diff --git a/austin-crime-reports-analysis.ipynb b/austin-crime-reports-analysis.ipynb
index e674480..62f8520 100644
--- a/austin-crime-reports-analysis.ipynb
+++ b/austin-crime-reports-analysis.ipynb
@@ -1 +1 @@
-{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/robertgrantham/austin-crime-reports-analysis?scriptVersionId=147989343\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"5a0c12d3","metadata":{"_cell_guid":"afe7265d-6e1f-4021-a439-37c51619fb2c","_uuid":"7a0c641a-6db4-4ea2-8803-5abce812fd35","papermill":{"duration":0.004242,"end_time":"2023-10-25T16:44:28.036211","exception":false,"start_time":"2023-10-25T16:44:28.031969","status":"completed"},"tags":[]},"source":["# Analyzing Austin PD's Crime Reports Dataset\n","\n","The Austin Police Department's Crime Reports dataset provides valuable information about reported crimes in Austin, Texas. In this analysis, we will explore the dataset and gain insights into various aspects of crime in Austin.\n","\n","## Table of Contents\n","\n","I. Introduction  \n","II. Data  \n","III. Methodology  \n","IV. Outcome  \n","\n","## I. Introduction\n","\n","The Austin Police Department's Crime Reports dataset offers a comprehensive view of reported crimes in Austin, Texas. By analyzing this dataset, we can extract valuable insights into the nature and patterns of crime in the city.\n","\n","## II. Data\n","\n","### 1. Project Context\n","\n","This project is self-paced and not affiliated with any work or educational institution. My goal is to uncover valuable insights that can benefit the Austin law enforcement community, news outlets, and anyone interested in understanding and addressing crime-related issues in the Austin area.\n","\n","### 2. Data Collection\n","\n","Initially, I attempted to import the data into this notebook using Sodapy's Socrata API method. However, I found it to be insufficient as it didn't import the complete dataset and added redundant columns. As a result, I decided to manually download the entire dataset and repeat the process each week after the updates.\n","\n","### 3. Data Scrubbing\n","\n","Before conducting any analysis, it is crucial to clean and preprocess the dataset. The data scrubbing process involves addressing missing data, removing irrelevant columns, and ensuring data types are appropriate.\n","\n","## III. Methodology\n","\n","Once the dataset is scrubbed and prepared, we can perform an exploratory analysis to gain a deeper understanding of the crime data. The exploratory analysis may include the following:\n","\n","### A. Crime Rates by Area\n","\n","#### 1. Crime Rates by Zip Code\n","\n","We start by examining the crime rates by zip code. The following code calculates the total number of crimes for each zip code and displays the top 10 zip codes with the highest crime rates.\n","\n","```python\n","crime_by_zipcode = df[\"zip_code\"].value_counts().head(10).to_frame()\n","display(crime_by_zipcode)\n","```\n","\n","#### 2. Crime Rates as Percentages\n","\n","In addition to the total crime counts, it is also informative to analyze the crime rates as percentages relative to the total. The following code calculates the crime rates as percentages for each zip code and displays the top 10 zip codes with the highest crime rates.\n","\n","```python\n","crime_rates_by_zipcode = df[\"zip_code\"].value_counts(normalize=True).head(10).to_frame()\n","display(crime_rates_by_zipcode)\n","```\n","\n","#### 3. Visualizing Crime Rates by Zip Code\n","\n","To visually represent the crime rates by zip code, we can create a bar chart. The following code generates a bar chart showing the top 10 zip codes with the highest crime rates.\n","\n","```python\n","plt.figure(figsize=(10, 6))\n","crime_by_zipcode.plot(kind=\"bar\", rot=45)\n","plt.title(\"Top 10 Zip Codes with Highest Crime Rates\")\n","plt.xlabel(\"Zip Code\")\n","plt.ylabel(\"Number of Crimes\")\n","plt.show()\n","```\n","\n","## IV. Outcome\n","\n","In this section, we summarize the key findings and insights obtained from the analysis. Discuss any significant patterns, trends, or observations related to crime in Austin based on the data and methodologies applied.\n","\n","By analyzing the crime data, we aim to contribute to a better understanding of crime distribution, trends, and areas of concern in Austin. This information can be valuable for law enforcement agencies, policymakers, and the community in efforts to address and prevent crime effectively."]},{"cell_type":"code","execution_count":1,"id":"fdf6626c","metadata":{"_cell_guid":"b3237cc4-51e3-49bf-9e86-7210dd60ca92","_uuid":"e34c5e34-3243-4448-8a64-56b4fcbc96bb","collapsed":false,"execution":{"iopub.execute_input":"2023-10-25T16:44:28.046384Z","iopub.status.busy":"2023-10-25T16:44:28.045838Z","iopub.status.idle":"2023-10-25T16:44:31.202006Z","shell.execute_reply":"2023-10-25T16:44:31.199149Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":3.164686,"end_time":"2023-10-25T16:44:31.205038","exception":false,"start_time":"2023-10-25T16:44:28.040352","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Redirecting output to crime_analysis_outputs/output.txt\n"]}],"source":["import os\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import folium\n","from folium import plugins\n","from folium.plugins import FastMarkerCluster\n","from geopy.distance import geodesic\n","from sklearn.cluster import KMeans\n","import seaborn as sns\n","import warnings\n","\n","BOOKING_OFFICE_COORDS = (30.274020, -97.746580)  # Latitude, Longitude\n","\n","# Create an output directory if it doesn't exist\n","OUTPUT_DIRECTORY = \"crime_analysis_outputs\"\n","os.makedirs(OUTPUT_DIRECTORY, exist_ok=True)\n","\n","# Redirect the output to a file\n","output_file = os.path.join(OUTPUT_DIRECTORY, \"output.txt\")\n","with open(output_file, \"w\") as output:\n","    print(\"Redirecting output to\", output_file)\n","    print(\"Some Example Output\", file=output)\n","    \n","# Set up pandas and seaborn\n","pd.set_option(\"display.max_columns\", None)\n","warnings.filterwarnings(\"ignore\")\n","plt.style.use(\"seaborn-white\")\n","sns.set_style(\"whitegrid\")\n"]},{"cell_type":"code","execution_count":2,"id":"09acec71","metadata":{"execution":{"iopub.execute_input":"2023-10-25T16:44:31.214972Z","iopub.status.busy":"2023-10-25T16:44:31.214287Z","iopub.status.idle":"2023-10-25T16:44:31.223448Z","shell.execute_reply":"2023-10-25T16:44:31.221355Z"},"papermill":{"duration":0.017939,"end_time":"2023-10-25T16:44:31.226905","exception":false,"start_time":"2023-10-25T16:44:31.208966","status":"completed"},"tags":[]},"outputs":[],"source":["def load_data(file_path):\n","    # Load data from a CSV file\n","    try:\n","        df = pd.read_csv(file_path)\n","        return df\n","    except Exception as e:\n","        print(\"Error loading data:\", e)\n","        return None\n","\n","\n","def calculate_distance_to_booking_office(df):\n","    # Calculate the distance from each crime location to the booking office\n","    df = df.dropna(subset=[\"Latitude\", \"Longitude\"])\n","    df[\"Distance to Booking Office (Miles)\"] = df.apply(\n","        lambda row: geodesic(\n","            (row[\"Latitude\"], row[\"Longitude\"]), BOOKING_OFFICE_COORDS\n","        ).miles,\n","        axis=1,\n","    )\n","    return df\n"]},{"cell_type":"code","execution_count":3,"id":"0a8a561a","metadata":{"_cell_guid":"ffbb0abe-b063-4084-b56a-918be9d0086d","_uuid":"fa23bad8-db53-4a64-a730-b6d686caa7df","execution":{"iopub.execute_input":"2023-10-25T16:44:31.23764Z","iopub.status.busy":"2023-10-25T16:44:31.236947Z","iopub.status.idle":"2023-10-25T16:44:31.250391Z","shell.execute_reply":"2023-10-25T16:44:31.249155Z"},"papermill":{"duration":0.02217,"end_time":"2023-10-25T16:44:31.25324","exception":false,"start_time":"2023-10-25T16:44:31.23107","status":"completed"},"tags":[]},"outputs":[],"source":["def clean_data(df):\n","    # Clean and preprocess the DataFrame\n","    columns_to_drop = [\n","        \"X-coordinate\",\n","        \"Y-coordinate\",\n","        \"Location\",\n","    ]\n","    df = df.drop(columns=columns_to_drop, errors=\"ignore\")\n","\n","    date_time_columns = [\"Occurred Date Time\", \"Report Date Time\", \"Clearance Date\"]\n","    for col in date_time_columns:\n","        df[col] = pd.to_datetime(df[col], errors=\"coerce\")\n","    df.fillna(\n","        {\n","            \"Zip Code\": \"Unknown\",\n","            \"Location Type\": \"Unknown\",\n","            \"Council District\": \"Unknown\",\n","            \"PRA\": \"Unknown\",\n","            \"Census Tract\": \"Unknown\",\n","            \"APD Sector\": \"Unknown\",\n","            \"APD District\": \"Unknown\",\n","            \"Clearance Status\": \"Unknown\",\n","            \"Category Description\": \"Unknown\",\n","        },\n","        inplace=True,\n","    )\n","\n","    categorical_columns = [\n","        \"Highest Offense Description\",\n","        \"Zip Code\",\n","        \"Location Type\",\n","        \"Council District\",\n","        \"APD District\",\n","        \"APD Sector\",\n","        \"PRA\",\n","        \"Census Tract\",\n","        \"Category Description\",\n","    ]\n","    df[categorical_columns] = df[categorical_columns].astype(\"category\")\n","\n","    df[\"Year\"] = df[\"Occurred Date Time\"].dt.year\n","    df[\"Quarter\"] = df[\"Occurred Date Time\"].dt.quarter\n","    df[\"Month\"] = df[\"Occurred Date Time\"].dt.month\n","    df[\"Day\"] = df[\"Occurred Date Time\"].dt.day\n","    df[\"Day of Week\"] = df[\"Occurred Date Time\"].dt.dayofweek\n","\n","    day_of_week_dummies = pd.get_dummies(\n","        df[\"Day of Week\"], prefix=\"Day\", drop_first=True\n","    )\n","    df = pd.concat([df, day_of_week_dummies], axis=1)\n","\n","    df.set_index(\"Occurred Date Time\", inplace=True)\n","    df.sort_index(inplace=True)\n","\n","    df[\"Time to Clearance (Days)\"] = (\n","        df[\"Clearance Date\"] - df[\"Report Date Time\"]\n","    ).dt.days\n","\n","    return df\n"]},{"cell_type":"code","execution_count":4,"id":"04c76313","metadata":{"execution":{"iopub.execute_input":"2023-10-25T16:44:31.262749Z","iopub.status.busy":"2023-10-25T16:44:31.2623Z","iopub.status.idle":"2023-10-25T16:44:31.274826Z","shell.execute_reply":"2023-10-25T16:44:31.27379Z"},"papermill":{"duration":0.020093,"end_time":"2023-10-25T16:44:31.277173","exception":false,"start_time":"2023-10-25T16:44:31.25708","status":"completed"},"tags":[]},"outputs":[],"source":["def filter_and_save_data(df, filter_condition, filename):\n","    filtered_data = df.query(filter_condition)\n","    filtered_data.to_csv(filename, index=False)\n","\n","\n","def plot_bar_graph(data, x_label, y_label, title, filename):\n","    plt.figure(figsize=(12, 6))\n","    data.plot(kind=\"bar\", rot=60)\n","    plt.title(title)\n","    plt.xlabel(x_label)\n","    plt.ylabel(y_label)\n","    plt.tight_layout()\n","    plt.savefig(filename)\n","\n","\n","def create_heatmap(df, outfile):\n","    coords_heat = df[[\"Latitude\", \"Longitude\"]].dropna()\n","    map_austin = folium.Map(\n","        location=[30.2672, -97.7431], tiles=\"OpenStreetMap\", zoom_start=12\n","    )\n","    map_austin.add_child(plugins.HeatMap(coords_heat.values, radius=15))\n","    map_austin.save(outfile)\n","\n","\n","def cluster_crime_locations(df, n_clusters):\n","    kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n","    df[\"Location Cluster\"] = kmeans.fit_predict(df[[\"Latitude\", \"Longitude\"]])\n","    return df\n"]},{"cell_type":"code","execution_count":5,"id":"a38754de","metadata":{"_cell_guid":"c80ce92f-034e-4215-87d4-ee09202c5178","_uuid":"177feb54-364c-44d5-a49e-40c0861b1eb1","execution":{"iopub.execute_input":"2023-10-25T16:44:31.28683Z","iopub.status.busy":"2023-10-25T16:44:31.286454Z","iopub.status.idle":"2023-10-25T17:04:04.609015Z","shell.execute_reply":"2023-10-25T17:04:04.60777Z"},"papermill":{"duration":1173.337122,"end_time":"2023-10-25T17:04:04.618225","exception":false,"start_time":"2023-10-25T16:44:31.281103","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The 'Occurred Date Time' column is not present in the dataset.\n"]},{"data":{"image/png":"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atRQ8eL2h1i5cmWehQMAAAAAAHnP4TIgNDRUe/fu1aOPPsoCggAAAAAAFEIOlwH/93//p3nz5qlFixb5kQcAAAAAAOQzh9cMqFKlilxdXfMjCwAAAAAAuAscLgNCQ0M1ffp0JSUl5UceAAAAAACQzxy+TGDMmDHKyMjQQw89pBIlSsjZ2dlu/9atW/MsHAAAAAAAyHsOlwGvvvpqfuQAAAAAAAB3icNlQI8ePfIjBwAAAAAAuEtyVQakpaXZFg1MS0u77VgWFwQAAAAAoGDLVRng7++vTZs2qUKFCmrRooUsFkuOMVarVRaLRfv27cvzkAAAAAAAIO/kqgz4+OOP5e7uLklatGhRvgYCAAAAAAD5K1dlQMuWLSVJV65c0datW9WrVy9VrVo1X4MBAAAAAID8UcyRwcWLF9dHH32k7Ozs/MoDAAAAAADymUNlgCS1atVKW7duzY8sAAAAAADgLnD41oIPPPCAZs2apQMHDui+++5TyZIl7fZ37Ngxz8IBAAAAAIC853AZMGHCBEnSggULcuzjbgIAAAAAABR8DpcBCQkJ+ZEDAAAAAADcJQ6vGQAAAAAAAAq3XJcBW7ZsUefOnZWWlpZjX2pqqrp06aJt27blaTgAAAAAAJD3cl0GfPzxx+rdu7dcXV1z7HNzc1OfPn20cOHCvMwGAAAAAADyQa7LgMTERAUFBd1yf5s2bbR37948CQUAAAAAAPJPrsuAM2fOqHjxW683WLx4cZ09ezZPQgEAAAAAgPyT6zKgcuXK2r9//y33JyYmqmLFinkSCgAAAAAA5J9clwFt27bVu+++q8uXL+fYd+nSJUVGRqp9+/Z5Gg4AAAAAAOS9W8/7v8HQoUP13Xff6eGHH9bTTz+tOnXqyGKx6NChQ1qyZImys7MVEhKSn1kBAAAAAEAeyHUZ4OHhoc8//1wTJkzQrFmzZLVaJUkWi0WBgYF644035OHhkW9BAQAAAABA3sh1GSBJ1atX14cffqiUlBQdPXpUklS7dm25u7vnSzgAAAAAAJD3HCoDrnF3d1fTpk3zOgsAAAAAALgLcr2AIAAAAAAAKBooAwAAAAAAMBnKAAAAAAAATCZXZUCPHj2UkpIiSYqKilJGRka+hgIAAAAAAPknV2XAoUOHbAXAe++9p/T09HwNBQAAAAAA8k+u7ibQuHFjjRs3Ts2bN5fVatVHH32kUqVK3XTs8OHD8zQgAAAAAADIW7kqA6ZOnarIyEj9+OOPslgs2rhxo5ycnHKMs1gslAEAAAAAABRwuSoD6tatq9mzZ0uSGjVqpIULF6pChQr5GgwAAAAAAOSPXJUB10tISMiPHAAAAAAA4C5xuAyQpGPHjunjjz/WoUOHZLFYVK9ePfXv31+1atXK63wAAAAAACCP5epuAtfbuHGjOnfurF27dqlhw4Zq0KCBdu7cqS5dumjz5s35kREAAAAAAOQhh2cGzJw5UwMHDtTLL79st33GjBmaMWOG2rRpk2fhAAAAAABA3nN4ZsChQ4f0+OOP59jeq1cvHTx4ME9CAQAAAACA/ONwGVC+fHnt27cvx/Z9+/ZxhwEAAAAAAAoBhy8TeOKJJxQWFqbjx4+rWbNmkqTffvtNH374oYKDg/M8IAAAAAAAyFsOlwHDhg2Tq6ur5s+fr1mzZkmSKlWqpOHDh6t///55HhAAAAAAAOQth8sAi8WigQMHauDAgUpLS5Mkubq65nkwAAAAAACQPxwuA65HCQAAAAAAQOHj8AKCAAAAAACgcKMMAAAAAADAZCgDAAAAAAAwGYfKgKysLPXr10+///57fuUBAAAAAAD5zKEywNnZWQcOHJDFYsmvPAAAAAAAIJ85fJlA9+7dtXz58vzIAgAAAAAA7gKHby2YlZWlL774Qj///LO8vb1VsmRJu/3jxo3Ls3AAAAAAACDvOVwG7N+/X15eXpKUY+0ALh8AAAAAAKDgc7gMWLx4cX7kAAAAAAAAd8kd31rw6NGj2rhxoy5duiRJslqtDh9jyZIl6tatm5o1a6ZmzZqpT58++r//+z/bfqvVqsjISAUGBqpp06bq16+fDhw4YHeMzMxMTZ48WQEBAfL19VVISIj++usvuzEpKSkaM2aMmjdvrubNm2vMmDG6cOGC3ZgTJ04oJCREvr6+CggI0JQpU5SZmWk3JjExUc8884yaNm2qoKAgRUVF3dHzBgAAAADASA6XAefOndOAAQP08MMP6/nnn9fp06clSa+99pqmTZvm0LGqVKmil19+WStWrNCKFSt0//33a9iwYbYT/g8//FALFixQWFiYli9fLg8PDwUHBystLc12jPDwcK1bt06zZ8/WkiVLlJ6eriFDhig7O9s2ZvTo0UpISNC8efM0b948JSQkaOzYsbb92dnZGjJkiNLT07VkyRLNnj1ba9euVUREhG1MWlqaBg0apEqVKmn58uV6/fXXNX/+fC1YsMDRlxAAAAAAAEM5XAZMnTpVxYsX14YNG1SiRAnb9s6dO2vjxo0OHatDhw5q27at6tSpozp16mjkyJEqVaqU4uLiZLVatWjRIoWEhKhTp07y9PRURESELl26pJiYGElSamqqVqxYodDQULVu3VpeXl6aPn269u/fr59//lmSdOjQIW3cuFFTpkyRn5+f/Pz8NHnyZP344486fPiwJGnTpk06ePCgpk+fLi8vL7Vu3VqhoaFatmyZrXhYvXq1Ll++rGnTpsnT01OdOnVSSEiIFixYwOwAAAAAAECh4vCaAZs3b9ZHH32kKlWq2G2vXbu2Tpw4ccdBsrOz9e233yo9PV1+fn5KSkrS6dOnFRgYaBvj4uIif39/xcbGqm/fvtqzZ4+ysrLUpk0b25jKlSurQYMGio2NVVBQkGJjY+Xm5iYfHx/bGF9fX7m5uSk2NlZ169ZVXFycGjRooMqVK9vGBAYGKjMzU3v27NH999+vuLg4+fv7y8XFxW7MzJkzlZSUpJo1a972ueWGk5NTrsYhb+X2/QEAAACAosLhMiA9Pd1uRsA1586dsztRzq3ExET17dtXly9fVqlSpfTee++pfv36+u233yRJFSpUsBvv4eFhKx3OnDkjZ2dnubu75xhz5swZ25gbj3HtuNeP8fDwsNvv7u4uZ2dnuzHVq1fPcYxr+25VBmRmZiouLu4fX4eSJUva7tKAuysxMVEZGRlGxwAAAACAPJGbc3OHywB/f3+tWrVKI0aMsG27evWqPvroIwUEBDh6ONWpU0erVq3ShQsX9N133+mVV17RJ598Ytt/4+0KczMlP7djrj/2rW6L6OiYG7m4uHCSX8A1bNjQ6AgAAAAAkCfi4+NzNc7hMmDs2LHq16+fbYr+9OnTdfDgQaWkpOizzz5zOKiLi4tq164tSWrSpIl2796tRYsWafDgwZL+/tS9UqVKtvHJycm2T/E9PDyUlZWllJQUu9kBycnJ8vPzs41JTk7O8bhnz561fbLv4eGhnTt32u1PSUlRVlaW3ZhriyVe/zhSztkLN2L6f8HG+wMAAADAbBxeQLB+/fpavXq1mjZtqjZt2igjI0MPPfSQVq5cqVq1av3rQFarVZmZmapRo4YqVqyozZs32/ZlZmZq27ZtthN9b29vOTs72405deqUDhw4YBvj5+en1NRU7dq1yzZm586dSk1NtY3x9fXVgQMHdOrUKduYzZs3y8XFRd7e3rYx27dvt7vd4KZNm1SpUiXVqFHjXz9vAAAAAADuFodnBkhSxYoV9dJLL/3rB581a5YeeOABValSRRcvXtSaNWu0detWzZs3TxaLRf3791d0dLTuvfde1a5dW9HR0SpRooS6du0qSXJzc1OvXr0UERGhcuXKyd3dXREREfL09FTr1q0lSfXq1VNQUJDGjx+vSZMmSZJef/11tW/fXnXr1pX090KA9evX19ixYzV27FilpKQoIiJCvXv3lqurqySpW7dueu+99zRu3DgNGTJER48eVXR0tIYNG3bbywQAAAAAACho7qgMSElJ0fLly3Xo0CFZLBbVq1dPPXv2VNmyZR06zpkzZzR27FidOnVKbm5uatiwoebNm2e7O8DgwYN1+fJlTZw4USkpKfLx8dH8+fNtJ+iS9Oqrr6p48eIaMWKELl26pFatWmnatGl2U79nzJihKVOmaNCgQZL+vqVhWFiYbb+Tk5Oio6M1ceJEPfnkk7bC4ZVXXrGNcXNz0/z58zVp0iT16tVL7u7uCg4OVnBw8J28hAAAAAAAGMZizc1qe9fZunWrXnjhBbm6utqm0O/du1cXLlzQnDlz1LJly3wJWhjt3r1b0t9rIeTW02/HKOGPs/kVCddpVL28Ph3R1egYAAAAAJBncnse6vDMgEmTJumRRx7RhAkTbJ++Z2dna+LEiZo0aZJiYmLuIC4AAAAAALhbHF5A8NixYwoODrabhu/k5KSBAwfq2LFjeRoOAAAAAADkPYfLAC8vLx0+fDjH9sOHD6tx48Z5EgoAAAAAAOSfXF0mkJCQYPt9//79FR4erqNHj8rHx0fS37fq+/TTT/Xyyy/nT0oAAAAAAJBnclUGdO/eXRaLRdevNTh9+vQc40aPHq3OnTvnXToAAAAAAJDnclUGrF+/Pr9zAAAAAACAuyRXZUD16tXzOwcAAAAAALhLHL61oCSdPHlSO3bs0NmzZ3X16lW7ff3798+TYAAAAAAAIH84XAasWLFCb7zxhpydnVWuXDm7fRaLhTIAAAAAAIACzuEy4N1339WwYcM0ZMgQFSvm8J0JAQAAAACAwRw+m7906ZK6dOlCEQAAAAAAQCHl8Bl9r1699O233+ZHFgAAAAAAcBc4fJnA6NGjNWTIEG3cuFGenp4qXtz+EOPGjcuzcAAAAAAAIO85XAZ88MEH2rRpk+rUqZNjn8ViyZNQAAAAAAAg/zhcBixcuFBvvvmmevbsmR95AAAAAABAPnN4zQAXFxc1a9YsP7IAAAAAAIC7wOEyoH///vrkk0/yIwsAAAAAALgLHL5MYNeuXfrll1/0448/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","text/plain":["<Figure 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","text/plain":["<Figure size 1200x600 with 1 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","text/plain":["<Figure 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","text/plain":["<Figure size 1200x600 with 2 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","text/plain":["<Figure size 1200x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# Load the data\n","file_path = \"/kaggle/input/10232023/Crime_Reports.csv\"\n","data = load_data(file_path)\n","\n","if data is not None:\n","    data = clean_data(data)\n","\n","    # Additional data analysis and visualizations can be performed here.\n","\n","    # Data Summary and Cleaning\n","    data_description = data.describe()\n","\n","    missing_values = data.isnull().sum()\n","\n","    data[\"Clearance Status\"].fillna(\"Not Cleared\", inplace=True)\n","\n","    latitude = data.get(\"Latitude\", 0.0)\n","    longitude = data.get(\"Longitude\", 0.0)\n","\n","    if \"Occurred Date Time\" in data.columns:\n","        data[\"Occurred Date Time\"] = pd.to_datetime(data[\"Occurred Date Time\"])\n","        data[\"Report Date Time\"] = pd.to_datetime(data[\"Report Date Time\"])\n","        data_types = data.dtypes\n","\n","        plt.figure(figsize=(12, 6))\n","        data[\"Occurred Date Time\"].value_counts().resample(\"M\").sum().plot()\n","        plt.title(\"Monthly Crime Trends\")\n","        plt.xlabel(\"Year-Month\")\n","        plt.ylabel(\"Number of Crimes\")\n","\n","        yearly_crime_stats = data.groupby(data[\"Occurred Date Time\"].dt.year)[\n","            \"Category Description\"\n","        ].count()\n","        monthly_crime_stats = data.groupby(\n","            data[\"Occurred Date Time\"].dt.to_period(\"M\")\n","        )[\"Category Description\"].count()\n","    else:\n","        print(\"The 'Occurred Date Time' column is not present in the dataset.\")\n","    # Crime Categories Analysis\n","    data = data[data[\"Category Description\"] != \"Unknown\"]\n","    crime_category_counts = data[\"Category Description\"].value_counts()\n","\n","    plt.figure(figsize=(12, 6))\n","    sns.countplot(\n","        data=data, x=\"Category Description\", order=crime_category_counts.index\n","    )\n","    plt.title(\"Distribution of Crime Categories\")\n","    plt.xlabel(\"Category Description\")\n","    plt.ylabel(\"Number of Crimes\")\n","    plt.xticks(rotation=90)\n","    category_plot_file = os.path.join(OUTPUT_DIRECTORY, \"crime_categories_plot.png\")\n","    plt.savefig(category_plot_file)\n","\n","    specific_category = \"Murder\"\n","    category_trends = data[data[\"Category Description\"] == specific_category]\n","\n","    crime_by_zipcode = data[\"Zip Code\"].value_counts()\n","\n","    crime_rates_by_zipcode = data[\"Zip Code\"].value_counts(normalize=True)\n","\n","    plt.figure(figsize=(12, 6))\n","    crime_by_zipcode.head(10).plot(kind=\"bar\")\n","    plt.title(\"Top 10 Zip Codes with the Highest Crime Rates\")\n","    plt.xlabel(\"Zip Code\")\n","    plt.ylabel(\"Number of Crimes\")\n","    zip_code_plot_file = os.path.join(OUTPUT_DIRECTORY, \"top_zip_codes_plot.png\")\n","    plt.savefig(zip_code_plot_file)\n","\n","    plot_bar_graph(\n","        data[\"Category Description\"].value_counts(),\n","        \"Crime Type\",\n","        \"Total Incidents\",\n","        \"Total Property & Violent Crimes (2003-Present)\",\n","        os.path.join(OUTPUT_DIRECTORY, \"total_crimes_plot.png\"),\n","    )\n","\n","    data = calculate_distance_to_booking_office(data)\n","    data = cluster_crime_locations(data, n_clusters=5)\n","\n","    plt.figure(figsize=(12, 6))\n","    plt.scatter(\n","        data[\"Longitude\"],\n","        data[\"Latitude\"],\n","        c=data[\"Distance to Booking Office (Miles)\"],\n","        cmap=\"viridis\",\n","        s=20,\n","    )\n","    plt.title(\"Crime Locations with Distance to Booking Office\")\n","    plt.xlabel(\"Longitude\")\n","    plt.ylabel(\"Latitude\")\n","    plt.colorbar(label=\"Distance to Booking Office (Miles)\")\n","    distance_plot_file = os.path.join(\n","        OUTPUT_DIRECTORY, \"distance_to_booking_office_plot.png\"\n","    )\n","    plt.savefig(distance_plot_file)\n","\n","    plt.figure(figsize=(12, 6))\n","    plt.scatter(\n","        data[\"Longitude\"],\n","        data[\"Latitude\"],\n","        c=data[\"Location Cluster\"],\n","        cmap=\"tab20\",\n","        s=20,\n","    )\n","    plt.title(\"Clustered Crime Locations\")\n","    plt.xlabel(\"Longitude\")\n","    plt.ylabel(\"Latitude\")\n","    plt.colorbar(label=\"Cluster\")\n","    cluster_plot_file = os.path.join(OUTPUT_DIRECTORY, \"clustered_locations_plot.png\")\n","    plt.savefig(cluster_plot_file)\n","\n","    crime_categories = [\"Aggravated Assault\", \"Robbery\", \"Murder\"]\n","    for category in crime_categories:\n","        create_heatmap(\n","            data[data[\"Category Description\"] == category],\n","            os.path.join(\n","                OUTPUT_DIRECTORY, f\"{category.lower().replace(' ', '_')}_heatmap.html\"\n","            ),\n","        )\n","\n","# Save the output file\n","output.close()\n","\n","# Display plots and results\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"id":"cdff9f74","metadata":{"papermill":{"duration":0.007363,"end_time":"2023-10-25T17:04:04.633934","exception":false,"start_time":"2023-10-25T17:04:04.626571","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"},"papermill":{"default_parameters":{},"duration":1192.534001,"end_time":"2023-10-25T17:04:06.071915","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-10-25T16:44:13.537914","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file
+{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/robertgrantham/austin-crime-reports-analysis?scriptVersionId=148484095\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"5dc54f74","metadata":{"_cell_guid":"afe7265d-6e1f-4021-a439-37c51619fb2c","_uuid":"7a0c641a-6db4-4ea2-8803-5abce812fd35","papermill":{"duration":0.002713,"end_time":"2023-10-29T17:53:51.440508","exception":false,"start_time":"2023-10-29T17:53:51.437795","status":"completed"},"tags":[]},"source":["# Analyzing Austin PD's Crime Reports Dataset\n","\n","The Austin Police Department's Crime Reports dataset provides valuable information about reported crimes in Austin, Texas. In this analysis, we will explore the dataset and gain insights into various aspects of crime in Austin.\n","\n","## Table of Contents\n","\n","I. Introduction  \n","II. Data  \n","III. Methodology  \n","IV. Outcome  \n","\n","## I. Introduction\n","\n","The Austin Police Department's Crime Reports dataset offers a comprehensive view of reported crimes in Austin, Texas. By analyzing this dataset, we can extract valuable insights into the nature and patterns of crime in the city.\n","\n","## II. Data\n","\n","### 1. Project Context\n","\n","This project is self-paced and not affiliated with any work or educational institution. My goal is to uncover valuable insights that can benefit the Austin law enforcement community, news outlets, and anyone interested in understanding and addressing crime-related issues in the Austin area.\n","\n","### 2. Data Collection\n","\n","Initially, I attempted to import the data into this notebook using Sodapy's Socrata API method. However, I found it to be insufficient as it didn't import the complete dataset and added redundant columns. As a result, I decided to manually download the entire dataset and repeat the process each week after the updates.\n","\n","### 3. Data Scrubbing\n","\n","Before conducting any analysis, it is crucial to clean and preprocess the dataset. The data scrubbing process involves addressing missing data, removing irrelevant columns, and ensuring data types are appropriate.\n","\n","## III. Methodology\n","\n","Once the dataset is scrubbed and prepared, we can perform an exploratory analysis to gain a deeper understanding of the crime data. The exploratory analysis may include the following:\n","\n","### A. Crime Rates by Area\n","\n","#### 1. Crime Rates by Zip Code\n","\n","We start by examining the crime rates by zip code. The following code calculates the total number of crimes for each zip code and displays the top 10 zip codes with the highest crime rates.\n","\n","```python\n","crime_by_zipcode = df[\"zip_code\"].value_counts().head(10).to_frame()\n","display(crime_by_zipcode)\n","```\n","\n","#### 2. Crime Rates as Percentages\n","\n","In addition to the total crime counts, it is also informative to analyze the crime rates as percentages relative to the total. The following code calculates the crime rates as percentages for each zip code and displays the top 10 zip codes with the highest crime rates.\n","\n","```python\n","crime_rates_by_zipcode = df[\"zip_code\"].value_counts(normalize=True).head(10).to_frame()\n","display(crime_rates_by_zipcode)\n","```\n","\n","#### 3. Visualizing Crime Rates by Zip Code\n","\n","To visually represent the crime rates by zip code, we can create a bar chart. The following code generates a bar chart showing the top 10 zip codes with the highest crime rates.\n","\n","```python\n","plt.figure(figsize=(10, 6))\n","crime_by_zipcode.plot(kind=\"bar\", rot=45)\n","plt.title(\"Top 10 Zip Codes with Highest Crime Rates\")\n","plt.xlabel(\"Zip Code\")\n","plt.ylabel(\"Number of Crimes\")\n","plt.show()\n","```\n","\n","## IV. Outcome\n","\n","In this section, we summarize the key findings and insights obtained from the analysis. Discuss any significant patterns, trends, or observations related to crime in Austin based on the data and methodologies applied.\n","\n","By analyzing the crime data, we aim to contribute to a better understanding of crime distribution, trends, and areas of concern in Austin. This information can be valuable for law enforcement agencies, policymakers, and the community in efforts to address and prevent crime effectively."]},{"cell_type":"code","execution_count":1,"id":"dd81d6e5","metadata":{"_cell_guid":"b3237cc4-51e3-49bf-9e86-7210dd60ca92","_uuid":"e34c5e34-3243-4448-8a64-56b4fcbc96bb","collapsed":false,"execution":{"iopub.execute_input":"2023-10-29T17:53:51.4464Z","iopub.status.busy":"2023-10-29T17:53:51.446061Z","iopub.status.idle":"2023-10-29T17:53:53.630615Z","shell.execute_reply":"2023-10-29T17:53:53.629611Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":2.189947,"end_time":"2023-10-29T17:53:53.632864","exception":false,"start_time":"2023-10-29T17:53:51.442917","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Redirecting output to crime_analysis_outputs/output.txt\n"]}],"source":["import os\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import folium\n","from folium import plugins\n","from geopy.distance import geodesic\n","from sklearn.cluster import KMeans\n","import seaborn as sns\n","import warnings\n","\n","BOOKING_OFFICE_COORDS = (30.274020, -97.746580)  # Latitude, Longitude\n","\n","# Create an output directory if it doesn't exist\n","OUTPUT_DIRECTORY = \"crime_analysis_outputs\"\n","os.makedirs(OUTPUT_DIRECTORY, exist_ok=True)\n","\n","# Redirect the output to a file\n","output_file = os.path.join(OUTPUT_DIRECTORY, \"output.txt\")\n","with open(output_file, \"w\") as output:\n","    print(\"Redirecting output to\", output_file)\n","    print(\"Some Example Output\", file=output)\n","# Set up pandas and seaborn\n","pd.set_option(\"display.max_columns\", None)\n","warnings.filterwarnings(\"ignore\")\n","plt.style.use(\"seaborn-white\")\n","sns.set_style(\"whitegrid\")\n"]},{"cell_type":"code","execution_count":2,"id":"400949d6","metadata":{"execution":{"iopub.execute_input":"2023-10-29T17:53:53.639403Z","iopub.status.busy":"2023-10-29T17:53:53.638935Z","iopub.status.idle":"2023-10-29T17:53:53.655925Z","shell.execute_reply":"2023-10-29T17:53:53.655241Z"},"papermill":{"duration":0.022451,"end_time":"2023-10-29T17:53:53.657797","exception":false,"start_time":"2023-10-29T17:53:53.635346","status":"completed"},"tags":[]},"outputs":[],"source":["def load_data(file_path):\n","    # Load data from a CSV file\n","    try:\n","        df = pd.read_csv(file_path)\n","        return df\n","    except Exception as e:\n","        print(\"Error loading data:\", e)\n","        return None\n","\n","\n","def clean_data(df):\n","    # Clean and preprocess the DataFrame\n","    columns_to_drop = [\n","        \"X-coordinate\",\n","        \"Y-coordinate\",\n","        \"Location\",\n","    ]\n","    df = df.drop(columns=columns_to_drop, errors=\"ignore\")\n","\n","    date_time_columns = [\"Occurred Date Time\", \"Report Date Time\", \"Clearance Date\"]\n","    for col in date_time_columns:\n","        df[col] = pd.to_datetime(df[col], errors=\"coerce\")\n","    df.fillna(\n","        {\n","            \"Zip Code\": \"Unknown\",\n","            \"Location Type\": \"Unknown\",\n","            \"Council District\": \"Unknown\",\n","            \"PRA\": \"Unknown\",\n","            \"Census Tract\": \"Unknown\",\n","            \"APD Sector\": \"Unknown\",\n","            \"APD District\": \"Unknown\",\n","            \"Clearance Status\": \"Unknown\",\n","            \"Category Description\": \"Unknown\",\n","        },\n","        inplace=True,\n","    )\n","\n","    categorical_columns = [\n","        \"Highest Offense Description\",\n","        \"Zip Code\",\n","        \"Location Type\",\n","        \"Council District\",\n","        \"APD District\",\n","        \"APD Sector\",\n","        \"PRA\",\n","        \"Census Tract\",\n","        \"Category Description\",\n","    ]\n","    df[categorical_columns] = df[categorical_columns].astype(\"category\")\n","\n","    df[\"Year\"] = df[\"Occurred Date Time\"].dt.year\n","    df[\"Quarter\"] = df[\"Occurred Date Time\"].dt.quarter\n","    df[\"Month\"] = df[\"Occurred Date Time\"].dt.month\n","    df[\"Day\"] = df[\"Occurred Date Time\"].dt.day\n","    df[\"Day of Week\"] = df[\"Occurred Date Time\"].dt.dayofweek\n","\n","    day_of_week_dummies = pd.get_dummies(\n","        df[\"Day of Week\"], prefix=\"Day\", drop_first=True\n","    )\n","    df = pd.concat([df, day_of_week_dummies], axis=1)\n","\n","    df.set_index(\"Occurred Date Time\", inplace=True)\n","    df.sort_index(inplace=True)\n","\n","    df[\"Time to Clearance (Days)\"] = (\n","        df[\"Clearance Date\"] - df[\"Report Date Time\"]\n","    ).dt.days\n","\n","    return df\n","\n","\n","def filter_and_save_data(df, filter_condition, filename):\n","    filtered_data = df.query(filter_condition)\n","    filtered_data.to_csv(filename, index=False)\n","\n","\n","def plot_bar_graph(data, x_label, y_label, title, filename):\n","    plt.figure(figsize=(12, 6))\n","    data.plot(kind=\"bar\", rot=60)\n","    plt.title(title)\n","    plt.xlabel(x_label)\n","    plt.ylabel(y_label)\n","    plt.tight_layout()\n","    plt.savefig(filename)\n","\n","\n","def create_heatmap(df, outfile):\n","    coords_heat = df[[\"Latitude\", \"Longitude\"]].dropna()\n","    map_austin = folium.Map(\n","        location=[30.2672, -97.7431], tiles=\"OpenStreetMap\", zoom_start=12\n","    )\n","    map_austin.add_child(plugins.HeatMap(coords_heat.values, radius=15))\n","    map_austin.save(outfile)\n","\n","    \n","def calculate_distance_to_booking_office(df):\n","    # Calculate the distance from each crime location to the booking office\n","    df = df.dropna(\n","        subset=[\"Latitude\", \"Longitude\"]\n","    )  # Replace with the correct column names\n","    df[\"Distance to Booking Office (Miles)\"] = df.apply(\n","        lambda row: geodesic(\n","            (row[\"Latitude\"], row[\"Longitude\"]), BOOKING_OFFICE_COORDS\n","        ).miles,\n","        axis=1,\n","    )\n","    return df\n","\n","\n","def cluster_crime_locations(df, n_clusters):\n","    kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n","    df[\"Location Cluster\"] = kmeans.fit_predict(df[[\"Latitude\", \"Longitude\"]])\n","    return df\n"]},{"cell_type":"code","execution_count":3,"id":"4fcbfab4","metadata":{"_cell_guid":"ffbb0abe-b063-4084-b56a-918be9d0086d","_uuid":"fa23bad8-db53-4a64-a730-b6d686caa7df","execution":{"iopub.execute_input":"2023-10-29T17:53:53.663792Z","iopub.status.busy":"2023-10-29T17:53:53.663543Z","iopub.status.idle":"2023-10-29T18:09:55.696736Z","shell.execute_reply":"2023-10-29T18:09:55.695815Z"},"papermill":{"duration":962.042866,"end_time":"2023-10-29T18:09:55.702924","exception":false,"start_time":"2023-10-29T17:53:53.660058","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The 'Occurred Date Time' column is not present in the dataset.\n"]},{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 1200x600 with 1 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","text/plain":["<Figure 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yC/a/3rr7+0bds23XPPPQ4rYRs2bFBkZKSlqn311a5dO0ly+udly5Ytuvnmm63uYZSkBx98UIZh5Ftxl67MnZUrV2rlypVavny5Xn/9dXl6euqpp57SH3/8IalwP49btmxR69at86z46N69u/766688S943btyoxx57TBEREVq8eLGqVatWoGv39/fPc91BQUE6duyY5ftt27YpICAgTxWxa9euBTpHQVydsyaTqUji/fDDD5Kkxx9/3G6fjRs36vLly7r//vut5lOFChUUERHxj3//AkBpxbJewEV8fHxUsWLFQj8+wd/fv1D9vb29rb53d3d32H7p0iVVrlxZ6enpMgzDbqLzT5Z3StK5c+dkGIbN67m6xPTqY3TOnDmjmjVrOoy3bt06DRs2THfffbeeeeYZ+fn5yc3NTR999JFWrVrl1BjPnDkjyfZnXqNGDeXm5urcuXNWS6uv/Yv31eWZWVlZkq5sRjN37ly9//77Gj16tLKzsxUQEKABAwbk+xfq1q1bWzbI2rx5s9q0aaOgoCD5+flp8+bNatCggbKysv5xcmorefDw8NClS5f+UVzpyv2THh4eeebfVSaTSUuWLNHcuXO1YMECTZs2TdWqVVO3bt00bNiwfHekLuw8yO9az507J7PZnO/8S09P1/fff6/mzZvbPH51LhXW2bNnVadOnTzt1/6MOFKhQgXLP4pIUlhYmG677Ta1a9dOb731lmbOnFmon8ezZ88WqN9V3377rS5duqRHH33U5r319hRkHp49e1Z169bN08/X1zff+AX9HXz06FFVrFjR7pwtrNOnT8vNzc3h7/Kr99I/9NBDNo///R/LAOB6QnIKuIibm5tuv/12/fjjjzpx4oTde56uVVT/ep8fHx8fmUwmffjhh3Y3a/onqlatqnLlyik1NTXPsVOnTlnGcPW/+VUYP/vsM9WtW1dvvPGG1Wf03nvvOT3Gq+e3N8Zy5cqpatWqhY5755136s4771R2drZ27NihhIQEvfjii6pTp47Cw8Ptvq9169ZauXKldu3apV27dmngwIGSpNtvv12bN2/WsWPHVKlSJYWGhhZ6TCXh5MmT2rt3ryIiIuzebypJderU0auvvirpSvV67dq1mjNnjrKzs/O9l7Co54G3t7fc3NzynX8+Pj4KCgrSsGHDbB539jEp1apVK9DPSGHVqFFDPj4+SkpKklS4n8fCjmns2LFau3at+vXrpzlz5hTp7sDVqlWzJHJ/Z6vtWm5uboqMjHT4O/jEiRPau3ev2rVrJzc3tyIZc/Xq1WU2m5Wammp3Xlz9DN966y3Vrl27SM4LAGUB//QGuFD//v1lGIZefvllq10/r8rJybHsulrS7rjjDhmGoZMnTyokJCTPKygo6B/Fv5pEffPNN5aqonRlF8vPPvtMtWrVsixfbteunX7++WfLskJbTCaT3N3drRKS1NTUPLtfSlcS67+f055GjRqpZs2aWrNmjdVy1IsXL2rdunUKCwv7R8+I9PDw0G233WbZ5TO/XThbt24tk8mkN998UyaTSREREZb2n3/+WZs2bVJERISlCu7ovJIK9BkUlaysLL300ku6fPmy1XNO89OoUSM999xzCgwMtPp87P0ZFmYeFISnp6ciIiL01Vdf6fTp03b73XHHHTp48KDq169v8+clv8qrvetp3bq1fvvtN+3du9eqffXq1TKZTIqMjHTquk6cOKEzZ85YKoyF+Xls3bq1tmzZkidh/89//qOKFSvmeTRQhQoVNHv2bN1xxx0aOHCgzZ2TnRUREaHk5GT99ttvVu1ffPFFgd7/7LPPyjAMTZgwQWaz2eqY2WzWhAkTZBiGnn322SIb89Wl3h999JHdPtHR0SpfvryOHDlicz79vRIOANcTKqeAC4WHh2vChAmaOHGievTooZiYGAUEBOjy5cvat2+fVqxYoYCAgDz3MZaEVq1aqWfPnho3bpz27NmjiIgIVaxYUampqfrll18UGBhoeaSCI1u2bLF5r2L79u31wgsvqG/fvurdu7f69u0rd3d3LVu2TMnJyZo5c6YlwXj++ef1ww8/6IknnlD//v0VGBiozMxM/fjjj3rqqafUpEkT3XHHHVq3bp0mTJigf/3rXzpx4oTefvtt1ahRw3Jf3VWBgYHaunWr1q9fL39/f1WuXFmNGzfOM8Zy5cpp5MiRGjFihPr376+ePXsqOztbCxcu1Llz5/Tiiy8W+nN98803deLECbVu3Vq1atXSuXPntHTpUrm7u9vdYfcqX19fBQQEaOPGjYqMjLQkxlFRUTp79qzOnj2b7yNprl6/JM2fP1/t2rVTuXLlFBQU9I+r4VcdP35cO3bsUG5urjIzM7V//36tWrVKx44d05gxYxxWzpKSkjR58mTdfffdatCggdzd3bVlyxYdOHDAKkEIDAzUF198oS+//FJ169ZVhQoVFBQUVKh5UFBjx47Vo48+qkceeUTPPvus6tevr/T0dK1fv14TJ05UlSpVNHToUG3evFkxMTHq1auXGjVqpOzsbKWkpOiHH37QxIkTHa6OsHc9Tz31lFavXq3+/ftr6NChql27tjZs2KBly5bp0UcfLdD951lZWdqxY4ekKwlXSkqKFixYIEl68sknLf0K+vM4aNAgff/99+rdu7cGDRokb29vff7559qwYYNGjhxpeUzN37m7u2vmzJl66aWX9Pzzzys+Pr5I7gt98skntWrVKvXr109Dhw6Vr6+v1qxZo99//11S/stfW7VqpXHjxunVV1/VY489pscff1y1a9fWsWPHtGzZMu3cuVPjxo2z2tX4n7r11lt1//33a968eUpPT9cdd9whDw8P7du3TxUrVlSvXr1Ut25dDR06VG+88Yb+/PNPtWvXTlWrVlVaWpp2796tihUraujQoUU2JgAoLUhOARe7+sD2JUuWaMGCBUpNTZW7u7saNmyorl276oknnnDZ2CZNmqTQ0FAtX75cH330kXJzc1WjRg21bNkyz0Yl9syYMcNm+3fffafbbrtNS5Ys0ezZszV27Fjl5uaqadOmmjdvntVGTjVr1tTKlSv11ltvaf78+Tp79qx8fHzUqlUry31pPXr0UHp6uj7++GOtWrVK9erV07PPPqsTJ05YngV71UsvvaSJEyfqhRde0F9//aXbbrtN77//vs1xduvWTRUrVtS7776r4cOHy83NTaGhoVq6dKlTf2ENDQ3Vnj17NGPGDJ0+fVpVq1ZVcHCwlixZooCAgHzfHxUVpYMHD1rdV1q7dm01bNhQf/zxh1q3bp1vjK5du+rXX3/VsmXLNHfuXBmGoe+++87mvXvOeP/99/X+++/Lzc1NVapUUd26ddWhQwc98sgj+T7+wt/fX/Xr19eyZct04sQJSVfubx49erTlebTSlUf2pKam6uWXX9aFCxdUp04drV+/vlDzoKCaNm1qmX+vv/66Lly4IH9/f91+++2WhL5GjRpauXKl3n77bS1cuFAnT55U5cqVVadOHbVt2zbf5d/2rqd69er6+OOP9frrr1vOXbduXcsjVgrizz//VM+ePSVdSdaubmoWGxtr9Q8iBf15bNy4sT7++GPNnDlTkyZNUlZWlpo0aaK4uDg9+OCDdsdRrlw5vfrqq6pcubJGjhypv/76Sw8//HCBrsGemjVr6oMPPtCrr76qV155RRUrVtSdd96poUOHavTo0TYT5Wv16tVLISEhWrRokeLj43X27Fl5e3urVatWWrZsmcOl9s6aNm2amjVrplWrVumTTz6Rp6enbr75ZvXv39/Sp3///mrSpImWLl2qL774QtnZ2fL391dwcLAeffTRIh8TAJQGJiO/rRMBAADKkNjYWK1Zs0Y///xzka0IAAAUPyqnAACgzJozZ45q1qypunXr6uLFi9qwYYP+/e9/a+DAgSSmAFDGkJwCAIAyy93dXQsWLNDJkyd1+fJlNWzYUGPGjLG6nxYAUDawrBcAAAAA4HI8SgYAAAAA4HIkpwAAAAAAlyM5BQAAAAC43HW1IVJiYqIMw5C7u7urhwIAAADc8HJycmQymYrlmcHF7cCBA8rOzi62+B4eHgoKCiq2+GXRdZWcGoahkt7fKTs7m63q4RBzBI4wP+AI8wP5YY7AkdIwP8ry3qvZ2dnKzv5LHm5pRR/b7FfkMa8H11VyerViGhISUiLnM5vN2rFjh5o1ayY3N7cSOSfKFuYIHGF+wBHmB/LDHIEjpWV+7N6922XnLgoebmm6pdbQIo+7/8RbkuoVedyyjntOAQAAAAAud11VTgEAAACgqBiSzEZuscQ1FXnUso/KKQAAAADA5aicAgAAAIAduSq7mzqVNVROAQAAAAAuR+UUAAAAAGwylKuiv+dUVGNtonIKAAAAAHA5KqcAAAAAYMOV3XqLvsrJbr22kZwCAAAAgB1siFRyWNYLAAAAAHA5KqcAAAAAYIeZymmJoXIKAAAAAHA5KqcAAAAAYIOh4rnnlFqsbVROAQAAAAAuR+XUBdKOnlb68TOqG3CTKntXcvVwAAAAANhRHI+SgW0kpyXEfNmsD6as0vLpnynnUo4kya18OXUfco+emfa43Mq7uXiEAAAAAOA6LOstIa90f00fTF51JTE1ciUjV+acy1o583Pd7RGj155729VDBAAAAHCN3GJ4wTaS0xKQ+P0e/fxloiUptWXdO9/rbs+eupxzuYRHBwAAAMAWQ1ceJVPULxYK20ZyWgJe7hZXoH7m7FzdV7VXMY8GAAAAAEofktNidvnyZWX/ddluxfRaOZcu67N5XxXzqAAAAAAUhNko+hdsIzktZnGPv1XgxPSqt4ctKZ7BAAAAAEApxW69xSz16OlCv8ecYy6GkQAAAAAoLDYwKjlUTovZQy92dfUQAAAAAKDUo3JazA4n/uHqIQAAAABwwpXdek3FEhd5ubRyumzZMnXr1k0tW7ZUy5Yt1bNnT/33v/+1HDcMQ7Nnz1Z0dLRatGihXr16KTk52YUjLrw7Hmvj6iEAAAAAQKnn0uS0Vq1aGjFihFatWqVVq1bp9ttv16BBgywJ6Pz587V48WKNHz9eK1eulJ+fn/r06aPz58+7ctiF0rBpvUK/546YqGIYCQAAAIBCMaTcYnhROrXNpclpx44d1b59ezVq1EiNGjXS8OHDValSJe3YsUOGYWjp0qUaMGCAOnfurMDAQMXHxysrK0tr1qxx5bALbeH+mYXq/+LC54ppJAAAAAAKwyxTkb9gW6m559RsNuurr77SxYsXFR4erpSUFKWmpio6OtrSx8PDQxEREUpMTFRMTIzDWCXh6nnyO1+dm2sroE1jJW86nG9M37o+cvcoX2LXgOJV0DmCGxPzA44wP5Af5ggcYX6gLHJ5cnrgwAHFxMTo0qVLqlSpkubOnaubb75Zv/76qyTJ19fXqr+fn5+OHTtmN152drZ27NhRnEPOY/fu3fn2efqNRzQmYlq+/YaveKbEx4/iV5A5ghsX8wOOMD+QH+YIHCkN88PDw8PVQ3AaGyKVLJcnp40aNdLq1at17tw5rVu3TqNHj9YHH3xgOW4yWU8Gw3D8R+nh4aFmzZoVy1ivZTabtXv3boWEhMjNzS3f/ivTF+gh32fsHh/w5pO6NaJVUQ4RLlbYOYIbC/MDjjA/kB/mCBwpLfNj3759Ljs3yh6XJ6ceHh5q0KCBJCkkJES7d+/W0qVL1a9fP0lSWlqaatSoYemfnp4uPz8/hzFL+gfQzc2tQOf09vHWv08t0GMNBirnrxyrY8MXDFCXvp2Ka4hwsYLOEdyYmB9whPmB/DBH4Ajz45/LNbhHtKS4PDm9lmEYys7OVt26deXv769NmzZZKqHZ2dnatm2bRowY4eJROq+an7e+vLDM1cMAAAAAgFLFpcnpzJkz1a5dO9WqVUsXLlzQl19+qa1bt2rBggUymUzq3bu3EhIS1LBhQzVo0EAJCQny9PRU165dXTlsAAAAADcIdtctOS5NTtPS0jRq1CidOnVKXl5eCgoK0oIFC9SmTRtJUr9+/XTp0iVNnDhRGRkZCg0N1aJFi1SlShVXDhsAAAAAUMRcmpy++uqrDo+bTCYNGTJEQ4YMKaERAQAAAMAVhkzKVbkij2uiGmtTqbvnFAAAAABKi+LYEIktqmwr+n8GAAAAAACgkKicAgAAAIAdxbEhEpVT26icAgAAAABcjsopAAAAANhgSDIbRV/PM4o84vWByikAAAAAwOVITgEAAADAjlyVK/JXfk6ePKkRI0YoMjJSoaGhuv/++7Vnzx7LccMwNHv2bEVHR6tFixbq1auXkpOTrWJkZ2dr8uTJioyMVFhYmAYMGKATJ05Y9cnIyNDIkSPVqlUrtWrVSiNHjtS5c+eK5oNzAskpAAAAAJQSGRkZevTRR+Xu7q758+friy++0JgxY1S1alVLn/nz52vx4sUaP368Vq5cKT8/P/Xp00fnz5+39Jk6daq++eYbzZo1S8uWLdPFixfVv39/mc1mS58XX3xRSUlJWrBggRYsWKCkpCSNGjWqRK/377jnFAAAAABsMhXLbr1yEHP+/PmqVauW4uLiLG1169a1fG0YhpYuXaoBAwaoc+fOkqT4+HhFRUVpzZo1iomJUWZmplatWqXp06crKipKkvTaa6/pjjvu0ObNm9W2bVsdOnRIP/74o1asWKHQ0FBJ0uTJk9WzZ08dPnxYjRs3LobrdozKKQAAAACUEuvXr1dwcLCGDh2q1q1b64EHHtCKFSssx1NSUpSamqro6GhLm4eHhyIiIpSYmChJ2rNnj3JyctSmTRtLn5o1ayogIMDSJzExUV5eXpbEVJLCwsLk5eVl6VPSSE4BAAAAwIaru/UW9cvRbr1//vmnPvroIzVs2FALFy5UTEyMpkyZotWrV0uSUlNTJUm+vr5W7/Pz81NaWpokKS0tTe7u7vL29nbY59oYV+Ne7VPSWNYLAAAAAHbkFsuyXvsMw1BwcLBeeOEFSVKzZs3022+/6aOPPtIDDzxg6WcymfK8ryCxC9Ln2tglhcopAAAAAJQS/v7+atKkiVVb48aNdezYMctxSXmqm+np6fLz85N0pUKak5OjjIwMh33S09PznP/06dM2K6olgeQUAAAAAGwwJJlVrshfjuqXLVu21O+//27V9scff6hOnTqSrmyO5O/vr02bNlmOZ2dna9u2bQoPD5ckBQcHy93d3arPqVOnlJycbOkTHh6uzMxM7dq1y9Jn586dyszMtPQpaSzrBQAAAIBS4sknn9Sjjz6qd955R/fcc4927dqlFStWaNKkSZKuLOft3bu3EhIS1LBhQzVo0EAJCQny9PRU165dJUleXl7q0aOH4uPj5ePjI29vb8XHxyswMNCye2+TJk3Utm1bvfzyy5bYsbGx6tChg0t26pVITgEAAADALrNRsotNW7RooTlz5mjmzJmaO3eu6tatq3Hjxum+++6z9OnXr58uXbqkiRMnKiMjQ6GhoVq0aJGqVKli6TNu3DiVL19ew4YNU1ZWllq3bq1p06bJzc3N0mfGjBmaMmWK+vbtK0nq2LGjxo8fX3IXew2SUwAAAAAoRTp06KAOHTrYPW4ymTRkyBANGTLEbp8KFSooNjZWsbGxdvtUq1ZNM2bM+EdjLUokpwAAAABgk0m5xbJNj2t2wy3t2BAJAAAAAOByVE4BAAAAwAZDktko+ipn/k8bvTGRnAIAAACAHWYWm5YYPmkAAAAAgMtROQUAAAAAWwwptzgeJcO6XpuonAIAAAAAXI7KKQAAAADYYMhULPecGjxKxiYqpwAAAAAAl6NyCgAAAAB2FMejZGAblVMAAAAAgMtROQUAAAAAO3Kp55UYklMAAAAAsMGQZC6GR8nwJBnb+GcAAAAAAIDLUTkFAAAAADtyeexLiaFyCgAAAABwOSqnAAAAAGCTqVjuORXVWJuonAIAAAAAXI7KKQAAAADYYEgyF0M9j916baNyCgAAAABwOSqnAAAAAGBHrsH9oSWF5BQAAAAA7CiOZb2wjU8aAAAAAOByVE4BAAAAwAZDJuUWw6NkDB4lYxOVUwAAAACAy1E5BQAAAAA7zFQ5SwyVUwAAAACAy1E5BQAAAAA7iuOeU9jGJw0AAAAAcDkqpwAAAABgg6HiuefUKPKI1weSUwAAAACwg2W9JYdPGgAAAADgclROAQAAAMAWwyRzcVRODR5PYwuVUwAAAACAy1E5BQAAAAAbDEm5bIhUYqicAgAAAABcjsopAAAAANhRLPecwiY+aQAAAACAy1E5BQAAAAA7ctlZt8SQnAIAAACADYYkczEsNmVDJNtcmpwmJCRo3bp1Onz4sDw9PRUeHq4RI0aocePGlj5jxozRp59+avW+0NBQrVixoqSHCwAAAAAoJi5NTrdu3arHH39cISEhMpvNmjVrlp5++ml98cUXqlSpkqVf27ZtFRcXZ/ne3d3dFcMFAAAAcEMxFdOyXpYK2+LS5HThwoVW38fFxal169bau3evIiIiLO0eHh7y9/cv6eEBAAAAAEpIqbrnNDMzU5Lk7e1t1b5161a1bt1aVatWVUREhIYPHy5fX1+7ccxmc7GO89rzlNT5UPYwR+AI8wOOMD+QH+YIHGF+FJ1cHnBSYkyGYZSK+3ENw9DAgQN17tw5LVu2zNL+5ZdfqlKlSqpdu7ZSUlL05ptvymw265NPPpGHh4dVjN27dys7O7ukhw4AAADADg8PD4WEhLh6GIW2e/dunc4+pc/Lv1vksbtdflbVPWqUyc+lOJWayumkSZN08OBBq8RUkrp06WL5OjAwUMHBwerYsaM2bNigzp0754nj4eGhZs2aFft4pSv/ErV7926FhITIzc2tRM6JsoU5AkeYH3CE+YH8MEfgSGmZH/v27XPZuYuCIclcDPeclorqYClUKpLTyZMna/369frggw9Uq1Yth31r1Kih2rVr648//rDbp6R/AN3c3PifAhxijsAR5gccYX4gP8wROML8QFni0uTUMAxNnjxZ33zzjd5//33Vq1cv3/ecOXNGx48fV40aNUpghAAAAABuZMWzWy9scWlyOnHiRK1Zs0Zvv/22KleurNTUVEmSl5eXPD09deHCBc2ZM0edO3eWv7+/jh49qlmzZsnHx0d33nmnK4cOAAAA4AaQa7AhUklxaXL60UcfSZJ69epl1R4XF6cHH3xQbm5uOnjwoFavXq3MzEz5+/srMjJSs2bNUpUqVVwxZAAAAABAMXBpcnrgwAGHxz09PfM8CxUAAAAASoIhk8wqjg2RWCpsCzVqAAAAAIDLlYrdegEAAACgNGJDpJJD5RQAAAAA4HJUTgEAAADAFkPKpZ5XYvikAQAAAAAuR+UUAAAAAOzIZWfdEkNyCgAAAAA2GJLMxbAhEnss2cayXgAAAAAoJWbPnq2goCCrV5s2bSzHDcPQ7NmzFR0drRYtWqhXr15KTk62ipGdna3JkycrMjJSYWFhGjBggE6cOGHVJyMjQyNHjlSrVq3UqlUrjRw5UufOnSuRa7SH5BQAAAAA7Mg1yhX5Kz8BAQHauHGj5fX5559bjs2fP1+LFy/W+PHjtXLlSvn5+alPnz46f/68pc/UqVP1zTffaNasWVq2bJkuXryo/v37y2w2W/q8+OKLSkpK0oIFC7RgwQIlJSVp1KhRRfvhFRLJKQAAAACUIm5ubvL397e8qlevLulK1XTp0qUaMGCAOnfurMDAQMXHxysrK0tr1qyRJGVmZmrVqlUaM2aMoqKi1KxZM7322ms6ePCgNm/eLEk6dOiQfvzxR02ZMkXh4eEKDw/X5MmT9f333+vw4cMuu27uOQUAAAAAm0zKLZYbRB3H/N///qfo6Gh5eHgoNDRUL7zwgurVq6eUlBSlpqYqOjra0tfDw0MRERFKTExUTEyM9uzZo5ycHKulwDVr1lRAQIASExPVtm1bJSYmysvLS6GhoZY+YWFh8vLyUmJioho3bpzvFWRnZ2vXrl1KSUlRVlaWqlevrltuuUX16tVz4vO4guQUAAAAAEqJFi1aKD4+Xg0bNlR6errmzZunmJgYrVmzRqmpqZIkX19fq/f4+fnp2LFjkqS0tDS5u7vL29s7T5+0tDRLn2tjXI17tY89v/76qz744AN9++23ysnJUdWqVVWhQgVlZGQoOztb9erV0yOPPKKYmBhVqVKlUNdOcgoAAAAANhgqnkfJGA6OtW/f3ur7sLAw3XXXXVq9erWl0mkyWY/JMBxFLFyfa2P/3cCBA7V7925169ZNCxcuVHBwsCpWrGg5/ueff2r79u1as2aNlixZovj4eKsKbn5ITgEAAACglKpUqZICAwP1xx9/6M4775R0pfJZo0YNS5/09HT5+flJulIhzcnJUUZGhlX1ND09XeHh4ZY+6enpec51+vRpmxXVq6Kjo/Xmm2/Kw8PD5vF69eqpXr166t69u5KTk3Xq1KlCXSsbIgEAAACAHbmGqchfhZGdna1Dhw7J399fdevWlb+/vzZt2mR1fNu2bZbEMzg4WO7u7lZ9Tp06peTkZEuf8PBwZWZmateuXZY+O3fuVGZmpqWPLY8//rjdxPRaAQEBhaqaSlROAQAAAMCugjz6pSjFx8erQ4cOuummm3T69GnNmzdP58+fV/fu3WUymdS7d28lJCSoYcOGatCggRISEuTp6amuXbtKkry8vNSjRw/Fx8fLx8dH3t7eio+PV2BgoKKioiRJTZo0Udu2bfXyyy9r0qRJkqTY2Fh16NChQJshSdLx48dlMplUq1YtSdKuXbv0+eef6+abb1bPnj2dunaSUwAAAAAoJU6cOKEXXnhBZ8+elY+Pj8LCwrRixQrVqVNHktSvXz9dunRJEydOVEZGhkJDQ7Vo0SKrzYfGjRun8uXLa9iwYcrKylLr1q01bdo0ubm5WfrMmDFDU6ZMUd++fSVJHTt21Pjx4ws8zhdffFGPPPKIHnjgAaWmpqpPnz4KCAjQZ599ptTUVA0ePLjQ105yCgAAAAB2FM+jZOybNWuWw+Mmk0lDhgzRkCFD7PapUKGCYmNjFRsba7dPtWrVNGPGDKfHmZycrBYtWkiS1q5dq4CAAH388cfauHGjXnnlFaeSU+45BQAAAAAUyuXLly33n27evFkdO3aUJDVu3NjyyJvCIjkFAAAAABuuPkqmqF/5P9Sl9Lv55pv18ccfa/v27dq8ebPatWsn6crmS9WqVXMqJskpAAAAAKBQRowYoeXLl6tXr16699571bRpU0nS+vXrLct9C4t7TgEAAADAFice/VLQuGVdZGSktmzZovPnz1s9T/WRRx5RxYoVnYpJ5RQAAAAAUGiGYWjv3r36+OOPdf78eUmSu7u7PD09nYpH5RQAAAAA7Cjp3XrLiqNHj+qZZ57R8ePHlZ2drTZt2qhKlSpasGCBLl26ZHl+amFQOQUAAAAAFMrUqVMVHBysrVu3qkKFCpb2u+66S1u2bHEqJpVTAAAAALCDyqltv/zyiz766CPL42Suql27tk6ePOlUTJJTAAAAALDBUPEkp9fDo2QMw1Bubm6e9hMnTqhy5cpOxWRZLwAAAACgUKKiovTee+9ZtV24cEGzZ89W+/btnYpJ5RQAAAAA7MgVy3ptGTt2rHr37q0uXbooOztbI0aM0B9//CEfHx/NnDnTqZgkpwAAAACAQqlZs6b+85//aM2aNdq3b59yc3P10EMPqVu3bjxKBgAAAACKGhsi2efp6amHHnqoyOKRnAIAAAAA8vXdd98VuG+nTp0KHZ/kFAAAAABsYLdea4MGDSpQP5PJpP379xc6PskpAAAAACBfSUlJxRqf5BQAAAAA7OCe05JDcgoAAAAANpmKKTktmwnv0qVL1bNnT1WoUEFLly512Ld3796Fjk9yCgAAAADI15IlS9StWzdVqFBBS5YssdvPZDKRnAIAAABAkTEkozgqp2V0R6T169fb/LqolCvyiAAAAAAAFBKVUwAAAACwI7eM3h9aXObMmVOgfoMHDy50bJJTAAAAAECBzJkzRzVq1JCvr68Mw/b6ZJPJRHIKAAAAAEXFUPE8SqaM3nIqSWrbtq1+/vlnBQcHq0ePHrrjjjvk5uZWJLG55xQAAAAAUCDz58/Xt99+q9DQUE2fPl3t27fXa6+9psOHD//j2CSnAAAAAGCHYZiK/FXW1ahRQ/3799fXX3+tWbNm6fTp03rooYcUExOjrKwsp+OyrBcAAAAA7CiOZb3Xk5CQEB09elS//fab9u/fr8uXLzsdi+QUAAAAAFAoiYmJWrVqldauXauGDRvqwQcfVLdu3VSlShWnY5KcAgAAAIAd18My3KI0f/58ffLJJzp79qy6deumZcuWKSgoqEhik5wCAAAAAArk9ddfV+3atXXPPffIZDLpk08+sdlv7NixhY5NcgoAAAAANpmK6Z7TsluNjYiIkCQlJyfb7WMyOXd9JKcAAAAAgAJ5//33iy02ySkAAAAA2GBIMoziiYu8eM4pAAAAACBf7777ri5evFigvjt37tSGDRsKFZ/KKQAAAADYkVuG7w8tar/99ps6dOigu+++Wx07dlRISIiqV68uSbp8+bJ+++03/fLLL/r888916tQpTZ8+vVDxSU4BAAAAwBajmB4lU0bX9U6fPl1JSUlatmyZRo4cqczMTLm5ucnd3V1ZWVmSpFtuuUU9e/ZU9+7d5eHhUaj4JKcAAAAAgAJp2rSpJk2apIkTJ+rAgQNKSUnRpUuX5OPjo6ZNm1oqqc4gOQUAAAAAO4rnUTJln8lkUtOmTdW0adMii8mGSAAAAAAAl6NyCgAAAAB2FMejZGCbSyunCQkJ6tGjh8LDw9W6dWs999xzOnz4sFUfwzA0e/ZsRUdHq0WLFurVq5eSk5NdNGIAAAAAQHFwaXK6detWPf7441qxYoUWL14ss9msp59+2urZOfPnz9fixYs1fvx4rVy5Un5+furTp4/Onz/vwpEDAAAAuN4ZurJbb5G/XH1hpZRLk9OFCxfqwQcfVEBAgJo2baq4uDgdO3ZMe/fulXSlarp06VINGDBAnTt3VmBgoOLj45WVlaU1a9a4cugAAAAAcMP73//+px9//NHyKBnjH6yDLlX3nGZmZkqSvL29JUkpKSlKTU1VdHS0pY+Hh4ciIiKUmJiomJgYm3HMZnPxD/Zv5ymp86HsYY7AEeYHHGF+ID/METjC/Cg6xfKc0+vAmTNnNHz4cG3ZskUmk0nr1q1TvXr19NJLL6lq1aoaM2ZMoWOWmuTUMAzFxcWpVatWCgwMlCSlpqZKknx9fa36+vn56dixYzbjZGdna8eOHcU61mvt3r27RM+Hsoc5AkeYH3CE+YH8MEfgSGmYHx4eHq4ewj/Co2Rsi4uLk5ubmzZs2KB77rnH0t6lSxfFxcWV7eR00qRJOnjwoJYtW5bnmMlkPSEclYo9PDzUrFmzIh+fLWazWbt371ZISIjc3NxK5JwoW5gjcIT5AUeYH8gPcwSOlJb5sW/fPpedG8Vr06ZNWrhwoWrVqmXV3qBBA7uFxPyUiuR08uTJWr9+vT744AOri/P395ckpaWlqUaNGpb29PR0+fn52Y1X0j+Abm5u/E8BDjFH4AjzA44wP5Af5ggcYX78czxKxraLFy/K09MzT/uZM2ecrpa7dEMkwzA0adIkrVu3Tu+9957q1atndbxu3bry9/fXpk2bLG3Z2dnatm2bwsPDS3q4AAAAAABJERERWr16tVVbbm6uFi5cqMjISKdiurRyOnHiRK1Zs0Zvv/22KleubLnH1MvLS56enjKZTOrdu7cSEhLUsGFDNWjQQAkJCfL09FTXrl1dOXQAAAAANwA2RLJt1KhR6tWrl/bs2aOcnBy99tpr+u2335SRkaGPPvrIqZguTU6vDrpXr15W7XFxcXrwwQclSf369dOlS5c0ceJEZWRkKDQ0VIsWLVKVKlVKfLwAAAAAAOnmm2/WZ599po8++khubm7666+/dNddd+nxxx+3uiWzMFyanB44cCDfPiaTSUOGDNGQIUNKYEQAAAAA8P8ZpuKpnF4n1Vh/f38NHTq0yOK59J5TAAAAAEDZs2rVKq1duzZP+9q1a/Xpp586FZPkFAAAAADsMIrhdT1499135ePjk6fd19dX77zzjlMxS8WjZAAAAACgtDFUPBsiXQ8J6rFjx1S3bt087bVr19bx48ediknlFAAAAABQKL6+vjb3EEpKSlK1atWciknlFAAAAADsuR7KnMWgS5cumjp1qipXrqyIiAhJ0tatW/Xqq6/q3nvvdSomySkAAAAAoFCGDRumY8eO6amnnlL58lfSytzcXN1///0aPny4UzFJTgEAAADAjmJ5lMx1wMPDQ2+88YZ+//13JSUlydPTU4GBgapTp47TMbnnFAAAAABKoYSEBAUFBWnq1KmWNsMwNHv2bEVHR6tFixbq1auXkpOTrd6XnZ2tyZMnKzIyUmFhYRowYIBOnDhh1ScjI0MjR45Uq1at1KpVK40cOVLnzp0r9BgbNWqke+65Rx06dPhHialE5RQAAAAA7DJcdM/prl27tHz5cgUFBVm1z58/X4sXL9a0adPUsGFDzZs3T3369NFXX32lKlWqSJKmTp2q77//XrNmzVK1atU0bdo09e/fX5988onc3NwkSS+++KJOnjypBQsWSJLGjx+vUaNGOXwMTFxcnJ5//nlVqlRJcXFxDsc/duzYQl8zySkAAAAAlCIXLlzQyJEjNWXKFM2bN8/SbhiGli5dqgEDBqhz586SpPj4eEVFRWnNmjWKiYlRZmamVq1apenTpysqKkqS9Nprr+mOO+7Q5s2b1bZtWx06dEg//vijVqxYodDQUEnS5MmT1bNnTx0+fFiNGze2Oa59+/bp8uXLlq/tMZmcWwpNcgoAAAAAdrjintNJkyapffv2ioqKskpOU1JSlJqaqujoaEubh4eHIiIilJiYqJiYGO3Zs0c5OTlq06aNpU/NmjUVEBCgxMREtW3bVomJifLy8rIkppIUFhYmLy8vJSYm2k1OX3rpJUt19v333y/qy+aeUwAAAACwyzAV/cuBL774Qvv27dOLL76Y51hqaqqkK88Y/Ts/Pz+lpaVJktLS0uTu7i5vb2+Hfa6NcTXu1T62dO/eXWfOnJEkderUyfJ1USE5BQAAAIBS4Pjx45o6dapee+01VahQwW6/a5fNGgW4MbagfRwtya1atapSUlIkSUePHi1QzMJgWS8AAAAA2GIU04ZIdmLu3btX6enpevDBBy1tZrNZ27Zt04cffqivvvpK0pXKZ40aNSx90tPT5efnJ+lKhTQnJ0cZGRlW1dP09HSFh4db+qSnp+c5/+nTp21WVK/q3LmznnjiCfn7+8tkMqlHjx4qV852vfO7776zG8ceklMAAAAAKAVuv/12ff7551ZtY8eOVePGjdWvXz/Vq1dP/v7+2rRpk5o1aybpymNjtm3bphEjRkiSgoOD5e7urk2bNqlLly6SpFOnTik5OVkjR46UJIWHhyszM1O7du1SixYtJEk7d+5UZmamJYG1ZfLkybrrrrt05MgRTZkyRQ8//LAqV65cZNdPcgoAAAAA9pTgo2SqVKmiwMBAq7ZKlSqpWrVqlvbevXsrISFBDRs2VIMGDZSQkCBPT0917dpVkuTl5aUePXooPj5ePj4+8vb2Vnx8vAIDAy279zZp0kRt27bVyy+/rEmTJkmSYmNj1aFDB7ubIUlSUlKSoqOjVa5cOe3du1e9e/e2bJBUFEhOAQAAAKCM6Nevny5duqSJEycqIyNDoaGhWrRokVWSOG7cOJUvX17Dhg1TVlaWWrdurWnTplmecSpJM2bM0JQpU9S3b19JUseOHTV+/HiH5+7evbs2btwoX19fbd26VTk5OUV6bSSnAAAAAGCHKx4l83fXPrLFZDJpyJAhGjJkiN33VKhQQbGxsYqNjbXbp1q1apoxY0ahxnJ1QyRfX182RAIAAAAAuAYbIgEAAACAq5TgPaelHRsiAQAAAICLuHpZb2nTrl07SWJDJAAAAACA68XFxencuXPavXu3TCaT6tevr6pVq/6jmCSnAAAAAGAPy3rzSElJ0aRJk7Rx40bLpkgmk0lt27ZVbGys6tat61RcklMAAAAAQIEcP35cPXv2VPny5fX888+rSZMmMgxDhw4d0kcffaSYmBitXLlStWrVKnRsklMAAAAAsIt7Tv/urbfeUqNGjbRw4UJVqFDB0n7XXXfpqaee0jPPPKO33npLr776aqFj2973FwAAAACAa/z4448aNmyYVWJ6laenp55//nn98MMPTsWmcgoAAAAA9nDPqZWzZ886vKe0Xr16Onv2rFOxqZwCAAAAAAqkRo0aSk5Otnv84MGDqlGjhlOxSU4BAAAAwB6jGF5lWKdOnTR9+nSdPn06z7H09HTNmDFDnTp1cio2y3oBAAAAAAUyePBg/fDDD7rzzjt13333qXHjxpKk3377TWvWrJG/v78GDRrkVGySUwAAAACwxTBdeRVH3DLK29tbK1as0MyZM/Xll1/q3LlzkqSqVauqa9euGj58uKpVq+ZUbJJTAAAAALDBkGQUwzLcMr6yV97e3po4caImTJhgWd5bvXp1mUz/LOl2+p7TI0eOaNasWXrhhReUnp4uSfrhhx8c3hwLAAAAALg+mEwm+fr6ytfX9x8nppKTyenWrVvVrVs37dq1S+vWrdPFixclSQcOHNDs2bP/8aAAAAAAoFRgQ6QS41Ry+vrrr2vYsGFavHix3N3dLe2RkZFKTEwsssEBAAAAAG4MTiWnBw8e1J133pmnvXr16k4/cBUAAAAASp2rmyIV5Qs2OZWcenl5KTU1NU/7/v37VbNmzX88KAAAAADAjcWp3Xq7du2qGTNm6M0335TJZFJubq5++eUXxcfH64EHHijiIQIAAABAyTNJMhXDPaLXQ+106dKlNttNJpMqVKig+vXrKyIiQm5ubgWO6VRyOnz4cI0ZM0bt2rWTYRi69957ZTab1bVrVw0cONCZkAAAAACAMmLJkiU6c+aM/vrrL3l7e8swDJ07d04VK1ZUpUqVlJ6ernr16mnp0qW66aabChTTqeTU3d1dr7/+up5//nnt27dPubm5atasmRo2bOhMOAAAAAAondhd16YXXnhBy5cv19SpU1W/fn1J0v/+9z+NHz9ePXv2VMuWLTV8+HDFxcXprbfeKlBMp5LTq+rXr28ZCAAAAABcd9jAyKY33nhDs2fPtsoHGzRooNGjR2vIkCH67rvvNHLkSA0dOrTAMQucnMbFxRU46NixYwvcFwAAAABQtqSmpury5ct52i9fvqy0tDRJUo0aNXThwoUCxyxwcrpv3z6r7/fu3avc3Fw1atRIkvTHH3+oXLlyat68eYFPDgAAAACllqHiWdZ7HSwVjoyM1CuvvKIpU6aoWbNmkq7kjBMmTNDtt98u6cojSOvWrVvgmAVOTt9//33L14sXL1blypUVHx8vb29vSVJGRobGjh2rW2+9tcAnBwAAAACUPVOnTtWoUaP04IMPqnz5K2ml2WxW69atNXXqVElSpUqVNHr06ALHdOqe00WLFmnRokWWxFSSvL29NWzYMPXt21d9+/Z1JiwAAAAAlC7XQZWzOPj7+2vx4sU6dOiQ/vjjDxmGocaNG6tx48aWPlcrqAXlVHJ6/vx5paWlKSAgwKo9PT29UGuKAQAAAABlV5MmTdSkSZMiieVUcnrXXXdp3LhxGj16tMLCwiRJO3bs0PTp09W5c+ciGRgAAAAAuByVU5vMZrM++eQTbdmyRenp6crNzbU6vnTp0kLHdCo5nThxouLj4zVy5EjLDk1ubm566KGHNGrUKGdCAgAAAADKiKlTp+rTTz9V+/btFRAQIJPpnz9yx6nktGLFipowYYJGjRqlI0eOSLryzNNKlSr94wEBAAAAQKnBc05t+uKLL/TGG2+offv2RRbTqeT0qkqVKqlp06ZFNRYAAAAAKFVMLOu1yd3dXfXr1y/SmE4lp7169XJYtnVmfTEAAAAAoGzo27evli5dqvHjxxfJkl7JyeT0lltusfr+8uXL2r9/v5KTk/XAAw8UxbgAAAAAwPWonNr0yy+/6Oeff9YPP/yggIAAy7NOr5ozZ06hYzqVnI4bN85m++zZs3Xx4kVnQgIAAAAAyoiqVavqrrvuKtKY/+ie02vdd999evjhhzV69OiiDAsAAAAAKEXi4uKKPGa5ogyWmJgoDw+PogwJAAAAALgBOFU5HTx4sNX3hmEoNTVVe/bs0XPPPVckAwMAAAAAV2O33v/TvXt3LVmyRN7e3nrggQccboT06aefFjq+U8lplSpVrAZiMpnUqFEjDR06VNHR0c6EBAAAAACUYp06dbKslO3UqVOR7dJ7lVPJ6bRp04rk5Nu2bdPChQu1Z88epaamau7cubrzzjstx8eMGZMn4w4NDdWKFSuK5PwAAAAA4JBRtAlYWfb3FbRDhgyx288wnCs3O3XPaadOnXTmzJk87efOnVOnTp0KHOfixYsKCgrS+PHj7fZp27atNm7caHm9++67zgwZAAAAAArHKMZXGZeQkGCz3Ww268UXX3QqplOV06NHjyo3NzdPe3Z2tk6ePFngOO3bt1f79u0d9vHw8JC/v3+hxwgAAAAAKB7vvfeeqlWrpp49e1razGazhg8fruTkZKdiFio5/e677yxf//jjj/Ly8rJ8n5ubq59++kl16tRxaiD2bN26Va1bt1bVqlUVERGh4cOHy9fX1+F7zGZzkY4hv/OU1PlQ9jBH4AjzA44wP5Af5ggcYX4Uoeugylkc3n33XfXp00deXl7q0qWLcnJyNHz4cB0+fFhLly51KmahktNBgwZJurIB0pgxY6wDlS+vOnXq5Gn/J9q1a6e7775btWvXVkpKit588009+eST+uSTT+w+siY7O1s7duwosjEUxO7du0v0fCh7mCNwhPkBR5gfyA9zBI6UhvnBoyavT8HBwZozZ46ee+45eXh4aOXKlfrzzz+1dOlS+fn5ORWzUMlpUlKSJKljx45auXKlqlev7tRJC6pLly6WrwMDAxUcHKyOHTtqw4YN6ty5s833eHh4qFmzZsU6rqvMZrN2796tkJAQubm5lcg5UbYwR+AI8wOOMD+QH+YIHCkt82Pfvn0uO3dR4VEy9kVGRmr69OkaOnSoGjdurPfff/8f5YhO3XO6fv16p0/4T9SoUUO1a9fWH3/84bBfSf8Aurm58T8FOMQcgSPMDzjC/EB+mCNwhPmBovT33Xr/zsfHR1WrVrXa6HbOnDmFjl/g5HTp0qXq2bOnKlSokO8a4t69exd6IAVx5swZHT9+XDVq1CiW+AAAAABghcqpxd/3HPq7tm3bFkn8AienS5YsUbdu3VShQgUtWbLEbj+TyVTg5PTChQs6cuSI5fuUlBTt379f3t7e8vb21pw5c9S5c2f5+/vr6NGjmjVrlnx8fKyehQoAAAAAKH5xcXHFGr/Ayenfl/IW1bLePXv2WCWyVy+2e/fumjBhgg4ePKjVq1crMzNT/v7+ioyM1KxZs1SlSpUiOT8AAAAAOETl1KHTp0/r8OHDMplMatSoUcnfczpnzhw9/fTTqlixolV7VlaWFixYYHct8rUiIyN14MABu8cXLlzozPAAAAAAoEiwIZJtFy9e1OTJk/Wf//xHubm5kq7c43z//fcrNjY2T65YEOWcGcjcuXN18eLFPO1//fWX5s6d60xIAAAAAEAZMW3aNG3btk3z5s3T9u3btX37dr399tvatm2bpk2b5lRMpyqnhmHIZDLlaU9KSpK3t7dTAwEAAACAUsfIm/dA+vrrr/XWW28pMjLS0ta+fXtVqFBBw4YN08SJEwsds1DJaUREhEwmk0wmk/71r39ZJahms1kXL15UTExMoQcBAAAAACg7srKy5Ofnl6fd19dXWVlZTsUsVHI6btw4GYahcePGaciQIVZbCbu7u6tOnToKDw93aiAAAAAAUKoYKp4Nka6D+1jDwsL01ltvafr06apQoYKkKwnrnDlzFBYW5lTMQiWn3bt3lyTVrVtX4eHhcnd3d+qkAAAAAICy66WXXtIzzzyjdu3aqWnTpjKZTNq/f78qVKjg9Ma2Tt1zetttt1m+zsrK0uXLl62O86gXAAAAANcDduu1LTAwUOvWrdNnn32mw4cPyzAM3XvvverWrZs8PT2diulUcvrXX3/ptdde09q1a3X27Nk8x/fv3+/UYAAAAAAAZYOnp6ceeeSRIovnVHI6ffp0/fzzz3rllVc0evRojR8/XidPntTy5cv14osvFtngAAAAAMClqJzadeTIEb333ns6dOiQTCaTmjRpot69e6t+/fpOxXPqOafff/+9XnnlFd19991yc3PTrbfequeee07Dhw/X559/7tRAAAAAAKC0MRlF/7oe/Pjjj+rSpYt27dqloKAgBQQEaOfOnbr33nu1adMmp2I6lZxmZGSobt26kq7cX5qRkSFJatWqlbZv3+7UQAAAAADgRrds2TJ169ZNLVu2VMuWLdWzZ0/997//tRw3DEOzZ89WdHS0WrRooV69eik5OdkqRnZ2tiZPnqzIyEiFhYVpwIABOnHihFWfjIwMjRw5Uq1atVKrVq00cuRInTt3rsDjfP311/XUU0/p3//+t8aOHatx48bp3//+t5588knNmDHDqWt3KjmtW7eujh49Kkm6+eabtXbtWklXKqpshgQAAADgumEUw8uBWrVqacSIEVq1apVWrVql22+/XYMGDbIkoPPnz9fixYs1fvx4rVy5Un5+furTp4/Onz9viTF16lR98803mjVrlpYtW6aLFy+qf//+MpvNlj4vvviikpKStGDBAi1YsEBJSUkaNWpUgT+WQ4cO6aGHHsrT3qNHD/32228FjvN3TiWnPXr0UFJSkiTp2Wef1bJlyxQcHKy4uDg988wzTg0EAAAAAG50HTt2VPv27dWoUSM1atRIw4cPV6VKlbRjxw4ZhqGlS5dqwIAB6ty5swIDAxUfH6+srCytWbNGkpSZmalVq1ZpzJgxioqKUrNmzfTaa6/p4MGD2rx5s6QrieWPP/6oKVOmKDw8XOHh4Zo8ebK+//57HT58uEDjrF69us2NcPfv3y9fX1+nrt2pDZGeeuopy9e333671q5dqz179qh69epatWqVUwMBAAAAgFLHhfeIms1mffXVV7p48aLCw8OVkpKi1NRURUdHW/p4eHgoIiJCiYmJiomJ0Z49e5STk6M2bdpY+tSsWVMBAQFKTExU27ZtlZiYKC8vL4WGhlr6hIWFycvLS4mJiWrcuHG+Y3v44Yc1fvx4/fnnn2rZsqUk6ddff9X8+fPVp08fp67XqeT0WrVr11bt2rWVlJSk1atXKy4urijCAgAAAMAN58CBA4qJidGlS5dUqVIlzZ07VzfffLN+/fVXScpTmfTz89OxY8ckSWlpaXJ3d5e3t3eePmlpaZY+tqqbvr6+lj75GTRokKpUqaJFixZp5syZkqQaNWpo8ODB6t27d+Eu+P8rkuQUAAAAAK43JhXP7rqmfI43atRIq1ev1rlz57Ru3TqNHj1aH3zwwf+932QdwTDyH2RB+1wb2x6TyaSnnnpKTz31lOV+13+6/5BT95wCAAAAAIqHh4eHGjRooJCQEL344otq2rSpli5dKn9/f0nKU91MT0+Xn5+fpCsV0pycHMsTVez1SU9Pz3Pe06dPO3W/aJUqVeTh4aELFy4U+r1/R3IKAAAAAKWYYRjKzs5W3bp15e/vb/Uc0ezsbG3btk3h4eGSpODgYLm7u1v1OXXqlJKTky19wsPDlZmZqV27dln67Ny5U5mZmZY+jqxatUqTJ0/WZ599JunKY2VatmypW2+9VX369NGZM2ecus5CLesdPHiww+OFeS4OAAAAAJR6Jbwh0syZM9WuXTvVqlVLFy5c0JdffqmtW7dqwYIFMplM6t27txISEtSwYUM1aNBACQkJ8vT0VNeuXSVJXl5e6tGjh+Lj4+Xj4yNvb2/Fx8crMDBQUVFRkqQmTZqobdu2evnllzVp0iRJUmxsrDp06JDvZkjz5s3TO++8o/DwcK1Zs0a//PKLvvvuOw0dOlTlypXT+++/rzfeeEMTJ04s9LUXKjn18vLK93idOnUKPQgAAAAAwJUlu6NGjdKpU6fk5eWloKAgLViwwLL7br9+/XTp0iVNnDhRGRkZCg0N1aJFi6zu9xw3bpzKly+vYcOGKSsrS61bt9a0adPk5uZm6TNjxgxNmTJFffv2lXTlETbjx4/Pd3yffvqppk6dqq5du2r37t165JFHNGvWLN19992SpICAAE2YMMGpay9UcsouvAAAAABuGEbxbIjkqBr76quvOnyryWTSkCFDNGTIELt9KlSooNjYWMXGxtrtU61aNc2YMSPfoV7r2LFjatWqlSQpJCREbm5uCgwMtBxv2rSpUlNTCx1X4p5TAAAAAEABXb58WR4eHpbv3d3d5e7ubvnezc1NZrPZqdg8SgYAAAAA7Cnhe07Lgt9++82qOnr48GHLTr3OboYkkZwCAAAAAArhqaeesnpuav/+/SVdWXJcmGelXovkFAAAAADsoXJq5bvvviu22CSnAAAAAIACKc6ns5CcAgAAAIAdxbJbL2wiOQUAAAAAe0hOSwyPkgEAAAAAuByVUwAAAACwg2W9JYfkFAAAAADglNOnT+vw4cMymUxq1KiRqlev7nQsklMAAAAAsIfKqU0XL17U5MmT9dlnn8lsNkuS3NzcdP/99ys2NlYVK1YsdEzuOQUAAAAAFMq0adO0bds2vf3229q+fbu2b9+ut99+W9u2bdO0adOciknlFAAAAADsoXJq09dff6233npLkZGRlrb27durQoUKGjZsmCZOnFjomFROAQAAAACFkpWVJT8/vzztvr6+ysrKciomySkAAAAA2GJc2a23qF/XQzU2LCxMb731li5dumRpy8rK0pw5cxQWFuZUTJb1AgAAAAAKZdy4cerXr5/atWunpk2bymQyaf/+/apQoYIWLlzoVEySUwAAAACw5zqochaHoKAgrVu3Tp999pkOHz4swzB07733qlu3bvL09HQqJskpAAAAANhDcmrTtm3bFB4erkceecSq/fLly9q2bZsiIiIKHZN7TgEAAAAAhdK7d29lZGTkac/MzFTv3r2diknlFAAAAADsMFE5tckwDJlMpjztZ8+eVcWKFZ2KSXIKAAAAACiQwYMHS5JMJpPGjBkjDw8PyzGz2awDBw4oPDzcqdgkpwAAAABgD5VTK15eXpKuVE4rV65stfmRu7u7wsLC9PDDDzsVm+QUAAAAAFAgcXFxkqQ6deqob9++qlSpUpHFJjkFAAAAADu459S2q8t7ixK79QIAAAAAXI7KKQAAAADYQ+W0xJCcAgAAAIAthoonOSXhtYllvQAAAAAAp126dKlI4pCcAgAAAIAdpmJ4XQ9yc3M1d+5ctW3bVuHh4frzzz8lSW+88Yb+/e9/OxWT5BQAAAAAUChvv/22Pv30U40cOVLu7u6W9sDAQK1cudKpmCSnAAAAAGCPUQyv68B//vMfTZ48Wffdd5/Klfu/tDIoKEiHDx92KibJKQAAAACgUE6ePKn69evnaTcMQ5cvX3YqJskpAAAAANhgkmQyiuHl6gsrAjfffLO2b9+ep/2rr77SLbfc4lRMHiUDAAAAACiUwYMHa9SoUTp58qQMw9C6dev0+++/a/Xq1UpISHAqJpVTAAAAALCHe05t6tixo2bNmqUffvhBJpNJb731lg4dOqR33nlHbdq0cSomlVMAAAAAsOc6SSaLQ9u2bdW2bdsii0flFAAAAABQKLt27dLOnTvztO/cuVO7d+92KibJKQAAAADYURwbIl0PJk2apOPHj+dpP3nypCZNmuRUTJcmp9u2bdOAAQMUHR2toKAgffvtt1bHDcPQ7NmzFR0drRYtWqhXr15KTk520WgBAAAAAJJ06NAhNW/ePE/7Lbfcot9++82pmC5NTi9evKigoCCNHz/e5vH58+dr8eLFGj9+vFauXCk/Pz/16dNH58+fL+GRAgAAALghsSGSTR4eHkpLS8vTnpqaqvLlndvayKXJafv27TV8+HB17tw5zzHDMLR06VINGDBAnTt3VmBgoOLj45WVlaU1a9a4YLQAAAAAAEmKiorSzJkzlZmZaWk7d+6cZs2apaioKKdiltrdelNSUpSamqro6GhLm4eHhyIiIpSYmKiYmBi77zWbzSUxRMt5Sup8KHuYI3CE+QFHmB/ID3MEjjA/is71co9oURszZowef/xxdejQQbfccoskKSkpSb6+vpo+fbpTMUttcpqamipJ8vX1tWr38/PTsWPH7L4vOztbO3bsKM6h5eHsblS4cTBH4AjzA44wP5Af5ggcKQ3zw8PDw9VDQDGoWbOmPvvsM33++edKSkqSp6enevTooXvvvVfu7u5OxSy1yelVJpPJ6nvDcPxPFx4eHmrWrFlxDsnCbDZr9+7dCgkJkZubW4mcE2ULcwSOMD/gCPMD+WGOwJHSMj/27dvnsnMXieK6R/Q6qcZWqlRJPXv2LLJ4pTY59ff3lySlpaWpRo0alvb09HT5+fk5fG9J/wC6ubnxPwU4xByBI8wPOML8QH6YI3CE+fHPsazXvt9//11bt25Venq6cnNzrY4NHjy40PFKbXJat25d+fv7a9OmTZZKaHZ2trZt26YRI0a4eHQAAAAAcONasWKFJkyYIB8fH/n5+VmteDWZTGUvOb1w4YKOHDli+T4lJUX79++Xt7e3ateurd69eyshIUENGzZUgwYNlJCQIE9PT3Xt2tWFowYAAABww6ByatO8efM0bNgwPfvss0UW06XJ6Z49e9S7d2/L93FxcZKk7t27a9q0aerXr58uXbqkiRMnKiMjQ6GhoVq0aJGqVKniqiEDAAAAwA0vIyND99xzT5HGdGlyGhkZqQMHDtg9bjKZNGTIEA0ZMqQERwUAAAAA/x+VU5vuvvtubdy4UY8++miRxSy195wCAAAAAEqnBg0a6M0339TOnTsVGBio8uWtU8u/r5AtKJJTAAAAALCD3XptW758uSpVqqStW7dq69atVsdMJhPJKQAAAACg+K1fv77IY5KcAgAAAIA9VE5LDMkpAAAAANhhMshO7Tlx4oS+++47HT9+XDk5OVbHxo4dW+h4JKcAAAAAgEL56aefNHDgQNWtW1e///67AgICdPToURmGoWbNmjkVs1wRjxEAAAAArg9GMb7KuNdff119+vTRmjVr5OHhodmzZ2vDhg2KiIjQ3Xff7VRMklMAAAAAQKEcOnRI3bt3lySVL19eWVlZqly5sp5//nktWLDAqZgkpwAAAABgh8ko+tf1oFKlSsrOzpYk1ahRQ0eOHLEcO3PmjFMxSU4BAAAAoJRISEhQjx49FB4ertatW+u5557T4cOHrfoYhqHZs2crOjpaLVq0UK9evZScnGzVJzs7W5MnT1ZkZKTCwsI0YMAAnThxwqpPRkaGRo4cqVatWqlVq1YaOXKkzp07V6BxhoaG6tdff5UktW/fXvHx8Zo3b57GjRun0NBQp66d5BQAAAAA7Cnh+023bt2qxx9/XCtWrNDixYtlNpv19NNP6+LFi5Y+8+fP1+LFizV+/HitXLlSfn5+6tOnj86fP2/pM3XqVH3zzTeaNWuWli1bposXL6p///4ym82WPi+++KKSkpK0YMECLViwQElJSRo1alSBPpaxY8eqRYsWkqQhQ4YoKipKX375pWrXrq2pU6cWKMa12K0XAAAAAEqJhQsXWn0fFxen1q1ba+/evYqIiJBhGFq6dKkGDBigzp07S5Li4+MVFRWlNWvWKCYmRpmZmVq1apWmT5+uqKgoSdJrr72mO+64Q5s3b1bbtm116NAh/fjjj1qxYoWl0jl58mT17NlThw8fVuPGjR2Os169epavK1asqAkTJvzja6dyCgAAAAA2mFQ895yaCjGGzMxMSZK3t7ckKSUlRampqYqOjrb08fDwUEREhBITEyVJe/bsUU5Ojtq0aWPpU7NmTQUEBFj6JCYmysvLy2oJblhYmLy8vCx9HOnUqZPNe0vPnTunTp06FeIK/w+VUwAAAACwx4UbGBmGobi4OLVq1UqBgYGSpNTUVEmSr6+vVV8/Pz8dO3ZMkpSWliZ3d3dLQvv3PmlpaZY+18a4GvdqH0eOHj2q3NzcPO3Z2dk6efJkAa4uL5JTAAAAACiFJk2apIMHD2rZsmV5jplM1vVXw8g/iy5on2tj/913331n+frHH3+Ul5eX5fvc3Fz99NNPqlOnTr7nsYXkFAAAAADscNWjXyZPnqz169frgw8+UK1atSzt/v7+kq5UPmvUqGFpT09Pl5+fn6QrFdKcnBxlZGRYVU/T09MVHh5u6ZOenp7nvKdPn7ZZUb1q0KBBkq4kx2PGjLE6Vr58edWpUydPe0FxzykAAAAAlBKGYWjSpElat26d3nvvPauNhySpbt268vf316ZNmyxt2dnZ2rZtmyXxDA4Olru7u1WfU6dOKTk52dInPDxcmZmZ2rVrl6XPzp07lZmZaeljS1JSkpKSknTTTTdp8+bNlu+TkpK0Z88eff311+rQoYNT107lFAAAAADsKeHK6cSJE7VmzRq9/fbbqly5suUeUy8vL3l6espkMql3795KSEhQw4YN1aBBAyUkJMjT01Ndu3a19O3Ro4fi4+Pl4+Mjb29vxcfHKzAw0LJ7b5MmTdS2bVu9/PLLmjRpkiQpNjZWHTp0yHenXklav359nrZz586patWqTl87ySkAAAAAlBIfffSRJKlXr15W7XFxcXrwwQclSf369dOlS5c0ceJEZWRkKDQ0VIsWLVKVKlUs/ceNG6fy5ctr2LBhysrKUuvWrTVt2jS5ublZ+syYMUNTpkxR3759JUkdO3bU+PHjCzTOd999V3Xr1lWXLl0kSUOHDtW6devk7++v+fPnq2nTpoW+dpJTAAAAALCjpO85PXDgQL59TCaThgwZoiFDhtjtU6FCBcXGxio2NtZun2rVqmnGjBlOjXP58uV67bXXJEmbNm3STz/9pAULFmjt2rWaPn26Fi1aVOiYJKcAAAAAgEJJTU3VTTfdJEn6/vvvdc899yg6Olp16tTRI4884lRMNkQCAAAAAFsMSYZRDC9XX9g/V7VqVR0/flzSlUfKtG7dWtKVDZ3MZrNTMamcAgAAAIAdrnqUTGnXuXNnjRgxQg0aNNDZs2fVrl07SVd2823QoIFTMUlOAQAAAACFMnbsWNWpU0fHjx/XyJEjVblyZUlXHlnz2GOPORWT5BQAAAAA7KFyapO7u7uefvrpPO1PPfWU0zFJTgEAAAAA+fruu+/Url07ubu767vvvnPYt1OnToWOT3IKAAAAAHaYcl09gtJj0KBB2rRpk3x9fTVo0CC7/Uwmk/bv31/o+CSnAAAAAIB8JSUl2fy6qJCcAgAAAIA93HNaYkhOAQAAAAAFlpubq08++UTffPONjh49KpPJpDp16ujuu+/W/fffL5PJ5FRcklMAAAAAsIPnnFozDEMDBw7Uf//7XzVt2lSBgYEyDEOHDh3SmDFjtG7dOr399ttOxSY5BQAAAABbDElGMWSnZTjh/eSTT7Rt2zYtWbJEt99+u9Wxn376SYMGDdLq1av1wAMPFDp2uSIaIwAAAADgOvfFF19owIABeRJTSWrdurWeffZZff75507FJjkFAAAAADtMRtG/yrIDBw6obdu2do+3a9fO6Z18SU4BAAAAAAWSkZEhX19fu8d9fX2VkZHhVGzuOQUAAAAAe8p4pbOomc1mlS9vP410c3OT2Wx2KjbJKQAAAACgQAzD0JgxY+Th4WHzeHZ2ttOxSU4BAAAAwI6yfo9oUevevXu+fZzZqVciOQUAAAAAFFBcXFyxxSY5BQAAAAB7iuM5p7CJ5BQAAAAAbDCpeJb1moo+5HWBR8kAAAAAAFyOyikAAAAA2MOq3hJD5RQAAAAA4HJUTgEAAADADh4lU3KonAIAAAAAXI7KKQAAAADYYkjKLYbSKdVYm6icAgAAAABcjsopAAAAANhDlbPEUDkFAAAAALgclVMAAAAAsIPdeksOySkAAAAA2GRIRnFkp2S8trCsFwAAAADgclROAQAAAMAOlvWWHCqnAAAAAACXo3IKAAAAAPZQOS0xVE4BAAAAAC5Xqiuns2fP1pw5c6za/Pz8tGnTJheNCAAAAMANw5BMxbFbL9VYm0p1cipJAQEBWrx4seV7Nzc3F44GAAAAAFAcSn1y6ubmJn9/f1cPAwAAAMCNKNfVA7hxlPrk9H//+5+io6Pl4eGh0NBQvfDCC6pXr57D95jN5hIZ29XzlNT5UPYwR+AI8wOOMD+QH+YIHGF+FJ1iWdYLm0yGUXo/7f/+97/KyspSw4YNlZ6ernnz5unw4cNas2aNfHx88vTfvXu3srOzXTBSAAAAALZ4eHgoJCTE1cMotN27d+v48bOKn7axyGOPHhOtm26qViY/l+JUqiun7du3t/o+LCxMd911l1avXq0+ffrYfI+Hh4eaNWtWEsOT2WzW7t27FRISwr2wsIk5AkeYH3CE+YH8MEfgSGmZH/v27XPZuYtMqS3lXX9KdXJ6rUqVKikwMFB//PGHw34l/QPo5ubG/xTgEHMEjjA/4AjzA/lhjsAR5gfKkjL1nNPs7GwdOnSIDZIAAAAAlAzDKPoXbCrVldP4+Hh16NBBN910k06fPq158+bp/Pnz6t69u6uHBgAAAAAoQqU6OT1x4oReeOEFnT17Vj4+PgoLC9OKFStUp04dVw8NAAAAwA3ARKGzxJTq5HTWrFmuHgIAAAAAoASU6uS0LDqZdk4LV/6k9VsOyiRD0a2aaHCvO+RbrbKrhwYAAACgsLhHtMSQnBah/6Wc0aMvLrJq+3pjkjZsTdYnc/rJx5sEFQAAACgzDMmUWzxxkVeZ2q23NEs5lqaY/5+YGvq/+WZIupRt1riZn7tqaAAAAABQ6lE5LSKPDH/P8rXpb+0mXUlQdx04KsMwZDKZrn0rAAAAgNKKZb0lhsppEbE3Za+25xrSpezLJTUcAAAAAChTqJwWIVs10auV0/LlTKrgwccNAAAAlCkUTksMldMiZGveXm3r3PYWlvQCAAAAgB0kp0WkTq2qkqwT1Ktfm0zSSwP+VeJjAgAAAOA8kySTYRT9y9UXVkqRnBaRlW/2U8UK7pKsd+uVSfpwxpMqV46PGgAAAADs4SbIIrR+6VDtPnhMC1dt1vkLlxTVsrEe6NhC1avxfFMAAACgTGK33hJDOa+IhQTW1htjH9KCKY+r74OtSUwBAACAsiy3GF4ObNu2TQMGDFB0dLSCgoL07bffWh03DEOzZ89WdHS0WrRooV69eik5OdmqT3Z2tiZPnqzIyEiFhYVpwIABOnHihFWfjIwMjRw5Uq1atVKrVq00cuRInTt3rtAfT1EiOQUAAACAUuLixYsKCgrS+PHjbR6fP3++Fi9erPHjx2vlypXy8/NTnz59dP78eUufqVOn6ptvvtGsWbO0bNkyXbx4Uf3795fZbLb0efHFF5WUlKQFCxZowYIFSkpK0qhRo4r9+hxhWS8AAAAA2PL/NzAqjrj2tG/fXu3bt7fzNkNLly7VgAED1LlzZ0lSfHy8oqKitGbNGsXExCgzM1OrVq3S9OnTFRUVJUl67bXXdMcdd2jz5s1q27atDh06pB9//FErVqxQaGioJGny5Mnq2bOnDh8+rMaNGxfxBRcMlVMAAAAAKANSUlKUmpqq6OhoS5uHh4ciIiKUmJgoSdqzZ49ycnLUpk0bS5+aNWsqICDA0icxMVFeXl6WxFSSwsLC5OXlZenjClROAQAAAMCeUrQhUmpqqiTJ19fXqt3Pz0/Hjh2TJKWlpcnd3V3e3t55+qSlpVn6XBvjatyrfVyByikAAAAAlCEmk/WTUo0CJNAF7XNt7JJEcgoAAAAA9hhG0b+c5O/vL0l5qpvp6eny8/OTdKVCmpOTo4yMDId90tPT88Q/ffq0zYpqSSE5BQAAAIAyoG7duvL399emTZssbdnZ2dq2bZvCw8MlScHBwXJ3d7fqc+rUKSUnJ1v6hIeHKzMzU7t27bL02blzpzIzMy19XIF7TgEAAADAnnyeS1rULly4oCNHjli+T0lJ0f79++Xt7a3atWurd+/eSkhIUMOGDdWgQQMlJCTI09NTXbt2lSR5eXmpR48eio+Pl4+Pj7y9vRUfH6/AwEDL7r1NmjRR27Zt9fLLL2vSpEmSpNjYWHXo0MFlO/VKJKcAAAAAYFexPErGgT179qh3796W7+Pi4iRJ3bt317Rp09SvXz9dunRJEydOVEZGhkJDQ7Vo0SJVqVLF8p5x48apfPnyGjZsmLKystS6dWtNmzZNbm5ulj4zZszQlClT1LdvX0lSx44d7T5btaSQnAIAAABAKREZGakDBw7YPW4ymTRkyBANGTLEbp8KFSooNjZWsbGxdvtUq1ZNM2bM+EdjLWokpwAAAABgTyl6lMz1jg2RAAAAAAAuR+UUAAAAAGwxVDyVU4qxNlE5BQAAAAC4HJVTAAAAALCHe05LDJVTAAAAAIDLUTkFAAAAAHtyXT2AGwfJKQAAAADYYWJZb4lhWS8AAAAAwOWonAIAAACATUYxbYhENdYWKqcAAAAAAJejcgoAAAAA9uRS5SwpVE4BAAAAAC5H5RQAAAAAbDFUPPecUoy1icopAAAAAMDlqJwCAAAAgD0857TEkJwCAAAAgD0kpyWGZb0AAAAAAJejcgoAAAAA9vAomRJD5RQAAAAA4HJUTgEAAADAJkMycosnLvKgcgoAAAAAcDkqpwAAAABgD7v1lhgqpwAAAAAAl6NyCgAAAAC2GCqe3XopxtpEcgoAAAAA9rCst8SwrBcAAAAA4HJUTgEAAADAHiqnJYbKKQAAAADA5aicAgAAAIA9VE5LDJVTAAAAAIDLUTkFAAAAAHtyc109ghsGlVMAAAAAgMtROQUAAAAAm4xiuueU+1htoXIKAAAAAHA5KqcAAAAAYIuh4qmcUji1ieQUAAAAuN6YT6ii+yEp92bJzdvVoynbcskkS0qZWNb74YcfqmPHjgoJCdGDDz6o7du3u3pIAAAAQKljmFOVe7qvTKc7qGmNl6X0aOVmzpBhmF09NCBfpT45/fLLLxUXF6eBAwdq9erVatWqlfr166djx465emgAAABAqWEYuTLO9JWyf7K0mXRJuvCujPNzXTiyss0wcov8BdtKfXK6ePFi9ejRQw8//LCaNGmil156SbVq1dJHH33k6qEBAAAApUf2T9LlA5JsVEkvLpZhXCrxIQGFUarvOc3OztbevXv17LPPWrW3adNGiYmJdt9nNpfMsoWr5ymp86HsYY7AEeYHHGF+ID/MEeSRfUBSOZlkozJnXFBuzjHJrX6JD6vM457TElOqk9MzZ87IbDbL19fXqt3Pz0+pqak235Odna0dO3aUwOj+z+7du0v0fCh7mCNwhPkBR5gfyA9zBFd5e15SY1/bS0ZzjfLaveeoco3TJTwqycPDo8TPibKpVCenV5lMJqvvDcPI03aVh4eHmjVrVhLDktls1u7duxUSEiI3N7cSOSfKFuYIHGF+wBHmB/LDHEEeRjMZpz+QctOtqqeGysnk2V0tQluX+JD27dtX4ucscsXxKBnYVKqTUx8fH7m5uSktLc2qPT09XX5+fnbfV9K/oN3c3PifAhxijsAR5gccYX4gP8wR/J+KMnwWyTjTX8r9v81DTRXayeT9kkzlmCco3Ur1hkgeHh5q3ry5Nm3aZNW+efNmhYeHu2hUAAAAQOlkcg+Syf87Gd4L9L/TA2X4/EflfN6VqVwlVw+tbDIMKTe36F9UY20q1ZVTSerTp49GjRql4OBghYeHa/ny5Tp+/LhiYmJcPTQAAACg1DGZ3CSPNjr9V2XVLx/o6uGUfSSSJabUJ6ddunTRmTNn9Pbbb+vUqVMKDAzUu+++qzp16rh6aAAAAACAIlLqk1NJevzxx/X444+7ehgAAAAAbjBGru0dkFH0SvU9pwAAAACAG0OZqJwCAAAAgEtwz2mJoXIKAAAAAHA5KqcAAAAAYE8uldOSQuUUAAAAAOByVE4BAAAAwBbDkIxi2K2X+1htIjkFAAAAADsMlvWWGJb1AgAAAABcjsopAAAAANhTHMt6YROVUwAAAACAy1E5BQAAAAAbDBXPPafcxWoblVMAAAAAgMtROQUAAAAAe7jntMSYDOP6ecjOr7/+KsMw5OHhUWLnzM7OLtHzoexhjsAR5gccYX4gP8wROFIa5kd2drZMJpNatmzp0nE4Y/fu3frrYpbOnjxX5LGr1ayqipU8FRISUuSxy7LrqnJqMplK/Jyu/oFH6cccgSPMDzjC/EB+mCNwpDTMD5PJ5JK/oxeFq59fxUaexRof/+e6qpwCAAAAAMomNkQCAAAAALgcySkAAAAAwOVITgEAAAAALkdyCgAAAABwOZLTApo3b55iYmIUGhqqW2+91WafY8eOacCAAQoLC1NkZKSmTJmi7Oxsqz4HDhzQE088oRYtWqht27aaM2eO2JPq+vP7779r4MCBioyMVMuWLRUTE6MtW7ZY9SnIfMH1bcOGDXr44YfVokULRUZGavDgwVbHmSPIzs7W/fffr6CgIO3fv9/qGPPjxpWSkqJx48apY8eOatGihe6880699dZbef78mSM3tg8//FAdO3ZUSEiIHnzwQW3fvt3VQwLydV09SqY45eTk6O6771ZYWJhWrlyZ57jZbFb//v3l4+OjZcuW6ezZsxo9erQMw1BsbKwk6fz58+rbt68iIyO1cuVK/fHHHxozZowqVaqkvn37lvQloRj1799fDRs21HvvvSdPT0+99957GjBggL755hv5+/sXaL7g+vb1118rNjZWw4cP1+233y7DMHTw4EHLceYIJGn69OmqUaOGkpKSrNqZHze2w4cPyzAMTZo0SQ0aNNDBgwcVGxurv/76S6NHj5bEHLnRffnll4qLi9Mrr7yili1b6uOPP1a/fv30xRdfqHbt2q4eHmCfgUJZtWqV0apVqzztGzZsMJo2bWqcOHHC0rZmzRojODjYyMzMNAzDMD788EOjVatWxqVLlyx9EhISjOjoaCM3N7f4B48SkZ6ebgQGBhrbtm2ztGVmZhqBgYHG5s2bDcMo2HzB9SsnJ8do27atsWLFCrt9mCPYsGGDcffddxvJyclGYGCgsW/fPqtjzA/83fz5842OHTtavmeO3NgeeughY/z48VZtd999tzFjxgwXjQgoGJb1FpEdO3YoICBANWvWtLRFR0crOztbe/bssfSJiIiweuBudHS0Tp06pZSUlBIfM4qHj4+PmjRpotWrV+vixYu6fPmyli9fLj8/PzVv3lxSweYLrl/79u3TyZMnVa5cOT3wwAOKjo7WM888o+TkZEsf5siNLS0tTbGxsZo+fbo8PfM+/J35gWtlZmbK29vb8j1z5MaVnZ2tvXv3Kjo62qq9TZs2SkxMdNGogIIhOS0iaWlp8vPzs2rz9vaWu7u70tLS7Pbx9fW1HMP1wWQyafHixdq3b59atmypFi1aaMmSJVqwYIGqVq0qqWDzBdevP//8U5I0Z84cDRw4UO+88468vb31xBNP6OzZs5KYIzcywzA0ZswYxcTEKCQkxGYf5gf+7siRI/rggw/06KOPWtqYIzeuM2fOyGw2W/6OeZWfn59SU1NdNCqgYG7oe05nz56tOXPmOOyzcuVKu385uJbJZMq3vSB9UDoVdL4EBwdrwoQJ8vX11YcffihPT0/9+9//Vv/+/bVy5UrVqFFDEnPhelTQOZKbmytJGjBggP71r39JkuLi4tSuXTt99dVXiomJkcQcud4UdH4kJibq/Pnz6t+/v8O+zI/rjzN/Lzl58qSeeeYZ3X333Xr44Yet+jJHbmzX/jkbhsGfPUq9Gzo5ffzxx9WlSxeHferWrVugWH5+ftq5c6dVW0ZGhnJyciz/cmXrX6zS09MlKc+/bqH0Keh82bJlizZs2KBt27apSpUqkqTmzZtr8+bNWr16tZ599tkCzReUPQWdIxcuXJAkNWnSxNLu4eGhevXq6fjx45IK9jsFZUtB58e8efO0c+fOPP8w2qNHD3Xr1k3x8fHMj+tUYf9ecvLkSfXu3VthYWGaPHmyVT/myI3Lx8dHbm5ueSrk6enpearpQGlzQyen1atXV/Xq1YskVlhYmN555x2dOnXKUhnbtGmTPDw8FBwcbOkza9YsZWdnW+473bhxo2rUqFHgJBiuU9D58tdff0nK+y+WJpPJUjEryHxB2VPQORIcHCwPDw/9/vvvlkdT5eTk6OjRo5ZdFJkj15+Czo+XX35Zw4YNs3x/6tQpPf3005o1a5ZCQ0MlMT+uV4X5e8nVxLR58+aKi4tTuXLWd2oxR25cHh4eat68uTZt2qS77rrL0r5582Z16tTJhSMD8sc9pwV07Ngx7d+/X8eOHZPZbNb+/fu1f/9+SwUkOjpaN998s0aNGqV9+/bpp59+Unx8vB555BFL9axbt27y8PDQ2LFjdfDgQX3zzTdKSEhQnz59WGZxHQkLC1PVqlU1ZswYJSUl6ffff1d8fLyOHj2qO+64Q1LB5guuX1WqVFFMTIxmz56tjRs36vDhw5owYYIk6e6775bEHLmR1a5dW4GBgZZXw4YNJUn169dXrVq1JDE/bnQnT55Ur169VKtWLY0ePVqnT59Wamqq1eos5siNrU+fPlq5cqVWrlypQ4cO6dVXX9Xx48ctt40ApZXJMAzD1YMoC8aMGaNPP/00T/vSpUsVGRkp6UoCO3HiRG3ZskWenp7q2rWrRo8ebbU774EDBzRp0iTt2rVL3t7eiomJ0aBBg0hOrzO7d+/WG2+8oT179ignJ0cBAQF67rnn1L59e0ufgswXXL9ycnI0c+ZM/ec//1FWVpZCQ0M1btw4BQQEWPowRyBJKSkp6tSpk1avXq1bbrnF0s78uHF98sknGjt2rM1jBw4csHzNHLmxffjhh1q4cKFOnTqlwMBAjR07VhEREa4eFuAQySkAAAAAwOVY1gsAAAAAcDmSUwAAAACAy5GcAgAAAABcjuQUAAAAAOByJKcAAAAAAJcjOQUAAAAAuBzJKQAAAADA5UhOAQAAAAAuR3IKACh1UlJSFBQUpP379xdL/KCgIH377bfFEhsAADiH5BQAkMeYMWP03HPPuez8N910kzZu3KiAgABJ0s8//6ygoCCdO3fOZWMCAADFq7yrBwAAwLXc3Nzk7+/v6mEAAIASROUUAFAoW7du1UMPPaTg4GBFR0drxowZunz5suV4r169NGXKFE2fPl233Xab2rRpo9mzZ1vFOHTokB599FGFhISoS5cu2rx5s9VS278v601JSVHv3r0lSREREQoKCtKYMWMkSR07dtSSJUusYt9///1W5/vjjz/0+OOPW861adOmPNd08uRJDRs2TBEREYqMjNTAgQOVkpJSJJ8XAAAoGJJTAECBnTx5Us8++6xCQkL0n//8RxMmTNDKlSs1b948q36ffvqpKlWqpBUrVmjkyJGaO3euJSnMzc3VoEGDVLFiRf373//WpEmTNGvWLLvnvOmmmyzJ5ldffaWNGzfqpZdeKtB4c3NzNWTIEJUrV04rVqzQxIkTNWPGDKs+f/31l3r37q1KlSrpgw8+0LJly1SpUiU988wzys7OLszHAwAA/gGW9QIACmzZsmWqVauWxo8fL5PJpCZNmujkyZOaMWOGBg0apHLlrvybZ1BQkAYPHixJatiwoT744AP99NNPatOmjTZu3Kg///xT77//vmXp7vDhw9WnTx+b53Rzc5O3t7ckydfXV1WrVi3weDdv3qxDhw5p/fr1qlWrluVc/fr1s/T54osvZDKZNHXqVJlMJklSXFycIiIitHXrVkVHRxfyUwIAAM4gOQUAFNihQ4cUHh5uSeIkqVWrVrp48aJOnDih2rVrS7qSnP6dv7+/0tPTJUm///67atWqZXVPaYsWLYptvDfddJMlMZWk8PBwqz579+7VkSNH1LJlS6v2S5cu6ciRI8UyLgAAkBfJKQCgwAzDsNv294S1fHnr/72YTCZLP8MwrPr+E7bi/P3+V1vjvfY9ubm5at68eZ7lvpJUvXr1IhglAAAoCJJTAECB3Xzzzfr666+tEsxff/1VlStXVs2aNQsUo3Hjxjp+/LjS0tLk5+cnSdq9e7fD97i7u0uSzGazVXv16tV16tQpy/fnz5+32sjo5ptv1vHjx3Xy5EnL+BITE61iNG/eXGvXrpWvr6+qVKlSoGsAAABFjw2RAAA2ZWZmav/+/VavRx55RCdOnNDkyZN16NAhffvtt5o9e7b69Oljud80P23atFG9evU0evRoJSUl6ZdffnG4IZIk1alTRyaTSRs2bNDp06d14cIFSdLtt9+uzz77TNu3b9fBgwc1evRoq3FERUWpUaNGlnNt3749z7m6desmHx8fDRw4UNu3b9eff/6prVu3asqUKTpx4kQhPzUAAOAsklMAgE1bt27VAw88YPWaPXu23n33Xe3atUv333+/JkyYoIceekgDBw4scFw3NzfNnTtXFy9e1EMPPaSXX37Z8v4KFSrYfE/NmjU1ZMgQvf7664qKitLkyZMlSf3799ett96q/v3769lnn9Wdd96p+vXrW95Xrlw5zZkzR9nZ2XrooYf00ksvafjw4VaxK1asqA8++EC1a9fW4MGD1aVLF40bN06XLl2ikgoAQAkyGbZuyAEAoAT98ssveuyxx/TNN99YJZcAAODGwT2nAIAS980336hSpUpq0KCBjhw5oqlTp6ply5YkpgAA3MBITgEAJe7ChQt67bXXdPz4cfn4+CgqKkqjR4929bAAAIALsawXAAAAAOBybIgEAAAAAHA5klMAAAAAgMuRnAIAAAAAXI7kFAAAAADgciSnAAAAAACXIzkFAAAAALgcySkAAAAAwOVITgEAAAAALvf/AMfq1e+W2CzQAAAAAElFTkSuQmCC","text/plain":["<Figure size 1200x600 with 2 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","text/plain":["<Figure size 1200x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Load the data\n","file_path = \"/kaggle/input/10232023/Crime_Reports.csv\"\n","data = load_data(file_path)\n","\n","if data is not None:\n","    data = clean_data(data)\n","\n","    # Additional data analysis and visualizations can be performed here.\n","\n","    # Data Summary and Cleaning\n","    data_description = data.describe()\n","\n","    missing_values = data.isnull().sum()\n","\n","    data[\"Clearance Status\"].fillna(\"Not Cleared\", inplace=True)\n","\n","    Latitude = data.get(\"Latitude\", 0.0)\n","    Longitude = data.get(\"Longitude\", 0.0)\n","\n","    if \"Occurred Date Time\" in data.columns:\n","        data[\"Occurred Date Time\"] = pd.to_datetime(data[\"Occurred Date Time\"])\n","        data[\"Report Date Time\"] = pd.to_datetime(data[\"Report Date Time\"])\n","        data_types = data.dtypes\n","\n","        plt.figure(figsize=(12, 6))\n","        data[\"Occurred Date Time\"].value_counts().resample(\"M\").sum().plot()\n","        plt.title(\"Monthly Crime Trends\")\n","        plt.xlabel(\"Year-Month\")\n","        plt.ylabel(\"Number of Crimes\")\n","\n","        yearly_crime_stats = data.groupby(data[\"Occurred Date Time\"].dt.year)[\n","            \"Category Description\"\n","        ].count()\n","        monthly_crime_stats = data.groupby(\n","            data[\"Occurred Date Time\"].dt.to_period(\"M\")\n","        )[\"Category Description\"].count()\n","    else:\n","        print(\"The 'Occurred Date Time' column is not present in the dataset.\")\n","    # Crime Categories Analysis\n","    data = data[data[\"Category Description\"] != \"Unknown\"]\n","    crime_category_counts = data[\"Category Description\"].value_counts()\n","\n","    plt.figure(figsize=(12, 6))\n","    sns.countplot(\n","        data=data, x=\"Category Description\", order=crime_category_counts.index\n","    )\n","    plt.title(\"Distribution of Crime Categories\")\n","    plt.xlabel(\"Category Description\")\n","    plt.ylabel(\"Number of Crimes\")\n","    plt.xticks(rotation=90)\n","    category_plot_file = os.path.join(OUTPUT_DIRECTORY, \"crime_categories_plot.png\")\n","    plt.savefig(category_plot_file)\n","\n","    specific_category = \"Murder\"\n","    category_trends = data[data[\"Category Description\"] == specific_category]\n","\n","    crime_by_zipcode = data[\"Zip Code\"].value_counts()\n","\n","    crime_rates_by_zipcode = data[\"Zip Code\"].value_counts(normalize=True)\n","\n","    plt.figure(figsize=(12, 6))\n","    crime_by_zipcode.head(10).plot(kind=\"bar\")\n","    plt.title(\"Top 10 Zip Codes with the Highest Crime Rates\")\n","    plt.xlabel(\"Zip Code\")\n","    plt.ylabel(\"Number of Crimes\")\n","    zip_code_plot_file = os.path.join(OUTPUT_DIRECTORY, \"top_zip_codes_plot.png\")\n","    plt.savefig(zip_code_plot_file)\n","\n","    plot_bar_graph(\n","        data[\"Category Description\"].value_counts(),\n","        \"Crime Type\",\n","        \"Total Incidents\",\n","        \"Total Property & Violent Crimes (2003-Present)\",\n","        os.path.join(OUTPUT_DIRECTORY, \"total_crimes_plot.png\"),\n","    )\n","\n","    data = calculate_distance_to_booking_office(data)\n","    data = cluster_crime_locations(data, n_clusters=5)\n","\n","    plt.figure(figsize=(12, 6))\n","    plt.scatter(\n","        data[\"Longitude\"],\n","        data[\"Latitude\"],\n","        c=data[\"Distance to Booking Office (Miles)\"],\n","        cmap=\"viridis\",\n","        s=20,\n","    )\n","    plt.title(\"Crime Locations with Distance to Booking Office\")\n","    plt.xlabel(\"Longitude\")\n","    plt.ylabel(\"Latitude\")\n","    plt.colorbar(label=\"Distance to Booking Office (Miles)\")\n","    distance_plot_file = os.path.join(\n","        OUTPUT_DIRECTORY, \"distance_to_booking_office_plot.png\"\n","    )\n","    plt.savefig(distance_plot_file)\n","\n","    plt.figure(figsize=(12, 6))\n","    plt.scatter(\n","        data[\"Longitude\"],\n","        data[\"Latitude\"],\n","        c=data[\"Location Cluster\"],\n","        cmap=\"tab20\",\n","        s=20,\n","    )\n","    plt.title(\"Clustered Crime Locations\")\n","    plt.xlabel(\"Longitude\")\n","    plt.ylabel(\"Latitude\")\n","    plt.colorbar(label=\"Cluster\")\n","    cluster_plot_file = os.path.join(OUTPUT_DIRECTORY, \"clustered_locations_plot.png\")\n","    plt.savefig(cluster_plot_file)\n","\n","    crime_categories = [\"Aggravated Assault\", \"Robbery\", \"Murder\"]\n","    for category in crime_categories:\n","        create_heatmap(\n","            data[data[\"Category Description\"] == category],\n","            os.path.join(\n","                OUTPUT_DIRECTORY, f\"{category.lower().replace(' ', '_')}_heatmap.html\"\n","            ),\n","        )\n","# Save the output file\n","output.close()\n","\n","# Display plots and results\n","plt.show()\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"},"papermill":{"default_parameters":{},"duration":975.913539,"end_time":"2023-10-29T18:09:56.730206","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-10-29T17:53:40.816667","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}
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