{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9011523,"sourceType":"datasetVersion","datasetId":4404813}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Clustering is a fundamental technique in unsupervised learning where the goal is **to group** a set of objects in such a way that *objects in the same group (or cluster) are more similar to each other than to those in other groups*. Unlike supervised learning, clustering **does not require labeled data**. It identifies patterns and structures in data based on inherent characteristics. There are several types of clustering algorithms, including:\n\n- **Partition-based methods**: Such as K-means and K-medoids, which divide the data into a predetermined number of clusters.\n- **Hierarchical methods**: Like Agglomerative and Divisive clustering, which create a tree-like structure of clusters.\n- **Density-based methods**: Such as DBSCAN and OPTICS, which form clusters based on the density of data points.\n\nClustering has a wide range of applications. One prominent example is **customer segmentation** in marketing, where businesses group customers based on purchasing behavior, demographics, or other attributes to tailor marketing strategies and improve customer satisfaction. Other applications include image segmentation, **anomaly detection**, document classification, and social network analysis. By uncovering hidden patterns in data, clustering helps in making informed decisions and discovering new insights across various domains.\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\ndf = pd.read_csv('/kaggle/input/rfm-dataset/RFM-Scores.csv')\ndf = df.drop('Unnamed: 0',axis=1)\ndf.info()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:41.934889Z","iopub.execute_input":"2024-07-28T17:49:41.935754Z","iopub.status.idle":"2024-07-28T17:49:42.022382Z","shell.execute_reply.started":"2024-07-28T17:49:41.935716Z","shell.execute_reply":"2024-07-28T17:49:42.021161Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2089 entries, 0 to 2088\nData columns (total 8 columns):\n #   Column           Non-Null Count  Dtype  \n---  ------           --------------  -----  \n 0   ID               2089 non-null   int64  \n 1   Recency          2089 non-null   int64  \n 2   Frequency        2089 non-null   int64  \n 3   Monetary         2089 non-null   int64  \n 4   Gender           2089 non-null   object \n 5   Recency_Score    2089 non-null   float64\n 6   Frequency_Score  2089 non-null   float64\n 7   Monetary_Score   2089 non-null   float64\ndtypes: float64(3), int64(4), object(1)\nmemory usage: 130.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"X = df[['Recency_Score','Frequency_Score','Monetary_Score']]","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:42.024080Z","iopub.execute_input":"2024-07-28T17:49:42.024403Z","iopub.status.idle":"2024-07-28T17:49:42.030293Z","shell.execute_reply.started":"2024-07-28T17:49:42.024376Z","shell.execute_reply":"2024-07-28T17:49:42.029186Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"### K-Means Clustering\n\nK-Means is a popular partition-based clustering method used in unsupervised learning to divide a dataset into a **predetermined number of clusters**, denoted as K. The algorithm works by minimizing the within-cluster variance, ensuring that data points within each cluster are as similar as possible.\n\n**How K-Means Works:**\n1. **Initialization**: Randomly select K initial centroids from the dataset.\n2. **Assignment**: Assign each data point to the nearest centroid, forming K clusters.\n3. **Update**: Recalculate the centroids as the mean of all data points assigned to each cluster.\n4. **Iteration**: Repeat the assignment and update steps until the centroids no longer change significantly or a maximum number of iterations is reached.\n\n**Advantages of K-Means:**\n- Simple and easy to implement.\n- Efficient for large datasets.\n- Works well when clusters have a spherical shape and similar sizes.\n\n**Limitations of K-Means:**\n- Requires the number of clusters K to be specified in advance.\n- Sensitive to the initial placement of centroids.\n- Can struggle with clusters of varying shapes and densities.\n\n**Applications of K-Means:**\nK-Means is widely used in various fields, including market segmentation, image compression, document clustering, and pattern recognition. It helps in identifying natural groupings in data, allowing for better data analysis and decision-making.\n","metadata":{}},{"cell_type":"markdown","source":"### K-Means Clustering Parameters\n\n#### `n_clusters`: int, default=8\nThe number of clusters to form as well as the number of centroids to generate.\n\n#### `init`: {'k-means++', 'random'}, callable, or array-like of shape (n_clusters, n_features), default='k-means++'\nMethod for initialization:\n\n- `'k-means++'`: Selects initial cluster centroids using sampling based on an empirical probability distribution of the points' contribution to the overall inertia, speeding up convergence. The implementation is \"greedy k-means++\", which differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them.\n- `'random'`: Chooses `n_clusters` observations (rows) at random from the data for the initial centroids.\n\nIf an array is passed, it should be of shape (n_clusters, n_features) and give the initial centers.\n\n#### `n_init`: 'auto' or int, default='auto'\nNumber of times the k-means algorithm is run with different centroid seeds. The final results are the best output of `n_init` consecutive runs in terms of inertia. Several runs are recommended for sparse high-dimensional problems (see \"Clustering sparse data with k-means\").\n\nWhen `n_init='auto'`, the number of runs depends on the value of `init`: 10 if using `init='random'`; 1 if using `init='k-means++'` or `init` is an array-like.\n\n#### `max_iter`: int, default=300\nMaximum number of iterations of the k-means algorithm for a single run.\n\n#### `tol`: float, default=1e-4\nRelative tolerance with respect to the Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.\n\n#### `random_state`: int, RandomState instance, or None, default=None\nDetermines random number generation for centroid initialization. Use an int to make the randomness deterministic.\n\n#### `algorithm`: {\"lloyd\", \"elkan\"}, default=\"lloyd\"\nK-means algorithm to use. The classical EM-style algorithm is \"lloyd\". The \"elkan\" variation can be more efficient on some datasets with well-defined clusters, by using the triangle inequality. However, it is more memory-intensive due to the allocation of an extra array of shape (n_samples, n_clusters).\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score, silhouette_samples\n\n# Initialize the KMeans model\nkm_model = KMeans(n_clusters=5, init='k-means++', n_init='auto', max_iter=300,\n                  tol=0.0001, random_state=111, algorithm='lloyd')\n\n# Fit the model\nkm_model.fit(X)\n\n# Predict the cluster labels\nlabels = km_model.predict(X)\n\n# Compute the silhouette score\nsilhouette_avg = silhouette_score(X, labels)\nsilhouette = silhouette_samples(X, labels)\n\nprint(f'Silhouette Score: {silhouette_avg}')\n\nX_kmeans = X.copy()\nX_kmeans['cluster'] = labels\nX_kmeans['silhouette'] = silhouette","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:42.032285Z","iopub.execute_input":"2024-07-28T17:49:42.032960Z","iopub.status.idle":"2024-07-28T17:49:43.212761Z","shell.execute_reply.started":"2024-07-28T17:49:42.032923Z","shell.execute_reply":"2024-07-28T17:49:43.211551Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Silhouette Score: 0.3779251489384814\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Within-Cluster Sum of Squares (WSS) Index\n\nThe Within-Cluster Sum of Squares (WSS) index is a measure used to evaluate the **compactness of clusters** in a clustering algorithm. It calculates the sum of squared distances between each data point and the centroid of its assigned cluster.*Lower WSS values indicate that data points are closer to their respective cluster centroids, signifying more compact and well-defined clusters*. The WSS index is commonly used in the \"elbow method\" for determining the optimal number of clusters. By plotting the WSS values for different numbers of clusters, one can identify the point where the rate of decrease slows down, forming an \"elbow,\" which suggests the optimal number of clusters for the dataset. The WSS index helps in understanding the **internal homogeneity of clusters** and ensuring that the clustering solution is both meaningful and efficient.\n","metadata":{}},{"cell_type":"code","source":"wss = km_model.inertia_\nprint('WSS: ', wss)\nkm_model.cluster_centers_","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:43.214778Z","iopub.execute_input":"2024-07-28T17:49:43.215794Z","iopub.status.idle":"2024-07-28T17:49:43.226237Z","shell.execute_reply.started":"2024-07-28T17:49:43.215750Z","shell.execute_reply":"2024-07-28T17:49:43.225079Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"WSS:  2736.57097030841\n","output_type":"stream"},{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"array([[3.63273453, 3.80638723, 4.11177645],\n       [1.97755611, 1.1446384 , 1.23441397],\n       [2.05139186, 1.29550321, 4.43468951],\n       [1.62619808, 2.85303514, 2.49840256],\n       [3.7002457 , 2.56019656, 2.30712531]])"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\n\n# Define the range of cluster numbers to test\ncluster_range = range(2, 9)  \n\n# List to store WCSS values\nwss = []\n\n# Calculate WCSS for each cluster number\nfor n_clusters in cluster_range:\n    kmeans = KMeans(n_clusters=n_clusters, init='k-means++', n_init='auto', max_iter=300,\n                    tol=0.0001, random_state=111, algorithm='lloyd')\n    kmeans.fit(X)\n    wss.append(kmeans.inertia_)\n\n# Plot the elbow curve\nplt.figure(figsize=(10, 6))\nplt.plot(cluster_range, wss, marker='o')\nplt.title('Elbow Method for Optimal Number of Clusters')\nplt.xlabel('Number of Clusters')\nplt.ylabel('WSS')\nplt.xticks(cluster_range)\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:43.228243Z","iopub.execute_input":"2024-07-28T17:49:43.228900Z","iopub.status.idle":"2024-07-28T17:49:43.549466Z","shell.execute_reply.started":"2024-07-28T17:49:43.228865Z","shell.execute_reply":"2024-07-28T17:49:43.548392Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"### Silhouette as an Evaluation Index for Clustering\n\nThe silhouette score is a metric used to evaluate the quality of clusters created by a clustering algorithm. *It measures how similar an object is to its own cluster (cohesion) compared to other clusters (separation)*. The silhouette score ranges **from -1 to 1**, where a value close to 1 indicates that the object is well-matched to its own cluster and poorly matched to neighboring clusters. A value near 0 indicates that the object is on or very close to the decision boundary between two neighboring clusters. *Negative values indicate that those points might have been assigned to the wrong cluster*. The silhouette score can **help in determining the optimal number of clusters** by comparing the scores of different clustering results. It provides an intuitive measure of the compactness and separation of clusters, aiding in the interpretation and validation of cluster analysis.\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score, silhouette_samples\n\n# Define the range for the number of clusters\ncluster_range = range(2, 9)  \n\n# Lists to store silhouette scores for each number of clusters\nsilhouette_scores = []\n\n# Loop over the range of clusters\nfor n_clusters in cluster_range:\n    # Initialize the KMeans model\n    kmeans = KMeans(n_clusters=n_clusters, init='k-means++', n_init='auto', max_iter=300,\n                   tol=0.0001, random_state=111, algorithm='lloyd')\n    \n    # Fit the model\n    kmeans.fit(X)\n    \n    # Predict the cluster labels\n    labels = kmeans.predict(X)\n    \n    # Compute the silhouette score\n    silhouette_avg = silhouette_score(X, labels)\n    \n    # Store the silhouette score\n    silhouette_scores.append(silhouette_avg)\n\n# Plot the elbow plot\nplt.figure(figsize=(8, 6))\nplt.plot(cluster_range, silhouette_scores, marker='o', linestyle='-', color='b')\nplt.xlabel('Number of Clusters')\nplt.ylabel('Silhouette Score')\nplt.title('Silhouette Score vs. Number of Clusters')\nplt.xticks(cluster_range)\nplt.grid(True)\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:43.551652Z","iopub.execute_input":"2024-07-28T17:49:43.551974Z","iopub.status.idle":"2024-07-28T17:49:45.048070Z","shell.execute_reply.started":"2024-07-28T17:49:43.551946Z","shell.execute_reply":"2024-07-28T17:49:45.046949Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"pip install yellowbrick","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:45.049445Z","iopub.execute_input":"2024-07-28T17:49:45.049856Z","iopub.status.idle":"2024-07-28T17:49:58.170891Z","shell.execute_reply.started":"2024-07-28T17:49:45.049820Z","shell.execute_reply":"2024-07-28T17:49:58.169580Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Requirement already satisfied: yellowbrick in /opt/conda/lib/python3.10/site-packages (1.5)\nRequirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from yellowbrick) (3.7.5)\nRequirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from yellowbrick) (1.11.4)\nRequirement already satisfied: scikit-learn>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from yellowbrick) (1.2.2)\nRequirement already satisfied: numpy>=1.16.0 in /opt/conda/lib/python3.10/site-packages (from yellowbrick) (1.26.4)\nRequirement already satisfied: cycler>=0.10.0 in /opt/conda/lib/python3.10/site-packages (from yellowbrick) (0.12.1)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.2.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (4.47.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.4.5)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (9.5.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (3.1.1)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.9.0.post0)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->yellowbrick) (1.4.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->yellowbrick) (3.2.0)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.16.0)\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom yellowbrick.cluster import KElbowVisualizer\n\n\n# Create a KMeans model\nkmeans = KMeans(init='k-means++', n_init='auto', max_iter=300, tol=0.0001, random_state=111)\n\n# Create the KElbowVisualizer with the model and the range of clusters to test\nvisualizer = KElbowVisualizer(kmeans, metric='silhouette', k=(2, 9))\n\n# Fit the visualizer and show the plot\nvisualizer.fit(X)\nvisualizer.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:58.172820Z","iopub.execute_input":"2024-07-28T17:49:58.173304Z","iopub.status.idle":"2024-07-28T17:49:59.961846Z","shell.execute_reply.started":"2024-07-28T17:49:58.173259Z","shell.execute_reply":"2024-07-28T17:49:59.960769Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x550 with 2 Axes>","image/png":"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"},"metadata":{}},{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"<Axes: title={'center': 'Silhouette Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='silhouette score'>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Interpreting Clusters in Customer Segmentation\n\n## Importance of Interpretation\n\nInterpreting clusters helps understand their characteristics by comparing feature distributions within clusters to the overall data. This process is crucial for selecting the optimal number of clusters and naming them effectively.\n\n## Selecting the Optimal Number of Clusters\n\n1. **Analyze Characteristics**: Compare the distribution of input fields (e.g., Recency, Frequency, Monetary scores) within each cluster to the overall dataset to ensure meaningful differences.\n2. **Adjust Clusters**: If clusters are not distinct or actionable, consider adjusting the number of clusters to improve interpretability.\n\n## Naming Clusters\n\n1. **Identify Features**: Use comparative analysis to determine key characteristics of each cluster.\n2. **Assign Descriptive Names**: Label clusters based on their defining features (e.g., 'Frequent High Spenders') to reflect their behavior and facilitate targeted strategies.\n3. **Specify Target Groups**: Identify clusters that represent specific target groups for further analysis or profiling based on demographic characteristics. This can help tailor marketing campaigns and improve customer engagement.\n\nInterpreting clusters is vital for selecting the right number and naming them meaningfully. This helps uncover patterns, validate cluster quality, and align insights with business goals.","metadata":{}},{"cell_type":"code","source":"import warnings\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Suppress specific warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Define the scores and clusters\nscores = ['Recency_Score', 'Frequency_Score', 'Monetary_Score']\nclusters = sorted(X_kmeans['cluster'].unique())\n\n# Loop over each cluster\nfor cluster in clusters:\n    # Set up the matplotlib figure\n    fig, axes = plt.subplots(1, len(scores), figsize=(15, 4))\n    \n    # Loop over each score and create a count plot\n    for i, score in enumerate(scores):\n        ax = axes[i]\n\n        # Combine the all data and cluster data into one DataFrame for plotting\n        all_data = pd.DataFrame({score: X_kmeans[score], 'Type': 'All Data'})\n        data_cluster = pd.DataFrame({score: X_kmeans[X_kmeans['cluster'] == cluster][score], 'Type': f'Cluster {cluster}'})\n        data_combined = pd.concat([all_data, data_cluster])\n\n        # Plot the count plot for the combined data\n#       sns.countplot(x=score, hue='Type', data=data_combined, ax=ax, palette=['darkred', 'pink'])\n        sns.histplot(x=score, hue='Type', data=data_combined, ax=ax, palette=['pink', 'black'], bins=10, kde=False, stat='count', alpha=0.7)\n        \n        # Set the title and labels\n        ax.set_title(f'{score} Distribution\\n(Cluster {cluster})')\n        ax.set_xlabel(score)\n        ax.set_ylabel('Count')\n    \n    plt.tight_layout()\n    plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:49:59.963175Z","iopub.execute_input":"2024-07-28T17:49:59.963605Z","iopub.status.idle":"2024-07-28T17:50:05.801238Z","shell.execute_reply.started":"2024-07-28T17:49:59.963571Z","shell.execute_reply":"2024-07-28T17:50:05.800097Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x400 with 3 Axes>","image/png":"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KZo3b546duxoa0teXp6GDh2qbdu2aezYsZozZ45CQ0M1ePDgfMWCffv2acmSJRo1apQWL16sa665Rp9++qkeffRRRUREaP78+Xrqqaf02Wef6Z///GeRfQAAeK7c3Fy7H8MwJEkrV67UkSNH9OKLL2rGjBmSpBdffFFz585Vjx49tHjxYvXs2VMzZ87Uu+++azve/v37NXLkSNWtW1fz589Xz549NXr0aOXk5JSofaNGjdJ7772nwYMH6/XXX1ebNm301FNP5fvek+TkZE2ZMkVDhgzR7NmzdeHCBY0YMcKugDB58mTNmDFD7dq106JFizRx4kRVrFhRmZmZMpvNiomJ0bp16+yO+/nnn+vUqVPq3bt3kW298cYbFRoaqmnTpmnbtm06f/58odsWdT2zdetWjRw5UvXr19eCBQv00EMPafXq1XrqqafyHWvChAm6/vrrtWDBAj344IPKycnR4MGDtX37do0ePVqvvfaabrrpJg0bNkwHDhwosh8AAM/Qvn175eTk2CZ6Dx48qAMHDqhbt2522+3bt0+DBw9WxYoVNWfOHI0dO1bbtm3T0KFDlZeXZ7dtUe+R//jjD/Xr109nz57VtGnTNHPmTJ0+fVqDBg2y5fr33ntP0sUbtd577z299957atKkiSTpr7/+0v3336+FCxdqypQpslqt6tevX74bzU6dOqUpU6Zo0KBBeuONN9S4cWP17NlTGzZssLsDPC0tTVu3bnW4WN6kSROtXr1aa9euVXJycqHbvfnmmxo3bpyaNGmi+fPn68UXX1SdOnV0+vRpSdKJEyd0//3368yZM5oxY4YSExP1888/6/7779e5c+fsjlXSayrA6QwATjN37lwjNjbWsFgshsViMf78809j9OjRxs0332z89ttvhmEYRv/+/Y177rnHsFqttv2SkpIMs9lsbN++3TAMw/jiiy+MiIgIY+PGjYWey5Hj/PHHH0ZERIQxcuRIu33vv/9+44EHHrC9fvLJJ42WLVsaGRkZBZ4rLy/PaNeunTF9+nTbsgMHDhgRERHGF1984dDYZGRkGIMHDzYiIiKMiIgI45ZbbjGefPJJY/fu3Xbb3XPPPUa3bt2M3NzcAo+zZcsWIyIiwvj3v/9tW5aTk2O0a9fOGDFihF0fmzRpYhw/fty2zGq1Gu3btzeefPJJu2Pu2LHDMJvNxn/+8x+H+gIA8Bxz58615Z6//2zYsMGIiIgwunXrZpdLf//9d8NsNhurV6+2O86MGTOM1q1bG3l5eYZhGMbo0aONDh062OWrtWvXGhEREcbcuXNty9q3b28kJibaHeuzzz4zIiIijD/++MMwDMPYuXOnERERYXz++ed2240ePdro3bu37fW4cePy5atdu3YZERERtnx66NAhw2w2G4sWLSp0TNasWWNERUUZaWlptmUjRoww7rnnnkL3udymTZuMuLg4IyIiwmjUqJHRs2dPY+HChcb58+dt2zhyPdOjR4985129erURERFh/Prrr3Z9nDhxot1277//vtG4cWMjKSnJbnnfvn3zXfsAADzPpffXhnHxPevYsWMNwzCMV1991ZY77rrrLmPcuHGGYRjG8OHDjXbt2hk5OTm2Y3z++edGRESEsXXrVsMwHH+P/PTTTxsdO3Y0srOzbctSU1ON2NhY46233rIti4iIMN58880r9iM3N9fIysoyYmNj7a4vxo0bZ0RERBh79+612/7IkSN27+sNwzBWrlxpREdHF/qe/XIHDhwwOnXqZLvu6dChgzF58mTbtYdhGEZ6eroRExNjTJgwodDjvPTSS0ZsbKxx5swZ27KDBw8aZrPZWLlypW1Zaa6pAGfjznLAyTIzM9WkSRM1adJE7du31+bNmzV9+nTdeOONysrK0vfff6/bb79deXl5trvb6tatq2uuuUY//fSTJGnnzp0KCQlR9+7dCzyHo8e5JCEhwe71TTfdpL/++sv2eteuXerSpYsqVapU4Pn8/PzUu3dv/etf/7J9NG3dunW67rrrbB+vKkqlSpVss/XDhw9Xw4YNtXnzZt1///22GfysrCz98MMP6tGjR4GPa5Gkb7/9VpUqVVKbNm1sywIDA9WpUyd99913dttGRETommuusb0+fPiw/vzzT3Xt2tXu7sJbbrlFfn5++e5MBwB4h+DgYL3//vt2P23btpUk3XrrrTKZTLZtv/rqK0lS586d7XJFfHy8kpOTdeLECUnSDz/8oPbt29vlq9tvv71E7fvyyy9VpUoVtWzZMt859+/fb3c3XM2aNdWgQQPb6/r160uSTp48KeliTjcM44p3nnXv3l0BAQG2x62dPn1a27Ztc/huNUnq1q2btm3bppdffln/+Mc/dObMGc2ePVu9e/e23Tle1PXM+fPntX//fnXp0iXfsSXly+vt2rWze/3ll18qIiJCdevWzTdul18LAQA82x133KGtW7cqOztbH3/8cYG55dtvv1XHjh3tvqcqISFBlStXzpdTinqP/OWXX6pDhw7y9/e35ZfKlSurcePGDr1v3Lt3rwYPHqwWLVqocePGiomJUWZmpo4cOWK3XZUqVRQTE2O3rE6dOrrlllvsPgW2fv36K75nv1xERIQ++ugjLV68WAMHDlRYWJhWrVqlu+66S/v375d08dPqWVlZV8z/3377rVq0aGH3afKbbrpJDRs2zDemJb2mApyNL/gEnCw4OFhvvfWWDMPQkSNHNGvWLI0bN04bN26UYRjKy8vT1KlTNXXq1Hz7XkoWaWlpqlGjhl2i+bv09HSHjnNJWFiY3evAwEC7j4mnpaUV+jyzS/r06aOFCxdqx44duvXWW/Xhhx/qvvvuK/YXqkRHRys6OlrSxY+2DRgwQDNnzlTfvn2Vnp4uq9V6xbakp6cX+M3d1atXtz1D9e/L/u7MmTOSpOHDhxd4bJI1AHgnPz8/RUVFFbju8pxy5swZGYahli1bFrj9iRMndN111yk5OTnfvpUqVVKFChWK3b4zZ84oLS3N9tHtyyUnJ6tWrVqSpMqVK9utu1QQuPSM77S0NAUEBBSYKy8JDQ3VHXfcoffff1/9+/fXhx9+qMDAQHXt2rVY7Q4PD1ePHj3Uo0cPGYahuXPnauHChXr//fc1cODAIq9nMjIyZBhGvraGhYUpKCgoX14vKFa//PJLgeNW2KQ7AMAzJSQkKDAwUHPmzNGxY8cKzFmFvVesVq1avpxS1HvkM2fOaMWKFVqxYkW+4/29GF+Q48eP68EHH1RkZKQSExNVs2ZNBQYGatiwYXbfySHlf896yd13363x48fr9OnTOnXqlH755Zd83/lRlKCgILVt29Z2g8Dnn3+uYcOGacGCBZo/f77tkTBFvf9u1KhRvuUFjWlJr6kAZ6NYDjjZ39+QR0dHq169err77ru1YMECjRs3TiaTScOGDdNtt92Wb9+rrrpK0sXZ5eTkZBmGUeAbzLCwMIeO46gqVaro1KlTV9ymVq1aatOmjdatW6e8vDydOXNGvXr1KtZ5Lnf99dfr9ttv17Jly5SSkqKwsDD5+fldsS3h4eH5vgxVklJSUhQeHm637PKxuzQbPnHiRFvB/u+KmjAAAHify3NFeHi4TCaT3nnnnQLfDNerV0/SxS+3vDwfnTt3Lt+b4KCgILvniUvK9+YyPDxcVatW1eLFiwtsY9WqVR3rjC7mutzcXKWmpl6xYN63b1+99957+vXXX7V+/Xp17dpVFStWdPg8lzOZTBoyZIgWLlxo+24SR69nLj0X9ZKMjAzl5OQUmdfDw8NlNpv14osvlrjdAADPEBgYqM6dO2v58uVq1apVgUXmwt4rpqam5sspRQkPD1fbtm1133335VtXVL78/PPPlZmZqfnz59smuXNzc/Plfyl/brukc+fOmjx5sj788EMdO3ZMN9xwg2655ZZi9eFybdq0UcOGDe3ytHTxuemXJuUvd6UxrVu3rt2ykl5TAc5GsRxwsaioKHXv3l3r16/XiBEjFBsbq0OHDhV6h5skxcfH64033tAnn3yS70tLpIt3hDlyHEe1atVKmzdv1tixY6/4sa6+fftq1KhROn36tFq1alWsWeCUlJQCL2iOHDmioKAgVa5cWUFBQYqNjdW//vUvPfjggwXeFdasWTMtWbJEX3zxhe2jc7m5udqyZYuaNWt2xTbceOONqlWrlv744w/179/f4bYDAHzHpceLpaWlqUOHDoVuFx0drW3btumZZ56x5atPP/0033a1atXK98XWl76g7JL4+Hi9+eabCgwMVMOGDUvV/pYtW8pkMmndunV6+OGHC90uKipKjRo10pQpU3TgwAE9//zzDp/j3LlzCggIUHBwsN3ySx8tr1GjhqSir2cqVqyoRo0a6dNPP9WgQYNsyz/55BNJKjKvx8fHa8eOHapZs6auvvpqh9sPAPBMffv2VWpqqu6+++4C1zdr1kxbt27V+PHjFRBwsRz25ZdfKj09vciccrlWrVopKSlJjRs3vuKnlQIDA/NNlGdnZ8tkMtnaIF3MbZceaeqIoKAg/eMf/9DatWuVkpKiQYMGFVpYL0hB77+zs7N14sQJ2yPc4uLiFBISonXr1hV4M5l0cUzXrFmjs2fP2iYcDh06pAMHDhT5peCOXlMBzkaxHHADjz32mD7++GOtWLFCTz/9tB544AGNHj1a3bt3V+XKlfXXX3/pq6++Uq9evdSiRQvFx8erbdu2evbZZ3X06FHFxMQoLS1Nmzdv1uzZsyXJoeM4asSIEdq+fbvuu+8+PfTQQ6pRo4Z+++03ZWVlaejQobbt2rVrp6uuukp79uzRK6+8UqwxeO6555SXl6fOnTurbt26OnfunDZv3qxt27bpgQceUFBQkCRpzJgxGjRokAYNGqT77rtP4eHh+vnnn3XVVVepT58+ateunaKjo/XUU09pzJgxql69ulatWqVTp05p7ty5V2yDyWTS+PHjNXbsWGVmZqpdu3YKCQnR8ePHtWPHDj3xxBPMbgOAj6tXr5769++vp59+WkOG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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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CA8Ph4uLC+Li4jB9+nRkZmZixIgRauX379+P58+fY9CgQbCwsMCaNWswfvx4HD582OCj4YjI8JydneHv74+ff/5ZdWPTiRMnkJWVha5du2Ljxo1q5QVBwEcffaTqvPby8sLJkyfxxRdfqG4Ge5kmbWNN8rKrqysiIiKwbNkyDBo0CC1atADwvxvC4uLikJubi8GDB6N69eq4dOkSNm3ahIcPH2LZsmVqMRWVywICArBy5UocOHAAQ4cOVZUtuHDQuXNnje9gdnZ2xsWLF3Hjxo1Sp6VbsWIFli9fjoCAAEREREAsFiMxMRG///47goODAfzv2R5BQUEYPHiwKudcvny5UI59+vQpRo0ahW7duqFnz55wcnIqc39GWfXs2RPx8fE4ffp0sblz+/btmD9/PsLCwjB8+HDk5eXh+vXrSExMRI8ePdCpUyekpKRg//79mDFjhuq78fL55O+//46DBw8iPDwcNWrUKPKmgZd98skncHZ2xqRJk5CQkICNGzciMzNT1fGtKU1ie1l6ejreeecd5OTkYNiwYahRowZ2796Njz76SHXB4mWanGeSkRGIjMjOnTsFd3d34fTp00JGRobw4MEDIS4uTnjzzTcFHx8f4cGDB6qyI0aMELp37y7k5eWplimVSmHQoEFC586dVcu++eYbwd3dXfjll18K7U+pVAqCIAh79uwRPD09hXPnzqmt37p1q+Du7i78+eefqmXu7u6Ct7e3cOfOHdWyq1evCu7u7sLGjRtVy6ZOnSp4enoKly5dKna/sbGxgru7u5CUlKRaJ5PJhNatWwvTpk0r/YD9v5ycHCEsLExwd3cX2rdvL0yfPl348ccfhfT09EJlw8PDhYCAAOH+/ftFxiQIgvDxxx8L3t7ewt27d1XLHj16JAQEBAjh4eGqZQWf1+DBg4X8/HzV8uzsbOGNN94QPvvsM7V9pKWlCS1atCi0/FW///674O7uLhw8eLDYMj179hRatmxZKJZ//vlHEARB+PXXXwV3d/cij3+BjIwMwd3dXVi2bFmhddOmTRPc3d2FxYsXF7muffv2qtf//POP4O7uLvj6+goPHz5ULU9MTBTc3d2F6Oho1bKhQ4cKQ4cOLXWbJcW2bNkywd3dXfW64Ps3a9YstXILFy4U3N3dhTNnzqiWtW/fXnB3d1f7rmdkZAg+Pj7CwoULC+2LiMxbwd/Wov4Jwv/+Xnfo0EHIyclRvU+pVAqdO3cW3n//fbX8kpOTI4SGhgrvvfeeatnHH38sNGvWTC0vJSUlCV5eXmp/6wr+1u7cubNQnK/+vZw5c6bQpk0b4cmTJ2rlPv30U6FFixaqWAvi79Kli9r5xA8//CC4u7sL169fFwRBEPLz84XQ0FChffv2wrNnz9S2+XL9Jk6cKAQHBwsKhUK17MqVK8XGXZwbN24Ivr6+gru7u9CrVy9h/vz5wq+//ipIpVK1cpmZmUJAQIAwYMAAITc3t8i4ZDKZEBgYKHTv3l2tzNGjRwV3d3fhm2++US0rLvedO3dOcHd3F/bu3au2/MSJE0Uuf1VB3srIyChy/bNnzwR3d3dh7NixarG8nBfnz58vNG/eXO1841UHDx4U3N3dhd9//73QuoL8d+LEiSLXvXzeVfC979OnjyCTyVTLV69eLbi7uwuHDx9WLSsuV7+6zZJie/XcYP369YK7u7vw008/qZbJZDJh0KBBgr+/v5CVlSUIwv9+E61atRKePn2qKnv48GHB3d1dOHLkSOGDRERmo+Bv2aVLl4RNmzYJAQEBqvwXEREhDBs2TBCEF3+vRo8erXpfQTvq22+/Vdve+PHjBQ8PD7V2sKZtY03z8qVLl4rNmS+fZxRYtWqV4OHhoXYOUVI7btCgQcKAAQPUlv3yyy/F/n0uTnx8vODl5SV4eXkJgwYNEr744gvh5MmTajlDEAQhJSVF8PT0FMaOHat2biAI/8vTGRkZgre3t/D++++rldm0aZPg7u4u7NixQ7Vs6NChgru7u7B161a1bZWlP6Mo06ZNE/z9/Ytd//fff5farv3oo4+Ebt26lbifNWvWqLXZX+bu7i54enoKN2/eLHLdy7m24Lziww8/VCsXFRUluLu7C1evXhUEoWznjiXF9mpO/89//lOoTZ2dna06Vyz4HDU9zyTjw2lYyCi9++67qik9IiIiYGtri//+97+oW7cugBdXU3///Xd06dJFdbfbkydP8O+//yI4OBgpKSmqocW//PILPD09C13dA6C6KyguLg6urq5o0qSJaltPnjzBm2++CQCqYbIFgoKC0LBhQ9VrT09P2NnZqaZDUSqVOHz4MNq3b1/kHK8F++3SpQusra3VhkrHx8fj33//Rc+ePTU+XjY2Nvjxxx8xcuRIAC/uIJs1axaCg4Mxb9481dCrJ0+e4Ny5c+jXr5/qbvRXY1IoFDh16hQ6duyIBg0aqNbXrl0b3bt3x59//qkaXl9g4MCBavOFnz59GpmZmejWrZva8bS0tISfn1+h41keEokEz58/L3a9vb09AODYsWMVmrN98ODBGpft2LGj2l2Dvr6+8PPzw/Hjx8u9f00UbP+9995TW/7++++rrS/g5uam9oAfR0dHNG7cuNA0R0REBWbPno1169ap/XtZ79691UbfXL16FSkpKejRowf+/fdfVR6QSqUIDAzEuXPnoFQqoVAoEB8fj44dO6rlJVdXV9XdVmUlCAJ++eUXhIaGQhAEtTwUHByMrKwsXLlyRe09ffv2VZuP8tXp3/7++2/cu3cPw4cPLzTk+uU7jHv16oXHjx+r5bl9+/bBxsYGnTt31rgOTZs2xZ49e9CzZ0/cv38fGzZswNixYxEUFKQ2r/epU6fw/PlzjB49utDdcAVx/fXXX8jIyMDgwYPVyrz11lto0qRJoSHDQOHcFxcXB3t7e7Rp00bteHp7e0MikVQ4rxfMI1tSXndwcEBOTo7aNAJl5eLigrZt22pcftCgQWp38w0ePBhVqlTReV4/ceIEatWqhe7du6uWicViDBs2DFKpFOfOnVMr37VrV1SrVk31urjpC4nIfHXp0gV5eXk4evQosrOzcezYsWKnYDlx4gREIlGhKTbef/99CIJQaIqV0trG5cnLRXn5PEMqleLJkycICAiAIAhFToFWVDuuV69eSExMxN27d1XL9u3bh3r16qFVq1alxlCgTZs2iI2NRWhoKK5du4Y1a9Zg5MiRaNeundq0sIcPH4ZSqcTYsWMLPculIE+fPn0acrkcw4cPVyszYMAA2NnZFco5VlZW6Nu3r9qysvZnlJWmefrhw4cVmgasZcuWcHNz07h8eHi42uuCEQNFTQOkTcePH4evr69am7pq1aoYNGgQ7t+/j6SkJLXypZ1nkvHhNCxklGbPno3GjRsjKysLO3fuxLlz59T+uNy9exeCIOCbb77BN998U+Q2MjIyUKdOHdy9e7fUBuqdO3dw69atYoc6v/qQjnr16hUqU61aNdWcYk+ePEF2dnapU1o4ODigffv22L9/Pz755BMAL5J1nTp1VIlNU/b29pg6dSqmTp2K+/fv48yZM/j++++xadMm2NnZ4dNPP1X9MS5pqNiTJ0+Qk5NT5PAqV1dXKJVKPHjwQK1ur06pkpKSAgCFhgkXeHUO9fKQSqWoWrVqsetbtWqFsLAwrFixAuvXr0erVq3QsWNH9OjRQ+MHdFSpUkV1gUYTr732WqFljRo1wsGDBzXeRnncv38flpaWaiepAFCrVi04ODiopjEqUNz3t6LzzhJR5eXr61viAz6LywPTpk0r9j1ZWVmQyWTIzc0t8u9n48aNy9Up+eTJE2RmZmLbtm3Ytm1bsWVe9uoF5IIO8YK8rkn+BF40nmvVqoW9e/ciMDAQSqUS+/fvR4cOHcqc+xo3bowvv/wSCoUCSUlJOHbsGNasWYPPP/8cLi4uCAoKUjX2SzrfSE1NVW3vVU2aNCk0h3xRue/OnTvIysrS+DyprAqmcCsprw8ZMgQHDx7EqFGjUKdOHbRp0wZdunRBu3btNN5PWaeAe/V7WbVqVdSqVatQXtW2+/fv47XXXivUsVIwbUvBZ1rg1bxe0HH+6ly3RGS+HB0dERgYiP379yM3NxcKhQJhYWFFlr1//z5q165dKG8V/A3StG3xctu4rHm5KKmpqVi2bBmOHDlSqN3y6s1cxbXjunbtiujoaOzduxfjxo1DVlYWjh49infffbdM02sBL86NVqxYAZlMhmvXruHw4cNYv349JkyYgD179sDNzQ13796FpaVlsdNuFdQLgGqKrgJWVlZo0KBBoeNdp06dQu3ZsvZnlJUmeXrUqFE4ffo0BgwYgNdeew1t2rRB9+7dVdPpaKKiebphw4awtLRUe46ZLqSmphaamhb432eYmpqqds5Y2nkmGR92lpNRerlR3rFjRwwZMgSTJk1CXFwcqlatCqVSCeDF1e3i7hB6teOwJEqlEu7u7qqHP77q1UT78l3ULxNeeeCJJnr37o24uDhcuHAB7u7uOHLkCAYPHlyogVQWzs7O6N+/Pzp16oSOHTti3759xc7trQ2v3s1WcBy++OIL1KpVq1D54o6fpuRyOVJSUkrsHLCwsMCyZcuQkJCAo0eP4uTJk5g5cybWrVuHbdu2lZjoC1hZWVXocygLhUJR4W1oeoJX0eNPRPSqV5/pUJAHpk6dCi8vryLfI5FIyvTQqeL+xr3697PgHKFnz57o06dPke959RkQxf2tL2teF4lE6NGjB7Zv346oqChcuHABjx8/LtNosaK2WfCgKX9/fwwfPhz79u1DUFBQubdZkqJyn1KphJOTExYvXlzke4qb01NTBQ/DLunczcnJCXv27EF8fDxOnDiBEydOYNeuXejduzcWLVqk0X5Ke/aINmkjr2tKm+elRFR5de/eHZ9//jnS09PRrl27Ih9OWR6l/Q0qT15+lUKhwHvvvYdnz57hgw8+QJMmTSCRSPDo0SNMnz5dtY8CxbXjqlWrhvbt22Pfvn0YN24c4uLiIJPJKpSnrays4OvrC19fXzRq1AgzZsxAXFwcxo0bV+5tlqSoXFbW/oyy0iRPu7q6Ii4uDseOHcPJkyfxyy+/YMuWLRg7diwiIiI02k9F8/Sr54qanjvqmrbOM0l/2FlORk8kEmHixIkYPnw4Nm/ejNGjR6umBxGLxaU2Fhs2bIibN2+WWubatWsIDAws8xXlojg6OsLOzq7U/QJA27Zt4ejoiH379sHPzw85OTno1atXhWMAXpwMNGjQQBVHwXErSHbFxW5ra6v2pOsCt2/fhqWlZZF3D7ysYD9OTk46acwXPCRVkyH6/v7+8Pf3x6effop9+/Zh8uTJOHDgAAYMGKCVz/plRT3ROiUlRe3BJNWqVStyuNWrd4mVJTZnZ2colUrcuXNH7a6F9PR0ZGZmlvpgFCIibSvIA3Z2diXmAUdHR9jY2BT59/PVPFTc3bKv/v10dHRUXVjXVg56OX+Wts1evXrh+++/x5EjR3DixAk4OjqWe0qZV/n4+AAAHj9+DOB/jdabN28WeXc+8L+7mZKTkwvdcZacnFzobqeiNGzYEGfOnEHz5s110uG8d+9eACh1ihQrKyuEhoYiNDQUSqUSUVFR2LZtGz7++GO89tprOsnrL4/0e/78OdLS0tTuZn/57skCMpkMaWlpasvKmtevX78OpVKp1sC+ffs2gMJ3qBERaaJTp06IjIxEQkIClixZUmy5godMZ2dnq91dXvA3qKxti7Lk5eL+Vt64cQMpKSlYtGgRevfurVpenqm5evXqhY8//hiXLl1SPZS7tBHhmioqTyuVSty6davYmwcK/qbfvn1bbRpUmUyGe/fuaXQuo+3+jFft3bsXFhYWxT6Eu4BEIkHXrl3RtWtXyGQyjB8/HjExMRgzZgysra11kqdfPmZ37tyBUqlU3aGu6bkjULY8Xb9+/WL7SwrWk2njnOVkElq3bg1fX1/88MMPyMvLg5OTE1q1aoVt27apEtHLXh7G1blzZ1y7dg2//vproXIFV/K6dOmCR48eqc0DWiA3N1c17EhTlpaW6NixI44ePYrLly8Xu1/gxRCxbt264eDBg9i1axfc3d3h6elZpv1du3atyKFr9+/fx61bt1RDrx0dHdGyZUvs3LmzUIIoiEkkEqFNmzb47bff1IYvpaenY//+/WjRokWpQ8nbtm0LOzs7rFq1qsj5wjUZZleca9euITo6GtWqVSs0R9nLnj17VuhKbcEJSsGdjLa2tgC0N/zp8OHDqrnyAeDSpUtITExUa1Q3aNAAt2/fVjsG165dw4ULF9S2VZbYCp5q/8MPP6gtL5hTuGA9EZG++Pj4oGHDhvj++++LnN+y4G+gSCRCcHAwDh8+rJaXbt26hfj4eLX32NnZoUaNGjh//rza8i1btqi9FolECAsLw6FDh4q8OFyeHOTt7Q0XFxds2LCh0N/lV3ONp6cnPDw8sGPHDvzyyy/o1q0bqlQp2/0p58+fLzJ/FkxLU5DXg4ODUbVqVaxatQp5eXlFxuXj4wMnJyfExsaq3cl//Phx3Lp1C2+99Vap8XTp0gUKhQLffvttoXX5+fkVyqP79u3Djz/+iICAgGKHjwPAv//+q/ba0tJSdSfiq3k9Kyur3PG8bNu2bWqfw9atW5Gfn18or7/6ndy+fXuhu9bKElu7du2QlpaGAwcOqJbl5+dj48aNkEgkaNmyZbnqQ0TmrWrVqoiKisL48eMRGhpabLl27dpBoVBg8+bNasvXr18PCwuLMk1/BZQtLxfXBiq4cPhyzhUEARs2bChTLMCL+tWoUQNr1qzBuXPnynVX+e+//17kXcEFebpgOo6OHTvC0tISK1euLHT3e8H7g4KCIBaLsXHjRrVt7tixA1lZWRq15bTdn/Gy7777DvHx8ejatSsaNWpUbLlX87SVlRVcXV0hCIIql2o7T7/6Hd20aRMAqL6jmp47ljW2kJAQXLp0CRcvXlQtk0ql2L59O5ydncs07zoZJ95ZTiZj5MiRmDBhAnbt2oXBgwcjMjISQ4YMQY8ePTBw4EA0aNAA6enpSEhIwMOHD1V3KY0cORKHDh3ChAkT0K9fP3h7e+PZs2c4cuQI5syZA09PT/Tq1QsHDx5EZGQkzp49i+bNm0OhUOD27duIi4vDmjVrSpyrtSgTJ07EqVOnMGzYMAwcOBCurq5IS0tDXFwctmzZojbsrXfv3ti4cSPOnj2LyZMnl/nYnDp1CsuXL0doaCj8/PwgkUhw79497Ny5U3VFt8Bnn32GwYMHo0+fPhg0aBBcXFxw//59HDt2DD/99BMA4JNPPsHp06cxZMgQDBkyBCKRCNu2bYNMJsOUKVNKjcfOzg5RUVGYOnUq+vbti65du8LR0RGpqak4fvw4mjdvjtmzZ5e6nfPnzyMvLw9KpRJPnz7FhQsXcOTIEdjZ2WHFihVFTvFSYPfu3di6dSs6duyIhg0b4vnz59i+fTvs7OxUydPGxgZubm44ePAgGjVqhOrVq6Np06alzklbnIYNG2Lw4MEYPHgwZDIZNmzYgOrVq+ODDz5Qlenfvz/Wr1+PkSNHon///sjIyEBsbCzc3NzUOpTKEpunpyf69OmDbdu2ITMzEy1btsTly5exe/dudOzYsczz3xMRVZSlpSXmz5+PUaNGoXv37ujbty/q1KmDR48e4ezZs7Czs0NMTAwAYPz48Th58iTCw8MxePBgKBQKbNq0CW5ubrh+/bradgcMGIDvvvsOs2bNgo+PD86fP1/knT2TJk3C2bNnMXDgQAwYMABubm549uwZrly5gjNnzuCPP/4oc32ioqLw0UcfoXfv3ujbty9q1aqF27dvIykpCWvXrlUr//LUIOVphK9evRpXrlxBp06dVB3Cf//9N/bs2YPq1aurngliZ2eHGTNm4LPPPkP//v3RvXt3ODg44Nq1a8jNzcWiRYsgFosxefJkzJgxA0OHDkW3bt2QkZGBDRs2wNnZGe+++26p8bRq1QqDBg3CqlWrcPXqVbRp0wZisRgpKSmIi4vDrFmz8Pbbb5e6nUOHDkEikUAul+PRo0eIj4/HhQsX4OnpWewzaAp89tlnePbsGd58803UqVMHqamp2LRpE7y8vFSjqry8vCASibB69WpkZWXBysoKb775JpycnEqNrShyuRzvvvsuunTpguTkZGzZsgUtWrRAhw4dVGUGDBiAyMhIjB8/HkFBQbh27Rri4+NRo0YNtW2VJbZBgwZh27ZtmD59Oq5cuQJnZ2ccOnQIFy5cwMyZM7Xy7BciMk/FTYPystDQULRu3RpLlizB/fv34eHhgVOnTuG3337DiBEjyjTdaQFN83LDhg3h4OCA2NhYVK1aFRKJBL6+vmjSpAkaNmyIRYsW4dGjR7Czs8OhQ4fKdbFWLBajW7du2LRpE0QiEbp161bmbcyfPx85OTno1KkTmjRpArlcjgsXLuDgwYNwdnZWPYDztddew4cffohvv/0WQ4YMQefOnWFlZYXLly+jdu3amDRpEhwdHTFmzBisWLECH3zwAUJDQ1U5p1mzZhqdR2ijPyM/P1/VHyCTyXD//n0cOXIE169fR+vWrTF37twS3z9y5EjUrFkTzZs3h5OTE27fvo1NmzYhJCRElbe8vb0BAEuWLEHXrl0hFovRvn171QNEy+revXv48MMP0bZtWyQkJGDv3r3o3r272s2Hmp47liW20aNH4+eff8aoUaMwbNgwVKtWDXv27MG9e/ewfPlyvU3lSrrDznIyGZ07d1bdpTZw4EC4ublh586dWLFiBXbv3o2nT5/C0dERr7/+OsaOHat6X9WqVbF582YsX74cv/76K3bv3g0nJycEBgaiTp06AKC62rt+/Xr89NNP+PXXX2FrawsXFxcMGzasyIdilaZOnTrYvn07vvnmG+zbtw/Z2dmoU6cO2rVrV2gIs4+PD5o2bYpbt26Vq1HduXNnPH/+HKdOncLvv/+OZ8+ewcHBAb6+vnjvvffUOks9PT1VcW3duhV5eXmoX78+unTpoirTtGlTbN68GV999RVWrVoFQRDg6+uLL7/8ssgHWRSlR48eqF27Nr777jusXbsWMpkMderUwRtvvFHo6d3F2bhxI4AXJzT29vZwdXXF+PHjMXDgwFLnR23VqhUuX76MAwcOID09Hfb29vD19cXixYvVhmrNnz8f8+bNw4IFCyCXyzFu3Lhyd5b37t0blpaW+OGHH5CRkQFfX198/vnnqF27tqqMq6srFi1ahGXLlmHBggVwc3PDF198gf379xfqvClLbPPnz4eLiwt2796Nw4cPo2bNmhgzZozO5sojIipN69atsW3bNnz77bfYtGkTpFIpatWqBV9fXwwaNEhVztPTE2vXrsWCBQuwbNky1K1bF+PHj0daWlqhzvKxY8fiyZMnOHToEA4ePIh27dphzZo1he5IrlmzJn788UesXLkSv/76K7Zu3Yrq1avDzc2tXBelgRejpn744QesXLkS33//PQRBQIMGDTBw4MBCZXv06KHKN76+vmXe15gxY7B//36cO3cO+/btQ25uLmrVqoVu3brh448/VstjAwYMgJOTE7777jt8++23qFKlCpo0aaLWCd63b1/Y2Nhg9erVWLx4MSQSCTp27IgpU6ZoPGft3Llz4ePjg9jYWCxZsgQikQjOzs7o2bMnmjdvrtE2oqKiALx41kmNGjXg5eWF6OhojR6+3bNnT2zfvh1btmxBZmYmatWqhS5dumD8+PGqRmmtWrUwZ84crFq1CrNmzYJCocCGDRvK3Vk+e/Zs7Nu3D8uWLYNcLke3bt3w2WefqQ3VHjhwIO7du4cdO3bg5MmTaNGiBdatW1foIkRZYrOxscHGjRuxePFi7N69G9nZ2WjcuDEWLFig8TkUEVF5WVpa4r///S+WLVuGAwcOYNeuXXB2dsbUqVPx/vvvl2ubmuZlsViMhQsX4uuvv0ZUVBTy8/NVf/tiYmIwf/58rFq1CtbW1ujUqRPCw8PLNYVpr169sGnTJgQGBqq11TQ1depUxMXF4fjx46pRSPXr18eQIUPw0UcfqeXWCRMmwMXFBZs2bcKSJUtga2sLDw8PtbjHjx8PR0dHbNq0CQsWLEC1atUwcOBATJw4EWKxuNR4tNGfIZPJMHXqVAAv7rJ2dHSEj48Pxo4di06dOpXaATxo0CDs27cP69atg1QqRd26dTFs2DB8/PHHqjK+vr6YMGECYmNjcfLkSSiVSvz222/l7ixfunQpvvnmG3z11VeoUqUKhg4dqqpDAU3PHcsSW82aNREbG4svv/wSmzZtQl5eHjw8PBATE6PRiD0yfhYCZ5QnMgq9e/dGtWrVCk2lQUREZI6WL1+OFStWFOowNwVPnjxB27Zt8fHHH6tdwCciIiLDu3btGnr16lVoDnQiIoBzlhMZhcuXL+Pq1atM1ERERJXA7t27oVAotPbAbiIiItKe7du3QyKRoHPnzoYOhYiMEKdhITKgGzdu4MqVK/j+++9Rq1YtdO3aVW29QqEo9UFkEokEVatW1WWYREREpIEzZ87g1q1biImJQceOHeHi4qK2Pjc3t9QHR1WrVq3UKUmIiIio7I4cOYKkpCRs374d4eHhhabYeP78eakPw3R0dIRIJNJlmERkYOwsJzKgQ4cOYeXKlWjcuDG+/vprWFtbq61/8OCB2kOkijJu3Di1B3gSERGRYXz77be4ePEiAgIC8Pnnnxdaf+DAAcyYMaPEbWzYsAGtW7fWVYhERERma/78+UhPT0e7du2KbEN///33WLFiRYnb+O233wpdDCeiyoVzlhMZsby8PPz5558llmnQoIHag76IiIjIOD1+/BhJSUkllvH29ka1atX0FBEREREV+Oeff/DPP/+UWKZFixaFbnIjosqFneVEREREREREREREZPb4gE8iIiIiIiIiIiIiMnvsLCcyIatXr8bbb78NpVJZrvcvX74cHh4eWo7KNJw4cQIBAQGlPjCViIhIW5i3y495m4iI9I15u/yYt6kyYWc5kYnIzs7GmjVrMGrUKFhaqv908/LysH79egwYMAAtWrRAs2bNEBYWhrlz5yI5OVlvMe7btw/r16/X2/5eJZfL0bVrV3h4eGDt2rVq69q1a4eGDRti1apVBoqOiIjMCfN26Zi3iYjIWDBvl455m8wFO8uJTMSOHTuQn5+P7t27qy1/8uQJBg8ejAULFsDJyQkRERGYPXs2OnTogCNHjqBHjx56i3H//v3YsGGD3vb3qk2bNuHBgwfFrh80aBC2bduG7OxsPUZFRETmiHm7dMzbRERkLJi3S8e8TeaCneVEJmLXrl0IDQ0t9OTtGTNm4OrVq1i2bBliYmIwYsQIDBgwAFOnTsUvv/yCYcOGGShi7VAqlcjLyyu1XEZGBlauXIkPPvig2DJhYWGQyWSIi4vTZohERESFMG+XjHmbiIiMCfN2yZi3yZyws5zIBPzzzz+4fv06goKC1JYnJibi2LFj6N+/P8LCwgq9z8rKCtOmTSt2u/fu3YOHhwd27dpVaJ2HhweWL1+uep2dnY3//Oc/CA0NhY+PDwIDA/Hee+/hypUrAIBhw4bh2LFjuH//Pjw8PODh4YHQ0FDV+2UyGZYtW4ZOnTrBx8cHISEh+OKLLyCTyQrtd+7cudi7dy+6deuGZs2a4eTJk6Ueo8WLF6Nx48bo2bNnsWWcnJzg4eGB3377rdTtERERlRfzNvM2ERGZDuZt5m2il1UxdABEVLqLFy8CAF5//XW15UeOHAEA9OrVS+cxREZG4tChQxg6dChcXV3x9OlT/Pnnn7h16xa8vb3x4YcfIisrCw8fPsSMGTMAAFWrVgXw4mr1Rx99hD///BMDBw6Eq6srbty4gR9++AEpKSn49ttv1fb1+++/4+DBgwgPD0eNGjXg7OxcYmyXLl3Cnj17sGXLFlhYWJRY1tvbG4cPH67AkSAiIioZ8zbzNhERmQ7mbeZtopexs5zIBNy+fRsA4OLiorb81q1bAAB3d3edx3D8+HEMHDgQ06dPVy0bNWqU6v/btGmDDRs2IDMzs9DJxL59+3D69Gls3LgRb7zxhmp506ZNERkZiQsXLqB58+aq5cnJydi3bx/c3NxKjUsQBMybNw9du3ZFQEAA7t27V2L5Bg0a4N9//0VGRgacnJxK3T4REVFZMW8Xj3mbiIiMDfN28Zi3yRxxGhYiE/D06VNUqVJFdeW4QMGDM15drgsODg5ITEzEo0ePyvzeuLg4uLq6okmTJnjy5Inq35tvvgkAOHv2rFr5li1bapS4gRdzy924cQOTJ0/WqLyDgwMA4N9//y1DDYiIiDTHvF085m0iIjI2zNvFY94mc8Q7y4lMmJ2dHQDg+fPnqqSkK5MnT8b06dPx1ltvwdvbGyEhIejduzcaNGhQ6nvv3LmDW7duITAwsMj1GRkZaq9fvaJfnOzsbHz99dcYOXIk6tWrp9F7BEEAgFKHjxEREWkb8zbzNhERmQ7mbeZtMk/sLCcyAdWrV0d+fj6ys7NVCRsAmjRpAgC4ceOG2nArTRWXwBQKRaFlXbt2xRtvvIFff/0Vp06dwtq1a7F69WosX74cISEhJe5HqVTC3d1dNbfaq+rWrav22sbGRqP4165dC7lcjq5du6qGgz18+BAAkJmZiXv37qF27dqwsrJSvSczMxMAUKNGDY32QUREVFbM20Vj3iYiImPEvF005m0yV+wsJzIBBUn63r178PT0VC1v3749Vq1ahb1795YreVerVg3A/xJagdTU1CLL165dG+Hh4QgPD0dGRgb69OmDmJgYVfIu7mSgYcOGuHbtGgIDA7V6hfnBgwd49uwZunXrVmhdTEwMYmJisGfPHnh5eamW37t3DzVq1ICjo6PW4iAiInoZ83bRmLeJiMgYMW8XjXmbzBXnLCcyAQEBAQCAv/76q9Dytm3b4scffyzyidMymQyLFi0qdrt2dnaoUaMGzp8/r7Z8y5Ytaq8VCgWysrLUljk5OaF27dqQyWSqZba2toXKAUCXLl3w6NEjbN++vdC63NxcSKXSYmMsybBhw7By5Uq1f3PnzgUA9O3bFytXriw0xOzKlSvw9/cv1/6IiIg0wbxdNOZtIiIyRszbRWPeJnPFO8uJTECDBg3g7u6OM2fOoH///mrrvvjiC7z//vsYN24c2rdvj8DAQNja2uLOnTs4cOAAHj9+jGnTphW77QEDBuC7777DrFmz4OPjg/PnzyM5OVmtzPPnzxESEoKwsDB4enpCIpHg9OnTuHz5strTur29vXHgwAEsWLAAzZo1g0QiQWhoKHr16oWDBw8iMjISZ8+eRfPmzaFQKHD79m3ExcVhzZo1aNasWZmPi7e3N7y9vdWWFQwPc3NzQ8eOHdXWZWRk4Pr16xgyZEiZ90VERKQp5u2iMW8TEZExYt4uGvM2mSt2lhOZiH79+uGbb75Bbm6u2hxjjo6OiI2NxZYtW3DgwAEsWbIEcrkczs7OCA0NxfDhw0vc7tixY/HkyRMcOnQIBw8eRLt27bBmzRq1h4PY2Nhg8ODBOHXqFH755RcIgoCGDRsiMjJSLREOGTIEV69exa5du7B+/XpVDJaWlli5ciXWr1+Pn376Cb/++itsbW3h4uKCYcOGoXHjxto/YEX45ZdfYGVlhS5duuhlf0REZL6YtyuOeZuIiPSFebvimLepsrAQCh5VS0RGLSsrCx07dsTkyZMxYMAAQ4djknr37o1WrVph5syZhg6FiIgqOebtimPeJiIifWHerjjmbaosOGc5kYmwt7fHyJEjsXbtWiiVSkOHY3JOnDiBO3fuYMyYMYYOhYiIzADzdsUwbxMRkT4xb1cM8zZVJryznIiIiIiIiIiIiIjMHu8sJyIiIiIiIiIiIiKzx85yIiIiIiIiIiIiIjJ77CwnIiIiIiIiIiIiIrPHznIiIiIiIiKiSmj58uXw8PBQ+/f222+r1ufl5WHOnDlo3bo1AgICMH78eKSnp6ttIzU1FaNHj4afnx8CAwOxaNEi5Ofn67sqREREelHF0AEYi4sXL0IQBIjFYkOHQkREZkoul8PCwgIBAQGGDsXoMW8TEZGhmUrebtq0KdatW6d6LRKJVP8fHR2N48ePY+nSpbC3t8e8efMwbtw4xMbGAgAUCgXGjBmDmjVrIjY2Fo8fP8a0adMgFosxceJEjWNg3iYiIkPTNG/zzvL/JwgCBEHQ2rZkMpnWtmdKzLnugHnX35zrDph3/c257oB266/NXFTZMW9rhznXHTDv+ptz3QHzrr851x0wz7wtEolQq1Yt1T9HR0cAQFZWFnbu3Inp06cjMDAQPj4+iI6OxsWLF5GQkAAAiI+PR1JSEr788kt4eXkhJCQEEyZMwObNmyGTyTSOgXn7BVOOHWD8hmTKsQOM35BMOXbAMHmbd5b/v4Ir3M2aNavwtqRSKa5evQo3NzdIJJIKb8+UmHPdAfOuvznXHTDv+ptz3QHt1v/y5ctaiqryY97WDnOuO2De9TfnugPmXX9zrjtgnnn7zp07CA4OhrW1Nfz9/TFp0iTUr18ff/31F+RyOYKCglRlXV1dUb9+fSQkJMDf3x8JCQlwd3dHzZo1VWWCg4MRFRWFpKQkvP766xrFIBaLIQgCXF1dK1yfnJwcpKSkwNnZGba2thXenj6ZcuwA4zckU44dYPyGZMqxA9qNPykpCRYWFqWWY2c5ERERERERUSXk6+uLBQsWoHHjxkhLS8PKlSsRHh6Offv2IT09HWKxGA4ODmrvcXJyQlpaGgAgPT1draMcgOp1QRlNyeVyXL16tQK1UZeSkqK1bembKccOMH5DMuXYAcZvSKYcO6C9+K2srEotw85yIiIiIiIiokooJCRE9f+enp7w8/ND+/btcfDgQdjY2Og1FrFYDDc3twpvp+Auw0aNGpncXZKmHDvA+A3JlGMHGL8hmXLsgHbjT0pK0qgcO8uJiIiIiIiIzICDgwMaNWqEu3fvIigoCHK5HJmZmWp3l2dkZKBWrVoAXtxFfunSJbVtpKenA4CqjKYsLCy0OvWPra2tyU4lZMqxA4zfkEw5doDxG5Ipxw5oJ35NpmAB+IBPIiIiIiIiIrPw/Plz/PPPP6hVqxZ8fHwgFotx5swZ1frbt28jNTUV/v7+AAB/f3/cuHEDGRkZqjKnT5+GnZ2dVu4SJyIiMja8s5yIiIiIiIioElq0aBHat2+P+vXr4/Hjx1i+fDksLS3RvXt32Nvbo1+/fli4cCGqVasGOzs7zJ8/HwEBAarO8uDgYLi5uWHq1KmYMmUK0tLSsHTpUoSHh2s07ysREZGpYWc5ERERERERUSX08OFDTJw4EU+fPoWjoyNatGiB7du3w9HREQAwc+ZMWFpaIiIiAjKZDMHBwYiMjFS9XyQSISYmBlFRURg0aBBsbW3Rp08fREREGKpKREREOsXOciIiIiIiIqJKaMmSJSWut7a2RmRkpFoH+aucnZ2xevVqbYdGRERklDhnORERERERERERERGZPXaWExEREREREREREZHZY2c5EREREREREREREZk9zllOREQmQSaTITEx0dBhFJKbmwuxWGzoMKgCZDIZrly5AqlUChsbG0OHo8bPzw9WVlaGDoOIiIhMlDbPoXNzc5GcnKy1cyae5xCRMWJnORERmYTExERcOXQE3q5uhg5Fzc2bN1ClkTN8fX0NHQqV0+XLl5F97RbEIlvA0ngG3V25lQQAaNmypYEjISIiIlOlzXNosVIJlzwB4rsPK3zOxPMcIjJW7Cw3YR4eHhqV27BhA1q3bq3jaIiIdM/b1Q0tfYyrU1qhVOKGIsfQYVAFeTZugpbezSASiQwdChGRXgQEBGhUjm0JMleVqb2trXNohUKBrOxs2NvZ8ZyJiCotdpabsC+++ELt9U8//YRTp04VWu7q6qrPsIiIiIiIyMjNnz9fbfoDtiWI1LG9TURknthZbsJ69eql9joxMRGnTp0qtJyIiIiIiOhl3bp1g0QiUb1mW4JIHdvbRETmyXgm5iStmzZtGlq3bg25XF5o3fvvv4+wsDDVaw8PD8ydOxd79+5FWFgYmjVrhr59++LcuXOF3vvo0SPMmDEDQUFB8PHxQbdu3bBjxw6d1oWIiIiIiPSHbQmikpXnN3LgwAFMmjQJrVu35m+EiMhIsbO8EuvVqxeePn2K+Ph4teVpaWn4/fff0bNnT7Xl586dQ3R0NHr27ImIiAg8ffoUH3zwAW7cuKEqk56ejoEDB+LMmTMIDw/HrFmz0LBhQ8yaNQvr16/XR7WIiIiIiEjH2JYgKll5fiNffvkl2rRpg48++oi/ESIiI8VpWCqxN998E3Xr1sXevXvRvn171fKff/4ZSqWyUPK+ceMGdu7cCR8fHwAvhma+/fbbWLZsGVasWAEAWLJkCRQKBfbt24caNWoAAAYPHoyJEydixYoVhbZJRERERESmxxBtiXfeeQc2NjZ6qiFRxZTnN7J582ZYWFjAy8sLvXv35m+EiMgI8c7ySszS0hI9evTAkSNHkJ2drVq+d+9eBAQEoEGDBmrlAwICVCe3AFC/fn106NAB8fHxUCgUEAQBv/zyC0JDQyEIAp48eaL6FxwcjKysLFy7dk1v9SMiIiIiIt0wRFviypUreqsfUUWV5zfy+uuvq17zN0JEZJx4Z3kl17t3b6xevRqHDx9G7969cfv2bVy5cgVz5swpVPa1114rtKxRo0bIycnBkydPYGlpiczMTGzbtg3btm0rcn9PnjxBvXr1tF4PIiIiIiLSL0O0JYhMCX8jRESVDzvLKzk3Nzd4e3tj79696N27N/bu3QuxWIwuXbqUeVtKpRIA0LNnT/Tp06fIMg0bNsSjR48qFDMRERERERmevtsSHh4eFYqXSN/4GyEiqnzYWW4GevfujYULF+Lx48fYv38/3nrrLVSrVq1QuTt37hRalpKSAltbWzg6OgIAqlatCqVSiaCgoCL3JZVK2VlORERERFRJ6LMtQWSK+BshIqpcDD5neWhoKDw8PAr9Kxi2lJeXhzlz5qB169YICAjA+PHjkZ6erraN1NRUjB49Gn5+fggMDMSiRYuQn59viOoYpe7du8PCwgL/+c9/8M8//xT7EM6LFy+qzYH24MED/Pbbb2jTpg1EIhFEIhHCwsJw6NAhtSd2F+CQMCIiIiKiyoVtCaKSleU3cvXqVdVr/kaIiIyTwe8s37FjBxQKher1zZs38d577+Htt98GAERHR+P48eNYunQp7O3tMW/ePIwbNw6xsbEAAIVCgTFjxqBmzZqIjY3F48ePMW3aNIjFYkycONEgdTI2jo6OaNu2LeLi4uDg4IC33nqryHLu7u4YOXIkhg0bBisrK2zduhUAMH78eFWZSZMm4ezZsxg4cCAGDBgANzc3PHv2DFeuXMGZM2dw7NgxPdSIiIiIiIj0QZ9tiT/++EMfVSLSqrL8Rj7++GN07NgRzs7O2LFjBwD+RoiIjI3BO8sLhhsV+O6779CwYUO0atUKWVlZ2LlzJxYvXozAwEAALzrPu3btioSEBPj7+yM+Ph5JSUlYt24datasCS8vL0yYMAGLFy/GuHHjYGVlZYhqGZ1evXrh6NGj6NKlS7HHpGXLlvD398fKlSuRmpoKNzc3LFiwAJ6enqoyNWvWxI8//oiVK1fi119/xdatW1G9enW4ublh8uTJ+qoOEREZSGhoKO7fv19o+ZAhQxAZGYm8vDwsXLgQBw4cgEwmQ3BwMCIjI1GzZk1V2dTUVERFReHs2bOQSCTo3bs3Jk2ahCpVDH5aQkRERWBbgqhkmv5GXn/9dSxfvhxPnjzhb4SIyEgZVatUJpNh7969eO+992BhYYG//voLcrlcbb4uV1dX1K9fX9VZnpCQAHd3d7VGeHBwMKKiopCUlITXX39d4/0LggCpVFrheuTk5Kj9V18mT56MyZMnF1mHgoeFdO7cudg65ufno2PHjujYsaPa8lfL29raqvb1KkPV3ViYc/3Nue6AeddfX3XPzc2FWKlUG41kDJTKF/Foo/6CIMDCwqLC29EljggjIqqcZs+ejdmzZxe5TiwWA0Cx00sU6NmzZ6llnJycStwXkbHSxm+ka9euaNy4Mby8vCCRSIosw98IEZFhGVVn+eHDh5GVlaV68nN6ejrEYjEcHBzUyjk5OSEtLU1V5uWOcgCq1wVlNCWXy9XmEKuolJQUrW2rojZu3IjatWvDxsam2Do+efJEa/U3probgjnX35zrDph3/XVd9+TkZLjkCcjKztbpfsoqNzcPsLbQWv2NfUQUR4QREZmfH3/8EQ0aNECLFi0MHQqRUeJvhIio8jCqzvKdO3eiXbt2qFOnjkH2LxaL4ebmVuHt5OTkICUlBY0aNYKtra0WIiu/uLg43Lx5ExcvXsSUKVNKvNPe0dERXl5eFdqfMdXdEMy5/uZcd8C866+vukulUojvPoS9nZ3O9lEeNjbWgCDTSv2TkpK0FJV+GHpEGBER6dbPP/+M69ev49ixY5g1a5bRj34i0jf+RoiIKh+j6Sy/f/8+Tp8+jeXLl6uW1axZE3K5HJmZmWp3l2dkZKBWrVqqMpcuXVLbVnp6OgCoymjKwsKi2KFQ5WFra6vV7ZXHjBkzIJFI0L9/f7z77rslzgdbpUoVrcVrDHU3JHOuvznXHTDv+uu67jY2NoClJUQikc72UR6WliJAoZ36m1oDy9AjwrQ1fVpubi5EABRK45riR6FUQp6bq5U6Fsecp5ACzLv+5lx3wLzrX5a6T5w4UfVsid69e5f49yg/P1+nf6+0RZufvSlMn0a6VfAb6d+/P4YMGWLocIiISAuMprN8165dcHJyUntytI+PD8RiMc6cOYOwsDAAwO3bt5Gamgp/f38AgL+/P2JiYpCRkQEnJycAwOnTp2FnZ6eVu8RN3fXr17VajoiIqIChR4Rpa/q0Bw8ewAWAVGpcnWZSqRT3kpP1cgHOnKeQAsy7/uZcd8C8669J3bds2aL6/5s3b5ZaTptTWuqauUyfRrpV1va2KVxQIiIyd0bRWa5UKrFr1y707t1b7c5ne3t79OvXDwsXLkS1atVgZ2eH+fPnIyAgQNVZHhwcDDc3N0ydOhVTpkxBWloali5divDwcJ64EBER6YgxjAjT1vRpT548AVJSIZHYQmRpPCMXJBIJGjesW+Ep0kpizlNIAeZdf3OuO2De9TfnugParb+pTZ9GREREpTOKzvLTp08jNTUV/fr1K7Ru5syZsLS0REREBGQyGYKDgxEZGalaLxKJEBMTg6ioKAwaNAi2trbo06cPIiIi9FkFIiIis2IMI8K0NX2ajY0N5ABEliKjmuZHZGkJkY2NXu4sN+cppADzrr851x0w7/qbc90B85w+jYiIiEpnFJ3lwcHBxQ5fsra2RmRkpFoH+aucnZ2xevVqXYVHREREL+GIMCIiIiIiIqqMjKKznIiIiEwHR4QREREREZExk8lkSExM1Mq2cnNzkZycDKlUChsbmwpvz8/PjzcKGTF2lhMREVGZcEQYEREREREZs8TERFw5dATerhV/xpFYqYRLngDx3YeApWWFtnXl1ovnXbRs2bLCcZFusLOciIiIiIiIiIiIKhVvVze09PGt8HYUCgWysrNhb2dnVM84It1gZ3k5lTScQ9vDMwroYpjG2bNnMXz4cJw7dw4ODg7YtWsXoqOjcf78ea3uh4iIiIiIXtDm0HBNsS1BpqSytrf37t2LRYsW4dSpU1rdDxERaQ87y8uppOEc2hyeUaAiwzQuXryIIUOGoG3btvjuu+8qHIuHh4fq/21tbVG7dm00b94cQ4cORZMmTcq0rWHDhsHT0xOzZs2qcFxERERERKZAm0PDNWEqbQkfH58ybYtticqL7e0XtPEbqVGjBoYFhlQ4LiIic8HO8goobjiHsQ3P2LFjB4YOHYodO3bg0aNHqFOnToW3uWDBArRt2xZ5eXlISUnB9u3bMXDgQERGRsLNTT8n/UREREREpkpbQ8N1TZ9tiejoaPTu3bviQVOlwPY2fyNERIagncuwZLSeP3+OAwcOYPDgwXjrrbewe/durWzXwcEBtWrVgouLC4KDg7Fs2TL06NEDixYtQnZ2NgDg33//xcSJE9G2bVv4+fmhR48e2L9/v2ob06dPxx9//IENGzbAw8MDHh4euHfvHhQKBWbOnInQ0FD4+voiLCwMP/zwg1biJiIiIiIizei7LTF37lw8e/YMANsSZBpM4Tdy6NAhDI2aCY/+3XHv8aMXv5Fvv0HoxyPhO6QvwiLG4Ieff9JK3ERElQE7yyu5gwcPokmTJmjSpAl69uyJnTt3QhAEnezr3XffxfPnz/HXX38BeDHPnLe3N7777jvs378fAwcOxNSpU3Hp0iUAwKxZsxAQEICBAwciPj4e8fHxqFevHpRKJerWrYtvvvkGP//8M8aOHYslS5bgwIEDOombiIiIiIgKM0RbomAuZ7YlyBSYwm+kffv2WDFpBuJXb0Q9p5pQCgLqOtXENxOn4+cl32Js/3ewZMsGHDh9UidxExGZGk7DUsnt2LEDPXv2BAC0bdsWWVlZ+OOPP9C6dWut76tgvvK0tDQAQJ06dTBy5EjV+mHDhiE+Ph4HDx6Er68v7O3tIRaLYWNjg1q1aqnKiUQiREREqF43aNAACQkJiIuLQ9euXbUeNxERERERFWaItsT9+/cBsC1BpsEUfiNWVlaobm+PWjVqAABEACIGhave16BOXSTcuIa40yfRNait1uMmIjI17CyvxG7fvo3Lly9j5cqVAIAqVaqga9eu2LFjh06S96tX0BUKBWJiYhAXF4dHjx5BLpdDJpNp9MTyzZs3Y+fOnUhNTUVeXh7kcjk8PT21HjMRERERERVmqLaEhYUFALYlyPiZ9G/k4H7sPPorUtPTkCeTQZ6fD89GjbUeMxGRKWJneSW2Y8cO5Ofno23b/10dFgQBVlZWmD17Nuzt7bW6v1u3bgEAateuDQBYu3YtNmzYgJkzZ8LDwwO2traIjo6GXC4vcTs///wzFi1ahGnTpiEgIABVq1bF2rVrkZiYqNV4iYiIiIioaIZqS7i4uABgW4KMn8n+RuKPY9HG7zFt+EgEuHuiqq0t1u7dhcSb17UaLxGRqWJneSWVn5+Pn376CdOnT0ebNm3U1o0dOxb79+/H4MGDtbrPH374AXZ2dvDx8QEAXLhwAR06dECvXr0AAEqlEikpKXB1dVW9RywWQ6lUqm3nwoULCAgIQHj4/4aG3b17V6uxEhERERFR0QzZlggKCgLAtgQZN5P+jVy/igB3T4S/3U217O7DB1qNlYhMj0wmM8oLy7m5uRCLxXrdJzvLK6ljx47h2bNn6N+/f6Er2p07d8aOHTsqlLwzMzORlpYGmUyGlJQUbNu2DYcPH8a8efNQtWpVAMBrr72GQ4cO4cKFC6hWrRrWrVuH9PR0teTt7OyMxMRE3Lt3DxKJBNWrV8drr72GPXv24OTJk3BxccFPP/2Ey5cvq66gExERERGR7hiqLbFo0SI4ODgAYFuCjJsp/UbOnz+PtNf98STzGarb2eO1uvWx5/gRnEz4Ey616+Kn40dw+dZNuNSuU+54icj0JSYm4sqhI/B2dTN0KGpu3ryBKo2c4evrq7d9srO8Aq7cSipyuUKphFQqhUQigcjSUmv78nZrqHH5HTt2ICgoqMihX2FhYVizZg2uXbtW7nhmzJgBALC2tkadOnXQokUL/Pjjj2jcuDGuXr0KAPjoo4/wzz//YOTIkbC1tcXAgQPRsWNHZGVlqbbz/vvvY/r06ejWrRtyc3Px22+/4Z133sHVq1fx6aefwsLCAt26dcOQIUNw4sSJcsdLRERERGRMimtL6GpfptCW8Pb2VpVhW4LY3tbOb+TChQuYtnIpZPly/PbtWrzTuQuuptzGp19/AQsLoFtwCIaEdcWJi3+WO14iqhy8Xd3Q0kd/ndKaUCiVuKHI0es+2VleTn5+fsWuk+fm4l5yMho3rAuRBg/X0IS3W8MS9/mqmJiYYtf5+vri+vX/zUf28v/37dsXffv2LXHbL5d/lVQqVf1/9erV8e2335a4rcaNG2Pbtm2Fli9YsAALFixQWzZp0qQSt0VEREREZArKcl6vDabSlngZ2xLmrbK2t3v27ImmTZuWuG1t/0aioqKApLtqHWALxn6CBWM/USs7KfxdjfZLRFTZsbO8nKysrNCyZcsi1xVc5fby8oJEItFzZEREREREZMxKaksQEdvbRERkONoZs0REREREREREREREZMLYWU5ERERERERUyX333Xfw8PDAf/7zH9WyvLw8zJkzB61bt0ZAQADGjx+P9PR0tfelpqZi9OjR8PPzQ2BgIBYtWoT8/Hx9h09ERKQX7CwnIiIiIiIiqsQuXbqE2NhYeHh4qC2Pjo7G0aNHsXTpUmzcuBGPHz/GuHHjVOsVCgXGjBkDuVyO2NhYLFy4ELt378ayZcv0XQUiIiK9YGc5ERERERERUSX1/PlzTJkyBfPnz0e1atVUy7OysrBz505Mnz4dgYGB8PHxQXR0NC5evIiEhAQAQHx8PJKSkvDll1/Cy8sLISEhmDBhAjZv3gyZTGagGhEREekOO8uJiIiIiIiIKqm5c+ciJCQEQUFBasv/+usvyOVyteWurq6oX7++qrM8ISEB7u7uqFmzpqpMcHAwsrOzkZSUpJf4iYiI9KmKoQMgIiIiIiIiIu37+eef8ffff2PHjh2F1qWnp0MsFsPBwUFtuZOTE9LS0lRlXu4oB6B6XVBGU4IgQCqVluk9RcnJyVH7rykxROy5ubkQK5VQKBQV3pZCqVD7b8W2pYQ8N1cr3wlN8btjOPzuv7wt/X73NT322jxe2qT8/2Ouje+OIAiwsLAotRw7y4mIiIiIiIgqmQcPHuA///kPvv/+e1hbWxs6HMjlcly9elVr20tJSdHatvRNn7EnJyfDJU9AVna21rYplVa800oqleJecjIkEokWIiobU/zuyOVy3LhxA1euXDF0KIW4u7tDLBZrVJbffcN990s79ro4XtqQm5sHWFto7btjZWVVahl2lhMRERERERFVMleuXEFGRgb69u2rWqZQKHDu3Dls3rwZa9euhVwuR2Zmptrd5RkZGahVqxaAF3eRX7p0SW276enpAKAqoymxWAw3N7fyVkclJycHKSkpaNSoEWxtbSu8PX0yROxSqRTiuw9hb2dX4W0plApIpTmQSGwhshRVaFsSiQSNG9aFl5dXhePSlCl/d06dOoXsa7fg7+UFywoee236+1YS5I0bw9fXt8Ry/O7/j76/+5oee20eL22ysbEGBJlWvjuaTh9mFJ3ljx49wpdffomTJ08iJycHr732GqKjo9GsWTMAL26TX7ZsGX788UdkZmaiefPmiIqKQqNGjVTbePr0KebNm4ejR4/C0tISnTt3xqxZs1C1alWdxCyTyZCYmFjkutzcXCQnJ0MqlcLGxkZr+/Tz89PoCkhZeXh4YOXKlejYsaPWt01EREREROpKakvoCtsS5ufNN9/Evn371JbNmDEDTZo0wahRo1CvXj2IxWKcOXMGYWFhAIDbt28jNTUV/v7+AAB/f3/ExMQgIyMDTk5OAIDTp0/Dzs6uzB3fFhYWGt9JWdnb27a2tnq7q9TGxgawtIRIpL0OVpGlqMLbE1laQmRjY5A7y/V5/LXFxsYGno2boHUzf61+lhUlsrQEyvA58rtvuO9+acdeF8dLGywtRYBCO98dTaZgAYygs/zZs2cYPHgwWrdujdWrV6NGjRq4c+eO2lO6V69ejY0bN2LhwoVwcXHBN998g5EjR+LAgQOq4WSTJ09GWloa1q1bB7lcjpkzZ2L27Nn46quvdBJ3YmIiRo0aBXt7+0LrFAoFcnJyYGtrq7UvWVZWFlavXo2WLVuW6X1paWmIiYnBsWPH8OjRIzg5OcHLywsjRoxAYGCgVmJ72fnz5zFq1CicOHFC5z98mUyGAQMG4Nq1a9izZ49er0gTEREREZVXSW0JXTCVtsTZs2cxfPhwnDt3rtA82tomk8kwePDgSt2WsLOzg7u7u9oyiUSC6tWrq5b369cPCxcuRLVq1WBnZ4f58+cjICBA1VkeHBwMNzc3TJ06FVOmTEFaWhqWLl2K8PBwnXQsF2B7u2h6/Y3I5RgwYyKupSRjz5fL4NW4iU73R0RkLAzeWb569WrUrVsXCxYsUC1r0KCB6v8FQcCGDRvw0Ucfqa7EfvHFFwgKCsLhw4fRrVs33Lp1CydPnsSOHTtUd6N/9tlnGD16NKZOnYo6deroJHZ7e3tUr1690HKFQoEqVarAzs7OoFdk7t27h8GDB8PBwQFTp06Fu7s78vPzER8fjzlz5iAuLs5gsZVGEATVcSzOF198gdq1a+PatWt6jIyIiExxRBgRkbEpri1hLCp7W2Lp0qVsSwCYOXMmLC0tERERAZlMhuDgYERGRqrWi0QixMTEICoqCoMGDYKtrS369OmDiIgIncfG9rbuCIIAhVKJKiUcvy82fo/aNRxxLSVZj5ERERmepaEDOHLkCHx8fBAREYHAwED07t0b27dvV62/d+8e0tLSEBQUpFpmb28PPz8/XLx4EQBw8eJFODg4qBrpABAUFARLS8tC86uZkzlz5sDCwgI//vgjwsLC0LhxYzRt2hTvvfee2jF+2dmzZ+Hh4YHMzEzVsqtXr8LDwwP37t0DANy/fx8ffvghWrZsCX9/f3Tr1g3Hjx/HvXv3MGrUKABAu3bt4OHhgenTpwMAlEolVq1ahdDQUPj6+qJnz55qJw8F+z1+/Dj69u2LZs2a4c8//yy2bsePH8epU6cwbdq0Ch8nIiLSXMGIMLFYjNWrV+Pnn3/GtGnTihwRFhUVhe3bt8PW1hYjR45EXl6eqszkyZORlJSEdevWISYmBufPn8fs2bMNUSUiIiqCIdoSw4cPBwC0bNlSp22JhIQE/P7772bZlti4cSNmzZqlem1tbY3IyEj88ccfSEhIwIoVKwrNRe7s7IzVq1cjMTFRddxKuhBhLsrzGzl//jyGDBmCrKws1TJ9/Eb+Tr4Nj/7dcfzCefSdOgHNBvfBn1eLf1Dk8QvncSrxIqYNH1nh40REZGoMnuH++ecfbN26Fe+99x4+/PBDXL58GfPnz4dYLEafPn2QlpYGAKr50Qo4OTmpHiySnp4OR0dHtfVVqlRBtWrVVO/XhCAIkEqlGpXNzc2FQqGAQqEotK5gWVHrykuhUCA3N1fj+J49e4aTJ09i7NixAFDofVWqVFFblpeXB6lUqurIyMnJUZ0A5ebmqv4rlUoRGRkJuVyONWvWwNbWFrdv34alpSWqVauG6OhozJw5E7GxsXBycoK1tTWkUinWrFmDAwcOYObMmWjYsCEuXLiAKVOmQCKR4I033lDt98svv8TEiRPh7OwMBweHIuubkZGBzz77DF9//bVqWVmOjS7l5OSo/decmHPdAfOuv77qnpubC7FSqdW/rdqgVL6IRxv1FwRB43nUDMWUR4QREZFmnj59ipMnT+LTTz8tcmrFikz/MHfuXMjlcmzatAkSiQRJSUmQSCSoV68eli9fjvHjxyMuLg52dnaq+ahXrVqFvXv3Ys6cOWjUqBHOnTuHKVOmwNHREa1atVJt+6uvvsK0adPQoEGDYmPMyMjAmjVrsGzZMq3Od03mxZR+Iy+fW361eT2mDR+JBnXqwqFq0Q/xS3/6Lz6PWY6VUz+Dzf9Pe0tEZE4M3lkuCAJ8fHwwceJEAMDrr7+OmzdvIjY2Fn369NFrLHK5HFevXtWobHJyslqHclG02XGUk5OD5ORkjecBT0pKgiAIEIvFGtXpn3/+wdWrV3Hnzh0AwPXr11VD4VNSUlTbfPbsGVJSUtCqVSvk5+cjKytLdefBjRs3VB3WWVlZUCqVAKDqWJ8xYwZq1KiBrKwsNG3aFEFBQVi3bh2qVq2q2m+PHj1QrVo1ZGdnIzs7G6mpqWpxCoKAL774AiEhIbC0tFQ9yfb27duq/RmDgmNmjsy57oB511/XdU9OToZLnoCs7Gyd7qescnPzAGsLrdVfl/N/asORI0cQHByMiIgInDt3DnXq1MGQIUMwcOBAAKWPCOvWrVupI8I6deqk93oREdH/3L17F4IgoEkT7c9RnJqairCwMHh4eABQv+BaMErJyclJ1dkok8mwatUqrFu3DgEBAar3/Pnnn9i2bZtaZ3lERATatGlT7L4FQcDs2bPRoUMHeHt748mTJ1qvH5kHU/qNvPPOO6r3RwwaijZ+AcXuWxAETF+xFO907oJmbk1x7/EjrdePiMjYGbyzvFatWnB1dVVb1qRJExw6dEi1HnhxB0Dt2rVVZTIyMuDp6QkAqFmzZqETnfz8fDx79qzQELKSiMVijZ/oLZVKYWtrCzu7wldjdfHAkfz8fDRu3FjjB8/k5+cDAFxcXDR6T4MGDeDl5YXnz58DePHE7oKHqVhavpitx83NDfXr18e7776LBQsWICkpCa1bt0aHDh1UD4gp+BwaNmyo+rxu3bqFvLw8LFq0SG2fcrkcnp6eavsNCwtT+5xftWXLFlhYWGDatGkQiUSqk4UmTZqoTiYMKScnBykpKWjUqBFsbW0NHY5emXPdAfOuv77qLpVKIb77EPZF/N01JBsba0CQaaX+BRcAjZmpjggrSW5uLkQAFErjGrWgUCoh1/HIKXMeFQOYd/3Nue6A4etf0ihVXXh5lKomdS9YVzD6tDRlGaU6aNAgLFiwACdOnCjUlijq/bdu3UJOTg7ee+89tX0WtCVe3q+bm1uJ8W7ZsgXZ2dno1asXcnJyCsVWVqYwIox0QxAEnW17+PDhiIqKQnx8PIKCgtC5c2dV30dR7ty5g5ycHLz//vtqy+VyeaG+gGauJfd3bDywD89zczCmz4DyV4CIyMQZvLO8efPmSE5Wf2BESkoKnJ2dAbzo7K1VqxbOnDmj+kOfnZ2NxMREDB48GAAQEBCAzMxM/PXXX/Dx8QEA/P7771AqlfD19dU4FgsLC43v3LaxsYFIJCqxM7y09WUhEolgY2OjcXweHh6wsLDA/fv3NXqPtbU1JBKJqqPn5X0V1KFg2dChQ9GhQwccO3YMp06dwtChQzFt2jQMGzYM1v8/TMvW1lb1/oI7vr/77rtCQ+utrKwgkUhU73N0dCwx3gsXLuDSpUto3bq12vLw8HD06NGjUIe8obxcf3NjznUHzLv+uq67jY0NYGlp0Ac5FcXSUgQotFN/U2hwm+qIsJI8ePAALgCkUuPqNJRKpbhXhlFlFWHOo2IA866/OdcdMFz9NRmlqk1FjVItqe65ubmwsLDAH3/8gXr16pW6/YJRqnfv3gUAXLt2TXVT0a1btwD8b5Sql5cXlixZgosXL+LixYtYu3Ythg4dirCwsCJHuRZcSJ48eTJq1Kihtt+CUbQF77t79y4yMjKKjfPIkSO4fPmyat7nAkOGDEGbNm3w0UcflVrXVxn7iDDSjddeew0WFha4fft2md5XcK73cme7XC5XKzNgwAAEBwer2tvfffedqr1dlIILPatWrSqyvV0wFzoA2JYy9dDvf11Cwo1raDZY/Zyu37RP0KPtW4j+eEIpNSQiMn0G7ywfMWIEBg8ejJiYGHTp0gWXLl3C9u3bMXfuXAAvksnw4cPx3//+F6+99hpcXFzwzTffoHbt2qq5UF1dXdG2bVt8/vnnmDNnDuRyOebNm4du3bqZ7byn1atXR3BwMDZv3oxhw4YVamhnZmYWOY9awZ1+aWlpqru2i3pCfL169TB48GAMHjwYX331FbZv345hw4apTvhfvkvG1dUVVlZWSE1NVRsmWR6fffYZPvnkE9Xrx48fY+TIkViyZAn8/PwqtG0iIiqdqY4IK8mTJ0+AlFRIJLYQWRrPxRiJRILGDetqPKqsPMx5VAxg3vU357oDhq9/SaNUdeHlUaqa1j0wMBBHjx7FhAkTCpXLyspSjUIF/jdK9eUbYApyRcFFzYJRqgWCg4MBAMuWLUN8fDw++eQTtTvEq1evDuDFiNX58+fDysoK7du3LzLWokbHFmXevHnIyMhAamoq6tevj6ysLHz88cdYtGgRmjVrVua2oymMCCPdKG97u+CCT1paGurWrQugbO1tsVgMoGzt7Zc7y0vz2fuj8cngoarXj588wcj5s7Fk4jT4NTX8SG4iIn0weGe5r68vVqxYga+//horV66Ei4sLZs6ciZ49e6rKjBo1Cjk5OZg9ezYyMzPRokULrFmzRnUyBgCLFy/GvHnzMGLECFhaWqJz58747LPPDFEloxEZGYnBgwdjwIABiIiIgIeHBxQKBU6dOoWtW7fi4MGDhd7TsGFD1YNDPv30U6SkpOD7779XK/Of//wH7dq1Q6NGjZCZmYmzZ8+qTobr168PCwsLnDhxAp07d4a1tTXs7Ozw/vvvY8GCBRAEAS1atEBWVhYuXLgAOzu7Mt2J+PIJNgDVSUnDhg1VJxtERKQ7pjoirCQ2NjaQAxBZam9EmDaILC0hKsOosoow51ExgHnX35zrDhiu/pqMUtWmokapllb3uXPnYvDgwRg+fHipbYmCUaoeHh6oV68e1qxZo2pLbNq0CcD/Rqm+2pa4cOEC3NzcIJFI4OrqCgsLC5w9exYhISGwtrZGrVq18P777+Orr76CWCwusi1R1OjWori6uqJevXoQiUTw8vJSXbht2rQpGjduXObjagojwkh3ytPebtCgAZycnLBq1SpMnjy5zO1tZ2dnWFhY4NixY6rfSGntbRcXF43rVL+W+pSoEpsXF8oa1qmHuk419TZ1FBGRIRm8sxwA2rdvX+xdAsCLk5AJEyZgwoTih/xUr14dX331lS7CK1ZWVlaRywvmLM/Pz9faCXBx+ypJgwYNsGvXLsTExGDRokV4/PgxHB0d4e3tjaioqCLfIxaL8dVXXyEqKgo9e/ZEs2bN8Mknn6gde6VSiblz5+Lhw4ews7ND27ZtMWPGDABA7dq10a9fPyxfvhxRUVHo3bs3Fi5ciE8++QSOjo5YtWoV7t27B3t7e7z++uv48MMPy3U8iIjIMDgijIhIO8pzfq/PfRmiLVGnTh2MHz8eX331FWbMmMG2hJmrrO3tcePGYcuWLfyNEBEZKQtBl0+mMCGXL18GADRr1kyj8jKZDImJiUWuy83NRXJyMho3bvxijl0t8fPzM/o58aRSKa5evQovLy+zvEvJnOtvznUHzLv++qr7uXPngKS7aOmj+Z3H+vD7pQTcUOSgf//+Fa5/WXORoRw9ehRff/01UlJS4OLigvfeew8DBw5UrRcEAcuWLcP27dtVI8IiIyPV7tp7+vQp5s2bhyNHjqiNCCuYo7Y02jxWJ0+ehPzabYS0bG1Ud5af++sS4NYQLVu21Nk+zPlvF2De9TfnugOGr39JbQldKWhLGLruhqbN+ptK3jYGbG+/YIjfnzbPoRUKBbKys2FvZ1fhcyZ9nOe8ypT//pn6+Sq/+y/FpefvvqbHnu3t/zGKO8tNkZWVVbFfbKlUColEYpJ/gOl/ytOIKThxk0qlWj1xe5UpXDghosrLVEeEEREZi5LaEkTE9jYRERkOO8uJipGYmIgrh47A21Xzh8eJlUq45AkQ330IWFrqJK4rt148SIgNLCIiIiIiIiIiIu1hZzlRCbxd3co0BEWbQ3OIiIiIiIiIiIhIf3Rz6ysRERERERERERERkQlhZzkRERERERERERERmT12lhMRERERERERERGR2WNnORERERERERERERGZPXaWExEREREREREREZHZq2LoAIiIiIiIiIiINCGTyXDlyhVIpVLY2NgYOhw1fn5+sLKyMnQYRERUAewsJyIiIiIiIiKTcPnyZWRfuwWxyBawNJ7B8lduJQEAWrZsaeBIiIioIthZTkREREREREQmw7NxE7T0bgaRSGToUIiIqJIxnsuwREREREREREREREQGws5yIiIiIiIiIiIiIjJ77CwnIiIiIiIiIiIiIrPHOcuJiIiIiMgoyGQyJCYm6nw/ubm5SE5OhlQqhY2Njcbv8/Pzg5WVlQ4jIyIiIiJDYmc5EREREREZhcTERFw5dATerm463Y9YqYRLngDx3YeApWaDba/cSgIAtGzZUpehEREREZEBsbOciIiIiIiMhrerG1r6+Op0HwqFAlnZ2bC3s4NIJNLpvoiIiIjIdHDOciIiIiIiIiIiIiIye+wsJyIiIiIiIiIiIiKzx85yIiIiIiIiIiIiIjJ77CwnIiIiIiIiIiIiIrPHznIiIiIiIiIiIiIiMnvsLCciIiIiIiIiIiIis2fwzvLly5fDw8ND7d/bb7+tWp+Xl4c5c+agdevWCAgIwPjx45Genq62jdTUVIwePRp+fn4IDAzEokWLkJ+fr++qEBEREREREREREZGJqmLoAACgadOmWLduneq1SCRS/X90dDSOHz+OpUuXwt7eHvPmzcO4ceMQGxsLAFAoFBgzZgxq1qyJ2NhYPH78GNOmTYNYLMbEiRP1XhciIiIiIiIiIiIiMj0Gv7MceNE5XqtWLdU/R0dHAEBWVhZ27tyJ6dOnIzAwED4+PoiOjsbFixeRkJAAAIiPj0dSUhK+/PJLeHl5ISQkBBMmTMDmzZshk8kMWCsiIqLKhyPCiIiIiIiIqLIyis7yO3fuIDg4GB06dMCkSZOQmpoKAPjrr78gl8sRFBSkKuvq6or69eurOssTEhLg7u6OmjVrqsoEBwcjOzsbSUlJeq0HERGROWjatCni4+NV/7Zs2aJaFx0djaNHj2Lp0qXYuHEjHj9+jHHjxqnWF4wIk8vliI2NxcKFC7F7924sW7bMEFUhIiIiIiIiUjH4NCy+vr5YsGABGjdujLS0NKxcuRLh4eHYt28f0tPTIRaL4eDgoPYeJycnpKWlAQDS09PVOsoBqF4XlNGUIAiQSqUVqM0LOTk5av81J5Wp7rm5uRArlVAoFBq/R6FUqP1XFxRKJeS5uVr5rmpTZfrsy8Oc66+vupfnN6kPyv//vWuj/oIgwMLCosLb0bWCEWGvKhgRtnjxYgQGBgJ40XnetWtXJCQkwN/fXzUibN26dahZsya8vLwwYcIELF68GOPGjYOVlZW+q0NEREREREQEwAg6y0NCQlT/7+npCT8/P7Rv3x4HDx6EjY2NXmORy+W4evWq1raXkpKitW2ZmspQ9+TkZLjkCcjKzi7ze6VS3XUaSqVS3EtOhkQi0dk+KqIyfPYVYc7113XdK/Kb1KXc3DzA2kJr9TeFzuKCEWHW1tbw9/fHpEmTUL9+/VJHhPn7+xc7IiwqKgpJSUl4/fXXDVElIiIiIiIiIsN3lr/KwcEBjRo1wt27dxEUFAS5XI7MzEy1u8szMjJUd7TVrFkTly5dUttGwdyoRd31VhKxWAw3N7cK1uDF3YUpKSlo1KgRbG1tK7w9U1KZ6i6VSiG++xD2dnYav0ehVEAqzYFEYguRpaj0N5SDRCJB44Z14eXlpZPtl1dl+uzLw5zrr6+6l+c3qQ82NtaAINNK/U1h+rDKOCIsNzcXIuh2VFB56GMkkTmPigHMu/7GWnd9jSIqz2hAYx3dV1bG+tnrizbrbyojwoiIiEhzRtdZ/vz5c/zzzz+oVasWfHx8IBaLcebMGYSFhQEAbt++jdTUVPj7+wMA/P39ERMTg4yMDDg5OQEATp8+DTs7uzJ3fFtYWGj1bl1bW1ujvftX1ypD3W1sbABLS4hEZe/0FlmKyvU+zbZtCZGNjdEe38rw2VeEOddf13WvyG9SlywtRYBCO/U3hQZ3ZRwR9uDBA7hAt6OCykOfI4nMeVQMYN71N7a663sUUVl+98Y+uq+sjO2z1zdzGhFGREREmjN4Z/miRYvQvn171K9fH48fP8by5cthaWmJ7t27w97eHv369cPChQtRrVo12NnZYf78+QgICFB1lgcHB8PNzQ1Tp07FlClTkJaWhqVLlyI8PJwnLkRERDpWGUaEPXnyBEhJ1emooPLQx0gicx4VA5h3/Y217voaRVSe0YDGOrqvrIz1s9cXbdbfFEaEERERUdkYvLP84cOHmDhxIp4+fQpHR0e0aNEC27dvh6OjIwBg5syZsLS0REREBGQyGYKDgxEZGal6v0gkQkxMDKKiojBo0CDY2tqiT58+iIiIMFSViIiIzEZlGBFmY2MDOXQ7Kqg89DmSyJxHxQDmXX9jq7u+RxGV5Xdv7KP7ysrYPnt9M5cRYURERFQ2Bu8sX7JkSYnrra2tERkZqdZB/ipnZ2esXr1a26ERERHRKzgijIiIiIiIiCorg3eWExERkengiDAiIiIiIiKqrNhZTkRERBrjiDAiIiIiIiKqrCwNHQARERERERERad+WLVvQo0cPNG/eHM2bN8egQYNw/Phx1fq8vDzMmTMHrVu3RkBAAMaPH6968HaB1NRUjB49Gn5+fggMDMSiRYuQn5+v76oQERHpBTvLiYiIiIiIiCqhunXrYvLkydi1axd27tyJN998E2PHjsXNmzcBANHR0Th69CiWLl2KjRs34vHjxxg3bpzq/QqFAmPGjIFcLkdsbCwWLlyI3bt3Y9myZYaqEhERkU6xs5yIiIiIiIioEgoNDUVISAgaNWqExo0b49NPP4VEIkFCQgKysrKwc+dOTJ8+HYGBgfDx8UF0dDQuXryIhIQEAEB8fDySkpLw5ZdfwsvLCyEhIZgwYQI2b94MmUxm2MoRERHpAOcsJyIiIiIiIqrkFAoF4uLiIJVKERAQgL/++gtyuRxBQUGqMq6urqhfvz4SEhLg7++PhIQEuLu7o2bNmqoywcHBiIqKQlJSEl5//XWN9y8IAqRSaYXrkZubCxEAhVJR4W1pk0KphDw3t8Q65uTkqP1XH3JzcyFWKqFQVPx4FRxzbRx7TY6Xthni+GuLKX/vAX731bel3+++psdem8dLm5T/f8y18d0RBAEWFhallmNnOREREREREWmVTCZDYmKiocMoJDc3F2Kx2NBh6NX169fxzjvvIC8vDxKJBCtXroSbmxuuXr0KsVgMBwcHtfJOTk5IS0sDAKSnp6t1lANQvS4ooym5XI6rV69WoCYvPHjwAC4ApFLj6vCUSqW4l5wMiURSatmUlBTdB/T/kpOT4ZInICs7W2vb1MaxL8vx0jZ9Hn9tqQzfe4Df/RfbMMx3v7Rjr4vjpQ25uXmAtYXWvjtWVlallmFnOREREREREWlVYmIirhw6Am9XN0OHoubmzRuo0sgZvr6+hg5Fbxo3bow9e/YgKysLhw4dwrRp07Bp0ya9xyEWi+HmVvHvw5MnT4CUVEgkthBZirQQmXZIJBI0blgXXl5exZbJyclBSkoKGjVqBFtbW73EJZVKIb77EPZ2dhXelkKpgFSao5Vjr8nx0jZDHH9tMeXvPcDv/sv0/d3X9Nhr83hpk42NNSDItPLdSUpK0qgcO8uJiIiIiIhI67xd3dDSx7g6pRVKJW4ojOvOTF2zsrLCa6+9BgDw8fHB5cuXsWHDBnTp0gVyuRyZmZlqd5dnZGSgVq1aAF7cRX7p0iW17aWnpwOAqoymLCwstHInpY2NDeQARJYiiETG02kosrSEyMZGozra2trq7a5SGxsbwNJSq8dKG8e+LMdL2/R5/LWlMnzvAX73X2zDMN/90o69Lo6XNlhaigCFdr47mkzBAvABn0RERERERERmQ6lUQiaTwcfHB2KxGGfOnFGtu337NlJTU+Hv7w8A8Pf3x40bN5CRkaEqc/r0adjZ2WnlLnEiIiJjwzvLiYiIiIiIiCqhr776Cu3atUO9evXw/Plz7N+/H3/88QfWrl0Le3t79OvXDwsXLkS1atVgZ2eH+fPnIyAgQNVZHhwcDDc3N0ydOhVTpkxBWloali5divDwcI3mfSUiIjI17CwnIiIiIiIiqoQyMjIwbdo0PH78GPb29vDw8MDatWvRpk0bAMDMmTNhaWmJiIgIyGQyBAcHIzIyUvV+kUiEmJgYREVFYdCgQbC1tUWfPn0QERFhqCoRERHpFDvLiYiIiIiIiCqh6OjoEtdbW1sjMjJSrYP8Vc7Ozli9erW2QyMiIjJK5ZqzvEOHDrh27VqR627cuIEOHTpUKCgiIiLSHuZtIiIi08G8TUREZDjl6iy/f/8+ZDJZketyc3Px8OHDCgVFRERE2sO8TUREZDqYt4mIiAxH42lY8vLykJOTA0EQAADZ2dl4+vRpoTKHDx9G7dq1tRokERERlQ3zNhERkelg3iYiIjIOGneWr169GitXrgQAWFhYYOTIkcWWHTduXMUjIyIionJj3iYiIjIdzNtERETGQePO8o4dO8LZ2RmCIGDmzJn46KOP0LBhQ7UyYrEYrq6u8PLy0nqgREREpDnmbSIiItPBvE1ERGQcNO4s9/T0hKenJ4AXV7pDQkLg6Oios8CIiIio/Ji3iYiITAfzNhERkXHQuLP8ZX369NF2HERERKQjzNtERESmg3mbiIjIcMrVWZ6bm4tvv/0Whw4dwsOHD4t8UvfVq1crHJypkslkuHLlCqRSKWxsbAwdjho/Pz9YWVkZOgwiItIj5m0iIiLTwbxNRERkOOXqLJ8zZw7279+P7t27w9XVFWKxWNtxmbTLly8j+9otiEW2gKWlocNRuXIrCQDQsmVLA0dCRET6xLxNRERkOpi3iYiIDKdcneVHjx7FtGnTMHToUG3HU2l4Nm6Clt7NIBKJDB0KERGZOeZtIiIi08G8TUREZDjluu1ZJBKhUaNGWg6FiIiIdIF5m4iIyHQwbxMRERlOuTrLBw8ejJ9++knbsRAREZEOMG8TERGZDuZtIiIiwynXNCw2Njb4888/8c477yAwMBAODg5q6y0sLPDuu++WebvfffcdvvrqKwwfPhyzZs0CAOTl5WHhwoU4cOAAZDIZgoODERkZiZo1a6rel5qaiqioKJw9exYSiQS9e/fGpEmTUKVKuapHRERUqegqbxMREZH2MW8TEREZTrl6kxcvXgzgRSd1QkJCofXlSd6XLl1CbGwsPDw81JZHR0fj+PHjWLp0Kezt7TFv3jyMGzcOsbGxAACFQoExY8agZs2aiI2NxePHjzFt2jSIxWJMnDixPNUjIiKqVHSRtwFe5CYiItIFXeVtIiIiKl25WqXXrl3TahDPnz/HlClTMH/+fPz3v/9VLc/KysLOnTuxePFiBAYGAnjRed61a1ckJCTA398f8fHxSEpKwrp161CzZk14eXlhwoQJWLx4McaNGwcrKyutxkpERGRqtJ23AV7kJiIi0hVd5G0iIiLSjFHcwjV37lyEhIQgKChIrbP8r7/+glwuR1BQkGqZq6sr6tevr+osT0hIgLu7u9oda8HBwYiKikJSUhJef/11jeMQBAFSqbTC9cnNzYUIgEKpqPC2tEmhVEKem6uVOhYnJydH7b+mLDc3F2KlEgqF5p9jwWeuy89eH59jeVSmz748zLn++qp7eX6T+qD8/9+7NuovCAIsLCwqvB1d40VuIiIiIiIiqozK1Vl+7ty5Usu0bNlSo239/PPP+Pvvv7Fjx45C69LT0yEWiwvN0ebk5IS0tDRVmZc7ygGoXheU0ZRcLsfVq1fL9J6iPHjwAC4ApFLj6jSTSqW4l5wMiUSi832lpKTofB+6lpycDJc8AVnZ2WV+ry4/e31+juVRGT77ijDn+uu67hX5TepSbm4eYG2htfrrorNYm3kbMJ6L3ERERJWRtvM2ERERaa5cneXDhg2DhYUFBEFQLXv1TjhNOp0fPHiA//znP/j+++9hbW1dnlC0SiwWw83NrcLbefLkCZCSConEFiJLkRYi0w6JRILGDevCy8tLZ/vIyclBSkoKGjVqBFtbW53tRx+kUinEdx/C3s5O4/colApIpTk6/ez18TmWR2X67MvDnOuvr7qX5zepDzY21oAg00r9k5KStBSVOm3lbcC4LnJzRFjFmfOoGMC862+sddfXKKLyjAY01tF9ZcURYcY/IkybeZuIiIjKplyd5Xv27Cm07NmzZ4iPj8cvv/yCOXPmaLSdK1euICMjA3379lUtUygUOHfuHDZv3oy1a9dCLpcjMzNTreGdkZGBWrVqAXjRwL506ZLadtPT0wFAVUZTFhYWWrlb18bGBnIAIksRRCLj6SwXWVpCZGOjlzuSbW1tjfbOZ03Z2NgAlpbl+gx1+dnr83Msj8rw2VeEOddf13WvyG9SlywtRYBCO/XX1RQs2srbxnaRmyPCtMecR8UA5l1/Y6u7vkcRleV3b+yj+8qKI8JStLI9XYwI01beJiIiorIrV2e5p6dnkctbt24NGxsbbNu2DW+++Wap23nzzTexb98+tWUzZsxAkyZNMGrUKNSrVw9isRhnzpxBWFgYAOD27dtITU2Fv78/AMDf3x8xMTHIyMiAk5MTAOD06dOws7PTyl3iREREpk5bedvYLnJzRFjFmfOoGMC862+sddfXKKLyjAY01tF9ZcURYcY/IkxbeZuIiIjKTusP+GzevDnWrl2rUVk7Ozu4u7urLZNIJKhevbpqeb9+/bBw4UJUq1YNdnZ2mD9/PgICAlSd5cHBwXBzc8PUqVMxZcoUpKWlYenSpQgPD+dDwoiIiEpRlrxtbBe5OSJMe8x5VAxg3vU3trrrexRRWX73xj66r6w4Isx4R4SVpCx5m4iIiMpO653lhw8fRvXq1bW2vZkzZ8LS0hIRERGQyWQIDg5GZGSkar1IJEJMTAyioqIwaNAg2Nraok+fPoiIiNBaDERERJVVWfI2L3ITEREZlrbb20RERKSuXJ3lH374YaFlcrkcycnJePDgAaZMmVLugDZu3Kj22traGpGRkWod5K9ydnbG6tWry71PIiKiykyXeftVvMhNRERUMfrM20RERKSuXJ3lz58/L7TM2toaQUFBCAsLQ9u2bSscGBEREWmHLvM2L3ITERFpF9vbREREhlOuzvJXG8ZERERkvJi3iYiITAfzNhERkeFYVnQDubm5ePz4MXJzc7URDxEREekQ8zYREZHpYN4mIiLSr3I/4PPo0aNYsWIFrl69CkEQYGFhAS8vL0RERCAkJESbMRIREVEFMW8TERGZDuZtIiIiwyjXneWHDx/Gxx9/DLFYjOnTp+Orr77CtGnTYGVlhY8++giHDx/WdpxERERUTszbREREpoN5m4iIyHDKdWf5ihUr0K1bNyxevFht+YgRIzB58mSsWLECHTt21EqAREREVDHM20RERKaDeZuIiMhwynVn+e3bt9G7d+8i1/Xq1Qu3b9+uSExERESkRczbREREpoN5m4iIyHDK1VlerVo1JCcnF7kuOTkZ1apVq1BQREREpD3M20RERKaDeZuIiMhwyjUNS9euXfH111/DxsYGYWFhcHBwQFZWFuLi4rB06VIMHDhQ23ESERFROTFvExERmQ7mbSIiIsMpV2f5pEmTkJqais8//xyzZ89GlSpVkJ+fD0EQ0LlzZ0ycOFHbcRIREVE5MW8TERGZDuZtIiIiwylXZ7mVlRWWL1+O69ev4/z588jMzES1atXQokULeHh4aDtGIiIiqgDmbSIiItPBvE3aJJfLkZuVjadPn1Z4WwqlElKpFAqFAiLLcs3qq5KVlQ0bubzCMRERaZvGneUpKSmYOHEiJkyYgJCQEACAh4eHWrI+fvw4ZsyYgW+++QYNGjTQfrRERGUkk8mQmJio8/3k5uYiOTkZUqkUNjY2Gr/Pz88PVlZWOoyMzBXzNhERkelg3iZduXnzJnKTbsJaqPi2BEF40VEuEsHCwqJicd26CRtrICgoqOKBERFpkcad5d9//z0kEokqcRclJCQEa9aswdq1axEVFaWN+IiIKiQxMRFXDh2Bt6ubTvcjVirhkidAfPchoOFdFlduJQEAWrZsqcvQyEwxbxMREZkO5m3SJZFIBLG4XBMLqBEEwMLCAlWqVEEF+8ohEokqHA8RkS5o/Nfy1KlTGDduXKnl+vXrhxUrVlQoKCIibfJ2dUNLH1+d7kOhUCArOxv2dnY88SOjwLxNRERkOpi3iYiIjIPGk0w9evRIo6FeLi4uePToUYWCIiIiooph3iYiIjIdzNtERETGQePO8qpVq+Lff/8ttdzTp08hkUgqFBQRERFVDPM2ERGR6WDeJiIiMg4ad5b7+PjgwIEDpZb7+eef4ePjU6GgiIiIqGKYt4mIiEwH8zYREZFx0LizfMiQITh48CBWrFgBhUJRaL1SqcSKFSsQFxeH8PBwrQZJREREZcO8TUREZDqYt4mIiIyDxg/47NChAz744AOsWLECsbGxCAwMRP369QEADx48wJkzZ5Ceno6RI0ciNDRUZwETERFR6Zi3ydzIZDIkJiaW6T25ublITk6GVCqFjY2NjiID/Pz8YGVlpbPtE5HpY94mIiIyDhp3lgPA5MmT0bJlS3z//fc4dOgQZDIZAMDa2hrNmzfH/PnzERISopNAiYiIqGyYt8mcJCYm4sqhI/B2ddP4PWKlEi55AsR3HwKWGg+4LJMrt5IAAC1bttTJ9omo8mDeJiIiMrwydZYDQEhICEJCQqBQKPD06VMAQPXq1SESibQdGxEREVUQ8zaZE29XN7T08dW4vEKhQFZ2Nuzt7PibICKjwLxNRERkWOW+hUYkEsHJyQlOTk5M3EREREaOeZuIiMh0aCtvr1q1Cv369UNAQAACAwPx8ccf4/bt22pl8vLyMGfOHLRu3RoBAQEYP3480tPT1cqkpqZi9OjR8PPzQ2BgIBYtWoT8/Pxyx0VERGSsdDPelIiIiIiIiIgM6o8//kB4eDi2b9+OdevWIT8/HyNHjoRUKlWViY6OxtGjR7F06VJs3LgRjx8/xrhx41TrFQoFxowZA7lcjtjYWCxcuBC7d+/GsmXLDFElIiIinWJnOREREREREVEltHbtWvTt2xdNmzaFp6cnFi5ciNTUVFy5cgUAkJWVhZ07d2L69OkIDAyEj48PoqOjcfHiRSQkJAAA4uPjkZSUhC+//BJeXl4ICQnBhAkTsHnzZtW86kRERJVFmecs17YtW7Zg69atuH//PgCgadOm+Pjjj1UPLsnLy8PChQtx4MAByGQyBAcHIzIyEjVr1lRtIzU1FVFRUTh79iwkEgl69+6NSZMmoUoVg1ePiIiIiIiIyChkZWUBAKpVqwYA+OuvvyCXyxEUFKQq4+rqivr16yMhIQH+/v5ISEiAu7u7Whs8ODgYUVFRSEpKwuuvv67RvgVBULujvbxyc3MhAqBQKiq8LW1SKJWQ5+aWWMecnBy1/+pDXl4eLAQBglDxbQn/v5EX/7Wo8Lby8vK08p3QlCGOv7aY8vceMMyxz83NhViphEJR8WNWcNy1cfw1PWbaoumx1+bx0ibl/x9zbXx3BEGAhUXpf7sM3ptct25dTJ48Ga+99hoEQcCePXswduxY7N69G02bNkV0dDSOHz+OpUuXwt7eHvPmzcO4ceMQGxsL4H9DwmrWrInY2Fg8fvwY06ZNg1gsxsSJEw1cOyIiosqFF7mJiIhMk1KpRHR0NJo3bw53d3cAQHp6OsRiMRwcHNTKOjk5IS0tTVXm5TwOQPW6oIwm5HI5rl69WpEqAAAePHgAFwBSqXF1eEqlUtxLToZEIim1bEpKiu4D+n+pqamop1RqdY55bXSmKZVKPEhN1cp3oqz0efy1pTJ87wH9Hvvk5GS45AnIys7W2ja1cfzLesy0pbRjr4vjpQ25uXmAtYXWvjtWVlalljF4qzQ0NFTt9aeffoqtW7ciISEBdevWxc6dO7F48WIEBgYCeDGfWteuXVVXuQuGhK1btw41a9aEl5cXJkyYgMWLF2PcuHEaHQQiIiLSDC9yExERmaY5c+bg5s2b2LJli0H2LxaL4ebmVuHtPHnyBEhJhURiC5Gl8Ty0XCKRoHHDuvDy8iq2TE5ODlJSUtCoUSPY2trqJa4///wTFrfvaeWmBEEQoFAoIBKJNLo7sySWlpaoX79+icdL2wxx/LXFlL/3gGGOvVQqhfjuQ9jb2VV4WwqlAlJpjlaOv6bHTFs0PfbaPF7aZGNjDQgyrXx3kpKSNCpn8M7ylykUCsTFxUEqlSIgIECvQ8KIiIiodLzITUREZHrmzp2LY8eOYdOmTahbt65qec2aNSGXy5GZmal2d3lGRgZq1aqlKnPp0iW17aWnpwOAqowmLCwstHInpY2NDeQARJYiiETG02kosrSEyMZGozra2trq7a5Sa2tryC0sUMG+7f/3YiMWWtiehYUFrKyt9X53LaDf468tleF7D+j32NvY2ACWllo9Xto4/mU9ZtpS2rHXxfHSBktLEaDQzndH04t8RtFZfv36dbzzzjvIy8uDRCLBypUr4ebmhqtXr+ptSBjAOdS0wZTnAHtVeeZr0uY8VsXvQ7/zW2nKWD97fc27VZ7P3lg/y7LS12fPOdSMDy9yExERGTdBEDBv3jz8+uuv2LhxIxo0aKC23sfHB2KxGGfOnEFYWBgA4Pbt20hNTYW/vz8AwN/fHzExMcjIyICTkxMA4PTp07Czs9PKneJERETGxCg6yxs3bow9e/YgKysLhw4dwrRp07Bp0ya9x8E51LTHFOcAe1VF5mvS5WdvqPmtNGVsn72+590qy2dv7J9lWen6s+ccasaDF7n1gxe5y4YXucvGWD97XuTWPV7kNq+L3HPmzMH+/fvx7bffomrVqqpca29vDxsbG9jb26Nfv35YuHAhqlWrBjs7O8yfPx8BAQGqzvLg4GC4ublh6tSpmDJlCtLS0rB06VKEh4ebxHkLERFRWRhFZ7mVlRVee+01AC+ubF++fBkbNmxAly5d9DYkDOAcatpgynOAvao88zVpcx6r4uh7fitNGetnr695t8rz2RvrZ1lW+vrsOYea8eBFbv3gRe6y4UXu8jG2z54XufWHF7lTtLI9Y+8s3rp1KwBg2LBhassXLFiAvn37AgBmzpwJS0tLREREqD2cu4BIJEJMTAyioqIwaNAg2Nraok+fPoiIiNBfRYiIiPTEKDrLX6VUKiGTyfQ+JIxzqGmPKc4B9qqKzNeky8/eUPNbacrYPnt9z7tVls/e2D/LstL1Z8851IwHL3LrBy9ylw0vcpeNsX72vMite7zIbV4Xua9fv15qGWtra0RGRqp1kL/K2dkZq1ev1mZoRERERsngneVfffUV2rVrh3r16uH58+fYv38//vjjD6xdu5ZDwoiIiEwAL3LrBi9ylw0vcpePsX32vMitP7zIbT4XuYmIiEhzBu8sz8jIwLRp0/D48WPY29vDw8MDa9euRZs2bQBwSBgREb0gl8uRm5WNp0+fGjoUNVnZ2cgXC4YOQ294kZuIiIiIiIgqK4N3lkdHR5e4nkPCiIgIAG7evIncpJuwNrJ+6dtJSXhQVWzoMPSGF7mJiIiIiIiosjJ4ZzkREZGmRCIRxGLjSl2WRja8XNd4kZuIiIiIiIgqK0tDB0BEREREREREREREZGjsLCciIiIiIiIiIiIis8fOciIiIiIiIiIiIiIye+wsJyIiIiIiIiIiIiKzx85yIiIiIiIiIiIiIjJ77CwnIiIiIiIiIiIiIrPHznIiIiIiIiIiIiIiMntVDB0AERERERERAMjlcuRmZePp06c63Y9CqYRUKoVCoYDIUrP7h7KysmEjl+s0LiIiIiIyLHaWExERERGRUbh58yZyk27CWtDtfgRBeNFRLhLBwsJCs9hu3YSNNRAUFKTb4IiIiIjIYNhZTkRERERERkMkEkEs1m0zRRAACwsLVKlSBRr2lUMkEuk0JiIiIiIyPM5ZTkRERERERERERERmj53lRERERERERERERGT22FlORERERERERERERGaPneVEREREREREREREZPbYWU5EREREREREREREZo+d5URERERERERERERk9thZTkRERERERERERERmj53l/9fencfHdO//A3/NTCYbESVRVCuUGSE7bSSiEYpa2osIadVeVaUoWtpriy1qaS2hltrb2sp1i5ZbfriqqCpVrboJgjQqC5HEZJnMfH5/pOZrJJGZZPZ5PR8PjzZnzpx5f847mfec9+ecM0RERERERERERETk9NgsJyIiIiIiIiIiIiKnx2Y5ERERERERERERETk9NsuJiIiIiIiIiIiIyOmxWU5ERERERERERERETo/NciIiIiIiIiIiIiJyelZvlq9evRqxsbEIDQ1FREQE3n77bVy9elVvnaKiIiQkJCA8PByhoaF45513kJWVpbdOeno63nzzTQQHByMiIgIfffQRSkpKLDkUIiIiIiIiIiIiIrJTLtYO4Mcff8SAAQMQGBgIjUaDjz/+GMOHD8f+/fvh6ekJAJg3bx6OHTuGJUuWwMvLC7Nnz8aYMWOwbds2AIBGo8HIkSPh4+ODbdu2ISMjA5MnT4ZcLseECROsOTwiIiKHsnr1avznP//B1atX4e7ujtDQUEyaNAlNmzbVrVNUVIT58+fjm2++QXFxMaKiojBjxgz4+Pjo1klPT8fMmTNx+vRpeHp6olevXpg4cSJcXKz+0YSIiIiIyKao1WqoVCrk3LsHmdTq573q5OXlw12ttnYYRCZl9SPSdevW6f08f/58RERE4LfffsNzzz2HvLw87Nq1C4sWLUJERASA0uZ59+7dcf78eYSEhOD7779HSkoKNmzYAB8fH/j7+2PcuHFYtGgRxowZA1dXV2sMjYiIyOFwkpuIiIiIyLJSUlJQcuMmart5QCKRWDscneQryXB3AyIjI60dCpHJ2M501N/y8vIAAN7e3gCAixcvQq1W6/3hPfvss2jYsCHOnz8PADh//jwUCoXeGWtRUVHIz89HSkqK5YInIiJycOvWrUOfPn3QvHlztGjRAvPnz0d6ejp+++03ANBNck+ZMgUREREICAjAvHnzcO7cOV3dfjDJvXDhQvj7+yM6Ohrjxo3DF198geLiYiuOjoiIiIjINslkUsjlcsjlLjbzTyaTWXu3EJmc1c8sf5hWq8W8efMQFhYGhUIBAMjKyoJcLketWrX01q1bty4yMzN16zzcKAeg+/nBOoYQQkClUlVnCACAwsJCyABotJpqb8uUNFot1IWFJhljRQoKCvT+a88KCwsh12qh0Riexwc5N2fuLZHHqrDV3Fclj1VRldzbai6NZancFxUVQSIEhDDryxjv73hMMX4hhE2dKWIIYye5Q0JCKpzknjlzJlJSUtCyZUvLDoKIiIiIiIgINtYsT0hIQHJyMr788kurvL5arcalS5eqvZ1bt26hEQCVyraahiqVCmnXrukukzen1NRUs7+GuV27dg2NigTy8vONfq45c2/JPFaFreW+OnmsCmNyb+u5NJa5c5+eno4GWq3NfXmzVmgBSE02fnu6dRgnuc2Lk9zG4SS3cWw195aaGBV/v0Dpfw2bpBRCoKioyOZyaSxL5d5SJywYS/v337uzTnITERHR49lMs3zWrFk4evQoPv/8c9SvX1+33MfHB2q1Grm5uXoH3tnZ2fD19dWtc+HCBb3tZWVlAYBuHUPI5XI0a9asOsMAANy5cwdITYenpwdkUtu5JMXT0xNNnqkPf39/s71GQUEBUlNT4efnBw8PD7O9jiWoVCrIb/wFr5o1DX6ORquBSlVg1txbIo9VYau5r0oeq6IqubfVXBrLUrk/e/YsJFfTbO4LIKWS0juamWL89nbrME5ymxcnuY3DSe6qsbXcW3pi1JhGrlarxa30dJO879gCc+fe0icsGKqwsAhwkzjlJDcRERFVzuodByEEZs+eje+++w5btmzB008/rfd4QEAA5HI5Tp48ia5duwIArl69ivT0dISEhAAAQkJCsGrVKmRnZ6Nu3boAgB9++AE1a9Y0qvktkUhMciDj7u4ONQCZVGZT92+SSaWQubtb5GDNw8PDZg8KDeXu7g5IpVXKoTlzb8k8VoWt5b46eawKY3Jv67k0lrlz7+bmBrVEAps7gevveEwxfns6O42T3ObHSW7jcJLbOLaae0tNjAohoNFoIJPJDH7vlUqlaNiwoc3l0liWyr2lTlgwlru7GyCKnXKSm4iIiCpn9WZ5QkIC9u3bh5UrV6JGjRq6y6+9vLzg7u4OLy8vxMbGYv78+fD29kbNmjUxZ84chIaG6prlUVFRaNasGd5//3289957yMzMxJIlSzBgwADO9BMREZkQJ7kth5PcxuEkd9XYWu4tNzFa+gISI15LIpHA1c3NpvZXdZg795Y+YcFQUqkM0DjfJDcREREZxurN8q1btwIABg4cqLc8MTERffr0AQB8+OGHkEqlGDt2LIqLixEVFYUZM2bo1pXJZFi1ahVmzpyJ/v37w8PDA71798bYsWMtNxAiIiInwEluIiIiIiIiclRWb5Zfvny50nXc3NwwY8YMvQb5o5566imsXbvWlKERERHRIzjJTURERERERI7K6s1yIiIish+c5CYiIiIiIiJHJbV2AERERERERERERERE1sZmORERERERERERERE5PTbLiYiIiIiIiIiIiMjpsVlORERERERERERERE6PzXIiIiIiIiIiB3TmzBm89dZbiIqKglKpxKFDh/QeF0Jg6dKliIqKQlBQEIYMGYLU1FS9dXJycjBx4kSEhYWhTZs2+PDDD3H//n0LjoKIiMhy2CwnIiIiIiIickAqlQpKpRIzZswo9/G1a9diy5YtmDlzJnbs2AEPDw8MHz4cRUVFunUmTZqElJQUbNiwAatWrcJPP/2E6dOnW2oIREREFsVmOREREREREZEDio6OxrvvvovOnTuXeUwIgc2bN2PUqFF48cUX0aJFCyxYsAAZGRm6M9CvXLmC48ePY86cOQgODkabNm0wdepU7N+/H7dv37b0cIiIiMyOzXIiIiIiIiIiJ5OWlobMzExERkbqlnl5eSE4OBjnzp0DAJw7dw61atVCYGCgbp3IyEhIpVJcuHDB4jETERGZm4u1AyAiIiIiIiIiy8rMzAQA1K1bV2953bp1kZWVBQDIyspCnTp19B53cXGBt7e37vmGEkJApVJVI+JShYWFkAHQaDXV3pYpabRaqAsLHzvGgoICvf9aQlFRESRCQIjqb0v8vZHS/0qqva2ioiKT/E4Yyhr731SKi4shh2n2vSkZmkdr7PvCwkLItVpoNNV/r3jwfmOK9x1D3itMydB9b8r9ZUrav/e5KX53hBCQSCr/+2GznIiIiIiIiIjMSq1W49KlS9Xezq1bt9AIgEplWw1PlUqFtGvX4OnpWem6j36Jqjmlp6ejgVaLkpISk23TFM00rVaLW+npJvmdMJYl97+pZGVloQFMs+9Nydg8WnLfX7t2DY2KBPLy8022TVO87xjzXmFKle17c+wvUygsLALcJCb73XF1da10HTbLiYiIiIiIiJyMr68vACA7Oxv16tXTLc/OzkaLFi0AAD4+Prhz547e80pKSnDv3j3d8w0ll8vRrFmzakaN0nhS0+Hp6QGZVFbt7ZmKp6cnmjxTH/7+/hWuU1BQgNTUVPj5+cHDw8MicZ09exaSq2lwcal++0cIAY1GA5lMZtDZmY8jlUrRsGHDx+4vU7PG/jeVkydPAjf+Msm+NyVD82iNfa9SqSC/8Re8atas9rY0Wg1UqgKTvO8Y8l5hSobue1PuL1Nyd3cDRLFJfndSUlIMWo/NciIiIiIiIiIn06hRI/j6+uLkyZO6pk1+fj5++eUXvPrqqwCA0NBQ5Obm4uLFiwgICAAAnDp1ClqtFkFBQUa9nkQiMcmZlO7u7lADkEllkMlsp1kuk0ohc3c3aIweHh4WO6vUzc0NaokEpumvlm5EYoLtSSQSuLq5WfzsWsCy+99UXF1dIWCafW9KxubRkvve3d0dkEpN+j5hivcdY94rTKmyfW+O/WUKUqkM0Jjmd8fQiSY2y4mIiIiIiIgc0P3793Hjxg3dz2lpabh06RK8vb3RsGFDDBo0CJ9++ikaN26MRo0aYenSpahXrx5efPFFAMCzzz6L9u3bY9q0aUhISIBarcbs2bPRo0cPPPnkk9YaFhERkdmwWU5ERERERETkgC5evIhBgwbpfk5MTAQA9O7dG/Pnz8eIESNQUFCA6dOnIzc3F61bt8Znn30GNzc33XMWLVqE2bNnY/DgwZBKpejSpQumTp1q8bEQERFZApvlRERERERERA4oPDwcly9frvBxiUSCcePGYdy4cRWuU7t2bSxevNgc4REREdkcqbUDICIiIiIiIiIiIiKyNjbLiYiIiIiIiIiIiMjpsVlORERERERERERERE6PzXIiIiIiIiIiIiIicnr8gk8icmhqtRqFefnIyckx6+totFqoVCpoNBrIpIbNQ+bl5cNdrTZrXEREREREREREZBg2y4nIoSUnJ6MwJRluwryvI4QobZTLZJBIJIbFdiUZ7m5AZGSkeYMjIiIiIiIiIqJKsVlORA5PJpNBLjfv250QgEQigYuLCwzslUMmk5k1JiIiIiIiIiIiMhzvWU5ERERERERERERETo/NciIiIiIiIiIiIiJyelZvlp85cwZvvfUWoqKioFQqcejQIb3HhRBYunQpoqKiEBQUhCFDhiA1NVVvnZycHEycOBFhYWFo06YNPvzwQ9y/f9+CoyAiInIOrNtERERERETkqKzeLFepVFAqlZgxY0a5j69duxZbtmzBzJkzsWPHDnh4eGD48OEoKirSrTNp0iSkpKRgw4YNWLVqFX766SdMnz7dUkMgIiJyGqzbRERERERE5Kis3iyPjo7Gu+++i86dO5d5TAiBzZs3Y9SoUXjxxRfRokULLFiwABkZGboz2a5cuYLjx49jzpw5CA4ORps2bTB16lTs378ft2/ftvRwiIiIHBrrNhERERERETkqF2sH8DhpaWnIzMxEZGSkbpmXlxeCg4Nx7tw59OjRA+fOnUOtWrUQGBioWycyMhJSqRQXLlwo92C+IkIIqFSqasddWFgIGQCNVlPtbZmSRquFurDQJGOsSEFBgd5/7VlhYSHkWi00GsPz+CDn5sy9JfJYFbaa+6KiIkiEgBDmfR3x9wuU/ldi8HOKiopsLpfGslTuLZVLo/0djynGL4SARGLY748tsnTdJiIiIiIiIjIlm26WZ2ZmAgDq1q2rt7xu3brIysoCAGRlZaFOnTp6j7u4uMDb21v3fEOp1WpcunSpGhGXunXrFhoBUKlsq2moUqmQdu0aPD09zf5aj96f1h5du3YNjYoE8vLzjX6uOXNvyTxWha3lPj09HQ20WpSUlFjk9YyZXNFqtbiVnm6S9x1bYO7cWzqXhtIKLQCpycbv6upqku1Yg6XrNie5q89WJzqrgpPcxrHV3HOS2/wslfuq/E1agvbvv3dOchMREVF5bLpZbmlyuRzNmjWr9nbu3LkDpKbD09MDMqnMBJGZhqenJ5o8Ux/+/v5me42CggKkpqbCz88PHh4eZnsdS1CpVJDf+AteNWsa/ByNVgOVqsCsubdEHqvCVnN/9uxZSK6mwcXFvG93QghoNBrIZDKDD5qkUikaNmxoc7k0lqVyb6lcGksqKb2jmSnGn5KSYoqQnAYnuU3H1iY6q4KT3FVja7nnJLflmDv31fmbNKfCwiLATcJJbiIiIiqXbXUcHuHr6wsAyM7ORr169XTLs7Oz0aJFCwCAj49PaXP6ISUlJbh3757u+YaSSCQmOZBxd3eHGoBMKoNMZjvNcplUCpm7u0UO1jw8PGz2oNBQ7u7ugFRapRyaM/eWzGNV2Fru3dzcoJZIYP6TfkpfQGLEa0kkEri6udnU/qoOc+fecrk00t/xmGL89n52mqXrNie5q89WJzqrgpPcxrHV3HOS2/wslfuq/E1agru7GyCKOclNRERE5bLpZnmjRo3g6+uLkydP6j6U5ufn45dffsGrr74KAAgNDUVubi4uXryIgIAAAMCpU6eg1WoRFBRktdiJiIicjaXrNie5TcfWJjqrgpPcVWNrueckt+WYO/fV+Zs0J6lUBmg4yU1ERETls3qz/P79+7hx44bu57S0NFy6dAne3t5o2LAhBg0ahE8//RSNGzdGo0aNsHTpUtSrVw8vvvgiAODZZ59F+/btMW3aNCQkJECtVmP27Nno0aMHnnzySWsNi4iIyCGxbhMREREREZGjsnqz/OLFixg0aJDu58TERABA7969MX/+fIwYMQIFBQWYPn06cnNz0bp1a3z22Wdwc3PTPWfRokWYPXs2Bg8eDKlUii5dumDq1KkWHwsREZGjY90mW6VWq1GYl4+cnByDn6PRaqFSqUpvxyGVmiWuvLx8uKvVZtk2ERERERGZltWb5eHh4bh8+XKFj0skEowbNw7jxo2rcJ3atWtj8eLF5giPiIiIHsK6TbYqOTkZhSnJcBOGP6cq9602Oq4ryXB3AyIjI82yfSIiIiIiMh2rN8uJiIiIiExBJpNBLjf8460QpRM8Li4uZrtHtq3dr5mIiIiIiCpmnutNiYiIiIiIiIiIiIjsCJvlREREREREREREROT02CwnIiIiIiIiIiIiIqfHZjkREREREREREREROT02y4mIiIiIiIiIiIjI6bFZTkREREREREREREROj81yIiIiIiIiIiIiInJ6bJYTERERERERERERkdNzsXYARERERNakVquhUqmQc+8eZFLbOY8gLy8f7mq1tcMgIiKyKazbRERkTmyWExERkVNLSUlByY2bqO3mAYlEYu1wdJKvJMPdDYiMjLR2KERERDaDdZuIiMyJzXIiIiJyejKZFHK5HDZ0zA2ZTGbtEIiIiGwS6zYREZmL7VyzRERERERERERERERkJWyWExEREREREREREZHTY7OciIiIiIiIiIiIiJwem+VERERERERERERE5PTYLCciIiIiIiIiIiIip8dmORERERERERERERE5PTbLiYiIiIiIiIiIiMjpsVlORERERERERERERE6PzXIiIiIiIiIiIiIicnou1g6AiIiIiIiIiIiIyFTUajUK8/KRk5NT7W1ptFqoVCpoNBrIpNU77zgvLx/uanW1YyLzYbOciIiIiIiIiIiIHEZycjIKU5LhJqq/LSFEaaNcJoNEIqleXFeS4e4GREZGVj8wMgs2y4mIiIiIiIiIiMihyGQyyOXVb30KAUgkEri4uKCavXLIZLJqx0Pm5VDN8i+++ALr1q1DZmYmWrRogWnTpiEoKMjaYREREVE5WLeJiByXKS9/N6W8/HyUyE1wmqETYt0mIiJn4DDN8m+++QaJiYlISEhAcHAwNm3ahOHDh+PAgQOoW7eutcMjIiKih7BuExE5NlNe/m5KV1NScKuG3Nph2B3WbSIichbVuyu9DdmwYQP69euH2NhYNGvWDAkJCXB3d8euXbusHRoRERE9gnWbiMjxPbj83Zb+SXn5e5WwbhMRkbNwiGZ5cXExfvvtN72b40ulUkRGRuLcuXNWjIyIiIgexbpNRERkP1i3iYjImUiEEDZ2YZzxbt++jRdeeAHbtm1DaGiobvmCBQtw5swZ7Ny5s9Jt/PzzzxBCQC6v/iV5hYWFKFYVwFUuB6p5439TUqtLoJEALi7mu/uOKb8h2NpKSkogEzDuyyAEICAggcRsubdEHqvCVnNfWFgIGQC5zLb2FwCoNSXQAHB3d7d2KNViqdzbai7VmhJIXFzg7e1d7fGr1WpIJBKEhYWZKDrbZGt1Oy8vDyWFRZDb2PuqJd4jbPW9uyps+T3CFt/rbTX3tppHwHZzaSzWbdZtY7FuG8aQ9whrvPfa8t+ipd9TbbX2GaKwsLC0P2GHv/cAf/cfZunffUP3fZV6YBagVpdA6iqHp6enxeq2be0BK3qww03xR+vh4QEPD49qb8fUbC8iqgrm0TheXl7WDoFMxBlyKZFI7O6Du7WYsm7XqlULqFXtzZCVOcN7hDNgHh2HM+SSddtwrNvW4wx/i86AeTQe95ljMGUPzNC67RDN8ieeeAIymQzZ2dl6y7Ozs+Hj42PQNh6eISciIiLzYd0mIiKyH6zbRETkTBzinuWurq5o1aoVTp48qVum1Wpx8uRJFmUiIiIbw7pNRERkP1i3iYjImTjEmeUAMHToUEyePBkBAQEICgrCpk2bUFBQgD59+lg7NCIiInoE6zYREZH9YN0mIiJn4TDN8u7du+POnTtYtmwZMjMz4e/vj88++8zgy8KIiIjIcli3iYiI7AfrNhEROQuJEEJYOwgiIiIiIiIiIiIiImtyiHuWExERERERERERERFVB5vlREREREREREREROT02CwnIiIiIiIiIiIiIqfHZjkREREREREREREROT02y4105swZvPXWW4iKioJSqcShQ4cqfc7p06fRu3dvBAQEoHPnzti9e7cFIjUPY8d/+vRpKJXKMv8yMzMtFLHprF69GrGxsQgNDUVERATefvttXL16tdLnffvtt3jppZcQGBiIl19+GceOHbNAtKZVlbHv3r27TN4DAwMtFLFpffnll3j55ZcRFhaGsLAw9O/fv9I8OkLeHzB2/I6U+0etWbMGSqUSc+fOfex6jpR/e8e6zbrNus26zbrNus26bT/svW7bc92197ppz7XP3uuWI9Ude68bhsRvS/t/+fLlZWJ56aWXHvscS+x7F5Nv0cGpVCoolUrExsZizJgxla5/8+ZNjBw5EvHx8Vi0aBFOnjyJqVOnwtfXF+3bt7dAxKZl7PgfOHDgAGrWrKn7uW7duuYIz6x+/PFHDBgwAIGBgdBoNPj4448xfPhw7N+/H56enuU+5+eff8bEiRMxYcIExMTEYO/evRg9ejR2794NhUJh4RFUXVXGDgA1a9bEgQMHdD9LJBJLhGty9evXx6RJk9C4cWMIIbBnzx6MHj0a//rXv9C8efMy6ztK3h8wdvyA4+T+YRcuXMC2bdugVCofu56j5d/esW6zbrNus26zbrNuP46j5d/e2Xvdtue6a+91055rn73XLUepO/ZeNwyNH7Ct/d+8eXNs2LBB97NMJqtwXYvte0FVplAoxHfffffYdRYsWCB69Oiht2z8+PFi2LBh5gzNIgwZ/6lTp4RCoRD37t2zUFSWk52dLRQKhfjxxx8rXGfcuHHizTff1FsWFxcnpk2bZu7wzMqQse/atUu0bt3aglFZ1nPPPSd27NhR7mOOmveHPW78jpj7/Px80aVLF3HixAnx+uuvizlz5lS4rjPk316xbrNus26zbpfHUfP+MNZt1m17ZO91297rrr3XTXuvffZet+yt7th73TAmflva/8uWLROvvPKKwetbat/zNixmdv78eUREROgti4qKwvnz560TkJX06tULUVFRGDp0KM6ePWvtcEwiLy8PAODt7V3hOo6af0PGDpSeWRETE4Po6GiMGjUKycnJlgjPrDQaDfbv3w+VSoXQ0NBy13HUvAOGjR9wvNzPmjUL0dHRiIyMrHRdR86/M2D+SrFu/x9HyD/rNus263bFHDn/zsBR8meLddfe66a91j57r1v2WnfsvW4YEz9gW/v/+vXriIqKQqdOnTBx4kSkp6dXuK6l9j1vw2JmWVlZ8PHx0Vvm4+OD/Px8FBYWwt3d3UqRWYavry8SEhIQEBCA4uJi7Ny5E4MGDcKOHTvQqlUra4dXZVqtFvPmzUNYWNhjL/UoL/9169ZFVlaWuUM0G0PH3qRJE8ybNw9KpRJ5eXlYv3494uPjsX//ftSvX9+CEZvG5cuXER8fj6KiInh6emLFihVo1qxZues6Yt6NGb+j5X7//v34/fff8dVXXxm0viPm35mwbrNuO9rfL+s26zbr9uM5Yv6dib3XbVutu/ZeN+2x9tl73bLnumPvdcPY+G1p/wcFBSExMRFNmjRBZmYmVqxYgQEDBmDv3r16t6Z6wFL7ns1yMqumTZuiadOmup/DwsJw8+ZNbNy4EQsXLrRiZNWTkJCA5ORkfPnll9YOxeIMHXtoaKjeTHJoaCi6d++Obdu2Yfz48WaO0vSaNGmCPXv2IC8vDwcPHsTkyZPx+eefV/gBwNEYM35Hyv2tW7cwd+5crF+/Hm5ubtYOh8jsWLcdD+s26zbrNpHtstW6a+910x5rn73XLXutO/ZeN6oSvy3t/+joaN3/t2jRAsHBwYiJicG3336LuLg4i8byMDbLzczHx6fMDEdWVhZq1qxp87Pc5hIYGIiff/7Z2mFU2axZs3D06FF8/vnnlc66lZf/7OzsMjNh9sKYsT9KLpfD398fN27cMFN05uXq6orGjRsDAAICAvDrr79i8+bNmDVrVpl1HS3vgHHjf5Q95/63335DdnY2+vTpo1um0Whw5swZfPHFF/j111/LfAGJI+bfmbBul8W6bb9/v6zbrNsA6zbrtmNzxLpt7bpr73XTXmufvdcte6079l43qhL/o2yp7teqVQt+fn4VxmKpfc97lptZSEgITp06pbfshx9+QEhIiHUCsgF//PEHfH19rR2G0YQQmDVrFr777jts2rQJTz/9dKXPcZT8V2Xsj9JoNPjf//5nl7kvj1arRXFxcbmPOUreH+dx43+UPee+bdu22Lt3L/bs2aP7FxAQgJdffhl79uwp94OHM+TfkTF/ZbFu21/+WbfLYt1m3WbddkyOmD9r1V17r5uOVvvsvW7ZS92x97pRlfgfZUu/9/fv38fNmzcrjMVS+55nlhvp/v37ejMcaWlpuHTpEry9vdGwYUMsXrwYt2/fxoIFCwAA8fHx+OKLL7BgwQLExsbi1KlT+Pbbb7F69WprDaFajB3/xo0b0ahRIzRv3hxFRUXYuXMnTp06hfXr11trCFWWkJCAffv2YeXKlahRowYyMzMBAF5eXrqzFt5//308+eSTmDhxIgBg0KBBGDhwINavX4/o6Gh88803uHjxokGzq7akKmNPSkpCSEgIGjdujNzcXKxbtw7p6elWvZSmqhYvXowXXngBDRo0wP3797Fv3z78+OOPWLduHQDHzfsDxo7fkXJfs2bNMvc59PT0RO3atXXLHT3/9o51m3WbdZt1m3WbdZt1237Ye92257pr73XTnmufvdcte6479l43qhK/Le3/jz76CDExMWjYsCEyMjKwfPlySKVS9OzZs9zYLbXv2Sw30sWLFzFo0CDdz4mJiQCA3r17Y/78+cjMzMStW7d0jz/99NNYvXo1EhMTsXnzZtSvXx9z5sxB+/btLR67KRg7frVajY8++gi3b9+Gh4cHFAoFNmzYgLZt21o89uraunUrAGDgwIF6yxMTE3WXvNy6dQtS6f9dsBEWFoZFixZhyZIl+Pjjj+Hn54cVK1Y89ktGbFFVxp6bm4tp06YhMzMT3t7eaNWqFbZt22Y391x7WHZ2NiZPnoyMjAx4eXlBqVRi3bp1aNeuHQDHzfsDxo7fkXJvCEfPv71j3WbdZt3+P6zbrNuAY+feEI6ef3tn73XbnuuuvddNe6599l63HL3u2Pr+r4wt7/+//voLEyZMQE5ODurUqYPWrVtjx44dqFOnTrmxW2rfS4QQwqRbJCIiIiIiIiIiIiKyM7xnORERERERERERERE5PTbLiYiIiIiIiIiIiMjpsVlORERERERERERERE6PzXIiIiIiIiIiIiIicnpslhMRERERERERERGR02OznIiIiIiIiIiIiIicHpvlREREREREREREROT02CwnIiIiIiIiIiIiIqfHZjmRBS1fvhxKpVL3Lzw8HK+++iqOHTtm7dBswvnz5/HGG2+gXbt2CAoKQseOHTF27Fj88ssv1g6NiIgc1KO1+cG/nj17Wjs0u1ZQUICkpCR0794dwcHBCA8PR2xsLD755BNrh0ZERA7iQQ1v3749tFptmcfj4+OhVCoxZcoUK0RXavny5fj555+t9vqP8/XXX6Nv375o3bo1wsLC0K1bN/zzn/9Edna2tUMjsioXawdA5Gzc3d2xadMmAEBGRgZWrVqFt956C1988QXCwsKsHJ31nD17FoMGDUL79u2RkJCAGjVq4Pr16zh06BAuXLiA4OBga4dIREQO6uHa/PAyqrqxY8fiwoULGDlyJPz9/ZGbm4tff/0Vhw4dwrvvvmvt8IiIyEHI5XLcvXsXZ86cQXh4uG75n3/+ifPnz8PT09OK0QFJSUnw9PS0uWP9tWvXYvHixRgyZAjGjh0LIQSSk5Oxd+9eZGRkoG7dutYOkchq2CwnsjCpVIqQkBDdz8HBwYiOjsaePXtsroBa0tatW/HUU09hxYoVkMlkAICIiAjEx8eXe5aAqWk0Gmi1WsjlcrO/FhER2ZZHa3NFCgsL2UQ3wPXr1/Hf//4XH330EXr16qVb3rVrV0yYMMEiMTBXRETOQS6XIyIiAvv379drlu/fvx/NmzeHVOo4N1QQQkCtVsPV1bXa29qyZQt69+6td9Z9dHQ03njjDYscf7NOky1znHcNIjv15JNPok6dOkhPT9ctO3fuHAYNGoSQkBC0bt0aEydOLHMpVHFxMT755BN06tQJAQEBeOGFF8pcXlbZdtLS0qBUKvHvf/8bs2bNwnPPPYeoqCh89NFHKCkp0dvWlStXMGbMGDz//PMIDg7GK6+8gn379gEA3nnnHcTHx5cZ25dffonAwEDk5ORUuh9yc3NRp04dXaP8YY9+wDl37hyGDRuGsLAwhIaGIi4uDidOnNA9npOTgw8++ADh4eEICgpCfHw8zpw5o7eNgQMHYuTIkfjXv/6Frl27IjAwEH/88QcA4OjRo4iLi0NQUBDatm2LGTNmQKVSVToGIiJyLEqlEmvWrMHChQvRrl07REREACg9WF23bh26du2KgIAAdOrUCRs3bizz/EOHDuGll15CYGAg+vbtiwsXLqBNmzZYvny5bp2OHTti1qxZZZ6nVCqRlpamW1ZcXIyPP/4YMTExCAgIQLdu3bB37169502ZMgU9e/bE6dOn0atXL4SEhKBv3764ePGi3nparRYbNmxAt27dEBAQgHbt2mHs2LHIy8vD5cuXoVQq9eoqUDqp3L59eyxYsKDS/Xbv3j0AgK+vb5nHHq3phnye+c9//oN//OMfCAwMRFRUFBITE1FUVKR7/PTp01AqlTh69CjGjh2LsLAwjBs3DkDp54uZM2ciKioKAQEB6NOnD77//vtKx0BERPajZ8+eOHjwINRqtW7Zvn37yr2l2pkzZxAfH4+goCCEh4fjgw8+0DteNfYYedSoUWjdujVCQkLw5ptv4saNG7rHlUolAGDBggW627ydPn0aALB+/XrExsaidevWiIiIwMiRI3Ht2jW97T+o68eOHcMrr7yCwMBAHD58GO3atSv3tmbjx49H3759Ddpnubm5qFevXrmPPVqr9+zZg169eiEwMBDh4eEYMWIE/vzzT93jly9fxvDhw3V9h7Fjx+r1Nx7si+p8piKyJDbLiazs/v37uHfvHho1agSgtBE8cOBAeHl54ZNPPsHs2bPx66+/4u2339Z73jvvvIONGzciNjYWa9aswfvvv6/X0DV0OwCwZMkSSKVSLFmyBPHx8Vi/fj127typezw1NRX9+/fH9evX8c9//hOffvop+vTpoyuAcXFxOHfuHK5evaq33V27dqFz586oXbt2pfuhVatWOHfuHJYsWYIrV65UuN7Zs2cxcOBAFBcXY86cOVi+fDk6deqki0Wj0WDEiBE4cuQIJk2ahKVLl8LT0xNDhw4t0yy4ePEi1q1bh3HjxmHNmjVo0KABDhw4gFGjRkGhUCApKQnvvfcevvvuO/zzn/+sdAxERGS/SkpK9P4JIQAAmzdvRmpqKubOnYuFCxcCAObOnYtly5ahV69eWLNmDXr37o1FixZh69atuu1dunQJY8eOhZ+fH5KSktC7d2+MHz8excXFVYpv3Lhx2L59O4YOHYrVq1ejffv2eO+998p870lmZibmzJmD4cOHY8mSJSgqKsKYMWP0GgizZ8/GwoUL0aFDB6xatQrTp09HjRo1oFKpoFQqERwcjF27dult9/jx48jIyEBsbGylsTZt2hSenp6YP38+jhw5gvv371e4bmWfZw4fPoyxY8eiWbNmWLFiBd544w1s27YN7733XpltTZs2DU8//TRWrFiBYcOGobi4GEOHDsXRo0cxfvx4fPrpp3j22WcxcuRIXL58udJxEBGRfYiJiUFxcbFuojclJQWXL19G9+7d9da7ePEihg4diho1amDp0qWYNGkSjhw5ghEjRkCj0eitW9kx8s2bNxEfH4979+5h/vz5WLRoEe7cuYMhQ4boav327dsBlJ6otX37dmzfvh2tWrUCAPz11194/fXXsXLlSsyZMwdarRbx8fFlTjTLyMjAnDlzMGTIEKxduxYtW7ZE7969sWfPHr0zwHNycnD48GGDm+WtWrXCtm3bsHPnTmRmZla43meffYbJkyejVatWSEpKwty5c9G4cWPcuXMHAHDr1i28/vrruHv3LhYuXIiEhAT89ttveP3115Gfn6+3rap+piKyOEFEFrNs2TIREhIi1Gq1UKvV4s8//xTjx48Xzz33nLhy5YoQQogBAwaI/v37C61Wq3tecnKyUCqV4ujRo0IIIb7//nuhUCjE3r17K3wtQ7Zz8+ZNoVAoxNixY/We+/rrr4vBgwfrfp4wYYJo27atyMvLK/e1NBqN6NChg1iwYIFu2eXLl4VCoRDff/+9QfsmLy9PDB06VCgUCqFQKMTzzz8vJkyYIM6cOaO3Xv/+/UX37t1FSUlJuds5dOiQUCgU4r///a9uWXFxsejQoYMYM2aM3hhbtWol0tPTdcu0Wq2IiYkREyZM0NvmsWPHhFKpFP/73/8MGgsREdmPZcuW6WrPw//27NkjFAqF6N69u14tvX79ulAqlWLbtm1621m4cKFo166d0Gg0Qgghxo8fLzp27KhXr3bu3CkUCoVYtmyZbllMTIxISEjQ29Z3330nFAqFuHnzphBCiJMnTwqFQiGOHz+ut9748eNFbGys7ufJkyeXqVenTp0SCoVCV0+vXr0qlEqlWLVqVYX7ZMeOHSIwMFDk5OTolo0ZM0b079+/wuc8av/+/SI0NFQoFArh7+8vevfuLVauXCnu37+vW8eQzzO9evUq87rbtm0TCoVC/PHHH3pjnD59ut56X331lWjZsqVITk7WWx4XF1fmsw8REdmfB8fXQpQes06aNEkIIcQnn3yiqx2vvPKKmDx5shBCiNGjR4sOHTqI4uJi3TaOHz8uFAqFOHz4sBDC8GPk999/X3Tq1EkUFhbqlmVnZ4uQkBDx+eef65YpFArx2WefPXYcJSUloqCgQISEhOh9vpg8ebJQKBTi/PnzeuunpqbqHdcLIcTmzZtFUFBQhcfsj7p8+bLo3Lmz7nNPx44dxezZs3WfPYQQIjc3VwQHB4tp06ZVuJ158+aJkJAQcffuXd2ylJQUoVQqxebNm3XLqvOZisjSeGY5kYWpVCq0atUKrVq1QkxMDA4ePIgFCxagadOmKCgowM8//4yXXnoJGo1Gd3abn58fGjRogF9//RUAcPLkSXh4eKBHjx7lvoah23kgKipK7+dnn30Wf/31l+7nU6dOoWvXrqhZs2a5ryeVShEbG4t///vfukvTdu3ahaeeekp3eVVlatasqZutHz16NFq0aIGDBw/i9ddf183gFxQU4JdffkGvXr3KvV0LAPz000+oWbMm2rdvr1sml8vRuXNnnD17Vm9dhUKBBg0a6H6+du0a/vzzT3Tr1k3v7MLnn38eUqm0zJnpRETkGNzd3fHVV1/p/YuOjgYAvPDCC5BIJLp1f/jhBwBAly5d9GpFZGQkMjMzcevWLQDAL7/8gpiYGL169dJLL1UpvhMnTqB27dpo27Ztmde8dOmS3tlw9erVQ/PmzXU/N2vWDABw+/ZtAKU1XQjx2DPPevToARcXF93t1u7cuYMjR44YfLYaAHTv3h1HjhzBRx99hH/84x+4e/culixZgtjYWN2Z45V9nrl//z4uXbqErl27ltk2gDJ1vUOHDno/nzhxAgqFAn5+fmX226OfhYiIyL717NkThw8fRmFhIb755ptya8tPP/2ETp066X1PVVRUFGrVqlWmplR2jHzixAl07NgRMplMV19q1aqFli1bGnTceP78eQwdOhTh4eFo2bIlgoODoVKpkJqaqrde7dq1ERwcrLescePGeP755/WuAtu9e/djj9kfpVAosG/fPqxZswaDBg2Cl5cXtmzZgldeeQWXLl0CUHq1ekFBwWPr/08//YTw8HC9q8mfffZZtGjRosw+repnKiJL4xd8ElmYu7s7Pv/8cwghkJqaisWLF2Py5MnYu3cvhBDQaDRITExEYmJimec+KBY5OTnw9fXVKzQPy83NNWg7D3h5een9LJfL9S4Tz8nJqfB+Zg/07dsXK1euxLFjx/DCCy/g66+/xmuvvWb0F6oEBQUhKCgIQOmlbQMHDsSiRYsQFxeH3NxcaLXax8aSm5tb7jd3+/j46O6h+vCyh929excAMHr06HK3zWJNROSYpFIpAgMDy33s0Zpy9+5dCCHQtm3bcte/desWnnrqKWRmZpZ5bs2aNeHm5mZ0fHfv3kVOTo7u0u1HZWZmon79+gCAWrVq6T32oCHw4B7fOTk5cHFxKbdWPuDp6YmePXviq6++woABA/D1119DLpejW7duRsXt7e2NXr16oVevXhBCYNmyZVi5ciW++uorDBo0qNLPM3l5eRBClInVy8sLrq6uZep6ebn6/fffy91vFU26ExGRfYqKioJcLsfSpUuRlpZWbs2q6Fixbt26ZWpKZcfId+/exaZNm7Bp06Yy23u4GV+e9PR0DBs2DAEBAUhISEC9evUgl8sxcuRIve/kAMoesz7Qr18/TJkyBXfu3EFGRgZ+//33Mt/5URlXV1dER0frThA4fvw4Ro4ciRUrViApKUl3S5jKjr/9/f3LLC9vn1b1MxWRpbFZTmRhDx+QBwUFoUmTJujXrx9WrFiByZMnQyKRYOTIkXjxxRfLPPeJJ54AUDq7nJmZCSFEuQeYXl5eBm3HULVr10ZGRsZj16lfvz7at2+PXbt2QaPR4O7du+jTp49Rr/Oop59+Gi+99BI2bNiArKwseHl5QSqVPjYWb2/vMl+GCgBZWVnw9vbWW/bovnswGz59+nRdw/5hlU0YEBGR43m0Vnh7e0MikeDLL78s92C4SZMmAEq/3PLRepSfn1/mINjV1VXvfuIAyhxcent7o06dOlizZk25MdapU8ewwaC01pWUlCA7O/uxDfO4uDhs374df/zxB3bv3o1u3bqhRo0aBr/OoyQSCYYPH46VK1fqvpvE0M8zD+6L+kBeXh6Ki4srreve3t5QKpWYO3duleMmIiL7IJfL0aVLF2zcuBERERHlNpkrOlbMzs4uU1Mq4+3tjejoaLz22mtlHqusXh4/fhwqlQpJSUm6Se6SkpIy9R8oW9se6NKlC2bPno2vv/4aaWlpeOaZZ/D8888bNYZHtW/fHi1atNCr00DpfdMfTMo/6nH71M/PT29ZVT9TEVkam+VEVhYYGIgePXpg9+7dGDNmDEJCQnD16tUKz3ADgMjISKxduxbffvttmS8tAUrPCDNkO4aKiIjAwYMHMWnSpMde1hUXF4dx48bhzp07iIiIMGoWOCsrq9wPNKmpqXB1dUWtWrXg6uqKkJAQ/Pvf/8awYcPKPSusdevWWLduHb7//nvdpXMlJSU4dOgQWrdu/dgYmjZtivr16+PmzZsYMGCAwbETEZHzeHB7sZycHHTs2LHC9YKCgnDkyBF88MEHunp14MCBMuvVr1+/zBdbP/iCsgciIyPx2WefQS6Xo0WLFtWKv23btpBIJNi1axfefPPNCtcLDAyEv78/5syZg8uXL2PGjBkGv0Z+fj5cXFzg7u6ut/zBpeW+vr4AKv88U6NGDfj7++PAgQMYMmSIbvm3334LAJXW9cjISBw7dgz16tXDk08+aXD8RERkn+Li4pCdnY1+/fqV+3jr1q1x+PBhTJkyBS4upe2wEydOIDc3t9Ka8qiIiAgkJyejZcuWj71aSS6Xl5koLywshEQi0cUAlNa2B7c0NYSrqyv+8Y9/YOfOncjKysKQIUMqbKyXp7zj78LCQty6dUt3C7fQ0FB4eHhg165d5Z5MBpTu0x07duDevXu6CYerV6/i8uXLlX4puKGfqYgsjc1yIhvw9ttv45tvvsGmTZvw/vvvY/DgwRg/fjx69OiBWrVq4a+//sIPP/yAPn36IDw8HJGRkYiOjsaHH36IGzduIDg4GDk5OTh48CCWLFkCAAZtx1BjxozB0aNH8dprr+GNN96Ar68vrly5goKCAowYMUK3XocOHfDEE0/g3Llz+Pjjj43aB1OnToVGo0GXLl3g5+eH/Px8HDx4EEeOHMHgwYPh6uoKAJg4cSKGDBmCIUOG4LXXXoO3tzd+++03PPHEE+jbty86dOiAoKAgvPfee5g4cSJ8fHywZcsWZGRkYNmyZY+NQSKRYMqUKZg0aRJUKhU6dOgADw8PpKen49ixY3j33Xc5u01E5OSaNGmCAQMG4P3338fw4cMRHBwMtVqN1NRUnD59GitXrgQAvPnmm+jbty9Gjx6NV199FWlpaVi3bl2Z27B07doVM2fORFJSEkJDQ3Hs2DGcP39eb5127dohJiYGb7zxBt544w0olUoUFBQgJSUF169fN+rM6SZNmiA+Ph5Lly7FvXv3EBERgcLCQhw9ehTvvPOOXlM5Li4Os2bNQpMmTYxqIly7dg2jRo1C79690bp1a3h6eiIlJQVr166Fl5cXevfuDQAGfZ4ZM2YMRo8ejUmTJuGVV17BtWvX8Mknn6Br165QKpWPjaNXr17Ytm0bBg0ahGHDhsHPzw95eXn4/fffoVarMXHiRIPHREREti8oKEhXh8vz1ltvIT4+HiNHjsTAgQORlZWFxYsXIygoSHcrEkONHTsWffv2xfDhw9GvXz/4+PggKysLP/74I9q0aYOePXsCKD0h6/Dhw2jTpg08PDzQpEkT3W1HPvjgA8THxyM5ORkbNmwocyu1yvTr1w+bNm2CTCYz+qrul19+GTExMYiKikK9evVw+/ZtfP7557h79y4GDx4MoPQKr9GjR2PRokUQQqBTp07QarU4ffo0evTogcDAQAwZMgS7d+/GsGHDMGrUKBQVFWHJkiVo0KCBrt5XxNDPVESWxmY5kQ1o2rQpunfvjq1bt2LkyJH48ssvsXz5cnzwwQdQq9WoX78+2rZti8aNG+ues3z5ciQlJWH79u1ISkpC3bp10a5dO93jYWFhBm3HEH5+fti2bRsWL16MhIQEaDQa+Pn5lTkjzcXFBR07dsSBAwfQuXNno15jwIAB2LNnD1avXo3MzEy4u7vjmWeewdy5c/WKbJs2bbB582YsWbIEH3zwAaRSKZo3b47x48cDKL0H6Zo1a7BgwQIsXLhQ94Wq69evR0BAQKVxdOvWDbVq1cKqVauwd+9eAMBTTz2F9u3bV3i/OCIici5Tp05FkyZNsH37dqxYsQI1atRAkyZN9L7As2XLlli6dCkWLVqEMWPGoHnz5vjkk08wfPhwvW3FxcXhxo0b2Lp1KzZu3Iju3btjwoQJZRq5y5Ytw5o1a7B161b8+eef8PLyQvPmzat0y7Pp06ejUaNG2LlzJzZt2oTatWvjueeeK3PZeOfOnTFr1qxKzwx7VOPGjdG/f3+cOHECO3fuxP379/Hkk0+ibdu2eOutt/SuPKvs80ynTp2wdOlSrFixAm+//TZq166Nfv36GdTodnV1xebNm7F8+XKsWrUKmZmZqF27Nlq2bFnuZfNEROTYAgICsH79enz88cd455134OnpiY4dO2Ly5MlGf5dF48aNsXPnTixZsgQJCQlQqVTw9fXFc889pzeZO336dMybNw8jRoxAYWEhNm/ejPDwcCQmJiIpKQkjR46Ev78/li5dqjumNVSzZs3g5+eHZ555xugrqMaMGYMjR45g/vz5uHPnDp544gkolUps3LhR7x7iI0aMQJ06dbBx40bs3r0bNWrUQGhoqO5Wbg0aNMCWLVuwYMECTJo0CVKpFO3atcOUKVMM+rJRQz5TEVmaRAghrB0EETkGrVaLF198ETExMZg2bZq1wyEiIrI5bdq0weDBg/HOO+9YO5RKffXVV5gxYwaOHj2qu3UKERER2YYbN26gS5cuWLp0Kbp27WrtcIgcBs8sJ6JqKy4uxh9//IGDBw/ir7/+4v2+iYiI7FhaWhquX7+OlStXolu3bmyUExER2ZC7d+/i2rVrWLFiBRo2bIhOnTpZOyQih8JmORFVW0ZGBuLi4lCnTh1MmzYNTZs21Xtcq9VCq9VW+HyZTGbUl5EQERGR+SQlJWHfvn0IDQ3FlClTyjz+uC8gk0gkRl/KTkRERIY7cuQIPvzwQzRu3BgLFy7U+6JQANBoNHjcTSQeXZ+I9PE2LERkdg/uR1qRxMTEKt1zlYiIiCzvcV+s+dRTT+H//b//Z8FoiIiI6GEDBw7Ejz/+WOHjhw8fRqNGjSwYEZF9YbOciMzu9u3byMjIqPDxRo0a4YknnrBgRERERFRVv/76a4WPubq6PraZTkREROZ19epV3L9/v8LHlUolXF1dLRgRkX1hs5yIiIiIiIiIiIiInJ7U2gEQEREREREREREREVkbm+VERERERERERERE5PTYLCciIiIiIiIiIiIip8dmORERERERERERERE5PTbLiYiIiIiIiIiIiMjpsVlORERERERERERERE6PzXIiIiIiIiIiIiIicnpslhMRERERERERERGR0/v/2A4C6coC6cAAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nscores = ['Recency_Score', 'Frequency_Score', 'Monetary_Score']\n\n# Set up the matplotlib figure\nfig, axes = plt.subplots(1, len(scores), figsize=(15, 4))\n\n# Define the colors\npink_color = 'pink'  \ndark_pink_color = 'brown'  \n\n# Loop over each score and create a horizontal box plot\nfor i, score in enumerate(scores):\n    ax = axes[i]    \n    \n    # Create a DataFrame for all data\n    df_all_data = X_kmeans[[score]].copy()\n    df_all_data['cluster'] = 'All Data'\n\n    # Combine the all data DataFrame with the original DataFrame\n    combined_df = pd.concat([X_kmeans[['cluster', score]], df_all_data], ignore_index=True)\n\n    # Sort the clusters including 'All Data'\n    combined_df['cluster'] = pd.Categorical(combined_df['cluster'], categories= clusters + ['All Data'], ordered=True)\n\n    # Create a horizontal box plot\n    sns.boxplot(y='cluster', x=score, data=combined_df, palette= [pink_color] * len(clusters) + [dark_pink_color], ax=ax, showfliers=False)\n\n    # Set the title and labels\n    ax.set_title(f'{score} Distribution')\n    ax.set_xlabel(score)\n    ax.set_ylabel('Cluster')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-28T17:50:05.802599Z","iopub.execute_input":"2024-07-28T17:50:05.802918Z","iopub.status.idle":"2024-07-28T17:50:06.638343Z","shell.execute_reply.started":"2024-07-28T17:50:05.802890Z","shell.execute_reply":"2024-07-28T17:50:06.637218Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x400 with 3 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"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\n\n# Group by cluster and calculate the mean for each score\ncluster_summary = X_kmeans.groupby('cluster').mean().reset_index()\n\n# Calculate the number of records and percentage for each cluster\ncluster_counts = X_kmeans['cluster'].value_counts().reset_index()\ncluster_counts.columns = ['cluster', 'count']\ncluster_counts['percentage'] = (cluster_counts['count'] / len(X_kmeans)) * 100\n\n# Calculate the overall mean for each score\noverall_mean = X_kmeans[['Recency_Score', 'Frequency_Score', 'Monetary_Score', 'silhouette']].mean().to_frame().T\noverall_mean['cluster'] = 'All Data'\noverall_mean['count'] = len(X_kmeans)\noverall_mean['percentage'] = 100.0\n\n# Rename columns to indicate they are mean values\ncluster_summary.columns = ['cluster', 'mean_Recency_Score', 'mean_Frequency_Score', 'mean_Monetary_Score', 'mean_Silhouette']\n\n# Merge cluster_summary with cluster_counts\ncluster_summary = pd.merge(cluster_summary, cluster_counts, on='cluster')\n\n# Append the overall mean to the cluster summary\noverall_mean.columns = ['mean_Recency_Score', 'mean_Frequency_Score', 'mean_Monetary_Score', 'mean_Silhouette', 'cluster', 'count', 'percentage']\ncluster_summary = pd.concat([cluster_summary, overall_mean], ignore_index=True)\n\ncluster_summary","metadata":{"execution":{"iopub.status.busy":"2024-07-28T18:14:42.605315Z","iopub.execute_input":"2024-07-28T18:14:42.606315Z","iopub.status.idle":"2024-07-28T18:14:42.636316Z","shell.execute_reply.started":"2024-07-28T18:14:42.606277Z","shell.execute_reply":"2024-07-28T18:14:42.635198Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"    cluster  mean_Recency_Score  mean_Frequency_Score  mean_Monetary_Score  \\\n0         0            3.632735              3.806387             4.111776   \n1         1            1.977556              1.144638             1.234414   \n2         2            2.051392              1.295503             4.434690   \n3         3            1.626198              2.853035             2.498403   \n4         4            3.700246              2.560197             2.307125   \n5  All Data            2.674007              2.348492             3.038296   \n\n   mean_Silhouette  count  percentage  \n0         0.442381    501   23.982767  \n1         0.405051    401   19.195787  \n2         0.354799    467   22.355194  \n3         0.329690    313   14.983246  \n4         0.335488    407   19.483006  \n5         0.377925   2089  100.000000  ","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>cluster</th>\n      <th>mean_Recency_Score</th>\n      <th>mean_Frequency_Score</th>\n      <th>mean_Monetary_Score</th>\n      <th>mean_Silhouette</th>\n      <th>count</th>\n      <th>percentage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3.632735</td>\n      <td>3.806387</td>\n      <td>4.111776</td>\n      <td>0.442381</td>\n      <td>501</td>\n      <td>23.982767</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.977556</td>\n      <td>1.144638</td>\n      <td>1.234414</td>\n      <td>0.405051</td>\n      <td>401</td>\n      <td>19.195787</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2.051392</td>\n      <td>1.295503</td>\n      <td>4.434690</td>\n      <td>0.354799</td>\n      <td>467</td>\n      <td>22.355194</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>1.626198</td>\n      <td>2.853035</td>\n      <td>2.498403</td>\n      <td>0.329690</td>\n      <td>313</td>\n      <td>14.983246</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>3.700246</td>\n      <td>2.560197</td>\n      <td>2.307125</td>\n      <td>0.335488</td>\n      <td>407</td>\n      <td>19.483006</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>All Data</td>\n      <td>2.674007</td>\n      <td>2.348492</td>\n      <td>3.038296</td>\n      <td>0.377925</td>\n      <td>2089</td>\n      <td>100.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"cluster_name = {'0': 'loyal_customers',\n                '1': 'passed_customers',\n                '2': 'valuable_inactives',\n                '3': 'churn_customers',\n                '4': 'need_attention'}\ncluster_summary['cluster'] = cluster_summary['cluster'].astype(str).replace(cluster_name)\n\nrfm_weights = {'R': 0.2,\n              'F': 0.3,\n              'M': 0.5}\n\ncluster_summary['CLV'] = (cluster_summary['mean_Recency_Score'] * rfm_weights['R'] + \n                          cluster_summary['mean_Frequency_Score'] * rfm_weights['F'] + \n                          cluster_summary['mean_Monetary_Score'] * rfm_weights['M'])\n        \ncluster_summary.sort_values(by='CLV', ascending=False)","metadata":{"execution":{"iopub.status.busy":"2024-07-28T18:22:54.240821Z","iopub.execute_input":"2024-07-28T18:22:54.241242Z","iopub.status.idle":"2024-07-28T18:22:54.261184Z","shell.execute_reply.started":"2024-07-28T18:22:54.241210Z","shell.execute_reply":"2024-07-28T18:22:54.260196Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"              cluster  mean_Recency_Score  mean_Frequency_Score  \\\n0     loyal_customers            3.632735              3.806387   \n2  valuable_inactives            2.051392              1.295503   \n5            All Data            2.674007              2.348492   \n4      need_attention            3.700246              2.560197   \n3     churn_customers            1.626198              2.853035   \n1    passed_customers            1.977556              1.144638   \n\n   mean_Monetary_Score  mean_Silhouette  count  percentage       CLV  \n0             4.111776         0.442381    501   23.982767  3.924351  \n2             4.434690         0.354799    467   22.355194  3.016274  \n5             3.038296         0.377925   2089  100.000000  2.758497  \n4             2.307125         0.335488    407   19.483006  2.661671  \n3             2.498403         0.329690    313   14.983246  2.430351  \n1             1.234414         0.405051    401   19.195787  1.356110  ","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>cluster</th>\n      <th>mean_Recency_Score</th>\n      <th>mean_Frequency_Score</th>\n      <th>mean_Monetary_Score</th>\n      <th>mean_Silhouette</th>\n      <th>count</th>\n      <th>percentage</th>\n      <th>CLV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>loyal_customers</td>\n      <td>3.632735</td>\n      <td>3.806387</td>\n      <td>4.111776</td>\n      <td>0.442381</td>\n      <td>501</td>\n      <td>23.982767</td>\n      <td>3.924351</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>valuable_inactives</td>\n      <td>2.051392</td>\n      <td>1.295503</td>\n      <td>4.434690</td>\n      <td>0.354799</td>\n      <td>467</td>\n      <td>22.355194</td>\n      <td>3.016274</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>All Data</td>\n      <td>2.674007</td>\n      <td>2.348492</td>\n      <td>3.038296</td>\n      <td>0.377925</td>\n      <td>2089</td>\n      <td>100.000000</td>\n      <td>2.758497</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>need_attention</td>\n      <td>3.700246</td>\n      <td>2.560197</td>\n      <td>2.307125</td>\n      <td>0.335488</td>\n      <td>407</td>\n      <td>19.483006</td>\n      <td>2.661671</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>churn_customers</td>\n      <td>1.626198</td>\n      <td>2.853035</td>\n      <td>2.498403</td>\n      <td>0.329690</td>\n      <td>313</td>\n      <td>14.983246</td>\n      <td>2.430351</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>passed_customers</td>\n      <td>1.977556</td>\n      <td>1.144638</td>\n      <td>1.234414</td>\n      <td>0.405051</td>\n      <td>401</td>\n      <td>19.195787</td>\n      <td>1.356110</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Hierarchical Clustering and the Agglomerative Method\n\n## Hierarchical Clustering\n\nHierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. It can be divided into two main approaches:\n\n1. **Agglomerative Clustering**\n2. **Divisive Clustering**\n\nIn this document, we focus on the agglomerative method.\n\n## Agglomerative Clustering\n\nAgglomerative clustering is a bottom-up approach where each data point starts as its own cluster. The algorithm then iteratively merges the closest pairs of clusters until a stopping criterion is met (e.g., a specified number of clusters or a distance threshold).\n\n### Steps in Agglomerative Clustering\n\n1. **Initialization**: Start with each data point as its own cluster.\n2. **Distance Calculation**: Compute the distance between all pairs of clusters. Common distance metrics include Euclidean, Manhattan, or cosine distance.\n3. **Merge Clusters**: Identify the pair of clusters with the smallest distance and merge them into a single cluster.\n4. **Update Distances**: Recalculate distances between the new cluster and all other clusters.\n5. **Repeat**: Continue merging and updating until all data points are in a single cluster or the desired number of clusters is achieved.\n\n### Distance Metrics\n\n- **Single Linkage**: Distance between the closest points of two clusters.\n- **Complete Linkage**: Distance between the farthest points of two clusters.\n- **Average Linkage**: Average distance between all pairs of points in the two clusters.\n- **Ward's Method**: Minimizes the variance within each cluster.\n\n### Dendrogram\n\nA dendrogram is a tree-like diagram that records the sequences of merges. It visually represents the hierarchical structure of the clustering process and helps determine the number of clusters by cutting the dendrogram at a desired level.\n\n### Advantages\n\n- **Intuitive**: The hierarchical structure is easy to understand and interpret.\n- **No Need for Pre-Specified Number of Clusters**: The algorithm does not require specifying the number of clusters in advance.\n\n### Disadvantages\n\n- **Computational Complexity**: Can be computationally expensive for large datasets.\n- **Sensitive to Noise**: Outliers can affect the clustering results.\n\n\nAgglomerative clustering is a versatile and intuitive hierarchical clustering method that builds clusters by progressively merging them. Understanding its steps, distance metrics, and the use of dendrograms can aid in effective cluster analysis and interpretation.\n","metadata":{}},{"cell_type":"markdown","source":"### Parameters\n\n- **n_clusters** `int` or `None`, default=2  \n  The number of clusters to find. It must be `None` if `distance_threshold` is not `None`.\n\n- **metric** `str` or callable, default=”euclidean”  \n  Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or “precomputed”. If `linkage` is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix is needed as input for the fit method.  \n\n- **linkage** `{‘ward’, ‘complete’, ‘average’, ‘single’}`, default='ward'  \n  Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observations. The algorithm will merge the pairs of clusters that minimize this criterion.  \n  - ‘ward’ minimizes the variance of the clusters being merged.\n  - ‘average’ uses the average of the distances of each observation of the two sets.\n  - ‘complete’ or ‘maximum’ linkage uses the maximum distances between all observations of the two sets.\n  - ‘single’ uses the minimum of the distances between all observations of the two sets.  \n  \n  For examples comparing different linkage criteria, see Comparing different hierarchical linkage methods on toy datasets.\n\n- **distance_threshold** `float`, default=None  \n  The linkage distance threshold at or above which clusters will not be merged. If not `None`, `n_clusters` must be `None`.  \n \n- **compute_distances** `bool`, default=False  \n  Computes distances between clusters even if `distance_threshold` is not used. This can be used to make dendrogram visualization, but introduces a computational and memory overhead.\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.metrics import silhouette_score, silhouette_samples\nfrom scipy.cluster.hierarchy import dendrogram, linkage\nimport matplotlib.pyplot as plt\n\n# Initialize the AgglomerativeClustering model\nha_model = AgglomerativeClustering(n_clusters=5, metric='euclidean', linkage='ward',\n                                   distance_threshold=None, compute_distances=False)\n\n# Fit the model and predict the cluster labels\nlabels = ha_model.fit_predict(X)\n\n# Compute the silhouette score\nsilhouette_avg = silhouette_score(X, labels)\nsilhouette = silhouette_samples(X, labels)\n\nprint(f'Silhouette Score: {silhouette_avg}')\n\nX_ha = X.copy()\nX_ha['cluster'] = labels\nX_ha['silhouette'] = silhouette\n\n# Plot the dendrogram\nlinked = linkage(X, method='ward')\n\nplt.figure(figsize=(10, 7))\ndendrogram(linked,\n           orientation='top',\n           distance_sort='descending',\n           show_leaf_counts=True)\nplt.title('Dendrogram')\nplt.xlabel('Samples')\nplt.ylabel('Euclidean distances')\nplt.show()","metadata":{"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"      Recency_Score  Frequency_Score  Monetary_Score  cluster  silhouette\n0               3.0              1.0             5.0        2    0.483883\n1               3.0              3.0             3.0        3    0.242441\n2               4.0              4.0             2.0        3    0.435512\n3               2.0              2.0             2.0        0    0.284173\n4               2.0              1.0             5.0        2    0.552188\n...             ...              ...             ...      ...         ...\n2084            2.0              3.0             3.0        1    0.391455\n2085            2.0              3.0             2.0        1    0.220381\n2086            3.0              4.0             3.0        3    0.152216\n2087            4.0              3.0             2.0        3    0.610477\n2088            4.0              4.0             4.0        4    0.734659\n\n[2089 rows x 5 columns]","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>Recency_Score</th>\n      <th>Frequency_Score</th>\n      <th>Monetary_Score</th>\n      <th>cluster</th>\n      <th>silhouette</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0.483883</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3</td>\n      <td>0.242441</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>3</td>\n      <td>0.435512</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>0.284173</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0.552188</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2084</th>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>0.391455</td>\n    </tr>\n    <tr>\n      <th>2085</th>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>0.220381</td>\n    </tr>\n    <tr>\n      <th>2086</th>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3</td>\n      <td>0.152216</td>\n    </tr>\n    <tr>\n      <th>2087</th>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>3</td>\n      <td>0.610477</td>\n    </tr>\n    <tr>\n      <th>2088</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4</td>\n      <td>0.734659</td>\n    </tr>\n  </tbody>\n</table>\n<p>2089 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\n\n# Group by cluster and calculate the mean for each score\ncluster_summary = X_ha.groupby('cluster').mean().reset_index()\n\n# Calculate the number of records and percentage for each cluster\ncluster_counts = X_ha['cluster'].value_counts().reset_index()\ncluster_counts.columns = ['cluster', 'count']\ncluster_counts['percentage'] = (cluster_counts['count'] / len(X_ha)) * 100\n\n# Calculate the overall mean for each score\noverall_mean = X_ha[['Recency_Score', 'Frequency_Score', 'Monetary_Score', 'silhouette']].mean().to_frame().T\noverall_mean['cluster'] = 'All Data'\noverall_mean['count'] = len(X_ha)\noverall_mean['percentage'] = 100.0\n\n# Rename columns to indicate they are mean values\ncluster_summary.columns = ['cluster', 'mean_Recency_Score', 'mean_Frequency_Score', 'mean_Monetary_Score', 'mean_Silhouette']\n\n# Merge cluster_summary with cluster_counts\ncluster_summary = pd.merge(cluster_summary, cluster_counts, on='cluster')\n\n# Append the overall mean to the cluster summary\noverall_mean.columns = ['mean_Recency_Score', 'mean_Frequency_Score', 'mean_Monetary_Score', 'mean_Silhouette', 'cluster', 'count', 'percentage']\ncluster_summary = pd.concat([cluster_summary, overall_mean], ignore_index=True)\n\ncluster_summary","metadata":{"execution":{"iopub.status.busy":"2024-07-28T18:39:22.751845Z","iopub.execute_input":"2024-07-28T18:39:22.752250Z","iopub.status.idle":"2024-07-28T18:39:22.782137Z","shell.execute_reply.started":"2024-07-28T18:39:22.752211Z","shell.execute_reply":"2024-07-28T18:39:22.781061Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"    cluster  mean_Recency_Score  mean_Frequency_Score  mean_Monetary_Score  \\\n0         0            2.151625              1.297834             1.474729   \n1         1            1.878049              3.356707             3.265244   \n2         2            2.177645              1.313373             4.317365   \n3         3            3.720554              3.140878             2.501155   \n4         4            3.941392              3.912088             4.443223   \n5  All Data            2.674007              2.348492             3.038296   \n\n   mean_Silhouette  count  percentage  \n0         0.370984    554   26.519866  \n1         0.218129    328   15.701292  \n2         0.294830    501   23.982767  \n3         0.308124    433   20.727621  \n4         0.709452    273   13.068454  \n5         0.359923   2089  100.000000  ","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>cluster</th>\n      <th>mean_Recency_Score</th>\n      <th>mean_Frequency_Score</th>\n      <th>mean_Monetary_Score</th>\n      <th>mean_Silhouette</th>\n      <th>count</th>\n      <th>percentage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2.151625</td>\n      <td>1.297834</td>\n      <td>1.474729</td>\n      <td>0.370984</td>\n      <td>554</td>\n      <td>26.519866</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.878049</td>\n      <td>3.356707</td>\n      <td>3.265244</td>\n      <td>0.218129</td>\n      <td>328</td>\n      <td>15.701292</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2.177645</td>\n      <td>1.313373</td>\n      <td>4.317365</td>\n      <td>0.294830</td>\n      <td>501</td>\n      <td>23.982767</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>3.720554</td>\n      <td>3.140878</td>\n      <td>2.501155</td>\n      <td>0.308124</td>\n      <td>433</td>\n      <td>20.727621</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>3.941392</td>\n      <td>3.912088</td>\n      <td>4.443223</td>\n      <td>0.709452</td>\n      <td>273</td>\n      <td>13.068454</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>All Data</td>\n      <td>2.674007</td>\n      <td>2.348492</td>\n      <td>3.038296</td>\n      <td>0.359923</td>\n      <td>2089</td>\n      <td>100.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"The interpretation of clusters according to the distribution of input fields within clusters and comparison with overall data can be done to select the number of clusters, name clusters, and specify target groups for further analysis or profiling based on the demographic characteristics of customers.","metadata":{}}]}