From 368b8c8f9b4ad924224f601eaae2473dc619b1f2 Mon Sep 17 00:00:00 2001
From: Jeremy Auclair <jeremy.auclair@cesbio.cnes.fr>
Date: Mon, 7 Aug 2023 08:42:40 +0000
Subject: [PATCH] Merge Vincent modifs
---
.gitignore | 13 +-
1_format_ref_samir_xls.py | 124 ++
2_compare_samir_xls_vs_modspa.py | 165 ++
DEV_inputs_test/pix_outputs_10.nc | Bin 0 -> 1880338 bytes
DEV_inputs_test/xls_ET0_10.tif | Bin 0 -> 82218 bytes
DEV_inputs_test/xls_NDVI_10.nc | Bin 0 -> 42333 bytes
DEV_inputs_test/xls_Rain_10.tif | Bin 0 -> 82516 bytes
DEV_inputs_test/xls_outputs_10.nc | Bin 0 -> 3024556 bytes
.../SAMIRpixel_Reference_Simonneaux2012.xls | Bin 0 -> 749568 bytes
SAMIR_xls/SAMIRv5_juin2015.xlsx | Bin 0 -> 642851 bytes
dev_samir_xarray.vr.ipynb | 1239 ++++++++++++
fichier_travail_vr.docx | Bin 0 -> 10429 bytes
test_samir.py | 1754 +++++++++--------
13 files changed, 2441 insertions(+), 854 deletions(-)
create mode 100644 1_format_ref_samir_xls.py
create mode 100644 2_compare_samir_xls_vs_modspa.py
create mode 100644 DEV_inputs_test/pix_outputs_10.nc
create mode 100644 DEV_inputs_test/xls_ET0_10.tif
create mode 100644 DEV_inputs_test/xls_NDVI_10.nc
create mode 100644 DEV_inputs_test/xls_Rain_10.tif
create mode 100644 DEV_inputs_test/xls_outputs_10.nc
create mode 100755 SAMIR_xls/SAMIRpixel_Reference_Simonneaux2012.xls
create mode 100644 SAMIR_xls/SAMIRv5_juin2015.xlsx
create mode 100644 dev_samir_xarray.vr.ipynb
create mode 100644 fichier_travail_vr.docx
diff --git a/.gitignore b/.gitignore
index 1ffce04..6636260 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,19 @@
+# Directory & Files to ignore
tests.py
test_numpy_xarray.py
-*__pycache__*
*config_modspa.json
dl_S2.csv
test_S2.csv
test_S2_one_tile.csv
DEV_inputs_test/output_*
+
+# Byte-compiled, backup..
+*__pycache__*
+__pycache__/
+*.pyc
+
+# Jupyter Notebook
+.ipynb_checkpoints/
+
+
+# Directory & Files not ignored: !
diff --git a/1_format_ref_samir_xls.py b/1_format_ref_samir_xls.py
new file mode 100644
index 0000000..8bb03e7
--- /dev/null
+++ b/1_format_ref_samir_xls.py
@@ -0,0 +1,124 @@
+# -*- coding: UTF-8 -*-
+# Python
+"""
+11-07-2023
+@author: jeremy auclair
+Adapted : vincent Rivalland
+
+Convert input forcing from xls reference file to spatialized and formated files adapted to run modspa-pixel
+
+"""
+
+
+import xarray as xr
+import os
+import pandas as pd
+import numpy as np
+
+data_path = './DEV_inputs_test'
+
+size = 10
+
+# Original sets
+ndvi_path = data_path + os.sep + 'ndvi_' + str(size) + '.nc'
+
+rain_path = data_path + os.sep + 'rain_' + str(size) + '.tif'
+if os.path.isfile(rain_path):
+ os.remove(rain_path)
+
+ET0_path = data_path + os.sep + 'ET0_' + str(size) + '.tif'
+if os.path.isfile(ET0_path):
+ os.remove(ET0_path)
+
+
+# Validation sets
+xls_NDVI_path = data_path + os.sep + 'xls_NDVI_' + str(size) + '.nc'
+xls_Rain_path = data_path + os.sep + 'xls_Rain_' + str(size) + '.tif'
+xls_ET0_path = data_path + os.sep + 'xls_ET0_' + str(size) + '.tif'
+xls_outputs = data_path + os.sep + 'xls_outputs_' + str(size) + '.nc'
+
+# Modspa excel file - Reference Simmonneaux !
+xls_file_path = './SAMIR_xls/SAMIRpixel_Reference_Simonneaux2012.xls'
+
+# %% Lecture de l'ensemble des variables au format dataframe
+df_samir_xls = pd.read_excel(xls_file_path, sheet_name=0, header=10, index_col=0)
+df_samir_xls = df_samir_xls.loc[:, ~df_samir_xls.columns.str.contains('^Unnamed')]
+# df_samir_xls
+
+dates = df_samir_xls.index
+
+# %% Genere NDVI 3D
+# Open empty dataset to get structure and reindex with correct dates
+empty_dataset = xr.open_dataset(ndvi_path)
+empty_dataset = empty_dataset.reindex(time=dates)
+
+# Transpose dimensions
+empty_dataset = empty_dataset.transpose('time', 'y', 'x')
+
+# Get the numpy array for 'ndvi'
+zero_values = empty_dataset['ndvi'].values
+
+# Transpose the numpy array for 'ndvi'
+zero_values = zero_values.transpose([0, 2, 1])
+empty_dataset['ndvi'] = empty_dataset.ndvi.transpose('time', 'y', 'x')
+
+# Assign the transposed numpy array back to 'ndvi'
+empty_dataset.ndvi.values = zero_values
+
+# Drop ndvi to get empty dataset
+empty_dataset = empty_dataset.drop_vars('ndvi')
+
+# Datasets
+ndvi_val = empty_dataset.copy(deep=True)
+rain_val = empty_dataset.copy(deep=True)
+ET0_val = empty_dataset.copy(deep=True)
+outputs_val = empty_dataset.copy(deep=True)
+
+# Inputs
+ndvi_val['NDVI'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype='uint8'))
+rain_val['Rain'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype='uint16'))
+ET0_val['ET0'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype='uint16'))
+
+# Outputs
+
+liste_variables = df_samir_xls.columns[0:-2]
+# for var in val_results.columns:
+for var in liste_variables:
+ outputs_val[var] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype='int16'))
+
+for x in ndvi_val.coords['x'].values:
+ for y in ndvi_val.coords['y'].values:
+ # Inputs
+ ndvi_val['NDVI'].loc[{'x': x, 'y': y}] = np.round(df_samir_xls['NDVI'].values * 255)
+ rain_val['Rain'].loc[{'x': x, 'y': y}] = np.round(df_samir_xls['Rain'].values * 1000)
+ ET0_val['ET0'].loc[{'x': x, 'y': y}] = np.round(df_samir_xls['ET0'].values * 1000)
+
+ # Outputs
+ # for var in list(outputs_val.keys()):
+ for var in liste_variables:
+ outputs_val[var].loc[{'x': x, 'y': y}] = np.round(df_samir_xls[var].values * 100)
+
+# Add precip
+outputs_val['Rain'] = rain_val['Rain'].copy(deep=True) / 10
+
+# Reorganize dimension order
+ndvi_val = ndvi_val.transpose('time', 'y', 'x')
+rain_val = rain_val.transpose('time', 'y', 'x')
+ET0_val = ET0_val.transpose('time', 'y', 'x')
+
+# Save datasets
+# Inputs
+ndvi_val.to_netcdf(xls_NDVI_path, encoding={"NDVI": {"dtype": "u1", "_FillValue": 0}})
+rain_val.Rain.rio.to_raster(xls_Rain_path, dtype='uint16')
+ET0_val.ET0.rio.to_raster(xls_ET0_path, dtype='uint16')
+
+# Create encoding dictionnary
+for variable in list(outputs_val.keys()):
+ # Write encoding dict
+ encoding_dict = {}
+ encod = {}
+ encod['dtype'] = 'i2'
+ encoding_dict[variable] = encod
+
+# Outputs
+outputs_val.to_netcdf(xls_outputs, encoding=encoding_dict)
diff --git a/2_compare_samir_xls_vs_modspa.py b/2_compare_samir_xls_vs_modspa.py
new file mode 100644
index 0000000..5f1da19
--- /dev/null
+++ b/2_compare_samir_xls_vs_modspa.py
@@ -0,0 +1,165 @@
+# -*- coding: UTF-8 -*-
+# Python
+"""
+Created on Wed 2023.
+
+@author: auclairj
+Adapated from notebook: rivallandv
+"""
+import os
+import xarray as xr
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+import matplotlib.dates as mdates
+
+# Settings for plots
+plt.style.use('default')
+rcParams['mathtext.fontset'] = 'stix'
+rcParams['font.family'] = 'STIXGeneral'
+rcParams.update({'font.size': 18})
+# Date format
+date_plot_format = mdates.WeekdayLocator(interval=6)
+date_format = mdates.DateFormatter('%Y-%m')
+
+
+def plot_time_series(ds1: xr.Dataset, var: str, ds2: xr.Dataset = None, label1: str = 'Dataset1', label2: str = 'Dataset2',
+ scale_factor1: float = 1000, scale_factor2: float = 1000, unit: str = 'mm', title: str = 'variable comparison') -> None:
+ """
+ Plot times series of a uniform modspa dataset.
+ Select first pixel (upper left corner) and plot
+ its value over time.
+
+ ## Arguments
+ 1. ds1: `xr.Dataset`
+ first dataset to plot
+ 2. var: `str`
+ name of variable to plot
+ 3. ds2: `xr.Dataset` `default = None`
+ second dataset to plot, optional
+ 4. label1: `str` `default = 'Dataset1'`
+ label for first dataset
+ 5. label2: `str` `default = 'Dataset2'`
+ label for second dataset, optional
+ 6. scale_factor1: `float` `default = 1000`
+ scale factor for first dataset to
+ divide the time series in order to
+ plot the correct variable values
+ 7. scale_factor2: `float` `default = 1000`
+ scale factor for second dataset to
+ divide the time series in order to
+ plot the correct variable values
+ 8. unit: `str` `default = 'mm'`
+ unit of value
+ 9. title: `str` `default = 'variable comparison'`
+ title of plot
+
+ ## Returns
+ `None`
+ """
+ plt.figure(figsize=(14, 7))
+ plt.grid(color='silver', linestyle='--', axis='both', linewidth=1)
+ plt.gca().xaxis.set_major_formatter(date_format)
+ plt.gca().xaxis.set_major_locator(date_plot_format)
+ (ds1.isel(x=0, y=0)[var] / scale_factor1).plot(label=label1, color='b', marker='o', markersize=2, alpha=0.8)
+ if ds2:
+ (ds2.isel(x=0, y=0)[var] / scale_factor2).plot(label=label2, color='r', marker='o', markersize=2, alpha=0.8)
+ plt.title(var + ' ' + title)
+ plt.ylabel(var + ' [' + unit + ']')
+ plt.legend()
+
+ return None
+
+
+# %% Path and Size
+
+# IN/OUT working path:
+data_path = './DEV_inputs_test'
+
+# Test size
+size = 10
+
+# Reference meteo forcing
+Rain_path = data_path + os.sep + 'xls_Rain_' + str(size) + '.tif'
+ET0_path = data_path + os.sep + 'xls_ET0_' + str(size) + '.tif'
+
+# Modspa-pixel output
+pix_outputs_path = data_path + os.sep + 'pix_outputs_' + str(size) + '.nc'
+
+# Excel output (validation or reeference)
+# File generated by: 1_format_ref_samir_xls.py
+xls_outputs_path = data_path + os.sep + 'xls_outputs_' + str(size) + '.nc'
+
+# %% Open/Read REFERENCE and MODSPA datasets
+pix_outputs = xr.open_dataset(pix_outputs_path) # modspa pixel
+xls_outputs = xr.open_dataset(xls_outputs_path) # reference xls
+
+# %% Forçage meteo: inutile car dans les outputs
+# Rain = xr.open_dataset(Rain_path).rename({'band_data': 'Rain', 'band': 'time'})
+# Rain['time'] = pix_outputs.time.values
+# ET0 = xr.open_dataset(ET0_path).rename({'band_data': 'ET0', 'band': 'time'})
+# ET0['time'] = pix_outputs.time.values
+
+
+# %% Compute differences
+pix_variables = list(pix_outputs.keys()) # list of output var from samir-pixel
+xls_variables = list(xls_outputs.keys()) # list of output var from samir-xls
+
+# recherche de l'intersection => variables communes de part et d'autre (xls & pix)
+intersect_variables = set(pix_variables).intersection(set(xls_variables))
+
+diff = pix_outputs.drop_vars(intersect_variables).copy(deep=True)
+for var in intersect_variables:
+ # NOTE vr: Pourquoi pix*10 et pas xls?
+ if var in ['E', 'Tr', 'SWCe', 'SWCr', 'DP']:
+ diff[var] = (pix_outputs[var].copy(deep=True)*10 - xls_outputs[var].copy(deep=True)) / 1000
+ else:
+ diff[var] = (pix_outputs[var].copy(deep=True) - xls_outputs[var].copy(deep=True)) / 100
+
+ # Get values
+differences_sum = {}
+differences_mean = {}
+for var in intersect_variables:
+ differences_sum[var] = round(float(diff[var].sum(dim='time').mean().values), 3)
+ differences_mean[var] = round(float(diff[var].mean(dim='time').mean().values), 3)
+
+print('Difference on sum :', differences_sum)
+print('Difference on mean :', differences_mean)
+
+
+plt.figure()
+plt.bar(range(len(differences_mean)), list(differences_mean.values()), align='center')
+plt.xticks(range(len(differences_mean)), list(differences_mean.keys()), rotation='vertical')
+plt.rc('xtick', labelsize=4)
+plt.title('Difference on mean')
+plt.show()
+
+# %% Figures Specifiques
+# Dataset pixel, Tr, E, SWCe, SWCr, Irr, DP : scale factor: 1000
+
+plot_time_series(ds1=xls_outputs, ds2=pix_outputs, var='E', label1='Excel values',
+ label2='Pixel values', scale_factor1=100, scale_factor2=1000, unit='mm')
+
+plot_time_series(ds1=xls_outputs, ds2=pix_outputs, var='Tr', label1='Excel values',
+ label2='Pixel values', scale_factor1=100, scale_factor2=1000, unit='mm')
+
+plot_time_series(ds1=xls_outputs, ds2=pix_outputs, var='SWCe', label1='Excel values',
+ label2='Pixel values', scale_factor1=100, scale_factor2=1000, unit='mm')
+
+plot_time_series(ds1=xls_outputs, ds2=pix_outputs, var='SWCr', label1='Excel values',
+ label2='Pixel values', scale_factor1=100, scale_factor2=1000, unit='mm')
+
+plot_time_series(ds1=xls_outputs, ds2=pix_outputs, var='DP', label1='Excel values',
+ label2='Pixel values', scale_factor1=100, scale_factor2=1000, unit='mm')
+
+# %% All Figures
+
+plot_variables = intersect_variables
+plot_variables.remove('E')
+plot_variables.remove('Tr')
+plot_variables.remove('SWCe')
+plot_variables.remove('SWCr')
+plot_variables.remove('DP')
+
+for var in plot_variables:
+ plot_time_series(ds1=xls_outputs, ds2=pix_outputs, var=var, label1='Excel values',
+ label2='Pixel values', scale_factor1=100, scale_factor2=100, unit='??')
diff --git a/DEV_inputs_test/pix_outputs_10.nc b/DEV_inputs_test/pix_outputs_10.nc
new file mode 100644
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diff --git a/dev_samir_xarray.vr.ipynb b/dev_samir_xarray.vr.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Build a validation ndvi and weather dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "data_path = './DEV_inputs_test'\n",
+ "\n",
+ "size = 10\n",
+ "\n",
+ "# Original sets\n",
+ "ndvi_path = data_path + os.sep + 'ndvi_' + str(size) + '.nc'\n",
+ "rain_path = data_path + os.sep + 'rain_' + str(size) + '.tif'\n",
+ "ET0_path = data_path + os.sep + 'ET0_' + str(size) + '.tif'\n",
+ "\n",
+ "# Validation sets\n",
+ "val_ndvi_path = data_path + os.sep + 'val_ndvi_' + str(size) + '.nc'\n",
+ "rain_path = data_path + os.sep + 'val_rain_' + str(size) + '.tif'\n",
+ "val_ET0_path = data_path + os.sep + 'val_ET0_' + str(size) + '.tif'\n",
+ "\n",
+ "val_outputs = data_path + os.sep + 'val_outputs_' + str(size) + '.nc'\n",
+ "\n",
+ "# Modspa excel file - Reference Simmonneaux !\n",
+ "modspa_excel_path = './SAMIR_xls/SAMIRpixel_Reference_Simonneaux2012.xls'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Lecture de l'ensemble des variables au format dataframe\n",
+ "df_samir_xls = pd.read_excel(modspa_excel_path, sheet_name = 0, header=10, index_col=0)\n",
+ "df_samir_xls = df_samir_xls.loc[:, ~df_samir_xls.columns.str.contains('^Unnamed')]\n",
+ "#df_samir_xls\n",
+ "\n",
+ "dates = df_samir_xls.index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Lecture du fichier xls SAMIR reference\n",
+ "modspa_excel = pd.read_excel(modspa_excel_path, sheet_name = 0)\n",
+ "\n",
+ "# Get input data\n",
+ "val_data = modspa_excel[modspa_excel.columns[1:4]]\n",
+ "val_data = val_data.iloc[10:, :] # retire les 10 premières lignes pour acceder aux données\n",
+ "val_data.columns = ['ET0', 'rain', 'ndvi']\n",
+ "val_data = val_data.astype('float64')\n",
+ "# val_data\n",
+ "\n",
+ "# Get output data\n",
+ "# numeros de colonnes dans le xls correspondant aux noms de colonnes\n",
+ "val_results = modspa_excel[modspa_excel.columns[[6, 7, 15, 17, 18, 23, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]]]\n",
+ "val_results = val_results.iloc[10:, :] # retire les 10 premières lignes pour acceder aux données\n",
+ "val_results.columns = ['FCov', 'Kcb', 'Zr', 'TAW', 'TDW', 'Irr', 'DP', 'diff_rei', 'diff_rep', 'diff_dr', 'Dei', 'Dep', 'Dr', 'Dd', 'SWCe', 'SWCr', 'fewi', 'fewp', 'Kri', 'Krp', 'W', 'Kei', 'Kep', 'E', 'Ks', 'Tr', 'Tei', 'Tep', 'p_cor']\n",
+ "val_results = val_results.astype('float64')\n",
+ "# val_results\n",
+ "\n",
+ "# Dates\n",
+ "dates = pd.date_range(start = '2006-02-06', end = '2006-11-29', freq = 'D')\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Open empty dataset to get structure and reindex with correct dates\n",
+ "empty_dataset = xr.open_dataset(ndvi_path)\n",
+ "empty_dataset = empty_dataset.reindex(time = dates)\n",
+ "\n",
+ "# Transpose dimensions\n",
+ "empty_dataset = empty_dataset.transpose('time', 'y', 'x')\n",
+ "\n",
+ "# Get the numpy array for 'ndvi'\n",
+ "zero_values = empty_dataset['ndvi'].values\n",
+ "\n",
+ "# Transpose the numpy array for 'ndvi'\n",
+ "zero_values = zero_values.transpose([0,2,1])\n",
+ "empty_dataset['ndvi'] = empty_dataset.ndvi.transpose('time', 'y', 'x')\n",
+ "\n",
+ "# Assign the transposed numpy array back to 'ndvi'\n",
+ "empty_dataset.ndvi.values = zero_values\n",
+ "\n",
+ "# Drop ndvi to get empty dataset\n",
+ "empty_dataset = empty_dataset.drop_vars('ndvi')\n",
+ "\n",
+ "# Datasets\n",
+ "ndvi_val = empty_dataset.copy(deep = True)\n",
+ "rain_val = empty_dataset.copy(deep = True)\n",
+ "ET0_val = empty_dataset.copy(deep = True)\n",
+ "outputs_val = empty_dataset.copy(deep = True)\n",
+ "\n",
+ "# Inputs\n",
+ "ndvi_val['ndvi'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'uint8'))\n",
+ "rain_val['tp'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'uint16'))\n",
+ "ET0_val['ET0'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'uint16'))\n",
+ "\n",
+ "# Outputs\n",
+ "for var in val_results.columns:\n",
+ " outputs_val[var] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))\n",
+ "\n",
+ "for x in ndvi_val.coords['x'].values:\n",
+ " for y in ndvi_val.coords['y'].values:\n",
+ " # Inputs\n",
+ " ndvi_val['ndvi'].loc[{'x' : x, 'y' : y}] = np.round(val_data['ndvi'].values * 255)\n",
+ " rain_val['tp'].loc[{'x' : x, 'y' : y}] = np.round(val_data['rain'].values * 1000)\n",
+ " ET0_val['ET0'].loc[{'x' : x, 'y' : y}] = np.round(val_data['ET0'].values * 1000)\n",
+ "\n",
+ " # Outputs\n",
+ " for var in list(outputs_val.keys()):\n",
+ " outputs_val[var].loc[{'x' : x, 'y' : y}] = np.round(val_results[var].values * 100)\n",
+ "\n",
+ "# Add precip\n",
+ "outputs_val['rain'] = rain_val['tp'].copy(deep = True) / 10\n",
+ "\n",
+ "# Reorganize dimension order\n",
+ "ndvi_val = ndvi_val.transpose('time', 'y', 'x')\n",
+ "rain_val = rain_val.transpose('time', 'y', 'x')\n",
+ "ET0_val = ET0_val.transpose('time', 'y', 'x')\n",
+ "\n",
+ "# Save datasets\n",
+ "# Inputs\n",
+ "ndvi_val.to_netcdf(val_ndvi_path, encoding = {\"ndvi\": {\"dtype\": \"u1\", \"_FillValue\": 0}})\n",
+ "rain_val.tp.rio.to_raster(rain_path, dtype = 'uint16')\n",
+ "ET0_val.ET0.rio.to_raster(val_ET0_path, dtype = 'uint16')\n",
+ "\n",
+ "# Create encoding dictionnary\n",
+ "for variable in list(outputs_val.keys()):\n",
+ " # Write encoding dict\n",
+ " encoding_dict = {}\n",
+ " encod = {}\n",
+ " encod['dtype'] = 'i2'\n",
+ " encoding_dict[variable] = encod\n",
+ "\n",
+ "# Outputs\n",
+ "outputs_val.to_netcdf(val_outputs, encoding = encoding_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Compare `modspa-pixel` and `modspa-excel` outputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Difference on sum : {'E': 0.417, 'Tr': 10.561, 'SWCe': -6.31, 'SWCr': -8.219, 'Irr': 0.0, 'DP': -75.81}\n",
+ "Difference on mean : {'E': 0.001, 'Tr': 0.036, 'SWCe': -0.021, 'SWCr': -0.028, 'Irr': 0.0, 'DP': -0.255}\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import xarray as xr\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib import rcParams\n",
+ "import matplotlib.dates as mdates\n",
+ "\n",
+ "# Settings for plots\n",
+ "plt.style.use('default')\n",
+ "rcParams['mathtext.fontset'] = 'stix'\n",
+ "rcParams['font.family'] = 'STIXGeneral'\n",
+ "rcParams.update({'font.size': 18})\n",
+ "# Date format\n",
+ "date_plot_format = mdates.WeekdayLocator(interval=6)\n",
+ "date_format = mdates.DateFormatter('%Y-%m')\n",
+ "\n",
+ "def plot_time_series(ds1: xr.Dataset, var: str, ds2:xr.Dataset = None, label1: str = 'Dataset1', label2: str = 'Dataset2', scale_factor1: float = 1000, scale_factor2: float = 1000, unit: str = 'mm', title: str = 'variable comparison') -> None:\n",
+ " \"\"\"\n",
+ " Plot times series of a uniform modspa dataset.\n",
+ " Select first pixel (upper left corner) and plot\n",
+ " its value over time.\n",
+ "\n",
+ " ## Arguments\n",
+ " 1. ds1: `xr.Dataset`\n",
+ " first dataset to plot\n",
+ " 2. var: `str`\n",
+ " name of variable to plot\n",
+ " 3. ds2: `xr.Dataset` `default = None`\n",
+ " second dataset to plot, optional\n",
+ " 4. label1: `str` `default = 'Dataset1'`\n",
+ " label for first dataset\n",
+ " 5. label2: `str` `default = 'Dataset2'`\n",
+ " label for second dataset, optional\n",
+ " 6. scale_factor1: `float` `default = 1000`\n",
+ " scale factor for first dataset to\n",
+ " divide the time series in order to\n",
+ " plot the correct variable values\n",
+ " 7. scale_factor2: `float` `default = 1000`\n",
+ " scale factor for second dataset to\n",
+ " divide the time series in order to\n",
+ " plot the correct variable values\n",
+ " 8. unit: `str` `default = 'mm'`\n",
+ " unit of value\n",
+ " 9. title: `str` `default = 'variable comparison'`\n",
+ " title of plot\n",
+ "\n",
+ " ## Returns\n",
+ " `None`\n",
+ " \"\"\"\n",
+ " \n",
+ " plt.figure(figsize = (14,7))\n",
+ " plt.grid(color='silver', linestyle='--', axis = 'both', linewidth=1)\n",
+ " plt.gca().xaxis.set_major_formatter(date_format)\n",
+ " plt.gca().xaxis.set_major_locator(date_plot_format)\n",
+ " (ds1.isel(x = 0, y = 0)[var] / scale_factor1).plot(label = label1, color = 'b', alpha = 0.8)\n",
+ " if ds2:\n",
+ " (ds2.isel(x = 0, y = 0)[var] / scale_factor2).plot(label = label2, color = 'r', alpha = 0.8)\n",
+ " plt.title(var + ' ' + title)\n",
+ " plt.ylabel(var + ' [' + unit + ']')\n",
+ " plt.legend()\n",
+ " \n",
+ " return None\n",
+ "\n",
+ "\n",
+ "data_path = '/mnt/e/DATA/DEV_inputs_test'\n",
+ "\n",
+ "size = 10\n",
+ "\n",
+ "# Inputs\n",
+ "rain_path = data_path + os.sep + 'val_rain_' + str(size) + '.tif'\n",
+ "ET0_path = data_path + os.sep + 'val_ET0_' + str(size) + '.tif'\n",
+ "\n",
+ "# Modspa-pixel output\n",
+ "outputs_path = data_path + os.sep + 'val_outputs_pixel_' + str(size) + '.nc'\n",
+ "\n",
+ "# Excel output\n",
+ "val_outputs_path = data_path + os.sep + 'val_outputs_' + str(size) + '.nc'\n",
+ "\n",
+ "# Open datasets\n",
+ "outputs = xr.open_dataset(outputs_path)\n",
+ "val_outputs = xr.open_dataset(val_outputs_path)\n",
+ "rain = xr.open_dataset(rain_path).rename({'band_data': 'rain', 'band': 'time'})\n",
+ "rain['time'] = outputs.time.values\n",
+ "ET0 = xr.open_dataset(ET0_path).rename({'band_data': 'ET0', 'band': 'time'})\n",
+ "ET0['time'] = outputs.time.values\n",
+ "\n",
+ "# outputs = xr.where(outputs < 0, 0, outputs)\n",
+ "\n",
+ "# Compute differences\n",
+ "variables = list(outputs.keys())\n",
+ "diff = outputs.drop_vars(variables).copy(deep = True)\n",
+ "for var in variables:\n",
+ " diff[var] = (val_outputs[var].copy(deep = True)*10 - outputs[var].copy(deep = True)) / 1000\n",
+ "\n",
+ "# Get values\n",
+ "differences_sum = {}\n",
+ "differences_mean = {}\n",
+ "for var in variables[:6]:\n",
+ " differences_sum[var] = round(float(diff[var].sum(dim = 'time').mean().values), 3)\n",
+ " differences_mean[var] = round(float(diff[var].mean(dim = 'time').mean().values), 3)\n",
+ "\n",
+ "print('Difference on sum :', differences_sum)\n",
+ "print('Difference on mean :', differences_mean)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1400x700 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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cA0LvgcViQceOHWOe85RTTsFrr70GQH4PwivI4lEq68Kb/xMREbU0Vj4RERG1M8qUovr6+pQ8/4033qj+Qjt37lz18VdffRVnnnmmOo0tGp/Ph1dffRUTJ07E0qVL8eijj0adItZaqqur8fDDD2PKlClwOp145ZVX0KlTpyafz2g0qlPDampqkj6uNd5DZXW88Cl1iYRXVXk8npj79e7dO+oxLSlRMiiW8CRMtATW0qVLMX36dDz55JO45ppr8Lvf/a6pQ0xat27dsGjRIhQWFsLpdOLPf/4zhgwZoqnOUijX0+v1xr0GbfEeEBERNRWTT0RERO2M2+0GAOzbty8lz5+fn4+rrroKAPDKK6+ov/DPnz8fv/rVr2Ie98MPP2D48OF4+eWX8e677+JPf/pT3EqPlvbaa6+hT58+qKqqwhdffIFrrrkGNput2edVehrl5uYmfUxrvIdK8mv//v1JHxNe0RVvWpbZbFardiwWSxNH2DpycnLUr/Pz89WvDx8+jHPOOQdXX301HnzwQbzwwgtxp262tNGjR+Onn37CxRdfDADYuXMnzj77bMyYMQNer1fdT3kPhBAR/anChVeQHW3vAREREZNPRERE7YzyC/bixYsT7vv999+3ynQbpelxVVUVFixYgJUrV8Ln88WsYlq1ahXGjRuHiooKvPvuu2rPobbyzDPP4Je//CWOP/54zJ49W+0l1BJ8Ph+AUG+dZLTGe6jnnIoePXqoX69fvz7mfkIItTKqZ8+eSZ+/LSi9m3r37q022q6oqMDYsWPxv//9D2+99RZGjhyZkrEVFRXh7bffxtKlS3H88ccDAObNm6dpGp7se6AkLIGj7z0gIiJi8omIiKidGTZsGABgzZo1ar+kaEpLS/HHP/6x2f2Sohk6dCgmTZoEQG48Pm/ePNx8880xp3zdeuutcLlcmDRpklop1FYOHz6srtKnNG1uSUqj7LPOOivpY1rjPVQSLO+9955mtbfGvvvuO7z88ssA5NXQlMqbRYsWxTxm37596pSwtpwmmYwdO3YA0F7/hx9+GLt27UKnTp00/araSuM+XuPHj8e3336LX/7ylwDkKkElmXfaaaep+8V7D/bs2QNAbjA+YcKEFh4xERFR8zD5REREdJQLr2pJpu/NBRdcoH49Y8YMHDp0KGKf6upqnH/++RGNoZvaVycapXrju+++w3vvvYdrr7026n6VlZVYu3YtgOhNvZVl6cOnIukV73V9/fXXatVI4+d3uVw4cOBAk5+/tLQU27dvR25uLm655Zakj2vOexjLZZddBkDue3TZZZdF7Se1bds2/Pa3v1WngnXu3BmXXHIJAOCbb77B5s2bo55bqaYqKSmJuZpeKrhcLixZsgQmk0lTTRQvoafEG9B6Mffzzz/j+++/1zxmNBrx5JNPApCr5ZQV7s4//3wUFxcDAN54442YvbeU9+Dyyy9v88pBIiKiRJh8IiIiOsodOXJE/TpezxfFWWedhRNPPBGA3GT6+OOPx7x587B9+3Zs2rQJ8+fPx3HHHYeuXbvioosu0hzrcDiift0U06dPV6f/TJ8+HQUFBVH3s9lsanXNxx9/jB9//BGAPI3or3/9KzZu3AgA2Lp1K5xOp1rhAYQaSCdq5h3vdSnN0QG5Sku53keOHMGNN96o9jJas2YNAGieXxE+5SncI488AgB46aWXIiq6lOQCEJoapmjOexjLVVddheHDhwOQV0MbMWIEXnjhBaxevRorV67EY489hlNOOQWzZs1CZmametzs2bNRVFQEIQR+//vfR5zX5/PhmWeegdFoxPz582EyaRdTVpJcsVYLdLlcAGK/h3V1dQDiJ4L2798fderhM888g9raWvz5z3/WTHtU3vPDhw9rmuJ/+OGHeP3119V/r1mzBnV1dWq/q/CG5cnGXKzvo0cffTTiMeValJSUqI3uzWYz5s+fD4PBgH379uFvf/tbxHH79u3DggUL0KVLFzWBpYgVmwrl+gJc8Y6IiFqRICIioqNWZWWlGD16tAAgAIjs7Gzx008/JTxu9+7dolevXupxjf+bOHGiqK2t1RzjdrvFGWecoe5z//33i0Ag0KzxP/744wKAWLlyZdz9zj77bPV5JUkSxcXFIiMjQ9xxxx3iueeeU7cVFhaK/v37CyGE2Lx5s+jSpYu67T//+U/M87/yyivqfsOGDRNVVVXqtpqaGtG5c2d1u8ViET169BBZWVliwYIF4sILL1S3FRUVieuvv149durUqQKA6Nq1q/jqq6/U61VbWyvuvfdekZmZKRYsWBAxHr/fLx588EH1vMcdd5yoqanR7NOU9zCRnTt3xjyn0WgUc+bMiXrchg0bRHFxsQAgbr75ZlFdXS2EkOPz8ssvFxkZGeK9996LOG7z5s3CZrOp792RI0c028vLy0VeXp4AIMxms1izZo1m+549e9T3xmAwiHXr1mm2T5gwQT126tSp6nafzyfmzZsnzGaz+PWvfx0Rx0888YTmtRcVFYkOHTqI8ePHi5UrV6qPZ2VliYKCArF161ZRXl4upkyZom678847hc/ni3q9tm3bJiwWiwAgrFar2Lhxo2a78n128803q7FYWVkpzj33XGGxWMQXX3wRcc5///vfwmazCZPJJObOnSu8Xq8QQogtW7aIESNGiJKSErFp06aI45555hl1zP/6178itk+ePFndvmXLlqivh4iIqLmYfCIiIjoKbdq0SXTv3l0YjcaoiYKOHTuKJ554Iu45qqqqxH333ScGDBggbDabyMrKEieffLJ48cUXI35pvvLKK4Xdbo94nvz8fPHll182+XUcPnxYTJ8+Pan9rr76alFYWChycnLE5MmTxf/+9z8hhJzIGTdunLDb7eKss84Se/bsERdddFHU61JQUCBeffVV9bzvv/++KCwsjNjPbDaLW265Rd1vzZo1YtKkSSIrK0t06tRJXH755eov8mvXrhW9e/cWubm54rbbbhMNDQ3qcT6fT7zzzjviiiuuECUlJaKoqEgMGjRIjBo1Stxzzz3iwIEDEa910aJFIisrK2JMFotFXHTRRZp99byHyaqqqhL33nuv6Nevn7BYLKKgoEBceOGF4ocffoh7nMPhEA899JAYOXKkyM/PFwMGDBAjR44Uv//978X+/fsj9h83bpwwGAwR112Jh+nTp6sJmvDEY+/evYUQQtxxxx1R3+O+ffuqz6EknyZMmCDefPNNMWzYMNG1a1dRUlIizjzzTDWGGvP5fGLWrFmiR48ewm63ixEjRohnn31Wvaa33nqryMjIECNHjhSLFy8Wf//736N+L2ZmZopbb71VPe/BgwdFt27dhCRJEfsOHz5c3S88yStJkujSpYvIy8sTZ511Vtz3Yffu3eLWW28VvXv3FgUFBWLIkCHi5JNPFk899ZQmLpX367bbbhOZmZnqc+Xl5YnbbrtNHDp0SGzYsEFMnz5dM8Y+ffqI2bNnx40DIiKippCEaMHmDkREREREbWTixIlYunQpJkyYgCVLlqR6OERERBQDez4REREREREREVGrYfKJiIiIiIiIiIhaDZNPRERERERERETUaph8IiIiIqK0VF9fr/k/ERERHZ2YfCIiIiKitLNx40b89NNPAID169djw4YNKR4RERERxcLkExERERGllUmTJmHYsGFwuVwAAI/Hg+HDh6Nz5854++23Uzw6IiIiakwSQohUD6K9CgQCOHDgALKzsyFJUqqHQ0RERERERETUIoQQcDgc6Nq1KwyG+LVNpjYa0zHpwIEDKC4uTvUwiIiIiIiIiIhaRWlpKbp37x53HyafWlF2djYA+Y3IyclRH3c4HFi3bh1GjBih7kMUC+OF9GC8kB6MF9KD8UJ6MF5ID8YL6cF4OXrU1taiuLg4qfeB0+5aUW1tLXJzc1FTU6NJPgUCAbjdblit1oSlaUSMF9KD8UJ6MF5ID8YL6cF4IT0YL6QH4+XoESvnEQ3fqSTV1tbiiy++aJFzGQwG2O12fqNQUhgvpAfjhfRgvJAejBfSg/FCejBeSA/GS3pqN+/WsmXLMGXKFOTk5CA/Px/jx4/H559/rvs8W7ZsgcFggCRJmv9yc3NbrGm40+nE5s2b4XQ6W+R81L4xXkgPxgvpwXghPRgvpAfjhfRgvJAejJf01C6STx988AEmTZqElStXIi8vDw6HA8uXL8cZZ5yB119/Xde5Zs+ejWgzEQcNGoTJkye3yHh9Ph8OHz4Mn8/XIuej9o3xQnowXkgPxgvpwXghPRgvpAfjhfRgvKSntE8+7d+/H7fddhtefvll1NbWYt++fThw4ACmTJkCAHjooYeSPtehQ4fw3nvvYdeuXaiqqtL8t379+harfCIiIiIiIiIiOlak/Wp38+bNw4IFC3DyySerj3Xq1An/+Mc/0L9/f+zbty/pcz333HO46qqrUFJS0hpDJSIiIiIiIiI65qR95dOFF16oSTwpunXrBgAYPHhwUuepr6/H888/j6+//hr33HMPPvnkE7jd7hYdKxERERERERHRsSbtk09Dhw6N+viPP/4ISZLwl7/8JanzzJ8/H5WVlVizZg2efPJJnH322ejSpQsef/xx+P3+lhwyLBYLevbsCYvF0qLnpfaJ8UJ6MF5ID8YL6cF4IT0YL6QH44X0YLykJ0lE666d5hoaGjB9+nRcf/31uOKKK5I65vvvv8e6deuwa9cuLF68GN988426bdq0aVi4cCGMRqOucdTW1iI3Nxc1NTXIycnRdSwRERERERER0dFKT84j7Xs+Nfb555/jjjvuwI4dOzB48GCcd955yMzMTHjc6NGjMXr0aPXfa9euxa9//WusWLECH374Ie6//3488sgjcc/hdrs1U/Vqa2sBAA6HQ9OsXJIkeDweZGVlRZ3al52dDUBOojWuurLZbDCbzfB4PBHHGo1GZGRkQAiBurq6iPNmZmbCYDDA6XRGrAxgtVphsVjg9Xrhcrk02wwGg3oNHQ5HxHkzMjJgNBrhcrng9Xo12ywWC6xWK3w+X8RSmJIkISsrCwBQV1cXscqg3W6HyWSC2+2Gx+PRbDObzbDZbPD7/WhoaIgYk3IN6+vrEQgENNviXUOTyQS73Y5AIID6+vqI82ZlZUGSpKjvTbxrqLw3QPRrGO+9Ua5tRkZGxPUNf2+iXUPlvdF7DcPfm2jXUHlvWvoaKu9NvGvYnPiOFofJXsN48d2caxjtvUl0DePdI0wmE5xOJ+x2e9RY4j1C1p7uEfGuYaL4tlgsqK+vh9VqjbgOvEfI2ts9ojmfI6xWK+rq6mA2m6Nu4z2i/d0jmvM5wmazoba2FiaTKWLRHt4jZO3tHtGczxFWqxW1tbUwGAwRf/DnPULW3u4RzfkcYTabUV1dDZPJFBEvvEfI2uoeES3eYmk3yaf6+nrce++9+P7777F37154PB4899xzWLVqFb7++mtYrVZd5xs5ciSWLVuG6667Dq+++iqeffZZ3H333cjLy4t5zGOPPRZ1mt+6des0CbD8/HxUVVVh8ODB2LRpU8T+EyZMAABs2bIl4s0cOHAgioqKcOTIEezYsUOzLT8/H8OHD4ff78fq1asjzjt27FhYLBbs3LkTFRUVmm29e/dGcXExqqurI8aUlZWF448/HgCwZs2aiG+YE044AZmZmdi7dy/Kyso024qLi9G7d2/U1dVh3bp1mm0WiwVjx44FAGzYsCHim2LEiBHIy8vD/v37UVpaqtnWuXNnDBgwAC6XK+K1SpKE8ePHA5CvYeObx+DBg9GxY0ccOnQIu3bt0mwrKCjA0KFD4fP5ol7DU045BSaTCTt27EBVVZVmW9++fdGtWzdUVlZiy5Ytmm3Z2dkYNWoUAEQ974knngi73Y49e/bg8OHDEa+1rKwMffv2jXjPbTYbTjrpJADA+vXrI25YI0eORG5uLkpLS7F//37Ntq5du6Jfv35oaGiIGJPRaMSpp54KANi0aVPEzW7IkCEoLCxEWVkZdu/erdlWWFiIIUOGwOv1Rn2t48aNgyRJ2LZtG2pqajTb+vfvjy5duqC8vBzbtm3TbMvNzcXIkSMhhIh63jFjxsBqtWLXrl0oLy/XbCspKUGPHj1QXV2NjRs3arZlZGSoiee1a9dG3GBHjRqF7OxslJaW4sCBA5pt3bp1Q9++fVFXV4e1a9dqtpnNZrUf3U8//RTxA27YsGHo0KEDDh48iL1792q2derUCYMGDYLb7Y76WuPdI3r27Im9e/eiuLg44vuG9whZe7tH9OzZE7169UJtbS02bNig2ZboHtG/f39s27YNnTp1ijgv7xGy9naPaM7niGHDhmHDhg3qXzjD8R4ha2/3iOZ8jhgxYgQ2bNgAm80WEd+8R8ja2z2iOZ8j+vfvjw0bNkCSJF33iO7du6vnbfx9YzabcdxxxwGQ7xGN43vgwIHIyclBaWkpDh48qNnWsWNHlJSUoKGhAT/99JNmmyRJ6nu+ZcuWiF/e+/btq743je89eXl56N+/PzweT8R7DsjxYjKZsH379oj47tmzJ4qKilBeXh5x78nMzMSQIUMAyPfZxoYPHw6bzYZdu3ZFvDfdunVDt27dUF1dHfF9Y7VaMWLECADRr+GgQYOQnZ2NvXv34tChQ5ptnTp1Qq9evVBfXx/xfWM0GtWfC5s2bYpI2PTr1w/5+fk4cOAAfv75Z822/Px8dO3aNerv0YAcLwaDAVu3bo2I7169eqmfefbs2aPZlp2djUGDBiEQCES9hiNHjoTFYol6/+7evTu6du2KqqoqbN++XbPNbrdj2LBhAOS8QON7xJAhQ5CZmRn1/l1UVISePXvC4XBg8+bNmm3h8f3TTz9FJOn69++v/gxsfP8uKChAnz594HK5sH79egDQfN/puUdES17F0i6n3dXX12POnDm477774Pf7MXfuXNx8881NOpfT6cSAAQNQWlqKpUuXqh9GoolW+VRcXIyff/5ZU4LmdruxceNGjBw5EgZDZNuto/GvEfyLpSwVf43wer3YsGEDRowYEZHZP1r/GsG/WKburxE+nw/r16/HsGHDIqoTeI8IaU/3iOb8xdLv92PdunUYOnRoRN8E3iNk7e0e0ZzPEUIIrFmzBkOGDIn4ox7vEbL2do9ozucIJckzaNAg2O12zXbeI2Tt7R7RnM8RgUAAq1evxsCBA9VYV0S7hoFAADU1NXC5XPD7/RBCRFx75dzK/o1JkqTGauNj4207Gs97NI6pNc8rhIDH44HZbI6orOQ1bNp5le/v3Nxc5ObmAkjuHlFbW4vu3bsnNe2uXSafFA8++CAeeOABXHzxxXj77bebfJ677roLs2fPxltvvYXLLrss6eNizX90OBxYvXq1+pcQongYL6QH44X0YLyQHowX0oPxQnroiRe/34/S0lK43W7k5uYiKysLRqMxIglB7Zff74fL5YLNZtPdl5m0hBDw+/2oq6tDTU0NrFYriouLk76ux3TPp3DXXHMNHnjgAVRXVzfrPH379gUANg0nIiIiIiJKofLycrjdbvTo0SOiqo6ODX6/H4FAgMmnFpSVlYXc3Fzs27cP5eXlKCoqavHniJzz1Y507NgRgDyPtTmUMtiBAwc2e0yAXB5ns9miTrkjaozxQnowXkgPxgvpwXghPRgvpEey8SKEgMPhQG5uLhNPxzhWurU8u92OnJwcOByOqNP8mqtdT7tbu3YtjjvuOLz33nv4xS9+0eTznHXWWaioqMB3332n6zg9JWhEREREREQUm8fjwc6dO1FcXKz2xCKillNXV4fS0lL06dMnoh9oNHpyHmn/p4g9e/ZE7UYPAE899RTGjx+P888/X31s0aJFGDp0KGbPnq0+duDAATz88MP46KOPIs6xaNEiLF68GP/4xz9afOxERERERESUHKWxMqdaEbUO5XsrWhPz5kr75NPkyZMxatQonH766VixYgX8fj9qamowc+ZMHDp0CB9++KGmfPOZZ57Bxo0bMWvWLPWxd999F3/+858xbdo0TJ06FZs3b0YgEMB7772HG2+8ER9++KG6nGZLqKurw8qVK6OuEkHUGOOF9GC8kB6MF9KD8UJ6MF5ID73xwilXxzalQXbjldio+Vrzeyvtk0/33nsv+vTpg+XLl+Oss87Ccccdh9///veYOHEivvjii4jSr0svvRTZ2dm45ppr1Meuu+463HLLLejevTuWLFmCk046CWPGjMG6devw3XffYcqUKS06ZiEEvF5vq8yjpPaH8UJ6MF5ID8YL6cF4IT0YL6QH44X0Yqykn7Rf7W7GjBmYMWNG0vtfffXVuPrqqzWPZWdnY86cOZgzZ05LD4+IiIiIiIiI6JiW9pVPRERERERERER09GLyiYiIqBm8DV7sX7Uv1cMgIiIiOqZs2rQJf//73zF58mQ8+uijqR4OfD4fVqxYgQceeAAjRozAsmXLUj2kowqTTymQkZGBkSNHIiMjI9VDoTTAeCE9GC9tb8X5T6Lm9F9g8xurUz0U3RgvpAfjhfRgvJAejJe2M3PmTHTp0gWSJCX937XXXpvqYWsYDAYsXboU//rXv3Dvvffiq6++gsfjSfWw8OKLL+L555/Hgw8+iPXr17fKinHpjMmnFDAajcjNzeUSoZQUxgvpwXhpe4bSPQCAqh92pHYgTcB4IT0YL6QH44X0YLy0nUcffRSlpaUYP368+tinn36KNWvWqP/9+OOPWLx4MZ599ll06dIlhaONTpIknHvuuXj66acxderUVA9Hdeutt+K1115DXl5eqodyVEr7huPpyO12o7S0FMXFxbBarakeDh3lGC+kB+Ol7Rk8bgCAv8qR4pHox3ghPRgvpAfjhfRgvLQtk8mEsWPHqtPCzjjjjKj7TZw4Eb1798Y777zTlsNLKBAIwOPxwGKxID8/P9XD0ZAkCbm5uaiurk71UI46rHxKAY/Hg/379x8VpYF09GO8kB6Ml7Zn8LoAAP7q2hSPRD/GC+nBeCE9GC+kB+Ol7dlstqT2mzp1Kk466aRWHo0+Qgh4vV4IIVI9FNKBySciIqJmMHrlyifUpF/yiYiIiCges9mMW265JdXDoHaAySciIqJmMAYrn+Bg8omIiIjaj8cffxxLlixR/71161b069dP04x8zJgxAIBhw4apjw0bNiziXF6vF08//TQmTZqESZMmYfDgwbjiiiuwefPmiH1ramrwhz/8Aeeccw4GDx6MHj164O6770ZDQ0OzXs/HH3+MnJwcdZz5+fn417/+pW7/73//iw4dOkCSJJSUlMDlcqnbPvroI0yZMgVnnHEG+vTpg+OPPx4LFy5M6nl3796NPn36qM87a9Ysddvpp5+OzMxMdVs0b731FqZPn47x48ejqKgIZ599NtatWxex33fffYcpU6bgtNNOQ+/eveOeMxWYfCIiImoGo1+ufDLUMflERESUakIATmf7+i8Vs8v279+PF154QfPYgAEDsG3bNlxzzTUAgNzcXKxcuRIAsGTJEpjNZrz//vvYsGGD5jiHw4GJEyeitLQUn3/+ORYvXoy33noLb731Fk444QTN/ocOHcKYMWMwatQofPzxx9i0aRNmzJiBp556ClOnToXX623yazrnnHPw/fffq33FHnjgAVx33XXq9vPOOw/z589HUVER1q9fr05NnD17NqZNm4abb74Zn332GTZv3oyOHTviwgsvxJdffpnweUtKSrBjxw6ccMIJEdsWLVqEDz74IOaxd9xxB9555x288847WLZsGb766it8/fXXGDt2LH744Qd1v59//hlTp07FzTffjK+++gq7du3Cs88+m/S1aQtsOJ4CZrMZXbt2hdlsTvVQKA0wXkgPxkvbM/nk5JOpIf2ST4wX0oPxQnowXkiPlowXlwsYN64FBnUUWb4csNtb7/wTJ07U/LuiogIbN26M2lNJkiTMmTMHX3/9NXbu3IkFCxbg8ssvx4MPPog//OEPOO+88yKOueaaa+ByufC3v/1NfWzEiBEYNmwYNmzYgAULFqjVUjfddBMGDhyISy+9VN33nnvuwRNPPIEVK1ZgwYIFuOKKK2A2m5tU1TNgwABcd911mDt3LlasWIHf/e53mu1r167Fr371K2RnZ6uPPfbYYwCAIUOGAAAsFgtmzJiBzz77DC+//DImT56c8HklScKgQYM0CSNF7969ox6zcOFCPP/889i7d6+aMBsyZAguu+wy/POf/8S9996LRYsWAQA++eQT1NTU4Oyzz1aPv/322/Hmm28mHFtbYeVTCthsNvTr1y/pJm90bGO8kB6Ml7YV8AVgDMjNUc3O9Es+MV5ID8YL6cF4IT0YL6m1ZMkSzX8bNmzA+vXrkZeXF3X/jIwMzJs3D5Ik4a677sIXX3yBlStX4v7774/Y97vvvsPChQvxm9/8JmLbs88+i8svvxy//OUvAQCbNm3CBx98gHPPPVezn9VqxahRowAAH3zwAQwGA2w2GwyGpqUzfvvb30KSJHzwwQcoLy9XHxdC4I033sANN9yg2b9r167IyMhARkaG+lhBQQEAoKysLOnnjTXeWEm0xx57DKNHj0ZRUZHm8bFjxwIAvvzyS3UqotKs/5///Kdm38suuyzp8bU2Vj6lgN/vR0NDAzIyMmA0GlM9HDrKMV5ID8ZL23LXutWvre70Sz4xXkgPxgvpwXghPVoyXmw2uVKoPUlFTm7o0KG4+OKLY26fOHEibrrpJrzwwgs499xzsXr16qiVa0pfpEGDBkU9R3jV1WeffQYAeO655/Daa69p9i0vL0fPnj0hhIAQAoFAoMnJp4EDB+K0007Dl19+iVdffRV33nknADkJN3DgQBQXF2v2/+abb9DQ0IDCwkIAQFVVlTpWn8/XpDEkUllZiR9++AGdOnWKqExzOp3o2bMnAODIkSPo2bMnfvGLX+C+++7DHXfcgV27duGhhx5CTk4Ofvvb37bK+JqCyacUaGhowOrVqzFq1ChNOR9RNIwX0oPx0rbcNaFGlCa/G546DyxZlhSOSB/GC+nBeCE9GC+kR0vGiyS17hS1Y8nxxx8fd/sTTzyB//73vygrK8OmTZvUaWnhtm7dCgDIz89P+Hw7d+4EADz55JOYMmVKzP3Ck5VNdeutt+LLL7/EvHnz1OTTyy+/jBkzZkTsq1Q9/fTTT3j22WdhMBgwYMAAAIg6NbEl7Nq1C0IITJ48GW+88UbC/bt06YKFCxfivPPOw7PPPot3330XzzzzTNwEYlvjtDsiIqIm8jjcmn879qdf9RMRERFRNDfffHNE1U24nTt3qpU/v/71r1FRURGxj8PhACA3xE5EmToW7Twtbfr06ejWrRs2b96M5cuXo66uDqtWrcI555wTsW9dXR2uu+46XHTRRbj99tsxd+5cHHfcca06vqZci8mTJ2PdunU47bTTcODAAVxyySW44oor1HOlGpNPRERETRQ+7Q4A6g8y+URERETtz5IlSzT/drvduPbaa/Hxxx/j5JNPxuHDhyOadwNAx44dAYSm1EVz+PBhzb4rVqyIue+RI0dw8OBBnaOPZDKZcNNNNwGQ+yS98847uOSSS2AyaSeHeb1eTJkyBa+//joWLlyoNkZvbcq1+OGHH+Imj9avX6/5d58+ffDll1/i3XffRefOnfHWW2/hT3/6U6uONVlMPhERETWR1+HS/Nt5iMknIiIial+2bduGv/71r5rH/vSnP2Hy5Mk48cQTMX/+fFitVrz++uv43//+p9nv5JNPBgA8//zz2LdvX8S59+3bhyeeeAIAcNJJJwEAXnrpJezatStiX6/Xi2uvvbbF+sjNmDEDZrMZ7777LubMmRPRaBwA/vvf/+Kbb77BkCFDNH2rmjLdTulRVVur/bxYXV0dcd6+ffuioKAAFRUVmlUCw7300ktqUvCpp55Sk3gAcOGFF+Krr76CwWDAJ598onusrYHJpxSQJAlGo7FJS0PSsYfxQnowXtqWr15b+eQ6nF7JJ8YL6cF4IT0YL6QH46XtBQKBpPYTQuA3v/kNJk2apD62fPly/Oc//8FDDz0EQG7g/cADDwCQp+rV1NSo+1555ZXIy8tDbW0tJk+ejGXLlmnOc/755+P3v/89AOCss85C9+7d0dDQgDPOOAOrVq1S9929ezemTZuGESNGoFOnTurjSlWQ3+/XewnQpUsXXHDBBXA6ncjPz0dJSUnEPrt37wYAdVU5hdLLKlpVUqwxdevWDQCwYMECdRri9u3b8eCDD8Jisaj/BuTvCSUZ9sc//hHPPPMM3G75c6fL5cJTTz2Fxx9/XK3e8vl8ePXVVzXPN2jQIOTn5yMnJyep69HqBLWampoaAUDU1NSkeihERNQKfnrpW7Ex43j1v2///GGqh0RERNRuOZ1OsWnTJuF0OlM9lLR30003CQACgPjpp5+i7rNz505x6aWXCgDixx9/FEIIceDAAVFcXCzmzZun2dfr9YrMzEwBQJx77rma92jhwoXCZDKpz5eTkyOysrKExWIRH3/8seY8ixYtElarVd23qKhIdO3aVUiSJKZOnSo8Ho+6b0NDg+jXr58AIC644IImXYclS5YIAOKdd96Juv2rr75Sx/LAAw+I1atXi7lz54oLLrhAABBZWVli6dKl4rPPPhNCCLF161ZhMBgEAPH3v/9dc661a9cKo9EoAAiLxSK6du0q7Ha7WLp0qXrtevbsKf73v/8JIYRwOBxixIgR6vNnZGSIXr16CavVKvLz88Xq1avVcz/22GMiOztbLF++XH3s7bffFpIkiffffz/p66H3e0xPzoOVT0RERE3kq9NOu/NWpFflExERER1bVq5ciSeeeAL//ve/1cdOOOEEjBo1ChMmTMCECRNw4oknomvXrujTpw8WLFiAgoICHHfccfj4448xcOBAlJaW4k9/+pOm39C0adNQX18PAPjoo49QUlKiTrM7//zzsWTJEpx++unIyspCIBDA+PHjsWrVKpx99tma8U2ePBnLly/H2WefjdzcXFRVVaGgoABPP/00Pv74Y5jNZgDyNL4ePXqolUILFy5Ev379dE8xGz9+PC644AKcf/75UbdPmjQJf/3rX9GlSxc88cQTuPXWW5GTk4N3331XbU7+8MMPo1evXrj++usxcuRItarsjjvuwKhRo1BWVgYAGDFiBN5++20MHjwYJpMJvXv3xuLFizF+/Hiceuqp+M9//oMdO3bgrLPOAgBkZWVh6dKluPPOO9GjRw94PB643W5ceeWVWLt2bUTTc4fDgfHjx2PgwIE48cQTMXv2bHz66ac477zzdF2T1iIJ0UprAxJqa2uRm5uLmpoaTalbfX09Nm3ahMGDByMzMzOFI6R0wHghPRgvbevHxz6H/eGZ6r/Lz7sR49/8VQpHpA/jhfRgvJAejBfSI9l4cblc2L17N0pKSmCz2dpwhHQ08fv9cLlcsNlsLdb/iWR6v8di5TyiYeVTCgQCATQ0NCQ9z5aObYwX0oPx0rb8DdqeT4Hq9Kp8YryQHowX0oPxQnowXkgvxkr6YfKJiIioiRonn1CbXsknIiIiIqK2wOQTERFREwUatD2fJAeTT0REREREjTH5RERE1EQBp1z55LR1AAAY65l8IiIiIiJqjMmnFLDb7RgyZAjsdnuqh0JpgPFCejBeWtGePYDXq3lIqXxyZXcEAJga0iv5xHghPRgvpAfjhfRgvJAeBoMBNpsNBgPTGemE71YKmEwmFBYWwmQypXoolAYYL6QH46WVfPcdcNFFwNNPax4WbrnyydehEwDA7Ha0+dCag/FCejBeSA/GC+nBeCE9JEmC2WyGJEmpHgrpwORTCng8Huzbtw8ejyfVQ6E0wHghPRgvraNm48/Ytw84sHKPdoNTrnwShXLlk81dAxEQbTy6pmO8kB6MF9KD8UJ6MF5Ij0AgALfbzRXv0gyTTyngdruxe/duuN3uxDvTMY/xQnowXlrHti1+1NUDpdsatBtccvLJ0FlOPkkiAGels62H12SMF9KD8UJ6MF5ID8YL6SGEgMfjgRDp8wc/YvKJiIgooYDHBwAwuholn4Ifkk0FufAbzAAAx881bTo2IiIiIqKjHZNPRERECQivHwBgdNdrHpfccuWTMcMGtzUHANBwKL36PhERERERtTYmn4iI6Jjl9QJvvw3s3x9/P+ENVj55tFPqJE+w8inLBq9NTj45y1j5REREREQUjsmnFOBqDqQH44X0YLzo88knwOy/+vCPf8TfT/jkyieLtx4I6y9g8MiVT6YsG3wZcvLJXZ4+lU+MF9KD8UJ6MF5ID8YL6SFJEkwmE1e7SzP87k4Bu92OIUOGpHoYlCYYL6QH40Wfnz/fhBe33ogNuTcCuD7mfkrySfgDcrmUxQIAMHjlyidjhhWBrGDy6XD6VD4xXkgPxgvpwXghPRgvpIfBYIDdbk/1MEgnVj6lAJeGJD0YL6QH40WfrK8/hVl40PXI2rj7KdPuhAB8NaG+T0ryyZxlhciWk0++qvSpfGK8kB6MF9KD8UJ6MF5IDyEEAoEAV7tLM0w+pUB9fT2++eYb1NfXJ96ZjnmMF9KD8ZK8hgagU+kPAADJ54u/c9h2Z0VoxTujT552Z862ATly8slfVdvCI209jBfSg/FCejBeSA/GC+kRCARQX1/PZGWaYfKJiIiOSdu/r0YP1zYAgBSIn3xSKp8AbfLJ5A0mn7KsMOTLySdRkz7JJyIiIiKitsDkExERHZMO/e9H9WvJn6Dyye9Xv/RUhyWfgpVPlhwbTMHkE2qZfCIiIiIiCsfkExERHZP833yvfq0n+eSulKcEBHwBGAMeAIAl2wpzgZx8MtQx+UREREREFI7JJyIiOiblbAslnwwJkk/h0+48NU75/3Ue9TFrrg3WjnLyyVjP5BMRERG1P0IInHnmmSgpKcGBAwfa7HkdDgc++ugj3HbbbSguLm6z543n4MGDeO2113DppZfivPPOS/Vw0oIp1QM4FmVlZWHcuHGQJCnVQ6E0wHghPRgvyancchj5tXvVf+upfPJWy5VP7lq3+pg1xwpbx2z4AJic6ZN8YryQHowX0oPxQnowXtrO4sWLsWDBArz00kvwer0AgMGDByMnJwe1tbVwOBwYOnQozj77bFx99dXIzc1Vjy0vL8dnn30GAFixYgUuvvjiVh9vQ0MDZs+ejUWLFmHFihUAAIPBgKysrFZ/7li2bNmC1157DW+88Qb27t2LCRMmpGws6YSVTykgSRIMBgNvrpQUxgvpwXhJzv4P5FXuApIRQOKG4+Gr3Xkdwconhzt4DhOMFiMyusgfzqyu9Ek+MV5ID8YL6cF4IT0YL21n0qRJmDt3Li677DL1sdWrV2PVqlXYuHEjvv32W5SUlOD222/HgAED8MUXX6j7dezYEbfccgvOOOMMTJ06tU3Gm5GRgVmzZuG5555TH5MkSf0vFQYOHIhHHnkEv/3tb1Py/OmKyacUaGhowNq1a9HQ0JB4ZzrmMV5ID8ZLcpzL5eTT4aLhABJPuwuvfArUypVPnlq52bjPZAMAZHbOBgBYvHUI+NJj6V/GC+nBeCE9GC+kB+Ol7fXq1Uv92mq1ql936dIF//jHP3Deeefh0KFDuOCCC3Dw4EF1+5w5c/Dpp59qKqLaQn5+vvq13+9HQ0MD/GGfz1IhfEyUGJNPKeD3+1FTU5PybxZKD4wX0oPxkgQhkLHhOwCA67ixAACDjsonv0P+YBxKPskf2LK75aj7OA44Wmy4rYnxQnowXkgPxgvpwXhpewZD/FSAMqWuvr4eH374YVsMSRfGSvph8omIiI4pYv8BmCvKEIAROaedACBx8im8J1SgTk4+eR1y8slvliufTDYTvKYMAED9wfSZekdERETUWKdOndSvKysrI7YHAulR5U1HDyafiIjomFL+yffwB4BdWcPQc4jcrDJh5VPYX9dEvZx88tUHez6ZQqXqbptc/dRQxuQTERERpa89e/aoXw8cOBAAUFFRgblz5+LUU0/F66+/rm7/29/+htzcXLUPU2ZmJp5++ml89dVX6uOZmZl46KGHIp7n888/x4UXXojJkyejqKgIEydOxOLFi5s9/tNOO03TG+r444/XJMyU7SaTCf/4xz/Ux48cOYJ77rkHp556Kk499VR0794dN9xwQ9QEXDT33nsv7HZ7RE+qd955B3379lUfnzVrVsSxBw8exG233Yazzz4bffr0Qb9+/fDoo4/C59N+TnW73bj//vsxZswYjBkzBpmZmTHPeTThandERHRMqflS7vdU1Wc0LBnyj0E9yScE+1F469wwA/BbbOomrz0HqCuD8xCTT0RERCkhBOBypXoULctmA9qwubbb7VYTMoMHD8bZZ5+Nzz//HG+88Qb+/e9/w+Px4MYbb1T3v+OOO3DxxRfjpJNOwoEDB3D99dfjzjvvBADMmjULL730EhYtWoSioiLN8zzzzDN455138PHHHyMvLw/79+/HySefjNNPPx3//e9/ce655zb5NXz11Ve49tpr8corryAvLw8//vhjxPYRI0bgyiuvxG233QZArvAaPXo0CgsL8fXXX8Nms2HJkiU47bTTsGPHDixdujTh8z7++OM49dRTMW3aNM3jF198MS6++GKMGzcOX3/9dcRxW7duxZlnnolXXnkF48ePRyAQwM0334w//vGPWLduHRYsWKDuO3PmTCxbtgzLly+HzWbDgQMHcMYZZzTlMrUpJp9SwGazoX///rDZbIl3pmMe44X0YLwk5t+8DQBgHjUMJpv8Y1AS8fsGhE+7g1NOPvnrXTADCIQln/wZcuWT+0h6JJ8YL6QH44X0YLyQHi0aLy4XMG5c889zNFm+HLDbW/1pvF4vVq5ciXvuuQfr1q3Dcccdh/fffx8WiwVTp07F1KlTUV1djQ8++CDi2O7du+Oll17CmWeeiX//+9945JFHIEkS5syZg/fffz8i8fTDDz/grrvuwo8//oi8vDwAQLdu3dSEy5133olzzjkn6op2kiTBarUmXO3u4Ycfxptvvonq6mps3LgRQ4YMUbfV1dXh0KFDuOWWW9TH3nzzTezduxcnnXSSGosTJ07EkCFDsGzZMuzcuRN9+vRJeB0HDx4cc1vv3r0jkk+BQACXX345zj//fIwfPx6A3JNr1qxZmDdvHt5++23cfvvtOPXUUwEA//73v3HllVeqY+zatSueeuoprFq1KuHYUonT7lLAbDajS5cuMJvNqR4KpQHGC+nBeEnA54P5wF4AQKcxvWG0ysknY8CHuK0LwiqfDEryqUGedifMoWl3gSw5+eStSI/kE+OF9GC8kB6MF9KD8ZJa48aNw6mnnoqhQ4fid7/7Hbp27YqXXnoJ33zzDXr06KHZN94Kb2eccQauvfZalJeXY+bMmZg5cyZ+9atfaZI+iieeeAJdunTBiBEjNI+PHSsvBrN9+3Zs3rw56vMYDAZYLJaETdO7d++OCy+8EADw0ksvaba98847mD59OrKzs9XHunTpAgAoLCzU7FtQUAAAKCsri/t84eOLJVrC7NNPP8WaNWsiKr26deumXv/whJ/H48HChQtRXl6uPjZlypSIcR9tWPmUAl6vF+Xl5SgsLOQNlhJivJAejJf4xL5SeFx+uAwZ6HVSEYyWKgCAAX74vAIWa/S/oElh0/KMrnoAgK9eLukX1rC/0ubIySdfZXoknxgvpAfjhfRgvJAeLRovNptcKdSetHIF4fIWvF5PP/00Pv30U8yZMwcnn3wy/v73v0fsI4TAF198ASEEJk6cqNnm9XrRs2dPAEBVVVXU5wgEAvD5fDCZTAkTULfddhv+/e9/49VXX8Vjjz0Gi8UCAHj55Zcxe/Zszb4XXnghSktL0bVrV/WxVatWobS0FAAiei+1lM8++wwA8Mc//jGiL5bNZkPPnj3h9XrVx6655hrMnj0bY8eOxdy5czF58mQYDAb8+te/bpXxtRQmn1LA5XJh27ZtyMrK4g9jSojxQnowXuKr/GEXAgHgoL0Ew3pJ8FWGfgz6PX7AGv3HohRe+eRxAgACwconWEOVT1KunHzyV6dH8onxQnowXkgPxgvp0aLxIkltMkWNosvPz8ezzz6LSy65BLt374bD4UBubq5mn4qKClRXV+Pkk0/GkiVLdD+HEAJutxtGozHhvqeeeiqGDx+O9evXY+HChbj00kuxa9cuOBwOnHDCCRH7d+/eHW63G6+88go++eQTTJ06FQUFBdi1axeEELrHmoydO3cCAF577TX069cv4f6PPPIIduzYgf/+9784/fTTccUVV+Bvf/ubZoXCoxGn3RER0TGj8oddAICGzr1hNkPt+QQAPlfsv2aF93wyu+XKp0BDsJlp2F8jjfly8gk16ZF8IiIiImpp3377LXJycnDgwAH8/ve/j9ju8XgAyEmotqD0dXrxxRcBAK+88oqmYXq4Tz/9FP3798fPP/+Mt99+G7fccgsyMjJadXx6r4fVasXChQvx7LPPIjs7G2+++SaGDh2Kr776qjWH2WxMPhER0TGjYaOcfJL69AagI/kUNu3O5A1WPjmDyaewyidjfrBvgMPRIuMlIiIiSifLli3D0qVLsWTJEpjNZsyfPx+LFi3S7NOhQwcYDAbs3LkThw4dinmu9evXt8iYrrrqKuTk5GDx4sXYvn073n33XVx55ZUR+33wwQc455xzMG7cODz44INtVrXZsWNHAMCKFSti7rNnzx44wj5fSpKE22+/Hdu3b8dVV12FI0eOYPr06Th8+HCrj7epmHwiIqJjhrRbTj5lDJOTT5LZBKXLk98dr/IpbNqd3wP4fBBOedqdZAslnwwWc3D/1ukJQERERHS0qqurw4033ogXXngBxx13HGbOnAkAuOmmm1BfX6/uZ7PZMGLECPh8PsyaNSvqub744gu89tprLTKurKwsXH311RBC4Prrr8eJJ54YMRUQAGbOnIlAIICLLrpI87je6Xbhfahqa7XV8NXV1RHnPOmkkwAAs2fPjtrnyuFw4IYbboA1+AfPu+++W91WVFSE1157DZdffjnq6+tbtH9XS2PyKQWMRiNyc3OTmqNKxHghPRgvcfj9sB2SV7rrOLpEfsxgkHtDIH7lkyGs8kkIwFPdIC/lDECyh6bdScbgj9W4S+cdPRgvpAfjhfRgvJAejJe253Q6o36diNL4OrwBtuLOO+/Eeeedh1GjRgGQkzlDhw7F7t27cd9992n2nTFjBgBg7ty5uPfee1FXVwcA8Pv9eOWVV3DttdfirrvuUvdXpqYp++iNlVtvvRUA8PXXX6vP3dju3bsBQJMo8/v92LFjR8QYwv/tD/sjJQB07txZHZ/S1Nzv9+PZZ5/FTz/9BADqOQHg8ssvR1ZWFg4ePIgzzjgDmzZtUrdt2LABkyZNwqWXXqo2S3/nnXc0YwSAk08+GQCQE1z85mjUbpJPy5Ytw5QpU5CTk4P8/HyMHz8en3/+ue7zLF68GFOnTsVJJ52EUaNGYfLkyU1qghZPRkYGRo4c2epzR6l9YLyQHoyX2Dw7S+F3+eA22NHzpM7q436DPPUuXuUTAtoPFc6KBsAtVz4ZMqIln7T7H60YL6QH44X0YLyQHoyXtrd27Vr166VLlyZ1jN/vV5Mna9as0Wx7/fXX8frrr+OBBx5QH7NYLGr10//93//h9ddfV7fddNNNmDp1KgDgr3/9KwoLC1FSUoKcnBz86le/wksvvYSioiJ1/2XLlqlfb9myBRkZGboSUIMHD8bEiRMxZMgQNVHTmNKA/OGHH8bixYuxbNky3HTTTejQoQMAYOHChXj//ffV6iSlymjHjh1wBz8XKq/7ggsuAAA8+OCD6Ny5M7Kzs/HFF1/g5ptvBgC89dZbuPrqq+F2u9GxY0fMmTMHkiTh+++/x5AhQ9C9e3cUFRVh+PDhGD58OG666Sb1/GVlZbjpppvUpKHD4cDrr7+O0aNHY9KkSUlfk7bWLpJPH3zwASZNmoSVK1ciLy8PDocDy5cvxxlnnKEJ8ETmz5+PyZMn44ILLsC3336L1atXY/r06Tj99NPx6quvtth4hRAIBAKt1i2f2hfGC+nBeInt0KpdEACOZJagY1Hox18gieSToVEyyVUZqnwyZEYmn6Q0uf6MF9KD8UJ6MF5ID8ZL25k9ezZ69+6tKdSYNm0ajjvuuLgNq//4xz+iuLhY7cP0/PPPY/Dgwdi+fTtuuOEG/PKXv4TT6cSll16qHrN27Vq14bgQAldffTWuuOIKAHK12wcffIAHH3wQffv2RSAQQF1dHc4991x8//33OOOMMwDIKyGOGjVKk3w5+eSTcf755+uOl9tvvx133nlnzO3z58/HxIkTsXfvXlx11VV488038eCDD2L27Nno0KEDPvnkE2zfvh3btm1Dnz591FxDWVkZevbsiYcfflg91wsvvIArr7wSBQUFkCQJv/vd7/Cf//wH+fn5uPvuu7F792689tpr6lS6q6++Gp9++ikmTJiAzMxMVFZWolevXnj55Zfx0ksvRYz1zTffRNeuXXHiiSfilFNOwQknnIAvvvgCJlP0lZuPBpJI8+/w/fv3Y8yYMXj00UdxxRVXwGg04vDhw7jqqqvwxRdfoH///ti6dWvC83z77bcYN24cJkyYgC+++EKzbdKkSfjmm2+wZs0aDBw4MOmx1dbWIjc3FzU1NZryN4fDgdWrV2PUqFHIzs5O/sXSMYnxQnowXmJb/5t5MM2fi50Dz8W0H2epj6/tMAkWtwMZ/3sPvSb0jHrs6sIpsDlDc/Cz3v4Xdv/5JXTcuhyO3/4ZJz16HgDgx8c+h/3hmSjvcTzGb36hVV9PS2C8kB6MF9KD8UJ6JBsvLpcLu3fvRklJCWxhq83SscXv96OhoUF39RMlpvd7LFbOI5q0r3yaN28eFixYgKuvvloNvE6dOuEf//gHAGDfvn1JnWfWrFnwer24/vrrI7bdcMMNcLlcmhJCIiJKL67NcrNxQ9/emseFjsonvyTv6652qtPujBmhhuOSKfgBSKRHzyciIiIioraQ9smnCy+8MOqczW7dugGQ53Ymsn//fnz66acAgIkTJ0ZsnzBhAgDg/fffR01NTTNGS0REqWLYKzeRzB5eonk8uWl38janSf6LjqeqHgavPO3OlBU27c4gNy+X0qThOBERERFRW0j75NPQoUOjPv7jjz9CkiT85S9/SXiOlStXAgDy8vLQpUuXiO3FxcXIycmBx+Np8ebjRETUBvx+ZBzeAwDoNLaPZlPAKCefAp7EySePVZ4K4K11wuCRk0+sfCIiIiIiii/tk0/RNDQ04C9/+Qtef/11nHvuuQn337hxI4BQtVQ0Xbt2BQC1uz8REaWP6k37Aa8XXsmKHid21mxLZtqdJORpd96MXPn/NfUwBpNP5uwoDcfTZLU7IiIiIqK2cPS2Qm+izz//HHfccQd27NiBwYMH47zzzkNmZmbcYyorKwEgbnM7pXnW4cOHY+7jdrs1SyzW1tYCkBvoSZKkPm40GjFmzBgYjUY4HI6I8yjjaGhogN+v/QXGZrPBbDbD4/Fonks5b0ZGBoQQqKurizhvZmYmDAYDnE4nfD7tL1lWqxUWiwVerxeu4ApOCoPBoF7DaONVGr25XC54vV7NNovFAqvVCp/Ppy4FqZAkCVlZWQCAurq6iNUK7HY7TCYT3G43PB6PZpvZbIbNZlObzTWmXMP6+noEGk1/iXcNTSYT7HY7AoEA6uvrI86blZUFSZKivjfxrqHy3gDRr2G898ZsNmPMmDGQJCni2PD3Jto1VN4bvdcw/L2Jdg2V96alr6Hy3sS7hs2J72hxmOw1jBffzbmG0d6bRNcw3j3CYrFgzJgxEEJExMuxfI/Ys+gnWARQmdcTflEPIHSP8EsGQAD1NQ44HI7Ie4QQcjJJAF6b/Dp9NXXytDsBeA1+9XVLJjn5JPz+iGvRWveIeNcwUXzbbDaMGTMGgUAg4nl5j5C1t3tEcz5H2O12jBkzBj6fT1d8p8M9orH29DmiOfeI5nyOyMzMxJgxY+DxeCLGzHuErL3dI5r7OWLMmDFwu90R8RJ+DZVr4ff74ff7IUkSDAaDulpeY0pP4MavU3leSZKirrLXnPMqxzblvMqY9J43Va81lefNzMyEECLi+OZew0TvTXu6htHOG36uZO4R0X4mxdJukk/19fW499578f3332Pv3r3weDx47rnnsGrVKnz99dfqEobRKDd7i8UScx/l+Gg3cMVjjz0WdZrfunXrNAmwTp06YdCgQXA6nVi9enXE/kqPqS1btkS8mQMHDkRRURGOHDmCHTt2aLbl5+dj+PDh8Pv9Uc87duxYWCwW7Ny5ExUVFZptvXv3RnFxMaqrq7Fp0ybNtqysLBx//PEAgDVr1kQE7wknnIDMzEzs3bsXZWVlmm3FxcXo3bs36urqsG7dOs02i8WCsWPHAgA2bNgQ8YNzxIgRyMvLw/79+1FaWqrZ1rlzZwwYMAAulyvitUqShPHjxwOQr2HjH46DBw9Gx44dcejQIezatUuzraCgAEOHDoXP54t6DU855RSYTCbs2LEDVVVVmm19+/ZFt27dUFlZiS1btmi2ZWdnY9SoUQAQ9bwnnngi7HY79uzZE5Hg7NmzJ3r16oXKykps2LBBs81ms+Gkk04CAKxfvz7iQ83IkSORm5uL0tJS7N+/X7Ota9eu6NevHxoaGiLGZDQaceqppwIANm3aFPGBaMiQISgsLERZWRl2796t2VZYWIghQ4bA6/VGfa3jxo2DJEnYtm1bRA+1/v37o0uXLigvL8e2bds023JzczFy5EgIIaKed8yYMbBardi1axfKy8s120pKStCjRw9UV1erlY6KjIwMjB49GoC8HGzjG6yy6kppaSkOHDig2datWzf07dsXdXV1WLt2rWab2WxW+9H99NNPER+0hg0bhg4dOuDgwYPYu3evZptyj3C73U2+R+zfv5/3iLB7ROVXq9EvEEBlXiesWbNGc49w+/wIBALYs2MXnKv9EfcI4fOj0C//YHaazAAAn6MeVrf8AXj3wX2oXC3/bMgVcvLJ74l871r7HlFbW9vke8SOHTt4jzjG7hFN/RxhtVqxffv2dnePCNdeP0c05x7R1M8RVqsV69ev5z3iGLpHNOdzhNVqxbfffhv3HnHo0CFkZ2fD5XIhEAhoEnjxEtROpzNmgtrr9cZMrgYCgbgJamUc4Ww2GwwGA3w+X8zkqhAi6nmV8brd7pgJar/fHzfJH+28mZmZkCQJbrc7ZoK6OdfQ4/FE3D+aew2VRHKs5GpzrmG09yY8yd/Ua+jz+XS/N0pyNd41bOn4jncNw/dN5h4RLz/SmCSipfzSXH19PebMmYP77rsPfr8fc+fOxc033xxz/9tvvx3/93//h7Fjx6r9nxobO3YsvvnmG9x+++149tlno+4TrfKpuLgYP//8s2bZQb/fj/3796NXr15RM5hH618jAP7FMhV/sQwEAvj555/Rs2fPiGvEyqcQ/sUyZN++fSguLtZUXALH9j3ihzMfQdGaRfj5/Jsx9oXLNPeItUNnIP/QNtQ8+FcMvfnEiHuEv8GNAyVnAgD2jrkQPb99DxVTLkPOVwth9rlh/ewtdBopT+Xb+84mGG6/DVUd+mD4pnlJX8NUVTVIkoS9e/eie/fuMBi0M/F5j5C1t3tEcz5HGAwG7NmzB127doXJpP37ZbrfIxprT58jUlX5ZDQasWvXLnTp0gVms1mznfcIWXu7RzTnc4TBYMCuXbtQVFQUUTTQuPKprKwMvXr1gs1mOyorTlj51PrnBeTfvU0mU8TnF1Y+Ne+8LpcL+/btQ0lJiVplGK7xPaK2thbdu3dHTU2NJucRTbupfAqXmZmJu+++G06nEw888AC+/PLLuMmnzp3lXxqi3UQVyraioqKY+1it1qgVVtnZ2ZopfQ6HA+Xl5ejRo0fcqX7Kh4xoLBZLzEotSZLintdut8fcZjabIz4ghIt3XpvNBpvNFnWbyWSKe6zywzWaWNcVkL/h4p033pTLeNfQYDA0+b1pzjWM9t4kGy/HyjVsTnw3Jw7jxXdzrmG896Yp1zCZeDkW7xG2A/sACSg8cYDmeTIzMyGZLYAEWAzaMSjx7fEbgWAez9whT/6ivgEmvweQgPzOHdTjDMGG4waImK+npe8RiqZcw2TihfeIkPZwj1A05XNEMvGSrveIWI62+Aba9h6haMo1TCZeeI+QtZd7hKIpnyOSiRfl+hkMBhiNRvUXb2VM4f9uLN62xsmLcOl23qNxTK1xXr/fD5/PB4vFEvP41hpve7mGsc4b/u9k7hF6apnaZcNxxTXXXAMAqK6ujrvf4MGDAQAHDx6MuY+ybciQIS0zOCIiahMBXwBZFXsAAEVje0dsFwlWu/O5Qo8b8uSG41JNNSTIP2ytuZENx7naHRERERFRSLtOPnXs2BFA/FXsAGD8+PEwGAwoLy/HkSNHIrYfOnQIFRUVMBqNag8AIiJKD85KJ4x+eUpC4aCOEdv1JJ/M+fJftaWaUK8Wa07oL84Gk7LaHZNPREREraUddo4hOiq05vdWu04+KY0Gp02bFne/goICTJkyBQCwfPnyiO3Lli0DAEydOhUdOnRo4VESEVFr8ntCc9WV5FA4EexdE/BGzpcPfzwAIyx5cvmxxSGvkhqQjDDZQjPYlconSUQ/FxERETWdMoUoWo8bImo+5Xsr0fTOpkj75NOePXuwZs2aqNueeuopjB8/Hueff7762KJFizB06FDMnj1bs+/MmTMhSRL+9a9/RZxn/vz5MBgMmDlzZouM2Wq1oqSkJO4KfEQKxgvpwXiJJPyhKiSjJXJOvFL5JLzRK5/8bvnxgBRKPlkb5Monv1F7nUPT7tLjL7KMF9KD8UJ6MF5Ij2TjRekZFa9XL7V/kiTBYrFELK5DzedwOBL2HmyqtE8+TZ48GaNGjcLpp5+OFStWwO/3o6amBjNnzsShQ4fw4YcfarJ2zzzzDDZu3IhZs2ZpzjN+/HjMmjULH330EebNC61Q9MILL+Czzz7DQw89pC4r3VwWiwU9evSI2cSPKBzjhfRgvEQK+ELJp2iVT1Cm3cVKPgUrpwIGE8x5ctNXk1delchn0jaPVZJPhkB6/EWW8UJ6MF5ID8YL6ZFsvCjNzmtqaiJWHqRjh8FggNVqbZXqnGOZ0+lEbW0tsrOzWyWxl/ar3d17773461//iuXLl+Oss85Cr169cOKJJ+KSSy7Bo48+GrH/pZdeimXLluGXv/xlxLb7778fI0eOxNNPP4358+dDCAGbzYYPP/wQ5557bouN2efzobq6Gnl5eRFLFRM1xnghPRgvkZTkkZBifEAJXicRo+dTePLJlm9H+ILWPrM2+WQwByur0qThOOOF9GC8kB6MF9JDT7wUFhbC6XRi3759yMnJQXZ2NoxGI6tgjiFCCPh8PphMJr7vzSSEgN/vh8PhQG1tLaxWKwoLC1vludL+J8GMGTMwY8aMpPe/+uqrcfXVV8fcPn36dEyfPr0lhhaT0+nExo0bMWrUqLjLlBIBjBfSh/ESSZl2F5BiLEOrfMj1RU8+KY3I/QYTrB0yNcmngKnRtDuD/AFISpPkE+OF9GC8kB6MF9JDT7wYjUYUFxejvLwcDocj4crm1P4EAgF4PB5YLBZWP7UQs9mMvLw8FBYWwmiM8Zm5mdI++URERBRP0pVPMabdKQ3HhcEIW4cMOMLPbYle+ZQuySciIqJ0ZDQaUVRUhE6dOsHr9SLAVWaPKfX19di4cSOGDBmCzMzMVA8n7RkMBpjN5lavImPyiYiI2jU1eRSr8in4151EDceFwYiMwgzNNmHWVj4pPaXSpecTERFROlMaT9Oxxev1QggBi8UCm82W+AA6KrBGjYiI2jWl4XjAECP5ZE5ytTuDCfZcC0TYj86ANXrDcSA9VrsjIiIiImoLTD6lgMFgQEZGBuenUlIYL6QH4yVSqPIp+jWREky7U6ftGYwwGCV4TKHqJ2FJ78onxgvpwXghPRgvpAfjhfRgvKQnTrtLgczMTIwePTrVw6A0wXghPRgvkZSG4zGn3SVoOK4kpYRB3s9ryYDVVydvbJx8SrOeT4wX0oPxQnowXkgPxgvpwXhJT0wVEhFRu6ZMu2tqw/HwyicA8FnCGls26jOgVD6lS/KJiIiIiKgtMPmUAnV1dfj6669RV1eX6qFQGmC8kB6Ml0ih1epiTLszJ1n5ZJT381vsoY226D2f0iX5xHghPRgvpAfjhfRgvJAejJf0xORTCggh4Pf7IQQb0lJijBfSg/ESKdFqd4mST+rxSvLJHqp8kuwxKp+QHsknxgvpwXghPRgvpAfjhfRgvKQnJp+IiKhdU3s+xVjtLunkU/B4YQs1HJds0RuOSyIAEeAHIiIiIiIigMknIiJq5xKudpf0tLtg8skeSj4ZMqJXPgFg8omIiIiIKIjJJyIiateaXfnkCT4enHYnZYYln+zayiel5xMQanRORERERHSsY/IpBTIyMjBq1ChkZGQk3pmOeYwX0oPxEilhw3FLcsknNXmVGer51Dj5ZLSEElytkXz6+es92L9qX4udj/FCejBeSA/GC+nBeCE9GC/pyZTqARyLjEYjsrOzUz0MShOMF9KD8RJJ+AOQELvyyWCSH5f88Xs+wST/yDRmhVa7M2Y2Wu3OIIWOa+Hkk6fOg/Jzr0HAYEKXw19opvg1FeOF9GC8kB6MF9KD8UJ6MF7SEyufUsDlcmH79u1wuVypHgqlAcYL6cF4iaQmjxL1fIqRfBI+bcNxQ3ao8smU2XaVT85KJyzeetjcNXBVt8z7y3ghPRgvpAfjhfRgvJAejJf0xORTCni9Xhw4cABerzfVQ6E0wHghPRgvkRonjxozBKfdxap8Esp0vGDlkyk7VPlkyordcNzv8TdtwDH43aHxeRzuFjkn44X0YLyQHowX0oPxQnowXtITk09ERNSuqQ3HjQmSTzFXu2s07S4nrPIpTvKppVe7C09mees9LXpuIiIiIqLWxOQTERG1a0ryKda0OyX5hED0SiXhVVa7k5NX5txQc0tzduzkkzrdr4W0RuUTEREREVFbYPKJiIjatUSr3SWedqetfLLkhyWfsrQ9n+SG43LT8Zbu+RSefPLWMflEREREROmDyacUsFgs6NatGywWS6qHQmmA8UJ6MF4iqZVPCXo+GWIkn+DTVj5Z80LJJ0u2NWJ3IbVO8im8kspX3zLJJ8YL6cF4IT0YL6QH44X0YLykJ1OqB3Asslqt6Nu3b6qHQWmC8UJ6MF4ihRqOx658CiBO5ZMy7c4kJ59sHTKgpH4sObaI/QOSEUYRCCW9Wkh48qmlej4xXkgPxgvpwXghPRgvpAfjJT2x8ikF/H4/ampq4Pe3bD8Qap8YL6QH4yWSOm0uRuWT0RqcdhdIMO3OKO9n65Co8kn+0dqaq935G1qm8onxQnowXkgPxgvpwXghPRgv6YnJpxRoaGjA2rVr0dDQkOqhUBpgvJAejJdIagVSgtXuDDGST8q0OylY+ZTdLQcecxbclhxkFGZE7K4kn1p6tbvWmHbHeCE9GC+kB+OF9GC8kB6Ml/TEaXdERNSuJZp2p1Q+xer5FNFwPMuCwo9ehmQ0wGiJTGgpyaeWXu0u/Hx+Z8tMuyMiIiIiagtMPhERUbuWqOG4mnyKVfkULOmWzKEfmd1P7RX7+SSj9nlbiGbanZOr3RERERFR+uC0OyIiatdEQEk+xWg4nrDnk3baXcLnUyqfWnq1O09ofAEmn4iIiIgojTD5lAKSJMFsNkMKLsdNFA/jhfRgvEShNhyP/iPPlKjyqdG0u0TUnk+tuNqdv6Flpt0xXkgPxgvpwXghPRgvpAfjJT1x2l0KZGVl4eSTT071MChNMF5ID8ZLpNBqdYmm3cXo0RSsfDKYU1/5pIxAuFqm8onxQnowXkgPxgvpwXghPRgv6YmVT0RE1K4lvdqd8EFEWaBOUhqRJ1v5ZGidhuNqEg0tl3wiIiIiImoLTD6lQH19Pb799lvU19eneiiUBhgvpAfjJZJINO3OJieVjMIHEYjMPolgw3G9lU/RztUc4cmsgLtlpt0xXkgPxgvpwXghPRgvpAfjJT0x+ZQCgUAALpcLgUDLTsmg9onxQnowXqIIxK98UqbdAYDPE3ndpKY2HG/hyqfwhuNwulrmnIwX0oHxQnowXkgPxgvpwXhJT0w+ERFRu5Zs5RMA+FxRmo4HK58kc7LT7uQklZ6G4yvveBur7n4v/nnDpt3B0zKVT0REREREbYENx4mIqH1LUPkUmXyyanfQmXwC5JVXkk0+NZQ3IO/FJyEkCd6/TIc5wxx1v/DKJ9FC0+6IiIiIiNoCK5+IiKhdS1T5pDQcBwC/O7LySWk4Hr5f3OfTWfnkrnEBEJBEIHrllXJeb2ib5GmZaXdERERERG2ByacUsNvtGDZsGOx2e6qHQmmA8UJ6MF6iCFYuxap8kowGQJKrleJNu0u64bjO1e689aEqJr8n9jHh0+6kFpp2x3ghPRgvpAfjhfRgvJAejJf0xGl3KWAymdChQ4dUD4PSBOOF9GC8RBGcdhevYbiQjJCEL3rlU0Bfw3HoXO2uKcmnlur5xHghPRgvpAfjhfRgvJAejJf0xMqnFHC73dizZw/cbneqh0JpgPFCejBeIgml51OMaXcA4DfIf4uJlnwy6J12pySffMlVPvkaQomkeNVS4dPuDN6WmXbHeCE9GC+kB+OF9GC8kB6Ml/TE5FMKeDwe7N27Fx6uVkRJYLyQHoyXKJQkkDH2j7xAMPnki1Z5pHfanVFfz6fwyqe4PZ/Cp915W+b9ZbyQHowX0oPxQnowXkgPxkt6YvKJiIjaN2XaXYyeTwAggsmnQJxpd8lWPuld7c7v8obGEeeY8OSToYWST0REREREbYHJJyIiat/8yVc+RV/trnUrn/zOJHs+hU27M/q42h0RERERpQ8mn4iIqH0LJo/iVT4FjHF6PumtfFJ6PjWh8iluzydfWM8nHyufiIiIiCh9MPmUAiaTCZ06dYLJxMUGKTHGC+nBeIkimdXulGl3nig9lwLByqdkG44HG5vHSySF01Q+RUl+qcKm3Zl8LdNgk/FCejBeSA/GC+nBeCE9GC/pie9WCtjtdgwaNCjVw6A0wXghPRgvUSQz7S5u5ZN8vNGS3LQ7pfIJQiQ3PGeSq92FVT6Z/C2TfGK8kB6MF9KD8UJ6MF5ID8ZLemLlUwoEAgE4nU4EAslNyaBjG+OF9GC8RKGn4XiUyidl2p3RmuTfa5pR+RT3mLDKJ6Pfk3RyKx7GC+nBeCE9GC+kB+OF9GC8pCcmn1Kgvr4e3333Herr61M9FEoDjBfSg/EShZp8Slz5FC35pK52l2zDcYNR87wJh+dKMvnkD20T0PaKairGC+nBeCE9GC+kB+OF9GC8pCcmn4iIqH1LYtqdiJN8MiqVT0lPu5PkcybZcDzgTq7hOHzasblrW2bqHRERERFRa2PyiYiI2jdltbs4DccRrFaKWvkk9DUcR3B6X/LJJ/2VTwDgreeKd0RERESUHph8IiKi9k3ISSBDvNXugqulCG+0aXf6Go4rq90JX3I9n0QTK588DlY+EREREVF6YPKJiIjaNUmpGDLE+ZEXa9qdEKFpd8k2HA+udieSbAguXKEkEpNPRERERNQeJflJmlpSdnY2JkyYkOphUJpgvJAejJcogo2/4zUMj9XzSQQElBRS0tPu9FY+eUKVT3GPaTTtztfQ/Gl3jBfSg/FCejBeSA/GC+nBeElPrHwiIqJ2TZk2F7fyKca0O7879G+TNcmG48GeT0iy55MI7/kUpedUaDCNkk/1rHwiIiIiovTA5FMKNDQ0YPXq1WhoaEj1UCgNMF5ID8ZLFMlUPiWTfLIlO+1O32p38CTXcFzya8fmrWt+8onxQnowXkgPxgvpwXghPRgv6YnJpxTw+/1wOBzw+5ObkkHHNsYL6cF4iSJY+SQZE/d8ikg+eULXUXflUyDJ5JO3adPu/A3NTz4xXkgPxgvpwXghPRgvpAfjJT0x+URERO2aMu1OirPaXaxpdz5X6N/Jrnan9nxq6cqnQKPkk7P5PZ+IiIiIiNoCk086HD58GMuXL0/1MIiISAcpiWl3MXs+BSufBAwwGKXknlBv8inpyqdGiTH2fCIiIiKiNNFukk9+vx/PPPMMRo4cCbvdjpycHEycOBEff/yx7nN9/vnnkCQp4r/OnTujc+fOrTB6IqI2VlsLVFWlehRtQ8hJoLjT7szxez75DSallVNiSmPzJKfdSZ5QEile8qlxz6eWmHZHRERERNQW2kXySQiBSy+9FHfccQc2btwIt9sNh8OBpUuX4txzz8Vzzz2n63xPPvlk1MenTp2Kfv36NXu8NpsNAwcOhM1ma/a5qP1jvJAeScVLIABcdhlw6aWAL87qau2E5E/c80lKUPkUkJJsNg6EVT4l14dA8iVX+aS8Dq/BKo/J1fxpd7y/kB6MF9KD8UJ6MF5ID8ZLemoXyad//vOf2LhxI5YsWQKn04m6ujp8/PHH6N27NwDg7rvvxv79+5M619q1a7F582YcOXIEVVVV6n/V1dX43//+1yLjNZvNKCoqgtlsbpHzUfvGeCE9koqXqioc/ukwyjZXAnV1bTe4FJGUyqc4PZ/Ubb4YySdDkv2egFDD8SSn3UnesJ5PSVQ+ec0Z8r7O5lc+8f5CejBeSA/GC+nBeCE9GC/pqV0kn55//nl88sknmDBhAkwmEzIyMnD22Wfjgw8+gNlshtvtxieffJLUuZ566inceeedKCwsRF5envpfbm4uDIaWuVwejwf79++Hx8NmsZQY44X0SCZeGvZXobwCqKwE6qvaf1wpjboNpjj3cKXyqVHyJ+CREz5CT/JJmZ+X7LQ7X+g9iFv5FHwdvmDySbian3zi/YX0YLyQHowX0oPxQnowXtJT2ieftm7diqlTp6JXr14R24YMGYITTjgBAFBRUZHwXPv27cOCBQuwcOFC3H///Vi6dGmrLN/odruxY8cOuN3s10GJMV5Ij2TipXZvqNeTu8bVFsNKKT2r3TWufFKSTwGDjml3wcqnZBuOG8Kn3XliT4NUk0/WTHlMLTDtjvcX0oPxQnowXkgPxgvpwXhJT2mffOrduzceeOCBmNt79OgBAOjZs2fCcz3zzDPw+Xz4+uuv8dBDD2HixIkoLi7GvHnzWmy8RESpVlcaSj5564+BvxiJxKvdSeboySel4biuyielt1SSf7wwhFc+xTlGCgSbn1vt8r4tUPlERERERNQWdPwp9+hkNpvjzvUsKyuD1WrFlClTEp5r2rRpGDBgALZt24YvvvgCGzZswMGDBzFjxgysXr0ac+bMacmhExGlhPNgNZRUiqeu/Sef1MqneA3HYySfAl6l51PyPy4lZYq2EEntH558QhLT7gJ2ufIJ/GsfEREREaWJtE8+xeNyubB27Vr88pe/REFBQcL9J02ahEmTJqn/Xrp0KW677TZs3LgRzz//PI477jjMmDEj5vFut1tT+ldbWwsAcDgckMLW6Fb2CQQCcDgcEefJzs4GADQ0NERM+7PZbDCbzfB4PBFlhkajERkZGRBCoC5KE+HMzEwYDAY4nU74Gv2CZbVaYbFY4PV64XJpp+EYDAZkZmaqr6WxjIwMGI1GuFwueL1ezTaLxQKr1Qqfzwen06nZJkkSsrKyAAB1dXUQjX5Rs9vtMJlMcLvdEfN5zWYzbDYb/H4/GhoaIsakXMP6+noEGvVdiXcNTSYT7HY7AoEA6uvrI86blZUFSZKivjfxrqHy3gDRr2G890a5pn6/P+LY8Pcm2jVU3hu91zD8vYl2DZX3pqWvofLexLuGzYnvaHGY7DWMF9/NuYbR3ptE1zDePUJ57V6vNyJelGvoKauAPfgyHeVV6n7t9R6BgB8QgNvnVsfX+B7hFQFAAL7gGJV7RF1NHcwCCECCw+FIKr5hMAAC8LpdmusR6xpKPjcgAEhyz6eY94iATx6j2Sqfv74BDocj7jVMFN9K/Hg8nojn5T1C1t7uEc35HKG89mjTHNL5HtHeP0c05x7RnM8RisbPCfAeoWhv94jmfI5QRPt+5D1C1t7uEc35HKGI9t7wHiFrq3tEtHiLpV0nnxYuXAiLxYLHHnusScdPmDABP/zwA6ZPn44vvvgCjzzyCG644YaYjccfe+wx/OUvf4l4fN26dWrAAUCHDh2Qn58Pv9+PtWvXRn1eANiyZUvEmzlw4EAUFRXhyJEj2LFjh2Zbfn4+hg8fDr/fj9WrV0ecd+zYsbBYLNi5c2dED6zevXujuLgY1dXV2LRpk2ZbVlYWjj/+eADAmjVrIr5hTjjhBGRmZmLv3r0oKyvTbCsuLkbv3r1RV1eHdevWabZZLBaMHTsWALBhw4aIb4oRI0YgLy8P+/fvR2lpqWZb586dMWDAALhcrojXKkkSxo8fD0C+ho1vHoMHD0bHjh1x6NAh7Nq1S7OtoKAAQ4cOhc/ni3oNTznlFJhMJuzYsQNVVVWabX379kW3bt1QWVmJLVu2aLZlZ2dj1KhRABD1vCeeeCLsdjv27NmDw4cPa7Z17doV+fn5cDqd2LZtm2abzWbDSSedBABYv359xA1r5MiRyM3NRWlpacSKj127dkW/fv3Q0NAQMSaj0YhTTz0VALBp06aIm92QIUNQWFiIsrIy7N69W7OtsLAQQ4YMgdfrjfpax40bB0mSsG3bNtTU1Gi29e/fH126dEF5eXnEa83NzcXIkSMhhIh63jFjxsBqtWLXrl0oLy/XbCspKUGPHj1QXV2NjRs3arZlZGRg9OjRAOTVLhvfYEeNGoXs7GyUlpbiwIEDmm3dunVD3759UVdXF/G9bDabcfLJJwMAfvrpp4gfcMOGDUOHDh1w8OBB7N27V7OtU6dOGDRoENxud9TXGu8eUVJSgvz8fNTU1GDfvn2abco9wnu4EtbgD6hdG7fhSHf5B2h7vUf4PR4EAgHs3L0Llavro94jyqsrURgIoL62BtXV1aF7xLYd6BcIwO2X4y6ZewRMRgREADWV1Zp9Yt0jMjwuBEQABskA4Yt+/z7xxBNh8PsgRAAOv1/+sFBZgdWrV6Nnz57o1asXamtrsWHDBs1xie4RgwYNQn5+Po4cOYJDhw5ptvEeIWtv94jmfI4YOXIk8vPzsX//flRXV2u2pfM9or1/jmjOPaI5nyNGjRqF/Px87N69OyK+eY+Qtbd7RHM+Ryg/j7Zu3cp7xDFyj2jO54iSkhLk5uZGXAeA9whFW90joiWvYpFE4+/udqK+vh4jRozACy+8gMmTJzfrXIcOHUKfPn1QX1+PvXv3qn2kGotW+VRcXIyff/4ZOTk56uPp+NcI/sVSxr9GyPgXS9nR/teIjRs9WLzYhCuv9CIjI3QNV427G7k/LgYAuB59BP1uODnpa5iO94jVHc9CpqsS4o1/osfpfTXXUHlv1sz8LwrnP4MDgydiwqrH1Gu4Yd5K2O+6CxUFfTFy0z+Tiu/lV8xF4X/n48Cpv8BJ792e8Bru6nYmzD43IAHl02/AcS9eFXHezMxMrC2cApurBj+fehG6f/0uDg8eh+MXP8h7RBDvESH8HCHj5wgZ7xEy3iNCeI+Q8R4h4z1CxntEiJ57RG1tLbp3746amhpNziOadpt8uu666zB27FjcdNNNLXK+iy66CO+99x5WrVqFMWPGJHVMbW0tcnNzI94IIQT8fj+MRqOmLJkoGsYL6REeL3+4B/jhiyr8ZlYHnH9+aJ9vht+EnJ3yXzn8Dz6KYb+fmprBtpHVHU6HzV2NzA8XoOdpfaLu8929/0HWc4/i0MAJmPTjbPXxn15YAcOdv0VFp0EYt/u1pJ5v2VUvonDhizg87iJM/PTehPtvzDoRUrAp+pGzr8WEd34ddb+1eRNh8dbh0NnXo+h/L6Gi/8kYt+bZpMYUC+8vpAfjhfRgvJAejBfSg/Fy9IiV84gm7Ve7i+bhhx9Gv379WizxBMhljgASXtBk1NXVYcWKFVEzqkSNMV5Ij/B4GbXiOfzf9qnwfNOo1NkRKuP2u46BhuPBxI7Rkni1O8kfveG4MOpoOK40Nm/0F6ho/N6AOj75gdgNxw3B1e6kLPkvmwaPK+a+yeL9hfRgvJAejBfSg/FCejBe0lO7Sz7NmzcPNTU1mDlzZouet6GhATabDb169WrR8xIRtZbCCnkevHnzes3j1rpK9etAQ/tfMU0Sya92JzVe7c4T/LchduIqgiH55FPj1QZFnOST8jpM2XLySfK2/8QhEREREbUP7Sr59Pbbb+O7777Dk08+GXX7hx9+2ORzL1q0CNOmTVPn0hIRHe0MLnnOuTgU1lgyEIDZVav+039MJJ/kJJDBHDuBZLAEK5saJX+EV04+CWPyySc9lU/e+kYJJK8v+o4ADMHkkzFH7hvA5BMRERERpYt2k3z65JNP8N///hfPP/98xLaGhgY89NBD6ko+r7/+OgYPHoy33npL3WfLli14+OGHsXTp0ojj58+fjwMHDsRMahERHY1MHrmBoKk8tDKMq6waIhBq9Rc4BqbdGQJy0sZgiv0jT0k+xZp2Bx3T7tTKpzhVTIqI5FOMYwK+0PQ8U478RxCjt/nT7oiIiIiI2kK7SD4tX74cF154IRYsWACr1QqTyaT5LzMzE0888QQuu+wyAMDjjz+OzZs349FHH1XP8eKLL+LPf/4zJk6ciCuuuAKlpaXweDx48cUX8cgjj2DJkiXo2bNnql4iEZFqzeyvsLz/9ShbfSDufiaPXPlkrTkMZWmJmr3Vmn0CLlY+AbGTT2rlk45pd82qfIqRfPJ7Qo+bc4M9n3ztP3FIRERERO1D2ieftm3bhnPPPRdOpxN+vz/qfwBwySWXqEsRXnnllcjKysJVV4WWs77nnntw5ZVXoqioCO+88w6GDRuG008/HdXV1Vi3bh1GjhzZYmPOzMzE2LFj1SUXieJhvFBjNW9+hIL967H79RUR25R4ycjIhMUrVz7lug9DWXW3dl+VZv+As2nJJ7/HjzWzv0LF1vImHd+WlOlqzap8MjWh8imJ5JPPqV1SN9Yx4cknS17LJZ94fyE9GC+kB+OF9GC8kB6Ml/Sk49P00al///6oqanRdcx9992H++67T/NY586d8frrr7fk0GIyGAywWCxt8lyU/hgv1JjklRMWfkdDxDYlXlwuwBqQt+f4K3Go1IOcIRY4f67U3vjdTUtgbJy3Ctb778GG/5yBiSseadI52oI8xVAu+4q32p3RaoIAIAVaYLU7U/B5kkk+NTS6/r7oPZ98rtDj1g6ZcAMw+Zs/7Y73F9KD8UJ6MF5ID8YL6cF4SU9pX/mUjpxOJ3766Sc4nc5UD4XSAOOFGlOqc/yO+ohtSrxUlNXCKEIJi+rtRwAAroPayifRxGl3zv3yinmGmqoEe4Y9lwBuvRX41a+Sysu0iIAv9ETxpt0pq90ZGk+7U5JBTWk4nkTPp4jKpySm3Vnz7PKQ/M2vfOL9hfRgvJAejBfSg/FCejBe0hOTTyng8/lQUVEBX4y/cBOFY7w0z7JlwOefp3oULSyYIBFRkk9KvDSUOzSPO3bICy54DldrD3A3LfkkfMFkiI4sUnU1EFj0FaSvvkR5G83WC0/axJt2Z7TG6vmkv/IJSvJJiPj7AfDVN7r+sRqOK9P/IKnJJ4Pf1+wsHu8vpAfjhfRgvJAejBfSg/GSntJ+2h0RUSyBAPDHe/3we/w45RQL2su0cIMvWC1TH5l8UrgqGhBeq9Ow5zAAwFcuVyo1mHKQ4auFaOK0u1DyKXF1j6LqoAu3HZgJSQgc2bsYnTplNOm59dBUPiXR88kQiF75pE6lS4Kk9nxKfG38Li8kzQPRj1Gm3QUkIyzZVnlsAPwNbhiz7EmPjYiIiIgoFVj5RETtlsspMHPrL/HYjovhrEs+SXK0U6tzGiJ7Pik8Ndp+QN79cvIJlXLyyZldJJ/L07zKJ0lH8qlmVwWMwgcD/KjderBJz6tXePIpUc8nILLnk/AE/62j4biSqJKSqEryOxsl/2JcT787mHwyGGHNDvU4cDu44h0RERERHf2YfCKidstd50VP11Z09O6Hv1LfwgRHMyX5ZGioi7mPt7pRYqqsTD6mRu7V5CnoLJ+r2cmn5Kd91ZdWqF8r0wBbm8+tb9pd48ontRJJT88nQ7CWqQnJJylG+bgy7S5gMMFiNyIQrGvz1jXt/SMiIiIiaktMPqWA1WpF7969YbVaUz0USgOMl6bz1Iaqf8JXC0t3SnWOwRVZ+aTEi2gIThcLPm6okCufTI5q+YGiZiafvMHrqaPyyRVsUg4A7n1lTXpevdTpgUiu8ili2p3yOvVMu1P2FUkkn1yNGo4nqHwSkhEGA+A1yveD5iafeH8hPRgvpAfjhfRgvJAejJf0xJ5PKWCxWFBcXJzqYVCaYLw0nbs29Iu58st7e6CsyGZ0RfZ8UuKltG47AMBsATwewFp9GEIAlgZ52p2pW3Danbd5PZ/0TLvzlIWST4H9bZN8Cp92p1YkRaEkphq/HjV5paPhuLLaXTJVYQFXo8qnGMeEVz4BgM9ohdXfAE9d86bd8f5CejBeSA/GC+nBeCE9GC/piZVPKeD1enHkyBF4vd7EO9Mxj/HSdF5HqPIposIkjRn88msxeSKTT0q8eKrl1e782fkAgDxXGaoqArC75emH9hK58snga2LljF9/8sl/ODTtTjrcNsknZbU7IRniJ5+ClU/GiIbjwddp1p98SmraXbKVT8HXEQgmwQImue9TcyufeH8hPRgvpAfjhfRgvJAejJf0xORTCrhcLmzatAkulyvxznTMY7w0Xfgv5ulQ+eQ44MCGF1ZqqnWiUabdmaMkn5R4UZJPdZ1KYDQCOf5K7Pi2AhICkABk9OoEADB4267nk6gIVT5ZKtto2p1fHl9Aij9tTp12J3wQImyDrwkNx5XkUxLT7oRLvv5+g5xMUpvJNxLwhKbdAYA/OO3O39C85BPvL6QH44X0YLyQHowX0oPxkp6YfCKidktT+ZQGyacff/l3GO/8DdY8uSjufkpfIou3ASIgou4TqHMCANwdusJgMQMADq+Qp+K5bTkw52XK5/I1cdpdEyqfDNWh5FNGbVkyhUHNFl75FI+62h0E/N6wgSmvU0/PJ2XanT/xtQm45b/Y+Uy2uMco0+6EIZh8Mgd7PtVztTsiIiIiOvox+URE7VZ45ZNSOXI0k47ITcEdy9fG3S/UFFvAWemMuo+oDzYjz8yEK1eucmpYvQUA4MnIgzlTrrQxNnXanVL5JJJPPplqQ8mnfO9hVJaHkjw//wxcdBHw/vtNG04sShWZSFD5ZLKFKpvCm9MLXxMajiuVT4ieGAwn3HLyyGe2y8cmaDgemnYnJ5989ZHvnwgILDn+Tiw5eWbSYyYiIiIiak1MPhFRu+WrP/pWu/O5fPj2vvdx8PufI7ZJHnm8xh1b4p4jfEU2Z0XkincAgAY5KSVlZcLXQW4ubtwun9efnQ9Tppy8MDW751Ny5UtCALZ6ueeTBMAofKjYHkpGLVnkQ8ma/2DluweaNp4Y1IqhBJVPsZJPUjD5JDVh2l1ylU/B5JMlfvJJnXYXrHwSZjl5GG3aXfXuKnTasgyd1n0OVzXL0YmIiIgo9Zh8SgGDwYCsrCwYDLz8lBjjpen8YVUhyvSmVNs4bxWyn30YW295JmKbwSOPN7dsW9y+T8ZA6LU4y7V9n5R4kZxy8smYZYfUWU4+dayQk0+B3HyYs6zBc3mgbXKUHKGz8snhALK9crLJYpMbf9dsDfV9Miz6HNeVPYpxPz6jeyzxhFaJa2rlk3y8wayj8kmpkkqi5xM88nvpT5R8Cr4OBF9HwBy755OrKlQNF77iYzS8v5AejBfSg/FCejBeSA/GS3riu5UCmZmZOP7445GZmZnqoVAaYLw0XaAhVPVxtEy785TXAgCMYVPQFErzb5PfhQPf7It6vAgISGFJDVeFNvmkxIvRLZ/LkJMJU1d52l2hV64qkgryYcmyqOcTniYk5nT2fKos88AeqIPBANR37AUAqN8ZSj5JWzYDkHtBtSSl4XiiaXcGkwHKWnjh/cHUBuB6Kp+Cq+olc22UaXcBq137fI3382lXuxMWOfkUcEX2fHJXharhPLXxK594fyE9GC+kB+OF9GC8kB6Ml/TE5BMRtVv++qMv+aSMQ6lyChfe/LtsSfSpd42nD3qqIle8AwCDS05AmHMyYOtZpNlmKsyHNccaOmdDE5pWK9PRkqnuAVC7R062GcwmOLv3AwB49smJJiGAjP1yM3SLs0b/WOII9XxK8ONOkuA3yIkdTXN6f9Mrn6RkKso88rUXSvIpRiWZOu1OTT7FnnbnqQmrfKrhtDsiIiIiSj0mn1LA4XBg2bJlcDgcqR4KpQHGS9MFnEdfw3F1GleUXktGXyhRUL96a9TjI5JP1dqeT0q8SA1yvJhyMpDdV5t8MnfKhyXTDBGs9XE7mpB8UpIySVY+1ZfKySd3ZgcYu3aWT3HwEADgUJlAl3o5+ZThrYG7iW2oogmtEpf4x10gWvJJaThuTr7yyWAK9nxK5tooySebMu0uejKv8bQ7KJVPzvjJp0SVT7y/kB6MF9KD8UJ6MF5ID8ZLemLyKUVEE3qs0LGL8dI04dPumjS1rBUEvHIyw+iNlnwKPSZtTa7yyVsdWfkkhIDBIycgLHkZyO3XSbPd3jUfZosEnyRXz3gdTaiO8evr+eTcLyefvNkdYOkhJ5+Mh+XKp31rKpDllyue7IE61FQmv4JeIslOuwOAgBSv8kl/w/Gkej55g3Fpj1/5pCQtlconWIPJJ3dk4tBbE0pIJvPe8v5CejBeSA/GC+nBeCE9GC/ph8knImq3jsrKp+A4jFEqn8JXnss+sBUiEPlD1efUJtF8NdGn3Zk9cgLCkidPuzOG5V4yuuVDkgCfUU5geOpav/LJfVBOPgXyOiCzj5x8slTKyafK73Zo9nX83HJT7/RUPikryUVLPqlNxJOgrnaXzEqAwconJflkiNHzKTTtLjgOa7BnV5TKJ58jVPnUpMQiEREREVELY/KJiNov19HX80kEK59MPm1SQASEvPJckNVTi0NrD0Yc37jyyV8bI/nklZNP1vwMIC9PU7mT3SNfPtYUrHyqb3ryKdmeT75DFfIXBQXI6S8nnzIchxAIAM4N2zX7Nhyo1j+eGNTkUzKVT1Gm3SkNwCU90+7MSs+nxNdG8srXXrLbgsfE7/mkZBGlYOWTcEVJPtWGKp98dUw+EREREVHqMflERO1W+C/mStIn1ZRxGP3apIHP5VOTFY7srgCAg0si+z5pqnIA+OsaIvYJBACLLyz5ZDDAlRuaepfTI08+1iQnMJqUoFAbjidX+RSokCufTJ06IH9QZ0gAsn2VqD7khmGHNvnkLGu5yid12p0hieRTcEpbeKJSST7pajiuY9qd5JMr2ZTkU6xKssbT7iR7MPnkiUwc+h1MPhERERHR0YXJpxTIyMjACSecgIyMjFQPhdIA46UZwjpXB46W5JMyXU344feEEg3u2tBYnf1GAAAc30f2fWqcfBK1dZp/Z2RkYOig42ARclIio1COG1+B3HTcZASMBXnyuYLJpyZVPgVCryMZxiq58snSuQNM+dkIWORkS/nGQ8g4KE+7s5jlfV2HUjXtLnblU1OST8lVPsnvuyErQc8nJX6DSTRDcNodolQ+BepD0+78dc6I7eF4fyE9GC+kB+OF9GC8kB6Ml/TE5FMKGI1GZGZmwmhM/pcZOnYxXprBHVb1cZQ0HEdYEiw84eRxKF9LsJwwDAAgtiSufEKDtvLJaDQCzmDyA3LPJwCQOsmVT15bFmAJ9gsKTrvzNehPPkn+UJIk4EucZDHWVgEA7N06AJKEhhx56l3pN/tR1LAbEgBXlxJ5jOWprXzyh1c+KUk2SxNWu9NR+WTMlt+nRJVPMCmVT8Fpep5oyadQTPjr41c+8f5CejBeSA/GC+nBeCE9GC/picmnFHC5XNi6dStcLk6HoMQYL01nCEs+HTXT7nxh1U41ofEpySef0YLCUwYCADJLE1c+oV7b88nlcmHrmk3ycxlNkIIVMuZiufLJl52v7hswy5VPiRIUUYUln/zexEkWa51c+ZRRXAAA8BTIyafaRd/BJLwQGRmoKx4kj7Gi5ZNPkJKvfBKeaKvdNaXheOKqMINPTvyZgpVPBhE9Thsnnwy2YOVTlGl3aAhVO4Wv+BgN7y+kB+OF9GC8kB6MF9KD8ZKemHxKAa/Xi7KyMni9R0klBh3VGC9NJ3mOvobjSq8kILzaCfDWyV/7jVZ0n9gXgIQMZzkqt1doDm+cfDI4tcknr9eLw7sPAALwmkOlyP1OKUJhAdBjRGTyqUmVT2GJFWVqWywNDUCmR+75lNOrAwBAFMnJp7yfvgYA1HfuCyk/T95WVa17PLEoUxuTmXYXreeTIRCcdtekyqfESwAbgpVPpiyl4Xgg6iqHavI0+Bc+o11OPknuKB+6nMknn3h/IT0YL6QH44X0YLyQHoyX9MTkExG1WwZPyzccXzP7K/zwyGdNPj688klJOIV/7TPbkFGYgZr8ngCA/V9pp941TqI1Tj4BgL9O/kHstYSST5axx6NTTzvyzxwTGotZTmD4GyKnbiXkD40jvHdVNJVH/Mjy18AgAbaucvLJ2EWuxCpy7gYABHr3hakgVx5XTctVPimVS0pVUzwiSvIJTZh2F+r5lETlkz9Y+ZRtD40jWvJJiRsl+ZQZTFZ5oyQOnaFpd8LJvwgSERERUeol/2maiCjNKM2cAWgqjprK5/LBNOtPsAa8KL/gOBQO7pT4oIiTJKh8CjYBd/ccAFTtQfU3W4CbT1b3a1z5ZHRHWe0ueF6/NawJY9++wJIlavICAGANTrtz6k8+hfd8SpR8qtlTBQkCBrNBrW6y9eqs2cc2rB8kgwQAMNZW6x5PLOq0u2QajkerfGpCw3Fl32R6PhmD0+4suXYoV9Hn8sGSZdGOTUmeKtPulMqnKMkngytU+cTkExEREREdDVj5RETtlsnbsj2fHPtrYQx4AAjs/3ht004Slnzy1bsjvg4Em4Cbhsl9n/ybtJVPfpe2vNjkiax8EsHV6zTJJ0CbeAIgLHLyKeBqwmp3YYmVRA3H6/bJU+489jw1CZTZR5t8yj+xHywd5conY32KVrtrqcqnYBItmeSTwS+/n+Zsm/pYtGSe8Gmn3ZmCySeDNzK5ZHCFJSTZC4GIiIiIjgJMPqWAxWJBcXExLBZL4p3pmMd4aTqDr2Urn+rLHKGvV65r2knCKoa8jlBiQE0+Bfsw2XvK09IMjaqAlMSI1yQnlsyNkk8WiwXZRisgAQFbguVng5VPgSZMu9PT88n5s9y3ypvdQX0st39R6FwAOp/SB/YueQAAc0MrNBxPYrU7KMmnsNej9nxqpconk1++9pbc0LS7qJVkwWl3kimYfAr2iFIalofTVMMlSD7x/kJ6MF5ID8YL6cF4IT0YL+mJ0+5SwGq1onfv3qkeBqUJxkvTmXxhlU8tkHxqKKsNnXvj2qadJEblk7/BDTOAgCWYVLCaAQCSX1vppCSfXPY8mB0NsHgbIAJCrbaxWq3IMcnnEPZEySf5B7ZwNWXaXfI9n1wHq+Sx54WSTwWDi6C0Uq/P6QxLQTYyuuSiHoDVWQMhAEnSPawISq+kZCqflKqiaA3HjRYdyadgw3FDgtXuREDAGJDfX2teguSTX7vanSnDAoEYySdPaNpd1IbkYXh/IT0YL6QH44X0YLyQHoyX9MTKpxTw+Xyorq6GrwV+Gab2j/HSNEIApvDKpxaYducuD1U+ZZdtR6Aust9SQmHvY3ijb79TTiIoTcCNNiX5pB23WvmUkRt8RMBZGUo2+Hw+OCvkZA8SJJ8kpfLJ3bzV7tTqohh8h+Q0k+hQoD5myrTCaZeTUc6ufQEAWd3zAACZvho4GxKvFJeMgI7KJxFM7IRP0VRep9HalNXu4l8XOckkv05rbmjaXbRKMnVM5lDyCdAmWBUmbyguEyWfeH8hPRgvpAfjhfRgvJAejJf0xORTCjidTqxbtw7OsOWwiWJhvDSNzytgCYT94t0CP5zCk0/CH0DZop+aMLAYyad6eaxKHyal8snQqKG0knwK2LMgJPkW3nAkNPXO6XSicv9BOaeRmRl/LDb5udCUyqdA8g3H/eVyzydjxw6ax525ct8n0a+fPJyiXEgSYIAftQcje1k1ibJKXFKVT5E9n4xNqHxSV7tD/OSTpy703lqyLAhI8nP4XFFiNVj5ZAhOu1N6RBmjVD6ZvaF7hcETP/nE+wvpwXghPRgvpAfjhfRgvKQnJp+IqF1y1/u0v/x7vbF3TpK3olbz7/Iv1+o/SXjSJiz5FFBWnAsmn5TKJ0PjaXdu+d/CaILHLCeXXJXaCixjcIUzQ6Yd8RiaM+1OR88nQ6Vc+WQu0iaffH3lpuoFU46Xz2mzqj2v6va3TN8nddqdsYmVT0J/w3ElUZWo8snbEHpvLVkWiGDyKWolWaNpd8pqeEa/Ry7zU3bz+INN8WWJkk9ERERERG2BySciapdcNY0SKv7mVz75quTKJ58kJ4b8P6zVfQ4pvPKpPpQYUJNPtmBFiy16zyc1MWIywWeWp9W5KrRVQlJw9TopK37lk8EuJ3okTzMrn7zxkyzGWrnyyd5Nm3w6ZeHdyPn0HQy+7iT1MY9Nnk7oPFite0zRNKXheHjyydCEaXdK/y0g/kqASuWTkAwwmAwIGOTniN5wXB6T0nDcnBlssBkIaKrpwqdgAoAxymp4RERERERtjcknImqXwleSA7RJn6YKVMuVT3sKTwAA2Hf9pFm9LilhSTA14QQgEEwYIbhqh8kerHwKRO/5JIwm+KxycslTpU0+mdxyAsKUnaDyKZh8Eh79PZ/Cx6VUF8ViqZOTTxnFBZrHzRlmdB9XoknWeDLzAAANB1uo8imgJJ+S+HEXpfKpSQ3Hw1bGE4HYvat8DfJ19xvk9zwQTJBFrSRrlHyy5ASbyiPULwwAnBWNquCYfCIiIiKiowCTTykgSRIsFguklljKido9xkvTeBwtX/kkauTkkzRyJJyGTIj6Bng2btd1DiksWRU+3U0Ep8opfZiUhtKNe/qEGk+b4bNFJp8kSYIx2CfKkJ2g8skmP4fkbr1pdx4PkOGSk0/ZPTvE3E/hz8oDAHiPVOseU1R6ej6ZtcknuWpJTh4ZmtBwHIjfD0tNPhmD0x+DPbz87tg9n6TgGC2ZZnWTuzb0/rmrtZVP0RqSh+P9hfRgvJAejBfSg/FCejBe0hOTTymQlZWFsWPHIisrK9VDoTTAeGkaT23LVz7BIU+7K+yTi715IyAEcOjzdbpOEb56nSb5FPxaCiaflGlehkDsaXcBu5xc8taEql2ysrKQbQAgAebc+KvdmTKbPu3OkGTyqbI8gFxfJSQJyCxOnHwSOfK0O295y1Q+qdPekuj5JDWqfApvPG7SU/kUlnyKN+3O55Tf21DyKXHlE4KVT1abBK8kH+etC0s+VTUEzyWPIVHyifcX0oPxQnowXkgPxgvpwXhJT0w+EVG7FP4LOQBIvuY3HDfUyZVPlsIcuPqPAADUrdCXfApvOK5p9B2sPjLY5elUyrQ7Y4yG4zCZIGxycslbXafZx+iSExCJkk9qz6coK6YlojTiBuInWKr31UJCACYjIHXIT3ziXDn55K9smeSTOi1Sz7S7YKLH5w69Rj09n5JOPimVT6bgtDtltb1oySdltbtg5ZPBAPgM8vvnrQ+9f54aufLJZc2T9xO+6KvnERERERG1ISafUqCurg6rVq1CXV1d4p3pmMd4aRpfXaPKpxaYdmd0ypVPloJs2MaMlB/7aa1mtbFENONwhY0x2HdJmQqnNJQ2BryavkHCF6p8QvCvPQFHqPKprq4OgfpqQACWvASVT8GpfYZWrHxy7JWn3HnsOYDZHHM/hbGDnHwS1S272l0ylU/KtDtlql74lDmTtRUqn4LJJ2GUr0u8yidlmqMy7Q4IVUyFJ1qVKjh3ZijR56qOXf3E+wvpwXghPRgvpAfjhfRgvKQnJp9SQAgBj8cDoeMXVjp2MV6apjWST2anXPlkL8pB58lDEIARxvLDQFlZ0ucI75WE8F5LwUSUUo2kVD4BQpvACJt2hww5ueSvDfV8EkLA7JWrX6wd4vd8UqbdGXxNST4l13Dctb8CAODJSjzlDgBMBXLySaqp1j2mqAL6p90pU9zCK4b0JJ/Cm5PHSz75XXIVWyBY+SSSaDge3szcbwpWPkVJPvkycwHIfRDcNbGTT7y/kB6MF9KD8UJ6MF5ID8ZLemLyiYjaJV99o2l3LZB8srqCyadO2Rh0nA17bQPg9gDOVWuTPochfBxhySel75KafLKFKly8DaGpd6GG4ya1obio0652Z/EFk0958Ve7U5NP3tabdoeqKgCAOzO55JO5Y548proWrnxqwrQ7JQkkYIBkTP7HZfjqfcKfuPIpYApWPgWTT9EajitJy2jJJ+U8QCjpKuyZ8JrkKZyN+58REREREbU1Jp+IqF3y1wcriYKJACnQvOST3+OH2SdXlWR1zUF+PnCgy/EAgIrPfkj6POFJsPBG38rXxgw5oWDJsqjbPHWh5IKSGJFMoeQT6kPJJ58nAKtffu22ggSVT+qKek2ofApPPnliX1tRH6zEsSXXENLWOU8eW1217jFFpafySZnS5tVWPgUMRuhZTCXmtLtVq4CZM4FaOYkZcAWn3TWqfIqasArGTfi0O6ViKjzR6g9OwRQ2O3xMPhERERHRUYLJJyJql/xO+Rdyj0VOwBj8zWs4XnfQoX6d1SVbfo5RowEAvm91JJ/CkmCSNyz5FGz6rVQjhU/d0lTCeIKvw2yCMTvY06kh1PPJWeWCkiexd4hf+WTODj6XX1/yqXGlU7zqHiXBIlksMfcJZ+8sT7uzOluo8klJPiVR+aQmdoIJPqXnU0BKvtm4+rzB1ebC+0Z5X30TDf/9HGLxEnmbM5h8MiduOK4kLSVT2JQ+s/z++Rsik0+w2+Ezy+8/k09ERERElGpMPqWA3W7HiBEjYLfH/8WQCGC8NFUgWPnktcoVN82ddld3QK5W8Zoy1MRQl7NGIgAjsH8/cOBAUucJ75Vk8LgivlYqnySDBL9Bno7lc4YlzsIqn0y5wcSaM1T5JLnkai9hMMCcbYs7FkuWknzSN+0uPKECxE8+CWfwNSaZfMrsGkw+uWvUoqXmCDUc1598Uiq6Aobk+z2pzxv88RreLH79d27s2QPs/r5cPm9w5UIl+YRgwipaDy3JHzntTkk+hfc3C9TLUy6RkYGAWX7/vY7YySfeX0gPxgvpwXghPRgvpAfjJT0x+ZQCJpMJeXl5MJn0/zWdjj2Ml6ZRKp/81mCCppnT7pyH5conty1HfWzUqRnYZR8CpxPwrEyu+im84bghrPLJGPxaqXwCgEAw+eStD0sOKcknixmW/Mjkk7faDUgSfCY7Es0VU1fU83vjJpAaa5x8ijftTp1aZo2fCFNkd5eTT5aAC/VV+ntRNaYkbaQkvn8kS3CfYKJSqThTVqHTQ6l8Cq9ictXLX1fvllcAVK+NuVHD8SjXU+35ZAm9Dk8wFkVNbeh5g9McpUw7/Bb5mjduvh+O9xfSg/FCejBeSA/GC+nBeElPTD6lgNvtxq5du+B26++zQscexkvTCCX5lCFXPhmaWfnkPBSsfLKHkk/duwM/dz4BQgCHP0ku+RSeBDOGJZ+UFefMWaHkk98Yp/LJYlKTT0Z3KPlUd7gaIiDgtST+S5AlOO1OIDQFLBm6Kp/cwWl31uQqnywdstQpco7S6qTHFPP5dUy7MwQrn6RGDceV6XB6KNVS4VMUlSmXgSPa5BPMwYbjxtir3SmVe+HJJ29WPgDAX1EV2tEpVz4ZszIQSCL5xPsL6cF4IT0YL6QH44X0YLykJyafUsDj8aC0tBQeT/P/qk/tH+OlaYRL/oU7YA8mn5pZ+eSpkCuf/PZs9TFJAoxj5L5P/lXfA0ks92oIr3wKa/RtDPZ8Ck8+KZVPmp5PwcSIwWyCJU/u+WTyhHo+NZQ7IISA15w4+WTNCT2X29H05FO81e5E8ENBssknSBJctjwAQMOB6qTHFFMTpt1JjSufmjDtDsHOW+GJJKUKC5XB5FNw2p0yJVEYldX2oiSf1Mqn0FikDsHk05Hw5JMcC4bsDIgkkk+8v5AejBfSg/FCejBeSA/GS3pi8omI2iel11BmsDoo0LyG455yufJJZGVrHi8+Zzi8kgWBw0eAffsSnic8CWYKSz6ZfPJ4NcknpfKpIcq0O7NJXc3O4q4Lba6VK198lvgr3QGAxW5UexPpaUqtSYYBEN44iT2l8slujb1PI167PPWu4WALNB0PVj5JSax2Z2g07U6tfDI0vfIpvOeTktQy1sjJJ+EKvv/ByicY4lQ+KcmnsNXujIVy8klUhpJPUjD5ZMqyq1MdlZUfiYiIiIhShcknImqfgpVPUnbLVD75KoPJp+wczeOjxlqxwz4cLifgXP59wvNopt2FrTKnfB0t+eR3hSXO/KHkk71QTjCZfU61+shbIycfAkn0WJIkwGuU9/PUtc60O8kjvy5DspVPAHyZcvLJfbgFkk/+5CufQskn+RjldYomJJ+i9XxSpt2ZHRXyPp7GlU/BhFXUyiel4i2URLMU5cnbqsOST2457k3ZdsAmv7eBBiafiIiIiCi1mHwiovYpmPRQkk9G4WvW6mmBGnnanZSjrXzq3Bk42H00BIDy/yWRfBKhxILJ74YICPg9fvVxpQ8TAARMkcknpR+RwWpGRkeluknAVR2cZlgnVz4F7IkrnwDAb5QTH5rqqgQaV+ZES5ao2zz6K58C2XLyyXOk+cknJTGmp/Ip1POp6dPulORTeGJOmXZncdYAPl/o2iiJOUPs5JOStDSGTbuzdekgb6sNJZ+MLjn5aM7NYPKJiIiIiI4aTD6lgNlsRufOnWFWploQxcF4aRopWPlkyJGTTxIC8Hmann1SVhQz5OVEbLOOk/s+ie9/QLwMl1ydpO0L5W3wwl0bqoAK78MkolQ+hTeetuZY1SRHwxG56biod0OSJCAjI6nX5TfJiQ+vQ8e0u8Y9n6JME1PHG0ywGG3JVz6JHDn55KtofvJJma4m6ah8Uq5xwNP0huNq5VOUhuN+HxCoqAKClU9K8il+5VPkancZ3eRpd5a6sOSTO5h8yrED9sTJJ95fSA/GC+nBeCE9GC+kB+MlPTH5lAI2mw0DBgyAzZbc0uN0bGO8NI0y3cuYm6U+5nM1Y+qdQ658MuZnR2zqddZguA12+CprgB07Yp4i2vO7ql3wOELJJ0tWKEkTMMtfq42pAXXancFigmSQ4DHLFU6uCjn5JLk8kAwSDFmR44zGb5LjylvfjMqnJKbdGe3JJ5+k/Dz5eSqrkz4mJmXanSmJyqfglLZQ8qkZlU9KFVPYtVFWXBQAavdUAo0rn4JJrkCcyqfwaXfZPeTkk9lVqyY9jV658s3y/+y9eZhcZ3nmfZ+19u7qbu27ZNmWJS9CeJPBS2yWhLAMkwXmA7NlZQaSMDM4YwZiCBAYCBMSMiwBfyRkIeQLZIAQQgDbAWNsY2TZsmwhy1qsrVvqpbprPfv3x3ves9R+aunq6n5+16VL3XWqTr11ztNvnbrrfu43m4Tgik9OubH4RPMLEQWqFyIKVC9EFKheiChQvQwnJD4NAMuyUCwWYVmN3QIEwaF66QwueihjvRGfxAJzPqkTtc6nF94g42eJF6BSAYr//ljDfdR7fj2veeKTJSoQZX9a5qufhdvu2M/cAWMqzOFUmXWznvJFpm4kW692BwC263wyi+0vVVsTON6kNgXDdT4l22+7EyeybL+5wQSOc4eSJz71zPnkH6f8qaD4xAPH3Va9uuITu02K+WMZ3TLqPofjCXWKweoglk1AdMUnnn9WD5pfiChQvRBRoHohokD1QkSB6mU4IfFpAJRKJTz22GMolUqt70yseKheOkMy3NXjsn72UTfik1Ri4lNsda34ND4OTG9nrXfT3z3QcB/BdjVLdNvdCprnOrKksEDjuJlPQedTsO0OAMwYe33aDFvxzlpYgG3bsNtsc+PuKqvUvvgUJfNJNKI7n5QJJqqI+Vzbj2mIJz610XbnCjti1Wp36FHmUzBsvnh6Fo7hCome88l1S+m1dSo4teJTdkJCURqBA6BwNgfHdqCYzPkUH09CTLYWn2h+IaJA9UJEgeqFiALVCxEFqpfhhMQngiCWJVz0kEeSENzbqh07DXnwQeA//2fg/HnvJrnCxJ3YqvrtbKv2bQEAzD833XC3wefXVSYa6XkN+gITB0y5yh1Up+2OCyOi65Yx42w/fJU7p8TEByHVpvNJYQJFN4HjaCI+CWZ051NsDROfpPzirnYnVTmfHB443oZrqoYW4pN2biawEqDrfOLiU51v8eoFjisKUIqz0PH8yVkYRR2Cw54vMZGElOLiUzn6+AmCIAiCIAiihywb8cmyLHzyk5/E3r17kUgkMDIygttuuw3f+ta3Iu/rq1/9Kl784hfjpptuwjXXXIPXvOY1OHjwYO8HTRBE3/CcT5k4bNFtX2tTfNK+/FXk73sU9r/+m3ebWmHOp+S6WucTAGzdw9r7jLlCw/2aGhMVHEGE5QpNRkGDUXDb7uT2nU/cAcNXtTNyLPNJLDMRSsy0Jz45ag+cT00ynyRXfJKT7TufEuuY+KSUeth2107mk9rA+dRN213gWIlOQHycnIXAnU/cpSa7z1Ov7c51PgUznwBAT7Hcp9LZOZRn/G//4tm4Jz4JGq12RxAEQRAEQQyWZSE+OY6D173udXjXu96Fw4cPQ9M05PN5/Pu//zte+cpX4lOf+lTb+7rnnnvw+te/Hr//+7+Phx56CI8//jh27tyJ/fv343vf+14fXwVBEL1ENn3xyYooPh15aBanTwPP3XcKAMvtiRlMVEqtq+98iq9i4pNcbiw+8QwhW5Bhc/EpX/Hyluxq55MrRjh60PkUznxy4izzyZh3xSd3tTO5XfFJYc9pl/vTdsdFwE7EJ7XSC/HJXe1OjLDaHXc+mV2sdsfzm2x/dcOg88m6OOvnYSWq2u6qjqdt2p6jSY6Hx2JlmPhUPj+H8ixzOJlSDKIsQk4z8Ukk8YkgCIIgCIIYMMtCfPr85z+Pw4cP44EHHkC5XEahUMC3vvUt7NixAwDw7ne/G2fPnm25n6997Wv4wz/8Q7zlLW/Bq171KgCAKIr4X//rf2HTpk14/etfj5mZmZ6MWRCE1nciCBeql+b85AP/goc3vBbPP3Dcu00y3cDxdMxzPpltik/OXA4AoP3sJAAgf94XlDIb6zufEquZ+KRo+Yb75eKXLcqwXNHHLGqwykyEsJUq8UmtbbvjwoiX/ZNmz2u54pPktljJI8mG4wjixNxxRGi7qwkcbyY+WWy/Sqb91UjSm7IAgJieh2U0dlW1g+C2sLXjfOLHVKxqu+vE+eS13fFj4ziegAQAmJ2F4AqJrcSnYFZYtfPJyWYBAPpUDlqOnXvDDaH3xCe9ufhE8wsRBaoXIgpUL0QUqF6IKFC9DB/LQnz6zGc+g29/+9u49dZbIcsykskkXvGKV+Ab3/gGFEWBpmn49re/3XQfjuPgve99LwDgbW97W2ibLMt485vfjJmZGXzsYx/reryZTAa33HILMpn2lkInVjZUL60pfvM+jMyfxvN/9yAA1mmlWOwDd2w0DscVn+w2xSe1OAcAUM6dBBwHxfOs5c6QEzXOE05iDTs/kqEBZv3n4SKCLcqwVSYMWCUNZoGNtUZ8UljbXdj55IpPcTcnKMmEBrvAHE+KUYEoiRhZN97WaxVcgcuptO98qmmzs5u03XHxKdW+82l0axa2qECAg6knJtt+XF3csVWLNvWoFp/8trsOAsfdkHK+2p1jWnB8ExSk+VmIDZxPsBqLT8HMJwAQx5nzyZyegzbHasBUmOuNi0/cfVYPml+IKFC9EFGgeiGiQPVCRIHqZTgZevHpZz/7GV72spdh27ZtNdv27NmDa6+9FgBaOpYeeughPPPMM0gkErj++utrtt96660AgC996Uuwm3zQIghi8eHtS9Y0E430kgkR7AO7OhL32qbaarvTdUhu65qQXwByOZQmmfikxxq/waXWsOwl2wacfP3WO9/5JHntbmZR89ruHLUN8anK+SRm2PM6Bdf5pLsi1Gibzie1f213jgPIlhv8HqHtTpRF5Me3AgCmfny8xb2bI9h8xbo2AsdjvO3OFQk951Mnq92xb+NsV6izq1awU/IzEHkYOxcS5dbOp2rxU1rNREZnZg7GAnM+WSo799xt1kx8IgiCIAiCIIjFYOjFpx07duCee+5puH3LFrYC1datW5vu56GHHgIAXH755ZDqfNC48sorAQCTk5M4dOhQp8MFABSLRfz0pz9FsVjsaj/EyoDqpTV81TBnmonM2oIvpMRHY7BF9uG+HfHJns15xhPTBIqHT6JykbXSGfH6LXcAkB6VoIkJOAAq0/XFJy7a2KLsiT5WSfOEnxrxSXXFJ81viWskPsGtD1kvwrZt2In2pnch7opPlfbb7mrEpjqrswGAoTuQHbZfNdP+ancAYGxmbdPzj3cnPkVxPnFhR+Jtdzpf7a6TzCfuYmLPHxSQACBemoPk5pJx55PQwPlkVvy6rRaf1LXM+STmZr0VDy03B0wdccUns7H4RPMLEQWqFyIKVC9EFKheiChQvQwnHQRZLC0URYHiugPqMTk5iVgshpe+9KVN93P48GEAwMaNG+tuHxsbQyKRQLlcxlNPPYVrrrmm5j6apkHT/A+9CwvMLZHP50M9qZqmoVAowDRN5PO1+TDcPlgqlWBVfQiJx+NQFAW6roeeCwAkSUIymYTjOCgUaj/8plIpiKKIcrkMs6otKBaLQVVVGIaBSiX8QUUURaRSKe+1VJNMJiFJEiqVCgzDCG1TVRWxWAymaaJcDi/3LQgC0m5eTaFQgBPsSQGQSCQgyzI0TYOuhz8YK4qCeDwOy7JQKpVQDT+GxWKxxqnW7BjKsoxEIgHbtutOZul0GoIg1D03zY4hPzdA/WPY7NwYhoFCoQDDMGoeGzw39Y4hPzdRj2Hw3NQ7hvzc9PoY8nPT7BjWrW8300bIzaJcLmNucgZwAAhA0dBgu0KAXiy3PIbzz5xhj3WZevQEKloGSQB6IhV6PK9vy7Kg6yWUxBRiVhkXT0xhfH225hgWcguQHMAWJNgKExz0hQIs20bGAUxJRj6f946hoMiAAxgVf9yi28YmxWR2DGPsPomnf4Lv3/j7WGsUAQcQU2LNa613DE1JYM9RKMC27bbmiNJCAZnAMTIC4ljwOfOzBgTHAQRATavR5ogdm4ADgPGz49656WSOgG0BDqAZemhs9eaIslFmx84VnyxdBxzAguM9tt36tgHAAcqFIvL5PMrT4fB027SRKl0EHEB3DPY36IpPhlYJjXXBzSADgIpWgVX2/26EMTfXKZ+DNpeH6gCmrCKfz8OQ2Gvn4fv1jqFlWSgUCqhUKjV/58tqjkB774H13qvanWeb1Xc3x7Befbc6hv26juC3l8vlmvHSdQRjKV5HNDuG/byO4PVCc8TKmSO6+axh2zYKhQIK7vVIEJojGMttjujmOqJZvdAcwVisOaJevTVi6MWnZlQqFRw8eBBvetObMDEx0fS+s7OzANC0bzSTyaBcLuPChQt1t3/kIx/BBz7wgZrbn3jiCa/gACZkAYCu63j66adr7s9b/I4cOVJzMnft2oW1a9fi4sWLOHbsWGjb2NgYrr76aliWhQMHDtTsd//+/VBVFc8991xNG+KOHTuwefNm5HK5mjGl02m88IUvBAA8/vjjNX8w1157LVKpFE6dOoXJyXA+y+bNm7Fjxw4UCgU88cQToW2qqmL//v0AgEOHDtX8UVxzzTXIZrM4e/YsTp8+Hdq2bt06XH755ahUKjWvVRAE3HLLLQDYMayePHbv3o3Vq1djamoKx4+HXRUTExO48sorYZpm3WP4ohe9CLIs49ixY5ibmwtt27lzJzZu3IjZ2VkcOXIktC2TyWDfvn0AUHe/119/PRKJBE6ePFlTX+vWrQPAJpXqcx6Px3HDDTcAAJ588smaCWvv3r0YHR3F6dOna0L3N2zYgEsvvRSlUqlmTJIk4cUvfjEA4Omnn66Z7Pbs2YNVq1ZhcnISJ06cCG1btWoV9uzZA8Mw6r7Wm2++GYIg4OjRo5ifD38gv+yyy7B+/XpMT0/j6NGjoW2jo6PYu3cvHMep2a9VdIWBhVkcP34czz16HDtsG6ak4MDjj0MGE38LuXzNY5PJJK677joAwMGDB1F88Cg2Bibv3MHnYKy9BI7toAAp9PiNGzdi586dKBQKOHjwIHQxjlHbxtOPPImRtIabbroJAPDUU0+hUqlg9sg5XGrb0Cwbpis0FGbmUMjnscq2kTd0HDhwAGvWrMEVV1wBS2IXYwvTs97zjpk6IDLx6ciRI7goVTBh20jlJ5E6OAlBFKBLKuRk7TxQb46YKxax3raRn56GaZptzRGnTz6PXYFjlJ/PeT8H54jClIGttg1REiGnYjh+8kTbc8T8uIhxx4Zymv2NdjpHOKYJ27ZxbvI8DPc1N5ojyudL2GTbEEQHtmmjtJCHbdvIl8ve8Wp3jjBMC7Zt4/Sp51E5EIN+voANjgMIIopiGkljHtA0OACOnzkJ44johaIvzMyG9l08UcA2hznmTj1/KjRHTJZnMe44UAtzmJ2ZR8q2UbDZPFM+W8Imx4ZsabBNu+4ccdlllwEALly4UDP3LKc5AgBuvPFGxGIxHD9+HNPT06Ft27dvx5YtW5DL5bwvozjVc0T1Rdi+ffuQyWRw+vRpnDt3LrSteo4IoihKzRwR5KqrrsL4+DjOnz+PU6dOhbbxOULTtLqvtV/XEVdddRUA4MyZMzXnhq4jGEvxOmLr1q3Ytm0bFhYWatz7/byO4F/Unjhxoqa+aY5gLLc5opvPGvz96Gc/+xnNEStkjujmOoJ3N1UfB4DmCM5izRFR3GfLWnz6p3/6J6iqio985CMt78sLXlUb55LE3FWhGh3gu+++G//1v/5X7/eFhQVs3rwZ11xzDUZG/HYdTdMwNzcHVVW9SaIeu3btqqs0AsDq1atD+wTgtQtKklR3v7K7bPsll1xS04bIX1s2m615rBjISnnBC15Qs18+pq1bt2LDhg2hbfx4ptPpmv0G3WBXXXVV3W8jAPZHt3r16tA27naLx+Mtj2G9byMAYO3atci6K0Vx+DGSZbnufvkx3rlzZ91vIwBgfHy85rHBVs56++WP3bZtGzZt2hTaZhgGJicnkUqlmp6bq6++uq6SDrA35rVr14a28WOYTCabnpvdu3fXVdIB9sbMxVQOP4aKotR9rXzfl112WcP6XrVqlafkc/gxFAShZr+H3MMQK8xgx44dcJ5jDh47xo7ZITdfKSYpuHLf1aHHBo/h3r17ceKR2dBt9smzMJXVEEQB8TVrQ89dXd8Pp7MQtTPYlF2LbVf7z3PllVfCtm08+6wMURQhx+Kw0kyQTogyYukRiKKI9PgE9u3b59dhPAZRFDHi1rljOzjjnho5LmPX9l245J5L8FR2FYzz7ALFtm3Mb4rj1s1rsXFHuJbqzRGPbniWPUcs1vYcsWHtutAxyiT8fKngHDH1zBx0UYQgAIKqRJoj5qUJLPzFFzA6dwKVkt3xHCGC1cLm7Vuxu049BueI0sUiZkQREAC9oCOuqBBFEZnAvNjuHCHH2LnbuH4Drty3D7mjF5EXBFiCBC01jvSC/0Z+xTV7sGnXVjwmPQYAGEmFj8UFcRKawLLCLqmaI85Is3A+IkCtzEPWTYiiiIQ7B5W2lDDjrrqnF/S6cwT/G1yzZk3N3LOc5ojg69mxY4d30cpp9z2QX5QGCc6z/MsCTrvvgXyOCMKP4fr162u+QOPHMBaLLep1BH/tmzZtwiWXXBLaRtcRjKV4HcGP4cjIyKJeR/B9bd++3TsXHJojGMttjujmswZ/fZdffrl3zKrHRHPE8pojOJ1cR3Dn0a5du2rqheYIxmLNEbzbqx2WrfhULBbxvve9D1/+8pdbup4A/+RUq7lB+LbqAufEYjGv8IJkMpm6jipRFJs6rRo9D8AKsZFQJghC0/1WXwAEadXG2Gy/8XjcK8ZqZFlu+tjqP/4gjY4rwCaJZvsNOs6qaXYMuzk33RzDeueGq82tXutKOYb16ptn9CQqc4gpMaiODFMAbCXO7iuzfQlW8+OfTqeBuTIgAHkpi4yVg3LmFIqrWf6QPD5W9/H8GNqpEWAWEEtW6HzwYxiTFEAAICsQ4uxcC7oJyW1Nk1Op0P7FeIzd7jjIZDIs+8d9n5FibGVPJIEXvf/V3mPyeebuUhSl4WsNHsP4SBoQAMmyvTfHVnOEyl8Hf/2BX4LPmROK0AXAllRAECLNEekbdiGvyIiZZTz/kylcduv6hmNqVt+87S+RStY9HsH6TqfSOJMYR6Iyi5Pffgai7Z6XeLzmsa3qW5DZMYopKjKZDDQljzwAS5BhZCaAvP8NVHbNGJLJJGuzBCBX1XhezUED4AhSzbnZuDuBM2ArEEoXL7Dxuu85qUQKM+6p0eYrGN2arRknn19UVW34epbDHBGkWX13817VrL67OYbN6nuxryN4vcRisYbPS9cRjKV0HcFZ7GPI6yWRSDR8XpojGMtljuB08lmD10syWf/9GqA5grNc5ghOJ8ewnXqhOYLR7zmiWkxrxtAHjjfiHe94B+666y7ccccdbd2fq4z1+js5fFu1qhuVeDyO3bt3NywugghC9dIayWS94IJjI39mHkae2U4thR0z2xWfqlccq4d+IQcAODPO2gXis2chzLG2XCnbOHAcAOwEe0Mx5poHjjui7AV9O5oGx7WB89s4ohs4DvfbnWbB05yo9SIm3OfU6692d+6R0zj74+dDt9UEjjdYAdQouqsQSu2vdMcRFBml1exbpamHT7S4d5P9uN/WCFLrtztBFJC/nK2QeuFbP/Ffp9zJ9zRM9XF44Li30qEMazT8hYiScgPH5fqB48Gg+mrSWRklya3LM8zuLqTYhYIoi7BEtm9tvn7oOM0vRBSoXogoUL0QUaB6IaJA9TKcLEvx6UMf+hAuvfRS/OZv/mbbj9m9ezcA4Pz583W380AzgPWgdoOiKFi9enVTxZogOFQvrZEsv4d//uQczCITUmxXfOKrldlaY2cjx5xm7WvSrktREZOwDBvp51k2gTTeXHyC+22GOVc/eM/2Vk+TIHDRp1xh/wD/Nhcx7oo2rjhllPzxNxKfotaLlGTPKdYRn4ySgamffzMu/sKbQs9dLeI5jVa7K7B9diI+AYC1lbX15J94rqPHA0yQBALCTgviL2biE376mCcCtfvYEK51u1p8cgQJGB8P3VVOKOHnqTqeQeGqGlEEtEQWAKBcZDkBQtL/Rs6U2d+AvlBffKL5hYgC1QsRBaoXIgpUL0QUqF6Gk2UnPn3hC1/A/Pw83vOe90R63M/93M8BqB9yB/ir4U1MTODqq6+u2R4FXddx+vTpmtA7gqgH1UtrZMsXTgqnZmAW2Adt2816gis+OUZr55MzmwMAJDeOYXZkG/t5ioVFKmONbakAPPHJWqjvfOJOGkeSWUsdAGga+wf4t7nwNizBZMIPFyGAxuJT1HrxnU+19794aBIxfQGqUUB51l+5o9r5JDQQn8yS63xS6lt9WxHbtZ09/tkunE82G5sot/d2t+WXWCDk+Okn4eTd89iB88lxWxjrOZ+kNWHnk5pu7nyydO58qi+C6WmWhZDMMfFJyvgWaVNpLj7R/EJEgeqFiALVCxEFqhciClQvw8myEp/+4R/+AY8++ig+/vGP193+zW9+s+Fj9+7di127diGfz+Pxxx+v2f6DH/wAAPCrv/qrodCwTtA0DcePH69ZNpIg6kH10hzHdkLOp9KZWVgl1/mkus4nVzwIijeNEHLM+aSuyUJfv43tx+0qi61u7nwSR5j45DQQn3znkwwx5dqEg+JTtfMp5jpiXPGJt905gthQTIlaL3KSCR+iUXv/uad9J6hZ9p1P7bbdeQ40uTPnU/aFzPkUO/McIrSTh7GjOZ/WX7cJxdRaiI6J+OHHIj02hBv0zY+VJyBJMpS1YeeTknS/tWvRdufUcT4BgDXC9ie45zAoPlmu84m3olZD8wsRBaoXIgpUL0QUqF6IKFC9DCfLRnz69re/ja9//ev4zGc+U7OtVCrhgx/8IKampgAAf/M3f4Pdu3fjy1/+cuh+3C31xS9+MXS7bdv4y7/8SyQSCfy3//bf+vQKCILohKAoAgDauRlYRfZB21GjO5+kfA4AEF8/BumSbaFt8dXNnU+e+NQgO46LCJBkSK7QJOgaYDDxjLfAeWOJ88ynsPPJFnq3VoTXdldHfCoc85czbiY+1YhRLp4I2KHzad3+7RAArCqcwMx0Z+qT4ERzPgmigOJu5n4ayblZV52IT9z5ZLNx89pzRAnxDb74ZAuSN7aGmU+6/9h6ONnwKjBS2m+7s9Tm4hNBEARBEARBLAbLQnz64Q9/iF/6pV/CV77yFcTc5cKD/1KpFD72sY/h9a9/PQDgox/9KJ555hn80R/9UWg/d955J972trfhc5/7HL797W8DYMsKv+9978PRo0fxuc99rmZpYYIgBoteCNttzQu+88mJh51Pdhvik1JkzqfkhiySV4SXCU6ua+58UsaY+CQ0EJ88AUKSPdFH0DWIGhMGvIwnFynhupKqxacGDphOUNKu+GTW2pa1k77zKegaq2m7sxuIT2W2T1vusO1u52ZIMQlxu4RTP7nQ0T742KK4l1K3XRfeRzdtd9z5xDOfRBmpzb74ZEn+sRH5GO36mU+NxCdxPBv6XRnxxSfu/uOtqARBEARBEAQxCIZefDp69Che+cpXolwuw7Ksuv8A1i7HlzZ8wxvegHQ6jTe+8Y01+7v33nvxF3/xF/jABz6Am2++Gddffz0OHjyIBx54AHfeeeeivjaCIFpTLT45M7OwS+4Hbdf55IkHeovAccdBrJwDAKQ3ZbHq2m2hzan1zcUn2c2EEkrN2+4gSQHxqcLcTwDkdHjFDlENZz5x91EvxSev7c6sdT7ZZ855P4ecT1UintOg7c4TARssM9wSRUFlDRMALz7cWeg4DxwXlfbFp22/fG14Hx05n9zAcffY+LlNMka2BcUnPyiTP49gVh1f02/Zq4e8Oux8Ukb9tjuHO58WyiAIgiAIgiCIQdG7TzAD4rLLLsP8/Hykx9x99924++67G25/y1vegre85S1djqwxsixjYmICckfLdxMrDaqX5vAV1TjC3Cyc8jr2S8IVc9p0PtnzeTgmEwtGtmSh7sziNAQIYK1T6fXN2+7UcSZwS+XWgeNyyl9ljrtkGrXdiVbY+RQULKqJWi9KOgYdgFxHfBIvtJf5VC2WcLjzyWt/7ABn23bg9HEUDp0AcFPkx3viU5ttdwCw+sq1eG50C0bmWdsdD36P9sQC+98NHA+63rKXTGDBvVtwJUChifNJQmPnk7quSnwKOp9i7G+At6JWQ/MLEQWqFyIKVC9EFKheiChQvQwnQ+98GkYSiQSuvPJKJBKJ1ncmVjxUL80ximHnk7QwC7vMhBQh5ooecnuZT/nTOTgAKmIS2TUqRlapWEhtAACYUtwPhm5AbIKJT3KlUeaT73ziLifR1CC5eUtckPJei9t2J1SJT42Cp4Ho9eK13Vm1bXexOT/zKRTWXu10auR8qrj7VDp0PgFIXMlanZ3nOnQ+8bY7KdrbXeVK3/0kqB1c2Eiu86l6tTtJRmo8Bl1Kur/7NeU53az6mVpOA+dTfH04wDw2lgz8wurMcwNWQfMLEQWqFyIKVC9EFKheiChQvQwnJD4NANu2oes67AYf2AgiCNVLc6qdT7H8DFBhH7SFKueTo7cWnwCgEsvyh6CyjrV9abHmricASKxm4pOqNXA+8eeXZU/0kQwNosHGy1vgONXOJ96216j9CoheL0qKPadsaQguKWebNlKFKf93vTbzyRJV/qR19+24IiA6bbsDMHHtdgBAYvIEGhismtJJ2x0AZG73c586a7sLZz55YfOiBEEAyskJdrtcx/nkhI+nt9pdg/Oe3pQN/R7LBi7E4s2dTzS/EFGgeiGiQPVCRIHqhYgC1ctwQuLTACgWi/jxj3+MYrE46KEQQwDVS3PMkhtqLbAP7vHyLJyK63zi4pPCRJxWzqfSuRwAQEsG2pi2bmPPE29DfFrjZj6ZOqDXOomcgPPJE59MDZLb8qZkwplPUoyJDdVtd82cT1HrRc242VOO7eUSAcDMkYsQHf/3cOB4uP1PsOofV1tzj0Gs87a7sRfugCgC68vH8fyp6CveiXa01e44O37lhf4+Omm7a7TanSsgGRlWY0HxiQtk1cez1Wp3mS3htrvERMD5xEP3y/XFJ5pfiChQvRBRoHohokD1QkSB6mU46VmT5PPPP9+rXXnIsowNGzb0fL8EQSwfjCITborptcjkz0GyDYiz0wAAMRFuu4PRPHC8cn4OMgArk/VuS1yxDbgPMFKjLceSWp3EAgTYtgN7oQBxVbgdystKCjifZLMC2xUV+G0cJaWiDEAKOJ8ENHc+RSU24j+nlteRdAWvmUPnQvezKoFjx51PUgwwijVOHU4vnE/iti1Q4yLsUhHP//QidlyyJtLjBVdAi+p8GrtkHM9M7ER25hjEWPN2y7q44hPcFjpPQHLPnTU6AZwDnHqB4w3a7tBAfBrdmsVs4Peg+MQFWLuB+EQQBEEQBEEQi0HPPsFs27YNAg9Y7eE+n+sw54MgiJWBXdIggDmTjHISilmCOsOEEynFPnh7gdEt+ra0qRxkAPao7yS54h134OD3foiRN7665VjSIyIqYhIJu4jShQLSNeJTnbY7S4Nou+JTqkHbne2LTxLCgkW3qCl/X/pCBclVTLjIHz2PVOB+thYQn7ig4jmfwmIJx3GdT0K8c+cTFAWF8S2QSydhHzsOIKr41FnbHQAk/9vbceFLX8c1b7wx8mN55hNvSfRa57iANMFqww6EsXvOp6rAcU+4ahCqmRhRoMlpxMwCHEEMZZN57j8SnwiCIAiCIIgB0tN4+I0bN+KSSy7pyb5OnToFx4neYkEQxMrCLOlQAFhKDOXkBJSFElLzbJU2b/U4V3xyWohP5sU59sNY1rttdMsobj3wJ22NRVUBTU4joRdRvlhAuvoOgba72KgbOO5YgOvO4S1wHDnBRISg84mJT72bukVZhCUqkGwjFN5eOTkZFp/qZD55Ydl2C/Ep1rnzCQCKY5sweuYkhKnJ1neuotO2OwDY+7u3Ar97a+THAfBWu+OB49UCkrR6wv29jvOpgfjUyPkEAJXkOGILBRhyAoLofxEkJl3xqULiE0EQBEEQBDE4eup8evbZZyFJHQSz1kHXddxwww092RdBEMsXq8zEJ0dWYWTGgYXTUKwygKDzyf2A3yLzyZ7NscdNjDW9XzP0WAbQp1C+WBs67rVPKXKo3Y3DBSkOd7CIjgnbtGtat3qFKcUg2Qb0gi8+WWfOh+4Tarvjzie5eeA4NL7qYHfik5NkMphdKEV+bDfOp66ocj75eV/s3KkbV7PbE77E18j55K921/g1GOkssPA8DCUZul1MkfhEEARBEARBDJ6eik+9Ep4AQFVVjI11/gFwKZNOp/GiF72op8eLWL5QvTTHdnOFHFWFpWaAs/42OeWGabfZdifkmPNJWZ3teDxmPA3kAW06X2cje35BlqGmawWZakGKO58AwKyYbYlPndSLLamAARh5X6AQpsLiU6jtzn0dXHyqFks83NB1L3urU5JMULEK5cgP9cSnDpxPXcEDx6ucT1yU2vO7L8FPDz2LTb/9Sv8higQbtYHjnmjZbJXDkTHgHGAp4SWHJdf5JGj1xSeaX4goUL0QUaB6IaJA9UJEgeplOOmZ+NSPFrnl2nYnCALkBtkdBFEN1UtzrDITOBwlBmE8nLEUVXwSF3IAgNi6zoVvK8Ga7fTZ5s4nQRRgiSok2x2/IEJSw2+gwewes2J6AlCj7B+gs3oxlThQQajtLjbNcrNMKQ7ZqoTa7jznk9JCfHKdT2K8O+eTmGSCilOM5nxybAcCBis+8WPFWz75uctsyOC2f7kr/BAuPjlVzifDzwprhJNlNWvFws4nOd1cfKL5hYgC1QsRBaoXIgpUL0QUqF6Gk55cjZfLZfz93/99L3YVoh/7XAqUSiU8+eSTKJWit5AQKw+ql+Zw5xNiKkQ3R4fjffBuU3ySC8z5lFif7Xw8SSY+GXO14pPnfHK/pTFlv83OlOI1dw+KT0ZR9wUguXHgeCf1YktMHDJLrovMdpDKs3ylwthmdptRKz453PnUIHBc0F3xqUvnk5hmgopTiuZ8sk2/HVCKLfIFCnc+uV+iOEZr95Kosm2N2u7Q5Ns9vrKiFa8vPol6ffGJ5hciClQvRBSoXogoUL0QUaB6GU56Ij6tWrUKb3/72/HXf/3XmJub68UuAQBr167t2b6WEpZlYW5uDlaDD2wEEYTqpTl2xXXrqDEoa8POJyXjfvBW2xOfYqUcACC9KdvxeJxMhj1Vro745J5DLoaZsi/KBH/miLIIR2DTtFEyYBut2+46qRfuYDILTCzKnZhzHVkC9NUb2X3qrHbncOeTUz/zSTDctrtkd+KTlGGCilCOdoFh6f4xWHTnk+Q+H89rMnjLZRMBiWc+VR1PT3xq8g2fvCrL7quGRUxPfDLqi080vxBRoHohokD1QkSB6oWIAtXLcNKTq/FXvvKV+P73v483v/nNWLt2LV7ykpfg05/+NM6ePdv6wQRBEF1ga1x8UhHfUF98ElTmFKrO0gntp6JD1pm4MbIl2/F4hDRzPtkLTZxPrvhkBwQnq474BACWyMZulg04euv2q06wFXaceAvjzFMs76mYWg0k3Ja3es6nFm13osHELKnLtjs5w8YQVXwKOp8WW3wSeNsdDxw3W5873nYpVmc+GeG8qHps/pUbUUytRezlt4Vu538DUgPxiSAIgiAIgiAWg558gvnKV74CXdfx3e9+F1/72tfwzW9+E/fddx/e+c534tprr8V//I//Ef/hP/wHXH755b14OoIgCJ+Kv6JacvMEgjKImmGCjtfOZBrVj/Yons3BAeBAxOjGdMfDEUfYY52FZoHjTESwFF9w4q1v1ViSCtnSXPHJHX+PxScuIplFdiznnzmHBIBKdj3grhToPTfgO8jU5uITdz7x7K1OkUbYinBCpXPxqTpPq+/wtjtvtbvW7iXf+dQg86mJ+LTlth3YcuFbNbcrmThMkPhEEARBEARBDJaefRWsqip+8Rd/Effeey8mJydx//334x3veAempqZw9913Y/fu3dizZw/e97734ac//WmvnpYgiBWO53yKxZDeEnY+xUar2u6aOJ8Wns8BAEqxLJRY51OjJz4V2nA+BcQnS63NfAIA23U+WRWjreDpTnBUNg59eh4AUD7OnE/WmvXec9lB8YmLTa74hEZtdyY7N1KiO+eTMsqcT5I2TG13rlBkte984uKTWC3mWa2Fq0bEsuzYySaJTwRBEARBEMTg6MvVuCiKuPXWW/Gnf/qnOHnyJB577DH8j//xPwAAH/7wh3H99ddj69at+L3f+z088MADsO36H1yWK7FYDDt37kQs1uXy48SKgOqlBYEV1bI7qsSnkSrnUxPxqXAmBwDQE9muhqOMMfFJKDYRn1znkx0QnIJCVBDbzXcyywHxSWkcON5JvYh7rwYAOP/0f+HYDswzLGxc2OCLTwi03XkB4y2cT5LJzo2S6k58UkdZ5pOkRQsct4zBOZ8EKbzaHdrIbWrpfGqSF9UIdYTVmGKW4di1K8jS/EJEgeqFiALVCxEFqhciClQvw8mifBW8b98+fPjDH8bhw4dx5MgRfOhDH8K6devwqU99CnfccQfWrl2LX/u1X1uMoSwJVFXFxo0boardfSAjVgZULy1wnU9CPIbkqiQs0T9Oapr93I74VD6fAwAY6bGuhqOMs8BxsVRHfLLDgeNOQHDiK8fVPMRtx2vX+dRJvVz9h78MU4pj7OJRPPWFhyFMMudTbNt6QK3TdscFFVcEq3HquEhmb9ruVNf5JBvRnE9eUDcAQRS6GkNkeOYTX+3ObB047mU+NXA+CZ04n1z3n+DYMCu19U/zCxEFqhciClQvRBSoXogoUL0MJ4vchwBcdtlluPvuu/HII4/g+eefxyc/+UlcddVV+Ou//uvFHsrAMAwDU1NTMIzG+TMEwaF6aYHurqgWVyGIAsrJCQAsK4m7T8RY68BxbYqt1GmPZLsajjrOnE9yqTbzSagSIHi7W/XPQWypTtud0liE6KReRreMYvbW1wIA5v70S1CmmfiU2rneEzycwEqB3OkkxN0xN2i7484nOdndhUF8nDmfZL3MtZy24G13jiAuvvhU7XxqQzj02u6cqsBxLqI1Oe+NiGd9d10lV9t6R/MLEQWqFyIKVC9EFKheiChQvQwnPROfLly4EPkxGzduxDvf+U7cd999mJyc7Mk+h4FKpYIjR46gUqEMDqI1VC/NEXQ3cNwVQvQ0a70zJf9DtxRjH9pFq/EblHExx34Y6875FJtwxadKY+eTl0EVsAo7sQaZT3JQfGLjb+aA6bRedn/4DXAEEatO/gSjs8cBAGO713srBaLOand8W0Pnk8WEQSXdnfOJi08xq4Qo1xiOm7dkC4scNg5A4JlPvK3cCrve6sGdT4Jjh1vkuGjZJHC8EXJchiOwt3ptvrYmaH4hokD1QkSB6oWIAtULEQWql+GkZ+LT61//+q4ePz4+XnNbt/skCGIF4K6oJiaYwGFmmfPJVHwxh4s91UvYB7GnmfNJHM92NZzEaiY+KXoB1TYd7nyqJz6hQc+642Y+WWXdF4A6cMC0Ys3V63Bx38+zcbpOplVXrguIT77q4zmfYm7mk1MrPtk2INmu+NRl5lN8jLXdqU4F5WL7GYFB59OiI7hOKy4+tdN2F/PPa0h88truootPgih4Qqy+QBdoBEEQBEEQxGDo2RW5E6UXYoD7JAhieSG6zidvRbUxJmTbsi/mtJP55ORyAAB5dXfOp9RaJj7BsrwwdA+ryvkUD7idGvSs8ywoWzcDAkbvxScA2H7Pm7yfy/ExxLNx36lj1K52x4Upoc6iEYbuQHGY+MSztzpFyiQ9Lac0276Awp1PziCcT3KV88lsLRwGQ9FD+UxVqyRGxZRJfCIIgiAIgiAGS8/EpzNnzvRqVwCAhYUFzMzM9HSfBEEsPwTX+cTFJ2EVE58spbbtTrAbi0/iPHM+xdZmuxpPalUCDkTYNmDmwq13/Pk9MSzehvMp0HbXrQjRiq137MTFS18EAChn17Pn4s6nOplPYtwNdK/jfNIKvlilZrpciUT187u02WLbD+Or3Q3C+eStdlclPoltik/ctRV8bCer3QG+C5DEJ4IgCIIgCGJQ9OwTzHPPPYfLL78cGzdu7HpfjuPgZz/7GeLx+hkow44kSchkMpA6yO8gVh5UL80RqlZUU9a4zqfASnJe5lNAfNIWNDzyn/8KySu24pp33Q45nwMAxNd36XxKCyhJaaSsBZSm8hhZt8ofqxVuvQqKT0Ki/nzHxSe7orclPnVbL5v/6O2Yu/Np4PY72HOptc4n/jq4+FSv7c4ouFlcCLjSOkUQYChJSGYBWq7c9sNsnY1zIG13YlXgeITV7oAq8Ym33XV4Tm1XfDILteITzS9EFKheiChQvRBRoHohokD1Mpz09OvzZ599Fs8++2zP9rdt27ae7WspkUwmsW/fvkEPgxgSqF6aIxlu2527olp61yYAgDnq58jJcTbVSYHA8ac//yOs+qe/AP4JeOJPViGls9XpUhuzXY1HlgFNZuJT+WIBI4FtQlXgeEh8ijdwPimu+KQZngDUTHzqtl52vGIXnIv/5q0O5zl1rCbOpzqB41x8gij0xKllKklI5QK0uVLbj/ECx8X+OMWa4TmfePt4O8JhK/FJ7ex1WCoTn4x8rfhE8wsRBaoXIgpUL0QUqF6IKFC9DCc9uyI/ceJEr3blIfcp14QgiOWDyJ1PSSbeXPmbN+Gx8/dg12tf6N2nrvPpwjy43JMsT3u3ZzZnux6TEU8DGlCZDrfd8cBzUWEiAw9JB/zw7hq488nwM5/EmNL1GJvBhafgcwl1Ase9VkfUZj7pBXZeTCnmh293gRVjoePGfPvik20Oru0OUpXziQt2SuNv6ILH3TZq2+6atew1w3bFJzPfvmuMIAiCIAiCIHpJz9SdrVu39mpXy558Po8DBw5g3759yGQygx4OscShemmOZDKHDV9RTZRFXP/BV4XvU0d8sgrsg/iFPbcj/erbUf7rf4SdTOOKK1Z3PSYrngbmAW2mKvPJFZ/4eKRUYEW+RINcJNf55GiG5z5q5p7pdb144lMd55MnPjk2HNsJiSdmkZ0XS+qy5c7FiiUBAPp8hLY7V8BxxAFkPonhzCehDeeTIAqwBQmiY4UCx73VBTvMfHJc8ckqaTXbaH4hokD1QkSB6oWIAtULEQWql+GErEUEQQw1osWdT41FDk98ckw4DjPi2EVXxBgdxbXv/XngvT/fszFZCbbinT6TD93OA8e5iCAlA7lUqQYZd4qf+cQFDLHD9qtO8J6rifgEMJdRsG3MKLLzYsldho27OHHmfDIXorfdDWS1O6/tznWFVa902ABHkADHCjuf2nxsw32q7BxYFb2jxxMEQRAEQRBEtwygF4EgCKJ3SK74pKQbixxSggk4omPBtlgGjyc+JRM9H5OdYuKTUbPanSvacOdTQHzi+Uk1cOeTbviB5X1a7a4envPJ9NvuuIMsKD6FMorgO59suTfOJyfOnE9RWscG6nziofLc+WS3JxzyfCreMgjUBtVHxl1J0a7jfCIIgiAIgiCIxYDEJ4IghhfHgczFp1RjkUOO+R/4eTuTU2QOGrEP4hPSTHyyqsQnT7SpIz4Ffw7BxSfT9NxHg3A+iWZt5lPQbVYjPpXYebF75XxKMvHJLnSS+TSAlVB4zhUPC2/z3NkiG2vY+dSl+KSy82SXagPHCYIgCIIgCGIxIPGJIIglxdGjwKFD7d3Xqhhw1xKDmmksciiJWvEJZeagEdPJTobZFMHtPbcX6otPPHRaTvljDv4c2hcPItd0PzMq3t/A8SD8uYKZT3x1u+A4gk4dICg+9cb5JLjikxVBfHJM7nxafPHJCxZ3V7sTorTdAbC0QJtjl213iLOWTqdCzieCIAiCIAhiMFDm0wBIpVK4/vrrEYv1xhFALG9WUr3YNvDbvw1UKsD3vgckW+hCet7/MK2mmzif4s3Ep947n8QR5nyqFZ/CbXdKJg4uMcjpRplP7L7BtrtmIkSv60VUZTgARCvgfHLY61DSMfAzUO18sstsi6P0SHxKsWJwojifDAsCBtN2B/c5vbDwNt1L9ZxPQtUqiVER4qwWHK0282klzS9E91C9EFGgeiGiQPVCRIHqZTgh8WkAiKKIRKIPrT7EsmQl1UuhALz56d9HxsphIfcZJJPNRQMeau1AhJpo/ME8KNZwR4lQZiKG3AfxSRpl4hMKzdvu5FTMF58aBKYLqusuMtoTn3pdL1JMhglAsIKZT27bXSLgfDIatN2pPRLBUu5rKkULHGfi0wCdT9WZT7EWzqd64pPdnfPJE5/qOJ9W0vxCdA/VCxEFqhciClQvRBSoXoYTarsbAOVyGc888wzK5faDc4mVy0qql9xkBdflv49dpZ/CnJppeX/ufDJFFaIkNL6jIHjtTNz5JGjseMojvW+7k7NMfBKKVYHjTtj5FGwVbNR2J/K2O8OA2Ib41Ot64a11XDgDwq/DEdjbiGWE2+648wk9cj557ZERXhdf7Q7CAN7qeOaTu9odb52T2my766n4xGuoUpv5tJLmF6J7qF6IKFC9EFGgeiGiQPUynCwZ8enuu+8e9BAWDdM0ceHCBZim2frOxIpnJdVL8WzO+znYUtcI7nyypNYCB19FzCwzB4/kik/KSO+/NVHGWeaTWA6LTxJ3PqlMYAiu0Ndotb6w88kdexP3TK/rhbubxDqZT6IiwXHfRqqdT3bFdaX1yPkkZ9h54o61duCtgINou+POp5rV7lq0ztV1PnXZdicm3ZZOvbbtbiXNL0T3UL0QUaB6IaJA9UJEgeplOFkS4tPk5CQ+85nPDHoYBEEMmOK5ee9nLiw1g7d2mW2sqMbFJ952J2lMxOiH+KSOM+eTHBCfWCA3C5+u53xqLD65QlPA+bSogePuWEU70HbnOp/kuO98aiQ+8ZXWusVzqFWiO58G0XYnSO7bq+t88sLmWzmfxDqB4wGxrxNEvpKiRoHjBEEQBEEQxGDoSebThz70IXzlK1/BXXfdhTvvvBMA4DgOrr/+eszMNG+dMQwDU1NTsCyr6f0Iglj+VCZz4E1wZqH1svBGgX2YtttxPkkyYATEJ4OJGGq292138VWu+FTxxScv6By+8yk26oeMN1qtT4q7r800fPdMp6uedUB1211QRBNlEbYoQ7L1mrY7L1+oR+KTMpqEBV80bAv+vjKAtjsuPnkr1Vlh11sjuPjEV+oD0PV5lxKstgSdxCeCIAiCIAhiMPTkE8z//t//G7lcDp/+9Kc98UkQBFx33XX47Gc/29Y+BKFJXgtBECsC/ULO+9kstv6gzJ1PlhLd+aS44lMs23vnU3xVGjoARS8AjgMIQkh84qvvxbNxaGoGkmUgvT5Td19ijIk/gmHUBJYvBrztTnJb/oKr2omK5DmfgmIJEMh86tEqJOpoAmVEE5+4G8uRBuh84kJdmwKSLbHtTORD+LEdOp+kBBMABb21oEsQBEEQBEEQ/aAnn2D+/u//Ht/4xjfw1re+NXT7G9/4RjzyyCO49957MTY2VvexhmHg8ccfxxve8IZeDGUoUFUVW7duhdojRwCxvFlJ9WJO5/yf2xGf3PvYcjuZT0xEsXUTju1AMZmI0Q/xKbUuAx1u21e5DCSTIdGGi0+iLGL07z4LWzcRG2kUOO6KT2Z74lOv68Ubq8OOW/B1SKoEu05GEQA4br4QX2mtW9RsEmUAsh5ttTsAwADb7rjzibfOtRIO6zufumu7k9PMYScata2sK2l+IbqH6oWIAtULEQWqFyIKVC/DSU/Ep5e97GV42cteVnP7TTfdhDvuuAN79+5t+vidO3fiox/9aC+GMhTEYjFs27Zt0MMghoSVVC/WTM77mbuamt6/rEMC4LQjPkm+88koGRDcLJ74WO/Fp/S4ilkIgOPAWChDSSZDGT7B1qsdv3B503354pPuuY+4G6keva6XoFhi6VaNiMZXZ6tuu+P5Qt5Ka10SG2PtkYrRRHx66CHga18D3vMeYHzcE3AGETjuiU8888lpr+2OC2W2Xhvw3qnjTXIzn0SjVtBdSfML0T1UL0QUqF6IKFC9EFGgehlO+n5F/sEPfrCt+331q1/t80iWDqZpYnZ2ltL5ibZYSfXi5PzAcbvUukXIKjOBym6j7c5x2+5s3UR51g+tToz3wfmUFqCLzG1SmmMf+L2V1wQRgth+mzHPXBIssy3nU6/rRUn6QpdRMmqcT43a7qD11vnExSfVKsMw6t9H/9KXUfyXB2D/4EE2piXgfKoOHG/X+RR0knXbbiknmQBYT3xaSfML0T1UL0QUqF6IKFC9EFGgehlO+i4+xdy8jyNHjuDHP/6xd/uhQ4fw+7//+7h48SIArCjlslwu49ChQyiX21+1iVi5rKR6EeZz3s9mqXXbneXex1Fau2ucgPOpMsvcM5aotnaidIAoAqbE5j4jX/GeFwBsIZqAwJ1PoqF77hneClePXtdL8LnMihlycImy6K/OpleLT+zciPHeOJ+4Qy1ml1Eu2nXvc/iRIk6dAp59kr12X3xafOcTb5ETbBtwnLbdSzyfKtR253TZdpditSiZtYLuSppfiO6heiGiQPVCRIHqhYgC1ctw0vcr8n/5l3/Brl27sGfPHrzxjW/0br/qqqvwkpe8BPv27cP3vve9fg+DIIghQMrnvJ/tcuu2O7vC7uOobQSOS0zEsTQTWo69URlK711PHMsVn/QFV3xyxRkefN4u3PkkGb5wsKiB40HxqWzUOLjsOhlFAAA380lM9Mb5pIwmwdelKM/Vd8WZC0UAwMKUKz4ZTCgbRNsdH6xgW3As240db+Pc1XU+sZ+biY7NUEeYC08yW/9NEQRBEARBEEQ/6OsV+QMPPIDXvOY1OHr0KBzHgeM4oe0vfelL8aEPfQivec1r8Mwzz/RzKARBDAFKKef9bFfacD65K6o5bYQNcueTrZvQ55k4YSrJDkbZHqbCPvDXOJ8itoBx8SkYtN2pCNEJgih4bi2zYnqiiOfg4m13VtiNJLjh1nylta6Jxbx2xWDbZBCpwo4Rb9n0xiQt3vHi+C4lJ+QWa3Xu6jmfuOOtU+eTknadT1brvymCIAiCIAiC6Ad9FZ8+8IEPYOPGjfjwhz+M++67Dxs3bqy5z5133glBENrOhiIIYnniOECsnPN/byPzyXGdT0IE8cnRDeg5t+1O7Z/zyZbdtruCm/nkiU/RhBAeLi4HWqYWU3wC/DFbmhlwcLkiSYO2O8HNF+qZ+CQIMF2nmjZXP3ScC3Se+GQPru3OCxy3a0Pam1IVOG6bthda3mmLqJJi50C2NDi20+LeBEEQBEEQBNF7+voJ5plnnsHjjz+O9evXAwAUpXaFJlEUsWbNGnz/+9/v51CWFKIoIh6PQxxEKwgxdKyUeqmUHaSMnPe704bzKUrbXdD5ZCyUoaC/4pOlus4nV3zyHEMdtt0ppu/2aSZg9KNeLEmBbFVglg3PfcRXuXMatN0JbtsdX2mtF5hKEmql5LVNViO7K+E5JXc7H9MgMp9kf7U7sxJwPsWaC0jc+QRXOLNN31HWabulmol5Y7E0M7Ra4kqZX4jeQPVCRIHqhYgC1QsRBaqX4aSv4tOVV17pCU+NKBaLOH/+fE1L3nImlUrhhhtuGPQwiCFhpdTL/FQFiuNn0jha63waLlAJsTacT3Kt+GTH+9d2Z7vik1kIt905EdvufKGAzZGOIHrCRj36US+26OZlVQzP0VPtfAqKJAAgmj12PgEwY0moecCYr+N8MgyIpgEbgFOubrsb3Gp3XPDhSEqLiyQukrrCWVC46tT5FM/GvZ+1vB4Sn1bK/EL0BqoXIgpUL0QUqF6IKFC9DCd9lQqz2SwWFha83+sJTB/60IegaRq2bt3az6EQBLHEKZzJhX5vx/nEBSoh3obzSWYfuG3dhLngtmfF++d8crj4VNS85wUAp8O2O07U1fJ6gR1YKZA7uDznE898MsJL3Ypu5hNfaa0n44gxsbCe+GQXy+AdZULFDRwfpPMpsNodF58sQYYoCc0fWJX5FKllrwFK0q8hPU+5TwRBEARBEMTi09cr8t/6rd/CL/7iL+Kpp54CAAiCf9F98eJF/Jf/8l/wsY99DIIg4E1velM/h7KkKBQKeOihh1AoFAY9FGIIWCn1UjqXC/0uaG1kPmntO58g+5lPVsFty+qr+MREF54/1L3zidGqba8f9cKf0ywbfvsgz9DiYolV7XzqceA4ACveWHwqTxf9XypLwPnkticGnU+OIEFooT154pMrVvZCfBJEAZbIzoM2H/67WinzC9EbqF6IKFC9EFGgeiGiQPUynPT1K/SXvvSlOHz4MF7wghdgw4YNyOfzuPnmmzEzM4PnnnsOpmnCcRy8/OUvx1133dXPoSwpHMeBYRgrqtWQ6JyVUi/lyXlkgjdobTg0XOeTGI8gPhkm7KIrPiX713aHGBOfrJK7Ip8ZFm3ahYdFc1qJT/2oF0fy2+64o6c686m67Y6vrFY9/q5wxUIrX0d8CqyA5wmXlivcDEB88pxPjg1Tc11M7bjeRL56oPuYQMseF7Q6wZTjkHQdZinczrpS5heiN1C9EFGgeiGiQPVCRIHqZTjpey/C7/3e7+HHP/6x15P5ox/9CEeOHIFlWdi7dy8+/elP41vf+lbdMHKCIFYO2lQufIPROvPJE6hibbR2BQLH7YIrXiT753xCnLXdde18qnK7RA0s7wV28NjpVa+jQdud5GY+9bLtzkkwsdBzrgXQZn3nExefBtp2J9dmPnHBrimuSIqqzCdbkLoUn9h5oLY7giAIgiAIYhAsyqeYa6+9Fv/wD/8AAJiZmYGu6xgfH0esnQ+MBEGsCIyLOQCAJAKWDQh66w/J/D5ttXYFnE98NTSxr+ITm994+LVtWJDgr7rXLsG8HiC6c6oXcOeTrQczn1zRSWrkfGLiIV9prRcI7vnyxMMAes6/TdRd55O9FALHLU98aks4rMp86nSVxGpsmf2N8AwygiAIgiAIglhMevYp5vbbb/d+3rJlC/7iL/4Cqlr7gXBiYqJXT0kQxDLCns0BAIzkCMTCAiS9deaT4LqjxEQbAofiik+mCZRd8Sndv7Y7IeE6n8pu253rDIoqPi0J55Pst91xoc97HWJYLAGY5qP0oe1OSLHz5bVNBtDmfPFJ0t3AcS4+Ddr5pEcQkKoDx6O4pppgu84nI9/674ogCIIgCIIgek3PrsgfeOABCIKAP/mTP8Ff/uVf1hWeCEYymcTevXuR7GfeDLFsWCn14uTmAQCV0XUAfGGpKUb7mU8Cb2fSDaDEhAox3T/nk8hX4HPDr712tYjiEwuL9t1PrVbL60e9OMG2O+584qITzygKiE9GxYII9ruS7p3zyRMLS7XOp2AIuWRUtd1Jiy8++c4n2xMe23KtcZeW1Vvnk+WuvmiVw39XK2V+IXoD1QsRBaoXIgpUL0QUqF6Gk559ha4oCv7xH/8RY2NjvdrlskWSJIyOjg56GMSQsFLqRZjPAQDMVWuBs0chGq3bg/h95GQbYnfA+SRUmDNG7qP4JCTZh32nwsbIRQR00DZniwok22A/y83z8fpSL+5z2prhCTpe5hMPHA+sdqflfYEjlundFxESP191xaciuDdINl1nlDsmYQBtd5LqB477eV9tnHu5yvkUxTXVBFthImB1291KmV+I3kD1QkSB6oWIAtULEQWql+GkZ18HX3755V0JT4888kivhgIAuP/++/GNb3yjp/tcWFjAd7/73a73o2kajh07Bq2d1byIFc9KqRcxn2P/r13D/jfbEZ+YyCElW7trPOeTYULQXPFppH/flkiu+OSFXxudBY4DgCW173zqR714zifNqA0c58JO0PlUZOdFQJt5XG3iOZ/KteKTlfdb8WSzAjgOYA9utTsILBxcdCxfQGpDePTqtGq1O7uDugniKOw88NUXOStlfiF6A9ULEQWqFyIKVC9EFKhehpOeiU/dOp5+53d+pyfjOHDgAF7+8pfj9ttvx4EDBzrax5EjRyCKIgRBCP0bHR2FIHS+2hBH13WcPXsWut5GWxGx4lkp9aIUcgAAeeNa9r/ZOpuGC1TtOJ8Eha8iZkJyxSdlpI9tdzyHSqtyPsmdOZ84rdr2+lEvjlyn7Y5nE9XJfDIKmr+th8KPMsrEJ7FSKz7ZeX+1O8cGHE33BbEBtN35zifHXwmwHQGJH9eqtrtORMsgjsrqka++yFkp8wvRG6heiChQvRBRoHohokD1Mpz0rO1O6uIDxvHjx3H48OGunj+Xy+Hzn/88pqam8PDDD3e1r0984hNwHKfm9iuuuAJ33HFHV/smCKI+sXIOAJDYthYOAMls/WYSxfkEhQk4jmlC0ph40U/xSUr3zvlkS+2LT32Bt93pgbY7L3C8NvNJL7DzYkq9XdFUHknCBiBWagPHrbwvSDkAjIWyt9rdINrueOA44MDSTIho1/nkjpXnROkRWvaaEauf+UQQBEEQBEEQi0HPPsX8+Mc/xo4dOyI/Ttd1TE5O1hV7ojA6Oop3v/vdAIAzZ87gK1/5Skf7mZqawle/+lUcP368xs2VTqd74nwiCCKMaTiI6yxwPH3JWuQByJYGx/G6l+oiWuyDdBTnk2AYkAwmXqij/ROf5BQTXgSdO59c90snzqeg+NQi86kvcOEu4HxCVdudE8h8MkvsvFhybxeeUEYT0ABIeq3zySmGb9PmK17r2iADxwEm+IhoUzjkK/NVtd1163xCzHU+lcmeThAEQRAEQSw+PROfNE3DyZMnO358t6JO8PHxeLzj/XzqU5/CG9/4Rmzfvr2r8RAE0T4LU2UoDhMsRnauQR6AYmswDQeK2nhukN22OzXdvvjkGCYUV3yKjfUv80lOsXlI5CuvuQ6WTtrQgiHjTgfiVdfI/rHznU9V4lOdtjtb6q34FMsmXfGp1vlUHUJemVsqzicmPilAW213XnuoKz7VBLx3irsCLYlPBEEQBEEQxCDo2aeYtWvX4ud//ucjP65YLOLAgQM4ceJEr4bSMcViEZ/5zGewdetW3HXXXfi5n/s53H777YjFets6oigKNmzYAEUZgIOBGDpWQr3kT+cAAI6sIrE+CwAQYEMvW1DUxtOUxJ1PqdZ/oyLfj2VCMZlQEcv2z/mkpNmYJCPcdteJ88kJOJ9arZbXl3rxnE9+4LgnpPC2O8sXn8ySDgGAJfd27lSzTCxU6jifqkPI9YWK58YSBpj5BAB2hdVpO84nr+0ukPnUbsteU+LsXDiVcObTSphfiN5B9UJEgeqFiALVCxEFqpfhpGfi065du/DFL36xo8dqmobNmzf3aigdc++992J2dhazs7N4/PHH8fGPfxxjY2O466678O53v7urXKsg8Xgcl156aU/2RSx/VkK9lM7lAACVRBZKxncuagsaUqMNpinbhmgbcAAoqQhtd+UyBIeJEvGxPrbdpeOwAUiGG75tdhE4rvivr5WA0Zd64eJTPecTd0WZftudVdIgIzzuXpAYZ+dLtUowzfChFOuIT4LFhbIBtN2JvmPPqhjshzbeQ2rEJ91sv2Wv2X4T7O/KqYQzn1bC/EL0DqoXIgpUL0QUqF6IKFC9DCc9E5+swLfeUYnFYrjiiit6NZSO2b9/Pz7/+c/j+PHjuP/++/Hwww9jbm4Od999Nx566CH80z/9U1MBStO00HKPCwsLAIB8Ph9qCxRFEbZtIx6Po1KpXdErk8kAAEqlUs1xjcfjUBQFuq7XLC0pSRKSySQcx0GhUKjZbyqVgiiKKJfLME0ztC0Wi0FVVRiGUTMmURSRSqW811JNMpmEJEmoVCowDCO0TVVVxGIxmKaJcjncKiMIAtLpNACgUCjU5H4lEgnIsgxN02pWMlAUBfF4HJZloVSqdUHwY1gsFmHbdmhbs2MoyzISiQRs20axWEQ1PPer3rlpdgz5uQHqH8Nm50aWZZimCVVVa45D8NzUO4b83EQ9hsFzU+8Y8nPTq2M4d2ISWQfQE6MQVAWCwFYsm5ucgZK1Q8eQ17dd1uBqSNCgw7btpvUtqExAEfI5lkgNwFRMFIvFto5hs/qudwxNiTlWJLOCYrEIo1wGHMB0HOTz+ab1XX0MLUHwxmwF5pJ6dagoCgzD8P4P0ukcYQrsyR3dgKnrgMPGkc/nYdo2G5s7jnw+j/zMAsYcwJQU5PP5ns0RpmoBDhCzy7gwtYA1a5PeMUSp4B0jAChOz3ttdwbsmr+7fs8Rmql549EW8sg4gCWw949mc4QgS+z46hry+TzKhSIyDuC4j61X382OIZ8jxJjK6q9YDB2LWCwGTdMgSVLdWloqcwSHn5tmx7Cb98BmxxDo7RzR7jFsZ46opl/XEfy6RRCEmuNA1xGMpXgd0c4cAfT+OiKRSNQ9tgDNEZzlNkd081kjHo+jVCp513NBaI5gLLc5opvrCFVVvRqsrheaIxiLNUfUq7dG9Ex8unDhQleP//d///cejaRzrrvuOlx33XXe7wcPHsQ73vEO/OhHP8I3v/lN/MEf/AE+/OEPN3z8Rz7yEXzgAx+ouf2JJ57wCg4AxsbGMDc3h927d+Ppp5+uuf+tt94KADhy5EjNydy1axfWrl2Lixcv4tixY6FtY2NjuPrqq2FZFg4cOFCz3/3790NVVTz33HOYmZkJbduxYwc2b96MXC5XM6Z0Oo0XvvCFAIDHH3+85g/m2muvRSqVwqlTpzA5ORnatnnzZuzYsQOFQgFPPPFEaJuqqti/fz8A4NChQzV/FNdccw2y2SzOnj2L06dPh7atW7cOl19+OSqVSs1rFQQBt9xyCwB2DKsnj927d2P16tWYmprC8ePHQ9smJiZw5ZVXwjTNusfwRS96EWRZxrFjxzA3NxfatnPnTmzcuBGzs7M4cuRIaFsmk8G+ffsAoO5+r7/+eiQSCZw8ebLmb2ndunWYnJzEzp07a855PB7HDTfcAAB48sknayasvXv3YnR0FKdPn8bZs2dD2zZs2IBLL70UpVKpZkySJOHFL34xAODpp5+umez27NmDVatWYXJysqZldtWqVdizZw8Mw6j7Wm+++WYIgoCjR49ifp6FjJ978mcYsW1UEmlAEGCKCgRTw6GfPIGx0ggAtqjA3r174TgODhw4AGuuhDXuJPvMc09jzbabEIvFcPz4cUxPT4eec/v27RBVGY7jwJmfgW3bMEUFTzz1BJLJpPd3f/DgwZoJdt++fchkMjh9+jTOnTsX2rZx40bs3LkThUIBBw8eDG0rTGnYDpZL9dRTT2HmwkWssW0slFir8VVXXYXx8XGcP38ep06dCj12zZo1uOKKK6BpGg4cOICyYXlvKIXAG3C9OWLr1q04deoUNm/eXPN30+kckSsUsN5x4BgGtCK7MCyW2d9evliA7dhe293jjz+Oc8+dwKhto2yy5+nVHGEuLGDMsQBbwGMP/hS3vPwF3hxhF+Zh2zZsCBDh4MLz5yG6wtjF6enQ612MOeL4yeNQ3XN24ex5jNs2TFeNajpHyBI7vgt5HDhwABdPPo9dtg3bvbCqN0ds3boV27Ztw8LCAg4dOhTaxucIIRGDbdsozM6Gxn3ZZZfh6NGjWLNmTc1+l9IcERzv+vXrMT09jaNHj4a2Vc8R1dx4441N54gtW7Ygl8vVrL7brzlCURTcdNNNAICnnnqq5iI4yhxRTb+uI6666iocOnQIo6OjNeeGriMYS/E6op05Auj9dcQ111yDAwcO1P2yleYIxnKbI7r5rHHZZZfhwIEDdcVtmiMYy22O6OY6YsuWLTV/MxyaIxiLNUfUE68aITjdLjPnIkkSnnnmGVx22WW92F1XvOUtb8Ff/dVf4Z577sH73//+rvZl2zbe+ta34ktf+hLS6TROnz6NbDZb9771nE+bN2/GmTNnMDIyErrf4cOHsXfv3hqlFlia30bQN5aMQXwbYRgGDh06hGuuuabGebdUv42Iegwf+b2vYcOXP4WLL3gJbv3RR3Fw/HaolQXY//BX2HrrltAx5PVdODmDuRt+GbYgYOPp72M0m25a34c+fh8Sf/ReFOITSJdnUI6N4LLnv963byNmThRQ2v8qCAKw5dx9ePjN92L9d/4W537uV3DD3//nSN9G/OTF/xPrnn0IAHDuhb+Al/zwgwDqzxGmaeLJJ5/EVVddVdMH3+kc8ehbv4j13/4SLtz2Okhb1mHir/4U5655CW74t/+JR37pU9jw4Ndw8bW/gVv/9reQz+fx+Ie/i1V/9ke4uONa7Pvxx3s3R1gWzm66A7YFCN/9Gi69YYN3DA9veiXixTnMy+MYNWdR+h/vQ+FbD2HNU9/Hhbf8Hl740deE9t3vOaKwUMDz624DAEy99m1Y+0//LyZ334bbH/vjpnPEw3d9DSN//kc4f8UtuP6BD+DJT9yPsY/9IS5ueyFuffpzHX9j+dC7/gHZz30M53ffhuvvv8fbblkWnnjiCVx55ZVQ1XCb5FKaIzj0jSVjUK4Gx3Hw+OOPY8+ePTV5mHQdwViK1xGDcjXwD3BXXHEFEolwmzvNEYzlNkd081nDtm0cOHAAu3bt8mqdQ3MEY7nNEd1cR3Dxrl690BzBWKw5YmFhAZs2bcL8/HxI86hHz5xPjuPg9ttvx7vf/W684hWvWDY9mKIo4rOf/Szuv/9+nD59Gk8++aSndFcTi8XqhpNnMhnvBFbvu97tnOo/pCCqqtZ8UOAIgtB0v9UXAEEURWka3NZsv/F4vOFKg7IsN30s/8OpR6PjCrBJotl+g46zapodw27OTTfHsN654W8grV7rMB9DKV8CBEAYGwcAWEoM0ADFrq0bXt+GkAcEwBRjyI6NgHejNapvnvmU0NnjLDVVs+9mx7BZfdc7hs56FSUAjgPEBRkSREAAlHgidN9m54YfQzEWB9zXJwfGUK8Oeb0oitLw+EedI5Sk+7thQHAACICsxpDJZCCrKhub+8aUyWQgWey1CvFk6Hl6MUeYShKiXYKkiZDd0KdYLIaYwWqoklqF0YVZCJrltd3FkomG++5XfafSKf+c2WA/uxlYzeYIQZYAAZDAjqUqyOyxbuZTs/m72TGUkqyGZMsM3YfXi6qqjR+7BOaIapoewy7eA7t5r4o6RwRpdgzbmSMa0evrCF4vsVis4fPSdQRjKV1HcBb7GPJ6SSQaz8E0RzCWyxzB6eSzBq+XZDLZcN80RzCWyxzB6eQYtlMvNEcw+j1HRPEy9SyF9Z577sFv/MZvIJfL4ctf/nKNujbMJBIJ/Oqv/ioA1FjhCILoHns2BwCQxkfZ7zJ7kzEKjZeF59ssSfWEp2ZIMfbhXbLZ3GSp/QsbB4BYxn+j1OYrAP+GpJOFC4LJ2h0ElneL6OZlwTQBHpzuBY6z/71AdfiruyHW28BxADDd86bnAt/+2DZEk9WDkWECpl0sAzYbkzCgwHFHcFcC1Nzj0c5qd+7KfIJbL95x7TJwXEqwcyHojf+mCIIgCIIgCKJf9OxTzD333NP6TkPMzp07AaCllawdBEGAJEmhEHKCaMRKqBdhgfVjy6uzAABbZuq8VWoiPhXZB3pTqq/kVyPGwt9u9Ft8UmMCDCEGxdFC4hN3YEUi+M1MC/GpH/UiBMQnp0p8ErgIZflWX7vsnrcer3YHAKaahIoZ6PO+pdnKl8C/dLGz48BZwCpWPOeTt4LcIuNAhAAb4F/GtCEcemN1hTNbY/Zrp0vRUUqyvxPBCP9NrYT5hegdVC9EFKheiChQvRBRoHoZThb/K/QhhfdY7tq1q+t9pdNpL2CNIFqxEupFzOcAAKorPlkqs5yaxcbiE9/GXVItn0MNT3d2vLGNtBcIAmDIcSiGBj2veW1pHQkhAbuxJwQ1oB/14glmhlEjPoG7imzf+eQ5feLtCYNRsGO1zqfSNPvZhgQxy74gsEsVCO4xF5UBiU+CCDiB49GGgMTrlI/dmHaDMkdGuxqLnGLnQqxyPq2E+YXoHVQvRBSoXogoUL0QUaB6GU4WvxdhSPnud7+L6667Djt27Bj0UAhi2aEUcgCA+LosAMBW2AflpuJTyW2fk9t1PlWLT/11PgH+2PSFHjqfOnl8l3iuMcOAU9U+6IlpgbY7p8LOm9Ag56AbuGho5H3nU2WWiU8VOQUpzc6rUypD4ILYANruAHhtd904n6wZttKNMJbtaixymgm6vD2RIAiCIAiCIBaTZSk+8YT56sR3zve+9z1ceeWV+MQnPuHddu7cOXzoQx/CP//zP9e9//3334//83/+T0/GVywW8ZOf/CTSsoTEymUl1ItazgEAEuuz7g1MtPDat+rAxSenTeeTVOV8wiKIT6bMPvAbBa1L8cl/jNBCwOhHvfAxC1Yg84mLJF7bXR3nU4Nww25wXPHJWvCdT1x8MpUkxCQ75k7Zb7sbqPMJANyVS9pxvfGxesLZXA4AIE1kuxqLnGR/J1JV291KmF+I3kH1QkSB6oWIAtULEQWql+Fk2YlPMzMzePDBBwEAP/rRj2qWQQSAT37ykzh8+DDe//73e7f94z/+I973vvfhVa96FV72spfhmWeegW3b+OpXv4pf//Vfxze/+U1cd911PRmjbdsolUoNxTGCCLLc68WxHcQrOQBAeqPbWqS2znziriirXfGpyvmEJqs39ApLccWnfMD51EHbXdBB1Eq86ke9eC2LphFoH3QFKTcg2wlkPjmu00eM9975BHeFESvvi0/aLLvwMNUkBL4yX7kMOHZojIsNF5+8kO8IzifedifM5wAAituS2ilKmv1NSVXOp+U+vxC9heqFiALVCxEFqhciClQvw8myEZ8sy8K1116LrVu3eivS3XfffdiwYQNe+9rXhu77ute9DplMBm9+85u929761rfi7W9/OzZt2oQHHngAN9xwA2688UY88cQTePTRR/HSl750UV8PQawUChfLkBwmzIxsyQIAbDfzqZnzySq7ziels8BxJPvvfHKC7YNcTOjA+RTMeerIOdUl/NgJZp3MpzrOJ/C2uz6sduck2VKydsEXn3j4uBVLQky5zqeKn/k0aPGpG+eTxFtS1491NRZPfLKo7Y4gCIIgCIJYfJZN4LgkSXjsscfauu+dd96JO++8M3RbJpPBpz/9aXz605/ux/AIgmhA/nQOAGBJMagjTDjg7VrNxCe+zWlzRbVq55O4COITF9HMQgWwunE+DVZ8kuKu+GSZNcHp3v+BwHGecSQmet92J6bcTKeiLz4ZuSIUAFY8ibgrPglaBc6AV7uzRffYmOx4tHPuvMBx93iqBZb55LWkdgj/25Ip84kgCIIgCIIYAMvG+UQQxHBSOpcDAFTiWe827pjhwdX14M4nW21P4KgRn9L9b7tz3LGZJQ1CF5lPQsC1JVRnVy0C/gpsptc+iCrxKdh2x9vM+tF2J6TYeXNKfuC46YaPO/EkJFd8ErUyBGewq90BbPlfwYiw2l2V8ynmtqRmtnTnfFLT7FyIjgnbJIs6QRAEQRAEsbiQ+DQAEokE9uzZg0Si/84LYvhZ7vVSPp8DAOjJrHebEGeiTTPxya64H+g7Fp8Woe3OHZtdrHirl4kdiEdiwPkkthCv+lEvvO1ODGY+8dXu3JY2wfLz9TzxqQ/OJ76aHUq+88mcZ5lPTiIJJeOKT/rg2+6480l0xad2HFjB46ktaFBMJqxlNme7Goua8c+FNl/xfl7u8wvRW6heiChQvRBRoHohokD1Mpwsm7a7YUKWZaxatWrQwyCGhOVeL5WpeaQAWOlR/8YEz+1p3XYHtbO2O3kRxCfE3eyqUsVzPnUiPoWcTy3Ep37UixRX4KCq7Y6PgwsqgcBH7vSRkr0Xn5RRd7W7ou988sLHk0koI+y8inoFtsief1Btd+CB45ab+dRO250iwQYgOBYWns8BAGxBRmpNqquhxEb8c6EXdCQm2HFc7vML0VuoXogoUL0QUaB6IaJA9TKckPNpAOi6jueffx66m4tCEM1Y7vVi5goAADuZ9m4TeVC11lh8cjT3eMTaEzjkeJX4lFkE8SmYXdUr51N1cHoV/agXLtyJltEw8ykoPokGO29Sovdtd+k1TDSxFwrebbab/ySkfOeTpFe81jVRHmzguOQej6iZTzwPrRLPQhCFrsYiyiJsge1bz/t/V8t9fiF6C9ULEQWqFyIKVC9EFKhehhMSnwaApmk4ceIEtCYfrAmCs9zrhYtPSAfEJ96u1eQ125Vozqdq8Yk7aPpK0PnUReB4UHBqJV71o158QaRO4Ljovo0E2+7cgG051XvnU2Y7+5ZLXZiG47DbHHflOzGTgjrKREXJrHiC2KCcT457bESrg9XuHBulsyxsPNiS2g2mzM5HUHxa7vML0VuoXogoUL0QUaB6IaJA9TKckPhEEMRAsefz7IdMrfjEs4Pq4jqfeDh5K2rEp5H+O58Et33Qrmi+C6cD5xNfba7Tx3dLyPlkhkW0us4nV3zqh/Np9LI1AICsNoW8WzoosswnMZXwVnVTjDIEh41pcM4nN/PJcmu1jXMnqfwxJiqTOQCAmekubJxjSezvyijQhRpBEARBEASxuJD4RBDEQHHyzPkkBsWnJBMQ0ER84m13PJy8FdXiE3fI9BMuPqHsO586WXktKOIMQnySE0z8kiyjxk3kOXVcRxTgt5nJyd6LT7HNayFJQNqax/QZNzi7zPKfpJEUYqNu4LhlQOKiz4ACxyGwVjk+jlZh8ew+3PlkQZ9izid7JNuT4ZgKOzYkPhEEQRAEQRCLDYlPBEEMlgITn6RRX3ziooXY1Pnk5uh06HyKjfW/7Y47uJyKBtHqPHA8+JhBik+ibXoOLs/x5LaW8dsB3+mjpHvfdod0Gk6MCYdzP7vAnq/MnE/KaBLxMbbNAaCYbjveAI4ZADjuaneeCBZBfBJtC+Z0jt041hvnky2zvxWzSOITQRAEQRAEsbiQ+DQAeDq/LNNig0Rrlnu9CEUmPslZX3ziq6Tx4Oq6uAGDQqI9gUOQRC8AGgBi2f47n7z2Qa1L51Og7S74cz36US9e212dzCf+epxA251s9s/5BEFAZZS13hWOu+JThYlMymgS8REVDpjjSNVdV92AA8dFu33hkR9rwbFgzzDnkzie7cl4bDfzKSg+Lff5hegtVC9EFKheiChQvRBRoHoZTuhsDYBEIoE9e/YMehjEkLDc60UsM4FAnch4t8mpGBwAQjPxyXU+iW06nwC2ZL3kMNGKO2T6iZRy2+40jYV1o8PMp4QK7ivi4kQj+lEvXtudbdQEp3v/B5xP3OmjZvrgfAJgjq+Fev4Uys8z8UnSmPikZpNQVAGGGIdql/0WwQG13fHAcU47wiMXykTbAnI5AIC8OtuT8dhqrfi03OcXordQvRBRoHohokD1QkSB6mU4IefTALBtG5qmwQ44BQiiEcu9XmRXfIpNBNru0ky0kZqIT4IRPdTaFn3hJjHef/GJr/Ym6hUmJqC1eFSPKIHj/agXLj4BDmCyldu89jAlLD7ZlgPZdtvuUn1wPgHA2rUAAPP0JBuf7otPAGDK8dDdO3Gb9QQh/BYbqe3OMSHmcwCA2JpsT4bjqOy4WCX/72q5zy9Eb6F6IaJA9UJEgeqFiALVy3BC4tMAKBaLePjhh1F0V2giiGYs93qRtTriE898cldNq4srPonJ9t01XHyyRNVbVayfcOeToFd851MnbXcBwaqVeNWPegnmZYk6C/nmIpjnKnLf/PWyBQHs536JT9J61naHC8z5xMUnnuNlKmFhcWBtd9XOpwir3QmODSXP2u4SG3qT+eQo7HwExaflPr8QvYXqhYgC1QsRBaoXIgpUL8MJiU8EQQwU1RWfEqt98YkHVctmpeHjeBi5FG9f4LAk5uAxlP67ngD/dYi61pXzKSjidPL4bgmKT5IrPjVqu9MLvmAYG+lP2118K3M+idMXANuGbLLV7uITKQCApSwN5xMPHI8yjuD5jRdnAADJDdnejMdtu7PKFDhOEARBEARBLC4kPhEEMTAs3fJWJEuuqRWfJKtJ253riuKtbe3guM4nU+n/SndAsH2w4oVOd9t21ypwvB+EnE9umHijtjs9765CiP6NNbWDiU/x+SkY+QrgOAD8VkpLXRriEwQh9Gs75z7oyIvpCwCAzJbeOJ8QY38rNolPBEEQBEEQxCJD4hNBEAOjeMG3yqbW+uKTOsLEA9nSuK5QA18JL1Lmk+S23amL5HzKsNchmhoEp3Pnk5+5NBjnkyj7KwXKRpXzyW274+KTUWDnxZIUQOzPW8zopaztLlWcQv4Ccz05EJGaYMfbqRafBtZ2V+V8itB2F2Rk82hvBuSKTw6JTwRBEARBEMQiQ+ITQRADo3SBtdyZUgxK0hdY1DQTlCTHhKHVDxKUzOih1tz5tGjiU6B90HM+dZA1FTw2QRfSYmKJbAy8FdJzPvHsJ575VGTnxZL603IHACOXroUgABkrh/NPs1wkXUpAkpnTyI4tFedT+C22nXNffR9dSYfOf1eQ84kgCIIgCIIYEIP5FLPCSafTuPnmmyFUtWQQRD2Wc72ULzLxSVfTodvVjC9caAsa1HitWCR20nbnOp/s+OK03akjcZQASKbWVdtdFOdTv+rFFhXA0iBb4cBxURZhwXc+mZ7zqU8r3QEQRjKAGgM0DbMHTmICgKH659SJLY3A8WrnVzur3VWLT1oi27PhCHHX+aT54tNynl+I3kP1QkSB6oWIAtULEQWql+GEnE8DQBAEiKJIfyxEWyzneqlMu86nWFh8klMx8FfLM4Sq4XlQfGW8drDdwHG7jpjVD3jgtmgZAFj/YLfOp1biU7/qha8UKDjM4eRlPfG2O7et0Cy5zie5f84nCAJKIyz3qfzMSfa8AfEJ8aXSdlflfGpDeGRj9c+dnu5R3hN88QkV/29qOc8vRO+heiGiQPVCRIHqhYgC1ctwQuLTACiVSjh48CBKpdKgh0IMAcu5XrTpPADATGRCtwuS6IkdPEOoGsly2+7S0Z1PWCTxiWdXBenE+aSmVUxvvx4zG69BZkOm6X37VS98pUBOo7Y7s8jOly33z/kEAMYYE5+cEyfY+GIpb5tTdX4H1nZXlfnUrvBoB9r17Ey2d8NJuH8rAefTcp5fiN5D9UJEgeqFiALVCxEFqpfhhNruBoBlWZifn4dlWYMeCjEELOd6MeYKUAFYyXTNNlOOQdFNaPWcT7YNyTJgI5rzyROfkovTdhcfqR1bJ5lNgijg5if/j/dzM/pVL3Yj8UmudT4pAGylj84nAM7qNcBRIH6eiU92oNVOSIZFv07cZj2h6tu4dgLHAcARJMA9nk62d84nT3zS/b+p5Ty/EL2H6oWIAtULEQWqFyIKVC/DCTmfCIIYGMYca7tz6ohPPLDaqCM+OeUKbHcVPGWkfReTLz4tjvNJVkWYQliA6lQImWJj4AAAzkVJREFUEUShpfDUT3hYO6em7c7NfLLKzJFmK/11PonrmfNpLHeSPV/Cdz4JiSXSdidVOZ/adL3ZgWMtjGV7Np56zieCIAiCIAiCWAxIfCIIYmBY80x8QrqO80lhAoJVqv2grOeYxdaBgMRoBOeT7IZkL5L4JAiAKS8RF06XeMKdCxefuKDCs6D4+XL63HYX28LEJ9lhYhcS/jmtPr+Dcz6F32Lbdb3ZgXY9aSLbs+FISSY+CTqJTwRBEARBEMTiQuITQRAAAMd28Nw/P4PiheKiPac9zzKfhEyt+MTFC6Oo12yrzJUBAJqYQCIZwQ0ks9YxMb04bXcAax/kOII4uJXXuqS67Y4LOvz1iNz5VGHny1H723aX2LYm9LuT9J1PUmppOJ+qV7uL1HbnoqzO9mw4XHwSSXwiCIIgCIIgFpnh/BQ05MTjcVx22WWIx2vDiAmimsWql2e/dgja6+7ET171h319niBOnjmfxNHaEG2+WppZqNRs03JMfDKlePXn++bP57aCSZnFE5+sgPPJFvofs9evemnkfPLa71zxyebOpz633Y1eujb0uxAQFKX0EnE+1ax212bgeMD5FF/fu8wnOeWKT4YvPtH7EREFqhciClQvRBSoXogoUL0MJxQ4PgAURcH69esHPQxiSFisepl99BhGAMRPH+37c3kUmPgkjdZxPrnOmbptdwtMkDKUaCLS2t95HaY+JeCq3/q5qCPtmGDwti32XwTpV704coPAcf6/G5Btu84nxPrrfBrbtRazgd+FVEB8qnI+DSwrq0p8ktvMfHICdZJYn+3ZcOqJT/R+RESB6oWIAtULEQWqFyIKVC/DCTmfBoBhGDh//jwMwxj0UIghYLHqRZ+cAQCk8lNweJp3nxFKTHxSxmrFJ1ttkvk07zqf1GjZTVe8YR9ue/ijmLh8VdShdoylBpxPYv/1/n7VS7X4xLOe/NXuWOaTJz6pfc58Wj0SypWSAs4nOe0fc0cQl4z41EngeHpTtmfD8cQn0/+bovcjIgpUL0QUqF6IKFC9EFGgehlOSHwaAJVKBUePHkWlUttORBDVLFa92BeY+CTZOnIn5vr6XBypifjE27b46mlBeOC4HVF8GgROyPnUf/GpX/VS3XbnZT7xtjvX+eRUXGGjz+ITBAHFjN96J4344pOSCbY6DjDgvcb51N5Ygs6nka29b7uTAs4nej8iokD1QkSB6oWIAtULEQWql+GExCeCIBizfhPTzOHJRXlKucwCx2OrajOfnBhvu6t9UzHyzPlkq0u/zzsYvO0MUgjpEkepHzjuiVCOBccJiE/x/rbdAYCR9UPH5Ux98ckRBvg2J4XPd7viE18lzxZkpNakWty5fdSMKz5ZFDhOEARBEARBLC4kPhEEAQCQctPez/lnF0l80pjzKb6q1vkEV7RxyrUflM08E6Ts+BA4n2IBF440xDF7LQLHAcCxbDgac6oJfc58AgBrle98UrMB8WnEr4uBCn4B55MtSBCl9tr/uPOpEs/2tGVQSbNzIgfa7qT77kP28cd79hwEQRAEQRAEUQ8SnwiCAACo+Rnv5/LJqcV5Tp2JT4nVdcQn1znjZQgFsArM+eTEF2/Vuk4Jik/OIgSO9w25Qdud7L+NWIYNaEzYEON9brsDIK73xSdl1K+F2OgScT4FxKcoIhgXKfVktqfD8ZxPts5y3ebmoH7gD7H1818ALKunz0UQBEEQBEEQQYb4a/jhRZIkjI6OQpKG+IMosWgsSr04DhJFX3zST/ff+WSUDMhu+09yTa34xJ0zXhtXAKvoik+Jpe98QjwoPvV/yu1bvVS13clx9lq4CAUAlm75zqdFaLtTNvptd7ExX3xSR4POp6UhPlkRzj0XKc1M7/KeAF98AgCjqEM4ehwnnwMcKNho2T19LmJ5QtcvRBSoXogoUL0QUaB6GU5IfBoAyWQSe/fuHfQwiCFhMeqlNFOGbAWylc73X3wqThW8n+vl2nDxwinXZj45hZJ7n6Wf+RQUYRbD+dS3emmR+QQAtmFB0JlYKCyC8ymxzXc+xcb9GopngysMDvCiJHBBFCls3h2zPZLt6XBiI34tVuY1FH78HAxTACBBgdL4gQThQtcvRBSoXogoUL0QUaB6GU6o7W4AOI4D27bhOIuznD0x3CxGveSemwn9Lk/3X3wqXWDikyEnQwIGxxNttFrnk+2GkIuppe98EhKLm/nUt3qparvj7XahtjvdAgzmfJIS/Xc+ZXb64lN8zK+FoPg00Mwnwc9riiI+eSLlWG+dT3Jc9pxgel7D/BMnvG16sba9lSCqoesXIgpUL0QUqF6IKFC9DCckPg2AQqGAH/7whygUCq3vTKx4FqNe8ifD4lN8vv+ZT+WL7PXosTp5Twg4n7TaD8VOibXdCclhEJ8CzqdFEJ/6Vi8B55MjiF4QdqjtzrAh6Ox8LUbm0/guv+0u2LqpJGRYAjvWjjjAtzm5M+eT4zqmxPFsr0cEU2L1aBQ0mEeeAxzAtmzMT+d6/lzE8oOuX4goUL0QUaB6IaJA9TKckPhEEASKzzPxaWF0CwAgWZ6GXuivE0KbYW8WZgPxSeSijV5nWfgyE5/E9NIPHBfjiys+9QtB9cUnW/BfR9D55JgWBIOdLznZf/FpdNsYLtz+n3Dx9tcjtdqvBUEATJm5nwbpfBKCwleU9j+3TuTV2d4OCIAVEJ/kM77zyaqYPX8ugiAIgiAIguAM7ychgiB6RuXcDOIAKpt2IpWfhGTrmDlyEeuv3di359Sm84gBMBP1xScpyT4ki1pt5pNQYeKTlFr6mU9iYIzDLD5B8cdenaPkCCIEx4alWxDctjtxEdruAOC2b/63urebcgIxozBY51PguaM4n+z1G4ETwNi1O3s+JEuOARpgnZuCND8HLjmZZaPnz0UQBEEQBEEQHHI+EQQBc9Jtu5uYQDGzDgCQ+1l/W+/0WeZ8shuIT1y8EOo5n1zxSc4s/bY7ORkQYQYZft0lQiDzqXoFOe6Esk0b0iI6n5phKYN3PoXEpwjC44u+fhdGvvOP2PWfXtDzIVkyq8fK48/AtAK3V0h8IgiCIAiCIPoHiU8EQcC+yMQnac0E9HEmPhWO9Td03Mwx8clJ1Ref5JQr2hi17X/iEIlPYnJ5OJ+CbXfVgg4Xo2zDgmiy8+WdvwFhqaw2Bup8kjpru1OSCja9eJuXq9VLbIWdl/KBZ0K3U9sdQRAEQRAE0U+G95PQEJNKpXDjjTdCUWhpa6I1i1EvwiwTn5R1E6isWQecACon+ys+WfNuQGC6RdudUet8EnUmPikjS198ktOB1kCp/y6cftWLoAbb7sJvHbYoARZb7c4TnwbtfFJd59MA3WZCh213/YSLT8oxV3wSAFESITvD68ojFg+6fiGiQPVCRIHqhYgC1ctwsjSuhlcYoigiFhusK4AYHhajXqR5Jj4lNk1AP8OWr7fO9ld8svNMfBIyjZ1PDgDJqM18kl3xSR0dLvFpMZxP/aoXMRg4XpP5xH63TRuSycRCJTVY8clZAuJTsO1uqbjePPFpbgp24HZHt+o/gCAC0PULEQWqFyIKVC9EFKhehhNquxsA5XIZhw8fRtldsYsgmrEY9RLLM/EptWUCsa2s7U6Y6q/4BFd8Ekeat92JddruJGN4xCclHXhjXATnU7/qRYgF2u7qBI4DbLU7yXIznwbcdufEXdFPGNzbnCAHjtMinPt2cFR2XmxXebIgwbZtlBdoqWKiNXT9QkSB6oWIAtULEQWql+GExKcBYJompqenYZqUsUG0pt/14tgOEiUmPo1sn0DqEiY+qbPRxadT9z2Hf/+lP8PCmYXWdy6wD7vyWKbuZi5ecCdNaNsQiU8hEUbuv/ulX/UiBla7qxafuBPK0i1IFhMLQ6LbIIhx59MA3+YEP7NpqTifuPjEmUpsAxxAL9YJ9ieIKuj6hYgC1QsRBaoXIgpUL8MJiU8EscLJn8tDdNjEnd0xjuzlrO0ukY++2t2J996L1f/6JRz+399peV+x5IpP2frOJzXjik9W1Ydiy4JosZW54mNLX3xSRwKZT4sgPvULMdYkcJyLT5oJ2Wbik5oecNtdnAeODzDzKeB8WiriEwIW9dnYBgijTPx1dLp4IwiCIAiCIPoHiU8EscLJHZsGAGjqCNS0iondTHxSjSIKk9FacaRpJljp5y62vK9YZvtWx+uLT9w5o5jhzCerUIbjsJ+HQnzKLG7bXb8QA4Hj1UIKF6P0Bf9cDdr5JCQG33YXWu1uqZz7gPhUWrsdcB1ttmYMakQEQRAEQRDECoDEJ4JY4RSenwUAVFLjAIDEeAKV2CgAYOZwtNY7pcD25czmWt5XLucBALGJFs4nW4dtOd7t5VnWcudARGJk6a9wERtdJs4ntXHbHW9t0+f9vvtBO5+EpOt8GqDoI0hL2/nkbN8BW2Z/Q7ZGzieCIAiCIAiif5D4NABisRi2b99OCf1EW/S7XkqnWd6TkZnwbiuPMPfT/NForXeJItsX5uZa3lfRmPMpsaZ+5lPQMWSUfFdGZY4JHJqUgKIKNY9basRGFjfzqV/1IjYNHGe/m3lXfBIESLHBii1i0hX9Btl2Jwbqc4kIj0Lcr4vEnh2AEoMgChBMu8mjCIJB1y9EFKheiChQvRBRoHoZTkh8GgCqqmLLli1Q1cE6A4jhoN/1op1jgpE95otPxgQLHS8eb9/5pC1oUMwSAECan215f1V3xafV9Z1PQdFGm/fbubi7xpATwTznJUvI+aT0X4DoV71I8SbikxgWnyxJxaBPjud8GmTgeDDzaQmKTxPX7YCgqhAEAWLAXUgQjaDrFyIKVC9EFKheiChQvQwnJD4NAErnJ6LQ73oxJ1nmE1av8m9cy8Qn/VT74lPuuC84KYXmzie9oEOymZspuaa++CTFZDhuXo+W173btZwrcChLP+8JACRFhCUy4UZYhBawvq12F8p8qt92Z+WZ+GhKg/8WSkovBedTMPNp6YlPm27eDsgyHMeBWabV7ojW0PULEQWqFyIKVC9EFKhehhMSnwZAuVzG4cOHUS6XW9+ZWPH0u16cGSYayavHvdvkTUx8cibbb7vLn/LFp1g51/S+xSkeZC4guSrZ8H5cxDAK/gdj7nwy1eEQn4CAGLMI7pd+1UvI+VQdOM5Xuysyh5olDf5bqPHrdgIAnO3bBzYGYQkGjosJVov51DqMrk/CEkU4tgOjRO9HRGvo+oWIAtULEQWqFyIKVC/DydL4KpYgiIEhzLK2O3W933aX2M7EJ/Fi+86n4vMz4PJETF+AWTEhx+tPMcVJFjauK0mIcmMN3JJjUMxySHwy8mWIAOwhEp8sOQ4YBQjy0hAgOkGKK7D4Lw3a7uwiuwCw5cGLT5f/6jWYfcF3sGv72MDGEBKflkjbnZxlTsPyRlecU9xzZdBqdwRBEARBEET/WBpXwwRBDAx5gYlPiU2++JS+ZC0sALG59sWnyrlZBNeemz+Vw8Tlq+rfd5o5n/RY/bBxjsWdT3k/88nMV6ACsGNDJD4pMaAMCIuQ+dQvggHiNSvIue2RTom13dny4NvuAGD80onWd+on4tITn/a883b89Ojz2PrrLwPg16Sjk22dIAiCIAiC6B/UdkcQK5x4nmU+pbf5QtH4buZ8ShUuwG5zFSx9Mhwynn8+nPv05KcfxGMf/g4AX3wyY/XznjiWK2KYJT/ziecKOfEhEp9klj+0XMQnLjZxuBjlLCHn01Ig6HxaKq639Lo0bv3/3oHtL78MACC4WV4OOZ8IgiAIgiCIPjK8n4RacP/99yOfz+PVr371oIdSgyiKSCaTEAe5ChMxNPSzXmzTRlzLAQBGtvmZT+OXrcKkqECyDZz6/jHvg2ozrIth8al4xhefzIoJ/I/fR9LS8NS6LPTZAuIArERz8clWXPGp6Lfd8RXVhkl8slWe+dR/AaJf9SInAplPcv3MJ1SYQ42ft5XOUmy7q0FRAQEQDHI+Ea2h6xciClQvRBSoXogoUL0MJ8vubB04cAAvf/nLcfvtt+PAgQMd7eP+++/Hy172Mtxwww3Yt28f7rjjDjzwwAM9G2MqlcJ1112HVCrVs30Sy5d+1sv8qRwExwYgIBvIxpFUCTO7bwEAnPrUN9rb2WxYfKpM5vxNR6chW0xAyr//E9AusG12sj3xySr54hPPFUI83t64lgC2ysYqLoLzqV/1EnI+Va8g5zqhhLLrSlPI+QRUuZ2WqPikJBMQRRGS7Qx6KMQQQNcvRBSoXogoUL0QUaB6GU6WjfiUy+Xw8Y9/HH/3d3+Hhx9+uOP93Hvvvbjjjjvw2te+Fo888ggOHDiAV7/61XjJS16CL33pSz0cMUEMntwx1nJXjo9BUsOCwsTbXgMAGP3Rv0Av6DWPrUacY9lRjitEaJO+8yl39IL38+jscQh/+7fsvunmmU+e86ngZz7ZJfazkGq8St5Sw3HFp8VwPvWLoPOpZuU293ex4opPKjmfAEAQBf/nJXruBdU9r9R2RxAEQRAEQfSRZSM+jY6O4t3vfjf++I//GL/wC7/Q0T4eeeQRvP3tb8cdd9yBt7/97d7tv/u7v4ubb74Zv/Vbv4UjR450PdZCoYAHH3wQhUKh9Z2JFU8/66VwiglGWro2mHnPr92IYmotYvoCDn3qgZb7UvLM+bSQ3QIAMC/4Tqj8syy43BaY+2Nk/nm2Id3c+cRFDKvsi1/c+SQkh6ftzlnLMrQSm1f3/bn6VS/NxCfedidqbuYTiU8AwoLTUs37MkUHtmXDqtBSxURr6PqFiALVCxEFqhciClQvw8myEZ8Ewf+GOd5hO8773/9+GIaBt73tbTXbfu3Xfg2VSgX33HNPx2PkOI4Dy7LgONTmQLSmn/VSOccEImNkvGabKIsov+RVAIDC33295b7iRVfI2ngJAMCe8Z1PleeZ82n6qp/D3Go/P0rItCk+FX3nE9wV1cTU8IhPL/zLd8L+0z/HNb9za9+fq1/1IscD4kkj55PuCoMqtd0BSzNwvBpB4c4nynwiWkPXL0QUqF6IKFC9EFGgehlOlo341C1nz57Fv/7rvwIAbrvttprtt97KPjT+3//7fzE/P7+YQyOIvqGdc1vlxuovSX/Zf2eB/auPP4qpg+cb7sesmIjpCwAA8VImPiGX87Yb55j4JGxYj/EP/3fvdmm0ufiEkRG2/9kF/zbXoSGmhifzKbMhgyt//UaI8vBOuUoy4HyqCRxnr0tyxSeQ+ASgSnxaos4nkWd5UdsdQRAEQRAE0UeG95NQj3nooYcAANlsFuvXr6/ZvnnzZoyMjEDX9Z6GjxPEIDGnWOaTsHpV3e3r9m3A9PbrATj42R9/s+F+cieYy8kRRKT2bAMAiAu+8wmTUwAAZdNaXPGGfbjwwlcAAEavvbTp+IQJ5sgKraTnrqgmp4fH+bQcaOp8ctvuZC4+xajtDhgO8YlnPgkWiU8EQRAEQRBE/yDxyeXw4cMAgI0bNza8z4YNGwAATz311KKMiSD6iuNAPfYMAEBeV198AoDkf2LB4/HvfgO2ade9z8JJJg6V42NIbmYuKjXvC0byDBOfElvXAABuue/9GP3e13Dlb+xvOkR5tbsCX2AlPcF1PkmZ4QkcXw6IsuiFyTdqu5NNt+0uRs4nABCVpZ/5xJ1PAjmfCIIgCIIgiD6yNK+GB8Cs++E2k2m8+taI2wJ04cKFuts1TYOm+UvCLyywVqF8Ph/KpBJFEfv27UM8Hkc+n6/ZDx9DqVSCZVmhbfF4HIqiQNf10HMBgCRJSCaTcBynbvhaKpWCKIool8swzXC+RywWg6qqMAwDlUoltE0URW8Zy3rjTSaTkCQJlUoFRtUHGFVVEYvFYJomyuVwoK0gCEi7gdOFQqGmZzeRSECWZWiaBl0Pr7amKAri8Tgsy0LJzQAKwo9hsViEbYcFk2bHUJZlJBIJ2LaNYrFYs990Og1BEOqem2bHkJ8boP4xbHZuZFnGvn37oKpqzWOD56beMeTnpt4xnP3aj5E9+ThMQcXqX7kutO/gudnxtutw8uNppPOTOPAXP8Tld+7zzg0/hjNHzyDlAJXkGEZXJ2EAiJXmvH2quSnAAeSNGTiOA1EWMXbNKhSK4Trl58Y7huNJwAEwN41SqcRej1YGHMBShdCY26nvenXY7jFsVt/16jB4DOvVYbP6blWHzeYIRVGwb98+KIpSUy/dzhG2IENydJiwQ/u2wI6ZarFzY0piaPtKnSMquoaUO2TDsaDrel/miGbHsFV9K8kERFGEaJk1z9vsGLZb372eZ2vmiAC9eA9cCXNEN9cR8Xgc+/btgyAINfVC1xGMpXgd0c0c0ew6otUckUwmsW/fvrpjpjmCsdzmiG4+a/B6sW27pl5ojmAstzmim+uIZDKJvXv31q0XmiMYizVH1Ku3RpD45MJPptokqyTmtpLUO0EA8JGPfAQf+MAHam5/4oknvIIDgDVr1uCKK65AuVzGgQMHau7P86WOHDlSczJ37dqFtWvX4uLFizh27Fho29jYGK6++mpYllV3v/v374eqqnjuuecwMzMT2rZjxw5s3rwZuVwOTz/9dGhbOp3GC1/4QgDA448/XvMHc+211yKVSuHUqVOYnJwMbdu8eTN27NiBQqGAJ554IrRNVVXs38+cL4cOHar5o7jmmmuQzWZx9uxZnD59OrRt3bp1uPzyy1GpVGpeqyAIuOWWWwCwY1g9eezevRurV6/G1NQUjh8/Hto2MTGBK6+8EqZp1j2GL3rRiyDLMo4dO4a5ubnQtp07d2Ljxo2YnZ2tWRUxk8l4F2D19nv99dcjkUjg5MmTNeLm1q1bsW3bNszOzuLQoUOhbfF4HDfccAMA4Mknn6yZsPbu3YvR0VGcPn0aZ8+e9Y+RYWDsA38CCcDPrns91gjncP7AOW+7JEl48YtfDAB47sxzOL9xD3Y892Oc+LdHUNwD7NmzB6tWrcLk5CROnDiBCweewRW2jbwch+rMIw0gpi/gJw//BACwqngBtuPgjD6FPY4DQRBw9OjRmvy0yy67DOvXr8f09DSOHj2KGWMBo7YNZ3YKR48exd69eyFpJdi2jbMz52EEjuWNN96IWCyG48ePY3p6OrTf7du3Y8uWLcjlcp7LkZNMJnHdddcBAA4ePFgzwe7btw+ZTAanT5/GuXPnQts2btyInTt3olAo4ODBg6FtiqLgpptuAsDcktVvcFdddRXGx8dx/vx5nDp1KrSNzxGapnU8R5w9e7bnc4QtyhAtDfP5QujxpcDfrm3bmC3kQ9tX6hxx+uxpXOZeCEzNTOPixYt9nSMWFhYizxGS6AACYGm1x2nDhg249NJLUSqVarYF54inn3665oKoeo4IsmrVKuzZsweGYdR9rTfffHPbc0SQ0dFR7N27F47j1N0vzRGMbq8jMpkMnnrqKbqOGMLriE7miEbXEUB7c0Qmk8FPfvITmiNW0BzRzWeNTCaDH/zgBzRHrKA5Auj8OiIej+Phhx+uea00RzAWa45opI3UQ3CWYUT8W97yFvzVX/0V7rnnHrz//e9v6zHvfOc78ed//ufYv3+/l/9Uzf79+/Hwww/jne98J/7sz/6sZns959PmzZtx5swZzzUFAJZleR9CqosPWJrfRtA3loxBfBvhOA6mpqawfv36mmPU6bcR5c/8DWY+cC9m5dXIfOcfsH1PeDzVSvqPX/nH2PDINzH52l/HdZ99Q823ET/5na9g3Vc+i3N7X4b93/mfOLXuNgAOMg99FZZmo3Tbr8AWRGw+812MZEfa/jbi9H3PAf/p11GKj2H3ma8jmUzisbWvQDJ/Afq9n8Mlr/RXzqNvI/znPX/+PNatW1fzuG7niKfW/QLi2jzOveQ/4Ya/+U1v26Ov+BjWH/g2+8UBLrz+t/HCP32dt32lzhHP/PVPkXjXuwAAM7/7HtzwgVcuuW8sn/mrx5F697swv/oSXHX43tA2cj4xltsc0c11hCRJOHv2LFavXg2pqv2WriMYS/E6YlCuBlmWcfr0aUxMTEDhK2u60BzBWG5zRDefNSRJwunTpzE+Pl5jBqA5grHc5ohuriMEQcDzzz+PiYmJmnqhOYKxWHPEwsICNm3ahPn5+ZDmUQ9yPrnwD2r1Co3Dt61du7bu9lgs5rmjgmQymVA7Xz6fx7lz57Bu3bqmbX58AqmHqqoNXVqCIDTdbyLROKhZUZSaC4QgzfYbj8cRj9dfgUyW5aaP5X849Wh0XAE2STTbb9BxVk2zYyiKYsfnpptjWO/ctFsvbR/DCxew8Nm/gwPgp/vfid+5sfE+AXYM5XQaEABJN0Nj4MdQyhUAAVDWrkEqk0IlNoq4loM9Y0KfLQICUEqvxejYqPfYdo7hut2bMSkACX0ecZXVlmyUAQEYXTdR93g0q+9u6rBZfXdTh83qu5M6bKdeOp0jbJHVtRKLhx4vBetdAGKjmbr7X2lzRDKdguN2XCfSKW9fvZ4jOJ0cQ0GVWSusZTR87LDNs928B66EOYLTyXVEO/MLXUcwltJ1BGexj2E79UJzBGO5zBGcTj5rtFMvNEcwlsscwenkGObzeZw/fx7r169vuG+aIxj9niOieJkocNxl9+7dAIDz5xsvJ8+37dmzp+F9CGKpo33iz5G/WMaxxNW49n2/0NZjhBSbZJ1ype52Z4Zlpkmr2ep0WpIFhRdPz6JwnFl7tdE1kcc6ujXLnt+xsXCa2WYVN9Q6NkaB44uNLbnh1HJV4Lgc/h5DilPgOMBC2jlLPXBcpNXuCIIgCIIgiD5C4pPLLbfcAlEUMT3NcjmqmZqawszMDCRJ8np8CWLocBzM/+N3YdvAg9f+HvbfJLR+DAAx4arwVTZSjjDH+vpjG9hKd0aGiU/lc3Mon2Lik7WqvmOwGZIqoRJjbqmFU3OwNQOCxWyu8Wz9bwaI/mFL7jdE1eKTGH4rERP1v11ZaQiSf1wkVWpyz8Ehxdk5FS2zxT0JgiAIgiAIonNIfHKZmJjAS1/6UgDAD3/4w5rtP/jBDwAAL3vZyzA+Pr6oYyOInmEYyE0zh8Mdv7EDQnvaE8Rkc/FJWWDOp/gG9rdhZ7IAAO1CDsbZKQCAsC66+AQAWpLts/D8LLSc//zxscaWV6I/OK74JFRlvaDqdylBzicAEBX/uCxV55OccJ1PNjmfCIIgCIIgiP6xLMUnHvJVHbrF+d73vocrr7wSn/jEJ0K3v+c974EgCPjiF79Y85h7770XoijiPe95T9fjU1UVGzdubLqyHkFwelkvdlkDz8e74Zb23SlS2hV6tPptd7ECcz6lNrvC7BhzPpkX5oBJJj4pG6O33QEBF9XZWVRy7PltSIhnGve6r2T6Ob80brsL/y6nyPkEAEF1V1SXpvgUSycgCAIkcj4RbUDXL0QUqF6IKFC9EFGgehlOlp34NDMzgwcffBAA8KMf/agmiR4APvnJT+Lw4cM1K+HdcssteP/7349//ud/xhe+8AXv9s997nP4zne+gw9+8IPekpDdEIvFsHPnzobBXwQRpJf1UllgKx04EBFPt/9hWEox55NYR3yyTRtxLQcAGNnGxCdxgglG9vQspFnWdpfY1pnzyR51c6Qm56DPM2HZkBPVnV6ESz/nF6eB+CRUnQwpSXMbEHY+LdW2u8RIGoIoQHLI+US0hq5fiChQvRBRoHohokD1Mpwsm49vlmXh2muvxdatW3Hu3DkAwH333YcNGzbgta99bei+r3vd65DJZPDmN7+5Zj9/8Ad/gK9//ev4m7/5G+zfvx833ngjvvzlL+Ob3/xmT1xPfKzz8/M1yxYSRD16WS96ni1Vaggq1FibPXcAlBHmfBL0WvFp4fQ8BIe5DLPbmegkr3UdUHNziOeY8ymzszPxCW6bq3lhFpU5V3xSKGy8Ef2cX7y2u2rnU1XbnZykb6GA4Qgcl+Ii4K52hwirlRArE7p+IaJA9UJEgeqFiALVy3CyNK+GO0CSJDz22GNt3ffOO+/EnXfe2XD7q1/9arz61a/u1dBqKJVKOHjwIPbt29d0aUOCAHpbL0aeiUemFIvkHJIzTHyStNrMp/kTLO+pEhuFHGdTSmxNlt0/N4NkiQX4Zy/rrO1OXOW6qGZ855OlUNh4I/o5v3DnU03geHXbHYlPAIYjcFx3DNi2AxEOHMuuFRYJIgBdvxBRoHohokD1QkSB6mU4WTbOJ4IgWqMXWNudKUezqCoZJvZIeq34VDjF8p601IR3W2IDE4wyF56D4NhwBBHjl07UPLat5+YuqtlZGAuu+KRS2PggcBR3ZbQqF0912x1lPjGCzqelmvkkJ/zsNFuj1juCIAiCIAiiP5D4RBArCKPI2u6siOKTOuo6n4zatrvyWeZ8MtJj3m2pTexn1SgAAErJ1aEP4lGIr2P7khYC4lOMxKdB4CRSAAIB9Jwqt4yaJucTUJX5FFua4pOS9MUnLk4TBEEQBEEQRK8h8YkgVhBmoUPxaYQ5n2Sz1vmknWfikz3mO5syW8ZC96lkO8x7ApDcxJxPSmEOZp49v0Pi00DY9qFfx8VXvQ17/vOtoduF6swncj4BAATRz1VbsuJT3D93RplWvCMIgiAIgiD6w9K8Gl7mCIIARVEgCO0HPhMrl17Wi1lgziVH6VR8qnU+mReY+MSDwQFgdGsWk4H7WBOdi0+pTWNYABAvzUJzxSebxKeG9HN+2fbSS7HtpZfWPic5n+oyDM4nWRFhCApisGBVqO2OaA5dvxBRoHohokD1QkSB6mU4WZpXw8ucdDqNm266adDDIIaEXtaLWdIhArAjik/xMSb2iI4Fo2SEWnWcGSY+Sat98UlSJWjqCGL6ArthbWdh4wAwspWJT6pRRGFmnt2YIPGpEQOZX6SwiZbEJ8YwZD5lMmkISgywSjDLJD4RzaHrFyIKVC9EFKheiChQvQwn1HZHECsIq8Ta7iKLT1l/dblKLux+EmZZ4Li6PhworiX91jtlY+fOp8yGDGyBOUis58+xG0l8WlJUt92pGWq7A8Kr3cmxpbuKnCUyMdkoUuYTQRAEQRAE0R9IfBoAxWIRjzzyCIrF4qCHQgwBvawX0w0cd9SIq90lFU8AqsyFc5+kBeZ8im8YD90eDCBPbOtcfBJEAeUE27c4eZbdliTxqRGDmF+CbXeOIIbazVYyQefTUm27KxaL0B0BcABLo8wnojl0/UJEgeqFiALVCxEFqpfhhMSnAWDbNiqVCmzbHvRQiCGgl/Vil9zMp1h0Z4opM/eTvhB2PsUKzPmU2hwWn6wRX3xK7+i87Q4A9BTbV2yGOZ+EFIlPjRjE/BJ0+JgSuZ44w9B2Z9s2DNf5RJlPRCvo+oWIAtULEQWqFyIKVC/DCYlPBLGCsCtuW01E5xMAmDITfILik2M7iJfnAACZrWHxCWO++DS2q3PnEwCYI2zfqcIUAEAk8WlJEXQ+2RLlPXGCDrCl3HbniGxslPlEEARBEARB9AsSnwhiBWFXWNsdOnE+qa74NO+33ekFHZLNPrCmN4yE7i9OMPHJEURMXL6qk+F6OFkmPgkO+3ZDTpP4tJQIik+mTM4njiD6K7DI8aXpfAIAS2Rjs8qU+UQQBEEQBEH0h6V7NUwQRM+xy0x8EuLRBQJbYW13Rt53PpUu+n3WifGwICSvZuJTKbkq1H7UEeNjoV9FEp+WFMG2O5vEJ4/kqiTK8XHYkrykVwC0JFd8oswngiAIgiAIok+Q+DQAEokErrrqKiRoxS6iDXpaL2VXOOpAfLJi7PnNvO98Kk8z8cmQkzUCU3wTcztVsus7GWkIeXW4pY+cT40ZxPwSaruTl67IstjIcRk7D/wDBEnsXoDtE4lEAnIiBeQp84loDV2/EFGgeiGiQPVCRIHqZTgh8WkAyLKM8fHx1nckCPS2XhyNtdWIHYhPjuo6nxZ88akyw8QnPZauuf/u33wxHv7312Ht/3NHJ0MNoawJO5/UkXjX+1yuDGJ+CYpPlkLOpyCjW7ODHkJTZFmG6GbA2RqJT0Rz6PqFiALVCxEFqhciClQvw8nS/Cp2maNpGk6ePAlN0wY9FGII6Gm9aF203cWY4GMV/bY7bZaJT6aaqrl/PBvHbd96N654w75ORhoisTH85qKMJrve53JlEPNLMFjbIefTUKFpGjTbARBYkIAgGkDXL0QUqF6IKFC9EFGgehlOSHwaALqu49SpU9B1utAnWtPTenEnaDHRgTslzmytViEQOD7HxCcrXis+9ZLkprD4pI6SxbYRg5hfgplPjkLi0zCh6zoTnxzKfCJaQ9cvRBSoXogoUL0QUaB6GU5IfCKIlYTGXEtishPxiTmf7JLvfNJnC+y2eH+dSJktVW13JD4tKYJtd45KbXfDhi0r7H9quyMIgiAIgiD6BIlPBLGCENxvB+ROxCc30M8u++KTOc+cT3ayNvOpl4xuC4tP8SxlPi0lQm13Kjmfhg1HYufP1kl8IgiCIAiCIPoDiU8EsYIQDNZ2J3UgPgkJJvg4Rb/tzsqX2A+p/rbdxbNxGLLvroplyfm0lAi23YGcT0OH4zrXHMp8IgiCIAiCIPoEiU8DQJZlrFmzBrJMiw0SrellvYiu+CSnogsEYsoVfCq+88nKM+dTv8UnAKgkffdTYpzEp0YMYn4R1cBzkfNpqJBlGXKCCbuOQZlPRHPo+oWIAtULEQWqFyIKVC/DCZ2tAZBIJHDFFVcMehjEkNDLehF1V3xKRhcIPPGp7DufnAWW+SRm+i8+6elxYOEsLFGBkqCpqxGDmF+CzichRuLTMJFIJJAcGwMEarsjWkPXL0QUqF6IKFC9EFGgehlOyPk0AGzbRrlchm3bgx4KMQT0sl5E0xWf0tEzk6SU+5iA8wlF5nxaDPHJGmHOJ0Mm11MzBjG/hNruYtR2N0zYtg1bFAAADgWOEy2g6xciClQvRBSoXogoUL0MJyQ+DYBisYhHH30URfeDO0E0o5f1Ipmdt91x8UnQfOcTF5+k0f6LTxgbBwBYCoWNN2MQ84sU851o5HwaLorFInKFPOAAoOWKiRbQ9QsRBaoXIgpUL0QUqF6GExKfCGIZcvCTD+BH29+IMw+eDN3OxSclHV184m4pMSA+iWU24SuLID4Jq7j4RM6npUao7S5Ozqdhw3HzEijziSAIgiAIgugXJD4RxDIk9/f/irELR3Dqbx8M3S5bTHxS09HdKXKGiT6i7rfdiRVXfMr2X3xSVrO2O1NNtrgnsdiIiuT/nCDxadhwFNe5ZlDbHUEQBEEQBNEfSHwiiGWIUGHuJCtf8m+0LAiWBQBQR6K3rqmjTHySAuKT7IpP6lj/xafEtrUAACs90vfnIqIREp/i1HY3dMjs/DkUOE4QBEEQBEH0CVoyiiCWITyXySn64pNV0uC4P8dGortTlEwcFgDJDIhPGhOfYuP9F5/2/NaL8fCB38DG193c9+cioiHK/vcY5HwaQlzxUDAo84kgCIIgCILoDyQ+DYBMJoNbb7110MMghoRO6oW3xgXFJ23eF41imejuFHU0gQoA2fAznxSDiU+JVf0Xn9S0ilv+9rf6/jzDziDml6DzSUqQ82mYyGQy2LR9GyAAMCnziWgOXb8QUaB6IaJA9UJEgeplOKG2O4JYhnitcWVfKNLyzNVgCCoUVYi8z9goa9WTXeeTbdpQTLb/xRCfiKULiU/DjdcqSZlPBEEQBEEQRJ8g8WkAlEolHDhwAKVSqfWdiRVPJ/UiGq74VPKXHzUKLGzclGIQO/jLj2XdzCfbgKVbKE3740muJvFpqTCI+SXYdienqO1umCiVSpicvgg4AEwSn4jm0PULEQWqFyIKVC9EFKhehhMSnwaAZVnI5/Ow3PBngmhGJ/Uiu+KTWPGdT3qeiU+W1Jk4EM/6IeXl2TLK00zYskSlo9XziP4wiPmFnE/Di2VZ0B1WKyJlPhEtoOsXIgpUL0QUqF6IKFC9DCckPhHEMkR22+HEiv9tgFlw2+XkzsQBJanAEdiUoc1XPPHJUJLdDJVYBgTFJ3I+DR+C6l4KWJT5RBAEQRAEQfQHEp8IYpnh2A4UN5dJ0nzxySgyV4OpxOs+rhWCKMCU2GO1XBmV6QLbr0otdysdSfXFJyVFzqehI8bWHhGp7Y4gCIIgCILoEyQ+EcQyQy/oYAEugKwHnE9F1nZny507U0yZiU/6QgXaLHM+mfF0x/sjlgehzKckiU/DhiAz8VAg8YkgCIIgCILoEyQ+DYB4PI5du3YhHu/MgUIsbY4fB558snf7i1ovlVzF+1ky/MwnT3xSuxCfVBY6rs+XYeTczKc4OZ+WEoOYX4Jtd0qa2u6GiXg8jo3bNgECIFqU+UQ0h65fiChQvRBRoHohokD1MpzIgx7ASkRRFKxdu3bQwyD6gOMAv/3bQD4PfOc7wMhI9/uMWi9azhecVKMIx3YgiAKsYgUSAKfDzCcAsBQmPhn5Coz5EuIAnASJT0uJQcwv1HY3vCiKgol1q2EBECjziWgBXb8QUaB6IaJA9UJEgeplOCHn0wDQdR1nz56FrtO3zMsNXQd+9dD7cNex38D0VG9WX4haL/qC73wSHQtGibXSWGX2eDvW+TcEtsoeaxYqMHMs88lJkvi0lBjE/BJsuyPn03Ch6zrmigsAANGitjuiOXT9QkSB6oWIAtULEQWql+GExKcBoGkajh07Bk3TBj0Uoscs5GzctPBtXF56HKVj53qyz6j1EhSfAKA8w3KfzJL7+C7a7uyY63xaKMPKs7Y7pEh8WkoMYn6JjcSQW3Up5sd3IL2OMsCGCU3TMDUzBTgkPhGtoesXIgpUL0QUqF6IKFC9DCfUdkcQPaQwmfd+Lk/OA9i86GPQ58uh38szJYxuzcL2xKfO26Ic1zVlFStwXPFJSJP4tNIRZRE3PPs3cGwn5IIihgNBZedMpswngiAIgiAIok+Q+EQQPaQ8ueD9XLmw0OSe/cPIs2wnDs+Asiuu+BTr3PnkxJnzySqU4RSY+CRmSHwiwrlPxHAhuOdOsCnziSAIgiAIgugP9BU1QfSQ8gXf+WRczA1kDGY+7HzSZt32OC4+dbMqhPtYq1AGimy/0giJTwQxzIgxJj5JtsFWTSAIgiAIgiCIHkPi0wCQJAljY2OQJHIKLDe0i77byZztjfMpar2YhXDmE2/Ds8tMfBLiXQRCu+KTU65AKJH4tBSh+YWIgiRJGF87zn5xHNimPdgBEUsaml+IKFC9EFGgeiGiQPUynFDb3QBIJpO4+uqrBz0Mog8YM77gZM3O92SfUevFKlaJTzkWOO5oXHzqPPNJSLK2O7tUhlRm4pOSJfFpKUHzCxGFZDKJq/ZdjecF9rtZ0qGOJgY7KGLJQvMLEQWqFyIKVC9EFKhehhNyPg0Ax3FgmiYcam9Ydphzftsd5nsjPkWtl2rxycoz8Ym33YldOJ+EpNuyV65AqjDxSR0j8WkpQfMLEQXHcSDGBO93s0wr3hGNofmFiALVCxEFqhciClQvwwmJTwOgUCjgRz/6EQqFwqCHMvSUpkt4+kuPwdKtjvdx/rGzOPndZ3syHnPOdz4JC70Rn6LWi10Ki0/mgis+6a7zKdl55pOUch0RlQokjcSnpQjNL0QUCoUCHnnsYcC9djPKFDpONIbmFyIKVC9EFKheiChQvQwnJD4RQ82jb/0M8Pbfxk8/9O2OHu/YDs684jeQ/49vxtxzs12Px5n3nU9ioTfiU1TsYjhwnDufBLftTkp07nwS0674VC5D1dhkH58g8YkghhlREmCICgDAKJHziSAIgiAIgug9JD4RQ41y6AAAoHzkVEePL8+WkSpegGTrOPXPh7of0ILvfJJLvQkcj4pTDjuf7ELY+SQlOs98klPMNSVoZSgGcz4lVqc73h9BEEsDW2QRkGZJH/BICIIgCIIgiOUIiU/E0GKUDIxOPwcAcGY6cy3NPTvt/Tz/48Ndj0ks+IJTrJTren8dUebOJ5bjYpfY76IrPsmpzp1PcoY5n6TiAkSHtTomJpId748giKWB7TqfrAo5nwiCIAiCIIjeQ+ITMbSc/vfjEB03nyQ319E+Cs/7opXw1FNdj0ks+m13cX0B5iDiUyrM+VSJjbLfC8yhJBqu8ynVeeaTnGaPjeV90S65isQnghh2LImJT2aFMp8IgiAIgiCI3kPi0wBIpVLYv38/UinKyumGiz884v0sz3fmfCo+P+P9nDnzDGzT7mpMctl3PiXsAhbmOg9C50SuF1d80lLj7HfXCeWJT9203bniU6LEjpshJyHKNI0sJWh+IaLA68WWmCOSVrsjmkHzCxEFqhciClQvRBSoXoYT+tQ4AERRhKqqEEU6/N1QPvgz72el0Jn4VDnni08xI4/zPznT1ZiUSj70e+Fc97lPUetF0JjYZIxMsBvKLPOJi0/dtN2po6ztjrfc6SpN+EsNml+IKPB6cWSW+WSVKfOJaAzNL0QUqF6IKFC9EFGgehlO6GwNgHK5jKeeegrlcrn1nYmGSMd851O83FnbnXF+OvT7uX/rvPXOtoG4xsQmPg8Wz3W/4l3UehE15nyyR8fY7xVXfDKZ+KRmuhCfRsIte2aMxKelBs0vRBR4vfDAccp8IppB8wsRBaoXIgpUL0QUqF6GExKfBoBpmpiZmYE5kECg5YFt2hidPOr9rphllGejTz7ONHNMOQL7Uyg+2nnoeGHBRsIusP0lWA5Saap751PUehF19zhMMOeT5IpPsis+KZnOM59i2UTodytGeU9LDZpfiCjwerEkV3zSqG6IxtD8QkSB6oWIAtULEQWql+GExCdiKDn70CnIVgWmFIclsgyj+ZPR3U/CLGu7m9n6QgCA/LPOxafiFBOeRAEoZjcBAPSpXMf76xRJZ84naTXLfJI0Jj5Jlis+pTrPfIqNhoUrO5HueF8EQSwdbInNC+R8IgiCIAiCIPoBiU8RWFhYwHe/+91BD4MAMPUDlvc0v+4ylJNMZCmcbiw+6QUdx//lSE2guDTPxCfptpsBAKNTP4NR6uzDV/E8czlZStzLW9Ivdt92FxXJZOKTup6NQdZLcCwbksVeVzdtd/GxsPPJTlDbHUEsC2QJAGBXKPOJIAiCIAiC6D3LSnx67rnn8KY3vQkveMELcNNNN2Hfvn34/Oc/H3k/R44cgSiKEAQh9G90dBSCIPRh5ERUij9leU/Wzl3Q00x8Kp6uHzo+/cxF/OSqt6HyK2/EgY/+W2hbLM8yn9a85Gpo6ggk28Cp7z3b0ZjKF1jYuBYfgTMyCgAwZ7tvu4uKbDDxKb6eZT4pRglWWYfjbu8q8ymtAgj8DdAKEwSxLLBlhf2vkfOJIAiCIAiC6D3LRnw6ePAg9u3bB8dx8Oijj+Khhx7Cpz71KbzrXe/Cb/7mb0ba1yc+8Qk4jlNz+xVXXIE77rij67HGYjHs2LEDsVjnIsBKRzjKnE+JF+yClWEiS+V8rfPp1PeP4cStb8HYBSZWFX7ytLfNsR0kSkywGr1kFRa2XAkAuPhAZ613lWkmPpmJDITREQCANde98ylqvciu8ym1hTmfFLOMSq7i72+k87oTRAGGHGi9I/FpyUHzCxEFXi9QWL1Q5hPRDJpfiChQvRBRoHohokD1MpwsC/Fpfn4er371qyHLMj796U9DUdg3uC960Yvw7ne/G5///Ofxl3/5l23ta2pqCl/96ldx/PhxzM3Nhf49+eSTPXE+qaqKzZs3Q1U7z95ZyTi2g8w5JiatfvHlcMaY88mYCjufDn/xUeT+49uQKk7BFliYLs6e87bnz+UhOuyDVnbHOIQr9wAAtAOdiU/GtNt2lxyBOJFlY811Lz5FqRfbtCHZrG1mZNu4e6uDwlk2DhsS1ITU1XhM2W+9EzIkPi01aH4hosDrRVDZxRs5n4hm0PxCRIHqhYgC1QsRBaqX4WRZiE+f/exncfr0afzyL/8yMplMaNvb3vY2AMB73/teGEbri+pPfepTeOMb34jt27cjm82G/smy3JPxGoaBixcvtjUeopapg+cR0/OwBRmbb90BYZw5n8yLvvjk2A4q//29UMwSprfsQ/muewAAyrQvPuWOsZY7TR2BmlaRfRETn2LHOhOfdFd8ctIZKOPM+SQudC8+RamXoMNpZEsWvEWO52GZUgzd6qem4jufJBKflhw0vxBR4PViS+xywNEp84loDM0vRBSoXogoUL0QUaB6GU6Whfj0hS98AQBw22231WzbvHkzduzYgbNnz+J73/te0/0Ui0V85jOfwYMPPoi77roL3/72t6FpWs/HW6lU8PTTT6NSqbS+M1HD+fuZ62l+1SVQkgrkNa7DZ9Zvu8udmEOiMgtAwPU//jOsu20XACCVOwvHZi2VheeZWFVJscdvfvluAMDo3EkUJguRx2XlWNudkxmBsoplPomF7jOfotRLZa7s/iQgNhKD4bqUimdc8UmON3hk+9hB8WmExKelBs0vRBR4vTiSGzhOzieiCTS/EFGgeiGiQPVCRIHqZTgZevHp4sWLOHbsGABg9+7dde9z5ZUsy+ff/u3f6m7n3HvvvZidncXjjz+Oj3/843jFK16B9evX46Mf/Sgsy+rtwImOyT/G8p6MS5igpK5j4pGQ851PFw+eBQAUU6sRz8axZu8Gdl+jiPw5JhKVTrOV7owMy0Yau2Qc+cwGAA7+v3c/ij/9U+BTnwLc8mqJnWNCkzA6gvhaJj4pxVyHr7IztHk2ARtynOUzKUkAgD7FxCdL7t6aaql+2508SuITQSwHHDdw3NEp84kgCIIgCILoPUMvPh0+7LdIbdy4se59NmxgwsNTTz3VdF/79+/H5z//edx999248cYbAQBzc3O4++67/3/27js+qip9/PjnTk/vAUKPdBAQ6Qg2FCuKutjrWta+rm2tX8u6lt9aV7GsbZW161pYKxZEVHqXIj1ASO/J9PP742YmGWYSctMmCc/79fJlmHvnzLkzT07uPPc55zJr1ixJQHUQaoNe+WQbORiou6ubpaKu8ql8oz69riZVjwl7op3qGD3JFEhMufbqySd/SlrweTWHHArAhI9uI+uha1jz7I/84zF/0zpWoSe1TMkJxHSvTT45Qyuf3JXuYOVVW3CX68mnQIWT11abfMovBcBvafmifH5bXeWTLUWST0J0CbVrJii3VD4JIYQQQojW1zqLGEVRcXFdtcv+6z0FJCbq6+/k5+c32ta4ceMYN25c8N+rVq3iuuuuY9GiRXz22Wfce++9PPTQQw0+3+VyhUzTKy/XEw8VFRUhC5UH9vH7/VTUJizqCxxHdXV1WMLL4XBgtVpxu91hUwLNZjOxsbEopaisDJ82FhcXh8lkoqamBq839Oq23W7HZrPh8XjCyhdNJhNxtXc1i9Tf2NhYzGYzTqczbN6tzWbDbrfj9XqpqakJ2aZpGvHx8QBUVlaG3WEwJiYGi8WCy+XCXW8dkpjdeuVT+pQh+Hw+TOl2UGCrKAr2r2bLHmIBT3q34GNVyT2IrS6ieF0Oh5wyFPfePFDgTUkK7tP//11K8f956LH5Bya5FjM8ZwlLfzyNioo/AxAfH4+maRE/G1VWqh9XUhwkmUGBw1lKWVkFVquZ4pUFFJ94HvlTTmHCe9c1+bMJvKc+ny/s/a//2VRWVlK2rwiL0iufKioq8Nr1KiVPfpF+rGZrsA2r1YrD4cDn81FdXd3gZ1NVVYXfX5eA81rrqqdMiY6wPlksFmJiYvD7/VRVVbG/xt7DQHxHisPWiO9Icbj/e7h/HDYlvo2+h9BwfMOB38PGxojAsXs8nrDP5mAZI6Dx+Ia69zDSZ9PYONuS+G7sPQx8NhD5PWzss2nsPTxQfAf6qMwmUOCpqmrxGAF1n01rv4cyRuhaMka05DwicOyRliSQMULX1caIwGdj9D2sf967/2uCjBEBXW2MaMl5RECk30cZI3RdbYxoyXlEQKTPRsYIXXuNEZHirSGdPvlU/0NoaLX7wC0YI72xjRk9ejQ//vgjl156KW+88QbPPPMMt956K8nJyRH3f/jhh7n//vvDHl+9enUw4ABSU1OJj4/H6/VGrMY68sgjAdi4cWPYhzlkyBC6desWMt0wICUlhZEjR+Lz+VixYkVYu5MmTcJms7F161aKiopCtmVnZ9O7d29KS0v57bffQrbFx8dz+OGHA7By5cqwX5ixY8cSFxfHzp072bdvX8i2wJpblZWVrF69OmSbzWRi0vr1MH48ayP8UowaNYrk5GT27NlDTk4OAD63j8zKfJSmkXl4b5xOJzvLc+nj92OvLmLZkmWYrWb8OXrlU0ViXPC9KI1JIM3vp3T9dgBcuXn4/X5K8Af3SUtL47BvHsO9cyebbv072qdL6Z63Mrh9ypQpWCwWtmzZQklJSUh/Ka9dV8lhZkv+dtL9fqz+an7+YRndeifh/GALyT4XphWLWbFicshTx48fT0xMDDt27AhLkvbo0YP4+Hiqq6v5/fffQ7Y5HA4mTJgAwJo1a8hdt5XBfj9OZWLFihWo2vWZnPv24ff7qfZrwWPJyspi4MCBVFdXh8WL2WzmiCOOAOC3334L+T2r9PpQSqFpGlXKGfbc9PR0hg8fjsfjiRiHU6dORdM0Nm/eTFlZ6ILsgwYNokePHhQWFrJ58+aQbUlJSYwePRqlVMR2J06ciN1uZ9u2bRQWFoZs69+/P3369KG0tDSkWhL0AT+QdF61alXYADtmzBgSEhLIyclh7969Idt69uzJgAEDqKysZNWqVSHbrFYrkyfrn/O6devC/sAdeuihpKamkpuby86dO0O2ZWZmMnToUFwuV8RjbWyM6N+/P/Hx8ZSVlbFr166QbZ1ujLDZmDRpEgBr165t0hgR0L17dwYPHozTGR6jmqYxbdo0QH8P9z/BGDZsGBkZGeTl5bFt27aQbWlpaYwYMQKv1xvxPWxsjBgwYAA9e/akuLiYjRs3hmxLSEhgzJgxABHbbWyM6Nu3L/369aO8vJy1a9eGbNt/jNj/pGbIkCHEx8dT7vfh9/spLShs8RgBMHz4cNLT09m3bx/bt28P2daVxojMr7+m78iRxJ59dqcZI1pyHjFq1Cji4+PZvXt32GcjY4Suq40Ro0ePJikpiZycHPbs2ROy7UBjxGGHHUZ8fDzbt28Pi++DZYyAznceEa3vGoG/R5s2bZIx4iAZI1pyHtGvXz9iY2PD3geQMSKgvcYIIzkWTe3/293JfPjhh5x11lmAfiUuUgLqjjvu4JFHHuHQQw9lzZo1hl+jpqaGwYMHk5OTw4IFC4KDzf4iVT717t2b3bt3B6uvoHNejTB6xbJwXT6J/ZKJT41vMJNumTcPy6OPYxp1KDXP/bNJVyNKd5RQMeEM0GBwyWIwQ1lhGfv6Hw9A8opPSOiZyMpJt5O+fQklN9zJyDuPA2DJJa/S44s32XfEGRzz9Z38cOh1ZG79laLr/8rou2fofar32ez7cRWlJ11JmSWVobs+xGxuPJO+fPjVZBZtRD3xD/qccxg5vaaDT2GZ/wG9Rmey5IxnyFz4AVWOVIbs+LDJn42RqxHrX/yFhHvuoLDbEA5b8zzLp91N5uZFFGSPI2PrUvJ6H8bYZU8Azb8asfikx8ha8YX+2X/5LmmjM0M/V7liCXScqxH1daQxIkCuWOqifcXyu9nP0X3ea+ROncX4D24ApPLpgO9hURGxZ5yByWJB++knnH6/jBEyRnTZMaK9qxq6xBhRS84jdDJG6GSM0MkYUaezjxHl5eX06tWLsrKykJxHJJ2+8ql79+7BnysrK0lNTQ3bJxA83bp1a9ZrxMTEMHv2bB5//PGwbGR9drs9WGVVX0JCQsQpgSaTqcGpgkBwAInEZrM1WOmlaVqj7cbExDS4zWq1YrVaG9zeWLsOhwOHw8HO77ZSc+rZ7Bx+DEcteQzQA3z/5xZ9uoj8LUBNMUNrf3Eiqf++lpUWggZOWyJmm353ptRuqey0J2B3V+Av9JAwJAF7kZ55Tx3RL/i68YP7wZdgztOvmFgrikGD5OyssL6ZTCa6De5FqQYJvjKUP56E5LoS8kifjd2pD/RxPVJJSErCY0/EWlMGZT5iY2Mx7dSvfMS4yoiPi0czaWFtNPbZRHoP64uPj8fiATTAEavvG6MPdNbKstDH6zGbzY22W79qD8BSb9+krJQGn9uS+G4sDlsS3015DxsSiO9IjL6H9TU0bkDXHSMiacln09h72JLPprH3sK3iGxp/D9sqvs0xMaCB2R/++p3tPWy3MSI3F0wm8Pth924c2dkyRiBjREBXGyO60nso5xF1ZIzQdaX4BhkjAmSM0LX1GGGklqnTLzg+dOjQ4Lzy3NzciPsEHh8+fHizX2fAgAEAB8zmNUVFRQU//vijofmRnUnBz/rUsJRNv+L3NrBYd1kZNT8uQSnwljT9fajO1Usn3Y6kkMddMfqi4xU7i/F7/cRV6Amm9FF1i9AnDNYXnrcX6okpe6VeDhzfLz3ia5lTkzCbwISP8twDlxPaXPpxxHZLqO2T3seafXqf4/P05JNJ+ajKb3p5opF48VbomXZVu9YTtYOjLXDXPVvLFxzXYuoGxbhuDQ+gIjq6+vgiWlcgXnyaPlZrHvcBniGCSkvrft5vukZXJeOLMELiRRgh8SKMkHjpnDp98ik1NZWRI0cChM0fDgjMuzz66KOb/TqBMrchQ4Y0u436Ovlsx0a5cvVF4K3eavYt3xNxn5ovfqCiTP+yY3VV6leOmyCQyPHEhiafPAl6xVvN3hIK1uVhUj78moW0IRnBfVKG68mnuPJcfG4fMU59HnVSdhoR2e34bHrypnJPWeR9armdfhze2uRTdz1B6Y3T++jKL6N0ewkOV2lw//KdJWFtNKap8eKr1stHlb02QRSvZ8EdNfrrqVZMPvk1C9bYhq+6iOjpyuOLaH1KKTRb7e/yfuXeohH11+LYby2FrkzGF2GExIswQuJFGCHx0vl0+uQTwDnnnAPAwoULw7bl5eWxefNmUlNTOf7445v9Gt988w3jxo0jOzu72W0cLLxFpcGfc78PXwQOYM+/5xMYL5TPj6oKn0sdibtATwL54kOTT74kPfnkyi2meJ0+NbIysQcmS12IZ4zohtJMmP1udn67BU35AY2kvskNv15t9VL13tIG9wGo2FeFhkKjrvLJX9tHd2E5uT+HLjZYubvx9prLX1U7d7m2ZNMUqyfPTH79C6XmaHnyyRSrt+2xxkacOiiE6HwCySe13xoGohEHYeWTEEIIIURzdYnk05VXXklGRgbvvvtu2EJcr732Gn6/n5tvvjk4P3P+/PmMGDGCxx9/PLjf3r17+dvf/sa8efPC2p8/fz7ff/89zz33XNseSBehiuquBlcu3xS+Q3k52tIl+r5oKAWuwqaVTHoKSgHwJ4Qmn0jRp9158kso36BXW7nSskJ2sTgsVMXp637lzdfv5FDjSAmuHRXx9eKT9bbyShvtV9U+vf8+ix2To3Zecu0UTW9RGaXLQ5NPNXuNVT41lb+28ikw3c6UsN/83wbm+xphjtfbdttlyp0QXYXJoSefNKl8arr6yaf97iwphBBCCCFCdYnkU2pqKm+++SYVFRVcd911wVXily5dysMPP8xJJ53E7bffHtz/qaeeYv369dx3333Bxz744APuueceTj31VI4//ng2bNiA3+/nww8/5PLLL+ezzz4L3iJRHED9qQibwiuf9vznB1w1PnbHDKTColcsBZI3B+Irrp3+lpwc8rgpXW/HX1iMa7te+aR69GR/zlQ9IeVaqt/10BXfwJS7Wv4E/XUOlHyqySvX93PUWxOsto/+kjJcG0NvEerc13h7DcnfWMyvd3yCqyLyuiyqRk8+BabGBRJFQa2YfPLZG17YTgjRuWhWPQmveWXNpyar/7dOKp+EEEIIIRrVJZJPADNmzODXX3+lqqqKiRMnMnXqVK666ioeeOABPv30U8zmuuqWs88+m4SEBC6++OLgY5deeilXX301vXr14ocffmDChAlMnDiR1atXs2TJEo477rhW62tsbCxjx45tdPX4zsxcXndCHr9nE8ofOh9333/mA1A+bnowWVOd37Tkkyop1V8jNbTyyZKpVz5RXIx/t558svQJrXwC8NcmpBxb9MonT1LjySdVm0DyFpY2up8zX08+eR11dwswpeh9VKVlmHZsBcBn0quiAhVcTRGIl61rYcORV5H4zIOsfDC8Qg9AVesLjmu10+0siaEx1hrT7uwZtRVdCSktbku0vq4+vojWFYgXe7weL1L5ZED9yqf8fNjvls1dkYwvwgiJF2GExIswQuKlc7JEuwOtaeTIkbz77rsH3O/CCy/kwgsvDHksISGBOXPmMGfOnLbqXpDZbG70doidnbWyLvkU4yyhcEMBGcMzAX39o5i1+pS7/pcfS+lfFkElOGsrhw6oXK98Mqclhzzs6KFXPpnKS9DK9GRXzCHhySdz7yxYDIll+lVqlZza6MuZkvUEkr+k8QXHXYUVWAFfbF3yyZKqJ2m08jLi8vTKp5JeI0jftQJfvXWxDsRsNvPLz7HsufQuxlXq7TjXbYm8c+2Xn8C6TPsnn0wxLU8+Db10IotXXEOvP0xucVui9XX18UW0rkC8WGvHDHyy5lOTlew3fTonBwYOjE5f2omML8IIiRdhhMSLMELipXPqMpVPnYnT6WTTpk04u+hVUnvtndW8Zj3Rsfe7uql36+csAK+XwqRDOGxWP7y1yRpXUdMqn0wVehLIlhFa+RTTU08i2SqKcZTolU9Jw8Kn3TmyQxNSWkZ6o68XTHLVv8IdgadIT5754uqm3QX6aM/bSYxTvwOgGjVa/39x09d8evNNN19eNpdxJV9jqU0Xa3t2R965Rq98MtVOjbMmtX7yyRZvY+prl5F9Uuvc+VG0rq4+vojWFYgXX21xsMkrlU9NFvi7oNXeeOEgmHon44swQuJFGCHxIoyQeOmcJPkUBR6Ph3379gXXpupKvE4vdreeSCrJHgtA2dK6Rccr3vsCAHXMdEwmUHF68snTxOSTpVJPPjm6hSaf4nvrU8BiKvOJqyoAIH1keOVT4tDQhJSlW+PT7iwZyQCYyksb3c9XWtv/hLrKJ3ttH1NK9MXGKxKycPTX+6SVNd5eQFUV/O/h9VxY+DQpyYr4U47W2y6InHzSXPoAbK6tYrAlha75ZIptefJJdGxdeXwRrS8QL8qiJ1BMsuZT0wUqnw45RP//zp3R60s7kfFFGCHxIoyQeBFGSLx0TpJ8Eq2qbGcpAEozYZk8HgD/b3rl074Ve0nfrk+5G/yXk/X9AneEK2la8slWo7cf0z00+ZTYT698snprAIXHEkNS3+Sw56cdGpqQsmc1nnyyZ+ptmCtKG93PG0g+JdZVPsXslyBz9sjG3q22vfKmVT6tXO7n6j33Yjd7ST3nKEy3/AWA+PK9+L3+sP0DySdLvJ58sqeGlqOaW6HySQjR9ZjttXe788lJXJMoVVf5NHKk/v+DoPJJCCGEEKK5utSaT6L1/XTlG/h//jX4b6VpJF0+m9E3Hhlx/4pdelLFaU8idcpQ1L8hNkevfNr8xDzSgYJ+4xk2Rk8CmRLiAfCXNmHNJ6VwOPXKp9is5JBNCVkJKM2EpvSETFVSFppJC2sidWAauSYbZr9+dT+2d+PJp5geyVQB1urSxvtWpvfflFyXfIrPSqS6/j7Z2cRkpeADrFUHaK/W719uZYInF1OiFfdtt5GZlkGhZsGkvBT+lk/myO4h+5tc+rS7YPIpOYaqetvNUvkkhIjA7NBPBzRZ86lpKirAX3sBYORI+Ogj2LWr4f3nzNHvgHreee3SPSGEEEKIjkYqn0SjvFt2kL59SfC/jG2LqXrypQb3r9qtJ5/csSn0OW4wAPGV+yjdXoLjm08BiDvvtOD+pmR9mpq/vPKAfXGXVIHfB0BCr9CqIpPFRI2j7u5r7ozw9Z4C+1Ul9gj+O75v48mnuCz9dRw1pSjVyI4VeuWTOblu2l18r+SQXWKG9Seup/5YYF2sA6n6YSkAlQMGQmwsJouJykQ9cVe4KnzqnckTWvkUkx5a+WSJk+STECKcOUa/E6dZKp+aJlD1FBsL2dn6zw0ln/Lz4dVX4cknobo68j5CCCGEEF2cJJ+iwGaz0bt3b2w2W7S7ckA9rz+D6jsfovrOhyiafQ0A1pqG7/xWs1dPqnjik4nLjKMisRcAq295k/jKfbhsCRx6/VHB/S0ptZVC5QeufKrco7+u2+QgPi08ieKOq3fnuh7h6z0FuNLrElNJ2Y0nnwIJpHhvKdVVDWefTJV6/61pdZVP9uQYlMkc/HfquENI7KsnyGyeKjzVjX/JKyuDlK3LQIPU4w8PxosrQ39PKzaEJ5/Mbr3yyZqor/UUkxq65pNUPnV9nWl8EdEXiBdHvD42mHyy5lOTBJJPKSnQp4/+c3GxvlDf/gr0dQhRCrZubZfutRUZX4QREi/CCIkXYYTES+ckyacosNvtZGdnY7d3/ETAwFkjGHvXDMbeNYPsK6cDYHM1vD6TO09PPvmT9ERQTV/9jmhpX70FQNmkE7En1h23NVWvFNKqDrzmU9VePfnktCdhihC53oS6yidr34aTT6o2MeXXLCRkJTS4H4CjezImDTT8lO9tuDrLXKUnn+zpde1pJg2nva5CK2tyP+K7x6M0vfOB9bEasnyJjyHVy7Hbod8fTgrGi+qpJ5+cW8KTT5bayidbol75ZI214tfqEmDW+I4fc6JlOtP4IqIvEC8xifoUaKl8aqJA8ik5GeLj9SQURF73qaio7uctW9q6Z21KxhdhhMSLMELiRRgh8dI5SfIpCrxeL6WlpXi9nWttjbjuemLF5qmKuNg1gLegdjpZ7Ym4eZg+9c6k9GPtc/1pIfsHkjXmJiSfqveWAuCOSYq43Z9cV/kUNyjytDsASx89+VQTm4rJcoBfAZsNry0WgIqc0obbdOr9t2ckhjwe6GtlfA9i0/Vpc057sv7Y7obbA9j+xUZi/FVYUuIp7dYtGC/W/nrySeVESD559eSTPVmveNJMGh5r3dQ7mXbX9XXW8UVERyBesNbe7c4vcdMkJaF/6+jdW/9/pKl3hYV1P//+e9v2q43J+CKMkHgRRrQ0XtxumD8fampauWOiQ5LxpXOS5FMU1NTUsHr1amo62egY36OuqqdyX+QqIH+RfkJuStNPyJMnDgluK8kYTPaJg0P2d2TobQaSN41x5euVT9645Mg7pNUln5KHNVz5FDdE/5LgSsw84GtCXQKpOrfh6YZ2p175FNMtNPnkjdWfW9O9f117sclA3fpYDan5UV/vyTdqFKvXrg3GS/wQPflkzQtNPim/wuILrXwC8Frrpt7ZEiT51NV11vFFREcgXnxmfT09s9+D8je2wJ0AQiufoG7qXRevfJLxRRgh8SKMaGm8PPQQ/PWv+hJ7ouuT8aVzkuSTaDKLw4LHoicyqnIbWKOp9oTckpEMQK9j65JN2qzTwnYPJGtszgOv+eQu0JM//oTIlU/mtLppd5mHNVz5NPzKKRSccBFp999wwNcE8NQmu5z7SiNu9/sUdreePAtUhwX4An095JC69uLD21ty3+cs7nYqq19fCehLhGTu0pNPyccdFtJmyqF68imuZHfIl0RPtSd4tz97Ur3kU23lFsi0OyFEZBZH4Oa3Cr/HF9W+dAolJSjg7S+S+ctfqEs+7dwZvm9xcd3PW7bQ+N0rhBBCGLV2LRS99RXP/H4CRf/9MdrdEUI0QJJPwhC3XU+uVOdFrlQyl+kn2fZueiIo5ZBUCvuPpyKxJ4feemLY/nHd9HVGTB6XXi/bCG9hKQAqMXLyydZdr3xy2pOITY+NuA+ALd7GkR/ewNDzxzT6egG+hGQA3PmlEbdXFVSjoSd94nqEVj7ZJo3Fr1nIOH1K8DF/bXuueu05P5hHQmUupbc+xN5dXpb/4mZw9SocDrBNCe1ntzF6Ys3mqaR8d13SzlnqDP7sSK5LPvnsde+FVD4JISIxO6zBnw90M4SDztat8NZb4KuXlCstxe2Gjfkp/PgjlCQ0sfKpvLxuAXIhhBAt5vfDm3dv4orc+0n2FjJ4zfuUNTxZQQgRRZJ8EoZ4HHryyVkQOflkrdSnksX2qpsCN23dHMbl/Dfi4t4JPeJRaCgF7uKGF/QG8BXrf0m0lMjJp/iBPQCoSu/X+EEYpJKSgbrk1/4CVWA+ky0suTP5n+cyOO9Hhl00tu7B2jVCvAV17cXk61fLM6p38N9z32PrJ+uwKhfmjFRUv34hbdoT7VTFZgBQsLJu6p2rTE8++TVLvSoG8Duk8kkI0ThrrCSfGvSPf8ATT8CCBXWPlZTg9UClORmATTW1yacG1nzyKwjWO3XydZ+EEKIj+fqDck5ZcCt2zY3VCkOql7PyZ5mKJURHJMmnKNA0DZvNhqZp0e6KYb4YPYHkKoycfLJX68mnuF4pIY83tLB3bLwJp1lfELsy9wDrPtVexjClJEfcPPTCsZT96a/0ef6OxtsxqnZNj0Dya3+BKrBAVdj+rHGhtwA1pertBdbHqimuIa4qDwCzGSaueQnfV/P1f48fi2YyhcVLTZo+9a50Xb3kU6n+h9ZTb40nAH9MXfLJkSi3I+3qOvP4ItpfIF6sDgsKPWa8Tlm8M0RgKl399ZpKS/F4odys/61bVaiPyZSV6dVN9fgLiti6FVbldtMTUJ143ScZX4QREi/CiObES3Wln+pb/490z17iBmRh6tENq3Kz5+OlbdhT0RHI+NI5SfIpCuLj45k0aRLx8fHR7ophvnh9WpmnODxR5HP7sLv1k+6E3slNas9kApdNb7NqX+PrPmnlpQBY0yNXPpksJiY9fhZ9jx3QpNduKkua/nqqpDTidme+3m+3IzHi9rD2MmoTc7XrY+Uu1q+Uu+xJpE0eQoy/kukl76EBGSeNixgv/h76F52a3+uST+5yvfLJa6mbcgdAMPmkhVQ3iK6pM48vov0F4iUhMQG/SR8fvNWNT4E+qHg8ddPk6q/nVFv5VFFb+bT691hIT9e37Tf1zrWvGI8HFqsJeDw0Xvn03nvw+eet1/9WJuOLMELiRRjRnHhZ9Kc3GFK4EJPDRrd/PwbTjqzdsKiNeik6ChlfOidJPglj4vRfcE9ReKKoPCdQGaSR1De5yU16HXqbzvzGK5/MFXr7tsymt90arLWLp2tlpRG3BxYO98ZFTortL9B/U5le+VS8YgcAlRn9SX/kFjL0GXXExoF9ytgILYC5r5588u2sSz55KvTkk2+/yidi9H97zHY0k1wdEEJE5jfp03W9NTLtLig/v26B8B076h4vLcXjgQpLMgDr14PqHWHdp+pqfJV6Vera+In6LcAbSj7l5MBjj8H994PTGXkfIYQQAFQXVpP52SsA+G66FdvIIXQ/U19jtc/uRRTky80dhOhoJPkUBZWVlfzyyy9UVja+xlGHlKhX9/jKw/tevkNfbNxpT2pwml0k3li9TWcDU/kCrNV68imme9OSPK0lkCyyVJZG3F6zQ58y50vLbFJ7jh4pIe1Vb9Cvpvt69YXRo0k/93j69IHuo7pBr14R4yVmoJ58MuXWSz6V619wfLbQyictXp/W6DPLek8Hg049voh2Vz9efGa98knWfKonN7fu55079ZVt3W6orsbjrVvzqboaSmOz9P327q17TlERXi+4TDFsiRmpJ5927NArqva3YYP+f58Ptm1ri6NpMRlfhBESL8IIo/Gy7slvsHprKE/qw7A7TwcgbtrhWGNtpHn2sX7e9jbsrYg2GV86J0k+RYFSCrfbjeqEt1s2JenrGqnS8Mqnqt16JY8rNiVsW2P8cXqbnqLGk0/2mlIAYnu0b/LJ0SMZAGtVacTtnpx9AGg9ujepvdgsvT1b7fpY/m07ALAM7Ke385ebiD9+CvYbrwZNixgvySP05FNMUV3yyVtZu+D4/smnWL3yyWeV5NPBoDOPL6L91Y8Xn1lfE87nkjWfguonn5xOfQpe7ZRpj9dElSkBs1nfnOOrTT7t2VP3nKIivB4oM6dRZOlGqTdeTy7Vr6IK2LSp7ucOui6UjC/CCIkXYYTReHG9/wkA3pNm1lX2OxxUDdNnDZTO+6lN+ik6BhlfOidJPglDzMm1i2pXhCeKavbqyRRPgrHkk4oPJJ8aWfPJ7cbk0ZMrcVntm3yKq0122ZwN3Ld1n558svZpWvIpoY/+/jicpSi/wrp3h/748L76DhkZ8PTTcMopDbaRPlpPPsVVF+AqdwF1lU9qv+STOUFf88lnkeSTEKJh/trKJ1+NrPkUVD/5BHrSqDb5VKKSQdMYP17ftKmqp/5D/cqn4mJ9YXJLKmgav/sH6LP4Ik2927ix7ucOmnwSQoiOIOfH7aTtWYPSTAy9NfR8OWb6Efr/l0vySYiORpJPwhBrqp4o0irDk0+u/FIA/AnJxhpN1Nv0ljVcNuncV4pSoDCR1LN9F5aL75UMQJy7FLfTH7bdUqxPu4vLblryKbAelkn5qNhbQUKxPu0ufWy/JvcpqXciLqv+PuSt0K+y+6pqK5/skZNPfkk+CSEaoWTNp3D7J5927oTSUvwKitEvJBx9tL5pTUGEyqfCQrweKLXoi5HvsA7E5SI8uaRUaOVTY4uSCyHEQW7bU58CUDD4CNIGp4ds63PuFDSgV+Eqcn+XKVlCdCSSfBKG2NL19ZlM1eHJJ2++XvlEirHKJ1Oy3qYqa7jyqWqvXnVUaUkmJrZ9F82Oy0pCAzT8lO8N/yMWU6pXPiUOalryyRZvw2PRE0I532zE4nPh1yx0P7xnk/ukmTSqk/X9S9bqU+8Ci9riCF1w3JJYm3yy2prcvhDi4OO31FY+OSX5FBRIPmXWrum3YweUlODx6Os9xcTAxIn6pmW5PfEr9GpYn09/sKgIjxfKLGlYLLDLPpAaJ+HJp/x8VGkpu3fXvuTmzXULnQshhAjyOr3EL/gfAMkXnRa2PWZAT6oy+6HhZ9vbi9u7e0KIRkjyKQpiYmIYNWoUMTExB965g7Gn61VK1urwRJG/UF9w3JSeaqhNS2AqX3nDaz5V7tGTTy5HElo737DN5LDhsekJnIqc0pBt7ko3MU79uNOGNy35BOCMSQag6PvVerspvTHbzBH3bShePN30qXeVG/Xkk7+69u5IjtDKp8wjBuHXzHgHDmty/0Tn1ZnHF9H+6seLqp1253fLmk9BgeRTIMNUO+3O64VycwqZmdCjh37NpZB0nF6rvih5nl4RG1hwvNSSxrhxkGMfEPmOd5s24XTCZlcfiktNeIvKoKiovY6yyWR8EUZIvAgjmhova+csJMZZTI0jlRFXTYm4j3us/njxe/P514t+/vUv+PLLVu+yiCIZXzonST5FgcViITk5GYvFEu2uGBaTWZt8ckZIFJXolU+WTGOVT41N5Qtw5unJJ09s+673FOCqTRZV54au+1S4Xv+C4TU7SMhKaHJ7nji9PbVyld5+934N7ttQvGi99eSTZ3tt8qmmNvm03yDc56hs+m35liM/v73J/ROdV2ceX0T7qx8vgepIWfOplt8fXNOPSZP0/9dWPnk9UGFJpls30DQYPhyUZqLY3kPfr3bdJ39hcW2iKpWjj4bd9kNw1qAvXF5W7+/Jxo3U1MCWmJHk2XpHro7qAGR8EUZIvAgjmhov5W/qC41XHnUKFkfkfVNPmwrAIdu/YdDts9jx8Fv8/Y6KkNnNonOT8aVzkuRTFLhcLrZt24bL5Yp2VwyL7aYnWGzuCpQ/dEqAqaIUAHtmsqE2bWl6m6aqhpNPrjy9bV98dJJPnthkQF97qr7SjfoXk6rE7nV32mgCX7zeXvKutQCY+vdtcN+G4sV+iJ580nbnAKBqk09aTGjlE0B893hD/ROdV2ceX0T7qx8vyqyfwEnyqVZhoT59zmyGsfrdk8jPh7178XigorbyCfTkE8AeVbvuU23yybWnEIByWzpHHAFOcxy7VRZ+P6FrPG3ciLMGdjoG61PzIlVHdQAyvggjJF6EEU2Jl6JNhaT//jMAh/wlfMpdwODzDqfs/GuJ7Z7IoPg9XFL8BP/Yejrblxe3er9FdMj40jlJ8ikK3G43OTk5uN2d7wQ/Pktfn8ns9+CuDO2/tUKvfIrJMlb5ZM/Q27TUNLzmk6dQv0LsT4hO8slXu4h6IAkWULlFTz65U5s+5Q5AJevvkdVbDYBjSL8G920oXjKPHApAyo6VOEudUJt8MsWGJ5/EwaMzjy+i/dWPF3+sfhMDT2lVlHvVQQSm3HXrps+rC6xnuGYNHi9UmPXKJ6hLPm2pDl103JOnT50zZaSRmakvHbUpZjROJzB/ft1rbdpEjRN2OIaQ4xigV0d1wOSTjC/CCImXtlG2s5Qfz32e4t873tTclmhKvGx+czGa8lPSfRi9pjR84VYzaUx66VL6rfsfPZ65E2tGMvG+MmoWr2mLrosokPGlc5LkkzAkJjUGpelhU7EnNFlkr9aTT3G9jCWfYjL0Lzy2SFP5avmKSvUfkpMNtd1aVJL+up6C0pDHnTv15JPKNJZ8IiU59J+H9TPcp/4nDKYiIQuLz8mGV35Gv1QOpjiZ+yyEaIbapLgncPOIg11t8snfvQcVFUC/fvrju3fj9UC5JSUs+bS5uqe+1nhuLiiFql0L0dYjLbjfwqRT9WsFX36pj9ulpfhy83C5YKd9EDm1lU+qA067E0JE3+q73iP901dYc/Hj0e5KiygVXLGjySq+WwqANmF8054QEwNnnEHVyMkAuDduM/aCQohWJcknYYhm0nDZ9Gly1Xl1ySK/14/DpVcnJfYztuB4XA+98snqrtTX2IjAX6K3re2XtGk3tUkvf3FpyMP+vfqaT6asboaaM6cmh/y7+4SGr940RDNpOKdMB6Dsw/nol9LBFCeVT0II47Q0fez2Fci0BCA4dW7R1h4cdxwUxtWN0x6vfre7wLS7pCTo1QsKrVl6YmnPHqioCN45MLaX/t4OHw4bYg8nz5wF1dXw3Xf6YuM1sM/Wh8QecexLGIjPD+6N28Eri78LIUL5d+tjU8r6hbjKO++Uoy++gOOOgw8/bNr+bpciYaOefOoxc5yh13IMywbAskuST0JEkySfhGEeu558qsmvSz6V7SwF9DWgEnsbmxoX1712oW6/H095TeSdyvXkkyUtOtPuzKn666rS0AXHtXy98snR11jlk61bXXVYjSPV0GLl9fW86FhAPwExVda+R/FS+SSEMM6SUTsuFUvyCYB9+1DA0t098HphTXm/4CZ9zae6aXcAQ4ZAvrUnLid64qqoCI8Xqk0JpPXQF3MPLEz+bVztWiWffKIvNu7U13saNQoyDu2O0xSLs8IDu3a12+EKIToHrVhfS87qreG3l3+Ocm+ab9nHu7l6z918/9qOJu3/29e7SXLloVkt9J05ytBrJY/Rk0/xBdv06lQhRFRI8ikKrFYr3bt3x2q1RrsrzeKJ1SuVXIV1yaeKnFL9MVsiZpvZUHvxaXa8mn5iXrk38rpPptrkkzU9Oskna0ay/kNZacjjtmI9+RQ/wFjyyd4tOfhzVWa/xl+7kXg55NRhVMb3wOqtIS1nFQCWeKl8Oph19vFFtK/68WLvoVfnmMpk2h0Aubm4XbDbp9/BbmWxXvnkV/o65PsnnwYOhAJrFk4X+t3scnPxeqHMkhaskBo6VL873qfqVLx+E6xYAfPnU1Ojr/c0fDgMG2Fit31Ah7zjnYwvwgiJl7ZhLatb66nso29DtlXuq2T3TzvauUfGKQW9v3+DSeVfMmXpk+zYceB4yflIr3qqHjDS8PqmKYdnY9Kgu3MHe3Iiz7IQnYuML52TJJ+iwOFwMHjwYByOzpkk8MfoazS5CuoSRVW79S8rrlhj6z0BmEzgsultVu2LvO6TpaoUAHtmdJJPtto7+Flq7+gHoPyK2Ap92l3SIGPT7uovyu7v3a/RfRuLF82kUTNZr37SlP7HVJJPB7fOPr6I9lU/XmJ66sknS4VUPgGQm0t1DRRa9eTTz3v7oQCvPpMOd2wyCfWKVgcOhEpzEuXeWP2BdevwevTkU0aG/lB8vL50VIk1kz19JuoPbtig3+nOPpjhw/XqqBz7gA55xzsZX4QREi9tw15ZGPw5Ze2PwRsAuSvdrB17KeUzzmLBOXPC7krdkRQUQPfi9QCMrPyFRf/NP2C8eH5ZBkDs1LGGX8/UKwtTjA2rcpO7bE/zOy46DBlfOidJPkWBz+ejqqoKXyet+/TH65VPntLK4GM1e/XkkyfeePIJwO3Q2wxM5Vv7wiJ+GnQZO+atA8BWrVc+xXSPTvLJ3j0ZAGttEgygYm8FVq8+TTB9uLHkU1zP5ODP1oH9Gt33QPGSddH0kH9L8ung1tnHF9G+6sdL4GYR9iqpfEIpPflUXZd8yvFlUeO14vGC0xRLWg8bmlb3lAEDAE1jtz8LpYC1a/F4obRe5RPAMcfo/3+n5nQU+vpRHi/kxA5myJC65JPTCb5NHSv5JOOLMELipfV5nV5inLUXfG0JWL3V/PbKLwAs+cs7JJVsByDjs1dZMPXusDtTt8hvv8Ell8DTTwfXxItk2+cbWTjoMjZ/0PCd5davcNHHqY9vGn4q35nXaLzs2a3ouXcpGtD3LGPrPQFgMlFTO9OgdOV2488XHY6ML52TJJ+ioLq6mmXLllFdXR3trjRP7aVeX3Fd5ZNrn/6H0JdkbLHxAF+M3qazQE8+lTz9Bql71lB25S348wqwuvTHY7OSm9vrFonroSe9rDV1az4Vrden3NU4UrEn2g21l9i3LkmXMKLxxcYPFC8DThtOVVxd8suaIMmng1mnH19Eu6ofL4GbRThcpfi9B/m0hJIScLmortEosnYjPh78mplCR2+8Hn2x8W77XXPo3h1iYyHfnIXbDWrt2rDKJ4CzzwabDT4qnEqFJQVnDZRYMskcnEJMjL5weXHaQJSCmtUdK/kk44swQuKl9ZVs1StTlWaibMrJAJR++C3FvxeR8O7LAOSPnoFfM5O56isWH34N5bsjL2lhlOf5lyn5aR1FT79J0dTT2XDKreR8uzlsv13/713S9qwh765nGmwr94dNaPhJTAANGLLpEzZtqGowXlZ9tI0EXwnWBDux40c0q/++focA4Ppta7OeLzoWGV86J0k+CcO0xNrkU1ndFLngrblr7wpnlC9Ob9NdWI6r3EXKbv1qibWskD1n/wW/t3Yx816Jzex1y8T3SgYgxlUW/FJWvrk2+ZRkrOoJIL57PD6Tvs5V5oT+LeqbZtKomnRs8N/2ZFlwXAhhXFLfZECfwlueU9b4zl3dvn14vZDvT8dvsnLGGfrD21U/PF4oN6eEVDOBPoV8wADIt/XE6QJ/eSV+BWXm0Mqn1FSYORN8mpX51pOoqYHtjqEMH65v1zRIHDMAAPfuPKiIPB1dCHHwKduqT7mrjkkj65LjAUhes4A1lz6J1VtNcfdhTFvwIKbnnsVtjSdt9ypWnPX3lr9wVRUF834lNxcWlY8kb58f9f337DvvL2HT++xb9Ol0abtXsXvRzojN1SzVZzZ4DhuPJTmODM8efntzdYMvX/C5vt6Td8Rh0Mw1fmxDas+3d0jlkxDRIsknYZgpqXaRi/K6E2J/kZ58MqU1b9qdqk0+eUoq2fLhasx+D5XmJJymWKpXbEABNaZ4ElMtLep7cyX00iufNPyU79GPu2qbnnzyphlbbBzAZDHhvOVuSi75C93HZLW4f1kX1k29syVK5ZMQwjizzYzLpif4y3d2gKl3Ph9cdRVcfz14ve372rXrPRVZezBgABxxhP7wuoq++p3uLOGVT6AnnwqtWbic9daGSkhj/yUpLrhAT1Y9WXkF/026mPczr2XYsHrtHJZAsaUbzg647pMQInqqdurJJ1dCOgNOH0FVXCY2TxWZK78EIPOxWzFZTAy7eBwJc59HaSYy185nw9zlLXpdz3cLqShys8/Wh2/Pe4XPL34Pv2YhrnIfeaty6/qXX0VScV1yZ+sTn4S15feD9Xc9QRU7bSyeo2cAoH36OQCVlXDTTXDCCTBnjn7TT/saPfmUOqMZU+5qJY7WK5/icqXySYhokeSTMMySVlt9VO9qrJanJ2KsvTIjPeXAEvU2vcXlFH6pLyhYOXIKn42+N3hL1GprUtgJfHuxxlqpSNCTRNs+WAGAZ5d+zHQ3nnwCGPd/JzHlufNapX8DTh9BYZ8xlKYNIG1IxoGfIIQQETjj9Kl3lbs6wKLjO3ZQ/v1yKr7+BT74oH1fe+/e4HpPo0fDsGFgNsMiplDhtLE+dnzE5NPAgZBv1SufPLX5MlNGWth+vXrBscdCjTmef9mvZ489mxH1ZpIMGwY7HYP1O95t3NgmhyiE6HxqcvTkky85HZPFRNX4Y4Lb8g8/iUFnHhr89yGnDKVg6pkAlNz1D3zu5q+Ns+eN+fh88FvWdJ54UuOWOdmUdh8CQM5nq4L77fxyA6DQJ9NB/A/z8DpDLx7s3Al9ytdjMkHGUcPpff3paMCAnB/YskLj2msdLFwIhYXw6qtw5iw/gyqWY7VCxknNTz4FZhqkV26nrOQgn1ouRJRI8ilKtPqrlHYytlS9SslUWTeH3J63C4DEYb2b1WZgKp+/rALTcv3qRvzR4zju0el8laonaCrim5fkaS3OSfof+LKP5gOgcgMJN+PT7ow6ULyYLCaOWPsCk3e9g8URneow0XF05vFFtL/68eJJ0JNPNXuin3wqX7mVPXsgZzeUPvoilJa234vn5lJTm3waNQocDhg6FDbHjubCvgv4Mu38sGl3oCefCgKVT7Xft2w9wpNPABdfXPez3Q7Z2XX/Hj4ctsUMx+UC98r1rXhgLSfjizBC4qV1uffqySeVlg4QnHrnscQw4sXrw/Y/7MU/4bIlklz4O0vu+rh5L1pdjf+nnwFIOWs6ZrP+sHfEaACqFq0K7lq8UB+v8odOo8aRSoyzmHUv/RzS3KYlZWR6duNwgGn4UBLGD6U6awBWv4ctj/1G5e/7GJywlwf+tJdjhuxlZNUvxPgrsafFow0Z3LxjABwDeqHZ9Dve7VmWe+AniA5PxpfOR5JPUZCQkMC0adNIqH+P5k7Elqb321yjVz753D7iy/TblmYc3qdZbZpTat+LfftI3af/4ep75lgmT4acWTfwco97+XLk7S3secsE7iqXum4hrnIX5sI8AGL6t21SrKnxYrLIr7Po/OOLaF/7x4s/UZ86HbiJRDQVLNlOYCWRvC0VlD36fLu9tnf3PpzOusongFGj9P/7NH29kUiVT4ccAgW2LDxecLn0x2J6RU4+DRkC48fX/Wypd90gLQ1Ke+qLQFUt7TjJJxlfhBESL63PX1AE1FVUDjprJDV3/52YV54jfWh45XtSnyRqLvkTAPZX5jRr8fHiTxbiLNen3B15+cC6tqfpg6JtY91aTd7V+nhlGTOKyiNrF0T/98ch7RUu0PfxZvXRZz5oGv6Zp4EGF5a/zAt7ZvF6yUxOemEmj22cyXOWG+neDbqfOIZg5qs5TCYqM/rpx7RMpt51djK+dE7ybVUY5sjQf8mttcmn/NW5mJQXn8lG+rDmTbuz1iafkjcvRlN+KhJ70W20fnvrm2+3sG/8TMafk91YE20ucFc5q7eaDa/+gqNUr3xKGBjdiiwhhGg1qXrlkzc/+pVPNeu3AbA84Sh8fih86b+414XfWaktlG3KRQG+zB7BmdWBJFRApORTQgKkZMVSYU6mshIUGgl9Gl4L8frroU8f+MMfwrfFjdMXgfLt3A1lB/kC8EIIXZFe+WTtkR586PA7jmfQWSMbfMqER8+kLDUbh6uMFX96yfBL5ryuV/zvGz6dXr3rKk36naYnn5KLtgWTWnE7ahcbP3IEh/zlNAAyNv1E0abC4PMC1ZymQ+vmGg+9+WTK0gcQm2Kn72A71ji7XhJqt+NIspPaPwnbuWca7vv+vL31qXfV62XRcSGiQZJPUVBVVcXy5cupqqqKdleaJSZTTxTZXHryqXCFPuWuMrlXs6tvrLXrSFm8TgBqRtTN6e7ZE956S1+gNZrq31Wu9L2via0uACBlSNtOu+vs8SLal8SLMGL/eAncNCJwE4mo2q4nn9SsM1mdcRwup5+1lzwOSh3giS3n3rEXgO6H9Qg+Fqh8ArDZgksVhhk4sHbRcRdUmpPJ6N7wlfqhQ+Gjj/SFdfc3YEwi+2x99EXH13eM6icZX4QREi+tz1KiJ3FieqcfYM86ZpuZ+HtuAiD5p3lhd6drjL+yGtvSRQD0vHh6yLaUQ1IpT+oDKHZ8tpaiTYXEVeWhNBN9Zwyh1xH9KOo5Ek352fCP/wHgdkNsbYIq5YjhwbbisxIZ+dsrFH3yDM75X8OiRaH/ffstTJnS5H43xDJIX3ScLVL51NnJ+NI5SfIpCvx+P5WVlfj9nXOxu7jutcknTyV+r5+K33IAcHVr3pQ7AEd6fMi/E45p/oKCbSlwV7n0Nd+iKT9+zULqwMhTKlpLZ48X0b4kXoQR+8eLtZte+URxlCufPB4ctWsJDpyRzZA5N+DVbMRsWM6yOUva9rULCvCU6Cez2ZPrKltTU6F37bKG3bpBQ0tNDBgA+baeAJRZ0sho5j0gRoyAbY7h1NQAv/3WvEZamYwvwgiJl9Znr9CTT3F9m558AhhywVj8mgWbp5KCdXlNft7mV39CudwUx/Ri4oUDw7Y7B+tZ+ZIFq9n9tT5OlaX2JzY9FgDbH04HwPrfd9m4pJzNmxT9q9ZjMUPylOEhbbVHvMQfqlc+2fZK5VNnJ+NL5yTJJ2FYfI+6ubWV+ypxb9G/IGh9W5B8ygy9hJw9e2yz22pLgdvampR+x5Cq+ExZa0kI0WU4svTkk7k8uskn7/YcPC4fTlMsfcdlcthJPag5+iQAVs35ObieUlvwv/Me1TWwJWYkI8bFhGwLTL2LtNh4QKDyCaDUktbovo0ZMgS2xw7H44WaZR2j8kmIrsLjgbvugqefrrs5QEen/IqYan3Np8T+xi58WmOtlKfpiZfcH39v0nP8fsh9/SsAqidPx+4Iz7g7Jo4GQFu9itJF6wBwD6hLKo265TiqU3qSUJPPmtPv4aNn95LgK8EWZ0EbPMjQMbSGjAn6Eh4pJdvxuiVpIUR7k2/NwjBrrBWv2QFA1b4KtN165ZN9QPPudAcQ260uoVWWmk3KIakt62QbMVlMVE2qKzt2pch6T0KIriMmS592Z62M7rS7/F+3oRTkxWbTI0v/wnPYVeOxWqBX7lLefLONXriqiqIX3sfvh+/7XMyAAaGbjzhC///gRm64NGAAbHXoX75y7AObnXyKjQVX7Zc45/L17TLdUIiDxU8/wdb/ruHrV3K44QaorIx2jw6sfHc5JqVnylIGGK+6d/fRB7Ty5QdOPtXUwL/+8DXdNi0AYOB1MyLu1+tkvfIpZfc6tNWrAHCMrVvLyZESw7B5/4+YRBsjyhYx+sN7APD0G6TPX25naaN64Tdbsfqd5K7c1+6vL8TBTpJPollcdj1ZVJNfgSNfr3xKOrT5lU/1q6ncozrmlLuAHucfG/zZnyHJJyFE1xHfR0/8O6qjW/lUvExf78nVMzs4vc02eSyZ3aCPazPv/auMvKbPHGmymv98RHFOJbm2fky6bWrYjZWOOQbefhtuuKHhNvr0gdWpR3NH9rt8lHUdycnN70/yhMH4MePJL4Z98kVJiNay/MMd3LPzcv6+7Rx8337PZZdBbm60e9W40i36lDuXLRFbvPHEjWWoPm3Ot2lLo/sVFcHd521j/NcPommgXXYZ/Y8Pn3IH0HNyX5z2JMx+N+m7VgCQeXTodLrY0YPo8/wdpCTDgJo1ANgOG75/U+1Cs5ipTO0LQNESWfdJiPYmyacocDgcDBs2DIfDEe2uNJvXoSeLqnKKia/QF2ZNP6z5lU/x3evWfEqa3rGTTwPPOJSqOP1StpbV9smnrhAvov1IvAgj9o+XxL565ZPNU4W70h21frk26Mkn84D+dQ+mppI4OpvYWMguWc4zz7Tyi3o85D7xFj4fLBt+EWf+IfwUSdP0aXUWS8PNWCzQP1tjj/0QUjIsmFpwpjVstI1djkH6uk8dYNFxGV+EER01XtxuUF99jYaf3t1c3LzvNgYvncuFFyhefRVKOsD9FlzlLhaMuJYFZ9YNdBXb9eSTM97Yek8BSYfrlU/2nIaTT1VVcNWF1Zz0w23EajVknDSOoU//qcH9NZNGef+6uzH4TDb6HH1I2H7m00+l+9Wz6NFDXz+v14zw5FN7xYurpz71rmL1tjZ9HdG2Our4IhonyacosFqtZGRkYLVao92VZvPG6Mmn0sWb0JQfr9lB+tBmrqqKPp2tuMcIahypDDinY673FGCymHCdfg4AmadMaPPX6wrxItqPxIswYv94SchKwK/p5T5lO6L3Dcy0Q/9SED8q9EuMNm4c3bvB8OqlfPUVrFzZeq+Z++oXuHIKKLFkcMyjJ4RVPRkxsLZIoLlT7gKGD4etMcNx1oB/XfSTTzK+CCM6arz88guMLpqP1QIpk4bSv5/i0vKnmLXxYf79zzJOPhkeeAA2b45eHze+vZKM7YtJ++o/uMr1Re5qduvrPXmSmpd86n6EnnxKLNnR4MWF775VnLzyQfr4d9Dn8AzSX3iIA2XQLYePDv5c2mMIFkfk7Lx2262kHDmK7v1jMU8aH7a9veLFPHwIAP7lrfgHRLS7jjq+iMZJ8ikK3G43OTk5uN3Ru6rcUv54fYFw3xr9ZLgitQ+aqYFb/zTRhDX/YsT2z0KqoDqqKS9cyCG5ixh6weFt/lpdIV5E+5F4EUbsHy+aSaMmRp96V74jSlPvvF5iCvTp3JkT+oduGzcOhwOmJy8F4OabYcWKpjft90deOkn5/OQ+9gYK2DXlPMZNadlaJIE1obKyWtQMhxwCu+KH4/ND9ZLoJ59kfBFGdNR4Wf7+Nnq6thGfbEF7fg7W226iX3+NC2I/4uXdJ3Lhzr+x8r3fOe88uPJK+O478Pnat48lv2wEwKR87Px6EwCuPXrlkz+1ecmn9KEZuGyJaMrPnkU7Iu6z8e2VTCj/hqQ0MzFPP6qXKR1A5vGjgz/7ho5oeEebDV56Cb76KmJmvr3ipdcfJgGQsnVpMLEnOp+OOr6IxknyKQpcLhfbtm3D1Za362ljKl6vfIrbqZ8Me7o1f8pdgDXWij3R3uJ22oNm0tqtr10hXkT7kXgRRkSKF3ecPvWuek90Kp8qfsvB7/HiMsXQd3y30I1jxoCmMci2gymDCigvh2uugc8/P3C7RUVw5plw8cXhXyTXzlmIY98OnJZ4jnz6jBYfw2mnwZ/+pH9xbQmLBUyH6tNT3Kt/a/9vwPuR8UUY0RHjxe0G31fzAbBMnQgJCXD++ZieeZqkCUMY0NfNxckf88+ic7ko7/+xarmP226D667Tk9ftxbNuU/DnwgX6ubY3T08+aRnNSz5pJo2K7nr1U+Ev4YuOV1RA6sKPAbDOOhVGjmxSu/1OGILPpCfs48cfYC0nsxliYiJuaq946XvsAKriMrH4XGx+e3mbvpZoOx1xfBEHJskn0TwJtcmn6gIAtL7NX2xcCCFEx+FN1K901+yJTuVT7s/bAShJ6k984n6nKYmJMGQIZhP845xlTJ+u3yb93nv1C+oN3RDO54M77oCcHPjtN5g/v26b8ivK/vlvAMqPO4usgXEtPoa4OLj8cn3x8ZbqNqEfTlMs7jInbN/e8gaFOIj9+iuMKvoWqwXSZtfdvZjJk+HNN9FeeYXYU6fTqydcnfYu/0q6hSRrNUuXwrx57dNHpSBm18bgv92rftN/KNCTT5buzUs+Afiz9TnB1avDk08/fVHB2NL52O2QcunpTW7TFm+j6MgzKEvNZtDFk5rdt/aimTSqRuu3Li365Kco90aIg4skn0SzmJISQv7tGNjyyichhBDRp5L15JN7X3SST+Ur9TsQeXpnR95hnH5TCuuqpfz973olE+jJp//7P72yYX/PPadPz3P4qrD7q/n3v+sSVZveW03anjX4TDZGPnxuax9Oiw0/1MR2xzBqnHSIRceF6MyWfbCDXq6txCVbMB01LXSjpsGoUfDII/DYY1hjbYwqX8jL2hUkewp49ll9QW6jlIIdO2DTpsj/FRaG7r9vaxWpVTnBfzu26r/3phJ9R3tWmvFO1IoZWbsg3dbwRcfz3vgKq3JDdra+4JwBR827hUk575HYK7HZfWtPySdPASBm5SKUv4GrFkKIVifJJ9Es5pTQPy7JI6XySQghuoRUfdqdrzA60+5cm/TqHsugBpJPY2tvSrFsGSYTXH893HWXvibu55/r02PKy+t2/+47eOMNsPur+VDN4h87ziLvt0IWL9a35z2mVz0VTTqF1IHN/1LXVoYPh20xw3E6wbdidbS7I0Sn5XaD90u97NE6ebxeSdmQY46BF1+ElBT6ujbxUMGVOAsqeOUV46/71ltw1llw/vmR/zvlFNi5s27/HV/pU+58sXr/Est2Ub67HFuZnnyK69v8yqfU8fq0u7i9oZVPlZXQbfEnAMRfcLqeiOvCBp0/Dr9mIaF8D3t+2RXt7ghx0GjkZsGirVgsFtLS0rA0dq/mDs6SHLooeOZYST61la4QL6L9SLwIIyLFiyVDr3xSRdGpfLLu1CufEkY1kHwaPVpfN2TvXv2/rCxmzYIePeC22/QKp3POgV699N031s5eueGIlXT7uRgVB9fv/itvvvYCPap3krFpIaAx8P4L2vzYmqNnT9iWORFV9G+cn39L3B23QZRuLS3jizCiI8WL0wlvvgmjC+djsUDa2dMP/KRDD4XXX8f0pz8xvCqHq/b+H8/+5x+cfrqpyVNqCwrg0ye38uecOaRaK9D2u+zv8UCe1p0vP7mLq27Q1xIt+XUTSYBz6GH4tmwnsWwXu778jZgqPfmU0L/5yaeeU7PZgUZsTRGl20tI7q9fbFj+9mb6Vm/A4rDQ7eITm91+S7RnvMSmx1Lcbwzp25ew861F9JrSt81fU7SujjS+iKaTyqcoiImJYcSIEcQ0sOBeZ2DPqLta5LHEBv94idbXFeJFtB+JF2FEpHixZurjuVba/pVPfo+PuCK9BKD75AaST7GxMKL2jkpLlwYfnjgRXn0VunWD/Hw9CbViBVRXw2GHwVl99X3TUmFwzSqyP3+WlTe9AUD+8KPpOaljXkTRNLBNOpxCaxau4iq9lCtKZHwRRrRXvPj9sHJl5Clx+zaV8eENC7hz0vdsePRTerm2kJhixnTMUU1rvGdPePRR4lOsTOVHjs9/kyeeaHrf/vWPMq7d8mem+BZwYvcVnJgZ+t+RCSuYUvY5he/MD04F9q7XM+b2UYOpydanvxV9tQyrtxqAlIHNTz7FpsdSkdgTgL0/1k29K39Tr3qqGTsNUqJzTt/e44v5SH3dJ9+CunWfioth8+Z2eXnRQvL3qHOSVGEU+P1+vF4vFosFk6lz5v/s6QkEZkhXpvZBM3Xt8txo6grxItqPxIswIlK8xPTUK58s5e1f+ZS/Yjeaz4vH7KDn4d0b3nHcOFi9Gn78Ub+1XK0BA+Cdd2D58robw9lsemLKfImefLKefDyJr3/NicVzUcX6Mfe6+5K2OqRWMWyEiQXJM8mueoHUjz+Gk06KSj9kfBFGtFe8PPIIfPSRnpeeORNmz9bXUfr+n+s44qObGOotYShgtUJqN0g58QBT7vY3bBjceivd7vs7s7c9xyNfDeeLL8Zy4gEKhNat8ZP92j2ke3JJO7w32h3Xhk1ni/l2Idoz8+i/dT5bt55M//4Qu0ufdpdxxBAqSISVX2D79UcAvGY7semxRt6eMDW9BpLw225KlvwOF4+jsthNj9VfAJB2+ektarsl2nt86XfBEZS9/gSpO1ZQXVjNxl2x3HKLPgXxrbf0vyei45K/R52TJJ+ioKqqihUrVjBmzBgSEhIO/IQOyJGRQE3tz54eHfNqcVfRFeJFtB+JF2FEpHiJ7ZWKG7BVtX7yyev0svrJ76h64wMcxXvDtpu9bmKAirT+mK2NnEwefzy8/LKefNq1K+S2cgkJcNRR++1fVlZ3Ofvmm7GZu8MTb6Dhp7DP4UybOazFx9aWjj4arptzKmcWvIh78QpsOTnQu/1v9CHji6jcV8nyGXeiLBbSrjyL4X+ciMliIj8fPvhAv5vcyJF6AiglpWXxsuGtleTfPwfzJRdyxB3TIu7z2Wew6/Xv+Fvhy2yIO5z5b8zmnXd6M7b8O67eezdW5caX0YOM4Zl06waaww7X/Mn4gc+ahX3NGjJfm8e1e+7kb/83l8GDM8luoEDT74elf3qFCVU/E59mJ/GFx2DgwLD9bP37E//KPEZU/cqC/1WijrXSo2YbJhN0O3IIFZYUvK9CUvE2AGri0lt8wdc0eCD89j2eDXrl06r7PibVW44zIZOhZ0xsUdst0d7jS9aE3uxO7EVC+W7eu2UJC5bHc0nBe1iVi28/e5gBN7UsySfalvw96pwk+SSaJbZbXfLJ1FfudCeEEF1FQp8UigBHTQnKr5r8Rcfr1b94mkx6pVH9C5Gl20tY+8B/ccx7n7jqAg5UJO87bGzjO2Rnw9SpsHAhzJ0Ld97Z+P7Ll+u3nMrOhrQ0Mu67lq3/20jC5mWk33FFk44vmgYNgsHTurEmdxIpRT/T47PP4Jprot0tcZBRfsWyU/6PzC0/6w/85UeWPNCXXYeexNrfY/AryMLC96sm8e67vRk3zs6IEQkcdpjx18pfs4+a624hw1WG56Hf+Hf1v7nw/gEh48rmTYqVf5nL9XufITNdMTV2M2fmv8NK30gG1awhOUkRe/wRJD33d70sqiU0Df76V9I3bqL6u9+5cttfue0vL/Hv/1iIiwvf/ZcnfmH86pcwmSDpkTsjJp4AyM7GMrA/lpXbyXv/R3Zq/cjCj0pOwdQtg77HJ/O7Zsak9FJOd0Lzp9wFJBw2AP4Llh2/s/DSV0l7bw4ANSefhWY+eCpINJNGzZgpJPzwLod9cBfjlQu7DVxu+N/b81B/nt3V110Xot11qeTT1q1buf/++1m7di0xMTE4nU6uvvpqrrjC2Inl999/z8MPP0xZWRkej4eUlBTuuecejgq7lHrwis9KpKj255ghskifEEJ0FUn99OST2e+hKr+K+O7xje5fWqL46EPFhx/qay0BZGXplQ9jk34n98l3SFv2FWl+NwA1jlSqTjyLrD9MiXhHJUuMlalHH3Lgjl58sZ58mjcPrroK0hq5U92yZfr/A3fKM5s5fNEzlO8oJm1o5oFfqwO4+GJ45avTGLX3Z9I/+gzrVVfpC683l1IEF5mB0GyhEBEs+tObZG5YgM9kpWjiKSQv/ZrE0p2MWPg8I9DzO0mJUJGn8RNT+LJ8Ns/MH843X9k4/3w/J52sERt34G/z7ioPm874K2muMswWE3hd9H72Nu6tfJO7/h6H3Q6VZT6WzHqUs/Z+RHw8pF9yClppCXGLFpHlW42mgfmc2XDzzS37PanP4UD7f4+Rdc4FuNetYcryZ3jggb/wyCOhQ9nKx78j4YF70FBUzziDpPNObrTZlNnTKV31L/r+Pp+NHx9BFuDJHgyahj3BRmnmIFLzNgDgS2l58ilz8gDKQG/zPb3d/OnnMe3FS1rcdmeTdtoR8MO7WJWLpO4xZE4ZyJaP1jBi6yf8/vtsBg2Kdg+F6Fq6TPJp1apVHHnkkcycOZMlS5ZgtVpZtGgRM2bMYOnSpbz00ktNaueVV17hiiuu4LnnnuPqq68G4Omnn2b69Om8+uqrXHTRRW15GJ1GTGoMSjOhKT9JI6TySQghugpHsgOPJRart5qy7cUNJp+2f7WZnY++Q+qyr5joczERsJhBAb6NwHfgAwKpneLuw7BffC6jb5mONdba8o6OGqXP71mzBt5+G667ruF9AwuTjxsXfMgaY+k0iSeAww+H58ZPo/zTZEp+LyDzl1/giCOMN7Rxo74w1tdf4612U1IKpaWQ12ssk1Y8h9nWSl/URZeyYe5yUt56FoCqq2/lqMfOoLrwJlY/NA/zxnUc0l/p+d+iIlKWLOFM908cXbSQ4hI/2m4zzId1jgzi/nE/wy8d3+hr/fyHJ8nMXYfTlkiv/z4Lt96KtmEXg96+n6MX/I0JFfM5ofg/jHFuwmrVSH/oJrQ/nqtngHbtwvLpp9C3L5x6auu/Eb17Y/37A/S85mZO2PkWz/73UP7iPo7zz4fDxyh+vvY/pLzxNKAoGHQEk/99ywGbdJw8nfiH/sWIql9ZvkG/k2XM6CHB7d5Bw6E2+dRokr2JeozrRZHZgcXnRGkmyq64haOenN3idjuj4X+cyIJfbyStm4Wed54Cfj+x80+gb9kmlv1nE4PuHxztLgrRpXSJ5FNZWRkzZ87EYrEwZ84crFb9pHbKlCnceuut3HfffUyePJlLLrmk0XYWL17M1VdfzbHHHhtMPAHceOONfPzxx1x11VWMHz+eIUOGNNLKwcFkMVGYPR5H/i5GHdtAKbEQQohOyRmbgrW8msqcEpjUhxWPzad8wcrgdtO230nftSKYWHI4IDW1bg3fsjL9rkFOt5mCkceSdfO5TJk1onVvTqFpejnQzTfri81ceikR578UFsL27fr+hx/eeq/fzjQNLrjUys8LTiK55C1S3nwb65gxodOJNm2CRYtgyJDQuY9eL9te+Z7qV98hfc9q/SEfVFTUFT+lbl/Gkrs+ZtL/O7Odj0w0i1Lw8cf6Z3z88dCGd3wqWJdH9Y13EKP85I87hSMfmQXod06b9ORsYL/Exa5d2N99l4yPPyY2rgiPJ5bSUhPxzgL811/P4i13MeGhmWGv4/HAors+J3PBewBYHnqAzKOGwWuPEnfu5Zh3fsfw348jxq/f1s5vi8H65IPEXnJUXSN9+jSeiG4NRx5J7J8upvvT/+aPuQ/y0/srWfS+xi7rPobkLwAg/8jZTP345qYlc7OzsQzoi3XVTiaUzwcg44i6pEfsuOGw8AMAzN1aXvlkspgoHn0MSWt/wvTg/Uy+bmqL2+ysTGaNo1+9MOQx39SjYN43eD/8BHXfbTL1TohWpClVv+a6c3r00Uf561//ypVXXsmLL74Ysi0nJ4c+ffrQs2dPtm/fHkxMRXLiiSfy5Zdf8tZbb3HuueeGbJs7dy4XXnghs2fP5t13321Sv8rLy0lKSqKsrIzEenfVUErh8/kwm81onXhEU36F3+uXq6RtrKvEi2gfEi/CiIbiZeHAS0nbuxbXg/8PT2kV8Y/fF/5czUTBoceSddPZDDyuX+QTdIdD/6+t+P36/L4dO+DGG+HCC8P3+fJLuPtuPSEzd27b9aUd+P1wzQnbuGHRbDIyILF7LM7jT8MzYCgxX/4X63o9QWi1gta3L5x9NlRVkffc+xRvyEcBfswsTjyOb1Jms8/WhyFDYIZnHoO/fAqnPYkBa/5LYq/IdwKT8aUDeest/I8/gdcLKi6BmhmnYzr7DyQOyQrft6pKL29DjyGPIwF7Rvhn7K50Y7KYsDjqrk1v/2ozhRf+mbiqfErTBzJm9Ws4kpv2O63cbnzl5ZjNZjwuxc9nPk7mqq8AKDj1Mgb842rQNNxumD8fCp59l9O2PK7fCGDmH5n2dt2FYN5/H/XIo/j8oNIz8Zx2FtbZs7BmpjT9PWtNPh9ccw2un5dTXAJlpeBXABoll9zE5H+eayjZ7nzyebbf8wpKgdUCA9f9N3hTgV0/bKPyZD3BV/HneyMm7oxSfoXP7Qv5rKOto4wvzh9+Zfsp11FpSiT5ly8ZONwWtb6IhnWUeBEN5zwi6RLJp4EDB7Jly5aISSOAQw45hG3btvH5559zYgP3Rd2zZw+9evUCYO/evfTo0SNkeyCJZbPZyM/PJykp6YD9MvJBCCGEEB3FD4f/hcyNP5I/9SzSFn2K2e8mf+RxaLU3mDAnxjP4xhPIGN4Bpq19+ik88AAkJ8OcOYQt0vHgg/DJJ3pi6sYbo9LF1vTRR/DtX7/mzIIX6O7eFbLNj5n1ceM5VK2me2I1SUlQVKQXf1WYU9g17kwqjj8TZ0IGFoteHHXooeD3+FhyyLkkFW8j/+hzOGregacKiShatYqK869i7y4fJVoqiT79zpRKM1Ew9Ei63XgOQ84bg7Z5kz7F8quv8Do9lJZAcQl4fKH7bf9yE7see4e05V/htcRQfszpDLnnD+Qv3o7/tr/qU3BT+pE97590G93jAJ1rmN/rZ+EFL5Dx2asA5NgH8nXqOfyaeBxnFTzPjOK3sVigavrpjHr3TkyWeuuQKQVffw0WCxx5pP7/aKus1MeWsjKcTtiwAexHTmTYBWOMt/X77+w64lwqK8GRGkv2zh+ClYt+r5+1aUdh9Vbje+IZDr1qcusehwjl9/PbgJmQt49tlz3EKf+cEe0eCdGhHVTJp4KCAjIz9ZPfVatWMWrUqLB9TjvtND799FP+/Oc/8+STT0Zs5/3332f27NkkJydTUlIScZ+kpCTKy8v5+OOPOe200w7Yt4Y+iOrqarZs2cKAAQOIbendN0SXJ/EijJB4EUY0FC8/TP8bmb98HPx3wYDJTF3+VOiXwY7C49Gn323erE89euQRmDKlbvvMmbB3LzzzDEzu/F/a3G79RnebN/oZVrGYYwveIdOdw7Lk6fyQdhb5ZGLxVDO1dB7HlH+MEzvfJZ/J0BuP58rrbA1OIVn/2hK0665BaSYS5r1Dn6PC7yEv40sHUFxM3rHnUby5kF8SZ/BKvwcZXbWI6QVvM7h8SXA3d0I6aaoQ0PM2JU4HfqV/+HZ/TXC/6ph0YmsKw15Gafrvuqb8FPYdy+ivH2uwIq4hDcXLkrs/Je65xzB7nQB4NSvxNg8pqRB3+/VYLrso4s0IujSlKJ1+JoUrdpFw5Bi6fRy6Vu2Cc+Zg/WUhIxf/64A3geisOtL4su6GlzC98hK7uo1nxtY5B104dgYdKV4OdkaSTx3gskHLrF+/Pvhzz549I+6TlaWXIK9bt+6A7TTURqCd8vJy1q1b16TkU0N8Ph8lJSX4fL5mtyEOHhIvwgiJF2FEQ/GipacGf66M787ozx7smIkn0OeYvfgi3H47LFkCN92kr/kydCiUl+uJJ7MZRo+Odk9bhc0GL78MYAIm1f4HRwI3o8+w+uyzWN59dzbzc2ZjNsNdd+k5uMYMv3Q8Pzx3FJkbfmDHNY9Refsfw/apqalh586dePqWEtOGawyJhvlefBnz5kL22vqz55K7+OEBE1brVGAqu37YxraH3yX1l/9hqyikDDNLEqfzVeq5bI0ZwbBhcM45MNS+jZ2PvUPqL/8jtqYQv2amcPRxZN10NjV7Syh78R3St+uJrPzxpzLlf3c26yYBDY0v4/82E247Sq8aeu89yM3VA/v+++G441rhXeqENI3k804muex5OCG8curId64Brmn/frWjjnT+kn3jqex49V/0yVvCkke+IyErIdpdEvvpSn+PrIkxDJw1ItrdaBedPvlUXFwc/DkhIfLAEMjA5QfuAd1IOw210ZR2XC4XLpcr+O/y8nIAKioqQuaiBvbx+/1UVFSEtRPoQ3V1ddgA7HA4sFqtuN3ukNcCMJvNxMbGopSisrIyrN24uDhMJhM1NTV4vd6QbXa7HZvNhsfjwel0hmwzmUzE1S7iGqm/sbGxmM1mnE4nHo8nZJvNZsNut+P1eqmpqQnZpmka8fH61ZvKykr2L8KLiYnBYrHgcrlwu90h26xWKw6HA5/PR3V1dVifAu9hVVUVfr8/ZFtj76HFYiEmJga/309VVVVYu/Hx8WiaFvGzaew9DHw2EPk9bOyzCbynPp8v7Ln1P5tI72HgszH6Htb/bCK9h4HPprXfw8Bn09h72JL4jhSHTX0PG4vvlryHkT6bA72HjY0RgWP3eDxh8SJjRJ2uNEY09h4eKL4DfXS73SGvq5L1991nspL84sOYUkwh2zvkGPHww8Q+/TTq00/xP/VUyDb/0KGYHQ5McFCMEeeck8Ds2fDTT04SErwMGKAIfHyNxXffp/5E5YmLSN+5DK5eFtZuDDDI769dy2a/UoD6/4xUU681YVuk7Z2t3aY+t5ntmgGnFkPODX/jxpt9OJ0VOJ36GNHnqGwyJvyZkp0X8fv76ynuNoTYxAz+YLEwdCgMGuTF6awBMkj/4Hoq9lzM7nm/kX3yaEYMzaiN735wyWHsWbiDii0FHHH1NCxW4+Ns/fPe/X+nAGJiY7FceCHuP/wBz6JFqIwMVP/+UFFx8J5HnHkmpr598Y8eDbXfHaJxHhGt7xoBkf5mt/t5RGY8RdljSdu6lIS/3dapxoiotRtpexuOs036e9RJ3sOCtP4MnPV+hzqPMDJGRDpvbUinTz7V/xBstsgLwtntdoCIb+z+7TTURlPaefjhh7n//vvDHl+9enXwjxJASoq+OKLb7ea3334L2//II48EYOPGjWEf5pAhQ+jWrRsFBQVs2bIlZFtKSgojR47E5/OxYsWKsHYnTZqEzWZj69atFBUVhWzLzs6md+/elJaWhvUpPj6ew2vvELRy5cqwgXvs2LHExcWxc+dO9u3bF7Ktd+/eZGdnU1lZyerVq0O22Ww2Jk3Sr9iuXbs27Jdi1KhRJCcns2fPHnJyckK2de/encGDB+N0OsOOVdM0pk2bBujv4f5/HIcNG0ZGRgZ5eXls27YtZFtaWhojRozA6/VGfA+nTJmCxWJhy5YtYdMzBwwYQM+ePSkuLmbjxo0h2xISEhgzRr+SFand8ePHExMTw44dO8KSm927dwf0uNv/M3c4HEyYMAGANWvWhA1Yo0ePJikpiZycHPbs2ROyLSsri4EDB1JdXR3WJ7PZzBG1t/D+7bffwga74cOHk56ezr59+9i+fXvItvT0dIYPH47H44l4rFOnTkXTNDZv3kxZWVnItkGDBtGjRw8KCwvZvHlzyLakpCRGjx6NUipiuxMnTsRut7Nt2zYKC0OnEPTv358+ffpQWloaUi0J+gnNuNrbr69atSpsgB0zZgwJCQnk5OSwd+/ekG09e/ZkwIABVFZWsmrVqpBtVquVybVTfNatWxd2onXooYeSmppKbm4uO3fuDNmWmZnJ0KFDcblcEY+1sTGib9++AJSWlob93sgYoetqY0Tfvn3p168f5eXlrF27NmTbgcaIQbXrI+Xn54e0Wz0xjd0/jCH9j2fT79h+nWeMuOceVHY2ha+/jlZ7EqbMZvaOH88wj+egGiNMJoiP/43y8grq73Kg84jqq2/F9+57uJ3hCQOr1YbH48akmfCr0JNci9mK2WLG7/Ph8YbGmaaZgudXLlfocQLYrHY0k4bX48HnD31/zWYLFosF5ffj9oSOARoattpzM7fLhdrvrN5mtaGZTHi9Xny+0C/CZpMZi9WK8ivcntAv2AB2u76ottvtRu13rFaLFZPZjM/rw+sLPVaTyazf3EYpXO4I7drsoGl43B78KvRYLRYrZnPj76HPGsPOE6eTfcw+Vq6sG0/DxohpdlLZDmwPjhHFxRHGiKmJpA/NAPYbI+KAUWaqqquafR4RWApj+/btYfEdHCPy89lusUBJif4fB/F5xOrVeiVnbfvROo+I1neNwN+jTZs2dYjzCHXFTIqfqkZz1XSqMQIaGGdtdjRNw+Px4G9gnPX7/XgaGWf1ZKSRcdaCxRp5/AYt+N068ntow2Q24fN68e7XrslkxmI2R2hTZ7c5QAOP2x3+t6r2PfT5fHj3ew9NmgmrzQYKXO7w9zD42UR4Dy1mC2aLBb/Pj8e733sY8tmEv4f+DH3d6Y52HgFNGyMay7Hsr9Ov+fThhx9y1llnAfqHGSl5dMcdd/DII49w6KGHsmbNmojtXH/99Tz77LNMmjSJn3/+OeI+kyZN4tdff+X666/nmWeeCdseqfKpd+/e7N69O2T+o8vlYv369YwePTok0x/QEa9GSFWDLlqVT2vXrmXUqFGYzaF3FpTKpzpRv2LZQa5GeL1e1qxZw6GHHhp2d08ZI+p0pTGipZVPq1evZsSIEWF/P2WM0HW1MaIl5xFKKVauXMnw4cODXxoCZIzQdbUxoiXnEYEkz9ChQ8OmxcgYoetqY0RLziP8fj8rVqxgyJAhYWv4yBih62pjREvOIwIXASPFi4wRuvYaI8rLy+nVq9fBseD4okWLgldfi4qKSE1NDdsnkFiaPn0633zzTcR2HnroIe6+++5GE1SHHnoo69at429/+xt33XXXAfvW0OJbbrebgoICMjIyGq20EgIkXoQxEi/CCIkXYYTEizBC4kUYIfEijJB46TgOqgXHhw4dGry6kpubGzH5lJubC+glvg0ZNmxYyL6RNKWdprDZbI0ubC5EfRIvwgiJF2GExIswQuJFGCHxIoyQeBFGSLx0Th301jVNl5qaysiRIwEirp8EdXeyO/rooxtsZ9q0aZhMJgoLCykoKAjbnpeXR1FREWazObhWSHN5PB7y8vLCyuqEiETiRRgh8SKMkHgRRki8CCMkXoQREi/CCImXzqnTJ58AzjnnHAAWLlwYti0vL4/NmzeTmprK8ccf32AbaWlpHFd7e9dI7fz4448AHH/88RGrq4xwOp1s3LgxbJ6pEJFIvAgjJF6EERIvwgiJF2GExIswQuJFGCHx0jl1ieTTlVdeSUZGBu+++27YQlyvvfYafr+fm2++ObjY4fz58xkxYgSPP/54yL533nknmqbx2muvhb3GK6+8gslk4s4772y7AxFCCCGEEEIIIYToYrpE8ik1NZU333yTiooKrrvuumD53dKlS3n44Yc56aSTuP3224P7P/XUU6xfv5777rsvpJ1p06Zx3333MW/ePF5++eXg4y+++CJfffUVDz74YHBxcyGEEEIIIYQQQghxYJ1+wfGAGTNm8Ouvv/LQQw8xceJEYmNjqaqq4oEHHuC6664LuUX92WefzY8//shFF10U1s69997L6NGjeeKJJ3jllVdQSuFwOPjss8845ZRT2vOQhBBCCCGEEEIIITq9LpN8Ahg5ciTvvvvuAfe78MILufDCCxvcPnPmTGbOnNmaXQthNptJSEgISYgJ0RCJF2GExIswQuJFGCHxIoyQeBFGSLwIIyReOidNKaWi3Ymuqry8nKSkJMrKykhMTIx2d4QQQgghhBBCCCFahZGcR5dY80kIIYQQQgghhBBCdEySfIqCiooKFixYQEVFRbS7IjoBiRdhhMSLMELiRRgh8SKMkHgRRki8CCMkXjonST4JIYQQQgghhBBCiDYjySchhBBCCCGEEEII0WYk+SSEEEIIIYQQQggh2owkn4QQQgghhBBCCCFEm9GUUiraneiqGrrtoN/vx+VyYbfbMZkk/ycaJ/EijJB4EUZIvAgjJF6EERIvwgiJF2GExEvH0VDOIxJLO/VJ1GMymYiJiYl2N0QnIfEijJB4EUZIvAgjJF6EERIvwgiJF2GExEvnJGnCKKipqWHDhg3U1NREuyuiE5B4EUZIvAgjJF6EERIvwgiJF2GExIswQuKlc5LkUxR4vV7y8/Pxer3R7oroBCRehBESL8IIiRdhhMSLMELiRRgh8SKMkHjpnCT5JIQQQgghhBBCCCHajCSfhBBCCCGEEEIIIUSbkQXH21DgRoLl5eUhj1dUVFBVVUV5eTlys0FxIBIvwgiJF2GExIswQuJFGCHxIoyQeBFGSLx0HIFcR1M+B0k+taGKigoAevfuHeWeCCGEEEIIIYQQQrS+iooKkpKSGt1HU5IqbDN+v5+9e/eSkJCApmnBx8vLy+nduzc5OTkkJiZGsYeiM5B4EUZIvAgjJF6EERIvwgiJF2GExIswQuKl41BKUVFRQVZWFiZT46s6SeVTGzKZTPTq1avB7YmJifLLIppM4kUYIfEijJB4EUZIvAgjJF6EERIvwgiJl47hQBVPAbLguBBCCCGEEEIIIYRoM5J8EkIIIYQQQgghhBBtRpJPUWC32/m///s/7HZ7tLsiOgGJF2GExIswQuJFGCHxIoyQeBFGSLwIIyReOidZcFwIIYQQQgghhBBCtBmpfBJCCCGEEEIIIYQQbUaST0IIIYQQQgghhBCizUjySQghhBBCCCGEEEK0GUk+CSGEEEIIIYQQQog2I8knIYQQQoiDSOBeM3LPGSGEEEK0F0k+CSFEB+X3+6PdBdHBKaXw+XwALFmyJMq9ER2dUgq/34+maQDB/wvREElQCiGEaC2SfGqGwIm+EAciyQNhVEVFBTNmzGDJkiWYTCYZb0SDfD4fmqZhNptZu3Yt33zzDR6PR74siogC8WIymdi8eTOTJk3iscceAyTBIELVP3epn6CUOBGRyLmuMErObQ9eknwyKD8/n48//pjS0tJod0V0YIE/xCaTCbfbzapVq8jPz49yr0RnsHXrVr755hvuueceAMxmc5R7JDqawPhiNptxOp3ccccdjBo1iocffhir1SrVLCLE/vFyyy23MGTIEBYvXsymTZvweDwSMwIIPXepqanh9ddf53//+x+LFy8GpFJOhAokI00mE06nk6+++or169dTUFAQ5Z6Jjky+Sx/cJPlk0AcffMBll13GggUL5AqQaJDJpP9qvf7664wdO5aTTz6ZXr16cdVVV/HLL78AcqVIRJaYmIjFYuGbb77hv//9LwBerzfKvRIdSWB8ee211+jZsyePPvooAFdeeSVer1fGFgHoXwyVUsF4efnll8nKyuKJJ57A4XDQp08fNm3ahKZpcj4jgLqx5d///jeTJ0/m008/5dprr+XII4/k6quvpqioKMo9FB1JIBn5yiuvMG3aNJ566immTp3K+PHjufPOO9m6dSsgFXMilHyXPrhJ8smATZs28dJLL1FRUcErr7zC3r17o90l0QEppaiurub666/nsssuY926dVitVrxeL//+97956KGHqKysDJ7kCVHfunXrSEtLA+Dmm28GwGKxyB9oEaSUYubMmfzxj3+kpKSEY489lp9//pknnngCi8UiY4sA9C+Gmqbx448/cvjhh3PllVdSWlrK2WefzYYNG3jxxRdZvHgxRUVFkoASKKUoKiriggsu4JtvvuHtt9/mo48+4osvvuDiiy/mxRdf5J133pE4EUFKKW699VY+/fTTYKx89NFHDBgwgEceeSS4hIBUzIkA+S4t5Ay1idxuN59//jkTJ07E4XAwb9485s2bh8fjiXbXRAejaRqrV69mzpw5jB8/nq+++ir478GDB7No0SJWrVoV7W6KDiZwQp+YmMg999zD2LFj2bFjB//4xz8AqZQToSwWC7169eLNN9/km2++YeLEiSilpEpOBHm9Xu69916OOuooVq5cybhx4/jyyy95++236du3L9XV1aSkpPDVV18BMqXqYKdpGosXL8btdjN37lyGDBmCz+dj6NChXHXVVQwbNoyPP/5YEpUiaOvWrcyfP597772XXr164fV6Oeqoo3jppZe4/PLL2bZtG3/84x/55ptvot1V0QHId2kBknxqsp07d3LooYfywgsv8MgjjwDw/PPPs2XLlij3THREn332Gccccwy//PILxx13HElJSfzxj3/k2muvpaysjLi4uGh3UXQwgS9+S5cupbq6mrlz5wJw5513UlBQgNlslgUaBaDHSlFREXfeeSfnn39+MDGpaRoWiyW4n3xBPLg5nU6WLVuG2Wzm2WefZfHixRx//PHBuJg8eTIej4d9+/YBEi8CHn744eAdEf1+f7CKcujQoaSnp1NSUoLL5ZJEpQDg119/ZfXq1djtdqBu2mb//v158sknGTJkCOvXr+eJJ54ILjkhDl7yXVqAJJ+AunUR9v+5voEDBzJ9+nQArrjiCkaPHs2aNWt46623qK6ubtf+iuhqLF4CP5eUlHDKKaegaVqwEsFqtTJ8+HCOPvpoevbs2f4dF1HRlPElsA0gLS2N3r17M2jQIM4++2y8Xi9//etfAWQ61UHgQPESSECeeOKJ5OXlsWXLFhYtWsS//vUvrrvuOv7xj3/w9ttvU1ZWJl8QDwINxYvf7yc+Pp7s7GzOPfdcrrnmGqDujnd+v59u3bpx2GGH8e2330at/6L9NDa2+P1+vF4vPp+PXbt2UV5ejslkQtM0fD4fMTExxMTE8Ic//CGYaBBiw4YNAMEEduAcxefzERcXx5NPPklCQgLff/89r7/+OuXl5VHrq4g++S4tQJJP+P3+4LoIe/bsCf7xDdj/xN/r9RITE8Ndd90FwL/+9S9WrFjRrn0W0XOgeAmcqHk8Hs4991wgdL2erKws8vPzmT9/PitWrJC5zl2ckfElkCjYunUrO3fuBAhOuXvttddYtmwZmqbhdrvb8QhEe2pKvATuflhWVsYHH3zAxRdfzHnnncdVV13FnDlzuO222zj//POZPn06H3zwQbQORbSDxuIl8CVw4sSJwb9F9ePHZDLh9XoZMWIEW7duZdeuXZKs7MIONLaYTCaUUmRnZ7N06VLuvPPOYKWKpmkUFBTgdDqDsRQg1XJdU6Ca9vPPPw8mBSJN/Xc6nQDBc5ZATAXGmRkzZnDxxRfjdrv5+uuv+fzzz9u876L9NTVeAuS79MHtoE8+mUwmCgoKuPLKKznzzDMZO3YsU6ZM4dVXX6WkpCR4Mhb4AxuY0nDmmWcyc+ZM8vPzefnllykuLo7aMYj2c6B4Af2P7hVXXBHyvMBVxnfffZf169dzzTXXMGHCBE499VRWrFgh06m6qKaOL1A3xsTFxXH44YcD0LNnT+677z4A/vKXvwBgs9lC9hddR1PiJfC5jxw5kvXr17N8+XLKy8vp3bs3EydOpF+/fiQmJrJ8+XKuuuoqvvnmGxlfuqjG4iVwThIfH8/QoUMBwsYbi8XCgAEDKC4uJjc3NyrHINpHU85drFYrF1xwAcOGDeOFF17g+OOP55hjjuHyyy/n4osvxu/3M3fuXD788MPgeYskLLsmk8nE9u3befjhh3nppZeA8PEDIDs7G4A5c+YAdUknqEtE3XDDDQwbNoydO3cyf/58CgsL2+UYRPs5ULzsT75LH+TUQcrv9yullPr000/VYYcdph5++GFVVlam5syZoyZNmqQ0TVMzZsxQS5cuDXuu1+tVSim1fPly5XA4lNlsVu+//77y+Xztegyi/TQUL88991xIvCxZsqTBNgoKCtSECROU2WxWgwcPVqmpqUrTNHXEEUeo7777rr0ORbSDpsZL/fElMH5cc8016s033wxpq2fPnkrTNPXFF18opZR65plnQvYRnVtz4uXDDz9U55xzjnr00UfV119/rTwej6qoqFAej0f99NNP6swzz1SapqmjjjpKbd68OVqHJtpAU+LluOOOUytXrjxgG1u3blWapqkPPvhAKVV3fiO6hqaOLYsXLw4+5+uvv1annnqqSk5OVpqmRfwvNjZWXXjhhWrRokXROjTRhiorK9Xs2bOVpmlq9OjRauvWrUopFfY9Z82aNSorK0tpmqbefffdsH0C8ffII48oTdPUiBEj1PLly9vpKER72T9etmzZopQKj5f65Lv0weugTT4FzJ49W73wwgshjxUUFKipU6cqTdPUqFGj1JdffqmUinxSdtNNNylN09Qxxxyjdu7c2S59FtETKV7y8/OD8TJ69OiI8VJZWanuuOMOdd1116mcnByVl5enli5dqs444wxlMpnU9ddfr8rKytr1WETbMzK+uFwu5ff71QUXXKDWr1+vlFKqqqpKKaXU559/rjRNU6mpqapv375K0zR1/vnnq4KCgvY9INGmmjK+fP7550oppQoLC9WOHTuUy+UK7uvxeEKed/PNNyuTySSJyi7KyPhSPzbq27ZtmxoyZIi68MIL27y/InqaEiuBsUUppZxOp9q6dat6++231ZFHHqkmTZqkzjvvPDVt2jSVnZ2t7Ha70jRNDR06VK1atUop1fgXTdG5LFy4UB133HGqd+/eStM0deutt0bcb+fOneqMM85Qmqapo48+WlVXVyul6pJOgZgoLS0NthVIdEu8dB1NjZeGyHfpg8tBnXxasmSJ0jRNffrpp0opfSAMJAx+/vlndfLJJytN01R2draqrKwMeW5g0MzLy1N9+vRRmqapp59+OuSLgOhaGouXRYsWNRovXq9X5eXlhfxbKaW2b9+uTjvtNNWvX7+w54jOrbnjy5/+9Cf1v//9L6St7du3q5SUFGWz2YKJp8aqGkTn05LxpSHr1q1Thx12mDr66KPbrN8iOlpy/rK/KVOmqGOOOUYVFRW1eb9F+2tqrPTv3z8YK263O/j8WbNmqR9++CF4flteXq7++9//qlmzZilN09S5557bzkck2tL27dvVCy+8oFatWqVef/11FRcXp7KystRPP/2klAq/EP/000+r9PR0FRMTo+bMmaOUqks+1d//73//u9I0TZ133nntdCSiPRiNl/rku/TB6aBe82nz5s0AIXf/CMxXnjRpEtdeey1Dhw5l+/bt3HHHHSHPNZlM+P1+MjMzufXWWwF44YUX2LhxYzsegWhPjcXL5MmTufbaaxk2bFjEeDGbzWRmZgL6InyB5/Xr14/LLruM3NzcYPuia2jO+FJZWcmmTZuYMGECoC8q/ac//Yns7GxKS0vxeDwAPPXUU4wePTp4J0XR+bVkfGnI4MGDOeaYYygsLKSsrKxtOi6ioiXnLwGBNVmmT5/OypUrg+vJia6lqbGyY8eOYKxYrVZAvwHG2rVr6dOnDzabDZ/PR0JCAqeffjpz586ld+/eLFq0iG3btkXhyERb6NevH1dddRWjRo1i2rRpzJw5k9zcXJ577jlAP59V9e6WePLJJzN27FicTifPP/88O3bsCN58B+pufnDCCSeQkJAA6OfBStat7BKaGi+RyHfpg9NBmXwK/BIE7tLw2WefAXUL5QW2T506lSuvvBKAZ599lp9//hmoW8E/sJjatddey4QJE9i4cSNvvvkmFRUV7XQkoj00NV6mTZsWEi+LFi0Cwu/4EPhDHJCSkkL37t1JSkpqoyMQ7cnI+HLVVVcBerwsXLiQ+Ph4hg0bRnl5Oc888ww9e/bkpZdeonfv3rz66qtceumlANx4440hbYrOq7XHlwCfz4fFYiE5ORm/3y/jSxfR3PFl//OX+s8ZOnQolZWV/PDDDyFtiM6tpbEC8OWXX5KXl4fD4QhJWvl8PmJjY7nqqqtwuVz07NmzfQ5KtKv+/ftz7rnnkpWVxSeffMI777wD6LET+A50yCGHcP755zNo0CDWrVvH3/72N6AuzgL7xcfHY7fb8fv9mEwmWay+C2osXhoi36UPPgdl8ikQ6D179sThcLBt27aQLGv9gXLWrFmcfPLJADz00ENAXfIgkNnXNI177rkHgFdffZUff/wRgJUrV0o1SxfQ1HiJi4vj9NNPD8bLww8/DIQnmwICV4U0TWP48OFkZWW12TGI9mNkfKkfL//4xz9wu918//33TJs2jT//+c94vV5uuOEGFixYwCWXXMLTTz+Nw+Hg7bff5vvvv0fTtEZvZys6vrYaXwKPe71ezjrrrDbrv2hfzR1f/v73vwOR46V37944HA7WrFkT0obo3FoaK6CfpwQSk4G/N/Xvcrdjxw7GjBmD2WyWv0VdTCBhMGnSJM4++2xqamp4/vnnKSkpCVasBPY57bTTuPTSS4mLi+PVV1/l7bffDrbjdrsByMjIoLKyktGjR4e0L7qGpsRLJPJd+uDTpZJPNTU1hgazhIQEHA4HK1euZM2aNRGf27NnTy666CLsdjtffPFF2NXmQGb/pJNO4tRTT6WkpITrr7+e3r178+c//1kG1w6srePl888/b7Q6IRA7v/76K1dffXXwyqLomNo6Xj777DP27NnD7Nmz2bNnD7NmzWLBggU89dRT9OvXD5fLRXx8PPfddx8A//znP4GGkw8iutpzfAkksuvTNA2v18u6des49thjm38gol1E8+/R5MmTycjIYMuWLUDkeBIdR3vEysKFCwF96i7AX//6V3bs2IHJZMJsNmMymdi5cyd5eXnceOONWCwW+VvUQRmNl4BAgjE9PZ0zzzyTUaNGsXDhQl5++WWAkOqlhIQErrjiCm6//XYAbr75ZubOnQsQnM67dOlSjjjiCC644IKQ9kXH0pbx0hD5Ln1w6RJ/KTweD6+88grvv/++oTK9KVOmMHLkSEpLS5k3bx75+flh+5jNZsaPHx+8IvTf//4XCP0lCpzIBU7w8/LyuOaaa1iwYEHwD7foOKIdL6WlpWzdupU9e/bwt7/9jby8PI455hhA/hh3RO0ZL6+++ir33HMP77//Ph9++GFw7Se/3x88gbvtttt49dVX+eijj1rh6ERri8b4ElhTYePGjWzdupUVK1aQm5vLAw88wKGHHsrUqVNb5+BEq4v23yPQk00TJkzg448/Dj5PdDzRiJUZM2ZwwQUXkJOTw/nnn8+jjz7Kb7/9xu23385FF13EjBkzmDFjRuscoGhVzY2XSEaNGsX555+Ppmm8+uqrwQq6+ons1NRU7r77bm6//XY8Hg+XXnopp512Gl9//TUPPvgg9913H+eee65M0eyg2jte9iffpQ8enT75VF1dzUMPPURRURFnnXUWiYmJTXpe4MrenXfeCcA777zDzz//HPGKX69evZgyZQo2m438/HxcLlfIdpPJxH/+8x9uu+02LrroIvbu3dvkBWFF+4pGvARKjl0uF9deey2DBg1i3LhxDB06lL1793LvvfcSGxvbSkcoWlN7x8u2bdvw+XyceeaZIe0ErjAG/jhfcsklLT000QaiOb6sX7+eSy+9lDFjxjB27Fiys7PZu3cvN998cysdnWhtHeH8BfTEQ1paGueccw4ej0euMndA7R0rhYWFVFVVAfo0vKeeeorc3FzefPNNLr30UkpKSvj000+55pprWukIRWtqbrw0JC4ujpNOOonjjjuOTZs2MWfOHCDyhfgHH3yQ+fPnc/3111NdXc2zzz5LWVkZn3/+eXDdStGxRCNe9iffpQ8irXjnvKh47bXX1IUXXqjKy8uVUqG392yqwG1mjz/+eLV58+aQbYH2fvzxR+VwONTw4cPDnl9dXa3eeOMNtWTJkmYcgWhP0YwXn8+nZs6cqWJiYtSJJ56oli9f3oIjEe0hmvHSnNcS0RXNePniiy/UhAkTVFpampo5c6ZasWJFC45EtIeOcP4SuA223Nq6Y+sIsVJVVaUqKipUbm5uM45AtKfWiJf9eb1e9cYbb6ikpCSVmZmp5s+fr5TSz23r2/+1KioqWvzaom1FM14C5Lv0waNTJ5/WrVunevTooV599dVmPT9w0rVs2TKlaZrSNE098MADqqCgQCkV/ss3atQoNXDgQFVeXi5fDDuhaMVLWVlZ8LlVVVVhJ32iY5LxRRgRrXgpLS0NPr+srExt3LixBUch2ouML6KpOlKsSOx0fC2Nl8Y+45ycHHXJJZcoTdPU6aefrjweT3O7KToIiRfR3jr1tLtXXnkFr9fLrFmzDD0vUG4cWCfj8MMP5+677wbgpZde4oMPPgD09XeUUng8HrxeLzabjT59+pCQkCBr83RC0YqXxMTE4BoasbGxDBw4sBWPSrQVGV+EEdGKl6SkpODzExMTZW2ETkLGF9FUHSlWJHY6vpbGS/3PeP/pmb169eLss8+mb9++fPXVV7z55puAPuWuurq6hT0X0SDxItpddHJeLVdTU6P69OmjTj/99OBjB7pC4/P5Qh773//+p2666SallH5l6JxzzlF2u1317NlTzZ07N6yNSZMmqS+++KLVj0W0PYkXYYTEizBC4kUYIfEimkpiRRjRWvFyyy23hLUd2KewsFDdeuutStM0lZmZqW666SbVr18/NW/evNY8FNEOJF5ENHTa5NPSpUuVpmnqmmuuUV6vV7nd7uC2+j8HBMqOlVJq48aN6rLLLguWH3/77bdKKaV27Nih7r//fqVpmnI4HOqxxx5T27ZtU2vWrFFnnHGGOvfcc1VxcbGUHXdCEi/CCIkXYYTEizBC4kU0lcSKMKI142XBggVh+wfW63nttdeC+40ZMyYYW6JzkXgR0WCJduXV/vx+f6Or4QcsW7YM0FfHN5vNmM1mCgsLef7558nNzaWiooJJkyZx6qmn0rt3b8xmM+Xl5Tz++OO8+OKL5Ofnk5WVxf333x+8zX3fvn2599576dOnD++88w6PPPIIn332GU6nk5tvvpmzzz67TY9dGCfxIoyQeBFGSLwIIyReRFNJrAgjohEv06ZNC2vfZDLhdDp54403SE1N5cknn+TCCy9s9eMVLSPxIjq0aGe/GvLhhx+qn376Sa1bty4k+xpYrOyll15Smqapk08+WVVWVqq5c+eqKVOmqLPPPlsdddRRStM0ZTKZ1JgxY9TKlSuVUko9/vjjwczrHXfcEZLB9fv9YSvwl5eXq40bNza4Mr/oOCRehBESL8IIiRdhhMSLaCqJFWFENOJlf/fee6+65ZZbZPHoTkDiRXREHS759Mknn6hjjjlGzZgxQw0bNkzFxcWpmTNnqm+++SZkv0DJ8FFHHaX++c9/qvvvv1/t2bMnuP3dd99VRxxxhNI0TY0dO1a5XC61e/dudcUVV6ht27YF92vol0HKjTsHiRdhhMSLMELiRRgh8SKaSmJFGNER4iWQnHS5XG10lKK1SLyIjqxDJZ/mzp2rjjjiCLV8+XKllD4v/c4771Q2m01pmqbmzJmjqqqqlFJKfffdd8HM69lnn60KCwuVUnVzVN1ut1qwYIHq0aOH0jRNPfXUUyGv5fV65SpPJyfxIoyQeBFGSLwIIyReRFNJrAgjJF6EERIvoqPrMMmn6upqdfzxx6v/9//+n1KqLvBdLpf617/+pTRNUwkJCerRRx9VFRUVavPmzWrUqFFK0zR12mmnKaUiX8F5+umnlaZpqmfPnsE2pfSv85N4EUZIvAgjJF6EERIvoqkkVoQREi/CCIkX0RkceDWydrJ3716++eYbUlJSAIILpdlsNi6//HIuv/xyKisree655/jggw/o378/gwYNQtM0CgsL2bFjB5qmoZQKaXfWrFmMHj2avXv3smjRIgAslg63zrowSOJFGCHxIoyQeBFGSLyIppJYEUZIvAgjJF5EZ9BuySe/3w8QFtABW7ZsAWDXrl14vV7MZjMAPp8PgDvvvJOpU6eSk5PDU089xb59+7jooouIjY1ly5YtfPXVVwBomhbSbnp6OkcffTQAiYmJrX9gok1IvAgjJF6EERIvwgiJF9FUEivCCIkXYYTEi+gK2jz5FPhFMZlM5OXlkZ+fT1VVVdh+Ho8HTdPYtWsXfr8/+ItlNptRStGvXz+uuOIK+vTpw5o1a3j22Wc5+eSTOemkk8jPz2fu3LnBbGzglwwgJiYGh8NBfHw8sbGxbX24ooUkXoQREi/CCIkXYYTEi2gqiRVhhMSLMELiRXQpbT+zT59zes8996hRo0apsWPHqqysLHXzzTerdevWBff59ddflaZpKiYmRuXk5Cil6lbKD8w/ra6uVpdddpmyWCxq2LBhasuWLWr16tVq2rRpStM0NWPGDFVZWRlsM/D8xx9/XJ1//vntcaiiFUi8CCMkXoQREi/CCIkX0VQSK8IIiRdhhMSL6CraPPm0bt06NXHiRHXzzTeriooK9cMPP6g//vGPStM01b9/f/XKK68EFy+bOnWq0jRN/fnPf1ZKqZAV9AM/f/3116pfv34qLi5OvfDCC0oppRYuXKgmT54cXDBtyZIlwecFbhP55ZdfKqXktrIdncSLMELiRRgh8SKMkHgRTSWxIoyQeBFGSLyIrqTNk08PPPCAuuyyy8Iev/XWW1ViYqJyOBzqkUceUUop9cgjjyhN05TD4VAbN25USum3cdzf+eefrzRNU9dee23wsV27dqlzzz1XJSYmKk3T1OzZs9WUKVPU2WefrVavXt1GRydam8SLMELiRRgh8SKMkHgRTSWxIoyQeBFGSLyIrqRNk0/l5eWqd+/e6uqrr1ZK6RnXQNZ1z5496p577lGapilN09RPP/2kFi1apCZOnKg0TVMXXHBBWHuB537//fdK0zTVt2/fsFs9Llu2TL3++uvq6aefVh999FFbHp5oZRIvwgiJF2GExIswQuJFNJXEijBC4kUYIfEiupoWJZ/ql/JF8vvvv6u4uDh10003KaXCM6+7du1SZ5xxhtI0TR177LFq3bp16t5771UxMTFK0zT1+eefR3yeUkodfvjhKisrS+3YsaMlhyDakcSLMELiRRgh8SKMkHgRTSWxIoyQeBFGSLyIg02z73bn9/sxmfSnL168mEWLFvHbb79RUVER3Ke6uprq6mrmzZtHRUVFcLX9gN69e3P77bcTGxvLd999x/fff88pp5zCCSecAMBNN92Ex+MJ3ioywO12069fPxwOB+np6c09BNGOJF6EERIvwgiJF2GExItoKokVYYTEizBC4kUclFqSuVq7dq064YQTlM1mU3FxcSo+Pl4df/zxatu2bcrn86mysjI1ePBglZ6erj744IMG27n33nuVpmlq4MCBKicnR3333Xdq4MCBStM0deONN6qCggKllJ61DWRuJ02apE488cSQx0THJvEijJB4EUZIvAgjJF5EU0msCCMkXoQREi/iYGM4+RQoD3z33XdVbGys0jRNJSYmKpvNphwOh9I0Tc2cOVMtW7ZMlZWVqenTpyuTyaRuuukmVVpaqpQKXyV/586d6pBDDlGapqlnnnlGKaXUvHnzlKZpymq1qj//+c+qsLAwuP+uXbvU8ccfr7755ptmH7hoHxIvwgiJF2GExIswQuJFNJXEijBC4kUYIfEiDmbNqnxyuVzq8MMPV5qmqTvvvFNt2LBBffvtt+qZZ55RJpNJWa1W9eCDDyqllLrvvvuUpmnqsMMOU999913E9nw+n3rssceUpmlqzJgxqqqqSiml1Ny5c9WUKVOCmdw5c+aoJ598Uh155JHqvvvuO+A8WdExSLwIIyRehBESL8IIiRfRVBIrwgiJF2GExIs4WDUr+fTJJ5+o9PR0tXDhwrBtjzzyiIqNjVXTpk1TSim1e/fu4C0bb7vtNpWXl6eUCs/Y/vTTTyo7O1sNHDhQrVmzJvh4aWmpevnll9Xs2bPVxRdfrK688kq1ffv25nRbRInEizBC4kUYIfEijJB4EU0lsSKMkHgRRki8iINVs5JPH3zwgZo+fbqqqakJ3vIxMFe0oKBAHXvsscrhcKgtW7YopeoyttnZ2er999+P2ObevXvVMccco2w2W/B5+88/dblczemuiDKJF2GExIswQuJFGCHxIppKYkUYIfEijJB4EQerZt3tbufOnYwePRqHw4GmaZhMpuDq++np6Rx33HE4HA4sFgsA9957L4MGDWL79u3MnTuXFStWBBY7D7bZo0cPhg4disfjoby8HCBsZX6bzdasRdVFdEm8CCMkXoQREi/CCIkX0VQSK8IIiRdhhMSLOFg1K/l04okn4na7cTqdaJoWfDzwCzBr1iyUUsFfGE3TeOKJJ0hOTubLL7/k+eefp6SkBE3T8Pv9uFwuABITE0lJSaFfv34tPCzRkUi8CCMkXoQREi/CCIkX0VQSK8IIiRdhhMSLOGg1p1yqpqZG7du3r8HtP/zwgzriiCOU1+sNWcjsiSeeUP369Qve9nF/5513nnrooYea0yXRgUm8CCMkXoQREi/CCIkX0VQSK8IIiRdhhMSLOFg1q/LJ4XDQrVu3SIksALp3707//v3x+/2YTCb8fj8AF198MS+99BJJSUk888wzXHTRRXz55Zfs2LGDq6++mr1793LGGWe0IJUmOiKJF2GExIswQuJFGCHxIppKYkUYIfEijJB4EQcrS2s2FigbXLx4MYMGDcJqtaKUwmTSc1zLli1j9OjRfPfdd8ydO5d3332XLVu24Ha7ufjii3n++edbszuig5N4EUZIvAgjJF6EERIvoqkkVoQREi/CCIkX0dW1avJJKYWmaeTn5zNy5EgAPB4PZrMZs9nM0qVLSU1NZezYsRx22GH8/e9/p7S0lLi4OOLj41uzK6ITkHgRRki8CCMkXoQREi+iqSRWhBESL8IIiRfR1TVr2l1DAqWC+fn5wVJCm82G2WxmyZIlPPbYY+zcuRMAn8+H3W4nMzNTflkOUhIvwgiJF2GExIswQuJFNJXEijBC4kUYIfEiurpWTT6ZTCYKCgrYt28fEyZMAKCgoIDLL7+ciRMnUlFRwfbt24G6Wz/WX+FfHFwkXoQREi/CCIkXYYTEi2gqiRVhhMSLMELiRXR1rZp8Ati9ezcTJ04E4KGHHqJXr168+uqrANx7773ccsstrf2SohOTeBFGSLwIIyRehBESL6KpJFaEERIvwgiJF9GVaSpQ39dCgTmqn332GU888QT79u1j06ZNAJx33nk89thjZGVltcZLiS5A4kUYIfEijJB4EUZIvIimklgRRki8CCMkXsTBoNUWHA+U/H311VcsWLAAgHHjxvHkk08yefLk1noZ0UVIvAgjJF6EERIvwgiJF9FUEivCCIkXYYTEizgYtFrlE0BlZSVDhw7F6XTy2GOPcemll7ZW06ILkngRRki8CCMkXoQREi+iqSRWhBESL8IIiRfR1bVa5RPo5YI33HAD119/PQ6HozWbFl2QxIswQuJFGCHxIoyQeBFNJbEijJB4EUZIvIiurlUrn4QQQgghhBBCCCGEqK/V73YnhBBCCCGEEEIIIUSAJJ+EEEIIIYQQQgghRJuR5JMQQgghhBBCCCGEaDOSfBJCCCGEEEIIIYQQbUaST0IIIYQQQgghhBCizUjySQghhBBCCCGEEEK0GUk+CSGEEEIIIYQQQog2I8knIYQQQgghhBBCCNFmJPkkhBBCCCGEEEIIIdqMJJ+EEEIIITqgK664gm7durF69epod0UIIYQQokUk+SSEEEII0QG99dZb5Ofn8/XXX0e7K0IIIYQQLSLJJyGEEEKIKNu3b1/YY3fccQdTp05l9uzZUeiREEIIIUTr0ZRSKtqdEEIIIYQ4WJWXlzNr1iy+/fbbaHdFCCGEEKJNSOWTEEIIIUQUXXHFFWzdujXa3RBCCCGEaDOSfBJCCCGEiAKlFLfccgvvvfdeo/v5/f526pEQQgghRNuQ5JMQQgghRBS88cYbzJ8/H9DXfDrqqKM46qij+PTTT6msrOTNN9/khBNO4O9//3vwOdXV1Xz22WdcffXV9O3bl82bN/P9999z7LHHEh8fT3p6OnfffTcAPp+P5557jjFjxhAbG8shhxzCp59+GrEvS5cu5ZxzzuG4444jKyuL8ePH88EHH7T9myCEEEKIg4Ks+SSEEEIIESU//PADRx/9/9u7d9AqsgCOw/8NIoqgBh8oBEIQxYAgFiI+Gr0giQiWik0IIgERTLQQRYmF2iikEMVolU7QwkKUgEgsbCxC1EZQiY8UKkqM4iPxcbeQvWsWZdllJ1mS72sunDMznDPV5cedO+tTW1ubx48fJ/kegs6dO5eLFy9maGgo7e3tOXLkSGXu1q1baWtrS5K0tLQkSbZu3ZpyuZympqYMDAyko6Mjt2/fzubNm1NfX185Z8qUKenv78+CBQsqa7h06VLa29vT3d2dmpqavHnzJqVSKb29vTl9+nR27do1pvcEAJh4/PIJAOB/ZOXKlTl//nyampp+Otfa2pr58+cnSerr63P27NmsX78+GzZsSGtra5Lk0KFDOXbsWLZv354VK1Zk9+7daWhoyKdPn3L58uXK9Z49e5bm5uYcP348NTU1SZLZs2dn3759SZKDBw/m/fv3xW4YAJjwxCcAgP+h6urqX85Nnz49SbJ8+fJR40uXLk2SzJ07N3V1dT+du3//fmXs1KlTGR4ezsaNG0cdu3r16iTJ0NBQbt68+S93AADw3ZTxXgAAAP+NGTNm/O3c27dvK2Pd3d2pqqpKY2PjqGPL5XJqa2uTJO/evStgpQDAZCI+AQBMIj++Pe/Ro0eZM2dOenp6xm9BAMCE57E7AIBJamRkJIODg/H+GQCgSOITAMAkNW/evHz8+DG9vb2/PObu3btjuCIAYCISnwAAxklV1fevYj8+CjeWVq1alSQ5fPjwT3/9dO/evZw4cWKslwUATDDiEwDAOPnjjXavX7/O169fk/wZoj5//jzq80dfvnz56fX+OHdkZOSX5/wYmXbu3JkkuXbtWpqbm/Pq1avK3JUrV9LQ0JD9+/f/s00BAPyF+AQAME4WL16c6urqfPjwISdPnsz169ezd+/eJMmdO3eSJH19faOC0fPnz/PixYskyZMnT0Zdr7+/P0ny8uXLDAwMVMbL5XL6+vqSJA8fPqyMNzY2ZseOHUmSrq6uLFy4MHV1dZk1a1a2bNmSo0ePZtmyZf/xrgGAyea3sn+YBAAYNxcuXEhbW1tGRkaybdu2LFmyJB0dHaPCUl1dXbq6utLT05OOjo4MDg4mSaZOnZpSqZSrV69m06ZNuXHjRoaHh5MkM2fOTEtLS/bs2ZM1a9bk6dOnlestWrQonZ2dKZVK+fbtW86cOZPOzs48ePAg06ZNy7p163LgwIGsXbt2bG8GADAhiU8AAAAAFMZjdwAAAAAURnwCAAAAoDDiEwAAAACFEZ8AAAAAKIz4BAAAAEBhxCcAAAAACiM+AQAAAFAY8QkAAACAwohPAAAAABRGfAIAAACgMOITAAAAAIURnwAAAAAojPgEAAAAQGHEJwAAAAAKIz4BAAAAUJjfATqjyYGVLm5jAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1400x700 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1400x700 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Dataset pixel, Tr, E, SWCe, SWCr, Irr, DP : scale factor: 1000\n",
+ "\n",
+ "plot_time_series(ds1 = val_outputs, ds2 = outputs, var = 'Dr', label1 = 'Excel values', label2 = 'Pixel values', scale_factor1 = 100, scale_factor2 = 100, unit = 'mm')\n",
+ "plot_time_series(ds1 = val_outputs, ds2 = outputs, var = 'Tei', label1 = 'Excel values', label2 = 'Pixel values', scale_factor1 = 100, scale_factor2 = 100, unit = '/')\n",
+ "plot_time_series(ds1 = val_outputs, ds2 = outputs, var = 'diff_rei', label1 = 'Excel values', label2 = 'Pixel values', scale_factor1 = 100, scale_factor2 = 100, unit = 'mm')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "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",
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+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+ "<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",
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+ "</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[data-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 !important;\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: 'â–º';\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: 'â–¼';\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-index-preview {\n",
+ " grid-column: 2 / 5;\n",
+ " color: var(--xr-font-color2);\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",
+ ".xr-index-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",
+ ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
+ ".xr-index-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-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,\n",
+ ".xr-attrs 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",
+ ".xr-no-icon {\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'><xarray.Dataset>\n",
+ "Dimensions: (x: 10, y: 10, time: 297)\n",
+ "Coordinates:\n",
+ " * x (x) float64 7e+05 7e+05 7e+05 ... 7.001e+05 7.001e+05 7.002e+05\n",
+ " * y (y) float64 4.7e+06 4.7e+06 4.7e+06 ... 4.7e+06 4.7e+06 4.7e+06\n",
+ " * time (time) datetime64[ns] 2006-02-06 2006-02-07 ... 2006-11-29\n",
+ " spatial_ref int64 ...\n",
+ "Data variables: (12/19)\n",
+ " Kcb (x, y, time) int16 ...\n",
+ " Zr (x, y, time) int16 ...\n",
+ " TAW (x, y, time) int16 ...\n",
+ " TDW (x, y, time) int16 ...\n",
+ " Irr (x, y, time) int16 ...\n",
+ " DP (x, y, time) int16 ...\n",
+ " ... ...\n",
+ " W (x, y, time) int16 ...\n",
+ " Kei (x, y, time) int16 ...\n",
+ " Kep (x, y, time) int16 ...\n",
+ " E (x, y, time) int16 ...\n",
+ " Ks (x, y, time) int16 ...\n",
+ " Tr (x, y, time) int16 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-62d5c5d4-da1a-4dd4-87a2-5378024466d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62d5c5d4-da1a-4dd4-87a2-5378024466d4' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li><li><span class='xr-has-index'>time</span>: 297</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8b854b60-5227-4b0c-965e-3e5698e6faa8' class='xr-section-summary-in' type='checkbox' checked><label for='section-8b854b60-5227-4b0c-965e-3e5698e6faa8' class='xr-section-summary' >Coordinates: <span>(4)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7e+05 7e+05 ... 7.001e+05 7.002e+05</div><input id='attrs-97ee40a6-8663-4962-97fe-7601359c9d82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97ee40a6-8663-4962-97fe-7601359c9d82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c9a44e2-5b03-4876-8c69-c15ab33e661f' class='xr-var-data-in' type='checkbox'><label for='data-6c9a44e2-5b03-4876-8c69-c15ab33e661f' 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([699970., 699990., 700010., 700030., 700050., 700070., 700090., 700110.,\n",
+ " 700130., 700150.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.7e+06 4.7e+06 ... 4.7e+06 4.7e+06</div><input id='attrs-164eb2e6-0a4d-40b4-b363-b3e7a2861a90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-164eb2e6-0a4d-40b4-b363-b3e7a2861a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-841b70d0-e159-4612-ae1c-0413650ebeed' class='xr-var-data-in' type='checkbox'><label for='data-841b70d0-e159-4612-ae1c-0413650ebeed' 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([4700030., 4700010., 4699990., 4699970., 4699950., 4699930., 4699910.,\n",
+ " 4699890., 4699870., 4699850.])</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'>2006-02-06 ... 2006-11-29</div><input id='attrs-d8c5b195-587a-4176-bcc5-bde2216f2edb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8c5b195-587a-4176-bcc5-bde2216f2edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eea170a-cb5e-4391-b6d3-4bcb43274696' class='xr-var-data-in' type='checkbox'><label for='data-9eea170a-cb5e-4391-b6d3-4bcb43274696' 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(['2006-02-06T00:00:00.000000000', '2006-02-07T00:00:00.000000000',\n",
+ " '2006-02-08T00:00:00.000000000', ..., '2006-11-27T00:00:00.000000000',\n",
+ " '2006-11-28T00:00:00.000000000', '2006-11-29T00:00:00.000000000'],\n",
+ " dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0c85149-6d58-4238-a915-8503bc4268ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0c85149-6d58-4238-a915-8503bc4268ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ef2b121-f10c-4b64-8d00-62e19db51693' class='xr-var-data-in' type='checkbox'><label for='data-8ef2b121-f10c-4b64-8d00-62e19db51693' 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>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d1c6182-ad3e-42be-8513-b145b8fcd38b' class='xr-section-summary-in' type='checkbox' ><label for='section-9d1c6182-ad3e-42be-8513-b145b8fcd38b' class='xr-section-summary' >Data variables: <span>(19)</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>Kcb</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4d8b9c82-ddc0-46b9-9a08-0b679847deb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d8b9c82-ddc0-46b9-9a08-0b679847deb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fafded8-d540-4df7-883d-b89950337e10' class='xr-var-data-in' type='checkbox'><label for='data-4fafded8-d540-4df7-883d-b89950337e10' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b7eb7c0-accf-4961-bdc8-cde2842d8e79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b7eb7c0-accf-4961-bdc8-cde2842d8e79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b0362d-036b-46b6-9310-bda23c4942a7' class='xr-var-data-in' type='checkbox'><label for='data-24b0362d-036b-46b6-9310-bda23c4942a7' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAW</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ff0f6ba-2683-4577-aa7c-97240ac1d88c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ff0f6ba-2683-4577-aa7c-97240ac1d88c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-020b79c6-989e-412a-9f10-68fa81152779' class='xr-var-data-in' type='checkbox'><label for='data-020b79c6-989e-412a-9f10-68fa81152779' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TDW</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3cf72ebd-f867-4f4d-98b9-330900250d1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3cf72ebd-f867-4f4d-98b9-330900250d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f04bb1-cb76-47df-a021-e39748c5a820' class='xr-var-data-in' type='checkbox'><label for='data-f9f04bb1-cb76-47df-a021-e39748c5a820' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Irr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd4bbbf5-a5d7-48dd-b662-ee6132696e3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd4bbbf5-a5d7-48dd-b662-ee6132696e3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37e159b1-1e43-48ff-8c4f-e70c9af658a3' class='xr-var-data-in' type='checkbox'><label for='data-37e159b1-1e43-48ff-8c4f-e70c9af658a3' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DP</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31d80c43-e3a9-4515-a812-95b818f057b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31d80c43-e3a9-4515-a812-95b818f057b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-654e1fe7-57c0-4c29-87ba-b7bd1ae4a3cf' class='xr-var-data-in' type='checkbox'><label for='data-654e1fe7-57c0-4c29-87ba-b7bd1ae4a3cf' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dei</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-635aa2a3-496e-4495-be5c-0b71725b1680' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-635aa2a3-496e-4495-be5c-0b71725b1680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d635fdd7-e848-4a9c-afcd-727af70b6e75' class='xr-var-data-in' type='checkbox'><label for='data-d635fdd7-e848-4a9c-afcd-727af70b6e75' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dep</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45bdc4e1-d98a-450e-96c2-02384cfdb043' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45bdc4e1-d98a-450e-96c2-02384cfdb043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d379099b-7f7f-446e-8d6c-d7b6588f127f' class='xr-var-data-in' type='checkbox'><label for='data-d379099b-7f7f-446e-8d6c-d7b6588f127f' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cc25feb-5a44-4e85-be17-8c6a357b6728' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cc25feb-5a44-4e85-be17-8c6a357b6728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b338142-5c87-4832-a362-0d6a44971fbd' class='xr-var-data-in' type='checkbox'><label for='data-5b338142-5c87-4832-a362-0d6a44971fbd' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWCe</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa527b65-6aef-4581-8442-669bd02b32ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa527b65-6aef-4581-8442-669bd02b32ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c936aa-9a1b-42fc-8eaa-540af2a29d7c' class='xr-var-data-in' type='checkbox'><label for='data-a6c936aa-9a1b-42fc-8eaa-540af2a29d7c' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWCr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b30a21e-0d80-4872-a88e-58aed308b9a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b30a21e-0d80-4872-a88e-58aed308b9a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06116abd-c499-4264-87a3-61c38269fedb' class='xr-var-data-in' type='checkbox'><label for='data-06116abd-c499-4264-87a3-61c38269fedb' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fewi</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6526bd20-792c-4850-9666-7e047af9a8f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6526bd20-792c-4850-9666-7e047af9a8f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d27047-eaa5-4a67-98a7-4f13743cdc5b' class='xr-var-data-in' type='checkbox'><label for='data-e8d27047-eaa5-4a67-98a7-4f13743cdc5b' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fewp</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6fa84755-c66b-45f7-a165-b13dd0ef8dbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fa84755-c66b-45f7-a165-b13dd0ef8dbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20e4a3f7-b1d0-43c6-b8fb-d3f3ef96f6ed' class='xr-var-data-in' type='checkbox'><label for='data-20e4a3f7-b1d0-43c6-b8fb-d3f3ef96f6ed' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-77e7de54-80f0-4e3b-aa08-1b2dd39b0ace' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77e7de54-80f0-4e3b-aa08-1b2dd39b0ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-072fa7a9-9f07-429d-96a5-9ebddff9ebc5' class='xr-var-data-in' type='checkbox'><label for='data-072fa7a9-9f07-429d-96a5-9ebddff9ebc5' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Kei</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-609dc19a-5e4d-4192-94a3-0b704e158c40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-609dc19a-5e4d-4192-94a3-0b704e158c40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8755ce53-7bef-4de5-a035-268d1dd669ae' class='xr-var-data-in' type='checkbox'><label for='data-8755ce53-7bef-4de5-a035-268d1dd669ae' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Kep</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93455cf6-e58c-4e5b-88ee-11f8c2a8817b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93455cf6-e58c-4e5b-88ee-11f8c2a8817b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb3023f9-5930-4f87-a58e-ae725d8d8a38' class='xr-var-data-in' type='checkbox'><label for='data-eb3023f9-5930-4f87-a58e-ae725d8d8a38' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>E</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e23c9f10-c06f-4d4f-a060-ab67a9c8beaa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e23c9f10-c06f-4d4f-a060-ab67a9c8beaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd7823cd-feb3-465c-9ec9-3c3da388e84e' class='xr-var-data-in' type='checkbox'><label for='data-dd7823cd-feb3-465c-9ec9-3c3da388e84e' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ks</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-733702e0-9854-4e0f-b9a1-f876dfd960eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-733702e0-9854-4e0f-b9a1-f876dfd960eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-874d23e3-05b2-4f29-9797-5a34ae3bb127' class='xr-var-data-in' type='checkbox'><label for='data-874d23e3-05b2-4f29-9797-5a34ae3bb127' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Tr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6148adb-89e9-4f36-b036-dfbba5a2e2ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6148adb-89e9-4f36-b036-dfbba5a2e2ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3329a3be-3bf3-47f5-b775-93539af9f87c' class='xr-var-data-in' type='checkbox'><label for='data-3329a3be-3bf3-47f5-b775-93539af9f87c' 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>[29700 values with dtype=int16]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea073ca6-c6e1-4586-823f-8e4d5006938f' class='xr-section-summary-in' type='checkbox' ><label for='section-ea073ca6-c6e1-4586-823f-8e4d5006938f' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8b241b31-ba51-4562-a993-88198428ae6a' class='xr-index-data-in' type='checkbox'/><label for='index-8b241b31-ba51-4562-a993-88198428ae6a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([699970.0, 699990.0, 700010.0, 700030.0, 700050.0, 700070.0, 700090.0,\n",
+ " 700110.0, 700130.0, 700150.0],\n",
+ " dtype='float64', name='x'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-43cbbbeb-4c36-4c01-9f25-9b4d74c2f453' class='xr-index-data-in' type='checkbox'/><label for='index-43cbbbeb-4c36-4c01-9f25-9b4d74c2f453' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4700030.0, 4700010.0, 4699990.0, 4699970.0, 4699950.0, 4699930.0,\n",
+ " 4699910.0, 4699890.0, 4699870.0, 4699850.0],\n",
+ " dtype='float64', name='y'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c5ac228e-625f-4f92-ba75-5dfe6ee6f5c8' class='xr-index-data-in' type='checkbox'/><label for='index-c5ac228e-625f-4f92-ba75-5dfe6ee6f5c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2006-02-06', '2006-02-07', '2006-02-08', '2006-02-09',\n",
+ " '2006-02-10', '2006-02-11', '2006-02-12', '2006-02-13',\n",
+ " '2006-02-14', '2006-02-15',\n",
+ " ...\n",
+ " '2006-11-20', '2006-11-21', '2006-11-22', '2006-11-23',\n",
+ " '2006-11-24', '2006-11-25', '2006-11-26', '2006-11-27',\n",
+ " '2006-11-28', '2006-11-29'],\n",
+ " dtype='datetime64[ns]', name='time', length=297, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99ab75ab-16ac-4b4e-8ac3-8f374c6804d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-99ab75ab-16ac-4b4e-8ac3-8f374c6804d4' 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.Dataset>\n",
+ "Dimensions: (x: 10, y: 10, time: 297)\n",
+ "Coordinates:\n",
+ " * x (x) float64 7e+05 7e+05 7e+05 ... 7.001e+05 7.001e+05 7.002e+05\n",
+ " * y (y) float64 4.7e+06 4.7e+06 4.7e+06 ... 4.7e+06 4.7e+06 4.7e+06\n",
+ " * time (time) datetime64[ns] 2006-02-06 2006-02-07 ... 2006-11-29\n",
+ " spatial_ref int64 ...\n",
+ "Data variables: (12/19)\n",
+ " Kcb (x, y, time) int16 ...\n",
+ " Zr (x, y, time) int16 ...\n",
+ " TAW (x, y, time) int16 ...\n",
+ " TDW (x, y, time) int16 ...\n",
+ " Irr (x, y, time) int16 ...\n",
+ " DP (x, y, time) int16 ...\n",
+ " ... ...\n",
+ " W (x, y, time) int16 ...\n",
+ " Kei (x, y, time) int16 ...\n",
+ " Kep (x, y, time) int16 ...\n",
+ " E (x, y, time) int16 ...\n",
+ " Ks (x, y, time) int16 ...\n",
+ " Tr (x, y, time) int16 ..."
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "val_outputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+ " /* 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",
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+ "\n",
+ ".xr-header > div,\n",
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+ " margin-right: 10px;\n",
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+ " display: contents;\n",
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+ " display: none;\n",
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+ " 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",
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+ " font-weight: 500;\n",
+ "}\n",
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+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
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+ ".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: 'â–º';\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",
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+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
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+ " display: none;\n",
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+ " padding-top: 4px;\n",
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+ " grid-column: 2 / -1;\n",
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+ ".xr-section-details {\n",
+ " display: none;\n",
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+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
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+ " grid-column: 1;\n",
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+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
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+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
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+ "}\n",
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+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
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+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
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+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
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+ " font-weight: bold;\n",
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+ " 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",
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+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
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+ " 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",
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+ "\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",
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+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
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+ " 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",
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+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-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",
+ ".xr-index-data-in:checked ~ .xr-index-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
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+ ".xr-var-data,\n",
+ ".xr-index-name div,\n",
+ ".xr-index-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-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,\n",
+ ".xr-attrs 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",
+ ".xr-no-icon {\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'><xarray.Dataset>\n",
+ "Dimensions: (x: 10, y: 10, time: 297)\n",
+ "Coordinates:\n",
+ " * x (x) float64 7e+05 7e+05 7e+05 ... 7.001e+05 7.001e+05 7.002e+05\n",
+ " * y (y) float64 4.7e+06 4.7e+06 4.7e+06 ... 4.7e+06 4.7e+06 4.7e+06\n",
+ " * time (time) datetime64[ns] 2006-02-06 2006-02-07 ... 2006-11-29\n",
+ " spatial_ref int64 ...\n",
+ "Data variables: (12/19)\n",
+ " E (x, y, time) int16 ...\n",
+ " Tr (x, y, time) int16 ...\n",
+ " SWCe (x, y, time) int16 ...\n",
+ " SWCr (x, y, time) int16 ...\n",
+ " Irr (x, y, time) int16 ...\n",
+ " DP (x, y, time) int16 ...\n",
+ " ... ...\n",
+ " W (x, y, time) int16 ...\n",
+ " Kcb (x, y, time) int16 ...\n",
+ " fewi (x, y, time) int16 ...\n",
+ " fewp (x, y, time) int16 ...\n",
+ " TDW (x, y, time) int16 ...\n",
+ " TAW (x, y, time) int16 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9d26800f-53f8-4456-a321-31c9d6eb5b5e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d26800f-53f8-4456-a321-31c9d6eb5b5e' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 10</li><li><span class='xr-has-index'>y</span>: 10</li><li><span class='xr-has-index'>time</span>: 297</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e99bb7e4-e758-4c3c-b59a-ca229e575e48' class='xr-section-summary-in' type='checkbox' checked><label for='section-e99bb7e4-e758-4c3c-b59a-ca229e575e48' class='xr-section-summary' >Coordinates: <span>(4)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7e+05 7e+05 ... 7.001e+05 7.002e+05</div><input id='attrs-08ed0ba8-3b96-4360-ac75-f8e8937ede77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08ed0ba8-3b96-4360-ac75-f8e8937ede77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a497a69-9dab-493f-a381-1fe645dd9436' class='xr-var-data-in' type='checkbox'><label for='data-6a497a69-9dab-493f-a381-1fe645dd9436' 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([699970., 699990., 700010., 700030., 700050., 700070., 700090., 700110.,\n",
+ " 700130., 700150.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.7e+06 4.7e+06 ... 4.7e+06 4.7e+06</div><input id='attrs-606478ff-d78c-43e4-86d7-e40b72aa4315' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-606478ff-d78c-43e4-86d7-e40b72aa4315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a16cc52a-af7a-45c0-98d3-ac4a7061c6e6' class='xr-var-data-in' type='checkbox'><label for='data-a16cc52a-af7a-45c0-98d3-ac4a7061c6e6' 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([4700030., 4700010., 4699990., 4699970., 4699950., 4699930., 4699910.,\n",
+ " 4699890., 4699870., 4699850.])</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'>2006-02-06 ... 2006-11-29</div><input id='attrs-788a9286-c40d-4ac4-a8d1-630fdf1a520d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-788a9286-c40d-4ac4-a8d1-630fdf1a520d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5792e191-6a70-46ea-90c1-2d884fe61451' class='xr-var-data-in' type='checkbox'><label for='data-5792e191-6a70-46ea-90c1-2d884fe61451' 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(['2006-02-06T00:00:00.000000000', '2006-02-07T00:00:00.000000000',\n",
+ " '2006-02-08T00:00:00.000000000', ..., '2006-11-27T00:00:00.000000000',\n",
+ " '2006-11-28T00:00:00.000000000', '2006-11-29T00:00:00.000000000'],\n",
+ " dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-429ff474-5cf5-48a7-af33-91fbb7eb62bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-429ff474-5cf5-48a7-af33-91fbb7eb62bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85dfee1d-10f7-4669-b093-bfa42cd31729' class='xr-var-data-in' type='checkbox'><label for='data-85dfee1d-10f7-4669-b093-bfa42cd31729' 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>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2861e1b-8f87-4102-a228-4f5e88f1b7b2' class='xr-section-summary-in' type='checkbox' ><label for='section-d2861e1b-8f87-4102-a228-4f5e88f1b7b2' class='xr-section-summary' >Data variables: <span>(19)</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>E</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcacaa25-44af-425c-9216-c232cc58c9b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcacaa25-44af-425c-9216-c232cc58c9b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-330d4eed-f58f-4c61-b3bd-c6214809a687' class='xr-var-data-in' type='checkbox'><label for='data-330d4eed-f58f-4c61-b3bd-c6214809a687' 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'><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>Evaporation</dd><dt><span>description :</span></dt><dd>Accumulated daily evaporation in milimeters</dd><dt><span>scale factor :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Tr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62fefb75-5253-4d26-92e0-7ab22b6ab1e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62fefb75-5253-4d26-92e0-7ab22b6ab1e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05afdf02-0bae-46fe-adb1-ea88f148195e' class='xr-var-data-in' type='checkbox'><label for='data-05afdf02-0bae-46fe-adb1-ea88f148195e' 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'><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>Transpiration</dd><dt><span>description :</span></dt><dd>Accumulated daily plant transpiration in milimeters</dd><dt><span>scale factor :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWCe</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d7325cc1-5b8f-4939-a5ff-e5db6db18190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7325cc1-5b8f-4939-a5ff-e5db6db18190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d100c17b-c199-4be9-bcc9-39198a557f64' class='xr-var-data-in' type='checkbox'><label for='data-d100c17b-c199-4be9-bcc9-39198a557f64' 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'><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>Soil Water Content of the evaporative zone</dd><dt><span>description :</span></dt><dd>Soil water content of the evaporative zone in milimeters</dd><dt><span>scale factor :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWCr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae560a78-ffa2-4604-8304-0c0ce8dcbf1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae560a78-ffa2-4604-8304-0c0ce8dcbf1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed86e8d8-b882-436a-a489-8e3d407361d7' class='xr-var-data-in' type='checkbox'><label for='data-ed86e8d8-b882-436a-a489-8e3d407361d7' 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'><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>Soil Water Content of the root zone</dd><dt><span>description :</span></dt><dd>Soil water content of the root zone in milimeters</dd><dt><span>scale factor :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Irr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-08f35d6f-9f36-4998-a067-0bcea9e93bb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f35d6f-9f36-4998-a067-0bcea9e93bb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e913d4b-61cd-48ad-9870-bd7289308ee0' class='xr-var-data-in' type='checkbox'><label for='data-2e913d4b-61cd-48ad-9870-bd7289308ee0' 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'><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>Irrigation</dd><dt><span>description :</span></dt><dd>Simulated daily irrigation in milimeters</dd><dt><span>scale factor :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DP</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7cebccb2-52cc-4507-ac6e-c49ce1f7601b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cebccb2-52cc-4507-ac6e-c49ce1f7601b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed1ff364-0766-48cb-843e-b89a3908f218' class='xr-var-data-in' type='checkbox'><label for='data-ed1ff364-0766-48cb-843e-b89a3908f218' 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'><dt><span>units :</span></dt><dd>mm</dd><dt><span>standard_name :</span></dt><dd>Deep Percolation</dd><dt><span>description :</span></dt><dd>Simulated daily Deep Percolation in milimeters</dd><dt><span>scale factor :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5bbd7f63-9b1e-4799-91fb-9c612ce5e0db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bbd7f63-9b1e-4799-91fb-9c612ce5e0db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5739452d-879c-44ca-8662-a884bad304fd' class='xr-var-data-in' type='checkbox'><label for='data-5739452d-879c-44ca-8662-a884bad304fd' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dei</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fded7664-6287-4619-86e0-b558f219f908' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fded7664-6287-4619-86e0-b558f219f908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c3a10f-d559-4c6e-a872-b1c63d55b9d7' class='xr-var-data-in' type='checkbox'><label for='data-07c3a10f-d559-4c6e-a872-b1c63d55b9d7' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dep</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-90073955-eec3-4de0-b3ae-da4e373d5b5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90073955-eec3-4de0-b3ae-da4e373d5b5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c52a296d-33e2-41ac-97c1-f463a94dced4' class='xr-var-data-in' type='checkbox'><label for='data-c52a296d-33e2-41ac-97c1-f463a94dced4' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dr</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0245d679-c038-46da-85f0-9952bf6b46f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0245d679-c038-46da-85f0-9952bf6b46f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2257590-7407-48ad-8a6f-e4cbc046c520' class='xr-var-data-in' type='checkbox'><label for='data-d2257590-7407-48ad-8a6f-e4cbc046c520' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Kei</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0263e530-cc00-4ddc-a063-0607571a8df8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0263e530-cc00-4ddc-a063-0607571a8df8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d1b7177-55a9-46ea-bc49-e9404e6c17a7' class='xr-var-data-in' type='checkbox'><label for='data-5d1b7177-55a9-46ea-bc49-e9404e6c17a7' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Kep</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5334872a-9e75-4759-a4b0-a9f5b7b73b48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5334872a-9e75-4759-a4b0-a9f5b7b73b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4414b97-5f52-4c6f-a875-4fedb4f32af2' class='xr-var-data-in' type='checkbox'><label for='data-a4414b97-5f52-4c6f-a875-4fedb4f32af2' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ks</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-010cdb77-4158-4167-9493-7a51f2ee2e94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-010cdb77-4158-4167-9493-7a51f2ee2e94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0288f6bf-aed0-446a-bdce-c606c6700731' class='xr-var-data-in' type='checkbox'><label for='data-0288f6bf-aed0-446a-bdce-c606c6700731' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e90e925d-cf43-4434-a72c-f620852f4af3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e90e925d-cf43-4434-a72c-f620852f4af3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc36d2b-5f66-46d3-8fbd-03b2f17e69c9' class='xr-var-data-in' type='checkbox'><label for='data-9fc36d2b-5f66-46d3-8fbd-03b2f17e69c9' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Kcb</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a80d3e2-c1a2-4f29-b462-bdd265614142' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a80d3e2-c1a2-4f29-b462-bdd265614142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1be248f-4eef-4f1b-a1c7-1c5a0731326b' class='xr-var-data-in' type='checkbox'><label for='data-e1be248f-4eef-4f1b-a1c7-1c5a0731326b' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fewi</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0772a3ee-b916-4e0d-a603-290b18d92427' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0772a3ee-b916-4e0d-a603-290b18d92427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b848ca96-101f-4e82-b377-fdcdc119d098' class='xr-var-data-in' type='checkbox'><label for='data-b848ca96-101f-4e82-b377-fdcdc119d098' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fewp</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c03f64b8-46b6-4757-8e84-de2c2275cf36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c03f64b8-46b6-4757-8e84-de2c2275cf36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0e294a4-24c1-4f2e-acec-5cc298962a04' class='xr-var-data-in' type='checkbox'><label for='data-c0e294a4-24c1-4f2e-acec-5cc298962a04' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TDW</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d2ca67c-d021-4936-9577-5f991260e11a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d2ca67c-d021-4936-9577-5f991260e11a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dcacfcc-8a90-4ce3-b9ab-7f270d99d786' class='xr-var-data-in' type='checkbox'><label for='data-4dcacfcc-8a90-4ce3-b9ab-7f270d99d786' 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>[29700 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAW</span></div><div class='xr-var-dims'>(x, y, time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be94c399-43ac-45bb-bf1d-007730108422' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be94c399-43ac-45bb-bf1d-007730108422' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-168e22ea-ca3a-4784-8cd2-8a61b435b109' class='xr-var-data-in' type='checkbox'><label for='data-168e22ea-ca3a-4784-8cd2-8a61b435b109' 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>[29700 values with dtype=int16]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-014b0fb1-5046-4bce-8f02-e8d0a8d08ea5' class='xr-section-summary-in' type='checkbox' ><label for='section-014b0fb1-5046-4bce-8f02-e8d0a8d08ea5' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54e558d5-6e69-47dc-a75b-9aa98858614d' class='xr-index-data-in' type='checkbox'/><label for='index-54e558d5-6e69-47dc-a75b-9aa98858614d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([699970.0, 699990.0, 700010.0, 700030.0, 700050.0, 700070.0, 700090.0,\n",
+ " 700110.0, 700130.0, 700150.0],\n",
+ " dtype='float64', name='x'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a8071114-cba1-4362-b78e-9a10d13312f6' class='xr-index-data-in' type='checkbox'/><label for='index-a8071114-cba1-4362-b78e-9a10d13312f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4700030.0, 4700010.0, 4699990.0, 4699970.0, 4699950.0, 4699930.0,\n",
+ " 4699910.0, 4699890.0, 4699870.0, 4699850.0],\n",
+ " dtype='float64', name='y'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a2adf37-1148-4544-be13-971cdef881a3' class='xr-index-data-in' type='checkbox'/><label for='index-2a2adf37-1148-4544-be13-971cdef881a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2006-02-06', '2006-02-07', '2006-02-08', '2006-02-09',\n",
+ " '2006-02-10', '2006-02-11', '2006-02-12', '2006-02-13',\n",
+ " '2006-02-14', '2006-02-15',\n",
+ " ...\n",
+ " '2006-11-20', '2006-11-21', '2006-11-22', '2006-11-23',\n",
+ " '2006-11-24', '2006-11-25', '2006-11-26', '2006-11-27',\n",
+ " '2006-11-28', '2006-11-29'],\n",
+ " dtype='datetime64[ns]', name='time', length=297, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-095f2fa6-4e0a-4653-9f93-11eb28ffd465' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-095f2fa6-4e0a-4653-9f93-11eb28ffd465' 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.Dataset>\n",
+ "Dimensions: (x: 10, y: 10, time: 297)\n",
+ "Coordinates:\n",
+ " * x (x) float64 7e+05 7e+05 7e+05 ... 7.001e+05 7.001e+05 7.002e+05\n",
+ " * y (y) float64 4.7e+06 4.7e+06 4.7e+06 ... 4.7e+06 4.7e+06 4.7e+06\n",
+ " * time (time) datetime64[ns] 2006-02-06 2006-02-07 ... 2006-11-29\n",
+ " spatial_ref int64 ...\n",
+ "Data variables: (12/19)\n",
+ " E (x, y, time) int16 ...\n",
+ " Tr (x, y, time) int16 ...\n",
+ " SWCe (x, y, time) int16 ...\n",
+ " SWCr (x, y, time) int16 ...\n",
+ " Irr (x, y, time) int16 ...\n",
+ " DP (x, y, time) int16 ...\n",
+ " ... ...\n",
+ " W (x, y, time) int16 ...\n",
+ " Kcb (x, y, time) int16 ...\n",
+ " fewi (x, y, time) int16 ...\n",
+ " fewp (x, y, time) int16 ...\n",
+ " TDW (x, y, time) int16 ...\n",
+ " TAW (x, y, time) int16 ..."
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "outputs"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (Spyder)",
+ "language": "python3",
+ "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": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fichier_travail_vr.docx b/fichier_travail_vr.docx
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diff --git a/test_samir.py b/test_samir.py
index de08cb7..473ef54 100644
--- a/test_samir.py
+++ b/test_samir.py
@@ -7,923 +7,968 @@
Test file
"""
-if __name__ == '__main__':
-
-
- import xarray as xr
- from dask.distributed import Client
- import os
- import numpy as np
- import pandas as pd
- import rasterio as rio
- from typing import List, Tuple, Union
- import warnings
- import netCDF4 as nc
- from tqdm import tqdm
- from parameters.params_samir_class import samir_parameters
- from config.config import config
- from time import time
- import webbrowser # to open dask dashboard
-
-
- def rasterize_samir_parameters(csv_param_file: str, empty_dataset: xr.Dataset, land_cover_raster: str, chunk_size: dict) -> Tuple[xr.Dataset, dict]:
- """
- Creates a raster `xarray` dataset from the csv parameter file, the land cover raster and an empty dataset
- that contains the right structure (emptied ndvi dataset for example). For each parameter, the function loops
- on land cover classes to fill the raster.
-
- ## Arguments
- 1. csv_param_file: `str`
- path to csv paramter file
- 2. empty_dataset: `xr.Dataset`
- empty dataset that contains the right structure (emptied ndvi dataset for example).
- 3. land_cover_raster: `str`
- path to land cover netcdf raster
- 4. chunk_size: `dict`
- chunk_size for dask computation
-
- ## Returns
- 1. parameter_dataset: `xr.Dataset`
- the dataset containing all the rasterized Parameters
- 2. scale_factor: `dict`
- dictionnary containing the scale factors for each parameter
- """
-
- # Load samir params into an object
- table_param = samir_parameters(csv_param_file)
-
- # Set general variables
- class_count = table_param.table.shape[1] - 2 # remove dtype and default columns
-
- # Open land cover raster
- land_cover = xr.open_dataarray(land_cover_raster, chunks = chunk_size)
-
- # Create dataset
- parameter_dataset = empty_dataset.copy(deep = True)
-
- # Create dictionnary containing the scale factors
- scale_factor = {}
-
- # Loop on samir parameters and create
- for parameter in table_param.table.index[1:]:
-
- # Create new variable and set attributes
- parameter_dataset[parameter] = land_cover.copy(deep = True).astype('f4')
- parameter_dataset[parameter].attrs['name'] = parameter
- parameter_dataset[parameter].attrs['description'] = 'cf SAMIR Doc for detail'
- parameter_dataset[parameter].attrs['scale factor'] = str(table_param.table.loc[table_param.table.index == parameter]['scale_factor'].values[0])
-
- # Assigne value in dictionnary
- scale_factor[parameter] = 1/int(table_param.table.loc[table_param.table.index == parameter]['scale_factor'].values[0])
-
- # Loop on classes to set parameter values for each class
- for class_val, class_name in zip(range(1, class_count + 1), table_param.table.columns[2:]):
-
- # Parameter values are multiplied by the scale factor in order to store all values as int16 types
- # These values are then rounded to make sure there isn't any decimal point issues when casting the values to int16
- parameter_dataset[parameter].values = np.where(parameter_dataset[parameter].values == class_val, round(table_param.table.loc[table_param.table.index == parameter][class_name].values[0]*table_param.table.loc[table_param.table.index == parameter]['scale_factor'].values[0]), parameter_dataset[parameter].values).astype('f4')
-
- # Return dataset converted to 'int16' data type to reduce memory usage
- # and scale_factor dictionnary for later conversion
- return parameter_dataset, scale_factor
-
-
- def setup_time_loop(calculation_variables_t1: List[str], calculation_variables_t2: List[str], empty_dataset: xr.Dataset) -> Tuple[xr.Dataset, xr.Dataset]:
- """
- Creates two temporary `xarray Datasets` that will be used in the SAMIR time loop.
- `variables_t1` corresponds to the variables for the previous day and `variables_t2`
- corresponds to the variables for the current day. After each loop, `variables_t1`
- takes the value of `variables_t2` for the corresponding variables.
-
- ## Arguments
- 1. calculation_variables_t1: `List[str]`
- list of strings containing the variable names
- for the previous day dataset
- 2. calculation_variables_t2: `List[str]`
- list of strings containing the variable names
- for the current day dataset
- 3. empty_dataset: `xr.Dataset`
- empty dataset that contains the right structure
-
- ## Returns
- 1. variables_t1: `xr.Dataset`
- output dataset for previous day
- 2. variables_t2: `xr.Dataset`
- output dataset for current day
- """
-
- # Create new dataset
- variables_t1 = empty_dataset.copy(deep = True)
-
- # Create empty DataArray for each variable
- for variable in calculation_variables_t1:
-
- # Assign new empty DataArray
- variables_t1[variable] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'float32'))
- variables_t1[variable].attrs['name'] = variable # set name in attributes
-
- # Create new dataset
- variables_t2 = empty_dataset.copy(deep = True)
-
- # Create empty DataArray for each variable
- for variable in calculation_variables_t2:
-
- # Assign new empty DataArray
- variables_t2[variable] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'float32'))
- variables_t2[variable].attrs['name'] = variable # set name in attributes
-
- return variables_t1, variables_t2
-
-
- def prepare_outputs(empty_dataset: xr.Dataset, additional_outputs: List[str] = None) -> xr.Dataset:
- """
- Creates the `xarray Dataset` containing the outputs of the SAMIR model that will be saved.
- Additional variables can be saved by adding their names to the `additional_outputs` list.
-
- ## Arguments
- 1. empty_dataset: `xr.Dataset`
- empty dataset that contains the right structure
- 2. additional_outputs: `List[str]`
- list of additional variable names to be saved
-
- ## Returns
- 1. model_outputs: `xr.Dataset`
- model outputs to be saved
- """
-
- # Evaporation and Transpiraion
- model_outputs = empty_dataset.copy(deep = True)
-
- model_outputs['E'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
- model_outputs['E'].attrs['units'] = 'mm'
- model_outputs['E'].attrs['standard_name'] = 'Evaporation'
- model_outputs['E'].attrs['description'] = 'Accumulated daily evaporation in milimeters'
- model_outputs['E'].attrs['scale factor'] = '1000'
-
- model_outputs['Tr'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
- model_outputs['Tr'].attrs['units'] = 'mm'
- model_outputs['Tr'].attrs['standard_name'] = 'Transpiration'
- model_outputs['Tr'].attrs['description'] = 'Accumulated daily plant transpiration in milimeters'
- model_outputs['Tr'].attrs['scale factor'] = '1000'
-
- # Soil Water Content
- model_outputs['SWCe'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
- model_outputs['SWCe'].attrs['units'] = 'mm'
- model_outputs['SWCe'].attrs['standard_name'] = 'Soil Water Content of the evaporative zone'
- model_outputs['SWCe'].attrs['description'] = 'Soil water content of the evaporative zone in milimeters'
- model_outputs['SWCe'].attrs['scale factor'] = '1000'
-
- model_outputs['SWCr'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
- model_outputs['SWCr'].attrs['units'] = 'mm'
- model_outputs['SWCr'].attrs['standard_name'] = 'Soil Water Content of the root zone'
- model_outputs['SWCr'].attrs['description'] = 'Soil water content of the root zone in milimeters'
- model_outputs['SWCr'].attrs['scale factor'] = '1000'
-
+import xarray as xr
+# from dask.distributed import Client
+import os
+import numpy as np
+# import pandas as pd
+import rasterio as rio
+from typing import List, Tuple, Union
+# import warnings
+import netCDF4 as nc
+from tqdm import tqdm
+from parameters.params_samir_class import samir_parameters
+from config.config import config
+from time import time
+# import webbrowser # to open dask dashboard
+
+
+def rasterize_samir_parameters(csv_param_file: str, empty_dataset: xr.Dataset, land_cover_raster: str, chunk_size: dict) -> Tuple[xr.Dataset, dict]:
+ """
+ Creates a raster `xarray` dataset from the csv parameter file, the land cover raster and an empty dataset
+ that contains the right structure (emptied ndvi dataset for example). For each parameter, the function loops
+ on land cover classes to fill the raster.
+
+ # Arguments
+ 1. csv_param_file: `str`
+ path to csv paramter file
+ 2. empty_dataset: `xr.Dataset`
+ empty dataset that contains the right structure (emptied ndvi dataset for example).
+ 3. land_cover_raster: `str`
+ path to land cover netcdf raster
+ 4. chunk_size: `dict`
+ chunk_size for dask computation
+
+ # Returns
+ 1. parameter_dataset: `xr.Dataset`
+ the dataset containing all the rasterized Parameters
+ 2. scale_factor: `dict`
+ dictionnary containing the scale factors for each parameter
+ """
+
+ # Load samir params into an object
+ table_param = samir_parameters(csv_param_file)
+
+ # Set general variables
+ class_count = table_param.table.shape[1] - 2 # remove dtype and default columns
+
+ # Open land cover raster
+ land_cover = xr.open_dataarray(land_cover_raster, chunks=chunk_size)
+
+ # Create dataset
+ parameter_dataset = empty_dataset.copy(deep=True)
+
+ # Create dictionnary containing the scale factors
+ scale_factor = {}
+
+ # Loop on samir parameters and create
+ for parameter in table_param.table.index[1:]:
+
+ # Create new variable and set attributes
+ parameter_dataset[parameter] = land_cover.copy(deep=True).astype('f4')
+ parameter_dataset[parameter].attrs['name'] = parameter
+ parameter_dataset[parameter].attrs['description'] = 'cf SAMIR Doc for detail'
+ parameter_dataset[parameter].attrs['scale factor'] = str(
+ table_param.table.loc[table_param.table.index == parameter]['scale_factor'].values[0])
+
+ # Assigne value in dictionnary
+ scale_factor[parameter] = 1/int(table_param.table.loc[table_param.table.index ==
+ parameter]['scale_factor'].values[0])
+
+ # Loop on classes to set parameter values for each class
+ for class_val, class_name in zip(range(1, class_count + 1), table_param.table.columns[2:]):
+
+ # Parameter values are multiplied by the scale factor in order to store all values as int16 types
+ # These values are then rounded to make sure there isn't any decimal point issues when casting the values to int16
+ # TODO vr: formule trop longue, trouver un moyen de rendre lisible
+ parameter_dataset[parameter].values = np.where(parameter_dataset[parameter].values == class_val,
+ round(table_param.table.loc[table_param.table.index == parameter][class_name].values[0]
+ * table_param.table.loc[table_param.table.index == parameter]['scale_factor'].values[0]),
+ parameter_dataset[parameter].values).astype('f4')
+
+ # Return dataset converted to 'int16' data type to reduce memory usage
+ # and scale_factor dictionnary for later conversion
+ return parameter_dataset, scale_factor
+
+
+def setup_time_loop(calculation_variables_t1: List[str], calculation_variables_t2: List[str],
+ empty_dataset: xr.Dataset) -> Tuple[xr.Dataset, xr.Dataset]:
+ """
+ Creates two temporary `xarray Datasets` that will be used in the SAMIR time loop.
+ `variables_t1` corresponds to the variables for the previous day and `variables_t2`
+ corresponds to the variables for the current day. After each loop, `variables_t1`
+ takes the value of `variables_t2` for the corresponding variables.
+
+ # Arguments
+ 1. calculation_variables_t1: `List[str]`
+ list of strings containing the variable names
+ for the previous day dataset
+ 2. calculation_variables_t2: `List[str]`
+ list of strings containing the variable names
+ for the current day dataset
+ 3. empty_dataset: `xr.Dataset`
+ empty dataset that contains the right structure
+
+ # Returns
+ 1. variables_t1: `xr.Dataset`
+ output dataset for previous day
+ 2. variables_t2: `xr.Dataset`
+ output dataset for current day
+ """
+
+ # Create new dataset
+ variables_t1 = empty_dataset.copy(deep=True)
+
+ # Create empty DataArray for each variable
+ for variable in calculation_variables_t1:
+
+ # Assign new empty DataArray
+ variables_t1[variable] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='float32'))
+ variables_t1[variable].attrs['name'] = variable # set name in attributes
+
+ # Create new dataset
+ variables_t2 = empty_dataset.copy(deep=True)
+
+ # Create empty DataArray for each variable
+ for variable in calculation_variables_t2:
+
+ # Assign new empty DataArray
+ variables_t2[variable] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='float32'))
+ variables_t2[variable].attrs['name'] = variable # set name in attributes
+
+ return variables_t1, variables_t2
+
+
+def prepare_outputs(empty_dataset: xr.Dataset, additional_outputs: List[str] = None) -> xr.Dataset:
+ """
+ Creates the `xarray Dataset` containing the outputs of the SAMIR model that will be saved.
+ Additional variables can be saved by adding their names to the `additional_outputs` list.
+
+ # Arguments
+ 1. empty_dataset: `xr.Dataset`
+ empty dataset that contains the right structure
+ 2. additional_outputs: `List[str]`
+ list of additional variable names to be saved
+
+ # Returns
+ 1. model_outputs: `xr.Dataset`
+ model outputs to be saved
+ """
+
+ # Evaporation and Transpiraion
+ model_outputs = empty_dataset.copy(deep=True)
+
+ model_outputs['E'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+ model_outputs['E'].attrs['units'] = 'mm'
+ model_outputs['E'].attrs['standard_name'] = 'Evaporation'
+ model_outputs['E'].attrs['description'] = 'Accumulated daily evaporation in milimeters'
+ model_outputs['E'].attrs['scale factor'] = '1000'
+
+ model_outputs['Tr'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+ model_outputs['Tr'].attrs['units'] = 'mm'
+ model_outputs['Tr'].attrs['standard_name'] = 'Transpiration'
+ model_outputs['Tr'].attrs['description'] = 'Accumulated daily plant transpiration in milimeters'
+ model_outputs['Tr'].attrs['scale factor'] = '1000'
+
+ # Soil Water Content
+ model_outputs['SWCe'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+ model_outputs['SWCe'].attrs['units'] = 'mm'
+ model_outputs['SWCe'].attrs['standard_name'] = 'Soil Water Content of the evaporative zone'
+ model_outputs['SWCe'].attrs['description'] = 'Soil water content of the evaporative zone in milimeters'
+ model_outputs['SWCe'].attrs['scale factor'] = '1000'
+
+ model_outputs['SWCr'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+ model_outputs['SWCr'].attrs['units'] = 'mm'
+ model_outputs['SWCr'].attrs['standard_name'] = 'Soil Water Content of the root zone'
+ model_outputs['SWCr'].attrs['description'] = 'Soil water content of the root zone in milimeters'
+ model_outputs['SWCr'].attrs['scale factor'] = '1000'
+
+ # Irrigation
+ model_outputs['Irr'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+ model_outputs['Irr'].attrs['units'] = 'mm'
+ model_outputs['Irr'].attrs['standard_name'] = 'Irrigation'
+ model_outputs['Irr'].attrs['description'] = 'Simulated daily irrigation in milimeters'
+ model_outputs['Irr'].attrs['scale factor'] = '1000'
+
+ # Deep Percolation
+ model_outputs['DP'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+ model_outputs['DP'].attrs['units'] = 'mm'
+ model_outputs['DP'].attrs['standard_name'] = 'Deep Percolation'
+ model_outputs['DP'].attrs['description'] = 'Simulated daily Deep Percolation in milimeters'
+ model_outputs['DP'].attrs['scale factor'] = '1000'
+
+ if additional_outputs:
+ for var in additional_outputs:
+ model_outputs[var] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d]
+ for d in list(empty_dataset.dims)), dtype='int16'))
+
+ return model_outputs
+
+
+def xr_maximum(ds: xr.DataArray, value: Union[xr.DataArray, float, int]) -> xr.DataArray:
+ """
+ Equivalent of `numpy.maximum(ds, value)` for xarray DataArrays
+
+ # Arguments
+ 1. ds: `xr.DataArray`
+ datarray to compare
+ 2. value: `Union[xr.DataArray, float, int]`
+ value (scalar or dataarray) to compare
+
+ # Returns
+ 1. output: `xr.DataArray`
+ resulting dataarray with maximum value element-wise
+ """
+ return xr.where(ds <= value, value, ds, keep_attrs=True)
+
+
+def xr_minimum(ds: xr.DataArray, value: Union[xr.DataArray, float, int]) -> xr.DataArray:
+ """
+ Equivalent of `numpy.minimum(ds, value)` for xarray DataArrays
+
+ # Arguments
+ 1. ds: `xr.DataArray`
+ datarray to compare
+ 2. value: `Union[xr.DataArray, float, int]`
+ value (scalar or dataarray) to compare
+
+ # Returns
+ 1. output: `xr.DataArray`
+ resulting dataarray with minimum value element-wise
+ """
+ return xr.where(ds >= value, value, ds, keep_attrs=True)
+
+
+def calculate_diff_re(TAW: np.ndarray, Dr: np.ndarray, Zr: np.ndarray, RUE: np.ndarray, De: np.ndarray,
+ Wfc: np.ndarray, Ze: np.ndarray, DiffE: np.ndarray) -> np.ndarray:
+ """
+ Calculates the diffusion between the top soil layer and the root layer.
+
+ # Arguments
+ 1. TAW: `np.ndarray`
+ water capacity of root layer
+ 2. Dr: `np.ndarray`
+ depletion of root layer
+ 3. Zr: `np.ndarray`
+ height of root layer
+ 4. RUE: `np.ndarray`
+ total available surface water = (Wfc-Wwp)*Ze
+ 5. De: `np.ndarray`
+ depletion of the evaporative layer
+ 6. Wfc: `np.ndarray`
+ field capacity
+ 7. Ze: `np.ndarray`
+ height of evaporative layer (paramter)
+ 8. DiffE: `np.ndarray`
+ diffusion coefficient between evaporative
+ and root layers (unitless, parameter)
+
+ # Returns
+ 1. diff_re: `np.ndarray`
+ the diffusion between the top soil layer and
+ the root layer
+ """
+
+ # Temporary variables to make calculation easier to read
+ tmp1 = ((TAW - Dr) / Zr - (RUE - De) / Ze) / (Wfc * DiffE)
+ tmp2 = ((TAW * Ze) - (RUE - De - Dr) * Zr) / (Zr + Ze) - Dr
+
+ # Calculate diffusion according to SAMIR equation
+ diff_re = np.where(tmp1 < 0, np.maximum(tmp1, tmp2), np.minimum(tmp1, tmp2))
+
+ # Return zero values where the 'DiffE' parameter is equal to 0
+ return np.where(DiffE == 0, 0, diff_re)
+
+
+def calculate_diff_dr(TAW: np.ndarray, TDW: np.ndarray, Dr: np.ndarray, Zr: np.ndarray, Dd: np.ndarray,
+ Wfc: np.ndarray, Zsoil: np.ndarray, DiffR: np.ndarray) -> np.ndarray:
+ """
+ Calculates the diffusion between the root layer and the deep layer.
+
+ # Arguments
+ 1. TAW: `np.ndarray`
+ water capacity of root layer
+ 2. TDW: `np.ndarray`
+ water capacity of deep layer
+ 3. Dr: `np.ndarray`
+ depletion of root layer
+ 4. Zr: `np.ndarray`
+ height of root layer
+ 5. Dd: `np.ndarray`
+ depletion of deep layer
+ 6. Wfc: `np.ndarray`
+ field capacity
+ 7. Zsoil: `np.ndarray`
+ total height of soil (paramter)
+ 8. DiffR: `np.ndarray`
+ Diffusion coefficient between root
+ and deep layers (unitless, parameter)
+
+ # Returns
+ 1. Diff_dr: `np.ndarray`
+ the diffusion between the root layer and the
+ deep layer
+ """
+
+ # Temporary variables to make calculation easier to read
+ tmp1 = (((TDW - Dd) / (Zsoil - Zr) - (TAW - Dr) / Zr) / Wfc) * DiffR
+ tmp2 = (TDW * Zr - (TAW - Dr - Dd) * (Zsoil - Zr)) / Zsoil - Dd
+
+ # Calculate diffusion according to SAMIR equation
+ Diff_dr = np.where(tmp1 < 0, np.maximum(tmp1, tmp2), np.minimum(tmp1, tmp2))
+
+ # Return zero values where the 'DiffR' parameter is equal to 0
+ return np.where(DiffR == 0, 0, Diff_dr)
+
+
+def calculate_W(TEW: np.ndarray, Dei: np.ndarray, Dep: np.ndarray, fewi: np.ndarray, fewp: np.ndarray) -> np.ndarray:
+ """
+ Calculate W, the weighting factor to split the energy available
+ for evaporation depending on the difference in water availability
+ in the two evaporation components, ensuring that the larger and
+ the wetter, the more the evaporation occurs from that component
+
+ # Arguments
+ 1. TEW: `np.ndarray`
+ water capacity of evaporative layer
+ 2. Dei: `np.ndarray`
+ depletion of the evaporative layer
+ (irrigation part)
+ 3. Dep: `np.ndarray`
+ depletion of the evaporative layer
+ (precipitation part)
+ 4. fewi: `np.ndarray`
+ soil fraction which is wetted by irrigation
+ and exposed to evaporation
+ 5. fewp: `np.ndarray`
+ soil fraction which is wetted by precipitation
+ and exposed to evaporation
+
+ # Returns
+ 1. W: `np.ndarray`
+ weighting factor W
+ """
+
+ # Temporary variables to make calculation easier to read
+ tmp = fewi * (TEW - Dei)
+
+ # Calculate the weighting factor to split the energy available for evaporation
+ W = 1 / (1 + (fewp * (TEW - Dep) / tmp))
+
+ # Return W
+ return np.where(tmp > 0, W, 0)
+
+
+def calculate_Kr(TEW: np.ndarray, De: np.ndarray, REW: np.ndarray) -> np.ndarray:
+ """
+ calculates of the reduction coefficient for evaporation dependent
+ on the amount of water in the soil using the FAO-56 method.
+
+ # Arguments
+ 1. TEW: `np.ndarray`
+ water capacity of evaporative layer
+ 2. De: `np.ndarray`
+ depletion of evaporative layer
+ 3. REW: `np.ndarray`
+ fixed readily evaporable water
+
+ # Returns
+ 1. Kr: `np.ndarray`
+ Kr coefficient
+ """
+
+ # Formula for calculating Kr
+ Kr = (TEW - De) / (TEW - REW)
+
+ # Return Kr
+ return np.maximum(0, np.minimum(Kr, 1))
+
+
+def update_Dr_from_root(TAW: np.ndarray, Zr: np.ndarray, TAW0: np.ndarray, TDW0: np.ndarray,
+ Dr0: np.ndarray, Dd0: np.ndarray, Zr0: np.ndarray) -> np.ndarray:
+ """
+ Return the updated depletion for the root layer.
+
+ # Arguments
+ 1. TAW: `np.ndarray`
+ water capacity of root layer for current day
+ 2. Zr: `np.ndarray`
+ root layer height for current day
+ 3. TAW0: `np.ndarray`
+ water capacity of root layer for previous day
+ 4. TDW0: `np.ndarray`
+ water capacity of deep layer for previous day
+ 5. Dr0: `np.ndarray`
+ depletion of the root layer for previous day
+ 6. Dd0: `np.ndarray`
+ depletion of the deep laye for previous day
+ 7. Zr0: `np.ndarray`
+ root layer height for previous day
+
+ # Returns
+ 1. output: `np.ndarray`
+ updated depletion for the root layer
+ """
+ # Temporary variables to make calculation easier to read
+ # tmp1 = np.minimum(Dr0 + Dd0 * (TAW - TAW0) / TDW0, TAW)
+ # tmp2 = np.maximum(Dr0 + Dd0 * (TAW - TAW0) / TDW0, 0)
+
+ # TODO vr: Updated version from xls
+ tmp1 = np.maximum(Dr0 + Dd0 * (TAW - TAW0)/TDW0, 0)
+ tmp2 = np.maximum(Dr0 + Dr0 * (TAW - TAW0)/TAW0, 0)
+
+ # Return updated Dr
+ return np.where(Zr > Zr0, tmp1, tmp2)
+
+
+def update_Dd_from_root(TAW: np.ndarray, TDW: np.ndarray, Zr: np.ndarray, TAW0: np.ndarray, TDW0: np.ndarray,
+ Dr0: np.ndarray, Dd0: np.ndarray, Zr0: np.ndarray) -> np.ndarray:
+ """
+ Return the updated depletion for the deep layer
+
+ # Arguments
+ 1. TAW: `np.ndarray`
+ water capacity of root layer for current day
+ 2. TDW: `np.ndarray`
+ water capacity of deep layer for current day
+ 3. TAW0: `np.ndarray`
+ water capacity of root layer for previous day
+ 5. TDW0: `np.ndarray`
+ water capacity of deep layer for previous day
+ 6. Dd0: `np.ndarray`
+ depletion of the deep laye for previous day
+ 7. Zr0: `np.ndarray`
+ root layer height for previous day
+
+ # Returns
+ 1. output: `np.ndarray`
+ updated depletion for the deep layer
+ """
+
+ # Temporary variables to make calculation easier to read
+ # tmp1 = np.maximum(Dd0 - Dd0 * (TAW - TAW0) / TDW0, 0)
+ # tmp2 = np.minimum(Dd0 - Dd0 * (TAW - TAW0) / TDW0, TDW)
+
+ # TODO vr: Updated version from xls
+ tmp1 = np.maximum(Dd0 + Dd0 * (TAW - TAW0)/TDW0, 0)
+ tmp2 = np.maximum(Dd0 + Dr0 * (TAW - TAW0)/TAW0, 0)
+
+ # Return updated Dd
+ return np.where(Zr > Zr0, tmp1, tmp2)
+
+
+def format_duration(timedelta: float) -> None:
+ """
+ Print formatted timedelta in human readable format
+ (days, hours, minutes, seconds, microseconds, milliseconds, nanoseconds).
+
+ # Arguments
+ timedelta: `float`
+ time value in seconds to format
+
+ # Returns
+ `None`
+ """
+
+ if timedelta < 0.9e-6:
+ print(round(timedelta*1e9, 1), 'ns')
+ elif timedelta < 0.9e-3:
+ print(round(timedelta*1e6, 1), 'µs')
+ elif timedelta < 0.9:
+ print(round(timedelta*1e3, 1), 'ms')
+ elif timedelta < 60:
+ print(round(timedelta, 1), 's')
+ elif timedelta < 3.6e3:
+ print(round(timedelta//60), 'm', round(timedelta % 60, 1), 's')
+ elif timedelta < 24*3.6e3:
+ print(round((timedelta/3.6e3)//1), 'h', round((timedelta/3.6e3) %
+ 1*60//1), 'm', round((timedelta/3.6e3) % 1*60 % 1*60, 1), 's')
+ elif timedelta < 48*3.6e3:
+ print(round((timedelta/(24*3.6e3))//1), 'day,', round(((timedelta/(24*3.6e3)) % 1*24)//1), 'h,',
+ round((timedelta/(24*3.6e3)) % 1*24 % 1*60//1), 'm,', round((timedelta/(24*3.6e3)) % 1*24 % 1*60 % 1*60, 1), 's')
+ else:
+ print(round((timedelta/(24*3.6e3))//1), 'days,', round(((timedelta/(24*3.6e3)) % 1*24)//1), 'h,',
+ round((timedelta/(24*3.6e3)) % 1*24 % 1*60//1), 'm,', round((timedelta/(24*3.6e3)) % 1*24 % 1*60 % 1*60, 1), 's')
+
+ return None
+
+
+# @profile # type: ignore
+def run_samir(json_config_file: str, csv_param_file: str, ndvi_cube_path: str, Rain_path: str, ET0_path: str,
+ soil_params_path: str, land_cover_path: str, chunk_size: dict, save_path: str,
+ additional_outputs: List[str] = None, additional_outputs_scale: List[float] = None, max_ram_GB: int = 2) -> None:
+
+ # Test inputs
+ if len(additional_outputs) != len(additional_outputs_scale):
+ print('\nadditional outpus name and scale list length do not match\n')
+ return None
+
+ # Turn off numpy warings
+ np.seterr(divide='ignore', invalid='ignore')
+
+ # ============ General parameters ============#
+ # TODO vr: config_params jamais utilisé...?
+ config_params = config(json_config_file)
+ # TODO vr: serait il possible de décrire le pourquoi de ses 2 listes?
+ calculation_variables_t2 = ['Diff_rei', 'Diff_rep', 'Diff_dr', 'Dd', 'De', 'Dei', 'Dep', 'DP', 'Dr', 'FCov', 'Irrig', 'Kcb', 'Kei',
+ 'Kep', 'Kri', 'Krp', 'Ks', 'Kti', 'Ktp', 'RUE', 'SWCe', 'SWCr', 'TAW', 'TDW', 'TEW', 'Tei', 'Tep', 'W',
+ 'Zr', 'fewi', 'fewp', 'p_cor']
+ calculation_variables_t1 = ['Dr', 'Dd', 'TAW', 'TDW', 'Zr']
+
+ # ============ Manage inputs ============#
+ # NDVI (to have a correct empty dataset structure)
+ ndvi_cube = xr.open_mfdataset(ndvi_cube_path, chunks=chunk_size, parallel=True)
+
+ # SAMIR Parameters
+ param_dataset, scale_factor = rasterize_samir_parameters(
+ csv_param_file, ndvi_cube.drop_vars(['NDVI', 'time']), land_cover_path, chunk_size=chunk_size)
+
+ # SAMIR Variables
+ variables_t1, variables_t2 = setup_time_loop(
+ calculation_variables_t1, calculation_variables_t2, ndvi_cube.drop_vars(['NDVI', 'time']))
+
+ # ============ Prepare outputs ============#
+ model_outputs = prepare_outputs(ndvi_cube.drop_vars(['NDVI']), additional_outputs=additional_outputs)
+
+ # Create encoding dictionnary
+ for variable in list(model_outputs.keys()):
+ # Write encoding dict
+ encoding_dict = {}
+ encod = {}
+ encod['dtype'] = 'i2'
+ encoding_dict[variable] = encod
+
+ # Save empty output
+ model_outputs.to_netcdf(save_path, encoding=encoding_dict)
+ model_outputs.close()
+
+ # ============ Prepare time iterations ============#
+ dates = ndvi_cube.time.values
+ ndvi_cube.close()
+
+ # ============ Create aliases for better readability ============#
+
+ # Variables for current day
+ # var = da.from_array(dataarray, chunks = (5, 5))
+ Diff_rei = variables_t2.Diff_rei.to_numpy()
+ Diff_rep = variables_t2.Diff_rep.to_numpy()
+ Diff_dr = variables_t2.Diff_dr.to_numpy()
+ Dd = variables_t2.Dd.to_numpy()
+ De = variables_t2.De.to_numpy()
+ Dei = variables_t2.Dei.to_numpy()
+ Dep = variables_t2.Dep.to_numpy()
+ DP = variables_t2.DP.to_numpy()
+ Dr = variables_t2.Dr.to_numpy()
+ FCov = variables_t2.FCov.to_numpy()
+ Irrig = variables_t2.Irrig.to_numpy()
+ Kcb = variables_t2.Kcb.to_numpy()
+ Kei = variables_t2.Kei.to_numpy()
+ Kep = variables_t2.Kep.to_numpy()
+ Kri = variables_t2.Kri.to_numpy()
+ Krp = variables_t2.Krp.to_numpy()
+ Ks = variables_t2.Ks.to_numpy()
+ Kti = variables_t2.Kti.to_numpy()
+ Ktp = variables_t2.Ktp.to_numpy()
+ RUE = variables_t2.RUE.to_numpy()
+ SWCe = variables_t2.SWCe.to_numpy()
+ SWCr = variables_t2.SWCr.to_numpy()
+ TAW = variables_t2.TAW.to_numpy()
+ TDW = variables_t2.TDW.to_numpy()
+ TEW = variables_t2.TEW.to_numpy()
+ Tei = variables_t2.Tei.to_numpy()
+ Tep = variables_t2.Tep.to_numpy()
+ Zr = variables_t2.Zr.to_numpy()
+ W = variables_t2.W.to_numpy()
+ fewi = variables_t2.fewi.to_numpy()
+ fewp = variables_t2.fewp.to_numpy()
+ p_cor = variables_t2.p_cor.to_numpy()
+
+ # Variables for previous day
+ TAW0 = variables_t1.TAW.to_numpy()
+ TDW0 = variables_t1.TDW.to_numpy()
+ Dr0 = variables_t1.Dr.to_numpy()
+ Dd0 = variables_t1.Dd.to_numpy()
+ Zr0 = variables_t1.Zr.to_numpy()
+
+ # Parameters
+ # Parameters have an underscore (_) behind their name for recognition
+ DiffE_ = param_dataset.DiffE.to_numpy()
+ DiffR_ = param_dataset.DiffR.to_numpy()
+ FW_ = param_dataset.FW.to_numpy()
+ Fc_stop_ = param_dataset.Fc_stop.to_numpy()
+ FmaxFC_ = param_dataset.FmaxFC.to_numpy()
+ Foffset_ = param_dataset.Foffset.to_numpy()
+ Fslope_ = param_dataset.Fslope.to_numpy()
+ Init_RU_ = param_dataset.Init_RU.to_numpy()
+ Irrig_auto_ = param_dataset.Irrig_auto.to_numpy()
+ # Kcmax_ = param_dataset.Kcmax.to_numpy() # Inutile?
+ KmaxKcb_ = param_dataset.KmaxKcb.to_numpy()
+ Koffset_ = param_dataset.Koffset.to_numpy()
+ Kslope_ = param_dataset.Kslope.to_numpy()
+ Lame_max_ = param_dataset.Lame_max.to_numpy()
+ REW_ = param_dataset.REW.to_numpy()
+ Ze_ = param_dataset.Ze.to_numpy()
+ Zsoil_ = param_dataset.Zsoil.to_numpy()
+ maxZr_ = param_dataset.maxZr.to_numpy()
+ minZr_ = param_dataset.minZr.to_numpy()
+ p_ = param_dataset.p.to_numpy()
+
+ # scale factors
+ # Scale factors have the following name scheme : s_ + parameter_name
+ s_FW = scale_factor['FW']
+ s_Fc_stop = scale_factor['Fc_stop']
+ s_FmaxFC = scale_factor['FmaxFC']
+ s_Foffset = scale_factor['Foffset']
+ s_Fslope = scale_factor['Fslope']
+ s_Init_RU = scale_factor['Init_RU']
+ # s_Irrig_auto = scale_factor['Irrig_auto']
+ # s_Kcmax = scale_factor['Kcmax'] # TODO vr: variable jamais utilisée?
+ s_KmaxKcb = scale_factor['KmaxKcb']
+ s_Koffset = scale_factor['Koffset']
+ s_Kslope = scale_factor['Kslope']
+ s_Lame_max = scale_factor['Lame_max']
+ s_REW = scale_factor['REW']
+ s_Ze = scale_factor['Ze']
+ s_DiffE = scale_factor['DiffE']
+ s_DiffR = scale_factor['DiffR']
+ s_Zsoil = scale_factor['Zsoil']
+ s_maxZr = scale_factor['maxZr']
+ s_minZr = scale_factor['minZr']
+ s_p = scale_factor['p']
+
+ # input data
+ with nc.Dataset(ndvi_cube_path, mode='r') as ds:
+ # Dimensions of ndvi dataset : (time, x, y)
+ NDVI = ds.variables['NDVI'][0, :, :] / 255
+ with nc.Dataset(soil_params_path, mode='r') as ds:
+ Wfc = ds.variables['FC'][:, :] # TODO vr: modifier FC en Wfc dans fichier sol
+ Wwp = ds.variables['WP'][:, :] # TODO vr: modifier WP en Wwp dans fichier sol
+ print('soil Wfc and Wwp:', Wfc[0, 0], Wwp[0, 0])
+ with rio.open(Rain_path, mode='r') as ds:
+ Rain = ds.read(1) / 1000
+ with rio.open(ET0_path, mode='r') as ds:
+ ET0 = ds.read(1) / 1000
+
+ # Create progress bar
+ progress_bar = tqdm(total=len(dates), desc='Running model', unit=' days')
+
+ # ============ First day initialization ============#
+ # Fraction cover
+ FCov = s_Fslope * Fslope_ * NDVI + s_Foffset * Foffset_
+ FCov = np.minimum(np.maximum(FCov, 0), s_Fc_stop * Fc_stop_)
+
+ # Root depth upate
+ # TODO vr: il semblerait que dans le xls, Fcmax soit le Fc max de la simulation, alors que dans python, c'est un parametre
+ Zr = np.maximum(s_minZr * minZr_ + (FCov / (s_FmaxFC * FmaxFC_)) * (s_maxZr * maxZr_ - s_minZr * minZr_), s_Ze * Ze_ + 0.001)
+
+ # Water capacities
+ TEW = (Wfc - Wwp/2) * s_Ze * Ze_
+ RUE = (Wfc - Wwp) * s_Ze * Ze_
+ TAW = (Wfc - Wwp) * Zr
+ TDW = (Wfc - Wwp) * (s_Zsoil * Zsoil_ - Zr) # Zd = Zsoil - Zr
+
+ # Depletions
+ Dei = RUE * (1 - s_Init_RU * Init_RU_)
+ Dep = RUE * (1 - s_Init_RU * Init_RU_)
+ Dr = TAW * (1 - s_Init_RU * Init_RU_)
+ Dd = TDW * (1 - s_Init_RU * Init_RU_)
+
+ # p_cor
+ p_cor = s_p * p_
+
+ # Irrigation TODO : find correct method for irrigation
+ Irrig = np.minimum(np.maximum(Dr - Rain, 0), s_Lame_max * Lame_max_) * Irrig_auto_
+ Irrig = np.where(Dr > TAW * p_cor, Irrig, 0)
+
+ # Kcb
+ Kcb = np.minimum(np.maximum(s_Kslope * Kslope_ * NDVI + s_Koffset * Koffset_, 0), s_KmaxKcb * KmaxKcb_)
+
+ # Update depletions with Rainfall and/or irrigation
+
+ # DP
+ DP = - np.minimum(Dd + np.minimum(Dr - Rain - Irrig, 0), 0)
+
+ # De
+ Dei = np.minimum(np.maximum(Dei - Rain - Irrig / (s_FW * FW_), 0), TEW)
+ Dep = np.minimum(np.maximum(Dep - Rain, 0), TEW)
+
+ fewi = np.minimum(1 - FCov, (s_FW * FW_))
+ fewp = 1 - FCov - fewi
+
+ De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi, fewp])
+ De = np.where(np.isfinite(De), De, Dei * (s_FW * FW_) + Dep * (1 - (s_FW * FW_)))
+
+ # Dr
+ Dr = np.minimum(np.maximum(Dr - Rain - Irrig, 0), TAW)
+
+ # Dd
+ Dd = np.minimum(np.maximum(Dd + np.minimum(Dr - Rain - Irrig, 0), 0), TDW)
+
+ # Diffusion coefficients
+ Diff_rei = calculate_diff_re(TAW, Dr, Zr, RUE, Dei, Wfc, s_Ze*Ze_, s_DiffE*DiffE_)
+ Diff_rep = calculate_diff_re(TAW, Dr, Zr, RUE, Dep, Wfc, s_Ze*Ze_, s_DiffE*DiffE_)
+ Diff_dr = calculate_diff_dr(TAW, TDW, Dr, Zr, Dd, Wfc, s_Zsoil*Zsoil_, s_DiffR*DiffR_)
+
+ # Weighing factor W
+ W = calculate_W(TEW, Dei, Dep, fewi, fewp)
+
+ # Soil water content of evaporative layer
+ SWCe = 1 - De/TEW
+ # Soil water content of root layer
+ SWCr = 1 - Dr/TAW
+
+ # Water Stress coefficient
+ Ks = np.minimum((TAW - Dr) / (TAW * (1 - p_cor)), 1)
+
+ # Reduction coefficient for evaporation
+ Kri = calculate_Kr(TEW, Dei, s_REW*REW_)
+ Krp = calculate_Kr(TEW, Dep, s_REW*REW_)
+ Kei = np.minimum(W * Kri * (s_KmaxKcb*KmaxKcb_ - Kcb), fewi * s_KmaxKcb*KmaxKcb_)
+ Kep = np.minimum((1 - W) * Krp * (s_KmaxKcb*KmaxKcb_ - Kcb), fewp * s_KmaxKcb*KmaxKcb_)
+
+ # Prepare coefficients for evapotranspiration
+ Kti = np.minimum(((s_Ze*Ze_ / Zr)**0.6) * (1 - Dei / TEW) / np.maximum(1 - Dr / TAW, 0.001), 1)
+ Ktp = np.minimum(((s_Ze*Ze_ / Zr)**0.6) * (1 - Dep / TEW) / np.maximum(1 - Dr / TAW, 0.001), 1)
+ Tei = Kti * Ks * Kcb * ET0
+ Tep = Ktp * Ks * Kcb * ET0
+
+ # Update depletions
+ Dei = np.where(fewi > 0, np.minimum(np.maximum(Dei + ET0 * Kei / fewi + Tei - Diff_rei, 0), TEW),
+ np.minimum(np.maximum(Dei + Tei - Diff_rei, 0), TEW))
+ Dep = np.where(fewp > 0, np.minimum(np.maximum(Dep + ET0 * Kep / fewp + Tep - Diff_rep, 0), TEW),
+ np.minimum(np.maximum(Dep + Tep - Diff_rep, 0), TEW))
+
+ De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi, fewp])
+ De = np.where(np.isfinite(De), De, Dei * (s_FW * FW_) + Dep * (1 - (s_FW * FW_)))
+
+ # Evaporation
+ E = np.maximum((Kei + Kep) * ET0, 0)
+
+ # Transpiration
+ Tr = Kcb * Ks * ET0
+
+ # Potential evapotranspiration and evaporative fraction ??
+
+ # Update depletions (root and deep zones) at the end of the day
+ Dr = np.minimum(np.maximum(Dr + E + Tr - Diff_dr, 0), TAW)
+ Dd = np.minimum(np.maximum(Dd + Diff_dr, 0), TDW)
+
+ # Write outputs
+ with nc.Dataset(save_path, mode='r+') as outputs:
+ # Dimensions of output dataset : (x, y, time)
+ # Deep percolation
+ outputs.variables['DP'][:, :, 0] = np.round(DP * 1000)
+ # Soil water content of the evaporative layer
+ outputs.variables['SWCe'][:, :, 0] = np.round(SWCe * 1000)
+ # Soil water content of the root layer
+ outputs.variables['SWCr'][:, :, 0] = np.round(SWCr * 1000)
+ # Evaporation
+ outputs.variables['E'][:, :, 0] = np.round(E * 1000)
+ # Transpiration
+ outputs.variables['Tr'][:, :, 0] = np.round(Tr * 1000)
# Irrigation
- model_outputs['Irr'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
- model_outputs['Irr'].attrs['units'] = 'mm'
- model_outputs['Irr'].attrs['standard_name'] = 'Irrigation'
- model_outputs['Irr'].attrs['description'] = 'Simulated daily irrigation in milimeters'
- model_outputs['Irr'].attrs['scale factor'] = '1000'
-
- # Deep Percolation
- model_outputs['DP'] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
- model_outputs['DP'].attrs['units'] = 'mm'
- model_outputs['DP'].attrs['standard_name'] = 'Deep Percolation'
- model_outputs['DP'].attrs['description'] = 'Simulated daily Deep Percolation in milimeters'
- model_outputs['DP'].attrs['scale factor'] = '1000'
-
- if additional_outputs:
- for var in additional_outputs:
- model_outputs[var] = (empty_dataset.dims, np.zeros(tuple(empty_dataset.dims[d] for d in list(empty_dataset.dims)), dtype = 'int16'))
-
- return model_outputs
-
-
- def xr_maximum(ds: xr.DataArray, value: Union[xr.DataArray, float, int]) -> xr.DataArray:
- """
- Equivalent of `numpy.maximum(ds, value)` for xarray DataArrays
-
- ## Arguments
- 1. ds: `xr.DataArray`
- datarray to compare
- 2. value: `Union[xr.DataArray, float, int]`
- value (scalar or dataarray) to compare
-
- ## Returns
- 1. output: `xr.DataArray`
- resulting dataarray with maximum value element-wise
- """
- return xr.where(ds <= value, value, ds, keep_attrs = True)
-
-
- def xr_minimum(ds: xr.DataArray, value: Union[xr.DataArray, float, int]) -> xr.DataArray:
- """
- Equivalent of `numpy.minimum(ds, value)` for xarray DataArrays
-
- ## Arguments
- 1. ds: `xr.DataArray`
- datarray to compare
- 2. value: `Union[xr.DataArray, float, int]`
- value (scalar or dataarray) to compare
-
- ## Returns
- 1. output: `xr.DataArray`
- resulting dataarray with minimum value element-wise
- """
- return xr.where(ds >= value, value, ds, keep_attrs = True)
-
-
- def calculate_diff_re(TAW: np.ndarray, Dr: np.ndarray, Zr: np.ndarray, RUE: np.ndarray, De: np.ndarray, FC: np.ndarray, Ze_: np.ndarray, DiffE_: np.ndarray, scale_dict: dict) -> np.ndarray:
- """
- Calculates the diffusion between the top soil layer and the root layer.
-
- ## Arguments
- 1. TAW: `np.ndarray`
- water capacity of root layer
- 2. Dr: `np.ndarray`
- depletion of root layer
- 3. Zr: `np.ndarray`
- height of root layer
- 4. RUE: `np.ndarray`
- total available surface water
- 5. De: `np.ndarray`
- depletion of the evaporative layer
- 6. FC: `np.ndarray`
- field capacity
- 7. Ze_: `np.ndarray`
- height of evaporative layer (paramter)
- 8. DiffE_: `np.ndarray`
- diffusion coefficient between evaporative
- and root layers (unitless, parameter)
- 9. scale_dict: `dict`
- dictionnary containing the scale factors for
- the rasterized parameters
-
- ## Returns
- 1. diff_re: `np.ndarray`
- the diffusion between the top soil layer and
- the root layer
- """
-
- # Temporary variables to make calculation easier to read
- tmp1 = ((TAW - Dr) / Zr - (RUE - De) / (scale_dict['Ze'] * Ze_)) / FC * (scale_dict['DiffE'] * DiffE_)
- tmp2 = ((TAW * scale_dict['Ze'] * Ze_) - (RUE - De - Dr) * Zr) / (Zr + scale_dict['Ze'] * Ze_) - Dr
-
- # Calculate diffusion according to SAMIR equation
- diff_re = np.where(tmp1 < 0, np.maximum(tmp1, tmp2), np.minimum(tmp1, tmp2))
-
- # Return zero values where the 'DiffE' parameter is equal to 0
- return np.where(DiffE_ == 0, 0, diff_re)
-
-
- def calculate_diff_dr(TAW: np.ndarray, TDW: np.ndarray, Dr: np.ndarray, Zr: np.ndarray, Dd: np.ndarray, FC: np.ndarray, Zsoil_: np.ndarray, DiffR_: np.ndarray, scale_dict: dict) -> np.ndarray:
- """
- Calculates the diffusion between the root layer and the deep layer.
-
- ## Arguments
- 1. TAW: `np.ndarray`
- water capacity of root layer
- 2. TDW: `np.ndarray`
- water capacity of deep layer
- 3. Dr: `np.ndarray`
- depletion of root layer
- 4. Zr: `np.ndarray`
- height of root layer
- 5. Dd: `np.ndarray`
- depletion of deep layer
- 6. FC: `np.ndarray`
- field capacity
- 7. Zsoil_: `np.ndarray`
- total height of soil (paramter)
- 8. DiffR_: `np.ndarray`
- Diffusion coefficient between root
- and deep layers (unitless, parameter)
- 9. scale_dict: `dict`
- dictionnary containing the scale factors for
- the rasterized parameters
-
- ## Returns
- 1. diff_dr: `np.ndarray`
- the diffusion between the root layer and the
- deep layer
- """
-
- # Temporary variables to make calculation easier to read
- tmp1 = (((TDW - Dd) / (scale_dict['Zsoil'] * Zsoil_ - Zr) - (TAW - Dr) / Zr) / FC) * scale_dict['DiffR'] * DiffR_
- tmp2 = (TDW *Zr - (TAW - Dr - Dd) * (scale_dict['Zsoil'] * Zsoil_ - Zr)) / (scale_dict['Zsoil'] * Zsoil_) - Dd
-
- # Calculate diffusion according to SAMIR equation
- diff_dr = np.where(tmp1 < 0, np.maximum(tmp1, tmp2), np.minimum(tmp1, tmp2))
-
- # Return zero values where the 'DiffR' parameter is equal to 0
- return np.where(DiffR_ == 0, 0, diff_dr)
-
-
- def calculate_W(TEW: np.ndarray, Dei: np.ndarray, Dep: np.ndarray, fewi: np.ndarray, fewp: np.ndarray) -> np.ndarray:
- """
- Calculate W, the weighting factor to split the energy available
- for evaporation depending on the difference in water availability
- in the two evaporation components, ensuring that the larger and
- the wetter, the more the evaporation occurs from that component
-
- ## Arguments
- 1. TEW: `np.ndarray`
- water capacity of evaporative layer
- 2. Dei: `np.ndarray`
- depletion of the evaporative layer
- (irrigation part)
- 3. Dep: `np.ndarray`
- depletion of the evaporative layer
- (precipitation part)
- 4. fewi: `np.ndarray`
- soil fraction which is wetted by irrigation
- and exposed to evaporation
- 5. fewp: `np.ndarray`
- soil fraction which is wetted by precipitation
- and exposed to evaporation
-
- ## Returns
- 1. W: `np.ndarray`
- weighting factor W
- """
-
- # Temporary variables to make calculation easier to read
- tmp = fewi * (TEW - Dei)
-
- # Calculate the weighting factor to split the energy available for evaporation
- W = 1 / (1 + (fewp * (TEW - Dep) / tmp ))
-
- # Return W
- return np.where(tmp > 0, W, 0)
-
-
- def calculate_Kr(TEW: np.ndarray, De: np.ndarray, REW_: np.ndarray, scale_dict: dict) -> np.ndarray:
- """
- calculates of the reduction coefficient for evaporation dependent
- on the amount of water in the soil using the FAO-56 method
-
- ## Arguments
- 1. TEW: `np.ndarray`
- water capacity of evaporative layer
- 2. De: `np.ndarray`
- depletion of evaporative layer
- 3. REW_: `np.ndarray`
- readily evaporable water
- 4. scale_dict: `dict`
- dictionnary containing the scale factors for
- the rasterized parameters
-
- ## Returns
- 1. Kr: `np.ndarray`
- Kr coefficient
- """
-
- # Formula for calculating Kr
- Kr = (TEW - De) / (TEW - scale_dict['REW'] * REW_)
-
- # Return Kr
- return np.maximum(0, np.minimum(Kr, 1))
-
-
- def update_Dr(TAW: np.ndarray, Zr: np.ndarray, TAW0: np.ndarray, TDW0: np.ndarray, Dr0: np.ndarray, Dd0: np.ndarray, Zr0: np.ndarray) -> np.ndarray:
- """
- Return the updated depletion for the root layer
-
- ## Arguments
- 1. TAW: `np.ndarray`
- water capacity of root layer for current day
- 2. Zr: `np.ndarray`
- root layer height for current day
- 3. TAW0: `np.ndarray`
- water capacity of root layer for previous day
- 4. TDW0: `np.ndarray`
- water capacity of deep layer for previous day
- 5. Dr0: `np.ndarray`
- depletion of the root layer for previous day
- 6. Dd0: `np.ndarray`
- depletion of the deep laye for previous day
- 7. Zr0: `np.ndarray`
- root layer height for previous day
-
- ## Returns
- 1. output: `np.ndarray`
- updated depletion for the root layer
- """
-
- # Temporary variables to make calculation easier to read
- tmp1 = np.minimum(Dr0 + Dd0 * (TAW - TAW0) / TDW0, TAW)
- tmp2 = np.maximum(Dr0 + Dd0 * (TAW - TAW0) / TDW0, 0)
-
- # Return updated Dr
- return np.where(Zr > Zr0, tmp1, tmp2)
-
-
- def update_Dd(TAW: np.ndarray, TDW: np.ndarray, Zr: np.ndarray, TAW0: np.ndarray, TDW0: np.ndarray, Dd0: np.ndarray, Zr0: np.ndarray) -> np.ndarray:
- """
- Return the updated depletion for the deep layer
-
- ## Arguments
- 1. TAW: `np.ndarray`
- water capacity of root layer for current day
- 2. TDW: `np.ndarray`
- water capacity of deep layer for current day
- 3. TAW0: `np.ndarray`
- water capacity of root layer for previous day
- 5. TDW0: `np.ndarray`
- water capacity of deep layer for previous day
- 6. Dd0: `np.ndarray`
- depletion of the deep laye for previous day
- 7. Zr0: `np.ndarray`
- root layer height for previous day
-
- ## Returns
- 1. output: `np.ndarray`
- updated depletion for the deep layer
- """
-
- # Temporary variables to make calculation easier to read
- tmp1 = np.maximum(Dd0 - Dd0 * (TAW - TAW0) / TDW0, 0)
- tmp2 = np.minimum(Dd0 - Dd0 * (TAW - TAW0) / TDW0, TDW)
-
- # Return updated Dd
- return np.where(Zr > Zr0, tmp1, tmp2)
-
-
- def format_duration(timedelta: float) -> None:
- """
- Print formatted timedelta in human readable format
- (days, hours, minutes, seconds, microseconds, milliseconds, nanoseconds).
-
- ## Arguments
- timedelta: `float`
- time value in seconds to format
-
- ## Returns
- `None`
- """
-
- if timedelta < 0.9e-6:
- print(round(timedelta*1e9, 1), 'ns')
- elif timedelta < 0.9e-3:
- print(round(timedelta*1e6, 1), 'µs')
- elif timedelta < 0.9:
- print(round(timedelta*1e3, 1), 'ms')
- elif timedelta < 60:
- print(round(timedelta, 1), 's')
- elif timedelta < 3.6e3:
- print(round(timedelta//60), 'm', round(timedelta%60, 1), 's')
- elif timedelta < 24*3.6e3:
- print(round((timedelta/3.6e3)//1), 'h', round((timedelta/3.6e3)%1*60//1), 'm', round((timedelta/3.6e3)%1*60%1*60, 1), 's' )
- elif timedelta < 48*3.6e3:
- print(round((timedelta/(24*3.6e3))//1), 'day,', round(((timedelta/(24*3.6e3))%1*24)//1), 'h,', round((timedelta/(24*3.6e3))%1*24%1*60//1), 'm,', round((timedelta/(24*3.6e3))%1*24%1*60%1*60, 1), 's')
- else:
- print(round((timedelta/(24*3.6e3))//1), 'days,', round(((timedelta/(24*3.6e3))%1*24)//1), 'h,', round((timedelta/(24*3.6e3))%1*24%1*60//1), 'm,', round((timedelta/(24*3.6e3))%1*24%1*60%1*60, 1), 's')
-
- return None
-
-
- # @profile # type: ignore
- def run_samir(json_config_file: str, csv_param_file: str, ndvi_cube_path: str, rain_path: str, ET0_path: str, soil_params_path: str, land_cover_path: str, chunk_size: dict, save_path: str, additional_outputs: List[str] = None, additional_outputs_scale: List[float] = None, max_ram_GB: int = 2) -> None:
-
- # Test inputs
- if len(additional_outputs) != len(additional_outputs_scale):
- print('\nadditional outpus name and scale list length do not match\n')
- return None
-
- # Turn off numpy warings
- np.seterr(divide='ignore', invalid='ignore')
-
- #============ General parameters ============#
- config_params = config(json_config_file)
- calculation_variables_t2 = ['diff_rei', 'diff_rep', 'diff_dr' , 'Dd', 'De', 'Dei', 'Dep', 'DP', 'Dr', 'FCov', 'Irrig', 'Kcb', 'Kei', 'Kep', 'Kri', 'Krp', 'Ks', 'Kti', 'Ktp', 'RUE', 'SWCe', 'SWCr', 'TAW', 'TDW', 'TEW', 'Tei', 'Tep', 'W', 'Zr', 'fewi', 'fewp', 'p_cor']
- calculation_variables_t1 = ['Dr', 'Dd', 'TAW', 'TDW', 'Zr']
-
- #============ Manage inputs ============#
- # NDVI (to have a correct empty dataset structure)
- ndvi_cube = xr.open_mfdataset(ndvi_cube_path, chunks = chunk_size, parallel = True)
-
- # SAMIR Parameters
- param_dataset, scale_factor = rasterize_samir_parameters(csv_param_file, ndvi_cube.drop_vars(['ndvi', 'time']), land_cover_path, chunk_size = chunk_size)
-
- # SAMIR Variables
- variables_t1, variables_t2 = setup_time_loop(calculation_variables_t1, calculation_variables_t2, ndvi_cube.drop_vars(['ndvi', 'time']))
-
- #============ Prepare outputs ============#
- model_outputs = prepare_outputs(ndvi_cube.drop_vars(['ndvi']), additional_outputs = additional_outputs)
-
- # Create encoding dictionnary
- for variable in list(model_outputs.keys()):
- # Write encoding dict
- encoding_dict = {}
- encod = {}
- encod['dtype'] = 'i2'
- encoding_dict[variable] = encod
-
- # Save empty output
- model_outputs.to_netcdf(save_path, encoding = encoding_dict)
- model_outputs.close()
-
- #============ Prepare time iterations ============#
- dates = ndvi_cube.time.values
- ndvi_cube.close()
-
- #============ Create aliases for better readability ============#
-
- # Variables for current day
- # var = da.from_array(dataarray, chunks = (5, 5))
- diff_rei = variables_t2.diff_rei.to_numpy()
- diff_rep = variables_t2.diff_rep.to_numpy()
- diff_dr = variables_t2.diff_dr.to_numpy()
- Dd = variables_t2.Dd.to_numpy()
- De = variables_t2.De.to_numpy()
- Dei = variables_t2.Dei.to_numpy()
- Dep = variables_t2.Dep.to_numpy()
- DP = variables_t2.DP.to_numpy()
- Dr = variables_t2.Dr.to_numpy()
- FCov = variables_t2.FCov.to_numpy()
- Irrig = variables_t2.Irrig.to_numpy()
- Kcb = variables_t2.Kcb.to_numpy()
- Kei = variables_t2.Kei.to_numpy()
- Kep = variables_t2.Kep.to_numpy()
- Kri = variables_t2.Kri.to_numpy()
- Krp = variables_t2.Krp.to_numpy()
- Ks = variables_t2.Ks.to_numpy()
- Kti = variables_t2.Kti.to_numpy()
- Ktp = variables_t2.Ktp.to_numpy()
- RUE = variables_t2.RUE.to_numpy()
- SWCe = variables_t2.SWCe.to_numpy()
- SWCr = variables_t2.SWCr.to_numpy()
- TAW = variables_t2.TAW.to_numpy()
- TDW = variables_t2.TDW.to_numpy()
- TEW = variables_t2.TEW.to_numpy()
- Tei = variables_t2.Tei.to_numpy()
- Tep = variables_t2.Tep.to_numpy()
- Zr = variables_t2.Zr.to_numpy()
- W = variables_t2.W.to_numpy()
- fewi = variables_t2.fewi.to_numpy()
- fewp = variables_t2.fewp.to_numpy()
- p_cor = variables_t2.p_cor.to_numpy()
-
- # Variables for previous day
- TAW0 = variables_t1.TAW.to_numpy()
- TDW0 = variables_t1.TDW.to_numpy()
- Dr0 = variables_t1.Dr.to_numpy()
- Dd0 = variables_t1.Dd.to_numpy()
- Zr0 = variables_t1.Zr.to_numpy()
-
- # Parameters
- # Parameters have an underscore (_) behind their name for recognition
- DiffE_ = param_dataset.DiffE.to_numpy()
- DiffR_ = param_dataset.DiffR.to_numpy()
- FW_ = param_dataset.FW.to_numpy()
- Fc_stop_ = param_dataset.Fc_stop.to_numpy()
- FmaxFC_ = param_dataset.FmaxFC.to_numpy()
- Foffset_ = param_dataset.Foffset.to_numpy()
- Fslope_ = param_dataset.Fslope.to_numpy()
- Init_RU_ = param_dataset.Init_RU.to_numpy()
- Irrig_auto_ = param_dataset.Irrig_auto.to_numpy()
- # Kcmax_ = param_dataset.Kcmax.to_numpy()
- KmaxKcb_ = param_dataset.KmaxKcb.to_numpy()
- Koffset_ = param_dataset.Koffset.to_numpy()
- Kslope_ = param_dataset.Kslope.to_numpy()
- Lame_max_ = param_dataset.Lame_max.to_numpy()
- REW_ = param_dataset.REW.to_numpy()
- Ze_ = param_dataset.Ze.to_numpy()
- Zsoil_ = param_dataset.Zsoil.to_numpy()
- maxZr_ = param_dataset.maxZr.to_numpy()
- minZr_ = param_dataset.minZr.to_numpy()
- p_ = param_dataset.p.to_numpy()
-
- # scale factors
- # Scale factors have the following name scheme : s_ + parameter_name
- s_FW = scale_factor['FW']
- s_Fc_stop = scale_factor['Fc_stop']
- s_FmaxFC = scale_factor['FmaxFC']
- s_Foffset = scale_factor['Foffset']
- s_Fslope = scale_factor['Fslope']
- s_Init_RU = scale_factor['Init_RU']
- # s_Irrig_auto = scale_factor['Irrig_auto']
- s_Kcmax = scale_factor['Kcmax']
- s_KmaxKcb = scale_factor['KmaxKcb']
- s_Koffset = scale_factor['Koffset']
- s_Kslope = scale_factor['Kslope']
- s_Lame_max = scale_factor['Lame_max']
- # s_REW = scale_factor['REW']
- s_Ze = scale_factor['Ze']
- s_Zsoil = scale_factor['Zsoil']
- s_maxZr = scale_factor['maxZr']
- s_minZr = scale_factor['minZr']
- s_p = scale_factor['p']
-
+ outputs.variables['Irr'][:, :, 0] = np.round(Irrig * 1000)
+ # Additionnal outputs
+ for var, scale in zip(additional_outputs, additional_outputs_scale):
+ outputs.variables[var][:, :, 0] = np.round(eval(var) * scale)
+
+ # Update previous day values
+ TAW0 = TAW
+ TDW0 = TDW
+ Dr0 = Dr
+ Dd0 = Dd
+ Zr0 = Zr
+
+ # Update progress bar
+ progress_bar.update()
+
+ # %% ============ Time loop ============#
+ for i in range(1, len(dates)):
+
+ # Reset input aliases
# input data
- with nc.Dataset(ndvi_cube_path, mode = 'r') as ds:
+ with nc.Dataset(ndvi_cube_path, mode='r') as ds:
# Dimensions of ndvi dataset : (time, x, y)
- ndvi = ds.variables['ndvi'][0,:,:] / 255
- with nc.Dataset(soil_params_path, mode = 'r') as ds:
- FC = ds.variables['FC'][:,:]
- WP = ds.variables['WP'][:,:]
- with rio.open(rain_path, mode ='r') as ds:
- rain = ds.read(1) / 1000
- with rio.open(ET0_path, mode = 'r') as ds:
- ET0 = ds.read(1) / 1000
-
- # Create progress bar
- progress_bar = tqdm(total = len(dates), desc = 'Running model', unit = ' days')
-
- #============ First day initialization ============#
+ NDVI = ds.variables['NDVI'][i, :, :] / 255
+ with rio.open(Rain_path, mode='r') as ds:
+ Rain = ds.read(i+1) / 1000
+ with rio.open(ET0_path, mode='r') as ds:
+ ET0 = ds.read(i+1) / 1000
+ # ET0_previous = ds.read(i) / 1000 # TODO vr: variable jamais utilisée!
+
+ # Update variables
# Fraction cover
- FCov = s_Fslope * Fslope_ * ndvi + s_Foffset * Foffset_
+ FCov = s_Fslope * Fslope_ * NDVI + s_Foffset * Foffset_
FCov = np.minimum(np.maximum(FCov, 0), s_Fc_stop * Fc_stop_)
-
+
# Root depth upate
- Zr = np.maximum(s_minZr * minZr_ + (FCov / (s_FmaxFC * FmaxFC_)) * s_maxZr * (maxZr_ - minZr_), s_Ze * Ze_ + 0.001)
-
+ Zr = np.maximum(s_minZr * minZr_ + (FCov / (s_FmaxFC * FmaxFC_))
+ * s_maxZr * (maxZr_ - minZr_), s_Ze * Ze_ + 0.001)
+
# Water capacities
- TEW = (FC - WP/2) * s_Ze * Ze_
- RUE = (FC - WP) * s_Ze * Ze_
- TAW = (FC - WP) * Zr
- TDW = (FC - WP) * (s_Zsoil * Zsoil_ - Zr) # Zd = Zsoil - Zr
-
- # Depletions
- Dei = RUE * (1 - s_Init_RU * Init_RU_)
- Dep = RUE * (1 - s_Init_RU * Init_RU_)
- Dr = TAW * (1 - s_Init_RU * Init_RU_)
- Dd = TDW * (1 - s_Init_RU * Init_RU_)
-
- # p_cor
- p_cor = s_p * p_
-
- # Irrigation TODO : find correct method for irrigation
- Irrig = np.minimum(np.maximum(Dr - rain, 0), s_Lame_max * Lame_max_) * Irrig_auto_
+ TAW = (Wfc - Wwp) * Zr
+ TDW = (Wfc - Wwp) * (s_Zsoil * Zsoil_ - Zr) # Zd = Zsoil - Zr
+
+ # Update depletions from root increase
+ Dr = update_Dr_from_root(TAW, Zr, TAW0, TDW0, Dr0, Dd0, Zr0)
+ Dd = update_Dd_from_root(TAW, TDW, Zr, TAW0, TDW0, Dr0, Dd0, Zr0)
+
+ # Update param p
+ # TODO: Calcul p_cor différent entre la doc ou l'excel et le code samir parcelle
+ # p_cor = np.minimum(np.maximum(s_p * p_ + 0.04 * (5 - (E + Tr)), 0.1), 0.8)
+ # Calcul p_cor différent entre la doc ou l'excel et le code samir parcelle
+ p_cor = s_p * p_ + 0.04 * (5 - (E + Tr))
+
+ # Irrigation TODO : find correct method for irrigation
+ Irrig = np.minimum(np.maximum(Dr - Rain, 0), s_Lame_max * Lame_max_) * Irrig_auto_
Irrig = np.where(Dr > TAW * p_cor, Irrig, 0)
-
+
# Kcb
- Kcb = np.minimum(np.maximum(s_Kslope * Kslope_ * ndvi + s_Koffset * Koffset_, 0), s_KmaxKcb * KmaxKcb_)
-
- # Update depletions with rainfall and/or irrigation
-
- ## DP
- DP = - np.minimum(Dd + np.minimum(Dr - rain - Irrig, 0), 0)
-
- ## De
- Dei = np.minimum(np.maximum(Dei - rain - Irrig / (s_FW * FW_), 0), TEW)
- Dep = np.minimum(np.maximum(Dep - rain, 0), TEW)
-
+ Kcb = np.minimum(np.maximum(s_Kslope * Kslope_ * NDVI + s_Koffset * Koffset_, 0), s_KmaxKcb * KmaxKcb_)
+
+ # DP (Deep percolation)
+ DP = - np.minimum(Dd + np.minimum(Dr - Rain - Irrig, 0), 0)
+
+ # Update depletions with Rainfall and/or irrigation
+
+ # De
+ Dei = np.minimum(np.maximum(Dei - Rain - Irrig / (s_FW * FW_), 0), TEW)
+ Dep = np.minimum(np.maximum(Dep - Rain, 0), TEW)
+
fewi = np.minimum(1 - FCov, (s_FW * FW_))
fewp = 1 - FCov - fewi
-
- De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi , fewp])
+
+ De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi, fewp])
De = np.where(np.isfinite(De), De, Dei * (s_FW * FW_) + Dep * (1 - (s_FW * FW_)))
- ## Dr
- Dr = np.minimum(np.maximum(Dr - rain - Irrig, 0), TAW)
-
- ## Dd
- Dd = np.minimum(np.maximum(Dd + np.minimum(Dr - rain - Irrig, 0), 0), TDW)
-
+ # Update depletions from rain and irrigation
+ Dr = np.minimum(np.maximum(Dr - Rain - Irrig, 0), TAW)
+ Dd = np.minimum(np.maximum(Dd + np.minimum(Dr - Rain - Irrig, 0), 0), TDW)
+
# Diffusion coefficients
- diff_rei = calculate_diff_re(TAW, Dr, Zr, RUE, Dei, FC, Ze_, DiffE_, scale_factor)
- diff_rep = calculate_diff_re(TAW, Dr, Zr, RUE, Dep, FC, Ze_, DiffE_, scale_factor)
- diff_dr = calculate_diff_dr(TAW, TDW, Dr, Zr, Dd, FC, Zsoil_, DiffR_, scale_factor)
-
+ Diff_rei = calculate_diff_re(TAW, Dr, Zr, RUE, Dei, Wfc, s_Ze*Ze_, s_DiffE*DiffE_)
+ Diff_rep = calculate_diff_re(TAW, Dr, Zr, RUE, Dep, Wfc, s_Ze*Ze_, s_DiffE*DiffE_)
+ Diff_dr = calculate_diff_dr(TAW, TDW, Dr, Zr, Dd, Wfc, s_Zsoil*Zsoil_, s_DiffR*DiffR_)
+
# Weighing factor W
W = calculate_W(TEW, Dei, Dep, fewi, fewp)
-
+
# Soil water content of evaporative layer
SWCe = 1 - De/TEW
# Soil water content of root layer
SWCr = 1 - Dr/TAW
-
+
# Water Stress coefficient
Ks = np.minimum((TAW - Dr) / (TAW * (1 - p_cor)), 1)
-
+
# Reduction coefficient for evaporation
- Kri = calculate_Kr(TEW, Dei, REW_, scale_factor)
- Krp = calculate_Kr(TEW, Dep, REW_, scale_factor)
+ Kri = calculate_Kr(TEW, Dei, REW_*s_REW)
+ Krp = calculate_Kr(TEW, Dep, REW_*s_REW)
Kei = np.minimum(W * Kri * (s_KmaxKcb * KmaxKcb_ - Kcb), fewi * s_KmaxKcb * KmaxKcb_)
Kep = np.minimum((1 - W) * Krp * (s_KmaxKcb * KmaxKcb_ - Kcb), fewp * s_KmaxKcb * KmaxKcb_)
-
+
# Prepare coefficients for evapotranspiration
Kti = np.minimum(((s_Ze * Ze_ / Zr)**0.6) * (1 - Dei / TEW) / np.maximum(1 - Dr / TAW, 0.001), 1)
Ktp = np.minimum(((s_Ze * Ze_ / Zr)**0.6) * (1 - Dep / TEW) / np.maximum(1 - Dr / TAW, 0.001), 1)
Tei = Kti * Ks * Kcb * ET0
Tep = Ktp * Ks * Kcb * ET0
-
+
# Update depletions
- Dei = np.where(fewi > 0, np.minimum(np.maximum(Dei + ET0 * Kei / fewi + Tei - diff_rei, 0), TEW), np.minimum(np.maximum(Dei + Tei - diff_rei, 0), TEW))
- Dep = np.where(fewp > 0, np.minimum(np.maximum(Dep + ET0 * Kep / fewp + Tep - diff_rep, 0), TEW), np.minimum(np.maximum(Dep + Tep - diff_rep, 0), TEW))
-
- De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi , fewp])
+ Dei = np.where(fewi > 0, np.minimum(np.maximum(Dei + ET0 * Kei / fewi + Tei - Diff_rei, 0), TEW),
+ np.minimum(np.maximum(Dei + Tei - Diff_rei, 0), TEW))
+ Dep = np.where(fewp > 0, np.minimum(np.maximum(Dep + ET0 * Kep / fewp + Tep - Diff_rep, 0), TEW),
+ np.minimum(np.maximum(Dep + Tep - Diff_rep, 0), TEW))
+
+ De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi, fewp])
De = np.where(np.isfinite(De), De, Dei * (s_FW * FW_) + Dep * (1 - (s_FW * FW_)))
-
+
# Evaporation
E = np.maximum((Kei + Kep) * ET0, 0)
-
+
# Transpiration
Tr = Kcb * Ks * ET0
-
+
# Potential evapotranspiration and evaporative fraction ??
-
+
# Update depletions (root and deep zones) at the end of the day
- Dr = np.minimum(np.maximum(Dr + E + Tr - diff_dr, 0), TAW)
- Dd = np.minimum(np.maximum(Dd + diff_dr, 0), TDW)
-
+ Dr = np.minimum(np.maximum(Dr + E + Tr - Diff_dr, 0), TAW)
+ Dd = np.minimum(np.maximum(Dd + Diff_dr, 0), TDW)
+
# Write outputs
with nc.Dataset(save_path, mode='r+') as outputs:
# Dimensions of output dataset : (x, y, time)
# Deep percolation
- outputs.variables['DP'][:,:,0] = np.round(DP * 1000)
+ outputs.variables['DP'][:, :, i] = np.round(DP * 1000)
# Soil water content of the evaporative layer
- outputs.variables['SWCe'][:,:,0] = np.round(SWCe * 1000)
+ outputs.variables['SWCe'][:, :, i] = np.round(SWCe * 1000)
# Soil water content of the root layer
- outputs.variables['SWCr'][:,:,0] = np.round(SWCr * 1000)
+ outputs.variables['SWCr'][:, :, i] = np.round(SWCr * 1000)
# Evaporation
- outputs.variables['E'][:,:,0] = np.round(E * 1000)
+ outputs.variables['E'][:, :, i] = np.round(E * 1000)
# Transpiration
- outputs.variables['Tr'][:,:,0] = np.round(Tr * 1000)
+ outputs.variables['Tr'][:, :, i] = np.round(Tr * 1000)
# Irrigation
- outputs.variables['Irr'][:,:,0] = np.round(Irrig * 1000)
+ outputs.variables['Irr'][:, :, i] = np.round(Irrig * 1000)
# Additionnal outputs
for var, scale in zip(additional_outputs, additional_outputs_scale):
- outputs.variables[var][:,:,0] = np.round(eval(var) * scale)
-
+ outputs.variables[var][:, :, i] = np.round(eval(var) * scale)
+
# Update previous day values
TAW0 = TAW
TDW0 = TDW
Dr0 = Dr
Dd0 = Dd
Zr0 = Zr
-
+
# Update progress bar
progress_bar.update()
-
- #============ Time loop ============#
- for i in range(1, len(dates)):
-
- # Reset input aliases
- # input data
- with nc.Dataset(ndvi_cube_path, mode = 'r') as ds:
- # Dimensions of ndvi dataset : (time, x, y)
- ndvi = ds.variables['ndvi'][i,:,:] / 255
- with rio.open(rain_path, mode ='r') as ds:
- rain = ds.read(i+1) / 1000
- with rio.open(ET0_path, mode = 'r') as ds:
- ET0 = ds.read(i+1) / 1000
- ET0_previous = ds.read(i) / 1000
-
- # Update variables
- ## Fraction cover
- FCov = s_Fslope * Fslope_ * ndvi + s_Foffset * Foffset_
- FCov = np.minimum(np.maximum(FCov, 0), s_Fc_stop * Fc_stop_)
-
- ## Root depth upate
- Zr = np.maximum(s_minZr * minZr_ + (FCov / (s_FmaxFC * FmaxFC_)) * s_maxZr * (maxZr_ - minZr_), s_Ze * Ze_ + 0.001)
-
- # Water capacities
- TAW = (FC - WP) * Zr
- TDW = (FC - WP) * (s_Zsoil * Zsoil_ - Zr) # Zd = Zsoil - Zr
-
- # Update depletions
- Dr = update_Dr(TAW, Zr, TAW0, TDW0, Dr0, Dd0, Zr0)
- Dd = update_Dd(TAW, TDW, Zr, TAW0, TDW0, Dd0, Zr0)
-
- # Update param p
- # p_cor = np.minimum(np.maximum(s_p * p_ + 0.04 * (5 - (E + Tr)), 0.1), 0.8) # Calcul p_cor différent entre la doc ou l'excel et le code samir parcelle
- p_cor = s_p * p_ + 0.04 * (5 - (E + Tr)) # Calcul p_cor différent entre la doc ou l'excel et le code samir parcelle
-
- # Irrigation TODO : find correct method for irrigation
- Irrig = np.minimum(np.maximum(Dr - rain, 0), s_Lame_max * Lame_max_) * Irrig_auto_
- Irrig = np.where(Dr > TAW * p_cor, Irrig, 0)
-
- # Kcb
- Kcb = np.minimum(np.maximum(s_Kslope * Kslope_ * ndvi + s_Koffset * Koffset_, 0), s_KmaxKcb * KmaxKcb_)
-
- # DP (Deep percolation)
- DP = - np.minimum(Dd + np.minimum(Dr - rain - Irrig, 0), 0)
-
- # Update depletions with rainfall and/or irrigation
-
- ## De
- Dei = np.minimum(np.maximum(Dei - rain - Irrig / (s_FW * FW_), 0), TEW)
- Dep = np.minimum(np.maximum(Dep - rain, 0), TEW)
-
- fewi = np.minimum(1 - FCov, (s_FW * FW_))
- fewp = 1 - FCov - fewi
-
- De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi , fewp])
- De = np.where(np.isfinite(De), De, Dei * (s_FW * FW_) + Dep * (1 - (s_FW * FW_)))
-
- ## Dr
- Dr = np.minimum(np.maximum(Dr - rain - Irrig, 0), TAW)
-
- ## Dd
- Dd = np.minimum(np.maximum(Dd + np.minimum(Dr - rain - Irrig, 0), 0), TDW)
-
- # Diffusion coefficients
- diff_rei = calculate_diff_re(TAW, Dr, Zr, RUE, Dei, FC, Ze_, DiffE_, scale_factor)
- diff_rep = calculate_diff_re(TAW, Dr, Zr, RUE, Dep, FC, Ze_, DiffE_, scale_factor)
- diff_dr = calculate_diff_dr(TAW, TDW, Dr, Zr, Dd, FC, Zsoil_, DiffR_, scale_factor)
-
- # Weighing factor W
- W = calculate_W(TEW, Dei, Dep, fewi, fewp)
-
- # Soil water content of evaporative layer
- SWCe = 1 - De/TEW
- # Soil water content of root layer
- SWCr = 1 - Dr/TAW
-
- # Water Stress coefficient
- Ks = np.minimum((TAW - Dr) / (TAW * (1 - p_cor)), 1)
-
- # Reduction coefficient for evaporation
- Kri = calculate_Kr(TEW, Dei, REW_, scale_factor)
- Krp = calculate_Kr(TEW, Dep, REW_, scale_factor)
- Kei = np.minimum(W * Kri * (s_KmaxKcb * KmaxKcb_ - Kcb), fewi * s_KmaxKcb * KmaxKcb_)
- Kep = np.minimum((1 - W) * Krp * (s_KmaxKcb * KmaxKcb_ - Kcb), fewp * s_KmaxKcb * KmaxKcb_)
-
- # Prepare coefficients for evapotranspiration
- Kti = np.minimum(((s_Ze * Ze_ / Zr)**0.6) * (1 - Dei / TEW) / np.maximum(1 - Dr / TAW, 0.001), 1)
- Ktp = np.minimum(((s_Ze * Ze_ / Zr)**0.6) * (1 - Dep / TEW) / np.maximum(1 - Dr / TAW, 0.001), 1)
- Tei = Kti * Ks * Kcb * ET0
- Tep = Ktp * Ks * Kcb * ET0
-
- # Update depletions
- Dei = np.where(fewi > 0, np.minimum(np.maximum(Dei + ET0 * Kei / fewi + Tei - diff_rei, 0), TEW), np.minimum(np.maximum(Dei + Tei - diff_rei, 0), TEW))
- Dep = np.where(fewp > 0, np.minimum(np.maximum(Dep + ET0 * Kep / fewp + Tep - diff_rep, 0), TEW), np.minimum(np.maximum(Dep + Tep - diff_rep, 0), TEW))
-
- De = np.nansum((Dei * fewi, Dep * fewp)) / np.nansum([fewi , fewp])
- De = np.where(np.isfinite(De), De, Dei * (s_FW * FW_) + Dep * (1 - (s_FW * FW_)))
-
- # Evaporation
- E = np.maximum((Kei + Kep) * ET0, 0)
-
- # Transpiration
- Tr = Kcb * Ks * ET0
-
- # Potential evapotranspiration and evaporative fraction ??
-
- # Update depletions (root and deep zones) at the end of the day
- Dr = np.minimum(np.maximum(Dr + E + Tr - diff_dr, 0), TAW)
- Dd = np.minimum(np.maximum(Dd + diff_dr, 0), TDW)
-
- # Write outputs
- with nc.Dataset(save_path, mode='r+') as outputs:
- # Dimensions of output dataset : (x, y, time)
- # Deep percolation
- outputs.variables['DP'][:,:,i] = np.round(DP * 1000)
- # Soil water content of the evaporative layer
- outputs.variables['SWCe'][:,:,i] = np.round(SWCe * 1000)
- # Soil water content of the root layer
- outputs.variables['SWCr'][:,:,i] = np.round(SWCr * 1000)
- # Evaporation
- outputs.variables['E'][:,:,i] = np.round(E * 1000)
- # Transpiration
- outputs.variables['Tr'][:,:,i] = np.round(Tr * 1000)
- # Irrigation
- outputs.variables['Irr'][:,:,i] = np.round(Irrig * 1000)
- # Additionnal outputs
- for var, scale in zip(additional_outputs, additional_outputs_scale):
- outputs.variables[var][:,:,i] = np.round(eval(var) * scale)
-
- # Update previous day values
- TAW0 = TAW
- TDW0 = TDW
- Dr0 = Dr
- Dd0 = Dd
- Zr0 = Zr
-
- # Update progress bar
- progress_bar.update()
-
- # Close progress bar
- progress_bar.close()
-
- return None
+ # Close progress bar
+ progress_bar.close()
+
+ return None
- data_path = '/mnt/e/DATA/DEV_inputs_test'
+
+# %% MAIN
+if __name__ == '__main__':
+
+ # data_path = '/mnt/e/DATA/DEV_inputs_test'
+ data_path = './DEV_inputs_test'
size = 10
- ndvi_path = data_path + os.sep + 'ndvi_' + str(size) + '.nc'
- rain_path = data_path + os.sep + 'rain_' + str(size) + '.tif'
+ NDVI_path = data_path + os.sep + 'NDVI_' + str(size) + '.nc'
+ Rain_path = data_path + os.sep + 'Rain_' + str(size) + '.tif'
ET0_path = data_path + os.sep + 'ET0_' + str(size) + '.tif'
land_cover_path = data_path + os.sep + 'land_cover_' + str(size) + '.nc'
- json_config_file = '/home/auclairj/GIT/modspa-pixel/config/config_modspa.json'
- param_file = '/home/auclairj/GIT/modspa-pixel/parameters/csv_files/params_samir_test.csv'
+ # json_config_file = '/home/auclairj/GIT/modspa-pixel/config/config_modspa.json'
+ json_config_file = './config/config_modspa.json'
+ # param_file = '/home/auclairj/GIT/modspa-pixel/parameters/csv_files/params_samir_test.csv'
+ param_file = './parameters/csv_files/params_samir_test.csv'
soil_path = data_path + os.sep + 'soil_' + str(size) + '.nc'
save_path = data_path + os.sep + 'outputs_' + str(size) + '.nc'
-
+ if os.path.exists(save_path):
+ os.remove(save_path)
+
# Validation sets
- val_ndvi_path = data_path + os.sep + 'val_ndvi_10.nc'
- val_rain_path = data_path + os.sep + 'val_rain_10.tif'
- val_ET0_path = data_path + os.sep + 'val_ET0_10.tif'
-
- val_save_path = data_path + os.sep + 'val_outputs_pixel_10.nc'
-
- additional_outputs = ['Zr', 'Dei', 'Dep', 'Dr', 'Dd', 'Kei', 'Kep', 'Ks', 'W', 'Kcb', 'Kri', 'Krp', 'fewi', 'fewp', 'TDW', 'TAW', 'FCov', 'Tei', 'Tep', 'diff_rei', 'diff_rep', 'diff_dr', 'rain', 'p_cor']
- additional_outputs_scale = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
+ xls_NDVI_path = data_path + os.sep + 'xls_NDVI_10.nc'
+ xls_Rain_path = data_path + os.sep + 'xls_Rain_10.tif'
+ val_ET0_path = data_path + os.sep + 'xls_ET0_10.tif'
+
+ output_save_path = data_path + os.sep + 'pix_outputs_10.nc'
+
+ additional_outputs = ['Zr', 'Dei', 'Dep', 'Dr', 'Dd', 'Kei', 'Kep', 'Ks', 'W', 'Kcb', 'Kri', 'Krp', 'NDVI',
+ 'fewi', 'fewp', 'TDW', 'TAW', 'FCov', 'Tei', 'Tep', 'Diff_rei', 'Diff_rep', 'Diff_dr', 'Rain', 'p_cor']
+ additional_outputs_scale = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
+ 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
chunk_size = {'x': 250, 'y': 250, 'time': -1}
@@ -932,9 +977,12 @@ if __name__ == '__main__':
# client = Client()
# webbrowser.open('http://127.0.0.1:8787/status', new=2, autoraise=True)
- # run_samir(json_config_file, param_file, ndvi_path, rain_path, ET0_path, soil_path, land_cover_path, chunk_size, save_path)
- run_samir(json_config_file, param_file, val_ndvi_path, val_rain_path, val_ET0_path, soil_path, land_cover_path, chunk_size, val_save_path, additional_outputs = additional_outputs, additional_outputs_scale = additional_outputs_scale)
+ # run_samir(json_config_file, param_file, ndvi_path, Rain_path, ET0_path, soil_path, land_cover_path, chunk_size, save_path)
+ run_samir(json_config_file, param_file, xls_NDVI_path, xls_Rain_path, val_ET0_path, soil_path, land_cover_path,
+ chunk_size, output_save_path, additional_outputs=additional_outputs, additional_outputs_scale=additional_outputs_scale)
+ print('')
+ print('Writting Output:', output_save_path)
format_duration(time() - t)
- # client.close()
\ No newline at end of file
+ # client.close()
--
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