diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml
index 5498ca5..6cde90c 100644
--- a/.github/workflows/documentation.yml
+++ b/.github/workflows/documentation.yml
@@ -36,7 +36,8 @@ jobs:
 
     # Deploy the newly generated html pages to the GitHub Pages associated with this repo
     - name: Deploy to GitHub Pages
+      if: github.repository == 'brysongray/neurotrack'
       uses: peaceiris/actions-gh-pages@v4
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
-        publish_dir: docs/build/html
\ No newline at end of file
+        publish_dir: docs/build/html
diff --git a/.gitignore b/.gitignore
index 0ad1cc3..7ae10e8 100755
--- a/.gitignore
+++ b/.gitignore
@@ -135,3 +135,16 @@ dmypy.json
 # Training data and model weights
 *.pt
 *.csv
+*.tif
+*.swc
+
+*.html
+*.pdf
+*.png
+*.jpg
+*.jpeg
+*.gif
+*.zip
+*.out
+*.json
+*.ipynb
diff --git a/README.md b/README.md
index cf30b03..d6840bc 100755
--- a/README.md
+++ b/README.md
@@ -33,4 +33,13 @@ No non-standard hardware is required, but this library uses pytorch which can us
 
 Examples for usage are given in Jupyter notebooks in the notebooks folder of the github repository.
 
+## Example neuron tracking results
+
+Gray surface is the true neuron, red surface is the result of automated tracing.
+
+![hippo](https://media.giphy.com/media/ZEcTAh6nwSrbRMdMDS/giphy.gif)
+
+![hippo](https://media.giphy.com/media/7Vu79HIrW4XROOVX03/giphy.gif)
+
+![hippo](https://media.giphy.com/media/Qlkpz2BHlb1bjyHEmo/giphy.gif)
 
diff --git a/bin/classifier_train.py b/bin/classifier_train.py
index 60183fc..a468496 100644
--- a/bin/classifier_train.py
+++ b/bin/classifier_train.py
@@ -3,12 +3,15 @@
 """
 
 import argparse
+from glob import glob
 import os
 from pathlib import Path
 import sys
 import torch
-
-sys.path.append(str(Path(__file__).parents[1]))
+from torch.utils.data import DataLoader
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent  # Go up two levels
+sys.path.append(str(parent_dir))
 import models
 from solvers import branch_classifier
 
@@ -25,19 +28,24 @@ def main():
     -o, --out: Path to output directory.
     -l, --learning_rate: Optimizer learning rate.
     -N, --epochs: Number of training epochs.
+    -w --weights: pretrained model weights 
     """
     parser = argparse.ArgumentParser()
     parser.add_argument('-s', '--source', type=str, help='Source directory containing labels as csv files and input images folder (observations).')
     parser.add_argument('-o','--out', type=str, help="Path to output directory.")
+    parser.add_argument('-c', '--channels', type=int, default=3, help='Number of input channels in the images.')
     parser.add_argument('-l', '--learning_rate', type=float, default=0.001, help='Optimizer learning rate.')
     parser.add_argument('-N', '--epochs', type=int, default=15, help='Number of training epochs.')
+    parser.add_argument('-w', '--weights', type=str, default=None, help='pretrained model weights')
 
     args = parser.parse_args()
     source = args.source
     out_dir = args.out
     lr = args.learning_rate
     epochs = args.epochs
-    
+    weights = args.weights
+    channels = args.channels
+
     source_list = os.listdir(source)
     training_labels_file = [f for f in source_list if 'training_labels' in f][0]
     training_labels_file = os.path.join(source, training_labels_file)
@@ -49,30 +57,91 @@ def main():
     if not os.path.exists(test_labels_file):
         raise FileNotFoundError("Source directory must contain a csv file with `test_labels` in the filename,\
                                 but none was found")
-    img_dir = os.path.join(source, 'observations')
+    img_dir = [d for d in source_list if 'observations' in d][0]
+    img_dir = os.path.join(source, img_dir)
     if not os.path.exists(img_dir):
         raise FileNotFoundError("Source directory must contain a folder named `observations`,\
                                 but none was found.")
 
     transform = branch_classifier.transform # random permutation and flip
     training_data = branch_classifier.StateData(labels_file=training_labels_file,
-                            img_dir=img_dir,
-                            transform=transform)
+                            img_dir=img_dir,transform=transform)
     test_data = branch_classifier.StateData(labels_file=test_labels_file,
                             img_dir=img_dir)
 
-    training_dataloader = branch_classifier.init_dataloader(training_data)
-    test_dataloader = branch_classifier.init_dataloader(test_data)
+    training_dataloader = branch_classifier.init_dataloader(training_data, batchsize=64)
+    test_dataloader = branch_classifier.init_dataloader(test_data, batchsize=64)
     
     if not os.path.exists(out_dir):
         os.makedirs(out_dir, exist_ok=True)
 
-    classifier = models.ResNet(models.ResidualBlock, [3, 4, 6, 3], num_classes=1)
+    # classifier = models.ResNet2D(models.ResidualBlock2D, [3, 4, 6, 3], in_channels=36, num_classes=1)
+    classifier = models.ResNet3D(models.ResidualBlock3D, [3, 4, 6, 3], in_channels=channels, num_classes=1)
     classifier = classifier.to(device=DEVICE, dtype=dtype)
 
+    if weights is not None:
+        state_dict = torch.load(weights, map_location=DEVICE)
+        classifier.load_state_dict(state_dict)
+        print(f"Loaded pretrained weights from {weights}")
+
     branch_classifier.train(training_dataloader, test_dataloader, out_dir, lr, epochs, classifier, state_dict=None)
     
     return
 
+
+# # TODO: Change to online data sampling from swc and image files
+# def main():
+#     """
+#     Main function to train the branch classifier model.
+
+#     Arguments
+#     ---------
+#     -s, --swc_dir: Source directory containing the neuron trees in swc format.
+#     -i, --image_dir: Source directory containing the input images.
+#     -o, --out: Path to output directory.
+#     -l, --learning_rate: Optimizer learning rate.
+#     -N, --epochs: Number of training epochs.
+#     -w, --weights: pretrained model weights 
+#     """
+#     parser = argparse.ArgumentParser()
+#     parser.add_argument('-s', '--swc_dir', type=str, help='Source directory containing the neuron trees in swc format.')
+#     parser.add_argument('-i', '--image_dir', type=str, help='Source directory containing the input images.')
+#     parser.add_argument('-o', '--out', type=str, help="Path to output directory.")
+#     parser.add_argument('-l', '--learning_rate', type=float, default=0.001, help='Optimizer learning rate.')
+#     parser.add_argument('-N', '--epochs', type=int, default=15, help='Number of training epochs.')
+#     parser.add_argument('-w', '--weights', type=str, default=None, help='pretrained model weights')
+#     args = parser.parse_args()
+#     swc_dir = args.swc_dir
+#     image_dir = args.image_dir
+#     out_dir = args.out
+#     lr = args.learning_rate
+#     epochs = args.epochs
+#     weights = args.weights
+#     if not os.path.exists(swc_dir):
+#         raise FileNotFoundError(f"Source directory {swc_dir} does not exist.")
+#     if not os.path.exists(image_dir):
+#         raise FileNotFoundError(f"Source directory {image_dir} does not exist.")
+#     if not os.path.exists(out_dir):
+#         os.makedirs(out_dir, exist_ok=True)
+#     if not os.path.exists(weights):
+#         raise FileNotFoundError(f"Pretrained weights file {weights} does not exist.")
+    
+#     swc_files = [f for x in os.walk(swc_dir) for f in glob(os.path.join(x[0], "*.swc"))]
+#     img_files = [f for x in os.walk(image_dir) for f in glob(os.path.join(x[0], "*image.tif"))]
+
+#     classifier = models.ResNet3D(models.ResidualBlock3D, [3, 4, 6, 3], in_channels=3, num_classes=1)
+#     classifier = classifier.to(device=DEVICE, dtype=dtype)
+
+#     if weights is not None:
+#         state_dict = torch.load(weights, map_location=DEVICE)
+#         classifier.load_state_dict(state_dict)
+#         print(f"Loaded pretrained weights from {weights}")
+
+#     transform = branch_classifier.transform  # random permutation and flip
+#     branch_classifier.train(swc_files, img_files, out_dir, lr, epochs, classifier, transform=transform)
+    
+
+
+
 if __name__ == "__main__":
     main()
\ No newline at end of file
diff --git a/bin/collect_branch_data.py b/bin/collect_branch_data.py
index 1cd0860..8b1b9d0 100644
--- a/bin/collect_branch_data.py
+++ b/bin/collect_branch_data.py
@@ -5,12 +5,13 @@
 import argparse
 from datetime import datetime
 from glob import glob
+import numpy as np
 import os
 from pathlib import Path
 import sys
 
 sys.path.append(str(Path(__file__).parents[1]))
-from data_prep import load, collect
+from data_prep import load, collect, tree
 
 DATE = datetime.now().strftime("%m-%d-%y")
 
@@ -42,6 +43,7 @@ def main():
     parser.add_argument('-o','--out', type=str, help="Path to output directory.")
     parser.add_argument('-n', '--name', type=str, help='Output filename base.')
     parser.add_argument('-a', '--adjust', action='store_true', default=False, help='Set to true if neuron coordinates were rescaled to draw images.')
+    parser.add_argument('-r', '--remove_soma', action='store_true', default=False, help='Remove edges near the soma from the swc.')
     parser.add_argument('--n_samples', type=int, default=100, help='Number of samples to collect from each image file.')
     args = parser.parse_args()
     labels_dir = args.labels
@@ -49,13 +51,24 @@ def main():
     out_dir = args.out
     name = args.name
     adjust = args.adjust
+    remove_soma = args.remove_soma
     samples_per_file = args.n_samples
     
     # get sample points from swc files
     files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], '*.swc'))]
     swc_lists = []
     for f in files:
-        swc_lists.append(load.swc(f))
+        swc_list = load.swc(f)
+        swc_list = np.array(swc_list)
+        if adjust:
+            min = np.min(swc_list[:, 2:5], axis=0)
+            max = np.max(swc_list[:, 2:5], axis=0)
+            vol = np.prod(max - min)
+            scale = np.round((5e7 / vol)**(1/3)) # scale depends on the volume
+            swc_list[...,:3] = (swc_list[...,:3] - min) * scale + np.array([10.0, 10.0, 10.0])
+        if remove_soma:
+            swc_list, _ = tree.remove_soma(swc_list, max_radius=7.0)
+        swc_lists.append(swc_list)
     fnames = [f.split('/')[-1].split('.')[0] for f in files]
     
     sample_points = collect.swc_random_points(samples_per_file, swc_lists, fnames, adjust=adjust)
diff --git a/bin/process_neuron_data.py b/bin/process_neuron_data.py
new file mode 100755
index 0000000..9e36cef
--- /dev/null
+++ b/bin/process_neuron_data.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+"""
+Process neuron data by applying scaling, inhomogeneity correction, and cropping.
+
+This script processes TIFF images and corresponding SWC files by:
+1. Loading and cropping images
+2. Applying scaling transformations
+3. Performing inhomogeneity correction
+4. Saving processed data
+
+Usage:
+    python process_neuron_data.py --tifs_path /path/to/tifs --swc_path /path/to/swc \
+                                  --tifs_out /path/to/output/tifs --swc_out /path/to/output/swc \
+                                  --scaling_dict /path/to/scaling_dict.npy \
+                                  [--plot] [--correct_inhomogeneity] [--sync] [--save_out]
+"""
+
+import argparse
+import os
+import sys
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import tifffile as tf
+import torch
+from glob import glob
+from tqdm import tqdm
+
+# Add parent directory to path to import local modules
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from data_prep import load, draw, save, data_utils
+from data_prep.draw import NeuronRenderer, DrawingConfig
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description='Process neuron data with scaling and inhomogeneity correction')
+    
+    parser.add_argument('--tifs_path', type=str, required=True,
+                        help='Path to input TIFF files directory')
+    parser.add_argument('--swc_path', type=str, required=True,
+                        help='Path to input SWC files directory')
+    parser.add_argument('--tifs_out', type=str, required=True,
+                        help='Path to output TIFF files directory')
+    parser.add_argument('--swc_out', type=str, required=True,
+                        help='Path to output SWC files directory')
+    parser.add_argument('--scaling_dict', type=str, required=True,
+                        help='Path to scaling dictionary .npy file')
+    parser.add_argument('--scales_df', type=str, default=None,
+                        help='Path to scaling CSV file (required for plotting)')
+    parser.add_argument('--plot', action='store_true',
+                        help='Enable plotting visualization')
+    parser.add_argument('--correct_inhomogeneity', action='store_true',
+                        help='Apply inhomogeneity correction')
+    parser.add_argument('--sync', action='store_true',
+                        help='Skip files that already exist in output')
+    parser.add_argument('--save_out', action='store_true',
+                        help='Save processed files to output directories')
+    
+    return parser.parse_args()
+
+
+def process_neuron_data(tifs_path, swc_path, tifs_out, swc_out, scaling_dict_path,
+                       plot=False, correct_inhomogeneity=False,
+                       sync=True, save_out=True):
+    """
+    Process neuron data with scaling and inhomogeneity correction.
+    
+    Parameters
+    ----------
+    tifs_path : str
+        Path to input TIFF files directory
+    swc_path : str
+        Path to input SWC files directory
+    tifs_out : str
+        Path to output TIFF files directory
+    swc_out : str
+        Path to output SWC files directory
+    scaling_dict_path : str
+        Path to scaling dictionary .npy file
+    plot : bool, default False
+        Enable plotting visualization
+    correct_inhomogeneity : bool, default True
+        Apply inhomogeneity correction
+    sync : bool, default True
+        Skip files that already exist in output
+    save_out : bool, default True
+        Save processed files to output directories
+    """
+    
+    # Load scaling dictionary
+    scaling_dict = np.load(scaling_dict_path, allow_pickle=True).item()
+    
+    # Get list of files
+    tif_files = os.listdir(tifs_path)
+    swc_files = os.listdir(swc_path)
+    
+    # Process each TIFF file
+    for i in tqdm(range(len(tif_files)), desc="Processing files"):
+        background_threshold = None
+        
+        if plot:
+            plt.close("all")
+            fig, ax = plt.subplots(1, 2, figsize=(20, 20))
+        
+        # Load image
+        img_path = os.path.join(tifs_path, tif_files[i])
+        print(f"Loading image: {img_path}")
+        
+        if save_out:
+            name = tif_files[i].split('.tif')[0]
+            tif_out_path = os.path.join(tifs_out, name, name + "_image.tif")
+            swc_out_path = os.path.join(swc_out, name, name + ".swc")
+            
+            if sync:
+                tif_exists = os.path.exists(tif_out_path)
+                swc_exists = os.path.exists(swc_out_path)
+                if tif_exists and swc_exists:
+                    print(f"Skipping {tif_files[i]}")
+                    continue
+            
+            # Create output directories
+            os.makedirs(os.path.split(tif_out_path)[0], exist_ok=True)
+            os.makedirs(os.path.split(swc_out_path)[0], exist_ok=True)
+        
+        # Load and process image
+        img = tf.imread(img_path)
+        
+        if img.dtype == "uint16":
+            # Convert to uint8
+            img = img - img.min()
+            img = img / img.max()
+            img = np.round(img * 255).astype(np.uint8)
+        
+        # Get matching SWC file
+        swc_file = [f for f in swc_files if tif_files[i].split('.')[0] == f.split('.')[0]][0]
+        swc_list = load.swc(os.path.join(swc_path, swc_file), verbose=False)
+        
+        # Crop image and adjust SWC coordinates
+        img, swc_list = load.crop_and_adjust_coords(img, swc_list)
+        
+        # Apply scaling
+        scale = scaling_dict[tif_files[i]]
+        if scale != 1.0:
+            img, swc_list = load.scale_and_adjust_coords(img, swc_list, scale)
+        
+        # Inhomogeneity correction
+        if correct_inhomogeneity:
+            if background_threshold is None:
+                img_flat = img.flatten()[::27, None]  # Downsample for efficiency
+                # Global intensity normalization
+                img_flat = img_flat.astype(np.float32) / img.max()
+                mu, sigmas = data_utils.kmeans(img_flat, k=2, tolerance=1e-3, 
+                                             init_means=np.array([1.0, 0.0]), max_iter=100)
+                del img_flat
+                background_threshold = mu[0] + sigmas[0] * 8
+            
+            img = data_utils.inhomogeneity_correction(img, background_threshold=background_threshold)
+        
+        # Save processed data
+        if save_out:
+            tf.imwrite(tif_out_path, img, compression='zlib')
+            save.write_swc(swc_list, swc_out_path)
+        
+        # Plotting
+        if plot:
+            
+            # Draw density
+            sections, section_graph = load.parse_swc(swc_list)
+            branches, terminals = load.get_critical_points(swc_list, sections)
+                        
+            # Use the new NeuronRenderer with clean config
+            renderer = NeuronRenderer()
+            config = DrawingConfig(width=3.0, rgb=False)
+            density = renderer.draw_neuron(sections, shape=img.shape, config=config)
+            
+            # Get a random branch point
+            branch_point = branches[torch.randint(0, len(branches), (1,)).squeeze(0)]
+            
+            big_image = img.max(0)
+            big_density = density.data[0].numpy().max(0)
+            if big_image.dtype == np.uint8:
+                big_image = big_image.astype(np.float32) / 255.0
+            
+            ax[0].imshow(big_image, cmap='gray', vmin=0.0, vmax=big_image.max())
+            ax[0].set_title(f"{tif_files[i]}", fontsize=8)
+            
+            # Plot seed point
+            seed = swc_list[swc_list[:, 6] == -1][0, 2:5]
+            ax[0].scatter(seed[2], seed[1], c='r')
+            
+            # Draw rectangles
+            rect_size1 = 19
+            rect_size2 = 35
+            rect1 = plt.Rectangle((round(branch_point[2]) - rect_size1 // 2, 
+                                 round(branch_point[1]) - rect_size1 // 2), 
+                                rect_size1, rect_size1, linewidth=1, 
+                                edgecolor='r', facecolor='none')
+            rect2 = plt.Rectangle((round(branch_point[2]) - rect_size2 // 2, 
+                                 round(branch_point[1]) - rect_size2 // 2), 
+                                rect_size2, rect_size2, linewidth=1, 
+                                edgecolor='g', facecolor='none')
+            ax[0].add_patch(rect1)
+            ax[0].add_patch(rect2)
+            
+            # Small image views
+            small_image = big_image[round(branch_point[1]) - rect_size2 // 2:round(branch_point[1]) + rect_size2 // 2, 
+                                  round(branch_point[2]) - rect_size2 // 2:round(branch_point[2]) + rect_size2 // 2]
+            small_density = big_density[round(branch_point[1]) - rect_size2 // 2:round(branch_point[1]) + rect_size2 // 2, 
+                                      round(branch_point[2]) - rect_size2 // 2:round(branch_point[2]) + rect_size2 // 2]
+            
+            ax[1].imshow(small_image, cmap='gray', interpolation="none", vmin=0.0, vmax=big_image.max())
+            ax[1].imshow(small_density, cmap='hot', interpolation="none", alpha=0.25)
+            
+            rect1 = plt.Rectangle((small_image.shape[1] // 2 - rect_size1 // 2, 
+                                 small_image.shape[0] // 2 - rect_size1 // 2), 
+                                rect_size1-2, rect_size1-2, linewidth=1, 
+                                edgecolor='r', facecolor='none')
+            rect2 = plt.Rectangle((small_image.shape[1] // 2 - rect_size2 // 2, 
+                                 small_image.shape[0] // 2 - rect_size2 // 2), 
+                                rect_size2-2, rect_size2-2, linewidth=2, 
+                                edgecolor='g', facecolor='none')
+            ax[1].add_patch(rect1)
+            ax[1].add_patch(rect2)
+            
+            plt.show()
+            plt.close()
+
+
+def main():
+    """Main function to run the script."""
+    args = parse_args()
+    
+    process_neuron_data(
+        tifs_path=args.tifs_path,
+        swc_path=args.swc_path,
+        tifs_out=args.tifs_out,
+        swc_out=args.swc_out,
+        scaling_dict_path=args.scaling_dict,
+        plot=args.plot,
+        correct_inhomogeneity=args.correct_inhomogeneity,
+        sync=args.sync,
+        save_out=args.save_out
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/bin/run_processing_example.sh b/bin/run_processing_example.sh
new file mode 100755
index 0000000..7a3537d
--- /dev/null
+++ b/bin/run_processing_example.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+# Example usage of process_neuron_data.py script
+# This script demonstrates how to call the Python script with typical arguments
+
+# Set your paths here
+TIFS_PATH="/home/brysongray/data/neurotrack_data/gold166/gold166_tifs_scaled/"
+SWC_PATH="/home/brysongray/data/neurotrack_data/gold166/gold166_swc_scaled/"
+TIFS_OUT="/home/brysongray/data/neurotrack_data/gold166/gold166_tifs_processed"
+SWC_OUT="/home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed"
+SCALING_DICT="/home/brysongray/data/neurotrack_data/gold166/scaling_dict.npy"
+SCALES_DF="/home/brysongray/data/neurotrack_data/gold166/scaling_gold.csv"
+
+# Run the processing script
+python /home/brysongray/neurotrack/bin/process_neuron_data.py \
+    --tifs_path "$TIFS_PATH" \
+    --swc_path "$SWC_PATH" \
+    --tifs_out "$TIFS_OUT" \
+    --swc_out "$SWC_OUT" \
+    --scaling_dict "$SCALING_DICT" \
+    --scales_df "$SCALES_DF" \
+    --correct_inhomogeneity \
+    --sync \
+    --save_out
+
+# Alternative usage with plotting enabled
+# python /home/brysongray/neurotrack/bin/process_neuron_data.py \
+#     --tifs_path "$TIFS_PATH" \
+#     --swc_path "$SWC_PATH" \
+#     --tifs_out "$TIFS_OUT" \
+#     --swc_out "$SWC_OUT" \
+#     --scaling_dict "$SCALING_DICT" \
+#     --scales_df "$SCALES_DF" \
+#     --plot \
+#     --correct_inhomogeneity \
+#     --sync \
+#     --save_out
+
+echo "Processing complete!"
diff --git a/bin/sac_train.py b/bin/sac_train.py
index de8b470..573cd33 100644
--- a/bin/sac_train.py
+++ b/bin/sac_train.py
@@ -5,17 +5,24 @@
 import argparse
 from datetime import datetime
 import json
+import numpy as np
+import os
 from pathlib import Path
 import sys
 import torch
 from torch.optim.adamw import AdamW
 from torch.optim.adam import Adam
 
-sys.path.append(str(Path(__file__).parents[1]))
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent  # Go up two levels
+sys.path.append(str(parent_dir))
 from environments.sac_tracking_env import Environment
+from neurotrack.data.neuron_data import Dataset, DataLoader, DataGenerator, DrawingComplexityConfig
+# from environments.neuron_tracking_environment import NeuronTrackingEnvironment
+from environments.neuron_tracking_environment import NeuronTrackingEnvironment
 from memory.buffer import PrioritizedReplayBuffer
-from models.resblock import ResidualBlock
-from models.resnet import ResNet
+from models.resblock import ResidualBlock3D
+from models.resnet import ResNet3D
 from models.cnn import ConvNet
 from solvers import sac
 
@@ -34,8 +41,8 @@ def main():
     
     JSON Configuration Parameters
     -----------------------------
-    img_path : str
-        Path to the input image.
+    data_dir : str
+        Path to the input data directory.
     outdir : str
         Directory to save output results.
     name : str
@@ -78,7 +85,7 @@ def main():
     with open(args_json) as f:
         params = json.load(f)
     
-    img_path = params["img_path"]
+    data_dir = params["data_dir"]
     outdir = params["outdir"]
     name = params["name"]
     step_size = params["step_size"] if "step_size" in params else 1.0
@@ -93,29 +100,46 @@ def main():
     n_episodes = params["n_episodes"] if "n_episodes" in params else 100
     init_temperature = params["init_temperature"] if "init_temperature" in params else 0.005
     target_entropy = params["target_entropy"] if "target_entropy" in params else 0.0
+    repeat_starts = params["repeat_starts"] if "repeat_starts" in params else True
+    section_masking = params["section_masking"] if "section_masking" in params else False
+    branching = params["branching"] if "branching" in params else 0
+    rng_seed = params["rng_seed"] if "rng_seed" in params else 1
+    start_complexity = params["start_complexity"] if "start_complexity" in params else 0.0
     patch_radius = 17
+    in_channels = 2
 
     if "classifier_weights" in params:
         classifier_path = params["classifier_weights"]
-        classifier_state_dict = torch.load(classifier_path, weights_only=True)
-        classifier = ResNet(ResidualBlock, [3, 4, 6, 3], num_classes=1)
+        classifier_state_dict = torch.load(classifier_path)#, weights_only=True)
+        classifier = ResNet3D(ResidualBlock3D, [3, 4, 6, 3], in_channels=in_channels-1, num_classes=1)
         classifier = classifier.to(device=DEVICE, dtype=dtype)
         classifier.load_state_dict(classifier_state_dict)
         classifier.eval()
     else:
         classifier = None
-
-    env = Environment(img_path,
-                    radius=patch_radius,
-                    step_size=step_size,
-                    step_width=step_width,
-                    max_len=10000,
-                    alpha=alpha,
-                    beta=beta,
-                    friction=friction,
-                    classifier=classifier)
     
-    in_channels = 4
+    rng = np.random.default_rng(rng_seed)
+    # Create dataset
+    dataset = Dataset(data_dir=data_dir, rng=rng)
+    # Create dataloader
+    dataloader = DataLoader(dataset=dataset, complexity=start_complexity, rng=rng)
+
+    # Create environment
+    env = NeuronTrackingEnvironment(
+        dataloader=dataloader,
+        radius=patch_radius,
+        step_size=step_size,
+        step_width=step_width,
+        max_len=1000,
+        alpha=alpha,
+        beta=beta,
+        friction=friction,
+        repeat_starts=repeat_starts,
+        section_masking=section_masking,
+        branching=branching,
+        classifier=classifier
+    )
+    
     input_size = 2*patch_radius+1
     init_temperature = 0.005
     actor = ConvNet(chin=in_channels, chout=4)
@@ -125,36 +149,62 @@ def main():
     Q1 = Q1.to(device=DEVICE,dtype=dtype)
     Q2 = ConvNet(chin=in_channels+3,chout=1)
     Q2 = Q2.to(device=DEVICE,dtype=dtype)
-    Q1_target = ConvNet(chin=7,chout=1)
+    Q1_target = ConvNet(chin=in_channels+3,chout=1)
     Q1_target = Q1_target.to(device=DEVICE,dtype=dtype)
-    Q2_target = ConvNet(chin=7,chout=1)
+    Q2_target = ConvNet(chin=in_channels+3,chout=1)
     Q2_target = Q2_target.to(device=DEVICE,dtype=dtype)
 
+    log_alpha = torch.log(torch.tensor(init_temperature).to(DEVICE))
+    log_alpha.requires_grad = True
+
     if "sac_weights" in params:
         sac_path = params["sac_weights"]
-        state_dicts = torch.load(sac_path, weights_only=True)
+        state_dicts = torch.load(sac_path)#, weights_only=True)
         actor.load_state_dict(state_dicts["policy_state_dict"])
         Q1.load_state_dict(state_dicts["Q1_state_dict"])
         Q2.load_state_dict(state_dicts["Q2_state_dict"])
+        
+        # Load target networks if available
+        if "Q1_target_state_dict" in state_dicts:
+            Q1_target.load_state_dict(state_dicts["Q1_target_state_dict"])
+        else:
+            Q1_target.load_state_dict(Q1.state_dict())
+            
+        if "Q2_target_state_dict" in state_dicts:
+            Q2_target.load_state_dict(state_dicts["Q2_target_state_dict"])
+        else:
+            Q2_target.load_state_dict(Q2.state_dict())
+            
+    else:
+        Q1_target.load_state_dict(Q1.state_dict())
+        Q2_target.load_state_dict(Q2.state_dict())
 
-    Q1_target.load_state_dict(Q1.state_dict())
-    Q2_target.load_state_dict(Q2.state_dict())
-
-    log_alpha = torch.log(torch.tensor(init_temperature).to(DEVICE))
-    log_alpha.requires_grad = True
+    # Initialize optimizers
     Q1_optimizer = AdamW(Q1.parameters(), lr=lr)
     Q2_optimizer = AdamW(Q2.parameters(), lr=lr)
     actor_optimizer = AdamW(actor.parameters(), lr=lr)
     log_alpha_optimizer = Adam([log_alpha], lr=lr)
 
-    criterion = torch.nn.MSELoss()
+    # Load optimizer states if available
+    if "sac_weights" in params:
+        if "Q1_optimizer_state_dict" in state_dicts:
+            Q1_optimizer.load_state_dict(state_dicts["Q1_optimizer_state_dict"])
+            
+        if "Q2_optimizer_state_dict" in state_dicts:
+            Q2_optimizer.load_state_dict(state_dicts["Q2_optimizer_state_dict"])
+            
+        if "actor_optimizer_state_dict" in state_dicts:
+            actor_optimizer.load_state_dict(state_dicts["actor_optimizer_state_dict"])
+
     memory = PrioritizedReplayBuffer(100000, obs_shape=(in_channels,input_size,input_size,input_size), action_shape=(3,), alpha=0.8)
 
+    logdir = script_path.parent.parent / "logs" / name
+    os.makedirs(logdir, exist_ok=True)
     sac.train(env, actor, Q1, Q2, Q1_target, Q2_target, log_alpha,
           actor_optimizer, Q1_optimizer, Q2_optimizer, log_alpha_optimizer,
-          memory, target_entropy, batch_size, gamma, tau, outdir, name,
-          show_states=True, save_snapshots=False, update_after=256,
-          updates_per_step=1, update_every=1, n_episodes=n_episodes, n_trials=5)
+          memory, target_entropy, batch_size, gamma, tau, outdir, logdir,
+          name, show=True, pause_after_episode=False, show_live=False,
+          update_after=256, updates_per_step=1, update_every=1, n_episodes=n_episodes)
     
     print("Done!")
     
diff --git a/bin/save_spherical_patches.py b/bin/save_spherical_patches.py
new file mode 100644
index 0000000..366a5bb
--- /dev/null
+++ b/bin/save_spherical_patches.py
@@ -0,0 +1,38 @@
+import argparse
+from datetime import datetime
+import numpy as np
+import os
+from pathlib import Path
+import sys
+import torch
+
+sys.path.append(str(Path(__file__).parents[1]))
+from data_prep import load, collect
+
+DATE = datetime.now().strftime("%m-%d-%y")
+
+def main():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-s', '--samples', type=str, help='Path to samples_points.npy file (contains swc files).')
+    parser.add_argument('-i', '--images', type=str, help='Path to images directory.')
+    parser.add_argument('-o','--out', type=str, help="Path to output directory.")
+
+    args = parser.parse_args()
+    sample_points = args.samples
+    img_dir = args.images
+    out_dir = args.out
+
+    if not os.path.exists(os.path.join(out_dir, 'observations')):
+        os.makedirs(os.path.join(out_dir, 'observations'), exist_ok=True)
+
+    sample_points = np.load(sample_points, allow_pickle=True)
+    sample_points = sample_points.item()
+    radii = torch.arange(6,55,6)
+    collect.save_spherical_patches(sample_points, img_dir, out_dir, radii)
+    
+    return
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/bin/setup_sac_train.py b/bin/setup_sac_train.py
new file mode 100644
index 0000000..1120d53
--- /dev/null
+++ b/bin/setup_sac_train.py
@@ -0,0 +1,206 @@
+#!/usr/bin/env python3
+
+""" Setup input directory for training SAC neuron tracking model.
+"""
+
+import argparse
+import json
+import numpy as np
+from pathlib import Path
+import shutil
+import sys
+import tifffile as tf
+from tqdm import tqdm
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+from data_prep import draw, load, tree
+
+def parse_args():
+    """ Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Setup input directory for training SAC neuron tracking model.")
+    parser.add_argument("--swc_dir", type=str, required=True, help="Path to the SWC directory.")
+    parser.add_argument("--output_dir", type=str, required=True, help="Path to the output directory.")
+    parser.add_argument("--image_dir", type=str, required=False, help="Path to the image directory (contains tiffs).")
+    parser.add_argument("--save_seeds", action="store_true", help="Save seed points.")
+    parser.add_argument("--save_branches", action="store_true", help="Save branch points.")
+    parser.add_argument("--save_density", action="store_true", help="Save neuron density maps as compressed TIFF files (requires image directory).")
+    parser.add_argument("--save_section_labels", action="store_true", help="Save section labels.")
+    parser.add_argument("--random_seeds", action="store_true", help="Use random seeds from SWC.")
+    parser.add_argument("--copy_image", action="store_true", help="Copy image files to output directory (requires image directory).")
+    parser.add_argument("--remove_soma", action="store_true", help="Remove soma from SWC.")
+    parser.add_argument("--sync", action="store_true", help="Sync with existing output.")
+    parser.add_argument("--use_symlinks", action="store_true", help="Use symlinks for output files.")
+    return parser.parse_args()
+
+
+def setup(
+    image_dir,
+    swc_dir,
+    output_dir,
+    save_seeds,
+    save_branches,
+    save_density,
+    save_section_labels,
+    random_seeds,
+    copy_image,
+    sync,
+    use_symlinks,
+    remove_soma
+):
+    """Set up input directory for training SAC neuron tracking model.
+    
+    Args:
+        image_dir: Path to the image directory containing TIFF files.
+        swc_dir: Path to the SWC directory.
+        output_dir: Path to the output directory.
+        save_seeds: Whether to save seed points.
+        save_branches: Whether to save branch points.
+        save_density: Whether to save neuron density maps.
+        save_section_labels: Whether to save section labels.
+        random_seeds: Whether to use random seeds from SWC.
+        copy_image: Whether to copy image files to output directory.
+        sync: Whether to sync with existing output.
+        use_symlinks: Whether to use symlinks for output files.
+        remove_soma: Whether to remove soma from SWC.
+    """
+    tif_files = []
+    swc_dir = Path(swc_dir)
+    swc_files = sorted([f for f in swc_dir.rglob("*.swc")])
+    
+    if image_dir:
+        image_dir = Path(image_dir)
+        tif_files = [f for f in image_dir.rglob("*image.tif")]
+        if not tif_files:
+            tif_files = [f for f in image_dir.rglob("*.tif")]
+        if not tif_files:
+            raise FileNotFoundError(f"No .tif files found in {image_dir}")
+
+    output_dir = Path(output_dir)
+    if not output_dir.exists():
+        print(f"Creating output directory: {output_dir}")
+        output_dir.mkdir(parents=True)
+
+    swc_lists = []
+    sections_list = []
+    
+    for swc_file in tqdm(swc_files, desc="Processing SWC files"):
+        fname = swc_file.stem
+        file_dir = output_dir / fname
+        seeds_out = file_dir / f"{fname}_seeds.txt"
+        branches_out = file_dir / f"{fname}_branches.txt"
+        density_out = file_dir / f"{fname}_density.tif"
+        image_out = file_dir / f"{fname}_image.tif"
+        section_labels_out = file_dir / f"{fname}_sections.tif"
+        section_graph_out = file_dir / f"{fname}_section_graph.json"
+
+        if sync:
+            save_seeds_ = save_seeds and not seeds_out.exists()
+            save_branches_ = save_branches and not branches_out.exists()
+            save_density_ = save_density and not density_out.exists()
+            save_section_labels_ = save_section_labels and not section_labels_out.exists()
+            copy_image_ = copy_image and not image_out.exists()
+            if not any((save_seeds_, save_branches_, save_density_, copy_image_)):
+                continue
+        else:
+            save_seeds_ = save_seeds
+            save_branches_ = save_branches
+            save_density_ = save_density
+            save_section_labels_ = save_section_labels
+            copy_image_ = copy_image
+
+        # Load the SWC file
+        swc_list = load.swc(swc_file, verbose=False)
+        seeds = np.array(swc_list[0][4:1:-1])
+        
+        if remove_soma:
+            swc_list, seeds = tree.remove_soma(swc_list, max_radius=7.0)
+            seeds = seeds[:, ::-1]  # Reorder seeds to match image format (z, y, x)
+        
+        if random_seeds:
+            random_seed_ids = np.random.choice(len(swc_list), 10)
+            # new_seeds = np.array(swc_list)[random_seed_ids][:, 4:1:-1]
+            # seeds = np.concatenate((seeds, new_seeds), 0)
+            seeds = np.array(swc_list)[random_seed_ids][:, 4:1:-1]
+
+        # Parse the SWC file to get sections and section graph
+        sections, section_graph = load.parse_swc(swc_list)
+        # Get branches list from sections_graph
+        # This returns reordered coordinates, i.e. xyz -> zyx.
+        branches, terminals = load.get_critical_points(swc_list, sections)
+
+        swc_lists.append(swc_list)
+        sections_list.append(sections)
+
+        if save_branches_:
+            branches_out.parent.mkdir(parents=True, exist_ok=True)
+            with open(branches_out, 'w') as f:
+                for branch_point in branches:
+                    # Convert the branch points coordinates to string and write to file
+                    f.write(f"{branch_point[0]} {branch_point[1]} {branch_point[2]}\n")
+
+        if save_seeds_:
+            seeds_out.parent.mkdir(parents=True, exist_ok=True)
+            with open(seeds_out, 'w') as f:
+                for seed in seeds:
+                    # Convert the seed points coordinates to string and write to file
+                    f.write(f"{round(seed[0])} {round(seed[1])} {round(seed[2])}\n")
+
+        if save_density_ or save_section_labels_:
+            if not tif_files:
+                raise ValueError("Image directory must be provided to save density maps or section labels.")
+            matching_files = [f for f in tif_files if fname == f.stem.split('_image')[0]]
+            if not matching_files:
+                matching_files = [f for f in tif_files if fname == f.stem]
+            if not matching_files:
+                print(f"Warning: No matching image file found for {fname}")
+                continue
+
+            tif_img = tf.imread(matching_files[0])
+            shape = tif_img.shape
+            del tif_img
+
+            if save_section_labels_:
+                renderer = draw.NeuronRenderer()
+                labels = renderer.draw_section_labels(sections, shape=shape, width=5.0)
+                section_labels_out.parent.mkdir(parents=True, exist_ok=True)
+                tf.imwrite(section_labels_out, labels.data.numpy(), compression='zlib')
+                with open(section_graph_out, 'w') as f:
+                    json.dump(section_graph, f, indent=4)
+
+            if save_density_:
+                renderer = draw.NeuronRenderer()
+                config = draw.DrawingConfig(width=3.0, rgb=False)
+                density = renderer.draw_neuron(sections, shape=shape, config=config)
+                density_out.parent.mkdir(parents=True, exist_ok=True)
+                tf.imwrite(density_out, density.data.numpy(), compression='zlib')
+
+        if copy_image_:
+            if not tif_files:
+                raise ValueError("Image directory must be provided to copy image files.")
+            # Find matching image file
+            matching_files = [f for f in tif_files if fname == f.stem.split('_image')[0]]
+            if not matching_files:
+                matching_files = [f for f in tif_files if fname == f.stem]
+            if not matching_files:
+                print(f"Warning: No matching image file found for {fname}")
+                continue
+                
+            tif_file = matching_files[0]
+            file_dir.mkdir(parents=True, exist_ok=True)
+            dest_file = file_dir / f"{fname}_image.tif"
+            
+            if use_symlinks:
+                if dest_file.exists() or dest_file.is_symlink():
+                    dest_file.unlink()
+                dest_file.symlink_to(tif_file)
+            else:
+                shutil.copy(tif_file, dest_file)
+
+def main():
+    args = parse_args()
+    setup(args.image_dir, args.swc_dir, args.output_dir, args.save_seeds,
+          args.save_branches, args.save_density, args.save_section_labels,
+          args.random_seeds, args.copy_image, args.sync, args.use_symlinks, args.remove_soma)
+
+if __name__ == "__main__":
+    main()
diff --git a/bin/setup_sac_train_v1.py b/bin/setup_sac_train_v1.py
new file mode 100644
index 0000000..e5474b1
--- /dev/null
+++ b/bin/setup_sac_train_v1.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+
+""" Setup input directory for training SAC neuron tracking model.
+"""
+
+import argparse
+import json
+import numpy as np
+from pathlib import Path
+import shutil
+import sys
+import tifffile as tf
+from tqdm import tqdm
+
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+from data_prep import draw, load, tree, save
+from neurotrack.data import DataGenerator, DrawingComplexityConfig
+
+def parse_args():
+    """ Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Setup input directory for training SAC neuron tracking model.")
+    parser.add_argument("--swc_dir", type=str, required=False, help="Path to the SWC directory.")
+    parser.add_argument("--output_dir", type=str, required=True, help="Path to the output directory.")
+    parser.add_argument("--image_dir", type=str, required=False, help="Path to the image directory (contains tiffs).")
+    parser.add_argument("--remove_soma", action="store_true", help="Remove soma from SWC.")
+    parser.add_argument("--rng_seed", type=int, default=1, help="Random seed for data generator.")
+    parser.add_argument("--complexity_range", type=float, nargs=2, default=(0.0, 1.0), help="Range of drawing complexities to use.")
+    parser.add_argument("--subtrees_per_swc", type=int, default=1, help="Number of subtrees to draw per SWC.")
+    parser.add_argument("--dataset_size", type=int, default=100, help="Number of synthetic neurons to generate if no SWC directory is provided.")
+    return parser.parse_args()
+
+
+def setup(
+    image_dir,
+    swc_dir,
+    output_dir,
+    remove_soma,
+    rng_seed,
+    complexity_range=(0.0, 1.0),
+    subtrees_per_swc=1,
+    dataset_size=100
+):
+    """Set up input directory for training SAC neuron tracking model.
+    
+    Args:
+        image_dir: Path to the image directory containing TIFF files.
+        swc_dir: Path to the SWC directory.
+        output_dir: Path to the output directory.
+        save_seeds: Whether to save seed points.
+        save_branches: Whether to save branch points.
+        save_density: Whether to save neuron density maps.
+        save_section_labels: Whether to save section labels.
+        random_seeds: Whether to use random seeds from SWC.
+        copy_image: Whether to copy image files to output directory.
+        sync: Whether to sync with existing output.
+        use_symlinks: Whether to use symlinks for output files.
+        remove_soma: Whether to remove soma from SWC.
+    """
+    if remove_soma:
+        for swc_file in list(Path(swc_dir).rglob("*.swc")):
+            swc_list = load.swc(swc_file)
+            swc_list, seeds = tree.remove_soma(swc_list, max_radius=7.0)
+            # setup temporary directory for soma-removed SWCs
+            temp_swc_dir = Path(output_dir) / "temp_swc"
+            temp_swc_dir.mkdir(parents=True, exist_ok=True)
+            save.write_swc(swc_list, temp_swc_dir / swc_file.name, exist_ok=True)
+        swc_dir = str(temp_swc_dir)
+
+    complexity_config = DrawingComplexityConfig()
+    rng = np.random.default_rng(rng_seed)
+    data_generator = DataGenerator(cache_dir=output_dir, complexity_config=complexity_config, rng=rng)
+    data_generator.generate_data(output_dir, subtrees_per_swc=subtrees_per_swc, complexity_range=complexity_range, swc_dir=swc_dir, img_dir=image_dir, dataset_size=dataset_size)
+
+    if remove_soma:
+        # remove temporary directory
+        shutil.rmtree(temp_swc_dir)
+
+def main():
+    args = parse_args()
+    setup(args.image_dir, args.swc_dir, args.output_dir, args.remove_soma, args.rng_seed, args.complexity_range, args.subtrees_per_swc, args.dataset_size)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/bin/simulate_neurons.py b/bin/simulate_neurons.py
index 8ea4e3e..6c35ea4 100644
--- a/bin/simulate_neurons.py
+++ b/bin/simulate_neurons.py
@@ -12,10 +12,13 @@
 import os
 from pathlib import Path
 import sys
+import tifffile as tf
 import torch
 from tqdm import tqdm
 
-sys.path.append(str(Path(__file__).parents[1]))
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent  # Go up two levels
+sys.path.append(str(parent_dir))
 from data_prep import generate, draw, load
 
 
@@ -29,52 +32,58 @@ def main():
     ---------
     -i, --input : str
         Path to input JSON file containing configuration parameters.
-    -h, --help : str
-        Display help string. 
     """
     
     help_string = """
-Generate and save simulated neuron images, either from existing neuron swc files or
-by generating new simulated neuron trees based on the provided parameters.
-
-Takes one argument, `--input` which is a JSON file listing the following configuration parameters:  
-
-JSON Configuration Parameters
------------------------------
-labels_dir : str, optional
-    Directory containing SWC files of existing neuron trees. If not provided, neuron trees will be simulated.
-out : str
-    Output directory to save the generated neuron images.
-width : int
-    Width of the generated neuron images in voxels.
-random_contrast : bool
-    Whether to apply random contrast to the neuron images.
-dropout : float
-    Density of intensity dropout points for the neuron images.
-random_brightness : bool
-    Whether to apply random signal to noise ratio to the neuron images.
-noise : float
-    Amount of noise to add to the neuron images.
-binary : bool
-    Whether to draw the neuron images as a binary mask.
-seed : int
-    Seed for the random number generator.
-count : int, optional
-    Number of neuron trees to simulate. Required if `labels_dir` is not provided.
-size : int, optional
-    Size of the simulated neuron trees. Required if `labels_dir` is not provided.
-length : int, optional
-    Length of the simulated neuron trees. Required if `labels_dir` is not provided.
-stepsize : float, optional
-    Step size for the simulated neuron trees. Required if `labels_dir` is not provided.
-uniform_len : bool, optional
-    Whether to use uniform length for the simulated neuron trees. Required if `labels_dir` is not provided.
-kappa : float, optional
-    Kappa parameter for the simulated neuron trees. Required if `labels_dir` is not provided.
-random_start : bool, optional
-    Whether to use random starting points for the simulated neuron trees. Required if `labels_dir` is not provided.
-branches : int, optional
-    Number of branches for the simulated neuron trees. Required if `labels_dir` is not provided.
+    Generate and save simulated neuron images, either from existing neuron swc files or
+    by generating new simulated neuron trees based on the provided parameters.
+
+    Takes one argument, `--input` which is a JSON file listing the following configuration parameters:  
+
+    JSON Configuration Parameters
+    -----------------------------
+    labels_dir : str, optional
+        Directory containing SWC files of existing neuron trees. If not provided, neuron trees will be simulated.
+    out : str
+        Output directory to save the generated neuron images.
+    width : int
+        Width of the generated neuron images in voxels.
+    random_contrast : bool
+        Whether to apply random contrast to the neuron images.
+    dropout : float
+        Density of intensity dropout points for the neuron images.
+    random_brightness : bool
+        Whether to apply random signal to noise ratio to the neuron images.
+    noise : float
+        Amount of noise to add to the neuron images.
+    random_noise : bool, optional
+        Generate noise with a random amplitude relative to the maximum intensity value in the range [0.05, noise).
+    binary : bool
+        Whether to draw the neuron images as a binary mask.
+    rgb : bool
+        Whether to draw the neuron images in RGB format.
+    seed : int
+        Seed for the random number generator.
+    sync : bool, optional
+        Whether to process only the neuron trees that are not already in the output directory.
+    count : int, optional
+        Number of neuron trees to simulate. Required if `labels_dir` is not provided.
+    size : int, optional
+        Size of the simulated neuron trees. Required if `labels_dir` is not provided.
+    length : int, optional
+        Length of the simulated neuron trees. Required if `labels_dir` is not provided.
+    stepsize : float, optional
+        Step size for the simulated neuron trees. Required if `labels_dir` is not provided.
+    random_len : bool, optional
+        Whether to use random length for the simulated neuron trees. Required if `labels_dir` is not provided.
+    random_width : bool, optional
+        Whether to use random width for the simulated neuron trees. Required if `labels_dir` is not provided.
+    kappa : float, optional
+        Kappa parameter for the simulated neuron trees. Required if `labels_dir` is not provided.
+    random_start : bool, optional
+        Whether to use random starting points for the simulated neuron trees. Required if `labels_dir` is not provided.
+    branches : int, optional
+        Number of branches for the simulated neuron trees. Required if `labels_dir` is not provided.
     """
     parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter)
     parser.add_argument('-i', '--input', type=argparse.FileType('r'), help=help_string)
@@ -93,17 +102,25 @@ def main():
     noise = parameters["noise"]
     binary = parameters["binary"]
     seed = parameters["seed"]
+    random_noise = parameters["random_noise"] if "random_noise" in parameters else False
+    sync = parameters["sync"] if "sync" in parameters else False
+    rgb = parameters["rgb"] if "rgb" in parameters else True
     rng = np.random.default_rng(seed)
+    adjust=False
 
     if labels_dir is not None: # Load existing neuron trees as swc files
+        adjust=True
         print(f"Loading existing neuron trees as swc files...\n"
               f"    labels_dir: {labels_dir}")
         files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], "*.swc"))]
+        if sync:
+            files = [f for f in files if not os.path.exists(os.path.join(out, f.split('/')[-1].split('.')[0]))]
+        print(f"    Found {len(files)} neuron trees to process.")
         swc_lists = []
         fnames = []
         for f in files:
             swc_lists.append(load.swc(f))
-            fnames.append(f.split('/')[-1].split('.swc')[0])
+            fnames.append(f.split('/')[-1].split('.')[0])
         print("done")
 
     else: # Generate simulated neuron trees
@@ -111,7 +128,10 @@ def main():
         size = (parameters["size"],)*3
         length = parameters["length"]
         stepsize = parameters["stepsize"]
-        uniform_len = parameters["uniform_len"]
+        random_len = parameters["random_len"]
+        random_width = parameters["random_width"]
+        if random_width:
+            width = None
         kappa = parameters["kappa"]
         random_start = parameters["random_start"]
         branches = parameters["branches"]
@@ -120,7 +140,8 @@ def main():
               f"    size: {size}\n"
               f"    length: {length}\n"
               f"    step size: {stepsize}\n"
-              f"    uniform_len: {uniform_len}\n"
+              f"    random_len: {random_len}\n"
+              f"    random_width: {random_width}\n"
               f"    kappa: {kappa}\n"
               f"    random_start: {random_start}\n"
               f"    branches: {branches}")
@@ -132,16 +153,22 @@ def main():
                                     length=length,
                                     step_size=stepsize,
                                     kappa=kappa,
-                                    uniform_len=uniform_len,
+                                    random_len=random_len,
+                                    random_width=random_width,
                                     random_start=random_start,
                                     rng=rng,
                                     num_branches=branches) # make simulated neuron paths.
             swc_lists.append(swc_list)
-            fnames.append(f"img_{i}")
+            if sync:
+                num_existing = len([f for f in os.listdir(out) if f.startswith(f"img_")])
+                n = num_existing + i
+            else:
+                n = i
+            fnames.append(f"img_{n}")
         print("done\n")
 
     print(
-        f"Drawing neuron images and saving to {out}..."
+        f"Drawing neuron images and saving to {out}...\n"
         f"    width: {width}\n"
         f"    random_contrast: {random_contrast}\n"
         f"    random_brightness: {random_brightness}\n"
@@ -152,6 +179,10 @@ def main():
     )
     
     for i in tqdm(range(len(swc_lists))):
+
+        if not os.path.exists(os.path.join(out, f"{fnames[i]}")):
+            os.makedirs(os.path.join(out, f"{fnames[i]}"), exist_ok=True)
+            
         color = np.array([1.0, 1.0, 1.0])
         background = np.array([0., 0., 0.])
         if random_contrast:
@@ -159,20 +190,46 @@ def main():
             color /= np.linalg.norm(color)
             background = np.random.rand(3)
             background = background / np.linalg.norm(background) * 0.01
-        swc_data = draw.neuron_from_swc(swc_lists[i],
-                                        width=width,
-                                        noise=noise,
-                                        adjust=True,
-                                        neuron_color=color,
-                                        background_color=background,
-                                        random_brightness=random_brightness,
-                                        dropout=dropout,
-                                        binary=binary) # Use simulated paths to draw the image.
         
-        torch.save(swc_data, os.path.join(out, f"{fnames[i]}.pt"))
+        if random_noise:
+            noise_ = np.random.random() * (noise - 0.05) + 0.05 # min: 0.05, max: noise
+        else:
+            noise_ = noise
+        renderer = draw.NeuronRenderer()
+        config = draw.DrawingConfig(
+            width=width,
+            rgb=rgb,
+            neuron_color=tuple(color.tolist()),
+            background_color=tuple(background.tolist()),
+        )
+        swc_data = renderer.neuron_from_swc(
+            swc_lists[i],
+            config=config,
+            shape=None,
+            dropout=dropout,
+            adjust=adjust,
+        )  # Use simulated paths to draw the image.
+        
+        # torch.save(swc_data, os.path.join(out, f"{fnames[i]}.pt"))
+        if not os.path.exists(os.path.join(out, f"{fnames[i]}")):
+            os.makedirs(os.path.join(out, f"{fnames[i]}"), exist_ok=True)
+        tf.imwrite(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_image.tif"), swc_data['image'].numpy().astype(np.float32), compression='zlib')
+        tf.imwrite(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_density.tif"), swc_data['neuron_density'].numpy().astype(np.float32), compression='zlib')
+        tf.imwrite(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_sections.tif"), swc_data['section_labels'].numpy().astype(np.float32), compression='zlib')
+        # tf.imwrite(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_branches.tif"), swc_data['branch_mask'].numpy().astype(np.float32), compression='zlib')
+        with open(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_branches.txt"), 'w') as f:
+            for branch_point in swc_data['branches']:
+                # Convert the branch points coordinates to string and write to file
+                f.write(f"{branch_point[0]} {branch_point[1]} {branch_point[2]}\n")
+        with open(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_seeds.txt"), 'w') as f:
+            for seed_point in swc_data['seeds']:
+                # Convert the seed point coordinates to string and write to file
+                f.write(f"{seed_point[0]} {seed_point[1]} {seed_point[2]}\n")
+        with open(os.path.join(out, f"{fnames[i]}", f"{fnames[i]}_section_graph.json"), 'w') as f:
+                json.dump(swc_data['graph'], f)
 
     print("done")
 
 
 if __name__ == "__main__":
-    main(parser)
\ No newline at end of file
+    main()
\ No newline at end of file
diff --git a/configs/sac_inference_neuromorpho_with_artifacts.json b/configs/sac_inference_neuromorpho_with_artifacts.json
new file mode 100644
index 0000000..91965b2
--- /dev/null
+++ b/configs/sac_inference_neuromorpho_with_artifacts.json
@@ -0,0 +1,9 @@
+{
+    "img_path": "/home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/neuromorpho_with_artifacts",
+    "outdir": "/home/brysongray/data/neurotrack_data/inference_outputs/neuromorpho_with_artifacts_inference_output_test/",
+    "name": "neuromorpho_with_artifacts",
+    "sac_weights": "/home/brysongray/data/neurotrack_data/model_weights/sac_weights/neuromorpho_with_artifacts_training_outputs/model_state_dicts_neuromorpho_with_artifacts_06_17_25.pt",
+    "classifier_weights": "/home/brysongray/data/neurotrack_data/model_weights/classifier_weights/neuromorpho_branch_classifier_with_artifacts_06-16-25/resnet_classifier_06-16-25_checkpoint-90.pt",
+    "step_size": 2.0,
+    "step_width": 4.0
+}
\ No newline at end of file
diff --git a/configs/sac_inference_simulated.json b/configs/sac_inference_simulated.json
deleted file mode 100644
index f28aae3..0000000
--- a/configs/sac_inference_simulated.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-    "img_path": "/home/brysongray/data/simulated_neurons/3d_no_artifacts_b-1",
-    "outdir": "sac_inference_outputs",
-    "name": "simulated_dataset",
-    "classifier_weights" : "/home/brysongray/tractography/pretrained_models/neurom_no_artifacts_classifier/resnet_classifier_01-25-25_checkpoint-12.pt",
-    "sac_weights": "/home/brysongray/tractography/outputs_test/model_state_dicts_3d_no_artifacts_b-0_01-23-25_it-6000.pt",
-    "step_size": 2.0,
-    "step_width": 3.0
-}
\ No newline at end of file
diff --git a/configs/sac_inference_straight_no_artifacts.json b/configs/sac_inference_straight_no_artifacts.json
new file mode 100644
index 0000000..0366c35
--- /dev/null
+++ b/configs/sac_inference_straight_no_artifacts.json
@@ -0,0 +1,8 @@
+{
+    "img_path": "/home/brysongray/data/neurotrack_data/simulated_neurons/de-novo/straight_no_artifacts",
+    "outdir": "/home/brysongray/data/neurotrack_data/inference_outputs/straight_no_artifacts_inference_output/",
+    "name": "straight_no_artifacts",
+    "sac_weights": "/home/brysongray/data/neurotrack_data/model_weights/sac_weights/straight_no_artifacts_sac_training_output/model_state_dicts_straight_no_artifacts_2_06-04-25.pt",
+    "step_size": 2.0,
+    "step_width": 3.0
+}
\ No newline at end of file
diff --git a/configs/simulate_neurons_de-novo.json b/configs/simulate_neurons_de-novo.json
index 0cbf543..06134eb 100644
--- a/configs/simulate_neurons_de-novo.json
+++ b/configs/simulate_neurons_de-novo.json
@@ -1,13 +1,14 @@
 {   
-    "out": "/home/brysongray/data/simulated_neurons/3d_no_artifacts_b-1",
-    "count": 10,
-    "size": 101,
-    "length": 20,
+    "out": "/home/brysongray/data/neurotrack_data/simulated_neurons/de-novo/curves_without_artifacts_grayscale/",
+    "count": 1000,
+    "size": 151,
+    "length": 50,
     "stepsize": 3.0,
     "width": 3,
-    "noise": 0.00,
-    "uniform_len": 0,
-    "kappa": 20.0,
+    "noise": 0.05,
+    "random_len": 1,
+    "random_width": 1,
+    "kappa": 15.0,
     "random_start": 1,
     "binary": 0,
     "branches": 0,
diff --git a/configs/simulate_neurons_from_swc.json b/configs/simulate_neurons_from_swc.json
index 7191de6..641156c 100644
--- a/configs/simulate_neurons_from_swc.json
+++ b/configs/simulate_neurons_from_swc.json
@@ -1,11 +1,12 @@
 {   
-    "labels_dir": "/home/brysongray/data/neuromorpho_sub1",
-    "out": "/home/brysongray/data/simulated_neurons/neuromorpho_sub1_with_artifacts",
+    "labels_dir": "/home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/swc_trees/neuromorpho",
+    "out": "/home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/neuromorpho_with_artifacts",
     "width": 3,
     "noise": 0.05,
     "binary": 0,
     "random_contrast": 1,
     "dropout": 1,
     "random_brightness": 1,
-    "seed": 7
+    "seed": 7,
+    "sync": 0
 }
\ No newline at end of file
diff --git a/configs/train_sac_neuromorpho.json b/configs/train_sac_neuromorpho.json
index 6cecaee..29e5fd0 100644
--- a/configs/train_sac_neuromorpho.json
+++ b/configs/train_sac_neuromorpho.json
@@ -1,20 +1,22 @@
 {
-    "img_path": "/home/brysongray/tractography/data_prep/training_data/neuromorpho_with_artifacts",
-    "outdir": "/home/brysongray/tractography/sac_training_output_test",
-    "name": "neurom_dataset",
-    "classifier_weights" : "/home/brysongray/tractography/pretrained_models/neurom_with_artifacts_classifier/resnet_classifier_01-24-25_checkpoint-16.pt",
-    "sac_weights": "/home/brysongray/tractography/outputs_test/model_state_dicts_neurom_with_artifacts_01-25-25.pt",
+    "img_path": "/home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/neuromorpho_with_artifacts",
+    "outdir": "/home/brysongray/data/neurotrack_data//model_weights/sac_weights/neuromorpho_with_artifacts_training_outputs",
+    "name": "neuromorpho_repeat_starts",
+    "sac_weights": "/home/brysongray/data/neurotrack_data/model_weights/sac_weights/neuromorpho_with_artifacts_training_outputs/model_state_dicts_neuromorpho_with_artifacts_06_17_25.pt",
+    "classifier_weights": "/home/brysongray/data/neurotrack_data/model_weights/classifier_weights/neuromorpho_branch_classifier_with_artifacts_06-16-25/resnet_classifier_06-16-25_checkpoint-90.pt",
     "n_seeds": 1,
     "step_size": 2.0,
     "step_width": 3.0,
     "batch_size": 256,
     "tau": 0.005,
     "gamma": 0.1,
-    "lr": 0.001,
+    "learning_rate": 0.001,
     "alpha": 1.0,
     "beta": 0.2,
     "friction": 0.0,
-    "n_episodes": 50,
+    "n_episodes": 2000,
     "init_temperature": 0.005,
-    "target_entropy": 0.0
+    "target_entropy": 0.0,
+    "repeat_starts": 1,
+    "section_masking": 0
 }
\ No newline at end of file
diff --git a/configs/train_sac_simulated_with_branching.json b/configs/train_sac_simulated_with_branching.json
new file mode 100644
index 0000000..ab4a264
--- /dev/null
+++ b/configs/train_sac_simulated_with_branching.json
@@ -0,0 +1,22 @@
+{
+    "img_path": "/home/brysongray/data/neurotrack_data/simulated_neurons/de-novo/curves_with_artifacts_b-1",
+    "outdir": "/home/brysongray/data/neurotrack_data/model_weights/sac_weights/curves_with_artifacts_b-1_training_outputs",
+    "name": "curves_with_artifacts_b-1",
+    "sac_weights": "/home/brysongray/data/neurotrack_data/model_weights/sac_weights/curves_with_artifacts_training_outputs/model_state_dicts_curves_with_artifacts_06-04-25.pt",
+    "classifier_weights": "/home/brysongray/data/neurotrack_data/model_weights/classifier_weights/neurom_with_artifacts_classifier/resnet_classifier_01-24-25_checkpoint-16.pt",
+    "n_seeds": 1,
+    "step_size": 2.0,
+    "step_width": 3.0,
+    "batch_size": 256,
+    "tau": 0.005,
+    "gamma": 0.1,
+    "learning_rate": 0.001,
+    "alpha": 1.0,
+    "beta": 0.2,
+    "friction": 0.0,
+    "n_episodes": 2000,
+    "init_temperature": 0.005,
+    "target_entropy": 0.0,
+    "repeat_starts": 1,
+    "section_masking": 0
+}
diff --git a/data_prep/collect.py b/data_prep/collect.py
index c57d856..3684eb5 100644
--- a/data_prep/collect.py
+++ b/data_prep/collect.py
@@ -1,14 +1,20 @@
+from datetime import datetime
 from glob import glob
 import numpy as np
 import os
 import pandas as pd
 from pathlib import Path
 import sys
+import tifffile as tf
 import torch
+from tqdm import tqdm
 
 sys.path.append(str(Path(__file__).parent))
-import load
-from image import Image
+import load, draw
+from image import Image, extract_spherical_patch
+from interp import SphericalSampler
+
+DATE = datetime.now().strftime("%m-%d-%y")
 
 
 def swc_random_points(samples_per_file, swc_lists, file_names, adjust=False, rng=None):
@@ -40,25 +46,340 @@ def swc_random_points(samples_per_file, swc_lists, file_names, adjust=False, rng
         rng = np.random.default_rng()
     sample_points = {}
     for fname, swc_list in zip(file_names,swc_lists):
-        sections, section_graph, branches, terminals, scale = load.parse_swc_list(swc_list, adjust=adjust)
-        rand_sections = rng.choice(list(sections.keys()), size=samples_per_file)
-        points = []
-        for j in rand_sections:
-            section_flat = sections[j].flatten(0,1) # type: ignore # 
-            random_point = rng.choice(np.arange(len(section_flat)))
-            random_point = section_flat[random_point]
-            # random translation vector from normal distribution about random_point
-            translation = rng.uniform(low=0.0, high=1.0, size=(3,))*8.0 - 4.0
-            random_point += translation
-            points.append(random_point)
-        points = np.array(points)
+        if not isinstance(swc_list, np.ndarray):
+            swc_list = np.array(swc_list)
         
-        sample_points[fname] = points
-    
+        # sections, _ = load.parse_swc(swc_list)
+        # rand_sections = rng.choice(list(sections.keys()), size=samples_per_file)
+        # for j in rand_sections:
+        #     section_flat = sections[j].reshape((-1,4)) # type: ignore # 
+        #     random_point = rng.choice(np.arange(len(section_flat)))
+        #     random_point = section_flat[random_point]
+
+        random_points_ = rng.choice(swc_list[:, 2:5], size=samples_per_file, replace=True)
+        translation = rng.standard_normal(size=(samples_per_file, 3)) * 2.0
+        # translation = rng.uniform(low=0.0, high=1.0, size=(3,))*8.0 - 4.0
+        # random translation vector from normal distribution about random_point
+        # translation = rng.standard_normal(size=(3,))*2.0
+        # translation = np.concatenate((translation, [0.0]))
+        random_points = random_points_ + translation # randomly shift the points
+        # some random points may be out of bounds after translation
+        max_coords = np.max(swc_list[:, 2:5], axis=0)
+        out_of_bounds_ids = np.any(random_points < 0, axis=1) | np.any(random_points > max_coords, axis=1)
+        # where random_points_ is out of bounds, use the original random_points
+        if np.any(out_of_bounds_ids):
+            random_points[out_of_bounds_ids] = random_points_[out_of_bounds_ids]
+        # points.append(random_point)
+        # points = np.array(points)
+
+        sample_points[fname] = random_points
+
     return sample_points
 
 
-def collect_data(sample_points, image_dir, out_dir, name, date, rng=None):
+def random_points_from_mask(mask, branches, samples_per_neuron, rng=None):
+    
+    if len(branches) == 0:
+       raise Exception("Branches list must not be empty.")         
+    if rng is None:
+        rng = np.random.default_rng()
+
+    # create branch mask
+    branch_mask = Image(torch.zeros_like(mask, dtype=torch.bool))
+    for point in branches:
+        branch_mask.draw_point(point[:3], radius=point[3].item(), binary=True, value=1, channel=0)
+
+    # get a random sample of branch coordinates
+    branch_coords = np.argwhere(branch_mask.data[0])
+    branch_coords = rng.choice(branch_coords, int(samples_per_neuron/2), replace=True, axis=1)
+
+    # get a random sample of non-branch neuron coordinates
+    neuron_nonbranch = mask[0] & ~branch_mask.data[0]
+    neuron_nonbranch_coords = np.argwhere(neuron_nonbranch)
+    neuron_nonbranch_coords = rng.choice(neuron_nonbranch_coords, int(samples_per_neuron/4), replace=True, axis=1)
+
+    # background is voxels not in the neuron mask or branch mask
+    background = ~(mask[0] & branch_mask.data[0])
+    del mask
+
+    # get sample_per_neuron/4 background samples
+    # background mask has too many voxels to use argwhere. Instead, sample randomly and keep foreground coordinates
+    background_coords = []
+    shape = background.shape
+    i = 0
+    while i < int(samples_per_neuron/4):
+        # randomly sample a point in the image
+        z = rng.integers(0, shape[0])
+        y = rng.integers(0, shape[1])
+        x = rng.integers(0, shape[2])
+        # if the point is in background, add it to the list
+        if background[z, y, x]:
+            background_coords.append([z, y, x])
+            i += 1
+
+    del background
+
+    non_branch_coords = np.concatenate((background_coords, neuron_nonbranch_coords.T), axis=0)
+
+    return branch_coords.T, non_branch_coords
+
+
+def save_square_patches(sample_points, img_dir, out_dir, radius):
+    out_dir = os.path.join(out_dir, f"observations_{DATE}")
+    if not os.path.exists(out_dir):
+        os.makedirs(out_dir, exist_ok=True)
+
+    obs_id = 0
+    for fname, points in tqdm(sample_points.items(), total=len(sample_points)):
+        img_path = os.path.join(img_dir, fname)
+        img = tf.imread(img_path)
+        img = img / img.max()
+        img = Image(img[None])
+        
+        for point in points:
+            patch, _ = img.crop(torch.tensor(point), radius, pad=True, value=0.0)
+            fname_out = f"obs_{obs_id}.tif"
+            tf.imwrite(os.path.join(out_dir, fname_out), patch[0].detach().cpu().numpy(), compression='zlib')
+            obs_id += 1
+
+    return
+
+
+def save_spherical_patches(sample_points, img_dir, out_dir, radii, resolution=(180,360), batch_size=50):
+    """
+    Parameters
+    ----------
+    sample_points : dict
+        dictionary with file names as keys and Nx3 numpy arrays of coordinates as values.
+    img_dir : str
+        directory where images are stored
+    out_dir : str
+        output directory
+    radii : torch.Tensor
+        Radii of concentric spheres from which to sample the image.
+    """
+
+    obs_out = os.path.join(out_dir, f"observations_{DATE}")
+    if not os.path.exists(obs_out):
+        os.makedirs(obs_out, exist_ok=True)
+
+    permutations = [[-3,-2,-1],
+                    [-3,-1,-2],
+                    [-2,-1,-3],
+                    [-2,-3,-1],
+                    [-1,-3,-2],
+                    [-1,-2,-3]]
+    # initialize spherical sampler
+    patch_radius = int(torch.amax(radii) + 1)
+    patch_shape = (patch_radius*2+1,) * 3
+    spherical_sampler = SphericalSampler(input_shape=patch_shape, radii=radii, resolution=resolution)
+
+    obs_id = 0
+    for fname, points in tqdm(sample_points.items(), total=len(sample_points)):
+        img_path = os.path.join(img_dir, fname)
+        img = tf.imread(img_path)
+        img = img / img.max()
+        img = Image(img[None])
+        for batch in range(int(np.ceil(len(points)/batch_size))):
+            patches = []
+            for point in points[batch*batch_size:(batch+1)*batch_size]:
+                patches.append(img.crop(point,radius=patch_radius)[0])
+            patches = torch.stack(patches) # shape (N,1,D,H,W)
+            spherical_patches = []
+            for perm in permutations:
+                patch_perm = patches.permute(0,1,*perm)
+                spherical_patch = spherical_sampler.map_coordinates(patch_perm) # output shape (N,len(radii),1,H,W)
+                spherical_patches.append(spherical_patch.squeeze(2))
+            spherical_patches = torch.cat(spherical_patches, dim=1) # shape (N, len(radii)*len(permutations), H, W)
+            for j in range(len(spherical_patches)):
+                fname_out = f"obs_{obs_id}.tif"
+                tf.imwrite(os.path.join(os.path.join(out_dir, f"observations_{DATE}"), fname_out), spherical_patches[j].detach().cpu().numpy(), compression='zlib')
+                obs_id += 1
+    
+
+def save_spherical_patches_v0(img, branch_coords, non_branch_coords, out_dir, resolution=(180, 360), start_id=0, annotations=None):
+
+    obs_id = start_id
+    if annotations is None:
+        annotations = {}
+
+    # Sample spherical patches from the corresponding neuron image
+    # img = tf.imread(img_path)
+    img = img / img.max()
+    permutations = [[0,1,2],
+                    [0,2,1],
+                    [1,2,0],
+                    [1,0,2],
+                    [2,0,1],
+                    [2,1,0]]
+    # Create meshgrid for spherical coordinates
+    theta_res, phi_res = resolution
+    theta = np.linspace(0, np.pi, theta_res)
+    phi = np.linspace(0, 2*np.pi, phi_res)
+    theta_grid, phi_grid = np.meshgrid(theta, phi, indexing='ij')
+    
+    # Convert to cartesian coordinates (points on a unit sphere)
+    x = np.sin(theta_grid) * np.cos(phi_grid)
+    y = np.sin(theta_grid) * np.sin(phi_grid)
+    z = np.cos(theta_grid)
+
+    # Sample branch positive patches
+    for i in range(len(branch_coords)):
+        spherical_patches = []
+        for r in range(3,55,3):
+            for perm in permutations:
+                patch = extract_spherical_patch(img, x, y, z, branch_coords[i], radius=r, permutation=perm)
+                spherical_patches.append(patch)
+        patch = np.stack(spherical_patches, axis=0)
+        fname = f"obs_{obs_id}.pt"
+        torch.save(torch.from_numpy(patch), os.path.join(os.path.join(out_dir, "observations"), fname))
+        annotations[fname] = 1
+        obs_id += 1
+    # Sample non branch patches
+    for i in range(len(non_branch_coords)):
+        spherical_patches = []
+        for r in range(3,55,3):
+            for perm in permutations:
+                patch = extract_spherical_patch(img, non_branch_coords[i], radius=r, permutation=perm)
+                spherical_patches.append(patch)
+        patch = np.stack(spherical_patches, axis=0)
+        fname = f"obs_{obs_id}.pt"
+        torch.save(torch.from_numpy(patch), os.path.join(os.path.join(out_dir, "observations"), fname))
+        annotations[fname] = 0
+        obs_id += 1
+
+    return annotations, obs_id
+
+
+def save_coordinates_and_annotations(swc_dir, img_dir, out_dir, samples_per_neuron=100, seed=0, branch_radius_filter=None):
+    rng = np.random.default_rng(seed)
+
+    sample_points = {}
+    annotations = {}
+    swc_files = os.listdir(swc_dir)
+    swc_files = sorted(swc_files)
+    img_files = os.listdir(img_dir)
+    img_files = sorted(img_files)
+    for i in tqdm(range(len(swc_files))):
+        swc_list = load.swc(os.path.join(swc_dir, swc_files[i]), verbose=False)
+        img_name = [img_file for img_file in img_files if img_file.split('.tif')[0] == swc_files[i].split('.')[0]]
+        try:
+            img_name = img_name[0]
+        except IndexError:
+            continue
+        img_path = os.path.join(img_dir, img_name)
+        img = tf.imread(img_path)
+        shape = img.shape
+        del img
+
+        sections, sections_graph = load.parse_swc(swc_list)
+        branches_, terminals = load.get_critical_points(swc_list, sections)
+
+        # optionally filter out large branches by their radii
+        if branch_radius_filter is not None:
+            branches = branches_[branches_[:,-1] < branch_radius_filter]
+        else:
+            branches = branches_
+
+        segments = []
+        for section in sections.values():
+            segments.append(section)
+        segments = np.concatenate(segments)
+
+        renderer = draw.NeuronRenderer()
+        sections_dict = {0: segments}
+        density = renderer.draw_density(sections_dict, shape)
+        mask = renderer.draw_mask(density.data if hasattr(density, 'data') else density, threshold=5.0)
+        del density
+
+        branch_coords, non_branch_coords = random_points_from_mask(mask, branches, samples_per_neuron, rng=rng)
+        sample_points[img_name] = np.concatenate((branch_coords, non_branch_coords))
+        current_size = len(annotations)
+        for i in range(current_size, current_size + len(sample_points)*samples_per_neuron):
+            k = i - current_size < len(branch_coords)
+            annotations[f"obs_{i}.tif"] = k
+
+        # overwrite sample points and annotations after every file
+        np.save(os.path.join(out_dir, f"sample_points_{DATE}.npy"), sample_points)
+        # save annotations
+        # split into test and training data
+        name = "gold166"
+        data_permutation = torch.randperm(len(annotations))
+        test_idxs = data_permutation[:len(data_permutation)//5].tolist()
+        training_idxs = data_permutation[len(data_permutation)//5:].tolist()
+        training_annotations = {list(annotations)[i]: list(annotations.values())[i] for i in training_idxs}
+        test_annotations = {list(annotations)[i]: list(annotations.values())[i] for i in test_idxs}
+        # save
+        df = pd.DataFrame.from_dict(training_annotations, orient="index")
+        df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{DATE}_training_labels.csv"))
+        df = pd.DataFrame.from_dict(test_annotations, orient="index")
+        df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{DATE}_test_labels.csv"))
+
+    return
+
+
+def spherical_patch_dataset(swc_dir, img_dir, out_dir, samples_per_neuron=100, sync=False, seed=0):
+
+    rng = np.random.default_rng(seed)
+    obs_id = 0
+    ncomplete = 0
+    if sync:
+        existing_files = os.listdir(os.path.join(out_dir, "observations"))
+        ids = [int(f.split('.')[0].split('_')[1]) for f in existing_files]
+        obs_id = max(ids) + 1
+        ncomplete = obs_id//1000
+
+    annotations = {}
+    swc_files = os.listdir(swc_dir)
+    img_files = os.listdir(img_dir)
+    for i in tqdm(range(ncomplete, len(swc_files))):
+        swc_list = load.swc(os.path.join(swc_dir, swc_files[i]), verbose=False)
+        img_name = [img_file for img_file in img_files if img_file.split('.tif')[0] in swc_files[i]]
+        try:
+            img_name = img_name[0]
+        except IndexError:
+            continue
+        img_path = os.path.join(img_dir, img_name)
+        img = tf.imread(img_path)
+        shape = img.shape
+        # del img
+
+        sections, sections_graph = load.parse_swc(swc_list)
+        branches, terminals = load.get_critical_points(swc_list, sections)
+        if len(branches)==0:
+            continue
+        segments = []
+        for section in sections.values():
+            segments.append(section)
+        segments = np.concatenate(segments)
+
+    renderer = draw.NeuronRenderer()
+    sections_dict = {0: segments}
+    density = renderer.draw_density(sections_dict, shape)
+    mask = renderer.draw_mask(density.data if hasattr(density, 'data') else density, threshold=5.0)
+    del density
+
+    branch_coords, non_branch_coords = random_points_from_mask(mask, branches, samples_per_neuron, rng=rng)
+    annotations, obs_id = save_spherical_patches(img, branch_coords, non_branch_coords, out_dir, start_id=obs_id, annotations=annotations)
+
+    # save annotations
+    # split into test and training data
+    name = "gold166"
+    data_permutation = torch.randperm(len(annotations))
+    test_idxs = data_permutation[:len(data_permutation)//5].tolist()
+    training_idxs = data_permutation[len(data_permutation)//5:].tolist()
+    training_annotations = {list(annotations)[i]: list(annotations.values())[i] for i in training_idxs}
+    test_annotations = {list(annotations)[i]: list(annotations.values())[i] for i in test_idxs}
+    # save
+    df = pd.DataFrame.from_dict(training_annotations, orient="index")
+    df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{DATE}_training_labels.csv"))
+    df = pd.DataFrame.from_dict(test_annotations, orient="index")
+    df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{DATE}_test_labels.csv"))
+
+    return
+
+
+def collect_data(sample_points, image_dir, out_dir, name, rng=None):
     """
     Collect data from images and save labels.
 
@@ -72,31 +393,38 @@ def collect_data(sample_points, image_dir, out_dir, name, date, rng=None):
         Directory to save the output data.
     name : str
         Name for the output files.
-    date : str
-        Date for the output files.
     rng : numpy.random.Generator, optional
         Random number generator for data collection.
     """
 
     if rng is None:
         rng = np.random.default_rng()
-
-    os.makedirs(os.path.join(out_dir,"observations"), exist_ok=True)
+    
+    obs_out = os.path.join(out_dir,f"observations_{DATE}")
+    os.makedirs(obs_out, exist_ok=True)
     image_files = os.listdir(image_dir)
     annotations = {}
     obs_id = 0
     for f in image_files:
-        points = sample_points[f.split('.')[0]]
-        data = torch.load(os.path.join(image_dir, f), weights_only=True)
-        img = data["image"]
+        points = sample_points[f.split(".")[0]]
+        img_file = glob(os.path.join(os.path.join(image_dir,f), "*image.tif"))[0]
+        img = tf.imread(img_file)
+        if img.max() > 1.0:
+            img = img / 255.0
+        if img.ndim == 3:
+            img = img[None]
         img = Image(img)
-        branch_mask = data["branch_mask"]
+        branches = glob(os.path.join(os.path.join(image_dir,f), "*branches.txt"))[0]
+
+        with open(branches, 'r') as f:
+            branches = torch.tensor([[float(x) for x in line.strip().split(' ')] for line in f if line.strip()])
         for point in points:
-            patch, _ = img.crop(torch.tensor(point), 7, pad=True, value=0.0)
-            i,j,k = [int(np.round(x)) for x in point]
-            label = branch_mask[0, i, j, k].item()
+            point[:3] = point[2::-1]
+            patch, _ = img.crop(torch.tensor(point[:3]), 17, pad=True, value=0.0)
+            distances = torch.linalg.norm(branches - point[None, :3], dim=1)
+            label = float((distances.min() <= 7.0).item())
             fname = f"obs_{obs_id}.pt"
-            torch.save(patch, os.path.join(os.path.join(out_dir, "observations"), fname))
+            torch.save(patch, os.path.join(obs_out, fname))
             annotations[fname] = label
             obs_id += 1
 
@@ -109,9 +437,9 @@ def collect_data(sample_points, image_dir, out_dir, name, date, rng=None):
     test_annotations = {list(annotations)[i]: list(annotations.values())[i] for i in test_idxs}
     # save 
     df = pd.DataFrame.from_dict(training_annotations, orient="index")
-    df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{date}_training_labels.csv"))
+    df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{DATE}_training_labels.csv"))
     df = pd.DataFrame.from_dict(test_annotations, orient="index")
-    df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{date}_test_labels.csv"))
+    df.to_csv(os.path.join(out_dir, f"branch_classifier_{name}_{DATE}_test_labels.csv"))
 
     return
 
diff --git a/data_prep/data_utils.py b/data_prep/data_utils.py
index c825be6..c79b2f3 100755
--- a/data_prep/data_utils.py
+++ b/data_prep/data_utils.py
@@ -2,6 +2,7 @@
 
 """Utils.py : Helper functions."""
 
+import numpy as np
 import torch
 from torch.nn.functional import grid_sample
 
@@ -72,5 +73,149 @@ def interp(x, I, phii, interp2d=False, **kwargs):
     out = out[0]
     return out
 
+
+def inhomogeneity_correction(image, background_threshold=None):
+    # piecewise intensity normalization
+    patch_size = 21
+
+    if isinstance(image, torch.Tensor):
+        # Convert PyTorch tensor to NumPy array
+        image = image.cpu().numpy()
+    elif not isinstance(image, np.ndarray):
+        raise TypeError("Input image must be a NumPy array or a PyTorch tensor.")
+    # Convert to float32 for processing
+    image = image.astype(np.float32)
+    # first do global normalization
+    global_max = image.max()
+    if global_max > 0:
+        image = image / global_max
+    else:
+        image = np.zeros_like(image)
+    
+    background_threshold = np.quantile(image, 0.9995) if background_threshold is None else background_threshold
+
+    # Get image dimensions
+    d, h, w = image.shape
+    
+    # Calculate padding needed to ensure all patches fit
+    pad_d = (patch_size - d % patch_size) % patch_size
+    pad_h = (patch_size - h % patch_size) % patch_size
+    pad_w = (patch_size - w % patch_size) % patch_size
+    
+    # Pad the image
+    padded_image = np.pad(image, ((0, pad_d), (0, pad_h), (0, pad_w)), mode='reflect')
+    
+    # Initialize normalized image with same size as padded
+    normalized_image = np.zeros_like(padded_image)
+    
+    # Process each patch
+    for z in range(0, padded_image.shape[0], patch_size):
+        for y in range(0, padded_image.shape[1], patch_size):
+            for x in range(0, padded_image.shape[2], patch_size):
+                # Extract patch
+                patch = padded_image[z:z+patch_size, y:y+patch_size, x:x+patch_size]
+                
+                # Create mask for foreground pixels (above background threshold)
+                foreground_mask = patch > background_threshold
+                
+                # Only normalize if there are foreground pixels
+                if np.any(foreground_mask):
+                    # Find maximum value among foreground pixels only
+                    max_val = patch[foreground_mask].max()
+                    
+                    # Normalize patch (avoid division by zero)
+                    if max_val > 0:
+                        normalized_patch = patch / max_val
+                    else:
+                        normalized_patch = patch
+                else:
+                    # If no foreground pixels, keep original values
+                    normalized_patch = patch
+                
+                # Store normalized patch
+                normalized_image[z:z+patch_size, y:y+patch_size, x:x+patch_size] = normalized_patch
+    
+    # Remove padding to return to original size
+    normalized_image = normalized_image[:d, :h, :w]
+    
+    return normalized_image
+
+
+def kmeans(image, k=2, tolerance=1e-3, init_means=None, max_iter=100, verbose=False):
+    """
+    Perform k-means clustering on the input image.
+    
+    Parameters
+    ----------
+    image: Array
+        3D NumPy array representing the scalar-valued image.
+    k: int
+        Number of clusters for k-means.
+    tolerance: float
+        Tolerance value used to determine convergence. Tolerance is relative to the range of values in the image.
+    verbose: bool
+        Run in verbose mode.
+
+    Returns
+    -------
+    means: Array
+        Mean values for k clusters. 
+    """
+
+    if init_means is not None:
+        if not isinstance(init_means, np.ndarray) or init_means.ndim != 1 or init_means.size != k:
+            raise ValueError("init_means must be a 1D NumPy array of size k.")
+        mu = init_means
+    else:
+        # Initialize means randomly within the range of the image values
+        mu = np.random.uniform(size=(k,), high=image.max(), low=image.min())
+    
+    tol = (image.max() - image.min()) * tolerance
+    mu_ = np.zeros_like(mu)
+    
+    # Flatten image for faster computation
+    image_flat = image.flatten()
+    
+    n = 1
+    while True:
+        if n > max_iter:
+            if verbose:
+                print(f"Maximum iterations {max_iter} reached without convergence.")
+            break
+        if verbose:
+            print(f'it: {n}')
+        # Compute distances more efficiently using broadcasting
+        distances = np.abs(image_flat[:, None] - mu[None, :])
+        clusters = np.argmin(distances, axis=1)
+        
+        # Update means more efficiently
+        for i in range(k):
+            mask = clusters == i
+            if np.any(mask):
+                mu_[i] = np.mean(image_flat[mask])
+            else:
+                mu_[i] = mu[i]  # Keep old value if no points assigned
+                
+        if np.allclose(mu_, mu, atol=tol):
+            break
+
+        mu = mu_.copy()
+        n += 1
+    
+    sigmas = np.zeros((k,))
+    for i in range(k):
+        mask = clusters == i
+        if np.any(mask):
+            sigmas[i] = np.std(image_flat[mask])
+        else:
+            sigmas[i] = sigmas[i]  # Keep old value if no points assigned
+    
+    # order means and sigmas by means
+    order = np.argsort(mu)
+    mu = mu[order]
+    sigmas = sigmas[order]
+
+    return mu, sigmas
+
 if __name__ == "__main__":
     pass
\ No newline at end of file
diff --git a/data_prep/draw.py b/data_prep/draw.py
index b3986ef..6b4cbc6 100644
--- a/data_prep/draw.py
+++ b/data_prep/draw.py
@@ -1,286 +1,699 @@
+"""
+Refactored draw.py - Simplified and more readable neuron drawing functionality.
+
+This module provides clean interfaces for drawing neuron structures with clear
+separation of concerns and improved readability.
+"""
+
 import numpy as np
 from pathlib import Path
+from dataclasses import dataclass, field
+from typing import Optional, List, Dict, Tuple, Union
 from skimage.filters import gaussian 
-import sys
 import torch
+import imageio
+from scipy.ndimage import gaussian_filter, median_filter
 
+import sys
 sys.path.append(str(Path(__file__).parent))
 from image import Image
 import load
 
 
-def draw_neuron_density(segments, shape, width=3):
-    """
-    Draws neuron density on an image based on given segments.
+@dataclass
+class DrawingConfig:
+    """Configuration for neuron drawing parameters."""
+    width: float = 4.0
+    rgb: bool = False
+    random_width: bool = False
+    neuron_color: Optional[Tuple[float, float, float]] = None
+    background_color: Optional[Tuple[float, float, float]] = None
     
-    Parameters
-    ----------
-    segments : array-like or torch.Tensor
-        A list or tensor of neuron segments, where each segment is represented by a set of points.
-    shape : tuple of int
-        The shape of the output density image (height, width, depth).
-    width : int, optional
-        The width of the line segments to be drawn, by default 3.
-        
-    Returns
-    -------
-    Image
-        An image object with the neuron density drawn on it.
-    """
+    foreground_mean: float = 0.8
+    foreground_std: float = 0.1
+    background_mean: float = 0.2
+    background_std: float = 0.05
+    mask_threshold: float = 0.1  # Fraction of max value for foreground/background mask
+    spatial_noise_scale: float = 10.0  # Scale for spatial noise features
+    spatial_noise_amplitude: float = 1.0  # Amplitude multiplier for spatial noise contribution
+    noise_method: str = 'gaussian_convolution'  # Method for spatial noise: 'gaussian_convolution', 'fractal', 'sparse_kernel'
+    blur: float = 1.0  # Sigma for optional Gaussian smoothing applied during post-processing
+    sharpness: float = 1.0  # Sharpness parameter for line drawing edges
+    vignette_magnitude: float = 0.2  # Strength of vignette effect (0 disables)
+    width_correlation: bool = False
+    width_correlation_rho: float = 0.0  # target lag-1 correlation for widths when enabled
+    segment_intensity_correlation: bool = False
+    segment_intensity_correlation_rho: float = 0.0  # target lag-1 correlation for per-segment intensity when enabled
     
-    # create density image
-    density = Image(torch.zeros((1,)+shape))
-
-    if not isinstance(segments, torch.Tensor):
-        segments = torch.tensor(segments)
-
-    for s in segments:
-        density.draw_line_segment(s[:,:3], width=width, channel=0)
+    def __post_init__(self):
+        """Validate configuration parameters."""
+        if self.neuron_color and len(self.neuron_color) != 3:
+            raise ValueError("neuron_color must be a 3-tuple of floats")
+        if self.background_color and len(self.background_color) != 3:
+            raise ValueError("background_color must be a 3-tuple of floats")
+        if not 0 <= self.mask_threshold <= 1:
+            raise ValueError("mask_threshold must be between 0 and 1")
+        if self.spatial_noise_amplitude < 0:
+            raise ValueError("spatial_noise_amplitude must be non-negative")
+        if self.noise_method not in ['gaussian_convolution', 'fractal', 'sparse_kernel']:
+            raise ValueError("noise_method must be one of: 'gaussian_convolution', 'fractal', 'sparse_kernel'")
+        # Validate correlations
+        if self.width_correlation and not (-1.0 <= float(self.width_correlation_rho) <= 1.0):
+            raise ValueError("width_correlation_rho must be in (-1.0, 1.0) when width_correlation=True")
+        if self.segment_intensity_correlation and not (-1.0 <= float(self.segment_intensity_correlation_rho) <= 1.0):
+            raise ValueError("segment_intensity_correlation_rho must be in (-1.0, 1.0) when segment_intensity_correlation=True")
+
+
+@dataclass 
+class GifConfig:
+    """Configuration for GIF generation."""
+    save_gif: bool = False
+    gif_path: Optional[str] = None
+    gif_axis: int = 0
+    gif_frames: int = 100
+
+
+class NeuronRenderer:
+    """Main class for rendering neuron structures."""
     
-    return density
-
-
-def draw_neuron_mask(density, threshold=1.0):
-    """ Create a binary mask from the neuron density image.
+    def __init__(self, rng: Optional[np.random.Generator] = None):
+        """Initialize the renderer with optional random number generator."""
+        self.rng = rng if rng is not None else np.random.default_rng()
+
+    def draw_density(self, sections: Dict, shape: Tuple[int, ...], 
+                    width: Optional[float] = None, mask: bool = False, scale: float = 1.0) -> Image:
+        """
+        Draw neuron density image from sections.
+        
+        Args:
+            sections: Dictionary of section_id -> segments
+            shape: Output image shape
+            width: Line width (auto-detected if None)
+            mask: Whether to create a binary mask
+            scale: Pixel scale factor
+            
+        Returns:
+            Image object with neuron density
+        """
+        density = Image(torch.zeros((1,) + shape))
+        segments = self._consolidate_segments(sections)
+        
+        for segment in segments:
+            line_width = self._get_segment_width(torch.from_numpy(segment), width, scale)
+            density.draw_line_segment(segment[:, :3], width=line_width, mask=mask, channel=0)
+        
+        return density
     
-    Parameters
-    ----------
-    density: torch.Tensor
-        Neuron density image.
+    def draw_mask(self, density: torch.Tensor, threshold: float = 1.0) -> torch.Tensor:
+        """
+        Create binary mask from density image.
+        
+        Args:
+            density: Neuron density tensor
+            threshold: Threshold for mask creation
+            
+        Returns:
+            Binary mask tensor
+        """
+        peak = density.amax()
+        mask = torch.zeros_like(density, dtype=torch.bool)
+        mask[density > peak * np.exp(-0.5 * threshold)] = True
+        return mask
     
-    threshold: float
-        Threshold value for classifying a voxel in the neuron density image as inside the neuron.
-        The threshold value is relative to the width of the neuron. Specifically, the mask will label
-        as neuron voxels within one standard deviation from the peak neuron value, where the neuron
-        intensities are assumed to be normally distributed around the centerline.
-    """
-
-    peak = density.data.amax()
-    mask = torch.zeros_like(density.data)
-    mask[density.data > peak * np.exp(-0.5 * threshold)] = 1.0
-
-    return mask
-
-
-def draw_section_labels(sections, shape, width=3):
-    """
-    Draws discrete labels for each section on an image.
+    def draw_section_labels(self, sections: Dict, shape: Tuple[int, ...], 
+                          width: float = 3.0) -> Image:
+        """
+        Draw discrete labels for neuron sections.
+        
+        Args:
+            sections: Dictionary of section_id -> segments
+            shape: Output image shape
+            width: Line width
+            
+        Returns:
+            Image with section labels
+        """
+        labels = Image(torch.zeros((1,) + shape))
+        
+        for section_id, section_segments in sections.items():
+            for segment in section_segments:
+                segment_width = self._get_segment_width(segment, width)
+                labels.draw_line_segment(
+                    segment[:, :3], width=segment_width, 
+                    channel=0, mask=True, value=section_id
+                )
+        
+        return labels
     
-    Parameters
-    ----------
-    sections : dict
-        A dictionary where keys are section labels and values are lists of segments.
-        Each segment is a numpy array with shape (n, 3) representing the coordinates.
-    shape : tuple
-        The shape of the output image (height, width, depth).
-    width : int, optional
-        The width of the line segments to be drawn, by default 3.
-        
-    Returns
-    -------
-    Image
-        An image object with the drawn sections labeled.
-    """
+    def draw_path(self, img: Image, path: Union[List, np.ndarray, torch.Tensor], 
+                 width: float, binary: bool = False) -> Image:
+        """
+        Draw a path on an image.
+        
+        Args:
+            img: Target image
+            path: Path coordinates
+            width: Line width
+            binary: Whether to use binary drawing
+            
+        Returns:
+            Modified image
+        """
+        path = self._ensure_tensor(path)
+        segments = torch.stack((path[:-1], path[1:]), dim=1)
+        
+        for segment in segments:
+            img.draw_line_segment(segment[:, :3], width=width, mask=binary, channel=0)
+        
+        return img
     
-    # create discrete labels for each section
-    labels = Image(torch.zeros((1,)+shape))
-    for i, section in sections.items():
-        for segment in section:
-            labels.draw_line_segment(segment[:,:3], width=width, channel=0, binary=True, value=i)
+    def draw_neuron(self, sections: Dict, shape: Tuple[int, ...], 
+                   config: DrawingConfig, gif_config: Optional[GifConfig] = None) -> Image:
+        """
+        Draw complete neuron image with all effects.
+        
+        Args:
+            sections: Dictionary of section_id -> segments
+            shape: Output image shape
+            config: Drawing configuration
+            gif_config: Optional GIF generation config
+            
+        Returns:
+            Rendered neuron image
+        """
+        segments = self._consolidate_segments(sections)
+        if shape is None:
+            shape = self._calculate_shape(segments)
+        bg_mean = getattr(config, 'background_mean', 0.0)
+        img = Image(torch.ones((1,) + shape) * bg_mean)
+
+        gif_frames = []
+        gif_steps = self._setup_gif_steps(len(segments), gif_config)
+
+        # Optionally prepare correlated sequences for width and per-segment intensity
+        n = len(segments)
+        width_seq = None
+        value_seq = None
+        if config.width_correlation: # and abs(float(config.width_correlation_rho)) > 0.0:
+            ar = self._generate_ar1_sequence(n, float(config.width_correlation_rho), start_val=0.0)
+            base_w = float(config.width)
+            # 50% variation around base width, clamp min at 2.0 px
+            width_seq = np.clip(base_w + 0.5 * base_w * ar, 2.0, 14.0)
+        if config.segment_intensity_correlation: # and abs(float(config.segment_intensity_correlation_rho)) > 0.0:
+            ar = self._generate_ar1_sequence(n, float(config.segment_intensity_correlation_rho), start_val=0.0)
+            fg_mean = float(config.foreground_mean)
+            # 40% variation around fg_mean, clamp min at 0.15
+            value_seq = np.clip(fg_mean + 0.4 * fg_mean * ar, 0.15, None)
+
+        # Draw segments
+        for idx, segment in enumerate(segments):
+            width_override = float(width_seq[idx]) if width_seq is not None else None
+            value_override = float(value_seq[idx]) if value_seq is not None else None
+            self._draw_single_segment(img, segment, config, width_override=width_override, value_override=value_override)
+            self._maybe_capture_gif_frame(img, idx, gif_steps, gif_frames, gif_config)
+        
+        # Compute masks once (based on raw drawn density) for post-processing
+        peak = img.data.max()
+        threshold = bg_mean + float(config.mask_threshold) * (float(peak) - bg_mean)
+        foreground_mask = img.data > threshold
+
+        # Apply post-processing
+        # img = self._apply_color_effects(img, config)
+        img = self._add_spatial_noise(img, config, foreground_mask)
+        img = self._add_gaussian_noise(img, config, foreground_mask)
+        img = self._add_vignette(img, config, foreground_mask)
+
+        # Final clamp and optional Gaussian blur
+        img.data = torch.clamp(img.data, 0.0, 1.0)
+        cfg_blur = config.blur if hasattr(config, 'blur') else None
+        if cfg_blur is not None and cfg_blur > 0.0:
+            cpu_arr = img.data.detach().cpu().numpy()
+            for c in range(cpu_arr.shape[0]):
+                cpu_arr[c] = gaussian_filter(cpu_arr[c], sigma=cfg_blur, radius=1)
+            img.data = torch.from_numpy(cpu_arr).to(device=img.data.device, dtype=img.data.dtype)
+
+        # Save GIF if requested
+        self._maybe_save_gif(gif_frames, gif_config)
+        
+        return img
     
-    return labels
-
-
-def draw_path(img, path, width, binary):
-    """
-    Draws a path on the given image.
+    def neuron_from_swc(self, swc_list: List, config: DrawingConfig, 
+                       shape: Optional[Tuple[int, ...]] = None, 
+                       dropout: bool = False, adjust: bool = False) -> Dict:
+        """
+        Generate neuron image from SWC data.
+        
+        Args:
+            swc_list: SWC neuron data
+            config: Drawing configuration
+            shape: Output shape (auto-calculated if None)
+            dropout: Whether to add signal dropout
+            adjust: Whether to adjust coordinates
+            
+        Returns:
+            Dictionary with image, density, labels, etc.
+        """
+        # Parse SWC data
+        sections, graph = load.parse_swc(swc_list)
+        branches, terminals = load.get_critical_points(swc_list, sections)
+        scale = 1.0
+        
+        if adjust:
+            sections, branches, terminals, scale = load.adjust_neuron_coords(
+                sections, branches, terminals
+            )
+        
+        # Prepare segments
+        segments = self._consolidate_segments(sections)
+        
+        if shape is None:
+            shape = self._calculate_shape(segments)
+        
+        # Generate images
+        img = self.draw_neuron(sections, shape, config)
+        density = self.draw_density(sections, shape, width=config.width)
+        section_labels = self.draw_section_labels(sections, shape, width=2*config.width)
+        
+        # Apply dropout if requested
+        if dropout:
+            img = self._apply_dropout(img, section_labels, config.width)
+        
+        # Prepare output
+        root_key = min(sections.keys())
+        seed = sections[root_key][0, 0, :3].round().astype(np.uint16).tolist()
+        
+        return {
+            "image": img.data,
+            "neuron_density": density.data,
+            "section_labels": section_labels.data,
+            "branches": branches,
+            "seeds": [seed],
+            "scale": scale,
+            "graph": graph
+        }
     
-    Parameters
-    ----------
-    img : object
-        The image object on which the path will be drawn. It should have a method `draw_line_segment`.
-    path : list or numpy.ndarray or torch.Tensor
-        The path to be drawn. It can be a list of coordinates, a numpy array, or a torch tensor.
-    width : int
-        The width of the line segments to be drawn.
-    binary : bool
-        If True, the line segments will be drawn in binary mode.
-        
-    Returns
-    -------
-    object
-        The image object with the path drawn on it.
-    """
+    # Private helper methods
+    def _ensure_tensor(self, data: Union[List, np.ndarray, torch.Tensor]) -> torch.Tensor:
+        """Convert data to torch tensor."""
+        if isinstance(data, list):
+            return torch.tensor(data)
+        elif isinstance(data, np.ndarray):
+            return torch.from_numpy(data)
+        return data
     
-    if isinstance(path, list):
-        path = torch.tensor(path)
-    elif isinstance(path, np.ndarray):
-        path = torch.from_numpy(path)
-
-    segments = torch.stack((path[:-1],path[1:]), dim=1)
-    for s in segments:
-        img.draw_line_segment(s[:,:3], width=width, binary=binary, channel=0)
-
-    return img
-
-
-def draw_neuron(segments, shape, width, noise, neuron_color=None, background_color=None, random_brightness=False, binary=False, rng=None):
-    """
-    Draws a neuron image based on provided segments and parameters.
+    def _setup_gif_steps(self, n_segments: int, gif_config: Optional[GifConfig]) -> set:
+        """Setup which steps to capture for GIF."""
+        if not gif_config or not gif_config.save_gif:
+            return set()
+        return set(np.linspace(0, n_segments-1, gif_config.gif_frames, dtype=int))
+
+    def _generate_spatially_correlated_noise(self, shape: Tuple[int, ...], scale: float = 10.0, 
+                                          method: str = "gaussian_convolution") -> torch.Tensor:
+        """Generate spatially correlated 3D noise using stable methods.
+        
+        Args:
+            shape: Output shape (depth, height, width)
+            scale: Correlation length scale (higher = more clumpy)
+            method: Method to use - "gaussian_convolution", "fractal", or "sparse_kernel"
+            
+        Returns:
+            Normalized noise tensor in [-1, 1] range
+        """
+        depth, height, width = [int(s) for s in shape]
+        
+        if method == "gaussian_convolution":
+            return self._gaussian_convolution_noise(depth, height, width, scale)
+        elif method == "fractal":
+            return self._fractal_noise(depth, height, width, scale)
+        elif method == "sparse_kernel":
+            return self._sparse_kernel_noise(depth, height, width, scale)
+        else:
+            # Fallback to Gaussian convolution
+            return self._gaussian_convolution_noise(depth, height, width, scale)
     
-    Parameters
-    ----------
-    segments : list of ndarray
-        List of segments where each segment is an ndarray of shape (N, 3) representing the coordinates of the neuron segments.
-    shape : tuple of int
-        Shape of the output image (height, width).
-    width : int
-        Width of the neuron lines to be drawn.
-    noise : float
-        Standard deviation of the Gaussian noise to be added to the image.
-    neuron_color : tuple of float, optional
-        RGB color of the neuron lines. Each value should be in the range [0, 1]. Default is (1.0, 1.0, 1.0).
-    background_color : tuple of float, optional
-        RGB color of the background. Each value should be in the range [0, 1]. Default is None.
-    random_brightness : bool, optional
-        If True, random brightness will be applied to each segment. Default is False.
-    binary : bool, optional
-        If True, the image will be binary. Default is False.
-    rng : numpy.random.Generator, optional
-        Random number generator instance. Default is None, which uses numpy's default_rng.
-        
-    Returns
-    -------
-    Image
-        An Image object containing the drawn neuron.
-    """
+    def _gaussian_convolution_noise(self, depth: int, height: int, width: int, scale: float) -> torch.Tensor:
+        """Generate noise by convolving white noise with Gaussian kernel."""
+        # Generate white noise
+        noise = self.rng.normal(0, 1, (depth, height, width)).astype(np.float32)
+        
+        # Calculate sigma for desired correlation scale
+        sigma = max(0.5, scale / 3.0)  # Scale controls correlation length
+        
+        # Apply 3D Gaussian filter for spatial correlation
+        from scipy.ndimage import gaussian_filter
+        correlated_noise = gaussian_filter(noise, sigma=sigma, mode='reflect')
+        
+        # Convert to tensor and normalize to [-1, 1]
+        t = torch.from_numpy(correlated_noise)
+        std_val = torch.std(t)
+        if std_val > 0:
+            t = t / (3 * std_val)  # Normalize to roughly [-1, 1] (3-sigma rule)
+        return torch.clamp(t, -1.0, 1.0)
     
-    if rng is None:
-        rng = np.random.default_rng()
-
-    img = Image(torch.zeros((1,)+shape))
-    value =  1.0
-    for s in segments:
-        if random_brightness:
-            y0 = 0.5
-            value = y0 + (1.0 - y0) * rng.uniform(0.0, 1.0, size=1).item()
-        img.draw_line_segment(s[:,:3], width=width, binary=binary, channel=0, value=value)
-    if neuron_color is None:
-        neuron_color = (1.0, 1.0, 1.0)
-
-    img_data = torch.cat((neuron_color[0]*img.data, neuron_color[1]*img.data, neuron_color[2]*img.data), dim=0)
-    if background_color is not None:
-        img_data = img_data + torch.ones_like(img_data) * background_color[:,None,None,None]
-        img_data /= img_data.amax()
-    sigma = img_data.amax() * noise
-    img_data = img_data + torch.from_numpy(rng.normal(size=img_data.shape))*sigma # add noise
-    img_data = (img_data - img_data.amin()) / (img_data.amax() - img_data.amin()) # rescale to [0,1]
-    img = Image(img_data)
-
-    return img
-
-
-def neuron_from_swc(swc_list, width=3, noise=0.05, dropout=True, adjust=True, background_color=None, neuron_color=None, random_brightness=False, binary=False, rng=None):
-    """
-    Generate a neuron image from an SWC list.
+    def _fractal_noise(self, depth: int, height: int, width: int, scale: float) -> torch.Tensor:
+        """Generate fractal noise using octave-based approach."""
+        noise = torch.zeros((depth, height, width), dtype=torch.float32)
+        
+        # Multi-octave fractal noise
+        amplitude = 1.0
+        frequency = 1.0 / max(1.0, scale)
+        
+        for octave in range(4):  # 4 octaves for good detail
+            # Generate noise at this frequency
+            octave_noise = self._generate_octave_noise(depth, height, width, frequency)
+            noise += amplitude * octave_noise
+            
+            # Update for next octave
+            amplitude *= 0.5
+            frequency *= 2.0
+        
+        # Normalize to [-1, 1]
+        max_abs = torch.abs(noise).max()
+        if max_abs > 0:
+            noise = noise / max_abs
+        return torch.clamp(noise, -1.0, 1.0)
     
-    Parameters
-    ----------
-    swc_list : list
-        List of SWC data representing neuron structure.
-    width : int, optional
-        Width of the neuron lines, by default 3.
-    noise : float, optional
-        Amount of noise to add to the neuron image, by default 0.05.
-    dropout : bool, optional
-        Whether to add random signal dropout, by default True.
-    adjust : bool, optional
-        Whether to adjust the SWC data, by default True.
-    background_color : optional
-        Background color of the neuron image, by default None.
-    neuron_color : optional
-        Color of the neuron, by default None.
-    random_brightness : bool, optional
-        Whether to apply random brightness to the neuron image, by default False.
-    binary : bool, optional
-        Whether to generate a binary image, by default False.
-    rng : numpy.random.Generator, optional
-        Random number generator, by default None.
-        
-    Returns
-    -------
-    dict
-        Dictionary containing the following keys:
-        - "image": torch.Tensor
-            The generated neuron image.
-        - "neuron_density": torch.Tensor
-            The density map of the neuron.
-        - "section_labels": torch.Tensor
-            The section labels of the neuron.
-        - "branch_mask": torch.Tensor
-            The branch mask of the neuron.
-        - "seeds": list
-            List of seed points.
-        - "scale": float
-            Scale of the neuron.
-        - "graph": dict
-            Graph representation of the neuron.
-    """
+    def _generate_octave_noise(self, depth: int, height: int, width: int, frequency: float) -> torch.Tensor:
+        """Generate a single octave of noise using interpolated grid."""
+        # Create a coarser grid based on frequency
+        grid_scale = max(1, int(1.0 / frequency))
+        grid_d = max(2, depth // grid_scale)
+        grid_h = max(2, height // grid_scale)
+        grid_w = max(2, width // grid_scale)
+        
+        # Generate random values at grid points
+        grid_values = self.rng.normal(0, 1, (grid_d, grid_h, grid_w)).astype(np.float32)
+        
+        # Interpolate to full resolution using scipy
+        from scipy.ndimage import zoom
+        zoom_factors = (depth / grid_d, height / grid_h, width / grid_w)
+        interpolated = zoom(grid_values, zoom_factors, order=1, mode='reflect')
+        
+        # Ensure exact output shape (zoom can sometimes be off by 1)
+        if interpolated.shape != (depth, height, width):
+            interpolated = interpolated[:depth, :height, :width]
+            if interpolated.shape != (depth, height, width):
+                # Pad if needed
+                pad_d = depth - interpolated.shape[0]
+                pad_h = height - interpolated.shape[1] 
+                pad_w = width - interpolated.shape[2]
+                interpolated = np.pad(interpolated, 
+                                    ((0, pad_d), (0, pad_h), (0, pad_w)), 
+                                    mode='reflect')
+        
+        return torch.from_numpy(interpolated)
     
-    if rng is None:
-        rng = np.random.default_rng()
-
-    sections, graph, branches, terminals, scale = load.parse_swc_list(swc_list, adjust=adjust)
-
-    segments = []
-    for section in sections.values():
-        segments.append(section)
-    segments = torch.concatenate(segments)
-
-    shape = torch.ceil(torch.amax(segments, dim=(0,1)))
-    shape = shape.to(torch.int)
-    shape = shape + torch.tensor([10, 10, 10])  # type: ignore
-    shape = tuple(shape.tolist())
-
-    img = draw_neuron(segments, shape=shape, width=width, noise=noise, neuron_color=neuron_color,
-                      background_color=background_color, random_brightness=random_brightness,
-                      binary=binary, rng=rng)
+    def _sparse_kernel_noise(self, depth: int, height: int, width: int, scale: float) -> torch.Tensor:
+        """Generate noise using sparse random kernels - good for clumpy patterns."""
+        noise = torch.zeros((depth, height, width), dtype=torch.float32)
+        
+        # Number of "clumps" based on volume and scale
+        volume = depth * height * width
+        n_clumps = max(10, int(volume / (scale ** 3)))
+        
+        # Kernel size based on scale
+        kernel_size = max(3, int(scale // 2))
+        
+        for _ in range(n_clumps):
+            # Random center position
+            center_z = self.rng.integers(kernel_size, depth - kernel_size)
+            center_y = self.rng.integers(kernel_size, height - kernel_size)
+            center_x = self.rng.integers(kernel_size, width - kernel_size)
+            
+            # Random intensity
+            intensity = self.rng.normal(0, 1)
+            
+            # Create Gaussian blob around center
+            z_range = slice(max(0, center_z - kernel_size), min(depth, center_z + kernel_size))
+            y_range = slice(max(0, center_y - kernel_size), min(height, center_y + kernel_size))
+            x_range = slice(max(0, center_x - kernel_size), min(width, center_x + kernel_size))
+            
+            # Create coordinate grids for this region
+            z_coords = torch.arange(z_range.start, z_range.stop, dtype=torch.float32) - center_z
+            y_coords = torch.arange(y_range.start, y_range.stop, dtype=torch.float32) - center_y
+            x_coords = torch.arange(x_range.start, x_range.stop, dtype=torch.float32) - center_x
+            
+            zz, yy, xx = torch.meshgrid(z_coords, y_coords, x_coords, indexing='ij')
+            
+            # Gaussian kernel
+            sigma = kernel_size / 3.0
+            kernel = intensity * torch.exp(-(zz**2 + yy**2 + xx**2) / (2 * sigma**2))
+            
+            # Add to noise
+            noise[z_range, y_range, x_range] += kernel
+        
+        # Normalize to [-1, 1]
+        max_abs = torch.abs(noise).max()
+        if max_abs > 0:
+            noise = noise / max_abs
+        return torch.clamp(noise, -1.0, 1.0)
+        
+    def _add_spatial_noise(self, img: Image, config: DrawingConfig, foreground_mask: torch.Tensor) -> Image:
+        """Add spatially correlated noise contribution to foreground/background means.
 
-    density = draw_neuron_density(segments, shape, width=width)
-    section_labels = draw_section_labels(sections, shape, width=2*width)
-    mask = draw_neuron_mask(density, threshold=5.0)
+        Masks are precomputed once in draw_neuron and passed here for consistency.
+        """
+        background_mask = ~foreground_mask
 
-    if dropout: # add random signal dropout (subtract gaussian blobs)
+        # Get noise method from config (with fallback)
+        noise_method = getattr(config, 'noise_method', 'gaussian_convolution')
+        
+        # Generate spatially correlated noise (Z,Y,X) -> (C,Z,Y,X)
+        noise_shape = img.data.shape[1:]
+        spatial_noise = self._generate_spatially_correlated_noise(
+            noise_shape, config.spatial_noise_scale, method=noise_method
+        ).unsqueeze(0)
+
+        result_img = torch.zeros_like(img.data)
+        amp = float(getattr(config, 'spatial_noise_amplitude', 1.0))
+        fg_mean = float(getattr(config, 'foreground_mean', 1.0))
+        bg_mean = float(getattr(config, 'background_mean', 0.0))
+        if foreground_mask.any():
+            result_img[foreground_mask] = img.data[foreground_mask] + (amp * fg_mean * spatial_noise)[foreground_mask]
+        if background_mask.any():
+            result_img[background_mask] = img.data[background_mask] + (amp * bg_mean * spatial_noise)[background_mask]
+
+        img.data = result_img
+        return img
+
+    def _add_gaussian_noise_tensor(
+        self,
+        img: torch.Tensor,
+        foreground_mask: torch.Tensor,
+        fg_std: float,
+        bg_std: float,
+    ) -> torch.Tensor:
+        """Add zero-mean Gaussian noise with different std for foreground/background.
+
+        Args:
+            img: image tensor (C, Z, Y, X)
+            foreground_mask: bool mask same shape as img
+            background_mask: bool mask same shape as img
+            fg_std: standard deviation for foreground gaussian noise
+            bg_std: standard deviation for background gaussian noise
+        Returns:
+            Tensor of same shape as img with noise added.
+        """
+        if fg_std <= 0 and bg_std <= 0:
+            return img
+        device = img.device
+        dtype = img.dtype
+        fg_noise = torch.randn_like(img, device=device, dtype=dtype) * float(fg_std)
+        bg_noise = torch.randn_like(img, device=device, dtype=dtype) * float(bg_std)
+        gauss = torch.where(foreground_mask, fg_noise, bg_noise)
+        return img + gauss
+
+    def _add_gaussian_noise(self, img: Image, config: DrawingConfig, foreground_mask: torch.Tensor) -> Image:
+        """Add Gaussian noise using foreground_std/background_std.
+        """
+        img.data = self._add_gaussian_noise_tensor(
+            img=img.data,
+            foreground_mask=foreground_mask,
+            fg_std=float(config.foreground_std),
+            bg_std=float(config.background_std),
+        )
+        return img
+
+    def _add_vignette(
+        self,
+        img: Image,
+        config: DrawingConfig,
+        foreground_mask: torch.Tensor,
+    ) -> Image:
+        """Apply a vignette effect: attenuate intensity farther from the center.
+
+        The vignette is applied multiplicatively as a smooth radial falloff. By default
+        the background is dimmed slightly more than the foreground to preserve neuron
+        visibility while still creating a realistic peripheral fade.
+
+        Args:
+            img: Image object (C, Z, Y, X) in [0, 1] range typically.
+            foreground_mask: Bool mask (C, Z, Y, X) identifying foreground voxels.
+            magnitude: Base vignette strength in [0, 1]. 0 disables the effect.
+
+        Returns:
+            Image with vignette applied in-place and returned.
+        """
+        mag = float(max(0.0, config.vignette_magnitude))
+        if mag <= 0.0:
+            return img
+
+        # Build a normalized radial distance map r in [0, ~1] with shape (Z, Y, X)
+        _, Z, Y, X = img.data.shape
+        device = img.data.device
+        dtype = img.data.dtype
+
+        z = torch.linspace(0, Z - 1, Z, device=device, dtype=dtype)
+        y = torch.linspace(0, Y - 1, Y, device=device, dtype=dtype)
+        x = torch.linspace(0, X - 1, X, device=device, dtype=dtype)
+        zz, yy, xx = torch.meshgrid(z, y, x, indexing="ij")
+
+        cz = (Z - 1) / 2.0
+        cy = (Y - 1) / 2.0
+        cx = (X - 1) / 2.0
+        # Normalize distances along each axis by half-extent, then combine
+        rz = (zz - cz) / max(1.0, cz)
+        ry = (yy - cy) / max(1.0, cy)
+        rx = (xx - cx) / max(1.0, cx)
+        r = torch.sqrt(rz * rz + ry * ry + rx * rx)
+        # Scale to roughly [0, 1] across the volume (sqrt(3) at corners)
+        r = torch.clamp(r / np.sqrt(3.0), 0.0, 1.0)
+
+        # Smooth falloff curve; higher exponent keeps center brighter
+        exponent = 1.5
+        base_falloff = r.pow(exponent)
+
+        # Compute per-voxel attenuation factors for FG and BG
+        # Apply slightly weaker vignette on foreground to retain neuron contrast
+        fg_mag = 0.6 * mag
+        bg_mag = mag
+        factor_fg = torch.clamp(1.0 - fg_mag * base_falloff, 0.0, 1.0)
+        factor_bg = torch.clamp(1.0 - bg_mag * base_falloff, 0.0, 1.0)
+
+        # Broadcast factors to (C, Z, Y, X)
+        factor_fg = factor_fg.unsqueeze(0).expand(img.data.shape)
+        factor_bg = factor_bg.unsqueeze(0).expand(img.data.shape)
+
+        factors = torch.where(foreground_mask, factor_fg, factor_bg)
+        img.data = img.data * factors
+        return img
+    
+    def _draw_single_segment(self, img: Image, segment: np.ndarray, 
+                           config: DrawingConfig, width_override: Optional[float] = None, value_override: Optional[float] = None) -> None:
+        """Draw a single segment with all effects."""
+        # Calculate segment properties
+        value = value_override if value_override is not None else self._get_segment_value(config)
+        width = width_override if width_override is not None else self._get_segment_width(torch.from_numpy(segment), config.width)
+        
+        # Draw the segment
+        img.draw_line_segment(segment[:, :3], width=width, mask=True, channel=0, value=value, sharpness=getattr(config, 'sharpness', 2.0))
+
+    def _generate_ar1_sequence(self, n: int, rho: float, start_val: Optional[float] = None) -> np.ndarray:
+        """Generate an AR(1) sequence with lag-1 correlation rho and unit variance.
+
+        x_t = rho * x_{t-1} + sqrt(1-rho^2) * eps_t, eps_t ~ N(0,1), x_0 ~ N(0,1)
+        """
+        n = int(n)
+        if n <= 0:
+            return np.zeros((0,), dtype=float)
+        rho = float(rho)
+        seq = np.zeros((n,), dtype=float)
+        seq[0] = start_val if start_val is not None else self.rng.normal(0.0, 1.0)
+        sigma = float(np.sqrt(max(0.0, 1.0 - rho * rho)))
+        eps = self.rng.normal(0.0, 1.0, size=n-1) if n > 1 else np.array([], dtype=float)
+        for t in range(1, n):
+            seq[t] = rho * seq[t-1] + sigma * eps[t-1]
+        return seq
+
+    def _get_segment_value(self, config: DrawingConfig, default_value: int = 1.0) -> float:
+        """Get brightness value for segment."""
+        if config.foreground_mean:
+            value = config.foreground_mean
+        else:
+            value = default_value
+        return value
+
+    def _get_segment_width(self, segment: torch.Tensor, default_width: Optional[float] = None, 
+                          scale: float = 1.0) -> float:
+        """Get width for a segment, handling various cases."""
+        if default_width is not None:
+            return default_width
+        elif segment.shape[1] == 4:  # Width included in segment data
+            return ((segment[0, 3] + segment[1, 3]) / 2).item() / scale
+        else:
+            return 3.0
+
+    def _get_segment_sharpness(self, config: DrawingConfig) -> float:
+        """Get sharpness value for segment."""
+        if config.random_sharpness:
+            sharpness = self.rng.normal(1.0, config.random_sharpness)
+            return np.clip(sharpness, 1.0, 6.0)
+        return 1.0
+    
+    def _maybe_capture_gif_frame(self, img: Image, idx: int, gif_steps: set, 
+                               gif_frames: List, gif_config: Optional[GifConfig]) -> None:
+        """Capture GIF frame if needed."""
+        if gif_config and idx in gif_steps:
+            arr = img.data[0].cpu().numpy()  # Fixed index
+            mip = arr.max(axis=gif_config.gif_axis)
+            mip = ((mip - mip.min()) / (mip.ptp() + 1e-8) * 255).astype(np.uint8)
+            gif_frames.append(mip)
+    
+    def _apply_color_effects(self, img: Image, config: DrawingConfig) -> Image:
+        """Apply RGB color effects."""
+        pass  # Not implemented
+            
+        # Apply neuron color
+        if config.neuron_color:
+            neuron_color = torch.tensor(config.neuron_color, dtype=torch.float32)
+            img.data = neuron_color[:, None, None, None] * img.data
+        
+        # Apply background color
+        if config.background_color:
+            background_color = torch.tensor(config.background_color, dtype=torch.float32)
+            img.data = img.data + torch.ones_like(img.data) * background_color[:, None, None, None]
+        
+        return img
+        
+    def _consolidate_segments(self, sections: Dict) -> np.ndarray:
+        """Consolidate all sections into segment array."""
+        segments = [section for section in sections.values()]
+        return np.concatenate(segments)
+    
+    def _calculate_shape(self, segments: np.ndarray) -> Tuple[int, ...]:
+        """Calculate image shape from segments."""
+        shape = np.ceil(np.max(segments[..., :3], axis=(0, 1)))
+        shape = shape.astype(np.uint16)
+        shape = shape + np.array([10, 10, 10])
+        return tuple(shape.tolist())
+    
+    def _apply_dropout(self, img: Image, section_labels: Image, width: float) -> Image:
+        """Apply random signal dropout."""
         neuron_coords = torch.nonzero(section_labels.data)
         dropout_density = 0.001
         size = int(dropout_density * len(neuron_coords))
-        rand_ints = rng.integers(0, len(neuron_coords), size=(size,))
-        dropout_points = neuron_coords[rand_ints]
-        dropout_points = dropout_points[:,1:].T
-        dropout_img = torch.zeros_like(img.data)
-        dropout_img[:, dropout_points[0], dropout_points[1], dropout_points[2]] = 1.0
-        dropout_img = gaussian(dropout_img, sigma=0.5*width)
-        dropout_img /= dropout_img.max()
-        img.data = img.data - dropout_img
-        img.data = torch.where(img.data < 0, 0.0, img.data)
-
-    branch_mask = Image(torch.zeros_like(mask))
-    for point in branches:
-        branch_mask.draw_point(point, radius=3, binary=True, value=1, channel=0)
-    # set branch_mask.data to zero where mask is zero
-    branch_mask.data = branch_mask.data * mask.data
-
-    seed = sections[1][0,0].round().to(int).tolist() # type: ignore
-
-    swc_data = {"image": img.data,
-                "neuron_density": density.data,
-                "section_labels": section_labels.data,
-                "branch_mask": branch_mask.data,
-                "seeds": [seed],
-                "scale": scale,
-                "graph": graph}
-
-    return swc_data
+        
+        if size > 0:
+            rand_ints = self.rng.integers(0, len(neuron_coords), size=(size,))
+            dropout_points = neuron_coords[rand_ints]
+            dropout_points = dropout_points[:, 1:].T
+            
+            dropout_img = torch.zeros_like(img.data)
+            dropout_img[:, dropout_points[0], dropout_points[1], dropout_points[2]] = 1.0
+            dropout_img = gaussian(dropout_img, sigma=0.5*width)
+            dropout_img /= dropout_img.max()
+            img.data = img.data * (1. - dropout_img)
+        
+        return img
+    
+    def _maybe_save_gif(self, gif_frames: List, gif_config: Optional[GifConfig]) -> None:
+        """Save GIF if frames were captured."""
+        if gif_config and gif_config.save_gif and gif_config.gif_path and gif_frames:
+            imageio.mimsave(gif_config.gif_path, gif_frames, duration=0.05)
+
 
 if __name__ == "__main__":
-    pass
\ No newline at end of file
+    # Minimal example
+    renderer = NeuronRenderer()
+    config = DrawingConfig(width=5.0, rgb=True)
+    print("NeuronRenderer ready for use!")
diff --git a/data_prep/generate.py b/data_prep/generate.py
index b72ef25..ab66dcd 100644
--- a/data_prep/generate.py
+++ b/data_prep/generate.py
@@ -5,12 +5,91 @@
 import scipy
 import sys
 import torch
-from typing import Tuple
+from typing import Tuple, Union
 
 sys.path.append(str(Path(__file__).parent))
 import draw
+from draw import NeuronRenderer, DrawingConfig
 import load
 
+
+def rvmf(n, mu, k, rng=None):
+    """Random sampling from the von Mises-Fisher distribution
+    
+    Parameters:
+    -----------
+    n : int
+        Sample size
+    mu : numpy.ndarray
+        Mean direction
+    k : float
+        Concentration parameter
+        
+    Returns:
+    --------
+    numpy.ndarray
+        Matrix of random samples
+    """
+    if rng is None:
+        rng = np.random.default_rng()
+    d = len(mu)
+    
+    if k > 0:
+        mu = mu / np.sqrt(np.sum(mu**2))
+        ini = np.zeros(d)
+        ini[-1] = 1.0
+        d1 = d - 1
+        
+        v1 = rng.normal(0, 1, (n, d1))
+        v_rows_norm = np.sqrt(np.sum(v1**2, axis=1))
+        v = v1 / v_rows_norm[:, np.newaxis]
+        
+        b = (-2 * k + np.sqrt(4 * k**2 + d1**2)) / d1
+        x0 = (1 - b) / (1 + b)
+        m = 0.5 * d1
+        ca = k * x0 + (d - 1) * np.log(1 - x0**2)
+        
+        # Modified rejection sampling
+        w = np.zeros(n)
+        for i in range(n):
+            accepted = False
+            while not accepted:
+                z = rng.beta(m, m)
+                u = rng.uniform(0, 1)
+                w_i = (1 - (1 + b) * z) / (1 - (1 - b) * z)
+                ta = k * w_i + d1 * np.log(1 - x0 * w_i)
+                if ta - ca >= np.log(u):
+                    w[i] = w_i
+                    accepted = True
+                    
+        S = np.column_stack((np.sqrt(1 - w**2)[:, np.newaxis] * v, w))
+        
+        # Rotation logic from ini to mu
+        if np.allclose(ini, mu):
+            x = S
+        elif np.allclose(-ini, mu):
+            x = -S
+        else:
+            # Implement rotation matrix calculation
+            # This is a simplified version - would need proper implementation
+            a = ini
+            b = mu
+            ab = np.sum(a * b)
+            ca = a - b * ab
+            ca = ca / np.sqrt(np.sum(ca**2))
+            A = np.outer(b, ca) - np.outer(ca, b)
+            theta = np.arccos(ab)
+            rotation_matrix = np.eye(d) + np.sin(theta) * A + (np.cos(theta) - 1) * (np.outer(b, b) + np.outer(ca, ca))
+            
+            x = S @ rotation_matrix.T
+    else:
+        # Uniform distribution on sphere
+        x1 = rng.normal(0,1,(n,d))
+        x = x1 / np.sqrt(np.sum(x1**2, axis=1))[:, np.newaxis]
+    
+    return x
+
+
 def get_next_point(q0: np.ndarray, q1: np.ndarray, kappa: float, step_size: float=1.0, rng=None) -> np.ndarray:
     """
     Generate the next point in a path using a von Mises-Fisher distribution.
@@ -36,24 +115,83 @@ def get_next_point(q0: np.ndarray, q1: np.ndarray, kappa: float, step_size: floa
     
     if rng is None:
         rng = np.random.default_rng()
-    last_step = (q1 - q0) / step_size
-    vmf = scipy.stats.vonmises_fisher(last_step, kappa)
-    step = vmf.rvs(1, random_state=rng)[0]
+    last_step = (q1[:3] - q0[:3]) / step_size
+    # vmf = scipy.stats.vonmises_fisher(last_step, kappa)
+    # step = vmf.rvs(1, random_state=rng)[0]
+    step = rvmf(1, last_step, kappa, rng)[0]
     # step[0] = 0.0 # for paths constrained to a 2d slice
     step = step/(np.linalg.norm(step) + np.finfo(float).eps)
-    next_point = q1 + step * step_size
+    next_point = q1[:3] + step * step_size
 
     return next_point
 
 
+def clipped_normal(mu, sigma, low, high, rng=None):
+    """
+    Generate a random number from a truncated normal distribution.
+    
+    Parameters
+    ----------
+    mu : float
+        Mean of the normal distribution.
+    sigma : float
+        Standard deviation of the normal distribution.
+    low : float
+        Lower bound for truncation.
+    high : float
+        Upper bound for truncation.
+    rng : np.random.Generator, optional
+        Random number generator instance, by default None.
+        
+    Returns
+    -------
+    float
+        A random number from the truncated normal distribution.
+    """
+    
+    if rng is None:
+        rng = np.random.default_rng()
+    x = rng.normal(mu, sigma)
+    x = np.clip(x, low, high)
+    return x
+
+
+def path_length(path: Union[np.ndarray, list]) -> float:
+    """
+    Calculate the length of a path defined by a sequence of 3D points.
+
+    Parameters
+    ----------
+    path : Union[np.ndarray, list]
+        A sequence of 3D points defining the path.
+        
+    Returns
+    -------
+    float
+        The total length of the path.
+    """
+    
+    if isinstance(path, list):
+        path = np.array(path)
+    if not (isinstance(path, np.ndarray) and path.ndim == 2 and path.shape[1] >= 3):
+        raise ValueError("Input 'path' must be a 2D array with at least 3 columns (x, y, z coordinates).")
+    diffs = np.diff(path[:,:3], axis=0)
+    seg_lengths = np.sqrt(np.sum(diffs**2, axis=1))
+    total_length = np.sum(seg_lengths)
+    
+    return total_length
+
+
 # compute neuron segment end points 
 def get_path(start,
              boundary,
              kappa=20.0,
              rng=None,
-             length=100,
+             length=500,
              step_size=1.0,
-             uniform_len=False,
+             width=3.0,
+             random_len=True,
+             random_width=False,
              random_start=True):
     """
     Get the neuron segment endpoints starting at a seed point,
@@ -70,15 +208,15 @@ def get_path(start,
         Concentration parameter for step direction distribution.
     rng : np.random.Generator, optional
     length : int, optional
-        Path length in number of segments. This is the expected path length
-        if uniform_len is set to False. The minimum length is 10 if uniform_len is False.
-        Default is 100.
+        Path length in pixels. This is the expected path length
+        if uniform_len is set to False. The minimum length is 50 if uniform_len is False.
+        Default is 500.
     step_size : float, optional
         Length of each path segment in pixels. Default is 1.0
-    uniform_len : bool
-        If false, the path length will be a normally distributed random number
-        with the expected value set by the "length" argument. Otherwise the
-        path length will be equal to "length".
+    random_len : bool
+        If True, the path length will be sampled from a normal distribution with mean given by length, by default True.
+    random_width : bool, optional
+        If True, the width of the path segments will be randomly sampled from a truncated normal distribution
     random_start : bool, optional
         Whether to start the path with a random direction. Default is True.
     
@@ -90,39 +228,51 @@ def get_path(start,
     if rng is None:
         rng = np.random.default_rng()
 
-    if not uniform_len:
-        sigma = length // 5
+    if random_len:
+        sigma = length / 5
         length = length + rng.standard_normal(1)*sigma
-        length = int(round(length.item()))
-        length = length if length > 10 else 10
+        length = max(length, 50)
 
     # first step
     if random_start:
         step = rng.normal(0.0, 1.0, 3)
-        step[0] = 0.0
         step = step / sum(step**2)**0.5
     else:
         step = np.array([0.0,0.0,1.0])
     q1 = start + step * step_size
+    if random_width:
+        w0 = clipped_normal(width, 0.5, 2.0, 12.0, rng=rng)
+        w1 = clipped_normal(w0, 0.5, 2.0, 12.0, rng=rng)
+    else:
+        w0 = width
+        w1 = width
+    start = np.concatenate((start, [w0]))
+    q1 = np.concatenate((q1, [w1]))
     path = [start, q1]
 
-    for i in range(length):
+    while path_length(path) < length: # length in pixels
         next_point = get_next_point(path[-2], path[-1], kappa=kappa, step_size=step_size, rng=rng)
         if any(next_point > boundary.max(axis=0)) or any(next_point < boundary.min(axis=0)):
             break
+        if random_width:
+            w = clipped_normal(path[-1][3], 0.5, 2.0, 12.0, rng=rng)
+        else:
+            w = width
+        next_point = np.concatenate((next_point, [w]))
         path.append(next_point)
     
     path = np.array(path)
 
     return path
-
+ 
 
 def make_swc_list(size: Tuple[int,...],
-                length: int,
+                length: float,
                 step_size: float = 1.0,
                 kappa: float = 20.0,
-                uniform_len: bool = False,
+                random_len: bool = True,
                 random_start: bool = True,
+                random_width: bool = False,
                 rng=None,
                 num_branches: int=0) -> list:
     """
@@ -132,16 +282,18 @@ def make_swc_list(size: Tuple[int,...],
     ----------
     size : Tuple[int, ...]
         The dimensions of the 3D space.
-    length : int
-        The length of the path.
+    length : float
+        The length of the path in pixels.
     step_size : float, optional
         The step size for each move in the path, by default 1.0.
     kappa : float, optional
         The concentration parameter for the von Mises-Fisher distribution, by default 20.0.
-    uniform_len : bool, optional
-        If True, the path length will be uniform, by default False.
+    random_len : bool, optional
+        If True, the path length will be sampled from a normal distribution with mean given by length, by default True.
     random_start : bool, optional
         If True, the path will start at a random position, by default True.
+    random_width : bool, optional
+        If True, the width of the path segments will be randomly sampled, by default False.
     rng : numpy.random.Generator, optional
         A random number generator instance, by default None.
     num_branches : int, optional
@@ -159,46 +311,47 @@ def make_swc_list(size: Tuple[int,...],
     start = tuple([x//2 for x in size]) # start in the center
     boundary = np.array([[0,0,0],
                          [size[0]-1, size[1]-1, size[2]-1]])
-    path = get_path(start, boundary=boundary, kappa=kappa, rng=rng, length=length, step_size=step_size, uniform_len=uniform_len,
-                    random_start=random_start)
+    path = get_path(start, boundary=boundary, kappa=kappa, rng=rng, length=length, step_size=step_size, random_len=random_len,
+                    random_width=random_width, random_start=random_start)
     graph = [[i+1, i] for i in range(len(path))]
+    graph[0][1] = -1
     paths = [path]
     branch_points = []
     for i in range(num_branches):
         start_idx = rng.integers(0, len(path)-1)
-        branch_start = paths[0][start_idx]
+        branch_start = paths[0][start_idx][:3]
         branch_points.append(branch_start)
         branch_start = tuple(int(np.round(t)) for t in branch_start)
-        new_path = get_path(branch_start, boundary=boundary, kappa=kappa, rng=rng, length=length, step_size=step_size, uniform_len=uniform_len,
-                    random_start=True)
+        new_path = get_path(branch_start, boundary=boundary, kappa=kappa, rng=rng, length=length, step_size=step_size, random_len=random_len,
+                    random_width=random_width, random_start=True)
         graph.append([graph[-1][0]+1, start_idx+1])
         for i in np.arange(graph[-1][0], graph[-1][0] + len(new_path)-1):
             graph.append([i+1, i])
         paths.append(new_path)
     paths = np.concatenate(paths)
 
-    swc_list = [[graph[i][0], 0]+list(paths[i])+[3.0, graph[i][1]] for i in range(len(graph))]
+    swc_list = [[graph[i][0], 0]+list(paths[i][:3])+[paths[i][3], graph[i][1]] for i in range(len(graph))]
     
     return swc_list
 
 
 def save_images_from_swc(labels_dir, outdir, sync=True, random_contrast=False, rng=None):
     """
-    Save images generated from SWC files to the specified output directory.
-    
+    Saves images from SWC files using the improved NeuronRenderer API.
+
     Parameters
     ----------
     labels_dir : str
-        Directory containing the SWC files.
+        Path to directory containing SWC files.
     outdir : str
-        Directory where the output images will be saved.
+        Path to output directory.
     sync : bool, optional
-        If True, only save images for SWC files that do not have corresponding output files in the output directory (default is True).
+        Whether to skip files that already have outputs, by default True.
     random_contrast : bool, optional
-        If True, apply random contrast to the neuron images (default is False).
+        Whether to use random contrast, by default False.
     rng : numpy.random.Generator, optional
-        Random number generator for reproducibility. If None, a new generator is created (default is None).
-        
+        Random number generator, by default None.
+
     Returns
     -------
     None
@@ -207,6 +360,9 @@ def save_images_from_swc(labels_dir, outdir, sync=True, random_contrast=False, r
     if rng is None:
         rng = np.random.default_rng()
 
+    # Initialize the renderer once for better performance
+    renderer = NeuronRenderer(rng=rng)
+
     files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], '*.swc'))]
     if sync:
         outdir_fnames = [f for x in os.walk(outdir) for f in glob(os.path.join(x[0], '*.pt'))]
@@ -215,6 +371,7 @@ def save_images_from_swc(labels_dir, outdir, sync=True, random_contrast=False, r
     for labels_file in files:
         swc_list = load.swc(labels_file)
 
+        # Configure colors
         color = np.array([1.0, 1.0, 1.0])
         background = np.array([0., 0., 0.])
         if random_contrast:
@@ -223,21 +380,27 @@ def save_images_from_swc(labels_dir, outdir, sync=True, random_contrast=False, r
             background = rng.uniform(size=3)
             background = background / np.linalg.norm(background) * 0.01
             
-        swc_data = draw.neuron_from_swc(swc_list,
-                                        width=3,
-                                        noise=0.0,
-                                        dropout=False,
-                                        adjust=True,
-                                        background_color=background,
-                                        neuron_color=color,
-                                        random_brightness=False,
-                                        binary=False,
-                                        rng=rng)
+        # Create clean configuration object
+        config = DrawingConfig(
+            width=3,
+            rgb=True,
+            neuron_color=tuple(color),
+            background_color=tuple(background)
+        )
+        
+        # Use the new cleaner API
+        swc_data = renderer.neuron_from_swc(
+            swc_list, 
+            config=config,
+            dropout=False, 
+            adjust=True
+        )
         scale = swc_data.pop("scale")
         name = os.path.splitext(labels_file.split('/')[-1])[0]
         torch.save(swc_data, os.path.join(outdir, f"{name}_scale_{scale}x.pt"))
         
     return
 
+
 if __name__ == "__main__":
     pass
\ No newline at end of file
diff --git a/data_prep/image.py b/data_prep/image.py
index 2fcb345..b75d869 100755
--- a/data_prep/image.py
+++ b/data_prep/image.py
@@ -10,14 +10,17 @@
 '''
 import torch
 import numpy as np
+from scipy.ndimage import map_coordinates
 from skimage.draw import line_nd
 from skimage.filters import gaussian
-from skimage.morphology import dilation, footprint_rectangle
+from skimage.morphology import dilation
+from typing import Literal
+import warnings
 
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-def draw_line_segment(segment, width, binary=False, value=1.0):
+def draw_line_segment_old(segment, width, binary=False, value=1.0):
     """ Generate an image of a line segment with width.
 
     Parameters
@@ -38,11 +41,14 @@ def draw_line_segment(segment, width, binary=False, value=1.0):
     """
     
     segment = segment[1] - segment[0]
-    segment_length = torch.sqrt(torch.sum(segment**2))
+    if isinstance(segment, np.ndarray):
+        segment = torch.from_numpy(segment)
+    # segment_length = torch.sqrt(torch.sum(segment**2))
 
     # the patch should contain both line end points plus some blur
-    L = int(torch.ceil(segment_length)) + 1 # The radius of the patch is the whole line length since the line starts at patch center.
-    overhang = int(2*width) # include space beyond the end of the line
+    # L = int(torch.ceil(segment_length)) + 1 # The radius of the patch is the whole line length since the line starts at patch center.
+    L = int(max(abs(segment).tolist()))
+    overhang = int(np.ceil(2*width)) # include space beyond the end of the line
     patch_radius = L + overhang
 
     patch_size = 2*patch_radius + 1
@@ -57,7 +63,7 @@ def draw_line_segment(segment, width, binary=False, value=1.0):
     # if width is 0, don't blur
     if width > 0:
         if binary:
-            X = torch.tensor(dilation(X, footprint_rectangle((int(width),)*3)))
+            X = torch.tensor(dilation(X, np.ones((int(width),)*3, dtype=np.uint8)))
         else:
             sigma = width/2
             X = torch.tensor(gaussian(X, sigma=sigma))
@@ -66,6 +72,129 @@ def draw_line_segment(segment, width, binary=False, value=1.0):
     return X.to(device=segment.device)
 
 
+def draw_line_segment(segment, width, mask=False, value=1, sharpness=1.0):
+    """ Generate an image of a line segment with width.
+
+    Parameters
+    ----------
+    segment: torch.Tensor
+        array with two three dimensional points (shape: 2x3)
+    width: scalar
+        segment width
+    binary: bool
+        Make a line mask rather than a blurred idealized line.
+    value: int
+        If binary is set to True, set the line brightness to this value. Default is 1.
+    
+    Returns
+    -------
+    X : torch.Tensor
+        A patch with the new line segment starting at its center.
+    """
+    if isinstance(segment, np.ndarray):
+        segment = torch.from_numpy(segment).to(dtype=torch.float32)
+    else:
+        # ensure float dtype for subsequent math
+        segment = segment.to(dtype=torch.float32)
+    start = segment[0]
+    direction_vec = segment[1] - segment[0]
+
+    # the patch should contain both line end points plus some blur
+    # L = int(torch.ceil(segment_length)) + 1 # The radius of the patch is the whole line length since the line starts at patch center.
+    L = int(max(abs(direction_vec).tolist()))
+    overhang = int(np.ceil(2*width)) # include space beyond the end of the line
+    patch_radius = L + overhang
+
+    patch_size = 2*patch_radius + 1
+    # Create coordinate grid as float tensors on same device as direction_vec
+    x = [torch.arange(patch_size, dtype=torch.float32, device=direction_vec.device),] * 3
+    X = torch.stack(torch.meshgrid(*x, indexing='ij'), -1)
+    translation = (torch.tensor([patch_radius,] * 3, dtype=torch.float32, device=direction_vec.device) + (start % 1))
+    X = X - translation
+    seglen_sq = torch.dot(direction_vec, direction_vec)
+    P = torch.outer(direction_vec, direction_vec) / seglen_sq
+    P_ = (torch.eye(3, dtype=direction_vec.dtype, device=direction_vec.device) - P)
+    P_X = torch.matmul(P_[None,None,None], X[...,None]).squeeze()
+    dist = torch.linalg.norm(P_X, dim=-1)
+    segTb = torch.matmul(direction_vec[None,None,None,None], X[...,None]).squeeze()
+    dist_to_end = torch.linalg.norm(X - direction_vec, dim=-1)
+    dist_to_start = torch.linalg.norm(X, dim=-1)
+    dist = torch.where(segTb > seglen_sq, dist_to_end, dist)
+    dist = torch.where(segTb < 0, dist_to_start, dist)
+    
+    width = np.maximum(width, 1.0)
+    sharpness = 1.0 if sharpness is None else sharpness
+    if mask:
+        X = dist < width / 2
+        X = X.to(dtype=torch.int16) * value
+    else:
+        X = torch.exp(-0.5 * (dist / (width / 2.35))**(2 * sharpness)) * value # FWHM = 2.35 * sigma -> sigma = FWHM / 2.35
+    
+    return X.to(device=direction_vec.device)
+
+
+def extract_spherical_patch(volume, x, y, z, center, radius, order=1, permutation=None, normalize=False):
+    """
+    Extract a spherical patch from a 3D image volume and project it onto a 2D surface.
+    
+    Parameters:
+    -----------
+    volume : 3D numpy array
+        The 3D image volume
+    x : numpy array
+        x components of sample points as a meshgrid.
+    y : numpy array
+        y components of sample points as a meshgrid.
+    z : numpy array
+        z components of sample points as a meshgrid.
+    center : tuple of 3 ints
+        The (z, y, x) coordinates of the center of the sphere
+    radius : float
+        The radius of the sphere
+    resolution : tuple of 2 ints
+        The resolution of the resulting 2D projection (theta_res, phi_res)
+    order : int, optional
+        The order of the spline interpolation (0=nearest, 1=linear, etc.)
+    permutation : list or tuple of ints, optional
+        Permutation to apply to volume and center point axes before patch extraction.
+    normalize : bool, optional
+        Whether to normalize the output to [0, 1]
+        
+    Returns:
+    --------
+    2D numpy array
+        The 2D projection of the spherical surface (equirectangular projection)
+    """
+
+    if permutation is not None:
+        # Apply permutation to volume and center
+        volume = np.transpose(volume, axes=permutation)
+        # Create a new center with the permuted coordinates
+        new_center = []
+        for i in range(3):
+            new_center.append(center[permutation[i]])
+        center = new_center
+    
+    # Scale by radius and translate to center
+    coords = np.array([
+        center[0].item() + radius * z,
+        center[1].item() + radius * y,
+        center[2].item() + radius * x
+    ])
+    
+    # Sample the volume using interpolation
+    values = map_coordinates(volume, coords, order=order, mode='constant', cval=0)
+    
+    # Reshape to 2D projection
+    projection = values.reshape(x.shape[0], x.shape[1])
+    
+    # Normalize if requested
+    if normalize and projection.max() != projection.min():
+        projection = (projection - projection.min()) / (projection.max() - projection.min())
+    
+    return projection
+
+
 class Image:
 
     """ 
@@ -121,9 +250,19 @@ def crop(self, center, radius, interp=False, padding_mode="zeros", pad=True, val
             padding : ndarray
                 Length that patch overlaps with image boundaries on each end of each dimension.
         """
-        i,j,k = [int(torch.round(x)) for x in center]
-        shape = self.data.shape[1:]
-
+        i,j,k = [int(x.item()) for x in center]
+        if self.data.ndim == 4:
+            shape = self.data.shape[1:]
+        elif self.data.ndim == 3:
+            shape = self.data.shape
+        else:
+            raise ValueError(f"Image data must be 3D or 4D, but got {self.data.ndim}D data.")
+        if any([i < 0, j < 0, k < 0, i >= shape[0], j >= shape[1], k >= shape[2]]):
+            warnings.warn(f"Center {center} is out of bounds for image shape {shape}. Translating to the nearest valid index.")
+            i = np.clip(i, 0, shape[0]-1)
+            j = np.clip(j, 0, shape[1]-1)
+            k = np.clip(k, 0, shape[2]-1)
+            
         # get amount of padding for each face
         zpad_top = zpad_btm = ypad_front = ypad_back = xpad_left = xpad_right = 0
 
@@ -156,14 +295,16 @@ def crop(self, center, radius, interp=False, padding_mode="zeros", pad=True, val
 
         if pad:
             patch_size = 2*radius+1
-            patch_ = torch.ones((self.data.shape[0], patch_size, patch_size, patch_size), device=self.data.device) * value
+            if self.data.dtype == torch.uint8 and not isinstance(value, int):
+                value = int(value)
+            patch_ = torch.ones((self.data.shape[0], patch_size, patch_size, patch_size), device=self.data.device, dtype=self.data.dtype) * value
             patch_[:, zpad_top:patch_size - zpad_btm, ypad_front:patch_size - ypad_back, xpad_left:patch_size - xpad_right] = patch
             patch = patch_
 
         return patch, padding
     
 
-    def draw_line_segment(self, segment, width, channel=3, value=1.0, binary=False):
+    def draw_line_segment(self, segment, width, channel=-1, value=1, mask=False, sharpness=1.0):
 
         """ Add an image patch with the new line segment to the existing image in the specified channel.
 
@@ -189,10 +330,13 @@ def draw_line_segment(self, segment, width, channel=3, value=1.0, binary=False):
         """
         
         # create the patch with the new line segment starting at its center.
-        X = draw_line_segment(segment, width, binary, value)
-
+        X = draw_line_segment(segment, width, mask, value, sharpness=sharpness)
+        if self.data.dtype == torch.uint8:
+            X = X * 255.0  # scale to uint8 if necessary
+            X = X.to(dtype=torch.uint8)
         # get the patch centered on the new segment start point from the current image.
-        center = torch.round(segment[0]).to(torch.int)
+        # center = torch.round(segment[0]).to(torch.int)
+        center = segment[0]
         patch_radius = int((X.shape[0] - 1)/2)
         patch, padding = self.crop(center, patch_radius, interp=False, pad=False) # patch is a view of self.data (c x h x w x d)
         old_patch = patch[channel].clone()
@@ -200,17 +344,17 @@ def draw_line_segment(self, segment, width, channel=3, value=1.0, binary=False):
         X = X[padding[0]:X.shape[0]-padding[1], padding[2]:X.shape[1]-padding[3], padding[4]:X.shape[2]-padding[5]]
 
         # add segment to patch
-        if binary:
-            # set the new patch to the minimum values between arrays X excluding zeros.
-            patch[channel] = torch.where(X*patch[channel] > 0, torch.minimum(X,patch[channel]), torch.maximum(X,patch[channel]))
-        else:
-            patch[channel] = torch.maximum(X, patch[channel])
+        # if mask:
+        #     # set the new patch to the minimum values between arrays X excluding zeros.
+        #     patch[channel] = torch.where(X*patch[channel] > 0, torch.minimum(X,patch[channel]), torch.maximum(X,patch[channel]))
+        # else:
+        patch[channel] = torch.maximum(X, patch[channel])
         new_patch = patch[channel].clone()
 
         return old_patch, new_patch
     
     
-    def draw_point(self, point: torch.Tensor, radius: float = 3.0, channel: int = -1, binary: bool = False, value: int = 1):
+    def draw_point(self, point: torch.Tensor, radius: float = 3.0, channel: int = -1, mode: Literal["mask", "gaussian"] = "mask", binary: bool = False, value: int = 1):
         """
         Draw a point on the image data with a specified radius and value.
         
@@ -222,30 +366,49 @@ def draw_point(self, point: torch.Tensor, radius: float = 3.0, channel: int = -1
             The radius of the point to be drawn. Default is 3.0.
         channel : int, optional
             The channel on which to draw the point. Default is -1.
+        mode : str, optional
+            The mode to use for drawing the point. Default is
+            "mask". Options are "mask", which draws the point 
+            as a uniform cube with value determined by the
+            "value" argument, or "gaussian", which draws a
+            gaussian blurred point.
         binary : bool, optional
             If True, the point will be drawn as a binary value. Default is False.
         value : int, optional
             The value to assign to the point. Default is 1.
-            
+
         Returns
         -------
         None
+        
+        Raises
+        ------
+        TypeError
+            If binary is True and self.data.dtype is not a boolean type.
         """
         
+        if binary and self.data.dtype != torch.bool:
+            raise TypeError(f"Binary mode requires boolean image data, but got {self.data.dtype}")
+            
         c = round(radius)
         patch_size = 2*c+1
         if binary:
-            X = torch.ones((patch_size,patch_size,patch_size)) * value
-        else:
+            X = torch.ones((patch_size,patch_size,patch_size), dtype=torch.bool)
+        elif mode == "gaussian":
             X = torch.zeros((patch_size,patch_size,patch_size))
             X[c,c,c] = 1.0
             X = torch.tensor(gaussian(X, sigma=radius))
             X = (X / torch.amax(X)) * value
+        else: # mode == "mask"
+            X = torch.ones((patch_size,patch_size,patch_size))*value
+        
+        if self.data.dtype == torch.uint8:
+            X = X * 255.0
+            X = X.to(dtype=torch.uint8)
+
         patch, padding = self.crop(point, radius=c, interp=False, pad=False)
         new_patch = X[padding[0]:X.shape[0]-padding[1], padding[2]:X.shape[1]-padding[3], padding[4]:X.shape[2]-padding[5]]
         patch[channel] = torch.maximum(new_patch.to(device=patch.device), patch[channel])
 
         return
-    
-if __name__ == "__main__":
-    pass
\ No newline at end of file
+    
\ No newline at end of file
diff --git a/data_prep/interp.py b/data_prep/interp.py
new file mode 100644
index 0000000..6d11aa9
--- /dev/null
+++ b/data_prep/interp.py
@@ -0,0 +1,120 @@
+import torch
+import numpy as np
+
+class SphericalSampler():
+
+    def __init__(self, input_shape, radii, resolution=(180,360)):
+        """
+        Initialize the interpolation grid for spherical sampling.
+        This method sets up a grid of points on concentric spheres with specified radii,
+        and precomputes the weights and indices needed for trilinear interpolation.
+
+        Parameters
+        ----------
+        input_shape : tuple
+            The shape of the input volume, with the last three dimensions representing
+            the spatial dimensions (z, y, x).
+        radii : array_like
+            List or array of radii for the concentric spheres.
+        resolution : tuple, optional
+            Resolution of the spherical grid as (theta_res, phi_res), where theta_res
+            is the number of points along the polar angle (0 to π) and phi_res is the
+            number of points along the azimuthal angle (0 to 2π). Default is (180, 360).
+
+        Notes
+        -----
+        The method computes the following attributes:
+        - weights000, weights001, ..., weights111: Interpolation weights for the 8 corners
+          of each voxel containing a sample point.
+        - points_i000, points_i001, ..., points_i111: Linear indices of the 8 corners for
+          each sample point, flattened to 1D for efficient access.
+        The coordinate system is set up with the origin at the corner of the volume, and
+        the points are scaled to match the specified radii.
+        """
+
+        
+        self.input_shape = input_shape
+        self.radii = radii
+        self.resolution = resolution
+
+        # compute sample points
+        theta_res, phi_res = resolution
+        theta = torch.linspace(0, np.pi, theta_res)
+        phi = torch.linspace(0, 2*np.pi, phi_res)
+        theta_grid, phi_grid = torch.meshgrid(theta, phi)
+
+        # Convert to cartesian coordinates (points on a unit sphere)
+        x = (torch.sin(theta_grid) * torch.cos(phi_grid) + 1) # ndim is 2, i.e. shape is equal to resolution
+        x = torch.stack((x,) * len(radii))  
+        x = x * radii[:,None,None] # ndim is 3 with radius varying along the first dimension
+        y = (torch.sin(theta_grid) * torch.sin(phi_grid) + 1)
+        y = torch.stack((y,) * len(radii))
+        y = y * radii[:,None,None]
+        z = (torch.cos(theta_grid) + 1)
+        z = torch.stack((z,) * len(radii))
+        z = z * radii[:,None,None]
+
+        points_f000 = torch.stack((z,y,x), dim=-1).reshape(-1,3)
+        points_i000 = torch.floor(points_f000).long()
+        p000 = points_f000 - points_i000
+
+        # compute sample weights
+        self.weights000 = (1.0 - p000[...,0])*(1.0 - p000[...,1])*(1.0 - p000[...,2])
+        self.weights001 = (1.0 - p000[...,0])*(1.0 - p000[...,1])*(      p000[...,2])
+        self.weights010 = (1.0 - p000[...,0])*(      p000[...,1])*(1.0 - p000[...,2])
+        self.weights011 = (1.0 - p000[...,0])*(      p000[...,1])*(      p000[...,2])
+        self.weights100 = (      p000[...,0])*(1.0 - p000[...,1])*(1.0 - p000[...,2])
+        self.weights101 = (      p000[...,0])*(1.0 - p000[...,1])*(      p000[...,2])
+        self.weights110 = (      p000[...,0])*(      p000[...,1])*(1.0 - p000[...,2])
+        self.weights111 = (      p000[...,0])*(      p000[...,1])*(      p000[...,2])
+
+        # Vectorize points
+        n = input_shape[-3:]
+        self.points_i000 = points_i000[:,0]*n[1]*n[2] + points_i000[:,1]*n[2] + points_i000[:,2]
+
+        # Find new sample points
+        self.points_i001 = self.points_i000 + 1
+        self.points_i010 = self.points_i000 + n[2]
+        self.points_i011 = self.points_i000 + n[2] + 1
+        self.points_i100 = self.points_i000 + n[1]*n[2]
+        self.points_i101 = self.points_i000 + n[1]*n[2] + 1
+        self.points_i110 = self.points_i000 + n[1]*n[2] + n[2]
+        self.points_i111 = self.points_i000 + n[1]*n[2] + n[2] + 1
+    
+
+    def map_coordinates(self,img):
+        """
+
+        Parameters
+        ----------
+        img : torch.Tensor
+            The input image with shape (N, C, D, H, W).
+
+        Returns
+        -------
+        Iout : torch.Tensor
+            Output stack of 2D spherical projections of the input image with shape (N, N_radii, C, H, W). 
+        """
+
+        if img.shape[-3:] != self.input_shape[-3:]:
+            raise Exception(f"Input image shape along last 3 dimensions ({img.shape[-3:]}) does not match expected shape ({self.input_shape[-3:]}).")
+        
+        if img.ndim != 5:
+            raise Exception(f"Input image must have 5 dimensions (N, C, D, H, W) but found {img.ndim}")
+        
+        Ivec = img.reshape(img.shape[0], img.shape[1], -1)
+        Iout = torch.zeros((img.shape[0], img.shape[1], len(self.points_i000)))
+        Iout.addcmul_(Ivec[...,self.points_i000], self.weights000 )
+        Iout.addcmul_(Ivec[...,self.points_i001], self.weights001 )
+        Iout.addcmul_(Ivec[...,self.points_i010], self.weights010 )
+        Iout.addcmul_(Ivec[...,self.points_i011], self.weights011 )
+        Iout.addcmul_(Ivec[...,self.points_i100], self.weights100 )
+        Iout.addcmul_(Ivec[...,self.points_i101], self.weights101 )
+        Iout.addcmul_(Ivec[...,self.points_i110], self.weights110 )
+        Iout.addcmul_(Ivec[...,self.points_i111], self.weights111 )
+        Iout = Iout.reshape(img.shape[0], len(self.radii), img.shape[1], self.resolution[0], self.resolution[1])
+
+        return Iout
+        
+        
+        
\ No newline at end of file
diff --git a/data_prep/load.py b/data_prep/load.py
index b590c6e..862d15b 100644
--- a/data_prep/load.py
+++ b/data_prep/load.py
@@ -1,6 +1,7 @@
 import numpy as np
 import os
 from pathlib import Path
+from scipy.ndimage import zoom
 import sys
 import tifffile as tf
 import torch
@@ -8,6 +9,18 @@
 sys.path.append(str(Path(__file__).parent))
 from data_utils import interp
 
+
+# Rotation matrices
+def Rx(a):
+    return np.array([[1, 0, 0], [0, np.cos(a), -np.sin(a)], [0, np.sin(a), np.cos(a)]])
+
+def Ry(a):
+    return np.array([[np.cos(a), 0, np.sin(a)], [0, 1, 0], [-np.sin(a), 0, np.cos(a)]])
+
+def Rz(a):
+    return np.array([[np.cos(a), -np.sin(a), 0], [np.sin(a), np.cos(a), 0], [0, 0, 1]])
+
+
 def tiff(img_dir, pixelsize=[1.0,1.0,1.0], downsample_factor=1.0, inverse=False):
     """
     Load a stack of TIFF images from a directory, downsample them, and optionally invert the pixel values.
@@ -62,7 +75,7 @@ def tiff(img_dir, pixelsize=[1.0,1.0,1.0], downsample_factor=1.0, inverse=False)
     return stack
 
 
-def swc(labels_file):
+def swc(labels_file, rotate=False, verbose=True):
     """
     Load and parse an SWC file.
     
@@ -83,8 +96,8 @@ def swc(labels_file):
     If it fails, it retries with 'latin1' encoding.
     Lines starting with '#' or empty lines are ignored.
     """
-    
-    print(f"loading file: {labels_file}")
+    if verbose:
+        print(f"loading file: {labels_file}")
     try:
         with open(labels_file, 'r') as f:
             lines = f.readlines()
@@ -92,129 +105,287 @@ def swc(labels_file):
         with open(labels_file, 'r', encoding="latin1") as f:
             lines = f.readlines()
 
-    lines = [line for line in lines if not line.startswith('#') and line.strip()]
-    lines = [line.split() for line in lines]
-    swc_list = [list(map(int, line[:2])) + list(map(float, line[2:6])) + [int(line[6])] for line in lines]
-
-    return swc_list
+    lines = [line.split() for line in lines if not line.startswith('#') and line.strip()]
+    # Columns are: index, type, x, y, z, radius, parent_index
+    # Index, type, and parent_index should be integers, while x, y, z, and radius are floats.
+    # Handle cases where all columns are saved as a decimal number.
+    idx_type = 'int' if lines[0][0].isdigit() else 'float'
+    if idx_type == 'int':
+        swc_list = [list(map(int, line[:2])) + list(map(float, line[2:6])) + [int(line[6])] for line in lines]
+    else:
+        swc_list = [list(map(int, map(float, line[:2]))) + list(map(float, line[2:6])) + [int(float(line[6]))] for line in lines]
 
+    if rotate:
 
-def parse_swc_list(swc_list, adjust=True, transpose=True):
-    """
-    Parses a list of SWC data and constructs sections, section graph, branches, and terminals.
+        choices = [0, 90, 180, 270]
+        angle = [np.random.choice(choices), np.random.choice(choices), np.random.choice(choices)]
+        rotation = np.eye(7)
+        rotation[2:5,2:5] = np.matmul(np.matmul(Rx(angle[0]), Ry(angle[1])), Rz(angle[2]))
 
-    Parameters
-    ----------
-    swc_list : list
-        A list of SWC data where each element is a list representing a node in the SWC format.
-    adjust : bool, optional
-        If True, scales and shifts the coordinates (default is True).
-    transpose : bool, optional
-        If True, transposes the coordinates (default is True).
+        swc_list = np.matmul(swc_list, rotation.T)
+        swc_list = swc_list.tolist()
+        
+    return swc_list
 
-    Returns
-    -------
-    sections : dict
-        A dictionary where keys are section IDs and values are tensors of section coordinates.
-    section_graph : dict
-        A dictionary representing the graph of sections.
-    branches : torch.Tensor
-        A tensor of branch points.
-    terminals : torch.Tensor
-        A tensor of terminal points.
-    scale : float
-        The scaling factor applied to the coordinates.
-    """
 
+def parse_swc_list_original(swc_list, transpose=True):
     graph = {}
     for parent in swc_list:
         children = []
         for child in swc_list:
-            if child[6] == parent[0]:
-                children.append(child[0])
-        graph[parent[0]] = children
+            if int(child[6]) == int(parent[0]):
+                children.append(int(child[0]))
+        graph[int(parent[0])] = children
 
     sections = {1:[]}
     section_graph = {1:[]}
-    i = 1
     section_id = 1
     for key, value in graph.items():
-        if len(value) == 0:
-            sections[i] = torch.tensor(sections[i]) # type: ignore #
+        if len(value) == 0: 
+            sections[section_id] = torch.tensor(sections[section_id]) # type: ignore #
             if transpose:
-                sections[i] = torch.stack((sections[i][...,2], sections[i][...,1], sections[i][...,0]), dim=2) #type: ignore #
-            i = key+1 # go to the section whose first segment corresponds to the next key
-            section_id = key+1
+                sections[section_id] = torch.stack((sections[section_id][...,2], sections[section_id][...,1], sections[section_id][...,0], sections[section_id][...,3]), dim=2) #type: ignore #
+            section_id = key+1 # go to the section whose first segment corresponds to the next key
             section_graph[section_id] = []
         elif len(value) == 1:
-            sections[i].append([swc_list[key-1][2:5], swc_list[value[0]-1][2:5]])
+            sections[section_id].append([swc_list[key-1][2:6], swc_list[value[0]-1][2:6]])
         else:
-            for child in value:
-                if child == key + 1:
-                    sections[i].append([swc_list[key-1][2:5], swc_list[child-1][2:5]])
-                else:
-                    sections[child] = []
-                    sections[child].append([swc_list[key-1][2:5], swc_list[child-1][2:5]])
+            # Edit 2/5/25: Every branch spawns new sections and terminates the parent section
+            if len(sections[section_id]) == 0: # The section is empty. This might happen if the root node is also a branch.
+                # In this case do not terminate the section
+                for child in value:
+                    if child == key + 1:
+                        # add segment to the same section
+                        sections[section_id].append([swc_list[key-1][2:6], swc_list[child-1][2:6]])
+                    else:
+                        # add segment to a new section
+                        sections[child] = [[swc_list[key-1][2:6], swc_list[child-1][2:6]]]
+                        section_graph[section_id].append(child)
+            else:  
+                for child in value:
+                    sections[child] = [[swc_list[key-1][2:6], swc_list[child-1][2:6]]]
                     section_graph[section_id].append(child)
+                # end the section and go to the next one
+                sections[section_id] = torch.tensor(sections[section_id]) # type: ignore #
+                if transpose:
+                    sections[section_id] = torch.stack((sections[section_id][...,2], sections[section_id][...,1], sections[section_id][...,0], sections[section_id][...,3]), dim=2) #type: ignore #
+                section_id = key+1 # go to the section whose first segment corresponds to the next key
+                section_graph[section_id] = []
+                
+    return sections, section_graph
+
+
+def undirected_edge_list(swc_list):
+    """
+    Parse a list of SWC lines into an undirected edge list.
 
+    """
+    edge_list = {}
+    graph = []
+    # swc_list -> (id, type, x,y,z, radius, parent)
+    node_ids = [int(i[0]) for i in swc_list]
+    for line in swc_list:
+        if line[6] not in node_ids:
+            continue
+        graph.append([int(line[0]), int(line[6])])
+    graph = np.array(graph)
+    # Make graph undirected
+    graph = np.vstack([graph, graph[:, ::-1]])
+    # for node id in the first column, find all connected nodes in the second column
+    ids = np.unique(graph[:, 0])
+    for i in ids:
+        adjacents = graph[graph[:, 0] == i, 1]
+        edge_list[i.item()] = adjacents.tolist()
+
+    return edge_list
+
+
+def parse_swc(swc_list, transpose=True, verbose=False):
+    """
+    Parse a list of SWC lines and return an directed adjacency list of neuron sections,
+    and a dictionary of sections with their corresponding coordinates. Sections are defined
+    as the nodes between and including branching points or terminal points.
+
+    Parameters
+    ----------
+    swc_list : list of lists
+        The list of SWC lines
+    transpose : bool, optional
+        Whether to transpose coordinates (i.e. (x,y,z)->(z,y,x)). (default is True)
+    
+    Returns
+    -------
+    sections : dict
+        The dictionary of sections with their corresponding coordinates.
+    sections_graph : dict
+        The directed adjacency list of neuron sections.
+    """
+    swc_list = np.array(swc_list)
+    if swc_list.size == 0:
+        return {}, {}
+    
+    # Compute undirected edge list
+    edge_list = undirected_edge_list(swc_list)
+    # Make list of branching nodes
+    branchings = [i for i in edge_list.keys() if len(edge_list[i]) > 2]
+    sections = {}
+    section_ends = {}
+    while len(edge_list) > 1:
+        terminals_ = None
+        if 'terminals' in locals():
+            terminals_ = terminals
+        # Make list of terminal nodes
+        terminals = [i for i in edge_list.keys() if len(edge_list[i]) == 1]
+        # check if the list of terminals has changed
+        if terminals == terminals_:
+            if verbose:
+                print(f"Warning: The neuron tree has disconnected sections.")
+            break
+        # from each terminal node walk along the tree until you reach a branching node
+        # or another terminal node
+        for terminal in terminals:
+            if edge_list[terminal] == []: # if terminal node is the only node left
+                break
+            section = []
+            node = terminal
+            while True:
+                next_node = edge_list[node][0]
+                section.append([swc_list[swc_list[:, 0] == node][0,2:6], swc_list[swc_list[:, 0] == next_node][0,2:6]])
+                edge_list.pop(node)
+                edge_list[next_node].remove(node)
+                node = next_node
+
+                if node in branchings or node in terminals:
+                    # complete the section
+                    section = np.array(section)
+                    if transpose:
+                        section = np.concatenate((section[:,:,:3][:,:,::-1], section[:,:,3,None]), axis=-1)
+                    sections[terminal] = section
+                    section_ends[terminal] = [terminal, node]
+
+                    # repeat with a new list of terminals until the edge list only contains one final node
+                    break
+    
+    # make directed sections graph
+    sections_graph = {}
+    for section in section_ends:
+        sections_graph[section] = []
+        # find all sections that share an end
+        for other_section in section_ends:
+            if other_section == section:
+                continue
+            if any(x in section_ends[other_section] for x in section_ends[section]):
+                sections_graph[section].append(other_section)
+
+    return sections, sections_graph
+    
+
+def get_critical_points(swc_list, sections, transpose=True):
+    # filter branches
     # get average segment length
     lengths = []
     for section in sections.values():
         for segment in section:
-            lengths.append(torch.linalg.norm(segment[1] - segment[0]))
-    avg_length = torch.median(torch.tensor(lengths))
+            lengths.append(np.linalg.norm(segment[1,:3] - segment[0,:3]))
+    avg_length = np.median(np.array(lengths))
 
-    branches = []
-    terminals = []
-    for key, value in graph.items():
-        if len(value) == 0:
-            terminals.append(swc_list[key-1][2:5])
-        elif len(value) > 1:
-            # check the remaining length of each section starting from the current key
-            lengths = []
-            for child in value:
-                # get the last position of each section starting from the current key
-                # length = 0
-                i = child
-                while len(graph[i]) > 0:
-                    i += 1
-                    # length += 1
-                lengths.append(np.linalg.norm(np.array(swc_list[i-1][2:5]) - np.array(swc_list[key-1][2:5])))
-                # lengths.append(length)
-            # if at least two sections have avg_lengths greater than 1, then the current key is a branch
-            if key == 1:
-                if sum([l > 2*avg_length for l in lengths]) > 2:
-                    branches.append(swc_list[key-1][2:5])
-            else:
-                if sum([l > 2*avg_length for l in lengths]) > 1:
-                    branches.append(swc_list[key-1][2:5])
-
-    branches = torch.tensor(branches)
+    edge_list = undirected_edge_list(swc_list)
+    branches = [i for i in edge_list.keys() if len(edge_list[i]) > 2]
+    # for each branch, walk along each section to which it connects,
+    # branches_to_remove = []
+    # for branch in branches:
+    #     # check the remaining length of each section starting from the current branch
+    #     num_long_sections = 0
+    #     for child in edge_list[branch]:
+    #         node = child
+    #         prev = branch
+    #         l = np.linalg.norm(np.array(swc_list[node-1][2:5]) - np.array(swc_list[prev-1][2:5]))
+    #         if l >= avg_length:
+    #             num_long_sections += 1
+    #         while len(edge_list[node]) >= 2:
+    #             next_node = [n for n in edge_list[node] if n != prev][0]
+    #             prev = node
+    #             node = next_node
+    #             l += np.linalg.norm(np.array(swc_list[node-1][2:5]) - np.array(swc_list[prev-1][2:5]))
+    #             if l >= avg_length:
+    #                 num_long_sections += 1
+    #                 break
+    #         # if the length of at least two sections is greater than 2*avg_length, continue
+    #         if num_long_sections > 2:
+    #             break
+    #     # else, mark the branch for removal
+    #     if num_long_sections <= 2:
+    #         branches_to_remove.append(branch)
+    # # print(f'removing {len(branches_to_remove)} branches')
+    # for branch in branches_to_remove:
+    #     branches.remove(branch)
+    swc_list = np.array(swc_list)
+    branches = [swc_list[swc_list[:, 0] == i][0][2:6] for i in branches]
+    terminals = [swc_list[swc_list[:, 0] == i][0][2:6] for i in edge_list.keys() if len(edge_list[i]) == 1]
+
+    branches = np.array(branches)
     if transpose and len(branches) > 0:
-        branches = torch.stack((branches[:,2], branches[:,1], branches[:,0]), dim=1)
-    terminals = torch.tensor(terminals)
+        branches = np.concatenate((branches[:,:3][:,::-1], branches[:,3,None]), axis=-1)
+    terminals = np.array(terminals)
     if transpose:
-        terminals = torch.stack((terminals[:,2], terminals[:,1], terminals[:,0]), dim=1)
+        terminals = np.concatenate((terminals[:,:3][:,::-1], terminals[:,3,None]), axis=-1)
+
+    return branches, terminals
+
 
+def adjust_neuron_coords(sections, branches, terminals):
     scale = 1
-    if adjust:
-        # scale and shift coordinates
-        max = torch.tensor([-1e6, -1e6, -1e6])
-        min = torch.tensor([1e6, 1e6, 1e6])
-        for id, section in sections.items():
-            max = torch.maximum(max, section.amax(dim=(0,1))) # type: ignore #
-            min = torch.minimum(min, section.amin(dim=(0,1))) # type: ignore #
-        vol = torch.prod(max - min)
-        scale = torch.round((5e7 / vol)**(1/3)) # scale depends on the volume
-
-        for id, section in sections.items():
-            section = (section - min) * scale + torch.tensor([10.0, 10.0, 10.0])
-            sections[id] = section # type: ignore #
-        if len(branches) > 0:
-            branches = (branches - min) * scale + torch.tensor([10.0, 10.0, 10.0])
-        terminals = (terminals - min) * scale + torch.tensor([10.0, 10.0, 10.0])
-
-    return sections, section_graph, branches, terminals, scale
+    # scale and shift coordinates
+    max = np.array([-1e6, -1e6, -1e6])
+    min = np.array([1e6, 1e6, 1e6])
+    for id, section in sections.items():
+        max = np.maximum(max, section.max(axis=(0,1))[:3]) # type: ignore #
+        min = np.minimum(min, section.min(axis=(0,1))[:3]) # type: ignore #
+    vol = np.prod(max - min)
+    scale = np.round((5e7 / vol)**(1/3)) # scale depends on the volume
+
+    for id, section in sections.items():
+        section[...,:3] = (section[...,:3] - min) * scale + np.array([10.0, 10.0, 10.0])
+        sections[id] = section # type: ignore #
+    if len(branches) > 0:
+        branches[...,:3] = (branches[...,:3] - min) * scale + np.array([10.0, 10.0, 10.0])
+    terminals[...,:3] = (terminals[...,:3] - min) * scale + np.array([10.0, 10.0, 10.0])
+
+    return sections, branches, terminals, scale
+
+
+def crop_and_adjust_coords(image, swc_list):
+    # Get the bounding box from the SWC file
+    swc_list = np.array(swc_list)
+    max_coord = np.max(swc_list[:, 2:5], axis=0)
+    min_coord = np.min(swc_list[:, 2:5], axis=0)
+    # Calculate the crop size
+    crop_max = np.ceil(max_coord + 11).astype(int)  # add 10 pixels margin plus one to include the max coordinate
+    # ensure we don't exceed image dimensions
+    # remember swc coordinates are in x-y-z not slice-row-column order
+    crop_max = np.minimum(crop_max, np.array(image.shape)[::-1])
+    crop_min = np.floor(min_coord - 10).astype(int)  # subtract 10 pixels margin
+    crop_min = crop_min.clip(min=0)  # ensure we don't go below zero
+    # Crop the image
+    cropped_image = image[crop_min[2]:crop_max[2], crop_min[1]:crop_max[1], crop_min[0]:crop_max[0]]
+    # Adjust the coordinates in swc_list
+    adjusted_swc_list = swc_list.copy()
+    adjusted_swc_list[:, 2:5] -= (min_coord - 10).clip(min=0.0)  # adjust coordinates to the new origin
+
+    return cropped_image, adjusted_swc_list.tolist()
+
+
+def scale_and_adjust_coords(image, swc_list, scale, anisotropy_factor=None):
+    swc_list_corrected = np.array(swc_list).copy()
+    img_scaled = image.copy()
+    if anisotropy_factor is not None:
+        img_scaled = zoom(img_scaled, (anisotropy_factor, 1.0, 1.0))
+        swc_list_corrected[:, 4] *= anisotropy_factor
+    img_scaled = zoom(image, scale)
+    swc_list_corrected[:, 2:5] *= scale
+
+    return img_scaled, swc_list_corrected.tolist()
+
 
 if __name__ == "__main__":
     pass
\ No newline at end of file
diff --git a/data_prep/save.py b/data_prep/save.py
new file mode 100644
index 0000000..9cc5cc5
--- /dev/null
+++ b/data_prep/save.py
@@ -0,0 +1,57 @@
+import numpy as np
+import os
+
+def paths_to_swc(paths):
+    """
+    Convert paths list to swc list.
+
+    Parameters
+    ----------
+    paths : list
+        A list of lists of 3D points.
+    
+    Returns
+    -------
+    swc_list : list
+        A list of lists in swc data format. 
+    """
+
+    id = 1
+    swc_list = []
+    for i,path in enumerate(paths):
+        if len(path) < 2:
+            continue
+        # set first point
+        point = path[0]
+        if i == 0:
+            parent_id = -1
+        else:
+            # The first point from a child section should match a point on the parent section.
+            match = 0
+            for k in swc_list:
+                if k[2:5] == list(point):
+                    parent_id = k[0]
+                    match = 1
+            # If not, move to the next path
+            if not match:
+                continue
+        swc_list.append([id, 0, point[0].item(), point[1].item(), point[2].item(), 1.0, parent_id])
+        parent_id = id
+        id += 1
+        # now the rest
+        for point in path[1:]:
+            swc_list.append([id, 0, point[0].item(), point[1].item(), point[2].item(), 1.0, parent_id])
+            parent_id = id
+            id += 1
+
+    return swc_list
+
+
+def write_swc(swc_list, out, exist_ok=True):
+    if os.path.exists(out) and not exist_ok:
+        raise FileExistsError(f"The file {out} already exists.")
+    if not os.path.exists(os.path.split(out)[0]):
+        os.makedirs(os.path.split(out)[0], exist_ok=True)
+    with open(out, mode='w') as f:
+        for line in swc_list:
+            f.write(' '.join(map(str,line)) + '\n')
\ No newline at end of file
diff --git a/data_prep/tree.py b/data_prep/tree.py
new file mode 100644
index 0000000..40d2bd6
--- /dev/null
+++ b/data_prep/tree.py
@@ -0,0 +1,206 @@
+import numpy as np
+from pathlib import Path
+import torch
+import sys
+sys.path.append(str(Path(__file__).parent.parent))
+from data_prep import load
+
+
+def split_paths_into_sections(paths):
+    """
+    Splits paths into sections based on their origins.
+    Each section is defined as a segment of the path between intersections with other paths' origins.
+    """
+    path_origins = np.array([p[0][:3] for p in paths])
+    sections = {}
+    # divide each path wherever it intersects with the origin of other paths
+    # create a new section for each segment of the path
+    for path in paths:
+        intersections = [0] + [i+1 for i, point in enumerate(path[1:]) if point[:3] in path_origins]
+        if len(intersections) > 1:
+            sections |= {len(sections) + i+1: path[intersections[i]:intersections[i+1]+1] for i in range(len(intersections)-1)}
+            if intersections[-1] != len(path) - 1:
+                sections[len(sections)+1] = path[intersections[-1]:]  # Add the last segment
+        else:
+            sections[len(sections)+1] = path
+
+    return sections
+
+
+def get_single_stream_length(section_id, section_graph, sections, visited=None):
+    """
+    Recursively calculates the total length of a section and its downstream sections.
+    Prevents infinite recursion by tracking visited sections.
+    """
+    if visited is None:
+        visited = set()
+    if section_id in visited:
+        return 0
+    visited.add(section_id)
+    # total_length = len(sections[section_id])
+    # total_length = np.linalg.norm(sections[section_id][0,:3] - sections[section_id][-1,:3])
+    total_length = (torch.sum((sections[section_id][1:, :3] - sections[section_id][:-1, :3])**2, dim=1)**0.5).sum()
+    for connected_section in section_graph.get(section_id, []):
+        total_length += get_single_stream_length(connected_section, section_graph, sections, visited)
+    return total_length
+
+
+def get_all_stream_lengths(sections):
+    """
+    Calculate the lengths of each section.
+    """
+    # Make section adjacency graph
+    section_graph = {}
+    for id,section in sections.items():
+        section_graph[id] = []
+        for other_id,other_section in sections.items():
+            if id != other_id and all(section[-1] == other_section[0]):
+                section_graph[id].append(other_id)
+    # Calculate total length of each section and its downstream sections
+    stream_lengths = {}
+    for section_id in sections.keys():
+        stream_lengths[section_id] = get_single_stream_length(section_id, section_graph, sections)
+
+    return stream_lengths, section_graph
+
+
+def get_longest_stream(section_id, section_graph, section_lengths):
+    """
+    Get longest stream, starting at the given section id, where a stream is a list of section ids of consecutive sections.
+    """
+    stream = [section_id]
+    while section_id in section_graph:
+        next_section = max(section_graph[section_id], key=lambda x: section_lengths.get(x, 0), default=None)
+        if next_section is not None:
+            stream.append(next_section)
+            section_id = next_section
+        else:
+            break
+    return stream
+
+
+def get_hierarchical_streams(stream_lengths, section_graph):
+
+    hierarchical_streams = {}
+    # list all sections in descending order of their stream lengths
+    section_ids_sorted = sorted(stream_lengths.keys(), key=lambda x: stream_lengths[x], reverse=True)
+    # start with the longest stream
+    while section_ids_sorted:
+        hierarchical_streams[section_ids_sorted[0]] = get_longest_stream(section_ids_sorted[0], section_graph, stream_lengths)
+        # remove the sections that have been collected into a stream
+        for section in hierarchical_streams[section_ids_sorted[0]]:
+            if section in section_ids_sorted:
+                section_ids_sorted.remove(section)
+    
+    return hierarchical_streams
+
+
+def restitch_sections(hierarchical_streams, sections):
+    restitched_sections = {}
+    for i in hierarchical_streams.keys():
+        restitched_section = np.concatenate([sections[section_id] for section_id in hierarchical_streams[i]])
+        # remove duplicate points, keep order of first occurrence
+        restitched_section, indices = np.unique(restitched_section, axis=0, return_index=True)
+        order = np.argsort(indices)
+        restitched_section = restitched_section[order]
+        restitched_sections[i] = restitched_section
+    
+    return restitched_sections
+
+
+def restructure_neuron_tree(paths):
+    """
+    Restructure neuron tree by splitting paths into sections, calculating stream lengths,
+    and restitching sections based on hierarchical streams.
+    """
+    
+    # Split paths into sections
+    sections = split_paths_into_sections(paths)
+    stream_lengths, section_graph = get_all_stream_lengths(sections)
+    hierarchical_streams = get_hierarchical_streams(stream_lengths, section_graph)
+    restitched_sections = restitch_sections(hierarchical_streams, sections)
+
+    return restitched_sections
+
+
+def remove_soma(swc_list, max_radius=7.0, verbose=False):
+    """Remove soma from neuron tree based on radius threshold.
+    
+    Parameters
+    ----------
+    swc_list : list
+        SWC neuron tree data in list format.
+    max_radius : float
+        Maximum radius to consider for soma removal.
+    
+    Returns
+    -------
+    swc_list : list
+        Updated SWC data with soma removed.
+    seeds : np.ndarray
+        Coordinates of the seeds after soma removal.
+    """
+    buffer = []
+    buffer_size = 5
+    edge_list = load.undirected_edge_list(swc_list)
+    nodes_to_remove = []
+    visited = [1]
+    branch_beginnings = []
+    temp_branches = []
+    seeds = []
+    swc_list = np.array(swc_list)
+    # walk along edge_list
+    # assuming first node is 1 and is the root node
+    node = 1
+    temp_branches = [n for n in edge_list[node] if n not in [node - 1, node + 1] and n not in visited]
+    branch_beginnings += temp_branches
+    while True:
+        radius = swc_list[swc_list[:, 0] == node][0, 5]  # Assuming radius is at index 5
+        if radius >= max_radius:
+            nodes_to_remove.append(node)
+            nodes_to_remove += buffer
+            buffer = []
+            branch_beginnings += temp_branches
+            temp_branches = []
+        else:
+            buffer.append(node)
+            if len(buffer) > buffer_size:
+                seeds.append(buffer[0])
+                # If buffer exceeds size, move to next branch beginning
+                node = branch_beginnings.pop(0) if branch_beginnings else None
+                if node is None:
+                    if verbose:
+                        print("Done")
+                    break
+                else:
+                    buffer = []
+                    # reset temp_branches
+                    temp_branches = [n for n in edge_list[node] if n not in [node - 1, node + 1] and n not in visited]
+                    branch_beginnings += temp_branches
+                    visited.append(node)
+                    continue
+
+        next_node = node + 1
+        if next_node not in edge_list[node]:
+            branch_beginnings += temp_branches
+            temp_branches = []
+            node = branch_beginnings.pop(0) if branch_beginnings else None
+            if node is None:
+                if verbose:
+                    print(f"Reached end of the tree without stopping. Adjust max_radius to prevent this.")
+                break
+            buffer = []
+            temp_branches += [n for n in edge_list[node] if n not in [node - 1, node + 1] and n not in visited]
+            branch_beginnings += temp_branches
+            visited.append(node)
+        else:
+            node = next_node
+            temp_branches += [n for n in edge_list[node] if n not in [node - 1, node + 1] and n not in visited]
+            visited.append(node)
+
+    seeds = np.array([swc_list[swc_list[:, 0] == seed][0, 2:5] for seed in seeds if seed not in nodes_to_remove])
+    # remove nodes from swc_list
+    for node in nodes_to_remove:
+        swc_list = np.delete(swc_list, swc_list[:, 0] == node, axis=0)
+    
+    return swc_list.tolist(), seeds
\ No newline at end of file
diff --git a/docs/source/branch_classifier.ipynb b/docs/source/branch_classifier.ipynb
deleted file mode 100644
index 0c06797..0000000
--- a/docs/source/branch_classifier.ipynb
+++ /dev/null
@@ -1,306 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Branch Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "from glob import glob\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "import sys\n",
-    "import torch\n",
-    "\n",
-    "sys.path.append(\"../\")\n",
-    "from data_prep import collect, load\n",
-    "from solvers import branch_classifier\n",
-    "import models\n",
-    "date = datetime.now().strftime(\"%m-%d-%y\")\n",
-    "dtype = torch.float32\n",
-    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Collect branch classifier training data\n",
-    "Training data consists of volumetric image patches chosen randomly from the neuron node coordinates given\\\n",
-    "in the SWC file with an added small random translation. Image patches are labeled 1 if they are centered on\\\n",
-    " a branch point and 0 otherwise."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get sample points from swc files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading file: /home/brysongray/data/neuromorpho_sub1/beining/CNG version/35dpi_ipsi_infra_06.CNG.swc\n",
-      "loading file: /home/brysongray/data/neuromorpho_sub1/campos/CNG version/Astro-1.CNG.swc\n",
-      "loading file: /home/brysongray/data/neuromorpho_sub1/allen cell types/CNG version/646805498_transformed.CNG.swc\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load SWC file data into python lists\n",
-    "labels_dir = \"/home/brysongray/data/neuromorpho_sub1\"\n",
-    "files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], '*.swc'))]\n",
-    "swc_lists = []\n",
-    "for f in files:\n",
-    "    swc_lists.append(load.swc(f))\n",
-    "\n",
-    "# Collect random sample points from SWC data\n",
-    "samples_per_file = 50\n",
-    "fnames = [f.split('/')[-1].split('.')[0] for f in files]\n",
-    "sample_points = collect.swc_random_points(samples_per_file, swc_lists, fnames, adjust=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Save sample patches and labels from image files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save sample patches from the images centered at the sample points\n",
-    "image_dir = \"/home/brysongray/data/simulated_neurons/neuromorpho_sub1_with_artifacts\"\n",
-    "out_dir = \"classifier_data\"\n",
-    "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir, exist_ok=True)\n",
-    "name = \"neuromorpho_test\"\n",
-    "\n",
-    "collect.collect_data(sample_points, image_dir, out_dir, name, date)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### View some example input images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "observations = os.listdir(os.path.join(out_dir, \"observations\"))\n",
-    "training_annotations = pd.read_csv(glob(os.path.join(out_dir, \"*_labels.csv\"))[0])\n",
-    "ids = np.random.choice(len(training_annotations), size=9)\n",
-    "sample = training_annotations.iloc[ids]\n",
-    "\n",
-    "fig, ax = plt.subplots(3,3)\n",
-    "ax = ax.flatten()\n",
-    "for i in range(len(ax)):\n",
-    "    img = torch.load(os.path.join(out_dir,\"observations\", sample.iloc[i,0]), weights_only=True) # type: ignore\n",
-    "    ax[i].imshow(img[0].amax(0))\n",
-    "    ax[i].set_title(f\"label: {sample.iloc[i,1].item()}\")\n",
-    "    ax[i].set_axis_off()\n",
-    "\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train branch classifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Instantiate dataloader for training and test datasets\n",
-    "Dataloaders use a weighted random sampler to balance classes. Additionally, the training dataset\\\n",
-    " adds a random permutation and flip to the image patch at retrieval."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set source data files paths\n",
-    "training_labels_file = \"classifier_data/branch_classifier_neuromorpho_test_02-07-25_test_labels.csv\"\n",
-    "test_labels_file = \"classifier_data/branch_classifier_neuromorpho_test_02-07-25_training_labels.csv\"\n",
-    "img_dir = \"classifier_data/observations\"\n",
-    "\n",
-    "# instantiate training and test datasets\n",
-    "transform = branch_classifier.transform # random permutation and flip\n",
-    "training_data = branch_classifier.StateData(labels_file=training_labels_file,\n",
-    "                          img_dir=img_dir,\n",
-    "                          transform=transform)\n",
-    "test_data = branch_classifier.StateData(labels_file=test_labels_file,\n",
-    "                          img_dir=img_dir)\n",
-    "\n",
-    "# instantiate dataloaders\n",
-    "training_dataloader = branch_classifier.init_dataloader(training_data)\n",
-    "test_dataloader = branch_classifier.init_dataloader(test_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## View balanced data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(3,3)\n",
-    "axs = axs.flatten()\n",
-    "\n",
-    "X,y = next(iter(training_dataloader))\n",
-    "for i, ax in enumerate(axs):\n",
-    "    ax.imshow(X[i,0].amax(0))\n",
-    "    ax.set_title(f\"Label: {y[i]}\")\n",
-    "    ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1\n",
-      "-------------------------------\n",
-      "Accuracy: 40.0, Loss: 0.405724  [   30/   30]\n",
-      "Test Error: \n",
-      " Accuracy: 50.0%, Avg loss: 24.919770 \n",
-      "           Precision: 0.500, Recall: 1.000\n",
-      "Epoch 2\n",
-      "-------------------------------\n",
-      "Accuracy: 76.66666666666667, Loss: 0.828855  [   30/   30]\n",
-      "Test Error: \n",
-      " Accuracy: 53.3%, Avg loss: 22.108765 \n",
-      "           Precision: 0.533, Recall: 1.000\n",
-      "Epoch 3\n",
-      "-------------------------------\n",
-      "Accuracy: 80.0, Loss: 0.543504  [   30/   30]\n",
-      "Test Error: \n",
-      " Accuracy: 51.7%, Avg loss: 23.350894 \n",
-      "           Precision: 0.517, Recall: 1.000\n",
-      "Epoch 4\n",
-      "-------------------------------\n",
-      "Accuracy: 70.0, Loss: 0.260397  [   30/   30]\n",
-      "Test Error: \n",
-      " Accuracy: 42.5%, Avg loss: 32.415985 \n",
-      "           Precision: 0.425, Recall: 1.000\n",
-      "Epoch 5\n",
-      "-------------------------------\n",
-      "Accuracy: 93.33333333333333, Loss: 0.181836  [   30/   30]\n",
-      "Test Error: \n",
-      " Accuracy: 48.3%, Avg loss: 26.552392 \n",
-      "           Precision: 0.483, Recall: 1.000\n",
-      "Done!\n"
-     ]
-    }
-   ],
-   "source": [
-    "out_dir = \"classifier_weights/\"\n",
-    "if not os.path.exists(out_dir):\n",
-    "    os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "lr = 1e-3\n",
-    "epochs = 5\n",
-    "classifier = models.ResNet(models.ResidualBlock, [3, 4, 6, 3], num_classes=1)\n",
-    "classifier = classifier.to(device=DEVICE, dtype=dtype)\n",
-    "\n",
-    "branch_classifier.train(training_dataloader, test_dataloader, out_dir, lr, epochs, classifier, state_dict=None)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tractography",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/branch_classifier.ipynb b/docs/source/branch_classifier.ipynb
new file mode 120000
index 0000000..e484366
--- /dev/null
+++ b/docs/source/branch_classifier.ipynb
@@ -0,0 +1 @@
+../../notebooks/branch_classifier.ipynb
\ No newline at end of file
diff --git a/docs/source/make_simulated_neurons.ipynb b/docs/source/make_simulated_neurons.ipynb
deleted file mode 100644
index 22ac643..0000000
--- a/docs/source/make_simulated_neurons.ipynb
+++ /dev/null
@@ -1,4473 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Make Simulated Neurons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glob import glob\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import sys\n",
-    "\n",
-    "\n",
-    "sys.path.append('../')\n",
-    "from data_prep import generate, draw, load"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate simulated SWC file data.\n",
-    "This is a list of nodes, each node being a list:\n",
-    "[sample_idx, structure_id, x, y, z, radius, parent_id]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "swc_list = generate.make_swc_list((101,101,101),\n",
-    "                        length=20,\n",
-    "                        step_size=3,\n",
-    "                        kappa=20.0,\n",
-    "                        uniform_len=False,\n",
-    "                        random_start=True,\n",
-    "                        rng=None,\n",
-    "                        num_branches=1) # make simulated neuron paths."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set random background and foreground (neuron) colors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "random_contrast=True\n",
-    "if random_contrast:\n",
-    "    neuron_color = np.random.rand(3)\n",
-    "    neuron_color /= np.linalg.norm(neuron_color)\n",
-    "    background = np.random.rand(3)\n",
-    "    background = background / np.linalg.norm(background) * 0.01"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Draw neurons from SWC format data; one without and one with added noise and artifacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "neuron_no_artifacts = draw.neuron_from_swc(swc_list,\n",
-    "                                width=3,\n",
-    "                                noise=0.0,\n",
-    "                                adjust=False,\n",
-    "                                neuron_color=None,\n",
-    "                                background_color=None,\n",
-    "                                random_brightness=False,\n",
-    "                                dropout=False,\n",
-    "                                binary=False)\n",
-    "\n",
-    "neuron_with_artifacts = draw.neuron_from_swc(swc_list,\n",
-    "                                width=3,\n",
-    "                                noise=0.05,\n",
-    "                                adjust=False,\n",
-    "                                neuron_color=neuron_color,\n",
-    "                                background_color=background,\n",
-    "                                random_brightness=True,\n",
-    "                                dropout=True,\n",
-    "                                binary=False)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Draw the images.\n",
-    "    a. Without artifacts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "img = neuron_no_artifacts[\"image\"].data.permute(1,2,3,0)\n",
-    "\n",
-    "fig, ax = plt.subplots(1,3)\n",
-    "ax[0].imshow(img.amax(0))\n",
-    "ax[0].set_title('xy')\n",
-    "ax[1].imshow(img.amax(1))\n",
-    "ax[1].set_title('xz')\n",
-    "ax[2].imshow(img.amax(2))\n",
-    "ax[2].set_title('yz')\n",
-    "for x in ax:\n",
-    "    x.set_axis_off()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    b. With artifacts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "img = neuron_with_artifacts[\"image\"].data.permute(1,2,3,0)\n",
-    "\n",
-    "fig, ax = plt.subplots(1,3)\n",
-    "ax[0].imshow(img.amax(0))\n",
-    "ax[0].set_title('xy')\n",
-    "ax[1].imshow(img.amax(1))\n",
-    "ax[1].set_title('xz')\n",
-    "ax[2].imshow(img.amax(2))\n",
-    "ax[2].set_title('yz')\n",
-    "for x in ax:\n",
-    "    x.set_axis_off()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulate from existing SWC file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load real neuron morphology data from an SWC file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading file: /home/brysongray/data/neuromorpho/quinlan/CNG version/KQa4-12-2015-tracing.CNG.swc\n"
-     ]
-    }
-   ],
-   "source": [
-    "labels_dir = \"/home/brysongray/data/neuromorpho/\"\n",
-    "files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], '*.swc'))]\n",
-    "\n",
-    "f_idx = 4\n",
-    "labels_file = files[f_idx]\n",
-    "# load and parse the SWC file data\n",
-    "swc_list = load.swc(labels_file)\n",
-    "sections, section_graph, branches, terminals, scale = load.parse_swc_list(swc_list, adjust=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot SWC raw data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
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-           "ticks": "",
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-          "yaxis": {
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-     "metadata": {},
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-   ],
-   "source": [
-    "# Create a DataFrame for plotting\n",
-    "data = []\n",
-    "# Iterate through the sections dictionary\n",
-    "for section_id, section_data in sections.items():\n",
-    "    # flatten the section into one list of consecutive points instead of segments (point pairs)\n",
-    "    for segment in section_data:\n",
-    "        point = segment[0] \n",
-    "        data.append([section_id, point[0].item(), point[1].item(), point[2].item()])\n",
-    "df_sections = pd.DataFrame(data, columns=[\"section\", \"x\", \"y\", \"z\"])\n",
-    "\n",
-    "fig = px.line_3d(df_sections, x=\"x\", y=\"y\", z=\"z\", color='section', )\n",
-    "fig.update_layout(scene_aspectmode='data')\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulate a neuron microscopy image with artifacts from the SWC data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set random colors for the background and foreground voxels. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.default_rng()\n",
-    "neuron_color = np.array([1.0, 1.0, 1.0])\n",
-    "background = np.array([0., 0., 0.])\n",
-    "if random_contrast:\n",
-    "    neuron_color = rng.uniform(size=3)\n",
-    "    neuron_color /= np.linalg.norm(neuron_color)\n",
-    "    background_color = rng.uniform(size=3)\n",
-    "    background_color = background_color / np.linalg.norm(background_color) * 0.01"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Draw the neuron image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "swc_data = draw.neuron_from_swc(swc_list,\n",
-    "                                width=3,\n",
-    "                                noise=0.05,\n",
-    "                                dropout=True,\n",
-    "                                adjust=True,\n",
-    "                                background_color=background,\n",
-    "                                neuron_color=neuron_color,\n",
-    "                                random_brightness=True,\n",
-    "                                binary=False,\n",
-    "                                rng=rng)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "img = swc_data[\"image\"].data.permute(1,2,3,0)\n",
-    "\n",
-    "fig, ax = plt.subplots(1,3)\n",
-    "ax[0].imshow(img.amax(0))\n",
-    "ax[0].set_title('xy')\n",
-    "ax[1].imshow(img.amax(1))\n",
-    "ax[1].set_title('xz')\n",
-    "ax[2].imshow(img.amax(2))\n",
-    "ax[2].set_title('yz')\n",
-    "for x in ax:\n",
-    "    x.set_axis_off()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tractography",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/make_simulated_neurons.ipynb b/docs/source/make_simulated_neurons.ipynb
new file mode 120000
index 0000000..7d17725
--- /dev/null
+++ b/docs/source/make_simulated_neurons.ipynb
@@ -0,0 +1 @@
+../../notebooks/make_simulated_neurons.ipynb
\ No newline at end of file
diff --git a/docs/source/sac_tracking_inference.ipynb b/docs/source/sac_tracking_inference.ipynb
deleted file mode 100644
index c377586..0000000
--- a/docs/source/sac_tracking_inference.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tracking Inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import sys\n",
-    "import torch\n",
-    "\n",
-    "sys.path.append(\"../\")\n",
-    "from environments.sac_tracking_env import Environment\n",
-    "from models import ResNet, ResidualBlock, ConvNet\n",
-    "from solvers import sac\n",
-    "\n",
-    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "dtype = torch.float32"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config_file = \"../configs/sac_inference_simulated.json\"\n",
-    "with open(config_file) as f:\n",
-    "    params = json.load(f)\n",
-    "\n",
-    "img_path = params[\"img_path\"]\n",
-    "outdir = params[\"outdir\"]\n",
-    "name = params[\"name\"]\n",
-    "step_size = params[\"step_size\"] if \"step_size\" in params else 1.0\n",
-    "step_width = params[\"step_width\"] if \"step_width\" in params else 1.0\n",
-    "alpha = params[\"alpha\"] if \"alpha\" in params else 1.0\n",
-    "beta = params[\"beta\"] if \"beta\" in params else 1e-3\n",
-    "friction = params[\"friction\"] if \"friction\" in params else 1e-4\n",
-    "patch_radius = 17\n",
-    "\n",
-    "if \"classifier_weights\" in params:\n",
-    "    classifier_path = params[\"classifier_weights\"]\n",
-    "    classifier_state_dict = torch.load(classifier_path, weights_only=True)\n",
-    "    classifier = ResNet(ResidualBlock, [3, 4, 6, 3], num_classes=1)\n",
-    "    classifier = classifier.to(device=DEVICE, dtype=dtype)\n",
-    "    classifier.load_state_dict(classifier_state_dict)\n",
-    "    classifier.eval()\n",
-    "else:\n",
-    "    classifier = None\n",
-    "\n",
-    "env = Environment(img_path,\n",
-    "                radius=patch_radius,\n",
-    "                step_size=step_size,\n",
-    "                step_width=step_width,\n",
-    "                max_len=10000,\n",
-    "                alpha=alpha,\n",
-    "                beta=beta,\n",
-    "                friction=friction,\n",
-    "                classifier=classifier)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate actor network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "in_channels = 4\n",
-    "actor = ConvNet(chin=in_channels, chout=4)\n",
-    "actor = actor.to(device=DEVICE,dtype=dtype)\n",
-    "if \"sac_weights\" in params:\n",
-    "    sac_weights_path = params[\"sac_weights\"]\n",
-    "    sac_state_dicts = torch.load(sac_weights_path, weights_only=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Perform tracking"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:12<00:00,  1.22s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "num branches: 1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sac.inference(env, actor, outdir)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tractography",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/sac_tracking_inference.ipynb b/docs/source/sac_tracking_inference.ipynb
new file mode 120000
index 0000000..33d1044
--- /dev/null
+++ b/docs/source/sac_tracking_inference.ipynb
@@ -0,0 +1 @@
+../../notebooks/sac_tracking_inference.ipynb
\ No newline at end of file
diff --git a/environments/neuron_tracking_environment.py b/environments/neuron_tracking_environment.py
new file mode 100644
index 0000000..78a4a94
--- /dev/null
+++ b/environments/neuron_tracking_environment.py
@@ -0,0 +1,534 @@
+#!/usr/bin/env python
+
+"""
+Neuron tracking environment for SAC reinforcement learning
+
+Author: Bryson Gray
+2024
+"""
+
+import numpy as np
+import os
+from pathlib import Path
+import sys
+import tifffile as tf
+import torch
+from typing import Dict, Tuple, Literal
+import warnings
+
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent
+sys.path.append(str(parent_dir))
+
+from data_prep import load
+from data_prep.image import Image
+from environments import env_utils
+from neurotrack.data.neuron_data import Dataset, DataLoader, DataGenerator
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class NeuronTrackingEnvironment:
+    """
+    Enhanced neuron tracking environment using DataLoader for neuron sampling.
+    
+    The environment automatically samples neuron data from the dataloader when reset() is called.
+    It loads TIFF images, SWC subtrees, and reward masks from file paths, then sets up the 
+    tracking environment with appropriate seeds and visualization channels.
+    """
+    
+    def __init__(self, dataloader: DataLoader,
+                 radius: int = 17, step_size: float = 2.0, step_width: float = 4.0,
+                 max_len: int = 10000, max_paths: int = 1000, alpha: float = 1.0,
+                 beta: float = 0.2, friction: float = 0.0, branching: bool = False,
+                 repeat_starts: bool = False, section_masking: bool = False, classifier=None):
+        """
+        Initialize the enhanced SAC tracking environment.
+        
+        Parameters:
+        -----------
+        dataloader : DataLoader
+            DataLoader object for sampling neurons
+        radius : int
+            Radius around the center to randomly place starting points
+        step_size : float
+            Step size for tracking
+        step_width : float
+            Step width for tracking
+        max_len : int
+            Maximum length of the path
+        max_paths : int
+            Maximum number of paths allowed
+        alpha : float
+            Alpha parameter for tracking
+        beta : float
+            Beta parameter for tracking
+        friction : float
+            Friction parameter for tracking
+        repeat_starts : bool
+            Whether to repeatedly restart at the beginning of a completed path
+        section_masking : bool
+            Whether to mask out all sections except the current section and its descendants
+        classifier : optional
+            Branch classifier for tracking
+        """
+        self.dataloader = dataloader
+        self.current_neuron_info = None
+        
+        # Store initialization parameters without calling parent __init__ yet
+        self.radius = radius
+        self.step_size = step_size
+        self.step_width = step_width
+        self.max_len = max_len
+        self.max_paths = max_paths
+        self.alpha = alpha
+        self.beta = beta
+        self.friction = friction
+        self.branching = branching
+        self.repeat_starts = repeat_starts
+        self.section_masking = section_masking
+        self.classifier = classifier
+        
+        # Initialize other attributes that will be set when neuron data is loaded
+        self.img = None
+        self.true_density = None
+        self.seeds = []
+        self.paths = []
+        self.roots = []
+        self.path_labels = []
+        self.prev_children = []
+        self.finished_paths = []
+        self.head_id = 0
+    
+    def _process_file_paths(self, file_data: Dict) -> Dict:
+        """
+        Process file paths from dataloader.sample() into neuron data format.
+        
+        Parameters:
+        -----------
+        file_data : Dict
+            Dictionary containing file paths with keys:
+            - neuron_name: name of neuron
+            - img_path: path to image TIFF file
+            - swc_path: path to SWC subtree file
+            - reward_mask_path: path to reward mask TIFF file
+            - complexity: neuron complexity level
+            - morphology: morphology complexity category
+        
+        Returns:
+        --------
+        Dict containing:
+        - image: processed image tensor
+        - subtree: subtree data
+        - reward_mask: reward mask tensor  
+        - metadata: additional information
+        """
+        # Load image
+        img_array = tf.imread(file_data['img_path'])
+        if img_array.ndim == 3:
+            img_array = img_array[None]  # Add channel dimension
+        image_tensor = torch.from_numpy(img_array)
+        
+        # Load subtree
+        subtree = load.swc(file_data['swc_path'], verbose=False)
+        
+        # Load reward mask
+        reward_array = tf.imread(file_data['reward_mask_path'])
+        if reward_array.ndim == 3:
+            reward_array = reward_array[None]  # Add channel dimension  
+        reward_tensor = torch.from_numpy(reward_array)
+        
+        return {
+            'image': image_tensor,
+            'subtree': subtree,
+            'reward_mask': reward_tensor,
+            'metadata': file_data
+        }
+    
+    def _setup_environment(self, neuron_data: Dict):
+        """
+        Setup environment with neuron data.
+        
+        Parameters:
+        -----------
+        neuron_data : Dict
+            Dictionary containing:
+            - image: processed image tensor
+            - subtree: subtree data
+            - reward_mask: reward mask tensor
+            - metadata: additional information
+        """
+        
+        # Setup image
+        img_tensor = neuron_data['image']
+        self.img = Image(img_tensor)
+        
+        # Setup reward mask as true density
+        reward_tensor = neuron_data['reward_mask']
+        self.true_density = Image(reward_tensor)
+        
+        # Generate seeds from subtree endpoints/branch points
+        subtree = neuron_data['subtree']
+        edge_list = load.undirected_edge_list(subtree)
+        
+        # Find endpoints and branch points as seeds
+        n_endpoints = sum(1 for neighbors in edge_list.values() if len(neighbors) == 1)
+        seeds = []
+        for node_id, neighbors in edge_list.items():
+            if len(seeds) >= n_endpoints//2:  # Limit number of seeds to half the endpoints
+                break
+            if len(neighbors) == 1:  # Endpoints only
+                node = next(row for row in subtree if row[0] == node_id)
+                seeds.append([int(node[4]), int(node[3]), int(node[2])])  # z, y, x to match image coords
+        
+        # If no seeds found, raise an error
+        if not seeds:
+            raise ValueError(f"No valid seed points found in subtree. Edge list: {edge_list}")
+        
+        self.seeds = seeds[:min(10, len(seeds))]  # Limit to 10 seeds
+        
+        # Initialize paths and other state variables
+        self.paths = [torch.tensor(seed).float().unsqueeze(0) for seed in self.seeds]
+        self.roots = [torch.tensor(seed).float() for seed in self.seeds]
+        self.path_labels = [0] * len(self.paths)
+        self.prev_children = [[]] * len(self.paths)
+        self.finished_paths = []
+        
+        # Add channel for path visualization
+        if self.img.data.shape[0] == 1:
+            self.img.data = torch.cat((
+                self.img.data, 
+                torch.zeros((1,) + self.img.data.shape[1:], dtype=self.img.data.dtype)
+            ), dim=0)
+        
+        self.head_id = 0
+        if self.paths:
+            self.img.draw_point(
+                self.paths[self.head_id][-1], 
+                radius=(self.step_width / 2.35), 
+                channel=-1, 
+                mode="gaussian", 
+                binary=False
+            )
+    
+    def _step_prior(self, sigmaf: float = 1.5, sigmab: float = 1.5) -> float:
+        """
+        Calculate the prior probability for the current step based on path smoothness.
+        
+        Parameters:
+        -----------
+        sigmaf : float
+            Standard deviation for forward smoothness penalty
+        sigmab : float  
+            Standard deviation for backward smoothness penalty
+            
+        Returns:
+        --------
+        float
+            Prior probability value
+        """
+        prior = 0.0
+        if len(self.paths[self.head_id]) > 2:  # ignore the prior for the first step
+            q = self.paths[self.head_id][-1]
+            q_ = self.paths[self.head_id][-2]
+            q__ = self.paths[self.head_id][-3]
+            prior = - torch.sum((q - q_)**2).item()/(2*sigmaf**2) - torch.sum((q - 2*q_ + q__)**2).item() / (2*sigmab**2)
+        
+        return prior
+    
+    def _get_status(self, new_position):
+        """
+        Determine the status of a proposed new position and whether to terminate the path.
+        
+        Parameters:
+        -----------
+        new_position : torch.Tensor
+            The proposed new position
+            
+        Returns:
+        --------
+        tuple
+            (terminate_path: bool, status: str) indicating whether to terminate and the reason
+        """
+        status = "step"
+        terminate_path = False
+        
+        # Check if out of image bounds
+        out_of_image = any([x >= y or x < 0 for x,y in zip(new_position, self.img.data.shape[1:])])
+        if out_of_image:
+            terminate_path = True
+            status = "out_of_image"
+        else:
+            # Check for turn around (sharp angle change)
+            turn_around = False
+            if len(self.paths[self.head_id]) > 1:
+                s = torch.stack((self.paths[self.head_id][-1], new_position)) - self.paths[self.head_id][-2:]
+                cos_dist = torch.dot(s[1]/torch.linalg.norm(s[1]), s[0]/torch.linalg.norm(s[0]))
+                angle = torch.arccos(cos_dist)
+                turn_around = angle > 3*np.pi/4
+
+            # Check if path is too long
+            too_long = len(self.paths[self.head_id]) > self.max_len
+
+            if too_long:
+                terminate_path = True
+                status = "too_long"
+            elif turn_around:
+                terminate_path = True
+                status = "choose_stop"
+
+        return terminate_path, status
+    
+    def reset(self, move_to_next: bool = True, dataset_index=None):
+        """
+        Reset environment state by sampling new neuron data from dataloader.
+        
+        Parameters:
+        -----------
+        move_to_next : bool
+            Deprecated parameter, kept for compatibility. Does nothing.
+            New neuron data is automatically sampled from the dataloader.
+        """
+        if move_to_next:
+            # Sample new neuron data from dataloader
+            if dataset_index is not None:
+                if not isinstance(dataset_index, int):
+                    raise TypeError(f"dataset index must be int but got {type(dataset_index)}")
+                if dataset_index < 0 or dataset_index >= len(self.dataloader.dataset):
+                    raise ValueError(f"dataset index {dataset_index} is out of range [0, {len(self.dataloader.dataset)-1}]")
+                file_data = self.dataloader.dataset[dataset_index]
+                self.dataloader.current_idx = dataset_index
+            else:
+                file_data = self.dataloader.sample()
+        else:
+            file_data = self.dataloader.dataset[self.dataloader.current_idx]
+        
+        # Process file paths into neuron data format
+        neuron_data = self._process_file_paths(file_data)
+        self.current_neuron_info = file_data
+
+        # Setup environment with new neuron data
+        self._setup_environment(neuron_data)
+    
+    def get_state(self, terminate=False):
+        """Get the state for the current step at streamline 'head_id'."""
+        if self.img is None:
+            raise ValueError("No neuron data loaded. Call reset() first.")
+            
+        if terminate:
+            patch = torch.zeros((self.img.data.shape[0],)+(2*self.radius + 1,)*3)
+        else:
+            center = self.paths[self.head_id][-1]
+            patch, _ = self.img.crop(center, self.radius, pad=True, value=0.0)
+            patch = patch.clone()
+            if patch.dtype == torch.uint8:
+                patch = patch / torch.tensor(255.0, dtype=torch.float32)
+
+        return patch[None]
+    
+    def step(self, action, verbose=False, training=True):
+        """
+        Perform a single step in the environment.
+        
+        Parameters:
+        -----------
+        action : torch.Tensor
+            The action to be taken, representing the direction of movement
+        verbose : bool
+            If True, additional information will be printed
+        training : bool
+            Whether in training mode (affects branching behavior)
+            
+        Returns:
+        --------
+        tuple
+            (observation, reward, terminated) - the new state, reward, and termination flag
+        """
+        if self.img is None:
+            raise ValueError("No neuron data loaded. Call reset() first.")
+        
+        terminate_path = False
+        terminated = False
+
+        direction = action
+        new_position = self.paths[self.head_id][-1] + direction
+
+        terminate_path, status = self._get_status(new_position)
+
+        if terminate_path:
+            reward = self.get_reward(status, verbose=verbose)
+            observation = self.get_state(terminate=True)
+            
+            # Remove the path from 'paths' and add it to 'finished_paths'
+            self.finished_paths.append(self.paths.pop(self.head_id).cpu())
+            self.path_labels.pop(self.head_id)
+
+            # Check for max branches
+            if len(self.finished_paths) > self.max_paths:
+                terminated = True
+            elif training and self.repeat_starts and len(self.finished_paths[-1]) > 4:
+                # If the path took more than three steps, add a new path at the same root
+                self.paths.append(self.roots[self.head_id][None])
+                self.roots.append(self.roots[self.head_id])
+                self.path_labels.append(0)
+            elif len(self.paths) == 0:
+                terminated = True
+
+            if not terminated:
+                self.head_id = (self.head_id + 1) % len(self.paths)
+
+        else:  # Take a step
+            # Add new position to path
+            self.paths[self.head_id] = torch.cat((self.paths[self.head_id], new_position[None]))
+            
+            # Draw the segment on the state input image
+            segment = self.paths[self.head_id][-2:, :3]
+            old_patch, new_patch = self.img.draw_line_segment(segment, width=self.step_width, channel=-1, mask=False)
+            if self.img.data.dtype == torch.uint8:
+                old_patch = old_patch / torch.tensor(255.0, dtype=torch.float32)
+                new_patch = new_patch / torch.tensor(255.0, dtype=torch.float32)
+                
+            # Get reward
+            segment_vec = segment[1] - segment[0]
+            L = int(torch.abs(segment_vec).max().item())  # Radius is the whole line length
+            overhang = int(2*self.step_width)  # Include space beyond the end of the line
+            patch_radius = L + overhang
+            center = segment[0]
+            density_patch, _ = self.true_density.crop(center, patch_radius, interp=False, pad=False)
+
+            # For now, use simple density without section masking
+            # TODO: Add section masking support if needed
+            true_patch_masked = density_patch
+
+            step_accuracy = -env_utils.density_error_change(true_patch_masked[0], old_patch, new_patch)
+            reward = self.get_reward(status, step_accuracy, verbose)
+
+            observation = self.get_state()
+
+            # Create new branches during training
+            if training and self.branching:
+                distances = torch.linalg.norm(torch.stack(self.roots) - new_position, dim=1)
+                if not torch.any(distances < 12.0):
+                    self.paths.append(new_position[None])
+                    self.path_labels.append(0)
+                    self.prev_children.append([])
+                    self.roots.append(new_position)
+
+        return observation, reward, terminated
+    
+    def get_reward(self, category: Literal["step", "out_of_image", "out_of_mask", "too_long", "choose_stop", "bifurcate"], 
+                   step_accuracy: float = 0.0, verbose: bool = False) -> torch.Tensor:
+        """
+        Calculate the reward based on the given category and step accuracy.
+        
+        Parameters:
+        -----------
+        category : Literal
+            The category of the action taken
+        step_accuracy : float
+            The accuracy of the step taken
+        verbose : bool
+            If True, prints detailed information about the reward calculation
+            
+        Returns:
+        --------
+        torch.Tensor
+            The calculated reward as a tensor
+            
+        Raises:
+        -------
+        NameError
+            If the provided category is not recognized
+        """
+        if category == "out_of_image":
+            reward = 0.0 
+            if verbose:
+                print('out_of_image \n', f'reward: {reward}\n')
+        elif category == "out_of_mask":
+            reward = 0.0 
+            if verbose:
+                print('out_of_mask \n', f'reward: {reward}\n')
+        elif category == "too_long":
+            reward = 0.0
+            if verbose:
+                print('too_long \n', f'reward: {reward}\n')
+        elif category == "choose_stop":
+            reward = 0.0
+            if verbose:
+                print('choose_stop \n', f'reward: {reward}\n')
+        elif category == "bifurcate":
+            reward = 0.0
+            if verbose:
+                print('bifurcate \n', f'reward: {reward}\n')
+        elif category == "step":
+            prior = self._step_prior()
+            reward = self.alpha * step_accuracy + self.beta * prior
+            if verbose:
+                print(f'step \n accuracy: {step_accuracy}\n prior: {prior}\n reward: {reward}\n')
+        else:
+            raise NameError(f"category: {category} was not recognized.")
+
+        return torch.tensor([reward], dtype=torch.float32)
+
+
+# Convenience function to create a complete setup
+def create_neuron_tracking_environment(data_dir: str, 
+                                     complexity: float = 0.0,
+                                     rng_seed: int = 0, 
+                                     **env_kwargs) -> NeuronTrackingEnvironment:
+    """
+    Create a neuron tracking environment setup.
+    
+    Parameters:
+    -----------
+    data_dir : str
+        Directory containing neuron data CSV file and associated TIFF/SWC files
+    complexity : float
+        Initial complexity parameter for neuron sampling
+    rng_seed : int
+        Random seed for reproducibility
+    **env_kwargs : dict
+        Additional arguments for environment initialization
+    
+    Returns:
+    --------
+    NeuronTrackingEnvironment
+        Configured environment ready for use. Call reset() to load first neuron.
+    
+    Note:
+    -----
+    The environment will automatically sample and load neuron data when reset() is called.
+    Data is loaded from file paths returned by the DataLoader.
+    """
+
+    rng = np.random.default_rng(rng_seed)
+
+    # Create dataset
+    dataset = Dataset(data_dir=data_dir, rng=rng)
+
+    # Create dataloader
+    dataloader = DataLoader(dataset=dataset, complexity=complexity, rng=rng)
+    
+    # Create environment
+    environment = NeuronTrackingEnvironment(
+        dataloader=dataloader,
+        **env_kwargs
+    )
+    
+    return environment
+
+
+if __name__ == "__main__":
+    # Example usage
+    data_dir = "/home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset"
+    
+    # Create environment
+    env = create_neuron_tracking_environment(data_dir, complexity=0.2)
+    
+    # Reset to load first neuron from dataloader
+    env.reset()
+    
+    # Environment is now ready for training/inference
+    print(f"Loaded neuron: {env.current_neuron_data['metadata']['neuron_name']}")
+    print(f"Image shape: {env.img.data.shape}")
+    print(f"Number of seeds: {len(env.seeds)}")
diff --git a/environments/sac_tracking_env.py b/environments/sac_tracking_env.py
index ab2e241..17248e3 100755
--- a/environments/sac_tracking_env.py
+++ b/environments/sac_tracking_env.py
@@ -7,20 +7,23 @@
 2024
 """
 
+from glob import glob
+import json
+import numpy as np
 import os
+from pathlib import Path
+import sys
+import tifffile as tf
 from typing import Literal
-
-from matplotlib import category
 import torch
 
-from pathlib import Path
-import sys
-sys.path.insert(1, str(Path(__file__).parent))
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent
+sys.path.append(str(parent_dir))
 from data_prep.image import Image
-import env_utils
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+from environments import env_utils
 
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 class Environment():
     """
@@ -35,9 +38,12 @@ def __init__(
             step_size: float = 1.0,
             step_width: float = 1.0,
             max_len: int = 10000,
+            max_paths: int = 100,
             alpha: float = 1.0,
             beta: float = 1e-3,
             friction: float = 1e-4,
+            repeat_starts: bool = True,
+            section_masking: bool = False,
             classifier=None):
         """
         Initialize the SAC tracking environment.
@@ -54,14 +60,20 @@ def __init__(
             Step width for tracking, by default 1.0.
         max_len : int, optional
             Maximum length of the path, by default 10000.
+        max_paths : int, optional
+            Maximum number of paths allowed, by default 100.
         alpha : float, optional
             Alpha parameter for tracking, by default 1.0.
         beta : float, optional
             Beta parameter for tracking, by default 1e-3.
         friction : float, optional
             Friction parameter for tracking, by default 1e-4.
+        repeat_starts : bool, optional
+            Whether to repeatedly restart at the beginning of a completed path, by default True.
+        section_masking : bool, optional
+            Whether to mask out all sections except the current section and its descendants, by default False.  
         classifier : optional
-            Classifier for tracking, by default None.
+            Branch classifier for tracking, by default None.
             
         Attributes
         ----------
@@ -89,8 +101,14 @@ def __init__(
             Step width for tracking.
         max_len : int
             Maximum length of the path.
+        max_paths : int
+            Maximum number of paths allowed
         classifier : optional
             Classifier for tracking.
+        repeat_starts : bool, optional
+            Whether to repeatedly restart at the beginning of a completed path.
+        section_masking : bool, optional
+            Whether to mask out all sections except the current section and its descendants.  
         seed_idx : int
             Index of the current seed point.
         r : float
@@ -114,42 +132,32 @@ def __init__(
         """
     
         
-        if os.path.isdir(img_path):
-            self.img_files = [os.path.join(img_path, f) for f in os.listdir(img_path)]
-        else:
-            self.img_files = [img_path]
-
-        self.img_idx = 0
-        neuron_data = torch.load(self.img_files[self.img_idx], weights_only=False)
-        img = neuron_data["image"]
-        self.img = Image(img.to(device=DEVICE))
-        neuron_density = neuron_data["neuron_density"]
-        self.true_density = Image(neuron_density.to(device=DEVICE))
-        section_labels = neuron_data["section_labels"]
-        self.section_labels = Image(section_labels.to(device=DEVICE))
-        self.mask = neuron_data["branch_mask"]
-        self.seeds = neuron_data["seeds"]
-        self.graph = neuron_data["graph"]
-        
         self.radius = radius
-        # make copies of the branch and terminal points so these can be changed while saving the originals
         self.step_size = step_size
         self.step_width = step_width
         self.max_len = max_len
+        self.max_paths = max_paths
+        self.repeat_starts = repeat_starts
+        self.section_masking = section_masking
         self.classifier = classifier
 
-        self.seed_idx = 0
-        seed = torch.Tensor(self.seeds[self.seed_idx])
-        self.r = 0.0 # radius around center to randomly place starting points
-        offset = torch.randn((1, 3))
-        offset /= torch.sum(offset**2, dim=1)**0.5
-        r = self.r * torch.rand(1)
-        seed = seed[None] + r * offset
-        seed = seed.to(device=DEVICE)
-
-        self.paths = [seed] # a list initialized with 1 path, a 1 x 3 tensor.
-        self.roots = [seed[0]] # a list of path start points.
-        self.path_labels = [0] # a list of path labels. 0 means the path is not yet labeled.
+        if os.path.isdir(img_path):
+            self.img_files = [os.path.join(img_path, f) for f in os.listdir(img_path)]
+        else:
+            self.img_files = [img_path]
+        self.img_idx = 0
+        self.__load_data(self.img_files[self.img_idx])
+
+        self.paths = list(torch.tensor(self.seeds).unsqueeze(1))
+        self.roots = list(torch.tensor(self.seeds))
+        if hasattr(self, 'section_labels'):
+            self.path_labels = [int(self.section_labels.data[0,
+                                                            int(round(r[0].item())),
+                                                            int(round(r[1].item())),
+                                                            int(round(r[2].item()))].item()) for r in self.roots]
+        else:
+            self.path_labels = [0,] * len(self.paths)
+        self.prev_children = [[],] * len(self.paths) # Keep track of the previous section's children for computing a section mask in reward calculation.
         self.alpha = alpha
         self.beta = beta
         self.friction = friction
@@ -157,10 +165,10 @@ def __init__(
         # we will want to save completed paths
         self.finished_paths = []
 
-        self.img.data = torch.cat((self.img.data, torch.zeros((1,)+self.img.data.shape[1:], device=DEVICE)), dim=0) # add 1 channel for path.
+        self.img.data = torch.cat((self.img.data, torch.zeros((1,)+self.img.data.shape[1:], dtype=self.img.data.dtype)), dim=0) # add 1 channel for path.
         
         self.head_id = 0 # head id keeps track of the current path since there may be multiple paths per episode 
-        self.img.draw_point(self.paths[self.head_id][-1], radius=(self.step_width-1)//2, channel=3, binary=False)
+        self.img.draw_point(self.paths[self.head_id][-1], radius=(self.step_width / 2.35), channel=-1, mode="gaussian", binary=False)
 
     
     def __step_prior(self, sigmaf: float = 1.5, sigmab: float = 1.5) -> float:
@@ -189,7 +197,7 @@ def __get_status(self, new_position):
                 s = torch.stack((self.paths[self.head_id][-1], new_position)) - self.paths[self.head_id][-2:]
                 cos_dist = torch.dot(s[1]/torch.linalg.norm(s[1]), s[0]/torch.linalg.norm(s[0]))
                 angle = torch.arccos(cos_dist)
-                turn_around = angle > 3*torch.pi/4
+                turn_around = angle > 3*np.pi/4
 
             too_long = len(self.paths[self.head_id]) > self.max_len
 
@@ -204,6 +212,45 @@ def __get_status(self, new_position):
         return terminate_path, status
     
 
+    def __load_data(self, path):
+        img_file = glob(os.path.join(path, "*image.tif"))[0]
+        img = tf.imread(img_file)
+        # data type conversion moved to get_state()
+        # if img.max() > 1.0:
+        #     img = img / 255.0
+        if img.ndim == 3:
+            img = img[None]
+        elif img.ndim != 4:
+            raise Exception(f"Image must be four-dimensional with channels in the first dimension, or three-dimensional and scalar-valued but found shape {img.shape}")
+        self.img = Image(torch.from_numpy(img))
+        density_file = glob(os.path.join(path, "*density.tif"))[0]
+        density = tf.imread(density_file)
+        if density.ndim == 3:
+            density = density[None]
+        elif density.ndim != 4:
+            raise Exception(f"Density must be four-dimensional with channels in the first dimension, or three-dimensional and scalar-valued but found shape {density.shape}")
+        self.true_density = Image(torch.from_numpy(density))
+        seeds = glob(os.path.join(path, "*seeds.txt"))[0]
+        with open(seeds, 'r') as f:
+            self.seeds = [[int(x) for x in line.strip().split(' ')] for line in f if line.strip()]
+        section_labels_file = glob(os.path.join(path, "*sections.tif"))
+        if section_labels_file and self.section_masking:
+            section_labels_file = section_labels_file[0]
+            section_labels = tf.imread(section_labels_file)
+            self.section_labels = Image(torch.from_numpy(section_labels))
+        elif self.section_masking:
+            raise FileNotFoundError(f"Section masking requires section labels but no file ending in 'section.tif' found in {path}")
+        graph_file = glob(os.path.join(path, '*section_graph.json'))
+        if graph_file and self.section_masking:
+            graph_file = graph_file[0]
+            with open(graph_file, 'r') as f:
+                graph = json.load(f)
+                # Convert all keys from string to int
+                self.graph = {int(k): v for k, v in graph.items()}
+        elif self.section_masking:
+            raise FileNotFoundError(f"Section masking requires a section graph but no file ending in 'section_graph.json' found in {path}")
+    
+
     def get_state(self, terminate=False):
         """ Get the state for the current step at streamline 'head_id'. The state consists of an image patch and
         streamline density patch centered on the streamline head.
@@ -219,10 +266,13 @@ def get_state(self, terminate=False):
             Tensor with shape (c x h x w x d) where the first channels are the input image.
         """
         if terminate:
-            patch = torch.zeros((self.img.data.shape[0],)+(2*self.radius + 1,)*3, device=DEVICE)
+            patch = torch.zeros((self.img.data.shape[0],)+(2*self.radius + 1,)*3)
         else:
-            patch, _ = self.img.crop(self.paths[self.head_id][-1], self.radius, pad=True, value=0.0)
+            center = self.paths[self.head_id][-1]
+            patch, _ = self.img.crop(center, self.radius, pad=True, value=0.0)
             patch = patch.clone()
+            if patch.dtype == torch.uint8: # perform data type conversion here so the whole image can take less memory.
+                patch = patch / torch.tensor(255.0, dtype=torch.float32)
 
         return patch[None]
 
@@ -285,10 +335,10 @@ def get_reward(self, category: Literal["step", "out_of_image", "out_of_mask", "t
         else:
             raise NameError(f"category: {category} was not recognized.")
 
-        return torch.tensor([reward], device=DEVICE, dtype=torch.float32)
+        return torch.tensor([reward], dtype=torch.float32)    
 
 
-    def step(self, action, max_paths=100, verbose=False):
+    def step(self, action, verbose=False, training=True):
         """
         Perform a single step in the environment.
         
@@ -320,18 +370,29 @@ def step(self, action, max_paths=100, verbose=False):
 
         terminate_path, status = self.__get_status(new_position)
 
-
         if terminate_path:
             reward = self.get_reward(status, verbose=verbose)
             observation = self.get_state(terminate=True)
             # remove the path from 'paths' and add it to 'ended_paths'
             self.finished_paths.append(self.paths.pop(self.head_id).cpu())
             self.path_labels.pop(self.head_id)
-            # if that was the last path in the list, then terminate the episode
-            if len(self.paths) == 0:
+
+            # check for max branches
+            if len(self.finished_paths) > self.max_paths:
                 terminated = True
-            # otherwise, move to the next path
-            else:
+
+            elif training and self.repeat_starts and len(self.finished_paths[-1]) > 4:
+                # if the path took more than three steps, then add a new path at the same root.
+                self.paths.append(self.roots[self.head_id][None])
+                self.roots.append(self.roots[self.head_id])
+                # i,j,k = [int(round(x.item())) for x in self.roots[self.head_id]]
+                # self.path_labels.append(int(self.section_labels.data[0, i, j, k].item()))
+                self.path_labels.append(0)
+
+            elif len(self.paths) == 0:
+                terminated = True
+
+            if not terminated:
                 self.head_id = (self.head_id + 1)%len(self.paths)
 
         else: # otherwise take a step
@@ -339,35 +400,51 @@ def step(self, action, max_paths=100, verbose=False):
             self.paths[self.head_id] = torch.cat((self.paths[self.head_id], new_position[None]))
             # draw the segment on the state input image
             segment = self.paths[self.head_id][-2:, :3]
-            old_patch, new_patch = self.img.draw_line_segment(segment, width=self.step_width, binary=False)
+            old_patch, new_patch = self.img.draw_line_segment(segment, width=self.step_width, channel=-1, mask=False)
+            if self.img.data.dtype == torch.uint8:
+                old_patch = old_patch / torch.tensor(255.0, dtype=torch.float32)
+                new_patch = new_patch / torch.tensor(255.0, dtype=torch.float32)
             # get reward
-            center = torch.round(segment[0]).to(dtype=torch.int)
-            segment_length = torch.linalg.norm(segment[1] - segment[0])
-            L = int(torch.ceil(segment_length)) + 1 # The radius of the patch is the whole line length since the line starts at patch center.
+            segment_vec = segment[1] - segment[0]
+            L = int(torch.abs(segment_vec).max().item()) # The radius of the patch is the whole line length since the line starts at patch center.
             overhang = int(2*self.step_width) # include space beyond the end of the line
             patch_radius = L + overhang
-            true_patch, _ = self.true_density.crop(center, patch_radius, interp=False, pad=False)
+            center = segment[0]
+            density_patch, _ = self.true_density.crop(center, patch_radius, interp=False, pad=False)
 
             # mask out competing paths
-            # true_patch_label, _ = self.section_labels.crop(center, patch_radius, interp=False, pad=False)
-
-            # new_label = int(true_patch_label[0, patch_radius, patch_radius, patch_radius].item())
-            # current_label = self.path_labels[self.head_id]
-            # # here mask out any sections that are not the current section or its children
-            # if current_label != 0:
-            #     section_ids = [current_label, *self.graph[current_label]]
-            #     section = torch.zeros_like(true_patch)
-            #     for id in section_ids:
-            #         section += torch.where(true_patch_label == id, 1, 0)
-            #     true_patch_masked = true_patch * section
-            #     if new_label != current_label and new_label in section_ids:
-            #         self.path_labels[self.head_id] = new_label
-            # else:
-            #     true_patch_masked = true_patch
-            #     if new_label != current_label:
-            #         self.path_labels[self.head_id] = new_label
-
-            true_patch_masked = true_patch # don't mask
+            if self.section_masking:
+                labels_patch, _ = self.section_labels.crop(center, patch_radius, interp=False, pad=False)
+                end_point = [x//2 + v for x,v in zip(labels_patch.shape[1:], segment_vec)]
+                new_label_idx = (0, int(round(end_point[0].item())), int(round(end_point[1].item())), int(round(end_point[2].item())))
+                new_label = int(labels_patch[new_label_idx].item())
+                current_label = self.path_labels[self.head_id]
+
+                if current_label != 0:
+                    # Pre-compute the section IDs
+                    prev_children = self.prev_children[self.head_id]
+                    graph_current = self.graph[current_label]
+                    section_ids = [current_label] + [x for x in graph_current if x not in prev_children]
+                    
+                    # Create mask using vectorized operations
+                    section_mask = torch.zeros_like(density_patch, dtype=torch.bool)
+                    for id in section_ids:
+                        section_mask |= (labels_patch == id)
+                    
+                    true_patch_masked = density_patch * section_mask.float()
+                    
+                    # Update label if needed
+                    if new_label != current_label and new_label in section_ids:
+                        self.path_labels[self.head_id] = new_label
+                        self.prev_children[self.head_id] = graph_current
+                else:
+                    true_patch_masked = density_patch
+                    if new_label != 0:
+                        self.path_labels[self.head_id] = new_label
+
+            else:  # don't mask
+                true_patch_masked = density_patch
+
             step_accuracy = -env_utils.density_error_change(true_patch_masked[0], old_patch, new_patch)
             reward = self.get_reward(status, step_accuracy, verbose)
 
@@ -376,15 +453,18 @@ def step(self, action, max_paths=100, verbose=False):
             # self.head_id = (self.head_id + 1)%len(self.paths) # only move to the next path if the current path is terminated.
 
             # decide if path branches
-            if self.classifier is not None:
-                out = self.classifier(observation[:,:3, 10:25, 10:25, 10:25])
-                out = torch.nn.functional.sigmoid(out.squeeze())
-                if out > 0.5: # create branch
-                    distances = torch.linalg.norm(torch.stack(self.roots) - new_position, dim=1)
-                    if not torch.any(distances < 3.0):
-                        self.paths.append(new_position[None])
-                        self.path_labels.append(0)
-                        self.roots.append(new_position)
+            # if self.classifier is not None and training:
+            #     # out = self.classifier(observation[:,:-1, 10:25, 10:25, 10:25].to(DEVICE))
+            #     out = self.classifier(observation[:, :-1].to(device=DEVICE))
+            #     out = torch.sigmoid(out.squeeze())
+            #     if out > 0.5: # create branch
+            if training:
+                distances = torch.linalg.norm(torch.stack(self.roots) - new_position, dim=1)
+                if not torch.any(distances < 12.0):
+                    self.paths.append(new_position[None])
+                    self.path_labels.append(0)
+                    self.prev_children.append(self.prev_children[self.head_id])
+                    self.roots.append(new_position)
 
         return observation, reward, terminated
 
@@ -404,41 +484,28 @@ def reset(self, move_to_next=True):
         """
         
         if move_to_next:
-            # reset the agent to the next image or seed and reset the path.
-            self.seed_idx += 1
-            self.seed_idx = self.seed_idx % len(self.seeds) # type: ignore
-            if self.seed_idx == 0:
-                self.img_idx += 1
-                self.img_idx = self.img_idx % len(self.img_files)
-
-                # load the next image
-                neuron_data = torch.load(self.img_files[self.img_idx], weights_only=False)
-                img = neuron_data["image"]
-                self.img = Image(img.to(device=DEVICE))
-                neuron_density = neuron_data["neuron_density"]
-                self.true_density = Image(neuron_density.to(device=DEVICE))
-                section_labels = neuron_data["section_labels"]
-                self.section_labels = Image(section_labels.to(device=DEVICE))
-                self.mask = neuron_data["branch_mask"]
-                self.seeds = neuron_data["seeds"]
-                self.graph = neuron_data["graph"]
-
-        seed = torch.tensor(self.seeds[self.seed_idx]) # type: ignore
-        self.r = 0.0 # radius around center to randomly place starting points
-        offset = torch.randn((1, 3))
-        offset /= torch.sum(offset**2, dim=1)**0.5
-        r = self.r * torch.rand(1)
-        seed = seed[None] + r * offset
-        seed = seed.to(device=DEVICE)
-
-        self.paths = [seed] # a list initialized with 1 path, a 1 x 3 tensor.
-        self.roots = [seed[0]]
-        self.path_labels = [0] # a list of path labels. 0 means the path is not yet labeled.
+            self.img_idx += 1
+            self.img_idx = self.img_idx % len(self.img_files)
+            self.__load_data(self.img_files[self.img_idx])
+
+        self.paths = list(torch.tensor(self.seeds).unsqueeze(1))
+        self.roots = list(torch.tensor(self.seeds))
+        if hasattr(self, 'section_labels'):
+            self.path_labels = [int(self.section_labels.data[0,
+                                                            int(round(r[0].item())),
+                                                            int(round(r[1].item())),
+                                                            int(round(r[2].item()))].item()) for r in self.roots]
+        else:
+            self.path_labels = [0,] * len(self.paths)
+        self.prev_children = [[],] * len(self.paths) # Keep track of the previous section's children for computing a section mask in reward calculation.
         self.finished_paths = []
-        self.img.data = torch.cat((self.img.data[:3], torch.zeros((1,)+self.img.data.shape[1:], device=DEVICE)), dim=0) # add 1 channel for path.
+        if self.img.data.shape[0] == 2 or self.img.data.shape[0] == 4:
+            self.img.data = torch.cat((self.img.data[:-1], torch.zeros((1,)+self.img.data.shape[1:], dtype=self.img.data.dtype)), dim=0) # Replace the last channel
+        else:
+            self.img.data = torch.cat((self.img.data, torch.zeros((1,)+self.img.data.shape[1:], dtype=self.img.data.dtype)), dim=0) # add 1 channel for path.
 
         self.head_id = 0
-        self.img.draw_point(self.paths[self.head_id][-1], radius=(self.step_width-1)//2, channel=3, binary=False)
+        self.img.draw_point(self.paths[self.head_id][-1], radius=(self.step_width / 2.35), channel=-1, mode="gaussian", binary=False)
 
         return
     
diff --git a/memory/buffer.py b/memory/buffer.py
index 1b37180..68e8e1e 100644
--- a/memory/buffer.py
+++ b/memory/buffer.py
@@ -1,6 +1,7 @@
 
 import random
 import torch
+import numpy as np
 
 from memory.tree import SumTree
 
@@ -62,7 +63,7 @@ def push(self, obs, action, next_obs, reward, done):
         self.idx = (self.idx + 1) % self.capacity
         self.full = self.idx == 0 or self.full
     
-    def sample(self, batch_size, replacement=False):
+    def sample(self, batch_size, replacement=False, transform=False):
         sample_range = self.capacity if self.full else self.idx
         if replacement:
             idxs = torch.randint(sample_range, size=(batch_size,))
@@ -76,6 +77,25 @@ def sample(self, batch_size, replacement=False):
         rewards = self.rewards[idxs].to(device=DEVICE)
         dones = self.dones[idxs].to(device=DEVICE)
 
+        if transform:
+            perm = torch.randperm(3) + 2
+            obs = obs.permute([0, 1, *perm])
+            next_obs = next_obs.permute([0, 1, *perm])
+            i,j,k = [x.item() - 2 for x in perm]
+            actions = torch.stack((actions[:,i], actions[:,j], actions[:,k]), dim=1)
+            if torch.rand(1)>0.5:
+                obs = obs.flip(-1)
+                next_obs = next_obs.flip(-1)
+                actions[:,-1] = -1*actions[:,-1]
+            if torch.rand(1)>0.5:
+                obs = obs.flip(-2)
+                next_obs = next_obs.flip(-2)
+                actions[:,-2] = -1*actions[:,-2]
+            if torch.rand(1)>0.5:
+                obs = obs.flip(-3)
+                next_obs = next_obs.flip(-3)
+                actions[:,-3] = -1*actions[:,-3]
+
         return obs, actions, next_obs, rewards, dones
     
     def __len__(self):
@@ -178,7 +198,7 @@ def push(self, obs, action, next_obs, reward, done):
         self.full = self.idx == 0 or self.full
         
 
-    def sample(self, batch_size):
+    def sample(self, batch_size, transform: bool=False):
         """
         Samples a batch of transitions from the buffer.
         
@@ -186,6 +206,8 @@ def sample(self, batch_size):
         ----------
         batch_size : int
             The number of transitions to sample.
+        transform : bool
+            Whether to randomly flip and permute images and actions. Default is False.
             
         Returns
         -------
@@ -213,6 +235,10 @@ def sample(self, batch_size):
         real_size = len(self)
         assert real_size >= batch_size, "buffer contains less samples than batch size"
         
+        # Handle edge case where tree.total might be 0
+        if self.tree.total <= 0:
+            raise ValueError(f"Tree total is {self.tree.total}, which should not happen. Buffer may be corrupted.")
+        
         sample_idxs, tree_idxs = [], []
         priorities = torch.empty(batch_size, 1, dtype=torch.float)
 
@@ -221,6 +247,8 @@ def sample(self, batch_size):
             a, b = segment * i, segment * (i + 1)
 
             cumsum = random.uniform(a, b)
+            # Ensure cumsum doesn't exceed tree.total due to floating point precision
+            cumsum = min(cumsum, self.tree.total)
             # sample_idx is a sample index in buffer, needed further to sample actual transitions
             # tree_idx is a index of a sample in the tree, needed further to update priorities
             tree_idx, priority, sample_idx = self.tree.get(cumsum)
@@ -240,6 +268,25 @@ def sample(self, batch_size):
         rewards = self.rewards[sample_idxs].to(DEVICE)
         dones = self.dones[sample_idxs].to(DEVICE)
 
+        if transform:
+                perm = torch.randperm(3) + 2
+                obs = obs.permute([0, 1, *perm])
+                next_obs = next_obs.permute([0, 1, *perm])
+                i,j,k = [x.item() - 2 for x in perm]
+                actions = torch.stack((actions[:,i], actions[:,j], actions[:,k]), dim=1)
+                if torch.rand(1)>0.5:
+                    obs = obs.flip(-1)
+                    next_obs = next_obs.flip(-1)
+                    actions[:,-1] = -1*actions[:,-1]
+                if torch.rand(1)>0.5:
+                    obs = obs.flip(-2)
+                    next_obs = next_obs.flip(-2)
+                    actions[:,-2] = -1*actions[:,-2]
+                if torch.rand(1)>0.5:
+                    obs = obs.flip(-3)
+                    next_obs = next_obs.flip(-3)
+                    actions[:,-3] = -1*actions[:,-3]
+
         return obs, actions, next_obs, rewards, dones, weights, tree_idxs
     
 
@@ -265,7 +312,21 @@ def update_priorities(self, data_idxs, priorities):
 
         for data_idx, priority in zip(data_idxs, priorities):
             priority = priority.item()
-            priority = (priority + self.eps) ** self.alpha
+            
+            # Check for NaN or invalid values
+            if not isinstance(priority, (int, float)) or np.isnan(priority) or np.isinf(priority):
+                print(f"Warning: Invalid priority {priority} detected, using eps instead")
+                priority = self.eps
+            
+            # Ensure priority is non-negative before transformation
+            priority = abs(priority) + self.eps
+            priority = priority ** self.alpha
+            
+            # Check again after transformation
+            if np.isnan(priority) or np.isinf(priority):
+                print(f"Warning: Priority became invalid after transformation, using eps instead")
+                priority = self.eps
+                
             self.tree.update(data_idx, priority)
             self.max_priority = max(self.max_priority, priority)
     
diff --git a/memory/tree.py b/memory/tree.py
index 1e8b4dd..10c5e5a 100644
--- a/memory/tree.py
+++ b/memory/tree.py
@@ -54,6 +54,17 @@ def update(self, data_idx, value):
         to reflect the change in the tree structure.
         """
         
+        # Check for NaN or invalid values
+        if not isinstance(value, (int, float)):
+            raise ValueError(f"Tree update value must be numeric, got {type(value)}: {value}")
+        
+        import math
+        if math.isnan(value) or math.isinf(value):
+            raise ValueError(f"Tree update received invalid value: {value}")
+        
+        if value < 0:
+            raise ValueError(f"Tree update received negative value: {value}")
+        
         idx = data_idx + self.size - 1
         change = value - self.nodes[idx]
         self.nodes[idx] = value
@@ -79,13 +90,25 @@ def add(self, value, data):
         increments the count, and adjusts the real size of the tree.
         """
         
+        # Check for NaN or invalid values
+        if not isinstance(value, (int, float)):
+            raise ValueError(f"Tree add value must be numeric, got {type(value)}: {value}")
+        
+        import math
+        if math.isnan(value) or math.isinf(value):
+            raise ValueError(f"Tree add received invalid value: {value}")
+        
+        if value < 0:
+            raise ValueError(f"Tree add received negative value: {value}")
+        
         self.data[self.count] = data
         self.update(self.count, value)
         self.count = (self.count + 1) % self.size
         self.real_size = min(self.size, self.real_size + 1)
 
     def get(self, cumsum):
-        assert cumsum <= self.total
+        if cumsum > self.total:
+            raise ValueError(f"cumsum ({cumsum}) exceeds tree.total ({self.total}).")
 
         idx = 0
         while 2 * idx + 1 < len(self.nodes):
diff --git a/models/__init__.py b/models/__init__.py
index 8322430..4872850 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -1,3 +1,3 @@
 from models.cnn import ConvNet
-from models.resblock import ResidualBlock
-from  models.resnet import ResNet
\ No newline at end of file
+from models.resblock import ResidualBlock3D, ResidualBlock2D
+from  models.resnet import ResNet3D, ResNet2D
\ No newline at end of file
diff --git a/models/cnn.py b/models/cnn.py
index ae83ea9..acdf288 100644
--- a/models/cnn.py
+++ b/models/cnn.py
@@ -5,37 +5,94 @@ class ConvNet(torch.nn.Module):
     ''' Base CNN class
 
     '''
-    def __init__(self,chin=4,ch0=16,chout=4):
+    def __init__(self, chin=4, ch0=16, chout=4, use_layer_norm=True, activation='relu'):
         super().__init__()        
         k = 3
         p = (k-1)//2
         s = 2
-        self.c0 = torch.nn.Conv3d(chin,ch0,k,s,p)
-        self.b0 = torch.nn.BatchNorm3d(ch0)
-        self.c1 = torch.nn.Conv3d(ch0,2*ch0,k,s,p)
-        self.b1 = torch.nn.BatchNorm3d(2*ch0)
-        self.c2 = torch.nn.Conv3d(2*ch0,4*ch0,k,s,p)
-        self.b2 = torch.nn.BatchNorm3d(4*ch0)
-        self.l0 = torch.nn.Linear(5**3*64,64)        
-        self.l1 = torch.nn.Linear(64,chout)
-    def forward(self,x):
+        self.use_layer_norm = use_layer_norm
+        
+        # Choose activation function
+        if activation == 'relu':
+            self.activation = torch.nn.ReLU()
+        elif activation == 'elu':
+            self.activation = torch.nn.ELU()
+        elif activation == 'swish':
+            self.activation = torch.nn.SiLU()  # Swish activation
+        elif activation == 'leaky_relu':
+            self.activation = torch.nn.LeakyReLU(0.01)
+        else:
+            self.activation = torch.nn.ReLU()  # Default fallback
+        
+        # Input normalization
+        # self.n0 = torch.nn.InstanceNorm3d(chin, affine=True)
+        
+        # Convolutional layers
+        self.c0 = torch.nn.Conv3d(chin, ch0, k, s, p)
+        self.c1 = torch.nn.Conv3d(ch0, 2*ch0, k, s, p)
+        self.c2 = torch.nn.Conv3d(2*ch0, 4*ch0, k, s, p)
+        
+        # Normalization layers - choose between BatchNorm and LayerNorm
+        if use_layer_norm:
+            # LayerNorm is more stable than BatchNorm
+            self.b0 = torch.nn.GroupNorm(1, ch0)  # GroupNorm with 1 group = LayerNorm
+            self.b1 = torch.nn.GroupNorm(1, 2*ch0)
+            self.b2 = torch.nn.GroupNorm(1, 4*ch0)
+        else:
+            # Original BatchNorm (less stable)
+            self.b0 = torch.nn.BatchNorm3d(ch0)
+            self.b1 = torch.nn.BatchNorm3d(2*ch0)
+            self.b2 = torch.nn.BatchNorm3d(4*ch0)
+        
+        # Linear layers with normalization
+        self.l0 = torch.nn.Linear(5**3*64, 64)
+        self.ln0 = torch.nn.LayerNorm(64)  # Normalize linear layer output
+        self.l1 = torch.nn.Linear(64, chout)
+        
+        # Initialize weights to prevent NaN issues
+        self._initialize_weights()
+    
+    def _initialize_weights(self):
+        """Initialize network weights using Xavier/He initialization."""
+        for module in self.modules():
+            if isinstance(module, (torch.nn.Conv3d, torch.nn.Linear)):
+                # He initialization for ReLU activations
+                torch.nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
+                if module.bias is not None:
+                    torch.nn.init.constant_(module.bias, 0)
+            elif isinstance(module, (torch.nn.BatchNorm3d, torch.nn.GroupNorm, torch.nn.LayerNorm)):
+                if hasattr(module, 'weight') and module.weight is not None:
+                    torch.nn.init.constant_(module.weight, 1)
+                if hasattr(module, 'bias') and module.bias is not None:
+                    torch.nn.init.constant_(module.bias, 0)
+    def forward(self, x):
+        # Input normalization to stabilize training
+        # x = self.n0(x)
+        
         x = self.c0(x)
         x = self.b0(x)
-        x = torch.relu(x)
+        x = self.activation(x)
         
         x = self.c1(x)
         x = self.b1(x)
-        x = torch.relu(x)
+        x = self.activation(x)
         
         x = self.c2(x)
         x = self.b2(x)
-        x = torch.relu(x)
-        
+        x = self.activation(x)
         
-        x = self.l0(x.reshape(x.shape[0],-1))
-        x = torch.relu(x)
+        # Flatten and linear layers
+        x = self.l0(x.reshape(x.shape[0], -1))
+        x = self.ln0(x)  # Normalize before activation
+        x = self.activation(x)
         
         x = self.l1(x)
+        
+        # Optional: Add a final check for NaN values
+        if torch.isnan(x).any():
+            print("WARNING: ConvNet output contains NaN values!")
+            x = torch.where(torch.isnan(x), torch.zeros_like(x), x)
+        
         return x
     
 if __name__ == "__main__":
diff --git a/models/resblock.py b/models/resblock.py
index 649269b..e8338b1 100644
--- a/models/resblock.py
+++ b/models/resblock.py
@@ -1,9 +1,9 @@
 from torch import nn
 
 
-class ResidualBlock(nn.Module):
+class ResidualBlock3D(nn.Module):
     def __init__(self, in_channels, out_channels, stride = 1, downsample = None):
-        super(ResidualBlock, self).__init__()
+        super().__init__()
         self.conv1 = nn.Sequential(
                         nn.Conv3d(in_channels, out_channels, kernel_size = 3, stride = stride, padding = 1),
                         nn.BatchNorm3d(out_channels),
@@ -25,5 +25,31 @@ def forward(self, x):
         out = self.relu(out)
         return out
     
+
+class ResidualBlock2D(nn.Module):
+    def __init__(self, in_channels, out_channels, stride = 1, downsample = None):
+        super().__init__()
+        self.conv1 = nn.Sequential(
+                        nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = stride, padding = 1),
+                        nn.BatchNorm2d(out_channels),
+                        nn.ReLU())
+        self.conv2 = nn.Sequential(
+                        nn.Conv2d(out_channels, out_channels, kernel_size = 3, stride = 1, padding = 1),
+                        nn.BatchNorm2d(out_channels))
+        self.downsample = downsample
+        self.relu = nn.ReLU()
+        self.out_channels = out_channels
+        
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+        if self.downsample:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+        return out
+    
+
 if __name__ == "__main__":
     pass
\ No newline at end of file
diff --git a/models/resnet.py b/models/resnet.py
index 37e2ea1..c24c015 100644
--- a/models/resnet.py
+++ b/models/resnet.py
@@ -1,19 +1,20 @@
 from torch import nn
 
 
-class ResNet(nn.Module):
-    def __init__(self, block, layers, num_classes = 3):
-        super(ResNet, self).__init__()
+class ResNet3D(nn.Module):
+    def __init__(self, block, layers, in_channels=3, num_classes=3):
+        super().__init__()
         self.infilters = 64
+        self.n0 = nn.InstanceNorm3d(in_channels)
         self.conv1 = nn.Sequential(
-                        nn.Conv3d(3, 64, kernel_size=3, stride=1, padding=1),
+                        nn.Conv3d(in_channels, 64, kernel_size=3, stride=1, padding=1),
                         nn.BatchNorm3d(64),
                         nn.ReLU())
         self.layer0 = self._make_layer(block, 64, layers[0], stride = 1)
-        self.layer1 = self._make_layer(block, 128, layers[1], stride = 1)
+        self.layer1 = self._make_layer(block, 128, layers[1], stride = 2)
         self.layer2 = self._make_layer(block, 256, layers[2], stride = 2)
         self.layer3 = self._make_layer(block, 512, layers[3], stride = 2)
-        self.avgpool = nn.AvgPool3d(4, stride=1)
+        self.avgpool = nn.AvgPool3d(5, stride=1)
         self.fc = nn.Linear(512, num_classes)
 
     def _make_layer(self, block, filters, blocks, stride=1):
@@ -33,6 +34,7 @@ def _make_layer(self, block, filters, blocks, stride=1):
         return nn.Sequential(*layers)
 
     def forward(self, x):
+        x = self.n0(x)
         x = self.conv1(x)
         x = self.layer0(x)
         x = self.layer1(x)
@@ -45,5 +47,53 @@ def forward(self, x):
 
         return x
     
+class ResNet2D(nn.Module):
+    def __init__(self, block, layers, in_channels=3, num_classes=3):
+        super().__init__()
+        self.infilters = 64
+        self.n0 = nn.InstanceNorm2d(in_channels)
+        self.conv1 = nn.Sequential(
+                        nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1),
+                        nn.BatchNorm2d(64),
+                        nn.ReLU())
+        self.layer0 = self._make_layer(block, 64, layers[0], stride = 2)
+        self.layer1 = self._make_layer(block, 128, layers[1], stride = 4)
+        self.layer2 = self._make_layer(block, 256, layers[2], stride = 4)
+        self.layer3 = self._make_layer(block, 512, layers[3], stride = 2)
+        self.avgpool2d = nn.AvgPool2d(3, stride=1)
+        self.avgpool1d = nn.AvgPool1d(4,stride=1)
+        self.fc = nn.Linear(512, num_classes)
+
+    def _make_layer(self, block, filters, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.infilters != filters:
+
+            downsample = nn.Sequential(
+                nn.Conv2d(self.infilters, filters, kernel_size=1, stride=stride),
+                nn.BatchNorm2d(filters),
+            )
+        layers = []
+        layers.append(block(self.infilters, filters, stride, downsample))
+        self.infilters = filters
+        for i in range(1, blocks):
+            layers.append(block(self.infilters, filters))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.n0(x)
+        x = self.conv1(x)
+        x = self.layer0(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+
+        x = self.avgpool2d(x)
+        x = self.avgpool1d(x.squeeze(2))
+        x = x.view(x.size(0),-1)
+        x = self.fc(x)
+
+        return x
+    
 if __name__ == "__main__":
     pass
\ No newline at end of file
diff --git a/neurotrack/__init__.py b/neurotrack/__init__.py
new file mode 100644
index 0000000..5d906aa
--- /dev/null
+++ b/neurotrack/__init__.py
@@ -0,0 +1,25 @@
+"""
+Neurotrack - Reinforcement learning system for automated neuron tracing
+
+Author: Bryson Gray
+2024
+"""
+
+__version__ = "0.1.0"
+
+# Import key components for easy access
+from neurotrack.data.neuron_data import (
+    Dataset,
+    DataLoader, 
+    DataGenerator,
+    DrawingComplexityConfig,
+    create_neuron_data_components
+)
+
+__all__ = [
+    "Dataset",
+    "DataLoader", 
+    "DataGenerator",
+    "DrawingComplexityConfig",
+    "create_neuron_data_components"
+]
diff --git a/neurotrack/data/__init__.py b/neurotrack/data/__init__.py
new file mode 100644
index 0000000..64138e7
--- /dev/null
+++ b/neurotrack/data/__init__.py
@@ -0,0 +1,22 @@
+"""
+Data handling components for neurotrack
+
+Author: Bryson Gray  
+2024
+"""
+
+from .neuron_data import (
+    Dataset,
+    DataLoader,
+    DataGenerator,
+    DrawingComplexityConfig,
+    create_neuron_data_components
+)
+
+__all__ = [
+    "Dataset",
+    "DataLoader", 
+    "DataGenerator",
+    "DrawingComplexityConfig",
+    "create_neuron_data_components"
+]
diff --git a/neurotrack/data/neuron_data.py b/neurotrack/data/neuron_data.py
new file mode 100644
index 0000000..8845cfa
--- /dev/null
+++ b/neurotrack/data/neuron_data.py
@@ -0,0 +1,973 @@
+#!/usr/bin/env python
+
+"""
+Neuron data handling components for SAC tracking
+
+Author: Bryson Gray
+2024
+"""
+
+import csv
+import json
+import shutil
+import numpy as np
+import os
+import pandas as pd
+from pathlib import Path
+import sys
+import tifffile as tf
+import torch
+from typing import Dict, List, Tuple, Optional, Union
+import warnings
+from dataclasses import dataclass
+
+# Add parent directories to path for imports
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent.parent
+sys.path.append(str(parent_dir))
+from data_prep import draw, load, generate
+
+
+@dataclass
+class DrawingComplexityConfig:
+    """Configuration for complexity-based drawing parameters."""
+
+    width_correlation_rho_range: Tuple[float, float] = (0.5, 1.0)
+    segment_intensity_correlation_rho_range: Tuple[float, float] = (0.9, 1.0)
+    # Foreground parameters
+    foreground_mean_range: Tuple[float, float] = (0.5, 1.0)
+    foreground_std_range: Tuple[float, float] = (0.0, 0.35)
+    # Background parameters
+    background_mean_range: Tuple[float, float] = (0.0, 0.02)
+    background_std_range: Tuple[float, float] = (0.0, 0.04)
+    # Spatial noise parameters
+    spatial_noise_amplitude_range: Tuple[float, float] = (0.0, 1.0)
+
+    # Vignette magnitude [min, max]
+    vignette_magnitude_range: Tuple[float, float] = (0.0, 2.0)
+    
+    def interpolate_config(self, complexity: float) -> 'draw.DrawingConfig':
+        """
+        Interpolate drawing configuration based on complexity (0.0 to 1.0).
+        Higher complexity = more artifacts and variation.
+        """
+        complexity = max(0.0, min(1.0, complexity))
+        
+        def lerp(min_val, max_val, t):
+            return min_val + t * (max_val - min_val)
+        
+        return draw.DrawingConfig(
+            width=3.0,
+            blur=1.0,
+            sharpness=2.0,
+            mask_threshold=0.1,
+            rgb=False,
+            width_correlation=True,
+            width_correlation_rho=lerp(*self.width_correlation_rho_range, 1.0 - complexity),
+            segment_intensity_correlation=True,
+            segment_intensity_correlation_rho=lerp(*self.segment_intensity_correlation_rho_range, 1.0 - complexity),
+            foreground_mean=lerp(*self.foreground_mean_range, 1.0 - complexity),  # Less mean for higher complexity
+            foreground_std=lerp(*self.foreground_std_range, complexity),
+            background_mean=lerp(*self.background_mean_range, complexity),  # More background for complexity
+            background_std=lerp(*self.background_std_range, complexity),
+            spatial_noise_scale=3.0,  # Fixed scale
+            spatial_noise_amplitude=lerp(*self.spatial_noise_amplitude_range, complexity),
+            vignette_magnitude=lerp(*self.vignette_magnitude_range, complexity)
+        )
+
+
+class Dataset:
+    """
+    Dataset class that stores neuron file paths and complexity information.
+    
+    Complexity is represented as a tuple containing:
+    - scalar: degree of contrast artifacts in the image (0.0 to 1.0)
+    - category: overall complexity of neuron morphology ("simple", "moderate", "complex")
+    """
+    
+    def __init__(self, data_dir: str = None, rng=None):
+        """
+        Initialize Dataset from directory containing CSV file.
+        
+        Parameters:
+        -----------
+        data_dir : str, optional
+            Directory containing a single CSV file with neuron data
+        rng : np.random.Generator, optional
+            Random number generator for reproducibility
+        """
+        self.entries = []
+        self.rng = rng or np.random.default_rng()
+
+        if data_dir and os.path.exists(data_dir):
+            data_path = Path(data_dir)
+            
+            # Find CSV files in the directory
+            csv_files = list(data_path.glob("*.csv"))
+            
+            if len(csv_files) == 0:
+                # No CSV files found
+                raise ValueError(f"No CSV files found in {data_dir}. Directory must contain exactly one CSV file.")
+            elif len(csv_files) == 1:
+                # Exactly one CSV file - load it as data entries
+                csv_file = csv_files[0]
+                self.load_from_csv(str(csv_file), data_dir)
+            else:
+                # Multiple CSV files - error
+                csv_names = [f.name for f in csv_files]
+                raise ValueError(f"Multiple CSV files found in {data_dir}: {csv_names}. "
+                               f"Directory should contain exactly one CSV file.")
+        else:
+            warnings.warn("No valid data_dir provided. Dataset is empty.")
+    
+    def load_from_csv(self, csv_path: str, data_dir: str):
+        """Load dataset from CSV file."""
+        df = pd.read_csv(csv_path)
+        required_columns = ['neuron_name', 'complexity', 'morphology']
+        
+        if not all(col in df.columns for col in required_columns):
+            raise ValueError(f"CSV must contain columns: {required_columns}")
+        
+        data_path = Path(data_dir)
+        
+        for _, row in df.iterrows():
+            neuron_name = str(row['neuron_name'])
+            
+            # Construct file paths based on neuron_name
+            img_path = data_path / f"{neuron_name}_image.tif"
+            swc_path = data_path / f"{neuron_name}_subtree.swc"
+            reward_mask_path = data_path / f"{neuron_name}_reward_mask.tif"
+            
+            # Verify files exist
+            if not (img_path.exists() and swc_path.exists() and reward_mask_path.exists()):
+                print(f"Warning: Missing files for {neuron_name}, skipping entry")
+                continue
+            
+            self.entries.append({
+                'neuron_name': neuron_name,
+                'img_path': str(img_path),
+                'swc_path': str(swc_path),
+                'reward_mask_path': str(reward_mask_path),
+                'complexity': float(row['complexity']),
+                'morphology': str(row['morphology'])
+            })
+    
+    def save_to_csv(self, csv_path: str):
+        """Save dataset to CSV file."""
+        if not self.entries:
+            print("No entries to save")
+            return
+            
+        with open(csv_path, 'w', newline='') as csvfile:
+            # Use the standard format with neuron_name
+            fieldnames = ['neuron_name', 'img_path', 'swc_path', 'reward_mask_path', 'complexity', 'morphology']
+            
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(self.entries)
+    
+    def get_complexity_distribution(self) -> Dict:
+        """Get distribution of complexity levels."""
+        morphology_counts = {}
+        complexities = []
+        
+        for entry in self.entries:
+            morph = entry['morphology']
+            morphology_counts[morph] = morphology_counts.get(morph, 0) + 1
+            complexities.append(entry['complexity'])
+        
+        return {
+            'morphology_distribution': morphology_counts,
+            'complexity_stats': {
+                'mean': np.mean(complexities),
+                'std': np.std(complexities),
+                'min': np.min(complexities),
+                'max': np.max(complexities)
+            }
+        }
+    
+    def __len__(self) -> int:
+        return len(self.entries)
+    
+    def __getitem__(self, idx: int) -> Dict:
+        return self.entries[idx]
+
+
+class DataLoader:
+    """
+    DataLoader that samples neurons based on their complexity with adjustable sampling priority.
+    """
+    
+    def __init__(self, dataset: Dataset, complexity: float = 0.0, rng=None):
+        """
+        Initialize DataLoader.
+        
+        Parameters:
+        -----------
+        dataset : Dataset
+            Dataset to sample from
+        complexity : float
+            Complexity parameter (0.0 to 1.0) determining sampling priority.
+            0.0 = prioritize simple neurons, 1.0 = prioritize complex neurons
+        rng : np.random.Generator, optional
+            Random number generator for reproducibility
+        """
+        self.dataset = dataset
+        self.complexity = complexity
+        self.current_idx = 0
+        self.rng = rng or np.random.default_rng()
+        self._update_sampling_weights()
+    
+    def _update_sampling_weights(self):
+        """Update sampling weights based on current complexity parameter."""
+        if len(self.dataset) == 0:
+            self.weights = []
+            return
+            
+        weights = []
+        for entry in self.dataset.entries:
+            # Combine artifact level and morphology complexity
+            complexity = entry['complexity']
+            
+            # Weight based on how close the neuron complexity is to target complexity
+            weight = np.exp(-8*abs(complexity - self.complexity)) # exponential decay
+
+            # Ensure positive weights
+            weight = max(1e-4, weight)
+            weights.append(weight)
+        
+        # Normalize weights
+        total_weight = sum(weights)
+        self.weights = [w / total_weight for w in weights] if total_weight > 0 else weights
+    
+    def set_complexity(self, complexity: float):
+        """Update complexity parameter and recalculate weights."""
+        self.complexity = max(0.0, min(1.0, complexity))
+        self._update_sampling_weights()
+    
+    def sample(self) -> Dict:
+        """Sample a neuron based on current complexity weights."""
+        if len(self.dataset) == 0:
+            raise ValueError("Dataset is empty")
+        
+        if not self.weights:
+            # Fallback to uniform sampling
+            idx = self.rng.integers(0, len(self.dataset))
+        else:
+            idx = self.rng.choice(len(self.dataset), p=self.weights)
+        self.current_idx = idx
+        
+        return self.dataset[idx]
+    
+    def __iter__(self):
+        """Iterate through dataset in order."""
+        self.current_idx = 0
+        return self
+    
+    def __next__(self):
+        """Get next item in sequential order."""
+        if self.current_idx >= len(self.dataset):
+            raise StopIteration
+        
+        item = self.dataset[self.current_idx]
+        self.current_idx += 1
+        return item
+
+
+class DataGenerator:
+    """
+    DataGenerator handles generating neuron images from SWC files on-the-fly.
+    """
+    
+    def __init__(self, cache_dir: str = None,
+                 complexity_config: Optional[DrawingComplexityConfig] = None,
+                 rng: Optional[np.random.Generator] = None):
+        """
+        Initialize DataGenerator.
+        
+        Parameters:
+        -----------
+        cache_dir : str, optional
+            Directory to cache generated data
+        complexity_config : DrawingComplexityConfig, optional
+            Configuration for complexity-based drawing parameters
+        rng_seed : int, optional
+            Random number generator seed
+        """
+        self.cache_dir = Path(cache_dir) if cache_dir else None
+        self.complexity_config = complexity_config or DrawingComplexityConfig()
+        
+        # Set up RNG
+        self.rng = rng or np.random.default_rng()
+
+        self.renderer = draw.NeuronRenderer(rng=self.rng)
+        
+        if self.cache_dir:
+            self.cache_dir.mkdir(parents=True, exist_ok=True)
+    
+    def get_subtrees(self, swc_list: List, target_path_len: int, num_subtrees: int, 
+                    mode: str = "no_branch") -> List[List]:
+        """
+        Extract subtrees from SWC data using the get_subtrees function from the notebook.
+        
+        Parameters:
+        -----------
+        swc_list : List
+            List of SWC nodes
+        num_edges : int
+            Number of edges in each subtree
+        num_subtrees : int
+            Number of subtrees to extract
+        mode : str
+            Branching mode: "no_branch", "one_branch", "any_branch"
+        rng : np.random.Generator, optional
+            Random number generator for reproducibility
+        """
+        edge_list = load.undirected_edge_list(swc_list)
+        
+        # Identify branch points and endpoints
+        branch_points = [node for node, neighbors in edge_list.items() if len(neighbors) > 2]
+        end_points = [node for node, neighbors in edge_list.items() if len(neighbors) == 1]
+        critical_points = branch_points + end_points
+        
+        subtrees = []
+        swc_list = np.array(swc_list)
+        
+        if mode == "no_branch":
+            for start_node in self.rng.permutation(end_points):
+                attempts = 0
+                max_attempts = 100  # Prevent infinite loops
+                while len(subtrees) < num_subtrees and attempts < max_attempts:
+                    attempts += 1
+                    current_node = start_node
+                    path = [current_node]
+                    subtree = [swc_list[swc_list[:,0] == current_node][0].tolist()]
+                    path_len = 0.0
+                    while path_len < target_path_len:
+                        neighbors = edge_list[current_node]
+                        next_nodes = [n for n in neighbors if n not in path]
+                        
+                        if not next_nodes:
+                            break  # Dead end
+                            
+                        next_node = self.rng.choice(next_nodes)
+                        path.append(next_node)
+                        current_node = next_node
+                        subtree.append(swc_list[swc_list[:,0] == current_node][0].tolist())
+                        path_len += np.linalg.norm(np.array(subtree[-1][2:5]) - np.array(subtree[-2][2:5]))
+
+                    if path_len >= target_path_len:
+                        subtrees.append(subtree)
+                        break
+        
+        elif mode == "one_branch":
+            for start_node in self.rng.permutation(branch_points):
+                attempts = 0
+                max_attempts = 100
+                
+                while len(subtrees) < num_subtrees and attempts < max_attempts:
+                    attempts += 1
+                    
+                    # Get all neighbors of the branch point
+                    neighbors = edge_list[start_node]
+                    
+                    # Initialize the path with the branch point
+                    path = [start_node]
+                    subtree = [swc_list[swc_list[:,0] == start_node][0].tolist()]
+                    total_len = 0.0
+
+                    # # Calculate edges per branch - divide equally among neighbors
+                    # edges_per_branch = max(1, num_edges // len(neighbors))
+                    # remaining_nodes = num_edges
+                    len_per_branch = max(1, target_path_len // len(neighbors))
+                    remaining_len = target_path_len
+                    
+                    # Walk along each neighbor's path
+                    for neighbor in neighbors:
+                        if neighbor in path:
+                            continue
+                            
+                        # Start walking from this neighbor
+                        current_node = neighbor
+                        path.append(current_node)
+                        subtree.append(swc_list[swc_list[:,0] == current_node][0].tolist())
+                        branch_len = np.linalg.norm(np.array(subtree[-1][2:5]) - np.array(subtree[0][2:5]))
+                        total_len += branch_len
+                        remaining_len = target_path_len - total_len
+                        # remaining_nodes -= 1
+                        # Continue walking this branch until we hit the limit or an endpoint
+                        # while len(branch_path) < edges_per_branch and remaining_nodes > 0:
+                        while branch_len < len_per_branch and remaining_len > 0:
+                            next_neighbors = [n for n in edge_list[current_node] if n not in path]
+                            
+                            # Stop if we reach an endpoint or have no more neighbors
+                            if not next_neighbors:
+                                break
+                                
+                            # Move to next node
+                            current_node = self.rng.choice(next_neighbors)
+                            path.append(current_node)
+                            subtree.append(swc_list[swc_list[:,0] == current_node][0].tolist())
+                            step_len = np.linalg.norm(np.array(subtree[-1][2:5]) - np.array(subtree[-2][2:5]))
+                            total_len += step_len
+                            branch_len += step_len
+                            remaining_len = target_path_len - total_len
+                            # remaining_nodes -= 1
+                    
+                    # Add subtree if we've collected enough nodes
+                    if total_len >= target_path_len * 0.7:  # Ensure we have at least some minimum path
+                        subtrees.append(subtree)
+                        break
+        
+        elif mode == "any_branch":
+            for start_node in self.rng.permutation(critical_points):
+                attempts = 0
+                max_attempts = 100
+                
+                while len(subtrees) < num_subtrees and attempts < max_attempts:
+                    attempts += 1
+                    current_nodes = [start_node]
+                    path = [start_node]
+                    subtree = [swc_list[swc_list[:,0] == start_node][0].tolist()]
+                    path_len = 0.0
+                    
+                    while path_len < target_path_len:
+                        # Get all neighbors for current active nodes
+                        all_next_nodes = []
+                        for current_node in current_nodes:
+                            neighbors = edge_list[current_node]
+                            next_nodes = [n for n in neighbors if n not in path]
+                            all_next_nodes.extend(next_nodes)
+                        
+                        # Remove duplicates and nodes already in path
+                        all_next_nodes = list(set(all_next_nodes) - set(path))
+                        
+                        if not all_next_nodes:
+                            break  # No more nodes to explore
+                        
+                        # Randomly select next node to add
+                        next_node = self.rng.choice(all_next_nodes)
+                        path.append(next_node)
+                        subtree.append(swc_list[swc_list[:,0] == next_node][0].tolist())
+                        path_len += np.linalg.norm(np.array(subtree[-1][2:5]) - np.array(subtree[-2][2:5]))
+
+                        # Update current_nodes - keep nodes with unexplored neighbors
+                        new_current_nodes = []
+                        for current_node in current_nodes:
+                            neighbors = edge_list[current_node]
+                            available_neighbors = [n for n in neighbors if n not in path]
+                            # Keep node active if it has unexplored neighbors and isn't an endpoint
+                            if available_neighbors and current_node not in end_points:
+                                new_current_nodes.append(current_node)
+                        
+                        # Add the new node as an active node
+                        new_current_nodes.append(next_node)
+                        current_nodes = new_current_nodes
+                    
+                    # Accept any subtree that reaches the target length
+                    if path_len >= target_path_len:
+                        subtrees.append(subtree)
+                        break
+        
+        return subtrees
+    
+
+    def crop_around_subtree(self, volume: torch.Tensor, subtree: List, 
+                           padding: int = 10) -> Tuple[torch.Tensor, List]:
+        """
+        Crop image around subtree coordinates and shift subtree coordinates.
+        """
+        # Get coordinates from subtree
+        coords = np.array([(node[2], node[3], node[4]) for node in subtree])
+        
+        # Calculate bounding box with padding
+        min_coords = np.min(coords, axis=0) - padding
+        max_coords = np.max(coords, axis=0) + padding
+        
+        # Convert to integers and ensure within bounds
+        volume_shape = volume.shape
+        min_coords = np.maximum(0, min_coords.astype(int))
+        max_coords = np.minimum(volume_shape[1:][::-1], max_coords.astype(int))
+        
+        # Crop the image
+        cropped_image = volume[:, 
+                              min_coords[2]:max_coords[2], 
+                              min_coords[1]:max_coords[1], 
+                              min_coords[0]:max_coords[0]]
+        
+        # Shift subtree coordinates
+        shifted_subtree = []
+        for node in subtree:
+            shifted_node = (
+                node[0],  # node_id
+                node[1],  # node_type  
+                node[2] - min_coords[0],  # x
+                node[3] - min_coords[1],  # y
+                node[4] - min_coords[2],  # z
+                node[5],  # radius
+                node[6]   # parent_id
+            )
+            shifted_subtree.append(shifted_node)
+        
+        return cropped_image, shifted_subtree
+    
+    def generate_reward_mask(self, subtree: List, shape: Tuple[int, ...], width=3.0) -> torch.Tensor:
+        """Generate reward mask from subtree."""
+        # Parse subtree into sections format
+        sections, _ = load.parse_swc(subtree)
+        
+        # Create reward mask (same as true density)
+        # reward_mask = self.renderer.draw_density(sections, shape, width=width)
+        reward_mask = self.renderer.draw_density(sections, shape, width=width, mask=False)
+        return reward_mask.data
+    
+    def simulate_neuron_image(self, subtree: List, shape: Tuple[int, ...], 
+                             config: draw.DrawingConfig) -> torch.Tensor:
+        """
+        Simulate neuron image from subtree using draw.neuron.
+        
+        Parameters:
+        -----------
+        subtree : List
+            Subtree SWC data
+        shape : Tuple[int, ...]
+            Output image shape
+        config : DrawingConfig
+            Drawing configuration
+        """
+
+        # Parse subtree into sections format
+        sections, _ = load.parse_swc(subtree)
+        
+        # Generate simulated neuron image
+        img = self.renderer.draw_neuron(sections, shape, config)
+        return img.data
+    
+    def load_files(self, swc_path: str, img_path: Optional[str] = None) -> Tuple[List, Optional[torch.Tensor]]:
+        """Load SWC file and optional image file."""
+        # Load SWC
+        swc_list = load.swc(swc_path)
+        
+        # Load image if provided
+        img_tensor = None
+        if img_path and os.path.exists(img_path):
+            img_array = tf.imread(img_path)
+            if img_array.ndim == 3:
+                img_array = img_array[None]  # Add channel dimension
+            # img_tensor = torch.from_numpy(img_array.astype(np.float32)) / 255.0 #TODO: Keep original dtype?
+            img_tensor = torch.from_numpy(img_array)
+        
+        return swc_list, img_tensor
+    
+    def generate_data(self, subtrees_per_swc: int = 1, 
+                     complexity_range: Tuple[float, float] = (0.0, 1.0),
+                     swc_dir: Optional[str] = None, img_dir: Optional[str] = None,
+                     dataset_size: int = 100, output_dir: Optional[str] = None) -> Dict:
+        """
+        Generate processed data from SWC files or synthetic data and save to output directory.
+        
+        Parameters:
+        -----------
+        subtrees_per_swc : int
+            Number of subtrees to generate per SWC file (or per synthetic neuron)
+        complexity_range : Tuple[float, float]
+            Range of complexity values to sample from (min, max)
+        swc_dir : str, optional
+            Directory containing SWC files to process. If None, synthetic data will be generated.
+        img_dir : str, optional
+            Directory containing corresponding TIFF images for SWC files
+        dataset_size : int
+            Number of synthetic neurons to generate (only used if swc_dir is None)
+        output_dir : str, optional
+            Output directory to save generated data. Uses cache_dir by default.
+        
+        Returns:
+        --------
+        Dict containing:
+        - processed_files: Number of files/neurons processed
+        - total_subtrees: Total number of subtrees generated
+        - output_dir: Path to output directory
+        """
+        if output_dir is None:
+            output_dir_path = self.cache_dir
+        else:
+            output_dir_path = Path(output_dir)
+            output_dir_path.mkdir(parents=True, exist_ok=True)
+
+        # check for existing csv in output dir
+        existing_csv = list(output_dir_path.glob("*.csv"))
+        existing_neuron_names = set()
+        if existing_csv:
+            # get neuron names from existing csv
+            existing_df = pd.read_csv(existing_csv[0])
+            existing_neuron_names = set(existing_df['neuron_name'].tolist())
+        
+        # Determine data source: files from directory or synthetic generation
+        if swc_dir is not None:
+            # Use SWC files from directory
+            swc_dir_path = Path(swc_dir)
+            # Find all SWC files in the directory
+            swc_files = list(swc_dir_path.rglob("*.swc"))
+            if not swc_files:
+                raise ValueError(f"No SWC files found in directory: {swc_dir}")
+            
+            # If img_dir provided, check for matching TIFF files
+            if img_dir:
+                img_dir_path = Path(img_dir)
+                if not img_dir_path.exists():
+                    raise ValueError(f"Image directory not found: {img_dir}")
+                
+                # Filter SWC files to only those with matching TIFF images
+                matched_swc_files = []
+                for swc_file in swc_files:
+                    # Look for corresponding TIFF file
+                    tiff_patterns = [
+                        swc_file.stem + "_image.tif",
+                        swc_file.stem + ".tif",
+                        swc_file.stem + "_image.tiff",
+                        swc_file.stem + ".tiff"
+                    ]
+                    
+                    tiff_found = False
+                    for pattern in tiff_patterns:
+                        tiff_path = img_dir_path / pattern
+                        if tiff_path.exists():
+                            matched_swc_files.append((swc_file, str(tiff_path)))
+                            tiff_found = True
+                            break
+                    
+                    if not tiff_found:
+                        print(f"No matching TIFF image found for {swc_file.name}")
+                
+                if not matched_swc_files:
+                    raise ValueError("No SWC files have matching TIFF images in the image directory")
+                
+                swc_files = matched_swc_files
+            
+            data_items = swc_files
+            use_synthetic = False
+        else:
+            # Generate synthetic data
+            data_items = [f"simulated_img{i}" for i in range(dataset_size)]
+            use_synthetic = True
+        
+        processed_entries = []
+        total_subtrees = 0
+        
+        print(f"Processing {len(data_items)} {'SWC files' if not use_synthetic else 'synthetic neurons'}...")
+        
+        for item in data_items:
+            if not use_synthetic and img_dir:
+                swc_file, img_path = item
+                item_name = swc_file.stem
+            elif not use_synthetic:
+                swc_file = item
+                img_path = None
+                item_name = swc_file.stem
+            else:
+                # Synthetic data
+                item_name = item
+                swc_file = None
+                img_path = None
+            
+            try:
+                # Sample complexity from range
+                complexity = self.rng.uniform(*complexity_range)
+                config = self.complexity_config.interpolate_config(complexity)
+                
+                # Determine subtree extraction mode and set complexity parameters
+                subtree_mode = self._complexity_to_mode(complexity)
+                morphology = self._complexity_to_category(complexity)
+                target_path_len = 50.0 + {"no_branch": 100.0,
+                                           "one_branch": 500.0,
+                                           "any_branch": 5000.0}[subtree_mode] * complexity
+                
+                if use_synthetic:
+                    # Generate synthetic SWC data
+                    if subtree_mode == "any_branch":
+                        num_branches = self.rng.choice([2, 3, 4])
+                    else:
+                        num_branches = 1 if subtree_mode == "one_branch" else 0
+                    
+                    # Estimate reasonable shape based on path length
+                    estimated_shape = (300, 300, 300)  # Default shape for synthetic data. This will be cropped later.
+                    kappa = 1000.0 if complexity < 0.1 else 20.0
+                    swc_list = generate.make_swc_list(
+                        size=estimated_shape,
+                        length=target_path_len,
+                        step_size=1.0,
+                        kappa=kappa,
+                        num_branches=num_branches,
+                        rng=self.rng
+                    )
+                    img_tensor = None
+                    subtrees = [swc_list]  # Always 1 subtree per synthetic SWC
+                    padding = 0
+                else:
+                    # Load files from disk
+                    swc_list, img_tensor = self.load_files(str(swc_file), img_path)
+                
+                    if morphology == "full":
+                        subtrees = [swc_list]  # Use full neuron
+                        padding = 10  # Padding only for full neurons
+                    else:
+                        # Extract subtrees from loaded SWC
+                        subtrees = self.get_subtrees(swc_list, target_path_len, subtrees_per_swc, mode=subtree_mode)
+                        padding = 0  # No padding for subtrees
+                
+                # Process and save each subtree individually to avoid memory issues
+                for i, subtree in enumerate(subtrees):
+                    # Create unique prefix for this file and subtree
+                    file_prefix = f"{item_name}_subtree_{i:02d}"
+                    if file_prefix in existing_neuron_names:
+                        # try incrementing i until unique
+                        j = 1
+                        while f"{item_name}_subtree_{i+j:02d}" in existing_neuron_names:
+                            j += 1
+                        file_prefix = f"{item_name}_subtree_{i+j:02d}"
+                        existing_neuron_names.add(file_prefix)
+                        print(f"Adjusted file prefix to avoid duplicate: {file_prefix}")
+                    
+                    # Generate data for this single subtree
+                    if img_tensor is not None:
+                        # Use real image - crop around subtree
+                        cropped_image, shifted_subtree = self.crop_around_subtree(img_tensor, subtree, padding=padding)
+                        reward_mask = self.generate_reward_mask(shifted_subtree, cropped_image.shape[1:], width=float(config.width))
+                        result = {
+                            'image': cropped_image,
+                            'subtree': shifted_subtree,
+                            'reward_mask': reward_mask
+                        }
+                        # Since real images have natural artifacts, the complexity only reflects morphology
+                        # Adjust complexity to reflect this.
+                        complexity = 0.75 + 0.25 * complexity
+                    else:
+                        # Simulate image from subtree
+                        # Estimate shape from subtree coordinates
+                        coords = np.array(subtree)[:, 2:5]                
+                        shape_estimate = tuple(int(x) + 20 for x in np.ptp(coords, axis=0))[::-1] # Reverse for z,y,x
+
+                        # shift coords
+                        subtree = np.array(subtree)
+                        subtree[:, 2:5] = subtree[:, 2:5] - coords.min(axis=0) + 10.0
+                        subtree = subtree.tolist()
+
+                        simulated_image = self.simulate_neuron_image(subtree, shape_estimate, config=config)
+                        reward_mask = self.generate_reward_mask(subtree, shape_estimate, width=float(config.width))
+                        
+                        result = {
+                            'image': simulated_image,
+                            'subtree': subtree,
+                            'reward_mask': reward_mask
+                        }
+                    
+                    # Save immediately to free memory
+                    self.save_data(result, str(output_dir_path), prefix=file_prefix)
+                    total_subtrees += 1
+                    
+                    # Track entry for CSV - one row per subtree
+                    processed_entries.append({
+                        'neuron_name': file_prefix,
+                        'complexity': complexity,
+                        'morphology': morphology
+                    })
+                    
+                    # Clear result to free memory
+                    del result
+                
+                print(f"Processed {item_name}: {len(subtrees)} subtrees generated")
+                
+            except Exception as e:
+                print(f"Failed to process {item_name}: {str(e)}")
+        
+        # Save entries to CSV
+        if processed_entries:
+            # check for existing csv
+            if existing_csv:
+                # append to existing csv
+                df_new = pd.DataFrame(processed_entries)
+                df_combined = pd.concat([existing_df, df_new], ignore_index=True)
+                csv_path = output_dir_path / existing_csv[0].name
+                df_combined.to_csv(csv_path, index=False)
+                print(f"Appended entry data to existing CSV: {csv_path}")
+            else:
+                # create new csv
+                csv_path = output_dir_path / "generated_data_entries.csv"
+                df = pd.DataFrame(processed_entries)
+                df.to_csv(csv_path, index=False)
+                print(f"Entry data saved to: {csv_path}")
+
+        print(f"\nProcessing complete!")
+        print(f"Total subtrees generated: {total_subtrees}")
+        print(f"Results saved to: {output_dir_path}")
+        
+        return {
+            'processed_files': len(data_items),
+            'total_subtrees': total_subtrees,
+            'output_dir': str(output_dir_path)
+        }
+
+    def _complexity_to_category(self, complexity: float) -> str:
+        """Convert numeric complexity to category string."""
+        if complexity < 0.33:
+            return "simple"
+        elif complexity < 0.67:
+            return "moderate"
+        elif complexity < 0.9:
+            return "complex"
+        else:
+            return "full"
+    
+    def _complexity_to_mode(self, complexity: float) -> str:
+        """Convert numeric complexity to subtree extraction mode."""
+        if complexity < 0.33:
+            return "no_branch"
+        elif complexity < 0.67:
+            return "one_branch"
+        else:
+            return "any_branch"
+    
+    def save_data(self, data: Dict, save_dir: str, prefix: str = "neuron_data"):
+        """Save only image, subtree, and reward mask to disk."""
+        save_path = Path(save_dir)
+        save_path.mkdir(parents=True, exist_ok=True)
+        
+        # Save image
+        img_path = save_path / f"{prefix}_image.tif"
+        tf.imwrite(str(img_path), data['image'].numpy())
+        
+        # Save reward mask
+        mask_path = save_path / f"{prefix}_reward_mask.tif"
+        tf.imwrite(str(mask_path), data['reward_mask'].numpy())
+        
+        # Save subtree as SWC
+        swc_path = save_path / f"{prefix}_subtree.swc"
+        with open(swc_path, 'w') as f:
+            for node in data['subtree']:
+                f.write(f"{node[0]} {node[1]} {node[2]:.3f} {node[3]:.3f} {node[4]:.3f} {node[5]:.3f} {node[6]}\n")
+    
+    def empty_cache(self):
+        """Empty the cache directory."""
+        if self.cache_dir and self.cache_dir.exists():
+            shutil.rmtree(self.cache_dir)
+            self.cache_dir.mkdir(parents=True, exist_ok=True)
+            print(f"Cache directory {self.cache_dir} emptied.")
+        else:
+            print("No cache directory to empty.")
+
+
+# Convenience function to create data components
+def create_neuron_data_components(data_dir: str, complexity: float = 0.0,
+                                 complexity_config: Optional[DrawingComplexityConfig] = None,
+                                 cache_dir: str = None, 
+                                 rng: Optional[np.random.Generator] = None) -> Tuple[Dataset, DataLoader, DataGenerator]:
+    """
+    Create neuron data handling components.
+    
+    Parameters:
+    -----------
+    data_dir : str
+        Directory containing neuron data with a single CSV file
+    complexity : float
+        Initial complexity parameter
+    complexity_config : DrawingComplexityConfig, optional
+        Configuration for complexity-based drawing parameters
+    cache_dir : str, optional
+        Directory for caching generated data
+    rng : np.random.Generator, optional
+        Random number generator for reproducibility
+    
+    Returns:
+    --------
+    Tuple[Dataset, DataLoader, DataGenerator]
+        Configured data components
+    """
+    # Create dataset
+    dataset = Dataset(data_dir=data_dir, rng=rng)
+    
+    # Create dataloader
+    dataloader = DataLoader(dataset=dataset, complexity=complexity, rng=rng)
+    
+    # Create data generator
+    data_generator = DataGenerator(
+        cache_dir=cache_dir,
+        complexity_config=complexity_config,
+        rng=rng
+    )
+    
+    return dataset, dataloader, data_generator
+
+
+if __name__ == "__main__":
+    # Example usage
+    swc_dir = "/home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset"
+    output_dir = "/home/brysongray/neurotrack/generated_data_output"
+    img_dir = None  # Set to path containing TIFF images if available
+    
+    # Create data generator
+    data_generator = DataGenerator(
+        cache_dir=None,  # No caching for this example
+        complexity_config=DrawingComplexityConfig(),
+        rng=np.random.default_rng(42)  # Fixed seed for reproducibility
+    )
+    
+    print("DataGenerator created successfully!")
+    
+    # Example batch data generation from directory
+    if os.path.exists(swc_dir):
+        print(f"Processing SWC files from: {swc_dir}")
+        results = data_generator.generate_data(
+            output_dir=output_dir,
+            subtrees_per_swc=2,  # Generate 2 subtrees per SWC file
+            complexity_range=(0.2, 0.8),  # Medium complexity range
+            swc_dir=swc_dir,
+            img_dir=img_dir  # Optional image directory
+        )
+        
+        print(f"Processing results:")
+        print(f"  - Files processed: {results['processed_files']}")
+        print(f"  - Total subtrees: {results['total_subtrees']}")
+        print(f"  - Output directory: {results['output_dir']}")
+        
+        # Example of loading the generated dataset
+        print(f"\n--- Loading generated dataset ---")
+        dataset = Dataset(data_dir=output_dir)
+        dataloader = DataLoader(dataset=dataset, complexity=0.5)
+        
+        print(f"Generated dataset size: {len(dataset)}")
+        print(f"Complexity distribution: {dataset.get_complexity_distribution()}")
+        
+        # Sample from the generated dataset
+        if len(dataset) > 0:
+            sample_entry = dataloader.sample()
+            print(f"Sample generated entry: {sample_entry}")
+    else:
+        print(f"SWC directory not found: {swc_dir}")
+        print("Generating synthetic data instead...")
+        
+        # Example synthetic data generation
+        results = data_generator.generate_data(
+            output_dir=output_dir,
+            subtrees_per_swc=1,  # Generate 1 subtree per synthetic neuron
+            complexity_range=(0.2, 0.8),  # Medium complexity range
+            swc_dir=None,  # No SWC directory - use synthetic data
+            dataset_size=50  # Generate 50 synthetic neurons
+        )
+        
+    # Example of creating dataset from existing generated data
+    print(f"\n--- Example: Loading existing generated dataset ---")
+    test_data_dir = "/home/brysongray/neurotrack/data_dir_test"
+    if os.path.exists(test_data_dir):
+        try:
+            dataset = Dataset(data_dir=test_data_dir)
+            dataloader = DataLoader(dataset=dataset, complexity=0.5)
+            
+            print(f"Test dataset size: {len(dataset)}")
+            if len(dataset) > 0:
+                print(f"Complexity distribution: {dataset.get_complexity_distribution()}")
+                sample_entry = dataloader.sample()
+                print(f"Sample entry: {sample_entry}")
+        except Exception as e:
+            print(f"Failed to load test dataset: {e}")
+    else:
+        print(f"Test data directory not found: {test_data_dir}")
diff --git a/notebooks/branch_classifier.ipynb b/notebooks/branch_classifier.ipynb
new file mode 100644
index 0000000..2ebc1fc
--- /dev/null
+++ b/notebooks/branch_classifier.ipynb
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Branch Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from glob import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "import torch\n",
+    "\n",
+    "sys.path.append(\"../\")\n",
+    "from data_prep import collect, load\n",
+    "from solvers import branch_classifier\n",
+    "import models\n",
+    "DATE = datetime.now().strftime(\"%m-%d-%y\")\n",
+    "DTYPE = torch.float32\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Collect branch classifier training data\n",
+    "Training data consists of volumetric image patches chosen randomly from the neuron node coordinates given\\\n",
+    "in the SWC file with an added small random translation. Image patches are labeled 1 if they are centered on\\\n",
+    " a branch point and 0 otherwise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First define input and output directories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Location of neuron morphology swc files \n",
+    "labels_dir = \"/home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/swc_trees/neuromorpho_sub1\"\n",
+    "\n",
+    "# Location of neuron images. These images should be the output of \"simulate_neurons.py\"\n",
+    "# which are in a specific format.\n",
+    "image_dir = os.path.expanduser(\"~/data/neurotrack_data/simulated_neurons/neuromorpho/neuromorpho_with_artifacts_sub/\")\n",
+    "\n",
+    "# Output directory for the classifier\n",
+    "# This directory will contain the data, labels, trained model and training logs.\n",
+    "out_dir = os.path.expanduser(\"~/data/neurotrack_data/branch_classifier_data/neuromorpho_classifier_data/neuromorpho_test/\")\n",
+    "if not os.path.exists(out_dir):\n",
+    "    os.makedirs(out_dir, exist_ok=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get sample points from swc files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading file: /home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/swc_trees/neuromorpho_sub1/beining/CNG version/35dpi_ipsi_infra_06.CNG.swc\n",
+      "loading file: /home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/swc_trees/neuromorpho_sub1/campos/CNG version/Astro-1.CNG.swc\n",
+      "loading file: /home/brysongray/data/neurotrack_data/simulated_neurons/neuromorpho/swc_trees/neuromorpho_sub1/allen cell types/CNG version/646805498_transformed.CNG.swc\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load SWC file data into python lists\n",
+    "files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], '*.swc'))]\n",
+    "swc_lists = []\n",
+    "for f in files:\n",
+    "    swc_lists.append(load.swc(f))\n",
+    "\n",
+    "# Collect random sample points from SWC data\n",
+    "samples_per_file = 300\n",
+    "fnames = [f.split('/')[-1].split('.')[0] for f in files]\n",
+    "sample_points = collect.swc_random_points(samples_per_file, swc_lists, fnames, adjust=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save sample patches and labels from image files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save sample patches from the images centered at the sample points\n",
+    "name = \"neuromorpho_test\"\n",
+    "collect.collect_data(sample_points, image_dir, out_dir, name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View some example input images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "observations = os.listdir(os.path.join(out_dir, f\"observations_{DATE}\"))\n",
+    "training_annotations = pd.read_csv(glob(os.path.join(out_dir, \"*_labels.csv\"))[0])\n",
+    "ids = np.random.choice(len(training_annotations), size=9)\n",
+    "sample = training_annotations.iloc[ids]\n",
+    "\n",
+    "fig, ax = plt.subplots(3,3)\n",
+    "ax = ax.flatten()\n",
+    "for i in range(len(ax)):\n",
+    "    img = torch.load(os.path.join(out_dir,f\"observations_{DATE}\", sample.iloc[i,0]), weights_only=True) # type: ignore\n",
+    "    ax[i].imshow(img[0].amax(0))\n",
+    "    ax[i].set_title(f\"label: {sample.iloc[i,1].item()}\")\n",
+    "    ax[i].set_axis_off()\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train branch classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Instantiate dataloader for training and test datasets\n",
+    "Dataloaders use a weighted random sampler to balance classes. Additionally, the training dataset\\\n",
+    " adds a random permutation and flip to the image patch at retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set source data files paths\n",
+    "training_labels_file = os.path.join(out_dir, f\"branch_classifier_neuromorpho_test_{DATE}_training_labels.csv\")\n",
+    "test_labels_file = os.path.join(out_dir, f\"branch_classifier_neuromorpho_test_{DATE}_test_labels.csv\")\n",
+    "img_dir = os.path.join(out_dir, f\"observations_{DATE}\")\n",
+    "\n",
+    "# instantiate training and test datasets\n",
+    "transform = branch_classifier.transform # random permutation and flip\n",
+    "training_data = branch_classifier.StateData(labels_file=training_labels_file,\n",
+    "                          img_dir=img_dir,\n",
+    "                          transform=transform)\n",
+    "test_data = branch_classifier.StateData(labels_file=test_labels_file,\n",
+    "                          img_dir=img_dir)\n",
+    "\n",
+    "# instantiate dataloaders\n",
+    "training_dataloader = branch_classifier.init_dataloader(training_data, batchsize=64)\n",
+    "test_dataloader = branch_classifier.init_dataloader(test_data, batchsize=64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View balanced data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4TD9etDmTwSteIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouYeImIiCxKupwoZY/cAcK/N0H+7pRLK5x0+Zb4lm/L5Qb9JXpnkwxPvxj7QeW/i7FxKY1i7K9TQ+qcf7vnfDHmCstlU7GgV4y5d+ldMlyj9G4hBBy6UO5u8lD5W+rYj/rLxVh0QD58PmySO1X1D8ifNwD8Nq1SjB3emSPGKn8nH2cDuXrJX8MBuVNLfo38OhuicuxHY/5NnfNfei9R4wQ4SslQLFcuFwIAf0O7vN1pxWLsxgmbxNj/6tLPN6My5VKkpnS5o09k/Ggx5mvsVOeEVz6eokVKB6IueV/j3fp513Hk83kyeMVLRERkERMvERGRRUy8REREFjHxEhERWcTES0REZBETLxERkUVJlxNB65LTG1aHOn65K4XxyGVB6fvl7fbn+9U5d0+Rb3vPU255z3L1ibE/Duidgpr75Nv70/fKHZr8dYfEmDxqUGz/wQTPoH+Y8bYYW978bXXstpZCMRbcKq/BlANKiUOp/n033iKXXVRtk9cujLxavN0Rdc7AXrmLy+R1t4mx1y79VzEWMXrJ39xxercvAkxhvhhzN8olngAQK5XHpu+Vu+88+cEMMfbX52xR5wzH5M882KiUVKbKqSh8llwSBAC+nfI50P3ZPnWsxFMulzcBAFwnds3KK14iIiKLmHiJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisijpOl7T0yvHwgnqeDPk+laz94AYS+mW5wwWlatzfvSbcWJsW7Vcm5k6bkCMFXr1VlH1dQVibHSr3JrOhOQ54da/G7kL5Fo9GlTg7RRjv98yRh1b8o78/gcPyDW17jY5lv0nvb4VUXmtODG5/jKWLbeQ8zZ3qVOWvywfa7sXyjXx3/u3H4uxx89fpc75bx1nq3ECsEeuQzUBvdWj+6DcFjBVaUWasU3e7rvN31LnjKbKteRmjLJ2ffJvPeRu0/OLq2yUGIv75WNNq/81wYA6p/m8QY0nwiteIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouYeImIiCxKupwo3t0txkxUb5dnBpRyGUfO/SY7Q4xlbZf3BwDcA3JrtcZsOfZI338UYxdW7FHnzNomv51OTGnL5pLbZSEs34IPAGZAv9WegP9n0/fEWOZWuawCANJ2yyVkrrC87qN5SgmdR/++q5X+mMNyzNUrt7REbpY6p9MXEmOBJnl9dmXIpSdLds1T5/zXar3ciABXunyuincrLSKhl3H6Dsljc7bLJTjdZ+kpw/89ucVpS4e8P30RuXzHiculRgCQ/Zm8PgON8utUS4ZaO9Q5nbJSNZ4Ir3iJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisijpciJXaupxT+Kkyrdtx7uUMqXde+X9SZM7sQBA0Cff7h04II8dGCuX/Xz06wnqnCVb+sWYp0OOQSu38uq30iMud7KhQWMfk9/fWEAv19K+msY+3SnGvGVnibF4WoJjqUMuGXK8cvmTVtbnKNsEACidbkJycyL4W+U3qMmfq075TN53xNi0MnUoAXBGF6tx0yJ3J0Kv0rUnzS/GAgF97bb9Vu78hqDcucg7Ri776XX0c70rLB8Troi8vyn7m8WYkymXsgKAadc71SXCK14iIiKLmHiJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILEq6nEjtoJOACcmdTxyPsgteOZaoK088Rb5dPqLc1u7xyeU5CZpkoKdYvg0/Z5/c7SKqdJxxV1Woc5oDTfpOEdxN8nvv5GepY51+eZ05SskQIkppj1bmAQBGLnEySvVYrLVNjHnOLlenDJ+VLcYCh+TjZSBbPi+4Qvr3+k87lNITGqSUjzmdeoc2BJTuO8r53DUgr93gXr0jEhy5m1LbePmc3H9YPndmVXWqU/aF5LWb0qm8f+fI51Zfm9LpC4BLOb6TwSteIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouYeImIiCxi4iUiIrIo6TreeK/c1s7xybVSg2N75bF+uX7LiR1/y7u+UXLRbTRbrsHKDsgt5PoStKeKBOXaONOtvAc+pUC4Va5BBQBXbo4aJyDeJtfNOqlyOzwAgLa2e+R1FO/olOdMcLyguEDe7ucNYsyj1BVH6/epU2ol6v4Sud7WuOU17+vSv9cfiBTJwUvVoWcOt/IeRuT6agCINR0SYy6lDaTTI5+rXNq5CkBGc6cYM47cqvVwWN5ud4JjtHCq3N4v9i15ffa+kC/GfO0JfrdC+1ySwCteIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouYeImIiCxyjDH6PelERER00vCKl4iIyKJTnnifffZZOI6DDz/88KRsz3Ec3HbbbSdlW1/e5v3333/c4yORCJYsWYLy8nL4/X5UV1fj8ccfT3p8T08Pbr/9dhQXFyMlJQVTpkzB6tWrj3t/6OQ4E9buPffcg3nz5qGkpASO4+D666//WuO5doenM2HtjuTzbtK/XEWyW265Bc8//zweeOABTJs2DW+//TZ+8pOfoLu7Gz/96U8Tjp8/fz42b96Mhx9+GGPHjsXKlSuxcOFCxONxfP/737fwCuhM9eijj6KmpgZXXHEFfvnLX37t8Vy7dLqM6POuOcWWL19uAJjNmzeflO0BMLfeeutJ2daXt1lbW3tcY7du3WocxzEPPfTQkMd/9KMfmUAgYNra2tTxv/71rw0As3LlyiGPz5492xQXF5toNHpc+0Un7pu+do0xJhaLHfnfwWDQXHfddUmP5dodvr7pa3ekn3eHxb/xhkIh3HHHHZgyZQoyMzORk5ODiy66CK+99po45qmnnsLYsWPh9/sxYcKEY/6JoKmpCYsWLUJpaSl8Ph8qKiqwZMkSRKPyb+x+Xa+++iqMMbjhhhuGPH7DDTegv78f69atU8e/8sorSEtLw1VXXXXU+MbGRnzwwQcnbV/p5BvJaxcAXK7jPwVw7Y5sI3ntjvTz7rD4U/PAwADa29tx5513oqSkBOFwGOvXr8f8+fOxfPlyXHvttUOe//rrr2Pjxo1YunQpgsEgnnjiCSxcuBAejwcLFiwAMPjhn3/++XC5XLjvvvtQWVmJTZs2YdmyZaivr8fy5cvVfSovLwcA1NfXq8/bunUr8vPzUVg49Ifka2pqjsQTjR8/fjw8nqEfxZfHT58+Xd0GnT4jee2eKK7dkW0kr92Rft4dFok3MzNzyAcSi8Uwa9YsdHR04Oc///lRC6C1tRWbN29GQcFgF5e5c+di0qRJ+Kd/+qcjC+D+++9HR0cHtm3bhtGjRwMAZs2ahUAggDvvvBN33XUXJkyYIO7TVz8QSVtbG3Jyju4QFAwG4fP50NbWlnD82WeffdTjX2wz0Xg6vUby2j1RXLsj20heuyP9vDss/tQMAC+99BIuvvhipKWlwePxwOv14plnnsH27duPeu6sWbOOfPgA4Ha7cc0116Curg779+8HALz55puYOXMmiouLEY1Gj/w3Z84cAMD777+v7k9dXR3q6uqS2nfHkVtIabGTNZ5Or5G8dk8U1+7INpLX7kg+7w6LxLtmzRpcffXVKCkpwYoVK7Bp0yZs3rwZN954I0Kh0FHP/+qfF7782BffVJqbm/HGG2/A6/UO+W/ixIkABr+9nQy5ubnH/HbU29uLcDh8zG9lyYxvbx/sIZtoPJ1eI3ntniiu3ZFtJK/dkX7eHRZ/al6xYgUqKirw4osvDvmmMTBw7Kb0TU1N4mO5ubkAgLy8PNTU1ODBBx885jaKi4tPdLcBAJMnT8bq1avR1NQ0ZGFu2bIFADBp0qSE41etWoVoNDrkzyzJjqfTaySv3RPFtTuyjeS1O+LPu6f0nmmT3G3t8+fPN+PGjRvy2MGDB01aWpr56i4CMIFAwDQ1NR15LBqNmurqalNZWXnksR/+8IemuLjYtLe3J9xHnITb2h9++OEhjy9atCip29rXrl1rAJjVq1cPefzyyy9nScZp9k1fu1/1dcuJuHaHr2/62h3p511rV7zvvffeMe9Umzt3LubNm4c1a9bglltuwYIFC7Bv3z488MADKCoqwq5du44ak5eXh0suuQT33nvvkbvrduzYMeTW9qVLl+Ldd9/F9OnTsXjxYowbNw6hUAj19fVYu3YtnnzySZSWlor7W1VVBQAJ/71h4sSJuOmmm1BbWwu3241p06bhnXfewS9+8QssW7ZsyJ8sli5diqVLl2LDhg2YMWMGAGDOnDmYPXs2br75ZnR1daGqqgqrVq3CunXrsGLFCrjdbnV+OvW+qWsXGPw3t5aWFgCDN9c0NDTg5ZdfBgDMmDED+fn5R/aJa3fk+aau3RF/3j2lad385ZuX9N+ePXuMMcY8/PDDpry83Pj9fjN+/Hjz9NNPm9ra2mN+87r11lvNE088YSorK43X6zXV1dXmhRdeOGrulpYWs3jxYlNRUWG8Xq/JyckxU6dONXfffbfp6ekZss2vfvMqKyszZWVlSb3GcDhsamtrzejRo43P5zNjx441jz322FHP++L1bNy4ccjj3d3dZvHixaawsND4fD5TU1NjVq1aldTcdOqcCWt3xowZ4uv78jrl2h1ZzoS1O5LPu+xOREREZNGwuKuZiIjoTMHES0REZBETLxERkUVMvERERBYx8RIREVnExEtERGQREy8REZFFSf9y1eUFt4gxJz2ojjUe+VdAnN5+eaDfJ2/T71XnRCx+fHNqXSliMX1O5ddOzDF+dPyIUbny7nT1qlOazDQxtm7LMnXsmWJO1V1yMMFnanr7xFi8vEiMuVu75I2Gjv1buEe2WyD/QPvB72SLscPfkrcbSNfnTFmfLsbSDsrvUfpHjfJG3Qm+17vk+Ft1/0Mfe4Yof/5nYmxSufLeA7gwe48Ye37NLDEWaJJ/2mHUB8q6BuDqUc6tCicqrzGTIucBAIinpcj7s/uAPDAnS441680cnMwMMfZWw6PqWIBXvERERFYx8RIREVnExEtERGQREy8REZFFTLxEREQWMfESERFZlHQ5keOTy3di2Xo5kbutW4zFu+SYS7nd2+lIcNu6V95frbTHSZfLc0x6qjql0ym/FsejvNXdcskKEnVt3HdQjxMQjogh09Ojj/X7xZD7YLu83WBAjMVGZapT9pXI66z7fHndX1vz72Js/cFx6pzthXJ5RNE7h+SBSslQ7GCzOqd7VL4aJ8CE5RLF1ZWvq2M3huTP1LvgbTH2/71/qRhz4vI2ASB7l3y8eDuUkspwVJ4zQUmlu0/Zbpqcm4xXPic7Qf1cn7CcNQFe8RIREVnExEtERGQREy8REZFFTLxEREQWMfESERFZxMRLRERkERMvERGRRUnX8fbWlIix4A6lzg9Q26C58uQWaGr7NKVOd3DDcnu/+Gi5nZurXm615SitBgcHK99jlJjpk+t4nUSvs2iUHie1VtfJ0mtqtfaSJhw+rv1xEpRmt02Qazf/ZsKfxdiS/G1izOvo7Q+fGS0fh/u/WyzGSp7dLsZcZaXqnCae4HgiZI2Sfxug9tAF6tj/UfixGAvFW8TYou+8J8ZeKDlPnbPtHbltZcZeOd2k7lN+A0H5DQlA/x0J96HDYiyaJdfaI1OJAfA2yjX8yeAVLxERkUVMvERERBYx8RIREVnExEtERGQREy8REZFFTLxEREQWJV1O1DdKfqqvS76FHAC8+9vkYERuB6UxGXorQuOWy4ncTfL+xJUSkXiBXFIFAK59SllVbpYc08pSHPl1AAA6u/Q4wcnOkoNRvczGBOQ2Z47SVgzdciszl0cuFwIAVyxdjH3eI7fS64vL66gjorc5c7fJJRtp++X3SG2fluDYNgnKRAg4vFcud/vVvvPVsWuLJoixH1f/Toy5HLnMa3RWpzrnzu/In2nkY7nlauCgfA0YKcpS5/R+LrdGNTny++dpkUuY0KW3C01QEZgQr3iJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisij57kTFclmLK5qijs3dq9x8HVPKOdxy2YXT26/Oif6QHPP5xJBL6VZjOuUSkURzqvurlWQk6uDCkozElJKseLZc4gAATrPShcST9OEzRNd4vfwunCkfLy39chndQ61TxdgbOyerc2bWKbGPlTI5Zf0ZpaQKAExxnhonqHUrGTv1srS+rgwx9tiuuWKsdKrcoe2Ho+UyJAD4WfvlYiyjXun05ZavAT118v4AesmQ06GUWyprN9beqc7pLjyxrnC84iUiIrKIiZeIiMgiJl4iIiKLmHiJiIgsYuIlIiKyiImXiIjIoqTrIQKt8n3tOR8p3YcAvSTGJed+rQOReps49K4pxq+U4BxSXkuW3DUGAJx0uTQlUpwjxjw798kbTVSywnKihExvnxhzaWVngN4dSnnvI8VyyVBXuV4GkjulWYz9bOwaMfbYgUvFmHu73s0ro17pkKV0YTLK++MkeG+dNnbWSqRobIsYa86Ty4UAIPCxfA5M3yufkxvSC8VY7aEr1DljvfJaGdUWEWOuT3bJGz2rWJ9zuzzWUz5aHmiUWq0p49Q5ze4DajwRXvESERFZxMRLRERkERMvERGRRUy8REREFjHxEhERWcTES0REZBETLxERkUVJ1/HmbJVrIdW6WADok1viOVlyLZrTp9QBajVYABCJytsNy/VkJltpMdXWqU5psuXX4mmRaxa1+l/T3aPO6XTpcdKZdL2+FU1yHSUy5M8t7pW/08bkrpQJ7Q7L7ch2tuWLsWCjfrz4W+VjNJ4q77C7uVPeaKIadI9ez0zAVWd9JMYmj1Hq/wEsyZVrbg9sLxBjozbJtdmRVL0FbN4ncitIV59cK+6kK7+RoPzWAwA4502Sg00dYijeKv9mgys0oM5plBySDF7xEhERWcTES0REZBETLxERkUVMvERERBYx8RIREVnExEtERGRR0uVE3RUBMZa5S2n7B2BgWqUY8zfJ5TCuHrmcKHK23LoKALwNchlI9Kw8ec5++TbxaL7ehssVicmxTrm8SSuNcgLy+w6AJRlJiDUfEmPuBGUBplxuSeb0ySUH3la5/C7Q4lfn7OqTSza29ZWIsb4+ebs+pbshALh65NcSy1ZabAaV9am1A0WCckECAOwLye1Er8v4VB37w7LfirGHDs8RY9GAXCZX+OsGdU71M1fOc1qxm9Mrl7oBADLkNRhXWrk6IXn9OQG9bOpE8YqXiIjIIiZeIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouSLifqrJJzdFeZ0lkCwKiP5ZKNzolZYszXI9+a7uuQO10AQP9EuQwknCGX4HSNlm+lj+lVIMhokPc36pffo/zfNskb7ZW7GgGA40+wU4TD//lCMebt1bv2BPcq5W5KBywTlMsRPKEEnbX+KHfI2rBefi2ZcjUb0vcp5WwATEDpQLRHXp9Oirz+jF9vw2QCXLuJvP2S/Hm/cc5kdWxmulyGE90nd+VS10qCErHjZbTSngQdxFxdSrlR+2E5pnTGM74EHfcSdI1LhFe8REREFjHxEhERWcTES0REZBETLxERkUVMvERERBYx8RIREVmUdDnRP37/ZTH24Edz1bFNPrm7STQol1aM+lD+XtBZJW8TAEI58najaXIsWNEhxkrTu9U56xrz5e1+LHfQiBTK5SO+mH77vjnB29rPBOff/kcx9uamb6ljcz+SSw7S98klQ8Yjr91oQG8V5FKqObI/k8vo/PvktYsufZ04LuU7eFA51pSSqliWfow6kVNTmvJNMvrlg2LMeVF//zrPkzu4ZeTKazB1u1w+FivMVed07Zc7gZlu+fzpypc7xmFALx11tK5HEWXsYfmYMCXyuRwAXJl6p7pEeMVLRERkERMvERGRRUy8REREFjHxEhERWcTES0REZBETLxERkUVMvERERBYlXcd7fYZcn7V/0h/UsS94p4kx53O5DV9c2TtPn95aLTZNbhWV4pfbFF599sdibFJgvzrnCp/cwuvj1ioxFvlMbkHl26+/zui4s9Q4ASX+TjE25Zzd6thPwpVirGOS/Lml1cvfaQey1SkR98qfeThLPihStvfJG9XqdAHAoxxsSis4kyG3bPO06nXvxpv06eeMZdLk+n+nQ39/szbJ56vAmAJ5TqXm25XgM4uXjpLH7lPqjt3K+kzQijCerrxHPcoxYeTtxoJ6S8t4yomtXV7xEhERWcTES0REZBETLxERkUVMvERERBYx8RIREVnExEtERGRR0vdE33Noshjb3Famjo0qJUNZn8njUpvksh9XRN/10Ba5JVmoQL6NfEVMLn0qz5NLSxLxt8nfcfwtvWLM9IfU7Xo/l9uG0aAbsv4kxv4xd5c69grjFmPt/fIa89bExFhnn1z+AACdbfLx0t0ilzkEKuQSEVdY3h8AcHUr6ywmj3U65ZIWk5WuzgmlnRsNcjW3izGTqMym87AY82vvfZbS8k4rzwHgVuLRqhIxZhy5TaG3WX4dAODqlktHjUtpwakcEt69reqcJ4pXvERERBYx8RIREVnExEtERGQREy8REZFFTLxEREQWMfESERFZlHQ50aurvi3GXHLVDwCgZIf8BG9vVI5pZTYe5ZZ3ADk75Fhst3yLeThDLuVoStHLIyLyUJT+Rr7N3iidOZwT6SpDAIDbGq4QYx5HL8nY0SiX6Jw3eq8Ym5TeKMbeba5W54zG5c/88AQ5FkuROwW5BtQp4Yoox5OyBN0DcllKwXr5PQAAhBOcOAgIpIghRynzAgBXpVLmeVjuQISovN1os9ylDgA8xUVizK10q4rtlo8lM+5sdc54mvweufYorzMmH/v94wvVOVP+LO9vMnjFS0REZBETLxERkUVMvERERBYx8RIREVnExEtERGQREy8REZFFSdeilL4n3wru6tE76DjdcilNPFcpY4jIpUaBOr17REqK3MUlkiuXXWT9sVOMmTS9q4zTH5bHpnjlcb3K+6eUEwBQO8fQoK3rxokxj95sBfFiueQgVCIfPuel7hZjC8Z+pM7ZGZfXbl/cL8Z+9MG1YuyyMdvVOZtCcqncn/aVijHXXvmYcIeK1Tlz/71FjRMApTtZaLzc7QcAUvYo50ils1G8o1OMecpHq3NGCrPksYflLkLuTDkPmD371Dndebny2OxMOZYqH0spDZ3qnMjN0uMJ8IqXiIjIIiZeIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouYeImIiCxKuo7XtbdZjDmO3GZvcBZ5GldblxiL7j8gxtxZcn0WAKBYbufm3SLXWCIvRwy5mtvVKU1aqhhzmtqUObPlbTbr9cpw+N0pkdL35PaSfUV6nXRmvRzb2TJGjN0+TWknWKK3FOuLynW84bh8LOVly7X2/gS9O6/I/5MYm5wht/eLT5SP/V/XfUed0wTk10mDjNKm1L8lQWs6pd1odPQoMeZRztdG+X0EAPDsPiiPLZTrbR0j1xW7iuRjCYBak+x0y8e+06MU8Sf6/YQ++TcmksGzNhERkUVMvERERBYx8RIREVnExEtERGQREy8REZFFTLxEREQWOcYYc7p3goiI6EzBK14iIiKLTnniffbZZ+E4Dj788MOTsj3HcXDbbbedlG19eZv333//cY+PRCJYsmQJysvL4ff7UV1djccffzzp8T09Pbj99ttRXFyMlJQUTJkyBatXrz7u/aGT40xYu/fccw/mzZuHkpISOI6D66+//muN59odnrh2Ezuda5dXvCfBLbfcgp/97Ge49dZb8fbbb+PKK6/ET37yEzz00ENJjZ8/fz6ee+451NbW4q233sK0adOwcOFCrFy58hTvOZ3pHn30UbS1teGKK66Az/f1f0mKa5dOl5G8dpP+yUg6tm3btuGZZ57Bgw8+iLvuugsA8Fd/9Vdoa2vDsmXL8OMf/xg5OfLPUK5duxbvvvsuVq5ciYULFwIAZs6ciYaGBtx111245ppr4Ha7rbwWOvN0d3fD5Rr8/v38889/rbFcu3Q6jeS1OyyueEOhEO644w5MmTIFmZmZyMnJwUUXXYTXXntNHPPUU09h7Nix8Pv9mDBhwjH/RNDU1IRFixahtLQUPp8PFRUVWLJkCaLRE/udzS979dVXYYzBDTfcMOTxG264Af39/Vi3bp06/pVXXkFaWhquuuqqo8Y3Njbigw8+OGn7SiffSF67AI6cuI4H1+7IxrV7+tbusLjiHRgYQHt7O+68806UlJQgHA5j/fr1mD9/PpYvX45rr712yPNff/11bNy4EUuXLkUwGMQTTzyBhQsXwuPxYMGCBQAGP/zzzz8fLpcL9913HyorK7Fp0yYsW7YM9fX1WL58ubpP5eXlAID6+nr1eVu3bkV+fj4KCwuHPF5TU3Mknmj8+PHjj/ph8i+Pnz59uroNOn1G8to9UVy7IxvX7mlcu+YUW758uQFgNm/enPSYaDRqIpGIuemmm8y55547JAbABAIB09TUNOT51dXVpqqq6shjixYtMmlpaaahoWHI+EceecQAMNu2bRuyzdra2iHPq6ysNJWVlQn3dfbs2WbcuHHHjPl8PvP3f//36vgxY8aYyy677KjHGxsbDQDz0EMPJdwHOjW+6Wv3q4LBoLnuuuuSfj7X7vDFtas73Wt3WPypGQBeeuklXHzxxUhLS4PH44HX68UzzzyD7du3H/XcWbNmoaDgL62i3G43rrnmGtTV1WH//v0AgDfffBMzZ85EcXExotHokf/mzJkDAHj//ffV/amrq0NdXV1S+661RUzYMvEkjKfTaySv3RPFtTuyce1+/djJMCwS75o1a3D11VejpKQEK1aswKZNm7B582bceOONCIVCRz3/q3/W/fJjbW2DfW+bm5vxxhtvwOv1Dvlv4sSJAIDW1gR9bpOUm5t7ZM4v6+3tRTgcVm+s0sa3tw/2/k00nk6vkbx2TxTX7sjGtXv61u6w+DfeFStWoKKiAi+++OKQbxoDAwPHfH5TU5P4WG7uYLPlvLw81NTU4MEHHzzmNoqLi090twEAkydPxurVq9HU1DRkYW7ZsgUAMGnSpITjV61ahWg0OuTfG5IdT6fXSF67J4prd2Tj2j19a3dYXPE6jgOfzzfkw29qahLvrtuwYQOam5uP/P9YLIYXX3wRlZWVKC0tBQDMmzcPW7duRWVlJc4777yj/jtZC+C73/0uHMfBc889N+TxZ599FoFAAJdffrk6/sorr0RPTw9+9atfDXn8ueeeQ3FxMS644IKTsp90aozktXuiuHZHNq7d07d2rV3xvvfee8e8U23u3LmYN28e1qxZg1tuuQULFizAvn378MADD6CoqAi7du06akxeXh4uueQS3HvvvUfurtuxY8eQW9uXLl2Kd999F9OnT8fixYsxbtw4hEIh1NfXY+3atXjyySePLJZjqaqqAoCE/94wceJE3HTTTaitrYXb7ca0adPwzjvv4Be/+AWWLVs25E8WS5cuxdKlS7FhwwbMmDEDADBnzhzMnj0bN998M7q6ulBVVYVVq1Zh3bp1WLFiBesgh4Fv6toFBv/NraWlBcDgibShoQEvv/wyAGDGjBnIz88/sk9cuyMP1+4wXbun9NYt85e766T/9uzZY4wx5uGHHzbl5eXG7/eb8ePHm6efftrU1taar+4iAHPrrbeaJ554wlRWVhqv12uqq6vNCy+8cNTcLS0tZvHixaaiosJ4vV6Tk5Njpk6dau6++27T09MzZJtfvbuurKzMlJWVJfUaw+Gwqa2tNaNHjzY+n8+MHTvWPPbYY0c974vXs3HjxiGPd3d3m8WLF5vCwkLj8/lMTU2NWbVqVVJz06lzJqzdGTNmiK/vy+uUa3dk4drdeOR5w3HtsjsRERGRRcPi33iJiIjOFEy8REREFjHxEhERWcTES0REZBETLxERkUVMvERERBYl/QMac876iRgzaanq2KaZ+WIs96r9YizoCYux7ohfnTPy+NG/K/qFtO1H/0bnF5yuHjEWbWoWYwDgzsuVg1ovSo/yMYxStgkAB1vE0Lq2X+hjzxAX/e0/izEnrlfTpe2R10OoUF73vg557UbSveqcsYD8fTiUJRf252+Uj6XOC0rUOX2H5fWZukv+fd3YgYPyRidUqXO6O+X39q3PH1HHnikum3KvHNxzQB3rCsrrs2/KaDF2uFw+H+VuP/o3nL9sIFte2+GgvK5z1u8WY47Pp84Jl9LQQPkhDNPdK8Y6ZleqUwZa5ePl/6z7R3UswCteIiIiq5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouYeImIiCxKvh+vT75N3OntV4dG0uTbvaNxOfeXp8llP1s69IbKvYXybeTpf9RviZeo5UIAHK/8HhmlnMgMyKUnzv4mfc7MDDVOQOv3+sSYa3uaOtb/424x1rhZfu+zdiglGX8+rM7ZXxIUY56QUv4Ui6vb1Ri3fIzGctPFWKhaLhVMeedjdc7QjHMS79gZrn1KthiLXCzHAMAjL3vElTN//kdymVckQy/tST0g54JgJCYPTJfXfPv5Beqc6XvkOT3tcsmQKZHXbljJWQCQuV3PeYnwipeIiMgiJl4iIiKLmHiJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILEq6jjfWKNeTuspK1bEp7XLtoccl1x5+O32nGHt/v95yLJqu1CXmZ4kxd4dct+m4AuqcprVdjsXkGjbHK38MJhxR53S0doMEAEjZJNfq9l8g1/kBQN8v5XrxXCOv65R2+XOJpum1kDG//H04ba9cPxgryBJjnn69xje1Xq4tNh55f1L3yDXxoUumqHOm/EE+vmlQ9srNYqz976apY/M2K+1PQ/JvB7R8u0iMpR3Uz0eN35FrvrsnynNm5crr0/+/1SlhfPL67JiaJ8bSDgyIsYL35XarADBQmqnvVAK84iUiIrKIiZeIiMgiJl4iIiKLmHiJiIgsYuIlIiKyiImXiIjIoqTLidwl8i3mscxUdWzML8fSvXI5QomnQ4xNHtWozvnZIbllVneVXF6SuV7frmqU3DYw/nm9GPMUKm2v/MqbB8D0n1h7qjOBp08u+8nNklugAcB/uf9VMfazn/+dGIsE5RaRneP1w65qlbxPcZ/c7hKO3spMpbSfHDh/jBjrGCevz6K3D6pTtv+nCYn36wznqiwXY/4uvUSsbap8Psre2iXGcpRYqEA/1+dtkUuG/vHvXxFj//zZpWKs9Md16pyN/yqXlma/LZeshc6tEGPy0ft/x+Yk31H3WHjFS0REZBETLxERkUVMvERERBYx8RIREVnExEtERGQREy8REZFFSd8THa3fJ8bc7jJ1bNwjd6wYky53gbgwRS6d+CRLv8X8j6WTxFjJRrmEyUmRyyNiSvchAHBnyGVKrlT5NnzT2ydv1KWXiJgIuxMlkr1L7kLS8rZSygXgoZhcMtRVLXecSi2VS4KKnpfXCQCEs+Q1mLJP7iLUfp7ciSV9v/weAEDk3Ep5zr2dYiyYJc8Zy5OPewAIJuh0Q0D3BLkkyBWRy+SABJ+5csnlxOTtRtKUcjYA7dXyhv/fnbPEWKpPXgsHevROQG5lfwemyCVDnn75+O34lryuASCUc2LXrLziJSIisoiJl4iIyCImXiIiIouYeImIiCxi4iUiIrKIiZeIiMiipMuJPEVK2UVUvi0bAOJKq4ffNZ8txnry/yDGPuk5S53TJ1ddwN0t32Zv4nLHD63UCABMT68Yc+XLZQGmU95ZJ0u/lT5Wv1eNExD3yt8vC/9lkzq27cYLxVhwn1xaEerLEGPt1XqJWOCQvL8pB+Sx4XQ5FvPrZSAar1c+TQRa5G40PWcF1O2m79Y7QxGQvkMpYYzr5URaKVLb+BQxVvQruVQz3auvo8NnyyVkKV659HHJmNfE2PLmb6tzbimVc1PMKx8TwYPy+5d6SC91S9uvd4ZKhFe8REREFjHxEhERWcTES0REZBETLxERkUVMvERERBYx8RIREVnExEtERGRR0nW8pq9fjuXKNYsA4OmX66XyA3Lt68/bp4ix3x6Q638BIC534UM0W64v9LXJ30WcDL3NGXxywbLp6JTHFY2SxzW1qlO6E9T5EuDtkmtN49+Zog9WSm6zd8p1ifv+k1znlzGxS53SuzJHjEXy5JaCBb/rEGO9Vfox6jssv5bwqKAY87bJLS0zD3Sqc/ZMzFfjBPRWZoux7lK9prbg3+T10Fklr7HWy+UWke6wXjvskg819ITk30EIGfncub1NPj8CQEaD/DsSrqi8v73FPjGW9al+jMLo70MivOIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisoiJl4iIyKKky4lih+Xbqz1++bZsAPD2yLfEf/p7uSzo8/FyW6ueVrnEAQDKPpXbOrl75HveTYZcrgFPgtZqzUrpj9IW0OkLibFoZ6c6paeiTN8nwkC2XMaQ0iKXyQFA/qqdYqx9fo0YS/9UPrQGcpQ+mQA658ptK7u3KqVwXXKrt4wGuVwIAHoL5WM4Y49cMtQxOUuM5XystLQD4O3R24kS0Pht+ZxT9bxcLgQADVfKJUOXfnezGPv1jkliLJCq1AsB6G2T6zj/5+T/Lcb+ee9lYqxPKUMCgPAY+T1KaZHLfjLrlfawbr11Z0gpsUsGr3iJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisijpciK3Ug5j0pRWQAB8PXKnltSD8q3gPWVKGch+vSQjGlA6VjS2yAP75dKegWlj1Dm9AXmfnJBczmEONMnjPPrrRFx+b2lQV5myzF1yeQ4AmMKJYqy3WC45yL/0gBibXbBDnfPd5mox1ryzRIyltMulEy3n6Osoki6PdZRWX7m/bxRjXd8qUuf0d8glfzRozDOHxFi4RO9M5pGrwLD+tWlizBkvD/yvE95W52yNyl2wZinn5FfS2sTYnvfL1TnzPpHXkVEqQCNB+bxgHL2cyHdYL6tKhFe8REREFjHxEhERWcTES0REZBETLxERkUVMvERERBYx8RIREVmUdDmR45XLEUxbpzrWPZAnxmIp8v3eqR/JpR6hPLn8AQACLfLt3o5P7sRicpRb9PUp0TE+XYxl1sm36Huzs+Qpe3vVOc3hbn2nCPlPbhJjzlS5XAgADk2TyyN6y+QSseoU+XP7ad5n6py/aa0SY7+84XExdut/u02M9Y+TO7EAgInL5ROBDfLC7z6nUIx5+vRSt0QlGwSEi+Xzka9JP/aL/4/8mbdOkdd14JB83v3zuNHqnAdCWWLsb9rkcsw/75S3W7JV72J14K/kNHb2y/J75G6VO+5FiuWOegAwkKt3TEqEV7xEREQWMfESERFZxMRLRERkERMvERGRRUy8REREFjHxEhERWcTES0REZFHSdbzRA3L7L5dfr2kK7pDb8MVSCsTYoXPl7wXpe9Qp4e6TaywRlWNOl1x/2TdFbo0IAFGlO2J3uVwblxqQ26d5f79VndPxJP0RnrHcE8aKMRPTi7ONS641Tf9cfu//w4w6MbY/2qPO+Q+j3xFjb3XXiLGUK5vFWLE6I9DxvlyPG8qR6yi9Wq2u0d9bb4fcgpMGef/wqRw8S/9U+4uCYqxg40ExFsuWx63LuVCfs1BeD+dN3SXG8n8vH0veHuVcDqB0vRzXanW1tpUmwSVpNOXEatB5xUtERGQREy8REZFFTLxEREQWMfESERFZxMRLRERkERMvERGRRUnXorhzlDZJLrm1HwDEM+Q6m9T9cru8lDK5zZ5WugMAAzlyiVNgr3zLuynIEWMxv34LeThDjvu65dIK3yG5hMlJUDLg9ParcQIaL5XbUuZt0UtaHKUkpmR9pxh7Km2uGHu8aqY6py9FLo+ozG8VY1ed9ZEYuyyolKUA+OvP/4sYi5wjr7GM9XLpSbBZb+cWzTqx1mpnhLHlYij2yQ51aNAlX1fFU1PE2OGx8nk3pU0vEUvfK8faXisXYxlGbmG47xJ9nZS8L7eA7TpXLhnSck84W58zY9thNZ4Ir3iJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisij51jZaF5wB+VZwAHD2y11TYpPKxJhRqnf8h/Xb2v1tcpmIKZLLS5xeeVxKZ5o6Z9wtl1U5SmWF8Srj9rapcxovuxMlkvOpvD7DGfr7l/eJXErTOTFTjEXGyaUKgU/0Wrj+SfKcn34yWt6fsXIHrH9pvFSdc9z4/WKs4TfyMTrqN/Kx3Vcll+YBgG9/pxonwPjk9elMm6yOjbnlE2goXy4n8nXL5Zb+Lv28q/E3dYsx45GvAbN3+NTtBnY0ibGUgFwWFCmWj1/3gNJ1CwCUUq1k8IqXiIjIIiZeIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIouSrkUxh7vEmBPUyyOcNLmDScwv53633HQCrog6JXrK5DkzNshdPZws+RbztG1yZxgA8JVmiTF3r7zDrn3y7fDwedU5oXTPoUEpW/aJsVhFoTo2rpR6Ze7sEWPZP5HLbBr/dow65+gV8mHZUyQfLwdjo8RYsFn/jl3XcZYYi1fIJXah0VliLPXzDnXOeJpc/kSDzIdbxVjX9y9Ux2YpHXT2XS6/9yUb5G22TdBTRsXKRjkYk2squ6fKXdiyPpOPMwCIFstla5EMuRQpGpSP7dT9csc4AAjny/klGbziJSIisoiJl4iIyCImXiIiIouYeImIiCxi4iUiIrKIiZeIiMgiJl4iIiKLku8pp7VBys3Wx3bLNVHenqgSk2tY4wnKW9M/k2vYkCfXfZlOuV4ZBXI7QQDwtsvt3Jw+uTWdo7RcjB7Sa4c9ZaVqnIB4cb4Yc3fJNaoAECnNEGP9o+Qawb5pVWKs6Nkt6pymulyMpTTK9eC+3iwxltosr00AiPnlmsa4R4752uT2h8avn17cHXKbOPq/LqwRQ9lrt6tDnYBcq1v2ht7iVFLxP+vV+KG5Z4uxvH+X67rjHrmFodYyEADcHfIadCJye7/28fJvNvTnyMc9AASb5byVDF7xEhERWcTES0REZBETLxERkUVMvERERBYx8RIREVnExEtERGSRYwz7yhEREdnCK14iIiKLmHiJiIgsYuIlIiKyiImXiIjIIiZeIiIii5h4iYiILGLiJSIisoiJl4iIyCImXiIiIov+fxL16IytkGssAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(3,3)\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "X,y = next(iter(training_dataloader))\n",
+    "for i, ax in enumerate(axs):\n",
+    "    ax.imshow(X[i,0].amax(0))\n",
+    "    ax.set_title(f\"Label: {y[i]}\")\n",
+    "    ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training epochs: 100%|██████████| 5/5 [00:15<00:00,  3.19s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# instantiate and train the classifier\n",
+    "lr = 1e-3\n",
+    "epochs = 5\n",
+    "classifier = models.ResNet3D(models.ResidualBlock3D, [3, 4, 6, 3], num_classes=1)\n",
+    "classifier = classifier.to(device=DEVICE, dtype=DTYPE)\n",
+    "\n",
+    "# Training logs are saved to the output directory\n",
+    "branch_classifier.train(training_dataloader, test_dataloader, out_dir, lr, epochs, classifier, state_dict=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training started at 2025-06-26 15:36:28\n",
+      "Learning rate: 0.001\n",
+      "Total epochs: 5\n",
+      "\n",
+      "Epoch 1\n",
+      "-------------------------------\n",
+      "Accuracy: 45.3125, Loss: 0.700314  [   64/  720]\n",
+      "Accuracy: 56.25, Loss: 0.793185  [  704/  720]\n",
+      "Test Error:\n",
+      "Accuracy: 52.8%, Avg loss: 22.499360\n",
+      "Precision: 0.528, Recall: 1.000\n",
+      "Epoch 2\n",
+      "-------------------------------\n",
+      "Accuracy: 45.3125, Loss: 0.849842  [   64/  720]\n",
+      "Accuracy: 53.125, Loss: 0.673285  [  704/  720]\n",
+      "Test Error:\n",
+      "Accuracy: 59.4%, Avg loss: 9.390966\n",
+      "Precision: 1.000, Recall: 0.027\n",
+      "Epoch 3\n",
+      "-------------------------------\n",
+      "Accuracy: 62.5, Loss: 0.649782  [   64/  720]\n",
+      "Accuracy: 57.8125, Loss: 0.647150  [  704/  720]\n",
+      "Test Error:\n",
+      "Accuracy: 50.0%, Avg loss: 10.020075\n",
+      "Precision: 0.482, Recall: 0.964\n",
+      "Epoch 4\n",
+      "-------------------------------\n",
+      "Accuracy: 70.3125, Loss: 0.642213  [   64/  720]\n",
+      "Accuracy: 59.375, Loss: 0.614109  [  704/  720]\n",
+      "Test Error:\n",
+      "Accuracy: 47.8%, Avg loss: 0.919053\n",
+      "Precision: 0.571, Recall: 0.083\n",
+      "Epoch 5\n",
+      "-------------------------------\n",
+      "Accuracy: 64.0625, Loss: 0.610191  [   64/  720]\n",
+      "Accuracy: 65.625, Loss: 0.634910  [  704/  720]\n",
+      "Test Error:\n",
+      "Accuracy: 63.9%, Avg loss: 0.335915\n",
+      "Precision: 0.595, Recall: 0.843\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load and print training logs\n",
+    "logs = os.path.join(out_dir, \"training_logs.txt\")\n",
+    "with open(logs, \"r\") as f:\n",
+    "    lines = f.readlines()\n",
+    "    for line in lines:\n",
+    "        print(line.strip())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tractography",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/data_demo.ipynb b/notebooks/data_demo.ipynb
new file mode 100644
index 0000000..285cc74
--- /dev/null
+++ b/notebooks/data_demo.ipynb
@@ -0,0 +1,8288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d2bcf416",
+   "metadata": {},
+   "source": [
+    "# Data Generation and Loading Demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c7e8eae6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "115206ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from dataclasses import dataclass\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import plotly.express as px\n",
+    "import sys\n",
+    "import tifffile as tf\n",
+    "from typing import Tuple, Optional\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "from data_prep import load, generate, draw\n",
+    "from neurotrack.data import neuron_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af82b3e7",
+   "metadata": {},
+   "source": [
+    "# Data Synthesis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fff7355b",
+   "metadata": {},
+   "source": [
+    "Load some real data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f01406cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 30 tifs and 30 swcs\n"
+     ]
+    }
+   ],
+   "source": [
+    "tifs_root = Path(\"/home/brysongray/data/neurotrack_data/gold166/gold166_tifs_processed_subset\")\n",
+    "swc_root = Path(\"/home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset\")\n",
+    "\n",
+    "tifs = sorted(list(tifs_root.glob(\"*.tif\")))\n",
+    "swcs = sorted(list(swc_root.glob(\"*.swc\")))\n",
+    "print(f\"Found {len(tifs)} tifs and {len(swcs)} swcs\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ef2039b",
+   "metadata": {},
+   "source": [
+    "Look at an image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "241e57e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading /home/brysongray/data/neurotrack_data/gold166/gold166_tifs_processed_subset/080926a_image.tif\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "928fda28a9dc46c295fdc1ca02ac0767",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tif = tifs[0]\n",
+    "print(f\"Loading {tif}\")\n",
+    "img = tf.imread(tif)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(np.max(img, axis=0))  # max projection\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8979ad3d",
+   "metadata": {},
+   "source": [
+    "Look at the morphology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9835f4a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_sections(sections):\n",
+    "    # Create a DataFrame for plotting\n",
+    "    data = []\n",
+    "    # Iterate through the sections dictionary\n",
+    "    for section_id, section_data in sections.items():\n",
+    "        # flatten the section into one list of consecutive points instead of segments (point pairs)\n",
+    "        for i,segment in enumerate(section_data):\n",
+    "            point = segment[0] \n",
+    "            data.append([section_id, point[0].item(), point[1].item(), point[2].item()])\n",
+    "            if i == len(section_data)-1:\n",
+    "                point = segment[1]\n",
+    "                data.append([section_id, point[0].item(), point[1].item(), point[2].item()])\n",
+    "    df_sections = pd.DataFrame(data, columns=[\"section\", \"x\", \"y\", \"z\"])\n",
+    "\n",
+    "    fig = px.line_3d(df_sections, x=\"x\", y=\"y\", z=\"z\", color='section', )\n",
+    "    fig.update_layout(scene_aspectmode='data')\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c17dc2ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/080926a.swc\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "section=129<br>x=%{x}<br>y=%{y}<br>z=%{z}<extra></extra>",
+         "legendgroup": "129",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines",
+         "name": "129",
+         "scene": "scene",
+         "showlegend": true,
+         "type": "scatter3d",
+         "x": {
+          "bdata": "////////IUD4D+m3rwMSQBZIUPwYcw9A2Yr9ZffkDUCWKhiV1AkOQDkjSnuDLxBAZmZmZmbmEkBT46WbxKAUQD0K16NwPRVA3nGKjuRyFUD4oGez6vMVQDi0yHa+nxhAKvaX3ZPnMUC18/3UeIk1QHjHKTqSyzZAGp5eKcuQN0BkiGNd3KY5QJfdk4eFGkFA",
+          "dtype": "f8"
+         },
+         "y": {
+          "bdata": "fT81XrrXkUB9PzVeuteRQEoMAiuH05FAg8DKoUXIkUDD9Shcj7mRQAAAAAAArJFAmZmZmZmjkUAK16NwPaCRQEjhehSunpFAsHJoke2ZkUD0/dR46Y6RQE5iEFg5gJFAUrgehet3kUBkO99PjWqRQGiR7Xw/YpFAfT81XrpfkUBxPQrXo2ORQCuHFtnOXZFA",
+          "dtype": "f8"
+         },
+         "z": {
+          "bdata": "U5YhjnVBbUAUrkfhevRtQLC/7J48sG5AR1D8GHNXb0BQjZduEu9vQNxoAG+BRnBAiNLe4AufcEDW52or9v1wQMDKoUW2XXFARdjw9Eq7cUCEDU+vlA9yQLN78rBQW3JAc2iR7XyFckBYF7fRANByQJvmHafoKHNAi/1l9+SFc0CF61G4HuNzQHWTGARWBHRA",
+          "dtype": "f8"
+         }
+        },
+        {
+         "hovertemplate": "section=139<br>x=%{x}<br>y=%{y}<br>z=%{z}<extra></extra>",
+         "legendgroup": "139",
+         "line": {
+          "color": "#EF553B",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines",
+         "name": "139",
+         "scene": "scene",
+         "showlegend": true,
+         "type": "scatter3d",
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+            "ticks": "",
+            "zerolinecolor": "white"
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+           "yaxis": {
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+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+          },
+          "title": {
+           "x": 0.05
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+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
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+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "swc = swcs[0]\n",
+    "\n",
+    "swc_list = load.swc(swc)\n",
+    "sections, sections_graph = load.parse_swc(swc_list)\n",
+    "\n",
+    "plot_sections(sections)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48ac7cde",
+   "metadata": {},
+   "source": [
+    "Goal is to synthesize data with a range of complexity in terms of morphology and contrast\\\n",
+    "with the most complex data mimicking the real data as closely as possible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7344f1c",
+   "metadata": {},
+   "source": [
+    "## Morphology"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "673f1429",
+   "metadata": {},
+   "source": [
+    "Simplest: A straight line in a random direction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c3ea4b08",
+   "metadata": {},
+   "outputs": [
+    {
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+          "geo": {
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+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+            "gridwidth": 2,
+            "linecolor": "white",
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+            "ticks": "",
+            "zerolinecolor": "white"
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+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "swc_list = generate.make_swc_list(\n",
+    "    size=(100, 100, 100),\n",
+    "    length=50,\n",
+    "    step_size=1.0,\n",
+    "    kappa=1000.0,\n",
+    "    num_branches=0,\n",
+    "    rng=None\n",
+    ")\n",
+    "\n",
+    "sections, sections_graph = load.parse_swc(swc_list)\n",
+    "\n",
+    "plot_sections(sections)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33dc2b01",
+   "metadata": {},
+   "source": [
+    "Increase complexity by extracting sections from the real tree\\\n",
+    "Greater complexity = larger sections & more branches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8a322e08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/080926a.swc\n",
+      "Using subtree extraction mode: any_branch\n",
+      "Using target path length: 4550.0\n"
+     ]
+    },
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+           "#EF553B",
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+           "subunitcolor": "white"
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+           "align": "left"
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+          "hovermode": "closest",
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+          "paper_bgcolor": "white",
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+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+          "shapedefaults": {
+           "line": {
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+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "swc = swcs[0]\n",
+    "swc_list = load.swc(swc)\n",
+    "\n",
+    "complexity = 0.9\n",
+    "data_generator = neuron_data.DataGenerator()\n",
+    "\n",
+    "# Determine subtree extraction mode\n",
+    "# possible modes: \"no_branch\", \"one_branch\", \"any_branch\"\n",
+    "subtree_mode = data_generator._complexity_to_mode(complexity)\n",
+    "print(f\"Using subtree extraction mode: {subtree_mode}\")\n",
+    "# Determine target path length based on complexity and mode\n",
+    "target_path_len = 50.0 + {\"no_branch\": 100.0,\n",
+    "                            \"one_branch\": 500.0,\n",
+    "                            \"any_branch\": 5000.0}[subtree_mode] * complexity\n",
+    "print(f\"Using target path length: {target_path_len}\")\n",
+    "\n",
+    "subtrees = data_generator.get_subtrees(swc_list, target_path_len, num_subtrees=1, mode=subtree_mode)\n",
+    "subtree = subtrees[0]\n",
+    "sections, sections_graph = load.parse_swc(subtree)\n",
+    "plot_sections(sections)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e4e5a36",
+   "metadata": {},
+   "source": [
+    "## Image Contrast"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f29b67",
+   "metadata": {},
+   "source": [
+    "For image contrast, the simplest contrast is a curve with a constant width, ones in foreground\n",
+    "zeros in background, and added blur."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "37a10939",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords = np.array(subtree)[:, 2:5]                \n",
+    "shape_estimate = tuple(int(x) + 20 for x in np.ptp(coords, axis=0))[::-1] # Reverse for z,y,x\n",
+    "\n",
+    "# shift coords\n",
+    "subtree_ = np.array(subtree)\n",
+    "subtree_[:, 2:5] = subtree_[:, 2:5] - coords.min(axis=0) + 10.0\n",
+    "subtree = subtree_.tolist()\n",
+    "sections, sections_graph = load.parse_swc(subtree_)\n",
+    "\n",
+    "# Initialize the NeuronRenderer with a fixed random seed for reproducibility\n",
+    "renderer = draw.NeuronRenderer(rng=np.random.default_rng(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a9c1daa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass\n",
+    "class DrawingConfig:\n",
+    "    \"\"\"Configuration for neuron drawing parameters.\"\"\"\n",
+    "    width: float = 4.0\n",
+    "    \n",
+    "    foreground_mean: float = 0.8\n",
+    "    foreground_std: float = 0.1\n",
+    "    background_mean: float = 0.2\n",
+    "    background_std: float = 0.05\n",
+    "    mask_threshold: float = 0.1  # Fraction of max value for foreground/background mask\n",
+    "    spatial_noise_scale: float = 10.0  # Scale for spatial noise features\n",
+    "    spatial_noise_amplitude: float = 1.0  # Amplitude multiplier for spatial noise contribution\n",
+    "    noise_method: str = 'gaussian_convolution'  # Method for spatial noise: 'gaussian_convolution', 'fractal', 'sparse_kernel'\n",
+    "    blur: float = 1.0  # Sigma for optional Gaussian smoothing applied during post-processing\n",
+    "    sharpness: float = 1.0  # Sharpness parameter for line drawing edges\n",
+    "    vignette_magnitude: float = 0.2  # Strength of vignette effect (0 disables)\n",
+    "    width_correlation: bool = False\n",
+    "    width_correlation_rho: float = 0.0  # target lag-1 correlation for widths when enabled\n",
+    "    segment_intensity_correlation: bool = False\n",
+    "    segment_intensity_correlation_rho: float = 0.0  # target lag-1 correlation for per-segment intensity when enabled\n",
+    "    \n",
+    "    def __post_init__(self):\n",
+    "        \"\"\"Validate configuration parameters.\"\"\"\n",
+    "        if not 0 <= self.mask_threshold <= 1:\n",
+    "            raise ValueError(\"mask_threshold must be between 0 and 1\")\n",
+    "        if self.spatial_noise_amplitude < 0:\n",
+    "            raise ValueError(\"spatial_noise_amplitude must be non-negative\")\n",
+    "        if self.noise_method not in ['gaussian_convolution', 'fractal', 'sparse_kernel']:\n",
+    "            raise ValueError(\"noise_method must be one of: 'gaussian_convolution', 'fractal', 'sparse_kernel'\")\n",
+    "        # Validate correlations\n",
+    "        if self.width_correlation and not (-1.0 <= float(self.width_correlation_rho) <= 1.0):\n",
+    "            raise ValueError(\"width_correlation_rho must be in (-1.0, 1.0) when width_correlation=True\")\n",
+    "        if self.segment_intensity_correlation and not (-1.0 <= float(self.segment_intensity_correlation_rho) <= 1.0):\n",
+    "            raise ValueError(\"segment_intensity_correlation_rho must be in (-1.0, 1.0) when segment_intensity_correlation=True\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "da5b02aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b380cf2a89b34bdfb4001a27122e46f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=1.0,\n",
+    "    foreground_std=0.0,\n",
+    "    background_mean=0.0,\n",
+    "    background_std=0.0,\n",
+    "    spatial_noise_amplitude=0.0,\n",
+    "    vignette_magnitude=0.0,\n",
+    "    width_correlation=False,\n",
+    "    width_correlation_rho=0.0,\n",
+    "    segment_intensity_correlation=False,\n",
+    "    segment_intensity_correlation_rho=0.0,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb10e725",
+   "metadata": {},
+   "source": [
+    "Add complexity by adjusting several factors:\n",
+    "- variable path width\n",
+    "- variable edge intensity value\n",
+    "- Foreground mean\n",
+    "- Foreground standard deviation\n",
+    "- Background mean\n",
+    "- Background standard deviation\n",
+    "- Spatially correlated noise\n",
+    "- Vignette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3efe9522",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "779b2003f5274c44862747e482d58bfa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Variable path width\n",
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=1.0,\n",
+    "    foreground_std=0.0,\n",
+    "    background_mean=0.0,\n",
+    "    background_std=0.0,\n",
+    "    spatial_noise_amplitude=0.0,\n",
+    "    vignette_magnitude=0.0,\n",
+    "    width_correlation=True,\n",
+    "    width_correlation_rho=0.7,\n",
+    "    segment_intensity_correlation=False,\n",
+    "    segment_intensity_correlation_rho=0.0,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "plt.close('all')\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "96f98b3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92aefa8c4bfa4fc6a866e9f7d1131e42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Variable edge intensity\n",
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=1.0,\n",
+    "    foreground_std=0.0,\n",
+    "    background_mean=0.0,\n",
+    "    background_std=0.0,\n",
+    "    spatial_noise_amplitude=0.0,\n",
+    "    vignette_magnitude=0.0,\n",
+    "    width_correlation=False,\n",
+    "    width_correlation_rho=0.0,\n",
+    "    segment_intensity_correlation=True,\n",
+    "    segment_intensity_correlation_rho=0.9,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5aa27b40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e052ac4c5e9f4950a6568a001888eb17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Lower FG/BG contrast\n",
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=0.6,\n",
+    "    foreground_std=0.0,\n",
+    "    background_mean=0.4,\n",
+    "    background_std=0.0,\n",
+    "    spatial_noise_amplitude=0.0,\n",
+    "    vignette_magnitude=0.0,\n",
+    "    width_correlation=False,\n",
+    "    width_correlation_rho=0.0,\n",
+    "    segment_intensity_correlation=False,\n",
+    "    segment_intensity_correlation_rho=0.0,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3389f649",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da3c7d28b18941d8ab373d0afd78d8d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Increase foreground/background noise\n",
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=1.0,\n",
+    "    foreground_std=0.3,\n",
+    "    background_mean=0.1,\n",
+    "    background_std=0.3,\n",
+    "    spatial_noise_amplitude=0.0,\n",
+    "    vignette_magnitude=0.0,\n",
+    "    width_correlation=False,\n",
+    "    width_correlation_rho=0.0,\n",
+    "    segment_intensity_correlation=False,\n",
+    "    segment_intensity_correlation_rho=0.0,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6c307da4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86e2f14274f54cf8b7b99efbf83727a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Add spatially correlated noise\n",
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=1.0,\n",
+    "    foreground_std=0.0,\n",
+    "    background_mean=0.0,\n",
+    "    background_std=0.0,\n",
+    "    spatial_noise_scale=2.0,\n",
+    "    spatial_noise_amplitude=2.0,\n",
+    "    vignette_magnitude=0.0,\n",
+    "    width_correlation=False,\n",
+    "    width_correlation_rho=0.7,\n",
+    "    segment_intensity_correlation=True,\n",
+    "    segment_intensity_correlation_rho=0.9,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "41f5e9f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5de33fc3f0b740b78cf87c66b2b451fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Add vignette\n",
+    "config = draw.DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=1.0,\n",
+    "    foreground_std=0.0,\n",
+    "    background_mean=0.0,\n",
+    "    background_std=0.0,\n",
+    "    spatial_noise_amplitude=0.0,\n",
+    "    vignette_magnitude=2.0,\n",
+    "    width_correlation=False,\n",
+    "    width_correlation_rho=0.0,\n",
+    "    segment_intensity_correlation=False,\n",
+    "    segment_intensity_correlation_rho=0.0,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cb6b829b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6922933e570842e480139b12f3ef7de3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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jXc7fFXfOBMvaqOX0Eiyf0fI6h0oimSJeZwVEUUAX5tFkMn4bCJv8UVYQVW3E32rNcX8MswcsABnmaeMQdQBdyxQlBm9QvN+k+Da7bnuOK24OVcOuHUfIkb66Lr4Y7B2acH3XIdJT3wOHEIpCbt2/UgJKQ5Y1UALYlKbUi830reut5Y9r/DHMzrAAZJingUPH/KW2AYsOMXQlHiQObVfcte/r2kwRNb6LNyTS/LqKV0W7pr37CbuEviD492uim14UuRGAsi34XFamj6/WwHqzLfJcVtBNwzX+GOYAsABkmJvOVYm/vYa8mUkpk/AthK6okWIoKt1zfK5bnHjrOninkbHrS3X7tpY/kecQs5m5z00DfVFvW7ut20SPTvixy5dhDgELQIZhdsImPIwVQr5Ua6Ar2lLd1yFX8HWLNopQZvLYWq14bYUY1V1lp9clZOWLubzHhpwVEEdHEEcL82WnboDzC5Pha/v42jFZ/DHMwbg5gT4MwwzZx/rn1vS7pJg+qhDyTmVEvDEni5N9klr8+0Otn8pEvi7BOGXey1zjAcrbdH1/5zPoWQGdZ+Y939b203Vl4vwUW/4Y5tCwBZBhbhqXVePvACIwVquvVwLFO2dggYqs5aCdL6hkkNjxMShhc9Xt1qZYBJ1tY+32drbS7iG8RZFDzucQD+5BHy2g5gXkujQWQPIEwSKQYQ4IC0CGuUkcUvzFXHQHJLXwc1BkpLo1qeNcAUZd75iwcc+nrICxNfn39LIKVKfMMSHmruPQa429Du52ISGkgFwuIJZL6OMldJEZf1RVA5uSLu7M4o9hDgoLQIa5Kewr/sasQYki8FCZvL7VjxSKbnLFruJk6vUeUvj4FsYUIRYquzNFbE4lVKh5l3sxFmtpx6UEMtr3QZZBLBbAcoHmeAahYXr7biroqjLuX6VZ9DHMJcICkGFuAymFj52HckppkOTWbP4apoiKMaugOx4xbi8Bxb9u3xJFZfLu2srMjrcLl2WZDSRimHsULtPiv6ajwj9lzTHxnc0glwvo+yfQR3PoIoM8LyEuNtBPnrRZv1U7jnePfUFo97NQZJjJcBIIw9wVDlwa5NDHBdkjiSXqdvZF86HFGPUzdk6IPayW3etsE1cCY7mWWve9ccgSP7bkC+ZzoMihMwHRtG3eNrbOX8wSSghCFn8MsxMsABnmJnAZtf5GMltjwmwv0ea6+sITpI01Yi3rzUGM2dvvX5MU2x9/zliWrxtfaH+o/aFzp4hN99gDZ/+62dahJJFYeZ+dsFm/y0Ub9ychVxXE+Qr67By6LLfWvxCXlSTFMHcMdgEzzHWzywMtNV4uIDRSBF5MHLjHCCmC84yKwClWsbHkh9QYSMd9K4SAHrgViTGnQF1XzP3snkft27UWYG/ofq3GqQL/UFZAkWVAnkMXOXSb1SvWlen2sVqnr8v9zLAFkGF2ggUgw9wxDtoabAJUweiDMDUb1xF/yVACLiQSs4weP3MEmBUtdswpcYFSmLFl+n/fQikzZ1tU+TreA+aLQmtxFQJCa6DWEFUNXdfQFZH5yzDMpcECkGGuk13dWaHyJdR+dzonzitFAIQygicJuVaQBZNGKHzX7dR6ewkW0oEIC53vr9uKQT+mMpOAlBDLBZDnEFICWpt57BxtqzPRKNPpAg051mAtzvXLk2OI2Qz6eAlI645WrbiSQJ5BZ0ZkaSkBCcizDeR6A/X6G9BladbgXlfoPTTYtINobNeulW5FXw2xKU3Wr9LQ5xfG+tcE6v+NwYkgDLMTLAAZ5rrYN5YpJooOlNDgW+2Cwi+1NAi1Pv86ploJY8WeqbmVjs/hj0eN24pAkUkT11bkpqxJUUDfO4IuMqgiAxoN0TQQVStuRGsFqxuIpoGsasAmPdS1SYDYbKCbxtTCs5cwW5qWaYsF9DP30CxyNCczqEwCAkZISQFVSDRzAZ0JaAmoTAACmJ0eIb9oMDs5glxtTL09pdr+ugp6ve66b1wq9j3fKAi0177ZtH1/2frHMFcJC0CGuQ4OFch+wE4UY2VfktezC/Y6/DVY61+IUG1B93xgeI9Sroty+/r7i8IIs+US+v4x1LJA9cwCzUKimUnISkPWGvnKCCstjDiDNqJNbow1UCgNUdYmG/b0HFivgfMGWmuTOXu0BO4do3lwhPL5JeqlRHkioXIj9IQCVA6oGVAvBXQOqAzQGYwAfEOiOMtxdC9H8aSG3DSQlRGmomogX38MvSm3VrjAde/kOiati7or+GzKvhxAeHKnEIaZBAtAhrlqDpnFuIf4S+3gMTh2344XgQLBAPrWOV+8UcKOEiO7uCldsTdmIQQglwtTyuSFZ9Hcm6N8Zo7NMxmqpUD5jOjEV3EGZGuNxRvGUqcygfK+QFMYkSYUAG1+8rVGvtI4/vw95E82yL70BACg8wzllz2LzXMF1s9IlPcEVAGoAtC5mUcLQGcaqgDqezVQaGRHNbQCVC2xPi2QrQXOz3JkmxyyAuQGkLVGVgJHrz1AcV4j/9IKoqxMXN7ZBbDZQK3Wvdd7l2SSrvjzcgFRFEDRPnoaNUzC6Z9oJ4pP4I7BQpBhkmAByDC3iEsP7r+MjhSpJAizg+BmCNvltnMLIdoyJkvokyNUzy5R3Suwfi7D5oFEswSqEwACgAayEpA1OpeslkAzF2hmQDOH6YBhp5gLNHOgWOXQUkBsGkACepZj9aYC62clNs8JNAtAZdoIv8wIPwhjAdSFgjiukc8aHB9tUDUZqipDWUs0uYTOBWQlIBpAbszvsgaAHMVFhnkhka0bZJsGMpMQ5xmE1l3coE/y+62t/yds/b8ihwj1/KWIfenwxZ/9l0Ugw0RhAcgwl80VPIz8OL29kjb6J04/Zqx+3hhW6FGxfZchbu08VvhlWZdQIZQyr51Qxt2b5xAP7qF59h6qZxe4eKlAeSKweVagPtZQM0DlGrISyNYCogZEDchKGwGYCYhGQyjRiT8tjCVP5UC9EJBVhtlcAuIYzUyiWUo8/osZymc0qgeeq1RqU81VmH9FrpDPasxmDRazCrJWEEKjmuXQUqOZCTRCm0kbAVELiFKgOpHI1gLFS3PkF9YSOUPxeI3si0UvecQnxSIoZjOIoyX0g5MuMUWUXr2/WJwotX8MFoEME4UFIMNcJq5FAtjtgbRnj98p4i9a+y/14ZtShmVMyLnWvrEkj9AcoXO6MirSlCPJMmOhms9MCZc8255bGZGitTbFi+czVM8fo3wwQ3XPuHx1BsgKKJ6YhAvRCGQbIFtpzE8V8rXG7LQyde8EkG1MBwyVtS5gAdRz0cXzzc418pWCrJU5rtGQFSAqQFQCOm9F30xB5ApCaqjKZgNb66XGIq+htYBS5hgtADSiVZxmnaIWEArQUkPNBGpt4hRVDmRlDjU7wlwIyCKHXJfQVbW9p3VbvqUsR2M1xayAmM3QzAtTDscK7EwaS6uQABxxOSnjm7OAGWYXWAAyzFUyNf4v1OPXE1mhGnuUdWaSNXBKprFfmoYSBK6oc8XZLiIvhHt9fs0/V/S1cWnIc4g8hz5amBIqRQYomJIlm9KcrzTU/SM0RwU2z89Rnsgu2QIAsjUgGw2hzO/5WqG4UMjPG8iyQXZetvdFIFtt/9sVVQMIgWZZoD7KoAoJ0WhkpYIoFTKYuMFsDWRrATWXaBYKutAQmUKWKwipoGsJrYS55VpAa4FcKuRSoZIKIjM196CM8IMybmBh/xZoYxQBMTfrrI7bsjPVHAUAUc5N0ea2/ZqoTDkXBbTWwYg1UGbtvZXQbeaybMUfsmybCb1PiAB3CGGYSbAAZJjL4BAPI+phONLebbtL9Fp9uQQLMgsJIQNWRoqYOJQCUJGHuVsXz05vY+5CyRgplkPvPCEExPERxGwGLBfQeWYSEOrGlEHJM6ijGZplYcRJW0pFSxiRUrWWMyGweZChmQuUJ8Jk8NbA/A2NfKOQnzXIV0bsybM1UNUQtsSL1ibLtbWSZW1BZt0oswYARZGjmM/NOvPMbK8bQEoU8wLF2TNYvTjD6gWJ1ZsyNEuNqpSolw1QKKCUxqVbSVQaaBqJN2ZLLIsKzyzXAICLYo61mgNnObKN6CWgFGfGaplfmPur25jCeimxfi7H5tm82w4Yj3O+MgJ39udPIL7wOvTFyghBh65LjDbXKhoFSGGsobPClINZb9r7kRATOLULCruBGSYIC0CGuSW4wm7nUh1T46x8Eej+bQWba93zRZ3zN5kNSolAuz2EdfHarNP796BOFqgfLKFmpjxLcVabMiiNRn1coD7OoArRuWmtOxZ2GgHj7rUWv9Jk7c7fqJFfNMifbCBWpYlrW2+g634dPzTN9voaIz5719s0RiyWFZCZAtLdtZc5iiIHcAJgBpVlqI9MAkdzYVzQxmIJiAZo6hxqk+GRAJ4UNfJcodzkqNY5xFmO4lQgK8X22jSQXwCyNK5mbQ2CbaigKkzWsi0zY88pLiSKhYSsjpFXNWSem3qCyhS/FoCpk1jMgGfuQR3N0Zy0AzQaOjdFq68ksYdhmAEsABnmMhj0l93hIbdPP9rRoSNt2XYRgYO/2zg6ao4xcUqVe4HjxgWgMTJGlhlr2rMPUL1wD+UzM1y8mKOZmyzcxesZZucKxZMG1b0M5YlEU5jxZQ00szYxo9jW7NPturKNxuxMY/a4xuLzZxAXG+DxmSmZUtdxS5Z/7a74LUvo9Ya8H3K1xmxdIlvfh1BHqI4EqpM2s1iIrt6fzTJWM6C8WKAqNMpCQ1QC+VpgdipQnBmxp53WdMW5hmjMGC5aAE0hUN0TqBdAfdK9AKgugOKJhNBzLPV9ZIs55IUpKC1a6yXyDOp4ifrZJaqTHM1CIisV8osGusggNvJyEnsYhhmFBSDDXDa7uoN3EH4xy5+fHdwTgdZy54u5UEu1sWOnPNSpWEDv7y5pQwWEqWNtFFkG+cwD6JMjbL7sAcoHOcoTidWbTJmVZmFarzUzM7YVfmpm4u2EbsVeGxMnKyDbAMWFMuLvtEZxWkI+vgAePwHKyrg+td6uz3NxCyGMyHPd2GNFrB10WQJvnCKrapxsKuhZDjXf/vetcwGVmTIvqhDQUpiOIK27VWhA1gr5hUK2aSBq3bm6dS5bNze6c7U0v6us3Wb359vXSuUCzUJgc09CiwWy5+cQ6h7kRiHbtAksM4nNgwzVsUQzN+dkG4niIsORECgAyDcKoCwBVP5ltxe3Z4cQ9/PH7mCG6WAByDCXyRWKv/hwCeuICTr7dyjub9f1pmQxU/eQElKt+BOzAvp4CXV/ic0zRvzVR0CzAJq5KdXSzICsQCeOhHPpWm6tflIJZBtTFmX2uEFxUSN/Yw35ZAVcrKAuVp1LF0C/76+3/k4EOmu2+6LFkIGuRRy0ggSAPIPMM4h2Lp1nQCZNAouUxronvfuqFOS6NrGFWncJL7rIoOY5dC7QzDPj/s7MTxcZoLCNGRTmX1vGRuVAdSTRzE28ZL6RyNamTmEzE1g/J1EvTPFqSCMCAaBZSmSbAlmem9IwVwHHBDJMBwtAhrksdhF/Y0IqVidtB2IJIR2u2JoahO8y1qKNyAzuiT83bs4Vfza7eVZAHB0BD05Q/oUH2DxT4PylzIi/OaBmuk3sMDF82cYkMmgJyEoAkFCt5zorTQu3bGMycvMnFYo/f2xE3/kFVChpIWT5dK2XfmZygJ5gBKCr1r18vuqNEzrXxY6jfHG6mEPOZ5DP3AfmM8jjGeqTGZrF9nURyri9hbIWQuMa7uIGGyOo62xrOcwzjWYuUC+B1YutyBVoS84AgEmqAYD85Ahis4FYrS63iHl3QSwCGQZgAcgwh2dM+O0joiaIvlBZmGg5GGp86qGcGic4NcDfTxxx6FnJ3OzhTJqacssFxPES6njRWrK886UpeZKtgfkjjcWjBrPX1yYrNZMonmSddUs2CqLWkGVjevSuSugn58YKV1WjFju7Rl+IuaJOaz0qAgckFsXW0hOB/rH2tasqY9R7cg6xqUyGMgDZ5G1MoalPqHJjuZNl231EAsU5kFVGJNt2dIARz9laoT7OIJRAfWbON+KwPU5ujzc3RrbvPUJU79t6kGEYEhaADHNdXNODbeeuIMOBtutPcOVOHj4U9+cLxCwzrt/FAno5R3M0M5aqzJRrsYWPAZPgUZwBi0cN5l/aIHv9DLYocZ6167flWbQ2yQxVDb3ZmDInTTMew7dLy7pAcsjB8K2r7WunGwXo0lzvrIKQApkQW9dyG0NoYwWLC9G5iItzE0uYbVSXICOUEc2yUhDNDEJlqJcCzawVga2F1ZbZCTHIZmcRyDAHhwUgw1wlY+2tDuDi3VvgTck+nrLGlO4fsancRIr2bxQFxHwGMZ9DPziBWpguG7JUKAAAEkIBlRaAaK1/jzUWX1wj//PHUK990QweiEHTthWcdT/7Fjg/yQPoibeelY8Qs6RVUxEWwkyQ8YUk3vzumH03vOz26bKCrmuIuoa4WCObz5DZjihadyLZxhpCSqCsTF2/yil3U1WtqFRYtuV38osTrJ/NUZ6ItgZgO2xrXexZcyP1K4PvNRaGDLMTLAAZ5tDYB2YqO1g3QvX+Dib+7O/RQs8TyrnsshStTS9eArd9mzg+gp7PoJYF1Cwz9eVEm8jRaGRrAS01ZCmQrzTmpw3kWQms1v36fHZsQQgoIEmgDs5xCWUwA3StwzFGaiKOuqn919C2vhPC1DC0FlE7jhAQed6JwW0x66Y73xS8bq2neQ4hBfLzBfKlhMqlcRVnJvlG1m3pmasuA8PxfwwDgAUgw1wdMWtZqNwKeagY/N4lc0TGidb+C7FLLUKvuweAnuUOwPahb0WmL2aaBhBtRw4CkefAfA48cw/NyRJqmaNZ5OZ4KTqhITQwO1eYnQPZWiNfNSheX0M+OoW+WKVfk72uiNgKxfO5QjYpdjBx3KgrmjqOwrc6tv19o8TeC257wvXalORZVyieZBANkG9smRnT7zhfbTOoIQUZAhiF+qxEe2ez+GMYCwtAhjk0+7aBG2nx5hPMnPQsi9HCz9vB6GN8a2Aw25XOPh3FHy9wD2yZF/HsM9DLOepnlqiPctMjd2bOEXZObbJ8s7WC3DTIT9cQ6wrifAV9fm7ETmR95L6EItWDNXvZvDGoYwduXJ8pMYehY6243SVBKSCMdVUDmw3k6QqzRiO/yKGsdVYCaDRkbeYReQa5XJhC2I0abwsXWt/Y+jkDmGE6WAAyzC3gYIkdhyQlsYESegFBIYQw4m+xgDpeQp3MUJ8UqJeyzVQVXTs0WbdlXNYK+XkFeVYaq19ZQa3XXSZvSJyF3MKhTOQQU8Vf8JwUK96+UOV+LKmhAS5NY+IE16Y/cFbmkFIa8SeEcdUrGHfyrDCWwEZBoErrC7wrLAIZBgALQIbZj32tff5wXrcOf9+U7e3Oyw2SD83tuni7bXJcrDhFnV2E09e3eeYEmxeXaJYS1VJ2BYmFAmRlXIvFaQm5qiHPVhBr02JNnZ93wkII0RNcboJGz+Lmr4MQgjHrX2ws14XbrUEI00otRTiG2uzF7vFYJ5Le4iPuf3dfyC1e19CNgmgaiCI3MZt5btz3RQ49n5li1MsZ9DyHODmCPJ1BrzcQp6cmxnBqfCAnhDBMMiwAGeYGcfCkjptGgrvSFT9CiK6vrzg5hrq3RHN/hvrI1PkTytSiE42GrFqL31mJ7PEKoqxM6Za6NqVcbAmXXsJFxJKVUGhZax21JHbXnHjtpGXqUCViXHE9RVjFRNWYNVArc/+1BkQNMTNdnIU0XwZ0LqEW7WNIaWRVDdEmkJjPQoIlcJ+6mgxzh7mi/jsMA3z4wx/uLC/256WXXur2a63x4Q9/GK+88gqWyyXe85734A/+4A+uccUjTM30nTr8IcRfykNRq/hxtkjvoQpXWyEU6PsLOBY6KSEWc4jFHOreMer7C1QnOeql6XmbVRrZSqF40mD2qMTsSxfIv3AKfPF1qC++Dv34FOrsHGqzIYTUjv/9STk4d0q2cJBYlvDoubovNF1G7jW53imv9dixTQNdlqaWYlkCNslEmtZ1zSJHfVSgPplBH82hl3NjMbQldqTofjrse9J9fx24DiXD3HbYAshcKX/5L/9l/Pqv/3r3d5Zl3e8/9mM/hh//8R/Hxz/+cXzFV3wFfvRHfxTf+I3fiD/8wz/EvXv3rmO5Tyexnr5j1hzqHGrbmIBIOY/6G1sxJfIcYrkAnrkPdTRH9dwR1EwCGpg9URCNRnFeI3+8gViVEOsNsCm3BZt9a9qEFmx2HYNjXeEnpall7Laoi12jFWlEyZduLsoNDUwvtD2WpOOuI+WLhm9l899TqYKxacyPLRfTaFNEWsAU61YKolHxbjX7wvF/DAOABSBzxeR53rP6WbTW+Mmf/En88A//ML79278dAPAzP/MzePjwIX7hF34B3/u933vVS7273BBLiu3uoY7maI7nRvyhTfBYNRCVQnZeQZyvIS7W0JsNUFbGykSN54u5rvzIFGGlhsdLCeGLQG/OvS2E9jUJxf2lQrWSG4shTdmWiNZ622mlURBKdV1HhNYQtQpaQg/WJ9h/H7AgZO4oN+N/eubO8Ed/9Ed45ZVX8Na3vhV//+//ffzJn/wJAOAzn/kMXn31Vbz3ve/tjp3P5/jar/1afOpTnwqOt9lscHp62vu584zVG5z6ALcWnpCVx7oY3Z/Qfn85TjjAgFkB9cw9lC8co3xuBtV2+Jg92mD2/x5j9rnXkX3+i8AXH0GdPoE+O++JPxufZ+ehtgMwgsMVHY7AI+P6Rly1vWtqxzK/0/cgOtcYKcW4qTnd1zPUdSMm/sbeE7H12uzgsoIoa2QXFfLzCtlpCXGxBtabbb/ktivIwcQfxYETuRjmaYEFIHNlvOtd78LP/uzP4r/8l/+Cf/fv/h1effVVvPvd78aXvvQlvPrqqwCAhw8f9s55+PBht4/iox/9KB48eND9vPnNb77Ua7guYg/C3jbqoTzlIX0A618ndvYZYzZrrX8F1Ex2JV5k2UCerSGenBvBd7Eyos+WDfHj80LlZBIe+r3jfKufFY2+ePTZJ64PiAvrseP97S5TX+fU+d3YPEpEattnuW2x1xiXryhryLKNDWxrPcr5HHK5MJbg1PfTDbFeM8zTALuAmSvjfe97X/f7O97xDnzN13wN/uJf/Iv4mZ/5GXz1V381gKGbLtoJAcCHPvQhfPCDH+z+Pj09vTUi8KBWDz+GjErq2LX3cChmzss2TSltsk38WEAvZlDzHDoDIAChNGTZQKw20GuTUOB2rRBOPOlkiD693fYIlIVxfK64FdAda7S9XOK4vf2hWn8pTM0gtvO4HUK0Nm5fpU3LuFoZ12/dwLaZE0UBLTMIZWI5dVVjepuQ+Dr6+7g2IHP3YAHIXBvHx8d4xzvegT/6oz/Ct33btwEAXn31Vbz88svdMa+99trAKugyn88xn88ve6n7uYmuwCqRHDQfK/bbH3C4ze8EIvvixBcug/ZviAilLINczCEWC2A+Q/P8PTRHOaqTHFoKyFJj9voa8nQFvVp3Vr+eqHTi87TWPaEy9kWiO3fMoucxRfj5AjjUczgWSzjYb1/PUHcPy1i839RC0/uUXlEaarOBLEuITELJJfRCAsghmhlQK4iqFfbta4zVGnrVkHUye9vGOoQwDNPBnwrm2thsNvjf//t/4+WXX8Zb3/pWvPTSS/jEJz7R7S/LEp/85Cfx7ne/+xpXuSdX8OAhy2SE2MV647vyEt2B06xiEmK5hH7mHuoX72P9pgU2zxaojkw/YNFoyFUFsXHKiNjz3HlcATfVDZ0g/GKWuFTX8pQxd7L8qb74Df5+COz7wr5HQu8NP2tYafM61o1p3Scl1CyHLjIgb8fJJFDkEPO5CQko8nYoz0vgXpO7nv5BXCeQYTzYAshcGT/4gz+Ib/mWb8GXf/mX47XXXsOP/uiP4vT0FN/93d8NIQQ+8IEP4CMf+Qje9ra34W1vexs+8pGP4OjoCN/1Xd913Uvfuocuufbf5LEv46Hml/o4UCmOzgpHuFtFJqHvH2Pz8ATr53OU91rhp4DFIwVZadPD92Jt+sW2oqFzGTpZuAM39K70YgmJLF+7PyIc/TX5VsAUgRdyC4+ev0uxZ1/o+2LS3+efM2W+qgYKUyRaFRJqngEbQNpSMLPCmCiWc5MtXJZG7GkV7ZjTE4Es+hgmCAtA5sr43Oc+h3/wD/4BvvjFL+JNb3oTvvqrvxq//du/jbe85S0AgB/6oR/CarXC933f9+HRo0d417vehV/7tV+78zUAyYedjemL9WztD5I2GdXzNdE96IudUD/dgfibzSCWS5RvOsb5ywVWb5KojwBZAvM3NLJSIVvVwKYEKi/ho1uH7LbH2rSl7Ottd5JABFU2xq/bF8AVcVYEjoo/14Xthm+OfQnZNU7PH8P9NzaPH1+aWFha1+Y1FRcbZABklZs6gLUy9z0T0FICizlE3UDMZuac1FDAqeLP3leOBWTuCELvVHuAYW4mp6enePDgAd6Dv41cFIef4BosgL4A7FxgsR6tY2sJFRaemhgS6k9MJHz4lqtOEC3mEA/u4+IvvYQnb85x8aJAs9DILwSWr2nc+1yF+RdXkP/3VehNCV2WvRIrHYSVrpfFG9sPQgBaC6PZSdcNTBSAsfmCuEJKxi2A5Lgpwj1k3UuFEoAU1PsyyyDnc4jnnoFezqHnhbnPbWKImhdAJiCqBvLJquvq0n0BwEii1K7Wv0t4JNa6wm/gP+Lx48e4f//+wcdnmF1gCyDDpBISf6FOG2OJFINdreUs1XpDPXhdt9eYmzh0vnuOe4zfKYSKPZMi2d3ZiZdiBj0rUB9J1AsBNUMXnZxVQH7RQF6U0Jtt4oeZry++gjF4jmCLWQRD5wwEAeHyHbPKjSWhkAKOsOSFk0Gs21NANM02AYcQkT0LbUQIBrO2B2Nmw/NTss7bFnE4OzdxgEpDFzkgATUvoOcZtBDIalsypulbRS+zNiDD3AFYADJMCrtkAU+IQRpL4Aju9x+0VCxXSHROtVDGjldhoTc6bCahpYRoNPK1sfxBCBRnwOxMIbswBYJ103RWvJCbmbTwUd07qHVQrzFxLaMZxYmExugJrwR3bm+coKAVw2MtihCL8O5laA1+X+eJokxrbcRfu0Y9zwApoQVMizhzUP8cpS+nRRzD3DFYADJMjF0f9CGx5Ak26kEWDXDfF/ugP4R7OiAcQgQtSlkGZBKy1sjWQHEGCKVRnAHFaQ151tb9a+LBX/GEiAQhTolE77yuQwUR75hq4evN57qVnbkGIjB1zMEcI6+Pb81zkz6oc6f0JKa+jFDY11UI6CJrewMLaAEINvIxzKXBApBhQqSKv5iVb8QKGLJm7C0CqQd1SAxQ9eJC+MdOWKNvURKZNOKvyKGlRLZWmJ82yDcSxXmD/KLB7LVziMdPoC5W3amxjNiBIItZ/lrB1Y0xYkk7aLi0XZdbvNoRgYO5WrFNXmN3TOBcGSlP41rufEteqEWce557boiUmpJlBRQ55DqHzgR0JtGczKBzIwSLWkGsFsBqnZ4EEgrNYBgGANcBZJirhbCGhIRecm2/FMbG8R/4+1oIU4RklkEs5tDzmXH9aUBWJus3v2iQn1XG9VtVnZVoIGQC8X09URcUPzLuPr2kgtCRQcKCNdBHuSOyVmpto2J2cg3FCV8GPPGnmwa6biDWJUTVQDQaOpPQuYDK2mzgbPg6TvpshGJdyWPZvczcDdgCyDAhtL6yh8FOcU1TLBwxK81YPTUL1V82kKnaDRFw+4osg1guIO6doH6whJrnEFqbgs8bID+rIM/W0KdPOvcvmZ1r/wb6Ishuc12rVBZvi2lPtkesINUr2N8fmj+hnmBwXmL82HGkZfFQpFjZCIu4LitAnwNVCTEroLMF1DyDKgS0FNBWxFvB7mZET7GU+3NzazjmjsMCkGFCxB64U4rMXlZB6LEYq5T6bLHjfSgRGchUperWuXFz8v496HvHqJ8/QfnAuPpEA2SbBrJUkI/OIM5XUG3R5574M4NtLWZK0Q9rmwDilnAB4kLLFYFEXJ5LNO7PL3YdmtNmt3qCYy+ROVbAundOTFQGQgP8bGKJ4XsrKq68fbrt3qJ0F/9n5gQEnCQRO9/UL0u+6EuF6wIytxx2ATOMi//gbeuS0cde7sdHK51m3biB8U1USRYhBESWAUUBLBdQJ0tU92eojzPUCwmdmeQPeVFBrNrEj7qOWu6muGkth4zlG1rUCCukjy8MXQFLiTrZU9P0FxM7TnDO8UziuIVR9P+NDpbwuaBCIVwBrLVJAHGX7cZ7XmUJGHYJM7cUtgAyTIgUoTBWgoU8hX6g2Ida1LqR2gIutAZq7LGWXpH4s14tuRbS5SvamL+jI4jjJcq/8CzKZ2ZYPZ9BS0DWgKw10GjITQV9sYLebMzaMjF0+1phHhOArgvfjRUk2saJLKPH8sWXHbcdp8Md0xWsjnu3N6c/F5HA0W9pN6xLSFogQ/cj8p5Kjl+MFf2GnuZipfYrBVHVQNVA1PaLV+sGltL0Bu5OHXEDxxJPdvnCxC5h5hbCFkCG2YXQA2aPzEMq6eOm1ztLFw8SosghjhbQx0vUJ6bws5oBOgdUDghlLIBoVFv0V01PRrBYIRR4aPcsXmOu1eHJ5HndmIGYvjErG110WfkHdda+XRJPJs8fwbaz630JSE228L802Ne8rICygiwb0/+50X0rIOjPxOjn5JKt9QzzNMKfCobZFa22P5NOS6ybl2IJTJvQ/Buz/vlMEF5j9e+EdVsWM+ijBZp7c1QnputHU5gfnQHQrQBUymSGThEk1trmWd2chYTPobAuVeocdzx/Tv+4CQS7lLhJLG5MX2Buv8+wfQ36WcP9rN2DuMVD7xn3veqWmnH3aQXdKOiqgtiYn04ETl3b1HaGDHNHYRcww7gc0s0TefBcWjcDas7QOlILBBN/604HJbgWiwJiVkCcHKE5WaA+KqAyAdFoFOdAVmlkpUZxXkOuKoiy6ow+0Vp+bleQxAzewELNv0QdvmBmcKg3cLsu/3eyLmGgCHR/mvZOOEWweyVuXDHoz+1eh985xXnvDTp+xN6XY63lyIuIuem9ZJCmtW42JgZQKA1RKsiyBqo6MkykhNGhYmTZDczcMvirEcP4+KKGg8CHjFiPXPeqyCREUUAv51AzU95DaEA2W/GXbTTkpoEoa6Bu+pm/24l2K9BMFXqmxt0hoeQgBHoLH6S24FQmfim5lDUKAZ3LNvbPibsMZDRzT2CG2Q22ADIMxY4Ptr07eIwFzu9jzWi7b0xzrQayP9U2ESE+p4JYLqFPjlA/e4R6mUHnAlmpoNtyMflaIVsr5I/XEGcr6IvVeHLHYJPuJ2UMrkMOrXEpxZNT4gOtlZCy7vnjRcrMdMd5CSsuvfvds9zR5Wr8Oox+55RD0BvH7wlsSwvZ923ssyHaRI8iR3M0Q31k3iuyFD3r2+TPF3cEYRgSFoAMc0UkuXydB9RluIl3eegHe/i6PWOdYwfMCuj5DNVJDjWTULkwrj1oQAuIurX+rTbQZWl+PNcliZRx0ddb67YeICWCQi3lOsEWq/dnBrAnD4UmtZZUdzVVi3Bq0kpsLTFi7mA54va1WPEVCkOgMnOFAOy0GiYRpG5Mogi2X7LsZ2OSILws9zDDPIWwC5hhDsih3FF7dwZJwQ/Ijw3tJxCMju2sJcugiwxqJtHMBJT92tnWeZO1hqxVlwHqxruR+LX2YmKKEGY7xwpafNHjirIRkWUyZlPq5AVKwPiE+v+G5u5NQSSHdOPq/r+HJpSoIWSvGLRQMO8NpQBFvy/2+pI09TPD4SDMLYItgAzjsut/8GNdNDAi6jxLSPS41CzHUCcHe5oN3odjySOseoMkgQA9t6kr0poGojE9XiEEVC4gG0BWGvmqQfF4A3m+gV6tjPjLMohWBGqtO2MQGYSfZcN9Y0LPT/Sg3K7+uVRbwGDfXrO9O9q34AWyfQd9iami0ObA/rZISZjJFt/Q6+u7dFPOcfFdw8HEpAaiMdZa0WhjolAKom6gq7o7nyqXdGWxgJwMwtwS2ALIMLviPsS8B9ql1u+z9Qb9uoNjaLV9gPZEhAiKv+QlhWLmAKBuIDY1so2CaIxVBxoQtUa2aiAvSojVxpl/a9Ubi9Ujy7X4VsGxWoApJVxiXwx8seaLPDfmL7Cu6P0bs3JGSEkmmSQS7fvNlpFJFV2pxzUNUNWmBEzTji8ldCaj70tOBGGY6bAFkGEsqdY/X/h5FrmQ+IuKwkPFIvmWFv/3wfGy1xljl/l64oW4h3qzgShyZBc1srl5mMtaI9so5KdriMdn0Ou1efhLAQEBba1wbg08P3lCStoyB/Rdv861drhWQt/yNgVqHXZ97rhZ1h+bmucAtQSD57sW1bGyLT5+LGDs/RTrhpMg0nRZAZvSfClY5BAyg8olZJYBeW7W4XiC9xZ+vkWTYwKZOwQLQIYBxsVfzNLm7bOuXlLwXdXDxrXoBY9x3J5CmJZrDqRAICyFvQQDX3AJYUrA5BlEo1CcVijOBbKL2jzk33gCfX5u3HtSAk3iA50QN4PtgBFefleQWIFo6jr8v6k2blYEun+PzdVtFuFEE2/e3r2O1A+MYccgM4Ipoecl+eiYLlUTRCvxZUU3DbBeQz4+Q15kaJoZIAX0ooA8PoJYrY3oc0IlKBEYdAlzTUCG6WAByDDXTFIPYHPAFazGELUOEevsyrB4vXNFkbeWGwnRKGQrBShAXmwg1hvo9dqIPz9JIzZ/SKQBtGWPEkpj1rV9H/BUHGGgWPPoWkLFnVPFXyv2YpnVwUxvcjj69T8ISgNNA71eQ6yPIHOJ5mgGXWTQi7l5PzUNdB0p4RP6HB36CxeLQOYphwUgc3eZkvCRkOQBTC/1EjxvH7E3ZvlT2lhxmibSfixSAgRDEdC1H3OscTLLTPu3xQzIpOnysS6BsoI+v4Cq6771zu164Wf6biemf4+sc9BSjUqmGHPhuusbE2x2LFt6xhIThaFxKPe8rcEoI4k3/ny+q98KZOfeBF3D/nshVoYmUC+SJGCh01UN3ZwjWy4hM4nqwQKQBdQsQ3G+MpnCT54MTnU/Q6T1b+wzNVUgsvhjnnJYADJMKvtm+toxLpMpQflS0DFhU+KqnCzUnoWpKCCOlsDREmo+M9vbMi96vYYuy63VjxJ4VPxfrBYfJRitKA0JsZg4o0QOZXkMCVU/NtGPaaSw53iCdSC2Q29BysLoXVe0td5gvMD99oW0lOOle0JQMYNaGVdy0wCNgioEVJFBFBL5cg5UVW+IvRKu9vk8sgWQecphAcjcTS6xnpcVgaHYJP+4SVZD+6BMifFLZLTMy1h/WEfYWIEhZgWwXEDdW0LPcqBWxuW7KaE3JS0YqDIrqW7O0LEpwsQXmo5o6oStn0BClWlJKfA8JhioOEVgUKbGvmaxAs+DjichS2pCEpD/JSFaO7BnLQy8T8eEl27vsdbQmYBu41P1fAZZFPFzU+GED+aOwwKQuXvsI/4SXcEx8UfG/PkdEdx5Qg8qKuOX2m+PsRnLbikP6tjYOCkIAbFcQt0/QvXcEloIZJsG2WMNrYmyLTF8qxx1LlVChXL3jglK4nztnjMm7mK1A33xZQUXlYHszhdYrxWmAyHmWPiiMYLOXKFWceaw/nurlzjSiuvBGvz3I/WZGWt5CEBvTHmgfNWgXmaolxLNgwUAQJ4+gdpsoKt69AsU6RrmZBCGYQHI3EFCpUMue9qQUAsJysuyUMRKdcSgLDwhq1dbC09lpsagriaUWqH2T8l8TewJ3NvmzpE6lnvuFPxyMLH34pTxJ7d688oX7ZgcsncCSKieZtMAVQW5biDmGbQE6mUGURXIlgtTLJyw8HJNQIZJgwUgczcJPbSuWhgKOW7Jc7HWFFeMRUp3bF2YniuO6ubgu+4QEAA2iaTdH4pVG65d03F/oTi6bq2tWzkmRlJ68FJzeBa6QdJIrPzLSKcRMpuZKhGTMo7H4D748YbOMb2+xgSxrF5bLiapN3OIsT7AAPke1mUJSIHsyQbNMgdEhvJBDp0JLB+dQNS1qRs4OXlj5PgE6yTD3AZYADJ3j1gZkV2HHMtADB1vA963O+gT3AdSyI3r4iR5dEixnct3y9n1OMLPdyUmJx9YMVYryNqIRaG06ebgJyGkuleV2rpjKfcp7NSE4EoZH4S4cZMbpggfyu08VkfQPS+2Vkds28xrEXBDjorggGB27+FY6Zje8aFi0f4XnMR2ciarXEFUNWSlILrsZwHkWXt/Ra8wdLc2IgY3yTLo9CROFoHsBmaeUq6usBjD3ARiD8VBPJXcqRyLTewIJXhcaps4y5Q5/L6qfqmVXdycSkEoBVEryEalWTdbt3FszF7yhffQ9YVLV5rGnuv/EGMM5rProtY6di2WqfcvdB8CFrzJLtp9O4ykQL3/pNjNsqaM+LNtBIW9nMBn036+3JjbndzCV1h3k2GuA7YAMneXmCVwpN2bj/uAmSz63LFj7jLfChg7fgw3mxgB65d/CmEl0lrDPbP7W0qgUcg2DXQmIDYNRFUHuov4CQKeRYWynrmu1EQBNKiZ55aDUQp6e8L4Gi1uDFpI9Pku7lg9wlgpF29f7zUjxGHwXgcsp2Qyh9PtxcU9dixxJBp6MIZSEI2CXFeYnWYQjUa2riFWG2itIPK81xnEh8zG99ew72eJYZ5SWAAyzDUkhOxEygMqJjT9B6EUcTchQJc7QVh8iCwz7deKHMiMuJLrBqKsAVv+xRV4O1ijeuVN/Bp3zjGDdVJz+fGIlGii4hZ9BuIpUvcvVI7Fnhc73t+eKvwC41Du3u3xdBzoaNkZcu4Jca4W1caMNg3kukZ+XkHUyryXtIbIc2AxN3PXdVJGMAkLP+aOwgKQYShsnNIOLqtYHcAxS2GP2EOT6Mnbw0/qSBB/vYd8KPuVKJDsiwOR59CzAjpvYwhXJcSmNAWgm4TSJAmktEPrWfzMhu08rjWNEnex2MGxEjNUJxAq0cV1r/vnxgiUxgkJv0Hx5wn32d7DYJcQRERkyA1MZpOHYlkVsCkhhEBWN51FEIApNp7n26SRqk68qi3BnsEMcwdgAcgwe+I+REKibvJDhsjIDR4TKtTsbnf2D4RRahYvuQa5FWFSQsxmEPMZ1KyAzjITsN8G8+uqJl2ZyRnZjrt1cA3+qb4wdEWab4H0hZgViISbuFuvW2C5tVL15ooJP3c97pyh6/fFpT9PqBtLiITCz5MhvmAESRF+AKAVdAPoi5Xp/nGeQWQSyDLo5XybpFTX238j7mB6ipH7kPIlkBNAmKcUFoDM3eGSXL1Txd2VJIH4c16mm7tn0WoTGLL2B9iWfwH61raUmn7EMdPamRHHxR7YbtauPzdVvy+UAepvHyv1EhondK8Cr2fwdXbOpcRfKAuYgnT1HrAzzWC+tme0yDLoIt/GnNr3W54DdW32ozGZ7rskmyQWeWeY2wILQIYJQT1EJvQCHhV6oWD0FEbbs7UP5NhxsXiz1G4dSpmYP/dcYCuOrAWQSuSw1kC/fIw/lteerRvfnkP9TXXl8F2yjrjqCSBfBHrXq7U2sY7uWtx1hGL8IuVrJrW92/f8wXB9a2Swy4gDWSfQFYHO+y6lmPRwAtX9q6va/AtAzE3MnykrBHMvihxCz4FGAWXZxgMeMOuXsgKy1Y+5BbAAZJgD44rAHU5Gr9CzT8h9FhJ6PVfyMF5smPmZWEzZOdYVVaIooI+XqJ5ZGPcvgGzM+mXH8AVMIAFlWK5nRAxS53jr0KEC1O6Y7j3za/w5gpIUkylQx7rJJL7oiNUW9IgJMK11vG3cyLh+sehkUi2G1Bcjp5SPziREkUMcHwGZhCgrqNYV7Mfc7hzv54vAS6glyjBXDQtAhrkEJotAqtBzYsHc3r6E2EE/aD+UDGL3DZJDqIQJGydXFMB8hvq4tZApmGzgkAAbe4D6+6e4sq1oSznHv66YcNu3/VuMkAikLJe7jJWI7xL2k0Hsdv8cDWLbrtY/Crf0j/3JMiO08wxCa2ghIcoSugGApvc59Ptx7w0XgWaeYlgAMsx1Eas/5oq/WHxVQg2zsZi5MevQ4O+mCZdXKXKoRY7qSEJoQNaOAHOPa5o0cRKwwnV/Uw/gUCydTQBx/3bX5f5NWSP9awgkb/TuWewaQ1nC1LyUazwQT9kT9d61+Nm8pJXXuR/ulwH/ePpcJzHJaRnYbUthYG3z3jeNBOqmKzWkFjMTbyoE5KyAWG2Azca4g4lewQzDGFgAMkwKE4PDd7b+TSXUTzU0FWW1SbRgJB3bdv/IVxpCaSMAywpoMzUnW6QoC6DdFsseHhQjDiRz2H1jVrVYcWqKUNauC2XVo9zbobjGQLHpWNs2EkoMT2TbccWu1Q8tsK/ZhEQL6jiloBsF0TQQjYLONJAJaClM2aF5YdpbLRcQAISfoZ18QQEByjC3CBaAzN0h1R3ocx0PgKlzenX9gj1xgc5VPCUz2FoRRcxKJoTp3bquMH9UQjTKdAA5u4DelN4avHP98ajyKVQZF3c9vtVrTKSluEjde+THfVHFnmNxiykJNu57NHSP3WND49n7EbkHo0WyyeUNLYFa6zTrnpPYQb6/XUt34P2vGwUhG+iqgigK0xMYgM4ldCGhRAHkErI8NrGJWkOvN/t9ydrnXIa5wbAAZJgxEqwWOyd9pAi9HeKVfGvdwEIz4fzuYU+JNru9KCAWC+jlHHpRQG5qyFUFcbE2BaDdIr2UKBmLt/PdyNaF7Mft9S+CdtmGriOyrec6tRYlV5hShES4FXlZRlsfR76o2Axk7SWfOIul5yeu5yChAdR7ikpKSv1SEwpraLfrqgbEBmgFnjiaA0WGZp5BnQhAF5jNMuRFDgmgKUugcYcZSQyZKvgoUc4wTwEsABlmTy6lrh8V9xeznKTgPvhiAoMI+CcOcuY3ZVREnpkC0IVNAFFAWZnuH1bUuO7MlBqAzlw9q1NkPSYZYXhN0UdzRBAlW0rHjou5rKn9sTI9U0hpSZfIpKSOWAkiKpt9pCi0+7uua4i6NpbAuhWGUqCZS2gByDJDNi8giqL32vti76BdQFj8MU8ZLAAZZg9i4i8pEzgk6vxYqkhZmLHA/u6BbUWl7Iud0bZvY/ubxlizZgX0skBzlAMKEFXTtx6GYthiQtARZr3kBmtBs8LSGTulb22vA0ho/nbNvWxoN5HETLZdT+x6qFjAWFyi/7c7tluDkIJyjwfiBXvbXDe6lx3uJ4P01xZJUkrpEOK/x6nPg91mhWDTQK3WEFUNce8Ysp5B5QLNTEALoFlkULMMstg+4pLFnj8Xw9xSWAAyzCWRbBmMuZhTrYDuISk1/Fwo4RKzDrnHu6JBtWU5FCCUBhr7t+of7zKx1Ip2xwNo4RUQg4OxQ8kV7liOCBy4cN01+OP7sYr74F1XL7OVEnuhWoLu+tx1B0rM+OVfgpa/q+yl62cIa21iTasG2bqBKgQgAFkpCA3otnUcGgUht+ddSv9fLgnDPGWwAGSYCezl7k3pK2qh4qCmnE/hPPS64r+xGDoKVzwJMbRiNSb7F6odn8rA3LWIbkiAhY7zf7dzB+YdiBzqGv19MQujay2kxg1h1xgrCu3O4x+Tir/GXcZIEVJ+hxB3O3VcyOodsjTWjSMAZSsAdXefRZYBWQNdO5nSKUWhUz5vVIFoFoHMUwILQObusEsGMBDOSPSCyZPGmCLiXFeUe767TWlTfNdpuxXELYg7ltjhb/Otblr3hUxjMjPlxQbCdtaom62FLlRCZSwW0LXuBS1QXsmVUBatP5bd166xE38pZUNGkihE66IOjjlmRQxBiUJLrOOKf5wQ/TXa++GWk/FczcKZLxhWEGIslCHluLa7h1mMU6twvYGQAoXWEM3SZAS3dQFR5OanaUyrOJtIsmtNQh+yXSSLQObpgAscMQyQnqF4WfgPkpT1uOcQDzTbomt0mJiwClm/qDp3dW1isqrG1F8TAsjz8WQSG6Pm/th9FlfcUONNeeC68X/7EBN/LmNlX3xBbAbZbX3WKrnLl52p1uDu2O2Xj0mhBz62d3WqOFO6dc035stGbWoDivZ8LQBt78W+rzXD3ELYAsgwllgHgpaQ5SC59Rs1vlsfzZ+XsjBQbeMAuJ0XQh0b3LiuaEu4kBvV6Q7hupB10xgLUVkZQTDLgTyDXsyAixwCgK6dUjA2tsy1khFCaeCWnSr+nAe/qWXYXntgvm4OKinDTUCJxQ0WuePyDCRceGuzfwshtn2JfZfvWKymb5WMWU21pu97TCj51j8H11oonPeIOS9gIaQ+S6HPn/85sH8rCVQ1UNjSPKYoNIT5HRLDz9kh2Tcsg2GuERaADAPs/Z94Urbv9uDxY2MuY3ebkGRsVCgRxBeGvcxhqpgyQa+GnCsumwaidXU2xzNzbDNHXlbQF2vjhnNr17WioROk/kSuW9Zfi+/uDTAQu4O4skAMXUgEWpomOLduAmuNzeutWWttjmmaocijhGDU9S/pBBzXLU2J0kDpGOEJwVGrH/XZSOkIEgp98A9r3buibiA2DSQAraQpQl7R7vzknsDuGsa+jDHMUwYLQOZusIdratcG8slWwfEFjG93Y6eIOZMe1r64iMXnUQLGEWXNIoMWgNAZsvkMom6gL8JTR8efkpQQYNi5ImKZ254Uz+gdi0kMHeO6efWIcKfmHYk/HBxrxWSIA9zfg7FTjOw2NlC0iUhaOF8Q2lJJqv38Up/JpM/4mGW+dyzHATI3HxaAzO1nn7ikRJLFXqzGWQjSfSa7fT03qfSEDsIu4G76KTFSoVg8O39bD3DzTA6Vm/3F4yWkUsAbzhhuNwtqjpDFz+9+4a7HtWi17lRXGPesiRG3c4/Y/pDr1LfaUeNQcY7Wsmb/7rlRiWu210sJwpSkmbFOJt0ydPQ90mWUIxJPOgUrAlM+K1JAzOddFxo1M/X/mqMccpFBlgWK1cYIu7Nzs0bns+p+Zknxd92xwQxzibAAZBgK5z/+VMvfXoWfQySIyqkP3V5hYx8qucPHzwJ2j81MBqYqBFT7v4vOZViEu5bGWNmV0Dpif4dw57MkupQH48S2j1mB3LjClKLYPlSyCbUmdx37WPu88d330GThN/YZmPAZEfMZ9GIGtcihljmaeYbNgwyyySBLjfyNBURZTVvfvrD1j3kKYAHI3G52sf7t8a1fR9xMQfwYvtSSGfaYHdzMvQe2LxrGxF/o2DbjUmcZVI7OAqiloC2HbrFldzzqNROib+GjLFKOiNp2DIm4FFPeGyFX9JR7FBuT2herYxjaZmvehZJmKGulb8ENrHuQHOSKQD/hI3D+QSyDRPs4IQQwnwGzAmpRoF5kqI8yrJ+VEAqQFbD88xmyi6Ln5qW+rPVqA7oxtn5yFsf9MbcEFoDM7cSWfxgruts7Z5rw8x8grqUwaDUMxe25uH+HCuOOiT6v5dvg6JAliLAU9dzGVI08V9DUDeSmwuJRAy0EhNbIH68h1pt+dmvMRemPaY+VAUuiL27cc7Xqu3+BYaat2UivI9Vi5nZFmdJZZcz1PhJrOdyl+/e2Cw0grtcXm77LmcB19+7MlC84/vH+Z07rtr6fsUg3S4nqWKB8ICArINtoNPO2JVwr5kJfznqfWRZ7zB2ABSBz+0kRgU9TrM9Ui58rToCtmPJdhgHxZ/8NJpC449Q1UFbIzxtoaQQgqto8pLv1B6xcMTEUqgnodwdRql/A2BF/2+xlAaGwzbQNWdT8ed153Htp15T8RWOH4/wyNCGmCOvQ+SNMbTXYtzZPtIzH/rY0TScmm5lEvRBo5u0plQAyMXrPD9oajt2/zFMCC0CG2VH8ue5eyqWU9FBxuxuMrWeS1W/rVtYSfQtgKDPUe/jH3Ha+Ra3rmbteA1pj9loBXWTQuYSoTfmXXjJK77poEdaVcPHEKtmybWTdvYSQLAPQ9EWge/2R2nmx9Q72UeP4FkJfhIfG9bpy9ISea+32j6HWZ9dGJMNQdSIpsef3Cab2jxIKffDorYf4TOn1BjheQmcS5YlAeV+gPtEQSpg3Plm9ZSRe13cBp1oEWfwxTxEsABlmamIGMIgnsr+7D5XROMB9rA4TY/900wwzOSnL28A6R8zj1ILrnQdAV7URm1VtthUCusi7+oCdMPAflIQlMmRpCmbzWiucX6NvIHT11jLo3gP32imLkSvCQsWWqdp8IWviWKawcwxVvLsjJDrGEksIiy+ZIR75u5vHvY9uDGZozRMZjOG3PhQCOhdQBaBmgCo0mrmxBOq8Ff+eeEsu+ZJamobFH/OU8RT5vRjmEkmM9RFSHKa238R5e3gPLte1GeyhCvMQ1U2ztTyF6AnCiGixoskrgaKr2lj90CaA5NnQgjXGlPIsVBxbyCrYPtgHsXIU7vW1ZWVcq5crPrzJtudT+8fQxOubVGJo3NXZO27sfeDiv87+Nv/33nSJ1x/qstMJVcJaDph7IyVUJqClsXjrDNCZhsrb96AQvS9qIQ7++WaYGwxbAJnbzZSH746WwFDmr/ugEVKg69phSbEAuhbFSDbl1l3qnUu5zELFhq0FzXH9aa1NTJ2bses+7CnXox0nE9BFBjXLISunDRxVcHlw3Z5QCxGrYUjFfrZJIYNECddq6I3ZCTA389S+LvaG+5Y/XwyNxTlGrtEkOhBWRvfe+6+nvS56wOBc7pzB1oA+IyIyagF036OhLzDUdscyLWYzqCIHpEC+asWfkMgqIFsDslamU4ib5dtCbWOYuwILQIYB9koC2eshEostojIe7XZXjABbYeFn6Y5ZM0ZKwAzGp3CFSJZBZBI6MyLQdAMZzy7t5phikbLjhertuSVQpOxZQgfrpsZ2CRQJFkIPBQ4Vqzgm/KjSLVNIOT8lE7ldTy9cYMes3yTXr5/xPnaM+zlV2iR42JaCjUZxoU2AqRYQjUZWAnLTQLRfQPzPqv+5DcYGpriB/aQdhrnhsABkGE/8xboDhB4Qqdt6uJaNfZvKtyInJfYqKOpiSQOhbNjtoOafLAPyHMgz6MwRYGP14qi1pFjRrGj0xZy/XqWGvYbd40IJFNLrR+zd48E6BsMnJEjE7kuouLOd0wp+994QsX1Jr/VUiGzx9HND1kmv5l4KmTlW1grzN2oUZwL1mYTQgKw0sicbwCkEPfaFjS2CzF2BBSDDTGSnYs/mRHQt3CgmdghxEwNsJi7d3WMkYcRP0IBnATI77Y6h2HHnLHKIooAqMkAB2bqGWJUQ6xKasnZZgeMXeXb32c1eHOGoe5hanz+Hf5yT0dwT0876TC08AFL1xeFguGESSzDxZEwEuti1UHP7wnmskHTMsjtBJJLXGcN/P7pfhFLRCloL496tashNAyhAZgKyVG0haAWx2kBX/U4goy3g9mXMDc8wNwBOAmEYD5vZe9AHg5B7uZkHw0194JLJIdskgOj5McvgYLvpBayzzPzv0mjjfqtr2GK9k4gUJB4QyrIVfUskSeSBTc3VlSSZkEgRvMc2ZjI1icPlEJa8kXmjXWPc7WM1BmPsk3jhJonY4RoNWTaQm9oIZqqA+VWw6+vKMFcAWwCZu81lFYAmmtZ3D6iYm4t6EAY6ILgZqVoTsWiBcSjLlBuvNrD+AVuh4SaEEBY4MZ9BHy3QHBcQjYZc19DnK2Czodfku3ezrG+5orJ5x2rw+aVh/N9D8W0xa53FdXv2Dw6OMUimcCyN1qqZLIupeEr/tXLFrG8FdC24bueSKe5cSui5YQUjojT45SUlIaQ3kPvaCqh5jvKZAqow8X+z0xpibQqTo67jY6UQ+r+Cu4UwTyksABnGZ5c4JJdYIWelt/F+qVnHhyhLEUgocR/GY1mfWmuT0OHX2HOPzSR0lqFZZJC1mSPLM+hK9EWYixDhMjGRYsNBUmrVtWOSEsNx/2pXVIbW4AomKxodkTUQVCk18lItpamdPeyaXFHrrtOpvUgvZ6T7hyO6YwJy0uuYSpZBz3LURxmqYwmVAfka0EKY7OlAAWmGueuwAGTuLpdh/Rsbc1cR6E8z5UEqhyKPbPE2JQs3OJcEcgk1l9A5AAFgVgBlSR8fKuMSKp6cMn/MQthNm5DdnDIHZW20f/ukbttH/LnJMJT10i2vA5iC3sQ962UwUzGkvnW1WzqR1bvLF5gpgk2YckPNUqKeC+gMyNqQP6E1dBMWpZPCPKjPamqBaI4FZG4gLACZuwlVfsVuGxFkg+SPFNEXW4M9RnmWCqfbwSBBYyIxl6+bRBIr/dGNESh7AqWgMwm1yLF6LofQGrLKUHzpGLJR0OcX7mDb36l5rUXQiVEkr9u1zDkWPqpzxpS4yWByhx3bum5di5+9LvdapgjqURf+jgkvFkoA23GtVdSZp3uPUO/fhKxntyZl0r2fKsYAAG0JmKpBtlIoCgEtBfKVgrT9pw9duP1p6hvOMBH4nczcTSg3b8K3eTLz1z1v13igPV1UVF9W261iJ7fbrpaxLDMisACawrTmMtu9JAFrFXGF1iESGtqxppclSZybGjv1XrmJEr5VKLbeXd2mfrmcKec5bvLR9w81dqhcTnScXa/TSbIQphuIyo0Q1CIhlvCy4QQQ5obCFkDmdqJ1a86IHaO22bm7ij93LIqpnT8IC0Oq9Y8SgTFinUWScVypQgjoRYFmkaGZAbIBdNM+hC1U4gJpDVKD3zVV3oSqT9ieo/15QtgYuJg71rNSarltDaep+EDXRdxaBcl+vlT9QZuc4ZNSjNt3RQfO6SyqsYQOa4XVetv7mbpHoXHQT1CK9jPu1jsi2MiC6RI6l9C5QD03Xzq0BLJ1hqwIW7Qns4/lT4gJmT4MczWwBZA5CL/5m7+Jb/mWb8Err7wCIQR+5Vd+pbdfa40Pf/jDeOWVV7BcLvGe97wHf/AHf9A7ZrPZ4P3vfz9eeOEFHB8f41u/9Vvxuc99brcFTf3WvUOZlmC5GPchZd26qVYIu4aA2LQPU/fH3Ze89pSsYXJ9ov/TWovEYgG8+DzWL5/g4mGBZi4ABRTnCvJ8Db3eDIWCP5bLrmVF/LE8y2J332xPZDuXn9jiQ2wL3sOmIYVd8Hh/bpsJTd0D6l65mc9UHKJ1WWfZQIyS+NZJu55ut/fes8LT/+mWLNLEX3cP0j+7QgjoWYFmWaC8l6F8ILB5RmD9nMT6uQybFxYQx0uIxZw+v+39yz2AmbsIC0DmIJyfn+Mrv/Ir8bGPfYzc/2M/9mP48R//cXzsYx/D7/zO7+Cll17CN37jN+LJkyfdMR/4wAfwy7/8y/jFX/xF/NZv/RbOzs7wzd/8zWiuq4ZXBKpDiLchdnJ8vxP7l7weJ55vb6seuSZHjHjiRGQSYj6DeuYYmwcZynsmEF82QH6hIDZlvwyHK7jccS/TVaaIos1+fTv/Gu2aQqJrzKpEWfd8y6bjuky6fqq2oSVlDP/6UucLYGMgY19MdmKKGMskVCFRLwTqJdAsgPoYqI4FqmMJPZ+Z7jR2zZEvY5NFIMcDMk8x7AJmDsL73vc+vO997yP3aa3xkz/5k/jhH/5hfPu3fzsA4Gd+5mfw8OFD/MIv/AK+93u/F48fP8ZP//RP4+d+7ufwDd/wDQCAn//5n8eb3/xm/Pqv/zq+6Zu+6fCLnvCft/9g8NtJBWMDqTkS6/8FszC9RJFQoeKdS24QiQ/+79a1J4SAeHAfzQv38eStx7h4KFEvgdljjcUbDZZ/dgZ9fgFdVoOSI+RcvrChkjzsdvf40LbYdXnxbTq2Lvc0t2RN67JMKhdD4VvsPDd+b03+tVI1DN2uKk6dv951UpnCIaHndw1Rqr82paF936bsr50qNTSaBCIFkXkcyOSVEqoQaGYwsacLc5wsBWQtoU7myNZbC+CYyNurt3cIzgJmbiD89YW5dD7zmc/g1VdfxXvf+95u23w+x9d+7dfiU5/6FADg05/+NKqq6h3zyiuv4O1vf3t3zMHZIWHj4A8G/0EXchknuJLdpI+dEz8Idyk1j/uvns+MC+5EopkBEEC+aq1/q7J7+HUxZ1aAxMpjUFbBFKsVFY/mu1L3eRj7glF5QirlvrvuczguUsLNOtq5JMSUDirumtzzI69Rr3jzRJKsg6H3OfHlSTSN6flbArJuhd9GICuBrNQQVQM06Z/15M+4b8lnayDzlMEWQObSefXVVwEADx8+7G1/+PAh/u///b/dMbPZDM8+++zgGHs+xWazwcbpMnF6enqoZQPYWgvch0JyL+DYAyHlwRl7EPkWyYgg2KWEDGkN61kCt8H6elGgPilQ3TMB+EIDxUoju6ghNmWvj25gMvOv22XEny/lfMfapVPObYs0d/dkzKVLWK96cXChe+uXuLF/N00r/FpBrdo8gTFLpzsuZSn1aZNWujWEsph9Cy2VkEKVf/E7eHiMWaN75WZC73l3u5O0pbWGqBtkmwb5WkNuzDyyAvJzjeJCQ2zqfggCVQJqKlPP5SQQ5gbCApC5MqhaYGOCZOyYj370o/iRH/mRg6yPnH8fi1/oIeEKQ8rtpTTtPg6JRqW7mmudJcmt7ZbSmmtqwoVyMjulhM6AZg6oOQDt1IBrFGzP3J4oGxNL5JyEyzNinRq831wR5Gb0BtYwyJImMpMH6/HOd9czOLcTnq2Qtskjfl1BZ8zud+su9u+Xvy4rDENxj/65VDKJHcOJ9xtAvDdTvmxEj4l99hwRKMoK8qLC7LQwJWAygWKlMHujxuyNDfDGKdTFyrme8OfB/7yT7uDQ+dwSjnnKYJs1c+m89NJLADCw5L322mudVfCll15CWZZ49OhR8BiKD33oQ3j8+HH389nPfna3RV71f96pJWEOASGqem7clEB/72/tiQEt2vprmYbOWkGaIvKIbFdSFMTiEi8B9/4M1kOJqYSxKIJJE6mW0Ng9nlqE+gDXM4XoOCkWctHGNioN0TTINgrFucL8SYP5IyP+5OMLYLUGqmp0OEr8McxthgUgc+m89a1vxUsvvYRPfOIT3bayLPHJT34S7373uwEA73znO1EURe+Yz3/+8/j93//97hiK+XyO+/fv934mQWTkpvzHn+T+HYsJCsX5+esD6AeiPV56cX+UpcxHSpPMYF18CZZYssxHby5AZ4DKYQrxZmZcbcWFJ2pElqXFLMZau7klTqzocbqHkAIrJkAH8YNUck9ALHnXSRaM9uMR7Xqp0jEhKOtjqigLZDPrpumXxgnN41k3zXEaKTGqwNai333xCCafOC5lP07WWv4yCRQFkEmIxoQcLF7b4Oj/rTD/7BvIPv868IUvQa3W0K4LOIHg59v/UsZxf8xTDLuAmYNwdnaGP/7jP+7+/sxnPoPf+73fw3PPPYcv//Ivxwc+8AF85CMfwdve9ja87W1vw0c+8hEcHR3hu77ruwAADx48wPd8z/fgB37gB/D888/jueeeww/+4A/iHe94R5cVfKk4/5GnuH210uGHhPtQSCwyPTqOj5MZTIon+2Al3XXeuH5GqDfmwA3vxbSJqkFWKuTnxhIoNFCcN8jWtbG8WJHjuab7SyDi6rw1heYniVgMXdduv3VZPwnDWpe6c/x5XSE4IsAG7mTfQjclqYOq9defrH+cGx/ox/d5a+yNGUqccbKBg63isL2HlAU5KPjtWLHMd/u5KAqI+Qz6aAG1yKFnEnLTQFQNxMUaer022eeHTPY6RPwgw9wQWAAyB+F3f/d38XVf93Xd3x/84AcBAN/93d+Nj3/84/ihH/ohrFYrfN/3fR8ePXqEd73rXfi1X/s13Lt3rzvnJ37iJ5DnOb7jO74Dq9UKX//1X4+Pf/zjyNyyGzcIMhkkVPaFaj0X6xISOm5sTbuWf3GsVsITEGMxhLBB+CsNwApABbmqopaXoNszJvAC+0a7mgSEpxlzm4Thrqt/L8R2P5VQEcmWpv42wkn2r3mCyzZ4ve41hpJvPOvtTu8ZXwQGOEhNStca6Pwu8hyiKNDMC6h5DpVJSFUDtYLelNBlBV1Ns/wlw5Y/5hYg9KVUjWWY6+H09BQPHjzAe8S3IRdF+okJ/6FTGcH+PnI8qlSEjNSgs4d69dYGSMeV1sbj+eIt6j719vWsYpSVKVCwWb7peTTP38P5l59AFQKi0Tj5kzNkj55AfeFLw8QGYozeuny8+Xv3pWmG1q1Q6ZiACHRFnjnOKcESSgjxxyPus3GxB1z3iAjgUCKIf01+trD7ulGve6j2n39fYvGWVA3Bbngvhs5PgvGtensi79+HODlC9fIzULMMKheYfWkNeb4GvvA69GYDXZaj48Ri/0a9AYmWwFpX+A39K3j8+PH0MBWGuSTYAsgwiUzKCE54MMQsL6MWGTk9bm6s5ElPPMbq7zlCSzhtwrJSQVYCstaQ6xKoajqr1Bsjdf1WqAyEU1JdOSJmsV1TJ3ynevVsyZkUN3uAgUiKZeGmjBsS/Pt8z49ZT6dwWUkVGhCNhhSttXqCJdWGciSXdmKYWwQLQIaxLto93TpJWYR2ntYa4tfoC4rCUGzURNdhzNLYjUdZAH3B1jQmAL/IoeeFEUO1Rl42kOsG4mwFvV73z9kuwru2yH2PZcdSxDKI3bFsaRRHXGnPwugXZu5iHm1IghQmE1UPS8lshfYBrV5UDKK9Fhd3+5g49YUxVW7GJzRuAuHSOI6b199O1Qe0x2kNWdbQjQQqAVHWEI2CDsUlBr7Ehbp/9LZT/0eELP29SdMzrBnmKmEByDB7BnRboZfUQooSma4Y9Gv6ucdQ+IkbMZckYa0auHvbvwcPanct1mI2KyCWS9QPlmiWOXQmIDcNsvMN9GplLIAhetemenMC2AqqMUtoTPykEInTC6G1Nhmo3msZcul3lkUrStwi2r4wiNQ3JCFeF/LvkBXPf7/4QtDLrB4IRHf84BKH9zQqBN0vO+6/wNbynWUQswK6yKGLzAg+IaDbzHKoVth7Am1KGMfgmF2/IAoBkyK/2+kMc1mwAGSYBKgHx97uIvcB4/8e6PHrEip4vDOTEwEyoMihigwqMw84WTYQ6wqo620HECoLdR9iVs+YdSqULe1YOPfpoTw41yZZdMk0ds7hMeRaYzGH9phdSH0tdrH0HcLiGfqy0wlop4RRbsTfwOJny+r4YpLAz+g/eLtHhrmhsABkGI/YwyClkfxBuIKHUC8eUMp+cH/r5hRCBA0XIs+gZwV0bjJkiycVstfPoE+fmPIbLpR1yklgsZ1CutZomrKUEnF8vlvX/TdU288fw57jCbbgXF0XDtVPGHHPdd20baLKQAj646ZY89ykl5SMXx/fShxyx08Rl724ykuMobPiUghg1oYfZBl00a65MQWhUdVQTnvIDrd7yGWsc5+STwxzDbAAZJjLwn8g3IDSEcGkBc/F1ztGa+NyyyTEYmHEoRDQx0uoo7mp/ae0qftXVv2+qyn49fcO8QylsncpN+Z2YrQnxOMMHdelUN59CrVrs9hxs2wrGmPWytA+16LqjusSS9ygMowTakOS40Qgay2S770dvuxkGXSeQRdtPGajkW0aiHVpMn/Ziscwo7AAZBigJ85SXUBJxaD9BJMEl1Q7+H6CcWorMItbsNlBCAHkOfS9Y2BWQOcSepZDzVpLYa0g1jV0RdRei4iZgUCjsnztPnqAoBUr3moscG8nxBD2SsWMZck695S8vtTyPT4px7lWPyqj2BWqjjXT0o9njMf6JVcVm9IKkeidLaSEzjMgb1/rRkOsKmC9AXzrX0pG/q6C8QDJYwxzXbAAZO4Wl+ymIUWhV/uv657gBbf3UOkPFfIBbUVJrNxLCMpSNp8Dz97H5sueQbOU0FJAVhqyVJCVQrZpIC/alluxZARvbu0LjqmidUT8Dca3c7iJLl4coDtW93q5Ismz9HVuay+xw2YLR6/RG3P7/gisyb8OdxxfkIaSgnyx6r5PXLc8hmLVz1ofjEe4opNFof+ZoD6rsl1fnkPNCzQL8wiTVQN5dgF9fjF0/xLjHCTOz47Jbl/mKYW/ujB3jx2+sWulBz+XSsiyGIldOlgyiMWWPclzk3E5K6BmEs1MQuVGcMhKIVvXxvpSVumxaPsQSoQIFXnepYZd7Bw/89phn8Qce06vV+5UxmL4Jo459TpidS3jFlnR/7c7UfaO6d6LixnUMkd9lKGZZ1BFG5YQKpp+aFj0MbcAtgAydwe/Ry9w8P/Ie9Y/f2wlTW6DX/NMaTp7cixQPVQ7DQgWgu7WSQmnUM235QJ6OYde5F1uhqw08vMa+eka8skKKCvoi5VJ/ojFn1H1BFMJCQhne2eNs7hZ205LQa31MGs3Il4GiSHuNRL3P9QHN9SZpNeNpLVYCQXokPvcjwOk1u9aHqnj/esLWByTXOmRY4J1LUMZw9TrJ03RcXF0BHG8RP3sMTbPz1Hek5BtxEH+eL5NWnJds4T1jwrdiH2pG9QDZJhbAAtA5naiNeD/H0/FFF1l/I5WYdcu8UDqx8cR7mJbf7A9LlbCJMkNFxBtIs87IZKvGmQbBblpkD/ZQD5ZQZ8+ASqn7IvbSSTk/k0pQeIfa0VMoFWZyLy4y24oT8Chf89IOldqe4/dZGDnGC2l2SeHySa9OSnXtLOW7bzaZBeHGLuHobqIEbT/Gk0pP+RnkEsJ0bYl7I5x7oNwXcvO+ze4HnteW/dP3DuGun+E8tk56qWEygSyUkE0GmjShNkuGcB6zDXNME8h7AJmbi/XWX1/ygPiACUpYlaa5BismNvQCpjKiD/j9i2N5W/d9lx1rXlTXI1CpJUdicTECVv0l0gYCE/rlMEZu0eh18iKHWouKpEmQO81UoHxDo21ZKa0T3NFXKiIs8fAuuqIwdi96Fun29+zzMT9nSxQn8xQ3cvQzACdGXEulN6+FlcBJ34wtwC2ADK3k5ioCImzif+pk5YEaozQfCNFc0nrX+A4Idq+vClJH744c4P5qbZpeQZd5FCzHMgEoNqs37oxSR/umG4SgZuUMFzUcD2UEAuVWfG6XGhpWrL5dfkG7dzs/bAuyNA6HXexmSOejBHc6yecEMWdraVQe9fU4Vv9/HqHLlRmr7+emOUwdL+9sQcFr63Vt01g6VkBqTX6YwyW41hUbezf8RFWL59g80yO85clRGNCEY6+oJCtalMCZofM953jeS8pjIRhrgoWgAwzgdF2byE3s5sx6FupxiyAY8WnpyQMjCU3eO5AUeQm/m9ZoD5u273Vjkv2EEkfIbETg3JVxmK4QiVKrFh13daulcobs8vQpdY+QlDg+VBxe7uW9aGw9y7B3UsSWkuodqArrgPu+1jpGQBAJqGLHPVxhvJEoD4CZAkILTDaYo1dtgxDwgKQYRJwrX1Ry9+YdXHfvsOekBm42dqHM9nz1z0O6MfWudut1arIgWIGdTRDdW+G8kEO2WjojXOeW/bFrzfXriPZwW1FiW/F8i169jjPCuXH+UUZ9HkVPUvddknbuErqvg86f5BzqXB3kRRSrmtq/cAxoe0K49h+n1CtQ2/74EsL8b7t3fcsA2YFymOJ6p5AdU8jPxOQDcKxvi6eCLRf4i6lIwjDPCWwAGSYBFKax3snpAxqHkxUVrDFb1AfsmKZndtEjBCENYZMHlHK1P67d4z1wyXWz2TYPCtRnGnMzlpLYd2Yos9WpLkWQSuo3Pgy18pGrT9WD9AXHP59GHFzu/dlINrcWn7d/FuhEhPTnUjBMMs4KqBcxkSbtcx6cXR71U+0xKyArls/Zr101hP8cuJhOql4bnrPdUyGQAjzowUAaf7V/hcar3QMZRkOfXapnt8Mc1vhSFaG2ZHeQ0Kr3ax7u1oEE5IOetP4D/AxlxtM9i9mBarjDNWxQHUCNHOYGoDmpN0TbWxiSSu+OouQH4d46KB+Iq6tbx0N1BKM0HMLSyMkg+ccyo0LDC1oU9nl/jpfGqgSN2P3ityfck9Uu1aNzuWrBUxZImpK6Qn62Jp2sQKyS5m5BbAFkGEsV9HWiXIFu5bAlAeXL/5iLcY8YscM9s0KqOM51s8IbJ4V2Dyjka0F8gv0hcNYbJ0bY0cIFTI2znUrOyVGBttG6HXWgCdAsqxvIVIagJvJTFhkqfX69QCFBKSCQBY+b4zYsfuKSCpWz7Uq+q9RoDZgV3rIOyfW+9eKfV9A+mOYtXhfsOoGstYQTWulywCdmy8keuwzMyLYJlv+OAuYuQWwAGSYHYlaDtwHRCwmye8RbHeHkhY8UkXFlI4OQgiI2Qzq3hLls3NsnjVB9xCArIB8oyE29TYDuH+y+ddNKIm1JUtbEL2Nil20ONaoXpFlCl/ouUIu0dLTu7/2nFZc9l4jKuM5dH2xv/2x7N/+vaWEtHvOVLw5p9adjL5fY2vSGqJukG80slJDOFZAYS2DVBtCpSdZ60aTvBjmFsECkGEOScwy4O8LiL8BoUzhWCcQeEH0kW0DpARmBZqTOTYPMlQnGroAoIGs1Mg2CqKshvGGKYWeqTIzVBYuYZ3qF8YOlDkhr8cRdEBYEPjHSdEzCNLnJFqCxrqjTBDoO60hlKGbQmDtu7SJ68X1Oe/bYdII8bdSkKWGrADRoBOB0IBolPlCYq+PEnGXlQ3M5WCYpxQWgMzdZYqFx31YjZWBoYSejRGkBCIh8AbFhd1jJvYD9l1yY31ZxawAnn2Ai1cWOPuyDNVzDeRKYP66xMmfVVj82QX0G6fmgRvqPOELOJsgQtTn6463IjBgyYMUEDbgy4oJ0nXoZZxCtG7Z1t3rWeV6CSBCAnnexfFpmQFaQdQ1KSrchAXhv5VsPKBqw9bca7drtiKayqT2k15igosS1hQBce1ej9utgywxFOn0EYKO/Qt8sQGGCTlaAY1CVipkG4lsIyFqINsA+UpBXrStCJtILO6Ez3qSFdAfj8vNME8ZLACZuwv1H/hNHjdAqrvYQmb9ug/xPIdezlAtJeoloKWGaCTyCyA/byDP131ry3bg8cn93rH+OX7tOxeyX3Lg3vq1BTOQ7sBkK1Yb1zepQ0dPtBPu2ZA1LnZfx+oBBoufTw8VmGrhSxlzL7SGrJSxQq9MOEK20cguamORPmSCzehaqDqILP6YpwsWgAyzI6Gm8iMn9V1G9ndf3JBZvttjqED7pGzV2Dk2Zi7PUZ/MUR0D9ZGGUALZBpi/oVG8sYZ4cm7cv1TXjxh+MkjIGuiIQFfc+oWZ7b7BvfBEktYawi0DQ4mhzp3eWpCUNBmmdj0WOVxDb0wpoBuny4VbTsZdF3WvQtbUMSIWvd5+31oaKsvj4h7jvMZkJxCPkDXa7QM8XCsR1mDvedNArhsU5xnmj6Wx/q018jfWEOcrqJj1byLJFn+GeYphAcgwwKiVbqwOIFVYthOIqa3nQi4xQihqrbu6cwIYFV/RwtB+/F1RoLo/Q3UiUB8p6JmCKiRUBui8dZH6rdJ8fLHjW/5iKGXGjxR3dmMBB8cQljb/mKhYppJbIsTuLVUqxTt4uz3Ups1nrKuGiy84qfdJTIwD4/GDhOAOQQp2gI75c5JzdF0D6zXyxyuIRgF6gWyjkK1ryDeeQF+szLHUl6tusuH6d/oSNwV7fYcuZ8QwB4AFIMPcopIOpHvXMtZ2TkpT+6/IUS8l1AzQMw1kGlrashsSyAL3aywWbMxd69cA9LN7B/15CYEZc6v6pWCoIsFqG9M3OHZXxgpCu/smlLjZm1TXrG9N3dGlmxSmMCjLs12DrmrI8xUyrTEHIDc1RFlDr1bQZdkfh/pMB7qBDNZ52aKQYW4ILACZu433oEgNAA+5iNyHh1tbjHygBOrMdfvs9pBADQiLoEvYLVBMWeiUgjhaonlwjM0DifpYQy8aiFxBzbQRhfMMelb4E9LrC1mT3ESPWMcPv/6few8bX7hFLKBW+IWEq7/Epunm1llm7pkrHqwQ1cokHfTWofvlX6jr9NywZFu5WIILdV/dTGm7firOMmQl3KeckHfvqXZ6sb/Nupz3OyHMdWOSQNQXvgQIgazIoRsF3bTdaHyLX+yzFcF+loP/B0x1MXf3QmC0ZzHDXDEsABnGYZd4n7EispdqTUgIfJ9UgHg+g1rkqBdAMwPETKGY1ygXOZqFhMrk0DLnrsW3ZlH4RYdj7snecROuw6uxN2x117f02WOC90o7CSAyMZvUj/sLHNNflxoK324NnnimlkkV6A5l6u5jaaTiLAOvd3KSEhX356GbZjuezUa2oswPlehOol2/lwZnAzNPCSwAGSaAL9xClr6DE3s4RWqn+Rmck4rutiJLFznUPIOaCaiZQpYrzOcVytnMuIRl4HwfP8aQWGcnjmKlSRxxOIj1C8X+BWsQEpYox/3c/U2VXHFKx3SlaFJFBBVjN5bNG8IvHp0q7MZqNMbK8/gQpWPsv6kiMKkeZTefc7+VhoYGGkf09RZDxP7BPyTs+j0YvgjkGEDmBsICkLl77Fi49SCN4m09uhC+5WLseAfdqO7YZKufL0QyadpqaUA0QFNLrC7mEOcZijMgXzWm5Ebvmry4NT/L11+nW4swy4zL0o3v8xMi7L8289g9joIQIcNCwyMJDc6c2ndpEm5YV9C49QAH7l1vbtJd74ox97744pQSlm6BbP/+pQpOql6he669fut29uouUtdFvR9H36O+FZByE4daK0Y4SLePsf9DBqWg2AXM3DxYADLMRKgHyMFdSlNdSLvMH7D2CG1abclSoF5nqFWO2alEcaohLyqgrIaWqOjaCBdcr8AzIWRSBGwsc3UqIfe11rRFkMC1hJkh0i3EZLu4bimtiEoejWCq+OsvIJ5k47x+7rV3vYJTBPnoui5HPU224rN7l7lFsABk7h6J/4G7os5/UISsCMn9gVPWNyU7ORAzFT7eqwfnuk81IBqN4lxAIMfsDYHFFzTu/98S2eun0OfnzlpbcWRFwlhnEEdIkCVeYqVNKPFhO4ykdKRIqVMIQGTZVqD47e6cOVy3Zq8tXntMNGPWF1Sua9iO5VoVXasqFRs45v4OxV16UG7ase4h7vFjWcJTi5ZPfl93E41b+VM/0z1Sun+wQGSeElgAMkwCVMyf+8BIsiRYl24k2zElhokk8SHZPaA9AWC36yyDzgR0JiArIH8icPI5heUXGyw++xj67By6rLZCIlS2ZEqNuu0iguvuEircc4njBxmorjDz3KcDMUK4MttBe9m62+ECrlVHpNEZr0Tmrz+fNxa1ziBUvJ/dZl3KEYuecK2fsfmI9xA1TtQiOlaaKIT/fneF2I4xgJNgkcfcAm5PATSGuQZ26gRiSX0QTW0/1hLq+eu6Ku1PhwRULqAyEwOYbYDFowbzL62BN06h15u+qBoj1posMS7PXgN5Pc45U2r2+WMGy5v0Yg7F9mdkzSlz98ryxIhlSVNZv/3J6NfATyLxayRSaB0UjlPc3n6XlB5toscg9q+bSG1/BgPLSV+euMMHc9dhCyBzdxl5WPhJH6HMXyFFf6yYdYCa0z2eqmXmnu4H1ff6zYYzMEPbey47KaGLDDoXUHMACshKYPZGBfn4wnRbcDqQDIRF6OEfs0i5BKyE2tsnhDDb2iSSEKQlz45l75VrOdKtq9NzwXYWO5uQozSAJlyTz1kntZ6te1kPxOcgFnCsRA51HwOZwr33Ti/xJ9uKO+qaEgS/8AWlu68t1zLsikK9B0YE5NQELsJF61ruJ2fzcwwgc4tgCyDDjHAtXQFS5wyIv94D2f/xj+tle8JkG2hAKGMFFFUDUTd90bBjN4ggY3FhSo3GoVFZqKMlYaiHeSiezq0FOEK0HZwLZVUc62rSW5PuW1Pdc7xt5DoGxcIDjwTivXNl2C857j2aGk/LMMwAFoDM3YUK3h55YPTcRodoIUfNNyL+JpV4cQlZaez2LvbLCD9ZA7LWpveqW4LEz5DdFV+8TCXYWk73LXljIrBzO9Jxhb3jmwbQiox3o9zJA6ufI2Q7q2KWba2L1LWl3CdXINvf7fX4Fr/tosPjua+znzDkExOG7rWS+/UkUS0y/z7FXNbj4o+K6506BsM8rbALmGEsib1CnQP2F4ETz/fLbNjf/X2j2bNA362a58CsQH1/hupEQueAKAFZGgsgas/laQXCWAyaPXZ4If21hUSEX1+w+9VL4GhFTm8Fdn2uYPVj+rrTPReol4jhdvQQ8GrztUkapNVvrJAyJWAoK17o79Ac7jxdzJ1zf8ZEd6j0DTWXXyNwMBSRWWy3p9TEpIqfW1c81fFjgpvWz/QffOZTQzsY5imELYAMsw9uG6rU4xMsjS6+9SSaFOGjhtYqbzAT07dcQJwcozrOUS8klE0WbQDRRKxPqa7gXd2Hu7iaA6Jxu0mEhQdVymYwPJ28ktbVIpJssUtSgl+0OcVVu4/V1ueyXMNT78UupZMGQ3BSCHO3YAsgw7hEHiDBWMBdLYGhh5Zn+UjNsIxlyEbrsy0XwPPPoHzTMc5ezlHdE9A5AGVcwD0XorWQuYkGIQubxS+wbI9zs0qHFzM817Mudckg9ni/nmErbnvWOJsAYu95KMHBP4dKjiCSNLSUEJnsuoFo/3h42crt/Nq62f1r9hNS3LVRuPeVGo/a5lhyba/dwTHeNXTnufdmJIu7d36wtEw4UcQkrxDXTn3+duz2sz3dmZeFIXNLYQHI3F5SOjiEykn0/qTH6GUQpmT+7uFCipVzoYrripjlz4/puneM8sUTnP2FGVYvCjRLjWYGZBuBqhJoTubIV/PhuWYR5PhJBX/9enMUvvBq3bHd+FRtQKoO36DmYF8cB9dGZbWGrq0TN3qYaetO3dViJDJjXVKSQFzhFYyzc+6hPX4kgzlI5EvGQRhzCVNfmkJfvhKKQMf6Ag8+975r+QCfa4a5TtgFzNxOxrJAE6HEn1a6V0Yi2UIQsxJG3MIh8bdXn1U7diahj+YoH+RYvSBRPqtRPtBojhTqJVAdCTRHBfSsSBqvY6zGHdV2LZUxtzZxfP/vbfJHNFbPri3kRnZxrqcTf37ihmeR7Y7zM5zdBI7YmlyRuourPFYE2p/L/j4CWe5lZM6dmJA8Fft8CimiX/AiJ0aXxzBPA2wBZG43By5XMskdNPaQCDWyT3i4uHGAgGvpCgfWB7uAzHLUS4n6GKjvNxCLBrJQ2MgZVCFRPsiRP5mbWMGU5IG2gPJAXLnnuu3b3ILLXZB/JIklZF2LWDyFEKaAtU3oaDyLqXUVEy3rfEvZoLtIbI1eoWVqbR2+6HOTbFyXO3Wdvks+VSBTrejsdvez44pN6vUMdJaxv2+HGb7/QgwSRkL1Mrs1EAkcWtECLzFRhN2/zG2Gv8Ywt5cDiD/3ARB6GEwuJBvrWDCp/y8RgB8pHh07TwvzA6khcoW8qKEzDU0tZ+wBHrMC+dYrf6xdX7OQKBpJ0Bgk0gR69Ap/vbGyLIdMsrgM3Nefeg+F6iC6+8a2jTDJgnsZ7Oq29T+f7P5lnmLYAsgwloD4GnMhDc4NZQbHxF1kn9+tg+qz68f8jcapuaVitIawh+YaWd6gKBpsBCAUICsNUfeTHcgYMkrM9YpMq84a110blYThlWCxa0x2RY6ta3B8a01V1tLUzqXUtuuJS0y8+BZBr3RN8DQq2YP6OwZlUY0lgPhlYmKWxRC+O54o3dIfzrO8etfXJXpMsdrFMoATy7iQJWDCB6cdxzA3HBaADJMIFTjeBYunWAKoB5UvFt197jz2We08cAWRFRtcu7+vcxlKqHkOlcP4A2qBusyhlET2JMPsVKB4UkOsymHShRm4Px6wjUmjxIefxOGvKVYzcBerkTummwBhd7fu4N79CSXcUJ1QqDqIAddvVPxQ98p161LC3xd0Wvezmv3YQNeFT7mXx8b3txPX4lqMQ3UqXcjsdKogtv9FYSxUwncXH9JSx1Y/5pbAX2UYZiK+y3dynNAOtQCtGCTbnU3BFzF5DjXLoDJhXMBKQFcSzTpHVgJyA8hNbVrB+YyVfKHm9NeeUujYHYdKmEjB76zhnT+avDC2Pl8AA0NLJrV+HOB1JMYcMJZ0Ezs/svYrYd8598m+jySJMMzTDlsAmdtLShmYKcM5pSHGykiEziMZcylZawdRIy3kHh7NxJQS4ugIeHCC8pkc9RGgcw1RCqDKINcSxROB4kJDVGqYFNCtjbAe2eNcq40vhlLcqO41udbHlPIx1DW7v7tjuZaukU4WneXMn9t3iVMxg4RbNmolm5LM4Vr7fEtkyPobsvAlQnWeEcT7j3QRA8Maf3a/W//SvodC9f9StiUQ+xxzIghzW2EByDCWhAxcv3VUinVgL/HnH2NbYFGkiD/AiL88hzheQp0sUR1JNHMBVWjopQKEhlICQol+KzifmJDwRUgsKcT9fey4sULCY1DrCiWkwBUmkcSI2FrdWMBIF5Bo0gyFX5x67IuOuw5KvI4wWoycCEdItWz2xJ/9N/Q+59ZsDHMwWAAytxs35inp+LQyLAeBmicm8KyQ9I6Z5EKU0sSCFTn00QL1yQzVUqCZAyoH5LKGEEBTSUBJZKWGKCsiBisQN0YlcFBY8eILOiJpQjtix7Zx0+4Y9lybsEGVcwnF0FHiNFTsmDrOL1sT66Jhr4l6jacWfbZ/U/OFupuMjXeZ+Mkhg+sP/D2lxZsnCO2XtViiVlKC13BHcE6GeZpgAcgwPiMPnZ1jglKC0SOtsAbbiWzJlN7AIs+BPIee5dCFhKwBWQFZCZSVNFVaaoF8DczOFMSTc+jVirZ+hTqDUC5XXzBFkiCo0jXbUiz+PXKSIELrct3OoUSW0HX5mbXu9pAw9rN6XbdvQyTvONnGumkGCSCkRc1NtKFEH2WRpa4hQQSOvq/sWPY1psZsi4ObZCL3i0IorpL4HPbOk/0vbO7xofp/CURbPjLMLYKTQJjbz4GLQR+MsQcK9SBSevvj/j1l2l7mKiAb3SZ8CIjzHDjPkZ1LZCuNbNVAV9VAtIwSiitzawBGzrVJGX4sWXKyhjufP3eowPHg1Eg3k33i5lKTQNzM6ZCoSknMoDKVXahagAlE789gjrRyOP0JImsKveftOYlW/DGRuHf833XXO2SYCGwBZBiXXd2/KX1BU4LWp8zvuatjcVe9h65SQF1DVA1kpZCvNZpzjVkmIJoMQgP5BXD0pRqzxyXguoCpeL9Q7F4sXs93306t3efObaGSH/zyKjGh4xfH9hNsUuen9vmJL/71ufUGKcEWSqhJFRikRc5xW1MlfkLnB4pl+7iJLYOuHt24I+sXsu8yp7p9UL/HGPniFRR9u5aToZKGGOYGwAKQYVwmJIIkJ3fEHhq+cKTmp4LiiTFjlrFBvTWloYWAzgU29yXKBwLlfUDlGlkpkG00srWG2FRdC7WgpYiqf+cSchP3rjEhySLWsSPEWBwitS5HOI72Cg6NOeYa9wQ0WRA7JMio7PY9M3pHM45jruKxzGlqOyWy/Pc49fmiPkvU5/WAGcJ7MSWTm2GuGHYBM4xP4oNi784BYxYL39W7nTjtPM893LlPtTZjSEBlEvVSoF4C9VJD54AWaOMClckAnoLvkvStVSnlXyi35ljpmDHGhNxY2ZyxcVMYq194CEsRkUyTzNQOKwFCBZ+TkGL7MxgkUcBxrB7DJMEWQOb2s0s9wMRs4GAnEP/8MRfxFNcv5YpyE0JimcTuGEJAS0BnRvQJAKIxP1AANPrj+GVHYqJqpLZeDzeRoY13E7Y+n3WN2i4WY9ZGL6Gie+3ten0rou9a9VrVAW0WMnVNdrxY8slYgWjKkunO49f2C9Uh9ElJ2KGOD5WWcWMQnbVOiccMHhupc2nms9c+3QU7WoMTmNYGjmFuESwAGSZEYgmK4EPmskvKhLIjU2gaiKpBtlHIL4w7WGgBoYBsYzKCpS0ATUEJm6ldOfwMV/98qkyMS2i+UP9bQsBMctGFYvxChZbdfTGRSJGSqTxWNzF1zlBdQKoP8mUQKocUOjbly1Z7TLSUizMOi0DmLsICkLndXFEGcDAu0BeRh+pLascjsiu7MhvEA81a1nTTQJQVslWF+eMZslKiXrfu3wrI1wqyaiAaZWruxZIgAi6/gSVNawjXoueseRQpgarq/+2fm1LOBaBrF05JgiASInoJOE3Tzy4mhGrvtXLd237iCiVmx+6ZK0x3dW2bxW3HihbJ1kPB5tX9CyaBpOC/l0Mi0O4bI/BZHBWB7FpmbhksAJnbTWoh6FjSxiEsedQYUx5awGiQvBVdZK01n6aBqGqIdY18raEKDaEEmqVxC6tCQOUSOs/6rlJf/ASsTL2ZWxHUuXWdws6acD0K+3eo/dwYlCUx9LcrvmwnFWrMiGixxZ2FasUNJeJcV7NSW7fyWNFm1yUbEoO+eI2NTSSLuILMFbLC7ByO4bR9G32feXNss6oTwhS6NbfHuZ+h0BepsSQRogc3W/6YuwongTCMz45ib7Tw7D4WhIkuXisEXcsLaW3LBFQuoDIBlQNaApDtv9lIzb4pXSRCImasjp0vMFNbn/nxgg7B+nVdgozunx+CiBf0Ea41zmXKvZtyLFXjcNcxD9ElxBNXSdY/qkj0rp+dyGdZK3354o8zgJkbDFsAGSZkmaO2uXX3EovI9o6b8iAbszz67bUikP1nF3PU9+ZYPW8ygZuFSQDRAlCZgJYCyAirEz0BvZ1q1TZYCGGlpYSe/Z0aJ5Rs4rtlpdy6JSGgrZWRelDb431LpCP8tm5fPbQmOhZOkWXDkjKhbGk3bpASsbG4v8R4TEqI2WvqXVvTDN87XgLOoP4k1dYt1ALOZ6xDyIQM/S45i/gMXYnVj8Ufc8NhAcgwl5SssXPLuO0A48fICd0YHLTWEHkGNctQLwWqE6A+1sjWAtkaEBoQSgNVPbTSxWoBdmsPWNhGYuOi57tQhaPdMSlRZ+PtYkkbPm59wpQ4Nr/4syeUAM/FTBWaDgnZkBAbK1ZNLpO+v/51+UJ3245PhceJib1UIThYMPElLZJZv/OXLoa5Q7AAZG43u5R/CY51RRET/kNySrzUBHRmCkGrGdAsNerj9iHfCEADolIQVb0VLH73j26gCYkGlAh0xZpvDfSFoidyBtanBBHYb4WXeF+d9ZBZy9Y97VvqHEvgoKuIE4/XS5Lwr2HCOoVbVPqmWKCoL0LuezqlL7BPIOuXYrK1L/SFcEoC10259wwTgQUgwwCXL/xSO4MAw6zK0AMsYmGMxlpJabJxpTRuXsDUApxp4FwgK4Hl6zXyRxfQj59skwwoFy3Q1e/z5+iJF18suZ1FYla5kfZt3XVS4svFFYNj2cSUGKXcvvZ8F8qy6Se82CW5bmFq3Fi5mkBZHB0T6TZpo9sthoLaO6Z3LIWfcEIkKVEZwKKtQRllSpvEgDVwJ/Fn/93ncz+lvBDDXBOcBMIwN5EDWPyCyQ5ZBjGfQc2yNtvXJH1ooY3lrwGytQKqGrquR9aZWCsQbtkQJwbOxs2Fki4CBYjJv/chJJy6TSOWZGotE0SApsSdFcixAtIp6+gtaTwZhUogIiHc27FOHpR72W9PGF54wqPqUOWV7M8+sPhjngLYAsgwMUayCLeHEQ/Lse4f24H65/h/UwSsf4NYLWd7F+Sf5xAnx2iOC9RLCTUzFkAAkI2ArIFsVUOsS+iqosXGmPiyljvP+tetzy1REhqfiouLzUtZ3/yYPDj3wrX+hWoDUvg1AX1hSyRiDFzVIKy0RLby4DjXqmrnsW51/zjfSuuUbxnM62Zppwhr4jWjrnGwfmvd9u8RJf7sZ2FHMXal5V2oGE2GueGwAGSYGJEH0E5lX1JawaU88Jz4KeoBGyzELASQ51APjnHx4gyrN0mUzyiohQZyDS1grIC1J3KsmBuJSesJ0EASRA+vK0hg0O0x9pwUQRhYJ+X6JLGiSmtTONvPPrb3I8GqRsYMxs5Rqp/t7LafoxizOFHWQ1cQRwo9T7F+hkTgIN5vEDIg6NAHv+A5VRPQ/u2u/Tpr+7H1j3lKYAHIMJfAoD0cZdXbxWXlP/jsQ1MRVhYfpYFMQGQSapZ32b/NsQJyBWTapP9CmH9CLtmrYEoMFVVGZhf8cWIWu9DaqNg9ipHYxsF8h8ITktr7NyjgKFJd8PuKsdAXramF1EdwP68DAcnJH8wthAUgc7uh3GeTxxhaAaMPC2d7tEdwrAdq6KFJnUMdqzQZZG8f7iqXqI4FygcaR286hzHYCTTZzB7YrmfawzVaM87/3d/mn+OWYLH/Wsuiu53obkG+1taillLCxcng7f3tnu9ZErtrt8kdvqt4DD/xJXQfYmsPuZXtdsf1TtXu051Bdut+HiSOOOOa6yS+cLjbAvGAg/fKlGx3N1QiUAaG+lzazyO1j/wcT6rbqYfvIYa5wbAAZO4GB47P2cvFlFLcOTxxmsVjxD0tGtdtZ/5tGglZCmSlhijrYfu3iWIwek6oZIqFEj3XGWMVskg6IrEXZylEv5wLdR2x63fH989PWWdovN6hTowhkbHbXceYKzhU5mWEYQzkxM/USFkWSgQGv5TtOAfDPM1wFjBz9whlnEbP2e0hYB9ApGCMPYgO3TbOmd/EfJkftLeiaSSaOoMsgWwDiE0N3aiwFWvHRAESV+S41r5QUsPU1y/1eDfj1v5t544RuBciZJXz1zUWH2j3UxnXVAwiNQ/FnoWjR13FSsdFnbt/TPyNjQUkZ+9eSnzglOLiDHNDYAHIHITf/M3fxLd8y7fglVdegRACv/Irv9Lb/4/+0T/qlZcQQuCrv/qre8dsNhu8//3vxwsvvIDj42N867d+Kz73uc8dZoH7/oe8R5zRQAQmxxOpnYXgoLyGW5qjaSDWNYozYP6GwMVrxyg/d4zsTxZ48BmFe3+6Ad44hV6vOxfg4GFPiQe3bEmWbcWJ/XG32eOzbLvdLQtDCZ6Q6Ax1JrE/dm7X/TxmWXPX6Yq00LluVnO3BMdVbNfgJnYMXKCSvpZQkol7H2P3IHZ9WQaRZduSLN44boyg/7MTlJALfTmyP+5++5mgPhvetp1qAFLjTvns7/LlkmGuCRaAzEE4Pz/HV37lV+JjH/tY8Ji/9bf+Fj7/+c93P7/6q7/a2/+BD3wAv/zLv4xf/MVfxG/91m/h7OwM3/zN34yGeLheKZcQZA5g/7pnqfjuvTbrM99o5BdA8TjD7JHE4nWB+aMa+eMNUFZpVr5Q/F1qlm1szCnE5vM6bQQFk2XKQ5w4tieQUq1sY+sZ2z7VVUwJercUDA6QhLJrK8SxpI+rgN2+zB2AYwCZg/C+970P73vf+6LHzOdzvPTSS+S+x48f46d/+qfxcz/3c/iGb/gGAMDP//zP481vfjN+/dd/Hd/0Td90uMXukwwCRMWZG3cUrA1orQxjIi/UjgoYDZgP9XTtaBSKswbzGaCFRHGmsXhDYfnZU4jTc6iyNELRF06h7h4+gR66UagadHZOorZedF73/JYuLs//QuEmX7jJHzEB5Atfv96g70qOFcwO4SeFUGtws3qp8WPlZty1R66VrEdI7CePmSoCvS8r+4jQUCJIMlNFICeAME8ZbAFkrozf+I3fwIsvvoiv+IqvwD/+x/8Yr732Wrfv05/+NKqqwnvf+95u2yuvvIK3v/3t+NSnPnUdyzX49flGRNvoA+cQlgX/oeq6y2IPXCkhZjOI5RLNs0eoTjLUS4FmjvZHQC0LoCC+F8bi73xx5rpf/b9j2103qW+lcwWXLyh9t7L7ExMn7rmxJA+qM0egQ8eg24lPyNLnroOaOyYe7b3z1+Xfv1CcYSBz2O/UEUoCGXUL79jKsF92h3DLHqJjxxhXMQfDXBNsAWSuhPe97334e3/v7+Etb3kLPvOZz+Bf/st/ib/5N/8mPv3pT2M+n+PVV1/FbDbDs88+2zvv4cOHePXVV4PjbjYbbDab7u/T09PwIqZYo4DL/49/ly4HO7jVeg/xWQG9nKM6KVAdCdRLATUz4q9eAGqWQxZ5XxT4QiLmrpwqfELj+fF3lOiz+6dAWQhj2bfUOimxmNItZUq3jdCaQhY9G0cZuR+9DiiHgOj7m3Ss/ZuK8aPOuSp3bGoXHoa5JbAAZK6E7/zO7+x+f/vb346v+qqvwlve8hb85//8n/Ht3/7twfPIemEOH/3oR/EjP/Ij6QuZIgL3CQYfDBVwC7siMKWw7VjNNWpuew+FgHhwH9Wb7uHsL8xw/rJAdU+jPlHIzyTUTOD41Rmy1bxX583tEqFNynCkJEhfLHZWnNg5KbiuWcqNC0Rf10GvXWsddK2WKaIskBncK6BMlHxxu4iYsisjIjAmTCkhSVkeA909hF3XmHgmOoYEGXT6SBDmgfqVA6Z8PganTqwHuI/oY7cv85TBX3GYa+Hll1/GW97yFvzRH/0RAOCll15CWZZ49OhR77jXXnsNDx8+DI7zoQ99CI8fP+5+PvvZz17Ogg9gDQjWH/MzD8csHiklMXyyDKLIoY4WqI9y1EdAfaTRHGnohYIq0PUDtg+y2AO/56Kj4tt2SeSgoDJg9x3bj89z4wwpF68/f4r1zxK6l7G4Pbs/BX/uwFqC7u8JkIWb/bCDXd6bKfifj1DGLgH1udNKH7YcDIs/5imEBSBzLXzpS1/CZz/7Wbz88ssAgHe+850oigKf+MQnumM+//nP4/d///fx7ne/OzjOfD7H/fv3ez+jXMJ/1mPFZScVnwXGH2ypDz9bdifLgGIGdTJDfZKhOhZojjTUUQO5rKEL0wdY1KrfBxh98UD1Fx6IwcA6KHEy6Jjh4yeepBIScLHC1KFyNT6xnrxKbc/bxc0bq9lnf2LxfNTfDmRtwpgITRaisdjTQHyqH7saimEl3udWwLkizt12cIEXg8Uf85TCLmDmIJydneGP//iPu78/85nP4Pd+7/fw3HPP4bnnnsOHP/xh/N2/+3fx8ssv4//8n/+Df/Ev/gVeeOEF/J2/83cAAA8ePMD3fM/34Ad+4Afw/PPP47nnnsMP/uAP4h3veEeXFXyTuZSHTSxGMMEi2RNrRQ6xmEMVGbQUyDZAfiYBJaDWEvM3JOZvaOTnNbAph+dbpES3NZJx67on/fZh7v7eHL7rtC3f0nXU8BNQqDp6/tpagdlzwboJI+7xVBFo62qOuWyVQu/Vt8e6Y2ht5qXm8K89MAe5LSZod7UiRoglgpix4tnpoyhiHKIbB/WFKpb1G3MFRz+7XA6GucWwAGQOwu/+7u/i677u67q/P/jBDwIAvvu7vxs/9VM/hf/1v/4XfvZnfxZvvPEGXn75ZXzd130dfumXfgn37t3rzvmJn/gJ5HmO7/iO78BqtcLXf/3X4+Mf/zgyt3juXSMlUUQrQMkRK0wGFDmauYTKBSAAWQNZCQglITeALAFZNRB1g0PJ2aS4vxQBEor7A8bLpYCIJU21msWsiH48qf+3EP12er4wpBgRbYPrGBsvNIdf5iYw72gbOJepVm4/bjAGt2RjmIMj9N7VPhnm5nB6eooHDx7gPfjbyEURP/gSMoLH6gCSvUjdsalA97HMRCsSvXHdh7Z84Xmo5+/j8f/vAcr7AuvnxDbuTwCzN4DlFxWe/f1TyEdPoP78C/QFEkkeo+IgVovO7rdJCTGrni+m3LHc5AzXYmi3ucc0zdbCmMmgxapzT/v/RY7Ve3O/sFBrjrhhk+4nBTX2vmMi7v739wOg76UfI7jTQtLFn2vR8z9rkyz1yR17xsesdYXfwH/E48eP08JUGOYKYAsgczfZJyN1T6KFoilC1g8r/FxR6GQEdw/+ooBezNAczVAvBeqFgMoBnQMq04AE1EygngvowmtX5gst15KFRMuQOXD7e5bRrlxqrDE3Zsz6FXL19sYfq9u4g2AZWzM1puMSHy3iTRXFjhSvDgk3d67Q62iP2akXcLc2J0t9SpYw0H+PB2IB/c+R/RK2l/hjmDsAJ4EwzAHxg9J3ImZtTMl+9DIxRSaBWQG1yNDMATUzlj8r/szvgCoAlUvozCkzYomJKGqZoVg9N+s21gs3VDw6hVCCBJGpHBMxo+I2pXSMvw63n3AshrI3TaAmo78/cTxq3GQxN+Zu3rX9G8VIGabY52ty0tXY3AxzC2ELIMOksEvRZgwtFEkPpli9s5Q1SE9wFTPoWY5mZs4VNZBfAKoQ0LkRgNnG/MiyhqhqEwMYqCPXW07EctTRNMO6e2aw7b9+okiWDWvcBdbkLZB2J7vnuTXwlDJJJv4xMXettYCGsn2pbiWxrF1vf7C1GjWud/xgnsBclKUxZA0U7msxIppjLuBee7cpySIBMTZF5CV/GWPhx9whWAAyzCVDxv0ND6K3WcE3Jvw8F1s/u9YKSmFKvWhANgAEgAaABoozjfkTBbmquizg7vSReoD+fKTLcKyTBmVNm5LJ6o7rCktKcPr47lNfqKYSKBK9D4P76ArbsfszIjopEdjjUB1DpnJgEcauX4ahYQHIMKns0Ilge2pEBDoPvMFxKZZHR/T12n1ZAVDXXTKDlkYEQgHZGhANUFxoLN5osHhtA/HoFPr8ArpR/XgtQlj6LsRRlymVsGFOHgrEqcLP/b3XQ9Zzj8Za0rlr8BNIqOtw2SOzN2ksd32UNdKxYkbj+rxSNLbLS+8cImln51xBSb9fBtwU0ed3HfH3+ds5h5J5imEByDBT2dEd3CPwoIlmCFMEhJmwD+2mAdYKYlUiX5uMVFUAzQLIV0YEFhcaxWmD/NEF9GoFXZbkHINL8BMEqNp+FL6Q8RNMqGzgbi0jVjZfTLrj+8LGF4uU8IplAIfOodacksySIhTHOofYOo2h41yrqCsED2W1TOnta4+bmhCSSFD8xT63KQKU3cPMLYOTQJi7yb7f3A/1MEip8bfDXD3rXKMg6gaiaq09GdDMdVcGRigNWTYQ6xK6qo3175LodQ0BdnO17itWqNhCO+5lZYenCkV/PVMhMp+7cjYpnT1iSSbdFCIuGH1xl9Ie7gCtFoPdP6jP0MR2cgxzG2ELIMNcEtG4P2uNSK41FnA/t+7ZbSaops/RGqLdpzOYHsC5SQRpviCgc78wsui7AwNuwGACQgpEZ5DosSERRQlKr1zN6BoCJVR6+2P1//zOH5ZQJjVAx0Lu09kjIjLJ185z/7rbQkyyFFLWQMoKZ49L+TxEjhnt6mHnZxgGAAtAhrk0RpM/Lvth5D5ohYCWAhCAbn+gzY/OAJ0J6CJvM0cjGawtZJyZI1JCMYFd4sFYVqzPWNu0kJs31skj0J84ed6QKHXF2tR4QZdES2kw/rKdxxV1IRFvt/uFn0OZwqNcVuIFCziGORjsAmbuLlPryw3O3+9hRFkrdipXMeZmkwI6a+v7ufkSyvxoKUx7uDyjs3ft8bFiwDFRY8uRTMkqdV+bULkVuy1mtQq5da3429XlOzZne63W5U0KJ2pbStKJcy97wi3UtSTLdsroJdec4s4NDuhY/0Ku2p2G3bp+967/xzB3CLYAMkws6cAn1KKNPJQec6xYdGrGcNCdZlEaIpMQJ8dQzxxj83yBzbNAfaQhFCArAVm1Q2UCal5AzgqIujaxgFQZQuc+CSLeDOiLksnJBbGEDapeH3X+mNjx10TV+gsd7wvekW4c5PW71+jWPbTjp1gNA1ZZ4VtWI+9t9/WJlYQh9/kJHSmi0H2vUufs2e836ctTqJXipIk485e5HbAFkGF25RAPk13nSUVKiMUC9ckM5YlEs9Bt4ocRf7I0SSBwn2lCIKWjQ7I7cBcrWyhRI7KG4Hp27SJy2e0C/RqFKescidHrWRvde7hLso1DVMhfhrt31yLoobH8lom7wuKPuUWwAGQYIP0/9pjA8/ZRWYkpVopJbix/Pb47OMugH5xg/UKBixcl6mMNNdNdN5DiTCMrAVkpYz0Sxr0YeuD77kw/w5RORukLkmSroCtgvDHcdQx+b5qwuzhlPrPI4fqnloTxXd8xV3jK2uz99dzyg84dfmJOex4lkP2Yv4My1q6QOi4k1NyC6M7+YNYvwzCjsAuYYSxTXMG980asMokWkknCL6U+YOsCVkczVEuJetmeWgnk5wLZRiOrgGyjkG0URFmbotFuFiuwfVi7brvYWp3C0ZMJuVZHcN2Uwo3vs25WV0C6c4QSSiiR5ruKU5NC/P2hBJjE6zRTE0k4QG+ff16KxXbUFexeXqzenvv72Hs1xTIXyIInM3+njMcwdxgWgAzjMlUEHuhBcnDxZ//NMjTzDM0MUDMAGhANIGubBKIhaw1RK6BujPgLFhvW/d9lX4xQDOLSyHEDcX8RBkWoY4RKqMRcoylrosrYhMaLZSOP4F/nrl05QuLOL+Yt1B6dPw4lrFigMcylwwKQYQ7JIeKMfFwripfk0TuGml+YGn9CG9EnS9Fm/gLNTEDUrSCsTCFo1TS0G9cnJlidfcluxVBGbkQ8+m3LhO/CTbH0hdbijuOyazydXy7HbqPGC9VeJMq69Kfor5USyL4IHCTyREr+BAn1rI5Z/2JdQHyXsLuNEIb2y9NOGfRToWo3MsxTCscAMsxtw4u/E7WCrIBss7X86dxYBFVhxOBA6PgPU9vdwe3ycJkQmcWTrVKR8ilJUOLvMkhdU8jCGInZTCalSwgwzP4FwiJvn6SNCecHO4CMjc8wdxy2ADLMVMbimg7RK3jqevy51daKJ2uFYqWQn2WoFwI612hmGqIWEI0pEK0FwlYZObQiddMlZOmOxakJN7bOs5RpLx5xEPsWiruLufJTXNJu4oaFco+nlKbx1+Ouq7UokdfVO327f9B/mThuOD0dMxiKF+xZCv33Q0qMp2+p9t9bB2oNFxJ+o11Bdi05w9Y/5hbBApBhfHZNBrFMEH/JsX++sHO3h+bWCroskT26wGKZm04gUqJeCDRzYxHM1tpkAFdtDGDkARd0F44khgzERxtn1iMQc+cmPQQTFELiy3Wvhlx3vjvWHW+Ku9dP5kgRhs4x9tq01l0STkw4dyIwkngibG1BYJjYE2Hn+D8L9UXiwO7ZMYvfpbSEY/HH3DLYFs4wFMk17nb/CE3uWkA1tB87VmuI9QbZeYXirDGZv2WbANIAsoFJAlEqTSSMuSv36RQRmc8tWJzk7qTqCAasZqNiP1ajb6xo81gLO/+YCfduUO9v0jmeONv1daPew1PH0upqkz44wYRhALAFkGH2x3UnhZIxCEZ7BdMnTVqXbhroR4+RFTnmUmD9XGYSQOYCsjEiEBpAo6GbhDgwp5RJP0GBiCGUW9G2PW6kZdzgGogOIF7P4e2hkYSGlJ6/oUxhfyyfWNkYrbdj284cbjmYQVmYbakdqhPLcHmEe9aOQ66VsoIe4NiphN7He3YDMUNE3L8s/himgy2ADBNiH5fPVT1o/MK5vjVFaeiyhFiXkGdlZ/1TBVAvgHopoGYSusjaBI/wfwmuxSko/mJJIqnxcv1J++dPSZhAgpXMZghPEYu95SW+R6hWbtbdO3CR75Fo457rFOg2fxNicYql7rKTf9wvT4EvUDbhI/TF6dJ6AbP7l7mFsAWQYaYS6lRAuWgnuIhdq0XSg2xs7HZ+3Sjo9RryokBWKkBlUO0nv9FAsxDQhYTIc1z6Y45IbhB+cgRVYBkYulad2Dnq+JSexF2dQqoMjTuvJ179biiDHryWWNkQQpROqvfnZeT6RaJ758eEXmut9c9PIjRuan/gCfhWvdFED4ZhorAAZBif0ZiwgLA7gPvq4GgFZBlQVsDFCsVpg+JIQmiB6p5GdQ84u8gAvcTx+j7ExcqcVtXm/ISafqRwkISQCcQYdgItJPyGE25dqxjWxfMFDJVZS4pGwkLZE4heCzZyHHesngAbuolj2bxJVtLAa0OK4ljdPU/8dWNlI4KQ6hDj7wOmfRGKHOsKvpDwiwrCXT+bbP1jbiksABlmF2IPtcsuATNl/PZYrTVQ123PXxNfpmYaOgOqY4l6KaHnBUSeA2W5Pd/r+BHtOzv1Mhyh0hNaQFwADcQNLc52onVvH6Sunl2bv60lOMcuhaYd0t3SV+TSpYh9WSJ6ajMMc3hYADLMrrgxSztYF1w3b7L7N6WvKpWI0jRAoyAaBWhAZxrNsQJyhereDOWxQHOUI5sVwCaDlnW/ZhuxpliXiTF3opvV23NZNk2w3EuwpZx10bqdNdzyLmbwtAQX2MN13yrmC7qEdmm+a5gqh0OtoZepmyjSyGQQ6tyAtc5NONlLAMdEnZBBS+Tk8AeGYfaGBSDD+NiHaepDcEfXEmXZmPTwcx+mMRGqNJAB0ApCaQilAVgRAkBoiDYTGG4rOHf8VlD4sW8xYqKEdp0O5wiNFxVnFF42MZmZ3I7ju1P7awzX3euN589BHePjCtSI1StWH7ATz4mt+kIi/yBxgBOg3vcxUbiTVdBPloox9f8AhnkK4SxghrlNhB5sWQbkOXQmoTPnoaYBoURbF7AtRBwteRLPHCXr9E3JNg0d51uMdmkNRz3Mp7SG69YyvD/DmLthxnJ3b1zrpP1xx0m9V1PWTo0ZygpO7daRim+1TijifGMsghz/x9xi2ALIMCF2tQJQzewPhRt7mPKAlgJyPod4cB/qwQlWD+fY3BfQGSA2EthIFKfA7IlC9mQDbDbQdR0drze3b5kJ1aXzj425KGPzevTEZs+C5ok0IoM42XpIQInPzgoXaiWXylhdvmxYridktewEpVOTMUk4j7j/e1DxsNT7Xisgodb4TrUx/fnG6nHa7b02iiz2mLsFC0CGSWHX9nCX0RfYHzPizhJ5DnHvBOr5+yifX2L1nER1IgBoZGsBoYF8pZGVrRg6pPUnNgaRwaql93fiA5mMrXPKtQxaplHxe7H4QuKYwRp9oRQrDt1ffPjCBmsJj793+7aUOV3GWhJeVTZ8rKD01HOpzzeLQuYWwwKQYW4IkywfKQ9YKSCKHPr+Mcrnl7h4WGDznOkDDJhewLIUbT9gDVi36hTxR1mIJpwfjbdLYFCPz7pUrThyYhqFIwrtOl3RORCS24WZf0PdQjzRrCVReoYSg36ZmG66RAFM1CSMMfn+BsrMROe7jFJIlLVuH25aqSaGuSZYADLMGPaBt4sVMNH6lyr+bGxUyvHy6Aji/gnWr9zH2SszrN4kUB2bTiDFE4HiCZBfaBy/WmP2+hpitYEei/8L9X4Fxov/htzB9tq87NOYYImKEK/7B2UZ7K1JEuLPF2xSmkxqagziut2MZnctvkXRT2xJcdFS5/S2++55r4xPshs4MOd2I+FmndiqMFbypfceb4/buyTMVPEXK+TNME85nATCMFPQevszASFF9zN5SuKhN/ogFBJiMYc+XqJ8kKM6AeojQOfmvGwNFGca81OF2WmF7LwEqnr8ulIFXiK+sIgKk13i6cYIFbp226illJCxLdjGXl+/PRsx964lWFLOG02eSbmOQ9bl28VVe9m494fFH3OLYQsgw+zCgctDUG2uYvvdbQNRKaRx/T57H9WbjnHxJon1cwLlAw3ZAKIx4u/oCzVmb5QoPv8GsFpDX6za2DJBW+tcCyBVSiSVkcSRferQ+bX3wkki/cQIsy5JulUFEW8XW9+gwLWgr9ethdjN364z6pL2xhnM7cVSdscGEnc6Qq/pZcSxJhDr+dsRasHoH5M0IQs/5m7BApBhLhPbj3cPq8lYz1PfbSYWc8iTY6xfuofVi4Vx/Z5oqLmCWElT+qUBsrVCdl5Cn12Y7N+y7NcAHCn3MmpJSrm2wDikcGpbyY2JrxB+qzrS3Uv0GI6KysDc9vyQ+BusBxgkdgxqFRJWQ6o24qh49l/XXWI4x5IvUqx2xDFW8Lkt35JEoP93L7uXY/4YhoJdwAxz2RzgAeQ/BGPuZJFJ4/p9Jsf6GYHqvkaz1J37FwCEBmSjINYV9HptxF+z4zpdt2GKGxSBeoEeIbFEiZ7w2pxWeN787n7Kopuc+BAgJv6C22OFpkdc4FvxPqxBGOSGdt3wu+T4tQEZhtkftgAyzE3Csxi6D8Lk+MH5HOreEVbPS6yfF6jv14ACRCMADUAAqgC0FSg2U1YrQMkki1BqyzeXFMtZbyy3RVliW7Qxt2nkxHYeusVbr4BzIPt2zOJHjdlrg2fnoXoijyXn2Pkkke1sGanhGLT8jblQXRdxwped1A44vjUwaf7tyWz5Y5gR2ALIMFPZtR7gJT+Qeg9RCagM0DmATANZawG0Bq8GELUyma1u/T8gOaYvWWRNtNxsrXPu9YjBMVQv4t72gBUsGBdI7adIGRcB8eeKtUgxab8vMfUTYlSMU1ZaVxSmtJG7hpjAIK7Ys58zFn8MM8oN+hQzDOMzZv0gLSdWiAhjEUKugEIDuYaWbRZwqZFtGohNtRUM7kM91sGDKnnin+MfOyYCJ7gtk4TnpDZpESvbId2OYxnU7U8n8ALWSEtMYIasmMlt+i7JNTyl//Ukty9b/BhmMuwCZpirZKxFVfRUOlO45yaezYD5DGpRQBUCOteQhYJWAlpLFE8k5o+A5RcbyMcX0BcXyS3lfCuhht5upyxHnssxGIcH9N2qARdlLFs3lHRBzjW4tuFr0bln/e4b9ngpIXyBRnUN2TNDuhsjVn8xcH70mkNZvqEOH9T+UPu1CClhDDvF+u0r/jjrl7mDsAWQYZ4CUh+KYlZAz2eoTnLUS6CZa2R5AyE0oATyC2D2RKN4XJrCz2VlJ4i7z0LzT2kd51i5gIQagG5284HL7hyKg62LENCTE08SxF/ymFPdqDvG/gG4fncyiz/mjsICkGGuA+qB6QWz20zfUL2/QXakkBD37qF68R5O31Lg4ssa4JU1nn9wDiEBcZHh5P8p3PvTDYrPvAr1+iPo9WZ8rakWGU/gdYSsV2okpq21Lg7KoTj/BpNQxgo3++M5UIkkScKJKPC8nSetbuIgjtE/157vx+xJQZ/rEO+eMvIFYOp2gqj177pEIIs/5g7DLmCGuSmMPATHikVDCuijBaoHpucv7lc4OtqgURLNJkNxLlCcN8jPSqjzC5MA4s491eKT+tAei31LSTqwbt8RYddzA6eKwF0gMm39nsMDIteZZEkkhPVY3UNSIO8T13gZRaGd9x2XemGYq4MFIMNcNVTJjAkP1WD9PyGglzNUxxnK+xrzowpHswplnQEbifxCoDhrIC9KKMry52dT2jXt0vVjNOmjjR20pWfcbYNjHatfSFw5QpISPimdNZIJlVlx8ItOU/NOcvHG7qdfMiY2xyEE1gFFYFKh57G17Hwui03mbsMCkGGm0mXN7vDgoh6cgYep3+HDbqO2I8sgZjNUJzPUCwGdAXWV4clqjovTBeav5Tj6c43Zl1YQp+d0MooUWzHmP1j9pAF6wfFropJElJs4QYgV6dXFC7RrA+i6et3SJraXi5ZomXKuK/48d7NvKQzWHwS29RBDOPeEvE7XZUy9liFBt2tbtcEwwzAGt9vHxMF2WkN/DBZ/DMMCkGGukh2sJ34xaLKUhjBxYDqXbTFgoNpkUI2AOC1QPAFmpxrifA29KaetJ0X8mcWlXtKlMCbQUiyAMVF5UBLEH30e4TL3spGDZXl24QDlVdz3K7t4GebmwAKQYa4aqnn9RBfwoFeqlECeQ+VGHMhSQDzJobXA8s8ljv5c4+jPS+CNJ9Dn5ymThLf7nR+mxAKGSsXY/d72WDxfJ86CgnjrGg22knNKu/SEk5dR61vWYu3ogmKuXUfUQgf05o4KQ1uKZyqUq92+jpQVd0cReClij1rH2PrY2scwJCwAGWZXtN7NDeyTkPwxGielFFDXkLU2RZ7XAsWphGgEln+ucfSFGsUXL0zfXzf5w0xg/nU3p7oEp7BLLGGAsRZrkNK8NkQMoD9O136NWufInKluZTJZxI1T9Nu0+ezQqWU7oePm9c/1Bb0fB+oeFxp3B5LbvKXABaAZZie4DAzDPOVopTtLmKgVZKWRlWiTPoDFY4XitIQ8XwFV1cv+DT6AQw9VqudqCiEBO/LwpkrEjLc6k1FhTlnsQmOOtV0bzEuMHyPYrxc4bBeSGNfout8rAWTnSW9mTUmGuWrYAsgw+xATBykPmsSHr28FHMQCKg3dNMjWNYqzAvNHEloA+Vrj+E8vkH/xCfSjN6Ab1Y3nju2Oe6g1d4T6zo4d7yQuhFynQoitG9RNGmkJJnMEuo0k1TD0x7Jr2aHsTLCOoT93KEOawr9/g0SfkXFCrmCXkOvfOS/VyheKaz0Ih7LSM8wthAUgwzyFDGoCZhJiNoOSAkID2UYj32jk5wrZo3Po8wvT9aN9QI8+dGMP9ykCcExQhsYKCTTEs32h9HjGbCoTrVNTk0UGtfmuwxp2yYTeY6NfaA66CBaBDEPBApBhbgNFAcwK6FwC2sQBzh/VKE5L4PU3oFdr6LLsnRK09o1ZjFITP3YVNAHxl1zKJdYjd6wotXd81EKXMmcgueNSSb3GGGOJFQmvf8iy7Jd/cXtac5Yww1wdLAAZ5rJIqRcYymokD40nPgghIMsGxRMgXzcovngBcb6CWq2hqzptzVTx59C6YyIg1P5t4n4/eYMsbEzFzbmCL1V4pRxHjZ845kBMeu3cRsVmivs25Lr11zsle3uKEHSEY7BgeSv0qNaGSXNOwSYCcSYwwwxgAcgwl80lu6CE7QcLQFQNpNZAKSAu1sbyV9VpD/GpD93UGoKXafkii0sfzuoWbKGWeF2UqKMykpMTR0IiMKG39OT9FL5l8MCFohmGuTpYADLMTWPCg1lIYY7NMtMNZF1BKAVR1dBvPO7i/sji0fbc7Ybt75Tbd8pad3mgu+KmFVg7FWEOJXaMCLbJfXND1sfI2L3xifWE6hqSx4fEYKqgHxOHsfNSusGQu4mknNT3GsMwB4UFIMNcNrtY/1JFoJAQswJisYA+WgB5BpQKqGpo+zNFjFHzpj6QpySJpIiyLqt3JEN2bFwqm5ZwER+0g4Y/ZmytwDZxJeQGpWr4uX+7oo96vaj6fy6R13jgrk0UeVEXL/H3pcCuX4YJwnUAGeYyuYLsQ1HkwMxJAhECuq5Nvb8p1hSqAHDK+b7ocH+3gitW4Hhsf+r2XQVFapFle51j92RKLKH799jch2AHC11v+77nx9aVcm+nwOKPYaKwBZBhLpPLLkEhBZDn0IsZ1NEMOhPIagUhJbQcfr8bBOZ7LcC21ptLdMOFEl9CrsAdSsWE2rkFj7e4FseUpIspiS0UsSSgsW4sbsJOivg9QOeO/nDxOZMtfKHEFXYFM8ylwgKQYS6bXUUg4Y61cXsik4CUEHkOkefmYVsrCAWgbqCVIvvnBucZbKJLeEShYsN8l6aQ9HGU63lKj+ERksq4hNzGPlNc3FNJdPsns0Of6SvHbz93jZ1JGOYuwQKQYW4yvrUny4zom82ArE3+yHNAa4iqgdAaoqyg69q0MZsgRMbcd5O6hLgPct89HLNsHSJr1U/M2KUTSSy5ImVOy5S4yJTxqfhFKhbQnXtPUXUlonCqCBwrscTuX4YZhQUgw1wFKTUBIwgpIPIc8tlnoI+X0EdziEYbK9+mgqhqiKoGygp6s4FerZOtUDbIP+bmSxIBKVY96jhqH3XeqChQ4WSJFNfurla7Xa2FqfcL2F5DqphNrS+Z6Gad9D44BKMZzGMdZgSLQIYZgQUgwzwNCGm6fSwX0McLNMczyEpBVA1QNxBlZVy/67XJAJ6aABKc9pIf+GMdJw6FL5Sm9Na9KlJqKobgeDmGYSbCApBhroI9E0FsqRd1/wj1vTmqkxz5qkG2FsjOYSx/6w3UxYXph9s025Mdy9Kgh3DA8ncl+MkO+7gqffdhrFOHa00bKzFzKJFIjZlq0Yzt21X4UQWcI2PZ9wjVwYNhmKcTFoAMcxXs6gIW0oi02QxiPoMSAqJWyFcN8vMKYlNBrEvoysT9QelJosA+0INJH7t2CfGuwZlwuI06boqFLlT3jsp4tvMc0voX6gpyCCHpi8BQuZ2x7NlQTOZE9hZ/V5Xkwe5fhhmFBSDDXCW7ZARnmanzl2cAAFkriEZBnm+M5a8sgbICqqpv+bOMPHCpAr2DB71vIdrV+pSS5GHntiIwJhoP1IqswxdtUwXLoYRe6BiKQ/T09fYdPOaPEq6XJQRZ/DFMEiwAGeaqSRSBwmb8LpcQRQEIAXm2gqhN3J9erYC6hi4r0/GDEn/ATlaXTgTGXI0p3SRSMnv9v9VI/bvQOmIlaNzjXPzkEP+81PtG3XvlWeWmZDArvb3vbvkclz2zfMfcuWPiz90/mm1+WbGe/meJxR/DJMMCkGFuIkJ25V7EvC35Aphkj7IyLt/VGtB6K/5CLsEdxMGU1l+TjomsZSA6U9jlOl2RFRN//jkxkTNW8sZ30cbWekXJKfuIv8lchlvaF3ss/hhmEiwAGeam0T4UxWwGLBfA0dI83LSGvlgDmw3Uag1dV+Ex9nCzTX74jz28KVGU2k4tJIZ2cR/61+WLuik9h0OkWrpCIpAqnJ0yV6qAImIFKevd1PeAmyQyYILreRRK5LHwY5idYAHIMNfBiBtYZJkp+iwEtBSm5l+jgM0GuizD7t7eIFcg/naFEErd3Lt2BKFITfy4jpIwY1m+lzVn9yt9zbu+B6YUHWcY5vrhnjsMc120Vr2eBaPN+nWL/grVFnxWCrppoBv1dNd9i63dF0SXkYSwy/6rJJTpewWw+GOYuwMLQOYgfPSjH8Vf/at/Fffu3cOLL76Ib/u2b8Mf/uEf9o7RWuPDH/4wXnnlFSyXS7znPe/BH/zBH/SO2Ww2eP/7348XXngBx8fH+NZv/VZ87nOfu8pLuR6ENEkfRd7194XSpqXb+YX5uViZhI8U69+kqfvB/PZnytonW+liwibWVs3/SRmPmts93z/Xn9/+THGzhu5JKE7TXVdsvaHrn4B9fW0NSPdnyvk7vVdcdnnfMAxzMPjTxxyET37yk/in//Sf4rd/+7fxiU98AnVd473vfS/Oz8+7Y37sx34MP/7jP46Pfexj+J3f+R289NJL+MZv/EY8efKkO+YDH/gAfvmXfxm/+Iu/iN/6rd/C2dkZvvmbvxnNgUXPjUEIQDrCbzYDiqJ1Acut67KuTbmXS7AI7RQDdhmWqSnZwodaS6iGIEWKWPGF2dRkmitgHzf/ZLE39rrdkHvCMHcRoTVH0DKH5wtf+AJefPFFfPKTn8Tf+Bt/A1prvPLKK/jABz6Af/bP/hkAY+17+PAh/vW//tf43u/9Xjx+/BhvetOb8HM/93P4zu/8TgDAn/3Zn+HNb34zfvVXfxXf9E3fNDrv6ekpHjx4gPfgbyMXxaVe40EQwlj+5nMj+op8K0Bm7fqVhl6vTexfWU4Ye/fvd3v3BR6fgK7hR1n/poiEWGma1IxTd21T7iE1l72e0BeYsQLOmHbfD9apw7tXO1v5Dinw3LjZp+yxVesKv4H/iMePH+P+/fvXvRyGAcAWQOaSePz4MQDgueeeAwB85jOfwauvvor3vve93THz+Rxf+7Vfi0996lMAgE9/+tOoqqp3zCuvvIK3v/3t3TE+m80Gp6envZ+nClvuZbmAOFpuy77MCggpIewDT018kLo15Hqbp7d+8918IfffXu5Ai+9yTREgritxV4tTaIx9i127pWZixyW2YRu7v/uKP+2K7ti9P1RpoOC5gev0Y2YZhtkZFoDMwdFa44Mf/CD+2l/7a3j7298OAHj11VcBAA8fPuwd+/Dhw27fq6++itlshmeffTZ4jM9HP/pRPHjwoPt585vffOjLuVRMwoeNGROm64dj5dCNAuraPPQOaIWZEu+VcsxBXYNTicXVpXCI/sNX5MpMtQJealLGdbhtWfQxzMFhAcgcnO///u/H//yf/xP//t//+8E+4ZU+0VoPtvnEjvnQhz6Ex48fdz+f/exnd1/4VSPEVvhJx7XVij1dVUBVQm828U4fFFSHDaQneRxSRCSP4yc3OFY5sj+xf+4upMb1TdkXuPf7sI/l1n/NQ6/t2P5257SF74rwPg8MwxwcFoDMQXn/+9+P//Sf/hP+23/7b/iyL/uybvtLL70EAANL3muvvdZZBV966SWUZYlHjx4Fj/GZz+e4f/9+7+epoXM56q6nrxF71bbFm93ud/rYda4rwBcPoXjCnUVmqiC7LCujZSxpZcwyOCGb99Bi3H9NQq/ZYN5YhvNlMLVvNsMwybAAZA6C1hrf//3fj//wH/4D/ut//a9461vf2tv/1re+FS+99BI+8YlPdNvKssQnP/lJvPvd7wYAvPOd70RRFL1jPv/5z+P3f//3u2NuDUJs6/0B0HUNVHUrBKv272rY5s2tHXhXLCNTBQchqvxYxW5c/7z/f3t3FyJV/f8B/P39nnNmZjWVVsvdjRSJjSgXISVTqKRMErQfGWQIsYIE0gMsKRF1oRexRFDd2AN10QMUdmVdFJXQM92IFJhEGBlauEhifx92d2bOOZ//xffM7OzsnJkzM2ceds77BcvqzJlzznx3a95+vk9t0PIu2hri7P4novmLO4FQLJ588kl89NFH+PTTT7Fo0aJipW/JkiXo6+uDUgpjY2MYHx/H8PAwhoeHMT4+jgULFmDXrl3FY/fs2YN9+/Zh6dKl6O/vx/79+zEyMoLNmzd38u3Fq1DVCHb6AGCqfL4Uu3mVVjNdvqWzH5udBdnEFnFxmhNCymcFNxDGSgNLtVnMdQnrbi7f47eJ+y1fh7H872HPhT2utJpz7ijnqXRvNQ6qHKKb2rmFoZOoXRgAKRZvvvkmAGDTpk2zHn/33Xexe/duAMCzzz6LqakpPPHEE7h48SLWr1+Pr776CosWLSoe/9prr8G2bTzyyCOYmprCfffdh/feew+WZbXrrbRHcZkQXRLqSmZeVhvuV/iQrKd7rNkP5laLaZycOZVECkWz2iTs+tUej6E9Y5u1W+G81SqN1dqnzhvoWCWViJrDdQCpp8yLdQCDyR865RQDoLgu4Hnmez3nafpe4guFtUJEHOvTRVkTr1YlsOymCi9q+t6qaXTf3WrvpdZYy3oCXcWfXVjIrbaUTrPt2KMfR1wHkLoRK4BE7VI+A9rzgcIMZ89r/ZirSuvOxVgZjPX+awSz2BY8rnKNsOtFDphxBKKy60SZYFNVyM+74s+u3speK9b9I6KWYQAkagdturDnVFp8gSDoqutU11mlHTlacpk6QludoayJm4p0ndLvkbpLY2rD0mtFDdg1l3Dp9uEARNQWDIBErRR09yrHNlu9KQXleUH1z58Jfo1UQOodC1jtgz9sV4qw42MMjbW6dRupLIYuY1L2fNR7K39Npe7W4mN1tEXUEFttLF/hfloaSlvxjxNW/Yg6igGQqMWUZUGn02aXD0ubnT08H5LLQcGD+LrGrI8aSmcJ1zy2xqSGSosYx1gtqhR4Ks16bXhSAuILf+X3US2oxtYdXSZKG7Tq2i3F8EfUcQyARC2kLAvK0mZvX9sGbBuSz0MVJn0AJgSKqu9DUdV5fBeJbRkShFfo4lQeKJseh1fyukozlpu5NyKiqBgAieJQ6Oq1LEArM7FDaxP6Ug7U4kUQx4akbSjXB/Iu1H+XofI5yHQWAIIFn+sMgUB8QTBqN1/E4+oNJ3HOaq10bLW1AeMKpGFacf66Zu5We035682Bdd8PEc0vDIBEjdKW+WBX2lT5LMuEvsLevpYF5TiA40AyKYhjQVI2xPKhtFkGRgCokp0pxM3PnL9asOtU9a/O8BftlPFtc1a4btTw2UxXc+l1ym6k4fPVo6VVv1ZNFJmnVWuiXsQASFSvknX8VCoFOLYJekHVr3iMY0NsC3Bs+AtS8B0LYmsoz4dyNPRVx4RF2zLh0XWBacxMDkGDk0PiFDY5pBFtmH1aXtGLEgTLQ2DYThxRr9+RrdYi/mwiTRRp9OccNha107/DRFQRAyBRVIXgl0lDpRyoTAbIpCEpB37GLDqtPAHckq5cS0O0BkSgXR8+AJ33oPLBpI8gICrLMrODM5lgkogHP5uNvjh0+Yzg0g/dZhaMbiC0NTOrtfyYZse4Fc5RzwzjZq5V+r3RLuyuGddXb3dwHAuTE1HbMAASRVVYzqUvYyp+C/pM127Khp8x/ykp14fKKTPOzzMfoMr3AVdBNKB9HyrvmecBQGtTJbQ0lC/BsZ6ZKBJMEkE9u4O0qtoSZRHgFlT3mg1jYY9X6vptJniFBcmoM5KbDntxLTpd6Rzc2o2oJzEAEkWhLSjHhu7LmAkdfWn4C1IQrSCWhp8OFnp2fdh5D/B8qKtTxUCmbCuoIJbM3g3Cn6QciGMBloLvWNDTLnTOhQKgpqbh5V3Ab9EyMeWBMey4KAEjCIGVgpX4Er6tWAMVxmYDU71b1sUdRMuvE7laGKW9qlXuOhHm5vGMdaJexgBIFIGyLDOj10kVA5toBSVB1a9Y7ROo6TzUdBYyNQUUFnq2bfOBrJWpHtrm9bA0oAHYGr6tISkNHzagASuTNlWkbBaSR+MLRgPhITDKh3OToSHursx2dY0WKnjVrhfnvUSakVwt/JWH9PKw2O7wx9BH1NUYAIkiUFbQ/Zty4Du2qdhpBbimS1cXunRFoCanIdNZyNVJiAjgi5klrM1MYXWNNmP/CoFMKYhlvjxHwwLgwzaTTFzPTDQBoo8HDBPXuMAw9XYBl25N1sjrq5036n63FV8uc/4cR9Wx0nWq7fHb0PXCAl+7wh9DH9G8wQBIFKZ0mZdUCnBSgG1BiZjg5/pQ2TzUdA6Ymi5u7+ZPTUNcF5LLmfOIQNyZmcNmBxCYqqGlgbwLy/WhHQtKUiYwer4JjI4NlclAKWXWCbSsYlWxqW3kWqFW92S1EBLn+MGSc9UTpKKuHRinhiehRAnNcY0LJKKexABIVIlSM7t4WMEyLYUw4PtQeZhqXzYP5PKQyaniQs6Sd+cu6ixmWRcRgcq7EKVMkPQtKM8P1gO0oYPKnHnOhDtlaYhjm9mslmXO7ZlxhpAgGM66TpepFUIaCX8RxsI1HN5Kzt3MLh1tUa0dGP6IqAoGQKJySkHZDlQmDZVOmbF/weNwPaipbHHsnExNA1PT8Ccna+/kEYRDH4ByXYhlzSwaPZ2CsjSsdGqmezabm5lEUugGtizz2nzeBEkRIJcDfAmu38RkkTi6hVu5k0Qd+xSHzq4Nu78q5+74ciyVFO5ffAY9ImoIAyBRQWE7N8eGsoOu15SZsAFfANeF5EzFr9D1Krk8JJeLvo2b70FyPiTYNk4AMzEklzdVwemZySLIB+P9LAsqnTITSexgtrFSQCoFJQJJp8y9eb6pDPqm0gjPq297uWqzhbtBHaEyNLSFdYu2oLu0FeMGu1o3Vp+JKBQDIBEwE/60MuHPsc3SLYVdOhBU1lzXTMQoBC23QndvLSKAeBDfKwYuCa4tuXxxL2HxfNMF7ThmP+GSxaWhghnEAJRrm7UCXXdm7UHPK45BrHp/nVo3sNbrWr0XbaXqYa0Q2OrqZqOTaKIorRhGOnedSwcx/BHNOwyARDDLvCBY6kWlgmqb1qbyB8+M88vlINks/Olsc+vylSp8cIo3e9Jm4buroFzXjAd0zDI0JpjakAUZiNYmGGZzUK5n1hbUZr1BPTlt7vf/LpsQ6HfRWMF2dGFWC1X1Ph71vLMOi1p5LZkNHTUENhL+oqp3D+pO/y4RUUMYAIkCqrTiIaa6p4K9fSWXA/IuJO+2d8yViAkSrmsmhPgCQRrKts0Wc7Y2E0tc000tjm2+0hZ0ygGyaWgRsyyNW3L/lT60u7n7dz6Io0JY/rsVR7WxECyj/t5GrfwR0bzGAEhUKgh88MxWbgKYMJjLQ+odUxcX34NkPTNG0LKgtQLSKcBSELvQDWzG/Unahp9x4F6Tgr7Gh8pl4PgClTJrE/qTk9HWE6y0ZmD5fsPtUBpa6qmOKV1XeIo0Xq9S1bLabhtRq4lRnm82CHKiCBGVYQAkAopLqSgA4s8s6gzfh3h+EPw6vOaeSLC+YN50+U7loNxgUWog2FVEQywNsRXyaRs6LdDuIqhsBirrwrrSZ8Ls5GQx1Ba7hsOqgq1YQDpq8Gn0fJW6U6ucs67JGo1ssdZskGOAI6KYMQASBcSXYG29IAyULrgc15i/GIjnQeVzUNmc6f4VCcKq+a5EoLxgGzOt4KUtaEtBORa075sxhH5QyRQ/GdmilW+ynnM3MtmDiKgFGACJgJmZuXMqSd037knyLnx/CtoXMzs4kzF7C9sWVF5DA3BcH37K7Dfspyz4KQtKbNhaQ03loQEzptHzzPjCarplbGDU8NTK/W/rDXu1Hq93dm4cyrvyu335HyJqCQZAolJdGPjm8E1Q9bPBDGHXnZkd7PlQ0wpiW9BBl7Df50BsbcYQ5lyovAu4XnGf4qpavX9wHCp1/1ZTrWu40a3sSl/bbPd1K5X/frd7eSAi6hoMgETzkYhZlkZpKM8zewfbttmmTqmZGc2WBe32QRwbsHUxABbXMqyroiVzJ4SUqhQQ464ulVcBm10OpVZ4jNpd20tj+xj+iBKBAZBovip0W2eDRZ8Lu5iUBi6toa5OzuxpHCxc7U9NN7akTb1rxLVKI8Gpla/pxiBXqtbizQx9RInDAEjUCwphMA+zjVwp3zfhT+vi9nBV1wPsdt0etrrJfPz5ElFbMAAS9RLfm9urWWuSRyt167jBghbsATwvFLrmGRCJEovrERBRss33ZVkKywBVerzW64goseb5//mIiAgAAx0R1YVdwETUGq3q/o1zZnG3d/9WmrxR6fGw4wuvYTgkojKsABJRa0QJHfUGk6QHmbDu3lqvISIqwwogEbVOWLWufAmSWseEPdftk0waUfreWL0johZhBZCI2ivKhIWwrsxC4Cv9c6PXjPq6RqpujYq6wDYRUZNYASSi1iqtaMUxM7U0ELViH9tq4+taGcaibtNGRBQDBkAiao9mw1+lreiiBrJax4Vdu9KOGVGvGTaBI8p1iYhajF3ARDT/1NsFHKadkyqaqfDF9X6JiAKsAFJPkeBD1UUeYHGlB8UYgiIFsLBlVyLcR8WxjhEuWe0+ws5LXc1FHsDM/5+IugEDIPWUy5cvAwB+xOcdvhNqiXZ/foZdr1vug+aVy5cvY8mSJZ2+DSIAgBL+k4R6iO/7+P3333Hrrbfi7NmzWLx4cadvaV66dOkSbrzxRrZhg9h+zeulNhQRXL58GUNDQ9CaI6+oO7ACSD1Fa40bbrgBALB48eJ5/8HRaWzD5rD9mtcrbcjKH3Ub/lOEiIiIKGEYAImIiIgShgGQek46ncaBAweQTqc7fSvzFtuwOWy/5rENiVqLk0CIiIiIEoYVQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQOopb7zxBlatWoVMJoO1a9fihx9+6PQtda2DBw9CKTXra2BgoPi8iODgwYMYGhpCX18fNm3ahJMnT3bwjjvv+++/x/bt2zE0NASlFD755JNZz0dps2w2i6effhrLli3DwoUL8eCDD+Lvv/9u47vonFrtt3v37jm/k3feeeesY5LcfkRxYgCknvHxxx9jbGwML7zwAn7++Wfcdddd2Lp1K86cOdPpW+tat912G86dO1f8OnHiRPG5l19+Ga+++ioOHTqEY8eOYWBgAPfff39xv+Ukunr1KtasWYNDhw5VfD5Km42NjeHIkSM4fPgwfvzxR1y5cgXbtm2D53ntehsdU6v9AOCBBx6Y9Tv5+eez9/VOcvsRxUqIesQdd9whe/funfXYLbfcIs8991yH7qi7HThwQNasWVPxOd/3ZWBgQF566aXiY9PT07JkyRJ566232nSH3Q2AHDlypPj3KG3233//ieM4cvjw4eIx//zzj2it5YsvvmjbvXeD8vYTERkdHZX//e9/oa9h+xHFhxVA6gm5XA7Hjx/Hli1bZj2+ZcsW/PTTTx26q+536tQpDA0NYdWqVXj00Ufx559/AgBOnz6NiYmJWe2ZTqdxzz33sD1DRGmz48ePI5/PzzpmaGgIq1evZrsGvv32W1x//fW4+eab8fjjj+P8+fPF59h+RPFhAKSe8O+//8LzPCxfvnzW48uXL8fExESH7qq7rV+/Hh988AG+/PJLvPPOO5iYmMDGjRtx4cKFYpuxPaOL0mYTExNIpVK49tprQ49Jsq1bt+LDDz/E119/jVdeeQXHjh3Dvffei2w2C4DtRxQnu9M3QBQnpdSsv4vInMfI2Lp1a/HPIyMj2LBhA2666Sa8//77xYH3bM/6NdJmbFdj586dxT+vXr0a69atw8qVK/HZZ59hx44doa9j+xHVjxVA6gnLli2DZVlzqgDnz5+fU5GhyhYuXIiRkRGcOnWqOBuY7RldlDYbGBhALpfDxYsXQ4+hGYODg1i5ciVOnToFgO1HFCcGQOoJqVQKa9euxdGjR2c9fvToUWzcuLFDdzW/ZLNZ/PbbbxgcHMSqVaswMDAwqz1zuRy+++47tmeIKG22du1aOI4z65hz587h119/ZbtWcOHCBZw9exaDg4MA2H5EcWIXMPWMZ555Bo899hjWrVuHDRs24O2338aZM2ewd+/eTt9aV9q/fz+2b9+OFStW4Pz583jxxRdx6dIljI6OQimFsbExjI+PY3h4GMPDwxgfH8eCBQuwa9euTt96x1y5cgV//PFH8e+nT5/GL7/8gv7+fqxYsaJmmy1ZsgR79uzBvn37sHTpUvT392P//v0YGRnB5s2bO/W22qZa+/X39+PgwYN4+OGHMTg4iL/++gvPP/88li1bhoceeggA248oVh2dg0wUs9dff11WrlwpqVRKbr/9dvnuu+86fUtda+fOnTI4OCiO48jQ0JDs2LFDTp48WXze9305cOCADAwMSDqdlrvvvltOnDjRwTvuvG+++UYAzPkaHR0VkWhtNjU1JU899ZT09/dLX1+fbNu2Tc6cOdOBd9N+1dpvcnJStmzZItddd504jiMrVqyQ0dHROW2T5PYjipMSEelU+CQiIiKi9uMYQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKEYQAkIiIiShgGQCIiIqKE+X8qGH39KZReDQAAAABJRU5ErkJggg==",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Put them all together\n",
+    "config = DrawingConfig(\n",
+    "    width=3,\n",
+    "    foreground_mean=0.6,\n",
+    "    foreground_std=0.35,\n",
+    "    background_mean=0.02,\n",
+    "    background_std=0.04,\n",
+    "    spatial_noise_scale=3.0,\n",
+    "    spatial_noise_amplitude=1.0,\n",
+    "    vignette_magnitude=2.0,\n",
+    "    width_correlation=True,\n",
+    "    width_correlation_rho=0.7,\n",
+    "    segment_intensity_correlation=True,\n",
+    "    segment_intensity_correlation_rho=0.9,\n",
+    "    blur=1.0\n",
+    ")\n",
+    "\n",
+    "img = renderer.draw_neuron(sections, shape_estimate, config)\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe0fc2b7",
+   "metadata": {},
+   "source": [
+    "## Given complexity, $c \\in [0.0, 1.0]$, generate data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5947ad49",
+   "metadata": {},
+   "source": [
+    "Specify a range for each factor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "70962031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass\n",
+    "class DrawingComplexityConfig:\n",
+    "    \"\"\"Configuration for complexity-based drawing parameters.\"\"\"\n",
+    "\n",
+    "    width_correlation_rho_range: Tuple[float, float] = (0.7, 1.0)\n",
+    "    segment_intensity_correlation_rho_range: Tuple[float, float] = (0.9, 1.0)\n",
+    "    # Foreground parameters\n",
+    "    foreground_mean_range: Tuple[float, float] = (0.6, 1.0)\n",
+    "    foreground_std_range: Tuple[float, float] = (0.0, 0.35)\n",
+    "    # Background parameters\n",
+    "    background_mean_range: Tuple[float, float] = (0.0, 0.02)\n",
+    "    background_std_range: Tuple[float, float] = (0.0, 0.04)\n",
+    "    # Spatial noise parameters\n",
+    "    spatial_noise_amplitude_range: Tuple[float, float] = (0.0, 1.0)\n",
+    "\n",
+    "    # Vignette magnitude [min, max]\n",
+    "    vignette_magnitude_range: Tuple[float, float] = (0.0, 2.0)\n",
+    "    \n",
+    "    def interpolate_config(self, complexity: float) -> 'draw.DrawingConfig':\n",
+    "        \"\"\"\n",
+    "        Interpolate drawing configuration based on complexity (0.0 to 1.0).\n",
+    "        Higher complexity = more artifacts and variation.\n",
+    "        \"\"\"\n",
+    "        complexity = max(0.0, min(1.0, complexity))\n",
+    "        \n",
+    "        def lerp(min_val, max_val, t):\n",
+    "            return min_val + t * (max_val - min_val)\n",
+    "        \n",
+    "        return DrawingConfig(\n",
+    "            # Fixed parameters\n",
+    "            width=3.0,\n",
+    "            blur=1.0,\n",
+    "            sharpness=2.0,\n",
+    "            mask_threshold=0.1,\n",
+    "            spatial_noise_scale=3.0,\n",
+    "            width_correlation=True,\n",
+    "            # Interpolated parameters\n",
+    "            width_correlation_rho=lerp(*self.width_correlation_rho_range, 1.0 - complexity),\n",
+    "            segment_intensity_correlation=True,\n",
+    "            segment_intensity_correlation_rho=lerp(*self.segment_intensity_correlation_rho_range, 1.0 - complexity),\n",
+    "            foreground_mean=lerp(*self.foreground_mean_range, 1.0 - complexity),  # Less mean for higher complexity\n",
+    "            foreground_std=lerp(*self.foreground_std_range, complexity),\n",
+    "            background_mean=lerp(*self.background_mean_range, complexity),  # More background for complexity\n",
+    "            background_std=lerp(*self.background_std_range, complexity),\n",
+    "            spatial_noise_amplitude=lerp(*self.spatial_noise_amplitude_range, complexity),\n",
+    "            vignette_magnitude=lerp(*self.vignette_magnitude_range, complexity)\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e48f5fde",
+   "metadata": {},
+   "source": [
+    "DataGenerator class takes DrawingComplexityConfig as input at instantiation.\n",
+    "When generating a new image, a scalar complexity is converted to a vector of complexity parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2314c9f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "complexity_config = DrawingComplexityConfig()\n",
+    "data_generator = neuron_data.DataGenerator(cache_dir=\"../data_cache\", complexity_config=complexity_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ad007b03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfa10966dda84a44a45e87c128f9d1e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Simulate a single neuron image with specified complexity\n",
+    "complexity = 0.9\n",
+    "config = complexity_config.interpolate_config(complexity)\n",
+    "img = data_generator.simulate_neuron_image(subtree, shape_estimate, config)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img.data[0].amax(0), vmin=0, vmax=1)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81caebab",
+   "metadata": {},
+   "source": [
+    "Generate a batch of synthetic data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cca65c70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing 30 SWC files...\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/3_CL-I_MT_X_MYR-GFP_ddaE_MT-mCherry_membrane-GFP.swc\n",
+      "Processed 3_CL-I_MT_X_MYR-GFP_ddaE_MT-mCherry_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c5.swc\n",
+      "Processed 140921c5: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091201c1.swc\n",
+      "Processed 091201c1: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/1_CL-I_X_OREGON_R_ddaE_membrane-GFP.swc\n",
+      "Processed 1_CL-I_X_OREGON_R_ddaE_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091204c2.swc\n",
+      "Processed 091204c2: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c4.swc\n",
+      "Processed 140921c4: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c1.swc\n",
+      "Processed 140921c1: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c9.swc\n",
+      "Processed 140921c9: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c14.swc\n",
+      "Processed 140921c14: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/2_CL-III_X_Myr-GFP_v'pda_MT-mCherry_membrane-GFP_A_1024.swc\n",
+      "Processed 2_CL-III_X_Myr-GFP_v'pda_MT-mCherry_membrane-GFP_A_1024: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c3.swc\n",
+      "Processed 140921c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/080926a.swc\n",
+      "Processed 080926a: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c7.swc\n",
+      "Processed 140918c7: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091202c2.swc\n",
+      "Processed 091202c2: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c9.swc\n",
+      "Processed 140918c9: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaD_Membrane-GFP_F-Actin-Red.swc\n",
+      "Processed 2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaD_Membrane-GFP_F-Actin-Red: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/3_CL-I_MT_X_MYR-GFP_ddaD_MT-mCherry_membrane-GFP.swc\n",
+      "Processed 3_CL-I_MT_X_MYR-GFP_ddaD_MT-mCherry_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaE_Membrane-GFP_F-Actin-Red.swc\n",
+      "Processed 2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaE_Membrane-GFP_F-Actin-Red: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c12.swc\n",
+      "Processed 140921c12: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c3.swc\n",
+      "Processed 140918c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/100110c4.swc\n",
+      "Processed 100110c4: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091226c2.swc\n",
+      "Processed 091226c2: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c6.swc\n",
+      "Processed 140921c6: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/1_CL-I_X_OREGON_R_ddaD_membrane-GFP.swc\n",
+      "Processed 1_CL-I_X_OREGON_R_ddaD_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c8.swc\n",
+      "Processed 140918c8: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c16.swc\n",
+      "Processed 140921c16: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/3_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby.swc\n",
+      "Processed 3_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/100108c3.swc\n",
+      "Processed 100108c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/110203c3.swc\n",
+      "Processed 110203c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/1_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby.swc\n",
+      "Processed 1_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby: 1 subtrees generated\n",
+      "Entry data saved to: ../data_cache/generated_data_entries.csv\n",
+      "\n",
+      "Processing complete!\n",
+      "Total subtrees generated: 30\n",
+      "Results saved to: ../data_cache\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'processed_files': 30, 'total_subtrees': 30, 'output_dir': '../data_cache'}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "swc_root = \"/home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset\"\n",
+    "data_generator.generate_data(swc_dir=swc_root, subtrees_per_swc=1, complexity_range=(0.0, 1.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "77ae6a88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cache directory ../data_cache emptied.\n",
+      "Processing 30 SWC files...\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/3_CL-I_MT_X_MYR-GFP_ddaE_MT-mCherry_membrane-GFP.swc\n",
+      "Processed 3_CL-I_MT_X_MYR-GFP_ddaE_MT-mCherry_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c5.swc\n",
+      "Processed 140921c5: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091201c1.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 6.16698988  0.6065     95.326     ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 6.89510961 14.177      89.182     ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.30130691 161.           0.628     ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.44182125 161.           1.9615    ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.44182125 161.           3.4615    ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.44182125 161.           4.9615    ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.44182125 161.           6.4615    ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.44182125 161.           7.9615    ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.44182125 161.           9.4615    ] is out of bounds for image shape torch.Size([6, 161, 95]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.805  9.118 67.811] is out of bounds for image shape torch.Size([15, 96, 82]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.982 18.46  64.346] is out of bounds for image shape torch.Size([15, 96, 82]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.997 27.888 61.109] is out of bounds for image shape torch.Size([15, 96, 82]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.511 36.899 56.837] is out of bounds for image shape torch.Size([15, 96, 82]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 7.    51.438 82.751] is out of bounds for image shape torch.Size([15, 96, 82]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 7.354 96.133 34.315] is out of bounds for image shape torch.Size([15, 96, 82]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 56.2615 154.836    0.53  ] is out of bounds for image shape torch.Size([56, 154, 90]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 56.011 151.783   4.49 ] is out of bounds for image shape torch.Size([56, 154, 90]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [44.065  9.154 90.47 ] is out of bounds for image shape torch.Size([56, 154, 90]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  1.94166667 117.684      126.991     ] is out of bounds for image shape torch.Size([4, 117, 126]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  1.94666667 117.671      126.84      ] is out of bounds for image shape torch.Size([4, 117, 126]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.    51.663  0.517] is out of bounds for image shape torch.Size([4, 117, 126]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.    51.208  3.601] is out of bounds for image shape torch.Size([4, 117, 126]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 091201c1: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/1_CL-I_X_OREGON_R_ddaE_membrane-GFP.swc\n",
+      "Processed 1_CL-I_X_OREGON_R_ddaE_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091204c2.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [25.2732  2.035   6.605 ] is out of bounds for image shape torch.Size([25, 23, 63]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 091204c2: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c4.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [26.57142857 42.5023     13.        ] is out of bounds for image shape torch.Size([26, 65, 70]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [26.57142857 42.8353     14.667     ] is out of bounds for image shape torch.Size([26, 65, 70]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.85714286 35.5023     70.        ] is out of bounds for image shape torch.Size([26, 65, 70]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [26.25428571 35.6133     41.        ] is out of bounds for image shape torch.Size([26, 65, 70]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [26.57142857 38.8353     33.333     ] is out of bounds for image shape torch.Size([26, 65, 70]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [18.     65.5023 34.    ] is out of bounds for image shape torch.Size([26, 65, 70]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.58181818 63.281       0.732     ] is out of bounds for image shape torch.Size([5, 63, 20]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 5.04848485  2.015      10.812     ] is out of bounds for image shape torch.Size([5, 63, 20]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 140921c4: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c1.swc\n",
+      "Processed 140921c1: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c9.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 60.71314286 210.436      161.105     ] is out of bounds for image shape torch.Size([60, 266, 161]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 60.70371429 211.882      151.293     ] is out of bounds for image shape torch.Size([60, 266, 161]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 60.59685714 213.437      141.489     ] is out of bounds for image shape torch.Size([60, 266, 161]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 37.56057143 266.517       51.482     ] is out of bounds for image shape torch.Size([60, 266, 161]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 60.71428571 213.431      128.365     ] is out of bounds for image shape torch.Size([60, 266, 161]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 2.97428571 58.349       0.5       ] is out of bounds for image shape torch.Size([9, 58, 88]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9.17485714  0.553      50.22      ] is out of bounds for image shape torch.Size([9, 58, 88]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9. 11. 55.] is out of bounds for image shape torch.Size([9, 58, 88]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 6.19428571 38.58       88.472     ] is out of bounds for image shape torch.Size([9, 58, 88]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 140921c9: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c14.swc\n",
+      "Processed 140921c14: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/2_CL-III_X_Myr-GFP_v'pda_MT-mCherry_membrane-GFP_A_1024.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 28.83642496 105.029      257.4165    ] is out of bounds for image shape torch.Size([32, 105, 257]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 32.29300169  85.7075     235.0335    ] is out of bounds for image shape torch.Size([32, 105, 257]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 32.28161889  79.214      221.559     ] is out of bounds for image shape torch.Size([32, 105, 257]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 32.21332209  74.6165     207.339     ] is out of bounds for image shape torch.Size([32, 105, 257]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 25.099  80.438 285.925] is out of bounds for image shape torch.Size([26, 80, 285]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 26.974  75.017 274.27 ] is out of bounds for image shape torch.Size([26, 80, 285]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 2_CL-III_X_Myr-GFP_v'pda_MT-mCherry_membrane-GFP_A_1024: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c3.swc\n",
+      "Processed 140921c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/080926a.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 16.8808 180.9274 115.3708] is out of bounds for image shape torch.Size([21, 180, 115]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 21.3232 170.0194  94.1548] is out of bounds for image shape torch.Size([21, 180, 115]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 21.82   167.4334  88.7548] is out of bounds for image shape torch.Size([21, 180, 115]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 21.7768 164.6644  83.4568] is out of bounds for image shape torch.Size([21, 180, 115]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 21.4492 162.202   78.0028] is out of bounds for image shape torch.Size([21, 180, 115]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [21.63333333 38.209       0.306     ] is out of bounds for image shape torch.Size([21, 45, 168]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [21.18181818 31.652       7.853     ] is out of bounds for image shape torch.Size([21, 45, 168]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  6.67878788   0.601      168.101     ] is out of bounds for image shape torch.Size([21, 45, 168]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  0.3969697  45.961     145.808    ] is out of bounds for image shape torch.Size([21, 45, 168]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 080926a: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c7.swc\n",
+      "Processed 140918c7: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091202c2.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  2.4 145.6  85.2] is out of bounds for image shape torch.Size([25, 145, 97]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  2.4    145.1332  85.3998] is out of bounds for image shape torch.Size([25, 145, 97]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [18.6 76.6 97.8] is out of bounds for image shape torch.Size([25, 145, 97]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [25.1664 88.5742 52.623 ] is out of bounds for image shape torch.Size([25, 145, 97]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [25.5462 86.9944 58.347 ] is out of bounds for image shape torch.Size([25, 145, 97]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [25.521  85.9048 64.2426] is out of bounds for image shape torch.Size([25, 145, 97]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [13.     0.89   0.114] is out of bounds for image shape torch.Size([12, 8, 79]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [12.667  3.22   4.674] is out of bounds for image shape torch.Size([12, 8, 79]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [13.     7.11  19.894] is out of bounds for image shape torch.Size([12, 8, 79]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [13.     8.    26.004] is out of bounds for image shape torch.Size([12, 8, 79]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 091202c2: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c9.swc\n",
+      "Processed 140918c9: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaD_Membrane-GFP_F-Actin-Red.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  9.69139966  90.51       267.4345    ] is out of bounds for image shape torch.Size([12, 90, 267]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 12.23608769   6.         143.5       ] is out of bounds for image shape torch.Size([12, 90, 267]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaD_Membrane-GFP_F-Actin-Red: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/3_CL-I_MT_X_MYR-GFP_ddaD_MT-mCherry_membrane-GFP.swc\n",
+      "Processed 3_CL-I_MT_X_MYR-GFP_ddaD_MT-mCherry_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaE_Membrane-GFP_F-Actin-Red.swc\n",
+      "Processed 2_CL-I_Membrane-GFP_X_F-Actin-Red_ddaE_Membrane-GFP_F-Actin-Red: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c12.swc\n",
+      "Processed 140921c12: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c3.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 14.06227656 117.55735     17.361     ] is out of bounds for image shape torch.Size([14, 234, 260]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  0.96121417 234.29345    260.2413    ] is out of bounds for image shape torch.Size([14, 234, 260]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [14.04221754 98.25495    17.1101    ] is out of bounds for image shape torch.Size([14, 234, 260]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 14.07268971 105.96265     23.0147    ] is out of bounds for image shape torch.Size([14, 234, 260]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.87878788 30.8991     48.222     ] is out of bounds for image shape torch.Size([6, 30, 48]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 6.93939394  9.0101     11.111     ] is out of bounds for image shape torch.Size([6, 30, 48]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [6.93939394 6.7881     6.333     ] is out of bounds for image shape torch.Size([6, 30, 48]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [6.93939394 3.8991     2.222     ] is out of bounds for image shape torch.Size([6, 30, 48]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  3.56771429   0.534      117.974     ] is out of bounds for image shape torch.Size([20, 105, 117]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 14.53971429 105.778       21.758     ] is out of bounds for image shape torch.Size([20, 105, 117]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 20.57142857 101.111       35.98      ] is out of bounds for image shape torch.Size([20, 105, 117]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 20.57142857 101.111       42.98      ] is out of bounds for image shape torch.Size([20, 105, 117]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 20.57142857 101.111       43.98      ] is out of bounds for image shape torch.Size([20, 105, 117]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 140918c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/100110c4.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 49.   108.21 102.16] is out of bounds for image shape torch.Size([48, 108, 102]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 47.5105 108.21    98.195 ] is out of bounds for image shape torch.Size([48, 108, 102]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 100110c4: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/091226c2.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [20.534  0.68   6.18 ] is out of bounds for image shape torch.Size([20, 179, 49]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [20.102  5.675  6.18 ] is out of bounds for image shape torch.Size([20, 179, 49]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  5.  159.   49.5] is out of bounds for image shape torch.Size([20, 179, 49]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  0.692 179.46   18.155] is out of bounds for image shape torch.Size([20, 179, 49]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9.94330303 41.805       0.543     ] is out of bounds for image shape torch.Size([11, 41, 42]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.64657576 31.829       0.858     ] is out of bounds for image shape torch.Size([11, 41, 42]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.78827273 21.917       2.104     ] is out of bounds for image shape torch.Size([11, 41, 42]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 091226c2: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c6.swc\n",
+      "Processed 140921c6: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/1_CL-I_X_OREGON_R_ddaD_membrane-GFP.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.    71.695  2.68 ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.    64.695  2.68 ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [15.    57.695  2.68 ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.85166667 40.584       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 39.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 38.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 37.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 36.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 35.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 34.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 33.695       4.68      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.66666667 32.806       4.57      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [11.48166667 32.139       4.35      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [10.92666667 31.806       4.01      ] is out of bounds for image shape torch.Size([15, 71, 4]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 33.75142857 180.842       85.737     ] is out of bounds for image shape torch.Size([33, 180, 87]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 19.88285714 140.566       87.19      ] is out of bounds for image shape torch.Size([33, 180, 87]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 20.04  139.795  87.284] is out of bounds for image shape torch.Size([33, 180, 87]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 17.92  129.891  87.539] is out of bounds for image shape torch.Size([33, 180, 87]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 1_CL-I_X_OREGON_R_ddaD_membrane-GFP: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140918c8.swc\n",
+      "Processed 140918c8: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/140921c16.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.21212121  0.471      52.667     ] is out of bounds for image shape torch.Size([9, 25, 52]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9.3030303  3.471     38.667    ] is out of bounds for image shape torch.Size([9, 25, 52]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9.3030303 11.582     26.778    ] is out of bounds for image shape torch.Size([9, 25, 52]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9.3030303 15.026     23.222    ] is out of bounds for image shape torch.Size([9, 25, 52]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 9.3030303 16.915     18.111    ] is out of bounds for image shape torch.Size([9, 25, 52]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 3.24242424 25.471       0.778     ] is out of bounds for image shape torch.Size([9, 25, 52]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 140921c16: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/3_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 1.891 21.6   39.   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 1.7506 21.6    37.8   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 1.5182 21.6    36.6   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.7184 21.6    35.4   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.2274 21.6    34.2   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [1.10000e-02 2.15988e+01 3.30000e+01] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [2.38000e-02 2.15988e+01 3.18000e+01] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.0522 21.5964 30.6   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.1094 21.5928 29.4   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.2504 21.5856 28.2   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.484  21.5676 27.    ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 1.2864 21.5316 25.8   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 1.7828 21.4428 24.6   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 2.011  21.2964 23.4   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     19.1652 13.8   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     19.1268 12.6   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     19.0344 11.4   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.    18.876 10.2  ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     18.3576  9.    ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.   17.97  7.8 ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.    17.682  6.6  ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     17.1708  5.4   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     16.7976  4.2   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.    16.542  3.   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     16.0968  1.8   ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.  14.4  0.6] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     13.0668  0.9036] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     12.      1.4016] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 4.     10.8     1.7316] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.   9.6  1.95] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.     8.4012 2.0892] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.     7.2024 2.5704] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.     6.0036 2.8668] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.     4.8084 3.    ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.     3.6216 3.    ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.   2.43 3.  ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [4.     1.3332 3.    ] is out of bounds for image shape torch.Size([4, 21, 39]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  0.489  134.1735 142.757 ] is out of bounds for image shape torch.Size([10, 134, 143]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  1.551 134.816 137.887] is out of bounds for image shape torch.Size([10, 134, 143]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 3_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/100108c3.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  6.699   4.307 143.232] is out of bounds for image shape torch.Size([10, 134, 143]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [10.5   22.111 28.022] is out of bounds for image shape torch.Size([10, 134, 143]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 100108c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/110203c3.swc\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 0.78148 44.0632   0.60964] is out of bounds for image shape torch.Size([18, 44, 23]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 110203c3: 1 subtrees generated\n",
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset/1_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby.swc\n",
+      "Processed 1_CL-III_X_LifeActRuby_vpda_membrane-GFP_actin-LifeActRuby: 1 subtrees generated\n",
+      "Entry data saved to: ../data_cache/generated_data_entries.csv\n",
+      "\n",
+      "Processing complete!\n",
+      "Total subtrees generated: 30\n",
+      "Results saved to: ../data_cache\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  2.33333333 210.362       49.71      ] is out of bounds for image shape torch.Size([8, 210, 55]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  0.85185 112.584    55.6    ] is out of bounds for image shape torch.Size([8, 210, 55]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [  2.33333333 106.473       55.6       ] is out of bounds for image shape torch.Size([8, 210, 55]). Translating to the nearest valid index.\n",
+      "\n",
+      "/home/brysongray/neurotrack/notebooks/../data_prep/image.py:261: UserWarning:\n",
+      "\n",
+      "Center [ 8.62966667 41.806      26.27      ] is out of bounds for image shape torch.Size([8, 210, 55]). Translating to the nearest valid index.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'processed_files': 30, 'total_subtrees': 30, 'output_dir': '../data_cache'}"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# You can also use the data generator to chop up the original images into smaller sections for training\n",
+    "data_generator.empty_cache()\n",
+    "img_dir = \"/home/brysongray/data/neurotrack_data/gold166/gold166_tifs_processed_subset\"\n",
+    "data_generator.generate_data(swc_dir=swc_root, img_dir=img_dir, subtrees_per_swc=1, complexity_range=(0.0, 1.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "26e681a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cache directory ../data_cache emptied.\n",
+      "Processing 30 synthetic neurons...\n",
+      "Processed simulated_img0: 1 subtrees generated\n",
+      "Processed simulated_img1: 1 subtrees generated\n",
+      "Processed simulated_img2: 1 subtrees generated\n",
+      "Processed simulated_img3: 1 subtrees generated\n",
+      "Processed simulated_img4: 1 subtrees generated\n",
+      "Processed simulated_img5: 1 subtrees generated\n",
+      "Processed simulated_img6: 1 subtrees generated\n",
+      "Processed simulated_img7: 1 subtrees generated\n",
+      "Processed simulated_img8: 1 subtrees generated\n",
+      "Processed simulated_img9: 1 subtrees generated\n",
+      "Processed simulated_img10: 1 subtrees generated\n",
+      "Processed simulated_img11: 1 subtrees generated\n",
+      "Processed simulated_img12: 1 subtrees generated\n",
+      "Processed simulated_img13: 1 subtrees generated\n",
+      "Processed simulated_img14: 1 subtrees generated\n",
+      "Processed simulated_img15: 1 subtrees generated\n",
+      "Processed simulated_img16: 1 subtrees generated\n",
+      "Processed simulated_img17: 1 subtrees generated\n",
+      "Processed simulated_img18: 1 subtrees generated\n",
+      "Processed simulated_img19: 1 subtrees generated\n",
+      "Processed simulated_img20: 1 subtrees generated\n",
+      "Processed simulated_img21: 1 subtrees generated\n",
+      "Processed simulated_img22: 1 subtrees generated\n",
+      "Processed simulated_img23: 1 subtrees generated\n",
+      "Processed simulated_img24: 1 subtrees generated\n",
+      "Processed simulated_img25: 1 subtrees generated\n",
+      "Processed simulated_img26: 1 subtrees generated\n",
+      "Processed simulated_img27: 1 subtrees generated\n",
+      "Processed simulated_img28: 1 subtrees generated\n",
+      "Processed simulated_img29: 1 subtrees generated\n",
+      "Entry data saved to: ../data_cache/generated_data_entries.csv\n",
+      "\n",
+      "Processing complete!\n",
+      "Total subtrees generated: 30\n",
+      "Results saved to: ../data_cache\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'processed_files': 30, 'total_subtrees': 30, 'output_dir': '../data_cache'}"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Or generate data from scratch without any input SWCs or images\n",
+    "data_generator.empty_cache()\n",
+    "data_generator.generate_data(dataset_size=30, complexity_range=(0.0, 1.0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85257293",
+   "metadata": {},
+   "source": [
+    "Data generator writes a CSV file to save complexity values of generated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "b1cd313c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>neuron_name</th>\n",
+       "      <th>complexity</th>\n",
+       "      <th>morphology</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>simulated_img0_subtree_00</td>\n",
+       "      <td>0.459293</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>simulated_img1_subtree_00</td>\n",
+       "      <td>0.728718</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>simulated_img2_subtree_00</td>\n",
+       "      <td>0.577886</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>simulated_img3_subtree_00</td>\n",
+       "      <td>0.125195</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>simulated_img4_subtree_00</td>\n",
+       "      <td>0.881911</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>simulated_img5_subtree_00</td>\n",
+       "      <td>0.939202</td>\n",
+       "      <td>full</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>simulated_img6_subtree_00</td>\n",
+       "      <td>0.793544</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>simulated_img7_subtree_00</td>\n",
+       "      <td>0.408677</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>simulated_img8_subtree_00</td>\n",
+       "      <td>0.066658</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>simulated_img9_subtree_00</td>\n",
+       "      <td>0.149516</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>simulated_img10_subtree_00</td>\n",
+       "      <td>0.491563</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>simulated_img11_subtree_00</td>\n",
+       "      <td>0.771897</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>simulated_img12_subtree_00</td>\n",
+       "      <td>0.960195</td>\n",
+       "      <td>full</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>simulated_img13_subtree_00</td>\n",
+       "      <td>0.666314</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>simulated_img14_subtree_00</td>\n",
+       "      <td>0.325065</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>simulated_img15_subtree_00</td>\n",
+       "      <td>0.802070</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>simulated_img16_subtree_00</td>\n",
+       "      <td>0.887100</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>simulated_img17_subtree_00</td>\n",
+       "      <td>0.386370</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>simulated_img18_subtree_00</td>\n",
+       "      <td>0.451699</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>simulated_img19_subtree_00</td>\n",
+       "      <td>0.172048</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>simulated_img20_subtree_00</td>\n",
+       "      <td>0.522255</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>simulated_img21_subtree_00</td>\n",
+       "      <td>0.752266</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>simulated_img22_subtree_00</td>\n",
+       "      <td>0.991397</td>\n",
+       "      <td>full</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>simulated_img23_subtree_00</td>\n",
+       "      <td>0.851683</td>\n",
+       "      <td>complex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>simulated_img24_subtree_00</td>\n",
+       "      <td>0.527032</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>simulated_img25_subtree_00</td>\n",
+       "      <td>0.666568</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>simulated_img26_subtree_00</td>\n",
+       "      <td>0.188018</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>simulated_img27_subtree_00</td>\n",
+       "      <td>0.536255</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>simulated_img28_subtree_00</td>\n",
+       "      <td>0.664016</td>\n",
+       "      <td>moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>simulated_img29_subtree_00</td>\n",
+       "      <td>0.289996</td>\n",
+       "      <td>simple</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   neuron_name  complexity morphology\n",
+       "0    simulated_img0_subtree_00    0.459293   moderate\n",
+       "1    simulated_img1_subtree_00    0.728718    complex\n",
+       "2    simulated_img2_subtree_00    0.577886   moderate\n",
+       "3    simulated_img3_subtree_00    0.125195     simple\n",
+       "4    simulated_img4_subtree_00    0.881911    complex\n",
+       "5    simulated_img5_subtree_00    0.939202       full\n",
+       "6    simulated_img6_subtree_00    0.793544    complex\n",
+       "7    simulated_img7_subtree_00    0.408677   moderate\n",
+       "8    simulated_img8_subtree_00    0.066658     simple\n",
+       "9    simulated_img9_subtree_00    0.149516     simple\n",
+       "10  simulated_img10_subtree_00    0.491563   moderate\n",
+       "11  simulated_img11_subtree_00    0.771897    complex\n",
+       "12  simulated_img12_subtree_00    0.960195       full\n",
+       "13  simulated_img13_subtree_00    0.666314   moderate\n",
+       "14  simulated_img14_subtree_00    0.325065     simple\n",
+       "15  simulated_img15_subtree_00    0.802070    complex\n",
+       "16  simulated_img16_subtree_00    0.887100    complex\n",
+       "17  simulated_img17_subtree_00    0.386370   moderate\n",
+       "18  simulated_img18_subtree_00    0.451699   moderate\n",
+       "19  simulated_img19_subtree_00    0.172048     simple\n",
+       "20  simulated_img20_subtree_00    0.522255   moderate\n",
+       "21  simulated_img21_subtree_00    0.752266    complex\n",
+       "22  simulated_img22_subtree_00    0.991397       full\n",
+       "23  simulated_img23_subtree_00    0.851683    complex\n",
+       "24  simulated_img24_subtree_00    0.527032   moderate\n",
+       "25  simulated_img25_subtree_00    0.666568   moderate\n",
+       "26  simulated_img26_subtree_00    0.188018     simple\n",
+       "27  simulated_img27_subtree_00    0.536255   moderate\n",
+       "28  simulated_img28_subtree_00    0.664016   moderate\n",
+       "29  simulated_img29_subtree_00    0.289996     simple"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "entries = pd.read_csv(data_generator.cache_dir / \"generated_data_entries.csv\")\n",
+    "entries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956464f6",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8fa6273",
+   "metadata": {},
+   "source": [
+    "Dataset class stores data paths and complexities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "c6a2c942",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "simulated_img0_subtree_00\n",
+      "simulated_img1_subtree_00\n",
+      "simulated_img2_subtree_00\n",
+      "simulated_img3_subtree_00\n",
+      "simulated_img4_subtree_00\n",
+      "simulated_img5_subtree_00\n",
+      "simulated_img6_subtree_00\n",
+      "simulated_img7_subtree_00\n",
+      "simulated_img8_subtree_00\n",
+      "simulated_img9_subtree_00\n",
+      "simulated_img10_subtree_00\n",
+      "simulated_img11_subtree_00\n",
+      "simulated_img12_subtree_00\n",
+      "simulated_img13_subtree_00\n",
+      "simulated_img14_subtree_00\n",
+      "simulated_img15_subtree_00\n",
+      "simulated_img16_subtree_00\n",
+      "simulated_img17_subtree_00\n",
+      "simulated_img18_subtree_00\n",
+      "simulated_img19_subtree_00\n",
+      "simulated_img20_subtree_00\n",
+      "simulated_img21_subtree_00\n",
+      "simulated_img22_subtree_00\n",
+      "simulated_img23_subtree_00\n",
+      "simulated_img24_subtree_00\n",
+      "simulated_img25_subtree_00\n",
+      "simulated_img26_subtree_00\n",
+      "simulated_img27_subtree_00\n",
+      "simulated_img28_subtree_00\n",
+      "simulated_img29_subtree_00\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'morphology_distribution': {'moderate': 12,\n",
+       "  'complex': 8,\n",
+       "  'simple': 7,\n",
+       "  'full': 3},\n",
+       " 'complexity_stats': {'mean': 0.5678134736538217,\n",
+       "  'std': 0.2671941910509159,\n",
+       "  'min': 0.0666583169193615,\n",
+       "  'max': 0.991396743342269}}"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = neuron_data.Dataset(data_generator.cache_dir)\n",
+    "for e in dataset.entries:\n",
+    "    print(e['neuron_name'])\n",
+    "dataset.get_complexity_distribution()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76a7f6f3",
+   "metadata": {},
+   "source": [
+    "DataLoader class performs weighted data sampling using the given target complexity to weight data entries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e86973c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DataLoader methods\n",
+    "\n",
+    "def _update_sampling_weights(self):\n",
+    "    \"\"\"Update sampling weights based on current complexity parameter.\"\"\"\n",
+    "    if len(self.dataset) == 0:\n",
+    "        self.weights = []\n",
+    "        return\n",
+    "        \n",
+    "    weights = []\n",
+    "    for entry in self.dataset.entries:\n",
+    "        # Combine artifact level and morphology complexity\n",
+    "        complexity = entry['complexity']\n",
+    "        \n",
+    "        # Weight based on how close the neuron complexity is to target complexity\n",
+    "        weight = np.exp(-8*abs(complexity - self.complexity)) # exponential decay\n",
+    "\n",
+    "        # Ensure positive weights\n",
+    "        weight = max(1e-4, weight)\n",
+    "        weights.append(weight)\n",
+    "    \n",
+    "    # Normalize weights\n",
+    "    total_weight = sum(weights)\n",
+    "    self.weights = [w / total_weight for w in weights] if total_weight > 0 else weights\n",
+    "\n",
+    "\n",
+    "def sample(self) -> Dict:\n",
+    "    \"\"\"Sample a neuron based on current complexity weights.\"\"\"\n",
+    "    if len(self.dataset) == 0:\n",
+    "        raise ValueError(\"Dataset is empty\")\n",
+    "    \n",
+    "    if not self.weights:\n",
+    "        # Fallback to uniform sampling\n",
+    "        idx = self.rng.integers(0, len(self.dataset))\n",
+    "    else:\n",
+    "        idx = self.rng.choice(len(self.dataset), p=self.weights)\n",
+    "    self.current_idx = idx\n",
+    "    \n",
+    "    return self.dataset[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "d8c87bfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = neuron_data.DataLoader(dataset, complexity=0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "167f78c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sampled image path: ../data_cache/simulated_img9_subtree_00_image.tif\n",
+      "Sampled complexity: 0.1495164864569415\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a2dc262d7b74d4ba64c0bf375952588",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
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+       "                    Figure\n",
+       "                </div>\n",
+       "                <img src='data:image/png;base64,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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataloader.set_complexity(0.0)\n",
+    "\n",
+    "entry = dataloader.sample()\n",
+    "img_path = entry['img_path']\n",
+    "print(f\"Sampled image path: {img_path}\")\n",
+    "print(f\"Sampled complexity: {entry['complexity']}\")\n",
+    "img = tf.imread(img_path)\n",
+    "\n",
+    "if img.dtype != np.float32:\n",
+    "    img = img.astype(np.float32) / img.max()\n",
+    "\n",
+    "plt.close('all')\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img[0].max(axis=0), vmin=0, vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26ade4ef",
+   "metadata": {},
+   "source": [
+    "# Commmand line usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "978c86f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear the data cache first\n",
+    "data_generator.clear_cache()\n",
+    "\n",
+    "!../bin/setup_sac_train_v1.py --swc_dir /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed_subset --output_dir ../data_cache --complexity_range 0.0 1.0 --subtrees_per_swc 1 --remove_soma --rng_seed 1"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tractography",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/make_simulated_neurons.ipynb b/notebooks/make_simulated_neurons.ipynb
new file mode 100644
index 0000000..f3d15c5
--- /dev/null
+++ b/notebooks/make_simulated_neurons.ipynb
@@ -0,0 +1,9602 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Make Simulated Neurons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glob import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import plotly.express as px\n",
+    "import sys\n",
+    "import tifffile as tf\n",
+    "import torch\n",
+    "\n",
+    "sys.path.append(os.path.expanduser('..'))\n",
+    "\n",
+    "from environments.sac_tracking_env import Environment\n",
+    "import json\n",
+    "from data_prep import generate, draw, load"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate simulated SWC file data.\n",
+    "This is a list of nodes, each node being a list:\n",
+    "[sample_idx, structure_id, x, y, z, radius, parent_id]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "swc_list = generate.make_swc_list((121,121,121),\n",
+    "                        length=100,\n",
+    "                        step_size=3,\n",
+    "                        kappa=30.0,\n",
+    "                        random_len=True,\n",
+    "                        random_width=True,\n",
+    "                        random_start=True,\n",
+    "                        rng=None,\n",
+    "                        num_branches=1) # make simulated neuron paths."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set random background and foreground (neuron) colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_contrast=True\n",
+    "if random_contrast:\n",
+    "    neuron_color = np.random.rand(3)\n",
+    "    neuron_color /= np.linalg.norm(neuron_color)\n",
+    "    background = np.random.rand(3)\n",
+    "    background = background / np.linalg.norm(background) * 0.01"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw neurons from SWC format data; one without and one with added noise and artifacts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# neuron_no_artifacts = draw.neuron_from_swc(swc_list,\n",
+    "#                                 width=3,\n",
+    "#                                 noise=0.0,\n",
+    "#                                 adjust=False,\n",
+    "#                                 neuron_color=None,\n",
+    "#                                 background_color=None,\n",
+    "#                                 random_brightness=False,\n",
+    "#                                 dropout=False,\n",
+    "#                                 binary=False)\n",
+    "\n",
+    "neuron_with_artifacts = draw.neuron_from_swc(swc_list,\n",
+    "                                width=None,\n",
+    "                                noise=0.25,\n",
+    "                                random_sharpness=True,\n",
+    "                                adjust=False,\n",
+    "                                neuron_color=None,\n",
+    "                                background_color=None,\n",
+    "                                random_brightness=False,\n",
+    "                                dropout=False,\n",
+    "                                rgb=False,\n",
+    "                                binary=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Draw the images.\n",
+    "    a. Without artifacts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    b. With artifacts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8277t2+Knfuqn4uu//uvjH/yDfxARET/xEz8RP/ETP9F5/7M/+7N7x7aHBwuXl5fRNE05KCeiawTj1HIw0OXlZQyHw3j06FHs7OyUslA7jBxYQ0mqnSAM5/39/Xj8+HFcX1/H2dlZpww3YuM84mzhwJEho7+IrjOQHVtnqnZ2duLm5qYcVjUajUoGmgORptNpcQ4w+nG2cIY8LpwCSrUjooyTktGLi4vyHI6JHc6muT1Yajqdxvn5eck+4jz5PRxUfjh8azQalSwkwPx86BHBBLLF2TGj3BWnDIf08vIyrq+vS1m6s3wEKYxHwAcSQUOnp6cF/y7HXa1WxenNpbrQlYMUZOGZHzRUc8xw9nwgktfYBzD5gClK7wluuNSacnvoPpfi59JzO+sOnvCenWWypRxARnuMg/Ex5pubm04wxaXdrjSgrYjoOMS0nzPMfE5lQHZqcfTvC71j20MPPfTQQw+fAHzRF31RJyIODIfD+OAHP1j+/3t/7+99GkfVQw//3wCM3FqmlM/ttDqbi0OQD0DivVwe7M9rbdTKZ22Y5zYoiQVypg8H0vPwWIBc6pnxQP840nlseYzOxtVKaj1WBxCc8fPnzvR6P2WeY3acav3mZ2t4jdhkTl1uXjthmbbIGjsYQr95L+5rra/HlvdL52xlLYCR8Zv3W/Osn3N2F7pivHmtc/92pmvfG7/bKhI8zswvuT23a15wxjyP2e9lOs94wWnFofW+3W34vC/0jm0PPfTQQw899NBDD58S4DoROyQEgjCAKUPNexK5GoVsIE4yRjHZVTJKzma17e2+SLJ82YGhfx8iRfkpe1Zph0OneM6HLFE22zRNyfj5Ghxnll2qaieRvZpnZ2cl84qzZifAjrz35uIs4VTQh8tQz8/PCx5ns1ms1+vOlSvOmkVE2fvpg5oi7l4fYyfZawMYty7LJqvNWMkqt+3tAUjD4TCef/75aNvbfbSnp6edMfDbuGKdwRNX7OAU24lnbDkTy5qR8eU5Dk5yNhwcc03NtuBFxCa7PZlM4uDgIG5ubuJjH/tYuWbJQRTWwZnTHHxgzMzXpc8RUbLXHkuuaqjtMTfQ597eXjRNU+5od/m350ybzqAbD23blquOnn/++XKN1OnpaQyHwzg4OIimaeL8/LyUSPsQr/tA79j20EMPPfTQQw899PApAQz2nJGN2BjOODs4JM5+Olubs7/ZOM9ZXEo8c8Y37wX0WOxMsPcyZ8ry84D78Bxoj75zxhqHxc6XnRk7H27b5auvlrFlbC6B3ebQ5P/zPlOP2e9kvOS19fowHmfSs5OF08T/dj7t0EVEKaHN+6SdWc7489iNc+MgZ39zFjw/S9s5Y8vnPsyp9p6B78wTOdPuZ2vOX15XB3YcCInYnLRcywKbvvLJ2hkXOZvvdjxuHGAOVbOT7DLrPI/Xgt6x7aGHHnrooYceeujhUwI5I+pTfH0oUAY7HDhXZA7JzGFwk1V0Zo2rZJy19CFDfIZhzanMGNfOgvk0Zkonj4+PIyJKVtAOR77eBuDgIGcO7ZA525mvOlosFiUjPR6P4+rqKi4vL+9kcvnBGQWm02lMJpPO+Jxtw0meTqcFj23bxnQ6jdlsVvYOk/ljHXJggPmQzXWmPGf2GBd7PW9ubmJ3dzfm83nJlDvjaaeJtQRnZOb9HdfaOHjg/o2ziLuBieVyGefn58XBhG7pA5rz73zwVd6Lulgs4vz8vOBjOp2W05Tt6Oegjh1v+ANazxUCtQBSRHT2Xzu7an7z3/xQibBer2M2m3We9RVH7Ccmy0pf3sdsmnGwi73i2Xl+1sOjBq/9SA+f6bBYLOLzPu/z4vM+7/NisViUz588eRJvectb4ku+5EviB3/wB6Npmvj1X//1O++/733vi+FwGL/7u7/76Rx2Dz300EMPPfTwOoPLZJ2J42qf8Xgck8mkOF65vBWDnud9ZY0zdvnQp+vr63Igke9BzVeKUPLsA3toE4eatr0v8Pz8vDg9zixh4FNejaOHM319fV369OfOvmanhjJbnAKfBI1zwLx9IjO4xwmbTqfFCaG/vDa7u7vloCpwMp1OC27ol4O+uEcWqGVOvReYMXEg1mw2K6WurAPjxHmPiPKO33dmD0fPWU7G6NOZgZxZdlDA44aGnEkEb9Dv3t5ezOfzmM/nsb+/H3t7e4VO7cxH3AZSnjx5EkdHRyVQAV2btk1PzCvP3/iu0YD7taNovsm8RL8ueb66uipBDcYLjuBNZ+IZg3lzMpmUK6543+ME11wX5YCNx/Za0Gdse3hNmE6n8e/+3b+LL/3SL41v//Zvj+/7vu+LiIi//bf/dhwfH8f73//+eMtb3hJ/5+/8nfjRH/3R+MIv/MLO+z/6oz8af+pP/an4A3/gD7wew++hhx566KGHHl4n4GoTsme+ozRi40AOh8PikNoBwWDP19HgwORsHOCsIA6Zn7exjBPj/XxN05RsW+3QoslkUp7z9xFx5/oYl9XynR0fnFlfZZOdMBxQMoHO6GYHLe9HBl8+Tdrlo3bMvS7M/erqKq6vrzuZaM8JZ9/ryp5nHzZUK83Gwac9nPh8hRFjYd5kcJ1hZ98zc/H6M1b69fVJrJ8P12I8VBxwDZXxVDvsiyxtPhQLJw78e6+0+85l0A605O9whhmLcetMNDhx2Tef+xTrjA+vFw4qp0RnZzrvXwb8Pvh3EMI85/74na/cei3oHdse7gV//I//8fiH//Afxvd+7/fGX/pLfyk+9rGPxX/4D/8hvv/7vz/+yB/5IxER8Y53vCN+7Md+LP7ZP/tnhYB//dd/PX7zN3+zXHXRQw899NBDDz185sDp6WlxDK6vr4ujwL5IjOumaeL6+jrOz8/LZzgE19fXxfnNxr0dGRvROaNkg9uOH8Z/PqHWTkF2bAeDQczn8072EiCjGrG5x9VjoGzV2WmyqBcXF50SYeYesdmrfH19HRcXFx2nDkcsBwu8P5LAApnSiKg6JHbOmR9ZWcq2XaqdS29dSpudEpdcU6bssnJogxLkvJ5XV1dl7ru7u8WZpT0OxWJeOMo4qKyP2wTHOM12DqfTaTx69Chubm7i5ZdfLvMBLwRh7NS7HNf90QdZcZ7Hkc/7fF0+nMuU+T2dTssVTi+//HK0bRt7e3udK4RYL/ON13A6nXacToIfOJ9+luy0qxHIzjq4Yn4EHCxg3Wvjc8bcPHlf6B3bHu4N/+Sf/JP46Z/+6fiar/maODs7iz/5J/9kfNM3fVP5/j3veU/82I/9WHzgAx+IL//yL4+I22ztdDqNd77zna/XsHvooYceeuihh9cJMHTtiGVw5ipnb+w04Hj43k87dPTjfYS8a8c277XkPdqyE5A/A/jOn+fslKFWGlpzBFy2W9snmUs0PS87bPyfM725r5xtzH2TnfP1Os6S3hfyvI0XOzN8lteMd/O9pn7Ov4fDYccRzSXBrIcPVHKm0OtChn44HJZsbB6T34+ITuDEWcga3sC752a6zw5tXjfml3nG8zC9eW5kwL3GOJ/5FHE+85gyDdJu7jPTnvFXWz+P8Vmgd2x7uDfs7u7GD/3QD8Xb3/72mEwm8W//7b/tEPNXfMVXxFve8pb40R/90fjyL//yWK/X8WM/9mPxF//iX4z9/f3XceQ99PCphX/xL/5FRETZN4LgJ2rM3jH2Ol1dXcXx8XHs7u7Giy++GG3bxiuvvBKr1Spms1kpyfOdc95TE9G9W242m8VqtYonT56UfVhEbnd2djoHqVAeRvQV3kRhESn3wSFcEr9arcr+K3h/uVyWQ03yHh0g7zPih/1T7LNaLpdxcXHRKU/CmPBhIMvlsuCD33k8EdG5jD5ikz1h/xh4oZ2rq6s4OzsrkXr2/DiDwhzylSTs/2NuNzc3Zf8d77/44ouxu7tb6OLo6Khz+AvvEhVfrVZlX9O2qDUZn5oBYzoh6xIRJTvGGoEPMmoYLbu7uwUPXNFAloS2yao9efIkFotFTCaTTmbk5uamHDwCbDOEwBX7sABnioCMD4+LPnxACvskr6+v4+zsrGSNnB0Cly+++GLMZrN48uRJudrCmTl4aTKZxJvf/OZYr9fx8Y9/PJbL5R1+8QE16/U6JpNJzOfzuL6+jtPT0w4O/8bf+BvVNX6jA2tJNs7Ok/ds5ithfHBQxC3/npycRNveXv8C31kW2JFz1hCZmB0Kso7Qtvkgl4dGdMs1kTP0X8susf5kqCK6/GlHxHtt8xUn5hlKoJ3h4ofsHm0PBoMiQ31dDfPzoU6Wie7TQQTmOpvNYjQaFX0DHoyzWlbcPJmdpIjoZHHtMFom0Q9jz3gfDoext7cXEVH210IXxttisSj9QC9kfvlBbu7s7MRb3vKWaJom/t//+3/x9OnTMhf2IZNh5zooDhRD77CuzpjbcQRnXAt1fn5ecJkdWoIU8Ar0Sz/Wa5keHfxYrVZxfHzcyRpPp9M4ODgoB2fZDuHqLXgqImI+n5cx0zf0ZbuHtQIfNfrIQRrafBboD4/q4ZngZ3/2ZyPiVlj89m//due74XAYf/Wv/tX4iZ/4ibi8vIwPfOAD8Xu/93vx7ne/+/UYag89fFphW8QxZwkc6cz/R3Sjs47EGrIxY6PQ/eTnaStnMjweflzets1pAvIeL/pxezXHLBt4EXHnQJA83wzO8OQocQ0PtUj3toh2xpf7ynPI79g48vvG9bZ3vS7b+sk4yPPO/9dw5/7yM9mY3/b/tjFte28b1Hgl46uGf96t9W1jsIb3zBeAg1LZIM/zpW3zy6vNqza+2vcPCeAhjH3jqcardgadxc3OX/6hvSzjcLa8LzDLVx+ylAN0mRYZr+Vulr3+zPPLWdvcj2k2nyRsWvRBVpa1dlrcf8YHYzGNAzlD6MOLcqCoFkzaxiv5/21yvSanARxU6yaP0/RSc5rcP/PKGVv/2IHmEC2Xw/Nexod1QM6gez1q8jPLZa9V/sk6O/NSbQ3s3OLQ+jCzGo5NG75Cy7ZC1pWm17ze2/DtsWWavS/0Gdse7g0f/vCH47u+67vi677u6+JDH/pQ/M2/+TfjN37jN+Lw8LA88573vCf++T//5/Gf//N/jv/6X/9rvPjii/Fn/syfeR1H3UMPn3rIwhiFQaYCRYjy8ImBJycnEbHZ3+X9QzYEybYaiKCen5+X/yOiY/g5qj6ZTEpmlIgqJ06iDIluHxwclIgz36PEbEg5Gmsl37ZtHB0dlfaaprlzLUbTNOX/8/Pz2N3djRdeeCHato2Tk5OCk7z3zQeMkI3AACHiTfY5YpOhdLaXQzd81QHvEoGPiLK3iYNDPH9KuLjigYwP70yn086+OdbfTjyGUnaseAc8kZHEWCO6zrpx4uTFxUVcXFyUrC8ZbIxSO+g+edSGjY1E76Nirm3blnVlP1fmAWjORhjrM51Oo2mawgNkO4jk48QwBrK+w+GwZKtYM+bkMcAD3v9GRp0MlrPhZNrB99HRUZycnHSyL7zL2MgKnpycxGCw2W9JhmVbya15jX2j2UF4aIDc8j5QPmO9812pbbs5XMnGME5GduxyIC1iIw/tJPEOlSeMxU6rTyj2nk9fq2LHCWBdR6NRoXEfIMQztYCK6Xi9XnfGOR6PY7lcxtnZWXE87Khk58ffrdfrePr0aTTN7TUzHN6Vad7OpJ0z2gB3zAGcRWwqY1gD9mgyf/fhva84fuiAi4uLuLm5KXIuO6k560x/+ZAmZK37v7m5KVfJMI/Mdw4mkI303tajo6MYDAalkgAc+BoqKgC89t5b6yuQWG9wy1VM6C1OCrfuRV9ZN5OVtvysBU78N3h3hRPZVebImMGp23GFBGsODZgvyN5mG8GyMgcRIjZVEOaZ+0Lv2PZwL7i5uYmv/dqvjbe+9a3xAz/wA/GRj3wk3v72t8c3f/M3xw/90A+V5972trfF2972tvg3/+bfxC//8i/H13zN19wpheihh4cKjjriBNl4QRn6IJJ8qiLKEMPDmaCsBDBEKFvLpVY2eLKh5oxTjqTS1s7Ozh2jwZmA7OiiCFHcnpdx5H4wYG5uborzgVHbNE0xMKwQB4NBx2CqRYhr0eFaFpS1Yo7OtALeWwb+bQTy4zZd2odT7P5t2OaSLNOFacrP5TkydsbmeeZ9UjZOsuOQjQjTUV57P7ctsp/p0ZDXtLZuxlvO9Llf48l4hPZt0NqBAg+DwaCMN5/8moNWnhMO/2QyKXSZjewa2IGrOUkPCZxNRTaYNhyY4HmMb+OU70zr2cGLuFumnh1JHJHJZFLK3KEX+kB+Y5DTrmU6fTt7FREdeqB0k3HQTqZj3qNdHCCCkVdXV3FyclLkJGA5XstcR0SZnw/r8XaErAMyb1nm8J2dS59ObOckZ37X69vy7dVqVUrIyYLy3vX1deegLzuApg+vga/C8fhMC34Gh9byj36gPVdsgCcONXMZt59nPMZDLr3NVyvloKYPx9rf3y/bJ3D4WXs7igTxfKKz1yrzAWtp3RtxW1I8mUzunEyeA6K8zzrBz9gM1hW5osFQk5Xm/Tzm+0LvcfRwL3jve98bH/rQh+Lnf/7nY39/P972trfFd37nd8Z3fMd3xFd/9VfHn//zf748+573vCf+/t//+xERfRlyD58RgMAny4oBwQ97Bi8uLu5kHFBUKFgAw4ZMAcrFe/TOzs6Kso7YGDNEfokWN01Tsoo2xlC0KCbab5omTk9Py7jsyGEIOlPL9xhQV1dX0TS32bzZbFYMCdoAL3bUMFCOj4/L+IwXK1EbYBhLrEPExll3FD3i9uqyiCi4cFYPnM/n8zJG8GKn10EEjG7vXR6NRjGZTDoOKtlX78FlnID/hj48DiLijCUfnrNcLksmgVNKj46OOgZ7dlgxoDw/Owg2GhkL77CmZ2dnEXHXeAYnPo00Ox7eE8neRu9HG41Gsbe3V4IbNtbgG+jEzjt0tbu72ymtA6dk7p2FpiqC7C7tgB/2WoOHy8vL4oAwJ57DyHNQynjHcaZiIBugDw1Mf9lYzg5L5gk7AH7f2TbkgPfiOdjhCozcLri3Q8b7XiucOAfRcmCGtcZ5N//mAIznazqDXzl1FhpDvpienGHMOKoFYeA3+NpzyZlLgqBUUtgJjog7Y7ZTmJ8x33tOVLjwnuVj7s/BJf7O1Tb+3riwI22d42DEtgAZ6xQR5S5b5uYxIfdqwULkjoNslrMZV4zP2WZkVZbVrrDJTqDnRtDbctL05/Uh+8u4PD7atczibwIV1l2+V9iATcI4cjCR93KQ9j7QO7Y9vCb82q/9Wrzvfe+Lb/zGb4wv+7IvK59/27d9W/zUT/1UfP3Xf338r//1v+LRo0cREfGud70rvvVbvzX+8B/+w/HFX/zFr9Ooe+jh0w/O9jn6yHdWTChYK8iaoiPjh2LGcYqIOwrYRh8KId91yHPOPljJojApRcp74pwVcJaJqLWdCw4YwXHAoagZKxGbexZzJgZFPhwOO2WCzljzLO040wDgRNiB492IKGV/Lgez4WdFa8c2Zzy8/i6d4x1w9GpZEvrjOe+3s4PPO6w1/VO+azy4PYxnr0GmkWzAY2ThNJK9cOYiG/SmKcaJo5r3IWcn3FeJ5GytMy2M0UEeZ39dssq8bdRRVgddQ4+8l+/UNF9hMDozRMmq19Lr6bW0sfhQwfLC9O+gAkGmHPgh0IADdH19XejP2dTr6+vCsy5L3xbUyPg2jxFocdCM8fOss844aGwHWK1WpUoDWmMMnt9isSgZWB++A48wf2gHZ87BRgdYLKNqtGWZCa6tfwjcecvAaDSKy8vLwgMuOd3Z2SnlzQY7+Jn3wcl4PO5sa4i4DTzOZrPONgbLOPOxy73hXctq5IP1mDO10CV8zDitT1kn1tC60m0RJCSY6nWGxpumifPz8+Ic0x80ZLvBW22gS2fiCUCzXt56lB1m5B8Ba98d62uWwAe4Z5zM07or6y/GxCGQ6/W6BBWPj487QUHPYTqdxu7ubpyenpaDDX2CtzPzzwK9Y9vDa8IXfdEX3dnbF3HLiB/84AfvfA5h9tnaHj6ToG1vT1l0BBpDlv09bdvG/v5+cfacEbCzh8Ngx4s2j4+P4/T0tBg/w+GwZCLhU5wODCdH6SkF8+EQzmjkUjUUGpk071Xk8/Pz86LAIzZlYGTt7AR4ryj3MRK9teGAseT+7ZDYwfS+XfCBUZrL/uygY0TY2CPLaWM8Z2Cc4bABx3NW5E3TxGw2q2Z6bLhhLBGUsNHMs16PnNH1aaYOUjDGiE2whDJJMrzscZ3NZjEej+Ps7KycBgx+aNeOhPGajUo7dK4OMH1koxpjzbjm5NLFYtE5edoObUR0nJ988BPPLxaLkokyXUCDzn7ZkKZ9Z0bAB04SRtje3l6HThgzvOgMM7/p/6FCDqhtC+zkQJkN9BwMNG3ZcXPbtbYc2Mpywf+bv92Wy6Od4WVuzM/ve/4Gzz/Tq5/xyczgyo67+8jv+/9c2p1lkefPZx5XbT7OQvJ9LUPttfXnzrTXnvHfOQBHP7UAnNupBXWzw5j7rgUQ89i9hrWAoPG4jdbyeHNAseYQej787/78XN5WU4PsOOY5+CePadt7tXXM84W+TB81OfisQb/ese3hkw7vf//7Y7VaxV/7a3/t9R5KDz182gAn1RlABDKGxM7OTrkOZTabda6vsHEWEZ1MX8TGCfIVBRjpXG3AM1y4zuE4HoezUjaOUI4+uIJMlqPtWVFimO/s7BQH29linhkMbg/Y8Z57IsNkoe104vA7++lSPWcmsgHB4VFcVZCVs7PY4BuHyKXJ4NcGDsrdzqKdUpwwO5c4tmRxagYtbVAiTZbSWSk70fRPRiIHRnKk258RXFgsFrFcLsscJ5NJ7O/vF7p0NhX8ujwu4u4BLNlxYI707TZcyk4WjiCGS+y8x8xrbeOLQI0dfMaDMc/1MD5wjCoBG082anMVRv6xg0qAqGmaglu2GxCoYZ38m8z6QwXv/ffaRkQJzo3H49jd3b2TubUjx3POJPF8RHQCa7k6wbKB9yOirA+0Q7Yp7xXkO/a8shUEmepMmKtqmDO/8zojSx3s4Tc05iu/zMOMC8iOsXnLWbfr6+s4Pz8vbfIsZcKMgWvc7NxZHoL7y8vLwr+eL2voQK71GTqO+ZnHc3DJB/0RwHIAMetQ3ndmFbmDXoUW8xVm7pc51rKW4ICgbi3g4bU1/vKauarDes5rSBDXDiaBMx8uSXUA1wyCa+tNt+sqF4+Ltct60pVApg/rq1yxk/EWcRtQ5Lq7g4ODTqUD8twy9r7QO7Y9fNLgF37hF+I3f/M343u+53viHe94R3zO53zO6z2kHnr4tMBrRV/tBNmA8enIKDyMQP62UWZjz9FYDAkffoOSoS87GVlperx2Lq2EcNpxJm0wusyOsdecQU5v9NUbLrE2kKmkVBHF6jYYW0R0nGM+39vbK0rdc2aufM4JwnZK7Yg56myHyJlSBwcwPmwkNk1T7gzG6alFvlHkOPz5QDDf74iT7r1dzqCYlmwYgx8bfFdXVyWjiXOfcWbjFWfQRo/7xtg3bTBnAiAue/cJwcyDLD0ZeWePMXYwXKGnnJ2Bjm3A5YCPS/HoJzv1OWPE514Hl4Caz1wm6UPZeJ9A1EOFWjAiIjo0CU07u5UB5yJnzIz3GtT68bN2JhkvvMxa5jHQZ17nHESrZbo8P/rMTqnfy5UYOXhlGVKT59Y/o9Gos792G37g4VpbHrMDjIDlPu3kwBvvW8aYV70m5mnzF+26vfyb+XhcDpplmnQ/yMZta+q+GDP4qI3H86qtt3Ft3etKBVe92A7IWVwHgzz3V8tAZ12U14t2nGXNNoUP7aoFWoxr2wA+LNJ9PqtTG9E7tj18EuG7vuu74n/+z/8ZX/qlXxr/8l/+y9d7OD308GmDmiFWiyTnAztQBESknSHl+VweiWNiJecDd3gPpYeDGLFREnb2amOnDytolwDTlktMnUV01tlzpfSVtsiW+Gh/+rdjizNggyYbGBw0RCQ+Isppm2Q1fRCM5zCdTmN/f79EkGvK3gZdxOa6HRw4G1A+0dpzms1mxXj3+ma6Wa/XxbnM13I4EEJwxHv48iEmngfjzAYT60SGxidRZ6Oeg7G8F8/ReNOgaRuaa5qmk9lnfdq2LVdMZTo0Lnkn8w2RfvY7mvfML5TOMx/m5ECM6c1BHNbY9Mea+/AwcGeHxzhn/xnjPTg4eNCObXbM7LS4/Jx1Yb2z81rL3rAu8DfvOlBm+rm6uipnFmBMEzixfPFYIrqHlZ2ennaytF5zPqttCyBrmR2sHBzyM95/i0xgX6t5ijaQpWQaLZ8sQ7J8z04RY8mQg2Q7Ozsxm806ThRZO3BM4CwiyjaInDW0Q4ss428CaRcXF+UZVzQR/HSQ0c6ecW0adAY448Il7gTzkIvgYTwex2w2u4Mf4xocGW+mYwcRTDemvdlsVnSb6cs6mMwzbbuShCAhOEbWUVHi8xKcfY3YZO3tuG9zhL0Hm35s5/Cu99E6c58DT+bHZ4Hese3hkwa/+Iu/+HoPoYceXheg/BAF5sMcbBxgpNgxQKBTzgXkwzMwWDAGbchZiURsDJJtTqvLqlD+LjXKDk1E3cnBEafsDuWVS9Fy1iC/3zTNHeeQvihV4iCTbBChpLmL1ePDOa4ZyqwbOD07Oyvv29itBRY8Psbgaxwyvvj79PS0M9fs/Dpj5Ds8jRc75nmdMZJ5lznkDArP2vk0DRm3tJ+zXD7Exm1Pp9OYTqexWCxKBthGHAYiwQsbgeDba4UBjlHEc9BuNljZV23aNq1TMl9zPlgPZ19539UTdvBzab9Ll80TGPoR0Sl9JhDzkB3bTKfOJhGIIGsP3QKZ33IAxWWO0BXOk9cV2sl3K+eTqZFfGOg+uRsHhDYIMhEEcTkyWzp8t7XLh83H4CJnv+ArtlagM3BqjUvoh6CM5+igSo1nDNkxy04vYL2Ugwj85jaAy8vLclUO9wf7gCJnx81HlNMS9FgsFuXQKfQr77kENtOF8eD5WMYZF8Y/Y3FglmDszs5OqQryVUpuB9npyirrfTvfNb0LHg8ODkpQLgfMHNwlCOAANH3acXSA2LrFh1kxL2gcGs1ZZ+ujWqURa0UlmatcXL2FzkZOe/1qtLoNese2hx566OETBEoJycjhJHGSJArFJa9cC+JDeCKiE023smcPD0rj8vKyc/WKFXRWZIZthgQH6pA9c/8RcUe5sG+KqPXNzU0cHR11lHa+yiQ7cxgvGIt2dJkDkX6MIhszGAWMm+gyJ6Yy3+yogScytaenp/H06dOOwYNxeHFxcaesyj++roTvbRQal2SB9vb2OlFpBxcwVOz0RUQn6o8z5Gwm/ThrQ7aENc1GHNfrcCVTdmw9J38fEZ3Dz2wg7e/vx6NHj+L4+LhjXEGnq9XmyqC9vb3OHm7o36fSmhdsaDtTyppFRJyensbl5WXZy45xbaNuZ2cnlstluarIWTVnRfic+x1tgO3t7XXawIHx2MmCLJfLwis3NzfF4bej/5AdWyA7p6y7eTQHd/y9gxHmmczbNrzdp3/TlmWl1xwn1BkqB9YAyzTuZLUDYsdo27VkmbY9V5zsmvzxdpPsAORKCcCBurw2dlbdZtYttBPRLa91MAE+apqmUylzdXXVGYMz2RmnEZuqH/N8DjZ6+wNjpm9XGmV9aBrKARRnULO+dIWRg7n83laF43HguE2n01Jx4kC18cghiw5uWF+4cob1M98Yp+YJvme8+aDGHNjwfLKOqMloeCYHLGkPZznzsLPJz+LQAr1j20MPPfTwCYL3hThDFdGNBKPYcmki3w0Gg1KWyCmqOZsV0b03zk6BITuU7t9RYmfs6GNbhoXxorB4B6Wcy53cj8EZLXBkx8b9WBm6BMtZNBQ9Y3LGxgqXvt0eRqMdl+y82mhi3FbcOEo+1Is5ug+352xQxrOzrTZojU8yBDWjIxs1NWd5vV7fKQHMTnA2bMFfRDezz/N20p15szG8Xq87BhTtOLLPvGyQMwY7Ph4PtE0W0Nd7eKx2NGr8YkPPOMzVF9m4c4mks0nmhezEGH81Hn5oUMuSM3/Lk4juPZfGE7Thk9nhJVfD0JcdD9rEIbAzErFZ7+l0Gnt7e+U0+4jbYBR8zphMkwQ25/N52dLAYWWcik3gzXyPHMoygGCfHQXLxIgo1Qer1aqcdg8dIQfJomWHLcsN5PdkMilbOF6NLllH8xmQA2fOUJO55bAwrxtOr+UcOgVHnfV10NVlyXagkSHIBctur7dP1gc/y+Xyzh3G6JWs+5Dx0JIPjaNN0zAyajQaxeHhYYxGo3j69GnRHw6gELQ13Xnsl5eX5V55V6XkIKYrAlh/ZBO8wbYAX78D2DEGHw5Cmz/Nl2wlskyEpsCn+cA4z9VE94Xese2hhx56+ATh5OQkmqaJs7OzEoV16WLExmD2wUd2lFC+f/AP/sHY29uLj33sY3F2dlZOQUbJ21Fj75WzVjkqbYeDcl2MLUrmvH8m7+0EiOL7YB32NZ2cnJTTLYfDYczn844y8n4ljJrpdFr2u2FI2HBDOdvIwVC9vr4u2W8Ok8Ig3dnZif39/Wjbthh7zMn7mCkNu7q6ivF4HAcHB3F1dVWuubGDxhwcjHBZ+N7eXpyensbZ2VkxkihTY7xeH5zyHEywszYejzsnhmaHl6qAfM+wHTj+JwvK6cxkoV2y7DspXTbJuBiD93HZUMrZZ5+SHRHlJFZwFrE5xAknxUYl/GDDp5ZxwXm+uLjoOBIYzuCftaa80fQIeL+bnS3Gn43uiM0hZ/AH5Yq0hQFrw87rRH/PYri90QBarO0fdHaNz21A17JGObvHZ3bGHHAxH9doKTuoObPE2J0pzBl2+jUvO7jmzJrbdoDJbVnGZCfFgbMcLLPjl4NONdwZP6bHjF//j97yujgIZ3w4mOHvLKe2BXscYHPwNX+Xs+iZTjL+AOZbC7jYETR9em4153/bOPzj5+woej1NJ+4j84n1WQ38fHauM00wFhzvWr8ZB+advIbGbQ1HxkdN/m2jrdeCezu2//pf/+syeKIEjpBwrDSnSrIwKJGITUkBBos9/PV6XWrvHfl31GI4HMbh4WHZp9Y0TbzyyiudqxVms1nMZrM4Pz8v+5lsPJjJMb7Oz89juVzGCy+8EPP5PJ4+fRpHR0cFsSjott1sHqc/K0AM26urq5hOpyXayPO1zAERH/Bioeo+VqtVqbNfLpedwy+sBFgXb7znb6JX7FnLUR2X+EVEKaHa29vrHBmOcQl+UN68k5kGHHofhiMylGXt7e11lAWMANOyByfvifGeEzI5b3nLW2I4HMZHP/rRzgXrWamBo7xOEZt9M0SVzs/PO07JfD6P/f39ODs7iydPnpTDa1zGyCETjgxaYTkDxemfRGBZYyLMtHFxcVGe9b7Lm5ubuLy8LHseodesfGxs0W7e6wDf4rTgNHhNZ7NZjEajTnR622Xz8PM3fuM3vraweQNCzRCygPd6Y/jmCDf87qsPakoGXo3onj7MOPjc7Vq5QX92VP0e40QxOSqbFVfExuGrKcttysjZEsaNUjfOwAPj9Phpx4rR+PBYUbru37onP28lbAPC42MNoPu8JsaNPzd++MyZTN+XGrFxtpyJ4V0bNTXDIBsUPkTGa5oDMPk7O1/gytlW0zEOJM4kY/S8txlpxh1jdFkka+d1BE824j0enrHud8a2Nh7TjfWn5bD78DhdOmlnPfOP5+rM9EOEXGoKoM+wEZB1VE9wnU62JyLuViZg21hO+kTubFc504s9QTl5zYhfLBYlCJhtBmQAthl9D4fDjn5nvD4B1raU7TACd3xHVjAi4uDgoNgJzrYxFu8jpj/GYr4mo2abhTlA03m/cw40WcbkAI23KVj2+T3b6ASPsqMKfUAX9gtYGwKj5k/zJXPjb9bO8zK/E5zmtHivtzPLectGzQbITq1L4G2j7+/vd2jFwRjLDOtlZ4VZd/Sdg7PgGH8HO9HvkeXe3d2N+XxeKgGsIwHbqGz78NVbZJ/Pz8/LVgsHCzwfZ8yZr20mbID7wr0dWxDl0g0QCPJh5tls1iEiFA9MaYWRa7RhdA5TQABQp31xcVEMKNpk/woZktPT005mYjKZlL0wg0G31M9CjhO9WCjG2jRNZ941J9VXRBAldvT41RQ0ShaGnc1mMR6PiyB1qcbJyUkHtzCjT5qDQSaTScEDY+NdC3MTrOdkJo+Igkv2CPGsS//m83kRNL6vjrvfvA+O8YInnEKcVJjZpQwux8D5Yi8jSnEwGBRnLDvUGFsW7j5J0VAz6CI2itX7aPb39wv90k/TNGXduE/TV1nY+JlMJvHcc8/F9fV1HB8flyBODlZYQUBjBISgJd5B2IEH8xO07hJEG7cIOSsf6CLfH0iWB3qhfUeo9/b2qob3QwHkhefooAU8vFgs4vT0NMbjcczn8/I8tLNer+N3fud3OgbEZDKJnZ2dkt1E8UHvlgfZKGMdfEovmSwMI2Qp/IiB47shd3d375RDZaeoaZpiiEV078yFLoDFYhEXFxcdOQSP+Tob5Pl6vY7nn3/+zn5JH9jB/BzQgZ55B3zk7NvNzU2MRqMSVPVa5tJlZBtOIjIkHwi1Xm/254Jfl+ytVqtiSF9dXcXl5WUcHh7Go0ePYrFYxPHxcaEt5gTuwQ+Zb9bHpdDmYeTY6elpZxzQDn8js7x+dgoxttbrdZycnMRqtSoGIPKeMjb0+N7eXpkn73LytNceXNsZIFjIWtp5QR6xXjZWLft8DyWyEoMOXcPcc1CGO6Jt2FkXwePD4ebKHugBHIMP9thaj2CzYNc8VHAwJusB8wd6xplO1jHr5wzQh2WTnVo7OKx35nHr55ohH9F1liLijhzMhwLl4BltbAsA2i60zZhtMzsC4Cxicy1bDtg4YAUgR3wwlnGeS4ztOHq9Mq7ymuREiueUdSY/OVCYnUjLJP7neb9n2OZosr52itGvfO42rNvch/Fcc3JzG6wXdrSDYzkYkHHFWtT4ws/SjoN55jkHCs1rVAw5aAvf0Icz2TnJxrO0kX094yQHnj1ur9t94d6OrQ0wR30ygYPADDVi8rs8k6M1FkooxOw0u18IzqV+mbidpVqvN/t9ssPocVswZSFlQndJFQovC/SacKF/45S2PTccbBMjbZj5cahhmrwGnqPH5edebRxWPO7X5Vi8ZyZizTITRGz2KN1HUHg8ZmzvGYiIO4LZ0fSIrpJzf3zn9cl4NmO7XM1jd5bHAtLZcX77pD/mAmRaRqCbFmqCoKbETPtWTKYp07Tp18LUV8xkpZJ5ODs2Dw1qpWQRd2nAawgtZKPA/MrvLDdrAt99ZP7NY6LfLMf4njXms0wbNjpxGDyWbbI3y1P6qCniPM9M65bHedxATYGibN2vdQ/P56qUmnHFu/CTxxHRPWCF/mwQ5fK3bW3ZcPV3uQTZePT/OVKeyzJreHPwlnHb+DUO8jpZn2UHuaYPHWCrGbvb+qr9diCCNc+f8Xz+P+M96wPjMANrmTMTyD/rkKznHjp4z2ZElKwiQU/sCvTp+fl5CRCYR6AjgsGj0agEq+mHZ4HsKEVEsREI1PjqLPeDfchn8CFOgu2rmh3Fb94j0J5p07SRq9EAywCC6saZM3iWN3wGzUHj4DEH2yO6PNQ0m/2wlk8EB7ONwftes7wmGV88Tx887zF4rzSJL+xNzw+8ZhuTufK9cYZt7bW/uLjozNGywAmTLH8InmU5kJ1JaJtAmXVu1lduwzrXCQ5ouSabrKNd1UA2ljUiAEk2drlcdpKJTl44+O2qFtYUOZcDMODdUCuLtg9Xm9OrwTNnbOnchOijxZkUz5rQuOuKU8AYMBGmHC2JiM5JmTWF7r4MFgyUj7z5zW+OiM0m+ewU5+sifBiIy/YMNuAQgnbwPA7S/IydMWeDBWJCWJsx82EAfgbiILtDZjXfy2ji3WZAev1YIzKbZi47jgQeGK8jRAhil8igRCi1PTs76zDefD6P0WgUZ2dnnZI275nytSrgjjHbELWBhgBwiRzlGETe27Yt2ZZcysR4yRRnoWWBmwM1VszgkiwEgpTxkrHJzgLvwiO1UjmEJXMAT64eAE+0bR70+vD848ePYzQalZJsskHT6TQePXpUDjFA2XI/Y9tuMmgPFWz82Nl3xQhZbbZARGx4JmJj5FCST/WK96HiNCAHUMYo7OxQst7ZGId3nGFAfuTyJ2e8mKc/39/fjze/+c1xeXkZH//4xzsGPrK+limF75xxY26MZ71el6g5MpT2wY/LccnMovB91yxVEDs7O3F8fFxO5sWw8TUx6/VtRtLjAafz+bzjhNmw4H2uqKAqAh3JXmT6hSaapilbeT7+8Y93siisB9k+DFoMG/qxIcA4+IFemCM0hz6YzWadrC/yEHDA2EZQNsSRT+hr1pdKI+T4dDrtOPZkfX3lErL+9PS0o1OtA20wsR5UPIELqoEoKQTvlqWsHfwEPqAjaB0cWMaj/8iGU2FxcnIS19fXMZ/PSzWOszPg33z1UMEnaMMvVCHhNGEfWFc7ABuxufcZQ52sEnLA278iunIOHe3gN5n1fC2W6TwHIB0oqgV1DW4TPZoDY7azTFe1YLMdWwef/F6mpRwAs4PuhIQrVRzUwebhvABvO8LBtCNDG8wJOzMHkvL4wLWdy4x/5BJylXkZr3YK+T8HurBbKcOm8sw6A1qpjbdmJwHeHoZ89vYjdAdyhOtxjO+MT2dM0WWMxcGDWvDTc27bTcUJeiOvg21s1jtic5I/pdNOjGSapk+3me3krDO8XswpB5fvC8/k2DIRLyyObe0Op7xIRIaItLn+3tElT9gLauLJ0ZjaxG2YDYe3B5qs1+s4OjrqRAJ41+VPNrqy0+C+EBIwmLOOdhwQll70XDYIU8MsOZvCOzljyhxXq1UpCbYh6f07KBKvp5kEyI5tjjpZEJsgXy1z4WPejXvwgLHhknU+v7y8LFdD0I+dMjNZjQZpL3+HcPT3Nt6ttEzHML8NPgtaB2Zylsm4cRQu8wMBCVcUZJ6CbmgToUq23orCc/G65MwMfTmzZL51pHi1WhXDkTU235KFRrA/VDA9g/OIbsYNvs18z99eU69xLUNm/ncb2aDxs5Yl/KZt950zzFkZ2VCz87Stn4yj/Ez+yYZVrXIht5NxkI3CbfKB93L/23gGw4BnbHTX5pnHlmWos0IR3QNqLJdMDxlPNZlaoyN/nummBpmmrPMsl+77Tk3n1ozd/L2fsezK/bI+WebnuW9bn1q7WfchF3N/5hO/ax1bK3Hld82Afkjg7T/gwkElHDWvu3GOYR6xMfLzPtJsb1mfW5/ZiWIctrus5x2k5B3sLeZifRfRlbuMN+tu25S84+dtS6JrTct2DKCnnDgBDxF393Cbp0iCYCuCT4+dgHumX9scjMXrYBzX9JyfaZqmY9fxHHyV13Q0GpXgmrdcmI9NRw4WZllkB5WtH9n5Yn55nfPJ7+Db6wANYjtio/Oe/Q7LEn4TIOfaH8sh72v12CK65zOQMOJz+w/uL8tHxuRqBtOTacD2K4kw6yraY93M95mXanrrvvBMpyJno61t204UkkFnpPmeRqIEHHRDtA3BhZOMgWzDIRvzNqQs2GwY0IYj0M7A8Z1L07Y5sI6O5f2pXjj6dtaWudEfETbj03gEVzCc5+vxIlC8d5JI6HA4jMePH5ej5tkPmrOIOEC+W9GlOICdWPAC8QI+EAZc8NtCMRu3NmC9GZ6gBJFY5g3+2PO7bY8nDAj+cRbBr2mbE0PzHHIpStu2ZS1d+mEG9zUFCDnjzVkaSkR8V53pPQtjC1TmdHZ21jlcgT3QucwpG6XshYMvrIgsaNbrdbz88ssxGAxKiQo0f3NzUyoM6JOsIyVluWTpoYH37zs6idIhOJMPJyGLGLEpvTk7O4v1el1O+KWNfFiYnaBMV153R4qpMmB9I+7uX7ED2zRN7O/vlygvwYvr6+uSGWvbNp4+fVrW38aVKz2yEWRjwfrCcjuiW54WsZHFjrzz+eXlZfndNE1nb+1yuYyjo6PCB5bblssYcDayBoPBnaAhUW2ChbSxXq/vVFsgNzi0zfomYpNZ4h0beR4Dv20MmkehKZfCIQ+apumUE6KbjXf6Z28pmQzmGLHZbmEDhnEhu3NAz/SDkWWjhey5rzBiXdq2LXttARuF9MEd0peXlx0jznQNPYAzH5ZinqDahdJW0wv4PTg4iJubm7LWGGvcN0nAgmz6ZDKJ+Xwel5eXcXp6Wmicc0me1Xh7IwFXh9ix9QFKlh0OaEAzBFaRexFRPeCI9+BRtmTZFjCeTWcRG7riMFTsVGiQZ7E36JfAreUw517kg+VqvAtP40RG3FYmTCaTcip+RNcpt23atm3JrFExAC7QQ+AjZ0exC3nWtiZ8jMxkDnZGyJzDTxEbBxc82KbOCRPbS7UMOJ+Zp+hzNptFxMY5R75aFoN/9Kl9EtYdnTEajeLg4KDITdOg8cgPVyPZ9r+4uCgny7PO0DrnIbh60TROv+4v4rZK6NGjR+WWBOTuer0uVZngzPadg0enp6eFhpy8sR3h8WR9jB1CH/AkNMDZQqytz9JxQIazcQaD2/MHsA98eJnp6/cjF5/ZsbWzl5Was1UIKXv3zmINBpt7GnkXAUUmyI7PtnE4asqzzjCYCOnLxt96vS5GeY4Q0YbbZKEtSB2N83uOVCEYSe3bYaGdbMjYGcS5NfHRj9uiZJG+5/N5HBwcxPHxcSkDys4oTAX+rRiM2xxBAaf+LO9h9Vy2OTcOKEAnzNNBD+MIBkFpORrrcfDdeDzuCBob0hFRnGQEhcdaG7eFclZajAv+sNGYcehDtSjFznQOzTIO8EQ/3I8XEaUEEr4El/4xPpir6dWKzYEIjDHmZCM9Ijp8jnJxgOohg2UANIbTbxnk9TUdA64A4NA02jdf8X4tgmwZZzDdZ1lOezYu+czyjnYiNsYbxh8Oid+HNxzkYxy5X/8Y7KBbnuTx873371HySp9ZPtXA7fo368pnrCljZD2zXsztOHBQw33+zFVLft/PZTrINOCAVnYabUzYCHFmDRnsjAzjNQ6sc22g1uiCPvjf+MzgoOq29UO+Y1dkHDkIkunNBmE2Zk1zrLEzsMZ5RLdc1vig7JG1tHx1IOohgjOhllMRd8+ycEAmyyIgywnTZJYj/K6VqmaedvsOqrDu0Lttpm3vbwOPLfNqplnbNdvsKstjIPNRbTw1WZO/s+z0emSbl3dd0u35Weab3/L7Di45eGV8vdaccn9ZXrs9O8BZf/K8ZbirLvOaZ1nvfrIzb31i+zGvh3VATthlnZJxYPlfW5Paetf4x/1ss4OdvMi8mOfudwCvQ/7JGf/7wL0dWwiilr2y8cNVH9fX18VQRwmw34r3aozMRDiJEGfUafCIu5fDEyly5NrKcDgc3jk9EYRxNQ/lDM7u5qsXbAjA4HY2wEeuu2c8CEoTdC47puSTa1SyYWfitjFnggZXlI2SWeBz1oPoecRt1okoDGPgXZcSYYyz5jgw4DQi7kQIwauVFpnMfFqes6CmOWdqoAlOTGUfHMLQkbj1el32P2Hwel8NP96PaufQpzJngcgaYrRg1HLVFOvuPZE2eogUEnGsGdLGQdu2JepHJiYiSgSMq5MccbUQgZa8l45xQ0OUxRCBow/WKWefs8FKpt2KIt+J+tCAE6wvLi7i/Py8GNHwNls2jAOU59HRUdnD3DSb0iJXrhAoWCwWnf205nVHmwlwRXSVmmUfvMszOThE0INTuq38yCQ/efKk0OVoNIrnn38+1uvNPlDolj2URLPBh7OeVBtw/Yej+BFR9Ad4RS6Zz6Bv5AF6hoxYrkJx4NV0THbPRg6ZOU75RW7mwJfbyGXL3r4Azhw0cBsu/XOQz5ljr1t2mgliZqPUgULvNXWmgywjuESH53Flg5i2LeMtW/IeSMsNaI9tSnkvq9fJPEHbtj9sdzgoxFpatnscPOPqofF4XII3DqaShZzNZmWONtDInEGnBH+Q9cYVmeOHCj4F21VN2B/Gl0+t9jkayCMb6P6BFrJdBC2w1rZNbANEbBwQMly7u7vx6NGjIg+RO8hhG93mH+yAiK7zCZ1nOwTw+9YlnLrvts0f1rHQlwNT2Tk3zrIj6OctQ5kPAJ8gZ6bTaTz33HMRcWtLMr6cfYQnfZCWA5HD4TBOT0/vVF0wl/V6s7+erCBr7MoS5KyDxegW1sN2EIF6r9VyuezcIABtIMexOW3HWk8im1gj29Kc5YJcsw8BzlhvnzNzcHBQaMX2IM83zeYcHdO2bd0sa/KBZQ4y5gx/DjJih4Mb26y8z3f5eiv+xma2rQuOp9PpM9+qcW/HNjuS2dCBscbjcedwB5QZpUQ4mLXoviNhGNVGnh3MHPE0oeXIC4tgI79pNvdy+oAVMwJC1pFbRyfAC4vurBrjcdQBYzcfLIQRCvHjWJroPWcYxYKbOWfGPD8/j8Vi0Sk/WC5vr9eZzWaFSZfL2+uQmqYp99YyV8oFsoHAOt3c3JQgBgeEUWbuy+npF8GGg+dSW3DJuoO/vC5kCq+urkopFwwCwEwoAox/lBzCCmWFY5uDBQhLlAnMB94wfkzD0D40T1kSAh8DEYVN26bbnK1DQE2n05jNZvH06dO4uLgogSAbX3nPK/hwNJS1hCdz9h2F6/Is831tX5QzHS4fI3P2UAEjDeMnYqPA+dsOQcSGPl1lAZ/yOfIQ2QLPmBdNGyi0fFiXZaXlo8eZI8d2cuB3y1TozZkPgmWuejCdWXHljA3yyaVJWS744Au/a8eEtgy+oipHimtBVsZrRxGcUmaf6T5Hle1o2rgwP5vPvVa8W1tDG6WsGf1loyqf0m595XfswJuPkdfgwrhykNtjzLKLH8ufbGB5HZFHnmvEJstrYzsHIxgnfGSeM2743/rS+DBOnFXO8s9BAnQMa4bORydhE9j+sSP1kIH5OmgNPWWZCE6tI23sm26yTsm6kvfMyzasHRhkTASN2NKFfeg7Vl1BV+MBBxPpK8sAwPYOY6IPnCYnMpin+cM4c/lwLQhtqPGTZZsDQ8arn3WAH5uBdTOOjCvrHs+rZg/msXr+dqTMs9vmaDqE79y3ZYTH5aCe6SzrX+OY97PDyjM5geP3DA6SEPRpmu6p3qyD22YNauvn/sBZzT7L8hX5nhNrNb6szQN8umTZuszjtFzYNr5tcG/HlgHkfYg2duyEeeMwRjRGkE9dtNAh8pMdyqy4DBANvx3JYtwg2so2ohvRYawgEIeXDds+Mc4L47JD+rAQjuhu4PZYaS87cq7/x4kbjUYlC47CdD/ZSIQ4nB12NCdHl6zkMeAIJvgyap6xEsfYI+DBnPnOWQoE5WCwudvUZUrMIQsKG384kigAmNrGsOnB+DGt4oS4tNYRd57l/kE7gzbwtykLG39eaxvsViDO6prWYHD4C+cYw4qoN/i3AcqcGEMODvBdzcAGp5xszFhcfoXQdaDCgo4xe5/9QwQuKs976bNwN13kKodXXnmlI1sxzL2vtmluI/Ln5+dlzch22ghBDmZesgzCCCFzyjvHx8cd58CGuNuA3qyQ7Tg68m8ZSpCHoIsrEyK6ZyA4KAKtn5+fl8g748f4tPPMWHnHzpwVsg25bBhZ+dI2/bdtW/aWGc8YpLS5u7sbb3rTm8p4r6+vq/vUnWWxMU82w/uFbSTWzmrI+/WZi3Vt0zTlXuD8HGADIx9QwpwJXjEm6MjBTPbH0h72AcEy9uFxKnLTNAW3teqp7PC3bds5kR15mYM11u3WE+Yb0/Hp6WnJCnFHuzM06/VtJh8ZCv3Spu/PpQqDucAD5sWHCsgPBywi4o4NGdFNZHgLQ8Rm/7htLs54yI6DZQpbwPg7G/oR3WoD61GyzbbnfLOEed+Gv8/ZyJUldkIvLy+LDMt0bb6zjcT/6HN0gB0t+Mx4Yfy8b/52EI7nbbv4HWga2Q2vPX36NCK6pzZvc1oYsz+nSgkeM5hGWH9wAx6oCMo2oysMa+CxIn/Qe1RQ8Tf0iw2FnOJeb+Qqz1u32FljHgRdec5ndIAXbPa2vb0tw/a62+J/J9XAv4MP4MJ+hNuBhm9ubjrnpKzX687VT9aj0JCrXSI2+tD2Kf1gxzM+xtK2m5seBoNBqRq8LzzzHtuDg4PY2dmJo6OjuLq6KmlwNjCjoO3k5sNKckmJT8i6vr7uZL9MRHksFm4s1unpaUcQOMpAPywEVx8YEDoQLEYViLUxz8LkKORgsMlYOlO6LbtgY2O1WnX2q5FZnc/ncXx8XMoCKbcyHiM2QttBAWfkxuNxuUbH447YECJ7NhEwdto5UIGS15ubmxiNRrG/vx8R0ck+eQ0REjYKptNpLJfLcggEBq/30Tp4AM44ep51pn2cQ3AeEeUgHitPFAoHJ0Fz+Woa+qb0kENBEEQ4EjZ+vaaOtOH0YaB6H5aFJFe7mD6MQww25uo9tpQsZcGRgxTr9boYtF7jnFEkYk156dHRURmzs4L8Zn7gwv1mpfzQAAMV/PtamppBwlpADxFRqh588APOITzjbK4DPJZtKIns2OYxmK8A5OJgMCiHRrm8z06hnT3ezU6H+QDF5oOEHATiPRte5mnKmCgFRNnbMXI7Ng6gaQxKZxsd9PL/2TGEP8xDNcPABiX9QhcY4P4Og8cBLTtm8CFjz1dHWJZjmGGcZdnJHGu61/vHABstfG9aq62pfwjOUtHgyivGvrOzU67AQiYzJ+sMxuOMm9fJATavidcVWnNA2m3YCOTvprmtvHFVjAOklCRTAeaxOkPEGAlGsea1gP1DA9soyJGclPB6gn/jBVrLQXkO5gO8vjmg67ZsI7EGDv4wrnzQEPLG87KMpQ07rx6vtx9gZ3GwkcfnQBH8Qpt2pF3ZmHGO/DMvmD7d17b1Ml757SAAchTnDhzRjh0g95vxSADSSbOMV+aAnWE566DacDjsHOoEzu1bWA7zt2nEATKy4N5axPixozgQan9/P6bTabGtsJ1tD/EDnZjOkYseV05uIZPMC9ajGe/Wt7RPZSj2hG1YkjmLxaIkUvLcTSO1JKR9Jdv9lsM41wTWzU/oACoznwWeybFlsSE0JofThHPBhCDMnNUy80dsIu4R3X1jRnQ2emoAolxGy0LCiETgcgSJqIizUXYybcDUHO0cPfGc7dDaILBxZYEKA+Q5+Boc48Nt8n3OKDqTyph8pySfQVCOCNWEgNshqg7OWEO+538MdkeR/Kxxl/uzYiH6iuHnZ2E+wALbBq2DJswTnDuz1DRNEVAWSuDc65yVQ8SmVN3Pe01Y6xyoyYIcGmNuDlqQObBDAA6NH9Yfg8y04meygMwGgA1dj9285Pk4m/RQwRUcNthsXBhH6/Um8w1uHABzJUtEd5tG27adPaPO6pEZy8YdvMd3TdOUgBLt23GO2Bh/fM67rD0nQlrxEC0noIJBUDuAhT7ox3uwGBPZRxQc45nP5x294Wg6gT+3aVybzjEYbADjuNiBY1zmSxxIO3gOjNpxIcqOLOHUVYy5bPAQJAF3Ed1tFozPBjhO4GAwKKf5Mm5vbTEvOuNhOW85BTAWByDzOtIfuHBmALltGcx42KuNg2Ldx/zRMcg7HHzGQKCIcZheeMcBgZpT4JOf7eDaOIQ/qBpgfz30R7A76022zKAL27btBKgfsoy0vQRP2YkwwLc1h8h/W67SvvmTfl09BkDjts0I1DsIFrGxeU3rOQDkz5mf902z3vw4SOTAnGkmyyrGkB28HNjiHdOZ70FHRljf1OwKA32ZZx1oh35dbYOcN9/RB7rBe17zfPMYbC+blvgO29y6zmuS7fosu2p2prcV5fUzz1r/Y1tHRCdrb1swB09sZ+V1zOM3LeRKANuUzAd5jBzlt+8+Z54Eygmm2OF11RhzRydiN9AX427bzaF9Dh55zcEL74GHHOB/Fngmx7Zt204J3GQyKdEyfjAGsrDxRFEqTJI9nXbsHGFismQ3mmazjwCgz729vZhOp+V6GxCGw0upnUulWaT5fF4uocagOjs7i93d3ZjP56V8FQKw0+eDIhAcjio5gsc7GIMQkwmOKzVgfjKPAH14fw9CCUIzIeaMBpHniI3QZY8pJ+bStrO3Ni5QQODL64zBiABxBD6XhzFvG/gRm8w+38E0l5eXd0oTYDaujWB9ssFAVYAZzaWRNmxp4+TkpCN4IjYBmFrWKzva/G3nHvxQ6QDt2ij0WloR+0oM8IqD4UyNjT/aYk5E4ubzecnC5+wBuINOmCu8R4UGB43Z0bVRm5X0QwTmDa/bEMiBFhSFSxb9vytJbKQAOLAoHn47+mvjImKz99ZZCPMQjlQ29nF4s5OOEpvP50VWIdMw4LMCNECPWYGb/sAp/Er/ZGr5QVa7+gIHgnnAd95/Bs6hUfiV0kBK+1gLZ/7AuR0lyxRkHWMgk0FbZBOI8gPMEeecv21kMJdstDu7yVquVqtyNc1qtSpls+AlO6deHxso/h556SxZzbElm2Ej184iATbkNmN3NsB8QbaT8w7yWuJIQK9ZX9lgzI4ta+cDw3gfGc5YHZgdj8fx3HPPxXq9jidPnhR+gS5dYkdQwLIdOufKlYcKBNzAiY1pl9I7gGCashMb0d32gP6DN23Yc+aLnWXLHutzO4DT6TSm02mnFBPIhnbWb8gHX7/DmTHwG7qWajBos9Ze5teITYDaWUhwYj5FT9iBYXuAnWHbKjnRxBpB064sNB7xCRwUs81EeyQlODSzlgG3rqIPHxaIfLL9S+aboJ4dPmgj4pbfTk5OOtWItGV8cwiXgwmsi8/FcTUScoPqyP39/VitVnF2dhar1eaMF+tn1hU9ajvaTqHnwZw9JvwUggbYZuh+bwtC5mID2PnFbscOYf7eAuDzBRgrNkHe8pkz7wY7sZzXw/hcDZu3yb0WPLNjmyNCOVJiA8apb4MNDkcDQCqMaOFvI60W0ULR5Iidn7dwdLTO6XwrXysxC12IMaK7Zy4iOsqPcXvfrZ0OO201pcaz7o//Hb3K7xsHGR813Hm8tOc58znv57by2ppps9FnQyS3yWcwrpWdv2ednK20MZ7XyWN0AMH/m34cYHHEsRY9ttAzXngWfsj0y2+EAwIRJvfYa8Y/75vmHQWzQLJQzGPNYB5BqGSaz/SDscm62MB3xPghG24IePBnWWj5B05c5ZAPlcsOX16/9XpzCnnmN68XNGVDjx+XwkNvzghGbOg0GycOqBHcABgDjm4+rMnPOYvBZ/TrH4wZZB/Gsd9B6TJ3r4Oj4ObDzCvGt/nP+4NYw6ZpijHtMt5cheOtANBJXidHsXNAFNlmuQmOcOSQV9YpjBO+JGjpOXkd7Hyaz6FXAgx5PZxxsKOAgQxOMKiNwyz7cjSfdl2KbZr23cERm8PtcuDPgXTv6bPMp207GHbuXT6aM84E0O0QOxBqR96GmmW7qwYeIkDzWVdl26SWrOBvINtx2fbJkOVLtilN79CD+9k2H96p2Ub5mZq9mnFTGzPjzBlD48/OfHbWsx3gtl9tfnkOr2VDGpdZzmb7DZ4yHyI//axxa3vEbXmcNdmecWzaqckay/1sv+Z1Yw6WuZlmjR/PJ+Myf24/Ks/DbVnnZF8l4zgHMJFNtSBNbsd95/Fv47kaPxi/2L9ZD38y4JkOj4qITkTICGDPJEBEBqVhIiI6w37Yw8PD8lnTNCX765psG461BXQ0G8cRhEN4PEMkgIuOHz16FNPpNC4uLuLi4qKzRwnCIXLrC6iJFA4Gg85ptzYQuJR9sVjEkydPSlSYqF3EZmN7Fno+lMOK0YatM4Yo7xqx1Rx/5sa+r+xgmQDpp+bw2oDIJTVEBi10mDNGJ7jOB3pwgJgj3pQktu3tMeePHj2Kq6urUsrGXHOWkTFzdDuRodFoVLKdMHku28sCgd/Z4AcfCLvB4Haf4nC4OeUTvDAn9jxzoEzuHzrxGtjAOjs7K/shLPQo8XFZOTwAb+RSWPj2+vq6HPjStm3JKNWMiKa5PVCOy8NRQKyVr/76ZAmt/y8CUXeySHZEzXNkEy3j4HPWiAg8kW1HlZFbFxcXnQg5hjG0Aj/6kLccsEOxEBxCpnhbBfRvJe/Ajx0mKy5fIp8VWESUcl3LmJw94bcPGqIywIES7y0HX8j8vAc2y8uIjaNSU7asqXHK7/l8HuPxOI6Pj0tE3ttxWB/LeqLn+UA+O4mMDVrKjj3jJ6IOXnIpG/rp5ub2KoWIDQ+zLryL7DeNWB6S4W/btpT8OiCCfjEOoRt0jPHP+ByVd/AFHCCXCZ4ZH1wJSNsHBwcxHo/jpZdeKmO0w845CmThaB+axpi0Y2u95tJ0HPWbm5s4OTkpcxkMBp3ydNMXOLHuc9byIUPOwPqwJJe25uo2b1+CjzkDgEAGmT/rJ/pyxs3B9YiuDWI5RT/oM/Stg3SW69bndmR8RQ+8bB2I7eP2bCPA89jW8B7jNC+BF9tuOXhKlR1jcN+WP1SI2AllHrniDt4gmwvOZrNZyWYyBnjRZyaAU88LmWE52jS3h7fZHiZr3LZt2QMPXg32XbALfZhdRLfsGMBW5DeyZr1ed7K7ri7FjkWmQasclEpWFnBwj8w1so73yY7Sfs5IU+G5XN7ebJLn7DJp+gT43L4G8itXI9pvoA1w5gQnvABPmefZIoQu2d/fL5Wuroxw8MNy8r7wzNLUzoONASMMZsuKOv/ttrLDWov2RNw1Qmrjs1Fkw8zIyY5WjlQzBuZg58rjoX1+tkWqDMaTx+Jx20FwxB4DJpfVZKHtklY+t+EIIzGOPF87c1mIG3jHgjEbqmYIr1OOKG6jGQtcRy7zGvqd7IB5fT1mf1dbl2zwehy5zNn4yQ6N/67NPwceTAsZhxb+ftdrZlzWhFMtSgfUaODVINOt29zGpw8NLFu8fjkqjnKlrBLHwLTDe7n6oyZn7LC5T39OMIz3TNec/O6yzXydgOnI9GjHACOVueBQYrTmrAPzMb2jTL0FImJzVQ/jyHuFXJKdzwUwb2d5X6NZDBMbyR63A6Z2YHJZufHD+hHUxKgzXsz/4Dfzb+ajnDGljyw7vF7WbeDQWeSspzH6MeYw5DzPXKHE2KEHHOQs82z48S6fm/ZtRDXN5i7cnEm342GjzDoQwxScWAdAS+4Tgy1n2lk7DFOvV14DXycIjh2sMJ88VDBtQTeulKrxV0S37BfcO7ufs38Zh6ZLO2n5M+jDji395L22Wa/X5uq27Qz6GcYHf1gnMyboG7vPASO3ZX1hGwgHEWeLMddslyzn8xy34Zggl7ci2bl2xRf6iGCqqxpq9rLXxYEIeIhxEQBh3WoAjtBXlnnmTeMD+Wj8GzfWIfxkuz1iU26fg16M3fa38WO8Z9nGj/HgrRWMD8hVLJ6ng9WWb54jz2fa89g8L+tgcFDz43KwdRvNPYt8vLdji+L2puTcEYvEcdBcNcI7ubQXA+j6+rpcyEzkLUfzslLNURlHnB1NtePnTfMoyPX69rj+9XpzyqX3rxJh4BTSF198Mdr2dq8xESPjiAwlmTGiWOxrGI1GMZ/PO5lIIlj5lGYiRefn5+V+0tls1qlDZ46cKglxHB4elog2kUEO7ri5uSn1+Ca+zKwQmA0Vf8c6ITjBC8It04+NvZx14TuvD8Yq+1AcvWUc7JWA8K18WEP2soIb1oJ7fJ3dtNHBPkH2+pJZoU32hdnZzMqAqgXjET6iqsGC0kELly9aEDnr4/3gNuBsoDl4UHPKGQslf96/abBj7zmdnJwUJ8RCCEG+Xq87RvhDBFdQ5AyDeZo1dwYK3FG1ks8PsNMHDpGH19fXneu7IqLQ9Hq9LocsQatk9wHkwOXlZbkT+eDgIAaD2wOPbETmuSDbqUrhPADagIdxirzf2qWpdkJ4F/pbr9fl3lhwiuONDCXy78yGdQ7OOjrARmA2KJ1lJbKObPNJ1227qYpA/rG+8MV6vakc4TkyGWR40TlUCjEe5IyNqazcccBtOMPb8Lfljg1IxkNGwgeJuB8+IwO1Wq1K6a0Nq9VqsyfK9GIcoZvRi227uQKQ2xbo39eguOx9Op3GwcFBLJfLODo6KrQVESULi4wEwOlyuSz06dPETcvoyL29vWKcUsHCcwSAfL6BjXnbStAvJ4ySVbu8vCzVOmQwsk3zkMDBHgCa9QFw4ABj3Wd38Ax4tZz0Hmr352C4K4+apuns2US2Yld5PzSBJwf6HMCJ2CQLPEfoAGeGPefYjdDcer3uOJ68j6zwXM2fdhytH1ylZrvOwRj2Xq7X62LLYmM5MGq54sCN8eh9tw4UOdBEUI/nyUK6yoF1zNWL3suL04iN6CuS7OAzZo+PcTjpAz5zxhr8+TTk7MDRL7qX99CDPqfBlVKmT68Tv3NlAPZfDj5SsWC68Ngs/w30gz/hIEwOiFpG5jaMD8bK+ljXOBhgfl2v10XvOPBj/cnaPatsvLdjmyMVDMBIyAghyuHSBSPFC8vkctaqFhHIC5gjCFbefGZEI3AgZAwWojieH4RDn2Y6z9mEakfHxq0/dwZjm9HPc3Zqao5m/o42wX8mcI/ZgtBz8hzze7kNQzbAMl629RFx9w7e7GB7zBEbJWdj02Ou9ZvxSzuZucG7Mz2ObOUMEJCjnrV5eqzQhkvhsmCiv/y/x2AF4vat2Iy7bYIs07z5KLcFOBLqqB/f8d62dXgIUKt+qNFuTZnlE9azgeRILUoDGvXR+1auXjMrVXiGZzCyMTRwRm2oZ3nL34w1omuU2qByRsTr74wLwUTvB3ZEF9qy42BZahkI/eXMRi4VdISa8dGuA0rWJ9mozLxhuevSdHCC0ePIedt2I9XGbZYfNqJNSzZE3VcNl5YTdpitH/MaAsZ/1vXuO+s6PnPALT9v/nCQkAAQAT8czZpM8Xoaf1kPmE4yL1r+eZ0iNhk7H/aDcQ3f1OjE+wedtcDg/UwI/EV0sz3gl+AzARp0iXHpd41f0wFrZgeGzx1UidhkzzCYfVgS7eOEAqyRadu2mQP3lteeM8F0EhOmjfxs09yWnO7t7ZXERtbVEV2569LdbQ4PzzhAxFgIeqIT/L7Hlu1RZIznkd9lnSM2h4JOp9NSckvbPmeH9/P2Pw6rpE3wCO5ti/CeZbNtXtbMMgdH1SejE3R0RZAdbcvz8/PzWCwWMZ/PO1dIZsc2O6v8uHzbCZ1MiyQPkYvmDQJDNbuf39CJZWiuxqk5vf7Jji04ctCGPi2fHbDJAWb6dnClZkO/GjzzPbYeXDa8IqJcsOySMgh3f3+/KABHmmwAODphZBK14TtH5AGPazKZxOHhYSn9Q7CxGHYMXEbGXtoc2UZQcKqkT7wbDDZ72SxcMNggMoT30dFRMUqzI8D4+YxFJ6sLk5ONxSjKESGyeOzlc7swICd1WvhEdAnJhrqVhO96bds2Tk9PYzAYxGw2K6dSE8WH8S00TDtZGWVl5ioBBwYQwICFkw8zQijhBFAKSBmc96xiQLGmOSNi3K5Wq87hVc7cIHzYD+ErPXBm2JfCCXqz2axEsSKigzevH3N1gMaOgpWC99Ewx6Zp4vnnny8ngDNn6BR8531fOWqGwOXeNvCEYeJxPWvE7Y0GvibEMs/C2n8Tid7Z2YmDg4OIiJJltZEVscn8YOiQXZ1Op/H48eNYLpdxenoaTdOUvUNkgJCbPr6ffWzD4e3+tOPj4yJj1ut1vPLKK8Xgc3Se8bDGPlAHfodefa9vDXxS4+7ublxeXhYlxynkPjwJvDFH9AuyhDnZ6cL4ME/hQHBvJCe+TyaTmM1msVgs4unTpzEej+P5558v8tqOt42hiOhcCUeUPu/lZD2fPn0aEVGyy5yKbNnGsx4/OsXyEB1mR8CyhYxpzclCn4Bn6MJVMcg/GyrD4bDQGHrC1VXOsFm/8Z1P4meNvLaWY5x/cHBwEOfn52UPVk0v05/tC9aMczCQR9CtjS/a8K0LVDbhWJ+enpYTpq+uruL4+LhUYJnH8kmqjJsrntDX5k8yOw8VnPnLFT+Z5nku25n8hoftBNpeBHJwa9s2Mju90BXfs1bow+yA5uCTHXY73a46wP5kjADyj/bgac5asAywPes5gesclGROtJ+zrOZH3s3yh3ZwRqmo8fpsc6Kww4xjxgG+vJZeW561w+NKOH/n5INxBc5ZP9bbATfbv3yHQ4xc8PYhHOjsq2AHYjsi/yxnPNesr7CTvZ5O3pg+kGFeL+ODai7w7zVCJhtX9vEyDWXH3GPhfwfOkXWma9rMVQa2j4BX++614N6OrQnIiDPRO2JiB4rnHSmzx29vvDYBEGGC5/Nt7di5YUFrc6LPHN3Jz7PYdiRNCHk8LIqZKhsMOMUee54fhGQmhRA9dwtE1gXCxbHzfJkLgiMbVWYAPuNZj5f5Ecw4ODiI0WjUORzFisL/874z+mZ0IDOrhTTrYEXhOdnIcAkKDJePtjfO/bvG4DVB7nlYENIO60TJjcdPIMRKxDRhZWEH1+vlH88FYM3t0FtIOeCUMzEZHK3mOgPaciRw27o+JMgZIM/ZdF/jrYh6pQOfZ2VvYC0dFOKdTBs27j022slyyNUJeZz575yx9fP5d8aBac7PeA6MyeOGr/2eZQk0jnxiHSwzWbuIjWHhtmr6wLrPffEeMt7vZ2O59q7bz3jY5vTQdj4YJvNa5kXj1rrDcgWH1bJ/m36u4cffOTvh76FF6Iw1Qb9Zh9bkYM0mMNSMNNY+G+W131mm2tBi7M4sMM6sT/0+z2Ve3bbGDwFwRAgymB4jNvyGbeRgifUmtk9uA7y6DDU7ud6bbQM7YnMHcl5XgoFN05SDbgiYOLEAvWB32VZj6xKHuOG4YAPgKOUg1OnpaWecZHyHw2GcnZ11xmz5Y5sH3GCHUJ2D4xGxCVCDG1dPOsmzXt8ewPbCCy/EarWKj3/8452DkOgfPJieV6tVORyT6yRZ77btVuEAHjtrij3P4YmmJ9aOgKzt/9Vq1Qloke0nkeUtD15TbLX9/f2O7QoevfbeNpkPYmUMzAt5atmEzuB+eGxUV6t4vWnD8sT0T6KJK/lMT23blq0RpivTo7O+Hotx5TkZB7u7u/Ho0aNomiaOj487VwfBNxFRtuLUdIgTVfYZ7gPPtMcWQWOwwvbviG40ZLValRO77MFHRKe2nz4cSfDi46Sx7+rs7Kzsr8KRgRk53RgBwJ5JxulInPfH8nfbtkXYrNebDJyJ3vMwUxpPZC1hZvYoeQ8wuLSR5chUjhQ64tE0TUcYoQAgThtsFhQoVebEPjyYkfHwPgKa8jADzOb9D54H62djE6cuG32O8jvSayMahq4ZOVnYQ7+1kiQzDLhE4LO+/CDMnLm24WdG531O7MxCzeUjEVFOdiY6aNonSAAdwkeOxrm8FFpnzDZ2me/p6Wlnj7MDL+wBJdtso4M2TEcXFxdxfn7eKZX1O6/mmD0UqBnqKNXlclnu6qb0C+OmaZpCIxb8bpO/UQgEyqCNpmnKerNfivbtWDXNJoKLXPQ5AuyHZN8NGTBnTl1dEbGhazsNzqaSyc0OYtPcBn3gKct86AhadYYSPLA3PesG7xOHhlerVXz0ox8teG/btvAQSnq5XJZMMQEf3z1r+kXGsS92NpvF/v5+wSnnKjC+4XBYTvSkPQwr7tnkPmva5jn6syNtehgMBqVt5u+qKbIrnntEd38ua2tnn1NGLy4uOpnJjHvWZ71ed/Qohjw0RkYyn+aPrENHIvNns1ns7u7G2dlZvPTSSzEYDIoR68CMadGlb1mXYljVKkoA+Ik2rZfQeTgsTdOUk06fPn1agicRUYxE75+E3geDQamScCboMwG2BT9yQM1Gf9atObhg49fBmtx+7v++/5vO/b8DJPm5HKTI7zjAzjiz7cC4HZR3kMb9ZLnqvrMdZPsXWe75OeBi2VrDgdvLQSfPK88/B4vyGHLftfWpBbMyTWTnvjaePJ+IuwcxelxOfrhf4ynTnp/Na+62a3jMYzPU1qdGd/TLLSO10739vPHk9a09y981+47ATo1W/Ld9DMaaISdoXgvu7diy2C7ZYBAIexvvNQJF8DuDWpssit97lTJB+QAfxuE9pVZiCEQyc0QLshK0UYFR6ogUBhvCyWPywpspMNJQjDhGNUGdhTOGrEsCAROBMzP0i+Fk4st45n9nQCF6O0HGO+NzwMGOpAWL32U9CB7QH3OoCc/XErIm9jy/zGSsg7MaNpCMezv+/nH2fZsghxZoM9Ma70Gn9ImxNR6PyxUcNko9Hpe950MzIjYOL8aux2mHyIfVkLnGyM3lSTUFAB4uLi5KOaf7Irhk3nvIkIV15uls8JgWIu5mOrMytgMCoDxQDsg7l5TSVubPrIg9Vkdvc8lblinb8MAz24yUbXNyO4y9ho8MlqmWVxmH/t4GdFasDk6CF3/v77KRnvGF3Mj48ftZbmWHumZI5Ki21zobuLW13oZL02zNUK/J3vvgw46k+/CzjNeBDetmy6KaEZlxw3ceL/3UylNr7dbW3KXcDtS6PeMjG8uGbbz0kCDjBBsCvYq8ITjiNWHLAPQyHA475fTYhBn/HELmclbTmatSzA+st417xsAashXKsmk0GpVx0QYH5DAuB6+Zu5MYzINnOUSUwCUBMTJxi8WitOnAqu1n+nG2m6t5+MGuYJy8h63BM0+fPi14YR7MBRsf+8PlylluuaqOALwDbbk6kud3d3djNpuV9WEdzWNOIO3t7RX8+TaRnABBb5I087qxpcTJFPpFhtimy3RoO4sAnufHM84IR3R9FOa4rRzeSSOSIbQ3Ho/jTW96U6zX6/joRz8aV1dXZZtFttUtpwgO02+uNsnjczIMOrFMJ/FBn/YFCNJ7ixE0lysDXgvu7di6dMAHjbBQ7FnEILciYPBW7I52uXQ5R7J4jwwhk3XZj4maDdU+gh/iy6fA+Q49iAHGhGk5CRKHNDt23vdkgZnL67xJ3waSDUWi6DC7FbidQwNtIbDcP9kHFAUbzQkGMC6Y16UzrBFKHRzihDlDaKPv7OysrDf4NjOwXkTzPYdsPOT7urJwxAm8urqKk5OTjvDDoWQNOH2TfpgnwPp7LjnSC+M6cACOvXbseYQeUXg1JWIaBaecqJeVkk/fc7BoOByWu1E5bRUn3vsCwSVrCzRNU/Zt2njmGZylHEDh2YODg7JPEhzCV6yHjY6HCNnxpwqFdUXW2PiN6O49ssKiOmQ0uj1B1/usoBeUAW1gHEVEHB4exnA4LFlFsl3eU22ZNRwOyxkIuRrE8m84HJaAH/9btmKc2ZCznDMdwj/IBct4KzoMxWwkmJdQ5PA9tOhMnY1a8yrrgwKPiE7ZILzN+GjPd5ty2i1ztMPUNE3H4KNNglQY3uyxp/1c0ZKzBJx4fXZ2VgLBw+HtHlhnV81/3ltmuvVYI7r3XR8eHnZkGsDz+TobdJTlB+cZcOI191qDW8rkkBc+QyNn7mslfcYPbXDyqw0jG74YyaZ5t2NDFbqCTqzT8n3DrKeD6ub54XAYh4eHnblsCzA8FMiGOJ8B2dG0rdW2m0o961DkT0Tc4RW3DY6z7LVMymPJa4Gd6u9t49musw0LvbmMNMtw23e2BxkT8hWZjbyiKsL9O9GBbM484ySV7WfsBcuvLB+wk9yWs8nGbbZvMy3YCbJdYd1gO8S2pivVcp842S6ltc2U8ZvtEm8TswyDx43rbJvW6CaPj7H4ewdHvF5+L+JuBj63b1+I9eSsAM7Q4HvwWKNt48gBJ3SIt5N6XXKQIwdT+O3AM/3mpJB9mU+ZY2vlkq8lQEEvFouYTqcdA8GLYqFjQnYmzIdHGWH5IBsrIkeBceTcv7OmjH+9Xnf2uOZsB84gWVYbevTZtm3HCbRizE4o/xNptBB26WAu5fTG8Mys9GsBk/ekMJfJZBJ7e3uldBvCbttNuSzGN4xNNNLRGM/HRM8aLxaLYoy61DA7/MzFa22HEnx4fQBoZzwex6NHj+Ls7CyePn3aUYi+CsBGik+Zo22+wzDxHWcGO6AWqBiTzMFXZ9j4d9QNY8lzt+KwQGf+rA+CA6HF+lKCCG48b/iHNbeBj+HgQ9+M59ohFV6P2WwWh4eH8corr5S9LxZyzP+hO7b+bSWWFWHGg6OevBvRPRQsK00H5OAly1HasaLK/7v/WuTZ79Buzmx4rlZyjso6IAKOagp023PGCX9nw4jfvIdMtfzMa8Xc+d+Giue4zYGyEs/R+jx3xpOfQe8gG4wXr2XGi+UGBhd6Ej3qkjPjjTayo5yNezKlGHkuQ6+tR17bjGP0m5/jXWQqutFrmdt3hiOvl8fPmuHEeu1tSBqPmTdpJ8vRvCb+jHYcfDc4QGrj/SHLx+eff77oGZ9uHXF3C1I27CO6FWr5AB9+m6+sZ61bCXgMh8NSteQ1zHYlwULo13Q0HN5uMYjYOD4O7tkYdxbW9GQasd6NiHIVF9lHbFOPazAYxKNHjwqOaMtnsmTdBE17e2HOvHEVHTix85pxzdq5Lwe4udLK53vY9nKmmLXydaHZluN/O3m1A5SQJYybAzqXy2UnAWD7Nmd+wWeWeQbPG9uXQEQ+YMsOJGsEXbkqIdNx9lFMx9hs2a7Dl1gsFoVewZ9hm25hzQHwyLjNp+hAHGjLV7aV+DRw4wP8zmazzhzwQ6CBLGtfDe7t2FICkRmAjc4QKZkiE4IjqLTlSCbPwax2LvNi2LnyOEzQMDQMCxMRFbbRbeHnuwhBJPPK5XKOlNmIsYDLCov+bHSZeB2Rg2gcQbOhDEEZHxAvzNI0TblTeL1el8g+AtSEGrHJ+jr76yhnZuC2vXvRvAU2Y6oZQcylFrRwtt3CmUMHyKxfXV3F06dPY7lclju9mBvj8r4mlCA0kOnGhylBB1koQduOANaCGPP5vCMkWEsb43YS3QbCxCedMic71N4LDk+xv8sGv/HuKCTrgxDySbURm3KYbMAxfwcRjo6OigHgjI0N0ocMNsjW63XZc2zlHxEd/vKp2BEbumfdyUKiLJENtUgrSjyX8HFKeZa1yG2P3eVWNljYN4rMpDzJc84KHMhBzfPz81JyD32wx5eAKXyMoqN/wNkzZHYOiFpvmA/z1SIRUfiCOeQAgfnchhX7ePncwVPWkH4dtY7oyhZHuO2EWy8ha/29g4Y+iObq6qqUGaKToBtONGauNg5renG1ut17bT3ggCxzyXPFkLGBiF51EMB04oCycehtDNBL1oEOijubilHHeK2vwGE28jHkocOdnZ1yu8D19XWxhZDn+/v7BaeM17IWh96BUe6uBizXHyKQoX769GnRExGbxIMDY9l+sB3ltYPucJZtU9h+cBDQThM8Rz92IBzs8MFzptnhcHOHMZUxGPjAYLA5LIl+IroyxYkN/z8ej8t1Pz4TwIGgvb29UgVxenpanIschLPj4qAP87OT52oxO7K8b75jvSyXLKvm83ns7e11khs5Q5xta3CBHKBt/kZnOknkw5nAu6vo4EvubEfXMRbsHq8x/drBc/AlryVjZ91wVP0MbbvP+Xx+J4jCuFlvb3fA+XUZOHIvV4U6cABdWZeaLjJkx9Y8ZRvWOjEHlCKi3OZwdnZWkmi2fbHtuemFKitoEd1lGnktuLdjy2EYLvmgHp2ypxwdt2Dh2YjNtRY8A9gRtWNrAYTyom2iB3nf7nJ5exk7xLFarYoycUbJBh/94QC6Dp7x5lIumB4iAw9873la2PAcxkaegx1bZ3IIHPiKCJ7n+4gNUVKWvVgsyuEw2XE0GKcR0REWOFbe/4lCoUwPBszOanbubeC7XKZmnPPbeytwbMkQ+goQjOGIKOuCIefj5VF4njfA3Gzgsj5cFWKhbNofDAbF2EHges+DFTACB6FlRx96ZYz0D52ytlyFMRqNygFp2QjNTrTniRDJmWyy786ymJ/JIC8WixIBdR8W5J8pjq15Gz5w8MlBHeg+80Y2eMA5+LVicbTfhpavoInoRvMjosMr0KDvSnTADfrkcwec/B148O/a3Jyh9F5g6JLPkCnwrvGMUeKSX/Bq/NiQsxGdM6yMCxlBm5aPOcuyzTnLOLGOAzxfBxE9R+bB9zkg5kCjZTnyxoGv5XLZyZhyqJflsQ0qy4tth414HjYSTa82BM0XGXc2hnJQNQfn+B6wrhsMNlfv1QKOyGA7UHaWGCc6EDvEY/LeOAfELUsz7UOrBEKcqbB98VCBAAP6KyLu0K7pCMh85MqGTP+uDHKw2A5IxCYg5MyfecdyF6O75uRFbOxBAiJ2zpmjnSAHjGx3Zv633qzZJry7Xm8ymlnuuT/etwOY5a1x6/J66Nx8aDD9OtDn/j0fbCXsJ8/bspP1y0FIyxhoxbKRuXiLBLZ83u+cnX7Lan++Tf6Zh7NczPqS5+0QYqsyD9MaNGy85wBuxlW27zwX5uN9rv6MdrLutq/jdvyebd88DnjN2WrjI9sM5gdoxGt9H7i3Y0t9tg1wHEiyYY7O23EwIRks+I0Q9rbCWFm4eLFt8CAwMNBc/mFDKRNnFqSeo5UoAsIC09FZ/2Sl5oVk4QATsefD+xgofO5sMbgwoeJoMzbjGpzacEUZ5P1GjogZhzb+wBFt7e3tdYiSZ2AY8APBesyenxUL7+LM8z3zcVQq04OVKU6u+zLD5OhmLufNTG+haqeDOfC/gxrsjckC3HzjuZtXoHlnZ1h/aD0bg9A8c/CR9s7MmUbATTYIeB46JdsREZ3rfiyQvbf4IQP4Yq0PDw9LeTi45jCLiCiBNtMsModTiVFiZAMiNrLKFSXwhLM+eX8LdEf5nQ0+xg5vExGez+clA0mEHNrNxlFEdz8O64+zwR7UnZ2dODw87Lxj5ypiE0CDh3JJLeMmwMdn/t9yz/IpG7s4G3ZUXBIV0Q3Q0ibyi72jLhl0Gy6/Y53ol4CrZb950VF3j8eZCMZih3c8Hpe1pH3mfn5+3tFN5lXzqU9tR5Y6SJINnKbZlOCBB2+RgAcI1lm32lA3fdpQtUwkaEm/fM4e4HxHsYMouSrH2WB4wDILHHLlh4PNVKCdnZ0VHmuaTVCJ8ecKLJdWYjdZVz9EePLkSaGhfMgg9ITTgVNi3cvaeT8/dIIN44A7VQaAgxyWEQ7oYKs6OzYcDkv1le+ctp0UEdXrDW3zRHRlDe2wZSsiOvrYts9gMCj7ul2Bgiw+PT3tOKbwLvoXWiXokw9Q8h3KTXObYIGXjH8HEYHsFGJfMH/LKeZDmTJ6xHKEtfEp7vAK+pB5OjiLDCTTOxwOi+1DoooxMS6qJ+xYmxd5znLcsiE718yF5x3k4j2qjzwOnhuNRrG/v18SNhHd++OdUICGsh1pHZKBNc8+i9fNZyvwTtbDtvl9howdWfsy6H4+rwUGwB+03rZtHB0dxfn5eTk87Vng3o4tWVmcCBaUhcqZXBaekiKIx8oMBMF8IGN/fz8eP34cZ2dn8bGPfSyGw2ExFMgcA87ywtQ3NzflkAc7qVbMXmyMP4wV77W1kILoceb9LsrMeyvtlFpQUoNO/8y7th8M5rWDuVgs4uTkZGuEpWmacrG9yyEwrHLmGuGK43dychLX19elPAB8UKJyfn4eT548KcqcNbeyf+mll+Ly8rJ8b8XCuiG8cIhcCg6tOCp1cXFRsvDMNWdPwLuFKQYLhgd9elM9RqqdBTLpXINkgcbaoKjIXLPWZDs5AAiemM/nMR6PS1kG42edDV5XG3jgNO+3YE486yoJB3tsZOUAS/4f2neAinWCrw8PD0u5ka8cIYPIu9sE7kOALNO4Pw4DweVyOIsuR0TRRNye4ujILPRnoylHnfkeBZ6VCE6GqwcsZ0y7Nrp2d3fj6Oio9OUMk5Wfvzc+LFuvrq7Kfhs7rrybA1k5IJoNf2SmZYHfsaNtw8JBKHjYMt/VKJ6XT9VnnKvVqgQRmSN7uWy0eYwRm8PFvO7WCZY1OZODrKXNbMxg3CHrs1NofNh599qh542nnCnLTimnbBKIcSmZ9RwOjm0BwNVOnp8DEzkwCxAoQp+ZhpzVZQ6Mw/LJ2VmvB3PB8YZOcCyGw2E5TdW4s8OU9bz5yFVSDxHsgGbnzVD73z+vBQ7a5MCL14L+gVdzCNymbQ/e4zs/a33NPKBZB989L95x1tC8wPfQjO0bO1Gmq/xctjs9R8ve/HcN8hrmNv1Z7jvLjtzONl7INlimD7fhIH/N+cwOeq2NPNZt9Jnx6PWuPZvnndeZ/2v43Da+jCP/vy1hkoGx5Cxzllc8m/uo0QwyzzxmfG5bwzyuZ5GP93ZsMZTcAcTjATMJIiY8T7TYnj4IcclxxK1yYy8WbebyMBOA9y7yDOMzQNTZcbLAadu2OJIupyJa5ZIT2nKmjfE6kpP3z+QFtJFBfxEbxwknhnd8QjPK1IqTceFk+DsAAWpjxpG0GgOZQMEz86St7GSCXzt3fIcj59Iwrx9zzodq8X6OyFohZAe3FtViPt5bZ8a2cnDWJwtW5ucx1xSbaRW+cfTLisn4tyJkreE948A0Dz7z6dCZtsx72aBkTnZm+N44cMDLp0Wbb3Nk9qGB6XowGBT5tbu7G/v7+2VfXsRm7zEBLpwzsu6c6Ivx7+AddOmqmZqRb94aDDYHmuG8IR9oM2fTmqYpBvtgsNkuQTbA0XYbZK7SoU2+J4KOUwtt8A54ZI4R3bJcHBZnasAn74MzB5xqdGdH0zxseWvnxA5mRHScYfOLT79u27ZTesozTdN01oKAQpYllp2WCcYpztp4PO6sS9M05aYCtwVYtkVECWJcXFyUIK11PTRmes3bTawbWGvWCtpwBsQykG0VyBkcf/QX69C2m1JVZJzv43Z2liAr/RJwHI029xvn0+szHeWtPN56gn2CMwy9gAOX2TNu6CFfbeEs9EMF618HOeAv/zatoUcsBwHko6uJCCxGbALftke9jqy315J1sx0S0a2EsD5vmqZUt3C7AIEOeJ2g8nw+78hQAp4RGznoDJZpG3qxswiYdpEBZLehL96jHw6fAue8Zz3PWHLQi+/dfy5VRQZF1K9mzHvPI7p+Bn0ZP3kc9Ik8sZ0CLvf390sm1CXxbtP0CDjzbf/A87AtlNuC5pAzDu6TJMq4ybKFMQLobePRNqqTI8gcB+ra9u7hW9aTzNn2ofW2ZXFE95AwaDDLax8a5UAebdomd1ujUffu9GeBezu2p6enBVlGVM6YMnnujhoOb/esLpfL+OhHPxpN08QLL7wQs9msIBjlAfMtFos4Pj4ufeMUN83m6gU7jRiBfOboLgsPchCSGB18Z2G2t7cX+/v7sVgs4vz8vAg0HG478xztz0EiOdvLRnWXRljAN03TMYYcLWHjPcKSPQIYidxN1TS3l8RzZxsGJURlpQCxu4wAPDozk6M0WRBjPDJ2Z/FsrBink8mkjCdH7ylfw8iIiKKAnn/++djb2yt9uyTS+ORv45TPwAEZbJjWJS8WWuv1unPFgBnXhrgDIZTwNE1Trlcxs5PpdzkaZfe0hcDOgSL4DgVig8mC14ofvuFvcM3a+IRoDDfaJdvKu1kZ5AzOYrHotAFuc1DnIQOKBXwul8t485vfXE5i9AFL0KiVZd7/iCNnh8GKM6K77cPAugI3N7fXykD3OG6MebValT34lELDj5x0zzs2gLJj60wJTkdEdIJTdmxRYDYqPG47tjhwOQAGDTpI6mva3Abv2bFdLpflBHwHeuA7yxHmwjM2hCwHXcFkx4WqGOQ0fISccBCV/32/NMYuOgvdM51Oi7xEfnDSeTZ4anRLhdNisaherWDHljnnK7xYUx/Okx1bZ7aZ02BwW3FCdpd1tEHFOG34gzMfXOIgoSt0Im510+XlZUyn0xiPx51DZMAltELAAYf/8vKyYxQzN69XpjXGYecK3vNa4+h+poCN7Ii4w5c5uBXR3Q7jz7e1mavwfFqrg63IMJcLZ/qyMY4DmMcArTiwnSsyHHAD/J0DlcwpV4r5uyzTLHdNg8zPwW3LCcaQS4NzMKxmJxn/DuC50nFbtRZyKQfJHPhgXNmJpE/PgXXyZ+AV+912InPyXDN+rXf9Xg4qQLN+D35nTtYXOVlm+vXcs7OdaSbTRk6OOJgGjvJYbGvahrS9Z1wZjJ/cHmPKyU/Pi/k6IMu7yPK8deo+cG/H1qU7OIMerI1fG/M5MmwwM1iAZALLzAvRsJDecwTUkJyj5xagtcxrXnC3xcLnhfB7HksWEFl4IXCIyHnBcwTNv1mXPDYbQIAj3hlfHncWsIwjZ2cttLOgN6O6HTOVy8VyeZvxZGbPwsuCIq/dq/24LQsmxpmFuL/Pa2zByrPZSc3GP2B6zOvkiHbGpwU97WxjfM+jhgfjMAtKt5n32FiQeuw5++x3HiqALwxkDNWrq6uSvbWzg4GFg4uBDn+6dAq5a+M7VznwvCP8yBSPCyeIe0ShPzsGljGZ7ix3rYwyHTroko186J+2suGDTMmGKO06oxNxV6ma73gu84czqs6i8r51l/kW2eOM+Wq1Oc0ShzM7fXZWyd4g6334C2OnosW8yP+ej+nIshEnyydfY+BZftIWgVP0YJZ3pgUb/hF3T48neIxTgXPOWI0704CDNeDUEX0br+AjG6DGITRLkIKAc9u25do73nEpMv8zVgdUTQ9ZxmV9YeOUMQG+o70max8aQCtkER1YozrO5zREbAIu0BPr79PTWc/8HTTG5+YJgkpsteKka/p09YszhttsC+QrgTcytdatTXNb7YLcte2ZbWfbIaZry1locjqdlj3A0LDtYQepbN+6GsWZUJ6zjrLMhU4Hg0G5gWGxWJTsL0F6xmkesS7D1oXXLy4u7vCR1w3asY1iWbC/v18Chk42kWzzidE5yNy2bQlsWvaDHycorJsICJtm23ZTQZL3ifN9RJQkh215+zZsvQQ4Wdt0bJuDRA96yPPKlTUeV97WwXrZsWU98/kEft50nG1CEmjoA8tr4916z/1nW/w+cG/HFoU3n89jd3c3jo+PO+lh722FuNkH6ogxBOkTyhA+7FXCMGDxrRhhVIh8f3+/oxidRQAwNjj8geccPV8uN5deY2A68uMj37NSM3HaceM7Z2ojNgYdSvfk5CQiouxVxMiwwAAHPkRqvV7H06dPI6JrqEVEaYPotPcxOwqYx2XcIvBgmL29vU4mdL1el4MzYEjeQXH50AI+u76+LgL58vIyXnnllSI8Wcumacp8UWo5UGJj24ckWcEBNcdzudzc04higjZtjLg0AqXAPqvRaNTZy+YoLXQDTokaQ9tkVMhyssYoKugNJUO1APutXQLnAzG8to7YWmGAU4yLpmkK39KOeZ/vWWvTGnh29DVfp5CDKA8RoFkbCmRfwTeyDfnHtTar1aoYPQcHB52MtzPk0IPLVyOi/O+9tZYz0PnBwUFpHwXvK09ygCiiu2fLUWiX6wI2FiwbnZlEHkdssmimc+aMgcjx/xGbAybgIQfccEIYB3jBQLNyhD8pCYMf4W9HrnMGkBPA+Zx9l9CAeQm8uYSQ74fDYedgMMslZAGAkQ//QUvg0NkJsvPIEipVBoNB59wA6zeuEyGTYOOWMdvx9HiJqDtLBn2gV7kPHgPW8q5pNveOY4i5tNBGGP0aP8ZJDmygTxaLRYdvjo+PO3YCa06VxMnJSTl5n4C49YR5w+XubrMWTEHnwPM+jOihA7TlverQNQYu9okPSbLeAt9UFA2Hw2KcO8gDP1geRXQTCXt7e4UfOLvFcjNi49jaYM+OLbqQOdiZts2xXC47hwIhs7K8tOOGI2VnH57DjprNZrFer4uuQQfxHHOyMwlubLvWAgo5CeR1xBdYrVaFv1wFmB0Ry1OPA/50cBV8oGM8Nv9Nm7PZLMbjcTx58iROTk5KBQrymWol76l3IJMKkZwQyo4t+BiNRiUo4nmCc3STadA48ffoFeOIs0lc1TqdTjvnO7Au3s7iA6WgX9YEusZOo6rM/oxpPwfqcmAV/DCfHJjhPV/T6bOIIjYl2WybyY66g0PPAvd2bM1wFjY5ywPUmN+Lx29HY83Y2bhylMDg/jOBbUMIjJPnkiMU2Tnmc7ftNvwMY8j/85wznDmy63f9XnZGLbgyAxq/eV450ux+DIzL6+yx3CeKUpu78eL1zlF8hDuGyja64F3jLgc43K/byG3Wftd+YF7PKztvNQHgeTK/bbSVcZjnmPHr8VrIOBtE/zm4kekdYedAgg3XGr/YKP5MA0fCXSrkoJiNJa+Jr3GCZhzwcx+5z4hNNsCGit93BoO9PovFonPoH2AFZiWK0WEHB2MvKz8UrGUW48l83TTd7SUOmNC3g5FZXtiRYa78gCN/T+AJ4zdnqT0/Zzj8OUalDRLrAAIGWTfYSWS97MhhsNsAJbgLrhmzZTKlv+DY9MiY4V30Rc1wAyemo5oMdYUJa573ATM+gnA435bBzJN+WXOvN3RhGcj/tJedl1wBY3o1zVk/Zpuk5migm3wqb9NsbgLIe5O9T5hACjyQsy45Q/LQwLxsuQYebcDi8Httne3ONoU/zzZSzT5z1SGl96b/iO5+Tsu4LPt8YBjXCGUeZGzeo5idZP6uBYBte1luIifBn0vykQfWH+ZNJ2DgRRwOy07fwGC+ZL68k23obWvh9TMNZOcaeeXSbScsPMam2dxjS7CSAF+mNeuhXPWTbUbGiszJz9Rsa7+T5WzEprrFc3WbGW/WPdBPllnGcb7Jw3TMvI1bnF7jPWKzvxjcQh95jb22mQb8OThwqT/PMifrdMtL+OJZ5OO9HVsfcuB9kpRU4WFD8JwMSTbLgslXDjBhCI0JuDyVRSUyY6JCUHB5uhmEvyHIHJWDSZ21a5qmZDkvLi46e4sNEAlRdDIwVnxW5DbQTMSMo21vM3Lsc4uIYrBgSBlXFsyDwSAePXrUOUnVKX8fILKzs1MOzvBhMczJjMie5N3d3XIJuC+nZ/5Eu8BDNiCZvwUy4x+NRvH88893nFeMofPz807knnVx5JZsU3acs6Bgbs58ZVw628PcTKcAVQnsu/I8cSC8t8L061OSs4Fq2jJtM14yuRcXF3F1dVVKbWg/l7ASGWUcERGz2Sx2dnZKRp++s0FLJtERW59wDf5tCPI9WWgb8jU8PiQA7+zDA4cYO1SjRHRPLF6v14XnyfIh+7LBYLrK8oN+oSnuCvfZAxG3Vx94LWxQwjPwMjSYy0Rd1ulTtjH0lstlkROME1lC9Ba+hs4cNaY9DCecAGclrSQte11tgqwxLxHFJ+NrWZAzlL5ewjwC/5mucToxyjP/1Zyw4fD2WqjpdBonJyedvYDM7/r6upx9AH0gO+h/b28vZrNZp4qKsbNuyE1KLo0fdNZwOCyntWcas072NV+j0aicJ4BMAU+Mi1P8acvGoqtAyM5YppApR38iQ1kH0y+AvkQe2hlB57AHmx+yQvkAKOPB872+vo6Li4vY3d2Nx48fx3q9jpdeeqlj58xms5hMJnF+fl5oGdygD1inh+7Y2qF3UIJqAdsSbN8wv+TAsA3gmnMBj3nPvOlnONzcv2656rMvkNc4C7Z7CFw8evQomqYpfAOdQtvwFlUAT58+7fABYEeFeTBHV6Q4s4lsoZILHfDo0aOYzWadYJLtI+xDO/TIUdvQ2NsRUWSe2zk6OuoEAyyjsfFs2/hvr13bbq4hBAeTyaToEpx0tg/4Sh/WiHN8yORjT0dsrkezLrHMtx51htw8z/gceMgBGesC+x0OQrhqA32X7TzrIvqzXMUu4Dfz2t3djcPDw8Jn1jUufUa/U0njahj0ADejeJuk7eXMZ8yf8UV07Wholu1FDg76pH4nXTznVzsjogbPnLG1c+bPYUI7dCAjRzayx59/Z8/fY4BosiBAGfIc/ZoQa1E/z8FzcfnfqxnlHjOLlw35bZEyl3QxPoSxszHMz30yP8/Z/WanEpzl7zy2/GNH1xvQ3Y4VjdeF79xHxpkjSNnZz2tiw95GTo5wGs8R3cio+7Yh7fXLTrGNQ7dhOuSzHKnKkPFrHOb1yk5hzfnNfOhx1OYLPnImyjh2O15XQ43GAEcqazz/UMEK0QrT9IlydVYgr7NlaTZ2LYuQbQ6cRdT3NGMcQcsYXv6MObRt2zFuyDjRTo1+t8lqxpe/s8I2rUB/zgwjD/2s6Y+AKjjAaGD8VvC8z7Pm18z/lms58IdhYPltIwaj03xlPLhdnGBnP1DsLvVmrpbbDmJ6P6r50hl5491zX6/XnQCio/YE4exs5rVkvJZb+fkaP5hGwaX1mWnbsilH87Pe8DqaLz3fPD7bN4B1msfiwD7lpfwNX9KWT8A17Zrn3P9DBts/xj00mmVYzcas6a1X00e1MWT5Be2Ylmr2Sy3YwY8dDtOAx/1qczN9WxbZpvLYLX8cePTzWZ7Rdk6w8F4NT55HTbfk9ak57Pyu2ZzG6autUc0O8Vi2+Qw8Q2AAsP6xM+n5uP2sm2v2ImD7yjK/Ni4/s23uma5t02V6yrak26y1l9+vQQ23Ge/+LH+edbt9oNr8LA9+v3bjvR1blzAxiOFw2BHy/DgyiVFlhMxmsxKNdmai5qjxPtkpRz46E0kn1vE9ipnxGjKjQJA+5ptMpTfhN81mzxRlYpPJJCaTSZkDxk3e/8jYUXxNs7kwmaxGxC2Bev8bhhMRTxgFY4dMOlGkHFywIQuOaDNf+k2k0cLA0Z5txipGTjYaMaQtFJbL5Z0oPusDTl3SA0MQhfReh4juwVRmNOjCgog2fb8weIAO2nazXycLBXC3s7NToorbThIl0uS9xk+fPi3rlfcWrdeb7Bt05IyI9x+v1+uyd+Tw8LATeWNe3p9iBcwhC6ZLKydOeKYPHB7jg/tWnTFDFrAn3gbhQwb2iZKRJ/LpKDjRyOvr60I7jrTnABH8mfe+ZqPeBoKVHtC2bdnLQukd/fO9swFPnz6NwWAQzz33XHW/Y3bS8ufwnXVADpIwJ2QaY9jZ2Yn9/f2OLCGjy5kM9DGZTArv8B3nCjx69ChWq1UcHR3FarXZZ5T34CJjoGHLIe9NYpzr9ToePXoUo9Go4DIHyDhAhjl5PxcZOqobnjx5Eufn553MEvP2GpPZbprNnmOc5/Pz87JnNKJ7hUXeI+ezKmxAsOeWLCt3hjtz5UCnaY2MkYNarjoYDAYdfBj4nzMSmqbpOPPIKvQpOhedDH7gPfZy5VJ+64CIzTVE2ZmniskncFtv0Db0gJxkvNDlcDgs13ZRtWFayvjJh409NGBuLpd30MB4849lnw15cIZOpT0HN2rQtm2pSBiPx/Ho0aM742vbtqy/g07QAhm8wWBQZDeyADuEPZH0meVzdgYsD32IIDxg+UuA33sToX8qao6OjoqucfYYWmQeuWqCtUBnEcRDptsRIRPK+DgZOmJzO0Yeu2nANr6DYsanYTqdlowf+PHZKuhGbBHm5WvinHU2/ieTSezv75cspffN297OtqTpFzzt7e2Vk9ShNQdnoV0qE4Ha9iPTOlVG6Kn5fF7on/ViPKyz58BaUmGLDWh7GryaPvLhT9iIpmtXhuWgIf/77CSqeKnSQP8iVyPuXsX0LE7uvR1bM6ajAnZo82KbUZyFcOlyjnbkwTtS4siXmYJ2s5efHbCcCcmKhPYhLoRb7XAe2rCT6IOncpQpRyWyA58jejYQcklpjijB0AhFCw2vmXGUo8cQtB1hO42r1eYKI68V7RjPnoOFkNfMhwJ4/SI2JR45OuuxuOTFgqDmdG+LUuWIF+3kLG8t2GIDvhZpzjQA45+dnXVKPGgDhxXc0E6OJrpd0wP0Qbvb8MHz3u8DLrKwMr7zuJrmtmTfdGp8uy3j5SGDZYpxWMsk1eh0m/yM6DqQNaMN+oDeakY543NUPfflH5cUbVvDbCBko8F7KG28QOvwsoNQWZdY5jE/64Oa/KnhMsvEPKfM38iYmiFEm243y5LaONx3NoxytsNBCstCV4vkcXgenq/lCP3l562D8nrkLE9e7yzvvL526OzYmpaNv2zs5jXkGe9Jz3iv4cH914ylGk4yrWcdUqMf6+TMr7Vna1mWhwpZl/IZa55pMW8tqPGL8Wb7kN/ZpmJduMop4q7NwsGVtvX8G3nr6gF4hB9gm7wHzNcRm60qlm0OUFn+MQZwRcDNCQCe95YCcJWTHuDUfFKjU2wv4xtnu22791TnYBZten413WPZ4d+2/2jD5eZ+DzsWfyMHYj12B9dqa5R5Ps8LAA/OpGfaBtcOQJs2cyCEuVjOUuIOvsxP2b7z+jpIxBhdJWSbI9ue+cf0UdM7vOcAo+0BJ5iMV/isJm/vA8+0x9aI9gQsYKxkffes38MBI/rJ51mZRUQRPCcnJ52oFJPmEBTvWVwul+VUNIgFRjTCESC0RxsXFxflxLAcQWGMRH5ok+PXzczgxceIu42cBYeATIQmBt515J2sGxkAauzPz887BzA4Op+JDAfWhyo4ou2MA0zl0+Us3Iio8T6/GTtMTzSNiKiZE4eJPnEE9/f3y32xjB/GRrBZ8dnA8r7e/D84RADO5/PSvw0glAVOvhmbrCrj8F431iNiE/G3oCJC5X0EXoPT09OyVijcLJyJgG1TgPyfL8oGvI/ENAq9WehC15Q/+jh+Vy0QMa4pt4cIxjnrQbUJkWRkCrRvg8mVChbsLg3Nh84gN6yUyZANBoNyuut6fVsJ4IyoTxs339AfJ+XWlJgNO4/Vxl3TNGXv/5MnTzr8kysL9vb2yl5FZC/tcGo42UPvr2IfM2OH3/gM3CIzyFyYd9ABVF+AF7J3tM2ee9pG95iXIzZ7PGvBIvCETB2NRuVEz1zlYKPWRg18en5+ficjayMW/nZ2y1c9YFhERDkdlpOLd3d34/nnn+/cjU4g0lUwpn32T1vuXl1dFXnPeRuMKWJTCQRdU+lARQvAeFn34XBYToPlNHFOpwaHzvyRWUJvu0Td+PM5FRFxx7k3btFX+TR5vmcONqzRl9gadtg+EyAHq40vqrisU+DhHFSP2Jxn0DRNkXfOGkVsHE/bJtDzer2pwoCPapknZJyv6GJ88IErCemPk4J9FoBlunGAXWSH0uDnnKBALtrJdWYNO8v2qR257CiiR4BXC2i5emxvb6/YUtAycySoxZzgMc+NNukLXNuB993fzhYzzhw04jP8EDt7PIu8Yg8xNiuyBtq0XEF+gkuA+VFZt1wuO34TNIityLidoSeRxtkt6CsnrviOhJd9DPamW/fZ38hVr9Y3tj2MG1dYMEaqGbwNA7uYfdmWr9jE2LTI4RzwREfVaOxZ4N6OrQmRSfK5lamFASUBCB3ecamVIy9MxoxENotyCzJkEDZItSI3w5uYHeXPwslK0E4oc83Mh6HE3HyJsJUUxFeLKucfCMc4pf+cmfC+Jz8DI3BVAUC/CHOvRxa2Xh+vU16vHFWJ2DhnNnDzGoOT8XjcKQ/hOSuXbAhv22NrYwpcQguMkx+EFs9jnGIoU0KEIHVAwKW99OXSU8oqMLZYTytQ6BMBY0fXuGT8lLnu7e2Vd/iOMdlh9ZpaIUbEnWyxaQDadyDCuAWXuUTKfbI+8A99PXTH1jxhvsmyLeJuNsh0mwN7vGflYwc6r6+DZDbqHZRCUXrbgHkG3qtl/7dF22vP5Hn6mdrftJczHhl3xil9EWh6tah87sf/W35k48djMO/X3mdM/GQ5aV6KuHtSZG7TfbrCyXiqOQsZTJ/Z6OF7yhhNK7U1ro3R71gn5LXk8yxDnanw2Px8je69Nn43r1fu33rBeteObI1OPUfrnojuNX6eX16bPJfPJMj0gZ2Gc2HHKOLu+SARG/7FAdnb27tT1pkdEEo/M3+z7Ywkg3nRtk9EdGwrAjI5GeFAoa/6g6aYv0vPceTYXkTfPmEZWvF74Ab85TnjhIAr8zFbUQgc4pTYvorYXPXHGAzgcW9vL+bzeSwWi+JY+Tof28w4KXZoLM8ASonBMePk0Mx8R2xO2IAf1jhXijAfbF6XDUOfrizNZdE54eV2IzZXWVkvQ0MEMKBfAt77+/ude3abpumUl0On+ELgAFv66uqqc7iY+ca0nA80NF0wL2jBths0OB6PYz6fFz6wvbe7u1u2zIATkmyuaoAe4QvAgUMSJ/DGs8C9HVsrQDORIw82iB3p8SmKGRyNy44nUfPB4Pa+2ohNHToKYzqdxt7eXseRI0OME5L3kjG2XOedFQ1MlRkoYiOsvJeubTclLDYYs6HrUx4RYPmEWuObhfUhSyb8iE35AZ97r2POBIBz8JINpuxoEznz/JmXs85WWgh83/9rg5lxrNfrjuPvNfbpsTs7O3F9fV3u/GV9dnZ2OoLH5UP0DyN5jLS7Wq0Kbn2vMQLRTjHzR2EQWWZumXaMT5wM1or9Is6IWOCynoPBpkqB8boygDFZcLNmERsBa2GNoPTcbYCjSJg/40K4OAqMAjVecbRRltlIfIjgKz8cbMrX3pAZx9CyIYMRwdrZISbaSj98Dm35JEjvraI92oQOzbve/0MmrGmactKkA13L5bKcJ+C9SFlJ0d/x8XFEdK9gsYNtOXhyctJResYXPMEJ0zs7O6Xkbjwex2d91meV01SZs3UFRilAn7QFf1jmuASOOaxWqxJx5zt4iMAWJ+ba2eK+deuCiCj9Y1hZdmVjFdkOH2bDyjKWPq6vrzulcWS54HnodTabdfbBYnChQyyfHEzwuiCvHGiArqlggU6ZvzPdNTmG3cAc0F3L5TKOj48LTfJMDjB4TKyXs78HBwed76Fbtow4sGmd6GADvPb48eNS8bVcLjt0AW/Ugt+fSQ6uaZT//bcDow4iRGxO+odm0Fk866BH7q9pujdFOOhu/URb2GrIvlqAJeLu9iPsNRv7tps8H7/vbR8uRca2dLWAf9uRNo1CdzlA5fna7jMPOLDE85mHGCvr4oyw1ybjF12WceB+mZNtYttGnitzB+92YnOQ11lWt5t1lmkHHAPZRrbz6qwjYP0eESVB48Ay7boPflh34zEHDmnH1Vw8k+06PnO/zCEH8kxPHiPr7fmaJ3xmhtfI40KHETyo8VLuc5sPWYPft2Pr8jUQXCO+iChOB987sokxkMuEmQxXBz169Cjato2Tk5OOYjo8PIzxeByvvPJKybxhCHEENkf7o6htKCDIcLDsPNo5sSB01AllCw7oD0b3wtIukTlKH3iOyBt9OYrMdxhXCAdKyHImDmJnjDlzPRqNilOcy3EA5s8hLURoWMssvBEudqTMFM7CmoaIipkRIjaZfRudHHxjI4f1BLf+nD4IHniNGRefObppIytfOg6NWanWsq1ZwZqWOM7egs/Chv+bZlPi5/LuHIHlPRvcViIoOpcxglsEOrTjQwrcPsawD8KCb3y9iAMNjCcb4Q8NPHcMsIh6Zs9VGVmh+bkcHKEf2jXdAw50WAbbmbS8ylkmaMVGJc/XFLGVvGnWRmfWC56j8WCDs0bXWecgOwkmYgRkfERsHPpsZG0bi9fCOGcNnN2gbZxwPrMhYifQa5oNd4+/Zmhg7GRjo0YvxpXpBZn7aoZCjqLncVtHun3jLAe6s7z2+nqeNoI8BgfQIqJjSNfmYro0PfvvvE/Q/JBxmOeJjK05E4w5r1Euvcs0+JABHBFod5AcmhwOh8V2itiUBsPr6CvbCWSxHIS3HWJ7C5vFmUL/UM6eHS3WMsvE7Lggi5bLzWnZdrBsxJuWeN42BVeBeVuLg/Dsp+UaS2fBCOTB5/zmUCbm6nGZ/gh+sy3E9gNOiTOOBCRtl2CXuiIuYlMJh93lqkbWOSLKMzlhAI5sZ+RrDB3U8mFO5+fnMRhsyrd98CWBPegqy5s8Bmwf29GWRW7L8pfESZYlWU5gt2adWMuE5y1oOVG4u7sb8/k8IiKOjo46pcCeE/1hx9ghtuNquUuih4AjwXXon0P9HASgPdbfQabsCDOuZ4FnKkXOgt+GCHXXpMMjNvftZYchomtU8b8RDINy8pedDS80d8RBZBARBnrbbk7Bo11nkywk1ut1OYGRkzhR6o4KW3DaGUSZuyy55rBkoW1j1HiBEZxlph9wCoFaScCIzrzBuOyhIoretpssU3bEaM8n6FpwIhToC+czYmMEwzzMx5lLBKKZxGNgv0cumVytVoV5+N8ChLW3IQ2DOohiAUU/NsYZC0YIZSTQY9u2pR8LlmyA87kFDkEVKyM7Q5QKuSSHko6IjbNtPmQupjv/nw1m1gveIVjDes3n8yKgeB6az4IpR3FtODbNprLgoYKDAOYdDJ3FYhGLxaIYJsvlMk5PT4vQtvFh2nKU1Q6QS+KpVjHN5b2EKBd+O1PmvVBt2xZDCTomkEEpGPKN9bUycqbFc2Ielo2r1Somk0nZk8O4KaeK2NCogyMYL8gaG2vQO/87u5llDQE1qoIwdKgwstJG5vMe+8nZenB5ednpi/ZshERsjBJkmk9SRbcgtx2YtGx0QBCc24CG9pCDNl68jz4iCu5PT0/j6dOndwLUOCI1eek+0EnsEXMAHJyAM4wf5A7ywXiy4Wf5jGNvGWS9wLizke6tO6yp+QTcIXORu2dnZ7FYLMoeaDLFzMWBdM4BMX1bL4EfG44OAD5kyPSTgwUOzNQCEJnmIjb7W2sOanaa3W92KuyI+beDLxGbioLa+N0O881ByGwT+P8cSDINGze5Pb6vgel627hq67TtO4/FYwBPBAtqgao8V9sm/slzNW624TTjMuPk1XBZ+ww9mPHm3w725T7zOhmfxoHbNs3l4HFOCuTgLXRpvZvnZCfR48hypxaM8/jy2NFnzrzmoFFeA0Mea412fj/w+3JsQVDbtsVAn0wm8fjx4zg+Pu6Ug3FYyXq9LqVtPm7ckQcmakXPdTvsA7DSiLg9VKpt25hOp0WpEqkhy0WUhtI/HDoOXOKKDgyk6XQas9msUxKBc+hLt+0Y4JzkrAaOJbiLiJJFc627SxMwKGjX5YCsBcYx5XvOCNooI6I2n89jf38/zs/Pi1NIJJB9EawHeIGoOEyLA7nAg50xz81RPCt+G0JskAc/g8Ggc8pu02wubOfocoj94uKilIpBh0TLiEJijJpuIzaHJGG82LmkDdbLmf+2bWM+n8fu7m4JpuAM8g5rnvcmAqxrRJQySjuAjmTv7OzEo0ePikOAEXRxcVGcE4QJ9Om5mE+cPbGQxNFYr9exv7/fcVLB0dXVVYkkZ2PVPJ6z7rQDLeU9UA8N4NU8R0fDXc5pekMReD8OvFPL7PodFKyPyI/oVmFkWVtTfjYg7STDOx4L42Pda8ZObtcOnbNVdjIcQHQ7VpQR3bK9iK6jj8xzWTdjhrcYO/P1dx6fszLWfVmmEMH2+uYsA3OycUHb7q9m1Lr80Aa6Ax0GGwTZCLLuNn7Ozs7i4uLiDs3V5Fg2Kh1INR3VjCLadokj8s8OPc87O11zXO5jCIHDPO8cWDCtee3Z54Xsy9kDnnWmj3e9XuDI87ED9pDBZzyg98AFASHwRlDXety4wr7z1TLwYkQ3U+bEgmknOyzQIoFIMo3+ngCvs1k25rPOZavX+fl5kRE+14X31ut1CfBluxA7KidpuOYv24TIE9MouoVnXHJrPUTAzltMwIl1QJaV6Auvm2kf8NyzLrH+4hpBxulAnrc40FbbtuVqLVe9ONlin4I26c+l4/SPbWTc0J/lCTRdk9e1IAh4JBiK3ctn2OS2JbKT6aCx7f6sf7HJcqLF/hfPkKDCF7J9zJiyjptMJvHcc8+V7Y7Y6r6qCdqmisH2KXNx4NV0TIWmKx3uC/d+GuSaOD2Q5XIZZ2dn5RTAiI0B5tS7BT/A4kPMjsbnDfhZGbDYNpZo346iHStKDrKxUzNwIAqXRNgAy84+Y+C3icynI2PU0UY2iD0unGqeXa/XxSnMhosVdzZgLSSNd4QFRqoDBxHRKYFBuZ+dnXUMTmdPLUA8PhuftRLK2qmbMJKjs85mAjWB5/l7ja6vrzsneNI+wsrlErTF+GDumrPh51FEHpNpEbzWjFmexUmHFnOpoj/PCo4Twc1rppdcEoJzAc7hnVy+PhhsqidspPldC1HmT8b/IYNlmwMSBAZ8wuFgMCinuqI8nTWyccCPS3Vog3UhaEeQCmWZS9BccfPo0aMOP3o/vI1+B0fozzIe4weahf6ZP05mxMYJhPY5WdjyDjyiHC0PIrqHezAG+NZlgIPBZm8rRg93rs5ms5hOpx2cOgu4s7MTBwcHZSxt23ay8g48gkMO8aAdqmKyYclvPs+VS87mbTNymDe0ZqOcChs7UKZPZBmf0T9yHvm6t7dXyvcsh3EofL4Ca2u9yj5yKns4I8EGH0FNl8AhW0yXnKUBTu0UowfgPctdBzEYqw0s6A2ngi0/PrMjl0PSR65S8F3Jpo1chmc6hd5eqzT8jQ7ZIfKa2WbjM+RHLoOM6PI7cskVcjkAYufq1cB2gGnJsiv3wfj4jHHYMcuOhwM9Tli4grBpms6p58hkOyk4+IwV/nYFB/3lIFJNrzjQ5wA17WYnys8i020juk/blK7gs20UEZ1DOB188liyjRFRPxXZdr/x6H6tWyzbbee4LdvhzMnz8jqYTmwb4jDyHZ/hnAOW3+aBPL9Mw37ePMezrmxxsBQ5hA4YjUblDBjWKAce2aaIf+C1ZP2c+KAvJ1q8bp6fx2Uc3geeyQ22sUM2DYVONgnlQNSIkjsThQWHGcNX9NiJGI1uj5Bmcih9O2MoK1+HYsKkr+FwWGrNj4+PyxhN7DCBjTI7wxAK0RaEiRnNjAJODg4O4vr6Op4+fVrwYGPYzrkNWAtR9pIdHR2VcdG/+x6PxyVL1jS3USsy6dPptFOKjEJGcPmgJRM7bV9dXcXZ2Vkn25KDF5TpARz1bYfHfbZtW/bJINw8JxO7jRzvu4FubEgCjJOTFzHQzCwIfCseO/55v0leZ9aa+cMv2RHmb5f4ZcPGRjhBBGdTIqJcJ2Glg5PDlS70T9SNd32AESV2KDh4lyuKUDQ2+mrjsUI0bjBsjaeHBtnxADdUtDgSjxzCGTS/w7vD4bCsWURXYWXjDXpD1q7Xm0PjcKaJBkMvfMd4rPyzwWgDBcMIGY98xAkBHC33HGiPd4hcZyfRDpPxyt8eD21D+yhj5BM6A0DZcvYC87a8ceTe0Wr0UaZleMTK3UZPDjJaF5qPvH/JvOS5sz42utANu7u7nS0/POcxuG3LaObA9R2PHj2K4+PjQlMA7zirY4PTPOBr6UxPNeOYNXBAzsG1XFZugx4aMK9koykbejYsfdBTxKYKgHlYl7gdcGkZ7CCkgxm04c/c/0MGX0dmZ8PBaXSQnYBsAFvH850za5lnsjOGzYTsgbYciHPg1nYh25BIvJinkaXQN8Fc6/bsFDdNt7KMvZKMiWCk6dsBJtPaNjuE93jXzrmTM4zd4wLvEV0eoA1X7AA4QeZft+GxWT4yDsslZJflodfS72X7ypBtOj6D5iyr1+vbylJ4Ezs0B6+sF/MVavYFLA+tt2xT2863k22HlMNG8XNo2zLOAVPzhOdremBPNGtj/cOhg9C6dbbllnXieDyOxWJRtjoCBDW3yWzoA52PfWk7iWrO+8IzObY1ovJ3VgrZKCEqYcMsl95G1BWbFw/IhA0j1gQZi50VMO1ikLifHIXZxkx5nhbcWcE6mmd80b4VnyPR9GNhnp1RAMJz9gTmtJPPsyYsl1tlowkiduRrm7D2jw1WG6MWeDilWQhkA95GrBVIFmg1Acb3zDFic/drDmjwrIUu+Mo4twByyYTxYQVYEzQRXTp3VsVMn0uB7ATwjN8FT87AsnZui2dp10Lbc7RBYYVTW9M8p2cRSm9EwNkxj0MLOH0oZnjJh4VYiVFaB41YyWcFw/pTjcE4MJK8X5RgB/1BX95mEbExXAgAWl4yn+zcQQfs2bTy87vQCo63D77geQepsgNE/6Y/y2+Xo7VtW05l5lmcVQc/fQ9uRLesvG3bOxlLxpSzEqvVqlxRQCAn38nH+mXnzniIiGJE8bfliEsVcdLBG8YFATjkHePO1UIuOwNv0MDFxUXHSQa2Gargy0FpZ3MwvFinbJAxxhxM4H/fb+z+XarYNE3ZukKf5gMCHbZVIqJzN2TEJmjIFqcsEwm281nbbjL65peso0y7LkfMuvmhgSuCIqJjoEds9C3btJB/5kl+wyc+nNN6zm3mvX8ErMbjcbnj25lGByVYKzs1DlzZzvH9y8hin2Luw5HgEZzs3d3dzsGczBlZkm0H49FBIc/bgRjzvWU+9iHJGWgVORIRnTNAkHGM3dlV5s7WQLaq1WwVJ3Js3+TPkCvGG89mXqll9rPcMj5Mh/RBP3xG8q5t28Kn/EBz6DHbTQSzCTb6Pdv26CDLC3DGZ4xlNLq9xYREhQM94AQnMGJj27oiKNuclPrSNg4x641OgM9sM25zbNu2vXNGRj7TKPtIzJvPjD9shU+ZY2vj2U4Kn7OAnFBnJwfk5lN/iRggaNbrdedQCTNRjtxBoCzowcFBTKfTOD8/7xwW5awi73JaFxHZ8XhcMp/MgXJVspsciGVnDycwO9EIZQgUYeKri+z45eyzHUkzG3gbDofllGgr+ojNqXwcNERmzkIBQqIUjwgV9fUYJhA3czo/P4/FYlEOobFTlCN7WShBIy43v7q6KkzF2kfc3YdlA47xOftrRjGdeM0Z13w+j/F4HE+ePImPf/zjhSEjNlfjcNARwoV3YS7vw2N9r66uYnd3tzC3I07gJaIbnbPjyzwRTpRT0j8RPvr36dA2HjEcj46OOuuNsGEt2S9MVBgBjRE4GGyupSL6d3p62sELCssGK/xqpzkHcR4iwPPQBevFGrO+2UCwcHf204qadUXWOABGv5YhNmCg/4ODg1JaiuJAflxdXcVkMomDg4NOW7PZrJRIEbVGdqD0HOCxYnU2AFpgzBicvkbGStdOkA24HOhyhseOqpUuhhY4ffToUcfIxrF1IDHLSoyE2nYUjBvLK+QBwQJ4yDonB6kwiK38sxFt/ZADkM7YIk+gQ+uaXF6LQ2maY/18KJd1LjjPmUjWyFH+nLlE51i2em7040oqcOtbDXgnBwQxOpFp/L9eb24ssHFNiRz3jjMOHNtHjx7FwcFB2Ttmm4bqMmiRNgDbAPRlXck4wbmdkYcG4MB2oeUbvJcdRge97PxEdA+uobTbcoR1wvFwUNHlrbZjLauyzAIsK3JAwrYP6+lKGHSqHfYcwDZtei6mJYNpkv7Mdw7IAbbH7RBanhtsdzt473W181mzcSzPGJMPUzP+kJluM9OB5+/vrVMZr+dlvelKNQcps1OOjLf8QYbW7FRfsck48t/2HUxrrI/xC81iI/CcbSsnIjKP1dYYnWFbg3nDk5mf3C9+QLZXqaa0g8u6M35vA3TVoenA1UzPGvh7th250S1JMOEyaSMwE7mJxoP0OzZe+M5tQYAwSVaqfgewMHAU1VECR/2sLHP/noPnVgMzr9vIEQszmp31bPBlJnBbtPda0S33wffOpNsgra2R+4rYRKDdnsdSY7L8eYYcaXNb9GHBY6Fq2vQ7uS2vpcft97dBpn8MejLjtfFmZckzr+bwZf7KggVc1caaFUDGvfkgR04zzWdazAor4i4fZDp56MAdpL7X2pmrWpmdFXjERgGzRj74KAdEMi/Ah0T9UcQ+jMLGXabdprk9QChikwFjawkOuA1DSkuzkZZlAca8I9E8t1ze3vX53HPPle0Nnr8NgyxfrEMs8+xoWB6wLmyJIBrPflK3DV44sBDHyOvB3HKZqvkVpW2Zng9uYR0xMLy3zEFLr7MrAZg/ThJ04HJyQz6w0E4CGXvrw6wP7AwzDrLkzv4i27K+zAEXyw9wRj9ZNvtwpqxHaJvvs8FmPGX+ycFy8yK0noPgBJo8l9yfqzdsKOZs9Ks5LQ8FXD5pGxD5w8nx0A24grbIlpLlbpqmc2sA17nktaWEEweybdtygFXE3UyyacuOkeUN/zvhgXwhw8Tc1uvb0tarq6sSJCHARLDGVTXQrmUt40O+2/lHrpIgGAwGJTnkpI7XgWoOVxnZHo64eyAdz7Ddg7XxWGwDwKuWkwT9KVXd29uL6XQaV1dX5YYAeGW12hxqWrOdsrNmHOVsZ840eiwnJyed6jbmB+75bH9/P1arVUmK7e3tde5yd7Dk0aNHMZ/P4+Liohzu6swrY4NezQvIVW/hgHY5dT2Xf7siIVeWOMiS/S4HN9wWY3EFDG3Bm8Ph7c0xjx8/jtFoFMfHx7FYLGI0GsULL7xQ9KzpyIHgx48fx2AwiLOzsw79Wz7A1+jRZ4F7O7YQMClrIszsBwM5IMsRBhADsg4ODjqR4pojnEteIVbKO5577rkYjUbl6geuooB4HBXlVGSfsobQGwwG5ZRdCNRGP8ziUlNnEzDwMnFCZLPZLPb390uZhvEJI5AhPD09jZubm7Ih24oe/GSHKUfkfdUSm7pdKgEjWhGTfWUvlQ0wOyYYgQjYnZ2dUo5lx7JpNicJI9T4zVp57A4qtG1bMtuMj3HDUMyNfYOMx8YWBoxLj2mbyBJZb7Kd+TAahGEW9lay19fXMZvNYjabxWKxiJdffrnjOHuOpikLoFy+jPFn4eQf6NsVDThJzo7TJg7Xer0ugpnMBdUJNpYR2L7fLSI6WZi23Vxlg6KGZ11mYwPhITu57DknE356eto5CMLrH7HhXYybiI0z6j1MliVZwUd0r1VarValWoa9tchg943RiEzHACMjb0fBgDMI3fGs1xeDhmyZnUH41EbHfD6Pw8PDODk5KZU25pMcRLGSdoDJmZ08PoKLg8GgyFjuiKTsDhmP3CGLhx6zIcWYMGaNW6+L8YHe8D49O+NUo3ifLBUsdqQtL3Jwj74Ymx185D2ynhsK+P65554rpW44yYzPwRj0O2vbtrel2ldXVyXDj268j2OLnLOTZ6PdkXwcd5d3e3zO1vt+eTu2zpxD+3xn3kJfYczSxmQyicPDw5KhyHzlQAd2j0s7LRMd2HTQ6yFCDsRlR8q8noNadm4MNdzb9vPntiGzI1drO9NWdqqyo2CaBiwv8jxoowbZac3ztwzyGD1OnvPv3Hb+3HP0OHPwx33h+DHWPObM0xm3GY+mj5rdYBuzNm/azXjJ4/YcMz3mYJjnkdsxjiwnjePa3DMu/UzmEZ63DRyxKb/OY+TdGr630XumxdpzWTabDh3UJmjiUmvs0ozHvIZetxr/3BeeqRQ5L5IHY2FSW8yIu/eAoSj5rkYoEL4nx+eOpLHwfObxZacrI8gRVDsl24SKF9Tz4++agDNkQswLua2PLHxq79J2xk0eYzaOs0LZJuh4NuMDZsp95DZszGRGM704u1Trq4an3E5NsJsBMZhqgji/D27zmhovPIPBktfThpRxn3EN2ICtKVjwtK2vmoLKws6QPwNP4AWjoUavjNPGrOnooRtuGPs4VRjXgHGUecklP5lP7Agb1zbSaCMHTrxOueyZz+0Uu2QuYhMcAhxwsdOQ2zSv1GSJnZirq6tyHQZOMNtQcIIJDrlf65DMsx6zS3MZu53tXGng4JDLBsFdLrGzg+bPWdPMb5YhZH1wqo1D/meOzCHLcX4z7lx9QT/IHZ8GHBEdXJBFshGS5ZpxzZ5SZ3udKc9BA/OCyyw9R29pqm3zMW29mrFjevAe7OysZz3j+UZsTqpl3rQzGAzKPd+0a97O+r1pugf9eOzPYrC9UQH8UrroU/IJHLDlgeetj0xDZNssHwi6kRFFVhF8JWi1Xq87Jey5tLPGz9lWdQCDZ31gnZ0P2yyXl5fx8ssvl8Avso4gOVsRsnz1IT8u5yXQgwPBtosc4KcdfvsMA3/HliNf72PZy/PgFnmxt7cX+/v7JWmBDszBi5psyja+s/U5GADvkNGkH/Oa28rZQv9NAJjkTLaVrH8JqkVEyVZTOUAFh3Uw2eb1en0nY248+tTsrCey3eYKGhIRjx8/jvF4XO7aZnsEvOFKMQKmyCHW/+bmJsbjcRweHpax8Z3lFTrNe4Zvbm7ipZdeKp8T2BwMbrOtb3nLW2K5XMbv/u7vxtXVVdEHbdt2DiadzWYl4Zb1eU6m3Bfu7diyoD5pGAbJqWQApnX6HaVBGZoJtAzq/0+4RGdNKI4OYdRT471N0dkgsYIkU2JhhjD0Jmz6YS4Rm/tabVzxOcw3Go3i6uoqXnnllY6A5vTfw8PDiLi9ixfB633IZCSzUWThiQAFTwgWMunseYbpbUTwLkzPwS9kNZgXStuEHREdHCJc855fxu31sYFphYWAALcIE9aMPVIwd9tuMhMIMZ+SXIsIA94zCD3DfPP5vDCujSCEBMzr034Rds4oW3hTHcA+ZoRRBjsb+/v7HRoEfzam4AkMeAcCMt6hExvllP5ERMmYRWyUyHK5jOPj49jd3Y03velNxTigDAnB6OuN+MHgJZNounhowEFLLrdBybRtWw7qsNxCDsEHEd2MDoa9y4Nwhlh7jBsrVdYtYkNPV1dXHXrjHRQztNw0d09+hHYYD/cMWj5kB8F7abJTa+Me2oA34HXv3XfFhwMlEXf3tNE/co42fZiGnW/2HKP4vX99NptF0zSFhrl+w+N3oApDYnd3txhL4IZ55+wAz6DA7eiiE1xqFnE3k256IbgCsAacFXF0dNTBHeWcNp7BJfQ2HG7uM+T7iCg6GYOGE+dZq+l0Gvv7+7FYLOLk5KSzRr4eygYtjg/3fFMVY1vAxk5Edw9dDVhLgijMgTUER5l+0C/QEN+hX7nlAAPRNI2MzYY986Vflyc/dMjBDhxM5EqWCxF3gzfgzLxifENXlkfZUeRzyzYHOrIey3YLc4nYZM2gVejItoadE9uw8Je/e7VApgNCPu8AnveNJa6YqrXn4I0zf2QFAVcgmv5tf9hupWqklqF7rUBUbYxef9twOdllhygHmLLtZ7nja5l4bluwE1nsYIMPJ2P9sIug1byOjDknOTLdvRb+XGXoaiPw4wCg6YD27SQjf12x6lOVadPybL1el+1Kh4eHnW022MDoXMZrf862hP1DV/E6uPAscG/H1oRk457/M0H6xxEyBsvntUg0CwTCTcA2Sralt/ksj93vM46IbkkBz7OHxkaAnXcLz9ymiSjjxwLSUUIYx4rVbdFeTWFumzdt39zc3FH+/tvCwp/ZGeedzKhWILXghsexjTgdEbIjyRisnIwzCzErIeM3C7U8nixMjIM8jix0zA9EuGwUGhfQsksOMdDcHoARkPFh4ZmF3zZ8mO4t+GgXgWZ+svNr+vT6m6esTEzzfG7D8CFCpo1tssnrguLYJlsiunxj+rTDiAyFJ7KcxvAyH/O7bdvOQT12imnTCtj07myWv/c8fdcp43XGI4/XBkCWhdCQ97y6MgX6zHzNuEyvuXLI/JNLF/NJlF4D48ZGb5bbnovHYxoxn3udjM+IbtbGgGEKjv2uy44tU8Cr5ab1luWYAxmebw6A8JvAbO2gP4/Za89aY3R5b3qW46Z3xudxAhj9GFkOxNiesNPBWC2zoS1omsoCAhp5uwk4ymtrXGR5/VABnLN1zKX4AHYX+hGww+G1JSDI/ci8j0yDP5BXJAksM3LSxYH7iOhspYNO4SPzNdu+CF5ALxF1uyln7n0qOvwFPQ2HwxLkBh84m5ZBribINu5r2YvggnY8Bjvxpm8HMVlXEh15nyrrF7HZchMRJVhGENHjzPv7WSt/R8LNuDFd8T94Qe/M5/PSX/YDtq2bHVtvOciyPutLzz0HTkxDDkrbJnblnreJIHMsg6BVdDrzhsasK5F73nZmOmLcjMsBVdOSkzDGuRNBBFvAJ5lb/mc8BDcJxtOnK3zuA8+0x7bG2Nn5yM4tC8JvEESmh4lh6DtN7z2U2VgnAkwfdoKJ8E4mk6KUsiNsyMrNwg3Gzk4fRinRahuixocdBisvoucevwW48eqFx+FhbysZWp9k6rbMrNPptOCUsXptvUfJd7wyJyJd0MBoNCrPZeHCcwh8Its24Imee02I/kVEOWTAESSyBxbgo9Ht1Qs3NzdxfHxcxhqxyazPZrNSAkVUMhubAPcuMzf2X1NFYIcwH4tvHEDXw+GwHOzgTfa1QAaCzQYne/1yNsG86b22lMmwlihGvluv12Uu5+fnnYoLO8AIOtrhpGXKXBA2FxcXd7I5TXNbWnZ2dvagDTYAuWXHBV7N/Ij8YE0pY+Y55BX7lu2wRNyW883n845D6EoaO39k5CI2UX94hn58wAXPWcFSzseYUa5UH0RsnDnvdW3b9s6BLtPpNKbTaaEvSpBMw8gHZwssV8GLM6MYgOCQd2gTOQx9YhyQqbWzjZ5CVrhaqGk2B4RxVYdlOnLAJY7Zsc087rJGG0XWS14/H4TlNcXQobKF9Wjb9s5+UMaCAcSaMxZOAXbGEmfDVToRm6slsrF3fn5e7k7P9oH1iY0v1uvi4qLIYQD9bmfQhrurufzDAT3ccsCcmVPbtp2bHIz/8Xgcs9msHEJ0dnYWT548KfoUR8bXvACUx9pZzo5tDuY+VMAGWq1Wd2QCAO3nU2WdRYrY2JOsnct40cd2psj6U/LM4V/ZRuM7O17YLXboaI/ST+ylwWBQDoqyHZkdtojuCeLwKTQJzyIjoFvbhjxnBxrbxEmnGm1ZR/u3nWTLWwfDvW7MiWoO8DIY3N78MR6PO6fwg2P0BjKG6gfash2EDmWclrXY+Mh38JhLrNFbrpCiKs83BIArl1/bQc6OrXWVaTnrrZxEoT1+044z4VQeurwXHuJAqezcmjdsDxgP1h/0D0/CP84E84ztAa+Fg4XGBbRtGwf6so1fCypjH6BPrT/vC8/k2Nr4zgvEZLcJaEdHcgQnK0Qbh06HW8nni4VdToERYiekli3MDFpTwHmONibpn8itlW7Nwa85fzCSCTdnFxgTC4+yNDNjUFLqk+9FhClr2Qwb2xiyxl0ek6P5xjuGUR6/szPMw4I8R77Yf2LHmt85GsfccjlJzYBEKLFOOVjB3GAm3+vpkg3jtJbttAAGR7zLoTCz2ay077mYzxBYGGJ5/3geuwUKzqqv9XCmx0rMmRnPIePH9zSaT/nfeLGjA+4fMuTKEhvdOWgAZNnJeqBAaoo1OztuqyZ/zC8uw800asPbgUvzbJax/tuGUFbylvV5vBFxx7jMhlStsiLTvOfpzI5/LGfyOmXHIwdTGU/OhmB8OTDnDAFra1wDeX1rc83v+9lXoyvmYMPKho4j9qZRZzWML7/DjzMb/K45r5b/rHEuUa8FznN/ed2BjAfzSKZF9FPGG39bTlo3m/7RlXk7SV4H04kdEj7zPDI/PzSwIQ2YhiM2Rjlba6y/TQtug/cckHJb5imPARrI/0N/ub1sw7lP3qMfdP22YIblYU2+oJvNEwSTMt3X5LXpPvOOg2jZVvd8PF8Hz3iO9y23si17dXVVSnY9vrwFDH7KugWbzmuVcWr+sfyoOeQ8xzM5A80Yt1WIZPniRJpxabs74xL82j7I9JGDIcZz0zQd+5w19fU5zDnLOOtH2sKRNR+AJ4+HfrB3l8tlCRAQSHVlrv0SO+zZPnDQ1var7V7r0fvCva1NZ1LtNObTHW3MZiXO6b+cyGiFGdG97uL8/Lxk4mBe2oGYcGAd7R0Oh3ccs0y43qdghgPhLJQjvFwOz4m6ZoKIzWXaRGlZUKIgOBvMwQaj5+2sApFLFhn8mmlpB0PB11lQpsFY2GtL9s7ZFgjXhqKz4DBlRLeUELqwMCLqwnjZe3V5eVkyNuxjrhlz2RF1yW4W2vTtyCpzNkNAl2RabEizjigP1ofAg/HrrAwRQ+bmDC58Af7IXERsIvk4ur6o3sYgNGx6hq/Ai8t1bBRyKi7ZFp/QORwOO9FVaNxRb6Koy+WyZMHBC3RjfhkOhyUz4jnMZrMOjT9UYH84csI8Y4Hv6Gp2ulgrAgjs8ydI5UzcxcXFnQxlRDco1Lab02+n02nZQ49xgZwg+0vFzNHRUckUQA+UB1rBcWCKnUPG7LKzwWDQyS7Cm8jv8XhcMnREc/mNTDP/wwPr9bqUK8GryEVO74dm2XvnsxEiNnvtUfCOEvM+cyIrg+6iGgFdEREdxW8ZCy0QgacNX4vgsjKMFTuKjMl8Dx6zQ+fDQ2x4wv8YjcfHx3FzcxP7+/tFZiA3HVigXUfUwXU++ZnxurIHHXh+fl4MIhu28Aaly1SZANYNzurU+sXIcgYbXDq7BTRNU9b28PCw6CbLLK8l2b/f+73fK21RFms9aRwADkhb18LzDxUsF3Jw2kY4OvXg4OBOJYptJIOr8rA9kTs4VzxHn7ZRLT+x49h7yfiQ3xFRZIJ1NO8NBoNSnWV7CtsUuZWdH2xBzjyw04X9aZvIdgvzow3kg0/jRg74LIHsHHqvJXRK1Zxtfusc3rdthR60XeZsuu8FZ9289x18cKiQD3rKiRPWDdnJVU62fZzxRNb6s2x3ZkcefGRfBZ7n1hVkCDIM/8HyALrCpkfvoj+9Zc36KyKK/MV+Rz5TwWlZU0sOsebIGh98BV/lcmUHOJGj3EAznU7jD/2hP9Sp7oS/HdhAR9eC18hyxo5ehU7gG2eQ7wP3dmxzdrUWqYLASaPbaUSpZmQ7GkG7lPUQUbDAd9YJ5wtmtjNmxWXmjeheIVQDnGOIhitiTBzMDWHH4uWohoXEtgyOnXC3zTi4R88Rnm3RI5iF1D+EipDB4bWTkSNwGJKMtRa5hNi99lYoPhyEGn4OaWG8CDWX73mtan24L6+vy+ksdAGUhGnD5RvOuiLEbDyaZs2cjlDaiXEZivuK2DiX0I8VE/OhTytP8wAGtvGVDVhKxXFw2nZzEJGNb8pMHQwYjUYxn8/j6uqqOLZWbA441JQlz+EwPPSMrSFHJQHLPiutrFhr7TnrlZ/1+uf+vB5WsB4L/2M0uArGNOn3srwC+NvGKHOoPc+4HCxzhsx0l3+MN8sG3vUYs5GQx2+e8/+ZFy0HasE22sxr6TH7+SwzcrDDhoVliceT16RGCzUcGP/bfkw/fj/THnNxIDTTYna+WVtnKzP912gt0yJ0Y2fXtJ7bqfVDG8gyHJesd7ABCKZ4fUzrmT6te2q4+UyAmqzIa2JaM9ToPNNBhiwrvS4eT7YT0OWmqTzmLIOwH8wfNVrN8rSGE5ycTHfwjXnWbUXczdR5HNt+ang2/rLdZbmYdY/XJuPOc8rf2d7ctt41+Vb7XcN77f085szvWVbavsprVpO3Wc9tk9Meh3Vunk+WvzV/wP1lfb9tTHnc0F/W3f7M+iv7WjnQmuk3z89yO6J7Pgl9brONtsG9rU1fXRHRvfoBZyYzHP/zLs5h0zSdaLRLtzIycdRwVPJ1BTaMQGLtlEIvdC75rQkCIvfewIyj6Xd9Km7OnuDwcOrno0ePOgKTvRg+nTmiS1xEhWzMkC1xBJGxmyjsqNEGmToLKGcgrYi31dQzRjKUngOMiaOJobCzs1Mike7D44Om8p5PolyUibmMmXGBD9YHPJNdAL9kv2oGI86Xs+EWIAQLCKIQkWrbtkRpAeg4R1qZI+MmAmecE+Vkj5nfb5qmZCSgh4joROqgX/CS57izs9PZv8nz8BFtcDq2Awbui/d9QAyZMuYPXWwzPh4KEGV0lQMAniKiw8O+j/Pm5qacng6eoOm9vb0Yj8clQGR5YEAeRWyuH2JvlsuNGJv32FIdMxzentYOPcKPyDey/RHdg0mAfKI5dOV9upZttElpvucGjSNj0BPQLnLPwU7Tc43eCCo58ERGYn9/P87Pz0tVDuMkEs+6In9oyxVAtUM7cF7btu2cStw0TTx69CgODw/LKcA2rJ0xBKeWtexVy44UAC9bXjJW1hSZnHU2tMI62YABB+CPcc/n89jb24uzs7POCevgcmdnJ55//vkSlW/btlTwIFPY43h6elqyu2SeuCscPDAmZO/p6Wlnrug69B7P2ziF35CLeYtILpFbLBZxfHxc9FJE10ZpmqZke52NWK1WZQ6cP2FZC54eKuS91jXHCjqmzNtZnIgNbcJL0IFlA2A5wDainPVB7tr2MQ/bhrTN6Wyd9657PozJchNadmUXe3Jtsz59+rTYULaXGV9EdM6BsYyBX2tON/ON2ByoZbzzPEEbzyHzivFsHnH1mWUnawff5sAT+KdfJw2oNMpXgGXbHpxBK5Zr9EubyFbmgp16fX0dx8fHpdIDWmWO+cwf2nAwkD3aDto544vOIRPNeCeTSUyn07JdzbZAPjdhuVwWGUogju/Ap/Uf8ou5gm9sbePJtv5gMCg2HXTDuK6vr+OjH/1oTCaTePz4canIZb6ca8MZFTko4nGxX908yXlBl5eXRSffF+7t2CIUmBjIpQzLDimLt16vy4BXq1U8efKkCHh7+RCKhR4TdykVShRhYYfOGTCcKpd7OJ3NHOyARnSj6hiRGKz0SXkDwuLo6KgwgTOBJuKbm5vY29srd0U1TVMEJ8LARJyZxploK1ML/OzoeBx2JhH0zhSwTwhj2Moir4sjbRjnFmgYD7yPE0bmPSsMxuFgA+vmbJ+zOTnCn5kWHDZN07k3D4G9WCyKQWWFBe5yVgL6mM/nMR6P4/T0tJTocKjU3t5eh18IamRHw8qqbW/LGLlOBBxiuF9eXsZgcHuCX3ZsfWIy5cw2ZOGTXCoOPs3DNrRtqPvwrhz4sHFLH+A0GyjZgHmIkGkSsFFgJZaVidfKe8G9brkKwe/Worj0lSOtfAcv5UyDA01Wcg4QWVHRT83IdKVCbQz+DmMRyJFij5ufHJQ03hlr/s6ZBivqbHBZDteCCLU5eV55zekP/PCZq3pcGWXjxGOotVtbd88pZ5Ksc2tBD7+P7HXZpnUyzxt/1mf+H/lEGxjh0E7WNaZv48T95ey83wM3VFNZ3hu3BE1zYMpztbw0TbqvjDv+N42ZXrO981Ahl0hG3OVR4xynNVeEWT5tkyfZwfT61GQE+LdR7e/z89m2s4zxu8zZDidBD5w1nFp4wM4GfcF/Hl+tNNOOP/aHZbjls3k3yzzmZV4xLnjeyaRsUzMe71nP68i71k/Yf/TH+9gTXtusyywjzV+ep3nQ7+zs7JSAVo2ncxAvy0bLWOQHa2r/xO0zT1dIUfmWAzUOloFn7ETG7/UCB9ZLrE3eLpnXgzEyXng2B5lWq80VeGwbcVs4+HkcPGdcZLuDfgno5/V4Lbi3Y4vzk40bE4EXzIIdg31/f7/DBEQOnN0jm0Sb3phtB46T7QCUDmACgxgsGEEqhOUsI4KVeXhuPrkzIopT4s8cTWYxiLA4Suu9tUS0EYAQO4KPZzJuvR4GxoLz1TRNiRJ6PlYQZlDWZzQadbK8+blMdAhlhBARLPaSmXGcqedvC7pc1u2AiHGcAxwoBwQRAp5ACxF+6MJRUXBnxmMuOHpeg1wW7OglbUJzdmQmk0nnvlDa3RbZitjsV8SRJRuRlUXGiwF88K4DEdCFTxW00q8Zb3lubCGArmmbNh8qOMsTsdljTUbMe5prQTnkAWC6cxTb2cvValX2pTq6zPuO1NvYYYsEsogTInNQK6JrWPmO0cvLy5hMJrG3t1eCMA5KEalHpnnMbXsb+T4/P4/d3d3OdhP6i9icNO7zFFD+tO8xZ/qM2BjUWSmi3M/OzkplzsnJSUdG0TYZOoI23m/GOCM296U6s061Dtnw5557Ltq2jZdeeikuLy/j6Oioc6I4dMI82U7A+kdsZD7jODk5iYuLi05GyFnkiM11GgA6CDlihw755r20Dhh7P+hgcFuJhGPufceWGQ6wWV5lIHNNMJc5sC9vvV4XPUYbyF7Wxuc5HBwcxMXFRblv0U76YDAoOoXgIWtO25wbgFy9vLwsZzS4GsfvYTvYYKVa4fj4uKOrwZF59yFCPtl2myNqh9G2gHWK+cVbz4xX23xsvbJuRTdDt+hD6zwHciI2jhx9Rmzsv7wVqqYvycoSWEb3M5Yc3LBzh9y3bWGcRWx0jvVubi/P0bZkrmKx7ezbL/LeUMvuWjAgBwq8nraz7BM4SEb1B3hyEAwZ4bGzTk5w2c61o+kgvGUMbYInxoLctP1MNUh2pI1/9w+Q4YQWfHo09OfkDp95/39OFMJntjVNE9gKrlyyD1ejwRx4sV5HX7JH2jqZysOITUYeXYO9YvszVzfawWcv8H3h3o7t2dlZR9nlxXUUCCRSasehNM8991w0TRNPnz4t2V5fBePsr8t0WQwIm3I5NjL7FK2svByVwMBz1tUOkktLcXg9V5Q6Cws+EACOMGaBjFKzY2XHPmKzOZuDPI6OjspVHPSB0UMGEELGsKRf5oahxN7aHGU0MRtvEZu72Xyw0LbMMTiihAIihw64EsY4Ndg4ZQ70xbqYphAGCCif/guTYBCZWXd2dmI6nZY9v1YejiZ7XBwMYUOVudkYhD5s7LBOFnxte1sCOJlMOuXuXhee83gQaMfHx+UwKNbQQho8OFrGb4w0+oQHUQA4Urk9cMG60nfTNKUUBiMQMH1mRf/QIAf8+Im4u9crG/Xg03g2rjA0zJuWBxg+KBQbVjl4ZZnH2ma6rzmJDjRZbsCPDuqZ/q2Abfg5EOnAiYMx8Bx8anpzCadxnGGbkWnlDF4czPT7+eBE3rHBRmDShhYOLorbJYg8w9yctQUvZMkzvfA3uHIVjsvvGJcdXVcpWW/RFnhG1tvAz8EJxkVknSCBg6Omw21r4bkxd8bEPOyoOPDptvkOWby3t1fkD/xjvQ4NwD+ulDC/uLySsnXsAesW3nVG3ga0A9M1p7722UMBHx5l+VJzcF25kHUQQTQcVtbSjpxtHLKkOSmA7KL0Myc/kF3mR9tHdr7IXGWnNjtyHCBKu3aC4CHmb9nNd1mv2M4yj9i5cyDBPFzT705S2GkjmEQ/fOaAK3opg/Ue4L7t2IKHLA9wbGezWTl4yXZ7RLdMmbnnbYxej2yfM06+h1aRwbnM29ljZItxaUewppfwQ+gH2ww7G1tvf3+/ZHGRPfThAAk/ttH4H7zwLvrT8pytKTnowPzy2iFXwfvZ2VmhE1cg2rHFFzFOWEN0IXMmiHh9fV22MT4L3NuxJWIDgbMgzgTUDCL+X6/XZZ+FIyJM1iUTEA1KEwURsXFovL/CCCJSgEAjCmTYVh5nw8BMhwDzguaojBWr5zwYDDoOJe1FRBEMzIXxE53AYPE4Xc+PUVNT7tnJtsEImLgiNlF1DBtwleedSxf53kKE7xBMuWTHeGc9vFcuM5hxwN/gjv8xoO30OtpG2y75tENngRYRHaPERpYd6FyW5yh9TWDyPPSOQ8uP18NtZcPIWdaMG4SX18POk/kKesW4M568XwWHwgI7l0g5WGLFsy2Y8ZAAPHu/OsqDfTnGl/Hr/enwHvjOgTorGxvpNmQsU1wV4rIlO1JPnjwpAbf1enM3trc3NE0T5+fnpeoEpcyz0Bg0ZHlkmnHwijHkzIh/b6PdyWQS5+fnsVgsYjKZxHPPPReXl5dxcnISEd0yUwPOrJUspf0ONsLbOKPZcbZcImJOEAx+Hw7vngrOWs9ms04Wxjzm/Vir1SpOTk46uMsRbXSyDW7zoQMjPpnUPMpcvE52BExTNlZoi4wHwWXrwZoB6uvI7Iy7wsDrwHcel0vd0LPQmgOS3ufPXMHbiy++WE6Bb9u2lNaRnSer7jkxHm5scEDf9Avt2tHFkSaYaN5/yGAHy/owOwD+Lss863XAjjB6DxrNQRWPxXISWZodUTue/s40YCc4ohukyzaM5UAOcGyrLDFvmp9fDc+AeTnrhzw2j5F+s+ylvWyvEQRzECjbzx5blqVu03am7auMm20BMv+4MrPmnBlX6Osa/rI85bsazu1YR2y/eSXP3TYVawQ+ve614IkDIQ4W5/nZjtxWQWees93A2Ix786iDS77VAR2fbXlo0L6FAxHG/7bgwKvBvR3bw8PDQiyDwaCTqXSpJ2CjmUV6+eWXo23bcugFZVB45jikLDTGthmOyRLFyXtLF4tFyapZaVpBmvksiFhQDgmijNfPOiPoyKIdTsbJ3tr9/f2yx5OxUrblPaeUqXBYDNcKOHqO8nb63oIKg+D8/LxkOV06l4UkyhaDtm3bEhljTMPh7QEdvoQefGBc5Kih1+P09LTDfBHdkgWyGvP5vGP8mclyZJz5UwLB5wgz/s6HLJCppJyRbLAVGP3nY/sBhCCHTED/5g87FFkgtm3bKbkjGkf2jUh0Fgj0gYGKUs4wHN5embBcLuP09DQioipkEFpkN3yIFaWZdmApZ/ThJxFRaNvGH4KPfn3p/UME6JuoN2WhzkxmxcDfDupBO3YGaoEpK11/b55iHMPhsBM8s5KjqsFZASpgsnGJjMYpc0AjYsNzWdnWFDo8bDlgpZfnyDv0QT9cO4CT8Wpg+UiwLctHB0hx/HDife2QDR6CqETcvWZ2UiM2BixVDlSWAMgW+sAxsyHhaLydwOzM2yhAn1oWbvssy2nLihxMJpiDDMzlmF435u/tHPkZ0wzGcnbQqShhDODYZanIy0yf7mcwGMT+/n7J1q5Wq3IAFfgmy5azUshgZ7BrTpDloYPj6J6Li4uO3nrowBo62JkDApY5BI1Ifjg4Q3vIpf39/VLqb8fWRrx5yNeVOSAe0b3zOMuVXDnnoKC3fuXAOLaIM2QEslzyznOcT2K9YdlseQXe+I3zzDkdbN3wYajW0cY5Mgh8weMObME76A0OCeIwrVxSDJ8yd/pn3V3uz/Y3cORx2J8w5OQDa2In0c8yZ+TDZDIp9if9ZFzxWQ5aWPZGbBx9qvEitu8tdzDUJefIlZOTk9KH+cX9W+9kOWM7nX3dZ2dnxQZ25RO0YFnkg8SgdQd9mSsHX11cXMTJyUlMJpN48cUXo2maOD09LRW/0CbrgX3IlaDQeC7th+bvC/eWpiATpxMk5oyXGSR73HZOshdu4rAQ4Nkc4bLAsTAiUwXR+pkcFXXpkdsDiY6kYOAzLitI+rMgzM8wL/DiIADzRIjd3Nx0BITxwhwcwQFo2wRgoyqvnRnEQv1ZCMgGutfVxqnHZAPBDhBjdwSHMdt4ydEq/5hxbBzlcdA/Qg3a2xbt8lyheeOSfqzErCT8bh4rQtg0U1sbr7PpyYEg49H3//k9GxE2JFBYrzZvr5UzEVYcFvY8Q38P2bEFrzm7zmeZT21MGdq2LYrfAT6+835C8EkAB8ULru3E5ns5HVnF6KE/sh6W43bWUaZ2SvOhK9m4571c4UBwyryDvPKc2rYth7cxFwwisrQ+TMOy3sYH80eOW+ZjHPAOcoggKoocHE+n0xIUdCCB/uyQwVsEeOzQOhhqPUKWlf2q2WHF0eQzyw/aZe+3547M2aajLOMYz2AwKJVE2QmwvDVdunQ7BybZDmTnGJpz0ALZj8Owu7sbBwcHxSA0PxHkzNkI8wE6lvubrQsw1obDYXz84x+P6+vrEvTJ+FutVp0rACOirK3tIfO39TUy0/N/yJCDHRH1qorsNPgZcJUDCAbbSsimbCeaL23f5bHwf01vZb6pvftan3vOjMmBEMvTPNZtejri7mFctXlsayfjvPZ3lu35O69hnl9eMz7L48i0YjzUbKGIu9cl2uHPNrHtGNtjfJ/n4DFlvDqonOk2y8kazWac+n8HMTI+rTfsT2wbX6b/bM+57Rr/1Wglb+FD1+PwGv+1MbjtbLvX1v++cG/H1krfgyXbagNqOp0W58wLhjLLTOqMJAaFT3fFeaaNiI3RR6qeY/rp4/z8vBysQvQIJQ/iMOjInDIeR4oiooyDw1LMNPTPAqMgUaBEyVBiEd0Tje0sM57z8/M4OjoqShylTmRkuVyWI7QzQ9gZZ5w4P84MYIzZ2HK20/BqzJyVDHOzM922m6wfNfhEq9u2LftviOQeHBzEYDCIi4uLTlm5jS5HZPnf2QzviyNYsFwuS6bk8vIyzs/PS1nYen2bsR4ON+WDed4Ye543+LMh68AMGXayGpTH2RmBv1g375u0M2OnZjC4zSSNx+O4vLzs7CWG96BxO1Ksh41z9lEfHx+XE56tuHOJKDgmiu4DaHCMqOiAxj4TDLeIrnL1npj7KGk+z4dysN7eD4nxH7FxuDCWHQS0wxjRPYE571uHNvLWDfolYuv1xrGrlTa5f3jE5aTwAxkxxuGgkHUFmS5vf/FVaXYWPAbatOPnwAt85UCDo+c4UOz9sdO5s7PTySbZgWJ+znL4DAl0CmtpnQBY12T6cHCK75gfctWHdECTxrFp12tmWcFaMHbWg98+nMVjRV95fPRhh5s2lstlqRYCp84SoGcfP35c6Nf6J9+/nnEIHsheUG4MjbtK4enTp3F0dFTkY54DtGcjM+/ZzmC85UD9Qz9cL2fMIjbBQJ/DENG1L8jkTafTcq86B5TRznQ6LVVOvooJuycH06hyI0iWnWXGgVzwQWir1apzkwF6G/pxtjM7bJZXdgYI6GDbmiaQM9Yhlm+2o5knupasJc/lEmLAtqMryGxzjMfjzrVcyAGexVbDjkTeO4Cak0SU2o5Go2JzMRaXJGPr+cBL8IFsJrPP+PEpmqYpcqtmv/mqUgKs3obngJ0DDfzGNkU+5oB/zZG3DocOXfEFbTOW7FRmBxfwGHIVS9u2hW8csHTGlSDz2dlZueGCSkqPCdyMx+N44YUXih27WCzi8ePH8eKLL5Z9t9h/rgDlt3Vx27al6gGfyvP4lGZsDdl4YBA2IEB+hhz5sEIuA5My8zP0lyFHVrJB5zm4TTsKZjxnwGjffaDAbUjQprOD/m0l6r7yOM2c2XGtOZn+3BHyV4tsek45YgKuMp4i7l7SnKMr26JjNi487hzN8ljBI+9ZeHu8zNV4rEWH8pwcAMj4MA5q/fl5K0aA+daMRr+T8cPY/G5u3/NBWfnd2jr53UxTfI7AIzgDXp11Mk6yIeLAQqaP3N9DA8rjoE/wQNDFmdy8dhFR5SEH2zINWuGu1+tSNu5TfRmPwcodpe7yYdMnBokztdnhxgiwgYjzELEpPXVpPnMHL5S4ZxnLnG3wUQbIeNnWAn7twBi3GDfQsEudanzoQIKdGStcn6RqQw/ezZUdKH872+a7Gj/yN217bb3n3T82xPI9hebNmuzNGSzLsXxqZf6hXdsANvL+f+S9SXMkyXaefSITKAyZmKp64CUvTZRMC231//+BFtpzI5pRd+ruqsKYmDPjW4CPxxMvIqtRlMnsI+RmsCogMzzcj5/hPYO7MwePA71l22CaAX5xBCkXdgCX/uDBtHc+IIpTynFCOZyRvbXmC5wl6zfL4jZdSzCRv/lKF34Mwqfwz3trtl+2r7YZxkP87mCqHQDTHh7Fcey64WApf8ZzrPNUJt7rYAegatgDiANBuSzriC5wlVjSICvQjEHgL/6WVVBTdtRya33GOH36+ZQt3oYrHWQnWE1fxhrouHQCoZsDcE42eH0JatopJFDk7YDMz/vv0ScECK1XGCe+hNeW+eLc8R54z/y4je78jfEwB29ZyZa4yeN0BY7tkW1Z8mhiMCcIkQ/s5lQZtZ/bbF5uAHh4eGhrbb3q9zN2Duhja8/+/n799NNPdXt72+6i9jjMN6YzY7C98Nyn9Pq32psdWzo183oxiX6lA8OCQYgUZvo0wVxqRss6dr5rIwsYIwp0enraDCORdzOrIye7u7utlC0Jy5UUCNNsNhsd+W/BxQhPHfqEYs7ICUrY0TIYMQGswaYdHGcVOIWYdfMR21YwgD0fnNJ13VY6ZNACpeU+iILzLOCPfcsYFhSUHU7WkMgf4wCYsC+VNePCe1cHAMJcvomjZkVoJcLciBKiQF1e6XVKY+I9N9ABRwcll2XwrDGKDWPMOrCm0AceApyv1+u2p4VoKnf0zucvp4ab5y3HVugGk4yJPvLkO2ddLaeMzeUn6TS890ZU34c2zWazpvDRf/B/OrZTwZl0CuywWHc9Pz83x9bGvWrIOtDgnW9dmQPAALSdnp423eGspgFa1aAPHOX98uVLq5TAMTGvwbuOzFeNDTZVMhhoIsE4xabhFABEHrtuqDJw2XI6tBhyOznQ0kDcVQ/ct4ezzhr5jAacQxvyBO806ypnmqqGLSuACtaUOSGHrCHvZ25pR90YD/zFc9g6V7S41G8K6JmGrDeONuNA93mriXnWDizrD6/bZrhPy0vVsJ+ZPZgOrADkvn79Onov8mHechbJgQL4Bf6lRB36XV1dNblIG8r6ZGDjvTUDWBprSBYTnTnlBGegyHg0rzrj7zzn5+2kpS3nX94LL2XjoDTkPWWFZ/jX529gl6fOpKka85nnmk6uA5wO9leNky6u0ECOjRvdl3F3OlmubINmjM/OLc4hug2b5HU3lnfgAZ2H/TTm9o/H5Ox4Ym6waTp/vBd7xPPodp98bN0M7u/7vp0r4jFxIjwVTcahfM+Yib/5PXZ2sVumgQO75m3jSPNeroXnaZvr/qh0oOovHW+qC3wWjM9gOj8/r9vb27q4uBidLeTMMLoWnGlb6T30Oba3tu/O2CbwsKE3UT0QO5787jYVEaHxzFTpVEZQzPwW9FRafIaQZ2ZtalwZOfHnU8TPCEf2/y36GDBiED3HfJ4+UoBRZDlempWx/6VZ+SXNkw/8uZVvfmZamWf8rA1DZhA8djvcXkMLo5/NSFW+P9vUszZcDsx4fax883fTy4be7zIw9rM5Bxtk08PRL/r2e3Le23jTJUu5BikbqTCTV79XMf1HaxgbglEYgKkSTpoDZ/Th8mVnH6HvfD4fOVCOSldNR+KdEfTa21F1EIJ3UoqbYNygjvf6OziQjAMa7O3ttcwYvOHAYzp4gKQ86MNlf3b07cyk3LL/EhBquiSgyb2vqSMsc1O2zU4O8mYQbbBHXziDGWxiHH0/BDM9P2gPEPP8Kd0mIDql98yTpiOAw981n3ZdN+Jrxkt/DuR5bQAt5n3bhcyST+kqR/v9A73g6czUAtjg7dls1srg6cO8Zll0IDJLZ52pg3/s7EBP5md+cID6vTdjDOaM/BMgsvNU9frMAusseMpbgeg7nT1oXzWUn/J34wxXZfBe+Monqjs44s8s4/TJe4+Ojurk5KSenp7q/Px8ZIuNL5AJHJ6pA9CMeRKnWC/Bb5YzO0nwLfpvahsh60Dwjj4y8IbTRZ/oNWdMGWPVuOIHWjkw7PJi2wnzBsEnTmRHh9Ina8x7rZdsk6Gb94xa1/A+dCDBUVeXwAu+ntIZ875/2TJJMoZtad6+iI3HAaYcGH2UzqjpmFUvfd+PrvaBFsbZyJ6DS1wvtFqtRnMmcHp0dNSuq2R9OYSPICGOLc9YluFpO7bezkPAHTxkXntre7Nj65ImCzeCuNlsWpawanyYisv0qoYSKojKdQsIzHw+nIIHkxOZBzjSbEwNmMheEX2y4TH4qKpRFL1qiMLSrERhdpjRRhblYIVDf1UDIOAET+9bNsOdnZ3Vzs5Oy8jxHiuJ3LNI+ZOj9KxLAg8DXUc6zTwGshZeWjparDlzBCxwMlwCkqloJUqHLCM0h0+8fxumB7CiAIkyHR8f13w+byAcMEJ2BQHjX8Af2SbmYEVSVaOyUsA6Edynp6e29zlBdp5encr28fGxrq+vG995jclYYdgd9EA+UIpZjgfNURbO7hiQUZVgPl4sFiM5ZH1YO3gd/uQzA7V0NN5rQ4d4/xZ7nNgHbd1lIH1yclLr9bp++eWXen5+bucEAMBc5oaRo1wII07GOIN6ABGfUgl/wAuWWfT1bDZrOhfe4l/OVagajCp7quFJ34vH5wcHB+20dppLzdDB7C3ngKrlctn0IWWd6/V6xH/meYM2dDoHTC0Wi1eZUubO+QXcNYlTCM1SH/J+/sZ3/H4DJuZE5B4A4SAIAKVqAEqW7arxfnyvG+AH/bK3t9euQQJIp963zYR2OBlHR0ejIAqlgdfX1yOAbaDLj7ECDdDCus9mQ4WPgyU4kIw1A33Igk/kxxFwNoAS+uVyWaenp+0z9thy6wB2AN0/n8/r8+fPdXt7O5Lfu7u70d246HPGy9kE7tO2jUyOZcl7Nd+zjnSmzAF3O1ZVrzGIdZbxoJ1Qy34GVKYCOFNYxqWXlnXLvvnZ1TT8bgxnXWxn3rbS37Eut8OY8/Pc6MMYDhp43p4D8zAu9/wS14E3qdhwBYrXybiB52geW2KDpJWDAlkh5MPoHIygX2doEwMZlzh44M/4cVKENadfbw+yjbXdxL7ZxrjPrARyYMP8bj/D+h5HNYMwxtWMEftuDMt3vN5+Zio4Y1k5ODio09PTUXUeNomzI8jU2ncwPsg5QpNsxhDfox+/+1RkiJfANb3qjCBBgKrX9dZ2IlIRTSks+sWoTEWtzEAY0YzW8IzLH6ai8G5mmKmFSGcxBQ/w4sXNsROtwkH1e/3/VFYWeAvIt5wL/y3nkwo1HfWpvlAurLkVlDMk+W73mcLE776WISNqBpiZnbLSBgQ6ujWlWP2sFS1j5QeDmPsDGIdpa6c0aWieog/kIZUrf7NMVr0+uMhjZb6AzqS/M1cAL+YEH3k+PJOK0fTMz99zY54o4NRxfId1qxpkAwNg3WFe4YAK8yngZSrTY3ngGetu1jQPZjC4tMwYoKFHyVDwXfgt9ZQDjaaR++R3IrY03u+AmDMzjNWAhKwcY3DmwOMiEm1dwFhxLtNeuEQt9WLqDVeQJCDy2mOjTGsCeS4p5Jmq8R41792EZuiOx8fHWq1Wjb+8Hu7XNDffMA7rEvNSOvo8nxU0yYNT/JEB6KQtfUNb6zQ7j+brw8PDlgU0f3NAnsv3WA+CmwQ6oAegHafMANhrAU86AzM1hwSO711PEpRx8gCcwnoDiP03H/7INiS2ga1Wq8Y3YDxvCdmW9OC7tuHs8+e51LVVQ4DJQBy5dtLGB+VU1WgbFkEP60WyYBzO5NJ1+M3Yo2rA4+ZhPretz3MxmPfh4WEri766umrVCalLHGRkK4SxnelCwKjv+3alDN+zzHs7DMkX0xxbQJCYuRJU8lgcXHKljXVuvt8BLLa12LHCVjlRBRZi3yg2ZbPZtLvU0TUkc/CJCHrh4/iKStaMd8EnzlJX1WidXQ4NzjJusG26u7tr2XcHVOB7807VOPkFbY1Du66rT58+1X/9r/+1VR4gt1UvwVhk1QE++NEHm/F9klRPT08taGjc7eqht7Y3O7ZWGHZGKQ+YArEIJQrJmbKqQTDIANhAAVAANldXV6PFhwAIEHcrQijKsIjKsqCMywvF4gFoyPZ6Hxrj4QAN7y12tJp3UIPOAsMsXjiaMxY0p+ZpgKqu65rCMS0dxfLcmHNG8qA5a+tsApkQR88dLXcUPUFO1QCOGSuCRyaEcg0EjGfSWadPFAKVA+mw8n9vgp/NZu20bOj7/Pw82osGLfb29urTp0/1/Pxcl5eX1fd9W2PWFwGFx6Ar9+FBq/V6Xb/++mvbe0CpBVke70eF17quq9PT09HcfFeogRVRMQNWAyXGVVV1fHw8+h0eQYa8P9AODf24MsGRNwN96IhR58RpFDq8NhVYeS8N+pLlNJhw1BbZhN4AC/pA32H44eG9vb26urpq9xKTTUR3pEPoMlIHDNHHrD0AygCAShJ0H3Jpp5ZyKpdjIX9Vg0MC35+fnzeeWywWTecfHBzU8fFxuzeZ8aMrqoYSbYw77wP8eFyU+tHQLYAKaMDp7MghRtcODRlVaHt4eNhkNiPgBhjWK6yxq1CqhtPjfQe0QVFVNfnnOQDXyclJs1M4YtAM+qBffQ+iAT56/Pj4uIEpdF5VtcysdTx0yKCgK2oIyjIX1s4BNfMHYBT9SfASoAkNd3d32xkV8IgPBLO+Zo/rcrlsGWHr1eVyWT/99FOtVqv6/Plz42HoOZu93NG5XC7bOKjOYc1cosc4oZcBtmWdygV+t8OSQcP31rz2iRMzgJN2Ip05441tds/BhHxnBl6nfqbGkAFmN8aWQXHGZMeDd3s8bh67+/HvdmQz6J1j3fa8EzDbGt9zgmjKweB7GdBy/9t4fNv3p96R755ap239JD8kvzlZB67x3v+pAKbtvNcknzG9Mxjt9XIgzDaZv6PLzW85R4/FCZn8jun+lma62DZlMibHn3TKtcrAQ44vef4t7c2OrctrbcgBvlORIybk2umc5JQh4P8sEMYT449R6bquHXCC4YPQZlpHCaeEHGE1QHAEi4iN++bHJbcZDbdDkwvPd0wL/m9gNCXIBhlJSytO//A+Kx/AIs2lPhbCLO+x8KWATZVYOEoN6M4SPGjD2vO8+zRAMHCfMpqAQA7Tgk44pSn88/nLoSgPDw/15cuXBtRS+ZnGLp3zvioCILu7u3V2dtbmb/oD8r2XC/51NNZ8AeDOkxgZRz5r8OWoKOsACCca6ews9GFeqZC95g4q4AzwvLNx77mlcUynNuW0avrAGMsLzwEonAn6Fl0TuGXwJ7O9BDLSyNgRTKP0LRCyzaAzduaZAIy/mxZ+f+5j81z8Xeuk5NkExFPz4FlnAaCXs9sJXqBlzsPrn3P3IUN2eKy385kpOifAMsCc0pGmub+XgGJqjRM026n2Xi9+/He/w/PZBlrMs7mPPMdKoIKtTQTaPG4c/y9fvtTd3d0oqJ40Yb1t803vXAM7Fx67aezxs07wx3tuOT/TtWpcEWFb5e+g83ytE3rRmIf39f2wx9AH/fCDveq6riU/piqmrKdc4WFeYbz+rq9RnM1etnDc3t7Wzs5O2+4Eduy6rgW4CY7SvzEx+oJmZ4gsIXLG9iL3Bd3J5GFDzMNk3aqG/cgOWPIZ73HgGzqwtYDx7e3t1dHRUVVVqyJhPF5b+kCOCTjRJwFC3k3AjOwvY+RvVTU6wAje4CpQ79u14zafv1w7eXBw0E74rapRkoTn8H9ubm5qPp+PcHUGY3zXNfOCtth1/s+a2jFlfOA+V5iYJ+ATAobWRcavrJ/lKQMepjHvYWsV+JXg5nK5rI8fP7brVuEl+stDb5Ep8zjBauuEdM5/r73ZsaVjX6zOYqXRtiEic8pnDB6GeHp6aobIigJG5DROSg/4HOcEIMD+K9+xCJNwiqOZxY4U44fAdmTJ7iGk7KPxSV5EmpO5nM2mWZHh3NiRRbCJHFOGQ5oeYUfQXAbSdV27k5ffLcwoPT7jHVyn4Si5N5PTh8vMiKp777Xfx1429tRZcWJo0uGljMUlcAawBjLwyMPDQ1OCNkKOtK3Xw/2xrDPrRn+3t7f166+/VlWNMqVVw6mavM+nZKPUMcB2oj1mMhHmPxxS1scHDkBbyxXjzcCD97sTJGB/oktFnUGy7Po9/hzFuy1KPeW0wfvwGHK7zQl6Ly1PKkQOXCqa4N0Zb3RZ13Uty39zczMqu3L26PT0tB3yYUBhR9cGzvLJe+ER84wNPFUJ9O39kfCG9YHBP8bMYBUdaiNnR7WqXvER76t6ARUuyUYO7ZSTocRZpwzLenI2mzV74eof87kzlugpdC46c4rvoQOyBx2cmUMHf/jwck/5zs5OXV5e1uXlZaOBHUSeY3zoG96b5WfW6fTHv3YSDHbdtwNt1lHwtfdcVw1XPZEptZ2HN7hPk2ZbBJgx4DKPsM5k+ilRdMngzs5O/fjjj83uODhkHXx4eFi//fZb/Y//8T/avKuqVZdAB6pwnDFxoBEAC68wVq8Hth1wmsEGTmp+fn456fs9O7fmTzsF/O51dIaf71p3mj+Q83QgqsYlnqxh1Xg/LLrPgWEHLxwkstNqPebnEoMkLnKCBoxIP2AiKmU8DnjVB18xRwN/09nBIOtI+Ntn4lQNVUBT9IT/sUEOyjBOlz1nCXTXDYfN+bo2eN6OF+uKjqIvn/WR22h8XRC08q0O5h+wVtIr58qJ/bzXjqkDIF5bjyntPWtiPcBcE0NjM7Mff+6KD/O89RV8RP8OxBk3uH/GZN42T1eNfQV0NjpusViM6ML3PHfea1lywq2qRofC2c6+pX33HlsAvRnWDqaFH8FGsRg4MWEUmE8adAnstuyl9zbgLDiCQH84PSgFnGkrIytZFhWhBehvNptWvmeDiRJeLBajiJGF2wJDnzgwNu4wAM6p91KZHjhqVdUcbTs7ZhoLCc4P0TEr/tls1px4MoLOwlvgeQbnx0LJv+yHINhgfjHwsCDiUDposdlsWimzDT/zZC19NUPVeC8TiowSddY2wSEBEfYwAIo5mY6yOgIx8KWDBgQ8DOINVHGweRaZYT42tpl99+eMB7oARvmeT/s0H2dzxNRj93pl5hqaOWvBHLzGdrRstN5jy8wgcoLx8Vq42eAkCEqgNfVDHzQbLb9j6vv+zjZg6H+tW9yv5Z4GX9phMphEJ9Fv1fg08G3vR9amjLFlyIB3il7m1XwvfRGUyKyaA5ism2Vgaj1yLsi3v2dZ95xM0ymQk0A06cK48m/+e/7rlnPKQJfnbzrlOk7Rg98NmvxcAjG/L3mA8nK/y7TFtpGB2Ww2LSCKfvoWr3k8Doy7squqRmAznZypsTGn99zAKwm27SSYFsY+DsjaZppfEntS3UQAxJVHDiha91pXkBgxsOc9BHvAlIyZ72Hf0R9+D9+/vLwcOS/W/TwPpjbPuaTfNiN1eVab2AnlPXbYkR+wTuqGKT1ouUj5Me9DGwK0HEKVOgDnhnGRAEibgzxZ/unPSQZnhVM30px1pn/sCCf4k7ypGpzXtEGsV+pB+M57vsHw1t/gXejgioXUY3yHZAnY0jrSeJ8gp8drf2p3d7dtWSPTnjxpOUWHMgZvJcyEEn05kGrZndLraS/swL+1vdmxhXG9+TejAjhfuf+L7J8XHCFH+VCiAcMakLuEDSYzA9A3INxZChbCQCcjRc42GOgnyFuvXy6HJ5pTVS2DyvynymLsMKSAMT7Th4V0WQIONZFtn9pMHzAj+4EIKCTtfXJw13Wv7vdNEMgP0XNHz1zq0vfDXWK3t7dtLY6OjloZCacGO9tnw8Qa7uzsjDLeVmRToCzLaRwJIoKX2Wf2bPmkSmdJ4GODXAukBS4jeeY/DKGVNIbC0VgMB8rFAQqvoQ0RmSHvQzPvmqaWY5e4MBZH43B04WvWv+uGsvsph6JqOEDA9PpexfQfrUEPANXJyUnt7e3Vly9f2r5Yr0cC46oBHAG6DbJYZ+j/+fPnkRzQn0+ttCywP92Hm1RVy0zt7Oy0A1RsaKqGoAhl/Mwzf+c7Xde1U5KhDeDy9PS0jo6OWqCpqpp+Pjk5adcFVFWjpa9m8XkJzJmMHnIwBQQINOVJjYvFok5PT+v6+rouLi6aDiBohmy43Cuj3NDUmSZX2kAfgxiywWQ51+v16PwIaGmH16WNjAPA+PT09Cq4aZ1n4G276SyWg3Osu2kFr/K5TwAnm+MfdAoBQQNgxoUtOjg4qMViUVdXV62SiPWiUsb36K7X69GVJLPZy77Y09PTdt2EAXPfvxxo8+XLl5H+sh5jzvRJthV96GwD67i/v18//vhj45nn5+f65ZdfWmUPcrVcLpuuJ9jrAD7ZrPfacGqMS8yrVa+xhnmVM0f4nmXSCQR4frFYtKoXDpnCRsGvODUG8Mjb7u5uHR4ejqpTDNapoPN9zLzDVRrYXnQHeuri4qL6vm8JEeMV77+3HKH7jX3soKLvXfmAA+458y5kkf/7WiycL/Bmyq51q7FL1TgDbxyETFpfkWRxVQ7z8bkEOd/M/DnRxffhCfSiA3K2cZlkoR94xzjH+sG0A/tir8F26D1Kho19ec59m7+qBsfbgUvzMfzOesE7BAU4Jwjn2nJn+/njjz9WVbWbGbx+9r2en5/r9va2VqtVSxoyfhJD6buZ/2nOptsXy+At9PrerWzf5dg6ikLWCiFwZsZlmywuUTSyGETOPnz4UHd3d/X4+Ng+Z5IIOQtsZrfxsWKyAYJZcFwc+XKkij4c+bYC4BmMKe8g24ogAPQMUuzApGNmhkCZOnLIv+wFofTQkULGTPAAJYhRNYCuqkaPvb29direxcVFe7/H5bFCJw5+8iFaVigIDwoRRYnA3d3dNYDiE+B4B4GRqqrr6+uRY+vm9ctol9fP/Vu4DLp895qVHGWOgBvzmQ0HDqUdCyt01tjOHQrVRt3Z+IxS2allHKyxD0xjHLzb0W3TMSssoIf5H8Pu0nmMq/kjAxXQxYbrve+xrXpdyuQsrYEBv+dzmRmYWnPzu8GJm9cmAZkDGCnr2Z+BT9d1oyCjDZB1hPvl/R77FJCwfNrYMx5nCgBqDg4YaNAcGGCOBjR+x7ZsuvvdFvjL9eHvDmDl57YngGDT0JmUHL/HmeNgLLZjU2Pw3LJPO8H5TvNVvj95Dcea75nu5rPMtLF2CUJTd3pcONtT23rSdnMwIGM235pvzKNT/ODfHfRI2+3PXTXjcbHm79mxZW4ZTIVfDbhTt1SNT+uGruZR09/853WYAs3up2p8qj3vt/xgQ6n8yq0+xlq8z7xN3xzYaUc/Ey+8y04Z40+dmvKcesQy6/laHz09DXerUoZvbGocO6V3E1ubfl5Ljw/M4yo2+jIdTR/T2Rgvn8ksof+1roIvp3CiaepxpS6dooVtGt9lfZl7jiPpmryTOKFqCDzbnlhGoK1pZWeS+fN74paUIfBsVbWAnH0Hzi1I2524IOlF4/3GqCTQ3tre7NjiwDjalsDEZZ4Gcmmkco8EBslRZRbd0Q0zvQXTf0NoHHE3g9sJ8jhtmO3Q8m6IjbPuQ7N4n8GMQZmj2QZnMH1mvTPq5L4y88Z6MC7m6T0O/pfvEJEyaOBfr6EVNWXHBiMJipwNcsQKuvpvPMP8fQoxSt/ROVoaAJzhvh8CH4yNNUGROBrGGHgW2mMUXR4PrR2FJDtKVIwIJ4aBPi0DBuUunTd/el4e55RSxigY+P0e73suCb68jzGdcXjRcm4FzppAm64b9mmmsXhvzXtJn56e6pdffhmBLsokfXgZa+ygIXxvxW5+7/u+XVcCn1UN/IUBMP19kAdgBZ5l72LVYHTzcDV0HM6DZcj79Wk2fPDZcrlsn11fX49K5fq+b3KwXr/cobqzs1OfPn2qruvq6uqqfe7INTrw+flljyI6xIE+8/eUnqbcbDab1dnZWaP1VPAQmYL/Hcwz2HLFzenpaZufAYLvkETG0uGy/nHQkO/xHNmldEQZB2ts0J9R8tzPBK0dIPXWB3gt7Qr9wSu5J9D2Edr2/bDt4/b2th4eHuro6KjpVIM+R+/RVX/84x9bpof7pF1OeX19Xefn5y1gyLwJ3nXdsI+PMRKUS5tmW4Mt+9Of/lSz2bjkz5U/5gv+jz2yDXnPji28hU0hO2j+Me+mI0fFAxlUZBx9iINEtp8EhG19Vlvxd2NYSnGrBt5kLLb/TjRMObNOjPAcvEzpp7Ef1SjOEHMYmrEP9EAHQaPNZjhUiz7hK2PkzGxWDcGl6+vrdtASY0Cul8tlk0frYeiEbk18wZgZD+NDt2ATXbILvziQgH30O4yHU7/u7Ow0GnNYlWllOtCQW98GYN3sdfDhn9Aq8RmJEjd44ODgoN0YQb+u0oNmPsuF8ToIAN2NEdDN+B2c3YLN8YGlyASVMw66wC9gWQI62Fvupmf7Hv389ttvDWOADdg/btyY/9rhn82G20YWi0WdnJxMBvG3te9ybLtu2G+HgjKIRsFUvXZsWQgW0IJB9pd9shhEgx+E2OVZNDMdzAtRuFuL99toG5TbiUMZ2cnZ29trG+5RIhhGlBZ0QGHBOIBenGFO/UI5AWCIlpmJrfyctQR8wYQoe+hh+vI3Ggbi/Pz8hQlUFw89Upjo0045c3VAAbBTNThYVjaUpTk6C63I1FLiBujI8g87cwncXApeVY1PKU0iQ0t/Hz58qOVy2a7GcHCDu87saLNPe7PZ1MnJSZ2dnTUj/fj42IwDJW8ALRQNQnt7ezu6py2zUoxtqjEe+MknMzNfl0o54mfnaH9/vz1nXt3b26vb29v6/PnzSNHCU4wLQ4MxR34pK+QeTfj0vQO3qkEX4UwSkXdG3M18nc5uRqgdaHO0F9lzIMOBuNzn7AAWMgwo9PvsqPgd5oXMvBkoVg0Ofzr+Dt4xd55FJ6bzl7RiThh0B1y8FjluP4tu4Q5CVyhMBdSYEzTj7wmA/F1klZZ0y+BRBtb8mXmBd3hLzxRgh6dMB48VXsA5sB1h7XKc6EiPy0AW+vonbbQbettg0kDG87Zd5AAmSt8dSOQ5HBDW2oFCV7C4MUb4KbNoHgv23IHedOZz7TLwbZ54jy15r+r1Hs0M4E59x7ooZRkdsc1ZTjCNjHh8UxmyfN7fz3EyltSbTsyk4+vEijGV5SbHOUXPKZrnPHOsbqmbszGOqbFso0nqhLQpSc9cIz/7rfH7eeNYr2fSFv7I/rIvj2Eb7fys35l020ZD81zSljFjc02j5Pep72QAPcdatf0GFj6bosOUPKATp5JfOe58V/7dvye//F777lORDUCqthtEZ9RwZKgJRylhxJz6xtAgYHzuktc0siYCURL2Kbnck3/TCOJseH6p0PIU3WRSR6j4nP67bnyCmfdb8X4bUT6DtgbNMCB0ynuFoa/r/K3kp4TYRtbjYr3YE+R5+1n3ncrN64xCYU9cnpQ5dagX7zFdLZDM2w6052DhzCwFP0T7XBLkPqApDqMdQO5JNMgEACXw67rhHuIp/k3H0fxnowONM/uUfGtgjmNhByErA3gHa4Zc+J1uzooge7yLz+2c/J5R+I/cMBrQl4guuoNMghU+8oqOOz09rarhegLzIWXE6DXvG0Om2FtWNVypAG+gDwhkkJk1LxFooc/lclk7OzvtBGFnoZgbPwS87MhCl6pp4GGegh442Ov1ui4uLqpq2HPO/JPncw+Px5WAGT73vefQgy0xRK+ZL3PNPVS2H763lWfX6/XolGayAV03Dgzzffq8ublp64wdYY27blz2aHvM35bLZR0dHdX19XV9+fJlFHW3nqoa7giuGgKRdvLgE+YJHQWDM+kAAQAASURBVBkX9GBOLpGHl3I7Q9X4YB3/TiAuK78ctES+nM1AN/LZhw8f6vLysp3Kb1qZFwk4sHWGAN/t7e0oaGcnI89LgD/Y/834KclL2TP49L6699x8Jgn6hkwSAeTkPWfWCBB2XdfOxnCwAVvnQx2dAbIM0KauiOFZr5lP8a0aglGMvWoI8vocEletWNdtNpsWDPGefvTbyclJdV3X9JEdZGM0l6Biu62fjOksuzxLn97PDA19qnrVcK+1MftyuWz6GLzA3K+urka3kyRWBpu6wsI63E6RHTJ0uvEv8yM5hr3AZtg+oquOjo7aOB2YAg+iZ5krY/CeUGhonoPHyWxmlQ166uHhoVUZJc+kU2u86+ANf/eWNOt52w1nZ51kMa+g2zxmY2av3+HhYas2wUdwRYIdZQeCE+MbUzJXny4NzbCjb21vdmwp7eJlTIpyoPv7+1YWB1G9iLu7L3d6el/l58+fWwnS8/NzyyKR6aKf/f39Wi6XjbAw7VS0AgfaDjfEfH5+buOYzWbtHQg/iiKj2zBcZg1MD+8f5vvcZYrg4xSxt9VX9vgnmdrzpI/Dw8Nar9d1eXnZnDKP+/DwsIEt1iHLLxBkGC8PB+Ier/Pz81cZCQc2DBgTYAIcGAM8c3h42OjAGNlTCw14llJ1Cxp/h0a+Dop1sQKwI4kBoZSEw2sojfLhD4wHhffw8FBnZ2d1dHRUx8fHdXx83NaaPlB2znQ7++2x2OABcjlohHXg2dVq1ebtUkj6geeRT97NuByAurm5GSkwg0MAHdUAGOjM3Ll6g+wkjhV8TRYsS7beW3Nk3TxjByYzRpbvquEqtby7DtlysMrlr+gK9ELXdaODdarGAUjW2+CG9Tc/oeMp1/VckfnUBxl4oq+plo4t/wdQ4GDClwZENpwEPi0rUwE8xodxpkLEDqQP03KQtGp8wFJm56wH7dihWwiAUSJOX54D+gi5ZK7ofFc9TDm2ntvx8XHjN+yo18fBUgAgug9AxPv5Pc85sB2Zz+eN1zMgB685EMbcp4JxCWb9DPxh4O539H3fyuPQXbn1yC0DenbSObfANtl84Pn3fd8wkstmvS6uQHPQkzm958a6IsOctYEdyRslLJOsS1ajIGPGS05QODngAK55IUtFjRlcrWUHkv8ToEfuq4b93pY33pUBldRJOGjccetDzjK76wCl8a3nYT2VWJJ3M8/ZbHwfcB5mdnNzMzpEq6raIaVU+uzu7tZisajn5+e6urpqesNjMi94G5aTW0mvlFnrB/sjTnCkjsFxNtY1prLNYi2NhSz70Jz1Tr1I0M0YNp1z/CK2I+T8p2hg/rF9A2dV1SunlHk7KM27nNH2/FJvZcNZNv7GvzHf+fvIyJTudPCbMUAnbCXbIN/avuu6HxOFv3mwDCajo+m4+XQu/93EoPEuGCQJnZlWDLEdCJwMGxEvaEYm/F2UG4yfmYhtiw/joMQcnXfGYVtkJOfkCKVpbmVhxzqBtCPO/KSjYoEGPDNPHBRnRh1ZN/jOOeZ6+TtTc3EzP6VR8nfyWf7Guyy8256BPgZLU+/33igbHGgHnewo0HKeyQceWyp2K4BtQC3nQ/N+WfefUVHzIt9P479NwRrQ26j4mffaMpsFDQigZODJ8ukD5xxoMqizIcpm589RTo+janyv7WazaQfRZRYUuebQNHQFP4Aqrym8kNd/5aFh+/v7tb+/X3d3d/Xw8ND2HpPB2Ww2r66Xsm4jgm4ZNS0dyCEjUDUEg7zPLk/8dBTbOgwet442sGLcVLZkAILvA0Bcfup9Z/P5vJ2Sav45OjpqdgR6OMNh21D1ss+Jk3kdrPS4vU/VujgzJ573NhsF7/msAT+DY4+jb8ch95CxPnyXhsO+s7PT9nb99NNPLTvgigIC1tfX1y0oYzvgQPXV1VV7B/aaMWcVltc+sUPXdW1Pn3kXWiDL6Apkzhn+96wfjd+YK6ewIzfgNjsZxpq2x1U1CjqYvumsudKCz5E9eD+Dt4BpZCttsp0z5kcAimbs6qRI2l2wF32Bu3gGuuBQ+H3+myul4HMcaHja2A09xb5iqnxsRxzcPz4+bsF91g3aWTdBax8wO4UZEjclfTLwmzqcsVdVs0m7u7vtXAP0BzgNXGYcb35MTOSqKbZtYU84n4JmrOvtYFQlcL82fWMLWCcHNuCvqmr60ts8kREHq0kcpG9mmpvW9tdYQzvD1kX0Q8Dw9PS0fvjhh6Z3GTuBWXiItXQQNmUDe2uckk6vfYa3tjc7tmz+pfyAEp+q8aZnTwLCczIuUevb29u2udhpbZf7Wjn1fd8ur2ehnCnd2dlpe8lgJhZtb2+vRa85LOni4qJms5frJSzQCM79/X09PDzU4eFhLZfLNjcIDGBKJ95MyoI9PDyMrrhxVIxn8m82gJQYoEQcWWbxEbzZbNYycTBoRpxZFzMXtCLyxvMAgoODgzo+Pq6rq6sW4eJ5Nv/DzDA/oNFgvmooT+374coLM7XnBC9wKAIKF3CM0jcYsQB0XfcqU+nvJ5h3RAtgZaPHPA4ODur09HR0ByLrtFqtarVa1eXl5ciZNNDLjC30d5QaJee/2+GxorKxt0Np4Pz169fq+350nP5sNmsVF2QoHDi5v79vwBpFbyfH88uAF6W3zGEKML+n5nmmUXW5j3+gNTKNEczyLUdYDfrc4AnKITGaCSToG8fSmbvUu9bHBhrIRK4p8tV1wyFWLputqqbr0WfokIeHhzZ2ZNhBO+QC3cd+eWyBdRnABSACDSn5qqpR2TDPZGTbgUTkKQNBrB80Yf7+zLL8rUg1c4ceq9Wq5vN5HR0d1fPzc3358mXkfHqc1ilUT/m9jMvzZz12d3fb3enwqoNc/jFQ4oe+j46OWvUW/dtJpMybNQWUM5/1erjiziXSGRg4PDysxWLRKq+w68gHjtLt7W2rXOAdHPrDmnGdx2KxaDLJ+kwBKQeImR9Ox2KxqPl8OP0YuU5QxzjALP8vOLbwA5iAvdEEFxwocxaMv6FbwFAEr0iUOOBeNRyGyFkWVGgYI9heoSfgxwze+fuWW3QQ/MqWuQyiWG9Zx3oMOLbYTutO+Jtn0F84WOYdeMlVlHaGnD0Hn+/s7DS8Y4wBJjw5OanlctmCRnzmAzAdlKBv2zJjPNsa5m5bZQeWOYN/7OhStv3169eG2ReLRd3d3dUvv/xS6/W6jo6Oms1BLzrTbhto+plfDw4ORoGL/f39WiwWI3+DseX5Q1SygXvxD3xFJO/G+QWzIhvHx8dtayB6GH66vb1tlUFkor1tw7qbORlbPD8/t2oT62MH+tCRe3t79fHjx/rpp5/q9va2/vSnP9XT01MLyDLndGztTBuf+zylrFDMsXxPe7Njm4KIkXbJhR0GBs4iGcjZGawa76XgOROV7yRR/E5nHswojI05VA0La0BkMJJGHWCXCo4+UHQJJKcMVSrKdGw8L8aXY6J/z9ERmHxvghHT2M/6+85IpPPueTiIYeCXtPf7c00TfJs22wDWNnqkg+dxpBNr2ruZRnYeUdZEz1BK/nGZfNX4hGmPw1knOw7Ij/mStUhQ7XF7nHx3ilYpG55z0td/t6Phz/337KtqfH/t1HvfS0t5MQDr+yHbxgFkjv7CI5TmOYDh/qYcS653Qg87qrvZbFowkeg5oB4j6TXNLBSg29/dbDatWiEdHCoZMohm/e6DfKpeAqY4Jo5m892q4eBCXz2B4SdKT4CNLDTjdaDBWT3sEqAY+kCLdGKtSxzItQ6nNI+xz+fz+vHHHxvYYb7Wt9+KRKc9Za29tvP5/NX+UAOZ1D9e877v2z6+DMJZBzsrSl84yR47DiVAl5M/nRGwnFRVXVxcNNtBpto2DJrCQ7PZrO23ZA7QiewGYBuegud9owA6HP5IW8C6E3w3TQmCIE9kfe3QWsfbnhtsMmfe854dWwfj4GEHUqGXg/teC29rgXb048BOYir/zQ5Lyq6xqP/1Z8YojNP8Y93MmHme/uBh9GMGO6vGZ1/QEhMmBvRcEzd6vsas0Bt5AeM4QeM1c1Z8Cn8kxjHt/V07/VPPeM7Wv5YbO5GML3G155F6BfvjoKt5gLVFLr2VjnWyfZ7yN6w/jIm8zqxHzg29jX7I6sAp3p2iPeXybsaTXo+05zj9VNU4a4y9sMNpzAsOTp6ZojV05O/oduP/79WNb3ZsOdgEA+dLxlHgZK0cIQPs9H3fLqV21qzv+7a3cn9/vxlr9toS4U3F41T9ZrOps7OzWiwWoygtTEtUFkAE2HJ2g4gOxntnZ6ftFfBmeoNWFCrRjIODg5FRZbEAajAo+xC9aNDFCpVIijNfdlxgJEoRoBFREEc7HL2DFqwLoAOG8p4yxrVararv+xbZRtBubm6q67rGHyls0ADFwMZ+73Hj+xYMrzd9WLn7kCPTJw96yGAHfIpy8KmxvB/+RCGwxh8/fqzFYlGnp6etwsDH45OxxekAhPEe8z0R0NVq1Q6LWSwW9fDwUFdXV6PIMutBI+qL4uBd0MV8lUbWkTw7NYBRfggUdV3X5CfLr3DSeJZ+zTfeS/T/AnCzsSIDBp+yVoCh29vbVgXD733fjw4kS0BcNb7PFdDvAAngvu/7dmIses9OkMurHBjhHRja4+Pj2t/fb7wOX+RBEx8+fKjj4+ORE8pnrjhxRNd7OqmYIfLrKLIDSXyP58jiWfbgdxxgMg0ANyqP0IvOEjNv9EQab8uYAR1l1Xd3d3V5eVkfPnyon3/+uZ6fn+vXX39t8uBgj8vg6N9gz4Euynxvbm6q7/s2h6pq2SKf6eAgnkt87cASTMmAoG062VZ4qqpaJQf0xY573ZfLZR0fH7esmQMdYAfKLj99+jSq2mLc+/v7dXx8XPf393Vzc9P4Ix1BnHR4zBkkywtrhm6nZJ3DoowvyAoeHh6OsuDw3PHxcTsEBhmH9/t+uK4FWTJtcLrJIm47Af+9NJ/XgA4gsAfPgUlsx+CDxWLRMnGskysG0a2ZHaRfMBF6K9cZvrTs8HwGLbF7zpC56ubh4aH29/fr06dPNZ/Pm8zf3NzU9fV1W3dX1OG4W3dnJYoDmNYTdgr43cFE6xNjG/TJ+fl5PT8/1+HhYcuiu8SUOXBgoXEnMu2y/XSQPDZo7nGnTBj7WZejM6gyW6/XrRItA5DWeZ6P7YGdUfOObetisWiZU+wGMo7cW2d6nvgV1nlVg2+B78KaYOesD9Af8AkywhztyJumrMXJyUl9+vSp7u/v6/z8vDab4fwPrw9z97pCg59//rkF9+AhblThXBpjPfQ49thXyeWearLotlOu7KXs/f+aY5tCY0MMQ04xtJ/NbBANZWLlngtGg4kdCbIAOlqfzxh8+lmP0X2h6FiUb83B/btP92VF7ciandtUREmPbB5X1ThyltGyjHxM0ahqfHWJo0n0b2XDHPjdAYt8ryOGWQIJbbbN2wbGtHKkLnkyaey1zTXP8dIwXhgjslXQ3M4gBtslTAhwRtig41SJqueQf5uiCT/M2fPhe3YMGLuBsyOXpj3f9TsdaMi1cBTQfJl0fW9tmxH3WvM9Sr5Q+AQ/0DMYHpcymv484xL7bWuXJ/k68wvos7GvGuQfg0uADVAKv/FuO4Hoi8xMpv5L8Ml34C/GXDUOFmArrJsIMOHImy+hPwDUWaGuG7KEVS97U11uxt+hmbOrBrq59o5y49DbUZrNZm3vJwEv6IK+MF+sVqsWiEjaG1QBfKxPnV1gLauGU2qhE+OET1yyWFWtZNHBRJ+CDL0IPOBgX15eNl5IGnl90JnmeWgPzxGkQQ9zuwEBkNzzl3sbDS75G+NwxZH1mYOojIcgHsEVB527rmuHwLnEj2a9T//OOL7XZruQmCXt4pRMTeHBtHNuaf/SQUzMah5OR8njmHIm/Jl1wtT3E7f4u9aDU/iZeW2jx1QgdBtu9POuMrMO97zd71RfSSv/a0z4reaxbsO+idOQRYKtvMs2M+mYWH/qHTyPbnLwwLyybV65RkmDxNbJr7TMzvK3b9HODbuQ5dbbnmd9PT7rc+tB08L6y3xs/Tv1vsTwxsyJfafmt6292bFlPyQCQN25CUL0lQwEmR0Gu1gsqu/7Fj33oPm/DY+Bh08hM1Dn3ev1ukWUACZV48gNoAliE3XDKcF54bv83WWDLCDGCbDhLNrDw0OL1rhkDvqkM2YlZKcLMEQDYFUNhzFAK+bkfg2oum44bZc5kIVN0OET5ujD5VhVr5UoezHSaDkLA61RRo7az+fzdv0P/AFYJrpox8oRcMYHXQyOASXMCXoxb4NkIvIIFpnl//yf/3MdHx/X3d3dNwHT3d1dXV1djdbX+8TgWfiZI8yJArIu+/v7jaY4HxZynBUyF1b2jMfGyUoD/vbJu1ZY8DJ/NwjFebAjjU7wnkveA32Z73t2bBNIJFgzHcm4Uf7K+tvAmbYO6FQNkWDLgNeP9/Z9P9q3ipygq6qqRaWdecWh8h5zl6s6w4EDmtkQ80rVwGM2kmnIHSlGDxm8mKdx/InGE8n/9OlTPT09NXsFr+a1HubTvb29Wq1WdXFx0eYyxbPQkPF6L5WzKziBVdVOvieA8PHjx+q6rukSO5rQEDBycHBQj4+PLfvLXndkj3eiI+AL6yaCGT7QZD6f16dPn0ZO8OXlZbv3ndJz3oMuJsPDQSU+SMW6neuGVqtV/fLLL6NsCDRCF7PWOKPp1JDZODw8rNPT03Yv/cHBQQtiUJXCPmH2j8Nz1nNULTEWZMHBDHQddDPgo3+q0+AtbEhVtWuyfvvtt1aVYR1AAMIH9bx3/QiNsrIH2kOfbXqUAJCf53Pf4+wS06rB7vZ93/AOfJbrwu8G82Seql7W1Qe7OaA2m70ckMZ6zmazur6+HunEruvaTRDIPlkpcKp1eTp3xpnYEjJ4z8/DdTzoKaolq4bS2zyAyXiPPvLd2axb3CeYpWrA6d7Xmj8O6BCkmgqCeaxVA/Y1H0AzqorgLVeOWv+xzumQm3embhQhu8gaG2e7TBnd4Rs3oFHyL2vkalKq5ByYhPa2ZQQE4YvEiGSWjRMSx1UNuAFac2r5bDZr2W38GdtBaMB6o6+hs/UqPiPvM86B/wmYolddwfE97c2OrR2AqsFQmxkYvKORGCmcYSaBoHuSLJb78juqXkdCrBBxagzuvHj8jfe4DMxjz+9W1cg4Vg0lLGZmxp0gk/flWNymokcASMaX38/fk4aOotjpYIysaSrQpJPH5HcZhOdBGZmpNLgxL6E0TC/Gbn5xZjNLXP2sx0lfOSevlek+lZmZzV7KoE5OTkYKLueP0SUw4z1gfreVKtdJoKhYY0pV/Lud1OQxxuCMD9/PSKMV2NSa2mgmzzkSOkUDv8PA9T0DNloeR08wxxlR08gOGuCZtWJtXCnCWrkP86iNHd837XnePMHfncn15/BDZiYd6AJAOJJrmTLAdDmSeYL/GxTwXvOYm/nOegM9BADNKyp4FsBimTB49NwAbrzD+sI6w46g582/AFnolQEL6233QWCLsmFnx/09dKIDCJTsebyMxbQ1gKTftF8JOl2+bTpY95v21jepr6FxZjFwYL1NqapG22OSn7x2liXbRINkBzq8xxC75PI5nFLr3TwoCyfVAUPAH30TCHaw8z3rSdYNHOKgjB1M22TLBgHlpJODOg44m6/p14dislbwrAPnOA8ZsMBJotyYoFpVtQOYHBBC9/A+eBj8wpjQ395ylLxgWqGn4B8cYhwx09SOOBg3T/u2TDmYbXs21VgzzxlaGat5u5jxufWMDz1KGiRmZq45PgccTDMHKRLzme8yqJA6Evy3DcODexxQu7+/f5U1pf/EhIwFR84OazqArBfjdaUP32O83gZnfsyKQa973/evTisH00wFKYw16D/5yPoYGsA3xoysw8HBQdsu5G0wb2lvdmwdTTahWbD7+/s2Idf2U45k8JX3NMIUGSWmdV3XlAxGhL/79MSqqqOjo3aSMQsCIICg6WBVjZnLgIW9IQb6pgN9wIgAv6pBUSAM1I4DnHzCHDRgrgbDMKeVButhkFz1urwYZjTos+BZ0TgqZCcnHUgrIJj15OSk+r5v0R0iVOYLaGSDkc4Ta+HxVL0+3Aaht3CYDhgJornsgyUzQjRtZ+fl+HIibGRKOHGP54lg3d3d1U8//VRnZ2d1eXlZFxcXtVqt2h46R4yJ1M1mswZkoJl5DB5C2XAQDtkF1gCjDd184iPAkP0NDizN5y8nq87n8xbVXS6Xtbe316JrGNu8UxDlnAEGeB9Z5TOMLPOdysy9x8Zec9bBlRzwH/zlfWDr9bqdMYCeOD09Ha2pqw98mjx0tZ6C7g5O+HvWz3wvT1R0xQTrbFkka+Y9ttb9XTdUBvi6AsuhdRf74xiz98/bkbMhrRoHwBgn+9XZK4YOTkCDXqK0Ff5mbjw3m433VaEzsUVPT09tvzw6xcAkq0TYRw1ASaDuskDkCnv25cuXms1e9qPu7OzU+fl5y/ZUvZRS39zc1GKxaPtvnSnzqZNk8nk/pb3sfTTQ4YRp9BGyTsUWh4Hh9AGcc72qpkvmmKMPB+LfH374oX766acmNzgT7LmtqpEskHl6eHioxWIxqsC6ublpNyJQNZD7uI+Pj9v/4dW7u7s6PDxsmR/WDf5yCT+0mc/no1Lt/DvZ8aOjo1GQ4702bF8epmObjS2Brpb9qsFu2o4il76uxHznALaDgWmTHFzP7/gkcY8P/Js8zbtc+cS4cMiWy2X7rO+HDLFlP50Gv4M+0UkEs5ycwQ77PXZcGasxk7FZOjqpQ30Ggb9nOhq35Y8xdeIEy4IdaDvt5itjHW+LQ8+CuXMuyXvmK/MNfzc/Wec40GWfwgET09vfs4PPHMni+n08b5nCTjuYCF+wjq4swhanM88cjVPReYzJ1UzMwTQjQATN6BMe2abfjIVMfx8AeHx8PPnstvbdpyJTUoqj5SyRB2ni5wEymSm0U5v9wNA2JigUR4QgNmVAVQOz4ww7KsvfvQhTSo0FcakZY2Z+zBGmAbjCTMyDMTsjl8LE9xijs5rQi+/wfsaa0SXPwUycQp1K2UJrZ9ItFW+WDQICECrW0+UWHoNpjpJw5JvPGS/rladj8jlrzfs43MknnwLGfGURz3BNFGWZOH70sVwu24Ejj4+PLXtg0J0RSn9uBWdFAX94Ta3soWXV+IRVZxOm1tUb+Nfrde3t7Y32zFluM5hh42sj5bF7TRw8SWP4XlvKo4NF0AEw4axCOv88UzWWRctL0tdrwnM0885UNNwONXJnfVg1vuJk24/fBW/wHsbJ+KxnUwe6H7/T86QhK9bFzjwnePX/bbd4/1Qmgc/dh2XQtEe2Elx/q/k75gvrXtN0Chh6fcw3thuO7ntMpjVOYeoor4Gfy+fNW2nzphw38MTOzsuBigB76w2fyO11wS4yhr4frh+xvTGYZTw5f48bWbDeQrZz/2+CcPMs753KfE2t+XvXj6yVTx/n7xwI5eyQs3yJzbLCi+ulCOJbbggyGStMyQoOJ/xWNQSnvTWIIAZrnduEaA782AkloHFyclKz2axtSXBAGT2W9pZmG+AkkveZIi9Zwpm23vLtq/9IBoH7WRfrVO/Hd9CbOfT9cFjcNufWsm05Sv/AY/lW9R88YD1KMMq6LR196Gk6ptNmPepgAhgx50FQ0HNK/J22Gp2IgwjG5HP3xZz39/fbIU78ncClk2LMn8TN3d1dC2qzZvDj/v5+O9MA/qY0mIC2aQa+5HuWT6+XbUjiZZ/f0XVdC4Avl8t2tdtb25sdW17ue7EAQQYLDJh/5/Nh7yTESOZ35MFlFTaYWeKZJ1Pyvaenp7q8vBwJP2OBcI5WATgtbAY8KFiXdNDM8FMG0mPDOaoashi8i75wBokCe9+RwaZLU10mQ98IOAoJZcz3fJKZ91UQFPB6TjFfKqaqau9gjFYAVlhWQJT1cDqc9yMZvORcs6TDNKEZpHofgqOYRK7I3lDuwPuz9AxFfn5+Xnd3d3VxcVFfv36tu7u7kTDbiWB/iRWpgatpyu/sYcvyHANb6MmcMTzeSwdPzWbDfh+M92q1GmXToQ9ztGyx/2XKYbbBQX7tSPmkvPcM3s7Ozmq9XtfFxcXokCXmfHh42E5N90nv0Im16rru1Qm+6El4x+DExnfKEcbg3NzctIoFH/+Pjqt6idaStZrNhsOrnG11lgr5hHcdmc076Si58/UtDggaMDkogN7GltjhI8tokMd7AQbQJvdpAV54rmrYB8ieOubCfidkiSoHl5pXvdgedBj9W1bps2qcYfA4+AFYVI0DflMACT1l24a9hJcc1DBfOtgLCLWe5llnvR1UsfPK371fFNsPrxmkffjwof7whz/U/v5+yzDDcz752nzH/mXLCZ//+uuvoyoo63TeeXp62vgXfnTj9FMaAcD7+/u6vLwc8bozxfCjs2Xwh3Us9CcQ7Bsb3rN+RMflXmI7CdYDU1m5KYeU72Z57VRfiRvc/LsDRpZRxp74J3WvnSc7gev1cDgbc0LOsBd22h0kTWztd3u+2xwHnvF3aaZz8qpl3smdKT2UGNiO6RT9jfNcPgwGdkAAPG662zmfWgu+7/2m1mtT9Mm19Hr7O9As55wynMFE41k389SUjf+WbjDey3EZC3jsGYzJ9fJ8EtfZZtp+2S5k8D6Tmf6/5dO05nvg2v8rji2DSMUEs8OoU4qHzJBPA00G9CQN2GFgOydVQzlzpuERiKrXTJdMtQ2ss+h2KjFQU6DeDOUFZnzOErJIacxwnjHuZBgdxaCvpB1jgcHswDuLAQDCUaG8DBDkiEyWojB+R1J5L8qnatgPBh0yu+ESP8rBoBfAGuAypWjox41xmh6MD6cZGpoeBqSU43qvgunuiOT19XVdXFy04/v7fihJskECdJm/mL8BMeOxo4ssGNhPKVX+nuCAgA0ONyXiXDPgigsMwlRJC3xpZZvNwIIxu8LCivG9Noy26Vk1DvLREhz47zxrHrehmTLuUzIyZTx5hys4+K4NntffwGWK7zzX/CyNFGNNw5mfu4807qadS8DcR2bzTB8/azpsAxqmucdrOm2jw9Qcp8Bf/i1Bl+mPrCdv0Hc6A6ZfjsttypZ4nWzfPTbrG/+gW6aA42w2a9khdI5LjdEfXMGXWc2kDzZvKtCTtEoZ8XryNwN492naWnYzMP6ttXZf5rvfA6//0RsB3jwluuq1jiToXTU+CNFOsL+PTQNb8RnJEgemq2pkT+FRB+ScDAGngE/oM51lH7RJX2Q92faTgae+70fXBbL/Hj1MBhvZcMO2IztPT091cXExwjB2FtKGmGedLMptInnXsJMIOFMkYXZ3d0cHylYNe6CdtXPwkWvkSPZwNZ0Pbzs/P3+FybKUms/Qj8b79O2ftA8OVE05n9DD/MLzpqv1CXop9aXfmzYEvZDnB3iPbuoVDsuD/uk7eVsMWI3MvP0W+Mq+RNd1bUsHf+MaVioPuq5ryR4aB/ni93nODrSSnTZ2dNCH9bNOfkt7s2OLwmAB2CxvQwdQcGlwgi4bDpjdzg4TzDKMNAwZvU8jyiJORRfow0YYRpgy6l037K/KMhic9Nw3jKPgE0ztYDqKQX8oxilFT4Q3N8Y7A2Al4qwGTjk/ZIwQPDtYeQCDI4AGlwnccLgpXcgTVHHayfxAMxQxPOWINz8HBwft5D9ngACwBgnQwWXfjiQlD2bA4ODgoM7OzpqxNN+6RMeK2/+aduZHZ9Z9rZVPiU0nOJ0e6JSKlz7JbtiIe85eT7KKNrg844OQCDTwjNeFoIhlBr6eivx+T8TtP2KbzWbNCCTg7/t+VFpTNYDoLG/LjClOwM3NTa1Wq/rw4UM7hIQ+HRFlDWysKI3jPkgfgEJ2iTtoKTVi7yTjB4jQrL99KnrXDXtseR4gh2y6hK5qOLE2I8k8a1l3NoeGnKOj6YsAS1ayoJd9P2vf9+2AmNStVdX2s1J9AO2ogmF/qm1W2oTcOgF/WGahLe/wHYxV1QCw6eC5uX+/339nPbCzPs3UAS7WFt6mD4MUrklCH6HrvVbQ8vn55b7M//bf/lvNZrO2Txjdv1qt6u7urn788cdaLBaNX1jbx8fHdpo8Oo/7c52FhxcJRjNHn6hqMAm4ykoDTrk/Pj6uH3/8sW5vb9vfCPJCF3gPTOAy8Cm7bT3PerzXllii6nU5u/8+FbDyv9mPcRPv82ffstn5Pstmfndbsxwj+37ejnTKvpND8LvHkU6W+dY/fiZpa3k1jRLrOiDlMU61tDem4bfW1vMxfdLpo2U2Mp8zTbJ/vpNzmBon489AWo7nW/ObesfU/Lb1wVi3ZXbdl/nVuGyqz6kxbpvXFG39nvRPEud4/EmHb62bf68aY1dXHL2lfZdjizGZz+e1XC7r4OCgRQumsksZqUinwqdN2lA7y5mESINkYlhA6cOAytngVCxVQ5YPIEhfGFSn/OkLB4pSP54FIPjIaoIDOCdkE5lDZt3aIv3bPlDKdmmz2Wx0Zx7z9hwBs7e3t+3gIIMQABH/d0tnsO/HpefQBzrM5y9XOuHgEBk0YPTJnXZ+d3d327O+hL3v+1YOxiER7KWyIBuwzOfzdmgT65Qnw9EMwD58+NDq+X2/JbzCHhsyDaybjYYF2vzLWBeLRYuWPT8/t3lzMNWUYTMfwDtW1DifBquHh4e1Xq/bJeKWH+bCviTWztUCjIODeLqua/xK8OP29rb1wbjMxw6EbFPS76VZPqrGF6WbBxzRN32yD8owq17vg8xs4bdAoYGOjUg+Y77zwRPm56m1zL4SLPnfzEpN9TEFCLfReoqG+fmUcZ0at5/j8/w/Mmc5zOfS4XY//lvOf6ql/jUfeQ4JElJnM66ki2nI99IeG+TZJk7RetsauD+CcARALy4uXgEzj9mZFWdk4FdAj4PXfD9BOTw9tQbpIBikYVtYd/PUNj5KUJk6wLyQa/CeGwE3sBG2n3W1DakaeJygBDxkmjujS0u5d5aIPqnSItDlvrz9wfxtnAsvwGvwF39jnNh2vxteNk6ayp7xDo/TuMbVClxd5j5dKWXcyloQqJvNZi34Z6zKd6kwM52ZM3vkXZllWeXH2076/qUMP/EJ2GKz2bSgpCslUz4ZI5leB5GY62KxaLqMNWfLQuJ15g7u9Jytn5gflSX+jrEdV2yCafF5Ujcy95wXOgf6r9fr0TWdrI0PfqRvY1QC6Og46OxgKnwG/QkGOnAIvbi6lW2frAH7dskGu7rB9sr6Gj2P/4QfAB3to721vdmxhQAwFg4KRzFDGJxfADLEZgGs1KvGXjkM6SgxTGAnlncwDqLmdgiI0tuQAIJgePqgT2eOHeVz9NqRV8bsZ33qH4Lsw2CgIe+h2cAloPEGcANLPuu6buTUIMQ4MDs7O82R4V0ej0ul+d3RL4BDAuqqoYwWJY7jzbpAPwcL7AQ6s8TR6AZRKAoAG/RDIFIR+E47XxFQVaPSQ5QvfdAn2XHKb6GTnTWXiRhkTkV4bRhsBKFZ3w+lFvzOT4IwO+/IJHOws0vQxEraBg6DzfMoPlcjQI/NZtP2lvm9aZwBHaYjcmE5f6+NLJKz3uv1up26a16E3nkVDUY35Rz9ur+/X8fHxy1rhQz5jlOyWJbZqsHRYF+1KwTgNZ+O7Ow7e//R6TSXlxlUwg8GIwTgsAfJbwAEnrUxOzo6ajyeTpm3XcDntgWcQmwA48Dizs5OHR0dtaAZn1n2qFiwPjeYvb6+bvviTU9XO2QEG34wbfNcAlcx9X3fMrUA0dT52eAxAl3YxKoaVY0wT9bOJ2878MX6ObtLxtK6l9JE5gog39/fr7Ozs9rZ2WkADF1PkPPo6KhVzFRV29sKj5j219fXbV+z97QDcglmmi/Nb9brrDfZeIAftHl+fq6rq6vRe+AV9CPOemZnAWqssQMKlsX3rB9p0No0hI+qXjukdvbgS9YoHawMuLgZq2G/6NNYbyrrZNzjoH0GMBgLThkymQetItPbAlwOihLgdIWZqxXRI1UDjkP3G3PR7Ej4PZmldbKHNeLqMHjVuCrt/lSAyO+rGqrGEmMbm2XgbJtj60o46EEfVDxZdn3CO2PPhALJDieDpsZjvM/zOLYktfw+b9NK2fAZNqYpjjf6mfE5YEilFXbQfOvATib7/H7LEc9Qyo1No4LS5+NwKJxv2bB8W8bSn4Pnvd3U1cCJL97S3uzY+jAMGAOj4gMaDg8PW2kOhCQrSBYtlTsE5Rh/JunonBWBjXvu2ayqBuA4yAPA5P0DgC36IKriU+T29/dbtIcSWjNFVbW5OwKyv7/fMtkwBYrBi5sldTAPc0KxOrPpCB6CwHeSIZ2NJZKSAgwtqwZnlPJlZ5aqaiTErBvrhLEhAkdjzDA3yoJnUW4AN4CSS2nhCUd/stSWvSgcuIIxycNImLcND84GoBjBZZ1ubm5GTsfV1VXd3NzU3t7e6DoJ/vX/WV+P13N5enqqm5ubRheMrTMFvibLc/FdewZiKCOALcodAEmwxxFB9rlwIA9Kdb1e12+//dbGsbPzcj2SjRjg0NFkqiUAuLk//b01K3IbEoyH9+s7qFP1ej9mAmNkHfpbZhydNzAjKGF9wzgdPOM9DqQ4U4ARYg7pKBhA+l0GPQaTjsIamBiwORCIoTMNbTP4noGGA1bYGoMv5ubKIdbEAYWqejUu5ubvYRemwGqCJQM9xmz6pw63zmV98ro0R939ftPJjinzMr/QDCZoCZDcp3UpGQ7AtvWgK2KmHEcHVTmECb2O81E1zs4hBw6OGqRO8Zn5zTQ1mHUQ1Vlrnz1hJ4e5Ygvcl4Gn7fW2n/fa4Fmvh+lpHFY1Thwgb2A+6O0AfNUYmLufqiGYy/+zsso8YpuWuDOdXPq0w2Wess2zLrFuJai3v7/fnCB0unnRY/U4rNur6pWOtf61HcqgODT9liOdDnHS2LJp/QZtum64ztLY1xgzAwCWj6enp+b4zGbDIWyu4oMeXmPoiM5gztAJPWt7gLwyZwc7wD+Z6GCM/O4TiekngyM01sm/Mz5n2hNrgvOYR/IJ9GT/LfrWmVT0LXzkzKxvmkn5wQHF8cYXMQ/m2FMPsP6MHT/Fwd7vxY7fdY+tF21b5ISJV1VjEL6HQrLR90R53kJnxQJAt6DAkHzX+zDdR9VQpuTIOv9aESI0MK6dDRt4KwxnORw1MQhwlBxa0tJ5nwKMNsjOlNgowmDQC0ZjvI4YuSzYzSDPiglh9TgYQxr85Ak7agnyXAJuZWYnieAEyoPv8E7ob+CdIJV3OErF544q4RQyPoAaZdBpGAxyzEdWVLzHhgEawJ9WmjZczrY6mwPw9Vhp5qWqeuWU+B1TgMrAGbp4Px58Y/mys0DfXJXgzPl7bNCATBRGt2qIIDuy62itHUo7aTh0GDjviXVEs6pa1qiqRjxkcGMetN5MufOpzIzJet8OJJ8n2HF1gcERMrRcLuvw8LAF/wg80of1jvkLfvR9ehhm87SdYMZheSCbxynNs9lsMiOBzqPxrqpqAcccq/UbssLv6Bbex2cJcL1vOYOpmdWwnOKI2Q5CO/+k/LOtwPIM3Szn8KXXhQwR68d3ePfe3l6dnJzUfP5yOrftLf0cHh6OKlg8Vu4kfnp6ahlcbA7r6mocqidS1/Z9/8oGs8Y4z5l1wlkHPNJ2d3fr+Ph4VJECCCMLDy1Y9zy/Yjabtb3y6Wi9t8a5Awb0PrwGPrYsoIuofGEfu/e6OzPlAIfpjGw4o5mY0w4QzfoVfqCZR2wbwVruk4Ytt2OAHiaJ4uQJARr35/GkQ8lPOm8E7GezWUu4EMCGbvSXWc+UCWhbNVT2ubG2d3d3rdoHO4je46wGdA/4Oh0xaIUdQwegf7EDff9SEstVSqwz72Nd0ccut7X+gK7YFjuM/LjKiOA99iuDlS55dkBhas1yPml7vDXT+Bg958ql5D34E/1qh5NnWCP4DToeHBy0Mz98iwYyxLhcKcnap6+YcpXzt2NrXkrZe0t7s2O7WCxqvV63rGZGyQxeMPYHBwcj5uAaBQTi4ODg1YXQCZhdJvvhw4f6+PFjzWYvh05QmmLCsg/SwNCZUUdNaRhmFA8XE6NEMagIJc/4vQC8h4eHdsALoH69frk3lENj+BvCVfU6a5GMmY4UdEZw+B5ltAQV7u/v25UCKDjKK2AggyZA4Xq9HgFrDA80z6wBRsr7ZQyqUNhWJvRLNhFl5xOvu65r+7kvLy/r9va2zcHOOr8zJyvddBYy68U4OUCEU/i816Kq2inI9M8Phhhwh6I0eIWmfIfPvXeCSBWZT+bP2lDWwn5u+BDaOtvBe1k3wCCgC0AwpZDhP+ScPomOUpJXVaMxIlsoUMDf09NTff369V07ttAMo0k5pJ0+Ox3Q2/LgfqqGCD8G2sEMAw2cgapxmTjN8paGFACRASg7P3YQ0qllfDSMrnUSY6yqkREmgkzVi0uRLLOmS9qadLr4m3Ur7/eYyAQia8ibAWECOZ41LbOSxnN1wM+0Td1q53xvb68Wi8XoTAB0qfsxGMu5GRhtaxn0ZWwOciSY4P3YOr/LNKfih/WifM3BC553YPjDhw9N9/ODTiMA8vT0NLJ5Bj6ZVUkHgzU2T/DvlLNjPZ7BW+wVwREHnpApmueTDr0r0t6zY5sOve0NzbLgZrqwhg7c2oFN5zl5NINQ1mt+l9+Z75/qC72MPnJ/8It1WM4RZ8FBagfmMoDsknwH06qG6kr6Tb7j78buOTY7eqwfz7kSIQPoU33wbnSt+cFrz7/GEqa/7Zrpku9Lpwn8DqbkuapxgN+VpLzP+oVxmYbuy+ttG4GzbJtqXWP95PmYTmnjkmZeQ7+fSlHPx9UIyJNLzj0WV365DwdDqobyZifr0gH3Wia/mHb52ZS8/F777ut+rLAhTmal8pk0kizqVGQ5Fd3U/6cUlQ2ZQVf2YQbJcZkhvjUHM5IZs2p87YedKQu5y2v8fAqVs4upSJMmWVPvsXtuNgBW8H4/n2dpqbMyjqr52ZyLFUGuE+93mW7VcMCEab0tEuX13PY573CFQCofFAAO4TZ+SCG3oubHY0EB2GDlu23wTSOXpCaNUbJ+FjolTapeR5ineMm8NjVOFH/ynktF7cBsAwHvsVk/EkCaz4e9gwB3wGwarKrByMKnyDSlW5Yplznz/jSS8AiG1U6q38tYXOJXNVyVlc18bseQ91ZV2xJgh6Hv+xbB7/u+Li4uqqpaCZ63VdDM5zSXdWWlCHzuaL8b5z2QySA4uL+/XwcHB1U1nDpMlslRamgNfdGNvI/PmTPZJehE5Qnfs05FD0ILZ26m+APe8tp6K4hpnVU70MnVA3ZGsc1831F6Xw83mw1biw4ODkZbELAPmRmwDSAwzFguLy9HB6AQPOQ0YmcCaNDDzqfpZAchA8nOLFvHuQQO/c3zff8SSDo/P29rDG93XdfKIg0W9/b26vr6uu7u7kZBjazeeK+NpEbV2BbR7Jyk7TIgJ6Dgv1sfwBvpONmmUaHB96y/vFWDMWTwjKCYMZ4zWDgS6WgyVmjgufvkd+NDxm1e9vcJ/Fj3w4fIjAPoxg4cJuQgt2kEjauqZeE+fPjw6mBOZJyT9MGOyDlbnM7OzhqtSJAxz9xy6MMtwWSMk/k7GOSzZbzHdrPZ1Pn5eVVVHRwc1HK5bEmNzWZTh4eH7TBPbIP5z+uOLkY3caAuOm2bHM9ms1oulyM85HFmqa7xLnzlq51sa3geBxT+5/vcgmDa8h62n3GAKbK1s7PTzk5iXY1RCOKRBOz7vr58+VK3t7ctwG45dRA68YR9OQdsjY0tS29tb3ZsWbSjo6Pq+74d2EB09v7+fnQgRC78zs5OO0nry5cv9fDwUDc3NyPQn87LlKNMn95bmwaCAwY4KRZhy5JXO24oTfpDyfCcM8dd17UyDjNqVTXjjmB6HybMfHV1VVU12ieMYGBIYWCYDKGbOvRjPn85pdrlG1OCw3gp9eLvLpEk+8ocERCUsvc+m2F9TQLrAxjabDYjcG5GtqCuVqs2Pj5zkCId8KmgSjpPKGgUmA87M+BaLpf18ePHkcFx1tMg2UCb7K8Pa6oalKP/pXk+VtIuFfGmfdbZjjmOJMaGDCE8RUkJfbDvfcpppz/GYzDO99frdVsfyjeRDzLqt7e37f4y9lhMOXDvsbFOjmZjbKBn1Ti6a3l3kAaZozkibydlKlpOy0ACOmxbiSoVAzhfBpO5bsiuqyI8xtlsNnISnTFAD9ze3tbNzU3t7++3e6uzfDiDeg6aOMPB+3GcHBC0PmQtcAaREfQ0d3hjiyyPBssOjqFzvS/LTpQzHKlr7fA7EGuAa7APX6DTmD/vR7fBR1RMPDw8jE6vtF7CUeQ9LtGzfTUNHa130I0SQ/pkHM4MAALpAxDOXFarVSslrhpOY12tVi0QMhWMZgzYDQf4nC1NmTOA5HM7DtaLfofv1+V76EsOtQL3gAGQBZ/v4GqI99wyKJ7rti3oaRs1FbTjedvJbf1M9ZtBcQddksf5jpufgffyu7zHlSBT/TiLWjVOZjipwvdd1eaGLrHeScfpW3OGxqarZSODD+7TOMyOHOOa0vO8b2p+/D/xinWsaZTVNVWDvQAbmf/8u4Nifj4xoG3xFI+YL3me9cjth/zrPrBlKRcOPEzZev84wE7QwPwAXTibhh90Fno+dS0NWjnQQkVNYvGUdf+YTtva9zq1Vd/h2G57oV9qx6Pq9YRy4SwMCf7d3If36U4JZzrC/nsqvykiT/0dMIFB8/emaGHhSwGeUm5JLwCxHaBUXilYv0e3/LufmzIQjN8lRH53RkT9TPb1LVpP0TEVPAKIYmHMScMpYU+QaeUAH/owAtOS/l2eyN/TyU7e85pPrZGVdL4v14BmxQQtbMQM5kzXbYYn52H6WOEn7Qww/F3Aq2XTgOY9t6QN/OkDoghi+TolMoZd1zVnkMCS9+2nzHg9q4Y9OFmGSZsy+KwpBuz4+PjV3+3MJQigysGOkANa5jEfgGf9jZPjg2Mygs3YPRYMf1WNHMvHx8c2Lr/HVSEuuWXNnp6earVajQ4qIiAKGLETZofVdsHAgnWY0s92HK13nDHyXi/rXOZEhQl8QqDWssweRdMLp7fruhah9/r0fT95MiXP42AfHR3Vzs5O22q0XC4beOQ0aL6PM5dBC7LSNzc3LQht8EeQs++HMysAT9Zrtp3wFLSdAqAEhrwX1nLFeMmcW7+y9tCHYAI2JNeUTEbXdfXx48cRCPf2nPfc7DQg02SE2AJkGwOve58/eyinKvHgZXDaarVqoB07SVLAgRcCtD4AlT5ZU5/Q/fT0VDs7O+0aF9aPZ9F7iYvR/b6Dmn99MGvVsM/RDpaD8FVje+NTa7vu5RR3smnM2dlVV8OZ1ugS63GXrbry0LzLcw40phOF3LL26EXrJa/l1Pp6C4/HTv/06UNazX/YWpd8OyGTGGUKD2ag1DxNwHG1WrWMJkFi7AM8RF/GYA54MR+wZ+5HT9uF3eME6J9//rkl9pAD8/F6vW77ur22JOaqhrM6CDwzJm4AYNvner2ur1+/ti0iiTV8ToHp7bXN5KFtjvn+re3Nji1G6fb2dgRCXDIAATBWPuXNhoPIrkuZECZHx20geP/Xr19b3wiTjT19sD/WpR0oHWchDZwyasWYDg8P6w9/+EM9PDzUr7/+OsrM5RxYOG/gJxOCUPCvy2YAsij53KPE3/lbbioHHC+Xy5ECgg4wEHt/YVyi5DCxAaWvbbCAm5a8h8/zEADvueUz6M5ngBD2JFtR931f5+fnraSGwzZ8HRTKAcGD1szBB05AByJZ7Glj3zGGxuv/l7/8pVarVTtlG0Xedd0o+21H0JlbWipJG4kMFqAQrLx9zcdmM5TRGNShQFCkVlKsnd8BnVarVSsjgT6LxaKen5/b/lzmglJHCeYaEv1D9gDdUwD/vTQ7lIDdqtf74zGuPhAN2U0jaCDiyHTV+BRO6GtDaIfXmbjMQCGX+/v7dXJyUpvNpi4uLkYnEk45tpYh9IF1WWbG0MvOMDAOnzyJzqiqka6zY+USTwDt/v5+O0/A4Ac64pR6bzg6Az6ljJe9tlQgMdajo6Oaz+ftlHScv7wT2vo7o97Mi/kCZgzUDNAoe2SdmUPXdXVycjKyEz6oDUcV3nI1FLSz/qkarvCDbwC56DLGQJUW7897H72fFmcEgG3HFjl5fn6ur1+/toyztzxwmv/+/v7oWibbK+bEPLOc1HaC7/OTTlIGiijX46R805DDxrAbdqIceMVZPzs7q48fPzb7xVr9v+DYWn6hiwMU5k8H5tBXAHR0aDod8PRisRgF64xDXC3gxIGDJbbjdjyrqmFf+gJX2KbSF81OblbRpRwyD88tdW9WHUJHOwvgOyq5qoaSVtOa8ToTa7xmWcEWGYOiYxinZY65e/0zy8d8kzfcrMez+st8wmfeckbzGBnzlG2ZerdtJeP1fG3LoB1BFVcZwnPc8eogmO0V/OjgssdoZ9nzs+3kWrWjo6O6vLxs2Dlv1/ABYoyFIKTPkMH3wE6b78DXt7e3rarUzQG/DHxXDc48yUPzs+fsSoi3tDd/OwltwePlGCszqhvKxovKQpphzTBmODMiRLPisFK0o+OMAoKVwutmIfW7UuHQLMgGt6ZbAko+83gt3Km0MwI2tT5WRDkfR5bsoHiu6dh6nl4TBMnf3UZHr6n783jyb6a1QSBCx3igTfY71QzsbVRQ/lb2DgqwHwSj5jGa19PhSCd2ao4G/u7T0W07lPkcYMrz5/9TZTVTMulnDJ7944BByiX92nB6zbfpgvfWvI5Vgy7CsST4ZTnDULjEdD6fj648s3ym3qRt46V07hiXnTnrIBwGR+nhM4BN6nifYJuAIR1xj8mBPvNO1bjaoWpwggzAcPihN7IKP2Yw1aCs6vVVED6QznqRgAOG2eCK7ySYStto++S9m85g4vzhJFtnGny4ZbksfXOKux0/j8H0gAegH2sM//HM7u5uywShL6EB+7N4lnEAwnAIHbSwc2O7mT8EM8liwQ+WudS1vIOWGQKvje0Y37UMsDasP4FNyyB0Ns+ZHlU1orczHb9nt95LYysMNpwTVh1cMs0zQwPfWv8YMNPH9fX1Kznj+QzSVY2voElMZ/xhnuBdiQ8Zt20AOJPnrTfSMfP+bsurqwPQtz59mfcbv2FbvN3C388xIIsed+pvfkeWoBnjMd2mnETWlMSTg3rZvK0qMYzxauo4+Ah7ynPMy3z2FpycmN72bQrn83/Plb7hD3SItzPyXdtQ9K4rZ6x3WIejo6OGHcC0fE5FhOnsPd92Rruua063fTp/9+HhoV3xikOLTwifpJ+RwQywBGNxJYzHAk2xUd/T3uzYnp+fV9/3zdvH2NhJAGwgpHd3d7Wzs1NnZ2dVVS1yiXC6dApCEgmD+Zy2N+P5NFkrH4hFNILypgRdBm8sCIuYJRV3d3f1r//6ryPwlyCSaEOWvlhZW/gcJWSRr6+v6/b2ti0sQtAW698ikpS0ODPnsROAgJnY8L9cLhugJZMMDfzDuvgwELIALpP0HBBmrnW4urpqQkIkcH9/v9XiO0IDPQGmx8fHI+VtBncpOEJrJxABw2BhUL13bj5/2VR/dHRUHz9+rL//+7+vx8fHuri4qJ2dnfr48WMrr7i+vm7ZWsCrTzb1nbysGQoIvvJcTk5OWvkTTg38wt7s6+vr9jl92GD46oTVajUClGSZeBaQ7NLMNGbwO2PAoLBfljG4dKbrupaR2RYMsQHxur3H5mCD6UFlCtkoAL+dLxwGTk333dgGOQ7KJHgyvwHUDPpo/J9rd2joA95rB9w6zc4VQBLesHGH78wzzobAs4zDc7DuzWAB9HMJNPvRAY6bzWakRxmHg6g8hx1BR07pNA7fwBmhT+Ts4OCg7S3NYESCSQNOdN7z83O7ziRBAvtk0Tt2Ulkv9DSA0eWzfN8H0Zk/GW+uObKNTd7d3a2PHz/W3t5enZ6eVt/3bd/uyclJA2k86z22BqIua8N+TdEMOpCNd1DBgHqqUTUC7zMXgJ+D1tgzOzGbzXBlxWKxqMPDw2Y3qWSx3SKzfXNzU1XDvmAy2L6a6u7uru7u7tr5ItscgffW2ObgO0zRc4nhHAChGfja+U05omLD4BrecnDDf2dcXn/bMlfW0Y+dEMZnveZxOitPv4zFdthnavgzl/o6wM5csDvofHgefbdeD+djoIONL5wVJyAGXY1x0SdgcnACpfaMYYqXoQ0Y+v7+vl3xZf5nzj7DBdn0d6A9Y+DvYEQqE+2cJvaZ0h+24TzrQIYrk6Cn8U7yL/rF9tmVglXV7EpeHzebDTeGONBhP6Lrujo9Pa0ff/yx6VN4m6wx76HBT2RZbaeg+3K5bOf2QANuBYHGj4+PDavyvLFm+lPpN6E7s1pjap19oORb2psdWxy6zJi6BMcMigAwUTOVS5kNlJyRMEOl4p8yCCaKwQzKZOq7VpBWFGYalIBP9ctn+B2mNvB0S+bMqBFA1jTIzAxOcGbEXFpXNY5SI0wHBweNIe0ETmV8XI7gDG1GjRzhRnk5q+p12dnZGWVyrLSgB6C864bIIM18kwKSjqSDHLzDGQkOGNvf36/lctn2vjiTBEB0kMBKw8qfMfG5AzEGbbzb5YV2/jODYCNtPgBAOosM+DKvmd/8fytH84Gjwz59NI2ceSy/Y8XEO3Ne763l6aquLnB5vtcCHcK/6JjMNkzJSsqMwRt/sw5K/ZgOoxufWXasL7Nv5mAQsQ3A2XmhX3jK+5em3m+Zyj7sPPO+5Nm0KR4/e2wtF+hUdDLzhF524lMGGKuBK4Y8I/A8x+8uu+LvVQP4dvmjwZbtmU+rhnZTa4v8AsRsQ+jn8PCwXYFHQJPx0Fgzj5d3OfDHd1erVSv/JoOZd72yXtDXtob+3Xiv5cc2n/n5WetW/419y9bxHo/XH/l1IC/XJXFI8rxx1XtsrJkdAzfrR+sWB4bsjFrf+cc2x/8ik/SZtE6eTfvJ+zNDmHyUGNM/9O8fO29TOJdn7fSYh9Mmmy7WU8l7aZOTHg5IuTImaet5+TPbJf4+VQ5tW+W13YZXPL6cs+2qP/NaWVa3yZt1D2PMeTrAbB7w2mVLXGdetQ1Lenr9PFd0Ezh9qnm+Doyi87uua4Fh4wPrLYJGZGzBxQ7Y5PrkPJPeUzw0xYPQP/XF77U3O7bU6jvL2HVdM3ir1aoBM5iCf/m7DYGjRjbAnkjfD3tg0lHEEclIAEDdpWG812ADRWdlRcTb301BNAN2XdeMnLN5MAMZOWcVUSabzWbEjAkGmQdRc0DN1dXViEmtrDebTYvwABDok74AEXx/Npu1KDoODs8QXYaZySpBs81m07LS7OXiCHn6nnKU+N2ZGhRt3/ct22Lgj2M+m82aYPX9y6EjBoMpZKwPYOf4+LjOzs6a4042nozV9fV1/e///b9HDoLvAWPtMDamWRo7j6eqRlFqr49/UskYXMFbmTn2erj8xMrJGaxUoHbKLOOucvAcyfSn8rYsMh6+t63k6L009jeSMT88PKyDg4O6uLio1Wr1aj8qMo3ue35+rt9++63W63UrK8osgB06R5CrhrXJO2Hzmh8cK2TKfdDQdRwswZ5Sj8f62I4A40D/OcMAf3v88DcZLuTQ2T34zecZONgEz7lqyPf68gMdiOibJ8k62OnzPbtdN+yB9p42Z7rJuhKRr6rRd+fzeZ2entZ8Ph/tw3Jp2Ww2a3tUkR3rXsbjqqO8t92HdbC21mG8w3Nhv74POuLu9R9++KEODg7q5OSkqqpl+rEFjClLyuzY+s70h4eH+stf/tLufH96ernmx04tWWKfEZGAfCrbA82rqp3qbzAI37pEmHFVDXtrCYo7oJF4BlvGujA21oU5s5bcMW9e5cyI6+vrkRP33ho0g54OPlh+CRxBM+/fJmsJ7yOv5pu0S6zJ3t5eqwTjzAjjLdYP/nAwv2qovmGNnJ2rqlc6kbF7TeFhZJT/26nje/yLjH7+/Llms1kLLqWzzZg2m03TD5kIAYuRYJhyIHkn+/vBWvRhGjuQkA5/JsCoaECW2AdMwM9rgdyhO3LPsn0Cyx2JA5/qD9Z6fHxs+pn+Mwhi++Ws/NS7Pd8MJE/xTvIj++yhAb6Oz0xg7KwHOv3u7q7m83mdnZ3V3t5eHR0dNf8snXwSOHd3dy27Cs8dHx/X0dFRrVar+tvf/tbwAXRlPzBnbnC4InxhP8R8bz8OnOxKAM9vKlnF+LBZWan0lvZmxxYDwWIa5HCUvcvP/N2McNpAZWTHDMB3zfjZZ5vIznDPF0SnHwNzMxnCkwwN2LDjnUyaStEAlfcDYJ2JRhG7z4zWmjnpe2dnpxl5K0GUmkvNrIAcNMAomFEMEMyUBqc+WS2vwKEE1g49zQ4btPUaOhNiJmculJQhEC5P4Xs+Lc7rzTwcNe/7lygVJZ98zjUSVS/O/J///OfabDbt+p8URj/L+tgge10cpcSgGwBNjRtHwHwDrzkLn9Eyr2m+n/Wx4mIdbSDSefJa0lyK7XJH5jXVZ8ree2up4J+fn0eZ2qrxfq40ilWv92mxDug2Awbzgd/rjKKb19rrP7UmPG95tb5KncrcrPMzyu13mff43Y6iHZQMeE7JjsedxpFAH898ix+t+zy37J9xe2weI7o/HSOCPXwPIAbNDNDdLyDJOp45MSavM8/4swwwpZ72d5ylZC8YwRbrBWd7HSAG9PIdBwDOz8/bPi22tjg4Y/uaQHqb/pjiJ54z5vDnDjAn8PT3kza2Z7wj96b7OzlO62fb3Azcv7dmJ4J5Z0vaJg5KHjDNrW+mmvljm84zrt02PvRyjsnBZT63TvSPHVreZwc16VE1vl3ANhe9YvyUMmr9bTxtfez3bfs88U/O//e+6+1a3hYytQ5TPx675dnPpa3KRIftg+VvyiY4uGi95D4SI1n38bepZqw4xd+Wk7QHPGs+yrVN2QGbO5lCENfBPcYEziAR5upFB5angjG5jvCj5W9q7lPr8O/FjW92bA2GDLBubm5GWaYPHz7U0dFRizJnVsFM5TIcGwgMPCd1sneCCEHXvWSK5/N5O/k4hfDx8bFd6M77URzfAmhuzq7wPBGVLNMkSg5d2PeEs2sFZIbwmF3+ZEb0RnMcW9bCoNcMzLgy8gWDmYmcUaka3xOJYDNvokxWIgZNmdUA4Dvi5Ii7M0dTjq8zNQlsyeoYqGS9fgIVaEsGzQ4I84aOq9Vq5MD7sBayCQ5imLdTuXDgCjzMu7z3z9E5xsx6GKxyrQGnVhugsW+ZQwHMEwBpr99isaj9/f1WCkjwwkoQ5cncuMt6qtSO+cDrzta/58bdm/Dq1dVVWyuO3SdTyCXx19fX1XXDib1kFOAdX1nhPZTwtSs54D+uWckDFxwcIki2LSPvTCBradlFV1uHk6mmL+taywTNQT62YDBm+ibKy14p9tHCb8wlwclsNmvZsZ9//rmqqv761782G4WeZXyMZ1sGMEEQ9DCwsd3xfjkyMz6Rsu/7pnNYEz+TYM/BUvry1SW2q+ipx8fHdq4BWWoH6a6urpo+YB2en5/bM4zj+Pi4fvzxx5FtcBZ6Z2enfvrppxaEZN3I1hPlx5n98uXLKMgA7VzFZf2czkBmQ2wLcb7R087cw0PMgSwwgJl3IV/Yc8aIjrc9Ra+xt5bPOEwr98wxXmzfZjNkdfN++vfWfJ4GoNzZxKpB3myvsDEOgtPXlNzn/+1wstXA24v4cYWLgb4dlalqEzAMFQ83NzevstM+/Mxz9Ng5rwPedMCFYDzvIZkAFnEmz/sZ6cP8ih6zQwL90ZnQaLPZtDMioId1E7TF5j0+PrZqQPr0eJBzzhlhryeHEDFeO+fGmVnJZDo6iwtmcyaUGwhubm5qZ+flKqTZbPbqFHYaegEapf3C0TNdnSBKh880AyOTnJtyWtNnMma0zq6qdv7LbDarT58+1WbzUqHHQWr06fOKZrOXSqzb29uml9HriQem7Dp+jYObroA1HR3syUC8sc22Ofuzt7Z/l2NbNQg53jwvRggx3GwU5zM7Yy4NtvGGCDs7O+2Cee4UxLhTmrKNgUidIyBWdv5+MjTNIM/PGEj63ZTSodQo48KoO7qUEUYW0qV0CDlMQ5keY2YtXBrB351FZt4IOkrH77bxNYCzIDkzzPgBWVY+zqqipH3PH/0yV5xElLR5zNkbZ7CYH0rVjO8MsAXSguexM04rX3jS17AwNjKVPjQK4YNOCWR4Pxv2s7TCawcf0Lye/Ozt7bWSGjviGLnn5+dWesxa29jBg7ybQ728LpYLR5cNAn3nJPyGYrNyA2Buk7X30KYMI/Q2iMfIGtRUvd4DlgDIfU79+L0J7ixTKT/WE1NjT8c0M/E0O9EGS2nIeDaDa/7JLKf/bpp47taHU/xuMGqa+ztpkD2XKZ2S87P+zG0dU+vN81Pz4rteB8C9TxbelqVEn+ZnjNXBKAcn00HAmUR/m36uHvCcKWEDNDnKDxhMx5T5sOYZhE5aQjvmYlptk4f8PTGD6cL7ptYkn0/Z9/wsfyljVQPPZbbuvTWXy9ppoFm+7HCanx1UtuOSPJuOLX37nBhXEtB/6h3wFvzg8n/sLU4dW84orbe84MQ505VBRebl4KVp5+0adrgcrHKlSNXr7J7nkjwKtt3f36+np6cWGEfXuCw3bYyvP+S5tA3oBzAfh7ty7osDraaxx0cg1/ySlYpT7+Xv0Jb1cFB0yp5O+QvMPzGj7WTabDcHxpzISfuSNsn08HyqhoMgDw4OarFY1Hq9blugCHI6O8uYONPg9va2bTdyBQ3vZwugeS7tOvw7pRdTx7s5sZUYAmc8P3tL+y7H1koGRkJYWbD1et2yq1YE9JEKrWq48N1GnIXGcYYJcEb8N5eKOsOAAkQhEokmIub78KwIHS2mP0d0HVFMQO9IFdlkFsilvSgSK2OYEAGh9Jj9N33fv9oo7rIHfjcTQQevo8FfrnHVoPzy1GrGaeVLHynUzHuxWLS9WAZ9ODsuSXFQAwcOwX5+fh5FU11K5meISqVhYx/CbDZr0UHGkncPQzuEkD5Za5Sy52RjwjNVNRL2vh8OFeJdBuGsjecI3e1I+J5fZzHgM3+Xfx2AYL2qXpTizc3NKBsG3RiHs0KmN1c2mK9cxmijenJyMgkk30uzweV3DP3j42O79oS9ipyAXTU+ybiqRpk7aAidCVxMRTDN/wSYDBK6rms6iUwyPJTA0sYVHWDdYUNjZxXgha7l9HYHLR0sY97oXXh4Z2enTk9PazabtSwApwNfXl6OrimAPlw4//T01Kp1fvvtt6oa5NFGGXptNpuWqfTeJwM3aGcZw26xHpy0vlqt6suXL81+VA37Z7kL13diGmABUpyNh9YuzbadQw6RU4N1AlbYaGfaPZc81Orw8LCOjo5qvV7Xn//851osFvWHP/yh8Vbfv1RrQQcHxz5//lwXFxdtrZ05hsfR+/A6/LlcLmtvb290OAlBQ+YGQCbzwXxZH8sk7zHABawl4AIb0D9rDj86IG+dncEFqhd4P5+xXpzSvLe3VycnJ6+yHO+xoa/sINGmgjzYKniNvYKWBzt0Ds77O/RtkOxETAYpAPvoI4LzXj9k1fgCvVtVrQIHx9a6b8qZcWAIXWmZhB7pvEPPqW1G6UgYw4HlveUJXMf8ySYaG2fQ0Flv5pjXJjn5wPkrztAaF0MfYxDzQuLu5CGvtTEZdOSshqoaHYLkagBomsGopDvvt212EmzKz3EfDtBkWS/201lqnzRvPeRxY/fI3nNQbFZAYdOwNdDBOs122r6dcUnXdW2vNJ/bD2BeYBH6RXa7rmu4CDpYn6ejm2v+rfZdji2Lh5HGUD4/P4+iUhg8C6udPwNcAxsWwe+jvIJJOo1vR8EReZfcQcTd3d1mqFEeGELKqAzOiUJZYeL8GBBUDUrHcwMg2WmzY0s5hBUXR2lzAAl9oAj29/cb2AO04vTaoTZDkl2cChowh4yMMR7KUAAZGW2CGQ3+mC/Cfnp6WoeHhy1za7DlI+nhLSstsvKM1Zv/fVKmHVvm6KDBfD6v4+PjOj4+rsvLy1bi6agtPOH72GgZGeao9gQjOOsOXiwWiybEVeNggTMe8LwjWFktwP/hCRSGA06sh9+ZTqqNJOvishIrXtaJfuzcAqiRFTvl1hUfPnyos7OzSWX/XpoNjHml6vWpivxMBY3cLKv+jsFb9svvaYwYl2WXMUxlx9wnzYYlDbz/lv+62TDmnDweeMl0ZU6OHk+NGZpXvb5HMMcH31teLAO5rv7JvhzcTCOc68Q8cv5+T65pjmEKaPEvtMsxZ9CL/qG5QazlH3sE/xiA45yiRzhIEn7z/ly/dxsP5edT6zb1Y6fXPJJBGv617p2i7xSAnvp7OrbpvCTeqRqfN7It4P+eGrySeJD16bohGGe+gC5svwG3VVULojnQxxrk1hsDbXAIwQo+7/u+VQYSCL+/v28Vh9wXmjqgqkaZLraN0TeVTdZjxkvoIctX13XtGjH+ZprRcEbBPBxax5zseKZdwpEg+E51pfGLHRfsCfYjsZK3zIHl0CtO8lDRQQCPPmyf0EGmszEnThrOvHkps/BcGQadWRP3CeZ3kiAzkInxvW52zODjbzV8EvjSWJqKPHyWnZ2durq6qpubm1a9abmBThzGhw+GP5VVdfgfYL/EoKmXnWizvpzNZu0qPPjGvhJ9c4ifz8TBb4PHq8YHLVpnm8fe2r7ruh87EV6gBCEZ8YdB/HnVALK9zxCC8nzuFePd2SeLY0G1gWGxZ7NZOwwDJp4qH3V0wo4Xi0W/MDEKxs61T5bzWA3eGCfgAuZj8avG+0PzgC7+NaP3fd8cWoM3Is+OmgMGaO4HgUDhOjjBmrNXAWXpOn2EDXpOZQQN3HIfCmvB2qBIfb0G8yBDlFElFAiZSZxcAh+OeLLfjzEwPjsCVdX2Z/m0N59UaB50pMoRMQyRgZIDFOn4eK0NhlhDv4uARhpgeAyemgKKdpQZz9HRUXtX3w8ZCWdnkaOqsUFFca9Wq+9WTv+RGoAHHYA+tK7xvW9TQIZ/4UHr1KenpwbuOLm2alyOVjWc8orBJ4hE43RxyxP92OHq+yHjBP9QPUPViDMYVS/rTiYVx4Z3e087gANbkHeu8j5OvneFBXMCNDlTyPuh687OTh0dHVVVjfbyMje2BmSgEnnmtFuMNt/hQCrTNOlNlQw6HlkBOAOqHSy1/EJbxjyfz9sp1ZazKWeVzDHVPqwDIK/rurZNh72kp6en7YReMsnmi9vb2/pf/+t/tWwAPAzANZ9sNi+VOvf3923vvveTofuqXpemXl1dVdVw3ykZH/Q44BngxBj5nbGzVYSsBXxrO4HsGLPwrr7vm8xhP/KwKwcmsNcGe7Z30MaYZ71e1/n5edPX71k/pq2hmXedsZsKODiAW/X6KjG/C/7NoIPH4DG5RNN8XDW+ys2Bwm0BlsSS1l383YkY23nGbEzr8YA90sGYosPUXBkTDpR1vrOhGWDwPDxONz43hjS2cUVjfn8bf3j8xotJT76TAQfe46RJBv6dcd0WtPLvXTdkGtfr9WT1RzqH/G4dT6CAz7AJ2FaXoCft0OXGB06eGV+Yr22TjZWzZaWqf2ynWb+sek0Mnwko+3cOOKQ8/Hud2zc7tgYqNqYuF2USSTAia4AuFgFD5P2ynjhGIJUfi4pD6WwVB2sQ2bODcHNzU3t7e/UP//AP7W8csuGFcxSFd8IogC3mzcEAjnY5qwcY9OI7Owy9HN3i88Vi0YzkbDaUcLgPG0RAWNVL1HC5XNbNzU0rh3x4eGiGGkXBekxFBZ1BZ/xm+L29vTo7O6uqocSINYUhid7bwTN4s5LCGCXzM9f7+/vRxfaMy2XnLhcEuH348KHu7u7q9va2fv755/r48WPrnwMP2FfiMqWp6BVz4sCQqpdoMsqNtYLnmZMjll5nH0JAxM6Gw8GKjAJ6PRIUu6zTwB3wxZygLfKDY8vv+/v79eOPP45KnllfHHODb/jZ+4gpvXvPbSo7VjWU3LD+HBKUzn/VEEG3g2X+SwNiOfCa8nsaegMEGw3LkudiIwNPEMiZMuJV45OL/Xlmpqxr3T8N/V81ZP7hc7/btOC9BoNpm6xvLN+ZKXDGcgrQISeWecZqR43PGIfLDJE7gzvTyfNiLuaJzLzzd853cFDAtsf0INBJNYy3w6Qzgd12tYz53zyZID6BOL8nH7k0kLlax/GMdUzyqMGVg2v8zTyQvJHAGx3u6hofwuK15//YV2MX857HTKDaGOc9NleFMF9neKqGA3kMmLFPDqJw9gU20XqMZwher1arFkzyGlu2CJ5UjZ1DzgY5OTmpvu9beSfP7u7uvgrsW18j22BHZG2zGa7fo1rQMu7SYCrlsL/MhcOq5vN5c7CQdeYHLkKGLM/7+/v16dOnNi/4mnH6ek8nPrxuqcPW63XLHtvuUZlHZs7YOvUn6wPNeM6Bo+QTWsoP6wsmBWft7u7WyclJzefzury8bHNiXHbWXO5uXXRyctIqLsFabLu5vb0dHfJaNT5Y7uDgoPkLDw8PDdMul8sWiIWGBB6znPfDhw/1D//wD7VcLutPf/pT/fWvfx05rmDZtP/wI2tmmTTdsAded2Q4S8cZO/bSVYHmK/sYXdc1WwJ9oDeBd5IAGVx4S3uzY+tJWxmZQG5T3rwnxMQNys1AaSgNQAyCvGcjIzjOxCLwOH8ISBpC3pERCcYPMLAys9PjSASRar+DMU8pQJqVOgoNoczonIXZNCUTYMXhyA8OSQJQ0y2zn9DMDhFZ3QQvjMe0YY7OCvrdPJNAwOvi9TDt07HwmqWDYGcd44His+DxLw5cGkYrVp+0aKXNWPixc2ugZbpjSPiM37PM0SUbfkc+03VD9t384YbCS5nbbDZNXsyrjDOjoeks5f/fa4Pe3qf4/PzcStpw7qd0CwYf2sFfrjzhOYDwYrEYrVlWq+TeRQJaVJa49CpP1sag2QHA4FDpwDwBIOyXJyNG5paSfebsChPrf8sxwSAbZsszc9lsNu306aphK0DXdS2wxLYYDDN92Ci78sBg5vr6uo2JYJV1EzLPswRVyYA+Pb2cmAsN5/PhHlve+eXLl7q9va2jo6NaLBZtqwX2o+/70R5fAB6AlnWrqpaFPjk5qbOzs7q/v6/FYjGSS2SYv1HyRknYx48fa39/v75+/VqfP3+u1WpV5+fnr4IJm81QambbyjtY6+Pj46ZbDbChPQFiQOt8Ph+dLM0BM8zTQWf4AqfEW4qgGye+I5uWLXiA8TA/eIKKHtYQ/nG5p5tBJfyYwSsa4B15NnZ5j824jP8nxrNudEss4N/9Hfiraij1nApOJ2bKcdnW05cxSGIPz21q3P5ujt1zTfxlvMMz4F1O+bU8WraNkabmBo+DZ9JBtE72OBN3um8+Z7xVw2niieNN46n+7Ohsu/PXcpVBWK+HaZ1YmH+xobmezCXtFYmdPNgVe5jZX3TP3t7eqCyagC1OvPHD7zV4vGp83SB6zYEj0znXyHOmORg7teZeQ/tGiXf9nim5hk4u37Ze2Da+36XN93wZYXAjGmKhtOfO7zbm6aABBlzG570MBuOcOoxTxiEcvvyc/jFAKTwYarJ7KEDmmBGJDx8+vNpTmSVMjqoBaIiu8Q7vQe66rhnsLN9lzOyZuLm5qevr60YXgA/OqRv0xlljzWA8gKk38FtoAZaz2XAUujf4r9frBnL7vm+ZSwuVFXHOiXVyvb35wrxmZ9mBCDuFRDRzL50VkPfuPD+/3FsLcOGeLq6mYF2cOeGoffYteM8AYwJAGygZhCIfLjs04Ifum83m1cE4lNz5EK6qwZmeyuTgaABsKR+GJqwnzzw/D4c74Bzxnc+fPzeamsbM30bMwQs7Vi79eY/NjhkNXYbjiry6lN5gzLyQDpczRlXV1pAgDXLhUyxtkNAd1svOzhtsMl4HxqxH0jk0eIGH6c+HpWWEOA2kHU2cZoMDGmCEDAq6y04Wuj9pbWNrUERLx405QVv0dc7b6+gMUIJknHK/kz58LoAzTfRrEGA6uG8qhXJvP9FzTm3lMCfAGX0tFos6Pj6u6+vrkW6kD+jg9Un6s2bwHPyLrgegew6AV/SJr8cyVrAzSstAEN8Hc3hPngMktr0ZPEQ+vN3Ez3tt08Fy4MDgjWYckcHJ99rSMTo4OKijo6PabDYNw1m3wCdkwZALH4Rp24W9pX+f2M8+VVeSgR3YnmUb1vd925dJYBe9nAd+OkM5n89bn9bfzmyir8j0wm/QKANWzhT2/VDpwVwcFMhgOTQybgczPDw81Pn5+Wht0HN9P+ybT8cZmYJW0N0Yp6pGuBS5BMeY5711wPx/cnJSy+WyDg4O6ocffqiqqt9++61ub2+bfnUl2NHRUdNp0Aabw3YEts7ZRh0dHbUsKeO+vLwcOarw497eXn38+LHpFNNvd3e3jo+P275sb8Hb2dmpxWLRtkI4OcZBeWDtr1+/jviC93OoF470ZrOpf/mXf6mu6+ri4qJdY5TXhtnvwJ7YT0J/215V1SjTbn5EDrCvXnP4AdohE/CsAxG2S4zBFQ7wKutknfmW9mbHNrNCGIaMxjjbZQNips2oSn4/gca2aJ0jERldykjFFLjhPXZKp4CUhdvPOPph59FlVnbqskzO38nxmU6eN2NBOHjOhpm5ZJTGyiSDC35nRlxy3TLa5jkmbdP4p4Gxscq+vP45rvyuHQvGbIc99wt67VCS5js3R/y8jtui8dk8dhuApCHjNX+YlgQmzKfJO1MKAGWUGY80gr4v2H9nTMi6M19plPN5A5r33BaLRVUNe1yvr6/bnlp4izV3sKaqRifVOsBj2fbhbf7hZOM8EdbR/qohAOkAEDznigQCK6njq4agWTYHcKpe+BDjy9aNDJbh8AKyzLt2yM1fzhDayCYgthzloSkOcOXppwQKkA9ARdd1LWDpRl8csvfjjz/W3/3d39XV1VX98ssvjTZkcNHL6/W60YMDaY6Pj9uJzl5rO5K8n6Dc6elp7e3tNdADLR38XK1WLRAJcKqqOjs7GzmNq9WqAaSDg4M6Pz9vfHB8fPxKz3Vd14JmWdbm7THemgJQh3ZV1apBmCcZWpepOYgAf5iHXR7qrKzH5Wv6nF3AgaIvb61KPID8Mg7/vrOz0044/vz5cwPSgNPn5+c6PDysg4ODtk1qZ2enfvzxxwbCE3e8p5a2msoCy5WBMAdF+eR9ZJi1tIOUuMo3XthmVg32zLYQ/vE+eg7OhH/t1DEXdDe/E7h0IMRJEJwT60ueTSxpvrb+qhrukza+TsxlmjlYXTXYFPQ/fZP0wAn1Nj3o5D3o/C1xjekK7nKpb9WAO5MOff8S+Pj06VMtl8v6h3/4h2aXcLZ43vyE3rCuIMjv82WcUCBJAZ1wln1ug/UTJbdfv35tARmSHdx2kLSoGoKOTh7xfxIpbBu0M2md5+DCZrOp3377bWTXN5vNKwcVh9z0St+p6nWiDjoQ9DQutV8Hj0FrrwnrlLfe+N/0d+wjJQ/bF/m99mbH1pEjSs6yhCvBruv77aD6QBM7akRtbQB5DuDG3132yoRhQisD3ucx+qS3zWYzKltDcBy5dbmCo8NVw9UcHBKDkPmYbcbmvXUwihfLe28pT0D52vl3RgBG4DAQFPLt7W1Tyi59Zi+ps65cp+HIKAYfgOCyUwwAzGhhyf27MDCRb+99Zh6np6dtHIAzDIn7YO4YiFz7+Xze9oYAijCSx8fHDfChJFerVe3t7dXp6WlTVjaW8Af7y52p8VrCIwZ/rKV51Fkfooh2MKqGbI8NAX2hVG9ubl4ZcviZK0tYDxQjUUrzK4fnPDw81OHhYR0fHzcniTXf2dmp5XJZm82mzs/Pa7PZNHpZeVVVK7O5vb1t2V/o8t732GKIMJJERzHGZAAyCANfEQWdzYasFd9jLSz/VeOMVNLXPIy8TslM1VBqB78BeBiPy+eRv3RyzO/WH1SFcLojsutgojNiHlcaTviS8WS5oY14VY2AlLPNXTfs5fSJvegZO2JToJB3Mgf2I/3888/1X/7Lf6k///nPrxxbqi24jgFacfcg9oNx07C1lnXW3WcwoFNWq1VzROEL9tBSWTKbzeof//EfW1AE2b66uqrb29va3d0dXS/BYU3ObsIfVdWu9zP4BsgQdc8AHjoOW8CZGZxZcX193eSDNU/+peUZFg462y6hv7El0JK15XvJDylXniu/sx7z+bw+f/7cqiLsRPEd9CPX/Tw9PY3Kvd9jy4oqSrbBFPCVA8Yuq83SSjc7Eqx/VjMZTBtQG1RX1Qi3sPeU7089m7jQwbMMUKKjXF1FQ0+g/6uGqkQ7h3aY0imx/mSuDpLxzrQjrjzh+XTM/K+xN/PiswwEeTsdY3bGkrGjB3hutVrV3/72t3Y3cFXV58+fXwVdz87Oquu6Ojk5aU4qZ6r87W9/q8fHxzo6OqrDw8OmN3GCHagwv7F9AmxkumCb7ZxT7fbrr7+26iQCc5xvw7u8Fqw1fkdWLVhuvJ7Ija/ogd9c7Wes3Pcv+7LT58igBOtonndlpX0+cHAGBF3izu8ETBivdXnyM89Bh+TBt7Q3O7bbOoU5vWAWoKoaKSQzvY1dKh0mxE86rI4gZaSKz/x3mhc1owB+n8fvsfC9VEw5h5yj3520gn4WbBtmZ14MVj3mHEu+N6Mi6XQlfb+1xtuiJ3Zw3XLN+JsN/1QUZ2rd+HuC36QF/5qGSQ/T3Tw3RUevbRqsqTVOUGTDtK1NKRYb6+QPHG+/zzTk7zxjkJVyM2Xk871TvJZGPkv2UK4ey3tsGBQcTGTM+wsB0KyRM7espR0IGgbB6+aDuyhD5v04DjilgAkHDQ2uMkLqahC/085x1Wt9Bq+kc9j3wyntjC/lAIBA8IeAUlU1p4fxO+rtDF3f9w34WH96rDi7VfUKYFonMibr8HTmWZPVatUCos/Pz+2wvOVy2U4b/vu///uqqvrrX/86qg4h08peYeSH7S8fPnyo4+PjtrYew9evX0flcAQPr66u6m9/+1vd3t7W5eVlzefzdg0E6/GXv/yl7cllbfnXJegGxzjHrAv08xVm5geXKlIO5+qFrhucYwJq0GCz2bSg5P7+/ug8CPMmAaGULdYH3mMMzs4nqLM8sebO9NlxMA8jLwQNum4IfBt4Pjw81OXlZXNy+75vgR+fvPseGxlAZ7EJxjtgAm6zbbKj6nJLr4tP8e77fhQcp7/cRpdBEHQKjhjbrBiLA+z8uPwdfmJc6QDCg99ybB18dgIo5YvxOAmTJcKbzabpJTuyyAwBK+78zgQC9E9sM5VUYjw4YqYP2Xf6dwIHRzB1/cXFRf3yyy8jGlvv0+9PP/1Uu7u7tVgs2mGmnz59qvPz87YdkK1k6LYPHz60+8S9xY4qi48fP1bVS+kzvgv6xoeA2vG7v79vJ7ovl8va39+v+/v7Nq/VavUq8MvWIK759Enw8BHjo6G7zOPpaHZdNzqUjDM1KLmmCsJ8kT4U44QfySITZGVsBDXX63ULKoBNbB+QKX7Q9RyOlX4XcyBJ6ADDW9p3lyLTyDgxgCmnBmJ7r9BsNmvXFqxWq9YHEVn2/xweHrZSqt3d3To7O2tKbbPZ1JcvX9qJxl5wR6OcTfJC5WESMKv3CyN0vM/315pBMUh3d3ctO0PZA8Lk2njTkrEeHh5W13Wj+85ms1m7KoGxYAh96qL3yXZd1/aJohQ5HdnlCowfYUOZZ4aGZ4j4N6b5N7qw5s7yca+ulZcdMN7lOV1cXLQ+ql7vywPAMiZnfavG97SiSKDt9fV12/Pg/WQfPnyoH374oV0DRGkYY0VxVA2HkgCUWVsaETsAKQpmNhuOZGfNyKRDCwQd4/z09FTX19dNIezu7ra7i1HUZHFQzNDDMmAjiYxCF969t7c3ighCOz5fLpdVVaMD0ABovBeFxvh9QTv0wKi9Z8fW++mrhi0B8B4ynRkJQIb5zo5sBqmqhvudycgBFsiCzGaz+vTpU4tcr9frUWkdEeRtTpt52wFCSrqsY50xtYNucGVDnkE8v8dRXHRb3/ejaw0MIpFlO62mddXYbqEXDQj9mefBmBy8yiARYBt5fnh4qIuLi7aXarFY1M8//1yHh4etnM5gYLPZNH15fX3dsq1nZ2cNrO3v77eT530P+PPzc/366691f39fR0dHbTvF4+NjXV5etv8j04wZvfDXv/61qqqOj49H18p5P5p1Lgcxea8Wa5lBFGibji1yYj7znnBXF202m3bGBIetZLaEeSE/OEqslysNjAkMlJyBYBzmSYM8nIsMCiMvqT/tLLB+3AmMPuaWg9wb996aMRPrm9eNZQCav/O8f6rGJ5Bbjv0+y7mD3QQb7BBnYHdbcDgxbj7jMwIcNJ7SNcz5W0F18zz61X1Ojcn9uZ+cpx3ZDLZmmwq8MIeUFdM9Pzc29xrahzBecDbZcwBf4z+AIa+vrxuWdsCftbHeB58bH1sngc0Yr3W+n/H8wEZ2fm0LzaPmReNp67rUd3zXPLDN6XMf2Y+b9SbrkfvZPS4/s+09llM76VPzMX9ZR7gy8XsCf292bJN4jsSnsPkZJgzBHDXaRhQWzZNxGUGCFYhgRYSgpDBXDREnC54JmAxjADgFNE0POyqpzHLuKKQ0ru4TYGAhMK1crgrzZaTZkcmp+VhB+j3+HMXH76mITFsrpzRofg/gx1GzFFSviZW8BdB85LJtaAidck1dHgFPTBkDaJjlQLlWzGubIOYzSQ8bOxtirwuRtimFBr/4XVOyMrUW/q7pY56jDyvBnAvjddmN3/FeWzpD6dzDOwZcGI4Ec2kQpiKqPtCkanwOAHoAB4UACkEv85J5hn5o6BaXuqUuQdeknmAelGaTBbTT7vm6f4zn8fHxSKcyN9PDjoj5l8/tMJiWLp22PXCDFi5B7rqhXJXnvQcprwwjy/qv//qvzVknEMV4Dg8P28F0ODqsjwMj1r9TwM9jgwbY56l9cV7j1BcEcVlLgibWlWTHDVwI/MFzjJu+ODDRdhe+Nq0NVBlX6lb6cenm8/Pz6IRNniVTk1k7mveZMw7zk20ImRZAtWUBvkkgbplxkJvvmI/fY8u9f5Rpwv+z2aw5BPC/A8JVg13hUCd0STp2BAu7bjipnCCP+Xpvb6+VZ2Yw34dcErDwwUPpAMIv9EmAGkfJWAceIHlh3UaDn5BPdH2eFcL8qmpSDxtLpHPsbQB937frVTwG64nEB8gYwTAfjsSWArKk0JU1ZbucAzvoCe+PzjGwVvAKWXxodX5+3pJLV1dXLUCGHfbhTev1ui4uLtrp99CY3ylF5sT4zeYloWYaTR0Wxv5U5oq9tv3BPkNH+IM1Iflmu2++8HNuxmbpU02tq3nB3+GeXuv1u7u7VpXCeI15rRMdrMMmOiGVeML8wboThP8eh5b2ZscWBWBhg4AoEBbfjgenhW02r0ssLJRVg5BuNpu6vLys2Wy4UJ4sFkQEGKDUyAYQFYVJUIY2NBYQg34UEJkyFnw+n7cyLOaNwaUvavahB2CHvpzlZAExbtTMQy+Ub9WQHYXJ0oE1kLPTYWH3nausi/fYek9A13VNgcPs0LQxzb8pLMbJ+js7y52+0NP3jrEpHdpDH4MOA18E6xXz7gwnzjJ35kBW6/T0tH2Hd/kuOd9xxrUOpqlLcjDAt7e3oz2N0BNAh7FmHpSqVFXba0n2FUNI4KaqRld0PD091ZcvX0Zri5LwqY5k/atelwJaGdoQk4FD4VpO4HErVGQvnQc7Hi5FoloDur/nhrwhl/ARZVN2QOEVgBMnavM5cuSDlnxK/GazaeWuADjWBh2EoeA+R0qzDg8Pa39/vwFLvzcPRoE30GXIlIEG4DIDSvAg46Msy9n7NNBUO1A9wWmYv/76a8t0oc+J1k+V0DmYwpx84r6/Rx8EGuzAoMuQIcAa48jSOO4ktOE/PT2t1WpV//Iv/1J934+qlZ6fn5t++vz5c7NbrAs/zuxji5Avr5O/w3pS6sWhRefn5yNnYOqAGgMN9ILLpXmvS4dxMDebTSuhxs6hA9lr7DFjF9A9qeuphAI02wmHT1lbl7f5lFVsDiDe84X34VP0mPUiupe98oeHhyObaAwzFYi1PLBG2AfoBgh/r83BU/M1zfgsA8PWb+nEZuMzB+OQU8sU+tnBW5qDknYsM2DhlmudTurU71N9bZvbVICa9zjgs40engO/mwYOzuT4ki5TDriTOTln0x362Bnib1U12oaTY8i1o0LHQdzb29vmoFMR4HE5CImc5/5e4ywHYY2VGZtl1n9br9evMG3yNvMwXuPvU+swJRfb+DHfaX/H9KDlO+1gMkccVye5jAnzPTSec3AkHfIp+jgY8y2Zn2pvdmztKFkRWOAye2bBgdEcaXYky0YET92ljVMHqpgAGGA7UACZNDxmnqp6BYbsoGUpAn/neWfI+J4dbkcnUuhd/uFMnGmUGa9c+ClFwu+O/E99DpBjXcyQHr+F233wLKCUtQb0eBx2vDOKZGHyWvOZecnztTNo3mD8ADtAHTzGGrk0gjVg7SzYNBxwoqxJaxsaInX8YCC5WB1aQLN81uAKpxingncz/lSgaQz8L5/ThxWa5bdqLGPZr9fNfVQNxgP+cObuvTbvPTWIqhoiwZb3bUbRwRRolqXBOHZ2eqYAH8af5kj1bDY+FMg6KvW5gSH9wUPJGzknB1Y8T+biPq2bM0jikmfmjcymQ7sNULnR93z++nAmnk09hZ5I22dZ9Ro8Pj7Wly9fqqpagBTH2IfPIcMEGh1ocpAT2nr7Ds8zZvOVwSq6wnrTMk9fnu82nsqyRWevebf1PM8xFzvHBknWQ1lNxRhtJ1hDY5OkBbqev2XJs4GuaWZ9TB/wXO7DzfJ3aJ60pZm3bXffs34kMOdDnRykx+46UF1Vr/iMoB405F/o7cAba4Pzwxp7T59P5SagYzzZ931LKtBXOoG8q6paoNJOi9cffkvMy/uorHEwy824wzLSdcN9zsiZbYl1qfmbeTE+DrEj8OJAZFaD2ba4cnOz2dTFxUWbrwOj4HuSCZvNpgWtbFPQ5dYh1jOMnVP3+75vCRQSaMyXYO7h4WEtl8sWcMfPSJ3Bv2SPbXdSt3q7Ym65spxnCfSUE8d3nPnkb7Y9tl22rVkRkDadeZgHXY1EY54ECj0++zSMwTwBXvV7cWqTR8GHljnrU3iHJMz3tO8uRUZB2XFEIVxeXtZyuayzs7N6ehruSYV4JycnVVUjhZQnHcLMMLfLfuyQsphEZVCWVTU6/Zb+7BBl2RECw7+UhPEM76c/3tt1XduEXlVNuOxIExGqqtFR3pvNppU8EPHHMDujTSbOWWavSd6XCKPjwDmKAgiEqS8uLhoDOatkAGJGpoTKxhjlT7kOzA9TrtfDflP2dHqtMpLE+22I2ESOgJDhZF2cxWHv6XK5bPSDZ21QaZQo+UAX84qBosGSMz8oQPMtcyHQYvo+PT2NMisG6jYgzipBFzuS/O69roB06Gf+MO/wLhQzfAe/QFOMgIGZP5/P5y3zTZkPmWSXWE1FCd9TY7+69z5Dc4y390FiKKyboCdGyXJfNRgDrha4u7ur8/PzyQCPy8RcLYAxOTw8rKOjo5ZlrHq9r4rgDLyOEbKuxliia9IJy+htGkYytMiInVqXqfGZacphSVylQP/Qgf6t6+kDHXt2dlYnJyd1f3/f9o46IOhn0c/ei+XMsenRdS/76b98+dL0Ede9cLAIOmmzeSlh/OGHH+rm5qaurq7aibnIH+/lJx1xB/7QJZz+udlsRvdYd13X9i2jrzlnIPk1A2JTa02Jrx2UjNAzLgeoCa50XdcqfHzew87OcM84awYIfn5+bqX1x8fHDYPQ0NU+GwFHBplAbjKwDQiEPowDm2+Mgn1DXuBXnq2qNjc7OvzNADbl7z01aJHBqvybdYVlj88AuVMybb2ZMrstsJZOtp0A7Dhl9a5Cc5DQzc4C70vHzM4o7wCDOcmQGI7+rX+ZP+OsqpFjZ1lF/p0kwYFZr9et0oFkAON39tPNAW0ng5Cz9XrdTgoHPzIH9APPO4ibQUr6zLFjO0yDDFq5qgZ8hCz7KjLjZzvuzN28YR51pVE6yVPY1s5sNvN/OrYO7k1VOkADAiL+zM6n/Rzkgvd4Ta3fHeBO+2qnn3GTPWcdHMTBX0hbbrq6WhX76sTBW9p3O7aOABncUtqG8Z7P56/2x2BMudYFRnBGMt+ZqXxHlKvGwAXD6CislULOIRUORhXA5IVwpsJRHRtgO/qM1QYewICjRXkUp7YZDOP8IDRTAlg1OJIGO1bMtHwWRT2fz+vo6Gj0XUem6I93UWJm58vGvWp8jDtjz3vbLCB2fjxWGplXrzHgkPfyPOtBObVPmoMP04AasFspeK09Jitg5p8RYZwCK2WMDHwC6HLGKjMmDlb4O5kBwBA6ymkHJ+WA5wnEoOxzvKwtY4ZGAAnoyql+Hk/O5T07trSkcxpig3mvdxoo/vVa0idGx4G2NKh2mGn8zvo488SzKZd+h8dqRyCzJinT6AE+53mDSM8BgOr3MW/6MIjKTPOUzs/18fttXHOeVeOyRsukaeZ1toxkNpMgEUE4xoBtfH5+Hu1dTb3vn8wkeK4JCk0TxunvOZqfetDgNf9mfqI/AzrsZ77XPMl6ek7WISkb6CmvIe+2PvI8cn3MY+Yt08VAzjRK/oIudoas79Dz6VCkTn/PjcC3sYPlomq8192OMLaH9fDBleaZlDXrTq9f0t6BaGMUxkqfDro4S5f6IHWC5cUOhBtjysN53Lf/tUxYR/BZVkjhVGLXoddUtYCDjXnNpnU2WyLAYZ4zCZCqakkDaGubw7jYsoRNSgcT2vKvsbttHnxFIoXg3uHhYQu+VVX7fbVaNQyZ64busn2xD2Ed4Z/EZ1P2x2uUp8pX1cg20AdztV4xbscmpq2iX9tgEhBJa48h+QLaZJIsdbrn6fXyeBKXmn4EqS2TWbnwe+3fdY+tDRpCNZ/Pa7FY1Gazqc+fP78Sehy5qhpFhnEaDabt4HnfJ1nh9XrdFtUnBDNO+iLS5lIhlEfV4BTauGEYYTgutscJdEZss9m0I75ZIB9fDY3SEfJiVg1XdCAMjAuamUkSwHif3BToIdvRdePLni2wACkEjYBE3jfMnoY0FlWDkSJjle/AQULROHrPXFiXLONlDQk4MFf/n6DByclJozF84rtdb29v6+rqqg4ODlqJMgLucZu3MHDcSYYTbOOZ5TZpWFHuZJvpM5UE8yHyD23TSCM3zNFBGPN8RsUB0Ox3zKivDTuyhDFO2adM5/DwsO2r4yTe3N/iO53fY2OeU2Crajh1+vj4uD59+lT39/d1fn4+ilrzPR+0ZB1LZhI9WDXICrrVPMS4DLLhD9aI3zF2dpSQdYySs3mcRNt1Xct2YgP6vm8VL+jD6+vren5+br8bGABUycgdHR3VZrNpe48x/NCBcn7v47TTBc2snzz3vu8bv/Z9Pzq/weVc8D2y7UNukDGXWJEtgJboOtpms6k///nPtbOzU//4j/9YZ2dno+0P2Q/7LjNynuVtfi995Gn+djQNgKAdP1xVcXd3NzrB2MFF9Db9+BAabC57/xeLxeiMgQRb8AHjZY5PT0+jzBONCpv5fDh0iO/AW+g2n92AnDCXo6OjtoYO2kEXaGv5psomgwKUbTIXbKBBMBVZrLezJenQvMdGaepUcAidiT3EIakaHJ2qAWhTrpwVAdhA+N5JD9tT7+OsGt+qYDyJDXMQr+/7Zu/Q4XYOvrWG8IGxsceX+0uNZZ1RZkzQDn2AjaVSzRVt3iqWzrt5Gl7k3Vlei87Y3d2tjx8/1t7eXv32229Nvi3PVdX0iDPK2BEOKFqv123/vvWCZaOqRgdn+XuM2wGT3d3dZieOj4+bvKOv+Z3r0NKJA/c4eObgSJ7IP5VoMM7nc/5G9tiYEbuCrrG9ctAGfQu2pxlPp6+ROBOcZgxo/jUW9NozTvhmKotrOhgTQZcpbG36WgZZB/PsW9qbHVuIghCaYIBilPf5+XkTIjMLUSCXPFaND58xgEdocSh9UAkTdb24+7CSc/kGhrjv+xZxMtM5s4dwUI/v0xb59+bmZpQtywW24nXUxUaN8UAvK3+XQbgx5zxEy+91ZgCGgq6eK6XCTv07W+DsXR7Rb0BpYSPL59P/XErmEsmM8qOEEWoMlctXHHWF5rPZrGW/r66umtNH1vb5+eUgLfbAJNiAhg7i2OA4KonAWvFMKW+Pv+u6Ec8lP7tEHjpAWzs8rLMjcIB/xkTFAfv2HDTY2dmpm5ub9h07YRm0smPrKDqOdde9XI6+WCxGig96wh8uz3rPDcORRtL/t56rel0+6+a/sUboOug7lXXN95tvraf8k+NIp9jGx3qM7zt4aGPpZ5MOftZzyP/zXX9/6udb9DMdt33ufuBhR8bz/e7PTqKze/TFvxmgcp9TY0Xu+L7/zWe2/T7FawZODu5OravXw8Eb+vYz8Gfqk+zrW/R3tiztFmPN+ZoeSdPMkE3xo+fIdzLg+JbmYHbypt/zrfG8t5b6IP8/9bfkcXjO/DCFfayn7Djm+6xbjBEtB9aX5iXvDXR/2+bi95mXGatl0brDWJTfGQMtM6HMx3ZhSr5zzPTp9+f/PZepgNmUbp1aj210mpLJtCGmWb7H/aCLn56e2inDOaep8fi96Ui7b4J4XjfzpwMmiVc9Xmg4pWP5nm13OvNTtiidUdMGfJjOdmIIV4FVjat7cD6NhXOdcv3BuLSU3fy+7Urq799rb3ZsffqtGTS9cYA6ThIA2AOnWenwg7NVVc0JItP2/PzcfuddNvoACsbnvYGMwwLK+30oDw4AkXAcgCliuy/XwnMhMvOfAvlWZFNGlj7ttE8xmP/1M0Svn56e6uLiovq+n7xH146+hcnKNDewW9nzXTITZFYZL2tvZ4j9F84wTgEW08vKw9lJ/n9wcFCLxaLOzs5GAkRmhn5wqPu+HznX8I4NzjYwbT624vPzjsh6Tr7zMsuOfPx7VY2UMc+bR6zorOw8H9O274e9hUQFnaVLQ57ZR0eN4QvmzH63qhpVGdg5eM/ADZo6awW9Hbh4enpqF79jOKCNQULVGNhtNpsW5WWfoNcjAUZuUUBOrF/hU/gus78nJyc1m81e7UUiOEImjiyLs3ppH7wP07JhMAUPkylEFtJ+oIcsq+gDy5uznXzXdFmv16P9uw4EAoY4Wb3rupZ1tqNrW+LqGNYE3eoyy83mZc8rW1jgj9ls1vard91wP6sPULQ8I/fW54yHQ/329vbamRH0YV2XJ5/f3t6OrvUxLaHvFN/aJrvEzVVT0Ct1TNW45BS6sH/a62G9k4E32y743TyHXFinE4hMIAmNTk5O6vDwsC4uLpodTcDlrD00rap2v7D51Q176KzNe215rY55FboYv/lvU3jDFRNUKEyta9Vru+mgbQaaeL+zZ+ZZcCUZ6Ozff5sKILoKp2rYn+5932xVYX4HBwe1XC4bHnUwns/W63X7zJnLDCyjN6bwHnIL7gbXzWazZnPogz3u6CpuPuGH8bEWDtzjI5hWzhpDM2jlg5Ayu8g6kkCBzujxnZ2d+vLlS6MjFXMuJXd1D7JaVSMMzeF/zIW+VqtV063oG+hIUgV97ySXeX/qee6jXy6Xr/Z/85y3iKHXoItPiMcmQ2N0vNcgEw/2TeAPMuZUpKQPA2+5Dz+7WCzq+Pi4nc7//PzcsupgCfiH/mxbvqe92bGllDIzf34xg7HRTafMzWAjlRqG4uDgoB4eHurr16/V9y8lZFVVV1dX9fw83FmH8vHJtTc3NyMmskGuGpQcpUocoMFhTuv1uh3w5GxeOrZ2+LlCBcUwlZXwyZBVQ/kLY7Jju7e31/qkGfDaCHj+AOyLi4u6urqq5XJZx8fHje44rqyVQRgK0TSdz+dNuWVkbjab1WKxaCUlADUApA2Z7yiDxj7JOh1bnEGUjwE6393b22vlJp8+fRqVM/oaHBTU3d1dO1mRtbcjmBnJKcfWwYoE8gbvfo65UP7scg6ULPuDcRbhD2huhxt6uOwkeRx+5V+MJ3Q032WGxIopo3Qozfl83q7kcJDL8oVie8/ALaOVDhZYptOo0/jcQMN9swYZXHHFRI7H/JKfJYjIcWY/BmnfCn54XO7PfGPnLMdKn55T9vOt7+QcpuZj+wWt/ZxBt8dn4JY63X17LTI4Zh3hYIFlJunuDAC6c2p+ntuUvZ2ikyuu3J/HwxzTjpvn09Z5Dl7bb40n2xTfu9981qDI62BglHyU/XicuQY5j5RFA7KpuTkYMjXv96wfzV9TdDamsUz/Hu8kHpta32262PzizNeU3nEDszgj9y39m/Kd48z3meemEj5817rX40yHlb/xr8eU9JriWf+kLeO5LFuFtu5jah3tOCXNbUtS/rfJKs3vtGPK3tUpXe6/uR/zD++yjTBOApMxbgdmpnic58FJxpFTc3P/DqrZXnkuU/qF8Rpv00z7tHH+nWrYqbEmnzB+ML9tonF+NvNiYpjfa292bO10Zp20HZfn5+cG6vMkYyaJE5hHidMPbb0ejj7PlD7CbsMCobIsgn/pIwnH+3Fw0iB7EQyoiIjnOwDyfl8y5pTCzNJX0z0VNZm/vJybefm9MBrKGCXpO8AsdLnXzvPh8nGycozTEWmcJhzFVJTQGHCHoPF+AB/8leOhsW77+/t1cnJSBwcHdX9//+qOzvl8XtfX13V1ddWcd6+DT+YGOCYQTSPS98M+CehhILTN0KXyc6bfIDIVdzr9Gbl0iTjjZd08h6TjNuNgOXeGp2rYy5GRddYJQ+L5TinR99SQQ04Yhv99eJz5HN2xXq9fRXNd8u19qci9M0Snp6ftb3ZQAB3IpcveHb2vGvZwZyDw/Py8+v4lmMip5I5E575AgjGz2XD/OM/AB75v3KfMJvC0wUM2cv8be32fnp4a3VNPWq6wWeglAlscnmaniP36XdeNDqgjeLnZbFoEG93wrcygr/iqqhHQIkPDuxaLRasuIdtKn13XtX3MZExoDnDxXoJ0zqQnUEqdRtbA++Lh0/n8ZR9u1XD/OmvKfl6CqpywmuCw7/tRoJK5ISOMFx5ibj5ABFBn/U0WwqfDY18cpCRgzD5mdJkBK/+/vr5uckHWzGDS9g3+gR4ORBAsZ29bnkROVdh7bfCfW550atpbpmwLWVN0nSsIfQPFFDb0ewjUZ9bf+9mzmskYx6f4orOdyAErWQe60a+dIvYOI3fwaVW1BIv5qqpGyRfP23iD/mmJB7BdPtuEvn0oKE4JVQYc0Hl7e9uqbCzftmm2O2AF3zfN+jqT7kA7a4N+5W/oO/rg7BTreM6mYG+0ZZds5/Pz82hPNWM2BjY2ur6+btU1+Zx5B13Be6lEIfkEDreN7rqu8Q5r7SCox5ROYfo6m82whc5VDHy2LRjpKknGxNjhA3//Ww0shM3Cv2Fe3ooJ35lH4b3vaW92bCEgjI1RR5jJFLq0CUPnDMV8Ph8BJcorKLtEoCl1MwDo+/Gl8C5NYMEB8750uWpQVHa0ISCEdlrc91fZiGZ0BrBFSR5peis6KwTmx6J6PNDQhtgRO95rkEGZHE5q1Thi5ygJpXUc3kSpNsrcY5/NZq1Pl14sFos6Pz+vf/3Xfx3RxQYFEDKbDRlar4MztQRCAAQYAnjBfeG0Wgne3d3V8fFx/eEPf2hAhL9D4/l8Xp8/f67ffvttxIfz+cv+C0pBALooIRRjliXRPnz4UCcnJ3Vzc9MAPcLpch/mMhX9hN4OZtgATEWtcHTgCxxr+rJRwFjCuy7h9Logc3wXvgD4w2MoYr7DOwFtHz58qNVqNTLOKMr33FgrVz0ksDJAc2BlKnLOMw4cVL0+mTgrGDIYku+fqqBx5NnjQn9jZNMhSoPq8U45M/wdIGEgMGVgsw8DDr+HsXs+6VhOjf1bRtkyakBre+AKBgf4oHfOxc063mvna3Yc9LXecPbLmSa/zzxQVSPbl7Qwf3jtptY2AV4GyFgn306QkXrmlOtgsI8j6HdkVpn/W29CK3hsSt+hlzyeKRraKcFJN81dLZH0Y73Np+hS5Mp9pD5+b82g1XxdNXYIphww7CR/85Yy1on+fGjilP7BacgAIJ8nFuOd6WQ7Ewe/28nAcZtykqb4FwcTR6SqWhkpZcpgW2OT1COutHOzvCev8RzNzoarCjNRw/g4g4a1tM603wDO40Ri+sSGgcWxUdAXh9tOIfNxgL/qBZNRrcccKEn2tXDQwIECb7dkzOYb+xoEN8GvrLl5whgJbM16EZQx/7k8PQOCrvTib2kjkybwNzQzf/p580N+x7rT7+A9b3E4HXClvN7BE/rkO+nE2+a9tb3Zsc2yxVxoR1tp26IB/J9/M/IwBTbsCEwB8wQCjNnAzqBjypA4o8FeKhbOyneqeY+HDVxmLM3AnoeVsxcdJkiaWGFaCJ2tTqBiQALDWHAdqLASRynN5/M6Pj5uTqNpPmXUed7v5vOki40Fn00BWCttHG4cQwBU13VtTzanx+JoebyMH5BD3/CJab8tmuxSaz5jbKYLmaLsg3U2eE66mbbmZeaCYaRBS9Y3wbPBQYI9K3yUK+My78HzBCnsqBiI8Pf3XooMrQG/BP6gKUbcmTD4jIwdPAKAIKMAYEMfwUfsVXHQzIeXWecYhBAU4/A0giI4sMgFEeU0dpym6FMY7XSlYU+jTPP+JfdBxJ25+MoJ8zPgZTab1fHxcd3f39fNzc0r58vOCI4P9KC6w84Q9EGn+4A+1tqBHJqzEtAC8OKAR9+/nMR8f3/fQAxz47q8r1+/tqye9WLSkoyVdZfBPiAKWUYuWTv0pU/IxK6gG3EyAYU8azDqqh+CdNgTxmHb7KtTuq5rQUvvHeQ5ggjcUEB2KHmdtTg4OKjd3d1W7UWAr+/7UWDUARC2DkFrX08H/3gPGO+FX6qq0ZiAvYEyTotPozePv2entmrgGewW9qZqnGUisO/qFlriKActaOgf9KSfM4iGf1l3+N4AmnXKoOFUIJI1rxpvWwM7uXIAvcjzllXowPN2BDxPHBY7szlO49EMIji4lHS2k52ZVzv4V1dXo4NCsXeMzxjGjr6r+dC96CFXcbrP9CsyUJBBhapBP1iWSUDZtlJhYcfUODTXKZ1X5srziYkdvIAOfd+3Q0wdfKRf23O/x+vqIPe2QK7/llvrkDPT3/6IZRFbkcFF0x1ed1ACW5J2yYE/n2Fk2ZgKsL61vdmx5XAglyGmhw0ItgB4UI5qGbwb9FohMDkLDJ/nVQAwCJlWxkxmkM9YDDfeTWQM0LZYLNp+R5Qep8z6Koy+79tBERcXFy0zSjbNJTceh+fcdV0DeNDNpX7Q2sCg7/vRlQxd17XyEN5lpyajhy5v7LquRZXOz8/r4eGhlZQ9PT3V9fV1/d3f/V398Y9/bHPPyIvBrdfPe4j53c4cYwQ4ocytMJwZRPj29vZqsVjU/v5+XVxc1MHBQf3444+1Xq/r4uKi7u/v6/Pnzw24o4RdtstBCdDLAAZac/2A+YQs6e3tbTMwVnJ5EA/VAK4SwNDl6cgYqiyPcnmrI5sZqbVhN5BnnRgXfTiqiMGkFDBlzWD69PS09vf36/Pnzy1Lyzgo0V6tVi2K+r1Rt/9ozcDIYKNqCAIgf1WvD4nD8GPIpoIlvIe/53VgfJaZQ/gfXUD2yM8ZXDB2Gy76w9kwODOgTANkMGQwip52UISxJf8bpKURxIH04UoGFfn/zWYon0Xno+sYD4Y89ZxpnPTJABCyaNDKnDnEzeu02Wyao0sVid/n8fvvWTLJZ3wXWsIDXocc/1SA2oB5s9m0wCb8mCfzZzCBBh1sJ9J59AE28/m86UcHXW0zzFvmbWfbsB1TfMt6QB/mYJAJLdHD5ieXJtOnHV/jHuttg+Pk0/fYfBAism++rRruVCfAUbV9Dz04yk4QthwsYb4FB6I/eF8GFnxWig8BtSxXDXiHZ5Fnl9AzB/ACP3ZY4GMODKJibDabjQ63TJ1KQJDD1UxP61j+z9wdPICfk6/39/fr+Pi44UvzJTyfQSJj+arxNURgtePj49H4OHgJ/W2nMPWGeWAqQGB7SRDZwbyu62q1WrWA4tevX+v5+blh/LQRxkhTTpe3GmbwnhtA7Gjbgdzd3a27u7u21ce2EztGwNYlxeguO7RVQ4DOmDWdwql1RreydcOBBWyFeZbScQKZDlIgG+ksV9XoAE30vYPz1ofmJXjVzv9b25sdWzMQvzuC7s+srDKy5WedOTNgsVfPotMcNU9j4HEkA3hM/O6W4+X/BkpTzyO8Bil+NiOK/r+jFqajv5vRMt7P/AF9ztBZ2aPMEhRNARcb8SmAhPIFeFhJ2amCyZlTCpnp4me8tn4GWmZE8sOHD7VcLkeOPH0jFChiC40dLM/ZgNsAhTGYP/nc9/qaN7bxjPnC42UdWEPGlryToNayMSULBrDuY1tU0TLJHF0B4fljHLfpgKlxv9fGPhbWELnFANt529/fr8fHx5ZdJILLydIGTHZIn5+f6/r6uhkig7WMvhvAbDYvJy8S1MGY+bRwjFrVoOuRKwyZ5YNxwRO8y6eM8y9A0TwAb2Ms4SVoyHjW6+GcBf6OzuVZKjM2m00DtlPXXJknAQ92uOFzIvyMa7VaNT3qYIXnS8P5ur6+HukU5rxYLGo+n9ft7W0DwpwUiSzlienwRJ7gS1UK40VveitP7r3nznKy89DAmRY7klXjPX/w3Gw2a8EqO42sE8HLnD8g0gfooU/29/dHwNjbWNiyxFqaHgZ/yCIZGYJsi8WibXkyKPb7+37IorBdynvaTR/bNbLOzq6kbjCfOIhJ8J3DDN9rcwbWeqpqfECZbaZ/N3h30NwtccQUpqoa3wpBEJ7xGR/ZXht/TTnlaTvBS7b3zNlBvLTpHsNUSx3q+SSuNS0JAplm+X5jLgcW0AXIurdKGBdtC9IkprQdsbz7sylaOOjrtUieShxrne3seeKg/PkWdvdzDtQlJrPeSBrzPQfBE/uaL7/lU5ke+Z5ckyn60R+BT+YBtp9auylfIecx1TyuKXolzv/3tDc7tmQGeSk1/yaGhahqAEwZyfWzVS8RrYeHh1HJDgabaJQFwQYJwOUo7fPzcysxI3LDvgEMtBcLUOM+WDSi00QEMWI8TwlXVTVjDigjIuLIFe/vuq4pCI5pd8nElFI1fSlf8yFSRP7Ym/z09FRHR0d1eHjYsoseO+/JUgwzusfx+fPn+ud//ue6v7+vs7OzkaML2GEcuRfatAaoQg/2jEJ7H5ZTNSgjwClj/fTpU/3TP/1Trdfrxp+UHh8dHdX+/n59+fJltIZkRNIBpaQMwAZfwmvOIjA2R9b4O04KgBEQhCLMkjsU7IcPH1p1AFEzO+MAPfgsHXeybFXTzrqVFXOzs0XZkx1uZAuwenBwMAoKcFewHQmvM+uP/vg/UVT/f2+O7Jr2Ce6J6HtfNHrAJ4bT4Kv5fN6OxOcZsgS8xwYzq2OywqRqqPqAjxyYqxoCGThQzC0jzPA/TnPf9yNZpSXgx3b4WfMozhK86n1Kfd83R4gDlqCLt5BAE6L3tPv7++bkOdqMfnaVEGtpW1Y1LvdmrQDKdmQM/JgDJak7Ozut9Bp7hr5LwGAwjc1y+bABFs5mAkqqKqATMu6gwePjY6tSyaAMThm6ju+gs9AH6Wxiv22Lbcet87tuyHrQCCIArJ3NoKFTcWqhiW9XyIB6Zoe8j4/32nEwze1Y264nsEfnmz/o05UG79mx5QwOr7OdGQesbBvRJ4eHh+3wLu9RzgA+lUnwPryY615VTX84k59VcgQFGTtBG2/jsLMNT8OHdtKQfztBzIfADvi4qpp+tR1xn8wVuWB8dtzInpEFRrfYqfcZK/v7+y1QyHPoaHDncrlsQX10IPOfKpuGLuhbxnZ4eFhnZ2d1f39f5+fnrxJDVWPd6YoadBZ2xjwAzTh0jwP6uCHE55mw3uBm3smY7QzavvscHvAYa8vf7VBzwwV6dzabtVtKeJ+D4mBm73VOZ9SJEG5zIfM65cgmFrH9Bm8eHx+3LTrwJfSCRn7OetaBCuYOrmGeaRccrGXe0BE+/ve0Nzu26aTCzF6MKeAy5ZFnlo+/V02X5Ngw8+78PBfSCs9g08xpBsjP6MelYvTryBTvAZTRT0ZFtkVJaAYkNqbQJp3RbBZGK9qkLd/Jd7Mu+YznjiIn2sz6V43Lu6foPBUZ89ySR7ZFdRxgQOG6T4xj1RB1zAiuHcYUeI/ZZROp4Bzp9RySp2gAUa/PlMxMzd+0ASAC8JOGyRsGlrm2KZ9pWHIO5l9+d0lWNoPPqXm+p3Z2dtb4L2ltGgNcCGRZ7tJxtExj2FxuZmNnxwl+zfKfqvE90zismYFirN7vVDW+F9F8z5aA+Xze7vpkLumgAfxonjtZRJfPEXC0k+69UDzPPHmHDw/s+76VQjr4wxYEHGz32/d9y6qyJgCSzCp4Lt6q43VhrQGtfOf8/Lw5ON4TZt2W+ohgIkABoM0cABNeP/phSxFACzoA0DMownrhDNuhYL8aPMB6ew2Zq0uA7dBaBpgT/Xv9HAAimAh9raum8AMBWBwkB6AJYrLmBq7QA2fBzhLjYXxV47tCzQOmD+sIICdDPKW731NLPFM1fQjYtgyagbIzXcZxru4DFFtejeFoSXfeb0ySyQXs2rbgCHbRODedkaygsm5Bp7kCIDEUfU057B6HbTZ87LmzHp4XvO4srW1DYh9j5FzXpAn0ZA4+XX+KjtlX4kRjLv5mnqBPeCedJcaFHXQfOQ7rYP+N9WUsUz4GawRP2lH33PK5qapPYwp4xjoHXvH3HZyznzLlz+GD+B3pU/k97rNq+nqmKbrm35Ke/yf68M2OraOUMPe2l0NUgAITJbp7fX3dMmD0SRYDMAMTkumBocwgVlLOqmEsczM3zzr6TjPjOhrDPFFkAKYEpGThiEjC+DhhZMQAOYCtvh8Orjg5Oand3d36/Plz3dzcNLBCxNnMBZ1oCABKgkOVqoZrEGBQM63B89evX0eK3Y5iGh5HnL0W0IfoITR3NspRRRsQAwPmSGSPrPP19XUdHx/XH//4x+q6rr58+dK+O5u9HDzz+PhY//Iv/9IysGR1iMQzVowPtCGr49IzIkiAkLu7uzo4OBg5EAcHB3V8fNzW2KDOh1kRQXTU3odckI1z1NnRVhuErhsOfCFamIrUSsdyC587EwPvEPE0sIXPXAUAsOM7zq4YuPkUwvfcTk9Pa7PZtIhwVb0yHnYI+r5vemDKsbV+dVUIIANHIQ2sTxDfbIYraVjPqiHIgoPkCL51HY4LY3PGyX+n1JPf/a/Hic70d5AVSnLtMHlfGE4TMuNIPXSxgSTa7BM1HYh1VpHn/RnOkG0F83fE382VEzbuZGWrXq6Dsv7lLICPHz/W6elp01cJaq03vO+JeWAndnZ2WvDCe7IAzxwIdnFxUZvNpk5PT0fXHdmxhZbO1meAZr1e12KxaPRj3bxFwUAfHYbuh5+T1/wv60MJPyX7lqmqccCNBt9gazmoDV7g6iLAPPaIzBZZvdxXhp7kffCtZYO+XCFmB2K9XrerhDL7/N6aT1afwmGsEzwLFsDGPD4+1sXFxYgHU6+av12Kju0kMGH85mCFnWd+bI+rhiCj9wZi77CpyObR0VHTPWDTw8PD1g/Yz/1WDUkCZNpbE2yv0Y3oCgezqga+RB/abhhTOgOO3iRTe3JyUh8+fKjLy8u2DYbtBMZPVNT5LB7wUJ4n4IAsn6UT7Qwqtg862FElC+xgHDQ8PDxscocuJ9MIzdOBtcN+eHjYTqX2IVf2G+z4UZUA39C3A1vQBF1ueliHOqBcNQQfrPfslDqQ0nWvD+FKR9M0QGfjJFuvWWd7fYw7NptNwxKWIcu5g9PQxDT3fL2ti8qITBj8XnuzNk0AZQLndxw98cJAEBOUz4gUMbnsG+I6SuaJmoDJAAZWGfHZ9uM+PY4pp5i/84ybF9oM6UiMjWWm6h3BSKBgxgeU0GAKGNzPTs3f0SyY1/Mx3d0fgBKFyPftsCZtPKcpHvKcHHGj//n8pfSVUmB4iTk8Pb3ca3l7e1vL5XIyumphcvaDd0xF5OBDz4+fNIiOXpl/p3gHhZLPpNGFPqZz0sd/y3ekY2se8PtTcZrnptYxZdF9OJDwnp1b70GtGnh+KuAGPbIKJvdw+lAxR1Pp184xn1tGE4ABHrNNyXTV2OBaXr1do+oFcHz9+rXu7u5aRhKHGqDj4BoG3jqd8lTrtm16gd+R9XS+rTeYkw/KSl1gHrbOSv3FeFjTtBVTes3AynMzrQ8ODkZbOHCQfv7553p6emrB3bStqbtdPcX4HKzz/HgWvmXNEvRXDScYG0ybB33IFjxHubIDZKahAxEOOCR2YByALwMvgJTPezCf+PARtjqx3cOBYGQN/OAgInLLvBx0Yj3gP29DMf/A6zghCUTNY++1+fDCtLvmA//YkWAbhoNE5peqAfM4eGLdZhm33PCs+/La2IYzbuSM8VNBxlkGdvJYa4Lkm81mFOjHEflWgNN2vmpcPYWN9XkriQVcUch3cLKRXw5LYpwfPnyoxWJRHz58qPPz87aFjLWEFvzYxiFDnhf/sg441XaY0INeG/4lWOfzI3B+nVVmfg5+Qm+XM09hT3QCc6BU3frLFVBODvmQw9Sz0IXDRo2JnbH2PnLre/pyn9b/+Qw601UDudXQ3/VNJ97qZofT1V+2r33ft+0W8IDnRX+mFbLrcdMva8q1XA6KvLW92bEl0sGdUM6AWcHAzCYyxvDk5KTm83m7Z8pRBZjJ1yccHBy0Gvy//e1v7W9d17WFgAFhPkeAnCllTyaMbqMP8VAICBeRAoNPA5K+71/tDcKIToEbFs6nSvLM09NT29fgbAOHS1xdXTXmdGQzHRMytZQNWyjM1DYaCPtisai+71sE/ubmZnQv8f39fV1eXrZoNfs62ONWVfXx48fa2dlpEXjohfDAPygNBMvRzKrx0eQ8f3JyUj///HN9+vRpVLLNz/39ff35z39uwIo1tEHD4PF+jOuHDx9a1vX6+vpV0AC6+Qoc+nV5dpaXJLC0o8LzjubyL/tcrIDpw3t8bYCzzCaVnXkeHgB8sg6+O9qH6Gw2m1Y2lxmT5DHAnQ1dOtrvrXkftwMnKPzlcvkq8o+jhzywNvTje5txmABuABDAtAEya2kHaspQVo0PyONdDiJVDft8HaxA/pDr6+vrenp6OQUb0NR1XS2Xy8a7GFgDVebmUyQZl/WmHR4DEMsHY3O2l36osCB74ii018sBqgwm8X8camTHusIOG3NFdnhf1aDfAI+cynx5eVm//PJLHR8f13/6T/+pVqtV/eUvfxlVxdiueM0BwQbSXJtDRD6DUnm+BSDEd1EbyBJMgf7woQPTmRWyUwxdfXCZdVo6H4BZ7DhruV6v21kdlMETPKGxn/36+rpub29rd3e3jo6O6vHxsel44w+emc2GDDLnfFhek2fI0h8dHTX+pD/mCGgl++u9mfT9nlsGiQxy7dBtS5ogN2Tzkv/RiVTDOLPmYBLvoS/AuwM/6EAHLxhDru825wJ+5TPkki0AbgRlrK/hfQeY7KBN0SltrOUsg+nr9XAuiW2Gs9xV1Q44dOICx8PBXGgDnjIWSWfcQXWvL5gmT8XOYJjHh25yMgZ8x7PgJCox0d8OqNl58zk6zMFO8tR2o1xPB0rQGdj4KdzkfzPwYhuYTq3flbJhvGseMI/Cd8yPIKt1E+8nMOcMqvU1feTaMY+pgAD0BGda3oxN/Oxb2psdW5dqQEgzkJ3YjJTZKBv829ilwsOgUuJmo2iCWUFmP2nkKfvwtT+OZORcHTWiWTnCVP7d0YVUjDzvEhYrxTzhFHrjcKRB5b0eF8bT+ySmnA/3D8OzLoCsPOqdrErVcJACgQav2e7ubota824DHgTUZY1Wjqyds++bzcuJpycnJw2kmu8cHABo2EmYWkM+dzkf77KxNbBNelYNWQU7cjybUS5nqczXdhgywwwINlhPsMiPAzH5dytq09kyBUCghN6OrUshCerYYNKnnQT//T07t15Tr7+BcIKi1Ds2IoB9/93vSKDogMIUz2P4KQ2bkjP4MEGT9Y75El7xuHzNRt/3o+Aauib1D/LnQFcaZDtlPMe8M6rMv7ZF1gWegwEZsoOT5sAc86ka731LsGF+cOP7BmlVw/5P393rQ7Lm83mdnZ21bTmO7puvmAOOO/yBzrXe8Vy8Vtgd2+zkC6/H1BpAa/7GwV/mR2i8jefdn4MoyI3thfWX+yeYiDyQKfXpw5ZF08VrOlX14HfwLHM1LmEOliHzA383PnqvzVlPfseesCa5jYvGWgOqHfg35jKARoaoIkl5hPZUtYHzvC7wIc4CSQOqwRyMxE7bQaXSAn7nQFPGx3M4A+gfaEXwgz7gMbAQ8zeO8ffBNFX1yhmj2s1zzUqLvu/r69evTTfyHmSKsXjrBPLkShBXKbD2tifovb5/2ab4+Pg4qvZgTVx1UzVgXX/HWAc6ELwjm04wNnmOfnyYH2viAHJWW9B4xn2iD6l6IdgAH9smuB/jO3QPfMu/vI9gpHUQAUX0l/nKATn8ChJO4Oubm5uWyHAiaG9vr+7u7pqdYQ7cUEBihPVCPunHNIAvHx8f2/3ttv/G4d+7VePN30ZAXMcPI5PhMagwaHM2NKNjJoYN1Ww2aydHVr1kAsli9P2wNwGjc3h4ONpfBENAVJiZfRhVw2XNaSgNBLxHC5BPBtWONTRBmIj0moGhD7RBUJmTHWqMpvuFcZ31duQX5wemtQNtg02Uiyg+68Q4WVvKp6oG4I5SsxLY3d2tH374oR4eHury8rIxvAMfu7u7bQ19kjD8UlV1fX1dVcNpxFXDHr4PHz7UyclJffr0qQ4ODtr+iaenlzt2//a3v73ah2in35EpgCtRfB+ogrAbODpaxNpAFwIP3gfiNTb/GXxjeLx28CS8xfwR7nRibYAIQvg51qfvh3003gO9s7PTsi52RBmjFRJ9+pAdn4jrKJ1LEvMKm/fa0sjZgTGQJ9PkEwxpGenkcwyJjZyz7KwLGQHAhh0PIrFkNRzUS2c5wR18ZQDIXAgiIXPcVQjfclXO/v7+KGMNqCQYBD2QS97X930DGowTwIZNuru7a7LsUlJoDQ34OzrOOgZdio6vGvZ2AdrQi2SjyWrifBmAunKC9U/ZZG2fn59ruVw2gHFyctJ0+d7eXv3TP/1TPT4+1l/+8pe6vb1tYNNrXFUtIwFtkHcDfsaAzqEPqgNwGqATdDQ4Q+/47AFXabEOZOXTWaTSB97CXlVV41Oc+MViUcvlsm5uburLly8jGvq0VvMpNMGR2N3drcViUU9PT/Xrr782nb/ZbNqhYownM3vw/JR8Y0eg09XV1Ui3AqKhB/TnWTtWU0D5vTUHlCwrGTCxbH7r+arXh8/432+NIzNS5kW+w/hovCsDwu6TefDd1KPbbLmxpB0Q02WqHDODdf67aZOBbvO5m+eTP/x9KjifNEiaue8MClXVqz79PdNy23pCN2xDbnXIoAPfd4Al13IqGG9dxueei/nDMk3/aQNy7paNTIz5eb8v+8s1NK+n3CRPG7tnsz9lOk71lTJZNc5c5xp/Sza+tfbb2ndlbAEbDBIDaEDGdw3mkmm8P2w2m7XafowizADI2dvbqx9++KE2m007qdJR7fX6pe6fqAp9eREceQMQER1KAbcx5x0eMwax7/vmYDkzjIDt7e01kJNC6igVxp/3OTrhrIGjJy7HMhPY0DqCbqMPCMbZgd52whB8M6H3lkEXlAjA7Pz8fBQhRWA4cOn29rY559AB3uI5Cw2gmbK25XLZIkc43pvNpjnUdrbgN/61oYBPTA+ACDTc5pB5XDbQ0Aje998z+5MRbAcyKEmnjHPKuLCWAGCXvpnHvN+B8VXVaJ+a6ZJy4H8dMECOpuhjULyt7/fWbHz4l+CDt2s4IMBz6MfMVkFnG7B8j4EhnyefOaPA+3EmHZRhbXMcfm82A7wpoznlTPGcgz/wcToW5v8pmqM3ff2Aacf70xBvA6yM0zTflhn0+qGXmZMBm99hPcDnXde169jINvAZgWOX5DqQYGeMDJJ5BhuTuoz3ZxWN1zozJh6X19PAiOdozligE5xFmNJrac84NIZxOztju246wAupO3lPrgPNmdmsqLBO4z0Ojjj7D218qM025yfl5z026O3giTNx8LSTE6m/wGEOmCcdvQasuQMQXgOC0FUDJkgdxLisxwiUVI0dM/rNw0uRVWfo4CWCGgT45vOhFNZ4i+fRA/SB/jYtzJM0B1W8HnzP86waH+CTlVqWEesR1sl6k2Z9a7qwVmQHeQ4c7KxzBtkt+2QMF4tFnZ2djXQVW+WoHum64QR+glAEGDebTSsXPzw8HB2k5B9v02KrQh5Q6K05zNf22duK4L2+fwkCcxYAc7TtsdNtmwZOZv1IGPqEdwdP+KE6r+u6tv3NGXPeyRVsJEOwu7YNzh67SgC+5uAxaOXvX19fv8Ifxrnf0777KD6I6cWz82bgkplCGMTgA+JS0mrj64wCpzhCLPbX8PtqtRrtXW0T/DenC+NaVS2LAMGtyDBYaeRtuCE+f08juNlsmmLGkTC4ok+MNbS04s6IGoLAHLbtFaMvstJ2gqz0q8YHOlQNWXnP3QJhAAco/stf/lIHBwf1xz/+sX1OGcPT01NzRA8PD2t/f7+Wy+XoIJX9/f12VYpPlbSRQfmwrkTvUQreZwcfGkw7iMLaODrkzfxkIsyzlPz4dD/WypkKqgEIwKDkaO6TdSb7jFxUVctQOapfVaMya88RnqU56+RqAJdbA5iRU5elZCbZALdqyOzTL8rTGTjvl1iv123N33uz49R1Q6aUcijKfdbroWwUQ3J8fFyz2ayur69HDqd5NbMd/oz3O0DmkrzValU7Ozv18ePHdpLx7e1t20fFM4C28/PzFvwB+HmezlSmc23wgiGDvxysIqtmoIRu9X711NHQhYwYp6FzFoHvxjbooEQrdRuBSAAkYLPrhsx5OmHWAwASj5kqofl8XovFosldZomenp7qy5cv9fj4WAcHB7VYLEb73a+urur5+bndd852D8vj8/NzHR8f12KxqNVqVTc3N20rD7rFNoj1RE9xZgJzsq5gvowd0Fs13qIE2HNVFzr+9PS0Hh4e2n2VBO+yJBldDl/f3Ny0u8mrhuuJWFMDQ9aGoA1rAzg0T7EdyTYPPu37vt3nyLgAyPwfvrLMUR1zfn5ez8/PdXp62qoQsOl5WA50fM9ObdXr7BH0toMKL9m25fNu6Vz5d/M6n2Vz4C4xj0G1cSxyax2SuO5bDrfHy/PwkwNEDoRaLxqHuo8MjOR4si/P2+Pke2n73S//ZqDPNPJ8TV+/K5Njfm4qWJrr7vHwd+MlZ0uzIoI18Q/zz/X0OzLggR3LBB80d5B/igeTflNBRvNdOtjux/4S7/RcjHH9jPkNHFj1eqsU/TnD7HXK9Up6MD4HyxPLsIbfotNb2r/rjPkUVgaJ02NlZcEzgfiuQZAZif9jbC8vL0eE8D2FVcPdfs7cIig+kApjnkrLzZEvDGxmLyF+OrY2YBYkM5cjGV3XtTJqH36RQArHloOFyFybWc2kPmXTpzk6AuRTHKuGA8KYJ3+3AHgsz8/P9csvv9SnT5/qv//3/94ytpRXVVUrJcOxJepImdrR0VH94z/+Y/X9cA0U88I5zvevVqv6/PlzW1u+Yzob/PZ9345up9wYnjTooQ9nM+FBA147tXnNzeHhYbuaAIPlsaNoAH9cPP/w8NAihQBsHFscDUfTUrHb8cA5X69fyuOqqu0PIrP+5cuXenh4aHt9cLRwSquGKKzpj/JzZtqHHNm5RSc8PDy8OpDrvbY0xFOAzorbxsBZJNbE8p/gwP3b+PF3AwYbuJSR7M+Gi2bdS0uQlXOyTk/jlwDUYMb9TzU7lRkA9Od+h8dhvT9lmHPMCSCcdbMzONW2gTCPFYfu8fFxFBCybOMMmWYJ1gw6Egggu3yWmeScN2PzHFNPet34vWoAQARACSgmDxrMmLbe70h/xhuZMXW/CZwTnCU/5DrzXejtU12pUkoes250v8yPvuCTKZ4wHd9jy+yo7Qr8glyy7ahqfHYIjUMmTUP4zZVQyBX848Mp0QNJc4I5d3d3r04y52omr3fqaPjR8rLZbNr6M17rC/gYrIYtNS8zL4I65ndvc6EhH660ZByJXV1RQJ+J7ZM//R0HJHgG3ONkBbiTYC/rRsYQX4D3sH7WccYYPgeB+V1fX9fXr19HcwfrMg4wiXlnPp/X6elp27JBv/gZdvq93s/Pz3V9fd0wnX0dgq7oW8/PGXD6An93Xde2FNGfM9fmG9sz+1vWkcb1tl845WRjLWvgwEzepAOLPkPP5zpbJrpuOMjL21l5nqyxkzKc90KG/a3tzY6tvWwWDYXhvVN8J7OyEJgBO1rOd2FYoqMWFpwyxmFA7ugBqW0DHzsfjlxY0TDmqnE6HafJzMpC+NmqsbPr/q00ZrPZ6H4znnUEzVnsg4ODtofMynJvb2+0f9fr0/f9qxJlfqDtzs5OizDb4e264Q5aRyq9nhZisiT//M//XPP5vAmko9IINNkjomqUFOcJg6wbhshO6tXVVds39/j4cqchc7XSsRPOd+EfGz/PO5+h8T3mA1hiHjY+0AmFsS2qaQXHMfqsRfIWWRUrdVcapANiRW+6VlU70AIeOjg4aGXdGFTvVWb+dgJYF+hFpgq+s8MPz02BiPfWDNCRHa5vQV+yblXVoqvICKU4uc/QYCSBDsEWH4gHSOr74Z4/MnIcVoSuw/hWDUCc0zKRYesu9DVBRLKLDoJZlhxEwjgDHJArZAs7Ah1TdhwUMlCuGvQ6gOfm5uZVH9gHMnp2sHIdHf32YRgEuDabzagChe0VCTiRFQfJGK/n6ii5wSsgHz3w/Pzc+OnLly+jg0HI1BoEsf6AUPSSy96o2ADUYYPv7u7aHeIcvsKYWA/WDjBme0lZ4MPDQ11cXLwqrayqpsu52oEs7OHhYQvo2V4xLoATfRh4sebs67azgM519gG+MJ0IxHLPMJUMVLqgYwkW9n3f9Djyen9/X/f39/V3f/d3dXp62iqZDLqtF96zfnQw3wH4qnFpe1W1/fboMdtR+BP6Y5scbKoazl5xEiUPO3SDd8A+VFu4b9Yd3GLMmGWbDtrY4fD7zK/Mh4atd6Vd1XClFFUaGXBOLGB+hw6pX+3wpP42bRw8h4f9L83yRL8EWbEp4FfWD0fXJ+sio8blft7yzPdvb2/r+vq65vP5yJ647NcVJdAWLIQeBFuTSHCmkT7ha2//SzyWwTgHEdD5tjf4Lt6WB9976xvrwjv8Ht5lH8vPWc6omgK3M4YpR9rvdDDT/AQ9XAFlLOpKHiflSMbAO6YtNvH/qmPL5BAiPPvMDtjoYbQo8QTYsFiAv6Ojo1a2Z1BeVY2xKEHmQCGMPIsKgKsaMsMp8I4I+R2ONMNglA55/+5sNmulZal4EX4WxYqH8Z2dndVmM+wLZSw8D/A6Pj6u4+PjOj8/b84N16gcHByMMrf0b2eIDACMAi3I1AGOURwIUzq2zAVaMQ4iXw8PD/U//+f/bGWOrKtpDXPimFnRe+8JSq3v+zo7OxspwNVq1TJ/XfeyL+3r16+jrE1GlfgMAEXQAp4G5KUyNn/Y4QfsOsJsoGLA6DFZ2VTVCBxeX183PiHry5iZNzxGGSLGF+Np/rZjY8PV933by0LQhKtGcHiYgwF2Rnu5XoM9K9Dt+vq6OWcGIqz//wvNgSrWO7No/v9U0Iy/0/ydbNsACL/b0OU4HbDw36e2SFiuABOWs9Sl5n3G6e/7M/8t52la5dgNjj0/jyfH5fHncxkENA3TwbZ9MniYmrflP4G16YeeRa5dnQE483q4Csf8ZpplFVXSfxvtpwJmBiL07X4cmU/w7HVIfvO7DHro1xkhP5PYwPYvQfqUzU8+mOIzrxO4ZYp/LWsOAE0FTkzPXK/32jJY6zW045bOTNXrOy8T7DuoR7Ajg7qsB3+j8ZkzXhnsML+yvikfHqsDll03bNlArj2uqX6mbAjzyX8ZJzRLvZznhUzZBNOAPmnomqw2QF+l7uCz3EbjzCGYwsFNnuU560/jNWezWSeP05nC1C2MYUr348TaSYbHyCamL2HehcapBxyQZgxpG/iBVonnmXsmMuAvH05LgIB3W79AR5+rMGWfp+iWcmsZ5BnW0o46jq353uttPmJrnOlQVW2v8ve077rH1sA9DTqKwcrHB0TglFaN9wRuNpsG1lFSjgKnwoeQgG1HAyCKmcBOR0b3siTDytaOxpQiwxli87TvvUOxsWAGH5vNcBJjKnVHfqqGaDuZG76LQuj7vp3uaCbkX+aHQ82ds8yRzEwaYECVs/IuHakaLqO24YBWOzs7dXR0VFXVSoBRKDzjLFNGDom08Rx8RaSNcfiEY2jNswhvgnfPwWAJOiUoM99kyXzyFn9DOdq4Jag0P+AkE7FyNjyVAP93wCSVo4MSfJ8fAjZeW8sqihwlySm65hPm7XISj8VraYP33sGb9RH8h3LnTk1o78ZaGRQ4E2D+svxgxHm36c+d4RcXFy3jiMwQuOK7CTrJhrJFgvEA9BzNJegC/yDnnIbtA1EIhri0Cd1SNa5w4Xfr3IxKE5zhd4Mvspyu1OB9s9nLifvseUaHuFIB+fN4rKehSfI5a+UqFNsOg4wEBk9PTy3wRKCJIJdPb4cup6entdls6vPnz7Ver9t2A9sHAy1H/Z0ZqKpRtZQDG+v1ut1RTJCXTCpnVaBvlstlGzv0ubm5aWV+BEItH842GRitVqtarVbtoB7wg3U84zk9PW1ryjjv7+9bIJWAHIfIELRjPbbpWmTXJawG2qw5Qed0YJGRi4uLtqc4A+C2G++5wcd2XH2Yp/mANUFvYhONU3Z2durTp0+1u7s74mHkHv1pu+gKuarBjoKN0C12kPgef4M/rWvd7Fygi9CBHpex2ZTjmY5CNuNu2xUqBhgruhzeQz49Xmf8/C7Gy7/oO+ZifudZV//ZaUXPYruckTSNqUBCZl1ZeHV1NdpXb5wBf8znw40hNzc3IxviCkLLXlW1w/tMW8bAYU7oZONE+z1Oaq3Xw4FgaRvsf5jn0ZnmAWNy+zzowdlsuIt8b2+v3UqAnoIvCICY5/weJ2z8Y+xshxh74it9HEThs/l8fL94YlLj9DxUjuZqobe2Nzu2i8Wivfz5+bnde8SkAcMQLA07qX0azND3fR0dHdXBwUFdXV3V9fV1K0UxoZmwS6wMGHi/T+xyihshR8H5mdy7hBKg1jwFEKHZ2dmpP/zhD7W7u9v2ZJAF4z1mMBT6xcXFyJHBUEIf/4swc0UIzMH8Tk9P2+I7AsacYJq9vb369OlTc5YfHx/r69evL0yg+RM0sJJZLBatTAPgQDmYgatr809OTtpJxru7uyOntOpFSAC/8BH0YB/o4eFhi0baSWU9AHPQB4casGd+M39icDCoBES8b9sRLmgLPa04KOXg/RwWghFF+TgAQHSKOaAkjo6O6vHx8dXhQTj3GRHLzImVHrySBgDHg/3LHPpEHxz0wjN3d3dtvzBy60gt/OOsCc4x7b07tG52sNAl29bLzcZvKqLM73aQqobgi0EXetLBQpr1jQEVY//We/1OZAFjZsc89aV1Me/JIIjf6+DPlGNrWUK3JmB1cDHnxvjg46kAkX8c+KQv9+e1NZ2maJnz5Ic+KTUmoASoz8AWsgwogT7M24GQqnqlv/hx0A7ZJmiSFTvmZ/OraQ59nOkxffjXa0UzUEaG0C0A5czuwQM7OzstuGwAaR7LoKRtXjqmgMgsW/eYM4iZ82GsVLa4hNV8aBl4z8185x9/XvW62oOW+g/5zb+lPso2pWcsHwb88Jf/lk4ofVkver5Tc5pyWHOsrsjIwGbVODDvMfGcnSi/bypRs41GtitT65XzsvxZd/onnSr3u403PEfPZ9sae56ZDGNcqff9XI4hbYIdv/x8yqaZrp4388p/t80tx5q4MgMv7nfK7tgR3Wb/8h3G4f6c91u3pq3PuWaJt3UibSpo+HvtzY4tTOGBWrDtzVeN72ZKI5uEJhphY8TnBi3JMCZ2Gi1nEGn8P8sD3LfBJwLgOfAMwpJX5uDUZMmSFyz/7gyOmRKGc8krc3B5qOcOIPB8vQ7+MQP532RIghJW2M40eH28sR/AhDAQGOGwISLwRMbsZOIsG2w5qtb3w9UBAJyuG05i8364VFoWYtPAkfhcB68FYAbjYZ5y9phx5rrDay7hZ+x8133bABssWjm6Xxtor6HHYMXuKoc08lXVDizL/WCWU75rJ+b3jOd7anYo7DxCW58kbf72+vE5B+Gxvs4qev9KngJcNcg8d2oSYDFA81i8X4lAD3sZHYwyr/A7PEaQzsCSPpF9snvJD95vRQaO7A42gTmcnJzU3t5e3dzcNN3hMwYIjvGsnQ6yzavVqm5vb9uWEF+HYF3FvGhd140Ok+m6rgXtkBl0P/TgOQdTs+rGdgLwZVvK2s7n8zZnovJ3d3ftcI2koTO38MV6Pb5r1brMgT8322Z0ou+mNl94q4Y/J5jLu1h322fWx3e/EiTFsfX+N+jV98MNCWQGPH9OAqf/3PO8DSiyJtgc7JrxidcImlYNW2s4Wfn8/LztrUVuEud8L3D7j9Y816qhhLZqAK4+mdv2KvGidVxVtcDP4+Njff78eYSHHGTJjCg8ul6v28nbxjVVL1Vjy+WyqobrIb0X0sFnqgLgR2ezLEfmN2f8TKuu6+ro6Kj29/fr5uamLi4uRvYeHjc/0RdnNEAXV0pQxeCxGwul8+OT0d3SCXPWzTJBUsSfod8sP9CK6h07U4klHZQ0JkX3kKDwtre+f8n4cmWNs+iMizEwZ9trglP8EHx0MJH5oG/o2/TKwCHvs3yY350Npjn5Rvk9ds+2nfdix3wQlg/UhWf6fjhY17acPkg8cdaD9daU7JJEQ1+DQcw7YA5nZTO4lD7KW9qbHVtO/krAwMtspNPhIWtkxgQ4zGazBjZcToCg5MFCMAOLRfkV91Q52+oIclU1BmJ8/hci41CgnBhLOkf0jcJh8W5ubtrdVXaSXcrhvVKZwvceY04Dc+QJpcQJfczZp/k9PT21jLozCRy0BHBGQKCpo2FmrLu7u7q/v2/vN/hlrDiSy+Wy9vb26suXL3V/f1+np6etpKeq6rfffqu//vWvjYa8n73WzkABVHGEf/vttzo/P2/P3N3dNWCPoK5Wq5rNZi3ryDiYFyV90AF+REFUjUvC7Vxw4vbNzU0D26wLwPL6+vpFsCJzkg4oh5OQmd1sNiMnxRkh+BSDkPel2VkFXAGCeRZgDaBFEdmZBZg7eLC7u1t/+MMf6unpqf70pz+NSuKteHDIMZxkhh1Bf8/NMlo1DkY4WGCeyewX30+g64AF7wLs40hhlL3e7scgAb52dJfPPQc7RQkWq8ZAkyCNy1j9jDN+NtIGJPTlyhQAK7qRa33Qt3ZQrK8NnugXAwrA42Ahf8e87HHa0DNW6O5MADo+95RDi6mot+2a9Q7PwStUIuW1RHbkbGvTWSLAghOWGYspYDtl6wEuLu0zzbK0cbPZtNJDB34cnJvSZe6HtaY/O4nQBRvL+RGAT6/tVEY752yZNE23Bev4DBmE97jjcmpbVQLZ964fPb/UN1OBhW3P+zmXLcMX8Lf5JvvPlnbKcl81rhTJMdCs1zLDmUFmzzkDxB4nDlJWOXgMiSs8FtuXfGc60tv4jznlu/yZbZ2TNMbi+S7rnNSN+Z5c+wwK8Zn1J/rD40Pv2lG0jeT9rLedtwxGpX1OGoLvbX95LhNKOV/zlzOgU812yr6EbRTjc9LE83eA0rbeY+e5TBaap9CPHs/U+mWfJMJMx+SPKRn4VvvuE108uRSqBCz5O5NksDhegC0IOQVM6C+VDt8BEPmZZH4T2L/zNzOZ32ujTssFsFG3gKYyNEMloEiGARBPKSEb3vz7FNgAAOAQVQ0XiU/RNWllxjbTew5ZYpkChZJP54gfBz66rmvlsuzL8+mjALltipDPcEhTyTAuG600VjZ0U8Ak6QDPVo33DZuefm5b1P5bhsbPe7xe81RsXsdUWFNrnXRKnttmeBwk+dYc3mPDGcHZcLNOgC5Efa1fkEcuZycD5b3MBD3IVviEdRwWG2XLtasL7NwCRpwhRR7Rq6mnnVHhGQJe1jW8h7JoO492BrO8yQEc7nVGD5DVy1NvzfvoNO/f9efX19ejE6MJMrIWzkITGPO938zR2UPvqUIHEbEnQ3B0dFRd19X5+XnTZQTGnP3lX/fJu7zFZT6f1w8//FDHx8ftLncHUQkwsfbePtT3fcuypsOIjvVeYp7leX9WVe1KviwZ77quBUIdcGMM8AC8tF6vR9eO2YGH1g7mEohjDTwHdJn3ePGsM+mLxeKVo181ZJKddeNaNA7RoyTcexyrXvbewqueCzYJm5aO2Hts3hLggDqf9X3fbn9Av8xmL4d0ugqA7T9VNUqyZJUUgT2As/WN+ciYqWoc3EKfE6wm+cIz8IPxhnUwY7Ez5YSAbbHfDc+h79ATVcO2MQLTyCW6ZrPZNL6zLnGVFWOGT5FjbymyTWAOzNv6jXG7QqlqfBYOe12RNSedTH//y3o54cBBqcba6GT0ntcZHEnSxUmx5AnLPDqb/sx/XEOFXGOnaNZBWTHDfKGT/R1ohu4iCWe7Rf/YAwf1zL/wP7YTG+CgMzRAx3s/suWStcX+2IfwnLx2zM+niGfWnnV8enpq1WiJswlQWFe8tb3ZsbVAMUk7JRhIJo/Cdzbr8+fPLSOLkd/f36+vX7825VFVI8BgpsMg4Ki5xO3/I+/PliNJtvNseEUmZiSmqu7NPZAmmcQT3f+1SCcymVEiubt3dxWmxAxk5neA//F44oWju4q/8YCgm8FQlYj0cF++hncN7n5yclLHx8d1d3fXBCCP+HeZKoTjHTAFc3P0AyYw8DfYsaLklFlAhiPPMKsj4OnYwrBVNblLDWaAIXxAgb/LITXsO2W9ODhje3u7ldYwdwNMC5EPGmE9yShDd+jBOAFdnFqNArPT34u4zmazJngIxMXFRQPhRGMRBPql5A8lhsJi//Dnz59rZ2enfv3117q5uWlry/rCn+nYmy96ZSsWfjspmXWwo+FyJIxkroPBlcv20jG18TdQYp9ZHjDASYGWreR9xk45I7Tl9G6Aso0kfF81Hh//8vLSrhX6XoX0H7Wlk0azM5eBEZeCWU+gn5AD5BLnh2tEkGXWvQeqLG92XGz87eRm1pU5AHjsdNl5cIk0+tF94RynUwPtEmBgiDebTTvEii0ezv76ULoMviB/VTUpsbL+Zh+/gRrfZy4+kwEZ8jis3w0isEHWh5ymT2ACGlJODG2RURxMxgTf2LFjDRgHhw3yfTvFGXF35tT84jVnnAaI6GPbC8r1CDjwPb6LTvJPrhv9eV62cV4f+JO1zHM//A7zGPYM2WJ8zNcyx+dU+IBvyKZRAUSwyYFjHHJsqG1Olor3ArQfqVnfGAgbB2HfoYudWH/H5apZfcKzAHCy9+Cqnv6pept95O8vL+O1PzgzXj/4BHnb29ubHExm58T20SWj6DLGYcfWus5OEzzsykDelQd72nE3zyMDdmicuDBWzWbagwmqxqpIY2nWFL1l/JZ9ZmBhGIZGT6o+oJMdWxx5fmwHqqZOIzrA9ixxlelCPxxkZ3/CQUPGlfrMrWfzPH9+GB/vsgxkCb8xFngZ3mS7TOoa5Ghvb29SLeuD3BykqJqeHJ24zmNnbNbL+A2sk2nsihbrbPqxDfzW9t0ZWzPf7/3ASP6xMauqN8aqqr/nxIxq5QNjmpndh/vnmXzW3+mNJd/v53NBrWx4N+/DUJtxUmn4fZ5X0i3HYVCSn6dh93pgUPyeFCSvuR23fA9gB6ZnLI6y5fqYgWH2jILyfAJwO1jJHxbiBNT5/vd4AZr3lJQNRa6T+4POpoGBe8/4vDcuzyXnAB0xTOaRnsy6z/wsHezcN/Pe+psu7uOjg7aq6XYH1nuz2TQwA418qFbSzNk9GzhoRxTU2SvAD/zlyGdPVvk8dZTlDiDB3whieV+ky9h5FkcOEAOYY1zOapjnoRn9YDwZO33iWFHJgQNnWYGm1kfQzg6Tzya4v7+vra2tN6egXl9fN3DH2qY+zHflwYXQC7n1niuXxRIYqBrvIq56BWz/8i//0t43n8/bHluD22EY2hYT+nBwN/dx+XOAD+vp4IV1MdtzCB6SFSDw0DvPwCDQgWQCMdDCgL1qDG672sF3V5rulg9nE/IeSgNwApLoT7ba8H9XEDgovrU13seO7KWeZC8aFUbee4x+dkn8f4bWA/gJtJFJY0VsubM2zqjZ9iCrwzC0QG7V2y0V/GbfdgJyxgRPsmYG3LS0rdYLxoT0nddDMu7EQtYZyKKfN28yrjzPxLjRgS2Pp2rUa8aCpqvnwvs9L8+354DQF/MxRjCmm81m7WpQZO7l5fVkdWTGctlLRuS6wCfw0GKxqKenpxasZ11NY2y07wr2NkV0DE618Zx50XbJGez35tybV+J368Mepkr7xvyN35A9H6bLWK2zbX+rprcUGFOD82y3k/7wVTrZ9kOczPR8/i348ZsdWwyhhc6TQ2gtnHnK7/7+fu3u7rbNx8vlcqLgmSRRHi++T5dl8iYAB4oYLDrTaIFA+cEkLgtg0bwvtOdYOmro56wYdnd3W6SX/bJEfV5eXurr16+1Xq8nhyShoDCMzlCaYWCGjOQ5Gwz49D2tzHc2m03Kw4icAPQMKuxMwrwIvzOV6/W6fv7559re3q5Pnz61/bGAUk5HrqqW5STrBLC2UmXN8qARmo1Q7qf2QR6M9+joaJKdRXl6/zBzrRqva8hMNqd2OwLP5vsEz1wszbocHx/XfD6v5XLZ7ubabF6zUicnJw2wA+Sq6k25nIEnc7i/v6+9vb3WB1dKZXCF8aBcTG/z98vLS7vKxWVhs9lskrGyXHE9EwaCNd3a2mrf/ajNCn8+n0+cMfZSO8NuPYSyd3UGtHJklLWAv6pGp8+ObS+QQ7MOcTmYAy3oYviDa1SOj48nzl1W1hi4OYMxn8+b02WH3eVQmVGwYeU9h4eHzX6s1+tWugiNvRYGBNDOgU4ycZwfQKUNjYO5WIMMwKbBhddZ88PDw7aOdnBdyuiMJD/OLFS96leuzOH8gsVi0daPTO98Pm/2pKome26pvrBO9aE38CO0R7Yd3DLw4//Y26Ojo0lwImlkpw+sAJ6wbkdn8vzz83M718A0xK4hLwZrrLX505UQnO7v6p2q8WCVzKKYd6rGzDU8Ao34v2WLwIIz6+heHzIG3fO9H6kZ8Fe91WV2IPf29mqxWNRqtarz8/N2ZoirgbJvOydbW1vN1mNnWRPjIAC3nRTwAplXA33exRwM3j2Hqrd7bukbrEGQxracjBkYxWXwduJTvxmTItv5PPOitBvdQEBvPp/Xzc1Nwx6MHf2WTpExp7Got2vwmZ0d9/XyMh60ip4iw8j4n56eugcP0jdrn/YO+lWNZcrb29v1+fPnur29rcvLy/ZOO1bQA37yZ1Wjk4juZc1fXl7q6uqqm+1N7G19urW11bCyqzw9T+wpWxqYl+cPPdCfPqcFnnOlKiXutpGspR3n1LPpONt5RZ55hgAouhzeduCb5zjEMYMByYff2r7ZsYU5EcQ0YiwU/7ehsYFPAJOTcIkQffm7vahZ1VSQHN024yJkVkzpsHosPMN4eZb5ug8zAvPyojta5cNSzDA0K+Ecq9t7AMtj89ysaP3eHj0MLHk+55Zjrqp2YAdgB4F0ZMc8YBpnpjKjvLzTGVJHsUxT3pGZGq8ra9IzGLn2CXispOjLSr+nJOCtPHXPYJR52TAyBpcKeSzQg2f823Ng3MhBGl6/i3Gx5pZPRyb9fgeIkvd6gYmP1DLaaJDugI0dGYAx/EGAxwELjB9r38uUGmz31pvflitHgCmf9fqY3wlYuEypJ7uW8ZQdxpfBENsAeMagbBjGPVI2fhhIZD0zGozHe7+YKw6l9Q0G2LyP08HYbPsMYDNo4cg+dMfpdbDMTpcziWQKfPCR9dnFxUVtb2/XP/zDPzQQ+Pz8XPv7+/Xjjz/W3d1dC5qxNcRyadDM2jnQxbjRIwTxnGWdzcb7XR08xVE2JgAI9vS8txbRDzzBurMeDra4bwdrWPPEJ4ApADU8liehGnhCbwLBliHrcdabuZHZ9j5zb//w2Pz594K3/6jNOC0/t27p2eMeXvPa2OFJm239kH9PvOdn0665Je7wXOwM92jgvsy/doxyTuYz0yTxYW+cHpfHbCxlndXDnMaFngs/tg+9tcsxGjsZjyUdLeeW96SxcQvPYQNIEPE95Pq3xuusueluzJQ4Hdvdw1V+PvFsb44ei2nda0mrnoNs+prPkh/degGEDFbwXvOw8QDz8NqnD5Fz9Xx7fPdb7ZsdWwZL3TTGikHb0HgiZCgx7iZILl4C94wS94jPd4joUV7CXlMiPmaeBHvex1A1Ghs72uv1a9bv+Pi4qsaafpcEYgQRUgSI6A4AcrlcNpBjJ4d/E7VizgbJyTAoOoCFT/m0AWE8ZCgfHh4mx39nAMAgMMtXUvFZAeDIsm+a9fJpymdnZ5NonfetJhi1c+VyEF9rwVoBjAz8nSWCJs/Pz3V4eFifP39uoANASd/wJHODdowFAM467u/vN9oSuRyG8boV75dhDvADfMlp2mdnZ7VavZ58bR4jQ4Jy9t5DaHl9fd3mZFpaThOkpwwCIF2BsVq9Xolg+fMVBzYSAFxo1HPGP1pLQEx1BnxuUEYElhJYQDIHU3BvMJFO+kIOibayXujYzWbTzhcwMHKQxU4ta+V9uavV67kFjjhzMnvPEBtEWs78edV4ej26uGqa5XRE2eXFs9msZX2vrq7q6emp7eG0g+N9Y1XTcj7GU/V62NbOzk5dX183W8acvIeXKoOq6SnpnosdLTJz7LfkO/A+2Tzeh53CLjgD7/1iZHf4+/Pzc11eXtbW1lb95S9/qb29vXbX+eHhYZ2entbFxUV7F9ktAwM7ttZjWcVR9RrUOD4+bjqGdZvNZu0gLCqL4KU8bMRnY6D70MtUkLB/0ft0XcJvEMZv6zgHg30mAvPALvnAITvW3j9tGcnMMTTc2dlpZ1Ugz8gPNKcvn6rsADdzYE0+smPrwI0DJsiGK4DsvLl6wwEj8yj6wPaF80kcOKAf8IbxaO/cFcZiO+g75ZlXz/m1k2E8bLtIg4eZQ5b0g2WNsVNHQh/3Z+cNWhN4MQ8SBEMXO0BHNtf7n2nM27reya2q6Z2wzjRWjecuWP6cMHA/xibIIlVO1rdUvO3t7bXsNPaL9eQGFTKRVEylU2y9bFtRNW4BYouDx8vfsM2MyzqAMc9ms0mmHBqQ7fReW/sn6eAzP7ArlVCWFTBD2i7zafpzyKl9sKenp0lfjMk21z6B15B3Wj97m0fPibdO+Nb23XtsbVQYLMDAjMHfcOqq3gIWT9h9eREdXTDz2MlDaAA7MJX/72dpCTazb77DnAAhVVMFzdzMKB57Akuf2Ef/VWNWywdTuNwhFYdp8x79eb+ZxQYE5ZWg1A5vL7OZNGS9eD/7i+yMVo17lHg3QM50MoMDLnLtM4LDv638+X/yG2t5cnIyud6Gsmkb1l70MmkNn5vH+J4BI/16v4t5ENBOQMZKAKCIIUFp5rU6Li/xOHoRR/NnGhD/Hb5BaTp7i+I2f7mP5L2P3My7VTWRM/MQf3cwCSNdNQ3yOTCGfPQcSYA140h5QZdQUWGgcXd318Zu3ZF6zKcoEkiDPx1Yso63/mDO3ocF8LchTgNrQOVoO3To2Qs74gauDkpZ7/KcaU1/aWsM6Agm2oF187j9f9sWAJKBiIO6jNMZ3GEYWoky9LGORu/yHjuypoPnZn70Gti5YD2c4bKuM01xgFNnm6+sO5ibg6kO0vF/vtfLSECnzJjwHr6HEwG/ZVCV/ds4vxyc6O+AZwiM8rl53XyQTshH14duzvpUTe1M1bh+6Etom1jDzTrO/GiMk/yVWMl9JKh20AweyQBEPm/ZyfczTuM4jxt65JYsb/+wLJmnk67pUCQ+s37LwJ2/Y/xmh8rNcu15eWy97yFvYHWPL9cduTXuqBorKqwPTBsCXZwBAL/1gnjmkcQxDmTwfXjUegg9CMbzQU/WsdbF6BkfqpV4IXnTvOd/M05Xu9iHMU+4L/NS0j7tggNCpqdp6Xn2ZCR5GZ5JGicG+db2zY4titjRifV6PDXQpVUMBOcFI0KkiGgEAKkNZmtrYgw8aYTR2TyMRdXrPbsY+tlsLFsDeEE4BJn+DXogNkoREGZA+csvv0wYHwZiXlbWzAnFxN4whIFICJEjhJboONk7MqxpvMnaQQ8r32QwA8gff/xxokDJMrKJ3Vk+R2IcATTIsJBCGzIURPkdXYM3uCj77//+71tG0BlcGyDmbKVhIbMzxrogeHZCnTVlfnnSMfNiDmSM3hP+nhFz+TXRWBxRnAOy0OwxwMGAb4lKsm8xlTeKdWtrq46Pj9vpuDy32YxROp+0u1qturI2DEPLJBrkQUsHcXi/94ZZefKOxWJR6/W6nUr7URtrvVgsaj6f1+3tbaO1nVDky86Gg0HD8HrdDyfJoye8h9sRXfonOOSMUNXIy7u7u3V4eNgi1uw3vL+/r8vLy5rNZpMAl40O49/b26uDg4OW0a0aK3d2d3fr8fGxLi8vaxhey6qdqYMf0ZHYC/bMkaFLA0kGGR3rTARtZ2en0Suj0gY3VeN+Kztr3ufJ/tjVajW5MqPq7dUvBwcHk6qLl5fXfenOvuS2lAw8IUNkneAPZ7BcmoxTNp/P629/+1udn5+3ceBgr9ev1zxgb8gk25HmAC7a4eHh5MTzp6enphfhY3SbeW0+n7e15n5vO6a213w/QZb1Qu69htaAPtsD901br/sH9LixZ459cQax7F/mOqijo6NWGeOy781m0/aN877MwqHbs6ycvqCB5/TRm4Oi2bCPttvIDvuih2FoVRHYyZeXl7Y3EUcC/kHW3eAJ+IfgEHgibffp6WlVjbYTvjY+8o/1dDrQdmoM8KveL8X2uRsO2Fnv8Hw6uk6mIBOWTWxHZmrpB7py9ZDnQPNZC8yHcVoGjE2QN/SVK0GrpltWeJ5qkNlsvI+d95ydndX+/n4tFos6Ojp6g+WGYajb29v69ddfmz3xlUkOXMEztt2mObKNTiK4D417zzMe6G6+tG24vb2dHErF35304SwIsBefGx+mU2mZ82cOZtPsi1SN1aCcOo/OI5vLd3iOdXUVjJNN5lMHWvgumIHqs39L+27HloF5QFkma6Hz3w3uZrNZAxlmZpcV5fsMCOif5nubOCDChybBbHZCGhEEgmhWAjDvw8NDOynT32EhWRwrOTNy3ncF2AJAQR+ADEYVcOexuY9hGCbKycqReWNcd3d36+joqAFcOzIc8JPlNfSHErXCzmbeMK1hfOhExGtnZ6c+f/7clLedT/dJv+lMw/geK4qHeXs9EDLm7DmxHqwhiodDJBB0814KKM0BAMaz2WxaGSPfs9NyfX09UXjwLQed5AEtzJ0sL0oEejnKShmdD1SxQbVD/vDw0Ep4LK8o3Nvb24lz4GCKI3go4efn57q6uvrQ4C2rL6reZhLsMFo+fyu7Zb1kXuU5R3ur3u6tNVhi7R0J7TXLm8dKEIg1px9Hv9OgMke/z89gyACkCfR43nRIHecgnd9RNY362m4Y2Nk2oacSYPIbx9bPOSNvm+HP0R22d6YP/bjqx5Ue1mkeJ3IGD9j54xAeaGTdio11NZPto2mYToYDwehy238DsQy00FzC1pMb0z/X0vrKfadcuT/Pxe/HwYRmBA/ADQB9r7fpad6CfsYFjMtObPI57SMH/qreHrZEY95ODJgvq6qV7hOQMx7Bztkpc9DwvYbdon+cGpxXgkJHR0dVNcqB5Tt1pYM2/Fg3wiPmX+v31DlVNbGz3OlNgwbWjfw2vcE9PojNAbSXl5dJEJK+XbYNbvCp/VXVkkgOLNgRs8NovOD3pANjvZ1OsgO6yNmnT5/q9PS04SkCk/QLRv7y5cvkRHhjd2yat/MwXuZFg7/wDQhAek0t79aNmfwy7if5g47NtSRoarq5zx5f8f9eIgMeYDzws+0jwWICqDi1rG3VeIis7SB2CTtlnmZODjAzD/B2+kvfox+/6x7bnhA7SuG0PWB6NptNjuu28FIrn/XZu7u7zTn1iVwGOCgwMqbUzfsZjxcGxvAgaDCIDbSNFsABg08fzuQOw9AYEmFh8XDgGCt9OtpBBsT75jgB8OrqqhnbpDF9V42K2ZGizWbTshlVo4Ik0pz7hFFOKAPWz3s4oQdKCYCAsvBeFfpGaDAaFr7Hx8f613/910ZfO1C84/7+/s1BO3YAeD992ph4HAgqRtMOuPtivR05yzsb7fDhCFeNASDWnDXhB1piKOAbnP7V6nVvrRU/htzNivHu7q71s1qNe34J7vi7LmllneBnO+7Q35E3+Id1hA425kRnCeSwp9Ig4iM2MnSOjBPMqJpu2bCewVGwzDrD4+g5J6yjH8kQ7u7u1tnZWVW9Vq6s1+tJZBv+JXjkq02qpocTOaDlrGzVq57iLuzt7e22jQCdsV6v39yj64AJBtsltwcHB3V8fDyxK67oQadkhYjHxbzQmw7OUd5tR2qz2bQ71NmLZ3DBXve8ioUxej+oS66cbUT+nGW1HqFBe3SBM7XeDwrAdkDYe15fXl5PjT46Opo479vb23Vzc1M///xzW69hGFpfy+WyjZFqITK/tnvoMfYeW+655cCZVr7PD3aedSLrhu1zoK1qCtiRI+yVAxGMqWrcSpJl7WQCAY6com1nnznYBnGLAzSEL7DNPk/BzfsTt7a2WjDcQS4HZ+yQf9RGZZmBflaZOUufdDG4tx5wQNXBFX8v+Zj3G0ugD8ER5itXWTBeeABMZKfBAa10dqtqwtP5N94NpnjP7mfwyPMznsmso+lhWwXP2t6Yjsaxdkr4e69CI7PbziomfYwnPE/WysHLntxgM0la0Adzgw4EBryf2O9w/+Y5O4Jpxz1fnoMexsPGz7ZjzB3bmFt1jCfAzKYRTjV4n7/BE55nr2XACZ7KAHIGtsHZfM+2dxiGSYbf2zigd/J2T2Z+KzD1W+27M7YGZRB0tVq92YOCg1pVLRPn/VfDMLTUtu+LqqqWVWQ/WHr0XgAOWgJEWFlBQEfL+C4AAgaYzcZ777zQKCEUqt/PT1U1h4VrI8y0Dw8Pb4xpOnA4Q5vNppWF+YAnhIXoEGOHeVAy3ksFwGVPK8rSJVS8H1BnBcua2rGF0VyeakeOUkzfOWYhs9KgBJcS8sPDw4kjRZTMTqvpbifLmStHankn4NhKIaNIVkoII/ybexjNT7wPvmFc9OnyCoCjBTfLvQFQKGFAgYGuBZ6yQWiH/Dw+PrbDpKyY7WQ6EMVaGhSgsDabzQT8MV8HEQwo4cfb29uJPH3UZjBhPjJ/ZQTXetQ86+9n5Jd/m2dYgwQ1PO/3VU2Nl4MzWdrJdzNY5nX3fPiOdQjvdbYtn0vamV7uy7Rx4NI2J530fGca1p6hdWA0/5bvd0DLY6aP/D766T1w7kxBNo/VvwF1ST90WAalDYhtUxM05vscyO2tm/+f9Mt5pSObdtu/7UTn+rDufjb532CLft7jR9tN63Lbba+bnQDLu8GZedd6P4H0R26U80J3rrGqGmVib2+vYTmeRU6wLcZJ2LP3HEDjBR/KBm/hRGDj1uvxADl/tlwu38gwgUV4u2oM3oGjjJnhnarxRO2qatlBeBJcaF41j3h7ivfevry8TGw9z0JLaOWzFrDNPs/FTpV52YE8B5LAy+ArHHawgm0/B1j19EcmZjwGZMgYK595enpqyS0Ct2ytIzC4Xq/bQXhgRtPLzid4Bl+hl6y7v7+v+XwsY3eVgRN08BBBNwcKPffj4+N2FShBB97t8y2QGxxJEhg7OzsNKxqzWieb7g5koEN9oJf1GTLLWPMQNJ8/xPs4uwZ/xnrXfh38aR1t+f+3tG92bG24qt6W71pYid7aeNlAOCrkqBeL/fz83O5ONADYbDYtg2vDhWDRt51VLxqMx287SX6OZuOPkDqKb+NtoeaETO+trBqzqtDTFx9nFIQ9B0QGbVx5n9clHTj+j5K1cLj2nXlakUJTnLDNZrwXa7Va1d7eXitnZpzOYDMnTsR0xMxr4j0uBhZ21ntGyMYEOqB4XObLGtGggQFaOiPQ1H3wHkf9fMogkSwLp+WGKGKCtV4Ahjk4C8bnmbGyQ4LCx/gjPwnCDKKTxw0Wqqqd2pt0sFPMfBI4Wkag/Udu8KwDP+gkrxmBQJ5dr9ftvlR0Fpkefju6TOZruVzWfD5vGbovX75U1QgSXW6F/rKxZstGVTVQc3x8PAlKuMQVA55OlJ2kdBSYI3oIgIH+oc+8O7Fqel+owQbjQg94LxZ2wN/he97PRNbTVSiAw52dnTo6Omp6CD0GbVkD9DJjSiPNv/3d+XzeSoPZ4+pKCQCN7x10QAEgSkPmCICydQVe2mxetzoASB0MQFcQ9ATUM1bG4vXNahzAF+MziAZ8s8ZUEmDnOKnUW4+sE81fDkQ7A+P7vnmG4OFqtar9/f0mh7u7u5Oqha9fv050vEv5baO9XSqz8zhVPouhqibBS9tZPjMvZ1XER23WeenIQyfsve2ecWcGXODp94JI5nda2nXrKfCJbVziXmMGOx7W8TR/x3gj35/ZWX+Wfb0X3OL9GRx6D3PkWP1Mztd4DZzQ69Nj9Q9y6QBaj7Y5BuMcrxX/doWIHTL6dsAB/kpbYn5ibHb8sqTdjrBpY54wzXNtLP+/13p0eY/GOXa+k3yS/eV6Jj2SVl4X08X4rzcuB8udsTfuhLZOCuXz39r+/3JsPRjXW7ssyYYJw+nSRgwWUTI7VBAEgOboGZHoqpoY0XSSHXmgpATCEX1hXhZcorYui9ve3m7AJEs8TQ8MMNkzSt4uLy8bE3g8ACjmwHHhOFCUy8EYRIOshABZHHGOMQX0kDG2Y2sgY+cYBiLqxqEx0HaxWNSnT5/q4eFhctCRhfz5+fVKEpdWGJSiKHvOXZb9Qku+C0Ax+IPmvQvCq6Z7TFwuax5LJUAWOEuk9vf36/T0tPVxe3vbss6ekxUn0eLDw8PJXtk0yMwBGlop8nwKe+55Wa/XbU8rffIdBwbgOaKI0IFDfHzq6nw+7n2wgQKoIjdZpgwdkLmP2tJA9wBZGmb/O41sGrH83HS342YDY8Nj5yszkh6H+cuf9TLufJ5BE38vaeHAh//OnN4zZHy3Z9B7gDADLDlXO6Tux/9OoJJrlzRhjd8Dw0lP2xr3lX/PsXkMzKWq2jUMDjDyLLopg80OUiWPJoDLz82XBudeV+whzqFp1KNhVgck3dMR8LjsDLh/xuG/ZTap9w7GCTB3lUPSItfO65JzSd74z9B+/fXXZkvJorHOJCs4RyTxAPRy1RTBOewt3/M+UAfjwIzGF+Yl3k2WrWoE9Zb3YXgNVHAeBociZrVI1bj+Dry4fHcYxgAJWTc+4z09uUk6OtuZeNbYq2rEqWBI6xc+x54wFoJJXCnn2xEc0LQ/YAy12YyHF4I7wV2uPvP7wCPGq5mQOjw8rD//+c8tcDWfz+vm5qbhGTAf47q7u2vnlbB1K51B21IOo6K6wP6H8f3BwUFtNtOtEvTnG1AIMOdVlfAXCT2vl3WR/28Mx99fXl7aAWsk1ewPwcuMxcE5xpznS0DHYRgrbGn0ZX0ONrduZk5cwWRak/llC5D7RHaQyX8XxzYzLqnQHel35jEjlLPZrJWcsn/UzFvVP9GLjCFCgQD4NLsUKo+1qiZ7u+jXEZ6eYXS5HUrRn9uAonAYu6P96/V6kuWrqjdKzoYwjaENq8ET7zKgRSnhxPBOl0oAanDaPWf+nUKFMFE+DM2hB8LFu3t7Bhg7fOGoGALLuDNzyVwcNGH9vKEe4MKzuZ78ZszOwjNHxurf3kvlq4FQwNDVgJrxuaLAUSrTOudkmrmvlI+MiKUDkM5EOhx87n7zOWcmElCmg4NhSR5MufxIzRnBYXjdc+9T0E0jl/js7OzU2dlZbTabVq5UVS2wUNU/RdYBHe9dT2ctt3/Af+zXBVxUjXeeOiPq7/BDwM17NvmhOiUzUQAxyubJegJIMPjOmCC7NrabzRgQtGFm/rzT1UFZZfBbfEjGlDYM08CR+3aQzLbO2zAwyC7fo3kbDplsTmVGX9Mn2VUykj2Zuri4qNvb2zo5OalPnz617242mzo9PW2HBzJG+MhzcGM9sVsZPIGn/X/zC5natFvWfy4BhaasqbcXObvL2N7LQmcl0N3dXV1cXLyxKTxvsA54Yw7IB3QCzAKO7Riw55ZTWbkr2dVKjA95cHntR27eD06ygJYZ2l52z04m/GNd0QvQGNtVvZ/5g7fhKQe+bR/5gTdd6ptZqByHHQjkMmXH78mx9/5NP8iIHYL3aNBzkDzWdB5ss6rGqhLjfOuh1Enu3wHQdABz7umI2bYxJhJN2JBheK1U8t5obEDVeEd4Zm2N+f0OMCxJE+t+b8WjUtNVlRlc9ffRo5nYMXZPHnXzGnutzcfeSmHHM7GIdbGxomlj7JH89V6m1n1C7zwg0r6FK9mcYEF2fHbHt7RvdmydcQFQwTQ4cUSL8cJxOu0cbm1t1Q8//FC7u7stgnJ8fNz2LDiSCoNRJufoL0zhyAsOr6NIFg4Y0IbQgDxB/sPDw6RMjn0MBnDQhWwrV3Vw/RDOwO7ubp2cnNRqtWoH6mAYYQIYAIfRBtGlzavVWALs/agWHAwwfZluNASew728t6Oq3tAG8EmksqeQmQuZaiKnGHFn6MmgAmaIlAIyjo+Pa3t7u5bLZTuIiBN7fWoczilROUdGE3QhQIybsmqMFb/TeauqWiwWtbOz07LvBsl5Z3I6BQ54uEEHl/rxHPTwelmZ8F3KKk0P1pG1zAx31VvH1kEA/k//Ly8vzYmB12lWPvCQdQJ64iM7tjk3Ozo26sixjTz60tmHDAhW9aO3vYCBDQ192ClgfOZVG1X6MK/mGJDlDMK4LMvN2Q6MnB2h5MP33s/Y7SD1xpsZPdMNm9ADDI7YW/85E5IA3IEl+udAJm8TSFvodbYz5oNpPLeerTJtqHTiNHPTCgfKDrZBTgIp/6AHWNPUqX4OUOVsGc6c18Nr8B74NhjrBd1MC/Ob1xaaEzTJrSo83wPZ2R92zPii930Ha5O/XM5shyf54qM1Zw5JZrD/0Nm7XrC7arxSxmt4f3/f9v2BeQ4PD6tqrF4wD6Uurnp77ye8ayfltxwR/g+P23Fh7F5bVwDyXgcRwQrYhdSBTn448+yfdNosK9DaY8pAeSYjHMDnedPTSQL6YquB7dCXL1+aDQQ/eM6WWfDier2eXBPktWSfMKdlz+fzuru7awkzcK0rMa1Hvf6mrTPEd3d3kwPFcp8wusUYnL2+6HL7Qg7E0IdPXHYCwvbb9Db2sq23Dub5qukBlHyHd3mPrG0bawTu53njTNa3arwKK3FkjpHgOX8znyamN5/nNpzfa9/s2PqFEDGBhJnbxt7AxWUoZlYrOBPCUfBeZMkMybsNrhz9cZ92cD0HKz6DTr83jbGZKPfTMK50WHsRFIOwHjMbRPacE75rpsh1qZoKM333gFLSGAXuvnr84R+DQYMj3oFiYK1Y+2EYS9INwBE6nHacLQNMO3QJVly+Ce0N7L3OWcbDGlJmbZ7z3HvORiqMdFpQID3gRrOCIdBi/vC86QOl6QieecW0zHLhlAfGYuVsJW/ZMu/9ZwBuVSNoq5reX22nB5pbni8uLmo+n9fJyUn7v7c6QENnsxyw433DMF7r5MqWqtegDFcvEaSgJIpKhDxZ3E4QTuxs9noCvQNbgFL6SrtgvUH/BPacEaCSJ7N40M26nGwJIJlMW/KgnUODYj5zRQHNW2g2m7E8i7kAojx3z9/7U3mHgfp6vW5XZrF2BwcHDQxaTgmWVk2zXvytajwoBEC6u7tbP/74Y11fX7dTj9FZlFBaz+GE0mdmZJkfc4IvfFrxarVqFTqst/UitMT+AVRwKOCbdB4IkDozBV05Ld57slgv5si6EFT2wUR8h/eu1+tJaWkCKfrDMckyQ7LzzuS5kocKBdaPPjIw8hEbvOpAC8Fg23iCoLn32oE35Nyln1XVtlttNq9X5xHMtd6ommIksJFlPgMmdugcTEsZwq4bP9nR5Jmq6R2k7/XlDBfPmE5p69MJtuNqfeXvmh7Gnz4vBucCHeOtUk7AGH/7IDDsDUmh09PTiQ62Ew9uZv5kWTeb8fwY1pwDo8xT9/f3LaHEvn7WDf5ILGxsaYzIuG1DfMMI84bP0GG3t7f19PTUaGAdZYee3+YJr0v6LfZLPAcHQe1U+jt+J839+qBWcIMD7swhg612WKkIyoSMZYmEnOnPGtnB95pAl38Xx9adGvQ4+4axsaePYbZC4FJ0wBrRl3Ri/N6sv04wTaSNEjeuQLGjBBP7NGAzGo4UB05sb2/XwcFBixjYANNvntzsGnk7rQhAT8Gms2hA6wylgbMFJkHX1tZWmwsKKSMwpjHGAPrk+GBel8PlnFzKY8fRER2XGjJPTlwmGga/DMNQX79+rZeXlzo4OGjlQev16xUhP/74Y339+rVFAV3SZcVtI7XZbFoWmYguPPz09NSu2WHvjveEEL1jTXxAWEYgWTvTDgV5dnbWsr4+hRrgaEBppQytiGijeNfrdSsXtMNdVc0IuQ+ALobj5OSkjo+P6/Lyss7Pz9v6uDSUPrLUydFiG7rHx8e6u7ubGMh0mj9aQ5YyyJaAo+otsHp8fGxlqNCS7AR99oJ6vMfvYo0cdEAvuFyfd6cD57HzfVeBzGazSSaODAQ6xVFX08UOLLKIvuF99O/97BmYSv6rGkunHWxzQM3/t56lzwQ2Hg/r0wNvuTbMk8/p22uezrkBPDo+eYXvZLk7tEAPe+z7+/uTE1cB6Dh9SS/Tw2vlOZqW8Jr7Thr6x8562ouqMWPWC6Khrx3ssTPqILKdZ9tXB++83slb4Bl/1/aKvl1dZkdkNptNwDBrhwPsDC0Og/n0o7bka3gRYA1tqqYZHtPFZ1NUTbdo2EbyPdbLQcCerFunWCeBTZ1R9XurpmcaYI8Tvzqo7OB+OqJgBvAQ9hTa2MZYjvOAox5fWwaN06qmOotSXuupDKKmbrbDznvQVZY5xslc+S7P+fvpSPlvbAngRhTmRUAJ54zAlzEg2wW8Zcay6nXxeiY9eR67OZvN2voZo9mp81x6VR3GUuYd6x3T2kEU86EdWfME38tgj/VQr0rMAZa0BV4X95u8Td/wrCtnkm8T07znmP9W+2bHtmcEYRoPKpWJB2bHycrFCsV989uTpL3nwfccMxPI76APHAiPy4DlPSWYzrfHkADKwuzv9OZsxZOR42SYnJP/xvpgnDOak/PynpIcW+/9BjdW+l67/Mzvt3Ckk4yiIMrO9wHTjt4lcPI4c917IM38YnBl+qZyS3lIo5nlGObfNIrOinhNksccQOqtMXzcW6+M8PaeyX7z/+/xam8tPe+ewf1oDZBL4IfmQI/BLIDdFRzn5+dVVW3vqw/2gsY4n1Vjhtg8QwmsL07nsBPvCRyGoQEDMie5xu4bIEFQhs9TT+IUYAzZZwwtMhhCcJCAU9XIP2RucdAyS3p6etqutQKwWC8AdkwvjzOdCcbF95k/ASzk04fXGeQ4OOU7/WwjfdeqI9SskfVx7uVEvh3Yqqp2wrVLgP/1X/+1VbOQyUVPoJ9wwhwMy8oN6FBVkwMLWU94wzYL+qzX67auXHUHgOJcA3jbVVxV1a6ugD9ms1ktFot6eXk9rO89XcI4rKPt+HOdBocxpl7PwLn1tnWiQacDjA44mi/SkXMwJrHFR2yJCXDaLLOWPWfc4RefhYE+daXAev16uA9/rxozQt4r7nWwra+qydYx+I8x+BT13uGVdsZtQ0mS3N/ftwSJKxrQRz4nxvyAHWA+VDzc3t42nVxVb/Cb+cnBKjsifoYSWnQXfaKzXUZtPMv8WC/oiPPNOlPRgB7waeWUqPr6TraN2WbNZrM6Pj6uT58+NfozztVq1Q5aZfui53h1dVUXFxe1u7tbf/jDH2o+H898sL7geScSXEniZ9H55m3wKfoAuqATN5vN5ECkXtCa/6Nz7+/vJ9tb4OPVatWSebZ3dn6ZAwkKn0/kgID9MOQptwiS8ACH8uPzYRIn2hZjS0hiWb7SZ/RVdd9b0fLNji2ClQaFKJuBmy8yR+hXq9fDUEwQFFrurTDIX69fyxKOjo6qqlr5yXuRTvbFzmZjBtB3LhlIpgKiLy8CJXcuF6gaDXI6oDZUdligDwxmUGqnC1oj9ADJ29vbBkL5ro2rnUIDt4ODg6bQ+XveX8u6GjwBIKG/I88uH/DeUkAoYNBrT0mZs4rQj8ia7zNGmczn81bmcHZ2VmdnZ3V0dNSAHIZtsVhMxlE1gg87hRya4zmzvmdnZ5N1AFCiAACjzlK9vLy09wOY4GNAWVXV2dlZbW9v1+3tbS2Xy9rb26vFYtFAM3tFsqE0GC88BJ/gTHnfKyCqajx4x4fFsA6MjxIqHC1ApktC3TJza2VIAOXg4KBloVyu9FEb8mAnCINe9faABwyMwRx775Fll+uxDqyXA3FVb/dzAU5wknFgnIHCcPhUbOtEO3boUTJf7wWyMrrPfDLQ42AaMpsH+bjs2rTks93d3To9Pa2rq6tWKWOjDXi1XmQM5keeQ2fzXK+8i7E4gMXa9rLvBivQ4uXlpckrfbpCAtkzWPFY6RPa26nF5pyfn7dnM/DlbKx5zTzFc14/HAvrV4Nkxp72HHo4MId+9BkZ5l9O4uf8AJfxeu+kdTzyZeeUzwgmABTRy4lneoHkDN77WRwSArFpHx2gdObKpemew0dt7wUiaL/n2OffDaJ7Adh0pN1HZn3NJ/48A/I5nt7c0H3wQY6NhrwlXkze8/d79PCPs7oeW/ZrrPce3TMYbd0IrZJmVW/331pfWX/3xsnvDLJmgojP0IEOqllW3UcGqvJvnlPSKfmCufTGxTvsL/QSWh67P+ffSfff4jf/u/fjsee8MqFoWjgJlGPK/tMW9OhrG+v3Wedmvx7H97R/UylyEiAXPr15l+84kmnhMMP2sk75WY9pDTwyKmojbyctx5/vwEDxfTu/poMXz5EfO709Ybbg85n78XffY+ikAYIE06Dw+MnIVI/Wnqs/s0DkOlhQ0hl+r+UYUuEyDgNT0xVHworrt4xRZi57dHHktaeEU6nSl5W/P+M7Nnip8DNLnDTp0Sr5BL73XBKMZ78As+TzfC4Vd7beM4zvtwzoR2lpEFziVDUtPeI5tkoA2Pk/UWg7KvykfJn/CDSlk5s6pmq8u3azGTMY6Av0tx0ZnBBK+HHOeVeCCoIjXIVgvbFer5tT7Qgyjr0PxqNvH2aCk/38/NyutTo+Pq6q1y0rzo4Z9JkmzDUNJvJOADI/s/1AN5OhnM1m7fDAo6OjSRaW9/iAOr87g6xkJr3mvNcZBp6F5vAAziSHMrKfOQ+kceYsQUbVuG+RZppatzlbQD/8ENTmb3Zos0qFxkGBDmywplw95+vyyAjNZrPJtYIubyV4wLzgC/qwvrf+Ql7haebrdbEsIh9kMQ4ODt5U9BAsWCwWre/fspP/0VvOzVkw/m4b4rJJfiNzvh88Mzn0kXabtbaNJshtvkcmXX3B895y5j2JVaP+Q6/Cn2l7+TvZRPfprHViSScjqsbqCTKDBJ6yWsXjqxrPCYF2zAX+ZQ+qqzaMI5Fnkku8y9ubCMISwMO+OVnF+23P0G3oN9bM+1qdnEInzOfz+sMf/lAHBwf1+PhYX758qfV63XSvs+PHx8c1DENLbkBLaNXTgdZNrjihX+NR9BAJKaqw4A2vad41n/LhsVN1gz8FL3lt4YfUz7bf6K75fN62AdrWWX4Iotv34d89m4B9sc3ZbMagqbfAuMolD/Xlx+cq/VaQqde++1RkmJt9j8vlcpKl8p5JBJdN3CwOC2OjsVqt3ig6L7oztGlcIbqj8CgPyhzMJI5EG6DAHDbKMJKzqzCQDRqCzBzJkOzv79fJyUk9Pz83Y+5M4Gw2q5OTk5blcVQdISfavF6v292iWVYDY/ogDwCyswgGXawLawvw5vh0ToQ7PDys/f39yf4FAhVWuHaKWAfubXW2CeVpkAMgs/Np4R6GYXLaHGWI//iP/1h3d3d1fn4+iZL7ihDoSXaqaizR4G+r1aqurq6qajSm8MNisaj5/PV6qvv7+wYc+fvLy3h/WPIWZRx3d3eTA2M40ZF1Qeh7Csn3xqFQ4NsEbygOMjSAXWfFoS1rfHd31zLqBsusbRpcxoehMn2pwMDgOADQc4g/SrPDaqPN31hnGw4yqJzKDVD3QSbQj0xfOmKO+Fu20Bs2CF47Soyenp4auHP1TdUI/sn6Gqi5NJhx2LFFtjhfgaoLeNL3kFdVM4T8xnja4YJ28DgnX1L98PDw0E4rxwlB53t/MLYmA5yMHZ62/jT4ysAgMso1SAcHB3V8fFy3t7f19evXNxnJdOh4r9+fAC6d0arxHnZH1eEXKmE+f/7cAhIZ5LTTmNlc/g6fsP6sdY/3DVYcVOaQP1fncCAMLf8Nz6Gz0WPDMDRbBD8ydpweVy04U4wN3t7ebucn4AiYHszJwSMHzFnzxCIGrK464P5G1pSqMs5xwPH/yI5ttrQHKYfWK36Gz1gfPv+9jI6DFO7LQZ2qacKCd1keMgBofcHz4ChXYDlggt12YM2Yw/Nz0Mg0s73l/7kNo+ptebLtkw9jQ79ZHkw783jaE69d4nF0sBMhHkfSO4OItktZUeTABnoKXI7N5F0OPNjJfo8vco7GpSmnrAn0wweoGgMf2LLemuahSfZFkvbGU6l3/Iz7oF/o5wPZjIEZF+OGtqxj2l/Wy3xiefF8/Dl9Yadsw3gffff2OX9L+2bHNonpfTvvMToT84FHOBHZ/B03FsZKxO/zYrsvA0MbJH8HJyMZxnNJhqbvZLYcsx1Gl4HRl8GIF92KPA1t1biXz+80c8MwBoMOGiB4zNvKxU5RKhaipLlezs7Tp59xdoV165Wt8bnXJdcZZeySZYCBwXaO0Q5vBlMs+C6x7PE6ytKKi75cXuqxMx4rCvOz+cXvTBrj8NjwJDjI75uWNs52SgxG6S/lwYC3J9+pgM2HvUzxR2xcM5FrmQDYTi88kaAG+nIAHPtkZ7PZZFuBdYDXk/cMw9CCMM42Mi62hrA+Kd8GPgTemKN5IZ3anu7k8LgM1sBXzAGdwXgsI7k3iHfiLDiTiVNqefe6JDDsgSXGmCCPNbLRRS59jQ7OJO/abMYTJhmTg0FZsoaeISDiQElP1j0vgmU41vDOjz/+WE9PT3V5eVnPz89t76vlFbphM1g365IE2fyYv9lfa2Dk4EbVGDyB9vAqOh4aYLdWq1Vzem2bsK2+GsRZXdaeQAvr6juBGYfXOZ0Z09vBw2EYsyUcoGnH3tdcQB9OqYaHP7KeNH5xAsLNn/O8EwrImQ9ZrBpxkgO/BD281zHllsMTHbSB57m6zpkny5h5yBgKPW1ZNw3gf/bapiPC+NK2Z7ONIJHEXnTrSe+zt12wA27bYNrbLlCV4rlWTQOryB+y5uwtiRBonAGjrH7wVgzmQJKEw0Xp+/n5ub5+/dpORLaTZtnuBZeN6Q8PDyefJ9/O569bIukbv8InLWODvU0NfjJ2gx74J15TBzN5h+0d9GeM2HivTR7OZP413iZpk0klHP/5/PW2gtlsVjc3N5PP3az7SeDBW4zZssDfvYfb9su8at751vbNjq2P+meSVdM9fC6tQ9CqXtP/BugJCGDkZEa+k+AkwQ5GZLMZSxmI3rNX1oRKYqP8lstlPT09tfF7/xelRZS8AUqZv/cRcdgJfdzc3ExAPguIQJBRhgZkZ1Cu3pdq52Y2m7XxkKnN8j5neuyEoiheXl7q8vJyAmgQRtaUvb4ePw6cyyFms1lTOpy+S+aPkjaX1tEXYMPOt0GQy1W2t7fr5uam/umf/qmBnZeX15OTKd/jWZdvPDw8TDJFXlvG4WPKzcfOvgFmXJVQVZPSTAck7ICYfw18rGgTOFe9dfhd+cCp2fCbr1BIBcV3/e7FYjHpC8PhMi07tlXVFBw85vWEL+Et9lx/9HZ0dFSbzZhpqqoJ7xg4sy4AEQxr0tPnCby8vNRisWh8bqfLAZUsIeOAiiyvur+/r+Vy2f0b/TogSXkd8/JzVdPAiQEK/394eJjsD/Zp7T7UwsYuo8GUfzFPxmFZZiyuzGCcthuuyjFQgX8Zs0Eh3+W9mZkDILucLh3bqproNBwzxmqgladEo9symJoBKWh6f39fX79+bXZrZ2enDg8Pm153QIuMOvoCm+hMvQ8vsaOQji38aocC3reT7GCFK4/29vYaYPUawRtgD/TgyclJzWazury8nJzqap7AvnJ3KnIJRjGY8pzAD9ADXrLji8xxHRc6mb7gB9srwFpWK33UZifUCQWaMR/4BBuIXBos48TCc/AyFQFgR6oo+L/XHnzloBt2jsOj7u/v6/r6ujnJrlrqOcsJ0N2MgdEt7I83RnAg6L3GHAn0V9XkbBkOZQKnwNP0aZl1Bs5rxXrZ2bIOzVN9M2BpfcC6Mz50mpM8xkUc9sXaDMNQP/zwQ3PEHCjbbDZ1eXlZt7e3TZ7spEFPz988sl6/HnKHbnAJrNcEJ29r6/VMFN9VazpQvQnP2L8xHocezlBWjTclMEcHEi0n1tXQ00kh6zPWDLpsNpvGJ+ZV28Cq8bo5bERi9wyGeAyZCEksjN8IdkU/Ite2/+kY/177Zsc2I4o+sQwiG6hU1YSREWY7WfTr0k4vhCMQyZA0KxLeSb8AQzt0BgX0lUAoGdpgzSXAGHsMcjpM9OeyWCsWmCkP8IA57IRZqdtJzwM1mJ/BhCP9vB8jYMZjzFYICGIqNb+HRqnZbDYtPVytxoM7YGQzuoGS1wYa+1mi+YCNw8PDpkA8djv4CDPALJ1PAgLmAQNL74dzZMoGmPVJ/jTv+lkUeAq++ZJ3uPIAPrHipm+CHu4vo3WsF3QwCKY0ltI/B0HS4eagGwMLj9cOdg+Mf7SGjCGPZFkxeoBkB0nyFGBHQZEL1hvglg4ffOfPLMvIQfInPF81vXYMsGC+obnsjVI8ItkE2zC26E1nV30QFuWqNmCeh4Nndk6Rbeva1A+epw/Agt8tt75mImXEtsc0te4jq+CgVK/cLOfXO0UygX4CP8aXjjbPQovr6+tGO2cNKX21/gZwGRBZ9/MbXsJWGiBBV+ZgvQ7gYl3MN3b8E6jSl7EC88QG8307ydCIuaM3HVR343k7LAaFtk/QxmuNHM9ms+as8HdjB+TYmIN/mwc+asvrCqveVuOl7HmNE0DjUIAHoCWBCgd2WX8HzMwL6ELWBXnw/dBep54ds36wE29+zL/xvd7f7ExVjYe00mwrUi+lU2w8YPxjJ4bf8G/Kp/UQdDMmTRuSzgy6xron8QHN64XjRnbx+Pi4/R1dxFz5Ti/zz9/hFQdXcKjszFm/OrhmHOx5mNb4Qsa0rGmPr9GrBPOqxkoW61rWyP6UeZn3eg7Q2/4GdPU627n2u9brdQuYsIZuPT7Ov1kGzA+se2JpVz2wPt+rH7/7VGQflAGRYCQWK09QdhTH+2V6zhH9+ORZPwsjE1XxItlpAKCbEdMRQDmw33G9Xjen3HerIkQ4bDQDE0rhiCT6aHiEk72t7Kex4fT8LTieC/8nqgTwgCHm8/nkMBCP01lt3rNcLmuz2bQDXiz0OTbeA3C38wcfAKroAxpvbW21LDr8AWDiHev1umUkXCIDEIIuLgUehjG7gxN8cHBQVaPiR4GnYDMXAw/Tijnz43UBlNO3S7V912JVvVFUBHe8PozXpW02sjYI/D0BwtbWeDl5OrVVY0bCDhig0BFK+JOAEzIH/XAs7Jy4LBv6wCcGoR+5oS9OT09ra2urvnz5Uk9PT3VwcNDuFCV7jcLmEBwAcSpv1oiABVcZ2JiwbpZTyzJ90NCBAEM+4//r9frNpfTJ95SJkfnju9ZH8CfjJdtMtp8gmAGnwSF6dBiGSTlpVdXBwUHt7OzU1dVVC7A6A8OcAKdV04oSsmpUwlClQ/TaZV2WJcpcAUEEHp+fn5suSyBF1QNjQBYYBzTj717XDAAihzhS6CXo59L18/PzOjs7q7/85S/NPu3t7dXR0dGkPJ3rNapqQhcHNAnQkF0iK0YlEsATXc1vrinBHmxtbbV74Z0pxTnBBrDGBELRmazT6elpVY02h2omIv30RSn0/f19k708IIbf0NTgCt1uYA89DEgJrnKQmfdY2pmi8sF203L5kZ1beIa1ykApfOTMEzawauQT21xKU8ENq9WqYRrbPRxVn6ZdVU2enXVDVn2KPJVcTljQ4BfLazqvdjhd2VLVPwQ1T6MH/xmbwUvePkWzvrfThz3gzBNwuZ0f9ArZSPQD46es3k4pNsHOETLoYByJFOiBfiRLNwzjtgfsF/K1u7tbP/74Y3369KmWy2Ur4YevwERkqXm36eDsNuu0WCxaUgpd4EAGmHKxWNRqtarr6+umy7CX2DpkGD3MmTbmHa81OgcbDc+RsLFvhA7kzB76IYBjHWlbAc/hf1RVXV9fd8/gMJ84CHlxcVGz2es5LVTiZUUgzQkMsIADgw5g+d92fNM2GLt/a/vmp1lsR1EyKs0gLVhESSB21TSakN48kQIUnL15FsjAzdEtLwjKwM6Ps8SOhiAIdoZdfuQsgE/Kc/QLIecKFzM7DjN7b1AMMIQjksn8dm6tGFkH770jsmO6OUpn4LRavZZMoziqpkEKGwZohhPjSArOId/HKfI7Z7NZ2wuyv78/OSmV+W82m7aXxcbGSpAfR9cc3cNZtULzng87u9CUtXYkyQ6wI7ysCUqNkjuUFKCSOdEH4yLLBP3cXKaW5TzJHxyy4/FiDPJ6Kvdj5QKv39zcTEqOXR2QWW/G4ECJsyR2spAj72n5z9Asf/ClT77kmV5Lxwj9hk50mQ/NOsj6sHeyMrLj4JQjpQZnfNfrap3gwJef6YE6+I7322ha7pJPEvDxmeWVq6t8VQ79eDweh9fIc4PHCaoaBLkvr4vngF4wiE77lmNM/cJYMwuAzmc9AflJH2hv+vP91Wo1uVYM24ej6cysg7LwQTobjMV2xTYYO+1Aq3Vm1XgqM/NPoJ92BF6rGkEY8mEAxDh4NzxvsO33eI4OrpiXvR7GExlcp08HX702/N069T+LfvQ6Vk3Luvl/yqN/+3usbU+fpqz2dEnygMfgd+baeIzf0vId6XR5PL3P7Qjns70x+t+pO3uO9G+938/2ftxPYgTL9Hv8bn5wn05IsM2P7/bwtwN8fsbz/a01sy5PnZy62vo18fJ7vNPioU+EAADwTUlEQVTT8aZHT8ekLctmG5DrlbbJ8uA189z8f/PBb8lXrzHulEH+lrwDHTKj3OvrW9t3XfdjQveEwADJ3jyGh2f4dx5zzfdwOqvGKDbAxYbJC2vw78Xd2tqq09PTen5+bqfe4tAyNgjKmDGEjjTQXPbk+dixxFEgmsT8fSCGBdGA0fR5eXlpV2Awb4/Z0SKDDBt3siwZscQZq6rJ556vgxTO4DB2QDsAxuDWUTAAXWZRXTJSVROHl/nOZrMGujJYwf+3t7fbPbKc9Ewz8MqSk57CcNbA4zVgcyCD/5MRcBSadzg4QZaUd6fT6nVOI4IyXCwWTSYIbCAzHIDAu+FXny4O7R01S/nxD7JIAAi6ptMAbxCJBYg7Av+R22YzBr5wGAgcuCzTQD7X106qeYgoOlUP8DGRfPiRdeXeZHQOe6Y/ffpUZ2dndXNzU1dXV5NgoU+ZrRpLLCnJ83h5PxUtPgjLfMf+HHh1sVjU3t5e3d3dNbowh3RwshyecSHjx8fH9cMPP7QTxw1k0K3Q1Ns6fABdGm1kiGcYV9Xo0LOGVVM5ODg4qNPT01ouly2b6X1oppuzrc7MpixZH7J38/T0tAWOrCcTaMIfjPXh4aH++Z//ua3xfD6vL1++TPT/YrFoFQb39/ctU4tTXFVt3fI0f4KEtjmcQsxBPFdXVxOgYl2b4AtbRkaDOZJp6wWz2Rs5DEPbc8dnPqsi5Y91urm5qaenp1osFrWzs9MqsSy/rAfZstzXhr6neogMjgEzNpyMTs+B+kjNAd+Dg4MJ/ci0W+aqXtefq5JcQeCAoTGocRgNufHfLFPoy3R4Dw4Omh5wJZmDhA5egBWGYWh/S6eFRIBtQA/ouyx6GIZ2mjfynEFx5mRc4YCox+BtJsabzIGgXgainDF3cA26MxdsiQOx6J8MHlH5Y53vYCp65uzsrNkZV5OBpUjS+CR1GrbRQVzbW2yXfQafm0MiCp2DnXXiir6xl2zHc9KE9e5l4Z09NdZi/JYH9DB8gh/A+qKLsLeupHFlA3zDevgaNPdpbIjMGg/bJqZTn/THDjjoAM8eHBy0ilZuODFfvJcMeK99s2Pbi7T0nFsbfped+MflPzCnjaPLZmFylBiKCGLDFHm0P9/b3d2tk5OTdghA1ejYuvx4NpvV/f19PT8/1/7+fvssgVWCLebHHWLL5bIeHx9bCaKVpw+Agukzuu5IjjMRGU2kDxwqp+2tzNhLe3Nz04wLYyCKDcPb6TNTG2R7PaxAc58wJS8GX6y7QV3VqzBub2+3UglAIXTAKXf5h0vUd3d36+zsrB4eHurq6moCwl2SsdmMpTiU2yHkNAM4DrVKxxZhBXjhODiD7b1irK3XyODSNM+ImhuGhMMXAPgOFGEonH0H2NsAOmuSWSADagd3vD/axpW5uRSJe+JSgX3U5hLYqum1Dw7U8Lekn0ELRqbq7QmV6cC4fwOlXnTZsmves1G305EBJoMmg0p0HOPjOQdoMoDJeOA7O7XvRbEZk8fhwA39MTfTzxFs/5iO6CbAXD5rh9MAmT783Zyzx5/zso7xHP0Oy05vDqan1y+Dh/AD9sqgNGlhAFg1VtbYKah6v7LFOi9tpksyzbfmU1ePeF65xy+DavSBnkM3/ZZ+hUY8817wws3yl/OD/vm8ecL8mmv50ZrXxWsPj4B3rBsM3m2noZcxhx1M0xiH2PgDOnvLVdX0oEecD+sN3gF/9NbyvZb4odd6PMR8cVJ+qyQzA2GpQ3jG4+3Ju8dgh8Xz6OF+7Fg6qOBxP0tgp4cvceBYc9+FCx52Ugte8Kn4SfeeTvNa9rAs8zF2Sd1rfM8zXqfe+TrWjbzTlXw5Jvo0luoFOUx/4xDGbZv2Hu8wPtPOtOrZHH6nPXXfrtZKXc/8HORLG/m9uvG7Do9ikPzfygcCwJRV4+EoLJgVg5nSxGBSOIEAdSIDSdgETESJcI7TcewRyfujULJ2wGwwvXnaYMfM2BujHTmYLxWnFZK/y1x6Y2c83gtUNZ6evNmMm8ydZXZ/Hn/V2+scoA2RHer12cPmMWFEvM+BHwAswp4gDMeb/ux4Mj87hTi8zgoTDfYdnER3zRtuWWrn6zUItHicXiv4zoaAPlkfjLZLSZlLOrY0wLojo9DNAQhnMmwMGKujvji48IGBhEEw/OggEWvrd7rM1UqTtbbsOTL7Eduf/vSnenl5qb/97W/1+PhYh4eHdXx83O5eZu13dnZaZQKyQqDi9PS0hmFoV7EgwwQp5vPxHtCq6VU2GHWixQRnHHnd29ur29vbur6+bhFSApBeKyLJRKnzpFq2FdjhQ0exp9p7cKpqsreVTDHBtb29vXZyLIGq9fq1csIOie8On8/nbe8k98aah10pQF/DMEbV6RMQgkxwkjCyAm2rxntjE5Cg3y4vL9t+JAeY3FL3sYeNfc2WuQR9yJv5ydkfA3zedX193dbGAbAff/yxNptNy4CgU+A3aEX2Ff5Zr8e9xlRAWe4NaNgbZz2dVTnwnnUsnz8+Pra9z6wVe/yYBzwHXdivXlUt2+otIshNbu0huMvasCfep+pafxm8WufZDsBjLuHGSbOzZb74qA08VzUt5aeBvcwbBtPoCwfH/ZxxJWvovh2AABuARXg3Y7MDVVWtQor/O2hpQL/ZjBVy6MCq6dY7V2jA52AS9K51elbKMAZ0sT9nL7CDWemA+m/oUuSnaoq5jHl617PxN8+N9WA/PgmEqtEWuWrMMkP/2BxwJtVH19fXdXd3N7mKiQoL6MHf0FN28I2JmSPjwC7jpDIPxujMJHOBRuhyfqdeRncStOZecWjqAA/vROeTwe0FG0gMWkaMs4zrzV/YGtYSmWA9CaI4GYKM+ABLBwEIIOVedeNlEkAeG+vD1WfG6oxxa2urXeP1re2bHVuX2/CbCcAwOLWUjQLCGKDT0ABoM54/x4FiEzVEYH8qxHEkg3e7xMyLACAxsRHa9XrdhMkMRuapajz1d7PZtM+dgbOzkzSirHgYxmtkXEaYip6+Xbbg+WAY8zoPgAKA8fb2tpbL5SQikoqfhqARzU+leH9/X7e3t/X58+f6/PlzAw70x7p5bThABCUKsKY8jPFsNpsGWHi3hcAACzr7oCwE++TkpIZhaL//3//7f03xuVwa+vA9hAsDAehbr9etHMpZJcaQRgBQSjTy6OiogV7vA7TM9LJmgGNHJ/MaDQdikFFKlqCVI4fDMF6tdH193cAmwST/IEusg9+VpYEoamSYQxPcx/cqpv+ozWCrl+mBjg4Y9ACS/+3vWtegQ3rNRtXv8d+QWesuP2un2AAq9afHa16uehuscYQ7A0Y0ByDToTAdXOmTz+V40+livsifxwvQfO/9pmUCDuQTZ8z08nd7Y/WcbddyLHYMmJvH5LlwGIkrCsw36FXLc9LZmR3m5Pebpm7WadadHq+Dp56/+8jv5Zz5yTH1bL1pkwDQdtFBbdPcvJT0zvHmetnByZY88dEaALgny/w4k8vfLKeA6qqaZNJMz9SrPOtnXGJqm1w1ZnH52dnZaddJ5dYFMKfHQNUUSYDNZnr/vB0esN/z83PLSlonurIms10EcdA12F3scY8O5mE+R/ZxQl1m7ebqB2x5JozAf/P5vE5PT9v5B/TLuMBtti+enwOlnIQ8n8/r4uJicjUmiRWCYC8vL5OrjhwkhH/S6WVN9vb2mmOLE8Z627Fl3k6mpH3JzKiDB2BR1pB14x0EFpMu5m/zfeLHbMzDc2B+prmDuMzPJ5lvNps3ji19My6wXgY+4BPbass/iR+CrPA9DjmYvKc332vfd9RUTbN5GBODIjtoTotDtJ6w8Qx7TrwXxt+xwrPj+h7wgfnyAIuMtphJcGidbbDRe88AWzmjiG0woYcXBwbwoR1+v8fnU3BN28wI8D6Uatb4u88sD/VaOPNncIODx+mmVvR21qFz9oVjj5D1MiMZleXHET5nM3zRPcoCBw6BgJ4ICgYIXvMasYasKQKN0mctAIVWNukIIOw4m86cmDecXcpyqiz1I6iRUW3oC+3dB2vA/m8AM/1m6ZXlJ+dk2TN4sEFifnb0P3L7+eefa71eN+NNabojr5TS4/RXjTR6fn6uX375papqYlwxOsiLeeDg4KD+9Kc/tVPdkYWqUYY4zA7Dv7+/32TCQIOx9BzsBFipD8n6mqe8t6qq2nVS6GJ0HcDH8smWEn739KYdosfHx/ry5cskoAOfMmb0AWDHwRjGDB2cda6alkbbSfOBcB5nDwjxLi6796mh0Jrf6/XrFhIA5GKxaHuPesE179s1KH14eKhff/21Pn36VP/lv/yXWq/XdXFxMeFb7kX2HOiLubMOZGot6x6/r/PbbF4DgxcXF290COtUVZPMtgPT+/v7bduJr5sgYM7a43BYv5rHDNx9GjNzcKba4JTAn7df8D2CBDR4jSqJw8PDpgc3m03je9tH2zR46SM7tl576IXNdEabcwGMW6qmGXB+97ZOOQDjd4MvLKPDMLQKggwSWXbBFO6LZ+Fj870dAa8z64/8m994D/3CO36nEwfIoZ1e+nEwwPI2DMObrRKMxRijalphZX7N9fTfq8ZkjG8t4cdluR6r8aX7QQ/d3Ny09SI5RsbTGXDm7YwputRVefYZsuLNgYiqEZs7y141ntibvDAM480Xppt9EmMhy4T1Ae83nWmMwxV37iv5mXG58sfVTxncBWezRsggmV7Py3TkvBtXhvKM/RSvs/UwwQrsjNfTFR/f0r4LbWZ0YRiGFjGBoC51cPp+vX69Xqbq1VAzeTPiYrFoB5tcXFw0pvb7vR/DfZugyUCUy8LcPkqcZ/kef7dDwsL0GBDmceTDZTMu03R2hWerqp0G7AvevbeW9LyFw1dzpBNTVe3aFxsS6ASwzn0NZiQ7UpSksf7Pz8/1888/T4yI51813nPszeYo+9PT06botra26ujoqDab8VRk5uI+HJggE8x1I44WmRf29vbq9PS08c1yuWzXjqAsbm5u2mb1BKW8j3I0K6z1el37+/t1dHQ0ucTdDgtgx440dGSNoT8HRJBZd8bG9zFDp8fHxzelNYzZ/JrBHa5D4NoPDjc6PDxsEUu+66io5cdBFtYUmvuwHBwe1ut7Im7/0RrK2E5/1XSrhY0oMufnHC11H1X9fX7IGxFZjDnPWw/mIXOMNceZ/+4F53imFy1Oo5eAiz4Nilz1YKOXoOy9aC86y7TNfgBcBsBuBgO2Zb1xmR5+n0GKx2f9aBBHs37M9eU71ivvrVN+F7AMbexQMq6dnZ1W2fNbjlXSJ+eaTgEA34DE9DAderQ1uIdH0T/GDrkeHq/pncA5+d4B+5xLBnnT+Un68P335MLNQfaP3OyYuPR+sxkrpU5PT2uxWNT19XULVJMBpNTU+gAnhwCE5TCdJoNjqpoODw9b5RlXNdkBwhYul8sahrG8FlvM+SV2Zno2j8MvOZAqAyw4Vc7gpd7EprskGHtLIM64LZM3R0dHDQMxf3idKkg+o5qOtXH5reXfWBhniLFQ7eYMtW0Tc3Ygn8+qxkDw8/Nz/e1vf2tJr8ViUU9PT3V7e9t8D5dPQyMCZJvNpp2Zk1sSqsatYgTE5vPx0Leqt4kiaMotJzxjP8JJsQwwuJrH2WR8Bejo6sRsrIMrQLMa1qXBvorSc7ZN95ZG+zYEU+jTutRzptoV5zZ1KeNKfMRcyZhzs0f6Ik5wfkv7Zse2V0aThoyF8r5Cg26aMxJ2oCAii2vj4/famzdoYEwG1jzv79LsgNsxTBBSNT2eG+NqI9kDcDlOKxtHUBL0eay9OSZ4TOCZih0F4agnwMF06AEVK698hvHa2BiMEDUnY4Rz6jHbGcx1MT0Yh3nCUTSikNAhr8wxz1Fai5NmHrQDmaUbVtIZ4MjS7QS8zjKl8fG8PGcbtc1mGhjxdwzaEnCnk5COiPt7T07p6z1lS998x++2wcrgy0dq0IRIt2mWhtyZwqp+JtARe4Ii8Ln3UnInHcE03s2+Xd4PD2dZm7P/KYfwG+MBkO3t7dX+/n4LuDFmAI3BfZbQcdpsbg2gkZmkpXPCPJ6fx3sCMbAAF9OScTjQwziOj4/bOHjW7/Ieo6qa9AnooxwwgzfWTcMwtD3ABvRVYwUFQATgU1XtRHwHi61nGFteLYSuAeTd3NzUTz/9VPv7+/X58+dJpcZf/vKXenp6qv/1v/5XXV5e1sHBQSubIwB6fn4+0fGev5sBLGvrZvtpnct31uvxWjwCgPAvc9ne3q7j4+PmcNimovOtk50NQE6hEZl75spnDnI6YE7fvYMC1+v1ZPsHwUvA5f7+fjthl/HAl/TR08sftf0Wnqmanp3yHl2sV+xs+e/IvHktAyLv9W9cZ763vsbemZ9zft/SjLH4Pp/3+mFeiacSB7r/xJieU2KyxKr5PujTe7ff19PFxtXGyvgOwzA99BO6YNOcqU165e+kZc6LZl3FmhrnefzZF99PfGO+7NEog26mm7Fi4n/jQ/Pe773vt/jR65Dy0OMP+nqPz4wJ0Z8koN7Dnz382vvbt7Rvdmy9PzMZumrcD0DW9eHhoZU9sXh48mTm0vD5agxn/jxRC5Wj72YSDCGlQQZZJiTRm9PT00ldPgJsI4yw0cfR0VEDqa7HZzEy8uesIo4Wiwy4ZJ7QFbDjendoZofMDg4ZY2i8t7dXh4eHtVwum0OHw7lYLGq9XjcwyhrbsXO0E1DsyP96/XpQ06dPnyaA6eLiop6enlpm8MuXL7VcLifBDGhwc3MzYWSfSpcOgOvv3QwkZ7NZXVxc1NbWVt3e3tZ8Pq/b29sW6UvnKyPt8MBisajZbNYAPfzBOlFuM5vN3pQ0pQIFOHHytsutDLYwHBnwGYahjo6Oant7u/VBRiQvAgeQWeG49I9xEdGEH+EPglIAV2SBe5hpdo4AszhZOGZZZv7RWwIy86/5I0u2E5RkRNXAzUbDGTkHT94rUat6ezaBjWhvLl5P+N9OAM+u1+sJz3i8/Pj6GB8Wx9/JXFSNe+H8/QSnzDO3QOTWDeZpJ5QSWxzDlNl05BwshKfRFfSLDjSY472bzaZt4UgQlM6rdV2e0pnNmQ+XsDFvdOxsNmtnHuBgc8ATzrWdbtaYa4aSHhnky2h8D5R7XIzd33UwJoPNZMms2zKo7L4T3EKftF/mv+QDZyV43vYDfOD1MW54eXmZHHbj7Ln7y21BH62xFrb/zlxtNptmo6uqBYKQASoLbPuti8ic8h54lWwdV7Zgr+fz+eSMEOtE+uacF7Yx5N84CKhq1OEEZhi7+Ymgepb9mofsYMAvDnbRvA3C58BUVcMDBACfn5/r+vq6vYf+MwDEmLx1zwfIMRdoZtlhHmBZ9J1xep7R4UQL6/KXv/ylFotFPTw8vCkB95YwsKT3L9MHAVnr4tRt0ABMbofNdi/1Ff8mY8zYmDN61Pw0DGPpNDwHX/l9ueVnvV5Prk1ED0FbB8UIrDm4yhpT/Qn2gyccOGBNfUZP3gyDb2H7ZllkLA5AehsQVwwa26IjLevWz9DXOvhb2jc7tha4nuefDqadUD7LDIW/lwYiAVwPMCZ4or98f47T701Q2TOKvN+KIWmT0YaMPGR/6VD1aOjx+29Wlu9FTGxk8/tZEplz7NGj1wz4UAq/FVnJdXpPoeQzPVBrA5XPV73Nirp/gEm+K5VX78eROdPOjkPO2WuWWV/T+D3gmmPyXHqgO2nWo6nf6awifzMt35tX/vBsZnrfk5uP1MxbaewxCr7my4rcgQvAAQfwEVhxBgzjTTbNfMFhexgYtn+k4wsfeo+Sm3mKoBaghGBYVU1AhfkPw5RBKkrhXNLP313W5D59SiMADDo5g813AU1VYzUOfTszyuecpG69AHhhzqwltOe7vL+q2rqxBeLx8bGtT5Y1s5Y+rNByaSfZZbIAEYN6TpxmbmwfIHjJ9hGDuOVy2XhqtXo9POXHH39sc7ez5S0IdtK9lzB16zCMZac4AHyXAIjp4gMLAVZk9smQsj/Z1T80wI95isqC5+fnury8bDyFk+SAZFbkoBdxpG2nejoXGYMv+b5px1phD3o/H73ZGbJd9Bq4vBYeA+xn1UTVW7tpWUFveE8rP042ZMWVx+Wql8QgKdfoHwC7cQH8/p6+9Y+xEXO0TYEveY+fcSAS+vn8kpxLYjEHytC5PEPA23Pv4cb0A0x3B139PuwXd4Fbl3hd7Vxn4NNr73nanphP/Lf8OxniXvP5Mtbp5iHsIOvuuVaNwRfW37/No94y6HnRMsDofsw/toH+HNmDl5DBHu19poTf4yCF7b2DIwQqjAUtZ05WeY49/+n32jc7tmTV8OJTKDDQAK5hGI+4ztNd7+7uWiTTWUCyWC7xIAKBcbm/v2+GzwvHxJ3dzPIzFrZXTuoNy2aOVMIAulRMKVQoFz7zASlEY1AQLCRjJ/riAz2qqh2cQUkTYBjwScTk+fl5Usvvi6v39vbq5OSkPWtBY52gNacGOlpq5kUQck9yVTXQdHd31/7G3moMlIEW62K+YWwGuIArsiTMMZVMbo4H8C+XyyZcBjBkbuDXYRgm10p4nyKNqNvd3V275sMKwbxPtNgHmyArrHEv2ABPA0arxmgt43Fmn+wLdLXRsxPL/NlP4vJF7gz2kfoeoxUyfXs80NLjSeDw0VqCNPMA9IGe6BxXXbiUlWi0D31ANhwktB7wvvKq8aoT+G2xWLS1qJqCrDToNooOVADU7VD6rl7PG11rJwQ5MEizvrXxtPOD3rReRX/ZIYHG9AVdbNS9/wpZACAiZ8iND/xxMAtae1wGIfv7+5PT/Hv6Cdq63BU7wXsZj21b1XjqJjQlK+VDxHCk7Og7O/rw8DA5GXR7e7vtX/PZBulQMwfWzkFJB/2qqu1BrBpLrh3ctu6DDnYst7e33zg22AG/NwEQtMZBYt8Wz9g2Y29t16A5z7mUNXkW2fdvry3jgTa+Ss7y9tEdW3gGHWK9YsfGzg4OmR0GMGjV6FyQcYKHwEPWY+zBdXUF9o5nqqplpezcWR7T7sHPrmRL590YB9n1LQHmb3ikagwaptNYVe10YPjKPIWtMN+hwxLfWkfSHDjzXk2cPOhgp9BrDMadzWbNpnlN/W+PweuyWCzeVPYg99i27e3tOjw8rKpquo+qPOhm2bVMmvbWscZfDkAkvvf30w7Y8UvH2DYGnUizA+sMpe0qvGr972wulZr8Hb7g39AaHjceYbzY9PQ/WBv27SJ3+CuMHRpx6B9YED+khzVo6OSeXf0e/fhd99jaePUAiQF1Et5GKEFfj9Ho18/Z009wlEQyYRw9qJpGMQw83jNcNAuxx9GjgceTc/UzCfZNN88552ZjisCyTgk2DBZs1L0hnr6SRnzusskEpTC4HRhHNO3s8w7GYxowFs896eVmxWVF1qNxZj16iixpC0/klSqei5UNvxNwWZlnZLDHC/6b1xhDnoLPew3+0oB57XrvZI0NuExbeIvf2UcvINQDfx+1AahQ9L7iCvogCwRDrq+vJ3KAbjVAqhpPHLSip6QePvRpm1VTB8yGlP6cOc6AhUGA15K+XfLU48N0gqxXbFQNdAyUvA/H86iqiSPLvKtG3gSs2kGlb8bijKOdXNNqPp/X8fFxVY1BAOTWwYT9/f1JcAIaLJfLVlK92WxamT8lXgZ27NNl/1juwTYo9v+hD1t4nCHywXI46S8vL/XP//zPtbW11YC/g3gELGheF9M6nUQHRw3IXJlAP96yY1uEw+MgJvQB4GbJuMfJeMy/72GJ9Xpd19fXE553kBkeJ7jqQ1MorWO7FPxqnW47B7jHQWLrQGYKfWDNR22ss28YSOeoajwAyM6v9ZzLcuELtuRYD6MTCMDDA97iQB9OgjBG1tTfNWZhPoDxlNfEFn6PHT5+vEe7ajzIB56meX7etpfYwi0PM6Ufxu6qLUqmzduJ45kf/VguLc/wOEFY1tRzQk5sM7m2hy2NyAwyREUTji1yDV16WLQXnDDeSUxkO+MzZLLvdNa8ztnoa7N5e0aB18T4MZt1LbQmsYButv7qObZVNamcMi8k3rT/hM7mfchpr+oPJ9tjSezreSPn3tYzDEMLwv67OLZ+eWY8Eyix4CwChhqCEmGGWTjdjHcgaJQ9IGy5qCxAL8JnpWmlZSc2HXCEAkHY39+vxWJRj4+Pk5I+FsU/7D8EKDnyzoKz9wJFSbkNDaPHgqPgWeSHh4e2B5nyOTLIlGkBUAwoGG8KOvTOuThKBxDz4TV8z0IBWCHbSr+MPQ2ZlZoj8QaBGApozlUlGDI77tAJg8H7fJAZvMhpzGTFoaEFGCH3uFCYBwcH7QQ3Tm88Pj5u/SdYt8CbN12OacFn3TCWvUNpkCnKNjebTTvNkFJEO02O4poXuPbEF5vzPIrPDi3jrhqNJfv1GP9sNt5ZbefLiuyjNYCQnSgHWaqq8SP7+YlEowes3KtGh4rDo2jeV2VDU1XvOqvssQf4YXzhdzurzjjBjzxvMGU+h39tI6qmgSJo4j1CDoagl6xjADyAPAeImLfBmvWMda3Hgt7gsKmnp6fmfPA961hODbXsbTab+vTp02TvJO9Yr19PQ/c80FfsO2Ku3ldoZxsd5kBglhzyQxWTaQ2wNL88Pz/XTz/9VNvb2805g7aU/Rqs8tv8jF6oetXHXvfMCmEHLRdUdWRA2M4ga/P8/NzuG0dP9ZrteDq1Btu8B7vJPjD0lEEuZdS3t7dNbvLGAzAKvMH6IBs+14CKJ3Sqr56hEusj60fWO4Mbdjas/xITVI3JFdshB7Sq+gcaJZD2fkvjEAfX/DeaeQqZoF9nzXoYM3+QA55L54t+/G7LvANI0AAbYbowN+Mw3pMHmCF/po2de8ZrR67n9Bh/ORCZGIbG1o29vb2JHkbPEthCh1m/cI6OaZfZTuNd6wjGYufVfOJ1ti3KhFPyC+tj5zT5oWrM3rJWxkneN2sMaT5wiTw8jRw4sOYxWyfa0bTflnbS83RFbcoeczfmMR+9RwOvl3062zlo9a3t33y5ZBoMBu5IPM3ZXi+6lZGjYvSb76GlI0Yzk9h5SmL+1mcGTlXVougG8zxrRnPGqxfJcUkTzQzB2AGOmS1hbN4rAW1hfEdNkgkMejKylwaV+eP8GViaTmZ+1va9H3+vN7akgx1MGnyTEXLmlA6k+zcdzWMoSAcgvD6pDOAJ9o5REsO1K8zBc8FY5zwt6NDPgRjT2ADX68aYkTF40HNNOeC7fp9LqKyMPe9cP2fLbHAcrHAQrBfB/CiNQALzr3p7urdlhYygaZNGxsbXx90Dmq1j4DEAsvdB8TzXVVSN+3R5xvqW7zoi7MDgMEyrPax3fThZ1fRERMbJ+yg9zYwIfMVnnFxMn4Ay25u0E74yw3oobRN92YgzJ2eN/DcapVUGbPwfeTw6OqrVatUO6CPb4AZtvWeObQ699zNexmneYQ1Nb3+OY0VgmSAzGWYccgf+yFhmwy5Yl6zX6xaYJrtiWchsCs37g9fr9STDxFYIdIrXrare8LoBo0vhHVTh33ZCHdRhnRzUpLwbvs35UFVBH8YNBLoNGo0TsizxozUHWeDZTE44QHt4eFibzXiKOMEAA23+ho7CIbCdcr/oVLKR8Djj89ohc3Yi6St1Ac+gk8AUjDe3Djgj7f6Mjd+z294GhHxw9d/x8XHN5/O2hczfISjDVoW9vb12eChbX8CoiTk3mzH7ZszBeAmKQyOe7wUPkAvjtZOTk/pv/+2/tYPt0Pm3t7ftYNLNZlM//fTT5Fol7umuGoPGBEVwwqC/nUfjEhw1nGqan0FG5/PxPANoDG/aBlJWbbyWSR3rIniNBBL0oy/KfqEZtCSweHNz086DgH9c1g42RU/Cb7ZFaR/39/dbkgT6GuOZX1NvOWBXVU0PWn6g1cHBQdtO6B90Nvzme5W/pX3XdT8Qcz6ft+yiBRHHjVItZ9x6hOG7EB9HAXBj4J2nbcJ8BoUsTNaqYxAxHhYeFCML4XKoqte9xT7Ihaxr3odFmYWjj1X9MimfxGfA7wiIAQPN9Pf+2PV6Xefn5xMAlyV/Ga1G8BlbVbU52YDYuUJBJIhwkAKQ6oiTHU1HJuELxmCBYC3X63W7B45SI/p1hJA1ZQ6sK7TmdOajo6N2BQb7bcm2OtPt9XBQAtCDs5FOvZXZbDZrGWwaBtvPuOLBexyYW15jYcNmRx+lz3tR/jaqadxxYJg3VRIYQ8Cv+Rbe9smyOPgoa5STs0wf2bGlogO5q5pGaS1/gPTT09MGvnm+11xiSsPp8H7InZ2ddp0Ln8EbPjDH/OCqmKpqenK9XjcjbmNaVe1AH3gUg21nI4EYMsM8HByyDrYsA1Aou4a2Ptk3+0U34OCnwTS48Digv2XY1T806/H7+/u6v7+fgAfbnoODgzo7O2slYmRODWChET8AqL29vTo7O2tOD7qXfrEBBu4OKjtjkrZ5Z2enHVTG/dWXl5dtTQG+BwcH9fj42E5UpsE/gC728d7e3tbz8+t9nfv7++0EaIMmbBEVHayDr37abDbNab6/v6+7u7tJCSp/XywWE1nA0enJDTbV20rYk40jzdwJ7tg2wWMuY01bRsUDwQHmRIUQdiSD+rbFH7U5KGX9YwyRmcbfapZn46QMJvJs/t+f9ZzIHoj2u/zD36yDcny9/v2s34EeymcTC743Jzt3DgQkFjXN4GNjGd7jMXp8DnQa92SAPNfDTjF6L2UDHWZHHj8gy2fpM5MTtr3MJf+WjnqPX/Jvnif0Tgc+kyTGbKZ/8hSYmL7tB+RzuY65tjkGO5Y9nurxZ65hyo2/n9+zjnM/9nmy9Xwn+vp3dWxhRO/lspChyL2ANl4mFlENyn5Q8M4OOKqRE0+CQAyMpxnaRp5ofka2E8Bh4NIwWvHwXgAac3FLxWAHpadgesyDAXAGxFEglzUiDMzbAsf6MHeAZFVNIk+OqDkrwve8HsncGZlJ/rCCZS6sOQqP8TBen2pow2jjiHNmnsDR29nZqcPDw7a/h/UC8GfJrX9nFBWnIQU2+RFwbqF0pN5RYwcT/H0UfCoZKy/oDrBmTuY9Z/OhrUGveQqaMX6fUmvgbiecNcHRNc8k+PiIzevQCwa5ZJHfDnBZL1RN5cpGzM5YgmEi8rzbh/E4EFU1NXLmCb8/x+T7bAmIeIzW2+hN5mc+qHp7zUvqRAMdl+b19I71i/nS/fr77wFJP2P5olQ8D8pwFNq2hT5Xq1Utl8tmx2az2aS8jjW0M+oArS+qZ33RcS7zNZ17ZXgJcrAXjAd7CN28P5f30od1FHMkMMJcCNQYhDJOlypa57s8Hd0BFqAknHJuZ2Sgh9fVABlaMgbzOHSwzTNIt37nhGUD1N3d3ZaJ7dkC1gP+tz2Hhxzw/cgNGaVSoAegSSI42+h1dQAI/UPf0Bid6MARCRgyQ/AA2y+Sl9Jm2ZGDP1w9AG+CKdAfVeMZAcZRpgffrxodg9x2R+NvjMUyU/VaNURQjC1bbKNwRQAHwBLctn5ERlN/8oz3cdoZ5lnkCR43DZk7+ubo6KgWi0XbarBarers7Kz29vbaFoCqcS/ozz//XL/++muTVVdJ2NlEj0KXw8PDhkv4Gw2bljiqZ4uGYWiHdqHbdnd3W4AtdZ11LmPkUKz7+/uJbYC/wQzemtmrnLJ9dlAN3iJYYKybGVvwmu2qedTYgef5DtUJTpBkUIQxOnBimw5fwLe281XVMIT58VvbNzu26cD1yhZQFovFYrLAGLFPnz7VMAztKG+UnKOaRJIvLy8nACGjEiyco9YwB86n3z8MQztqH6VE7T4KzQbRP5y05qxzOiFEmM2ENJic78BMzAnhdLaD6LOVKkoLJt1sNpOIs4Ugo0ZWPgkOE/yYMavG0+jcLwwPQGJvBDS+vLxsY3TWDtDkoAJzZr1QjnZ0XdphpX59fT3JKvq70Pzx8bG+fPlSV1dXbY4oS8+7Z/iYP7Qmy7RejyV3gF7WOQG9gb2VCoGHBKg2JA48+B4+xkV2dbFYTDJGabSYK7/hgVyPra3XAxnsAHlf3NbWePAMh9YAQH06s0thhmFoe2E+asuSW5S3nRCew+H02lVNFbmNgGVus3k9UZPKAwMsHClAA/s/Wdvn5+fGS/AOa8pnBud2wqtqcqAHzxkQsO6uZvAe16ppADL3z3ElDHoQYORSKmhgXWVDibykM+ogkf9vcIA8+hRUAmJVVZeXl7VarVrmm3I05gKgZL1Xq1V9+fJlYhORS/bp39zctOygz6h4fn6ui4uLNgbogT5jnM5wHRwcNHnmip88rwBa40SQcSbATKaWNeK9Dlqw5rybDL/7J0PtNUb/Y2+xyQaWADpA6MHBQR0fH9dyuayrq6vJSag4/naEbItZU5cqG3xS2WC5TdDI5ycnJ/X58+dWIrm9vV2np6f18PBQ19fXE5xiYI9spO7HoXJWOR3jj9SQeyqB7HhU1YSvbHPMO7bV1onGLAbXDlAYr9oBQC4ShP/ePPx81Xiwmx1bdK1lx9iL3+YZ5sDczROJC4zxrNv29/ebI8f3jHWcOHB/nmPiVwckMzHh53inkyv+rrH7wcFBHR0dNWcuA5XIKzb07u6ubm5uJk6Q19k/Dk5Q7dFLRqTT5D7ew8LQjvmgnxkHut9jc2Dn4ODgTcLJtDIdcywZ6DXW8PolX2Ryivcm7ewnePuP+yXZ4TN83sOuycP0Ac0JEHjd6c86NXnt99o3O7aOfmHcDdycGQK4AYDIIh0dHbWIP6fYbW1ttRJKavUfHh7a9UKOxk8GvjWeYmmhgOkzmg7Y8UJgXDDCvMfOlIE5DNczQt54noqXZw0U0hDzfFW1A0XyHQYsvoqm6m1kjXGk0k/GtvPFOFxeCP2gIWN0ZAklRXkYZWkGEY727O3tNVBlYeJ9LoV0tMyKb3t7u4GM/f39doURwCjLmakwSOeUoAvRWwCmeZ4gA9lweIXvkFE23TOzkQoVHrHTA186wwAI6kVY+S7j8IEUyT/5ftYePkcZuyqA72UAgDESveSH8RCwYp2sCz5qw7jZ0chsgzOc0B66VU0PtnClQ64d8p9ZcfOcD3nA+BkI2Uj5VE0b4aopqOmVe1p239M5zNXOOrwNeIFGBnoETDLCy+cGZjznSLVlibnwLHRz3wZhnn86eNgc08ZAN/Ww+cIOnPuxbEEzWjoAGfA0vZHhXO+0C858UbLOVUgAFn/Pc7PuwDl871nmnHrRtoeWAN/vcLDMfZgu5kkHZFjnHj/wPuMMcAzf5Xln5bB9udfcNKbleqBT/e6P7ti6Koi9fb5iseoVy5ydnbUsU48mAG6aA2u8B/5Gt1rGeMZr4SC7edq4CCzRq6pBV1lu7XSDibGP8/l4FoL3YdNn8h5BJ3+GnKYeqqqGpzlkcxim13S6AqznLJPdBk8Zn6xW4x2niRdx8tAp6/W4ZczvyYwwOGE2m9XFxUVdXV21PaYPDw/1008/vfE77JwzdvpjjeABY1nWmEPj7KTDL3yfzCRB4JwDQdzLy8sJz2WA3225XDZ8n5iRuVj/pE0xZoWPWWvsn+07PGO+zwoW8Lt5x32w3jRXVcCfrP97et8+UVVNzq9gnomPqR7i+e9p33V4lBePQTEABA1GhTEQ5ufn10vSAR1V4+ZlmM0RXkdRLODsMUW4zYg2KC5TMSBxNJDN5r6EOiMFnguLbAfRZSM8B2DwmGzYPV76N41xDJib98ehqDMylgrKxhMhcIabtYSO0MrgG2bK4IIVF8ECDA4Bifv7+3aogaNJ3tCfGSIHR1gfaEk20WWAwzC0fYB5aThzNE/yQ5CjagoUnf1PsENf9I2CMNBPQeUZCzwH+BDcsWJ18Aj+2Gw2zYGEr3mPnX6f6G1esIL0GnpOjmSyjhg3B2hYL+6mtEOVAMQHpdhYf9TG1TCsAfsN4QdOWGfdkbX1ej25CqaqmtNqXn95eWkg4ebmpt2b7IqXYRjv/WYfPY3S16p6I2OLxaKen5/r6uqqfQZvoJuHYWj3A9qI+XCdBEiWB/bFYlzhUWwA/FY18hUHYhBQIrp7d3fXgE/V9A7Jw8PDFnQxcLO+RP6enp5aaZgNNyCDwNv9/X3N5/OWWT8/P59c1WCD7SAvQK9qdGTJWJ6fn0+yK5wf4C0Cs9msG8S0jbR9A4CRWTctM1tyeHjYMvpkQm5vb2u5XNZyuWxg3LoEveSssPVRAmZoA82dBfBdpJ6L9RYA2Jlz6ydXOPmdLol+fn5u9wr71gHbenhsGIa2TuYJZOHLly9tzbE1zsDZbmATN5tNO5gGmV8ul63SyAfWfORmx5YAGToKuv3hD3+ok5OTSbVa1RT0OjCfwRfeYxBt588VYD3HtmoavMqMnPvi/cgIDqoxinnZe7gJ8IMPeE/VKC9VI/6yo4mtZizZNpvNJKHk2yLAj5m5s+OBjvCZAd4mZYc29Tl/o/qGseaBqzh8yIgdmKurq5Ysmc1eqy3Oz8+bXsWxhXbeKujgg4N+8I2dUjLFnLZunANtyEyS7EgacJI8Z7WQ1HGyL53M29vbyR3LrDmysV6vJ7aQsSfGR2fxPe/j95qaD53pZS7gDPSdMYt5zxj2/v6+bm9vJwEa23T7NnbAGRPjNX0Yk8cNfz09PU3s/be0f5Nj64i368LtOGJ4huG1Lp3SKkfAEEBAlw/joLTUQB1HZj4frxixk+eIu505lA8KDCAJ2IKZnOU1E2GcmL+Voa86sRNpZnLk3I6tI87Q1wxLxM3lC0TVs8TP0ZOqaUkMinR3d7dFme2kE9UzzVDCKGI7/gYXrBPvY24oNketMAB2wFgrFDGgB4Fy6Stjxhnb2hqv5ACE2pGsGkvn4TkMB6excTiKQTpgxwGJqtEpd1Q39/7i/GbwBx7ltNOLi4tWxu1sVkauAPhV1YI6Bkwua02A13MoPZ+qagEI3u+sKzwPAIA+7Flx9sKRUxwtB42+91S7/2jNhsIA11H8nH8GtDD2GFMrfDtLvM9gxEY8ecjvcoCt937+nXPLAB3PIbt21HvBGpr1UoLSHLMDJwkQstGXgZv1d2aufwv40JfBOH2bvg4E5hpnJinXJDPYjLEH5r0mPRlPmfZ7PT9/zjqg3xnz9vZ2C745W+G+emPxPA0yaXYirFv5DuNz0BhckQeM0R/86sAp48tMHOPytVmmmZ0L0ynnaZ5HH3qtkx6en2U5wd57fP1RWp59UTW9sjETC5kYyIBz6iP4zYkGns+gifu2U8aYsLGJJazjrEuqasKL5geC+IyRYDG4wXrG46Mvxm5MagfRTiL92Mk2JmYsZOl6+xaTh/lx5jV1qTPSiYM9H8aBo8iardfrhvn29/fbNhLGy7q+p9PTbti29GwisjsMQ8PTzMWYmXEyf97pOWw2mzfXgDngZb3Guy3rqbttl3p23cENJ1GsRyxH8C19M17bSvOe+Sn1mhM/DkxYpnifx8waO5hEnySvenxoh/579eN3lyI7JWwnAAH1gCAsTJEKyUoNwfGPI0tmdFoa2jTyZgTe5zEQXWNuVkI9QuKYmSYwM46976k0I1jQfT+qgaCFCqaBASlDhZHYD9ozGD2Qw3wRYt632Wwm0fPVajUpW8l+rFTND7zTe019r5rLR6qmDr8z8FbuzBcHabPZTKJd8I2VR47ZimG1WtVisagff/yxbm5u6vr6ura3t9u1GhmFT+VuRZFKh+ez2UGpGvc2whNeB/r3Ka2OeNpoOhNjIO/fpnnOwcAaEIuzCq8kXVNOiO5Ztr3GCe6SLz9SI0PI2voKBAJRvjLBugw9dHR0NKnAOD8/b3vB2QpA5ovvHRwcTAwZh4V4rapGQ0xm0E4NOu/09LQFiSwD6Dayi84gE7x08MMAlPlUjaXMvbkkELVus73wvc12WGazMbtpp4d+2d6w2WxahoCgC7zs+2TZy7nZjKdEEzV2VJ05ZAQcOvEd1gNan5ycTEq8COo6c+Cx0IcdP2iTzQ6decPOP/tVqV6ZzWZtfX/88cf2t4uLi/rrX/9aVdVAvfcVwwu204A8Hx7l+Ru4pVOSuuzx8bGur68n9pu1Ozw8nOh20xLdZJ3OdijzqQ9WqRoP1vGpyNAefYrDT9bCZXzWc6wRp/pDr+3t7To6OmrrS0btI+tHKrnQP8hy1dSeOkBeNVakQT/0hnGStxdR7WI9zFkqzrpVTQNv8CROimUM/IittHOYFYz0a0BPVpBM4fPzczs4yPth+T52wWcFOOiOozUMQ6sEMp6+urqq29vbSdUFzi6VKgcHB22vOvLrasDEUvv7+7W/vz/JtNO/txD6KiWcQ+bufZXo6s1m08702Nvbqz//+c91eHhYd3d3bS1dCgx9k+bwQjpj1kt8d71ev7l+rRfwdEmxD7BDf1AdwzVLzmzyTtbNmNVJFhp6wEFc3svnJNKcyQc7Z5DD/OQABthuGIZmc4ZhWklAwstJmc1m086UYM2hh/kS3UyDr8wv7vvw8LAWi0W7acE+jhMm31vx912HR2HkcTbSSSRqg3MD0XHCMnrCwmJEycahvKwcmGwCp16kwqDARKWEkgaYB3wwdozYarWaMPvLy0srj4OZmaPBH6fs2qGG6WBWBD4BpqO5zMX3OCKA3DF1cXFRm82mCQDNzrEBI4KKcAJoNptNO9TLwDAdNztfALdhGFqWGePuPdg2DKwXwmGnlew3ysNlajs7O+2wInjF2XhnMq2QDZJXq1V9+vSp/vt//+/1L//yL3Vzc9MMH3Pxnhw7do64OTpL/7zbDb4wgAMwIUPJJ5SnYYwcEMn9EwawVuJ2ErzXiOeRBxubxWJRR0dHzTGoGrM6dobpi3V6eXlpYMDOMkCDvel27j9iMxjP6OV7kWs7G249gGv+QF84kJUGPcuOUvf6B140YOj9eGwOIPben9/xnMyj38ITOOoeX46Fd9sZocF//r6BTAZ9+I7/ne90y+xsjw75jIGo1yHH3ft/RsRzjgmy8znbU99gUFUT0JWBOzcAHL/9/t48DbzsuFuXeb58jg6yw5/yk7T0963Lcg+39dJ7oJl3OQhr+vM381Dyu+nuLQVeJ8/rIzbroKqpzrAcYqfc3tMRlmmvdX6vtx49PvVn+Z1cn/xbfmb9wvydsOF7Pb3TswfQxzbkvTn9Vkvc/C3P96pLevz9nt5PeeF5r5t1W27BMZ7hu/793rj9rrQ5SfuUXY/9t1rqfNukpMdv9dGjmX+SR5K2yb+pS5NmOT/m37M5TqL09HT2bX3YG1tvnr1+6Ovf0r77VGRe1GMcT7zHPEzYkV0Mo/dzVY1ZLLILGLdUejaqVWMmyg4Ki+JsIn25hNKHIzFeC3UKIv16LpvNZhIZtMI1U3jcOZ6e0rHDa+cin3Pkin5cUsP8e+vjuRqMM6+87LkHfghIwBfJuKkgTUfX96cyhMbes0vE3fzyniDwntvb2/r69Ws9PDy0AApOOlkfK0BAda8kxMoWWuPo0nrg0N+HZnzusdKv55s8lEDKa59KrzcP+rKjap4zwEtliBFyEMWBlM1mM9lb93sK/j9yg27IDjqNwE3yS9Xb8lCCZjbmBB6GYajFYlFnZ2eTYAHBD+hMlpPs7/X19eReYrIayNNms2myzBrxfgcRh2GY/L+q2gEwlK8yZoJ1BguUt/JedB1jT7pYljPDB99676b5bhjG8wNYl5OTk4kDlYCK5qg3AU+eXywWNZvNJif+UrbvO1YJgLrkDhnh/Xd3dy2AZ5uY++Ys2+gixuFrZkw/xkbwkHHRvx23q6ururu7a9sa9vf3a3d3t25vb9tPjs3A03zMlqLb29t29ywnRS8Wi1qtVu2QO7ZXkHX1fdvw+uHhYT08PLRgup315+fn+vr1aw3DuF/X9x37TIzV6vW0cLJW6DpKH1n3qnF/OQe8eP82+8nQyS5xTVtqfuVZV2ThtHsf40duxiqW56pR/5Ghs/yav52Nso2jOTtUNVY4uBLMeMRYlvU3H3oLm/Wi35eHIjKvnZ2dOj09rWEYmg42L3BQJbLac/7gCU4PXq1W7VBO8I55mYyhqxH83vl83rK02I7E3uAraMC+dleXGO/PZrNWkeAqRWiSOoj1n81mdXZ2Vqenp5PED1nJX3/9tf7617+2Ch+3Hj52AJ5n4Bcyi2RZoQe/qU5Lm3V2dtZOruf8CdbJ32OPLVvFPN7kXeQd/mOdHPhg62EG5kiE+ZwEtnAyZ97tClJ0D7aIeVSNutzVSumsk5V1dRW2m2oVsGOWMcMD7hu/jrG4SgN+rBrP5bBe/db23XtsU9ghAkbeEzAwrhoVGgt5dHTUMjv39/dtszYKA2Pi+noTISMCjI+yCINpyuDMgDAKQg1gyFPNAKfseaXBlJvNpo6OjibPsPB8F8b0wqFEvE85s3QIJozvzfl8BlABCNMfgoOgonTSaMDwlGGRbWVvNIy6v79fnz59as7hZjNm46EzmVuULzX0LnP26cOs4cvLy6Q8B+Vn4MDcUKp5mANjx/glj1ZV/fTTT3V9fV0HBwftDjWuymG/KbxBBtsAGqF2/zjH0Mg8jwA7o+vxA8LslFjB0ucwDHV+fj4BQPRDHzbQyJuNODRERj0H5u6+XaIEfzjAwzuh8dPTU3N07NRRetXLpn2U5kAE/4c3MiPlIIS/b8Xe020OCmWkNYMeBnB+H/3bofS7PfYcYwbrDG7y/UkX+uo5lN+SacmACjrbmS/TyeMGWKBj0xHM97nPpKdpmvT2nBJoJS3o2314HEk3rws8kN/xM/z0QKCfQ7ZtJ5gL9tFr7fXovYfvIv+mFzznvlJn0bcrQHpzMLhCp4EL0MXIi6u+rLszWMI65tyS/wzQ0znr0do0y3Xi3x9ZN1a91QO5pvAMZ2dgW9K+e2383feCCz3a9nQj/biiyVVr1rN2ShxsSV21v79fs9lsctWd5WAYxvtQwYGmlecJjjC/Mh6+74BJ1XhQl+2GS4S9V9N6xTQB/zoIYHoZlzsgm3O1jmY8vs2CcfLv+/v7urq6ehNM6MmXaeV1cBIIuwe+5u+2IZ4Tf9vd3W04uGoMfDmY71LrXqDRY8l1NZ70c94i6ecIOEBXgtXMwYFSr0XOzfSi/54+hz/STtFPVgSmbDuI4OCkdafHmmOGJ9Im/l777oytjVFOyIPMBeRvbjlQGw9P3AaTPhJkvQf2GKsZi0WCyQ12bIT9nt/KkHp+ubB2UnHw8l0Jeh3dz3mmITW9oJFLXdzoz4ad5xJE90A1xsfgIPnDwpIMzhgcmLBgZ/Q7wYX5hvfwHWciDEoTQFiRQ2MuAmdvABFGGxqed1/837xnRY8w+3te3/dATkYoU54yC4xjm4bXSiFpmw0+tYPC7+Q7823O2UaOsXoMH7VRPs68bZANrGlbW1ttTyvBMbYb+FRiG5Wbm5tmSMmyYkx9cu4wjBfJuyKBu0tfXl7aSa1Vo1zkfaCcHEnUHHCA42A+QIb4LgEvGt81EOzpbeu1/NzZBINYDLz1PryIDANMXNGTxtxAx047wdzr6+vWJ0FY5MJ74BgThn+1WrX14Ts+tMSNbRYGrHm+gM9VYLwEIjnBmTVi/ug0O4HwBz/eEoTOYqvGw8PD5G551tC6Dd43sGYbzc3NzQR4oxNyiwS6jX2J0Ik9z1QLwSM8S7MNZC12d3fr8PBwsjbsKwZ0+4BCftCr0NDX08zn0313VfVmzyJjYA5eB9aJbUkf/To0MJCTCdY1VWNlhG0b/3YJu/WoMQSy2dMnDtC6T8t66gPrGOONDJh43JYJDimFH0hsWD/Bh3wPmcFxQFcR9IeWtvX8eH6poxP7essgdDaNHBA3PenbNE06VfX1IQkTbrIgYG8sjhx6v246Q3assGF5wJzxiU+Q5nnoYWcL3iHB8/z8XOfn5/X8/NzOskB/k3lFTzgQZ/5mnPaZqmqie6Clnezb29v2nZQbaEDVj6sMWT9onZiYd2WwMnnFAR50Vp6PwWfwhgMgvYAJdGe9aXkfu/sAMyQm/r32zY4tBIYApMHZUMzgvdEbIrNYFiSXo/F/fhBKhHyz2TTgniVLyVSAPO//I3KGkjg/P29ZTytRUt8wHWV7dgzSWYPhKSmhjIByNaI+zMFC5pI3+qZ0wCVdvsg6FT9KlmzzcrmcRMudbWQNmFNGnNOZ9aFCHAbyyy+/tAy7nV6UnR33HGvVa8mCwRdrOgxDKwtzGbkVB6AeQYXWRHr9d2enLagARfh5uVzWzc1NHR0d1T/8wz+0bDJ85/IaDCHvSoeRSKOVsUESc84DZ1wqZYXp61+smPb29lpJ+Gq1qsPDwzo8PKzb29u6uLiYKCMMBpnsjChCh/39/To4OKj7+/u6vr6eRHv9Xb4zm83eHNKCHEI/sjPw/kdumeVzEKXq7R7HPEiBH4MJ80XV9PAlB9nMGw44Vo0naVtGWH9H0qtq8jeXpfl7CUYxVD0DSbPxzj7SOXLL4CK6zoYacOdKIgdW6AN6JOg1GOsBQT5Hp6xW435k+D1LA6EDNDS4QHZ81ZedW4MZ9+NAZGZOPV6ccX9mu0HfvTXIn6oxaGZA5bG6eU34v6Pws9ms2XHLg3+YMxkl1hI7lsGP5NteAA+d74oogkceZzrtDnSg33neQQve4woI9KV1bpY9OuMBdvmojbXKq2OqpqXJiXHsqKXdhM4OHttZqBoTEgb9fp8dB/Ml/fuHOThZ4QPuLHcES6pG/iQgSR/YVvAhQB6HzkE4yl1xuqzPUsdltYNp7YASzgU6ykFt+sH2W1+ga5GJTHL48+xre3u7Tk5O2iGrjAk6grs4TMi41PKFPklHM+dKQAFfAFo7AA3fgHePj49rNpvV+fl5O4eF0mDex7YH8yx8lfrPz5iHcA7RWazXarVqWX70lB1wO7bmPXQm49zd3W12H1pAF35jLxyIBXvgR/h6x6rx+irwCPguAw8+a8n2ELnx+9GNlkn4At/qe9q/+XLJNESpiHiGz9wyDZ2gz9/3e7KPBCImahrAZCyDEH83x8x4cq69eZo2vb/3QN97dPVYTJukK33m98y8SdMEOdmspLy2jmx6rDnn3wNLpqmV1Ht0Ya165X25rklH6JT8AWBCaMwLCBqlt3bQcm2TFrl2yY89A+hns6/fmkv2BYBDUfr9BlkGFpa/ntwmbZO33uPX97I6H7X5nsHNZjx5F/46OjqqxWJRDw8Pbd8iGcCqVzqlESMggtHC+MKbBHL4ftXba4B8giL31QKqbVyqxgoFOwgGO/zdh/t5f5i/Bx34G0aLQBHgy433JGAAVNixX6/Xbf8mzz4/P7cgod9roIWBPD09rYODg7q9vW3f4f3ONjig6SvomIf1BTLgLGuWvQL2HPWuelumbOcc+QG4AUT5O6czE2y2HgX88A5H7atGQA+fAar39vbq7//+7ydBTPgImmf2KXULWZrcR2isAB8gP4yDcXmbh08qNk8ZLFqnGrCz1cl71wCQ1oHswfMWETIKq9XrHmFOViZbAdB3gNrjsVwxHubBvNOp+mgNmiCPgGh4MrFe0sKBI/NA1XTbzHvBIv+gRx0E4bnEhw4wMwbW1fYvdYBbjgu+d2AmA3Iej/vs2V7G632Vlk+/z1lmxoNNMW2t5yxT6Vx5Tp4vsknf6D5jEwckHGDA6Wbs3iub+M//tw3yZ3yfMZg/eC96aBiGptetK0ne+UyRXvlvj+70lYHuxPPmm0wk2ob0fKdMOmDrXDnmgGk2nnGlS6454zGvs3521GnId47ZeB/+wO6mT8N43/NV3mvf7NimQeQIfgaWXnUqdBsawPft7e3kEB9q4M00Ji4EnM1m7XAUjB1/c7YPZmUcLBZZOf5vRrUQpBNq8MkCcb2Gr8IgKkypEofC4CBBD59KPJvN6vr6uhlRGJTDNQAoVqJecLJ6MJSNN++BHqenpxOaZnCB0i5nBpg/7wRYu2xmPn/d0A7IZM3n83l7zozrSPt6PZZ0MXdoTWaQseaaDsPQNrHzmUtPnFlxJt/7UgHdjuz+4Q9/mAjxP/3TP7W7mFkTH5IC+LeiTgeUscML7Ml29N5XOcFHVdNSPystwNje3l791//6X+vu7q5+/fXXNp71el1XV1dvDJoVLvyCPM1mr4fUmKasF0CXNbJBtOzv7u7WyclJA5AfGbg5WFc1Zm+gzfb26xUfwzA05ww6ugQKwwoPGQjwfzutVDtgqFljdC3Oq/mdcZqfLAdVb/dsVv32ybBpkLI0kL9bvrLUn38bIMBjzjozJzIAtM1mLGnzPkv6dxR5e3u77a13GZv1YYJe3sXp6fwtg0nMjfdZ5yfIMDDxvw3irKdN7wTMOLgZuPPaJphCbqkuwoZzDQM22nrMWZIMoDIP6zre07MljtYDiNHVzMvVEHacE6ilToNPHUyhcgR6knHwXrGqsSScg6ugJRk2gijw2mazaUGBLANNPoRe6Am+k6D9IzU7leAHbnYA9yDbYAbzC3bJTkNVTdbXuiv3WZsnDw4OJhVPYCr6G4ahZQz39/dbBs/XV1VNbSJ8AA72GNF5eZsD8ui5W356ji386QwlFR1gUWjgagd0g3UXtKE0mGozxkxAyI4bWc2q6daoxOvoO7BO4ivO1gHj7O3t1Xq9boe8se0GbFVVba8rwa6q6f3IxlY0xgedHx4eWvbYdsL2jquA6Bu/JJ119CX6ER40j3obSer4zDDbzqJj37OzVePVmrY72Hi2gLhPMKoz6fSFnXBVitfY/ftMF8aT2+HgrarRfiUuALNS+ct2Ffwh3s97v6f9mzK2Jo4Bi4Uqmx1F74PoGXSEwiDMAmNjwfc8tgRiNDuyPJtjs6Pbc2zdX1VNjDuOtYFiKr/eWFOw7MBlVMgg12PLaKL7SzCA85VAKdfWn/cUrtfLzlFGXHp8kALDO9IRRLHn/L2m7sP/zr5S6UAPGw+/AwVKxNbla7yb7FcvopY87HV/7/82xM7GpZJLR8CKtHcCMXz6Hp16wDGVi9epqt4YNPdpw+l3fNTGHqgejQn0cNKq5cNyiFHBCAI4rB8z+GIHahjGPUCMweU9qTMsA/RfNe7DBPQ7So/x48e6zmCA9wLksoQOubMM8V5APkASUJa6kmyas2sOeNmRNa0AbAAAGjoXR6XnKLFOACxnXqz/UlfxjJ1gg6T7+/vmGAHGyC5mQJS58w62A3nPIM1lkIAG61XfQ7i9vV2fP39u9/f6YBt0Jw6c5wggZ11ZY9YnnV0CrlXVnEMAjg94YU8tvA4PwssAXuSFuVKB46wcGSnvXSM4Yt6mQTPo7mDr1tbWJEPI3BN82b4Dgh10N9j/6HtsaWnHbNuq3p7LQksMYP2ZDlX2k5iI9xrr2J76+x5L2jhk8z3MmPjKweucVw8/p31+b1698ffmkP/vYZPed5P2pnP2kzQyTYzBvGXC78jAQOK3HrZNOvX4p9eHMSbNTjzz9Pp47A5m5jt7/oNpm3b3Pd7tzef39ERvLP53j7d762k5y2d6fPBb7813mvb5k/39Fp++177ZsXVdPcaNjJ0zggZdPRDuKJp/PKH3FMd6vW6RLTLGjId+nIHCEfEGZxaDvv0+9kAcHh5OSgvzGTsBNqZV1YxqOrFEz1w+iCEEZFEXb0BFlJhLxgFizJ3/O+JshjMw8gEqGO7NZtwLnaWQAEr2TWf5YDKyAaOziaafQTqRO46yv7m5qarXLKv30+zu7rYIq2nqw2jMO8yJZ2w0UrjInLFe5tW7u7sGoHZ3d+svf/lLnZ6e1pcvX+ry8nISCXXmYbVatbI077FjjhbSnZ2dFq1krc/Ozmq1WrWThK3oHfWFj/ns8fGxfv755+YUmU+zLASZY78uB7XAh6zl1tZW27/NuhFNTSDPgUSuWkBPOFjyERt7YogAV9VE18AT6KaqtyX58DJgN50Q1pLI887OTss6uvoDPVg1Vm+4EsUNmXZwBF7hagkbHOuljPymw40OxrGdz+fvBl3gYRqBAHRrz8ghG0TpXdaEbkb/ULmBI+6yVjfPH7rn/lxnQpDDBEwZiOO70AV9hEPng2VwonP9ob11Gjbg8fGx6Sma6QCdfGUNvMRabm29HmiGXfSpyPAp37HcI9/JY7zXdIDX0JNkOw4ODmp7e7seHh7q8fGxnVjPIVIGouhZ7CfvwD4wbujoseKUuzoGWnmd8rAW1tnBDNuV2WxaMm9bko6tQS2O+0e/Dq1qihc4awN+IahRNQ2qOhnhMwkMigk2eD2QWbKzVeOVJvALcuP3ZSnwy8tLXV5etrEQACLgjZ111Qfys1wu23w5hI1gDVcbwSvvOcmmm3XR7u5uyxDDOw7Ag9cJTt3e3ragEzRCBlgH+N1BcAJxyBN6o+fEo/8t7y65B0eQTeRKr5eXl7q6uqrVajU5gRgcCIbioDV+eg5tr3LUegMZdZDXwQV8Cetw+yboovwu65e6H1o5CPf09FQ7Ozv1+fPnWq1er1xzvz2/I30CPkunHH7mej36oE9oZFnEPttu28H3mkInbzWqqnbYl6u50HHe/sSPM9Jg/B9++KHNIbH49+rG7zoVOSMiGDUr/4yq8Vz21Yui8Tf/7mXTUCQJJvweO3QwQm8MbnZU3S/NkRwLggFERnwYi8EgYIFxWPjNGKnUrXz8zqq3gNrK2Vm1jD7ZGJgONtiss5n1PUbjuSxzBhB4baFzloM4SMH/aRYyK3r6TR71noZetMrBEgdkUgGQQdje3q7lctl4w+vV40Pzjp0XBByAhjFhXxqgr6omji3f87v4zKU2PiU2nzVQ4/olK0WvuYNQKCH4OY0bjr6Bu2XiIwO3vEaJBh2QVYN9AJfl3f9n3VnfLK81iGY7B2Vz19fXzTjCBw6GWB/QJ5k6OwVeY8sbY8HB297enoANvpuylSdxAkyrxgAfdoTPrZerRucQPePTM20/DBZdLg+tMbYO9lRVKylz9tNBOmTGhhr6WGYIYlrustQrDwMDsPI3xmvQwbsArwar5r35fN7uocReOshk2+KsuLPYBL/IKDp4wRrjLBMssK5n/PRlu2jeMl/ASwTNsR+7u7sTpwDQ7cOtoKOdWmxvAnHPoQfGALn84AiwNx7aImcGfehyHFoy4xnQNy0+sn40HqwaT8yGvxKL+XvGY1Vvb5/oOYK2N+kAICPoJvREVU0cTeSf4IuDM/xmm5B5DNm3nqcSBJ3G88YYvYqapANybhvPD983JoFeDkQmPRMfWzfaCbQdT4ePMfbwlp3kYRhasPvg4GAiHziljMfBSuNE2yHLjPWbP3PVkjPmyS9pf233eNY2xvNOPnN/HgPBFeO86+vrN/0lrnUAmr/1zlMxLjMPJPY3D5hnoPF7fkKPVrZB6OqkQ/pVWSmJfLBdMPFi+ii/1767FNnAxQab3yygnd7VatWMrCMC/gFMGWhtNpvJNRIQzYSE6dlDRtbo8fGx7TtFedA3J735c4DCzs5OPTw81N3dXRPqJDJ9VFXLwtixRLheXl5qsVjU8fFx3d/f18XFxSvRt6YnkKUTgIMLyFmvXw+awbgD5sxErku38+dsOHNw6ZbXrGrq6LEGwzC0+nyvF2DYwMXXWUBvKzjmBH0Mnnmf98EOw3SPrQUFYSBKzxg8VxrGyGvjjO/t7W39n//zf2pvb6/+7u/+rkUWN5vNZJ8VgBsngPc6g19VjcegjU+IM7BOR4iTp6EDMoYDzLictfHeHz5zNj75lv00z8/PdXNz077PPDBqRFgta6YlcoPRhCehLXSysvqozXtpMhPoQAGyhaG2kXSZIry12Yz3OqPjzMt2NNnTPJvN6ubmpumVBCMAL8YJTy8Wi1qtVnVxcdEcFNpmM5Z4khFA1ni3S+fhc2SH99vp8XjI0FqHeC+XnVDvXwYckolz8AxDbXlwEAGeNl8zT3QL77eOOTs7a06WgY7LZtEFBIs4PT8zmKw3+pSsDvc42pknaME4ee/Dw0Nzrsx7yB/jwMHtBUSpilmv15M99Ht7e+2Uf9t0dDrgfnt7u87Pz+vx8XFSYWOH0sGb1Plkjh0kMe+QwfZd4wRzONcBPvE5B7PZrN1QYAcmg8uuuuEz+JXTVHd2duru7q4uLy/fnB9gLAOvISc3Nzd1f3/fKrKchUqQ+VFb2m6COGnnCJIYO/K3DMpa7vLUVP5uPuJzO3fwhK8+c0YYvVs1ve6kanTOq0Znm2b9AR++vLy0aifLL/9+rxrOjoHxDbLlii/TrKomuM1BFObXe58TKbyfz41X+Nz7Wv2MaUm1DlUjrLW3C0BPV69UjZl2cAh6146kHabEGfzfQX5syG85bTjC5pdcI5xyBwPQZfBA8hz2Dn8AGtInete+i9fMfGWey6onO7mZXHB//mH+6G7Thma9bVplX7k2OR/WgUqCvCnA3/t3d2yTKDZa/ullanwnXDKSF5934GR5onYeTWyY6PDwsI6Pj+v6+ro5xXaavQAGXAZZj4+PdXt720pNqt6WsPZOMDVdYAwcBPq3ks5yCC+gHUIycYAMnuc55o7AmLYZlbEChO6OhKFgDEp81H5G/Xxku8E69EZxsY7b29utrBqAb8fYSpzyQStx3mVQ+h7jW+j9HjvnzINrkI6OjurPf/7zpDzQe63gtd3d3RbJpU94zaUdpo3X1bR0hNilRaYxfWIg/N3MNvOZjZD3PHIoisu1rUTg5zwN0nPIIIdllblh8JK3P2Jjbji48Iyj59Dbjl3qhQQYPIcugd+9n9LBBa7ycuTcAQ7zHY3ncYadAbT+SP3FM5S6DcPQDhzz3Himarw+xvsN7bwxxh7/wV/OgNiJ7DkH1sueD316fy7z5m+ZuUSm0QnQPLNv8INpmCAyM4V87kAZjqazkAaplm0CC84AZTY7xzMMr9f2EdQg6OiAxnq9bsEvf854rR8t37ZPnoPpYJ7yGjkA4aw5ZclZndSrbLENMs96PDzr9XPgAYzgfe2r1aoBc2ibOMfvqBpPkoa2Bnw48SmTH62ZJ6E5wQ8CDwcHB5PT3m0zLL/p2HKAjx0WfnLLBGMxzrJji57i+/wNPjOuJahsWUxd49+Mk5b4JJ0my7qzwfAXzpMPQ4WXsL9gLZx7652eY4ttoRokq4aM1ZOG0M94m3mAW4bh9X50gjyeD45tbrezToTW1jXWZz07asfWtMYJ9Vitq3w4YTq2Xh+SQKwH2ynYGpJ6DTx8cXHR7ArZSmiVuMnYyzbDdHIwxeW/Ts4ljuPv9kXgLdu/pDV62TzAb+MF/82YEZ7iHBH0QFVNgozo4H9Xxxam8iI78t+LhpjRKJMEkLAIEJQIp9/F311OYCVJYzGurq7aXa429F4QCOfIk5koDZEX0FExKy4rz81m07LHs9nr6c9E7i0cALjfK7UgIgndmIsdC/Y9eX3srJjR0hDn/51VRRGbSS0kvI/xwrAGhwidndCnp6e274M9neaFzWbT9i2i1Hnn9vZ222/BXla+yw/ZTaKCzjbhcKG8EVLm+PXr1+5eKgMQ7+vrOW12dG0saJlhtQMEoCWCRcmWZY6+DRjN3zhYNtQoXwIO+/v7b6KfnEJo48UasjYGbHauEjg+PT21fcIZ9ftojXU4PDys+Xxe19fXk8ySDQ2y9d5eoAREBDv4DB7hO5w54ChwliGjC3vR12F4zSSSIUxnNg2V152g0O3tbR0cHNSf//zn2mw2Letr47TZbFpGcrlcThxb5gTfzOev1xMNw9CqYrznE2C8t7fXouaer51leNMAAdk5PDxsmUAcFfSNA3sAyNls1rKGyA4gDXqmnWS8bnnyKw7V3d1dLZfL2tvbq8ViUev1WAqJ3AE2kcs//vGPdXR0VNfX121fMoDLp2anc7C1tdUOizo5OZkAZGT47u6uLi4u6vHxcXI6tfU/1U3wt203+sqOhzECzcAUHW9H+uHhoa6urmoYxgPSWNPlcjlZW3S/Mw4OaLPG0NKA0BUXyBZjH4bXzPvx8XE9PT3V1dXVRI7gP54FgxwcHExOmLfjTLaNuX/UljYa25QBDwcHqqZBQOwWGVQH4Az4ze/wMfrC4J3xVI02rRd8yb2vyJZ1tSuxHHT233KOYA+aMSX/59l05t1fOpLp6KHDLE9+j9/hOVqX2hG2rJmefjf4g2cODw/r6OhosqXOfO9ToXNcDlaZh4x7MhCQ6+hmHZ8BLmPh/Ek+YTzOOsOj2DMn8pL2XiP/nfEZN0MP2xKP3X3le0wT85tlxmvh8WTf9GPetr0zb/OMsSPvRUc7sOlzYLJ9r378ZsfWC+sITmaLTPg0qAB0FsslZZvNZnIEfB7f7qtZ7FhmpIUTIh1VMEEdCchoMc/isFiY+XuWNfPdZDKi6FU12RBvYcv34YTd3d1NoiV2qFKYDS7trPOcmdFzMaOYAavG0385et2lUzbgVqbp8HvNX15ean9/f3LcONclJMDxdwG0rBdz2draauVxefiDFSvPbW1ttRIg79nwWjjDdXl5Wdvb23V8fNycXzsHdmzNY8lLKOyqavtJMvpkEI5MOKjgCCK868glsgfPIVvQ2g5O1TRzxjHrPqjAQQMcawxOKnXTsWf8KC/NeX7kZjnNPZGUsSVAyOCIZczgIAMV0D4j8VXjvn7Lk2Upy2bNu1lJkvLnlvqIQFgCBPp2WXoG2ng3Ro/vuBqF5xm/qxdopqWz5LZFdix7GQD/8LcEewYKvSy432v94rW1LNsZdJ9Zcmb9DS28J7QHKKxj/H+cL+vRBFsZ0WdcpqcBUTpq5qEESbk+5jUwgJ0cvuN1MX/11s/N/Oi5OZPKWtOXed8lc5TSpg1knrm9g/6sS3HuXIb3EZudE2jD1hXbDXSWkx/D8Hq13sPDQ+3u7tanT59qvV7X+fl54xFwhBMgVeNhPQQkqqolPry28H/VeAWTkxxV1Q7TW63G07V51+npae3t7TVMYrttW+CSUzvxm814bowDIzhKLk1ljL5e0tjUQXRwqq+ac0DVcg5GA5thwzeb6b3sPO/9mJltdNZta2urjo6O6k9/+lMNw1BXV1f1+Pg4KekGt6cMGOPSH/rSSTKwGQ2fgfFZN5EQcfMBrQ4UODhmethp5QAxniEp5swr/ThB4fMfbDscCFwsFrW1tTXZqsF96lRYgfvMH7zHGNHVDNAIubNfk7zpSiXbXv+Nqgv6Ze347fdyYCwVigSr9/f3JzT2WvZ0+W+1b3ZssxzAhgCHASbyfjqcDE/YyjwVPoSAwYio0p/3Srlc1IaCkgAMaEa7OPwnjZsBQ4ICK7F0Kg0GDTZo9Mc8/Jydcis+PkPIULY4CvRj5eAsb2YbTX8MiYUM2pmhfDiH5wzNkoa9zw3o7GStVqsm/PTN36Br3rvpuVAW5AMUTFeEg9/QOrOcNrQEBxg3+32gK3vEXd4EKLShM8jxuPx+sk3eH2SQl2uGoYO/6c8ZEq9RRtTsnMIfaSR4rzNXVsa5J9Rrkk4Mz2YU9CM3HDvmTVCk6nVt2bvIyYw8B0Bmrx585BPWMYLv7Y8HZDhTb+fDGdODg4O6ublpp37Dn+hwAnK+l5O5uEqGcRmYnp+fV1VNZNG2gDItDLsdbPpjPyo/h4eHVTUGyczLBEsty9Db/Ej5KOPhkDPWzHYImhiUGKQa2GGc0zG0Y46esP60/kAnoBNZw/v7+0mmyUEL+G29Hu+vZMzpXJoO9IU9v7m5afMnS8z96zzD9hcDcGhofWAQBf9yGrPPHDDQQ17YewoovLq6ahU98Pj+/n49Pj62eyahCWtp2tumODDBD89QnQTP5X2p0J87NwHge3t77T546OdruuA1KhAoVyTI4b32VEtYtj5a81r07MF74PW3ghN83xgsKyOyr5QHY7z37FMmRzz+HJ/tb8qgn7Nd93t6/fk7DuAnTfidONafZTAqv/979EinKAOeXkf0kUvwc53T4cx3vte355Hzt27wuxxIZQ0dzMo5pv1y/8ZYPRqm/vc7eabn2L7H6zm+5D2vt8fi73iuyQPv8b7nYizr6oLe2P235Glkle2NfGb7+p68fGv7ZsfWGUQrkqppdBOl7ehr7qsFdHCADREeKwyANRlDwB8ZAQwlTgGg3wcCVdXEMFa9Gl9KiHBUfNoo47STzdyYC8Q30GD8GGyMGd8HwLr/dMpx1qERfbi0lEuMHdHcbDbtpEg7rTguaeCd3eF3T1g5MIPnDfBMU8ZRVZPPoTfvBjQ4KmuhoUFbDs3p8QVOAmW7jjj53dDNYMX8aoDtQwrYr20e++GHH+ro6KjxGmt4fX1d19fXE0XvyBl8xDhXq1UdHx/XwcFBu/aA63ZwOlOZV43XwEAPb7xn7W0k4AEHSQg6ZbkxYwWI0ewc+TsAXAdD6It1cfvP4NhWvTWwnrODJjzzWwbdzq8BFbxnY0NLveVxIAfeN5Wyx3fMd66OSeBioGGDhx54D6ik0eoBKj7LwIn7smFkrIzTfWf/yIYzEDasvfExz5S/BDgeX28Ncp68w2Atx++1tXNGXziU+T73bRDK31ivHlCyPbSe8BYO08ZOhXk055vvMm8yHq8jv6EPgNB0Mr1t+0wz09pA23Tg7xn4M1h2pr/nHDAGeCXnSgDK/Jf0+2gN+rOPzicEG5eQFXSpL7Qjc/XLL7+070E3bM7h4WGt1+uWlZ3NZm17iMv0E2x7nF6j1WpVv/76a/s7WdLMehIEJMhhnOGkT9WIF3sywLut/6uqZes40DGD11VTOaeyywEkvuegi2WE4Ax4gvnyDtsrMOjW1tab09urxkQY1xzNZrNaLpdt7j57hfcZo0ETy1tiWmMzZJo5Wz9AHweW2MLmxApryPcIOpKEMDYzba13wMlel/V63c40YY67u7vtWsevX7+2DCjfQ8dl1aKzzdZjfr99rAyMgtvSmbavNp/PJ9nzxP9s/clTxX1TgLFAJv0Yk6uwvM5USDw8PLw5FPZb2zc7thCNDd92JtLos5g2vjYaBshZFuxDOapGpeCSLfqxYTdzZeSWkzGJqNpA4pAirCyET+71u1jIfC+/6ZNFT6fSSo2GEc3DihIUwhB2nOw4Mo7ed7OlQkthAFBsNmPW0zRlrXv0MW3sfDpSnuPsOT6pyJzVYH8oWVbvX805GWDzXgcyEgRaOQNgq6p73+3Ozk7t7+/X8fHxxKnslYTYEK/X4+nTLsWwM2p56o0vT6PNEtKekufvAAcDdJdAuhlsIouOquUR8V77zH79Z2nwqmmB3iNLBf19zU3VCNg4bTadHCoEDNL4tw8z6QXolstlLZfLpktolkd4g/GkcUU/Ms/kcQfevB/T73DZLfoZoECfHOhCmRPyyNisB3ES0D/oWINDZA5gAXDlfQ6SmWauXLEu4QT+m5ubSabS87RdYgz+nZkH/m1DTz/Isg/RQpcakCYYBHQAcOEL1me9Hssd0XXYXUo5ATRVr6dCe21pVLgsl8u6u7trp3jzXtad8k7rd+w+vJcH5NnZJFMKvxlgDsPQ7sCldNQgfrPZtBONGRdbJrB5s9l4qMnl5WXLHBOc5qTpy8vLptvsONu58Ljhtb29vTo6OqrHx8d21QdVCR+5ec3AkuZvY7cMasDLBF7BFRlYgd94l8/SsC7qObWM0ViHIHpVta1i1rk04zEHgqwP6P+36JNBJgejqqo5k6525Jm0E1WjLellBRMnghcykGOHKQMDxhz0ndjO5cKz2az5EMZ0qS9tMzwnj4k5QAOPI79bNU1y2ck3HvM8oAeVOTk/21iPz7iYOaY/gH3zc6Y5+gLH3zSzrfFa5/hss5OXrA+NydFV9tvSrnnufB+/p2cDPUavoe2b15UEoBMs78nre+2bHVvA7+npaTvan0iNicEAUEBmICZ/dHTU/p1ZKMqq+A6ZWgwwfSYQ5IeTyGAulxJhXG2k5vN53dzctMwoDsbu7m6LSmC8bEBhUDsBKOutra126bIVx8PDQ+3s7NTJyclkoVFWt7e3jW527pnj3t5enZyc1OPjYzucJQX7PYVQ9bb0hLkAmHhfHvVONpF9CKwlezGGYWh1/5QI09fx8fHkUBLzB4rZZYDp8AN6ef75+bkWi0X9+OOPtVwu68uXL208drg5UMQliczH4I/P0phUVSvVxCljb4gFH2MHv3CIDxUOPAv/sjeDyJtpe3x83NaDH2hlw+V3czWIx45yXa3Gw7/gdRQmpw8ydiJoNq7wCYqUNcwTf/f392t/f79ub29bOS2gAr3xvRG3/2gtZSv/ZsNCoMABnh5I6Bln/g0PpT76vbH5/z19kX+vmp5CngbOhtp9vefk5d9tVP1ug0HrDpzmPFnZY7EcWPf93rwd+fa6vQfi3C9/z+oPjyffbUDUG9d7POXIvoO+7pPnkL/sx/RxkNJnW+T4cXp7f3f/XtfUsR4DNDOdXR3Q4//3+Dx1PLzTA0QJktFxvfG/B6hNa8bqzJD77vWRjsf3Arf/aM0Bk81mvLYRzATvsw4EAfPuU6+Jbetms2nVU/CCHQH4l/+b7tjAqmoBFQdkySCx1slf4FN4lyCSs4l2hjabTcNYyJzlyzTDuUbXEVwHI5rPvK82z+fIwDJ2e7MZD/i0o5lOVtXbMmuqBzOAOpvNWjUklWnghtVq1bbTsS0Fxw2H3QGtdKyhq/cjs27r9XqyrYD1Yg2c7LDMbm9vtwBe8qr1o/0Q/u5AqGlUNeJa23frNejhoBjjc6aSBAY8yvddeYUskPhK55px9Gwlf6NvN7Ai8+E53keWNqty3Y/9AfPnfD5vPhYyijzbFtg3+db2zY4tBICYSaQUditrR6khFMyLYUaZGLD5vW4942Ymp1+csDTgBl0JOnNhbPhgogQ7EB6hs9CkMYeGHrcVkKNKqejo3yUNSe904AwKPeeq6WEcjLEHWtLwJrA1MMvDcbz/jDkRaHBkFeFgjO7jPWGDZzwuG44EOl5vPs+50n/Oi3UxzaywyHaQeU/B9FhyLlZE5qUETEkLZwR8ypzXvgesoJXBGWtgOvn9PONxuFwv7y028EgafMRmEFM10shH+bMuLr+xw5ROHnsJCRDwOdk1gnXuK0/+zGoLO4V2vtzsZPTWjX3lHg99GsBV1eRaKZ7FIJKRhX7Wi+v1umVKoCW65fPnz7VYLOr8/LwuLy/bOMkEWp/nVTSuAGL7hvWbg6nOKgOATJvb29sGuKuqFotFnZ2d1dXVVf31r39t82S9qqoFL62nLJP8zTLLfNhL6tIw2xl0CEB9b2+vDg8P2+nkdsANSIZhqOvr67q6uqrDw8N2zQUZfXQLZYd/+9vfWtAYnpvNZm3/6fb2drtP2TbQIAkdf3R0NAmKAHSxcfCJaZNXrDAHZOz6+rpljH19TAJhaLpardrp7QT4nMXnnlzWgsov9Bp9MRfe4cATmR/sEqeCvwcoP1rzDRgZpCDoSqDXZY3oM8C+ZcLrzjpw3RlZNmhv5zVBMnqD5jJodGY6InYMcDKqqgV5bfsNzq3jGMvz83O7EST1jt/98jJeiYIjbVziIEBmBz0O9A4JIFd2ucKLMeCQJabEfsDX6B+2cR0cHNTR0VHTQehiDqy8vr6u5XLZkj3whJ3HxBDQiwSDAxLPz8/NsbXup3n81h07Ozst0G8n0dWkzJ8+ccQtw36PMRTNSQc7+ukj2LH1VY8EIhg/ttX+ArbL1U3u37jOsmQ95PXHCeUZ2xCCPtbf8BLPwON2XEm4oEuz4sYHBRPY+Xd1bDeb1xNjTSgYzaWMOGR2Cg30eZ6DIh4eHurh4aGOj49rsVi0vbeOcuTEvAAmQlW1rN7p6Wm9vLy0/QUIH9+x8DAuomPMgQOqyIoBjPiOjZszdYAaxmmGItLlgzyGYWgn58KcMMLDw0Mrx7u9vZ28h8immciKkHe77BoF4Iu9WZtcc5S79xbzngS+s9l4Zy9Z8Pv7+7q9vW0n7u3v79enT59aKaXfjZC4315Wiv0RwzA05YwyRIAAI9DNhgC+9IEeDlL49FWMANnRvb29Wi6Xk2uGyHhC04eHh/rpp5/q/v6+zs/Pm5JhXXr8y8Ek5hUAE3uEUKrwIDzqjCt31P7666+T/pvA//8cJNOLNbADgtywr2ezGfd7sNZEEe/u7urm5qYZS5qj5Kn8P1pL45SBlAwQ+Xdmh8z7dnL4PDNHlr98R74P3ZXBGz+fQSz/PQ1N711JB5wAf26n570Akz/n3Taq7nO9Xk90MvRK3ZGBh5xf73fSwY6a1xgb42CbgzysXW9cSUP363mlLu4FN3s/GYS0DasagTLVQx4v+oZxv2cn+LvH6L95zZm39XEGxrJvnoHuabMsN1U1CUSYh5K+YJPMfDPXHq8xnwzw9uTSa4f89GToIzfLRNUUZMN75i3WAuwIzZxBhH6sRdLR6+iAoZ/1+/xer50xHN/H4TQf804nN6rGYFrVqNNcGWH58piwzz2Zoz/zm/UDnxGgSZ3TK4E1Lb3tIisVeY/1M5+DM/mMMRoL4LixLlU1wR7W9eguY+9MVkFDvtvTy/STfGZ87EPykkcdPM3yXgfvGMN7dsa22wdBOvmUPGoamyesw+Fz3mfbkHYeHW9sbf5MndabSw+r0I8xYPKW55CBfetz+rZd+Z72zY6ts2tWJGmwmUBVX5nx2wSEaWF6R1T8nBffCsvg2X+3o9rba2gmMPOlcasaBcfjh7Gt+HpGn+bIWQ80ZYbPi2sAAG2H4e2R5h6/52ThTEbtjcd0sXJwvxacXpYzf5jje3dqOrptRWDhsyL8LaCUdPBamKamv9+VQMTRP0egDA5Ny1TOmX1PgwU/mm+cwTaA9Zg9R+ad/Od3ZJae7/T4lvlnuaD5xLKTY+qN86M2dBDGwmW7jp6yH7uqvz/IffA5QRMHH1y5wXoS2LBOwhDzPJkTIvy8s6rv2FnnsGfSZYHMm0BV7pujn729vYmsUsmRZWXm+zSIBKm+fPlS19fXbzLOWUWBDTCN8zsOXqW94t0EERkn2RDLEAFIriA5Pj5uQV/zA/oJfeCTgx0FR+dsbW21/ZdpXzIzaDpsNpvJ3a8GQPyNuazX6zb/3d3dWiwW7cCU/f39Ojo6qoeHh3a/9z/8wz+0QJzBCIHqzWYMGhKQo/TMd3QzJwNFMs0Eitfr9WR7g3kzA3yufPDcCGg6687a57kb/M0HN7KVxtkV+nJQlTJYrw+Byf39/XZHMWNibT3mj9q8Jz/B983NTQ3DuCUL/qkaK2G4Z9qZWNaOfbTWF76qDiyJjkRHeU3hRzvZ1iU5F/Qx1QMElp+entp8XK65u7vbkiNUi1RVk/ceTgFzpN1lDHbW+B66dhjGLLfxGfRBfhNnVL0Gyk9PT2u1WtXFxcWbLXXIm7OVrAOZTALzyDfJAfQkN5Ngg6AH80AfEmC3HXRCordn1mdY4GhtbW3V/f19qwLi+9AGu0wm2bqDNp/P24nn8K/xnzHabDabVEXxDBVc6AvoZCzYcw7hR5JFrIO35AzD0PjRflTaDO4Tvrm5mdzmAc8kT7kq1UlM42Wyz4vFop03w2fYecbloAzJEZJ1DlxAU1cUfGv7ZscWxkvQ7qPt8cSdkTXTkfGhD4Otvb29enx8rPPz80kfCDwRLt4LsQBMl5eXdXd39yaL5EUC/BONcjkdf4eYRAifnp5qZ2enZX8xXlZ6dkoQSJgqnS8WE6NqxkMgUaxEv9iXzFzokz0MwzC00iYfRmRFz1wMaK1M7Lh6/igLDJMZ3HRzRpv3YgCqxiDHcrlsp1LbgV+v1y1jiVECpKOYKI9jLtDB4AWngXtrvS7Q4eXlpQ4ODurw8LAdxOOyFhr7Zy8vL+v6+rrtj318fKy7u7s6ODhoa8mdvAi098DA3wZwPvHNNDdPca+XD+9B+SALVeNdlC61cumqHdTM7HtvrQMVKHEbXpfxIJN2tiyrgBCMQBrHj9Yy4MBnCVgoA4UX7Tw6SGA9yfrxmQ1dBqCSxg52ZCDJAaCcR29uBms2hubdDAD6exklhnfQ9Y6CG1B6rpQxuRrF77PzZ57OiHQ+k1k5g0DT2TRxwA29jU3xgSnW26ZH1fSKNebu/YfWvVnu7uYxe7zoAmwxz2ITsUEO0jEGgBPyD0A9PDxstsGOgCufcgwAG9+JWDUtC7We4vvuH1sJ7TJDb/oiCw7Eu7STn8zg8Nulihk49lqx7ozV4NaVazs7O6100HLuLNR/hub1onk9aJY1nkkdm+thfZTr6X74vKcb8yff7ff0Psvx9XjG/Rsf2v7md5M/kuf9d9uLpDHfTT1o25FztB3z/22b0O++wjFpbz3u7zuBlBnZ9+xqyowdsEw6eX6eg22pP086J+1ms7f3kvM3/zbte3yT9Oita8++9daJvzuA8Xu8nzLI79R1vb6y9WTH69obSw8Ppk+QNPjW9l0Z26q3YCiFlWYDRMuoaE+wUsEkYegjGcLPsMC5n8HAiedw3Pz/7Mv/9zg99/dAqJ8BQLwXnTVDZX9pcHuC8h4DmN7OJGXGKBV10qf3DvfNb4/X60b2M6NIv8f0HkcKI4LcG2cCUa+Fo069uZn2OGUPDw91f38/Wccsi+K7OLlE5/IQFwyAA0LMIe99fo/2lhGcBNO3x7OWgSyn7xk3BzxyHOlYMQ5HvPOZj9rQNawJQJ4sBGvsoBeBJg67SLDB/31/K3tq4CvvyyJo5eoOtgQA6v1+ZzAcYU8Q5e0NgHEOsSPoYp7DGdtsNu3kYcaIXeAdztL0sgq819FnsnrsceW93pvHPHE0HRSyDoUvyU72shzIgEuALQO8j4CO9YKdUssTNPCVZ8ybdUfPMAeCeQTc2Bdq4O1mG+z38x6esdN/fn5eV1dX9enTp/r8+XM9PDzUxcVFy7BXVcucMOeff/657u/v3/AXDWe4aiw5ZL8w64UDy8Ey0GBnZ6cODg4m24GYF5kpaOB9ktaNfOfg4KA+ffrUtmqY3+xoD8O0EsrX1Hlt4RPeQVDVTv/z83NdXl7Wzc1NowXYhCCNg+0ftSGnBEwSgGNjvG2HAAs87yzpMLw9z8ON96AnHTzEPrGF5+npqVUFpI1CRm1PnXHlM58RYH1a9RpkZ8ypQ/b29iYnObOP1pjBe1h9jgFzIFCKnTBNeJ552R7B69AR3fj09FTn5+c1DEPTiwTi3RdzYR8t9sf6jXfd39/Xly9fGk1ym551O/yBHLm6Bb3ifbRV1cVyBFurxnMhwMA+tZ/9ut4KlpV/xk6MNzP7znwzzgzCORDhfcl8lpUllpGszDH+R1eRgXVSCV5gnN6GlvoPW+IEGJ+7WoLvpe9wd3c3OQGfcbo6zHQwZnXFKrY6/c5vbd/s2GaWyGVwNsRV1cAVpQSUZrDvEaIwcAgMARBGO3WZmve1BZnhMhBytNqOhZmVzzm8gI3vLpfiWH5n/qpG59zgp1d2ZnrRZ9UUWDBvritA8QIgKeEAjDpCjNDaQANCXS5hxzuddhQIB564xMHgi+icwZ+BDO816EBYWUeAkhWCeQM6cGCJHQeXYHB4DmsBr9lBYMzwiPdJUz4DgKkanU4b25eXl/q///f/1l//+tf6u7/7u/r06VMrzcTZgNeqXq9veH5+bsocnlwul/X4+Fh//vOfG3DkdMDb29va29ur09PTenp6ql9//bXW63XbC+0Ds3oOPqU2LklFeVK9wNwZO7xlZwja4Xi7pCTLjxxx9b4Zl4uZHz9qS/531UXV2y0VznRbvjJQha4BpFhXVo0nM6Iv4Wl0Gt/H0NmBRncCJPzO5C/0NDqaqwqcgXKAE51mXrWTaF3iUmnzCDT0AVDoC4y29ZODVd7nZaNtO4V8M19n8zLabJDtIJTtlEES8occoL9cZm3bw/jMK6Y7fTBHZ7MZo1sGFl3Fk0FFrw+HW+3s7NTx8XFz9HnWwQNsBLop99XSrIdwsKEPQJh+DF6hEfbbgWoDL2jg7GquM+9cLBbNsUxnyHYD/WcA6+CNMQD2DUcbfQmfUDqIk5DBG2TwIzu2WY3kjBu/Xc2R+Ah+oaw9gw+5ln5PBmWN2Qi8WaZpllPjH4KMyANjhw+8Nc4BOnCL5wxusJPHfKzPfWWi5wvfYZ+N6zwGOxRZdZOVD8gHeMbbYNw/dNnb26vFYtH0knUP64PDbj3oAJj7RO5sV6pGvQjNjKnT8bHeZ048azvEloMePezYpk5L7MuzDlr4PZlEYO3czGu9v+Va5vdYt9lsrHKk2YG2bUmZMPbLoBGfZeDSstLb9plj4fOqUeZT7qtGHxMZ+J72fU/X1DEy2MjomxnNisRlRjQLoBmop1jSkbbxcilCRhXMLD0wYOOec0WoDX4cWej9JLBLsJrz97+t0La2tiblbMyXOcCsLqkyqHXfPeFMAOJ59wyt18PCkQYkadpbxx7NMA4ZNbMQetz+P2Djt8ZtUOzoFLRMobaxub+/r8fHx/r06dOEPj2FxPoBdmx4N5vN5Fh6G0CchqQXAD9lxGucwaXkeUcGfQcmRhRed3/mLRvM3g/vyAMkejz/0Rqyx9YMwC00hq9eXl5quVy277G9YWtrqwVyHK31D3Jh4MFpu64iqBplmUCS5RN+wmh4befzeTuhcrlcvikThg+rpmWs3kO32WzaflRfQdEzXgRYNptNi/Q6cOLggEETDiQHfmRfOEjWxcMwtD2NbGWhPT8/1/n5eaOLHRl0PvrBYAoQ4WesGwwKGIMBgIPDBsaAZp4FIMxms5a55HlnSnk/NjqdeLfZbDxkz2PZbDbt4DsCf/Sxu7tbZ2dnVVX19evXts4Z8IWvTGMD9qrpFhbobH1YVS17lGAd2eEZ5mqAapoSCP3pp58mV7H51FmeNeDlVGZnbe10oPNw7qG9dTeBJgLVDoBwUCRB3o/a4EP40kGfdGJtk5wgyQyesWDacvp1lgv6ssZs4XGWs+cwpMNjPWa7zlpzkm06y8hH8reDKZvNZnKoJ8Fk3pfl7ujjl5eXVk0GvkA+TA8HY6xnmZ9vk7BDyBgcZCCBtb+/Pzm8ErlOp4QgFg6uHSxX1CDLPYzhZtr2sLznnmuITnWFm4PQ6T/4b/RpurIOxm2597uq2unLTvKAuywTrnAwhrAv0sP5qb+ornGA1D5bBiJwQjNpxDiZF/oeXVo1Jv3MT+h1y3rPR3P1hauQMmD1re2bHdvFYlGz2azdQcvhHQYfjirZoJ6dndV6PV7SnmVYFro0UixeZkRQOJRWACQBe74Kg2iyI8oWamehyGJ9/fp14sD53cwLYDWbzVrJH4Jixs3v+7qC7A+FSErf9Nne3q6jo6OJEPrvVVUnJye1vb1dt7e3TSlS8miAylgcnCAS72y37wQzY+/u7rZMJHSrqpZJt4GyEjUtmOv19fUkMk/jBG4DBTuhVWOpn+nbWy/GDV/4c3jIfZlHUvjPz8/r5uamDg8PmzNiZ84lOTiQZHJ//PHHqqqWlceocncyynY2m9Uf//jHlon13unDw8O2xs7gW26sMFh7BwQoLbIjzJ24XA2CXEP7o6Oj2mzGrE6WV1vp8h2Uau5n/2gNnse5wnjCj8gQkXWeBbyQTXI2jrUymMYpAxjzrgRvdhbeCyqks4lu9oniRIBpBmfOHqfxhAdcaprvZJxE3xPEwJvobWSJsj/kxvrRmQDLAu+jYiMBNXrSABOau/qEOTAuwJwrN9ysZ6zbcr7OwkDvLGGHlsgSMgZ4wV68Z7cS/A3D0DIyBDEMuK+vr9vBR3x3a2urHQ7y5cuXpqsS1Ngm5zgMCuELziBIx4HtH9gc84UDgrZjxhVVoz5+fHysq6uriRPkQIjlFB4/ODhoGTFkcbPZNHvh8WBnOSTHji16AV7iWiRKMk37j9gyu81hQkl7r5sD/K7aMqA3f9kxsI7KzD3PokPs2NJvL8jvqqaqV14AS9je7e3tNafZejt1X9V4SJnxGTxxfn4+OaCNecF/BAkI0rGHGzlMzMw65Biw2WAS1oFgvh1bsAbY0FldZHS1WrXya5fEghFXq9XkzA3j2ZTDXkA0nU7jWfrz/2kOcjIfZwUzUMUPgQrGbjxLcxCPisednZ1aLBaTeVCh5yCr8TXVSzyTmJjx9RxxZAMfg4AB+svz4n22V7PZrJ0bc3l5OQk62l9DJjmADD1pG0DA18Ehr6WdV3R2Vh4wNiokvqd9s2MLQGbRAD3poZvQgF+YHSGl8R0ziw0+z1RN95b6t7MUEBSQxTNeTBSEm4Ejjh0gBcOXjb4ANx6DI4MGLFVvDyNhcVFWZgAazASISTqYXmT8fPGxBaEXzfLccTYdPDA4dxTVTg0C5fW3IvJv0xDDZsHhWSKRALpeJqLnqJs+fifg1A4HoJC+cryet51CBJo1wXH13kroUjWWydnR9Lo6AwA9KJszYGONOSANo5zOtxW/15J5ZTkJYMMl7bw3329jnrSx3PYA9kdtBrN2VLJ8JwMq0BeHxafBVk331gAeDOAoc6Q5YltVk7sX1+v1hN+tH1nP9XpdFxcX7W++c7rqrSwzJoyanUJ0voMx3HVqvU3gBr7snXYMzZxhdnYzHVKMuA24dTTf5RoyDhv0vNgX6kOb7HjRJwDXJWluBiHp6OGsE6yw8bccml8IclkHAn69z5T1N4BBX/geRmhusPHyMr0mD5CGbUXH7e3t1Q8//FDPz891e3s7CQ6wr82BNfrHrjqQXTXqSeTAdOe7ACZvWbKd4jn65UDJzAJ5LTPY4uw39tSOF+CRuSATvfJK8yTjBPBnAPgjN2jFvB0gqnpbimw8yHM9Ryfln89slyw/tlv+vmXXfdoOVk23lST2AF+4WsSOMXOwY2cM7c8cyO85534n+tA8arzE8x6DsTbyaoxnfrbOJQjlgye9TclOFA4+ASqCksiCf5hnZi+9tj0swzPZjE/NF3asjGUz0WSeSJmGtjhdXkPbKdtn3ut/O3jqdXPVVlaipCPsLTJkUG2P7U+Yjz2/lDnzTDrWGXxKmtq2W8emzOX37XdkoClLn3+vfbNjyyJwfYA3gePcbW1tNYfKwo8gWemnM2mCO3tGdBqmIUMBMQCDAG+Y2RetE/HmvRxQARj1ntwEQ3bYUuDX63Xry+UBvJfN6DZa9OuoFREh+rTjBYAygDMTpOPGHasAYYNWKwToxTgSQFVNS7xsUMgoW5nS0tnZ39+fPGOAzxg/ffo0cWT5LnO2ckmjwrpa2IZhzD67PIdAC9kEwJONiYGTgTpznc1mLXpMv5QBzWazViIKKPOBK6x7ludRTokM4GSenp42Pl2tVvWnP/2pNptNcw7m83mdnp7W7e1tCxohQ9DD1wogt8hk8gPzhUa9Pes0B7H4Plkv7+ek5IeS2Y/afG2HdR20sMxWTcvBq8ZzA1KJWw84YorhRP8YPFVNHSjenQ6SHduqajro/Py8qqqOj49re3v7zX5E6x07tlXj3inkDt386dOnOjo6mlSD4FhZRhmrHQY7MBmddqDIusqZDXjaTgT0Pzk5qZOTkzo/P2/OKev2+fPn5ugT1a+ankmQTlgaZD5L8E7zPv0E8wYb0Jp+nUXhb1QQ0Yd5wICa8RDIQie5jDmzNF5r+mU8i8Wiqqp++umnVn1TVc3RdVAB+mV1F2tMWWPu3WbeAOv1eqwAg7YOwJhOrHXuQcwggkE0gJX3cTWKZYZKsMPDwya/dtLTcfHnPofEjsNHbsPwetDQ0dFRqwwi6DWfz2u5XL6pmsCuYo/QF1VjQA5n2bozHRKq3XAA4BsHnt28Nr69IOUdPOjSWpzaxEaHh4d1fHzcyuvtQGQgmTGR1ea6LJ8qzg+BP3CEA6G+tQGsgSwYh4E7l8tlS0YR4HEDl0C7YRjq9vZ2kt1jDOivp6enuru7axUexiDYIusndAb2Dh1uve/sak9ujGN75dv4FScnJ7W/v9/OGuEZByOhvzEpuhDcZR+FdeLgPdYSvQJ9vFfcPEDVB+vGzSzQ2yW+bGk7ODiom5ub+uWXX2qz2TS/DPqjB00zaGs9hf+WmXPLmzE5AUp0uB1t7C1Bj93d3Waf7Btib5B1fAYHMhjft7ZvdmxtdGkQl70/AHsGlE6sIwd2pNKBJBpE1NgTzAgCi0QEmr8Behy5Zw44G0dHR42pDCycSXN/joYxB8qdHUWBDil0PONSmqrpCblWNFYO7tsRPzv8VWMNP/RAOWcmwePwGtsJdiTKBsPlzVkqkQ4m0X6AEr9pKG+XIDDWjKyaNigA05U1JiOBEYKuACNnpQ00+bv3lBlg9pxs92HFwbpg5PgufMF3qQrw/Bj/0dFRG8d6vW5lQj///HPd3NxMlIrvR7QRACg4k5dBAdOOz3d2duro6KiVxZgHkA3LMmPEaXEACGDZi6p+lGZAnbR1sAHjiLzbqDviXTU6TpYbvueoJutVNRrx5N2qUd8Y7PGdHKu/z0F2jKMXkXV2z8ES311okMh8CXo4y2HeYZx2TqETWdWMmnve6VzQ+Nv9/f3kt6PgOLnM2/xvWvmdzDt1V2ZHvBYGGs6omB7z+bzZrbShfi/vJKhpuTNIAWxkA1B6XDhwOMWAQvrwQV5UmzBvB5ttCz1mf5ZBS5eNO3NBKaSDNhlYclVRnhhrW27nHRqxNeTl5aVdJWjet05zoNJ8QTDT/Ob1SvvzkZsD66y5Zea9Zn2EXFh+svVsjNfMPGLc8Ft9pg4xjyZPp2PrMVkvWFbf09OM3QERf8e4xXT1uHKO/unN0zjGc8DmgytzG1Lq1uRzVw++J/u9f3sO+X/bKM/bGNJ9Ju9B3wxA+D3GdLmO/oFe+c7E8J63fQ2P3XbjWzCTx5E0so34Pdmz7urxT84t1wxMbr8p+/8tHnwPt3wrHdy+2bHN0iGIBejlYIRcKDusGbll4R01gzAYet+hWTVm/BA6QI+/5/0VBn0AlKznt2F0BI4x8YyFCYbtCZgXhIg8DoIZnFI3/s9+VvbHmklSObMPg5aCTzQHgNJTIs6ImsZ+3mWLjrAxD5fIuG/GmiVlLjXxewFZvMeC7si9PzdwsfMA+KI5i+XAC+DPziTz95zsxA7DeI8se7aIROHoAywNXDiGnX0wJycntVgs2mnIBAAsU5zwbJmjOdLGvuq7u7uWMXG0bnt7u/74xz/WfD6vv/71r/Xw8FAnJydtj+9qtWqRdK6loNzZ/GFjDH1sxOBLwKyzHvT1UZt5Bvmzc1Q1RprtuPEswSJH8dGHOH15KizAn2zEarWqy8vLSSCL/uwgeE3IgCB3ngNycXh4WFtbWy16jF6qGqPZy+Wytre36+TkZGIgT05Oaj5/vWf88fGxgX0Azv7+fi0Wi7bnmwzYy8tLOzTo+Ph44iAwp93d3To5Oanr6+smKzbmVdOTu62vaNyrzXfY+zefz9t6OBhVNe7TNci1rmJt0ANEo7GHlgNnVVlPX5GEA7XZbFqVjL/r79NcjWGnm7FZH7miBh3IGRXwKM/6GhLGw1YR9KHtlu33bwFf6xHbd/Mj9tPOP+XFrJ8B/2azafvFXl5eb2YAD1g/IrdZpnp8fFzz+bxlFj1evs/aUhUDHXGg2XtIhU3Pgfnoe2tpVOxwAKPlc7lcNtkF15hfkEsykugrYzWCL/AbVSC7u7vts6xeMt1TlrMKhEZgDV40hjL+rBqv2ILnXFWFbDtInEE5aAKvGu9mUK3qrbNGwNt/w4Y4w5zbOZgP1XQcGHd0dNSu3aqqZtPJ8lmGXa1BBRs6GIzkxve9D90BpKoRh4HpsYsE8KgYdZDV38MmY3OhM+vlANwwjGXzxv7WlcMwVvdRxcj8wZ+Hh4dN/6/X66aTjLcp7V6vx1Ph+RsHE67X4yFhjMd7uAkuM04n1uzbOCGB7KTP1gsoe87QLPE0PMr8XIXj78L7jCETnxlEMP9+a/uuPbYGFfxAXJjNwDf3dLJATt1b0VvhMHnei2AQLaZMgTJevgNTwOAcsc/CY5irRkfXUUGAJODPC4kisxJKJen5IEAHBwd1f38/ufuMsjGYsKqaIYQpnflC2JylcdQjI2E4tp4H43OkiOerpnujYUiX6LjE1GCOZuazY+vSQ6+5x+5AgcsTqsb7y+wUWInbSWC8PvnS5V442t53hSNAiRsAkr4MuGgolpeXl1osFk14yRSjqKpGh5q+n5+f6/j4eHI/Y9V4txsydXt72+hgRcHY+DdO6eXl5aRkiX1vW1tb9eOPP9bOzk5dX183Z+Hw8LCVVZ+cnNTZ2VkzDlRiIIcOLJgGVkyAPQewelmhj9gc6LEOM/CoepuBYH2hn4NGPI++MNiyHFHWlI4B47JeyAAJuptnq6Z3hdt4ZVSagJTLluBLVxMk4CLo4e87wInOSJ0FfdCXZIDTuTP9rMP9/yzDc/DFdoExOpIO4EH+6ZcxWS866GPa8ttABdlx9U7+0AfyaD3Mu6Edus4gMUs2kz9sQ/1eAi/eFgNoYzwE5xhLL/rPOKCbdYV1nMcA/zv7CxYwfTP7xdqxnu/xQ9I47Tm8x7OWA3S+ed1rm0GWlD/T5SM3JxPgGXQFa05Q1HxjuTPvGfiC13jOeot7YnF+q0Y9RPPap6NoWXbfudbW5ZYdvyNL5a3XLTOZLGBeDhiljXGf/N0VMq4yMV9aJhNfbW29ntR/eHhYf/jDH+rz5891dXVVP/30U3NiWBdkr2rUyw8PD2/0GbRKO5bBX2jm+Xq8xnReH/jJwfcMSjF2Psvve20cVOAZ20Dsxnw+n1xBig6m8tQVH3xmnrVesx6CHk5uMN4MoNgBt35n3LYbOXfkKvVpOvN8x7xqmrG+/hxcmEEiBzStI633015+a/tmxxaHkom6nJK/O9plgtMYvLN+FoY0hCZkXnUCQSiPcoO41KVn5IIIswEZ78VAO7pjBWMja0XI37Kvl5fxyHYYhH6JsLBo3FXLGDKtT0QHQUgFXDUyqJUrjMZ6MAYylIAR+jLAxsnOzC/fcabKRioFlsy1D03IrDfr4nck4IAvmANzY+wJGlxyzN9NU8Ce+YQxZHQ0jRUCu9lsarlc1sPDQyvN4xRxXwK/Wq1qsVg0PiTyvFgsWh9V9aas3vLjNfH+zarpnkgc2oODgxZ1XK1WLdDi4/lZvzQ+nPiNk5wAmIYzD31dUovRS13wEZsVd/I28sA6cPffZjOekk5WA5lxhhR9xv4o7+kka2SAYANn5xUd4ooUAn/eD853HfhxVQzjvr29nQAD3zcOwMEmwG9kbNGN8BIZMB/iBM0MfCmvf3x8bKdvWjYNgJBh+uD0RrJoGNXFYtH2q3PtT9WYIUY/kR3f2dmpr1+/1uPjYytbZb1YA+sZgwOyefP5vL5+/Vp3d3ft9GHWOh1V1tT8YTuQgNXRb2SZipLNZqwKYZ3onzVzxJ+1vrm5aQFAtipsNpsWrGPrBHslnZ1i3N4yMwzj/fRUsvTmCs3sXLoCygCsasxGEXiwc8rJm+ij3j48A1M+29vbq+Pj47q9va2rq6s2RgLUrpagcg174MoLeNdzNHj/qM18wDyRK+smHxxnfOUAmIMY9JcgGNnAFvawowNYlpf1ej3BX/AIY7EuZK2xc56nnTYHgJhb1fRQIvedf+P/4C3LJs/wN2hkDGOaO1vL8+v1ulVpcSgcGHEYhnZquvfmUoWyXC4nW6HsB3hNnCTBDjlhVDXKoZ3z3O5nTOfkAxgFecOOeZ0dAKYP8En6A97G5vdje4w1N5tNw3ppa9E9nlPVeHAgz/LjMyWcIGCs4DK/K4N33NYBjavGwIz7d5LPspnBH8ZIy2SZf7D5jB1aY/PN0/a5SDSBKRzk+N72zY4thyfA8AYkGL67u7va29tre188IRSYFwQm4bQ0e+4IA0yIE5j3gmYJIM6OGckGnyjUbDary8vLibMGE1OOZWc4lac35a9Wqwbuc6GdZcvyNDK4gA7KpQAOdqTn83kDkvST76oayxfZhI+AUwJUNQYM6BeGgiFR6j7pzobBAIF1IAKH4mDc/JuSIINtRz0BCFWjIGQGhfk6+2Bl6fWi+QCsVLY94Tag9vVH5g8awvj8/FwXFxdtDcl0c4XObDaWIbkc5ObmppVTLpfLuri4aGU/VmpeZxwQH7DA52dnZ/X3f//3dXV1Vf/0T/9U29vb9Xd/93c1DENdX1/Xy8tLM1yU7jgI4XUGwLPWOBiWTzsc+/v7dXd31xws+vEWho/cAC2Wiarp/mU7PFXTK8yq3p447M+hZ2acbIjgfcuB+cbPetz+t3WcAZr1oOfJ84wlgZqdBI8n5S6zBciqZdl62KAnAWBvDvTjTLABMnbK+gWa2sl1kNV90R/rlHS3noKm+T07U2n4/Z3eO/03A1nT03PJrUXZl3nOgIzMrUGz6UXwAlomkGGNM8vuNckgm3/o02M0r9OHwSU/rlDKDIl/9xxM8zwAE77lc4PUXDf+jZPEOFxu/ZGbdZ1xHcEwnAtoCl3gE4ICdhDf45Gq6aFOZA6xY1X9MnB4CzvN+jgY5MSCkyfGm5Yj7LyDg7wL+cO5QyemM2pewo5m0Mj6i8/Ase7n7u5uct+yeZ7A1+HhYZ2dnU2+d3193a7ABDeAfa+vr9t2AFdiODhvmTM2fnh4aA51VbWgre0BvEPJrnVQbjM0fnWQtWrq/DJn8D/8BX/ivHosXhNf28O6cl0fa0PgFno4uIAfZBojE/zY3ntuYEqqVtnGgx7f2tqqs7OzOjg4qLu7uxaAYExUCLKWtmnOwpNlTb5ivnY8rZPhLyfZCBywbs4QV1XjOZ57eXlpGBU5Slz0W+2bHVsDBRwVn9JoJQ4jZumxn+05jCaclQTOqJud0fwcZyQNHYvmA0xsZP2MFdN8Pm8nH3qRDYLssPC5md5KmP4NqtLQQ6uqapEOBMLKIsFwKmqDRJxRGwv6z4x5ztFgkncns+EMVo0lZtCMSGFVtdIghMGAkvEYrLm/NBo9EGElyvPOcpiGGUxh/jjpVip+pqomAotBppycyKZBkEETAYibm5t2ch4OMco2Mz/wGO9AyTNm70P74x//+Ia3Pbc0xI5g09ft7W1Tmg5OmZdZL193kjxnxflRW27VqJqWkBGM4GqozWbT9i4CZJJG0Bgdyv6cjGpjxOFZHBDLhg2lD/uzLs5751hfsmn8H343sEB+OaUZfeJKHut+QCGnUXJA1Ww2m+zhqnqb7QFIsv+RTDaNucDTprErYigJg/YXFxctIg6gfX5+bieY8pPXYhgs85t5Pj+/npLOqdnQA7DBWNCPbJGwvkAfoj8d3IA/np+fJ6fDAprthBMIIOA1n8/r559/bkEygKT1ADIMjTjddH9/v05OTpoeAQhRrbK/v1/X19eNV+E/9IGzLl5XHAWy315T9ChB9szGowsPDw8nlQ2ZMWO8BJdtR5BdeBGef3p6quVy+SbYO5vNGpCDXx3EJAlARvvo6KjxkeWWPXgfvTlglEERl5rTHMzApmYwgn6rpmWlDqynbeczB9P8fz/fC2wbJzogZb1qW5C4w/NLfdsLUDoAyTN2luE1yy9jyqCPgynG304I8Tx6AH3Ne7E1zro6WOnAp98PTaGJ73dmTKaLx5m4zXyU/VaNFQG/hTv8vnxPJkEywAmfeI7pw2RfXt/kL2Ntr7PH1PNt/H94AR2LM2nH2eNOmpm+WT1jGvPv/DEfm/7+nudjh9tbezwG9/+t7bv22PqFpLoBv54sE+J4dsAOE+85Ci6pcsRus9m0axnW63VdX183ga56e/eZCesMGU7HavW6od59EAk8OjpqjplLVHZ3d+v4+LgeHh7ql19+mURoiSIBNlwOYWa0suFzFJEZ24xgx9bXNCAwbgmq7OixfhxrTgSRjHFGsawMDQIBSoyH9bPwsm/YTMvY1+t1y2JSdm0FzZhdFlY1lvyiAImWIRBWOI6GEc1i/pkBYp2JyuW+QM+xZ1wMdh8fH2tvb69OTk6qqiaAqaomUen1el0nJyd1cHBQv/zyS/31r3+tP/7xj/WP//iPtVqNBw2g6LgOgbXnrsqbm5vmyKLwHh4e6vj4uP7H//gfdXd3V//7f//vVgXg0uWMQK5Wq8lVXY+Pj/Xly5cGZjebTVvbdDTYP+7+V6tVK99D5j4ycEsDmtF7P8dnzgzQes86AGeHJ429v5djsi7K53tBG3/fjpp1LXrVuuM945N6qQccmFsa2d9r7hcdmkD3vfkmoEy6Gew64IZj5T2iPR2R70xa8r0eWM/gXM7Vf6eZvzw3vuusQNV4AJW3n7jPfDd6YhiG5qznvJPX/G/m3QOLbrk2SbseH/fonPyfTovHk3NNfki9R+NzZ4Et27/l1BDIzD4/WoO+rrJKOWQLztPTUytHhG7YfJ6vqgnvpDNjR4Lv2KGoes0AujLPuM7lwATDvD+Y7xFsciaKijy+52AiY3EwLBM5DmSRuWIft5MB2HXO9yADyJWP4FG3zMQRkAEfg2fBQFRm/vGPf6wffvihlstl/e1vf6unp6f6+vVr3d/ft8q1nZ2dVpb/yy+/TLa+0Sx/3PwA9lqvp9fXOWjGetsRMj0zmIzj3XMWPSaeB1+Zd8Cb6RyyDnw/k1rIvTPvfOZzD9BPxrsEx/y3zWa8mQBslraUOVCVQABuuVy2UnxXTZimwzAGqFl3sAYVOMiWx+YAJXzpay0J3jpgxVi9zY+1uri4mOBv6N7zd36vfbNjawADQRLoGLCl4YKYCercbMzs6GTGJwEaoD6jcyyCx+N3QSyPw8zLD8bcNfd2MC1Yjk5nnzAB4J9nHDQw4DDz5klqdkjcV0Zv0mHlc4NSvpsbyA2OUBAWfvft96cSMRPjSGZmCzpBQysI+nWUP50Cj5fnMVJEGwERGCcMkdefuTlilYorHQ7WEMWFgvbzjIVx3dzctD0rOLyXl5e1Xo/7MqAtJeVeNzJbPoUU5Yy8oERNC/p1ibzlASXlkhiXBCU45TvpQFgRJZD/iA3ehH8ACKzRMAztMxprgj7IzB/BGdbx5uamrq+v2zpT3v/y8nrqa+paxrVeryf3g7K+zjzyrA11njFgXUN0nz5dnsqzthHwG2OHn9nP9fz8XH/7298m2U3rIehCf6vV6/aPxWLRItP8nbLDqrelr7zT4MTZAgwydDcYJ5iGoT4+Pq69vb1W1eAKBWiJfHDauMF4/vA5oI0qpbRJ2B325PpvzhTisBo0M0cOgLm9vW3P5eFM/Ga/Phnsr1+/NqeArUfOFqVTyr5+9F7V9HBBzz+dBzsxqdMNApmD9Rd9Af55jrkZM7BNxqDN2X3rVxwwV6MwN4NRHAZOWCbrCw8DQm1fPmrLYJV5xJiv54R4TayvEtNV9QODvSyYn/G/7dSmrNree72MfdNJdsDDzUGWpI3lwfPI1nNs0N/IQmIsxm7agI0y2O2GrgfDuAov8ZOxv6tLjO9yLKajecEOJ7jovTH6/ca7vbEl3TKg2sPv1tU9nrUON5+7n7RlVdPTnq3Pc+zYMr7vbLrng82xD5H2g+Z5e7wZaM01yvH16JwyYrql3FdNDxpNny5p+XvtuzK2KHuiBhgONxsOg2BHEzCm6TAwIcAHAvT8/FxfvnypYRha5AtjA8EcCTFxAVVk/KrGq1LspEFAH5bEWO7u7ppBQpCpp2d+LpPJSG9VtdNvEyxS5jyfz+v29ra93w7J8fFxnZ2d1c3NTX39+rUBBYw4xncYhrY+h4eHtbu7O8nCsVYAbPbWQn+ABHt9XZ7GemGsfdob86iqBgbMN1wfwdwoYXGmNA1DVbV3cdALQNPRpAy4MA6XM87n8zo6Oqrd3d26urpqh6DAEwBrgAnAl4wte6A5wt7K+uXlpe17GIahXULO6cMASx8UUFX19evXBrqOjo7q7u6u/uf//J+TCJkjVyikYRjagVDmIYDw2dlZPT091a+//tpKNTGu8/l4dczV1VU9Pj6205EN+Mk2I49Ep+2sI0cu5aqalgNZnvJEyI/WLPMJNPjckceeYegZbH+fQAOOpUFbBl2yzwSNNii5dj1A5gBGb27pzHhsNnI9gJmOtQFIBqwYZ4LCBANJxwSL2W8PyLCfzpF30zJBDX/ze3J8SWuDLM/XgUd/xroYwGbZeQK3BC7oNtuqdJqZT/aLIzcMY8AN+05jXA5gOOvtdevRAB5LXugBovzc/Xk84Anzid+LDmWeCaJ7cvxbANv0tryg05OuyZ8frbGVK8+HMJa6vb1tFXlep55+IdM4m72eX0E2NysP4D2anU62bZgfeR681tMpqdeqxoowss3+jrcQ5N5C61wCWmDbXvAYuhDcqaqW1UIWqJziTBbbC+Z/dHRUP/zwQ8N0jDMxGVhkPp+3/Znn5+dtSwXZXSrwwMngULLNzM+/wVfgS94DHoFeYCtviWEN7MQxHoL7yBQYzOPl3ewpfnh4mBzACMasqlaRR1bbdtP6Eizuwxc9R3gHXQjPMlZoBVa2E4r+8DpyOJ37Ns6kTwcj8FW8pdL6yQEOxuWK3JQDEm5pT3d3d+v09LSqqh2s6nXjWbA19Ecf4CeCr7/3usjvuscWYw/REszZOLNgLArKxcoKwTVRkqg2qFZYqRggdG/y7pPv9gDTe8YGYfRcEkDk/PjcY7XhTkOc70ug1gNlNpDZrMBzvplly4ihlYYZEaHPdfa4eF8Ck97YeWdG7fJZO10JTDwvr4OjP1XTK3f4Ls5xbzxWKtl4xtlzFKcVomnaM2QZFUte89wAW9DDTgI8iXIgW2MFhkPLbwIF3HNr0Jr7GpypSwDeAx+/xQ8fvXm9V6tVAzzv6RuXdiZYJliGk8Bp1hzUB9hwZDwrB+jXgb9hGNo1DgCVYRgP+sKQOcgIUGIvp40rY4PPkEPzs7Nem82mgRQHHNEtHLiBzKSDBA+6igYaOOu4Wo0nLAIIfIBeOizQinK6u7u7idFPPXl9fV3X19dv9DjjIQBZNW538Xg3m03LBps/Uv6QMQKP0JI5PD09NeBlUD6fjyf2An6hNbyxWCzaWt7d3dXJyUkdHR21ckYCW8zdFTfn5+cta0m/u7u79Yc//KHOzs7q559/rp2dnXp4eGjbX6BT2lPoz9xxOsgKIwPPz89tj23qbBpOKnqSvccGfNAaG8AcHQROO8wYCfJzZzjBU6oELi4u2t5q9ti6xBLbQh+AzY/s2GJjCGrzGfTb2tpqPFfVz8Zad7IO8/m8bd3hx8/Z8bAtNZa1w2D9klVN3nbA98wTyB6yltUrjCftJO+D99Hh6LGkReI4B8BNT1fv8U7rptPT05aJraoW7Gau0IVxI/tUITEO094OE99jLIkdjckIlh0eHr4J2jM+nu1lwI2pWSMHAk13Yxmfxou+YL347ROTmY/tBu/3T2In1h9bapzo9cxkX2bRWZNeksDfR9faqedzghKJPz0304px2hdgLNjzxHdbW1t1dHRUw9DfRkAf6GZX6liuPefvad/89OfPnxtxEF5nNQEf1EXD5Bz8ZKPlkiEYGiZjkmQCLSBWAOm4wKBmKPYnHB4eNgZdrVaTCMRsNh7skyUBvN8glcgj46McjL0CV1dXDXQSTYdxfRIl860a71zldGje6dIE/uZMmJUzzABtHYFiHjBgRgRRJDzDOvAuRzgBo3koCEqESB2RGObiZ2BWBIkyNTKFPMOdrCcnJ7W3t1fX19eT/QIAgqqaZPINLnmWDKkNECDd+1As1L5uyGWajgp63x2RPQPGjNQjN+x5ZR8E+8jdB2OljO7XX39th+24hI/xsP+FqzhYs6qxxB1nib3Sq9X0EveULeRlNpu1yCkVFxg11hGQ7koL1p79uR+1vRfkgQcMMmz4/VwaFHgKY0X1g40lPJ6BHWiNnNtQ8sNBVDQbVPpHv9NnAizbgIyS0yw31uHWZym7BqJp2E23pHsGydLx93fSGNu4G4x4fbNvAg8JtgyG6TfX3IDG78pAqJ9jfAbryBvP5P/tIBuIML7ZbDbJXjkrkLaQ91VVy2gRYYem+/v7zTnmKqmUfdsEg9EEyXYIEtAlf5jn3a/BofkIOvCTcuo5Mzbo5aohvgv+oB/bzzxdlX697eQjO7a+rhFswdyXy2UDslTXUSVEBRj2kgMSU2+yDlWjbKKH7LBUjXLvmwHsZFg2eJ6/8TsdVWwwFWB2bMGd1us0+Ikx+l3whd9tXuVz6+uqMUBI9ivnvL29XcfHx+2gNusqDrDMfaAED5bL5ZttN54f2wMcFEg5zoP+oBWyafvms2lShj1n7w1G9nzeiffIVlXLAPpwQ1d1eHyM21UG6cTa1vs7zlbbAbQDa3/JDim6GJ7IQAs4/f9r78x6G0uaMx0kJUoktXV9S8MwfO1/ZsD/2NeGYbjdXaV9bYmcC+HJ85y3jqpV7umZaU0GIEgiz5IZGRkZb0RkpJ0WgEHramfV7na7EZhkDaY9zBcflWZnjvEQOpD3+Lv7+/v6+eefG49z/IxjaB+R+qohRTqDku+ldwPbbxka+be9kvzQWJ7xnveZbPD5Z+pzX58LWRqV+Vn2yZ/5t59RNa6CZu/2FM+SB6m8EvxZOfj5U2R+pED7bxuuyYvsa/729SgR0xSfmaSeaDYsp8ZqihfpebMBMcUH8xgjlHsxTDAC/Z4EGVP8NdkYzbZmn6zM06CZ6nsCCysSPkfJophZsLOAgx0SNs7fkgHzKfuX/J4a9/fI7EcgeAhf0zvtsbTBxILCgo8x560ANs5s6HlhxjADqPJMoklEW5EJ0oN2u10zRugDUUbk1/s0bZTghPF8dpSiqhrooZ8+dxajimjj8fFxq8Ztvckcub29HVXLTeM2o99Vw953DBkcS5w9W1Xtc/rCO91v3meDDt0M/0klc2aFoyk2tH2ObNWQiui+eN+ux5bfzHXG3n8DArPyNt9nxWlHzTFekVN0iI2yqtfI5HK5rB9//LHW63VLHbu9va3lclkvLy/Nwbm3t9cqKltXwseplDrLvbey2NFqPW0D1fOGPjw9PTUnnVMtZ7PhTPmcr+kYcDQ9q4uz7cPPtxzRJkfYLYMfmXCw0mfk20YrR9FQ6NJG/2q1qqOjo7q9vW36w3rHAMDrmseN+QWgs4GfwY6qAXQ65dfOcutGQA/rsHUhNkYCAeQPPWhQVDUUdrMTyNkaOAccPaXvvPfk5KSqhjn86dOndg639cp2O9QOqBqfTLHdbuvz5891cXHR+Fs1toXQfzjheYaDL9zDvDFoNVCHT7SBrWOAcc93xnS1WtVqtarb29u6vr6u/f39+vTpU5vbjhgSLOA7jmjkGJ3MxEMH4biw/rY9awcaesDOODtYGT8CB/TXWwtZv3E8Ms7IF7UNLCesnYxBppfP50PB24yIIl8O/Fj+LfueV5YJA3SKQeGUysi559T+/n4LBvE83s16+D068t3A9j/+4z9GwmzDnAFGWfsICISeAaHzCILTsBBomO/okcGiGQnDEA6qh3kwSBmyQqMvnrxeCL1Q2qPkKrsw2kYIk+b+/r6urq7ahHFZdBRKDhSV5UjXYRJ6EUcofdC8PeIoSf6nD97zhMJl3FhgphQr92JoeLyosOxULgQR4YbnGJIUwDEP6Zuj/p7cNzc3dXNz0xYlK2+nuHiR5DfPxDvG3wZ4/LConp2dfeWlTRlECfEMwN/p6WkzZLfbIcXMSsVjzXO9L5fx49kY5V4A6FtGHBh/nmWlOwUwUYiOttqQ9Vy4vb0dKVunBs3n87a/yHMM+fvo59hOGb7WIVVfO0Ws5Jk71kces3yWK1vac8xCj8Hs1CfkFLl8fHwcFcFh3L1HiHYidyzGyA3XsG/O5wrSDi9ofMf/dpwYNE/xy8e0GYSkgZUgLPUS8u5zBO0o4u+p+cL7/LkPpHchMI8ZvzOiDjGG7ouzYarG1eLpK0aO1zLGCzCH0W0DwSnrNqrR6y8vQ9E4ry8GcqSTV42rbJIKDZ8N1pPMY+vajG46QkJ7vAfacxD+0m/WXjLELKe8O+0Mj5Hbbn5YrtFx8NIZC9bP3hPt+Z0R/49G6bxPp451oOepARuym2NnuYH4zr+RJ9eGQDam2pkANfWCdRBty2wa7rPN6++su80by146ybnWz/T9llEc/ziYXMjTlZyranRkn+c4esQ2CZRrWY4zfE8Q5D5YByQvbN/wDNvlrs/ioqR2clofu63mrfWpxzTl1HxnfKYAl8fSP9knxo9n+dn5fPPN7fWYu/1eH31/2ihuj99v/Vs13r9u56TnX753isfcNyULuXYnlnwvvRvYAj6courG2VPlxQHjy5OOv1HsqQzMCE9wnucOpvfG3zNQGXFKYwWaWmS9UFYNRlkOenqhmWj26toDBOUkdjs8YaxQHS2Y6gs8Mc/M73yuJ1yOqYXU3lYMyJzotCsVFwqUe9y+TIHzmGPAGCRPKRMr8ileTHmLcgxQ6ilfU5PKcpE8YuyTh+axQaMVM995IXBUOg2vVJJcb8+w5Sf3h+QY54/noDMQptL2DI6SUmF/VEqZ996+qmppOjZ+cYpZJqyHptKYMJCcnpSg9OXlpQGtjPB7Htijvtvtvir0gAyxZ5u24+RAx1YNOhePNt5iUtwwRnLBtxxmCiCAjvaw5xIvNtEHjLWqcVVfeGjdybWZRuxrcUIlYJraF4RTwoU/ki+z2awd1eWIfoKq3e41GmhjFIeT56OdDdYP9qzThoz+ei+c9YCNEYxgnmlgXTUYOv/+7/9eP//8cx0cHNTh4WE9Pj7W58+f23VEDQBzvM/rIZEeHNQGfZ4X1qnIBAVdvI/57u6ubZuBp2yL8ph6n6U/550UYXx4eGh7jufzectW8JrpjAciNXZW0C7a7LlmR/lHJGeU4DTJaIyPtaF6dtXg+IX/Z2dnjXcJsuA3jj2Tt/YsFou6vr5uDnMDMzu8XbyI+YGD39cxjo5C2qmL/YL95Mwe2u37PMdxxBjMcB82Gc5K5gQ83+1ej+kjq4LMHYplLpfL+od/+Id2nOP9/X3TPY+Pj/XTTz+1DIz9/f2WbeB13wDTKd3wnOKc6Gba56CEU1BtSzuSR/uYgx4b1gLqLtgJsd1uR8EBr3049Lfbbdu6x4+r3aOH0U1eD77lrPQ6QDtcb4LsIdaRdAykLWBQ6wBQRlW9ZiYgRVarhsAPABLesVbZQcApABzvmLaf11zmIPOarDG2cDIGuW89g2RVr3ZHFqX9LXo3sD06OqrdbteMm+yQF1eUVS7E/q6qRoM7ZejAFA/YW0DXoCvPEK36utgV7/dvCO+uI8iOpnhvMINmJY1gOLK12+2aUeE25U9VtWgCaSZHR0d1eno6SpmBZ0wqH01Ae/b391vqjj11TtezUsLosBeePnHoPDxn4rB3krajZFl07GzA+5djZyMP/hsIolgsU4w3+3MZr9xzy1hBPIt+24tL2g4TH754sntCeq8yP8ge7SBl1IvZfD5vChhlnIrMoNSFX9j/4sWP/eWu5GgvrvvieZiGMIsaZ0ab1zYa2H+BnLLnnmsZw729vWao8KyPSnZweCyd5VA1RLTgnY0VO1+8mDHXrV9R+jbsMASqhv2PGO7pEJwyyBNokWWA3mQxRW8g/8iDn8Fn3sNjwISB6+gvPDCgsuykscS8Pzg4qM1m81VKrsfDhqC9x+lk8noGL/OoOeY+45ZtnTJW0a9OLbP+Y0zg9f7+fp2eno6cD56z1t/ptJtyPPK/+wTA4B73hTUPfWlDytdVVf33f/93zefz+qd/+qc6OztrBmEWSrHRTX/9rtlsqKIOv9xHOyDQT/P5OBoF4HSKPvdiwHn9Qk54NmPHOnp4eDiqEWKgja63UQ6P0YvWtc/Pr6cI8ExqiaSz+SNSZpJ5PqdeSmcr18D/fBbkucGPn2N58xpr/fQ9ZJvF67Hb674mCMr18K33G4hB8MjzKdceOwyts6xzecZUsIV5ggPQ7fC7PCeyDfkztUaapnjiZ8Nnjyn/u0/WD1WD/Pk9iSWm7JOUU97v9XTqfuveXHvf4tNbz5jCCFU1WtOmnm+bIfmbssNndujkc6ccA352juXUuxODpU7POetrv2d+fvdxP95s7Z/scC7YU43y9R58nmkFNDURkgm5AKcysfc0Gebn2hPnhdHtzCgjlN9V1Uh5Zl/5378BXCghogez2WyUZ+/+ZFqa92KYFxh06eWfUrp8h7FBm+BP1dfl9f1c99WK39ensvD487+NK7cdT6bvS6cJPJySOUfJMGQN/J2CBFCzcYkDgSgBvAWA00YWa3tkabP3W00tHjbO6Hc6eXiPIypc57G1Qs6x9yZ+9sf5PQYHfEfKf3pILctEFunPRyVkFBCbMuY9X3aYJFDJMfHCyHO9zzBTbRkrHApVAyD1lhDLr+XesmHAiex4wYasv3wMhNcEdLJlxSAAon9V42rtABd0FEDPER7mCvf4e3/neeFUbmcQVY1Tn73+GODlfLR+SGMOByHfeZ5VDYU9GBfkg/4afLpd2WfrVEedq6rpNgO9+XxeV1dX9euvv9Z6vR45YJEd1sOqIe04t4zwXhc1qRq2XSwWi1Z4DvBK2+CBHYBE+71H1esruoW9fzzLxp7BKPJpByly5uiA5xZ71lzNmnbjVPXey9lsOJbEY8u4ZOEkr9HfY7j92ejHH3+s3W7X9tESvWYeGGhSpLOqRvamnTPMZz7D6UsknDGwPDhDBn3G50SX0gadzWZ1fn7e7mMdT53srTy0jWCDbZX1et0c0FVDxBCyTe26CMxtrvW8zOwsHGjsKaemwsvLa9HK09PT0fGT3Mt9z8+vFc+xYdj+huw7eGEnIGCyqkYpwuic4+Pj2u2GytjodOY6/a8ab5OjToAdhlXV6jGwlqYudfv39/dbQVU+s+5GBqrGqePYgMxNAhY4bh1cwZl8enraIt4+X54+0FYHcLx2wBfeAw8zyGYcgPOB9c7OWPiBfLA9CBniB57ZQef3cayW5dHg13gkZZW5gzM/nYB8RvaDx8GZRu+ldwPbXAgYFO9hJKpG6sIPP/wwUvzei5QGvD9PT60XQgCePa5p/LGA4B1H4dzf37eJOQW2l8tlS7kwsPUiagOPQUsAnX1kQsI7Uh9cRMWgg0GmLbPZrC4uLpo3GqExyHChAhtXCDtGoQHUt7xwNpiqarRHt2rwCE7tTeJ/R2byXgt/7jn25ESwbQSu1+sWoaFgR1U1Psznw3leyAmGlY2TTN+g33xGtWCDdRZRF53Y2xsqO6Ow6Q9jT2SbqPXh4WFrY/KFRR6lgdxyPQs6wBI+2AHBeLsoDIuRIyhOjZnPXyPJnBntMUUJec6gCC3ffI6CMnj7yMS8c5GeNFy9hxV9OJ/PR/rI459z0J95nD2HXKSI/w1s7XDgXTa20J3WsYyto8+MuQ0N5ItrLRNeQNEHgOrcL5zFRlgPWJDtODo8PGxpohC6h98synZ6oaNtdNgpwfjwnX9j1HgNse7MCAi8qRrO1/YRNHYgEI3mPZCvyeiII8d2ds3n81ERHoxK+AYQmM/nTX+Q3kekhiwYA3kMVp7p6qovLy8NyNohiKHLOmZKGWJ8DZL9XmT/6OioXl5ei5S8vAxbVZBvZApgTAYUz/d8tROadWc+H85S9Dgyv46Ojurx8bGBBuaTTzfw2LMuVQ12Q8rJRyUy/uARY2M7BD45JTkzL2zMey1nLAFd7PG2bQlZd9lJbhvM9p2fBaVOwLbiGQbAXufRdV7jMzhi4Ixjht8GwdYJBki2easGhyO63qDODgV0LZkE6DjmCHzxs63zDPTtJCS7izHFWcEakCAPQh4y88FyYOdj2nF2ttupkcCQNgOwPIZ+n+XR23IsK/BxuVyOsEk68Zx9af2GnKJPDYJ5jh1h2T5nM6Q9bydfBn0ctPA4er7Ca8t6BjI8P/jO62VGgXmWx4/1n/vIyPkex9+7gW16hWEWk9UecFJfN5tNY5QHOUGlJ2WmVTnaZYPFC+GUANkQ2t/fbwsQwNb9aMzYG4okOU0O4v8UUk9uPp+ahPZy0HYMSd/LbyY+nqlU5DYIcu+kFS7Gb/I1wUYqK9rHtb7fRob77sVpsVh8dSwUZF5kVVaE36mwdqAcHBy04yR85iZKN6OfLHrIgVMQPYYeN3vg1uv1qAiKi5IAZEmDtyfKz0auXKABRQrPc+HEiHckjc887uzrs4eLNmJQsFDRJviAzDO+RJABSx4n74fgOW4L7aYvKGR7SD8qWQfY8Vc1rq7IbxsVyAhOKwjd6bkLeY7jRLDxkbKEoyN1CA4/5l4ulFWDbmFxYd57TqYTJPvmBRtKh2R6yZMMzq03AWiAEvcjHbK8h2J2pNEjy5Z32uxFP3lv0ML70pHje+3EgC+Mg8fdMuU2eOHnHU7jRbfYgDLfeR/fpSMN3QAItOGXzgLvlX5+fm5r1MnJyahtVeP92V67PUf4jVFIFOmtbBXG1sY5BrP1pA1DU65Btg3cJnjj+cM7PQ7mD2uY9aQzYpxKzzvSsP9I9PPPP1fVkHmAAyTnStVQbd0AwvPN8gNhezpzw3Zn2i1VNbI9rDfSNvU2NGQP2UQ3e16hTwByzjS5u7v7yulhveK2E6lyxiDOJBw/FCcFBNHO+/v7Wq/X9be//W2UmeEoJ7oInt7c3NTFxUWzlau+dtJ5zeddROTSfvLcfnl5qZubm3YPepstd57LaScb9JlHvibtPPPVe++9fhmMe+2wU8G6mOdbTtGFVcN+VXSoZcAOMcYj1xC+m81mI6emHS/IgvWeZRt9stlsar1ej3ibeMeBD/OF+eLsTN8Pb3Ld8jz2nCB4knMKcmYabYEv/vx76LuBLWALbww/hJRR7G44EQYabYPMnbFHiwWZ0H9uTneZaEeSvL+GQQVMOG2WNhlAuix41bBx35OZAeOcNRZzjASEwiA4PRs+g7Tq6722fObFmAliL5yvT9DpBdjRHCK4TD4mq+9hLLwoZ5vsSfW4up92RljhMEns+aEP9mrn4sbznp6e6urqagRqGY/0ONkgYszdH3skbZzO5/OWDoWDBLKB7T2NgF6PDXyi6AHyS9QAIGnFbQVkQ9JtRzbt8bSi4x1s8s9jWTjSxak2KFBkwOnUVdXAKWleBvo2Og3wcXAAkj8q2ZtqAAjBR6fEVVUzxgxYnMKDfnLRNs9XZAJgi7FiskPQeo85SqEWL1z+QZ9bpp0CaH3huZ3gw4DBMmvD32lHyBNzLQtIIHuk+N3f34+OgNntdqNjRuj/drutzWZTZ2dndX5+3oxtp3PBM947tVblmkDqn4+9sBd6NnstwLG3tzdK2TZvrDNsHFqn2OE3m83aSQDX19dtW4PXIHhKO/0Or1tkf3DuIxkFyDR60IY+a9/j42Odn583g5hxWS6XrWgIhVhsFCeIt32BrBs8MAY25OlvZkCZn9gRjJ9/FotFrdfrms/nLQPHmVg804XSPL/hB0WJvDcd/iDv2ErHx8eN17Z3Pir913/9V1WNK/lTSM2yzfpBmitON6+Nrubr9ZKCSCayVQyOkDmnhbNO0j7bNd6SxJjatkknGNFR6xH0AxlmyJadG8xZQA0ZWV7PXZRvu32NBn/69KkBU0Dm4+Nj/eUvf6l//ud/ru32NV0fe+Ph4aHW63U755XI2OXlZf3yyy9tvi4Wr6c8eL4nHwyyDcCcwYDdji7GJvA2lCxYaFvdjgjmaMoA44ZD37xC17JGuw/w3VkZviaBm4MA6Fba55Ra+sM6wLpDH2yHkxGAzjSv0llmTIKO8vpBoIWjkb58+dIcGZkZYCANr52lxd92sHOd5yxzxDYAPLI9ZEzEfMGJOkVcb8zwXvouYMsi6jQDzhWkcwx+evftlYLMHBjgCBSDt7e3V8fHxyMvAt9vNpvR4nd5eTlKN+I3isbeJAT/+Pi49vb26vb2drSYky6ayoeFEMDCO3LyZ+EUJoCjDl6ATfCtakiRnlL8vM+pAQgRwIuoLwrXXiUDF6duuF02ML2fqOrrNF0AoCv0AWoNwriW++Gtx4k0MN7JRARYGuTBH6ckGtzae5/tdyou42ZZd5oSjgnkEiWTKckJsmmXHR8Yjxh8uZ8DAtjCU+/DywwHL7JWmnaSoHCRA5QNiskFX9LztlgsWlonMofCzQXPTi5HQj4qMbcc3bGMoAMyOsr88wLA3KkaPKTIPLxNg9Djn+DIiw1nrdoT67lrkE6//NuLpFOl+M7j7P895yCDLD63fvDnNjbNP/jiPeae41yba4BTr7nHxpb1tN+L8ZTgzGPmz7gH/WJQCP+96KdDxE43rt3tds3wRRYMqqwDUpd7DOwA5V3Wm76GvxkvO7BY/3EGuPK3jZfNZtP2nqVMQSnXlhX/uC+0F/3owo2OcvgeR8TQ+VMyxrvtyH9rfkzJvyNufOfiapbxj0rmkY17O27Mc36mslWgKWM3n2Gdk9lFdrD4mX4u7eU7nm0nkecx89B2Jm3xGuzxdt+zL7wjdae3TTiyhg5DN+A0J2pGkMD6zxFNtxHd6r7YpskswJR78yznFf32Wuc55XGBPwZhuSZYJ7AemP/prLBNnjJoXZvjDfl5lh/3y+9I2XObeYftd7/L/E+dmDJugGl7wWPk9qXTwlupHLxI+TSg9VyzDZLzwP10/7M/nnuJRd5L7wa2DLoNpqohulA1RFe5jkIRdMxRr6lGYpxUVTunlXdYubCoQw6fVw1eDj6z58yeCLfdQp2pHymUs9nsqxQvnmEvCNfmT9V4T4I9in6HUz/T60RfEChPCITJkx+Hw2w2a2CcPuEhdPqEgbL3KKWnLPvqVDvG06lEOAXsCbKC8IR2NWLzx4Y4wm9DEUVi5ZMAgOd583rKkQEa16M0GH94jUIlcut37Ha7USoQ/SYiYSMZObXBzHsyEuFItvniRdcK33LCM1BgGOsY+aQswQe84YvFYhStZ5zddis1pyJ+ZILn9hjPZsN+bHhox0k6DHCWXVxctIUH2cZZ570qVfWVTKMXnfIGeJvP53V0dFSbzaZub29HXnKMfAwdy6xT43DA7O3t1fX1dV1cXIyAM+PN/wAH75e3fsI5mh7sxeJ1D2UaGX4Gz0Ev4QTzHjCPD88kO+f29rYV/8C5tFwuG/j68uXLJDgmjRk9w/vxuDM3PcakC+ZRGVOZL7SVtcS63iDz7OysDg4O2p5OHKAGq4xD6j8yKNii4C04XmMweG2gONpbNURg2L/3/Pxc5+fnba/t7e1t3d7e1mq1qr/97W91d3dXnz9/bqnJNpDoSzogrcesb5k/NvA3m01tNpvmIOcZdjbT7pOTk9rtds2Jmg545MyO0Sx8g85M4xdyFIs+XV1dNXldLBa12Ww+NLglC2Mq8kRGhG0Z61PLgIMI/G99kE5C9CHvqRqqvTsiVDWdep4OZ+wK5lQ6EDMjjjlPG6wXZ7PBMWiHNO3ILU+0PcHM9fV1699sNqu///3vdXZ2VlVVP/30Ux0eHtY//uM/1sHBQV1cXNTNzU09PDzUly9fRuDy+vq6tQc+cIyOdRR9IMsBG4/vaFfKPPOAftoR6dNPqmp00gLP89i6LoN5ls5IrreTzhks6Jqqocir22kZsHxgj7kNEA4r9F/VEJlEzxpD0b90wOW65ywS2uFrV6tVnZycND16f3/fbETmHTK32+3q+Pi4VqtVy1ioqjo9Pa2Dg4O6u7tr2ai5fvDOg4ODOjs7q/l8Xjc3N+3oPeOtqhpl+OW8saPQcm+++P/30ncDW+/jYUHCE3R3dzfKm7+8vBxNCEcGpwjDjeMbco+lwdBms6nFYjEq1OBFG4ax/woGOdqJcBsIEWFBaPFqmewByYpe3pxvQyS9DwZhntz27CIkd3d3be8IHmmOpuEdkEGfPV4YbVSZs5KCH67iB5GCtVwu6+bmphmhTBQ7C1B2GVlFKeOQODk5qfv7+7q6uhrxlPayEGDMZ2p71dfecUcL/b/l18YjSgQee0+ZJx/XIr8ADRtMkA1rDFneh8fUhtuvv/5am82mjo+Pm6ENv0mfsyKzgt7thoI4WWDFUcJMJ7HThj46BRNvL8DImQ8XFxcj0MqYAY7JcOBzxod02/RYfiSC9047tAEO/31t1RiYpnFgZwf8tsFV9fWWBC8Snh/87UXF33Ov35/jZSOFvuXiTrscmYT8Lv5Pz246p/iN09DGEnrGxox55f7RDuatMyWcQuXxyvb6+9Sv/M3nqRv4LKPxlgGDUM/ZlBV4Z0en53S2u2q8v9hy4u88vtavGKUYZIy1t3DQHvT9Wx5/O7nTOWO5cCZKGlVcY75YnhIA5zrLOKXjM5+R7/H7/GxfZ97bUCNrCt7RFvpo5+9HpNRJU301YPP8Zc3lGj/vrXfY3oK/OdYew7fa81ZfPGdSH04BAd47JS9T/UjiPuaM9UempLKHGxvCtQg8j30kloGT22de+TvaQqZW9sltzvnjPiXvpyKh+bypd/mZGRDw9R6/1HHZprzuvZSA039b5qwDpij7l/Lka2izdXGuH5Dblff7GfkzZb+lM8/Xu51TcyDl47fuzzX1W/TdUNgD9ZbxlIuY0boXZHva/AyDOwsWRpAZwzMcbePaqrERaQMExnmQeX5GvNJwqBobgRYeKwu/138bZKSy49l5jwGDlYkXcNpjz5+FxGDPUZ+qGvUvjS7abE+jJ6nJRrP30/B+yvzn/jobaXjg7bAwP6BvRQrdb/edz5MXTgvjc++38ZjO58OZsna4WK5ciIC2IqdOlWEPuD2lU6k07ruj6VOGb6bNu7+WKT9vtxsXpTGvMjo/tUB7sbQs+1kfOSKBB/nLly/NoQboQr74nLmM881HCFQNoJBxTIcBWQYGYXhcvccU7751L9FF9A9EW6qGseNZOLHYdsI1FHEjtXQ+nzeHDnsHiZLgNMl5iHPMkRjag1MRPvz444+1Wq3ql19+adFWikYxv/LYDvqIXqL9GRV09IZrva/YegrvtNv69PTU2uM9UlVDxgNzjGd7zbMOQV7gv1O7oN1uOBecPbaOUHptYwy326G+A/zCaZwO68PDw1ZRGbnFWEZPHB8f13w+b/qcPc7IyadPn9o7iFxQfZlIPlsl7JBBJnCOWpfaBvB6mWNNG6zTHQnEKLeu9FrF+6zHuN7pmb7PQNzP5LiR+/v75ozEOcKeuqurqw/t+EMmHf2Hf97qY2DiNSPTeOEr2SCMCWs2WTCMhYMV6BrbgmnzGBSkrcc9nrPILOud120+t46FDz4yDzl39NLHTxmA2M7CHvn06VPTyWShILfO4FmtVqP5wfyFH/v7+6PCkVXjwIHHjbajI8hkISvQRYwIoMCD1WrVglBOmXbUEtsOfpP5A8/hVdrwHitnYjg44bE0pqAt1ErAZoXP3E/7nPFn8IVDH56ynmRgj3vQq87qcsCCa1NuCYjN5/MWnMKJtlgs2l72jCzDdztar6+vR0E0+GxQzXtfXl7q8vKyjQ3zizXG6yjXs/3R21UTaNu+pc/eYvgeejewtdJOAOm/+YExUwu0K6hlGkl6Uh2h2O3GlRgBfLPZbLShPO/jM5SZJyuKa7d7jQIzMQ3c9vf32yZwlJMraVYN6S1s4MfYqBofPIwROpvN2hEwVo6z2aylEDiSRiEjnpFeNqcLMint9fY9TH5XZquqNgGyShn3LRavm9JRUvCwalAOKIDVatUiiW7rzc1Ni+yTsmNeErGkgIf38NnbSDvv7u5a1DUdGga1GG72nlNMC2VgQDGfz1uhF8sbExWjbrFYjCo3IqcUo4A3pKJkhgH8dR8N+C0X3OOsAuZTyuPDw0Orisw4pRx6Idhut+2YLsaKd7tAUNVQuI1562g0459pRzbKPyIZJNmoyQUhnRBeUDOKZr0K2WnjuZu6OR1xBgJQLphOr0+yvs4FbioC7UWpauzdRwcZ2KXDBJ5WfX1w+xTYpD/Indvjdpl3tNHRdBsOJjtv3CaDwymnGvc6wutn+u+3ogMeV/fJ8z2faT66//5/irymc43BYI6FDUQ7V5EnDFwboLznLV5N/Vh+PZbZ9qnP6IPbiBwz9vmTz0iw4za/xV+/03xKeuu9H4mSvzhIFotFO87DYAZdmGukndhOn2e9Qk6zmA1ywJzJqJ7HyuPtz6bky3MkMxC4lvXa0XoDcZ8La+c8/ZqaywZW6Jezs7O2TYIMN2wZZ78BFLGLSBVFR+MY8Pu4HiACzzJTxI5+svjIYHTVZq53fQPGwrY79mBVNWBsXUFfbNs7kOJ5lfhkav30OgwvsRWRvZyvqUsgxg8baT4fUp+99ll/Iudc4xTolDnut7PcGXoG3owHfGAcM5jhs4kNMLne88WA09mm9BuMQBanbW7GgX7bYeJ3IsOuEv0eejewzUXFAm0PzpQ3xADLg+NFxgNsAGnlYqYadPG9hTMZZ0Vlj5gXaHuO3U7fa14kXwB/NnQAItm2qYXb77BhaB7zY+DJ9W8ttDZ6USh+Xy7+TArGBW8T/HEfPCbmKYrAbeR6FigTk9+OCD/bi8rUb8uOf7vPVtwYaTkWHi/amkac2+KIbhpoKT8GHVaCadTaO0of3jL4vMBYNlCgU97vKXKfUmGn4errbdinM8FzMJXzRyPStDebzWhvfFV9JVNOV64aCoEgExhuJycndXBw0PZE5Zx25MEG43w+b8bi1dVVc5DhpPA+WD57enpq54HyfJ6B93ez2bRFMnUVCyuRW+8Fm81mjS/n5+fN6ZJGQs4Z93e329Xl5WXd3Nw0g5ioA7rq8PCwFRmsel0bLi8vR8CXdlhPcbyVMxMcSefeT58+1WKxqIeHh9H+0KOjo7YfONu/3b6mTLOX88uXL6M9T/QvK9NnSiNyk+OPUVc1BpY2iHEeVg1zmv7bIYeB5C07nuPeS/rrr7/W+fl5+9/6g4yC6+vr+rd/+7c6OTmpv//9783YsQ6d0vfwn7Z5jyPplcybXHuqhnNQvdd6Nnt1flMnIMEqz3ChQs9hV4nG+ZfAh3alc54IUEZB7NS10+Qjko3k+Xw+GsepeW9bpKpGxjvX4ix2RNURYcaKjBD2DPqdVWPAw7g7ypdAwrZrbkXzmoeetawZSADgkS10N/LDHHVgx58ZIMxms1YVfb1etyr63EOgBJ5xVCJg1zY6OsVz2vbxlK3N/eYpn6Vt6AJF6GfmOkEdgzt+Owsqt+3Ycco4uOJypvr7f94B/x15Z+6uVquRjWi71OuXeehnwgv0snWg7V7bb/TZwNxRcfiEzrR9xnixrnkt8VgZP6WsJRZK55wdG3zPNkX39eDgYFS/wyDZAQHI84v/fVzQe+i7IrZmRBrhLGZO6YThR0dHI6GAmOSuasznWRXZqaYGvk6DQLDZpO30Kybl3t5e/eUvf6n5fN7SMzDcfCyQj6ZxWN8pGh5YBhGvmQXLezirqj0jjV8fDI/w8myq7k6lPcxmszaJGQ/449QOp6Zst9sWVaQ/KGLG7+joqEWL1+t1nZ+f1+fPn5tSpd1MzqpqBcMYW6eIo8iJzFoWSKVxVId2YSSnw8QLEZPBMsX7MSTpOz+u6ObxZPwuLy+/iq4ie8gl77SixUtZNRhKTslcLpctwwBggaw4GyDT5DJFI5UR15IO6IU20z641vPbUSc8pU5XsQPL2RAHBwctFZKxwDNnhfSRDTcb0B4LK24vgnxvykXDYCsVvQ0Ng0K3IR2G6Yzx3M+2TBk11hH+PJ/J/3w25TTJv90+88ptsTMI/TE1R/IZUw4sr2dT4HqqfWn8JZ+mnDcGcOiGjBzYiej3uv3mrYnvp/YQTvE6x8Jtt66wgUg7HR23Lsl+G7yQMk4/vd885b1q7L135CD7nPxP2yLHwEYoz7YBRXvs1E1e2nFtecy+5Hzl/VP8Mjj4yPqRtZZtFRxLlfLtdd+Oh81m01LhfRSQDXWcMRj9s9msHXvy8vLSiizhsLM+ZFw5ZQMQmHMLYm5gp93c3DQHoo9oscxhA9qOZD54z7+LC9E/zj0GnB8eHn7l2MG5enJy0k49gMfYAz///HNdXV2NQBfZhh4DgJWjcHa421FjmwCyvoPPjOVqtaq9vdfig7e3t42PVcNWPviA3U077VisGuanHRLYjKenpzWbzery8nJ0iojtZ6fJYrNQv8cRxqOjo2ZD55qWNpVPIkGH8R76x7XYis5E9fYLb4dBpsge9fYReJVrNnzDDgckoo/43FFq0suRAcu8+857Z7Ohtgj7u92Hw8PDVjCW4qoeB/pgeyXlH3zzXvquiO3UgpnX5HWeeO95hheKqZ9cRG1cvfXcXHwywuU2vWUUIFhTffZ1VgZesHjPW4Zkvj+fnV6NKfJi/1tGGs90u9MTZ2Nxyjvle60Y02B4D9kY95iaX1PGSxqDUzICWcnmWOZktbc2xyVlMj93u97zXfYz5X3q3m8BEY//VBvfasNbbfpWH3J8fsvA/Kjk9C4fBI+H3NW3q14dHEdHR/X09FSfP39uC4vl+eHhoS0QBmde1BzVsrcZ/YZTyBkkWemduc993JOFx+gXC76jBqRaefsF/djtdnV+ft4cTJzh6T3rNt6yGGBG9TAccA7ZWcc5qgn8WYApnGeAwsKPkZVACt17fn4+ag/GFUYA+1K9VnA9/KCP9Adeks5vXWr+5brCmOKgJfJi3eXMIRwSvNt98Jy2bAEsMPrgD87azOJ5ax3+/PlznZ+fNyczAMdyggOWyqze/uL3zGazBiYYY+8lz+iUt7A4so2jmCKM7DOkv95LB+BgawrAx873qsG5A7hifE5OTmq9XtfDw0MzsOE7/bfD6CMS8kC/nfGR6yljXVWjcWV8ElB4jjnCZV1qO+WtOW6wZv1lPcm16EvmucEL1zuaZvCMHrc94uvSKZI8MT9ttyCzU04ZZ3DZlmQsvLZbP03RlJ1iIOK2sd+d9SLfkc5Cr0N2qrmdXrsARe6D117W5Cm70by1bPDsfC/vSzt+yqZ8y0b0uHvdMvC0cycpbcO0y6fw0FRU1GT+TzmA0U0OOGWbuBbd6uvcv+zXFDaEPM++l96tTf/1X//1ux/eqVOnTv8/0NRiXzWU/veiDrByVoYXSyi3YUC5iHmhdHoa7+E3BoCjZzyvanxkUVU1YADZuPSC40XaVdJtZAA0nFnjSKUdSmmkAEoNUjkGw7yvGrKAbGj5GtqPB9iZQlPbA5LXACsbXIwxfed+v5cxnPJKp9GRzro0nLJPAE+PO99nOiCfO+MmDV3Lrw06oic4T2jTlDHlPgI0cVp4n1/KHnJmIGt5dUp/8sz8QAadqp9gxYDfbc//AUxZ2wBK4JMGIA4nIhhvGbwf2fGHLsusOBveqZsM1nDwOTuJa8mCshPEzjF0GNlDpNtbLpyVhw6yU6dq0BW0hQgh7XSkyg7O09PT5shhbuTeSe815X1uJ9HpKecRa8lf//rXJmeO3r28vLSIslOMDQBpF8/LeZFAnLWkasiK42++r3rN4Lu6uqr9/f1RTRnvr+bZ3Gcd77FEH9gBBT+4n7Y/PT3V9fV1czJa11j/2VmCUzb3c6K/LAPO/EsnCf1zQS4DeRyQOLqfnp5aRh/9tJPTZPlwlJw1KOvjOFU/CRlAfqyDaCdjxDzCMQU/nSY+n78Wr7q8vGz3EaWmHel08fzOIyGtR6f2Gn+LPrabsFOnTp3+D1DuR8HQcrXLqgEcYtTt7e3VDz/8MEqxY8Fg8XOanc+etTHCQorx9+nTp7bAO7LBImTgkIDW1Yh3u12rPko07PDwsDabTXseVSN3u107+9iGAwbm3t7eyEiwkVc1VArFoPFCZ8ACbzOyAnk7AemHubeHfT82yjAU2JNH5I17XKESo9b7uXa7XTuDl9QwCgUyjrTVVVmdbm7DjMIcNijNj4wYQDa8aAd7nzn6zlWaMTgMIr8Fku10QPa9b5jo8+PjY0s9I5LmbALmw263q+vr69ZPopuPj4+t0rSdQ5Yf5BW5cfSG9jCWdrxwzdXVVesbMmV+ZLQDI3Rq6wHju16vW9+Ym3d3d61P/78SfM+90xnly6gUcmkjOsnONY9bRp+41sCONlQNejwjbtk+5MfAg/RVO63yne+dy9Zn6QTxZ+4351gD+pLvb0Xtcn1wXzN67PZOjW8+P51u1uPmRz576vmMp9+R+t/OMcYi06SnyGvqVITexb6+1d/8zrJA2y0LduL53ik++FluH23MeZPXW/ZSpqcyMm0bWN/73elkZU3xdXZW5/2/Jffv4fUUdWDbqVOnTr+T0kByGqiVOAsNC9F8Ph8BLHt5nVa2WCxasYiMvlWNFza8p47K0S5HvNLwclpUGu77+/vNoKfNXrBZvCjnzx4y70lyxNhGKAsnRmUavAZQvNN9ykgGDoHcc5Qeex8/BL+dHpZ7yrwn3+Nu4OZoS1WNFvlM64KXfl4a51NG3pTRl0aH+0Khnr29vRHomkoRo10pu0k26u1gIDrJnntAIqDUAMNjh3OBPV4Yw8ge77FsQrQ/AQ88MK9pBz/IK5Ej71tMIxAZzaNi+M0PfHP0hLltZ9K3xvOjEf1ljHxkmetiAKYYfxvMyJL3oDL2ziyxUU5BOZxqdhymcV81bCuwzFoXWuaZAzhbDFBwZiEH3h6A3FUNUVl0B/fxDgrt+Yg4CF1PUb8pEM2Ps0ncduvNjIZbrzqCyniiIwwu03FDdobnprOHchz4zXzBSVk1RPBwbFIEy2sec43PdrvhpBDLkmszOMvJOtEp5Gy94DQMsplwJvv5PqXD21UM6i0rdpJkKrDHmnFJJ63bjo711ih0nzOxrL8chU0nB23n3VPzwH0wv3mWsyEyHd/rD/oAm8GFDHG2v5c6sO3UqVOn30k+jot9RSw4FKNwqvB2u62ff/659vb2Gkg0kK0aCo9hEBskmbwvlkXi5uamZrNZK7TBAoehAXhh4WEhZ1FzyikLJIuxo4lV4/PxDKh4zm63a3so07j3kWAYdo4aGpDSBgD2YrGo4+PjloLFwvrrr7/W1dXVqC+MjUHy4+NjiyAb9GCcrdfrenkZqqkSDc+icxQqZE80fLcR6qjFfD4ulkJqdO5PRmacslY1ZAMwNhwtZ+OHyBh8oapnnpXstEMbNHzO/zYYHVlnzLnehUeQN8Zsb2+vtcORCjt6bDBCBpSz2awVo3IxRqd4J3B0JfDtdtsAPrLiPiGnzNPb29s2rs4+qPp6D6cBtgvnOSPCEWP4zDz4yAAXAGj9kFE0+O4jUQxGMipH5klVja6vGpyInMnKuBtMQAaojHEWVOKZdpTZeE+AZh2ILNNn5mZVjZx4zv6w7nZ9hASB6BLLT4Ii5oPniL/zGBnMG0iT/cB1/mFueW++9X9maSQoSlCbTgv6hu4C6HMt6eAZceU9dlLZuYx8GPjSLjt9vU5RlIsx9LrMe1I/W66gjNJa1lOOTXZEmF8Z0UR3Wy/ZcY3Tl2symp66yk7mKdDtdTqjvy7C5fvJHGItT4c2Dmoyf76HOrDt1KlTp99JgFPSiQ1C2F+FMseQvrm5aeljVvYsEngziXxlurMXJTzELMoAq3wGiwbGhtPW7JH1Ap174DKa4jQj98MAA4937vkBQBiUZ3S1amzsAEbW63XjrUG/veiA81x04QmABTDiVNTVatXSie3J9wKPYbdcLr8qJmTDLftkY80plhn9xGlBOq0/51kZGaIP7jPAF28432X6Ltfnj5+JUWTjjTZhrCAjBroYiFU1inC5z+6DDTOPi6t8wkuMtpQbZAynCk4Minw5ol1VLfqFXLI33Cm07i/EuKdjwMY2xpv3n2dU+qOST3MAiDg9HaeCtxlgVBvwoY+qhhMXDJINRjGqkb8pYMhvxsPbLniP98c6umwQOhWBMoifAl0GjrPZrEUmq8ZH/BkY8Tm6b7PZtFNHcGThjPHcSRl2351Fk+DFIJS9uIwNc4n+2EkL2ZFoZw76imc4wpg8YquB9S88mcr6SSeHeQk5E8prGf0wDwwEOWuYcfO6YccfbUTvOmJvnWN5tOOD6w3K4WeCXYPg3JvLWuf/HWXnHci4zzSGaB/ZPpabdICgW32d13g+8zpK21Lvw087Od9LHdh26tSp0/8GAmQAvjAqWFi8ABG9YiHz4lI1VEW2N5oF1WDHZ2dWDRFAFo0EVfxPZVve7cgg9wMOc3G1gVY1FOCoGkAEHnW+cwTbABwDkkUNQ8NACuMFA8J7J6lmWzV98LzHhrbbmKaSsY1I+odzYAqoOLXUBasweIkOJdiCp/CKaIv35zlakUYMYw2AtGzRTp7hKAZyYu89/bUDhH76nGH4CbC2MVhVX6U5GtDb8cJ+XxPX20C1fCDHPI9xxpng5zilz4YpfbFzxxGANPIdMWa+8VzemxkU8/lrZoYBDmNu+WZciM47ujE11h+FfFyNj5Lx79VqVavVqhnXNv45EqVqMIC9bSONecutz0bNiJfn+2LxetwPZ4ff3Nw0PV01GP1VQxYADkU/23MSBxl7zu2csu6juJL1DtcZUPPZ0dFRnZ2d1Xq9blkrFxcX9fT0VJeXl+0oS/hnHhnwuQ1VQ9Q8AbszW/gsjzDMNcRz2M5Giu+R+ePUXUfw4BH1G+iHAZW3Kbg/zFU7OiwjzqawniW12w5mIoZuC/c5A+r6+ro5IKlFwTFBjMNqtaqDg4O2594OFLJF7ByFDybbEZZlOwj4zHKeTjk+XywWDbS6zkc+++joqDlwKQiFg5J7WE+RY6+JCYRpp9dUyyrPd1veSx3YdurUqdP/JmLBnDJmcwHlbyKGnK8ISKVQDUYU11P4iGiSvbA+1xuA4Gicwcbh4WEzIlik/QwblenBTc95RlXgAwYlC+ZqtWrAAC+4gSFtgy/8z2KM4QUQzCgagDIBlL3CBv2r1arxwfc8PT21FGSnmE0BWwNi2pZpiNyXR8Fg5GWq61vA1pGgjCJ6vx7tBkzyXqfEG0DSF4Nap4gDMhhjy7NTmS0XVYOR7z1nU577qdRUgxCuh5cYyJZr761zJJl0VMbVUSAfwYV8WPaqauQsIB2RuWnAe3R0VPP5vAEg5Mxyytg5NfojA1poCoT6rHnGD4PZkU/IQNQGsOXJcwjA6eOj8l4DGGejeP7xfMtBkp/r/9G/yH46u2hHgkE+Z76ivwyq4J3P88XJBODwdYCM3JrhPaZvgSI723IfLro5s03m8/koQu9UU64zcONZ1vkGTU7bp5853iY7yfxs+OFopmWB9hoomw+Wn4x8OpMGHpnSYetrrIf9TEf7p0BtOhP92wAfOfP4MwZTDiLaYT1mfeZsA9cmSDng/flMyPz07/8JoIU6sO3UqVOn30lZAMVAzGCoaqzAbfT7qAh77gGNVYNxxeJSNRSU8OLEIsj+T67nf6dPkV7lxbBqWFicbmVjKxcnL0iuXGvDDODkBdTGqqMf5hOf27BztI/F2vuJeZ+f4X5hTALeWLjhKVEmop1p1LKgO/ppA9UgHX6lscx1tCMNmarBGMijMbwfzgCb7+wRB8ClMWujjXeRKeBzjJ3eZ5BmA8sAm4iMzzsGOKQhZL4lnwEms9msVeLmXU7zw2h3O5gnyIX3bmLMGRRk5M1zAQPYjhzuwyHlrAMb+hh3zDecD/6xMfsR6erqqqqqRSdXq1Wdnp7Wy8tLXVxctPlXNd5KgOPHOgWyQwsQ5WNZFotFi5IaBEFEGq13r66umu72NgbacnBw0Pbme9wAzgYYeSQQMjjVdjsSPUftiDK4se7+8uVLLZfL+vvf/17z+bx++umnur6+ruVyWavVapTZguz7PcfHxyPg7X4ZGCO7OIkoQkTk+vHxsVWBPzo6qr29vebkIRK62+3a0UNEyA0QvT4YRFW96j/GgKOVbm9vRzosnZy0HacdwMtOKZy6OPK8rnqtMCh1xJH3sPff66gLLyETyArZXfTfaxB9cKEsg0UT2SNTKb/e6285tqOgqlq20GKxaMUssT9oE/aDncDI6Xq9ruVyWQ8PD00GeD7ZFnd3d1/NYY+NC4xZr2d/30Md2Hbq1KnT7yRHTh2xY7Fx1MZpl/v7+3V8fFzb7balkl1fXzdjyADCBjOR0KpqhkvV+NiKqldjAAPj6emppUKxcHBUjz3lbv9uN1QnrBoAU0YyEqSSpmbjDX4YWLNwGlxltMYgEKBAH1msARYYqERe3AYDKe5lgca4hTDEeJajPfxN9CONCTsjvF8Lw8FOABvGz8/Pk2lbGIYnJyc1m82aE4N3397ejvasYqRBGBc4QACf8MxGIREz5MDRGDtV4KvBtM89XK/XdXl52VLQ2V/tvbcGmOavPfhEpA4PDxsQcuVtDGXk1BE4jEgMcDucADQUWTs9PW1ACKPT0TKnDPq8yOVyWWdnZ1X1Ct62221LZSR9EZnHYeCol2XqI5OBknVkAgfLgg38jHLyrIzS2bmHbKZspWMxnVIGPLSt6uu0VX+XUSq/m+/tsJuKTqVzA1lNB186xphT9M3PSh5PkZ1pbzlafK/HxfxMct8812n3VJpt8tjPpp1eB6cca1NyYb0y9bnlizUhddFblPLh+6YiqX6mx9iZVV4zv/VunpnZCNmufK/fbafPlNMVPgNoLcdeV9Mh7vd8a1z8O2lKDt5DHdh26tSp0+8kFL2LfKSH3fv1+GEPGIp9sRiOFjBwS2PCaVJeMHiOU5cBg06T9v1QGtn0Ic9ctRHn+6q+jvZ6wQWsOU2bZwJA8P7aqw5ItCfeEceqwePM8zLtzUYfANNRvTQ4iTJWjYH11ELr8fQ4VtVXUZK8J50X2V7Gi+iFZcxGOM4Cxt0e9QRSVa+OmHTA8A7G0U6MNJx9z1Rkw4YShtNUVNLOCgA3kQ6cO7SdiKuLl9FWG16Wbe9VTCcL/8PjqhpFfVO23ZeqYU8cZ0tjqDvCnQ4GHBcJwD46sGWfqsfh/v6+8cn6qmo6LRHZyntw2DCvcXrYgfjw8FCXl5dV9brfFz2W+1nX63XbM4mOyuhlAhM+e3l5qcPDw1ZN3fsPITuLrI8SONKW1WpVd3d3dX193fpM1PTk5KTtd9xut/Wf//mfLeOCLRasJTiFAMJ+p9PyiYQ6xRfekFmEIxQev7y8tPaxV9OZMMwZtqMwj125F10Bb7gfB6J1IWOd20G8Z3u32422X3j7xHK5bE5C9iLb0ULkHEed13HqMUA4meEP8z/XV+Y5GVM4lKuq3ZfZOqxNdprzzvxtHlonOxMiAaszr/gM/Um0mPahy5w9YP12f3/fnISMF+snxRerhnoM/PBM5MO88lpsB897qAPbTp06dfqd5P2ZjnZ5wUhjn0WeVGCiq6SNXl9ft0WXZxpYOk3ICyjPWiwWoxQyqskS1fQiZgPNbeb3bPaaXnZwcFAPDw+jVGP3m4WKBRNjhnYQMWTBXa1Wtdls6vn5tfrwer2u09PTen5+rvPz8xHQIjWPqDMG0v39fTO2OAoCQ+78/LwZUTYAKOhyd3dXd3d3I1DIczDyzJf0QFcNjgIbgYB10rIwEmx4PD8/1+Hh4ahI0243VNWEh1OVgqsGA8VFqNwn88yFlp6fn5sRcnx8PDonk8i177GBSzsxbMlGMG9teNLXqXMIAQP0a2/v9ZzIw8PDuri4qOvr69aO3W5Xd3d3LWI7m80aOAc0MV8yomBHiIEt4wwwWiwWdXZ2VsvlcrRP1g4m5hyZFi8vL/Xly5eaz+f16dOnFkF/eXmpzWbTjFii8S6+4j2Rb0UsPgoxt+yQ8j5QZz44kpRzq6q+0nV2JFhPAkwcKcSYppiT028x5tFxyJK3edCXdHoxLyg2ZX1vR5WjjlXjSG8Ce1KJ0bd2MKGfmN/39/d1cXHRwJy3nBickZLs+Qw/ycwxr/jbIIi5QjEhqjAvl8t2jBbzB94aUOf2B+s0+ONxzxRVz8sp529VjUC1nZSMH+fRppPCDg07oXiHzwuGF8xlj+sU/5B/0nnt/LK8wiuyVdAdrmVgubJswgM+z3UJmfN65vamPk59aUJ/7navWTMPDw91cHDQwL/lBZ1JthhjQ7+85SRrJDjD573UgW2nTp06/U7K1LWMrvo6R2y8wPs81aq3o4J854XcEag04DMykIttRov87AQ0GZ3Le6aM0VysAEb2cLs9GJzwyymhfofTmN/qr/lXNd7T6iikeUlfWZBtYJnfNtIymosxmWOJcZD8oh3fkp/smyOqHpOUA3husOcop9v1llwwhjbEUyYtX7k/KtvFPewzoz0YoUSPuDbfZfK95lUaZHmvgS3f2ZnjqLczDDK6gny66Ey22amXHueMBn5UYp96Ai0DHke8EwQ5auPsA7JgvLVht9u1lHoMZ+9ddDSfOYEuYt672JTlMsEQYMMZIt4mkvMYUMiz6BPOIN41teWE52+327ZnGWeh94/TPkc309nDs6qGyJ/T9onKWb9ap83nQ1YFjlKOsOPZb61f1oE5T1x5GRDEPHThK8hRvdlsOPKI+VY1ziKiTzj+6IOBLc/lb+SF660/kWmPsZ0a1k04AWmjnRw4Qu109u+p6LHXFOuvdBTkWBjkex65vQlirasgrwM4IB2F97FmuV0Jov84eC2zzAPWgw5sO3Xq1On/IHF8B0obgwqQZkVN5DQN5/Pz8xZFc7rPVNqvK4oaOOJRtreZ5/s8XCITPkeVhdYHs1cNYDArSbLI4lHFMPG1LHaAFKIPJycntdls6v7+vm5vb6uq2l7jn376aWRMkfJmBwCe8qenp1aMxvzH0Esw72gFC29WjcTwILpBqikFUXxerRd7Gyl5HueUAwJDzCCeduLBZ7xsBPNO0iYpymF5Ik2WNmQFXwpRXV5eNh5iCFIlGg868sU+XXgIWDBvM00ui944ajCfz+vs7Kxms1nbj0pU8/j4uB394qN54JPBBqlvpIBmkSCnr/pnsXgtXgPgADBkBM2OASJl8GqxWDS5pQ9QygntZTwNdEj3+6h0e3vbolNOEQbEklWBvDJ3kXWORrNxzDWc6Up6J2NRVS06m4XhvCUC/cHccqSxagBNq9Wq9vb2mu6pGiLRdpSRYpqOnIeHhxbZXK1WTV/bkWSHH/1IUP78/Fxfvnypq6urURaMiy4x53z2LG0C2NhRY73nrCHk3s4cp/o67ZZtNfP5kDFkQhfawZPAlrl7dHTU5jO8Zi2x3jWwJUMJftgh6nHfbrctdRrZ41l2nnk/KfoLXjiC6lRp+JAODOTE0XfrL2wF5Ne6g/Ujs46mnKWsAXZ62pFGe5EveGIbxfeYPCf8mfmf0WyvOQbgnh+s8RQg++WXX5ocsh58r37swLZTp06dfifl4jG1oLy1YHCNQYHvyeumnpVG+1S7MuLla/M932on3+ez3I+MYKaX24aHo3lveY+Tl7wjgeNbfeWaqejf1Lg4ysG9bxkKU/St8cjPsv3vHRMbbfksR6/hl99vw9LG67f6ONXuqb9/ix++PqOfNtY87m89c+r5NqCyXb8l/4y5+fpb8yblis+n5NDvmrrnoxKVdJE3G8EGulXT89H7pafk0AZ/1djYR5fgfKwaihcBLFMXTKV3YoRzvyN56DPv+UzZApwYcHkfb8qKHYv0kx/e4Uhs6lzf4/TTKXnkfq4BINnBageVn0Xf7eiEnDEyBYze+n9KT6dDIrMzpqKN2eapdcHfvwX8cy2yzrLjl3FLGfAY5rudJeLsHes/g0/zw3LFM93X1IeWbWffcE06HBwdnsqeSQcD78ysFffX99Evnp9p8lxrx9R7qAPbTp06dfqd9C//8i//t5vQqVOnTv9P0o8//li73a4uLi7q9va27UUlSuRoGga2gRZ78n3cjI1+pw/PZkPGgjNUfvjhhxaVZ3+/j42pqlZsCaBhIAMorRpqKjw8PLTIG9kcRGy9r5L7+SECRUVzjHjavNsNRe1cvC8r6xuc0HdHE6tq9Cx+MrV7NpuNANb9/X3t7e3Vp0+fan9/v25vb1sUPB2QHJuUQIcMB/aZ39zcjMD3lNOHz50WzTMpcuUMDKd0s2ed7QWue0Gb7DgDwPGsqmpySXYR6ezIHLympsHT01PL1IAfmd5cVaP95M6I2u12o7Ty5+fn0d57xossJWQHHm23269OOvB4u+92Mhh0EoE2cKQt5pePHqI/TlXPMfX7mKfw0CA9K+R7GwJgl/n5XurAtlOnTp06derUqdMfQhn5qxoAUqZYQo7CO6sgMxymonuZIQD4MaBxlDUzGvjbv5My2pefZfQyI4tVNQJuBnxvRVinIpr5XbbR0bdMZc0f7gHUwCPamsDIvM22Jd9NmbkxFWk2aPczcYC8lTbrCKWBNM/P66eySLyP2xHp5J3bZv7mGNhh4y0nBqO+1xFUPz/TeWl/Xsdv88R9/K354r7ne0yZXZYZTn6nn+vxwbEwpQMcsf0e6sC2U6dOnTp16tSp0x9Cv/zySzNSfaQTESwTezVns6EKrfcpTwETwKWPNvF+9+12W+fn56Nr7u/vW3EmonuuQ8BzAKI+i3yz2YwAD1G73e51z6+jYIABV8Mm0sh+RwMR75U3wPI1Bn0uUOcoJJ8ZcNEX9vnudkOxqwSRv/76a11eXrYoMs/LwlJOGXW9AKLAgBfuo89EIeFHgipXyXUk1OCNyLeju7lNxVs2MmWWWgMPDw/t+CneS/t8H3wgsuq0aPjH6QYvLy9tb7mP9LHTw9HLqX2xrv/gdlRVq1pPSrtlbcpJAi/IZjCPuN/fTdXYoL0u6mbynnHawvndLgbF/DHA5T1Oq2bfN/us30sd2Hbq1KlTp06dOnX6Q+j6+rqqalQ51cau95tS5Xc+n7eCTVXTESMDAQxhPrch/fT0VLe3tyMAAYj1sxw98+dVNQJtPj7LbT44OGjgwJVcAYI+15X2JUCiTYAa7s+Ia0bb8lq+cwQMkGTAD7DN6O5sNpxZa1AGmHc6aQIkp0pnzQQ7Kwy4/A746WhvprAa8OF0gKaiq1NRfp8Zf3d3N5IhOzIAcAa26WDhXQcHB3V4eFh3d3d1c3NTi8WiHS83lQ3gAkxZV8A8yQionQGO9E7VB7A8kOqMPAHm/b3rX/AcA3g+d6Se/5F77sfpYPkh3dsFuvI4RNoNuP0e6sC2U6dOnTp16tSp0x9CRGWdclg1AD6AA3sIHS3ysSEGcAZ5GM7shXTKMQA202Vpj1OEDaRog6NKTs/MdwM0Mt13am+j03p5Nnt6XQE2waHbTBvZiwhgnCq6linP7JOkja72zp5SeJdthAxgzQ/+BrSZjzgw+BsQBHCyo4HvHKU18AIAQz6OzGDUwNbjUVWt4j17Vc3//f39kaPEoG4qMmnwiEy42rbTot2ePEbJlMDVkWFHtO1sqBpOZWCvrDMIcLDw/Pl8Poqg+kSBKWcS/E+Hhv9mLjni7hRv+G5+5JFd7it7jt9LHdh26tSpU6dOnTp1+kMIwOk0RvZJYvBSWMlRzLu7u5bGavBpgInxi4HOZ96fZwPblVcBFoAoRxqrxtFPA7L8zOCCz3xMjYtCTe0dxaDn6CGK9Lh6LO01aH15eRkVsHKRqkxdNljhWY6UEqHcbDb117/+tV5eXurz588t4uYImoG0I2wGtvDC0UADU9ps5wXger1e13q9boWsnCrNGdfPz88tykpElPsZbxOfWW4oELVarero6KgdveXiZRzD47EFABusQfB6sVjU0dFRbbfb1gd4g/Mggd8UiGScLQse28PDwxEfnVJs8Iuz4vLysh4fH9txOqvVqk5PT6uq2neWFR9pRdsBmjg+PB+qxinUtIUx2G63jZ8U4UJu7YAhtT0zJN5DHdh26tSpU6dOnTp1+kMogaT/53uiTVMRT0eAMuLrdFvvMTRAdWokoMWRsgSMGeGDDEjzWj53JMufZbv8DANDA6AEjPQVIOCIcVb6NYg2/7JaLf/zTFJEszBQ9s/p2nzuPcEGgVPRtkyTnXqXx9RRW7fXfPnWmORz8/nmY0Yk/dwcp6mI9VttcN9dOdm/LauOVifPp1Krp9LRPd7wL+UljyrymLid5pXnqQlw6nPvc7yTF+YX/fL4Zn9/izqw7dSpU6dOnTp16vSHEJGjw8PD2t/fHxUaqqoWxaG4EmmTRIuIiL68vNTj4+ObEVtHBW0IT4GgBLMmp25mFNdHoXjfoCvTco0BBumtVdVSQXk2xj1Hmxh4OM2Td7r4UwIGUq8N6FxoiHRg9jEDZg8PD+vg4KC2221dXl62vcOOmHMfkUxHf5fLZW02m9rtdnV3d1fPz8+jvbw+Voe+5tm2BsbwKI+3AXRyHNFu91q8y89j/HhPgitHH+HNw8NDiy7u7e3V4+NjPT4+jo7OAdgRTc+9yrvdru0jhy8GgR5T9llvNptRZXCn4z8+PraiWT4bF1nieaTzc1YzUVeiso6S8pmB7cXFReuD93+/vLy0Qm5nZ2e1Wq3q4eGhbm5uvpozLjrlIlzMXe+VzXR5O5D4+/DwsDlZnHL+HurAtlOnTp06derUqdMfQhmxrRqn1wI2AE0AxaniOW89eyoqCOV+vvyM+zM6mlE0Pie65+dMtS+jUVlxNg18+LDdbkfpzRn9zTYBBBO4ObprXjryx/08j5RWg2lH2qdSj6vGR+s4uuooX/bVoN9jxfUZJczxA0A+PDx8Nf453ukE8Bi7kJKvyeglvIDHvtc/UxFJvy/3E6fs+D1+rueEeePoczo63N6qYY+s25fVpi1v9NNj+VZqvNvivc5T8uLrp+Yw7UxZfQ91YNupU6dOnTp16tTpDyFXhAXAEtFhfyvfs+e1akhrBLTt7+/XyclJi0wSZXIBqkzF5T6Mckc0AZBEuoj8sY/QQBwD29VyM8WVSC3vZZ/lwcFBizyxb5ToI0WK0ujPYlZVA5Bg3yvvzbRXCNDAc2g7lWmJvBqAGySxD/Tg4KCWy2U9PDyM+gZf+Lm5uWn3cewN0Uj6CA8Y6ymwkxFO98XtpAAWzzLI4rspUDUF0Bm/q6urms/no6OfAHKOiMJHZASZpc/cRxSXKCvR4Lu7uxZt9XvYG+sUYe9JZkxSJswPxpv9ve67q3On44MiUkSKq6odX+X07NzH7D3YyDXPol/IUToQGKv7+/uv5HmqoNZ7qAPbTp06derUqVOnTn8Iea8chi6FYaq+rlzrqC4RRFJbV6tVVY335ToCutvtGgBz1C5TiWnHcrlsoASD3qDKEShAc6aF0t5Mm6TI0GazaUB+Nps1I94VZh3BS574s6oaFZYCCADEp6KFAJOHh4d6enqqw8PDlkILmPf7iAbu7+83ELxcLluaMSmtBi4UY1osFrVer0dnt3I96bWAwuSxec3/eY3Jx8AYACFfyYtMKU7AS8GyvIdoKTzGqWLHBqnPBpXICY4ViiUho8jwbDZrMklEl/bQXp4DsHXaNU4LCpYR7cfpk9FWOyMA4rTPxx/h9MlUe4NPp1sb2DL+FM5yBNYp6GQbUCkZmfMc+F7qwLZTp06dOnXq1KnTH0oJWKrGZ4ISAQMQON0S4x5Q6OqtefamjxrxMx2h8vNd3biqRqm7ABYieL6fZxDRdKEkvn94eBid1em+8RmfmwdEc19eXlp01XwEHLov8NYAzp9nKm3VcLawQajTiAFygHauYcx4PoDE0cN8n9N7p0BLFt0ymATM85kBUha7gv+Wm4z8OiI4VWTLkcmM+nofuKOfVTUCgj7mClmn3Tg0LN85T5yFQD+Rf0fO2Q9r54jHxc/IPue+ZjsFnNaMc8l7jpFlO1CQGxxXHk/+xpFEBDvTy7mP9rzl2HiLOrDt1KlTp06dOnXq9IeRo18GRgCDqvHZpY50uQLwzc3NaB8oIMPpklx/cHDQ0oAzSso1gLCqasWEAHmkflYNxY54H+0h7ZJzUL1/c7fb1e3t7QiEAi4c/fV5o44C+xgig8O7u7tR5DUjy7zPRYIggywX6XFF5IeHh6oagArH4jg6DXGvi0fd3t62FGfv2wScGXxPRRMhnotjAn4vl8tRpPjp6amlo9M+nkm/HB1nvAF5jlgDWN+K+NphQgQanjs9122xnAHUHJXkN/ODwl44Cwwq4Stt8/FOzmBwhNfFnKaAO/JDFNlg3cDWANd7tg34XWCLvtNHnAdEcz02AFz4QvGu/8ke2+9PXu7UqVOnTp06derU6TtoKqJmIz33iU4BsoyeTZHBgp+R90ylOfqe96ZCZip1vnOqzdnX/PtbbXjruvdQXvettuc7f4vnGSHOvuX9743CTcnB9zwn+/ZbvPqe792vKVmeoreu+Zas+vlv/Xyrvb8l61P9eevdv3XtW/M4sxmyHTlOv9Wvt2i2+5/c1alTp06dOnXq1KlTp06dOv0/Qj1i26lTp06dOnXq1KlTp06d/tTUgW2nTp06derUqVOnTp06dfpTUwe2nTp16tSpU6dOnTp16tTpT00d2Hbq1KlTp06dOnXq1KlTpz81dWDbqVOnTp06derUqVOnTp3+1NSBbadOnTp16tSpU6dOnTp1+lNTB7adOnXq1KlTp06dOnXq1OlPTR3YdurUqVOnTp06derUqVOnPzV1YNupU6dOnTp16tSpU6dOnf7U9L8A9Au7LAnYoo8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = neuron_with_artifacts[\"image\"].data.permute(1,2,3,0)\n",
+    "\n",
+    "fig, ax = plt.subplots(1,3, figsize=(12,4))\n",
+    "ax[0].imshow(img.amax(0), cmap='gray', vmin=0, vmax=1.0)\n",
+    "ax[0].set_title('xy')\n",
+    "ax[1].imshow(img.amax(1), cmap='gray', vmin=0, vmax=1.0)\n",
+    "ax[1].set_title('xz')\n",
+    "ax[2].imshow(img.amax(2), cmap='gray', vmin=0, vmax=1.0)\n",
+    "ax[2].set_title('yz')\n",
+    "for x in ax:\n",
+    "    x.set_axis_off()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([60, 60, 81, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = neuron_no_artifacts[\"image\"].data.permute(1,2,3,0)\n",
+    "\n",
+    "print(img.shape)\n",
+    "fig, ax = plt.subplots(1,3)\n",
+    "ax[0].imshow(img.amax(0), vmin=0.0, vmax=1.0)\n",
+    "ax[0].set_title('xy')\n",
+    "ax[1].imshow(img.amax(1), vmin=0.0, vmax=1.0)\n",
+    "ax[1].set_title('xz')\n",
+    "ax[2].imshow(img.amax(2), vmin=0.0, vmax=1.0)\n",
+    "ax[2].set_title('yz')\n",
+    "# for x in ax:\n",
+    "#     x.set_axis_off()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulate from existing SWC file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load real neuron morphology data from an SWC file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading file: /home/brysongray/data/neurotrack_data/gold166/gold166_swc_processed/1_6_Ch2.swc\n"
+     ]
+    }
+   ],
+   "source": [
+    "# labels_dir = os.path.expanduser(\"~/data/neurotrack_data/simulated_neurons/neuromorpho/swc_trees\")\n",
+    "labels_dir = os.path.expanduser(\"~/data/neurotrack_data/gold166/gold166_swc_processed/\")\n",
+    "files = [f for x in os.walk(labels_dir) for f in glob(os.path.join(x[0], '*.swc'))]\n",
+    "\n",
+    "f_idx = 1\n",
+    "labels_file = files[f_idx]\n",
+    "# load and parse the SWC file data\n",
+    "swc_list = load.swc(labels_file)\n",
+    "sections, sections_graph = load.parse_swc(swc_list)\n",
+    "branches, terminals = load.get_critical_points(swc_list, sections)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot SWC raw data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('3e9664d1-4ac9-4c4f-aa7f-557bc68d2f56');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a DataFrame for plotting\n",
+    "data = []\n",
+    "# Iterate through the sections dictionary\n",
+    "for section_id, section_data in sections.items():\n",
+    "    # flatten the section into one list of consecutive points instead of segments (point pairs)\n",
+    "    for i,segment in enumerate(section_data):\n",
+    "        point = segment[0] \n",
+    "        data.append([section_id, point[0].item(), point[1].item(), point[2].item()])\n",
+    "        if i == len(section_data)-1:\n",
+    "            point = segment[1]\n",
+    "            data.append([section_id, point[0].item(), point[1].item(), point[2].item()])\n",
+    "df_sections = pd.DataFrame(data, columns=[\"section\", \"x\", \"y\", \"z\"])\n",
+    "\n",
+    "fig = px.line_3d(df_sections, x=\"x\", y=\"y\", z=\"z\", color='section', )\n",
+    "fig.update_layout(scene_aspectmode='data')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulate a neuron microscopy image with artifacts from the SWC data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set random colors for the background and foreground voxels. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_contrast = True\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "neuron_color = np.array([1.0, 1.0, 1.0])\n",
+    "background = np.array([0., 0., 0.])\n",
+    "if random_contrast:\n",
+    "    neuron_color = rng.uniform(size=3)\n",
+    "    neuron_color /= np.linalg.norm(neuron_color)\n",
+    "    background_color = rng.uniform(size=3)\n",
+    "    background_color = background_color / np.linalg.norm(background_color) * 0.01"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw the neuron image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "swc_data = draw.neuron_from_swc(swc_list,\n",
+    "                                width=3,\n",
+    "                                noise=0.00,\n",
+    "                                dropout=False,\n",
+    "                                adjust=True,\n",
+    "                                background_color=background,\n",
+    "                                neuron_color=neuron_color,\n",
+    "                                random_brightness=False,\n",
+    "                                binary=False,\n",
+    "                                rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = swc_data[\"image\"].data.permute(1,2,3,0)\n",
+    "\n",
+    "fig, ax = plt.subplots(1,3, figsize=(18,6))\n",
+    "ax[0].imshow(img.amax(0))\n",
+    "ax[0].set_title('xy')\n",
+    "ax[1].imshow(img.amax(1))\n",
+    "ax[1].set_title('xz')\n",
+    "ax[2].imshow(img.amax(2))\n",
+    "ax[2].set_title('yz')\n",
+    "for x in ax:\n",
+    "    x.set_axis_off()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tractography",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/sac_tracking_inference.ipynb b/notebooks/sac_tracking_inference.ipynb
new file mode 100644
index 0000000..d3c3282
--- /dev/null
+++ b/notebooks/sac_tracking_inference.ipynb
@@ -0,0 +1,592 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tracking Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glob import glob\n",
+    "import json\n",
+    "import sys\n",
+    "import torch\n",
+    "import tifffile as tf\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "sys.path.append(\"../\")\n",
+    "from environments.sac_tracking_env import Environment\n",
+    "from models import ResNet3D, ResidualBlock3D, ConvNet\n",
+    "from solvers import sac\n",
+    "\n",
+    "from environments.neuron_tracking_environment_v2 import (\n",
+    "    create_neuron_tracking_environment, \n",
+    "    NeuronTrackingEnvironment\n",
+    ")\n",
+    "from neurotrack.data.neuron_data import (\n",
+    "    Dataset, \n",
+    "    DataLoader, \n",
+    "    DataGenerator, \n",
+    "    DrawingComplexityConfig\n",
+    ")\n",
+    "\n",
+    "DEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
+    "dtype = torch.float32"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_path = \"../configs/sac_inference_dynamic_complexity.json\"\n",
+    "\n",
+    "with open(config_path) as f:\n",
+    "    params = json.load(f)\n",
+    "\n",
+    "data_dir = params[\"data_dir\"]\n",
+    "outdir = params[\"outdir\"]\n",
+    "name = params[\"name\"]\n",
+    "step_size = params[\"step_size\"] if \"step_size\" in params else 1.0\n",
+    "step_width = params[\"step_width\"] if \"step_width\" in params else 1.0\n",
+    "alpha = params[\"alpha\"] if \"alpha\" in params else 1.0\n",
+    "beta = params[\"beta\"] if \"beta\" in params else 1e-3\n",
+    "friction = params[\"friction\"] if \"friction\" in params else 1e-4\n",
+    "repeat_starts = params[\"repeat_starts\"] if \"repeat_starts\" in params else True\n",
+    "section_masking = params[\"section_masking\"] if \"section_masking\" in params else False\n",
+    "rng_seed = params[\"rng_seed\"] if \"rng_seed\" in params else 1\n",
+    "start_complexity = params[\"start_complexity\"] if \"start_complexity\" in params else 0.0\n",
+    "branching = params[\"branching\"] if \"branching\" in params else 0\n",
+    "patch_radius = 17\n",
+    "in_channels = 2\n",
+    "\n",
+    "rng = np.random.default_rng(rng_seed)\n",
+    "# Create dataset\n",
+    "dataset = Dataset(data_dir=data_dir, rng=rng)\n",
+    "# Create dataloader\n",
+    "dataloader = DataLoader(dataset=dataset, complexity=start_complexity, rng=rng)\n",
+    "\n",
+    "# Create environment\n",
+    "# alpha and beta removed to test new environment reward structure\n",
+    "env = NeuronTrackingEnvironment(\n",
+    "    dataloader=dataloader,\n",
+    "    radius=patch_radius,\n",
+    "    step_size=step_size,\n",
+    "    step_width=step_width,\n",
+    "    max_len=1000,\n",
+    "    friction=friction,\n",
+    "    repeat_starts=repeat_starts,\n",
+    "    section_masking=section_masking,\n",
+    "    branching=branching\n",
+    ")\n",
+    "\n",
+    "env.reset()\n",
+    "\n",
+    "actor = ConvNet(chin=in_channels, chout=4)\n",
+    "actor = actor.to(device=DEVICE,dtype=dtype)\n",
+    "\n",
+    "Q = ConvNet(chin=in_channels+3,chout=1)\n",
+    "Q = Q.to(device=DEVICE,dtype=dtype)\n",
+    "\n",
+    "if \"sac_weights\" in params:\n",
+    "    sac_path = params[\"sac_weights\"]\n",
+    "    state_dicts = torch.load(sac_path)#, weights_only=True)\n",
+    "    actor.load_state_dict(state_dicts[\"policy_state_dict\"])\n",
+    "    Q.load_state_dict(state_dicts[\"Q1_state_dict\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "num branches: 1\n",
+      "num long paths: 1\n",
+      "estimated return: -10030.58\n",
+      "Press Enter to continue to the next episode (or 'q' to quit)...\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "Interrupted by user",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msac\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutdir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mQ_net\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mQ\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msync\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/neurotrack/notebooks/../solvers/sac.py:649\u001b[0m, in \u001b[0;36minference\u001b[0;34m(env, actor, outdir, Q_net, n_trials, show, show_live, save_paths, sync, stochastic)\u001b[0m\n\u001b[1;32m    647\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    648\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPress Enter to continue to the next episode (or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to quit)...\u001b[39m\u001b[38;5;124m\"\u001b[39m, flush\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 649\u001b[0m     user_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mstrip()\n\u001b[1;32m    650\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m user_input\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    651\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuitting training.\u001b[39m\u001b[38;5;124m\"\u001b[39m, flush\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/tractography/lib/python3.9/site-packages/ipykernel/kernelbase.py:1282\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1280\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1281\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1283\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1284\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1285\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/tractography/lib/python3.9/site-packages/ipykernel/kernelbase.py:1325\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1323\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1324\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1325\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1326\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b980acd95f67447f887712ee8b216c60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=1500.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sac.inference(env, actor, outdir, Q_net=Q, n_trials=1, save_paths=False, sync=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# config_file = \"../configs/sac_inference_neuromorpho_with_artifacts.json\"\n",
+    "config_file = \"../configs/sac_inference_gold166.json\"\n",
+    "# config_file = \"../configs/sac_inference_simulated.json\"\n",
+    "with open(config_file) as f:\n",
+    "    params = json.load(f)\n",
+    "\n",
+    "img_path = params[\"img_path\"]\n",
+    "outdir = params[\"outdir\"]\n",
+    "name = params[\"name\"]\n",
+    "step_size = params[\"step_size\"] if \"step_size\" in params else 1.0\n",
+    "step_width = params[\"step_width\"] if \"step_width\" in params else 1.0\n",
+    "alpha = params[\"alpha\"] if \"alpha\" in params else 1.0\n",
+    "beta = params[\"beta\"] if \"beta\" in params else 1e-3\n",
+    "friction = params[\"friction\"] if \"friction\" in params else 1e-4\n",
+    "repeat_starts = params[\"repeat_starts\"] if \"repeat_starts\" in params else False\n",
+    "patch_radius = 17\n",
+    "inchannels = 1\n",
+    "\n",
+    "if \"classifier_weights\" in params:\n",
+    "    classifier_path = params[\"classifier_weights\"]\n",
+    "    classifier_state_dict = torch.load(classifier_path)#, weights_only=True)\n",
+    "    classifier = ResNet3D(ResidualBlock3D, [3, 4, 6, 3], in_channels=inchannels, num_classes=1)\n",
+    "    classifier = classifier.to(device=DEVICE, dtype=dtype)\n",
+    "    classifier.load_state_dict(classifier_state_dict)\n",
+    "    classifier.eval()\n",
+    "else:\n",
+    "    classifier = None\n",
+    "\n",
+    "env = Environment(img_path,\n",
+    "                radius=patch_radius,\n",
+    "                step_size=step_size,\n",
+    "                step_width=step_width,\n",
+    "                max_len=10000,\n",
+    "                max_paths=1000,\n",
+    "                alpha=alpha,\n",
+    "                beta=beta,\n",
+    "                friction=friction,\n",
+    "                classifier=classifier,\n",
+    "                repeat_starts=repeat_starts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate actor network and Q-network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_channels = 2\n",
+    "actor = ConvNet(chin=in_channels, chout=4)\n",
+    "actor = actor.to(device=DEVICE,dtype=dtype)\n",
+    "\n",
+    "Q = ConvNet(chin=in_channels+3,chout=1)\n",
+    "Q = Q.to(device=DEVICE,dtype=dtype)\n",
+    "\n",
+    "if \"sac_weights\" in params:\n",
+    "    sac_path = params[\"sac_weights\"]\n",
+    "    state_dicts = torch.load(sac_path)\n",
+    "    actor.load_state_dict(state_dicts[\"policy_state_dict\"])\n",
+    "    Q.load_state_dict(state_dicts[\"Q1_state_dict\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 108, 658, 787])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "env.img.data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(255, dtype=torch.uint8)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "env.img.data.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGsCAYAAACB/u5dAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjUsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvWftoOwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGdZJREFUeJzt3XuMVOX9wOHvAjIisIvcWVkQVCTKpV4p8VItFCGUeGmMpSRSYzRaMFrE6DapSqNZYtOmFwk1tZU2UbEkRVtbMXhZiBUooESUlgDBggpiMewuKCOy5/dH4/y6AsKs7+yFPk9yEuZc9rzzZjL7YebsTFmWZVkAACTQobUHAAAcP4QFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQTKuFxfLly2PKlClRWVkZZWVl8fTTTxd1/P333x9lZWWHLF27di3NgAGAo2q1sNi3b1+MHj065s2b16zjZ8+eHTt27GiynHXWWXHttdcmHikAcKxaLSwmTZoUDzzwQFx99dWH3Z7P52P27NlxyimnRNeuXWPMmDFRW1tb2N6tW7fo379/YXn//fdjw4YNceONN7bQPQAAPq/NXmMxc+bMWLFiRSxcuDDeeOONuPbaa2PixImxadOmw+7/6KOPxrBhw+KSSy5p4ZECAJ9pk2Gxbdu2eOyxx2LRokVxySWXxGmnnRazZ8+Oiy++OB577LFD9t+/f388/vjjXq0AgFbWqbUHcDjr16+PgwcPxrBhw5qsz+fz0atXr0P2X7x4cTQ0NMT06dNbaogAwGG0ybDYu3dvdOzYMdauXRsdO3Zssq1bt26H7P/oo4/GN7/5zejXr19LDREAOIw2GRbnnHNOHDx4MHbt2nXUaya2bt0aL7/8cvzpT39qodEBAEfSamGxd+/e2Lx5c+H21q1bY926ddGzZ88YNmxYTJs2La6//vr4yU9+Euecc0588MEH8eKLL8aoUaNi8uTJheN++9vfxoABA2LSpEmtcTcAgP9SlmVZ1honrq2tjcsvv/yQ9dOnT48FCxbEgQMH4oEHHojf//738e6770bv3r3jq1/9asyZMydGjhwZERGNjY0xePDguP766+PBBx9s6bsAAHxOq4UFAHD8aZN/bgoAtE/CAgBIpsUv3mxsbIz33nsvunfvHmVlZS19egCgGbIsi4aGhqisrIwOHY78ukSLh8V7770XVVVVLX1aACCB7du3x8CBA4+4vcXDonv37hHxn4GVl5e39OkBgGaor6+Pqqqqwu/xI2nxsPjs7Y/y8nJhAQDtzNEuY3DxJgCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgmRb/2vRSOvWevxyy7u25k1thJADwv8krFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAyRYXF/fffH2VlZU2W4cOHl2psAEA706nYA84+++x44YUX/v8HdCr6RwAAx6miq6BTp07Rv3//UowFAGjnir7GYtOmTVFZWRlDhw6NadOmxbZt275w/3w+H/X19U0WAOD4VFRYjBkzJhYsWBBLliyJ+fPnx9atW+OSSy6JhoaGIx5TU1MTFRUVhaWqqupLDxoAaJvKsizLmnvwnj17YvDgwfHTn/40brzxxsPuk8/nI5/PF27X19dHVVVV1NXVRXl5eXNPfVin3vOXQ9a9PXdy0nMAwP+i+vr6qKioOOrv7y915WWPHj1i2LBhsXnz5iPuk8vlIpfLfZnTAADtxJf6HIu9e/fGli1bYsCAAanGAwC0Y0WFxezZs2PZsmXx9ttvx6uvvhpXX311dOzYMaZOnVqq8QEA7UhRb4W88847MXXq1Ni9e3f06dMnLr744li5cmX06dOnVOMDANqRosJi4cKFpRoHAHAc8F0hAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQzJcKi7lz50ZZWVnccccdiYYDALRnzQ6L1atXxyOPPBKjRo1KOR4AoB1rVljs3bs3pk2bFr/+9a/j5JNPTj0mAKCdalZYzJgxIyZPnhzjx48/6r75fD7q6+ubLADA8alTsQcsXLgwXnvttVi9evUx7V9TUxNz5swpemAAQPtT1CsW27dvj9tvvz0ef/zxOPHEE4/pmOrq6qirqyss27dvb9ZAAYC2r6hXLNauXRu7du2Kc889t7Du4MGDsXz58nj44Ycjn89Hx44dmxyTy+Uil8ulGS0A0KYVFRbjxo2L9evXN1l3ww03xPDhw+Puu+8+JCoAgP8tRYVF9+7dY8SIEU3Wde3aNXr16nXIegDgf49P3gQAkin6r0I+r7a2NsEwAIDjgVcsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgmaLCYv78+TFq1KgoLy+P8vLyGDt2bDz33HOlGhsA0M4UFRYDBw6MuXPnxtq1a2PNmjXx9a9/Pa688sp46623SjU+AKAd6VTMzlOmTGly+8EHH4z58+fHypUr4+yzz046MACg/SkqLP7bwYMHY9GiRbFv374YO3bsEffL5/ORz+cLt+vr65t7SgCgjSv64s3169dHt27dIpfLxS233BKLFy+Os84664j719TUREVFRWGpqqr6UgMGANquosPizDPPjHXr1sWqVavi1ltvjenTp8eGDRuOuH91dXXU1dUVlu3bt3+pAQMAbVfRb4V07tw5Tj/99IiIOO+882L16tXx85//PB555JHD7p/L5SKXy325UQIA7cKX/hyLxsbGJtdQAAD/u4p6xaK6ujomTZoUgwYNioaGhnjiiSeitrY2nn/++VKNDwBoR4oKi127dsX1118fO3bsiIqKihg1alQ8//zz8Y1vfKNU4wMA2pGiwuI3v/lNqcYBABwHfFcIAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkU1RY1NTUxAUXXBDdu3ePvn37xlVXXRUbN24s1dgAgHamqLBYtmxZzJgxI1auXBlLly6NAwcOxIQJE2Lfvn2lGh8A0I50KmbnJUuWNLm9YMGC6Nu3b6xduzYuvfTSpAMDANqfosLi8+rq6iIiomfPnkfcJ5/PRz6fL9yur6//MqcEANqwZl+82djYGHfccUdcdNFFMWLEiCPuV1NTExUVFYWlqqqquacEANq4ZofFjBkz4s0334yFCxd+4X7V1dVRV1dXWLZv397cUwIAbVyz3gqZOXNmPPvss7F8+fIYOHDgF+6by+Uil8s1a3AAQPtSVFhkWRa33XZbLF68OGpra2PIkCGlGhcA0A4VFRYzZsyIJ554Ip555pno3r177Ny5MyIiKioqokuXLiUZIADQfhR1jcX8+fOjrq4uLrvsshgwYEBheeqpp0o1PgCgHSn6rRAAgCPxXSEAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJBM0WGxfPnymDJlSlRWVkZZWVk8/fTTJRgWANAeFR0W+/bti9GjR8e8efNKMR4AoB3rVOwBkyZNikmTJpViLABAO1d0WBQrn89HPp8v3K6vry/1KQGAVlLyizdramqioqKisFRVVZX6lABAKyl5WFRXV0ddXV1h2b59e6lPCQC0kpK/FZLL5SKXy5X6NABAG+BzLACAZIp+xWLv3r2xefPmwu2tW7fGunXromfPnjFo0KCkgwMA2peiw2LNmjVx+eWXF27PmjUrIiKmT58eCxYsSDYwAKD9KTosLrvsssiyrBRjAQDaOddYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGQ6tfYASu3Ue/7S5Pbbcye30kgA4PjnFQsAIBlhAQAk06ywmDdvXpx66qlx4oknxpgxY+Lvf/976nEBAO1Q0WHx1FNPxaxZs+K+++6L1157LUaPHh1XXHFF7Nq1qxTjAwDakbIsy7JiDhgzZkxccMEF8fDDD0dERGNjY1RVVcVtt90W99xzz1GPr6+vj4qKiqirq4vy8vLmjfoIPn+h5rFyQScAfLFj/f1d1F+FfPLJJ7F27dqorq4urOvQoUOMHz8+VqxYcdhj8vl85PP5wu26urrCAFNrzH/UrOMGfX9Rk9tvzrkixXAA4Ljx2e/to70eUVRY/Pvf/46DBw9Gv379mqzv169f/POf/zzsMTU1NTFnzpxD1ldVVRVz6hZV8bPWHgEAtE0NDQ1RUVFxxO0l/xyL6urqmDVrVuF2Y2NjfPjhh9GrV68oKytLdp76+vqoqqqK7du3J3+Lhf8wx6VlfkvL/JaeOS6t1p7fLMuioaEhKisrv3C/osKid+/e0bFjx3j//febrH///fejf//+hz0ml8tFLpdrsq5Hjx7FnLYo5eXlHtAlZo5Ly/yWlvktPXNcWq05v1/0SsVnivqrkM6dO8d5550XL774YmFdY2NjvPjiizF27NjiRwgAHFeKfitk1qxZMX369Dj//PPjwgsvjJ/97Gexb9++uOGGG0oxPgCgHSk6LK677rr44IMP4t57742dO3fGV77ylViyZMkhF3S2tFwuF/fdd98hb7uQjjkuLfNbWua39MxxabWX+S36cywAAI7Ed4UAAMkICwAgGWEBACQjLACAZI6bsPBV7mncf//9UVZW1mQZPnx4Yfv+/ftjxowZ0atXr+jWrVt861vfOuQD0/h/y5cvjylTpkRlZWWUlZXF008/3WR7lmVx7733xoABA6JLly4xfvz42LRpU5N9Pvzww5g2bVqUl5dHjx494sYbb4y9e/e24L1o2442x9/97ncPeUxPnDixyT7m+MhqamriggsuiO7du0ffvn3jqquuio0bNzbZ51ieF7Zt2xaTJ0+Ok046Kfr27Rt33XVXfPrppy15V9qkY5nfyy677JDH8C233NJkn7Y0v8dFWPgq97TOPvvs2LFjR2F55ZVXCtu+//3vx5///OdYtGhRLFu2LN5777245pprWnG0bdu+ffti9OjRMW/evMNuf+ihh+IXv/hF/OpXv4pVq1ZF165d44orroj9+/cX9pk2bVq89dZbsXTp0nj22Wdj+fLlcfPNN7fUXWjzjjbHERETJ05s8ph+8sknm2w3x0e2bNmymDFjRqxcuTKWLl0aBw4ciAkTJsS+ffsK+xzteeHgwYMxefLk+OSTT+LVV1+N3/3ud7FgwYK49957W+MutSnHMr8RETfddFOTx/BDDz1U2Nbm5jc7Dlx44YXZjBkzCrcPHjyYVVZWZjU1Na04qvbpvvvuy0aPHn3YbXv27MlOOOGEbNGiRYV1//jHP7KIyFasWNFCI2y/IiJbvHhx4XZjY2PWv3//7Mc//nFh3Z49e7JcLpc9+eSTWZZl2YYNG7KIyFavXl3Y57nnnsvKysqyd999t8XG3l58fo6zLMumT5+eXXnllUc8xhwXZ9euXVlEZMuWLcuy7NieF/76179mHTp0yHbu3FnYZ/78+Vl5eXmWz+db9g60cZ+f3yzLsq997WvZ7bfffsRj2tr8tvtXLD77Kvfx48cX1h3tq9z5Yps2bYrKysoYOnRoTJs2LbZt2xYREWvXro0DBw40mevhw4fHoEGDzHUzbN26NXbu3NlkPisqKmLMmDGF+VyxYkX06NEjzj///MI+48ePjw4dOsSqVatafMztVW1tbfTt2zfOPPPMuPXWW2P37t2Fbea4OHV1dRER0bNnz4g4tueFFStWxMiRI5t8kOIVV1wR9fX18dZbb7Xg6Nu+z8/vZx5//PHo3bt3jBgxIqqrq+Ojjz4qbGtr81vybzctteZ8lTtHNmbMmFiwYEGceeaZsWPHjpgzZ05ccskl8eabb8bOnTujc+fOh3yJXL9+/WLnzp2tM+B27LM5O9xj97NtO3fujL59+zbZ3qlTp+jZs6c5P0YTJ06Ma665JoYMGRJbtmyJH/zgBzFp0qRYsWJFdOzY0RwXobGxMe6444646KKLYsSIERERx/S8sHPnzsM+zj/bxn8cbn4jIr7zne/E4MGDo7KyMt544424++67Y+PGjfHHP/4xItre/Lb7sCCtSZMmFf49atSoGDNmTAwePDj+8Ic/RJcuXVpxZNA83/72twv/HjlyZIwaNSpOO+20qK2tjXHjxrXiyNqfGTNmxJtvvtnkuivSOdL8/vf1PiNHjowBAwbEuHHjYsuWLXHaaae19DCPqt2/FdKcr3Ln2PXo0SOGDRsWmzdvjv79+8cnn3wSe/bsabKPuW6ez+bsix67/fv3P+Qi5E8//TQ+/PBDc95MQ4cOjd69e8fmzZsjwhwfq5kzZ8azzz4bL7/8cgwcOLCw/lieF/r373/Yx/ln2zjy/B7OmDFjIiKaPIbb0vy2+7DwVe6ltXfv3tiyZUsMGDAgzjvvvDjhhBOazPXGjRtj27Zt5roZhgwZEv37928yn/X19bFq1arCfI4dOzb27NkTa9euLezz0ksvRWNjY+HJheK88847sXv37hgwYEBEmOOjybIsZs6cGYsXL46XXnophgwZ0mT7sTwvjB07NtavX98k4JYuXRrl5eVx1llntcwdaaOONr+Hs27duoiIJo/hNjW/LX65aAksXLgwy+Vy2YIFC7INGzZkN998c9ajR48mV8hybO68886strY227p1a/a3v/0tGz9+fNa7d+9s165dWZZl2S233JINGjQoe+mll7I1a9ZkY8eOzcaOHdvKo267Ghoastdffz17/fXXs4jIfvrTn2avv/569q9//SvLsiybO3du1qNHj+yZZ57J3njjjezKK6/MhgwZkn388ceFnzFx4sTsnHPOyVatWpW98sor2RlnnJFNnTq1te5Sm/NFc9zQ0JDNnj07W7FiRbZ169bshRdeyM4999zsjDPOyPbv31/4Geb4yG699dasoqIiq62tzXbs2FFYPvroo8I+R3te+PTTT7MRI0ZkEyZMyNatW5ctWbIk69OnT1ZdXd0ad6lNOdr8bt68OfvRj36UrVmzJtu6dWv2zDPPZEOHDs0uvfTSws9oa/N7XIRFlmXZL3/5y2zQoEFZ586dswsvvDBbuXJlaw+pXbruuuuyAQMGZJ07d85OOeWU7Lrrrss2b95c2P7xxx9n3/ve97KTTz45O+mkk7Krr74627FjRyuOuG17+eWXs4g4ZJk+fXqWZf/5k9Mf/vCHWb9+/bJcLpeNGzcu27hxY5OfsXv37mzq1KlZt27dsvLy8uyGG27IGhoaWuHetE1fNMcfffRRNmHChKxPnz7ZCSeckA0ePDi76aabDvlPhzk+ssPNbURkjz32WGGfY3leePvtt7NJkyZlXbp0yXr37p3deeed2YEDB1r43rQ9R5vfbdu2ZZdeemnWs2fPLJfLZaeffnp21113ZXV1dU1+TluaX1+bDgAk0+6vsQAA2g5hAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkMz/AY+0TJ7BnNo5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(env.img.data[0].flatten(), bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7263a6c4886449786e6b0a94c10ac05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=None)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.close(\"all\")\n",
+    "plt.imshow(env.img.data[0].amax(0))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/28 [00:13<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msac\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutdir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mQ_net\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mQ\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msync\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/neurotrack/notebooks/../solvers/sac.py:542\u001b[0m, in \u001b[0;36minference\u001b[0;34m(env, actor, outdir, Q_net, n_trials, show, show_live, save_paths, sync, stochastic)\u001b[0m\n\u001b[1;32m    540\u001b[0m     shell \u001b[38;5;241m=\u001b[39m get_ipython()\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m    541\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m shell:\n\u001b[0;32m--> 542\u001b[0m         \u001b[43mshow_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlive\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreward\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreward\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcpu\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNameError\u001b[39;00m:\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
+      "File \u001b[0;32m~/neurotrack/notebooks/../plot/tracking_interface.py:159\u001b[0m, in \u001b[0;36mshow_state\u001b[0;34m(env, fig, live, ep_return, reward, policy_loss)\u001b[0m\n\u001b[1;32m    156\u001b[0m     img_vol \u001b[38;5;241m=\u001b[39m img                   \u001b[38;5;66;03m# already 3D\u001b[39;00m\n\u001b[1;32m    158\u001b[0m img_proj \u001b[38;5;241m=\u001b[39m img_vol\u001b[38;5;241m.\u001b[39mamax(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)      \u001b[38;5;66;03m# (W, D)\u001b[39;00m\n\u001b[0;32m--> 159\u001b[0m path_proj \u001b[38;5;241m=\u001b[39m \u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mamax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m        \u001b[38;5;66;03m# (W, D)\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \u001b[38;5;66;03m# Show RGB MIP (left axis)\u001b[39;00m\n\u001b[1;32m    162\u001b[0m ax_left\u001b[38;5;241m.\u001b[39mimshow(img_proj, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgray\u001b[39m\u001b[38;5;124m'\u001b[39m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m, vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef3b158b50b1414ca69d9f34fb1a9f1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=1500.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=None)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sac.inference(env, actor, outdir, Q_net=Q, n_trials=1, save_paths=False, sync=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perform tracking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "num branches: 128\n",
+      "num long paths: 76\n",
+      "estimated return: 1596.43\n",
+      "coverage: 0.98\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 73/73 [2:34:22<00:00, 126.88s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sac.inference(env, actor, outdir, Q_net=Q, n_trials=5, save_paths=True, sync=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "\n",
+    "outdir = params[\"outdir\"]\n",
+    "out_files = os.listdir(outdir)\n",
+    "max_coverages = []\n",
+    "coverage_max_returns = []\n",
+    "\n",
+    "for out in out_files:\n",
+    "    out = os.path.join(outdir, out)\n",
+    "    inference_outputs = np.load(out, allow_pickle=True)\n",
+    "    coverages = inference_outputs[\"coverages\"]\n",
+    "    estimated_returns = inference_outputs[\"estimated_returns\"]\n",
+    "    max_coverages.append(np.max(coverages))\n",
+    "    coverage_max_returns.append(coverages[np.argmax(estimated_returns)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float32(0.9884575), np.float32(0.9788084))"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.median(max_coverages), np.median(coverage_max_returns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 17\u001b[0m\n\u001b[1;32m     14\u001b[0m     labeled_neuron \u001b[38;5;241m=\u001b[39m labeled_neuron \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;241m255.\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m     16\u001b[0m name \u001b[38;5;241m=\u001b[39m out_files[i]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_08-13-25*\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m][:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 17\u001b[0m reference_img_path \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mref_img_files\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_image.tif\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m     18\u001b[0m ref_img \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mimread(reference_img_path)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ref_img\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39muint8:\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkLxxDSYbNmyIOXPmRG1tbTQ1NcXOnTtP6LrNmzdHRUVFLF68eDxfFmBS0EAgVfoHpEwDgZRpIJCKggeTLVu2RFtbW7S3t8fu3btj7ty50draGq+++urbXvfKK6/EV77ylbjsssvGfbMA5aaBQKr0D0iZBgIp00AgJQUPJvfcc09cd911sWLFijjvvPNi48aNceqpp8aDDz54zGuGhobi85//fNxxxx1x1llnvaMbBignDQRSpX9AyjQQSJkGAikpaDAZHByMXbt2RUtLy1ufoLIyWlpaoqur65jXff3rX4/p06fHNddcM/47BSgzDQRSpX9AyjQQSJkGAqk5qZAnHzx4MIaGhqKhoWHU4w0NDbFnz54xr3nqqafigQceiO7u7hP+OgMDAzEwMDDy5/7+/kJuE6AoStFA/QMmI68BgZRpIJAy3wcDqRnXm76fqEOHDsXSpUtj06ZNMW3atBO+rqOjI+rr60c+Ghsbi3iXAMUxngbqH5AHXgMCKdNAIGW+DwamuoJ+wmTatGlRVVUVvb29ox7v7e2NGTNmHPX83/3ud/HKK6/EokWLRh4bHh7+2xc+6aR44YUX4uyzzz7qujVr1kRbW9vIn/v7+8USKLtSNFD/gMnIa0AgZRoIpMz3wUBqChpMqqurY/78+dHZ2RmLFy+OiL9Fr7OzM770pS8d9fxzzz03nn322VGP3XbbbXHo0KH4P//n/xwzfjU1NVFTU1PIrQEUXSkaqH/AZOQ1IJAyDQRS5vtgIDUFDSYREW1tbbF8+fJYsGBBLFy4MNavXx+HDx+OFStWRETEsmXLYvbs2dHR0RG1tbVx/vnnj7r+9NNPj4g46nGAqUADgVTpH5AyDQRSpoFASgoeTJYsWRIHDhyItWvXRk9PT8ybNy+2b98+8uZP+/bti8rKor41CkDZaCCQKv0DUqaBQMo0EEhJRZZlWblv4nj6+/ujvr4++vr6oq6urty3A0xSeWxFHs8EFEcee5HHMwETL6+tyOu5gImVx1bk8UxAcRSjF+ZfAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeQYTAAAAAAAgeeMaTDZs2BBz5syJ2traaGpqip07dx7zuZs2bYrLLrsszjjjjDjjjDOipaXlbZ8PMNlpIJAq/QNSpoFAyjQQSEXBg8mWLVuira0t2tvbY/fu3TF37txobW2NV199dczn79ixI6666qr45S9/GV1dXdHY2Bif+tSn4g9/+MM7vnmAUtNAIFX6B6RMA4GUaSCQkoosy7JCLmhqaoqLL7447r333oiIGB4ejsbGxrjxxhtj9erVx71+aGgozjjjjLj33ntj2bJlJ/Q1+/v7o76+Pvr6+qKurq6Q2wUSUopWlLqB+gecqGL3wmtAYLLK42vACA0ETkweG6h/wIkqRi8K+gmTwcHB2LVrV7S0tLz1CSoro6WlJbq6uk7oc7z++uvxxhtvxLvf/e7C7hSgzDQQSJX+ASnTQCBlGgik5qRCnnzw4MEYGhqKhoaGUY83NDTEnj17Tuhz3HzzzTFr1qxRof17AwMDMTAwMPLn/v7+Qm4ToChK0UD9AyYjrwGBlGkgkDLfBwOpGdebvo/XXXfdFZs3b47HHnssamtrj/m8jo6OqK+vH/lobGws4V0CFMeJNFD/gDzyGhBImQYCKfN9MDDVFDSYTJs2LaqqqqK3t3fU4729vTFjxoy3vfY73/lO3HXXXfGzn/0sLrzwwrd97po1a6Kvr2/kY//+/YXcJkBRlKKB+gdMRl4DAinTQCBlvg8GUlPQYFJdXR3z58+Pzs7OkceGh4ejs7Mzmpubj3ndt771rbjzzjtj+/btsWDBguN+nZqamqirqxv1AVBupWig/gGTkdeAQMo0EEiZ74OB1BT0HiYREW1tbbF8+fJYsGBBLFy4MNavXx+HDx+OFStWRETEsmXLYvbs2dHR0REREf/2b/8Wa9eujYcffjjmzJkTPT09ERHxrne9K971rndN4FEAik8DgVTpH5AyDQRSpoFASgoeTJYsWRIHDhyItWvXRk9PT8ybNy+2b98+8uZP+/bti8rKt35w5Xvf+14MDg7GP/3TP436PO3t7fG1r33tnd09QIlpIJAq/QNSpoFAyjQQSElFlmVZuW/iePr7+6O+vj76+vr8WB5wTHlsRR7PBBRHHnuRxzMBEy+vrcjruYCJlcdW5PFMQHEUoxcFvYcJAAAAAABAHhlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5BlMAAAAAACA5I1rMNmwYUPMmTMnamtro6mpKXbu3Pm2z//xj38c5557btTW1sYFF1wQ27ZtG9fNAkwGGgikSv+AlGkgkDINBFJR8GCyZcuWaGtri/b29ti9e3fMnTs3Wltb49VXXx3z+U8//XRcddVVcc0118QzzzwTixcvjsWLF8dvf/vbd3zzAKWmgUCq9A9ImQYCKdNAICUVWZZlhVzQ1NQUF198cdx7770RETE8PByNjY1x4403xurVq496/pIlS+Lw4cPx05/+dOSxj33sYzFv3rzYuHHjCX3N/v7+qK+vj76+vqirqyvkdoGElKIVpW6g/gEnqti98BoQmKzy+BowQgOBE5PHBuofcKKK0YuTCnny4OBg7Nq1K9asWTPyWGVlZbS0tERXV9eY13R1dUVbW9uox1pbW+Pxxx8/5tcZGBiIgYGBkT/39fVFxN/+CwA4liONKHAHPmGlaKD+AeNVzAZ6DQhMZnl4DRihgcD45KGB+geMVzEaWNBgcvDgwRgaGoqGhoZRjzc0NMSePXvGvKanp2fM5/f09Bzz63R0dMQdd9xx1OONjY2F3C6QqP/5n/+J+vr6Cf+8pWig/gHvVDEa6DUgMBVM5deAERoIvDNTuYH6B7xTE9nAggaTUlmzZs2oJfq1116L97///bFv376ixL8c+vv7o7GxMfbv35+rHy/M47nyeKaIfJ6rr68vzjzzzHj3u99d7lsZtxT6F5HPf395PFNEPs+VxzNFaOBUksd/g840deTxXHnoX0QaDczjv7+IfJ4rj2eKyOe58tDAFPoXkc9/f3k8U0Q+z5XHM0UUp4EFDSbTpk2Lqqqq6O3tHfV4b29vzJgxY8xrZsyYUdDzIyJqamqipqbmqMfr6+tz9RcaEVFXV5e7M0Xk81x5PFNEPs9VWVlZlM9bigam1L+IfP77y+OZIvJ5rjyeKaI4DfQasDjy+G/QmaaOPJ5rKr8GjEirgXn89xeRz3Pl8UwR+TzXVG5gSv2LyOe/vzyeKSKf58rjmSImtoEFfabq6uqYP39+dHZ2jjw2PDwcnZ2d0dzcPOY1zc3No54fEfHkk08e8/kAk5UGAqnSPyBlGgikTAOB1BT8K7na2tpi+fLlsWDBgli4cGGsX78+Dh8+HCtWrIiIiGXLlsXs2bOjo6MjIiJuuumm+MQnPhF33313XHnllbF58+b4zW9+E/fff//EngSgBDQQSJX+ASnTQCBlGgikpODBZMmSJXHgwIFYu3Zt9PT0xLx582L79u0jb+a0b9++UT8Cc8kll8TDDz8ct912W9xyyy3xD//wD/H444/H+eeff8Jfs6amJtrb28f88bypKo9nisjnufJ4poh8nqsUZyp1A/P49xSRz3Pl8UwR+TxXHs8UUfxzeQ04cfJ4LmeaOvJ4rjy+BozwdzWV5PFceTxTRD7PlccG5vHvKSKf58rjmSLyea48nimiOOeqyLIsm7DPBgAAAAAAMAUV5x2hAAAAAAAAphCDCQAAAAAAkDyDCQAAAAAAkDyDCQAAAAAAkLxJM5hs2LAh5syZE7W1tdHU1BQ7d+582+f/+Mc/jnPPPTdqa2vjggsuiG3btpXoTk9cIWfatGlTXHbZZXHGGWfEGWecES0tLcf976BcCv27OmLz5s1RUVERixcvLu4NjkOhZ3rttddi5cqVMXPmzKipqYlzzjln0v0bLPRM69evjw996ENxyimnRGNjY6xatSr++te/luhuT8yvfvWrWLRoUcyaNSsqKiri8ccfP+41O3bsiI9+9KNRU1MTH/zgB+Ohhx4q+n0WKo/9i8hnA/PYvwgNjJj8Dcxr/yLy2cA89i8inw3MY/8iNDBCA8tJA0fTwNLKW/8i8tvAPPYvIp8NzGP/IjQwYvI3sGz9yyaBzZs3Z9XV1dmDDz6Y/dd//Vd23XXXZaeffnrW29s75vN//etfZ1VVVdm3vvWt7Lnnnstuu+227OSTT86effbZEt/5sRV6pquvvjrbsGFD9swzz2TPP/989s///M9ZfX199t///d8lvvO3V+i5jnj55Zez2bNnZ5dddln2mc98pjQ3e4IKPdPAwEC2YMGC7Iorrsieeuqp7OWXX8527NiRdXd3l/jOj63QM/3whz/Mampqsh/+8IfZyy+/nD3xxBPZzJkzs1WrVpX4zt/etm3bsltvvTV79NFHs4jIHnvssbd9/t69e7NTTz01a2try5577rnsu9/9blZVVZVt3769NDd8AvLYvyzLZwPz2L8s08AsmxoNzGP/siyfDcxj/7Isnw3MY/+yTAOzTAPLSQNH08DSymP/siyfDcxj/7Isnw3MY/+yTAOzbGo0sFz9mxSDycKFC7OVK1eO/HloaCibNWtW1tHRMebzP/vZz2ZXXnnlqMeampqyf/mXfynqfRai0DP9vTfffDM77bTTsh/84AfFusVxGc+53nzzzeySSy7Jvv/972fLly+fdKEs9Ezf+973srPOOisbHBws1S0WrNAzrVy5MvvHf/zHUY+1tbVll156aVHv8504kVB+9atfzT7ykY+MemzJkiVZa2trEe+sMHnsX5bls4F57F+WaWCWTb0G5qV/WZbPBuaxf1mWzwbmsX9ZpoFZpoHlpIFv0cDSy3v/siw/Dcxj/7Isnw3MY/+yTAOzbOo1sJT9K/uv5BocHIxdu3ZFS0vLyGOVlZXR0tISXV1dY17T1dU16vkREa2trcd8fqmN50x/7/XXX4833ngj3v3udxfrNgs23nN9/etfj+nTp8c111xTitssyHjO9JOf/CSam5tj5cqV0dDQEOeff36sW7cuhoaGSnXbb2s8Z7rkkkti165dIz+qt3fv3ti2bVtcccUVJbnnYsljKyb7mSLy2cA89i9CA4/IYwPz2orJfq489i8inw3MY/8iNPCIyd6KCA08Fg0sjTw2UP/eksdWTPYzReSzgXnsX4QGHpHHBk5UK06ayJsaj4MHD8bQ0FA0NDSMeryhoSH27Nkz5jU9PT1jPr+np6do91mI8Zzp7918880xa9aso/6Sy2k853rqqafigQceiO7u7hLcYeHGc6a9e/fGL37xi/j85z8f27Zti5deeim++MUvxhtvvBHt7e2luO23NZ4zXX311XHw4MH4+Mc/HlmWxZtvvhk33HBD3HLLLaW45aI5Viv6+/vjL3/5S5xyyillurO/yWP/IvLZwDz2L0IDj8hjAyd7/yLy2cA89i8inw3MY/8iNPAIDSwPDXyLBpae/r1lsjcwj/2LyGcD89i/CA08Io8NnKj+lf0nTDjaXXfdFZs3b47HHnssamtry30743bo0KFYunRpbNq0KaZNm1bu25kww8PDMX369Lj//vtj/vz5sWTJkrj11ltj48aN5b61cduxY0esW7cu7rvvvti9e3c8+uijsXXr1rjzzjvLfWskKA8NzGv/IjQQiikP/YvIbwPz2L8IDWTy0MDJLY8N1D8mkzw0MK/9i9DA1JT9J0ymTZsWVVVV0dvbO+rx3t7emDFjxpjXzJgxo6Dnl9p4znTEd77znbjrrrvi5z//eVx44YXFvM2CFXqu3/3ud/HKK6/EokWLRh4bHh6OiIiTTjopXnjhhTj77LOLe9PHMZ6/q5kzZ8bJJ58cVVVVI499+MMfjp6enhgcHIzq6uqi3vPxjOdMt99+eyxdujSuvfbaiIi44IIL4vDhw3H99dfHrbfeGpWVU3NbPVYr6urqyv7/VRORz/5F5LOBeexfhAYekccGTvb+ReSzgXnsX0Q+G5jH/kVo4BEaWB4a+DcaWB7695bJ3sA89i8inw3MY/8iNPCIPDZwovpX9pNXV1fH/Pnzo7Ozc+Sx4eHh6OzsjObm5jGvaW5uHvX8iIgnn3zymM8vtfGcKSLiW9/6Vtx5552xffv2WLBgQSlutSCFnuvcc8+NZ599Nrq7u0c+Pv3pT8fll18e3d3d0djYWMrbH9N4/q4uvfTSeOmll0aiHxHx4osvxsyZM8seyIjxnen1118/KoRH/o/A395XaWrKYysm+5ki8tnAPPYvQgOPyGMD89qKyX6uPPYvIp8NzGP/IjTwiMneiggN/H9pYOnlsYH695Y8tmKynykinw3MY/8iNPCIPDZwwlpR0FvEF8nmzZuzmpqa7KGHHsqee+657Prrr89OP/30rKenJ8uyLFu6dGm2evXqkef/+te/zk466aTsO9/5Tvb8889n7e3t2cknn5w9++yz5TrCUQo901133ZVVV1dnjzzySPbHP/5x5OPQoUPlOsKYCj3X31u+fHn2mc98pkR3e2IKPdO+ffuy0047LfvSl76UvfDCC9lPf/rTbPr06dk3vvGNch3hKIWeqb29PTvttNOy//iP/8j27t2b/exnP8vOPvvs7LOf/Wy5jjCmQ4cOZc8880z2zDPPZBGR3XPPPdkzzzyT/f73v8+yLMtWr16dLV26dOT5e/fuzU499dTsX//1X7Pnn38+27BhQ1ZVVZVt3769XEc4Sh77l2X5bGAe+5dlGphlU6OBeexfluWzgXnsX5bls4F57F+WaWCWaWA5aeDYNLA08ti/LMtnA/PYvyzLZwPz2L8s08AsmxoNLFf/JsVgkmVZ9t3vfjc788wzs+rq6mzhwoXZf/7nf478Z5/4xCey5cuXj3r+j370o+ycc87Jqqurs4985CPZ1q1bS3zHx1fImd7//vdnEXHUR3t7e+lv/DgK/bv6f03WUBZ6pqeffjpramrKampqsrPOOiv75je/mb355pslvuu3V8iZ3njjjexrX/tadvbZZ2e1tbVZY2Nj9sUvfjH73//939Lf+Nv45S9/Oeb/To6cZfny5dknPvGJo66ZN29eVl1dnZ111lnZv//7v5f8vo8nj/3Lsnw2MI/9yzINnAoNzGv/siyfDcxj/7Isnw3MY/+yTAOPXKOB5aGBR9PA0slb/7Isvw3MY/+yLJ8NzGP/skwDp0IDy9W/iiyboj9jAwAAAAAAMEHK/h4mAAAAAAAA5WYwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAkmcwAQAAAAAAklfwYPKrX/0qFi1aFLNmzYqKiop4/PHHj3vNjh074qMf/WjU1NTEBz/4wXjooYfGcasA5aV/QMo0EEiZBgKp0j8gNQUPJocPH465c+fGhg0bTuj5L7/8clx55ZVx+eWXR3d3d3z5y1+Oa6+9Np544omCbxagnPQPSJkGAinTQCBV+gekpiLLsmzcF1dUxGOPPRaLFy8+5nNuvvnm2Lp1a/z2t78deexzn/tcvPbaa7F9+/bxfmmAstI/IGUaCKRMA4FU6R+QgpOK/QW6urqipaVl1GOtra3x5S9/+ZjXDAwMxMDAwMifh4eH409/+lO85z3viYqKimLdKjDFZVkWhw4dilmzZkVlZfnfokn/gFLSQCBVk61/ERoIlM5ka6D+AaVUjAYWfTDp6emJhoaGUY81NDREf39//OUvf4lTTjnlqGs6OjrijjvuKPatATm1f//+eN/73lfu29A/oCw0EEjVZOlfhAYCpTdZGqh/QDlMZAOLPpiMx5o1a6KtrW3kz319fXHmmWfG/v37o66urox3Bkxm/f390djYGKeddlq5b2Xc9A8YLw0EUpWH/kVoIDA+eWig/gHjVYwGFn0wmTFjRvT29o56rLe3N+rq6sZclSMiampqoqam5qjH6+rqhBI4rsnyI7v6B5SDBgKpmiz9i9BAoPQmSwP1DyiHiWxg0X+5YXNzc3R2do567Mknn4zm5uZif2mAstI/IGUaCKRMA4FU6R8w1RU8mPz5z3+O7u7u6O7ujoiIl19+Obq7u2Pfvn0R8bcfo1u2bNnI82+44YbYu3dvfPWrX409e/bEfffdFz/60Y9i1apVE3MCgBLRPyBlGgikTAOBVOkfkJqCB5Pf/OY3cdFFF8VFF10UERFtbW1x0UUXxdq1ayMi4o9//ONINCMiPvCBD8TWrVvjySefjLlz58bdd98d3//+96O1tXWCjgBQGvoHpEwDgZRpIJAq/QNSU5FlWVbumzie/v7+qK+vj76+Pr+7EDimPLYij2cCiiOPvcjjmYCJl9dW5PVcwMTKYyvyeCagOIrRi6K/hwkAAAAAAMBkZzABAAAAAACSZzABAAAAAACSZzABAAAAAACSZzABAAA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G5ti/lPJsYI79S0kDU5p4Daxl/8b8n+Q6ePBgbN++PTo6OoauTZ48OTo6OmLr1q0jPrN169Zh90dELFiw4Kj319poZvpTb7zxRrz11ltxxhlnVOuYFRvtXF/+8pdj2rRpce2119bimBUZzUw/+MEPor29PZYvXx4tLS1x3nnnxV133RUDAwO1OvYxjWamSy65JLZv3z70Vr3du3fHpk2b4oorrqjJmaslx1aM95ki8mxgjv2L0MDDcmxgrq0Y73Pl2L+IPBuYY/8iNPCw8d6KCA08Gg2sjRwbqH9vy7EV432miDwbmGP/IjTwsBwbeKJacdKJPNRo7Nu3LwYGBqKlpWXY9ZaWlnjhhRdGfKanp2fE+3t6eqp2zkqMZqY/dfPNN8fMmTOP+EseS6OZ6+mnn45vf/vbsWvXrhqcsHKjmWn37t3x7//+7/GpT30qNm3aFC+99FJ87nOfi7feeiu6urpqcexjGs1M11xzTezbty8+8pGPREopDh06FJ/5zGfilltuqcWRq+Zorejv748//OEPccopp4zRyf4ox/5F5NnAHPsXoYGH5djA8d6/iDwbmGP/IvJsYI79i9DAwzRwbGjg2zSw9vTvbeO9gTn2LyLPBubYvwgNPCzHBp6o/o35O0w40po1a2Ljxo3x2GOPRWNj41gfZ9Ref/31WLx4cWzYsCGmTp061sc5YQYHB2PatGlx//33x9y5c2PRokVx6623xvr168f6aKO2ZcuWuOuuu+Ib3/hG7NixIx599NF44okn4s477xzro1GgHBqYa/8iNBCqKYf+ReTbwBz7F6GBjB8aOL7l2ED9YzzJoYG59i9CA0sz5u8wmTp1atTV1UVvb++w6729vTF9+vQRn5k+fXpF99faaGY67J577ok1a9bEj3/847jggguqecyKVTrXr371q/j1r38dCxcuHLo2ODgYEREnnXRSvPjii3H22WdX99DvYDR/VzNmzIiTTz456urqhq594AMfiJ6enjh48GDU19dX9czvZDQz3X777bF48eK47rrrIiLi/PPPj/3798f1118ft956a0yePDF3q0drRVNT05j/f9VE5Nm/iDwbmGP/IjTwsBwbON77F5FnA3PsX0SeDcyxfxEaeJgGjg0N/CMNHBv697bx3sAc+xeRZwNz7F+EBh6WYwNPVP/GfPL6+vqYO3dudHd3D10bHByM7u7uaG9vH/GZ9vb2YfdHRDz11FNHvb/WRjNTRMTdd98dd955Z2zevDnmzZtXi6NWpNK5zj333Hj22Wdj165dQx8f//jH46Mf/Wjs2rUrWltba3n8EY3m7+rSSy+Nl156aSj6ERG//OUvY8aMGWMeyIjRzfTGG28cEcLD/0fgj79XaWLKsRXjfaaIPBuYY/8iNPCwHBuYayvG+1w59i8izwbm2L8IDTxsvLciQgP/Lw2svRwbqH9vy7EV432miDwbmGP/IjTwsBwbeMJaUdGviK+SjRs3poaGhvTggw+m5557Ll1//fXp9NNPTz09PSmllBYvXpxWrFgxdP/PfvazdNJJJ6V77rknPf/886mrqyudfPLJ6dlnnx2rEY5Q6Uxr1qxJ9fX16ZFHHkm/+93vhj5ef/31sRphRJXO9aeWLl2aPvGJT9TotMen0pn27NmTTjvttPT5z38+vfjii+mHP/xhmjZtWvrKV74yViMcodKZurq60mmnnZb+7d/+Le3evTv96Ec/SmeffXb65Cc/OVYjjOj1119PO3fuTDt37kwRke699960c+fO9Jvf/CallNKKFSvS4sWLh+7fvXt3OvXUU9M///M/p+effz6tW7cu1dXVpc2bN4/VCEfIsX8p5dnAHPuXkgamNDEamGP/UsqzgTn2L6U8G5hj/1LSwJQ0cCxp4Mg0sDZy7F9KeTYwx/6llGcDc+xfShqY0sRo4Fj1b1wsTFJK6etf/3o688wzU319fZo/f376j//4j6H/7PLLL09Lly4ddv/3vve9dM4556T6+vr0oQ99KD3xxBM1PvE7q2Sm9773vSkijvjo6uqq/cHfQaV/V//XeA1lpTM988wzqa2tLTU0NKSzzjorffWrX02HDh2q8amPrZKZ3nrrrfSlL30pnX322amxsTG1tramz33uc+l//ud/an/wY/jJT34y4v9ODs+ydOnSdPnllx/xzJw5c1J9fX0666yz0r/+67/W/NzvJMf+pZRnA3PsX0oaOBEamGv/UsqzgTn2L6U8G5hj/1LSwMPPaODY0MAjaWDt5Na/lPJtYI79SynPBubYv5Q0cCI0cKz6NymlCfoeGwAAAAAAgBNkzH+HCQAAAAAAwFizMAEAAAAAAIpnYQIAAAAAABTPwgQAAAAAACiehQkAAAAAAFA8CxMAAAAAAKB4FiYAAAAAAEDxLEwAAAAAAIDiWZgAAAAAAADFszABAAAAAACKZ2ECAAAAAAAUz8IEAAAAAAAo3v8HMdsnf28B5JQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x2000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ref_dir = \"/home/brysongray/data/neurotrack_data/gold166/gold166_soma-removed_training_set/\"\n",
+    "ref_img_files = [f for x in os.walk(ref_dir) for f in glob(os.path.join(x[0], \"*_image.tif\"))][0]\n",
+    "outdir = params[\"outdir\"]\n",
+    "out_files = os.listdir(outdir)\n",
+    "# random_ids = np.random.choice(len(out_files))\n",
+    "\n",
+    "fig, ax = plt.subplots(5,5, figsize=(20,20))\n",
+    "ax = ax.ravel()\n",
+    "for i in range(25):\n",
+    "    f = os.path.join(outdir, out_files[i])\n",
+    "    inference_out = np.load(f, allow_pickle=True)\n",
+    "    labeled_neuron = inference_out[\"labeled_neuron\"]\n",
+    "    if labeled_neuron.dtype == np.uint8:\n",
+    "        labeled_neuron = labeled_neuron / np.array(255., dtype=np.float32)\n",
+    "    \n",
+    "    name = out_files[i].split('_08-13-25*')[0][:-1]\n",
+    "    reference_img_path = [f for f in ref_img_files if f.split('/')[-1].split('_image.tif')[0] == name][0]\n",
+    "    ref_img = tf.imread(reference_img_path)\n",
+    "    if ref_img.dtype == np.uint8:\n",
+    "        ref_img = ref_img / np.array(255., dtype=np.float32)\n",
+    "    \n",
+    "    ax[i].imshow(ref_img.max(0))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tractography",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/plot/tracking_interface.py b/plot/tracking_interface.py
index 35beb64..5c08924 100644
--- a/plot/tracking_interface.py
+++ b/plot/tracking_interface.py
@@ -18,94 +18,250 @@ def manual_step(env, step_size=2.0):
                     'p': torch.tensor([1.0, 0.0, 0.0]),
                     'l': torch.tensor([-1.0, 0.0, 0.0])}
     
-    fig, ax = plt.subplots(1,3)
+    fig = plt.figure(figsize=(12, 8))
+    gs = fig.add_gridspec(2, 2)
+    
+    # Top row spans the full width
+    ax0 = fig.add_subplot(gs[0, :])
+    
+    # Bottom row has two plots side by side
+    ax1 = fig.add_subplot(gs[1, 0])
+    ax2 = fig.add_subplot(gs[1, 1])
+    
+    ax = [ax0, ax1, ax2]
     while True:
         action = input("Choose an action: ")
         if action == 'q':
             break
-        if action == 't':
+        elif action == 'r':
             env.reset()
+        elif action == 'b':
+            point = env.paths[env.head_id][-1]
+            env.paths.append(point[None])
+            # env.path_labels.append(0)
+            env.prev_children.append(env.prev_children[env.head_id])
+            env.roots.append(point)
+            env.img.draw_point(point, radius=env.step_width, channel=-1)
         else:
             action = user_input_dict[action]
             action = action.to(device=device)
-            action = action * step_size
+            action = action * getattr(env, 'step_size', step_size)
             display.clear_output(wait=True)
-            observation, reward, terminated = env.step(action, verbose=True)
+            observation, reward, terminated = env.step(action, verbose=True, training=False)
 
             # Show:
             # 1) Whole image with path overlayed,
             # 2) Cropped image with path overlayed,
             # 3) Cropped mask, true density, and path overlayed
-            img = env.img.data[:3].amax(dim=1).permute(1,2,0).cpu()
-            path = env.img.data[3].amax(dim=0).cpu()
+            img = env.img.data[:-1].amax(dim=1).permute(1,2,0)
+            img = img.squeeze()
+            path = env.img.data[-1].amax(dim=0)#.permute(1,0)
             ax[0].imshow(img)
             ax[0].imshow(path, cmap='plasma', alpha=0.5)
-            # patch, _ = env.img.crop(env.paths[env.head_id][-1], env.radius, interp=True)
+            # if len(env.path_labels) > 0:
+            #     label = env.path_labels[env.head_id]
+            # else:
+            #     label = None
+            # ax[0].set_title(f'path label: {label}')
+            # patch, _ = env.img.crop(env.paths[env.head_id][-1], env.radius, interp=False)
             patch = observation[0]
-            patch = patch[:, env.radius].cpu()
-            ax[1].imshow(patch[:3].permute(1,2,0))
-            ax[1].imshow(patch[3], cmap='plasma', alpha=0.5)
-
-            if len(env.paths) > 0:
-                mask = Image(env.mask)
-                mask, _ = mask.crop(env.paths[env.head_id][-1], env.radius, interp=True)
-                mask = mask[0,env.radius].cpu()
-                true_density, _ = env.true_density.crop(env.paths[env.head_id][-1], env.radius, interp=True)
-                true_density = true_density[0,env.radius].cpu()
-                ax[2].imshow(mask, cmap='Blues')
-                ax[2].imshow(true_density, cmap='Reds', alpha=0.5)
-                ax[2].imshow(patch[3], cmap='Greens', alpha=0.5)
+            patch = patch[:, env.radius]
+            ax[1].imshow(patch[:-1].permute(1,2,0).squeeze())
+            ax[1].imshow(patch[-1], cmap='plasma', alpha=0.5)
+
+            if not terminated:
+                center = env.paths[env.head_id][-1]
+
+                density_patch = env.true_density.crop(center, env.radius, interp=False)[0]
+
+                # labels_patch, _ = env.section_labels.crop(center, env.radius, interp=False, pad=False)
+                # new_label = int(labels_patch[0, env.radius, env.radius, env.radius].item())
+                # current_label = env.path_labels[env.head_id]
+                # # Here mask out any sections that are not the current section or its children. 
+                # if current_label != 0:
+                #     # The graph is necessarily an undirected graph. Here "children" means connected sections
+                #     # that have not been previously available to the path.
+                #     children = [x for x in env.graph[current_label] if x not in env.prev_children[env.head_id]]
+                #     section_ids = [current_label] + children
+                #     section_mask = torch.zeros_like(density_patch)
+                #     for id in section_ids:
+                #         section_mask += torch.where(labels_patch == id, 1, 0)
+                #     density_patch_masked = density_patch * section_mask
+                #     if new_label != current_label and new_label in section_ids: # only change label if the new label is a child section
+                #         env.path_labels[env.head_id] = new_label
+                #         env.prev_children[env.head_id] = env.graph[current_label]
+                # else:
+                #     density_patch_masked = density_patch
+                #     if new_label != 0:
+                #         env.path_labels[env.head_id] = new_label
+
+                density_patch_masked = density_patch        
+                density_patch_masked = density_patch_masked[0,env.radius]
+                # mask = mask[0,env.radius]
+                # ax[2].imshow(mask, cmap='Blues')
+                ax[2].imshow(density_patch_masked, cmap='Reds', alpha=0.5)#.permute(1,0), cmap='Reds', alpha=0.5)
+                ax[2].imshow(patch[-1], cmap='Greens', alpha=0.5)#.permute(1,0), cmap='Greens', alpha=0.5)
             else:
-                ax[2].imshow(torch.zeros_like(patch[3]))
+                ax[2].imshow(torch.zeros_like(patch[-1]))
                 env.reset()
-        
+        # Turn off axes for all subplots
+        for a in ax:
+            a.axis('off')
         print(f"reward: {reward}")
         print(f"terminated: {terminated}")
 
         display.display(plt.gcf())
 
 
-def show_state(env, z=None, finished=False, path_id=0, t=-1):
-    
-    if finished:
-        paths = env.finished_paths
+def show_state(env, fig, live=False, ep_return=None, reward=None, policy_loss=None):
+    """Show a single max-intensity projection (over first spatial axis) of the whole neuron.
+
+    - Base image: input neuron image (grayscale)
+    - Overlay: current path (plasma colormap, semi-transparent)
+    Also draws a red rectangle centered at the current position with
+    width and height equal to env.radius (in pixels).
+    If live=True, also show a second subplot to the right with the current
+    cropped state (env.get_state()) as an overlay: image (grayscale) + path (plasma).
+    """
+    from matplotlib import patches  # local import to avoid global dependency
+
+    print(f"image: {env.current_neuron_info['neuron_name']}")
+    display.clear_output(wait=True)
+
+    # Reset view; one or more axes depending on `live`
+    fig.clf()
+    if live:
+        # GridSpec: Left spans both rows, right has two stacked axes.
+        # Left is wider than each right axis; the midline aligns with the split between right-top and right-bottom.
+        gs = fig.add_gridspec(2, 2, width_ratios=[2.0, 1.0], wspace=0.05, hspace=0.05)
+        ax_left = fig.add_subplot(gs[:, 0])
+        ax_right = fig.add_subplot(gs[0, 1])
+        ax_right_bottom = fig.add_subplot(gs[1, 1])
     else:
-        paths = env.paths
-        path_id = env.head_id
-
-    state = env.get_state()[0]
-    true_density_patch, _ = env.true_density.crop(paths[path_id][t], env.radius, pad=True)
-    mask = Image(env.mask)
-    mask_patch, _ = mask.crop(paths[path_id][t], env.radius, interp=False, pad=True)
-    I = np.array(env.img.data.to('cpu'))
-    O = np.array(state.to('cpu'))
-    T = np.array(true_density_patch.to('cpu'))
-    M = np.array(mask_patch.to('cpu'))
-    if z is not None:
-        z_ = state.shape[2]//2
-        I = I[:, z]
-        O = O[:, z_]
-        T = T[0,z_]
-        M = M[0,z_]
-    else: # display a maximum intensity projection along z
-        I = I.max(axis=1)
-        O = O.max(axis=1)
-        T = T[0].max(axis=0)
-        M = M[0].max(axis=0)
-
-    fig, ax = plt.subplots(1,3)
-    ax[0].imshow(I[3], cmap='hot', alpha=0.5) #, int(paths[env.head_id][-1, 0])])
-    ax[0].imshow(I[:3].transpose(1,2,0), alpha=0.5) #, int(paths[env.head_id][-1, 0])])
-    ax[0].axis('off')
-    ax[1].imshow(O[:3].transpose(1,2,0), alpha=0.75)
-    ax[1].imshow(O[3], alpha=0.25, cmap='hot') #, env.radius//2])
-    ax[1].axis('off')
-    toshow = np.stack((O[3], T, M), axis=-1)
-    ax[2].imshow(toshow)
-    ax[2].axis('off')
-
-    display.display(plt.gcf())
-    plt.close()
-    
+        ax_left = fig.add_subplot(1, 1, 1)
+
+    # Prepare volumes
+    env_img = env.img.data.clone().detach().cpu()
+    if env_img.dtype == torch.uint8:
+        env_img = env_img.float() / 255.0
+
+    # Separate channels: img (all but last) and path (last)
+    img = env_img[:-1]                  # (C_img, H, W, D) or (1, H, W, D)
+    path = env_img[-1]                  # (H, W, D)
+
+    # Max-intensity projection of input image (img) and path
+    if img.ndim == 4:
+        img_vol = img.amax(dim=0)       # (H, W, D)
+    else:
+        img_vol = img                   # already 3D
+
+    img_proj = img_vol.amax(dim=0)      # (W, D)
+    path_proj = path.amax(dim=0)        # (W, D)
+
+    # Show RGB MIP (left axis)
+    ax_left.imshow(img_proj, cmap='gray', vmax=1.0, vmin=0.0)
+
+    # Overlay path as a transparent colored mask
+    ax_left.imshow(path_proj, cmap='plasma', alpha=0.5)
+
+    # Overlay seed points as red dots (projected using y=seed[1], x=seed[2])
+    h, w = img_proj.shape[0], img_proj.shape[1]
+    if hasattr(env, 'seeds') and env.seeds:
+        xs, ys = [], []
+        for s in env.seeds:
+            try:
+                y_s = int(round(s[1]))
+                x_s = int(round(s[2]))
+            except Exception:
+                continue
+            if 0 <= y_s < h and 0 <= x_s < w:
+                ys.append(y_s)
+                xs.append(x_s)
+        if xs:
+            ax_left.scatter(xs, ys, s=25, c='red', marker='o', linewidths=0)
+
+    if env.paths:
+        # Draw red box around current position with size env.radius x env.radius
+        pos = env.paths[env.head_id][-1]
+        y = int(round(pos[1].item()))  # axis-1 (rows)
+        x = int(round(pos[2].item()))  # axis-2 (cols)
+        r = int(env.radius)
+        h, w = img_proj.shape[0], img_proj.shape[1]
+
+        # Compute box extents; ensure width and height equal to r, clipped to bounds
+        half_down = r // 2
+        half_up = r - half_down
+        y0 = max(0, y - half_down)
+        x0 = max(0, x - half_down)
+        # Clip width/height so rectangle stays within image
+        width = min(r, w - x0)
+        height = min(r, h - y0)
+
+        rect = patches.Rectangle((x0, y0), width, height, linewidth=1.5, edgecolor='red', facecolor='none')
+        ax_left.add_patch(rect)
+
+    # Optional right subplot: current cropped state overlay
+    if live:
+        try:
+            obs = env.get_state()[0].detach().cpu()  # (C, H, W, D)
+            img_obs = obs[:-1]
+            path_obs = obs[-1]
+            if img_obs.ndim == 4:
+                img_obs_vol = img_obs.amax(dim=0)  # (H, W, D)
+            else:
+                img_obs_vol = img_obs
+            img_obs_proj = img_obs_vol.amax(dim=0)
+            path_obs_proj = path_obs.amax(dim=0)
+            ax_right_bottom.imshow(img_obs_proj, cmap='gray', vmax=1.0, vmin=0.0)
+            ax_right_bottom.imshow(path_obs_proj, cmap='plasma', vmax=1.0, vmin=0.0, alpha=0.5)
+            ax_right_bottom.axis('off')
+
+            # Bottom-right: overlay path_obs on cropped true_density MIP
+            center = env.paths[env.head_id][-1]
+            density_patch = env.true_density.crop(center, env.radius, interp=False)[0]
+            if density_patch.dtype == torch.uint8:
+                density_patch = density_patch.float() / 255.0
+            # Use first channel of density if multi-channel
+            if density_patch.ndim == 4:
+                density_ch = density_patch[0]
+            else:
+                density_ch = density_patch
+
+            # # Optionally mask out competing sections
+            # if env.section_masking:
+            #     current_label = env.path_labels[env.head_id]
+            #     if current_label != 0:
+            #         labels_patch, _ = env.section_labels.crop(center, env.radius, interp=False, pad=False)
+            #         labels_patch = labels_patch[0]
+            #         # Create mask using vectorized operations
+            #         section_mask = torch.zeros_like(density_ch, dtype=torch.bool)
+            #         prev_children = env.prev_children[env.head_id]
+            #         graph_current = env.graph[current_label]
+            #         section_ids = [current_label] + [x for x in graph_current if x not in prev_children]
+            #         for id in section_ids:
+            #             section_mask |= (labels_patch == id)
+            #         density_ch = density_ch * section_mask.float()
+
+            # density_proj = density_ch.amax(dim=0)  # (W, D)
+            density_proj = density_ch[env.radius]  # single slice through center
+            ax_right.imshow(density_proj, cmap='Reds', vmax=1.0, vmin=0.0)
+            ax_right.imshow(path_obs_proj, cmap='Greens', vmax=1.0, vmin=0.0, alpha=0.5)
+            ax_right.axis('off')
+            if reward is not None:
+                ax_right.set_title(f'Reward: {reward:.4f}')
+        except Exception:
+            # Fail quietly if state isn't available
+            ax_right_bottom.axis('off')
+            try:
+                ax_right.axis('off')
+            except Exception:
+                pass
+
+    ax_left.axis('off')
+    display.display(fig)
+
+
+    return
+
 if __name__ == "__main__":
     pass
\ No newline at end of file
diff --git a/solvers/branch_classifier.py b/solvers/branch_classifier.py
index 00de72a..e629a5a 100644
--- a/solvers/branch_classifier.py
+++ b/solvers/branch_classifier.py
@@ -2,12 +2,14 @@
 import numpy as np
 import os
 import pandas as pd
+import tifffile as tf
 import torch
 import torch.optim as optim
 from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
+from tqdm import trange
 
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-date = datetime.now().strftime("%m-%d-%y")
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
+DATE = datetime.now().strftime("%m-%d-%y")
 
 
 class StateData(Dataset):
@@ -26,17 +28,24 @@ class StateData(Dataset):
             A function/transform to apply to the labels.
     """
     
-    def __init__(self, labels_file, img_dir, transform=None, target_transform=None):
+    def __init__(self, labels_file, img_dir, transform=None, target_transform=None, seed=0):
         self.img_labels = pd.read_csv(labels_file)
         self.img_dir = img_dir
         self.transform = transform
         self.target_transform = target_transform
+        self.rng = np.random.default_rng(seed)
     def __len__(self):
         return len(self.img_labels)
     
     def __getitem__(self,idx):
         img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0]) # type: ignore
-        image = torch.load(img_path, weights_only=True)
+        image = torch.load(img_path)
+        # image = tf.imread(img_path)
+        # image = torch.from_numpy(image[None])
+        # large_image = tf.imread(img_path) # image with shape (3,21,21,21)
+        # large_image = torch.from_numpy(large_image)
+        # shift = self.rng.integers(low=-3, high=4, size=(3,))
+        # image = large_image[:, 3+shift[0]:18+shift[0], 3+shift[1]:18+shift[1], 3+shift[2]:18+shift[2]]
         label = self.img_labels.iloc[idx, 1]
         if self.transform:
             image = self.transform(image)
@@ -44,6 +53,44 @@ def __getitem__(self,idx):
             label = self.target_transform(label)
         return image, label
 
+# # TODO: Change to online data sampling from swc and image files
+# class StateData(Dataset):
+#     """
+#         A custom Dataset class for loading and transforming image data and labels.
+        
+#         Attributes
+#         ----------
+#         img_labels : pd.DataFrame
+#             DataFrame containing image file names and corresponding labels.
+#         img_dir : str
+#             Directory where image files are stored.
+#         transform : callable, optional
+#             A function/transform to apply to the images.
+#         target_transform : callable, optional
+#             A function/transform to apply to the labels.
+#     """
+    
+#     def __init__(self, swc_files, img_files, transform=None, target_transform=None, seed=0):
+#         self.transform = transform
+#         self.target_transform = target_transform
+#         self.rng = np.random.default_rng(seed)
+#     def __len__(self):
+#         return len(self.img_labels)
+    
+#     def __getitem__(self,idx):
+#         img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0]) # type: ignore
+#         large_image = tf.imread(img_path) # image with shape (3,21,21,21)
+#         # image = torch.from_numpy(image[None])
+#         large_image = torch.from_numpy(large_image)
+#         shift = self.rng.integers(low=-3, high=4, size=(3,))
+#         image = large_image[:, 3+shift[0]:18+shift[0], 3+shift[1]:18+shift[1], 3+shift[2]:18+shift[2]]
+#         label = self.img_labels.iloc[idx, 1]
+#         if self.transform:
+#             image = self.transform(image)
+#         if self.target_transform:
+#             label = self.target_transform(label)
+#         return image, label
+
 
 def transform(image):
     """
@@ -72,7 +119,29 @@ def transform(image):
     return image
 
 
-def train_loop(dataloader, model, loss_fn, optimizer):
+def transform_spherical_patch(image):
+    """
+    Apply random transformations to the input image tensor.
+    The function randomly flips the image tensor along each of the last two dimensions.
+    
+    Parameters
+    ----------
+    image : torch.Tensor
+        The input image tensor to be transformed. It is expected to have at least 4 dimensions.
+        
+    Returns
+    -------
+    torch.Tensor
+        The transformed image tensor with the same shape as the input.
+    """
+    
+    if torch.rand(1)>0.5: image = image.flip(-1)
+    if torch.rand(1)>0.5: image = image.flip(-2)
+    
+    return image
+
+
+def train_loop(dataloader, model, loss_fn, optimizer, logger=None):
     """
     Train the model for one epoch.
 
@@ -94,19 +163,20 @@ def train_loop(dataloader, model, loss_fn, optimizer):
     """
     
     size = len(dataloader.dataset)
-    positive_count = len(np.where(dataloader.dataset.img_labels.iloc[:,1] > 0.0)[0])
-    negative_count = size - positive_count
+    # positive_count = len(np.where(dataloader.dataset.img_labels.iloc[:,1] > 0.0)[0])
+    # negative_count = size - positive_count
     losses = []
     model.train()
     for batch, (X,y) in enumerate(dataloader):
-        out = model(X[:,:3].to(device=DEVICE))
-        out = torch.nn.functional.sigmoid(out.squeeze())
+        # out = model(X[:,:3].to(device=DEVICE))
+        out = model(X.to(device=DEVICE,dtype=torch.float32))
+        out = torch.sigmoid(out.squeeze())
         # out = torch.nn.functional.softmax(out, dim=1)
         # y = torch.nn.functional.one_hot(y, num_classes=3)
         y = y.to(dtype=torch.float, device=DEVICE)
-        weights = torch.where(y > 0.0, positive_count/size, negative_count/size)
+        # weights = torch.where(y > 0.0, positive_count/size, negative_count/size)
         loss = loss_fn(out,y)
-        loss = torch.mean(loss * weights)
+        # loss = torch.mean(loss * weights)
         loss.backward()
         optimizer.step()
         optimizer.zero_grad()
@@ -116,11 +186,15 @@ def train_loop(dataloader, model, loss_fn, optimizer):
             losses.append(loss)
             accuracy = ((out > 0.5) == y).type(torch.float).sum().item()
             accuracy = accuracy / len(y) * 100
-            print(f"Accuracy: {accuracy}, Loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
+            print_out = f"Accuracy: {accuracy}, Loss: {loss:>7f}  [{current:>5d}/{size:>5d}]"
+            if logger is None:
+                print(print_out)
+            else:
+                logger(print_out)
 
     return losses
 
-def test_loop(dataloader, model, loss_fn):
+def test_loop(dataloader, model, loss_fn, logger=None):
     """
     Evaluate the model on the test dataset and compute various metrics.
     
@@ -147,8 +221,9 @@ def test_loop(dataloader, model, loss_fn):
     test_loss, TP, TN, FP, FN = 0,0,0,0,0
     with torch.no_grad():
         for X,y in dataloader:
-            out = model(X[:,:3].to(device=DEVICE))
-            out = torch.nn.functional.sigmoid(out.squeeze())
+            # out = model(X[:,:3].to(device=DEVICE))
+            out = model(X.to(device=DEVICE,dtype=torch.float32))
+            out = torch.sigmoid(out.squeeze())
             y = y.to(dtype=torch.float, device=DEVICE)
             weights = torch.where(y > 0.0, positive_count/size, negative_count/size)
             loss = loss_fn(out,y)
@@ -165,11 +240,15 @@ def test_loop(dataloader, model, loss_fn):
             FN += FN_
 
     test_loss /= num_batches
-    precision = TP / (TP + FP)
-    recall = TP / (TP + FN)
+    precision = TP / (TP + FP + 1e-8)
+    recall = TP / (TP + FN + 1e-8)
     correct = (TP + TN) / size
-    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n\
-           Precision: {precision:>0.3f}, Recall: {recall:>0.3f}")
+    print_out = f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n\
+              Precision: {precision:>0.3f}, Recall: {recall:>0.3f}"
+    if logger is None:
+        print(print_out)
+    else:
+        logger(print_out)
     
     return
 
@@ -202,6 +281,13 @@ def init_dataloader(state_data, batchsize=64):
     return dataloader
 
 
+def log_training(filename, mode='a'):
+    def log_info(info):
+        with open(filename, mode) as f:
+            f.write(f"{info}\n")
+    return log_info
+
+
 def train(train_dataloader, test_dataloader, out_dir, lr, epochs, classifier, state_dict=None):
     """
     Train a classifier model using the provided dataloaders and parameters.
@@ -230,7 +316,13 @@ def train(train_dataloader, test_dataloader, out_dir, lr, epochs, classifier, st
     
     if not os.path.exists(out_dir):
         os.makedirs(out_dir, exist_ok=True)
-        
+    # Create a training log file to track progress
+    log_file_path = os.path.join(out_dir, "training_logs.txt")
+    logger = log_training(log_file_path, mode='a')
+    logger(f"Training started at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    logger(f"Learning rate: {lr}")
+    logger(f"Total epochs: {epochs}\n")
+
     if state_dict is not None:
         #load a previously trained model
         classifier.load_state_dict(state_dict)
@@ -239,14 +331,69 @@ def train(train_dataloader, test_dataloader, out_dir, lr, epochs, classifier, st
     classifier_optimizer = optim.AdamW(classifier.parameters(), lr=lr)
     binary_loss = torch.nn.BCELoss()
 
-    for i,t in enumerate(range(epochs)):
-        print(f"Epoch {t+1}\n-------------------------------")
-        train_loop(train_dataloader, classifier, binary_loss, classifier_optimizer)
-        test_loop(test_dataloader, classifier, binary_loss)
-        torch.save(classifier.state_dict(), os.path.join(out_dir, f"resnet_classifier_{date}_checkpoint-{i}.pt"))
-    print("Done!")
+    for i, t in enumerate(trange(epochs, desc="Training epochs")):
+        logger(f"Epoch {t+1}\n-------------------------------")
+        train_loop(train_dataloader, classifier, binary_loss, classifier_optimizer, logger=logger)
+        test_loop(test_dataloader, classifier, binary_loss, logger=logger)
+        if i%10==0:
+            torch.save(classifier.state_dict(), os.path.join(out_dir, f"resnet_classifier_{DATE}_checkpoint-{i}.pt"))
+    logger("Done!")
     
     return
 
-if __name__ == "__main__":
-    pass
\ No newline at end of file
+
+# TODO: Change to online data sampling from swc and image files
+# def train(swc_files, img_files, out_dir, lr, epochs, classifier, transform=None):
+#     """
+#     Train a classifier model using the provided dataloaders and parameters.
+    
+#     Parameters
+#     ----------
+#     train_dataloader : DataLoader
+#         DataLoader for the training dataset.
+#     test_dataloader : DataLoader
+#         DataLoader for the testing dataset.
+#     out_dir : str
+#         Directory where the model checkpoints will be saved.
+#     lr : float
+#         Learning rate for the optimizer.
+#     epochs : int
+#         Number of epochs to train the model.
+#     classifier : torch.nn.Module
+#         The classifier model to be trained.
+#     state_dict : dict, optional
+#         State dictionary to load a previously trained model (default is None).
+        
+#     Returns
+#     -------
+#     None
+#     """
+    
+#     if not os.path.exists(out_dir):
+#         os.makedirs(out_dir, exist_ok=True)
+#     # Create a training log file to track progress
+#     log_file_path = os.path.join(out_dir, "training_logs.txt")
+#     logger = log_training(log_file_path, mode='a')
+#     logger(f"Training started at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+#     logger(f"Learning rate: {lr}")
+#     logger(f"Total epochs: {epochs}\n")
+
+#     training_data = branch_classifier.StateData(labels_file=training_labels_file,
+#                                                 img_dir=img_dir,transform=transform)
+#     test_data = branch_classifier.StateData(labels_file=test_labels_file,
+#                                             img_dir=img_dir)
+
+#     training_dataloader = branch_classifier.init_dataloader(training_data)
+#     test_dataloader = branch_classifier.init_dataloader(test_data)
+
+#     classifier.train()
+#     classifier_optimizer = optim.AdamW(classifier.parameters(), lr=lr)
+#     binary_loss = torch.nn.BCELoss()
+
+#     for i,t in enumerate(range(epochs)):
+#         logger(f"Epoch {t+1}\n-------------------------------")
+#         train_loop(train_dataloader, classifier, binary_loss, classifier_optimizer, logger=logger)
+#         test_loop(test_dataloader, classifier, binary_loss, logger=logger)
+#         if i%10==0:
+#             torch.save(classifier.state_dict(), os.path.join(out_dir, f"resnet_classifier_{DATE}_checkpoint-{i}.pt"))
+    logger("Done!")
\ No newline at end of file
diff --git a/solvers/sac.py b/solvers/sac.py
index be3360e..4878b65 100644
--- a/solvers/sac.py
+++ b/solvers/sac.py
@@ -5,21 +5,29 @@
 updating the Q-networks and actor, performing target network updates, and training the agent.
 """
 
+import csv
+from collections import deque
 from datetime import datetime
 from itertools import count
 import matplotlib.pyplot as plt
+import numpy as np
 import os
 from pathlib import Path
+import re
+import select
 import sys
 import torch
 from tqdm import tqdm
 
-sys.path.append(str(Path(__file__).parents[1]))
+script_path = Path(os.path.abspath(__file__))
+parent_dir = script_path.parent.parent
+sys.path.append(str(parent_dir))
 from memory.buffer import ReplayBuffer, PrioritizedReplayBuffer
-from plot.show_state import show_state
+from plot.tracking_interface import show_state
+import ipywidgets as widgets
+from IPython.display import display
 
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 dtype = torch.float32
 date = datetime.now().strftime("%m-%d-%y")
 
@@ -43,7 +51,7 @@ def sample_from_output(out, random=False):
         A multivariate normal distribution parameterized by the processed mean
         and log variance.
     """
-    
+
     mean = out[:,:3] # component 0, 1 and 2
     logvar = out[:,3:] # logvar component 3
 
@@ -51,7 +59,7 @@ def sample_from_output(out, random=False):
     meannorm_ = torch.tanh(meannorm)*10 # maximum of 10
     mean = mean * meannorm_/(meannorm + torch.finfo(torch.float).eps)
     logvar = torch.tanh(logvar)*3 + 1 # no very low variance (std is order of 1 pixel) 
-    direction_dist = torch.distributions.MultivariateNormal(mean[:,:3], torch.exp(logvar)[:,None]*torch.eye(3, device=DEVICE)[None])
+    direction_dist = torch.distributions.MultivariateNormal(mean[:,:3], torch.exp(logvar)[:,None]*torch.eye(3, device=out.device)[None])
 
     return direction_dist
 
@@ -106,18 +114,31 @@ def update_Q(actor, Q1, Q1_target, Q2, Q2_target,
     with torch.no_grad():
         # sample next actions from the current policy
         actor_out = actor(next_obs) # set steps_done to start_steps so that this samples from the current policy
-        direction_dist = sample_from_output(actor_out)
+        direction_dist = sample_from_output(actor_out.detach().cpu())
         next_directions = direction_dist.rsample()
         logprobs = direction_dist.log_prob(next_directions)
-        # next_directions = torch.concatenate((torch.zeros(next_directions.shape[0],1, device=DEVICE), next_directions), dim=1) # for paths constrained to a 2d slice
+        next_directions = next_directions.to(DEVICE)
+        logprobs = logprobs.to(DEVICE)
         # get target q-values
-        next_states = torch.concatenate((next_obs, torch.ones((next_obs.shape[0], 1, next_obs.shape[2], next_obs.shape[3], next_obs.shape[4]), 
+        next_states = torch.cat((next_obs, torch.ones((next_obs.shape[0], 1, next_obs.shape[2], next_obs.shape[3], next_obs.shape[4]), 
                                             device=DEVICE)*next_directions[:,:,None,None,None]), dim=1)
         Q1_target_vals = Q1_target(next_states) # vector of q-values for each choice
         Q2_target_vals = Q2_target(next_states)
         targets = rewards + gamma * torch.logical_not(dones) * (torch.minimum(Q1_target_vals, Q2_target_vals) - log_alpha.exp() * logprobs[:,None])
+        
+        # Check for NaN values in intermediate computations
+        if torch.isnan(logprobs).any():
+            print("WARNING: NaN detected in logprobs!", flush=True)
+        if torch.isnan(Q1_target_vals).any():
+            print("WARNING: NaN detected in Q1_target_vals!", flush=True)
+        if torch.isnan(Q2_target_vals).any():
+            print("WARNING: NaN detected in Q2_target_vals!", flush=True)
+        if torch.isnan(log_alpha.exp()).any():
+            print(f"WARNING: NaN detected in log_alpha.exp()! log_alpha={log_alpha}", flush=True)
+        if torch.isnan(targets).any():
+            print("WARNING: NaN detected in targets!", flush=True)
     # compute q-values to compare against targets
-    current_state = torch.concatenate((obs, 
+    current_state = torch.cat((obs, 
                         torch.ones((obs.shape[0], 1, obs.shape[2], obs.shape[3], obs.shape[4]), 
                                     device=DEVICE)*actions[:,:,None,None,None]), dim=1)
     
@@ -143,6 +164,15 @@ def update_Q(actor, Q1, Q1_target, Q2, Q2_target,
 
     td_error = torch.maximum(Q1_td_error, Q2_td_error).squeeze()
     
+    # Check for NaN values in td_error
+    if torch.isnan(td_error).any():
+        print("WARNING: NaN detected in td_error!", flush=True)
+        print(f"Q1_vals has NaN: {torch.isnan(Q1_vals).any()}", flush=True)
+        print(f"Q2_vals has NaN: {torch.isnan(Q2_vals).any()}", flush=True)
+        print(f"targets has NaN: {torch.isnan(targets).any()}", flush=True)
+        print(f"Q1_td_error has NaN: {torch.isnan(Q1_td_error).any()}", flush=True)
+        print(f"Q2_td_error has NaN: {torch.isnan(Q2_td_error).any()}", flush=True)
+    
     return td_error
 
 
@@ -176,11 +206,14 @@ def update_actor(obs, actor, actor_optimizer, Q1, Q2, log_alpha, log_alpha_optim
     """
 
     actor_out = actor(obs)
+    # direction_dist = sample_from_output(actor_out.detach().cpu(), random=False)
     direction_dist = sample_from_output(actor_out, random=False)
     directions = direction_dist.rsample()
     logprobs = direction_dist.log_prob(directions)
+    directions = directions.to(DEVICE)
+    logprobs = logprobs.to(DEVICE)
     # get expected Q-vals
-    current_state = torch.concatenate((obs, torch.ones((obs.shape[0], 1, obs.shape[2], obs.shape[3], obs.shape[4]),device=DEVICE)*directions[:,:,None,None,None]), dim=1)
+    current_state = torch.cat((obs, torch.ones((obs.shape[0], 1, obs.shape[2], obs.shape[3], obs.shape[4]),device=DEVICE)*directions[:,:,None,None,None]), dim=1)
     Q1_vals = Q1(current_state)[:,0]
     Q2_vals = Q2(current_state)[:,0]
     # entropy regularized Q values
@@ -190,7 +223,22 @@ def update_actor(obs, actor, actor_optimizer, Q1, Q2, log_alpha, log_alpha_optim
     actor_optimizer.step()
 
     log_alpha_optimizer.zero_grad()
-    alpha_loss = (log_alpha.exp() * (-logprobs - target_entropy).detach()).mean()
+    entropy_term = (-logprobs - target_entropy).detach()
+    
+    # Check for NaN in entropy term
+    if torch.isnan(entropy_term).any():
+        print("WARNING: NaN detected in entropy term for alpha loss!", flush=True)
+        print(f"logprobs has NaN: {torch.isnan(logprobs).any()}", flush=True)
+        print(f"target_entropy: {target_entropy}", flush=True)
+    
+    alpha_loss = log_alpha * entropy_term.mean()
+    
+    # Check for NaN in alpha_loss
+    if torch.isnan(alpha_loss).any():
+        print("WARNING: NaN detected in alpha_loss!", flush=True)
+        print(f"log_alpha: {log_alpha}", flush=True)
+        print(f"entropy_term.mean(): {entropy_term.mean()}", flush=True)
+    
     alpha_loss.backward()
     log_alpha_optimizer.step()
 
@@ -239,14 +287,17 @@ def train(env,
           gamma,
           tau,
           outdir,
+          logdir,
           name,
-          show_states=True,
-          save_snapshots=False,
           update_after=256,
           updates_per_step=1,
           update_every=1,
           n_episodes=50,
-          n_trials=1,):
+          dynamic_complexity=True,
+          show=True,
+          pause_after_episode=False,
+          show_live=False,
+          pause_after_step=False):
     """
     Train the Soft Actor-Critic (SAC) model.
     
@@ -284,14 +335,12 @@ def train(env,
         Discount factor for future rewards.
     tau : float
         Soft update parameter for updating the target networks.
-    outdir : str
-        Directory to save model snapshots and logs.
+    outdir : str or Path
+        Directory to save model snapshots and checkpoints.
+    logdir : str or Path
+        Directory to save training logs.
     name : str
         Name for saving model snapshots and logs.
-    show_states : bool, optional
-        Whether to show the states during training (default is True).
-    save_snapshots : bool, optional
-        Whether to save snapshots of the best trials (default is False).
     update_after : int, optional
         Number of steps to collect transitions before starting updates (default is 256).
     updates_per_step : int, optional
@@ -300,140 +349,200 @@ def train(env,
         Frequency of updates in terms of steps (default is 1).
     n_episodes : int, optional
         Number of episodes to train for (default is 50).
-    n_trials : int, optional
-        Number of trials per episode (default is 1).
+    dynamic_complexity : bool, optional
+        Whether to use dynamic complexity adjustment (default is True).
+    show : bool, optional
+        Whether to show each finished episode and pause.
+    pause_after_episode : bool, optional
+        Whether to pause after each episode (default is False).
+    show_live : bool, optional
+        Whether to show the state of the environment after each step during training.
+    pause_after_step : bool, optional
+        Whether to pause after each step (default is False).
     """
 
     steps_done = 0
     last_save = 0
     ep_returns = []
-
-    if not os.path.isdir(outdir):
-        os.makedirs(outdir, exist_ok=True)
-    if show_states:
-        fig, ax = plt.subplots(3,3, figure=plt.figure(num=1))
+    
+    reward_cache = deque(maxlen=10000)
+    moving_avg_reward = 0.0
+    if dynamic_complexity:
+        last_avg_reward = 0.0  # Track previous average for improvement detection
+
+    # Ensure outdir and logdir are Path objects
+    outdir = Path(outdir)
+    logdir = Path(logdir)
+    
+    # Create directories if they don't exist
+    outdir.mkdir(parents=True, exist_ok=True)
+    logdir.mkdir(parents=True, exist_ok=True)
+    
+    if show:
+        fig, ax = plt.subplots(2, 3, figsize=(15,10))
         plt.ion()
 
     # Train the Network
-    for ep in tqdm(range(n_episodes)):
+    # Detect if running in a terminal or redirected (like with nohup)
+    use_progress_bar = sys.stdout.isatty()
+    for ep in tqdm(range(n_episodes), dynamic_ncols=True, leave=True, file=sys.stdout, mininterval=1.0, disable=not use_progress_bar):
+        # Print episode progress when tqdm is disabled (e.g., with nohup)
+        if not use_progress_bar:
+            print(f"Starting episode {ep + 1}/{n_episodes}", flush=True)
+        
+        env.reset()
         policy_loss = []
-        coverages = []
-        trial_returns = []
-        labeled_neurons = []
-        # run n trials per episode before moving on to the next image.
-        # The best trial is saved if save_snapshots==True.
-        for trial in range(n_trials):
-            obs = env.get_state()
-            ep_return = 0
-            ep_rewards = []
-            for t in count():
-                actor.eval()
-                if steps_done < update_after:
-                    action = torch.randn(3).to(DEVICE)*3
-                else:
-                    actor_out = actor(obs).detach()
-                    direction_dist = sample_from_output(actor_out)
-                    action = direction_dist.rsample()[0]
-                    
-                steps_done += 1
-                # take step, get observation and reward, and move index to next streamline
-                next_obs, reward, terminated = env.step(action)
-
-                ep_return += reward.cpu().item()
-                ep_rewards.append(reward.cpu().item())
-
-                # Store the transition in memory
-                memory.push(obs.cpu(), action.cpu(), next_obs.cpu(), reward.cpu(), terminated)
+        obs = env.get_state()
+        ep_return = 0
+        for t in count():
+            # Determine if we have enough samples to start learning updates
+            learning_started = steps_done >= update_after
+            
+            if not learning_started:
+                action = torch.randn(3)*3
+            else:
+                actor_out = actor(obs.to(DEVICE))
+                direction_dist = sample_from_output(actor_out.detach().cpu())
+                action = direction_dist.rsample()[0]
                 
-                if steps_done >= update_after:
-                    if steps_done % update_every == 0:
-                        # Perform updates once there is sufficient transitions saved.
-                        actor.train()
-                        for j in range(updates_per_step):
-                            if isinstance(memory, ReplayBuffer):
-                                obs, actions, next_obs, rewards, dones = memory.sample(batch_size)
-                                td_error = update_Q(actor, Q1, Q1_target, Q2, Q2_target,
-                                                    obs, actions, rewards, next_obs, dones,
-                                                    Q1_optimizer, Q2_optimizer, gamma,
-                                                    log_alpha, weights=None)
-                            elif isinstance(memory, PrioritizedReplayBuffer):
-                                obs, actions, next_obs, rewards, dones, weights, tree_idxs = memory.sample(batch_size)
-                                td_error = update_Q(actor, Q1, Q1_target, Q2, Q2_target,
-                                                    obs, actions, rewards, next_obs, dones,
-                                                    Q1_optimizer, Q2_optimizer, gamma,
-                                                    log_alpha, weights=weights)
-                                memory.update_priorities(tree_idxs, td_error.cpu().numpy())                    
-                            else:
-                                raise RuntimeError("Unknown memory buffer")
-                                
-                            # Perform one step of optimization on the policy network
-                            loss = update_actor(obs, actor, actor_optimizer, Q1, Q2, log_alpha,
-                                         log_alpha_optimizer, target_entropy)
-                            policy_loss.append(loss)
-                            # update target networks
-                            target_update(Q1, Q2, Q1_target, Q2_target, tau)
+            steps_done += 1
+            # take step, get observation and reward, and move index to next streamline
+            next_obs, reward, terminated, truncated, info = env.step(action, training=learning_started)
+
+            ep_return += reward.cpu().item()
+
+            # Show state after every step
+            if show_live:
+                try:
+                    shell = get_ipython().__class__.__name__  # type: ignore
+                    if shell:
+                        show_state(env, fig, live=True, reward=reward.cpu().item())
+                    if pause_after_step:
+                        try:
+                            print("Press Enter to continue to the next step (or 'q' to quit)...", flush=True)
+                            user_input = input().strip()
+                            if user_input.lower() == 'q':
+                                print("Quitting training.", flush=True)
+                                return
+                        except EOFError:
+                            pass
+                except NameError:
+                    pass
 
+            reward_cache.append(reward.cpu().item())
+            moving_avg_reward = sum(reward_cache) / len(reward_cache)
+
+            # Store the transition in memory
+            memory.push(obs.cpu(), action.cpu(), next_obs.cpu(), reward.cpu(), terminated)
+            
+            if learning_started and steps_done % update_every == 0:
+                # Perform updates once there is sufficient transitions saved.
+                for j in range(updates_per_step):
+                    if isinstance(memory, ReplayBuffer):
+                        batch_obs, batch_actions, batch_next_obs, batch_rewards, batch_dones = memory.sample(batch_size, transform=True)
+                        td_error = update_Q(actor, Q1, Q1_target, Q2, Q2_target,
+                                            batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones,
+                                            Q1_optimizer, Q2_optimizer, gamma,
+                                            log_alpha, weights=None)
+                    elif isinstance(memory, PrioritizedReplayBuffer):
+                        batch_obs, batch_actions, batch_next_obs, batch_rewards, batch_dones, weights, tree_idxs = memory.sample(batch_size, transform=True)
+                        td_error = update_Q(actor, Q1, Q1_target, Q2, Q2_target,
+                                            batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones,
+                                            Q1_optimizer, Q2_optimizer, gamma,
+                                            log_alpha, weights=weights)
+                        memory.update_priorities(tree_idxs, td_error.cpu().numpy())                    
+                    else:
+                        raise RuntimeError("Unknown memory buffer")
                         
-                if terminated:
-                    if save_snapshots:
-                        labeled_neuron = env.img.data[3].detach().cpu() > 0.3 
-                        true_neuron = torch.linalg.norm(env.true_density.data[:3].detach().cpu(), dim=0) > 0.94
-                        TP = torch.sum(torch.logical_and(labeled_neuron, true_neuron))
-                        tot = torch.sum(true_neuron)
-                        coverages.append(TP/tot)
-                        labeled_neurons.append(env.img.data[3].detach().clone().cpu())
-                        trial_returns.append(ep_return)
+                    # Perform one step of optimization on the policy network
+                    loss = update_actor(batch_obs, actor, actor_optimizer, Q1, Q2, log_alpha,
+                                 log_alpha_optimizer, target_entropy)
+                    policy_loss.append(loss)
+                    # update target networks
+                    target_update(Q1, Q2, Q1_target, Q2_target, tau)
+
                     
-                    ep_returns.append(ep_return)
-                    if len(policy_loss) > 0:
-                        episode_avg_loss = sum(policy_loss)/len(policy_loss) 
-                    else:
-                        episode_avg_loss = 0
-                    if show_states:
-                        try:
-                            shell = get_ipython().__class__.__name__ # type: ignore
-                            if shell:
-                                show_state(env, ep_returns, ep_rewards, policy_loss, fig)
-                                print(f"num branches: {len(env.finished_paths)}")
-                        except NameError:
-                            with open(os.path.join(outdir, f"{name}_{date}_log.txt"), "a") as f:
-                                f.write(f"episode: {ep},\n")
-                                f.write(f"image file: {env.img_files[env.img_idx].split('/')[-1]}\n")
-                                f.write(f"num branches: {len(env.finished_paths)},\n")
-                                f.write(f"episode return: {ep_return},\n")
-                                f.write(f"episode avg. policy loss: {episode_avg_loss}\n\n")
-                    env.reset(move_to_next=False)
-                    break
-            
-                # if not terminated, move to the next state
-                obs = env.get_state() # the head of the next streamline
+            if info["terminate_episode"]:
+                ep_returns.append(ep_return)
+                
+                if dynamic_complexity:
+                    # Check if model is improving based on recent step rewards (at least 50 rewards for stable comparison)
+                    if ep >= 50: # start updating complexity after 50 episodes
+                        improvement = moving_avg_reward > max(0.0, last_avg_reward)
+                        if improvement:
+                            current_complexity = env.dataloader.complexity
+                            if current_complexity < 1.0:
+                                new_complexity = min(current_complexity + 0.05, 1.0)  # Cap complexity at 1.0
+                                print(f"Improvement detected. Increasing complexity to {new_complexity:.2f}", flush=True)
+                                env.dataloader.set_complexity(new_complexity)
+                                if new_complexity > 0.33 and env.branching == False:
+                                    print("Enabling branching in environment.", flush=True)
+                                    env.branching = True
+                        # Update the last average for next comparison
+                        last_avg_reward = moving_avg_reward
+                
+                if len(policy_loss) > 0:
+                    episode_avg_loss = sum(policy_loss)/len(policy_loss) 
+                else:
+                    episode_avg_loss = 0
+                if show:
+                    try:
+                        shell = get_ipython().__class__.__name__ # type: ignore
+                        if shell:
+                            show_state(env, fig)
+                            print(f"num branches: {len(env.finished_paths)}", flush=True)
+                            print(f"reward min/max: {np.min(reward_cache):.2f}/{np.max(reward_cache):.2f} moving avg: {moving_avg_reward:.2f}", flush=True)
+                            if pause_after_episode:
+                                try:
+                                    print("Press Enter to continue to the next episode (or 'q' to quit)...", flush=True)
+                                    user_input = input().strip()
+                                    if user_input.lower() == 'q':
+                                        print("Quitting training.", flush=True)
+                                        return
+                                except EOFError:
+                                    pass
+                    except NameError:
+                        csv_file_path = logdir / f"{name}_{date}_log.csv"
+                        file_exists = csv_file_path.exists()
+                        with open(csv_file_path, "a", newline='') as f:
+                            writer = csv.writer(f)
+                            if not file_exists:
+                                writer.writerow(['episode', 'image_file', 'num_branches', 'episode_return', 'episode_avg_policy_loss', 'moving_avg_reward'])
+                            writer.writerow([
+                                ep,
+                                env.current_neuron_info["neuron_name"],
+                                len(env.finished_paths),
+                                ep_return,
+                                episode_avg_loss,
+                                moving_avg_reward
+                            ])
+                break
+
+            # if episode does not terminate, move to the next state
+            obs = env.get_state() # the head of the next streamline
         
-        if save_snapshots:
-            value, index = torch.max(torch.stack(coverages), dim=0)
-            coverage = value.item()
-            index = int(index)
-            best_return = torch.tensor(trial_returns)[index].item()
-            labeled_neuron = labeled_neurons[index]
-            paths_dir = os.path.join(outdir, f"episode_snapshots_{name}_{date}/")
-            if not os.path.exists(paths_dir):
-                os.makedirs(paths_dir)
-
-            to_save = {"labeled_neuron": labeled_neuron, "true_neuron": env.true_density.data[0].detach().clone().cpu(), "coverage": coverage, "return": best_return}
-            torch.save(to_save, os.path.join(paths_dir, f"ep_snapshot_{ep}.pt"))
-
-        # save model every 500 steps
+        # save model after at least 500 steps 
         if steps_done // 500 > last_save:
-            model_dicts = {"policy_state_dict": actor.state_dict(),
-                        "Q1_state_dict": Q1_target.state_dict(),
-                        "Q2_state_dict": Q2_target.state_dict(),}
-            torch.save(model_dicts, os.path.join(outdir, f"model_state_dicts_{name}_{date}.pt"))
+            model_dicts = {
+            "policy_state_dict": actor.state_dict(),
+            "Q1_state_dict": Q1.state_dict(),
+            "Q2_state_dict": Q2.state_dict(),
+            "Q1_target_state_dict": Q1_target.state_dict(),
+            "Q2_target_state_dict": Q2_target.state_dict(),
+            "actor_optimizer_state_dict": actor_optimizer.state_dict(),
+            "Q1_optimizer_state_dict": Q1_optimizer.state_dict(),
+            "Q2_optimizer_state_dict": Q2_optimizer.state_dict(),
+            "log_alpha": log_alpha,
+            "log_alpha_optimizer_state_dict": log_alpha_optimizer.state_dict(),
+            "steps_done": steps_done
+            }
+            torch.save(model_dicts, outdir / f"model_state_dicts_{name}_{date}.pt")
             last_save = steps_done // 500
-        env.reset()
 
     return
 
 
-def inference(env, actor, outdir, n_trials=5, show=True):
+def inference(env, actor, outdir, Q_net=None, n_trials=1, show=True, show_live=True, save_paths=False, sync=False, stochastic=False):
     """
     Perform inference using the given actor in the specified environment.
     
@@ -456,55 +565,119 @@ def inference(env, actor, outdir, n_trials=5, show=True):
     """
 
     if show:
-        fig, ax = plt.subplots(2,3, figure=plt.figure(num=1))
+        fig, ax = plt.subplots(2, 3, figsize=(15,10))
         plt.ion()
     actor.eval()
-    for i in tqdm(range(len(env.img_files))):
+    if sync:
+        # find image indices that are not yet processed
+        # get a list of image names that are already processed
+        processed_image_names = [re.split(r'_\d\d-\d\d-\d\d_inference.npz', f)[0] for f in os.listdir(outdir) if f.endswith('.npz')]
+        image_names = [entry["neuron_name"] for entry in env.dataloader.dataset]
+        img_indices = [i for i, f in enumerate(image_names) if f.split('/')[-1] not in processed_image_names]
+    else:
+        img_indices = [i for i in range(env.dataloader.current_idx, len(env.dataloader.dataset))]
+
+    if n_trials < 1:
+        raise ValueError("n_trials must be at least 1")
+    elif n_trials > 1 and Q_net is None:
+        raise ValueError("Q_net must be provided if n_trials > 1")
+
+    # Detect if running in a terminal or redirected (like with nohup)
+    use_progress_bar = sys.stdout.isatty()
+    for i in tqdm(range(len(img_indices)), dynamic_ncols=True, leave=True, file=sys.stdout, mininterval=1.0, disable=not use_progress_bar):
+        # Print inference progress when tqdm is disabled (e.g., with nohup)
+        if not use_progress_bar:
+            print(f"Processing image {i + 1}/{len(img_indices)}", flush=True)
+            
+        img_idx = img_indices[i] % len(env.dataloader.dataset) # -1 because the index is incremented when the environment resets
+        env.reset(dataset_index=img_idx)
+        coverages = []
+        estimated_returns = []
+        labeled_neurons = []
+        trial_paths = []
+        # Begin trials. Loop through n_trials.
         for trial in range(n_trials):
-            coverages = []
-            labeled_neurons = []
+            estimated_return = 0
             obs = env.get_state()
-
+            # Begin episode. Loop through steps.
             for t in count():
                 with torch.no_grad():
-                    actor_out = actor(obs)
-                    direction_dist = sample_from_output(actor_out)
-                    action = direction_dist.sample()[0]
-
-                next_obs, reward, terminated = env.step(action)
-
-                if terminated:
-                    labeled_neuron = env.img.data[3].detach().cpu() > 0.3 
-                    true_neuron = torch.linalg.norm(env.true_density.data[:3].detach().cpu(), dim=0) > 0.94
-                    TP = torch.sum(torch.logical_and(labeled_neuron, true_neuron))
-                    tot = torch.sum(true_neuron)
-                    coverages.append(TP/tot)
-                    labeled_neurons.append(env.img.data[3].detach().clone().cpu())
+                    actor_out = actor(obs.to(DEVICE))
+                    direction_dist = sample_from_output(actor_out.detach().cpu())
+                    if stochastic:
+                        action = direction_dist.sample()[0]
+                    else:
+                        action = direction_dist.mean[0]
+
+                next_obs, reward, terminated, truncated, info = env.step(action, training=True)
+                if Q_net is not None:
+                    current_state = torch.cat((obs.to(DEVICE), torch.ones((obs.shape[0], 1, obs.shape[2], obs.shape[3], obs.shape[4]),device=DEVICE)*action[None,:,None,None,None].to(DEVICE)), dim=1)
+                    q_val = Q_net(current_state)[:,0]
+                    estimated_return += q_val.cpu().item()
+
+                # Show state after every step
+                if show_live and show:
+                    try:
+                        shell = get_ipython().__class__.__name__  # type: ignore
+                        if shell:
+                            show_state(env, fig, live=True, reward=reward.cpu().item())
+                    except NameError:
+                        pass
+
+                if info["terminate_episode"]:
+                    labeled_neuron = env.img.data[-1].detach().cpu() > 0.3 
+                    # true_neuron = env.true_density.data[-1].detach().cpu() > 0.94
+                    # TP = torch.sum(torch.logical_and(labeled_neuron, true_neuron))
+                    # tot = torch.sum(true_neuron)
+                    # coverages.append(TP/tot)
+                    estimated_returns.append(estimated_return)
+                    labeled_neurons.append(env.img.data[-1].detach().clone().cpu())
+                    trial_paths.append([path.detach().cpu().numpy().tolist() for path in env.finished_paths if isinstance(path, torch.Tensor) and len(path) > 3])
                     if show:
                         try:
                             shell = get_ipython().__class__.__name__ # type: ignore
                             if shell:
+                                # Show final state and episode info, then wait for user input
                                 show_state(env, fig)
                                 print(f"num branches: {len(env.finished_paths)}")
+                                print(f"num long paths: {len(trial_paths[-1])}")
+                                if Q_net is not None:
+                                    print(f"estimated return: {estimated_return:.2f}")
+                                # print(f"coverage: {coverages[-1]:.2f}")
+                                try:
+                                    print("Press Enter to continue to the next episode (or 'q' to quit)...", flush=True)
+                                    user_input = input().strip()
+                                    if user_input.lower() == 'q':
+                                        print("Quitting training.", flush=True)
+                                        return
+                                except EOFError:
+                                    pass       
                         except NameError:
                             pass
                     env.reset(move_to_next=False)
-                    break
+                    break # Move to next trial. (same seed, same image)
 
                 obs = env.get_state()
-        
-        value, index = torch.max(torch.stack(coverages), dim=0)
-        coverage = value.item()
-        index = int(index)
-        labeled_neuron = labeled_neurons[index]
-        name = env.img_files[env.img_idx].split('/')[-1].split('.')[0]
-        if not os.path.exists(outdir):
-            os.makedirs(outdir)
-
-        to_save = {"labeled_neuron": labeled_neuron, "paths": env.paths, "coverage": coverage}
-        torch.save(to_save, os.path.join(outdir, f"{name}_{date}_inference.pt"))
 
-        env.reset()
+        if save_paths:
+            value = np.max(estimated_returns)
+            index = np.argmax(estimated_returns)
+            estimated_return = value.item()
+            index = int(index)
+            labeled_neuron = labeled_neurons[index]
+            name = env.current_neuron_info["neuron_name"]
+            if not os.path.exists(outdir):
+                os.makedirs(outdir)
+            # Convert paths to a serializable format
+            paths_to_save = trial_paths[index]
+            labeled_neuron_np = labeled_neuron.numpy()
+
+            # Using numpy's compressed format
+            np.savez_compressed(os.path.join(outdir, f"{name}_{date}_inference.npz"),
+                                labeled_neuron=labeled_neuron_np,
+                                coverages=coverages,
+                                estimated_returns=estimated_returns,
+                                paths=np.array(paths_to_save, dtype=object))
 
     return
 
diff --git a/tests/unit/data/fixtures.py b/tests/unit/data/fixtures.py
new file mode 100644
index 0000000..6628e05
--- /dev/null
+++ b/tests/unit/data/fixtures.py
@@ -0,0 +1,280 @@
+#!/usr/bin/env python
+"""
+Test fixtures and utilities for neuron_data tests.
+
+Author: Bryson Gray
+2024
+"""
+
+import numpy as np
+import tempfile
+import torch
+from pathlib import Path
+from typing import List, Tuple, Dict
+import csv
+
+
+def create_sample_swc_data() -> List[Tuple]:
+    """Create sample SWC data for testing."""
+    # SWC format: (node_id, node_type, x, y, z, radius, parent_id)
+    swc_data = [
+        (1, 1, 0.0, 0.0, 0.0, 2.0, -1),    # Soma
+        (2, 3, 5.0, 0.0, 0.0, 1.0, 1),     # Dendrite
+        (3, 3, 10.0, 0.0, 0.0, 1.0, 2),    # Dendrite
+        (4, 3, 15.0, 0.0, 0.0, 1.0, 3),    # Dendrite
+        (5, 3, 20.0, 0.0, 0.0, 1.0, 4),    # Dendrite
+        (6, 3, 10.0, 5.0, 0.0, 1.0, 3),    # Branch from node 3
+        (7, 3, 10.0, 10.0, 0.0, 1.0, 6),   # Continue branch
+        (8, 3, 10.0, 15.0, 0.0, 1.0, 7),   # End of branch
+        (9, 3, 15.0, 5.0, 0.0, 1.0, 4),    # Another branch from node 4
+        (10, 3, 15.0, 10.0, 0.0, 1.0, 9),  # Continue second branch
+    ]
+    return swc_data
+
+
+def create_linear_swc_data(num_nodes: int = 10) -> List[Tuple]:
+    """Create linear SWC data without branches for testing."""
+    swc_data = []
+    for i in range(num_nodes):
+        node_id = i + 1
+        node_type = 1 if i == 0 else 3  # Soma for first, dendrite for rest
+        x = float(i * 2)  # Space nodes 2 units apart
+        y = 0.0
+        z = 0.0
+        radius = 2.0 if i == 0 else 1.0
+        parent_id = -1 if i == 0 else i  # Parent is previous node
+        
+        swc_data.append((node_id, node_type, x, y, z, radius, parent_id))
+    
+    return swc_data
+
+
+def create_complex_swc_data() -> List[Tuple]:
+    """Create complex branched SWC data for testing."""
+    swc_data = [
+        (1, 1, 0.0, 0.0, 0.0, 3.0, -1),     # Soma
+        
+        # Main trunk
+        (2, 3, 5.0, 0.0, 0.0, 2.0, 1),
+        (3, 3, 10.0, 0.0, 0.0, 2.0, 2),
+        (4, 3, 15.0, 0.0, 0.0, 2.0, 3),
+        (5, 3, 20.0, 0.0, 0.0, 2.0, 4),
+        (6, 3, 25.0, 0.0, 0.0, 1.5, 5),
+        
+        # First branch from node 3
+        (7, 3, 10.0, 5.0, 0.0, 1.5, 3),
+        (8, 3, 10.0, 10.0, 0.0, 1.0, 7),
+        (9, 3, 10.0, 15.0, 0.0, 1.0, 8),
+        (10, 3, 5.0, 15.0, 0.0, 1.0, 9),
+        
+        # Second branch from node 4
+        (11, 3, 15.0, 5.0, 0.0, 1.5, 4),
+        (12, 3, 20.0, 10.0, 0.0, 1.0, 11),
+        (13, 3, 25.0, 15.0, 0.0, 1.0, 12),
+        
+        # Sub-branch from branch 1
+        (14, 3, 10.0, 12.0, 5.0, 1.0, 8),
+        (15, 3, 10.0, 12.0, 10.0, 1.0, 14),
+        
+        # Third main branch from node 5
+        (16, 3, 20.0, -5.0, 0.0, 1.5, 5),
+        (17, 3, 20.0, -10.0, 0.0, 1.0, 16),
+        (18, 3, 25.0, -15.0, 0.0, 1.0, 17),
+    ]
+    return swc_data
+
+
+def create_mock_edge_list(swc_data: List[Tuple]) -> Dict[int, List[int]]:
+    """Create undirected edge list from SWC data."""
+    edge_list = {}
+    
+    # Initialize all nodes
+    for node in swc_data:
+        node_id = node[0]
+        edge_list[node_id] = []
+    
+    # Add edges based on parent-child relationships
+    for node in swc_data:
+        node_id = node[0]
+        parent_id = node[6]
+        
+        if parent_id != -1:
+            # Add bidirectional edge
+            edge_list[node_id].append(parent_id)
+            edge_list[parent_id].append(node_id)
+    
+    return edge_list
+
+
+def create_test_image(shape: Tuple[int, ...] = (32, 32, 32)) -> np.ndarray:
+    """Create test image with some structure."""
+    image = np.zeros(shape, dtype=np.uint8)
+    
+    # Add some structure - a line through the middle
+    if len(shape) == 3:
+        z, y, x = shape
+        mid_z, mid_y = z // 2, y // 2
+        # Draw a line from one side to the other
+        for i in range(x):
+            image[mid_z, mid_y, i] = 255
+            # Add some branching
+            if i > x // 2:
+                for j in range(max(0, mid_y - 3), min(y, mid_y + 4)):
+                    image[mid_z, j, i] = 128
+    
+    return image
+
+
+def create_test_dataset_csv(temp_dir: str, num_entries: int = 5) -> str:
+    """Create a test CSV file with dataset entries."""
+    csv_path = Path(temp_dir) / "test_dataset.csv"
+    
+    entries = []
+    for i in range(num_entries):
+        complexity_level = i / (num_entries - 1)  # 0.0 to 1.0
+        if complexity_level < 0.33:
+            morphology = "simple"
+        elif complexity_level < 0.67:
+            morphology = "moderate"
+        else:
+            morphology = "complex"
+        
+        entry = {
+            'swc_path': f'/fake/path/neuron_{i:03d}.swc',
+            'img_path': f'/fake/path/neuron_{i:03d}.tif' if i % 2 == 0 else '',
+            'artifact_level': complexity_level,
+            'morphology': morphology
+        }
+        entries.append(entry)
+    
+    with open(csv_path, 'w', newline='') as f:
+        fieldnames = ['swc_path', 'img_path', 'artifact_level', 'morphology']
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(entries)
+    
+    return str(csv_path)
+
+
+def create_test_swc_file(temp_dir: str, swc_data: List[Tuple], filename: str = "test_neuron.swc") -> str:
+    """Create a test SWC file on disk."""
+    swc_path = Path(temp_dir) / filename
+    
+    with open(swc_path, 'w') as f:
+        f.write("# Test SWC file\n")
+        f.write("# node_id type x y z radius parent_id\n")
+        for node in swc_data:
+            f.write(f"{node[0]} {node[1]} {node[2]:.3f} {node[3]:.3f} {node[4]:.3f} {node[5]:.3f} {node[6]}\n")
+    
+    return str(swc_path)
+
+
+class MockRenderer:
+    """Mock renderer for testing without actual drawing functionality."""
+    
+    def __init__(self, rng=None):
+        self.rng = rng or np.random.default_rng()
+    
+    def draw_neuron(self, sections, shape, config):
+        """Mock draw_neuron that returns a tensor with predictable values."""
+        result = MockDrawResult()
+        result.data = torch.ones(shape) * 0.5  # Medium intensity
+        return result
+    
+    def draw_density(self, sections, shape):
+        """Mock draw_density that returns a binary mask."""
+        result = MockDrawResult()
+        result.data = torch.zeros(shape)
+        # Add some positive values in the center
+        if len(shape) == 3:
+            z, y, x = shape
+            result.data[z//2-1:z//2+2, y//2-1:y//2+2, x//2-1:x//2+2] = 1.0
+        return result
+    
+    def draw_section_labels(self, sections, shape):
+        """Mock draw_section_labels that returns labeled sections."""
+        result = MockDrawResult()
+        result.data = torch.zeros(shape, dtype=torch.long)
+        return result
+
+
+class MockDrawResult:
+    """Mock result object for draw operations."""
+    
+    def __init__(self):
+        self.data = None
+
+
+def create_mock_sections(num_sections: int = 3, points_per_section: int = 5) -> Dict[int, np.ndarray]:
+    """Create mock sections data for testing."""
+    sections = {}
+    
+    for section_id in range(num_sections):
+        # Create points along a path
+        points = []
+        for i in range(points_per_section):
+            x = section_id * 10.0 + i * 2.0
+            y = i * 1.0
+            z = section_id * 1.0
+            points.append([x, y, z])
+        
+        sections[section_id] = np.array(points)
+    
+    return sections
+
+
+# Pytest fixtures
+import pytest
+
+
+@pytest.fixture
+def sample_swc_data():
+    """Fixture providing sample SWC data."""
+    return create_sample_swc_data()
+
+
+@pytest.fixture
+def linear_swc_data():
+    """Fixture providing linear SWC data."""
+    return create_linear_swc_data()
+
+
+@pytest.fixture
+def complex_swc_data():
+    """Fixture providing complex branched SWC data."""
+    return create_complex_swc_data()
+
+
+@pytest.fixture
+def test_image():
+    """Fixture providing a test image."""
+    return create_test_image()
+
+
+@pytest.fixture
+def mock_renderer():
+    """Fixture providing a mock renderer."""
+    return MockRenderer()
+
+
+@pytest.fixture
+def mock_sections():
+    """Fixture providing mock sections data."""
+    return create_mock_sections()
+
+
+@pytest.fixture
+def temp_dataset_csv():
+    """Fixture providing a temporary dataset CSV file."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        csv_path = create_test_dataset_csv(temp_dir)
+        yield csv_path
+
+
+@pytest.fixture
+def temp_swc_file():
+    """Fixture providing a temporary SWC file."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        swc_data = create_sample_swc_data()
+        swc_path = create_test_swc_file(temp_dir, swc_data)
+        yield swc_path
diff --git a/tests/unit/data/test_neuron_data.py b/tests/unit/data/test_neuron_data.py
new file mode 100644
index 0000000..92ccc19
--- /dev/null
+++ b/tests/unit/data/test_neuron_data.py
@@ -0,0 +1,753 @@
+#!/usr/bin/env python
+"""
+Unit tests for neuron_data.py module.
+
+Author: Bryson Gray
+2024
+"""
+
+import csv
+import json
+import numpy as np
+import os
+import pytest
+import tempfile
+import torch
+from pathlib import Path
+from unittest.mock import Mock, patch, MagicMock
+import sys
+
+# Add parent directories to path for imports
+test_file = Path(__file__)
+project_root = test_file.parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from neurotrack.data.neuron_data import (
+    DrawingComplexityConfig,
+    Dataset,
+    DataLoader,
+    DataGenerator,
+    create_neuron_data_components
+)
+
+
+class TestDrawingComplexityConfig:
+    """Test the DrawingComplexityConfig class."""
+    
+    def test_default_initialization(self):
+        """Test default initialization of DrawingComplexityConfig."""
+        config = DrawingComplexityConfig()
+        
+        # Check default ranges are properly set
+        assert config.width_range == (1.0, 5.0)
+        assert config.blur_range == (0.5, 3.0)
+        assert config.foreground_mean_range == (0.6, 0.9)
+        assert config.foreground_std_range == (0.05, 0.2)
+        assert config.background_mean_range == (0.1, 0.4)
+        assert config.background_std_range == (0.02, 0.1)
+        assert config.mask_threshold_range == (0.05, 0.2)
+        assert config.simplex_scale_range == (5.0, 20.0)
+        assert config.simplex_amplitude_range == (0.5, 1.5)
+        assert config.vignette_magnitude_range == (0.0, 0.5)
+    
+    def test_custom_initialization(self):
+        """Test custom initialization of DrawingComplexityConfig."""
+        config = DrawingComplexityConfig(
+            width_range=(2.0, 6.0),
+            blur_range=(1.0, 4.0)
+        )
+        
+        assert config.width_range == (2.0, 6.0)
+        assert config.blur_range == (1.0, 4.0)
+        # Other values should remain default
+        assert config.foreground_mean_range == (0.6, 0.9)
+    
+    @patch('neurotrack.data.neuron_data.draw')
+    def test_interpolate_config_minimum_complexity(self, mock_draw):
+        """Test interpolation at minimum complexity (0.0)."""
+        config = DrawingComplexityConfig()
+        mock_draw_config = Mock()
+        mock_draw.DrawingConfig.return_value = mock_draw_config
+        
+        result = config.interpolate_config(0.0)
+        
+        # Check that DrawingConfig was called with min values
+        mock_draw.DrawingConfig.assert_called_once()
+        call_args = mock_draw.DrawingConfig.call_args
+        
+        # Verify key parameters are at minimum end
+        assert call_args.kwargs['width'] == 1.0  # min of width_range
+        assert call_args.kwargs['blur'] == 0.5   # min of blur_range
+        assert call_args.kwargs['rgb'] is False
+        
+    @patch('neurotrack.data.neuron_data.draw')
+    def test_interpolate_config_maximum_complexity(self, mock_draw):
+        """Test interpolation at maximum complexity (1.0)."""
+        config = DrawingComplexityConfig()
+        mock_draw_config = Mock()
+        mock_draw.DrawingConfig.return_value = mock_draw_config
+        
+        result = config.interpolate_config(1.0)
+        
+        # Check that DrawingConfig was called with max values
+        mock_draw.DrawingConfig.assert_called_once()
+        call_args = mock_draw.DrawingConfig.call_args
+        
+        # Verify key parameters are at maximum end
+        assert call_args.kwargs['width'] == 5.0  # max of width_range
+        assert call_args.kwargs['blur'] == 3.0   # max of blur_range
+        
+    @patch('neurotrack.data.neuron_data.draw')
+    def test_interpolate_config_mid_complexity(self, mock_draw):
+        """Test interpolation at mid complexity (0.5)."""
+        config = DrawingComplexityConfig()
+        mock_draw_config = Mock()
+        mock_draw.DrawingConfig.return_value = mock_draw_config
+        
+        result = config.interpolate_config(0.5)
+        
+        # Check that DrawingConfig was called with mid values
+        mock_draw.DrawingConfig.assert_called_once()
+        call_args = mock_draw.DrawingConfig.call_args
+        
+        # Verify key parameters are at middle values
+        assert call_args.kwargs['width'] == 3.0  # mid of width_range (1.0 + 0.5 * 4.0)
+        assert call_args.kwargs['blur'] == 1.75  # mid of blur_range (0.5 + 0.5 * 2.5)
+    
+    @patch('neurotrack.data.neuron_data.draw')
+    def test_interpolate_config_clamps_input(self, mock_draw):
+        """Test that complexity input is clamped to [0.0, 1.0]."""
+        config = DrawingComplexityConfig()
+        mock_draw_config = Mock()
+        mock_draw.DrawingConfig.return_value = mock_draw_config
+        
+        # Test negative value clamped to 0
+        config.interpolate_config(-0.5)
+        call_args_neg = mock_draw.DrawingConfig.call_args
+        
+        mock_draw.DrawingConfig.reset_mock()
+        
+        # Test value > 1 clamped to 1
+        config.interpolate_config(1.5)
+        call_args_pos = mock_draw.DrawingConfig.call_args
+        
+        # Both should be clamped appropriately
+        assert call_args_neg.kwargs['width'] == 1.0  # Min value
+        assert call_args_pos.kwargs['width'] == 5.0  # Max value
+
+
+class TestDataset:
+    """Test the Dataset class."""
+    
+    def test_empty_initialization(self):
+        """Test initialization with no parameters creates empty dataset."""
+        with pytest.warns(UserWarning, match="No valid data_dir or csv_path provided"):
+            dataset = Dataset()
+        
+        assert len(dataset) == 0
+        assert dataset.entries == []
+    
+    def test_initialization_with_nonexistent_paths(self):
+        """Test initialization with non-existent paths."""
+        with pytest.warns(UserWarning, match="No valid data_dir or csv_path provided"):
+            dataset = Dataset(data_dir="/nonexistent/path", csv_path="/nonexistent/file.csv")
+        
+        assert len(dataset) == 0
+    
+    def test_scan_directory(self):
+        """Test scanning directory for SWC files."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_path = Path(temp_dir)
+            
+            # Create mock SWC files
+            swc1 = temp_path / "neuron1.swc"
+            swc2 = temp_path / "neuron2.swc"
+            swc1.touch()
+            swc2.touch()
+            
+            # Create corresponding image file for neuron1
+            img1 = temp_path / "neuron1_image.tif"
+            img1.touch()
+            
+            dataset = Dataset(data_dir=temp_dir)
+            
+            assert len(dataset) == 2
+            
+            # Check that entries were created
+            swc_paths = [entry['swc_path'] for entry in dataset.entries]
+            assert str(swc1) in swc_paths
+            assert str(swc2) in swc_paths
+            
+            # Check that image file was found for neuron1
+            entry1 = next(entry for entry in dataset.entries if 'neuron1' in entry['swc_path'])
+            assert entry1['img_path'] == str(img1)
+            
+            # Check that neuron2 has no image
+            entry2 = next(entry for entry in dataset.entries if 'neuron2' in entry['swc_path'])
+            assert entry2['img_path'] is None
+            
+            # Check complexity values are assigned
+            for entry in dataset.entries:
+                assert 0.0 <= entry['artifact_level'] <= 0.5
+                assert entry['morphology'] in ['simple', 'moderate', 'complex']
+    
+    def test_load_from_csv(self):
+        """Test loading dataset from CSV file."""
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) as f:
+            writer = csv.DictWriter(f, fieldnames=['swc_path', 'img_path', 'artifact_level', 'morphology'])
+            writer.writeheader()
+            writer.writerow({
+                'swc_path': '/path/to/neuron1.swc',
+                'img_path': '/path/to/neuron1.tif',
+                'artifact_level': 0.3,
+                'morphology': 'moderate'
+            })
+            writer.writerow({
+                'swc_path': '/path/to/neuron2.swc',
+                'img_path': '',
+                'artifact_level': 0.8,
+                'morphology': 'complex'
+            })
+            csv_path = f.name
+        
+        try:
+            dataset = Dataset(csv_path=csv_path)
+            
+            assert len(dataset) == 2
+            
+            entry1 = dataset[0]
+            assert entry1['swc_path'] == '/path/to/neuron1.swc'
+            assert entry1['img_path'] == '/path/to/neuron1.tif'
+            assert entry1['artifact_level'] == 0.3
+            assert entry1['morphology'] == 'moderate'
+            
+            entry2 = dataset[1]
+            assert entry2['swc_path'] == '/path/to/neuron2.swc'
+            assert entry2['img_path'] is None  # Empty string becomes None
+            assert entry2['artifact_level'] == 0.8
+            assert entry2['morphology'] == 'complex'
+        
+        finally:
+            os.unlink(csv_path)
+    
+    def test_load_from_csv_missing_columns(self):
+        """Test loading from CSV with missing required columns."""
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) as f:
+            writer = csv.DictWriter(f, fieldnames=['swc_path'])  # Missing required columns
+            writer.writeheader()
+            writer.writerow({'swc_path': '/path/to/neuron1.swc'})
+            csv_path = f.name
+        
+        try:
+            with pytest.raises(ValueError, match="CSV must contain columns"):
+                Dataset(csv_path=csv_path)
+        finally:
+            os.unlink(csv_path)
+    
+    def test_save_to_csv(self):
+        """Test saving dataset to CSV file."""
+        dataset = Dataset()
+        dataset.entries = [
+            {
+                'swc_path': '/path/to/neuron1.swc',
+                'img_path': '/path/to/neuron1.tif',
+                'artifact_level': 0.3,
+                'morphology': 'moderate'
+            },
+            {
+                'swc_path': '/path/to/neuron2.swc',
+                'img_path': None,
+                'artifact_level': 0.8,
+                'morphology': 'complex'
+            }
+        ]
+        
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) as f:
+            csv_path = f.name
+        
+        try:
+            dataset.save_to_csv(csv_path)
+            
+            # Read back and verify
+            loaded_dataset = Dataset(csv_path=csv_path)
+            assert len(loaded_dataset) == 2
+            
+            entry1 = loaded_dataset[0]
+            assert entry1['swc_path'] == '/path/to/neuron1.swc'
+            assert entry1['artifact_level'] == 0.3
+            assert entry1['morphology'] == 'moderate'
+            
+        finally:
+            os.unlink(csv_path)
+    
+    def test_get_complexity_distribution(self):
+        """Test getting complexity distribution from dataset."""
+        dataset = Dataset()
+        dataset.entries = [
+            {'morphology': 'simple', 'artifact_level': 0.1},
+            {'morphology': 'simple', 'artifact_level': 0.2},
+            {'morphology': 'moderate', 'artifact_level': 0.5},
+            {'morphology': 'complex', 'artifact_level': 0.8},
+        ]
+        
+        distribution = dataset.get_complexity_distribution()
+        
+        # Check morphology distribution
+        assert distribution['morphology_distribution']['simple'] == 2
+        assert distribution['morphology_distribution']['moderate'] == 1
+        assert distribution['morphology_distribution']['complex'] == 1
+        
+        # Check artifact level statistics
+        artifact_stats = distribution['artifact_stats']
+        assert artifact_stats['mean'] == 0.4  # (0.1+0.2+0.5+0.8)/4
+        assert artifact_stats['min'] == 0.1
+        assert artifact_stats['max'] == 0.8
+    
+    def test_getitem_and_len(self):
+        """Test indexing and length methods."""
+        dataset = Dataset()
+        dataset.entries = [
+            {'swc_path': 'neuron1.swc', 'artifact_level': 0.1, 'morphology': 'simple'},
+            {'swc_path': 'neuron2.swc', 'artifact_level': 0.5, 'morphology': 'moderate'}
+        ]
+        
+        assert len(dataset) == 2
+        assert dataset[0]['swc_path'] == 'neuron1.swc'
+        assert dataset[1]['swc_path'] == 'neuron2.swc'
+
+
+class TestDataLoader:
+    """Test the DataLoader class."""
+    
+    def create_test_dataset(self):
+        """Create a test dataset with known complexity values."""
+        dataset = Dataset()
+        dataset.entries = [
+            {'swc_path': 'simple.swc', 'artifact_level': 0.1, 'morphology': 'simple'},
+            {'swc_path': 'moderate.swc', 'artifact_level': 0.5, 'morphology': 'moderate'},
+            {'swc_path': 'complex.swc', 'artifact_level': 0.9, 'morphology': 'complex'},
+        ]
+        return dataset
+    
+    def test_initialization(self):
+        """Test DataLoader initialization."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.3)
+        
+        assert dataloader.dataset == dataset
+        assert dataloader.complexity == 0.3
+        assert dataloader.current_idx == 0
+        assert len(dataloader.weights) == len(dataset)
+    
+    def test_initialization_empty_dataset(self):
+        """Test DataLoader with empty dataset."""
+        dataset = Dataset()
+        dataloader = DataLoader(dataset, complexity=0.5)
+        
+        assert len(dataloader.weights) == 0
+    
+    def test_set_complexity(self):
+        """Test updating complexity parameter."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.0)
+        
+        # Change complexity
+        dataloader.set_complexity(0.8)
+        assert dataloader.complexity == 0.8
+        
+        # Test clamping
+        dataloader.set_complexity(-0.5)
+        assert dataloader.complexity == 0.0
+        
+        dataloader.set_complexity(1.5)
+        assert dataloader.complexity == 1.0
+    
+    def test_sampling_weights_low_complexity(self):
+        """Test that sampling weights favor simple neurons for low complexity."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.1)
+        
+        # For low complexity, simple neurons should have higher weights
+        simple_idx = 0  # simple neuron
+        complex_idx = 2  # complex neuron
+        
+        assert dataloader.weights[simple_idx] > dataloader.weights[complex_idx]
+    
+    def test_sampling_weights_high_complexity(self):
+        """Test that sampling weights favor complex neurons for high complexity."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.9)
+        
+        # For high complexity, complex neurons should have higher weights
+        simple_idx = 0  # simple neuron
+        complex_idx = 2  # complex neuron
+        
+        assert dataloader.weights[complex_idx] > dataloader.weights[simple_idx]
+    
+    def test_sample(self):
+        """Test sampling from dataset."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.5)
+        
+        # Set random seed for reproducible test
+        np.random.seed(42)
+        
+        sample = dataloader.sample()
+        assert sample in dataset.entries
+        assert 'swc_path' in sample
+        assert 'morphology' in sample
+    
+    def test_sample_empty_dataset(self):
+        """Test sampling from empty dataset raises error."""
+        dataset = Dataset()
+        dataloader = DataLoader(dataset, complexity=0.5)
+        
+        with pytest.raises(ValueError, match="Dataset is empty"):
+            dataloader.sample()
+    
+    def test_iteration(self):
+        """Test iterating through dataloader."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.5)
+        
+        items = list(dataloader)
+        assert len(items) == len(dataset)
+        
+        # Check all items were returned in order
+        for i, item in enumerate(items):
+            assert item == dataset[i]
+    
+    def test_iterator_protocol(self):
+        """Test iterator protocol methods."""
+        dataset = self.create_test_dataset()
+        dataloader = DataLoader(dataset, complexity=0.5)
+        
+        # Test iter returns self
+        assert iter(dataloader) is dataloader
+        
+        # Test manual iteration
+        assert dataloader.current_idx == 0
+        item1 = next(dataloader)
+        assert dataloader.current_idx == 1
+        assert item1 == dataset[0]
+        
+        item2 = next(dataloader)
+        assert dataloader.current_idx == 2
+        assert item2 == dataset[1]
+        
+        item3 = next(dataloader)
+        assert dataloader.current_idx == 3
+        assert item3 == dataset[2]
+        
+        # Should raise StopIteration
+        with pytest.raises(StopIteration):
+            next(dataloader)
+
+
+class TestDataGenerator:
+    """Test the DataGenerator class."""
+    
+    def test_initialization_default(self):
+        """Test DataGenerator initialization with defaults."""
+        generator = DataGenerator()
+        
+        assert generator.cache_dir is None
+        assert generator.patch_size == 64
+        assert isinstance(generator.complexity_config, DrawingComplexityConfig)
+        assert generator.rng is not None
+        assert generator.renderer is not None
+    
+    def test_initialization_with_parameters(self):
+        """Test DataGenerator initialization with custom parameters."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            custom_config = DrawingComplexityConfig(width_range=(2.0, 6.0))
+            
+            generator = DataGenerator(
+                cache_dir=temp_dir,
+                patch_size=32,
+                complexity_config=custom_config,
+                rng_seed=12345
+            )
+            
+            assert generator.cache_dir == Path(temp_dir)
+            assert generator.patch_size == 32
+            assert generator.complexity_config == custom_config
+            assert generator.cache_dir.exists()
+    
+    @patch('neurotrack.data.neuron_data.load')
+    def test_load_files_swc_only(self, mock_load):
+        """Test loading SWC file without image."""
+        generator = DataGenerator()
+        mock_swc_data = [
+            (1, 1, 0.0, 0.0, 0.0, 1.0, -1),
+            (2, 3, 1.0, 0.0, 0.0, 1.0, 1)
+        ]
+        mock_load.swc.return_value = mock_swc_data
+        
+        swc_list, img_tensor = generator.load_files("test.swc")
+        
+        mock_load.swc.assert_called_once_with("test.swc")
+        assert swc_list == mock_swc_data
+        assert img_tensor is None
+    
+    @patch('neurotrack.data.neuron_data.load')
+    @patch('neurotrack.data.neuron_data.tf')
+    @patch('neurotrack.data.neuron_data.os.path.exists')
+    def test_load_files_with_image(self, mock_exists, mock_tf, mock_load):
+        """Test loading SWC file with corresponding image."""
+        generator = DataGenerator()
+        mock_swc_data = [(1, 1, 0.0, 0.0, 0.0, 1.0, -1)]
+        mock_load.swc.return_value = mock_swc_data
+        mock_exists.return_value = True
+        
+        # Mock 3D image data
+        mock_img_array = np.random.randint(0, 255, (64, 64, 64), dtype=np.uint8)
+        mock_tf.imread.return_value = mock_img_array
+        
+        swc_list, img_tensor = generator.load_files("test.swc", "test.tif")
+        
+        mock_load.swc.assert_called_once_with("test.swc")
+        mock_tf.imread.assert_called_once_with("test.tif")
+        assert swc_list == mock_swc_data
+        assert img_tensor is not None
+        assert img_tensor.shape == (1, 64, 64, 64)  # Channel dimension added
+        assert torch.allclose(img_tensor, torch.from_numpy(mock_img_array.astype(np.float32) / 255.0))
+    
+    def test_complexity_to_category(self):
+        """Test complexity to category conversion."""
+        generator = DataGenerator()
+        
+        assert generator._complexity_to_category(0.1) == "simple"
+        assert generator._complexity_to_category(0.32) == "simple"
+        assert generator._complexity_to_category(0.4) == "moderate"
+        assert generator._complexity_to_category(0.66) == "moderate"
+        assert generator._complexity_to_category(0.8) == "complex"
+        assert generator._complexity_to_category(1.0) == "complex"
+    
+    def test_complexity_to_mode(self):
+        """Test complexity to mode conversion."""
+        generator = DataGenerator()
+        
+        assert generator._complexity_to_mode(0.1) == "no_branch"
+        assert generator._complexity_to_mode(0.32) == "no_branch"
+        assert generator._complexity_to_mode(0.4) == "one_branch"
+        assert generator._complexity_to_mode(0.66) == "one_branch"
+        assert generator._complexity_to_mode(0.8) == "any_branch"
+        assert generator._complexity_to_mode(1.0) == "any_branch"
+    
+    @patch('neurotrack.data.neuron_data.load')
+    def test_generate_reward_mask(self, mock_load):
+        """Test reward mask generation."""
+        generator = DataGenerator()
+        
+        # Mock subtree data
+        subtree = [
+            (1, 1, 0.0, 0.0, 0.0, 1.0, -1),
+            (2, 3, 1.0, 0.0, 0.0, 1.0, 1),
+            (3, 3, 2.0, 0.0, 0.0, 1.0, 2)
+        ]
+        
+        # Mock parse_swc to return sections
+        mock_sections = {0: np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0]])}
+        mock_load.parse_swc.return_value = (mock_sections, None)
+        
+        # Mock renderer.draw_density
+        mock_density = Mock()
+        mock_density.data = torch.zeros(32, 32, 32)
+        generator.renderer.draw_density = Mock(return_value=mock_density)
+        
+        shape = (32, 32, 32)
+        reward_mask = generator.generate_reward_mask(subtree, shape)
+        
+        mock_load.parse_swc.assert_called_once_with(subtree)
+        generator.renderer.draw_density.assert_called_once_with(mock_sections, shape)
+        assert torch.equal(reward_mask, torch.zeros(32, 32, 32))
+    
+    @patch('neurotrack.data.neuron_data.load')
+    def test_simulate_neuron_image(self, mock_load):
+        """Test simulated neuron image generation."""
+        generator = DataGenerator()
+        
+        # Mock subtree data
+        subtree = [
+            (1, 1, 0.0, 0.0, 0.0, 1.0, -1),
+            (2, 3, 1.0, 0.0, 0.0, 1.0, 1)
+        ]
+        
+        # Mock parse_swc to return sections
+        mock_sections = {0: np.array([[0, 0, 0], [1, 0, 0]])}
+        mock_load.parse_swc.return_value = (mock_sections, None)
+        
+        # Mock renderer.draw_neuron
+        mock_image = Mock()
+        mock_image.data = torch.ones(32, 32, 32)
+        generator.renderer.draw_neuron = Mock(return_value=mock_image)
+        
+        shape = (32, 32, 32)
+        complexity = 0.5
+        
+        result = generator.simulate_neuron_image(subtree, shape, complexity)
+        
+        mock_load.parse_swc.assert_called_once_with(subtree)
+        generator.renderer.draw_neuron.assert_called_once()
+        assert torch.equal(result, torch.ones(32, 32, 32))
+    
+    def test_save_data(self):
+        """Test saving generated data to disk."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            generator = DataGenerator()
+            
+            # Create mock data
+            data = {
+                'image': torch.ones(1, 32, 32, 32),
+                'reward_mask': torch.zeros(32, 32, 32),
+                'subtree': [
+                    (1, 1, 0.0, 0.0, 0.0, 1.0, -1),
+                    (2, 3, 1.0, 0.0, 0.0, 1.0, 1)
+                ],
+                'metadata': {
+                    'source': 'simulated',
+                    'complexity': (0.5, 'moderate')
+                }
+            }
+            
+            # Mock tifffile.imwrite
+            with patch('neurotrack.data.neuron_data.tf.imwrite') as mock_imwrite:
+                generator.save_data(data, temp_dir, prefix="test")
+                
+                # Check that files were written
+                assert mock_imwrite.call_count == 2  # Image and reward mask
+                
+                # Check SWC file was created
+                swc_path = Path(temp_dir) / "test_subtree.swc"
+                assert swc_path.exists()
+                
+                # Check metadata file was created
+                meta_path = Path(temp_dir) / "test_metadata.json"
+                assert meta_path.exists()
+                
+                # Verify metadata content
+                with open(meta_path) as f:
+                    metadata = json.load(f)
+                    assert metadata['source'] == 'simulated'
+                    assert metadata['complexity'] == [0.5, 'moderate']  # Lists in JSON
+
+
+class TestConvenienceFunctions:
+    """Test convenience functions."""
+    
+    @patch('neurotrack.data.neuron_data.Dataset')
+    @patch('neurotrack.data.neuron_data.DataLoader')
+    @patch('neurotrack.data.neuron_data.DataGenerator')
+    def test_create_neuron_data_components(self, mock_generator_class, mock_loader_class, mock_dataset_class):
+        """Test create_neuron_data_components function."""
+        # Setup mocks
+        mock_dataset = Mock()
+        mock_loader = Mock()
+        mock_generator = Mock()
+        
+        mock_dataset_class.return_value = mock_dataset
+        mock_loader_class.return_value = mock_loader
+        mock_generator_class.return_value = mock_generator
+        
+        custom_complexity_config = DrawingComplexityConfig(width_range=(2.0, 6.0))
+        
+        # Call function
+        dataset, dataloader, data_generator = create_neuron_data_components(
+            data_dir="/test/data",
+            complexity=0.3,
+            complexity_config=custom_complexity_config,
+            cache_dir="/test/cache",
+            rng_seed=42
+        )
+        
+        # Verify components were created with correct parameters
+        mock_dataset_class.assert_called_once_with(data_dir="/test/data")
+        mock_loader_class.assert_called_once_with(dataset=mock_dataset, complexity=0.3)
+        mock_generator_class.assert_called_once_with(
+            cache_dir="/test/cache",
+            complexity_config=custom_complexity_config,
+            rng_seed=42
+        )
+        
+        # Verify return values
+        assert dataset == mock_dataset
+        assert dataloader == mock_loader
+        assert data_generator == mock_generator
+
+
+# Integration test with mocked dependencies
+class TestDataGeneratorIntegration:
+    """Integration tests for DataGenerator with mocked dependencies."""
+    
+    @patch('neurotrack.data.neuron_data.load')
+    @patch('neurotrack.data.neuron_data.tf')
+    @patch('neurotrack.data.neuron_data.os.path.exists')
+    def test_generate_data_simulated(self, mock_exists, mock_tf, mock_load):
+        """Test full data generation pipeline for simulated data."""
+        generator = DataGenerator(rng_seed=42)
+        
+        # Mock entry without image (simulated)
+        entry = {
+            'swc_path': 'test.swc',
+            'img_path': None,
+            'artifact_level': 0.3,
+            'morphology': 'moderate'
+        }
+        
+        # Mock SWC data - simple linear chain for "no_branch" mode
+        # Format: (id, type, x, y, z, radius, parent_id)
+        mock_swc_data = [
+            (1, 3, 0.0, 0.0, 0.0, 1.0, -1),  # Root (endpoint)
+            (2, 3, 1.0, 0.0, 0.0, 1.0, 1),
+            (3, 3, 2.0, 0.0, 0.0, 1.0, 2),
+            (4, 3, 3.0, 0.0, 0.0, 1.0, 3),
+            (5, 3, 4.0, 0.0, 0.0, 1.0, 4),
+            (6, 3, 5.0, 0.0, 0.0, 1.0, 5),  # Endpoint
+        ]
+        mock_load.swc.return_value = mock_swc_data
+        
+        # Mock undirected_edge_list - simple linear chain
+        edge_list = {
+            1: [2],           # Endpoint: connects to 2 only
+            2: [1, 3],        # Linear: connects to 1 and 3
+            3: [2, 4],        # Linear: connects to 2 and 4
+            4: [3, 5],        # Linear: connects to 3 and 5
+            5: [4, 6],        # Linear: connects to 4 and 6
+            6: [5],           # Endpoint: connects to 5 only
+        }
+        mock_load.undirected_edge_list.return_value = edge_list
+        
+        # Mock parse_swc
+        mock_sections = {0: np.array([[float(i), 0, 0] for i in range(5)])}
+        mock_load.parse_swc.return_value = (mock_sections, None)
+        
+        # Mock renderer methods
+        mock_image = Mock()
+        mock_image.data = torch.ones(25, 20, 20)
+        generator.renderer.draw_neuron = Mock(return_value=mock_image)
+        
+        mock_density = Mock()
+        mock_density.data = torch.zeros(25, 20, 20)
+        generator.renderer.draw_density = Mock(return_value=mock_density)
+        
+        # Generate data - use complexity=0.2 for "no_branch" mode which is simpler
+        results = generator.generate_data(entry, num_subtrees=1, num_edges=4, complexity=0.2)
+        
+        # Verify results
+        assert len(results) == 1
+        result = results[0]
+        
+        assert 'image' in result
+        assert 'subtree' in result
+        assert 'reward_mask' in result
+        assert 'metadata' in result
+        
+        metadata = result['metadata']
+        assert metadata['source'] == 'simulated'
+        assert metadata['original_swc'] == 'test.swc'
+        assert metadata['simulation_complexity'] == 0.2
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])