colorado_spills/archive/unused/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import geopandas as gpd\n",
    "\n",
    "import os\n",
    "\n",
    "# Database connection details from zshrc environment variables\n",
    "db_name = 'colorado_spills'\n",
    "user = os.getenv('DB_USER')\n",
    "password = os.getenv('DB_PASSWORD')\n",
    "host = os.getenv('DB_HOST')\n",
    "port = os.getenv('DB_PORT')\n",
    "\n",
    "\n",
    "# Create an engine to connect to the PostgreSQL database\n",
    "engine = create_engine(f'postgresql+psycopg2://{user}:{password}@{host}:{port}/{db_name}')\n",
    "\n",
    "# Function to load data from a table\n",
    "def load_table(table_name):\n",
    "    query = f'SELECT * FROM {table_name}'\n",
    "    df = pd.read_sql(query, engine)\n",
    "    return df\n",
    "\n",
    "# Load the spills and demographics data\n",
    "spills_with_demographics = load_table('spills_with_demographics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Document #', 'Report', 'Operator', 'Operator #', 'Tracking #', 'Initial Report Date', 'Date of Discovery', 'Spill Type', 'Qtr Qtr', 'Section', 'Township', 'range', 'meridian', 'Latitude', 'Longitude', 'Municipality', 'county', 'Facility Type', 'Facility ID', 'API County Code', 'API Sequence Number', 'Spilled outside of berms', 'More than five barrels spilled', 'Oil Spill Volume', 'Condensate Spill Volume', 'Flow Back Spill Volume', 'Produced Water Spill Volume', 'E&P Waste Spill Volume', 'Other Waste', 'Drilling Fluid Spill Volume', 'Current Land Use', 'Other Land Use', 'Weather Conditions', 'Surface Owner', 'Surface Owner Other', 'Waters of the State', 'Residence / Occupied Structure', 'livestock', 'Public Byway', 'Surface Water Supply Area', 'Spill Description', 'Supplemental Report Date', 'Oil BBLs Spilled', 'Oil BBLs Recovered', 'Oil Unknown', 'Condensate BBLs Spilled', 'Condensate BBLs Recovered', 'Condensate Unknown', 'Produced Water BBLs Spilled', 'Produced Water BBLs Recovered', 'Produced Water Unknown', 'Drilling Fluid BBLs Spilled', 'Drilling Fluid BBLs Recovered', 'Drilling Fluid Unknown', 'Flow Back Fluid BBLs Spilled', 'Flow Back Fluid BBLs Recovered', 'Flow Back Fluid Unkown', 'Other E&P Waste BBLS Spilled', 'Other E&P Waste BBLS Recovered', 'Other E&P Waste Unknown', 'Other E&P Waste', 'Spill Contained within Berm', 'Emergency Pit Constructed', 'soil', 'groundwater', 'Surface Water', 'Dry Drainage Feature', 'Surface Area Length', 'Surface Area Width', 'Depth of Impact in Feet', 'Depth of Impact in Inches', 'Area Depth Determined', 'Geology Description', 'Depth to Groundwater', 'Water wells in area', 'Water Wells', 'Water Wells None', 'Surface Water Near', 'Surface Water None', 'Wetlands', 'Wetlands None', 'Springs', 'Springs None', 'Livestock Near', 'Livestock None', 'Occupied Buildings', 'Occupied Buildings None', 'Additional Spill Details', 'Supplemental Report Date CA', 'Human Error', 'Equipment Failure', 'Historical Unkown', 'Other', 'Other Description', 'Root Cause', 'Preventative Measures', 'Soil Excavated', 'Offsite Disposal', 'Onsite Treatment', 'Other Disposition', 'Other Disposition Description', 'Ground Water Removed', 'Surface Water Removed', 'Corrective Actions Completed', 'Approved Form 27', 'Form 27 Project Number', 'GEOID', 'TRACT_NAME', 'total_population', 'white_population', 'hispanic_population', 'median_household_income', 'poverty_population', 'unemployed_population', 'percent_white', 'percent_hispanic', 'percent_poverty', 'unemployment_rate']\n"
     ]
    }
   ],
   "source": [
    "print(spills_with_demographics.columns.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Report Date            datetime64[ns]\n",
      "Date of Discovery              datetime64[ns]\n",
      "Supplemental Report Date       datetime64[ns]\n",
      "Supplemental Report Date CA    datetime64[ns]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "date_columns = ['Initial Report Date', 'Date of Discovery', 'Supplemental Report Date', 'Supplemental Report Date CA']\n",
    "\n",
    "for col in date_columns:\n",
    "    spills_with_demographics[col] = pd.to_datetime(spills_with_demographics[col], errors='coerce')\n",
    "\n",
    "# Check the result\n",
    "print(spills_with_demographics[date_columns].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oil Spill Volume               float64\n",
      "Produced Water Spill Volume    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def clean_and_convert(x):\n",
    "    if isinstance(x, str):\n",
    "        # Remove any non-numeric characters except decimal point\n",
    "        x = ''.join(c for c in x if c.isdigit() or c == '.')\n",
    "        return float(x) if x else np.nan\n",
    "    return x\n",
    "\n",
    "spills_with_demographics['Oil Spill Volume'] = spills_with_demographics['Oil Spill Volume'].apply(clean_and_convert)\n",
    "spills_with_demographics['Produced Water Spill Volume'] = spills_with_demographics['Produced Water Spill Volume'].apply(clean_and_convert)\n",
    "\n",
    "# Convert to numeric type\n",
    "spills_with_demographics['Oil Spill Volume'] = pd.to_numeric(spills_with_demographics['Oil Spill Volume'], errors='coerce')\n",
    "spills_with_demographics['Produced Water Spill Volume'] = pd.to_numeric(spills_with_demographics['Produced Water Spill Volume'], errors='coerce')\n",
    "\n",
    "# Check the result\n",
    "print(spills_with_demographics[['Oil Spill Volume', 'Produced Water Spill Volume']].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spills per year:\n",
      "Year\n",
      "1994       1\n",
      "2004       1\n",
      "2009       3\n",
      "2010       8\n",
      "2011       5\n",
      "2012       7\n",
      "2013      21\n",
      "2014    1099\n",
      "2015    1580\n",
      "2016    1373\n",
      "2017    1599\n",
      "2018    1581\n",
      "2019    1557\n",
      "2020    1166\n",
      "2021    1790\n",
      "2022    2155\n",
      "2023    2080\n",
      "2024     864\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Total spill volumes by year:\n",
      "      Oil Spill Volume  Produced Water Spill Volume\n",
      "Year                                               \n",
      "1994               0.0                          0.0\n",
      "2004               0.0                          1.0\n",
      "2009               0.0                          0.0\n",
      "2010               0.0                          0.0\n",
      "2011               0.0                      10200.0\n",
      "2012               3.0                      30700.0\n",
      "2013               0.0                      46115.0\n",
      "2014          404477.0                    1080000.0\n",
      "2015          553498.0                    1977935.0\n",
      "2016          590644.0                    1811414.0\n",
      "2017          637575.0                    1514706.0\n",
      "2018          571563.0                    1854170.0\n",
      "2019          601360.0                    2094208.0\n",
      "2020          447700.0                    1258209.0\n",
      "2021          416769.0                    1273508.0\n",
      "2022          512475.0                    1556782.0\n",
      "2023          400198.0                    1195050.0\n",
      "2024          134124.0                     383838.0\n",
      "\n",
      "Average spill volumes by year:\n",
      "      Oil Spill Volume  Produced Water Spill Volume\n",
      "Year                                               \n",
      "1994          0.000000                     0.000000\n",
      "2004          0.000000                     1.000000\n",
      "2009          0.000000                     0.000000\n",
      "2010          0.000000                     0.000000\n",
      "2011          0.000000                  2040.000000\n",
      "2012          0.428571                  4385.714286\n",
      "2013          0.000000                  2195.952381\n",
      "2014        368.040946                   982.711556\n",
      "2015        350.315190                  1251.857595\n",
      "2016        430.184996                  1319.310998\n",
      "2017        398.733583                   947.283302\n",
      "2018        361.519924                  1172.783049\n",
      "2019        386.229929                  1345.027617\n",
      "2020        383.962264                  1079.081475\n",
      "2021        232.831844                   711.456983\n",
      "2022        237.807425                   722.404640\n",
      "2023        192.402885                   574.543269\n",
      "2024        155.236111                   444.256944\n",
      "\n",
      "Spearman Correlation between reporting delay and spill volumes:\n",
      "                             Days to Report  Oil Spill Volume  \\\n",
      "Days to Report                     1.000000         -0.014038   \n",
      "Oil Spill Volume                  -0.014038          1.000000   \n",
      "Produced Water Spill Volume        0.006207         -0.254482   \n",
      "\n",
      "                             Produced Water Spill Volume  \n",
      "Days to Report                                  0.006207  \n",
      "Oil Spill Volume                               -0.254482  \n",
      "Produced Water Spill Volume                     1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Count of spills per year\n",
    "spills_per_year = spills_with_demographics['Year'].value_counts().sort_index()\n",
    "print(\"Spills per year:\")\n",
    "print(spills_per_year)\n",
    "\n",
    "# Sum of spill volumes by year\n",
    "yearly_total_volumes = spills_with_demographics.groupby('Year').agg({\n",
    "    'Oil Spill Volume': lambda x: x.astype(float).sum(skipna=True),\n",
    "    'Produced Water Spill Volume': lambda x: x.astype(float).sum(skipna=True)\n",
    "})\n",
    "print(\"\\nTotal spill volumes by year:\")\n",
    "print(yearly_total_volumes)\n",
    "\n",
    "# Average spill volumes by year\n",
    "yearly_avg_volumes = yearly_total_volumes.div(spills_per_year, axis=0)\n",
    "print(\"\\nAverage spill volumes by year:\")\n",
    "print(yearly_avg_volumes)\n",
    "\n",
    "# Correlation between time to report and spill volume\n",
    "correlation = spills_with_demographics[['Days to Report', 'Oil Spill Volume', 'Produced Water Spill Volume']].corr(method='spearman')\n",
    "print(\"\\nSpearman Correlation between reporting delay and spill volumes:\")\n",
    "print(correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average demographic characteristics of spill locations by year:\n",
      "      percent_white  percent_hispanic  median_household_income  \\\n",
      "Year                                                             \n",
      "1994      28.591783         23.557350             1.012500e+05   \n",
      "2004      79.166667         10.784314             1.436460e+05   \n",
      "2009      81.660008         20.347086             7.067033e+04   \n",
      "2010      77.983425         22.914365             6.457300e+04   \n",
      "2011      83.450494         28.346579             8.144260e+04   \n",
      "2012      84.676145         11.650869             5.971800e+04   \n",
      "2013      80.786704         26.118158             6.534219e+04   \n",
      "2014      82.974270         23.382431            -9.629444e+06   \n",
      "2015      83.703315         22.309731            -4.142396e+06   \n",
      "2016      83.478642         23.321560            -4.295124e+06   \n",
      "2017      83.914508         21.828941            -7.426691e+06   \n",
      "2018      84.082954         21.682858             7.731813e+04   \n",
      "2019      83.985918         21.211912            -7.630970e+06   \n",
      "2020      83.125641         21.580733            -1.478967e+07   \n",
      "2021      83.962007         22.303013            -5.504801e+06   \n",
      "2022      83.105479         23.252316            -6.414875e+06   \n",
      "2023      82.844350         23.542315            -6.971533e+06   \n",
      "2024      83.594628         23.552187            -3.005627e+06   \n",
      "\n",
      "      percent_poverty  \n",
      "Year                   \n",
      "1994        10.757540  \n",
      "2004         4.166667  \n",
      "2009         8.601781  \n",
      "2010         9.571823  \n",
      "2011         7.043807  \n",
      "2012         9.834123  \n",
      "2013        10.637816  \n",
      "2014        10.344828  \n",
      "2015        10.787674  \n",
      "2016        10.976589  \n",
      "2017        10.238045  \n",
      "2018        11.052733  \n",
      "2019        11.045891  \n",
      "2020        10.627531  \n",
      "2021         9.849063  \n",
      "2022         9.581029  \n",
      "2023         9.860368  \n",
      "2024         9.919254  \n",
      "\n",
      "Correlation between spill frequency and demographic characteristics:\n",
      "spill_count                1.000000\n",
      "percent_white             -0.008777\n",
      "percent_hispanic           0.048403\n",
      "median_household_income    0.012053\n",
      "percent_poverty            0.133947\n",
      "Name: spill_count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Average demographic characteristics of spill locations by year\n",
    "yearly_demographics = spills_with_demographics.groupby('Year')[['percent_white', 'percent_hispanic', 'median_household_income', 'percent_poverty']].mean()\n",
    "print(\"\\nAverage demographic characteristics of spill locations by year:\")\n",
    "print(yearly_demographics)\n",
    "\n",
    "# Correlation between spill frequency and demographic characteristics\n",
    "spills_per_tract = spills_with_demographics.groupby('GEOID').size().reset_index(name='spill_count')\n",
    "tract_demographics = spills_with_demographics.groupby('GEOID')[['percent_white', 'percent_hispanic', 'median_household_income', 'percent_poverty']].first()\n",
    "tract_analysis = pd.merge(spills_per_tract, tract_demographics, on='GEOID')\n",
    "correlation = tract_analysis[['spill_count', 'percent_white', 'percent_hispanic', 'median_household_income', 'percent_poverty']].corr()\n",
    "print(\"\\nCorrelation between spill frequency and demographic characteristics:\")\n",
    "print(correlation['spill_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of spills for analysis: 16886\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Ensure 'Days to Report' is calculated correctly\n",
    "spills_with_demographics['Days to Report'] = (spills_with_demographics['Initial Report Date'] - spills_with_demographics['Date of Discovery']).dt.days\n",
    "\n",
    "# List of EJ variables we'll examine\n",
    "ej_variables = ['percent_white', 'percent_hispanic', 'median_household_income', 'percent_poverty', 'unemployment_rate']\n",
    "\n",
    "# Remove any rows with missing values in these columns\n",
    "analysis_df = spills_with_demographics.dropna(subset=['Days to Report'] + ej_variables)\n",
    "\n",
    "print(f\"Number of spills for analysis: {len(analysis_df)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlations with Days to Report:\n",
      "percent_white             -0.033242\n",
      "percent_poverty           -0.011415\n",
      "unemployment_rate         -0.008741\n",
      "percent_hispanic          -0.003342\n",
      "median_household_income    0.005737\n",
      "Days to Report             1.000000\n",
      "Name: Days to Report, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "correlations = analysis_df[['Days to Report'] + ej_variables].corr()['Days to Report'].sort_values()\n",
    "\n",
    "print(\"Correlations with Days to Report:\")\n",
    "print(correlations)\n",
    "\n",
    "# Visualize correlations\n",
    "plt.figure(figsize=(10, 6))\n",
    "correlations.drop('Days to Report').plot(kind='bar')\n",
    "plt.title('Correlation of EJ Factors with Reporting Time')\n",
    "plt.ylabel('Correlation Coefficient')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of spills for analysis: 16886\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Create a copy of the DataFrame to work with\n",
    "analysis_df = spills_with_demographics.copy()\n",
    "\n",
    "# Ensure 'Days to Report' is calculated correctly\n",
    "analysis_df['Days to Report'] = (analysis_df['Initial Report Date'] - analysis_df['Date of Discovery']).dt.days\n",
    "\n",
    "# List of EJ variables we'll examine\n",
    "ej_variables = ['percent_white', 'percent_hispanic', 'median_household_income', 'percent_poverty', 'unemployment_rate']\n",
    "\n",
    "# Remove any rows with missing values in these columns\n",
    "analysis_df = analysis_df.dropna(subset=['Days to Report'] + ej_variables)\n",
    "\n",
    "print(f\"Number of spills for analysis: {len(analysis_df)}\")\n",
    "\n",
    "# Function to categorize tracts into quartiles\n",
    "def categorize_quartiles(series):\n",
    "    return pd.qcut(series, q=4, labels=['Q1 (Lowest)', 'Q2', 'Q3', 'Q4 (Highest)'])\n",
    "\n",
    "# Create categorical variables for each EJ factor\n",
    "for var in ej_variables:\n",
    "    analysis_df[f'{var}_category'] = categorize_quartiles(analysis_df[var])\n",
    "\n",
    "# Function to plot boxplots\n",
    "def plot_boxplots(variable):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    sns.boxplot(x=f'{variable}_category', y='Days to Report', data=analysis_df)\n",
    "    plt.title(f'Distribution of Reporting Time by {variable}')\n",
    "    plt.ylabel('Days to Report')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Plot boxplots for each EJ variable\n",
    "for var in ej_variables:\n",
    "    plot_boxplots(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results for percent_white:\n",
      "F-value: 3.4856\n",
      "p-value: 0.0151\n",
      "\n",
      "ANOVA results for percent_hispanic:\n",
      "F-value: 2.8655\n",
      "p-value: 0.0352\n",
      "\n",
      "ANOVA results for median_household_income:\n",
      "F-value: 7.6346\n",
      "p-value: 0.0000\n",
      "\n",
      "ANOVA results for percent_poverty:\n",
      "F-value: 3.3987\n",
      "p-value: 0.0170\n",
      "\n",
      "ANOVA results for unemployment_rate:\n",
      "F-value: 5.1023\n",
      "p-value: 0.0016\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to perform ANOVA\n",
    "def perform_anova(variable):\n",
    "    categories = analysis_df[f'{variable}_category'].unique()\n",
    "    samples = [group['Days to Report'].values for name, group in analysis_df.groupby(f'{variable}_category', observed=True)]\n",
    "    f_value, p_value = stats.f_oneway(*samples)\n",
    "    return f_value, p_value\n",
    "\n",
    "# Perform ANOVA for each EJ variable\n",
    "for var in ej_variables:\n",
    "    f_value, p_value = perform_anova(var)\n",
    "    print(f\"ANOVA results for {var}:\")\n",
    "    print(f\"F-value: {f_value:.4f}\")\n",
    "    print(f\"p-value: {p_value:.4f}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset size: 16886\n",
      "Dataset size after cleaning: 10916\n",
      "Number of records removed: 5970\n",
      "\n",
      "Correlation Matrix:\n",
      "                         Days to Report  percent_white  percent_hispanic  \\\n",
      "Days to Report                 1.000000      -0.012665          0.001356   \n",
      "percent_white                 -0.012665       1.000000         -0.641930   \n",
      "percent_hispanic               0.001356      -0.641930          1.000000   \n",
      "median_household_income        0.007872       0.113642         -0.259411   \n",
      "percent_poverty               -0.027037      -0.204533          0.049957   \n",
      "unemployment_rate             -0.002549       0.074651         -0.120400   \n",
      "\n",
      "                         median_household_income  percent_poverty  \\\n",
      "Days to Report                          0.007872        -0.027037   \n",
      "percent_white                           0.113642        -0.204533   \n",
      "percent_hispanic                       -0.259411         0.049957   \n",
      "median_household_income                 1.000000        -0.585739   \n",
      "percent_poverty                        -0.585739         1.000000   \n",
      "unemployment_rate                      -0.246535         0.382319   \n",
      "\n",
      "                         unemployment_rate  \n",
      "Days to Report                   -0.002549  \n",
      "percent_white                     0.074651  \n",
      "percent_hispanic                 -0.120400  \n",
      "median_household_income          -0.246535  \n",
      "percent_poverty                   0.382319  \n",
      "unemployment_rate                 1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yWzQaNRMnTjQHHXRQ8j2aOM59399f/OIXjSSzYsWKlO1HHXWUmTx5cvLvdD7HndHRb0OinhdddFFK+eXLlxtJZteuXcYYYzZs2LDf78jOhu6LLrrIRCIREw6HzXvvvWdOO+004/V6zWuvvZYst2zZMmO1WttctP7LX/5iJJnHH388ZZ9utzsl4EWjUXPYYYeZMWPGJLedddZZJicnx2zbti1ln6eccorJzc1NXnxJfHaPP/74NvVP53c0UbeSkpLkhVNjjKmsrDRWq9UsW7Ysue1Tn/qUKS8vNy0tLcltjY2NpqioqFdC974XQxPf1XtfqDOm9b07ZMiQ5N9r165tc7HIGGO2b99u3G63ueKKK9KqO5BtdC8H+rGpU6fK4XDI6/Xq5JNPVmFhof7+97/Lbm+drmHTpk3auHGjzjnnHElSNBpN/vvc5z6nXbt2tdsdNaGpqUnf//73NWbMGNntdtntduXl5SkQCLTpntpZ//rXvySpzSynX/7yl+XxePT000+nbD/qqKN08MEHJ/92uVw69NBDU7oxfvrTn9YTTzyhH/zgB3r22WcVDAbbfWyLxaJTTz01ZdsRRxzRpktkRxLPY8L06dM1cuRIPfPMM126//z582W325P3f/LJJxWNRvW1r30t5bVyuVyaMWNGp2aWHz16tCoqKvTUU09JktasWaNJkybpq1/9qjZv3qwPPvhALS0tev7553XSSScl7/fYY4/phBNO0IgRI1Ie+5RTTpEkPffcc8lyBQUFOvXUU1PKHXXUUSotLW1Tx868fvvzuc99TlbrJz9V48ePlyR9/vOfTymX2L5t2zZJnX8u3333Xe3cuVMLFixI6Wo5cuRITZ8+vVN1vP322zV58mS5XC7Z7XY5HA49/fTT7X5GPv/5z8tmsyX/PuKIIySp08/H3k477bSUv/fd19q1axUMBtt81srLy3XiiSe2+ax1xqc//WnV19fr7LPP1t///vd2Z+Tv7Hsp4UDPSeI1WrhwYcp7IS8vT1/60pf00ksvqbm5ud36rl27VqFQqMPPbk850DG89957+uCDD3TeeefJ5XK1u49AIKCXX35ZZ555ZspKCzabTQsXLtRHH33U5vt63rx5KX/v7/Ox93ss3c/xvtL9bTjQezXxHdjRd2Rn3XbbbXI4HHI6nTr00EP1xBNP6IEHHtAxxxyTcuwTJ07UUUcdlXLsc+fObXcFj1mzZqmkpCT5t81m01e+8hVt2rRJH330kaTW37VZs2apvLw85b6LFi1Sc3Nzm2EQX/rSlzp9TPtzwgknyOv1Jv8uKSlRcXFx8nkNBAJ67bXX9MUvflFOpzNZLtHNuzek8x6tra1NdjF/7LHHZLFY9NWvfjXldSotLdWRRx7ZpZVWgGxiIjWgH7vnnns0fvx4NTY26qGHHtIdd9yhs88+OzleMTGObenSpVq6dGm7+9jfMlYLFizQ008/rauuukqf+tSnlJ+fL4vFos997nMdBtsDqampkd1u17Bhw1K2WywWlZaWqqamJmV7UVFRm33k5OSkPP7//d//6aCDDtJDDz2kG2+8US6XS3PnztVNN92ksWPHJsvl5ua2OeHNyclRKBTqVN1LS0vb3bZvnTt7f7vdrqKiouT9E6/Xpz71qXbvv3fg2J9Zs2Zp1apVkqSnnnpKs2fP1qRJk1RSUqKnnnpKY8eOVTAYTAndVVVVevTRR+VwONrdZ+J9UlVVpfr6+pQTuPbKJXTm9dufIUOGpPydeNyOtidey84+l4nnvqPX9kBLT61YsUKXX365vvnNb+pnP/uZhg4dKpvNpquuuqrd8LHv85GTkyNJXfo8HWhfiWMbPnx4m/uOGDFCa9asSfsxFy5cqGg0qt/97nf60pe+pHg8rk996lP6+c9/rtmzZ0vq/Hupp44jHo+rrq5Oubm5bW4/0OvbUw50DIlx0vub+b6urk7GmA6PU1Kb75p0Ph97f8+l+zneV7q/DZ19jTv6juys+fPn63vf+54ikYjeeustXXnllTrrrLP0xhtvJH8LqqqqtGnTpk6/P/f33qmpqdFBBx2kmpqatF639sp2xYG+XxPvqb0vGiS0ty0TuvIdnpeXp6qqqg7rLkmHHHJIBmoLZA6hG+jHxo8fn5w87YQTTlAsFtOdd96pv/zlLzrzzDM1dOhQSdKVV16pM844o919jBs3rt3tfr9fjz32mK6++mr94Ac/SG5vaWlRbW1tl+tcVFSkaDSq3bt3pwRvY4wqKys7DEn74/F4dO211+raa69VVVVVstX71FNP1caNG7tc131VVla2u23MmDGdvn9ZWVny72g0qpqamuSJU+L1+stf/tKtVrhZs2bp97//vV555RW9/PLL+vGPfyxJOvHEE7VmzRpt3bpVeXl5KTPQDh06VEcccYSuu+66dveZOHlMTISUCPX72rvVJZs6+1wmnvuOXtsDuffeezVz5kz95je/Sdne2NiYTnUzInFsu3btanPbzp07k8+R1NoDwe/3tynXXvhavHixFi9erEAgoH//+9+6+uqrNW/ePL333nsaOXJkp99LPXUcVqu1wyUSD/T69tbax4nvukTLaHsKCwtltVo7PE5JKa9Zd3Tnc5yJ34a9X6f2viM7a9iwYcnfxGnTpmn8+PGaMWOGLrvssuQEY0OHDpXb7e5wQsd9n+P9fTck6l1UVJTW69aTE5jtT2FhoSwWS5uJ5KTOfb9l09ChQ2WxWPSf//wneZFmb+1tA/oyQjcwgCxfvlx//etf9ZOf/ERnnHGGxo0bp7Fjx+rNN9/U9ddfn9a+LBaLjDFtftjuvPNOxWKxlG3ptNbNmjVLy5cv17333qvLLrssuf2vf/2rAoGAZs2alVY991VSUqJFixbpzTff1C233KLm5uZ2W8C64r777kvpFvjiiy9q69atOv/88zt9/727Of7pT39SNBpNzoY8d+5c2e12ffDBBwfsfrj3c+52u1NumzVrliwWi6666ipZrVYdf/zxkqSTTjpJ3/ve97R161Ydf/zxKS098+bN0+OPP67Ro0fvd433efPm6cEHH1QsFtOUKVM6ddzZ0Nnncty4cRo+fLgeeOABffe7302eDG/dulUvvvjiAQOixWJp8xn53//+p7Vr17bpatrbpk2bJrfbrXvvvTc5M7LUGvz+9a9/6cwzz0xuq6io0J///Ge1tLQkj6empkYvvvhih2vvejwenXLKKQqHw/riF7+ot99+WyNHjuz0e6mzxo0bp7KyMt1///1aunRp8jUKBAL661//mpzRvD1Tp06Vy+Xq8LPbW6H70EMP1ejRo/WHP/xB3/3ud9sNDB6PR1OmTNHf/vY33XzzzcnPdTwe17333quDDjpIhx56aI/Upzuf43R+Gzor8R3Y0XdkV332s5/V1772Nd19991au3atpk2bpnnz5un6669XUVGRRo0adcB9PP3006qqqkq2uMZiMT300EMaPXp0sufCrFmz9PDDD2vnzp0p3xn33HOPcnNzO7XUYHd6vXTE4/Ho2GOP1SOPPKKbb7452Zrc1NTU7iznfcm8efN0ww03aMeOHZo/f362qwN0G6EbGEAKCwt15ZVX6oorrtD999+vr371q7rjjjt0yimnaO7cuVq0aJHKyspUW1urDRs26I033khZRmpv+fn5Ov7443XTTTdp6NChqqio0HPPPaff//73KigoSCk7ceJESdJvf/tbeb1euVwujRo1qt2ub7Nnz9bcuXP1/e9/Xw0NDTruuOP0v//9T1dffbWOPvpoLVy4MO3jnjJliubNm6cjjjhChYWF2rBhg/74xz/u92S8K1577TWdf/75+vKXv6zt27frRz/6kcrKynTRRRd16v5/+9vfZLfbNXv2bL399tu66qqrdOSRRyZPKCoqKvTTn/5UP/rRj/Thhx8mx+lXVVXplVdeSbboS9KkSZMkSTfeeKNOOeUU2Ww2HXHEEXI6nSouLtbEiRO1evVqnXDCCcnn4KSTTlJtba1qa2u1YsWKlLr99Kc/1Zo1azR9+nRdeumlGjdunEKhkLZs2aLHH39ct99+uw466CCdddZZuu+++/S5z31O3/nOd/TpT39aDodDH330kZ555hl94Qtf0Omnn95TT3mXdfa5tFqt+tnPfqbzzz9fp59+ui644ALV19frmmuu6VT343nz5ulnP/uZrr76as2YMUPvvvuufvrTn2rUqFHdCgs9oaCgQFdddZV++MMf6mtf+5rOPvts1dTU6Nprr5XL5dLVV1+dLLtw4ULdcccd+upXv6oLLrhANTU1Wr58eZvAfcEFF8jtduu4447T8OHDVVlZqWXLlsnn8yV7qXT2vdRZVqtVy5cv1znnnKN58+bpwgsvVEtLi2666SbV19frhhtu6PC+hYWFWrp0qX7+85+nfHY7+/r2pF//+tc69dRTNXXqVF122WU6+OCDtW3bNj355JO67777JEnLli3T7NmzdcIJJ2jp0qVyOp267bbbtH79ej3wwAM91kLanc9xOr8NnTV+/Hh99atf1S233CKHw6GTTjpJ69ev180339zhRZ/O+tnPfqaHHnpIV111lZ566iktWbJEf/3rX3X88cfrsssu0xFHHKF4PK5t27Zp9erVuvzyy1MuRAwdOlQnnniirrrqKnk8Ht12223auHFjyrJhV199dXIug5/85CcaMmSI7rvvPv3zn//U8uXL5fP5DljPdH5H0/HTn/5Un//85zV37lx95zvfUSwW00033aS8vLxu9VrLtOOOO07f+MY3tHjxYr322ms6/vjj5fF4tGvXLj3//POaNGmSvvWtb2W7mkDnZXMWNwBd09GSYca0zg6979JBb775ppk/f74pLi42DofDlJaWmhNPPNHcfvvtyfu1N3v5Rx99ZL70pS+ZwsJC4/V6zcknn2zWr1/fZpZSY1pnlR41apSx2Wwpy5G1N+tqMBg03//+983IkSONw+Eww4cPN9/61rfaLL3V0Uyp+84g/IMf/MAce+yxprCw0OTk5JhDDjnEXHbZZWbPnj3JMueee67xeDxt9pWYWXxv6mD28tWrV5uFCxeagoKC5KzQ77//fpt9dvQYr7/+ujn11FNNXl6e8Xq95uyzzzZVVVVtyj/yyCPmhBNOMPn5+SYnJ8eMHDnSnHnmmeapp55KlmlpaTHnn3++GTZsmLFYLG2WSbrsssuMJHPdddel7Hvs2LFGkvnf//7X5nF3795tLr30UjNq1CjjcDjMkCFDzDHHHGN+9KMfmaampmS5SCRibr75ZnPkkUcal8tl8vLyzGGHHWYuvPDClOejs69fexKzQt90000p2xPv072X6zGm489EZ55LY4y58847zdixY43T6TSHHnqo+cMf/tDhjMF7vzdaWlrM0qVLTVlZmXG5XGby5MnmkUceaXPfjo6nvX0eSEfPQeIx9l4KMHFsRxxxhHE6ncbn85kvfOELKUumJdx9991m/PjxxuVymQkTJpiHHnqozXHcfffd5oQTTjAlJSXG6XSaESNGmPnz57d5P3XmvZTuc/LII4+YKVOmGJfLZTwej5k1a5Z54YUXUsq0t2RYPB43y5YtM+Xl5cbpdJojjjjCPProo516H+6ro9nLO3sMa9euNaeccorx+XwmJyfHjB49us2M3f/5z3/MiSeeaDwej3G73Wbq1Knm0Ucfbfc4932/J75r9l62y5j2v/86+zluT2d/GzqqZ3u/Ny0tLebyyy83xcXFxuVymalTp5q1a9e2+3vTHrWzZFjC9773vZSlCpuamsyPf/xjM27cuOTnYtKkSeayyy5Lmak8sc/bbrvNjB492jgcDnPYYYeZ++67r81jvPXWW+bUU081Pp/POJ1Oc+SRR7b5LHb02U1I53e0o+Nt7/l6+OGHzaRJk4zT6TQHH3ywueGGG8yll15qCgsL261HR7oye3lnv6s7eu/+4Q9/MFOmTEl+HkaPHm2+9rWvpcxID/QHFmOMyWysB4D+beXKlVq8eLFeffXV5HhBAMDAZrFYdPHFF+vWW2/NdlV6VCQS0VFHHaWysjKtXr0629UBBgW6lwMAAAAD1HnnnafZs2cnh4Tcfvvt2rBhg371q19lu2rAoEHoBgCgDzDGHHAiKpvN1mszHw8msVhM++v4Z7FYUtbhBvqTxsZGLV26VLt375bD4dDkyZP1+OOPpywbCSCz6F4OAEAf8Oyzz+qEE07Yb5m77rpLixYt6p0KDSIVFRXaunVrh7fPmDFDzz77bO9VCAAwoBC6AQDoAxobG/Xuu+/ut0xPzGaMtt566y21tLR0eLvX69W4ceN6sUYAgIGE0A0AAAAAQIZYs10BAAAAAAAGKiZS66R4PK6dO3fK6/UyiQ0AAAAADHLGGDU2NmrEiBGyWjtuzyZ0d9LOnTtVXl6e7WoAAAAAAPqQ7du366CDDurwdkJ3J3m9XkmtT2h+fn6WawMAAAAAyKaGhgaVl5cns2JHCN2dlOhSnp+fT+gGAAAAAEjSAYcfM5EaAAAAAAAZQugGAAAAACBDCN0AAAAAAGQIoRsAAAAAgAwhdAMAAAAAkCGEbgAAAAAAMoTQDQAAAABAhhC6AQAAAADIEEI3AAAAAAAZQugGAAAAACBDCN0AAAAAAGQIoRsAAAAAgAwhdAMAAAAAkCGEbgAAAAAAMoTQDQAAAABAhtizXQEAAACgp8XjRjvqgwqEo/I47SorcMtqtWS7WgAGIUI3AAAABpRN1Y16cn2VPtjdpFA0JpfdptHD8jR3YonGFHuzXT0AgwyhGwAAAAPGpupG3fXCFtUGwhrucynX6VZzOKr1O/3a6Q9q8XEVBG8AvYox3QAAABgQ4nGjJ9dXqTYQ1tjiPHldDtmsFnldDo0tzlNtIKzVb1cpHjfZriqAQYTQDQAAgAFhR31QH+xu0nCfSxZL6vhti8Wi4T6XNlU3aUd9MEs1BDAYEboBAAAwIATCUYWiMeU62x9B6Xba1BKNKRCO9nLNAAxmhG4AAAAMCB6nXS67Tc0dhOpgOKYcu02eDkI5AGQCoRsAAAADQlmBW6OH5WmXPyRjUsdtG2O0yx/SmOI8lRW4s1RDAIMRoRsAAAADgtVq0dyJJRricer96iY1hiKKxuNqDEX0fnWThnicmnN4Cet1A+hVhG4AAAAMGGOKvVp8XIUmjvCpvjmiLXsCqm+OaFKZj+XCAGQFA1oAAAAwoIwp9uqQmXnaUR9UIByVx2lXWYGbFm4AWUHoBgAAwIBjtVpUPiQ329UAALqXAwAAAACQKYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMiQrIbua665RhaLJeVfaWlp8nZjjK655hqNGDFCbrdbM2fO1Ntvv52yj5aWFl1yySUaOnSoPB6PTjvtNH300UcpZerq6rRw4UL5fD75fD4tXLhQ9fX1vXGIAAAAAIBBLOst3Ycffrh27dqV/PfWW28lb1u+fLlWrFihW2+9Va+++qpKS0s1e/ZsNTY2JsssWbJEDz/8sB588EE9//zzampq0rx58xSLxZJlFixYoHXr1mnVqlVatWqV1q1bp4ULF/bqcQIAAAAABh971itgt6e0bicYY3TLLbfoRz/6kc444wxJ0t13362SkhLdf//9uvDCC+X3+/X73/9ef/zjH3XSSSdJku69916Vl5frqaee0ty5c7VhwwatWrVKL730kqZMmSJJ+t3vfqdp06bp3Xff1bhx43rvYAEAAAAAg0rWW7rff/99jRgxQqNGjdJZZ52lDz/8UJK0efNmVVZWas6cOcmyOTk5mjFjhl588UVJ0uuvv65IJJJSZsSIEZo4cWKyzNq1a+Xz+ZKBW5KmTp0qn8+XLAMAAAAAQCZktaV7ypQpuueee3TooYeqqqpKP//5zzV9+nS9/fbbqqyslCSVlJSk3KekpERbt26VJFVWVsrpdKqwsLBNmcT9KysrVVxc3Oaxi4uLk2Xa09LSopaWluTfDQ0NXTtIAAAAAMCgldXQfcoppyT/f9KkSZo2bZpGjx6tu+++W1OnTpUkWSyWlPsYY9ps29e+Zdorf6D9LFu2TNdee22njgMAAAAAgPZkvXv53jwejyZNmqT3338/Oc5739bo6urqZOt3aWmpwuGw6urq9lumqqqqzWPt3r27TSv63q688kr5/f7kv+3bt3fr2AAAAAAAg0+fCt0tLS3asGGDhg8frlGjRqm0tFRr1qxJ3h4Oh/Xcc89p+vTpkqRjjjlGDocjpcyuXbu0fv36ZJlp06bJ7/frlVdeSZZ5+eWX5ff7k2Xak5OTo/z8/JR/AAAAAACkI6vdy5cuXapTTz1VBx98sKqrq/Xzn/9cDQ0NOvfcc2WxWLRkyRJdf/31Gjt2rMaOHavrr79eubm5WrBggSTJ5/PpvPPO0+WXX66ioiINGTJES5cu1aRJk5KzmY8fP14nn3yyLrjgAt1xxx2SpG984xuaN28eM5cDAAAAADIqq6H7o48+0tlnn609e/Zo2LBhmjp1ql566SWNHDlSknTFFVcoGAzqoosuUl1dnaZMmaLVq1fL6/Um9/HLX/5Sdrtd8+fPVzAY1KxZs7Ry5UrZbLZkmfvuu0+XXnppcpbz0047TbfeemvvHiwAAAAAYNCxGGNMtivRHzQ0NMjn88nv99PVHAAAAAAGuc5mxD41phsAAAAAgIGE0A0AAAAAQIYQugEAAAAAyBBCNwAAAAAAGULoBgAAAAAgQwjdAAAAAABkCKEbAAAAAIAMIXQDAAAAAJAhhG4AAAAAADKE0A0AAAAAQIYQugEAAAAAyBBCNwAAAAAAGULoBgAAAAAgQwjdAAAAAABkCKEbAAAAAIAMIXQDAAAAAJAhhG4AAAAAADKE0A0AAAAAQIbYs10BAANDPG60oz6oQDgqj9OusgK3rFZLtqsFAAAAZBWhG0C3bapu1JPrq/TB7iaFojG57DaNHpanuRNLNKbYm+3qAQAAAFlD6AbQLZuqG3XXC1tUGwhruM+lXKdbzeGo1u/0a6c/qMXHVRC8AQAAMGgxphtAl8XjRk+ur1JtIKyxxXnyuhyyWS3yuhwaW5yn2kBYq9+uUjxusl1VAAAAICsI3QC6bEd9UB/sbtJwn0sWS+r4bYvFouE+lzZVN2lHfTBLNQQAAACyi9ANoMsC4ahC0Zhyne2PVHE7bWqJxhQIR3u5ZgAAAEDfQOgG0GUep10uu03NHYTqYDimHLtNng5COQAAADDQEboBdFlZgVujh+Vplz8kY1LHbRtjtMsf0pjiPJUVuLNUQwAAACC7CN0AusxqtWjuxBIN8Tj1fnWTGkMRReNxNYYier+6SUM8Ts05vIT1ugEAADBoEboBdMuYYq8WH1ehiSN8qm+OaMuegOqbI5pU5mO5MAAAAAx6DLQE0G1jir06ZGaedtQHFQhH5XHaVVbgpoUbAAAAgx6hG0CPsFotKh+Sm+1qAAAAAH0K3csBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECGELoBAAAAAMgQQjcAAAAAABlC6AYAAAAAIEMI3QAAAAAAZAihGwAAAACADCF0AwAAAACQIYRuAAAAAAAyhNANAAAAAECG9JnQvWzZMlksFi1ZsiS5zRija665RiNGjJDb7dbMmTP19ttvp9yvpaVFl1xyiYYOHSqPx6PTTjtNH330UUqZuro6LVy4UD6fTz6fTwsXLlR9fX0vHBUAAAAAYDDrE6H71Vdf1W9/+1sdccQRKduXL1+uFStW6NZbb9Wrr76q0tJSzZ49W42NjckyS5Ys0cMPP6wHH3xQzz//vJqamjRv3jzFYrFkmQULFmjdunVatWqVVq1apXXr1mnhwoW9dnwAAAAAgMEp66G7qalJ55xzjn73u9+psLAwud0Yo1tuuUU/+tGPdMYZZ2jixIm6++671dzcrPvvv1+S5Pf79fvf/16/+MUvdNJJJ+noo4/Wvffeq7feektPPfWUJGnDhg1atWqV7rzzTk2bNk3Tpk3T7373Oz322GN69913s3LMAAAAAIDBIeuh++KLL9bnP/95nXTSSSnbN2/erMrKSs2ZMye5LScnRzNmzNCLL74oSXr99dcViURSyowYMUITJ05Mllm7dq18Pp+mTJmSLDN16lT5fL5kGQAAAAAAMsGezQd/8MEH9cYbb+jVV19tc1tlZaUkqaSkJGV7SUmJtm7dmizjdDpTWsgTZRL3r6ysVHFxcZv9FxcXJ8u0p6WlRS0tLcm/GxoaOnlUAAAAAAC0ylpL9/bt2/Wd73xH9957r1wuV4flLBZLyt/GmDbb9rVvmfbKH2g/y5YtS0685vP5VF5evt/HBAAAAABgX1kL3a+//rqqq6t1zDHHyG63y26367nnntP//d//yW63J1u4922Nrq6uTt5WWlqqcDisurq6/Zapqqpq8/i7d+9u04q+tyuvvFJ+vz/5b/v27d06XgAAAADA4JO10D1r1iy99dZbWrduXfLfscceq3POOUfr1q3TIYccotLSUq1ZsyZ5n3A4rOeee07Tp0+XJB1zzDFyOBwpZXbt2qX169cny0ybNk1+v1+vvPJKsszLL78sv9+fLNOenJwc5efnp/wDAAAAACAdWRvT7fV6NXHixJRtHo9HRUVFye1LlizR9ddfr7Fjx2rs2LG6/vrrlZubqwULFkiSfD6fzjvvPF1++eUqKirSkCFDtHTpUk2aNCk5Mdv48eN18skn64ILLtAdd9whSfrGN76hefPmady4cb14xAAAAACAwSarE6kdyBVXXKFgMKiLLrpIdXV1mjJlilavXi2v15ss88tf/lJ2u13z589XMBjUrFmztHLlStlstmSZ++67T5deemlylvPTTjtNt956a68fDwAAAABgcLEYY0y2K9EfNDQ0yOfzye/309UcAAAAAAa5zmbErK/TDQAAAADAQEXoBgAAAAAgQwjdAAAAAABkCKEbAAAAAIAMIXQDAAAAAJAhhG4AAAAAADKE0A0AAAAAQIYQugEAAAAAyBBCNwAAAAAAGULoBgAAAAAgQwjdAAAAAABkCKEbAAAAAIAMIXQDAAAAAJAhhG4AAAAAADKE0A0AAAAAQIYQugEAAAAAyBBCNwAAAAAAGULoBgAAAAAgQ+zZrgAAIHvicaMd9UEFwlF5nHaVFbhltVqyXS0AAIABg9ANAIPUpupGPbm+Sh/sblIoGpPLbtPoYXmaO7FEY4q92a4eAADAgEDoBoBBaFN1o+56YYtqA2EN97mU63SrORzV+p1+7fQHtfi4CoI3AABAD2BMNwAMMvG40ZPrq1QbCGtscZ68LodsVou8LofGFuepNhDW6rerFI+bbFcVAACg3yN0A8Ags6M+qA92N2m4zyWLJXX8tsVi0XCfS5uqm7SjPpilGgIAAAwchG4AGGQC4ahC0Zhyne2PMHI7bWqJxhQIR3u5ZgAAAAMPoRsABhmP0y6X3abmDkJ1MBxTjt0mTwehHAAAAJ1H6AaAQaaswK3Rw/K0yx+SManjto0x2uUPaUxxnsoK3FmqIQAAwMBB6AaAQcZqtWjuxBIN8Tj1fnWTGkMRReNxNYYier+6SUM8Ts05vIT1ugEAAHoAoRsABqExxV4tPq5CE0f4VN8c0ZY9AdU3RzSpzMdyYQAAAD2IAXsAMEiNKfbqkJl52lEfVCAclcdpV1mBmxZuAACAHkToBoBBzGq1qHxIbrarAQAAMGDRvRwAAAAAgAwhdAMAAAAAkCGEbgAAAAAAMoTQDQAAAABAhhC6AQAAAADIEEI3AAAAAAAZknbovueee9TS0tJmezgc1j333NMjlQIAAAAAYCCwGGNMOnew2WzatWuXiouLU7bX1NSouLhYsVisRyvYVzQ0NMjn88nv9ys/Pz/b1QEAAAAAZFFnM2LaLd3GGFksljbbP/roI/l8vnR3BwAAAADAgGXvbMGjjz5aFotFFotFs2bNkt3+yV1jsZg2b96sk08+OSOVBAAAAACgP+p06P7iF78oSVq3bp3mzp2rvLy85G1Op1MVFRX60pe+1OMVBAAAAACgv+p06L766qsVi8U0cuRIzZ07V8OHD89kvQAAAAAA6PfSGtNts9n0zW9+U6FQKFP1AQAAAABgwEh7IrVJkybpww8/zERdAAAAAAAYUNIO3dddd52WLl2qxx57TLt27VJDQ0PKPwAAAAAA0Crtdbqt1k9y+t5LhyWWEmOdbgAAAADAQNfZjNjpidQSnnnmmW5VDAAAAACAwSLt0D1jxoxM1AMAAAAAgAEn7dAtSfX19fr973+vDRs2yGKxaMKECfr6178un8/X0/UDAAAAAKDfSnsitddee02jR4/WL3/5S9XW1mrPnj1asWKFRo8erTfeeCMTdQQAAAAAoF9KeyK1z372sxozZox+97vfyW5vbSiPRqM6//zz9eGHH+rf//53RiqabUykBgAAAABI6GxGTDt0u91u/fe//9Vhhx2Wsv2dd97Rscceq+bm5q7VuI8jdAMAAAAAEjqbEdPuXp6fn69t27a12b59+3Z5vd50dwcAAAAAwICVduj+yle+ovPOO08PPfSQtm/fro8++kgPPvigzj//fJ199tmZqCMAAAAAAP1S2rOX33zzzbJYLPra176maDQqSXI4HPrWt76lG264occrCAAAAABAf5X2mO6E5uZmffDBBzLGaMyYMcrNze3puvUpjOkGAAAAACR0NiN2aZ1uScrNzVVBQYEsFsuAD9wAAAAAAHRF2mO6o9GorrrqKvl8PlVUVGjkyJHy+Xz68Y9/rEgkkok6AgAAAADQL6Xd0v3tb39bDz/8sJYvX65p06ZJktauXatrrrlGe/bs0e23397jlQQAAAAAoD9Ke0y3z+fTgw8+qFNOOSVl+xNPPKGzzjpLfr+/RyvYVzCmGwAAAACQkLF1ul0ulyoqKtpsr6iokNPpTHd3AAAAAAAMWGmH7osvvlg/+9nP1NLSktzW0tKi6667Tt/+9rd7tHIAAAAAAPRnaY/p/u9//6unn35aBx10kI488khJ0ptvvqlwOKxZs2bpjDPOSJb929/+1nM1BQAAAACgn0k7dBcUFOhLX/pSyrby8vIeqxAAAAAAAANF2qH7rrvuykQ9AAAAAAAYcNIe0y21rtX91FNP6Y477lBjY6MkaefOnWpqaurRygEAAAAA0J+l3dK9detWnXzyydq2bZtaWlo0e/Zseb1eLV++XKFQiHW6AQAAAAD4WNot3d/5znd07LHHqq6uTm63O7n99NNP19NPP92jlQMAAAAAoD9Lu6X7+eef1wsvvNBmTe6RI0dqx44dPVYxAAAAAAD6u7RbuuPxuGKxWJvtH330kbxeb49UCgAAAACAgSDt0D179mzdcsstyb8tFouampp09dVX63Of+1xa+/rNb36jI444Qvn5+crPz9e0adP0xBNPJG83xuiaa67RiBEj5Ha7NXPmTL399tsp+2hpadEll1yioUOHyuPx6LTTTtNHH32UUqaurk4LFy6Uz+eTz+fTwoULVV9fn+6hAwAAAACQlrRD9y9/+Us999xzmjBhgkKhkBYsWKCKigrt2LFDN954Y1r7Ouigg3TDDTfotdde02uvvaYTTzxRX/jCF5LBevny5VqxYoVuvfVWvfrqqyotLdXs2bOTM6ZL0pIlS/Twww/rwQcf1PPPP6+mpibNmzcvpTV+wYIFWrdunVatWqVVq1Zp3bp1WrhwYbqHDgAAAABAWizGGJPunYLBoB544AG98cYbisfjmjx5ss4555yUidW6asiQIbrpppv09a9/XSNGjNCSJUv0/e9/X1Jrq3ZJSYluvPFGXXjhhfL7/Ro2bJj++Mc/6itf+Yqk1qXLysvL9fjjj2vu3LnasGGDJkyYoJdeeklTpkyRJL300kuaNm2aNm7cqHHjxnWqXg0NDfL5fPL7/crPz+/2cQIAAAAA+q/OZsQurdPtdrv19a9/Xbfeeqtuu+02nX/++aqvr9e3v/3tLlc4FovpwQcfVCAQ0LRp07R582ZVVlZqzpw5yTI5OTmaMWOGXnzxRUnS66+/rkgkklJmxIgRmjhxYrLM2rVr5fP5koFbkqZOnSqfz5csAwAAAABAJqQ1e/k777yjZ555Rg6HQ/Pnz1dBQYH27Nmj6667TrfffrtGjRqVdgXeeustTZs2TaFQSHl5eXr44Yc1YcKEZCAuKSlJKV9SUqKtW7dKkiorK+V0OlVYWNimTGVlZbJMcXFxm8ctLi5OlmlPS0uLWlpakn83NDSkfWwAAAAAgMGt0y3djz32mI4++mhdcskl+uY3v6ljjz1WzzzzjMaPH69169bpz3/+s9555520KzBu3DitW7dOL730kr71rW/p3HPPTdmPxWJJKW+MabNtX/uWaa/8gfazbNmy5MRrPp9P5eXlnT0kAAAAAAAkpRG6r7vuOn3zm99UQ0ODbr75Zn344Yf65je/qb/+9a965plnNG/evC5VwOl0asyYMTr22GO1bNkyHXnkkfrVr36l0tJSSWrTGl1dXZ1s/S4tLVU4HFZdXd1+y1RVVbV53N27d7dpRd/blVdeKb/fn/y3ffv2Lh0fAAAAAGDw6nTo3rBhgy6++GLl5eXp0ksvldVq1S233KLjjz++RytkjFFLS4tGjRql0tJSrVmzJnlbOBzWc889p+nTp0uSjjnmGDkcjpQyu3bt0vr165Nlpk2bJr/fr1deeSVZ5uWXX5bf70+WaU9OTk5yKbPEPwAAAAAA0tHpMd0NDQ0qKChovZPdLrfbrUMPPbRbD/7DH/5Qp5xyisrLy9XY2KgHH3xQzz77rFatWiWLxaIlS5bo+uuv19ixYzV27Fhdf/31ys3N1YIFCyRJPp9P5513ni6//HIVFRVpyJAhWrp0qSZNmqSTTjpJkjR+/HidfPLJuuCCC3THHXdIkr7xjW9o3rx5nZ65HAAAAACArkh7IrVEd29jjN59910FAoGUMkcccUSn91dVVaWFCxdq165d8vl8OuKII7Rq1SrNnj1bknTFFVcoGAzqoosuUl1dnaZMmaLVq1fL6/Um9/HLX/5Sdrtd8+fPVzAY1KxZs7Ry5UrZbLZkmfvuu0+XXnppcpbz0047Tbfeems6hw4AAAAAQNo6vU631WqVxWJRe8UT2y0Wi2KxWI9Xsi9gnW4AAAAAQEJnM2KnW7o3b97cIxUDAAAAAGCw6HToHjlyZCbrAQAAAADAgNPp2csBAAAAAEB6CN0AAAAAAGQIoRsAAAAAgAwhdAMAAAAAkCFprdO9t927d+vdd9+VxWLRoYceqmHDhvVkvQAAAAAA6PfSbukOBAL6+te/rhEjRuj444/XZz/7WY0YMULnnXeempubM1FHAAAAAAD6pbRD93e/+10999xz+sc//qH6+nrV19fr73//u5577jldfvnlmagjAAAAAAD9ksUYY9K5w9ChQ/WXv/xFM2fOTNn+zDPPaP78+dq9e3dP1q/PaGhokM/nk9/vV35+frarAwAAAADIos5mxLRbupubm1VSUtJme3FxMd3LAQAAAADYS9qhe9q0abr66qsVCoWS24LBoK699lpNmzatRysHAAAAAEB/lvbs5bfccotOOeUUHXTQQTryyCNlsVi0bt06uVwuPfnkk5moIwAAAAAA/VLaY7ql1pbte++9Vxs3bpQxRhMmTNA555wjt9udiTr2CYzpBgAAAAAkdDYjpt3S/e9//1vTp0/XBRdckLI9Go3q3//+t44//vj0awsAAAAAwACU9pjuE044QbW1tW22+/1+nXDCCT1SKQAAAAAABoK0Q7cxRhaLpc32mpoaeTyeHqkUAAAAAAADQae7l59xxhmSJIvFokWLFiknJyd5WywW0//+9z9Nnz6952sIAAAAAEA/1enQ7fP5JLW2dHu93pRJ05xOp6ZOndpmnDcAAAAAAINZp0P3XXfdJUmqqKjQ0qVL6UoOAAAAAMABdGnJsMGIJcMAAAAAAAmdzYhpT6QGAAAAAAA6h9ANAAAAAECGELoBAAAAAMiQHgnd9fX1PbEbAAAAAAAGlLRD94033qiHHnoo+ff8+fNVVFSksrIyvfnmmz1aOQAAAAAA+rO0Q/cdd9yh8vJySdKaNWu0Zs0aPfHEEzrllFP0ve99r8crCAAAAABAf9XpdboTdu3alQzdjz32mObPn685c+aooqJCU6ZM6fEKAgAAAADQX6Xd0l1YWKjt27dLklatWqWTTjpJkmSMUSwW69naAQAAAADQj6Xd0n3GGWdowYIFGjt2rGpqanTKKadIktatW6cxY8b0eAUBDC7xuNGO+qAC4ag8TrvKCtyyWi3ZrhYAAADQJWmH7l/+8peqqKjQ9u3btXz5cuXl5Ulq7XZ+0UUX9XgFAQwem6ob9eT6Kn2wu0mhaEwuu02jh+Vp7sQSjSn2Zrt6AAAAQNosxhiTzh0CgYA8Hk+m6tNnNTQ0yOfzye/3Kz8/P9vVAQacTdWNuuuFLaoNhDXc51Ku067mcFS7/CEN8Ti1+LgKgjcAAAD6jM5mxLTHdJeUlOjrX/+6nn/++W5VEAAS4nGjJ9dXqTYQ1tjiPHldDtmsFnldDo0tzlNtIKzVb1cpHk/rGiEAAACQdWmH7gceeEB+v1+zZs3SoYceqhtuuEE7d+7MRN0ADBI76oP6YHeThvtcslhSx29bLBYN97m0qbpJO+qDWaohAAAA0DVph+5TTz1Vf/3rX7Vz505961vf0gMPPKCRI0dq3rx5+tvf/qZoNJqJegIYwALhqELRmHKd7U8z4Xba1BKNKRDm+wUAAAD9S9qhO6GoqEiXXXaZ3nzzTa1YsUJPPfWUzjzzTI0YMUI/+clP1Nzc3JP1BDCAeZx2uew2NXcQqoPhmHLsNnk6COUAAABAX9XlM9jKykrdc889uuuuu7Rt2zadeeaZOu+887Rz507dcMMNeumll7R69eqerCuAAaqswK3Rw/K0fqdfeTn2lC7mxhjt8oc0qcynsgJ3FmsJAAAApC/t0P23v/1Nd911l5588klNmDBBF198sb761a+qoKAgWeaoo47S0Ucf3ZP1BDCAWa0WzZ1Yop3+oN6vbh3b7XbaFAzHkrOXzzm8hPW6AQAA0O+kHboXL16ss846Sy+88II+9alPtVvmkEMO0Y9+9KNuVw7A4DGm2KvFx1Uk1+muaggpx27TpDKf5hzOOt0AAADon9Jep7u5uVm5ubmZqk+fxTrdQO+Ix4121AcVCEflcdpVVuCmhRsAAAB9TmczYtot3XsH7mAwqEgkknI7gRRAd1itFpUPGXwX9gAAADAwpT17eSAQ0Le//W0VFxcrLy9PhYWFKf8AAAAAAECrtEP3FVdcoX/961+67bbblJOTozvvvFPXXnutRowYoXvuuScTdQQAAAAAoF9Ku3v5o48+qnvuuUczZ87U17/+dX32s5/VmDFjNHLkSN13330655xzMlFPAAAAAAD6nbRbumtrazVq1ChJreO3a2trJUmf+cxn9O9//7tnawcAAAAAQD+Wdug+5JBDtGXLFknShAkT9Kc//UlSawv43mt1AwAAAAAw2HVpne4333xTM2bM0JVXXqnPf/7z+n//7/8pGo1qxYoVmagjgH4sGo3rje11qgmEVeRxanJ5oez2tK/3AQAAAP1S2ut072vbtm167bXXNHr0aB155JE9Va8+h3W6gfQ9vaFKK1/Yoi01AUVicTlsVlUUebTouArNGl+S7eoBAAAAXZaxdbr3dfDBB+vggw/u7m4ADDBPb6jSsic2qjEUUZHHKbfTpmA4pveqG7XsiY2SRPAGAADAgJdWH894PK4//OEPmjdvniZOnKhJkybptNNO0z333KNuNpgDGECi0bhWvrBFjaGIDi50y+tyyG61yuty6OBCtxpDEd394hZFo/FsVxUAAADIqE6HbmOMTjvtNJ1//vnasWOHJk2apMMPP1xbt27VokWLdPrpp2eyngD6kTe212lLTUBFHqes1tSvGavVqiKPU5v3BPTG9ros1RAAAADoHZ3uXr5y5Ur9+9//1tNPP60TTjgh5bZ//etf+uIXv6h77rlHX/va13q8kgD6l5pAWJFYXG6nrd3b3U6bagNh1QTCvVwzABiY4nGjHfVBBcJReZx2lRW4ZbVasl0tAIDSCN0PPPCAfvjDH7YJ3JJ04okn6gc/+IHuu+8+QjcAFXmcctisCoZj8rradqgJhmNy2FpbvAEA3bOpulFPrq/SB7ubFIrG5LLbNHpYnuZOLNGYYm+2qwcAg16nu5f/73//08knn9zh7aeccorefPPNHqkUgP5tcnmhKoo8qgmEFY+njtuOx+OqCYQ1aqhHk8sLs1RDABgYNlU36q4Xtmj9Tr8Kch06ZGieCnIdWr/Tr7te2KJN1Y3ZriIADHqdDt21tbUqKel4puGSkhLV1TE+E4Bkt1u16LgKeV0ObasLqjEUUTQeV2Moom11QeW7HDp3egXrdQNAN8TjRk+ur1JtIKyxxXnyuhyyWS3yuhwaW5yn2kBYq9+uUjzOZLcAkE2d7l4ei8Vkt3dc3GazKRqN9kilAPR/ieXAEut01wbCctisGlfi1bnTWacbALprR31QH+xu0nCfSxZL6vhti8Wi4T6XNlU3aUd9UOVDcrNUSwBAp0O3MUaLFi1STk5Ou7e3tLT0WKUADAyzxpdoxthhemN7nWoCYRV5nJpcXkgLNwD0gEA4qlA0plynu93b3U6bqhpCCoRpFAGAbOp06D733HMPWIZJ1ID+o7dmurXbrfr0qKIe3y8ADHYep10uu03N4ai8Lkeb24PhmHLsNnmcnT7dAwBkQKe/he+6665M1gNAL2KmWwDo/8oK3Bo9LE/rd/qVl2NP6WJujNEuf0iTynwqK2i/JRwA0Du49AkMMomZbmsDYQ33uZTrdKs5HNX6nX7t9Ae1+LgKgjcA9ANWq0VzJ5Zopz+o96tbx3a7nTYFwzHt8oc0xOPUnMNLWK8bALKMgZXAIMJMtwAwsIwp9mrxcRWaOMKn+uaItuwJqL45okllPi6iAkAfQUs3MIgw0y0ADDxjir06ZGZer8zTAQBIH6EbGESY6RYABiar1cLFUgDoo+heDgwie8902x5mugUAAAB6FqEbGEQSM93u8odkTOq47cRMt2OK85jpFgAAAOghNGcBg0hfnuk2Go3rje11qgmEVeRxanJ5oex2rgsCAACgfyN0A4NMYqbbxDrdVQ0h5dhtmlTm05zDs7NO99MbqrTyhS3aUhNQJBaXw2ZVRZFHi46r0KzxJb1eHwAAAKCnELqBQagvzXT79IYqLXtioxpDERV5nMmW9/eqG7XsiY2SRPAGAABAv0XoBgapvjDTbTQa18oXtqgxFNHBhW5Zra3dyb0uqzxOm7bVBXX3i1s0Y+wwupoDAACgX+IsFkDWvLG9TltqAiryOJOBO8FqtarI49TmPQG9sb0uSzUEAAAAuieroXvZsmX61Kc+Ja/Xq+LiYn3xi1/Uu+++m1LGGKNrrrlGI0aMkNvt1syZM/X222+nlGlpadEll1yioUOHyuPx6LTTTtNHH32UUqaurk4LFy6Uz+eTz+fTwoULVV9fn+lDBLAfNYGwIrG43E5bu7e7nTZFYnHVBMK9XDMAAACgZ2Q1dD/33HO6+OKL9dJLL2nNmjWKRqOaM2eOAoFAsszy5cu1YsUK3XrrrXr11VdVWlqq2bNnq7GxMVlmyZIlevjhh/Xggw/q+eefV1NTk+bNm6dYLJYss2DBAq1bt06rVq3SqlWrtG7dOi1cuLBXjxdAqiKPUw6bVcFwrN3bg+GYHLbWFm8AAACgP7KYfRfrzaLdu3eruLhYzz33nI4//ngZYzRixAgtWbJE3//+9yW1tmqXlJToxhtv1IUXXii/369hw4bpj3/8o77yla9Iknbu3Kny8nI9/vjjmjt3rjZs2KAJEybopZde0pQpUyRJL730kqZNm6aNGzdq3LhxB6xbQ0ODfD6f/H6/8vPzM/ckAININBrX4pWv6r3qxpQx3ZIUj8e1rS6ocSVe/eHcTzGmGwAAAH1KZzNinzqL9fv9kqQhQ4ZIkjZv3qzKykrNmTMnWSYnJ0czZszQiy++KEl6/fXXFYlEUsqMGDFCEydOTJZZu3atfD5fMnBL0tSpU+Xz+ZJlAPQ+u92qRcdVyOtyaFtdUI2hiKLxuBpDEW2rCyrf5dC50ysI3AAAAOi3+szs5cYYffe739VnPvMZTZw4UZJUWVkpSSopSV0uqKSkRFu3bk2WcTqdKiwsbFMmcf/KykoVFxe3eczi4uJkmX21tLSopaUl+XdDQ0MXjwzom6LRuN7YXqeaQFhFHqcmlxdmJdwmlgNLrNNdGwjLYbNqXIlX505nnW4AAAD0b30mdH/729/W//73Pz3//PNtbrNYUtcONsa02bavfcu0V35/+1m2bJmuvfbazlQd6Hee3lCVDLmRWFwOm1UVRR4tOi47IXfW+BLNGDusT1wEAAAAAHpSnzijveSSS/SPf/xDzzzzjA466KDk9tLSUklq0xpdXV2dbP0uLS1VOBxWXV3dfstUVVW1edzdu3e3aUVPuPLKK+X3+5P/tm/f3vUDBPqQpzdUadkTG/VedaO8LrvKCt3yuux6r7pRy57YqKc3tP2s9Aa73apPjyrSKROH69OjigjcAAAAGBCyelZrjNG3v/1t/e1vf9O//vUvjRo1KuX2UaNGqbS0VGvWrEluC4fDeu655zR9+nRJ0jHHHCOHw5FSZteuXVq/fn2yzLRp0+T3+/XKK68ky7z88svy+/3JMvvKyclRfn5+yj+gv4tG41r5whY1hiI6uNAtr8shu9Uqr8uhgwvdagxFdPeLWxSNxrNdVQAAAGBAyGr38osvvlj333+//v73v8vr9SZbtH0+n9xutywWi5YsWaLrr79eY8eO1dixY3X99dcrNzdXCxYsSJY977zzdPnll6uoqEhDhgzR0qVLNWnSJJ100kmSpPHjx+vkk0/WBRdcoDvuuEOS9I1vfEPz5s3r1MzlwEDxxvY6bakJqMjjTJkpXJKs1taluTbvCeiN7XX69KiiLNUSvSUeN9pRH1QgHJXHaVdZgVtW6/6H7gAAACA9WQ3dv/nNbyRJM2fOTNl+1113adGiRZKkK664QsFgUBdddJHq6uo0ZcoUrV69Wl6vN1n+l7/8pex2u+bPn69gMKhZs2Zp5cqVstlsyTL33XefLr300uQs56eddppuvfXWzB4g0MfUBMKKxOJyO23t3u522lQbCKsmEO7lmqG3bapu1JPrq/TB7iaFojG57DaNHpanuRNLNKbYe+AdAAAAoFP61DrdfRnrdGMgeGVzjS7/05vyuuzyuhxtbm8MRdQYiuoX84+kpXsA21TdqLte2KLaQFjDfS7lOu1qDke1yx/SEI9Ti4+rIHgDAAAcQL9cpxtAZk0uL1RFkUc1gbDi8dRx2/F4XDWBsEYN9WhyeWEHe0B/F48bPbm+SrWBsMYW58nrcshmtcjrcmhscZ5qA2GtfrtK8TjXYwEAAHoCoRsYROx2qxYdVyGvy6FtdUE1hiKKxuNqDEW0rS6ofJdD506vYObwAWxHfVAf7G7ScJ+rzZKJFotFw30ubapu0o76YJZqCAAAMLD0mXW6AfSOxDrciXW6awNhOWxWjSvx6tzp2VmnG70nEI4qFI0p1+lu93a306aqhpAC4Wgv1wwAAGBgInQDg9Cs8SWaMXaY3thep5pAWEUepyaXF9LCPQh4nHa57DY1h6PtjusPhmPKsdvkcfLzMNhEo3G+EwAAyADOqoBBym63MlnaIFRW4NboYXlav9OvvBx7ShdzY4x2+UOaVOZTWUH7LeEYmJ7eUJXs/RKJxeWwWVVR5NGi4+j9AgBAdxG6AWAQsVotmjuxRDv9Qb1f3Tq22+20KRiOJWcvn3N4yQHX66ZVdOB4ekOVlj2xUY2hiIo8zuT74b3qRi17YqMkEbwBAOgGQjcADDJjir1afFxFcp3uqoaQcuw2TSrzac7hB16nm1bRgSMajWvlC1vUGIro4EK3rNbWCydel1Uep03b6oK6+8UtmjF2GBdVAADoIkI3AAxCY4q9OmRmnnbUBxUIR+Vx2lVW4D5gCzetogPLG9vrtKUmoCKPMxm4E6xWq4o8Tm3eE9Ab2+sYjgIAQBcRugFgkLJaLSofktvp8rSKDjw1gbAisbjcTlu7t7udNtUGwqoJhHu5ZgAADBycFQEAOiWdVlH0D0Uepxw2q4LhWLu3B8MxOWytry0AAOgaQjcAoFM60yoaicVpFe1HJpcXqqLIo5pAWPF4POW2eLz1tRw11KPJ5YVZqiEAAP0foRsA0Cm0ig48drtVi46rkNfl0La6oBpDEUXjcTWGItpWF1S+y6Fzp1cwXAAAgG5gTDcAoFMSraLvVTfK47SldDFPtIqOK/HSKtrPJCa+S8xIXxsIy2GzalyJV+dOZ0Z6AAC6i9ANAOiURKvosic2altdMGX28ppAmFbRfmzW+BLNGDuMtdcBAMgAQjeAPiEUiuqhN7ZpR11IZYUufWXywXK5+Irqa2gVHbjsdivLggEAkAEWY4zJdiX6g4aGBvl8Pvn9fuXn52e7OsCA8ovV7+qeFzaroSUmI8kiKT/Hpq8dN0qXzxmX7eqhHdFonFZRAAAwqHU2I9KMBCCrfrH6Xf3m2Q8UjX9y/c9I8rfE9JtnP5AkgncfRKsoAABA59AsASBrQqGofv/8h8nAbdnrnyRF40Z/eP5DhULRbFURAAAA6BZCN4Csue+1bWoOt64NbNnntsTfgXBc9722rVfrBQAAAPQUupcDyJp12+qS/7+/ySX2LgcAAAD0J7R0A8iaXKetR8sBAAAAfQ2hG0DWnDKxtEfLAQAAAH0NoRtA1jgdtgN+CVk/LgcAAAD0R4RuAFnjD0aV73bItu8sah+zWaR8t0P+ILOXAwAAoH9iIjUAWVPkccrrssvrsqkuEFZzOC6j1pnLPU6rCjxOSRYVeZxZrikAAADQNYRuAFkzubxQFUUevVfdqMNKvQqE44rG47JbrfI4rdpeH9K4kjxNLi/MdlUBAACALqF7OYCssdutWnRchbwuh7bXh2T5uDu5xSJtrw8p3+XQudMrZLfzVQUAAID+iZZuAFk1a3yJJGnlC1u0pSag2kBYDptV40q8Ond6RfJ2AAAAoD8idAPIulnjSzRj7DC9sb1ONYGwijxOTS4vpIUbAAAA/R6hG0CfYLdb9elRRdmuBgAAANCjaEYCAAAAACBDCN0AAAAAAGQIoRsAAAAAgAwhdAMAAAAAkCGEbgAAAAAAMoTQDQAAAABAhhC6AQAAAADIEEI3AAAAAAAZQugGAAAAACBDCN0AAAAAAGQIoRsAAAAAgAwhdAMAAAAAkCGEbgAAAAAAMsSe7QoAAAAAPS0eN9pRH1QgHJXHaVdZgVtWqyXb1QIwCBG6AQAAMKBsqm7Uk+ur9MHuJoWiMbnsNo0elqe5E0s0ptib7eoBGGQI3QAAABgwNlU36q4Xtqg2ENZwn0u5Treaw1Gt3+nXTn9Qi4+rIHgD6FWM6QYAAMCAEI8bPbm+SrWBsMYW58nrcshmtcjrcmhscZ5qA2GtfrtK8bjJdlUBDCKEbgAAAAwIO+qD+mB3k4b7XLJYUsdvWywWDfe5tKm6STvqg1mqIYDBiNANAACAASEQjioUjSnX2f4ISrfTppZoTIFwtJdrBmAwY0w3AGQQs+cCQO/xOO1y2W1qDkfldTna3B4Mx5Rjt8nTQSgHgEzgGwdAnxCNxvXG9jrVBMIq8jg1ubxQdnv/7ozD7LkA0LvKCtwaPSxP63f6lZdjT+libozRLn9Ik8p8KitwZ7GWAAYbQjeArHt6Q5VWvrBFW2oCisTictisqijyaNFxFZo1viTb1esSZs8FgN5ntVo0d2KJdvqDer+6dWy322lTMBzTLn9IQzxOzTm8hB5HAHoVoRtAVj29oUrLntioxlBERR5n8uTovepGLXtioyT1u+C97+y5iZYWr8uhvBy73q9u0uq3q3TI0DxO/ACgh40p9mrxcRXJnkZVDSHl2G2aVObTnMPpaQSg9xG6AWRNNBrXyhe2qDEU0cGFblmtrd3JvS6rPE6bttUFdfeLWzRj7LB+1dU8ndlzy4fkZqmWADBwjSn26pCZecypAaBP6D9nsQAGnDe212lLTUBFHmcycCdYrVYVeZzavCegN7bXZamGXcPsuQCQfVarReVDcnVYab7Kh+QSuAFkDaEbQNbUBMKKxOJyO23t3u522hSJxVUTCPdyzbpn79lz28PsuQAAAIMHoRtA1hR5nHLYrAqGY+3eHgzH5LC1tnj3J4nZc3f5QzLGpNyWmD13THEes+cCAAAMAjSzAMiayeWFqijy6L3qRnmctpQu5vF4awv3uBKvJpcXZrGW6WP23MFrIC59BwAAuofQDSBr7HarFh1XoWVPbNS2umDK7OU1gbDyXQ6dO72iX4YWZs8dfAbi0ncAAKD7CN0AsioRRhJhpTYQlsNm1bgSr86d3r/DCrPnDh4Dcek7AADQMwjdALJu1vgSzRg7bEB2y03MnouBa6AufQcAAHoGoRtAn2C3W/XpUUXZrgaQtnSWvuM9DgDA4MMldwAAumGgLn0HAAB6Bi3dADolHjeMTQbasffSd15X22vZ/XXpOwAA0DMI3QAOaFN1Y3IW7lA0JpfdptHD8jR3IrNwAwN16TsAANAz6F4OYL82VTfqrhe2aP1OvwpyHTpkaJ4Kch1av9Ovu17Yok3VjdmuIpBViaXvvC6HttUF1RiKKBqPqzEU0ba6YL9e+g4AAHQfLd0AOhSPGz25vkq1gbDGFufJYmntTu51OZSXY9f71U1a/XaVDhmaR1dzDGoDeek7AADQPYRuAB3aUR/UB7ubNNznSgbuBIvFouE+lzZVN2lHfbDby2IxZhz93UBe+g4AAHQdoRvow7IdRAPhqELRmHKdbsVicX2wp0lNLTHl5dg0emie3E6bqhpCCoSj3XocxoxjoGDpOwAAsC9CN9BH9YUg6nHa5bLb9PrWWr2zs0GBcFTGSBaL9KKzRhNG5Gu4zy2Ps+tfJYkx47WBsIb7XMp1utUcjmr9Tr92+oNafFwFwRsAAAD9Vlb7vP373//WqaeeqhEjRshiseiRRx5Jud0Yo2uuuUYjRoyQ2+3WzJkz9fbbb6eUaWlp0SWXXKKhQ4fK4/HotNNO00cffZRSpq6uTgsXLpTP55PP59PChQtVX1+f4aMDuq6vTF5WVuBWQzCiV7fUqTEUld1qlcthld1qVWMoqle31KkhFFFZgbtL+993zLjX5ZDNapHX5dDY4jzVBsJa/XaV4nHTw0cGAAAA9I6shu5AIKAjjzxSt956a7u3L1++XCtWrNCtt96qV199VaWlpZo9e7YaGz8JHEuWLNHDDz+sBx98UM8//7yampo0b948xWKxZJkFCxZo3bp1WrVqlVatWqV169Zp4cKFGT8+oCv6UhCNRuN6Z1eD4sbIbpOMjKLxuIxa/44bo3d2NigajXdp/+mMGQcAAAD6o6x2Lz/llFN0yimntHubMUa33HKLfvSjH+mMM86QJN19990qKSnR/fffrwsvvFB+v1+///3v9cc//lEnnXSSJOnee+9VeXm5nnrqKc2dO1cbNmzQqlWr9NJLL2nKlCmSpN/97neaNm2a3n33XY0bN653DhbopN6cvOxAVm+sVH1zWC67RaGoUdwkgr6R1SK57BbVN4e1emOl5h1Rlvb+9x4z3p6eGjMOAAAAZEufnVJ18+bNqqys1Jw5c5LbcnJyNGPGDL344ouSpNdff12RSCSlzIgRIzRx4sRkmbVr18rn8yUDtyRNnTpVPp8vWQboSz4Jou1fE3M7bWqJxnoliFb6WxSJxdUSNTJGslk++WeM1BI1isTiqvS3dGn/iTHjzR0cSzAcU47d1q0x4wAAAEA29dkz2crKSklSSUnq2qYlJSXaunVrsozT6VRhYWGbMon7V1ZWqri4uM3+i4uLk2Xa09LSopaWT4JEQ0ND1w4ESNPeQdTrcrS5vTeD6FCvU4me4zarZN2r5d1ijGJxKW5ay3VFWYFbo4flaf1Ov/Jy7Ckt+8YY7fKHNKnM1+Ux4wAAAEC29dmW7oR9u9caY9ps29e+Zdorf6D9LFu2LDnxms/nU3l5eZo1B7omEUR3+UMyJnXcdiKIjinO65UgWpyXI6tFMpL2/bRY1Lrdamkt1xVWq0VzJ5ZoiMep96ub1BiKKBqPqzEU0fvVTRricWrO4SWs1w0AAIB+q8+G7tLSUklq0xpdXV2dbP0uLS1VOBxWXV3dfstUVVW12f/u3bvbtKLv7corr5Tf70/+2759e7eOB+isvhRE/aGocnNsskiKxqVY3MgYo1jcKBpvDd6eHJv8oa53dR9T7NXi4yo0cYRP9c0RbdkTUH1zRJPKfCwXBgAAgH6vz3YvHzVqlEpLS7VmzRodffTRkqRwOKznnntON954oyTpmGOOkcPh0Jo1azR//nxJ0q5du7R+/XotX75ckjRt2jT5/X698sor+vSnPy1Jevnll+X3+zV9+vQOHz8nJ0c5OV1rvQO6KxFEE+t0VzWElGO3aVKZT3MO7711uos8ThW4nfI446oLhBWOGSUmTXfaLSrMdcphs6rI07Xu5Qljir06ZGaedtQHFQhH5XHaVVbgpoUbAAAA/V5WQ3dTU5M2bdqU/Hvz5s1at26dhgwZooMPPlhLlizR9ddfr7Fjx2rs2LG6/vrrlZubqwULFkiSfD6fzjvvPF1++eUqKirSkCFDtHTpUk2aNCk5m/n48eN18skn64ILLtAdd9whSfrGN76hefPmMXM5+rS+EEQnlxeqosij96obNaksX/XBqFpiceXYrCpw27W9PqRRQz2aXF544J0dgNVqyfhs7AAAAEBvy2rofu2113TCCSck//7ud78rSTr33HO1cuVKXXHFFQoGg7roootUV1enKVOmaPXq1fJ6P2nl++Uvfym73a758+crGAxq1qxZWrlypWw2W7LMfffdp0svvTQ5y/lpp53W4drgQF+S7SBqt1u16LgKLXtio7bXh1TkccqX61AwHNP2+pDyXQ6dO71CdnufHakCAAAAZJXF7DtTE9rV0NAgn88nv9+v/Pz8bFcH6FVPb6jSyhe2aEtNQJFYXA6bVaOGenTu9ArNGt/x3AgAAADAQNXZjNhnx3QD6DtmjS/RZ0cP1eqNlar0t6jUl6M5h5XK6bQd+M4AAADAIEboBnBAm6obk5O6haIxuew2bd0T1NyJvTepGwAAANAfEboB7Nem6kbd9cIW1QbCGu5zKdfpVnM4qvU7/drpD7KsFwAAALAfhG4AHYrHjZ5cX6XaQFhji/NksbTOnO51OZSXY9f71U1a/XaVDhma1+1Z1eNxw5JhANAJfF8CQP9C6Ab6sGg0rje216kmEFaRx6nJ5YW9OlP4jvqgPtjdpOE+VzJwJ1gsFg33ubSpukk76oPdmmW9ve7ro4fl0X0dAPbB9yUA9D+EbqCPam/G8IoijxYd13szhgfCUYWiMeU63e3e7nbaVNUQUiAc7fJj0H0dADqH70sA6J8I3UAf9PSGKi17YqMaQxEVeZxyO20KhmN6r7pRy57YKEm9Erw9Trtcdpuaw1F5XY42twfDMeXYbfI4u/ZV0pvd1wGgP+P7EgD6r97rpwqgU6LRuFa+sEWNoYgOLnTL63LIbrXK63Lo4EK3GkMR3f3iFkWj8YzXpazArdHD8rTLH5IxJuU2Y4x2+UMaU5ynsoL2W8IPJJ3u6wAwmPF9CQD9F6Eb6GPe2F6nLTUBFXmcslpTP6JWq1VFHqc27wnoje11Ga+L1WrR3IklGuJx6v3qJjWGIorG42oMRfR+dZOGeJyac3hJl1tVPum+3n5LudtpU0s01q3u6wAwEPB9CQD9F6Eb6GNqAmFFYnG5nbZ2b3c7bYrE4qoJhHulPmOKvVp8XIUmjvCpvjmiLXsCqm+OaFKZr9vjB/fuvt6e7nZfx+AUjxttr23WxsoGba9tVjxuDnwnoI/j+xIA+i++mYE+psjjlMNmVTAck9fV9rpYMByTw9ba4t1bxhR7dcjMvB5foibRfX39Tr/ycuwpXSYT3dcnlfm63H0dgw8zO2Og4vsSAPovQjfQx0wuL1RFkUfvVTfK47SldDGPx1tbuMeVeDW5vLBX62W1Wrq1LFhH+5w7sUQ7/UG9X906VjExadwuf6jb3deROX1xnWBmdsZAxvclAPRfhG6gj7HbrVp0XIWWPbFR2+qCKbOX1wTCync5dO70il5drzuTEt3XE62TVQ0h5dhtmlTm05zDaZ3si/piazIzO2Mw4PsSAPonQjfQByWWA0us010bCMths2pciVfnTu+9dbp7S6a6r6Pn9dXW5HRmdu7pHhtAb+L7EgD6H0I30EfNGl+iGWOH6Y3tdaoJhFXkcWpyeeGAaeHeVya6r6Nn9eXW5E9mdm5/PKvbaVNVQ4iZnTEg8H0JAP0LoRvow+x2qz49qijb1QAk9e3W5L1ndva6HG1uZ2ZnAACQLQOzyQwA0OP68jrBiZmdd/lDMiZ1ibDEzM5jivOY2RkAAPQ6LvkDADqlL7cmM7MzMPD0xVUSAKArCN0AgE7p6+sEM7MzMHD0xVUSAKCrCN0A+oRoND5oJo3rr/pDazIzOwP9X19dJQEAuorQDSDrnt5QlVweLRKLy2GzqqLIo0XHDbzl0fq7RGvyE29V6tUttWpqiSovx65PVQzRKZNK+8SJMDM7A/1XX14lAQC6itANIKue3lClZU9sVGMooiKPM9ly+l51o5Y9sVGSCN59zNaaZr304R69V92kcDQup92qWDyuCSPy+0ToRtfQ2wR9QV9eJQEAuorQDSBrotG4Vr6wRY2hiA4udMtqbT3B97qs8jht2lYX1N0vbtGMscM4+f9YticWenpDla599B3VBFo+Xv7CKBQ2WveRX9c++o4kLpL0R/Q2QV/xySoJ7c8N4XbaVNUQysoqCQDQVYRuAJ2SiVawN7bXaUtNQEUeZzJwJ1itVhV5nNq8J6A3ttexXrlaxzmuWl+pt3b41RyOKtdp16Qyn06e2DvduqPRuG57ZpOqGoIycSMjyUiyfPyvqiGo257dxEWSfobeJuhL+vIqCQDQVXxjATigpzdU6a7nN+u96sZkd+JDi71a/JlR3ToZrwmEFYnF5Xba2r3d7bSpNhBWTSDc5ccYKDZVN+qWp97Xe5WNiplP4u7m3QFtrGzUkpPGZjx4v76tVu9WNSoS/SRwJ1gkWeJG71Y26vVttZpyyNCM1gU9g94m6Gv6+ioJANAV/IIC2K+nN1Tpx4+s18tbalUbCKspFFVtIKyXt9Tqx4+s19Mbqrq87yKPUw6bVcFwrN3bg+GYHLbWFu/BLB43uv/lbXpze71i8bi8LruGeHLkddkVi8f15vZ63f/yNsXj5sA764Z3qxrV3BJTXKkt3JaP/45Lam6J6d2qxozWAz0nnd4mQG9IrJIwxOPU+9VNagxFFI3H1RiK6P3qpj6xSgIApIvQDaBD0Whcv1j9rqobQjJxI4fNKpfDJofNKhM3qm4I6Rer31U0Gu/S/ieXF6qiyKOaQFjxeOo+4vG4agJhjRrq0eTywp44nH7ro7pmvfRhjWwWqSgvRzl2m6wWi3LsNhXl5chqkV7+sEYf1TVntB6BUFSJV8kiyWLZ69/H2+Mfl0P/0JneJpFYnN4m6FWJVRImjvCpvjmiLXsCqm+OaFKZj+XCAPRLdC8H0KFXt9bqg+omSZLLYVXMSNG4kcXS+ncwEtcHu5v06tZaTRudfndiu92qRcdVaNkTG7WtLpgynrQmEFa+y6Fzp1cM+m6tH+4JyN8cUZHX2e5svr5ch2qawvpwT0AHF3kyVo/OtqNntr0dPWnv3iZeV9vPGb1NkC1jir06ZGZeVieOBICeQugG+rBsz1T96pZaReJGNovUHIlr797LVotks0iRmNGrW7oWuqVPJmhKzJxcGwjLYbNqXIlX505n5uQEY5Es6ui17533RJ7LLquU7F6+d7pO/K/143LoHxK9Td6rbpTHaUvpYp7obTKuxDvoe5sgPT018abVamFZMAADAmdGQB+1qbpRT66v0ge7mxSKxuSy2zR6WJ7mTizp1a51xkiRjxNVyvhd0/qvJ+LerPElmjF2GGsEd2DUUI8K3E7VN0dUkm9tM7GQvzkin9upUUMz18otSeNKvMp12tQcjrU/kZqk3BybxpXQ9bO/oLcJehrLzwFAW4RuoA/aVN2ou17YotpAWMN9LuU63WoOR7V+p187/cEuj2lLt/Vh8sEFbba113W4vXLpstutLAvWgfLCXE0dNURrNlSpJhCW12WXw2ZVJBZXYyiquDGadsgQlRdmtkXomIOHaFypV+t3+j9eMuyT8G+RkcVq0bhSr445eEhG64GeRW8T9BSWnwOA9hG6gT4mHjd6cn2VagNhjS3OS7Zqel0O5eXY9X51k1a/XaVDhual1dW8K60PVqtFdssnLd3tBW67RYyxyzCr1aIFUw9WdVOL3qtqVONeE5XZrBYdWV6gs6ccnPHXwW636qITxujaR99RTaDl45k4W+cxN5KKPDm6aOYYWkX7IXqboLtYfg4AOkboBvqYHfVBfbC7ScN9rnYnzRruc2lTdZN21Ac7Pdatq60P/mBUDrtVkUjHs5M77Vb5gx3PVp3tcekDxZhir5acNFar3qrUWzv8ao5Eleuw64iDfJo7sbTXhhwk3id3vbBZH+xuUiRq5LBbNGaYl+6j/Ry9TdAd6Sw/x/sMwGBD6Ab6mEA4qlA0plynu93b3U6bqhpCCoQ7tyxTd1ofCtx2xeJGVqnDMbzRuFGBu/2vkr4yLn1fPTXJT28bU+zVRSdkfzZfWkUB7Kszy8/VBsIsPwdgUCJ0A32Mx2mXy25Tczgqr8vR5vZgOKYcu00eZ+c+vt1rfWiN1haLkdtuUVwWGWNksVhklVEoaloXaW5nOrV0x6WnE4S7E5r7+yQ/fWU2X1pFAeyN5ecAoGOEbqCPKStwa/SwPK3f6ZfHaVNTS0zhWFxOm1V5OTbt8oc0qcynsoL2W8L31Z3Wh/pgRG6nVcGwUTgm2W2S3WpVzHz8t9WiXIdV9cFIyv3SHZeeThDuTmhmkh8AyAyWnwOAjhG6gT7GarVo7sQSbahs0JPvVCm21+LYNqtFh5Z4Nefwkk53Ke5O60ORxymvyyGvyy5/MKpQJKaoMbJYWsO6z22XZGlz33TGpb9X1djpINyd0MwkP+1jzD2AnrD38nNba5qVm2OTxdLaO6q5JSZfrpPl5wAMWoRuoC9Lro/98fJM7U0ffgDdaX3Y+76HFnsUCMcVicflsFrlcVq1vT6kcSV5be7b2XHpDc2RTgdhSd0KzUzy01ZfHXOP7OACDLpr1vgSrdter3te3KqaQFBxI1ktUl6OQydPLKUnEYBBi9AN9DGJrtmxuNHcw0vadC/ftDuQ1pJhe7c+bKsLprQQ1wTCync5Omx92Pu+2+tDKvI45XM7FAzHtL0+1OF9OzsufXNtU6eDsKRuhWYm+UmVqbXg0T915QJMf52QEJnz9IYqPbG+UjkOqwpzWy/axONGzZG4nlhfqaPKCwjeAAYlQjfQizpzkrp312yr1ap8d+rtXVkyLHGSkxgLXRsIy2GzalyJV+dO3/9Y6K7cd+9x6Xk59pQu5saY5Lh0i8WSVhDuTmhmkp9PZGotePRPXbkA098nJETP23sIz8gh7ja9qgbrEB4AkAjdQK/p7ElqTy8ZltCdZZ7SvW9iXPpOf1DvV7deQEi0ru/yhzTE49Scw0uSAb6zQbg7oTndbvYDuattT6wFHw7HtHpjpSr9LSr15WjOYaVydnBBBH1XVy7AMCEh2sMQHgDoGKEb6AXpnKT29JJhe+vOMk/p3ndMsVeLj6tIdlmtaggpx27TpDKf5hze2mU1Go2nFYS7MzNuOt3ssz3WOdOBv7sXdv64dovu/M9m7W4MKWaMbBaLbvK+p/M/O0oLp1X0WD2ReelegGFCQnSEITwA0DFCN5Bh6Z6kdrZrdmeXDMumMcVeHTIzr8MAme54866OTU/oTFf5bI917o3A350LO39cu0U3PfmuWqIx5TrtyrFb1BI1qmwI6qYn35Ukgnc/ku4FGFoz0RGG8ABAxwjdQIale5La2a7Z/aWrs9Vq2e/Y83TGjHdnbPre++ioq3y2xzr3VuBv78JOLBbXB3ua1BiKqqklqhPHFbe5sBMOx3TnfzarJRrTkFxH8v2c65RcdotqmyP6/fOb9ZVjyulq3k+kewGG1kx0hHW6AaBjhG4gw7pyktqZrtm9LZMzFaczZrw7Y9MPpCfGOndVbwb+fS/s1DeH9fYOvwLhmOKSbBapuqFFhR5nSqv16o2V2t0YUq7T3u4FpFynXdUNIa3eWKl5R5R1q47oHen2rKE1Ex3pzkoZADDQEbqBDOvqSeqBumb3pr42U3F3xqbv71jKCt0ZmcSuM3o78Ccu7Nzw+Ea9uqVWsbhkt1nktltktVhVE2hp01280t+imDHKsbf/HsyxW9QcNqr0t3S7fugd6fasOWK4T4W5Tm2tbdbw/Ljy3c7k+7WrrZkDedLCwaYneiMBwEBE6AYyrDtd7g7UNbs39MZMxb0V6g90LBd8dtQBu9o6bVY1BCPaWNnQowEhU7PW78/BBbl6r6pRVotU4LHLZrXKZrXIYrHIE7e26S5e6suRzdI6hju3nYbMlmjrpGqlvpweqyMyr7M9axKf0531zWpqiei96ohcjqCG+1zKsdu61JqZ7UkL0fMy2RtpIOAiEzA4EbqBDOvPXe56Y6biRBBuaA4rN8cmu80mY4zerWzo0eWHOnMsj725U58eVaR3djW029X2/eomyUgPvLxNLbF4jwaETM5a35HVGyu1p6lFnhyHchypwx/a6y4+57BS3eR9T5UNQbnsljYXkJrDUQ33uTXnsNIeqyN6x4F61ux9wao43yVfrkO7/CGFwnFt2dOsQo9TE4bnp9Wame1JC5E53emNNJBxkQkYvAjdGHQyOTa5I/21y12mZypOBOHdjS0y8bjqgxEZI1kskstuVUtjS48tP9SZY9lS06zTJx+kyoZQm66271c3qdIfUmm+S4Uep3Kd9h4NCNmYtT7d7uJOp03nf3aUbnryXdU2R1JmL28OR+Wy23TeZ0b1yUnUWFf8wDrqWdPeBSuvHBqS61RjKKKdDS0q87n023OOkcvVudOKbE9aiP5hILUKc5EJGNwI3RhUsjk2uStd7rJxgWBv6UwC15W6vrG9Tht2+RUMR2Uk2a0WWS1S3EjBaFwWxfXOTn+PLD/U2WNxO21tuto6bVbJSKX5Lh19cEFGAkI2Zq3vSnfxxPjuxDrdzeHWMsN9bp33mb65TjfrindPRxesrFarfLk5slpbhyL8b5e/05/TbE5a2Bdk+7s903riItdAahXuzEWmVW9V6thRLaprjgzI9wQw2BG6MWj0xtjkA0mny12mLxB05qSos5PAbdkT0KK7XtEHu5sUjsXltFk1elieFn9m1H7rWtUQUkMwKiMjp90q68cnIlZL679wNK6GUFRVDaFuH286E9rt29W2IRjRAy9vU6HHmdGA0Nuz1ne1u/jCaRX6yjHl/aLlmHXFuy8Ty4RlYw6DvqKvTUzZ03riItdAaxU+0EWmSCymu9du0X0vb5WRBtx7AgChG4NEb4xN7kmZvkDQ2ZOizkwCNzTPqQde3qaa5rBsltaW6kAspv9+VK9tj76z37rWBlq7N7e2cKeeiFgtrduicaPaQPdnw053Qru9u9purGxQSyyuXKddxhg1hqLJiwtel71HA8KYYq8qjvf0SitYd7qLO522Pr8sGOuK94xMLBOWjTkM+oK+cPE3k3riItdAHHqwv4tMm/c06fWt9WoOR1VW4Fahxzmg3hMAWmU/XQC9IJ2xydm27wUCr8shu9Uqr8uhgwvdagxFdPeLWxSNxru0/8RJ0U5/UBaL5HHalGO3JU+K/rh2S7JsYhI4r8uhbXVBNYYiisbjagxFtK0uqDynTaFwTLubWmQ1RlLrCZMkWY3R7saQfvPspg7rWpSXI6vVoljc6OO7JxkjxeJGNqtFRXndnw37QMeyvwntEgFhZ32zXttSp7Uf1ujlzTVa+2GNXttSp131wR4LCJuqG3XHvz/Ug69s16Nv7tSDr2zXHf/+UJuqG7u97/YsnFah780dp9J8t1oiMdU1R9QSiWm4z62lc8f161bgdNYVR8cSF6xqAmHFYjG1RGJqDkfVEokpFmudEHLUUE9ay4Ql5jDY5Q/J7PPhT8xhMKY4r0fnMMi2TH+3Z9u+F7lynTbZrFblOm0akutQSzSm3z+/WeFwbL/7SWfoQX+x90WmvcXjca3bVq9QJCaP06b8AfaeAPCJgXUJGehAJrpHZkomJy8Lh2P6v6ffV2OodQx1JGbUHI7LYbPI47SqORJr0/K3v0ngph4yRHc896Fi8bha4lLcfHJiYLVIdqv0flWTXttWq6mHDG1Tn2KvSz63Qw3BsFqicdltFtksFsWMUTRmZLValO92qNjrSvNZbF9XJ7QrK3CrINehNe9UyWmzyOt2yGGzKxKLq6ohqO11zZo9oaTbASFbXSr7U3fxdLCueM9IXLC69tF3tKGqKdmjJW6kmDEa6slJewWGbMxhkG2Znpgy29K5yLW/XjIDcehBRxNl7vKHVNcclt1qUW6OQ0576pwJ/f09AeAThG4MCvvrHmmMkb85IiMpFo8rHjdZPdHL5AWCFU+9p91NqfczksIxo0gwJk+Otd2Too4mgXvwtW0KtEQVM6372VvcSJGYFI9Htam6qd3QPbm8UONL8/XWDr9i8ZhaokZRY1pnL3dYZbNaNWF4flotaAfS5TVkkwdoUTgSVyQWV+s1Bosko+6+Y7LdpbI/dBdPF+uK95yRRbkaNdSjQEtULdFY8qKY225TxVCPRhalP5dBb89hkC2JGbj/95FfwUhMxd7232996eJvV/TURa6BOPSgo4tMNYGwwrG4vDl2DcltO2dIOu+JgT45H9Df9Z9vLKAbOhrPGwzHtLsxqN1NYTltVt39wlZ9UBXQ548cnrUTvkyMn5Raf5Dvf2Vrh7cbSYGWuJx2S7snRe1NAme1SNF90/Y++4ya1nLt2XsN84ZgREM8Vtk+7m4ejMTlc2dmDfN015DdUR9UfTCicaV5endXo3Y1BD/p+p6bo3HD81TXHOnWRGqDfTbnnpQIOSMLPSrMdWp3U4h1xbshcUEo12nT2Z8qV2VjSMFwTG6nTaVelz7YE+jyBaEDrQ/e3+09A/cOf1DNLVFtr21Wqc/d5sJqV7/b+4qeusiVjeUTe0N7F5licaMcu02+XEe7F9o7+54Y6JPzAQMBoRuDQjxudPiIfL29y693q5o0PN8pm92mzbsDCkZau0RH43G9sa1O63c26PkP9mjZGZOyErzTnfCrs9Zu3qOG0P7H0pmP/9PZlr+WSOfGme2vXOKE4A//+VDvVX8y+/m4kjwt/swhfeKEIRCOak9Ti7bUBLS7qUXRWOsY9JjFaHdTSJYqqaLI063ujnt3qcz0ZG17G0jr4EqtIWfVW5V6a4dfgUhU+W6HdjeGtCcQVl6OI+Prig/E1qa9LwjZbFaVFaRe9OnuBaGO1gfv7/YdLlKan6PtNQHVBMKKG6MRBbnJoNWd7/aEbH+Wu7oawr4G8tCDfS8yuexW/fjh9Xp/d5OG5Ma79Hs/0CfnAwYKQjcGvL1n6g7H4orHpfdDrcElEQUdNslpsyoWl1oiMf13W71ueGKjfrvw2F7/Yd+79XdbXTDlR7QmEN7vhF/78+ibOzv9+J1t+YvE99PMnWY5I4sisbgi0bgskuKm75xQuR02bd7TpF3+FskYOeyftMhHonHtrA/JmNZyXbX3ZG2V/hbVNocVjcdlt1o1JNepUl9Oj3epHEjr4Eqtx3PLU+/rvapGxfZ6z/k8TgVCMQXDUTWHlbF1xQdqa9NAHGObaR0NFzm2Yoj+8/4eNbZEVdkQ1EGFboUi8W59t0vd+yz31IWilNUQAhG5HDbZbRZFY0ahSEwuR+cvcvXU0INsX4hoz74XmRZ/ZlSXf+/728oswGBG6MaAtu/yJfluu1oiRo0tUUU/Pin3OPdaH9om2a1WNYfjWvtBjbbsbtIhJb0fPro64df+NB9gxtiEiqLcTrf8FXs71w1yf+We3lClHz+yXnuaWhT/eBbzlmhcr26t1eaagKTsXKXf+2StpqmltXUqbpTrtCZPoG0WyWK3KBgx2tPUokikc89xe3prsraEfVvh3A6XqhtDWvvhHr1X3aiLTxitQ0vye+SxekM8bnT/S9v05vZ6Oe2ts/86bBZFYq2f8xy7TWOLPZo+eqiGF7h6fKK4gdzaNBDH2GZaR8NFRg3NkyS9tqVW9c0Rba8NyuWwdeu7PfFZrmkKK99lV77LoXjc6K0dB56AsacvFC2cVqHqxhbd8+JWNbVEFP94eFGey6GvTRuZ1kWu7g496C8XFbvzez/QJ+cDBhJ+ITFgdbhGb44UicXUFP64JcwYaa+TIovFIofNomAkpjXvVunCLIRuqRsTfnXgmIML9dj/Drw00pmTD2p3e3utIer09GHtl4tG4/rF6ndV1RBS3HwcYj8uGokZVTWE9IvV7/X6Vfp9T9aq6kNqicRls0rRuGSzto4tjMSMonEjIykUieum1e/peyeP6/oJXaJxdp8x3a1/d3+ytoR9W+HqmiPasKtedc1hRWNxfbg7oJ8Fwrrq8xN0aGnfOTndn+11zXppc62sFouKPJ9MSJRjt8jpcaqqoUVVDWHNObxUI4s8PfrYA721aaCOsc2k/fUOGDW0dSm0/26v10njS3TEQb4uf7cnPsvbapsVjca1pSaQ7CFT6HYoEI52ON4+ExeKNlU3qjYQ1pRRBa0TdH4c5J02i2oDYW2qbkzr+7GrQw+ytRJEV3X1974/rcwCDHaEbvQ7ne0Kt7/lS/YONZGYkYnFklfknTarrFZJMakxmF53yZ4ez5nuhF/785XJ5br+8Q3a3zBsh7W13L46ag0ZUeBKLh3UEZtF2tPU/g/+q1tqtam6KRm4rRZLYjJwSUYx03ry9OqWWk0b03b280xo72StNhBuvTZjlWxWi6LxuCIxk3LcRtLzm/Yo9FhcV80bn/YJXWKytk9VFGqXv0V1zWE1tURlt1pVkt86HrS7k7Xt/ViJVri65ojWba9XMBxVnsshh8supz2qD6qb9OtnNumSWWPaPZZwOJayxNiJY4q1vqoha2OZN+8JqD4Y1rC8nHYnovPlOlTT1KLNewI9HroHemvTQB5jm67Odlc+UO+AlmhcI3xuzT28tFuf5x31Qf13e13r8KlITJaP33+JHjIOu01vbKtr872RiQtFe1/MO2y4r83FmUyvwNBePbKxEkRXdeX3PlMTrwLoeYRu9CvpdIXb3/Iljr1+aMN7hdCYkSLx1jHFdqt0aGleRuqWDW/sqJfP7dCeQKTDMocMy1NNKKLc3E9OEvfXGvJeVYMs2isn78Oi1usbxfntT8z26tZahWOtLbhWq+WTltyPA3g8ZhSOGb26tXdCd0cna2U+lxw2i6JxI4c1LmPav9AQicX02tZa3fbMB7r5y0dKUqe7RiZaxg4ZmqeDCnPbTKQWM0Zb9gR6ZNxs4rHcDpfe2VmnhmC4NRgYI4ss8uTYFY7GVBNoaffkdO95EmKmdUiARRa5nVa5HLasvfctRjLtvhOl9t+hPWMwtDYNluW99ied7sq91TugMRTRtppmNQTDag7HFIrGk523XHarcj9+TzaGUr/3M3GhqK+swNBX6tEbMjXxKoCeR+hGv5FuV7iOli8xRge8um3U2qJ58vjhB6xXNBrX757/UCtf3KJwNK4Sr1MeV06fGs+5qbpR97+yTTabVcV5Du1pimjvBm+HVTqyvEAl+a6UULd3a0h5gUv1waiaWqJy2qwqL3Dpw92B5Ame225R3FgUN0ZWi0VWi1EoapRjt2riiPbHBvuDrSeCieC+t73DfKJcT+moR0JHJ2sjCtwq9Di1u7FFzZH2Y53DalGRx6H6YFTPb9qtf7+3W2/vbOj0eMJ9W8by3amtY8GWaI+Nm0081qbqRm3a3aRY3KghFJWM5HRYledsDc7Dfe42J6eJeRKC4db6SEbNUSPJ6P+3d+dxUdT/H8BfszfLfcipongieN9p0WFSltXXvmlmqGWHqdlp92WXdpk/K62+lprVN/umlWUqaKQZKAoeqCiCCCqX3AvLnvP5/bHsyLInyMKq7+fj4aOY+czMZ+azOzvv+Vx6DQ9fhQS+CkmHf/ZjQrzhr5SiTq2Hwk9sFeTUqvUI8JIiJqR9a7mBq6e26Uqf3suR1jZXbm3rgJYtR1wdc6Bea0BVgxZ1WiMYY5CIOKH1UaOBh6apC0y91vJlnTteFHXWDAyO8mHLlTTwn7sGXiWEtD8KuslloS1N4WxNX9KoN6JeY4DO6LzWS2tg0OmNDh98duaU4eu/T2P/mSroeFPwajAYERHAIdBbdsn9Odujubq59rZBa0CAlxSNOgP8lQyNOiMYMwW1Yb5ydAtUguM4i6DOXBvCAcg+X2dx3WRiDj4KCUQiUy2n1sAgEZuCTwNjTX+L0D1YiZScMohPXLB6mOwd6iME1jzPLB7e+aa+0lxTuvbiqEVCVKCX3YfG/uG+qKjXgtn56MgkHDiOg1ImRm2jHp/vykegtwxhPnI0aPU4p9LidEU9zlap8dB1PTutZsx8rAAvKdLyK5oGwRJBZ+Bh4HnU64DKeqCLrwwSEQe1ziA8nJrHSVDrTAF6fYvB+XgGlNdpEeYrh3egV4f2Ze4aqMSYmGCkHC9DZb22aSA6EfRGHqpG04BOo2OC0TWw/Wu2rqbapit1ei9H2tpc2dXWAS1bjog5Dh/45uKha52Prq+QidCgM8Jo5KGQmAbFYDC9+JSJGDQGHmqdKV1z7nhR1HwGBnMXGYORh0QsQqBShgg3zMDgKB9Xy8B/7hh4lRDS/q6MOw654rWlKZzF9CVqPaRiDg1a3uVGpgzAR3+exOuTB9pcvzOnDIt/O45ylQY6HhDB1Dy9Qc/jTJVp1O1Ab5lF3oZ1C3Q5iG6v5urm2luJqKmps9Z4cbwumPqxl9ZpkH66EpMGRlgEdZUNOtQ06lDfNL+3iINQi6IzMlQ26CERAT2DlShTadGgM8DAGDgO8FFIEKiUobCyER8n55paD7R4mBwTEwxfhQQqjQEGBnDNgnpzwO2nkGBMTPv0g3XWWuLha3vanraL41DbqAcnAmCnT3yDjofeoIVCKoaBZ6jT6KGQivDzoQo06AxCsH6ytB4VDVqr6eg6vN9sUxN+I8+jXms6KRHHgWM8wHFQ64zILKxC92Bv4eE0+UQpSmrVMDgYF0BrZKhWGxDiK+/QvswiEYf7RndHuUqL3FIVVBoDzJ8isUiEwZG+uG90d7fUylJt05XtUporO2sd0HKGDfM88qV1jfhg+0kAcBh4F1WqTa23OEBjZABr9iKs2f26qFKNQVEXX/q440WR+WVeSk7ZxRkEFBLojQzlKg3OVasxsZUzMGg0BmzIKsL5ag2iAhWYNqw7FArHj67NX2AqpSKU1mmh1huhlIoR7ie/Igf+a++BVwnxBO09TlJno6CbXBba2hTO/LCyevdpnK1uFAI5wH4Pz+Zx1bkqjc00BgOPlal5uKDSQMxxTX2S0TS/NGAwMpyvboS/l0TIW+rJcnyyM8+lILo9R5Vt0BlQVNWAvPJ6aHRGi/NmML0oMDKgtE6LzMJqi239ZRKom4J0CWdKZ+7LLOYAAwOMPBCglOHmAaE4XaGGSmeAj1SC4poGHC2pBwD4ySVQyERWD5MzRkdjQv9Q/Hq4GEZmXSYiDpgQG4roIMsmwa29ERsMPPYXVuKDbSdRWa9FTLAXajVGqLQGyJuayp+t0eD3w8Xo1cUHO0+UW0zbVa/Rm6Y0Y4CPDLAzLhx0PKDXGpsGG2dIy6+EgTc1+RSLTU3vtXoj/jpZjk9TT2HhTX0ttjfXjP1xuBh78iqh0urhK5fi2j7BuHVQZLv1mz1f04gatR6xEb7Yc0oHnZFv6n9vyqtSKoaE41Cl1iPcn0eEnwIAUFzdCFdmntMaDADkNr+X7pw3t3eoL56c0AfbjpYi+3wt1DojlDIxBkUFuH2aIKptunJdanNle60D7M6wIQMUEtP376s9BZg2vJvdFlccTM3J9bZ+0Jjpt0zKmdI157YXRcIPrKnLidBJqOmtY2tGVvgo+aTV1GPLkvMw85poPDOxn93tzC8wM85U4r/7z0FrMArdoOQSMeKj/C7Lgf+utACEEEc8fZyktqCgm1wWLqUpXNLYHujZRYmF3x+CVMzBWybGuepGiwHULDQbFaxrkMJmkgNFplG3JSIOMomp2bp5Y8ZMzaLVeiNq1TpIJGIYjDx+O1QMnZGHn0IKX4UEOgOPk2V1VkF0e48qK5eIUFjZiLpGg71KWsHhc7VY/NsxLL4zHgBQrtYINbSGFk9Lzf+WSDicrmxERIAXRHUaZBZWo7ROK6xX640QiU1Nr1s+TEYFmZq122q3zXEcIgOVFg9Hrb0Rm9OfKKtDpUoHBiBLrbd4ASMTm7oDnKlUw7epFoUxhpIaDfRGI3je9LLByOwH3MDFj44IwJmKeuiMpv/XG03Ti4lFgEwMaI3ADxlnMffaXlYP0+n5lfgp6xxKVVqhyX1pXSMCvOXtFjQ26AyoqNeitLYRYjEga7oSIo4DA6A1GKHjOIT5KSCTiFBSp0G3ICXqta717zQ0VYW3/F7mlauwNbsU+89UoV5rgI9cgpE9gnDrwPB2O7feob6Y10n9jm+KDcO1vULa1DeXeC53NVd2NMOGSCSCUiZBeZ0GySdKcfugKJv7CPGRmVqocKaWRDwgxLqm1leme0iIj/VvY3u/KDK/zBvZI1BoKdSgNUAsEiHM3wvhfnLUuDgDw0fJJ/H5rnwYeAaZiINYZHrBW6c1dd0B4DDwLqxUo6BCDa3BaHoxLmJgjIPWYERBhRqFlepOG/yvLbX3zn73rsQAhVy93DGdoSegoJtcFpo3hVOIgVoNDz3PQyoSwV8hctoUTqUxQiYRQaM3okzlOHBoXpP7zI22f9TzyuuhNfDw85JAIhJBIjL3E7cMHPMvqKGQiSAVm24YCqkI1Wq9MNiYQsKhukFrEURnFlUh74IKPjIxdAYeAA8epgcqmaT1o8qW12lQo9Y6DbjNfso8hxcT+0OhkKBCpXe6HQMwKCoAvgop9uRdwJFztdAaLlaHijlT0Fmr1gNKwEsqFh4mfz92HpsPFYPjAG8pB13TNFwc19Q/ngGbDxXj8et7QyYTt/pG3Dy9XCxCyxIyB95aI0NZnRZyCYdTZSLoDEacdfI5sXctzOGVuqnSq/n1M/CAoSlNVYPW4mHaYOCxZFsOvt1baArQOdNgfjwDztdosLTp/Jz18XSFUipGRb0WKq2pP7e3zPTd0Bp48E2jkcslHOKj/MAYhBo8Dq7VqnAcZ9VENa9chbd+z8GRc9Vo0BqEmqvs8zU4UFjdpmnW7HG133F717rbGt26sKLxkmvZqYarc7lrvAVHM2wApu+gWsdQWqu1uR4AQv0U8JJJoDHw0NoYq0Qi4qCUSRDqZ/sFcns2S26vGRg0GgO+SSs0BdwwzUigMzZN6QlAxzOsTy/E/Ot62QxWzS+utQYjYsN9oTcyoa+8VMx16FgTLbWl9t7Z796hszXYerT0igtQyNXJHdMZegoKuq8QbR359HJhbgr3/MYjyDqnslrfxUfmsClcsLcMtY16NDqapLqFWwdGwMfOIDIKiRgcBxh5BqkYkIg5m4OzGQGodTzkUlOwqdbzkEtEpumwGKBuyk9umQpZZ6sR5C3D+n/O4EKdFiIRhL56ErEYMrEIXlIOUokIKo0Be06WY8+pMhTX6NA1SIFHrullMdWXWUlVo8O5uVtq0BmxIasIs66JgZ+Xa5+hmFAlJvQNxdq009DojRbTaZkvC8+Aeo0BColYeJjcc7ISF1QayCWc0K8YAMBMAaqYYzhfo8b6fYWYNbZHq27EzW/cXf3lyCuvt9m0kTX7r8bAcOpCg+sXqwURAD8vCaqdzO9uBADDxYfpnTllWL0rD+lnaoQ0PANEvKl2XASgUW/Eqr/yHTY1dZXpnDmIOQ6M51HVaEDzjy8H0zy/VfVahPsrhRo8ruV893ZojTyKqhuFJqoiEYfPUvOQll8BfYvviU5tQFp+hTDNWnvUSLsSpOaVq7DlcAn+ya+ASmOAr0KCcb1CkBgXjjqtvtUBSGtHt3bVzpwyrPmnAPkX6qEz8JBJROjVxQcPjOvZ7g/SdfVavLn1OM5VadA1SIHXbh0APx/b0/0B7fe74+kvFZqPt3CypA61Gj00Bh4KiQj+CilC/BRtaq5sb4YNM63BFCiG+9svA62Bh5eUwwVbcxgCMPAMjDHszCnHoK5at17b5i0CvGUSqLR6NOqM8JKJ4SOXuNwiYENWEeq1enAM0AJNN8yLRDBNgWb+nWqp+RgwPAPO1aih1fOQS0WIDvLu0LEmmhNq740MEpHphTQDUKexX3vfMgDhOA46Aw+xiEOYrxyltRp8k1YIuVSE7oEKNOh41DXqIRFd7DZ1uQYo5OrkjukMPQUF3VeA9eln8Oqvx6yWv3VnXLvUinmK4ppGVNhp23uhXodv957B2Oggm4Fn72BlqwLu2wdF4NP7htldPyI6ED5yCeq1BlQ6mPcaaArm9DyYnkewv1yoJRFzgJdUBLWOR73WgOzzNdiaXYrsc7WmptvNHjS0RiMaYER148VlK/46bXGc5TvyMWN0d7zzL8uB33bnl7t0zs2drzb1ZT96rtal9M/9dNRpGgbT4GtltRr4KaWmgI8DdEYeBjuX0Nx44PfsYgR5y4QbcYNWh+OlaiHdgHAlgpQynCitw6pdeRgdE4ySmnqk5VXACKCszn5NUXviYT2itz1GAOeqVHj+p8PYfqwUNTYCdR4A3/Sx5QCUqzS4//+2IaPyYprxkcDnc262+4KoJZ3OiG3HSqDRG1BS09jyeRaAqaz0PJB2uhq3xYnBeD2GvboFVS7O3KZSGxDsY5q3e09eOcJ9ZEg+VmYVcJvpjQzJx0tRVNkbPbpYjlR/5kINpn6RgVqNAf4KCb5/eASq1MxmgJZ2+izu+/KIzWN8NWuEEKTmlavw4qZsHD1Xg8Zm/SQyzlRj+c5TEItMcxx7ySXoG+qDB8fHOAxwm49uHR3ohb1nKlHXaICflwRjegSjsLrR5ujWFbVqzP/hIEpqtYjwl+Oze4cixP9iDb15sMaSKjWaX/qKWi2Kqkw3g+b5OlBYjGmrDgplqgAwuGeA1X5tmfX1PuzKrbi4oAD4KbMYCX1DsO7B0cLinw4fx7P/LbC5j3AfCebf1K9Vvzs7c8qw9I8jOHXh4r29TxcZXpg0yC21c19nHMSbm4otlq1+IBYT+lkHb82Fe8ux+/h5nFVZf2NmjO7u8IWKrebEVepGfLz9BNR6Hmo9j2q1HiKYvnsSERColEKl1kMq5fB5ah62HCnGkjsHIsDXVGO9O68QM1c7v+8Cpt/Ht7fkAACUEiDEzwtxkb5YcudAZJ6rxafbj+JIqQZGmFrhDApXYEFifKuvv7lFwJ8ny1Bc1YBS1cVuPOG+UkQGeeOm/s4HUjtfrYGjCUbMTejNv1MtmceAOVvVAFWzl7kqrREV9TXwlYvgLZe2ajq0SyXU3huZcH9tzmBkWLMnH9VqDcpqTS/Sn7mxH46Xq4TfvTqtHqdKG4TWUxIOUMrEqNcaIZPIcOx8LRqbfTy9xECIn6JTA5S2NKUnVzd3TGfoKTjG7E2Ac+VZuXIlPvjgA5SUlCAuLg7Lly/Htdde69K2dXV18Pf3R21tLfz8bM853BnsBdxmV0rgrdMZ0f+1bU6bOosATG8ReOaVq/Dsj4dw6Fydy8frHqjA7udvsrue5xme+d8h/Hyw2G4aRzgAfhLAx0cBtc4Ijd6ImC7eOFlaD4OdGgtXtQy8Z3yZhn9OVzvYwtriO2Ix65oYTF7xJ7KLG51v0AY9gpVYeH1vPL3RdpDUXKS/HCN7BGNvQaXDAJoDoJCKWvWC5Urh7EURcHFqorK6RmhadtL3AO9OicN9o3oIfw94davQGqQlpVQEmUSEARH+mHNtT8xZd8Dp/r+aNQI39AvFI98cwM4T5S4N6iQTcwj2kePtu+wHIWer1Pg4JRdZRdU4U6m2Wt8jWIlh3QPx1M19hWbvEz5KRd4F67S9uyix45kbYDDwmPZlOjKLauzmbXh0ADY8PBYSiQg9Xtji8DzM+7XFKuBuwRx4OzuGmau/OztzyhyWW/MXJYDjGvGW6/p38cHXewuEWvtHrumFAW8mO8zPmaW32Vy+4Pss/H6kxOG2j9/Y22bz4I+ST+KrXXlQu/YuzqkR0QE4UFjTPjtzQATgPy2uvyvWp5/Ba78es/nd4gC86cJnY/XuXLz9xymnx3plUh88dF1fq+UZBZVI+moftA7ucXIJh/VzRndYILou7TTe2JzTqoHkRACGdg/A+ZpGlNVpW7VtcwFeUiy5eyBujY9o4x7a5qPkk1j1Zx6av06WAHjMznflUl3pLT6vFhkFlXjmx8PwVUjg3fRSycDzkIhE8JGL0aAzQqUx4KOpgz2mptvVGPGqCbo3bNiApKQkrFy5EuPGjcMXX3yB1atX4/jx4+jevbvT7T0x6NbpjOj72jan6XLfvOWyv/Gs/ycPr/520uX05sDTXKN14Ex1q3+wnAXerj6AOqOUiiARAVoe0LZTsHjs1ZuhVErBGHDHil04Wtq6JtNHX70ZCoUEvV/e2i75seedKX3x8qZcl9L2DPZCQaV7XgBcKW6ODcU7U2xPcffTgbNY+Vc+dAYj9MbWjSLcUW6ODcJrkweb/n/ZX255MbB25gg8+M0Bl8c4AExdPHp28caq6cMgbtFEkzGgoKIeL248ggoHrV5CfKR4d8og9Az2xpy1GSiyU0sHAOF+Mjw7sR+e/Snbad4++PdALHIhHQB0C1Tgi6SRYM1Kv0Gtx9TV+1zavjU2PjoWUunFa9XyScNo5DHl83Sn+/np0bEQizkcOFOF34+UoKRWI8z9HOGvwG2DTIHEliMlKKlthMHI0KA1Qmts2730f3PHWvy9LCUX6fmVdlJbWj97JGQysXB1f9x/FpsOnm9TPjyBmAO+TBoBsdh2s/mW306jgcej32XC0aUXi4DPZwyHWMzZGjsTjAEpR0uxIeuc0/xNHdYVE+IsXwowAPVqHZ7Z6Pw78eHdA+HjJbNxJtZ5ankMR+tt2Zh5Fn+evOA8oZvMuyEGfVq0xrA6LyfnYbN7VouNzH9vO1aKP0/YP98b+3fBxAHhLh7Dcv+20uw7XYl/8iqg0lzsK++rkOKa3iEY1TPI4gCOTtO8d+dlfgmfGWZ5LOs82D5o82MyG8ewux+b+bJ9ni23t5dHcwJn5eXsGLaOwPMMmw8Xo0ylaepqaEoh4kzj0XAiDoO7BuDrWSM9pssEBd0tjB49GsOGDcOqVauEZbGxsbjrrruwZMkSp9t7YtD9yvd/4tsjzoMQuRjwVpianlrdIFukdfoltpHAakkbbgSOjsEAqF1sstucQiKCzsjjUiqOpWJOmGql+a3BXjNZQgghhBBCSPuRcKZueVIRh0cTermltURbuRojXhUdK3Q6HTIzM/HCCy9YLJ84cSLS0tI6KVeXzpWAGzBNT6S9DPs+XCqN4dJrjfU2RiQnhBBCCCGEdAwGwEcmglgkxuGzNTAYeI+p6XbVVRF0V1RUwGg0IizMshlSWFgYSktLbW6j1Wqh1V7sO1pX53p/YEIIIVceEQdIbIzgrmtjU+bO4iuXCJPUqzSOR9q/FIEtBrVsPtVWVSteBIs407YcTNMJmvHMNIMEYJpejzF2SS2bzMKbptcqrbPfBcCeSP+L27ZHXjxBTBdvu+uaNzzPb8XMD31CTQMmcjZarueW1bu8n/7h1gPYnSi1nuHEngERfnbz0VzL9Rw4h+tbOuLioKTuNDw6EFbZtDovx1qeN2B97vsKqlzO05gY6z65tvJgdf2b/q5QaXGqvB4ijkPzW7M5uZEHeMbQN8wHIT4Kq+0daZmEc2UjR3m2mcbxPp2XV/MF1jccW/u3Pi+HWXDKlc+E00M0S1DVoMPhszWQikWQiDjom2ZgCPSWIcJPgXqtgUYvvxy0/PAxxux+4JcsWYLFixd3RLY6xK/zxwn/f6k/HrY426ez9M7W37n8b7R2/OkAuRi1WiNkEkDbxudKEYCUZxIu5qvpvzd+tKttO+xA00d2xfwb+2D8e6mt3rZ3iBLrHx6DsUv+dEPOiDtF+cux81nLAbO2Hi3GSxuzIZeKbY6S7knOLL0NPV/Y4nHtS+QSEdY+MBJje4VYrWvN+A4ioFX9ydubTAJkL04U/m6vsSlsOfjaRLvrWnPcUF85ogK9rF54VDVocfpCAzgO6BmibBrR1sUh9h3Y+9JNrc6jWdqLpm2HLN7u8d81V0hEwJ/PXO9S2tZcr5SnE+yua81+tj153SVt/8cTrg2me6nc+T1zhRjAxseu6ZBjteZcf3hkzCUda/Xfp/HB9hMIVEohtvFC1MibZgb49/BueOhaxzMUEM+w9WgJFm8+ZvOeD1zeo5dfFUF3SEgIxGKxVa12eXm5Ve232Ysvvoinn35a+Luurg7dunVzaz7daXC3gM7OwiW5ZWAX/JrdukFIRvQMxK7cCvCMQ1ubiPfvokCvFlMYuYO3lEODvn3DjP4Rvuga6HiaIHsS40IQ4e94WhfimW7sEwKF1HLgxNviIrE8JQ+ldY0Qw2raW48zLMobmefbPl+6M2IRHA72ZEvvLt4YGR3kPKETPQKkOF1z6YFhWw2Pav1c4W3Rno3+pGIRGnVG+Cos9yoRi4QXtBKRCIp2aGrYpdnMe75SDqo23pf7dlEio+jybyE3PMp+LXdL3QKkOOvCZ7tbgPW0nsS9RnRz/3OMmUwE6Fy4v8ra4SbRHnPdE88S7C2ze88HgEadEVKxab7uy83l1Ri+jWQyGYYPH46UlBSL5SkpKbjmGttv/uRyOfz8/Cz+eZrXpkS2azpP9tadg1uVXgTgw7sHo4uvQmh+2BZr51zaW1hXbZw3ynnzm1aaNsw0Kv8U6xlVnJqf0L+dc3PpHhzbsdOdXK6ev3WA1TKZTIyHru0JuUTs0QH36gdiAQD/mTXaScq2CwJw78jurfq+KQE8PbGf3f5jnyb1cWk/nyb1wfePulbbdM+gQJfSLZ0W7VI6s89mjLT4u/1nwjbZNH+44/XzHE9vZ/bj3CHoEeyNygYdeN7ySd5bKjY1O+c4KKUcwv0U1k0nW5NpAL89dbEGduP81n0Om5/TyhmOz/9ysSrJ9Wuw4ZGxzhO5kG7lTNd+tOylW3ijay+bXU3XHpbd17m1rKuSRnXYsTbNH+k8USvSOTKxfzi6+Cqg1hms7g88z0OtMyDUT4GJ/a1HSieeaVi3QLv3fJ7nUdmgQ88Qbwzr5tpvpCe5KoJuAHj66aexevVqfP3118jJycFTTz2FoqIizJ07t7Oz1mYPjhraruk8mZ+PHAl9rZt12jN9dHcE+Crw2PW94CUVw86MJw51D1QgNMD2W35787m2RYi3FP0jQnBdK87Pmcdv7A2FwtSQZdmDrcvrjNHdoWzqi9me52mLq/v3lYvx2p3DoJA4L8ir5qZmw+2DIuBj5+1v0tgeWJTYD9FBHfeg2VoT+pkeTIP8vBAX6Z4a2aylt+Gdfw3EfaO7Q+7ijeETJ/MV3x7nWpBwe1xfhAf6IMLP8Rt6L6kIj04YCKmTD7NUBNw7NN6lYwNAXKQvgvwsW7Dsc9N3fEg3xw+5w7q79hJtVI8ozB7XA74KKYqqG6HS6GHgeag0epytaUQXHzlCfOQ4V6tFo4FHsLd1Laqr94QIPxnCAy/WCPYND3ZaBs01P6cQfyV6d/Hc75orbH1eHIkM8kWIjevfXIi3FJFBjr/bkwa49hLLXrqnJ9qej76t6drDlEGxbd7WhZ89h1pbjpcqPirU6XdO1JTuUjV/oVyl1kOtM8LI81DrjKhS66GQiDFnfM/Lftrcq4lEIrJ7zy+qboSfQopZ1/S47AZRA66iKcMAYOXKlXj//fdRUlKC+Ph4fPzxx7juOuv+QLZ44pRhZo76z7g7aOpos77eh125FXbXy8XAv0eY5ug2W59+Bv/ZfRrlKo3L8/46m6Pb7FL7aYV4S3Hg1Yv9Hmd9vQ+7cysuqT/r4zf2tjmVgit5Nc9v3pZtW6v5Z9PR/kO9xch49Rbh7/6v/GG3HEN9ZdAZeIg4DnIxUKMxoLGdm+27YkCED46XuD4YUHu4fVAEPr3Pee2hTmdE8olSvPfHCZytcT5QlFICqNuha2r3QIXDualt3atuW7Ebx4rtD4rEwfRCZta4nvjkzzyneWh5DLVajy/T8rF8R77dbfLevtXlH/fW3IvHvpuCkjrrPmk+MjF+WTAOvZvm1O3z0hbobTTVlIqAU++69h0CTA/eWxba/71zJe+u3gda87vj6jXbmVOGtf+cwZnKBuiNPKRiEXqGeGPWNT0AwGJdXaMBjXqjxX1ULhFB62BGiwg/GdJfutnmOntlYC+vzU34KBV5F9SON24FqQhO8+IKH5kYexZdjxlrMmx+x5x9XhwZ8VayzTnrW/7eOXOpzzae+GzU2t9S8+fy36v+wYHCGptpEvqGoKJe2+7leKliXthic/wKEYDT7Xz916efweq/C3BBpYGRmZqUh/opMGd8TySN7dGuxyIdw9E939FL8M5A83S3M08OugHg64yDeHNTsfD3a1Mir4gablvq6rV4c+txnKvSIMJfiggfL5Sr9egapMAj1/QSammbMwcapbVabNmbg4OVF9cN9Qd4Hx+UqfQI95Phy/uH263htsXWj+i/h0UhxFuKvPJalNcbERWowOM39sDj32ejokGPEG8p1j840uYb/+bnV15RhdPNfkfH+QP/2BkEVQHg0BuJQg23LU9/vQWbcm2vO/7aRJvXztl5NqjKsfWU631Upw4A3p9p/YNra/97n0+wqHkyO11ejXs+z0Cd1gA/uQT/mzsK3YP8kXW2GpUNOgR7yzAkKgBl9Vo06AzILi7Foh9PCds/MFSMtQdND+UiAD/MHQIDz+O+L4+4fB72nH53EkQiDjUqDWZ+tQtHSi0j1nnXyfDcpJtRo9LgxV+zcb5ag6hABR67vjvmfnME1Wo9Glu8VOgLIHnpbSitrsfY93ZZBBM9pMDvL9xst4bbkRqVBkPe2WlzXZwv8Pp9YzCsWyDqG3V47Pt9SC9w/CLhgXHdca5KgxC5CEfLVLjQYLD6Tu04eRoPrckRtln9QKxQw21LVV0jnvrpMIprNIgMUGDJXXFIOXFBuG7ThnUXPvN78otw/3+yrfYRBFMNtyNHz5fj7pX7oTWaXt5tnDeyTTUxvx/LxYL1Fz9rnyb1sVsTXlpdjwfWHkBpnRaB3lJ8ePdADIkOgUhkWbWVW1qJu1dmQK3noZSKsHHeKPQNtx61NauoBFNXZsH8ifMHMKRvMD7+92CXarpGv7AFZc3+DoN1TXjL3xozKQf8b95wpzXctjTPtwTAj/OG2awJNxh4i+/4sG6BwguRluv6d/HB13sLcK5KY/HbsHb/Ibyx8bywz0AxsPVZ2/eZ5nJLK3H78r1o+Zpkk528NldRq8b8Hw6ipFaLUG8xhnUPwukKFXblVkPPTJ+3O/p4YfOpRhiMgFJmKmMAdst9d14hZq4+Khxj2X0x+M+2M8ipMoU5Yb5S/O/R0YgM8MWO3GKsSMlHnVqPXl188PFUy89Dy++Yq58XR4qrVEj6er/T3ztn/jh+CvO+ufijtXJmX5drwgFgWXIqVvx58aXHwhuVHVrDbcumIzl4+vvTwt/L7ovBlEGxpvvBugMoV+kQ6ivDmlkjLD6XNSoNnvnpIDKL6sAAXN87CG/dOQh+Pqb+yu4ox0vVXvdVVzR/zgv3l2Ni/3Cq4b7MObrnexIKutuZpwfdhBBCCCGEEEI6jqsxoue9LiCEEEIIIYQQQq4QFHQTQgghhBBCCCFuQkE3IYQQQgghhBDiJhR0E0IIIYQQQgghbkJBNyGEEEIIIYQQ4iYUdBNCCCGEEEIIIW5CQTchhBBCCCGEEOImFHQTQgghhBBCCCFuQkE3IYQQQgghhBDiJhR0E0IIIYQQQgghbkJBNyGEEEIIIYQQ4iYUdBNCCCGEEEIIIW5CQTchhBBCCCGEEOImFHQTQgghhBBCCCFuQkE3IYQQQgghhBDiJpLOzsDlgjEGAKirq+vknBBCCCGEEEII6Wzm2NAcK9pDQbeLVCoVAKBbt26dnBNCCCGEEEIIIZ5CpVLB39/f7nqOOQvLCQCA53kUFxfD19cXHMd1dnaueHV1dejWrRvOnj0LPz+/zs4OaYbKxnNR2XgmKhfPRWXjmahcPBeVjWeicuk8jDGoVCpERkZCJLLfc5tqul0kEonQtWvXzs7GVcfPz49uHh6KysZzUdl4JioXz0Vl45moXDwXlY1nonLpHI5quM1oIDVCCCGEEEIIIcRNKOgmhBBCCCGEEELchIJu4pHkcjlef/11yOXyzs4KaYHKxnNR2XgmKhfPRWXjmahcPBeVjWeicvF8NJAaIYQQQgghhBDiJlTTTQghhBBCCCGEuAkF3YQQQgghhBBCiJtQ0E0IIYQQQgghhLgJBd3ELZYsWQKO4/Dkk08KyxhjeOONNxAZGQkvLy9cf/31OHbsmMV2Wq0Wjz/+OEJCQuDt7Y077rgD586ds0hTXV2NpKQk+Pv7w9/fH0lJSaipqbFIU1RUhMmTJ8Pb2xshISFYuHAhdDqdu07X450/fx73338/goODoVQqMWTIEGRmZgrrqWw6nsFgwCuvvIKePXvCy8sLMTExePPNN8HzvJCGyqVj7N69G5MnT0ZkZCQ4jsMvv/xisd7TyiE7OxsJCQnw8vJCVFQU3nzzTVypw7M4Khu9Xo/nn38eAwcOhLe3NyIjIzFz5kwUFxdb7IPKpv05+8409+ijj4LjOCxfvtxiOZWLe7hSNjk5Objjjjvg7+8PX19fjBkzBkVFRcJ6Kpv256xc6uvrsWDBAnTt2hVeXl6IjY3FqlWrLNJQuVzmGCHtLCMjg/Xo0YMNGjSIPfHEE8LypUuXMl9fX7Zx40aWnZ3Npk2bxiIiIlhdXZ2QZu7cuSwqKoqlpKSwrKwsdsMNN7DBgwczg8EgpLnllltYfHw8S0tLY2lpaSw+Pp7dfvvtwnqDwcDi4+PZDTfcwLKyslhKSgqLjIxkCxYs6JDz9zRVVVUsOjqazZ49m+3bt48VFBSwHTt2sLy8PCENlU3He/vtt1lwcDD7/fffWUFBAfvf//7HfHx82PLly4U0VC4d448//mAvv/wy27hxIwPAfv75Z4v1nlQOtbW1LCwsjN17770sOzubbdy4kfn6+rIPP/zQfReoEzkqm5qaGjZhwgS2YcMGduLECZaens5Gjx7Nhg8fbrEPKpv25+w7Y/bzzz+zwYMHs8jISPbxxx9brKNycQ9nZZOXl8eCgoLYokWLWFZWFsvPz2e///47KysrE9JQ2bQ/Z+Xy0EMPsV69erHU1FRWUFDAvvjiCyYWi9kvv/wipKFyubxR0E3alUqlYn369GEpKSksISFBCLp5nmfh4eFs6dKlQlqNRsP8/f3Z559/zhgzPUBJpVL2ww8/CGnOnz/PRCIR27ZtG2OMsePHjzMAbO/evUKa9PR0BoCdOHGCMWa6sYlEInb+/HkhzX//+18ml8tZbW2t287dUz3//PNs/PjxdtdT2XSO2267jT344IMWy6ZMmcLuv/9+xhiVS2dp+TDkaeWwcuVK5u/vzzQajZBmyZIlLDIykvE8345XwvM4Cu7MMjIyGABWWFjIGKOy6Qj2yuXcuXMsKiqKHT16lEVHR1sE3VQuHcNW2UybNk34nbGFysb9bJVLXFwce/PNNy2WDRs2jL3yyiuMMSqXKwE1Lyftav78+bjtttswYcIEi+UFBQUoLS3FxIkThWVyuRwJCQlIS0sDAGRmZkKv11ukiYyMRHx8vJAmPT0d/v7+GD16tJBmzJgx8Pf3t0gTHx+PyMhIIU1iYiK0Wq1Fk+qrxebNmzFixAjcc889CA0NxdChQ/Gf//xHWE9l0znGjx+PnTt3Ijc3FwBw+PBh7NmzB5MmTQJA5eIpPK0c0tPTkZCQYDEXa2JiIoqLi3HmzJn2vwCXmdraWnAch4CAAABUNp2F53kkJSVh0aJFiIuLs1pP5dI5eJ7Hli1b0LdvXyQmJiI0NBSjR4+2aOpMZdM5xo8fj82bN+P8+fNgjCE1NRW5ublITEwEQOVyJaCgm7SbH374AVlZWViyZInVutLSUgBAWFiYxfKwsDBhXWlpKWQyGQIDAx2mCQ0Ntdp/aGioRZqWxwkMDIRMJhPSXE1Onz6NVatWoU+fPti+fTvmzp2LhQsX4ptvvgFAZdNZnn/+eUyfPh39+/eHVCrF0KFD8eSTT2L69OkAqFw8haeVg6005r+v9rLSaDR44YUXcN9998HPzw8AlU1nee+99yCRSLBw4UKb66lcOkd5eTnq6+uxdOlS3HLLLUhOTsa//vUvTJkyBbt27QJAZdNZVqxYgQEDBqBr166QyWS45ZZbsHLlSowfPx4AlcuVQNLZGSBXhrNnz+KJJ55AcnIyFAqF3XQcx1n8zRizWtZSyzS20rclzdWC53mMGDEC7777LgBg6NChOHbsGFatWoWZM2cK6ahsOtaGDRvw7bff4vvvv0dcXBwOHTqEJ598EpGRkZg1a5aQjsrFM3hSOdjKi71trxZ6vR733nsveJ7HypUrnaansnGfzMxM/N///R+ysrJafd5ULu5lHqjzzjvvxFNPPQUAGDJkCNLS0vD5558jISHB7rZUNu61YsUK7N27F5s3b0Z0dDR2796NefPmISIiwqr1aHNULpcPqukm7SIzMxPl5eUYPnw4JBIJJBIJdu3ahRUrVkAikdh9Q1ZeXi6sCw8Ph06nQ3V1tcM0ZWVlVse/cOGCRZqWx6muroZer7d6c3c1iIiIwIABAyyWxcbGCiOVhoeHA6Cy6WiLFi3CCy+8gHvvvRcDBw5EUlISnnrqKaGlCJWLZ/C0crCVpry8HIB1bfzVQq/XY+rUqSgoKEBKSopQyw1Q2XSGv//+G+Xl5ejevbvwPFBYWIhnnnkGPXr0AEDl0llCQkIgkUicPhNQ2XSsxsZGvPTSS1i2bBkmT56MQYMGYcGCBZg2bRo+/PBDAFQuVwIKukm7uOmmm5CdnY1Dhw4J/0aMGIEZM2bg0KFDiImJQXh4OFJSUoRtdDoddu3ahWuuuQYAMHz4cEilUos0JSUlOHr0qJBm7NixqK2tRUZGhpBm3759qK2ttUhz9OhRlJSUCGmSk5Mhl8sxfPhwt14HTzRu3DicPHnSYllubi6io6MBAD179qSy6QRqtRoikeUtWCwWCzURVC6ewdPKYezYsdi9e7fF9C7JycmIjIwUApqriTngPnXqFHbs2IHg4GCL9VQ2HS8pKQlHjhyxeB6IjIzEokWLsH37dgBULp1FJpNh5MiRDp8JqGw6nl6vh16vd/hMQOVyBXD3SG3k6tV89HLGTNPu+Pv7s02bNrHs7Gw2ffp0m9PudO3ale3YsYNlZWWxG2+80eZ0CIMGDWLp6eksPT2dDRw40OZ0CDfddBPLyspiO3bsYF27dr1qpj9qKSMjg0kkEvbOO++wU6dOse+++44plUr27bffCmmobDrerFmzWFRUlDBl2KZNm1hISAh77rnnhDRULh1DpVKxgwcPsoMHDzIAbNmyZezgwYPCCNieVA41NTUsLCyMTZ8+nWVnZ7NNmzYxPz+/K3YqF0dlo9fr2R133MG6du3KDh06xEpKSoR/Wq1W2AeVTftz9p1pqeXo5YxRubiLs7LZtGkTk0ql7Msvv2SnTp1in3zyCROLxezvv/8W9kFl0/6clUtCQgKLi4tjqamp7PTp02zNmjVMoVCwlStXCvugcrm8UdBN3KZl0M3zPHv99ddZeHg4k8vl7LrrrmPZ2dkW2zQ2NrIFCxawoKAg5uXlxW6//XZWVFRkkaayspLNmDGD+fr6Ml9fXzZjxgxWXV1tkaawsJDddtttzMvLiwUFBbEFCxZYTH1wtfntt99YfHw8k8vlrH///uzLL7+0WE9l0/Hq6urYE088wbp3784UCgWLiYlhL7/8skWwQOXSMVJTUxkAq3+zZs1ijHleORw5coRde+21TC6Xs/DwcPbGG29csdO4OCqbgoICm+sAsNTUVGEfVDbtz9l3piVbQTeVi3u4UjZfffUV6927N1MoFGzw4MEWc0EzRmXjDs7KpaSkhM2ePZtFRkYyhULB+vXrxz766COLa0HlcnnjGGvqGU8IIYQQQgghhJB2RX26CSGEEEIIIYQQN6GgmxBCCCGEEEIIcRMKugkhhBBCCCGEEDehoJsQQgghhBBCCHETCroJIYQQQgghhBA3oaCbEEIIIYQQQghxEwq6CSGEEEIIIYQQN6GgmxBCCCGEEEIIcRMKugkhhBAXXH/99XjyySeFv3v06IHly5d32PFnz56Nu+66q8OO11Ycx+GXX365pH20vNa2tOb6t0eeCCGEkLaSdHYGCCGEkMvR/v374e3t3dnZIC4oKSlBYGBgZ2eDEELIVYqCbkIIIaQNunTp0tlZIC4KDw/v7CwQQgi5ilHzckIIIZe166+/Ho8//jiefPJJBAYGIiwsDF9++SUaGhrwwAMPwNfXF7169cLWrVuFbY4fP45JkybBx8cHYWFhSEpKQkVFhbC+oaEBM2fOhI+PDyIiIvDRRx9ZHbdl8+Zly5Zh4MCB8Pb2Rrdu3TBv3jzU19cL69euXYuAgABs374dsbGx8PHxwS233IKSkpJWne+HH36IiIgIBAcHY/78+dDr9cK66upqzJw5E4GBgVAqlbj11ltx6tQpYf0bb7yBIUOGWOxv+fLl6NGjh/D3X3/9hVGjRsHb2xsBAQEYN24cCgsLhfW//fYbhg8fDoVCgZiYGCxevBgGg8FinxUVFfjXv/4FpVKJPn36YPPmzRbrd+3ahVGjRkEulyMiIgIvvPCC1T6aKy8vx+TJk+Hl5YWePXviu+++a80ls2hefubMGXAch02bNuGGG26AUqnE4MGDkZ6ebrHNP//8g4SEBCiVSgQGBiIxMRHV1dUAAK1Wi4ULFyI0NBQKhQLjx4/H/v37La4hx3HYvn07hg4dCi8vL9x4440oLy/H1q1bERsbCz8/P0yfPh1qtVrYjjGG999/HzExMfDy8sLgwYPx008/tepcCSGEeB4KugkhhFz21q1bh5CQEGRkZODxxx/HY489hnvuuQfXXHMNsrKykJiYiKSkJKjVapSUlCAhIQFDhgzBgQMHsG3bNpSVlWHq1KnC/hYtWoTU1FT8/PPPSE5Oxl9//YXMzEyHeRCJRFixYgWOHj2KdevW4c8//8Rzzz1nkUatVuPDDz/E+vXrsXv3bhQVFeHZZ591+TxTU1ORn5+P1NRUrFu3DmvXrsXatWuF9bNnz8aBAwewefNmpKengzGGSZMmWQTmjhgMBtx1111ISEjAkSNHkJ6ejkceeQQcxwEAtm/fjvvvvx8LFy7E8ePH8cUXX2Dt2rV45513LPazePFiTJ06FUeOHMGkSZMwY8YMVFVVAQDOnz+PSZMmYeTIkTh8+DBWrVqFr776Cm+//bbdfM2ePRtnzpzBn3/+iZ9++gkrV65EeXm5y9fNlpdffhnPPvssDh06hL59+2L69OlC4H/o0CHcdNNNiIuLQ3p6Ovbs2YPJkyfDaDQCAJ577jls3LgR69atQ1ZWFnr37o3ExEThHM3eeOMNfPrpp0hLS8PZs2cxdepULF++HN9//z22bNmClJQUfPLJJ0L6V155BWvWrMGqVatw7NgxPPXUU7j//vuxa9euSzpXQgghnYwRQgghl7GEhAQ2fvx44W+DwcC8vb1ZUlKSsKykpIQBYOnp6ezVV19lEydOtNjH2bNnGQB28uRJplKpmEwmYz/88IOwvrKyknl5ebEnnnhCWBYdHc0+/vhju/n68ccfWXBwsPD3mjVrGACWl5cnLPvss89YWFiYS+c5a9YsFh0dzQwGg7DsnnvuYdOmTWOMMZabm8sAsH/++UdYX1FRwby8vNiPP/7IGGPs9ddfZ4MHD7bY78cff8yio6OF8wTA/vrrL5t5uPbaa9m7775rsWz9+vUsIiJC+BsAe+WVV4S/6+vrGcdxbOvWrYwxxl566SXWr18/xvO8xXXw8fFhRqORMWYqU/O1PnnyJAPA9u7dK6TPyclhABxe/+YAsJ9//pkxxlhBQQEDwFavXi2sP3bsGAPAcnJyGGOMTZ8+nY0bN87mvurr65lUKmXfffedsEyn07HIyEj2/vvvM8YYS01NZQDYjh07hDRLlixhAFh+fr6w7NFHH2WJiYnCfhUKBUtLS7M43pw5c9j06dNdOk9CCCGeifp0E0IIuewNGjRI+H+xWIzg4GAMHDhQWBYWFgbA1Ew5MzMTqamp8PHxsdpPfn4+GhsbodPpMHbsWGF5UFAQ+vXr5zAPqampePfdd3H8+HHU1dXBYDBAo9GgoaFBGHBNqVSiV69ewjYRERGtqrGNi4uDWCy22D47OxsAkJOTA4lEgtGjRwvrg4OD0a9fP+Tk5Li0/6CgIMyePRuJiYm4+eabMWHCBEydOhUREREAgMzMTOzfv9+iZttoNEKj0UCtVkOpVAKwLA9vb2/4+voK55mTk4OxY8cKtecAMG7cONTX1+PcuXPo3r27RZ7M5zVixAhhWf/+/REQEODSOdnTPI/m8ysvL0f//v1x6NAh3HPPPTa3y8/Ph16vx7hx44RlUqkUo0aNsrrOzY8RFhYGpVKJmJgYi2UZGRkATF0eNBoNbr75Zot96HQ6DB06tI1nSQghxBNQ0E0IIeSyJ5VKLf7mOM5imTnA43kePM9j8uTJeO+996z2ExERYdEH2lWFhYWYNGkS5s6di7feegtBQUHYs2cP5syZY9G021Y+GWMuH8fW9jzPA4Dd/TDGhPMXiURW6Vo2PV+zZg0WLlyIbdu2YcOGDXjllVeQkpKCMWPGgOd5LF68GFOmTLE6jkKhcDmfzQPu5nlvudzZukth7/MBAF5eXna3s5cfW+fV8hiOrov5v1u2bEFUVJRFOrlc7vyECCGEeCzq000IIeSqMmzYMBw7dgw9evRA7969Lf55e3ujd+/ekEql2Lt3r7BNdXU1cnNz7e7zwIEDMBgM+OijjzBmzBj07dsXxcXFHXE6ggEDBsBgMGDfvn3CssrKSuTm5iI2NhaAacT10tJSi8D70KFDVvsaOnQoXnzxRaSlpSE+Ph7ff/89ANO1O3nypNV16927N0Qi1x4pBgwYgLS0NIs8pKWlwdfX1yrYBIDY2FgYDAYcOHBAWHby5EnU1NS4dLy2GDRoEHbu3GlzXe/evSGTybBnzx5hmV6vx4EDB4Tr3BYDBgyAXC5HUVGR1bXt1q1bm/dLCCGk81HQTQgh5Koyf/58VFVVYfr06cjIyMDp06eRnJyMBx98EEajET4+PpgzZw4WLVqEnTt34ujRo5g9e7bDoLJXr14wGAz45JNPcPr0aaxfvx6ff/55B54V0KdPH9x55514+OGHsWfPHhw+fBj3338/oqKicOeddwIwjfR+4cIFvP/++8jPz8dnn31mMap7QUEBXnzxRaSnp6OwsBDJyckWQftrr72Gb775Bm+88QaOHTuGnJwcoTbcVfPmzcPZs2fx+OOP48SJE/j111/x+uuv4+mnn7Z5jfv164dbbrkFDz/8MPbt24fMzEw89NBDDmujL9WLL76I/fv3Y968eThy5AhOnDiBVatWoaKiAt7e3njsscewaNEibNu2DcePH8fDDz8MtVqNOXPmtPmYvr6+ePbZZ/HUU09h3bp1yM/Px8GDB/HZZ59h3bp17Xh2hBBCOhoF3YQQQq4qkZGR+Oeff2A0GpGYmIj4+Hg88cQT8Pf3F4K+Dz74ANdddx3uuOMOTJgwAePHj8fw4cPt7nPIkCFYtmwZ3nvvPcTHx+O7777DkiVLOuqUBGvWrMHw4cNx++23Y+zYsWCM4Y8//hCaNcfGxmLlypX47LPPMHjwYGRkZFiMnq5UKnHixAncfffd6Nu3Lx555BEsWLAAjz76KAAgMTERv//+O1JSUjBy5EiMGTMGy5YtQ3R0tMt5jIqKwh9//IGMjAwMHjwYc+fOxZw5cxwG7mvWrEG3bt2QkJCAKVOm4JFHHkFoaGgbr5Jzffv2RXJyMg4fPoxRo0Zh7Nix+PXXXyGRmHrlLV26FHfffTeSkpIwbNgw5OXlYfv27QgMDLyk47711lt47bXXsGTJEsTGxiIxMRG//fYbevbs2R6nRQghpJNwrDWdyQghhBBCCCGEEOIyqukmhBBCCCGEEELchIJuQgghxAP4+PjY/ff33393dvY81nfffWf3usXFxXV29gghhBBqXk4IIYR4gry8PLvroqKi3Dpw2OVMpVKhrKzM5jqpVNqq/uaEEEKIO1DQTQghhBBCCCGEuAk1LyeEEEIIIYQQQtyEgm5CCCGEEEIIIcRNKOgmhBBCCCGEEELchIJuQgghhBBCCCHETSjoJoQQQgghhBBC3ISCbkIIIYQQQgghxE0o6CaEEEIIIYQQQtyEgm5CCCGEEEIIIcRN/h8KvEbOv+4DwgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         Days to Report   R-squared:                       0.002\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     3.450\n",
      "Date:                Fri, 09 Aug 2024   Prob (F-statistic):            0.00407\n",
      "Time:                        14:17:50   Log-Likelihood:                -68649.\n",
      "No. Observations:               10916   AIC:                         1.373e+05\n",
      "Df Residuals:                   10910   BIC:                         1.374e+05\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                      85.5864     23.853      3.588      0.000      38.830     132.343\n",
      "percent_white              -0.5888      0.216     -2.732      0.006      -1.011      -0.166\n",
      "percent_hispanic           -0.2400      0.139     -1.722      0.085      -0.513       0.033\n",
      "median_household_income    -0.0001   7.03e-05     -1.588      0.112      -0.000    2.62e-05\n",
      "percent_poverty            -0.9954      0.266     -3.747      0.000      -1.516      -0.475\n",
      "unemployment_rate           1.1161      1.044      1.069      0.285      -0.930       3.162\n",
      "==============================================================================\n",
      "Omnibus:                    24581.151   Durbin-Watson:                   1.900\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        182543160.185\n",
      "Skew:                          21.262   Prob(JB):                         0.00\n",
      "Kurtosis:                     635.086   Cond. No.                     1.55e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.55e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variance Inflation Factors:\n",
      "                  Variable         VIF\n",
      "0                    const  365.555860\n",
      "1            percent_white    1.935037\n",
      "2         percent_hispanic    2.020569\n",
      "3  median_household_income    1.783219\n",
      "4          percent_poverty    1.906722\n",
      "5        unemployment_rate    1.212996\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Remove extreme outliers (above 8000 days), ensure positive days, and remove negative incomes\n",
    "analysis_df_cleaned = analysis_df[\n",
    "    (analysis_df['Days to Report'] <= 8000) & \n",
    "    (analysis_df['Days to Report'] > 0) &\n",
    "    (analysis_df['median_household_income'] > 0)\n",
    "]\n",
    "\n",
    "print(f\"Original dataset size: {len(analysis_df)}\")\n",
    "print(f\"Dataset size after cleaning: {len(analysis_df_cleaned)}\")\n",
    "print(f\"Number of records removed: {len(analysis_df) - len(analysis_df_cleaned)}\")\n",
    "\n",
    "# List of EJ variables\n",
    "ej_variables = ['percent_white', 'percent_hispanic', 'median_household_income', 'percent_poverty', 'unemployment_rate']\n",
    "\n",
    "# Correlation matrix\n",
    "correlation_matrix = analysis_df_cleaned[['Days to Report'] + ej_variables].corr()\n",
    "print(\"\\nCorrelation Matrix:\")\n",
    "print(correlation_matrix)\n",
    "\n",
    "# Visualize correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1, center=0)\n",
    "plt.title('Correlation Matrix of EJ Variables and Reporting Time')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Scatter plots with regression lines\n",
    "for var in ej_variables:\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.regplot(x=var, y='Days to Report', data=analysis_df_cleaned, scatter_kws={'alpha':0.5})\n",
    "    plt.title(f'Relationship between {var} and Reporting Time')\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Days to Report')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Multiple Linear Regression\n",
    "X = sm.add_constant(analysis_df_cleaned[ej_variables])\n",
    "y = analysis_df_cleaned['Days to Report']\n",
    "model = sm.OLS(y, X).fit()\n",
    "print(model.summary())\n",
    "\n",
    "# Residual Analysis\n",
    "residuals = model.resid\n",
    "fitted_values = model.fittedvalues\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=fitted_values, y=residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.title('Residual Plot')\n",
    "plt.xlabel('Fitted values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Q-Q plot for normality check\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sm.qqplot(residuals, ax=ax)\n",
    "ax.set_title('Q-Q Plot of Residuals')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Variance Inflation Factor for multicollinearity check\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"Variable\"] = X.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "print(\"\\nVariance Inflation Factors:\")\n",
    "print(vif_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows in original dataset: 16886\n",
      "Rows kept: 10916\n",
      "Rows removed: 5970\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 30\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# Compare distributions for key variables\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDays to Report\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_household_income\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m ej_variables:\n\u001b[0;32m---> 30\u001b[0m     \u001b[43mcompare_distributions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# Summary statistics for removed rows\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mSummary statistics for removed rows:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[25], line 26\u001b[0m, in \u001b[0;36mcompare_distributions\u001b[0;34m(variable)\u001b[0m\n\u001b[1;32m     24\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvariable\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     25\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend()\n\u001b[0;32m---> 26\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/pyplot.py:527\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    483\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    485\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    524\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    526\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/IPython/core/formatters.py:182\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    180\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m    231\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/IPython/core/formatters.py:226\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 226\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    168\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/backend_bases.py:2164\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2161\u001b[0m     \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m   2162\u001b[0m     \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m   2163\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2164\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2166\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3151\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3158\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/axes/_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3068\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3070\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3071\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3073\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3074\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/legend.py:780\u001b[0m, in \u001b[0;36mLegend.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    777\u001b[0m     Shadow(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegendPatch, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shadow_props)\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegendPatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m--> 780\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_legend_box\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    782\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlegend\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    783\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/artist.py:39\u001b[0m, in \u001b[0;36m_prevent_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m     renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[1;32m     37\u001b[0m     renderer\u001b[38;5;241m.\u001b[39m_rasterizing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/offsetbox.py:412\u001b[0m, in \u001b[0;36mOffsetBox.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;124;03mUpdate the location of children if necessary and draw them\u001b[39;00m\n\u001b[1;32m    409\u001b[0m \u001b[38;5;124;03mto the given *renderer*.\u001b[39;00m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    411\u001b[0m bbox, offsets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_bbox_and_child_offsets(renderer)\n\u001b[0;32m--> 412\u001b[0m px, py \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_offset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    413\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m c, (ox, oy) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_visible_children(), offsets):\n\u001b[1;32m    414\u001b[0m     c\u001b[38;5;241m.\u001b[39mset_offset((px \u001b[38;5;241m+\u001b[39m ox, py \u001b[38;5;241m+\u001b[39m oy))\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/offsetbox.py:60\u001b[0m, in \u001b[0;36m_compat_get_offset.<locals>.get_offset\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m params \u001b[38;5;241m=\u001b[39m _api\u001b[38;5;241m.\u001b[39mselect_matching_signature(sigs, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     57\u001b[0m bbox \u001b[38;5;241m=\u001b[39m (params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m params \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m     58\u001b[0m         Bbox\u001b[38;5;241m.\u001b[39mfrom_bounds(\u001b[38;5;241m-\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxdescent\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;241m-\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mydescent\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m     59\u001b[0m                          params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m\"\u001b[39m], params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m\"\u001b[39m]))\n\u001b[0;32m---> 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mself\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrenderer\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/offsetbox.py:312\u001b[0m, in \u001b[0;36mOffsetBox.get_offset\u001b[0;34m(self, bbox, renderer)\u001b[0m\n\u001b[1;32m    297\u001b[0m \u001b[38;5;129m@_compat_get_offset\u001b[39m\n\u001b[1;32m    298\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_offset\u001b[39m(\u001b[38;5;28mself\u001b[39m, bbox, renderer):\n\u001b[1;32m    299\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;124;03m    Return the offset as a tuple (x, y).\u001b[39;00m\n\u001b[1;32m    301\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[38;5;124;03m    renderer : `.RendererBase` subclass\u001b[39;00m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    311\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m--> 312\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_offset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    313\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset)\n\u001b[1;32m    314\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset)\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/legend.py:738\u001b[0m, in \u001b[0;36mLegend._findoffset\u001b[0;34m(self, width, height, xdescent, ydescent, renderer)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Helper function to locate the legend.\"\"\"\u001b[39;00m\n\u001b[1;32m    737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_loc \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:  \u001b[38;5;66;03m# \"best\".\u001b[39;00m\n\u001b[0;32m--> 738\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_find_best_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    739\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_loc \u001b[38;5;129;01min\u001b[39;00m Legend\u001b[38;5;241m.\u001b[39mcodes\u001b[38;5;241m.\u001b[39mvalues():  \u001b[38;5;66;03m# Fixed location.\u001b[39;00m\n\u001b[1;32m    740\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m Bbox\u001b[38;5;241m.\u001b[39mfrom_bounds(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, width, height)\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/legend.py:1171\u001b[0m, in \u001b[0;36mLegend._find_best_position\u001b[0;34m(self, width, height, renderer, consider)\u001b[0m\n\u001b[1;32m   1167\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39misaxes  \u001b[38;5;66;03m# always holds, as this is only called internally\u001b[39;00m\n\u001b[1;32m   1169\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m-> 1171\u001b[0m bboxes, lines, offsets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_auto_legend_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1173\u001b[0m bbox \u001b[38;5;241m=\u001b[39m Bbox\u001b[38;5;241m.\u001b[39mfrom_bounds(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, width, height)\n\u001b[1;32m   1174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m consider \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/legend.py:977\u001b[0m, in \u001b[0;36mLegend._auto_legend_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    973\u001b[0m     lines\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m    974\u001b[0m         artist\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform_path(artist\u001b[38;5;241m.\u001b[39mget_path()))\n\u001b[1;32m    975\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(artist, Rectangle):\n\u001b[1;32m    976\u001b[0m     bboxes\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 977\u001b[0m         \u001b[43martist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_bbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(artist, Patch):\n\u001b[1;32m    979\u001b[0m     lines\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m    980\u001b[0m         artist\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform_path(artist\u001b[38;5;241m.\u001b[39mget_path()))\n",
      "File \u001b[0;32m~/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/matplotlib/transforms.py:468\u001b[0m, in \u001b[0;36mBboxBase.transformed\u001b[0;34m(self, transform)\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;124;03mConstruct a `Bbox` by statically transforming this one by *transform*.\u001b[39;00m\n\u001b[1;32m    466\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    467\u001b[0m pts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_points()\n\u001b[0;32m--> 468\u001b[0m ll, ul, lr \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    469\u001b[0m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    470\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Bbox([ll, [lr[\u001b[38;5;241m0\u001b[39m], ul[\u001b[38;5;241m1\u001b[39m]]])\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Create a DataFrame of removed rows\n",
    "removed_df = analysis_df[\n",
    "    (analysis_df['Days to Report'] > 8000) | \n",
    "    (analysis_df['Days to Report'] <= 0) |\n",
    "    (analysis_df['median_household_income'] <= 0)\n",
    "]\n",
    "\n",
    "# Create a DataFrame of kept rows\n",
    "kept_df = analysis_df_cleaned\n",
    "\n",
    "print(f\"Total rows in original dataset: {len(analysis_df)}\")\n",
    "print(f\"Rows kept: {len(kept_df)}\")\n",
    "print(f\"Rows removed: {len(removed_df)}\")\n",
    "\n",
    "# Function to compare distributions\n",
    "def compare_distributions(variable):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    sns.histplot(kept_df[variable], kde=True, color='blue', alpha=0.5, label='Kept')\n",
    "    sns.histplot(removed_df[variable], kde=True, color='red', alpha=0.5, label='Removed')\n",
    "    plt.title(f'Distribution of {variable}')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Compare distributions for key variables\n",
    "for var in ['Days to Report', 'median_household_income'] + ej_variables:\n",
    "    compare_distributions(var)\n",
    "\n",
    "# Summary statistics for removed rows\n",
    "print(\"\\nSummary statistics for removed rows:\")\n",
    "print(removed_df.describe())\n",
    "\n",
    "# Count of rows removed for each reason\n",
    "reason_counts = {\n",
    "    'Days > 8000': sum(removed_df['Days to Report'] > 8000),\n",
    "    'Days <= 0': sum(removed_df['Days to Report'] <= 0),\n",
    "    'Income <= 0': sum(removed_df['median_household_income'] <= 0)\n",
    "}\n",
    "print(\"\\nReason for removal:\")\n",
    "for reason, count in reason_counts.items():\n",
    "    print(f\"{reason}: {count}\")\n",
    "\n",
    "# Check for any patterns in categorical variables\n",
    "categorical_vars = ['Spill Type', 'county', 'Facility Type']  # Add any other relevant categorical variables\n",
    "for var in categorical_vars:\n",
    "    if var in removed_df.columns:\n",
    "        print(f\"\\nTop 10 {var} in removed data:\")\n",
    "        print(removed_df[var].value_counts().head(10))"
   ]
  }
 ],
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