From 83eee84c2ae2d40b2442617efaedda29c7e7e4ee Mon Sep 17 00:00:00 2001
From: Zhe Feng <zhe.feng@pnnl.gov>
Date: Fri, 8 Mar 2024 19:02:19 -0800
Subject: [PATCH] Saag (#86)

* Added codes to work with SAAG WRF simulations.

1. Added Notebooks/test_idcell_reflectivity.ipynb to visualize the cell identification to an example radar reflectivity snapshot.
2. Modify pyflextrkr/preprocess_wrf_tb_rainrate.py to have options to work with SAAG WRF output precipitation.

* Added capability to track convective cells with composite reflectivity.

1. Added a function in pyflextrkr/idcells_reflectivity.py to work with composite reflectivity (2D) input data, with an option to pass additional variables specified in the config file from the input data to tracking output pixel-level data.
2. Added an option in pyflextrkr/netcdf_io.py to pass additional variables to output pixel-level data.
3. Modified Analysis/plot_subset_cell_tracks_demo.py to allow more flexibility to customize the quiclook plots.
4. Updated Notebooks/test_idcell_reflectivity.ipynb to visualize more cell identification settings and results.

* Added options to remove cells smaller than certain size.

1. Added flags in pyflextrkr/idcells_reflectivity.py to remove cells smaller than specified thresholds from the config file.
2. Added codes in pyflextrkr/steiner_func.py to remove cells smaller than specified thresholds.
3. Updated Notebooks/test_idcell_reflectivity.ipynb.
4. Adjusted reflectivity levels in Analysis/plot_subset_cell_tracks_demo.py.

* Updated advection function.

1. Updated /pyflextrkr/advection_tiles.py to work with Scikit-image v0.22, where phase_cross_correlation returns 3 items by default.
2. Updated environment.yml and requirements.txt to have scikit-image>0.22.
---
 Analysis/plot_subset_cell_tracks_demo.py      |  148 +-
 ...ot_steiner_convective_cell_functions.ipynb |    6 +-
 Notebooks/test_idcell_reflectivity.ipynb      | 1198 +++++++++++++++++
 environment.yml                               |    4 +-
 pyflextrkr/advection_tiles.py                 |    2 +-
 pyflextrkr/idcells_reflectivity.py            |   79 +-
 pyflextrkr/netcdf_io.py                       |   10 +
 pyflextrkr/preprocess_wrf_tb_rainrate.py      |   40 +-
 pyflextrkr/steiner_func.py                    |   26 +
 requirements.txt                              |    4 +-
 10 files changed, 1418 insertions(+), 99 deletions(-)
 create mode 100644 Notebooks/test_idcell_reflectivity.ipynb

diff --git a/Analysis/plot_subset_cell_tracks_demo.py b/Analysis/plot_subset_cell_tracks_demo.py
index e78d069..15eb3d1 100644
--- a/Analysis/plot_subset_cell_tracks_demo.py
+++ b/Analysis/plot_subset_cell_tracks_demo.py
@@ -240,7 +240,7 @@ def plot_map(pixel_dict, plot_info, map_info, track_dict):
     pixel_bt = pixel_dict['pixel_bt']
     xx = pixel_dict['longitude']
     yy = pixel_dict['latitude']
-    dbz_comp = pixel_dict['dbz_comp']
+    var_fill = pixel_dict['var_fill']
     conv_mask = pixel_dict['conv_mask']
     tn_perim = pixel_dict['tn_perim']
     lon_tn = pixel_dict['lon_tn']
@@ -255,6 +255,7 @@ def plot_map(pixel_dict, plot_info, map_info, track_dict):
     dt_thres = track_dict['dt_thres']
     time_res = track_dict['time_res']
     # Get plot info from dictionary
+    var_scale = plot_info.get('var_scale', 1)
     levels = plot_info['levels']
     cmaps = plot_info['cmaps']
     # titles = plot_info['titles']
@@ -264,6 +265,8 @@ def plot_map(pixel_dict, plot_info, map_info, track_dict):
     timestr = plot_info['timestr']
     figname = plot_info['figname']
     figsize = plot_info['figsize']
+    show_tracks = plot_info.get('show_tracks', True)
+    mask_alpha = plot_info.get('mask_alpha', 1)
 
     marker_size = plot_info['marker_size']
     lw_centroid = plot_info['lw_centroid']
@@ -310,69 +313,73 @@ def plot_map(pixel_dict, plot_info, map_info, track_dict):
     ax1.xaxis.set_major_formatter(lon_formatter)
     ax1.yaxis.set_major_formatter(lat_formatter)
 
-    # Plot reflectivity
+    # Colorfill variable
     cmap = plt.get_cmap(cmaps)
-    norm_ref = mpl.colors.BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-    dbz_comp = np.ma.masked_where(dbz_comp < min(levels), dbz_comp)
-    cf1 = ax1.pcolormesh(xx, yy, dbz_comp, norm=norm_ref, cmap=cmap, transform=proj, zorder=2)
+    norm_pcm = mpl.colors.BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+    # Scale the variable
+    var_fill = var_fill * var_scale
+    var_fill = np.ma.masked_where(var_fill < min(levels), var_fill)
+    cf1 = ax1.pcolormesh(xx, yy, var_fill, norm=norm_pcm, cmap=cmap, transform=proj, zorder=2)
     # Overplot cell tracknumber perimeters
     Tn = np.ma.masked_where(tn_perim == 0, tn_perim)
     Tn[Tn > 0] = 10
-    tn1 = ax1.pcolormesh(xx, yy, Tn, cmap='gray', transform=proj, zorder=3)
+    tn1 = ax1.pcolormesh(xx, yy, Tn, cmap='gray', transform=proj, zorder=3, alpha=mask_alpha)
 
     # Plot track centroids and paths
-    marker_style_s = dict(edgecolor='k', linestyle='-', marker='o')
-    marker_style_m = dict(edgecolor='k', linestyle='-', marker='o')
-    marker_style_l = dict(edgecolor='k', linestyle='-', marker='o')
-    for itrack in range(0, ntracks):
-        # Get duration of the track
-        ilifetime = lifetime.values[itrack]
-        idur = (ilifetime / time_res).astype(int)
-        # Get basetime of the track and the last time
-        ibt = cell_bt.values[itrack,:idur]
-        ibt_last = np.nanmax(ibt)
-        # Compute time difference between current pixel-level data time and the last time of the track
-        idt = (pixel_bt - ibt_last).astype('timedelta64[m]')
-        # Proceed if time difference is <= threshold
-        # This means for tracks that end longer than the time threshold are not plotted
-        if (idt <= dt_thres):
-            # Find times in track data <= current pixel-level file time
-            idx_cut = np.where(ibt <= pixel_bt)[0]
-            idur_cut = len(idx_cut)
-            if (idur_cut > 0):
-                color_vals = np.repeat(ilifetime, idur_cut)
-                # Change centroid marker, linewidth based on track lifetime [hour]
-                if (ilifetime < 1):
-                    lw_c = lw_centroid[0]
-                    size_c = marker_size[0]
-                    marker_style = marker_style_s
-                elif ((ilifetime >= 1) & (ilifetime < 2)):
-                    lw_c = lw_centroid[1]
-                    size_c = marker_size[1]
-                    marker_style = marker_style_m
-                elif (ilifetime >= 2):
-                    lw_c = lw_centroid[2]
-                    size_c = marker_size[2]
-                    marker_style = marker_style_l
-                else:
-                    lw_c = 0
-                    size_c = 0
-                size_vals = np.repeat(size_c, idur_cut)
-                size_vals[0] = size_c * 2   # Make CI symbol size larger
-                cc = ax1.plot(cell_lon.values[itrack,idx_cut], cell_lat.values[itrack,idx_cut], lw=lw_c, ls='-', color='k', transform=proj, zorder=3)
-                cl = ax1.scatter(cell_lon.values[itrack,idx_cut], cell_lat.values[itrack,idx_cut], s=size_vals, c=color_vals, 
-                                 norm=norm_lifetime, cmap=cmap_lifetime, transform=proj, zorder=4, **marker_style)
+    if show_tracks:
+        marker_style_s = dict(edgecolor='k', linestyle='-', marker='o')
+        marker_style_m = dict(edgecolor='k', linestyle='-', marker='o')
+        marker_style_l = dict(edgecolor='k', linestyle='-', marker='o')
+        for itrack in range(0, ntracks):
+            # Get duration of the track
+            ilifetime = lifetime.values[itrack]
+            idur = (ilifetime / time_res).astype(int)
+            # Get basetime of the track and the last time
+            ibt = cell_bt.values[itrack,:idur]
+            ibt_last = np.nanmax(ibt)
+            # Compute time difference between current pixel-level data time and the last time of the track
+            idt = (pixel_bt - ibt_last).astype('timedelta64[m]')
+            # Proceed if time difference is <= threshold
+            # This means for tracks that end longer than the time threshold are not plotted
+            if (idt <= dt_thres):
+                # Find times in track data <= current pixel-level file time
+                idx_cut = np.where(ibt <= pixel_bt)[0]
+                idur_cut = len(idx_cut)
+                if (idur_cut > 0):
+                    color_vals = np.repeat(ilifetime, idur_cut)
+                    # Change centroid marker, linewidth based on track lifetime [hour]
+                    if (ilifetime < 1):
+                        lw_c = lw_centroid[0]
+                        size_c = marker_size[0]
+                        marker_style = marker_style_s
+                    elif ((ilifetime >= 1) & (ilifetime < 2)):
+                        lw_c = lw_centroid[1]
+                        size_c = marker_size[1]
+                        marker_style = marker_style_m
+                    elif (ilifetime >= 2):
+                        lw_c = lw_centroid[2]
+                        size_c = marker_size[2]
+                        marker_style = marker_style_l
+                    else:
+                        lw_c = 0
+                        size_c = 0
+                    size_vals = np.repeat(size_c, idur_cut)
+                    size_vals[0] = size_c * 2   # Make CI symbol size larger
+                    cc = ax1.plot(cell_lon.values[itrack,idx_cut], cell_lat.values[itrack,idx_cut], lw=lw_c, ls='-', color='k', transform=proj, zorder=3)
+                    cl = ax1.scatter(cell_lon.values[itrack,idx_cut], cell_lat.values[itrack,idx_cut], s=size_vals, c=color_vals, 
+                                    norm=norm_lifetime, cmap=cmap_lifetime, transform=proj, zorder=4, **marker_style)
+
+        # Plot colorbar for tracks
+        cax = inset_axes(ax1, width="100%", height="100%", bbox_to_anchor=(.04, .97, .3, .03), bbox_transform=ax1.transAxes)
+        cbinset = mpl.colorbar.ColorbarBase(cax, cmap=cmap_lifetime, norm=norm_lifetime, orientation='horizontal', label=cblabel_tracks)
+        cbinset.set_ticks(cbticks_tracks)
+            
     # Overplot cell tracknumbers at current frame
     for ii in range(0, len(lon_tn)):
         if (lon_tn[ii] > map_extent[0]) & (lon_tn[ii] < map_extent[1]) & \
             (lat_tn[ii] > map_extent[2]) & (lat_tn[ii] < map_extent[3]):
             ax1.text(lon_tn[ii]+0.02, lat_tn[ii]+0.02, f'{tracknumbers[ii]:.0f}', color='k', size=fontsize*0.8, 
                     weight='bold', ha='left', va='center', transform=proj, zorder=4)
-    
-    # Plot colorbar for tracks
-    cax = inset_axes(ax1, width="100%", height="100%", bbox_to_anchor=(.04, .97, .3, .03), bbox_transform=ax1.transAxes)
-    cbinset = mpl.colorbar.ColorbarBase(cax, cmap=cmap_lifetime, norm=norm_lifetime, orientation='horizontal', label=cblabel_tracks)
-    cbinset.set_ticks(cbticks_tracks)
 
     # Plot range circles around radar
     for ii in range(0, len(radii)):
@@ -414,6 +421,7 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
     map_extent = map_info.get('map_extent', None)
     figdir = plot_info.get('figdir')
     figbasename = plot_info.get('figbasename')
+    varname_fill = plot_info.get('varname_fill')
 
     # Read pixel-level data
     ds = xr.open_dataset(datafile)
@@ -451,7 +459,7 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
             yy_sub = mask.where(mask == True, drop=True).lat.data
             lon_sub = ds['longitude'].where(mask == True, drop=True).squeeze()
             lat_sub = ds['latitude'].where(mask == True, drop=True).squeeze()
-            dbz_comp = ds['dbz_comp'].where(mask == True, drop=True).squeeze()
+            var_fill = ds[varname_fill].where(mask == True, drop=True).squeeze()
             convmask_sub = ds['conv_mask'].where(mask == True, drop=True).squeeze()
             tracknumber_sub = ds['tracknumber'].where(mask == True, drop=True).squeeze()
         else:
@@ -459,7 +467,7 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
             yy_sub = ds['lat'].data
             lon_sub = ds['longitude'].data.squeeze()
             lat_sub = ds['latitude'].data.squeeze()
-            dbz_comp = ds['dbz_comp'].squeeze()
+            var_fill = ds[varname_fill].squeeze()
             convmask_sub = ds['conv_mask'].squeeze()
             tracknumber_sub = ds['tracknumber'].squeeze()
 
@@ -482,7 +490,7 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
             'pixel_bt': pixel_bt,
             'longitude': lon_sub, 
             'latitude': lat_sub, 
-            'dbz_comp': dbz_comp, 
+            'var_fill': var_fill, 
             'tn': tracknumber_sub,
             'conv_mask': convmask_sub,
             'tn_perim': tn_perim, 
@@ -520,19 +528,33 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
     out_dir = args_dict.get('out_dir')
 
     # Specify plotting info
-    # Reflectivity color levels
-    levels = np.arange(-10, 60.1, 5)
+    varname_fill = 'dbz_comp'
+    # varname_fill = 'echotop10'
+    var_scale = 1     # scale factor for the variable
+    # var_scale = 1e-3    # scale factor for the variable
+
+    # Colorfill levels
+    # levels = np.arange(-10, 60.1, 5)
+    levels = np.arange(-10, 70.1, 5)
+    # levels = [1,1.5,2,2.5,3,3.5,4,4.5,5,6,7,8,9,10,12,14,16,18,20]
     lev_lifetime = np.arange(0.5, 4.01, 0.5)
     # Colorbar ticks & labels
-    cbticks = np.arange(-10, 60.1, 5)
+    # cbticks = np.arange(-10, 60.1, 5)
+    cbticks = levels
     cblabels = 'Composite Reflectivity (dBZ)'
+    # cblabels = '10 dBZ ETH (km)'
     cblabel_tracks = 'Lifetime (hour)'
     cbticks_tracks = [1,2,3,4]
     # Colormaps
     cmaps = 'gist_ncar'     # Reflectivity
+    # cmaps = 'nipy_spectral'     # Echo-top Height
     cmap_tracks = 'Spectral_r'  # Lifetime
+    show_tracks = True
     
+    # Put plot specifications in a dictionary
     plot_info = {
+        'varname_fill': varname_fill,
+        'var_scale': var_scale,
         'levels': levels,
         'lev_lifetime': lev_lifetime,
         'cbticks': cbticks,
@@ -540,10 +562,12 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
         'cblabels': cblabels,
         'cblabel_tracks': cblabel_tracks,
         'fontsize': 13,
+        'mask_alpha': 0.6,   # transparancy alpha for cell perimeter mask
         'cmaps': cmaps,
         'cmap_tracks': cmap_tracks,
-        'marker_size': [30,30,30],    # track centroid marker size (short, medium, long lived)
-        'lw_centroid': [3,3,3],         # track path line width
+        'show_tracks': show_tracks,
+        'marker_size': [10,10,10],    # track centroid marker size (short, medium, long lived)
+        'lw_centroid': [1,1,1],         # track path line width
         'radii': np.arange(20,101,20),  # radii for the radar range rings [km]
         'azimuths': np.arange(0,361,90),   # azimuth angles for HSRHI scans [degree]
         'figbasename': figbasename,
@@ -632,6 +656,8 @@ def work_for_time_loop(datafile, track_dict, map_info, plot_info):
 
         # Trigger dask computation
         final_result = dask.compute(*results)
-    
+
+        cluster.close()
+        client.close()    
     else:
         sys.exit('Valid parallelization flag not provided')
diff --git a/Notebooks/plot_steiner_convective_cell_functions.ipynb b/Notebooks/plot_steiner_convective_cell_functions.ipynb
index 5d7901a..a8ded4f 100644
--- a/Notebooks/plot_steiner_convective_cell_functions.ipynb
+++ b/Notebooks/plot_steiner_convective_cell_functions.ipynb
@@ -374,9 +374,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "flextrkr",
+   "display_name": "pyflex",
    "language": "python",
-   "name": "flextrkr"
+   "name": "pyflex"
   },
   "language_info": {
    "codemirror_mode": {
@@ -388,7 +388,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/Notebooks/test_idcell_reflectivity.ipynb b/Notebooks/test_idcell_reflectivity.ipynb
new file mode 100644
index 0000000..5137159
--- /dev/null
+++ b/Notebooks/test_idcell_reflectivity.ipynb
@@ -0,0 +1,1198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c28575c8-fa7e-499e-be50-fdf370824c4d",
+   "metadata": {},
+   "source": [
+    "# This notebook visualize the cell identification applied to an example radar reflectivity snapshot\n",
+    "## The parameters are read from the cell tracking config file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "54c4f1ea-d396-45cf-b92e-03dc839744f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import colorcet as cc\n",
+    "from pyflextrkr.ft_utilities import load_config\n",
+    "from pyflextrkr.steiner_func import make_dilation_step_func, peakedness\n",
+    "from pyflextrkr.steiner_func import mod_steiner_classification\n",
+    "from pyflextrkr.steiner_func import expand_conv_core\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "573186d7-0ba0-4de2-ac47-530511f78d5a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input reflectivity file: /pscratch/sd/f/feng045/SAAG/hist/auxhist/refl/2019/refl_2019-01-23_21_15_00.nc\n",
+      "Config file: /global/homes/f/feng045/program/pyflex_config/config/config_saag_wrf4km_template.yml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Input reflectivity file name\n",
+    "indir = '/pscratch/sd/f/feng045/SAAG/hist/auxhist/refl/2019/'\n",
+    "infile = f'{indir}refl_2019-01-23_21_15_00.nc'\n",
+    "\n",
+    "# Cell tracking config file\n",
+    "config_file = '/global/homes/f/feng045/program/pyflex_config/config/config_saag_wrf4km_template.yml'\n",
+    "\n",
+    "figdir = '/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/idcell_test/'\n",
+    "\n",
+    "option = 'option6'\n",
+    "\n",
+    "print(f'Input reflectivity file: {infile}')\n",
+    "print(f'Config file: {config_file}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b091ba4e-f8bf-4fa5-8360-e50febf2e13a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'run_idfeature': True,\n",
+       " 'run_advection': True,\n",
+       " 'run_tracksingle': True,\n",
+       " 'run_gettracks': True,\n",
+       " 'run_trackstats': True,\n",
+       " 'run_mapfeature': True,\n",
+       " 'run_parallel': 1,\n",
+       " 'nprocesses': 128,\n",
+       " 'dask_tmp_dir': '/tmp',\n",
+       " 'timeout': 360,\n",
+       " 'startdate': '20181101.0000',\n",
+       " 'enddate': '20190101.0000',\n",
+       " 'time_format': 'yyyy-mo-dd_hh:mm:ss',\n",
+       " 'databasename': 'refl_',\n",
+       " 'wrfout_path': '/pscratch/sd/f/feng045/SAAG/hist/auxhist/refl/2018/',\n",
+       " 'wrfout_basename': 'refl_',\n",
+       " 'clouddata_path': '/pscratch/sd/f/feng045/SAAG/hist/auxhist/refl/2018/',\n",
+       " 'root_path': '/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/2018/',\n",
+       " 'tracking_path_name': 'tracking',\n",
+       " 'stats_path_name': 'stats',\n",
+       " 'pixel_path_name': 'celltracking',\n",
+       " 'rangemask_varname': 'mask110',\n",
+       " 'feature_type': 'radar_cells',\n",
+       " 'advection_field_threshold': 10,\n",
+       " 'advection_med_filt_len': 9,\n",
+       " 'advection_max_movement_mps': 60,\n",
+       " 'advection_mask_method': 'greater',\n",
+       " 'advection_buffer': 6,\n",
+       " 'advection_size_threshold': 10,\n",
+       " 'advection_tiles': [1, 1],\n",
+       " 'advection_filename': 'advection_',\n",
+       " 'absConvThres': 60,\n",
+       " 'minZdiff': 10,\n",
+       " 'truncZconvThres': 55,\n",
+       " 'mindBZuse': 25,\n",
+       " 'dBZforMaxConvRadius': 60,\n",
+       " 'conv_rad_start': 4.0,\n",
+       " 'maxConvRadius': 8.0,\n",
+       " 'conv_rad_increment': 0.75,\n",
+       " 'bkg_refl_increment': 5,\n",
+       " 'convolve_method': 'ndimage',\n",
+       " 'radii_expand': [1, 2, 3, 4, 5, 6, 7, 8],\n",
+       " 'weakEchoThres': 15,\n",
+       " 'bkgrndRadius': 14.0,\n",
+       " 'min_corearea': 16,\n",
+       " 'remove_smallcells': True,\n",
+       " 'min_cellarea': 96,\n",
+       " 'echotop_gap': 4,\n",
+       " 'sfc_dz_min': 500,\n",
+       " 'sfc_dz_max': 3000,\n",
+       " 'radar_sensitivity': 0.0,\n",
+       " 'return_diag': True,\n",
+       " 'dx': 4000,\n",
+       " 'dy': 4000,\n",
+       " 'x_dimname': 'lon',\n",
+       " 'y_dimname': 'lat',\n",
+       " 'z_dimname': 'z',\n",
+       " 'x_varname': 'longitude',\n",
+       " 'y_varname': 'latitude',\n",
+       " 'reflectivity_varname': 'REFL_COM',\n",
+       " 'input_source': 'wrf_composite',\n",
+       " 'datatimeresolution': 0.25,\n",
+       " 'pixel_radius': 4.0,\n",
+       " 'time_dimname': 'time',\n",
+       " 'ref_varname': 'dbz_comp',\n",
+       " 'timegap': 0.5,\n",
+       " 'othresh': 0.3,\n",
+       " 'maxnclouds': 1000,\n",
+       " 'nmaxlinks': 10,\n",
+       " 'duration_range': [2, 60],\n",
+       " 'remove_shorttracks': 1,\n",
+       " 'trackstats_dense_netcdf': 1,\n",
+       " 'match_pixel_dt_thresh': 60.0,\n",
+       " 'feature_varname': 'feature_number',\n",
+       " 'nfeature_varname': 'nfeatures',\n",
+       " 'featuresize_varname': 'npix_feature',\n",
+       " 'tracks_dimname': 'tracks',\n",
+       " 'times_dimname': 'times',\n",
+       " 'fillval': -9999,\n",
+       " 'pixeltracking_filebase': 'celltracks_',\n",
+       " 'pass_varname': ['tb', 'rainrate'],\n",
+       " 'tracking_outpath': '/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/2018//tracking/',\n",
+       " 'stats_outpath': '/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/2018//stats/',\n",
+       " 'pixeltracking_outpath': '/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/2018//celltracking/20181101.0000_20190101.0000/',\n",
+       " 'cloudid_filebase': 'cloudid_',\n",
+       " 'singletrack_filebase': 'track_',\n",
+       " 'tracknumbers_filebase': 'tracknumbers_',\n",
+       " 'trackstats_filebase': 'trackstats_',\n",
+       " 'trackstats_sparse_filebase': 'trackstats_sparse_',\n",
+       " 'start_basetime': 1541030400,\n",
+       " 'end_basetime': 1546300800,\n",
+       " 'geolimits': [-90.0, -360.0, 90.0, 360.0]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load configuration file\n",
+    "config = load_config(config_file)\n",
+    "config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ae313f-3fcb-4551-a700-82f3e668d50a",
+   "metadata": {},
+   "source": [
+    "## Get cell identification parameters from config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "622182ab-53e9-4cd9-b728-e9fada5faf0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "absConvThres = config['absConvThres']\n",
+    "minZdiff = config['minZdiff']\n",
+    "truncZconvThres = config['truncZconvThres']\n",
+    "mindBZuse = config['mindBZuse']\n",
+    "dBZforMaxConvRadius = config['dBZforMaxConvRadius']\n",
+    "conv_rad_increment = config['conv_rad_increment']\n",
+    "conv_rad_start = config['conv_rad_start']\n",
+    "bkg_refl_increment = config['bkg_refl_increment']\n",
+    "maxConvRadius = config['maxConvRadius']\n",
+    "radii_expand = config['radii_expand']\n",
+    "weakEchoThres = config['weakEchoThres']\n",
+    "bkgrndRadius = config['bkgrndRadius']\n",
+    "min_corearea = config['min_corearea']\n",
+    "echotop_gap = config['echotop_gap']\n",
+    "sfc_dz_min = config['sfc_dz_min']\n",
+    "sfc_dz_max = config['sfc_dz_max']\n",
+    "return_diag = config['return_diag']\n",
+    "dx = config['dx']\n",
+    "dy = config['dy']\n",
+    "z_dimname = config.get('z_dimname', 'z')\n",
+    "fillval = config['fillval']\n",
+    "input_source = config['input_source']\n",
+    "geolimits = config.get('geolimits', None)\n",
+    "convolve_method = config.get('convolve_method', 'ndimage')\n",
+    "\n",
+    "# sfc_dz_min = config['sfc_dz_min']\n",
+    "# sfc_dz_max = config['sfc_dz_max']\n",
+    "radar_sensitivity = config['radar_sensitivity']\n",
+    "time_dimname = config.get('time', 'time')\n",
+    "x_dimname = config.get('x_dimname', 'x')\n",
+    "y_dimname = config.get('y_dimname', 'y')\n",
+    "z_dimname = config.get('z_dimname', 'z')\n",
+    "x_coordname = config.get('x_coordname')\n",
+    "y_coordname = config.get('y_coordname')\n",
+    "reflectivity_varname = config['reflectivity_varname']\n",
+    "\n",
+    "types_steiner = {\n",
+    "    'NO_SURF_ECHO': 1,\n",
+    "    'WEAK_ECHO': 2,\n",
+    "    'STRATIFORM': 3,\n",
+    "    'CONVECTIVE': 4,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf6f8ec-8e2f-49d2-869d-ea04abf634f5",
+   "metadata": {},
+   "source": [
+    "## Plot Convective Cell Expansion Radius Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e551e0f0-e3a7-49bd-b177-b3f891aa532a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25, 60, 5, 0.75, 4.0, 8.0)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mindBZuse, dBZforMaxConvRadius, bkg_refl_increment, conv_rad_increment, conv_rad_start, maxConvRadius"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dfa3a7f2-e1e2-4189-a2fd-74800ddd8837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  0.  25.  30.  35.  40.  45.  50.  55.  60. 100.]\n",
+      "[4.   4.   4.75 5.5  6.25 7.   7.75 8.   8.   8.  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert radii_expand from a list to a numpy array\n",
+    "radii_expand = np.array(radii_expand)\n",
+    "\n",
+    "# Make step function for convective radius dilation\n",
+    "bkg_bin, conv_rad_bin = make_dilation_step_func(\n",
+    "    mindBZuse,\n",
+    "    dBZforMaxConvRadius,\n",
+    "    bkg_refl_increment,\n",
+    "    conv_rad_increment,\n",
+    "    conv_rad_start,\n",
+    "    maxConvRadius,\n",
+    ")\n",
+    "# Repeat first value in array for plotting purpose\n",
+    "conv_rad_bin = np.insert(conv_rad_bin, 0, conv_rad_bin[0])\n",
+    "\n",
+    "print(bkg_bin)\n",
+    "print(conv_rad_bin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3cb729ff-7fb8-4e69-ad6d-45495796f7d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Steiner et al. (1995) step function\n",
+    "bkg_bin_steiner, conv_rad_bin_steiner = make_dilation_step_func(\n",
+    "    25,\n",
+    "    60.0,\n",
+    "    5.0,\n",
+    "    1.0,\n",
+    "    1.0,\n",
+    "    5.0,\n",
+    ")\n",
+    "# Repeat first value in array for plotting purpose\n",
+    "conv_rad_bin_steiner = np.insert(conv_rad_bin_steiner, 0, conv_rad_bin_steiner[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cae7faa8-3b84-4bc6-a234-e60b89ef5a90",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Calculate expansion thresholds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8b0006c1-fc9e-4b42-91df-e32d2560896e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Horizontal/vertical expansion: [4. 8.] km\n",
+      "Diagonal expansion x1: [5.65685425] km\n",
+      "Diagonal expansion x2: [] km\n"
+     ]
+    }
+   ],
+   "source": [
+    "dx_km = dx / 1000.\n",
+    "dx_km_diag1 = np.sqrt(dx_km**2 + dx_km**2)\n",
+    "dx_km_diag2 = np.sqrt(dx_km**2 + (2*dx_km)**2)\n",
+    "# print(dx_km_diag1)\n",
+    "# print(dx_km_diag2)\n",
+    "expand_hv = np.arange(min(conv_rad_bin), max(conv_rad_bin)*1.0001, dx_km)\n",
+    "expand_diag1 = np.arange(dx_km_diag1, max(conv_rad_bin)*1.0001, dx_km_diag1)\n",
+    "expand_diag2 = np.arange(dx_km_diag2, max(conv_rad_bin)*1.0001, dx_km_diag2)\n",
+    "print(f'Horizontal/vertical expansion: {expand_hv} km')\n",
+    "print(f'Diagonal expansion x1: {expand_diag1} km') \n",
+    "print(f'Diagonal expansion x2: {expand_diag2} km')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "45a45e8e-2838-4109-83e1-5b72c3483253",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/idcell_test/convective_radius_dilation_stepfunction_option6.png'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpl.rcParams['font.size'] = 15\n",
+    "mpl.rcParams['font.family'] = 'Helvetica'\n",
+    "\n",
+    "fig = plt.figure(figsize=[8,4], dpi=100)\n",
+    "ax = plt.subplot()\n",
+    "ax.step(bkg_bin_steiner, conv_rad_bin_steiner, '-', lw=2, color='g', label='Steiner (1995)')\n",
+    "ax.step(bkg_bin, conv_rad_bin, '-', lw=3, color='b', label='PyFLEXTRKR')\n",
+    "# Plot expansion threshold lines\n",
+    "for ii in range(len(expand_hv)):\n",
+    "    ax.axhline(expand_hv[ii], color='orange', ls='--', zorder=0, label='Cross Expand')\n",
+    "for ii in range(len(expand_diag1)):\n",
+    "    ax.axhline(expand_diag1[ii], color='r', ls='-.', zorder=0, label='Diagonal Expand (x1)')\n",
+    "for ii in range(len(expand_diag2)):\n",
+    "    ax.axhline(expand_diag2[ii], color='magenta', ls='-', zorder=0, label='Diagonal Expand (x2)')\n",
+    "# Label axes\n",
+    "ax.set_xlabel('Mean Background Reflectivity (dBZ)')\n",
+    "ax.set_ylabel('Convective Radius (km)')\n",
+    "ax.set_title('Convective Cell Expansion Radius Function', loc='left')\n",
+    "ax.grid(ls='--')\n",
+    "ax.legend(fontsize=12)\n",
+    "ax.set_xlim(-5, 70)\n",
+    "# ax.set_ylim(0, 6)\n",
+    "\n",
+    "figname = f'{figdir}convective_radius_dilation_stepfunction_{option}.png'\n",
+    "fig.savefig(figname, dpi=300, bbox_inches='tight', facecolor='w')\n",
+    "figname"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6172993-4919-49ca-bd44-1b22e8a4aac3",
+   "metadata": {},
+   "source": [
+    "## Plot Convective Core Peakedness Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "34f72950-d6f2-408a-a315-df411559e088",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Make background reflectivity array\n",
+    "refl_bkg = np.linspace(-10, 70, 1000)\n",
+    "mask_goodvalues = np.ones(len(refl_bkg))\n",
+    "\n",
+    "# Make peakedness curve\n",
+    "peak = peakedness(refl_bkg, mask_goodvalues, minZdiff, absConvThres)\n",
+    "# Steiner et al. (1995) peakedness curve\n",
+    "peak_steiner = peakedness(refl_bkg, mask_goodvalues, minZdiff, 42.23)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6639e392-d2e6-49c5-a00a-92069717309f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fee1aa8ca60>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpl.rcParams['font.size'] = 15\n",
+    "mpl.rcParams['font.family'] = 'Helvetica'\n",
+    "\n",
+    "fig = plt.figure(figsize=[8,4], dpi=100)\n",
+    "ax = plt.subplot()\n",
+    "ax.plot(refl_bkg, peak_steiner, '-', lw=2, color='g', label='Steiner (1995)')\n",
+    "ax.plot(refl_bkg, peak, '-', lw=3, color='b', label='PyFLEXTRKR')\n",
+    "ax.set_xlabel('Mean Background Reflectivity (dBZ)')\n",
+    "ax.set_ylabel('Reflectivity Difference (dB)')\n",
+    "ax.set_title('Convective Core Peakedness Function', loc='left')\n",
+    "ax.grid(ls='--')\n",
+    "ax.set_ylim(-1, 12)\n",
+    "ax.legend()\n",
+    "\n",
+    "# figname = f'{figdir}reflectivity_peakedness_curve.png'\n",
+    "# fig.savefig(figname, dpi=300, bbox_inches='tight', facecolor='w')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74153478-a476-4245-a757-8161ad14f045",
+   "metadata": {},
+   "source": [
+    "## Read sample radar data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9988b3a1-151f-4a86-bb0d-8632480f45e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
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+       ".xr-header > ul {\n",
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+       "  margin-top: 0;\n",
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+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    (lat: 110, lon: 94, time: 1)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2019-01-23T21:15:00\n",
+       "Dimensions without coordinates: lat, lon\n",
+       "Data variables:\n",
+       "    longitude  (lat, lon) float32 ...\n",
+       "    latitude   (lat, lon) float32 ...\n",
+       "    tb         (time, lat, lon) float32 ...\n",
+       "    rainrate   (time, lat, lon) float64 ...\n",
+       "    REFL_COM   (time, lat, lon) float32 ...\n",
+       "Attributes:\n",
+       "    Title:        WRF subset auxhist data\n",
+       "    Contact:      Zhe Feng: zhe.feng@pnnl.gov\n",
+       "    Institution:  Pacific Northwest National Laboratory\n",
+       "    created on:   Tue Feb 13 15:42:12 2024\n",
+       "    DX:           4000.0\n",
+       "    DY:           4000.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b3c5001a-813a-48f3-9813-c14d789d575c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3c5001a-813a-48f3-9813-c14d789d575c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>lat</span>: 110</li><li><span>lon</span>: 94</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fca4162f-dc19-4512-b234-ad56839ea20c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fca4162f-dc19-4512-b234-ad56839ea20c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-23T21:15:00</div><input id='attrs-ac035b44-110b-41e0-bafd-5e56f0733cc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac035b44-110b-41e0-bafd-5e56f0733cc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0a4208a-34b5-44f6-a52d-d9e7698300eb' class='xr-var-data-in' type='checkbox'><label for='data-d0a4208a-34b5-44f6-a52d-d9e7698300eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Epoch time (seconds since 1970-01-01 00:00:00)</dd><dt><span>tims_string :</span></dt><dd>2019-01-23_21_15_00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-23T21:15:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87aae7fa-fcba-406a-b835-f3a4862ae6c3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87aae7fa-fcba-406a-b835-f3a4862ae6c3' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37eb18c2-7359-4ffb-8892-897f8c89faf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37eb18c2-7359-4ffb-8892-897f8c89faf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4b02c98-e1a1-42ec-add7-c84d9b7be237' class='xr-var-data-in' type='checkbox'><label for='data-c4b02c98-e1a1-42ec-add7-c84d9b7be237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[10340 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48425ca0-563d-4a63-9594-ab0574d578b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48425ca0-563d-4a63-9594-ab0574d578b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0587ab2-4b13-4522-8267-261c33ad2c47' class='xr-var-data-in' type='checkbox'><label for='data-a0587ab2-4b13-4522-8267-261c33ad2c47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[10340 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tb</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d576ea46-b342-41cb-86d2-a450e3bb1e97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d576ea46-b342-41cb-86d2-a450e3bb1e97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f930ef9c-226e-43f7-bb9b-3aca9066f0c0' class='xr-var-data-in' type='checkbox'><label for='data-f930ef9c-226e-43f7-bb9b-3aca9066f0c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Brightness temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[10340 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rainrate</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41d089f0-406f-413c-84a6-c2dc1b269fa7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41d089f0-406f-413c-84a6-c2dc1b269fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d33dc9f6-4352-49b1-b785-e072ef1b29bc' class='xr-var-data-in' type='checkbox'><label for='data-d33dc9f6-4352-49b1-b785-e072ef1b29bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Precipitation rate</dd><dt><span>units :</span></dt><dd>mm hr-1</dd></dl></div><div class='xr-var-data'><pre>[10340 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>REFL_COM</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42453701-da80-49a8-b869-1079403f8ae2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42453701-da80-49a8-b869-1079403f8ae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-079acc8e-686e-455a-a540-b6a1a2136e09' class='xr-var-data-in' type='checkbox'><label for='data-079acc8e-686e-455a-a540-b6a1a2136e09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>units :</span></dt><dd>dbZ</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>coordinates :</span></dt><dd>XLONG XLAT XTIME</dd><dt><span>description :</span></dt><dd>COMPOSITE REFLECTIVITY</dd></dl></div><div class='xr-var-data'><pre>[10340 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c742ada-cfd1-4fb0-81bd-7782e11ad6bb' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c742ada-cfd1-4fb0-81bd-7782e11ad6bb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d59fbeb5-a237-4ac1-a57f-2e9c8eb4e1a5' class='xr-index-data-in' type='checkbox'/><label for='index-d59fbeb5-a237-4ac1-a57f-2e9c8eb4e1a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-01-23 21:15:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f2753ec-71d9-417a-a3a3-9602805baf78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f2753ec-71d9-417a-a3a3-9602805baf78' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Title :</span></dt><dd>WRF subset auxhist data</dd><dt><span>Contact :</span></dt><dd>Zhe Feng: zhe.feng@pnnl.gov</dd><dt><span>Institution :</span></dt><dd>Pacific Northwest National Laboratory</dd><dt><span>created on :</span></dt><dd>Tue Feb 13 15:42:12 2024</dd><dt><span>DX :</span></dt><dd>4000.0</dd><dt><span>DY :</span></dt><dd>4000.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    (lat: 110, lon: 94, time: 1)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2019-01-23T21:15:00\n",
+       "Dimensions without coordinates: lat, lon\n",
+       "Data variables:\n",
+       "    longitude  (lat, lon) float32 ...\n",
+       "    latitude   (lat, lon) float32 ...\n",
+       "    tb         (time, lat, lon) float32 ...\n",
+       "    rainrate   (time, lat, lon) float64 ...\n",
+       "    REFL_COM   (time, lat, lon) float32 ...\n",
+       "Attributes:\n",
+       "    Title:        WRF subset auxhist data\n",
+       "    Contact:      Zhe Feng: zhe.feng@pnnl.gov\n",
+       "    Institution:  Pacific Northwest National Laboratory\n",
+       "    created on:   Tue Feb 13 15:42:12 2024\n",
+       "    DX:           4000.0\n",
+       "    DY:           4000.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read input data\n",
+    "ds = xr.open_dataset(infile)\n",
+    "# grid_lon = ds[x_coordname].squeeze()\n",
+    "# grid_lat = ds[y_coordname].squeeze()\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e5d048c3-5c3a-4e58-b125-acf69b2104da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get radar variables\n",
+    "dbz_comp = ds[reflectivity_varname].squeeze()\n",
+    "    \n",
+    "# Make a copy of the composite reflectivity (must do this or the dbz_comp will be altered)\n",
+    "refl = np.copy(dbz_comp.values)\n",
+    "# Replace all values less than min radar sensitivity, including NAN, to be equal to the sensitivity value\n",
+    "# The purpose is to include areas surrounding isolated cells below radar sensitivity\n",
+    "# in the background intensity calculation.\n",
+    "# This differs from Steiner.\n",
+    "refl[(refl < radar_sensitivity) | np.isnan(refl)] = radar_sensitivity\n",
+    "# Create a good value mask (everywhere is good for WRF)\n",
+    "mask_goodvalues = np.full(dbz_comp.shape, 1, dtype=int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4f509e1a-79f3-4065-aa4d-c41a904a419b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fee182f82e0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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o6IhJkybh+PHj6NGjh9K+YORUQcmRkx8tp+cMW1KcnJywdu1aLFiwAPfffz88PDzg6emJkJAQLFmyBJMmTUJ6ejrs7e3NDrFFyZUFX0dSUhJ5z27X67A0nnwSExPRsWNHvP/++zCZTKhWrRrCw8Oxbdu2IiPMOG5ubmjSpAnef/99fPrppwDyQtJtGTVlq7GWVfKjJ2/cuGGOviyM0j4Xd+N5EoTCkImScEfIX9Fo1qwZcnJyLP4VljvlTvHqq69i4cKF5jHv378fH3zwQaF+JQVzzuTn8NEjP/KosNw+JcXe3h7jxo3D7t27kZKSgqSkJPz111944oknsHv3bgB54dX5qQIKkyvz897kO4nn5uaao5846enp5i8va1+HpfEAeaszffv2Nb+egQMH4uTJkxg1ahRZ1SvI1q1bsXr1ahw9erTQcw8aNMj8/3/++ceq12HNWMsz+XmMCqN69ermVeminov8KLf8++luPU+CoIdMlIQ7Qr6v07lz55Cdna3bJi0tDfPmzcO8efOKtaJ0u6LeDh48iI8//hhAXpj5rl270KxZsyKP8fT0ROPGjQEAO3bs0G1z7tw5sz9Mhw4dLI6jKOLj47FkyRIsWbKkUH+O9evXA0CJ/LKaNGliDu0u7HXs3r0bmqbBaDQWmVjRVixYsAC7du0CAEyePBkrV64sMhIKyJuA9e/fH2+//XahbQpOeouK8LvdYy1LjBw5Er169cKvv/5aaJv8SaWXl1eRearyad++PYDC76eMjAwcOnQIwK3n4k4/T4JQFDJREu4IvXr1goeHB65fv47w8HDdNu+//z5eeOEFfPbZZ2bfmqK4XVFv3333HTRNg7e3d6G+Gnrkh0R/9913ur4b+askLVq0MGdnLi2XL1/G8OHDMXz4cMTExCj74+PjsXLlSgB5qxrFxd7e3ryaVzBqriD5r6N37953JHJr8eLFAICePXti1qxZxTqmefPmAIC///67UD+agpNmnseqtJRmrGWJ+Ph4rF+/3vw6OLm5ueb7okePHsVaJct/Ln755RfdDPHLli1DVlYWqlevjo4dOyrH3YnnSRCK5HbWRxHKDvkFQGfMmKHl5uaat3/77bcaAM3Hx4e0z6/3pVfbqmDtK72aUV999ZUGQKtbty7Znl8w1NXVVfv888+1hIQELTc3Vzt79qz20ksvaQaDQQOgrVq1yiavubQ0bNhQA6C98847JTru4sWL5urskydPJvt27txpruH1008/Wewr//oXVustJydHq169ugZAe/bZZ8l7mpqaqnXp0qXIOllFERkZaa4N9vnnn5N9P/30k2YwGDSDwaDt27fPYl9169a1ql5ZbGxskQWKC2P37t3m46ZPn67sP3HihBYQEKAB0B588MEi+ypurbfSjrUwinvthg8frgUFBWnDhw8vsp2l51bTNG3x4sXmQrRLlixR9oeFhZn37969m+y7ePGiFhQUpAUFBWk///yzeXt6erpWu3ZtDYA2dOhQUvj21KlTWrVq1TQA2uzZs5X+bPU8CYI1yETpP0LByuEGg0FbtGiRpml3dqKUm5urjRs3jhTnLPhnZ2dnsZL77cZkMmn29vbm8djb2xf5V79+fXL8Z599Zn49ISEh2pQpU7ShQ4dqTk5OGgDt4YcfLtY4LE2UNE3TPv/8c/O57r33Xm3KlCna888/b/5S8vT0LLL4aVHkV3QHoD300EPa66+/rvXr1888gXrhhReK1Y+1E6Vt27aZx2HpvbC3tyeFfPOLquZfn1deeUWbPHmy9sgjj2gODg7ma/TPP/8UOYbiTpSsGasexb12+fdKx44di/06CrsvTCaT1rlzZ3O7jh07aq+99pr26quvavfee695+yuvvFJk//w6/fbbb+YfQi1atNAmT56sjRo1SnNzc9MAaK1bt9YyMzOVPm31PAmCNchE6T/CyZMntQceeEBzcXHRPDw8tF9//VXTtDs7UcpnzZo1Wvfu3TVPT0/N2dlZq1evnjZ8+HBtz549Vr9Oa7ly5UqhEzm9P73X+Omnn2ouLi7KJPDJJ5/Ubt68WaxxFGeipGmaNmvWLKXaPAAtMDBQO3DgQGkugaZpeV+YkydPNk8o8v+cnJy0V155haxgFYW1E6UVK1aU6P146qmnzMdmZWVp48ePV15D/t+DDz6onTp1yuIYijtRsmasetyNiZKmadr169e1Z599Vve6VapUSXv33Xct9q93nZYvX655enoqffbu3Vu7du1aoeOxxfMkCNZg0DQrUhULgqBLfi2xixcvokaNGmjfvr1NiozqkZCQgIiICFy4cAEGgwHNmzdHt27dbOKgHBsbi40bN+Ly5cvw8fFBp06dyl0odlxcHLZu3YqYmBjk5uaiZs2aaNeuHe655567PbQyTWxsLCIiIhAbGwtXV1fUr18fnTp1sipn0Y0bN/D777/j/Pnz8PT0RJs2bdCiRQuLx93J50kQODJREgRBEARBKASJehMEQRAEQSgEmSgJgiAIgiAUgkyUBEEQBEEQCkEmSoIgCIIgCIUgEyVBEARBEIRCcLjbAyiv5Obm4tKlS3B3d6+QxS4FQRAE26FpGtLS0uDt7Q07u7u3RpGYmAjgVqFywTKSHqCUXLx4sdzlkxEEQRDuLjExMahTp85dObfJZELzZs3h4OCAAwcP3NUJW3lCVpRKya2kay8BsFzAVRCEMkDtYGo/9Qi1P46m9uP+1P7kE6XLbx2XE7tvdhSx34qnBV0/r/W0Oq5BrN/tU6md5aweI5QzMgF8YlXCTmtZunQprl+/DpPJhBUrVmDw4MF3bSzlCZkolZJbcpsRgHyICUK5wM6V2kYPahvYl5gT2++hPuuujjQDukc2leKN6ewADyd1XA58HPzHl3zGVBTulqtGVlYWpoZNxbS3piE7OxtvvfkWBgwYAEdHx7synvKETJQEQfjvcN8L1D7M7P5sVjM9mdp29yldPp59g9jLHX8h9mu+l4l9Jmqh0sfa7z6nGzpVo/ZJeg5BKCnhX4TD2dkZw4YOg6Zp+PDjD7EofBHGjBtzt4dW5hGBUhAEQRAqMOnp6ZgxawbemfYO7O3t4eDggOlTp2P6u9Nx8+bNuz28Mo9MlARBEAShAvPZp5/Bx9sH/R/pb9722IDHUK1aNSz4bMFdHFn5QCZKgiAIglBBSU5Oxvuz38e7b79L/KPs7Oww4+0ZmPXBLKSlpd3FEZZ9xEdJEIT/DqufLVn7scz+sYtOn+HEvPgX9UmqeSyJ2GsOVFK6eKkzdfD99Ogm2iCFnTeEdXDibXVcggBg9vuz0bxpc3Tr2k3Z17tnbwQ1DMLHH36MqdOn6hwtALKiJAiCIAgVkvj4eHz62ad4d/q7utF2BoMBM9+ZiY8+/QgJCQl3YYTlA5koCYIgCEIFZOaMmejSqQvatW1XaJuQDiFo36493pv53h0cWflCMnOXktTUVFSuXBnAa5AcJ4JQQhocpvZVlqk4pYyUVwg5RW1jktpme3NiBsa/T+zvKrcg9v3XDyldVGMfIYkX2MdyA3bekOfYGILUcQlljJsA3kNKSgo8PDwstraWf//9F0FBQdi9fTeaNW1WZNsDBw8gpEsITp8+DR8fn9s+NgC4cOECpk2bhoiICCQmJqJu3boYOnQoJk+eDCcnnVxjdxFZURIEQRCECsa0sGl49JFHLU6SAKBVy1bo81AfTJ82vVTn2rp1KwwGg8U/f39/AMD58+fRrl07LFq0CEFBQRg+fDiMRiPCwsLQs2dP5OTklGoctwtx5hYEQRCECsSpU6ew9MelOHrgaLGPeTvsbbRs0xKTJk9CgwYNSnS+OnXqYMKECYXuP3HiBDZu3IhWrVoBAEJDQxEXF4c5c+YgNDQUQF7R4CFDhmD58uWYO3cuJk6cWKIx3E5EeislIr0JghWI9EYQ6e2/wJ2T3gYNHAQvLy8snKdmgS+KUc+OQubNTPzw4w82G0tSUhLuvfdeGI1G7N27F5cvX0ZgYCACAwNx6hR9vk6cOIHGjRujfv36OHPmjM3GYC2yoiQIwu3Hpwm1x7FQ5Ius/c7xah97a9p0SMWCT0D4BA8A+n9LzKh2HxK77fG+xO5x5R6liyG51J7XkIX712AXSCZGQiEcOHAAa39fi6hjUZYbM8JeD0Oj5o1w5MgRNGtmWbIrDiNGjEBsbCz27t0Ld3d3LFu2DADQp08fpW1wcDCqV6+Os2fPIiYmBr6+vjYZg7WIj5IgCIIgVBDefP1NjHt2XKmcsuvWrYsxo8bgrTfesslYli1bht9++w2TJ09G8+Z5K6+RkZEAgCZNmugeExwcDADKatPdRCZKgiAIglAB2L59O3b8vQOvvfJaqft4/dXXsWnLJuzatcuqsSQnJ+Oll15CvXr18MYbb5i35+drqllTf4XYy8sLAJCYmGjV+W2JSG+CINiewax+VFA8tTuz9lWYvdPWA7IRZ5oXY9sNap6iMtmGhgOVLjbY7Sv6vFfqFL1fEAC8+867ePGFF1GtWrVS91GzZk2EPheKGdNnYNnyZWSf0WiE0WgsVj/vv/8+4uPjsWjRInJMSkoKAMDZWd+3183NDQCQnZ1dmuHfFmRFSRAEQRAqAFcSrqBVy1ZW99OqZSscOnIIlStXJn+zZs0q1vHx8fGYO3cu6tWrh2HDhpF9+TmS0tPTdY/NzMwEALi6ulrxCmyLrCgJgiAIQgVBy9GgZVsXzK6ZNHh7e+PkyZNke3FXkz7//HOkp6fjhRdegL29PdlXo0YNAHnRcHrkS3N3KvFlcZCJkiAItietHrU/QdH2VmbfjQi328UFZldZqbZ5hG+QIrdCKckFYG2+RlNeHbjSpDHIycnBl19+CScnJ4wYMULZ37hxYwDAkSNHdI+PioqCg4ODuV1ZQKQ3QRAEQRBswubNmxEXF4cePXqYHbML0qNHDwDA+vXrlX2HDx9GbGwsQkJCzL5KZQGZKAmCIAhCRSEH0LI1q/6sWZH65ZdfAAAPPfSQ7v7mzZsjJCQER48eRXh4uHl7RkaGObt3WcrKDYj0JgiCIAgVBs0EaFYGjGmm0h+7adMmAEBISEihbcLDw9G2bVuMGTMGq1atgp+fHzZs2IDo6GiMGjVKNxnl3UQmSoIg2J6c3dSOYPun1qf2Ky6swWNqn/dupnYTdo7Fr1seF+8jsovlY6xldm9q1/1DbbOE2SUrtSUIZYKkpCScOXMGHh4eRfoYBQYGYv/+/XjzzTcRERGBbdu2oUGDBpg3bx7Gj9fJyn+XKRfS28aNG2EwGBARwT9t87hw4QJGjhwJX19fuLq6olGjRnjnnXeQlZWl237NmjUICQmBp6cnqlSpgm7duuHPP/+8nS9BEARBEG47mknLi3yz5s9Uuqg5Ly8v5ObmIiUlBQaDoci2AQEBWLp0Ka5cuYL09HQcOXIEzz33nMXj7gblYqI0b968QvedP38e7dq1w6JFixAUFIThw4fDaDQiLCwMPXv2RE4OFVsXLlyIhx9+GEePHkXfvn3Rt29fHDhwAA899BC++uqr2/1SBEEQBOH2kaMB2Vb+lXKiVFEpk9Kbpmk4dOgQjhw5gmXLlul6x+cTGhqKuLg4zJkzB6GhoebjhwwZguXLl2Pu3Llmx7DY2Fi8+OKLqFKlCvbv34969fJCmGNiYtCyZUuEhobioYceKlP5GwShXPI0s7lz6Ga2Opz5neU+vVgCusWXWYO51PTVKQoaubroY24H7ueprRdxXXYKpQvlHC0X0GyQHkC4RZlcUbpx4wZatWqFESNGFDlJOn36NNauXYvAwEDzJAnIy/8QFhYGAFiw4FYphc8//xyZmZl4+eWXzZMkAPD19cXTTz+NmzdvYtGiRbZ/QYIgCIIglEvK5ETJxcUFK1euNP916tRJt92WLVsAQNdDPjg4GNWrV8fZs2cRExMDIC+/AwD07dtXad+xY0fSRhAEQRDKHTnWpQbQsjWrot4qImVSerO3t8fAgbcKR65du1a3XWRkJACgSZMmuvuDg4Oxbds2nDp1Cr6+vjhw4ADs7OzQqFEjpW2+h/6pU6esHb4g/LfgkWQA4DuV2jyu4p5wasddobaHTuXwQ69QezDL/v3jS9SOCVT7uBNS22CWdjwomdr/0znmdRYZh7ZFn8OYUcJBAXBmtbV4H5k88lCHmxbqb1k6BwAY9Wt8lWhcma5Ft0mpWvQ5KjImANbWk5WJEqFMTpSKS35NmJo19csd5GcFTUxMRFpaGjIzM1G9enU4OKgvu2BbPTIzM83F+gAgNTXVqrELgiAIgq3RTHn13qzrQ5y5C1ImpbfikpKSAgBwdnbW3Z+fAj07O7tEbfWYNWsWqaLs6+tr1dgFQRAEQSj7lOuJkpOTEwAgPV1/KTd/BcjV1bVEbfWYMmUKUlJSzH/5fk+CIAiCUFbQTNb7KMHKFamKRrmW3mrUqAEgLxuoHvnSnI+PD6pUqQIHBwekpKRA0zQlqVXBtnoYjUYYjUZbDV0QKg562a1fZNl1E1pRO5PZLQdQm6cXAAC+iPvASzqN7gINDlN7ajK1A/dT+3BrtY+To9mGY9SszFwC9Hy4OJWvFb2f+wpxvx9boOePZMm/ivs5WfKLAoBU5pNU/WLR+/X8niqKX1PO3S1hUhEp1ytK+Q7YR44c0d0fFRUFBwcHNG7cGA4ODggMDEROTg5OnDih2xbIK9gnCIIgCIIAlPOJUo8ePQBAN9fS4cOHERsbi5CQELP/UX57vXIl69atA1B4xWNBEARBKPOY/l86s+ZPVpQI5Vp6a968OUJCQrB9+3aEh4dj9Oi85euMjAxMmDABAMxZuQFg7NixmDdvHmbPno3HH38cderUAQDs378fX375JXx9fUlaAkEQSsleFok6mIX2n3yO2t8w6a36SrXPnIep3e8Laq9+tvjjsyVValH7nkvUzv6H2lt0pLc09vorb6M2l5KcmXylJ8VxCYtTmvQAlvoo6X7A8jj1pDc+Vt4Hl9H49bqp81r5WHkfpbk+dwHNhDw/I6v6EB+lgpTriRIAhIeHo23bthgzZgxWrVoFPz8/bNiwAdHR0Rg1ahRJRhkUFIRZs2Zh0qRJaNasGR555BGkp6fjt99+AwB8/fXXhTpzC4IgCEJZJy89gJWdyIoSoVxLbwAQGBiI/fv3Y8iQIYiMjMT3338Pd3d3zJs3T7fI7SuvvIKVK1ciMDAQK1euREREBLp27Yq//voL3bt3vwuvQBAEQRCEskq5WFFatGhRkTXYAgICsHTp0mL3N3DgQJHYBMFW1Jlquc0O1oYXhr3A7Bruah/OTNLazDP2r7E8jsELqL2/PbXPlCKYw3s1tS+xc9RkGcT1ctUaQqnNpTY/nQK/BfHQiXCzJGmVBktSWqYns73UNlmsTWkitJyTqV35KrV9T1Oby2ipVdQ+r7CwSh6xx/eXVSnOpAFWSm8Q6Y1QLiZKgiAIgiAUA0kPYHNkoiQIgiAIFQTNpNmghImNBlNBKPc+SoIgCIIgCLcLWVESBME6Guts45m1f2J2MLMnMns78x0CgKvMb+kl5vvzCWvf5g21j8OTqJ16Sm1TFDUuqtv+/h+1H+5I7Qth1O6mU8Tbl2Uq5+H+3AeJZ+rmvkEAAAuVBLj/EPcd0iPNQh+KrdMnb2PUr6xwa3+y5XHxPrjtxPrgPkyAeo25HxP3z4oJVPsoA35LNkkPICVMCDJREgRBEISKggmlc5DnfQhmRHoTBEEQBEEoBFlREgShZPAisDE6bbi0toKF9h/0pvZqJpO9+q7aZ1tmj2T2ZKYBXvpD7aPxeWo3ZMV7eRg5l7j0pLca7AJwCcd3ODuHm9qHLwv/5+flMlpCS2rrheFbojjH6Ep6RfSRVQzpjZNWr+j9euO0JNe5s/fZg9l6MiM/xlnnvbbE1TrUvhuFdm3gzC3pASgyURIEQRCECoIm6QFsjkyUBEEQBKGiIAknbY74KAmCIAiCIBSCrCgJglA0XVZS24X52OhESaMus7XMkp3zz9nqtuBXqF2D9ekcTW2HILUPn03U5r5BHF7GQvEdghpqnsp8brgPjiX/GgBIq05tW4Thc7g/UWmwVLKkOD5KpRkHv6ZO7JrycfD2/D0CgOoHqF3tgNqmIHr3Ak8PcLNAkXUtE8gquktbkFcUVxJO2hKZKAmCIAhCBSEvj5KVnchEiSATJUEQBEGoKNikhIn4KBVEJkqCIFD6L6P2s/9Q24e11ysozzM4g0kdPDl1QquibQB4iNlOO6ltYrpGFR3p7d7R1I5iIeF1mTRX7aDaB4eH6l/sSm1FektW++DSEZfnLMloeiH2pUkZYAlLUpqSHkBnDLwPDwvjTNWRKvk1VKQ21t79XNHn0IO/B77FuBeKSi+RmwUklHwYwt1HJkqCIAiCUEHIyTEgO8tgVR+mHOuOr2jIREkQBEEQKgg5OQZkZVkX0J4jEyWCTJQE4b8Mz7INAA5Mauu+n9qmXdS++YLaB0tODXeWmduFFbiN7ULtYB05ptYRNo7j1LZjmbldFP0PaMvOu4tlAD8/gNo88olnbwZUuekqk+LSAqitJ1/xzNF65ymqD73IMVtIb5YkQEsyGgvey2tTwjGk6rwOvW1FkeBJ7eJE2qWy940X1tXL0l4wyg2gUXC51npYC3cLmSgJgiAIQgUhJ9uALCulN1lRoshESRAEQRAqCDk5kImSjZGJkiAIgiBUEPJ8lKydKNloMBUEmSgJwn8J7lfRerPaZhmLYc7m/jK1qKnjCoQ9zPZlPkqIpebJR6j9o06fBuZfZMdSCOxoT+14nT44jzH7BOvzOLMPFyOrNvevasD2++ocE8OO4ePgIfKWQuoBwGi5iUU8dNIOkP2WjtfZVtnCMSkW9hfnvBzu06Tn48RD93kKAu6vpZdhXc9vKZ8ck3LbV0S2b9+OGTNm4ODBg0hLS0NAQACeeOIJTJ48Gfb29uZ2Fy5cwLRp0xAREYHExETUrVsXQ4cOxeTJk+Hk5HQXX4GKTJQEQRAEoYJgixWl7FJKbytXrsTgwYPh6uqKPn36wM7ODhEREXjjjTdw9epVfPLJJwCA8+fPo3379oiLi0PXrl1Rv3597NmzB2FhYdi8eTM2btwIB4eyMz0pOyMRBEEQBMEqsu9SeoCEhASMHDkSNWrUwI4dO1C/fn0AwOXLl9G+fXvMmzcPEyZMgL+/P0JDQxEXF4c5c+YgNDQUAKBpGoYMGYLly5dj7ty5mDhxolWvwZbIREkQ/kt4sEKep5urbbK/prbDd9TO/YzaLKIeADCN2Vw201gW7XuY7tGumtrnvxaKzdZh7Xn2bwBoyOwrzOZ9BDP7YjEkL94Hl4n4fj12M/u4DUL9U5ldHPnKkkxmCb1zlFSu4+PWg8t1/Bh+zfX6PMEL7bI0Dzx7um4KB9axc4Giyjl3poCaTXyUskt+/Lx583Djxg188cUX5kkSANSqVQuhoaGYOnUq9u/fj+zsbKxduxaBgYHmSRIAGAwGhIWFYfny5ViwYEGZmihZN+0UBEEQBOE/z7p16+Di4oIBA9RfThMmTEBycjIGDhyILVu2AAD69OmjtAsODkb16tVx9uxZxMTE3PYxFxdZURIEQRCECoLN0gOUYHaQnp6OQ4cOoXnz5nB2dsaOHTuwZcsW3LhxA0FBQRgwYAA8PPKWCiMjIwEATZo00e0rODgY27Ztw6lTp+Drqxf5cOeRiZIgVGQqJxa935VXuAXcHd8httvNG8SOc2QHnNDptymz41iBWgdm80ze6rAAHrJcg9numdR2ZPIeAGQwCbCKhfPyiL7S/MjlEqBeQI8zs7lEuIXZPLiqOPJUaSLUitNvSfvk2yxJbXpj4K+fS2+8z+J83/JIwoMsM3dCMmuvI735/E7tmAJvpOnOxNzbJuEkoNlrSE2lF99oNMJoVMMp4+LikJOTAy8vLwwaNAgrV64k+ydNmoTvv/8evXv3RkJCnsxes6aeNg54eeW9D4mJFj677iAivQmCIAiCQLh06RIqV65M/mbNmqXbNikpz39r06ZN2Lp1K5YsWYKkpCRcu3YNCxcuRGpqKvr3748zZ84gJSVvVuvszH8d5OHm5gYAyM4uOyVfZEVJEARBECoIOSYDskrhjM378Pb2xsmTJ8l2vdUkAMjMzFvN1TQNixYtwkMPPWTe9+yzz+LEiROYO3cuvvzyS3OOpPT09CL7cnV11d1/N5CJkiAIgiBUEHJslB7AYDCY/Yos4e6eJ2k7OzujV69eyv6BAwdi7ty5OHHiBGrUyNPM81ehOPnSnI+Pnv5+d5CJkiD8l3DOoPb5n5UmTiyHCnf9ibNnWbZ5Fm49mOsGGjObR79z/yMAcGG2O0spoDGHIk3Hx6EScxByYz5LN72pncR+QeuNi11ScMWA+3Tx16G3jftO/Y/Zh5ltrS9RcSlNigFOSX2likh2XSiW0hrojYH7MaWydAEX2Rd7AsueDgDVDrBxFLgH72B6gGwrfZRMJcyj5O/vD4PBABcXF9jZqZO0fDlN0zQ0bpz38B85ckS3r6ioKDg4OJjblQXER0kQBEEQhFLj4eGBZs2aISkpCadOnVL279mT92uqRYsW6NGjBwBg/fr1SrvDhw8jNjYWISEh5slVWUAmSoIgCIJQQXDMMcCYZd2fYykyc48fPx4A8NxzzyEt7dbqbkxMDGbNmgUnJyeMGjUKzZs3R0hICI4ePYrw8HBzu4yMDEyYMAEAylSySUCkN0EoP/Rfpm5L/4fa698uug+emVuny6o0GwDSFP/NKGoGqb8glXQAbdl+Li15MX3PRUc249JaLjtGsyDFAWpGcAOT4pyYrFibSXEmnYzhqUy+41Hg3Nb71LWU2sDdQloDPXnqps42W8PD8q3N5K3Xp14mc0tSY3FSDFhCkeKY1HZVx8fGvQu1GxXIcp+llWIQJcfBBDhZmYnAoRQq4ZgxY7Bp0yasWLECjRo1QseOHZGdnY1NmzYhKSkJCxcuREBAXsqF8PBwtG3bFmPGjMGqVavg5+eHDRs2IDo6GqNGjdJNRnk3kYmSIAiCIFQQHHMBo5XuUA65JT/GYDBg2bJl6Ny5MxYuXIhVq1bB2dkZ9913HyZNmmSW3AAgMDAQ+/fvx5tvvomIiAhs27YNDRo0wLx588wrU2UJmSgJgiAIgmA1dnZ2GDt2LMaOHWuxbUBAAJYuXXoHRmU9MlESSkcNnbX+K8Wp9imUml+GqNuqbaJ2cybZXGMSljPLXdKESU0Aspj0dt25Et2gsSK52sPquDj3MBmMR85xmcyko5UoUW088zbbb9BJGW53tdAhAgByWRXcXJad2a6qeownk+PsmJ3DZDMHJrMBANhr4a/Vi0mEjiwii0feFbaNjMPCfj24pMOjx25Hn6WRzfjHE8+ozuU9QI3E5PCPt906UW/G5MKPN2UB+Lrw/TbC0QQYrZTeHO9MgF65QSZKgiAIglBBsIX05lgK6a0iI1FvgiAIgiAIhSArSoIgCIJQQbhbztwVGZkoCaXjgXXqtr9Z+mDxWbr9JOxgNvNlif2B2jVeofYFFv4OINqHOWLm7qe2gfn5aDpOJLXYMSaWxsB0qOg+oVMHysBqP/FUL4YLxHSyU32U3HOK9lFKc6xO7Kzc1rSBidkAgKbU5GkK7JjTTS57j/LOxPpgfkwGlqOBpwtw10lbwD/defZvnjFcD55lnPu+WKpbqucrw4/h47TkswTohO5bsHnaAj0fJQ7/+OJ9BvN08gDO6vgt5ZObjjvho+RgCx8lmSgRZKIkCIIgCBUE8VGyPeKjJAiCIAiCUAiyoiSUDoPOeniTXdSuzNZ/9cLbhdvL/cyOeY3aegVtB1LZLDCN2tGVuTy1Ve3D/g9i+mfRPmoz6YNLBYksIwEAJLBtPG1B7RSa1yAwXu2DJ8DmpBmpNBdXmb6OozWZpAggy9SbbsjtSG2DTkoBBSat8YzhSlJnJtXpFdrl2btdWMoB3TQFjAwm8WWwcXKJh6ck0JOALEltXBK0JO8BlmUyTnGkN/4RVxxPAl5IN7VA9m7tejE6sB5JD2B7ZKIkCIIgCBUEcea2PTJREgRBEIQKgoMJcCp5TVulD+EWMlESSoeejMYj4dL3UrsbkygieKVUwWp8mlB7Bdvfi0VH6ckJ/Z8kZppRlZsK0iPmHWVbAyaXuHH1iSlLdiygLVlNGI59rI+0TCq13fsv3V/dTe3DrTa1r7MANS+maHkz+a7qDTVqLtKPZipPtGMRe7kPsiN0dEUwiatYcl0BaiSo25RM5ryoMM9srhON58zG5cKlN7afFwjWK8xrSXorjtTGj6nJbC69cRlNL5KOZ/PmfRRH3uNtThSMgitNinGhLCATJUEQBEGoIEjUm+2RiZIgCIIgVBAcc3Nh1KybKTlqMlMqiKQHEARBEARBKARZURJuH+2Y/WdXai9nWaIB4PG3b9twzHBfKp5RvDxzZAAxa7tSr85759Lma+tvUrrwvxlK7FcrUceLmqB+TmcCVKeSGqwNPyaHOZkcMB0jdsNg1cGoM2oR24F9fMU3pX46jjofb/wYV3dnYh9Np+O4nkiPD9SLqGe+UZF+PxE7kf8czW2k00kgNbk/kZLdm/kk8fYAgGKE/1uC92tgPkj8EnPfH0fWHrCcQoCnOuDt9eB9NGQ295XS883jidwtuRTp9VGUX5MJwCkLfdoAx1wTjLp5GUrQB8SbuyAVYkUpMTERoaGh8Pf3h5OTE6pVq4Z+/frhwIEDStsLFy5g5MiR8PX1haurKxo1aoR33nkHWVncsVEQBEEQyhtpAK5Z+Xdncj6VF8r9ilJycjIeeOABREVFoW3btujRowfOnz+PX3/9FX/++SfWr1+Pjh3zEsCdP38e7du3R1xcHLp27Yr69etjz549CAsLw+bNm7Fx40Y4OJT7SyIIgiD8Z7kBIMliq6KRiVJByv2s4P3330dUVBReeeUVfPjhh+btP/zwA4YNG4Zx48bhxIm8NdXQ0FDExcVhzpw5CA3Nkxc0TcOQIUOwfPlyzJ07FxMnTrwrr6NCwCWsG29Q2509vDyk904xgaUtsB9K7fRY9ZjILrdvPPn0+4Lal/pRu7q6Qopfg4i5yr4lsR9lCk1EYAdit8UjSpd/sZD43gHU9l95hm54WP1QPWmkUlujlN20gT01+/IO7HTiyhN0QuAL4lz0bgDI9qIfebGOVM5r4ErTKxxwpa/tVHy00mcWU7h49u9EV67psFwIAJDrr24riF7hYUsYWCoIXlhXSRegd30trLTzFAP2LK2BXip0hxJm+9bLOs6xlFKAX3KetRxQC+3GMJunD9CjqOzdObgj0ptge8q99Pbnn38CAF566SWy/YknnkDDhg1x8uRJXLlyBadPn8batWsRGBhoniQBgMFgQFhYGABgwYIFd27ggiAIgmBzRHqzNeV+RUnT8oogGQxqKtLs7LyfGW5ubli9ejUAoE+fPkq74OBgVK9eHWfPnkVMTAx8fflPC0EQBEEoD1xH3mTH2j6EfMr9ilLnzp0BAB988IF50gQAixYtQnR0NEJCQuDq6orIyEgAQJMmTXT7CQ4OBgCcOiVro4IgCEJ5RVaUbE25X1GaNm0a9u3bh08//RRbtmxBmzZtcP78eURERKBOnTpYuHAhACDh//0batbUd4zx8vICkBdBp0dmZiYyM2/p7ampko7eIod5SQR27TvZIBWAx1Rqh+iURfnlGWJGOjYl9tQWNCR8bVelVDvQZjy1ub/QOlaepTTs+J7ap7rqtyvANObA8egutrL6MjW7BVFfoW4xahjxG1OYD8l5Vmp9NP0Q/bWN6sfywN+sBHxNNi7m94RB1Ink3Gs3wDlI3Ykw4ORvdEML5m/1uppiwPF+OnYPP2rHshQENZnjU2y1y0qf95yi/lSRfrRESdNE6qOU5sx9loBolwt0g+k51oKH6XP/I50wfDvWhpcbceAlTnQ+05QyKGoTil6aAoajsej9OWy/XnoA/s3F/ZjY7VesbzpLKQYs+TDpUTBdQiYANRuHUA4o9xMlo9GI+++/H3///TcOHz6Mw4cPm/fVrl3bvMqUkpL3Ye/srO/x6eaW96GaL9dxZs2ahbffvgM5fgRBEASh1EjUm60p99LbE088gY8//hidOnXC3r17cePGDZw7dw6TJ0/Gvn370KVLFyQlJcHJKW91Iz09Xbef/NUiV1ediBQAU6ZMQUpKivkvJqY4PycEQRAE4U4i0putKdcrSseOHcPPP/8Mb29vrFmzBpUq5S1516tXD++99x6SkpLw5ZdfYvHixahRowYAIClJf6adL835+Pjo7jcajTAaLSwZC3ee9vWpve5JtY3Bm5jLE5jUVo9KXkN+VwMDljVaTTc43obcBj6b2QYmvbm/rxzSxr4F3fDlfmLGHqdaifcWamedUFdQN79J7d4mtgqb+jExH16rfoxoGbRWVOJ8+txVnelF7EPHqdSm9VeltwHfMHnKl0ltL1HJCy10PuxZWHg2+wh0YSmcHUDlu7r2avy3e3AyHUYmtbNYKoRUnTDziFrbiB3lyqRcjWmV/Bblof8AACZ9ZzPbgdkGnlYbgMZlVR7uz+V1HQmQwyU/S8c46Lw2Lq2VdH9p4I+K3rD5x8LpAv8vToZxoUxSrleUjh8/DgC4//77zZOkgvTo0QMAEBUVhcaNGwMAjhw5ottXVFQUHBwczO0EQRAEofyRH/UmK0q2olxPlDw88n4BxcXF6e7Pd8yuVq2aedK0fv16pd3hw4cRGxuLkJAQs6+SIAiCIJQ/RHqzNeVaegsJCYGnpyd2796NJUuWYNiwYeZ9V69exezZs2EwGDBw4EA0a9YMISEh2L59O8LDwzF69GgAQEZGBiZMmAAAkpW7PHCURbk11W9GqE3X4T+Ib03sj+xostKJB9XVyU+ajiZ2rZNXaYPBLAv5++9Su1cxAgFeZ8d4jCPm+LMLlUN682gfFlXj9TNf76cymlOwqlH4+XHZi31M+PLn5EWlD4ML1YZc2lL5JNVEJcBGV2kRTuM3nZU+MfYQtUcx2acaG3dltYtsI30tGex6cCmOF+/Vw4WnBDd6EjMFycR2clX7NPJNBnZ/cemNS2CaTgZsVUEuGkNVnY08WzfP7s2kOUMpamY6sLFzibA4MpolxY/LZjqJ35XM7krkHBuni44kyJ/Hgi6vd2zuYStnbi6r/ncp1xMlNzc3fP311xgyZAiGDx+Ozz//HMHBwUhJScGff/6JtLQ0vP3222jWrBkAIDw8HG3btsWYMWOwatUq+Pn5YcOGDYiOjsaoUaN0k1EKgiAIgvDfpVxPlADg0UcfxcGDB/HBBx9g06ZN2Lt3LypVqoTWrVsjNDQU/fr1M7cNDAzE/v378eabbyIiIgLbtm1DgwYNMG/ePIwfP77wkwiCIAhCuSAN1n+1X4d+Qbz/JuV+ogTkZdVetGhRsdoGBARg6dKlt3dAgiAIgnBXuI5iZAYtRh8yUcqnQkyUKhRG5lNifE9tk/ofTnw5xsL+qVOVTX2GUmeNtTfoh8j5eLr/2pOeSh8fXGK+Gt1Zg4tMzy+OTxLncXbM8s+p3Vz1UfrMj2aSfmE09U240jWQ2P7fMd8FP9WRZd/ntM/AbHq9HGlyb1z/Vc1NZmD+HblDqN+XRwN2Xh6ZPlb1E0Nz9uHvRx1PUr1oIMY1eCpd8G3cjmc+ORlKugD1I5P7KFnya7qpk7z6aJ26dENO9SL7sAk8A7a9zsDsaGoNJVO3kpbABn4tjllF2wDgruOTVZAM5rSUzcapl3fYhfsg8azkzNb1UfJWt+VzO1IW6GF/HbArha9YQXKtPL6CUa6j3gRBEARBEG4nsqIkCIIgCBUF++vWf7NbDvb8TyETpbJG+7XU3hSqtslhhTkda6ltKirnmPbmy2QfJgsBwNqwfXQDSykwbzeVdOZpOklHs5lkdfE2yJ+DP6H2y1RGXHCOFc0F0CdqOLGrhN1DbB8mLf3wZDKxr5xXY5bfAb2/HHkNVxZFflVHJktlqkSLTFbA9jjLqL6BhcM70szeAHCtOZXWeAHbaJZF+yLUArbXEE3s9DT6+nOY+sKTQru6q3nWuPSWwjJzJ0TTb53Nesn/c3hGeSa9GbikxQaml5mbS2uWbB7aDqgXQLMktXHJRkfCUbJ9sz70JMCS9sHzBXDZy/2STp/svLmWxqGTk0Dpt4AUp19m1PbYw/pv9lK6OH355Zc4cUIt+gzkRanPmDHDbF+4cAHTpk1DREQEEhMTUbduXQwdOhSTJ082lxwrK8hESRAEQRAEq/nss89w7Ngx3X1Vq1Y1T5TOnz+P9u3bIy4uDl27dkX9+vWxZ88ehIWFYfPmzdi4cSMcHPSnJ08//bRNxmowGPD1118Xq61MlARBEAShouAA6x3H1QVdi2iahnPnzuGpp56yGIUeGhqKuLg4zJkzB6GhoebjhwwZguXLl2Pu3LmFJoBetGgRDAYDNM26yD6ZKJVnNj9G7e6j1DZef7ANt0EG6rKS2nxcd4srrCjp4AXU3v+oekybRGrXYrYHf22DdE58G65xHRaht4PZqaz9pWHgrA3YSexjN2hkXFWmrGWxJ35YPU+lz2tMwnJsSiPBXL6hUlLAJzqyz/dcGmF5yi6epfY/TMaooUbSVblOP72PuFGZ8SJotdnoBLX6bDJTRhLZF0pVlsHZyZXaN9NUqTLXRLftYUVw3dk54irrRPRl+1ObZ8k2uJfMBtTIL0tKEm8PAC5sm5Kdmj1LyneXnvTGI9Z4dBnfr5d1nIVJ2vlTm0fK8XHm6klv7DxcztS7xpb6cC/w7HBp73ZhD+snSibLTTiXLl1Ceno6GjZsWGS706dPY+3atQgMDDRPkoC8iUtYWBiWL1+OBQsWFFkpY+TIkXjrrbdKPsj/Z/r06cVOKQTIREkQBEEQKg62WFHiJV+KwZkzZwDA4kRpy5YtAKBbCSM4OBjVq1fH2bNnERMTA19fX90+PDw8ULduXd19xSG/TmxxkYmSIAiCIAhWkT9R8vHxwffff48TJ07AwcEBrVq1Qp8+feDomDd7i4yMBAA0adJEt5/g4GBs27YNp06d0p0orVmzBvXr19c5svg888wz6NatW7Hby0RJEARBECoKdrD+m90+z2coNZXq/0ajEUajjtSOWxOlRx55BImJVOps0KABfvrpJzRv3hwJCXlyZM2aNXX78fLyAgClj3w++ugj1KpVC59++ilq1KhRrJczf/58rFq1Cps3bwYANGrUCI0aNSrWsYBMlG4/PZnfSTDb/4kF35eItjobb4O/jEdfYo5fMI3YC5axSIa3y0h28B+Z78tTM9U2mZ7U3sl+SfBjuB8UAPzBwrcrs4c4Ra/yegH+VDOGK+HC/HPjArNX6fT7wn3EjHZ+jNncz+JFYnbUSZjif/YQ3XCdfUx8yuxjOn4ofVmbcd8QM9WH+kF5pDP/jcpql9lG2mdDFuqfBvq+RWWoPkpb2Wq9kb382inUrnqD2lk6kkQ0q/Tgf43al9hrScttrXaihJrz94375FRjnepkhGbjAEv6r7z1et8GPGUAz0bNz1uDZbDX9QVibbjvDg/9V1IjQL0ellD8nvT6ZNv4Nb5WRNbtfDyK8EPiuSduF7aQ3uzzfI4qV6Y379SpUzFt2jTdQ/InSl27dsXUqVMREBCAc+fO4d1338XSpUvRvXt3HDt2DCkpeQ+Zs7N+vgQ3t7zPhuxsff1v69atMBgM+Ouvv7BixQo88MADFl/OmTNnsG3bNovtCkMmSoIgCIJQUbCFM7cD4F3TGydPniSbC1tNAoAJEyZgxIgReOihh2Aw5JUoCg4Oxg8//IBr167hzz//xLx588w5ktLT1YANAMjMzJtQurq66u4HADs7O1y6dAmdO3fGBx98gAkTJpTo5ZUUKWEiCIIgCALBYDDAw8OD/BU1UerQoQP+97//mSdJBRk6dCiAPP+kfLksKSlJaQfALM35+OhlZs3j2WefxXPPPYfs7GxMnDgRgwcPxo0bNwptby2yonS7Wc8kqmPMge1vJsl8SEOe8csQ249JjxAqYS0ImEv3n/KkdsBspQv/IzRrdvRbLFbYksyox72bqR3Zhdr/Y6kSvmbtAVTVOhA7UWNy5tkfqB14RB1Hw73MZu9b5CZqVz9I7eNql02foh8oR6swXajBHHbEI2onjr2ofYnJAwOoOX7RfmLfryaaBqKZDLSWSQZcatORyTCMaTbBVJLwYBIX+I9HnVV3xxSqFblUK7pgrYOOuhLlwSNlaKj+qaosQzgjS9P5lav5EzPRjS7xR7t0pO1No9U+7Hg6ACb7cBkog7XXU3z4NkvR6XrfBlyuUzJcM/sCG6deYFIOk+M0fV+UW+jJ2uzN5fKdA5eDi5HJnPfJ0yVwqVLveiaxYwqaadbl/Sk2NpLebEl+lJm9vT0aN86rfHDkiM7nLICoqCg4ODiY2+nh5OSEjz/+GB06dMAzzzyDlStX4ujRo/j5558RFBRk28FDVpQEQRAEoeKQ78xtzV8JJ0p///03DAYDevfurbs/3z+oVatW6NGjBwBg/fr1SrvDhw8jNjYWISEhZl+lonj88cexd+9eBAcH4+TJk7jvvvuwcuVKi8eVFJkoCYIgCIJQalq3bo3q1atjw4YNiIiIIPsOHjyIhQsXwmg0YuTIkWjevDlCQkJw9OhRhIeHm9tlZGSYfY2KSjbJCQoKwt69ezF8+HBcv34dgwcPxsSJE2EylSJrZiGI9HaniWXRYx+8QO0wtiz9yx2KLlvHfgkMmkXt1S9Re/ArShfRziwybJcNxhUTSMw+p6YRuwFTeKrZMykKQDiTrhOdWBHh+p9R26TjZHimHzGn5dIT52RSGWiG43vEfjVF1e2rVvUktjtLh/tODj1nXFOdpXsuwXzJ9rO3zZkpA7HeakFlHzdaTPbjrlROac+iy+4POKSOqxuLRPn7L3YSpunwkgl6fpxsWzzouE7iDLF/0wuq0VixWdNAYqrxe9WVLSpUsol2eo7uzuXRVDoZnnkUF88CncOzbDPpiGdxBwCuaJ3WaWMJnsaGB5txaY5HyZ1lUhwAeHemtrNOZBxBJ6pSL2qtSCzIagCQwa7pTbafy8HF+fbMKeT/t5O7IL05OTnhiy++wKBBg9CzZ09069YNDRo0QGxsLP744w9kZ2fjiy++MCeJDA8PR9u2bTFmzBisWrUKfn5+2LBhA6KjozFq1CjdZJRF4eLigsWLF6NDhw6YMGEC5syZg/3792PFihWoVcv6ovGyoiQIgiAIFYX8qDdr/krho9S/f38cPnwYQ4cORVRUFMLDw7Fr1y706tULmzdvxpgxt3xYAwMDsX//fgwZMgSRkZH4/vvv4e7ujnnz5uGrr74q9UsfM2YM/v77b9SrVw87duxAq1atrEoLkI+sKAmCIAhCRcEWK0qlnBkEBwfj+++/L1bbgIAALF26tHQnKoIWLVrgwIEDGDFiBFavXo3u3bvD39/fqj5lRUkQBEEQhAqDh4cHfv75Z3z44YcAbiXDLC2yomQt7r0Bw/9756euKfnxq5+l9iWWafrAAvWYViX0W3pMzQp972fUZyayJguL5i41vlHU/pE5vwBAJXae3db7Vz2/M5zYUWxq/2ktVkE6l/nCAIALS2XAsvRqGYPo/hOqc8uiNnQci2OpA4Mb82d4rcZrxH6smlrXqNXKaLrBkzqajO5EH8+J6aqf07xaLPR8/FZqM78I7jY2L/kyOA960vQJE/cfoA02/0PMRVW4gxHg25z6lXRNZ74+3HWFd6HzizjWSH0NzsCT7r/EPgxp5H8eGkuNb0czmyth+SYWwlycCvJKKDrzj9HLKm1gvmY3WZqHJAs+SfE6w4hk9glm8z70kl3z5Oa8DU9iz1NF6PmJpbLX4lGP2tzPST+Bc8ng/kF6/kLc34r7JBVnlaYsfKPaA3BQPytKhN0dSmVQCr799lsEB/MSF/q8/PLLaNu2LR5//HHExcWV+pxl4W0VBEEQBMEWOBkAFysnSiX1lb+DPPXUUyVq3759e1y8qJYzKgkyURIEQRCEioKjAXC2wURJL9iwDLNv3z789NNPOHXqFG7evIkaNWqgffv2GDx4sFKzrqTIRMlajF8Ddv8//XZXQ9OVdACW2Msqo04p3bAIF8YrmyI9mFak0RDdscenEXuhJ1uKfayNep6vSziuGjqz/NiPiXmvwxJizzOxsPNHNlB7pU6EgxeVBLWE9+n+wzTuPLuN+gkxMrch3eDzBTEDr3cldjMmEbooscYAMtg19aZ6gONWOq4J3Vn1VQBfMj0ly4u9Nnua9mGXRsPhm5/6SenzL+wmdm8ukzEZaEQ1NUTmje/oa0txb0HseNDCqK2YFpej89EUz/SSg+k0y/hX3lRrS8vVqQ5uomHHTVPp+9aeSVjfBFPJMCv3d7VPXnBVyfpsodgqoKZ5sCS18eoPXGYDAPZoKLprKuvEw0vtg0trPFkyl+IsSXN62/h3GL/NuRQHWP7m4jIZl9G4zAZYlue4BFhSh+nrJWwvFJsXX3wRn32Wl+ZF02599vzwww+YMmUKvvjiCzz22GOFHW4RmSgJgiAIQkXBCdavKDlq5WZFKTw8HHPnzkXVqlURGhqKBx98ENWqVUNcXBw2bNiAr776CkOGDEHVqlXRpUsXyx3qIBMlQRAEQago2ER6MwC3r8asTfn888/h6emJyMhI+Pn5mbc3btwY3bp1wzPPPIN7770X7777bqknSpIeQBAEQRCEcsnJkyfRp08fMkkqSIMGDdC3b1/s27ev1OeQFSVrSfCGWbxufkDdH2tl+MB6G5Qw4X5PgOK3g+205Mb99zUgdjUWmj7jF53w0WqdqJ2wldp9ma/UXh0HhoM0/Dr0PhbSuWMxtQ+tovZTalXw54+9r2wryJn2NYjdMFfHhySB+aZ4hRGzSyzd/W5tdvxL/6p9PkHN8NY0JDzyRDSxPZPVOhUDYqhd05OmJfjUh/lssfD4hQ11HMsMdBxenkOI/eqIFNreS73m7+ZQn6wJ5w8Ru1c9moJgG2jKgQunkpU+/2U/ktcGjKUbuE+J1l7pw8mRlhcJvEL3H/akdlb2fLrBoFOZ3D5B3UaOYSkF9MpncJ+ka2w/u78Uf6PdUFnPfJB8NlO7GtufqeOjdJGF7p9gdjA7hvso6fnQ8sfekq2XtoB/pPGPWf7NxqsS6aVT0CsDY2kcHP56Cz4qOpWRbgtONlhRcrTy+DuIq6tevSO1jYND6ac7MlESBEEQhIqCI2wwUbLJSO4I3bp1w6ZNm3D9+nW4ubkp+00mE7Zu3YoHH3yw1OcQ6U0QBEEQKgpG5EUKWvOnLhKXWWbOnIns7GwMGzYMV6/SxMmXLl3CkCFDkJycjPffL1pZKApZUbIlh3VktpeYxPWJDaQ0W5DJYm5HUHPW3zTLcVSl1Ra7bJpEZZ7edvRXzQd/09B0NNPJOt6GXp80TKL7fZjUduE3Yn5rT6UnADjKipNPYRLNeyb2S+MAk/cABAbS1/Iwk0baBbdgJzlJ7R9ZCDkAPE/NMdkf0A2NphFzyDGeWhno0ZRKpA1Ztmq7S38Q+0AOtWszFQ0AstinwudM+n/Vnx3QRc3MHeHVgdijvag8Fc8+euJikoltp1OUc239unRDLpO8eDoAe7WOlPcN9kHKpJJoHprOs2pf05HNXNg2B/Ze51iQ1fS2MUlQkdrWM/vgebXPRuxZqcZcA4zFkN4SWrE+PKl9hUlxZwPYOdh+wHJKAZ6CoDgpBjg88wh/dHTuewUuoxVHEixqHDxFgVAqAgIClG3Z2dlYs2YNvL290bBhQ7i5uSExMRHR0dEAAD8/Pzz99NPYuXNnqc4pEyVBEARBqCjYIuqtDPso5ebmwmCg4/P09ISnpycAICMjAxkZeYmyCjp4X7rEfjGXAJkoCYIgCEJFoYI7c+evEt1JZKJ0u9nDM1j/764MwyInqOQV1SCC7r9MH5xON9UHaUsMiz74iWpcvT+h6+Gdqw5Vx3GUySVc6fCn41xuT+WEeJ30tx9XZ4VzW7EwUVdWPXUHzQYOAEPtaFreNpVbELv3l6x46nudqN2aayVQInfcHUcSe8gRmsgkrBktCgsAPgcv0A0NqWTawJtG8M2Op3JVu6YtlD4f3c90CgPLPMeSjL8VrGam2/HvDmJ3YIGEe1kt3l0B9D1od05N4uJkYGFD9lTqzTJE0/28PYBoVyrfRbuwYr0mmskcVx6htl5kVA1mpzGpjWeB1pPeuHJmKav2RSaj3cNkNgAIYNuM7KIb2cAyPdU+PM5RO5XJHu5MWss6aLnPVHbM1YCi9/dUu8CLrJjxp+yZ/ppdHyeeylyHLC49MtmRJ3b+4Fe1D7tP2IYC93GqCVhteRhWYwtn7jJc6+1uIBMlQRAEQRDKBdOnTy/VcQaDAW+99ZblhjrIREkQBEEQKgoVXHqbNm2asi3fZ6lgnTe+XSZKgiAIgiDkTZRcbFDCpIyyZcsWYmdlZeH5559HYmIiXn75ZXTo0AGVKlVCbGwsNm/ejK+++grdunXD7NmzS31OmSjdbv5mPknNmT+HXkqBskgzWiNn63GeKhg440czITu0pz5KXx5l6YO391LP05SlT/jfH6wB9SH5PuoYsdc2pBmeAQB2M6l9cAW1DU8ze47SRRu7FnQUhm6shT81ey6n9stqLPEBNxp335G9li9ogmxgb7LSB+6fTMw/HqK/mHpfoP4yH/Lo2CFHlS5vTqT+QZfq0D6qsRD5GTmqT9eQlH60jc8m2iCgGbVN44iZ5vyT0mcW9x/SmH+RxtIHQPXpgkNral9m/jDsVgBLzI36n6l98pTLF+h7Aj4snQwD8NHZVhDuo3RPOLW5PxIApLDke/GB1HZm4+Y+SwBgZBnlA5gPEvcnSmO2XsoB7teUxhy0eJ9L1HBwpDKfpKg3qN1cJ10CGZenui2NnWci81F6it3ndurnBM98T+7R3EwA7PoJJaZjx47EnjZtGi5evIjdu3ejadOm5u2tWrVC37598fTTT6Nt27ZYv349GjRowLsrFpJwUhAEQRAqCvnO3Nb8lZPf7wCwePFi9OzZk0ySCtK0aVM88sgjWLBAJ29fMZEVJUEQBEGoKFRwHyXO5cuXUb169SLbeHh44Ny5c0W2KQqZKN1pyovU1mE4tVc/S22d56hhcxbuf4gWIMUlpiXRSPY8/Ji08TeT69ZRaW5t0DS6/6hatHRVExq6f9RE47m3xtJ497Y6GXd7f8cymeNDaq74lJipj9Fw9x900vhmgIZrr3FmDcawBd91esUfaSrp3ibWCctKkFqZyjEejdR0wc4LaJuAf6mEOpklxH4xo5/ShwMPmV9FpVv0Z+UEbq4k5q7qOuUGtPHqtoJcZZrWaZ0245ifgiIdDaC2Hx2Xu+MUpcs0EzumGSs+a8d0tes6hXW56sWLvvLbhydDP32P2mdKVXVbUTjrSW9MnrvC0lNXTqR2NaYRZurInzwjOLd5BnEPHRktnR3TyJLUpiMBchQJ8BVq2zO5WPtU7SOeFWL+scD/b6YCmGd5HNZi0AA7naLlJe2jnODj44MNGzYgKysLTk7q96vJZMKWLVtQowb/UCo+Ir0JgiAIglAuGTp0KC5cuIBHHnkEp0/TX0dnzpzBoEGDcObMGQwbNqzU55AVJUEQBEGoKNgBsLdyRagcLaG88cYb2LNnD9avX49GjRqhdu3a8Pb2xpUrVxATE4Pc3Fy0b9++1KkBAJkoCYXBpbbiUO9bavOCopzoqeo2A8sKHV/CMbRXN/204xCxlwWz4rx1aKHUuXY6J23E6gTt60rMo61pxEyzh1h0o/dZpcs35zL98kcqm+1xo7KG2yi1+GzjhSw6bBKVzcalpBHbOZdmLh8YxqLAACw5sZ/YC11Zm0QacdROx5+huyf7aHmMVTr95Di127EOYljkGACMYZJMOxaVxLqEphMJ1oZFi21nYW17+AH0+qUpkXUADCw7ei7PZO5PbR2FS8nWzZM+80KxW9hrv65TcZVLb7wQNkcv6o1HxlVn1WZ5n/ycXJoDVHnOZ7PapqhzAMBNJkPzCD8Fdn30xsUlwD9YlOXHo6nNJUIASGDZzwtG1+Wq2eZvC3Y2kN6sPf4OYjQa8eeff+Lrr7/GwoULcejQIcTG5kVl+/v7Y9SoUXj11Vfh6OhY6nPIREkQBEEQKgp2mg1WlMrPRAnISyw5evRojB49Gjdv3sS1a9fg5uYGDw8dh9NSUI4W2ARBEARB+C8zaNAgfPfdd4Xud3Z2hre3d5GTpMWLF2PQoEHFPqdMlARBEAShomDALfmttH9lODvATz/9hEOHDlnVx6FDh7BqlY40XwgivQm2Y/Naavdn6QJ+GULtQTrh3i9/Tu03HmUNmhc9hmQ1THjZfQ/SDZFNqJ1IfaXCU9RPCeeW1P68Gg3/T8umpcVHvPsOsdu3ZGHoAEbvpT5J2yOpj0TbD+njqWXoLIcvnEFt5lLyeShr/yQd95knWaw/gMnBNAR8IXeN2kjNXQ+p4zpmpD4j98ZT37PIVHZA6zBqtxur9IndzC8nmoXEN2ZZ2VeysH0AcGXh6vbsfcnIZPuZf1aOmh4ABp5qmznKJdHM5iRkPB+WBQPMjQ7fMPscz4zPwvZLg54vUEl9kDyYnaqToiCFteF+UBzuj6Q3LmU/O4anOdBLnVCD+V9xH6QWLCWIXsoB7ueUUOCezc1QfdFuB/8B6W337t2lLo6bf3xJkImSIAiCIAjlht27d5d4ssPJL5hbHGSiJAiCIAgVBTsNsFMjZEvcRxmFF8W9E8hESbAdqWuozaU2zl6efhjA46woriWpjZP7qrrt1W1Fn6MeXWKfO0rnQyLOk9pLabblTnFUaqvGkpCPPsGKiwLAJPphFpLK5AReoHWy6pwYq1GJwYelAwBTp+Ib0nMkwlPpMw1MNru6g9iRvF7tGP6eAWnVaHx75CWacsBpGs2SO/Y8TdFwD081AeDi21QamenyMm0wlx3grsqKAJPaPmZvFFfr6laj9g2dpHVcTuFRyE+xMPOTLMwcAHzZidlLw0X1GpcJuISlpCRgchYA3GT3uV5G8KLaAyVPdVDScwKqrFiDFbTVK6zLpbeCmLKAaMuntZoKLr3xorh3ggrjzL19+3b07NkTNWrUgIuLCxo3boyZM2fCZDKRdhcuXMDIkSPh6+sLV1dXNGrUCO+88w6ysrIK6VkQBEEQygmGYjpsF+nMXXYnSneDCrGitHLlSgwePBiurq7o06cP7OzsEBERgTfeeANXr17FJ598AgA4f/482rdvj7i4OHTt2hX169fHnj17EBYWhs2bN2Pjxo1wcKgQl0QQBEEQBBtQ7mcFCQkJGDlyJGrUqIEdO3agfv36APIqCrdv3x7z5s3DhAkT4O/vj9DQUMTFxWHOnDkIDc0LB9I0DUOGDMHy5csxd+5cTJw48W6+HMFaTq5Ut3XibVhG8CNs/xSWvRkATkcQc3zUfcRuEkSjjhqxKKRrNVlEFoDT2/yJfRTJxM4GLUZ76IgqY3y5IoVueG4TMQPPdCN2pyNUjtpaV5WnotyeJHbtylR6C/2cOkHO7ciiGwHgrC8xX7n2Gh1mTXp9/OlLBSrrSDYsYG3kKRre23AMK3jbi8lmABDLZDAuyaz8mdq5LKpQr0DrIVY8tUFX1uAPaoboSM5Gdg13v662KY8UpzBvpoWoN72M4ZVLGD7Go950x2Ehwo/v99AZgzu7P5ySb/0/m6obt40yUsLk0KFDaNOmDdq0aYMdO+hnyIULFzBt2jREREQgMTERdevWxdChQzF58mTdwrZ3m3Ivvc2bNw83btzA7NmzzZMkAKhVqxZCQ0NRqVIl7N+/H6dPn8batWsRGBhoniQBeZ7vYWF5ockLFiy44+MXBEEQBJthrexmA+ktKysLTz75JLKz1dI658+fR7t27bBo0SIEBQVh+PDhMBqNCAsLQ8+ePZGTw3813X3K/URp3bp1cHFxwYABas6UCRMmIDk5GQMHDjR7yvfp00dpFxwcjOrVq+Ps2bOIiYm57WMWBEEQhNtCvjO3NX9WOnOHhYXh6NGjuvsKKjsRERH44osvcPDgQTz++OPYunUr5s7lURl3n3I9UUpPT8ehQ4cQHBwMZ2dn7NixA++88w5ee+01fPvtt0hNvZXVLjIyEgDQpEkT3b6Cg4MBAKdOnbr9AxcEQRCECsiuXbswe/Zs3cWL8qrslGsfpbi4OOTk5MDLywuDBg3CypXUP2XSpEn4/vvv0bt3byQk5Pli1Kyp4x8AwMsrL8tqYqJOVWkAmZmZyMy8lbW34CRMKEM01Qmj7sL8lta/S+36LKS+vxrK/5Edzcg8kef4N+xlR4ykZnKy0mdbd/r4tbscTexdNamujySW8RnAiIZ0HB9d9ST2Hyw/wD8NqK/UYHv1I2A6niH2PucNxPapQiukz73C/HgAwJ6+/g8rsf0rk6k9ju1PrQ+F+84Scwf/+LrA2nN/JAA41lbdVpAz/P7hnxc6nx/9n6N2Assg7vM7G0M7tY/TJUyDUV7gIfaAmr2bh+rXYKv6er5AvN9UNfN9kWR5qtt4pm0jy9LuxCShLD+1j8SAws+Zkw3gTHFGZx35JUysoZRLKOnp6XjqqafQoEEDzJgxQykTUhJlx9fXV2kDAJs3by7d4AB06dKlVMeV64lSUlJezopNmzahWrVqWLJkCf73v/9B0zSsWLECL7zwAvr3749jx44hJSXP6dXZ2Vm3Lze3vNwxepoqAMyaNQtvv11Gc5kIgiAIAvD/EyUb9FEKJk+ejHPnzmHHjh2637XFUXa2bduGU6dOFTpR6tatW4myaheEpwsqLuV6opS/wqNpGhYtWoSHHnrIvO/ZZ5/FiRMnMHfuXHz55ZdmT/r0dP3oh/y+XF116goBmDJlComIS01NLfSNFARBEIT/Eps3b8b8+fMxadIktG3bFtHR0Uoba5WdglSpUgXdunUrdPHDlpTriZK7e14xSmdnZ/Tq1UvZP3DgQMydOxcnTpxAjRp5mYDzV6E4+W+gj4+P7n6j0Qij0ai7TyhDPDZV2TRiyjRiv+dAMyPXPE1D5D9o6Kn0kcFC9TGMLW37sgzPe6hpqLRa6RMmKvntqrWUNaD3W1hbnvIZeJuphuEs0/YYE0118JUrzSwdeSJa6VOzDyF2fBANk15TmRWKTVc/1E7YsaKt+2m2bxxgK7efs/29qcwGAFPYW/DeldN0w2tMVjumI72VlMHMX2Lvu2qbTfx9Y9JbNZYe3ZL8V57gofu+UdTmhWb1tjmzX/lcurzEiloDwBHWhktvWV5F7/fQKWirJr4v2X69NgVfam4qAJ7W/jZgB8De+j40TVNcTAr7HkxNTcXIkSPRqFGjIovVWqvsAED79u3x999/IzExEb///jv69OmDxx57DL17975t39Hl2pnb398fBoMBLi4usLNTX0r+Rdc0DY0bNwYAHDnCk+bkERUVBQcHB3M7QRAEQSh32NngzwBcunQJlStXJn+zZs3SPeWLL76I2NhYLFq0qMjJirXKDpBXhePixYuYM2cOmjdvjh9//BEDBgxAjRo1MGzYMPz666/En9gWlOuJkoeHB5o1a4akpCTdaLU9e/J+1rdo0QI9evQAAKxfv15pd/jwYcTGxiIkJMQ8uRIEQRCEckf+ipI1f3aAt7c3UlJSyN+UKVOU0/3555/49ttvMXnyZNx3333K/oJYq+zkU7t2bbzwwgv466+/cPHiRXzyySdo2rQpli1bhkcffdTmk6ZyLb0BwPjx4/Hss8/iueeewy+//GKW42JiYjBr1iw4OTlh1KhRCAgIQEhICLZv347w8HCMHp0nv2RkZGDChAkAIFm5KwJJqtyy6CaVyarFU0fAHLYK/GluA6WPGXY0WmznPio/eeA6sRecYJm4a36hjjU3mNrTWXTUZPqAJ/yrJmL7IYjKOqfi6Xm1zA70gIeoOVon0TTa0et1JsKf2A/n0KKUSxyGK100Os2ie/5lYW9e9D344zHq3Pnpud1KnxsCmJw3xJPaTix1d3Od+o08szaXO/n3wCwm5XZWuwRLzI14Zg8KQYWBy2YNDhe9n8tqgI60Vq/o/dwGoMibXPK6l9l1mK0no/E23K6scwyHK0o3C/z/BtSiy2UYg8EADw/LeuPu3XnP6syZMzFz5kxl/86dO2EwGFC3bl0891xehKgtlZ3atWsjNDTUnJ9p5cqVWLFiBZYtW4Zly5bBzc3NLM/169ev2P0WpNxPlMaMGYNNmzZhxYoVaNSoETp27Ijs7Gxs2rQJSUlJWLhwIQIC8kI2w8PD0bZtW4wZMwarVq2Cn58fNmzYgOjoaIwaNUo3ZFEQBEEQyg22iHorwfFt27Y1LzYUJDU1Fd9++y28vb3x2GOPoUqVKujRowdeffVVrF+/Hh9++CFpn6/sdO7cudTKjt6k6fPPP8eyZcuwfPnyUmf9LvcTJYPBgGXLlqFz585YuHAhVq1aBWdnZ9x3332YNGmSWXIDgMDAQOzfvx9vvvkmIiIisG3bNjRo0ADz5s3D+PHj7+KrEARBEAQbYCNn7uLSq1cv3WCq6OhofPvtt6hXrx4+/fRT8/Y7oexcvnwZq1atwsqVKxEVlRdgYI2jd7mfKAGAnZ0dxo4di7Fjx1psGxAQgKVLeZSKIAiCIFQA8h2yraGUeZSKw+1Sdq5evYqffvoJy5cvx86dO2EymWA0GvHwww9j0KBBePjhh0s95goxURIEMxE6odcRNFHo7GoRdH8gc0z56xOli2uXqK9Ph9pLaIOcfdR+/AdqH6B+PXkwh0We0DSc+u0sOK/WQFqQQbNov5jFnCS4zwRPKJ+u43z57Eli7kE03a/RZfZ/0j9S+6jN7PtZlIs/9YN6HLQu1OQAdel9Qwar5v5lMj2lI/10fyxO9ZewY594n3qzlAt2r7MGQdTWK181m9nXWDqAK0N0DioncB+khhZ8khJaUlsvUzX3ObrKjpnSldossT4AgKewY+5+Fn2S9NL48EArS9+OarYO9ZiCbVgqj/8qtlR2EhMTsWrVKqxYsQLbtm2DyWSCk5MTevfujUGDBuGRRx4x+y1bg0yUBEEQBKGicIelt8Lw9/eHpumXUrFG2UlKSsLPP/+MFStWYMuWLcjJyYGjoyN69OiBxx9/HP369SuWE3pJKPVE6emnn0aPHj0wePDgQtssXboUkZGR+OgjnV+cgiAIgiDYlrtYwuROULNmTZhMJjg4OKBr1654/PHH0b9/f1SuXJywxNJR6onSokWL4OXlVeREad26dfjpp59koiSULRJYsdlurJpq7iXlkI9rMckhl4WqGx6h9gCaARt2OoUcDfxXD8tGHXuM2l3VXGHYTMfe3JemNrgGmnW8SmIysS+8wmRIAFvep0vVEyfTT92JY8fQAya/qfSxNvAdYvepSj953/2bSmuT2fEXT1yHQiv2vjxEU0HE/UTlz7n+TyldVDWdoBtyu1N7CJPadrEOtFVQ4O8TyqnU1kRNyaBIbzzcP5YVGeUZsNN0pDd+zEkmxVVnsnboIbUPdKOmPXvfNKZzRTXT6YPBpTQXZpfm27LgMdau8ggAgJycHHOi6ePHjyMsLAxhYWEWjzMYDLhwgVfOLh4leuvzw+zz+frrr/HLL7/ots3JyUFsbKzFxFGCIAiCINgIW0hvZXxSl19ehZdYuV2UaKJUsMidwWCwOFAfHx9ZTRIEQRCEO0UZj3qzltzc3Dt+zhJNlPIL1WmaBicnJ0yYMAGzZ/OQjzwMBoNu/TVBEARBEG4TZcSZuyJRoomSvf2tqz916lS0a9eObBOEcokxmdpJj6ltJnhS+z12DKsUgqusVMZA5pcBABl8wzpqLqflM9p1UH/m3R9H7RE8qp69lOyTtCq3X031+X0invqhbOxMI1e6h7A46jnqqnGf3jOI/XHDasT+JZ76TvVkXTpx/xAAuMr8X5xHUtuLfboHH1e6SExhP+yu/0HtGOYnVpHh/ke+UWobxSeJhe5nelKbh/5znyUAuEYLq7546Sdiu7HC8ddi1C4WNNxPN+S+S20DCwkvjgeIC/Nrsk9gDXhJHCe1DwNPaligjYPkB7gdREZGYtu2bfj333+RmZmJKlWqoHnz5ujWrRuqVKlik3OU2pl76tSpFtvMmDEDO3fuxB9//GGxrSAIgiAI1pFjsEO2lWpOjuHOy1sl5cCBAxg7diwiIyMBwJyKwGDI+0FpNBoxevRozJw50+pi91blUdq1axeWLVuG69d1IlSQV1X4TjlbCYIgCMJ/HZOdPXKsVHpy7XSKGZch/vrrLzz00ENIT0+Hs7MzOnXqhODgYDg7O+Pq1avYv38/Dh48iPnz52PTpk3YsmULatSoUerzlXqitHfvXnTs2NFcZM5gMJDkUvl2frVgQSiz8HDlrr3VNseYlPYDW/q/yGSz9NeorWYcUKW3vWyVlskFUTrPufdptiGdyXMpVDZzrHuV2LEePIU24JNKM0t3v0DlqHf96K+zrFY3wTl+jtqrcmkG8LGJdJW5Tk2aSjnHXad4ZTYNAR5whqYgWJUyirbXU9H86A+3qgaa9C7R8CRtH7+Y2s95q32ufpbaD7B7wZVlFN/XUO0jpaq6zVqM7AarnEhtnlWb2wBwjpW7L2k6gOo60ptG3/v0JLo7l731v9RVuxhxeBuxEypR7XttQ5aq24Vnadep+6WxhzSHPiswMFvj6cABGNj9UVACNN1Q298GTHb2yOYp6EtIThn2Ubpx4wYGDx6M9PR0jBgxAh9++CGqVlWfn1OnTuG1117Dr7/+iv79+2Pnzp2lPmepL8fcuXNhMpkwc+ZM/P777/D398ewYcOwYcMGLFiwAN7e3hgzZgw+++yzUg9OEARBEAQhn6+++gqXL1/G2LFj8c033+hOkgAgKCgIv/zyC1544QXs3r0bK1fq1cIpHqWeKO3cuRMdO3bEa6+9hl69eqFfv36Ijo5Gt27dMHbsWPz+++/49ttvsXHjxlIPThAEQRCE4mOCPXLgYNWfqQyHva1btw6VK1fGhx9+WKz2H374Iby8vPDjjz+W+pylXp+Lj49H7963JIq6deti8eJby9RNmzZFhw4d8NFHH6F79+56XQhC2YDLBx771TY+LLO2iUYMLdHor5Un0qcRe4rOj573OrE6SN6sICSLxkvsp9ZNWsUSgu+pTwuM3v/hGdrARDMY7+laX+lzO4v82s6CeyL3seidlq8offSxp5FwEy9TueXjelSu6/Qvrd57Sa8agdMbxFwVyLLsmljmcvWlAdp3xEzUWLFijWXzZgnYMV9HQ/2FRUddYhJqNxZp57tJZ2CM6kwGa83uv2PtqJ2iE91T+Ro7L4tq41Ibl9UANYqNS3FtvajdmB3Pi9MCwBl6Pb5s3J81yCTW+DMs0g7Aky1bEPv+64dogxs0A/sbWj9ir+EBbADc6GnRkUVizmKRmCeNNAs+ALwaRZ+3tfUHFrBYON9twgQ7ZFtZxtWEsuujdPLkSXTq1AmVKlUqVnsnJyd06dIFe/bsKfU5Sz1t9PT0RELCrQ8IPz8/JCUlISnpluB8zz33YO/evaUenCAIgiAIQj4JCQklrvhRo0YNXL161XLDQij1RKlVq1ZYv349zp7N+/XZpEkTaJqGTZtu/Vo6deqUJJ0UBEEQhDuEbaS3spsfsVKlSkhJSSnRMWlpaTAadRz4i0mp1+dCQ0Px+++/o1mzZpg/fz5GjBiBhg0bYty4cThz5gzOnz+PrVu34n//+1+pBycIgiAIQvHJgb0NpLeyu8Dh5+eH7du3l+iYffv2wdfXt9TnLPXV7NGjB7799lvMmzcPaWl5Pgvz58/HI488gtdffx1A3nJXcR2uBOGusbcm26A3uWcV4us1J+YwVpU6POEfYm+t/ZbaJfff6LmAmGFTaDnztxOWqX2wz7OjoKH9z1ahOc5qMLeKjlATsTkwn6QvazYhdqtMloLgKC+7Dmxt2ZbYnU3sxXamWbW3bp+m9KHwlAe1ZzKnkhq/Untbe7WP9guprf1NbfvJxHS6z3K18axcFr9e62tqT2M+XBtUny7lbeAZxE+z0H7ub9SE7QeANHov4GpPakezUH49rlKfNwQznySexP5eZrO3DADAbxeezVr7lJj1g2hWdwC4/9hRuuGHolcL3v2Z3ivvPqG2PxBG0za0ymTP/BzqR9do8l9KH2v20Gva9+ytrOPZ14H1RY7SNuT+/4qSNZTlFaVevXrhgw8+wKJFizBixAiL7Tdt2oR//vkHL774YqnPadXVfOqpp/DUU7ecH7t164YjR45g/fr1MBqNePjhh1G9enVrTiEIgiAIggAAGDduHD777DM899xzqFatGvr06VNo23379mHo0KFwdHS0KqdjsSdKmzdvttzo/7nnnrxfMkeP5s36u3TRiaYQBEEQBMGm5Ngk6q3srij5+flhzpw5eOaZZ/DII4+gV69eeOyxx9CsWTN4enoiPT0dJ06cwKpVq/Dzzz/DZDLhvffeQ/36emGwxaPYV7Nbt27mGiolxWQqu6GGglAqYtmyvANdct+KaXR/T53aiFxe+YRKMunjaIrijNp06R8AXK7RMPtI0GO+bEqzB4dlUcmm2+kdSp/doplecpKluD7IPjaGqeNan7ybbrjMUhs8zw7YtITanw1T+sR8FunCpTb7CcRs2qSf0kU7GjUOnsDYjn0/5DB1b6tOluioSiybt8bSFPQ/Qu1HVSkJ9jRtA84xea4tk8By2A/XfTo/RnmWbCNPg6EeosDfWqbeKVIbl5P1qka4sPfNFEnMdldpkdyJ8aq0i+H0OyXlRZre22M40zKnUXPvaKUiNe4PoOHmsYn0nk0eSmXsxr6s8C6A1Db0vFEFfI5NeoWebwO2kd7Kro8SAIwaNQqurq4YP348/vjjD/z5559KG03T4OTkhFmzZuGVV3Tk7hJQ7Kv55JNPlnqiJAiCIAjC7cc2ztxld0UpnyFDhqBHjx4IDw/H77//juPHjyM1NRVubm5o2LChOfm1NU7c+RT7ai5atMjqkwmCIAiCINiCqlWrYvLkyZg8ebLlxlZg3bRTEAR9etMM0JigI719EU7tk7Ro5hc1qRTwJNRfRj7VLhN72TmWiTuAppbeHUvlq2sNPZU+q2SyzNuVmfTxFJX33nZTMw6/h4F0wzNKE8ogJh2102nzAk3X3ePffsROYzLRrlpqceOjVWgUm7uB2lVv0PcgjQVHJeJldVyGh6itRVPbjhZwVYqrAkAuyzJcl0mP21kKdheWrZrV5QUA8Jqt3Oa3k15qmlRms5eqfINUZ8Wjc19Vurw3gUprkdXp+/Q3l6imqAWSM/+h21xCcpU2ReHkpGa5z/yX9uljojqtzxSadnzN562VPrKZ9H3I/Vb0a6qmoRbLOn47MNlgRSmnHKwoFUVycjI8PDxslsexbAuRgiAIgiAUGxPsrE44mVsOJko7duxAp06dMH78eGXfu+++i6pVq+KFF14gFURKi0yUBEEQBEEoNyxZsgSdO3fGX3/9hdxcdTWxatWqSE1Nxfz589GyZUucPHnSqvPJREkQBEEQKgj5ztzW/OWU4alBVFQUxowZA6PRiC+++AJz5sxR2rz22muIiYnB888/j0uXLqFv377IyFAjHYuL+CgJgi0IYSHh2YHU1vP/mEmzd4MVgM/EfGKvSGftAaQnU5v76Tx/hvokzWtyjtj3JqrZmUc0op20sW9B7IdM79IDquiEpu9k9jKeh20xNf9hdutv1D7tqW/LBn/mg2Si2b4Rw9NGA2AR3WnuK6ntOonYgaksM3elj5Qu0+ypP1qWHUuym8ucg3J1HIpimQ8SjzxvMI6YoTE0w3jWx2qX+6pSO5rZ7sxdJtpF9emCifmasdcKuFJTY/5XWkelyyz2rXPR7iDdcJalht+sZo9PqESlocx21K6WTtt7ZA4hdsv3Vyt9ZmdQ/6KfHWlGekdQnyQXJXcC0DeFptv4oLKn+f837TTgDvgo2SI9QFmW3ubMmYOsrCysW7cOvXr1KrSdt7c35s6diwYNGuDFF1/EF198Uers3GV32igIgiAIQonIgQnZyLHqLwdlN/fhli1b0KJFiyInSQUJDQ1Fw4YN8fPPP5f6nDJREgRBEAShXBATE4MmTZpYbliADh064MSJE5YbFoJIb8J/D15QlBMTWPR+PWKZ/OTOwqS/o0ViAQBP7la3FSBL20PsGU46GZ2dqUbzSjZNCvt0A1oFd+TNh4l970E1THrahKF0w6b+xIx0pBmwWx1lWcoB8JX7VHsqn0SAjuszVzru6Cy1y2jjH3RDBvtFyZVJnSza8GBZoe0+IGanGCq13UOTMcNJJ7uynQOVm9KTqL0qmH5AJ2KU2skqZl+k5osvUantrXqexB7wb7LS5es1WxC7IWgW95MO9ON/2yl2fQEsCORFgWn6BGhUZgxM20/sLrHUBtSiyz9UpePq29qP2I2usZQXAHyuseTH3J83jr1R2WxNwFFNnuwYS6W3R3GINhjLjhnKZEcAL3ejdqvKtcz/T4cJ+hq8bTHBhByoKRVKQq5yQcsOJpMJLi4lS3Pu5OSEtLQ0yw0LQSZKgiAIglBBMCFXyedUUsqy9FarVi0cP368RMecPn0aVatWtdywEER6EwRBEIQKQv6KkjX/csvwRCkkJAS7d+/GkSNHLDcGcP78eWzfvh33339/qc8pK0rCfw/f09T++3/W93mdymQ4x6qDHtaJKOIr91FvUHsA02Mc1Ki3Hu9ROaBLAJX4qoDKFrGoRWxMUYeF1kupbX8fMePBopBowuw8WEDQD/Ak9j/nDxHbP5m23+rL00gDyGWhYG4s0rAVSyyXe0Dtw47JSyxLdiJLkH2RqkKooxNh7OpJbSf2vg6P4+3Ve+GLUGq/6kA7ffUw+6h+PJmYW3YxSRFApj29r41+tI+m39OBRren5wQALYH5dUQz3ey+4dSuyqS2XUqXeIHZ26OSiX3yBrW7tbxH6aO9G9VE/ZOYVpnAvuj3sQ5O6qy4tGRvXG2qH//wJ40gjcpUJcGb0dSOx6178mYZlrPKE88++yy+//57PPbYY9i0aRPq1OGVmG9x7do1PP7448jJycGoUTqSdzGRFSVBEARBqCCYKnjU2wMPPICxY8fi9OnTaNq0Kd544w3s2rULycnJyM3NRXp6Og4fPoyZM2eiUaNG2L9/P/r164c+ffqU+pyyoiQIgiAIFYT89ADWYCrjq1/z58+Hvb095s+fj/feew/vvfeebjtN0/DYY4/h+++/t+p8sqIkCIIgCEK5wWAw4LPPPsOOHTswcOBAVKlSBZqmmf+qVq2KgQMHYuPGjVi+fDmcnJwsd1oEsqIkVHyMTxKz3cppxG6eTB0YVupkB0jcycLoH2FOJc2Yj8QplhrYXw3DBw/cuG82tSOYrxBNIg0A2OA7l9it0+i4WrnTlALcv6jpD2qY9NHJfKyfE+vrk9Rn6UAjNXOyBzuPC/uoaVCP+kp9lXiZdmDS8enSeGV6mgUZhmhq2/PQdgAac0IyvUzMo5X9mG1kHehkVjbQa9zjQgdiT/GlfmOtoKZTeJilS7j/MGszmUohCT2vEbvqFk+lT/7VsGkwdbjqylLB94VO+olzzEnrEP1tnWk/jdgnHd8hdotMna8YZ/parqsJrgnXkKxsS2M+b4q/nw/LL8HdEO9VQ/uxiY7rh2fovXDGFE3s64nqqk0SO+2ixFs+SqbSR6eXiFzkWp0ewFQK6e38+fMICwvDpk2bkJCQAE9PT9x///145ZVX0LEjzdB+4cIFTJs2DREREUhMTETdunUxdOhQTJ48uUSTmgceeAAPPPAAACAlJQU3btyAl5dXidMHWEJWlARBEAShgmCLzNwlnShFR0fj3nvvxZIlS1C/fn08/fTTaNmyJdatW4fOnTtjxYoV5rbnz59Hu3btsGjRIgQFBWH48OEwGo0ICwtDz549kZNTukle5cqV4e3tbXGS9NJLL8HBoWRrRDJREgRBEIQKgi3SA5TUR+nNN99EUlISPv74Y2zfvh0LFy7E+vXrzWVDJk26VUMxNDQUcXFxmDNnDiIiIvDFF1/g4MGDePzxx7F161bMnTu3sNPYDE3TWeEvApHehPLN4E+oHZ2sNKm9hcpL/iw7wDmmxiRirHqeh5kdQR/m2k3pOR5gKpEef9dbRuw4Z1ZQdJCOFMJxoEVaH3ZvQeya6YeIveIUDaE/2nS12mcPZufGEnNVQxpinxmlymQ9Aqn0xqW4gmHTxcZwiNoal0+4rNYaKv7UzHmC2n8xqW0US9GQ0ErtMm0FMQOYWnWSpdlupvOxe/+ms3TDK/RX9apM+sH+6K+WP+i1DNrGyYkds5N+GTaqpYa74wKTVZ+lMvaZsTRfQty/VDZpUVVdmRhQm0qRHmk0Q70dy+reiMmSgJr2QoF/z8eye+Mf9fodDatHbC75XU+gbywvSA0AcSxVxqUCdq6qclcINE3DL7/8gipVqiA0lEr//fr1Q8OGDREVFYUrV64gJSUFa9euRWBgIGlrMBgQFhaG5cuXY8GCBZg4ceKdfhlFIhMlQRAEQaggmGwS9VZ86S05ORm1a9dGkyZNYG9vr+zP31apUiWsXr0aAHRD9YODg1G9enWcPXsWMTEx8PX1Ld3gbwMyURIEQRCECoLpDjtze3l54cwZ/RW+TZs24eTJk2jfvj0qVaqEyMhIACi0qG1wcDC2bduGU6dOyURJEARBEATbk7eiZJ3OZ0IuNE1Damoq2W40GmE08khQyoYNG7BmzRqcO3cOGzduRFBQEL7++msAQEJCnuRes2ZN3WO9vLwAAImJiVaN39bIREnIo+1Uarsxv5N9DantruNwd/Ft68fRjfor4ANWcqIpi7F1pOZXGvUzAIDRvPwDC09GNjWnmsKVPqYfoCHyqDuOmMOT6e7ujWiIeLf+fyt9wpl9mC2gldgNP7K6Cwmqzw2vdl/Hj4WvsywFSrS23RfquAbR8H9ceYSY7jWo30rbQDU9gDvzSYpmfjqXz9ASFPDi41KrzsP0INvgT02NlTix81b7SG9PbV7VIJK995nsfsr0VPt06EfMhQ2Z71QOfXYuZak+Xe9UY2+MP42zGRDJ7hXnS8TMTVK/eAwu9JiWg9l9/xZzpqqnvo94k75Pacs/IPa1VVRq6X2SfonG1mYlcwCsuUSfcV7ypbs79WFqppNOocr1ZLqB3ee4wj4YdtPXHvuqp9LnaVZ3h8tXfJxXaYYGAMCpGtROtK97y7DPBRCjHlRGuXTpEipXpk5XU6dOxbRp04o87sCBA5g3b57ZzszMxJEjRxAUFISUlBQAgLOzfk4IN7e8ezA7O1t3/91CJkqCIAiCUEEw5ZqQY2UFElOuCd7e3jh58iTZbmk1CQBee+01vPTSS7h48SKWLVuGadOmYfDgwfDx8THnSEpP5zPbPDIz837oubrq5Lm6i8hESRAEQRAqCJrJhNyckoW/q33kwmAwwMPDo1THG41G1K9fH2+++SZu3LiB9957D+Hh4ahRI2/JLSkpSfe4fGnOx8dHd//dQiZKQh67uWxGl78RTaW52jVVDTxOY0v5TsOovYOllu6kI9WlMqmjyWe0Swea+XfAERpSP1rv+ZtMx/rXPzR2+MELtLz7M5oaAr7bQPt40oFWNH+CVVWPrb+d2Glf3VD6vLaP/uyr248uczf9mUpgR6sdVvq4xJTIayxjsY8jzXjdryl1olwFVeJ63VSf2C/VoB8THin0tWS4q8voLgnUubMeT8HQgG5Iy2WyWm5dqPhT0/47amvsGAOvUw8gg9nrD1C7HsvrwNMBeHCNEEBvtq1tELV7UnNJM7WLrs2prNppNpOgz7EvrM30ObHrrmYM/8GR3l9uleg9+ghPYRHK5FAAmVep/HRoFZVEQgI8if3Zciq1vZ3DMq4DGMkCo+7zbEHshogmdpXMZKUP7GcyoT8b+xqWIpClSjjN7yUAGSwdQBX2LMVn0pQWkTRxNwAg0ZnK504FNEHNkMtV/grB0qVL8eWXX+KJJ57AmDFjlP35mbMvXbqErl27AgCOHDmi21dUVBQcHBzQuHHj2zfgUiAJJwVBEAShgpCbm4tcU451f7klSzi5bds2/Prrr7r7LlzI86P08/NDjx55SdrWr1+vtDt8+DBiY2MREhJi9lUqK8hESRAEQRAqCFoukGuy7k8rwTypd+/ecHNzw/r16/H33zRo5fTp03j33XcBAE888QSaN2+OkJAQHD16FOHhtwInMjIyMGHCBAC47ckm27RpgyeffNJywwKI9CYUj26PEjPupqqBd9pJ5aklfnQZvncTuv9oyBD1PK1ZIdgcKilkgfa5tulIYl/jYXAAqrxOF7wfzGTpcxvSENhP49VhPRbgT+wnDrO8IWOoRFHzJJU9tAy10GP683Ss0VfqEPto9vPsiNeUPnqwZN5gL+1oZSoR/nGQRhANVXoE6rakmZA9MtlrZfpBtB0dNwB850SPiXZ6i9hVc6iE2ukSjW5so+PLmWtaSOz9TG3aWodHBeoU17zC7I+YtDaPZeLOZLIaVSXzCLZgt6MRaok8gziAWeeo1JZRn0rfvR2ZFAcmxXmpUvgTSewifsTOm/0tMa9ihNJHdRO9F0Leiib2WwH0Pv8jnkpt7VKULuEXSJ/hRqCyWY1slrVd53lEQya1sZ/82VPouHY60nuDZ0sHgCsmet5r/1JXgn3Mj9ldJwI/6CZ9IAtGmZpSAZZ//baQ+/8TJWv7KC5eXl745JNPMGbMGHTs2BE9e/aEn58fYmJisGHDBmRlZeG1114zF8YNDw9H27ZtMWbMGKxatQp+fn7YsGEDoqOjMWrUKN1klNbSt29f/P3330hMTMSQIUMwZIjOd08RVMgVpUOHDsHJyQkdOnRQ9l24cAEjR46Er68vXF1d0ahRI7zzzjvIysrS6UkQBEEQhKIYPXo0/vjjD3Tp0gW7du3CV199hd27d6NLly5Ys2YNZs2aZW4bGBiI/fv3Y8iQIYiMjMT3338Pd3d3zJs3D1999dVtGd/169eRnJxc6uMr3IpSVlYWnnzySd08DOfPn0f79u0RFxeHrl27on79+tizZw/CwsKwefNmbNy4scRVhQVBEAShrKCZgFzrEnNDK8WKVK9evdCrV69itQ0ICMDSpUstNywjVLgVpbCwMBw9elR3X1moWiwIgiAIt4s77aP0X6BCLZ/s2rULs2fPxoABA7BqFfU1OH36dLmsWnzXMDJntydY2P5ONbvwVgfqt1SH+YPU9qCi/pD5y5Q+1jb9jdhpzmzSm0Fjq9Oy6ftV1aAmMpvxIM0+3Z45kayLp/4fs6uuVvp4Jb4f3VCNNRhH/UEcfmJZonVotIvFqv9FfUi07Hfp/nYsFwCAnxu0IDbPJvxr5j/E5pm53XXSrdRkLy7emEwbMF+NDw6qdZ6WtqRh0simdiJWE/vBmgOJ/YyRX2DABdRn5I0TdFxbTTRbOs7qxOHz25aF7oNfc7AYer20MtxFa8BmYta+2ZXYI3VixEMD6Out+S/zSdrK/IvqsyWDRqpvnkI0cxj6cQExq3/OfeKA2CfpfTw7hi41sMTwQBVqHtNJh+N/hvoxuTTwJ3Y88yfqdPq42kll5qD2AzVXfBJA7Gss5UACC/UHgGsx9JrmMG+MlszOUbtAGvNbSizwtuWkVUwfpf8CFWZFKT09HU899RQaNGiAGTNmKPu3bNkCoHhViwVBEARBEIAKNFGaPHkyzp07h0WLFunWkSlO1WIAOHXq1O0bpCAIgiDcRkR6sz0VQnrbvHkz5s+fj0mTJqFt27aIjo5W2pTXqsV3jXlMapvDNIt/WbFaAE2NdN05gakFcU9SnWfDUjW+di5qE/voObqE3t7QgtjRjjTMd2m8uh7+ZjUq6ygZwjvS8O1O/6p6wf/8aLj2GVD54OdnkmmXLPPv/XupBAYAWMUm9N+p2bsJX6gx8zXb00e4US49T6s0KiccbUrTBWyEer3i2badTFtKZeHca5qqfWTlDKAbDFSjWeXwDbEfzWJSUqaa0fmkkYaqn3NOpg0cH6Y2l4UAgCf8/XUTtT2Y1PbdaGoPovcKAOBjWnx3yAkqtS1lchRWuah9/EivYVxnmmL+xAl6TNcN7N75Q005gGz2O7gdS5fQksnafurXAZc7u/hSWWx/5iFi102k7+NaHafiZf7Udo6PJrZDTTqOFV3VLO0+rICtQ2t6omvs+eRSW3qyZW9nZwvquYNO9gk7pqZnFVAIs+9QYHXuXXLmvlN89913lhsxLl9WP09KQrmfKKWmpmLkyJFo1KgRpk+fXmg7a6sWZ2Zmmgv25Z9XEARBEIQ7x4gRI2Aw6CSxKgJN00p8TEHK/UTpxRdfRGxsLHbt2lVkZWNrqxbPmjULb7+tU5tMEARBEMoIWgV35n7yySetmvSUhnI9Ufrzzz/x7bff4vXXX8d9991XZFtrqxZPmTKFRMSlpqbC19e3NMMuH3CJ4jBbU9ZZvDtaeTWxzzvQKJq3p9Cbe1FVNeRq2UHa5osAKvv4Rx+iB7CSQL1rUnkGAIbH/0Rs93r0HN8yack/lVXtBIAh+4j51y66tt2Xzb8bDWDr8m/qrFS2ZCFU7ZlUspN9WnEpBUD7w4fohmpszf0KjYZqei6a7m/jr/T5G8taHHmGSm27mCThxLKB5208yTbQHyBnWBTSUVcqCZ6GuvK79OQhYm8wsPuHvkWAeisA3zD7ApXJwINe76fmgJPq50PHiTTT9AtMiTxTzZ8Oq9u/6rjupdenNuh5ai9jN1hzdrxzMb4ZPdn9c5LdK/+qz2OV/fS8fevTIsodvegDuMabPksH/lUl56pMYbZjj0oGk/tyoOpI7uzB5y149GdOFreVLuFQ+O9sAKqclcULLAO4yr6/owvIrqZiBCbaAltMlMqyj9KiRYvu+DnLtTP37t15IbQzZ86EwWAw/9Wrl+dfs3PnThgMBvj7+5urEZe2arHRaISHhwf5EwRBEISyRG4Fd+b+999/rcqyXRrK9YpS27ZtzYX0CpKamopvv/0W3t7eeOyxx1ClShX06NEDr776KtavX48PP/yQtM+vWty5c+cyV7VYEARBEIQ86tWrhxdffBEfffQR2Z6cnIzU1FT4+fnZ/JzleqJUWMr06OhofPvtt6hXrx4+/fRT8/aQkBBs374d4eHhGD06L5rlTlYtFgRBEITbiS1KmJRlHyVN06Bpqkz89ttvY+7cuTCZbB+yV64nSiXlblQtLjf0nErtNo8Rc5q2h9gu01hVdQCDmN/AF8nU1+VvGukPcDcWABtq0LQDf2AMsR/391QPKsAanXD3yBo0vHhaLi1H7n+QlSf/QO33uGcysUNO0QdVy6b2hbfpOOp20XHkmcfsR6g/yCJ/+mk1YnJDtY/FLOyV+67YsQ+UWtT/g4d/A0AzUH+hVE/ql4LifA4Zoqmt0Wzon95MJvYfV6i9tY6a+R1Bi9kGfRn91hh0nE4MVantwDKA8ypGfjSvmvshtcsq8KQb0ul78lVCNLEbBvsrfbRiz44PC2+veYT5KB1g6QDsdHzguJtXNLsXGhXj49+16G/ck8wR7MClQ8SuzZKBA0Bt9hY4sfiZa0gmNs8UD1j2Y+K2HXM7dGNj0IP7MSWzzBDndAKo49hjXjBFSu4d8j/OyQWyrJwr5JThidLd4D81UcqvWvzmm28iIiIC27ZtQ4MGDTBv3jyMHz/+bg9PEARBEKwi214tVVRScsq197LtqZATJX9/f92lOaD8VS0WBEEQBOHuUSEnSgKjy0p125E51F5D46Y7xdKQ+qVsSfn5yp5Kl/5zaVbzWc2onBDUiYZRj1Tdy4C/g4g5PrcpsY/8Q6U5vqS+oAHLwg3gtTT6Wrp50uzCP7eky/Ttl1HJEAAar6HZl+FPC9T+2YoW6ez9+8+0/TadzMn1WdLSV+la/ohd7PFsqLN2f5PJSzxM3IP9YGCh/XrSG8fZnY6jK8v2nasjr2xhabEjq/sTO86RFl2O82ZFdE3MBgBsK3KcAJOD7bzVJjwWncs6rVkJoxwq7y1q+rLSZfS/1Kn0BBvGBy70nr03XpUVe7P3paNrB7rBjYbZ1+TZ0PXTw1E8WUbwZKavNNNJi5FO3/uMynSp4SiTyXJYrdp6/mqXDk60zxAjzXrfLWEHsddUo1IwoIb/c5vLoS6u9Nni2eUBICuTPwu0z5vstSUWQ77LKvBSc+/Qt22OHT1vqfrQuRX+y8hESRAEQRAqCNl2QKaVE51su3KeO8jGyLUQBEEQBEEoBFlR+i+wU43mq53cjdidWAJdLrVNYQrO2zlqkcFOoXSJPJEtfx+P300PWKIOdcgxGhrXMJDeov+y5e9FDejA3O3VPFizeA3SjVHU/ojKaBF/tlf6qFn5IN3ApI7eR5kExqS26AfVKMFroIWHWw0+RhvUYnIdl0oAoDb96XgtiL5xVdKT6X5XTzYGagOAA/tYcDJS2SIhmsoWEVRRBQAkcqXRcJVtYKFOGpfa9AoE8zZMRjMw/cqgRkshl4Uuea0gprvhHWK7meg49CSNrb40om9rLrWhRRCTZ6YGgCy2AnDaNZq1oM/WNS/6/Ll4qRKq/6lYuqERSw19kIV1mXSk3dq0zR47KpOdT6PPdI169PlrpJMe/dH0Q3TDYdrHzuZUGq+hk5n7CrtHrzEJmUfOpaRR+yZ95HW38WpXaSzKTe9e4E7UWQ63HgTNQUPxNFLryLG3jfSmU/O3zPDTTz/h0KFDZNuZM2cAAF26dNE9xmAwYNOmTbr7LCETJUEQBEGoINhkomRXtidKFy9exMWLqi8pAGzdulV3+3+6KK4gCIIgCHnYIj1Adhl25t6yZcsdP6dMlARBEARBKBd07Njxjp9TJkoVkQBW4fz0F0qTZzUquE8Lpn5MWVE0pH6qH/WRyLqkVgXf6p1M7BdiaZtOrL1DVXUp9L169CGonUVDwuOa0bByaH8S0ylHdQBZY6Q+D32NLCX4x3Qce3BI6aPbPazfE9SM9qFV039mPhOxl9Rl4k7e1LmnFU/e3Z6dU6/6eE1qngR9rT6u1JflAAuHj9fJZH4NdKxXzlOfpH/Z2+auk2FA8VHSivZJGnCahtg76SSEjmQlnIyszVFP7n8QqvTRNJneT02Yy1Jl9kuap584o/MeHPWmN0McTwiu0fcxWiesPJC5cF1yZfcLu3yp7H30V9JwA/41WB/xLHXEvezFVGYOgACya9CviBz2lfGwewti33/lEO3gNPMHBICbzJfsHuoc1AhniH0EanqAaEQTOyGT3sdZGSy0n/kf6fkoXeT+Rex55Pck91kCgDT23mcWeOM05OKO+CjZqT5vpelDuIVcDkEQBEGoIOTnUbLmryznUerSpQsWLFigbE9OTsa///6re8zrr7+OqlWLkfiqEGSiJAiCIAgVhHwfJWv+ssvwzGDr1q3mCLeCvP3226hXr57OEUBGRgaSk5NLfU6R3ioCbVlB2x+pqWXTwrIA8JmRxXT/Q7N3d2xUm9hNd9FMy++1Vm8dp+zXib3YgUofTmzlf1elr5U+YOhMzLg97MbvlETtbkOJmfizWrpmWTTVimo82ILYaawg6fGjNDMwAExlaQoOXKDr8HHxVObgUSdHa1FJDAA+SmLFZlcyfYWF/sMvQ+mDZw928aM62Bn40/2svV7B0Tqg98bemmycTNHRi7BJtKeFiLnUNsOeZi5/ohGVLv0TdCJaWHaEP6rSUPUFUV1pHzof9lV86WBbN21B7IusGK0/6LhOQv2Ajkuksk+iM9XRsliqgyh31cfCP5FJgsl0f5orlT9dmNSWDTUthvIz2IeO4+P69L1vCJoVHwD6xtL3vttZliYjnumwnh7Ubs2yzwOI96Lnvcb045PstRw1HVL6uJl2k9l0fzr7mIhmlyeaeSfowe/rqiyZd2IlNdt+JtNIswre95oJAK1eIJQPZKIkCIIgCBUEW+VREm4hEyVBEARBqCDYqoSJcAuZKFUEdr9NbX9qVstWD0nM+ZZuoLU/EdpgIbGvtRlM7EvHdcKSqr1AzMha7ej+jUx+mqh2gceY/d55tuE7akZQ+QUuajgLjzJacuYQsXOZJLisCZONAOB4NLVZpA4M+9RjyEnUcWVUptfU5TwbyB9UXkjpl6z0UXlEOLGvaVTScmfFP++PPkTseH9VevuDZev2cqVtjCYqNSXo1PuF5s9sKr0tT6TjalW1CbH9E1lWaQCIp7JG761UBur9BGufqfNpf5Hdt1X2q20KYqQSYDdHnY/MqvT6XE2m0tvPTJ1KM1IZGwB2BdCLWPUolcmcXJPpAZ7UjGWSIQDcrxOVVZCJ/7KIx+vX1EaxTEqrzLJ5e9K0hBHdmxHbQTerNn3vudx5NZnKm3oRalxaS2QBfJfYbX2dRaPx6DQAuO6sdyMX6LNSEbJaYRDpLQfAqUKbCmUXmSgJgiAIQgVBpDfbIxMlQRAEQaggZNsbAQfrvtpz7HUUg/8wMlESBEEQhApCDozIhY62WAJyoSYf/S8jE6UKCQ3LT3R7Um0SsIra03pRu+ojdHcyS0XdjGXXBYDs96lddzK13WdT+5JOcrC3S58UDACgrVQ2NWWuGA5e1D7Do+5zZqj98jb72OuvTytWv6JRx5Sna6pV1F2iWUprL+ZUkkFTHTiPYukDAGDxW8TslsSyef/LHvF0F3oKf9WRpSp3gGE0DaQ+Ju5H1NTcCZVouPvaBr2JfdRzILHvRwTt4KJOuHs4zWp8rSV9Y91yqI9J2i9qlvabH3gS24dnh/dlBwRT5xfHsWr6iceb02u8yJOOvV8CvV5XzildILoqHesxb7rfnT0qXT2pT9cRqJnywXyBeHoFxbEnTacMaksa3h9djaVxiGY+XEk0tcYHXp4646KkpCUT+zp7XnOYWxQAZDF3Pp61Xi9bfEH0Mr8nmuh7wMP/s3h2eT2K00awmm+++QarV68m265dy/OxCwgIUNrn7ystMlESBEEQhApCjma0fsKmqT8IyhKpqalITVVzdAFAdHS07naDQS2ZVVxkoiQIgiAIFQZnAEVH8FmGL9mVHc6f55HQtx+ZKJV1npqpbltMM2DDoy+1zzNt6QqzAaDKK9T2+ozaL9NQf+ymGbLd26nSyBOn6dL1yvOvETsRf9ADardUxxXAHoJaX1L77/+pxxRk3NPKpi+/ZGkJTCwkPIfFlXurmbkRx2KU5zLp7X6aXyHIzp/YjXay6qsAsJPJXt8xvSCGmkbT92ofTwVTe/ABag+lUhvqUfkqlmXhBoBEJLM2NHz78ik6zkXN9ap501+0fc7Q934XWx3fyYr59s3VKaa6h354H8+l16/DBPpxdj1ZTQ9Q9yHa5o9U6ovh8A+Vn7pXYeE/rly/UlMwVGHSZbqR7s/U+dRtwxYAmlWlhWD3p1NpzYUVXeYZxAEg1khP5JOQQhvYs1WDf3RWEZzpNfaPZWkI7On1eS6Tvkfxl5OVLquz0zgwxY8XIub7AVU6q5pd9H6OXkFbLtdl2jPplh2TprfowhctNGsnLKVAc7LBilLpfJRycnLw6aefYsmSJThz5gw0TUNQUBBGjBiB559/HnZ2t57JCxcuYNq0aYiIiEBiYiLq1q2LoUOHYvLkyXBy0nnT/5+6dXXSt9xmJK2UIAiCIAhWoWka+vXrh0mTJiExMRGPP/44+vbti+joaEyYMAFjxtwqpXX+/Hm0a9cOixYtQlBQEIYPHw6j0YiwsDD07NkTOTm2j7qLitL58VVMZEVJEARBECoMtpDe0i03YaxcuRLr1q1D+/btsWHDBri65q1qXb58Ge3atcM333yDoUOHomvXrggNDUVcXBzmzJmD0NC84CNN0zBkyBAsX74cc+fOxcSJehmJ87h+/ToWLlyIXbt2IScnB82aNcP48eNRu3Zt5OTk4Pfff8fp06eRlpaGGzduIDY2FuvWrUNKSkqhfRaFTJQEQRAEocJgBJe+S9dHyVi+fDkAYPr06eZJEgDUqlULkyZNwnPPPYdly5bBz88Pa9euRWBgoHmSBOQ5W4eFhWH58uVYsGBBoROl69evo127djhx4gS0/3c6X7NmDb766its3LgRzzzzDPbu3QsA5v35/ZcWmSiVdWK7qNu67aa211Rqt2A+Nxmq3w5OdWUbaNkFxDCfmuw/iZl2jYUeA/izLr0RX3XwJLYDhhP75Uvj1HG5s1jgcxZ8kjhfv62zzdJB71EzeqraxO4hYjpNpH5OA47S6zc6if2iG6H+QosbQusw1IqsXOQo01ap4e4eTyygG/5hTphxzA6kPjYRpmNKn1tjqbPG1jrMxyuQ+3zplXIIJNa2wL+I7ZRDX8vIHHo9j3dXS6vU7MhTHVAz9Ud6T9adTUPX83iWWK1dqS9E9Zrsw7Q3+8LJ5XkiAI9/6Xkz/KgzS2o8bT+9qb/SR/cb0cT+Oof6JLVjp93K0gHcjxZKn9d4mgdWUciH+xvZ6fiFpLO4exO9Ph+3o/e58yUa2189We1ybkP6PmY40uu1kH0tZZjUWP/UeLpNKXPCPkZ4puqsYmSeNnJ/5pv0nt3pzHNJANnMm6WH6VaajNxcIIkfUIE4e/YsAODee+9V9vn5+QEAYmJisGXLFgBAnz59lHbBwcGoXr06zp49i5iYGPj6qtd49uzZOH78ODp27Ihx48bB1dUVe/bswezZs/Hggw8iJSUFderUwcMPPww/Pz+4ubnBw8MDdeqofnzFRSZKgiAIglBhcLaBE7kRJY18++CDD5Ceno7KldUffLt27QIA1KxZE5GRkQCAJk2aKO2AvMnStm3bcOrUKd2J0urVq1G3bl2sX7/e7PTdp08feHt747nnnkPt2rURGRmJ6tWLUYuvmIgztyAIgiBUGJyQJ71Z82eEpmnmfEX5f5mZhUfD9ejRA/369VO2//XXX/joo48AAAMHDkRCQt6qY82aNXX78fLKi9JOTEzU3R8dHY0uXbookXH55+7WrZtNJ0mArCiVfWb+oW7rz7Jqe7CM11X8qd2CykYAgKosxUA21THeTPch9gOubYn9kMc+pcvoPjRedvI3rAEL4cXo3lDIbKtuu9PU1QnlN7Esvb2uELvqPCbZJDJbx2Wg9kx6Hi2M/hLLiae/6DbOUFMytPXYS+yDTI7q8zobx05qN7WnYegAMM2XCQQ5PLM7Sylg0MnSbkelszTTenYM+8DVqHYyN62f0uW7oVQGCnmRpT74lB0wlslGAOBMX3/1PizlgjN7o1zZTZuufmT+EFSf2LGXqCwWxX6Ots2JVvpINI6lGzSa9mFtPSaNR71DzJt11JQWdV39ie0Aev84+tzDbPW1ubCY+G65VKqdmMLSaLO3RLEBpDKpjaeoeDX6DLEX+atZ7U860TY8pQCX2ni6gO/91D4jWJqHmqD3ML8W7ixtBgB8dIbKvX0L3NZZ14GlyhG3AZsknDTi0qXTyurQ1KlTMW3atGJ1kZ2djffeew/vvPMOsrOzMWTIEDz88MOYO3cuAMDZWSdHAwA3Nzfz8XqkpqbqrlrVqpV3H1WpUqVY4ysJMlESBEEQBIHg7e2NkydPkm1GY/GcvDdu3IgXXngBp06dgp2dHV577TXMmJFXGip/JSg9XT+yLn/VqqBDeHHId9a2xmm7MGSiJAiCIAgVBlukBzDCYDDAw8OjREelp6dj/PjxWLx4MQCgTZs2mD9/Plq3vhUAUqNGDQBAUpK+a3u+NOfj46O7/24gE6WyRvb9xHw+Wp0dz4tmNW5GswihKkzW8HhX50QsOsyRyicz/mRpZ5uyw59XJRusZ30+vJnamewXwrEyILPp4eutbqvDiu1eCSdmIg+G8mdRgc+oGsTVKVReWdeeXvMRkUuIPQBtlD6SP6eRHN070OXsk11on42u0fdgy5lDSp9D2Gsx5tAiy25MaZtXVydj+A1WZJlfHzV4jMCTSANAfEu63F7zQ3aNvVh0VA116T66Mr1eHkxucc+mA3O8TjWbz7zUTOZRZ6jUFslu88gA6i+Rlf2y0gfs7mM2+4JimZLX1qMP5Fr7D5QumybSSMJAqhajJpMEnXR+wGexH/272aPRuXIHYldhWd2VyDsAF5lkdQVUvmtUjEULB9Yml/kd/8Qyv8ca6ftWIzta6fOkI3Uubsiyn+9g426k8/VZxZduc8u4df9k6pcmsz02kt5Kys2bN9GjRw/s3LkTXl5e+OijjzBixAhlhadx48YAgCNHjuh1g6ioKDg4OJjblQXEmVsQBEEQKgxG5K0oWfNX8onSBx98gJ07d6JOnTqIjIzEyJEjdWWwHj16AADWr1+v7Dt8+DBiY2MREhJi9lUqC8iKkiAIgiAIVpEvty1fvhz16tUrtF3z5s0REhKC7du3Izw8HKNHjwYAZGRkYMKECQBQZFZuAPjmm2+wevVqZbvBYChyX36up5IiEyVBEARBqDDc+czcFy9exLlz5+Dq6ooVK1ZgxYoVuu0aNGiA559/HuHh4Wjbti3GjBmDVatWwc/PDxs2bEB0dDRGjRqlm4yyIPnpCkqyTzJzlyd6sqzPX1DzvANNsOX/rvoWhXz7ALEf/2o6bcAnzQN0xtGcpa49zLLy9mL+Rg+so/bfQ3Q6ZUTqZBUvD1zUye6dxnJ6dNlAzGX3vEXsejdp+PaAUBpCDgCtBtAHd8TfbKn5Z+bIc0FNALc6nGba7gcaZt8ol37gXWvnSeysI6qzUL9m1FejKfPV2MrswBSacR0AouKG0Q28xiV3H2L+M1/2CAbHM+EEsbu3of4xGWxc0dDLzE0HksrsLHo5cY297XGX1ZDwSD9qR7uyseeyauf2PCs+ADvuF8eeR5Y+AQbupPOm0uVRj3+pXfkC64OmHKiqfaf0UZUlg3+ARuVjezCtElDP6E+PZ+9J3jZPYndg70Fsbepnl8b8yADAwYl+Lnp60z6msNvaLou+b8Pc1fQANdnXoTs7Lx93DUQrfbx9jY7jjQKPNE9hcPuwjTN3Sbj8/89Feno65syZU2i7jh074vnnn0dgYCD279+PN998ExEREdi2bRsaNGiAefPmYfz48UWe6/z58yUamy2QiZIgCIIgCKWmdevWpK5acQgICMDSpSXPLFW3bl3LjWyMTJQEQRAEocLgAGVFssToJGv9DyMTpdvNASa1sRVffzsqv/jHsePVBNgYtPMcbdLgNWLPvr87PeDPVmonD7xC7f6sYuYvTFr7u4TFae8YNHQdvs+rTWIC1W0lJaUqteNZ+PYjPxBz5pGfmK3qn2HV6IfR2x2YxFCdSXFfqDLZiKMscHUZazCGLqH/xrINrwpOVvr0TKDZl+Or0dDqY0eojBEVrOMrEK9uKnL/BmbvOq4c8sHUU9TOZuHFLES+zxlVJnNnmTN4BudLLOFvIquEEOWh82vWxDLM5zDJ2Z45tioyG4AMdn/lcOmDpQBxYba9Tnb0XNZGYxphLpXzEsGKHQNwMtLny8OT7q9spBsaMnnKX0f+rJKbTOxYO3p//cDkOp4RG1AlPkcjfSOzmZzH5TsuH+edh46LZ/Fpw/Z/mK5KgjksC8isAsNIzQE+Uo64HTiiNFFrFJkaFESuhiAIgiBUFAwOgMHKFSWDTA0KInmUBEEQBEEQCkGmjbebVjoRVAWIPkMd4Az1aDbd346NU47Zn3mI2LMdNtEGldkC7zPXdM7MsmKfY9JQNxrNgogymkXbg0VA2EJmKw5cijQyWSyNppbuFKeGpk73pJJNuh0tgPyhG4tM1GFtNr1/mrahMljd9+gS/IhXqYwx0k3Nqv3BaRaxVo0VPk1ikg6r5woAeJHZXLE6xuwl7H0M0Yl8+YsViG5PM2LDQEOS19Zbq/ZhOKFuI/tZKmqe4ThHjcaDoSG17Zm0xqW2DB2ZLI1JJTxKkMP7cAlS27gzOU9j4+CRdNwGEGdYTeyVtfvR/aZOxPa/SSXnTlHqsKqxYbh6UnmukpFKznrSm2MJv7p4HzxCElDluZ+5fGdSpTZOLnvfJhS4pJnq5b1N2EJ6Ex+lgshESRAEQRAqCiK92Ry5GoIgCIJQYZAVJVsjPkqCIAiCIAiFICtKdxv+DhieIua8c6pPxYbVLLHX6zQ9ADKZn05MMcbBM3NzH6aySuqauz2CPDJZXHDOE8TcWlvnmMiniTm75Z/Enl69P7FdXlUTuvXxoL5PB+rTrNpbPqS+GCMRSztorBOqfpb5C2V6Uvska7/wV7UP7u/i0J7ax1nI/D3h1K62S+1zC7MfvIfau1mf9V9Q+6jCxmXP/K+UDNjseIOOfxHfxv2HeKi/nq8Kz/ygZoKgsNtNt30a8yVz5ikGWK4EF52BafSYODuWr8RE75VoF5rK++8A5lcG4F6aMBxV01i2dGMysWNzqA0AOcx9L5clrXdgH2dOzNWsvXcLpc+D6YeK7DOHp5bQueafsGOGFnBrymZZzm8fjmrm9tL0IZiRiZIgCIIgVBhskXBSpgYFEelNEARBEAShEGTaeLfxZ+kD+iURc8Nsnfo59zKbyz7C3cf1PWrzosIAkMTSNuyh2dJdnb+i+5vQewMA+pynmZPXculofzS1n2dh9946BSbdmIT1z2hq53ixA3SKH6cx+YkndnccSu1Gv1N7p04m+JtMJmw5ktrnWYqLnjoZ6UexcfkwW0dZI+hJXDyUn3+qWioIrNfGEpakOb1xcDXFxVi0DQDuXK5j95cjl26pxBrlulPpMqrRZrrBjqZEcbKjxXuNYCkbALjdpDqWP3uUmjJF1YG9tJ2XDil9cqkti502ndkJOnVn+/INBc6brfe+3w4MNpDeJOqNIFdDEARBECoMIr3ZGrkagiAIglBRsMmKkjhzF0QmSmWN1c9SW+9+zWHRPhii00goU+gWFWZhMNWZDNaGyVH9lio9rH3xO7rhExaV9N0b1PZeT+3KOtmGjUziM86mtk7SbIt91GavTWOFdLnUppdh3VLW9QYzqJ1+SW0z7F12TFdqNy76FPAoxjaevJsV2kUKVHhdYW7XsTCu4lCT2SwSTImkA1SJz4Vpk87M9uBRhUzGBQCNZX7PfYSYWabT1LZXI+dqZ9JtlqS2GHabR1dRh5XF5xb8+liJydpFHuGuIRMlQRAEQagwiPRmaypE1FtOTg5mz56NFi1awM3NDZUqVUKrVq0wd+5c5ObmkrYXLlzAyJEj4evrC1dXVzRq1AjvvPMOsrIs19USBEEQhDKNyTEvd5c1fyaR3gpS7qeNmqahX79+WLduHerUqYPHH38cN27cwIYNGzBhwgQcPnwYX3/9NQDg/PnzaN++PeLi4tC1a1fUr18fe/bsQVhYGDZv3oyNGzfCwaHcXxJBEAThv0qOA5Bt5YqSSb4HC1Lur8bKlSuxbt06tG/fHhs2bICra56wfPnyZbRr1w7ffPMNhg4diq5duyI0NBRxcXGYM2cOQkPzwqo1TcOQIUOwfPlyzJ07FxMnTrybL0dl5ds6G8UnqUKSyfyNvmU+SXXHqcfwavehLEv2C/bUPnuW2lN0MnNXO0htd50UAhZhaYz/Zf5FZ0KofcUGTjhnmhdtA0AT5m/lz9IURDGfpVTmYxOrkwrB2QZ+TrcDS75S/LtUL+UAD2m3FOLOX5teJnPTb2xD06L7zK2rbIpy70jtJqyBiaWKYCkIYKApCHQxRBPTiT9rOmRpRTg2OeSieGUShLJGuZfeli9fDgCYPn26eZIEALVq1cKkSZMAAMuWLcPp06exdu1aBAYGmidJAGAwGBAWFgYAWLBgwR0cuSAIgiDYmFyR3mxNuZ8onf3/X8j33suzMAJ+fn4AgJiYGGzZklcoqk+fPkq74OBgVK9eHWfPnkVMjMz4BUEQhHJKDvJW/qz5K2kC1ApOuZfePvjgA6Snp6NyZb6uDOzalVdYs2bNmoiMjAQANGnC12jzCA4OxrZt23Dq1Cn4+vrevgELQj5dVlI7YhW1c94nZuCNhUoXkyr7E7uTw4vEjgfNrDw/+x1iL4vSyfzejMWRpzH5KYPFWt/UkRu4lKYng90NjrUt2m5wmNoNf6R2AHuPADVz+S4mz2WyTOY8dUJxUPpg74mecsnrWvO0BM7MLk48y01mc7nOgUltNf6vvXuPjrq4+wf+3iRkSciFCASBkIdrWgKYqICRyMNFpFqstUIFRCqBRp9SH7FaC56fB7D0QI+KAgVrK2ooHCuk6tOKCBhuakRAFMJNUi7BEKlcjElIINkk8/tjzZr5fGfZTXaT7Cbv1zn8Md/v7Ow3k4SdzGfmM4bQW8iP9LIl549MKTASFiEiY7gSod4QES6uvVY0YDgA2CYOILd10YrW7jGF2UR4zna+3jNWg6G34BT0A6Vx48YZr3/wwQdYsmQJAGDixIlYvXo1AOegySQuzvkf0cWLF433KysrUVn5/S9jaalMdEJERNTCauD7jFCt5yptSdCH3iSHw4GFCxdi7NixqKysxJQpU3DXXXehpMT551T79vLPKKeoqCjX600WL16M2NhY1z/OOhERUcCphXOg5Mu/GkurbVqrGii9//77GDx4MObNm4eamhrMnTsXa9asAQCEhzu3eFTIkw2/UzdbVH9BeH1PPvkkSkpKXP+4lomIiAKOP9YocaCkCfrQG+Ac/MyaNcsVXhs2bBhWrlyJIUOGuOrEx8cDAIqLzesDLlxw5sDv0aOH8b7dbofd7uP5OdR2fT3feq3LZL1cW6QV59U+pZWfFsswAAAHzujlSH3OPb6/frZDrMgWgA8Nbd73tV7eIdZ7xOvPhXOG2dVK03kYQUCupTovFv8M/MT6mmHimJgL1+vl0j56ubKj5+eQa5LkOjGZtuDIDdY2skUbYmkQxI+Okdzu399D/aOiXGZIPwHTtXo8pSAArEc7yaVQ14g1TGHiOJtaceYJAKhOok6qh4cwfJZY0g7U+8NbMalxsAr6gdKVK1cwbtw45ObmIi4uDkuWLMH06dNhs9m0egMHOpOb5OXlGdvJz89HWFiYqx4REVHQ8ccaJc4oaYI+9PbMM88gNzcXCQkJ2LdvHzIyMiyDJOD7Rd+bN2+23Dtw4ACKioowYsQI11olIiKioFMDht78LOhnlOrCbevWrUPv3oaTqr+TkpKCESNG4MMPP8SqVavwy186t/RevnwZs2fPBoDAy8pNwcP+C73cV/wsitsA8D/P/1Arz0nWwzy9isSfhaZ9CD/U66y36+kvHr1ySCufTdbDe+h/j7VNT1v5C5Oufr81KRHhmI/HW+vIVAiJ+Xo5arde7mRIgd1erp0UYf4LIrRWJLKBV4kwGwAcMVyrT0ZMvckYLnbQe3yNKW2BfF/z/prvmU7j8PTJFSFicdGibDPFsQWbTEEgwnWmUJrqYr3muufFe/oDZ5T8LqgHSmfOnMHJkycRGRmJ9evXY/369cZ6/fr1w8MPP4xVq1YhLS0NmZmZePPNN5GYmIgtW7agoKAAM2fONCajJCIiorYrqAdK//nPfwA4F3MvW7bMbb2RI0fi4YcfRlJSEj799FM89dRTyMnJwc6dO9GvXz+sWLECs2bNaq7HJiIiahp1oTdf2yCXoB4oDRkyBEoZMgtfRZ8+ffD66697rkjUEPJAWxmiOGGNvR3oqJdzoa+P+yZRD6OtKdTDaACwNPR/9AsOkRW64ud6eaT4gyBQMmYHM0+H8caKJLYxhqS2sd/o5XiRfiR+l16Wu+JMO+nkxi65M07uejOF0eQ1T+E6WTbtrPMUrvMUigM8f3LJgUK1CL2ZXq9EHSW+T1591Ij0Mqregb7KdOpwE2Doze+CfjE3ERERUVMJ6hklIiIiqoczSn7HGSUiIqLWohrOE3x9+efrQAvOkzJsNhtycnKM90+fPo2MjAz07NkTkZGRGDBgABYuXIiqqsBLzMkZJSJvDBPZqge/qJflNujcO/Ry1F8sTe4q1Rc97Lr2U72C3E4cmm59ruWi/Ioo/0BPQYA9U6xtUNOSKQZkGbAeKt/PQ2bzHlv1sjfZvktFJulSsc7pjCG9SoyoI88Cb8yRl7IN+bsjc/6aTpWSmbmlK/KCWH8UYcg50E58QIeKskwXYJPpwAGosquUvVl85QcOGL7+RrThoxUrVri9d+rUKaSnp+Ps2bO49dZb0bdvX+zevRvz5s3Dtm3b8P777yMsLHCGJ4HzJERERBSUlFLYv38/8vLy8Pe//92Y3LnOI488grNnz2LZsmV45JFHXK+fMmUK1q1bh+XLlwdUXkMOlIiIiFqLcgAlPrZhPjv+6m9bXo4bbjCcOSj8+9//xoYNG5CUlOQaJAGAzWbDvHnzsG7dOrz44oscKBEFnTARFvtUHIT6sQhRHE0TDcgygP97+urvOeKYXk4yhN6mibJs8ghDbUFJHsYrUwzEi333MeLgYsCaQsD+rbjfUS+HGw55LRMH+h4RrzkjQ3OiDfmeALBL1IkRH64io4XlMF8AiBflBqcLMB1wLq6F/UAvXyNCpjJ9AGA+bLeOTZ5I3UQuwxrebKhGDJQiIiKQnZ3tKq9cuRI7duyw1Nu+fTsAGBM8Jycno0uXLjhx4gQKCwvRs2djYrv+x4ESERFRa1EB3wdKjUj5FBoaiokTJ7rKGzZsMNbbt28fAGDQoEHG+8nJydi5cyeOHTvGgRIREREFJqUUSkv1EZfdbofdbpqN896FC85Zt65duxrvx8U5ZykvXjTM2LUQDpSIvGE6DFVT7vt7TH5BL7/9uV6O6mt9zR/EAbWlhhAfBR9PO+VkufNnntsslbvaRFjNFL6T12Q4T+rsRehNhvxkuO4FEYqTO+8AYLQoe9oFZwm9Gep4+jS8Ina9RXe21okW35faejvjQi55eAM/8ceMUgXw1VdfITY2Vrs8f/58LFiwwKemS0qcC6jatzfvAoyKcp5Q4HD4Yeudn3CgRERE1Fr4Y43SZaB79+44evSodtnX2SQACA93pmaoqDAvhKqsdKZFiYw05YVoGRwoERERkcZmsyEmxtPBfA0XH+9ciV9cbNg8gO9Dcz169PD7ezcWB0pERESthZ9Cb01l4EBnRtG8vDzj/fz8fISFhbnqBQIOlIgCxRu/ERf+phff+0WzPQoFGJkuQKYHkOkDAOu6JbuH9UOWNUwGPbZ5ruOJp/eRKQmyPayLAoCbRfkaUfaQ6ByAdR2TXPck1zWZdoadE+uW4uuVy30dvXiphXa9eWvcuHH43e9+h82bN+PZZ5/V7h04cABFRUUYPXq0a61SIOBZb0RERK1F3RolX/414YxSSkoKRowYgYMHD2LVqlXfP/bly5g9ezYABFSySYAzSkRERNSMVq1ahbS0NGRmZuLNN99EYmIitmzZgoKCAsycOdOYjLIlcaBERBToZDqAUi8O2pXhOYit/uHf6uWYk56fI1q04Snbd5khzCbrVInQWrR4DlOo7ogoyzXHcnmL3IluOBPX46ehr5+WZZ6r+IW/jjAx9ZGfJCUl4dNPP8VTTz2FnJwc7Ny5E/369cOKFSswa9aspnvjRuJAiYiIqLXwU3oAdPStiaysLGRlZbm936dPH7z++uu+vUkz4UCJiIiotagAoPzQBrlwoEREFGzOiV1wdsMnW6XY6hXzjaggph2i/+P5fa+IJIAlHg7vleE9wJrt+4LIxF11q16WWbgBINndA36n0MN9Exm+izXWapj6O+WaKTE3+R8HSkRERK1FBaypDhoqcE4PCQgcKBEREbUWly8Bl81Zr73H6a/6mEeJiIiIyA3OKBERBRu5RsmkUqQHMKUQqM/eBOmY409Zr1V11MtyjZLMsm36UuV6IrnLS2ZGkH5quPZPD6/x9Aye6lxpYPuNFVYOhH7rWxs15X55lNaCAyUiIqLWot0loJ2PoTcHB0r1caBERETUWoRdAsJ9HCgprlGqjwMlIqJgZwrFyVBbe5FCIEZs5TeF3tr7GI4ztSmze0sypOVNiEuG2mR50wW9XPuVtY3F1+llmWKgMekD6n9bqryoTwGJAyUiIqLWol259WiZhqplxsn6OFAiIiJqLdqVA3Zf1ygx9FYfB0pE1HbJ0NDkF/Tyhev1crsvrW289wu9LDNitxT5HJ6eyxQmq/QwsxArs30L2zZYr/V+Uy/L9TSeQl6ma3LXmzxCzPGKXg65xdqmfN8T8gBgcTjvzw3PJTOG1w+9VQBYY3iNv7W75PuMUiUXc9fHPEpEREREbnBGiYiIqLUIK/d911sYZ5Tq40CJiIiotWh3yfc1Su24Rqk+DpSIqO0YcUwvO8Rillv+qJflWphPTI2KtSxjZ+nlWrH4xbQWSG7vl+VSsdXfU5Ztb1yJ9FxHrmvqIvbdyxQD1UP08s0i6zYAzBDX5Db7ez0/luU1vxHljp/q5RpxvyTd2qZc53SXWJM0TtzvaXiu9qJcv4s59ghaHCgRERG1Fv5ID9CO6QHq40CJiIiotQjzQ+gt7BKAdn55nNaAAyVqOnf8TS/LbdRE/jRWxMXi3rPWeVKUe4jyNWV62RGul2+xW9ucIkI074r3zRb1jxg+xOQMgPygk1vqe/9FL5t+tzylA5D3Yy9a68R7CLUlntbLG/fo5U2G95XhKctjfqYXjxjCdzIR+Y9E5m3HLr0s0wH8zvBc80W5vyjLSKU3n571xxoy/NdU2l0C7OGe6121jXIAHf3xNK0C0wMQERERucEZJSIiotYirBwI93EOJKwcUB398jitAQdKRERErUW7csBe7WMbV3iIbz0cKFHjyPUgAJAu1mbMmauXR4k9vHu6+veZqHUbIxb7dDqklxeL+n0NJ8TLU+NrxdoWm1jbYRf5AboZtuXHd9bLPaL18nhR/5s4axuF4lqpWPeULdbp7BXln4mUBABwyLC2p74rXhy1Itck9T+gl09O0MtLxOtNnzD/FGW5hitGPLfpCJPnRbk6V1w4rxe/FukAfm1o01N3yLXNDi/q1Ofj2MVr7crNKSga1EYtB0r1cI0SERERkRucUSIiImotHOFApY9b+x2m6bK2iwMlapycNOu1imF6+RWxVfiMF6G2PmK/9k9/qZf/uUovnyzy3Cb5191ia/qlFM+viZYnsYvt7xdEuGWPYWv/q2L/dk+ZffkLvezIt7ZhE4n01H+J8rWivog/2CqtbYbqdW4M/aFWLrher37xnLK28UNRlmGa0aK8T4S8/iHKALBZlOPFXv1zt+tlU/gOIj3AXvH1Z4nqojst6RYAYKi4NkeEM78SKRjOGR7rB6KsRJu24Xo5Tjx3vCGuVBptvVaf/J54E0qrP95ork9bhx24InMwNFCVj6G7VoahNyIiIiI3OKNERETUWjjCPScb9aS6hqODetgV5D8fy+09Tze8jUiRgThfhtruaHibrZnMptzz33pZ7lpqL0JPMd9Y25ShtEkiDPZjEQIzzfLLDWdbRPkVUZZZkbeIUB0AVG/Xy2qnXg4VWaItqZSBR4v0r6VaRGSqRUTmirj/lTyMFUC4CMFEi9ekiy5fLjbJGcklIvJ/6uli69gMEQ41CA/RQ5VVMqZV1sf6otUv6uUbxO6yWhHfs4Q2u1jbtHxt4vv0XyIldt/rrG3IUJsSsUslvgnhIkwbYvgmXCNCb9WiLLO0XzZkaZfhuLCr3Gsq/hgoORwcHdTDriAiImotHHbgivWPhIa1ccVzuoQ2hGuUiIiIiNzgjBIREVFr4ZfQm4+H6rYyHChRYDkk0g4cMldrk0ynu/cTmZLlGiX5mgtir/pX/21t83GxLuVekb26Jk+8wLBWI2KIXr5R1LlR1P9vsSbJkmkZgG2vKIvsy2I9zKzj/7A0UR2qly+JpS5ZqWJ/e/Xj1ueQQv+mFX97Xl8P06erftT9yxBZtwFkXtZTG4RH99TKVcViPZES31fTWiB1j95Grfja4sUXf/gnhjZ+rJdtR7RiN4e+LqxKfKJcDEs2tNnFQ1msNXOIn2kAQAfDtfrE+qIQkYJArnEykZ+ODtFGQ1MVhXqu4hcOO1Dpa+jN8DvdhrW50Ft1dTWWLl2KQYMGITIyEt26dcN9992HL774wvOLiYiIAllVuPN4Gl/+cUZJ06YGSkopTJs2Db/5zW9QUVGB+++/HzfddBOys7MxbNgwfP755y39iEREREGptU5EtKnQ21tvvYU33ngDw4cPR05ODiIinHHcjRs3Yvz48XjggQeQlyfDCh6ETwFs303zVv7t6nWJGkIebCnDagAQLzIny1Bb0Ri9LA9bLbrV2qbcAn9ZhDHay9CRIcux3Dp9RdwfKsoylGRqU4ZobPpW/6QyPWRTIXaqA0DWYP01N58T6SbUv/Ry6Eyt+LtvxDZzAAM699PK/bvqYceu0EOXnyHK0sasL/UcCy/226pXkImSbT/XyyEydTeAs+L7dK38vumhtlGFIt0CgF8m6tvuV315+qr3X/ha/0CMjtJDdQDw/zr00spFNbu1csFB/YclK8WQEkT+LNQaQnxafTlDYggt2WRm7kbMqlwtHOfjqSJeq7b7aY2SIQv9VdRNRLzxxhvo3bs37r//fpw7dw7Z2dnYsGEDdu7cieuvv95zQwGoTQ2UXnjhBQDAs88+6xokAcCPf/xjDB06FHv37kVubi7S09PdNUFERBS4HHYgxB9rlBo2UGqSiYgA0WZCb+Xl5dizZw86deqE4cOHW+6PHDkSALBt27bmfjQiIqKg5mki4uDBg8jNNWzSCAJtZkYpLy8PDocDAwcONN6vu37s2LGGNVz1d5hTE7cgGX65Y521zhumAzDJrRHi5+LAb/VyZZxeloe+ykNhTWSY7HrRpr4RyumwKOeJzOaVHfWyKfuy9JgoPy/CFINFCMd0lmiE+Gt0uGgjWqTurhVhjhB56ikAJXYdVQ/WivnRK/VyynOWJmb9W4/5bUyQNR7RSnNL9VDbw53FobkAelw4rl+Qf4iLJNA77L0sbbzYb6J+4Yz4WZCRlC9Epx8xfBN+tlYr3nh+mla+WWz8ijGcWX1PbYFWnirDR+f0UNtU+V+h4RPmPeh9uClff4+PB+j1ZUZxAKiSoTabmEGR5w4r0T8263NZQm3VHnZ+hXkx4+KpjabgCIfP2SKrGxZ29GYiYu/evdi2bVtQRmzazEDpwgXnOoGuXc0n2MfFOT+ULl40bMEGUFlZicrK738xSktL/fyEREREPqqyAzU+ht5qGjbAa7KJiADRZgZKJSUlAID27c2zP1FRzgWWDoc8iMhp8eLFePrpRpxdRkRE1Fyqw4FqX88fadiMkq8TEYGuzQyUwsOd3/gK01YYwDVbFBlpHok/+eSTeOyx72MSJSUlSExMREMXvDULJXYMOWoNleQ2JLqqajGAVpdEOezq92vNP3c6MUspsyOaxvA1oizfp1bESrxJtFcjnqNc3JdNyDAHYA1LyBCEfI5a2Z/yTQEoeeCq+DkPlaeOijYBVMlvi5wYduhtVIr7ZSHW36VS2R/yvwTLWaqG38ca8c0tkz8Lor789TX9eJXqW+VqxHNWibJpo1Rpjf7NdcivVSZRlF+7YfNihV3/YhwevieqytBftaJDasUbKQ8dFGL4+QoRX1yN+MY1JrdQ/Z/7776nSpl+YfzDuTZoI4Ctnqp6UAm7/TpL5MRut8Nut842+ToREejazEApPj4eAFBcXGy8Xzci7tGjh/G+/AH5/gfoBf89pL/I//zfNFX6YzM8SCuyS16wZn3WyA8IL8YnFnJphnWpRtOQs+MTmul9/c66ZX6Vx9foi03lb/cLEBmyG+VLL67JHA2+2++hbLKkSf4QbOgPsmkWQi4KDp5FwmVlZYiN9f/3FwDWrVuHw4flwsXG2bx5s+U558+fjwULFljq+joREejazEApOdm5+O/gwYPG+/n5+QCAlJQUr9rr3r07CgsLoZRCYmIiCgsLERMT45+HbeNKS0vRs2dP9qmfsD/9j33qX22hP5VSKCsrQ/fu3ZvsPRISEpCQYNmh0CijRo2yDIpMs0mA7xMRga7NDJTi4+ORmpqK/fv3Y9++fbjxRv2wqY0bN8Jms+H222/3qr2QkBAkJCS4ZpZiYmJa7S94S2Gf+hf70//Yp/7V2vuzqWaSmoK7MJuJvyciAk2byaMEAP/7v/8LAHjiiSe0WOmLL76Izz//HPfccw969erVQk9HREQUfOomIs6fP499+/ZZ7jd0IiLQtKmBUkZGBu68805s374dgwYNwkMPPYSxY8fi17/+NRITE7Fs2bKWfkQiIqKg05onItrUQMlms+Htt9/GokWLAACrV6/G0aNHkZmZiV27djUqfmq32zF//nyvpyjJM/apf7E//Y996l/sz+DXmicibKop9yoSERFRm1BdXY1nn30WWVlZOH36NDp16oTx48djwYIFTbqIvalxoERERETkRpsKvRERERE1BAdKRERERG5woNRI1dXVWLp0KQYNGoTIyEh069YN9913H7744gvPL27jqqur8dxzzyE1NRVRUVHo0KEDbrjhBixfvhy1tfpxBadPn0ZGRgZ69uyJyMhIDBgwAAsXLkRVleFsBAIA7N+/H+Hh4bjlllss99if3vvwww/xox/9CPHx8YiIiMDAgQOxaNEi1NTo58awTz27ePEiHnnkEfTq1Qvh4eHo3Lkz7r77bnz22WeWuuxPCjiKGqy2tlZNnjxZAVC9e/dWmZmZ6qc//akKCwtT0dHR6rPPPmvpRwxYtbW1avz48QqASkhIUDNmzFCTJk1ScXFxCoCaMWOGq+7JkydVt27dFAB16623qgcffFClpKQoAGrUqFHK4XC04FcSmCorK9XgwYMVAJWenq7dY396b/369SokJERFRUWpyZMnq/vuu0/Fx8crAOrRRx911WOfelZcXKySkpIUAJWWlqYyMzPV2LFjFQBlt9vVjh07XHXZnxSIOFBqhH/84x8KgBo+fLiqqKhwXX/33XcVADV48OAWfLrAtm7dOteHeHl5uev62bNnVa9evRQAlZOTo5RS6s4771QA1LJly1z1amtr1aRJkxQAtWTJkmZ//kA3Z84cBecxtZaBEvvTO+fPn1cdOnRQ1157rTp+/Ljr+tmzZ1WfPn1UWFiYOnXqlFKKfeqNuXPnKgDqt7/9rXZ97dq1CoAaMGCA6xr7kwIRB0qNkJ6ergCo3Nxcy72hQ4cqAOqjjz5qgScLfPfcc48CoLZu3Wq5t3LlSgVAzZw5U+Xn5ysAKikpyVLv8OHDCoDq27dvczxy0Pj4449VaGiomjBhgmWgxP703vz58xUAtXbtWsu9pUuXqtjYWJWdnc0+9VJqaqoCoIqKiiz3+vfvrwCor7/+mv1JAYtrlBqovLwce/bsQadOnTB8+HDL/ZEjRwIAtm3b1tyPFhROnDgBAJaz9gAgMTERAFBYWIjt27cDAO68805LveTkZHTp0gUnTpxAYWFhEz5t8KioqMADDzyAfv364Q9/+IPlPvvTe++++y4iIiIwYcIEy73Zs2fj22+/xcSJE9mnXlLfZaCx2WyWe3UZnKOiotifFLA4UGqgvLw8OBwODBw40Hi/7vqxY8ea87GCxjPPPIO3337beDjkrl27AABdu3Z1nRc0aNAgYzt1hzCyn53mzJmDkydPIisrC+3bt7fcZ396p6KiAvv370dycjLat2+Pjz76CAsXLsTcuXPx2muvuQ7BBtin3ho9ejQA5+++qpe2LysrCwUFBRgxYgQiIyPZnxSwwlr6AYLNhQsXADg/zE3i4uIAOHd5kNW4ceOM1z/44AMsWbIEADBx4kSsXr0aAPvZG9u2bcPKlSvxxBNPIC0tDQUFBZY6/Ln1ztmzZ1FdXY24uDjce++9yM7O1u4/8cQTWLNmDe644w72qZcWLFiAvXv3YunSpdi+fTuGDRuGU6dOIScnBwkJCXjppZcA8GeUAhdnlBqopKQEAIx/tQPOKWQA2qGA5J7D4cDChQsxduxYVFZWYsqUKbjrrrvYz14qLS1FRkYGBgwYgN///vdu67E/vVNcXAwA2Lp1K3bs2IG1a9eiuLgY33zzDV566SWUlpbiZz/7GY4fP84+9ZLdbsdNN90Em82GAwcO4OWXX0ZOTg4AoFu3bq5ZJvYnBSoOlBooPDwcgHOK3qSyshIAEBkZ2WzPFKzef/99DB48GPPmzUNNTQ3mzp2LNWvWAGA/e+vRRx9FUVERsrKyrnqgKPvTO3X9oJRCVlYWpk6dio4dOyIuLg4PPfQQfvWrX6GyshJ//etf2ademjp1Kp5//nmMGjUKe/bsQXl5OU6ePIk5c+Zg7969GDNmDIqLi9mfFLA4UGqg+Ph4AN//5SnVTR/36NGj2Z4p2FRUVGD69OkYN24cjh07hmHDhmH37t1YvHgxQkNDAbCfvbFp0ya89tprmDNnDoYOHXrVuuxP70RHRwNwzmrcfvvtlvsTJ04EABw5coR96oVDhw7hrbfeQvfu3fHOO+9g6NChiIyMRO/evfHHP/4RDz74IM6dO4fVq1ezPylgcaDUQHULCg8ePGi8n5+fDwBISUlptmcKJleuXMG4ceOwevVqxMXF4dVXX8Unn3yCIUOGaPXqFsXn5eUZ28nPz0dYWJjbRfVtwSeffAIAWLRoEWw2m+tf7969AQC5ubmw2Wzo1asX+9NLvXr1gs1mQ0REBEJCrP891oV/lFLsUy8cPnwYAHDTTTehQ4cOlvt1axbz8/PZnxSwOFBqoPj4eKSmpuL8+fOuXRr1bdy4ETabzfjXKDl3vuTm5iIhIQH79u1DRkaGcdtw3X+gmzdvttw7cOAAioqKMGLECNcHV1uUlpaG2bNnW/5lZGQAALp3747Zs2djxowZ7E8vxcTE4LrrrkNxcbFxd9Xu3bsBAKmpqexTL8TExABwLpI3qVuY3blzZ/YnBa4WzeIUpF555RUFQI0ePVpVVVW5rtclTJwwYUILPl1g69Onj9tkndKIESMUAPXyyy+7rlVUVKiRI0cqAOqdd95pykcNWqdOnTJm5mZ/eucvf/mL6wiN0tJS1/Uvv/xSJSYmqvDwcHXixAmlFPvUk7KyMtWxY0cFQK1Zs0a7d+7cOdW/f39ls9nUgQMHlFLsTwpMNqXqJbYgryilcNddd2HDhg1ISkrCqFGjcOLECWzduhWJiYn4+OOPGUc3OHPmjOugy8zMTLf1+vXrh4cffhj5+flIS0tDcXExbr/9diQmJmLLli0oKCjAzJkzsWrVqmZ8+uBRUFCA3r17Iz09HR999JHrOvvTO0opTJ48GevXr0ePHj0wcuRIOBwObN26FcXFxXjppZfw4IMPAmCfeuOtt97ClClTUFVVheHDhyM5ORklJSXYtGkTysrK8PTTT2PevHkA2J8UoFp2nBa8HA6HWrRokUpKSlJ2u111795dZWZmGtP0k9PevXtd55Bd7d/IkSNdrzlx4oSaMmWK6tKli4qIiFCDBw9WK1asULW1tS33hQQ4dzNKSrE/vVVTU6P+/Oc/q5SUFGW321VsbKwaO3as2rx5s6Uu+9Szw4cPqwceeEAlJCSosLAwFRsbq0aPHq3efvttS132JwUazigRERERucHF3ERERERucKBERERE5AYHSkRERERucKBERERE5AYHSkRERERucKBERERE5AYHSkRERERucKBERERE5AYHSkR0VdOnT4fNZkNOTk5LPwoRUbPjQImIiIjIDQ6UiIiIiNzgQImIiIjIDQ6UiKjB9u/fj3vvvRfx8fGw2+3o06cPZs+eja+//lqrl5WVBZvNhhUrVmD79u0YM2YMoqOjERMTg9tuuw15eXkt9BUQEXmHAyUiapB3330XaWlp+Ne//oX09HTMmDEDCQkJWL58OVJTU5Gfn295TXZ2Nm677TYopTB16lQMHDgQOTk5uO2221BaWtoCXwURkXfCWvoBiCh4FBcX4/7770doaCg+/PBD3HDDDa57WVlZyMjIwLRp07B7927tdR988AHWrl2LqVOnAgCUUhg9ejR27tyJ9957D5MmTWrWr4OIyFucUSIir61duxbffvstpk+frg2SAGcagRtvvBF79uzB0aNHtXtjxoxxDZIAwGaz4e677wYAnDp1qsmfm4iosThQIiKv7dq1CwDwk5/8xHj/5ptvBgAcOnRIu56ammqp27FjRwBAeXm5/x6QiMjPOFAiIq+dP38eAJCQkGC836FDBwDA5cuXtesRERFu21RK+enpiIj8jwMlIvJaZGQkAODixYvG+4WFhQCArl27NtszERE1JQ6UiMhrKSkpAIBt27ZZ7jkcDmzZsgXt2rXD0KFDm/vRiIiaBAdKROS1adOmITQ0FH/6059w/Phx7d7zzz+PCxcuYNKkSbjmmmta6AmJiPyL6QGIyGv9+/fHM888g8cffxwpKSkYP348OnfujLy8POTm5qJv37547rnnWvoxiYj8hjNKRNQgjz32GN555x0MGzYMmzZtwquvvopz587h8ccfx+7du7k+iYhaFZvilhMiIiIiI84oEREREbnBgRIRERGRGxwoEREREbnBgRIRERGRGxwoEREREbnBgRIRERGRGxwoEREREbnBgRIRERGRGxwoEREREbnBgRIRERGRGxwoEREREbnBgRIRERGRGxwoEREREbnx/wF2eARQsWJEFgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dbz_comp.plot(vmin=0, vmax=70, cmap='gist_ncar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab6c315b-5038-4663-9660-b0f77a76ec7e",
+   "metadata": {},
+   "source": [
+    "## Run Steiner classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "88acd536-3416-44f8-aaef-04468c1a75fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "convsf_steiner, \\\n",
+    "core_steiner, \\\n",
+    "core_dilate, \\\n",
+    "refl_bkg, \\\n",
+    "peakedness, \\\n",
+    "core_steiner_orig = mod_steiner_classification(\n",
+    "    types_steiner, refl, mask_goodvalues, dx, dy,\n",
+    "    bkg_rad=bkgrndRadius * 1000,\n",
+    "    minZdiff=minZdiff,\n",
+    "    absConvThres=absConvThres,\n",
+    "    truncZconvThres=truncZconvThres,\n",
+    "    weakEchoThres=weakEchoThres,\n",
+    "    bkg_bin=bkg_bin,\n",
+    "    conv_rad_bin=conv_rad_bin,\n",
+    "    min_corearea=min_corearea,\n",
+    "    remove_smallcores=True,\n",
+    "    return_diag=return_diag,\n",
+    "    convolve_method=convolve_method,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b562db-51fb-4bc9-bee6-552cfbce6bda",
+   "metadata": {},
+   "source": [
+    "## Expand convective cell masks outward to a set of radii to increase the convective cell footprint for tracking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5dbe29fe-74dc-4994-a356-d5b446847c2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_expand, core_sorted = expand_conv_core(core_dilate, radii_expand, dx, dy, min_corenpix=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "07622db4-5750-4f25-b156-e52e0e659d50",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('2019-01-23  21:15:00', '20190123_211500')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_str = ds[time_dimname].dt.strftime('%Y-%m-%d  %H:%M:%S').item()\n",
+    "time_fig = ds[time_dimname].dt.strftime('%Y%m%d_%H%M%S').item()\n",
+    "time_str, time_fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e9d4ff8e-fc4a-46ea-a32c-326beedfbaf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/pscratch/sd/f/feng045/SAAG/hist/cell_tracking/idcell_test/idcell_20190123_211500_option6.png'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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dGwcPHpS2FSlSBAsXLrRI/UlJSbLRVtlFXFwcPv/8c/Ts2VMWWJ4yZYpNpnDMahmnAmzVqhX279+Ply9fIjg4GA8ePEB4eDjmzJkjm15y/fr1mDJlitn7tcZnjp9jIiIiIsppGAgjIiIiItJy6dIlNGvWDCNGjJDdqG3cuDH++OMPODjIJ1VISkqSlg2NsNKWMahkZ2fdn+ZNmzZF586dpfW5c+di6NChOkGToKAgDB06FEOGDNGpI0+ePJlqQ045V9YQHh6ODz74AC1btsS9e/ek7YULF8aePXtk08YZUrZsWVSpUgUlS5bUOY8XLlxA7969MXLkSIu1PbO2bduGKlWqYNGiRbLtn376KaZNm5ZFrbIt7UDYhAkTcODAAbRp00b2Xi5QoAAmTJiAs2fPyqabnDt3rtnBTWt85t7kzzERERER5UycGpGIiIiICGnPv/r666+xcuVK2bN7NBoNRo0ahTlz5uh9vo6rq6sUMEtOTjZ6f69fv5atu7m5mdly461ZswYNGjRAcHAwAGDVqlVYtWoVfHx84O3tjejoaNm0Z4ULF0bFihVx+PBhAICnp6esvmrVqhnc5+rVq1GvXj0A8hEh2flcDRkyBGfPnlXN88knn+CTTz4xWFdiYiIWLlyIb7/9FrGxsbK0tm3bYs2aNfD19TWpfQEBAdJySkoKrly5gvXr12Px4sXSufr1119Ru3ZtDB061KS6LenKlSsYPXo0AgMDZdvd3d2xcOFCvcHW3EgIgT/++AMA4OjoiBYtWqjmr1ixItasWYMOHToASAs8LV++HPPmzTN539b4zOWk7zwiIiIiIoCBMCIiIiJ6w6WkpGDhwoX45ptvEBMTI0urUaMGlixZgsaNGyuWz5Mnj3RT2JRnaMXHx0vL9vb2yJs3r4ktN523tzdOnTqFfv364d9//5W2P3v2DM+ePZPlrVChArZv347+/ftL20qUKCHLc+3aNYP71B5Vpz2izNxzBegG5Czt3r17Bo/t6dOnBuvZtWsXRo8erTMtXoECBTBnzhyLBILs7e1Ru3Zt1K5dG3369EGLFi2k8zV9+nQMGTLE5iNvoqKiMGnSJCxfvlwWVAaAbt264eeff0bx4sVt2qaspNFo8Pbbb5tUpn379qhSpQquX78OANi7d69ZgTBrfOZy0nceERERERHAqRGJiIiI6A129epV1K9fH+PGjZMFwQoWLIglS5bgwoULqkGw9LzptEdTGfLkyRNpuVixYrC3tzeh5ebz8fHBvn37cODAAXzwwQeoUKEC3N3d4ezsjBIlSqBbt27w9/fHf//9h4oVK8qmZCtbtmym9m2Jc2Vvb49ixYplqh3WFhUVhT59+qBLly6yIJiTkxPGjRuH27dvW2U0VL169fDZZ59J62FhYbh06ZLF96Nm+/btqFy5MpYtWyYLgtWoUQP79+/Hn3/++UYFwTKjadOm0rL2swpNYY3PXE77ziMiIiIi4ogwIiIiInoj7dy5E/369cPLly+lbS4uLhg7diy+/PJLuLu7G1VPhQoVcPXqVQDA/fv3jd5/SEiItJzZAJM5WrdujdatW6vmuXjxomxEV4MGDWTpGZ/5Y0iFChWk5fDwcLx+/RouLi4Gy2mfq5IlS+o8p83SDh06ZHbZ27dvo3Pnzrh9+7Zs+3vvvYfvv/8eZcqUMaqeR48eSYEkDw8PeHh4GFWuU6dOmDt3rrQeHByMOnXqGNn6zJk+fTpmzJgh2+br64tvv/0W77//Pp8JZSIfHx9pOeMILWNZ4zOXU7/ziIiIiOjNxSsRIiIiInrjHDhwAD179pQFwTp27Ihr165h1qxZRgfBAKBq1arS8uPHjxEREWFUufQpzwDdAFN2cfToUWnZx8cHlSpVylR92ucqNTVVupluSE44VwDw8OFDtGnTRhYEq1ixIg4ePIjff//d6CAYANSvXx/FixdH8eLF8fXXXxtdLuPzxkwNVppr5syZsiCYnZ0dxowZg6CgIHzwwQcMgplB+5ly2kExU1jjM5ebv/OIiIiIKHfi1QgRERERvVGioqLQr18/JCYmAki7YT9//nzs3r3bpEBFujZt2sjWDxw4YLDM48ePcfPmTWm9ZcuWJu/XVGvWrMHo0aMxevRo+Pv7G1Vm8+bN0nK3bt2g0Wgy1YaM52r//v0GyyQlJeHYsWPSui3OlbmGDBkim8Kub9++OH/+PFq1amVyXdrTz2m/VwyJjo6WrRcqVMjkfZvq+PHjmDZtmrSeP39+7N27Fz/++KNJQeXcqnnz5qhVqxZq1aqFxYsXG13u3Llz0nL16tXN2rc1PnM55TuPiIiIiCgdA2FERERE9EaZN28enj59Kq0vWbIE48aNM7u+xo0bw8vLS1pfsWKFwTLr16+XlgsWLGhWoMRUJ0+exMKFC7Fw4UIsWrTIYP4rV67gxIkT0nq/fv0y3YaSJUuiWrVq0vrq1atlz5HSZ/v27dLIPUdHR7z77ruZboc17Nu3D//++6+03r9/fwQEBCBPnjxm1ac9YubUqVNISEgwqpx2UMLBwQG1atUya/+mGD9+vDTyLE+ePNi/fz/efvttq+83p0hISMDly5dx+fJlbN261agyN2/exOnTp6V1c8+nNT5zOeU7j4iIiIgoHQNhRERERPTGSElJkd207d27N4YNG5apOh0dHTF8+HBp/cCBA6rPmAoPD8f8+fOl9ffffx+Ojo6ZaoMx6tatKy1fuHBBdYq05ORkfPzxx9J67dq1LXbjeuTIkdLyrVu3sHbtWsW8r169ko006t69u9lTxFnbsmXLpOVSpUph+fLlmRpB5+fnJy2/ePFCVr+Shw8f4ocffpDW27RpA09PT7PbYIz//vsPJ0+elNbnz5+P2rVrW3WfOU379u2l5YMHD+L48eOq+YUQGD16tBRcdHNzw/vvv2/2/i39mcsp33lEREREROkYCCMiIiKiN8apU6dkz7OZOnWqReodPXo08uXLJ6336tUL165d08n38OFDdOnSRRqR5unpifHjx1ukDYZ069ZNNjqpb9++smn80kVFReHdd9+VjQb78ccfLdaOwYMHo3Tp0tL6J598ovcmekxMDHr27IkbN24ASBvdNH36dIu1w5ISEhKwd+9eaX38+PFwdXXNVJ3t27eXTYc3fvx4bNmyRTH/5cuX0bp1a0RGRgIANBqN7Jld1vLXX39Jy76+vvjoo4+svs/M8Pf3h0ajkf5s8Z4aOnQoXFxcpPV3331X9vw9bfHx8Rg2bJjs/TRu3Dh4e3vrzX/o0CHZ8egLmFnjM5cTvvOIiIiIiNI5ZHUDiIiIiIhsRXskhkajQa9evcyqZ/Xq1ahXr560XqhQIaxatUqaQiwiIgK1a9fGwIEDUb9+fTg6OuL8+fMICAhATEyMVG7p0qU2G+FUoEABTJ48GV999RUA4OrVq6hatSr69++PevXqwc7ODpcvX8b69evx7NkzqdzYsWMt+jwfFxcXbNq0CU2bNkVSUhLi4+PRunVrvPfee2jWrBnc3d1x7do1rF27FuHh4VK5b7/9FpUrV7ZYOyzp8uXLiIuLk9Z/+OEHk54Fle6TTz7BJ598AiDt/bly5Uo0btwYycnJSExMRK9evdCgQQN06tQJJUqUgJOTEx4+fIjAwEDs3btXNuXdzJkzZdMrWov2Z+rFixeoWbOmWfWojVDM6UqWLIlZs2ZJU7A+ffoULVq0QKtWrdChQwcUKVIE8fHxuH79OrZs2YKwsDCpbL169TBlypRM7d8an7mc8J1HRERERJSOgTAiIiIiemNoj1gQQugdwWAM7aBHuu7du+Onn37CF198geTkZCQlJWHVqlVYtWqVTl47OzssWLAAffr0MWv/5po0aRJu3bqFNWvWAEgLXCxduhRLly7Vm/+jjz7C3LlzLd6O+vXrY/369RgyZAhevXoFIQR+//13/P7773rzjxs3DhMnTrR4Oywl4/vo7t27ZtWj/ew6IC0IsmnTJvTv3196Rtjp06dlz47KyN7eHjNnzsSkSZPMaoOptI/95cuXZn+mcruxY8ciOTkZX331FZKTkyGEwMGDB3Hw4EHFMo0bN8auXbssMo2gNT5zOeE7j4iIiIgI4NSIRERERPQGefjwoVXrHzVqFA4ePIjmzZsr5mnQoAEOHTqEzz//3Kpt0Uej0cDf3x+rVq1C2bJlFfNVrFgRmzdvxm+//QZ7e3urtKVXr144ffo0OnXqBDs7/ZclVapUwfbt22XPF8qOrPm+6tGjB86fP48ePXrAwUG5H6OjoyN69eqFs2fP2iwIJoTA48ePbbKv3GDChAk4d+4c/Pz8VD9XFSpUwPLly3HkyBHZ9IOZZY3PXHb/ziMiIiIiAgCNSH8CLxERERERWczt27dx8eJFhIWFITExEUWKFEGjRo1Qvnz5rG4agP+NiDt37hwiIiKQnJyMAgUK4K233kKtWrVs2pawsDCcOXMGDx48QFxcHAoVKoR69eqhRo0aNm1Hdvfy5UscP34cISEheP78Oezs7ODl5YUKFSqgfv36mX4uGdnOs2fPcPz4cdy7dw9xcXHIly8fChQogAYNGqBkyZJW3781PnPZ/TuPiIiIiN5cDIQRERERERERERERERFRrsSpEYmIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiCMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChXYiDMhi5evIiZM2eidevWqFSpEry8vODs7IzChQujWrVqGDx4MJYvX47o6OisbirZiEajkf6WLl2a1c3JFO1jMebPzs4OefLkQZkyZdCpUyfMnz8fjx49ytJjePr0KcaPH4/q1avDw8MDGo0GH330kZR+6NAh2THkZrZ8b5YqVUra15dffmnVfREREREREREREdGbhYEwGzh+/Djq1auHOnXqYOrUqQgMDERQUBCio6ORmJiI8PBwXLt2DWvXrsWwYcNQvHhxfPHFF4iPj8/qphNZjRACr169wr1797Bnzx6MHz8e5cqVw7x585CSkmLz9jx8+BB169bF/PnzcfXqVbx48QIAkJSUZPO2EBEREZF1hYaG4rvvvkPnzp1RqlQpeHp6wtHREd7e3qhSpQr69u2LJUuW4NmzZ1ndVIMydta6efNmVjcpR4mJicHmzZsxcOBA1K1bF8WKFYOzszM8PT1RqlQptG7dGl9//TVOnDiR1U3NcaZNmya9L/fu3as3T0pKCrZv344RI0agdu3aKFy4MJydnZEnTx4ULVoULVu2xLhx43D06FEIIWx8BLmPpTto+/v7S69xqVKlrNr27M7YDp6JiYlYuHAhmjRpAm9vb9jb26N8+fJS+v3792Xf6f/8848tmp8lWrZsKR1nnz59sro5RJTLOWR1A3Kz1NRUjBo1Cr/88otOmoODAwoUKAB3d3dER0fj6dOnUtrLly/xww8/YM+ePdixYwfKlStny2ZTNqM98mjJkiUYMWJEFrbGOPny5UPRokVV86SmpuLZs2d49uyZdEETHx+PCRMm4MaNG1i1apUtmiqZN28ewsLCpPW8efOiePHiKFy4sE3bYW0tW7bE4cOHAQC9e/fGpk2bsrhFxilVqhRCQkIAABMnTsT333+fxS0iIiKinCgmJgajR4/GunXr9Ha+ev78OZ4/f44bN25g06ZN+Pzzz/HBBx9g+vTp8PX1zYIWk7XExcXh+++/x/z58/H69Wud9MTERMTGxiIkJASBgYGYNWsW3nrrLfzwww9o3rx5FrQ459m+fTsAwNPTE61bt9ZJ37VrFz799FPpd762xMREvHr1Co8ePcLhw4fx448/okKFCpg1axZ69uxp7abnOsePH8fo0aNx7tw5venh4eGyTtpjx47F8OHDMXPmTLi6utq4tblXSkoKOnXqhAMHDsi2swMuEZH1cUSYlSQnJ2PAgAGyIJidnR0GDx6M/fv3Sz/ogoKCEB4ejoiICKxfvx61atWS8l+/fh1vv/02Hjx4kAVHQGQ+Pz8/XL16VfXv+vXrePr0KR4/fozvvvsOTk5OUvnVq1fD39/fpm0+efKktFyvXj08ffoU169fZ8CFiIiIKJd4+PAh6tSpA39/f1kQLG/evChdujTKly8Pb29vWUe05ORk/Pbbb6hRo4bs96I+uWna89zu4sWLqFixIr799ludIJi7uzvKlSuHEiVK6AQAzp07h1atWuHbb7+1ZXNzpLt37+LKlSsAgM6dO8PR0VGW/t1336FLly6yIJhGo4Gvry8qVqyIYsWK6Zz/W7du4b333sOQIUNUAwfTp0+XPos5oWOjNUfFpKam4rPPPkPTpk11gmAODg7w9fVFhQoVULBgQVlaegftt956C3fu3LFom95kO3fulAXBHBwcUKFCBZQtWzYLW2V5Oe0zSERvBgbCrGTChAnYuHGjtF6qVCmcPXsW/v7+aNOmjc6PQB8fH/Tv3x9nz57F6NGjpe0hISHo168fUlNTbdV0siEhhPSXE0Z6WUOhQoUwadIknZFJP/30k03bERkZKS23b9/+je/1Zsv35v3796V9MfBIRERE1pCYmIiOHTvi7t27ANJuPo4bNw7Xr19HbGws7t69i1u3bkkzFmzZsgUNGjSQyj979gxt27bFvXv3suoQyEKOHz+OVq1a4eHDh9K2MmXK4JdffkFoaChiY2Nx+/ZthISE4MWLF7hw4QJGjRolddxLTU3FlClT8M0332TVIeQIf/75p7TcvXt3WdrGjRvx1VdfSes1a9bEli1bEB0djUePHuHmzZt48OABXr58iQsXLmDixInIkyePlN/f319234T0Ywft7Ee7Q0XevHlx584dBAUF6YwQIyIiy2MgzAoOHjwou4lfrlw5HD9+HHXq1DFY1sHBAQsWLED//v2lbceOHcOaNWus0VSibKN79+5o3LixtH758mUEBwfbbP/JycnScoECBWy2XyIiIiKyvp9//hn//fcfAMDR0RF79uzB/PnzUblyZdkIMADInz8/evbsiVOnTmHChAnS9ri4OIwaNcqm7TZGy5YtZZ2YKlWqlNVNyrZCQ0PRuXNnxMTESNvGjx+P69ev49NPP0Xx4sVl+e3t7VG7dm389NNPOHv2LIoUKSKlTZs2Dfv27bNZ23Oa9ECYi4sLOnToIG1/+fIlxo4dK623b98eZ86cQc+ePeHh4SGrw87ODrVr18b333+P8+fPy16fJUuWKE7zR2nYQdu2jOngqd0Bt1atWihZsqROnlKlSsm+07U/P7nNoUOHpOPMKY9tIKKci4EwKxg9erT0zCN7e3usW7dO9oPZGIsWLYK7u7tsnSi3a9u2rWxd31zxtuDm5pYl+yUiIiIi6/jtt9+k5c8++wxvv/22UeXmzJkj66y1Z88e2Y1MyjmEEHj//fdlQbA5c+Zg7ty5cHZ2Nli+Ro0a+Oeff+Di4iJtGzFihN5nzb3pwsPDpZEvbdu2Rd68eaW0HTt24MmTJwAAV1dXrFmzRjZNvpKKFStixYoV0roQAgEBARZuee7BDtrZEzvgEhFlHYesbkBuc+DAAamnIQAMHDgQDRs2NLkeLy8vDBgwAEuWLAGQNof5s2fP4OPjo1ouNjYWhw8fRlhYGKKiouDl5YXSpUujWbNmsqkEzHHmzBmcPXsW0dHR8PT0RJ06ddCoUSOdHpQAcOHCBVy6dAnh4eHIly8fypYti1atWun0ODJGamoqTpw4gUuXLiE2Nhbe3t6oUaMGGjRoADs702O5EREROHr0KB49eoQXL14gf/78qFChAho3bmzUBZC2lJQUXLp0CZcuXZIuiH18fFC5cmXUr18f9vb2JrfPkm7duoWzZ8/iyZMn0Gg0KFasGN566y2UKVMmS9ulJOMDyMPDw40q9+rVKxw5cgQhISF4/vw5ChQoIL3vjbmoysjSr9uDBw9w/PhxPHnyBElJSShatChq1qyJqlWrmlVfYmIijh49ijt37iAyMhJubm4oWbIkGjRoYHLQnYiIiCi3Cw0Nxe3bt6X1Xr16mVR+woQJ6NatG4C0m5jHjx9H165dLdlEsoFt27YhMDBQWu/cubNsxJ8xqlevjnHjxmHWrFkAgHv37mHXrl18P2SwY8cOafRQxmkRtaeAa9GiBQoVKmR0ve3atUOtWrVw6dIlAMDhw4cz39hcylIdtP/66y+8ePFCWh8yZIjF2/qmYgdcIiLbYiDMwlavXi1b/+ijj8yu6/PPP0eFChWkde2eIxkFBQVh0qRJ+Pvvv/U+NNbJyQl9+vTBlClTUK5cOcV63n//famXT//+/bF+/XocO3YMH3/8Ma5evaqTv0qVKli7di3q1q0LANi/fz8+++wz3Lx5Uydv/vz5MW/ePHzwwQd6992yZUvph+ySJUswYsQIbN68GRMmTEBoaKhO/mLFimHWrFkYNGiQ4vFoO3PmDCZPnozAwEC9Q/rd3Nzw4YcfYtKkSQYf5pmamorFixdj7ty5inNl58uXD6NHj8b48eMVf+BoBxHTjxmQvw7aPv74Y3z88ccA0qbimD59uk6e9CHlM2bMQFBQkN791qtXD1988YXJNwGsLTExUbau3dtSn4cPH+Lrr7/G5s2bER8fr5OeL18+9O7dG9OnT9f7miqd5w8++EB6nw4ePBj+/v4mHMX/7N27F19//bXilB1VqlTBZ599hmHDhhkV1H3x4gVmzZqFJUuWIDY2Viddo9GgTZs2+O6771CvXj1pu7+/v94Lls2bN2Pz5s0A0i5CDx06JKsrnfZ788MPP8TKlSultPPnzxvVq/DHH3/EuHHjpPWgoCDp+61UqVLS6L+JEydK00hMnz4dM2bM0Klrzpw5mDNnDoC012fp0qUoXLiw1Lu3VatWOHjwoME2RUVFoVChQtJ35ogRI6TOB0RERJR7ZPy97unpaVL55s2bQ6PRSDeVHz9+bLG2pcuqDmxCCJw7dw7//fcfnj59Cnd3dxQvXhytW7eWjeIx1tOnT6VOh69evUKRIkVQuXJlvPXWW5lq58OHD/Hvv//i4cOHqFq1qhSYNIX26BiNRmP2rCsjR47Ed999J70f9u3bZ1Qg7MqVK7h06RKePn0KAChYsCDq16+f6aksExMTcejQIdy9exdRUVHw8fFBhQoV0LRp0yzrmJk+LaK9vT3eeecdWZr259HUzyKQdt2SHgizxmcRSJu+ce/evbh9+zZcXFxUn0f26NEjnDlzBvfv38fLly/h5uaGwoULo2rVqqhRo4bejsPWltUdtA2x9DkLCwvDqVOnEBYWhlevXsHLywvFihVD06ZN4eXlZVJdV69exYULF/D06VMkJyfDx8cH5cqVQ6NGjUzuOG2IpT+fN2/exJkzZ6QRlwUKFEDt2rVRs2ZNk87pixcvcPr0aQQFBSE6OhpOTk7Inz8/KlasiHr16ln8PJgqPj4eR48exf379xEZGSn9v9W8eXOTX++MHj9+jMOHD+PRo0dITU1F4cKF0ahRI5QtW9ZCrSeiLCXIoooXLy4ACACiUKFCNtnn6tWrhYODg7RftT83Nzexbt06xboGDx4s5e3fv79Ys2aNwbo9PT3F7du3xcKFC41qw6JFi/Tuu0WLFlKeJUuWiNGjRxtV36BBg0RiYqLqOZo5c6bQaDRG1efj4yP27t2rWFdCQoLo0qWLUXUBEPXr1xfPnz/XW5d2viVLluh9HZT+pk2bplNfXFycSW1r27atiI6OVj13xtKud/DgwWbVkfE1v3z5smLeXbt2ibx58xp1nB4eHmLjxo06dRhznrWPJTAwUJamJDk5WQwbNszo16FOnTriwYMHqucmKChIlCpVyqj67OzsxKpVq6Syq1evNlimRYsWsv0pvTePHz8uS5swYYJqu9PVrVtXKtOsWTNZWsmSJaW0iRMnStunTZtm9Ovz4Ycfyo7/8ePHBtu0cuVKWV2nTp0y6liIiIgoZzl69Kjs//zVq1ebXMeNGzfEf//9J/777z8REREhbTf3d7sQQqSmpooNGzaIihUrKpatV6+e2Lx5s2rbMv5GvXHjhk4e7d9bJ0+eFEIIsW7dOlGiRAm9+3V1dRXjxo0TCQkJRp2f06dPi9atWws7Ozu99ZUqVUp899134vXr16r1ZGxnfHy8GDlypOya1M/Pz6g2abt9+7bOdVBmrF69WixYsEAsWLBA/Pnnn4r5kpKSxK+//iq7T5Dxr3z58mLVqlUiJSVFsZ579+5J+Z2dnYUQQsTHx4uvvvpKeHh46K23QIECwt/fX6eujJ+Hzz77zKhjfvr0qex1mDx5st58MTExwsnJSQAQLVu21Elv06aNVEfp0qVVj1ufyMhI6bN47do1abv2OVL706b9+Z09e7YQQoilS5cKT09Pabunp6feduzbt0+0aNFC9T5D4cKFxddff633mtucazRj9e/fX1bP8ePHzapHiLTvvvT3+oIFC3Sus7SPo2TJkqp1ZfacZXT27FnRunVrxbrs7OzEO++8I65fv26wrg0bNohKlSop1uXm5iZGjRolnj17pliH0nWtMa+19rnL+F7es2ePatu3bdsmqlSpolr3L7/8IpKTk1XruXnzpujXr59wcXFRrCtPnjxi8ODB4vbt2zrlzfkMat8H7N27t2r7wsLCxJAhQ4Srq6veeu3t7UXXrl3FhQsXVOvRvs8wfPhw6djbt2+v+H9Y06ZNRVBQkGq9RJT9MRBmQaGhobIvyo4dO1p9n6tWrdL5z7l///7il19+EQEBAWLBggWiR48ewtnZWZZvzZo1euvT/iFYqlQpYW9vLwCIypUri2+//Vaqs06dOrL6ypQpI/2Y8fLyEqNGjRJr1qwRS5cuFQMHDpT9YHZxcRFPnjzR2bf2f4DaP0AqVaokpk+fLtasWSMWLVokhgwZovMf8yeffKJ4jqZOnSrLmy9fPjFs2DCxZMkSsX79ejFv3jzRoUMH6VgBCEdHR3HgwAG99Y0bN05WX7169cTs2bPF2rVrhb+/v5g+fbqoV6+eLE+XLl301qWdRzvY8NVXX4mqVauKqlWryvIUKVJE2v7rr7/K6kpKShLNmjWT5a9fv7749ttvxfr168WKFSvE5MmTdeps3LixePXqleL5M5Z2neYEwpKTk0XZsmWlOnx9fRUvinbu3Cl7T7m7u4sPP/xQLFu2TAQEBIiFCxeKXr16yX4g2dvbi61bt8rq0T7Pjo6OUt6iRYtK27/66ispv7GBsD59+sjyValSRUyZMkWsXbtWrF69WsyYMUPUr19flqd8+fLi6dOneut78OCBKFCggCx/s2bNxLx580RAQIBYvHix6Nu3r+wY7O3txcWLF4UQQmzfvl06Hjc3NymPp6entP3999+X7VPpvSmEkN2sKVWqlOJ5SBcUFKT6/aN0wfDrr7/qfX18fHx0Xp+MF/RKAXdt7dq1k/JXrFjRYH4iIiLKmTJepxUuXFh2Az0zskMHNlMDYUePHhW9e/c2ar/vvPOOwXMwY8YMozsdlitXTrWzm3Y7//nnH9GwYUOdOswJhK1YsUL19601RERE6G2/0l+bNm0UrwcyBsIeP36sc12n9Dd//nyd+ipUqGDUdZe2RYsWSWU0Go24c+eO3nwbNmyQ8v3000866R988IGsfaNHjzZ4g94YlgiEffzxxzr59QXCJkyYYPTrmv6+Dw0NldVhzUCYLTtoGxsIs8Q507Z27VrZPRy1Pzc3N7Fv3z699aSmporhw4cb3a6yZcuKkJAQvXXZOhCWlJQkBg0aZHTbO3bsKOLj4/XWtXXrVtUAWMa/vHnz6pxTawbC9uzZY3RHaAcHBzF37lzFujIGwrZs2SIF79X+PD09RXBwsGK9RJT9MRBmQRlvxBo7UsJcQUFBspvaVatWVfwxGhQUJPuh7OTkpLc3g74Lua+//lrnh2lSUpJo0qSJTt6WLVvq7SFz4MABWb5ffvlFJ4/2f4Dpf1OnThWpqak6eUNDQ0XlypVlef/991+dfIcPH5b16GjRooUIDw/Xe45OnTolihYtKuX18vLSGckVHR0t+w9yzJgxeusSQoiffvpJ1r7Tp0/r5NFOV7oYMyaPEEJMnDhR9kNPqedoamqqWLBggexi9dtvv1Ws11ja7TQnEDZp0iRZHT/88IPefGFhYcLb21vK16FDB1mvXG13794VNWvWlPJ6e3sr3kDQ/tGq1EvYmEDYkiVLpHR7e3uxcOFCve9hIYTYuHGj7P304Ycf6uRJTU2VfTacnJzEhg0b9NZ34sQJWdC7a9euOnmM/aGp9r77/vvvDb63tWkHoz08PERcXJwsXemCwdQ8ZcqUkfJkHHWWUUREhCyYOmvWLNX8RERElLNpj05P/001YMAAsWPHDvHixQuz680OHdhMDYRp11utWjUxa9YssW7dOrFy5Urx0UcfyTogARBr165VPP4vv/xSlrdkyZJiwoQJYvXq1WLt2rVi9uzZOiM2ChYsKG7duqW3Pu12litXTvabf8GCBWLDhg1i//79Jr5KusEXQ79fM+vly5c6IzSqVasmdfBcvny5mDx5siwgBUDUqFFD7/tR+wazg4ODrGNahw4dxMKFC8WGDRvE4sWLRYcOHWR1Ojo66ty8/e6772R5AgMDDR6TdlCvVatWivl69eol5bt//75O+s6dO3Wu+ytXrizmzJmTqQD1w4cPpc+cdidCBwcHaXvVqlVlZbTvf2i/32rWrCm++eYbsW7dOp1rr61bt8ra7unpKT7++GPx22+/iU2bNokVK1aIcePG6czm0b59e1k95nRWNIatO2gbEwiz1DlLd+nSJdm1nI+Pjxg7dqxYtmyZ2LBhg1i4cKHo06eP7No4T548et+P2gFeIK0j9owZM4S/v79Yu3at+O6773S+w2rVqqU3eKt0zar9WufLl0/Kky9fPml7u3btpPzGBsIydsD19fUVEydOFKtWrRJr1qwREydOlN3jAiDGjRunU09ISIjIkyeP7DPz3nvviZ9//lls3LhRrFmzRkyfPl00aNBAVpe3t7d4+fKlVI85n0Fj7k/s27dP9no7ODiIbt26iYULF4qAgADx888/i8GDB+sEymbMmKG3Pu1AWMWKFaW6PTw8xKeffipWrlwpAgICxDfffKPznmzdurXeOokoZ2AgzIJ27Ngh+4L8/vvvrbq/fv36SfvKmzevuHfvnmr+u3fvyqZNGDBggE6ejIEwtYBGQECAzn+CasPXtS8+hwwZopOeMRDWr18/1eO5f/++rMdKmzZtdPI0btxYSi9WrJiIiopSrfPUqVOy/2C//vprWfqePXtkbQwLC1OtT/uHwvTp03XStevKTCAsNDRU1u5t27aptksIIcaMGSP7IWpoeklDjH3fpEtISBChoaFi06ZNonnz5rLy3bt3VwweaU85WK9ePYPTqzx69Ej2o06pZ5AlAmEvX74U+fPnl9J//PFH9ZMghGxKUXt7e52pJv755x+Tvle0L2odHR11PpOWCIQ9evRI1vtu7Nixqm3SvqgcMWKETrqlAmHaP2g1Go3q53Pp0qWyvEq9+oiIiCh3CAwMVJzyyNHRUTRq1EiMHz9e/Pnnn3pnrzCGMb/bhbB8BzZTA2Hpvzv1dU4UQoh///1Xdq6Ubvzt3r1b1rYvv/xSJCUl6c178OBB2XRzb7/9tt58Gdvp7u6ut8OjqbSn4wNgkRkx1AwdOlS2vzlz5ui9vklNTRWzZs0yeC2lb6SFt7e3OHLkiN79T5kyRZb3m2++kaU/fPhQ9nv+448/Vj2eu3fvyupbv3693nyvX78W7u7uAkib/l2f1NRUnddD+69IkSKid+/eYtGiReLcuXOK7yk12tcFaiOi9HUE/v777xWvRYUQolWrVlLeatWqKXa0TUpKktWv0WgUv1tMmR7OEFt30DYmEGbpc6Y9orV8+fKK0xXevHlTFC5cWPF9npKSInx9faX0Hj16KI6O3LZtm+z7Tt/3tjHXrNrHp3TfxJhAWMbZoXr27Kn33siLFy9kndgdHBx0zv+MGTOk9Lx586p2FPjtt99k+/3999/15jP2M2jovR8RESF7DYsWLSrOnz+vt65Hjx7JOploNBq9QX59j2Bo166d3vuF0dHRolq1arK8vHdAlHMxEGZB69atM/riJ7OePHki66mnND93Rp999plUxsXFRec/yow/BG/evKlY19mzZ2V5R48erbrvHj16SHn19UrS/g9Qo9GIu3fvGjwe7WCORqMRkZGRUtqFCxdk7fvtt98M1ieEEO+8845UpnTp0rK0jRs3yupMn3pOyYEDB8S8efPEvHnz9F7AGfN+MSbP+PHjpTwNGzY06jifPHkiu8A9ePCgUeWUKF3ImPo3fPhwxQvTyMhIWfDzn3/+Maptn376qVSmefPmevNYIhD266+/yn6gGTPFx+vXr2Uj3LSf7SWEEF27dpXSChQoYDBg+ezZM9lFbcYffpYIhAkhROfOnaX04sWLK14snj59WlbX2bNndfJYKhAWHBwsuzhZsGCB4vFpX4yp9WglIiKi3GPjxo2KzxbJ+Fe6dGnRv39/sXLlSsXp6jIy5ne7NTqwmRMIW7hwoeo+/fz8pLx58+bVSU9NTZVNZ//5558bPI6MHUfPnDljsJ2Gno9mrNq1a8uug60pKChI9pt05MiRBstoX6fb29vrzN6S8ca4o6OjOHfunGJ9SUlJsmuMzp076+Tp2LGjlF6wYEHVa5eZM2dKefPly6c4vdrff/8t5Zs5c6ZifZGRkarBMO0/V1dX0axZM/HVV1+Jo0ePqgap0pkbCDMUEBRCyGb0UHu2uBBCREVFya7N0p/Pl5ElA2G27qBtTCDM0uesUKFCUrqh2W20Z2zJOK3/jRs3ZOfq2LFjqnVpB+D0jdazVSAsKSlJNv1ltWrVVAPGwcHBsv/7Fi9eLEtv27atlDZp0iTVcyCEkD0qJf3ZehlZKhA2efJkKd3Ozs7gaN7nz5/Lnn3ZtGlT1bYBaSOy1e6z/Pnnn7L8W7ZsUW0DEWVfdiCLcXR0lK3b29tbbV+HDh1CUlKStD5kyBCjyvXt21dafv36NU6dOqWYt2jRoqhYsaJiep48eWTr9evXV923g4ODtBwfH6+at3r16ihdurRqHgDo1q2btCyEwIkTJ6T1ffv2ScsuLi7o06ePwfoA+Tm6d+8eQkJCpPUKFSrI8g4YMABXr15VrKt169b44osv8MUXX+Dtt982av/m2L17t7TcokULo8oUKlQIlStXltZPnjxp8XaZqnTp0hgwYABcXV31ph88eBCvX7+W1ps3b25UvS1btpSWT58+jdTU1Ey1U4n269C0aVOjvgOcnZ3RsGFDaV37dUhNTUVgYKC03qVLF53vmYy8vb2xbds2rFu3DuvWrUOJEiVMOQSjaX/nPHjwQPH9ExAQIC3XrFkTb731llXaAwBlypRBkyZNpPXff/9db77w8HAcPnxYWh84cKDV2kRERETZR58+fXD9+nWMHDkS+fPnV8177949BAQEYOjQoShSpAj69u2LO3fuZLoNixYtQnJyMgCgYcOG6N69u8EyEydOhJ1d2qV7TEwMjh07lqk2lC9fHp999plqnrZt20rLL1++RFRUlCx9z549uHnzJoC037PffPONwf127doVVatWldb//vtv1fy1a9dGr169DNZrjNjYWGnZ09PTInUq+eWXXyCEAADkzZsXM2bMMFhmypQp0vVySkoKNm3apJp/8ODBqFu3rmK6g4MDWrVqJa0/ePBAJ88HH3wgLT99+lR23ZHRxo0bpeX+/fvDxcVFb74///xTWlZ7b+fPnx/79u1DQEAAmjRpAo1Go5g3Pj4eR48exaxZs9CsWTOULVsWv/32m8Wv6ZydnQ2+Vi9fvkTlypVRs2ZN1K1bV3adqU++fPng4eEhrWtfy1qL9nsdsP773RBrnLPo6GhpOSIiQrW+d999F/PmzcO8efMwevRoxXqMqWv8+PFSXe+8845qXmvauXOn7DM9c+ZM2f22jMqUKYN27dpJ66dPn5al58+fHzVr1kTNmjXh5+dncP8lS5aUlq35nk5KSsKyZcuk9T59+hi87+jl5YVRo0ZJ68eOHUNoaKhqmR9//FH1Pov2/4eA/u9TIsoZlL8pyWTu7u6y9Yz/qVqSdgCraNGiKFeunFHlqlSpIlsPDg5WDJxkDPpklPHHqo+Pj1FtACBdGChp1qyZUfVoBxCAtAvWdNrnqG7dujqvjxJ95yj9P/o6deqgdevWOHjwIADg2rVrqFGjBpo3b4727dujWbNmeOuttxQvDKwhJiYG169fl9b9/f0NXlSm0w7yGfpxYIp8+fKhaNGiqnmSk5MRFRWFp0+fStvu3buHZs2a4ZtvvsGUKVN0ymgHOjUaDerVq2dUe7QDrwkJCXj69CkKFy5sVFlTZAzEVqtWzahyjx8/lpa1X4fr16/jxYsX0rqxwdSuXbsalS8zunbtCh8fHzx79gwAsHnzZjRu3FiWJyUlRRaM+vDDD63erkGDBkk3h06dOoXQ0FCdYOCWLVukC2dXV1f07NnT6u0iIiKi7KFUqVJYtGgRFixYgOPHj+PAgQM4evQozpw5g1evXuktk5ycjE2bNmHnzp3Ytm2b7IaiqTLTge3atWsA0jpOaQc5TNWpUyfVwAMAFClSRLYeFxcHLy8vaV37OGrVqmX0zfaWLVvKjkONJW80a9/ktGaHVQD4999/pWU/Pz+DQVcAKFCgAFq3bi115gwMDMTUqVMV83fu3NlgndqvYVxcnE56165d4e3tjcjISABpncj0XW9cvHhRdr2p9Js+NTUVf/31F4C0YKt20FMfOzs79OvXD/369cOjR4/wzz//4PDhwzh27Bju3r2rWO7evXsYPnw4du3ahc2bN1vs2rtBgwYoUKCAap68efPi0qVLRtd5+/Ztq94X0seWHbSNYY1zVqFCBfz3338AgMWLF6N69eoYMmSI1GFAW8GCBfHFF1/oradMmTKwt7dHSkoKAGDkyJHw8fFB06ZN9eavW7euagDaVvbv3y8tu7m5oX379gbLfPvtt9J1b8Z7IYYC79pev34tnXtru3DhgvT9BBg/AKBPnz4YN26ctB4YGIjBgwfrzZsvXz5ZZ1p98uTJAw8PDynIrO/7lIhyBgbCLKhYsWKy9fDwcKvt69GjR9JypUqVjC7n6ekJV1dXKTCg/Z9KRtq9cIxhaKSKKTJeeClxcnKCp6cnYmJiAADPnz+X0sw9Rxl/FGQ8R7///jv69u0rXeAIIXD48GFphImTkxMaNmyIt99+G35+fqhRo4bR+zbH48ePZYHF8PBws9576efQEvz8/ODv729U3oiICCxZsgTffPON9AN06tSpaNy4Mdq0aSPLq/2aCiGki2hTxcTEWDwQ9vr1a1lP2aioKJ2es8bQfh20jxcw/nNhC46Ojujfvz8WLlwIIC24tGDBAtnFx4EDB/DkyRMAaaMy+/fvb/V29erVC59//jlev34NIQS2bNki+xEMpAXt0nXr1s3oIDkRERHlHg4ODmjRooUUjEpOTsaVK1dw4sQJHDt2DIcOHdL5TR0XF4du3brh8uXLKF++vMn7zC4d2IyZeSPjDA3po9jSaXcAu3btmtEdwNI7UQGGj6N48eJG1WkM7d971gxMREdHIygoSFo3NPpFW5UqVaRAWHBwsGpeU1/DjK8fkHbd2r9/f/z8888AgG3btmHx4sU6I0s2bNggLdetWxe1atXSu79jx45JI2qMGemorUiRIvjggw+kUWpPnz7FyZMnceLECRw5cgRnz56VrhXT/fXXXxg5ciRWrFhh0r6UZPb99vr1azx48AChoaEICQnBjRs3EBAQYLATsKXZsoN2Zpl7zsaPH49BgwYBSBs19OGHH2LmzJno2rUrWrRogSZNmhh1vV+wYEEMHjwYq1atAgA8fPhQ6tzcqVMnNGvWDA0bNkTevHkzf7AWpD2iq0mTJooz6mirVq2a0d/T6VJSUvDw4UOEhoYiNDQUwcHB2LZtm0VGRxtDu3O7g4ODYoAyoyJFiiBfvnzSe1/t+7RUqVJG1enq6ioFwvR9nxJRzsBAmAWVL19e1pvkwoULmaovMjISw4cPl9YHDx4s9YrT/jGTL18+k+rVDoSpTSegNrTa2rR7GxqSN29eKXig3bPR3HPk5uYmW894jry9vbFv3z78/fffWLFiBfbt2ycbcZSYmIgjR47gyJEjmDp1KurUqYP58+dnqtemGu3gX2YkJCRYpB5TFShQAFOnToWjoyMmT54sbd+0aZNOICw7H6s12pYxkFawYEGL7MNSPvjgAykQ9vjxYxw7dkw2XaX2RfO7775r0ufaXJ6envDz85OCXZs3b5YFwh4+fIjjx49L65wWkYiIiIC0a586deqgTp06GDlyJIQQOHnyJJYtW4aAgADpGi8+Ph6TJk3CH3/8YfI+sksHtoxT3JtDu8PWy5cvzeqgZug4LHk9WqxYMZw9exYA8OrVK7x8+dIqN7czvsbmdshU67AKWOY1BNJ+z6cHwiIjI7F//3506NBBShdCyEaLDB06VLEuY6dFNEbBggXh5+cnTdP27Nkz/P7771iwYIHsJvzKlSsxatQoVK9ePVP7A0x/v509exa///47Tp8+jaCgINlMJ1nJlh20TWWpczZw4EBERETgyy+/lB4ZEhISgkWLFmHRokUA0u7PtW7dGp07d0a7du3g7Oyst65ffvkFCQkJsin9z507h3PnzgFIG1FXt25dtGnTBl27dtWZlSgraH//WrKzbEhICDZu3IgjR47g+vXrCAsL0wlA25L2cZYsWdKk0Z+FCxeW7gmqfZ9a6ruUiHIGPiPMglxdXWU/wM6dO5ep+XIDAwOxdetW6U97SLv2jzRTbuqnpKTI5oy2xY1pa9M+Hm9vb2nZ3HOUMaChdI66dOmC7du3IyoqCocOHcKMGTPQunVrnUDahQsX0LZtW2maCEvL+INu2bJlEEKY/Ldt2zartM9YI0eOlI0qvHHjhk4e7WP19fU16ziFEFYZpZfxdZg0aZJZbVMLoGf1tBYZ1ahRA3Xq1JHWtUdavX79WnYxrHbRbGnpvQOBtIst7SlTt2zZIt2cKFy4cKamNiIiIqLcS6PRoHHjxlizZg2OHDki63G/a9cuxWkU1WSXTl2WCDBZ4lhs2RFP+9nIQOY7rS5ZsgQ9e/ZEz549ZR2rMo6+MbdDpqHnX1kqSFizZk3Url1bWs/4jN3Dhw8jLCwMQNr9jn79+inWtX37dgBp12kNGjSwSPvS+fj44JNPPsH169fx3nvvydK2bNli0X0Z8vDhQ3Tq1An169fH/PnzcfToUZ2AjkajQeXKlTF9+nSbtg34XwftdJbooJ3+Xu/Zsyd27txpch3WOGdjx47FjRs3MGbMGL2j+W7fvo1ly5aha9euKFKkCGbOnKk3qOPq6or169fjyJEj6Nevn87MSCkpKThz5gxmz56NRo0aoWLFimZ1hLAk7Q6zlugsm5CQgLFjx6J8+fKYNGkS9uzZg5CQEJ3z5evri2HDhhmc9tRSMjMAwNjv06wcAEBEtsdAmIV16tRJWn7x4kWmggva/7k6ODjIfkxqB3wePnxodJ137tyRDeO15HQTlmToIaXpoqKiZM9Q0j4v5p6j9Ic+pzN0jpydndGiRQtMnToVBw4cQFRUFAIDAzFixAgpsJOSkoLPPvvMKtMiaB8nYPy5y27c3d1lU3zou7jWPtbnz59b/AHJmeHl5SWbFtASr0NOeG21H7K9detW6cfyzp07pSB1mTJlrDYiUp/27dvLetRqX9BrB+sGDBiQ7YKLREREZFl37tzBgAEDpL/Lly+bXEfjxo3xySefSOuvX7/G/fv3Ta4nt3RgA+TH0rdvX7OOw1KBQWPUr19ftn706NFM1ffrr79KHVa1p0LMeFPV3A6Ztuywqv3cnT///BOJiYnSuvYomZ49eyo+C+7ixYvSZ8LPz0/xGXRz586VPovTpk0zua2Ojo5YtmyZrANlxut3awoPD0fTpk2xZ88eaZuvry/69u2L77//Hhs3bsSpU6cQGxuL69evm3WMmWXLDtrGsOY5K1u2LH788UeEhobi5s2bWLZsGfr166fzvPLnz59j6tSp6Nu3r2JdzZo1Q0BAACIjI3Hq1CnMmTMHnTp10gmM3bp1C++99x5+/fVXo9tpTZm9nhVC4N1338WCBQuk0XVubm545513MGXKFPj7+yMwMBBPnjzBo0ePsGzZMvj4+Fii6QaZ27kdyLrvUyLK3hgIs7ABAwbIfvT98MMPZt2sDwsLk13gdOjQQXZTvEqVKtLytWvX8PLlS6PqDQwMlK1nvCDILox9+OaZM2dk69q92bTP0enTp40OQmmfI09PT9l0Fjdu3MClS5dw6dIl2Ug0bU5OTmjZsiWWLFmCtWvXSttDQ0Nx69Yto9pgiuLFi8suSEy5uN+2bRvWr1+P9evXqz6Q2Fa03+P6etlqz2mdkJBg9EXPnTt3pONM76loaXZ2drKepqa8Dnv37pXad+XKFWl7xp6rFy9eNFhXamoqWrZsiVq1aqFWrVo4ePCg0e0wR79+/aQbIeHh4dKz8rQvmj/44AODD2S3JHt7e1lv1fTgV0hIiGw+daUH5hIREVHukZKSgoCAAOnv/PnzZtXTrFkz2brStYCanNDJyVjax5ITjqNNmzay4J32dZqpbt68KZsKUvu5NRlfY3M7ZNqyw2r//v2lcxMdHS09CzsxMRFbt26V8n344YeKdWhfY6lNi3jx4kXps7h69Wqz2uvl5SUbkWLOZ9FcX3/9tRTws7e3x7Jly/DgwQNs2LABEydORJ8+fdCgQYMsf6aUrTpoG8NW56xixYoYNmwYAgICEBYWhqCgIMyfPx8lS5aU8mzZsgUHDhxQrSf9GCdMmIBdu3bh+fPnOHXqFMaPHy9r44QJE2Sdsm3Jkt+/GzZswO7du6X1YcOG4cmTJ/jrr7/wzTffYPDgwWjZsiUKFSqUqf2YQ/s4Mz4/XU18fLzsGZTZdQAAEdkeA2EWVrlyZXTp0kVav3DhgjRHsSlGjRol9cYA0oZ9a9N+Dk9iYqLOFAZKtJ/ZU69ePZ0f6tnFvn37jOrxsX79emm5SJEiqFixorSufY4ePXqkEwTUJyUlRTZipF27drIb+L1790bt2rVRu3Ztoy6eunXrJlvP+MwnS7C3t5ddmP/zzz9GTdVy9epV9OjRAwMHDsTAgQOt0jZTaT/YV/u5a+kyjioy9gf9l19+KR3nypUrM9dIFdrtO3funFEPMo+MjISfn5/Uvtu3b0tpRYsWRbly5aR1Y4730KFDOHz4MC5fvozLly+jbNmyJh6Faby8vGTv882bNyM6Olrq8Wdvby/rZWor2kGuixcv4vbt2/j999+lgHjdunVNflgwERER5TzFixeXjR4xtsOdIeZMR5WbOrBp/47S7shlyPHjx6XjOHnypDWaplfevHllv1lv3bpldge59Gfkpuvfv7+0XKJECdkzZ06dOmVUnUIIqUMZAItPLagmf/786Nq1q7Sefj28e/du6RqxfPnysuvrjNKnRPf09FSdCUL72uTRo0cGn4VmDFs9Rzk1NVU2DePIkSMxbNgw1RE5jx8/tkXTdNiqg7Yh1jhn4eHhUudktZG5FSpUwLhx43Dx4kX4+vpK2/fv3y8tBwcHS3U9e/ZMbz329vZo0KAB5s6dKwWJgbSOuydOnFBtq7Vod5g1prMsAMybN0/qLDt69Ghpu/b9r9q1a2Pp0qWy+zL62Op9rd25/fnz50Z3LD98+LDs/W7L71Miyt4YCLOCH374QfYQxy+++EL2rBxDpk2bJvux8e677+r8mEyfmzjdN998Y3BU2IEDB2RTQAwfPtzoNtlabGws5s6dq5rnzJkz2Lhxo7Tes2dPWbqfn59sCPSkSZNk00Lq4+/vL/sxlfEcaZ9z7d5xSrSDGgB0huibSunH60cffSQtv3jxAj/88IPBekaOHCmt16xZE3Xr1s1U2yxB+weXvmBe3bp1ZaP+Fi5caHBKlQMHDsg+T9Z8VpV2L0khBGbMmGGwzJgxY6Sgb8GCBWWBdEA+9eChQ4dw7NgxxbpSUlJkU0nUqFFD1gMuI0tNLakd6Nq2bRs2btwoTanSsWNHiz7AFzCu3TVq1EDNmjWl9d9//132Iz8rgnNERERke25ubmjRooW07u/vj7i4OJPr0Z6Jws3Nzazf9bmpA5v29enTp09Vf6OmS0pKkp6pNXDgQJvfRP7iiy9kwYFRo0aZPKLjwoULWLFihbTeokUL2XWUg4MDGjduLK1v2rRJ73OJMgoMDJTdXO7QoYNJ7cos7WuOHTt2ICEhQdaJVu0aKjg4WAowd+nSRRZ4zqhjx47SckpKCpYuXWpyW2NiYmTTUZYvX97kOszx7NkzxMTESOva3ytKrD07hxJbddA2xBrnLCAgQOqcnPEekD5eXl6yIK729+eYMWOkur7//nuDdTVs2FA2MiqrvotbtmwpLWtPS6pECIFff/1V6iyrfZ/szp070nKzZs0MzuQSEhKC4OBgs9ptqowjsbU7wqvR/u7y9fWV3RcgojcbA2FWUL58eSxZskRaT05ORq9evTB27FjVYfvPnz/HwIED8c0338jq0v6hnU6j0eDrr7+W1kNCQuDn56fYi+X8+fOyh/hWqVJFtp4dzZw5E2vWrNGbdvbsWbzzzjvSRUWePHkwefJkWZ48efLIfqidOXMGgwYNUgwY7tu3T9Yzpm3btmjTpo0sj5+fn7R86NAhfPvtt4o35cPDwzFs2DBpvXbt2mYNydbuNXrgwAG9Uzy+8847qFevnrQ+Y8YMrFu3Tm99L168QL9+/WQ9DmfPnm1yu6xBOxD28uVL2Y/udNqfj2fPnsHPz09xOoB9+/ahR48e0jlr1KiRrLejpdWsWVP2Y3zVqlWYM2eO3tcsMTERY8aMkb1OM2bM0Hl2xIgRI2S9HPv06aN3SkghBMaPHy+7CfHpp5/q5NN+yOzZs2dlFybmatu2rfTefvbsGaZMmSKlWSrwqN3uI0eOyJ5doGTQoEHS8pIlS6SpkJydnVUf9E1ERES5i3bntujoaHz00UcmdQi6deuW7CZy165ddX6zZZTbO7ANGDBA1vlz+vTpBsvMnDkTT548AZD2e2zAgAHWap5eb731lizgExoais6dOxsVjATSRsd07dpV6lzp4OCABQsW6OTT7nB17949LF68WLXe1NRUWQe6kiVL2jwQ1q5dOxQrVgxAWqfU33//HX///TeAtONUm1Jcu9Ov2rSIQNr1WI0aNaT1OXPm4OzZsya1deLEibLZQ3r16qWa31Kd/zKOYlK695IuIiLCrGeEWaq9tuigbYg1zpl25+TLly/LAjlKMs66oq+uHTt2GOw4/fz5c9koxsx2dDbXoEGDpOdnCSHw7bffquZfvnw5QkJCpHXteyLar5Gh1yc1NRWjR482KrifsZw5ihYtinbt2knrP/30k8HpZm/cuCHrADt06FA+F5yI/keQ1fz888/Czs5OAJD+XF1dRdeuXcV3330nVq5cKdatWyfmzJkjevToIVxcXGR5K1WqJIKDg1X30bt3b1kZLy8v8dFHH4mlS5eKDRs2iJ9++kl069ZNODo6ytpw6tQpvfUNHjxYytejRw/Vfd+4cUO278DAQKPb2qJFC530Fi1aSOl58uSRluvXry++++47sW7dOrFkyRLx3nvvCXt7e9m+lyxZonefSUlJokmTJrK8vr6+YtSoUWL58uVi/fr1Yv78+aJt27ZCo9FIeby9vcXdu3f11lelShVZfRUrVhQTJkwQK1euFBs2bBA//PCD6NWrl3B2dpbyaDQa8e+//+rUZ8wxNGjQQJbP29tbVKhQQfz000+yfPfu3RPe3t6yvE2aNBHff/+9WL9+vVixYoX47LPPRIECBWR5Ro4cqfq6GUu7zsGDB5tVx6hRo2T1XL9+XW++cePG6bzvR4wYIX777TcREBAg5s2bJ9q0aSPLkz9/fhEUFKS475IlS0p5V69erTdPYGCgrE59oqKiRLly5WT5atSoIWbMmCHWrl0rVq9eLSZMmCDbHwDRtWtXkZqaqrfOnTt3yt6fLi4uYvDgwdLnfPbs2aJWrVqy+ho2bChSUlJ06po4caIsX548eUT58uVFv379ZPmMeW9q++qrr2RlAIhChQqJpKQkg2W1z8XEiRP15sn4Xefp6SkqVKggxo8fr1jvkydPdL4rAIi+ffsabBMRERHlLq1bt5b9HujYsaPqb0MhhEhMTBT+/v4if/78UjlnZ2dx6dIlvfk9PT2lfO+++67e33apqamiXr16Uj57e3uxdu1avfXFxsbq/AbavXu3Tr6Mv1Fv3Lihk8eY37ra9uzZI6vz3r17Onm++OILneuKxMREnXwpKSli7ty5st+zSr/5TG2nqV6+fCmqVasma3flypXFgQMHVMvt2LFDFC5cWFZu0aJFevMmJSWJSpUqSfkcHR3Fpk2b9OZNTEwUI0eOlNW7fv16nXz37t0z+HpkpP27v2TJkgbzT548WXbNmb7s5+enWq5x48bSNcrLly8N7icwMFB2nyRfvnzi119/FcnJyarlgoKCRI8ePWTnoU+fPnrzLliwQJZP6TOrff/DmGvYYsWKSfnLlSsnHj9+rDffgQMHRIUKFXSuQ/bt26c3v5+fn5SnVKlSIjo62mBbjLF69WrZ/h0cHMSYMWNETEyMYpnIyEgxYMAAWbny5cuL58+fG9yHvveZpc/Zy5cvZd+1DRo0EOHh4XrrTElJEd98843ie+Ho0aOytOHDh4uEhAS9dcXGxoquXbtKeQsUKKBzrWvMda0x77mMn/c9e/bo5Pnoo4+M+j46fvy4cHNzk90n0Na/f38pzcnJSRw7dkxvPbdv3xYdOnTQeX0mT56sN7+xn0Ht+4C9e/fWST98+LDs/44aNWoofv/duXNHVK5cWcpbsGBBERkZqZNv2rRpUh599yb1KVSokFRm2rRpRpUhouyHgTArO3LkiM4Nb2P+evToYdSPn9evX4tevXoZXW/evHnF/v37FevLLoGwSZMmifbt2xs8Ho1GI+bMmaO636ioKFndhv4KFSokLl++rHrc2v8JGvqzt7cXixcv1luXdj6lYMO6dev01qvvP99r166JMmXKGNUujUYjxo8frzdYYg7tus0NhM2cOVNWj9KPx9TUVDFp0iSjX4Ny5cqJixcvqu7bUoEwIYQICwsTdevWNbp9AwcOFK9evVJt35o1a/QGdfT91a5dW0REROit5+7du7LAuNJn0pj3prY7d+7IfqACEBMmTDBYTgjjLhgyXqQY+17r1KmTThlDNzqIiIgo93n+/LmoXr26zu/0Ro0aicmTJ4tff/1VbNiwQaxcuVJ8//334r333hMFCxbUyb9ixQrFfWRVB7asCoQlJCSIRo0ayfKVLVtWTJ48WaxevVqsXbtWTJ06VacjYb169RR/+1o7ECaEEI8fP9Z5LwAQFSpUEGPGjBELFy4UAQEBYvHixWLChAk6N+c1Go2YOXOm6j4uXrwo6xgJQDRq1Ejq4Lly5Urx5Zdf6ly7vfvuu3rrs0Ug7Pbt23p/b+/cuVOxzOPHj6WgVteuXQ3uI93y5ct19lOwYEExYMAAMW/ePLF69WoREBAgFi1aJMaNGycaNGig08m4Zs2aIioqSm/9Gd+/9vb2okyZMqJcuXKyfKYGwjJer7q7u4vRo0eL1atXi2XLlokJEybIgqA1atSQvf/9/PzEhg0bxLlz52T1GttZ0RzW7qBtKBBmjXM2a9YsnfM1aNAgsWjRIrFhwwaxZMkSMW7cOFG8eHGd6+6M2rZtK8tTrFgxMWrUKPHbb79JncuHDBki3N3dZflWrlypU5ctA2GxsbE63x+NGjUSc+fOFQEBAWLp0qWid+/esnsITk5O4sKFC7J6Ml5n29nZiT59+ojFixeL1atXi2+++UY0b95cutbPly+f6Nixo+z9sX79evH333/L6jX2M2goECaE7ufD1dVV9O/fX/p/+5dffhH9+/eXBfzs7OzEn3/+qbc+BsKI3lwMhNlAfHy8WLJkiU7Ps4x/7u7u4t133xVnzpwxqf7U1FQREBCgt/dM+l/evHnFBx98IB49eqRaV3YJhE2bNk0kJSWJKVOmCC8vL73H9NZbbxl9QzsxMVEsWrRIFC1aVPEc5c+fX4wbN07xx7S2hw8fivfff1/n4kb7T6PRiNatW4vjx48r1qOdXy3Y8NNPP4kqVaoIZ2dn4ezsLEqUKCHWrFmjN29MTIyYNGmS8PDwUG2XvhFqmaG9D3MDYf/8849OW9UuvAIDA3UuvrX/ChUqJMaPHy9iY2MN7tuSgTAh0oLUc+fO1bmBof1Xv359xd6h+pw/f160bNlSsb7ChQuLWbNmKfZiS7d3717RtGlTkSdPHuHg4CAKFiwoPvvsM1keY9+b2po3by4rZ6iXdTpjLhiEECIgIEDUqVNHuLq6CkdHR1GkSBExe/Zs1bo3bdoka1O5cuUUR94RERFR7hYVFSX69Omjek2m9FeuXDmD1ztZ1YEtqwJhQggRHR1tVAfG9L8OHTqIZ8+eKe7XFoEwIYR49eqV+Pjjj01+HxQsWFBs3brVqH0EBgbKRq4Y+uvWrZvi73hbBMKEEKJZs2ay/RQpUkR1pNbSpUvNfr3++usv1WslpT8HBwcxYsQI1Y6EycnJivdHtJkaCHv9+rVo1aqVUe1s3bq1iIiIEOPHj9dJW7BggaxeYzsrmsuaHbQNBcKscc5SUlJE9+7dTTqWNm3a6H3PPHnyRPVemr6/L7/8Uu+5sGUgTAgh7t+/L8qWLWtUm93c3BTvrWiPBlX7K1OmjDh79qzYtWuXTlrGkaPGfgaNCYSlpqaKMWPGGP36ODo66h1Zm46BMKI3FwNhNvb48WPx559/isWLF4tZs2aJBQsWiPXr14vjx48bNYWYIdevXxdr1qwRc+fOFXPmzBErV64UgYGBBm+MZwcZA2Hp4uLixN9//y1++eUXqcfStWvXzNpHamqqOHfunNTTc968ecLf31+cOHHC4FQM+rx48UIEBgaKpUuXijlz5ohZs2aJRYsWib/++ktxyL+tJCQkiAMHDojFixeLb7/9VsyfP19s2rRJhIaGZmm7rOH+/fti/fr1Yt68eWLWrFliyZIl4tChQxb5TGVWSkqKOH78uFi2bJmYNWuWmDt3rli/fr3RQSJ9Hjx4IAICAsT8+fPF7NmzxW+//SZOnz6dLY43u4mPj5ddVM6bNy+rm0RERERZLDAwULz77ruqndqAtGkQO3XqJDZu3Gj0tUJWdGDLykCYEGk3PJcvX656o71q1api8eLFBjsk2SoQlu7KlSti4MCBIm/evKrvhSpVqog5c+aIuLg4k+oPDQ012IGyWrVqBjvH2SoQtmrVKtl+lKY9S5ceBLW3t1cNcCqJjIwUs2fPNiooXKZMGTF+/Hi9jzDQ5/bt26J79+7C29tb2NvbC09PT1GvXj1ZHlMDYUKkBXbGjBmjM3Iq/a9WrVqy6U4fPnyoM61mxkCYEMZ1VswMa3XQNhQIE8I65yw1NVX89NNPokSJEqrHU6JECfHjjz+qzoQTHR0tPv/8c51RXxn/6tWrpzPySZutA2FCpP0/MnbsWNnjRTIGhXr16iXu3LmjWIcQaaM0M46CTv8rVqyYmD59uhRITElJ0emgq28KVWM+g8YEwtLt2bNHdeYdZ2dn0aNHD3Hr1i3VehgII3pzaYQQAkTZQMuWLXH48GEAaQ9mNeaBy0REag4fPoyWLVsCAFxcXBAWFgZvb++sbRQRERFlC4mJibhw4QJu3ryJqKgoxMXFIU+ePMifPz8qV66M6tWrw9XV1SbtOHbsGIKCgvD8+XO4uLigWLFiaNy4MYoXL271/VvShQsXcPHiRTx9+hR2dnYoVKgQateujZo1a2Z101QlJyfj/PnzuHnzJp49e4bExER4e3ujYMGCqF+/PooUKZKp+uPi4nD48GHcv38f0dHR8PDwQMGCBdGgQQOULFnSQkeRs4WFheHs2bMIDw9HdHQ0AMDDwwMlSpRAjRo1UKJEiaxtYAaRkZEIDAzEvXv3oNFoUKxYMdSsWROVK1fWyRsREYE//vgDz549Q7FixdC5c2cULFgwC1qd5smTJzh16hQeP36MqKgouLm5oUCBAihdujTq168PBwcHq+zXGucsNTUVN27cwMWLFxEREYG4uDjpeGrVqoWqVavCzs7OqPYlJCTgwoULuHbtGqKjo5GQkAB3d3cULVoUb731Vrb+rL5+/RqBgYG4e/cuoqOj4e7ujnLlyqFhw4bInz+/UXUkJCTgyJEjuHbtGuLj41GkSBGUL18eDRs21DmHiYmJ2Lp1K4KDg+Hp6Ym3335b7+toDffv38fx48cRHh4ufVeXKFECzZo1g5ubm03aQEQ5EwNhlG0wEEZEltazZ09s3boVADBkyBCsWrUqi1tEREREREREREREtmSdbh5ERERZLCQkBNu3b5fWx4wZk3WNISIiSUBAAF68eGFW2VatWqFixYqybTdu3MBPP/2EAwcOICwsDG5ubihevDg6deqEjz76CGXKlLFEs4mIiIiIiN5on3/+ORYtWgQAuHfvHkqVKpWp+vbt24elS5fi5MmTiIyMhLe3N8qUKYPevXtj8ODB8PT0tECr0zAQRkREuUJUVBRcXFzg6uqK6OhoDBkyBCkpKQCAzp07o3r16lncQiIiAoCvvvoKISEhZpVdvXq1LBC2ZMkSjBo1CklJSdK2hIQEREVF4cqVK1i4cCGWLl2KQYMGZbrdREREREREb6q///5bCoJlVnJyMoYNG4bVq1fLtj958gRPnjzBiRMn8OOPP2Lr1q2oW7euRfbJQBgREeUKO3bswEcffQRvb288f/5cuimq0WgwY8aMLG4dERFZQt68eaXlLVu24JNPPpHW3377bbRq1Qr29vY4efIkdu7cifj4eKknoZ+fX1Y0mYiIiIiIKEd79OgRhgwZYrH6Ro8eLQXBnJyc0KtXL9SqVQsvX77Erl27cPbsWYSEhKBNmza4fPmyRZ7TyEAYERHlGsnJyQgPD5dtmzRpksV6jxARUebdv3/f6Lz//fcfGjZsiFevXmHAgAHo2bMnACAmJgaffvqplG/VqlU6F2Z///03unfvjuTkZAwdOhTNmzeHl5eXRY6BiIiIiIjoTZCamoqBAwfi2bNnFqnvxIkT+PXXXwEA+fPnx7///os6depI6dOmTcOkSZPw/fffIyYmBkOGDMHBgwczvV+7TNdARESUDfj4+CB//vyws7NDvnz50Lx5cwQEBGDWrFlZ3TQiIjJDbGwsevbsiVevXqFWrVpYvny5lLZixQpEREQAAAYNGqS3d2KXLl3w/vvvAwAiIyNl5YmIiIiIiMiw2bNn4+DBg3ByckLt2rUtUl+6+fPny4Jg6b799ltUqlQJABAYGIgzZ85ker8aIYTIdC1EREREREQW1LNnT2zduhVubm64cOGC7NlgtWrVwuXLlwEAFy9eRK1atfTWcerUKTRq1Egqc/HiRau3m4iIiIiIKDc4efIkmjdvjuTkZMybNw9Xr17FmjVrAAD37t1DqVKlTKovMjIShQoVQkpKCgoUKICHDx/C0dFRb97vv/8ekyZNApA2leKCBQsydSwcEUZERERERNnKunXrsHXrVgBpF0DaQbAnT55IQbBixYopBsEAoEGDBvDw8AAAXL58GZGRkdZrNBERERERUS4RExODfv36ITk5GW+//TbGjRuX6Tr//fdfpKSkAADat2+vGAQDgLZt20rLBw4cyPS+GQgjIiIiIqJs4/nz59JF1ltvvSV7FhgAnDt3TlquX7++al0ajQY1a9YEAAghcP36dQu3loiIiIiIKPcZNmwY7t+/Dx8fH6xduxYajSbTdZpyLVe9enU4ODgAAG7evCkF0MzFQBgREREREWUbX3zxBSIiImBvb4+lS5fCzk5+yXLr1i1puUyZMgbrK1mypLR89+5dyzWUiIiIiIgoF1qxYgV+//13AMDKlSvh6+trkXpNuZZzcnJC4cKFAQBJSUkICwvL1L4dMlWaAACFCxdGXFwcSpQokdVNISIiIiILCg0NRZ48efDkyZOsboqOrl27Ijg42Ob7DQ0NVfzde+3atUzVffLkSaxevRoA0L9/f9StW1cnz8OHD6VlYy7IChYsKC1HRUVlqn2Uc/AajYiIiCh3ys7XaEDOv067efMmRo0aBQD4+OOP0bVrV4u0DzDvWi49ABYVFSXr5GgqBsIsIC4uDi9fxuP69YisbgoREVmDg7NympeHctqzJOU0T4V5kH2fKRYppolRTHMXCYpp4cn6tz939FIsgxCVHySvwpXTBAebU24Tn9UNUBQcHIw7d+6gXNlyNtvnneA7FpkSQ8lXX30FAHBwcMCUKVP05nn58qW07ObmZrBOFxcXaTkuLi6TLaScIi4uDnEvXyHkeuZ6jhIRUc6T4uWqmGYfZd5vu5QSTuY2x2LsQxOzuglE2cIrvMrqJqhKu067gXKllJ9/ZWl37idBo8n891RCQgL69OmDV69eoUqVKvjhhx8s0Lr/ycprOQbCLKBEiRL/HwT71GBeIiLKgQpVU077sIdy2vf3lNN6lNa/fckcxSI/OK5XTOuedFMxbcIT/ZGwn3x7K5ZBlyXKaYe+VE5LUL7oJMqZfkWJEgWyuhGKypUth6sXr9psf9VqVwM0mR/5pc+hQ4cQGBgIABg4cCDKldMf4EtO/t93mrOzSkeF/6cduMs4zSLlXiVKlEDI9TA00rTL6qYQEZGNRfZurJjmveyEWXXGLCtvbnMsxrPT7axuAlG2cFLsQ4kSxbK6GarKlXLEf4fNH71kquotQgCHspm+Tvviiy9w+fJlODs7Y+PGjXB1tew9nqy8lmMgjIiIiIgopxKASBI23R+sNCBs2rRp0vLYsWMV82lfjCUlqYy8/X/x8f/r+W1Mr0MiIiIiIqLMEUhFqk33l1k7d+7EL7/8AgCYM2cOatSokek6M8rKazl2iSQiIiIioix15coVHDlyBADQsmVLVKumPBLX3d1dWn7x4oXBurWn3/Dx8clEK4mIiIiIiHKfyMhIDBkyBADQsWNHfP7551bZT1ZeyzEQRkRERESUkyXb8M9Kfv31V2n5k08+Uc2r/YBk7YctK0l/uDIAlC6tMC0tERERERGRhQgAKSLVZn+ZHQ/24MEDREZGAgD27NkDOzs7aDQanb81a9ZIZUqXLi1t9/f3N2o/pl7LpedxcnJCkSJFTDgiXZwakYiIyJB6nymnXVZJ667yANdvovVvt6unWKR3kvKDQTc7/qmY9mXxJ3q337m1VLHM32tVnhHWUqUXzo3MPbyUiN48L1++xIYNGwAAHh4eeOedd1TzV6pUSVq+ceOGwfqDg4MBpM0przbSjIiIiHIHc58DFjlc+dliDogwtzl6nar1h8ll3hr+sWKaucdMRGRLGa/lunbtqpj3yZMniItLu8dUo0YN2fPCzMFAGBERERERZZldu3ZJU174+fnBxcVFNX+DBg3g7OyMhIQEnDhxAklJSXB0dNSb9+nTpwgKCgIA1KpVCx4eHpZtPBERERERUQ5XvHhxLFmi0iH6/61ZswanTp0CAHz77bfw9vYGADRq1Mio/TRv3lxaPnz4MCZOnKiYN33q/IzlzMVAGBERERFRTiUAkZT5ByObsj9L2759u7TcrVs3g/ldXV3Rpk0b7N69G7Gxsfjjjz/Qt29fvXkDAgKk5V69emW2qUREREREREbJ/ISFtuPt7Y0RI0YYzHfq1CkpENa/f3+UKlXKpP00bdoUXl5eiIqKwv79+xEaGooSJUrozWvpazk+I4yIiIiIiLJEUlISdu/eDQDQaDRo0aKFUeVGjRolLU+fPh2xsbE6eR48eIDZs2cDSAueDR482AItJiIiIiIiInM4OTlJAbekpCTFEWF79uzBzp07AQC1a9dGgwYNMr1vBsKIiIiIiHIqAYgk2/1ZulPjhQsXpCBWpUqVpKk1DGnXrp30LLFbt26hffv2uHDhAoQQSE1Nxe7du9G8eXNERKQ9z2PSpEkoXLiwZRtPRERERESkhwCQasN/2WnsmUajkf4OHTqkkz5+/HgUL14cALBp0yYMGTIEDx8+BAC8evUKv/zyC3r27AkhBDQaDRYsWGCRdnFqRCIiIkO2D7d8nUojzje1VmnHCsWksCNPFNMKXY3Su33nhTyKZca0Un4I6U//HVBMQ4xC+5spF8H1GSqJRJSbnT59Wlpu3Fj5AfX6rFq1Ck2aNMGtW7dw6tQp1K1bF25ubkhOTkZiYqKUz8/PD5MnT7ZYm4mIiOjNk7yjgN7t3stOKJbZ++iyYpqd7y2T23DucQXFtIZ+PRXTlNoOqLefiMgavLy8sHHjRnTs2BEvXryAv78//P394eHhgZcvXyI1NVXKO3fuXKNnDTGEI8KIiIiIiHIoIQCRLGz3Z+GuhtqBsPr165tU1sfHB2fOnEH//v3h4JDWv+/Vq1dSEMzNzQ1Tp07F1q1bYW9vb7lGExERERERqRJIEbb7s8rDnK2oSZMmOH36tKwzZGxsrBQEK1q0KDZv3owvvvjCYvvkiDAiIiIiIsoSAQEBsocgm8rT0xPr16/HvHnzcOTIEYSGhkIIgfLly6NNmzbw8PCwYGuJiIiIiIjeXOmjt9QII3tPVq5cGcePH8fVq1dx/vx5PHr0CK6urqhRowaaN28udXa0FAbCiIiIiIhyKiGAJBv2/hMCgPLUqVnF19cXvXv3zupmEBERERERAUA2e3JX9lWtWjVUq1bN6vvh1IhERERERERERERERESUKzEQRkRERERERERERERERLkSp0YkIiLbKHdZOS2imHJajLfl25IdTOmnf7tzlHKZMzUVk5bFPFNMa1Ktlt7tDUpdUiyzzkW5GbjfWjmtnEL7m32qUmFFlTQiMkQkZ3ULiIiIiHI+72UnLFpfw0s9FdNOPa5g0fqSdxRQTLP0cRGRYQJAig2nRuQkjIZxRBgRERERERERERERERHlShwRRkRERESUUwlAJNmw/x+7GhIRERERERmUyounbIUjwoiIiIiIiIiIiIiIiChX4ogwIiIiIqKcSgBIsvH+iIiIiIiISJEAkCL4jLDshIEwIiKynKLVlNM+nqacFqZS5/FP9G8/U8ioJmVbRyvq317usnKZ7qsVk241mqeY1vDaO3q3t3taSbFM31TlZvxSfoZyYkGFF1PpeImIiIiIKMtEDm+sd7v3shM2bknu49nptmJaw909Lbovvl5EROoYCCMiIiIiyqkEIJL5jDAiIiIiIqLsRKV/MWUBPiOMiIiIiIiIiIiIiIiIciUGwoiIiIiIiIiIiIiIiChX4tSIREREREQ5lQBEEqdGJCIiIiIiyi4EgBQbXjzxMs0wBsKIiMh0fRbr314xXLlMK5X68qukHTemQbnInZrmpSFOOSmoot7N+8orP6B5n91ZlX2peFrMvHJERERERJRtRA5vrJjmveyEDVuSOyXvKKCY5uAXYcOWEBG9GRgIIyIiIiLKwURSVreAiIiIiIiItKVwmFa2wmeEERERERERERERERERUa7EEWFERERERDmVAJDMZ4QRERERERFlJ6lZ3QCS4YgwIiIiIiIiIiIiIiIiypU4IoyIiEz3orT+7QtUyqilHVJJO1PIcHtIXYjC9vxblMv4qVU4IxONISIiIiKi7MJ72Qm92yOHN7ZxSyhd8o4CercrvVZERGQYA2FERERERDmUEIBIst18hUIAGpvtjYiIiIiIKOcRAFJseOXEGewN49SIRERERERERERERERElCtxRBgRERERUU4lAJFs2/0RERERERGRCgGk2vLaiddpBnFEGBEREREREREREREREeVKHBFGRERERJRjCcCGzwhL62rIp4QREREREREp4TPCsh8GwoiIyHTJp/Rv369SZlpZ5bQvXFUKvqd/c92DykWqKbQPANZMVtmXArV9nW9ten22Nr+j/u0l9yiXWa9SX7lMtYaIiIiIiGwocnhjxTTvZSdM2k6WYc75Ned1JCKiNAyEERERERHlVAIQSbbdHxEREREREamz5YgwMozPCCMiIiIiIiIiIiIiIqJciYEwIiIiIiIiIiIiIiIiypU4NSIRERERUU4lAJFsw/kKOTUiERERERGRKgEgVdhuakRephnGQBgREZnuA4XtySplDu5XTktYa3obvNyU09Y8USn4s3JS8Vv6t5/fbl592YX7Pf3bPVTK3LFKS4iIiIiIyAoihzfO6iYQERFlW1adGnH//v3QaDTQaEyLft64cQPDhw9HuXLl4OLigvz586NmzZqYNGkS7t69a1Qdr169wvz589GwYUN4eHjA1dUV5cqVQ69evfDPP/+YczhERERERNmLAESSsNkfuxrmfLxGIyIiIiKyvhRobPZHhll1RNgff/xhcpklS5Zg1KhRSEpKkrYlJCQgKioKV65cwcKFC7F06VIMGjRIsY5bt26hU6dOCA4Olm0PDg5GcHAwtmzZgl69emHVqlXIkyePyW0kIiIiIiLKiXiNRkREREREbxqrBcKuX7+ONWvWmFRmy5Yt+OSTT6T1t99+G61atYK9vT1OnjyJnTt3Ij4+HoMHD4anpyf8/Px06oiMjETbtm0RGhoKAChRogT69OkDX19f3Lt3DwEBAYiMjMTvv/+OpKQkbNu2LXMHSkRERESUVQSAJIO5LLs/yrF4jUZEREREZH0CGqRYdzI+nf2ROosGwoQQCAoKwl9//YV58+bh9evXRpeNiYnBp59+Kq2vWrUKQ4YMkeX5+++/0b17dyQnJ2Po0KFo3rw5vLy8ZHkmTZokXWB17NgRW7duhaurq5Q+ZcoUtGrVClevXsWff/6JtWvXqvZcJCIiIiIiyql4jUZERERERG86iwXCqlevjvv37+Ply5dmlV+xYgUiIiIAAIMGDdK5wAKALl264P3338eKFSsQGRmJ5cuXY8KECVL648eP4e/vDwDw8vLC2rVrZRdYAODj44OlS5eiadOmAIA5c+bwIouISJ+6B5XTik/Tvz1Rpb5KK5TTHj9VTvOI1L/90hfKZfqUVk7bNEY57UEFhYSflctkF30WKKdVjNa/vbNKfZM7qiQ2NKJBGTjHm17GEJdXpu8rwVU5Tc1rN9PLKLUPUG6js0oZNeYcc4LKMamdpxhv49pENiOSOUyL9OM1GhERUfYUObyxyWW8l52wQkuIyFpSBUdpZScWG5939epVsy+wAGDdunXS8pgxyjcphw4dKi1v3LhRlrZ582Zp3vrBgwfDx8dHbx1NmjRBxYoVAaRND3L58mWz201ERERERJQd8RqNiIiIiIjIgiPCtC+S0o0ZMwbPnj0zWPbJkyfShU6xYsVQq1YtxbwNGjSAh4cHYmNjcfnyZURGRsLbO61n8j///CPl69Kli+o+27Zti6CgIADAgQMHULNmTYPtJCIiIiIiyil4jUZERERERGTBQNiAAQN0tn399ddGXWSdO3dOWq5fv75qXo1Gg5o1a+Lo0aMQQuD69eto1qyZyfXUrl1bWr527ZrBNhIRERERZTdCACLJtvujnIPXaEREREREticApMB2UyPyMs0wi02NmBm3bt2SlsuUKWMwf8mSJaXlu3fvAgCeP3+OyMi058j4+PjA3d3d5DqIiIiIiIiI12hERERERJR7WGxEWGY8fPhQWvb19TWYv2DBgtJyVFSUxeowpGrVqnq3BwcHA/Awqg4iIiIiIosRAJJs2P+PXQ3fGLnhGs0BzkbVQURERERkaSkiW4xBov+XLQJh2g9wdnNzM5jfxcVFWo6Li7NYHUREpOV8a+W00Z/o3/6sjnKZBJW02j2U0z5Q2F5cuQgaj1FJzMHKXVZOmxatnFbhnP7tl99SLnPjQ5WGXFVO8ozUv91DYbshns9NL+P8SjktwfBvBJtQaqNzvHn1uagc82szjjnWWzmtQJjp5RJclcvEqOyLiLIMr9GIiHIW72UnbLavyOGNzSpnyzZmB2rnycEvQjEteUcBazSHiOiNli0CYcnJydKys7PhXnsazf/m17Szs7NYHYYozVNftWpVXL+u/B8YEREREZFVCEAkc0QYWV5uuEYLua4SuCciIiIishoNUm36VCrbPY8sp8oW4/NcXf/XSzgpyfDTvuPj/9dbOr1noSXqICIiIiIiIl6jERERERFR7pEtRoRpPzT5xYsXBvNrT7Hh4+NjsTqIiIiIiHIaYTi+QGQyXqMREREREZlHAEix4SgtTtxhWLYYEVayZElpWfuBykrCwv43xUXp0qUBACVKlJCm0jC3DiIiIiIiIuI1GhERERER5R7ZYkRYpUqVpOUbN24YzB8cHAwgbd74atWqAUibdqNEiRIICQlBVFQUwsPDUahQIYN1AEDNmjXNbToR0ZvpjML3a58vlMvc+FQ5bVUP5bQCW/RvT+6qXKbbMuW07cOV07K7/IWV0yo9Uk5Luql/e+BbymVeqLwmnoeV0wooPI/FJV7/dgDwiFROc3mlnKbEWWVfCa7KaeZQ25ely5lzLgDgtcL0YmrnQm1fMd4q5RSO67XKvtTOhdq+LP1aEpEMr9GIiN5skcMbK6Y5+EUopiXvKGCN5uRIaueJiIhsK1uMCGvQoIH08OQTJ06ozh//9OlTBAUFAQBq1aoFDw8PKa158+bS8uHDKjfpABw5ckRvOSIiIiKiHEMIiGTb/UFw0o03Ba/RiIiIiIjMlyLsbPZHhmWLs+Tq6oo2bdoAAGJjY/HHH38o5g0ICJCWe/XqJUt75513pOWVK1cq1vHkyRMcPHgQQFpPx+rVq5vVbiIiIiIiotyI12hERERERJRbZItAGACMGjVKWp4+fTpiY2N18jx48ACzZ88GkHZhNnjwYFl69+7dUaJECQDAvn378M8//+jd19ixY5GQkAAA+Pjjjy3SfiIiIiIiWxNCg6RE2/0JYbsHPlPW4zUaEREREZHpBIBUaGz2x3k7DMs2gbB27dpJvQVv3bqF9u3b48KFCxBCIDU1Fbt370bz5s0REZE2v+6kSZNQuLD8WSkODg748ccfpfXevXtj9erV0gVVcHAwevfujY0bNwIAKleujOHDc/CzYoiIiIiIiKyE12hERERERJQbOGR1A7StWrUKTZo0wa1bt3Dq1CnUrVsXbm5uSE5ORmJiopTPz88PkydP1ltHjx49MHbsWPz444+IjY3FBx98gA8//BB58+aV9WD08fHB1q1bpXnviYgog2LTTC9zTKWMh3ISQlTSCrrr3+7ySLnMwb9VKtypkqagz2LltHNNlNPu1DR9X2qKbFdOe6TSxkKl9W/X7dj/P5rPldMKhCmnlbilUqkCj+fKaS6vTK/PGpzjTS+TkE8lzUv/9kSVMsqP5zGPS7RymqfKg72L31ZOi/HWvz02v3KZp8WV05xVXn+lcgmuymVyISGAxETb9W3jI8LePLxGIyIiUzj4qfyOXGa7dmQHyTsKKKapnSfvZSes0RwisikNUmw6BokzdxiSbUaEAWkXPmfOnEH//v3h4JAWo3v16pV0geXm5oapU6di69atsLe3V6znhx9+wLJly1CoUCEAQGpqquwCq23btjh79iwqV65sxaMhIiIiIiLK2XiNRkREREREOZ1VR4Tdv3/f5DKenp5Yv3495s2bhyNHjiA0NBRCCJQvXx5t2rSBh4fakIL/GTZsGIYMGYKDBw/izp07iI6ORuHChdGsWTNUqFDB5HYRERERERHldLxGIyIiIiKiN022mhpRm6+vL3r37p2pOhwdHdG+fXu0b9/eQq0iIiIiIso+0qZGtN00GJwa8c3GazQiIiIiIsMEgBRhwynsbbannCtbTY1IREREREREREREREREZCnZdkQYERFlsaoqaR8obP9DpUwVlbSxKmlHm+jfHuGuXGZMaeW0BSr7qv+V/u2XxyuXiQ1SqdAMBcOU0050Vk7r2kI5LWSq/u1vF1IuU7yOcppHpErac/3bPVXKJORTToOzSppSfV7KaYlq+1Lxwox9qaYptEOtjHOUcpoa52gzyqjsSy3NSWFfxW8rl1F6zwBAbH6VdsTr3/5AZXq1BFfltByKI8KIiIgIACKHN1ZM8152wuRyqmWgvC81kcMLmLyvnEz1uJbZrh1ElDVSOQYpW+GrQURERERERERERERERLkSR4QREREREeVQHBFGRERERESUvQihQYqw5XWa7faVU3FEGBEREREREREREREREeVKHBFGRERERJRDCaGx8Ygwy+/r0aNH+Pnnn7F7927cvXsXKSkpKFiwIBo2bIj3338fHTt2tPg+iYiIiIiIrCmFY5CyFb4aRERERESUJXbu3IkqVapgzpw5+O+//xAXF4fXr18jNDQUv//+Ozp16oThw4er1nHjxg0MHz4c5cqVg4uLC/Lnz4+aNWti0qRJuHv3ro2OhIiIiIiIiLIrjggjInqTlbusnPZApVwVhe2/P1Iuc7GIctr2r5TTJszSv72hchEMUUmbWFU57dEe/dur3lMuU/4T5bQYb+U0z0j92wuGKZcpqPKieDxXTis+UP/2mLwqZW4ppym1HQDgrH/zs9rKRRK8VOozg7n1JeSz7L4SVdLM2deL0qaXAZTb6BxlXn3uKp8HD4W0xHzm1eei8nkwR0Qx5TS1zytZxZEjR9CjRw8kJSUBAKpVq4YePXrAw8MDZ8+exZYtW5CSkoLffvsNNWvWxCef6H7fLlmyBKNGjZLqAICEhARERUXhypUrWLhwIZYuXYpBgwbZ7LiIiIiym5jd5ZUTd5hen/eyE4ppkcMbm1yfOWUMtYOIiEgbA2FERERERDmUEEBiou0meRDCMvUkJCRg0KBBUgBr7NixmD9/PjSa/029OGjQILzzzjtISUnBtGnTMHToUDg7/y/YvmXLFllw7O2330arVq1gb2+PkydPYufOnYiPj8fgwYPh6ekJPz8/yzSeiIiIiIjIgFTByfiyE74aRERERERkUytWrEBISAgAoF27djpBMADo2LEj3n//fQDAs2fPsGvXLiktJiYGn376qbS+atUq/Pvvv5g8eTImTpyI7du3Y8eOHXBwSOv3N3ToUERFmTkKkoiIiIiIiHI0BsKIiIiIiHKotBFhGpv9WWpE2G+//SYtz549WycIlq5///7S8unTp6XlFStWICIiAkDayLEhQ3TnxO3SpYsUSIuMjMTy5cs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",
+      "text/plain": [
+       "<Figure size 1800x1500 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=[12,10], dpi=150)\n",
+    "ax0 = axes[0,0]\n",
+    "cm0 = ax0.pcolormesh(dbz_comp, vmin=0, vmax=70, cmap='gist_ncar')\n",
+    "ax0.set_title('Composite Reflectivity', loc='left')\n",
+    "plt.colorbar(cm0, ax=ax0)\n",
+    "\n",
+    "ax1 = axes[0,1]\n",
+    "# Steiner conv/strat/weak classification\n",
+    "cm1 = ax1.pcolormesh(convsf_steiner)\n",
+    "# Overplot Steiner core\n",
+    "Zm_core = np.ma.masked_where(core_steiner==0, core_steiner)\n",
+    "cm2 = ax1.pcolormesh(Zm_core, vmin=1, vmax=2, cmap='spring')\n",
+    "ax1.set_title('Steiner Conv/Strat Classification', loc='left')\n",
+    "plt.colorbar(cm1, ax=ax1)\n",
+    "\n",
+    "ax2 = axes[1,0]\n",
+    "core_mask = core_expand * core_dilate\n",
+    "Zm = np.ma.masked_where(core_mask == 0, core_expand)\n",
+    "cm2 = ax2.pcolormesh(Zm, cmap=cc.cm['glasbey_bw'])\n",
+    "ax2.set_title('Convective Cell Mask', loc='left')\n",
+    "plt.colorbar(cm2, ax=ax2)\n",
+    "\n",
+    "ax3 = axes[1,1]\n",
+    "Zm = np.ma.masked_where(core_expand == 0, core_expand)\n",
+    "cm3 = ax3.pcolormesh(Zm, cmap=cc.cm['glasbey_bw'])\n",
+    "ax3.set_title('Expanded Cell Mask', loc='left')\n",
+    "plt.colorbar(cm3, ax=ax3)\n",
+    "\n",
+    "fig.suptitle(time_str)\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "figname = f'{figdir}idcell_{time_fig}_{option}.png'\n",
+    "fig.savefig(figname, dpi=300, bbox_inches='tight', facecolor='w')\n",
+    "figname"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e2761312-e424-41b0-9412-302433788ec4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create variables needed for tracking\n",
+    "feature_mask = core_expand\n",
+    "# Count number of pixels for each feature\n",
+    "unique_num, npix_feature = np.unique(feature_mask, return_counts=True)\n",
+    "# Remove background (unique_num = 0)\n",
+    "npix_feature = npix_feature[(unique_num > 0)]\n",
+    "# Get number of features\n",
+    "nfeatures = np.nanmax(feature_mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0c90f457-a37a-4766-bf3e-361d516baa11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([209, 179, 143, 131, 115, 118,  99,  68,  53,  58,  50,  41,  43,\n",
+       "        35,  34,  36,  37,  24,  26,  32,  28,  27,  30,  28,  29,  24,\n",
+       "        30,  25,  23,  25,  25,  24,  21,  21,  25,  21,  24,  24,  16,\n",
+       "        25,  25,  21,  21,  24,  21,  16,  16])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "npix_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cb72387-bf72-41ca-b63a-0ec12cb06c29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyflex",
+   "language": "python",
+   "name": "pyflex"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/environment.yml b/environment.yml
index a14a69c..33e51a9 100644
--- a/environment.yml
+++ b/environment.yml
@@ -12,15 +12,15 @@ dependencies:
   - pytz>=2019
   - cartopy>=0.17
   - dask>=2.9
-  - scikit-image>=0.16
+  - scikit-image>=0.22
   - joblib>=0.14
   - ipython>7.0
   - pip
   - astropy
-  - wrf-python
   - colorcet
   - colormath
   - seaborn
   - scikit-learn
   - ffmpeg
   - xesmf
+  - wrf-python
\ No newline at end of file
diff --git a/pyflextrkr/advection_tiles.py b/pyflextrkr/advection_tiles.py
index 0f13f06..7725ef1 100644
--- a/pyflextrkr/advection_tiles.py
+++ b/pyflextrkr/advection_tiles.py
@@ -158,7 +158,7 @@ def movement_of_storm_fft(
             # Updated to use phase_cross_correlation to replace masked_register_translation
             y, x = -1 * phase_cross_correlation(
                 field_1, field_2, reference_mask=mask_1t, moving_mask=mask_2t, overlap_ratio=0.7
-            )
+            )[0]
 
             # plot_subplots = True
             if plot_subplots:
diff --git a/pyflextrkr/idcells_reflectivity.py b/pyflextrkr/idcells_reflectivity.py
index 23300b1..2d0183a 100644
--- a/pyflextrkr/idcells_reflectivity.py
+++ b/pyflextrkr/idcells_reflectivity.py
@@ -42,7 +42,8 @@ def idcells_reflectivity(
     radii_expand = config['radii_expand']
     weakEchoThres = config['weakEchoThres']
     bkgrndRadius = config['bkgrndRadius']
-    min_corearea = config['min_corearea']
+    min_corearea = config.get('min_corearea', 0)
+    min_cellarea = config.get('min_cellarea', 0)
     echotop_gap = config['echotop_gap']
     sfc_dz_min = config['sfc_dz_min']
     sfc_dz_max = config['sfc_dz_max']
@@ -54,6 +55,8 @@ def idcells_reflectivity(
     input_source = config['input_source']
     geolimits = config.get('geolimits', None)
     convolve_method = config.get('convolve_method', 'ndimage')
+    remove_smallcores = config.get('remove_smallcores', True)
+    remove_smallcells = config.get('remove_smallcells', False)
 
     # Set echo classification type values
     types_powell = {
@@ -141,7 +144,9 @@ def idcells_reflectivity(
             bkg_bin=bkg_bin,
             conv_rad_bin=conv_rad_bin,
             min_corearea=min_corearea,
-            remove_smallcores=True,
+            min_cellarea=min_cellarea,
+            remove_smallcores=remove_smallcores,
+            remove_smallcells=remove_smallcells,
             return_diag=return_diag,
             convolve_method=convolve_method,
         )
@@ -162,7 +167,9 @@ def idcells_reflectivity(
             bkg_bin=bkg_bin,
             conv_rad_bin=conv_rad_bin,
             min_corearea=min_corearea,
-            remove_smallcores=True,
+            min_cellarea=min_cellarea,
+            remove_smallcores=remove_smallcores,
+            remove_smallcells=remove_smallcells,
             return_diag=return_diag,
             convolve_method=convolve_method,
         )
@@ -198,6 +205,14 @@ def idcells_reflectivity(
                                        dbz_thresh=40, gap=echotop_gap, min_thick=0)
         echotop50 = echotop_height_wrf(dbz3d_filt, height, z_dimname, shape_2d,
                                        dbz_thresh=50, gap=echotop_gap, min_thick=0)
+    elif ('composite' in input_source):
+        # For 'composite' reflectivity (2D) input source, skip echo-top height calculations
+        echotop10 = np.full(shape_2d, np.nan, dtype=np.float32)
+        echotop20 = np.full(shape_2d, np.nan, dtype=np.float32)
+        echotop30 = np.full(shape_2d, np.nan, dtype=np.float32)
+        echotop40 = np.full(shape_2d, np.nan, dtype=np.float32)
+        echotop50 = np.full(shape_2d, np.nan, dtype=np.float32)
+
     del dbz3d_filt
 
     # Put all Steiner parameters in a dictionary
@@ -255,6 +270,9 @@ def idcells_reflectivity(
     out_nfeatures = np.zeros(1, dtype=int)
     out_nfeatures[0] = nfeatures
 
+    # Get passing variable DataSet from comp_dict
+    ds_pass = comp_dict.get('ds_pass', None)
+
     # Write output into netcdf file
     if return_diag == False:
         write_radar_cellid(
@@ -286,6 +304,7 @@ def idcells_reflectivity(
             out_nfeatures,
             config,
             steiner_params=steiner_params,
+            ds_pass=ds_pass,
         )
     if return_diag == True:
         write_radar_cellid(
@@ -320,6 +339,7 @@ def idcells_reflectivity(
             refl_bkg=refl_bkg,
             peakedness=peakedness,
             core_steiner_orig=core_steiner_orig,
+            ds_pass=ds_pass,
         )
     logger.info(f"{cloudid_outfile}")
 
@@ -609,32 +629,32 @@ def get_composite_reflectivity_wrf_composite(input_filename, config):
         comp_dict: dictionary
             Dictionary containing output variables
     """
-    # sfc_dz_min = config['sfc_dz_min']
-    # sfc_dz_max = config['sfc_dz_max']
     radar_sensitivity = config['radar_sensitivity']
-    time_dimname = config.get('time', 'time')
+    time_dimname = config.get('time_dimname', 'time')
     x_dimname = config.get('x_dimname', 'x')
     y_dimname = config.get('y_dimname', 'y')
-    # z_dimname = config.get('z_dimname', 'z')
+    z_dimname = config.get('z_dimname', 'z')
     x_varname = config['x_varname']
     y_varname = config['y_varname']
     # z_varname = config['z_varname']
     reflectivity_varname = config['reflectivity_varname']
-    # fillval = config['fillval']
+    pass_varname = config.get("pass_varname", None)
 
     # Read WRF file
     ds = xr.open_dataset(input_filename)
-    import pdb; pdb.set_trace()
-    # Drop XTIME dimension, and rename 'Time' dimension to 'time'
-    ds = ds.reset_coords(names='XTIME', drop=False).rename({'Time': time_dimname})
+    # Get dimension names from the file
+    dims_file = []
+    for key in ds.dims: dims_file.append(key)
+    # Find extra dimensions beyond [time, y, x]
+    dims_keep = [time_dimname, y_dimname, x_dimname]
+    dims_drop = list(set(dims_file) - set(dims_keep))
+    # Drop extra dimensions, reorder to [time, y, x]
+    ds = ds.drop_dims(dims_drop).transpose(time_dimname, y_dimname, x_dimname)
     # Rounds up to second, some model converted datetimes do not contain round second
-    time_coords = ds.XTIME.dt.round('S')
+    time_coords = ds[time_dimname].dt.round('S')
     # Get data coordinates and dimensions
-    # Get WRF height values
-    height = (ds['PH'] + ds['PHB']).squeeze().data / 9.80665
     nx = ds.sizes[x_dimname]
     ny = ds.sizes[y_dimname]
-    # nz = ds.sizes[z_dimname]
     # Create x, y coordinates to mimic radar
     dx = ds.attrs['DX']
     dy = ds.attrs['DY']
@@ -647,13 +667,18 @@ def get_composite_reflectivity_wrf_composite(input_filename, config):
     radar_lat = xr.DataArray(radar_lat, attrs={'long_name': 'Radar latitude'})
     grid_lon = ds[x_varname].squeeze()
     grid_lat = ds[y_varname].squeeze()
-    # Get radar variables
-    dbz3d = ds[reflectivity_varname].squeeze()
-    dbz3d_filt = dbz3d
+
     # Get composite reflectivity
-    dbz_comp = dbz3d_filt.max(dim=z_dimname)
+    dbz_comp = ds[reflectivity_varname].squeeze()
     # Get low-level reflectivity
-    dbz_lowlevel = dbz3d_filt.isel(bottom_top=1)
+    dbz_lowlevel = ds[reflectivity_varname].squeeze()
+    # Replace all values with NaN
+    dbz_lowlevel = dbz_lowlevel.where(dbz_lowlevel != dbz_lowlevel, np.NaN)
+    # Add a vertical dimension to make a fake 3D DataArray
+    dbz3d = dbz_lowlevel.expand_dims(z_dimname, axis=0)
+    # Make a fake height array
+    height = np.zeros(1)
+
     # Make a copy of the composite reflectivity (must do this or the dbz_comp will be altered)
     refl = np.copy(dbz_comp.values)
     # Replace all values less than min radar sensitivity, including NAN, to be equal to the sensitivity value
@@ -662,15 +687,22 @@ def get_composite_reflectivity_wrf_composite(input_filename, config):
     # This differs from Steiner.
     refl[(refl < radar_sensitivity) | np.isnan(refl)] = radar_sensitivity
     # Create a good value mask (everywhere is good for WRF)
-    # dster = xr.open_dataset(terrain_file)
-    # mask_goodvalues = dster.mask110.values.astype(int)
     mask_goodvalues = np.full(refl.shape, 1, dtype=int)
 
+    # Subset pass variable from input DataSet
+    if pass_varname is not None:
+        # Find the common variable names between the dataset and the list
+        pass_varname = set(ds.data_vars) & set(pass_varname)
+        # Subset the input dataset
+        ds_pass = ds[pass_varname]
+    else:
+        ds_pass = None
+
     # Put output variables in a dictionary
     comp_dict = {
         'x_coords': x_coords,
         'y_coords': y_coords,
-        'dbz3d_filt': dbz3d_filt,
+        'dbz3d_filt': dbz3d,
         'dbz_comp': dbz_comp,
         'dbz_lowlevel': dbz_lowlevel,
         'grid_lat': grid_lat,
@@ -681,6 +713,7 @@ def get_composite_reflectivity_wrf_composite(input_filename, config):
         'radar_lon': radar_lon,
         'refl': refl,
         'time_coords': time_coords,
+        'ds_pass': ds_pass,
     }
     return comp_dict
 
diff --git a/pyflextrkr/netcdf_io.py b/pyflextrkr/netcdf_io.py
index d9e6645..0a68c72 100644
--- a/pyflextrkr/netcdf_io.py
+++ b/pyflextrkr/netcdf_io.py
@@ -286,6 +286,16 @@ def write_radar_cellid(
         for key, value in kwargs['steiner_params'].items():
             gattr_dict[key] = value
 
+    # Check for optional keyword ds_pass
+    if 'ds_pass' in kwargs:
+        ds_pass = kwargs['ds_pass']
+        if ds_pass is not None:
+            # Get variable list from the DataSet
+            pass_varname = list(ds_pass.data_vars)
+            # Loop over each pass out variable and add to the output dictionary
+            for ivar in pass_varname:
+                var_dict[ivar] = (dim3d, ds_pass[ivar].data, ds_pass[ivar].attrs)
+
     # Define xarray dataset
     ds_out = xr.Dataset(var_dict, coords=coord_dict, attrs=gattr_dict)
     # Define variable attributes
diff --git a/pyflextrkr/preprocess_wrf_tb_rainrate.py b/pyflextrkr/preprocess_wrf_tb_rainrate.py
index 4212365..65cd8f4 100644
--- a/pyflextrkr/preprocess_wrf_tb_rainrate.py
+++ b/pyflextrkr/preprocess_wrf_tb_rainrate.py
@@ -11,6 +11,7 @@
 from multiprocessing import Pool
 from pyflextrkr.ft_regrid_func import make_weight_file, make_grid4regridder
 from pyflextrkr.ftfunctions import olr_to_tb
+from pyflextrkr.ft_utilities import subset_files_timerange
 
 #-------------------------------------------------------------------------------------
 def preprocess_wrf_tb_rainrate(config):
@@ -35,16 +36,32 @@ def preprocess_wrf_tb_rainrate(config):
     outdir = config['clouddata_path']
     inbasename = config['wrfout_basename']
     outbasename = config['databasename']
+    start_basetime = config.get("start_basetime", None)
+    end_basetime = config.get("end_basetime", None)
+    time_format = config["time_format"]
     regrid_input = config.get('regrid_input', False)
 
     # Create output directory
     os.makedirs(outdir, exist_ok=True)
 
-    # Find all WRF files
-    filelist = sorted(glob.glob(f'{indir}/{inbasename}*'))
+    # Identify files to process
+    infiles_info = subset_files_timerange(
+        indir,
+        inbasename,
+        start_basetime=start_basetime,
+        end_basetime=end_basetime,
+        time_format=time_format,
+    )
+    # Get file list
+    filelist = infiles_info[0]
     nfiles = len(filelist)
     logger.info(f'Number of WRF files: {nfiles}')
 
+    # Find all WRF files
+    # filelist = sorted(glob.glob(f'{indir}/{inbasename}*'))
+    # nfiles = len(filelist)
+    # logger.info(f'Number of WRF files: {nfiles}')
+    # import pdb; pdb.set_trace()
     # Check regridding option
     if regrid_input:
         # Build Regridder
@@ -116,6 +133,7 @@ def calc_rainrate_tb(filepairnames, outdir, inbasename, outbasename, config):
 
     regrid_input = config.get('regrid_input', False)
     write_native = config.get('write_native', False)
+    rainrate_method = config.get('rainrate_method', 'standard')
     
     # Filenames with full path
     filein_t1 = filepairnames[0]
@@ -164,12 +182,20 @@ def calc_rainrate_tb(filepairnames, outdir, inbasename, outbasename, config):
     # Calculate basetime difference in [seconds]
     delta_times = np.diff(basetimes)
 
-    # Read accumulated precipitation and OLR
-    RAINNC = ds_in['RAINNC'].load()
-    RAINC = ds_in['RAINC'].load()
+    # Read and compute accumulated precipitation
+    if rainrate_method == 'standard':
+        RAINNC = ds_in['RAINNC']
+        RAINC = ds_in['RAINC']
+        # Add grid-scale and convective precipitation
+        RAINALL = (RAINNC + RAINC).load()
+    elif rainrate_method == 'saag':
+        RAINNC = ds_in['RAINNC']
+        I_RAINNC = ds_in['I_RAINNC']
+        # The total precipitation accumulation from the initial time is computed (units: mm)
+        RAINALL = (RAINNC + I_RAINNC * 100).load()
+    
+    # Read OLR
     OLR_orig = ds_in['OLR'].load()
-    # Add grid-scale and convective precipitation
-    RAINALL = RAINNC + RAINC
 
     # Create an array to store rainrates and brightness temperature
     rainrate = np.zeros((ntimes-1,ny,nx), dtype=float)
diff --git a/pyflextrkr/steiner_func.py b/pyflextrkr/steiner_func.py
index dab6308..85a9220 100644
--- a/pyflextrkr/steiner_func.py
+++ b/pyflextrkr/steiner_func.py
@@ -535,7 +535,9 @@ def mod_steiner_classification(
         bkg_bin,
         conv_rad_bin,
         min_corearea=1,
+        min_cellarea=0,
         remove_smallcores=True,
+        remove_smallcells=False,
         return_diag=False,
         convolve_method='ndimage',
 ):
@@ -574,8 +576,12 @@ def mod_steiner_classification(
         Convective radius bins for dilation step function
     min_corearea: float, optional
         Minimum area to keep a convective core (km^2) (default 1)
+    min_cellarea: float, optional
+        Minimum area to keep a convective cell (km^2) (default 0)
     remove_smallcores: bool, optional
         A flag to remove convective cores smaller than min_corearea (default True)
+    remove_smallcells: bool, optional
+        A flag to remove convective cells smaller than min_cellarea (default True)
     return_diag: bool, optional
         A flag to return more fields for diagnostic purpose (default False)
     convolve_method: string, optional
@@ -632,6 +638,8 @@ def mod_steiner_classification(
             rid = np.where(tmpregions == rr)
             if (len(rid[0]) < min_corenpix):
                 score_keep[rid] = 0
+    else:
+        score_keep = score
 
     # Dilate convective radius
     sclass_new, score_dilate = mod_dilate_conv_rad(
@@ -646,6 +654,24 @@ def mod_steiner_classification(
         conv_rad_bin
     )
 
+    # Remove small cells with pixels < min_cellarea
+    if (remove_smallcells == True):
+        # Make a copy of the cell array
+        score_dilate_keep = np.copy(score_dilate)
+        # Convert min_corearea to number of pixels
+        # dx, dy are in [meter], min_corearea is in [km^2], convert all to [meter]
+        min_cellnpix = int(min_cellarea * (1000**2) / (dx * dy))
+        # Remove small cells
+        # Label connected cell pixels as regions
+        tmpregions, num_regions = ndimage.label(score_dilate_keep)
+        for rr in range(1, num_regions+1):
+            rid = np.where(tmpregions == rr)
+            if (len(rid[0]) < min_cellnpix):
+                score_dilate_keep[rid] = 0
+        # Replace cell array
+        score_dilate = score_dilate_keep
+
+    # Return values
     if (return_diag == False):
         return sclass_new, score_keep, score_dilate
     if (return_diag == True):
diff --git a/requirements.txt b/requirements.txt
index 5d873a1..e45708b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,17 +7,17 @@ netcdf4>=1.5
 pytz>=2019
 cartopy>=0.17
 dask>=2.9
-scikit-image>=0.16
+scikit-image>=0.22
 joblib>=0.14
 ipython>=7.0
 setuptools>=65.5.1
 PyYAML>=5.4
 pip
 astropy
-wrf-python
 colorcet
 colormath
 seaborn
 scikit-learn
 ffmpeg
 xesmf
+wrf-python
\ No newline at end of file