8DvAI4JMd8bzuM3AFwYl4YSpSnGKMqu+LKbM7ksaWOUFV9KE0kbo0QpzGtbkUYAVvg9X+lMC1RPVdcAgHNfNwFtI6J0itGyEl8ObEXe5e0YzchI6YovR3WmMHg7RolSWFa8FiwiXwKoH+SlB1T1w1BvCzItikPDpdrRD0A/5+k2EQl29M3fQQA2VuQz44TtipxX2+Z2u5oBjNGwnXNO8On774S7/X8NxavtArzbNrfbldox+vvvwH//G3lD5sypaPIb9//r8OHDMXz48Ejf5vbvrSxebZvb7UrmGHX7uyuLV9vm1XYB3m2b2+1qFmxi3BJfVT0ziretBNDE73ljAKuDzLdORBqo6hoRaQBgfRntGAFgRLgNEJHpqtqp/DkTi+2KnFfb5pV2MUZji+2KnFfb5pV2MUZji+2KnFfb5pV2JWOMeuW7C8arbfNquwDvts2r7fJaV+ePAPQQkcoi0gJAKwC/hJivt/O4N4BQR9WIKLYYo0Texhgl8jbGKJFL3Lqc0UUishLA8QA+FZH/AYCqzgfwLoAFAD4HcIuqFjnvedVvOPgnAZwlIosBnOU8J6IYYYwSeRtjlMjbGKNE3iMazeiKKUxE+jldRjyF7YqcV9vm1XYlC69+f2xX5LzaNq+2K1l49ftjuyLn1bZ5tV3JwMvfnVfb5tV2Ad5tm2fbxcSXiIiIiIiIUpnXzvElIiIiIiIiiikmvg4R6Soii0TkdxG51+32+BORZSIyT0Rmi8h0F9sxSkTWi0ie37RaIvKFiCx27g/0UNsGisgq53ubLSLdXWhXExH5SkQWish8EbnDme6J7y2ZMEbDaocnY5TxmR4Yo2G1gzEaWbsYozHk1Rj1Snw6bWGMRtaupIpRJr4ARCQTwEsAugFoC6CniLR1t1X7OU1V27s8NPhoAF0Dpt0LYLKqtgIw2XnuhtHYv20A8KzzvbVX1YkJbhMA7AUwQFXbADgOwC3Ob8sr31tSYIyGbTS8GaOjwfhMaYzRsI0GYzQSjNEYSYIY9UJ8AozRSCVVjDLxNccA+F1Vl6rqHgBjAVzgcps8R1W/AbA5YPIFAN5wHr8B4MJEtsknRNtcp6prVHWm87gAwEIAjeCR7y2JMEbD4NUYZXymBcZoGBijkWGMxhRjNAyM0cgkW4wy8TWNAKzwe77SmeYVCmCSiMwQkX5uNyZAPVVdA9iPH0Bdl9sT6FYRmet0EXG1m4WINAfQAcDP8P735jWM0eh5+bfG+EwdjNHoefn3xhhNHV6OUS/HJ+Dt3xtjNAJMfI0Emeal4a5PVNWjYN1TbhGRk91uUJJ4BcDBANoDWAPgabcaIiLVAbwPoL+q/uVWO5IYYzT1MD5TC2M09TBGU4uXY5TxGR3GaISY+JqVAJr4PW8MYLVLbdmPqq527tcDmADrruIV60SkAQA49+tdbs8+qrpOVYtUtRjASLj0vYlINmxlMEZVxzuTPfu9eRRjNHqe/K0xPlMOYzR6nvy9MUZTjmdj1OPxCXj098YYjRwTXzMNQCsRaSEilQD0APCRy20CAIhINRHJ9T0G0AVAXtnvSqiPAPR2HvcG8KGLbSnFF3COi+DC9yYiAuA1AAtV9Rm/lzz7vXkUYzR6nvytMT5TDmM0ep78vTFGU44nYzQJ4hPw6O+NMRo5UfVKLwd3OUOADwWQCWCUqj7ubouMiLSEHf0CgCwA/3WrbSLyDoBTARwEYB2ARwB8AOBdAE0BLAdwmaom/OT7EG07Fdb9QwEsA3Cj73yDBLbrJADfApgHoNiZfD/s/AfXv7dkwhgNqy2ejFHGZ3pgjIbVFsZoZO1ijMaQF2PUS/HptIcxGlm7kipGmfgSERERERFRSmNXZyIiIiIiIkppTHyJiIiIiIgopTHxJSIiIiIiopTGxJeIiIiIiIhSGhNfIiIiIiIiSmlMfNOciGwLeH6tiLwYp88SEZkiIjWCfXYMlj9QRO52Hg8RkdNjuXwiNzBGibyNMUrkbYxR8mHiS3EhIplBJncHMEdV/0pAE14AcG8CPocoKTFGibyNMUrkbYzR5MPEl0ISkWYiMllE5jr3TZ3po0XkUr/5tjn3p4rIVyLyX9iFrANdBeDDIJ8jIvIfEckTkXkicoXfa/c40+aIyJPOtBtEZJoz7X0RqRq4TFX9E0BtEalf0e+ByKsYo0Texhgl8jbGaHph4ktVRGS27wbgUb/XXgTwpqoeAWAMgOfDWN4xAB5Q1bZBXjsRwIwg0y8G0B7AkQDOBPAfEWkgIt0AXAjgWFU9EsBgZ/7xqnq0M20hgOtDtGWm85lEyYwxSuRtjFEib2OMEgAgy+0GkOt2qmp73xMRuRZAJ+fp8bBABYC3UBKMZflFVf8I8VotVS0IMv0kAO+oahGAdSLyNYCjAZwC4HVV3QEAqrrZmf8wEXkMQE0A1QH8L8TnrQfQMIw2E3kZY5TI2xijRN7GGCUArPhSZNS53wvntyMiAqCS3zzby3j/XhEJ9puTEPOL32f6Gw3gVlU9HMC/AOSEeH8OgJ1ltIco1TBGibyNMUrkbYzRFMbEl8ryA4AezuOrAHznPF4GoKPz+AIA2WEubxGAlkGmfwPgChHJFJE6AE4G8AuASQD6+M5rEJFazvy5ANaISLbTrlAOBZAXZtuIkhFjlMjbGKNE3sYYTSPs6kxluR3AKBH5B4ANAK5zpo8E8KGI/AJgMso+8uXvUwCnAvg9YPoEWFeTObCjXveo6loAn4tIewDTRWQPgIkA7gfwEICfAfwJG1ggN/CDnBXFIQCmh9k2omTEGCXyNsYokbcxRtOIqAarrhPFnog0gA0gcFYCPusiAEep6kPx/iyiVMEYJfI2xiiRtzFGvY1dnSlhVHUNgJHiXNQ7zrIAPJ2AzyFKGYxRIm9jjBJ5G2PU21jxJSIiIiIiopTGii8RERERERGlNCa+RERERERElNKY+BIREREREVFKY+JLREREREREKY2JLxEREREREaU0Jr5ERERERESU0v4fkvvzEoIGV3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,4, figsize=(16, 4))\n",
    "axs = axs.ravel()\n",
    "\n",
    "axs[0].plot(np.arange(0,24,1), np.nanmean(obs_EFbyClass_24hours[0,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[0].plot(np.arange(0,24,1), np.nanmean(model_EFbyClass_24hours[0,:,:], axis=0),'r', label='CESM2')\n",
    "axs[0].set_title('Mean EF:\\n'+classCodes[0]+' Days',fontsize=14)\n",
    "axs[0].legend()\n",
    "axs[0].set_xlabel('Hour (local)')\n",
    "axs[0].set_ylabel('EF')\n",
    "\n",
    "axs[1].plot(np.arange(0,24,1), np.nanmean(obs_EFbyClass_24hours[1,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[1].plot(np.arange(0,24,1), np.nanmean(model_EFbyClass_24hours[1,:,:], axis=0),'r', label='CESM2')\n",
    "axs[1].set_title('Mean EF:\\n'+classCodes[1]+' Days',fontsize=14)\n",
    "axs[1].legend()\n",
    "axs[1].set_xlabel('Hour (local)')\n",
    "\n",
    "axs[2].plot(np.arange(0,24,1), np.nanmean(obs_EFbyClass_24hours[2,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[2].plot(np.arange(0,24,1), np.nanmean(model_EFbyClass_24hours[2,:,:], axis=0),'r', label='CESM2')\n",
    "axs[2].set_title('Mean EF:\\n'+classCodes[2]+' Days',fontsize=14)\n",
    "axs[2].legend()\n",
    "axs[2].set_xlabel('Hour (local)')\n",
    "\n",
    "axs[3].plot(np.arange(0,24,1), np.nanmean(obs_EFbyClass_24hours[3,:,:], axis=0), 'k',  label='FLUXNET')\n",
    "axs[3].plot(np.arange(0,24,1), np.nanmean(model_EFbyClass_24hours[3,:,:], axis=0),'r', label='CESM2')\n",
    "axs[3].set_title('Mean EF:\\n'+classCodes[3]+' Days',fontsize=14)\n",
    "axs[3].legend()\n",
    "axs[3].set_xlabel('Hour (local)')\n",
    "\n",
    "# Set y-axis limits\n",
    "axs[0].set_ylim([-10,10])\n",
    "axs[1].set_ylim([-10,10])\n",
    "axs[2].set_ylim([-10,10])\n",
    "axs[3].set_ylim([-10,10])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:35:26.755689Z",
     "start_time": "2021-03-02T20:35:26.555654Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hour (local)')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1, figsize=(8, 6))\n",
    "\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_SHbyClass_24hours[0,:,:], axis=0), label=classCodes[0])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_SHbyClass_24hours[1,:,:], axis=0), label=classCodes[1])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_SHbyClass_24hours[2,:,:], axis=0), label=classCodes[2])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_SHbyClass_24hours[3,:,:], axis=0), label=classCodes[3])\n",
    "\n",
    "ax1.legend()\n",
    "ax1.axhline(0,color='gray')\n",
    "ax1.set_title('Diurnal cycle of absolute SHFLX Bias (model-obs)')\n",
    "ax1.set_ylabel('Bias [W/m2]')\n",
    "ax1.set_xlabel('Hour (local)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:35:53.684897Z",
     "start_time": "2021-03-02T20:35:53.510326Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hour (local)')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAGDCAYAAADZBDLOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAACyB0lEQVR4nOydd1gUV9fAf3eXpffeBRUVC2BBEXvvidHEdGN6770n75f65k0zvZmeWFKMiRo1sYu9K4gFLFTpHRZ27/fHLChKUxd20fk9zz7Lzty598zssGfuOeeeI6SUqKioqKioqFwcaCwtgIqKioqKior5UBW7ioqKiorKRYSq2FVUVFRUVC4iVMWuoqKioqJyEaEqdhUVFRUVlYsIVbGrqKioqKhcRKiKXaVBhBCfCiGet9DYq4UQt7XRWLOEEOtboV8hhPhaCFEghNhyHscfFUKMNrNM3wghXjFnnxczQoghQojkVup7gxCid2v0fcY4w4UQaS1s+5IQ4ofWHueM46KEEAnnM6ZK46iK/RLEpDQqhBAlQohCIUSCEOIuIUTd/SClvEtK+X+WlLOdMxgYAwRLKftbWphz5UIfApo6XgghhRCdz9hWT6mY2pQJIUpNr0LT9kYViBDiVyHE52dsWyiE+LCR9i8JIapPGyNJCDG9dr+Ucp2UsmuLT7qFCCGmACVSyp3m7ru9IaXcAxSaromKmVAV+6XLFCmlC9ABeAN4EvjKHB0LIbTm6Ked0wE4KqUss7Qg7ZhoKaWz6eXegvb3AtOFECMAhBBXA72Bp5o4Zl7tGMBDwA9CCL8LlLs57gK+b+Ux2hM/AndaWoiLCVWxX+JIKYuklIuAq4GbhBA9of6MqyFz9emzLlPbT4QQS4QQZcAIk1XgMSHEHiFEkRBinhDC3tTeQwjxlxAix2Sq/ksIEdwSeYUQWiHEM0KIIyaLw3YhRIgQ4iMhxNtntP1TCPGQ6e8QIcRvpjHzmpjFdRNCrBBC5AshkoUQM5qQJVAIscjU9rAQ4nbT9luBL4GBppngyw0c20kIsdIkS64Q4kchhPsZzWKFEImma/T1adfP23TNCk1jr6u1tgghIoXiyigUQuwXQlzWiOyNfqdCiDuA64EnTPL/edr5/mq6hqlCiAcauzaWQEqZBTwKfCGECAVmA3dKKUtbePwyoAToBGdbB4QQT5123yUKIa44bV9nIcQa072eK4SY19AYQghbYCSw5rRtLwkhFgghfjD1vVcI0UUI8bQQ4qQQ4oQQYuxp7Ru870z7HEz/jwVCiEQg9ozxz/s7FELYCSHeE0JkmF7vCSHszmjzjOn8jwohrj9t+0TTNSsRQqQLIR477bDVwKgz+1I5f1TFrgKAlHILkAYMOc8urgNeBVyAWoUxAxgPhANRwCzTdg3wNcqsNhSoABpUtA3wCHAtMBFwBW4ByoFvgWtPU3DewCjgZ6FYEP4CjgFhQBAw98yOhRBOwArgJ8DXNM7HQogejcjyM8o1CwSuBF4TQoySUn6FMivbaJoNvtjAsQJ43XRsJBACvHRGm+uBcSiKpgvwnGn7o6ZxfQA/4BlACiF0wJ/AcpP89wM/CiHOyZwspfwcZRb1X5P8U0zX9U9gN8r1GwU8JIQYdy59tzZSym+AI8AO4G8p5d8tOU4oTAJsgcRGmh1B+f9wA15Gmd0HmPb9H8p19wCCgQ8a6SMCMEopz3QnTEGZxXsAO4FlKP8nQcB/gM9Oa9vgfWfa9yLK/dIJ5d656bRzvNDv8FkgDogBooH+nLonAfwBb1PfNwGfn3bvfYXykOUC9ARW1h4kpUwHqgGzuz0uVVTFrnI6GYDneR77h5Ryg5TSKKWsNG2bLaXMkFLmo/ygxABIKfOklL9KKcullCUoDwTDWjjObcBzUspkqbDb1N8WoAjlxwrgGmC1lDIb5QcoEHhcSlkmpayUUjYUMDcZxXz+tZSyRkq5A/gV5cezHkKIEBQ/+pOm/nahzNJvbMlJSCkPSylXSCmrpJQ5wDsNXIMPpZQnTNfvVZQHDVB+BAOADlLKapMvWKL86DoDb0gp9VLKlSgPNNdy4cQCPlLK/5j6TgG+QLnO58MOk1WhUCj+84bM5ae3mX0Ofa8DvICWBILNMI1fBiwCXpNSFjbUUEq5wHQ/G6WU84BDKPcWKN9JByCwifsLwB3FKnCWzFLKZVLKGmABykPbG1LKapSH0DAhhHsL7rsZwKtSynwp5QkUq0UtF/odXg/8R0p50nTPvszZ9/vzpnt6DbDYJA8o16e7EMJVSllg+t86nRLTtVExA6piVzmdICD/PI890cC2rNP+LkdROgghHIUQnwkhjgkhioG1gLtomW8+BGXm1BDfAjeY/r6BU37MEOCY6UezKToAA85QONejzETOJBDINz2Y1HIM5Ro2ixDCVwgx12SWLEZRQt5nNDv9mh4zjQnwFnAYWC6ESBFC1CrFQOCElNJ4PjI1Qwcg8Ixr8wyKxeB86COldK99ocR5NNWmRSZjIUQE8BjwMfC2yYrRFPNN/TuizHJnCiEa9PcKIWYKIXaddv49OfWdPYFihdlicoHc0sh4BShWrTPJPu3vCiBXSmk47TMo/z/N3XeBnH3f1NLi71Aoq2JqgwqfOa3v0/s7/Z4EKDgjpuT0/dNRrGzHTC6LgWcM6QIUnimHyvmhKnYVAIQQsSg/Dg3NNMoAx9PaNqTozqVM4KMoZrcBUkpXYGht1y049gQmH2gD/ABcLoSIRjFvLzztmFAhhE0L+l5zusIxmaLvbqBtBuAphDj9RzoUSG/BOYBihpdAlOka3MDZ5x9yRt8ZAFLKEinlo1LKjigm3EdMptgMIESctrqhCZma+07P/D5PAKlnXBsXKeXElpxsWyCEECiz1/dQ3BBlKEGhLUJKeRRYinJNz+y7A8rs9j7Ay/Qwsg/TdyalzJJS3i6lDEQJBPtYnBH5b+KQSdTzfdhq7r7L5Oz7ppYWf4dSWRVTG7j42mljdzij74zTPnuY3Fln7ZdSbpVSXo7iIloIzK9tJIQIRHGBtMrSwksRVbFf4gghXIUQk1HMfT9IKfc20Gw30EMIESOUAK6XLnBYF5RZSKEQwhPFL9hSvgT+TwgRYfKLRgkhvABMfsutKDP1X6WUtTOdLSg/eG8IIZyEEPZCiEEN9P0X0EUIcaMQQmd6xQohIs9saDJzJgCvm/qLAm5F8U23BBegFOUaBAGPN9DmXiFEsOkaPQPMAxBCTBZKsJYAigGD6bUZRZk9YZJ9OIqSOiuegOa/02yg42mftwDFQognhRKgpRVC9DQ9EDaG1nRtal+2TbQ9J87o1950Le5GmUG/ZrJa3IpyLbq1sM9glJiQ/Q3sdkJ52Mkxtb0ZZcZee+xV4lQAaIGpreHMTkym9X9ouevpzOObu+/mA08LJUA1GOUBp5bz+Q5P52fgOSGEj1BiWF7gbHfHy0IIWyHEEBTX1gLT5+uFEG6m86+9Z2sZDqyUUla1+EKoNImq2C9d/hRClKA8xT+L4uO9uaGGUsqDKAE8/6DMOC40oct7gAOQC2wCWhTgZOIdlB+v5Sg/EF+Z+qrlW6AXpy0nMpk0pwCdgeMogUdXn9mxybw5FsXnmIHiSngTaCxa91qUYLwM4HfgRSnlihaex8tAH5S4gMXAbw20+QnlPFNMr9p14REo30UpsBH4WEq5WkqpBy4DJqBc24+BmVLKAw2ca3Pf6VcoPtFCIcTC065hDJBq6v9LlECyxngK5QGu9rWyibbnQtAZ/Vag+J1fA241XQeklInA2yhR8o1Zg66uNTmjPBRuQPlu6nFaXxtRHnp6mdrWEgtsNvWzCHhQSpnayJif0cJYjEZo6r57GcUEnopy7zT0fxBDy7/D03kF2AbsAfaiBCienqsgC+WhJgPlQeOu0+69G4GjJrfTXZxymYHi7vq0hTKotAChxNyoqFwcCCGGoswiws7wNauoWA1CWWp4v7zEk9QIIXoBn0spz/S5q1wAqmJXuWgwBUrNBXZLKf9jaXlUVFRULIFqile5KDD5wQtRloG9Z1FhVFRUVCyIOmNXUVFRUVG5iFBn7CoqKioqKhcRqmJXUVFRUVG5iGguYUe7wNvbW4aFhVlaDBUVFRUVlTZj+/btuVJKnzO3XxSKPSwsjG3btllaDBUVFRUVlTZDCHGsoe2qKV5FRUVFReUiQlXsKioqKioqFxGqYldRUVFRUbmIuCh87CoqKioqFzfV1dWkpaVRWVlpaVHaHHt7e4KDg9HpmqtCrKAqdhUVFRUVqyctLQ0XFxfCwsJovKbPxYeUkry8PNLS0ggPD2/RMaopXkVFRUXF6qmsrMTLy+uSUuoAQgi8vLzOyVKhKnYVFRUVlXbBpabUaznX81YVu4qKioqKygXy/vvv89BDD9V9vvPOOxk9enTd5w8++IAHHnig0eMXLlxIYmKiWWRRFbuKioqKisoFEh8fT0JCQt3nXbt2UVRUhMFgACAhIYFBgwY1eryq2FVUVFRUVNqY//73v8yePRuAhx9+mJEjRwLw77//8v7773Pw4EEqKiooKirC0dGRmJgY9u7dCyiKPT4+niNHjjB+/Hj69u3LkCFDOHDgAAkJCSxatIjHH3+cmJgYjhw5ckFyqlHxKioqKirtipf/3E9iRrFZ++we6MqLU3o02Wbo0KG8/fbbPPDAA2zbto2qqiqqq6tZv349Q4YMIS0tja1bt1JRUcGAAQOIiIggISEBX19fpJSEhIQwatQoPv30UyIiIti8eTP33HMPK1eu5LLLLmPy5MlceeWVF3wuqmJXUbESDEYD6aXphLqGWloUFRWVBujbty/bt2+npKQEOzs7+vTpw7Zt21i3bh2zZ8/m+PHjJCQkUFFRwcCBA4mIiOC1117Dx8eH+Ph4SktLSUhI4Kqrrqrrs6qqyuxyqopdpd2iN+ix1dpaWgyzIKXkhYQX+PPInyyYsoCunl0tLZKKitXS3My6tdDpdISFhfH1118THx9PVFQUq1at4siRI0RGRhIfH89nn31GZWUl9957Lz4+PiQmJuLj48OgQYMwGo24u7uza9euVpVT9bGrtEv25+5nwE8DWH50uaVFMQsf7vqQRUcWIZGsT19vaXFUVFQaYejQofzvf/9j6NChDBkyhE8//ZSYmBiEEMTHx7Np0yZycnLw9fVFCIGPjw9//PEH8fHxuLq6Eh4ezoIFCwDlgX737t0AuLi4UFJSYhYZVcWu0i7Zlr2NGmMNz65/lqS8JEuLc0HMT57P53s+Z3rEdDq7d2Zz5mZLi6SiotIIQ4YMITMzk4EDB+Ln54e9vT1DhgwBwMPDAx8fH3r0OGVRGDhwICdPniQ6OhqAH3/8ka+++oro6Gh69OjBH3/8AcA111zDW2+9Re/evS84eE5IKS+oA2ugX79+Uq3Hfmnx5Non2ZS5qc4U//Okn/F28LawVOfOyuMreXj1wwwOGsz7I97n7W1vs+DgAjZcuwE7rZ2lxVNRsRqSkpKIjIy0tBgWo6HzF0Jsl1L2O7OtOmNXaZck5ScR7RPN7BGzKaoq4qFVD6E36C0t1jmx6+Qunlj7BD28evDW0Lew0dgQFxBHlaGK3Sd3W1o8FRWVdoqq2FXaHWXVZRwtOsrOQ07YGkN4ZdAr7M7ZzX82/of2YoFKLUrlvpX34efox4ejPsRR5whAX7++aIWWTZmbLCyhiopKe8Viil0IYS+E2CKE2C2E2C+EeNm03VMIsUIIccj07mEpGVWsk+T8ZCSSjBwvXvkrkbFhY7k7+m7+OPIH3yd+b2nxmiWnPIe7/7kbrdDy6ehP8bT3rNvnbOtML+9eqp9dRUXlvLHkjL0KGCmljAZigPFCiDjgKeBfKWUE8K/ps4pKHUn5SrCcsTKIVck5rD2Yw13RdzGmwxje3v42G9I3WFjCxinVl3LPv/eQX5nPx6M+JsQ15Kw2AwIGsC9vH8V68ybgUFFRuTSwmGKXCqWmjzrTSwKXA9+atn8LTG176VSsmcS8RDRGF+I6hNHBy5FXFidiNMIrg14hwj2Cx9c8TmpRqqXFPItqQzUPr36YQwWHeHvY2/TwbngtblxAHEZpZFuWGhCqoqJy7ljUxy6E0AohdgEngRVSys2An5QyE8D07tvIsXcIIbYJIbbl5OS0mcwqlicxNwl9eSD9Onjy9IRuHMwuZd62EzjqHJk9cjY6rY77V95PUVWRpUWtozYBzabMTbw48EWGBA9ptG20TzQONg6qn11FReW8sKhil1IapJQxQDDQXwjR8xyO/VxK2U9K2c/Hx6fVZFSxLiprKkkpSsFQGUivIDfG9fCnf7gn7yw/SHFlNYHOgbw34j3SS9N5fM3j1BhrLC0yAO/veJ+/Uv7ivpj7uCLiiibb6rQ6+vj1Uf3sKirtjLCwMHr16kWvXr3o3r07zz33XKukjG0Oq4iKl1IWAquB8UC2ECIAwPR+0nKSqVgbhwoOYcSAsTKIXsFuCCF4flJ38sv1fLTqMAC9fXvzQtwLbMzcyNvb3rawxPBT0k98te8rrupyFXdE3dGiY+L840gpSiG7LLuVpVNRUTEnq1atYu/evWzZsoWUlBTuuOPs//naUq6thSWj4n2EEO6mvx2A0cABYBFwk6nZTcAfFhFQxSqpDZxzE2H4F+4CKekV7Ma03sF8vf4oJ/LLAbgi4gpuiLyBH5J+4LdDv1lM3n+O/cMbW95geMhwnhnwDEKIFh0XFxgHwJasLa0pnoqKyjnQVNnWG264oV5bZ2dnPv30UxYuXEh+fj6rV69mxIgRXHfddfTq1Yvnn3+e999/v679s88+W9f3hWLJIjABwLdCCC3KA8Z8KeVfQoiNwHwhxK3AceCqpjpRubRIzEtEGB253j0b8fUdcP0vEDGGx8d1ZcneTN5YeoCPru8DwKP9HuVI4RH+b9P/EeYaRh+/Pm0q647sHTy59kl6+fTiv0P/i42m5f9uXTy64GHnwabMTUzpNKUVpVRRaYcsfQqy9pq3T/9eMOGNJps0V7Z1/fr6dR5qc8MfOnQIgC1btrBv3z7Cw8M5evQo06ZN48EHH8RoNDJ37ly2bDHPg7wlo+L3SCl7SymjpJQ9pZT/MW3Pk1KOklJGmN7zLSWjivWxLzeRmopABuuUfxSSFgHg72bPncM6snhvJluPKreMjcaGt4a9RbBzMA+vfpiM0ow2kzOlMIX7V95PoHMgH478EAcbhxYdZzQqCXY0QkP/gP5sytzUbpLuqKhc7JxZtnXgwIF1ZVtr88Wfyen/v/379yc8PBxQ/PFeXl7s3LmT5cuX07t3b7y8vMwip1q2VaXdUG2o5nDhIWoqBhEhTYVfkpeC0QAaLXcM7cjcLSd45a9Efr9nEBqNwM3OjdkjZ3P94ut5YOUDfDfhu7osb63FyfKT3PXPXeg0Oj4Z/Qke9i3LsbR8fxaPzN/NikeGEuDmwICAASw7uozU4lQ6unVsVZlVVNoVzcysW4vmyraeSUlJCUePHqVLly7s3r0bJyenevtvu+02vvnmG7KysrjlllvMJqdVBM+pqLSEw4WHMcgaRKU/7gX7wC0EynIgbSsAjrY2PDG+K7vTivhjd3rdceFu4fx32H85VHiIZ9c/i1EaW03GEn0Jd/9zN0VVRXw8+mOCXYJbdJy+xsgri5MoraphS6picYgLUPzsmzLUZW8qKtZCU2VbT6e0tJR77rmHqVOn4uHR8MP9FVdcwd9//83WrVsZN26c2WRUFbtKu6E2cC5aq0PUVMDQx0CjgwN/1bWZGhNEVLAb//07mQr9qcjTwUGDeaTvI/xz/B8+2f1Jq8hXbajm4VUPk1KYwrvD36W7V/cWH/vj5mMczy9HCNh9Qll/H+ISQpBzkLrsTUXFimiqbCvAiBEj6NmzJ/379yc0NJTPPvus0b5sbW0ZMWIEM2bMQKvVmk1G1RSv0m5QAufsucyxCCqBTqMgfAgcWAxj/g+EQKMRPDepOzM+28gX61J4YFRE3fEzu8/kUMEhPt39KZ3dOzMuzHxPyEZp5LkNz7E5azOvDX6N+KD4Fh9bXFnNBysPM6izF5XVRvakFdbtiwuIY/nR5dQYa84p+E5FRaV1GDVqFNXV1XWfDx48WPf30aNHGz1u+PDhDB8+vN42o9HIpk2bWLBggVllVGfsKu2G/bmJ1FQG0E97GFwCwC0Yuk2C/BTISa5r1z/ck4m9/Plk9RGyiyvrtgsheGHgC0T7RPPc+udIyku6IHmM0khmaSYb0jfwUsJLLEldwoN9HjznKPbP1hwhv0zP0xMiiQ52Z19GETUGxV0wIGAAJdUlFyyrioqKdZGYmEjnzp0ZNWoUERERzR9wDqhTAJV2QY2xhoMFBzFUxNLBsBZCY0EI6DoRFj+qmON9u9W1f2p8JP8knuStZcn876rouu22WlveG/Ee1/x1DQ+seoCfJ/2Mt4N3k2NXG6o5VnyM1OJUUgpTSClKIbUolaPFR6moqahrd0PkDdza89ZzOq/Mogq+XJfK1JhAega5cSSnlDkbjBzMLqV7oCv9/fsDsClzE718ep1T3yoqKtZL9+7dSUlJaZW+VcWu0i44WnQUvbEKx0oPHPRpEHKXssM1EIL6Kub4oY/VtQ/1cuTmQWF8vi6FWfFh9Axyq9vn7eDNByM/YObSmTy06iHmjJuDrdaWEn0JqUWpdYq79j2tJA2DPOWvD3QKJNwtnL5+fQl3C6ejW0c6unesV361pby74iBSwqNjuwIQHewOwO60QroHuuLl4EUXjy5sztzM7VG3n8eVU1FRudRQFbtKu6A2cC5OY1Kwwf1P7ew2Cf79DxSlg1tQ3eZ7R3ZmwfY0/u+vRObeEVcvajXSK5JXBr/CY2seY/qi6ZRVl5FTcaqYkE6jo4NrB7p4dGFc2DhFebt1pINrB7Mtl0vOKuGX7WncMiicEE+lzw5ejrg56NiTVsi1/UOVcw6IY+6BuVTWVGJvY2+WsVVUVC5eVMWu0i5IzEsEqWOyXT7odRBwyrxOt8mKYk9eAv1PzWpd7XU8PKYLzy/cx7L92Yzv6V+vz3Fh48iryOPvo38T7RNNR/eOhLuG09G9I0HOQa0erPbm3wdwtrPhvpGd67YJIYgKdquLjAfFz/5d4nfsPLmTgYEDW1UmFRWV9o+q2FXaBXtz92OoCCBGHlKUuu60mat3F/DqrJjj+9c3V18bG8J3CUd5fWkSI7v5YmtTP170usjruC7yurY4hXokHMll5YGTPD2hG+6OtvX2RQe788maI1ToDTjYaunn1w8bYcOmzE2qYldRUWkWNSpexeoxSiMH8g8gKwPwL0uCkP71G9QG0R1dBxWF9XbZaDU8OymSY3nlfLfxaJvJ3BRGo+SNpQcIdLPnpviws/ZHh7hjMEoSM5VZu6POkSifKHU9u4qKlfHSSy/x8ssvExMTU7ft559/xtHRsW5J3N69e4mKimq0j127drFkyRKzyqUqdhWr53jxcaoMFfhV2aE1VEJw7NmNuk0GYw0c/uesXcO7+jKsiw/v/3uI/DJ9G0jcNIv3ZrInrYhHx3bFXnd2UoroYCXQb9dp5vi4gDgS8xIpqio6q72KiorlcHBw4NixY5SUlACQkJBAt27d2LlzZ93nQYMGNXq8qthVLklqA+fihUkpnzljBwjuB06+9bLQnc5zkyIp1xt4/5+DDe5vK6pqDPx32QEiA1yZ2juowTa+rvYEuNnXT1QTGIdEsjVraxtJqqKi0hCvvvoqXbt2ZfTo0SQnJ6PRaIiNjWXzZsWitn37du69914SEhIARbHHx8dTVlbGLbfcQmxsLL179+aPP/5Ar9fzwgsvMG/ePGJiYpg3b55ZZFR97CpWT1JeEkgbJmlywCVQSUxzJhotdJ0A+36Fmiqwsau3O8LPhWv7h/DD5uPcOLADnX1d2kj6+vy46Tgn8iv47pZeaDWN12ZXAugK6z739O6Jo40jmzI3MbrD6DaQVEXFenlzy5scyD9g1j67eXbjyf5PNtlm+/btzJ07l507d1JTU0OfPn3o27cv8fHxJCQkMHDgQDQaDcOHD+fpp5/moYceIiEhgRdffJFXX32VkSNHMmfOHAoLC+nfvz+jR4/mP//5D9u2bePDDz8027moM3YVq2dv7n4Mlf70qkmGkAbM8LV0mwz6Ukhd2+Duh0d3wdFWy6uLLZPFraiimg9WHmJwZ2+GdvFpsm10iDtH88opLFesFDqNjn7+/VQ/u4qKBVm3bh1XXHEFjo6OuLq6ctlllwEwaNAgEhIS2LJlC7GxsXTq1InDhw+Tk5NDaWkpHTt2ZPny5bzxxhvExMQwfPhwKisrOX78eKvIqc7YVawaKSWJeUnYVnbCtSqh/vr1MwkfCrbOijk+YsxZu72c7bh/ZGdeW3KAtQdzmlWu5ubTNUcoKK/mqQndmm1bm6hmT1pRnZwD/AewNm0tWWVZ+Dv5N3G0isrFTXMz69bkzCpuAHFxcWzdupX169czcKCyciU4OJi5c+cSH6/UjZBS8uuvv9K1a9d6x9aa8M2JOmNXsWoyyjIorymhQ5Xpn6kh/3otOnvoPMpUo73h0qw3xYcR6unIq4uT6vKxtwWZRRXMWZ/KFb2D6mXBa4zaNmf62UFJL6uiotL2DB06lN9//52KigpKSkr4888/AXBxcSEkJIRvvvmmTrEPHDiQ9957r06xjxs3jg8++AApJUBdcJ2Li0td4J25UBW7ilWTmJcIwBDKQWtbPzFNQ3SbDKXZkL69wd12NlqentCN5OwS5m07YW5xG+Wd5bWpY7u0qL2bg46OPk7sTjsVBR/hHoGnvaeq2FVULESfPn24+uqriYmJYfr06fXKtQ4aNIiqqipCQkIARbGnpKTUKfbnn3+e6upqoqKi6NmzJ88//zyglHlNTExUg+dULh2UwDkNE2SmotTPCIo7i4gxoLFRzPGN+OPH9/Snf5gn7yw/yGXRgbjY61pB8lMcyCrmlx1p3DY4nGCPlqejjQ52Z8Ph3LrPQggGBAxgc+ZmpJQNmgRVVFRal2effZZnn332rO0fffQRH330Ud3n4cOH183OQVkW11Btdk9PT7ZuNe9qF3XGrmLV7MnZj7HKl676Q/X867KmhqJFi6hKTa1/gIMHhA1WstA1ghCC5yZHklem56NVR1pL9DreWHoAFzsb7h3RufnGpxEd7MbJkiqyik6Vno0LiCO3Ipcjha0vt4qKSvtEVewqVosSOJeIa6UrNsaqejPw4r+XkfHEk6RMmEjK5VPJ/eQTqlJMSr7bZMg7BDmNr1mPCnZnWp8g5qxP5UR+eaudQ8LhXFYn53DfyM5npY5tjqgQdwB2nbbsLS5A9bOrqKg0jarYVayWk+UnKakupJPeFOR22oy9bMMGNG5u+D3zNBonJ3Len03KxImkXHY5OevzqCqyaTRZTS2Pj+uKRgPPLtxHcWW12eU3GiWvLz1AkLsDMweGnfPx3QNcsdGIegF0gc6BhLiEqMveVFRUGkVV7CpWS23GuWHGEnANqivJKqWkLCEBp4ED8Zw5k7CffqTzmtX4PfMMGhcXcr/4jpSlvqQ89TU5H35E1eHDDfYf4ObA0xMiWX8oh9Fvr+GvPRn1fGIXyp97MtibXsSjY7s0mDq2Oex1WroFuLD7NMUOyqx9a/ZWaow1ZpJURUXlYkJV7CpWixI4J5hQc7xefnj9kSPUZGfjFH+q0pnOzw/PmTcS9uMPdF69Gr9r4tBqysj96CNSJk/hyKTJ5Mz+gMqDB+sp75viw1h47yD8XO2576edzPp6K8fzLtw0X1Vj4K1lyUrq2JiGU8e2hKhgd/akFWE0npJ5QMAAyqrL2Je774LlVFFRufhQFbuK1bInZz9C70lwdXa99etlphzMTvENF1bQ+fnieffjdBiVR+fZd+H3/HPYeHqS+8knpF52OSmTJpMzezaVyclIKYkKdmfhvYN4cUp3th8rYMy7a/ho1WH0Nee/zv2HTcdJK6jgmYnd0DSROrY5YoLdKamsITWvrG5bf//+CITqZ1dRUWkQVbGrWC37chPxqjItDzvNv166YQO2HTpgG9zETNg3EjzC0WWvwfP66+nw/XdErF2D/4svYOPjQ+6nn5F6+VRSJkyk8Ndf0WoENw8K559HhjEq0pe3liUzcfY6NqfknbPctaljh0R4MyTiwrLbRYWcnajGw96Dbp7dVD+7ioqFeemll/jf//7HrFmzCA8PJzo6mi5dujBz5kzS09MtJpeq2FWskryKPAr1OXStqkZqbSFAqWcs9XrKt27DaVB80x0IAd0mQcoaqCwGwMbHB49rr6XDt98oSv6llxCODmS+9DKGwkIA/N3s+fj6vnw9K5bKagNXf76JxxfsPqdyr5+sPkJRRTVPjm8+dWxzRPi64GirZfeJ+uVa4wLi2JWzi/Lq1ovoV1FRaTlvvfUWu3fvJjk5md69ezNixAj0+rN/NwwGQ6vLoip2FaukNnBuuKEQERBTl5imfNcuZHk5TvHNKHYw1WivbrBGu423Nx7XXE3A//0fVFdTvGx5vf0juvmy4uFh3D28E7/vTGfU26uZv+1Es8F1GYUVzNmQyhUxLUsd2xxajaBnoNtZAXQDAgZQY6xh58mdFzyGiopKyzmzbOuZCCF4+OGH8ff3Z+nSpQA4OzvzwgsvMGDAAF555RWuuOKKuvYrVqxg2rRpZpVRzTynYpUk5SmKfaz+KITcUbe9bEMCaLU4DhjQfCch/cHRW0lW07Phfxz77t2x7diR4j//xOPqGfX2OdhqeXJ8N6bGBPHs73t54pc9/LI9jVen9iTCr+Gyr++sUNbOP9LC1LEtITrEjW83HqPaYESnVZ7Fe/v2RqfRsSlzE4OCGo41UFG5WMl67TWqksxbttUushv+zzzTZJvGyrY2RJ8+fThw4ACXX345ZWVl9OzZk//85z9IKYmMjCQnJwcfHx++/vprbr75ZrOeizpjV7FKdp/cj43eFU+Dvl5EfFlCAg5RUWhdWlBPXaOFruPh0HKoadiULoTAbcpkyrdtozojo8E2Xf1dmH/nQN6c3ouD2SVMnL2Ot5YdoLK6vkktKbOYX3ekMSs+7JxSxzZHVLA7+hojyVmnCkU46hyJ9olW/ewqKm1IY2VbG+J0655Wq2X69OmA8ptz44038sMPP1BYWMjGjRuZMGGCWeVUZ+wqVsm+3ET8q0x54U0R8YbCQir37cP7nnta3lG3ybDzBzi6Tqn81gCukyeT8/5sihYvxvv22xtso9EIro4NZXSkH68tOcBHq47w5+5M/nN5D4Z39QWU1LGu9jruHX5uqWObI8aUgW53WmE9835cQBwf7vqQgsoCPOw9zDqmioo109zMujVpaY2GnTt3MmqU8ptjb2+PVnsql8XNN9/MlClTsLe356qrrsLGxryqWJ2xq1gdRVVF5FVl0qOqEqNLELgGAlC2aRNIidOgczA9dxwOOscmc8fbhoTgEBND8Z9NZ6oDpab72zOi+fn2OGy0gllfb+Xen3bw+8401hzM4b4RnXFzNG9RmWAPBzwcdew+LbUsKH52gC1ZW8w6noqKSsM0Vrb1dKSUzJ49m8zMTMaPH99gP4GBgQQGBvLKK68wa9Yss8upKnYVq+NAvuI7G1GTiyb09DSyCWicnXGI6tXyznQOphrtSxqt0Q7KrL3q4EEqkxvPL386Azt5sfTBITw6pgsrErN5eN5ugtwduHFgh5bL1kKEEHWJak6np3dPnHRO6np2FZU2oqmyrY8//njdcretW7eyatUqbG0brw9x/fXXExISQvfu3c0up2qKV7E6agPn4qty6tavSykp27ABx7gBiHM1W3WbDEl/QuZOCGo40MV1wniyX3+d4r/+wr7rIy3q1s5Gy/2jIpgSHcj7/x5iWp+g80od2xKiQ9z5cOUhyvU1ONoq52+jsSHWL1b1s6uotCGNlW1titLS0rO2rV+/ntsbcf1dKOqMXcXq2HVyH3bVDngYjXX+9epjx6jOyGjZMrcziRgLQtukOd7GywunQfEULf4L2cTMviHCvJ149+qYC05G0xTRwW4YJexLL663PS4wjhMlJ0grSWu1sVVUVMxL37592bNnDzfccEOr9K8qdhWrY2/OfoKqdBg1tuCvJKYpNaWRdT4fxe7oCWGDmlTsAG5TplCTkUnFjh3nPkYrExXsDnCWn722jKs6a1dRaT9s376dtWvXYmdn1yr9q4pdxaoo1ZdysjKNaH05xoAYsFF8VGUbEtAFBaHrcJ4+7K6TIOcA5DZc6Q3AZeRIhIMDRS0IomtrfFzsCHJ3OCtRTUe3jvg4+KiKXUVFpQ5VsatYFckFSianYfqT2JgC52RNDeWbN+MUH9/ipSZn0W2iaYDGZ+0aJydcRo2i5O+/kQ2kgrQ00SFuZwXQCSEYEDCAzVmbMcrzL1qjotIeMGdZ5fbEuZ63qthVrIrawLkofXmdf71iz16MpaXN54dvCvdQxazfrDl+MoaiIkrXrz//sVqJqGB3jueXn5W3fkDAAPIr8zlUcMhCkqmotD729vbk5eVdcspdSkleXh729vYtPsZiUfFCiBDgO8AfMAKfSynfF0J4AvOAMOAoMENKWWApOVXalp3Z+7CvscXHYKyLiC/bsAGEwCku7sI67zYZVr8OpSfB2bfBJk7x8Wg9PCj+6y9cRo68sPHMTLTJz74nrbAuKQ6c8rNvytxEV8+ulhBNRaXVCQ4OJi0tjZycHEuL0ubY29sTHBzc4vaWXO5WAzwqpdwhhHABtgshVgCzgH+llG8IIZ4CngKetKCcKm3I3pxEOlQJqpwCsXMNAJQ0svY9e6J1d7+wzrtNgtWvQfJS6HtTg02ETofrhPEU/vY7htIytM5OFzamGekV7IYQsPtEUT3F7u/kT5hrGJszN3NTj4bPS0WlvaPT6QgPD7e0GO0Ci5nipZSZUsodpr9LgCQgCLgc+NbU7FtgqkUEVGlzKmoqyKo4Rl99CZpQJauaoaSEij17LswMX4tfD3Dv0Kw53nXyFGRlJSX/rLjwMc2Is50NnXyc69Vmr2VAwAC2ZW+j2lDd9oKpqKhYFVbhYxdChAG9gc2An5QyExTlDzRoMxVC3CGE2CaE2HYpmmYuRg4VHEJipL++CF0HRbGXb94MBsP5rV8/k7oa7auhqqTRZg69Y9AFB7coxWxbEx3szu60wrP8jHEBcVTUVLA3d6+FJFNRUbEWLK7YhRDOwK/AQ1LK4uba1yKl/FxK2U9K2c/Hp/USg6i0HbWBc92r9Kf86wkJCEdHHGNizDNIt0lgqILD/zbaRAiB6+RJlG3cSI2VPTRGh7iRW6ono6iy3vZY/1gEQk0vq6KiYlnFLoTQoSj1H6WUv5k2ZwshAkz7A4CTlpJPpW3ZnrUXe4MWb2kD/ko++NING3CKjUU0kXP5nAiJAwdPJXd8E7hNmQJGI8VLl5pnXDMR3UiiGjc7N7p7dVfXs6uoqFhOsQtlQfJXQJKU8p3Tdi0CaiOAbgL+aGvZVCzD3pz9dKoyUundC2xs0aelUX3suHn867VobaDrBDj4NzThj7br1Am77pFWl6ymW4ALOq04K1ENKOb4PTl7KK8ub3vBVFRUrAZLztgHATcCI4UQu0yvicAbwBghxCFgjOmzykWO3qAnozyVfvpi7MKV5VtlG5Q0smbxr59Ot0lQWQTHNjTZzG3SZCr37kV/9Kh5x78A7Gy0dA9wZc+JorP2DQgYQI2sYVv2NgtIpqKiYi1YMip+vZRSSCmjpJQxptcSKWWelHKUlDLC9J5vKRlV2o7DhYcxYqCnvgpbU+BcWUICNn5+2HbqZN7BOo4AG4cWRMdPAiEo+qvpdm1NVLA7e9OLMBrrB9D19u2NrcZW9bOrqFziWDx4TkUFIDEvEYAeVXoI6Y80GCjbtOnC0sg2hq2jUqP9wGJoIouVzs8Px/79Kf7zT6vKdhUV7EZpVQ0pufVLQdrb2NPbt7fqZ1dRucRRFbuKVbAjax/2BoGbjQ+4+FO5fz/GoiKcBg1qnQG7ToTidMjc1WQztymT0R87RuW+fa0jx3kQE+IOwK4GzPFxgXEcLDhIXkVeG0uloqJiLaiKXcUq2H1yP1301VQH9AMUMzyA08ALTCPbGF3Gg9DAgaaj413GjkXodBT9+WfryHEedPRxxslW23CiGn/FjbEla0sbS6WiomItqIpdxeLUGGtILztCjL4cl84DASVwzi4yEhsvr9YZ1MkLQuOb9bNrXV1xHj6c4iVLkTU1rSPLOaLVCHoFu5215A2gu1d3XHQubMzY2PaCqaioWAWqYlexOKlFqRioJrJKj21YHMayMsp37cLZnMvcGqLbJDi5H/JTmmzmOnkyhtxcyjZZj+86OtidpMwSqmoM9bZrNVqGhgzljyN/8EPiD1YVG6CiotI2qIpdxeLsz9sPQES1BL9elG3dCtXV5l/mdia1NdqbMcc7Dx+GxsWF4r+sZ017dIg7eoORA5lnp8Z9Ie4FhgUP482tb/LyxpfV/PEqKpcYqmJXsTjbM/dhawQXx65gY0vZhgSEnR0Offu27sAeYeDXC3b/DOWNr6rU2NnhMnYMJStWYKysbLRdWxIV7AbQoJ/dUefIeyPe4/Zet/ProV+5bflt5Feqq0ZVVC4VVMWuYnF2Z++jm16PJvjU+nXHfv3Q2Nm1/uBDHoacZPgkHo6sarSZ25QpGMvKKF3VeJu2JMjdAW9nW3annR0ZD6ARGh7o8wBvDnmT/Xn7ufava0nOT25jKVVUVCyBqthVLIpRGkkrO0iPqio8ug6iOisL/ZEjrW+Gr6XndLjtH7Bzge+nwt/PQPXZs3LH2FhsfH2tJsWsEIKoYPcGA+hOZ2LHiXwz/htqjDXcuPRGVh5f2TYCqqhcLBxcBkVplpbinFAVu4pFOVZ8jGqq6a7XYxcWdyqNbGsHzp1OYAzcsQb63wGbPoIvRkL2/npNhFaL66RJlK5bh6GwsO1ka4KoYDcO55RSWtV0tH5P7578PPlnOrl14sFVD/LFni/UoDoVlZZwMgl+uhrWvGlpSc4JVbGrWJTajHNBRldw8aMsIQGttzd2Xbq0rSC2jjDxLbj+FyjLgc+Hw8aPwGisa+I2ZTJUV1P897K2la0RokPckRL2NmKOPx1fR1++Hv81E8MnMnvnbJ5c9ySVNdYRL6CiYrWsfQuQcKJ95YVQFbtKi5EGA4ai5pXIubA1cx86o8TdNRppNFK2cSNOAwciNBa6NSPGwD0bofMYWPaMYp4vSgfALjIS244dKfrLOpLV1JZwbSiAriHsbex5Y8gbPNjnQf5O/ZtZf88iuyy79QRUUWnP5CTDvt/A0QtyDkBFoaUlajGqYldpMdmvvsrhkSPRHz9utj73Zuyga7Ue+7A4qg4cwJCf37Zm+IZw8oZrfoQpsyFtqxJYt/93hBC4TZlMxbbtVKenW1ZGwNPJlhBPhwZLuDaGEILbet3G+yPeJ7UolWsXX8venL2tJ6SKSntl7f9A5wgT/qt8Tm8/VRNVxa7SIgylZRT+Mg9jWTkZ991mlixsUkqOlx8mskqPb+SQ09LIWlixAwgBfW+Cu9aDVydYMAt+vwvXMcMBKFrS9Nr3tkIJoDt3K8qI0BF8P/F7bLW2zPp7FotTrKuCnYqKRck9DPt+gdhbIWKskn66HZnjVcWu0iKKv3sfqTfi2aOGioMnyH3+jgvuM700nUr0ROiN2AVFUbphA3YRndH5+ZpBYjPh1QluWQbDnoI987D9awYOkZ0otpLo+Jhgd9ILK8gtrTrnY7t4dOGnST/Ry6cXT617itk7ZmOUxuYPVFG52Fn3P9DaQfz9YO8Kvt1Vxa5ykWE0Ujj3Z3Qekg+u+ALnXm7kLkygfM6jTZY9bY7ajHNeIghjjZGK7Ttwim+lam4XglYHI55WFLzQ4Oq4k6qDB6lM2t/8sa1MU4lqWoKnvSdfjPmC6RHT+WLvFzy06iHKqsvMKKGKSjsj7wjsma/M1p1Nk4zgWEjfXi+Y1ppRFbtKs1QsfJfKkzWs7xjDD/vKuCPqGbRu9mR8tAjD74+c982+5cROtFLi49WX8m3bkXq95f3rTRHSH+5aj+vEiSAkxa/NhNxDFhWpZ5AbGtFwCdeWotPqeHHgizzV/ynWpK3hxqU3kl5q+RgCFRWLsP4d5WE+/oFT20L6Q1WxEkTXDlAVu0rT6Mso/O4L0ML7gdN5Z0Y0eRpHnupzO9XlNmR//hv8djvU6M+5630ZW+msr8a942DKNmxA6HQ49uvXCidhRuxcsLn+C5x6R1KUWIr8ZAhs/eqCLBcXgpOdDRG+Luc9Y69FCMH1kdfzyehPyCrL4tq/rmVbVvsJFlJRMQsFR2H3XOh7M7j4ndoe3F95T2sf5nhVsas0ieHf/1F8WLIjqAs3jIliWp9gFt03mJru0fzYdTRFRx0pXvIX/HwN6FtuwpVScrziKN31egJ7DaUsIQGHPn3QODq24tmYD7drb6GmTEOFJhoWP6IksSjLs4gsUcFu7EkrMkvSmfjAeH6a+BNudm7cvuJ2Xtn0CrtzdqsJbVRaxpInYPc8S0tx/qx7B4QWBj1Yf7tXJ3DwbDd+dlWxqzROUTrFc7/CWKNhRdRl3DuiMwD+bvbMv3MgZVfdRJJHB45t90e/dw18e1mTxVROJ7s8m1KhJ0hvh7ZaR1VyctulkTUDLiNHIhwcKKocAOPfhJRVsPRxi8gSHeJOfpmetIIKs/QX5hbGj5N+ZGL4RBYeXsgNS25gysIpfLb7M9VEr9I4VaWw9Qsle2N7pPA47PoR+swE14D6+4RQzPGqYldp9/z7MgWHbElz9WHWbZOx12nrdtnrtLxzXV+KH30OvUGwaWsUxoy9MGd8i/IqJ+YqGec8dR0p27gRAKdBVhg41wgaJydcRo2iePlyZJ9bYOC9SjKLnINtLkttoppzWc/eHK62rrw6+FVWz1jNf+L/g6+jLx/u+pDxv45n1t+z+O3Qb5Tozy4Zq3IJk7kbpFF5L2mHiY/Wv6ssaxv8cMP7g2Mh71CLJy+WRFXsKg2Ttp2K1b9Rla/jSPxEhnfzO6uJEIJZVw5Gf/dD+GRm8WXSRAxFGfDVuGYV3JbUDWikJMBnIGUbEtC6u2PfPbK1zqZVcJsyGWNREaXr18PA+5VkFmvfanM5uvq7YGujabYgzPngbOvMFRFXMGfcHJZNX8YDvR8gryKPFxNeZMT8ETy+5nHWpq2l2qjWfL/kydhx6u8j/1pOjvOhKA12fA+9bwC3oIbbhNT62a0/9kRV7CpnIyVy2TOcTHFHr7XhssduBaBUX8r+3P0YjIZ6zePuuQkxfCQD92zj/vRbqawshznjIG17o0Psz9xKeHU1fl0U/7rjwDjLpZE9T5zi49F6eFD055/g5KUsj9n3i5Lcog2xtdHQPcC10RKu5iLQOZDbo25n0dRF/DzpZ6ZFTGNT5ibu/fdeRi8YzZtb3iQxL1H1x1+qpO8A1yBw9oPD/1hamnNj/XvKe2OzdYDAPor/vR0E0LWvX1KVtmH/78iUzRQdtadwwHACg30AeCHhBa5ZfA0j5o/g2fXPsvzocsqqyxBCEPHGq9h6e3HdrrVMLniSAoM98tspcKThMqHHqtLpUmXA39aLmpwcnNuRGb4WodPhOmECpStXYSgtVZbH2NgryS3amJgQd/alF2Ewtr5SFULQ07snzwx4hpVXrWT2iNn09evLvOR5XP3X1UxbNI05++aoeegvNTJ2QFAf6Dxa+b8/YwJgtRRnwI5vIeY6cA9tvJ2dM/j1aBd+dlWxq9SnuhLjihc5dCIYXY2BPvfMAuBA/gFWHFvBhLAJDAoaxOoTq3l0zaMMnjuYO1fcybzMJTi8/AReBdk8lJvE2OJnOY4f8scZiu/5NHIrcikQerxr3NFvU8xa7Slw7nRcJ09GVlVRsuIfcPaBfrcoyS3yjrSpHFHBbpTrDRw+Wdqm4+q0OkaEjuCd4e+wasYqno97HmedM+9uf5cxv4zh9uW3szhl8VlWHpWLjPJ8ZalYYB/oPAoqCpSELu2BDbOVh5AhjzTfNqS/KVGNdd/PqmJXqc+mj9EUHefEIQ+MHcJx7tsHgI92foSLrQvPDXyO14e8zpqr1/D1uK+5IfIGMkozeH3L60w6/hTrhnrSaeNyHgw8yRXlz7CHCOQvt8CWL+qGSDy5GwBPuy6UbUjANiwMXWCgRU73QnHoHYMuOJjiP00V3+IfUJJbrHu7TeWIqg2gawU/e0txs3NjRtcZfD/xexZfsZi7ou/iRMkJnlr3FFf+eSUb0jdYTDaVVqbWvx7UBzqOUILQ2oM5viQLtn8N0deCR1jz7YP7g74UTia2umgXgqrYVU5RehLD2rdZk9uLoIJcAm64DiEEe3P2sjptNbN6zMLV1hUAG40N/fz78Wi/R/nzij/5c+qfPNbvMTZP6UiqnyDi6y/w6/A+N3t14wOHHlQsfRxWvwFSsjV5BQAhvvGUb93arqLhz0QIgevkSZRt2kRNTo6S1KLfLUqSi/zUNpOjo7cTLnY2Zo2MvxBCXUO5J+Yelk5byjvD36HKUMVd/9zFXSvu4mBB268ckFKyOXMzLya8yIniE20+/kVP+k7lPSAGHD2VCPJDKywqUotI+AAM1TD00QZ3pxdWcOUnCSzcaVrmGRKrvFu5OV5V7Cp1yJWvIKsrSD0WCnZ2uF02BYCPdn+Eu50710de3+ixYW5h3NTjJr6a/B39P/0JZ4MNDy/ToHNL5Av/YuJDQ7k36Uvm/3oNO7K30aG6mlACkZWV1p1GtgW4TZkCRiMZTz5JyapVyP73gMamTWftGo2glylRjTUhhGBMhzH8cfkfPBH7BHtz93LVn1fxUsJL5JTntPr4BqOBZUeXce3ia7lt+W38dug35h+c3+rjXnJk7ACvzuDgrnzuPBoydkJZrkXFapLSk0rWyKgZ4NnxrN0n8su5+rONbDtWwPebjikbPcLByUcp52zFqIpdRSFrH3LH9/xQOZJ+acm4jR+P1s2NXSd3sSF9A7f0vAUnnVOLuvKOjCHgqacJ2Z/DX8Z7+HDEZ/jZjGaXzpX/K0tkjzGb0CoN7impoNXi2L9/K59c62LXqRM+Dz5AZdIB0u6+h0NTriU7LZbK1Qug4FibyREd4k5SZjGV1dbn/9NpddzY/UaWTFvC9ZHX88eRP5j0+yQ+2f0J5dXlZh+vsqaS+cnzmbJwCo+teYwSfQkvDHyBvn59WZ++3uzjXfKk71D867V0Hg3IRoNnrYKED8BQBUMeO2tXam4ZMz7bSEllDeN7+LPzeAEFZXolUU2w9SeqURW7CkiJfslTlOBAYWkvtBXluF89A4APd36Ip70nV3e9+py69Lj2WpyHDSPvf+8woNKbpTe8zbUd5zHxWG8ezytgcHkYlZs24hATg9bZuTXOqk3xvvtuItasJvijD3Hs05v8DcdJXepJyrSryPv6G2pyW3/mEh3sRo1RkpRZ3OpjnS9udm48EfsEiy5fxOCgwXy862Om/D6FhYcXmiXArqiqiC/2fMG4X8fxf5v+DzdbN94Z/g6Lpi7iqi5XMTx4OIcLD5NVlmWGs1EBlKjy0iwI6ntqW0AMOHpbrzm+LBe2fgk9rwTvzvV2HT5ZwozPNlJVY+Sn2wdw57COGCWsPWSyMIXEQv4Rq7ZGqIpdBQ7+je3xdcw2XMn49L3Ydu6EQ+/ebM3ayuaszdzW6zYcdeeWw10IQcBrr6JxcSHj8SeQej33jYxgzJVv8XfhE5QEP0BlYiJO8QNb6aTaHmFri8uoUQR/8AER69bid0V3RFU+J998k0PDhnPizrsoXroUY9W5105vCdEh7oBlA+haSohrCO8Mf4fvJnyHv5M/z294nmsWX8OmzE3n1V9WWRZvbX2Lsb+MZfbO2UR6RTJn3Bx+mvQTYzqMQatRsiYODhoMoM7azUn6aYFztWg0SnT8kX+ts9Tpxg+hugKG1p+tJ2UWc/Vnm5AS5t4RR49AN6KC3fF0smV1skmx1xWEsV5zvKrYW4mKAyvIW/yyxap+tZgaPRWLn+aIMYCADmORSfvxmHFqtu7r4MuMrjPOq2sbLy8CX3uVquRkct55F4DR3f2Y8/y9XOViACnb5fr1lmDj4YHnU+8TPr6Qjo8PxuuWm6k8cID0hx/h0JChZL74EuU7d5o1mYu/qz0+LnZW52dvit6+vflh4g/8d+h/Ka4q5vblt3Pvv/eSUpjSouMPFxzm2fXPMuHXCfyY9CPDQ4bzy5Rf+HT0p8T6xyKEqNe+k3sn/Bz91Ah9c5KxQ4kp8e9Vf3vnMVCeB5k7LSNXY5TnK6t0ek4Dn651m/elF3HtF5uw0Qrm3RlHFz8XUotS+Xr/V/TtXMWagzlKnojA3sr5WrE53sbSAlyMlG/8Crtlj+GAkcLIybh37Nv8QRaiZsuXOBSn8rnDs9x/Yjuldna4XXYZGzM3suPkDp4d8Cx2Wrvz7t952DA8rruO/G+/xWnoEJwHDcJepyV/00Y0Li7Y9+xpxrOxMtyCoc+N2O34Ht8Hd+Hz0EOUbdpE0cI/KPrjDwrnzcO2Qwfcpl6O22WXoQtqJJVlCxFCEB3szi4riYxvKUIIJoRPYGToSH5M+pEv9nzBtEXTuLLLldwdfTdeDl5nHbMjewdz9s1hTdoa7LX2zOg6g5k9ZhLk3PQ1FEIwOGgwy44uo9pYjU6ja63TunRI3wG+kaBzqL+900hAwKF/6pvpLc2mj5Ula0NPFW3aebyAm+ZswcVexyczu7Ap9w+e2/wX+/L2AdDVdQD5ZVewJ62Q3qEeykOMOmO/RJAS/fKXcVz2CJuMkRikIGPTAktL1Tjl+dSsfJ11hp5MmDCDssV/4Tp+HBo3Nz7a+RH+Tv5Mi5h2wcP4PvE4tp06kfnU09QUFCClpGxDAk5xAxA2F/mzZW2KyvXvIbRanAcNIuit/xKxfh0Br76Kja8vOe/P5vCo0Ry7aRbFS5de0HDRwW6k5JRRXNn+crfbae24pectLJm2hBldZ/DLwV+Y9Pskvtz7JZU1lRilkZXHV3Ljkhu56e+b2J2zm3ui72H5lct5esDTzSr1WoYEDaG0upRdJ3e17gldCkipzNhPD5yrxclLMc8ftiI/e0UBbP4Mul+uPIwAW4/mc+OcDTh67KNbzAJu+mcKb2x5g2pjNY/1e4wZHS7nSPFWNDZlrDrdHJ++HQw1FjyZxlEVu7mo0WP49Q5sE95hrmEE5fHvsC+1E26pF/ZD3ZoUL3sVXXUJa8IfIubIdoylpbjPmMG69HXsyd3DnVF3Yqu1veBxNPb2BP3vLWoKC8l64QX0qanUZGa26/XrLcY9VElVueNbJcjIhNbZGffp0+jw/Xd0+mcF3vffR3VGBukPP0LFvv3nPVyUyc++tx2Z48/Ew96DZwY8w++X/06sfyzv73ifKQunMPWPqTy46kFyKnJ4uv/TLJu+jLtj7sbD3uOc+h8QMAAbYaOa481BfgpUFtX5139M+pGtWafNZDuPURSgtVRE2/QpVBXD0CcwSiPf7PiHWX8+jqbDy5S5f8PR4oPc2P1Gfr3sV3657BdmGHpz5WNLuWJtNeEdDrM6+aTST0h/qC6H7H2WPZ9GUBW7OagoxPj9NLT75vO/mhk4jHmN8LdfwHZLOT75J6jKOmBpCc9C5hzEcffX/MpIbps+hYL587Ht1An73r35cOeHBDsHc3nny802nn1kJL4PP0zJin/IfPY5oP2mkT1nhjyqlLPc8H6Du22Dg/G5917CfvoRgPLNm897qOhgN8C8JVwtRbhbOB+M/IA54+bg7+iPk40Tbw55k7+u+IvrIq8754DOWpxtnYnxjVED6MxBbeBcYB/25+7njS1v8MaWN07FjkSMUe59a1j2VlEImz4hpcto3s/4l+Fzx/D23ofRuuxmTNgoPh/zOcuvXM4j/R6hi0cXKg8c4Phtt0NpOeP2aNE4bmdPWhE5JVVKAh6wWnO8qtgvlKI05JxxyGMJPKy/m6DJz9Lnpw+ozswEoCTdnrQE6zPHn/ztCSqkLdVDn8E98yiVu/fgMeMqVqWtIik/ibui7zK7/9Fz1k04DoyjYudOdMHB2IY2UXDhYsKjA0RfA9u/UVJYNoKNjw+2YWGUbz3/Hwt3R1s6eDmy41jhefdhbcT6x/L9xO/5efLPTOw4ERvNhbtvBgcNJrkgmZPlJ80g4SVMxg6l8JFvJB/s/IAO2ZLsE8kk5Scp+wN7g4OnxdPL5lXk8cM/D3O1lz2XVx/kq71zyCvwwKN0Fkum/sPbI95gYODAutUTVUeOcPyWW9E4OuL7+GO4ltTgffgAQpfHmoM5iiXO2c9qA+hUxX4hZO1FfjmayrwT3Kh/kl4T72T0jiWUrl6N31NPYdutG1lpHtgeWmxpSetRkfwvfpmrWOB4NVcP70Ph/AXKUq3LpvDRro8Icw1jUsdJZh9XaDQEvvEGWg8PnEeOMHv/Vs2Qx5TUlY3M2mtxjO1H+fbtSMP5r+ke2c2Xf5Ky+Xxt2xaiaU/ULntTzfEXSPoO8I9ie+4edh9ez/99D/f9Jfn90O/Kfo1WCaI73PbL3qoN1SxNXco9/9zDqAWjeDN/G0Z7N6YE30XFkWfoaHiIP2bdT7C7e73j9MePc/zmW0CjIfTrOXhcfz04OTJkv8TdZx+rkk8qiWpC+lttCVeLKnYhxBwhxEkhxL7TtnkKIVYIIQ6Z3s/NgdZWHP4XOWcCRZUGplY8z6Ax05kh08j94ENcL5tC+eXDyIvtiMg1ElCQjLHguKUlVjAaKF74BCekD32uegqNvoqiRYtwGT+Of4u2cKjgEHdF32WWWVFD6Pz86LTsb/weOzvb00WNZ7gya982B0oaL2fqGBuLsaSEqoPnn0/9mYmRTIoK4LUlB3j/n0NqffQG6OLRBV8HX9UcfyEYaiBzNzKwN7N3zOay3fbYVxuITjWyY/MiKmsqlXYRY6DsJGTtaVPxXt38Kk+sfYLkgmRmukbyW1om94e+yrx/w+npH8wPtw3Aw6l+DFF1ZibHZ92M1OsJnfMVduHhaOztcRs7jviDGpycd7D24ElqDEYlgK7gqJKa1sqw9Iz9G2D8GdueAv6VUkYA/5o+Wxc7f0D+NINsrR/jSl5gzPCR3B5hT/pjj2PXpQvGx+/gpr9v4mXbZQgUc3zmpl8sLTUAGas+x6/iMOvD7iemYwDFS5ZiLC3F9cor+WTXJ3Ry68T4sDO/EvOidXVF2F54UF67Y8ijYNBDwuxGmzj26wdA+dZt5z2MTqth9jW9md4nmHf/Ocibfyeryv0MhBAMChrExsyN1BitM7LZ6sk5ADUVJLi4sy99O2O3GNjvGYZeo2XI5hJWHjf51TuNVN7bMDq+oLKARUcWcUXnK1g++VceSd6Is8dAZi2rpm+oB9/fOgA3h/quxpqcHI7PuhlDcTEhX36JfZcudfvcpkzGrtJAt0OZlHGUHccLlRk7WKU53qKKXUq5FjgzXPJy4FvT398CU9tSpiaREla9Dn/cyzGXPowueIpJg/ry8NBQ0h54AIxGHN58gTvW3k95dTnHfaDC142sE+4YkxZZWnqMFUU4rn+DnXRj4oy7ASicPx/bjh1Z45VNSlEK98TcU+dnUjEzXp2g1wyl8ERpwwVQdIGB6IKCKN92/oodQKsRvHVlFNcPCOXTNUd4+c9EjEZVuZ/OoKBBlOhL2Ju719KitE8ydiCBD/K2cHmyK47lleydcB2bw/sxbC8s2WOKLXL2VVLMHv63zUT7/fDvVBurmdl9Jtrt30BFAfekjSauoxff3BKLs119i2RNQQHHb7mV6pwcQj7/HIeePertdxwwAI23F0MTwc59l2KOD4gBjc4qzfGWnrE3hJ+UMhPA9O5rYXkUDNXwx72w5g2S/KYwOvs+pvTvxnOTupH9n/+jKjEJ11df4O4D/yG/Mp/PxnxGN69I9kTaYzwp8M/bbXGTTdKCl3GXhRQOfQk3J1sqk5Op2L0b16um8+mez+jq0ZXRHUZbVMaLnqGPKYUnNn7QaBPHfv0o37btgmfZGo3glak9uXVwON8kHOWZ3/cqmbNUAJRgKaFlXdo6S4vSPknfwUo3L5IKDjNpkyTJowPRE4dTc/mV2FdLnFdsIb3UVO40Yowys60obHWxDEYD85PnE+sfS2enACrXvscaQxSunQcyZ1Ysjrb1lbqhuJgTt96G/tgxQj7+CMc+vc/qU2i1uE+aRJ8jEk/7Xaw8kAk6ewiIhhPWFxlvjYq9RQgh7hBCbBNCbMvJaeXyj5XF8ONVsOtHdoTfyYRj1zCldwdendqTogW/UPT777jceSsP6b8nrTSND0d9SJRPFKNCR7E4JBeNUVKRaUv+joWtK2cT5KcfonPKd6x1GMXwEYqpvXD+AoROR0KUHceKj3FPzD1oRLu9JdoH3hHQczps+RLK8hps4hjbD0N+PvqUlqVVbQohBM9NiuT+kZ2Zu/UEj87fpfgHVXC1dSXaJ1r1s58nhoztfOjpzpTj3jjmFPJ71xGM6u7PiClD2ecVwrjtRhYdXKg07jwapAFSVrW6XBsyNpBems7VXa9m8/y3sNcXkBB8K5/P7Iu9rr410lhWxok77qTy0CGCP5iNU1xco/26Tp6CtkbSL7mEwyU7ySyqUMzxGTuViZ8VYY2/4tlCiAAA03uD01wp5edSyn5Syn4+Pj6tJ01xBnw9EY6uY1Ov/zAtaRgTegbw1pVRVO3bS/Yrr2A/aCDPddnDwfyDvDP8HWL9lTWOo0JHkRwEeldHsk64U757YevJ2QzH5j6OUQpCrnoDIQTGigqKFi3CaewYPjn6PT28ejAi5BKLVLcUQx9Xkls0Mmt3jFXunwtZ9nY6QggeHduVx8d1ZeGuDO7/eSf6GlW5g2KOT8pPIrfCeit1WSXVlSwtTeWw1HPlFhuyXH3RDh2Gm4OOyABXtvaegH8hHFoyF6M0QlA/sHdrk2Vvcw/MxdvBm4KMYDoe+ookh748eutM7GzOUOqVlZy4514q9u4l6H//w3nYsCb7te/ZA11YB4YlatC57VSKwgTHQk0FZFmXO8caFfsi4CbT3zcBf1hMkuz98OVoKEhlff+PuXZbZ0Z09eH9a3pDUSFpDzyI1teH96bAzrzdvD70dYYGD607vLN7Z0Ldw0ju4Yo+ywbfk5vbxBR1Jod3r6d3ySp2hswkvKMSEFL89zKMJSXsGeRPemk698bce1bBDJVWwqerUoBiyxcNZuTShYZi4+NzQQF0DXHviM48P7k7S/dlcdcP262ybntbU7vsLSEjwcKStC+qM3fxkZszEzI9sDuSztyOw5jQK7Buf/gVE8hxdGTA+jw2Z24Grc2pZW+tGMh5ouQE69PXMy1iOtkrv8RHFBMx4z/Y2tRXdVKvJ+3BBynfsoXAN17HddzYZvsWQuA2ZQrdjtfgJ/exIum41QbQWXq528/ARqCrECJNCHEr8AYwRghxCBhj+tz2pKyBOePBaGDdkO+ZucaZgR29+OSGvugwkv7Ioxjy85k3swMri7by0sCXzoomF0IwKnQUS0Py0FYb0WdrKd/f9mvaC1d/RJm0o+eVz5zaNm8euvAw3jMsI9onuu4HTqWNGPo46Mtg40dn7RJC4BgbaxY/+5ncOjic167oxarkk9z67VbK9Zd2RHg3z2542XuxPk01x58LC5N+Ik2n4/pdblS6erC6Qz/GdPev2z+lTyh/hQ0h6qhk5drvlI2dR0NJZqumYV2QvACN0BAs47mm+jfyfPpjE17/t03W1JD+2OOUrVmL/8sv4TZlSov7d5s8GSFh0AE9m7PXUuUUAC6BVhdAZ+mo+GullAFSSp2UMlhK+ZWUMk9KOUpKGWF6b/skw7vnwg/TwTWIDSPmcvPSSnqHevDFzH7Y67TkvP8+5Zs2seG6nsxjK0/EPsEVEVc02NXo0NHs7mCkxt6W7DQ3irb/1qanUlGUS8/8Fez2GIeru1IlqzL5IBW7dpE6LIKs8mx1tm4JfCOVQhSbP2tw1u4Y24+a7GyqT5ww+9DXDQjl7aui2Xgkj5vmbKGkHRaMMRcaoWFQ0CASMhMwGFULRkuoMlTxWfYGxp4wYLv7CEu7DqdfhC+ep60JD/Zw5OSQiei1GlwXraeoqkhR7NBq5vjKmkp+O/wbI0NHUrr+V/xEIW7jn6vXRhqNZDzzDCXLl+P3zNN1Japbim2HDthHRTE8UYPRaTtbUwuUWbuVBdBZoynesmTshN/vhNA4No34iZt/zyIywJWvb47Fyc6G4uXLyfviS1KHd+a9gD3cG3MvN3a/sdHuenj3wMPVj6PdPSjLsMczY50yU2sjDi37FHtRjfOQO+u2FS5YADods/320NevL3EBjQeMqLQiw54AfQls/vSsXeZYz94U0/oE88G1fdh5vJAbvtxMYbm+VcZpDwwOGkxRVVFdiU6VppmfPJ9sqeemHY7g5MQPvn0Y3zPgrHZj47uxOjSSwXtrWL73N3DxV8qdHmodxb7s6DKKqooY4TeRUXk/ke4SjU3HU65RKSVZL71M8aI/8XnoITxnzjyvcdymTCEku4YO5QdZmnRQUexFx5tMF93WqIr9TAJ7w/Sv2D7kS27++SDhXk58d0t/XO11VKWkkvn0MxR19OHZ2FRm9ZjFnVF3NtmdRmgYFTqK5aGF6CprMOQZqT7YRnmTjUZ8k39gj6Y7vfooldRqg+byBkRwVJPHfTH3qbN1S+HXAyKnKBWnzoi9sO3cGa2Hh9kC6BpiUlQAn97Ql6TMEq75fBO5pVWtNpY1MzBgIBqhUaPjW0B5dTlf7vmcsZmV6JLKSRk4jgpbB8b18Dur7cRe/izuOA77akife5o5/sQmZaWRmZmXPI9wt3BstmwhUORjP+opJfUrilLPfv11CufPx+vOO/G+q+nf7aZwnTgBtBqGJhr498RyJQMdWJWfXVXsDbDHYzQ3fbebADf7urSDxrIy0h64nyqNkafG5zO1+1U80veRFinF0aGj2Rxeg1GrIeeEK4Xb2iYLXeaOxfgbMsnqekOdnMXLlmEsLubbzhnEBcTRz79fm8ii0gjDnoSqorNm7UIIHPv1veBENc0xursfX83qx9G8Mq7+bCPZxZWtOp414m7vTk/vnmre+BbwY9KP5FcVcvPGaoRWwzf+A4jt4Imvi/1Zbd0dbekwoDdJAd70XpdFUs5+pYyrsQZS15hVrv25+9mbu5crO00nKvVrUu264hU9oW5/znvvU/Dd93jeNBOfhx68oLFsvLxwih/E0EQtJZpNHLPrBFpbOHH+VRnNjarYz+DwyVJmztmCu6OOH28fgI+LHVJKMp57jqqUFF6fXMWAmEk8N+C5Fs90+/j1wc7Ng4yuXhSkO+N87F+oaf3ZUdn6T8mRbsSMPeUqKJw3n4oADzb5l3BvzL2tLoNKM/j3gm6TYdPHSl3r03CMjaU6La2uUmBrMSTCh29v7k9WUSUzPttIWkF5q45njQwOGsy+3H3kV7ZNSI+UkjuW38Frm19rk/HMQbG+mK/3f814YyA2h+3RjB7N1lItE3r5N3rM1Jggfu0wCt8i2PzLR4rZ2s4VDpk3vey85Hk42DjQKSWPIE5SOuCRutl67qefkffZZ7jPmIHvU0+ZxULpNmUyXkU1ROal8VtikpKFzopKuKqK/QwC3OwZ3sWHn26LI8DNAYD8b7+lZOnf/DRU4D1kJK8MfuWc0q7aaGwYHjKcleFl2JVWIwqqkCnmfWI9k+q8o3Qs3MAmjyn4ergCUHnwIBU7d/JnryoGBw8hxjemVWVQaSFDH1eU+ubP622u87O38qwdYEBHL364bQAFZXpmfLqRo7ltFwdiDQwJGoJEttmyt61ZW9mYuZEFBxeQU97KCbbMxDf7vqFEX8ItGyuQBsGGAZcDML5n44p9VKQve0P6k+9qh+uideiR0HGYEkBnphUfRVVFLEldwqSwiYTv+ZKDoiM9hilBcfnffkvOe+/hetkU/F960WxuR5dRo8DeniH7JUuPLjYlqtkFNdYRq6Iq9jNwsrPhvWt6E+rlCEDZli1k//cttnQRZF0Rx/+G/e+86pSPDh3N+o5VSCHIS3OhcEfrRsenLf8QoxS4Db6jblvhgl8w2mhZFlnFfTH3ter4KudAYAx0mQAbP6zne7Tr2hWNi0urBdCdSe9QD36+I47KGiMzPtvIoeySNhnXGuju1R0PO482M8d/vf9rXG1dlfSnB+e3yZgXQl5FHj8k/cAk/1FoNufgHOnBr7k2xIS4102AGsJep2VcVBBLInoTmVrDunU/Kub44nSliIwZWHh4IVWGKkZVOBFoSCe1x91otBoKFy4k+/U3cBk7lsDXXkNozKfuNE5OuI4axaADgpOV66n076ukim7jCnaNoSr2JqjOzuboA/eR6S5Ze1M0s0d+gJ3W7rz6iguMQ+/uRE64JyfT3LA/vBRaa3lNdSXeh+axThNLfO9egJJlqXDhQrZ209Cv60h6ePdophOVNmXYE1BZCFu/qNsktFoc+/Rp1QC6M+kR6Ma8O+KQwNWfb7pklLtGaIgPiichI0HJlNaKHCw4yPr09dzU4yaGBA9hfvJ89AbrmOk1xlf7vqLKUMUtB30xVoHdhMHsSy9mYhNm+Fqm9g5ikd849DaQ9/33p5a9mcEcb5RG5ifPp7dPDF23/UCyDKHfuBvRHz1K1sv/wXHAAIL+9xbCxvxlqF0nT8KpwkBMWh4LSkzWBysJoFMVeyNIvZ7ke29HX1bCrzd34t0pn+Goczzv/uy0dgwJHsL6TpXYF+qxKSqB4xvNKPEpircvwMVYTFbXG7HRKl9xybJlyJIS/o4yqL51aySoD0SMhYQP6y2HdIzthz41lZrctkt5GuHnwvw7BwLw3MJ9l0zJ18FBg8mvzCcxL7FVx/l2/7c42Dhwdderub7b9eRX5vP30b9bdcwLIassi3kH5nFZ6CTE/L9w8Klis19fACY0sMztTOI6euHg5c32HmF02ZxJRlkZ+HY3SxnXjRkbOV5ynOmOHfGpPMrGoJvxdNCR/uSTCFtbAv/7ZquViHYePBjh7sag/fDLiQRwC7GaRDWqYm+EpJefQrvvEL9e6cf/3fANrrauF9zn6NDRrO6oRB3npzlRvqt1zPHlGz7liDGAASOn1m3LmfszWZ4aAoeMpatn11YZV+UCib0NKvKVXAom6vLGb9vepqKEezvx8OgINqfm82+SZasSthXxgfEIRKsue8sqy2JJyhKmR0zHzc6NgYED6ejWkR8Sf7DaB6jP9nyGESO3nOxKTU4BXpFl/HTCkx6BroR4Nj/Z0WoEl0UH8rP/aOxqYOdXbymz9mMboar0gmSbmzwXTzsPBm7/iyPGAHqMnkneF19QuXsPAS++gM7v7GV45kLodLhNmED/Q5BZtB59UF91xm7NHPz5C8SvS1kd78J9j83Fy8HLLP0OCR5CnrcthQFuZKR5QtKfYDSv2U+m78S/ZB9r3S+no68LAJUHDlC9czcrYuAedbZuvfh2V95zkus22XfvjnBwaFNzfC3X9A+lo7cTry9NuiQqwnnae9LDq0erKvYfEn9AIuuSWgkhuD7yepLyk9h5cmczR7c9J4pPsPDQQq7sPB35w2/Y+dhh1yOYjWl6JvZqfrZey9TeQRxy6MbhcGfcF2/EED4cjNWQuva8ZcsozWBt2lqme0bjW3KYX52uoUdZFjkffYzr5Mm4Tpx43n23FLcpU7CtMdLvcCl/O3grsQNF6a0+bnM0qdiFELNb8HqlrYRtC47uWkfFa+9wqIMtk/43D3+n5n1ILcVJ58TAwIFs6SKxy9VjW5xTb3ZmDnJWfUS5tMMzflbdtozZ71JmB9opY+ns0dms46mYEbdg0DlB7sG6TUKnw7F3TJtExp+JTqvhyQndOJJTxrxt5k9ta40MDh7M3ty9SgpUM1OsL+aXQ78wLmwcgc6nCqZM7jgZF1sXfkz60exjXigf7/4YG40Ns0qiqDp4EM/Ico7bdwOajoY/kx6BrnT2dWZNr3g8Cw3s3p4Its4XZI5fcHABAJMObuOo0Y+g/leR+eRT2Hh54f/8c80cbR4cevdGBAQweJ9gbqkp85wVmOObm7FfDmxv5jW9NQVsa9zDu7B/cDCRH31JqGe42fsfHTqaleFlCAlF6Y7o9y00X+fl+Xgc+YPFDGFsH6WKW8X+/VStXMvi/hpuiX/AfGOpmB8hlHrtp83YQTHHVx08iKGwsM1FGtvdj9gwD95dcZDSqou/YMygwEEYpZGNGeaPf1mQvICy6jJu7nlzve2OOkemR0zn3+P/klVmPWlJDxccZnHKYq7tdi013/+Cja8Pbn7ZrCkLpaufC518nFvclxCCqTGB/CaGkusmKPx5PoSf/7I3vUHPb4d+Y6h7JJ1yE/mSKxi69jf0KSkEvv4aWje3c+7zfBBC4HnZZUQfM3AiP5FSnYNV5I1vTrG/K6X8tqkX8FlbCNpWuLv5cd0nK4joHNsq/Q8PGc7RAA2lHk6cSPOmet8fZlvPWbH1e3RST2bXG3CwVdbZZ773LqX2gqrpYwh3M/+DioqZ8el2tmLv1w+kpHzHjjYXRwjBMxMjyS3V8/maI20+flvTy7sXbnZurEtfZ9Z+9QY9Pyb9SFxAHN08u521/9pu1yKR/HzgZ7OOeyF8tOsjHHWO3CAHUL51K56TBiC08Feu/znN1mu5PCYIo7Bnx8CuBCTnkq+LhMLjkHvonPtacWwF+ZX5zMhOI1164+fUg9Kff8Jj5o04xcefc38XgtuUyWgkDEzSs8I/wvpn7FLK95rroCVtVE7hYe9BX/9+7I7UYZNtwKHwOJw0QxSu0Uj15i/YYuzK8KEjAKjYtYuqdRtYNEBwU/+7L3wMldbHpwuUZNRbz24fFYWwtW2z9exn0jvUg8lRAXyxLvWiTzmr1WiJD4hnQ/oGsy57W5yymJyKnLNm67UEOgcyMmQkvx76lYqaCrONe77sz9vPP8f/YWb3mei/m4fGzQ2PnrYYhQ2JxtAms801RoinI307eLAscDxVNpC00rTm+zzM8fOS5xFq782g9L18WT6BUX9+iW2nTvg+8sg593Wh2HXuTE3HCAbv0/CbTqskqqm27P9Js8FzQohxQohbhRBhZ2y/pdWkusgZFTqKfzoUozUYKcm0x5i46MI7PbIS1/ITrHSeQq8gxQyV9f57FDsKyi4fpkbCtxe8Td/TabMYjZ0dDlFRFgmgq+WJcd2oMRp5Z/nB5hu3cwYFDSKvMo/k/OTmG7cAozTyzf5v6ObZjYEBAxttd13kdRRVFbE4ZbFZxr0QPtj5AW52blzrMJSSf/7B49pr0OTu5ZhNGEHeHnT1czmvfqfGBJKYH8TOGDdc1u2nxqnLOZdxTc5PZufJncwo15MnvOiWXIQoLCDwzTfR2J+ds74t8Jt2ORFZNWTn5nFSGCFzt0XkqKW54LnXgWeBXsC/Qoj7T9utpi47T0aFjiIpVFDlaMfRNF8q9yy84D5L1n1CjnQlYODVCCEo37aNyo2bWRgnuKX/PRcutErb4FOr2OsrFYfYflQmJmIotUyq11AvR2YODGPB9hMcyDJ/ZS5rYlCQUgnRXNHx69LWkVKUwk09bmoypWk/v3509ejKj0k/WnTp247sHWxI38AtPW+h8of5CJ0Oz+uvx5ixk42VHRjf0/+8U7NOigrERqNhf9xIdNWSw1n+cHQD6Ften2Bu8lzsNDqmnkhk0ZG+RB3chs999+LQ03JJtzymTEYiGJRoZKmTo8XN8c3N2CcDI6WUDwF9gQlCiHdN+9Ran+eJv5M/PXyjSIp0hAyJQ94ByLsA/2XBMZyP/8sC4ygu6xMGQPb771PkrKF40kB6evc0j+AqrY9HOGh0Z6XbdIqNBaORip2WWxJ1/8jOONvZ8PoS86QCtVa8HbyJ9Iw0m2Kfs28OAU4BjAsb12S72qVvhwsPsznLMpXCpJTM3jkbbwdvrvIaTdHChbhNuwIbUYSmqphdxo7ntMztTDydbBnWxYf1ZQPY30FDWUIasroKjrYspqFEX8LilMVMNNphrHAjeucR7KKi8brttvOWyRzo/Pwoi4xi8D4Nf7q6W3w9e3OK3UZKWQMgpSwEpgCuQogFQOuk87lEGBk6khVhRdhW11B+0hYO/HXefdVs+QojkBlxLR5OtpRt2kTl1m38Fge39FN96+0KrQ14dYac+iZvh5gYsLGxqDne3dGW+0dGsOZgDusPtV0mPEswOGgwu3N2U6y/MOvE7pzd7Di5gxu739iiGhMTO07Ew87DYkvfNmZsZHv2dm7vdTsVP/2CNBjwuvlmyFACN7Odu9Mj8MKSdV3eO4isAhsOjOiJY0ElhdluLTbHLzqyiIqaCq5OO8SeDYHYYST4rTdbJWXsueI37XICC2uoyYEjmVvNFhR9PjSn2I8IIYbVfpBSGqSUtwLJQGSrSnaRMzp0NLvDBdU6G1LTfKnc8/v5dVRdiWH7d6ww9GPcwL5IKTn53vsUuGrJHduHvqbUjyrtCJ8uZ5niNY6O2PfobpH17KczM74DwR4OvLYkCaPROjOlmYPBQYMxSAObMjZdUD/f7FOyVk6PaNmqYDutHVd2uZI1J9ZworhtcwfUztYDnAKYFjiegrlzcRk7FtsOHag6to0KaUvXXv0uuELamEg/nGy1pIRO4KQbHEv1bFHeeCklcw/MpRf2eB9ww/dkAfYPPoJthw4XJI+5CLp8EjUaLUP2G1ksyqHIcrkfmlPsVwFn2RSklM8BIa0i0SVCmFsYoT4RHOnqgj7dBrusneeXsShxIXb6Av52mER8Jy/K1q+nctcufhkoua2vOltvl3h3hYKjZ0XWOsXGUrF3L8YKy0VN29loeXxcVxIzi/l9p+UzbLUWUT5RuOhc2JBx/tXejhUf49/j/3J116vPqc7ENd2uQSu0/HTgp/Me+3xYeXwl+/P2c3f03ZT9shBjSQlet94KQGnqZvbJMMZHXfjPvoOtlnE9/Nl60I8NA1yxO15FZcqJZt2RW7K2cLT4KDcezCRnhz2HwqPofOuNFyyPudC6upLbM5b4RA1LHZwwHreMOwWaX+5WIaWsABBCRAkhLhNCTBNCTAMGtImEFzEjQ0eyMqwYhwo9lfk6OHDu0bBVGz/niDGA8NhJCAEnZ88m311L9oieDAxsPAJXxYrx6QrSCHmH62126NcPqqup2G3Z0pBTogKJCnbj7eXJVFa3UoVCC2OjsSEuMI716evPO5Dtu/3fYaOx4brI687pOF9HX8aEjWHh4YWUVbdNsKTBaODDXR8S5hrGpJBx5H/7LY5xcTj06gmGGpwLkjhs04WYYHezjHd57yBKKo0UjBhPpQ4yDrs0a46fe2AungYNEasdKdPaY//0C2arr24uPC6bgnu5Ac90DbtSl1tMjhblihdCzAHmoGSZm2J6TW5FuS4JRoeOZlsnMGoEx9J8lGQ150LGLuyytvODcTRXxYZQumo1VXv3MS9ecmufO63upldpIY1Exjv27QumFQ+WRKNRktZkFFUyZ0OqRWVpTYYEDeFk+UkOFpz7Er+8ijwWHl7IZZ0uw9vB+5yPvyHyBkqrS/nj8Dn+Jpwnf6X8xeHCw9wbcy/li5dSc/JkXUBaRfo+7GQVtqH90GjM85syqJMX3s52nCwZwLqegvKjDtTsXtpo+6yyLFadWMlj/5ZRnaflm4HXMmKQ9XmDe0yfSJnOnmH7JX/l7bKYHC0tAhMnpewnpbxJSnmz6aWuY79Aunl2w803mOMdXSlJs8fmRAKU5bX4eOOWL6jAjqywaQS42pHzwWxyvXRkDOnC8JDhrSe4Suvi1RkQZwXQaV1csIvsZtEAulriOnoxOtKPj1cdIa+0ytLitAq1y97Oxxz/84GfqTZWc1OPm85r7CifKHp59+KnAz+1en34lKIUXtv8GlE+UYwJHU3eV3Owi4zEaZCSwe3gzjUAdIoZYrYxbbQapkQHsDFZkDqmJ1oDFK7cCdUNu5l+PfQrndIMdN5hw+rQGCKuvAyd1vpqmOkc7DneM47YZFhZXUZ1pWWWhrb0ymwUQnRvVUkuQYQQjAwdyZqO5TiXVKIvFpDcQnN8RQFy7wJ+rxnElAGRlKz4h6qkA/w80MBtMXeiEdZ306u0EJ0DeISdteQNlPSyFbt2IfX6tpfrDJ6a0I2KagOz/z33lKDtAV9HX7p4dDnnZW/l1eXMTZ7LiJARF5TG+frI6zlWfKxVq82VVZfx0KqHsLex5+1hb1O+eg36lBS8br21zuJXcmQLxTjRq1cfs449NSYIvcGIQ/AE9oQJsg/aIVPOrvZWbaxm0Z6feeyPGgwuDnwUNZ1rYq03xMt54iTsqyVdUgXr9v9gERla+uv/LYpyTxZC7BFC7BVCWNbRd5EwOnQ0myKUJ/K0NG8M+1uYhW7nj2gNVfxhO5FRXbzJ+eADcnzsSBsYzpgOY1pRYpU2wadrvSpvtTjGxiKrqqjYt88CQtWns68z18SG8OPm46TkXFhdbWtlcNBgdmbvpFTf8vP7/fDvFFUVNZo+tqWM7TAWHwcffkpqnSA6KSXPb3ieY8XHeGvoW/g7+ZP35VfogoJwHa+sua+sNuBZtI+TzpFozTxDjgp2I9zbicNHw1nV3wFNuZaS385e5vfv8X+ZuDQftyKYHXsDA6PCCHR3MKss5qTv5aPIt3dh5D4jf6U27l5oTVr6Tc0BbgTGc8q/PqW1hLqUiPaJBl9vMoNdyE1zQaSuhspmSkYajRi2fMk2Y1d69RlE5T/L0R8+zI/x1dwSfRtajbZNZFdpRby7KMFzhvoV1Rz79QOwWN74M3lodBfsbDT892/zpF+1NgYHDaZG1rQ4YUyNsYbvE78nxieGGN+YCxpbp9Uxo+sMNmRsIKUo5YL6aohv93/LimMreLjPw/QP6E/59u1U7NyJ5803160LX5eURgQnsOtg/qJYQggujwlka2oZ7iMmctIdcpdvP6vd1vmzGbtTIuI6stylGzfEWcfytsbwcnVgf+RAeqbAttyjF5wL4XxoqWI/LqVcJKVMlVIeq321qmSXCFqNlpGhI0norMc1vxxDmaH5NZ0pK9EWpvJdzRhm9A4g98OPOOlvz7G+QUzqOKltBFdpXXy6gkEPhfX/zWw8PLCL6GzxALpafFzsuHNYJ/7en8W2o/mWFsfsxPjG4KRzarE5fMWxFaSXpl/wbL2Wq7pchU6jM/usfUvmFt7d8S5jOoypiwPI+/IrtO7uuE+fVtdu3/Z16ISBgO6DzDp+LVNjgpASHKrjWdpXQ1WmkcqNp6LjD6VuZ/S8VMo8jbzX525CPR0Z0vncgxHbGt24CWiN0PeAkX+Onn/N+fOlpYr9gBDiJyHEtbXL3UxL3lTMwKjQUWzorMzMstI8kc0UhZFbvqBAuJEZNAbfrWvQp6byXbyem6NubVF2K5V2gI+ptGcDfnaHfv2o2L4dWWMd9dFvGxKOn6sdry5JsmiO89ZAp9ERF9CyZW9SSr7e9zVhrmFmC171cvBiYvhEFh1ZZLaZX1ZZFo+vfZww1zD+b9D/IYSg6tAhSletwuOGG9A4KGbuqhoDlUeVB0ibkNZJdBXm7URMiDsJiY4cHRiM3kaSP+dTQLmex555DOcK8Lg2nmUZGq4bEGq2yPzWpN+oOApdnBi9z8DiQ7+1+fgtVewOQBUwFnW5m9np79+fogAX8r0dSU/zwHhoeeNFEQqOwcFl/FA9gum9Q8n96GOyg51IjfZlasTUNpVbpRXxjlDec842cTv264exvJzKJOvI2e5oa8OjY7qy83ghS/ZmWVocszM4aDBZZVnNmsM3Z20mKT+JWT1mmTV49frI66moqeD3Q+eZnfI09AY9j6x+hCpDFe+OeBcnnRMAObM/QDg44HH9qTX3CYfz6GI8RJW9L7gGXvDYjTE1JpDkrFL6dprGql4aihISqcnP5+Rv8wnamcXugQZ+D5yFrVbDVX2DW00Oc9IjyI0dHWPomCZIPbKbrLK2/b9orrrbtUIIr9OWuN2sLnczPzqtjmGhw9nSxYjzyXIor4QjKxtuvP1rJIKFmjEMP7aV6hMn+Caugpt6zsJOa9e2gqu0HvZu4BLQcABdP8XfaQ3L3mqZ3jeYbv4u/HfZAfQ1rbs8q60ZHDQYaL7a2zf7vsHL3ovJncw754n0iqSPbx9+PvAzBuOFJQR6c8ub7M3dyyuDXqGjW0cAStetp2TFCrzvvBMbD4+6tkv2ZtJHm4JNaOumpZ4cHYhWIygviOGfflowSHLefZecV14lMQQiJw7m2z2VTOzlj5dz+/iN02gEuSOvBCA+UbIkdUnbjt/M/g7AAiHEOiHES0KIAULNetIqjAodxbpO1WilJCfTHZL+PLtRdSVy+3eslH2J7R5JyRefkdXBlSM9PLiqy1VtLrNKK+PdpcEZu87PF12HUKvxswNoNYKnJnTjWF45P2y6uMJv/J386ezemXXpjVcgS85PZkPGBq6PvL5VHrBv6H4D6aXprE5bfd59LDy8kPkH53NLz1sY3WE0AEa9nuxXXsG2Qwc8bzkVF1BtMLIxMYVwMtAGta5i93a2Y3Bnb5bvKaNTRE8Sw6BwwS/UGKpZNlaS5nUnJVU1Vh80dyb9BkZT7aVl9D7J4pRzzyp6ITSXUvYNKeVIYCKwG7gF2GHyt88UQvi1hZCXAvGB8aSF2lPqYsfR494Yk5dCzRlrlRP/QFTk8XX1aK7N3UVNRiZfxZUys8dN55SLWqWd4NNNmbE34Nt1jI2lfPt2pNF6ZsfDuvgwuLM3s1ceoqii2tLimJXBQYPZkb2D8uqGXWTf7P8GBxsHZnSd0SrjjwgZQYBTwHlXfUvMS+T/Nv4fA/wHcH/v++u258/5Gv2xY/g99xwa21MFOzel5BFSZcpPENT7gmRvCVN7B5JeWEEvvyv5fYAGKeDLMRrGhUfxxV49Xf1c6NvBo/mOrIjBEd6khAbjlyupTE4mOb/tVo60yBEkpSyRUv4upbxTStkbeAXwAb5rVekuIRx1jsQHD2ZHVy0OWVVQVnR2jeKtX5CuDSLPtTeOC74nq5MHKV1cuabbNZYRWqV18ekC+lIoPrvYimO/fhiLiqg6ZD3JYYQQPD2xG0UV1Xy86nDzB7QjBgUNotpYzZass+tsZ5Zm8nfq30yPmI6bnVurjG+jseGabtewNWvrOSuIwspCHl71MJ4Onvx32H+x0ShL2arT08n99FNcxo7Fecjgescs3ZdFPxtTuuBA8yamaYix3f1x0Gk5lNaJ9I46bnlQy46e0LHrE+xLL+aGuNB2lyLbzUHHkejhICRD90sWp7bdrL05H/sfQojHhRCDhBB1j3NSykQp5dtSynGtL+Klg2KOr8TWUEPhSRdIOi06PmMXpG3ly8qR3Fexn5rsbD7vX8y13a/DxdbFYjKrtCLeppzxDQbQ1frZrcccD9Aj0I1pvYP5OuEoaQWNBIC2Q/r49sHBxqFBP/v3Sd8jkdzYvXUrjU2PmI691v6cZu0Go4Gn1j1FTkUO7w5/F097z7p92W+8AULg99STZxwjWb4/i5GuaUoGREdPWhsnOxvG9vDj730nmezeizIHweX2Ifx0QIejrZapvYNaXYbWwKPvWJz8qxieKFh6eHGrpweupbkZ+xeAB/AqkCWESBBCvCWEuEI1w5ufocFDORCmo9LOhkPH/TEmLYbaYJmtX6LX2LOkeiCd//mNrK7eHOnsyA2RN1hWaJXWo64YzNkBdLbBQdgEBlhVAF0tj43rggD+t+ziSVpjq7VlQMCAs5a9FVUV8evBXxkfPp5A59aLHAdws3NjSqcpLE5ZTEFlQYuO+Xj3x2zI2MAzA56hp3fPuu2la9dSsuIfvO++G11gfbm3Hs0nt1RPV8PhNpmt1zI1Joiiimo6+sxiAA5cMeBF/tyTwdTeQbjYt89lvPG9OlMZaodrsQGPg9nsPLmzTcZtzsf+l5TyGSnlcBTT+wNANvAWkNH64l1auNm50Td4APu62KLLqEaU5sCJzaa88L/wlxzMrWVHkHl5fNK/kBldZuBh3778TirngJMPOHg0uJYdFHN8+bZtVrd2PMDNgVsHh7NwVwZ705rJotiOGBw4mPTSdI4WH63btuDgAspryrm5h3kS0jTH9ZHXozfq+eXgL822XXV8FZ/v+ZxpEdO4ssuVdduNVVVkvfIqtuHheM06u0jN3/uyCLApwbEiA4LaTrEPjvDG08mWf1I9+PKmLazL8Key2sj1A0LbTAZz09XPhUMduoCN5MXiYfTxbZvr2ayPXQjhLYS4DGXW/j/gSuAflEA6FTNTa4530OspyXNUouN3/YSoqeD7ksEM2rqEzB7+HAm1Pe/KUSrtBCEUc3xOw2VDHWNjMeTloU892rZytYC7hnfC08mWV5ckWt2Dx/kyOLj+sje9Qc+PST8SHxhPV8+ubSJDJ/dOxAXEMTd5LtXGxgMUjxUf45n1z9DdqzvPDHim3r78OXOoPn4cv+eeRZwWMAdgNEr+3pfFdcG5yoY2nLHrtBomRwXwT1I2xZXV/Lj5GL1D3ekR2DpxC22BEIKqkP64BlVgs3oLsrptgkqb87EfAv4GegDLgMlSyjgp5V1Sym/bQsBLjZGhI9nVUUONVsPBE0FKFrqtX3LIrjt90jPRFBfxcWw+0yKm4ePoY2lxVVobny5n1WWv5VTeeOszx7va63hodASbUvJZeeCkpcUxC0HOQYS7hbMhXSnj+lfKX+RW5DKrx6w2leOGyBs4WX6Sf4/92+D+8upyHlr1EDYaG94d/m695Xf6tHRyP/0Ml/HjcR50dprYnScKySquZIxbOggNBES32nk0xOUxQVTVGHnpj/2k5JRxw4D2tcStIfx7DMWtQwXG8nKqEhPbZMzmZuxzgHRgOnA7cLMQop8QQq0y0kp4O3jTLaQ3Bzs5YEwzQlEa5KfwecFgLkteRVZ0EEcCNdzSUzWYXBL4dIPyPCjLPWuXbVgYWm9vq1rPfjrX9g+lo7cTry5OYsfxgoti5j4ocBBbs7ZSXl3O1/u+pptnN+IC4tpUhiHBQwhxCeGHpLNLgkopeWnjS6QUpfDm0DfP8vtnv/46aLVnBczV8ve+THRaQaeag4q1yM65Vc6hMfqEuhPq6chvO9Nxd9QxKSqgTcdvDaL7xGH001B+ax8cYmLaZMzmfOyvSykvl1L2A14CSlAU/F4hxJrWFEwIMd5UJvawEOKp1hzL2hgVOoq1nSpwKa+gotCOcp0nDskV2JaX8mG/PKZ0mkKAc/u/4VVaQBOR8UIIHGP7Ub51q1UqTZ1Ww0uX9SCzqJJpHycw5t21fL72CDklVZYW7bwZEjQEvVHP/7b9j6PFR7m5x81tvgxLIzRc1+06dufsZl9u/fK9Pyb9yNLUpdzf+37iA+Pr7StZvZrSf//F55670fn7n9WvlJIle7MY3MkLXeauNvWv1yKEYGqM8jByZZ9g7HXtfw7pZG/LMcdI3Ev3ttmYLVrHLoToCPQHBgBxKIF0Ja0llMki8BEwAegOXCuE6N5a41kbo0JHsT1CYATWZfTmg6oZXJGaQFa/MA77Gbm1162WFlGlrfDporw3YY6vycqiOv3ste7WwNAuPmx5dhRvTOuFq70Nry05QNzr/3Lbt9tYvj+LaoP1JNhpCX39+2KvtWfBwQUEOgUyNmysReSY2nkqTjqnerP27dnbeXvb24wMGcmtPev/Rhirqsh+9TVsO3bEc+bMBvvcl15MemEF0zsD5bkQ2PqJaRrimv6hDO7szaxBYRYZvzWoDuxHmOEoaVlt45ayaWqnEOJ3FEVeBGwENgAfSClb21HQHzgspUwxyTEXuBxoGweFhQl2CSYgJJKjoSlwrJryqjLs9RV82CeHcWHj6ODa/v1OKi3ENRh0jo0H0J22nt022DoLZLjY67imfyjX9A/l8MlSFmw/wa/b0/knKRtvZ1um9Qnmqr7BRPhZfz4GO60dsf6xrEtfx8weM+uSvbQ1zrbOTO08lXnJ83i076MAPLbmMYJdgnll8CtnWRHyvvyS6hMnCP16zlkBc7Us3ZeJViMY7nxC2WCBGTtAoLsDP9w2wCJjtxZBPYchj36NruAw+Pu2+niiKROeKRo+QUp5toOvFRFCXAmMl1LeZvp8IzBASnlfQ+3Dw8Pliy++2JYitjoZpRlUZabjVwBGIahxseeweyU9vHvgYONgafFU2pKMXaC1Ab+eDe6u2LEDrYcHtuHhbSvXBSAlFFboySmpoqC8GiklLvY2+LjY4+Vki9aKS3MWVBaQWZZJN89uZq3idq5UGarYm7MXf2d/SvWllFeXE+kVedbvg6yqomLvXrQeHth16tRof7tPFGJno6Gbfb6S7TB0oBJAp3LhSAlIs1/Pm2++ebvJVV6P5h4305pT6kKIPlLKHRckXQPdNrCt3hOIEOIO4A6AoKD2mZWoKTzsPTjooCh2DZJ0Fz3u9u6qUr8UsXWEysbXg2tcXDCUtJpnrFUQAjwcbfFwtKXaYCS3VE9OSSUpOaUczRN4Odri42KHq4P1JSbxsPewivwRdlo73OzcyCpVSoJ2dO/Y4O+D/vhxEALbkJBG+yrXG6ioNuDvZg/lJWDrpCp1cyIEDau1VhqumRn7bmA4TUv0ryl/vPmEEmIg8FJtylohxNOgBPM11L5fv35ym5VGBp8vUkqmLJzC/V/noevQgQf77Wfu5Ln08OphadFU2pq1/4OV/wdPp4Hd2ebqvG++4eQbb9J5zWp0fu03IaSUkt1pRczfdoI/d2VQUlVDqKcjV/UNZnrfYALd1YfaM9mUuYnbl9/OzO4zeTz28bP2l6xcRdo99+D7+ON43dr4Spq3lh3g49VH2PL0SHw+6gK9roLJ77Sm6CpmQAhxXjN2N2A7TSv2nAsRrBG2AhFCiHCU5XbXANe1wjhWixCCUaGjeOrK73CxzWCQ9yBVqV+qnJ5atoESmqf72d0mT2pLycyKEIKYEHdiQtx5flJ3lu3PYv62E7y94iDv/HOQa2JDeGVqL6s207c1cQFx/HXFX4S4nD0bN1ZWkv3aa9h27oTnzMbz2JdV1fDDpuOM7e6HT9UJqCq2mH9dxTw0qdillGFtJMeZ49YIIe5DSYqjBeZIKfdbQhZLMjp0NHP2zaGgqoA7o+60tDgqlsKnm/Kek9ygYrfv1hWNkxPl27a2a8V+Og6mwh9TewdxIr+cr9an8k3CUYQQvDq1Z7ur9NWaNBZMm/fFl1SnpRH6zTcIXeMujZ+3HKeoopq7hnWCjGXKxjbMOKdifiwT0tkCpJRLgCWWlsOS9PDuQaBTIMEuwfT2tczSExUrwCMcNLoG17IDCBsbHPr2sbpKb+YixNORly7rgaOtlo9XH8HZzoanJ3RTlXsT6I8fJ++LL3CdNAmnuMYjzPU1Rr5an0pcR096h3rAvh2gczplJVJpl1itYldRElF8O+FbNWDuUkdrA16dGqzyVotjv1hy1r5DTV4eNl5ebShc2/H4uK6UVtXw+doUXOxsuH9UhKVFskqklGS9+irCxgbfJ55osu0fu9LJLKrk9Wm9lA0ZOyAwBjTtPzHMpYwa9mjl+Dv542bXfosgqJgJ7y6NztgBHGNNeeO3bW8ridocIQQvTenBtD5BvL3iIF9vSLW0SFZJ6apVlK1Zi/f996Pza3zNtNEo+WxtCpEBrgzr4gOGasjaa7HENCrmo6WZ5wYJIZxMf98ghHhHCKFmSVFRaSt8ukFBKlRXNrjboUcPhL291eaNNxcajeC/06MY18OPl/9MZP62E5YWyaowVlSQ/cqr2EV0xvOG65ts+09SNodPlnLXsI6KW+NkItRUqoFzFwEtnbF/ApQLIaKBJ4BjwHetJpWKikp9fLqCNEL+kQZ3C1tbHGJiLnrFDmCj1TD72t4MifDmqV/3sGRvpqVFshryvviC6owM/J5/vsmAOSkln645QoinA5N6mepOpJvSkaiBc+2elir2GqkseL8ceF9K+T5g/fkfVVQuFrxNOeObMcdXHTiAoajxZDYXC3Y2Wj67sS99Qj14cO5OVie3v9Kwv2xP49+kbLP1pz92jLwvvsR1yhSc+vdvsu3WowXsOF7IHUM6YqM1qYGMHeDgCR5hZpNJxTK0VLGXmJLE3AAsNhVpsb6UUCoqFyveEYBoWrH3iwUpKd9h7kSQ1omjrQ1zbo6li58Ld/2wnc0peZYWqcUYjJKXF+3nobm7OFnSsHvlXKgLmLO1xffxx5pt/8nqw3g52XJVv9PWv6fvVPzr6mqDdk9LFfvVQBVwq5QyCwgC3mo1qVRUVOqjcwCPDo1WeQNwiI5C6HSXhDm+Fld7Hd/d0p8gdwdu/XYbe9IKLS1Si0jKLKakqoaSqhreWHLggvsr/fdfytauw+eB+9H5Nl1kJCmzmFXJOdw8KOxUWVR9ueJjV/3rFwUtUuxSyiwp5TtSynWmz8ellKqPXUWlLfHu2miVNwCNvT32UVEX7Xr2xvBytuPH2+Jwd9Qxc84WDmZbf978TSbrwvQ+wfy2M50tqfnn3ZexooKs117DrksXPK5vOmAO4LM1R3Cy1XJjXNipjVl7QRpU//pFQovWsQsh4oAPgEjAFiUbXKmUUl2HpaLSVvh0gZTVYKhR1rY3gGO/fuR9+SX/3959x1dd3Y8ff72zJ9kJgYQlEDZRGW4FtVrraLXO1oWobd1fd/31W/ttbevWalsHoDjqaN2tWhetoBKGgIYpkLCyEyCTrHt+f3zuhQA3d+WuXN7PxyOPe/O5n3s+J9fIO59z3ud9bC0tRCUnB7d/ITQwLYGXZ0/n/Ke+4qdzSvj7z45maFb4/vwlZQ38YssCzq/ZyeE7Wll30z8oOHwo0SnJRCUlEZWUTFRiIlHJSfbvrS9JTLResx+XuDjqnn6aropKBr/0ABLj+p/0bQ2tvPdNJVceM4y0pB6zqVu/sh71jj0ieFqg5kmseu1/B6YAlwFaHUKpYMoZA93tsGuLVbDGiaSpU6l/+mlaV6wk5bhjg9zB0BqalcxLs6dz4dNf8RN7cM9PC7/iTjabYdfS5Zy14l/Yhg1johEatm+hrmINcZ3tmPZ2zxuLjobubtLOOZukKQftBXKQOQs3EyVw1fE9tvjdtAAW/B4Kp0PqQB9+IhVuPK48Z4zZKCLRxphu4DkR+TKA/VJKHSjbXuazdn2vgT2xuBiio2ldtvSQC+wAo/NSmT9rGpc8W8JP55Tw+rVHk5USH+pu7Wd9dSMXL3uLzvRMit74B5KUxOXPLWXFlp18dttJZCfFYGtrw9bSiq21BVtrK6a11TrW2mo/bv9qa0Wiol1u8uJQ39zOa8u28cPiwfv+4NnyJbx6CWSNhItfDfBProLF08DeKiJxwEoReQCoBMJ3nEupSJRjX/JWtx44w+kp0SnJJIwfT8vnCzE33XRI1lOfVJDO3MuncNm8JVw2bwl/u/oo0sJoX/eNr73N2J1biP/lr/dOl9x71jhOe+xz/vDBWh65oJjolBSiU1L8et35X5bT3mXj2hNHWAe2L4eXL4ABg+GytyEp06/XU6HjaVb8pVjz6tcDLUAhcF6gOqWUciIhDVIGukygA0j/8XnsWbOGpg8/DFLHws/0EVk8femRbKhu4qrnl9La0RXqLgFga28n95U5bM0YzLBLfrz3+IicFK4+fgRvfr2DZeW+J9L1pqW9i/lfbeHUsXmMzE2FylXw0o8gOQsufxdSXGfSq/7F06z4LcaYNmNMozHmN8aY/zHGbAx055RSB8gpglrXy6PSzzuP+LFjqX7wQWxtbUHqWPg5qSiXxy86nK+37uTaF5fT3tUd6i7RMP8FBuyu45uzryDqgES362eOZFBaAr96ZzVd3Ta/XvfVpdusrVlPOgxq1sKLP4K4VLj8PRgwyK/XUqHnMrCLyOv2x29F5JsDv4LTRaXUXjlFUPcdGNPrKRIdzcBf3k1XRSX18+YFsXPh54yJ+dx/3iQWflfHja+s8HvA9EZXfT21Tz/N4oHjKDz5hINeT4qL4VdnjmNtZSMvLd7it+t2dNmYs3Az04dnckRyA7xwjrUN8OXvQvoQv11HhQ93d+w32R/PBM5y8qWUCqbs0dDRBI0VLk9LmjqV1O+fTv2zc+isPLRrqZ8/pZBfnzWOf6+u5o5/fIPN1vsfRYFU++STmLY25o4/k+kjnG+te/qEgRw/KpuHP95AbZMX2fEuvLuqgsrde7h5ShzMPwtsXXDZO70mYKr+z2VgN8ZU2h+3OL6w5ti32p8rpYIpx54Z76ICnUPebbeBMdQ89HCAO9W7jm3bqHv2WUxXaOe4rzx2OLeeOpo3V+zgzRU7gn799u++Y9drr1N65Cl0DCpkWFaS0/NEhHvPHs+ezm7++EHfK9LZbNZmL8fltnPUwiuho8UK6rlj+ty2Cl/uhuKPEpH/iMibInK4iJQCpUC1iJwenC4qpfbKsf+D7KJmvEPs4MFkXTWLxn/9KyT1401HB9tvvInahx+h8cN/B/36B3LMYX+0uiro165+8EGikpN5evgMpo/Icrla4bCcFGYfP4I3vt7O8i19S6T7dF0Nu2u285TtN0jbTrj0TRg4sU9tqvDnbij+SeD3wCvAZ8BsY8xA4ATgDwHum1LqQMk5kJDuUWAHyJo9m5i8PKrv+z3GFtz55donnqB97Vqi09OpnzMH4yIvIBhEhJPG5LJoY11QE+maF31By+cLibrsKjZ1xDJ9uPtlZTfMHEl+WgK/etv3RDpjDC99tpzXEv9AcnsN/OTvMPhIn9pS/Yu7wB5jjPnIGPN3oMoYsxjAGNP3MSKllPdE7Al0rpe8OUQlJZF7223sWb2a3W+9Hdi+9dCyZAn1c+aSfsEF5N5+O+3r1tGyaFHQrt+bmUW5tHZ0s7RsZ1CuZ7q7qbn/fmILC1lx5MkAHgV2RyLdmspGXi7Z6tO1v96whdtr7maI1CAXvwpDjvKpHdX/uAvsPf9UPHDdTGj//FbqUOXBkreeBpz5AxIPP5yaRx+lu7k5gB2zdDc1UXHXXcQOKSTvzjtIO+tMYvLyqH92TsCv7c4xI7OIi4nis3XB2b991xtv0P7dd+TeeiuLtzWTlRzHyFzPCs98f8JAjhuZzUMfraeu2ctEuvYm0t68iKKo7XSf/yKMONGH3qv+yl1gnywijSLSBEyyP3d8rxM1SoVCdhG01kOLZ/uPiwh5v/wl3XV11D/1VIA7B9W/+x1d1TUMfuABopKTkbg4Mi+/nNYlS2j7JrSrZJPiYjh6RBYL1gc+sHc3t1D7+J9IPOIIUk/7HiVlDUwbnulxNcCeiXT3e5NI19FK6/PnMWzPev499o/Ejz3Nx59A9VfusuKjjTEDjDGpxpgY+3PH9+FTo1GpQ4kXmfEOiRMnkHbuudTPf4GO8vLA9Ato/OADdr/zLtk//zmJkyfvPZ5+wQVEDRgQFnftM8fkUlbXQlldS0CvUz/nWbrr68m760527Gpjx642j4bhexqZm8JVx43g78u3s3yLB9MHnXvg1UtIqFzKneZ6jjvrch97r/ozT0vKKqXCRba9ZryHCXQOubfcTFRsLNUPPBiATkFndTWV9/6GhMmTyP7Ztfu9Fp2STMbFF9P0ySe0by4LyPU9NaPIKp8ayOH4zooKGp57ngFnnknipEmUbLay23tbv+6KI5Huf98ppdvVGvyuDvj75bB5AXd2XUP6tItJT4rz9UdQ/ZgGdqX6m7RCiE3yOrDH5OSQ/Yuf0/zZZzQv+sKvXTI2G5V3343p6GDw/fc73Rc889KfInFxNDwX2mp4Q7KSOCwnmQUBDOw1jz4GQO7/3AJASVk96UmxFOWlet1WcnwM9/xgLKsrGvlbSS/lQ7q74I2rYMOH/LPwdt42JzK759as6pCigV2p/iYqCrJHeTUU75Bx2WXEDhlC9R//gOns9FuXdr74Ii1ffkXeXXcRN2yY03NisrNJO/dH7H77HTqrg5O81puZY3IpKaunpd3/hXPavvmGxvfeI/OKK4gdZNVhLylrYOqwTKKifNtt7wcT8zl2ZBYP/ns99Qcm0tm64e2fw9p3aZnxW24rP5Jzem7Nqg45GtiV6o+yi9zu8uZMVFwceXfdScfGTex89TW/dGXPhg3UPPwIKTNmkH7B+S7PzZo1C9PdTcML8/1ybV/NGJNLZ7dh0cY6v7ZrjKH6/geIzsoi6+qrAajavYct9a1ez6/3JCL85uzxtHZ0c/+HPRLpbDb4583w7esw81c83XE6ezpt/MyxNas6JGlgV6o/yhkNjduhvcnrt6bMmEHyMcdQ+8QTdO3s23puW0cHFXfcSVRqKvm/+63bjO+4wkIGnH4au159je7Gxj5duy+mDsskJT7G78PxTR99TNvy5eTceCPRKdZe6yVl1uqF6cO9n1/vaWRuKlcdN5zXl23n6607obPNmlP/+gU44XZapt/M/C/LOXWcfWtWdcjSwK5Uf+QoLethoZqeRIS8u+/C1tJC3RNP9KkbtY8/Tvu6deT/7rfEZHkWuLJmz8bW0uK3EQNfxEZHcfyobBasr/FbRTxbRwc1Dz1E/KhRpJ937t7jizc3kBofw7hBA/p8jRtOHsXAAQk88tZCzHM/gLXvwffugxn37Nua9UTd3OVQp4Fdqf4o277kzYfheID4UaPIuPhidr76GnvW+9ZGy5IlNMx7jvQLLyR1xgyP35cwbhzJxxxDwwsvYGv3zw5mvpgxJpfqxnZWV/hn5GDnSy/TuW0buXfeuV/y4JKyeqYMyyDax/n1nlLiY/jD8bH8seEWuqtWw4UvwTHX09FtmLtwM9OGZ3Lk0Iw+X0f1bxrYleqPModDVIxPCXQOOddfR3RqKtV/+IPXd63djY1U3HUXcUOGkHfnHV5fO+vq2XTX1bH77Xe8fq+/nFSUA+CX4fiunTup++tfST7+eFKOO3bv8dqmdjbVtvi0zM2pTQs4adFPSI62cantXuoLTwWsrVkrdu/h53q3rtDArlT/FB0LmYf5fMcOEJ2eTvaNN9C6eDHNn366/4vtzTDnFPjuY6fvrfqtVV1u0IMPEJXkfAtSV5KOOoqECROonzcX0x28DVl6yk1NYFJBml+q0NX9+S/YWlrIu+P2/Y4vKbOvX+9D4txey+fDyz9G0grYdckHLO0YygMfrsdmMzz9302MGZi6948VdWjTwK5Uf+VlzXhnMi68kPhRI6m+/4H9h8W/ng/bl8Lqtw56T+P779P43ntWdblJk3y6roiQNXs2nVu20vTxJ752v89mFOWyYtsuGlo6fG6jffNmdr7yCukXnE/8qFH7vVZSVk9SXDQTBqf53kmbDT7+Nbx3Iww/EWb9m+EjxzDruOG8tmwbD3+8nu9qmvnZiYd5XK5WRTYN7Er1VzlFsLMMunyfp5aYGPJ++Us6t22jYf4L1sGuDvjySev5tiX7nd9ZVdVrdTlvpZ56CnFDh1L/7LMh29J15phcjIH/bvD9rr3mwYeISkgg54YbDnqtZHMDRw7NIDbax39qO9vgH1fAF4/BkVfCJa9DgpWEd+PJo8gbEM+fF2xicHoiZ07K9/lnUJFFA7tS/VV2ERgb1G/qUzPJRx9NyiknU/fUU1bhmG9fh6YKGHY81H8HrdZwsrHZqLjrbkxXF4MfeMBpdTlvSHQ0mbNmsWf1aloXL+5TW76aODiN7JQ4PltX69P7WxYvpnnBArKuvfagVQENLR2sr27yfRi+uQaePxPWvAvf+x2c+ShE7/vMU+JjuOcH4wC45oQRxPj6x4OKOPqboFR/5cNmML3Ju+MO6Oyk9tFHYNFjMHAinHin9eKO5QA0vPACrYsXk3fXncQNHdrnawKk/fAcorOzg7Y5TPvmsv2q3kVFCSeOzuW/62vo6ra5eOfBTHc31fc/QOygQWReftlBr++dX/clca5mHcw5Gaodme83gJNh9rMm5fP2dcdy6VH++e+hIoMGdqX6q+xRgHhdM96ZuCFDyLziCna//Q5tG8rhuFtg8BEgUbBtCXvWb6D2kUdJmTmT9PNdV5fzRlR8PJmXXUbLl1/Stnq139p1puHFl9h8xhlsPPFENs48mR3/cysNL7zI6bE7aWltZ8W2XV61t/vtd2hfu5acW/+HqPj4g15fUtZAfEwUkwq8nF/ftADmfs+aYrnyfRh7Zq+nigjFhek+l6pVkalvY2lKqdCJTYT0IX4J7ABZ117Drr/NpWpVLsOKzkJi4yBvPLbyxVQ8tsSqLvfb//N7glbGxRdR/8wzNMydy+BHHvFr2w673niT6vvuI2XGDJKmT6Nt5Spaly+n8f33KQDeiI6luXQUNScfQ2JxMYnFxS4L7thaW6l97DESJ09mwBlnOD2npKyeI4ZkEB8T7XlHv34B/nmLtYPfJa9DeqGXP6lSGtiV6t9yinyqPudMdO3X5E6op7Ikg8YPPiTt7LOhYBq1896hfUMcBU/91ePqcl5dNzWVjIsupH7ec+TcvJW4IUP82n7jBx9Q+atfkXzssQx+/DGi4vZtZdpZWUnbypW88cL75O/YSP3z86HTmhaILSwk8XAryCcVFxM/evTevIL6ufPoqq1l8OOPO/1DZ3dbJ2sqG7np5FEHveaUzQaf/R8sehQOmwnnz9+bJKeUt0IS2EXkfOBeYCwwzRizrMdrdwNXAd3AjcaYf4eij0r1CzlFsPm/1g5fUV7cGTqz6FHSxqeys2U8NQ89TOrJJ9PWmEvDmljSzz6V1JNO8kuXncm47DIa5r9A/XPPkf/rX/ut3aYFC9hx+x0kHnE4BU8+sV9QB4jNzyc2P5+OpNH87IN1LLr5GDIrymhbsYK2lStp+eorGt99DwBJSiJx4kQSJ02i4cUXSf3+6SQdcbjT6y4rb8AYD+vDd7bBWz+DNW9bme9nPLRfkpxS3grVb08pcC7wdM+DIjIOuAgYDwwCPhGR0caY0FSwUCrcZRdBdzvsLIesPlQdq1wFmz5DTv41eafOYMvFl1DzyKM0ffIRcand5J093m9ddiY2N5e0H57D7jfeJOe664jJzu5zmy1ffcWOm24mYcwYCp96iqjE3rcxnTkmlz98sI7/lDfy06OOIOmIIwBrt7bOHTtoW7GStpXWV/28eUhsLLm33tpreyVlDcRFR3H4kHTXnWyuhVcvhu3LrMz3o693miSnlDdCEtiNMWsBZ0NY5wCvGmPagTIR2QhMA74Kbg+V6if2ZsZv6FtgX/QYxA+AqVeRlJDGgLPPYufLL0N0NMPOsBFVt8ov3XUlc9Ysdv3jDRpefIncW27uU1utK1aw7brriRs6lMJnnyE6JcXl+SNzUyjISGTBuhp+2iPDXESIKyggrqCAtLOsJDZbayu21laXf3yUbK5ncmEaCbEuRlFq18PLP7aC+4UvwtizvPshlepFuGXFDwa29fh+u/3YQUTkGhFZJiLLamt9W4OqVL+XPdp67EsCXf0maxh4yixIsDK4c2+9lZicHHJvuZnEw6ceVKgmEOKHDyf1lFPY+cordDe3+NzOnjVr2HbNtcTkZDNk3lxiMtxviiIizByTyxeb6tjT6XqAMCopyWVQb27vorSi0f0w/BuzoXMPXPkvDerKrwIW2EXkExEpdfJ1jqu3OTnmtCSVMeYZY8wUY8yUnBytj6wOUYnpkDKwb4H9yycgKhaO+vneQ7F5eYz8zwKyZs+Gwqn7FaoJpKyrZ2NrbGTX66/79P72jRvZetVsolJTGPrcc8R48W/DjDG57Om0sXhzvU/Xdli+ZSfdNsP0ES4K03S2QXUpHHkFDD6yT9dT6kABC+zGmFOMMROcfLnazmk70HN9RwFQEag+KhURckb7XqSmqQpWvgzFl0DqwP1ekmj7MHLBNOtx+zICLXHSJJKmT6dh/nxMh3f12zu2bmXrlbMgJpqh8+YRO2iQV+8/ekQWCbFRfd7trWRzPTFR4nr71Jq1VtXAgRP6dC2lnAm3ofh3gYtEJF5EhgOjgMCPASrVn2UXWbu8+VJvffFfwdZlVTbrjaNQzfbg/K+YNXs2XdXV7H7vnx6/p7Oqiq1XXInp6GDI3LnEDRvm9XUTYqM55rBsPltf06fa9SVlDUwsSCMpzkUKU7W9GE+eBnblfyEJ7CLyIxHZDhwN/EtE/g1gjFkNvA6sAT4ErtOMeKXcyCmCjiZoqvTufXt2w7J5MO4c14l3ccmQNz4o8+wAyccdS/zYsdTPnYuxuS/z2lVfz9YrZ9G9ezeFc+aQMHq0z9eeMSaXbQ1tbKpt9un9bR3dfLN9l/v59epSiE2GjOE+XUcpV0IS2I0xbxljCowx8caYPGPMaT1eu88Yc5gxpsgY80Eo+qdUv+LIjPd2C9elc6G9EY692f25BdNgx9fWevkAExGyrrqKjs2baf7sM5fndu/axdZZV9FZWUnhM0+TOLFvd8Azx+QC8JmPw/Ffb91JZ7eb+XWAqlLIGwdR4TZoqiKB/lYp1d9lOwK7FxXoOvdYw/CHzYRBxe7PL5xmjQr0cf93Tw04/TRiCwqof3ZOr8Pi3c0tbL3mWjo2b6bgz0+SdGTfk9AGpydSlJfKAh93eyvZXE+UwBRX8+vGQPW3OgyvAkYDu1L9XUqutUzNmwS6VX+DlhprsxdPFEy1HoM0HC8xMWReeQVtq1bRtuzgpD3bnj1s/8Uv2LN6NYMfe5SUY49132hrAzw6ETb/x+VpM8bksrS8gcY9nV73u6SsgfGD0khNiO39pN3brWkQTZxTAaKBXan+TgRyxnh+x97dBV88bi2zGna8Z+/JHAFJWbB9qe/99FL6uecSnZlJ3Zz9t3Q1HR1sv/FGWpcuZdD995N68smeNVj2X9i9Fb772OVpM4py6LIZFn1X51V/93R2s2LbLvf7r1eXWo95E71qXylPaWBXKhJkj/Z8mHztO1YJ2uNu8bx8qYg1zx6kO3aAqMREMi/9KS3//Zw9660/WkxXFztuu52Wzxcy8Df3knbmDzxvsGyh9VjpuorekUMzGJAQ4/U8+6ptu+josrnff31vYB/nVftKeUoDu1KRIKcIWuvcF5ExxtpBLGsUFHkRFCGohWocMi6+GElKon7uHIzNRuU9/4+mjz4i7+67yLjgAu8aK+8R2F1k28dER3HC6Bz+s74Gm83zZW8lZQ2IwLRhHiTOZQyD+FSP21bKGxrYlYoEexPo3Myzb/oUqr6FY2/yPiM7iIVqHKLT08k4/3wa//U+Fbfdzu533iH7xhvIvPxy7xpqqrLq6WePtlYC7CxzefrMMbnUNXdQWrHb40uUlNUzZuAA0pJczK+DdceuiXMqgDSwKxUJPF3ytugxSB0Ek7y82wV7oZrooBWqcci84nIQofH998m8ahbZP/+5+zcdqHyR9Xj0ddZj5UqXp584OgcRz5e9dXTZWL5lp/v59Y4Wqzb/QJ1fV4GjgV2pSJBWCLFJ1l1pb7Yvs4ajj74OYuK9v0aQC9U4xObnk3vrrWTfcD25t93mbFdI98o+t3avm3QhRMdBxUqXp2elxDO5IN3j8rLf7tjFnk6b+8BesxYweseuAipU+7ErpfwpKgqyRroeil/0KCSkw5FeDmP3VDAVvnnNKlQT5WJLUj/LuvKKvjVQvgiGHgOxidYfJ24S6MAajn/k4w3UNrWTk+r6D6GSMivvYJq7wF71rfWoS91UAOkdu1KRIqeo9zv22vWw7p8w7Zq+JW0VToOOZvudZz/RWAENm/Yt7cufbAV2N/XgHVXo/rPe/V17yeYGRuWmkJXiZiSkejXEpUL6UNfnKdUHGtiVihQ5RbB7G7Q7qXP+xZ8gJhGmX9u3azgK1QRxPXufOZa5DXcE9mLYs8ta8ufC+EEDyE2NZ4GbwN7VbWNZeYP7MrJgT5wb7/kyQ6V8oIFdqUjhyIw/8K5993Zr+PyISyE5u2/XCEGhmj4r/9yagnAUhMmfbD26GY4XEWYU5bJwQx2d3b0vj1td0UhLR7f7jV+Mse7YdRheBZgGdqUiRU4vgf2rv1h7fx99fd+vEYJCNX1WthCGHbdveV/eeIiKdZsZD1Z52ab2LpaV7+z1nJKyegD3d+y7tlhL7TRxTgWYBnalIkXmCIiK2T+BrrUBlj8PE38MGX6a1w1BoRqf7dpqBdSepXNj4iF3rEcJdMeNyiY2WlwOx5dsbmBEdjK5qQmuG6uyV5zTpW4qwDSwKxUpomMh87D9A/uSZ6GzxSpI4y8hKFTjswPn1x3yJ1tL3twk0KXExzBteGav69m7bYYl5Q3us+HBXkpWrD8qlAogDexKRZKc0ft2eetogZKnYPTp1vCzv4SoUI1PyhdCYibkHBBMBxVDW4OVbOjGjKJcNtY0s62h9aDX1lU10rSny7PEuapvIeswqx6AUgGkgV2pSJJdBA1l0NUBK16ygpenW7N6KkSFarxmjLV+vef8ukP+4dajh+vZwXkVupLN1nSE28Q5sBLn/PkHllK90MCuVCTJGQOm2yot++UTMORoGHKU/69TOA12LLcK1YSrneXWHfnwEw5+LW+cNergpgIdwIicFIZlJTmdZy8pq6cwM5FB6YmuG2lvsurT61atKgg0sCsVSXJGW48Lfm8FNX/frTsUTA3/QjWO3dyc7Tkfm2hPoFvpUVMnFeXy1aZ62jr2/SFjsxmWlDV4eLe+xnrUpW4qCDSwKxVJskYBAhs+gNxxMOp7gbnO3kI1YTwcX7YQknP3LQM8kIcJdGANx7d32fhyU93eY9/VNLOztdN9fXiAanspWV3qpoJAA7tSkSQuCdILrefH3hy4CmeOQjXbwrRQjTHWHfuw43r/DPKLrT3sGyvcNjd9RCZJcdH7zbM71q8fNcKDO/aqUkhIg7QCT3qvVJ/oJjBKRZr8YkBgwrmBu4ajUE243rHXb4KmyoOXufU0qNh6rFwFaYNdNhcfE82xI7NZsK4GYwwiQklZA4PSEijIcDO/Dvv2YNdSsioI9I5dqUhz9p9g9ifWuvZAKpwK9RvDs1BN+efW4zAniXMOeRNAojyeZ585JpeK3XvYUN2MMYaSzdb6dbfbyNps1hy7DsOrINHArlSkScyAlNzAXyecC9WUL4KUgda68d7EJVnLAz3IjAc4qSgHsJa9ba5roa65nemeDMPvLLOKBGninAoSDexKKd+Ea6EaY6zEueHHux/6dmzh6oH8tETG5g9gwbqaHuvXPa04h96xq6DRwK6U8k24Fqqp2wAtNc6XuR1oUDE0V0FTlUdNzxyTw/KtO/loTRU5qfEMz/agilxVqTXkr6VkVZBoYFdK+S4cC9WU2efXXSXOOeQXW48e3rXPHJNLt83wn/W1TPdkfh2sO/asUdbaeaWCQAO7Usp3BdPCr1BN+UIYUAAZw92fO3AiIB7PsxcXZpCRZCUlejS/DtYdu5aSVUGkgV0p5buCKdZjuMyz22xW4pwn8+sA8SmQPcrjzPjoKOHE0VYS3VGezK/v2Q27t2rinAoqDexKKd+FW6Ga2rXQWu/Z/LqDFwl0ALOPH8HVxw9nZG6K+5OrV1uPWiNeBZEWqFFK+S7cCtWUL7Iehx3n+Xvyi+Hbv0NzLaTkuD19wuA0JgxO86ztKntGvN6xqyDSO3alVN+EU6Gass8hfQhkDPX8PT0r0Plb9bfWfvCp+f5vW6leaGBXSvXN3kI1IR6Ot9lgyxeuq805M9A+TF65wv99qiq17ta1lKwKIg3sSqm+2VuoJsSBvboU2nZ6tsytp4Q0yDzM48x4j9m6rdUCWphGBZkGdqVU34RLoRpX+6+7M6gYKr/xa3do2AxdbRrYVdBpYFdK9V04FKopW2hl6bvZqc2p/MnWsjR/5glU2fdg18Q5FWQa2JVSfRfqQjW2btjypW9367CvAl2FH+fZq0shKgZyxvivTaU8oIFdKdV3oS5UU7kK2nfDcC8T5xzyJ+1rx1+qSiF7NMTE+69NpTyggV0p1XehLlTjy/r1nhIzIGOYxxXoPFJdqvPrKiRCEthF5EERWSci34jIWyKS3uO1u0Vko4isF5HTQtE/pZSXQl2opnyhtdFK6kDf28gv9t8de2sDNO7QGvEqJEJ1x/4xMMEYMwnYANwNICLjgIuA8cDpwF9EJDpEfVRKeSNUhWq6u2DLV94vcztQ/mTYWW4tmeuraq04p0InJIHdGPORMabL/u1ioMD+/BzgVWNMuzGmDNgITAtFH5VSXgpVoZrKldDR5HvinIM/K9BpjXgVQuEwxz4L+MD+fDCwrcdr2+3HDiIi14jIMhFZVltbG+AuKqXcchSqCfZ6dsf+630N7F7uze5SVSkk50BqXt/bUspLAdsERkQ+AZxNeN1jjHnHfs49QBfwsuNtTs43zto3xjwDPAMwZcoUp+copYLIUagm2PPs5QshZ6xHG7i4lJQJaUP8U4Gu+ltNnFMhE7DAbow5xdXrInI5cCZwsjHGEZi3A4U9TisAKgLTQ6WU3xVOg1WvWuvKo4KQHtPVAVsXw+E/9U97g7zbwtWp7i6oWQfTr/FPn5TyUqiy4k8H7gTONsa09njpXeAiEYkXkeHAKCBM9oNUSrkV7EI1FSugs7Xvw/AO+ZOhYRPs2e17G/XfQXe73rGrkAnVHPuTQCrwsYisFJGnAIwxq4HXgTXAh8B1xpgQ1qhUSnmlcKr1GKzh+HLH/LqP69cPlH+49diXuvGOPdg1sKsQCdhQvCvGmJEuXrsPuC+I3VFK+UvG8H2FaqbMCvz1yhZaATQp0z/t5U+2HitX+b58rroUomKtqnNKhUA4ZMUrpSJFMAvVdLXDthL/DcODlYA3YHDfKtBVl1r14WPi/NYtpbyhgV0p5V/BKlSzfRl07el7YZoD5Rf3LTO+qlQL06iQ0sCulPKvYBWqKV8ICAw9xr/t5k+2/jBpb/L+vS110FylpWRVSGlgV0r5V7AK1ZQttHZlS8zwb7uDigGzbz91bzjeo4lzKoQ0sCul/CsYhWo626z2/Tm/7tCXCnR7a8RrKVkVOhrYlVL+VzgNdnxtFaoJhO1LobvD9/3XXUnNg5SBvs2zV6+23puc7fduKeUpDexKKf/bW6hmTWDaL1sIEgVDjg5M+4OKfcuM18Q5FQY0sCul/M9RqCZQ8+zlC60h84QBgWk/fzLUbYCOFs/f09UBtet0fl2FnAZ2pZT/ZQyHpGxrSZq/dbRa7fp7mVtP+cVgbPuqyHmibgPYOjWwq5DTwK6U8j8RKJgamAS6bYutADosAPPrDr7szb43cU4DuwotDexKqcAIVKGasoUQFQNDjvJvuz2l5lv7qXszz171LUTHQ9aogHVLKU9oYFdKBUagCtWUL4RBR0B8in/b7UnE+wp01ashdwxEh2QLDqX20sCulAqMQBSqaW+2ltEFcn7dIX+ylQzX2ebZ+dWlkKfr11XoaWBXSgVGIArVbF0MpjswhWkONKjYulb1avfnNlVDS63Or6uwoIFdKRU4jkI1u7b5p73yz60tUQun+6c9VxwV6CpWuD+32lFKVmvEq9DTwK6UCpxxP7SGsv9UDG9eC9V9LFhTthAKpkBckj9651paASRmepYZ71gWp0vdVBjQwK6UCpzhx8NNK2Hq1bD2Xfjr0fDyBVD+BRjjXVt7dltZ6sEYhgcrgc7TCnTVpdY+7kmZge6VUm5pYFdKBVb6EPj+H+GW1TDjHtixDJ4/A+aeCmvfA5vNs3a2fGUVjQlG4pxD/mSoWQtd7a7PqyrVu3UVNjSwK6WCIykTTrwDbi6FMx6C5hp47afw56mwfL774Fm+0Fon7lhGFwz5xWDrcp1A19VuVZ3TxDkVJjSwK6WCKy4Jpl0NN3wNP54HsUnw3o3w2ERY9Kg15O5M+UIrGS82IXh93VuBbmXv59Sus7Ln9Y5dhQkN7Eqp0IiOgQnnwbWfw6VvQ+5Y+OReeHQCfPy/0Fi579y2nVD5TfDm1x3Sh0JCuusEOk2cU2FGSyQppUJLBA6bYX1VrIQvHocvn4DFf4VJF8IxN0L9d4AJ7vy6o2/5k11XoKsuhZhEyDosaN1SyhUN7Eqp8DGoGM5/Dhr+F756Ela8ZH2l5lvBc/CRoenT4r9a27LGxB38etW31mhDVHTQu6aUMzoUr5QKP5nD4QcPW5n0J9wOna0w6hSIiQ9+X/InQ3cH1K49+DVjrDt2TZxTYUTv2JVS4Ss5G2beY2XTI6Hpw94KdCutIN9TU6U1/6814lUY0cCulAp/0bGhu3bGcIgf4DyBbm/inJaSVeFDh+KVUsqVqCjrTt3ZkjetEa/CkAZ2pZRyJ3+ydXfe3bn/8apSSBsCiekh6ZZSzmhgV0opd/KLobsdatfvf1wT51QY0sCulFLuOKtA19kG9Ru1MI0KOxrYlVLKnczDIC5l/wS6mrXWpjR6x67CjAZ2pZRyJyoKBk7avwJdtZaSVeFJA7tSSnliULFVZa67y/q+qhRik63lcEqFEQ3sSinlifzJ0NVmr1uPdceeN866m1cqjOhvpFJKeaJnBTpjrDt2HYZXYUgDu1JKeSJ7lLV3fOUq2L0N2ndr4pwKSxrYlVLKE1HRMHCiteSterV1TGvEqzCkgV0ppTyVXwyV31hfYM2xKxVmQhLYReS3IvKNiKwUkY9EZFCP1+4WkY0isl5ETgtF/5RSyqn8ydDZAmvehoxhEJ8a6h4pdZBQ3bE/aIyZZIwpBv4J/C+AiIwDLgLGA6cDfxGR6BD1USml9ueoQFezRhPnVNgKSWA3xjT2+DYZMPbn5wCvGmPajTFlwEZgWrD7p5RSTmUXQUyC9Xygzq+r8BSyOXYRuU9EtgE/wX7HDgwGtvU4bbv9mFJKhV50zL47db1jV2EqYIFdRD4RkVInX+cAGGPuMcYUAi8D1zve5qQp4+QYInKNiCwTkWW1tbWB+SGUUupAjuF4XeqmwlRMoBo2xpzi4al/A/4F/BrrDr2wx2sFQEUv7T8DPAMwZcoUp8FfKaX87ojLrOH49KGh7olSToUqK35Uj2/PBtbZn78LXCQi8SIyHBgFLAl2/5RSqlf5k+G0+0CcDTAqFXoBu2N3448iUgTYgC3AzwCMMatF5HVgDdAFXGeM6Q5RH5VSSql+JySB3RhznovX7gPuC2J3lFJKqYihleeUUkqpCKKBXSmllIogGtiVUkqpCKKBXSmllIogGtiVUkqpCKKBXSmllIogGtiVUkqpCKKBXSmllIogGtiVUkqpCKKBXSmllIogGtiVUkqpCCLG9P8dT0WkFmszGX/KBur83KbaRz/fwNLPN7D08w08/YzdG2qMyTnwYEQE9kAQkWXGmCmh7kek0s83sPTzDSz9fANPP2Pf6VC8UkopFUE0sCullFIRRAN7754JdQcinH6+gaWfb2Dp5xt4+hn7SOfYlVJKqQiid+xKKaVUBNHAfgAROV1E1ovIRhG5K9T9iTQiUi4i34rIShFZFur+RAIRmSciNSJS2uNYpoh8LCLf2R8zQtnH/qyXz/deEdlh/z1eKSJnhLKP/ZmIFIrIAhFZKyKrReQm+3H9HfaRBvYeRCQa+DPwfWAccLGIjAttryLSDGNMsS5l8ZvngdMPOHYX8KkxZhTwqf175ZvnOfjzBXjU/ntcbIx5P8h9iiRdwK3GmLHAUcB19n939XfYRxrY9zcN2GiM2WyM6QBeBc4JcZ+UcskY8znQcMDhc4D59ufzgR8Gs0+RpJfPV/mJMabSGPO1/XkTsBYYjP4O+0wD+/4GA9t6fL/dfkz5jwE+EpHlInJNqDsTwfKMMZVg/cMJ5Ia4P5HoehH5xj5Ur8PEfiAiw4DDgRL0d9hnGtj3J06O6bIB/zrWGHME1nTHdSJyQqg7pJQP/gocBhQDlcDDIe1NBBCRFOAN4GZjTGOo+9OfaWDf33agsMf3BUBFiPoSkYwxFfbHGuAtrOkP5X/VIpIPYH+sCXF/IooxptoY022MsQHPor/HfSIisVhB/WVjzJv2w/o77CMN7PtbCowSkeEiEgdcBLwb4j5FDBFJFpFUx3Pge0Cp63cpH70LXG5/fjnwTgj7EnEcAcfuR+jvsc9ERIC5wFpjzCM9XtLfYR9pgZoD2JetPAZEA/OMMfeFtkeRQ0RGYN2lA8QAf9PPt+9E5BXgJKzdsKqBXwNvA68DQ4CtwPnGGE0A80Evn+9JWMPwBigHrnXMByvviMhxwELgW8BmP/xLrHl2/R32gQZ2pZRSKoLoULxSSikVQTSwK6WUUhFEA7tSSikVQTSwK6WUUhFEA7tSSikVQTSwK6WUUhFEA7tSEUREmg/4/goReTJA1xIR+UxEBji7th/av1dEbrM/f0hEZvqzfaUilQZ2pZRb9i2ND3QGsCpIdb2fQLftVMojGtiVOkSIyFAR+dS+I9mnIjLEfvx5Eflxj/Oa7Y8nicgCEfkbVlWwA/0EJ2U+7XfyD4pIqYh8KyIX9njtDvuxVSLyR/uxq0Vkqf3YGyKSdGCbxpgtQJaIDOzr56BUpNPArlRkSRSRlY4v4P96vPYk8IIxZhLwMvAnD9qbBtxjjBnn5LVjgeVOjp+LVW51MnAK8KCI5IvI97H21J5ujJkMPGA//01jzFT7sbXAVb305Wv7NZVSLsSEugNKKb9qM8YUO74RkSuAKfZvj8YKugAvsi+wurLEGFPWy2uZxpgmJ8ePA14xxnRj7dD1X2AqcCLwnDGmFaBH3e8JIvI7IB1IAf7dy/VqgEEe9FmpQ5resSt16HJsFNGF/d8C+05bcT3OaXHx/i4RcfZviPRyvvS4Zk/PA9cbYyYCvwESenl/AtDmoj9KKTSwK3Uo+RJrK2Kw5scX2Z+XA0fan58DxHrY3npghJPjnwMXiki0iOQAJwBLgI+AWY45dBHJtJ+fClTa9+T+iYvrjUa3R1XKLR2KV+rQcSMwT0RuB2qBK+3HnwXeEZElwKe4vkvv6V9Y25duPOD4W1jD/quw7tDvMMZUAR+KSDGwTEQ6gPextuf8FdYWnVuwkvRSD7yQPeiPBJZ52DelDlm6batSyiciko+VjHdqEK71I+AIY8yvAn0tpfo7HYpXSvnEGFMJPOsoUBNgMcDDQbiOUv2e3rErpZRSEUTv2JVSSqkIooFdKaWUiiAa2JVSSqkIooFdKaWUiiAa2JVSSqkI8v8BD9jqjU0hQ2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1, figsize=(8, 6))\n",
    "\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_LHbyClass_24hours[0,:,:], axis=0), label=classCodes[0])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_LHbyClass_24hours[1,:,:], axis=0), label=classCodes[1])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_LHbyClass_24hours[2,:,:], axis=0), label=classCodes[2])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_LHbyClass_24hours[3,:,:], axis=0), label=classCodes[3])\n",
    "\n",
    "ax1.legend()\n",
    "ax1.axhline(0,color='gray')\n",
    "ax1.set_title('Diurnal cycle of absolute LHFLX Bias (model-obs)')\n",
    "ax1.set_ylabel('Bias [W/m2]')\n",
    "ax1.set_xlabel('Hour (local)')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-02T20:39:38.385262Z",
     "start_time": "2021-03-02T20:39:38.222900Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/Users/mdfowler/opt/anaconda3/envs/newBase/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 100.0)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1, figsize=(8, 6))\n",
    "\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_EFbyClass_24hours[0,:,:], axis=0), label=classCodes[0])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_EFbyClass_24hours[1,:,:], axis=0), label=classCodes[1])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_EFbyClass_24hours[2,:,:], axis=0), label=classCodes[2])\n",
    "ax1.plot(np.arange(0,24,1), np.nanmean(absBias_EFbyClass_24hours[3,:,:], axis=0), label=classCodes[3])\n",
    "\n",
    "ax1.legend()\n",
    "ax1.axhline(0,color='gray')\n",
    "ax1.set_title('Diurnal cycle of absolute EF bias (model-obs)')\n",
    "ax1.set_ylabel('Bias [W/m2]')\n",
    "ax1.set_xlabel('Hour (local)')\n",
    "ax1.set_ylim([0,100])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}