diff --git a/.vscode/settings.json b/.vscode/settings.json index 6bd136b..ef0cd5e 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,4 +1,4 @@ { - "python.pythonPath": "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\python.exe", + "python.pythonPath": "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\python.exe", "restructuredtext.confPath": "${workspaceFolder}\\docs" } \ No newline at end of file diff --git a/conflict_model/analysis.py b/conflict_model/analysis.py index e03a47b..6817c39 100644 --- a/conflict_model/analysis.py +++ b/conflict_model/analysis.py @@ -22,7 +22,7 @@ def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year, out_dir, s dataframe: dataframe containing column with boolean information about conflict for each year """ - print('determining whether a conflict took place or not...') + print('determining whether a conflict took place or not') out_df = extent_gdf.copy() diff --git a/conflict_model/env_vars_nc.py b/conflict_model/env_vars_nc.py index 06b2561..fd11798 100644 --- a/conflict_model/env_vars_nc.py +++ b/conflict_model/env_vars_nc.py @@ -1,14 +1,15 @@ import xarray as xr import rasterio as rio +import pandas as pd import geopandas as gpd import rasterstats as rstats import numpy as np import matplotlib.pyplot as plt import os, sys -def rasterstats_GDP_PPP(gdf_in, config, sim_year, out_dir, saving_plots=False, showing_plots=False): +def rasterstats_GDP_PPP(gdf, config, sim_year, out_dir, saving_plots=False, showing_plots=False): - print('calculating zonal statistics per aggregation unit') + print('calculating GDP PPP mean per aggregation unit') nc_fo = os.path.join(config.get('general', 'input_dir'), config.get('env_vars', 'GDP_PPP')) @@ -17,6 +18,46 @@ def rasterstats_GDP_PPP(gdf_in, config, sim_year, out_dir, saving_plots=False, s nc_var = nc_ds['GDP_per_capita_PPP'] + # years = pd.to_datetime(nc_ds.time.values).to_period(freq='Y').strftime('%Y').to_numpy(dtype=int) + # if sim_year not in years: + # raise ValueError('the simulation year {0} can not be found in file {1}'.format(sim_year, nc_fo)) + # sim_year_idx = int(np.where(years == sim_year)[0]) + + affine = rio.open(nc_fo).transform + + # gdf['zonal_stats_min_' + str(sim_year)] = np.nan + # gdf['zonal_stats_max_' + str(sim_year)] = np.nan + # gdf['GDP_PPP_mean_' + str(sim_year)] = np.nan + + nc_arr = nc_var.sel(time=sim_year) + nc_arr_vals = nc_arr.values + if nc_arr_vals.size == 0: + raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo)) + + list_GDP_PPP = [] + + for i in range(len(gdf)): + prov = gdf.iloc[i] + zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean") + # gdf.loc[i, 'zonal_stats_min_' + str(sim_year)] = zonal_stats[0]['min'] + # gdf.loc[i, 'zonal_stats_max_' + str(sim_year)] = zonal_stats[0]['max'] + list_GDP_PPP.append(zonal_stats[0]['mean']) + + print('...DONE' + os.linesep) + + return list_GDP_PPP + +def rasterstats_totalEvap(gdf_in, config, sim_year, out_dir): + + print('calculating evaporation mean per aggregation unit') + + nc_fo = os.path.join(config.get('general', 'input_dir'), + config.get('env_vars', 'evaporation')) + + nc_ds = xr.open_dataset(nc_fo) + + nc_var = nc_ds['total_evaporation'] + years = nc_ds['time'].values years = years[years>=config.getint('settings', 'y_start')] years = years[years<=config.getint('settings', 'y_end')] @@ -25,9 +66,7 @@ def rasterstats_GDP_PPP(gdf_in, config, sim_year, out_dir, saving_plots=False, s gdf = gdf_in.copy() - gdf['zonal_stats_min_' + str(sim_year)] = np.nan - gdf['zonal_stats_max_' + str(sim_year)] = np.nan - gdf['zonal_stats_mean_' + str(sim_year)] = np.nan + gdf['evap_mean_' + str(sim_year)] = np.nan nc_arr = nc_var.sel(time=sim_year) nc_arr_vals = nc_arr.values @@ -36,68 +75,9 @@ def rasterstats_GDP_PPP(gdf_in, config, sim_year, out_dir, saving_plots=False, s for i in range(len(gdf)): prov = gdf.iloc[i] - zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean min max") - gdf.loc[i, 'zonal_stats_min_' + str(sim_year)] = zonal_stats[0]['min'] - gdf.loc[i, 'zonal_stats_max_' + str(sim_year)] = zonal_stats[0]['max'] - gdf.loc[i, 'zonal_stats_mean_' + str(sim_year)] = zonal_stats[0]['mean'] + zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean") + gdf.loc[i, 'evap_mean_' + str(sim_year)] = zonal_stats[0]['mean'] print('...DONE' + os.linesep) - fig, axes = plt.subplots(1, 3 , figsize=(20, 10)) - - fig.suptitle(str(int(sim_year)), y=0.78) - - gdf.plot(ax=axes[0], - column='zonal_stats_min_' + str(sim_year), - vmin=2000, - vmax=15000, - legend=True, - legend_kwds={'label': "min GDP_PPP", - 'orientation': "vertical", - 'shrink': 0.5, - 'extend': 'both'}) - gdf.boundary.plot(ax=axes[0], - color='0.5', - linestyle=':', - label='water province borders') - - gdf.plot(ax=axes[1], - column='zonal_stats_max_' + str(sim_year), - vmin=2000, - vmax=15000, - legend=True, - legend_kwds={'label': "max GDP_PPP", - 'orientation': "vertical", - 'shrink': 0.5, - 'extend': 'both'}) - gdf.boundary.plot(ax=axes[1], - color='0.5', - linestyle=':', - label='water province borders') - - gdf.plot(ax=axes[2], - column='zonal_stats_mean_' + str(sim_year), - vmin=2000, - vmax=15000, - legend=True, - legend_kwds={'label': "mean GDP_PPP", - 'orientation': "vertical", - 'shrink': 0.5, - 'extend': 'both'}) - gdf.boundary.plot(ax=axes[2], - color='0.5', - linestyle=':', - label='water province borders') - - plt.tight_layout() - - plt_name = 'GDP_PPP_zonal_stats_' + str(int(sim_year)) + '.png' - plt_name = os.path.join(out_dir, plt_name) - - if saving_plots: - plt.savefig(plt_name, dpi=300) - - if showing_plots == False: - plt.close() - return gdf \ No newline at end of file diff --git a/data/run_setting.cfg b/data/run_setting.cfg index 52c9f14..574c0a6 100644 --- a/data/run_setting.cfg +++ b/data/run_setting.cfg @@ -21,4 +21,5 @@ zones=BWh,BSh code2class=KoeppenGeiger/classification_codes.txt [env_vars] -GDP_PPP=GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc \ No newline at end of file +GDP_PPP=GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc +evaporation=PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc \ No newline at end of file diff --git a/example/example_notebook.html b/example/example_notebook.html index 40243ef..d81d928 100644 --- a/example/example_notebook.html +++ b/example/example_notebook.html @@ -13102,6 +13102,7 @@

Import libraries and file with import matplotlib.pyplot as plt import numpy as np import datetime +import netCDF4 as nc import rasterstats as rstats import xarray as xr import rasterio as rio @@ -13363,6 +13364,140 @@

Applying functionsFunctions

+ +
+
+
In [9]:
+
+
+
def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year): 
+    
+    print('determining whether a conflict took place or not')
+
+    # each year initialize new column with default value 0 (=False)
+    list_boolConflict = []
+    
+    # select the entries which occured in this year
+    temp_sel_year = conflict_gdf.loc[conflict_gdf.year == sim_year]   
+    
+    # merge the dataframes with polygons and conflict information, creating a sub-set of polygons/regions
+    data_merged = gpd.sjoin(temp_sel_year, extent_gdf)
+    
+    # determine the aggregated amount of fatalities in one region (e.g. water province)
+    fatalities_per_watProv = data_merged['best'].groupby(data_merged['watprovID']).sum().to_frame().rename(columns={"best": 'total_fatalities'})
+ 
+    # loop through all regions and check if exists in sub-set
+    # if so, this means that there was conflict and thus assign value 1
+    for i in range(len(extent_gdf)):
+        i_watProv = extent_gdf.iloc[i]['watprovID']
+        if i_watProv in fatalities_per_watProv.index.values:
+            list_boolConflict.append(1)
+        else:
+            list_boolConflict.append(0)
+            
+    if not len(extent_gdf) == len(list_boolConflict):
+        raise AssertionError('the dataframe with polygons has a lenght {0} while the lenght of the resulting list is {1}'.format(len(extent_gdf), len(list_boolConflict)))
+    
+    print('...DONE' + os.linesep)
+
+    return list_boolConflict
+
+ +
+
+
+ +
+
+
+
In [10]:
+
+
+
def rasterstats_GDP_PPP(gdf, config, sim_year):
+
+    print('calculating GDP PPP mean per aggregation unit')
+    
+    nc_fo = os.path.join(config.get('general', 'input_dir'), 
+                         config.get('env_vars', 'GDP_PPP'))
+
+    nc_ds = xr.open_dataset(nc_fo)
+
+    nc_var = nc_ds['GDP_per_capita_PPP']
+
+    affine = rio.open(nc_fo).transform
+
+    nc_arr = nc_var.sel(time=sim_year)
+    nc_arr_vals = nc_arr.values
+    if nc_arr_vals.size == 0:
+        raise ValueError('the data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo))
+
+    list_GDP_PPP = []
+    
+    for i in range(len(gdf)):
+        prov = gdf.iloc[i]
+        zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean")
+        list_GDP_PPP.append(zonal_stats[0]['mean'])
+
+    print('...DONE' + os.linesep)
+
+    return list_GDP_PPP
+
+ +
+
+
+ +
+
+
+
In [11]:
+
+
+
def rasterstats_totalEvap(gdf, config, sim_year):
+    
+    nc_fo = os.path.join(config.get('general', 'input_dir'), 
+                         config.get('env_vars', 'evaporation'))
+    
+    print('calculating evaporation mean per aggregation unit from {}'.format(nc_fo))
+
+    nc_ds = xr.open_dataset(nc_fo)
+
+    nc_var = nc_ds.total_evaporation
+
+    years = pd.to_datetime(nc_ds.time.values).to_period(freq='Y').strftime('%Y').to_numpy(dtype=int)
+
+    if sim_year not in years:
+        raise ValueError('the simulation year {0} can not be found in file {1}'.format(sim_year, nc_fo))
+
+    affine = rio.open(nc_fo).transform
+
+    gdf['evap_mean_' + str(sim_year)] = np.nan
+    
+    sim_year_idx = int(np.where(years == sim_year)[0])
+
+    nc_arr = nc_var.sel(time=nc_ds.time.values[sim_year_idx])
+    
+    nc_arr_vals = nc_arr.values
+    
+    if nc_arr_vals.size == 0:
+        raise ValueError('no data was found for this year in the nc-file {}, check if all is correct'.format(nc_fo))
+
+    list_Evap = []
+    
+    for i in range(len(gdf)):
+        prov = gdf.iloc[i]
+        zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats="mean")
+        list_Evap.append(zonal_stats[0]["mean"])
+
+    print('...DONE' + os.linesep)
+
+    return list_Evap
+
+ +
+
+
+
@@ -13374,39 +13509,36 @@

Analysis per year
-
In [9]:
+
In [12]:
print('simulation period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end')))
 print('')
 
-X1 = pd.DataFrame()
-X2 = pd.DataFrame()
-Y  = pd.DataFrame() # []
+X1 = pd.Series(dtype=float)
+X2 = pd.Series(dtype=float)
+Y  = pd.Series(dtype=int) # not bool, because otherwise 0 is converted to False and 1 to True but we need 0/1
 
 # go through all simulation years as specified in config-file
 for sim_year in np.arange(config.getint('settings', 'y_start'), config.getint('settings', 'y_end'), 1):
     
     print('entering year {}'.format(sim_year) + os.linesep)
     
-    # add column whether there was conflict/non-conflict in one year in one region
-    out_df = conflict_model.analysis.conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year, out_dir, saving_plots=True)
+    list_boolConflict = conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)
+    Y = Y.append(pd.Series(list_boolConflict, dtype=int), ignore_index=True)
     
-    # add column with zonal statistics of GDP per year per region
-    out_df = conflict_model.env_vars_nc.rasterstats_GDP_PPP(out_df, config, sim_year, out_dir, saving_plots=True)
+    list_GDP_PPP = rasterstats_GDP_PPP(extent_gdf, config, sim_year)
+    X1 = X1.append(pd.Series(list_GDP_PPP), ignore_index=True)
     
-    # drop all rows with at least one MVs since sklearn does not like NaNs
-    out_df = out_df.dropna()
+    if not len(list_GDP_PPP) == len(list_boolConflict):
+        raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_GDP_PPP), len(list_boolConflict)))
     
-    print(len(X1), len(X2), len(Y))
+    list_Evap = rasterstats_totalEvap(extent_gdf, config, sim_year)
+    X2 = X2.append(pd.Series(list_Evap), ignore_index=True)
     
-    # create arrays with input variables X and target variable Y
-    X1 = pd.concat([X1, out_df['zonal_stats_min_' + str(sim_year)]])
-    X2 = pd.concat([X2, out_df['zonal_stats_max_' + str(sim_year)]])
-    Y = pd.concat([Y, out_df['boolean_conflict_' + str(sim_year)]])
+    if not len(list_Evap) == len(list_boolConflict):
+        raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_Evap), len(list_boolConflict)))
         
-    extent_gdf = out_df.copy() 
-    
 print('...simulation DONE')
@@ -13428,10 +13560,35 @@

Analysis per year + +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+

+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13456,13 +13613,37 @@

Analysis per year 
...DONE

 
-0 0 0
 entering year 2001

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+

+
+ +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13487,13 +13668,37 @@

Analysis per year 
...DONE

 
-384 384 384
 entering year 2002

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+

+
+ +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13518,13 +13723,37 @@

Analysis per year 
...DONE

 
-766 766 766
 entering year 2003

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13549,13 +13778,37 @@

Analysis per year 
...DONE

 
-1146 1146 1146
 entering year 2004

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13580,13 +13833,37 @@

Analysis per year 
...DONE

 
-1524 1524 1524
 entering year 2005

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13611,13 +13888,37 @@

Analysis per year 
...DONE

 
-1900 1900 1900
 entering year 2006

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13642,13 +13943,37 @@

Analysis per year 
...DONE

 
-2274 2274 2274
 entering year 2007

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13673,13 +13998,37 @@

Analysis per year 
...DONE

 
-2646 2646 2646
 entering year 2008

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13704,13 +14053,37 @@

Analysis per year 
...DONE

 
-3016 3016 3016
 entering year 2009

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13735,13 +14108,37 @@

Analysis per year 
...DONE

 
-3384 3384 3384
 entering year 2010

 
-determining whether a conflict took place or not...
+determining whether a conflict took place or not
 ...DONE

 
-calculating zonal statistics per aggregation unit
+calculating GDP PPP mean per aggregation unit
+
+ + + +
+ +
+ + +
+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+
+
+
+ +
+ +
+ + +
+
...DONE

+
+calculating evaporation mean per aggregation unit from C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc
@@ -13766,7 +14163,6 @@

Analysis per year 
...DONE

 
-3750 3750 3750
 ...simulation DONE
@@ -13793,75 +14189,220 @@

Machine Learning
-
In [10]:
+
In [13]:
-
X_df = pd.concat([X1, 
-                  X2], axis=1)
-
-Y_df = Y.copy()
+
XY_data = list(zip(X1, X2, Y))
+XY_data = pd.DataFrame(XY_data, columns=['GDP_PPP', 'ET', 'conflict'])
+print(len(XY_data))
+XY_data = XY_data.dropna()
+print(len(XY_data))
+
+
+ + +
+ +
+ + +
+
4246
+4224
+
+
-
-
-
-

Then, convert them to numpy arrays

+
-
In [11]:
+
In [14]:
-
X = X_df.to_numpy()
+
XY_data
+
+
+ + +
+ +
Out[14]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
GDP_PPPETconflict
02361.9342640.0423160
13104.0516870.0405200
21192.0252150.0392770
31275.8594900.0253050
41182.2020260.0363080
............
42413277.1567380.0609970
42423277.1567380.0686960
42431381.9669010.0483290
42441390.2114880.0521570
42451391.6898340.0528720
+

4224 rows × 3 columns

+
+
+ +
+ +
+
+ +
+
+
+
+

Then, convert them to numpy arrays

+ +
+
-
In [12]:
+
In [15]:
-
Y = Y_df.to_numpy()
+
X = XY_data[['GDP_PPP', 'ET']].to_numpy()
+X
+
+
+ + +
+ +
Out[15]:
+ + + + +
+
array([[2.36193426e+03, 4.23162297e-02],
+       [3.10405169e+03, 4.05202232e-02],
+       [1.19202521e+03, 3.92765536e-02],
+       ...,
+       [1.38196690e+03, 4.83292063e-02],
+       [1.39021149e+03, 5.21571179e-02],
+       [1.39168983e+03, 5.28718745e-02]])
+
+
-
-
-
-

The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided.

+
-
In [13]:
+
In [16]:
-
plt.figure(figsize=(10,10))
-sbs.scatterplot(x=X[:,0],
-                y=X[:,1],  
-                hue=Y[:,0])
-
-plt.title('n=' + str(len(X1)))
-plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)
-plt.show()
+
Y = XY_data.conflict.astype(int).to_numpy()
+Y
@@ -13874,15 +14415,13 @@

Machine Learning -
+
Out[16]:
-
- +
+
array([0, 0, 0, ..., 0, 0, 0])
@@ -13890,6 +14429,14 @@

Machine Learning
+
+
+

The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided.

+ +
+

@@ -13901,11 +14448,11 @@

Machine Learning
-
In [14]:
+
In [17]:
X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),
-                                                                    Y[:,0],
+                                                                    Y,
                                                                     test_size=0.5)
@@ -13916,7 +14463,49 @@

Machine Learning
-
In [15]:
+
In [18]:
+
+
+
plt.figure(figsize=(10,10))
+sbs.scatterplot(x=X_train[:,0],
+                y=X_train[:,1],  
+                hue=y_train)
+
+plt.title('n=' + str(len(X_train)))
+plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +

+
+
+
In [19]:
preprocessing.scale(X).mean(axis=0), preprocessing.scale(X).std(axis=0)
@@ -13932,13 +14521,13 @@ 
Machine Learning
 
-    
Out[15]:

+    
Out[19]:

 
 
 
 
 

-
(array([-7.59938755e-17,  1.38170683e-17]), array([1., 1.]))

+
(array([-4.71003707e-17,  6.39219317e-17]), array([1., 1.]))
@@ -13964,7 +14553,7 @@

Model

-
In [16]:
+
In [20]:
clf = svm.SVC(class_weight='balanced')
@@ -13985,7 +14574,7 @@ 
Model

 

-
In [17]:

+
In [21]:

 

     

 
clf.fit(X_train, y_train)
@@ -14001,7 +14590,7 @@ 
Model

 
-    
Out[17]:

+    
Out[21]:

 
 
 
@@ -14029,7 +14618,7 @@ 
Model

 

-
In [18]:

+
In [22]:

 

     

 
y_pred = clf.predict(X_test)
@@ -14046,13 +14635,13 @@ 
Model

 
-    
Out[18]:

+    
Out[22]:

 
 
 
 
 

-
array([1., 0., 0., ..., 0., 0., 1.])

+
array([1, 1, 0, ..., 0, 0, 0])

 

 
 

@@ -14063,7 +14652,7 @@ 
Model

 

-
In [19]:

+
In [23]:

 

     

 
y_score = clf.decision_function(X_test)
@@ -14080,14 +14669,14 @@ 
Model

 
-    
Out[19]:

+    
Out[23]:

 
 
 
 
 

-
array([ 0.87651796, -0.79900967, -0.9877144 , ..., -0.57555627,
-       -0.8306487 ,  1.00018643])

+
array([ 0.30562033,  0.78824461, -1.62267313, ..., -0.46900522,
+       -1.36638482, -1.35364663])

 

 
 

@@ -14115,7 +14704,7 @@ 
Evaluation
 

 

-
In [20]:

+
In [24]:

 

     

 
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
@@ -14137,9 +14726,9 @@ 
Evaluation
-
Accuracy: 0.4385026737967914
-Precision: 0.07642799678197908
-Recall: 0.9313725490196079
+
Accuracy: 0.599905303030303
+Precision: 0.09020902090209021
+Recall: 0.82
 

 

 

@@ -14160,7 +14749,7 @@ 
Evaluation
 

 

-
In [21]:

+
In [25]:

 

     

 
average_precision = metrics.average_precision_score(y_test, y_score)
@@ -14182,7 +14771,7 @@ 
Evaluation
-
Average precision-recall score: 0.10
+
Average precision-recall score: 0.12
 

 

 

@@ -14193,7 +14782,7 @@ 
Evaluation
 

 

-
In [22]:

+
In [26]:

 

     

 
disp = metrics.plot_precision_recall_curve(clf, X_test, y_test)
@@ -14216,7 +14805,7 @@ 
Evaluation
-\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;31m# create arrays with input variables X and target variable Y\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m     \u001b[0mX1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Variable 1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'zonal_stats_min_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msim_year\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m     \u001b[0mX2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Variable 2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'zonal_stats_max_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msim_year\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'target'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'boolean_conflict_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msim_year\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4402\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4403\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4404\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4405\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4406\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'Variable 1'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2007\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "0      0\n",
+      "1      0\n",
+      "2      0\n",
+      "3      0\n",
+      "4      0\n",
+      "      ..\n",
+      "381    0\n",
+      "382    0\n",
+      "383    0\n",
+      "384    0\n",
+      "385    0\n",
+      "Length: 386, dtype: int32\n",
+      "calculating GDP PPP mean per aggregation unit\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2008\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "0      0\n",
+      "1      0\n",
+      "2      0\n",
+      "3      0\n",
+      "4      0\n",
+      "      ..\n",
+      "381    0\n",
+      "382    0\n",
+      "383    0\n",
+      "384    0\n",
+      "385    0\n",
+      "Length: 386, dtype: int32\n",
+      "calculating GDP PPP mean per aggregation unit\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2009\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "0      0\n",
+      "1      0\n",
+      "2      0\n",
+      "3      0\n",
+      "4      0\n",
+      "      ..\n",
+      "381    0\n",
+      "382    0\n",
+      "383    0\n",
+      "384    0\n",
+      "385    0\n",
+      "Length: 386, dtype: int32\n",
+      "calculating GDP PPP mean per aggregation unit\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2010\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "0      0\n",
+      "1      0\n",
+      "2      0\n",
+      "3      0\n",
+      "4      0\n",
+      "      ..\n",
+      "381    0\n",
+      "382    0\n",
+      "383    0\n",
+      "384    0\n",
+      "385    0\n",
+      "Length: 386, dtype: int32\n",
+      "calculating GDP PPP mean per aggregation unit\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating evaporation mean per aggregation unit from C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "...simulation DONE\n"
      ]
     }
    ],
@@ -298,33 +920,30 @@
     "print('simulation period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end')))\n",
     "print('')\n",
     "\n",
-    "X1 = pd.DataFrame()\n",
-    "X2 = pd.DataFrame()\n",
-    "Y  = pd.DataFrame() # []\n",
+    "X1 = pd.Series(dtype=float)\n",
+    "X2 = pd.Series(dtype=float)\n",
+    "Y  = pd.Series(dtype=int) # not bool, because otherwise 0 is converted to False and 1 to True but we need 0/1\n",
     "\n",
     "# go through all simulation years as specified in config-file\n",
     "for sim_year in np.arange(config.getint('settings', 'y_start'), config.getint('settings', 'y_end'), 1):\n",
     "    \n",
     "    print('entering year {}'.format(sim_year) + os.linesep)\n",
     "    \n",
-    "    # add column whether there was conflict/non-conflict in one year in one region\n",
-    "    out_df = conflict_model.analysis.conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year, out_dir, saving_plots=True)\n",
+    "    list_boolConflict = conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year)\n",
+    "    Y = Y.append(pd.Series(list_boolConflict, dtype=int), ignore_index=True)\n",
     "    \n",
-    "    # add column with zonal statistics of GDP per year per region\n",
-    "    out_df = conflict_model.env_vars_nc.rasterstats_GDP_PPP(out_df, config, sim_year, out_dir, saving_plots=True)\n",
+    "    list_GDP_PPP = rasterstats_GDP_PPP(extent_gdf, config, sim_year)\n",
+    "    X1 = X1.append(pd.Series(list_GDP_PPP), ignore_index=True)\n",
     "    \n",
-    "    # drop all rows with at least one MVs since sklearn does not like NaNs\n",
-    "    out_df = out_df.dropna()\n",
+    "    if not len(list_GDP_PPP) == len(list_boolConflict):\n",
+    "        raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_GDP_PPP), len(list_boolConflict)))\n",
     "    \n",
-    "    print(len(X1), len(X2), len(Y))\n",
+    "    list_Evap = rasterstats_totalEvap(extent_gdf, config, sim_year)\n",
+    "    X2 = X2.append(pd.Series(list_Evap), ignore_index=True)\n",
     "    \n",
-    "    # create arrays with input variables X and target variable Y\n",
-    "    X1 = pd.concat([X1, out_df['zonal_stats_min_' + str(sim_year)]])\n",
-    "    X2 = pd.concat([X2, out_df['zonal_stats_max_' + str(sim_year)]])\n",
-    "    Y = pd.concat([Y, out_df['boolean_conflict_' + str(sim_year)]])\n",
+    "    if not len(list_Evap) == len(list_boolConflict):\n",
+    "        raise AssertionError('length of lists do not match, they are {0} and {1}'.format(len(list_Evap), len(list_boolConflict)))\n",
     "        \n",
-    "    extent_gdf = out_df.copy() \n",
-    "    \n",
     "print('...simulation DONE')"
    ]
   },
@@ -346,14 +965,153 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 82,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4246\n",
+      "4224\n"
+     ]
+    }
+   ],
    "source": [
-    "X_df = pd.concat([X1, \n",
-    "                  X2], axis=1)\n",
-    "\n",
-    "Y_df = Y.copy()"
+    "XY_data = list(zip(X1, X2, Y))\n",
+    "XY_data = pd.DataFrame(XY_data, columns=['GDP_PPP', 'ET', 'conflict'])\n",
+    "print(len(XY_data))\n",
+    "XY_data = XY_data.dropna()\n",
+    "print(len(XY_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
GDP_PPPETconflict
02361.9342640.042316[0]
13104.0516870.040520[0]
21192.0252150.039277[0]
31275.8594900.025305[0]
41182.2020260.036308[0]
............
42413277.1567380.060997[0]
42423277.1567380.068696[0]
42431381.9669010.048329[0]
42441390.2114880.052157[0]
42451391.6898340.052872[0]
\n",
+       "
4224 rows × 3 columns
\n",
+       "
"
+      ],
+      "text/plain": [
+       "          GDP_PPP        ET conflict\n",
+       "0     2361.934264  0.042316      [0]\n",
+       "1     3104.051687  0.040520      [0]\n",
+       "2     1192.025215  0.039277      [0]\n",
+       "3     1275.859490  0.025305      [0]\n",
+       "4     1182.202026  0.036308      [0]\n",
+       "...           ...       ...      ...\n",
+       "4241  3277.156738  0.060997      [0]\n",
+       "4242  3277.156738  0.068696      [0]\n",
+       "4243  1381.966901  0.048329      [0]\n",
+       "4244  1390.211488  0.052157      [0]\n",
+       "4245  1391.689834  0.052872      [0]\n",
+       "\n",
+       "[4224 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "XY_data"
    ]
   },
   {
@@ -365,32 +1123,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 88,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.36193426e+03, 4.23162297e-02],\n",
+       "       [3.10405169e+03, 4.05202232e-02],\n",
+       "       [1.19202521e+03, 3.92765536e-02],\n",
+       "       ...,\n",
+       "       [1.38196690e+03, 4.83292063e-02],\n",
+       "       [1.39021149e+03, 5.21571179e-02],\n",
+       "       [1.39168983e+03, 5.28718745e-02]])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "X = X_df.to_numpy()"
+    "X = XY_data[['GDP_PPP', 'ET']].to_numpy()\n",
+    "X"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'numpy.ndarray' object has no attribute 'to_numpy'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mY_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'to_numpy'"
-     ]
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 0, 0])"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "Y = Y_df.to_numpy()"
+    "Y = XY_data.conflict.astype(int).to_numpy()\n",
+    "Y"
    ]
   },
   {
@@ -400,14 +1176,32 @@
     "The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we can train and predict with the model, we need to scale the variable data and create trainings and test data for both variables and target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),\n",
+    "                                                                    Y,\n",
+    "                                                                    test_size=0.5)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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prK9vZ8OODsbkKoQNNeoRExERGaEKs/xccsRELjhsPBk+t4YjhyAFMRERkRHM7XaR6dYA2FClr4yIiIiIQxTERERERByiICYiIiLiEAUxEREREYcoiImIiIg4REFMRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDFMREREREHKItjkRERIYQay317RHW1LYR8LqZWJhJoTbqHrEUxERERIaQutYwp1z/MvVtEQCmjc7mzi8toDBbYWwk0tCkiIjIEBFPJvnbK5u6QxjAmro2lm5scLAqGUgKYiIiIkNEImGpagr1aa/up01GBgUxERGRIcLvdbNk0YRebR6X4cRZY5wpSAac5oiJiIgMIVNHZ3PrhRX88dn1BLxuvn3iNIpzND9spFIQExERGUJyg16OOXA088eNwhhDbtDrdEkygBTEREREhqC8DJ/TJcgg0BwxEREREYcoiImIiIg4REFMRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDFMREREREHKIgJiIiIuIQBTERERERhyiIiYiIiDhEQUxERETEIQpiIiIiIg5REBMRERFxiIKYiIiIiEMUxEREREQcoiAmIiIi4hAFMRERERGHKIiJiIiIOERBTERERMQhCmIiIiIiDlEQExEREXGIgpiIiIiIQxTERERERByiICYiIiLiEAUxEREREYcoiImIiIg4REFMRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDFMREREREHKIgJiIiIuIQBTERERERhyiIiYiIiDhEQUxERETEIQpiIiIiIg5REBMRERFxiIKYiIiIiEMUxEREREQcoiAmIiIjTiiaoDUcc7oMkd3yOF2AiIjI/pJIJKlqDvG7/6ylri3MksMmcOjEfPIyfE6XJtIvBTERERkxGjqinPKHl2gNxwF4eV0DfzxvPifPLnG4MpH+aWhSRERGjPeqW7pD2AdueWkjTZ1RhyoS+WgKYiIiMmLkZnj7tI3K8OJ1GQeqEdk9BTERERkxJhRkMm9cXvfjgNfFt088kKxA34AmMhRojpjsUnNnlLrWCGtqWzlobB4FmT79MBORIa0wy8/NF1Twfm0b29siLJiYT0GWJurL0KUgJv1qD8e45aWN/OGZdd1tvz9nLifNLsHrVkeqiAxdhVl+Cif7nS5DZI/oN6r0qzUc54/Pre/V9uOHVmrCq4iIyH6kICZ97GiP0NARJZG0vdpbQzGwu3iSiIiI7DUFMeklEk9w84sbWFvXxpzy3O72gkwfv/rsHIA+AU1EREQ+Hs0Rk17awnGeW13PQ29v44bz5vOvt6oJRWJcctQk/vz8em5fupkz5pZyytwy8jM1AVZERGRfKIhJL5k+N3PG5vLPyirO+8trnHJQKZccNYnzb3md7W0RAN7e2kxrOM6Xj5qEz6NOVRERkY9Lv0WFzkicqqZOHnl3G5sbOvn6cVOZNjqbzmiCJ5bX0BqKd4ewD9z1+haaQ5q4LyIisi/UI5bmrLW8taWJJX99o3vu11ePmcxtXzyEWMLidbsIxRJ9nleY7cejlapFRET2iXrE0tz21ghXP7ii1wT8PzyzjoSFsfkZjMkNkBf0ctpBpd3HvW7D1afMJD9T6/SIiIjsi932iBljAsALgD91/r3W2quNMROBu4F84C3gfGtt1BjjB24HDgYagLOttZtSr/U94CIgAVxprX0i1X4i8DvADfzFWnvNfv0spV+toRg7OiLsaO87xBjp0Qs2KtPH1afO5JKjDmBrY4g55bmaqC8iIrIf7EmPWAQ4xlp7EDAXONEYsxD4JfAba+0UoImugEXqv03W2snAb1LnYYyZAZwDzAROBP5ojHEbY9zADcBJwAzg3NS5MoDq2yLUt0e4+/WtnD6vrNex8QUZZO+0lVF+po+ZpbmcOGsMpXlBAl73YJYrIiIyIu02iNku7amH3tQ/CxwD3Jtqvw04PfXxaanHpI4fa4wxqfa7rbURa+1GYB1waOrfOmvtBmttlK5ettP2+TOTXdreFuayv7/J8uoW7qncytHTivj6cVOYOzaPMw8u584vLaAoW8OOIiIiA22P5oileq7eBrYDTwHrgWZrbTx1ShXwQbdKGbAVIHW8BSjo2b7Tc3bV3l8dlxhjKo0xlfX19XtSuvTjwbe3sWxrM+PzM0gkLRfdVsnKmjZOmjWGo6YUUZilECYiIjIY9iiIWWsT1tq5QDldPVjT+zst9d/+bqWzH6O9vzpustZWWGsrioqKdl+49GtLYyeJpOWOpZv57TlzGZMT4D+r6lhe3cJhkwo07CgiIjJI9mr5CmttszHmOWAhkGeM8aR6vcqBbanTqoCxQJUxxgPkAo092j/Q8zm7apf9JJG01LeF6YwmOPPgcm5/dTP/equampYwP/jUdOaU55Ib9PaZGyYiIiIDZ7c9YsaYImNMXurjIHAcsAp4FjgzddoS4IHUxw+mHpM6/oy11qbazzHG+FN3XE4BXgfeAKYYYyYaY3x0Teh/cH98ctKlJRRlbV0b379vOcf8+nkefa+GP543n4rxo4glkuQFvQphIiIiDtiTHrES4LbU3Y0u4B5r7cPGmJXA3caYnwHLgFtS598C3GGMWUdXT9g5ANbaFcaYe4CVQBy4wlqbADDGfAV4gq7lK2611q7Yb5+hUNUYoqY1zDOrtwPw5+c3cFB5LpcfPZmDx4/SnDARERGHmK7OquGnoqLCVlZWOl3GsHDj8+sI+jz86IHe+fbg8aO4ZUkFeRlaE0xERGQgGWPetNZW7NyuLY7SwOicAOMLMvG5XUQTye72syrKyQ1qOFJERMQpCmJpYPGUIm57ZRM3XXAw1z+zjoaOKJ9fMI7jZ44hGk/SHIoRiSXwelx0RhPYpCUv0zfihyxrW8KsrGlleXULx00vpjjbT2F2YECuFU8k2dEe4amV2wn63Bw5pZDinIG5lshQVdMSYk1tG+9WNXPsgaPJz/RRkhd0uiwRR2loMk00tEcIx5IkbBKPy0Vxjp9k0rJ0QyOX/f1N/nz+wfzhmXW8vrERgAPHZHPHRYdSNEDBxGnbW8P88IHlPLGirrvturMO4pTZJXgHYPmOrY2dnPjbF+iIdm0dNSYnwINfOVxhTNJGTXOInz2ykkfeq+1uu+YzsznloBIy/eqZl5FvV0OT2vR7hGoPx1hT28avHl/NvZVbSVooGxVkXH4mpXlBPC4XTZ0xvvKPtzigKIvNDZ3dIQxgdW0b9y2rZrgG9d0JxRK9QhjA/3tiDfUdfffd3FfxRJK/vLShO4QB1LaGeXbN9v1+LZGhKpZM9gphAL9+6n2aO2MOVSQyNGhocoR6a0szF9z6evfj6SXZ3HHRgl7DjZF4gtZwnDG5ATY1dPR5jRXbWoknLV53f2vuDm+JZN+A2dljo/P9yQIdkb6v3R6J9z1ZZITqMT21WziaoGsHPJH0pR6xEaixI8Kvn1zTq21VTRs1zaFebUGvm9LcAG9vbeaoqX13KvjcweV43SPzLZLhczOzNKdX25LDxpM3ADcveN0uvrR4Iq4ev2+CXjcnzSrZ79cSGar8HhcHlef2ajv/sPFk+rWTh6Q39YiNQNZCvJ8en8ROTQWZfv72xUO56u5lVG5q4toz5/Cn59YTTSS5/BOTmFWW2+c1RooxuUFuWVLB3W9sZeW2Vk6eXcKiSQVk+AfmW2JsfgaPXHkENz6/ngy/hy8feQBF2Vo2RNJHaV6QG8+v4J7KLSyvbuWEWWNYPKmQ3KC+DyS9abL+CGSt5YkVdVz69ze72yYUZHDvpYsozO57J2RDe4RE0hLwuojGLRZLfoYP9wjpDUsmk9S1RbrnwB06MZ+S3K47tWKJJKFogpyBWsajowGat0DdcpiwmEhwNC5vAK9nZPy/FelPTUuIVdvaqGsLs3hyIdlBN3nBrp89kViCjkic/BF+V7bIzrSOWBoxxrBoUgH/vnwR/3htC1OKszhjXlm/IQygYIT/QKxtjXD6DS+zvS0CwOgcP/ddfjileUG8bhfe4ACFolALPH8NvH5Td5P/s7fAjNPRrAAZqWpaQnz5jjd5t6oFgIDXxX2XH94dxPxeN/4BuDNZZLjSb4MRKifoZf64Ufzqs3P48lGT0nqZhH+9WdUdwgDqWiPct6x64C8cbYM3bu7d9sT3INQw8NcWcciG+o7uEAYQjiW57qn32d4WdrAqkaFLPWIjnMuVnnck1baEWF3bxuhsPw39LEnR0B7p51n7VyIRx73z0H+4pes2SpERpqYlREHQTXNn3++3llCM+M6TVEUEUI+YjEC1LSG+cc87XPjXN/jqXcs455Cxve5YdLsM5xw6bkBr2NEeYXl9HErm9j4wfwn4swf02iKDrbYlxPm3vM7CXzzFvHGjyAn0/hv//IXjKdUK+iL9Uo+YjDhNnTFeWd81/LeuvoN3q5v512WL+ONz6zHA5UdPongX8+X2l23NIS7792buOe9vFKy4lWDdMjomn4z/oM/h8WUM6LVFBtvbW5tZt70dgEw/PPCVw/nDM+uob4tw3oLxzBub53CFIkOXgpiMOKFo78VTv33ve3x2fhn/e/osfB4XeRkDf7t8RyTOtpYwx928mtNmn8b0sWeypdHNFSaHggG/usjg6rk6/kE/eYb7Ll3AD06eTiyRZEyuesJEPoqGJmXEKckLUJrb++aEWWW5jAp6SSYtVU2dLK9uoaY5RGSAVtOfVJzF6Bw/Xz1mCucunMSU8WWcd9iEAVkwVsQJ25pDrK5pZXNDB0dNLeLyIz4c7j/jz6+xsqZVIUxkD6hHTEacktwg/7z0MG58YQNbGjo5Y34ZiyYV0BaJ83+VVVz7xGqSFnKCHu780kJmD8DCtUVZXUtk3PLiBk7/4ytdbdl+7r30MMYXZO7364kMpqqmTs69eSlbG7t26zh9binfPvFA6joSNIdifGHRBCYXay6kyJ7Qgq4yYoWjcTpjCfIzu+aDVTd1csSvnqXnpgNzynO58fyDuxd43Z827ujg6P/3XK+246YX85uz55IdUM+YDE9NnVGueWw1//fG1l7t/75sEQcWZdKZsLtcs1Akne1qQVcNTcqIFfB5ukMYQFs4zs47P22o72Cg/hapa+27btK67e2EY/3sfiwyTHRGEt0T83taX99ORoZPIUxkLymISdrICXoZldG7J+q46cUEB2iV7wkFmfh32sro5Nkl5GqemAxjBVk+Tpo1pleb22U4dGK+QxWJDG8KYpI2CjK9/OPihcwfN4rcoJcz5pXy3ZOmMypzYO6izM/0cs+XD2NmaQ6jMrxcuGgCFy2eiE/7TMowFvC6OeWgUi49ahKFWT6mFGdx65KKAfuDRmSk0xwxSTt1LSHiScj0uwdlKYsPNlXPDnr1y0pGjObOKO2ROFgoz9faeCK7o02/RVJGD/It9SN9U3VJT3kZvkH5Q0ZkpNMYiYiIiIhDFMREREREHKKhSRm2wtEE9e0R7qncissYPldRTlGWH7+D87AaOyK8V9XCs2vq+cS0IuaU5/ZaQkNkKGvoiNAWinNP5VYyfG7OmF9OQcBLIKBfFSIDRZP1Zdja0tjJSb99gY7U3pLZfg+PXnUEYx2aONweifHrJ97nr69s6m67cNEEvnX8VLK0gKsMA+u2t/Gp379EJN611l1+po+Hv7qY0jxtVSSyr7Sgq4w4dy7d3B3CANoiXX/JO6U9nOCOpZt7tf196WbaIwOzn6XI/tQejnHTCxu6QxhAY0eUp1bWOViVyMinICbDVrKf3tzEzkvnD7Kdrz48+5slXfX3/ZNIaicIkYGkICbD1nkLxhPwfvgWDnrdnHPoOMfqyfS7ObuivFfbWRXlZPq1dpgMfVkBLxcfeQBet+luywl6OHFWiYNViYx8miMmw1ZnJMb2tii3v7oJt8vwXwvHU5zjJ+h1bmJxY0eE1zY08vTq7RxzYDELD8jXZH0ZNna0hWkOxbhj6RYyvG7OWzCO/EwfGX5N1hfZV7uaI6YgJjIArLUYY3Z/osgQlEwmcbk0YCKyP2myvsggUgiT4UwhTGTw6LtNRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDFMREREREHKIgJsNKMpmkpTNKIjF0V/uOxZOEY9rWSJzX2B6hMxp3uqQ/z0AAACAASURBVAwR+QhapU+Gje2tYR5bXsuLa+s5dGIBp80tZXROwOmyusUTSWpbwtz04gYaO6JctHgik4uzyNaG3zLIalvCvL21ifuWVTOhIJMliyZo426RIUpBTIaFhvYIv3hsFfct2wbAf1Ztp3JTIz87YxbF2UMjjO3oiHLyH16kNdTVA/HwuzX889LDOGRCvsOVSTqJxBI8saKWqx9c0d32+Ipa7r54ISUKYyJDjoYmZVgIx5I8+E5Nr7YnV9YRjQ+dIcrXNzR2h7AP/Om59XRENDQkg2d7W4S/vbKpV9vmhk62t0WcKUhEPpKCmAwPhl6bEUPXY8PQWcE+o5/NvTP9blxaZV8GkQEyfH3fiz6PftyLDEX6zpRhIdvv4ctHTurVdsFhE/r9heOUg8rzmFCQ0f3Y73Fx1bFTCQ6hGmXkK8/P4FvHT6Nn/l8wMZ+8DM1VFBmKtOm3DBvbW8NsbQrxyrodHDoxn4mFmRQPocn6APVtEZZuaKCxI8px00dTlO3D51EQk8FV1xKmNRzjyZV1TC7O4qDyPMbkDq3vFZF0s6tNvxXERERERAbYroKYhiZFREREHKIgJiIiIuIQBTERERERhyiIiYiIiDhEQUxERETEIQpiIiIiIg5REBMRERFxiIKYiIiIiEMUxEREREQcoiAmIiIi4hAFMRERERGHKIiJiIiIOERBTERERMQhCmIiIiIiDlEQExEREXGIgpiIiIiIQxTERERERByiICYiIiLiEAUxEREREYcoiImIiIg4xON0ASK7U9sSoiUUx+0yZAc8jM4JOF0SO9oiNHVG8Xlc5AS8jMr0OV2SDGEtoSgdkQQ72iOMyvDhdRvG5AadLktEhgAFMRnSalvCfPFvlaysaQVgwcR8fn/uPEfDWF1rmHNuWsrGHR0AnDhzDP97xiwKsvyO1SRDW1VTiHNvXkprKI4xcOUxUzj30LEKYyKioUkZupLJJP9+q6o7hAG8trGRpRsaHKspFk9yy4sbu0MYwOMrallf3+5YTTJ0xRNJtjWH+OH9K2gNxQGwFn7/zFqiCetwdSIyFCiIyZAVTVjW1LX1aV/VI5gNtkg8weravtdfu11BTHpLJi0rtrWSSFo2NXT0OmYtNHdGHapMRIYSBTEZsgJeN6fPLevT/qnZpQ5U0yUr4OUz88t7tRkDh08udKgiGaoaOiJc8Y+3CHpdHDe9uNex3KCXQg1liwgKYjLEzSrL4epTZlCWF2RCQQa/PfsgSvKcnax/1NQivnPiNMbkBJhSnMXfvnAIRVmarC+9xZOWqqYQT6+q4RufnMpZFeXkZ/qYP24U/7h4AZl+t9MlisgQYKwdnvMUKioqbGVlpdNlyCCIxBLs6OgaxhmT7cftdv7vh2g8SXMoissYCjJ9GGOcLkmGmIaOCEtufZ3l1a388jMzOHZ6CaFYEpcLyvIynC5PRAaZMeZNa21Fn3YFMRlKWkMx2iJdk5pLcvy4XM6HLoBwLEFrKIYxUJDpx+VS8JLeEokENa0RDBD0ucnP9LO1sZOr7n6bt7Y0MbM0h9+dM49JRZkK7iJpaFdBTMtXyJCxvTXM9c+u4/5l1RRm+bn6lBnMHZtHboazw36NHVFufmED/3h9C7lBLz8+dSYLJo4i0+91tC4ZOra3hlm+rYWfPryKhvYIn5lfzmWfmMTY/Az+sqSCeCKJ22W0xImI9DE0uhsk7UViCe5+Ywu3v7qZ1nCcDTs6+OJtlbSE447WlUxaHnl3G396fj0toRhbGju56LY3aGjXHW/yoVAswcW3v8nGHR20huP87ZVN3PtmFaFIjPxMH8U5AYUwEemXgpgMCY0dUZ5aub1XWyJpeWdrs0MVdWkLx3n43ZpebdbC65saHapIhqK3NjeRSPae5vHkilp2dMQcqkhEhgsFMRkSMvxuJhdn9Wk/oCjTgWo+FPC5mFGS06d9SnG2A9XIUDWpn/futNHZujNSRHZLQUyGhNygj298cirj8j+8m+y8BeMocng4x+9xc+knJjGpRyA8q6Kcsfm6600+VJTt59xDxnY/Hl+QwZXHTiE/U8ORIvLRdNekDCnbmkN0ROIEvG4CXhdF2c5v8A1Q3xahPRLH53GR6XOT5/ANBDL01DSHiCct4ViCTL+H0jztIykiH9JdkzIsDNVfXkXZfoqy1bshu1YyRN+7IjK0aWhSRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDNFlfBl00tYl3NJHE53ZRnOXH4xn4vwlaQzE6ol0r9Wf5PWQHtEWR7Fpda5hYIgkW3G5DSa4m44vI/qcgJoMqGkvwbnULl9/5FtvbIpTlBbn5goOZUZo7oNdt7IhwzWOruffNKgDOPmQs3zp+mradkX7VtIT4y4sbue2VTcSTluNnjOanp89idM7QWE5FREYODU3KoNrREeWyv3eFMIDq5hCX3/kWNc2hAb3uaxsauaeyiqSFpIW7Xt/KMoe3T5Kha3NDJ7e8tJF4atuiJ1fW8fA720gkEg5XJiIjjYKYDKpIPEl9e6RX26aGThIDvLDw8+/X921b07dNBGDphoY+ba9tbKTV4U3oRWTkURCTQeX3uBiz0/DO5OIs3MYM6HWPmz66T9ux04sH9JoyfB0+ubBP2+LJheQENJtDRPYvBTEZVEVZfm664GDG5ndNfJ5UlMkNn58/4KuSzx8/ii8smoDXbfC5XXzpiInMKc8b0GvK8FWeF+TKYyfj97hwuwxnzCvlhFljcLu1ibeI7F/aa1IGXTKZpLY1QiJp8bjMoG0N0x6J0xH58K7JTL96N2TXGtoihOMJLOB1uzRRX0T2ifaalCHD5XI5sqdklt9DlsKX7KEC7S0qIoNAQ5MikpYisQT1bRHawzGnSxGRNKbuAXFUTUuI1TVtuAxMG5PDmNyBH/6pb4uwtq6NpLVMHZNNcbaGnNJNbUuYm1/cwDOrt3PgmGy+f/J0xuZnOF2WiKQhBTFxTE1LiDP/9CrVqTXExuVncM+XFzJmAFcw394W5nN/fpXNDZ0AlOYGuO+KwzX/J43UtYb55WOrue/tagA27ujgveoW7rt8EUUK5SIyyDQ0KY65t7KqO4QBbGns5JH3agb0mk+uqO0OYQDbWsL8+62qAb2mDC2haIJHl/d+n1U1hWjTGmEi4gAFMXFMTUvf1fS3NYcH9JpVTX1ff2tjiOF697Dsvc5ovM/NIi4DvkHY71REZGf6ySOOOffQcb0euwx8rqJ8QK955sHluHZaO/a/Fo7DDPCCsjJ0FGb5+e5JB+Jzf/jj7yvHTCY3qE3gRWTwaR0xcUxje4SVNa3c8Ox6jIErj53CtNHZjMr0Ddg1OyJxVte2ct1T75NMdl1zdlkOWQH9Ek4XrZ0xGjojhKJJNjV0MKkok9ygd0DnJoqI7GodMQUxcVQklmBHexQAn8cM2mTp1lAMiyU32BX6EklLQ0cEmwS/10VexsCFQRk825pDJK3FAEWZfny+D1fG394aJpG0g7agsIikNy3oKkNOc2eU1zc28oP7lrOjI8KRU4r45WdnD0rPRE6PYahwLEHlpka++c93qGuNsGhSAb85e67upBzmqps6+cY97/DaxkZKcwP86nMHMb0km4LMroVai/X1FZEhQHPExDEdkTiX3/kW9e0RrIXn36/nuqfepzUUHdQ6WkIxLrqtkrrWCACvrG/g54+uoj2ihT6Hq7qWMFc/uILXNjYCXXfHXnxbJZFY0uHKRER6UxATx2xq6CCe7D00/sr6hkFfRqCuNUwk3vsX9MvrdtAZSQxqHbL/xJOW1zY09moLxRI0dgxuyBcR2R0FMXHM2FEZ7Hyz4pzyXDJ6zOMZDEXZftw73Up5UHkeAe/g1iH7j8vArPLcXm1etxnQG0FERD4OBTFxTJbfw49PmUnA2/U2nDY6m++fNJ1RmYO72XJOwMu1Z87pDoCTirL4yWkze80jk+GlJC/I/54+i4mFmQBk+tz86sw5eHdeu0RExGG6a1Ic1dIZpT2SIJZI4ve6KHFoCYFQLE5rKE40niTodVOYPbhhUAZGdVMn0XgSn8dNwGcoyNQEfRFxhu6alCEpN8NH7hDYazno9RD06tthpCkbNQTeXCIiH0FDkyIiIiIO2W0QM8aMNcY8a4xZZYxZYYy5KtWeb4x5yhizNvXfUal2Y4z5vTFmnTHmXWPM/B6vtSR1/lpjzJIe7QcbY95LPef3RvvNiIiISBrYkx6xOPBNa+10YCFwhTFmBvBd4Glr7RTg6dRjgJOAKal/lwB/gq7gBlwNLAAOBa7+ILylzrmkx/NO3PdPTURERGRo220Qs9bWWGvfSn3cBqwCyoDTgNtSp90GnJ76+DTgdttlKZBnjCkBTgCestY2WmubgKeAE1PHcqy1r9quOwdu7/FaIiIiIiPWXs0RM8ZMAOYBrwGjrbU10BXWgOLUaWXA1h5Pq0q1fVR7VT/t/V3/EmNMpTGmsr6+fm9KFxERERly9jiIGWOygH8BX7PWtn7Uqf202Y/R3rfR2pustRXW2oqioqLdlSwiIiIypO1REDPGeOkKYXdaa/+daq5LDSuS+u/2VHsVMLbH08uBbbtpL++nXURERGRE25O7Jg1wC7DKWntdj0MPAh/c+bgEeKBH+wWpuycXAi2pocsngOONMaNSk/SPB55IHWszxixMXeuCHq8lIiIiMmLtyQqWhwPnA+8ZY95OtX0fuAa4xxhzEbAF+Fzq2KPAycA6oBP4AoC1ttEY81PgjdR5/2Ot/WBX3suAvwFB4LHUPxEREZERTVsciYiIiAywXW1xpJX1RURERByiICYiIiLiEAUxEREREYcoiImIiIg4REFMRERExCEKYiIiIiIOURATERERcYiCmIiIiIhDFMREREREHLInWxyJiOwXTZ1R3q9t49HltRwyYRSHHVBAQZbf6bJERByjICYigyIaT/LPyq38/NHVANz2yiY+OX00vzpzDqMyfQ5XJyLiDA1NisigaA5Fuf6Zdb3anlpVR2cs4VBFIiLOUxATkUGTtP002v4aRUTSg4KYiAyK3ICXyz4xqVfb0dOKyPBphoSIpC/9BBSRQeH3uvn8oeM4qDyPh97dxoKJ+Rw1tUjzw0QkrSmIicigGZXpY/GUQhZPKXS6FBGRIUFBLA00tEcwgNtt8HvcBLxup0uSEawzEieemgyWtJa8DPV4iYjsioLYCLe9NUxbJM7aujYeW17L7LJcTplTyujcgNOlyQhU1xrmmVXbeXVDA0dOLWLx5AJqW0KMyQ06XZqIyJCkIDaCxWIJalrCPLtmO7/9z1oAHnh7G48vr+X68+YxJke/HGX/qWsJ89NHVvLwuzUAPPjONs4+ZCxfO3aKw5WJiAxdumtyBGsJx8gNern15Y292is3NxGOJh2qSkaqaCLJI+/V9Gr715tVJLQ8hYjILimIjWA5QS/GgM/d98vsMg4UJCOaMeAyvd9YbpdBbzURkV1TEBvBfB43uUEvXzl6Mhk+F988fgp3fmkB1509h5ygl4b2CLGEesbk4wvH4tS3honGEwQ8Ls45ZGyv4xcumoDfox8zIiK7YuwwHTaoqKiwlZWVTpcxLDS0hwnHLA+/u40Mn5vjZ4zhH69v4c3NTSyeUshn5pVRnKPJ+7J36lpC/P21LSzb0swRUwo5fV4ZLgvLa1p5ZX0DR04tZEpxlibqi4gAxpg3rbUVO7drsn4aiMQtp1z/Eo0dUe67fBHf+OfbvLyuAYCX1u1gVU0rP/r0DAqy/A5XKsPFtuYQ37znHV7d8OH7aE1dG98/6UCOPrCYow8sdrhCEZHhQWMGaeDhd2to7IgydXQW+Zm+7hDW83g4riFK2XPxRLI7hH3gwbe3EdFQt4jIXlGPWBqIxpP8/aJDyQ54cRmDx2W6F9wECGqBV9lDtS0hXlq3g4UHFOB2GRI93kcZPjdYTc0XEdkb6hFLA+ceUk5VU4jTbniZVTUtXP6JSRw/o5iyUV3zwq46bgq5AWVy+WhN7RFueXED3/rnuzR3RLlw0fhex7/+yankBBTqRUT2hn77poFIwvLrp94HIBxL8pn55Szb2sxVx00lN+jF73GRFfA6XKUMZTUtIVbXtLFwUiFfWHwAt760kfMXTuBTs0tZtqWJhZMKyM/wkR3UdkYiIntDQSxNRGIJvnr0JLxuF0f/+jk+uFn2osUTufwTk5wtToa0mpYQn/vzq1Q1hQAoywvyz0sP4/jrnue/DhvPV4+eTKaCvIjIx6KhyTSQG/Bw/sLxnDK3jJ88tJKeK5b89eWNhGIJ54qTIe/Bt7d1hzCA6uYQ/15Wxc/OmM2iSYUKYSIi+0A9YmkgM+BlyaIJxBKWxo5or2NJixZ1lY9U2xru01bXEub8BeOJ6G5bEZF9oh6xNFGcEyDT7+b0uaW92qeOziKguyblI5xVMZaeOxcZA+ccMo7cDJ8WAhYR2UfqEUsjeRk+vnnCNEpHBXlm9XZmlOTw1WOmUKKVz+UjFGb5uPvihfz+mbVYC1ccPZnibC3+KyKyP2iLozQUiSVo7IwS9LoJxxKEY0l8Hhdjcvxsb4/Q0hkjFE0wOjegkJZGwtE4jZ0xalpC5AZ9ZPrclOR9+PWvawljjWVMjt4TIiJ7S1scSTe/101uwEPl5mauunsZTZ0xxhdkcNfFC/nuv97lhbU7gK674+758kLKRmU4XLEMhqrmMGfd+Gr3PMLT55XxnROndYfx0bkahhQR2d80RyxNNYfiXH7nWzR1xgAoyQ2wdntbdwiDrrvjbnxhA52RuFNlyiCpawnxy8dX97qZ4/5l1TR1xBysSkRk5FMQS1Od0TjtPQLWAUVZbKzv6HPeph0dWt4iDUQTlq2NnX3aq5v7tomIyP6jIJamcgNe/vxf8/nDufOYXZbLkytqOWJqEW5X770CT59XRkGWJmaPVG2hKM2dUfIyPJw8u6TXMb/HxYzSXIcqExFJD5ojloZaQ1G2t0d45N0aXMbwy8/O5v3adoJeF7d94RCueXw1beE45y0Yx+IphU6XKwOgLRSlti3Cn55dTzie4JIjD+CsirFE40nuf7ua0TkBfvip6WT69LeaiMhA0l2TaWhtXRsn/e5F4smur73XbXj8a0cyqSgL6NrSJmkthZl+/FpjbETa3NDB8b95oXtBVmPgka8spjQvQFskgctAcZYfr77+IiL7xa7umtSfu2nozte2dIcwgFjCcvfrW7ofl+QGKcvLUAgbwR56p6bXqvjWwi0vb8TvdTM2P4OyURkKYSIig0BBLA1lBz4ckc4JePj5GbM58+By2sMDf4dceyRGfVuYUFQ3ADihrjVMTXOIo6b2HXLODnjZaYqgiIgMMAWxNHTOIWPJz/RRNirAo1cdwcYdHXzjnnf42SOr2NYc2v0LfEzbmkN891/v8Zk/vcJPH15BfVvfPQxlYDS2R6jc3Mg37nmbr961jG0tYZ742hHdx3MCHr64eAJ+r6aNiogMJs0RS0PxeJL6jggdkTi/fvJ9Hlte231sdlkuN19wMGP284r6O9ojfP7mpbxf197ddvSBRfz27LnkBn379VrS14b6dj75mxdI9BiSvuuSBXiMYXVtG8dOH01+hpeAT0FMRGQgaI6YdPN4XJTkBsnweXhiRW2vY+9Vt/SaO7S/hKKJXiEM4Lk19YRj+/9a0tfD727rFcIA/rF0C9NLszn/sAmU5gUVwkREHKAglubyMnr3RvncLjyu/f+28Lpd+D0fvq4xUJjl15ykQVKa17eHsyQ3iG8AvtYiIrLn9FM4jeVn+vjhp6djeoShK46ZRIZv/98tlxv08sNPz6A0L8D1n5/HPV8+jLsvXojbKIkNhsWTC5k6Oqv7cXG2nyWLxuPTnDAREUdpjliaa2yP0BqO8151CweOySYvw0tR9sBs7twWjtEeiXPx7ZUsr24F4PgZo/nFZ2Zr9f5BUNMcYuOODsKxBNNLcyjO8uF2a4kKEZHBsKs5YvpzOM3lZ/nJz/IzoTBzwK+V4fPw96VbukMYwJMr6zhvwTiOmlY84NdPdyV5QUr6GaIUERHnaGhSBk0skeSdquY+7e/1CGYiIiLpREFMBk3A6+Yz88r6tH9yxmgHqhEREXGegpgMqkMm5vOdE6dRkOmjLC/I786ZS0newMxJExERGeo0R0wG1agMHxctPoAzDy4HID/Tj1trWIiISJpSEJNB5/O4BuzOTBERkeFEQ5PiiPZIjB3tEaIDsIq/iIjIcKEeMRl0VU2d/OLR1ayubeOEmaO5aPFErSMmIiJpSUFMBlV9W4RzblpKVVMIgD8+105jR5QffXoGGX69HUVEJL1oaFIGVXsk3h3CPvDA29toj8QdqkhERMQ56oLYQ3WtYTY3dLJsaxOLJxdSnO2nKDtAfVuEyk2NtIZjHDm1iMIsP1638u2uBLwujIGeO2uV5gVwac9JmjujbG3spHJzE4dOzKcsL9i9KXtda5j19e0sr27pfp8VajhXRGTYUxDbAw3tEa5/Zi13LN3S3fbjU2dy0qwxnHXjq2xu6AQg0+fmsa8dybj8DKdKHfIyfR6uOHoy1z+zDgCv2/DzM2ZTkOVzuDJndUbi/PXlTfzu6bXdbd896UCWLJpAezjGLx5dzf1vVwPw80dX8+uzDuLU2SV4vdorUkRkOFMQ2wPhWII7X9vCcdOLufiIAwh63Wxq6KByY2N3CJtZmsNn55ezeUcHGT43SWvJz/DhUe9YLzlBL19aPJHPzi+nqqmTKcVZ5GX4MGnUI9bUESUSTxBLWDL8bgoy/bSF4/zpufW9zvvLixs46+ByQrEkn6sop7q5kzc2NQHwq8dXc9gBBZRq70gRkWFNQWwPxJOWbx4/lcMnFfKLx1ZT3xbhsweXc9rcUjwuOH1eOSfOGsMfn11PazjGeQvGkZ/pY0JBJrPLcnFpwdJe8jJ85GX4mDgIG40PNQ0dEWpbwvzt5U0s3djAnLJcvnfydHweF9HEh0t55AQ8/PuyRdz4wgYeX1HL+PwMfnb6bP6+dBN3LN1Ce1hz6kRERgJje07WGUYqKipsZWXlgF8nFI2zrTmMz+PiuOueJ9Jj3asffXoGsUSCQyYWcNafXyWe/PD/5c/PmM0L79fz09NnavFSAcBaS+WmJm54bh3Pranvbp82OpvbvngI/9/9K/jPqjoA/nLBwbyyvoFbX97Ufd6oDC8PXHE4R177HF9aPJGvHTeFrIB3sD8NERH5GIwxb1prK3ZuV4/YbrSG4zy9qpayURm9QhjA/W9X85cLKvjPqrpeIQzgseU1zB2bRywxPIPuQGsNxahvj/DO1mZmluYwOifQPTF9ONvRHmFDfQeNnVHmludRmPXh8HQ0kSTgdfP8+/W9nrOmro1YwvLLz87mPyuLWXBAAcbAD+5f3uu8ps4YOzqi/PrMORw5rUghTERkBFAQ2438oJcxucF+5+KU5wVJJJNk9/MLsXxUELcx+D2aI7azaDzJI+/V8L1/v9fd9s3jp/LFwyeSOYzXEmtoj3Dx7ZUs29IMQJbfw0NfXdw9BOv3uPF7XBRm+alvi3Q/z+9x4XW7KMjyc+TUIq68exnfOfFAyvKC1LVGel0jP8PH3PlluFx6X4mIjAT6ab4bXq+bwycXkBP0cvyM0d3tozK8fOuEaVz7+PsEvC4Om1TQfWx0jp//WjCeMw4u04rx/WjujPLzR1b1avvD0+toG+bzntbXt3eHMOhaM+03T62hs8caacU5fn5y6kw8qXmDxsD/96np5Aa97GgPU9UU4o1NTdz8wgZ+dMpMMnwf3hV5wWHj8XtcCmEiIiPI8O1+GESxhOW6J9dw1bFT+NpxU2jujFE+KghYKrc08Z81dfzstFlc8YnJdEbjTC/Jwdokfo+bzkhcK8bvxFroiPYOXdFEkkRyeA/j9uzl+sD2tkiv4em8DB+HTyrguf/+BFsaOxk7KoO8DC/NnVHuWLqZWWW5ADyxso4DS3J44mtHsqmhg5LcINkBD6NzNN9QRGQk0Z/Wu7G9Ncyza+o55aBSPvWHl7jg1tf58UMrOOG3LxL0ejhvwThaQ3GuvPttLv37m1z75BqqmkIcee3zLLrmGd7Y3EhymAeM/S3oc3PSrJJebQv/f/buOjzKK334+PcZt8xk4h6SYMElEJxSKrTUhbpS9+77q3eta922W9sq1KBChRrUKKWluLsHSIh7MhnX5/1jwpAhAUKAbkvP57p67c55ZJ5J4Jqbc+5z37lx6DW/7T+O/TMsUTNYANeP6obFEL10bTFoyLAaGJWXQGacAac3SCAk89qiPfRPtxDTGri/sKCI8c/8hMsXIDfBIIIwQRCEk5CYqjkCpy/AI59tZvZtI3npysG8u3wfsQY1L14xGKNWyZSCTMx6FZ+sqSAnwcDVI7L505dbAQiGZB77fAuf3zGaxBixRLmfWa/mr+f3JT81hoU76xieE8cNo7sRZ/zt/owaHF6mL9rLOzcM580lxTS5fFw4OJ0BGbFHvHZ1SSNDsmJRKxTMWFbMp3eM4oUfdlFr93L58CyGZFnFcqQgCMJJSgRiR9DiDi+hXfLacq4szOKeiT0IBEPEGTQYtGoMWrisIItJ/VLx+IJM/u8SGp2+yPW1LV5AzIgdLMGk5bbxeVw9IhuDRoXmN76pIRiS+W5LDT9sr+WCQenE6Cx8tq6cwVmxpHH4oqvlTS7yU0zcfkoez/9QxLI9Ddx3Wk+y4g1kWfUYtGJ3pCAIwslKBGJHkGTWkmjSUufw8sHKUj5YWcrt4/MY1i0uco5CIWE1aKgPeonVq6MCsfMHpWHQiB9zR1RKxUlRsgLAoldz8dB0Xvt5L6/+HK6Qn2zWEm/s+PPV2NwE5HBuwJSCTMY/tYBv7zuFifnJrN/XRIZVT6xOLYIwQRCEk5wo6HoEgUCI0iYXT8/bSUmDk8n9U5kyLJOkQxRprWx285/5u9haYeO0PsncMKqb2Dl5GC5vgGa3pgVpGwAAIABJREFUn+J6JxlWPYFgiPdXljIwI5bRPRJ+U42tG5xePlpVxtxNleQlmnhoUm8yrPp27Zsqmtw89sVmVuxtoH+6hX9dNACDGv7f7C3kJ5u4fnQOMRoVVrGcLQiCcNI4VEFXEYh1Qr3dQzAU3tkXb9IccYbL7vHT4vajUSpQKhXEHWJW5PcuGAzxc1E9N89cE9kx+f/O6Em9w8eMZSWMzI3j5auG/mp+fo1OHx5/EJVCwmpUo1a2b7gdCIawuf1o1UpMHeyWrbV5eHzOFr7fWhMZy4zT8+EtI9AoFQRDkGIRSfmCIAgnG1FZv4vKm1zc/cF6NlXY+Mu5fRiabaXK5iE/1UysQd0uKHN6AywuqueRzzZjc/sZmm3l5auGkPIr2fHm8ARocvkorrPTPdlMo9NHZbObvukWtEqJfY0u/EGZvETTCd9g0Ojy8/Cnm6LKVvx3wW4+uLmQGctKWL63EYfHf8ICsWaXj3qHj9JGJ71TzFgNavSHCLKrbG7ueG8d68uaiTWoefqSgYzOi29XmkTVWpj1UPfYU+vkkiEZXDk8i0c+20yVzUNZoxuvP0R6rOG4f0ZBEATh100EYofR6PDy96+2s76smXsn9mBvvZM/tu6IVCokZtwwjNHdE6KWnlrcfu6etT4SXKzd18ST32znHxf2/59XjfcHg/y0s5Z7PlzPF3eM5slvtzNnYxUQru7+1vXDeGLuNnbW2Emz6Pj8ztEntGRCSJapd0TX3vIFQ1FbG6KbSh0/Nrefl3/azfTFxQCoFBLvTh3OiNz4dkuJdo+fJ+ZuY31ZuFhrs8vPHe+vZfGDEzpdI66i2cXFryynusUDQO+UGJ67bBCXT1uBUaNEq2o/uyYIgiCc/H7bW9VOMLc/yNp9TQCc0iuRd5aVRI4FQzKPfr6Feocv6pryZne7wqSrihtxev93VeMbHF5q7R4anX7++OUWYrQq9GplJAgD8AZCPDd/F1OGZQBQafPwwcpSGp0HAqUml4/aFg9uf/C4PJdereS0/OSosZ7JJkIhmbtP7c51I7MjNbWOVTAkU2f3Um/34vYFcXj8vLGkOHI8EJJ5/IstNBz0+wRw+4KsKWmKGvMH5Xbthw7F4/MxY9m+SBAGsKPazq4aOwXZVv55UX8Mv/EaaoIgCELXiBmxw9CplQzOiuX7bTVoVAoOTqerafEgH1SaIiNWj1IhRQVjBd2sv1h1/RaPH5VCwqBR4fEH2V7Vwp++3Eqt3cPsW0dgc/vJijPQ7Pa3u7bG7sHaZhdjaaOL91eWUpgTT2KMhv/7ZBOljS4m90/l7lO7o1Yq8AaCJLbZuCDLMi3uAFq1Ap368LM8Zr2af1zYn7RYPT/vqqN/uoUHzuzFF+sr2FDWzIWD01EcNDt1NDz+IF5/CEmSKW9yEwjKxBo0zNtaTZ80cwe/Ty+hDkqN6DVKhmZb+W5rdWRMpZBINh9+6dbj8VDtCBJnUFPV7G53vMXtZ9o1QwmEQlh/wzXUBEEQhK4TgdhhxJu0/OmcPngDIWK0KrrFGyhpcEWOXzg4HZMienbIrFfz/GWDeOzzzbR4AgzKjOWRs/I7TNw+nlrcftaVNjF98V6sBg1/OL0nOrWSy15fgS8Yon+6BZUCJvRK4scdtaRYdMQZNVGlNs4dkMbiovrI68kDUnli7jZeXFDEJ7eOZFN5M/6gHJkZ7JMaw2frK7hkaAbjeiaiVihYsKOWT9aUkZNg5O5Te5AWq2u31NdWYoyWh8/qzZ0T8gCJm2auZmOZDYDFRfU8NKkXN43NRa3s/IyRLMtUNnt48cciVBLcPC6PB2ZvYmtlC2adikcn56OQJDKsesqbDgRIFw1JJ+agchF2jx+tSsGfz+1DebOLLRUtmPUq/n3xAMwdNHvfz+X1YfPIXPzqMqZdPZQrhmcxd9OBGUilQuKsfqmYdEo0KhGECYIg/F6JXZNH0GD3Iksy76/Yx7ieSby9tIRdNXbG9UzkosFp9I4Ngd4adY0vEKLe4cXpDbCvwcXOajuXFGSc0HyrRbvquPatVZHX+Skx3H96T255dy0Aj05MZ/LQ7lS1+PhyQyVxBjXnDUrnqXk7KGt0c96gNM7ul8LDn23G4w9y9YhsyhpdPPdDEQD/vLAfby0tYXetAwjXyHp8ch/unrUegL+d35ekGC23vrcu8gzJZi1z7x5zyFIfB9tVY+eM5xZFjaWYdcy5e3Sn7wHhtlTn/HcJtXYvL10xmC83VDB/e23kuEoh8dXdo3H7Q7y1pJiiWgdn9U/l6sKsSKJ9k8vH8j0NzFpVSrd4I3ed2h2VQsIbCKFUSFgNajSHyOvy+YN4/T62VDm5YvpKAFY8fCrrypqZvmgvOrWS+0/vQU68nkSzSNAXBEH4PRC7JrugyubmmjdXcefYTJQSTJ2xhouHZDA028qGsmZWF5XRe1h6u+tkZD5aXcYLC4oiY5+tr+DDW0Yct52I/mAIjy9IIBTCGwgHFG1V2jwktVk6a/SEA+6bZqxhUr8U4kxa5m+v4arCbLLiDMzbWs1ds9Zx/qAMTumZyAOzN7Gu9EBeVHqsnoY2ifXd4o1ROU/vryzl5nE5Uc9Q0+Kl2ubpdBBl0il587oCFJLEt1uqmb22DLNeFbU8GQyGcPiCGDVKVEoFdo8fpzc8K2nSKjHp1FTaPNS2NuC26NWRJPv9AiGZ8mYPT8zdxhXDM3nk7HwSY7SRWbdgSObbzVU8+vkWIDwzN29rNV/fM5a02MNXyfd4AjgCAYIBGbP+wIzZiCd/ZPVjExmQMRiFBGlih6QgCIKACMQOy+EJsLvWwcbyFm4fl8W7K8uYvngvABlWPY+cPgR0lnbX2Vx+3l2xL2psT50Du8d/XAKxOruXWSv3cWp+Mk99t4O8JFO7xtI2tx+jRsWUggw+XlPO7I313Dy6GzeNzeGZ73dFzvvjOfnM3VTJJ2vKAbh2pAqDRkFF84El2AtauwM0ucJ5ZWa9ivtO68ljn2+OnGM1aAgG28+uGjvZVaDZ5WP+1lqe/2EXwZDMtaO68fQlA0mM0UaKujY4vHyyppyfd9UxMi+ey4Zl8uKCIj5cXYYEXFWYxb2n9cSkVZEYo2Vy/1R8gRDDcuL4dvOB/C61UiLeqKGi2UXfNAuplujl0yaXj7eXlkQ9X63dS1mj67C/P7vbQ3WLn+fm76LR5ePpSwYyrkcCi1qXe0c/+RMf3jqCAWkxnfqZCIIgCCc/EYgdxv7+hwa9Fovk5qub+rKxNohSkumXZsai9EEH+U+SJGHWqaLyr9re71g0On3c9cE6MuMMfLaunEVF9WyrauH1awpYsL0WR+vuzD6pZqwGDY+enc9dE7rj8AZQaNRMGZLKxN5JbKqwMTjTilmvYkz3BEbnJTAwM5YEkwZ/IMAnt46k0eXHpFVh9/iJN2iYe/cY6u1eeiSbeHtpMXvrnUC49MVDZ/XC6w+hUkgEWjcqnJafhPWgGmBuXxCnL0CMVoW2TTJ/cb2TP8/ZGnn90o+7eev6AoZ1Cy/7trj9/OnLrXy9OZxn5fAGSIvV8/7K0sg1M5bvY3yvRAZlxvLGtQW8tbSYb7ZU8dCk3tTYvKwrbSLOqOHJi/rj9YdY+H8TiDNq2uWwKSUpajZrP6P28JsPWjwyF7y8FKcvPEM35bXlfHLbSCqa3ZQ2uRiRE49RI6FSib92giAIQpj4RjgMvVrJOQNSGZWXwOtrG7ikl5ahoZ2gNVHbmMrHpTJ3Tkhsd128UcMfz+nDTTPXRHbmnTswFZVCQpblwyavH4nbF2BlcSOn9k7im9agpN7h49n5O3l36nCK650km3X0SomJzCQd3M8xKRby0w7M5KVY9PRKMUdeF9f7mPDMQhJMGhyeAJ5AiB//33gue305EhBrVDPjhkJO7Z1MRZObEblxbCxr5qdddXx860g2lDWTbtWTYdVHFWOttnl4dv5O1pc2c0qvRG4ZlxeZYZrXZkfifnM3VjG+Z/jn6/IF+WbLgWT3nskmVhY3tLtmcVE9vVPMXDZtOR5/uArZir2NvDd1OFq1srUqvuaIyf8PTerFldNXRoLK0d3jSTjEbJjL6ycYglXFDZEgDKCqxcMDszfx4uUDGZJppsUXIM54+KVNQRAE4fdFBGKHEZJl7p3YA61Kyb4GF2Ne3ULvlBg8/gDF9TuYceOwDq9TKCQKc+P58Q/j+WF7LVnxBrz+IBe/uoyPbxtF+hHyjA5HqVCgUkgU1ToYkm1lY3l4h+HS3Q0s37OMRQ9MICPu2PKP1EoJSSKqRlqjy8fAzFiW7wkHG6c9+zOFOVZeunIIf527jf7pFj5bV8HXm6rokWyi3u7jzesP5CQ2OLxMnbGarZUt5CYYUSokfthezTn904jRqxmcZW33HAXdrCgV4YBJksKBsas10NlV4+CqwqzIkup+5w5I5ccdtZEgDKCi2c0z3+/kP1MGHbGkRp3dy5XTVzAgw8LHt45k7b4m+qSZ6Z0SQ/whSkzsrnOSHqvvMFCLM6qRFBKBEMSLIEwQBEE4iKgieRhfb67i9OcW8dR32xnXM5HzB6azs9pOeZOL60Zm07vNLNLBTFoV5U1uvt1SzV/nbOWeDzdQ0ezhtYW7aXKG840qm900u9oXED0ck1bJLeNy+WpTJZP7pzKuRwIQDlL+dE4fYjpYUjtaOpWSqwuzosZaXH6evKg/vVPC+U3xRg23jMvji/UV6NUKLhyczpl9k/EFQxTVOLh6RFZUwOnyBdla2cJNY3N4bHI+e+qcLNvdQHWLB18gSEG2ldP7HCjuOjI3jjPavLbo1TxwZq/I680VNvqlW7hyeCZKhYRKIfHE+X3JjDMQo2v/74tYgwblIWYiG1p/H3V2Lz/vrKWo1sGn6yq4YvoKvt1SRVGNvcM2Sy6vn8pmN3/7ahs2l5/uiSYGZhyYaTRqlNx/ek9i1Cr02mP/vQiCIAgnH1G+4jD+OndrJGn7wkHp3DOxO0FZRqVQYNQqowqZduSDlaU82iahHcJ1vMb3TOAvc7cB4dpVj0/OJ+4IBT3bLmk2ucL9IXdV2ynoFodCCvc4tOhV6NTHZ5Kzxuah0uZmc4WNkbnxWI0aEkxaqmxufK0lHOrtHmQksuMNxBm12Fw+XP5gJEeubR/OapuHK6ev4M/n9uH6d1ZHlmy1KgUL/t94ks069tY5cPmCyDLsrXMQZ9IwMCMWbyCEQpJQqyQaHD7W7WtiYGYsqRYdSoXU2rVAYtmeel5ZuIf/XDqQez5cz77Wmm8mrYpv7x1LZgczhXV2LzfPXMOGsmZO75NMjyQTryzcE3XOxUPS+ffFA1C1Wc70+YMopPDv5OFPN7K50s4HNxfiDcjsrnXQ4PAyLCces1ZBjF7UCRMEQfi9E+UrjlKzy8vFQzJ4Z1kJsgy9UmLYU+9gZ5WdFIuOcT2TjniPcT0TUCsl/G12E04pyODlhbsjrz9bV8FlBZkU5nb8Zd3o9LGpvJmvN1UxMi+e8T0TiTdpsRo09E1rv2PzePDZG4hr3EHivsX0yxiBpDeiMsXQ7PJR2uDi03XlFGRbOaNPCrFtZoosBg2HeiKzTsVfzuvL7LXlURXtvYEQ87ZUc+7ANK6YvjKywUGlkJh18wgenL2JBTtq0SgV3DQ2h2tGZHPJ0IyooChGp2bFngb+8PFGAO77aANPXzKQeocHly/EmO4JJJjaz2gFgyE+XlPGhtbyFqtLGrlpbA6v/byHtl2qrh6RHfV+tS0eGp0+3l+5D4tew32n9yYkh7j89ZW8evVQ+qSaUSsVHSb8C4IgCEJbIhA7BKc3SE2Lh3duGM6iXbXEGtTcNGNt5Hiv5Bjev7kwkhDfkQSTls/vGM2T3+7A7vEzdWwukgRbKlqizttcYaMwN77d9W5/kHeWlfBiaz2yT9aWc3p+Mk9fOqBdAv7xEgwGYec3qL+6C2hdu04diP+K2Xy9zc1jX4Rra328ppxZq8t449qCSBHUwzFoVQzOiuWnHbXtjlmNGoKyHLXLdGJ+Mot317Gg9XxfMMQrC/cwrFscCgUkmw8se/qDIWatPrB7srjeyZTXl/P38/tx9cjsQz6TNxBifZtaac0uP3M3VjHzxuG89NNufIFwz8u8JNOBn09Ipsrm4cJXlkaCtVmrSvnq7jFYVAqemreDB8/sTfYx5ukJgiAIvw8iEDsEs07Nz7vqsLn93DWhO7e/vy7q+M4aO3V272EDMZ1aSb90C69cNYRASCbOqOGzdeXtzhuRG8+eWgcxOhVJbarv291+pi/aG3Xu/O01uHxBTlQ90KC9Fs3S/0QPVm1E4Wli2qLKqOH1pc20eAKdCsQgPHN109hcPl1fjtcfwmrQoFFJjOmRgFqhYGi2NdJkvXuSiQ2lze3usbnChsMb4NyBBwIxlUKiV3L72lyxxugZqWaXj0anjzq7l9RYPRqlxNn9U/mhTdX991bs46YxObx29VBkiOq92djiwahXU97kjpoxa3D6WLK7nvduH4nN5SfJIpLyBUEQhM4RgdghOD0Bbhufx5Pf7WBdaROKDvK8O1uFou0S1Sm9krh9fB4zlpdg1Ki469TuLNhRy3Pzd3HewFT+eE7fqKKhHb3viSUd4oNJHQ4f7eMlm7XMu28cNrefKpuHnskxmHVqdGolL185mGmL9qJXKxmQbsZqUEeKoe43MMNCxUENtCVJ4pKhGcxeWx6pbTYo08KINrOMzS4f//5uB7NWlQHhvLHp1xaQk2Dk9lPymLmsBL1GycNn9cZq1GA5aFmx1uam2RNg1tpyeiSZWP7Iqdw7az2rSsKBo0KSkJBFECYIgiAcFRGIdaDF7ccny1z5+nKeuKAffVLNxOjU3NFmVqxvmrnDnXRHEmfUcO9pPbhmZLiX49vLSvhuS7iG1pyNVdx+SvdIIGbWq7ljQneenrczcv3Z/VLYXtWCxx8k3apHe4h+h13hc9lRSCH84x5B/cXNkfFQxjCCulhuGqvl8dalSYDCHCsx+qP7I+T2B/lw1YH2TxqlgvdvKmRYThx6jYpJ/VKZsawEfyjE1DG5bCxr5uvNVWhV4d2ipY3uqN2V+yWZdXx860hq7B6UColEkzZqpq7R6YsEYRAuCPvfH4sY3zOBy4dlcuPobkC4h6TqoBpjLo+PjRU2bnl3bSS/bWRePM9dPojRT/5EUoyWUXnxaNQiJ0wQBEE4OiIQ64DbH8DjD1Le7OHGd9agVyv56u4xzLhxGAu215IZZ2BghiW8W68L3Wp0aiWyLPPnOVvZUW2POlbT4iE/1Rw578rhWQzNtjJvSzU9k2NIMmu5Z9Z6/EGZ+X8YR3a88Xh8ZAAUTcWo3hiP95qv8N/4A9LWzwgm9UPR4zRu+Ggv43om8sZ1Bfy8s47+GRZO7Z10yNpah7I/ANrPFwzx8Geb+fS2kawva+aGd1ZHjn2/tYaPbh3BQ2f1xuEJUNnsJj/NTGKMlkanl0BIjsymASTEaA9ZdPXgLgcQri8WZ9Syr8HFuJ7tC/Pu1+Dy88y8XVGbDJbvacDpDfK3c/sysW8yCUbxV0kQBEE4eqKOWAcSTVr0aiVplnC+VmFuHJ9vqOCh2ZtpdPqYv62Gy6atYPrivQRCoSPcrWNmvZrJA1KjxvRqJT0PynWyGjWMyI2nf4aFD1aVMnXGGpy+IL5giA9Xl3G8+L1uFMtfBDmEdubZqL9/BFVsFkprNrsdOpbuaeBf3+7gL3O20uTysaaksUvLph5/KCq/CqCy2Y3bH+SVNrtJAUoaXOysdpBhNdA71cyp+cnEGzVsrbRx3VurOev5xTz93Y6oZuSHkhlnIEYbHSxN6pvC2n2N9Ew5dDTt9Ib7a7r8gXbHfIEQV47IJC1Wj1rMhgmCIAhdIAKxDigUCkza8JLZxPwkYvVqzDoV1S0evtpUxariRmQ53NBacdRZUmExOjWXFWRy32k96BZvYGRuPLNvH0liTMfLneVNLjZX2KLGjJrjtywpSQpCmgO7AylfDfMeJtS0L2qprrzJzVebqihpcKHoQqsmk1bZrrPAeYPS0KgUHVa9P3isyeVnyuvL2Vxho8Hp482lJbyzrAR/4PABcbxRw6d3jGJ093iy4w3cNj6X0/skc+PoXOKN7YOoapub5XvqefyLrSDLXDeyW9Tx3AQj8SYNSuXx+x0IgiAIvz9iPeUQrEYdJl2If1zQD38whFqpYPqiYupaZ19itCquH5WD4hiy6ZPMOm4Zl8ulBRloVcrD7sC8tCCTt5aW0OwKz9BYDWouGZrR5fc+mEqjJTjqHtjyMfjCCe+Y01H2mIhVoaFvmpmtleGyG0qFxEOTenWphEaCScuHt4zgX99sJ96k4ZReSQzJtiLJcOv4PJbvaYj0d+yVHENuQvTSa3G9M6p9EcBXm6q4dmS3qE0O7T6fUkHP5BheuWoIXn8ItVJCqei41pfPF2DF3gbu+yhcl2zu5goW/9+ppMXq+WJ9BXmJJq4dlU2qSMwXBEEQjpGorN9JsixTZ/fy085afIEQp/dJIcGkaZfYfaIEQzL1Di8/bK9BIlxnK8GkRXkct1UG/B4kZz3BrXOQtWZUPSaitISXT+vtXlbva6Ss0cUZfVNIMmkxaLsex9fZPXy2roKFO+sY1zOBcT0SeWtpMVcWZrNoVx0JJi390810SzBGBXzF9U4mPLMw6l6n5Sfx3GWDiNEd+/Jgtc2DPxjklnfXsr0qOn9v1aMTAdBrFMToTkwdN0EQBOHkJCrrHyNJkkgy67hsWNaRTz4BlAqJZLOOqwoPXaD0WKnUOojNQDn6jnbHEmK0nNUvtYOrjl6Lx8/fv97OlxvCdcl2VLfQN83Cp+sq+G5LNUOyrdjcft5fGeK9mwqjrrUa1Nw0Noc3FhcD4Xy+xyf3OS5BGISXgBNjtKg7CLArbR4GZcYel/cRBEEQBBCB2FGps3upd3jRKBVYjeoj9oc8Huod3siOv3ijptPFUzvLa29A5Wsh5GpEMqchGRNRqqL/WBzvz+32Bpm78UBx2CaXH0mCrDgDpY0uFrfWDnvh8kHEH1QiJNag4e5Tu3P9qG44vAHiDJrDLul2VlWzG6tRzfxtNfRKieG+iT2YOnNNZKdk3zQzyWbRM1IQBEE4vkQg1kk1LR4ue305Ja2NpMd0TwgHCsc5MGqrzu7lurdWsa0qnJs1MNPCm9cOO2SJhqPltdejWPgkyrXTUQLorQRu+B6SekbOOfhzj+4ez4uXDz62zy2BQaPC4T2wE/FvX23j/ZsKw0VZ6xxcWZhNn9SYSKPztix6DRb98VsarGp28/bSEhJjNAzMjOWO99cx/w/j+OaesczZUEm3BAPjeyaRYjl8k3dBEARBOFpi12QnBEIhZi4viQQjAEt217PloF2Mx9u3m6siQRjAxjIbP+1s36uxq5SeZtRrpx8YcDehmPcwXke4WnwgFOLd5fuiPvfS3Q1sOsbPHWtQ89CkXlFj43sm8cGqUhQS/OPC/ozMi8dygvpptlXbEm5XNGN5Cf/4ZgeDs2I5o08ypz+7iCe/3c4Fg9M4b2CaCMIEQRCEE0LMiHWCPxCiqMbRbnx3rYPxvZJO2Pu2DcL221HdfqyrQvbqdmOKpmIIhHeG+gMyu2rs7c7ZXetgwjF8bq1KyfmD0hmabWV3rYMBGbHh4rgSyDLU2r24fAFMWjUmXdf+iDo8ASqaXXy4uoysOAOTB6SSFBMdTDU6fVQ0eyiud/LBzSP4fF05N7y9iunXDuPxyfnIgEalQK8Rf00EQRCEE0N8w3SCXqNiSkEm32+riYxJEkzofeKCMIApBZntirZeMPj4laxQxueBxnigXAXgz78IpcEKgF6j7PBzTzwOn9usV5MS1NHg9HHeS0uwGjU8c8lA7vxgHbV2LyqFxKOT87l0aMZhE/Hr7V4CoXB5kbbLpVsrbVw+fQWyDEkxWgwaJWN7JKJSSsQbtTg8fv7z/U7eX1kKhHt6Pn/ZIPbUOxn71E9cOzKb+0/vgdUg8sIEQRCEE0csTXbS0G5W/npeX7LiDOSnxvD29cNIMp/Y5aqsOAPPTRlIXqKRvEQTr1w1hOw4w3G7v1+hJXD9d5A1AsxpBEbeizTiNlSaA8HH4KxYnmjzud+5Yfhx+9yBkMzjX2yhxRPgqsJsnp2/i1q7N3Ls719tw+5pX9EeIBSS2VHdwmXTljPiXz9y7VurKGlt+N3s8vHcD+GWRFaDmtevGcqXGyoZ9eSPnPvfJSzbXY/TG4gEYQAhGZ6dv4trR2Rz3chs7pzQXQRhgiAIwgknZsQ6KRSS8QVC3HdaD7z+EDuqW+ifYcF0DLW0DqfJ6WPaor3sqbNz+yl5WA0ahmZbOyxA2hXBlho0n01FoTXhnfgPJGM8sj4OrdESOcfhDfDTzlrWlDRy32k9UCsV9E01H7fPHAjJ7GvNP8uw6tstg4bk8PJhWmz7wqn1Ti83vL2aKpsHgK2VLdz+/lrevn5Y1HlXF2bx1tJilu1pAKCmxctNM9ew4A/jkVqXQvdrdProl25hWI6VBJPICRMEQRBOPBGIddLionr+8c32qLFeyeYTsjzpD4bYW+9k2uK9ACzYUQfAY2fnc+OYnM4XcXU1hpcdQwFQacGcBkDA70NeOQ11yWIAtDu/Db/vLUuo9OeiUiqJN2ppcft5cPYmQjLM2VQFwFn9Unj6kgGYjqFulz8Ywu7x0+D0UpgTx8riRjaUNTOuZyKfr6+InGfQKA9ZLd/rD0WCsP22V9mpsnn4z/xd3Do+j5XFjYzpkcisD9ZFXxsIUefw0if1QLcACHcv0KoUIggTBEEQfjEiEOuEUEhmUVFdu/Elu+uPeyAWCIbYW+dgcev7DcqM5eIhGRi1SqpsHjz+IMbOzEjZa2HNG7DkWQj6IaMApswEczpBjwNt+bJ2lwQr1vNMcZClu+t598ZC7B5/uwbdG8qacfmDXQ7EAsEQG0qb+WpTBRqVkofP6s1z83cxe20ZM24sxKBRsnbFGBwbAAAgAElEQVRfEykWHX84rSdWQ/T71Du8lDW6sOjVxBk1kRprAN3iDVQ0u9ld4yDRpGXOnaPRqRUMybKSGKNleE4c1TYPH68pJ96o4R8X9GPWqjKKau1MHpDKBYPST2g5EkEQBEE4mMgR6wSXL8CovPh24xN6JR7392p0+rhn1noGZcYypSCTO07J4+M1ZTz/QxFKhYTvCM2tI1x18PO/w0EYQPkaWPI8eB2odDH4cia2u0SROYyVxQ3UtHi584O1pFj0qA6afRuZF4/xGHYRNjp93PbeWtbsa6ZHcgy3v7eOUd0TeO3qoejVSq4f1Y3nLxvEVYVZxMdoCQQPRIL1Di+3vbeWC19ZxhNzt/Hvi/pHArWkGC1/u6Af0xft5bnLBvLA7I3cOGMNjQ4vfz2/LyqlxJPf7mDhzjpeuHwQSknixhmrsRjUPDtlENeP7CaCMEEQBOEXd8RATJKktyRJqpUkaUubsThJkuZLklTU+r/W1nFJkqQXJUnaLUnSJkmShrS55rrW84skSbquzfhQSZI2t17zotRRBc//MZcviEWv5uoR2WiUCrQqBTePzSUr3njki4+SNxBiZ42DkgYnU8d04/b317G5wkZpoyscSOzqZB2x6s3txyrWgqcFpVqNYui1hPpeDJICtGZ8k/7DNruBiqbwcl9RrROVAqZdM5QEU7ie16i8eB6a1LtzM3KH+XwNTh9bK1swalSc0TeZ5+bvQiFJ/LSjlo/WlDHphcXcPHMt4576idUljYRap+X21jlYUxKucbZwVx3vryzlnRuG8/3945h9+yheXFCEPyhT5/CyvcpOnd2L0xfitZ/3MGPZPqpsHpbvbeCqN1aCBI1OPxqlApNWifIX6hkqCIIgCG115hv1HeAlYGabsYeBBbIsPylJ0sOtrx8CzgJ6tP5XCLwKFEqSFAf8GSgAZGCtJElzZFluaj3nFmAF8A0wCfj22D/a8aPXKJmzoZLUWD0zpw5HluHHHTVoVcf/y1unVpAeq+fLDZVISAQPWhv8ZE05E3snHzlpP31I+7HcCWCIA0AVk4Tv7GdRnPF3ZBm+3e3l3jcjsTb90s2olArG9Uzkm3vGEpLDzxZ7jEVWdWol6bF6Kprd3PfReq4flcOcu8awvrSJodlWLnntwJJpMCTz2Odb+OyOUSgk2uWELdxVhycQZHT3BGK0KgZmxFLr8FDbEt55GWfUkG7V8+3m6HppNrefGruXl64YxPCceBJiRE6YIAiC8L9xxEhCluVFQONBw+cDM1r//wzggjbjM+WwFUCsJEmpwJnAfFmWG1uDr/nApNZjZlmWl8uyLBMO9i7gVyZGp+axyX3YXG7j8mkr+MPHGzi1dxKxhuOzg7GteKOWt68fhk6lJC22fYDQPdHUuQBQZ4XzXwa9NTzr1ecCGH4zqA/cU2OMRWVJQ2FOpWd6PLkJ4Rm+vmlmXr5yCPEmLSqlgiSzjhSL7piDsPDn0/DW9cPISzThD8os3V2P1aCmvMlFICS3y0m7/ZQ8vtxYyZh//0R2nAGdOvqznzcwncW76uibZqF3agz3TOjBKb2SeOO6Ap6+ZAAalYLMDkp+xBs1jOmReMJLkAiCIAjC4XR1jSlZluUqAFmWqyRJ2p+xng60rUBa3jp2uPHyDsY7JEnSLYRnz8jKyurio3dNikXHq1cPwesPoVBAnFHb+d2LR0GhkOiZEsNLVwzGGwxxSs9EFu4KJ+6nWXTcOj4XrVp55BuZEqHvRZB7Svi1SgfGhA5PVSok8lPNfHTrSIIdFEc9nhQKiV4pMXx06wgCwQPvdcHgDPbUOchLNLKnzolZp+KdG4Zj0auZ+OzPAPz3p928c8Nwpi/eS5PTxzUjsnH7gxg0KjZVNPO3r7aTZNby5R2j+e+CIjaW2zgtP4m/nNuHK6evxN7a2/KmMTloVcc+uycIgiAIx+p475rsKDKRuzDeIVmWpwHTAAoKCg553onyS35xm/Vq/vP9Ts4ZmMrtp+Th9gdRKRRU2tykWztZ1FVjCP/XSYcqFXEiJBwU6KVatLh9AV64fDBvLi7m7AEpvLW0mHMHpkXOWbC9lp3Vdq4Zkc25A1MxaFRcMX0FFwxKY2NZuP/liG5xrCttYmN5+PUP22vpkWTiu/vGUtnsIbG1yr6YCRMEQRB+DboaiNVIkpTaOhuWCuzPIC8HMtuclwFUto6fctD4wtbxjA7O/93zBUMU1Tl4bdFetCoFGqUCuzfA3y/ox7Bu7Xdw/tYZtWr6Z8Ric/l4/Jx87J4AX2+uYmrr7JW3dbdoeZObskYXVoMWvUbJzBsLaXL6yEkwce7AVLonmfh+a03UvV/9eS97653864L+xP2CwaYgCIIgHElXs83nAPt3Pl4HfNlm/NrW3ZMjAFvrEuY84AxJkqytOyzPAOa1HrNLkjSidbfktW3u9ZvT4PCyfE8D763Yx74GZ7iRdRcZtSquGBZefvUGQti9AVQKibE9Ol5ePFlYDBriTVrUyvBk6bRFe3n5yiHkJBhRKyUm90/lysIsml0+ZFkmMUZLz5QYBmRYeGbeLqa8tpyJ+cntlo2nFGQSaxRLkYIgCMKvyxFnxCRJmkV4NitBkqRywrsfnwQ+liRpKlAKXNp6+jfA2cBuwAXcACDLcqMkSX8DVree94Qsy/s3ANxOeGemnvBuyV/VjsnOanT6eOCTjfy4M5zPpZBg5tRCxnTvWuDU4vYTCIX42/n9+GhNKTFaNbeMyz0heWm/JnV2L5srbLh9AebcOZo/z9lGWaOTf17Yj5AMG8qaqG7xUNbkZtmeBkbkxZNo1DB7bTk7W1skrSpuYNbNhTw7fxdOb5AbRnejX7oFxUn+sxMEQRB+eyRZ/sVTrY6LgoICec2aNf/rx6DB4cXjD+H2BzmtNal8v/zUGN6bWtilxPc6u5dLXltGptXAaX2S8fiCfL6+gicu6Ethzsm3NAlQZ/dw6WvLKWntPxlrUPPJrSOxe/xc//ZqWjwBXrpiMB+uLmXJ7nDvSLVSYsmDE/jHNzuYs/HAqvZZ/VK4d2IPLAY1yTFaFApRJ0wQBEH435Ekaa0sywUHj4tvp2NQ0ezmjvfX8trPe2hy+dodt3sChLoY6Mbq1ZwzIJUlu+v5y5ytPPndDkobXXQ7AUVkfy2W7K6PBGEAzS4/H68uIyfBSIpFR4xWRYZVHwnCAPxBmZkrSrlwcPRm22+3VBMMyaRa9CIIEwRBEH61xDdUF7W4/XyyuowHJ+XjD4ZIitGSGaePOuf6Ud2wdnGnpVql4PpROdw8NofEGC2DM2P55LaR7Xovnkzq7e2D2RqHF6NWxT8v7E9SjBa3P0iMVsXlw8LtnwZnxvL91mp6JJv463l9yIzTk5do4qUrBpNiETsjBUEQhF83sTTZRXUtHiqa3Vw2bQVn9ElmZF48Q7OtfLS6jD11Ts7sm8zE/GSSu1Amwe0PUFLv4vWfdzOsWxyjuydi1CpJPMkrwJc1upj4n5/xBQ/00/zyztEMzIzF4w/S4vYRkqGmxcuHq0upavZwVv9U8lNiaHKH2yad2ScZvUZFilksRwqCIAi/HodamjzedcR+N4xaFQt21OINhDh3YBo5CUa+WF9BYoyW0/skY9KqaHL6uhSIVTV7OPe/SwiEZL7YUIVSITH//nEkxpyAD/IrkmTW8tXdY3juh124fUHumNCd3MTwUqxOrUSn1lNlc3PLu2uoaW1jtHBXHf+6qD8WvYqn5+3ktPxk0mL1h3sbQRAEQfjVEIFYFxm0qkiLo+5JJu78YB3bq+yR4zeNyWHq2Jwu3fvDVWUE2vT6CYZkPlhZyuPn9Dm2h/6V06qU9EyJ4ZlLByLLMiZd9DJsWaOT8iZ3JAjb752lJTx72UA+umWkCMIEQRCE3xSxdnMMJvdP46y+yQBRQRjAB6tKu3xfawf1ruJNv58aWEatql0QVu/w8vBnmzBo2v/bIUanIs6gYXhOHCat+LeFIAiC8NshArFjoFcruHRYRof1qSx6NV1Nv7tocDqJbUpeJJq0XDg44zBXnLwCwRAOjx+vP8jS3Y3oNUqG58RFjqsUEg9O6k2qmAkTBEEQfoPE9MEx2FXr4L0VZfzlvD5cPCSdT9dVRI49cGYvYnVHt8Ox3uFla4UNjz/Il3eNZk1JIyAxIjfuF+0D+WvQ7Pbh84fwBUMsLqrHolez+KEJPDNvB09e1J+SBif7GlxM6JVEnPHk3UkqCIIgnNxEINZF/mCIT9eWc/v4XHZWtXDz2FzO7p9KUY2Dwtw44owadBplp+9X7/By/dur2FLRAoBOreC7e8fSLcHU6XvUtHgobXSxfE8DI3LjyY43tNssUNviobzJzZLd9RR0s5KXaOrShoITyekN0OTwEZTh/JeW4PQFAchLNPLODcMZ+9RPTOqXzMOT8smw6lEpxcSuIAiC8NskArEuUkoSVxVmoZAg2WLhn19v48rCbMb2TGDdviYm9E46qpY6xfXOSBAG4PGH+NvX23nhskHt8qU6YnP5mLGshFcW7omM3TY+l9vH52FprWXm8Pj5eE0Zz3y/K3LOdSOzuXdiD+K6UP3fHwjR7PajVEjEHcc+jmqFjEalICTLLHloAjfPWM2aUht76pxsLGtm8YMT0KoUWI0aEYQJgiAIv2niW6yLFAoJvUZJIATPzNvBbad0Z9meej5bV8GgzFgM6s7PhkG4QOzBbC5/1O7Jw3H4gry5pDhq7K0lJTjaNB5v8QSiAjWA91aW4vYHqbV7aHK2L6h6KI1OHy8v3M35Ly3hxndWs7GsGa8/eMTr6u1eqmxu6u3eDo83ubzsqHFy96wNTHl9Ba8s3MvLVw1lQHq4jEWjy4dJqyLJrEMtgjBBEAThN058kx0DfyBEICQzZ2MVV0xbQXG9kyqbmyumr8QXPLpM/b5pZiz66Jmvm8fmEtvJyvyyLONvUwgVwB8KcfBT+ALR5wRDMoGQTOE/FzB1xhpKGpz4g0FqWzwU1zuoafFEBVgub4BGp5fZa8t4/ociKm0eNpQ1M+X15TQ4fWyrtLGkqJ7aFg/BNs8TCsnsrLYz5fXljPzXj1wxfQUVTS5a3H78rc/kDQRx+0Jc9voK1pU2UWXzMH3xXt5dWcrMqSMxaJSc2jsJo/boglxBEARB+LUSS5PHwGrU4LV5MetUtHgCzNtaA0CGVU9nJ2tcvgAlDS6+3ljJBzcX8u7yfVS3eLhhdA6DMmM7/Sw6tZKz+6fy1aaqyNikvikoFRL+QAiVUkIhhXdkfry2PHLOhF6J1Du8yDKsK23i+fm7uHpENjfOWE2LO4BRo+SN6wooyLbS4gnwzPc76ZNq5ssNlVHv7w2E2FjWzDPf72RPnROzXsWcO8fQLSE8k9Xg9DJ1xmrKm9yoFBL3n96T+dtqmL+9hvxUM7eMzUWnlimqseM+aGbtm01VTCnIYO5dY4g3adCoRCAmCIIgnBxEIHYM4k0avIEQT5zfj0c+24zbH8SsU/HCZYOIN3Yu56raFq6iHwzJvL+qlHMHpPK38/uRGWc4qmdJMGn50zl9GJptZenuekbkxnNm3xSufXMl79xYiFGtoNHl485Tu9Mvw8LionoKsq1cMDidS15dFrnPWf1TuWfWelrcAZQKiYfO6o0/KPP5hkp6p8SQGKOlyuYhO97A1sqWqGdIMus4Z0AqLyzYTYs7HLT9++IBGLUqvIEQ5U1uAC4YnM6WCltkmfT1q4Zi8/jBL3dYhiI73ohWpSQr7te1qUAQBEEQjpVYmjwGaqWSjFg9I/PimHffWObfP455949jQKalU4n6gWCID1eVEWzNA2t2+Xl3RSlvLS0+wpUdizdqyLLquWhIBjaXjwnP/ERRrZO5GypQKiVe+GE3E/6zkPJGFxcOTsftC7KquJF+6ZbIPeKMGiptHgBuHZfLvgYX1761igdnb+K8l5ZiNWjw+IM8cGbvqFpnFw5OR5ZlxvRIjIxVNrsJBEPU2b2EQjJJrSU4JuYn8UnrrNzqR05lTWkT5760lOFPLyRGq+KKYZlRz/Po2b1/dTs7BUEQBOF4EDNix0ipVJBs7lox0fImN/oOSlwkdmEHI4AM/LSzjvdWRlf1DzfRlgiGQoRCMG1xMRAO9p6bMjBSjd6oUWLVq+mfbmFzhY2J+clMeX151L1eWFDEe1ML+X5LFS9eMRiPP4hJp2JbZQsyoGwTf15ZmIU3EOLyaStItuj498UDeHD2JlzeIGadmjq7F78Md7y/DldriYpb313Ja9cUcsv4XJqcflIsOmL1ok6YIAiCcHISgdj/SIPDy30frecv5/Vj1qpSalt3ESaatFw0pGtV9FVKBdePzuHjNeWtwRcYNEouHJxBjE7N7afksWBHbaTif7xRw4jceEZ3T+COCXkYNCqsejWvXj2EP3y8EVmWI7N1+zk8AdRKBV9srORf3+3EpFXhDQTxB2WeuXQgqWYtfdPMXDE8k9N6J/H+qlL21jvZW+9EAp6+dAC5iUYeN+Zz44zVOLyBSBAGsKnCybinfmLRg6eQY9FhMeuOqgyIIAiCIPyWiEDsf8TjD1HS4OLRzzbz6tVD2V7VgizDqb2TSDYf3YyY1x+k3uFDrZRINmv59t6xvL20GLVKwfWjumHUKKhoctMtzsjcu8bw7vJ9xBk1XDUii1i9Gv1B/RkzrAZev2YoHn+QIVlW1pU2RY6dPyiNWIOKEbnxbK+yR5XHGJptZfmeeqYUZPLjjjqQYWe1I3J82Z4Glu1pYMdfz0SRJLHogQkoFRJGjTJStHX/fZAhJkYtgjBBEAThpCbJXW2I+D9WUFAgr1mz5n/9GEctHDR5CYRkXlm4h49WlyFJ0CfVTKxBzTOXDDyqvom1LR4+WVvOp2vLSTJrefTsfHokGlGrlASDIfbUO3jiq+3Utni5eGgGV7T2xly5t5EXFhQxrFscd5/ag4RDtFCqs3t5e2kxa/Y1MbF3EhcMTufdZSVMHpjGv77ZzqKiemK0Kh45uzdGjYp7P9oQuTYv0cRfz+vD1W+uiowtf3gCWypbeP6HIgJBmfdvGsaOagf3f7yROruXPqlmXrlqSGS3pSAIgiCcDCRJWivLckG7cRGI/bKqbW4WF9WTk2AkEJL5ZnMVP+6oJTfByN2n9iA3ydjpHZf+YIh3lhbzj292RMZ0agUL/nAK6VY9FU1uJj67EI//QD2vxyfno5Tgr19tj4ydNzCNf17Yr8MK/i5vAKcvQLPbT6xezVVvrGRXjYN/XdQPg1pFaqweXyCEyxfgtZ/3Rs2eAXx9zxh2Vdt5eeEeHji9B+lxRs7575Koczb9ZSJ2d4igHO5YkG4VDbwFQRCEk8uhAjGxa/IX1OL2sbHMxvaqFmxuP499vplgSOYPp/ekMDeeeVur0R1Fjax6u5cvN0bX8/L4Q2yuaAZgc0VzVBAG8MWGCvqmR9cn+3ZLVbvaXQD+YJClexoY+a8fOeO5RZQ0uNhVE15qTIrR8dgXW5jy+nKufnMlH6wsZVK/lKjrtSoFcQYN5w9K572pwzmtTzJzD3pegL/M2UG8QY1GKYIwQRAE4fdFBGK/oIpmDy5/kBG58Tzz/U6evmQgtXYvz/9QRJ3dy22n5GHUdj5tT6dWkN7BMmZa61haB8cyYg04PNHtlNJj9Ui0z8Vqcvp5cPZGAiEZWQZ1m3ytLRU2RnePj7z+uaiOzDg9fzynD3mJRobnxPHp7aOIM2pQKCRCMviCMrmJ7ZcccxOMqFUKUiwiCBMEQRB+X0Qg9gvaUd1CWqwOhzfApH6pPDh7I/3TLTx96QDumdiDhKMsW2E1anlwUm9iDQeWFCf1TSYpRku1zUOiScukvsmRY7EGNQ9M6kWj60BPSa1Kwb8vHkC8qX0rpaAs0+Q6ELStKmni4iHpAMxcvo/bxucxvmcikgTd4o2kmHVcVZjJR7eOZNo1Q+mXbsHlC1DR7CYky9jcfsb3TKRfujlyz7xEIxcPzUCpFNXyBUEQhN8fkSP2CypvcnH1Gyv5x4X9USsVxBrUGDRK4gxqDNqjr5XV5PQhI+P1hyhpcBJn1GDWq3n++yI+WVdGRqyemVOHIwFatRK9RolBrcIdCGJz+am2eciMMxBrUKProEl5o9PHLTPXsGZfOO9Lq1LwwuWDSLXo2VPnYHhOHDq1klBIRiFJxJs0SNKBWbOqZjdzNlbywoIiPP4gZ/VL4amL+tLik6mzewmGZFLMuqPanCAIgiAIv0UiWf84qXd4+XFHLVsqbFw4OJ3cBCOWTjbmdvsCbKls4Ym52zBqlVw8JIPT8pOxGjt3vd3jp8UTwB8IoVZKvL2shDeXFDMky8qrVw4CJNytDbR3Vrdw16wNvHVdAQaNis/Xl9M9KYZJfVNItkRXqW9y+the3cK3m6sYnhPPqLx44ltn56ptHv75zTZWFjcyJMvKn87p06nAqcXhptTma5eY/8CZPblmZBabSpvon2bF0sXitYIgCILwWyICseOgweHlpplrWF/aHBl7bspAzhuUjvIo6l01OsPlK+IMGlSd7A7e5PQxf3sNf5mzFZcvSI8kE9OuLeCeWevZW+fk+/vH8X+fbGT53gZUCompY3K4dkQWq/c1c1+bkhJ908y8eV1BJB/L6w/yxpJinp63M3LOmX2TefLiAVhbA0yH14/LG8SgUXa4s7IjvkCA91aW8cTcbVHjo/Li+c+lA9BrVMR2MoAVBEEQhN+6QwVioqDrUWh2+6OCMIDnFxQxpkciiYeow9WRuE6Wp2jL6QvwyGebI5Xui2odPDF3G49P7o1GpeD9lftYvrcBAIVCwu4NEAJe+ml31H22VrbQ4PRFAjGbx8+rrc2395u3tYY/nRvE2tp33KRVY+rk0mkoFKLe4cMXCDIyN77d8WHd4rAYNBg04o+eIAiCIIhk/aPRweRheELxxM8q1rR427Ub2lJhIzVWj0alZH1ZOED80zn5rH50IuN7JOAPdvxcbSdBZRmO16xoTYuHD1eXcfes9by4YDdGrZIXLxuIpnXWb1yPBK4YniWCMEEQBEFoJb4Rj0KsQc2gDAsbym2RsbtP7d6lGa6jlWLRoVEqIj0kAQpz46hoclPa5OLUXgn8cXI+zW4/0xbvZURuPEpJ4s5T8rj/442Ra/qkmkkwaWh0+li+twGby8fN43J5/oeiyDmn5ydj6CB5/3CaXT4+W1fOv78LL3GuLG7k56I6Pr9jND/+33hCsoxaqSDloPw0QRAEQfg9EzliR6HJ5aOs0cXyPQ2UNDiZ0CsJk1bFkGxrh7sOj6e9tXaKG1w88tlmau1eRubF8/QlA1hSVEelzcOUgkxmLt/HtEV7I9dMHdONa0dmU2v38fm6cnokx3BWv1QsOhVPfb+Tt5eWoJDgqUsGYNSoWLSrjhG58YzpkRBJ1u/089U5uOGd1exrcEWNf3r7SJLNWpSSQuyOFARBEH63RI7YMZBlGY8/hNMb+P/t3Xd8VFX+//HXudNb2qSSBAi9914UC6siiL2y2Neyru2n3++6xV13113X1dV15SvqWtZeUcGKhSJI7x0SQkJIr5Nk+sz9/TFjCC2CAkH8PB+PMTNn7r05k/N4DG/POfccps5YzKgubjITrPzloy1UNwWYf++EYxbEqhr9hMI6ZZ4Aq4vqeO3GkZgMGtVNAR7/Yju9MhPw+MJEdZ0XF+/a59yXlhQxfXRndlQ0Mm1UJ7qmObGYDFR4/Ly6tBiAqA73vL2e7ulO3rpp9GHfwdlabVOADi4L3bPNBwQxl8VETrLsGymEEEIcjMwR+w51zQEKqpoo9/gAGNs1lSUFNby3Zg/FtV6MhoOtSX90VDcGeGHxLsb8/Stykm18sK6US2Yu4boXV3LZ00s5u28mn2ws58P1pWhKEY7uu53Rt3PKemUm8OqyIiJ6lKpGP03+0AF3eRZUNeEPH7jNUVtqmgKU1HnxhiIYjYoHJw/l2WmDW94/vWc6ifYjXx9NCCGE+KmQHrE2NPpDeENRZi7YydzN5eQm23nw/H5kJVp4e9UeAH51WvdjMvncGwjhC0V4akHsjsa/frSVt24axZ56P7trvQztlIzFqLjnrB5oKAxKcd7ADry/du9ejuf2z0IpxQuLC7njzO74QlEumbmEcd3TuHZsZ/6v1d2Slw7LPaLPUeHxM3N+Ae+t3UO6y8Ifp/Sla5qDfjlJ/P2i/nRyO+jktpORIHPChBBCiEORINYGbzDCjK928M6qEgA2+Txc+Z9lzL3rFBJsZkZ1cVPW4KMpED6iPSIPR70vTFMg3HKH47juqfzt463MWV8GgMmgeG76MHpkunj2650s3FbN89cOZ3jnFBblVzOmq5sz+2Tw5ZZy7jmrJ2lOM0t21rKrxktxbREPXtCfmdOGsqqolnHdU+mfnUSi7fB6r5r8QV5fXswL3+yK1dUb4uoXlvPV/5uAAk7vlY6mwO2UECaEEEK0RYYm2xAIR/hqa9U+Zd5ghAqPn5qmAH+cvYkH5mw+JqtX1DYHsZkMpLksmAyKXlmulhAGEIroVDcHeXPlbp5btIsdVU2Mf3geu2qauX9yH8oafDy/qJDsJAe5yTbsFhPNgTAQmxd236wN/PXjLbisJoZ3TiblMOeGVXj81HlDzN1UsU95KKKzYU8DGS4TaS6rhDAhhBDiMEgQa4NJ0+iSduBE8xSHmY82lLGn3seFg7NxWI7+RP0ku4kZ83fw2g0jOaNnOtHogWmvs9txQCB69utCFuyo4pONFbyweBc5yTY0TaOuOUjXdOc+vV7FtV5Gd3VjNx9eT1iDL8iXWyqwmQwH/bt0dtvx+cJH+EmFEEKIny4JYm1IcZr5/eQ+pDpjvUVKwR1ndKfeG+K0nun8ZlJvrhzZ8ah3iPlCYdB1BuYk8/v3N/HzMZ3JTbGTvt/q/WaDIi/1wEDULc3J2K5uPrx9HEl2M++uKuG5RYWgw5u/GMW0kR2ZMiCLWbeMoVem67DqVN7g57NN5W9loe8AACAASURBVCzOr8GkRbj3rJ5kt1qO4soRuSTbzSQkyBIVQgghxOGSdcS+gzcQpt4XosEXwmkxsnJXLbPXldIlzcnWcg+FVc28f9tY0l1HbyiutN7Hmf9cwMe3j8OgaUSjOhWNfoyaxguLCymq9XJm7wzGdXOT4rBwxbNLKWvwA3DFiFxuObUrq4rqGNfNzRX/WU5+ZRMAmoLnrhnOql21NAUinNE7nfHd076zPpWNfv795Q62VTRxTr9MHpizmR1//hlVzWE8vhB2ixGTpmSdMCGEEOIQZB2x78njD/Ofr3dSXO1l8qAOGAwaC7ZXMW9bbO7YZcNzcR2lifoVDX4MBsWGkga8wQhby5v405xNNAXDzLhyCLe8sZoLh+QwMDeJ5YW1bNxTz/3n9ubdm8dQ5wtiNxupavRzzr8WMTIvmQ5JtpYQBrG5Yc8s3Mm4bqnMmF/ADePzvrNO5Q1+fKEIC7ZXU1zr5aZTunDJsBx6/+FzjAbFY+d3ZXC33Ja9K4UQQghx+CSItaGqMcDFM7+hpC62htgX2yq575xeXDWyI68sK+b0XunceWZ3bD9w+QqvP0xJg49fv7uBQCjMXy7oD8BLS3bxh/P6Mn9rJalOCzeMz6NXZgIOixGX1cioLm7WljSwaEcV00d35paXV7K1oonBuUn86fx+FO+3uCpAKBzFaTHy1FVDSLK3PUG/rN7H7z/YyG8n9aZHhpPiWi+3vrqaG8Z34dUbRpKdbMNi0EiTJSqEEEKI70WCWBuqmgItIexbLy8t4tnpw5g8oAOri+v4cH0ZN47v8v1/R2MAXyjCz59bRoUnAIDdbGBCjzSSHWb6ZCWwvLAGi1GjS5qTe99ZR3VTkOGdkxnXLZWZCwvYuMdDUyDCo5cNwmkxYtQU2cl2zEaNrERry7AlwM0TupKZYKWj246zjZ688gYfwUiUL7ZU8rvJfbjt9O5sLW+kpM7HzAUFmAyKaSM7knoUh2SFEEKInxoJYm2wGWP3MmQnWzlvYDY7K5vYXedjTXEdv3lvIxDb9PuHqG0OoJRqCWEA059fzhs3jkIpxTUvLCcc0Zk2shO3vbaaUCQ2p2/Frjr++slWzu3fgY17PHy4oYwPN8SWt/ji7lPi9Tfwyg0jeWvFbiobA0wd1IHcZDt/+nAT/7p88IGViWvyhzBqGmFNx6Ap7n17HU9eOZinpw0lEtVJsJmwmjQJYUIIIcQPJHdNtiHRbua1G0by3PTh2M0GzhuUzbNXD2XRjhoArCaNy4bl/qDfsbywFqtRw2Lc2xT13hCapmj0hyioaiYYjlLTHGwJYS3n7qxlTFf3PmX9sxNberoqGgNM+tfX7K7zYjMZuP+DTcxcWMB95/Q+5LBkeYOPjzeUM2tNCdGozts3jWLFrjpOfXg+87ZVYjVrpDrNMidMCCGEOAqkR6wNKQ4zvlCEc574umWF+5F5KTx0UX9SnGauGJGL3fzD1hAbmJvEssJa/jilD/fP3kQoovP3C/vj8YVxWWPre5U3+km2mzFoqmX/yNi5iSTaTDx/zTBmry2jd5aLqYOyyUy0UtccZN3uegLhKB9vKG855+ZTuwLw+vJiBuQkkp1kawll5Q0+Ln16KcW1sbllj87dzpxfjWPuneNYlF/L6K5uXFYTTqvsHymEEEIcDdIj1oayeh+PzN1G6xU+lhXW4g9F0fUoVzy7lM1lnh/0OzokWumZ6WJEnpv5905g7p2n0CPTRYLNSCgSZcqALHQ9NnH/4YsGtPR29c5ycccZ3bn06SW8sbyYyQMyuXJkRzITY8OF87dV0jnVgWq1t/fFQ3Pwh8Kc/a+vuW/WBs59YhHPLSrEGwhT4fHTHIytDzYoNwmAQDjKk1/lk+q0MH1ULjlJNrKkJ0wIIYQ4aiSItSGKji8YOaA8EI5NYvf4wqzdXf+9r98cCLOyqJ7rXlzBxMcW8Oc5W0iwGSmt8xMMRzAoxc/6ZvLs9KF0SLLRNzuB924dw7x7JnDTKV25/Y21VDYGmLu5kltfXUOjf++q9ssKa/l4QxkPnt+fFIcZTcG0UR157PMd+9Tho/WlVDcHueWVVZzx6AIenbuNuyf2YHR8yNMXihAIR/GFdVyHuRelEEIIIQ6PBLE2pNrNXDt237W2OrvtJNtNLZPrh+el4A8dGNYOR703yK2vrqK6KUhUh083lTNzwU76Zrt4b00pFY1+OiRZ+c/XhXy6qZzH5m7HaNAwGRQPzNlEYXVzy7XGdU/F0Ko1z+6XyXOLClm4o4pHLxnI6zeOIs1pwbtfXa8ek8fdb65ldXEsUO6q8XLnm2u5dUJsCPPG8V1wO60tw6RCCCGEOHokiLWh2htieOdkZk4bwll9M7nl1K68csNIdF0n1WnmD1P6EAhFMBnUd1/sIHZUNrH/FpKL86tp8IcZkZfC5lIPiTYTf79oAE9eOYTfTOrFc1/vpDkQ4sVrR9A7y4VBU0zomcafpvbF1Wo9s25pTu48sztLCmq466217KnzUtHoZ/KArH1+36DcJFYW1e1TVtscJMFqYtYtY8hzx5bBEEIIIcTRJ5P12xAMR5j0xCKmDurA1EFZlDf4mfSvr/no9vH87cIBLNxeyaT+WRi07xdUOqbYDygbkJOISVM8+VU+kwfGlqZYtKOKftmJVDUGmDKoAwlWM19uKeHhiwaSYDNS6fGjKbC36rVyWIykucw8ftkgdCAQjrC7xsv14/Lo7HawfFctA7Jjk/17ZbrYWt6491yzgTSXBZdFw2WzHFBHIYQQQhwd0tXRBrNBI9lu4oO1pdz66hr+9OEWkuyx+VYZCRZuP6MHGT9gVflgOMpdZ3bHHB9T7JOVwC9P68bry3dz36ReLNtZw0tLiuibnYjLauL/FhSwYU8DWUk2Lhqai1FTVHj85KTYydyvHskOM5P6d6CT206C1ciITin0yEzgoqe+YWu5hxGdUyhr8HP762t49NKBLee7LEaevHIIDpOEMCGEEOJYkx6xNpgMGv+6fDC3vb4ajy9MqjPWw2QzGRiQk/SDr//Wyt3owKs3jkTXoazBx5srdtPgC7GnzscfpvTh003lfL65gsX5NWQkWJjcvwMAGQnW7wyByXYzyXYzXeL7evvDUWxmA59tquCzTRUALSHwrxf2JzfZhtGgSLGbSLRLCBNCCCGONaXr+ncfdQIaNmyYvnLlymP6O3bXenl3dQnnD8omFIliNCheXlLEdePyyEk+cFjxSH2+uZwbX1q1T9kjlwxgTBc3mqa4/fU1/Pqc3mwq9aApGN89jY7u7/d7G/1BNuzxUFTj5b5ZG1rKf3laN/yhCO+t2cOc28ZhM2ukOCSECSGEEEeTUmqVruvD9i+XHrFD8AViS0FsLW9kwiPzW8ovGtIBk+HojOh2S3Ny0ZBs3luzBx04t38WuSl2UJBoM/G7yX34y5zNpCdauXBIDom279dc/mCIBl+Yf3+Zz9DOybx36xgKq5vpkGRjfUkDby4v5qmrhuC0aNITJoQQQhxH0iN2CDWNAYKRKOGoztzN5SwrrOWGcXmkOi2U1vvolu4kyW7GajLgD0Xw+EMoFCmO2Ar4h2P5rhqWFsS2KVJKsaKwllXFtTxwXj98wQgVHj+dUx1YjBopDjNKHfndmV5/iKrmIMt21lJU28yMeQU4zAZ+1jeD8wZmk5tiw2Yy4DBrJDlk70ghhBDiWJAesSPkC0fYVtaA0WAgFI7yywld+XxLJU9+lQ+AyaB46boRdE1z8vTCAt5cUUKC1cjvp/RhfLfUw9oGKNFq4vPNFfzz8+0t1/zoV+N5an4BLy8tAmL7Wb5102jczu/XU1XvDzNvayUfri/lscsGs7ywlhW76pi9rowku5nrxuaRaNVw2iSECSGEEMeb9IgdQl2zn5eX7qY5EObioTnYTAZO+ce8fdb96uy28/jlgzh/xjf7nDvvnlPJS3W2ef1AOEJRtZfKxgBlDT4qGwOM6eIm2WHeZygUYGBOIi9cO/yI5m41eIPUNAfZsKeB91bvYVz3VD5cX8qjlwxs+QwOixG33YjFLIu1CiGEEMfSoXrEZPmKQ1JcMjSHYDjKE1/uoCkYPmDx1bIGP9HogWcuLaj9zqs3+UOsL6lHR2d1cR01TQGS7CaqmwIHHFva4McfOsgvakOdN8QVzywjwWpiwY4q3E4Lkwd04K631vHasiIsRgMui0FCmBBCCNGOJIgdQrLDgqbg2nGdGZCTiM1kICd53w2vpwzMosLjP+DcXlmu77x+vTdMtwwXM77KJzPRxsQ+GTzz9U7C0diq/a2d2z/rsFfvr2r0Udbg46Ulu6ho9OPxh5jQI52731rL1zuquXhoDteN60KGy4TLZv7O6wkhhBDi2JE5Ym0IRKKc/fjXnNMvk+ZAhCcuH8zTC3eyo6KR8T3SuHJELgr2WZn+kqE5dDqMJSYMmuJ/3lnHA+f1I6pH6ZhiZ9bqPWwrb+SpaUOZuaCAXdXNTOqfxagubg53BLmsIcDMBTvJjYfG38zawJ/P78d14zoTCEfp1yGRZJv0hAkhhBAnAglibXh9+W68wQgefxiDQXHTK6u4dFguE3qmsW53PcsKaxnb1R3b59FqwmkxkmAzkWj77pDjtBrJTrJxxbNL+ej2cWwuayTVaWF1cT13vL6GS4fnMrF3BqO7uNla4SHZYaK2OUBJnY+iGi9DOiaT4jRhM+1twurGADPm5bNgexVv/GI0r68oxuMLc/db68hNtvHWTaPJTJRJ+UIIIcSJQoJYG769keGb/Gr+OKUvbyzfzYx5sbsmO7ntXDo8lwZfmLdXlVDdGCQz0cxvJvU5rGunOi08fPEA9tT7aAqEeWnJLu49qyd3v7WW0gY/j3+xg+vH5XFqzzTGd0ujKRDhD7M3M2ddKQBGTfHajSMZkefeW18gqoM/FOXBjzbz0nUjWZxfjd1s4Jx+maTYZShSCCGEOJFIEGvDVSM78eI3u5jQM51QJMo/Lx1IWYMfg6ZwO8385cPN3HpaN95eWQLEhhvvOKMnrsNYugIgzWUlEtUpqvHy9Y5qOqXYeefmMWwp89At3YnRoLGmqJ5JA7KorGluCWEA4ajOA3M289J1I1qWtkhzWfjlhK58uaWCFbvquGTmN5zRO4MHz+/3vZe/EEIIIcSxI0GsDQlWI5/ecQr5lY2s3FXLhlIP87ZWEtV1Sup8/PWCfry9cnfL8ZkJZuxmjdrmwGEtNRGORPH4w5TU+ZjQM41XlhXz9qoSuqQ6ePDC/vzuvY1cOiwHAF8wcsD5Yzq5cJpgZVEpSbYEHGaNrCQbH98+nteWF5PiMHPJ0FwSLHJPhhBCCHEikiB2CP5QmCU7a8hwWemdlcDCHVV0S3NyRq908iubGJGXQlFNc8vm2ZcMzeGuiT14cl4BO6uamDoomxF5KW1uzF3dFGRHRSOvLC3i+vF5XDmiI6UNfoZ0TKI5EKagqonTe2dQ1ejHbjbQIdFKaUPsLs0lvz6NQFhnYUEdPTISMRsUH6wr5aIh2fTKSuBPU/sdl7+TEEIIIb4/CWKH4PGFSHNZuObFFVw5IpeJfTL5eGMZzywsoHOqA7fDRCe3g39eOpDVxfXcOqErVz67lF01XgC+2FLJb8/tzbSRHbGZD/wzN/pCePwhnpyXz4Pn9+ehT7eyp87LmK5uTumeyrurSnj/l2OxmTQm/3sRKQ4zM64awitLi/jNpJ58tqmS37y3EQCl4G8X9OfSYTl4g0e23pgQQggh2o8EsUOIROG5RYU0+EKMyEtB13V8wQh/Ob8f/nCUjAQrD360mQZfiFO6ualpDraEsG+9tGQX5/TLJOcgQczjD/PWit0M7ZTCHW+u4bqxeXRPd1HTHEDT4NbTuuF2mvnHp9uo8ASo8AS45oUVzLplFP4QPPjRlpZr6Xrs9fjuqUT2X3VWCCGEECcsCWJtKI8PA7odFuZtq+Tsfpm8vrwYs0Fj+ujO3HtWLzz+EH2zEmgMhA8432E2ooCqRj8efzi+ubaBRLsZjz/Ey0uLeGraUDqm2FmwrYpKT4DpozsxdcZi3rxpNIkRneLaWLhTCr64YzyJdgMVTWGa95sz1hiIrfxvOswNx4UQQgjR/mQW9yGkucxcNCQ2UX53vZdAOMp9szaQlWjj7H6ZzFlfyqVPL+GB9zcR0XWsRo2x3fYuJaEU3HtWT5JsJi586hvOeHQBYx76ikfnbqeuOUiizYTVZOD6/65g4fYq+nRIwO00U90YINluxmxUWE0GLhqaQ7YNtj0wkaAepd4XIdluYNl9p+9T39Fd3Bg0RYfk715MVgghhBAnBtn0+xDK6n2s3V1PUa2XpQXVPHhBf2bMK2DOulLyUh08eeUQZi4ooKzBy+/O7csvX1vFc9eMYEuZh/yKRs7ul4XZqNHgDeIPR1lSUMNjX2wnFNH57M7xdHI72F7RyG/f20hBVROn9Uznf8/pxUfr9jB5YDaJViM2s5EdlY10SLRiMRloCoQJRXRS7Eb8oQhGg2LqjKUM65zMnWd0JyvJ9t0fTAghhBDH3aE2/ZYgdgildT4mPr6AUV3cTOiRjsWo0bdDQmzVfAX3f7CRjikOrh7TCYvBwFXPLaOwuplpIzty09gcNIOBaS+sprC6GYArRuTSr0Miv31/IzOnDeXsfpl4fEEafHuHNH2BMFXNAbqkOchKtBOKRKlv8hGMKiJRHZMGJqOGPxglwa7h8etEdR2nxXhYy2UIIYQQon0cKojJHLFDMBk1Lhmay4vf7OLLLZUADMxJ5OlpQ6nzBfEGI7z4zS4CoQj/76zuvHz9CP75+XbG5xpxly3gz9tzWkIYxLZLevn6LNwOM/1zEgFIsJkprvXx2aYypg7KwWo2kBgxY9RiI8a1TX6eW1zM84sKCUd1RuSl8MTlg4joOr4QpDpNNPgiEsKEEEKIHymZI3YILquRq0Z25P7JfZjQM42bT+3CE1cMxqAp6rwh/uesXnRNczJ3cwUeXwSbivD7Camc2cWOv6mezRX+A665u9bLs9OHkmzfu/J+xxQblwzLJRyJUtkYIIqOQlHT5KPcE+SZhTsJx++EXF5Yy0vfFJFkMxIIRYmEI22uUyaEEEKIE5v0iB2C1WQgM9HKlAGZjO3mxu0wo6OoaQ7Q6A+TkGTioQv7kWg3YTMZqA9G6EAAQ2MlCSULOKfHYNaVNLRcT1MwIs9NgtWILxih0R/GpCl0oMEbIr+qmU4pdv795Q5+c25vLEaN9bvrD6jXmt31+EIRkmxGHDbpCRNCCCF+zCSItcFlNeGymkhLsOHxh5gxL5+nF+wEYvtKPnXVEFKdFv79VT6lDT7uObMrfZOsGCs3cOnQIOXNWby9ppJUl4U/TumD2aCwGhX5VV521TTTOyuBRz7bxpdbY0OfdrOB2beNZeb8AtaV1PP4ZYMPqNP47qnYLUYSJIQJIYQQP3oSxA6TxxfimYU7W15Hojp/+WgLj14ykNeWFwMwf1sVA3MSmfXz90lZ/E/+N70Ht94ymaDJxf1ztrGlvJEHzuuLxxciL81JOBJtCWEAwXAUXzDKm/FNxJUe4U9T+/LI3G00ByJMGZDFxUOzJYQJIYQQJwkJYofJH4qw/w2mFR4/SsH/nNWTIZ2SqW0O8vKSIjY3O+k/8S+Yg15WFvj51ZuLW1a8v/mVVXx+96ks31lDh/2WmzAZNBoDoZbXZz3xDS9dM5RPbh8PCqwGcLtkiQohhBDiZCGT9Q+Drus0+sN0du+7WOrUQdl0SrFR7w3iD0XQFFw7phMpdjOY7VRFHHy1rRqnxUivTBdOi5GoDhtK6hmel0JWkg2XxUi6y0KPDCfBSBS3w0y6a2+P1/QXV/HSkiIsBgNulyzWKoQQQpxMZB2xNkSjUcoa/CTaTLy7eg8DchJ58ZtdbK9o5JTuaVw4JIckm5E6X4g3V+wmI8HK1EEdsBo1AuEooYhOJKoTikTZUuahV2YCS3ZWM6qLG1A0+oNkJtooqGyirMHP8LwUEm1GyhsCzJyfT2GNlykDOnDhkGzS5e5IIYQQ4kdL1hE7QjVNfvbU+3lg9ibumdiD03ulc83zy5nQK51BuUkUVjeRkWBhR0UTlz2zhG/32n55SRHv3DKa5YW1rC6qI9Vl4dG521uu++epfUl3WbCbDNR6jdz7zjoW5dcAYDVpvHPzGLaXezijTwbjsw3YbHYcLglhQgghxMlIhiYPIRiKkmI38eSVg8lNdWA1ajx79TA6pthJsZu5flwXGnwhnpyX3xLCAPbU+9hQ0sCmUg+T+mcxY17+Ptd96JOt+EJRDBpUNAZaQhiAPxTl759uZXTXVKxGA8kuBw5X4vH6yEIIIYQ4ziSIHUR1YwCLWUNpCn84SiSqEwhHsZsNnNo9ldHd3PzssYWHPF/XIclqRNMU/lB0n/eagxF0XUfTNOqagwecW9scJBSNMrhjEgZbwlH/bEIIIYQ4ccjQ5EFYjQp/WMcfinLzy6vYUdlEmtPC3y7sT58OLuqbgwTCUdYU1/G7c3tz22ldsZoMNPjCfL2jiiEdk2gOhrGbDYzp6uabgr29Xqf3SiMYibKmqI6emS4SbSYafHvvlLx0eC7pdjNWq+lgVRNCCCHESUSC2EEYDQq/P8q9b69jR2UTAFVNAe56cy2v3jiSNKeFzm47A3KS2FzmoaoxwP/NK8AfinD9+DwC4SivLC2mORDmiSsG8+6qEpYV1jC2WyrTRnVi/e56Hvp0K7N/OYZZt47hsbnbKfP4uXhIDqf1SpcQJoQQQvxESBA7iGBER1OK1cX7bjHUGAjjD0UIRaK8cM1w/vbpVqaN7MRtr61pOebxL3aQkWBF13W2VTQydcYi/nZBf64e0wlNwbZyD7PW7OF/z+nFxEcWMP/eU7h/Sm9CEZ00hxGz2Xy8P64QQggh2onMETuIYDgKus6Qjkn7lOe57WQkWAmHg6RaQgzJSWBpQQ2n9kjjD1P6cNfEHnRy21m4vYqBubFzR3dxMyIvhYiuYzRoeIMhzuqbyeebKnj6muF8urWG6qYgJoMmIUwIIYT4iZF1xA6iwuOnrN5Lgs3MTfE5YmO6unnwgn48/MlWtlY0MbFHEjeMysRhMvDmpibeW7MHt8PML07tQm1zkDlrS+nstnP12DwembuN1UV1DOuczN0Te1LR4AOlyK9sYmBOIkl2s6wTJoQQQpzEZB2xw6TrOkYNIlHQFDwzfSiaUhg1xWXPLKWkzgfAM9XN1Pki/H6EYv62EBv2NACwtLCGz+86lSUF1Uwfm8dtr61uGeLcVeNlT52fRy8dwO5aH6O7uDEZNAlhQgghxE+UDE3uJ6rrNPjCpLks/O79jZz2yAJO/cd8KjyBlhD2rdkbKvFrNs7ttjdI+UNRvt5Rxa9O704kEj1gntmSnTVEotDJbcdq0shIlBAmhBBC/FRJENtPgy+EyaDw+MMsbrXshEFTaGrfY7MSraiwn9LmfdcKS3dZKar1YtAUDrNhn/cSbEY0BS6LkVRZMV8IIYT4SZMgtp9wOIqu67gdZnpn7l1Qde7mCm47rWvLa5NB8dCkjiS705mzZW+v18i8FLqkOfjzh5sJhKL8bnJvVDzAKQV/mNwXh8WIQ5aoEEIIIX7yZLL+fuqbAzQFI1Q2+LBZTFz34grMRo10p5mHLx6IzaTweEMkWBUWPUSBBzokO9hd00Tf7CQUMG9bBbe9vg6ABfdOAGBnVTNd0x2YDQYyZThSCCGE+Ek51GR9CWKthCJRapsDmDWFL6yzuqiO/jmJeAMRgpEoVpPGxxvKKWvwceGQbFIdFuwWIylWA9W+MP/9Zhe1zUF+ProzKXYTp/xjPgCndnfz0IUDMBoUaQm2o1pnIYQQQpz45K7Jw6ApsBgUmqahwhGsJgN/nL2J03tl0CPTyc2vrKKswQ/AWytLeOX6EQzvlESNN8x5Ty6mNr535Kw1e3jjxlH8eWpf/jhnM784tRuJNiN2q6wTJoQQQoi9ZI5YKwZNIxDWieqxx+8/2MhZ/TKZu7mcsnp/Swj71r+/yiesw7LCmpYQBrFNv2cu2MnEPhl8cfcp9Ey3SwgTQgghxAGkR2w/evw/OlDVGKBzip0Hp/Rityd0wLEGTVHvDWExHJhnzUYNo6aRYtdItFuOeb2FEEII8eMjPWL70ZTi21lz/7qkH1YjXPvyWgyaIi/V0eo4uOmULtzz9joGd0omJ3nv3C+TQfGr07vhNCMhTAghhBCHJD1i+0lPsFLp8RGJ6gzLS+GMxxbTFAjjC4Z58orBLMqvpqopwM/6ZJCRYMVh0LCZNN6+eTRfbK6gqinIBYM64LIasVokhAkhhBDi0CSI7ccfjODxhdF1HZ8/TFMgTKrTTFSHyU8uYmReCok2E794eRWj8lJ48OKBzF5XypiuqVw4OBuTUcMXipBokzlhQgghhGibDE3upykQQmkKfzBCot2MQVMk2kzUeYPoOizdWctnmyqo94Yo9wTYU+9jVF4KVz+/nMLqRoISwoQQQghxmKRHrBVd1wlHdeq9QSo9fnISTTx4Xk8sRsXobqnYTAZ8oUjL8ZMHZPHh+lKuH9uZ924cTJIrAbNZ/qRCCCGEODySGloJRaKEo1HcDgsdk6zUBKKc3jsLfwQKq5t595YxPPzpVqqaAkwdlE2q08KaojosRg2HyS4hTAghhBBHRJJDK2ajgYqGADnJVkK6IjvBzIayRq76zzJCEZ0Xrx3OpP6ZeENRFmyrYnVxLbNuHYvDpLDZZNsiIYQQQhwZmSO2n05uOw5LLJ/W+sI8+NEWQpHYgha3v7EGu9nI4Nwkpo/uyCd3nEKG0ywhTAghhBDfiwSx/djMBkKR2HwxXdepabVivscX5rbX1wAwtGMSSVYTTpmYL4QQQojvSYJYK8FwhGg0FsCsJgPJVhOXDsvd55iMBAtpLgsWg4bdKiO7QgghhPj+JEm0Eo1+u6a+DrqOHgly2fAcEqxG5qwvo5Pbzp1ndCfNrLmA1wAABrxJREFUYcRkMrVrXYUQQgjx4ydBrBWr2Yi/KQAKgmEdzWTBpYe5amgOE3tnYDNpuKwGCWFCCCGEOCpOmKFJpdTZSqltSql8pdSv260imkJDw2BQhKPgw4gyamQkmElxWSWECSGEEOKoOSF6xJRSBmAGMBEoAVYopWbrur75eNclyR6bfN8cCGHQNKwmw/GughBCCCF+Ik6UHrERQL6u6zt1XQ8CbwBT27NCDotJQpgQQgghjqkTJYhlA7tbvS6Jl+1DKfULpdRKpdTKqqqq41Y5IYQQQohj4UQJYuogZfoBBbr+jK7rw3RdH5aWlnYcqiWEEEIIceycKEGsBGi9YFcOUNpOdRFCCCGEOC5OlCC2AuiulMpTSpmBy4HZ7VwnIYQQQohj6oS4a1LX9bBS6jbgM8AAPK/r+qZ2rpYQQgghxDF1QgQxAF3XPwY+bu96CCGEEEIcLyfK0KQQQgghxE+OBDEhhBBCiHYiQUwIIYQQop1IEBNCCCGEaCcSxIQQQggh2okEMSGEEEKIdiJBTAghhBCinUgQE0IIIYRoJxLEhBBCCCHaiQQxIYQQQoh2IkFMCCGEEKKdSBATQgghhGgnEsSEEEIIIdqJBDEhhBBCiHaidF1v7zp8L0qpKqDoGFw6Fag+BtcV7Uva9eQlbXvykrY9ef0U27aTrutp+xf+aIPYsaKUWqnr+rD2roc4uqRdT17SticvaduTl7TtXjI0KYQQQgjRTiSICSGEEEK0EwliB3qmvSsgjglp15OXtO3JS9r25CVtGydzxIQQQggh2on0iAkhhBBCtBMJYkIIIYQQ7USCWJxS6myl1DalVL5S6tftXR9xcEqp55VSlUqpja3KUpRSnyuldsR/JsfLlVLqiXibrldKDWl1ztXx43copa5uVT5UKbUhfs4TSil1fD/hT5NSKlcpNU8ptUUptUkpdUe8XNr2R04pZVVKLVdKrYu37QPx8jyl1LJ4O72plDLHyy3x1/nx9zu3utZ98fJtSqmzWpXL93c7UkoZlFJrlFIfxl9L2x4JXdd/8g/AABQAXQAzsA7o0971ksdB2+oUYAiwsVXZw8Cv489/Dfw9/nwS8AmggFHAsnh5CrAz/jM5/jw5/t5yYHT8nE+Ac9r7M/8UHkAWMCT+3AVsB/pI2/74H/G/tzP+3AQsi7fZW8Dl8fKZwC3x57cCM+PPLwfejD/vE/9utgB58e9sg3x/t/8DuBt4Dfgw/lra9gge0iMWMwLI13V9p67rQeANYGo710kchK7rC4Ha/YqnAv+NP/8vcH6r8pf0mKVAklIqCzgL+FzX9Vpd1+uAz4Gz4+8l6Lq+RI99O7zU6lriGNJ1vUzX9dXx543AFiAbadsfvXgbNcVfmuIPHTgdeCdevn/bftvm7wBnxHsvpwJv6Loe0HW9EMgn9t0t39/tSCmVA5wL/Cf+WiFte0QkiMVkA7tbvS6Jl4kfhwxd18sg9g86kB4vP1S7tlVecpBycRzFhysGE+s5kbY9CcSHrtYClcTCcQFQr+t6OH5I6/ZoacP4+w2AmyNvc3F8PA78DxCNv3YjbXtEJIjFHGyuiKzr8eN3qHY90nJxnCilnMC7wJ26rnvaOvQgZdK2Jyhd1yO6rg8Ccoj1cvQ+2GHxn9K2PxJKqclApa7rq1oXH+RQads2SBCLKQFyW73OAUrbqS7iyFXEh56I/6yMlx+qXdsqzzlIuTgOlFImYiHsVV3XZ8WLpW1PIrqu1wPzic0RS1JKGeNvtW6PljaMv59IbDrCkba5OPbGAucppXYRGzY8nVgPmbTtEZAgFrMC6B6/08NMbBLh7Haukzh8s4Fv7467GvigVfn0+B12o4CG+PDWZ8DPlFLJ8bvwfgZ8Fn+vUSk1Kj5vYXqra4ljKP73fg7Youv6P1u9JW37I6eUSlNKJcWf24Azic0BnAdcHD9s/7b9ts0vBr6Kz+ubDVwev/MuD+hO7AYM+f5uJ7qu36freo6u652J/d2/0nX9KqRtj0x73y1wojyI3YW1ndjchd+2d33kcch2eh0oA0LE/m/pemJzDL4EdsR/psSPVcCMeJtuAIa1us51xCaE5gPXtiofBmyMn/Mk8d0n5HHM23UcsSGH9cDa+GOStO2P/wEMANbE23YjcH+8vAuxf2zzgbcBS7zcGn+dH3+/S6tr/TbefttodderfH+3/wOYwN67JqVtj+AhWxwJIYQQQrQTGZoUQgghhGgnEsSEEEIIIdqJBDEhhBBCiHYiQUwIIYQQop1IEBNCCCGEaCcSxIQQQggh2okEMSGEEEKIdvL/AeIi28E5Wq06AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -420,45 +1214,27 @@
    ],
    "source": [
     "plt.figure(figsize=(10,10))\n",
-    "sbs.scatterplot(x=X[:,0],\n",
-    "                y=X[:,1],  \n",
-    "                hue=Y[:,0])\n",
+    "sbs.scatterplot(x=X_train[:,0],\n",
+    "                y=X_train[:,1],  \n",
+    "                hue=y_train)\n",
     "\n",
-    "plt.title('n=' + str(len(X1)))\n",
+    "plt.title('n=' + str(len(X_train)))\n",
     "plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)\n",
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we can train and predict with the model, we need to scale the variable data and create trainings and test data for both variables and target."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),\n",
-    "                                                                    Y[:,0],\n",
-    "                                                                    test_size=0.5)"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 1.60152382e-17, -5.33841274e-18]), array([1., 1.]))"
+       "(array([-4.71003707e-17,  6.39219317e-17]), array([1., 1.]))"
       ]
      },
-     "execution_count": 222,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -485,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -513,7 +1289,7 @@
        "    tol=0.001, verbose=False)"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -531,16 +1307,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1., 1., 1., ..., 1., 1., 0.])"
+       "array([0, 0, 0, ..., 0, 0, 1])"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,17 +1328,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.83420715,  0.5966746 ,  0.62393972, ...,  0.76362647,\n",
-       "        0.58796771, -0.18603833])"
+       "array([-2.43595112, -2.90152701, -0.41222322, ..., -0.47675971,\n",
+       "       -0.26396479,  0.03287877])"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -592,16 +1368,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.38467317806160783\n",
-      "Precision: 0.05465116279069768\n",
-      "Recall: 0.8867924528301887\n"
+      "Accuracy: 0.6875\n",
+      "Precision: 0.09971509971509972\n",
+      "Recall: 0.7142857142857143\n"
      ]
     }
    ],
@@ -624,14 +1400,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average precision-recall score: 0.07\n"
+      "Average precision-recall score: 0.11\n"
      ]
     }
    ],
@@ -643,12 +1419,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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jje8UkWDMzGwnKjYRHJH+rcgZF8Dx25opIu4D7ssbd3nO+wC+mb7MzKwFFJUIIuLjpQ7EzMxaRrG9hrpJuq6mC6ekayV1K3VwZmZWesWeLL4FWAOMSV+rgT+WKigzM2s+xZ4jGBQRZ+YM/0jS7FIEZGZmzavYI4L3JR1TMyDpaOD90oRkZmbNqdgjgi8DE9LzAgJWAONLFZSZmTWfYnsNzQY+IqlrOry6pFGZmVmz2WYikHRuRPxF0jfzxgMQEdeVMDYzM2sGjR0RdEr/dil1IGZm1jK2mQgi4sb074+aJxwzM2tuxV5Qdo2krpLaSnpY0ruSzi11cGZmVnrFdh89KT1BfDrJraOHAN8pWVRmZtZsik0EbdO/pwITI2JFieIxM7NmVux1BH+T9BLJRWRfkdQL2FC6sMzMrLkUdUQQEZcBHwUq0qeJraP+g+jNzKwVauw6guMjYpqkz+SMyy3y11IFZmZmzaOxpqHjgGnApwpMC5wIzMxavcauI7gi/fuF5gnHzMyaW7HXEfynpO45w7tL+knpwjIzs+ZSbPfRUyLivZqBiFhJ0pXUzMxauWITQZmk9jUDkjoC7bdR3szMWoliryP4C/CwpD+SnCT+IjChZFGZmVmzKfZ5BNdIeh44keTBND+OiAdLGpmZmTWLYo8IAOYBVRHxd0m7SeoSEWtKFZiZmTWPYnsNXQjcBdyYjtobuLdUQZmZWfMp9mTxV4GjgdUAETEf6F2qoMzMrPkUmwg2RsSmmgFJ5SQnjc3MrJUrNhE8Kun7QEdJnwDuBP5WurDMzKy5FJsIvgssA14ALgbuA35QqqDMzKz5NNprSFIb4PmIOBD4felDMjOz5tToEUFEVAPPSdqnGeIxM7NmVmzT0J7AnPTB9VNqXo3NJOlkSS9LWiDpsm2UO0tSSKooNnAzM9s5ir2g7EdNXbCkMuAG4BMkD7x/WtKUiJibV64L8DVgZlPXYWZmO66xJ5R1AC4BBpOcKL45IqqKXPbhwIKIeDVd1iSSx1vOzSv3Y+Aa4NtNiNvMzHaSxpqGJgAVJEngFODaJix7b2BJznBlOq6WpIOBvhExdVsLknSRpFmSZi1btqwJIZiZWWMaaxoaGhEHAUi6GXiqCctWgXG1F6GlvZGuB8Y3tqCIuAm4CaCiosIXspmZ7USNHRFsrnnThCahGpVA35zhPsAbOcNdgAOBRyQtAo4EpviEsZlZ82rsiOAjklan70VyZfHq9H1ERNdtzPs0sK+kAcBSYCzw2ZqJEbEK6FkzLOkR4NsRMavJtTAzs+3W2MPry7Z3wRFRJelS4EGgDLglIuZIugqYFRGNdj81M7PSa8rzCJosIu4juR1F7rjLGyg7spSxmJlZYcVeUGZmZrsoJwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMK2kikHSypJclLZB0WYHp35Q0V9Lzkh6W1K+U8ZiZWX0lSwSSyoAbgFOAocA4SUPzij0LVETEcOAu4JpSxWNmZoWV8ojgcGBBRLwaEZuAScDo3AIRMT0i1qeDTwJ9ShiPmZkVUMpEsDewJGe4Mh3XkAuA+wtNkHSRpFmSZi1btmwnhmhmZqVMBCowLgoWlM4FKoBfFJoeETdFREVEVPTq1WsnhmhmZuUlXHYl0DdnuA/wRn4hSScC/wEcFxEbSxiPmZkVUMojgqeBfSUNkNQOGAtMyS0g6WDgRmBURLxTwljMzKwBJUsEEVEFXAo8CMwD7oiIOZKukjQqLfYLoDNwp6TZkqY0sDgzMyuRUjYNERH3Affljbs85/2JpVy/mZk1zlcWm5llnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWVcSa8sttZp+kvv8NiCd4squ2HzFqa99A5fOLo/ZW3q7lfs/+EuHD24ZylCNLOdyInA6rnuoVeY++ZqOrYta7Ts2o1VAPznfS/Vm9azc3tm/cB3ETH7oHMiyICI4PnKVazftKWo8ms3VjFySC9uHn9Yo2UXL1/Hcb94hKe+fwLtcxLH1ffP4/4X39rumM2s+TgRZMCsxSs5+3dPNGmeg/buVlS5fnt0YtHVp9Ub367Mp5/MWgsnggyoab758ehhDO7dpah5hu7VtZQhmdkHiBNBK7VszUZeeXtNUWXnvrEagAP37sbB++xeyrBqba4O3lu/mReXrqo3bXDvznQo4vyDmTUPJ4JW6pt3zGbG/OJ69tTo0qH5vu7bZ74OwOn/9Vi9aWMP68vVZw5vtljMbNucCFqpdRurGN6nGz84bWhR5bt0KC+6WWhn+v15FXWGL5/8Iqs3bG72OMysYU4EH3ALl63lz08spjqizvjXV7zPAXt24fABPVoosm176vsnsGTleg7tVze+ax6o383UzFqWE8EHxPK1G/n2nc+xLq+L51OvrQBg993a1pvn4L7dmyW27dG7awd6d+3Q0mGYWRGcCFrAv096lvlvr60zbu6byQndD3Vtz4CenerN8+zlJzVLbGaWPU4ELWDy7DcY2LMTA3t1rh23YNlaNlVV84uzPsLHhvRqweiybcW6TTy9aEW98W+v3kDvLh3onndk9ugry+i7+2600dZxlSvfZ+JTr3PMvvVvr3H04J6Mqei70+M22xGKvLbnD7qKioqYNWtWnXGbN2+msrKSDRs2NDjfmg2bWfV+FXt170AbqcFy27KlOli2ZmO99vqmqg7o2rGcrh3qN/fs6ipXvg9AWe6Wk2BLNZS1gbZlbVixIZi2ZAsr3t/CjPnv8u2ThlCec4Haluqgc/tyjh68R6Prqw747fQF5H9jz1eu4rV319F9t7aU59wj6d21G3ekenV07VBOj07taocXLV8PwAF7FneNRoe2bbh+zAj6FzhC/CCo2lLNxqrqeuM3b6k/DqB9eRlty+r+722squbNVRvI/5dsI7FPj93I/0/dtKWaLdV1v831m7bwwtL36q3vzVUbKJPo1aV9nfFlbcSRA/fIXBdmSc9EREWhabvEEUFlZSVdunShf//+qIGN/KJ319Fhw2bKytsUlQgEfLhbB7rkbKzXbayietlaunZoS7vyHbtydo9O7erckiErNlcm/7C5G8iqLVHbk6h9eRu6rV3FRze9zWUPJd1jf/l/r+yUdfffY7fa9zUb5aMG7UH33bbGMvnZpXTfrR03nXdonXl/MnUeg3t35pSDPlxn/M/vf4lrzvoIXTtu/VfaXBW8vmJ9vSOC/pf9LwB9du/YaKxrNmzmyVdXMOGJRZxy4J51pnVsW0bfHnWXUbnyfea9ubre7//JV5fTZ/eOdT7v99Zv5rqHXmGfHrvRfgd+x/PfWdt4oQ+w3N/D8rWbWLOxiiMG9GD3nN/Ds0tW8vbqjZw8rO73/tiCd+nWsS2591msroal771Pz87tOax/49frbN4SdOlQzoXHDqw3bVDvTrQvb77twy5xRDBv3jz233//BpMAJBdVVVVX07l9eVGJYPWGzfTq0p49u239h1u3sYqFy9YyoGenOgnCdtzGqi20K2uDJCKCl156iX6D9uVn973E9089oE7ZAy5/AID/GndwUctuV96G44b0avE9wAXvrGXFuk1F9fRasmI9x14zveQxnZqX2JrivhfeYmDPTow9fGtT14bN1Vz30Ct8+6QhdGq/NTneOauSuW+u5pufGFJnL3/S00vo0akdXzp2QJ1l//uk2Qzs2YnRI/auHffOmg3cNvN1LjluED061f3/K2vThop+dTe+D8x5i/blbTh+/96146oDzrjhcQBGj9irdvzD895h7cYqytqIwTlNti+nF23u96G6Xa+DYN3GLRyR812u3rCZv897B4AhH+pMY155u+FEOu7wffjZZw5qdBlNsa0jgl0mERxwwAENzJGoSQQH7NmVtkXcB+eFpavo2bmdE0EL2dZ3unLdJlas38SgXo3/s7VmLy5dxar3615z8dDct7n1n4v44elD65yXANizWweG7VX3HlF/n/c2h+yze52jkACuf+gVrhw1rKj/BSvewmVr2afHbkV9rv9c8C6/njaf8UfVTYJXTpnDof1254bPHbJTY3MiwImgtSnmOzXbFdU0IXZpX7/l/oenD2XMYdvX2WBbicC7Aw2ICNZuqGLNhs21r23dxvmnP/0pw4YNY/jw4YwYMYKZM2dy5ZVX8r3vfa9OudmzZ9du4NauXcvFF1/MoEGDGDZsGB/72MeYOXNmwViOP/54Vq9eXTvunnvuQRIvvbT1Aq1FixbRsWNHRowYwdChQ7nkkkuori584q5YGzdu5JxzzmHw4MEcccQRLFq0qGC5Bx54gP3224/Bgwdz9dVX144/9thjGTFiBCNGjGCvvfbijDPOAGDq1KlcccUVOxSb2a7o4H2S64POruhb7zWwV4k6DkREq3odeuihkW/u3Ln1xuWbs3RVPLdkZWyq2tJo2YiI55asbPC1bsPmOmX/+c9/xpFHHhkbNmyIiIhly5bF0qVL46WXXooBAwbUKfvd7343rrrqqoiIOOecc+Kyyy6LLVuSmBYuXBhTp06tF8vUqVPj61//ep1xZ599dhxzzDFxxRVX1I577bXXYtiwYRERsXnz5jj22GPj7rvvLqq+Dbnhhhvi4osvjoiIiRMnxpgxY+qVqaqqioEDB8bChQtj48aNMXz48JgzZ069cp/5zGdiwoQJERFRXV0dI0aMiHXr1hVcbzHfqZkVD5gVDWxXd4leQ7l+9Lc5tXfbzLV+0xYigt3al9frklbIuvTWzR3blbHfh7vwvVOSvfiyNqrX0+LNN9+kZ8+etG+fdFPr2XNrb5Hu3bszc+ZMjjjiCADuuOMOHnzwQRYuXMjMmTO57bbbaJN2PRg4cCADB9bvQXDbbbdx0UUX1Q6vXbuWxx9/nOnTpzNq1CiuvPLKevOUl5dz1FFHsWDBgiJq27DJkyfXLv+ss87i0ksvJSLqnJh/6qmnGDx4cG3sY8eOZfLkyQwduvU+SGvWrGHatGn88Y9/BEASI0eOZOrUqYwZM2aHYjSzHeOmoW1o00a0kWhb1oZO7cvp1L6cDm3L6vVOOumkk1iyZAlDhgzhK1/5Co8++mjttHHjxjFp0iQAnnzySfbYYw/23Xdf5syZw4gRIygra7wny+OPP86hh27tznjvvfdy8sknM2TIEHr06MG//vWvevOsX7+ehx9+mIMOqt/zILe5Jvf197//vV7ZpUuX0rdv0iZZXl5Ot27dWL58eYNlAPr06cPSpUvrlLnnnns44YQT6Np1ax/6iooKZsyY0Wj9zay0drkjgis+Nazg+KaeLG6Kzp0788wzzzBjxgymT5/OOeecw9VXX8348eMZO3YsRx11FNdeey2TJk1i3LhxTV7+ihUr6NJla/e1iRMn8vWvfx1I9r4nTpzIIYckPQwWLlzIiBEjkMTo0aM55ZRT6i2vKRvfKNCZID8RFlNm4sSJfOlLX6ozrnfv3rzxxhtFx2JmpVHSRCDpZOBXQBnwh4i4Om96e+BPwKHAcuCciFhUyphKpaysjJEjRzJy5EgOOuggJkyYwPjx4+nbty/9+/fn0Ucf5e677+aJJ5JHRg4bNoznnnuO6urq2qahhpSXl9eWW758OdOmTePFF19EElu2bEES11xzDQCDBg1i9uzZ21zesccey5o19R9q88tf/pITT6z7sPk+ffqwZMkS+vTpQ1VVFatWraJHjx4Fy9SorKxkr7229tFevnw5Tz31FPfcc0+d+TZs2EDHjo1fXGVmpVWypiFJZcANwCnAUGCcpPyb518ArIyIwcD1wM9LFU8pvfzyy8yfP792ePbs2fTr1692eNy4cXzjG99g0KBB9OnTB0g22BUVFVxxxRW1e9Tz589n8uTJ9Za/33778eqrrwJw1113cd5557F48WIWLVrEkiVLGDBgAI89Vv8BMA2ZMWMGs2fPrvfKTwIAo0aNYgbPRJkAAAfNSURBVMKECbXrPv744+vt7R922GHMnz+f1157jU2bNjFp0iRGjRpVO/3OO+/k9NNPp0OHuncjfeWVVzjwwAOLjtvMSqOU5wgOBxZExKsRsQmYBIzOKzMamJC+vws4Qdu6PHgnKMXC165dy/nnn8/QoUMZPnw4c+fOrXMC9+yzz2bOnDmMHTu2znx/+MMfeOuttxg8eDAHHXQQF154YZ096RqnnXYajzzyCJA0sXz605+uM/3MM8/k9ttv3+n1ArjgggtYvnw5gwcP5rrrrqvtGvrGG29w6qmnAskRy29+8xs++clPcsABBzBmzBiGDdvaRNdQk9j06dM57bT6D743s+ZVsgvKJJ0FnBwRX0qHPw8cERGX5pR5MS1TmQ4vTMu8m7esi4CLAPbZZ59DFy9eXGddxVx8tGbDZl5fvp5he3fbZrkPojfffJPzzjuPhx56qKVD2WnefvttPvvZz/Lwww8XnO4Lysx2rpa6oKzQznd+1immDBFxU0RURERFr17bd4vmLh3atsokALDnnnty4YUX1rmgrLV7/fXXufbaa1s6DDOjtCeLK4Hca6H7APldRGrKVEoqB7oB9W8Gb7tcX/vDDjuspUMws1QpjwieBvaVNEBSO2AsMCWvzBTg/PT9WcC02M62qlI1cVnz83dp1rxKlggiogq4FHgQmAfcERFzJF0lqaZLyc3AHpIWAN8ELtuedXXo0IHly5d7A7ILiAiWL19er4eRmZXOLnH30WKeUGatR4cOHejTpw9t2/oOr2Y7yy7/hLK2bdsyYMCAxguamVk9vteQmVnGORGYmWWcE4GZWca1upPFkpYBixstWFhP4N1GS+1aXOdscJ2zYUfq3C8iCl6R2+oSwY6QNKuhs+a7Ktc5G1znbChVnd00ZGaWcU4EZmYZl7VEcFNLB9ACXOdscJ2zoSR1ztQ5AjMzqy9rRwRmZpbHicDMLON2yUQg6WRJL0taIKneHU0ltZf0P+n0mZL6N3+UO1cRdf6mpLmSnpf0sKR+hZbTmjRW55xyZ0kKSa2+q2ExdZY0Jv2u50gqzTNMm1ERv+19JE2X9Gz6+z61JeLcWSTdIumd9AmOhaZL0q/Tz+N5SYfs8EojYpd6AWXAQmAg0A54DhiaV+YrwO/S92OB/2npuJuhzh8HdkvffzkLdU7LdQH+ATwJVLR03M3wPe8LPAvsng73bum4m6HONwFfTt8PBRa1dNw7WOePAYcALzYw/VTgfpInPB4JzNzRde6KRwSHAwsi4tWI2ARMAkbnlRkNTEjf3wWcIKkUz7VvLo3WOSKmR8T6dPBJkifGtWbFfM8APwauAXaFe5QXU+cLgRsiYiVARLzTzDHubMXUOYCu6ftu1H8SYqsSEf9g209qHA38KRJPAt0l7bkj69wVE8HewJKc4cp0XMEykTxAZxWwR7NEVxrF1DnXBSR7FK1Zo3WWdDDQNyKmNmdgJVTM9zwEGCLpcUlPSjq52aIrjWLqfCVwrqRK4D7g35ontBbT1P/3Ru0SzyPIU2jPPr+PbDFlWpOi6yPpXKACOK6kEZXeNussqQ1wPTC+uQJqBsV8z+UkzUMjSY76Zkg6MCLeK3FspVJMnccBt0bEtZI+Cvw5rXN16cNrETt9+7UrHhFUAn1zhvtQ/1CxtoykcpLDyW0din3QFVNnJJ0I/AcwKiI2NlNspdJYnbsABwKPSFpE0pY6pZWfMC72tz05IjZHxGvAyySJobUqps4XAHcARMQTQAeSm7Ptqor6f2+KXTERPA3sK2mApHYkJ4On5JWZApyfvj8LmBbpWZhWqtE6p80kN5IkgdbebgyN1DkiVkVEz4joHxH9Sc6LjIqIWYUX1yoU89u+l6RjAJJ6kjQVvdqsUe5cxdT5deAEAEkHkCSCZc0aZfOaApyX9h46ElgVEW/uyAJ3uaahiKiSdCnwIEmPg1siYo6kq4BZETEFuJnk8HEByZHA2JaLeMcVWedfAJ2BO9Pz4q9HxKgWC3oHFVnnXUqRdX4QOEnSXGAL8J2IWN5yUe+YIuv8LeD3kr5B0kQyvjXv2EmaSNK01zM973EF0BYgIn5Hch7kVGABsB74wg6vsxV/XmZmthPsik1DZmbWBE4EZmYZ50RgZpZxTgRmZhnnRGBmlnFOBGYFSNoiabakFyX9TVL3nbz8RWk/fySt3ZnLNmsqJwKzwt6PiBERcSDJtSZfbemAzErFicCscU+Q3tRL0iBJD0h6RtIMSfun4z8k6R5Jz6Wvo9Lx96Zl50i6qAXrYNagXe7KYrOdSVIZye0Lbk5H3QRcEhHzJR0B/BY4Hvg18GhEfDqdp3Na/osRsUJSR+BpSXe35it9bdfkRGBWWEdJs4H+wDPAQ5I6A0ex9TYdAO3Tv8cD5wFExBaSW5sDfE3Sp9P3fUluAOdEYB8oTgRmhb0fESMkdQOmkpwjuBV4LyJGFLMASSOBE4GPRsR6SY+Q3BDN7APF5wjMtiEiVgFfA74NvA+8JulsqH127EfSog+TPAIUSWWSupLc3nxlmgT2J7kVttkHjhOBWSMi4lmSZ+WOBT4HXCDpOWAOWx+b+O/AxyW9QNKUNAx4ACiX9DzJIzOfbO7YzYrhu4+amWWcjwjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLu/wNBbHUDxaheVgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "
"
       ]