sparkify_churn_ready_ibm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sparkify Churn Project\n",
    "\n",
    "### Table of Contents\n",
    "\n",
    "1. <a href=\"#Overview\">Overview</a>\n",
    "2. <a href=\"#Load-and-Clean-Dataset\">Load and Clean Dataset</a>\n",
    "3. <a href=\"#Exploratory-Data-Analysis\">Exploratory Data Analysis </a>\n",
    "    1. <a href=\"#Data-Preperation\">Data Preperation </a>\n",
    "    1. <a href=\"#Insights-From-Processed-Dataset\">Insights From Processed Dataset</a>\n",
    "4. <a href=\"#Feature-Engineering\">Feature Engineering </a>\n",
    "5. <a href=\"#Modeling\">Modeling </a>\n",
    "6. <a href=\"#Conclusion\">Conclusion</a>\n",
    "    1. <a href=\"#Reflection\">Reflection</a>\n",
    "    1. <a href=\"#Improvement\">Improvement</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sparkify is a Music platform like <a href=\"https://fizy.com/\">Fizy</a>. Sparkify's most important problem is Churn. Churn defines cancellation of the subscription.\n",
    "\n",
    "In this case we are going to predict potential churner subscribers. If Sparkify detects the subscriber who will be churner, they can take actions to labeled subscribers like discount, no ads and so on.\n",
    "\n",
    "The data provided from Sparkify Company has customer logs in the platform. Also the dataset has variables like age, gender, location. First of all we analyzed the dataset and created some features about subscribers. After Feature engineering steps we went to modelling steps and tried some ML alogrithms for predict the potential churner subscribers correctly. In the evaluation step we used F1 Score because the dataset is imbalanced. Accuracy is not a good metric for this problem.\n",
    "\n",
    "Finally We achieved 0.82 F1 Score with Crossvalidated Random Forest Classifier. Also We prepared an <a href=\"https://abduygur.medium.com/churn-prediction-using-pyspark-a1f4ef0439b3\">article in the medium about this project</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Clean Dataset\n",
    "In this workspace, the file is `medium_sparkify_event_data.json` provided by Sparkify Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Setup Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for a Spark session to start...\n",
      "Spark Initialization Done! ApplicationId = app-20210129061231-0001\n",
      "KERNEL_ID = 62d57eec-4b0c-4ff9-bc55-d761c64d1800\n",
      "Processing ./.cache/pip/wheels/f9/d4/cd/cf12d0bb9578ac4fb9457c586dd2fe5a7430942968e638bc8c/httpagentparser-1.9.1-py2.py3-none-any.whl\n",
      "Installing collected packages: httpagentparser\n",
      "Successfully installed httpagentparser-1.9.1\n",
      "\u001b[33mWARNING: Target directory /home/spark/shared/user-libs/python3.7/httpagentparser-1.9.1.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n",
      "\u001b[33mWARNING: Target directory /home/spark/shared/user-libs/python3.7/httpagentparser already exists. Specify --upgrade to force replacement.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install httpagentparser\n",
    "\n",
    "import pyspark\n",
    "import pandas as pd\n",
    "from pyspark.sql.functions import col, udf, sum as Fsum, from_unixtime, \\\n",
    "    count, countDistinct, when, isnull, max as Fmax, min as Fmin, length, \\\n",
    "    month, datediff, first, year, concat\n",
    "from pyspark.sql.types import IntegerType, FloatType, DateType, TimestampType, LongType, StringType\n",
    "from pyspark.ml.feature import StringIndexer, VectorAssembler, OneHotEncoder\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.classification import GBTClassifier, RandomForestClassifier, LogisticRegression\n",
    "from pyspark.ml.evaluation import  MulticlassClassificationEvaluator, BinaryClassificationEvaluator\n",
    "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
    "\n",
    "\n",
    "import httpagentparser\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "\n",
    "\n",
    "import ibmos2spark\n",
    "# @hidden_cell\n",
    "credentials = {\n",
    "    'endpoint': 'STORAGEURL',\n",
    "    'service_id': 'API',\n",
    "    'iam_service_endpoint': 'SERVICE',\n",
    "    'api_key': 'TOKEN'\n",
    "}\n",
    "\n",
    "configuration_name = 'os_546af22583c84beb96dd0cca3d3dc17f_configs'\n",
    "cos = ibmos2spark.CloudObjectStorage(sc, credentials, configuration_name, 'bluemix_cos')\n",
    "\n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName('Sparkify_Churn') \\\n",
    "    .getOrCreate()\n",
    "# Since JSON data can be semi-structured and contain additional metadata, it is possible that you might face issues with the DataFrame layout.\n",
    "# Please read the documentation of 'SparkSession.read()' to learn more about the possibilities to adjust the data loading.\n",
    "# PySpark documentation: http://spark.apache.org/docs/2.0.2/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.json\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Some Options about pandas and matplotlib\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('display.max_columns', None) \n",
    "\n",
    "import matplotlib \n",
    "matplotlib.rc('xtick', labelsize=15) \n",
    "matplotlib.rc('ytick', labelsize=15) \n",
    "matplotlib.rc('axes', labelsize=20, titlesize=25)\n",
    "matplotlib.rc('figure', titlesize=25)\n",
    "\n",
    "#Disable Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_nulls(dataframe):\n",
    "    '''\n",
    "    Check null values and return the null values in pandas Dataframe\n",
    "    \n",
    "    INPUT: Spark Dataframe\n",
    "    OUTPUT: Null values\n",
    "    \n",
    "    '''\n",
    "    # Create pandas dataframe\n",
    "    nulls_check = pd.DataFrame(dataframe.select([count(when(isnull(c), c)).alias(c) for c in dataframe.columns]).collect(),\n",
    "                               columns = dataframe.columns).transpose()\n",
    "    nulls_check.columns = ['Null Values']\n",
    "    return nulls_check\n",
    "\n",
    "def grouped_session(df, agg_col='sessionId', group_col='yearmonth', col_suffixes='_monthly'):\n",
    "    \n",
    "    '''\n",
    "    Group the dataframe by UserId and count distinct values of agg_col, gives the name with col_suffixes\n",
    "    \n",
    "    INPUT: Spark Dataframe, Aggregate Column, Group Column, Column Suffixes\n",
    "    OUTPUT: UserId based groupped and aggregated column\n",
    "    '''\n",
    "    \n",
    "    #Group dataframe by UserId and group_col(default:yearmonth) and aggregate by CountDistinct by column(default:sessionId)\n",
    "    #Change the column name using col_suffixes arguement \n",
    "    session = df.groupBy('userId',group_col).agg(countDistinct(agg_col).alias('session_count'))\n",
    "    session = session.groupBy('userId').avg('session_count') \\\n",
    "        .withColumnRenamed('avg(session_count)','session_count'+col_suffixes)\n",
    "    \n",
    "    return session\n",
    "\n",
    "def grouped_session_length(df, agg_col='time', group_col='yearmonth', col_suffixes='_monthly'):\n",
    "    \n",
    "    '''\n",
    "    Group the dataframe by UserId and sessionId and take difference max and min of agg_col, gives the name with col_suffixes.\n",
    "    It is using for calculating average session length by user\n",
    "    \n",
    "    INPUT: Spark Dataframe, Aggregate Column, Group Column, Column Suffixes\n",
    "    OUTPUT: UserId based groupped and aggregated column\n",
    "    '''\n",
    "    #Groups the userId, sessionId and group_col(default:yearmonth)\n",
    "    session_length = df.groupBy('userId','sessionId',group_col) \\\n",
    "        .agg(Fmin(agg_col).alias('start_time'), Fmax(agg_col).alias('end_time'))\n",
    "    #Calculate session durations\n",
    "    session_length = session_length.withColumn('duration',(col('end_time') \\\n",
    "                                                           .cast(LongType()) - col('start_time').cast(LongType())))\n",
    "    #Calculate average session duration by user and group_col(default:yearmonth)\n",
    "    session_length = session_length.groupBy('userId',group_col).avg('duration') \\\n",
    "        .withColumnRenamed('avg(duration)','avg_duration')\n",
    "    #Calculate average session duration by user distinctly\n",
    "    session_length = session_length.groupBy('userId').avg('avg_duration') \\\n",
    "        .withColumnRenamed('avg(avg_duration)','avg_duration')\n",
    "    session_length = session_length.withColumn('avg_duration'+col_suffixes,col('avg_duration')/3600)\n",
    "    session_length = session_length.drop('avg_duration')\n",
    "    \n",
    "    return session_length\n",
    "\n",
    "def category_freq(df,cat_col,suffix):\n",
    "    \n",
    "    \"\"\"\n",
    "    Take most frequent value in category column by UserId\n",
    "    \n",
    "    INPUT: Spark Dataframe, Categorical Column, suffix\n",
    "    OUTPUT: Most frequent value by UserId\n",
    "    \n",
    "    \"\"\"\n",
    "    #group userId and categorical column and count it\n",
    "    most_frequent = df.groupBy('userId',cat_col).count()\n",
    "    \n",
    "    #Get the most frequent categorical column for every individual userId\n",
    "    most_frequent = most_frequent.orderBy('count',ascending=False).groupBy('userId') \\\n",
    "    .agg(first(cat_col).alias('most_frequent'+suffix))\n",
    "    \n",
    "    return most_frequent\n",
    "\n",
    "\n",
    "def groupped_dfs(df_pandas,col,target='target'):\n",
    "    \n",
    "    \"\"\"\n",
    "    Calculate the proportion of the categorical column values and their counts for graphs\n",
    "    \n",
    "    INPUT: Pandas Dataframe, categorical column, Target Column\n",
    "    OUTPUT: Value counts groupped by UserId and the proportion of the value\n",
    "    \n",
    "    \"\"\"\n",
    "    #Take the categorical columns value_counts()\n",
    "    counts = df_pandas[col].value_counts()\n",
    "    #Calculate the proportion\n",
    "    props = df_pandas.groupby([col]).target.value_counts(normalize=True)*100\n",
    "    props = props.rename_axis([col+'_',target+'_']).reset_index()\n",
    "    props.columns = [col+'_',target+'_','Proportion']\n",
    "    \n",
    "    return counts,props\n",
    "\n",
    "def prepare_data(features_df):\n",
    "    \"\"\"\n",
    "    Transform features to vectors with pipeline for modelling\n",
    "    \n",
    "    INPUT: Prepared Dataframe\n",
    "    OUTPUT: Dataframe with input vectors\n",
    "    \"\"\"\n",
    "    #Empty list which will be filled with numeric columns of Spark Dataframe\n",
    "    num_cols = []\n",
    "    \n",
    "    #take all non-string type field names\n",
    "    for field in features_df.schema.fields :\n",
    "        if field.dataType!=StringType():\n",
    "            num_cols.append(field.name)\n",
    "    \n",
    "    #remove target variable which is churn or not\n",
    "    num_cols.remove('target')\n",
    "    \n",
    "    #Index the categorical variables with StringIndexer\n",
    "    print(\"Indexer Process...\")\n",
    "    location_indexer = StringIndexer(inputCol='location', outputCol='location_index')\n",
    "    gender_indexer = StringIndexer(inputCol='gender', outputCol='gender_index')\n",
    "    level_indexer = StringIndexer(inputCol='current_charging', outputCol='level_index')\n",
    "    browser_indexer = StringIndexer(inputCol='most_frequent_browser', outputCol='browser_index')\n",
    "    os_indexer = StringIndexer(inputCol='most_frequent_os', outputCol='os_index')\n",
    "    print(\"Success!\")\n",
    "    \n",
    "    #Encode the Output of the Indexers with OneHotEncoder\n",
    "    print(\"OneHot Encoder Process...\")\n",
    "    onehot_encoder = OneHotEncoder(inputCols=[\"gender_index\",\"level_index\",\"browser_index\",\"os_index\"],\n",
    "                                   outputCols=[ \"gender_vec\",\"level_vec\",\"browser_vec\",\"os_vec\"])\n",
    "    print(\"Success!\")\n",
    "    \n",
    "    #Assemble all variables to one single vector with VectorAssembler\n",
    "    print(\"Assembler Process...\")\n",
    "    assembler = VectorAssembler(inputCols=num_cols+[\"gender_vec\",\"level_vec\",\"browser_vec\",\"os_vec\"],\n",
    "                                outputCol='features')\n",
    "    \n",
    "    print(\"Columns: {} assembled to the features column\".format(num_cols+[\"gender_vec\",\"level_vec\",\"browser_vec\",\"os_vec\"]))\n",
    "    \n",
    "    print(\"Success!\")\n",
    "    \n",
    "    #Create pipeline which is index the categorical columns, encode them and assemble all variables to assembler\n",
    "    process_pipeline = Pipeline(stages=[location_indexer,gender_indexer,level_indexer,browser_indexer,os_indexer,\n",
    "                                        onehot_encoder,assembler])\n",
    "    #fit the dataframe to pipeline and transform şt\n",
    "    print(\"Pipeline fit...\")\n",
    "    model_df = process_pipeline.fit(features_df).transform(features_df)\n",
    "    print(\"Success!\")\n",
    "    model_df = model_df.select(\"features\",\"target\")\n",
    "    return model_df\n",
    "\n",
    "\n",
    "def rf_tuning(train, test, numTrees=[20,50,100], maxDepth=[5,10,20] ):\n",
    "    \"\"\"\n",
    "    Tunes the RandomForestClassifier with given numTrees and maxDepth arguements\n",
    "    \n",
    "    INPUT: train set, test set, list of numTrees, list of maxDepth\n",
    "    OUTPUT: bestmodel after tuning\n",
    "    \"\"\"\n",
    "    #Model\n",
    "    clf = RandomForestClassifier(labelCol=\"target\")\n",
    "    \n",
    "    #Grid for parameter tuning\n",
    "    paramGrid = ParamGridBuilder() \\\n",
    "        .addGrid(clf.numTrees, numTrees) \\\n",
    "        .addGrid(clf.maxDepth, maxDepth) \\\n",
    "        .build()   \n",
    "    \n",
    "    #3 Fold Cross Validation\n",
    "    crossval = CrossValidator(estimator = Pipeline(stages=[clf]),\n",
    "                         estimatorParamMaps = paramGrid,\n",
    "                         evaluator = MulticlassClassificationEvaluator(metricName='f1',labelCol=\"target\"),\n",
    "                         numFolds = 3)\n",
    "\n",
    "    cvModel = crossval.fit(train)\n",
    "    predictions = cvModel.transform(test)\n",
    "    \n",
    "    #Model Evaluation\n",
    "    model_evaluator= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol='target')\n",
    "       \n",
    "    \n",
    "    f1_score = model_evaluator.evaluate(predictions.select('target','prediction'),{model_evaluator.metricName: 'f1'})\n",
    "    acc_score = model_evaluator.evaluate(predictions.select('target','prediction'),{model_evaluator.metricName: 'accuracy'})\n",
    "    pre_score = model_evaluator.evaluate(predictions.select('target','prediction'),{model_evaluator.metricName: 'precisionByLabel'})\n",
    "    rec_score = model_evaluator.evaluate(predictions.select('target','prediction'),{model_evaluator.metricName: 'recallByLabel'})\n",
    "    \n",
    "    print(\"RESULTS:\")\n",
    "    print(\"*\"*10)\n",
    "    print('The F1 score is {}'.format(f1_score))\n",
    "    print('The Accuracy score is {}'.format(acc_score))\n",
    "    print('The Precision score is {}'.format(pre_score))\n",
    "    print('The Recall score is {}'.format(rec_score))\n",
    "    print(\"*\"*10)\n",
    "    #Take the best Model\n",
    "    bestModel = cvModel.bestModel\n",
    "    \n",
    "    return bestModel\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(artist='Martin Orford', auth='Logged In', firstName='Joseph', gender='M', itemInSession=20, lastName='Morales', length=597.55057, level='free', location='Corpus Christi, TX', method='PUT', page='NextSong', registration=1532063507000, sessionId=292, song='Grand Designs', status=200, ts=1538352011000, userAgent='\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\"', userId='293'),\n",
       " Row(artist=\"John Brown's Body\", auth='Logged In', firstName='Sawyer', gender='M', itemInSession=74, lastName='Larson', length=380.21179, level='free', location='Houston-The Woodlands-Sugar Land, TX', method='PUT', page='NextSong', registration=1538069638000, sessionId=97, song='Bulls', status=200, ts=1538352025000, userAgent='\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36\"', userId='98'),\n",
       " Row(artist='Afroman', auth='Logged In', firstName='Maverick', gender='M', itemInSession=184, lastName='Santiago', length=202.37016, level='paid', location='Orlando-Kissimmee-Sanford, FL', method='PUT', page='NextSong', registration=1535953455000, sessionId=178, song='Because I Got High', status=200, ts=1538352118000, userAgent='\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36\"', userId='179'),\n",
       " Row(artist=None, auth='Logged In', firstName='Maverick', gender='M', itemInSession=185, lastName='Santiago', length=None, level='paid', location='Orlando-Kissimmee-Sanford, FL', method='PUT', page='Logout', registration=1535953455000, sessionId=178, song=None, status=307, ts=1538352119000, userAgent='\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36\"', userId='179'),\n",
       " Row(artist='Lily Allen', auth='Logged In', firstName='Gianna', gender='F', itemInSession=22, lastName='Campos', length=194.53342, level='paid', location='Mobile, AL', method='PUT', page='NextSong', registration=1535931018000, sessionId=245, song='Smile (Radio Edit)', status=200, ts=1538352124000, userAgent='Mozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20100101 Firefox/31.0', userId='246')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the dataframe\n",
    "df = spark.read.json(cos.url('medium-sparkify-event-data.json', 'sparkifychurn-donotdelete-pr-b0enq8ijgb8vvt'))\n",
    "df.take(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "In this part I analyzed raw data and I want to show the dataset details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- artist: string (nullable = true)\n",
      " |-- auth: string (nullable = true)\n",
      " |-- firstName: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- itemInSession: long (nullable = true)\n",
      " |-- lastName: string (nullable = true)\n",
      " |-- length: double (nullable = true)\n",
      " |-- level: string (nullable = true)\n",
      " |-- location: string (nullable = true)\n",
      " |-- method: string (nullable = true)\n",
      " |-- page: string (nullable = true)\n",
      " |-- registration: long (nullable = true)\n",
      " |-- sessionId: long (nullable = true)\n",
      " |-- song: string (nullable = true)\n",
      " |-- status: long (nullable = true)\n",
      " |-- ts: long (nullable = true)\n",
      " |-- userAgent: string (nullable = true)\n",
      " |-- userId: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(543705, 18, 449)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counts\n",
    "df.count(), len(df.columns), df.select('userId').distinct().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset including 543K Rows, 18 Columns and 449 Distinct customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logged Out\n",
      "Cancelled\n",
      "Guest\n",
      "Logged In\n"
     ]
    }
   ],
   "source": [
    "#Type of auths\n",
    "auths = df.select('auth').distinct().collect()\n",
    "print(\"\\n\".join(str(auths[i][0]) for i in range(len(auths))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "free\n",
      "paid\n"
     ]
    }
   ],
   "source": [
    "#type of levels\n",
    "levels = df.select('level').distinct().collect()\n",
    "print(\"\\n\".join(str(levels[i][0]) for i in range(len(levels))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F\n",
      "None\n",
      "M\n"
     ]
    }
   ],
   "source": [
    "#Genders\n",
    "genders = df.select('gender').distinct().collect()\n",
    "print(\"\\n\".join(str(genders[i][0]) for i in range(len(genders))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([Row(max(CAST(from_unixtime((ts / 1000), yyyy-MM-dd HH:mm:ss) AS DATE))=datetime.date(2018, 12, 1))],\n",
       " [Row(min(CAST(from_unixtime((ts / 1000), yyyy-MM-dd HH:mm:ss) AS DATE))=datetime.date(2018, 10, 1))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the max log time and min log time\n",
    "df.agg(Fmax(from_unixtime(col('ts')/1000).cast(DateType()))).collect(), df.agg(Fmin(from_unixtime(col('ts')/1000).cast(DateType()))).collect() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From October'18 to December'18 (Events in 2 Months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>110828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstName</th>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>itemInSession</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lastName</th>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>110828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>page</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registration</th>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sessionId</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>song</th>\n",
       "      <td>110828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userAgent</th>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Null Values\n",
       "artist              110828\n",
       "auth                     0\n",
       "firstName            15700\n",
       "gender               15700\n",
       "itemInSession            0\n",
       "lastName             15700\n",
       "length              110828\n",
       "level                    0\n",
       "location             15700\n",
       "method                   0\n",
       "page                     0\n",
       "registration         15700\n",
       "sessionId                0\n",
       "song                110828\n",
       "status                   0\n",
       "ts                       0\n",
       "userAgent            15700\n",
       "userId                   0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check null values\n",
    "check_nulls(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the null values table. There is some patterns.\n",
    "\n",
    "- We have distinct 2 values in null table which are 110828 and 15700\n",
    "- artist, length and song have a pattern and demographics info has another "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----------+---------+------+-------------+--------+------+-----+--------+------+-----+------------+---------+----+------+-------------+---------+------+\n",
      "|artist|      auth|firstName|gender|itemInSession|lastName|length|level|location|method| page|registration|sessionId|song|status|           ts|userAgent|userId|\n",
      "+------+----------+---------+------+-------------+--------+------+-----+--------+------+-----+------------+---------+----+------+-------------+---------+------+\n",
      "|  null|Logged Out|     null|  null|          186|    null|  null| paid|    null|   GET| Home|        null|      178|null|   200|1538352148000|     null|      |\n",
      "|  null|Logged Out|     null|  null|          187|    null|  null| paid|    null|   GET| Home|        null|      178|null|   200|1538352151000|     null|      |\n",
      "|  null|Logged Out|     null|  null|          188|    null|  null| paid|    null|   GET| Home|        null|      178|null|   200|1538352168000|     null|      |\n",
      "|  null|Logged Out|     null|  null|          189|    null|  null| paid|    null|   PUT|Login|        null|      178|null|   307|1538352169000|     null|      |\n",
      "|  null|Logged Out|     null|  null|          114|    null|  null| free|    null|   GET| Home|        null|      442|null|   200|1538353292000|     null|      |\n",
      "|  null|Logged Out|     null|  null|           34|    null|  null| free|    null|   GET| Home|        null|      292|null|   200|1538355024000|     null|      |\n",
      "|  null|Logged Out|     null|  null|           35|    null|  null| free|    null|   GET| Help|        null|      292|null|   200|1538355098000|     null|      |\n",
      "|  null|Logged Out|     null|  null|           36|    null|  null| free|    null|   GET| Home|        null|      292|null|   200|1538355178000|     null|      |\n",
      "|  null|Logged Out|     null|  null|           37|    null|  null| free|    null|   GET|About|        null|      292|null|   200|1538355186000|     null|      |\n",
      "|  null|Logged Out|     null|  null|           38|    null|  null| free|    null|   PUT|Login|        null|      292|null|   307|1538355187000|     null|      |\n",
      "+------+----------+---------+------+-------------+--------+------+-----+--------+------+-----+------------+---------+----+------+-------------+---------+------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.where('firstName is null').show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "userId has some None values, but not nulls that means we have no information about user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----------+-----+\n",
      "|userId|      auth|count|\n",
      "+------+----------+-----+\n",
      "|      |     Guest|   94|\n",
      "|      |Logged Out|15606|\n",
      "+------+----------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.where('firstName is null').select('userId','auth').groupby(['userId','auth']).count().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no demographic information in auth with Guest and logged Out. Because of logging off the information about users disappear in the current system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+------+\n",
      "|      auth| count|\n",
      "+----------+------+\n",
      "|Logged Out| 15606|\n",
      "| Cancelled|    99|\n",
      "|     Guest|    94|\n",
      "| Logged In|527906|\n",
      "+----------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select('auth').groupby(['auth']).count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----+------+---------------+-----+\n",
      "|      auth|level|method|           page|count|\n",
      "+----------+-----+------+---------------+-----+\n",
      "| Logged In| paid|   PUT|      Thumbs Up|19504|\n",
      "| Logged In| paid|   GET|           Home|13956|\n",
      "| Logged In| paid|   PUT|Add to Playlist|10033|\n",
      "| Logged In| free|   GET|    Roll Advert| 7337|\n",
      "| Logged In| paid|   PUT|     Add Friend| 6341|\n",
      "|Logged Out| paid|   GET|           Home| 5513|\n",
      "| Logged In| free|   GET|           Home| 5133|\n",
      "| Logged In| free|   PUT|      Thumbs Up| 4322|\n",
      "| Logged In| paid|   PUT|         Logout| 4184|\n",
      "|Logged Out| paid|   PUT|          Login| 4171|\n",
      "| Logged In| paid|   GET|      Downgrade| 3811|\n",
      "| Logged In| paid|   PUT|    Thumbs Down| 3771|\n",
      "|Logged Out| free|   GET|           Home| 2769|\n",
      "| Logged In| free|   PUT|Add to Playlist| 2316|\n",
      "| Logged In| paid|   GET|       Settings| 2240|\n",
      "| Logged In| paid|   GET|           Help| 2076|\n",
      "|Logged Out| free|   PUT|          Login| 1840|\n",
      "| Logged In| free|   PUT|         Logout| 1806|\n",
      "| Logged In| free|   PUT|     Add Friend| 1746|\n",
      "| Logged In| free|   PUT|    Thumbs Down| 1140|\n",
      "+----------+-----+------+---------------+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.where('song is null').select('auth','level','method','page') \\\n",
    "    .groupby(['auth','level','method','page']).count() \\\n",
    "    .orderBy(col('count').desc()).show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- auth has 2 distinct values already\n",
    "- level has 2 distinct values already\n",
    "- method has 2 distinct values already\n",
    "\n",
    "Let me check page attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "All Pages\n",
      "\n",
      "Cancel\n",
      "Submit Downgrade\n",
      "Thumbs Down\n",
      "Home\n",
      "Downgrade\n",
      "Roll Advert\n",
      "Logout\n",
      "Save Settings\n",
      "Cancellation Confirmation\n",
      "About\n",
      "Submit Registration\n",
      "Settings\n",
      "Login\n",
      "Register\n",
      "Add to Playlist\n",
      "Add Friend\n",
      "NextSong\n",
      "Thumbs Up\n",
      "Help\n",
      "Upgrade\n",
      "Error\n",
      "Submit Upgrade\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Pages when song is null\n",
      "\n",
      "Cancel\n",
      "Submit Downgrade\n",
      "Thumbs Down\n",
      "Home\n",
      "Downgrade\n",
      "Roll Advert\n",
      "Logout\n",
      "Save Settings\n",
      "Cancellation Confirmation\n",
      "About\n",
      "Submit Registration\n",
      "Settings\n",
      "Login\n",
      "Register\n",
      "Add to Playlist\n",
      "Add Friend\n",
      "Thumbs Up\n",
      "Help\n",
      "Upgrade\n",
      "Error\n",
      "Submit Upgrade\n",
      "----------------------------------------------------------------------------------------------------\n",
      "When song is null, {'NextSong'} pages can not be seen\n"
     ]
    }
   ],
   "source": [
    "all_pages = pd.DataFrame(df.select('page').distinct().collect())\n",
    "all_pages.columns = ['page_all']\n",
    "\n",
    "songnull_pages = pd.DataFrame(df.where('song is null').select('page').distinct().collect())\n",
    "songnull_pages.columns = ['page_songnull']\n",
    "\n",
    "set_diff = set(all_pages['page_all']) - set(songnull_pages['page_songnull'])\n",
    "\n",
    "print(\"-\"*100)\n",
    "print(\"All Pages\\n\")\n",
    "print(\"\\n\".join(page for page in all_pages['page_all']))\n",
    "print(\"-\"*100)\n",
    "print(\"Pages when song is null\\n\")\n",
    "print(\"\\n\".join(page for page in songnull_pages['page_songnull']))\n",
    "print(\"-\"*100)\n",
    "print(\"When song is null, {} pages can not be seen\".format(set_diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NextSong page doesn't have song information so song values are nulls in NextSong Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+---------+---------+------+-------------+---------+---------+-----+--------------------+------+---------------+-------------+---------+--------------------+------+-------------+--------------------+------+\n",
      "|              artist|     auth|firstName|gender|itemInSession| lastName|   length|level|            location|method|           page| registration|sessionId|                song|status|           ts|           userAgent|userId|\n",
      "+--------------------+---------+---------+------+-------------+---------+---------+-----+--------------------+------+---------------+-------------+---------+--------------------+------+-------------+--------------------+------+\n",
      "|       Martin Orford|Logged In|   Joseph|     M|           20|  Morales|597.55057| free|  Corpus Christi, TX|   PUT|       NextSong|1532063507000|      292|       Grand Designs|   200|1538352011000|\"Mozilla/5.0 (Mac...|   293|\n",
      "|   John Brown's Body|Logged In|   Sawyer|     M|           74|   Larson|380.21179| free|Houston-The Woodl...|   PUT|       NextSong|1538069638000|       97|               Bulls|   200|1538352025000|\"Mozilla/5.0 (Mac...|    98|\n",
      "|             Afroman|Logged In| Maverick|     M|          184| Santiago|202.37016| paid|Orlando-Kissimmee...|   PUT|       NextSong|1535953455000|      178|  Because I Got High|   200|1538352118000|\"Mozilla/5.0 (Mac...|   179|\n",
      "|                null|Logged In| Maverick|     M|          185| Santiago|     null| paid|Orlando-Kissimmee...|   PUT|         Logout|1535953455000|      178|                null|   307|1538352119000|\"Mozilla/5.0 (Mac...|   179|\n",
      "|          Lily Allen|Logged In|   Gianna|     F|           22|   Campos|194.53342| paid|          Mobile, AL|   PUT|       NextSong|1535931018000|      245|  Smile (Radio Edit)|   200|1538352124000|Mozilla/5.0 (Wind...|   246|\n",
      "|          Carter USM|Logged In|    Sofia|     F|          266|   Gordon|138.29179| paid|       Rochester, MN|   PUT|       NextSong|1533175710000|      162|       Airplane Food|   200|1538352125000|\"Mozilla/5.0 (Mac...|   163|\n",
      "|                null|Logged In| Maverick|     M|          190| Santiago|     null| paid|Orlando-Kissimmee...|   GET|           Home|1535953455000|      178|                null|   200|1538352176000|\"Mozilla/5.0 (Mac...|   179|\n",
      "|           Aerosmith|Logged In|    Lacey|     F|          107|Castaneda|220.39465| free|        El Campo, TX|   PUT|       NextSong|1537536032000|      442|       Walk This Way|   200|1538352215000|\"Mozilla/5.0 (Mac...|   175|\n",
      "|                null|Logged In|    Colin|     M|            0|   Larson|     null| free|Dallas-Fort Worth...|   GET|           Home|1537982255000|      497|                null|   200|1538352241000|Mozilla/5.0 (Wind...|   100|\n",
      "|       Amy Winehouse|Logged In|    Colin|     M|            1|   Larson|201.50812| free|Dallas-Fort Worth...|   PUT|       NextSong|1537982255000|      497|    Teach Me Tonight|   200|1538352259000|Mozilla/5.0 (Wind...|   100|\n",
      "|Drake / Kanye Wes...|Logged In|    Sofia|     F|          267|   Gordon|357.66812| paid|       Rochester, MN|   PUT|       NextSong|1533175710000|      162|             Forever|   200|1538352263000|\"Mozilla/5.0 (Mac...|   163|\n",
      "|           Bob Dylan|Logged In|   Gianna|     F|           23|   Campos|256.96608| paid|          Mobile, AL|   PUT|       NextSong|1535931018000|      245|Simple Twist Of Fate|   200|1538352318000|Mozilla/5.0 (Wind...|   246|\n",
      "|        The Bar-Kays|Logged In| Maverick|     M|          191| Santiago|164.23138| paid|Orlando-Kissimmee...|   PUT|       NextSong|1535953455000|      178|         Montego Bay|   200|1538352320000|\"Mozilla/5.0 (Mac...|   179|\n",
      "|          Black Kids|Logged In|   Payton|     F|           83| Campbell|251.48036| free|Los Angeles-Long ...|   PUT|       NextSong|1529027541000|       38|Look At Me (When ...|   200|1538352323000|\"Mozilla/5.0 (Win...|    39|\n",
      "|                null|Logged In|    Sofia|     F|          268|   Gordon|     null| paid|       Rochester, MN|   GET|      Downgrade|1533175710000|      162|                null|   200|1538352336000|\"Mozilla/5.0 (Mac...|   163|\n",
      "|                null|Logged In| Maverick|     M|          192| Santiago|     null| paid|Orlando-Kissimmee...|   PUT|Add to Playlist|1535953455000|      178|                null|   200|1538352376000|\"Mozilla/5.0 (Mac...|   179|\n",
      "|         Chris Isaak|Logged In|   Sawyer|     M|           75|   Larson|157.67465| free|Houston-The Woodl...|   PUT|       NextSong|1538069638000|       97|Solitary Man (LP ...|   200|1538352405000|\"Mozilla/5.0 (Mac...|    98|\n",
      "|          Crazy Frog|Logged In|    Lacey|     F|          108|Castaneda|169.50812| free|        El Campo, TX|   PUT|       NextSong|1537536032000|      442| Popcorn (Radio Mix)|   200|1538352435000|\"Mozilla/5.0 (Mac...|   175|\n",
      "|                null|Logged In|   Sawyer|     M|           76|   Larson|     null| free|Houston-The Woodl...|   GET|    Roll Advert|1538069638000|       97|                null|   200|1538352435000|\"Mozilla/5.0 (Mac...|    98|\n",
      "|         The Strokes|Logged In|    Colin|     M|            2|   Larson|187.34975| free|Dallas-Fort Worth...|   PUT|       NextSong|1537982255000|      497|  You Only Live Once|   200|1538352460000|Mozilla/5.0 (Wind...|   100|\n",
      "+--------------------+---------+---------+------+-------------+---------+---------+-----+--------------------+------+---------------+-------------+---------+--------------------+------+-------------+--------------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#None type userId's\n",
    "df.filter(df['userId'] != '').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preperation\n",
    "In this part we created some features related to the customers. After feature engineering steps there will be small part of visualisation part for understand the data better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for users os information\n",
    "udf_os = udf(lambda x: httpagentparser.simple_detect(x)[0].split(' ')[0], StringType())\n",
    "#Function for users browser information\n",
    "udf_browser =  udf(lambda x: httpagentparser.simple_detect(x)[1].split(' ')[0], StringType())\n",
    "#Function for users Location\n",
    "udf_location = udf(lambda x: x.split(',')[1], StringType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create target variable using Cancellation Confirmation and  Cancel pages\n",
    "# If system logged one of them then flag them with churn othwerwise not-churn\n",
    "\n",
    "\n",
    "label = df.withColumn('target', when((col('page').isin(['Cancellation Confirmation','Cancel'])) | \\\n",
    "                                        (col('auth')=='Cancelled'),1).otherwise(0))\n",
    "label = label.groupby('userId').agg(Fsum('target').alias('target'))\n",
    "label = label.withColumn('target', when(col('target')>0,1).otherwise(0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Users Os, Browser and location informations Using UDF's\n",
    "df = df.withColumn('os', udf_os('userAgent'))\n",
    "df = df.withColumn('browser', udf_browser('userAgent'))\n",
    "df = df.withColumn('location', udf_location('location'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert unixtime to Standard Time\n",
    "df = df.withColumn('time',from_unixtime(col('ts')/1000).cast(TimestampType()))\n",
    "#Convert unixtime to Standard Date\n",
    "df = df.withColumn('date',from_unixtime(col('ts')/1000).cast(DateType()))\n",
    "#Take month from date\n",
    "df = df.withColumn('month',month(col('date')))\n",
    "#Take year from date\n",
    "df = df.withColumn('year',year(col('date')))\n",
    "#Create yearmonth(YYYYMM) using year and month columns\n",
    "df = df.withColumn('yearmonth', concat(col('year'),col('month')))\n",
    "#Users registiration date\n",
    "df = df.withColumn('first_date',from_unixtime(col('registration')/1000).cast(DateType()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clear the userId with None Variable and Logged Out auth \n",
    "df = df.filter(df['userId'] != '')\n",
    "df = df.withColumn('userId', col('userId').cast(LongType()))\n",
    "df = df.filter(df['auth'] != 'Logged Out')\n",
    "\n",
    "#Dropna values if any\n",
    "df = df.dropna(how='any', subset=['userId','sessionId'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Last log date of each user\n",
    "last_date = df.groupBy('userId').agg(Fmax('date').alias('last_date'))\n",
    "\n",
    "# Last Charging Method for every users(Paid or Free)\n",
    "current_charging = df.orderBy('ts', ascending=False).groupBy('userId') \\\n",
    "    .agg(first('level') \\\n",
    "    .alias('current_charging'))\n",
    "\n",
    "#Create average and maximum length attributes for each user\n",
    "length_mean = df.groupBy('userId').avg('length').withColumnRenamed('avg(length)','avg_length')\n",
    "length_max = df.groupBy('userId').agg(Fmax('length').alias('max_length'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate page logs in monthly and daily averages\n",
    "#Create distinct pages and remove churn pages\n",
    "distinct_pages = [i.page for i in df.select('page').distinct().collect()]\n",
    "distinct_pages.remove('Cancellation Confirmation')\n",
    "distinct_pages.remove('Cancel')\n",
    "\n",
    "#Group user and Date and calculate visit count of each pages\n",
    "exprs = {x: 'avg' for x in distinct_pages}\n",
    "event_day = df.groupBy('userId','date').pivot('page').count()\n",
    "event_day = event_day.groupBy('userId').agg(exprs).fillna(0)\n",
    "\n",
    "#Group user and Month and calculate visit count of each pages\n",
    "event_month = df.groupBy('userId','yearmonth').pivot('page').count()\n",
    "event_month = event_month.groupBy('userId').agg(exprs).fillna(0)\n",
    "for column in distinct_pages:\n",
    "    event_day = event_day.withColumnRenamed('avg({})'.format(column),'event_{}_daily_avg'.format(column))\n",
    "    event_month = event_month.withColumnRenamed('avg({})'.format(column),'event_{}_monthly_avg'.format(column))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate users item count in each session\n",
    "item_per_session = df.groupBy('userId','sessionId').agg(Fmax('itemInSession').alias('max_item_cnt'))\n",
    "item_per_session = item_per_session.groupBy('userId').avg('max_item_cnt') \\\n",
    "    .withColumnRenamed('avg(max_item_cnt)','avg_item_per_session')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate daily and monthly average session counts\n",
    "monthly_session = grouped_session(df)\n",
    "daily_session = grouped_session(df,group_col='date',col_suffixes='_daily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate daily and monthly average session length\n",
    "session_length_monthly = grouped_session_length(df)\n",
    "session_length_daily = grouped_session_length(df, group_col='date',col_suffixes='_daily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Users most frequent os and browser\n",
    "most_frequent_os = category_freq(df,'os','_os')\n",
    "most_frequent_browser = category_freq(df,'browser','_browser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join all created features\n",
    "df_ready = df.select('userId','location','gender','first_date').distinct() \\\n",
    "    .join(session_length_monthly,on='userId') \\\n",
    "    .join(session_length_daily,on='userId') \\\n",
    "    .join(monthly_session,on='userId') \\\n",
    "    .join(daily_session,on='userId') \\\n",
    "    .join(item_per_session,on='userId') \\\n",
    "    .join(event_month,on='userId') \\\n",
    "    .join(event_day,on='userId') \\\n",
    "    .join(length_mean,on='userId') \\\n",
    "    .join(length_max,on='userId') \\\n",
    "    .join(current_charging,on='userId') \\\n",
    "    .join(last_date,on='userId') \\\n",
    "    .join(label,on='userId') \\\n",
    "    .join(most_frequent_browser,on='userId') \\\n",
    "    .join(most_frequent_os,on='userId')\n",
    "\n",
    "#Calculate Tenure\n",
    "df_ready = df_ready.withColumn('tenure_days', datediff(col('last_date'),col('first_date')))\n",
    "\n",
    "#Drop unnecessary columns\n",
    "df_ready = df_ready.drop('userId','first_date','last_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights From Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- location: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- avg_duration_monthly: double (nullable = true)\n",
      " |-- avg_duration_daily: double (nullable = true)\n",
      " |-- session_count_monthly: double (nullable = true)\n",
      " |-- session_count_daily: double (nullable = true)\n",
      " |-- avg_item_per_session: double (nullable = true)\n",
      " |-- event_Settings_monthly_avg: double (nullable = false)\n",
      " |-- event_Add Friend_monthly_avg: double (nullable = false)\n",
      " |-- event_Thumbs Down_monthly_avg: double (nullable = false)\n",
      " |-- event_Downgrade_monthly_avg: double (nullable = false)\n",
      " |-- event_Submit Upgrade_monthly_avg: double (nullable = false)\n",
      " |-- event_Roll Advert_monthly_avg: double (nullable = false)\n",
      " |-- event_NextSong_monthly_avg: double (nullable = false)\n",
      " |-- event_Error_monthly_avg: double (nullable = false)\n",
      " |-- event_About_monthly_avg: double (nullable = false)\n",
      " |-- event_Upgrade_monthly_avg: double (nullable = false)\n",
      " |-- event_Add to Playlist_monthly_avg: double (nullable = false)\n",
      " |-- event_Home_monthly_avg: double (nullable = false)\n",
      " |-- event_Thumbs Up_monthly_avg: double (nullable = false)\n",
      " |-- event_Logout_monthly_avg: double (nullable = false)\n",
      " |-- event_Submit Downgrade_monthly_avg: double (nullable = false)\n",
      " |-- event_Save Settings_monthly_avg: double (nullable = false)\n",
      " |-- event_Help_monthly_avg: double (nullable = false)\n",
      " |-- event_Settings_daily_avg: double (nullable = false)\n",
      " |-- event_Add Friend_daily_avg: double (nullable = false)\n",
      " |-- event_Thumbs Down_daily_avg: double (nullable = false)\n",
      " |-- event_Downgrade_daily_avg: double (nullable = false)\n",
      " |-- event_Submit Upgrade_daily_avg: double (nullable = false)\n",
      " |-- event_Roll Advert_daily_avg: double (nullable = false)\n",
      " |-- event_NextSong_daily_avg: double (nullable = false)\n",
      " |-- event_Error_daily_avg: double (nullable = false)\n",
      " |-- event_About_daily_avg: double (nullable = false)\n",
      " |-- event_Upgrade_daily_avg: double (nullable = false)\n",
      " |-- event_Add to Playlist_daily_avg: double (nullable = false)\n",
      " |-- event_Home_daily_avg: double (nullable = false)\n",
      " |-- event_Thumbs Up_daily_avg: double (nullable = false)\n",
      " |-- event_Logout_daily_avg: double (nullable = false)\n",
      " |-- event_Submit Downgrade_daily_avg: double (nullable = false)\n",
      " |-- event_Save Settings_daily_avg: double (nullable = false)\n",
      " |-- event_Help_daily_avg: double (nullable = false)\n",
      " |-- avg_length: double (nullable = true)\n",
      " |-- max_length: double (nullable = true)\n",
      " |-- current_charging: string (nullable = true)\n",
      " |-- target: integer (nullable = false)\n",
      " |-- most_frequent_browser: string (nullable = true)\n",
      " |-- most_frequent_os: string (nullable = true)\n",
      " |-- tenure_days: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_ready.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Pandas Dataframe for visualisation\n",
    "df_pandas_ready = df_ready.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>gender</th>\n",
       "      <th>avg_duration_monthly</th>\n",
       "      <th>avg_duration_daily</th>\n",
       "      <th>session_count_monthly</th>\n",
       "      <th>session_count_daily</th>\n",
       "      <th>avg_item_per_session</th>\n",
       "      <th>event_Settings_monthly_avg</th>\n",
       "      <th>event_Add Friend_monthly_avg</th>\n",
       "      <th>event_Thumbs Down_monthly_avg</th>\n",
       "      <th>event_Downgrade_monthly_avg</th>\n",
       "      <th>event_Submit Upgrade_monthly_avg</th>\n",
       "      <th>event_Roll Advert_monthly_avg</th>\n",
       "      <th>event_NextSong_monthly_avg</th>\n",
       "      <th>event_Error_monthly_avg</th>\n",
       "      <th>event_About_monthly_avg</th>\n",
       "      <th>event_Upgrade_monthly_avg</th>\n",
       "      <th>event_Add to Playlist_monthly_avg</th>\n",
       "      <th>event_Home_monthly_avg</th>\n",
       "      <th>event_Thumbs Up_monthly_avg</th>\n",
       "      <th>event_Logout_monthly_avg</th>\n",
       "      <th>event_Submit Downgrade_monthly_avg</th>\n",
       "      <th>event_Save Settings_monthly_avg</th>\n",
       "      <th>event_Help_monthly_avg</th>\n",
       "      <th>event_Settings_daily_avg</th>\n",
       "      <th>event_Add Friend_daily_avg</th>\n",
       "      <th>event_Thumbs Down_daily_avg</th>\n",
       "      <th>event_Downgrade_daily_avg</th>\n",
       "      <th>event_Submit Upgrade_daily_avg</th>\n",
       "      <th>event_Roll Advert_daily_avg</th>\n",
       "      <th>event_NextSong_daily_avg</th>\n",
       "      <th>event_Error_daily_avg</th>\n",
       "      <th>event_About_daily_avg</th>\n",
       "      <th>event_Upgrade_daily_avg</th>\n",
       "      <th>event_Add to Playlist_daily_avg</th>\n",
       "      <th>event_Home_daily_avg</th>\n",
       "      <th>event_Thumbs Up_daily_avg</th>\n",
       "      <th>event_Logout_daily_avg</th>\n",
       "      <th>event_Submit Downgrade_daily_avg</th>\n",
       "      <th>event_Save Settings_daily_avg</th>\n",
       "      <th>event_Help_daily_avg</th>\n",
       "      <th>avg_length</th>\n",
       "      <th>max_length</th>\n",
       "      <th>current_charging</th>\n",
       "      <th>target</th>\n",
       "      <th>most_frequent_browser</th>\n",
       "      <th>most_frequent_os</th>\n",
       "      <th>tenure_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>5.398750</td>\n",
       "      <td>3.571204</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>237.179840</td>\n",
       "      <td>655.77751</td>\n",
       "      <td>paid</td>\n",
       "      <td>0</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>Windows</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>3.512280</td>\n",
       "      <td>3.037352</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>61.153846</td>\n",
       "      <td>6.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>953.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.842105</td>\n",
       "      <td>54.485714</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>3.214286</td>\n",
       "      <td>3.461538</td>\n",
       "      <td>1.722222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>248.499650</td>\n",
       "      <td>2520.99873</td>\n",
       "      <td>free</td>\n",
       "      <td>0</td>\n",
       "      <td>Safari</td>\n",
       "      <td>MacOS</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>M</td>\n",
       "      <td>1.473993</td>\n",
       "      <td>1.120486</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>28.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>124.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.625000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>247.345358</td>\n",
       "      <td>562.20689</td>\n",
       "      <td>free</td>\n",
       "      <td>0</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Windows</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NH</td>\n",
       "      <td>M</td>\n",
       "      <td>4.636167</td>\n",
       "      <td>4.166661</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1.320000</td>\n",
       "      <td>81.185185</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>1.909091</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>74.250000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>3.538462</td>\n",
       "      <td>3.304348</td>\n",
       "      <td>5.933333</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>248.498211</td>\n",
       "      <td>978.44200</td>\n",
       "      <td>paid</td>\n",
       "      <td>0</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>Linux</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WA</td>\n",
       "      <td>M</td>\n",
       "      <td>1.733611</td>\n",
       "      <td>2.147037</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.130818</td>\n",
       "      <td>472.89424</td>\n",
       "      <td>free</td>\n",
       "      <td>0</td>\n",
       "      <td>Safari</td>\n",
       "      <td>MacOS</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location gender  avg_duration_monthly  avg_duration_daily  session_count_monthly  session_count_daily  avg_item_per_session  event_Settings_monthly_avg  event_Add Friend_monthly_avg  event_Thumbs Down_monthly_avg  event_Downgrade_monthly_avg  event_Submit Upgrade_monthly_avg  event_Roll Advert_monthly_avg  event_NextSong_monthly_avg  event_Error_monthly_avg  event_About_monthly_avg  event_Upgrade_monthly_avg  event_Add to Playlist_monthly_avg  event_Home_monthly_avg  event_Thumbs Up_monthly_avg  event_Logout_monthly_avg  event_Submit Downgrade_monthly_avg  event_Save Settings_monthly_avg  event_Help_monthly_avg  event_Settings_daily_avg  event_Add Friend_daily_avg  event_Thumbs Down_daily_avg  event_Downgrade_daily_avg  event_Submit Upgrade_daily_avg  event_Roll Advert_daily_avg  event_NextSong_daily_avg  event_Error_daily_avg  event_About_daily_avg  event_Upgrade_daily_avg  event_Add to Playlist_daily_avg  event_Home_daily_avg  event_Thumbs Up_daily_avg  event_Logout_daily_avg  event_Submit Downgrade_daily_avg  event_Save Settings_daily_avg  event_Help_daily_avg  avg_length  max_length current_charging  target most_frequent_browser most_frequent_os  tenure_days\n",
       "0       CA      M              5.398750            3.571204                    2.0             1.000000             85.500000                         1.0                           8.0                            0.0                          0.0                               1.0                            0.0                       129.0                      0.0                      1.0                        2.0                                4.0                    10.0                          7.0                       3.0                                 0.0                              0.0                     0.0                  1.000000                    2.666667                     0.000000                   0.000000                             1.0                     0.000000                 43.000000                   0.00                   1.00                 2.000000                         2.000000              3.333333                   3.500000                3.000000                               0.0                            0.0              0.000000  237.179840   655.77751             paid       0               Firefox          Windows           46\n",
       "1       CA      M              3.512280            3.037352                   19.5             1.285714             61.153846                         6.5                          20.0                            8.5                          4.5                               1.0                           36.5                       953.5                      5.0                      2.5                        2.0                               24.0                    45.0                         45.0                      15.5                                 1.0                              1.0                     7.0                  1.083333                    2.857143                     1.416667                   1.125000                             1.0                     3.842105                 54.485714                   1.25                   1.25                 1.333333                         2.285714              3.214286                   3.461538                1.722222                               1.0                            1.0              1.555556  248.499650  2520.99873             free       0                Safari            MacOS           76\n",
       "2       FL      M              1.473993            1.120486                    6.0             1.083333             28.666667                         0.0                           3.0                            1.0                          0.0                               0.0                           18.5                       124.5                      0.0                      0.0                        2.0                                3.0                     9.5                          4.5                       4.5                                 0.0                              0.0                     1.5                  0.000000                    1.500000                     1.000000                   0.000000                             0.0                     4.625000                 24.900000                   0.00                   0.00                 2.000000                         3.000000              1.900000                   1.285714                1.125000                               0.0                            0.0              1.500000  247.345358   562.20689             free       0                Chrome          Windows          182\n",
       "3       NH      M              4.636167            4.166661                   13.5             1.320000             81.185185                         7.0                          18.0                            7.5                         10.5                               1.0                           14.0                       891.0                      1.0                      3.0                        3.5                               23.0                    38.0                         44.5                      12.0                                 1.0                              1.5                     4.0                  2.000000                    2.400000                     1.875000                   1.909091                             1.0                     3.111111                 74.250000                   1.00                   1.00                 1.400000                         3.538462              3.304348                   5.933333                1.500000                               1.0                            1.0              1.600000  248.498211   978.44200             paid       0               Firefox            Linux           71\n",
       "4       WA      M              1.733611            2.147037                    1.5             1.000000             40.000000                         0.0                           0.0                            2.0                          0.0                               0.0                            7.0                        49.5                      0.0                      0.0                        1.0                                2.0                     1.5                          2.5                       1.0                                 0.0                              0.0                     0.0                  0.000000                    0.000000                     1.000000                   0.000000                             0.0                     3.500000                 33.000000                   0.00                   0.00                 1.000000                         2.000000              1.000000                   2.500000                1.000000                               0.0                            0.0              0.000000  240.130818   472.89424             free       0                Safari            MacOS           72"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pandas_ready.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate churn rate in all dataset\n",
    "churn_props = df_pandas_ready['target'].value_counts(normalize=True)*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate proportion and counts for each categorical variables\n",
    "os_count, os_prop = groupped_dfs(df_pandas_ready,'most_frequent_os')\n",
    "browser_count, browser_prop = groupped_dfs(df_pandas_ready,'most_frequent_browser')\n",
    "gender_count, gender_prop = groupped_dfs(df_pandas_ready,'gender')\n",
    "location_count, location_prop = groupped_dfs(df_pandas_ready,'location')\n",
    "level_count, level_prop = groupped_dfs(df_pandas_ready,'current_charging')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Churn Distribution\")\n",
    "sns.barplot(x=['Not Churn','Churn'], y=churn_props);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Churn Rate is around 20%. It is imbalanced data. In this case it is hard to predict potential churners and we know that we won't use accuracy metric for prediction. It is good insight for this case\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3eK6bjXl036q6UzIMLJ3kqCQfHXt3vCNDW+cBc9utFkjtTPvm+8bnWyX56EKPsq8+MowJmQwXI1cLcjai3bOjdrQd+K8ZelZ9TZK/q6pfrar7bdRtb3P+YdYzfBetNS7WbN2e2ga8dLU7A8bg8ubq/8+zXn5LrNYG/PsMF9a/LsP3+5SqOqaqdub/C9xCCaRgOvO9onbkKtDN9qzq7o9094u6+1HdffcM//CfkmTWhf0hWZmxb3c6bO7ndfUC6+6/SfKrGbp5/0CSVye5rKourao/qKoHrLmD9VutAbsjLlvn+s24XXIts/qsWv/u/lJWeuOtVv+17vGfDZZ6o5l1uvvVSX4nQ6P8RzKME3J5VX2sql5eVd9xM3UH4Baqu7/U3R/s7p/JcH742iRnV9XBq2yy1rl8du76/Oz28FXMLh4tnuv+OSsDZM/aHrPeUm9feH5gklTVPbMynMKNAqmdbN/MAqv9cuPeZIuP+Qs7B6xynBvR7lm3MWSYjSO53jbg9RnaBp/M8DmekWFYi+1V9daq+vlxnKldtVGfxVrtwL22DTha7e9iZj1twBtNmDbefvv4DH9X980wXthHkmyrqr+oqh+vhRkZ2fcIpGA6F839/O07sN23jc9XZiVgWlN3f6a7X5nh3u3ZSfhJE9y6961zP//rqqUWdPdsVpZnZLjd8HMZBs9+QpK31zBF7a6esDai2/qmzmS4AdZb/w09zu5+eobxQZ6d5LwMXbiPTvJfk1w4zswDwL5tNqDyIUlOWqXMes7lO3WuG285u2B8+cCF56WB1Nzzx5YNC7AT7ZtZz6CLF3uTrfHYuvTgJrxdb3RMhkAx2bE24IfGbR+TYcDtD2foOfegDLeSXbzGUALrNfVnsSfarDbg+Rn+Bn4yQ0/Ij2X4G394kj9O8oFxlk32UQIpmM47sjLF7GPWs0FVHZiV8YjePY71s27d/dms3Nd9WIbxGnanWQPyU6s1kFYzDnD90u7+oe6+U4b70P/nuPrkJD+/cdXcaXe7mfWzE+rilbjZ97bWbZOLs9NtpFl9jlitwHhL56w33uIUyrusuz/e3S/s7pPG9/meDI3zJHlaVT1io98TgL3K/EW39c7UO292rttSVV+7RrnZuXzZuW4xcHpAhv+gX5AMg4on+USSe4+z683KLd6u91U72L75zPh8z71wzJ2Hzf18wY5sOA46/4ZxwO1vztBePSXD2KRHZAgy9gRrBSd7bRtwtNbfxS4ZB0//4+5+Qnd//fhev5phQpxZzyn2UQIpmMg4k8csHPqRqvqGdWz2jAyzjyTDVaKdMX+/91pd2HdJVZ2cYbaUZJhqeJd09z91989mZQyExYHCZ+HelPehH1tVBy1bUVVHZ+VkfuHC6tkgn0sbA2PPtbXGHNjVY53V54Q1yhyfla7W/7CT77MuPcyw974MDfHZmGd72kDwAExr/qLPVTux/exct3+S49Yo96Dxedm5bhYs3auqHpzhFrp/nM0qNpoPrY5f2O5m3Uz7Zrbs1pmbBXBPV1WHJnna+PJfM8xyuNO6+wtjT/9fHRd9W1XND2GxGW3A5MZjh622bofagKPvWmPdRrUB71ZVX7+swDhm16z+u7UNmCTdfVl3vzgrA/drA+7DBFIwrdMyzNT2tUlet9bgzlV1Ylam831HkjctrP/+m7uvfuxhNZt+9pO99lTKO62qjsvK1b7PZGVq5PVsu9ZVzGT4vJKbdreeDS5+6HrfawPcNskvrbJu9l39R1YGTJ350Pj8Q6sM5PhTWbv31a4e62vH5++pqocsrqyq/ZP82vjyw9394Z18n5tY6/sdbyeYDTKqOz3Avu1H535e/E/9zeruf8zK8AjPWTYwdlWdlJX//L9myT4+mpUBmn9jfF4cyHoWPv3XrMwge8GS99qZ9s2FGcZQSpIXLMzmdxNVtd7Z7HabsQ5/lpV2zP+73h79O/AZJTf+nDajDZgkv7xskqBxPLDvHV/+6cLqWRvwLlX13Uu2vWOSn13jPXf1WN+alTG9Tl+lzM9lZfyym/xd7KxdaOOzDxFIwYS6+5+T/EyGf3i/OcN9008arywlSarq66vqzCR/keEK2SeS/Og4tsG8pyW5pKpeVlUPmh8AtKoOrqofzjAF7WywzbWmD95hVXV4VZ1YVa9O8rYM3Y23J3nYDgZff15Vvz/ua/5zOLyqnpOVXj1vXthuFpqcNOG951ckOa2qnjXrKVVVd6iq38kQKiXDVNNfWthudnL/xiSvml3lG7+nZyR5RYYgazWzYz25qg5bo9xq/iwrM+SdU1U/OhuzoqruMa7/nnH9f9uJ/a/l76rqd6vq+PnbD6rqLlX1sgxjSSU3/X4B2AdU1Z2r6vlZOY++L8l7d3J3sx4135/k9eM5LlX1NVX1Y1k5H78nK7eNL5oFTrPgajGQevvC+o9092dyUzvcvhnbeqdk6NF+ZIZz6MnzFyCr6q7jYNBvTfKiVY5htxo/z++oql/LEALObl18fncvBjJr+ZGq+tuq+rlxgPjZ/verqodmGOQ8Sd670LactYtm7d2pfF2SN83ucqiq/cc7BF4/rv8/Sd6wsM17snI76tlVdWwNblVVx2cIM9f6P/nsWO9bVfff0Qp39zVZCaIeX1WvqJVZJA+oqqdm5ULyn3b3+3f0PdbwqzXMMPkT4y2uGd/3a8fv7VfGRdqA+7Lu9vDwmPiRYbaVyzKMSzB7fDHDlYL5ZW9JsmWVfbxmoWxnCIT+c2HZ9RkaLLUT9Zzt48oMPZ8+k+SzS+p5Q4YeXEeusa8LxrKnr7J89rhifMwve12SWy1sd++5elw/1m3r+LjbkmM4/maO9fSx3AVL1p09rjs7Q2+jzjAmwH+Mxz57jz9crOfcPv5w4Zi2jfXuDPfOf/U9lmz7X+be5ysZrt5uTbJ1le/rJseaYWyDD8+V+fJYh/nfk19cpe5bxzJPWOPzW1r/uW1nvyfbxt+n+c/izM3+m/Tw8PDw2D2PufNrz7UlZo8vLpwP/jHJXdbYxwXreL9nLJybt43nvDXfY277J82V/UqSg5eU+chcmZevsp8LFo5tXe2bcdsHZ5j5dr4en89wK+P89v9jYbvjZ+s24HubnddnbazZ44qFz7czBC6PWmNfTxjLbV1l+ewxm/H3+rlllyU5Zsk+z58rsz0rbcCnLzmGs2/mWI+a29dRq32mSR6ZoWf3rN3+pYXP4B6r7P+hc9v1+D3O2rD/kmGmwaXfW4ZbUC+e2/Y/5o715PUea5Iz5/Zxw7if6+aWvT3JQTvzt7fa711u/LffSa7O0Ftr/vfnoiR33h3/9njsHY8bTc0ITKO7/2occ+gJGWaZ+JYMU/hem2FMnXcneU13v22N3fxEhtlIHpzkOzPMYrYlwwwt25J8PMM9/Gf30I19V9xufCTDyWt7kkszBBwXJjmnuz++k/t+apITM4z3cO8kd84w8OOnx33/YXcvXm1Kd39s7CL9rAxXKW+flTGQdue/bY/P0Mj86Qyf+VVJ/inJ/9fdf7zGdk9K8v4kTxy3u1WGsSLO6u5zqurs1Tbs7ndV1cOSnJphhsY7ZQd7uHb3ZVV1bIbBU384Q2+tAzJ8jxdkCIU+uCP7XKcfSfKQDKHaPTJ8v/tnaLi9L8mrunvx6jMAt0x3Wnh9XYaQ40MZepn8UXdfe5OtdkB3v6Sq3pkhmDpufM9rMvReOSfD+XqxJ/O8d8z9fGF3b19S5u0ZZoZbLD9vp9o34zG8dWwnnpJhsPD7ZLhl65oM/4F/b4ZxSReHCNgdbpWV760zXFS6LEMP/v+T5K+SvLW7b1i++Zr+IsPsaw/I0L75uiSHZ7i4+tEk52ZoJy3reX9yhuEGHpahN9ndx+W77Ta+7n7j2EvpmUm+L8NQDp/M0CvqBd29bZXt3lJV359heIfvzdD+uiRDD/X/nuQ71njPr1TVCRnCnQdluLVu1lv+wB2o+6lVdW6SXxjrcPsMn/MHM8x290e98TMzvirD78oDMtwZ8nUZ7qjYluSfMxz/K2/m75FbuOohvQQAAACASRhDCgAAAIBJCaQAAAAAmJRACgAAAIBJCaQAAAAAmJRACgAAAIBJ7c6p0fcqd7jDHfqoo47a7GoAALvJ+9///s9395bNrgcrtL8A4JZvtTaYQGp01FFH5cILL9zsagAAu0lVfWqz68CNaX8BwC3fam0wt+wBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMCmBFAAAAACTEkgBAAAAMKn9N7sCt2RHPfNNm12Fddl6xsM2uwoAAJPbW9pqu4s2IACbSQ8pAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUgIpAAAAACYlkAIAAABgUpsaSFXVY6vqL6rqsqq6sqreX1WPXyhTVfXsqrq0qq6pqndV1f2W7Os+VfW2qrq6qj5dVc+rqv2mOxoAAAAA1mOze0idmuTKJM9I8ogk70jy6qp66lyZZyY5LcmLkjx8LH9+Vd15VqCqDktyfpJO8sgkz0vyS0l+fYJjAAAAAGAH7L/J7//w7v783Ou3V9VdMgRVL6uq22QIpF7Y3WclSVW9N8nWJE9J8pxxu1OS3DbJo7t7e5K3VtXBSU6vqhePywAAAADYA2xqD6mFMGrmA0nuOP58/yQHJzlnbpurkpyb5MS5bU5M8paF4Om1GUKq4zayzgAAAADsms2+ZW+Z+ye5aPz5mCTXJ/nYQpmPjOsyV+7i+QLdfUmSqxfKAQAAALDJ9qhAqqpOyDAG1MvHRYclubK7r18oui3JAVV167lyX1yyy23jOgAAAAD2EHtMIFVVRyV5dZI3dvfZc6t6WfEl61Yrt2z57D2fXFUXVtWFl19++Q7VFwAAAICds0cEUlV1eJLzklyS5MfnVm1LclBV7bewyaFJru7u6+bKHbpk14dkec+pJEl3v6q7j+3uY7ds2bLT9QcAAABg/TY9kKqqA5L8ZZJbJ3nYOGj5zMVJ9kty9MJmi2NGXZyFsaKq6ogkt1soBwAAAMAm29RAqqr2T/K6JPdOcmJ3f26hyHuSbE/y2LltDkjy8Aw9qmbOS/LQqjpobtnjklyT5J27oeoAAAAA7KT9N/n9fy/JSUmeluTwqvruuXUf6O4vVdUZSU6rqm0ZejudmiFIe9lc2Vck+cUkb6iqFyW5Z5LTk5zZ3dt3/2EAAAAAsF6bHUg9ZHz+nSXr7pFka5IzMgRQz0py+yQXJnlwd392VrC7t40z9J2V5NwM40a9JEMoBQAAAMAeZFMDqe4+ah1lOskLxsda5S5K8sCNqRkAAAAAu8umD2oOAMC0quqxVfUXVXVZVV1ZVe+vqscvlKmqenZVXVpV11TVu6rqfkv2dZ+qeltVXV1Vn66q5y2ZIRkA4EYEUgAA+55Tk1yZ5BlJHpHkHUleXVVPnSvzzCSnJXlRhgllrkxyflXdeVagqg5Lcn6STvLIJM9L8ktJfn2CYwAA9mKbPYYUAADTe3h3f37u9dur6i4ZgqqXVdVtMgRSL+zus5Kkqt6bYXzPpyR5zrjdKUlum+TR40Qyb62qg5OcXlUvNrkMALAaPaQAAPYxC2HUzAeS3HH8+f5JDk5yztw2V2WYPObEuW1OTPKWheDptRlCquM2ss4AwC2LQAoAgAEX9M4AACAASURBVGQIoS4afz4myfVJPrZQ5iPjusyVu3i+QHdfkuTqhXIAADcikAIA2MdV1QkZxoB6+bjosCRXdvf1C0W3JTmgqm49V+6LS3a5bVwHALCUQAoAYB9WVUcleXWSN3b32XOrelnxJetWK7dsearqyVV1YVVdePnll+9wfQGAWwaBFADAPqqqDk9yXpJLkvz43KptSQ6qqv0WNjk0ydXdfd1cuUOX7PqQLO85le5+VXcf293HbtmyZZfqDwDsvQRSAAD7oKo6IMlfJrl1koeNg5bPXJxkvyRHL2y2OGbUxVkYK6qqjkhyu4VyAAA3IpACANjHVNX+SV6X5N5JTuzuzy0UeU+S7UkeO7fNAUkenqFH1cx5SR5aVQfNLXtckmuSvHM3VB0AuIXYf7MrAADA5H4vyUlJnpbk8Kr67rl1H+juL1XVGUlOq6ptGXo7nZrhYubL5sq+IskvJnlDVb0oyT2TnJ7kzO7evvsPAwDYWwmkAAD2PQ8Zn39nybp7JNma5IwMAdSzktw+yYVJHtzdn50V7O5t4wx9ZyU5N8O4US/JEEoBAKxKIAUAsI/p7qPWUaaTvGB8rFXuoiQP3JiaAQD7CmNIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADApgRQAAAAAkxJIAQAAADCp/Te7ArBeRz3zTZtdhXXbesbDNrsKAAAAsMfSQwoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJjUpgdSVXV0Vb2yqj5UVddX1QVLymytql54fGZJuftU1duq6uqq+nRVPa+q9pvkQAAAAABYl/03uwJJ7pvkpCTvS3LrNcq9OsnL5l5fO7+yqg5Lcn6Si5I8Msm9kvx2htDtORtYXwAAAAB2wZ4QSJ3b3W9Mkqp6fZI7rFLu37v7fWvs55Qkt03y6O7enuStVXVwktOr6sXjMgAAAAA22abfstfdN2zQrk5M8paF4Om1GUKq4zboPQAAAADYRZseSO2AJ1XVtVV1RVW9vqruvrD+mCQXzy/o7kuSXD2uAwAAAGAPsCfcsrceb8wwxtS/JfnGJM9N8u6q+ubuvmIsc1iSLy7Zdtu4DgAAAIA9wF4RSHX30+Zevruq3pPkg0memOSl80WXbF6rLE9VPTnJk5PkyCOP3JjKAgAAALCmvemWva/q7g8n+WiSb59bvC3JoUuKH5LlPafS3a/q7mO7+9gtW7ZsfEUBAAAAuIm9MpCaM9/z6eIsjBVVVUckuV0WxpYCAAAAYPPslYFUVX1Tkm9I8v65xecleWhVHTS37HFJrknyzgmrBwAAAMAaNj2QqqoDqurkqjo5yV2TbJm9Htc9rKpeU1U/VlUPqKqfT/KWJJckOXtuV69I8uUkb6iqB43jQ52e5Mzu3j7tUQEA7Nmq6uiqemVVfaiqrq+qC5aU2VpVvfD4zJJy96mqt1XV1VX16ap6XlXtN8mBAAB7pT1hUPM7JnndwrLZ63skuXQs89IMY0R9IclfJXn2fNDU3duq6oQkZyU5N8O4US/JEEoBAHBj901yUoaZjG+9RrlXJ3nZ3Otr51dW1WFJzk9yUZJHJrlXkt/OcOHzORtYXwDgFmTTA6nu3pphJry1nLDOfV2U5IG7WicAgH3Aud39xiSpqtcnucMq5f69u9+3xn5OSXLbJI8eLxa+taoOTnJ6Vb1YT3UAYJlNv2UPAIDpdfcNG7SrE5O8ZSF4em2GkOq4DXoPAOAWRiAFAMBanlRV11bVFVX1+qq6+8L6Y7Iwo3F3X5Lk6izMgAwAMLPpt+wBALDHemOGMab+Lck3JnlukndX1Td39xVjmcMyjN25aNu47kbGiWeenCRHHnnk7qgzALAX0EMKAICluvtp3f2a7n53d78qyUOT3CXJExeLLtm8li3v7ld197HdfeyWLVs2vtIAwF5BIAUAwLp094eTfDTJt88t3pZhJuRFh2R5zykAAIEUAAA7bL7n08VZGCuqqo5IcrssjC0FADAjkAIAYF2q6puSfEOS988tPi/JQ6vqoLllj0tyTZJ3Tlg9AGAvYlBzAIB9UFUdkOSk8eVdkxxcVSePr9+c5AFJfjzJXyb5dIZeUM9JckmSs+d29Yokv5jkDVX1oiT3THJ6kjO7e/vuPQoAYG8lkAIA2DfdMcnrFpbNXt8jyaVjmZdmGCPqC0n+Ksmz54Om7t5WVSckOSvJuRnGjXpJhlAKAGApgRQAwD6ou7dmmAlvLSesc18XJXngrtYJANh3GEMKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgElteiBVVUdX1Sur6kNVdX1VXbCkTFXVs6vq0qq6pqreVVX3W1LuPlX1tqq6uqo+XVXPq6r9JjkQAAAAANZl0wOpJPdNclKSfxkfyzwzyWlJXpTk4UmuTHJ+Vd15VqCqDktyfpJO8sgkz0vyS0l+fbfVHAAAAIAdticEUud29xHd/dgk/7y4sqpukyGQemF3n9Xd5yd5bIbg6SlzRU9Jctskj+7ut3b3KzKEUadW1cG7/SgAAAAAWJdND6S6+4abKXL/JAcnOWdum6uSnJvkxLlyJyZ5S3dvn1v22gwh1XEbU1sAAAAAdtWmB1LrcEyS65N8bGH5R8Z18+Uuni/Q3ZckuXqhHAAAAACbaG8IpA5LcmV3X7+wfFuSA6rq1nPlvrhk+23jOgAAAAD2AHtDIJUM40UtqiXrViu3bHmq6slVdWFVXXj55ZfvYhUBAAAAWI+9IZDaluSgqtpvYfmhSa7u7uvmyh26ZPtDsrznVLr7Vd19bHcfu2XLlg2rMADAnq6qjq6qV1bVh6rq+qq6YEmZqqpnV9WlVXVNVb2rqu63pNx9quptVXV1VX26qp63pO0GAPBVe0MgdXGS/ZIcvbB8ccyoi7MwVlRVHZHkdgvlAABI7pvkpCT/Mj6WeWaS05K8KMnDk1yZ5PyquvOsQFUdluT8DD3SH5nkeUl+KcNsxwAAS+0NgdR7kmxP8tjZgqo6IEOj6Ly5cucleWhVHTS37HFJrknyzgnqCQCwNzm3u4/o7scm+efFlVV1mwyB1Au7+6zuPj9De6yTPGWu6CkZZjV+dHe/tbtfkSGMOrWqDt7tRwEA7JU2PZCqqgOq6uSqOjnJXZNsmb2uqgO6+0tJzkjy7Kr6hao6IcnrMtT9ZXO7ekWSLyd5Q1U9qKqenOT0JGd29/ZJDwoAYA/X3TfcTJH7Jzk4yTlz21yV5NwkJ86VOzHJWxbaW6/NEFIdtzG1BQBuafbf7AokuWOGgGne7PU9kmzNEEjdKsmzktw+yYVJHtzdn51t0N3bxrDqrAwNpS8meUmGUAoAgB1zTJLrk3xsYflHMvRCny/39vkC3X1JVV09rjt3d1YSANg7bXog1d1bszJj3mplOskLxsda5S5K8sANqxwAwL7rsCRXdvf1C8u3JTmgqm7d3deO5ZZNILNtXHcjYy/2JyfJkUceubE1BgD2Gpt+yx4AAHusXrKslqxbrdxNlpvlGABI9oAeUgAA7JG2JTmoqvZb6CV1aJKru/u6uXKHLtn+kCzvOQWb7qhnvmmzq7Bptp7xsM2uAkASPaQAAFju4iT7JTl6Yfkx47r5csfMF6iqI5LcbqEcAMBXCaQAAFjmPUm2J3nsbEFVHZDk4UnOmyt3XpKHVtVBc8sel+SaJO+coJ4AwF7ILXsAAPugMVw6aXx51yQHV9XJ4+s3d/fVVXVGktOqaluG3k6nZrig+bK5Xb0iyS8meUNVvSjJPTPMcnxmd2/f/UcCAOyNBFIAAPumOyZ53cKy2et7JNma5IwMAdSzktw+yYVJHtzdn51t0N3bquqEJGclOTfDuFEvyRBKAQAsJZACANgHdffWrMyYt1qZTvKC8bFWuYuSPHDDKgcA3OIZQwoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASQmkAAAAAJiUQAoAAACASe2/2RUAAACAKRz1zDdtdhU2zdYzHrbZVYAb0UMKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACYlEAKAAAAgEkJpAAAAACY1F4RSFXVE6qqlzxOmStTVfXsqrq0qq6pqndV1f02s94AAAAA3NT+m12BHfTAJNfMvf7E3M/PTHJakl9JcnGSU5OcX1Xf1N2fma6KAAAAAKxlbwuk/qG7r1xcWFW3yRBIvbC7zxqXvTfJ1iRPSfKcKSsJAAAAwOr2ilv21uH+SQ5Ocs5sQXdfleTcJCduVqUAgP/L3p3HyVWV+R//POwg+6ZggKCobCpmURBE0WHYJCggizMyiCOOwujAiAI/UdwwIy4zCqiMgDI64IasAiKgrAoBwVEWEVkCKgQEMYSwhOf3x7ltOpXqLXTdW1X9eb9e9aLq3tvJN91N96nnnvMcSZIkaXG9VpC6MyKeiYjbI+I9g45vCiwA7mi5/tbqnCRJksbIPp6SJKlTemXJ3h8p/aGuA5YG9ge+GhErZeYXgTWAuZm5oOXjHgFWiojlMvOpWhNLkiT1D/t4SpKkcdUTBanMvBi4eNChCyNieeAjEfFfA5e1+dAY5hwRcTBwMMCGG244TmklSZL6jn08JUnSuOq1JXuDfR9YE5hMmQm1SkQs3XLN6sC8zHy63R+QmSdn5rTMnLbOOut0NKwkSVIfso+nJElaIr1ckBqQlOnhSwObtJzbtDonSZKkJWcfT0mSNK56uSC1F/AQcA9wDfAY8LaBkxGxErA7cGEj6SRJknrfQB/Pd1DGVb+g9PE8rDo/Yh/P1j8wIg6OiFkRMWvOnDkdjC5JkrpZT/SQiogfUBqa/4oyE2rf6vH+zHwWmB8RM4FjIuIRFjbUXAr4cjOpJUmSelsn+nhm5snAyQDTpk1r2+dTkiT1v54oSAG3AwcBG1AGOLcAB2Tm/wy6ZialAHUUsBYwC9gxMx+oOaskSVI/+z6wDy19PFtmSQ3bx1OSJKknClKZeTRw9AjXJPDp6iFJkqTOau3jefugc/bxlCRJw+rlHlKSJEmqn308JUnSc9YTM6QkSZJUP/t4SpKkTrEgJUmSpKHYx1OSJHWEBSlJkiS1ZR9PSZLUKfaQkiRJkiRJUq0sSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtbIgJUmSJEmSpFpZkJIkSZIkSVKtLEhJkiRJkiSpVhakJEmSJEmSVCsLUpIkSZIkSaqVBSlJkiRJkiTVyoKUJEmSJEmSamVBSpIkSZIkSbWyICVJkiRJkqRaWZCSJEmSJElSrSxISZIkSZIkqVYWpCRJkiRJklQrC1KSJEmSJEmqlQUpSZIkSZIk1cqClCRJkiRJkmplQUqSJEmSJEm1siAlSZIkSZKkWlmQkiRJkiRJUq0sSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtVqm6QCSJEmSJEmdNPnIC5qO0Ji7Z+7WdIS2nCElSZIkSZKkWlmQkiRJkiRJUq0sSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtbIgJUmSJEmSpFpZkJIkSZIkSVKtLEhJkiRJkiSpVhakJEmSJEmSVCsLUpIkSZIkSaqVBSlJkiRJkiTVyoKUJEmSJEmSamVBSpIkSZIkSbWyICVJkiRJkqRaWZCSJEmSJElSrSxISZIkSZIkqVYWpCRJkiRJklQrC1KSJEmSJEmqlQUpSZIkSZIk1cqClCRJkiRJkmplQUqSJEmSJEm1siAlSZIkSZKkWi3TdABJzZl85AVNRxiVu2fu1nQESZIkSdI4coaUJEmSJEmSamVBSpIkSZIkSbWyICVJkiRJkqRaWZCSJEmSJElSrSxISZIkSZIkqVYWpCRJkiRJklQrC1KSJEmSJEmq1TJNB5CkfjH5yAuajjBqd8/crekIkiRJkiYwZ0hJkiRJkiSpVhakJEmSJEmSVCsLUpIkSZIkSaqVPaQkSV2rV/py2ZNLkiRJGhtnSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtVqm6QCSJKk+k4+8oOkIo3L3zN2ajiBJkqQOcoaUJEmSJEmSamVBSpIkSZIkSbWyICVJkiRJkqRaWZCSJEmSJElSrSxISZIkSZIkqVYWpCRJkiRJklQrC1KSJEmSJEmqlQUpSZIkSZIk1cqClCRJkiRJkmplQUqSJEmSJEm1siAlSZIkSZKkWlmQkiRJkiRJUq36qiAVEZtHxKURMS8i/hARn4iIpZvOJUmS1M8cg0mSpLFapukA4yUi1gB+AtwC7AG8GPg8pej2kQajSZIk9S3HYJIkaUn0TUEK+BdgRWDPzHwMuCQiVgWOjYjPVsckSZI0vhyDSZKkMeunJXu7ABe3DHrOpAyQXt9MJEmSpL7nGEySJI1ZPxWkNgVuG3wgM+8F5lXnJEmSNP4cg0mSpDHrp4LUGsCjbY4/Up2TJEnS+HMMJkmSxiwys+kM4yIingY+mJn/1XL8fuAbmfn/2nzMwcDB1cuXAbd3POhztzbwUNMh+oyf0/Hl53P8+TkdX34+x1+vfE43ysx1mg7Rb8Y6BuvR8Ven9Mr/Oxp/fu0nJr/uE9dE/9q3HYP1U1PzR4DV2xxfjfZ37cjMk4GTOxlqvEXErMyc1nSOfuLndHz5+Rx/fk7Hl5/P8efndMIb0xisF8dfneL/OxOXX/uJya/7xOXXvr1+WrJ3Gy19CiJiA+B5tPQ1kCRJ0rhxDCZJksasnwpSFwI7RcQqg47tCzwB/KyZSJIkSX3PMZgkSRqzfipIfRV4EjgrIv6u6k9wLPCFlm2Ie51T3Mefn9Px5edz/Pk5HV9+Psefn9OJbaKMwTrB/3cmLr/2E5Nf94nLr30bfdPUHCAiNgdOALah9Cz4OnBsZi5oNJgkSVIfcwwmSZLGqq8KUpIkSZIkSep+/bRkT5IkSZIkST3AgpQkSZIkSZJqZUFKkiRJkiRJtVqm6QAaWkSsCzwvM++qXgfwbmBz4NLMPK/JfNJg1ffnJGAD4ObMfLzhSNKoRESkDRUl1SQi1gC2pPy+vDAzH4mIFYCnMvPZZtOpUyJihcycP8z59TPzD3VmUmdExPuA72XmnOr5cDIzv1JHLtUrIg4ALsjMh9ucWxN4c2aeXn+y7mJT8y4WET8CfpeZ769efwI4GvgdsAnwz5n5jeYS9o5q959XZOaZLcd3AT4FbAo8AHwpM/+zgYg9rfpl+xHgBUAC0zPzxog4C7jCz+noRcR3R7omM/epI0u/iIjTgUPbbT8fES8FvpmZ29SfrLdExPZjuT4zr+hUFqkXRcTSwGeAQ4AVWfT35QXArMz8WJMZ1TkRcRvwT5n5izbn/gn4YmauWX8yjbeIeBbYOjOvq54PJzNz6TpyqV4RsQDYJjOva3NuKnCdX3uX7HW7KcBlABGxFPBe4OjM3BT4NPBvDWbrNccABw0+UP0gOAdYG/gK8Gvg8xHxtvrj9a6IOAL4AvDfwBuBGHT6p8C+DcTqZeu0ebwMmAFsS/l+1djsAPwmInYafDAiDgNuApyRMDo/BS6v/jvw/PJBxy5veUha1HGUme6HAi9i0d+X5wC7NxFKtfktcFVEHBcRy0JZDRERZwOnAF9vNJ3GTWYuNVCEqJ4P95jwBYk+FsOcWwtY7EbpROSSve62GjAwxW8qsCbw7er1ZcC/NxGqR72GcldysA8AC4DXZea9ABHx9er49+qN19MOAT6amZ+t7v4Odjvw0gYy9azM3KHd8YjYAPgh8MV6E/WFLYAvAxdGxMmUAvQJwHTgY8DxDWbrJS8f9Hw94FTgIuAs4EFgXWAvYCdabgBIAuAA4MjMPK3N78s7KUUq9anMnBER76TcxNstIk6lzC5/hDIWvbbRgBp31VLcLwOnZObPm86jzouIPYA9Bh06JiLmtFy2AvA64PragnUxC1Ld7T5Kv6grgd2A2zLz/urcasCQ69C1mBcAd7Qc2xX42UAxqvID4Ju1peoPLwBuGOLcs5QfunqOMnN2RHwG+Cxg/7gxqJbq/VNE/AD4DmWGwm3AlMy8pdFwPSQzfzPwPCKOA07PzI+0XHZRRHyKMoP3J3Xmk3rA6pTCUzvLAc6U6HNVMfKXwLWUwtQvge2G6y2l3pWZ8yNiPxZOKFD/W5dFb+C9mPJeabCngB9T2sZMeC7Z626nAp+NiO8BHwJOHnRua+DWRlL1pkeBNQZeRMRmlBlnrT1OHgdWqjFXP/gd8Pohzm0P+IZ//CygNI7XGEXEJMoymaWAX1FmIuw07AdpOG8CfjbEuZ8Bb6gvitQzfs2id84H2wW4scYsakBEzAAuBP5Aman7CuA71UZG6k+XUVoHaGJ4Etg5M6dTxkN7Zub0lse2mfmugY3LJjpnSHWxzPxMRNxPWVbyr5QC1YA1ca35WPwCOCQizsvMZ4D3UJqJntty3WbA/a0frGH9J3BSRDwFfL86tm5EvAs4nDIbRaNUNeBvtRzle/OTOL13zCLiQMr36T3AqzPz5qp/1Kci4q3AgZn5+yYz9qA/U95YX9Lm3Fur85IW9SngBxGxIqU1QAJbVT+H3kPpFag+FRHfBN5BucH875n5eET8D3A6cEtEHJKZ32k0pDrhRODrEfE84EeUW+jNdQAAIABJREFUTZQW2VXM2dp95TTKxJGHKTfmV202Tvdzlz1NCBGxJWV69HzKbKkXA9/JzP1brvsZZWfDd9WfsndVjc0/SpldNtDAbx7w8cy0P88YVLuxtPvBHJRi1P4WT8YmIp6m9In6WGY+Pej4yyhLdLfIzFWayteLqp01T6AMrs9lYQ+pPSgzPQ7NzJOaSyh1p4jYh7L0esNBh++nFChG3GVVvSsiZgPvyswftxxfnrJZ0Qcyc9lGwqlj2uyyN3iMF7jLXl+JiIeAf87Ms6uv/Wsy05vJw7Ag1cUi4krKkrIrgWvabVmu0YuIF1Ma7a5GmRb/zcxcMOj8OpTd+L7VbntODS8iVgFeS9k14s/AtZn5l2ZT9Z6IaLf8cT5w36AechqDiHhNu222q3NLAR/KzJk1x+p5VePOo4FXUWZcP0PZtfC4zDy7yWxSt4uIl1J2Tf0zcHs6IO97EbHacOOiiHhtZl5TZyZ13hDjukVk5lBL4NVjIuJMSu/n24EplBY7jw91fWa+uqZoXcuCVBeLiC9TOvBvSamm/5pSnLoSuCoz/9hgPEkdEBEbAn8cPJNn0LllgPVbGvFLjaqKeusAczKz9U6wJKlS/R7fC9iO0n7jz5Rx/VlVSwlJPSwiVgLeC2wKvIsyk7x1l72/ycx31hSta1mQ6gERsRqlMLUdZS3qFGBZ4PeZ+ZIms/Wi6g3/dsALq0P3A1dm5uzmUvW2alvb7SkNt1t31cvM/Er9qXpTRCwAtmk3Sy8ipgLXObV7bKrlZcPxe1RSLSJifWB3yhik3e/LD9efSnWoGpf/mNLI/G5KL6HnA5OBm4G/z8wh37iq91U3cBbbfToz5zUQRx0WEXcBb8nMm5vO0s0sSPWI6g3/1pQ3/TsB2wB/zczVGg3WQyLiBcBJlKahrTtMPgucTel78qe6s/WyiNgOOIuy9KAd18aPQbXefOshClLbApdkpjtBjkGb/g2DJYDfo2MXEZOBfwReSvsB9j41R5K6WrX9+zcpfWPmULb+Hiwz80W1B1MtIuJblF2J9xzcUyYipgM/AH6Wme9oKp86IyKCslv6u4GN213jGEQTmbvsdbGIeDNlZtTrgKnAX4CrKDuzvB/4ZXPpektErEX53K0OfIJSfLqH8mZ0MqUR7/uBKyJi68x0h6jR+xJwJ7AjcEu7pWYaXkS8Athq0KFdI2LTlstWAPYBfltbsD6Rma0FaCJidUpx/8PA/ot9kIZVzdb7GTCbUpD6FaU/32TgPuB3jYWTutenKYWHf7Ev6IS0K+XG5yINjjPz+og4CvhyM7HUYe8HjqRsZvBpym6bC4D9KLsoH9dcNHVaRLwIOILFl+l+zk2KCmdIdbHqrv4TwCnAVzLz1oYj9ayI+E/KVuTbZOYfhrhmPcpOfGdl5uF15utlEfE45W7fxU1n6VUR8THgY9XLZOFOha3uAt6TmT+pJdgEEBEHA2/PzDc0naWXRMRllGLUQcDTwLTMvDEiXgucQfk+vajJjFK3iYhHgb0y89Kms6h+1Xhpv8w8r825GcD/ZubK9SdTJ0XEr4GTgRNZ9PflUsB5wP9l5pFNZlRnVDfvLqdsTnQ+C5fp7ka50bxDZt7YXMLuYEGqi0XEZyizo6YBcykzfK6oHjfaPHb0qjW8x2Xmf49w3cHAUZnZdkqtFhcR1wJfzcxvNp2lV0XEspS7ZAE8BrwRaN0i9ilnn42/iNgR+KFvAsYmIv5MmVn2Y8qd3u0GdoeKiIOA92fmVsP8EdKEExGnAvdn5jFNZ1H9IuJSYHlgp8x8fNDx51F+lj6RmX/XVD51RlWI3CUzr4iIJ6vnl1XndgO+npnrNRpSHRERl1PaxOwyuE9Y1fj8R8CzmfnGpvJ1C5fsdbHMPAogIpan9IzaDtgZOBbIiLgmM3dpLmFPWQ+4bRTX3Vpdq9F7L/CNiLjbbWuXTFVoGig2Lba8TJ1RzYr8d8rMM41NUoqkGREPAhsBA9uVzwbccENa3KHAKRHxdeAy4NHWCzLzR7WnUl3+nTJbYnZE/JgyW2JdyvLxAN7QXDR10MPAwE2ve4FXUf7/B1gDWLGJUKrFq4F9WpvWZ+a8iPgc8J1mYnUXC1I9IDOfjIibKD/MVqX88JoK/H2jwXrLQ5TeJleOcN3k6lqN3iXASsBlEfE0ZYbPIjJz3dpT9biIeCntdy30DcsYRcQcqublgywHrEKZRr1n7aF63y3Aiylvrq4FDouIWZQmzR+i9JWTtKiXUt6gbExZ7toqAZsb96nMvCkiXgJ8EJhO2W3vj8BXgS9kpuPP/nQ15ev9I+B/gWMjYk3K78tDAJfw9q8ngLWGOLcmZQw64VmQ6mLVbiwDTc03pwxU/o9SVJnJyMUVLXQx8KGIOGvwNOnBImJlyhupC2tN1vtOZPE3+1pCEbE55Y7J5rTvJeUblrFr9z06n9J8+6LMfLj+SD3vZEoBH+BoynKTgVmojwN7N5BJ6nanUW7a7EZp/N+6y576TER8lLIk6w8RsSHwR/sFTTjHAi+snh9H2WDpQMrMqEuAf20klepwATAzIn6fmVcNHKx2KP8MpYfYhGcPqS4WEfMpfWSurB5XuyvLkomIDYAbKDsbfBI4NzP/Wp1bGZgBHEOpVk/NzPuayqqJLSKupEzh/xBlFspib1gy8566c0nDqX6ObkMZYP88Mx9sOJLUddwEZOKJiAXA1tVOegsom+tc13QudVZEHABc4A2via3a5f0cyvhoDguX6a5LaXPwFr9HnCHV7VbLzCebDtEPMnN2ROwAfAv4H0oProHeDatTZqLcBLzRYtSSiYjlgJezcEvT/8tM7/6O3asou/Cc33QQqVVE7ErpGzcZ+BNwFmVTg7mUO72ShnYdsGHTIVSrOZQZz9dTxprOBJgYTqMUIR62EDlxVcWm7SJiZ8qyzfUoy3R/kZk/bjRcF7Eg1cUGilERsT7lh9rAG/1rM/MPTWbrRZn5G+BVEfF6YHtg/erU/cAVmXlFY+F6XER8CDiK0uNsYJnZXyLiuMw8vrlkPelO2vSN0pKrdjH8AKVX1FB9uexzNoKIeBtlOelvKQX8ycAJlH44H2oumdQzDqdsAvIEQzc1n7fYR6mX/QA4LSI+TylGXRwRzwx1sb+L+sYjLHyfYSFygsvMi4CLms7RrVyy18UiYmngy8C7WbRnzAJK/45/zcxnm8gmDYiIfwM+T2nK+R3KdNTnA/sC7wEOz8wvNZewt0TE3wGfBfbOzN83nacfRMQJlO/F8xl6GeTH687VayLiekox6h+zGjxUxehPACtn5pBvsiRBRAyM2YYcfGemPQL7SEQE5WbIZpSfladQ+he25e+i/hARZ1J6xd1Omfl+G6W/YluZ+eqaoqlGEbEZZcXTz6vXK1JaxGwOXJqZX24yX7ewINXFIuJTlJ04jmHxN/qfAI7PzI82l7A3Vc3iN2g3cyciPgjcm5nfrT9Zb4qIO4DvZub/a3Pu08C+mblJ/cl6U/Wmf0PKbpp30/4OugOXMYiIB4DPZubnm87SyyLiMUr/m58MOrYGZUvrl2XmHY2Fk3pARBzICDMlMvOb9aRR3SLicuC9mXnbiBerp0XESpTl7ZsC76LssDdnqOsz8501RVONqv/nrxl4jxQRJ1Ia2l9JWa3zMVeSWJDqahFxL/ClzPxcm3MfBN6fmfYiGKOIuJmy48liVemIeB/w7sx8Vf3JelPVfP/Ng9+kDjq3I3BeZroEbZQi4rSRrnHgMjYR8SDwD5lpj6PnoJrdsfXgPhjVTN6ngWmZeWNj4SSph1Qzp9YDHnR2aX+LiLsozatvbjqL6hURc4B3Zub5VfuIh4APZuZ/VytM3pOZmzWbsnn2kOpu6wK/GuLcr6rzGrtNgF8Pce5W4CU1ZukH9wJ/DyxWkAJ2rM5rlCw2dcR/A/tj0+3x8NqIWHvQ66UoMz62jYgXDL4wM39UazKpR9gbdOKqNob4GLAVpR3Hq4EbI+JkSj/TbzWZT+MvMzduOoMa8zzgser51tXrs6rXNwIbNRGq21iQ6m6/BfYD2nXh34+yLlljN4/S2LidDQB3NhybLwFfiog1ge+zcEvTt1Gmpb6/uWgSUL4n/6GaOn0Jiy+DzMz8Sv2xetIXhjj+Xy2vk0V7H0oT3nC9QauChL1B+1hEHACcCnwbOImyE9uAOyhLuyxI9aFqRty2wEtpv7HKSbWHUh1+TylEXQG8FfhltfMewNrAX5sK1k1cstfFImIf4EzKTiytb/R3oGwN/73mEvamiDgDmApsl5kPDjq+DnAVcGNm7t9Uvl4UEe+m3PFbn/JGNIA/AMdm5tebzNZrImLE/mWZuU8dWfrFoEbCQ0kbCY8sIsZ0Jy8z7+lUFqkX2Rt0YouI24GzMvOo1uXO1cyp0zLz+c2m1HiLiOdT3sttxsIxMgzqJ+cYpD9FxLuArwA3U5rbvzMz/6c69yVgs8zcscGIXcGCVJeLiL8HPg5MAZal/PK6gdIEzeUnSyAiNgR+DqxC2YLzj5R1/DtRZk5sm5mzm0vYm6q7P5Mon8s/AvelP2DGrJrF02pN4GWU5tG3Z+Yb600lSXqu7A06sVU9N3fNzMvaFKTeCFyQmSs2m1LjLSK+BWwM7APMBl5DKUb/I3AAsFtm3tlcQnVSRGwPTAduysxLBx0/Frg+My9oKlu3cMlel8vMHwM/joilKFP7HnI693OTmfdGxCuBwykzzbaivNH/MvDFzHyoyXy9qio+za4eWkKZuUO74xGxAfBD4Iv1JpKKqjnnqIvMmWmfQ2lR9gad2GZTZklc1ubcNOB39cZRTV4PfIBysxbKhJB7geOq93cnUW6Kqw9l5hWUJXutx4+tP013siDVI6oi1IMjXqhRycw5wFFN5+gXVYPWN1NmSLWujc/M/HD9qfpLZs6OiM8AnwXOazpPt4uIzYE7M/PJ6vmwMvOWGmL1uhMZQ0FK0mLsDTqxnQJ8LCIeAM6ujkVEvAn4EGXZpvrP6sCczHw2Ih5j0cLzNYBj5D4WEasD7wG2Y+FGFlcCJ2dma0/TCcmCVJeJiFPHcn1mHtSpLNJoRMRbgTMoDVofBJ5quSTxl+14WcDQDfm1qF9TGkleVz0fqpAS2IB7VLybJz1nnwLOrFoHtO0N2mA2dd5/UDbP+Sbl9zmUgsTSwNcy80tNBVNH3UVpZwHwG+AfgPOr17tTChTqQxHxYuBnwDrA1ZSdx59PKT4fGhE7uFzTHlJdJyKubzm0IeWb+MHqsW71mAPck5mvrjdhf4iIfSm73Ay124XT5kcpIm6l7A5zYGb6S/U5GmI2z3KUZpifBO61h9TIIuL1wA2ZObd6PpR1gAerKdWS1FH2BlX1JvVNlFYcfwYuy8zfNptKnVLNbl8nM/85InYBzqG8p3ua8j7vw+36yqn3RcS5lP5hO2fm/YOOvxC4ELgrM/doKl+3sCDVxSJid+A/gXdk5jWDjm9LubtyeGae21S+XhURb6dsu/sN4ODq+VLADEpT89Mz02nToxQRc4G3ZOZPms7SD6od4dr9YA7gemD/zPx9van6V0TsBXzXHW4kdVJELA/sDVyXmXfYG3RiiYgVgL8A+2bm2SNdr/4VEdOBt1JuiF+SmRc2HEkdUi3R/KfM/GGbc3tRdtZctf5k3cUle91tJvCRwcUogMy8OiI+Spn6a0Fq7I6gzDSZSSlInVTtcLIKcAkwr8lwPegayg5wFqTGR7um5vMpuxbe3+acJKnLVf3svg7sDNxhb9CJJTPnR8SDwDNNZ1GzMvN6yg1G9b/hWkIshX05AQtS3e5FDF0cmQdMri9KX3kJcHVmLoiIBcCqAJn514j4D8ouZk6dHUZErDTo5eHAt6uZUpdQZpktIjMt8o1SZv6s6QySpI74P0qrAH/OT0xfA94fERdn5tNNh1H9qvHzu4BNgT9RVmXc02wqddDlwCcj4vrBX+eI2IjSR+rSxpJ1EQtS3e1G4NiIuC4zB7YKHdjR7FhKzwGN3V+A5avn91N68/y0eh3AWg1k6jVzWbSqH8BpDF3pdznUGFV9Rl5NaYT5R8oyj3Y7M0mSesNhwDci4o/ARZnpbJmJZXVgS+DuiLiU0tR+8LjJXYn7RER8Htg9M1866NgqlJlRLwEeAVYD/j0iXm0Psb51GKXodEdE3MjCjSymArMpN/UnPAtS3e1gytbAd0fEDSxsaj4VeBj4xwaz9bJZwCuAiylLHj8aEc9Qdof7KPCLBrP1ioNwmmlHVAXnHwLTadnMICJmAW916Z4k9aSzgZUoTY0zIh6h5Xepm6r0tb2AJ6vnr2tz3l2J+8cOwLdajn2QMkPynzPz1IhYh7Ky4BjgHTXnUw0y866I2JTyvmk65SbzLZSb+N/IzNadySckm5p3uaoJ4sA38Qso0zuvpzRBe6LJbL0qIrYGNsrM70TE6pQG8btSZvFcD7zdLTjVlIg4n1Iw3a/NZgZnAL/KzDc3la9XRMQcRlc0XR5Y2abmkjotIo5lhJ9LmfnxetJI6pSI+DNlU6oLBh37NUBmbjno2DuAj2fmi+pPqU6q3sOfCxyXmT9tOE5Xc4ZUl8vM+cBJTefoJ5n5c+Dn1fNHgT2q3W+Wz8zHGg3XQyLiFcC7Kb3M/gSc5U4h4+KNwEFDbGZwJPDfzcTqOSfiLD5JXSQzj206g6RaLEPZkAaAiFiT0iLkxJbr7qZMOFCfqTYymI5tS0ZkQapHRMQywHKtx20WPTrVroSjuQ7KGv5PdjZRb4uINwIXUXpJ/RaYAhwUER/KzM83Gq73PQAMNfvxCeChGrP0LN/4SZKaFhG7Aldl5mPV82Fl5o9qiKXO+y3wBhY2rR6Y2X5xy3XrAn+uKZPqdy7wFmxePiyX7HWxiFgVOA7Yk/IDK1qvcZnJ6ETEs5Q384/T5vPYIu3hMLyIuJJy5+ctmfl4RCxF2Z3wncDq1XbWWgIR8W7gUGC3zLxv0PFJwPnASZl5clP5JElLLiL2pozrJgErtJ7PzFfXHkodU+3mvE1mXleNRZOhx6HpuL4/RMSBlBntX6HcaHw/8Fdgs8E7LEbE1yhtRHZuIqc6KyLeDhwPXAv8iMU3MrAIjTOkut3XKBX1r1MaoNn4bMn9HtiQsjPhmcAPXZ73nGwB/ENmPg6Qmc9GxKeBfwU2Au5qMlyviYjvthxaC7iz2pFjoKn5lOr53wEWpCSpx1Q9pD4K3IzjuoniMhbOet6YUox6E2UDnb82FUqdlZnfiIj1gEMouyveCBzSUoxaB9gDsG9c/xpobL9n9WiVuKTPGVLdrGqI96HM/HrTWfpBREwD9gP2AdamLDk7AzjfBvFjU93l2zozrxt0bGngaWBaZt7YWLgeFBE/ZfT9jjIz39jBOJKkDoiI2cD/ZObRTWdRPQbPkKpeL00pRE53rCT1t4jYaKRrMvOeOrJ0M2dIdbfHgftGvEqjkpmzgFnAByNie0px6gTg1Ig4F/haZl7RZMYes3FEzB30eqDC/6KImD/4wsy8pb5YvScz39B0BklSx62CvUQmmnbL80ZqHSGpD1hsGh1nSHWxiPg3yo5bb7EnT2dExHLAp4HDgHMzs910SrUY1AdhsVPVf3PQa3sijCAiTh3m9DOUpXpXZOaPa4okSRpnEfFV4K+ZeUTTWVSP1hnlziaXJo6I2A/YIDOPb3Pug8C9mdnatmPCcYZUd3sh8Erg9oi4HHi05Xxm5ofrj9X7ImJbygypvSl3LL9PaTyo0dmh6QB95uXDnFsaWA84OiKuAnbNzLnDXC9J6hItO6v9BPhsRKwNXMLi4zob3PandjfwnBEg9b+jKL2g25lXnZ/wBSlnSHWxiBipMXRm5otqCdMHImIKpQi1L/B8Sg+pMykzo+Y1mU0aSUS8hrJ97BmZ+W9N55EkjayaITNazijuM9XX/1HKbOcBa7c5BoC7PEv9IyIeB96cmZe3ObcDcF5mrlx/su7iDKkulpkbN52hX0TE7ZTdTS4DPgac5S57Sy4irgMOzMxbIuJ6hr/Tl5n5mpqi9a3M/EVEfAL4MGBBSpJ6g2O5ic0d1KSJax4waYhzGwBP1pila1mQ0kTxEmA+MBWYQpkyP+TF3qEa0W9YuI3xb3DqeV1uoczukyT1hrnAycDJmXlxuwsiYifgYOC9dQZT52WmBSlp4voJcExEXJyZDw4cjIh1gP8H2BsWl+x1narXwFWZ+VhL34G27DUwOhHxsbFc7wBC3SgiDgQ+k5nrNZ1FkjSyiPgk8GZgSg4x6I5yh+wG4BJ7g0pSf4iIDYGfU/oVXwT8kdIXdifKst1tM3N2cwm7gwWpLjN4N45R9B2w14A0QUTEesCVlN32Dmo6jyRpZFXLgC9k5tdGuO49wGGZuWk9ySRJnVbNhjqcsiHUWsDDwKXAFzPzoSazdQuX7HWfbYFfVs/tOyBNABEx3A4bSwMvoCw3nQ0cXUsoSdJ42Iiy3HoktwKTOxtFklSnzJxD2U1PQ7Ag1X2uBuZHxKzq+dXAtZn5cLOxJHXQOsOcewa4B/gWcHpmPl5PJEnSOHgCWHUU163Mwt6MkiRNCC7Z6zIRsSOwDfBa4DXAapSG0XewsEB1TWbe1lhISZIkjSgiLgV+l5nvGeG6rwGbZOab6kkmSRpvY9yJnMx8dT3JupczpLpMZl4CXAJ/a3K5BWUZ3zbA64B3AhkRj1AKUzOayipJkqRhnQh8NyKuycxvtrsgIg6gjO/2rTWZJGm8uRP5GDlDqodUBarXAx8EdgGwqbkkSVL3iojPA4dRdtK7CLiX8iZlQ8puS9MoDW4/2FhISZIaYEGqi0XE8yjL9l5bPbambBt5C3AtpbdU27ttkiRJ6g4RsTvwb5Tx3PLV4ScprRj+MzPPbyqbJGl8RMRHx3B5ZuYnOxamR1iQ6jIR8XYWFqBeATwK/Lx6XAv8IjPnNpdQkiRJSyIilqFs/Q3wcGY+02QeSdL4iYhnKUv2HgdihMszM9ftfKruZkGqy1TfxI8DpwMnZeZvGo4kSZIkSZKGERG/oyzHvhQ4E/hhZj7WbKruZkGqy0TEf1AamE+tDt1AtTyPskTvgaaySZIkSZKk9iJiGrAfsA+wNqV34BnA+Zn5xHAfOxFZkOpS1ZTuV1GKUwNL+CYBd7FogerGxkJKkiRJkqTFRMT2lOLUXsBKwLnA1zLzikaDdRELUj0kIl5IKUz9E7AzQGYu02goSZIkSZLUVkQsB3yasuPquZm5Z8ORuobFjC4XEcsD01k4S2obYJ3q9L1N5ZIkSZIkSe1FxLaUGVJ7A6sA3we+0mioLuMMqS4TEeuzsPj0WmArYDngGeAm4BrKFsFXZ+YfmsopSZIkSZIWiogplCLUvsDzKT2kzqTMjJrXZLZuZEGqy1S77CXwCKVP1DXV4zqboEmSJEmS1H0i4nZgY+AyShHqLHfZG54FqS4TEQcB12TmbU1nkSRJkiRJI6sml8wHHqdMMhlWZq7b8VBdzh5SXSYzT206gyRJkiRJGpOPNx2g1zhDSpIkSZIkSbVaqukAkiRJkiRJmlgsSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtbIgJUmSJEmSpFpZkJIkSZIkSVKtLEhJkiRJkiSpVhakJEmSJEmSVCsLUpIkSZIkSaqVBSlJkiRJkiTVyoKUJEmSJEmSamVBSpIkSZIkSbWyICVJkiRJkqRaWZCSJEmSJElSrSxISZIkSZIkqVYWpCRJkiRJklQrC1KSJEmSJEmqlQUpSZIkSZIk1WqZpgN0i7XXXjsnT57cdAxJktQhN9xww0OZuU7TObSQ4y9JkvrfUGMwC1KVyZMnM2vWrKZjSJKkDomIe5rOoEU5/pIkqf8NNQZzyZ4kSZIkSZJqZUFKkiRJkiRJtbIgJUmSJEmSpFrZQ0qSJI3K008/zX333cf8+fObjjKsFVZYgUmTJrHssss2HUWSJOk56ZXxF4x9DGZBSpIkjcp9993HKquswuTJk4mIpuO0lZk8/PDD3HfffWy88cZNx5EkSXpOemH8BUs2BnPJniRJGpX58+ez1lprdfVgKCJYa621euIuoiRJ0kh6YfwFSzYGsyAlSZJGrdsHQ9AbGSVJkkarV8Y2Y81pQUqSJC2RRx99lJNOOqnjf8/ZZ5/NLbfc0vG/R5IkqRf0yxjMgpQkSVoiYx0MZSbPPvvsmP8eC1KSJEkL9csYzIKUJElaIkceeSR33nknW221FYcddhhvetObmDJlCi9/+cs555xzALj77rvZbLPNeN/73seUKVOYPXs2n/zkJ9l0003Zcccd2X///fnc5z4HwJ133snOO+/M1KlTed3rXsdtt93GNddcw7nnnssRRxzBVlttxZ133tnkP1mSJKlx/TIGc5c9SZK0RGbOnMmvf/1rbrrpJp555hnmzZvHqquuykMPPcTWW2/NjBkzALj99ts57bTTOOmkk5g1axY/+MEP+OUvf8kzzzzDlClTmDp1KgAHH3wwX/3qV3nJS17CL37xC973vvdx2WWXMWPGDN785jez9957N/nPlSRJ6gr9MgazICVJkp6zzOToo4/miiuuYKmlluL+++/ngQceAGCjjTZi6623BuCqq65ijz32YMUVVwRg9913B2Du3Llcc801vO1tb/vbn/nkk0/W/K+QJEnqLb08BrMgJUmSnrNvf/vbzJkzhxtuuIFll12WyZMn/23b3+c973l/uy4z2378s88+y+qrr85NN91US15JkqR+0MtjMHtISZKkJbLKKqvw17/+FYC//OUvrLvuuiy77LJcfvnl3HPPPW0/ZrvttuO8885j/vz5zJ07lwsuuACAVVddlY033pjvfe97QBk03XzzzYv9PZIkSRNdv4zBLEhJkqQlstZaa7Htttuy5ZZbctNNNzFr1iymTZvGt7/9bTbddNO2HzN9+nRmzJjBK18WRymEAAAgAElEQVT5Svbcc0+mTZvGaqutBpQ7fKeccgqvfOUr2WKLLf7WlHO//fbj+OOP51WvepVNzSVJ0oTXL2OwGGra1kQzbdq0nDVrVtMxJEnqWrfeeiubbbbZc/5z5s6dy8orr8y8efPYfvvtOfnkk5kyZco4JFyoXdaIuCEzp43rX6TnxPGXJEnDG6/xF3TfGKzxGVIRsUlEfC0ibo6IBRHx0zbXREQcHRGzI+KJiLgiIrZqc93mEXFpRMyLiD9ExCciYula/iGSJGlUDj74YLbaaiumTJnCXnvtNe4DIUmSJC2u28Zg3dDUfAtgV+DnwHJDXHMkcAxwBHAbcDjwk4jYMjP/BBARawA/AW4B9gBeDHyeUnT7SCf/AZIkafT+93//d4k/9pBDDuHqq69e5NgHPvAB3vnOdz7XWJIkSX2t28Zg3VCQOi8zzwGIiO8Daw8+GRErUApSn8nME6pj1wJ3A4eysNj0L8CKwJ6Z+RhwSUSsChwbEZ+tjkmSpB524oknNh1BkiRpwunEGKzxJXuZ+ewIl7wWWBX47qCPeRw4D9hl0HW7ABe3FJ7OpBSpXj8+aSVJkiRJkvRcNV6QGoVNgQXAHS3Hb63ODb7utsEXZOa9wLyW6yRJkjQKEbFfRNwYEXMj4v6IOD0i1m+5ZlS9PiVJkgbrhiV7I1kDmJuZC1qOPwKsFBHLZeZT1XWPtvn4R6pzi4mIg4GDATbccMNhQ0w94vQxxu6MG44/oOkIkiRpAoiIGcAZwImUPp7rAZ8Czo+IaYNmuY/Y63M8dcuYbDw4rpMkTWS9UJACyDbHos25oa5rd5zMPBk4Gcq2w88loCRJUp95O3BjZh46cCAiHgPOAV4G3DqGXp+SJEmL6IUle48Aq0TE0i3HVwfmZebTg65bvc3Hr0b7mVOSJKkHXXTRRbzsZS9jk002YebMmU3H6WfLAn9pOTYwphq4MTjaXp+SJKmHdWL81QszpG4DlgY2AW4fdLy1Z9RttPSKiogNgOe1XCdJksbBeC+dGs3ypQULFnDIIYdwySWXMGnSJKZPn86MGTPYfPPNxzWLADgVODsiDgDOBl5AWbJ3eWbeUl0zXK/PfesKKknSRFL3GKxT469emCF1DfAY8LaBAxGxErA7cOGg6y4EdoqIVQYd2xd4AvhZDTklSVKHXXfddWyyySa86EUvYrnllmO//fbjnHPOaTpWX8rMC4ADKe0N/kK5Mbg0sOegy0bs9VlDVEmS1EGdGn81XpCKiJUiYu+I2Bt4IbDOwOuIWCkz5wMzgaMj4pCIeBPwPUr2Lw/6o74KPAmcFRF/VzUsPxb4QmY+Vus/SpIkdcT999/PBhts8LfXkyZN4v77728wUf+KiB0o46v/AnYA9gPWBH7Y0kphtL0+B/7cgyNiVkTMmjNnzjinliRJ461T469uWLK3LqXANNjA640pTTFnUgpQRwFrAbOAHTPzgYEPyMxHqmLVCZS+BY8CX6QUpSRJUh/IXLz2ERFtrtQ4+DxwbmZ+eOBARNxEaYWwB3AWg3p9tsySau31+TduKiNJUm/p1Pir8YJUZt7NwrtoQ12TwKerx3DX3QK8cdzCSZKkrjJp0iRmz579t9f33Xcf66+/foOJ+tqmwBmDD2Tm7RHxBPDi6tBoe31KkqQe1anxV+NL9iRJkkZr+vTp3HHHHdx111089dRTnHnmmcyYMaPpWP3qHmDK4AMRsRmwImUGO4y+16ckSepRnRp/NT5DSpIkabSWWWYZTjjhBHbaaScWLFjAQQcdxBZbbNF0rH71VeCLEfEHSnHp+cBHKcWoHwFk5vyImAkcExGPUGZFHc7ivT4lSVKP6tT4y4KUJElaIiNtEdwpu+66K7vuumsjf/cE8yXgKeC9wL9Q+nNeBRyVmY8Pum7EXp+SJGn8NDEG68T4y4KUJEmSFlP18PxK9RjpuhF7fUqSJA1mDylJkiRJkiTVyoKUJEmSJEmSamVBSpIkSZIkSbWyICVJkiRJkqRaWZCSJEmSJElSrSxISZKknnHQQQex7rrrsuWWWzYdRZIkacLoxBhsmXH7kyRJ0oRy7ydePq5/3oYf/b8RrznwwAM59NBDOeCAA8b175YkSeoV/TIGc4aUJEnqGdtvvz1rrrlm0zEkSZImlE6MwZwhJUmSJEnqWlOPOL3pCOPihuOd3SsN5gwpSZIkSZIk1cqClCRJkiRJkmplQUqSJEmSJEm1siAlSZJ6xv77788222zD7bffzqRJkzjllFOajiRJktT3OjEGs6m5JElaIqPZIni8nXHGGbX/nZIkSd2kX8ZgzpCSJEmSJElSrSxISZIkSZIkqVYWpCRJkiRJklQrC1KSJGnUMrPpCCPqhYySJEmj1Stjm7HmtCAlSZJGZYUVVuDhhx/u6kFRZvLwww+zwgorNB1FkiTpOeuF8Rcs2RjMXfYkSdKoTJo0ifvuu485c+Y0HWVYK6ywApMmTWo6hiRJ0nPWK+MvGPsYzIKUJEkalWWXXZaNN9646RiSJGmCmHrE6U1HGBc3HH/AEn9sP4+/XLInSZIkSZKkWlmQkiRJkiRJUq0sSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtVqm6QCSJEmSNJKpR5zedIRxc8PxBzQdQZIa5wwpSZIkSZIk1cqClCRJkiRJkmplQUqSJEmSJEm1siAlSZIkSZKkWlmQkiRJkiRJUq0sSEmSJEmSJKlWFqQkSZIkSZJUKwtSkiRJkiRJqpUFKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtbIgJUmSJEmSpFpZkJIkSZIkSVKtLEhJkiRJkiSpVhakJEmSJEmSVCsLUpIkSZIkSaqVBSlJkiS1FRE/jYgc4rFNdU1ExNERMTsinoiIKyJiq6azS5Kk7rZM0wEkSZLUtd4HrNpy7BPAq4Drq9dHAscARwC3AYcDP4mILTPzT3UFlSRJvcWClCRJktrKzFsGv46I5YBpwHcy85mIWIFSkPpMZp5QXXMtcDdwKPCRehNLkqRe4ZI9SZIkjdbOwBrAGdXr11JmUH134ILMfBw4D9il9nSSJKlnWJCSJEnSaO0H3A9cWb3eFFgA3NFy3a3VOUmSpLYsSEmSJGlEEbESsDtluV5Wh9cA5mbmgpbLHwFWqpb4tf45B0fErIiYNWfOnM6GliRJXcuClCRJkkZjd2BlFi7XG5Btro2hzmXmyZk5LTOnrbPOOuMcUZIk9QoLUpIkSRqN/YDfZeasQcceAVaJiKVbrl0dmJeZT9eWTpIk9RQLUpIkSRpWRKxGaVLeOjvqNmBpYJOW45tW5yRJktqyICVJkqSRvBVYnsULUtcAjwFvGzgwqNfUhbWlkyRJPWeZpgNIkiSp6+0H3JyZtw4+mJnzI2ImcExEPEKZFXU45abnl+uPKUmSeoUFKUmSJA0pItYG3gQcM8QlMykFqKOAtYBZwI6Z+UA9CSVJUi+yICVJkqQhZeZDwLLDnE/g09VDkiRpVOwhJUmSJEmSpFpZkJIkSZIkSVKtLEhJkiRJkiSpVhakJEmSJEmSVCsLUpIkSZIkSaqVBSlJkiRJkiTVyoKUJEmSJEmSamVBSpIkSZIkSbVapukAmrimHnF60xEAuOH4A5qOIEmSJEnShOIMKUmSJEmSJNXKgpQkSZIkSZJqZUFKkiRJkiRJtbIgJUmSJEmSpFpZkJIkSZIkSVKtLEhJkiRJkiSpVv+fvfsPs7Ws68X//rCRcBcI4jbKI2Lht338dVB3P0QxU0mh0LKwNPOAdsgKKym/AYIHUQQr86tgKdcpkXMyQ/MXJPoVEZVQcyumJRhmCP6Atp6tnA2YtL3PH88zslyszZ5Ze80zs2a/Xte1rpm5n3ut9Zm11sx85r3u53kEUgAAAAAMSiAFAAAAwKAEUgAAAAAMam4Cqar65ar6RFVtq6ovVdUFVfWDY3Oqqk6pqhuq6raq+mBVHbpSNQMAAABwZ3MRSFXVk5P8VZIrkzwlyR8keUySi6tq9Hs4KclpSV6e5Ogk25JcWlUHDlsxAAAAADuy50oXsEjPSPKJ1toJCwNVdXOSdyT5kSRXV9Xe6QKps1pr5/ZzPpzkuiQnJDl16KIBAAAAuLO5WCGV5G5JvjE29vX+Y/UfD0uyb5ILFya01m5JclGSI5e7QAAAAAAWZ14Cqb9IcnhVPauq9q2q/yfJS5O8v7X2mX7OxiTbk1w7dt2r+20AAAAArAJzEUi11v42ybFJzku3UuqzSdYleerItP2TbGutbR+7+tYk66tqr/Hbrarjq2pzVW3esmXLstQOAAAAwHebi2NIVdVPJXltklcluSTJ9yc5PcnbquoJIyFUm3T1HW1rrZ2XLuTKpk2bJl0X5sYjXnDBSpeQJPn4Hz1rpUsAAABglZuLQCrJK5K8s7X2BwsDVfXJJNekO+veW9OthNqnqtaNrZLaL8mtrbXbhywYAAAAgMnmYpe9dMeA+uToQGvts0luS/LD/dA16XbjO2TCda9Z7gIBAAAAWJx5CaS+kOThowNV9Z+T3D3Jdf3QlUluTnLMyJz1SY5Ot5sfAAAAAKvAvOyy99okr6yqL+eOY0i9KF0Y9a4kaa19s6rOTnJaVW1NtyrqxHSh2zkrUTQAAAAAdzYvgdSrk3wryW8keW6Srye5IsnJrbVbRuadnS6AOjnJAUk2JzmitXbTsOUCAAAAsCNzEUi11lqSP+svO5t3Zn8B2CXOXAgAALA85iKQAmD+CfgAAIAF83JQcwAAAADWCIEUAAAAAIMSSAEAAAAwKIEUAAAAAIMSSAEAAAAwKIEUAAAAAIMSSAEAAAAwKIEUAAAAAIMSSAEAAAAwKIEUAAAAAIMSSAEAAAAwKIEUAAAAAIMSSAEAAAAwKIEUAAATVdWeVXVSVV1bVf9eVV+sqleOzamqOqWqbqiq26rqg1V16ErVDADMhz1XugAAAFat1yd5fJIXJ7kmyX2TPHBszklJTkvygn7OiUkuraoHt9ZuHLBWAGCOCKQAALiTqnpSkl9O8l9aa5/ZwZy90wVSZ7XWzu3HPpzkuiQnJDl1mGoBgHljlz0AACZ5dpLLdhRG9Q5Lsm+SCxcGWmu3JLkoyZHLWx4AMM8EUgAATPLjSf65qs6tqpur6taqemtV/eDInI1Jtie5duy6V/fbAAAmEkgBADDJgUmOTXJoul33jkvyiCRvq6rq5+yfZFtrbfvYdbcmWV9Ve43faFUdX1Wbq2rzli1blq14AGB1cwwpAAAmqf7ylNba15Kkqr6S5ANJHpfkff28toPrTtzWWjsvyXlJsmnTpknXBQB2A1ZIAQAwydYkn14Io3pXJPlW7jjT3tYk+1TVurHr7pfk1tba7ctfJgAwjwRSAABMcvUOxivJt/vPr0myLskhY3M29tsAACYSSAEAMMnFSR5aVfcaGXtMkrsl+Yf+6yuT3JzkmIUJVbU+ydFJLhmoTgBgDjmGFAAAk5yX5LeTXFRVL0uyT5KXJ7m0tXZFkrTWvllVZyc5raq2plsVdWK6Nz3PWZmyAYB5IJACAOBOWms3V9Xjkrw6yZvSHTvqHUmePzb17HQB1MlJDkiyOckRrbWbBiwXAJgzAikAACZqrX0uyVE7mdOSnNlfAAAWxTGkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAgImq6tiqahMuzx2ZU1V1SlXdUFW3VdUHq+rQlawbAFj99lzpAgAAWPUel+S2ka8/P/L5SUlOS/KCJNckOTHJpVX14NbajcOVCADME4EUAAA787HW2rbxwaraO10gdVZr7dx+7MNJrktyQpJThywSAJgfUwVSVXXPJM9O8mNJ9k+ybsK01lp7/C7UBgDAEg3cpx2WZN8kF47c8C1VdVGSIyOQAgB2YMmBVFVtTHJ5kg1J6i6mtilrAgBgCsvYp/1LVR2Q5F+S/Elr7XX9+MYk25NcOzb/6iS/tMT7AAB2I9Mc1PyPk9w7ycuT/FCSu7XW9phwmfRuHAAAy2fWfdpX0h0f6leTHJ3ko0leW1XP77fvn2Rba2372PW2JllfVXvt6jcEAKxN0+yyd3iSv22tnTLrYgAA2CUz7dNaa+9J8p6RoUuq6nuSnFpVr1qYNuGqtaNtVXV8kuOT5KCDDppFmQDAHJpmhVQl+cysCwEAYJcN0ae9Jck9kxycbiXUPlU1vuJqvyS3ttZuH79ya+281tqm1tqmDRs2LHOpAMBqNU0g9fEkPzLrQgAA2GVD9mktyTXpDpp+yNi2jf02AICJptll74wk76mqx7bWLp9xPTtUVXsm+f0kz0lyUJItSd7cWnv+yJxKcnKS30hyryQfS/LbrbVPDlUnAOxurj/jIStdQpLkoBd9eqVLWA2G6NN+IclXk3wh3TGmbk5yTJKXJklVrU93vKnzlun+AYA1YJpA6r5J3pHk/6+qv0r3TtzXJ01srV2wC7WNe32Sxyd5cbp33O6b5IFjc05Kd+DNF/RzTkxyaVU9uLV24wxrAQBYjWbap1XV3yT5+ySfSrcS6pf6y2+31r6d5JtVdXaS06pqa+7ov/ZIcs6ufzsAwFo1TSB1frol2pXujCu/mjsfsLL6sZkEUlX1pCS/nOS/tNYmHhehqvZOF0id1Vo7tx/7cJLrkpyQ5NRZ1AIAsIqdn9n2aZ9N8ux0QdfC8ame1Vr7nyNzzk4XQJ2c5IAkm5Mc0Vq7aervAgBY86YJpI6beRU79+wkl+0ojOodlmTfJBcuDLTWbqmqi5IcGYEUALD2zbRP68/Wd5dn7GuttSRn9hcAgEVZciDVWnvDchSyEz+e5J1VdW6SZ6Wr+91JTmitfbmfszHJ9iTXjl336nRLywEA1rQV6tMAAJZsmrPsrYQDkxyb5NB0u+4dl+QRSd7WH8g8SfZPsq21tn3suluTrK+qvcZvtKqOr6rNVbV5y5Yty1Y8AAAAAHeYZpe9JN85g8pTkzwsyX5JvpHkE0ne1lq7ZTbl3XF3/eUprbWv9ff/lSQfSPK4JO/r540fI2HhuhO3tdbOS38GmE2bNk26LgDA3Bm4TwMAWLKpAqmqOirJG5LcM3cEPkkX+ryyqo5rrV08g/oWbE3y+YUwqndFkm+lO9Pe+/o5+1TVurFVUvslubW1dvsM6wEAWJVWoE8DAFiyJQdSVfXwJG9Nd+rfv0xyWZKvJPmBdKuVnp7kLVX1qNbax2dU59VJvmdSOUm+3X9+TV/TIenOCLNgY78NAGBNW6E+DQBgyaY5htQL073Ddnhr7VmttfNba+/pPz4ryaP77Xd5RpYlujjJQ6vqXiNjj0lytyT/0H99ZZKbkxyzMKFfrn50kktmWAsAwGq1En0aAMCSTRNIHZ7kza21j0za2Fr7aJK39PNm5bwkX0tyUVUdXVXPSPI/k1zaWruiv99vJjk7ySlV9VtV9fgkb073PZ4zw1oAAFarlejTAACWbJpjSN0jyQ07mXN9kn2nuO2JWms3V9Xjkrw6yZvSHTvqHUmePzb17HQB1MlJDkiyOckRrbWbZlULAMAqNnifBgAwjWkCqS8n+bGdzNmU7ngFM9Na+1ySo3YypyU5s78AAOxuVqRPAwBYqml22XtXksdV1UlVtW50Q1XtUVW/l+QJ/TwAAIajTwMA5sI0K6RekuTn0q1C+vWq+lC6d9kOTHegzIOT3JjkpTOqEQCAxdGnAQBzYcmBVGvtxqp6VJLXJTkiyf3Gprw3yXNba5aCAwAMSJ8GAMyLaVZIpbV2XZInVtV9kjws3QE0v5Hkqtbal2ZXHgAAS6FPAwDmwVSB1IK+qdHYAACsMvo0AGA1m+ag5gAAAAAwtZ2ukKqqv0jSkpzSWrup/3oxWmvtObtUHQAAO6RPAwDm1WJ22Ts2XaPz8iQ39V8vRkui0QEAWD7HRp8GAMyhxQRS9+8/fmnsawAAVpY+DQCYSzsNpFprX7irrwEAWBn6NABgXi35oOZV9aKqesxO5hxeVS+aviwAAJZKnwYAzItpzrJ3epLH7mTOY5L89yluGwCA6Z0efRoAMAemCaQWY88k316m2wYAYHr6NABgxS1XIPWIJF9dptsGAGB6+jQAYMUt5ix7qarLxoaOrarHTpi6Lsl9k9wvyV/tWmkAAOyMPg0AmEeLCqTy3cciaEkO7i/jvp3ka0n+Osnzd6EuAAAW57Ejn+vTAIC5sKhAqrX2nV37qurbSU5vrZ2xbFUBALAo+jQAYB4tdoXUqOOSXDXrQgAA2GX6NABgLkxzUPPXJzl51oUAALDL9GkAwFyYJpC6Ocl1M64DAIBdp08DAObCNIHUVUkeOOtCAADYZfo0AGAuTBNIvTzJUVV1xKyLAQBgl+jTAIC5MM1Bze+d5N1JLqmqtyf5WJIb051m+Lu01i7YtfIAAFgCfRoAMBemCaTOT9fUVJKn9pfkuxud6r/W6AAADOf86NMAgDkwTSB13MyrAABgFvRpAMBcWHIg1Vp7w3IUAgDArtGnAQDzYpqDmgMAAADA1KbZZS9JUlXr0x2X4GFJ9kvyjSSfSPK21totsykPAICl0qcBAKvdVIFUVR2V5A1J7pnuwJgLWpJXVtVxrbWLZ1AfAABLoE8DAObBkgOpqnp4krcmWZfkL5NcluQrSX4gyeOSPD3JW6rqUa21j8+wVgAA7oI+DQCYF9OskHphunfYDm+tfWRs2/lV9Zoklyc5Jckv7Fp5AAAsgT4NAJgL0xzU/PAkb57Q5CRJWmsfTfKWfh4AAMPRpwEAc2GaQOoeSW7YyZzrk+w7xW0DADA9fRoAMBemCaS+nOTHdjJnU7rjFQAAMBx9GgAwF6YJpN6V5HFVdVJVrRvdUFV7VNXvJXlCPw8AgOHo0wCAuTDNQc1fkuTnkpyZ5Ner6kPp3mU7MMmjkxyc5MYkL51RjQAALI4+DQCYC0sOpFprN1bVo5K8LskRSe43NuW9SZ7bWrMUHABgQMvZp1XVfZJ8Nsn3JtmntbatH68kJyf5jST3SvKxJL/dWvvk1N8IALDmTbNCKq2165I8sW9MHpbuAJrfSHJVa+1LsysPAIClWMY+7Y+SbEsXSI06KclpSV6Q5JokJya5tKoe3Fq7cRfuDwBYw6YKpBb0TY0ACgBglZlln1ZVhyd5UpKXpQumFsb3ThdIndVaO7cf+3CS65KckOTUWdw/ALD27FIgVVX3zZ3fedvZqYYBAFhms+rT+oOjn5PkjCRfH9t8WJJ9k1y4MNBau6WqLkpyZARSAMAOTBVIVdUDkvxpksdN2HZZkt9qrf3zLtYGAMASLUOf9twkeyd5TZJfGdu2Mcn2JNeOjV+d5JeWcB8AwG5myYFUVR2S5MokByT5lyRXpDtby8LZWx6f5IqqOqy19rkZ1goAwF2YdZ9WVQekO3PfM1trt3fHL/8u+yfZ1lrbPja+Ncn6qtqrtfatXfmeAIC1aZoVUmela3J+J8lrWmvfXthQVXskeV6SV6Y7xsDTZlEkAACLMus+7cwkH22tvesu5rQJY7WjbVV1fJLjk+Sggw5aRAkAwFo0TSD1+CTvaq2dM76hb3peVVU/neQJu1ocAABLMrM+raoelOTZSR5TVfv1w+v7j/eoqu3pVkLtU1XrxlZJ7Zfk1tba7RPqOC/JeUmyadOmSWEWALAb2GOK6+yV5JM7mfPJJHeb4rYBAJjeLPu0B/TzPpwueNqa7jhSSfLFdAc6vybJuiSHjF13Y78NAGCiaVZI/UPu3HSMOyTJp6a4bQAApjfLPu2KJD81NvakJH+Q5Kgkn0/yhSQ3JzkmyUuTpKrWJzk6/SooAIBJpgmkXpbkbVV1ZGvtkvGNVfUzSX4+yc/tanEAACzJzPq01tpXk1w+dv2D+08/1Frb1o+dneS0qtqablXUielW4d9pt0EAgAXTBFIHJLkkycVV9b4kH0xyU5LvT/KT6U4xfFGSe1XVs0av2Fq7YNfKBQDgLqxEn3Z2ugDq5P7+Nyc5orV205S3BwDsBqYJpM5Pd8aUSndAzEkHxXxyuqXaC6q/jkAKAGD5nJ9l7NNaa+f39zE61tKdje/MKeoFAHZT0wRSx828CgAAZkGfBgDMhSUHUq21NyxHIQAA7Bp9GgAwL/ZY6QIAAAAA2L1Ms8teku+c0vepSR6WZL8k30jyiSRva63dMpvyAABYKn0aALDaTRVIVdVRSd6Q5J7pDoS5oCV5ZVUd11q7eAb1AQCwBPo0AGAeLDmQqqqHJ3lrknVJ/jLJZUm+kuQH0p1K+OlJ3lJVj2qtfXyGtQIAcBf0aQDAvJhmhdQL073Ddnhr7SNj286vqtckuTzJKUl+YdfKAwBgCfRpAMBcmOag5ocnefOEJidJ0lr7aJK39PMAABiOPg0AmAvTBFL3SHLDTuZcn2TfKW4bAIDp6dMAgLkwTSD15SQ/tpM5m9IdrwAAgOHo0wCAuTBNIPWuJI+rqpOqat3ohqrao6p+L8kT+nkAAAxHnwYAzIVpDmr+kiQ/l+TMJL9eVR9K9y7bgUkeneTgJDcmeXi76KoAACAASURBVOmMagQAYHH0aQDAXFhyINVau7GqHp3ktUmOSHK/sSnvTfLc1pql4AAAA9KnAQDzYpoVUmmt/WuSJ1bVfZI8LN0BNL+R5KrW2pdmWB8AAEugTwMA5sGSA6mq+nySS1prv9U3NRobAIBVQJ8GAMyLaQ5qviHdu2wAAKwu+jQAYC5ME0j9U5IfnnUhAADsMn0aADAXpgmkXp3k6Kp66KyLAQBgl+jTAIC5MM1Bzb+Y5NIkf1dVr0vysXSnD27jE1trH9y18gAAWAJ9GgAwF6YJpC5P19RUkhMzocEZsW6K2wcAYDqXR58GAMyBaQKpM3LXzQ0AACtDnwYAzIUlB1KttdOXoQ4AAHaRPg0AmBdLCqSq6qAkP5runbePtdZuWJaqAABYEn0aADBPFh1IVdUfJ/nddMckSJJWVa9srb1gWSoDAGBR9GkAwLzZYzGTquoZ6Q6MWUmuSfLZ/vMTq+rpy1ceAAB3RZ8GAMyjRQVSSZ6T5D+SPKG19qDW2gOTPDHJt/ttAACsDH0aADB3FhtIPTTJ21tr718YaK1dmuQdSQ5djsIAAFgUfRoAMHcWG0jtn27597hrkuw3u3IAAFgifRoAMHcWG0jtkeT2CeO3546DZwIAMDx9GgAwdxYbSCXdKYQBAFh99GkAwFzZcwlzT6+q0ydtqKrtE4Zba20ptw8AwHT0aQDAXFlKI7LUJd+WiAMADEOfBgDMlUXtstda22Oay3IUXFX3qaptVdWq6vtGxquqTqmqG6rqtqr6YFU5swwAsKatpj4NAGCx5rEZ+aMk2yaMn5TktCQvT3J0P+fSqjpwwNoAAAAA2Im5CqSq6vAkT0ryx2Pje6cLpM5qrZ3bWrs0yTHpDvB5wuCFAgAAALBDcxNIVdW6JOckOSPJV8c2H5Zk3yQXLgy01m5JclGSI4eqEQAAAICdm5tAKslzk+yd5DUTtm1Msj3JtWPjV/fbAAAAAFgl5uJ0v1V1QJKXJHlma+32qjudGGb/JNtaa+OnNd6aZH1V7dVa+9YApQIAAACwE/OyQurMJB9trb3rLua0CWN1F9tSVcdX1eaq2rxly5ZdrREAAACARVj1gVRVPSjJs5O8uKr2q6r9kqzvN9+jqu6ebiXUPv1xpkbtl+TW1trtk267tXZea21Ta23Thg0blutbAAAAAGDEPOyy94Akd0vy4Qnbvpjkz5O8Mcm6JIck+ezI9o1JrlnuAgEAAABYvHkIpK5I8lNjY09K8gdJjkry+SRfSHJzkmOSvDRJqmp9kqOTnDdYpQAAAADs1KoPpFprX01y+ehYVR3cf/qh1tq2fuzsJKdV1dZ0q6JOTLdL4jlD1QoAAADAzq36QGoJzk4XQJ2c5IAkm5Mc0Vq7aUWrAoBl8ogXXLDSJSRJ3rbPSlcAAMC8WfUHNZ+ktXZ+a60WVkf1Y621dmZr7T+11u7eWju8tXbVStYJAAAAwJ3NZSAFAAAAwPwSSAEAMFFV/WJVXVlVX6uqb1bVZ6vq1Kraa2ROVdUpVXVDVd1WVR+sqkNXsm4AYPUTSAEAsCMHJHl/kl9LcmSSv0jywiR/MjLnpCSnJXl5ujMcb0tyaVUdOGypAMA8WUsHNQcAYIZaa68bG3p/Ve2b5Leq6nlJviddIHVWa+3cJKmqDye5LskJSU4dsFwAYI5YIQUAwFJ8LcnCLnuHJdk3yYULG1trtyS5KN2KKgCAiQRSAADcpapaV1Xrq+rRSX47yZ+11lqSjUm2J7l27CpX99sAACayyx4AADtzS7rd85LkgiQv6D/fP8m21tr2sflbk6yvqr1aa98a3VBVxyc5PkkOOuig5asYAFjVrJACAGBnDktyeJLfS/KUJOeObGsT5teOtrXWzmutbWqtbdqwYcPMCwUA5oMVUgAA3KXW2if6T6+oqq8meUNVvSLdSqh9qmrd2Cqp/ZLc2lq7fehaAYD5YIUUAABLsRBO3T/JNUnWJTlkbM7GfhsAwEQCKQAAluJR/cd/TXJlkpuTHLOwsarWJzk6ySXDlwYAzAu77AEAMFFVvTvJpUn+Kd3Z9B6V7jhSf91a+5d+ztlJTquqrelWRZ2Y7k3Pc1akaABgLgikAADYkY8lOTbJwUn+I8nnk5yc5LUjc85OF0CdnOSAJJuTHNFau2nIQgGA+SKQAgBgotbaaUlO28mcluTM/gIAsCiOIQUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxqz5UuAAAAYHdy/RkPWekSZuKgF316pUsA5pgVUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKD2XOkCAAAAANaq6894yEqXMDMHvejTM7stK6QAAAAAGJRACgAAAIBBCaQAAAAAGJRACgAAAIBBCaQAAAAAGJRACgAAAIBBCaQAAAAAGJRACgAAAIBBCaQAAAAAGJRACgAAAIBBCaQAAAAAGNSeK10AAAAArHXXn/GQlS5hZg560adXugTWACukAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAAACAQQmkAAAAABiUQAoAgImq6piqemdVfamqtlXVx6vq6WNzqqpOqaobquq2qvpgVR26UjUDAPNBIAUAwI6cmGRbkucneXKS9yd5Y1U9b2TOSUlOS/LyJEf38y+tqgMHrhUAmCN7rnQBAACsWke31r468vVlVfWD6YKqc6pq73SB1FmttXOTpKo+nOS6JCckOXXgegGAOWGFFAAAE42FUQuuSnLv/vPDkuyb5MKR69yS5KIkRy57gQDA3BJIAQCwFIcl+Uz/+cYk25NcOzbn6n4bAMBEAikAABalqh6f5ClJXtMP7Z9kW2tt+9jUrUnWV9VeE27j+KraXFWbt2zZsrwFAwCrlkAKAICdqqqDk7wxyTtaa+ePbGqTpu9oW2vtvNbaptbapg0bNsy6TABgTgikAAC4S1V1zySXJLk+yTNHNm1Nsk9VrRu7yn5Jbm2t3T5QiQDAnBFIAQCwQ1W1PsnFSfZK8jP9QcsXXJNkXZJDxq62sd8GADCRQAoAgImqas8kb07ygCRHttb+bWzKlUluTnLMyHXWJzk63YoqAICJ9lzpAgAAWLX+NMlRSX4nyT2r6idGtl3VWvtmVZ2d5LSq2ppuVdSJ6d70PGfwaufM9Wc8ZKVLmJmDXvTplS4BgDkjkAIAYEd+uv/4qgnb7p/kuiRnpwugTk5yQJLNSY5ord00RIEAwHwSSAEAMFFr7eBFzGlJzuwvAACL4hhSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxKIAUAAADAoARSAAAAAAxqz5UuAACGdP0ZD1npEpIkB73o0ytdAgAArBiBFMAqtxoCFOEJAAAwS3Oxy15VHVNV76yqL1XVtqr6eFU9fWxOVdUpVXVDVd1WVR+sqkNXqmYAAAAAJpuLQCrJiUm2JXl+kicneX+SN1bV80bmnJTktCQvT3J0P//Sqjpw4FoBAAAAuAvzssve0a21r458fVlV/WC6oOqcqto7XSB1Vmvt3CSpqg8nuS7JCUlOHbheAAAAAHZgLlZIjYVRC65Kcu/+88OS7JvkwpHr3JLkoiRHLnuBAAAAACzaXARSO3BYks/0n29Msj3JtWNzru63AQAAALBKzMsue9+lqh6f5ClJnt0P7Z9kW2tt+9jUrUnWV9VerbVvDVkj7K5WwxnhEmeFAwAAWM3mboVUVR2c5I1J3tFaO39kU5s0/S62paqOr6rNVbV5y5YtsywTAAAAgB2Yq0Cqqu6Z5JIk1yd55simrUn2qap1Y1fZL8mtrbXbJ91ea+281tqm1tqmDRs2LEvNAAAAAHy3uQmkqmp9kouT7JXkZ/qDli+4Jsm6JIeMXW1jvw0AAACAVWIuAqmq2jPJm5M8IMmRrbV/G5tyZZKbkxwzcp31SY5Ot6IKAAAAgFViXg5q/qdJjkryO0nuWVU/MbLtqtbaN6vq7CSnVdXWdKuiTkwXuJ0zeLUAAAAA7NC8BFI/3X981YRt909yXZKz0wVQJyc5IMnmJEe01m4aokAAAAAAFmcuAqnW2sGLmNOSnNlfAAAAAFil5uIYUgAAAACsHQIpAAAAAAYlkAIAAABgUAIpAAAAAAYlkAIAAABgUAIpAAAAAAYlkAIAAABgUAIpAAAAAAa150oXACvt+jMestIl5KAXfXqlSwAAAIDBWCEFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAAAAwKAEUgAAAAAMSiAFAMBEVXVIVb2uqv6hqrZX1eUT5lRVnVJVN1TVbVX1wao6dAXKBQDmiEAKAIAdeVCSo5L8c3+Z5KQkpyV5eZKjk2xLcmlVHThIhQDAXBJIAQCwIxe11u7bWjsmyT+Nb6yqvdMFUme11s5trV2a5JgkLckJw5YKAMwTgRQAABO11r69kymHJdk3yYUj17klyUVJjlzG0gCAOSeQAgBgWhuTbE9y7dj41f02AICJBFIAAExr/yTbWmvbx8a3JllfVXutQE0AwBwQSAEAsCvahLHa0baqOr6qNlfV5i1btixvZQDAqiWQAgBgWluT7FNV68bG90tya2vt9vErtNbOa61taq1t2rBhwyBFAgCrj0AKAIBpXZNkXZJDxsY39tsAACYSSAEAMK0rk9yc5JiFgapan+ToJJesVFEAwOq350oXAADA6tSHS0f1X94nyb5V9Yv91+9qrd1aVWcnOa2qtqZbFXViujc9zxm8YABgbgikAADYkXsnefPY2MLX909yXZKz0wVQJyc5IMnmJEe01m4aqEYAYA4JpAAAmKi1dl3uOGPejua0JGf2FwCARXEMKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGJZACAAAAYFACKQAAAAAGtaYCqap6YFW9r6puraovV9UZVbVupesCAFjL9GAAwFLtudIFzEpV7Z/k0iSfSfKUJD+c5BXpQrdTV7A0AIA1Sw8GAExjzQRSSZ6b5O5JntpauznJe6tq3ySnV9Uf9mMAAMyWHgwAWLK1tMvekUneM9b0vCldg/STK1MSAMCapwcDAJZsLQVSG5NcMzrQWrs+ya39NgAAZk8PBgAs2VoKpPZP8vUJ41v7bQAAzJ4eDABYsmqtrXQNM1FVtyf5/dbaq8bGv5Tk/NbaCydc5/gkx/df/kiSzy57obvuXkm+utJFrDEe09nyeM6ex3S2PJ6zNy+P6f1aaxtWuoi1Zqk92Jz2X8tlXn52mD3P/e7J87772t2f+4k92Fo6qPnWJPtNGL9HJr9rl9baeUnOW86iZq2qNrfWNq10HWuJx3S2PJ6z5zGdLY/n7HlMd3tL6sHmsf9aLn52dl+e+92T53335bmfbC3tsndNxo5TUFX3TfK9GTuuAQAAM6MHAwCWbC0FUpckeWJV7TMy9ktJbkvygZUpCQBgzdODAQBLtpYCqdcm+fckb62qJ/THJzg9yZ+MnYZ43lniPnse09nyeM6ex3S2PJ6z5zHdve0uPdhy8LOz+/Lc754877svz/0Ea+ag5klSVQ9Mcm6SR6Y7ZsH/SHJ6a237ihYGALCG6cEAgKVaU4EUAAAAAKvfWtplb65U1elV9dX+84Orqo1c/k9Vba6qp83gfh7c3+Zjd7noVWT08Zuw7bH99/zgoeuaB/1j16rq2h1s/1y//fRluO+DqurPq+pLVfXvVXVdVb2qqu41Ye6Dq+rtVfWVqrqtqv61qt60Wp7Xqrq8qt6yg22bq+r8nc1bprr26p/jQ4e6z6Woquf0r6//NDb+8n78mWPjR/Tjh/Uff3ZGdXznOdodVNX5VbW5/3zhd+TCZWtVXVFVjx+Zf2y/7ftWrmpYOVX11Kq6rKq+3v+9+ueqemlV3Wukb5vJ7yNWp5F+afxy6Yz/Hu1RVa+pqpuWq//iDkvtg0f/fs6Lqvrevmf+Wv+9HFtVx1fVz610bfOkf9w+3v9vvrWqrqqqP5nidu70fMygtlZVJ+zq7awGe650AXyX30/yd0n2TXJckr+uqltbaxevbFlz5xPpdhn4l5UuZBX7ZpL7V9Wm1tp3/shW1Y8muV+/faaq6kFJLk/yb0lemORf052V6YVJfraqDm+tfbmfe0iSjyT5+yQnpDul+AOSHJPkoUn+cdb1rSF7JfnvSa5L8smVLWWiK/uPhyW5cGT8sCS39h//19j4vyf5eLqfa2fsms5Lktx9bOxXknw+yQFJnp/k3VX1o6211fi6gcFU1SuS/G6S1yd5ZZKbkzwwyXOTPCjdzwu7h28kedLY2G3pfp/O6u/RU5P8ZpLnJPlMki/O6HbZsaX0wZP+fq52v5Hk6CTPSvKldP8TXZKuf377CtY1N6rq5HTP/R8mOSnJ3kkekeSZSU5c4s1Nej521SPT/S819wRSq8tnW2sfSZKqujTJw9O9gAVSS9AfQPUjK13HKndLuuDul5OMvuvzy0kuS/cLd2aqqtKFDFuTPHLkILcfqKqLk3wqyZ8mWXjn5rh0IcSRrbV/78cuS/K6/raYX9ck+d8ZCaSq6m7pXnNv6MdHHZZkc/868HM9pdbapObnU621f0ySqvpAkhuS/LckvzVkbbCaVNXR6f7ZeE5r7S9GNn2gqs5L8tO7cNuV5HtaazN/04dl8x8LvflSVNXdW2u3LXL6xiRbx15vLK9F98E7+Pu5JEt8PczCxnT/V/7NSA0D3v2acEKS17XWThkZu6iqXjzFbd3p+ZjWwmtpmt9Lq5Vd9lap1tq3061uODhJqupZ/S4V/7tfMvj+qto0fr2q+s2quqGqbqmqi5L8wLCVr7yasMte//XvVNXLqmpLVf1bvzz6e0bmTNwNcHRJZFX9RFX9R1U9e2T7PfrH/H+NX3eVe1OSpy0EPP3Hp/Xj31FVj6yqd1bVl/vX1Ser6lfGb6yq7ldVf1VVX62qW6vqU1X1jH7zY5IcmuSl42dcaq19Kcmrkzy5qg7uh/dL8vWRMGp0/lwe+K5fKn1ddbsf/m1V3Wdk28TdTGtsd7+FZePV7cb2qf75uKJffbbg//QfX1937F5w8GLvY7n1z9+H893B08OSVLpQ8iHVnzq+qvZI8uNJrqwJu8j0j+cfV9Xzq+qL/e/GN1XVfqP3Wd3un39XVd+sqqur6smTaquqp1XVp6vbPeeGqjqzqvbst+3djz9jZP5ZfU1PHhk7p6r+buTrk6tb/v/N6nbHeHdVHTj9Izid2skuB621bUn+Of3fnBH3r6r39q+1a6rqqRNu+4SqurZ/fD5XVc8f2356/3vhYVX1kf73w1VVdfiE2/q1qvqn/ra+UFX/73TfMUzt+Uk+MSkcaK1tb61dMjK0vqpeV1Xf6H8Hvbj/vZXku177j66qj6VbdXFMv22Hv2/67Qu7zT68/z19a//39+HV7f7x+v5+P19VTx+vtaqe0v+9+GZV3VhVf1hd+M8uuou/R6+oqtOq6ovpVtUt7I53Uv+7cWHXz/86cr3L063C2H/0b3a/7dCqel//3G+tqr+squ/vt+3Xv+YuGKvtnf19rF/ux2ENWGwffKe/n3UXPe/I6+NXquqCqvp6kov6bfev7nAUN1e3G9hF1e0VMHrbz+n/Dt7W3/4HaqTPq2634TdUt/vXrf3vh00j269Lt9ruYSOvqcvThWz/dWTs2Bk9jmvVfkluHB8c/z+kqs7uf5dv638m/7JG+rxJz0c/vrG6nvWG/nn8p6r63bG/IQu9+xP7n+1t6U4esqZ22RNIrW4H544fhIOTXJCukXlGuuW8H6yqH1qYXFVPSfKadCuqnprk00m823KH30vyg+mWWv5Rkl9P8jtLuYE+jf6jJK+sqoP64Ven+1l63uxKHcRbk3x/kkf3Xx+eZEOSt43Nu1+6XUl/Ld1y079JF3Z8pwGuqnunCxl+NN2up0cn+fMk9+2nPKb/+I4d1PL2dIHEQi2fSPJD1R1f6oHTfHOrzCPTvT5OTPdH6aGZfsn0Qeleg2cmeXqSeye5cKGhSvK4/uNL+/t9ZJKvTHlfy+XKJIdW1cIS+Eem2yXvH9OdnevH+/EHJblHutffjjwtyeOTHJ/kD5L8bJKXLWzs7+M9Sb4v3e/Olyb5/9I9jhmZ99NJ/jrda+8pSc5J91o+N0n6FQ0fS/dzsuAx6f7BHB/7UH+bz0pySpI/SfLEdCteP5fke+/i+1kRVbUu3c/rePP1xiTvTPLzSa5N8qYaOf5XVf23dI/VO9P93L85ySuq6qSx21mfbgXc65L8QroVkG8b/aepql6Q5M/S/Wz8bP/5S9ZKw8Xq1wc2hyV59yKv8odJtiX5xXSrgF/Ufz5q4bX/P9Lt+vX3O/t9M+YNSf4q3c9NJXlLur+vX+7v66NJLhj7uXxaur/xf5/kyUlenO535FmL/L4YUVV7jl7SPQ+TPCPJT6bb/e6X+rFzkpya7nTvP5Oux/qLkTDrN9M9n9/IyN/sqtqQ7jAH6/vbfV5/2++tqr1aa19P10/8avXHBaqq4/r7OLa1duusvv81bLF98HdZRM+74I/TvUl4TJKXVfcm+PuS/Od0q5GPTXL/dKsv79nf9mOSvDbd75Mjkzw7Xc90j5HbfXu6nuL3073O9kjy/pFg6+eTvCvdivSF19Rv9l+/a2Tsb/9ve+ceb0dR5fvvj/c7SBCEiRB5REBH3hlehsSgICABLqDoKAxXRzKggwMjjxESAZXHBYIoEBgk4AVFBMJDHoGEkADhdSEyBGFEkoiACQkkIAwkIev+sarPqXR6n7PPOfucfU6yvp9Pf3p3VXV1dXXt7lWrVq1qs3aCp4HvSDpGUv820m2Cy50H4VO9twImJbkKqp8HwN8BL+LP5kDgavxdfWrFNa4Bfo+/z6/pwj31TswstiZswGhgXvo9EDC8ka0GbAR8P4WdWHHuKindC8BZWfgTwD2ltFenfIY2+567q/4q4oame/50FmbAlFK68cBj7eVZfg64j55ngQdwQdKAA5tdJ51se7cDP0+/LwfGp9/z8OW6y+cqtb2xwKQs/Ce4+fNmNa55JW6OXqtMG6Z6PDUdr4YL65a2+cAvgd2aXX9ZmScDv60R9xQwLku3GNgyi9873dcBtdps1TWAccASYNss7NB07nbpeL10fGwpr7qu0UN1V5RlSDq+Cbgw/f4d6b2GK40NFxAHpt8HZ/nMwufhr5aFjQH+mh3/S6r/ARX1Py4Lewx4sFTO7wMfFuemdv5c+r0WrlT5Gek9ktrxh8BB6fhnwC3NbqtZ23mqVP87pv/aJsClef3igrIBx2V59E/t7/h0vAruC+Ha0rUuxztXa6Xj0Smvz2Vpdir9BzbAO/ajSnmdjSvJVm12Hca24m/Ax1K7/HY76Yr30fWl8OnAr7Pjou2PKKWr531T/AePydIcmMJ+kYX1S++4kelYwOyK/+VxuO+j/s2u576yZc+vvH2T6u/R68V7L4VtAyzNn2EKvx54snSdeaU05+EDNBtkYYPTdY/OwsYCc3BL4wXA+c2ut96+0UE5mOz7mY7bk3mL98NtpfDj8W/oVlnYAGARcHo6PgX4f22U/YCU975Z2LrAG/j0MqrKnMJaZNPY6monn8H9bFr6H8/AZZIN2jhnVVzR1CLj1noepfOK/tUZwMtZ+NCU1yUV5yzTP+3LW1hI9S5ux4WK+fgo/sX4CDGStpd0m6Q5uMCyGPgkMCjFr4p/jMoWKLf2TNH7BBNKx8/jH4IOYWaLcKd0Q/CO9H+a2d1dL15T+DVwRBq1OYKSmTKApI9I+qmk2Xi7W4yPtA7Kkn0OuNfMOmuJY8scmC0xsy/jHeYzceuZo4Bpkg7q5DWaydNmNrs4MLNHcOfugzuR1ywzy1eGeT7tO9yWm8gTuFBWTNvbCx9tBO+o5eF/NLM32sjrQTNbkh0/D2wiaY10PBgX7lqcxGb1D7S8P3fBrXtybsKVLsVo1lRghzSSuQcukF4B7JIsfYpR1sKiazpwoHwaz+BstKy3MB3/P8/BR9pPteUX0Wh5b5rZfLzeirY2ALc6raq3DYC/z8IW48rPgnK73RMXqm8uWSJMwkew+1L7Dvo+1n4SoD65wnBnwkCH3jcFE7PfL6X9pJbMzRbindFiGvgg3AL0NxX/pbWAXrFSbR9iIW4Jk2+P10g70Zb1DzYc78jeVnoWE3Er4ba+CYOBCZa5OTCzJ3DF1z5ZupPxb9E0fPbEWR24t6AOObiCemXesgXSYFwefLkISLLJI7Q+0+n41K5LJA3JZJk8jzfM7KEsj3fx2TH7EDQMM3sWt2Y7BFdWCu+TPKVsBWJJX5T0qKSFuGxbyJuDaAO5K4gfSnoJQJC4RwAAFwBJREFUH+BcjM9++ISy6duJFdqaLRRSvYvv4R+67YD1zOxkM/tQ7k9lAm4K+m+4SenuuOneWuncj+Ka1bmlPMvHKzMLSseLaK2/jvJ7XPBcE39J9VXuwC1qfoR3Bu+sSDMONwm+EHfmujs+FTSvu/60PS3sVWBDSRvUiB+YpWvBzJ41s3PN7Au4AvZ1XFnbG1iCj4RUsWqKL6j6H86lcz7eqtoxdL4t9zjmUwmmA3ulaSYDaFVITQP2SHPo96Lt6XpQXR/CLRnBLR5q1X/BxsDquGImpzjeKO0fwTuX++Dv4YfNbAbeYdkjhT1nPpUC/H9yBq5MfRyYI+mcXqSY+gr+f94G2NDMLqhI09Z7s2i/7dUbwNvmvhGBFsU+WV4bp/0MWhXfi4EHU3h5KkQQdAfz8Y7BFu0lTNQjV7yVtXeo/31TdY1FFWHl6xb/pbtZ9r80M4XHf6ljLDGzp/KNVl+NZcrPdGNcHljIss9iHC6ztyUDbFaRX3GNljZi7v/vLlwevcYqfG8GbVKPHFymPZm3oPz82n2mZvYAvrDPEHwQZ56kyyWtW28eQeMwsw/M7E4zO9HMdsCtI7fFB/GKVRnvwJVQX8cHFPZIp7cnl5+PW8RdhVu/7k5rH6d8btUzX2GIVfZ6Fy9ZtvRoxp54h+3zZtayxKykfD7xG3gHeJPSueXjoDbv09qJBdw6qEbak3DF4QvAZZKG5J2tvoKZvStf5e57wM1plKUFSWvhc6JPNLMrs/CyMns+bQtWU9L+EHxefJlD8I7+1DbKOkvSzfgUrN7AGyzvALpgM5ZVeFT9DzehVaApRlTLI2Eb4WbjjaAnrtERHsX9YuyFW30VdfE4sD7uK2Mb/IPdFf6K/1fL5M9kHt5JKD+nTdP+TXBLBEnP4oqnnXDfVAAPp7AW/1Ep/VJ8yfhLJH0c+Bou9L6KT2NtNjMsrbLXSYpn1ma91UmR9mCqBa8XO5BXEHQKM1ssX5Rgf9zvT0OyLR3X9b7pAsX5/ww8UxE/syIsaAzlZ/0mLpvvjVtKlWlr0Ph1qmWHTXGrcQDkzqxH4s/6B5J+ZWbLOWIOqmlPDq5BezJvS/al49dx35hlNiX735vZdcB1yY/Y4bgc8TZwGm23i66+O4J2MLNrJF1Aq1x5GN4f+LIVc++kLevM7kjgsnwwsI1ZIPVa7fZJwkKqb1A4/m0Z9ZC0F1ln2Mw+xC0ORpTOXW5FpKAmfwHWV7b6GRXLO0v6JN6p/AH+Mtkd/5D1Va7AR4SqOshr4qN7edtbH1cg5UwE9lda/aWCKXj7PDOd34KkzXDn8rcX09qSw8gqtqX3jBJMBXYttRck/QMuGOTKtV0yJ/hI2hsXKJ5IQYV57/ZZmo/jVmEdpZbFVCOv0QgewUePj6HVOgozewe3kjklBT3axes8iT+n3OFvUf/FNT/EBfwjS+cehXcipmVhU4Fh+EBBoWidgndgd6WGUtXMXjGz8/ApNyuCo37wNvUa1fX2Nr6wRr1Mw/3bbF62RkhbLYuEIGg0Y4DdlK2EViBfMe2ArmTewfdNZ3gRV3oPrPFfmt/F/IP6mYTLUP1qPItFbZz7OC5XtchMyRpjID4IUgwaXo8PjuyDKySu6p5bWaFpSw6uoj2ZtxaP4/LIJ4qAJEPuRXqmOWb2hpmNJbkLyPLYJDk/L/JYBx88Xi6PEl2ZGbLSUdUXSUrCfrT2RdYGFhfKqMRyK5HXYG2W7V+tiluur3SEhVTf4DHc2evVSSs7AHfI92op3Y+BWyVdga8QsS/u/G5FZQ1J5dVsoPbqJ+1xL94h+oWki/CVL45fJmN/WVyHj0RdbGZLJY0CzpX0u9yCra9gZpNZ1rdLHrdQvlT1WZLexoXl03Dz83z63SW4X62pkn4EvIIrPtY1swvMzCR9HZ9+My2141n4CMN/pPxOyPI7U9KO+Apff8DNqA/HVzI5hd7B9fgU2imSzsWdyG4PjMKVKPdlaecCd0kajQsD5+N+BO4F9yGQ6vkcSe/hgwVn0InRLjNbJGkmvpTxc7hl1LONvEaDKKbifZHlV7uchq9A8xb+/LvCtbjy+Hep/tfGl9guW4WNAu6TdC3uQ+LvU7qrc/9TuPLpO/g7+ekUNhX3+QeZQChpLF6/j+FtfBiuVK1aQaXPkd5/o4GxkuYD9+PfnZHAGSVfKu3ltSDldWkaXZyCt9FBwDAzO6zR5Q+CKszsTkkXA9ck5fXt+P99O1wmmEXXB6Hqfd90mPS/PBn4ZZomfw/eEd0KXwTjCIsV2HoEM3tR0pX46qQX4E6l18KtZAaZ2TfbOP1i/F16n6Tz8Wll5+GK/ltSmnPxaenDzey9pESdKulYMxvXLTe1AtKWHFyDNmXeNs4bh3//75F0Fu4TeDQuj4wFkPRD3HJ9cgrfGf+unpbKel+y4rxJvprtfFwuXht3rdEWL+CKtP3TeTNDQd0m/yXpdtxtzlx81fFTgPfwviC43HOSpDG4UnMvfDX3ergfOCH5kHoT7wet2bji9x3CQqoPYGZz8JG0j+GC0Um4UPRSKd1teEfpS/gKcjuT5riuoKyPOwUtb205QK6Jmc3Dl1UegNffP+JTinK+jwuOx2ZT9C7ErX+u60W+YRrJV3ET/+vxlbhuSb9bSE6n98YVdWNwfwb/DPw5S/McbkHyOC5UPYAro8YD/2Bmr2VZ3oCvnnYyLkxfj48OHW1mFzX8DjtB8ttQTNE6D1dAnY47pv1iaQrnNNzX2Bh8udbn8I5Bzlfx+vq/uHL5bDo/Tel43ProAdxCaPNuuEaXMLNXU1nE8hYB04rw0qhTZ67zHm699C7e8RuFt6vZpXQT8JGp3XCh4iTgIuDEUpaFBdQ0a3Wm/gzuU2Rmuq/8PobgSrG7cdPub5nZ+K7cU2/CzK4Gvovf213A0cDJyRqso3ldgL83voh/636FjzTWnMobBN2BmZ2M+07cFh8YuR9/b0zElQRdzb/e901n878Jt5jfCZeLbsWnuz9NqxVt0DOcgCsbv4F/B8bh1ixT2jinkKuG4YNKvwJ+jr8LP58GnvbGFaMnFlPezexRXJE1JrcKDhpLPTJvjfM+APbDFUPX4EqN2fhK6MXg4JO4vHslLleOxJVWl2ZZHYa/k8bg/2/hq9gu0y+s4Fx8kO836TpfavdmV27Oxi0Sf4orpc7BLfgHm9lMAPNFrU7F+4934MrDg+vM/zv4f/rnuM/R5/AVHFc61EVZPwiCIAiCIAiCIAiCIAg6RFhIBUEQBEEQBEEQBEEQBD1KKKSCIAiCIAiCIAiCIAiCHiUUUkEQBEEQBEEQBEEQBEGPEgqpIAiCIAiCIAiCIAiCoEcJhVQQBEEQBEEQBEEQBEHQo4RCKgiCIAiCIAiCIAiCIOhRQiEVBEHTkPQFSY9KekuSSRrf7DIFQRAEQRD0JCEPNQ9JsyTNanY5gmBlZbVmFyAIgr5F8dE2s4FdzGcgcDuwALgWeBt4oUuFWwloVP0HQRAEQdB5Qh5qLiEPBcGKQSikgiBoFvsBawEnm9mNzS5MEARBEARBEwh5KAiClZaYshcEQbPYPO1fa2opgiAIgiAImkfIQ0EQrLSEQioIegmSBia/AeMkbS3pt5LmS3pH0gRJn07pPirpKkmvS3pf0pOShlXk10/STyS9mNK9Jek+SftVpJWkY5L/gjdS+ldS+i+nNEMlGbAlsGUqa7GN68B9Fvn8MAU9mOUzNKWZnI7XkHRWuocP8utIGiDpZ5JeTnHzJd0hafca191U0jWS5kj6H0nT0z0PTdcaXUpf06eApNF5eUtx26Vn+Eoq1xxJN0r6ZEXacSmfgZK+Lem/Ut3PSc+4X0W9dan+S9ffVdItkuamss6WdLmkzSrSbirp/6Rn8a6kBen3OElbdeb6QRAEQVAm5KGQh3paHkr59kv1+Gq69vOSvitJpXR5+xwk6aYkRy3Nntsqko5PbfJvSW56UtJISauU8ntN0l8qyjM7XefMUviBKfzsLKxDMpqk/SXdLWleejZ/knShpA0r0s5K2waSLk6/F5fbSRB0lpiyFwS9j4HA48AfgHHp+DBgsqQ9gXtx/wI3ARsBXwHukTTIzP4MkD4ojwA7AE8CY4CNgaOACZJGmtnY7Jo/Ak4HZgK/ARYCmwG7A0ema83ChaaT0jljsvOnd+D+inyGAvsC16Uwsn3BLakM9wDjgbnp/nYBJqT7vw+4Nd3focDDkg4zs7uLTCT1Bx4FtgIeTttmwJUpn4Yg6YBUltWBO4GXgAHA4cBBkoaZ2dMVp14A7J/OmQAMA74FbAN8LqWZRWPqvyjrwXj9CvgtMBvYFRgJjJC0t5nNSmnXwdvT1sD9qZzChcER6fyXO1qGIAiCIGiDgYQ8VBDyUDfJQ4k1gAeADYFfp+P/BVwKfBI4oeKcrfH2+d/ADcDaeHsE+CXwVeAV4D8Bw9vu5cA+wNeyfCYBX5O0nZm9ACBpG2CLFD8cOCdLX9TDxJS2QzKapLPw+nsTuAtvS58BTgEOlLSnmRX3kdfPJLydTUj3ObOiToKg45hZbLHF1gs2XNCytP1HKe7MFP4mLjSsksV9PcVdkoWNTWFjAWXh2+LC1QfAwCx8PvAXYJ2Kcm1cOp4FzGrA/Y5OZRxaETc5xT1bcf3VcMHmfWDfUtzmwKvA68CaWfhV5TpK4bsBi1Pc6Hrvs6rswEeAt4B5wA6l9J8C/gY8XQofl/L5M7BF6R6npLjBja5/YL1Uzg+Bz5biTk3XnZCFfamq/lLcGsD6zf7/xBZbbLHFtmJsIQ8tExfyUDfKQ1k+hivn8rraCPhTihtSo33+uCK/o1Pc08B6Wfi6wFMp7qtZ+HEp7IQs7NspbEJqo+tkcc8A7wFrpOO6ZTRcwWe4UnLDUtpja7SNon4eANbtan3HFlt5iyl7QdD7mAWcVwq7Lu3XBP7dzJZmcTcCS4CdACStDvwj/sE/3cysSGhmfwR+in+gvlG6xmJcQbEMZjavszfSAM6suP5B+CjQZWb2UB5hZq/ho2sfw0eUivr4GvAOLjjl6Z/CR7UawTfwkbVRZvZ86TozgKuBnSXtUHHu2ZZGc1P6JfhKOwCDG1S+nBFAf+AmM5tairsIb4Ofl7RFKe5/yhmZ2SIze6cbyhgEQRCs3Mwi5KGCkIe6Rx7KOd3MPsiu/Satlkn/VJF+Dq3TLXOOS/vTzOxvWX7v4oN+AN/M0k9M++FZ2HDccqloo/tAi4XbjsDDZraodN16ZLTvpv23zGxBKe043MIst97KOTndQxA0lJiyFwS9j+lmVhaECkeX/13u/JvZh5Lm4KbQANsB6wCPpI9pmUnAD4Cds7AbgO8AMyTdDDwETDOzhV27lS7zREXYnmm/ZY3569um/fbA3bTWx9Qa9zMZOKZrxVymXDvWKNegrFzPl+Keqkj/Stp/pOtFW45d0n5SOcLMlkiago8A7oyPVj6Ej7SelqYH3I2bh1e11SAIgiBoBCEPtRLyUPfIQwVLcKuhMpPTfueKuN/nCqyMXYCl2bk5D+HKzpb8zGy2pJeBYcm/lOHTOB9I6ZfgCqpiCqNYVn7riIy2J65wPVLSkRXlWwP4qKT+ZjY/C38ft9ILgoYTCqkg6H0sJyQkJUFlXGIJPk8foHD8+HqNtEV47rjwe7hZ8nHAaWlbIulufETkpbpL31j+WhHWP+2rPqQ566V9UR9zOnCNzlCU61vtpFuvImxBRdiStF+10yWqTYfaiJm9LWkPfCTwENy/A8A8SZcD55rZ4m4oZxAEQbDyEvJQKyEPdY88VDCvxgBbUSf92ogr0w94s8KCqWi/84BNSlET8fraBVcYfRSYaGbvSHqSVuup4Vn6Is+OyGj98f7/qBplL1gPn75aMDe3MAyCRhIKqSBY8SiEtI/ViN+slI70Eb4UuFTSJrhp8FdwIedTkj5VYxSoW6nx8SvKPcLM7qgjmyL9pjXia9XTUnykqIrlViHJrrOjmfX2UaTOtJG/AP87rTazA+5U8wTgLHzF1jPLmQRBEARBEwl5qDp9yEPLs7GkVSuUUkWdVClAayloFgIbSVq9PFgnaTXc6XzZafgkXCG1H7AoCyv2p0vaCFdILcT9U7UWpH4ZbSHud22jGmWvRSijgm4jfEgFwYrHi7izw50kVZk3F0siV61ugpnNNbNbzewo/CO4NfDpLMmHdO8oVXs8lvafrTP9C7TWR9UI19Aa570FbJp8LpTZrQHl6iyNqP9n0n5oOSIJS/ukw+XaiDkzzOwy4PMp+NAulicIgiAIGk3IQ8sS8lBtVgP2qggfmvbPVMTV4hm8jz2kIm4IXuZym5uEK32G48qkl82sWMVuYsrv6/g0zMm13CXUIaM9BnxE0qc6cD9B0K2EQioIVjCSifANuLnt2XmcpK1xh4aL8SVpkbSmpOFpVCVPuzq+wgi4AFMwH59fvnb33EG73I6b058g6cCqBJL2TMvgkkanbgDWp+TEU9Ju1Hbe+AQuoCzjyFLSscDeFemvxU3NR0lazvGmpFUkDa11Ux2gEfU/Hl+h6Ohk5p1zEr4c9APWumz2pyUNrMinGGV9ryIuCIIgCJpGyEMhD3WQn0haszhIFkk/SIfXVp9SyS+y/NbJ8luHVif91+QnmNlcYAZen0PIpuThvq3eB85Ix8v4/+ygjHZJ2l8tafPyCZLWrZALg6BbiSl7QbBicho+MnWipN2BB3ET4aNwQeTEbORlbdxx4ixJjwOzgbXwkZXtgTvM7A9Z3hOB3YF7k/PrD3DHjnd2/225QCXpcOA+4HeSHsVXBXkP+Hgq21a4KX7xET4DH3U6KQldD6f4L+POHw+puNRluPB1haThuFPNHfERtLuAg0vlmi/pCOA24DFJE3HhYimwBe5Isj9et12hy/VvZn+TdBxwM/BQctz6Z2BX4Au4X4RvZ6fsB1yc6voFfOWXAfhqfUuBC7t4T0EQBEHQHYQ8FPJQPbyOr9z4nKQ7cD9kR+B1c7mZTak3IzO7UdIIvI3NkDQet346FPgE8Bszq1rRcCKtFni5j6gPJD1Chf+oRN0ymplNlHQa8BPgj8k32kxcabslsC/eJg6o936DoKuEQioIVkDM7E1JewKnA4cD/4YvB/sEcKGZTciSF8vQDsOFi0PxJYH/BIykdaSn4FzcZ8CX8JGcVfFlmHtEAAMws2cl7Yjf18G4oLQUFyiewZ01zsvSz5O0N/DjVO7dcFP+kfiy0ssJYGb2vKT9snOWAFNxQepwSgJYOmeipM8Ap+BOJT+L+wJ4DR/RuqXrd9+Y+jez21OdnJHK2g9XRF0JnJOWjC64DxiDj9qNADbA6/p+4GIzq1qZJgiCIAiaSshDIQ/VySJcsfNj3GfYxsDLuEXTZZ3I72h89bvjaB3g+wNwEXBFjXMmAv+KK68erIgbDswxsxmluA7JaGZ2flJwfRd30TAC9y31KnAVcGNHbjQIuorCYX4QBCszyWz8QeCHZja6uaUJgiAIgiDoeUIeCoKgGYQPqSAIgiAIgiAIgiAIgqBHCYVUEARBEARBEARBEARB0KOED6kgCBqCpNF1Jh1vZtO7sywrI82u/7TCy7F1Jh9jZgsaXYYgCIIgaDbN/h6v7ET99wxpiufQOpIuMLMx3VuaoC8TPqSCIGgIkup9mfyTmY3rzrKsjDS7/jPfE/XwCTOb1egyBEEQBEGzafb3eGUn6r9nSIq/UXUknW1mA7u3NEFfJhRSQRAEQRAEQRAEQRAEQY8SPqSCIAiCIAiCIAiCIAiCHiUUUkEQBEEQBEEQBEEQBEGPEgqpIAiCIAiCIAiCIAiCoEcJhVQQBEEQBEEQBEEQBEHQo4RCKgiCIAiCIAiCIAiCIOhRQiEVBEEQBEEQBEEQBEEQ9Cj/HwV2HZhWBDIIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(20,20))\n",
    "\n",
    "ax[0][0].set_title(\"OS Distributions\")\n",
    "ax[0][1].set_title(\"Browser Distributions\")\n",
    "\n",
    "\n",
    "os_count.plot(kind='bar', ax=ax[0][0]);\n",
    "browser_count.plot(kind='bar', ax=ax[0][1]);\n",
    "\n",
    "sns.barplot(x='most_frequent_os_',y='Proportion', hue='target_', data=os_prop, ax=ax[1][0]);\n",
    "sns.barplot(x='most_frequent_browser_',y='Proportion', hue='target_', data=browser_prop, ax= ax[1][1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most of users use windows for os and most of users use Chrome web browser\n",
    "- Ipad has 0 churn rate but proportion of Ipad users are very low\n",
    "- Linux, MacOs, Ubuntu and Windows users has nearly 20% churn rate but iPhone users 60% churn rate. Maybe there is a problem with user experience.\n",
    "- Users who are using Firefox web browser churn rate is nearly 30%\n",
    "\n",
    "Both OS and Browser attributes can be good predictor for churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(15,6))\n",
    "fig.suptitle(\"GENDER DISTRIBUTION\")\n",
    "gender_count.plot(kind='bar', ax=ax[0]);\n",
    "\n",
    "sns.barplot(x='gender_',y='Proportion', hue='target_', data=gender_prop, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no significant churn rate difference in Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,1,figsize=(20,15))\n",
    "ax[0].set_title(\"LOCATION\")\n",
    "location_count.plot(kind='bar', ax=ax[0]);\n",
    "\n",
    "sns.barplot(x='location_',y='Proportion', hue='target_', data=location_prop, ax=ax[1])\n",
    "\n",
    "ax[1].tick_params(axis='x', rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location can be good estimator because some location has nearly 100% churn rate. So keep this attribute in model data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(15,6))\n",
    "fig.suptitle(\"CHARGING METHOD DISTRIBUTION\")\n",
    "level_count.plot(kind='bar', ax=ax[0]);\n",
    "\n",
    "sns.barplot(x='current_charging_',y='Proportion', hue='target_', data=level_prop, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting that both paid and free customers have nearly same churn rate. I expected that paid customers have high churn rate but it is not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2160 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(50,30))\n",
    "ax = fig.gca()\n",
    "h = df_pandas_ready.hist(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above shows the all datasets distribution. Some of them highly skewed and we need to use a ml algorithm which is not sensitive to data skewness. I think, RandomForest is good algorithm for this case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indexer Process...\n",
      "Success!\n",
      "OneHot Encoder Process...\n",
      "Success!\n",
      "Assembler Process...\n",
      "Columns: ['avg_duration_monthly', 'avg_duration_daily', 'session_count_monthly', 'session_count_daily', 'avg_item_per_session', 'event_Settings_monthly_avg', 'event_Add Friend_monthly_avg', 'event_Thumbs Down_monthly_avg', 'event_Downgrade_monthly_avg', 'event_Submit Upgrade_monthly_avg', 'event_Roll Advert_monthly_avg', 'event_NextSong_monthly_avg', 'event_Error_monthly_avg', 'event_About_monthly_avg', 'event_Upgrade_monthly_avg', 'event_Add to Playlist_monthly_avg', 'event_Home_monthly_avg', 'event_Thumbs Up_monthly_avg', 'event_Logout_monthly_avg', 'event_Submit Downgrade_monthly_avg', 'event_Save Settings_monthly_avg', 'event_Help_monthly_avg', 'event_Settings_daily_avg', 'event_Add Friend_daily_avg', 'event_Thumbs Down_daily_avg', 'event_Downgrade_daily_avg', 'event_Submit Upgrade_daily_avg', 'event_Roll Advert_daily_avg', 'event_NextSong_daily_avg', 'event_Error_daily_avg', 'event_About_daily_avg', 'event_Upgrade_daily_avg', 'event_Add to Playlist_daily_avg', 'event_Home_daily_avg', 'event_Thumbs Up_daily_avg', 'event_Logout_daily_avg', 'event_Submit Downgrade_daily_avg', 'event_Save Settings_daily_avg', 'event_Help_daily_avg', 'avg_length', 'max_length', 'tenure_days', 'gender_vec', 'level_vec', 'browser_vec', 'os_vec'] assembled to the features column\n",
      "Success!\n",
      "Pipeline fit...\n",
      "Success!\n"
     ]
    }
   ],
   "source": [
    "#Feature Engineering \n",
    "#Index the categorical variables with StringIndexer\n",
    "#Encode the Output of the Indexers with OneHotEncoder\n",
    "#Assemble all variables into one single vector with VectorAssembler\n",
    "model_data = prepare_data(df_ready)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- features: vector (nullable = true)\n",
      " |-- target: integer (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Ready Data for Modelling\n",
    "model_data.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After preprocessing step we have data with one feature vector and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Column names for visualisation after prediction\n",
    "columns_ordered = [column[\"name\"] for  column in model_data.schema[\"features\"].metadata[\"ml_attr\"][\"attrs\"][\"numeric\"] + \\\n",
    "    model_data.schema[\"features\"].metadata[\"ml_attr\"][\"attrs\"][\"binary\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling\n",
    "\n",
    "Split the dataset into train and validation set, after split step we initalized models and evaluated the results. As i mentioned before dataset has highly skewed variables so that we prefered RandomForest and GBTClassifier in the first step. Because these models are more robust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly split data\n",
    "train, test = model_data.randomSplit([0.8,0.2], seed=123)\n",
    "\n",
    "#Create RandomForest, GradientBoosting and LogisticRegression Classifiers\n",
    "clf_RF = RandomForestClassifier(seed=123, labelCol='target')\n",
    "clf_GBT = GBTClassifier(seed=123, labelCol='target')\n",
    "clf_LR = LogisticRegression(labelCol='target')\n",
    "\n",
    "#Metric evaluator for predictions and targets\n",
    "evaluator= MulticlassClassificationEvaluator(predictionCol=\"prediction\", labelCol='target')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************************************************************************\n",
      "RandomForestClassifier is training\n",
      "\n",
      "fitted in 478.97 seconds\n",
      "RandomForestClassifier is predicting\n",
      "\n",
      "predicted in 0.1 seconds\n",
      "Evaluating...\n",
      "Evaluated in 700.3 seconds\n",
      "\n",
      "Test F1-score:  0.8010604453870624\n",
      "****************************************************************************************************\n",
      "GBTClassifier is training\n",
      "\n",
      "fitted in 605.47 seconds\n",
      "GBTClassifier is predicting\n",
      "\n",
      "predicted in 0.08 seconds\n",
      "Evaluating...\n",
      "Evaluated in 694.21 seconds\n",
      "\n",
      "Test F1-score:  0.7470399357816576\n",
      "****************************************************************************************************\n",
      "LogisticRegression is training\n",
      "\n",
      "fitted in 648.01 seconds\n",
      "LogisticRegression is predicting\n",
      "\n",
      "predicted in 0.07 seconds\n",
      "Evaluating...\n",
      "Evaluated in 682.62 seconds\n",
      "\n",
      "Test F1-score:  0.777245423340961\n"
     ]
    }
   ],
   "source": [
    "results = {}\n",
    "\n",
    "for clf in [clf_RF, clf_GBT,clf_LR]:\n",
    "    \n",
    "    model_results = {}\n",
    "    # Get the model name for representation\n",
    "    clf_name = clf.__class__.__name__\n",
    "    \n",
    "    # Fit the training data into the model\n",
    "    print(\"*\"*100)\n",
    "    print('{} is training\\n'.format(clf_name))\n",
    "    start = time.time() \n",
    "    model = clf.fit(train,)\n",
    "    end = time.time()\n",
    "    time_passed = end - start\n",
    "    model_results['train_time'] = round(time_passed,2)\n",
    "    print(\"fitted in {} seconds\".format(round(time_passed,2)))\n",
    "    \n",
    "    # Prediction\n",
    "    print('{} is predicting\\n'.format(clf_name))\n",
    "    start = time.time() \n",
    "    pred_test = model.transform(test)\n",
    "    end = time.time()\n",
    "    time_passed = end - start\n",
    "    model_results['pred_time'] = round(time_passed,2)\n",
    "    print(\"predicted in {} seconds\".format(round(time_passed,2)))\n",
    "    \n",
    "    \n",
    "    #Evaluate the results of prediction    \n",
    "    print('Evaluating...')\n",
    "    start = time.time() \n",
    "    model_results['f1_test'] = evaluator.evaluate(pred_test.select('target','prediction'),{evaluator.metricName: 'f1'})\n",
    "    model_results['accuracy_test'] = evaluator.evaluate(pred_test.select('target','prediction'), {evaluator.metricName: \"accuracy\"})\n",
    "    model_results['precision_test'] = evaluator.evaluate(pred_test.select('target','prediction'), {evaluator.metricName: \"precisionByLabel\"})\n",
    "    model_results['recall_test'] = evaluator.evaluate(pred_test.select('target','prediction'), {evaluator.metricName: \"recallByLabel\"})\n",
    "    end = time.time()\n",
    "    time_passed = end - start\n",
    "    print(\"Evaluated in {} seconds\\n\".format(round(time_passed,2)))\n",
    "    print('Test F1-score: ',model_results['f1_test'])\n",
    "    \n",
    "    results[clf_name] = model_results\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <th>GBTClassifier</th>\n",
       "      <th>LogisticRegression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_time</th>\n",
       "      <td>478.970000</td>\n",
       "      <td>605.470000</td>\n",
       "      <td>648.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_time</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_test</th>\n",
       "      <td>0.801060</td>\n",
       "      <td>0.747040</td>\n",
       "      <td>0.777245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy_test</th>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_test</th>\n",
       "      <td>0.842697</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.858974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall_test</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.853333</td>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RandomForestClassifier  GBTClassifier  LogisticRegression\n",
       "train_time                  478.970000     605.470000          648.010000\n",
       "pred_time                     0.100000       0.080000            0.070000\n",
       "f1_test                       0.801060       0.747040            0.777245\n",
       "accuracy_test                 0.847826       0.750000            0.782609\n",
       "precision_test                0.842697       0.842105            0.858974\n",
       "recall_test                   1.000000       0.853333            0.880000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first results look nice\n",
    "\n",
    "- Train Time: <b>Random Forest</b> is the Best\n",
    "- F1 Score: <b>Random Forest</b> is the Best\n",
    "- Accuracy Score: <b>Random Forest</b> is the Best\n",
    "- Precision: <b>Logistic Regression</b> is slightly higher than the <b>Random Forest</b> \n",
    "- Recall: <b>Random Forest</b> is the Best\n",
    "\n",
    "When we look at the F1 Score, <b>Random Forest beats the others</b>. On the other hand recall of the RandomForest is 1. It means that the model caught all churners correctly.\n",
    "\n",
    "\n",
    "\n",
    "I chose <b>RandomForest</b> for parameter tuning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULTS:\n",
      "**********\n",
      "The F1 score is 0.8208786231884058\n",
      "The Accuracy score is 0.8478260869565217\n",
      "The Precision score is 0.8588235294117647\n",
      "The Recall score is 0.9866666666666667\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "#Take the best model after parameter tuning\n",
    "best_model = rf_tuning(train, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F1 score increased from 0.8 to 0.82, our recall score slightly decreased but precision is around the 0.86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters : max depth:10, num Trees:20\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters : max depth:{}, num Trees:{}'.\\\n",
    "          format(best_model.stages[0].getOrDefault('maxDepth'), best_model.stages[0].getNumTrees))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vqmraLV5ZhvGfxm+bmm18sG3FXLfE7Tv29/em/H2v2Sr2n82e/ewlw5q2ID+X7kENW9INcH5aa+2aiTJLPedm893+de+qWkwPzNF1Nvjcaq3d3Fo7qbX2+621Q9L1evr1JN9K1zvv3f0thgtxWWbCi1k/04l1W/Mznc0fpnuy40FJ/mDK+tH3z1GttTe11r7VWpsMa7bWuTC6Jua6nmZbt1aO/2LN+z3RGx2XIe9ptV1LAKxxgjUAluqTmek18PS5Cs5i9ANq6oD0VXXnJA+bo/4ohJurN8mWKfubNNc+5tVa25LknH52yHFYS+Z66MJo3WVj46slydn962Fz1N2QmVvKZhvTaT4LOR9G58BX+gHip3ncwP3P55P967p0g6gv1GjMqH2ralke4tFau6q19u50vUaT7ra4BYUN/XhS/9nPzvWZjo7jzUn+Y0g7l6K/LkcP6vjjqpocC2y+75/1SYb8D4OFGF0Tc11PU3s+rZXjP8Atx6QfJuA2quqgzAT4Q74nVuJauuV/FtUCByIFYO0QrAGwJP2T8EZPQHxJVd1vrvL9U0THjZ6K+OOzVHlFukHlZ3Nl/7rbHGW+mu5WryT55Sltul2S/zVH/YV6W/96WFXNGa5NOQ5rya9U1YGTC6tqryS/1c/+48Tq9/avD6+qn51Sd4ckowc+fGnaUw4XaCHnw+icu9+0HoxVdXCSXxu4/zm11s5Pcno/+9qqussCq344M73d3tyPDTar8fNrAT1nxnvrLeZW29Fn+tSqetDkyj4Uf2k/+6+LeALqcntLkh+k66X44ol1833/zPWk46UaXSOPqqoNkyv7W1dfMkf9tXL8F2P0nvbNzFOWJx3Vv16auR/cMJuVuJauHPt7ru8mANYgwRoAy+GPknw/3Q/XM6rqWeO3VlbVXlX1lKr6YLoB48f9W//63Kp63uiHS1XtU1VvTPfD8Ptz7HsUwDx1Sm+UJElr7YYkH+hnX1ZVTxvbz4FJ/m9m/2G9GMdlZjymv6+q11TVLT3kqupOVbWhqo5NMtuTO9eCa9M9Ee9xo94XVfWT6X7k7pVuoPPJQOIDmTk2/1RVv1ZVO/Z1D+jXP7xf/9IMNzofHlhVj5ilzL+n60GyR5J3VdW+fTt2qqqn9evnGox+qX4/3TG8b5Izq+oJY8fiTlX1sKo6rqpu6TXXWrs23RNMW7rxnc6sqseP/9CvqgOq6req6jN92ZFHVNV/VtWLqur+o55A1XlEkrf25b6d6U9inc1b0z2cYsckJ1bV4WPb/rEkJ6V7Gub1SV6+iO0uq34g+eP62Rf2AfDI6Pvn5f131A7JLcfy3Umellv3eF1OH8hML7IPVNUvV9W6fv/3T/fU2Lke+LAmjv9itNY+k5nv6r+qqt8bBV/9vwlvz8z4h6/or4vF7mMlrqWvZuZhPM/Raw1gG9NaM5lMJpPplinJxnQ/ONoi6z0k3Y+81k83pxsH6KqxZS3JyRP1dkty7tj6m9L9kL25nz8u3QDkLcnxU/b738fK3pjkO0k2J9k8Ue6e6Z40N9rP9el6q7R0vQkePbZuw0TdDQs9JumCpY9NvOcrJt5TS3LDgM9mdBw2T1l35Gzrpny2mxawj2nHetT2Z6Xr8dGS/HDiM742yc/Psu190wVfo7LX9cdl/LN/4Sx1N/dljpznGO2Q5LyxbV42Oh+SPHWs3F9MfEaX9+dES/KNdD3Wpn7mCzyOc54zSX623+f4+XjZRJuePKXer/fH/JbzKF3PnWsn6v7JtLaM7evSvu74OXrogHPyQelChNF2rsnMdTU6H546S915j+MC9j86L25zvk6U26dvW0vy+rHl+6cbc2v8eI5/Lv8ryab+741zXBMb5tj3XPXvnW5crvHjNdr/dUmeONc+lnj852x7kvVjZdYv9jzPzHfSrPVn2eauY8ds9Jlcllt/f75+lrqzHuuVvpaSvGOszA+TfLM/f/9y6PlvMplMptUx6bEGwLJorX0+yQOS/F66nkuXpruF83ZJvpbk3enGHnvKRL3Lkzwi3ThIm9OFKzem+4H0q62158+z39OT/Hy/zyvSjW2zfz+Nl/t2unHU3pEuYEu628NOSPLQ1tppi37T09tzabpxjZ6U7mmm/5Xu6ZN37Pd7YrpjtH459rdCvpEuSP3rdIOH75Ru0PH3JHlIa+2j0yq11i5Mcki6AeU/nS4EuFO6Y/T3SX6itfaWpTSstXZjujGn3pHufNo5M+fDncfK/XGSZyT5TN+OHZOcn+S1/Xv7zlLasYB2/nu6Hmt/lm58r2vSnSOb0/U0+q10TyydrPeudGN+vSbdeFQ/SBdOX5vkC0mOTXf+HT1W7bPpel69Nd0TDC9NF16M6rwuyf1ba58Y8D6+lOSB6UKyL6S7dm+frkfmcUke2Fp7/6wb+BFprV2U5O/62d+tqn365d9Md07+bWY+82uTfCTJ41trf76V2/WNJAcnOSbd/5iofv/vT/KI1tq/zFN/TRz/xWjdLauHpRuvbFO64P7O6QLQDyR5TGttrltkF7qfH/W19LvpPqdRr9r90n0v7TWlLABrSLXWVroNAMAaUFWj/2h4TGtt00q2BQAAVgM91gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAhxfMYq+99mrr169f6WYAAAAAbDM+97nPXdpa23ul27FcdljpBqxW69evz9lnn73SzQAAAADYZlTVN1e6DcvJraAAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABdljpBqxFmw84YKWbsFWtv+CClW4CAAAAwKqnxxoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYYMWDtap6QFV9rKqurqrvVNVRVbVunjo7VdXrq+oTVXVNVbU5yj6pqr5YVddW1TlVdcTyvwsAAAAAtjcrGqxV1e5JTknSkjwpyVFJ/ijJq+epeqckz0lydZJPzrH9RyX5QJJTkxye5KNJ3lNVP7vkxgMAAACwXdthhff//CR3TPKU1tqVSU6uqrsk2VhVr+uX3UZr7fKq2qO11qrq95I8dpbtvyLJ6a21F/bzp1bVA5O8Msm/L+9bAQAAAGB7stK3gh6e5KSJAO296cK2R89VsbU26+2fSVJVt0/ymCT/NLHqvUkeXlW7Lr65AAAAANBZ6WDtoCTnjS9orX0r3S2eBy1x2/dJsuPk9pOcm+5932+J2wcAAABgO7bSwdruSS6fsnxLv26p286U7W+ZWH+LqnpeVZ1dVWdfcsklS9w9AAAAANuylQ7Wku7BBZNqluXLsf2abb+ttbe11g5prR2y9957L9PuAQAAANgWrXSwtiXJblOW75rpPdkWu+1M2f5ofqnbBwAAAGA7ttLB2nmZGEutqu6VZOfcdmy0xfp6khsmt9/P35zkq0vcPgAAAADbsZUO1k5M8viq2mVs2RFJrkly2lI23Fq7LsmpSX5lYtURST7VWrtiKdsHAAAAYPu2wwrv/7gkL0zywao6Osm9k2xMckxr7cpRoao6P8lprbVnjy07PF3PtoP7+af2qz7bWvtm//efJtlUVW9K8qEkP9dPT9iabwoAAACAbd+KBmuttS1VdViSY5N8ON24Z29MF66N2yHJuollb02y/9j8+/rXZyY5vt/+GX3g9pokv53kgiS/1lr79+V7FwAAAABsj1a6x1paa+ckeew8ZdYvZNksdT+UrrcaAAAAACyblR5jDQAAAADWJMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAA6x4sFZVD6iqj1XV1VX1nao6qqrWLaDerlX1zqraUlVXVNW7qmrPiTI7VdUrq+r8qrqmf311Vd1+670jAAAAALYHO6zkzqtq9ySnJDknyZOS3CfJG9IFfi+fp/o/JjkwyXOS3Jzk6CQfSnLoWJm/SPL8flufT/LQJK9JsluS31+u9wEAAADA9mdFg7V0odcdkzyltXZlkpOr6i5JNlbV6/plt1FVD0/y+CSPbq2d3i+7MMlZVfW41topfdFfS/LW1tox/fypVbVvkl+PYA0AAACAJVjpW0EPT3LSRID23nRh26PnqXfxKFRLktbaZ5Jc0K8b2THJFRN1L09SS2k0AAAAAKx0sHZQkvPGF7TWvpXk6n7dguv1zp2o944kv1VVj6yqO1fVoUl+O8mxS2o1AAAAANu9lb4VdPd0PcgmbenXDal377H5P07X++2MsWX/u7V21LSNVtXzkjwvSfbbb785dg8AAADA9m6le6wlSZuyrGZZvth6L0nyP5K8IN2tpS9M8utVNTVYa629rbV2SGvtkL333nvehgMAAACw/VrpHmtb0j2hc9Kumd4jbbzetORrt1G9qtor3RNAf7e19vZ+/elVdX2SY6vq2Nba9wa3HAAAAIDt2kr3WDsvE2OpVdW9kuyc6WOozVqvNz722r3TPbzgCxNlPp8uUNx/QHsBAAAAIMnKB2snJnl8Ve0ytuyIJNckOW2eevtU1aNGC6rqkHRh2on9om/2rw+dqPsT/evmgW0GAAAAgBW/FfS4dOOefbCqjk4XjG1Mckxr7cpRoao6P8lprbVnJ0lr7VNVdVKSE6rqxUluTnJ0kjNaa6f0ZS6uqg8lObqq7pDkP5Mc3G//fa21S35E7xEAAACAbdCKBmuttS1VdViSY5N8ON34aG9MF36N2yHJuollT+/L/l26nncfSRfSjfvNJK/sl98jyYVJ/ibJny7bmwAAAABgu7TSPdbSWjsnyWPnKbN+yrLLkzyzn2ard2WSF/cTAAAAACyblR5jDQAAAADWJMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAMH+sygAACAASURBVAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgAAAAAMIFgDAAAAgAEEawAAAAAwgGANAAAAAAYQrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAPssJTKVbVzkvsluXNr7RPL0yQAAAAAWP0G9VirqntW1QeSbElydpJTx9Y9qqrOqaoNy9NEAAAAAFh9Fh2sVdXdk5yV5ElJPpLkU0lqrMhZSe6a5IjlaCAAAAAArEZDeqy9Kl1w9rjW2lOSnDy+srV2Q5JPJHnk0psHAAAAAKvTkGDt55L8S2tt0xxlvpXkHoNaBAAAAABrwJBg7W5JvjZPmRuS7Dxg2wAAAACwJgwJ1i5Lcq95ytwvyUUDtg0AAAAAa8KQYO3MJE+sqn2mrayq+yZ5QsaeFAoAAAAA25ohwdrrk9whyWlVdXiSOyVJVe3cz384yc1J3rBsrQQAAACAVWaHxVZorZ1VVc9LclySj4yturJ/vTHJs1prX16G9gEAAADAqrToYC1JWmvvrKozkvxOkp9OsmeSK5J8OsmxrbWvLF8TAQAAAGD1GRSsJUlr7WtJXrSMbQEAAACANWPIGGsAAAAAsN1bdLBWVb9SVR+vqnvMsn7fqvpYVT1l6c0DAAAAgNVpSI+15yTZrbX2nWkrW2sXJrlLXw4AAAAAtklDgrUfS3L2PGXOTvLgAdsGAAAAgDVhSLC2R5LvzVPm+0n2WsjGquoB/a2jV1fVd6rqqKpat4B6u1bVO6tqS1VdUVXvqqo9p5Tbs6r+pqouqqprquq8qnrGQtoGAAAAALMZ8lTQS5Pcd54y901y+Xwbqqrdk5yS5JwkT0pynyRvSBf4vXye6v+Y5MB0t5zenOToJB9KcujY9u+S5PQkP0jygr7tD0iy03xtAwAAAIC5DAnWzkzyxKo6qLV23uTKqrp/upDswwvY1vOT3DHJU1prVyY5uQ/DNlbV6/plt1FVD0/y+CSPbq2d3i+7MMlZVfW41topfdGXJbl9kkNaa9f0y05d8DsFAAAAgFkMuRX0L9MFcmdU1Qur6n5VtXP/+vtJPpFkXV9uPocnOWkiQHtvurDt0fPUu3gUqiVJa+0zSS7o1408M8nfjoVqAAAAALAsFh2stdY+m+R30j35841Jzk1yZf96TL/8t1trZy1gcwcluVWvt9bat5Jc3a9bcL3euaN6VXVAkrsmubyq/rWqrq+qS6rqmKpyKygAAAAASzLkVtC01t5eVWekC9gelmS3dGOqfTrJW1tr5y5wU7tn+lhsW/p1Q+rdu/97n/71del6wT0hyY8neW2SG5O8dLJyVT0vyfOSZL/99pu/9QAAAABstwYFa0nSh2cvWIY2tCnLapbli6k36o335dbac/u/P15VuyR5WVVtbK1dfasNtva2JG9LkkMOOWS+/QMAAACwHRsyxtpy2pKut9ukXTP3U0VnqzfqOZckl/Wvkw8r+Hi6BxrcZ+HNBAAAAIBbG9xjrarWJTkw3W2Z66aVGX+4wCzOy8RYalV1ryQ7Z/oYauP1Dp2y/KAkH+r//nqS66eUqf715nnaBgAAAACzGhSsVdUrkrwoXc+yuUwN3MacmOQlVbVLa+2qftkRSa5Jcto89V5RVY9qrZ3Rt+mQdOOrnZgkrbXrq+rkJI+dqHtYuocjnD9P2wAAAABgVosO1qrqpUleneSKJH+f5L/SPQxgiOOSvDDJB6vq6HTB2MYkx7TWrhzb5/lJTmutPTtJWmufqqqTkpxQVS9O1/vs6CRntNZOGdv+UUnOqKp3JnlPkgcn+eMkf9pau25gmwEAAABgUI+15ya5MMlDW2uXLGXnrbUtVXVYkmOTfDjd+GhvTBeuTbZzsvfb0/uyf5durLiPpAvpxrf/mar6xSR/nuTXknwvyZ/18wAAAAAw2JBg7V5J3r7UUG2ktXZObnu75mSZ9VOWXZ7kmf00V92Tkpy0hCYCAAAAwG0MeSroxVnCQw8AAAAAYFswJFj7pyQ/U1W3X+7GAAAAAMBaMSRYe2WS7yZ5f1UdsMztAQAAAIA1YcgtnV9OsmOSeyT5uaq6It1DBya11tp9ltI41pbNB2zbOev6Cy5Y6SYAAAAAq8iQYO12SW5M8q2xZTWl3LRlAAAAALBNWHSwNu0JnQAAAACwvRkyxhoAAAAAbPcEawAAAAAwwJAx1pIkVXX7JD+ZZN8kt59WprV2wtDtAwAAAMBqNihYq6pnJXldkt1nK5KkJRGsAQAAALBNWvStoFX1hCTvSPLdJC9OF6L9c5I/SXJyP/++JM9avmYCAAAAwOoyZIy1P0ry/SSPaK29sV/2hdbaX7TWnpDkuUmekuTry9RGAAAAAFh1hgRrD03y4dbaVdO201r72yRnpuvBBgAAAADbpCHB2s7pbgMduTbJXSbKnJ3kYUMbBQAAAACr3ZBg7aIke4/NfzfJgRNldk2ybmijAAAAAGC1GxKsfTm3DtI+keSwqjo0SarqQUme1pcDAAAAgG3SkGDtxCSPrKp79POvS3JTkk1VdUmS/5dklySvWZ4mAgAAAMDqMyRY+5sk+ya5NElaa+ckOSxd4HZpkn9Pcnhr7V+Xq5EAAAAAsNrssNgKrbUbklw8sezTSX5huRoFAAAAAKvdonusVdUzqurB85R5UFU9Y3izAAAAAGB1G3Ir6PFJnjxPmScleeeAbQMAAADAmjAkWFuIdUnaVto2AAAAAKy4rRWs3S/Jlq20bQAAAABYcQt6eEFV/d3EoidX1fopRdcl2S/JoUk+uqSWwTZi8wEHrHQTtqr1F1yw0k0AAACAFbHQp4IeOfZ3S3JwP03TkpyV5EXDmwUAAAAAq9tCg7VRl5tK8o0kb0ry5inlbkqypbX2w2VoGwAAAACsWgsK1lpr3xz9XVWvTnLq+DIAAAAA2N4stMfauCOT3DXJ6cvbFAAAAABYO4Y8FXSvJFcsd0MAAAAAYC0ZEqx9Ocl9lrshAAAAALCWDAnW3pLkF6vqwcvdGAAAAABYK4aMsfbtJKckObOq/ibJZ5NclKRNFmytGYcNAAAAgG3SkGBtU7oQrZL8YaYEamPWDdg+AAAAAKx6Q4K1ozJ3mAYAAAAA27xFB2uttY1boR0AAAAAsKYMeXgBAAAAAGz3htwKeouqelSShyTZLckVSf6jtXbGcjQMAAAAAFazQcFaVT00yT8kOXC0KP24a1X1lSTPaK2dvSwtBAAAAIBVaNHBWlX9tyQfT3KXJGf0f383yd2TPCbJoUlOrqqfaq19bRnbCgAAAACrxpAea69IcuckR7TW3jexbmNVPTXJe5O8PMlvLrF9AAAAALAqDXl4weOSfGhKqJYkaa29P8k/9+UAAAAAYJs0JFjbK8l585Q5ry8HAAAAANukIcHaJUkeME+Zg5JcOmDbAAAAALAmDAnWPp7kiVX19Gkrq+qXkzwpySlLaRgAAAAArGZDHl5wVLrg7F1V9btJTk33VNB9kmxI8qgkVyV5zTK1EQAAAABWnUUHa62186vqcUlOSPLIfmpJqi/ylSS/2Vr72rK1EgAAAABWmSE91tJa+2yS+1fVI5I8NMmuSa5I8vnW2pnL2D4AAAAAWJUGBWsjrbVPJvnkMrUFAAAAANaMJQVrVbVjkvtnpsfaua21G5ajYQAAAACwmg15Kmiqas+qenuSy5N8Psmm/vXyqnp7Ve21fE0EAAAAgNVn0T3WqupuSc5Mcu90vdQ+k+SidE8FPTjJs5M8pqoe2Vq7eBnbCgAAAACrxpAea69NF6q9Kcn+rbXHtNZ+tbX2mCT7J3lzv/7Plq+ZAAAAALC6DBlj7ReSfKK19oeTK1prVyZ5UVUdkuQXl9o4AAAAAFithvRY2yXJGfOU+USSOw/YNgAAAACsCUOCtfOS3H2eMndP8pUB2wYAAACANWFIsPbmJEdU1YOnrayqg5M8Ld0YbAAAAACwTRoyxtoFSU5O8pmqOiHJ6UkuTnK3JI9O8htJTkyyuar++3jF1trpS2suAAAAAKwOQ4K1TUlakkrynCTPHltX/esT+2nSugH7AwAAAIBVZ0iwdlS6YA0AAAAAtluLDtZaaxu3QjsAAAAAYE0Z8vACAAAAANjuCdYAAAAAYIAhY6ylqu6Z5EVJDk5yzyQ7TinWWmv3WULbAAAAAGDVWnSwVlUbkvxrkjskuTHJxf3rbYouqWUAAAAAsIoN6bH2uiTrkjwjybtbazcvb5MAAAAAYPUbEqz9WJL3tNb+YbkbAwAAAABrxZCHF2xJctlyNwQAAAAA1pIhwdpHkjx6uRsCAAAAAGvJkGDtZUl2raq/rqqdl7tBAAAAALAWLHqMtdbapVX1hCRnJXlGVX01yRXTi7bDltpAAAAAAFiNFh2sVdUDk5yaZPd+0UNmKdqGNgrY9m0+4ICVbsJWs/6CC1a6CQAAAPwIDLkV9JgkeyZ5ZZL9k+zYWrvdlGndsrYUAAAAAFaRRfdYS/LwJB9srb1muRsDAAAAAGvFkB5r1yfZvMztAAAAAIA1ZUiwtinJTy1zOwAAAABgTRkSrL00yQOq6o+rqpa7QQAAAACwFgwZY+3lSb6U5M+SPLeqvpDkiinlWmvt2UtpHAAAAACsVkOCtSPH/j6gn6ZpSQRrAAAAAGyThgRrswVpAAAAALDdWHSw1lr75tZoCAAAAACsJUMeXgAAAAAA2z3BGgAAAAAMsKBbQavqpgHbbq21IWO4AQAAAMCqt9DgqwZse0gdAAAAAFgTFhSstdbcMgoAAAAAYwRmAAAAADCAYA0AAAAABhCsAQAAAMAAKx6sVdUDqupjVXV1VX2nqo6qqnULqLdrVb2zqrZU1RVV9a6q2nOO8k+uqlZVZy/vOwAAAABge7TQp4JuFVW1e5JTkpyT5ElJ7pPkDekCv5fPU/0fkxyY5DlJbk5ydJIPJTl0yn7ukOSYJBcvV9sBAAAA2L6taLCW5PlJ7pjkKa21K5OcXFV3SbKxql7XL7uNqnp4kscneXRr7fR+2YVJzqqqx7XWTpmo8pIkFyb5epIHbaX3AgAAAMB2ZKVvBT08yUkTAdp704Vtj56n3sWjUC1JWmufSXJBv+4WVbVfkpcm+f3lajQAAAAArHSwdlCS88YXtNa+leTqft2C6/XOnVLvDUn+qbX2H0toJwAAAADcykrfCrp7ksunLN/SrxtS796jmap6TLpbRu+3hDYCAAAAwG2sdI+1JGlTltUsyxdcr6p2SPKWJK9prV20kIZU1fOq6uyqOvuSSy5ZSBUAAAAAtlMrHaxtSbLblOW7ZnqPtPnq7TZW77n9/P+pqt2qarckOyVZ18/vOFm5tfa21tohrbVD9t5778W8DwAAAAC2Myt9K+h5mRgTrarulWTnTB9DbbzeoVOWH5TkQ/3fBya5Z5JpvdW2JPmNJP+wyPYCAAAAQJKV77F2YpLHV9UuY8uOSHJNktPmqbdPVT1qtKCqDkk3vtqJ/aJjkzxmYjopyVf7v09epvcAAAAAwHZopXusHZfkhUk+WFVHpwvGNiY5prV25ahQVZ2f5LTW2rOTpLX2qao6KckJVfXiJDcnOTrJGa21U/oy5yc5f3xnVXVkkr1aa5u28vsCAAAAYBu3oj3WWmtbkhyWZF2SDyd5dZI3JnnVRNEd+jLjnp6uV9vfJTkhyeeS/NLWbC8AAAAAjKx0j7W01s5J8th5yqyfsuzyJM/sp4Xu68hFNg8AAAAAplrpMdYAAAAAYE0SrAEAAADAAII1AAAAABhAsAYAAAAAAwjWAAAAAGAAwRoAAAAADCBYAwAAAIABBGsAAAAAMIBgDQAAAAAGEKwBAAAAwACCNQAAAAAYQLAGAAAAAAMI1gAAAABgAMEaAAAAAAwgWAMAAACAAQRrAAAAADCAYA0AAAAABhCsAQAAAMAAgjUAAAAAGECwBgAAAAADCNYAAAAAYADBGgD8f/buO0ySqmzY+P2wu2RYWCQjSXKSKAgoUbJEESUIGBAQTKCYERTEhLyfGF4EjCBBkqIIkhUMiAkEBJGg6IsoKznJnu+P5zRT29uzM9vsTE+4f9dV105X6K06XXXq1FMnSJIkSVIXDKxJkiRJkiRJXZjY6x2QJKV7V1ih17swpJa/555e74IkSZIkzVbWWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrrgqKCSpBHN0VIlSZIkjVTWWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC5M7PUOSJKkWXfvCiv0eheG1PL33NPrXZAkSZIGZI01SZIkSZIkqQvWWJMkSWPGWK7JZy0+SZKkkccaa5IkSZIkSVIXDKxJkiRJkiRJXTCwJkmSJEmSJHXBPtYkSZLGsLHc7xzY95wkSeota6xJkiRJkiRJXbDGmiRJksYda/J1ZrpIkjRrrLEmSZIkSZIkdcEaa5IkSZI0E2O5Jp+1+CTpxbHGmiRJkiRJktQFa6xJkiRJkmbJWK7FB9bkkzR4BtYkSZIkSZoNDDhK449NQSVJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCfaxJkiRJkqQhY99zGsussSZJkiRJkiR1wRprkiRJkiRJw2ws1+QbT7X4rLEmSZIkSZIkdcHAmiRJkiRJktQFA2uSJEmSJElSFwysSZIkSZIkSV0wsCZJkiRJkiR1wcCaJEmSJEmS1AUDa5IkSZIkSVIXDKxJkiRJkiRJXTCwJkmSJEmSJHXBwJokSZIkSZLUBQNrkiRJkiRJUhcMrEmSJEmSJEldMLAmSZIkSZIkdcHAmiRJkiRJktQFA2uSJEmSJElSF3oeWIuINSLiqoh4MiL+HhHHR8SEQWw3OSK+HhFTI+KRiDgrIhZpLJ8QEcdExE8j4t91uiIiNhraI5IkSZIkSdJ40NPAWkQsDFwJFGA34HjgKOC4QWx+LrAl8FbgIGAj4OLG8nmADwA3AQcA+wPPAT+LiA1mywFIkiRJkiRp3JrY4///UDIAtmcp5VHgJxGxIPDxiPhMnTeDiHglsD2wRSnl+jrvAeCXEbFtKeVK4ClgxVLK1MZ2VwF3AkcABw/lgUmSJEmSJGls63VT0B2By9sCaOeQwbYtBtjuwVZQDaCU8ivgnrqMUsrzzaBanfcs8Edgsdmz+5IkSZIkSRqveh1YWw24ozmjlHI/8GRdNujtqttntl1EzAVsANw2y3sqSZIkSZIkNfQ6sLYw8J8O86fWZbN7uw/X5ad3WhgRh0TEryPi1w899NBMvkaSJEmSJEnjXa8Da5ADF7SLfuZ3vV1E7EwG1o4ppfyp4xeWclopZcNSyoaLLrroAP+9JEmSJEmSxrNeB9amAgt1mD+ZzjXSBtpuoU7bRcRG5Cii/1tKOaWL/ZQkSZIkSZKm0+vA2h209YkWES8F5qNzH2r9blfN0PdaRKwC/BC4CjjyxeysJEmSJEmS1NLrwNplwPYRsUBj3j7AU8B1A2y3RERs3poRERsCK9ZlrXlLApcDdwNvLKU8Pxv3XZIkSZIkSeNYrwNrXwWeAS6MiG0j4hDg48DJpZRHWytFxJ8j4ozW51LKz8mA2bciYs+I2B04C/hZKeXKus08ZJBtYeCTwDoRsUmd1hum45MkSZIkSdIYNbGX/3kpZWpEbAOcCvyA7B/tC2RwrWkiMKFt3hvqumeSAcJLgXc2li8OvLz+fWnbtvcBy7+4vZckSZIkSdJ41tPAGkAp5TZg6wHWWb7DvP8AB9ep0zb3kqOESpIkSZIkSbNdr5uCSpIkSZIkSaOSgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK6YGBNkiRJkiRJ6oKBNUmSJEmSJKkLBtYkSZIkSZKkLhhYkyRJkiRJkrpgYE2SJEmSJEnqgoE1SZIkSZIkqQsG1iRJkiRJkqQuGFiTJEmSJEmSumBgTZIkSZIkSeqCgTVJkiRJkiSpCwbWJEmSJEmSpC4YWJMkSZIkSZK60PPAWkSsERFXRcSTEfH3iDg+IiYMYrvJEfH1iJgaEY9ExFkRsUiH9XaLiFsi4umIuC0i9hmaI5EkSZIkSdJ40tPAWkQsDFwJFGA34HjgKOC4QWx+LrAl8FbgIGAj4OK2798cuAC4BtgR+CHw3YjYbrYcgCRJkiRJksatiT3+/w8F5gH2LKU8CvwkIhYEPh4Rn6nzZhARrwS2B7YopVxf5z0A/DIiti2lXFlX/ShwfSnlnfXzNRGxJvAx4IqhOyxJkiRJkiSNdb1uCrojcHlbAO0cMti2xQDbPdgKqgGUUn4F3FOXERFzAVsB57Vtew7wyoiY/OJ3X5IkSZIkSeNVrwNrqwF3NGeUUu4HnqzLBr1ddXtju5cBkzqsdzt53Kt0sb+SJEmSJEkS0PumoAsD/+kwf2pd1s12KzbWocN6U9uWvyAiDgEOqR8fj4g/zWQfhtNLgH8N2/8WMWz/1YtkunRmunQ2fOlimnRmunRmunRmuszINOnMdOnMdOnMdJmRadKZ6dKZ6dKZ6TKjmafJcsOyD8Ok14E1yIEL2kU/87vZrv1z9DOfUsppwGkD/L/DLiJ+XUrZsNf7MdKYLp2ZLp2ZLjMyTTozXTozXTozXWZkmnRmunRmunRmuszINOnMdOnMdOnMdBkavW4KOhVYqMP8yXSukTbQdgs1tpvamNe+DgN8vyRJkiRJkjRTvQ6s3UFbX2oR8VJgPjr3odbvdlWz77W7gec6rLcaMA24s4v9lSRJkiRJkoDeB9YuA7aPiAUa8/YBngKuG2C7JSJi89aMiNiQ7F/tMoBSyjPANcDebdvuA/y8lPLIi9/9YTPimqeOEKZLZ6ZLZ6bLjEyTzkyXzkyXzkyXGZkmnZkunZkunZkuMzJNOjNdOjNdOjNdhkCUMlBXZkP4n0csDNwG3Ap8mgyMnQycUkr5SGO9PwPXlVLe0pj3Y3Jkz6PJGmifBv5ZSnlVY53NgWuBU4GLgZ3q+juUUq4Y0oOTJEmSJEnSmNbTGmullKnANsAE4AfAccAXgGPbVp1Y12l6A1mr7UzgW8DNwB5t3/8z4HXAtsDlwK7AvgbVJEmSJEmS9GL1tMaaJEmSJEmSNFr1uo81SZIkSZIkaVQysCZJkiRJkiR1wcCaNE5FRPR6H7o1mvd9JDD9umO6SWOb17g0fMbD9dZ+jOPhmDW6jadzdHYfq4E1AePrIhrvIuL1EbFPKaWMpt89InaPiG8CFDuHfFFa6RcRr4gI7wOD1Ei3tSJimV7vj0aHiNglInacxW1myJtHU349WjWu8V0jYu36t+k+SBGxYK/3YaSIiHl7vQ8jXeN6e2mv92WoNI5xt4hYYbSVvceCu+cH0gAAIABJREFUiJin1/swmjTO2dUjYq5e789QahzrShGxwIv9Ph+oRERE48RaPyLWHOsX0njSvIFHxKLk6LnfjYjtR8sNPiImAusDr4yI5Xq9P6NV27mwGPAL4Cs1fdWPtnRbA/gV8M6IWGp2f79Gv7bzZUngCOA7EbH5ILef1LgnLxMRi0fE5JpfW24bYhExN3Ai8GnwRc7MtJ3rewDnR8R6PdylnmlLi9cDX42IVXq4SyNWW1ptBVwUEW/q4S4NqRpwPgn4FJinDKf6UuvUiFi21/sy0rVdl68Avg98OCLm7N1eDY22Y90Y+Dnw9hcbXLOApma0dj/gEuAQYLGe7pRmm8bv+3rgc0ArGHBZROw+GoJrpZT/AucASwO79nh3RqW2APo2wH7An4C3ASf7wN5ZW7q9GlgcmAbsC7wjIpaYhe+a1Ph7gdYLjNFwDWrwGufL7sB7yfvpwsA5EbF1f9tFxGsjYqFSynP18/7A9cCvgRsi4hWllGlDfgDjXCnlaeDrwEtrIN3gdwdteeOiwDzAa4B3tWr7jRdtabEAWVbZHzg0Il7W050bYdrSajlgNWBZ4PCI2LOnOzd0Hgd+AKxSX7ZgmWtotAVM5gP2IfOl97XSXjNquy5fDqxAllv2A949loJrbce6HnnvWgh4M3BgRMzf7Xd7UQt4Iaj2NeBU4FullL/2eJc0G0XErmRg6i7gE8CBwA3AhRGx50h6sG8vbETEHBExoZRyG/Bl4LCIWKE3ezd6NW4iBwAXAysD5wNXAO8AThsp58BI0pZuPyYDu2cDtwEfBI4cqLAWERvW72oFTPYBfgJcHxFnRMQcvsEeWyLidcCFwBNkTYVjgEeBb0TEth3W3w34JnBcRMxTAzpfIvPtM4D/A64dww+ePdHK81r3ncb957vAMsAbwBomnTTyxlZ5YifgL2RA6ZMRsVYPd29YtaXFT4GNgQeBdwEfsMzSp5FWBwM3AlsDtwMbkPnf63u4ey9ahzxlYn0h8gUyWPF2AF+SDI3G+XUQcC6wOjCBLOd+JCKW7t3ejVyNdHsTmYdtC1xZF78POHqsBNfajvU64PVk4BuyVulBXQfXSilO43wia2D8HPg4MHdj/gZkhrRk/Ry93lenWf5tgwygfwe4CpinsWwdMrAyDdi5179x89yrn18JrNs277XAP4Bd6+cJvU7j0TQBSwC/A/4HmL/OWww4FHgaOM007ZhuiwN/runWzCO/WNPthFY+2WHbQ4D7gDfXz9vUbb5FFvr+BfwSWK7Xx+k0W86VACYBPySbUczZWLY9cDVwP/Dqtu0mkYG028maxW8GvgJMqsuXIgNvTwF79fo4R/sETGz9XvXfZTqs82GyVu+avd7fkTqRAaSnyMDx0jWvfCPwJHApsHav93EY0+JVwLPkQ+jLgBWBo2sZ6wxghV7v40iZgLWBR+p5M6XO2xy4FbgJeF2v97GLY4q2fzvlKSfV41uu1/s7licyKPQ08G5gPbLm1deBv5Mv6Jfq9T6OxAlYvqbRJ6nPi8BkMuj0F+ADrTLJaJ+A5cjnyROBeeu8OYHLgH+TXXjMP6vfa421cahDrZR5yELAH0opT0fEshFxAVmr5WdkH0wrlnrWaWRr/r4ltd6KTSILfa1lfyDfoD0K/CAidimlNzXXIuLjwI8jYqH6eRWyBuUvI+KkiNi57vMPyHPyE7UW2/PDva+jSYff8lnywWdqKeVxgFLKP4GzyBvpW4HPNt64WoMtzQcsCvyq5pETAUopR5K1144hm7F0ehN6M1kgOby+oV+FDJwcSDYn3Y2sgn5RRCw/xMehIdCe5wLPk/fVp0spz7aaAJdSLieDZ8sAZ0XElnX7uUrWZnwXWZNxJ7IJ6eN1PqWUv5MP7OeR/bVZc61LEfEx4ICImLve8zYF7o+Ib7XVlrmKfKh4Rd1uQg92d0RqnPMrkQGSc0spD5RSHiylfJd8CbYN8PHarGjMaqTFWuRD6VmllLtLKX8ppbSC5AcDx0TESr3azxFmcv33+lLKw7Vp1s/IdFqaLOPt3bvdmzUR8WFg51o7rUR2G/HHWiN9q8Y5chmwJrBR3c7n8KGxDvAA8L1Sym9LKVNLKQeTNZHfRPYbNlv6yB0LGufnXMD8wE2llKci+3t9hHxZ8nfgQ8BRo7nmWuNYFwQWAK4rpTxZr91nSyk7An8EjgfeHLPY55oX9DjUCpBFjnq1XCnlXuAa4EMR8W2y6ufqZCH/M9RO43u0u5pFjd938+gbhOIe8mb+srqs9aB3I1mr4j9k/z/bDncAtWZyt5JvlU6PiAVKKXeSVXPfQT5kfjkiLo/sn+gS8m34Ho3t1UHjXFiyPhROAO4Flo7GKEmllMfIAsdD5Bu+U5vbj1dRRyorpfyFfLO1bf3838a19RHgYeAA8mF9vsb2E0opN5Np+ghwGHA48EANej9PNoXZjwzeXRgOzjHqNK6zVpPfacDdwCYRMWcp5bno60/vArJJcZCdm7+ilPJM/ap5yXPlCvJt6hbRGGWxBsGPJq/V70XEG4flAMeeVn+ju9d74b1koHsV4ISI+EVE7ELW7v0a2Xxofl/kTKcVZHyCrPW8AOT9uOZ7VwGfJe/TR421gFKHYDpkQH1psoYaETEhspn/N8ja4G8j++Uct03RGsHpaeQ507qPTqjnzU3AO8kmk4dH9lM5okUOdrIPmVdsVWf/i8yrX0mWp66vwbZfki9XPhIRixSbgw6VRYGFSil/gxd+I0opR5FN//Ynn3ntcy21At0PAxOBVSG7L6kv/h4nXxBMI++fh4/iF02L1H/vB/5LfXHWVq4/gqyM8jZg72j0jzwQA2vjVERMJguWJ9RZXySrJ88PfLeUskYp5ULyBvA0WeDXKBHZofo1wEfrrBOAv5K1JCaXvr6e5icLNl8gO8k+tBkYGA61UHohWXtn67qPC5RSbiulnA7sQr7FbI6qtAGwZWN79aPWivkNWa37IeAi4CAyYNn0dF3veODgGMMjdA1G5AAP50bEyrUAcQmwYStdGsGQycCdwG/JpmOrRcRc9cEr6rq/J5vbPkZ21PzCQ2YNsP2aDK7NBVwaESsOxzFq9onsoPyXEXFEnfVp8vf/PvSdLxGxMBmUuIw8d46twYi3ktfmQuQD2Wn01dxoBtceIptjXAhMGYZDG3NKKWsBvyfLPXsCD5dSvk32n3gw2QzkZOAP5PU6F1kDyxom5CAb1H6iyCbydwFvjYjFGy8MIMscDwB7kzUwx4xGMH2niNi3zv4DebyH1nLW80CrfHI3MJUMGh1Ytx1XLwUjR2f8eq3t8mvyJf4JEbFyyQGqWp4jH/AXB94WOTDGiFVysJNXkbVcvhkR2wG3l1K+BmxGlsNbLy+vImtTzQFsCNaEnV0iYufoq8l9bs6KYyF/o0bQ5Cay9cZ2wBtrAHxcXYtNtaz7g4hYppYvziRrarVaCrXKulPIgPETZOuWlXuxvy9GRGxP9nO7BPAMWY7ateZNzWOdl7y3PQV8jOxGZ1DGfQFhHGuNULNGREypVbAPB/YppRwLLwRd9iSbtPy5Z3uqbjxC9iewWa2V+DRZsJ0C/Dwi9oi+kSFfRWYuvyffrs093Dtb39pdRlbR3hz4dn0ApZRyfynl6lLKK8kM7izyzfBbagFGM3cPmdd/EKCU8hkyDb8ZEYdExNL1wX0HsiD7Q7Jj/s1h/BX+G+YENgHWqQ9IXyKvqyMj4t3wwguKV5APAgeQtdo+CrwHWLW+AXtzRHy5lHIXcCQZwN4zIt7S/M9qcO1gsvbHxsNxgJqtHif7T9ukvh3/K5lfrRMRP4+IdSJiAzJ4szLZx97zZC3I08lA2sXAk/Uh8xgy0LYDcHyHmmv7l1K+NGxHN8pFxJciYvvWQ2wppdVh+qnk9ThvKeWfpZSflVJ2Jh8cvkXWQFmKvFe27lXjUuNe8F76OmC/lXyIPZh8IbNsXXcusmbAV4C3kAGSzYZ9p4dIq2Ye+YL6IIBSyi/IptyHAfvUsnWpgaR5gWPJ6/pDEbHqeHopWAPSOwCvBhYupTwLfJt8mf+piFizlPJ8Tasl67J3AjsyQu+HEfGViNgcoDaX24vsk/EbwGtqTZ+ppZQLSyk7kefFVWSwbS2yRQbWhH3xImIKeb9tNR9+gMyX9oyIVjo/U8/DOclr8edkmWzSeLoWO1gVWJe+4NG5ZOuVD0btGqGWP1Yla1zuS9Yofd3w7+qL9jIyT1mqPhefRsY43hsRb4AXYh9rkNfyzmSljrcN+n8oI6ADOaehnejrSHOO+u+E+u9Lyf61Ptphm1eQTZweAT7Y62NwGvzv25i/LfnA/8b6eU6yw9gryTfyT9d/P1qXfwr4FbBoL/a//j2JrKH2MLX2Rj/HthnZd9Un2r/DaYb0nYcMst5A30AkU8iO+J8lA2+31b+Prst/Bnyj1/ve64kMeNwBvLR+XrGel/8g+5u4uV5j76/LfwtcQNb8+xHZH8U08qVFq7P0tcnC9c3AwR3+z8V6fdxOgz4/ou3zPvX33qp+nr/mZ78nA2+PkjUXPlaXn1vnT2teb/QNWDCRDPz8Cfg8sOBA++DU8XeaTAbJtqyfJzSWXU8+RLyRtgF06vJWtxgPAnv2+lh6nI6tssb6wD9b94s674s1X7yW7K/zVLJmwwFkDfMngNf0+hiGIC12ILvS2L+x7GIysH4R2aToM/U634cM1D4BbNLrY+hBmi1Clu2+0Jj3bvJh/cF6vz2frElyBNlU9D/AW3q97x2OZSmyv8ut2uYvRLYW+TtZI2pih21XAY4jm6Bv2+tjGSsTGYh9Blirfl6jXot3kmXgvclB+p4in482rffejXq9772eyCDjdY3PO5KVbx4nuyv5aU3bVln3z61yzGibyGfg6+kbsGBLsnnwg2S5/Jp6rEeRtdXvBj4y6O/v9QE6Dd9E2ygoZDOVz9ab2rKN+SuTo4/dDBzRmD/HcOynU9e/71wd5rUCA8u0zX8VsEXjBrQB8H/A/xumfZ3Y+Hsy2TSqFfidi+mDa5Mb687R+PvEmhEu3Ou0H+kT+Xb0KeC9bfN3JpuVnQTs1Dg3HgTe0+v9Hqa0mSEwQd/Lh93JgQfe2Fj2klpg/hL5wPS6On+zeg0dSdZu+StZI+n41v/TOMfXIYNrvwEO6me/zG9H+EQd8bNxvkQtjF5F4wUF+cJgL7K/qY3reuuTAZ1/kTWnbgH27fDdE8kg+L3k29UxMSJXD3+r3YHXti2bIbjWdq9ZtJaHPt3r4xjmNOsYtCVH2PseWct8SmP+28lg8T/Jpn7vrvNfT9YgeVWvj2kI0mIpspba2cACjfkfrfnA4/X6PqrOP7DeG9bv9TENZ1o1rr8P1fR4ZWPZlmTT+dvJ/iUPqfN3qvnjzr0+pn6Os/VgvgewdWN+v8G1RhlgsZqnf7zXxzHapvbzi76XlnORL4UvpG/U++XrOXcPGdD+a+NaPJLsZ+tlvT6mXqRbK83qv/uQTfr3bixbhSzL/ohsHrp/nf/qel0e1OtjmsVjbZ0nb6vHul3bsR5Alt/OAA6o87cgn4dmeAne7//d64N3Gp6pXgj/Af4X2KIxfxuyI/jd29Zfjxp0qZ99yBvBE7B9vUnvTWMYbzJwcjfwpvp5zrbtFiabrd0HXNiYPyS1IIB12z6/nnxTcgPZ8WtzyOOZBtdq5ngrDlvensZ7kU0OFm+b/1my4LpaP9tNJpvs3A1cNNTnwkib6rWyL/CStvlXADfPZLsp5Mie97XSjew75V9kDY4rgFXq/IlMH1y7op7Dh/b6+J1m+XzZkXzLuWkr36rzDycL61vUz5PatptCBiAeqHnb4vX+fBnZR1Oz5kur5tqEeu8+sNfHPZon8sHrErKWwo5tywYKrn2NDJSMu8Am+bJ14bZ5r6npuFeH9V9CX7B543qun9Xr45hNabEMMF/bvIPJTrA3aZs/AViW7N8UciTIB4Bv9vo4himt1gc2aJu3CRlwen+H9VvlvznIZ5P7yNFme34sMznGBYDL67Xw6sb8wdRc+2bNj2Z4Ke40qLRfhr6AbasG6QfJMuw6HdZfib5WMJvW+/SZvT6OHqTbq2mrfU02A70FOLvD+q20nZcsJ98PXNDr4xjksW5Vr7/mvXwespVOv8dQr+vtuznWnh+00/BMZKDsULL20h9rhr8pfR3X/6a94NTYdlw8WI/miXywP5cMnl5LfVNcl50P3Nrp9yTf5rwLOKExb0iCqGQ17fuptTJqhvc02R/FJWRg8DZqYIO+4No/gUub5yfZxOp+4NJep/1Imsi+AL5aC3nXkgMRtAI5m5IPjgfWz62H9iAfADYhRys8fajPhZE21eP/RT2nbqnn3XJ12fpkTbR39rPtPmRg+Iz6eZFaANmEfCP6u5quqzX+r1ZBZS2yI919h+rYnIbsnHkXGVh7luyUer/GsuuBaxufo/Gbv7zmeVOB99Vlc5B9Gv6YDK41v2srGrU7Wt/X6+MfrRM5IMRZZJPcnduWXUcGw/enBkPq/JfV+9Apvd7/HqTXWvV+cilweJ3XuqecTbZ4WKrDdq2y5T3AeY35o/bcJbtImUY2K96jbdmV5IuSTs215yObyv4F+N5YSIsB0inqNfNvMjh2ItknU+u8OYHsZmaltvNpDrKP3/eTNUTPaXzniC2LkC/JLiJbBWzRmN8Krt1HNhlu1lxbmazRflKv9380TmRTzntruq9CX/B6zpreZzfWbab7S8jg2500AiZj9VpsS7MgK1P8ud7nLiefCxaty3cjuzZpr2jTKqMcSjYJ/XZj2Ui+LhcA/lbvQT+p12nzWKfSeDHE9M/Gh5G1rr81q8fa8wN3GpKTqd8MomYqe9eT7G/1IrmM7ANm27rOhOHYT6ch+X13IWs2PEW2iz+MfMC/k/4DA3M3/h6yTLIWrH5bC0xvqDe3T9H3Znt7MrB2J7WfqXqT3I0szLY/BL2m8feYvynOYlq/nAyw/aOm53vIYORnyML9vB22mcD0TcJH7A1ziNIsyKYn36nXz1Vk7aMpZMDj7H7SLag1Mck3Yz9tOzffUfPXy8kBDVrbrF8LOTM8iDmNrGmAPPdNZNDhMbIZwW5krdE/UJtVtBXY1iBr2z5E1iB9F31Bt1Zw7ZaaP+5HPny9qddpMNqmAX6zpckXUZ2CazeStY9WacxbHvjSYL57LE7ky4OzyGaNPyVrNs9T87sHgB3qehPatlud2qyvfh719xSy351LyYD6edQHM7IZ0T3UIHiHtHg107/wHPVp0U/6NPO6rWrZ42Hy5f1p5Iunl9fz6BNkM/n2pn0rAbuMtLQaIE9ZhxwBuj24Nplsnvgk05evFmf6fjXHVZ4yG36L+cm+HK8n+y79ErUWcs2f7qCtVnJj2/2BdzQ+j4jza4jTq71CxS7kS937yHjAFmQfwheTTSEX7HBdzg9sOtLTre1YF6t588/IYP75ZJB7iXqv/x8aLUma30Gj64JZOdZWYU5jREREaZ0VEa8gq8ouT75he7TkSDytdfcgq6a/h3y7eFEpZa9h32kNWtvvuw6Z+b0EuLqU8midPxeZQX4SWJO8gc9Hvk3dr+ToRcO933OWUp6tI4ZdRL4VCeC0UsoZdZ2JZP9ep5KFrc1KKQ/V41mylHJvP989RxmHI7W1nQuTyVpSD5E3leciYgGyUHcSWZCdQgZ39gU+THYg3DHdmt891rSl20JAIQNm/2issws5euOBZG3KOevnPUoplzTWe+Hci4jFydHh3ksG0j5dSrmqLnsHGUx5kHxbvyjZ/+H+pZQftu+XRo6282VV8l45pZRybWOdJckgwqfJGheTyXz3DOBdpZTn6noHAqeQ59RkchTm58mBCU4ppUyrIye+B9iafBj7SinlhGE41DGj7TfbiKx1NTdwWynlujp/aeBkMpj+htZ1WJftVUq5oP493f1lLN9vZpYHRcQiZEDyc8ByZGDpXWSw5L5SyjYz+67Rlm4DpMXSZNnqJLJWxF/IUQYvAa4opRwwwHePqrQYSNv1NqG0jXIZEUuR98UdyZdJXwb2JM+hrUopTw3mu3up7RjXJ2ucTQL+XHI02FaZ/JNkM+kdGnnNwuRxXlg/T5dGY+18mN36OwfqCJ+TyNYwryW74DiTHOzn7WSNtGNn9j1jOe3bztn5yA755ymlPNZY5zCyUkOrUsb65PPjFqWUOzp9V6fPvdbhWJ8ly/WPtH7jiDicfBm0K3nvX5d8mblxKeX3je9qv+fP2rEOZdTQqXcTGaG9l6wBdC/5Jm1fGp2rNtZdnXzYu5e2EW6cRuZEvnG5l+yA8VmyGcJebevMTb5F+39kja939WA/W9Wzm28QliKbj0yjrWkNWd14K7K2x73AEu3Le532I2FqS89WU8T/q+n6PhodStd1NiYLfE/UdH9Dr49hBKTb3sAPyYeia4C3tq07N/nwdClZy3Ia/YxORvaxc2fbutcD2zfWOYyssfkYGWA7rtfp4TRL585+ZA2z+8gXA+cyYxPN+cjO8c+u50CzhspKZHPCj1P7aCIDFVfXc+fdrfOT7JdpLWDDxvbmfbP+mx1I1qj6Pfl2eiqNTojpq7k2Fdi1w/bjJs3b8sZtyc73T6v51lyNZZPI/q/OJfuPerSe6xsO9z4PU1q8ul6bJ9AYxKYum0IGiG6o95Gnalqs0etj6FFa7Uj2RdjqALz9vFmIfKC9lr7RkEfVM0fNU/5O1iq+td4T3tZY3qq59minYxtPecoQnF+bkqPFfpi2EYbrPfP19V56TT23ppEvuMZdbcC2dNudHNDh12S5dL0O6+9LDlLwl5pur+/1MXR5rHvUe9PvyNp3W7atuxAZWPsFGYCd7c9DPU8Qp9k/1RPrEfIN0ST6+si4j6wi2xotpTlK3ZJk/0If7vX+Ow34+76u3rTfR9ak2b7+vj/vL4Og9mXR+t2HaT9fTr7RbjWTewtwcv176bq/f6HRn1BdNgdZW+MBbAI1UBrvSwZqPk++rbu4FvpOBRbpsP5G7QWS8TiRgeknyRoH+wHfZsZASGsEoXlrum3fz3dtRD4kHEXta4jsAP0eMmjSbBb6SrKZYHM0IgvaI3wig9dPkCPoLko2ZZ9GNtvcrJ9t1m77vFbNt1vN5lrn1+JkwPUhGsG1tm09Rwb3O01o/L0b2QTtmPp5B/oett7bWG8p8qFjGrB8r4+h1xMZOHiErH11Gfly9hIaXUY01t2l3msO6/V+D2FaPEy+uLyBfGj/aj/rHkT2tXhkr/e7x+fNd8ggZKurmVU7rLsO8GYazfFG6sT0nZ7vXs+HD9TPe5LNxp9py1PWrtfONGDpXh/DWJjq9fUAGRy6oqbtYcw4INuSZPcMVwIf6/V+93qqafEE2eTx7WQ/og/RNjJ2XXcpMng5Kvv8JSsUPUG2Cngv2ULnORrdPTB9zGMH4HWzfT96nRBOL/IHhBXbPi9J1sI4tn5epxbmv0w+BDxMBjgWqMubkd6zyGZ6cw71fjt1/XsvW2/YH6mf1yUHLDifjL7fReNNAzOOSDdsD2hkdfg/1fPq2HojPIK+UXyWJWv3/IYZ3wRPsEAyYPquS+2LqZGej5JvUe8jO0teuC7rNCLVuHxYJwNhdwBH188r1Wvo1nqOvq+x7oDpRo6S9G/g5W3zd63fewP9BDPH628wmiayyc8N9AVo1qm/6wVkTaefAps31n9hUJDmb0y+THgeeHtj3VZeuB4ZIL8JR4jt5jdqpn+Q/TidQ9+LnDVq+n6TDARNoxEMAl5KP/3xjKeJfND4B3BU/bwe+QLiafKBrDVa6pyNbZqDPIyZ/Kzm6/9s3Cc2I2uk/ZfpO9Vv1sqafyymxSDSamvyxXwrrdau19vTZGC2NXBPxzQZiWnF9LWFg3yhch7w2TpvTbK8dU7NV55uy1PWo0Pwwqmr3+K15LPr++vnTel7SfKRxj22GQTt+Pd4mmqedVcjP1+zll0eIANQuw+w/ajpb70e692NY12lHuvfap69W+tc6HQ+zM5zpOeJ4fQifryM4E9jxqqOx5LV15cmm9N9nQxUTCEfKG8HDqHRLLSehM8DJ/b6uJxm+ptPIR8M1iQfBu6ndoAKrFYLMzcyQmp6kbWB/q+ep59qzG/V1mgF135L/7Xtxl017kGm7fZknwjzk8Ghh8mmO/OQb86nkW+pWqOsmo6ZDtvWdFqoXjNT6+eV6au5NugaGLXQNw1YuX6ek76gynvIpto/pNGhsdPomcgaZd8lu0xYgawR+vXGufQ0jSYH/V1nZO3xM8lRubdsW/ZqsobjL2t+uOJQHMtYnMjap/8C3tOYtyBZg2a9+vvdS/Yz22re/bd6zc4woA/j9yFsXnL0xlPr57XJwMGZ5OjGU8mXs3PV5WP2BSzZb9oXgf9pS4tvAh8jg41nNNZvrzUzbu615LPF0cB36uc1yAfar5H9F91D1jKaoebaSJ3I0Q/vo9H1A9mX8RE1/1iyLv92Pf4N6/UxrZkPNbYdl3nKbPotFiJfzp9SP69OPuecQXZvMo1svTPDS9C6/ri5Fjsc+z5k4Heumm5T63W5ClnrbyqwU6/3czYcZ5DNgM+s9/hWHnRaLQNcTQbXth+Oc6LnCeL0In68vDguJWtLbNlh+SFkjZbV6QtkXERfpH+NxrpLM/0QuuM2MxqpE30P65Prvx8kA1IrN37f68g3EXfQGNWsB/vaeoO0fi2QPgR8D1indSz01exYlnyg/CNwUK/TebRM5Eg2G9e/LyT7NVmyfl6CrHlwF/WBstf7O1KmWiheu56Dl9S0W7wu27qRPx7ftl1/AZMpZDDkUvqCmK1z+wCyP7W7yIeyBcxbR8/UyHNbNT9PIpuwL1/PnwXI/rumkf1CLtHYdgMy8NYcWWo7sm+Pa6gFWjIwfjD5oLAK2XRh2PvDHK1TvZYvqPePZnBt0frvIfW3WbWx7HT6gmsbeE2+ECDZrabHQmTZ8WzyQWWemr+qidWsAAAgAElEQVS1+o6cp9f7O8RpMRfZnH89svbjHeTD/SRylLmbalqc0+t9HQkTsCrZj+s8NW3OJYPbk2qeOY3s82jNXu/rII9nDbIm8k1MH1xbov77zprvN0cO/jbwZ/OU2f5bzEF2f7MpWWvwL7UsNR/57PCbmubHYACzPe0WJ1u2TCBHub+A2v8yfRVzngD26fW+zoZjfVk91klkM+Dzqd3hNI51WPqXngONWqWUO8k3KFcCl0TE5m2rrE4WLm8vpfy3jhL4b3LkxR1KKbfBCyPUPFDqSEZ1RIwyfEeiwWj9JqVvVM+1gf+WUu6qv+/CZNOFI8i+Be7s0a5S+kZUuZOsWXUcmel9JCLWLem5iJhYSrmf7Eh+Elm7RwOoo9T8t5Tyy4iYm6yx9rvSN7Ll8uRb9T+Qo5Q93aNdHXFKKf8opdxCFvzXBH5fSnmwLn4M+BXwFTIYDMww4tBKEbF8RCxXv+9hslC9AvCpiFisdW6TD2VfJN+sHkAWxM1bR4lGnju1jkC2LvBYKeXeumw+8qH7DcAXSyn/BxARB5O1ey4GvhcR59TvuYLsvPtZ4NyI+AkZ3P0q2WTqbjIgPvfwHeXoVcsut5APVX8A3hwR7wYopbSu38XI4PeTdZvJ5HX5ObIT55u9JqHkKIU/KqXcTDaXX5AckfbpkqM2XkOm8Upk32pjVinlGeCSUspvgS3JssnJpZTnSin/JK/tW4GdI2Ln3u3piHFXKeWXZA3wJYFvllIeLTka8q1kjbXJ5Hk1otUy6W3kw/jjwCER8TaAVv5OVkSYm6zx0xr1cxHy5cgrzVNmn/os8aNSyo3ks8RTwGdKKU/UZ4dfkM89n2IUnF/DqZTyYCnld+Q1uSpweS2vQrZ2uoHM1xft0S7ONqWUu+uxLkYe61WllH/XxfeT5frvMwzHamBtFIrU+u0WIQvvk8iCejO49hNgjog4PiJWJquF7gYvFPBbQbTphsYuY3To4THoamD9iNg1IpYg+3TaBLixlHIe5LkyXDvT/L8iYnJELFpKebyU8vNSyqnAF8gabB+KiJcD1IDgemRTnnVLKWcP1/6OZm2Ftmnk9b8GQA2gr0LWZjy4lPKdOn/YzoVRYkGykLZIRCxY89SNyELaiaWUL7ZWbATV3kS++bsZ+FFEvKWu8j/kTXtj4KcRcQJZDf3TZKDkcjKYsuJwHJhmv3pfvBFYNyI2i4gFyT6pNiLz3K8BRMSKZMe5J5EDCX0O2C4irqrfcx4ZCDqavG4fBA4ppXyebBI6L1mbSjNR87NWWeVZstbzBOD9EXFIY9X7yTTdNyK2Ie+TG5EB9d/X77IsDNRACGRNhwXJTtmpLwlWoK9J+/m92cPhU0p5sv65BFkztRWYnZvshuMCssb4D3uzhyNH45lhEllrbWl44bxZjQx+vLyU8o2e7OAg1Tyl9Tz0GFlLcUngyIjYv7HqXWSAeY+I2AzYiXxJd00NMJqnzEaNa/ElwDJk9wutsu4CZPPs1VtprxlMIvOvFeGF83wDspue/erz2VixIHkNT2lcg+uRTdLf3izXD5WJQ/0faParD3klIt5M1gS6mSzwbwx8PyL2LKVcW+efDbyDHJ2xAJ8updzQ+C6DaKPXlWTfPxeTGeTCwCdKKX9qrTCcb80awYc3kqP1vDQivgecX0r5VSnlSzW28x4yuHYmWePj28AejWBvDOd+j2Y1MP5sRHwY+FZE3EL2AfUq4LhSyqN1PdO0TSnlrxFxHjnSY6v/iV3IwSAegBlqqq1LBkm+RBa6NwC+FhFTSimfjYgPkU1H9iL7d3qAHCnsjIjYq27zz2E9SM1ul5P9Bl1NNklZjmwy/DeAiNiE7ET3XuDsUso/IuJnZH88X4qIq0op29Q3q7+LiNNLKc9HxNwRsQMZjL22lHLW8B/a6NK4Lg8GPkO+ff8zsA1ZM3reUsoppZRvRcRGZNcJrWDc50op1zS+y3LQ9K4lH04+GhGXksGS/cmuGu6CcXVPuYGsbfWeiLi6/r0LsH/pa/Uxh+cQkPne78mao2uSLWTeSfZlOOLLIh3ylOvIfH4j4NiImLOUcmYp5fT6cvirZF9Ok4CTSim/aHyX58PsdxfZkuCIiLiSfAGwLfDd1nOP12JH95FN+PeMiDWAv5I1Mo8utQXUSL4uZ0Up5faIuJYcYX2diJhKjox6VKktU4b6WGMMpOO4VC+Oq8nRPr9cSvlXRGxJPiRuQo72cW1ETCH74FoVuL8G3Mx8RqD2i30wF39ELEoGVJcnq+NfXucP2+/bFnzYhews82KyNtA+ZE2CU1pvdiPiMDLQu0j9ii+XUo4djn0dbVppO9C5EBHzkn1QHEUOYnBtowbNmLhhDsZgj7XtnH0v2ffVBOCCUspXO6yzBvnAviFwRCnlsYhYhuzU+33AB0opn2l8/0KllP/Uv7ck+wS5sZTyxtl3tHqxurk26gPjZmStld+UUi6qb4BfQjaVmwjcXErZobHN3OQgF19uLouICWSw5zCyT6e/lFL2r8u8Rw8gIlYh+0L6ItkU95GI2IB84bgKeW85pa67A5nWT1kO6l8rTWow8gKyRsizwOebedx40Lj/7gF8o85+nmyKdlLv9mzkaaTVWuQgGOuSZcDTam3cUSEiVieDqZ8nB/J4pAbRziAHJvpCKeXrdd1dyZZfj5VSrqrzzFOGUER8krxXLkU21f1CKeXE3u7VyNXIz+ckW1C8kjxnv9WqqTZWnhGa115EnEo+G0Me6xfr/CE/VgNro1Rkk88ryOGcWxl6kNWuzyCDaXuUUn7WYVsz/hEsIjYE/lb6+nOY1e178vtGxEJk8GF94JOllKdqDY7vkE2bPldKubSuuwXZ780TZfpmyZ6XDRGxZOnrN20w689BDpH9XOvzeEzTiJhUsp+zfo8/sn+m5+vfE8l0azV9at6g1yQf3h8CriylvKPxHUuSb8beR779O7n13WQtj3eS/W/dUUp5fft3a2SoD1N/K6U8NpN1+i2QNQqvryLvv8uSTSwuaKwzN1nT5UzgtlLKJo1lU4C1SynXNb9vdhzbWFavzV+QtYcuaXu4P4NsrvXRUsqXO2w7btN4EC9qWufzImSfNZRSbm8uG6Zdne06vMCc6fE0zqmXka0Cni/Z99qoT4vZrXHeTCZfMMxfSrmvuay3eziwGpi/lny2urZVToiI1YDLyOD8x0uHZq2j5RhHilkJcrSVyTYhm/c/U2oLrPGa9o2y7szKJ61zOMjrcoFS+1objek2C+X6eckRm/8z0Hazk23AR6/HySrIS0JfBlULP98iawP9ICJe077haLuIxotIa5F9461Z502Y1e/pUVDtlWRNqU8D/65BtShZNX5fss+N90Xt6LeUcl0p5SKDav2LiK2B8yJi6cFuU0qZVvr6yRmX13o9x34GMz/+RkGDkgNBtIJq0dyulPJHsg+15YBtI2LVxrJ/AKeQ/Wl9LrJJLqWU50spj5MvPz5hUG3kqjVzfkX2pdRv3zhtD+PROnfqsmn135+SI3z+HTi8ef8tOYDIpWTttDOb31VKebgRVAvPkVkyNzmKI8DEWrC+lWz6OS9wdGQz7emMxzSOiNdFxC4DPczW4MgcpZR/lxz8qhVUG/XnZuvYI2KPiFh/oOOpQbUo2Tn2r8djUC0i9omIQ+vf/fbV2sgHH6nnTiuoNprOm6fILkoWas2owYs7yG51FiWbIh7RvuEoOsYRoXEtbhcRS9W/+7v/TmstK6X8opRydSOoNprOr9kmIvYmmyPPtNufVlm3xgieo2/QjVGTbhGxV0ScBYMq17fOkyeBZlPXYTlWA2uj1x/pK7wv3nZR3UY+KNxK1lzTKFAzvVvJfmLeW+c9P/OtZl7QGUYPkzXTlqWvieeE+pDzK2A/8s33hyNi9/aNR0vmPswWJ5t3Lgv9Fzia2s+FbgKzY8B8wNKRHZUP9CBQmuv099avlHIc8HHyHD4ysoP61rJ/kE3RvkS+8Ghu9+tSO/oeTw9io0kp5Say77v3188z/Y0awdgSEdtGxPsj4rMRsVtkf3s3AG8FVgA+0CG4dk4p5bTWd7Wfb4N9g68Xgt7nAidHxJrNlwpkk72HyE6L7+3B7o0okYNtvB14e2TtyZnqdB2MlXMzsoboh8l+0wbsa7qfe8J4ysu3Jstui3VzDrSCk0OwX7NV3cc7yZGaPxoRL69l8P+2VgEeJQP2/+rNXo4tNV86iyxfDRQ06bhsrORLXZgC7FVfDs5UM41af4+WdKvX5TLAqyL7Op6p1nnSLF8N57EaWBuF6gPac8ABZODs9MimLK0mTeuTndnvVTo0gdDIFBGT6p//D1glsm+mmQYGmhlHRBxQ32AM9X7OsD8lOw49iexf7ZiI2K2U8l9ykI1WcO0g8nwdj8GeWVZK+S7Z9ODEyI64B3zgb5wLr67fMWBgdgy6jhwBaW8Y+IbaSrd6Xq8eEfNFxEYRsXd9S7Zu/Z6TyADa3sB7OwTXjiml/E9//884exAbFSJiQg1YfwfYICJWqvNn+hBYz5eDyFES9yP7kjwRuCgiliqlXE3mdyuQNaa2b2z7fPN7Zu8RjUunk50xf7PWQHo+IuYim4H+BHhzcbRpSnYe/2OyD96Z1g5paV4HY+klTclmUNeQ/Q0tALOWFuPQ98lA9cYw8LnQdt4s2d8Lq5Gmvtz+L9knagAnRMTGNb+fh7xuzgW2KqWc08t9HUMeJ/PwtSNi2cFs0HZ+zTNUOzYK/Jx8ebQlzHJ+vnJEzDekezeb1LzjarKPw9fAwPlx2/PQco3n6yFnYG0UKn3VrW8nC/TrAZdGxGXkKKCfJfsC+ieM+wLBiNR8S1ofAppD3V9NNm3Ztc7vr918M+N4J/D1odznDv/nS2uGtVbdz9vIt07fAy6IiNfWc7UVXPslsFZp9Ds0njVqSU3oMK91E/gR2QRxlTq/Y57d9rscCVwb2Tx3zGpPi0hzlBz55xPAGyNi0wG+o1ngP4ocXfkIcsTdT5JvUr8dEZ8DKKV8jKyZtjfwrsh+d6jLnkQjVqeHwZJNdqeRgbWVgdfV+aVt2+lqNNZg6xfIlwQnllKWJZsevgo4NXL0uOvJ0WHXAj4RES8duqMbv0oORPAFsibJDRFxCRkI+DLZl90LzdF6tpPDrEPe2LqfnEKOVnwczDzY33ZPeT/w1YEe3Eai9t+9kRYnkKM5fgRmKS2Ojojjh2h3e6rDeTMBoOTAU3cDx9TP/b6wa0ur9wL/SzafHPFa50op5WIy/1gIuCwizgXOI1+s/bU0Rhfs1b6ORo376AvnWb3uvkeOsr7NYL6jcX69DTg0BlEDdzRrL7s0rss/kAPMvD+y5dqg87C63WJDt9fdmUkedAuZlxwVESvMLFDfdqzvI8vziw/dXk9v1N0kNb36Znw98gE8yGZQR5QxNtrHWBERb46IZepbMSJiH+DciHh7a51SygNkX2UHRMTG/XxPeyDlZODQUpudDZXG/7k/8ANyaPXrIuL8iFi7lPIXsvD1PbL2xi5tmX0r2GveU4NlQKva8nbAwRExdyPIehp5Xb8HOhf+O5wLXwDeVkr5+RDvf09ExDL1mFvptkPk6FzN9PkdOWDGFnWdGc63Dul2Avnm9P1kYO415MiPZ5E11Fr9px1PFrAPJJvHLDhUx6oXLyJ2j4hFS1+Htm+IiC9HxE6tdUopd5E1hQ+MiJXbtp+rBtPmaNxLDyMfuk4spZzbmHcncEIp5dm63U/Jod6/XEr569Ae6fjTeBA+j0z/j5G/y2NkOeiF0QjHUzmokTeuWj+37icBXASsFxHL1XVmCA605Y1HkLXRfz8aa902jmP5+rmVFk8DFwKbRkS/D5gd0uIzZIuQMaN1D2ucNxvWRc3f+3PAMtGhK4/G97Sn1eeAH7Ve8o90rXy+/n06cDRZtl6ZTIt3j9c8ZTZp5TWTmzNLKTcDXyO72Vim341nPL/+F3ioZBcLY07k4FgvBLIj4jURsQI5AEHL2WSz5DfUdQbKz48k8/OvllLuGdojGLzI5vnNPGjryO4dmkH8K4EngFfXdQZbrj+llPK3IT6EPqUUp1E8kSPZAS+M8DpPY9kcvd4/p+l+q82AfwCXA0vWebsD15N9wPwGOJTs6H8Z4BbyRv7C79z8revfR5JV9N86jMexJ/AMGXx4C9mh69/Jvv22quusTNYCmUY2Se55+o+kiQzKTAN2rp/nAW4C7if7BDoYWK8uO6Sm7eYdvqd5Lryzngtv6fXxDWG6HV3PtY3IF0OL1utpGvkG7j2NdT8G/Bt4yQDp9sI1RPZteDOwTGP5T4A7gA3bvuMkMoDZ83Rx6vd82YXs//ErwCJ13mH1GrsbuArYgeyr5OVks4rd6noTyJcEDwAL1nkT6/kyDXik8f9cRjZHfHn9vCnwAWDu/s47pwF/u4nNf2eyXrR9ntD2eVyUg4BPAYc0Pm9a7yc3Atu28jSyOdt/gOMGSs9G3vjmXh/fLKbFccBOjc9bkmWs7wEbApPr/PXI/rM63jPHQloMIq0+W/PHpevn1cjA9J+BY4GV6vwp5Aurb8xCWo3KskiHPGXOtuMbF3nKbErLo4HXttKM7K/vGbJFwDaN9V5LBqy3r5/b8/Exfy22He+JZFPPdevnpci+1f9LDn60R2Pds4Df9pNWI/66rPn1jY28ZlnyeXgacCrw+sa63wb+2M/3jIhj7XmCOs14UryYDLzTdziNnIkMfvyOfBBrFXQXAtYg+274PfAg8OaasdwJzNvPdx1RM9nhDKrNRfajdh45ZHNr/pK1IPYrYL46bzWySc6RvU73kTaRb1y+T9bge22dNx+wDvkW7g7gPrJ52ZvIB6R31PVmuLbrTWRYz4UepdvLgD/Va2PDOm/ump7nk7XUbgfeDWxfCyYfbc9HyaDY6Y10O6TOPxW4qbHej8iAyTqN323vDvtlfjtCJ7LGwW1k055WcG0JYDvg2no+/Y5shvJb4Jf1nAqysP+Xer4t2MhzP0aOHvfuet7dT18gfEGyxvFXgIV6ffyjaSIDHYc1Pu9PPoBNmoXvGHfXYr13nFXPzf3rvMXJJus/JgPJtwBvq/OPqffqNfpLO3rw0m42pcVCZD+bj1Af3MmyyNvJ8tWfgRuAHRvX6s+AZcdaWgwyvT5JPsCeVM+NSWTfkP9b75+PAccD69Y88glgs5l83xFjNa2cZm0iXxg/Vu+x29V5q5NlstvIl1vnAxvXZd8BbuzwPePiWmw75q2B58iWQWvXeROBw8mats/Wf/cDViVrrR0yk+8bsddlzVceIZ+JVqjzppAVDK4kRzG9ot7PtiIHZhyxx9rzBHV64URoZhwvqYWDufpbZ1a+z6n3E40H+3pjuIUs8C7dtt7qZJ8ft5IBgmlkMC7azpHX1WXD+saGDKz9Dji7MW9S/Xcd8k3UhxrLJvc67UfSBGza+Hsj4GKyRs1ubettWM+Tv9NXI+vvrZtO27oj8i3UbE63RRt/L1+vjVtrOrVqtixA1vT8OvBTcgCDp4DLGtsGWdg7tqZpaaZbvZE/Qwa6zyeDaq03hi8hmwt+hH6C3U4jYyJrAi/d+Px58iXFV4DF2tbdjgy6/bv+3v8Fdq/LJpFNgv9MBrqnkS89JpNB2Efr9NK6/pxkIPzv1ACH06B/swnAHjWdv1avxWmMwAeBkTiRo3F/iXzgOqht2U5kE8ZHyBq4t5APtXvW5e0vHt5V035U3lPIJvwXkjXztmtbth/5AP8o+dB6U02PLery9poy7x7NaTGTNJrY+Pv99Rg/25ZvLkvW4v412drit/Xf4/s5b9470tKK6cvNk4Ep/S13GpL0n1LvvXeQAZRWzbWVa37/u7r8RrIm1n3ADp1+G7If3GdH0vk1RGnWaom2GVke/TG1HFrnTyb7c/0+cBdZ0/7Bmq9Fh3Rr1bIfUTX82vKgzckA2o+BFRvzlwBeQQbYbqp5+r+BM/v5zsN6nQf1PGGdZjgp9iPfjt9bM5h3AS8bxHadarF4wxhBE9M353xnLcxdBizVYd3V6k3nNuD6Tt8F7DLM+z9HzbRPJ9/4LttYFnWfbgTO6rDtuD8Xa+H1+ubvTY5K1gquzfB7koGi/cl+cZ6hNj1sO5c+NpYLGmQg6y6yFlGrULZ8Laj9od502x+GViSbVf+h3mQP75CuV5AByZMb81cgazs8TtYmXKrOn0g+6N9HfRh1GnlTzYd2q7/fCm3LTibfnL9Qc61t+YbkQ/RfgQsa8yeSwbd769RqFroBWXv3/8haLweRfQo9RuPlgtMs/X5TyP4kp5IPUIfX+YOquc/0D9Ez/MZjbSIDaUc2Pi9CBo+fJQO87Q9YG5JNs35b88VbWudzY501a745qpq5k/1eNptHLQNcQj6Ibdth/R3J2lp/rWlxdYd1NiCD6v3WjhiNExm4/mRbOeKDNR0+w4y195Yjm9NeV6/NB2m87KrrLEmWZQ7t9fH1c8xvJGsm3kf2Q7tRY1m/5dO2PGXhXh/HaJjqObQOfeW1hcma33eQXS+0l9d2JfsTfqqeg1/p8J3r1vPusKHe/5EwNdJuc/4/e2cdZkeRtfHfiSsEJ0iEBA0SSIK7fLhLCG6LJri7OxtYHBZ33cV3YXG3RYK7BlgIAUKwkKnvj/d0bt2eO5OJzO0rXc9znrldLVN1urrq1HtMsv99wFKpa7oh8Pt8CoYYq6aumREpFitqj4BA/Mf8d+Jpt5LPL/cBC6Su7+hj4EwE7jcQufv7NTP7+rdTpn3Lmrk5FQ2KLfwDOg3F+znBF/17SZnsp+6LJ/4jiKyJcsr8nTa3YO9HClwjFU8GWbD9DAyN6pqNOTO9211iEdwGWXUcEQsayBLoEeCCyfW9Hgm5OS3lv/tE9TG4lsRcsxJj4WZgVNb9yIBvQxIhGHczTnhIAVwbUmq8IYAtcV1ulxrXc6FN5p/AyKh+W+A5JAiuhiyWDkMWcIdlzY+cWjRm5vO/A3BrMj/+K9KOx26h6e9sHaQRXS2qS8C1D3zOnjF6/n4+Dt9HG/nYAjKPxdPyd5YI1+uiwPI/AdfG76Al9/vvvfy7r9mNMMroNhK3tErVJ+Da9k3c2x5Zf3wYrznR+QWz7t8U8qIXcC2wcon6u9BmrRG45tfMhmTuD0jFMUUWqItn3b/pzKt2KG7r6slxdO4wSliuRec7oXh9r+NWa+mxl3X/orbE43l9JEdf69/Gj0hWXa/U9U08Yy8EZOdeGM3zfQ5kFbuCH8fg2oe+Vq5dam1EINJpyAJrpfTYAgZk3b8y8tEoDa7Flmvx+FzUz19H49iu3Vu7vVPYt7ZIzloxHiP+OwbX+jdx/yAfY+eX4EPm4TcyZ3BOkz6gTj6QLge6Rec+RNZBfZq6N/o9HPlk75t1n3Jq9J4Gow37MrHwgSwk0uBarEWcEwWsHJ5Ru9dF2o4jiYK3o8QFE5GFxhCkHd4dbYa2yqKt1UIoU+UbFG/AmwLXjIJZ+IoucAwpd5srgVDMibHA/FFdH0qAa6mFeme0yWy0SCNhLwHXzovqNwJu8vH8tc/BsWVIDphUIFEQRA258zYga7LYvakUuBbPuQujhAVbpp5dElzzcx1QnKs8edDUv7vk2x2MXHmPRYrFa6JrSiYmSMlBidvLjln3qQw86+x/NyEC/RFYVBJcoxhIeQ24Oc3PaiRcbkYWq8Oi+ibBNQphBDqi+Jzn1AIvWsCr5FvbHIU36BSdKwmupcbNLZSw8KtE8jV+ExQMPvleBvsa8CQub8V8KfF7eL3MKdOJ5wmf16N431ASXEutv/Mh66sD0u+hlokojiiK/Zi2JF6J0uBa/F2ehKzqKwpIm0y/1/d1KJ6DSoJrqb6eiLxKKk55lnkD6pVSk3Z7py+AA6P6eymO8dM3/thoLEzWdJylaiHkurZSdLwj2qCP8cX5BqLgrxTAtXtJuYUiE/zf43FRxn5sgQTzF/3vKIqzLh6ETLPH+d8x5C5QLeHrEBQ4+rlYUKMYXFvP6+JvfF2kdV066z5kxLflfQx+SuQeTzG4NpjCpiH5ux4K3j24ieeWBNf83EIoXk/PqK5mN1y1Rigg9wQXOONMrwm4dj6eNTYaL/1QgO5GrnA0BtcSt9A2qb91sRmYDu8nnt/SG4lZfC39gghc83MDKAApdS0HIUD3Cpct9o/qmwPXEmXN2Uip161c7W3lMdQDgWgTgC2i+ubAtYQXV6CsoZ1au82VQEj5kMTSO4OWgWvJ/La3r7k9y9nmqejjst6PD4GDU30YRAFcS7uU1fWcMp143w3F5htDMRDUFLgWK0PvAW7Lug9l4tPyqeNhzrd3kYXl3NEctTIFcG2J6J6EhxsizKAqLPsohO4Yg5TXTYFrsbyf9HUr59FCWfejUb+ybkC9E8pyMRy50T0HXOj196BMY0v48aIIkElAtrrLklINRCEl+Te+qM+DrJMOQ5lb9kTamH8R+cKjmGvvohgQXSjELNuLJtKbt2If2jhdiqwGuiDLufu8L0dE1y6FNA7bUhyYPwcfinmajnUzCMUreYnG4NodCECL48V0Q4FdH8y6L1ny0L+pF1Bw+DS49gYS1paL6jt6XbPadYrBtXOben/p95hTZRLFGvAT0eYqDa6dhbS71+CJghBwdgpwSzPPTsC1d3wuz12Dpv19bYYUSw8hhU2SDGI2BK59Dlzt3+mWCChfPfWMupWDXLY4HynwDorqE3DtFxonNJjD57tG8YyqmZDi6iYUW23LqD4B176lcUKDeXyOODvr9peBP/HeoTOSSScgkDUNrk1E7saxO31PX3+vLVebp6GvC6BkRr8Dp3tdOwpAxSAUx/g5XN4i31tNT/4vhDJvf0YUH4wCuPYGUhjHoNqCyBLp1KzbXwb+bIPckkf48RooztyVKObcl2iPMIRCkriVkeLvIWBQ9KweCCi+o9z9mEYedER7yM/wjOzRuZV8vn6QKDwB2g99C9ybdftL9inrBiDXiMoAACAASURBVNQbpSbt1X0x39Mnmgt8kn8eCZIL+XXtEMDySvwh+bnE7SHXplQIIVPmB/0dbox83mOLl62R6euDFINrhwPbpp7VPfrdamBValx2RwLXCUQCKNKc3IFcU5u0TGvNdlYTpXjagVQmSRR0vxS4tgLwMLBHzFM85Xat87gE39oni63zYXlKg2t9UVDT9De0QKlnl/i/Cbj2G/C3rPmQ01SNl3l8nkoHvj2J0uDaJcAOqWtniX6X/M58TV4HpbjfJmseVBul3tlGaON7G9rgfgLcj7t8o2y8RyBBejTSbp+Qet6h/oya3gBPZv5aCLk3lwLXLvfxv1BUPyNwdEueXYk0GV4MQXE1S4Fr9zsvYrBoboqT2FQVL6aRV92AfSgNrh3lvErHntu3Jc+uhD4i1/6rEEA2KQsuBXBtMFKEb5G67xDqIANlK/PeEFD2FI3BtR5oj/QVxYnQlgGuq7Tx1Up8WxK5Vb+H9vhbIcVeoshdECnvkiRdCbi2in+XG6WeV9KtuRKI4jU/sTpLXPE7IWu7UuDayk30ddWK7WvWDahXQu4ma6JgmIlLQw8EuDQAh3rdbChG0Dhgv9QzFveBWNPCZLUQxVqXXsjEvAF4mcZuLkP9XT9ASvOefpYfl2XioBC4/R2kNdozdb4nAtdeA47KmueVSqlFJLHGeB9Zoq5PASiKwbXto3vmbOrdV9oi0op82xRZE72GrC7W8vrmwLXuzTxvsmAkAtcSN5iVprYfOWUyXrZ2oexT5E59LMUZeBNw7XiiTXV0Ph2/q9nvDIFrvaa27TlNUuKcgZQ4yZw4HGVGf4wCuNYDafMPATaO7m9DwYJmn6z708q8isf6CigL7eEUbzAWpjS4NgdRMo4Sz64qRU2KF4NRrLBdKLbgWJrS4FofmsmoXm28mEJere7f2oUUh/XoSjG41jE6F/O0ZIzDrCnVx3n9HS8e1fUBbkTgWmKZFoNr6QynfVHyopqeU1qB90sgeXcrYOGIz02BazPTTNy6ShlfrcS3BFxaDCXaeQ9ZYh+UOt+HArgWW67NlX5WpfItNUY2QHEdH0IyWWJA1JmmwbW5Sj2rEvsaQg6sZcN0xQdpQOacSQbFOHPKs8gt8D204f6SYve7eJA2mS00p7K9z34UZ8dMsuH0QTE7fqV05q0tfcJ8DmlMyw6YpNqzJhJCb0RuoONQfLXBqXt6Anej2DeDytneaiNge+fpZSiA7pvIVXg4hQCvSyMLtdeB3Zt6P/VEzrfxaANwnPPnf8Amfj4B155BVkNpC6V4XE9RFl0U22mpaWl/Tq0+PtLC1TbI3e1Y5N5zgq+x51OsDU/qz0abyUQzvCWKG9SijFLVINxVOiEFw5tIAbV16txfkIX+ozSdGSxWZFVMNsIy8G0Hn/OechnxVeCM6HwCrn2PK2ib4lu1k/PiawTEfo6ULTGgmIBr36bHWK3xogW82tHHxH1ILv2a4sQVCbj2K3AuBaC7UUKgSiVgO6SI+xFZtt6JA2xI2X0DAtc2jvsU9TH524ESypecmuX9DggY+gAZE3yFg/kUW659RIl9Q3pNrVUCOqT+GoqlfROSeeMkWgnw24dCqJPlKc4YWvHfZWqM/IwsSC/1+fqt6BtNwLWPfP1PZzetir5m3oB6oPSEgdxVjnfh6F4K5pDJh9bdB9cJaIO5anRv1X1MtUxIk34VcL0f74Y2bsmC0g8h818By6THg7/fzF2JkJZvKIo7lMQbWh5tVh9ML4QICFyv3O2sJkLx0t4DDvHjuRBYORpZ1exFQXhdFm0OGgn/9UKRULsSxZq7Ps63TxFIGbtzrOjCRkl3DQSYXJ5cPxVtyufZCqVI6FzchbDkO1sUBb19HFlgXE6UVRtlOd4vOjaUNOQHYFdS1sXN/P+SGeRymjy//DjJ/taALNHSWve/IMXO06SA83qi1DjbCMmNSSD2FZC72jfARdF1C1Fw/1yyVsYnxWDqJkTgIUpS84evr8dF1w0B/uG86F8rvGgBr+I4k+sgIC0ZN0ujTXwD8O/oui4o3m8j989KJ2Qp9TuKqbkzCrHzhcsHa/g1/ZEVfAN59vpp5Xf8LW6KwMzDkCX3Fs7jiRSUoYZi3r2ALGpny6LdGfNsUeA03MvC5Y1bUTztRfz39xS7WidyTslQJ5VOFOT61ZHyI5Hr50cg2xhkpZbEj++MQiiNA4Zm3f6p6nPWDagnoji4+7zAMT7xnBjVt2/m/nyTV4Hkwu4faAMwAcW9ixed+YBHfFJc1usaCXdZCXw+2Tcggf3U1LkhCFx7iCaseOpFUJ0MD48C1ooWEUOuKdcgAGgBtHG/CJm/v44E3b3x2GvAHFn3IwO+7RULuEhTvIPzqYMLGz8i7daqyLpzPIWsqW0okZkseg93A/+ZwjbV/XiuVHKhNLayMJ+/bkVgf39k2XiFj58DKViozVfieduh2F73+xw+BtidyaSrpxjs2BxlN8vHTcve4ezR7z7I4uoDtNlPg297Iu31Zlm3OwM+LU9xjNWZEVh2lh8vgTao1yFX+e+Ac6LrF6UZ989qIgQOtouOZ0OW9UlA+sV9nbgFWX58Q2St57xcN+t+lIlXQ3EPCl8fOyN360ujcfET8He0sf8NuDO6vyuRG2U1EAqAfqPP5fE3Mzvy/nmZQsidBRHQOiLrdlcjpccGSmx2b/QtLoRAkRtQ2Jg/ibLcI5luy3K2uVIIKdtf8DXvcJdNhlMAzwYgt9D3geHRfcn5ZuWSSiFkHbtSdNwJZSC+yI8X9fn6MgSIv4+U6QP8fBegb9b9mOr+Z92AeiGEzv4APBDVzY0s1xoo1rDlAFqVEdr4N/ikOXeJ8wm49jlR1sJKIGQheTRyAbjVjy2azIe4IPYYbnWXUxH/uiH3g69QoM0E1JkBaWna+ru/HY+dhkCicX7fIX5NkVtCrRPSwH2O3AM2jOqX9DHXycfcLXhAeQR6NKDNwHap55UCq0cgq5geLeErxYDJqnickJyyJ7RBPNbf/0Wpc73971XIwnYuP04yjDUggbVbdM/6SBFyOLJ8XB0Bsb8Ae9CE5VpqjCTWHU3GbMqpiHcr+re7VlTXF21+X0PxstLg2oCs250Bn3akoKRLAIFuaBO2BAIM3sODfKMYau/4Gn5piedVrUzpfR6DQPDEu2NmXzcHoNAUH0W8WAS5fo4lUlrXAi9awKv/QwDr9Xi2YgQ6reXfVg/kfn2jz6c9kGtoA3B/NfAKhYbYKlXXAVktx0qXJBbVAJ9zjonOtcgqOadGvN8NhSYaGtXN7N/o4sgr4wuUwbkzsqj9xWnDEs+ruPFVBh6ugyy0/gSO97rYujSJufYhsHdUXxUW8kh+/x7Fk14mql8ZyfVdUQy1m5LvEDjd56CvgKWrfYy0IS/lKt8AJwMDzOxOgBDClwixPRE4zsyO9vqGzFqZlykuZmZI8LsOLeJnmtkc8TUhhI/QovQZ8Ej6fJYlhDAOBZM8GVlf7B1UJppZ2xDCi0gwWxkJsXmJSgjhZyTQvo0WxJXNrEMI4acQwiMoq11v5HLxtd/WHc0JBvwvhDAx+e6Drya1XkIIH6NkGZ2BQ8xsU69/xcfcHAiQfiiEMMZvG402Bg+jBTp+niQOs5nMLFnb3kBKjdlCCMG/1eSbLSpmZtEzDkBg6EzTsct5mYYSQvgVZc4+BNjFzC6PTn9mZh2BgcCnIYTRXj8RuRMeCjzp3yo+PlZB7teXhhCeCiE8EkLYCMXlOQcYZmY94jakxsgIv273EMK9rdPrmiufoZhq15vZ6jBpHlgHAelXAEvF32cI4U0o/c3WagkhXAP8C431ncxsBh+714QQXkPrDWj8EUL4BlmVfwZsmPA2el41y5Q3IODsCGCombUPIXwPXOVjYzO0cT8VIITwFkoK9Q2wh5ktGz+synkxufIYsl5cCrjAzHqEEH4HngkhvIQyLnZFWa9/DSH8gOIYvgasaWabxw+rJF6ZyswoicknqdPJPN/bzHoDhBAm+JzxNopVvUhycQjhp+SZZWh6LZVPED8PM7OhAP4t3hFCeB3FOh2NAO1fQwhPA/9G1uB3mVn/1NxeMeOrtUvU769xuR/YxMz6+l6rvcsXo1Bm0OeAI8zsYCjeF1TyHiGE8AqyTusPHGlmy3v9Ey7X90NGRbcm3yEwyukLBCzGz6u6MZIDa61QSk3WPoAuRYLQcmb2D68fjcC144ATzez0crY1L1Ne0u/XQagjUVyHYSj2x0gzmzN13UcoW872LghXTPHxeR4CeU8zs0O9PgHXnkfWVv/Msp2VVhIAx9/nMGTSfBH6xpP5tQ1yXent93RCruD3o8QQ15S73VkXM2trZm1CCE8gd73uwD5mtmF02Rxe3zaqWwgld9kjhHBpieduiYTop8zsTBTI/n/A4mbWKRFIInCkjf9NAyZnAHuFEJ6Znv3Oy9QVM2vj7ygJvn0isKuZnQWT3mdbJPQvama9zaw9As/mAK4NIVzozzIX1mZCboljvb69/7vhKKDuMcDmZtYlui8eI+eiMfL3MrCg6koTctBnaJ18Abi9BLjWDlnbLFPi3ordTEzPYmYdAEIIG6K5LAbXxvll/RBA8qXf0wmN5ytR1sNHyt7wViiuoBqLrEl/RODaNg6ufeeX9QPao00ZZtYdWYtfjVzOnit7wzMozpM/QgjHIM+DxYC/+bgZ75fNiHjT0e/piPh3H4r7dEcGTW9pSeb/tUIIL5jZWma2A0hORcq2IcB2DsAlc0ZHZLGWzPMxsFMXc8r0KL7+/QdZjX9NMbj2lV82Nxpj3/s9syDF6VHINfCDeuV51O9vUEy6Q5DV2p1m1i+EMAGXdR1cOxmFjPmuxOMqsphZO4AQwnWo/fMjpXm8ns+J5qAGvyeJvfcosEUI4YqyNro1SqgAs7laJeT2sGSqbgbkPvIVcHtUPzfazO1bzjbmNM3vdxtSyQdQ4MXxyNx+NqSdGEZj96WKM3FFwvrxaNI7KHWuarJDZcC3rZC1xRPOu3fRpj5xpz0OufbcgwT+8RSnu69Y0+4y8O1QZGnxK7IgWj86fzPaUF2CrJXGE2VOTfMNxbA4AsW2ewJZkjYgjfxryDJzX+Si0DP9DOR6NRHYLWve5FRyvOyALHOepBB8+4LU+deRi/FjPqYOic6vQiGJzDC0GR8azW3J38t9HHwPrOJ1ybe8P9LA52OkZe9sAKnYsUixcLfzd/Wofj60acs8oU9GvIrnooVQpu6fkdvnPkBXP7e8ryenIGvy3ZD748qlnlWNlOLFXCg5wS8oXtY2FNxCk0Dpe6EkQNsjq5kh0f01LbOkeDXY17F3UDD/yym4XC3uvLnNx9NwBDhtFt1fkbxCCsokXEZHZOHyXjxXUAgVMBKB8/Oi8BFVGwi9EojiLJRzIJntbSTnbhRdd7DP3zsjq8ltkEtjHF+8IsdXa/Et+j0nUn60j+qG+Xz2Ch4D1nm9jM95XbPuw1T2e33/7r5AclpiRJCcf8G/3ROBk/z73L4U36qRMm9ALZJ/GLP6BP8wjYM99kAZPxuAq6P6qvyI6pF84fgRbQx+R1Y0PaPzSVaT55AF03iiwMKVTAhcO8bH57FZt6fSCWmfJvgisZkLta/6orKKXzM3Mo9+w+eEug+cizZEE1wY3isS8p9BVhfJdVf4IvwaUTbH6HyTi7Dz/WHkwnACyhT5Hdqg7Zi6doS3JwdMKpCQC9AEF+qXRIDNRT5PXRZdtxnaWF2XCGtoUzaHv/djorHxEorxt1p0fwe/fytkyfFfCpv4Nf3/7V6OPlc7oYQEnyClYRpc64Xcrb+mGBCqigDNrcy3HVx+uAwpY95FcU6HUwBJ9nLZY5yfOyLrdrcSL3Z0Oetqn7//h4DzbSnE0TrFv8uxSNY6Kut2Z8Sr7ZB11iU+hz3jvLuWQkKD9RDYMQYp+A/Lut2T6VN/okDmCDzthkCz5xAoEW/K9/M5ZZyPle+AI7PuRy0QSnbxGYpj+rh/c29THHPtbrQ3GuN/j8663VkTAhifRwH6Lwf+LzqXgGuvoXA7w1A89i2ja6oGaAK2RnLaqSh29199TroHWMGv6Y4s1Eb7XHRQlm2e7jzIugG1TCj49XgknC+ROtcdbRYbgIe9rmo+nnomBJo+iTbiS6KsoO8iS4n+0XUr+WT5CMUZXir+PbvgkltQTp5PHdAG/gGKA6P38THyGQ6ueX1XoEd0XDfauxTf2roAdh+eFdXrV/Nv6UWigPDALE3xDSkqZiUKAOv1yabrROAp/90FWQ0vmrp2qM/Fu2bNm5yaHDPH+Zo5W1Q3B3Ckv7vzUtfH2uJE234a2nQt6McLIgD8BQTwroRcNH5EINoeaIPQx68fSAQC5TTZdzYjSj4yCmmmk28ysQzc1N/dN8DaqXvrdW7sjQCPUylkjO6EYhV9R7Hl2iIIcF46ur9m+IZchMb4t9/d6+Zxuepjii3XVkFhOGKQtmZ40QJezY6s1M4FOnhde+AstHm9JllDkfvngvE6WIm88j6N9DmkJwKcJwCr+vleSDnyClEyI5TgY10EUiwX1VdcH6uFkLXj90ixNbvXbYgUU69TDK5ti0DedeqR9ynZY00E8l6EwkG9iRTvW0XXDHUZ5Def+4/Lug9T2e/OwNMoMUGHqH4XpAy5j+KEBnPjiaZqaYxk3oBaIJq3mFgFuY3cBwyM6udFiO2ZRNqWnCqbUKyP0/zd9Yvql0TWSG9QDK51wTWFflw1EwdRevucmuXTvThw48fJpnEw2jC+ggCjdqn7Kh5gbUWedUBaumv9uC0FV7t10Gb7KVLZv9J8Q1ZFz/qi/T4CQuaMr0Mb0THxQh/d38ZpE2DjrPmSU7Nj5ki0QUyE+uT9zoO0wQ3Axekxkrxn/zvI5+ijKWw++wJ3+BiagFzqDvdzR6HN6lyt2bdaoKbmMwR8X+d8PAnoGJ1bFlk+PIXi1WXej6wJgWU/A+v6cbvo3FNoc7tPLFdE56tGvmghLwY7L1b042SNmAH4AFlDbhePqVrlRQt4NQtSGhzgxwmI3QFZfTcga5lS46ZiZRFf0z9CwMMfwJ4uLySAagyuNbmXqrfx0ArvYXkEEK2Wql/d5/YPiVyKc95PMsLYF1ltJcqQ1ZFr5DsUg5ELI6AyDo1QVXxDBhkfAqf5cez2OsLnoH9SQjlZyXPQlFKevGA6lJCMCrM1zGx/MzvEzGb2wKuPI8R6DeB0M1vTA2uuioT4y4IC/eUZaiq8eFDc45DWbMYQwodeb0GZULbzS283swUAQgi/hEJw7CRodjnaatHvGZNgrqXON1VCCH+29Np6LBFfXgRmNbMhUJgPgrJwvYa0p3cj65pJJbmuHksI4Q8UA2utJCsSkASo/xfabM+PAuTOl7o3mW+3Bf6ONGQbIMDkWGB/M+sZ8fdJtKD3K9GOBv8m7w4h3NUKXc3L9CuvIhBsLT9OEod8gdwMPkJZANeMxkg3T5KRZNx9GW3CdqIQKPhjFJdpEFKEDQwhnG5mgxGA8UgoZBnNS4mSSuywoJmtZmYreVDmH5BQ/QJy/z7Rr2uDLABHI+vUizNqfqWVL1FQ68GgddiTE4Dc3NqjzdpuUcIN/Nqqy6A2mfItsoJI5KmJZtYxKNnS3mhNPQDYvkRSqVrjxeTKT8hDZmGYlBWzo6+1+6JxtR5wVZIgIymVLIsEJSl6FH0PbwIvBmVR/9MTNnyG3P8nAvua2fZNPKfexsP0Lh2Qx8VvMCnxBUGJUs5AXhqnmNmO6RvrkfembMRfISD4m+AJRJxfpyN55XhPuEUI4e0Qwj1+nlhuqZYSlLn6dWAdTxY2IRon5/u5pYHjSiT3q9g5aEpLDqxNp+KTyX1I8DkDBc7+PzPrEpT5bk204F2LNuIXAY+GED5InlFLA6sWS1BGrr3RBm+gmR3i9QmQ8ioyge4CPGxmM6buL9v7jTY5w9C4fMXMRsbgT3OAWepcj1ZtbJWW6H3ejAT8g8ysb3LezLrigbiRZurL8rey8ko0tu5Dm+pTzGzeEMIEH5czIiuFS4Ezg7Lppp+xJHJJOCWEcDDaUKyGNhd7InAtWbh/QNr82ZtqU7UJMPVYQgj3ozFxmZmt4WBsIuDPhTLhLRKUuQwz2wAJcoeZ2TzRo45Am/UTomf/EkL4X1AW2N/MbCRyZ3g+hLC3Py9XMDRRovVmRxTT8HbgX8DTZrZTBK49CWxlZu/5NecBz/j5uuex9/834B/ARma2DkAI4Te/pBPwKYoV+H1QJrmaLM6L7xBovqNvVAkh/O6XtEVg0azAH/UsP5syt09AbpNDzWwPKOLVvGiNfBy42cG2ii9m1s7HwUTgBhRC5zAzWwImgYftHFzbFIE/JyWK7bxM1/I4UoZeYmazhBB+j4D9b5Es9wOyKsyLFHjXoSQ0C5vZDMkJxwROR1a3p5vZNumbq00mdUUZSG7qDJxmZp2TOcjMeqIQG3cCV4YQvs6mpWUoU2vqllOB0AL/AALV+qJF7CW06G9JIU7GQiij2IkorWxyf82YQNYKNfdOEED6b2SN1CiINbJ82DbrdqPMLD8jMPdiNKk9AqzXXD9Tz9gLuBBZ6GX+XiqNKLiYrYUyEP4LBe9cDIGwX+HxQOLrc5rEj8ORpdlDKIbHQigxyOd4HCy/Lu3aty5awLui4MZjgMv93HXI4uM0oLfXLd/afcmpLOOlHwJkfkfp3PfyMfQjxQkv2vg6fDsC18Ygq42Bfv5vyIV43ibG14HAifHzsu57pROwNp4cwtfADfxbbAD292tmQBbftyHwaNfo/lwOKvBiaWSd8yDuLoRAhV1RtvFGro+1ShSC7d8GrOl1XZGL4AVA56zbWCmErIauRy6yh3jd7Ei592Ay33l9RX5vTcikiQvwXgiMuI3i0DrJ+bnJs3+25rvZFMXL/DcwR8J7ZNl9HlF4nHqipr4lZF18ucsrOwCdUudXQcqmmgkHhZQ/fwPe8vW/B3LX3s7XtHkmx7dqpyRGSV6mspjZZkig7IkyEY3yekNxX+ZBAv19IYRfS9xfdeaetV5Sbi2rIxP0JI7Dv0MIX5nZYsA5SGi5OMhcvdlnlbOY2Uxo0l4aOCmE8Ku7Nt2I4n6dHkK4L93G1O/haILcOYRwTbn7UC0l4ZlbA14NzIZiDUwAzgghnJpl+yqxxPOemf0FgWnLIouzDsCpIYST/XzJb8jMlgkhPG9md6LFfLcQwmgz64Osgn9BLrgHh4LWLJ9vq7y4W/s+CGTogbTkl4QQTvfzcyAQ4rsQwg9uRTocBbJOsgu+ikDwvUIIl0fPbjTW8jHTfInmv/OQ2/uGQdbdmNmsyLL0YGD9EMID0X1dQgi/+O+cx6liZqshq8qFUBKccSjO0XHRWM9EvihHSckiw4CDEHDyCgJr10BZy89KX1/PxcwWB3YDdkeJWf5ECTFODiGc4tdUJK/SLuXI+6Mr8FJwq00z2we5/76ClB+jzGxzYDm0B/s9/ay8TJ/iVkl7orE1J7ImnRHYGGV2vMCvqxvep8Zsb6AdkkffiuqvR6DknsBtoWCBjJnNFWok1ESyjru3zoEITOyHPHdmRh4mJ2XZxnKUHFibhmJmnVHmpp2R0DMghPBTSmB8AU1ABwN3hYJpdl4qvJjZTsha620EnHZElhEbhBDedgHmbDRhXBlCuCirtsbFXSaeQVmzLg4hnB1NeIOQpc83KMDk/dF98QIxAmWX2j2EcEX5e5F9mRLhIOLv7AiEnRv4OoTwfHy+FZtbMcXdUiZGx00BYzG4NjOwDBJIvg+KTZm+ZgUEZDcEj4dmZl0Q4H1zCCGJ3bQ0chn8BLg/Bk7yUjvFzHoBAVnvfOB12yHQbUGUzOKlEMI+fm5lFNv0UOC/wIpI4FsCgXCNlAt5aXkxsyuBRVEWvvj7nw+5y3+AZKUJ8VyY87vpYmYLoXlxSzRWnwkhXOnnap5vKZlkWfTNboEsmv/TlEKz1sqUvmtXrC6MQpOMBUaFEG6ZmmdlUUzxU09AIEUvZKF2ZQjh335+b+QhNA4p0f4CnFAPm/asSiTjGjI22AEp778F7qiU/U9WxRTb72i03++OgvRfE8mqCbi2O+LXb6n7K/a7NLONEFD4QQuuTcZJexSiY13kyj26lDFHTZZQAWZz1UYUu8r1Q+BaA3BuVN8p+v0Ksp6oSzPZaiSU5XM0AkRn9brNkfvQd8B8XjcABVb9mCh1ecZtXwC4Cpkfn+517SiYyw9CZrrP4e5TqTE9Ak2Eu2Xdl4z4NyfQPTqer4X3NWUOXhduZKQyJ6LkA40ycbaEZ2m+oeDyvyJAuAGBw72Qqf1LwIN+XSe0eb+bKCNRTpVNzY2DKXjG1iiO0IlI8B/pY+We1PzW08+9DhyYdd+rjVK8nDH6fRxyuV3Aj+NsljchK8Fm54OcWvwO6mJN8b6m3bQnyTK1zgvc5ZdCBsyZp/F5Fc8rlOn7J+Ti3w25kDWgcBEbRNftgMCLp4G9s253PVCJb7Fjap6v+PHVSnzZGMXGPAYB/8OQ+/oryOMnue4qZEG6azyHVTKh2MUNwPlA36kZJ6lzNT9Gcou1FpaU5qxjUODGxAWiFwIj9gH+GkI42q/rFArmy0ODa4zyUvnFzDZELkNrhRD+63VtkLn5xUh7vHkIYZyZLQr0DyH8M4N2NmUNtDCyzNgB2DKEcKe334Iyaw1GweP3CSHcHt13CHAKcpGqO0s1/5ZPBV4JIZxjZrsjq8VFQgjvZ9u6yi1ulbIPMDaEcLKZ7YLc77YNIbw9Dc9ti1xB7kZBT59GsbOuQELL3sAQFN/jR5TCfHXg+BDCGf6M2taOVWlp7r1MqYWnz8HXITeLU03uwKPQGJkf+G8IYf3o+nbALCGEbybXlrwUSkoO2gRtgu8OIdxsZt2Q4mkc8H9BGcKS5BKX4JvkUIdW+82s0/m4I+dDqWJmA5D8dlUI4R1fU9cD9gwhfNfCZ1QVX82sH8r0/ZDP4wOR84vngAAAIABJREFU4vpppBD+HK3t9/v13dBG/Sc/rhvPgNYo0Z622XFT6rpqG2vTq7gF3zUoQdbWoRAGYVG07rVHwO/LLnfcAjwcqsjCz8yOBA5B8brPCyUSiuWlUPKsoC0oqcljM+AGM3sQuM7MFgjKSHM2CqQ6wsxOAWVyMrmLEgpm2DnPq6N0AwxpzjCzDiGEhhDC08AdyFJtRoAQwhsJqOaTbFlKalzOa2Z9TO6pOJhxAprEbzOzTV3gCO6q9xKysItBtb4oOOwB9QiqAfi3/D/gLDO7BWlpDkapsVtUyjkGKqiMQ9nZdjCzO5BwfAVy/2pRSfgW8y/IpewPf/6DIYSX/VtbDxgI/BW5/O2ILGJ+BkbkoFrll2juWsfMLjKzk8xsfT/XMLm1MvWddULZKK/0zdnLyP1wS6QlXtfMbo7+9585qDblJXpnO6IsfV8ja3wcSNsfuX88Y2abm9naKN7T1mizXNegmpnNaWbzm1lXlymCKw/qosTfrJl1N4UAiMdVLh8XSjdgZeB2MzsKramPojiRU1Uqjb+p8TAzciv8GLjVFK/qPhRCZwOUgGEQsJ+ZbQqacyJQzXJQreUlxftZTFkrZ4CijPclS3I+vq6O19D2KATCD25oYaZMtW8gd+VFURz2RO7YvFpANfOsr0Fxok9FQP9wl7Fa+gxr7rgmS2jClC2nxoRMkn9HlkzXofTDY1F2orYoUcGZyB3i9Kzbm1OL3mlT7ntz+nv8e1SXmOOvidzSFs+6/d6e7VCG0h+9zXcmbUPucjcg186NvS7JZGmpvx2IskbVG1HsevgIMtn+BzDb1IwnYCXqyB0RbQSewt3vSvG1hXxbzufSTX3snocsj3r5+cSleWkf7/cA83tdxyn5vzllPma2Ri4UL6EMup+hANTNvsPUeNkFxfFYzI+vBe4F5vbjGVCG7onAs1n3udoJhUn4HDgC6Jo61w5l930MbZJ/QS4xh5V6d/VEyJ39LZcZ30HZG+fxc03OVamxPmvW/ZhOvNgGxcYajZKIjMDdhGnGRSrFix5Z96MMfFoTxQr9EyWhavH3k+LV/Fn3ZTJt3REprLsn8hbayL8A9KUgs76MlN0v4hm/c5pm3m8DPOFr71N4NtkW3BePr66t0bZKpfQ3iBS8n0RyaNtITr0fAeJtKN5jVPQ6mHq/qwPrI3n7e5TUrs8UPmMgMEPW/SoHVZT2opKLmc2FTCHPQO5z24cQVkFZazZAi/wXyMLlGuBQM1uuLtDZKi0pTfI8bvXVBiCE8DXaOGxrZqd73Z+uXR6CFqEfMmr6pOIWlFcAdyFrgaMQ4HCrma0RZIF1HBLi/2FmWwXX6iV9j/7+EUL4PINuVEQJhSD5qwKLoAQQGyIrrJkmd39qPO2HgPdBrdbgCigpK7PE8uJFYEEzOxomWR81aZURuxWY2YEIzBwG3IpiWK6BAszv48+b6C4fLwDroAC6N5rZjCGyiAm59rqii5nNhtbO45BlxmooZs7BZnYilLZcS31nI5AlR4/gGbmR6+c3IYQv/XgeBGacjqzK8zJtpT8C0O4PIYxPKv2b/DOE8HoIYVVgI5QsYuNQsCBtk7y7eiomt9nL0Ny2vf+eF3jNzOZsaq5KjfX9gb8mVl7VVFLWMeshK9I30ffYGbn0X2cKnzKxlGVVie/+OLeyqani62DS/2/Qt/Y1sKnJQ2ayVo4pXu0LXO8eCRVRUuNhAJqbn0JgxLd+fjFkBfRxKCSGGo0CxP8thPBpFm2vpWJmQ9G3+CxK+PQf4AQzu8b53dR98fjaHTjblEiqZstk9vLPoPiuw82sVwhhos9jXZHBwvtBHk+T5vlKXwej97sdAgfXQLFSH0NhXg40hdwoWVJj5EDkPTVvqza6UkrWyF4lE8VBGfsi4XzdqO4e4FNgST/uGF27Qtbtz6nF73kHZNEwGgW0Xt7rZ0HulH8gN6NzEHD6Cy3U6rRyuzsCN6KMSXGw/dmBd5F2r5vXLYiE+hFZt7uSCVn/jQK28uORSFt8MM0EDqZx8offgb9k3Z9W5lXc5/nQBqCj/77Wx+Ax0TVtStyX5tsEFBtwHwRst0fg2unIEu7oEs9bvtZ5XWuEXDQvRAGpl4nqewNnIevbE6P6RPtbKsnKrlFdEufrXyhJwQz+TT9EpGGNn5NTi99ZYtl8OHKXT9aWtqnrSlrH1CPPkZVCB6QkuIJIY4+scT5D8Tsb8Sj1e7jPf7tn3aep4EGcaKAXsJl/4528rovP+e/4nNCuxDNK8WLX1mx31kTB4nYgMBQlm3oDWDgZW03cl54jG6jQRFRIobIBkmNnTZ07ClkzLwXMjADpD4m8KupxTpmOvJ8BeADJuHESmrec5mliTJUaX9tl3Z8y8m1D4EoUi3rjqP505MZ8I1IE90dJCsZVK3/8u3sDxYqLEzIeh2T18ylhuVZijExEsSEz71NZ+JZ1AyqN/IPYKjre0j+OAUhrtLbX3+dC0RJ+vDJyD027RuTuSBVM/r6/BE5CFl//QQDqln6+M7AWQunf8r97RfeXZWH3iWyrVF0H5CJ3c1TX3v8OcKEkBjbqwgx3GnjcBVnMnEjxBuhcBK4dlAggaHOQjJG6y6ia6vMw4HkUZzLZbPeiAK7FYNgayO0jbRa/r/PtDOA9JEBvG52f099LSXCtVLtyqhxKvxcEmjYgl/qVUud6oY33GODUJsZck98ZsCxShryDNMk/E7ki5jTN73JpJFTvF9UlIPdcSNGzWtbtrCRC8R/PiY7vQfJjErJhCJFrY2qsJ3PjLln3Ywr7vELqeAhSOI0FzvC6BDDv4t/8R8DA1H2lvvuq4sVU8G5F5G69XVS3pa+zbwALRfXLJDxrYtxUJAAJzOR9bEDJCRLZNQHwF0HATwOK1fo7cGTW7a5GQsqlAam6Hmjvc2RUdx9yaUzG03ype9Lf4p+VOr5aiY+boX3V08io5lPguOj8ocigocHll++qecxSCIm0tx/HxkZnej/PJlKmNTFf180YCSEH1tKDqD2Kl/Y20sru7ANnG4Tuf4A0jw/4gpDEdOkIHIBcv/pl3Y+cmn3H6Q3eYsiVKLE27Obv+CeU8SxZ7NugANmxZVirg6YogcLMKAbC0qlzbZFLybNE8Sb8njbItP6myfEgpwBKkX07skxcNf1+KViuXYZAzgYi0NKv2RdtOGsWVCvx/WyPwJHj8Y0UBcG4LwVw7QrgL863PVLPGO6L727AAj7WfyZlhYYsMU90Hh+fNS9ymqrxs2r0e1cfDzeQ0noil4GRfn7ZKRXW0Cb+Rp/b43T3+dw37e+wB3If+gLYPqpvj6y/P4zfc04BBPDe4L8TUC1RyvZ1fq7vx1WvqAF2QjET94/qBno/vweuiOqT2GpdECB+QHSu6nkxlfxbAVnZvpn6xhJw7U0Uf20YCkmSVroOr3ReIQv35YAnfUw08vJBSpadkSJmo6g+N1hoOZ/n93X0DmDBqL6nj6P9/PiB1Ly0MIo1PMiP6+5bpCDLJn/PRJaU7ZAX0N+QoiBWAPbxb3N9iq3xq3LM+nxzV8SDZK88u8sAY4DLEUaStiyu+TFSkmdZN6DSCJgbuNiFgj9QPLXk3Fq+ADTgGlmgK7LAGENqw5hT5RIKLL8VMt+9PXUucd34CQEuXaJzRRNtGdqZWAIkk9lawA7R+aEI8DmKyFURWdr9B7ionO2tVkIA0Xj/tjdI899/n4w0VG8DB5a4v2JdLlqJZ4shC4MDUt/ITMBM/nteZC7+sdOhqWfs6XzbJaqbD21EvyJyvfdzsyMT/AZgSNY8yGmKxsvcPgb+FtUlriTn4ckponN9gBVTdUNpoRsYja0Zq1KwrURCVmt3+Zx5EVJEnuvHh2fdvox40uQaiwDHsSiD8ce4+yfaoO2DQlAsl7rnoJaO9Uoj5AZ1H7LUi8G1xRG41kAkW/u52ZL1pMTzDqZGrWOaGjdIoXAPUkzF4NrmyGLmdxS+5NjUfYdVMq9QUqIh/ru9zyVvA//FFcST+ZbyeXzKeb42CmFzB+5K7PWXIKuqp4isRdEeaDhyP05bkB6MrLZqWtalGCSaFe31zwW2jup7UwhdcUozz6roMdvMHNQWOMa/z70ptljrh4yJriPy4vJz+5GS6+uJMm9AJRKwLbKK+BY4OarvjMCYn5BL4I3AzQhsi81pcxCjgglpwH7x9zgaaWnSGQc7oECNvyEz6kwmRiKXOWQZOQq5ym0TXXOsT2IjkVvAvMDuyLd/aNb8rlRKLZxdEYg6FribYlfQOEZMb4pjTyTjZT9gi6z71Iq8OqHE4rkMEvoTd6ZuSHP1DHIxGOb1MyHtaOy+0gZtKncmAoqj8/2QtvR1YJ3UuTmBZbPmSU6THTNp68auCEB7gggURZaeCbhWMisxBYVGb6K4Jjll+n4HIEDtQwSCP05xmISK3ky0Il/WQmElhuOhQXzcXuOy4gVe1w9Z6f5MBD75uV4ok9xe5Wz7dOp/Iq/0QcDQKIqt0AYAV1OIpzmDyyw7ImX2xqnn9UcA3d5Z962V+dYH6JyqW87lkXcpDo2wAErcs2rMdyQjVmwsXWBGBOJMAJbyunYIXHsfZYfunXU7a4V8TCQy6ir+fV0ZyWzzoHikDThghGJL74bAohGp5/VHgG7VzUvTwMPtEej7EVKKpD0uSoauqEZCIa32R9nakwynswD/RrL4sV43s8/Xz1PsxWU+Bx1NHcc9zrwBlUgu8OyMNGvv4mmuo/MDkJb2LhQTaIvoXF0Kk9VCaPP/TwSELInckT5FfvGJEByDa/eTQdBFX8D6RsfLetvnRVqkVyjWYu6HYgCOQ4Glq9q3vxX5GoNp3YgsrbxuGwS6XkWxFVbb5p5Vq+QL5WzITXaZ1LnVEBB5AHIFfQPF5zgDZQD6lVSMjhLvoBFfo3P9kWvuG8D/NXFNPt9WOPmc1SP6/QXw99Q1STygC2kijXtzYyWnsr7PNGDaBYEjsTKiLr9LpJQdj5R1P/p8mIQMWQS5Dv2ELAA+QuFFDo/uTwDkdnjw+mokJg+uLYqAxga0Wf2PX3NMiWd1aWpOqBUCBjsv9qAxuLYCslD7nCaUdxRb1nfIuj+T6esgtFH/gYKbYVsK4Npztf6+M+D5pggI+ZBC+IUF/NzSwIPIiOBRlFRlNHBEdH8ss/UtZ9sz4FXc1+URYHYFymD8I4rdul7qniZDV1QLIavqxNhkDNpjJp55c/iY+dJ58BYCaY9q4lkVPQe1NiWLeF5KFDPridyOVgRuDSEcHZ3rD3wZQvg1qmsTmkibnpfsi5mtiwTfXkj7+YaZtUca5gsRSLBSCGG8mbULIfyZxTv1NNdHoEDQ+3v7rgDWCiE8Zma9gDuRMHJOCOF6v28Jv6cH8EkI4Vmvz8dlqpjZ1si0eTa0mJwNPBxC+N7MtkWWV7cizdyvTT+ptouZdQgh/GFmXUIIv5jZWkjbeY6fPxZpN79AAsfu/t0MAe5FcVGen4b/3x8pMXoDB4cQ7pnWPuWlfMXn3JuQdfeFIYQ3zWwDBNTuGUK4Orr2AJR5edUQwhNZtDcvU1bitSX5bWYW6kywNLM2/vMGZLF7F7IGORO5tm8WQnjOzLqjuWwdBKx9mcyP1b5ON/XezawfskbtDVwZQhjp9YsiQH1T5E50aAjhTz9X1byY0mJmhoDGTZDMd1Nqb7Epmke/QdmSr8ikoVNQSs0N0bmBaK4fBKwRQnjZzNqiDKC3o037UiGEcRk0vaaKmQ1DVmoXIjCzJ/JouRt9cx/6dbshgOhb4K0QwiNeX1ffYlJ83toAWYceHkIY5/LMSUh5cnoI4YHo+j7Im+WpDJo7TcXM5kFWxA8gpfgyKEzLYsiA4yEzmwEl+1sbWS2+E0K4ze+vuzW/2ZI1slfphOLCXIks105A7qBbIDR3yazbl1OL32NH4FKkSf4ET0rg59oB6yLN6QsUMhuWTDFdpvbugQTvF5CQsScC0tr5+V7IbL7Icq3Ec+rScmAyvB3qPD0fuaLc6GPiSuRmaMhy7UfgelJWbfVCyMV4F1yDjiz8HkJazzhuTm+KM9p19PH7HlE8j2lox/zAi0SuMDlVPiE3lB2RBeibyIp2BwQ0nOdjafHUPUtk3e56o9Q61zZ1Ll8/Wsa3Lj7eHyfKiIqsbh9FrrLLtORZ1UgpXvRHVhvzA7N63fxI0ZK2XBuIEoz8hLsOVTsvpoRXJc5diSy9d6HYYn5Fn0MfI8oUWokUywJ+vB6yak9b4g1EFuljKcT2aoushBqFh8hpqt5FdwpxsLpF9Ru7DHwbHu+xifvrbv53+X9RZH32GZ7BODq/Ltp7PQGsXe18Q0kWDkTeWXF2z5UQ0PY/YM18jEwBT7NuQDUQsgK6HLnavY3iZJyUdbtymqr3OBK5HF2eOtcOIfFfIuCgXRZtTLXpCp/cX8FN5r0+yVSagGsv0gy4llMRT2dAWajOw11/vf5/vgGax4/bUEhIsFbW7c6IV6OQSfhQCuDa/MiV+vXUBilx/VkYZf8cBxw0HdsyY9b8yGmK3lfiTj+nC2fX+Pf0pc/BVyD3+339uiQzYGJFnwtr5X9nayPL3ZMQMJJ8082+C2ocCGkB37byteMmH9OJi1XyDfTz85/hsSFrlWcISP/UZeTPnB9L+rm+FNxC94vuGYDAtR8pkbiglohiAHJB5AK6MsWJp65C4NqeFFzo9/B5s6Ld8FDW7kspxCxug2KljkZJOjrFvABWRQqXLyiAa5kptGuNUHzbT4GzorpkXtoZ7YUuI6XgyimA4oQ1INfkvqlz63r9s0TZaquR/PtsQIrwmVLnVnT5bTSyLM28vdVAmTegWghlo9sZZZGMs4LkE38VULRhmx3FOfkkXmz8XDtgI2CnjNvazoWOy5Cm6QPklrhEfI3/nRd4zfuzQNZ8rnRCG/1vKI5PdxeKX5IIdnOjbFVt65GnKcH2EeQaMIxCDMJ+yPrgdRwY8fqlkDvo+0Sg2vScI/P5tvIJaUD3w2NEoRgufwJroM3keRQCJo+mRBy+nMr+zoYhC4aXUayd91BigmSdKQmupeaKLYFNs+5LGXgV93lNFIj9dt9oTQSeSl+LrDSfQoDJHFn3oZX4si5KxHAUsjpaFwXSb8AznqKYa/9EHiBxDKeF/drLsu5HmXi1o8scY50/L1BsBX6p1z/ufPkFGF5qDFYSAX/1MXAmHicN6IQA1Q9QFuhOqXtu9b42IPm8IvtWrYQA/yeBnn6cKEzm8HfSgDL49siqjZVEFMcrPNT5c2rCv+jc+i7rVmXSstQ6doP38xCiZAR+bgUUA3MC2hvl3+fkeJt1A6qZmhI2c6pMioTcOZEb4IfA2aWuSf8uV9tSdYlmaS9fAG8jSn0dnZ+bPPvn5Pg7i//tjEDIw/34XqRZX8KPByHr1H6p++vmW0egbuwq/SQtB9e2JDIbrye+5TTpnd+OANbngKW9bl/kOjEfck9ZHm0mG4gyHOeUyfvqjDbvh6Ksfd19rXkLhb8oCa6l1sr9EHi6Ydb9KSPf+qPMnycjy5xOPs6/BO5P8wlZ+26WdbtbgQ/JRv1C//bjsACP+Pq6WIpvj5Fy7QfmKjW2ao0Q4PiLf28r+lz4oMt4J0XX7YsAqQdIZSKsNErNBccj196zKFhvdkKuZgm4lljAd0fK4+E0kaAopynjf4lzI5AC60BcDvb62YE7gIOBfbLuQyURxeDasS6nnEJjcK131m2d2jFC47AP9yGr4Z2IvHn83Krk2dhbTHnygrzUVUmCLJrZnEiz+n/AAyGE/bNuk/9eEMVr6Qq8FEL4zev3QXEqXkHBa0eZ2eYoHftRIYTf08/Ki4qZbYRipu0KBOSW1tN/90FxEt4ys3YoocG2wK4hhDeyaXFlFDMbEkJ40X8/CSyEhP27gxJ8JEGp5wauD57QILo/H4s1XpoJWL4x+t7WQe5BnwGrA68C54cQJppZB2CFEMKj5WxzXgrFzLZELqCzA6eEQiD9mdG3PRhZlJwUomQ+qTVrBLJU2TuEcHkmHSlzMbMVgUuQZfP5IYQLvL4bsB1wJPBmCGFdry/6Tqo5ILiZHQc8E0J4KFX/APBrCGEzP74XWBzYIITwupmtCnwYQvjczGYIIfzk16UD29f0umFm5wJDEF/Gel0vBGIvBxwdQrjd69uiTf4EP67IcePJF9qGQvKJi5D3x43AJSGEj8ysEwJy5kdA/j9QLKvDgB1DCM/4vRXZx0otqbl4CZQ0pSPwbgjhTa+/GiXGuBhZQ45DcdaOBFYJIXyZfla9l1TyjWMRYHwKcHEIYXTq2ormW2qMbILA/YUQcP9qCOE/fu5faA7aDyVs/KXEs/Lvc3Ila2Qvp5ymBzEFGs7kWmS5djGKI7ViBfRhW6TR+wRpSG4hCo6JQJ93UVy1i5GFQKP09Dk14uvuzs/BfjwQxfVowDXBwMzIPeNHIpeLeiUk/DYggTepe5zGlmvzAf9GwMnAcrczp0zHSGyl0NvHQhwL0oCDUFKYe5CF8IvAvCWelVs2ZvD+kDX0nyjL2fJen1hC90DWJKPQhiKxXIvf+wjk/rhb1v0pM+/6oeQbv6JMl/G5Lig+1kfAf7Ju63Tudw8UV2gssHLq3A3Ac/77HuTqmFiC90SJgP6CQiwkMljNWqaV4F0b/+buRxnIk28w+a76+Tp6SRP3VzyvUNKFR5Gl4g8+N5yDu/sjwOdKXxN+RbFtj8q63bVAKDHQaJTkYjTwNJ4QxM+PdL7/4eNsApE7dk4leRpbriUx10ZSpQnNfIz8hsLf/NPX9ucpjnl5H9oT70rKci2nFvI56wbklNO0EALHukfHLYrXEwl2cyGNTdb92AqZzx+Osi9u55P4Q0izmVy3g0+ITyMLgczfQaWTC3MPukA7k9ct4RufUb5ReAzFXjsyPUbqkXwj9ASKR9gxqi8Fri1AlQdwzWmaxsr2Lsz/iFyc/kFxZsTVkEXTe9GclgNp2b2vGBjr7u9vPLLk7eT1iXvfjChGT6OslghUm0CNg2pNrQMITE7icx6QOtcFBWsfR4250KCkSfcg5dQqUf1g//6/RiD6Ql7fDgXff4c8ADbI8+Dn5HtCgFsCZl+GXLA7ZdW+aejXGgi0OQgpL+dD7sETUFKUvtF4WAZZtK0U3Z+vCVPP+80Q2J2EONmSAoAWJ5laDrn67QesF9XXrazbAt7G4NppRCBUpRIlFBf+Pb6FLESTfdAiPm6eJIr9iWKqNQALZ92XaqTMG5BTTlNLLuBdjwdKR5ZJE4hSBk/m/rTPeSYLO4WsYUf68UCf7O71Dc0LqUWwGzBD1u2uNCIVMyB+xyi+xOd4hrKI7zuhDIX7AuvWM0+R9jxeiPdF2q35U9c95uNye6IU7jG/c6oPQu4lv6M4JNsiwPVrBFgPi66bGcUSehc4OOt21xulvut0bJV2vnb+gWKPJhlaE3BtJlLxj/y9NyCX+cz7Vya+zYmUCHPhWYpRvLAk1uR+qXu7Aotm3YdW4kWS4fN73HLN5ZKjkLXMPUihNRABjOOZjlmiq4mATYGro+NlkdLqPqJsjCgO2W3A7Vm3eSr7uT/yuJgrVX+qzxVn04Tiux7lrenI955ImXWqHy+GlFx3AHejmI9NxuirZ97TQsvZauMRKSWY1w1AFqJrRXV3ISVIYlk8d3QuV5ZPLf+zbkBOOU0LIUuIxG3yd6SJaQSwNHN/2YGAlIA6MzADMo/vj7TgXyaCGLL2mIjc7RplXMui/ZVG6c0L0pwOJmWujTTmd7bgeVW1iE5HPlrqb3dkJn4p0AF3WfFzTyMXsrrLmprTpDHQFlk53UMx0N8TAWuvAP29LhlT8Riq+7mrTO8pXm829rXyOd/Er5jMk8iqKA2upUG45D3OAayTdd/KyMPtkFXmDwg4fihZdyiAa6OAEU3cX7VrSjIWSoylLsjK+2NgVa+bE4Ws+A741M+PAg4p9YxapBSP5gKOcxn1tKh+T58fn0fZBZdDbtnjqVKwGjgGGB8dx5bu/0Cb+kluoTlNl/E1u/89GmXcnsu/u+u8flkfU19TBZZW5eQbssaecWqeUcnzOVKSfelr9CSZHljF56HeXvcAjRO3nUIqVEcl97VSqQ15yUsVFjNrAxBCOBBZ0GyOXP1uDCFMbOEz4oCOK5lZ+1ZqblGJ/ueOKANlAA4LIXyANjdfAid4kMhHUcDv5YAjzax3qWfVazGzHYBXzWwLP07iAt0B3GJmi3pAaVD2tkFmtkFzzwx1GJjTzNYDnjWz1RAwAnJXeRKBu12Cgpd3BAghrIAy0b6XSYPzUrbiganTdW2QpdMA4LdQCETeIYTwFXLzWRBZsBFCCP6cickz633uKleJ1pvtEajWBsXxXBitmfubWY8QwqUoO98uwHn+LieWelYI4ZsQwr/K14vsigd7/jsCz3YALgJmAZ4xs6V93d4faf6Hm9lh6WdU65piZqcDh0RraDwXnIwyyv4I3Glmq4UQvg4hXIQs+w5ALmlDQwhn+fPa1Pp3n/re/o4y6o0DDvOg/oQQLgFOR8DjPShUxVEoScgVfn+jebfCy7+An8zsaH/Pv0cy9fsIyDgAgT95mcoSja/dgOfNbDYUl+9dlKRrLEoYRAjhOQSg/IT2DwOzaXX2JeLbdognbwH3mlmLEtelZJburdPK6VKeRRbm36BEFknfXwVeBs4xswfR+r9JCOE1TyyyMXINTa/5Vbl2ZVlyYC0vVVlCIVvLqmgyeAbYENjBzGaa3P0pUG0/FDtqUKs1mGJBycwGIMHqKWQV8K2fXwz4IYTwcVD2tdmRa8XRwN9CCJ+2ZhursHyCNjwjzWzLEMIPaEM/Ei1+/wX+amZrI7P435GgW42Ca6sU58PcCEi7FbjBzPb07+MEBKAcDhALyyGEO/z+fB2p7dLNzBYwsyU9ex0hhIagTMSPAwPMbCGv/8PM2oUQPkZz2/JQ2FBHwExNb64rocTfpZnNjSxKRgLbhxAU4kefAAAgAElEQVS2DiEsiixJDsQBUGSBeBBS8CxX3hZXVjGzNq5ISOKaHhdCuDuEcCKyOHoRuMvM+jm4diBykf8is0ZP/zIDcBKwq4Ovidw1EllGDAU2QPLXHa6YIYTwfQjhzhDCEyGEt/weq5dNmpmtiUJMPIpA1+VQFtk9zewygBDCLch9fiCwJrIAPd3vr0YA8h2UtGBzBM4TQphgyrb+B/qOhoQQnsquidVbUvuH/uj7uxSYGEL4zk/1979f+HUzIRfta1BohlfL1+LKKCm+rYW+w2eBq5Ay5Cwzu8rMZkhfHz8j2i/uA9ycXF9pJYQwKoTwppktA3zkxgcgcPV2NBctixKT/deVJkORpfG9IZXxNC9TUVrTHC6nnFqTkHvGKGArPx6J3NMOBmZu5r50VrPfibLnlKHdKyNh9EZg1tS5o1Bcq6WQm+j2aPKfN7qmpl0ppoKfg1GA7S+BLRIeIcXB3sDDztOzkftTA7Bs1u3OkF9Njh8UBPc859fTwLlIKHsI6JV123Mq+1jZG4EKv6P4lR8jt/UkccWmCPg/DZgnuq8Tcl+/3I/zOat872wYKRcXpHz6CXffBNpH5+5G8Sdn9uOOwICs+5EB344jij8T1T8NXO+/20b16yAg7cBkfBMlUqoV8m/7T9yVDLnz/QysH13TC8UM+wZYPes2Z03IYugdYJaobg6X7xqAkc3cW3VzJYV4jD0RuPYG8hzYAgGzvyDLxaLrc5oqXq+J4t8+jLv1Red29W91K2BJZMH2Ke7uV4+8j+bmOYBDUYKQ7l7Xxcfo+Ka+SRrvFxuAnbPu12T62hNZpP0DueVv5/VtgbNQWIMX/Nu8BcXKPCr9nJym8j1k3YCccpoa8gnxny7AxPF9zvWF5SAKAYY3A7b03+lJciJlzGqGAkF/7pPz0/gGJ5oQF0Fmyg0oEOzvRJkqc5rER4uEufn8fb/vi8iGqWvnQUDmGxHvG8WrqwdKjf/5gPmJEjpE5xZAFpXPOb8agM2zbn9OZR0r56PMuee6oL42cDFyNXkRzxiFEhf86gLrckAfpE3/Cdgm637UEyFQ7RUaJxyZ1efGONZVR//bzzcWjTYL1MkmDOiBrBjG4sH4o3N3Ai9Fx3HMsdeBW0s8r6Y2JsAZCFh/Crl+rl3imnmRS2MDURDseqJIjjvG5ZFZU/XzRGvqObU0XihkNp0NgbFvuvz6KXBo1u2rdnKZ13xcNTh/Exk4jl16oZ//FgGaR2XR3ox5tSGe+dKPl3WevAucXOL6PX1+WydVn94v/kmFx0D0vnzv8/FAZMDxI7JUB4FrWyI39Rdcpts6ur8u1vxWfQdZNyCnnKaUkIbhdqSxWdXr4pTIieXaZRQCxx6Tesa+PpGWDVTz/9sObT6f9MlvhRLX9AJ2Bo4gysyST3gl+bkbcgd9MBLqvyECgSLhY2bkelH3ABHagI9CwUv/RHGDlkhd0w5ZHu2PQOAX8WC5OdU2udD1LQKku0f1XRDA9j4CFeby+oNd0G8AxiAQ54is+1FvhKx0F/DfA4Ee/ntGZFH0BLB06p6BvhbVdRYwX3fv8bG7SlS/DAIeL0pd39VlkLOzbnuZ+HOCf99/xy1WvT6WvfrU+zhyPmzkvNraj2NLx6PQBv9H5F6ceXunY78TWSsB2ean2JI5l2GnnbftUVzpBpRxt5PXxyDQhshaba30/bVOwNLOm/MoGFcYimfdgLwvZk/dM6/LwgdFdZkaYbSwr+1Tx7OjTJ/HA928bglS4Fp0fTo5UV2MkVZ/L1k3IKecppSQe+R4nyQ3iOpjAe9kpCl7GziwxP0N5ZwkkcvUEP/d3if/t1EMsN5e35yLXj7hNebJYr4JOoxCdqT1UUDu0SgwZ3JtuzSP65WnwNb+/RyJYqvt7d/DrUTWa6nNwObO06Wybn9OrT4+zkVKh9VS9Ylg3wZYCVl/PhydXwAlutiECLyp1+8sg/cWf68DfaNwKa65Rwqd75Hr59pe1w3Y0b/t5bPuQ0Z8i9eEvghc+x63XEMB+g9BAehv8msWc76NR7GLMu9HmXh1ClLE7J9s3NI8jOpq+rtPjZtORJ4TXncFcpldLarriCyKzgAuAF6iSqz7SG3CW8qb5upyajE/N0cGBUkIhva+d/gcBZ0v8nwpcX9Nf4sl+rs7CmdybrT+tfVvbiKy6ooVhjMi1+1S1myH+LMqDVS7xdf3xBp2TWRM8hqNleQxuLZNVN82/y5b4d1k3YCccmoJpYSYrr7IjPVNQuwKGm8uelOsLUu0aPvhsbjK1PYZkQvFBByYQNZASyPLj5dIxUrIqUV8XdoF17T59krIxHkMsF7W7awEogCKLAw8j7tmAIv7d3Q/ctu4lwg8owBItkUb9cOz7ktOrTpOZkQxHT+imZg4QAdgL+RqsoPX1d3mulIozXv/Xi9DVoTn47GegDWA95DS6QVk5TueOgw3QLFLZyxfdEFWzx9TsIifFdjJ68cg4O1r6tPN6gwErh1ABK7VKyG3qgcoWM4fgZQPsyCQ9jdk7bc7ivH0E3JN2wJt8hfNug8t7OPeuAVsTq3O7zRoe4/LaRugDO3JGvyaz+UbR7JavuaKD3/xeSoG1wzFiP0VuWv3R+7Zu6AEG5vF7wCY07/fvbPuT6pvl/l4SBRk7YE7fE36jIIVY7zGLYHiHv5GhcaIqxXKvAE55dQUpRaXbsmCEtVt4xu7q+JzlNCspTceGfRlEArm/QMwKGknBXDtOaBP1jyvJkIA2s/Amn7cKTq3C7LCGosH7qw3Ak4FbkvV9UUBTedz+hq4ys8NdUHkOhq7iw12XlaU1i6n6TpeEs1nH+T2+zLFsTfS4M28CGhopOXNqfzvzX/3B/r777bIHeZdisG1xYDhSCn1N2Db6P662JSh+JFHUXCXia3d/4rAj1cRgBZbHM2KshtuAaxUb3yL+nsG2pweTA0mbJgCPgxDCqnrULzfJ5AV0V0IXGuHAoR/iqxFPgMO83v3829z/iz70ES/4jnFUDzjH1Bw/Bmm5hlZ96kaCXkX3ITcFxtcXtuECDhB4NqHyCumfZbtzZpKyChNgWuJW+gY5+0rNKE0psLCn3jbv8eTxERy2ywIcGtAmU8ToDWOvzfQ1/09s+5HLVPmDcgpp8mRLy5PINfJ55H2LMlgti0C164GOldAW9uU+u3HA1FMljS4NsQFr/frWUhtgp8jgI1TdbFV4qPIhLuDHycm8asBbwH/AnbJuh8Z8K2Tb3omAJelzs3pf/+GYv318uNZKQTGfZwoYy3SVj+ddb9yavVxk1j19kGuJv+lBLgW/X0WuMF/1xW4UAlE8eZ1G2SFdjaF2HdtKAbXmsuWXTfvD8WUbEDgRo+ofiRS1qyLLBnuJQWu1RrfaBxnp0UgCMoO2gAMzLoPGfDMUJD+l1CWvQTo6OZj6jPgWgqW4n19Tp3bj5dGWcyvzLovk+nndiiT+v3IomcMsrxrVk5NzUubo7icObg2ZbzfCIFCRyM3v21QxtUfELjW2a9rj/ZGPwJ9s253BnzaDugXHTcFro2k2JX2rz5/HQYsFl3fYpfnDPp6GQpJkIBq6fiWHYBrkFX6cRTkuRhcm6Vc7a1XyrwBOeXUHCErmj98U3Ao8hP/BJnzzukCzja+qFxPyqqtjO3skTpeD7lKdE7VJ+Da2EQgReDa8rhLVU6TeLUZhYyU11I6a92qyG3tGQqButsiQO6aeMGtNwJmQO56fwB/j+oTTfoTRBZtyALpdhec92nmuVW9kcxpsuOmpeDa4shdbses21zv5JuL8chqJgk3MCmQOIot8x6R5r7eCWUu/BPYz4/PQaDa+tE1vVDSh2+SzUytEK5giY77TemmkhLJl+qFUDKkr6Pxk1iIdEOx6Ean+YOyz+6PgLfbo/qKA51QvNoJwOHAisDqyNrlF2APmrBcoxhU25dULOScWsT7dghEeYTiWGCzoyzF3yNwLQaK6i4DN0qO0YDcIHtH9U2Ba0dEde3RPvIn5ObfqbXbO419nR34H/ABsCiNrayfQTEcZwZuQGBrDK6lEx1U3JxTK5R5A3LKqSlCwMCTSOMeZ6H6H7JUmseP21BISLBWBu08EQWR7BW15xEXrCZl7fFzhsCg74AvKIBrudl8Y74ORJkrPwW+QmDk0y7wJZrfjgh8fR9lMbwJgUMTgOH1xlOUwW5TpBFPTN/3IQWuef25yG1lQRRXaCdfjGeJrrF8bNYfURpcGxad7wwchCw26s5ipZIIJY34ECme4rVmFmDe5H36OvoxyurYIYu2Vhohl8YJKAbqj3jMmtQ181LIOF0VgeZb0O/+CEg82I93Qd4AC0zl8+pO2eLy6ZfAaX5sFCzmu/m42j91z0woY+MBlcw7l2HP9DExU+rc9QiA3oPGCuV0JsUJ5OEjpvYdXA+8luaty2vvIXB2AxoDJhU3nlqZT2v73H07UTid1FhsgzJl/o57CyXX+Ho4HimhK9qVFhjga/izwACvOxvFTVs3GiMJuDYKuaJXrBVeLVIb8pKXyi1d0KbhpRDCeAAzuwtNjgeEEL4ws7nRpuFGYKEQwkMZtLMbckkdbmZ9QggNyGLtZWS19hcz6wQQVB5DwNtcwH/NbPb4YcFnxrzwBgJ6xiH33yOB7ihG2L1mtjXaIN6C4q3dgtx3ZgBGhBAuSB5UDzw1s3OQ5u4WFLPvejMbGEK4EI3D7c3s79Et9yHh7BW0sbwYuCaEMCa5wMdriI9bvyd5ybqEECaaWdsQwifIchTgEDMb6r/XA44Frg4hvJpFG/MyqXRGoM99IYTfzKybmV2NLKPfNLPDQwgTgQO97okQwh/ZNbdySgjhMBSLcnnk8vZUcs7M2vg1nyMr3k1CCF9m0c5WKOOBnkg2uRltLq9Dm7YWFTOz5LfLPDVXzKxDE/VtQgg/IYXqPma2mS+VE/ySbkhp9VN8XwhhLPpOR0bPyZx38buESe9zJhRfaqxf095PD0dhNo4BNjezLskzEvnAzEYgxd1eIYRY5sjLZIqZtfWfrwAzmtmqPk4CQAjhXaTomgeFwFnQ70vmq8zHUzlLCOHfaH+wDnC2mfXx+pCMa+fJTcjSbyEoGq+7I8XJoOj7rcgSQngTganzABf4Or8nsFEI4QHvc5sQwvfoO30bxUUcnFWb67JkjezllFOaKARZ7ozcPg/343sRELCEHw9CgRz7pe4vi8aGYo3I8UiIOgvX+qI4V/cj090RFGIidEdC7HDg/9k773ArqqsPvwsQQcWCHTRGRbEES1Cjxt4LqOhnSYwNe+9dsTdAY48CKmo0liQaGxo11tgLYolGjWLvBbELv++PtY93GO69cvFy5pwz632e/dx7ppxnz5yZXdZe67fWK/p+12KhKYxpYXx19KTMvkNxceCJuIbawblzO+e/p9FLeg8+wEPCFsVDMF7DQzfmwAf6e+ErW5dmzlsJ15j4I7BVZnt4ppWsNPebM7nn2mN4WOF3wLGtnRtl2v5Gmd+mN77YNAQPb3wRD48/HtdRnEAzGmHxm01yL07BQ4UOIJPpsoV3oiH6FNyr4ZHUj/69LdeXew5XIBdW2giFXIKLFo5ZHBf2fxXYLm2bI/XDn5ESK9VLAVYHfpP+/x0eVbE1TZ4wlb/DU7vyCbB62lZpjw5I/UN4qv30/Z4kG3Fu39z4fOcukndS2t4VN+gOxBdKHiA8kAHWw+cKec+17FziXTKyFvVacM+1/6a2e89m9leueXaa8cKOMo1/n6IrECVKtuCCndcAM+Iea5VV5AfwFcAl0nGdcOPBoxSUrhx3I86KQl6YBiKDgYXSti64Z9B/8bCTFYFd8NDFlTPnNsRgfRrc3xnw1fS3gRUz+w5LncoYPDvZ87gHTZdMp1KKiWN67r4CVstt3y8NfitZU2ehybh2WSvfF89ig5fcgL4nrU8es8a1Mem9OyKzP56X6v9ma6c2cM70efM0abgbzwhWaQOXT33Sqi19V5Qf78kZuHHtwNbeh0YouJf/XLguzxN4cotDsvtbOTf7HB6Unq9li76maXCPsgkuWmsfV8E9hyak9nEMHpp2TNHX0IZr7YAbcr4iLZikfuEJfPydzYzbmbQQl8a2T9GkL7dOume7FX1N9VRw+Y5rgVG4rM0v0/Zl8YQRj+KeSSumcd0HuKTMsbhUypxFX0MtFCY1ri2S2d4Vd2R4g1zG+9z5ddMv4kb9ir70ZHPgfBteT9dW76XwCkSJki24W+5EYLn0eRlcj2wisHva1h3YIQ1e9imqrpk6D8Q13yoZeybg+iUV49r0uEjma7gR6APg6KLrXS8Fz/A5kSQ8CuyUPh+Ke2etCzxOMys3jV7SYPiRNPj6VdpWMYT0xFeUd84cPzNuXBsPXFF0/aMU8sxkJ8Zb416fR9BKVmUmNa5lPRvDqFb9329HXE/yciad8M6W/Q3xRYadcI/pXxdd73oouHHtazyjckNl6G5uYoUngFocD5N6ETg0s2+ydzvXdlQ0tPYo+tqm4T2rJLiYzNiauxdz4BpHF+GaRgNau4+1WtL1vgf0Tp9744bTx/CFy1XTuOtz3Ii2exp7/DIdvwy5Bb4ozd7nfNbUb/HolmdS2z4Sl7ap9LkPpd+hkpn1qLTvpHTOXEVfU60Umoxr9wHb416Yx+BG48OLrt8UXsNCzbXXzRy3JO5w8hCwVNH1jpJ+l6IrECVKtuBGqH+mTqYivr40bpl/FhdtvBfP0nVU5rxCrPG458B3uJD3MqlBvCANOIeS0l/jHna/wT3yVs2cXzeDroKfixH4ytyRuFHthPxAt6wlTYweSu/IUpntG6d7tUbu+JnTpGgisHbR9Y9S2HOzPa61dDqw0hQcn18Bjbar+r/ZJrjkwMFAj2b2V0K1fp0mvePJGEvKVJp5XqdojIAvik2kgZJy5CbyvXBNuVloEttfDI8UyBvX1sJDAo1Js9Dtiy8g7lyN+hd875r1ZMzd0xloxhBbq21k/l2gycO1L65tewwpvBBYENdufTONaz+kSZ7l6PTMTNYWRZmi36EzHuFyLE0ZPo/EtbGupUmgfmZgCdyY2SvzW70LnFv0dRR071psz/E541N4pMvX+NyxphOGZOq2W3rP1pySPgs3rr2BR3WFca0GSmUQFgRVJQljT8htM0kys4PwQcwmkp5O+xbGV8tWxVdoXpY0Ku0rTADWzA7AXYxXk/ROZvupuBfIWcCFkv7XzLk1IVxbJGbWTdIXU3DcpvhAoyNuVDtb0vi078f7mBXQLRNmthjuwTIXrptWCfM5X9IRzRw/C57F9tmqVjSoCcysL66/Nxi4JPMudcYNEl+X9V2qVZKo9eW4kWO7TJu3Be4x84SkJ81sEdzjYW687zkrHVeK39PM5pH0XubzwsDr+fHGT3zHbyX9e5pUsEDMbAfcy2UOfKFqJHCxpM9SH3I8vkB4Fz4xHYFHCgzPfMc+eIbZ3VUSYXozOwM3Zh8KDK+0l2nfLHjUwqbArsAr9fKemdlMwFfZcWgSRF8F6CPp67RtBlyntRcwVtLbZrYc3ofcKGmvqle+zjGzfrjR9iPcOPa3zL6D8WfpGeA4SS9m9s2Ge9RuAzwrabO0vWHb91xijJ7A59l3MHdsB0kT03s5L/7cfihpbHZ/tereVsxsXtyDeEE8Muu+n/pdzWwJPGrqQ1zz8OPWjg+mLWFYC6qKmf1K0nOZz2vjbuUvSPoqs/3FtG3zZr4m+32FNpJmdiy+ejdj+jy9pG/T/zcAv8UTFVzQnHGtzJjZYFz74FRJ707B8X/FV9D7pIFdww4kpoY0MboSmB/3RhgJ7C/pu9buVdHvUFB9zGx93DNnA3l25enS58XwEOJdJT0Uz0ZtkLKbdcAzIr8GnIi/52fjhpCJeFKcfpLuMLPV8MRoD6TzS/E7mlkvYE/gXUlDzWwg7rm3naT/TsX31fV9y01IK4aQS/HQvp1wb4dRwAmSPjGz3rhu2kb4M3WhpDMy37cXnrxkF0mXVvViCqY545qZzYxr5g7FNdVOLbKObSEZds7FExFcKemttH1eXFvtKkmHNXPe7LhHWz/gOUkD0vYYj7UBM1seH6Mtjnt+XpZ1OEjGtZ1wfeajJf0nbZ8e1/3rXlk0rfd2qjVybdjW+D25FzinYvht7Zwp2V4rZJxL5gSuAJYCtgUe+KmFITNbCpdQKlW7XIt0KLoCQXkws+2B0Wb2f+nzrLgh4G/AtWb2q7SCBnAy0Dd1/i1SA53J7cA4MzsmdW7fZtKSv4wbOA4EehRWw9pmbzxd/bwtHZBJP342IHyiRC13kEWQVjW3xwfF0wEjJX2X9rV4r2rgHQqqT2d8QP/b5HX7FC6A/ya+6vmP5PkTz0YBJEPaj8iZAPwb9465F89GOD0eHtQHDwU5JE3O7s8Y1axEv+OXuJfCrmZ2De5xdSVujJwisve+3u9bZkK6DDArnll9sKSbkkHkdlwb7AQz6y7pJTy8b2Vgo4pRzcw6pHHNRGBgGSdvkg7HFx+GAAPNrAf+Lg7FBf9Phcnf3RrmGbzd/x3wjJntZ2bLpEXOvwGrmtn8MNk78THeT/wlY1TrEOOxKcPM1jSzdSU9jnskPQscamYLSZpgZh0AJJ2JG1dWwnXWSNu/lTS4DEY1mKQN2x5fFBiNe3E1a1TLnjOl22uBXLvRA/cKnhc3fq/6U+2KpDGVdrmO2qCGJDzWgqqRVtEPwrUBDpJ0vZl1A3YGNsMHcyPxTv1h3EBwk6RDanWlIdX/Ilz/4AKl0Agz64SHVTyLhwY8WVgla4zcqtwxuPfF6cB5rXmuJUPszbhI9xqSPqpGfeuN5BZ+KR4Ouqki3LO0tDboNrNzcSP1c3hWwIGSvjGzTYCL8XfsperVNoDJVuh74xmyp5P0aNq2HR7K97GkK9K2GYA/A+9I2qeYmtcGZtYd12hdAQ9T2zxt/8kJaO7erwC8oUxYaT2SDECv4zqv10j6fWZfR1yuYgPcc+1ESZ/kzs/ek06SfqhW3WuR5Lm2Px56tQ7u7Xdy2leTRo7cbzg37t36kTwEeEFczuR3eLKjkbjx4nY8IdTw5r4ns60mr7kWMbM5cOP2f/FQz+/x+dCf8UWBzSW9bpPKmyyrJInTzPfV5LyovbESSVeY2Y7AqcD9ePTA4vizMUVhoUHxhGEtqCopHOFgYDU8TO2vybpueDrpLfDwyfOBBdLnlSU9UlCVW8SaYvnnBa7CDRlPA//AxTMPBnaSdG32+MIqXAOY2em45sHBagqZHYQbIVs0rmVcpPvh2VbPrWK16w5r0lybDdhS0jMFVykoEDNbB29XJwD/UdJzSc/Jp5LeT5874e3w7rhRNsLXCyINsAfhnkbf4F4iOwPPZwfXacFhY9yj9wBJV1W/trVBMhTNjnvzdcYNCMMlDa3sbymkJmd8OAhfBOzf0qS2XkjvdH/cs+orPFx4bGb80gH3xBqAa6vtVfF0bnTyz8OUTs6tSUP3aEmnpW01Ob7LPdfb4ka03nhExROS9k77VgPWAA7DPdlWwTOELo0b4ZT/vqB1WjBE7gRcCGwh6bb0fi5Lk3FtQPb9bO27yoI1sHRFzoi6OG5QOz+VT/AFoiF4Yrw/APfX2zWWDtVABoUojV2gKaMU3jgcjHfqH+ED1+yx85G0G/CJxEQyqctrrZAyjwFz4qnKn8dTZ48FDiu6frVU8BXzo9Jvegop61TaNyhtPxWYt6XnqLXPjVwq15q9Zn4isxE+6HgK75xnL/oaolTtWTkjtUWVZ+YPeFa7Z/Ewzw/xCfQMaX+lDVsED236AjfQFH4tZS24oewrfJK7Hu7V8G/gHVxnsvLb/h/wx/SbHlVknQu8V5P1A8A8+Er/X5g80+Vk7WauXd0X9yTZo+hra497kbZ3xsX1PwX+CsycvRd4UqARwPZFX0OV7tM8uc8Lk8siOwXfsVzm/5rNMpip4za44eZEYLnUbkzEowCyz/+8ad8YPLKk8LrXewF+BcyS+Xw5nslxvsrzk36T51M/vVDRda6lgi8MTAC2Bg5I9+gt4BJcFuHD/Dtd6yVd09yV3z/9XRPP5L1y7thfAE/i8+bV29pWRanyb1t0BaKUp+Air68D/0xlIvA+vnJTOabSwHTHRRu3KKKubbyuHwen6e8ilQ4zuz+KALoA++ET/dNoo3GtjCU36J27Ulo6Jrd9SWDroq8hStWelenS+zMR10qaDdfeOjK1qd2A7VI7/EjmvBWAR3EtqqwRojTG61op+ELUWbgBpHtm+23Aq8CimW2Hpd93YGZbafqbXNvYC5eTmAUPnQVfXLiGyY1ra+Ghb5a9X7hRbQIuJl749f2Me7EKHjp0OLAo0CVtHwCMA66nybg22SStkd/79JycCRySPg9Mbd+iU/l9Nf++4Yadp0nGd1yz6wvcO+Zd4Nbc8Z2yY4xGfh6qcO9XS/3x3/AIFvA5wmO4J9KMmWNXwOdEvyu63gXdqxbfJVxr7FvcwHR1pk3bJD3DvYuufxuus39qh88B5sxsXyL9/js1c86u6Tn6L7Bu0dcQpeXSiSCoAmbWBw/1GwJcJukDM9sYF68/z8wmSLpRHprQSZ6h6mqlFqUoF9/WQkcqZOo1MX1+OXN+mYSjfxK5htMwfIWuEp5znKTvJJ2YNDePT9tb1VwrC5l3YFs8I9n8wHRmNhLXzHlIkpoLFZD0PL4KWrOhKkH7Iel7MzsZH7SdCsyAr4DeoaSdZGbX4gPRP5vZJZJ2xldCL8DT0o9Kx8XzUgXy7216l5cFvsr8ZqNwI3k/Sf81s5XTez/YzK6stJNl+80ybeMOwEm49txYYKSZXSzpRTM7Hu9TdjazBXAv3hHA7un8ynfsg4fT7i7pkmpfy88lcy8G4h5H7+P6fIcD56Tx1A1mJlxHa5iZ7SZpXPZ7ShByVklw0S9Jk2yFL/a9NqVfkL1HdfK+dQHuBi41s4Vxo841eHbP/YEjzOwaSdsAyHX0KvIAjf48TJ38Cs4AACAASURBVGs6Ad/h4bffmtkAPMnU3bhn8nXA4wCSHjOzPpI+KKqyRaKmkMjJpCsk7WdmFzK5dMUv8Oin7wuqdpuRdHMav28IyMxOTb/5h8DbwLZmdr+kVzOnvYb3XV3IJLMIapCiLXtRylHwlZjxeIx8dvuqeCf/MZ6FqvC6Zuq2JbAXMGvRdWmEwqSeATPiLt0tea79gAuV9iy63rVQcK3Bb3FjyS64B9L7wH219t5EKb7gBrUj8QH9BDwRATSFEHbBDdtjyKyYZs6veS+MRihM6mW0KMlrBhiOe3V3xT3V3gCWSfvmwYXTd2npuxq95O7bcniI7Mm498IN+Kr+OSSPP3xSezEuLzEWODz3fXvRlPGy8Ov7GfdlJXySuR+wYNp2VGoDTsQn+R3TffoaF6ifruh6F3CfugOPpN/875ntP9nu5Z69FaiDEDRc17ZP+v8KXEC/Z/o8Mz6ZnwA8XHRdG6XknpOj8fnP5rj+47O4R+k4fHG0ufNL0QdTMukKJp3rnAX8B1/QqYSFLp/a5muAX1fOwQ3g1wAzFX0NUVovHQiC6jB9+vsDgJl1AZD0AJ5VczbgKjP7QzHVmzRFcfp/W9yQsYWZzTyV31Fqcvcg65XxJe4hc1gqJ6QMP0g6EddgOwRPYFFqklDrDviA7CRJI+SCydvh782+ZtaryDoGtUESIkfSV7hA8qH45HEbM+uiNEqT9A0eUtEbT7oyCaoPL4y6phlR8WuAPVPfeB2wNp6tdTFgY0mjU1uwMR4O/nr2+yrfVQYy920ZPLnDLcBgSTdJGoAbjDbE+5Xu8uy2R+OhohtJOiOd3yHd04pR7dICLqc96YUbGW+RVPHA6os/K9fLPZEk6Sbca+ZGSXXj6dEepAQXnfDf/ClgSTM7BLzdS/tbOjef4OLvuPdbTSNpvJqygy8CvC/p7fR5Plx773RcMD34mZjZlsBwM/tN2nQqcAewqqTNcON/P9xotFV6liahDH1wansn4F61R5nZbHjipGNxLbGF8CQyvYB/AUiaYJ6x+c/puBMlnZ2+r6bnXKn9+C793xf3VJwZ+D1wmJnNI+lx3AC7IR5VcDtwJe5o8LCaMqLW9LWWmQgFDdoNM9sXT0//j8y2jpImSHrAzB4HzjezpeQhgdOlQd1ruAbKG7hlvhAyA6Y/4EK/nXHPj8FARzP7i6QvWjo/N+jaAhhvZv8s04QnS+5+9Ae2NrNf4L/3ecCzks5K/cOQdNxx8rDQ48zsVkmPFVX/omgmnGtGXHvhBnla8Y7AREn/NLPj8En4isArBVQ3KJhcqE4l7ARJn5vZ5bh32qnAB2Z2gaT3k/FmITwk9Nsi6l12cv3NxbgH4T+S0fPONNkfjAvwf2FmvYH18d/yeEl3FVPz2sDMeuDe7p1wr49sSOOB+ELOBsAEMztR0ke58ysyDRPNbEQyOtUNLYToLYbrk/4vHZMNIX7WzFbFvSD/Cfw18ww2dLhfLnRzAt4Wbo4vTA0CdknHDEkT98lCqnPjmX1xT5t9VUdZY81spvRvT/Ns9l8Cv8b7geGSXk/HNfTzUCX+D+htZvdJOsbMrgd2NLO+kgaZZ2FdEzgO91wrHSqZdEWm/dgOGAZchi8CLYZnY+9gZqdLGmVmKwM74h5s3wH7S7oonR/vZy0zLdzgopSv4Bb2ialcQfPii2sA/wMeIoVX4mEJ++JZchaugevYGI/VPwIXAF4LuAnP0LY7SfC3mfOybt/7pfvQr+jrqYWCe1Z9jXtc/R0X4P4IX4maKR1zIPANbmCbPntPKY9LfC8yYTnAapn/r8c9jGZLn7PHPY57I0zyHEYpV8EH8rfjnk8HZ7bPjIeFTsBDCP+Ih8l9ChxRdL3LXPBshC+n/mb6zPa5cIPIFvhE43946PcLRHKJyrV3wsX4X8XDhhZI2yvJhDqkZ/11XFOtcxH1rMJ9WB1YIf2/Mb5AuQlNIcTLpn0z49pqg0nC32UoubFZqRNcpPqviE/UX8TH4uPJhUZHmbrnK7d9ITzU7008c+Vv8czO1+aO+2XR11B0oUTSFbi3+aupb8qGhV6Mh2SfRVNYaKfK/anHay1rKbwCURqjAMukwe1YfHXh09SJbEyTlsP0eLrkl3EX6L/gmc++B/bJfFchk4U0EB+MZ4iaLbfvz2kAsjs5zbXcwG3fdD27TOv61kPBhUVfxfVvuma2/41M9ht8Ff1g3CC5chF1Lfg+dQcuBa5Onweme7Fe+rxrGggPyj5/+Ir748DpRV9DlEKfn41w74MbU7s7Drgqs39mPLT6S9zIvRuZbLFFtbllL7jx7BWa9NNmTn3Ns/hizl6pbVgTn+j3zpxbmgF2S88n7lW+aRpv/JWmTJc/ZurGjWrbF30N0+CedMAnaV8Bx6RtPfDJ5/f4hH6ptH16XE7gTWDLoute0P3aATc0foXrGh1O0wJv1rh2fqb/3TX3HfvgE/+6Ht/hXjBXp3djp8z26Afadh+zY/8lgPVwL9lfpG0z4hqQD6f38rr0XO3dzHeVpj1v7ppxY/f+qe26iJzxHzdyfwssWXS9f+Y198A1xStzn6xx7ZrUvgylGe3GeD/roxRegSiNUfDV4+uA53BPrz1TR/IdnuZ7G6BbOnaeNHh5AA9J2KOgOk/WSOGi0a9lPldWNWfFw07eAnamSUgzb1Sr+0FXO93bysRmKXySv1b2fqb/78aNrBWvta6kCWbZCjAdbnT+Gl9FnohnzM3er8vS/ToPz343H66T8zklnSyVteTbrjQgPSn93x0PKxgPXJc5ZlbcuDYRWCKzvXQD+lop6R0eD/wpDaZfwhcijsJDzSZW2s7Wfv9GLrk+dhXcQHI4nuyhS9o+IPUz19NkXOtYhvuGC3+/RzK64l5Zb6ax2H54+PDJ6Tk7suj6FvTcRIKLye9Ph9Y+R2nTvawYbT9J7dCnaWyW9UI+EfcinZjGcT2KrndB9yr7XnbO7Zs1te2VZCsVz60uuEbm60Cvoq9hCq+zEnmTf88stS8XZ7ZV+rE5Ujv1Kj4X7VqNukZp59++6ApEqf9CkxFl4TR4Oymz71DgH6kzuZ1MiFLa3zn/PQXUf3XgN+n/3+HGs61pckOu/B2eGvxPgNXTtkqGmgNwI2IpjWr4CugGeJhBl8z2HmmgsVdmW6XDWQI3JP2upWeqbCU9YxNxL7SuaVvHzP4haVA2MT2nnwJHF13vKFV9RrID0wXxhYqhwEGZ7d2AnZjcuDYLsHzR1xBlkt9z3TRhuAPXjan0N8vgk7W1i65jLRTci+hz3CDyduqHjwMWSfs3Az7DV/0nk2ygzo1q+fpnxl19cSPaMZXxFB6G9ve0/VM8BHzv/LllKOk9WgfXNJo5s/3c9CydR5NxbQ5gfjJeMbhn4HT4YsWORV9PlNoquJfaV/hiyHJ40pnLcM+rA5l0cbRv6qv3K7reRRcaWLoi1f9scvJGmfb5GNx4tnNufx9c7uFWYLuiryPKVP7+RVcgSmMU3Ao/A+5a/jawYmbfYbghYAxuSHkeD2nrkhkcVn3Qy6ShFMembT2BJ4AHgTUzx3ZODfxWqdF7iqb493XS9e1W9O9Q0G8/FE9I8AMe4vvjxAb3nrkzlWVz5y2PT4TWL/oaaqGkwfspuEbhODxUuuIxmR2cLYYbTbbNPaOlmSxFEbiw7VjcwPAucFZu/4zpOfkUT3yRPz+elxopuCE0u8jUFfeC+B9hCAVYCdfl3A9YMG07iibPhk542OcmaYxxe7bNbKQCzJR/d3HttFeYVG6hS2oD5mNSg1Jp3nt8Ye+7ND67OrevIz5hfwmfBHdv5vzsIkanoq8nSu0UmhZABgP3AbNk9nXCs6x+S0YrN3te/v8yFRpYugL4DU164x8DJzTzDCyGZ7J+GjgkbZsd+APu1dg9c2zNXmuU5ksHgqAdkPMVcBWefnxNADPbCe9gDsdXKDbBDVkfSvpGKYuLUgtS5TpPlPQ+Prja28x6y1OQbwv8EjjDzAalTFr74yvmn+DJDBbAB6zgA/41JA2r9jUUjZmNwJMTnIaH6VyFi7ReaGad5Zl9TsY7myPNrPJczIjrC43HjWulR54h9wRcx29XXJ/wynQfvzezDum4FyVdJukqSfdAfWRECn4e2fTqZrYkblC4Gs+m9Qaws5ntUzlG0pd4eP6hwKZmtmH2++J5qR0kfSHpOwAzWw43ql0AXCTp8UIrVxv0wkNkbpH0WtrWF/f0u16e0VOSbsJDsG5M7WlDYWb98AXKw81svsyuI3Fj7AmVDWl89aWkt5QypmYyoZaFD/Dog9eAPma2APzYX07APYpuw70dB5vZJFnps+NS1VnW2GDaknk2ZgFml/Q5/PiO/YAvOD8JHGFmXSr9d+6Zqvq8pwiyY5fEIvhC4GZAf9z5YlMzuw4gtVcjcI/k6YEHJV2bvqtDjd+31/Df/RPcC/1I4B9m9iczW9bMukiq6CWPAQaZ2Wu4d94lwL/VlBE1sn/WIRa/WdDeJGPLurgA5SnAScAQSeMLrtckjVTFGGFmffGspNcAgyV9Z2YL4h3jCnio1WfAmZJON7OjcWPSWpLeqf6V1AZmNgwftA6Q9K+0rTNuSN0YT0LwcdreDw+9MFzDZDwegnuKpFMLqH5hmNlCuI5fi42vmU2P6wYNwwf+2+IrYJvi2WZ3qUZdg9rDzJbFhez74uFdn5pZHzy84NfA2ZIuyBw/EzC/pP8UUuFgikltw0h8cepCSX9M20szwG7uWs3sFNwjfM70eRS+MNNP0pi0+NVV0j+z59f7fWtmzDIfrr3XB1/YOwG4T9JoMzsX9wLfStKb9X7tbaWl601jkg3x9+puXCNtXGb81xHXVrtf0hVVrXRQF5jZ3LgxqAcePvyWpI/SvqPwhfd++UUQM7sC1xnumwy5pSPXHi+Ie6AdArwj6ay0vRvueHEecJukrdL2WYBF62VxqXKtZrYxcDPe7ryG6zPugHvPPoRrXb6YxmbL455qHwOjJV2d/a4iriP4eYRhLZhizKybpC+m4LhNgWtxV/sT8Ine+LTvR8+aohqO1Jh9lV25NbORuMdVH0lfp20z4GEXvYCxkt5O3gQ34Svie1W77rVCGuC/nMoWkl7O7OuL6+ptKOnZzAB2OWANXIvtFeChykC2LB5XZrYb7omyHnDvFBrX/oSHqzyE67wMlTSoCtUNaojksfgLPAz9a/z52Tazvw8u8Lscvgjwp+a+owzvWb2SJvl98TDGf6dtpfzNzGx14GtJj6WJyp/wrIx7AL8CNpX0tJnNjC/afAAMkvRNYZVuR3IT0r64sVV42NmCwJa4Z/MnuKbTaDwEdk9JwwupdEHk7tUquN7vPHiSgjckfWNmA/AF1DtwbaNxZtYxb/CICW2QxcwOwcesa+ALwt2Ae4HzJN1oZl3xLM5vAX+Q9FY6rzNuKJoH127+uszPlZntiM8Hu+Ehsn+RdFBm/4y41M5Z+NhmQO78uukHzWwuPJFOV/zZ+Rx/DvbDIwjeA0YBf1aKOsmdXzfXGjSDaiAeNUrtF1xH4Dxg3ik8/q/4gK9n+lwTceJAP1y35khgvsz2eXFtuMEtnDc7rrH2Mhm9olq5roLu5YrA+/hgvjdNmnOb4x3J88AjuCGpD02JHix73yiX5su8+KBsLD5Qa/X5wd3g18aNKQ+QEb0t87NXltLcb4x7y36YSl67ow8ejv0muUQxUeqvlPEdZ1Lt02PSth542Mz36dleKm2fHvcEeJMGzYyM6yS+j4fCfoKHfW+b7tOyeLjUeOB+3Kv5HWDOkj47pU5wEaV9Sxq7vo4bhFbHJWD2xnWYvwT2SMctn563p4Gd8YXTA0j6YEVfR0H3LjvGXzK1W6fh3mqPpvd0n9w5MwK7pHZsw6Kv4Wde/764h9qmmW274EbFO3FPtom4ZvfcRdc3Sjv+9kVXIEp9FNywNhHXy2rRuEaT8WQV3LX1xKLrnqvf/LjRb0yq337AMmnfucDDeNjUZIMs4KDs9VAig1Ar97MiKv1PPFX2r3Eh0oeBC/HV9ffShOgx4Lii61zgvap4CM+Jr1a9jRvXOk7BuZ2BuTKfS//slangxtU1Mp+3wj10rgeWyx27FC6Mu0PR9Y4SZWpLmoS9B/ROn3vhBrTnUr+9fhqPjMdDawqv8zS4B2ul/vQAYGnckHgJnrRh18xx8+ILf2PIZAcuUyESXERpx4JL2Xyc2pmZcvt+k/reicC2adsvcSH+t4Fv8KiMwzPnlNJoixv/D8b1YGdL2/rgUU0vk8lWnPbNBCxedL1/xvVmDYoP4+Gd0CTnMihzD/bDPYwLr3eU9isRChq0StZV3syOwQcop+Nu0O+2ct6seIz5bPiE8KNq1DdXh2x4wNy4C/JHkj5Lsf774C7an+D6G82GUrSg+RKuugkzWwlfdXkFz3bzF3xw/2XaPx+wBZ4J6A4lXYUykRWuNbOl8YnQbTRNEu/LP2PZc7P7IlSlPKTnZgbciPYqPgh9IO3bDl/weAD3tH0ic97sSvqGQVDLtFH7dCFc+3RRPIP3aOCvSpqCjdYvm9lBuK7mNpXxlrnA93LAJpKeq4zRzKwTLqL+fjquVP1Eag8PBTaT9L+07W/AMmlbVpZiS/xeXVRglYMaxczOwT2O+km6LbM9Ox9aBteQXgLXNXw8bV8S77O/kIvUN1y7NCWUXboijd32xj1m78E15E7B+7LJJJXK1l43MmFYC1rEzE7HVw8OlvRt2jYIOJ5WjGsZAcd+wEKSzq1itSepQ/p/W9yI1htfIXlC0t5p32q419BheAewCr5KvjRuhGsIEeRpjZmtiE+AZsENqc80c8xMKjiBRdEknYlT8bCdnsDieEjBDrRiXAvKSaYtXRR/Zl4HDpN0f9pfMa7di2fZyosnR7sV1AVt0D7tgnsezQaMU1PGy7qdhLWEmV0CrCRpifS5uaQNH0h6KXdeQ7/3LSx2ljLBRdD+mNkZuJH2QGBEZZE47cs+OxvjnldHSzqnhe8qzfPVwnu5Ha6bBq7HfH9mXx/gCGA1XIv7zKpVtgok/c/H8cQX+wGXVPqxoHHpUHQFgtokrX6Ow7OZDEpCnEg6ETesHQHsa2bz5s9NE0GTdEvFqFbx2KkWmY5vGzyz4p247sFDwJ5mdnOq4/3pmhbBUyQ/h4vDf5jtIMrSMU4tkh7Bw9Mm4GnrF6nsMxfkRk0JLKr6LBRJWrWr/L84MATPQLY3PpjYGNdb+zOwevb4IEhtaWdJ/8UNDL3w92u1tP9KXLNkfeCEJJo7yfnVrnMQtJW0CDcGODx5OFc4EheAPqGyQdI3kr6U9FbGqGaNZlRLjAY6mNmSZnYbnrRhk2Qo6o5P/Dev9LEVGv29z4zvVjezFdLmh4CvzWyTdK+WxPWNxqQJ7s7AOmbWJcZ2QWtIOhzPvnsmsJt51srKPmUiEG7FF+TXb2lcW6bnK/Nerm1ma6RtV+LjXeFzxuUyxz+L3+dn8DDuhiF5N47DvdS+xZNXhFGtBMQkLmgWST/gqwwHAIfjk7Y2Gdda+1wNzOxXeN1PkWdR/AgXj3wQdz++JVO/d/EVqnXVlAK6NAag9kDSY0B//N6eY2a90/YJueMafqBhZv3NbO4UdlJpZ+fBJ4l3SvpYzqN4qu33geHAqvlJUlA+zGxA8rRFHgLXWdIreMKQXsDp5lkTkXQV7nH7d0kfFFbpIJhCmulbR+N6NL8DxpjZ/ma2TOqX/4a3i/O3cG4j9ym34/3GI3jI2UaSRqex2Ca4Z/1z+T620TGzDubyHqPwBVNw4fjP8OelD+6p9rQ1ZddeG3hcDZI1Npi2SDoSN6wNAXZJHrWVfcq0Q1/hGriln0+bMyNwE3Bu8hJF0nW4ztoqwGE549oYXA/28iLqPK3ItMlP4IkK1k/e1kGDU/qGIGiZNAAZhntEHEobjWs1QBfgbuBSM1sY90i7Bk9TfxmwoZldUzlY0g8qqT5JeyHpYdwLa1XgMnOtvVJhZv3xzIxHmdmcGU+K9/Gwz97Z4yW9gQvlLowb19aqYnWDGsPMZsEzf15uZlvBZMa1DfGJ46Fmtk7aP0LSiHR+LAgENUsulKpv8lZbGtgdF3g+Hx9XXGVmh+CTtOWBDaChjWiTIellPMu2gLeAnuayCwfjGQuHSbq5wCoWgqSJaax2DrCPmfWW9A5+r97DMw6uYWbrA8fi9+pCSdcXVumg7kieaxXj2q7NGNd+CcwO3CrXOSx935vCZpcF5gKG5LzrD8PnBgeb2fKZcz6G2h67mNl06W/H9Hem1s9wJL0AXIlrrNXiPDloZ0JjLWgWy+iVpBWIXXHR4CF4Zsfv0r5BwCDcu+0cSW8XVOXJSA3fgnLR2iuA7sDukt5OoQH/wVeDH5O0UpF1rTXMbDpJ31uTMHKb9NHSStXikoZNw2rWLGZ2Lj4RvA04VdIHZjYncAeeLGN3Sa9mjl8H1y3sgr9Hw5v52qCByRkclsO9hfvhz8q1adDZUdIPKdRpA+B5YP00qQyCusHMdsLbvAl4uzceD/38C25o2wRf0GtR+7QsmNnK+CLn9PiE9SXgaklnp/0Npy+XJb/QaZHgIqgi5pprB+Pt0XBJ45OBZTc8ycHuSkmFyk5aAPzOzHrhnravMKku7LbAeWnfjvXgZW+uBbcucL2kN81sBzzU/CQ1k4ggc15FI3dhYClJN1SpykGBdCq6AkHtkBu8ZDUovjSzC3APxyHp2OMkfSfpxNTBHAvciKeargmSIejZ9HER4IWM4W8+4FM8G+gL1a9d7VLpRMxskk7EzFrtRLKkQUYle2FpvP8qgwpJ+5nZWbh3EWZ2mqT3zWx3XIT+FDMbLOmp5AW6JD4A2aUtBsygvrFMljFckP0HAElPmNnZeB99cXqHrgF+MNd7eQMPm+sWRrWg3jCztXBvo0F4xrQXgQuBK4AZ0sLC02Y2DPdymBUYKenDgqpcKJIeSotVswAzAh9Leg/KYSjKLDhMkuBC0pNm9gSwI+5ZhKT/mdnvKUmCi2DaI+nw5Ew1BJB5UpEN8MRBg8puVDOzAXi7fVXWuz551z6CS1ccKek+SVeZWVeAejCqJRbA25iVzOxR/HffE18MapFKu5UW0V+FpjFfmeZFZSMMawEwmbdEf2BrM/sFHht+HvCspLMynUvWuHacmd0q19iqOTIuuz1TyOqXwK+Bd/HVp9fTcdHQOVPVibREWrFp+HubrrHiydkXzwa0NfB7YIKZDZH0uJltjq+wL2Vmb+BhK5vhq3rjM9/V0PerzKQQ4Q8rRrUUDre1mY3DPWgvT8a1M/FFjktTO/ZvXGdtQ9xD43/p/HhegnpiGVwT61qlzOLJYDwW11qrTEDeNbNDgdlVcpkGSZ/ii4E/ku5FKQxFqY08FxhuZldKeivtOhLXMToBN8JWZEzAx3qV80tzr4L2J2NcOw34DT5mO03SH6G87ZI1SVdsYmbfS7ouZ1zbEPgXLl0xnaS7lGQr0vk1f98k3WKeVOdYXK9xkKSLUxTBT5Iz6M8NvFPr1xxMPaGxFgCTrAhuB1wHzISL/a+CC+juncIBz8I11w7EvW6mT1/xeDq/5p6pZKw4ENeuuge/nouAuypGtXRcNHR4J4J7D6yMh+oMknTxlJ6f7WzMrEf6zoa/t7l36EFcS+J24GVgDzzj3dySRuH39lY8BKorsL9SuvZ6GGgEU4+ZDQauNM8Si5ltAfwdmB/YCDjZzP4EIOlx/B0ciYeCPYTrBV1UMaql4+J5CeqJJYE5M0a1UbjBeDNJzyXvrF4Q2qet0cj3oplJayS4CNoNyyWJmhIjiVxz7Rx8wfRkudZ0xXBSquercr8kfQ6cii8WDzOzrdMh35tZpzSGeQAf2/yxMieoUOv3LTOnfQ4fq38MLG9mPZPTQKvJxrIGfTPbF0/qMM80rXRQKKGxFvxI8lC7B9c4OUUpNbCZ/Q03sB0h6bLkxrsX7rm2iqSHiqpzWzAXyzwQz+Lzb0mXpe0xWE9Yk3bJKrho9Pe4K/decm26bOhac+dnPR/3BVYH9lEKW2l0zDOVPYTfu8MzHmwX43pZ1wJnyMNCO8n1smaQ9FU6LkJVGhhzbcdV8IzE1+JGs31wz+Az8dClPVO5VdJO6bwZ8KyACwHvqUmvJJ6XoO5IfcPewBb4OKIP0F+e8bI7MAJfrBvcWn8TNCa5cURfXPRbwH3AgngCql1xvdLLcKPb7cCeCn3SoBXMbJ7seNRc/+r1trQzZracpCfS/6Xpg7Pj/8r4NbNvOdzpYgNgD7l0RcUTeQg+t+yW9VarJ8y1GxcAVsA99F4G9pPL5TT7DOTasX1wj9vd6vUeBFNGGNaCrDFlKdzTZjNJ/0puu9+nY+4GfgEsKxfu7Ar0ljS6wKq3mXwDWKZOsS1EJzJ1pNW4Z4FDkhG6c8a4VslI+0dgaN7YGAbexiZ5oc0i6fdmtilwA/BnfNJ4iqR703Fz4pPGg4CbJA1s4fui7QrqEjNbBDecdcQ9APrLkwx1xkPnjwUOUAkzXgZNWCS4CNoRc0H9PYF3JQ01s4F4NuLtJP13Kr6vFH2wJemKzOd+uNfej9IVafvy+LhlU2A/mqQrjgfWVJ1IV+TmMrMCPyijfWxmhwHb4/OifZXC0s31qcdJGtuMk8HZ+HzokipfTlBlai5sL5j2mNnyZraBma1oZl0yHcNH6e9iAPKskJVQz31xwf/+ad/XFaNaLYZ/tkS+EyxDp/hT5EI3ZzUP+f2fpHsknYELSi+CuzDPl3Fr7mNmC1S+I9eJnAPs2qhGtcp70cyz/y4++F8RQK410SXt2wd4H9doOCkZp3+klgcawc/DzIbjA7Hh6V35B+6t8wdgbdxTDYA0gB2GZ1reyMwub+47o+0K6hVJLwOb415Ib+H6pyvimfcuAIaFUa3cWFOCi9NwTcl5gTvx8cjOkp6WdAI+NnkSD9UaKteujL40aI4v8edo17TQOQK4EvcYnyKy4+UyljFkPAAAHmNJREFU9MFWQumKzFxma+B64GEzOyZ55SFpME3zovPMbNl0X+7EsxGTmw+djWeODaNaCagbg0jQPpjZUFxD7RbgZmBkCk8C+AZ4FBhgZssCSPo27ZsR+BZ3vZ+EMnQujUx0Im3DzI4EzjCzhXPej53TfRgOrGNmO8MkQsrzAp/hGfDuVwq1DhobMzsXNyKsC9wn/ZjM4wZ8YAqwh5ktUTlH0kfAxfig9A9mtna16x0E0xJJ/8LDhmbFEyTdgS86HJ0WdOpq0S5od7IJLp5JY9FmE1zgXmvryjWAp0gvKygf6VnZB08CshVwo6TzkxPBT7Y1uQXkFazBtbLS3PBeYD1gkJktjbfZxwPrAysBlwJbmNllAJKewUNCV8C9ATeQdFr6vppuz3NOBv3wa/sIeAXYHzjRzNaBH+dFI4HeeJjrn/AFoTsz33EYHqFSqsidshOhoCXCzEbgHmfHAmOAbXCvifuAgcm7ZnXc4HY78CdJ95jZjHgI28nAFpIeLeQCgnYlN0io6H/dhIdcrIKH6pwl6a50zCHAQKAH8B2+CjUo832H4SKmjWxU+w1pUI8Pzs4H7lbSvErHLAYMBXoCV6WQg9nxVfffA3+Q9Ek6tqZd4oOfh5n9ER+QXa4mvbSOwET4MWNuf+Af+Pt3gqQXM+fPCSygpOcSBI2Gmc0GzIIv3n1cCZEvS5hV0DxmdgmwkqQl0udReNKLfpLGmCe4+EDSS7nzok8NmiX1vbMDNwKdcUPtcElDK/vVgtZabrx8EB7y2F/S01WpfJUps3RFmvPuCsyJZ34db2Zb4mHon6Vtd6Zj18SjuT6WdFvaZunc64HrJF1QwGUEBRGGtZJgZsPwmPgBaaUYcz2T04GNgZUlfZy2V9KaG/AmHtq2Ot6YnlpA9YNpSHQiU46ZzYVn81wQ+Cfwf3h4wTW46/t/JH1jZr/GDSoDcA2hL/CVrRMlnZK+KyYADUxayNgGT/7RAzeyVn77iq6lJeNaZeB6HXB81riW+b66GZgGwc8h2sbAIsFF0A4015YkT7PZgEHAssAlkoakfZP1s81InZyFa2tdVI1rqDZJuuL3eMKte9MYZQCefRfcweKGzPFzALvhump3SNqh2nVuL5Jzya24l9pF2d843YNB+KL6qRWng9z5lbFdB6B7ij4ISkRNu2UG7YOZzYcL0I/FDWWA6z8BV+ErxT3SsR0k3YK7SV+Aeya9gWdbOrVyTFUvIJhmpE7kfWBH4E0lgU5J1wMn4YOPIzPuz/dIujJjVKukGf8I72wb2ahmkj7A3eC7A5cDv0p/t8G9PP9iZotJegqfFGwK/Cvt2ymMauXAzC7FvXzXwL08nwC2N7NjwMPnK+1oehb+gRthN8c1S5bMf2cY1YKyEG1jgPeZ8+ALE0sAGyWjWmc8acHSwHNhVAtaImcQ62VmK5vZLPjC8H+AE/CMsjub2aHwY9+8lpn9zpwOme+oSJ3s0cBGtbJLV7wB3A0sBcyVnoGOAOkenIh7Oh5nZuvnT66M0yRNDKNaOQmPtZJgLgz8D1yzYn/gVUk/mNnmeLryt3CvmieBi4AXJE2oxJxnOpbwmmggzGxBfKDQHzcYnQR0UFNK7QHAUbj+3smS7iioqjVD8lq7HuiK6018jk8A9sO1Xt4DRgF/lnRPM+fHO9TAJK/O0/H35l9pYPpL/N1aAbhS0snp2LznWmVVeCNJtxdzBUEQBMVjnsDgRly65GTcg35N4Bjc+/uMAqsX1AlmtgPe/86BOxiMBC6W9FmS7jge1/S7C88wOwKXNBme+Y598GQau6tB9bLKJl3RgjejAb/E50WrAZtJui9npK140R4i6e9VrnZQ44RhrUSY2Uq4ftpTuEfaQrgw5fO4wW1JPFxt9vT5VnnWpaABiE6k/Ugrl2cCWyZvI8xsF3zF7n6gF7AAbmAbKOn9ouoaVB8z6ynp7fR/xXi2AD45/Cnj2kLKZNAKgiAoK2a2Mi6zMD0wF/AScLWks9P+WKgKJiE3fl0O1w6+FHgM2Amf64zCDUOfmFlvXCNsI9yIdGHWaGtme+F6urtIurSqF1MlyiZdkXtGFsIj+KZLnoyYWU/c6WRZYHNJD+TPiXFa0BxhWCsZybhWiR9fDPgLcJCkL9P++XBNi43wWPmziqpr0H5EJ9I+5O7Jw0BXScuY2bZ42vbjJZ1oZn3wlfXvJf2pwCoHVaSlwWRmQNqicQ1fEFbm2JoemAZBEFQDiwQXwVRgZsvgXmpb4QvD49L2c/FogzuA45JxbQ48CmFmSc+n4zoAHYGdgW8kjaz+VUx7zKUrtgDWBj7Axyi/oZkxCvzoubYpHrlxE34Pny+i7j+X5M14HDADLn1zDZ7Q4kEz64EbZPvi+uQP5h0UmnNYCMpNGNZKSAoLvQYfqKwhT4+cP2YmJb2toHGITqR9SJ5+e+P38h48icEpuJDyF80dH/et3LRgXBuplIo+CIIgmDKiTw1aI41nXwc6AddI+n1mX0c8AcEGuOfaiUqZ2jPHZBdRO0n6oVp1ryZllq5IYa3X4lp7Y4Bvcc9EAQdK+mcar10IrIpH9PyrqPoG9UEY1kqKma0A3IZrqu0j6eW0fZJ00zF4aRyiE2lfzGxmPCvZIri+2iWSvi62VkEtkzOunYBnW75a0tEFVy0IgiAIGgIz64RrBw8FvgL6SRprk2ZtPBNPGnQXsJc8oVvpKKt0hZldjIeX71wxrJrZI0AXYGtJL6Vt8+PRXVdIGlZUfYP6ILI7lhRJj+GdznLAOUljAOUyLIVRraHoh7u+D5c0KhnNxgHf44KuSBoL7IEb3noVVdFaJxmgx+Feat8CX4dRLfgpMqGeY3HB5NG4ZlAQBEEQBG0kRRBMQvIwuxXXTpsPONPMZq4Y1VII8SG4Ue3+MhrVknGRilEt/T8xM0Y5Btel286ayWieeC37XbVK/hkxs67A8sCnGaPaKKAnsIOkl8xsKTPrIelNYMMwqgVTQnislZwUFnon8CzuzvtZwVUK2oFmQji7Av8GRksamLaNAn6Fr+Q9Y2ZLAR9JesfMujUX0hhMinnK8b8CzwHbS/qm4CoFdUBmtTfesyAIgiCYCnIhm6sAC+NZ2m8A3pD0TQpZvBxfWN5Z0rh8dE7+u8pOo0pXmNmiwFhJ35onbFgY2AS4Glga6J/mQwviHo0jJd2UOT+ekaBVatrCHEwZZjZd+tsx/Z1pSs+V9AieqGBkGNUah8xAY1Ezmz55Uz0FLGhm3czsZjwzUr9MJ3I87sFIZbLf3EpgIzK175CkF/CkBf8HzDvNKhjUFD+nzYWm97Ns71kQBEEQtBeZse5A3EPtaFya4xHgcDNbRNINwPbAusCw5LkWRrVWyHnXHwM8DOxmZqcUXLWpxsxWxT0UF0qbRgNLAI/iRrW103yoE558bHE8qudH4hkJfoowrNU55tkH9zWz+SVNMBenH2Rm3ab0OyQ9UHFxjQle4xCdyJQxte9Q5l25DthC0mvTuq5B8bRHm5unDO9ZEARBELQ3ZrYSMBg4FlhfUk9cW20QHsbYCbgZN65tClxXWRyrEH3w5DSgdMUYYCZc7gZJ5wM3Aovh2U0/N7NfALsC5wIjJN1bTFWDeqVT0RUIfjYLADsCK5nZo3jnsicwVRk9Mw1pdDL1T7YT2V/S+ckosCueoKDSiWwMDMFTZt9bVGULZKreoYzX0avAq9CU/CPeoYamXdtciNXyIAiCIJhKegHvALdkFjj74llBr5f0Q9JVu8nMtgdml/R9QXWtKzJzwtfNbPt6la4wz+z6uZmdCuxuZtdLelDS7mb2HbAe8DvcuWACnin2zHRujM+CKSY01hoAM9sDX6mZGzhe0slT2hDk9Al6SHpnGlc3qAKpE/nBzA4Bdgd2kvRg2nce3onMQVMnMkzS4LS/dJ3Iz3yHKkK48Q6VhGhzgyAIgqC6NNfPpvDE3STNmT6PoknqZEyK3ugqz3yf7X9LMdY1s+kkfZ9Z+J1JUikXAs1sWWAUcLak0zPblwEWxOdEH0oak7b/OL4PgikhQkHrGGvKwvIc0BX4GFjePHWyKvo/rZyf7WD2Bc41s3mmaaWDqiDPiARwN9ANWCWzb19ga2CXVDbPGNU61GuHOTW00ztUMarFO9TgRJsbBEEQBMWQ6T9XN7MV0uaHgK/NbBMzuw03qm2ajGozAzsD65hZl+z4tgxj3TJLV5jZ1mZ2pJktX9km6WngIuAIM1sss320pBsk3Z0xqlkY1YK2Eoa1Oibzwr8DbAGchWc4OT/TiDb7G+cmePsA5wC3S3qvClUPpgHRibSdeIeCthDPSxAEQRAUg5l1MLO5ca+j9dLmp4HPgL8BfXBPtafNbHpgALA28LjKmbW9Il1xVopguQyXLvlZHmvtU7VpR6pjX+Bg4CozG2pmvcz19kYAY3H9velaup56MSAGtUWEgtYZucnZrMAPWZdeMzsMF+h8GdhX0ltpex9gnKSxzXhNnI27UV9S5csJ2onUMZwBDAQ+wYU4L8I1JubBsyXdgguQ/lDmDiPeoaAtxPMSBEEQBLWDmZ0G7ASsLuklM+sF3AN8DgzDRfZXBQ4ATpF0WmGVLZgySFe0dD1m1hdPznYU8Abu3XgkPl9aFVhJ0rj8eUEwtYTHWp2RaeC2Bq4HHjazY8xsubR/MHAFsAhwnpkta2ZbAHcCi+a+ozLB2z0mePVFfoVFzmHA+vigYiDwVzyzzRd4quzNcJ2J0hrVIN6hoG3E8xIEQRAE1Sc/1s14hP8V+AjY0sw6S3oFWB34L7AbcA3wW+DwilGtJW/yRqUs0hW5evY1s35m9jszm03Sk5KGAksAdwBrAM8DE4HFgWOKqnfQmITHWp2Qazj6AdfiXkldcP2sx4GzJN2VjjkEN670AL4DLpI0KPN9hwGnEhO8uiPfiQDz4jpqt0v6NG2fBzgQ6A/MjD8rewBDkwGudMQ7FLSFeF6CIAiCoHjMbCbgq6xciZmNxPviPpK+Ttu6AB2B2XCP8XFpe2lF6M1sITwkdAVgO9y7fj9Jb7Z0X5qRrjgX97IfUcWqtwkz2wk4HU/I1in9PRi4T9LbyZDYFfdeWxcPFd1T0sUFVTloQMKwVmeY2YzArsCcwGmSxpvZlrhr62dp253p2DWB+YCPJd2Wtlk693rgOkkXFHAZQTsQncjUEe9Q0BbieQmCIAiCYkgLW+cCw4ErM3IL8wJPAFe1tmA8pWGPjUBZpSvMbC3gRmAQHhL8InAhfq17AldI+i5z/BLATJIeK6C6QQMThrU6wsxWx7WyXsG9IS7K7BuANyifAqdWvChy53eQNDG5B3eX9FGVqh60M9GJTB3xDgVtIZ6XIAiCIKgeeUOYmc2Ha2L1wReuTsAXkEeb2bnA8sBWyQOrNEa01kjSFbvgGsvX4hEtT6R9WePaicBCwAXAdpVFwnRcVrqiZj3VAMzsIGBTYBtJ76Zt1wHL4cksXmjl3NJ6MwbtT6eiKxC0iTeAu/HwvrmSJ0QHSRMk3ZCkCI4CjjOzjpLuyJ5caTjS35jg1TfL4JmQrs10It2AN4GHskY1gGynUvJOJN6hoC3E8xIEQRAEVaAFqRMBuwMLAlsCRwC7mdlluDzDXsAGwPCyGtWaka64FL8344H9gZXN7CxJd0kabGYTcemKe2iSrsga1SrSFTXtqZZhSWDOzHxoVNrWT9ILZrYq8IGkl/Inlng+FEwDwrBWo7Sw6vI6nuEGXD/rPkn3VY5NE70OwBBgxipWN6g+0Yn8BPEOBW0hnpcgCIIgKI6McSgrddIFNxAdiWe2vwHYJP3/FJ6I7wQzuxH4qIzGtcx9mxHohXua5aUrjkpjlzslDTWzJ2lZumJjYP86MaoBjAZ+a2ZL4uOxXwH9JY0xs+74+O1xMxssaUKRFQ0amwgFrUFyKw8L4Z3GdJL+k7b1BC4DlgU2l/RA/hxJ/yuo+kEVSC7aewNb4J1IH7wTGZ06kRG4uHopO5F4h4K2EM9LEARBEBRPK1InOwJ7SBqejpsXOAxYGxgp6axCKlwjlFm6wswWwec8HfHsp/0lPWtmnYHfA8cCB0i6ucBqBiWgVKmH64XMZG0H4C7gQWC0mV1uZqtIehvvYJ4E/p62Ka00UJngVT4HDcntuHbCI3ga6Y2SUa0zvpK3NPBcGY1qEO9Q0DbieQmCIAiCmiArdfKMpG/xzPdjgYcBkvTCu8ChwLoVo1rJ++CKdMVSJOkK8yRmSLoB11PrhktXrJ8/OStdUU9GNQBJLwOb42HDbwE9zWxFPKHbBcCwMKoF1SAMazWKmfUH/gRcDOwEbIgLdF5sZutJegfXHHgMuM3M1sq7P5fRHbosRCfy08Q7FLSFeF6CIAiCoHCakzpZEdhM0nNJ6qQXgKQfJL2fjitN4oIWDIiv49IVN+Ohj6tJmpBZALwBD6/tSQNKV0j6F661NytwHnAHMAA4WtIZ4F55xdUwKAMRClqjmNnFwFzAzpI+SdsewbUGtq5oZ5nZ/MBf8CyQw4qqb1AMZrYyMAyYHn9eXgKulnR22l/aRAXxDgVtIZ6XIAiCICiWkDppnZCuaB0zmw2YBTcefizpvbS9tPOhoHqEYa0GyK+ymFlX4N/AaEkD07ZRuBhjP0nPmNlSuEjnO2bWTdIXhVQ+KJzoROIdCtpGPC9BEARBUHuEXtaUkaQrjgNmAGYDrsEzoz5oZj3wzKB9gQFpW37cUyoPv7Jca1As4RJZA2RWERY1s+klfY1nulnQzLqZ2c00ZXx8xswWxLPhLJfO/yKdX2ZtgdIi6VNJr0t6PmNUs7IY1SDeoaBtxPMSBEEQBLVHSJ38NCFd0TbKdK1BsYRhrUZImgF3AQulTaNxUfpHcSH6tdMErxOwJrA4MC77HdFwBBXK+CzEOxS0hXhegiAIgqD2CL2sn6Qffk+GSxqV7tc44Hs8yQOSxgJ7AGNImnRBEExbytwo1RpjgJnwRhBJ5+PpphcDbgI+N7NfALsC5wIjJN1bTFWDoCaJdyhoC/G8BEEQBEENIukhYFVgXWBlYJMy6gfnPeOTdMXywKcZPdhReFKCHSS9ZGZLmVkPSW8CG4YebBBUh9BYqwHMrJOkH8zsENx1dydJD6Z95wHrAXPgqxETcDfowWl/xI0HpSfeoaAtxPMSBEEQBPVHWftgM1sUGCvpWzMbASwMbAJcjXvZ989IV5wJjJR0U+b8Ut63IKgmnYquQODpotO/dwOHAKsAD6Z9+5rZMsCC+CTvQ0ljoFwrNkHQGvEOBW0hnpcgCIIgqD/KaBxK0hVXAesD/8GlK/rj0hUz4dIVL4d0RRAUS3isFYSZbY1r+9wl6fHM9uOBA4AVJb3Yyvmx8hCUmniHgrYQz0sQBEEQBPWGmc0CvAZcKWn/tO1iXKriQuBEoAuwMTAEOE7SmQVVNwhKS2isFUCKl++LZ7i5ysyGmlmvtNIwAhee3M7Mpmsp61xM8IIyE+9Q0BbieQmCIAiCoN5I0hWfA6cCG5nZKgCSdsezpK6Le7Hdh49xTqwY1SJzeRBUl/BYqwIteTqYWV/cZfco4A3gIeBI4AxcsHMlSePy5wVB2Yh3KGgL8bwEQRAEQdAomNmywCjgbEmnZ7aHdEUQ1AhhWJvGZCd4aVI3L9ANuF3Sp2n7PMCBeLz8zHhGuj2AoZIOK6TiQVAjxDsUtIV4XoIgCIIgqFdCuiII6pMwrFUJM9sJOB3PMNcp/T0YuE/S22bWEeiKe1Ksi4ct7Snp4oKqHAQ1RbxDQVuI5yUIgiAIgnoihW+eAQwEPsEX/i4CXgfmAW4FbgGOB34IA1oQ1A5hWKsCZrYWcCMwCLgHeBEXm9we2BO4QtJ3meOXAGaS9FgB1Q2CmiPeoaAtxPMSBEEQBEGtE9IVQdA4dCq6AiVhGeBp4FpJ7wKYWTfgTeCh7AQPQNILlf8jRj4IgHiHgrYRz0sQBEEQBDXLT0hXPAk8aWZ/pkm64nncg21x4BggpCuCoIYIw1p1WBKYMzPBG5W29ZP0gpmtCnwg6aX8iTHBCwIg3qGgbcTzEgRBEARBzZIxqk0mXWFmWemKo4CTaJKuAHi1gCoHQdAKHYquQEkYDXQwsyXN7DbgV8AmksaYWXd8JWLzpPkTBMHkxDsUtIV4XoIgCIIgqGmSdMU5wGnAhsD8wG3A5cCGZtZZ0gRJ4yUdBeyAJy8IPdggqDHCsFYdbscFJx8BlgA2kjTazDoDmwBLA89JmlBgHYOglol3KGgL8bwEQRAEQVDrZKUrnpH0LR4O2qJ0RUUP1sxiHh8ENUS8kFVA0svA5oCAt4CeZrYinqHuAmCYpJsLrGIQ1DTxDgVtIZ6XIAiCIAjqgOakK1YENqtIV5hZ7+ZODOmKIKgtIitoFTGzlYFhwPTAXMBLwNWSzk77QzQ7CFoh3qGgLcTzEgRBEARBrWJm+wJ7A1sAQ4A+QP/kZd8dGAE8DgwOL/sgqG3CsFZlzGw2YBZgRuBjSe+l7THBC4IpIN6hoC3E8xIEQRAEQS1iZovghrOOwMf8f3t3F3JZVcdx/PtTK0RhJEnQTLtwAqPyokDDQkV8aXCytIvSxFEoJdQZia5UZkxFvBKdCC2C6aLwYsSXIRoIh2nmxiRxUHCMECeZYsrEUUjNl/l7sdejh8M+45ztnHle/H7gsJ9n7bX2+m94Hjj82GuvLlR7pr264jLgFmCNT9lLC5/B2gIwut2ypOn5P6Rp+PciSZIWgraBwcPA08DtwF7gHOBm4OdVddc8lifpABmsSZIkSZI0D3x1hbT4GaxJkiRJkjRPfHWFtLgZrEmSJEmStID46gpp8TBYkyRJkiRJkgY4bL4LkCRJkiRJkhYjgzVJkiRJkiRpAIM1SZIkSZIkaQCDNUmSJEmSJGkAgzVJkiRJkiRpAIM1SZIkSZIkaQCDNUmSJEmSJGkAgzVJkqQpJakP+aw6xLVsPVTzSZIk6QNHzHcBkiRJi9itE9p3HNIqJEmSNC8M1iRJkgaqqnXzXYMkSZLmj0tBJUmSZijJp5PcmWRnkjeSvJrksSTn9/RdluRnSbYk2Z3krSQvJXk0yRljfVclqfbrWWNLUde1PmeP/t4z364ku/qu244XJtnaaq6RPkck+UmSx5O8luT1JE8luS6J3y8lSdLHhk+sSZIkzUiSk4GtwOeB7cBm4CjgImBzkmuq6tcjQ04F7gC2AX8AXgFOAr4NfCvJyqra3PruoFuKuhb4B7Bh5DpbD0L53wMuBP4I3NfugSSfADYBFwB/A34PvAmcA6wHTgeuOAjzS5IkLXgGa5IkSQNNeBJsV1VtaD//FjgZ+EFVPTAy7hi68OveJI9W1b/bqZ3ACVX137F5TgSeAO6mC+eoqh3AjiRr25x9tXwUK4AVI0HenJvoQrVfAGuq6t1W4+HAr4Crk2ysqkcOcj2SJEkLjsGaJEnScGt72v4MbEhyGnAWsHE0VAOoqr0tEHsYuBT4ZWt/tW+SqtqdZCNwfZKTqurFg3kTEzwyHqq1ZZ7XAXuAG+dCtVbju0l+ClwFXA4YrEmSpCXPYE2SJGmgqsp+Tn+9HZdNeLLtM+146mhjkjOB1W38ccAnx8Z9FjgUwdoTPW1fAI4F/g7cnPTe/huM3ZMkSdJSZbAmSZI0G8e243ntM8nRcz8k+S6wke6dZX8Cngf+B+wDzqZ7Au5TM6i1z56etrl7Wk7/03pzjt7POUmSpCXDYE2SJGk25pZ1rq6qew9wzG3AW8DXqmrn6Ikk99MFa9PY146TvvMtG6lzXPW0zfV9qKoumbIWSZKkJcft0CVJkmbj8Xb85hRjTgGe7QnVDgO+MWHMPuDwCedeacfPjZ9IcgpwzBS1ATwH7AXOaLuDSpIkfawZrEmSJM1AVf0V2A5ckuTqvj5JvpzkuJGmXcDyJCeM9AndsssvTpjqZXqCs+Y54DXg4tF5khwJHOhTdO+rqneA9cDxdDuaHjneJ8nxSSbVKkmStKS4FFSSJGl2LgO2AL9JcgPwF7onvk4EvgJ8iW6Tgv+0/ncD9wFPJXkQeBs4ky5U2wSs7JnjMeD7STYBTwLvANuqaltVvZ3kHuCWds2H6L7/nQf8q32mdRtwGnAtsDLJFuCfdBstLG/13gQ8O+DakiRJi4rBmiRJ0oxU1e4kXwWuBy4FLqdbtrmHLnhaDzwz0v/+JP8H1gBX0u2wuR24qo3vC9ZW070P7VxgBd2KhFuBbe38WuB14EfAj9vcDwDrGBB+tbDuO8APgVXARXSbFbwEvEAX4v1u2utKkiQtRqnqey+tJEmSJEmSpP3xHWuSJEmSJEnSAAZrkiRJkiRJ0gAGa5IkSZIkSdIABmuSJEmSJEnSAAZrkiRJkiRJ0gAGa5IkSZIkSdIABmuSJEmSJEnSAAZrkiRJkiRJ0gAGa5IkSZIkSdIA7wGQ4skf4xUcBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importance = pd.DataFrame(list(zip(columns_ordered,best_model.stages[0].featureImportances)), \n",
    "                               columns= [\"Feature\",\"Importance\"])\n",
    "\n",
    "plt.figure(figsize = (20,10))\n",
    "\n",
    "plt.title(\"Feature Importances of RandomForest\")\n",
    "sns.barplot(x=\"Feature\", y=\"Importance\", data=feat_importance.sort_values(by=\"Importance\", ascending=False).iloc[:16], color=\"red\")\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After Tuning step, F1 score of the Random Forest decreased but the results are more confident than the first results.\n",
    "\n",
    "Finally:\n",
    "\n",
    "- I predicted churners with 0.82 F1 Score\n",
    "- Best parameters : max depth:10, num Trees:20\n",
    "- Tenure Days which is created in feature engineering step is the most important feature in the model\n",
    "- Also daily and monthly averages of the event_ attributes have important part in my model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "As a result we created a churn model with 0.82 F1 Score. We know that the churn rate is around 20% (Let's assume 30%). If we take churn actions to every customer:\n",
    "- precision will be nearly 0.3 (TP/(TP+FP))\n",
    "- recall will be 1 (TP/(TP+FN))\n",
    "- F1 Score will be nearly 0.46 ~ 0.5\n",
    "\n",
    "If we assume these scores as base, our last models f1 score is 0.82. It is better than the base model. Also our model predicted nearly all churners correctly, it means that we can take action to the all potential churners.\n",
    "\n",
    "## Reflection\n",
    "\n",
    "This is my first project in Cloud side. It is hard to learn the syntax and some operations in dataset. Because I felt more confident and safe in pandas but Pandas is not enough for huge dataset. Also churn problem is my favorite problem because everyday I'm trying to solve problems about the churner subscribers in my professional life.\n",
    "\n",
    "## Improvement\n",
    "\n",
    "It is the small dataset for spark maybe larger dataset can help to identify churner subscribers more accurately. On the other hand more domain knowledge provides huge improvements to the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7 with Spark",
   "language": "python3",
   "name": "python37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}