SPARSQL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPARK SQL (CLOUDS Fall 2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will learn how to use the DataFrame API and SparkSQL to perform simple data analytics tasks.\n",
    "\n",
    "# Goals\n",
    "The main goals of this notebook are the following:\n",
    "1. Understand the advantages and disadvantages of using DataFrame over RDD\n",
    "2. Analyze the airline data with the DataFrame API and SparkSQL\n",
    "\n",
    "# Steps\n",
    "\n",
    "- First, in section 1, we will go through a short introduction about the DataFrame API with a small example to see how can we use it and how it compares to the low-level RDD abstraction.\n",
    "- In section 2, we delve into the details of the use case of this notebook including: providing the context, introducing the data\n",
    "- In section 3, we perform data exploration and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. RDD and DataFrame APIs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our previous notebooks, we have worked with RDDs (Resilient Distributed Dataset), the basic abstraction in Spark. The main reason is due to the task at hand: we have been invovled in the design of distributed algorithms.\n",
    "\n",
    "In this laboratory session, we study (or revisit) the DataFrame API - an immutable distributed collection of data.\n",
    "DataFrames allow developers to impose a **structure** on a distributed collection of data, allowing higher-level abstraction; in addition, DataFrames provide a domain specific language (DSL) API to manipulate distributed, structured data. Ultimately, the goal is to make Spark accessible to a wider audience, beyond researchers and specialized data engineers.\n",
    "\n",
    "Let's take a small example to see the difference between RDDs and DataFrames. The two cells below contain short code snippets that perform the same task: count the frequency of words in a document, then sort the result in descending order. The first snippet is the same you have been working on in the very [first notebook](https://github.com/EURECOM-CLOUDS-COURSE/Introduction/blob/master/introduction-lab.ipynb) of the CLOUDS course.\n",
    "The second code snippet uses the DataFrame API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "<strong>Question 1.</strong> \n",
    "\n",
    "Run both approaches (RDD and DataFrame) detailed below, compare their runtime, and discuss. Is the DataFrame API approach faster? Can you explain why?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 137.22 sec\n",
      "[(29556020, 'the'), (18043957, 'of'), (14914748, 'and'), (12892052, 'to'), (9779124, 'a')]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "time_start = time.time()\n",
    "words = (\n",
    "            # read the text file\n",
    "            sc.textFile(\"/datasets/gutenberg/gutenberg_small.txt\").repartition(16)\n",
    "            \n",
    "            # construct words from lines\n",
    "            .flatMap(lambda line: line.split())\n",
    "            \n",
    "            # map each word to (word, 1)\n",
    "            .map(lambda x: (x, 1))\n",
    "    \n",
    "            # reduce by key: accumulate sum the freq of the same word\n",
    "            .reduceByKey(lambda freq1, freq2: freq1 + freq2)\n",
    "            \n",
    "            # swap (word, freq) to (freq, word)\n",
    "            .map(lambda x: (x[1], x[0]))\n",
    "    \n",
    "            # sort result by key DESC\n",
    "            .sortByKey(False)\n",
    "         )\n",
    "print(\"Runtime: %.2f sec\" % (time.time() - time_start))\n",
    "print(words.take(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+--------+\n",
      "|word|   count|\n",
      "+----+--------+\n",
      "| the|29556020|\n",
      "|  of|18043957|\n",
      "|    |16611988|\n",
      "| and|14914748|\n",
      "|  to|12892052|\n",
      "+----+--------+\n",
      "only showing top 5 rows\n",
      "\n",
      "Runtime: 97.08 sec\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import *\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.functions import explode, split, desc\n",
    "import time\n",
    "sqlContext = SQLContext(sc)\n",
    "time_start = time.time()\n",
    "\n",
    "\n",
    "# read the text file\n",
    "df = sqlContext.read.text(\"/datasets/gutenberg/gutenberg_small.txt\")\n",
    "\n",
    "# show 3 samples\n",
    "# df.show(3)\n",
    "\n",
    "result = (\n",
    "    # split into words\n",
    "    df.select(explode(split(df.value, \"\\s+|\\t+\")).alias('word'))\n",
    "    \n",
    "    # group by each word\n",
    "    .groupBy(['word'])\n",
    "    \n",
    "    # count the freq\n",
    "    .count()\n",
    "    \n",
    "    # sort the result\n",
    "    .orderBy(desc(\"count\"))\n",
    ")\n",
    "\n",
    "result.show(5)\n",
    "print(\"Runtime: %.2f sec\" % (time.time() - time_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of the above pieces of code work on the same task of word count and displaying the top 5 words, but the approach of using the DataFrame API (97.08 sec) achieved the task faster than the approach of using RDDs (137.22 sec).<br>\n",
    "This is because the SparkSQL and its utilization of the DataFrame API possesses features that enhance the efficiency and optimize the execution of the required operations of data. In SparkSQL, the <b>Catalyst Optimizer</b>, which is a component of SparkSQL, works on the optimization of the query implementation and processing. It optimizes the <b>logical plan</b> of the desired query by implementing several optimization rules on the <b>query tree</b> and also chooses the best <b>physical plan</b> of the query based on the cost model as well as code generation with <b>quasiquoutes</b>. So it has rule-based and cost-based optimization capabilities which is a big boost over the RDD approach implementation.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Analysis of flight data using the DataFrame API and SparkSQL\n",
    "\n",
    "## Use case\n",
    "\n",
    "In this notebook, we play the role of a data scientist working in the travel industry, specifically on air transportation of passengers. We want to explore the data collected by the Department of Transportation (DoT) to understand passengers' behavior, as well as the properties of all flights, across several airline companies.\n",
    "\n",
    "The dataset provided by the DoT has 29 features, that can be either categorical or numerical. For example, the ```src_airport``` (source airport) is categorical: there exist no comparison operator between airport names. We can not say \"SGN is bigger than NCE\". The ```departure_time``` feature is numerical, for which a comparison operator exists. For instance, \"flight departing before 6PM\" can be express by \"departure_time < 1800\".\n",
    "\n",
    "In this use case, most features are numerical, except carrier, flight_number, cancelled, cancelation_code and diverted. \n",
    "The data contains a header, that is useless when analyzing the data: it serves the purpose of an \"embedded schema\", to help data scientist figure out what information is available. Note that there are some features with missing values in some lines of the dataset. The missing values are marked by \"NA\". These values can cause problems when processing the data and can lead to unexpected results. Therefore, we need to remove the header and replace all \"NA\" values by empty values, such as they can be interpreted as null values.\n",
    "\n",
    "## DataFrame\n",
    "As we have seen already, there are multiple ways to manipulate data:\n",
    "\n",
    "- Using the RDD abstraction\n",
    "\n",
    "- Using the DataFrame abstraction. \n",
    "\n",
    "A DataFrame is a distributed collection of data organized into named columns. It is based on the data frame concept in R language or in Pandas for Python. In some sense, it is similar to a table in a relational database: each item is a list of values (the columns). Also, the value in each row of each column can be accessed by the column's name.\n",
    "\n",
    "To use DataFrames, the data should be clean (no invalid values). That means we cannot create DataFrame directly from the \"RAW\" data. Instead, we will first create an RDD from RAW data, produce a new, clean RDD, then transform it to a DataFrame and work on it. The RDD `cleaned_data` is an `RDD[String]`. We need to transform it to `RDD[(TypeOfColumn1, TypeOfColumn2, ..., TypeOfColumn29)]` then call a function to create a DataFrame from the new RDD. Please note that, package `com.databricks.spark.csv` can really help us to load the data efficiently without concerns about parsing, conversions, etc... However, the CSV package can have some problems when loading not well-formatted data or the data that has many kinds of missing values. The guidelines defined this notebook are a general way to help you work with any data.\n",
    "\n",
    "\n",
    "DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs.\n",
    "\n",
    "During the labs in this course, we will mainly work with CSV data files. So, in the next sections, we only focus on constructing dataframes from structured data files directly and from existing RDD.\n",
    "\n",
    "### Constructing a DataFrame directly from structured data file\n",
    "\n",
    "To construct DataFrame from a structured file directly, the file type must be supported. Currently, Spark supports csv, json, avro and many more. Among these types, the csv type is one of the most popular in data analytics. A DataFrame is constructed from csv files by using the package spark-csv from Databricks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "sqlContext = SQLContext(sc)\n",
    "\n",
    "df = sqlContext.read.load('/datasets/airline/1994.csv', \n",
    "    format='com.databricks.spark.csv', \n",
    "    header='true', \n",
    "    inferSchema='true',\n",
    "    nullValue='NA'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the function load parametrized with `com.databricks.spark.csv`, we ask the SqlContext to use the parser from the DataBricks package. Additionally, we can specify whether the file has a header, or ask the parser to guess the data type of columns automatically. The parsed data types is viewed using the function printSchema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- year: integer (nullable = true)\n",
      " |-- month: integer (nullable = true)\n",
      " |-- day_of_month: integer (nullable = true)\n",
      " |-- day_of_week: integer (nullable = true)\n",
      " |-- scheduled_departure_time: integer (nullable = true)\n",
      " |-- scheduled_arrival_time: integer (nullable = true)\n",
      " |-- arrival_delay: integer (nullable = true)\n",
      " |-- distance: integer (nullable = true)\n",
      " |-- src_airport: string (nullable = true)\n",
      " |-- dest_airport: string (nullable = true)\n",
      " |-- carrier: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print(df.dtypes)\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the automatically inferred data types are **not** as expected. For example, we expect that `CRSDepTime` to be of interger type. The type and the name of each column can be modified using function ```withColumn``` and ```withColumnRename``` respectively. Additionally, we can also compute and print basic descriptive statistics of numerical columns via the function ```describe``` (similar to Pandas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Year: integer (nullable = true)\n",
      " |-- Month: integer (nullable = true)\n",
      " |-- DayofMonth: integer (nullable = true)\n",
      " |-- DayOfWeek: integer (nullable = true)\n",
      " |-- DepTime: integer (nullable = true)\n",
      " |-- scheduled_departure_time: integer (nullable = true)\n",
      " |-- ArrTime: integer (nullable = true)\n",
      " |-- CRSArrTime: integer (nullable = true)\n",
      " |-- UniqueCarrier: string (nullable = true)\n",
      " |-- FlightNum: integer (nullable = true)\n",
      " |-- TailNum: string (nullable = true)\n",
      " |-- ActualElapsedTime: integer (nullable = true)\n",
      " |-- CRSElapsedTime: integer (nullable = true)\n",
      " |-- AirTime: string (nullable = true)\n",
      " |-- ArrDelay: integer (nullable = true)\n",
      " |-- DepDelay: integer (nullable = true)\n",
      " |-- Origin: string (nullable = true)\n",
      " |-- Dest: string (nullable = true)\n",
      " |-- Distance: integer (nullable = true)\n",
      " |-- TaxiIn: string (nullable = true)\n",
      " |-- TaxiOut: string (nullable = true)\n",
      " |-- Cancelled: integer (nullable = true)\n",
      " |-- CancellationCode: string (nullable = true)\n",
      " |-- Diverted: integer (nullable = true)\n",
      " |-- CarrierDelay: string (nullable = true)\n",
      " |-- WeatherDelay: string (nullable = true)\n",
      " |-- NASDelay: string (nullable = true)\n",
      " |-- SecurityDelay: string (nullable = true)\n",
      " |-- LateAircraftDelay: string (nullable = true)\n",
      "\n",
      "+-------+-------+-----------------+------------------+------------------+------------------+------------------------+------------------+-----------------+-------------+-----------------+-------+------------------+------------------+-------+-----------------+------------------+-------+-------+-----------------+------+-------+--------------------+----------------+--------------------+------------+------------+--------+-------------+-----------------+\n",
      "|summary|   Year|            Month|        DayofMonth|         DayOfWeek|           DepTime|scheduled_departure_time|           ArrTime|       CRSArrTime|UniqueCarrier|        FlightNum|TailNum| ActualElapsedTime|    CRSElapsedTime|AirTime|         ArrDelay|          DepDelay| Origin|   Dest|         Distance|TaxiIn|TaxiOut|           Cancelled|CancellationCode|            Diverted|CarrierDelay|WeatherDelay|NASDelay|SecurityDelay|LateAircraftDelay|\n",
      "+-------+-------+-----------------+------------------+------------------+------------------+------------------------+------------------+-----------------+-------------+-----------------+-------+------------------+------------------+-------+-----------------+------------------+-------+-------+-----------------+------+-------+--------------------+----------------+--------------------+------------+------------+--------+-------------+-----------------+\n",
      "|  count|5180048|          5180048|           5180048|           5180048|           5113308|                 5180048|           5101202|          5180048|      5180048|          5180048|      0|           5101202|           5180048|      0|          5101202|           5113308|5180048|5180048|          5157099|     0|      0|             5180048|               0|             5180048|           0|           0|       0|            0|                0|\n",
      "|   mean| 1994.0|6.579943853802127|15.723382486031017|3.9516016067804776| 1345.266130849149|      1338.6794068317513|1494.4365151585841|1495.874874904634|         null|920.2637743897354|   null|114.00258860558746|114.82526995888841|   null|5.662489742613603|  6.65992152242736|   null|   null|670.7402911985982|  null|   null|0.012884050495284986|            null|0.002337043981059635|        null|        null|    null|         null|             null|\n",
      "| stddev|    0.0|3.436720373974637| 8.788272360339212|1.9902384397190065|470.22426611386146|      463.61750114772104|  483.984955904281|472.6701494862173|         null|608.1342943927972|   null| 64.88861058427706| 64.20406191593874|   null|23.61815604081688|22.226297708488598|   null|   null|522.2696688890817|  null|   null| 0.11277435077764802|            null|0.048286464527841806|        null|        null|    null|         null|             null|\n",
      "|    min|   1994|                1|                 1|                 1|                 1|                       1|                 1|                1|           AA|                1|   null|               -40|                -5|   null|             -115|              -109|    ABE|    ABE|               11|  null|   null|                   0|            null|                   0|        null|        null|    null|         null|             null|\n",
      "|    max|   1994|               12|                31|                 7|              2400|                    2400|              2400|             2400|           WN|             3599|   null|               740|               578|   null|             1313|              1439|    YAK|    YAK|             4502|  null|   null|                   1|            null|                   1|        null|        null|    null|         null|             null|\n",
      "+-------+-------+-----------------+------------------+------------------+------------------+------------------------+------------------+-----------------+-------------+-----------------+-------+------------------+------------------+-------+-----------------+------------------+-------+-------+-----------------+------+-------+--------------------+----------------+--------------------+------------+------------+--------+-------------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = (df\n",
    "          # change type of column CRSDepTime by casting its values to interger type\n",
    "          .withColumn('CRSDepTime', df.CRSDepTime.cast('int'))\n",
    "\n",
    "          # rename the column\n",
    "          .withColumnRenamed('CRSDepTime', 'scheduled_departure_time')\n",
    "    )\n",
    "\n",
    "# print schema of the current data\n",
    "df.printSchema()\n",
    "\n",
    "# run jobs to calculate basic statistic information and show it\n",
    "df.describe().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing a DataFrame from an existing RDD\n",
    "\n",
    "Another way to construct a DataFrame is using data from an existing RDD. The main advantage of this approach is that it does not need a third party library. However, with this method, we have to remove the header ourself and provide a clear schema.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import explode, split, desc, col, udf, round, when, stddev_pop\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pyspark.sql.functions as func\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "\n",
    "sqlContext = SQLContext(sc)\n",
    "\n",
    "data = sc.textFile('/datasets/airline/1994.csv')\n",
    "\n",
    "# extract the header\n",
    "header = data.first()\n",
    "\n",
    "# replace invalid data with NULL and remove header\n",
    "cleaned_data = (\n",
    "        data\n",
    "    \n",
    "        # filter out the header\n",
    "        .filter(lambda line: line != header)\n",
    "    \n",
    "         # replace the 'missing data' by empty value\n",
    "        .map(lambda line: ','.join(list(map(lambda x: x if x != 'NA' else '',line.split(',')))))\n",
    "    )\n",
    "\n",
    "airline_data_schema = StructType([ \\\n",
    "    #StructField( name, dataType, nullable)\n",
    "    StructField(\"year\",                     IntegerType(), True), \\\n",
    "    StructField(\"month\",                    IntegerType(), True), \\\n",
    "    StructField(\"day_of_month\",             IntegerType(), True), \\\n",
    "    StructField(\"day_of_week\",              IntegerType(), True), \\\n",
    "    StructField(\"departure_time\",           IntegerType(), True), \\\n",
    "    StructField(\"scheduled_departure_time\", IntegerType(), True), \\\n",
    "    StructField(\"arrival_time\",             IntegerType(), True), \\\n",
    "    StructField(\"scheduled_arrival_time\",   IntegerType(), True), \\\n",
    "    StructField(\"carrier\",                  StringType(),  True), \\\n",
    "    StructField(\"flight_number\",            StringType(),  True), \\\n",
    "    StructField(\"tail_number\",              StringType(),  True), \\\n",
    "    StructField(\"actual_elapsed_time\",      IntegerType(), True), \\\n",
    "    StructField(\"scheduled_elapsed_time\",   IntegerType(), True), \\\n",
    "    StructField(\"air_time\",                 IntegerType(), True), \\\n",
    "    StructField(\"arrival_delay\",            IntegerType(), True), \\\n",
    "    StructField(\"departure_delay\",          IntegerType(), True), \\\n",
    "    StructField(\"src_airport\",              StringType(),  True), \\\n",
    "    StructField(\"dest_airport\",             StringType(),  True), \\\n",
    "    StructField(\"distance\",                 IntegerType(), True), \\\n",
    "    StructField(\"taxi_in_time\",             IntegerType(), True), \\\n",
    "    StructField(\"taxi_out_time\",            IntegerType(), True), \\\n",
    "    StructField(\"cancelled\",                StringType(),  True), \\\n",
    "    StructField(\"cancellation_code\",        StringType(),  True), \\\n",
    "    StructField(\"diverted\",                 StringType(),  True), \\\n",
    "    StructField(\"carrier_delay\",            IntegerType(), True), \\\n",
    "    StructField(\"weather_delay\",            IntegerType(), True), \\\n",
    "    StructField(\"nas_delay\",                IntegerType(), True), \\\n",
    "    StructField(\"security_delay\",           IntegerType(), True), \\\n",
    "    StructField(\"late_aircraft_delay\",      IntegerType(), True)\\\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert each line into a tuple of features (columns) with the corresponding data type\n",
    "cleaned_data_to_columns = (\n",
    "    cleaned_data.map(lambda l: l.split(\",\"))\n",
    "    .map(lambda cols: \n",
    "         (\n",
    "            int(cols[0])  if cols[ 0] else None,\n",
    "            int(cols[1])  if cols[ 1] else None,\n",
    "            int(cols[2])  if cols[ 2] else None,\n",
    "            int(cols[3])  if cols[ 3] else None,\n",
    "            int(cols[4])  if cols[ 4] else None,\n",
    "            int(cols[5])  if cols[ 5] else None,\n",
    "            int(cols[6])  if cols[ 6] else None,\n",
    "            int(cols[7])  if cols[ 7] else None,\n",
    "            cols[8]       if cols[ 8] else None,\n",
    "            cols[9]       if cols[ 9] else None,\n",
    "            cols[10]      if cols[10] else None,\n",
    "            int(cols[11]) if cols[11] else None,\n",
    "            int(cols[12]) if cols[12] else None,\n",
    "            int(cols[13]) if cols[13] else None,\n",
    "            int(cols[14]) if cols[14] else None,\n",
    "            int(cols[15]) if cols[15] else None,\n",
    "            cols[16]      if cols[16] else None,\n",
    "            cols[17]      if cols[17] else None,\n",
    "            int(cols[18]) if cols[18] else None,\n",
    "            int(cols[19]) if cols[19] else None,\n",
    "            int(cols[20]) if cols[20] else None,\n",
    "            cols[21]      if cols[21] else None,\n",
    "            cols[22]      if cols[22] else None,\n",
    "            cols[23]      if cols[23] else None,\n",
    "            int(cols[24]) if cols[24] else None,\n",
    "            int(cols[25]) if cols[25] else None,\n",
    "            int(cols[26]) if cols[26] else None,\n",
    "            int(cols[27]) if cols[27] else None,\n",
    "            int(cols[28]) if cols[28] else None\n",
    "         ))             \n",
    ")\n",
    "    \n",
    "# create dataframe\n",
    "df = sqlContext.createDataFrame(cleaned_data_to_columns, airline_data_schema)\\\n",
    "    .select(['year', 'month', 'day_of_month', 'day_of_week',\n",
    "            'scheduled_departure_time','scheduled_arrival_time',\n",
    "            'arrival_delay', 'distance', \n",
    "            'src_airport', 'dest_airport', 'carrier'])\\\n",
    "    .cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples\n",
    "\n",
    "Using the contructed DataFrame, we can answer some simple questions:\n",
    "\n",
    "<ul>\n",
    "<li>How many night flights do we have in our data?</li>\n",
    "<li>How many night flights per unique carrier?</li>\n",
    "</ul>\n",
    "\n",
    "<strong>NOTE:</strong> We define \"night\" to start at 6pm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1078203"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.scheduled_departure_time > 1800].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(carrier='DL', count=208224),\n",
       " Row(carrier='US', count=170397),\n",
       " Row(carrier='AA', count=142832),\n",
       " Row(carrier='WN', count=124024),\n",
       " Row(carrier='UA', count=113640),\n",
       " Row(carrier='NW', count=100453),\n",
       " Row(carrier='CO', count=94501),\n",
       " Row(carrier='TW', count=54771),\n",
       " Row(carrier='HP', count=44351),\n",
       " Row(carrier='AS', count=25010)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.scheduled_departure_time > 1800].groupBy(df.carrier).count().orderBy('count', ascending=0).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data exploration\n",
    "Next, we will go over a series of simple queries on our data, to perform exploration and compute statistics using the DataFrame API and SparkSQL. These queries directly map to the questions you need to answer. \n",
    "\n",
    "**NOTE:** finding the right question to ask is difficult! Don't be afraid to complement the questions below, with your own questions that, in your opinion, are valuable ways to inspect data. This can give you extra points!\n",
    "\n",
    "- Basic queries:\n",
    "  - How many unique origin airports?\n",
    "  - How many unique destination airports?\n",
    "  - How many carriers?\n",
    "  - How many flights that have a scheduled departure time later than 18h00?\n",
    "\n",
    "\n",
    "- Statistics on flight volume: this kind of statistics are helpful to reason about delays. Indeed, it is plausible to assume that \"*the more flights in an airport, the higher the probability of delay*\".\n",
    "  - How many flights in each month of the year?\n",
    "  - Is there any relationship between the number of flights and the days of week?\n",
    "  - How many flights in different days of months and in different hours of days?\n",
    "  - Which are the top 20 busiest airports (this depends on inbound and outbound traffic)?\n",
    "  - Which are the top 20 busiest carriers?\n",
    "\n",
    "\n",
    "- Statistics on the fraction of delayed flights\n",
    "  - What is the percentage of delayed flights (over total flights) for different hours of the day?\n",
    "  - Which hours of the day are characterized by the longest flight delay?\n",
    "  - What are the fluctuation of the percentage of delayed flights over different time granularities?\n",
    "  - What is the percentage of delayed flights which depart from one of the top 20 busiest airports?\n",
    "  - What is the percentage of delayed flights which belong to one of the top 20 busiest carriers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Basic queries\n",
    "\n",
    "### Question 2.1\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "How many origin airports? How many destination airports? Discuss the results, indicating any discrepancies or strange facts about the data.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of origin airports is:  224\n"
     ]
    }
   ],
   "source": [
    "no_src_airports = df.select(\"src_airport\")\\\n",
    "      .distinct()\\\n",
    "      .count()\\\n",
    "\n",
    "print(\"The number of origin airports is: \"\n",
    "      ,src_airports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of destination airports is:  225\n"
     ]
    }
   ],
   "source": [
    "no_dest_airports = df.select(\"dest_airport\")\\\n",
    "      .distinct()\\\n",
    "      .count()\n",
    "\n",
    "print(\"The number of destination airports is: \"\n",
    "      ,dest_airports)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "According to the results given by the two queries above, there is something quite strange about them and that is that the number of origin airports (224) is less than the number of destination airports (225) in our data.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.2\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "How many unique carriers are present in the data?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of carriers in the data is:  10\n"
     ]
    }
   ],
   "source": [
    "no_carriers = df.select(\"carrier\")\\\n",
    "      .distinct()\\\n",
    "      .count()\n",
    "\n",
    "print(\"The number of carriers in the data is: \"\n",
    "      ,no_carriers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "<ul>\n",
    "<li>How many night flights (that is, flights departing later than 6pm or earlier than 6am)?</li>\n",
    "<li>What is the percentage of night flights over the total volume of flights</li>\n",
    "</ul>\n",
    "<br>\n",
    "<strong>NOTE:</strong> Here we're using a more refined definition of what is a night flight, than in our earlier examples.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of night flights in the data is:  1121918\n"
     ]
    }
   ],
   "source": [
    "no_night_flights = df[(df.scheduled_departure_time>1800) | (df.scheduled_departure_time<600)]\\\n",
    "      .count()\n",
    "\n",
    "print(\"The number of night flights in the data is: \"\n",
    "      ,no_night_flights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of night flights is: 21.658447952605844 %.\n"
     ]
    }
   ],
   "source": [
    "percent_night_flights = df[(df.scheduled_departure_time > 1800) | (df.scheduled_departure_time < 600)]\\\n",
    "       .count()*100/df.count()\n",
    "\n",
    "print(\"The percentage of night flights is:\"\n",
    "       ,percent_night_flights, \"%.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Flight volume statistics\n",
    "\n",
    "### Question 3.1: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "How many flights in each month of the year? Plot the changes over months by a line chart and comment the figure.\n",
    "\n",
    "<br>\n",
    "From the result, we can learn the dynamics of flight volume over months. For example, if we only consider flights in 1994 (to start, it's always better to focus on smaller amount of data), we can discuss about which months are most likely to have flights experiencing delays.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame[Month: int, count: bigint]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ad0f05eb8>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group the months and aggregate over the number of flights\n",
    "# sort by month\n",
    "# convert the dataframe to a pandas dataframe\n",
    "\n",
    "flightsPerMonth = df.groupBy('Month')\\\n",
    ".count()\\\n",
    ".orderBy(\"Month\")\\\n",
    ".cache()\n",
    "\n",
    "pd_flightsPerMonth = flightsPerMonth.toPandas()\n",
    "\n",
    "# display the the number of flights for every month\n",
    "print(flightsPerMonth)\n",
    "\n",
    "# plot the number of flights per month\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.grid(True)\n",
    "plt.title(\"Number of Flights vs Months\",fontsize = 15)\n",
    "plt.xlabel(\"Month\",fontsize = 15)\n",
    "plt.ylabel(\"Number of Flights\",fontsize = 15)\n",
    "plt.xticks(np.arange(1,13),['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'],fontsize=15,rotation='vertical')\n",
    "plt.yticks(fontsize=12)\n",
    "plt.plot(pd_flightsPerMonth[\"Month\"],\n",
    "         pd_flightsPerMonth[\"count\"]\n",
    "         ,'-o'\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "\n",
    "\n",
    "According to the line chart, we mainly witness peaks (up to 449K)in the number of flights in the second half of the year, especially in July, August, October, and December. <b>These four months can be expected to most likely experience flight delays due to the increased number of flights.</b><br>\n",
    "For July (443736) and August (451086), the increased number of flights can be explained by the summer vacation when there is a high aviation density caused by mainly students, families, and workers .<br>\n",
    "As for October (449369) and December (446521), one of the reasons for the high number of flights is the huge number of people leaving for and coming from vacations.<br>\n",
    "On the other hand, lower numbers are observed during the first half of the year, especially in February (386954). This period will most probably experience less delays in flights than the later half of the year.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.2: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Is there any relationship between the number of flights and the days of the week?  \n",
    "\n",
    "Plot a bar chart and interpret the figure.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<strong>NOTE:</strong> You should study both global aggregates (irrespectively of the month of the year), and monthly aggregates (day statistics, for each different month of the year).\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "By answering this question, we could learn about the importance of the weekend/weekday feature for our predictive task.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group the days of the week and aggregate over the number of flights\n",
    "# sort by day of the week\n",
    "# convert the dataframe to a pandas dataframe\n",
    "\n",
    "flightsPerDayofTheWeek = df.groupBy('day_of_week')\\\n",
    ".count()\\\n",
    ".orderBy(\"day_of_week\")\\\n",
    ".cache()\n",
    "\n",
    "pd_flightsPerDayofTheWeek = flightsPerDayofTheWeek.toPandas()\n",
    "\n",
    "\n",
    "# plot the number of flights per month\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\"Number of Flights vs Days of the Week\",fontsize=15)\n",
    "plt.xlabel(\"Day of the Week\",fontsize=15)\n",
    "plt.ylabel(\"Number of Flights\",fontsize=15)\n",
    "plt.xticks(np.arange(1,8),['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],rotation='vertical',fontsize=15)\n",
    "plt.bar(pd_flightsPerDayofTheWeek[\"day_of_week\"],\n",
    "         pd_flightsPerDayofTheWeek[\"count\"],\n",
    "        width = 0.3\n",
    "        )\n",
    "plt.yticks(fontsize=10)\n",
    "for index, element in enumerate(pd_flightsPerDayofTheWeek[\"count\"]):\n",
    "    plt.annotate(element,(index+0.7, element+10000),fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9ad2a2ed30>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Xf4u/FPOue1xsm2Q/xFoEZPeMvzdVIlFLJNzbpmZvSp/F7iT/E2Wx8qYbJT8d/HX4bMbM+sZahDXyD/TE6u5dn9VRrx88aaTfC1jtC0hbZL8xVysyI94iWWEC6jod6J1ln8mY7hz7uWofHUTlKXc5XC+84ZfSfqVmR0q3wTwUTOb45ybH2e6yIVlTTh+Iss6RfEvBheWY163yNcOnRln3LcqedOioi8y61vJXmRb6IftXZ51TebufU08DyT7/dwrzrlHJD1iZs0lnS7pL/IdOF1Xjtmslt9n9WKCwJaSdoTane8XmcT8Itt7nPyzZLEigeAoSX9zzv0xMsLMTo7Ju1H+3FHVjpM/D0dqsU6TD97PjtQomVmH2InC79bj8uf85fI3AKeWsazVkuJ13NNSVX/sbpIPdG+OMy6vjGnfkw/oB8l3oPO+JDnndprZ5/LB3wCVfP3DJvkawAdV0oaoPLPkm5bGKogZ/r1CTaaZHe38ayoAlIIAEDXNvfLPFvw93kjnXJGZzZa/kLg3atRZ8ndh3483XSn26q5xAg/J1/r1kH+OMfYObKxz5TujiX3XXG35WtBzJP1WvkbwRjPrG2lmZmZt5JtDRv/Afizpx2b2m6imMCOSKXi4mPlAUlfn3E2lZP1Y0kUxF/2xy4gEet//WIcLqKPkO8CIKLEPylGO6GnmmNk18k0hu+mHZnXRVsgHj6PkayIjzpK/iJ6bzLJiyi4zqxPdlDZJ78s/H9XaOfdCOafdTVj3/8o/T2Uxo1fI1xhFO0EV7zT5HgAVmsOdIF/TI1XgukqVch6oCMl+P5NV6rnJ+ddQ3Ge+Z+PsPSirk79hMC2U1cLwnrwqYqH8s7UdnXOl9cJbV7ufD9K1ezN3yTdDHmVmh7oqegVLaHUxWb5586tRZd0V05wwUTPrh+SbPU6Sf75uQRmL/FD+UYFOkVrmcKwMCPMor735HXtN/nvzRZxa/LJEjuurJK12zi2PGve+fC1vd0l3x1lmT/nmxYluMLwmX7P3tXMu0bs4I3bJH7svSnrVzAaG5tcAEiAARI3inHtDuz8bE8+N8u9uelj+Wble8ncvHwjPUZRHrqQTzexE+TvPS8OzgnvjP/JNF4+Q760vITNrK//Mx/Vh3WPHvywfIP5W/sftc/nnbK6Xv6C+Ub5GpThqssnyFxiPh23UXb7nS8XkS+RaSa+ZWbF808I8+ed7Tpb0f6HJ4R2SfiHfbPUv8s3frpPv3TCyjFz54OM2M5so39Pp7+QvFKPlSmppvsv6efKdNCxLphxm9o78neR58he0F8v3RPqR4nDOFZvvBv8+M9so/9zIsfJ3mX+zB0Fcbvj7azObKd+0L6maO+fcllCWv4bA+C35GuMsScc558r7wu5b5Pd7rGcljQ376QX5mo4TyznvsuyUdEsI/FbJ1wocIOmvUqWsq1Sx54GKkOz3M1m5kk4O54B8+SDravna2zfka0oOlz9+y1P7J+fcAjN7TNJdZtZQPvC5WP7GSbwal7LmV2xmV0n6R5jfS/JBycHyvTyeGWoaZ0j6RXgGcJP8OST21Q3TQvr/wjGzUL41RZZzrlzrmUCGmUWacjaQD9DHS6on3wlP5HnXGZIuN7M75G/EDZDvmbYE59yH5l9bMlC+E5uyTJVvAvmSmd0g3/nJJPl9et8erFOJY8U5V1btXcTt8us108z+Jn9+bil/XL3jnEvYgsU5t87860GGK+oZ4OB9+Sb6ppI3QCbJn6NfMLMp8uvdRv6m0dTwWzhNvrOcN8zsz/K9gTaVf35yjXPuLzFl2WlmP5YP4F81s2PCTRIA8STbWwwfPpXxURm9soU88XrDO1u+tuY7+SDjFu3eY+cYRfXQF5W+TLt3S36w/A/GVkX1khmbL948VUpPkZL+Kd+1dqldUctf0H3fa2Gc8ZGeNY8Mwx3kOxf4Vr650Tj5h/z/E2e6JSHfO/LNY5ykkYm2Rcz0R4blbJMPqObLXyg0ispznHxNXoF888Ojw/Iuj8rTR/6Hfqd8b5JjVLI3xTryz06uC2Wcmmw55HsjnSsfHG6Rf6bs6CSOu8vC9vlO/sLiipjxCfdtTD6T9Ef5oKdYobdHJTiuFdVleVTa+fKdHeyU78XuQ0lXlrHcNxSnd8BwLDiV/L5cL9+te144NkdEr58S9JYZ7xiJs/8myX9Hjw7HQYF8IHRMnPKVuq6x805iP5Z1Hoi7XnHmU2a+OOs9RjHnGCXx/Uz22JAPTD6QP+5dOCZPka8ZWR+WsVA++EvY62Ep+7aefI+Ja8M+myXpxGSOs1KWNVy+997t8t/Zz+Sb50V6qG0pHyhsC8v9o354LUv0dmwq35X/urCeuZJ+Vcb3KO52jZPHhU+x/DljVjhuSvR0Kn8T6puwPq/K91RZYtkh7+/lb4DFfU1GnPwHy98szJMP2qYrqsfL8mz/eMdKebaTfMdYD0cdC8vkzxM9klj2I2E5Vyc47rYpzu+g/M2Gp+VvBOyUPx/fp6jfQkmN5G8ifaMfvuP/VniNUILvTZNw3H2iqN8rPnz47P6JvG8FQAUJvZUtl3/VQXl6EdyTZTWSD2Ducs7dWEq+8+W73j7Yla9ji/KUZaD8xd9g59zrlbEMYF+S7PcT+z4z+0i+5u2C6i4LAJSFJqBABQldsh8m33NnU+1ZU56ylnGp/J3rxfKdS1wp34RqSky+e+WbMG2Wb4r6W0kvVGTwZ2aT5Tt8WCP/rqeJ8jWCb1bUMoB9SbLfT+w/zKy3/Ksr+sg3XQWAGo8AEKg4reWbO66TdImrnOeQCuSfHWkv3/TlI0nHu90fvpd8AHpP+LtR/v1o11ZwWWrLN8FsKd+M6X/yzfn25HknYH+Q7PcT+4+P5ZuSXu+c+7i6CwMAyaAJKAAAAACkCF4EDwAAAAApgiagkpo1a+Y6duxY3cUAAAAA9lmzZ8/e4JxrXt3lQOkIACV17NhRs2bNqu5iAAAAAPssM+OZ530ATUABAAAAIEUQAAIAAABAiiAABAAAAIAUQQAIAAAAACmCABAAAAAAUgQBIAAAAACkCAJAAAAAAEgRBIAAAAAAkCIIAAEAAAAgRRAAAgAAAECKIAAEAAAAgBRBAAgAAAAAKYIAEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIggAAQAAACBFEAACAAAAQIogAAQAAACAFEEACAAAAAApggAQAAAAAFIEASAAAAAApAgCQAAAAABIEQSAAAAAAJAiMqq7AAAAoGwdr3shbvqyW0+u4pIAe6/XI73ips8dPbeKSwKkHgJAAAD2ZZMalUjq1al93KxcXFeveEE8ATyAqkYACAAAgMoT5yaFEtykAFD5CAABAACAKrCgW/e46TMH3R03/Rd/H1yZxUGKIgAEAAD7hUQX191zF1RxSQCg5iIABAAA+7W7L50ZN53aFQCpiNdAAAAAAECKIAAEAAAAgBRBE1AAAEpB80EAwP6EGkAAAAAASBEEgAAAAACQImgCCgClifcC40lbq74cVazXI71KpM0dPbcaSgIAACoSNYAAAAAAkCKoAQQAJIWXbAMAsO+jBhAAAAAAUgQ1gEANQe0KAAAAKhsBIFAN4nWw8WQ1lKMm4B1rAAAAVYcAEAAkdbzuhbjpy+pUcUEAAAAqEc8AAgAAAECKoAYQqEzx3iEnSZ3aV205AAAAAFEDCAAAAAApgwAQAAAAAFIEASAAAAAApAgCQAAAAABIEXQCU0PE64J+2a0nV0NJAAAAAOyvqAEEAAAAgBRRpQGgmXU1s8+iPtvM7HIzyzSzGWa2OPxtEvKbmd1pZkvMbI6ZHRE1r9Eh/2IzGx2VnmNmc8M0d5qZVeU6AgAAAEBNVaVNQJ1zCyX9SJLMLF3SSknPSrpO0mvOuVvN7LowPEHScEldwudISfdKOtLMMiXdKKm3JCdptpk975zbHPKMk/SBpBclDZP0UpWtJAAAQA224rq3S6S1vfXoaigJgOpQnU1Ah0j60jm3XNKpkh4J6Y9IGhn+P1XSNOd9IKmxmbWSdKKkGc65TSHomyFpWBjX0Dn3vnPOSZoWNS8AAAAASGnVGQCeI+mx8H9L59xqSQp/W4T0NpK+iZpmRUgrLX1FnPQSzGycmc0ys1nr16/fy1UBAAAAgJqvWgJAMztA0ghJT5WVNU6a24P0konO3e+c6+2c6928efMyigEAAAAA+77qqgEcLukT59zaMLw2NN9U+LsupK+Q1C5quraSVpWR3jZOOgAAAACkvOoKAM/VD80/Jel5SZGePEdLei4q/cLQG2g/SVtDE9FXJA01syahx9Chkl4J4/LMrF/o/fPCqHkBAAAAQEqr8hfBm1k9SSdIuiQq+VZJT5rZWElfSxoV0l+UdJKkJZJ2SLpIkpxzm8zsZkkfh3w3Oec2hf/HS5oqqa5875/0AAoAqDL0sAgAqMmqPAB0zu2Q1DQmbaN8r6CxeZ2kXySYzxRJU+Kkz5LUs0IKCwAAAAD7kersBRQAAAAAUIUIAAEAAAAgRRAAAgAAAECKIAAEAAAAgBRBAAgAAAAAKYIAEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIggAAQAAACBFEAACAAAAQIogAAQAAACAFJFR3QUAAPxgxXVvl0hre+vR1VASlOW2s0+Jm352pwlVXBIAAJJHAAgAALAf4iYFgHhoAgoAAAAAKYIAEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIugFFN+L1/28RBf0AAAA1SFeT65XPTG9GkqC/Qk1gAAAAACQIggAAQAAACBFEAACAAAAQIogAAQAAACAFEEACAAAAAApggAQAAAAAFIEASAAAAAApAgCQAAAAABIEQSAAAAAAJAiCAABAAAAIEUQAAIAAABAiiAABAAAAIAUQQAIAAAAACkio7oLAADx3Hb2KSXSrnpiejWUBAAAYP9BDSAAAAAApAgCQAAAAABIEQSAAAAAAJAiCAABAAAAIEUQAAIAAABAiiAABAAAAIAUQQAIAAAAACmCABAAAAAAUgQBIAAAAACkCAJAAAAAAEgRBIAAAAAAkCIIAAEAAAAgRWRUdwFQPW47+5QSaWd3mlANJQEAAABQVagBBAAAAIAUQQAIAAAAACmCABAAAAAAUgQBIAAAAACkCAJAAAAAAEgRBIAAAAAAkCIIAAEAAAAgRRAAAgAAAECKIAAEAAAAgBRBAAgAAAAAKYIAEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIqo8ADSzxmb2tJnlmtkCM+tvZplmNsPMFoe/TUJeM7M7zWyJmc0xsyOi5jM65F9sZqOj0nPMbG6Y5k4zs6peRwAAAACoiaqjBvCvkl52znWTdJikBZKuk/Sac66LpNfCsCQNl9QlfMZJuleSzCxT0o2SjpTUV9KNkaAx5BkXNd2wKlgnAAAAAKjxqjQANLOGko6R9JAkOee+c85tkXSqpEdCtkckjQz/nyppmvM+kNTYzFpJOlHSDOfcJufcZkkzJA0L4xo65953zjlJ06LmBQAAAAApraprAA+WtF7Sw2b2qZk9aGYHSmrpnFstSeFvi5C/jaRvoqZfEdJKS18RJ70EMxtnZrPMbNb69ev3fs0AAAAAoIar6gAwQ9IRku51zh0uabt+aO4ZT7zn99wepJdMdO5+51xv51zv5s2bl15qAAAAANgPVHUAuELSCufch2H4afmAcG1ovqnwd11U/nZR07eVtKqM9LZx0gEAAAAg5WVU5cKcc2vM7Bsz6+qcWyhpiKT54TNa0q3h73NhkuclXWZmj8t3+LLVObfazF6R9Ieojl+GSrreObfJzPLMrJ+kDyVdKOlvVbaCAAAAFaDXI73ipj/5/wpLpHXPXVDZxQGwH6nSADD4paRHzewASV9Juki+JvJJMxsr6WtJo0LeFyWdJGmJpB0hr0Kgd7Okj0O+m5xzm8L/4yVNlVRX0kvhAwAAAAApr8oDQOfcZ5J6xxk1JE5eJ+kXCeYzRdKUOOmzJPXcy2ICAABUvkmN4qd3ap/0LO6+dGYFFQZAKqiOGkAAAIDkJAqQJm2t2nKgTB2veyFu+rI6VVyQShRvHZfVOS9+Zo5R1FAEgAAApIgF3bobQ8yxAAAgAElEQVTHTd8XnyGL94zck9VQDiARjlHUVASAAACkuHhNCH/x98HVUBIAQGUjAAQA7L8SNB/sleD5qrmj51ZmaQAAqHYEgAAABHGbSA66u+oLAgBAJanqF8EDAAAAAKoJNYAAAKDapUIPkgBQE1ADCAAAAAApghpAACineF17S4k7EKnRPSxWwEuoa4r47+eqhoIAAFCDEQACQAqgeR0AAJBoAgoAAAAAKYMaQABJid+87ry4eVP1HWtxXyEg8RoBAABQYxAAAhWgMpvX1ejnx8qJd6ztn+Ido9K+e5wCALA/owkoAAAAAKQIAkAAAAAASBEEgAAAAACQIngGEPs0nj0CAAAAkkcAiCqRsJOUW0+u4pIAAAAAqYsAENVrUqM4aVurvhwAgKSsuO7tuOltbz26iksCANgTBICocXo90itu+pP/r7BkIq8QAAAAUbhJAZSOALAmi1c7JlFDBgAAAGCP0AsoAAAAAKQIagD3QfGaSM4dPTdu3kS9ZAIAAABIPQSA+4kF3brHH8EzcgAAYB8X74Y2r3wC9gxNQAEAAAAgRVADCAAAgBqBFk1A5aMGEAAAAABSBDWAAIAqw/u5AGDvcB7F3qIGEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIugEBsA+gwffgapz29mnxE0/u9OEKi5J1eI8A2B/Rw0gAAAAAKQIAkAAAAAASBE0AQX2IzRdAgAAQGmoAQQAAACAFEEACAAAAAApggAQAAAAAFIEASAAAAAApAg6gUFKiddJCh2kAACw70nVd1UCe4saQAAAAABIEdQAAgAqRby789yZBwCgelEDCAAAAAApghpAAACQkqilBpCKqAEEAAAAgBRBAAgAAAAAKYIAEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIggAAQAAACBFEAACAAAAQIogAAQAAACAFJFR3QUAUH63nX1K3PSzO02o4pJgT7EPAQBAdaAGEAAAAABSBAEgAAAAAKQIAkAAAAAASBEEgAAAAACQIugEBvslOtgAAAAASqIGEAAAAABSRJUHgGa2zMzmmtlnZjYrpGWa2QwzWxz+NgnpZmZ3mtkSM5tjZkdEzWd0yL/YzEZHpeeE+S8J01pVryMAAAAA1ETVVQN4nHPuR8653mH4OkmvOee6SHotDEvScEldwmecpHslHzBKulHSkZL6SroxEjSGPOOiphtW+asDAAAAADVfTWkCeqqkR8L/j0gaGZU+zXkfSGpsZq0knShphnNuk3Nus6QZkoaFcQ2dc+8755ykaVHzAgAAAICUVh0BoJP0PzObbWbjQlpL59xqSQp/W4T0NpK+iZp2RUgrLX1FnPQSzGycmc0ys1nr16/fy1UCAAAAgJqvOnoBPco5t8rMWkiaYWa5peSN9/ye24P0konO3S/pfknq3bt33DwAAAAAsD+p8hpA59yq8HedpGfln+FbG5pvKvxdF7KvkNQuavK2klaVkd42TjoAAAAApLwqDQDN7EAzaxD5X9JQSfMkPS8p0pPnaEnPhf+fl3Rh6A20n6StoYnoK5KGmlmT0PnLUEmvhHF5ZtYv9P55YdS8AAAAACClVXUT0JaSng1vZsiQ9C/n3Mtm9rGkJ81srKSvJY0K+V+UdJKkJZJ2SLpIkpxzm8zsZkkfh3w3Oec2hf/HS5oqqa6kl8IHAAAAAFJelQaAzrmvJB0WJ32jpCFx0p2kXySY1xRJU+Kkz5LUc68LCwAAAAD7mZryGggAAAAAQCUjAAQAAACAFEEACAAAAAApggAQAAAAAFIEASAAAAAApAgCQAAAAABIEUkFgGZ2RnhHX2S4k5m9Z2ZbzOwZM2tceUUEAAAAAFSEZGsAfyupYdTw3yQ1k3SrpCMk3VLB5QIAAAAAVLBkXwR/sKS5kmRmjSQNlXSac+4FM/taPhCM+8J2AAAAAEDNUJ5nAF34e6ykIkmvhuEVkppXZKEAAAAAABUv2RrAzyX9xMw+kPQzSa875wrCuPaS1lVG4QAAAADsu2bPnt0iIyPjQUk9RQeUVaFY0rzCwsKf5eTkxI3Rkg0AfyPpv5JGS8qXbwIaMVLSh3tTSgAAAAD7n4yMjAcPOuig7s2bN9+clpbmyp4Ce6O4uNjWr1+fvWbNmgcljYiXJ6kA0Dn3jpm1l5Ql6Uvn3Jao0VMkLd7r0gIAAADY3/Qk+Ks6aWlprnnz5lvXrFnTM2GeZGZkZjdIauCcmx0T/EnSp5LO3YtyAgAAANg/pRH8Va2wvRPGecm2w71RUtsE41qH8QAAAACQlPT09Jxu3bpld+7cuUfXrl2zJ02a1LKoqKhSl3nJJZe07dy5c49LLrlkt9hm+vTpDWbMmHFgZPiMM87o+PDDDzfZk2XcfPPNLX7605+2iwyfd955HQYMGJAVGb7llltajBkzpl38qUvXpk2bXqtXr072Mb64kp3Y9EMvoLHaStq8N4UAAAAAkFpq165dnJubO1+SVq5cmTFq1KiDt27dmv6Xv/xlVWUt89FHH22+fv36z+rWrbtbbDNz5swG9evXLzrhhBO27+0yjjnmmPwnn3yyaWR43rx5dYuLi62wsFAZGRn64IMPDhwxYkRsq8oqk7AG0MxGm9lMM5spH/zdGxmO+rwn6Z+S3qyqAgMAAADYv7Rp06bwwQcfXPbwww+3KC4u1sKFCw/Iycnpmp2d3T07O7t7pHZu5MiRnf75z382jkw3YsSITo8++mij6HkVFxfrkksuadulS5ceWVlZ2Q888EATSRo8eHDnnTt3ph1++OHdI2mStHDhwgOmTZvW/O9//3vLbt26Zb/88sv1JenNN9+sf/jhh3dr27Ztr+jawIkTJ7bs2bNn96ysrOwrrriidey6DBgwYMeyZctq5+fn28aNG9Pr1q1b3LNnzx0fffRRXUmaPXt2/eOOOy5fku65557MXr16de/WrVv2eeed16GwsFCS9O9//7vhj370o27Z2dndhw8ffvDWrVt3i9vy8/Pt6KOP7nLbbbc1K++2Lq0J6A5JG8PHJG2NGo58lkr6o6Rx5V0wAAAAAERkZ2d/V1xcrJUrV2a0bt268O233140f/78BU888cRXV1xxRXtJuvjii9dPnTq1qSRt3Lgxffbs2fXPOuusrdHzmTZtWuO5c+fWXbBgwRevvfbaohtuuKHt8uXLa82cOXNJpNbx4osv/r4FY9euXb+78MIL11966aVrc3Nz5w8bNixfktauXVtr1qxZuc8999ziG2+8sY3kA7MlS5bUmTNnzoIFCxbM/+yzz+q99NJL9aOXX6tWLXXv3n3n22+/feAbb7xxYE5OzvYjjzxy+1tvvVV/2bJltZxz6ty5865PPvmkztNPP505a9as3Nzc3PlpaWnu73//e9PVq1dn/OEPf2j11ltvLZo/f/6CI444YsfNN9/cMjL/bdu2pQ0dOrTL2Wefvemqq67aUN7tnLAJqHPuKUlPSZKZPSzpZufcV+VdAAAAAAAkwznfMvO7776zsWPHdpg/f37dtLQ0LV++vLYknXzyyfmXX355h5UrV2Y8+uijTU4++eTNtWrV2m0eb7/9doOzzjprU0ZGhtq1a1d45JFH5r/zzjv1OnTosLXkEhMbMWLElvT0dOXk5Hy7cePGWpL08ssvN3zrrbcaZmdnZ0vSjh070nJzc+sMHz48P3raI488Mv/tt9+uv3PnzrQBAwZs7969+7c33XRTqxYtWhTm5ORsD/NqMG/evHqHHXZYd0n69ttv01q0aFH4xhtvHPjll1/W6du3bzdJ2rVrl+Xk5ORHlavz5Zdfvmb8+PGbyrd1vWRfA3HRnswcAAAAAJIxf/78A9LT09WmTZvCq6++unWLFi12PfPMM0uLi4tVt27dnEi+s846a+ODDz6Y+cwzz2ROmTJlWex8IkHk3qpTp873M4rM0zmnyy+/fPU111xTas3bUUcdlX///fc3LygosKuvvnpdq1atChcvXlzn3Xffrd+/f//8MC8bNWrUxrvvvntl9LT/+te/Gg0cOHDbf//736Xx5t2nT5/8l19+udEll1yyKS0t2T49f5D0FGbW28z+YGbTzOzJ2E+5lwwAAAAAklatWpVx8cUXd7jooovWpaWlaevWremtWrXalZ6ernvuuadpdO+gl1566Yb77ruvpST17t3729h5HXvssXlPP/10ZmFhoVatWpXx0Ucf1T/66KNL7dylQYMGRXl5eelllXP48OHb/vGPfzSLPJO3dOnSWitXrixRqTZkyJD8zz777MBNmzbVatOmTWFaWpoyMzMLX3nllcbHHHNMviQNGzZs2/Tp05tEpl+7dm36okWLDhg0aND2WbNm1Z83b15tScrLy0ubM2dO7ci8//SnP63KzMwsvOCCC9qXVd54kn0P4HhJH0n6maRDJDWP8wEAAACApBQUFKRFXgNx3HHHZQ0ZMmTbn//851WSdPnll6977LHHmh522GHdFi1aVKdu3brFkenatWtXeMghh3x7/vnnb4w33wsuuGBLjx49dnbv3r3HoEGDsn73u9+taN++fWFpZTnjjDO2vPDCC42jO4GJ5/TTT982atSoTX369OmWlZWVfdpppx2yZcuWEoFj8+bNizIzMwuzsrJ2RtL69u27fdOmTRn9+vXbIUk5OTnf/va3v105ZMiQrKysrOzBgwdnffPNN7Vat25deN999y0755xzDs7KysrOycnpNnfu3DrR83/ooYe+KSgoSLv00ksTvaovoWRfA3G1pCmSLnXOlbrxAAAAAKAsRUVFsxON69WrV8GiRYvmR4ajm0nm5eWlLVu2rPbYsWPjPgOXlpam++67b4WkFbHjduzY8Wm8aQ499NDdlhfpCCbedBMnTlw3ceLEdYnKHrFkyZIvoodvv/32Vbfffvtur7i4+OKLN0d3SBMxYsSIvBEjRiyITV+5cuXcyP9PP/30srLKEE+yTUBbSHqM4A8AAABAdfnPf/7TICsrq8fFF1+8rmnTppX71vj9VLI1gC9JOlLSa5VYFgAAAABIaOTIkXkjR46cW3ZOJJIwADSz7KjBuyXdb2a1JM2QVOLN9c65+bFpAAAAAICao7QawHmSovtQNUk3SrohJp+FfGX2mgMAAAAAqD6lBYDHVVkpAAAAAACVLmEA6Jx7syoLAgAAAACoXOV/dTwAAAAA7CPMLGfkyJGdIsO7du1SkyZNDjvuuOM6V8T8r7zyytY33HBDy4qYV1VIqhdQMyvW7s8DRnOStkn6XNKdzrlnK6hsAAAAAPYjHa97Iaci57fs1pMTvkswom7dusULFy6sm5+fb/Xr13fPPvtsw5YtW+6qyHLsS5KtAbxS0kpJCyT9UdI1kv4kKVfSKkl/lVQk6WkzO78SygkAAAAAe2TIkCFbn3rqqcaS9Nhjj2WeccYZ379Efu3atenHH3/8IVlZWdmHHXZYtw8//LCu5Gv2Ro0a1bFv375d27Zt2+v3v/99i8g0EyZMOKhjx449BwwYkLV48eLakfTbbrutWc+ePbt37do1+8QTTzwkLy8vbfPmzWlt2rTpVVBQYJK0adOm3YarWrIBYGtJ7zrnejrnrnfO3e6cu84510PSe5KaOOeOl/SopGsrq7AAAAAAUF4XXHDBpieeeKLJjh07bMGCBfX69++/PTLu2muvbX3YYYftWLRo0fybb7555ejRo79vLrpkyZI6b7755qKPP/54wZ///OfWBQUF9vbbb9d79tlnM+fOnTt/+vTpSz7//PMDI/l/8pOfbJ43b96ChQsXzu/atevOO++8s1mTJk2K+/fvn/fkk082kqQpU6ZknnTSSZtr166dqIVlpUo2ALxI0sMJxj0s6YLw/xOSuuxtoQAAAACgohx55JE7V6xYUfuBBx7IPP7447dGj/voo48ajB07dqMkjRgxIm/Lli0ZGzduTJekoUOHbqlbt65r1apVYWZm5q4VK1ZkvP766/VPOumkLQ0aNCjOzMwsHjp06PfvSJ89e3bdnJycrllZWdnPPPNM0y+++KKOJI0bN2791KlTm0rSP//5z2bjxo3bUHVrv7tkA8AMSd0SjOseNZ/vJH27t4UCAAAAgIo0bNiwLTfeeGO7Cy+8cFN0unMlK+LMzElSdC1denq6CgsLLYyPu4xx48Z1uuuuu75etGjR/AkTJqwqKChIk6ShQ4duX7FiRe0XXnihflFRkfXp06faYqZkA8DHJf0/M7vSzLqYWePw92pJf5D0r5DvCPnnAgEAAACgxhg/fvyGq666alXfvn13Rqf369cv7+GHH24qSdOnT2/QpEmTwszMzOJE8xk8eHD+Cy+80Dg/P982b96cNmPGjMaRcTt27Ehr3779roKCAnv88cczo6c755xzNl500UUHn3/++dVW+ycl2QuopF/L1+79Xr7zl4gCSQ/IdwojSR9Keq3CSgcAAAAAFeCQQw7ZNXHixHWx6ZMnT1513nnndczKysquW7du8dSpU5eWNp+BAwfuOO200zb17NmzR5s2bQr69u2bHxl33XXXrerbt2/3Nm3afNe9e/cd+fn56ZFxY8eO3Th58uQ2Y8eO3RR/zlUjqQDQOfedpF+b2e8k9ZJ0kKQ1kuY65zZF5XujMgoJAAAAYN+XzGsbKtqOHTs+jU075ZRT8k455ZQ8SWrZsmXRa6+99mVsnttvv31V9PDixYu/iPw/efLkNZMnT14TO82ECRPWT5gwYX28crz22msNhg0btrlZs2ZFe7IeFSXZGkBJUgj23qyksgAAAADAfmf06NHtXn/99UbTp09fXN1lSRgAmtlJkt5xzm0L/5fKOfdihZYMAAAAAPYDjzzyyDeSvqnuckil1wBOl9RP0kfhfycp0csKnaT0BOMAAAAAADVAaQFgJ0mro/4HAAAAAOzDEgaAzrnl8f4HAAAAAOybSnsGsF55ZuSc27H3xQEAAAAAVJbSmoDmyz/blyyeAQQAAABQY6xZsyZ90KBBXSVpw4YNtdLS0lxmZmahJH322WcL6tSpU554p0w5OTld//a3v309YMCAnWXnrh6lBYA/VfkCQAAAAABIbFKjnIqd39ZS3yt40EEHFeXm5s6XpCuvvLJ1/fr1i2666aa1FVqGfUxaohHOuanOuUeS/VRloQEAAABgT82bN692t27dsiPDv/nNbw669tprW0nS3Llzaw8cOLBLjx49uvfu3bvrnDlzakvS/fff36RLly49unbtmn3kkUdmSVJeXl7a8OHDD87Kyso+5ZRTDi4oKPg+vjr33HM79OzZs3vnzp17XH311a0k6Zlnnmk4fPjwgyN5nnzyyYYnnXTS98NVobRnAM+T9HJ4+TsAAAAA7Pd+9rOfdZgyZcryHj16FPzvf/87cPz48e3ffffdxbfeemvrN998c2G7du0KN2zYkC5JkydPbt64ceOiRYsWzX/33XfrHnPMMd8HlXfccceKli1bFu3atUv9+vXrOnv27M0jR47cdtVVV7Vfs2ZN+kEHHVQ0derUZmPGjNlQleuXsAZQ0j8kdY4MmFmamX1tZj0rv1gAAAAAULU2bNiQ/vnnn9c/44wzDunWrVv2r371qw7r1q07QJL69OmTf+6553a6/fbbmxUVFUmS3n333QYXXnjhRkk66qijdh5yyCHfP/s3ZcqUzOzs7O49evTI/uqrr+rMmTOnbnp6us4444yNDz30UNO1a9emz507t97pp5++rSrXsbRnAGNf+m6S2ko6oPKKAwAAAACVq1atWq64uPj74W+//TYtIyPDOefUuHHjwshzg9Eee+yx5a+//vqBzz33XKPDDjusx9y5c7+QJLPYsMk3I73vvvtazpo1a0GzZs2KTj311E47d+40SRo/fvyG0047rbMknXrqqZszMkoLySpeaTWAAAAAALDfadeu3a7169fXWr9+ffqOHTvsf//7XyNJat68eVHz5s13TZs2rbEkFRUV6f33368rSQsWLKg9ZMiQ7Xfccceqhg0bFi5btqzWUUcdlTdt2rRMSXr//ffrfvnll3UlacuWLekHHnhgUZMmTYqWL19e66233moYWXbnzp13ZWZm7vrrX/960Lhx46q0+adUeg0gAAAAAOx36tWr537961+vycnJ6d6uXbuCrKys75tuPvHEE1+OGzeuwy233NJ6165dNmrUqI39+/ff+ctf/rLdihUrDnDO2bHHHru1T58+33br1u27s846q2NWVlZ2r169dvTo0WO7JB111FE7unTp8m1WVlaP9u3bF+Tk5ORHL//ss8/e9NBDD6UfeuihBVW97mUFgL80s9Xh/0jd5q/NLLbrVOecm1CxRQMAAACwXynjtQ2V6fbbb1+1W1EmTVo7adKkEq+EyM7O/u6dd95ZHJs+c+bMJbFpDRo0KH7ppZe+ire8//znP0sTleWdd96pX9Wdv0SUFgB+LWlgTNpyScfEyeskEQACAAAAQCm6deuW3ahRo8IpU6Z8Ux3LTxgAOuc6VmE5AAAAAGC/F6+DmapEJzAAAAAAkCIIAAEAAAAgRRAAAgAAAECKIAAEAAAAgBSRMAA0s/ZmVqsqCwMAAAAAFWXs2LHtbrrpphaR4YEDB3Y5++yzO0SGL7744raTJk1qmcy8rrzyytY33HBDUnn3xJ133tn0wgsvbF9Z848o7TUQSyX1l/SRmc2U9HPnXG5lFwgAAADA/qnXI71yKnJ+c0fPLfW9gkcddVT+008/3UTSuqKiIm3evDkjPz8/PTL+448/rn/uuedWy+sYqktpTUB3SqoX/h8kqWFFLdTM0s3sUzObHoY7mdmHZrbYzJ4wswNCeu0wvCSM7xg1j+tD+kIzOzEqfVhIW2Jm11VUmQEAAADsWwYPHpw/e/bs+pI0e/bsul27dt154IEHFq1fvz59586d9uWXX9YZMGDAjokTJ7bs2bNn96ysrOwrrriidWT6CRMmHNSxY8eeAwYMyFq8eHHtSHrfvn27jh8/vk2vXr26d+zYsefLL79cX5IKCwt1ySWXtI3M609/+lMzSVq+fHmt3r17d+3WrVt2ly5dekTy//Wvf23asWPHnn369On63nvv1Y/M/1//+lejQw89tFv37t2zBwwYkPXNN99kFBUVqUOHDj1XrVqVIUlFRUVq3759z9WrV5dWqVdCaZk/lfRXM5sRhn9pZqsT5HXOufK8CP7Xkhboh6BysqS/OOceN7O/Sxor6d7wd7NzrrOZnRPynW1m2ZLOkdRDUmtJr5pZVpjX3ZJOkLRC0sdm9rxzrlrftQEAAACg6nXs2HFXRkaGW7x48QFvvvnmgf369du+cuXKWjNnzqzfpEmTwq5du+588cUXGyxZsqTOnDlzFjjndPzxx3d+6aWX6tevX7/42WefzZw7d+78Xbt26Uc/+lH24YcfviMy78LCQps7d+6CJ554otFNN93UetiwYYvuuOOOZo0aNSqaN2/egp07d1qfPn26/fjHP9722GOPNRkyZMjWyZMnryksLFReXl7a8uXLa916662tZ8+evSAzM7NowIABXXv27LlDkk444YT8c845JzctLU233357s5tuuumgBx54YMWZZ5658cEHH8y84YYb1j333HMNu3fvvrNVq1aF5dkmpQWAF0v6k6RTJTlJQyQVJMjrJCUVAJpZW0knS7pF0pVmZpIGSzovZHlE0iT5APDU8L8kPS3prpD/VEmPO+cKJC01syWS+oZ8S5xzX4VlPR7yEgACAAAAKSgnJyf/9ddfP/D999+vf80116z9+uuvD3j33XcPbNSoUVHfvn3zX3755YZvvfVWw+zs7GxJ2rFjR1pubm6dvLy8tJNOOmlLgwYNiiVp6NChW6LnO2rUqM2SNGDAgO3XXHPNAZL06quvNszNza33/PPPN5GkvLy89Pnz59fp16/f9ksuuaTjrl270s4888zNAwYM2Dl9+vSG/fr1y2vdunWhJJ1++umbFi1aVEeSli5desDIkSPbrl+/vtZ3332X1q5duwJJGj9+/IYRI0Z0vuGGG9ZNmTKl2ZgxYzaUd3skbALqnMt1zv3YOddFkkka6ZzrlOBzcDmWeYekayUVh+GmkrY45yKR6wpJbcL/bSR9E8pTKGlryP99esw0idJLMLNxZjbLzGatX7++HMUHAAAAsK/o379//nvvvVc/Nze3bp8+fXYOGjQo/+OPP67/wQcf1B84cGC+c06XX3756tzc3Pm5ubnzv/7663lXXHHFBknydU/x1alTx0lSRkaGioqKTJKcc3bbbbd9HZnXypUr555++unbhg8fnv/WW28tbNOmzXdjxozpdNdddzUtbf6XXXZZ+5///OfrFi1aNP+uu+5aXlBQkCZJnTt33tWsWbPC559/vsGnn3564KhRo7aWd3sk+xqITpI+K+/MY5nZKZLWOeeiH9aMt9aujHHlTS+Z6Nz9zrnezrnezZs3L6XUAAAAAPZVxx57bP6rr77auHHjxkUZGRlq2bJl0bZt29I//fTT+scdd9z24cOHb/vHP/7RbOvWrWmStHTp0lorV67MGDx4cP4LL7zQOD8/3zZv3pw2Y8aMxmUt64QTTth67733Ni8oKDBJmjNnTu1t27alLVq06IA2bdrsuuqqqzacf/75Gz755JN6xxxzzPYPPvigwZo1a9ILCgrs2WefbRKZT15eXnr79u13SdLUqVObRi/jpz/96fqf/exnnUaMGLEpI6Ncj/9JKr0J6Pecc8vNLMPMzpY0UFKmpE2S3pb076jau7IcJWmEmZ0kqY78M4B3SGpsZhlhPm0lrQr5V0hqJ2mFmWVIahSWG0mPiJ4mUToAAACAFNO3b9+dW7ZsyTj99NM3RtK6deu2c/v27emtWrUqPP3007d98cUXdfr06dNNkurVq1f86KOPLh04cOCO0047bVPPnj17tGnTpqBv3775ZS3riiuu2LBs2bLavXr16u6cs8zMzF0vvvjil6+88pv9AusAACAASURBVEqDO++886CMjAxXr169okcffXRphw4ddk2YMGFVv379ujdv3nzXoYceuiNSk/h///d/q84999xDWrZs+V3v3r23f/311993QHPuueduveyyy9LHjRu3MXFJEksqADSzFpL+J+lQScskrZV/RcQvJH1uZkOdc2W2o3TOXS/p+jDPQZKuds79xMyeknSmpMcljZb0XJjk+TD8fhg/0znnzOx5Sf8ys9vlO4HpIukj+RrALmbWSdJK+Y5iIs8WAgAAAKhGZb22oTJkZGQoPz//0+i0Z555Zln08MSJE9dNnDhxXey0kydPXjN58uQ1sekfffTRwsj/rVq1Kvz/7d15fFT1vf/x94ewhUUkgIggRoUAAa00CG60Xq2IinUDC1cUrJbrBqi1xdb7s621XLlXsXKxdUWgtiK9SlG04EoRrcgmsoWlioKAsu9Eknx+f5wzMIYkTJKZJOS8no/HPHLme7bvNzNz5rznnPM9X3755WJJSktL09ixY79UkEUOGjp06JahQ4ceFtiGDx++Zfjw4YeVDxw4cPvAgQO3Fy2XpDlz5qR36NBhb9euXfcXN/5IEj1mOFrBtXc93H1urNDMzpT0Ujj++vJUIDRC0iQze1BB76PPhuXPSvpT2MnLVgWBTu6+1MwmK+jcJV/S7e5eENbpDkkzJKVJGufuSytQLwAAAACoFn75y18eP378+BbPPffcZ+VdRqIB8FJJd8SHP0ly97lm9gtJ/1vWFbv7TEkzw+FPdagXz/hp9kvqV8L8v1PQk2jR8tclvV7W+gAAAABAdTZy5MiNI0eOPOyIZFkk2glMPUm7Shi3S1LdilQCAAAAAJB6iQbADyWNMLOG8YXh8xHheAAAAABANZboKaA/lfSupLVm9oaCTmCOk3Sxgo5Xzk9J7QAAAAAASZPQEUB3/1hBT5tPSWoh6SIFAfAJSe3dfVHKaggAAAAASIqE7xzo7psl3ZvCugAAAABA0mzcuDHt/PPP7yBJmzdvrlOrVi3PyMjI//LLL+sed9xxB/71r3+l9I4BY8aMaTZv3ryGEydO/CKV6ymLst86HgAAAADKYXnHTjnJXF6n3OWl3lfw+OOPL8jNzV0mSXffffcJjRo1KnjggQe+WrFiRd0+ffq0L+96Dxw4oDp16pR39iqVaCcwAAAAAFBjFBQUqH///ie1a9eu87nnntt+9+7dJkndu3fvMGvWrAaStGHDhtqtW7c+TQqO5l1yySWnXHDBBe169uyZ9fnnn9fp1q1bh44dO2a3b9++8/Tp0xtJ0mOPPdYsMzOzy5lnntnhgw8+aBRb31/+8pcmp59+esdOnTpln3POOVlr166tXVBQoJNOOqnL+vXra8fq1LZt2y4bNmxI2YE6AiAAAACAyPniiy/qDxs27OvVq1cvbdKkScHEiRObHmmeBQsWNHrhhRc++/DDD1eOGzcu48ILL9yRm5u7bPny5Ut79Oix9/PPP6/z0EMPnfDBBx/kvvfeeytXrlyZHpv3oosu2v3xxx/nLl++fFnfvn23PvDAA8enpaWpb9++W5555pkMSZo6deoxnTp12teqVav8VLWbAAgAAAAgclq3bp13zjnn7JOkrl277l2zZk29I83Ts2fPnS1btiyQpLPOOmvPCy+80Pzuu+8+4aOPPkpv2rRp4axZsxqeddZZu0444YT8+vXr+9VXX701Nu9nn31Wt2fPnu2zsrKyx4wZc3xubm66JN16662bJ02a1EySxo0b13zw4MGbU9PiwBEDoJnVM7P7zOw7qawIAAAAAFSWunXremw4LS3N8/PzTZJq167tBQUFkqS9e/da/DwNGjQojA1fcsklu2fNmrWidevW3wwePPjksWPHNpMks2/NctAdd9zR9rbbbvt65cqVy8aOHft5Xl5eLUlq167dgebNm+e/8sorjRcuXNiwX79+O5Ld1nhHDIDunifpPknHprIiAAAAAFDVTjzxxLyPPvqooST9+c9/LvG00JUrV9Zt3br1gZ/+9KebBw4cuHnBggUNvve97+358MMPG2/cuDEtLy/PpkyZcnD+Xbt2pbVt2/aAJI0fP75Z/LJ+/OMfb7r55ptP/uEPf7i1du3U9tOZ6CmgcyQltcceAAAAAKhu7r333q+effbZFl27du24efPmEtPYjBkzGmdnZ3fu1KlT9tSpU5v+/Oc//+qkk046MGLEiPVnnXVWp/POOy/r9NNP3xub/r777ls/YMCAU3Nycjo0a9bsW9f4DRgwYMfevXvThgwZsiWVbZMSvw3EzyX9xcy+kfS6pK8kefwE7r63uBkBAAAAQDrybRtSafTo0etjwx06dPhm1apVB+8B+MADD3wVG+7atev+lStXLos9HzNmzHpJGjZs2BZJBwPa0KFDtwwdOvSwwDZ8+PAtw4cPP6x84MCB2wcOHLi9uLrNmTMnvUOHDnu7du26vxxNK5NEA+Cc8O8YSY+VME1axasDAAAAANHxy1/+8vjx48e3eO655z6rjPUlGgB/rCJH/AAAAAAAFTNy5MiNI0eO3FhZ60soALr7+BTXAwAAAACQYmXqYsbMshV0BnOipHHuvtHM2kn6yt13paKCAAAAAIDkSCgAmlkjSeMk9ZV0IJxvuqSNkkZK+kLSPSmqIwAAAAAgCRK9DcRoSedIulBSY0nxdzd8XVLvJNcLAAAAAJBkiQbAqyWNcPd3JRUUGfe5pJOSWisAAAAASIK0tLScjh07ZsceK1asqFt0mjVr1tTp3bv3KcXN37179w6zZs1qkPqaVo5ErwFMV9w9L4porMNDIQAAAAB8y+O3vJOTzOXd/sQFR7yvYL169Qpzc3OXlTT+wIEDyszMPDB9+vRPk1m36irRI4BzJd1Qwri+kj5ITnUAAAAAILXGjBnT7JJLLjnlggsuaNezZ8+sFStW1G3fvn1nSdq9e7f16dPnlKysrOzLLrvslP379x+8/O26665r26VLl07t2rXrfNddd50gSVOnTm180UUXnRqbZsqUKcf06tXr1MPXWj0kegTwPyW9ZWZvSfqrgnsCXmpmdykIgN9LUf0AAAAAoNzy8vJqdezYMVuSTjzxxLw333zzX5K0YMGCRp988snSli1bFsSfFvrwww8fl56eXrhy5cplc+bMST/33HOzY+NGjx79ZcuWLQvy8/N1zjnndJgzZ0765ZdfvuvOO+9su379+tonnHBC/rhx45oNHjx4c+W3NDEJHQF099kKOoCpJ2msgk5gfiPpFEk/cPe5KashAAAAAJRT7BTQ3NzcZbHwJ0k9e/bc2bJly8MuZZs9e3aj66+/fosk9ejRY19WVtbe2LgJEyZkZGdnd8rOzs5etWpV/UWLFtWvVauWrr322i1PP/10xubNm9MWLFjQqF+/fjsqp3Vll/B9AN39fUk9zSxdUlNJ29197xFmAwAAAIBqp0GDBoUljTOzw8pyc3Prjh07tuX8+fOXt2jRouCaa67J3L9/fy1JuvXWW7dcdtll7erXr++XX375tjp16qSw5hWT6DWA8fYruBfgviTXBQAAAACq1Hnnnbf7+eefz5CkuXPn1l+5cmUDSdq2bVtaenp6YUZGRsHatWtrz5w5s0lsnszMzAMtW7Y88Mgjj7T6yU9+Um1P/5TKEADN7FIz+0BBANwoab+ZfWBml6WsdgAAAABQie65556v9+zZk5aVlZU9cuTI40877bQ9knT22Wfv69Kly9727dt3vv766zNzcnJ2x8/Xv3//La1atfomJydnf9XUPDEJnQJqZv8h6Q+S3pY0XNLXko5TcH/AV8zsNnd/MmW1BAAAAHDUS+S2Dcm2d+/ehUXLhg0btkVxt7nr0KHDN6tWrVoqSY0aNfJp06YVe0uIl156aU1J65k9e3bj6tz5S0yi1wD+UtJT7n5rkfInzOwJSfdJIgACAAAAiJzOnTt3Sk9PL3zyySfXVnVdjiTRANhM0ssljHtJ0sDkVAcAAAAAji5Lly5dXtV1SFSi1wC+K+n7JYz7vqRZyakOAAAAACBVSjwCaGbZcU/HSHrGzJpJ+psOXQN4laRLJN2cykoCAAAAACqutFNAl0jyuOcm6T/Ch4fPY6ZLSkt67QAAAAAASVNaAPy3SqsFAAAAACDlSrwG0N3/UZZHZVYaAAAAABI1YsSI49u1a9c5Kysru2PHjtnvvPNOw5KmHTNmTLM1a9bUqeg6u3fv3mHWrFkNKrqcZEu0F9CDzKy2pLpFy919b1JqBAAAAKBGeuRHfXKSubyfvjjtiPcVfOuttxrOmDHj2MWLFy9LT0/3DRs21M7Ly7OSpn/++eebn3HGGfsyMzMPJFqPAwcOqE6dCmfGSpFQL6Bm1sTM/mBmGyTtl7SrmAcAAAAAVCtffvllnYyMjPz09HSXpFatWuVnZmYeuOeee1p16dKlU/v27TsPGDDgpMLCQj333HNNlyxZ0uCGG244pWPHjtm7d++21q1bn7Zhw4bakjRr1qwG3bt37yBJd9999wkDBgw46dxzz21/9dVXn7x7927r06fPKVlZWdmXXXbZKfv37z8YMq+77rq2Xbp06dSuXbvOd9111wmSNHXq1MYXXXTRqbFppkyZckyvXr1OVYolehuI8ZL6S5oo6VZJPy7mAQAAAADVypVXXrlz/fr1dTMzM7sMHDiw7WuvvdZIkn72s599vWTJkuWrVq1aum/fvlqTJk1qcuONN27r0qXL3okTJ36am5u7rFGjRl7asj/55JMGM2bMWP3qq69+9vDDDx+Xnp5euHLlymX333//hmXLlh08zXT06NFfLlmyZHlubu7S999/v/GcOXPSL7/88l2rV6+uv379+tqSNG7cuGaDBw/enNr/RuKngF4o6T/c/YVUVgYAAAAAkqlJkyaFS5YsWTZ9+vTGb7/9duNBgwadev/996875phjCkaPHn38/v37a23fvr12dnb2Pkk7yrLs3r17b4+FxNmzZzcaNmzY15LUo0ePfVlZWQcvkZswYULG+PHjm+fn59umTZvqLFq0qH6PHj32XXvttVuefvrpjNtvv33LggULGr388sufJbXxxUg0AH4hiWv8AAAAABx1ateurT59+uzq06fPrtNPP33f008/3XzFihUN5syZs6xdu3YH7r777hP2799f7NmRaWlpXlhYKEnat2/ft6Zp2LBhYfxzs8MvLczNza07duzYlvPnz1/eokWLgmuuuSYztq5bb711y2WXXdaufv36fvnll2+rjOsIEz0F9OeS/tPM2qayMgAAAACQTIsWLaq3ePHierHnCxcuTG/Xrl2eJB1//PH5O3bsqPXqq682jY1v1KhRwY4dOw7e47xNmzbfvP/++w0kafLkyU1VgvPOO2/3888/nyFJc+fOrb9y5coGkrRt27a09PT0woyMjIK1a9fWnjlzZpPYPJmZmQdatmx54JFHHmn1k5/8JOWnf0oJHgF099fN7AeSVpvZGknbi5mme5LrBgAAAAAVsnPnzrRhw4a13blzZ1paWppnZmbmTZgw4fNjjz02Pzs7u3ObNm2++c53vrMnNv0NN9yweejQoSf97Gc/K5w3b97y+++/f/0tt9ySOWrUqAM5OTl7SlrPPffc83X//v1PzsrKyu7cufPe0047bY8knX322fu6dOmyt3379p3btm2bl5OTszt+vv79+295/PHHa+fk5OxP3X/hkIQCoJk9LOlOSXMlrZb0TSorBQAAAKDmSeS2DcnWs2fPvQsXLswtWj5mzJj1Y8aMWV+0fPDgwdsHDx588IBX7969d69Zs2ZJ0elGjx79rXkbNWrk06ZN+7S4Orz00ktrSqrf7NmzG1dG5y8xiV4DeLOk+9z9v1JZGQAAAACIis6dO3dKT08vfPLJJ9dW1joTDYB7JVV6WgcAAACAmmrp0qXLK3udiXYC85ikIVZctzYAAAAAgKNCokcAm0vqIWmFmc3U4Z3AuLuPSGbFAAAAAADJlWgA7CspX1IdSRcVM94lEQABAAAAoBpL9DYQJ6e6IgAAAACA1Er0GkAAAAAAOCqNGDHi+Hbt2nXOysrK7tixY/Y777zTsKLLvPvuu0+4//77WyajfpUp0fsA3nakadz9DxWvDgAAAICaat297+Ukc3ltHup5xDsVvPXWWw1nzJhx7OLFi5elp6f7hg0baufl5UW2c8tEjwCOLeXxv+EDAAAAAKqVL7/8sk5GRkZ+enq6S1KrVq3yMzMzD7Ru3fq0DRs21JakWbNmNejevXsHKTiy169fv8zu3bt3aNOmzWkPPvjgcbFljRgx4vjMzMwu55xzTtaqVavqxcofeeSR5l26dOnUoUOH7IsvvvjUXbt21dq2bVut1q1bnxYLm1u3bv3W86qSUAB091pFH5IyJA2QtEhSdiorCQAAAADlceWVV+5cv3593czMzC4DBw5s+9prrzU60jyrV6+u/49//GPl3Llzlz/88MMn5OXl2XvvvddgypQpGYsXL142bdq01YsWLTp4Gul11123bcmSJctXrFixrEOHDvvGjBnTvGnTpoVnn332rsmTJzeRpHHjxmVceuml2+rVq+epbO+RlPsaQHff7u4vSnpC0pPJqxIAAAAAJEeTJk0KlyxZsmzs2LGft2jRIn/QoEGnjhkzpllp8/Tq1Wt7enq6t2rVKj8jI+PAunXrar/77ruNLr300u2NGzcuzMjIKOzVq9fBW+PNnz8/PScnp0NWVlb2Sy+91Gzp0qX1JWnIkCGbxo8f30ySnn/++eZDhgzZnNrWHlkyOoH5TFK3JCwHAAAAAJKudu3a6tOnz65HH310/f/8z/988be//a1pWlqaFxYWSpL27dv3rVwUf5QuLS1N+fn5JklmxZ+9OWTIkJPHjh37xcqVK5eNGDFifV5eXi1J6tWr155169bVe+211xoVFBTYmWeeuT9VbUxUhQKgmbWS9FMFIRAAAAAAqpVFixbVW7x48cHr9RYuXJjepk2bb9q0afPN+++/30CSJk+e3PRIy7ngggt2v/baa8fu3r3btm3bVuvNN988NjZu7969tdq2bXsgLy/PJk2alBE/X//+/bfceOONpwwcOLDKj/5JifcCuknBzd7j1ZXUWNJ+SVcnuV4AAAAAUGE7d+5MGzZsWNudO3empaWleWZmZt6ECRM+X7RoUf1bbrklc9SoUQdycnL2HGk555133t6rrrpqa5cuXTq3bt06r3v37rtj4+6999713bt379S6detvOnXqtHf37t1psXE33XTTllGjRrW+6aabtqaqjWWRUACU9LgOD4D7Ja2TNN3dtyS1VgAAAABqnERu25BsPXv23Ltw4cLcouWtWrXavWbNmiVFy0ePHr0+/vmqVauWxoZHjRq1cdSoURuLzjNixIhNI0aM2FTc+t9+++3GvXv33ta8efOC8rUguRIKgO7+6xTXAwAAAABqlEGDBp347rvvNpk2bdqqqq5LTKJHAAEAAAAAZTBhwoS1ktZWdT3ilRgAzeydMizH3f3CI01kZvUlzZJUL1z3/7n7r8zsZEmTFNxbcIGk6939GzOrJ2mipBxJWyT9yN3XhMv6haSbJBVIGubuM8Ly3pIek5Qm6Rl3f6gM7QAAAACAGqu0XkC3JPCoK+n88JGIPEkXuPt3JJ0hqbeZnSVplKRH3b29pG0Kgp3Cv9vcvZ2kR8PpZGbZkvpL6iypt6Q/mFmamaUpuF7xEgU3px8QTgsAAACg8hUWFhYWf+8EpET4/y4saXyJRwDdvV9J48ysraQRkvpI2qwgnB2Ru7ukWG85dcKHS7pA0r+H5RMk/VrSHyVdEQ5L0v9JGmvBzTeukDTJ3fMkfWZmqyV1D6db7e6fhvWcFE67LJH6AQAAAEiqJZs2bcpu0aLFjlq1ahXtVBJJVlhYaJs2bWoi6bDObWLKdA2gmbWT9AtJAyV9HQ4/6e77yrCMNEnzJbVTcLTuX5K2u3t+OMk6Sa3D4dYKz5l193wz2yGpWVj+Ydxi4+dZW6S8Rwn1GCJpiCS1bds20eoDAAAASFB+fv7NGzdufGbjxo1dVMF7kCMhhZKW5Ofn31zSBIneB7CzpPsk9VMQsIZLGufu35S1Ru5eIOkMMztW0hRJnYqbLLbqEsaVVF7cm6rYXxrc/SlJT0lSt27d+DUCAAAASLKcnJyvJf2wquuBQ0pN4WaWY2YvS/pEUldJN0tq7+5PlCf8xXP37ZJmSjpL0rFmFgujbSTF7r2xTtKJYV1qS2oiaWt8eZF5SioHAAAAgMgrMQCa2d8lfSTpZEn93b2Tu08Ij+CVi5m1CI/8yczSJf1A0nJJ70rqG042SNLUcPiV8LnC8e+E1xG+Iqm/mdULexBtH9Z1rqT2ZnaymdVV0FHMK+WtLwAAAADUJKWdAnpx+PdESY+b2eOlLcjdj0tgfa0kTQivA6wlabK7TzOzZZImmdmDkhZKejac/llJfwo7edmqINDJ3Zea2WQFnbvkS7o9FkzN7A5JMxTcBmKcuy9NoF4AAAAAUOOVFgB/k+yVuXvsVNKi5Z/qUC+e8eX7FVx3WNyyfifpd8WUvy7p9QpXFgAAAABqmNJuA5H0AAgAAAAAqDp0xQoAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIAAAAABEBAEQAAAAACKCAAgAAAAAEUEABAAAAICIIAACAAAAQEQQAAEAAAAgIio1AJrZiWb2rpktN7OlZjY8LM8wszfNbFX4t2lYbmY2xsxWm9knZvbduGUNCqdfZWaD4spzzGxxOM8YM7PKbCMAAAAAVFeVfQQwX9JP3b2TpLMk3W5m2ZLulfS2u7eX9Hb4XJIukdQ+fAyR9EcpCIySfiWph6Tukn4VC43hNEPi5utdCe0CAAAAgGqvUgOgu29w9wXh8C5JyyW1lnSFpAnhZBMkXRkOXyFpogc+lHSsmbWSdLGkN919q7tvk/SmpN7huGPc/Z/u7pImxi0LAAAAACKtyq4BNLNMSV0lzZHU0t03SEFIlHRcOFlrSWvjZlsXlpVWvq6Y8uLWP8TM5pnZvE2bNlW0OQAAAABQ7VVJADSzRpJeknSnu+8sbdJiyrwc5YcXuj/l7t3cvVuLFi2OVGUAAAAAOOpVegA0szoKwt+f3f3lsPir8PRNhX+/DsvXSToxbvY2ktYfobxNMeUAAAAAEHmV3QuoSXpW0nJ3Hx036hVJsZ48B0maGld+Q9gb6FmSdoSniM6Q1MvMmoadv/SSNCMct8vMzgrXdUPcsgAAAAAg0mpX8vrOlXS9pMVm9nFY9ktJD0mabGY3SfpCUr9w3OuSLpW0WtJeSTdKkrtvNbPfSpobTveAu28Nh2+VNF5SuqS/hw8AAAAAiLxKDYDuPlvFX6cnSRcWM71Lur2EZY2TNK6Y8nmSulSgmgAAAABQI1VZL6AAAAAAgMpFAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEQRAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEZUaAM1snJl9bWZL4soyzOxNM1sV/m0alpuZjTGz1Wb2iZl9N26eQeH0q8xsUFx5jpktDucZY2ZWme0DAAAAgOqsso8AjpfUu0jZvZLedvf2kt4On0vSJZLah48hkv4oBYFR0q8k9ZDUXdKvYqExnGZI3HxF1wUAAAAAkVWpAdDdZ0naWqT4CkkTwuEJkq6MK5/ogQ8lHWtmrSRdLOlNd9/q7tskvSmpdzjuGHf/p7u7pIlxywIAAACAyKsO1wC2dPcNkhT+PS4sby1pbdx068Ky0srXFVNeLDMbYmbzzGzepk2bKtwIAAAAAKjuqkMALElx1+95OcqL5e5PuXs3d+/WokWLclYRAAAAAI4e1SEAfhWevqnw79dh+TpJJ8ZNtc/tWAAAFN1JREFU10bS+iOUtymmHAAAAACg6hEAX5EU68lzkKSpceU3hL2BniVpR3iK6AxJvcysadj5Sy9JM8Jxu8zsrLD3zxvilgUAAAAAkVe7MldmZi9IOl9SczNbp6A3z4ckTTazmyR9IalfOPnrki6VtFrSXkk3SpK7bzWz30qaG073gLvHOpa5VUFPo+mS/h4+AAAAAACq5ADo7gNKGHVhMdO6pNtLWM44SeOKKZ8nqUtF6ggAAAAANVV1OAUUAAAAAFAJCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIoIACAAAAAARQQAEAAAAgIggAAIAAABARBAAAQAAACAiCIAAAAAAEBEEQAAAAACICAIgAAAAAEQEARAAAAAAIqJGBkAz621mK8xstZndW9X1AQAAAIDqoMYFQDNLk/S4pEskZUsaYGbZVVsrAAAAAKh6NS4ASuouabW7f+ru30iaJOmKKq4TAAAAAFQ5c/eqrkNSmVlfSb3d/ebw+fWSerj7HUWmGyJpSPi0g6QVlVrR1GouaXNVVyLFanobad/Rr6a3kfYd/Wp6G2t6+6Sa30bad/Q5yd1bVHUlULraVV2BFLBiyg5Lue7+lKSnUl+dymdm89y9W1XXI5Vqehtp39GvpreR9h39anoba3r7pJrfRtoHpEZNPAV0naQT4563kbS+iuoCAAAAANVGTQyAcyW1N7OTzayupP6SXqniOgEAAABAlatxp4C6e76Z3SFphqQ0SePcfWkVV6uy1chTW4uo6W2kfUe/mt5G2nf0q+ltrOntk2p+G2kfkAI1rhMYAAAAAEDxauIpoAAAAACAYhAAAQAAACAiCIDVjJntruo6JJOZFZjZx3GPzFKmPd/MplVe7VLPzNzM/hT3vLaZbUpWO81spplVqy6kzeyqsN0dyzHvM2aWHQ6vMbPmya9hxaT6Na0Oatp2qCRHamd1/HzFq8hnrQLrvNPMGlRwGfeZ2VIz+yT8XuhRjmWcb2bnVKQeRZZXadsbM2tjZlPNbJWZ/cvMHgs7rStp+oT+55X9uQ3fe4/EPb/HzH5dmXWIW3fS2x63/7LUzBaZ2d1mViX7zVHZJqPyEAAhM0tL4eL3ufsZcY81FV1gRetrZpXZ+dEeSV3MLD18fpGkL8uygEqubzIMkDRbQQ+8CTOzNHe/2d2XpaZaSVPh1xRIknJ91iroTknlDoBmdrakPpK+6+6nS/qBpLXlWNT5kpIWACuiLNtoMzNJL0v6m7u3l5QlqZGk35UyW4X+5wnWqzzfM3mSrq6OP9SVRSltj+2/dFawnb9U0q8qr2bJcRTuQ6ASEACrITNrZGZvm9kCM1tsZleE5ZlmttzMng5/kXojthMa/0u1mTU3szVx87wXLmtB7BfT8NfTd83sL5IWm9lvzWx4XB1+Z2bDUtS+NDP7HzObG/4C/B9xo48xsylmtszMnoj92mZmu83sATObI+ns+F9rzaybmc0Mh7ub2QdmtjD82yEsH2xmfzWzVyW9YWZ/iv1fw/F/NrMfpqK9kv4u6bJweICkF+LWm1B9w7Kfh++HRWb2UNzy+5nZR2a20sx6pqgNCTGzRpLOlXSTwp3S8L02K8HXtVofcYlTntf0PTM7I266983s9EqtdRlYkSPyZjbWzAaHw2vM7Ddx26iOYXlDMxsXfrYXxn/GqqvS2hlXdpOZPRr3/CdmNroSq3mYUj5rJb1ml5pZrpnNNrMxsenM7Ndmdk/cPEvC742GZvZauL1ZYmY/Cr8TTpD0rpm9W86qt5K02d3zJMndN7v7ejPLMbN/mNl8M5thZq3C+sw0s9+Hn6Ul4ecrU9Itku6y4AhNTzNrYWYvhe+9uWZ2blz7JljwfbnGzK42s/8O37fTzaxOXN1+Fm5LPzKzduH8pS33KTN7Q9LEMrT/Akn73f25sP0Fku6S9OPwf/5wWLdPzGxocf9zMxsQTrPEzEbFL9zMHgk/l2+bWYuw7NSwrfPD7VDs8zrezEaHy/3WchKUr6AXy7uKjjCzk8I6fBL+bWtmTcLXILb9b2Bma82szhHq+EcL9lc+NbPvh9uY5WY2vrLa7u5fSxoi6Q4LlLgfY8V8V1f39iGC3J1HNXpI2q3g9hzHhM+bS1otySRlKtjgnhGOmyxpYDg8U1K3uHnWhMMNJNUPh9tLmhcOn6/gSMbJ4fNMSQvC4VqS/iWpWRLaUyDp4/AxJSwbIuk/w+F6kuZJOjms035Jpyi4hcebkvqG07mka+OWu0ZS83C4m6SZ4fAxkmqHwz+Q9FI4PFjSOkkZ4fPvK/gFVpKaSPosNl8KXs/TJf2fpPrh/+F8SdPKWN9LJH0gqUH4PFY+U9Ij4fClkt6q4vfvQEnPhsMfSPpuGV/X+Pfxwde4Oj0q8JoOkvT7cDhL4WexOj7CNh5sU1g2VtLguNdmaDh8m6RnwuGROrRNOlbSSkkNq7o9FWjnTAXbl4YKtol1wvIPJJ1WxXUv6bN2WFvC9+laHdrevxD3fv21pHvi5lmi4PvgGklPx5U3iXvty/25VHC06+PwvfEHBdviOmEbWoTT/EjBLZxir8HT4fD3JC0pod5/kXReONxW0vK46WaH6/iOpL2SLgnHTZF0ZVy77guHb4j7/5S23PmS0svY/mGSHi2mfKGk4ZJe0qHtR0Zc3WLfdydI+kJSCwX7Cu/EtcElXRcO3y9pbDj8tqT24XAPSe+Ew+MlTZOUVoHPzzFh/ZpIukfSr8Nxr0oaFA7/WIe+b6dK+re41/mZBOo4ScE+0BWSdko6TcF+ynwd2h9Ketsl7S6mbJuklip5P6ak7+pq1z4e0X5wWLh6Mkkjzex7kgoltVawwZGkz9z943B4voIv6tLUkTTWgiMPBQp2PGM+cvfPJMnd15jZFjPrGq5robtvSUJb9rn7GUXKekk63cz6hs+bKAin34R1+lSSzOwFSecp2NEuUPDFeCRNJE0ws/YKNpjxv+6+6e5bJcnd/2Fmj5vZcZKuVrCTnl+uFh6Bu39iwS/WAyS9Xp76KggSz7n73nCZW+Omezn8m8j7IdUGSPp9ODwpfP6aKv66VivlfE3/Kun/mdnPFOwQja+UyqZO/Pvu6nC4l6Qf2qEjSvUV7jRXct2Syt33mNk7kvqY2XIFQXBxFVerpM9acTpK+jS2vVcQAIccYfmLJT0cHmGa5u7vVbC+kiR3321mOZJ6Svo3SS9KelBSF0lvmpkU/FC0IW62F8J5Z5nZMWZ2bDGL/oGk7HB+KTibpHE4/Hd3P2Bmi8NlT49rY2bR9YR/Y0d8S1vuK+6+L9G2h0zBdqG48u9JeiL2XVRkOx9zpoIfPDdJwdkr4Xx/U7C/8GI43fOSXrbgSPE5kv4a14Z6ccv7qwdHIcvF3Xea2UQFwTb+f3G2Dm0X/iTpv8PhFxUEv3cVHLn+QwJ1fNXdPXz9vop99sxsqYLX72NVXttjCyppP+aw7+qjrH2ICAJg9XSdgl/3csIvrTUKdqSk4Jz7mAJJseuQ8nXolN76cdPcJekrBb981lJwJCZmT5H1PqPg1+LjJY2rUAtKZwqOHsz4VqHZ+Tr8izH2fH+RDVlJ7f2tpHfd/apwB31m3Lii7f2Tgv91fwU75Kn0iqSHFfxC3yyuPNH6lrTTIB16TxSoCj/TZtZMwelNXczMFexouYKAlOjrejQp02vq7nvN7E0Fv/Jeq+DIUnUW/xmTvv05k4p/35mka9x9RYrrlkxHamfMM5J+KSlX0nOprlRpSvmsvaLi22IqWbHtd/eVYVC7VNJ/mdkb7v5AMuoffuZnSpoZ7vTeLmmpu59d0ixHeC4FbTi7aCALd4xjp5sWmtkBd4/NX6hvbzO9mOHSllv0OyURSxUcXY1f1jGSTpT0qUrezh+cvAzrcgX1317MD7Ex5WlDUb+XtEClfy5i7XpFwfspQ1KOgiOYDY9Qx9i2plDf3gcq+voVXV9S225mpyjY3n2tkvdjeuvw1/BI9agW7UO0cA1g9dRE0tdh+Ps3SSclMM8aBRtTSeobV95E0gZ3L5R0vYIdhZJMkdRbwS+MM0qZrqJmSLrVwmsvzCzLzBqG47qb2ckWXCPwIwWn7hRnjQ61N/7LtIkOdcgx+Aj1GK/g4nq5+9Iy1L88xkl6oJijBonW9w0F14g0kKTwy7O66Stporuf5O6Z7n6iglNrz1Pir+vRpDyv6TOSxkiaW8Kv+9XJ5wqOfNQzsyaSLkxgnhmShlq4dxyeUVDdJdROd5+jYCf93xV3zWcVKemzJhXfllxJp9ihXph/FLesNQpOH5WZfVfBaWwysxMk7XX35xX80PHdcPpdkhqrnMysQ3h0POYMBUeIW1jQQYwsuCasc9w0PwrLz5O0w913FFOPNyTdEbeeknaKS/OjuL//TOJy470tqYGZ3RAuL03SIwq+j96QdIuFnXbEbefj2zpH0vctuNY/TcGR33+E42rp0Pf/v0ua7e47JX1mZv3CZZqZfaeCbfiWcFs2WcH1qDEf6FDnRNcp3Oa7+25JH0l6TMGR5YIk1TGlbbfgmrsnFJx66Sp5P+aw7+qjoX2IHgJgNRJu9PMk/VlSNzObp2DDmZvA7A8r2Bh9oOAawJg/SBpkZh8qOP2zxF+E3P0bBadlTE7xUZlnJC2TtMDMlkh6Uod+5fqnpIcUXIfymYJQWpzfSHrMzN5T8ItczH8r+HXxfZUeduXuXynY8Uj5r/nuvs7dHytmVEL1dffpCn45nWdmHyu41qK6GaDDX6+XFHxZJfq6HjXK85q6+3wF13hU6RGk0sS2Q+6+VsFO3ScKtkkLE5j9twpOef0k/Gz/NmUVraBytnOypPfdfVslVLE0pX3WDmtLePTqNknTzWy2grNCdsTNlxFuV25VcG2eFFyH9FFYfp+C0zSloNOPv1v5O4FppOAU6WVm9omkbAXXNPWVNMrMFik45S2+h89t4XfbEzoUMl6VdJWFncAoOAWxmwUdcixT0ElMWdWzoEOq4TrUsUkylntQGB6uUtB51yoF/+/9Co4uP6Pg+r5Pwv/Dv4ezHfyfu/sGSb9Q8F29SMG1+1PD6fZI6mxm8xUcIY4dsb1O0k3hMpcqOAsh2R7Rt/c9hkm6MXyNr1fwP415UcE1rC/GlVW0jqloe3r4/loq6S0F4e434bhi92NK+a6uju1DhNmhMyFQ1cJfbp529+5VtP5aCk7j6Ofuq6qiDpUp/IVusYLuyHccaXqUjwWn9t7j7n2qui5VLTyqMlNSx/CofLVT1duhylKedlrQc+aj7v526mqWGmbWKLz+ziQ9LmmVuz96pPmqmgU9PN/j7vOqui4AUFNwBLCaMLNbFJxW9J9VtP5sBb2Nvh2R8PcDBUdW/5fwh8oQnvI1R0FPg9U1/FXpdqiylLWdZnasma1U0KnVURf+Qj8Jj0gsVXCa8pNVXB8AQBXhCCAAAAAARARHAAEAAAAgIgiAAAAAABARBEAAAAAAiAgCIACg3Mzs12bmYZf2xY1fHY7/dQrWPcTMriymfI2ZPZzs9QEAUBMQAAEAFbVf0slm1i2+0MzOlHRSOD4Vhkg6LAACAICSEQABABW1R9I7kvoXKe8flu+p9BoBAIBiEQABAMkwSdK14Y3GFf69Niz/FjO71swWm1mema01s9+ZWe248YPD00ZPM7M3zWyPmeWa2dVx08yUlCNpUDitm9ngIuu5y8zWmdk2M5tkZsfGjatjZg+b2RdhPdab2RQzq5vk/wsAANUKARAAkAwvS2op6bzweU9JLSRNiZ/IzHpJelHSAklXSPpfSfdIGlvMMv8i6RVJV0laJWmSmbUJx90mKVfS65LODh+vxc17raQLFZwmOkJSH0kj48b/QtJ1kv6fpIsk3Slph6S0MrUaAICjTO0jTwIAQOncfbuZTVdw2ud74d/pYXn8pA9Imunug8Ln08Px/2VmD7r7urhpH3X3cZJkZvMlfaUgyD3h7svMbI+kTe7+YTFVOiDpSnfPD+fPDut0Wzi+u6S/uPuEuHkml7f9AAAcLTgCCABIlkmS+ppZPUl9VeT0TzNLk/RdSX8tMt+LCr6Pzi5S/kZswN23SPpaUhsl5t1Y+Astk3Rc3CmeH0sabGY/N7PTrUhKBQCgpiIAAgCS5RVJjST9TlJDSa8WGd9cUh0FR/LixZ5nFCnfXuT5N5LqJ1iX4uY1SbEA+KCkxxUcEVwkaa2ZDU9w2QAAHLUIgACApHD3PZKmSbpL0qvh83ibFZyaeVyR8pbh362preEh7r7f3e9390xJWQqOQv7ezHpXVh0AAKgKBEAAQDL9UcGRvyeKjnD3AknzJfUrMupaSYWS/lnGdZXliGCJ3H2Vgo5o8iRlV3R5AABUZ3QCAwBIGnefKWlmKZP8StIMM3tOwTWCp0n6raSni3QAk4hcSReb2cWStkj6LLxW8IjMbIqCMLpQ0j4F1yzWljSrjHUAAOCowhFAAEClcfc3FPTG2U3BkcI7JT0i6Y5yLO5BScsV9N45V9LlZZj3A0lXKrjVxFQF9xS8xt3nlaMeAAAcNczdq7oOAAAAAIBKwBFAAAAAAIgIAiAAAAAARAQBEAAAAAAiggAIAAAAABFBAAQAAACAiCAAAgAAAEBEEAABAAAAICIIgAAAAAAQEf8fxN0t2hBmS1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group the months of the year and then days of the week within each month and aggregate over the number of flights\n",
    "# sort by month and then by day\n",
    "# convert the dataframe to a pandas dataframe\n",
    "MonthlyAggFlightsPerDayofTheWeek = df.groupBy(['Month','day_of_week'])\\\n",
    ".count()\\\n",
    ".orderBy([\"Month\",\"day_of_week\"])\\\n",
    ".toPandas()\n",
    "\n",
    "\n",
    "\n",
    "# plot the number of flights per month (faster than seaborn usage)\n",
    "plt.figure(figsize=(13,10))\n",
    "plt.title(\"Monthly Aggregates of the Number of Flights for each Day of the Week\",fontsize=15)\n",
    "plt.xlabel(\"Months\",fontsize=15)\n",
    "plt.ylabel(\"Number of Flights\",fontsize=15)\n",
    "plt.xticks(np.arange(1,13),['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'],fontsize=10)\n",
    "for d in np.arange(1,8):\n",
    "    plt.bar(np.arange(1,13) +0.1*(d-4),\n",
    "             MonthlyAggFlightsPerDayofTheWeek[MonthlyAggFlightsPerDayofTheWeek.day_of_week == d][\"count\"],\n",
    "            width=0.1\n",
    "            )\n",
    "plt.legend(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],loc='center left', bbox_to_anchor=(1, 0.5),title=\"Day of the Week\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# construct a color map showing the monthly aggregates of the number of flights for each day of the week\n",
    "MonthlyAggFlightsPerDayofTheWeek_reshape = MonthlyAggFlightsPerDayofTheWeek.pivot(\"Month\", \"day_of_week\", \"count\")\n",
    "figure, axes = plt.subplots(figsize=(14,10))\n",
    "axes = sns.heatmap(MonthlyAggFlightsPerDayofTheWeek_reshape,\n",
    "                 cmap='coolwarm',\n",
    "                 annot=True,\n",
    "                 linewidths=.5,\n",
    "                 fmt='d')\n",
    "axes.invert_yaxis()\n",
    "axes.set_title(\"Monthly Aggregates of the Number of Flights for each Day of the Week\", fontsize=15)\n",
    "axes.set_xticklabels(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], fontsize = 15)\n",
    "axes.set_yticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], rotation=0, fontsize = 15)\n",
    "plt.ylabel('Month',fontsize = 15)\n",
    "plt.xlabel('Day of the Week',fontsize = 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "\n",
    "\n",
    "Considering only the days of the week across all times, the results show that weekends (Saturday and Sunday) witness less number of flights (around 700K on average) than weekdays (Monday to Friday) (around 750K on average)<br><br>\n",
    "\n",
    "The monthly aggregates show quite different results than the global daily results where weekends have some peaks during some months like we can see in the numbers for January, July, and October. So the decreasing number of  weekend flights in the global daily results do not extend to monthly aggregates, but exists during some periods of the year like in February, March, June, August, September, and November.<br><br>\n",
    "\n",
    "On the other hand, some months do not abide by any of the two patterns discussed previously (April, May, and December).\n",
    "However, when constructing a color map for the monthly aggregates over the days of the week, more information and a new pattern can be observed.<br> Every month witnesses 2 to 3 consecutive peaks in the number of the flights (with the exception of February). These consecutive peaks shift each month backwards 2 to 3 days.<br>\n",
    "Moreover, we can emphasize the previously seen fact that the number of flights is higher towards the second half of the year.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "How many flights in different days of months and in different hours of days?  \n",
    "<br>\n",
    "<br>\n",
    "\n",
    "<strong>NOTE:</strong> Similarly to the previous note, you need to compute both global statistcs and monthly statistics.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "Plot  bar charts, and interpret your figures.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Days of the Month Flight Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 31 artists>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gxh1X1mktxRWcOzpv0tEnKe4zOQW4nGKq7pVs/v228ynu7TuPIhmbzOkU9xhtpNhHVZdQnMh/EfgWxQnxH2fm8kliPoxiOOElFJMatKZRf2JmbhrGl5nnUAyD/WOKe72eQ9FHOvkmxclxfeKIyymubp0EfIfipPo4Np8wb6WBfvJmis/xu2V99ux1w/KPC08H7qaYcv1cip8euIdi3/ZNZl5DMeX+IophpR+iaP/h3bbroF1fnEnd7qLYfwe3WfaXZR33ovjufK98/2HgkOzwG1/ltp8B/j+Kq1lnUcze+c7MPL626t0U9w/+OUXyvbCMfXMZZy3FlbNhiu/DOyj2ey/76KAy/nd6WFdSH8SWoykkSQ9UEfFdYOfMrP8+lTqIiB8Dv8zM6Qw50w4gIl5Kca/bnuVQ6YEQEV8C7srMN27rukgPFA41lKQHoHJ41NOA/6aYefAVFD+kWr/vRG1ExFKK33b7fTpPG67B8E2Kq32vof1kIzuciNiLYnj1723rukgPJCZekvTA9DuKIXvvppiM4FqKH9v96jat1Y7jcorpyd+dmZdv68qoOZmZEXE0xW9jDYoR4M2Z2ddho5K6c6ihJEmSJDXMyTUkSZIkqWEONexit912y9HR0W1dDUmSJEnbqSuuuOLWzHzkZOuZeHUxOjrK8uWTzVAsSZIk6YEqIlb3sp5DDSVJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkho2q4lXRJwSEbdExNWVsjMi4srysSoirizLRyNifWXZSZVt9o+IqyJiZUR8IiKiLH94RFwQEdeW/y4sy6Ncb2VE/CwinjKb7ZYkSZL0wDbbV7xOBZZVCzLzFZn5pMx8EvA14OuVxde1lmXmmyvlJwJHA/uUj1bMY4ELM3Mf4MLyNcDBlXWPLreXJEmSpFmx02y+WWb+ICJG2y0rr1q9HHhetxgRsQewIDN/VL4+DXgJcC5wGHBguerngIuBY8ry0zIzgUsjYigi9sjMm7u918TEBCtWrGi7bHh4mKGhIQDGx8dZs2ZNxzhLlizZ9HzVqlVMTEy0XW9oaIjh4WEA1q9fz+rVqzvGXLx4MfPnzwdgzZo1jI+Pt11v3rx5jI6ObnrdqT1gm2yTbQLbZJtsUzu2yTa12Cbb1M4DvU292p7u8XoWsDYzr62U7R0RP4mI70fEs8qyRwFjlXXGyjKA3VvJVPnvoso2N3bYZgsRcXRELI+I5evWrZtZiyRJkiQJiOIi0Cy+YXHF66zM3K9WfiKwMjP/uXz9IOAhmXlbROwPfBPYF3g88A+Z+YJyvWcB78rMF0fEeGYOVWKuy8yFEXF2uc0Py/ILy22u6FbXpUuX5vLly/vTcEmSJEkDJyKuyMylk603q0MNO4mInYCXAfu3yjLzHuCe8vkVEXEd8DiKq1Ujlc1HgJvK52tbQwjLIYm3lOVjwF4dtpEkSZKkRm0vQw1fAKzIzE1DCCPikRExt3z+GIqJMa4vhxDeGRFPL+8LOxL4VrnZmcBR5fOjauVHlrMbPh347WT3d0mSJElSv8z2dPJfAn4EPD4ixiLiDeWiVwJfqq3+bOBnEfFT4KvAmzPz9nLZW4D/AFYC11FMrAFwPPDCiLgWeGH5GuAc4Ppy/c8Af97vtkmSJElSJ7N+j9eOxHu8JEmSJHXT6z1e28tQQ0mSJEkaWCZekiRJktSw7WJWQ0naUY0ee3bfYq06/tC+xZIe6Pr13fR7KalfTLykAePJhrY1k1FJkrZm4iVJ0g7OP7hI2x//CKU6Ey9JegDyhECaPhNdSdNh4iVJkrYJExhta/4RSrPJxEvaBnbUk40dtd4aHJ4kadB5nJUGl4mXpIHnybokSdrWTLwkbRf8K6+2taYTdPu4JD2wmXjtQJr8T9sTAkmSJKk5Jl7aoZkwapA5RFLbA4+zktQfc7Z1BSRJkiRp0HnFS43zr6WSJGm6PI/QoDDxkjrwQC9JkqR+caihJEmSJDXMxEuSJEmSGuZQQ0mSJM2Iw/OlyXnFS5IkSZIaZuIlSZIkSQ1zqKEkSdIDgMMBpW3LK16SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIbttK0rIEmSJGn7MHrs2X2Lter4Q/sWaxCYeEmSJEk7EJOjHZNDDSVJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkho2q4lXRJwSEbdExNWVsuMi4tcRcWX5OKSy7N0RsTIifhkRL6qULyvLVkbEsZXyvSPisoi4NiLOiIhdyvIHla9XlstHZ6fFkiRJkjT7V7xOBZa1Kf94Zj6pfJwDEBFPAF4J7Ftu88mImBsRc4ETgIOBJwCvKtcF+Mcy1j7AOuANZfkbgHWZ+Vjg4+V6kiRJkjQrdprNN8vMH0zhatNhwOmZeQ/wq4hYCTy1XLYyM68HiIjTgcMi4hfA84BXl+t8DjgOOLGMdVxZ/lXg3yMiMjO7VWBiYoIVK1a0XTY8PMzQ0BAA4+PjrFmzpmOcJUuWbHq+atUqJiYm2q43NDTE8PAwAOvXr2f16tVbLF82smHT80vWzuGO+wKAfRduZK9d2zfljnuDS27ZnF8vG9nQtk3LRjZw9bo5jN1VxBzZNdlv4caObTpvbO6m5wcs2siCXYr3r8dufUYAC3ZODti9c8zJ2tSKPW/ePEZHR7eoeye9tmnFihVb7adOcW+8K7hm3Zye29SyZs0axsfH29a53X7qpU3t+l512077qZc2deonM+l7LfXYrX4P0+979djV79NM+161TfX36rSvpvt9qh4j6rGn2veqbap/5q3Y0+171Ta1iz2TvldVjb148eJNz6fb9+qxq8fymfa9aptaqsfydvWY7vep0/6E6fe9AxZ1/t7PpO+12lSPPW/evI5tqJusTdXY1WP5TPpeNfbixYuZP38+UBzLO9V1qt+nluqxvN020/k+eR4xu+cRdf08j2i1qYnziJFds+vxZKbH8nZ9pf59arWprr6fOp2Tw7Y5L5+O7eUer7dFxM/KoYgLy7JHATdW1hkryzqVPwIYz8z7a+VbxCqX/7ZcfysRcXRELI+I5evWrZt5yyRJkiQ94MUkF336/4bFFa+zMnO/8vXuwK1AAh8E9sjM10fECcCPMvML5XonA+dQJIsvysw3luWvobgS9oFy/ceW5XsB52TmEyPimnKbsXLZdcBTM/O2bnVdunRpLl++vK/tn4nRY8/uS5xVxx9qbGMbu0/xjW3sXmL3M76xjW3s7S92u/jGbv+ZD6KIuCIzl0623ja/4pWZazNzQ2ZuBD7D5uGEY8BelVVHgJu6lN8KDEXETrXyLWKVyx8G3N7/1kiSJEnS1rZ54hURe1RevhRozXh4JvDKckbCvYF9gB8DlwP7lDMY7kIxAceZ5f1aFwGHl9sfBXyrEuuo8vnhwPcmu79LkiRJkvplVifXiIgvAQcCu0XEGPA+4MCIeBLFUMNVwJsAMvOaiPgy8HPgfuCtmbmhjPM24DvAXOCUzLymfItjgNMj4kPAT4CTy/KTgc+XE3TcTpGsSZIkSdKsmO1ZDV/VpvjkNmWt9T8MfLhN+TkU93vVy69n81DFavkEcMSUKitJkiRJfbLNhxpKkiRJ0qAz8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhs1q4hURp0TELRFxdaXsoxGxIiJ+FhHfiIihsnw0ItZHxJXl46TKNvtHxFURsTIiPhERUZY/PCIuiIhry38XluVRrreyfJ+nzGa7JUmSJD2wzfYVr1OBZbWyC4D9MvP3gP8B3l1Zdl1mPql8vLlSfiJwNLBP+WjFPBa4MDP3AS4sXwMcXFn36HJ7SZIkSZoVO83mm2XmDyJitFZ2fuXlpcDh3WJExB7Agsz8Ufn6NOAlwLnAYcCB5aqfAy4GjinLT8vMBC6NiKGI2CMzb+72XhMTE6xYsaLtsuHhYYaGhgAYHx9nzZo1HeMsWbJk0/NVq1YxMTHRdr2hoSGGh4cBWL9+PatXr95i+bKRDZueX7J2DnfcFwDsu3Aje+2abWPecW9wyS2b8+tlIxvatmnZyAauXjeHsbuKmCO7Jvst3NixTeeNzd30/IBFG1mwS/H+9ditzwhgwc7JAbt3jjlZm1qx582bx+jo6BZ176TXNq1YsWKr/dQp7o13Bdesm9Nzm1rWrFnD+Ph42zq320+9tKld36tu22k/9dKmTv1kJn2vpR671e9h+n2vHrv6fZpp36u2qf5enfbVdL9P1WNEPfZU+161TfXPvBV7un2v2qZ2sWfS96qqsRcvXrzp+XT7Xj129Vg+075XbVNL9Vjerh7T/T512p8w/b53wKLO3/uZ9L1Wm+qx582b17ENdZO1qRq7eiyfSd+rxl68eDHz588HimN5p7pO9fvUUj2Wt9tmOt8nzyNm9zyirp/nEa02NXEeMbJrdj2ezPRY3q6v1L9PrTbV1fdTp3Ny2Dbn5dOxvd3j9XqKBKpl74j4SUR8PyKeVZY9ChirrDNWlgHs3kqmyn8XVba5scM2W4iIoyNieUQsX7du3cxaI0mSJElAFBeBZvENiyteZ2XmfrXy9wBLgZdlZkbEg4CHZOZtEbE/8E1gX+DxwD9k5gvK7Z4FvCszXxwR45k5VIm5LjMXRsTZ5TY/LMsvLLe5oltdly5dmsuXL+9Ty2du9Niz+xJn1fGHGtvYxu5TfGMbu5fY/YxvbGMbe/uL3S6+sdt/5oMoIq7IzKWTrTerQw07iYijgD8Enl8OByQz7wHuKZ9fERHXAY+juFo1Utl8BLipfL62NYSwHJJ4S1k+BuzVYRtJkiRJatQ2H2oYEcso7sP6o8y8u1L+yIiYWz5/DMXEGNeXQwjvjIinl7MZHgl8q9zsTOCo8vlRtfIjy9kNnw78drL7uyRJkiSpX2b1ildEfIli8ovdImIMeB/FLIYPAi4oZ4W/tJzB8NnAByLifmAD8ObMvL0M9RaKGRLnU9wT1rov7HjgyxHxBuAG4Iiy/BzgEGAlcDfwuuZaKUmSJElbmu1ZDV/7MA9gAAAgAElEQVTVpvjkDut+Dfhah2XLgf3alN8GPL9NeQJvnVJlJUmSJKlPtvlQQ0mSJEkadCZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYT0lXhHxxxHxhsrrvSPikogYj4ivRcRQc1WUJEmSpB1br1e8/g5YUHn9b8BuwPHAU4AP97lekiRJkjQwdupxvccAVwFExMOAg4CXZubZEXEDRQL21maqKEmSJEk7tqnc45Xlv88BNgDfLV+PAY/sZ6UkSZIkaZD0mnj9FPiTiNgVeCNwUWbeUy57NHBLE5WTJEmSpEHQ61DDvwW+DRwF/I5iqGHLS4DL+lwvSZIkSRoYPSVemfnDiHg08Djguswcryw+Bbi2icpJkiRJ0iDodTr59wIPzcwrakkXwE+AV/W9ZpIkSZI0IHq9x+t9wEiHZXuWyyVJkiRJbfSaeAWbZzWsGwHW9ac6kiRJkjR4Ot7jFRFHUUymAUXSdWJE3FFbbR7wROD8ZqonSZIkSTu+bpNr3A3cVj4P4LfA7bV17gXOBT7Z/6pJkiRJ0mDomHhl5leArwBExGeBD2bm9bNVMUmSJEkaFL1OJ/+6pisiSZIkSYOq1x9QJiKWAi+jmExjXn15Zr68j/WSJEmSpIHRU+IVEW8BTgBupfix5HubrJQkSZIkDZJer3j9NXAK8ObMvL/B+kiSJEnSwOn1d7wWAV8y6ZIkSZKkqes18ToXeFqTFZEkSZKkQdXtB5SfUHl5AvDpiNgZuAAYr6+fmT/vf/UkSZIkacfX7R6vq4GsvA7gfcB7a+tFud7c/lZNkiRJkgZDt8TrubNWC0mSJEkaYB0Tr8z8/mxWRJIkSZIGVa+Ta0iSJEmSpqmnxCsiNkbEhg6P+yPi9oi4KCJe2nSFJUmSJGlH0+sPKP9V+bgD+DbwG4rf9nox8FDgZOBZwFcj4qjM/EIDdZUkSZKkHVKvideewH9l5qtq5cdGxOnAwsx8QUScBrwLMPGSJEmSpFKv93i9Dvhsh2WfBV5TPj8D2GemlZIkSZKkQdJr4rUTsKTDsv9diXMvMDHTSkmSJEnSIOl1qOHpwD9ExE5svsfrkcBhwAfYfDXsKcCKfldSkiRJknZkvSZeb6e4mvUh4KOV8nuAzwB/U76+DLiwb7WTJEmSpAHQU+KVmfcCb4+I9wNPBIaBNcBVmXl7Zb2Lm6ikJEmSJO3Ier3iBUCZZH2/obpIkiRJ0kDqOLlGRBwSEQsqz7s+en3DiDglIm6JiKsrZQ+PiAsi4try34VleUTEJyJiZUT8LCKeUtnmqHL9ayPiqEr5/hFxVbnNJyIiur2HJEmSJDWt26yGZ7F5JsOzKCbVOKvD49tTeM9TgWW1smOBCzNzH4p7xI4tyw+mmJ5+H+Bo4EQokijgfcDTgKcC76skUieW67a2WzbJe0iSJElSo7oNNdwbuLnyvC8y8wcRMVorPgw4sHz+OeBi4Jiy/LTMTODSiBiKiD3KdS9o3V8WERcAyyLiYmBBZv6oLD8NeAlwbpf36GhiYoIVK9pP0jg8PMzQ0BAA4+PjrFmzpmOcJUs2z8S/atUqJibaz7g/NDTE8PAwAOvXr2f16tVbLF82smHT80vWzuGO+wKAfRduZK9ds23MO+4NLrllc369bGRD2zYtG9nA1evmMHZXEXNk12S/hRs7tum8sbmbnh+waCMLdinevx679RkBLNg5OWD3zjEna1Mr9rx58xgdHd2i7p302qYVK1ZstZ86xb3xruCadXN6blPLmjVrGB8fb1vndvuplza163vVbTvtp17a1KmfzKTvtdRjt/o9TL/v1WNXv08z7XvVNtXfq9O+mu73qXqMqMeeat+rtqn+mbdiT7fvVdvULvZM+l5VNfbixYs3PZ9u36vHrh7LZ9r3qm1qqR7L29Vjut+nTvsTpt/3DljU+Xs/k77XalM99rx58zq2oW6yNlVjV4/lM+l71diLFy9m/vz5QHEs71TXqX6fWqrH8nbbTOf75HnE7J5H1PXzPKLVpibOI0Z2za7Hk5key9v1lfr3qdWmuvp+6nRODtvmvHw6OiZembm63fOG7J6ZN5fvdXNELCrLHwXcWFlvrCzrVj7Wprzbe2whIo6muGLGnnvuOZM2SZIkSSr9y3f/h0vWrpxC0n/Nptf1ZPIdL3hccxVtSMfEKyIePJVAmXn3zKuzdTXavdU0ynuWmZ8GPg2wdOnSrGbFnQwNDW3xF5luqpl7N/Pnz6f+3uedel3bda9ZN4dr1vUUlvPG5nJSmzbVY4/dFYzdNXer9dqp/oWlXeyWO+6LLf5y0k27NnWK3WvMbm2qxx4dHeW8sWvarls1lTYNDw9v+stJp33Z0mvMdn2vU+zqfuqm1aZe+slU+15Lt34y3b7XLfZM+14nS5YsmXRfwtTaVD1GdIs91Tb1ejyZzvep/pnXY0+171V16ivT7XvdYs+077VTPZZP1lem2veqOsWeapva/Z9Xjz3d71P7fbn5ODuTY3mnfjKTvtcp9vDwcM91nc6xfLJ+Mp2+t6k+nke0Xbcf5xF1O8p5xNhd0XM/mc73abL/H2ZyLO+0P2frvHw6ug01/B1TS1p628PtrY2IPcorUXsAt5TlY8BelfVGgJvK8gNr5ReX5SNt1u/2HpIkSZLUqG6J1+uZ4tWiGTgTOAo4vvz3W5Xyt0XE6RQTafy2TJy+A/x9ZUKNg4B3Z+btEXFnRDyd4secjwT+bZL3kCRJkqRGdbvH69Qm3jAivkRxtWq3iBijmJ3weODLEfEG4AbgiHL1c4BDgJXA3cDryrrdHhEfBC4v1/tA5Yec30Ixc+J8ikk1zi3LO72HJEmSJDWq2z1erwbOqyQ0fZGZr+qw6Plt1k3grR3inAKc0qZ8ObBfm/Lb2r2HJEmSJDWt211ynwce23oREXMi4oaI2CqpkSRJkiR11i3xqs8QGBSTVezSXHUkSZIkafD0Ni+kJEmSJGnaTLwkSZIkqWHdppMH+IuIuLl83hp6+PaIWFtbLzPzmP5WTZIkSZIGQ7fE6wbgmbWy1cCz26ybgImXJEmSJLXR7Xe8RmexHpIkSZI0sLzHS5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDOiZeEfHoiNh5NisjSZIkSYOo2xWvXwFPBoiI70XEktmpkiRJkiQNlm6J13rgweXzA4EFjddGkiRJkgZQtx9Q/gnwrxFxQfn6LyLi5g7rZmb6A8qSJEmS1Ea3xOvPgI8ChwEJPB+4p8O6CZh4SZIkSVIbHROvzFwBvBggIjYCL8nMH89WxSRJkiRpUHS74lW1N9BpmKEkSZIkqYueEq/MXB0RO0XEK4BnAg8Hbgf+E/h6Zt7fYB0lSZIkaYfWU+IVEYuA84HfA1YBa4FnAG8FfhoRB2Xmb5qqpCRJkiTtyLpNJ1/1MeARwNMy8zGZ+YzMfAzwtLL8Y01VUJIkSZJ2dL0mXocAx2Tm5dXC8vW7gUP7XTFJkiRJGhS9Jl4PAu7ssOxOYJf+VEeSJEmSBk+videlwDERsWu1sHx9TLlckiRJktRGr9PJvxO4CLgxIs6nmFxjEfAiIIADG6mdJEmSJA2Anq54ZeaVwD7Ap4FHAi+kSLxOAvbJzJ82VkNJkiRJ2sH1esWLzLwVOLbBukiSJEnSQOr1Hi9JkiRJ0jSZeEmSJElSw0y8JEmSJKlhJl6SJEmS1LBJE6+IeFBEvCci/s9sVEiSJEmSBs2kiVdm3gO8BxhqvjqSJEmSNHh6HWp4GbB/kxWRJEmSpEHV6+94vQv4YkTcC5wDrAWyukJm3t3nukmSJEnSQOg18bqs/PcTwL92WGfuzKsjSZIkSYOn18Tr9dSucEmSJEmSetNT4pWZpzZcD0mSJEkaWL1e8QIgIp5AMcnGXsApmbkmIh4LrM3MO5uooCRJkiTt6HpKvCLiIcApwOHAfeV25wFrgL8HbgD+uqE6SpIkSdIOrdfp5D8GHAA8H3goEJVl5wDL+lwvSZIkSRoYvQ41fBnw9sy8KCLqsxeuBhb3t1qSJEmSNDh6veI1H7itw7KHAhv6Ux1JkiRJGjy9Jl6XA0d2WHY4cEl/qiNJkiRJg6fXoYZ/B3w3Ir4LfIXiN70OiYi/pEi8nt1Q/SRJkiRph9fTFa/M/CHFxBoPAv6dYnKN9wOPAV6QmZc3VkNJkiRJ2sH1/DtemflfwLMiYj6wEBjPzLsbq5kkSZIkDYhe7/GqmqD4La/1fa6LJEmSJA2knhOviDgkIi6hSLzWABMRcUlEHNpY7SRJkiRpAPSUeEXEm4BvA78D3g4cUf77O+DMcrkkSZIkqY1e7/H6W+DTmfmWWvlJEXES8B7gU32tmSRJkiQNiF6HGj4C+HqHZV8DHt6f6kiSJEnS4Ok18boIeE6HZc8BftCf6kiSJEnS4Ok41DAinlB5+QngPyLiEcA3gVuARcBLgYOBNzZZSUmSJEnakXW7x+tqICuvA3hT+cjydct5wNy+106SJEmSBkC3xOu5s1YLSZIkSRpgHROvzPz+bFZEkiRJkgZVr9PJbxIROwG71Msz8+6+1EiSJEmSBkyvP6D8sIj4ZETcDEwAd7Z5SJIkSZLa6PWK16kU08Z/BlgJ3NtUhSRJkiRp0PSaeD0feFNmfqnJykiSJEnSIOr1B5RvALyHS5IkSZKmodfE613A30XEo5usjCRJkiQNop6GGmbmORHxAmBlRKwCxtus89Q+102SJEmSBkJPiVdE/BPwDuBynFxDkiRJkqak18k13gi8JzP/ocnKSJIkSdIg6vUer7uBK5qsiCRJkiQNql4Tr38Fjo6IaKISEfH4iLiy8rgjIt4REcdFxK8r5YdUtnl3RKyMiF9GxIsq5cvKspURcWylfO+IuCwiro2IMyJilybaIkmSJEl1vQ413A14GvDLiLiYrSfXyMw8ZrqVyMxfAk8CiIi5wK+BbwCvAz6emf9UXT8ingC8EtgX2BP4bkQ8rlx8AvBCYAy4PCLOzMyfA/9Yxjo9Ik4C3gCcON06S5IkSVKvek28DgfuB3amSGrqEph24lXzfOC6zFzd5QLbYcDpmXkP8KuIWAm0ZlVcmZnXA0TE6cBhEfEL4HnAq8t1PgccxySJ18TEBCtWrGi7bHh4mKGhIQDGx8dZs2ZNxzhLlizZ9HzVqlVMTEy0XW9oaIjh4WEA1q9fz+rVq7dYvmxkw6bnl6ydwx33FZ/Pvgs3steu2TbmHfcGl9yy+cLmspENbdu0bGQDV6+bw9hdRcyRXZP9Fm7s2KbzxuZuen7Aoo0s2KV4/3rs1mcEsGDn5IDdO8ecrE2t2PPmzWN0dHSLunfSa5tWrFix1X7qFPfGu4Jr1s3puU0ta9asYXx8vG2d2+2nXtrUru9Vt+20n3ppU6d+MpO+11KP3er3MP2+V49d/T7NtO9V21R/r077arrfp+oxoh57qn2v2qb6Z96KPd2+V21Tu9gz6XtV1diLFy/e9Hy6fa8eu3osn2nfq7appXosb1eP6X6fOu1PmH7fO2BR5+/9TPpeq0312PPmzevYhrrJ2lSNXT2Wz6TvVWMvXryY+fPnA8WxvFNdp/p9aqkey9ttM53vk+cRs3seUdfP84hWm5o4jxjZNbseT2Z6LG8Xux/nEfXY2+K8fDp6nU5+7xm/U+9eCXyp8vptEXEksBx4Z2auAx4FXFpZZ6wsA7ixVv404BHAeGbe32b9LUTE0cDRAHvuuefMWiJJkiRJQGS2zzK3hfK+q5uAfTNzbUTsDtxKcUXtg8Aemfn6iDgB+FFmfqHc7mTgHIp71l6UmW8sy19DcSXsA+X6jy3L9wLOycwndqvP0qVLc/ny5U00dVpGjz27L3FWHX+osY1t7D7FN7axe4ndz/jGNraxt7/Y7eIbe/aPs9tKRFyRmUsnW6/X3/H688nWycxP9hJrEgcD/52Za8uYayt1+AxwVvlyDNirst0IRcJGh/JbgaGI2Km86lVdX5IkSZIa1es9Xv/eZVnrklk/Eq9XURlmGBF7ZObN5cuXAleXz88EvhgRH6OYXGMf4MdAAPtExN4UE3S8Enh1ZmZEXERxr9rpwFHAt/pQX0mSJEmaVE/TyWfmnPoDeDhFovRT4AkzrUhEPJhi4o6vV4o/EhFXRcTPgOcCf1nW5xrgy8DPgfOAt2bmhvJq1tuA7wC/AL5crgvF5B9/VU7E8Qjg5JnWWZIkSZJ60esVr61k5jhwRkQ8DPgUcOBMKpKZd1MkRNWy13RZ/8PAh9uUn0Nxv1e9/Ho2z3woSZIkSbOm1x9Q7uZXwKQ3k0mSJEnSA9WMEq+I2AN4J0XyJUmSJElqo9dZDX/D5kk0WnYBHgpMAC/rc70kSZIkaWD0eo/XCWydeE1QTOt+Xmbe1tdaSZIkSdIA6SnxyszjGq6HJEmSJA2sfkyuIUmSJEnqouMVr4j43hTiZGY+vw/1kSRJkqSB022oYS/3be0BHMDW939JkiRJkkodE6/MPKLTsoh4NHAM8IfArcDH+181SZIkSRoMvc5qCEBEPBZ4N/CnwC3l809l5voG6iZJkiRJA6HX3/HaF3gPcARwI/B24JTMvLfBukmSJEnSQOg6q2FE7B8RXwd+BjwZeCOwT2aeZNIlSZIkSb3pNqvhucBBFEnXKzPzK7NWK0mSJEkaIN2GGr6o/Hcv4ISIOKFboMxc1LdaSZIkSdIA6ZZ4vX/WaiFJkiRJA6zbdPImXpIkSZLUB10n15AkSZIkzZyJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1bLtKvCJiVURcFRFXRsTysuzhEXFBRFxb/ruwLI+I+ERErIyIn0XEUypxjirXvzYijqqU71/GX1luG7PfSkmSJEkPNNtV4lV6bmY+KTOXlq+PBS7MzH2AC8vXAAcD+5SPo4EToUjUgPcBTwOeCryvlayV6xxd2W5Z882RJEmS9EC307auQA8OAw4sn38OuBg4piw/LTMTuDQihiJij3LdCzLzdoCIuABYFhEXAwsy80dl+WnAS4BzO73xxMQEK1asaLtseHiYoaEhAMbHx1mzZk3HBixZsmTT81WrVjExMdF2vaGhIYaHhwFYv349q1ev3mL5spENm55fsnYOd9xXXLDbd+FG9to128a8497gkls259fLRja0bdOykQ1cvW4OY3cVMUd2TfZbuLFjm84bm7vp+QGLNrJgl+L967FbnxHAgp2TA3bvHHOyNrViz5s3j9HR0S3q3kmvbVqxYsVW+6lT3BvvCq5ZN6fnNrWsWbOG8fHxtnVut596aVO7vlfdttN+6qVNnfrJTPpeSz12q9/D9PtePXb1+zTTvldtU/29Ou2r6X6fqseIeuyp9r1qm+qfeSv2dPtetU3tYs+k71VVYy9evHjT8+n2vXrs6rF8pn2v2qaW6rG8XT2m+33qtD9h+n3vgEWdv/cz6XutNtVjz5s3r2Mb6iZrUzV29Vg+k75Xjb148WLmz58PFMfyTnWd6veppXosb7fNdL5PnkfM7nlEXT/PI1ptauI8YmTX7Ho8memxvF3sfpxH1GNvi/Py6djernglcH5EXBERR5dlu2fmzQDlv4vK8kcBN1a2HSvLupWPtSnfQkQcHRHLI2L5unXr+tAkSZIkSQ9029sVrz/IzJsiYhFwQUS0v9xUaHd/Vk6jfMuCzE8DnwZYunRpVrPiToaGhrb4i0w31b+wdDN//nzq733eqde1XfeadXO4pscc8byxuZzUpk312GN3BWN3zd1qvXaqf41oF7vljvtii7+cdNOuTZ1i9xqzW5vqsUdHRzlv7JpJY06lTcPDw5v+ctJpX7b0GrNd3+sUu7qfumm1qZd+MtW+19Ktn0y373WLPdO+18mSJUsm3ZcwtTZVjxHdYk+1Tb0eT6bzfap/5vXYU+17VZ36ynT7XrfYM+177VSP5ZP1lan2vapOsafapnb/59VjT/f71H5fbj7OzuRY3qmfzKTvdYo9PDzcc12ncyyfrJ9Mp+9tqo/nEW3X7cd5RN2Och4xdlf03E+m832a7P+HmRzLO+3P2Tovn47t6opXZt5U/nsL8A2Ke7TWlkMIKf+9pVx9DNirsvkIcNMk5SNtyiVJkiSpUdtN4hURu0bEQ1vPgYOAq4EzgdbMhEcB3yqfnwkcWc5u+HTgt+VQxO8AB0XEwnJSjYOA75TL7oyIp5ezGR5ZiSVJkiRJjdmehhruDnyjnOF9J+CLmXleRFwOfDki3gDcABxRrn8OcAiwErgbeB1AZt4eER8ELi/X+0Brog3gLcCpwHyKSTU6TqwhSZIkSf2y3SRemXk98H/alN8GPL9NeQJv7RDrFOCUNuXLgf1mXFlJkiRJmoLtZqihJEmSJA0qEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiT9/+3dedwdVX348c83CZAAYlgkoQQTLEgEUZSICC64ErTWolSgLRZwV+paBdyKC5alKq3i9hOKrQtQRYvIoggIiIAIKiBBIzyUAEGQhCUkgSTn98eZG4abu88MTySf9+t1X8995s5875m5c2bmO3PmjCRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGmXhJkiRJUsNMvCRJkiSpYSZekiRJktQwEy9JkiRJapiJlyRJkiQ1zMRLkiRJkhpm4iVJkiRJDTPxkiRJkqSGrRWJV0RsExEXRsQNEXF9RLy7GH5URNwWEb8qXq8sTXNkRMyPiBsjYu/S8LnFsPkRcURp+LYRcUVE/D4iTouI9R/buZQkSZK0rlorEi9gBfD+lNLTgN2Bd0bEjsVnn0sp7VK8zgYoPjsA2AmYC3wxIiZGxETgRGAfYEfgwFKcY4tY2wOLgDc+VjMnSZIkad02abwLAJBSugO4o3h/f0TcAGzdY5LXAKemlJYDN0fEfGC34rP5KaWbACLiVOA1RbyXAH9XjPN14CjgS73KtWzZMubNm9fxs+nTpzN16lQAFi9ezMKFC7vGmT179ur3Y2NjLFu2rON4U6dOZfr06QAsXbqUW2655VGfz52xcvX7y+6cwH0PBwA7bbqKbTZKHWPe91Bw2R8fya/nzljZcZ7mzljJdYsmsGBJjjljo8TTN13VdZ7OXTBx9fs9tlzFJuvn72+P3VpGAJusl9hjWveY/eapFXvy5MnMmjXrUWXvZtB5mjdv3hq/U7e4ty4Jrl80YeB5alm4cCGLFy/uWOZOv9Mg89Rp3StP2+13GmSeuq0nVda9lvbYrfUeRl/32mOX61PVda88T+3f1e23GrU+lbcR7bGHXffK89S+zFuxR133yvPUKXaVda+sHHvmzJmr34+67rXHLm/Lq6575XlqKW/LO5Vj1PrU7feE0de9PbbsXu+rrHuteWqPPXny5K7z0K7fPJVjl7flVda9cuyZM2cyZcoUIG/Lu5V12PrUUt6Wd5pmlPrkccRjexzRrs7jiNY8NXEcMWOj1HN7UnVb3il2HccR7bHH47h8FGvLFa/VImIW8CzgimLQYRHxm4g4OSI2LYZtDdxammxBMazb8M2BxSmlFW3DO33/WyLiqoi4atGiRTXMkSRJkqR1XaTUOcscDxGxMfBT4OiU0hkRMQ24G0jAJ4GtUkqHRsSJwM9TSt8opjsJOJucSO6dUnpTMfwg8pWwTxTjb1cM3wY4O6W0c2eqWPgAAB9BSURBVK/yzJkzJ1111VVNzOpIZh3xw1rijB3zKmMb29g1xTe2sQeJXWd8Yxvb2Gtf7E7xjf3Yb2fHS0T8MqU0p994a0VTQ4CIWA/4LvDNlNIZACmlO0uf/z/grOLfBcA2pclnALcX7zsNvxuYGhGTiqte5fElSZIkqVFrRVPDiAjgJOCGlNJnS8O3Ko22L3Bd8f5M4ICI2CAitgW2B64EfgFsX/RguD65A44zU76sdyGwXzH9PwL/2+Q8SZIkSVLL2nLFa0/gIODaiPhVMexD5F4JdyE3NRwD3gqQUro+Ik4HfkvuEfGdKaWVABFxGHAeMBE4OaV0fRHvcODUiPgUcA050ZMkSZKkxq0ViVdK6VIgOnx0do9pjgaO7jD87E7TFT0d7tY+XJIkSZKatlY0NZQkSZKkxzMTL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElqmImXJEmSJDXMxEuSJEmSGmbiJUmSJEkNM/GSJEmSpIaZeEmSJElSw0y8JEmSJKlhJl6SJEmS1DATL0mSJElq2DqVeEXE3Ii4MSLmR8QR410eSZIkSeuGdSbxioiJwInAPsCOwIERseP4lkqSJEnSumDSeBfgMbQbMD+ldBNARJwKvAb4bbcJli1bxrx58zp+Nn36dKZOnQrA4sWLWbhwYdcvnj179ur3Y2NjLFu2rON4U6dOZfr06QAsXbqUW2655VGfz52xcvX7y+6cwH0PBwA7bbqKbTZKHWPe91Bw2R8fya/nzljZcZ7mzljJdYsmsGBJjjljo8TTN13VdZ7OXTBx9fs9tlzFJuvn72+P3VpGAJusl9hjWveY/eapFXvy5MnMmjXrUWXvZtB5mjdv3hq/U7e4ty4Jrl80YeB5alm4cCGLFy/uWOZOv9Mg89Rp3StP2+13GmSeuq0nVda9lvbYrfUeRl/32mOX61PVda88T+3f1e23GrU+lbcR7bGHXffK89S+zFuxR133yvPUKXaVda+sHHvmzJmr34+67rXHLm/Lq6575XlqKW/LO5Vj1PrU7feE0de9PbbsXu+rrHuteWqPPXny5K7z0K7fPJVjl7flVda9cuyZM2cyZcoUIG/Lu5V12PrUUt6Wd5pmlPrkccRjexzRrs7jiNY8NXEcMWOj1HN7UnVb3il2HccR7bHH47h8FJFS55l9vImI/YC5KaU3Ff8fBDw3pXRY23hvAd5S/LsDcONjWtDqtgDuNraxjW3sP4P4xja2sY1tbGOPR/y6zUwpPanfSOvSFa/oMGyNrDOl9FXgq80XpxkRcVVKaY6xjW1sY6/t8Y1tbGMb29jGHo/442WduccLWABsU/p/BnD7OJVFkiRJ0jpkXUq8fgFsHxHbRsT6wAHAmeNcJkmSJEnrgHWmqWFKaUVEHAacB0wETk4pXT/OxWpCk80kjW1sY69bsZuOb2xjG9vYxjb2eMQfF+tM5xqSJEmSNF7WpaaGkiRJkjQuTLwkSZIkqWEmXo8DEbFdRHwlIn4dESsj4qKa4v5tRJwZEbdFxAMR8cuIOLCm2PtFxGUR8aeIWBYRN0bER4qOT2oVEVsX5U8RsXHFWAcXcdpfb6uprJMi4oiI+H1ELI+IBRHxuZpiX9Sl7Ckinlcx9gERcXWxnG+LiP+KiL+oo9xF/L+JiN8Uy+TmiHjfiHH61pXIPhQRt0bE0oi4OCJ2qSn2OyLih8V6nyJirzrKHRFbRcTxxecPFGX/+iC/wQCx14+I0yPipmJ53BUR50TErlVjdxj/hGK5/FsdsSNirMO63v2pmkOWOyJ2joizIuLeiLg/Iq7st1wGWN579ain59WwTLaKiP+MR7br10TE39exTCJiakScHBH3FLHPiYjt+sQdaD8TEW+OvF1cVozz0n5lHjR+ROwfEWdExB3Fcj64jtgRsUlEfLxYL+6NiIUR8b2IeGpN5f5yRMwrPl8UeVv1sjpit43/nmK5fKemcnfbF03uFneYckfEzIj4drEePliss3OrxI6IWT3qZc/nvQ64TDaJvP0bK8p8Q7HcOz0KadjYG0TEZ4v178GIuCQi+nbTHgMcp0U2yv5ykNgj7S/XdutM5xqPczsBrwQuB+pMXN4H3Ay8l/wQu1cC34qILVJKn68Ye3PgQuB4YDGwG3AUMB04rPtkIzkeeADYqMaYLwGWlv6/qaa4/wm8FPg4MI/8CIQda4r9DmCTtmGfAJ5F7vVzJBHx18C3gROBDwBbAZ8CzoqIOSmlVaPGLuLvCZwBnAz8M/Bc4NiIWJVSOmHIcIPUlSOAj5LnZR65HpwfEU9PKfU6aB8k9hvIzw88DxjmJEa/2LsC+wJfA64AppHr02VFuR+oEHtiUeZ/Bf5AXofeC1wQEc9KKfVa9wfeNkXEjsChwH29xhsh9reA8vbqoTpiFwcXlwD/C+xfDH4OMKVi7KuB9hMhTwZOA86pEjsiJpB7890c+CCwENgP+EZEPJhS+l6FclOU8enAu4F7gY8AP4mInVNK3X7XvvuZiDgA+DJ5nb4UOIS8fXlOSum6HmUeKH6xDGYBZwFv6hNvmNhPBt4MnAR8GNgQOBK4IiKekVK6tWK5pwBfAG4k/yZvBM6JiBeklC6vGBuAiNgS+Bhw1yALZIjYFwIfapt2edXYEbEN8HPg1+T1ZAmwC/3rZb/Yd7BmvZwC/Ij+9XKQZXIK8ELyMpkPvBj4LPkZtL1Ovg4S+z/IPXkfDtwCvIu8T3tmSumWHrEHOU4bdX85SOxR95drt5SSrz/zFzCh9P47wEU1xd2iw7BvATc3NB9Hkytg1BjzBcA95AP2BGxcMd7BdcTpEnsu8DCw42O03qxfLJsvVYxzKvDLtmF/XSynp9VQzvOAi9uGfbYo+/pDxupZV4DJ5APGj5WGbUQ+6PhUldjlccgHpwnYq6ZyTwUmtQ17avEd/1i13B2m2Zh8kPS+umID5wOfBMaAf6u6TIrhA8UaMfblwLeaiN1hmg8AK4G/qLiezC7WiVe3Db8aOK1i7OcVsV9SGjYNeBD45x5x++5nyInFyeWyANcC3xhg2Q0Sv1UvNy7m4eABf8uesYttx5S2zzcjnwj8l6rl7vD5ROD/gP+oKzY5afxv4CLgOzUt74FijRj7VPIJkQl1x+7w+d8W68tzK64nGxb1+5/axjkDuKJi7BnACuDQ0ucbALcBXxjhN1h9nEaF/WW/2MX/I+0v1/aXTQ0fB1LFKwo94t7dYfA1wJZNfB/wJ2q8YhcRE8lnuj9BPhO0tjsUuCCl9NvH6PvmApuSr1ZVsR5541u2uPjbs5nEgHYhH5SX/Yhc9qGaSA5QV/YgX9E5vTTNEuAHwD4VY49cV/tNl1JanFJa0Tbsd+SD3p71dcQyLQGW0ae+Dho7IvYDngYcM2gBmtruDRK7uDr3XB59Ja2W2F0cCPw0pXR7xdjrFX871deedXWA2LuQD/B+WprmTuA3wKt6xO25n4mIp5BPIpTr5Crgf+hTJweJX4o3tH6xU0pLUkpL26a5h3zVoV+9HHr/m1JaSf4t+9XLgWJHxHOA15OvagykyeOGAdaVJwKvBb447G86YrkPJCc4V1SMPYl8MmGUetkv9s7khPz80jTLyclp13rZQ/k4beT95QCxG93GjycTLw1rD6C2xCAiJkbEhhHxfPLl7y+l4hRHDd5GPiNzYk3xyv4QESuKdslvrSnmc4HfRcQXIuK+oi32GVHjvVJtDiCf9bqkYpyTgRdExBuKdupPJTc1vLCmJHIyazYPazVJeVoN8ctmk888/r5t+A3FZ382IuIZ5DOptdTXoi3/pIiYDhxHXk5Vk3YiYgrwGeCIYqddt0Mj4qHI99h8JyJm1hDzucXfTSPfP7IiIv4QEW+sIfajRMT25ObAlZc1cB25KeonImL7or4eDOxJbspXxWRgRXHwX7ac4etpeT/Tqnfz2sa5AdgsIp40ZOz2+HXrGbso73Yjfv8asUv1cvOIeC+wPXmbXCl2cW/RF4DjUkq3jRCva+zCK4p93IMRcV6xvaoa+9nkkwspIn4WEQ9Hvk/6yGJ+6ig3kO/JIicXo9bL1bFTboZ7OvDBiNglIp4QEX9FTnpHOX4pl7t131ynfejMiNiwX7Aex2mV95cNHwOulUy8NLDINzO/hnoTmSXF6xLymdIP1BE0IjYnN1t6X0rp4TpiFu4gt2c+CHg1+SDmy8UOr6rp5KaMu5CTokPI9+58b8SdRlfFxvbV5OZFlTZyKaUfksv9VfIZuxvJZ9heW7GYLfPJ986U7Vb83aym72jZFHigw8HjImDDaKDzlyYU9/L8O3mH+KOawh5Obgp7B/CPwCtT7/sDBnVkEfMbNcRq97/AO8n3TX6AfIX0kuLMeBXTi7//BXwTeDlwLvC1iHhlxdjtDiQv9+9WDVTU9X3I+/7fkevrV8nNkC6oGH4+MDkidm4NKJLqpzNEPe2wn9m0+Lu4bdRFbZ+PGr82A8b+DLmp4ak1xd6fvH7cTd7n7Z9SurKG2IeQ1/O+Hd2MEPun5PsA9wbeQr4X7pKImFUxdqtefoV8TPEKchL6KeDtNZS77G/ISc1Qv2OP2G8gn1y4hnyf65nAJ1NKX68Ye37x9zmlcaL4P8jN1PvpdpxWx/6ykWPAtdp4t3X0Ve+LGu/xaos7C7gT+F7NcZ8NPJ98Q+ZichOBOuJ+GTin9P/BNHdv1mnkS+RDtSnvEOch8g5589KwFxblfmnNZd6/iDunhlgvBu4HjgX2KmLfQL5xdmIN8d9MbsL0ZvKGfm/gj0X5D68Qt9N9Kh8GFnUpQwLWGzV22+cjt1kfpI4Xv8Uy+tx7MExs8kHNHHLCfg75QG/g+xG7LO9tyc0hdy8NG2PI+7IG3e4Vy30F8J4a1pMEHNM2/ALgkpp/y98CZ9WxnpATrrOA68ln0/ciX71cBsytGHt9cidDlwE7kDvZ+XqxvJcOGHcWbfsZ4O+LZf3EtnFfXgzffohyrxG/7fOh7vEaJnYxztuBVcC+dcUmbxPnkJuO/3dRn/aqEht4IrnjldeXhl3EkPdlDbJMivGmk/f/J1Qsd2tdObVt3JOBW2v+Lc8BrqtrPQG+CNxKPlZpdbKxFHhjDbEvJZ8Q3RV4ErmjpBXFspo2QNyOx2nUsL/sFrttnMfVPV72aqi+ImIz8kbm/4B/qDN2Sunq4u2lEXE38PWI+ExK6Q+jxoyIncj3S70wIlpnc1qX058YEStTW7v7ir5DPoiZRbXeDRcBN6WU/lQadik5IdsR+EmF2O0OAOanlK6qIdZngDNTSoe3BkTEr8hn715DvkG4ipOBZwJfIp+df5B89eXz5J1MnRYBT4iIienRZ/GmAg+meq+eNiIi3kE+a3hg6nPvwTBS7qFqYfEd55AP3o8gn6kd1THkbcu8Ul2dAGxQ/H9vKva8dUgpXRe56+dnVwx1T/H3wrbhF5B7F6tFRDyT3Ezv6JpC/hX5vo6nppRazYMuKnqCO4581W4kKaWHIvc++G0eaRZ4Kfmq4Ev6Td9jP9O6sjWVR98D01pf2q+EDRu/skFiR+799fPkk0W9eo8cKnZKaRHQ2o6fWzRN/wT54H3U2B8iJwE/KtXLScB6xf/3pzWvcgxV7rZ5WBgRP2PAetkjdq96eUhEbJK69645cLmLFjUvI/fCN7BusYtmlm8HXpFS+nEx+OKIeALwbxHxn6n/fae9yn0w+Z7I1npyPblFxD/xyDLrqttxGjXsL5s4Blzb2dRQPRVN0s4in818VWrmHoyWVgXctmKc7cntvH9O3jAs4pHL7gsY4Yb4AVU9QLyhy/AgnyWtRdHMqkrb9HazgV+VB6SUbiSfrfvLqsFTSitTSoeRz9Q9g9xTWqur5F5dJo9iHrmZZPuzh2az5j0ma52IeB15/f5gSum0pr4n5Y48rgWeUjHUDuQmqYtKr23I3QkvArauGL+bP4u6Sj5BspTcZLIOs8kHRO33ZFxDPXX1SnLdmQ1sl1J6Afkm/571tM9+plXv2u8ZmQ3ck1Lq2815k/uxQWJHxB7kJmlfTikdX2fsDq5hgHrZJ/YO5Kto5Xq5J7m32kX06dSowvLuWy/7xO5VL6FP3Ryi3PuRE9GBmxn2id1at3/VNtk15CRm8yrlTinNTyk9i1zHZ5M73NgAuHqEk4nl47S695d1HQOu1Uy81FVETCKfJdke2Cel9MeGv3LP4u/NFeNcSm7+Vn4dW3z2SvJzI+r0OnKzq6r3u5wFPCMitigNeyE5ifx1xdhl+5I3unUlXrfQdqYyIp5GfsbJWE3fQUppUUrp2pSfSfUO4LKUUt3J0GXk9vV/2xpQuh+u37NaxlXkh0t+k9xFcKX7Mgb4rsnk37xqXX0Ta9bVO8k3mr+YwZ8dNJCIeDr5oPKXFUNdRj4AbX+I70upt67uD/wg9X4O2zBuId97sUPb8F2pqa6m7MaU0h8idwzyMnKX5B3128+k/Jy43/HoOjmh+L9vnWxyPzZI7KIFxlnkq4nvqjN2h2mCnBT1rJcDxP4Ia9bLXwMXF++vrbnc08j7/571coB1ZYx8NadTvfxDr3o0ZLkPBK4c9KrMALFbxw7tV/x2Jd//1LVX5mHKnVK6qTgpujm5lU7XetlD+Tit7v1lXceAazWbGj4OFCt664burYFNInfPDHB2SunBEUN/sYj7bnLvUbuXPrsm5S5JRxIR55K7N72e3CvOnsD7yZ09VLrEnHL3qhe1fd+s4u0lVQ5iIuK7wJXkLpInkg+M9gfe1a8pwAC+St4x/yAiPg08gZwwnp9SurRi7LIDgF+nlLqdHRzWl4HPRcTt5I3tNPIDN8eAs6sGL9a755PPBm5C3untXQwbNlbfuhIRxwAfjYhFPPJAyAn0uVI6YOw55Cap2xTDX1Qk2mO9mn32iw3MBL5flPe0trp6V686NUDs15CvkJ4L3E6+d+cdxd/Pdos7SOxO8xwRy8j3Y1xUJTb5APEfyAe9t5PPwn6E3BTnlIrlfjAiPgEcFxGLyQ8gfx35RMmLqsYuxtudfOb3fb3iDRO7eP0f8P2i/HeRmx6+ntwJSaVyR8RHyevg3eSz6h8l32/z4zUCPmKQ/cxR5Ic8jwE/I3fusj3wd73KPGj8yI8H2JFHeoCbExEPkOvOT+muZ2zyvVLnku/d/Q9gt3ikn6T7Uu9eX/vF3o28zzyD/JtuTl4uu5MPfHvpt0zWeCh1sZ7f3a9eDlDuHcj3GP0POeF4MrmDnVXACRXLvZy8zn03Io4ndyy0F7kzrH5Nogc63imacr6AvOwH1W+ZXFW8To6Ij5ETj+cD7wH+vU9z60HW73eR70O/jVxvjiQnzz0Tr0GO0yrsLweJPdL+cq1X5QYxX2vHi7xipi6vWRXijjURt4j9SXLXxg+Q2+hfTW5vPFDnBSN838HU8wDlT5NvUn2Q3ATol8BBNZZzO/LB0RLyGfVTgE1rjL8FuQesI2qMGeT26b8pyn0bucORp9QUf1fyge0D5LNrPwR2HjFW37pSzM+Hyc1Sl5J7W3pWTbFP6fL5KVVil9bvJmI/u1jmC8ldEI8Vv+9OdSyTDtOMMdgDlPuV+xnk+yLvKtb5hcXy7/kQ4mHKTT7IuJl8H+a1wGtrjH0Cedu4Qc3r93bkg97byXXq18Bb6fPg+gFjn0Cu/8vJvakdTtuDvbv83oMsjzcXMZeT9xcDdTg0SHxyYtfp84uqxCYf9Hf7vGrsWeT7ixcUy2QB+STD8+pYJh2muYjBHqDcr9xbk/dxd5DrzZ/IPXbOrqvc5BMuNxTx5wNvqzH2exjgYeYjrIPTga+Rk9EHi/IfCaxfQ+zDydup5eR7944FNhyg3H2P0xh9fzlI7FO6zNcpgy77tfHVejq0JEmSJKkh3uMlSZIkSQ0z8ZIkSZKkhpl4SZIkSVLDTLwkSZIkqWEmXpIkSZLUMBMvSZIkSWqYiZckaWARcVREpOK1KiIWRcQvIuLoiJg+3uVrFxFbRcTZEXFvUea9uoz3wU6fFdMc1lDZ9iotx206fH5S8flFDX3/KyLiPR2GnxIRf74PKJWktZSJlyRpWPcCzwP2AA4AzgAOAq6NiF3Hs2AdfBh4JnAgucxXdxnvg+QH3o6HJcD+5QERsT6wL/kBo015BfmBsJKkx4CJlyRpWCtSSpcXr/NSSv8KPAO4AzgtIiaOc/nKZgNXpJTOLsp733gXqIMfkBPYsr2BicBFj3lpJEmNMPGSJFWWUlpMvmr0l8DLW8Mj4piIuDYiHoiIBRHxzXKTxIg4PiJuiogox4uIQyLioYjYott3RsS2EfH9iLgvIu6PiB9ExHalzxPwUmDfosneWJc4Y8DmwL+UmlHuVRplYkR8OiLuiog/RsSJEbFBW4wnR8SpEXFPRDwYEedFxA59F1x2KrBrRGxfGnYA8H1geYfy7hIRPym+Z1GxTKeVPp9VzMPrI+IrRTPLBRHx8YiYUIxzFPB+YGZpnk9p+56XR8RvImJJRFwaETsNOD+SpA5MvCRJdbkQWAHsXhq2JfBp4FXkZm1PAS4oXRX7GrAt8KK2WAcDP0gp3d3pi4rE5yfA04A3F+NvC/w0IjYrRnsecE1RrueRm+51si+5+eRJxXjtTRLfD/wF8A/A8cBbgXeXyrIZcCmwA/A24PXARsD5ETGly3eW3QRcSXHVq5jmr4Fvd5jvJ5Gvgm0I/B3wT+Rl9+OieWLZceSmivsB3wA+VryHvNy/BSwszfMnS9M+uZjXo8nNNLcETm9PkCVJg5s03gWQJD0+pJSWR8TdwLTSsENb74tk6+fAAmBP4OKU0o0R8TPgEIpmdRHxFOAF5OSjm0PIycFTU0o3FdNdQU5i3gr8a0rp8oi4D7gnpXR5j3JfExErgAVdxhtLKR1cvD8vIvYEXktObADeS060dkkp3VOU5WfAGHAocGKP+Wg5FXgTOfl5NbAMOL8YVvb+4u/erWaTEfE74ArgdTw6Wbs4pdQa/8cRMbco9+kppQURcQewvMs8bwbsmVL6ffEdE4DvkZPLeQPMjySpjVe8JEl1am8yuE9EXBYR95Kvhi0oPnpqabSTgNdFxMbF/wcDdwLn9vie3YCrW0kXQEppAfAz4PmV5mBNP2r7/7fAjNL/LwN+DNwXEZMiYhJwP/BLYM6A33E6MDsidiZf+fpuSmlFh/F2A35UvlctpXQlOclrn+9+5e5lrJV0laZliOklSW1MvCRJtYiIyeR7pe4s/n8OcCY52TqI3Jyt1QxxcmnS04FVwOuLpmxvAP6rS+LRslXre9rcSb5aU6fFbf8/xKPLvwW5V8KH214vBtboJr6TlNJt5OaKbwH2IV8B62SY+e5X7l46TcsQ00uS2tjUUJJUlxeT9ys/L/7fF7gL2D+llAAiYmb7RCmlJRFxKvlK1y3ATOCUPt91B9Cps4dpwD0jlL2Ke8gJ5ic7fHb/EHFOBb5ATqIu7jLOHeT7rdpNI19hkyStpUy8JEmVRcRU4FhgPvneJIApwMOtpKvw911CnARcDhwFXJ5SuqHPV14BvCEitk0p3VyUYWvys8WOGmEWhrka1O4n5A41rk8pLR0xBsB3yN3In59SWtVlnCuAt0fEE1JK98PqK4uzyFfMhlFlniVJQzLxkiQNa1JEtJoMPgHYFXg7uae9uSmllcVnPwbeExEnkJ9VtQe5Z8A1pJSuiIjryfcpvXWAMpwCHA6cExEfA1aSE667ga+MME/zgFdFxLnkngBvbCU2A/gseb4uiIjPA7eRr0C9CLg0pbRG74SdpJTuAv5mgO96O7mTj2OBjYFjgGuB7w5Y3pZ5wLSIOBi4Drg7pTQ2ZAxJ0oC8x0uSNKwnkpsTXgb8D490V75zSml1c7eU0tnk5Oh15KZ4LwL+qkfc7wNL6X5/02oppeXkTi3mka+WfZ3cTHGvVs+CQ/oAsAT4IfALcjI5kKLL+92LsnyO3KnFceTl9JsRytLru+4iN+lcRu7B8ETgEuDlKaWHek3bwenkBPY48jwfVVtBJUlriEe3AJEkaXxExJXkK00HjXdZJEmqm00NJUnjKiLmAC8BngO8c5yLI0lSI0y8JEnj7Rfk7suPTCn9YrwLI0lSE2xqKEmSJEkNs3MNSZIkSWqYiZckSZIkNczES5IkSZIaZuIlSZIkSQ0z8ZIkSZKkhv1/+54HhASJ05wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group the days of the month\n",
    "# aggregate over the number of flights\n",
    "# sort by day\n",
    "# convert the dataframe to a pandas dataframe\n",
    "flightsPerDayoftheMonth = df.groupBy('day_of_month')\\\n",
    ".count()\\\n",
    ".orderBy('day_of_month')\\\n",
    ".cache()\n",
    "\n",
    "pd_flightsPerDayoftheMonth = flightsPerDayoftheMonth.toPandas()\n",
    "\n",
    "# plot the number of flights per day of the month\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\"Number of Flights vs Days of the Month (Global)\",fontsize=15)\n",
    "plt.xlabel(\"Day of the Month\",fontsize=15)\n",
    "plt.ylabel(\"Number of Flights\",fontsize=15)\n",
    "plt.xticks(np.arange(1,32),fontsize=15)\n",
    "plt.bar(pd_flightsPerDayoftheMonth[\"day_of_month\"],\n",
    "         pd_flightsPerDayoftheMonth[\"count\"]\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Note: A heatmap was used since the bar chart for the monthly aggregate was unclear and it was hard for comparison.</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group the months of the year and then the day within each month and aggregate over the number of flights\n",
    "# sort by month and then by day\n",
    "# convert the dataframe to a pandas dataframe\n",
    "MonthlyAggFlightsPerDayoftheMonthPerMonth = df.groupBy(['month','day_of_month'])\\\n",
    ".count()\\\n",
    ".orderBy([\"month\",\"day_of_month\"])\\\n",
    ".toPandas()\n",
    "\n",
    "\n",
    "# construct a color map showing the monthly aggregates of the number of flights for each day of the month\n",
    "MonthlyAggFlightsPerDayoftheMonthPerMonth_reshape = MonthlyAggFlightsPerDayoftheMonthPerMonth.pivot(\"month\", \"day_of_month\", \"count\")\n",
    "figure, axes = plt.subplots(figsize=(14,10))\n",
    "axes = sns.heatmap(MonthlyAggFlightsPerDayoftheMonthPerMonth_reshape,\n",
    "                 cmap='coolwarm',\n",
    "                 linewidths=.5,\n",
    "                 fmt='d')\n",
    "axes.invert_yaxis()\n",
    "axes.set_title(\"Monthly Aggregates of the Number of Flights for each Day of the Month\", fontsize=15)\n",
    "axes.set_yticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], rotation=0, fontsize = 15)\n",
    "plt.ylabel('Month',fontsize = 15)\n",
    "plt.xlabel('Day of the Month',fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION (Days of the Month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "    \n",
    "The global statistics of the number of flights over the days of the month is quite stable and there seems to be no interesting pattern about the obtained plot. The decreased number of the last three days of the month is due to the fact that not all months are 31 days and February has less than 30 days (28 or 29)<br>\n",
    "However, when we study the monthly statistics of the days of the month, a pattern appears. This shows that studying the data globaly over the days of the month hides that pattern.<br>\n",
    "<br>\n",
    "The monthly statistics of the number of flights for the days of the month show a pattern which already appeard in the study of the monthly statistics of the days of the week. The number of flights takes higher values for 5 consecutive days and then drops for two consecutive days. This confirms the observations in Part 3.2 where the flight numbers drop during the weekend (Saturday and Sunday) and also what was seen in Part 3.1 since we can see higher numbers towards the second half of the year.\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hours of the Day Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 24 artists>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# alter the column of the scheduled departure time in the view only by\n",
    "# extracting the hours value\n",
    "# take the integer part\n",
    "# do a modulo(24) operation to take out the hour value '24' and include it in the hour value '0'\n",
    "# give the modified column as alias\n",
    "# group the modified column representing the hours of the day\n",
    "# aggregate over the number of flights\n",
    "# sort by hour of the day\n",
    "# convert the dataframe to a pandas dataframe\n",
    "flightsPerHouroftheDay = df.groupBy((((df['scheduled_departure_time']/100).cast('int'))%24).alias('Hour_of_the_Day'))\\\n",
    ".count()\\\n",
    ".orderBy('Hour_of_the_Day')\\\n",
    ".cache()\n",
    "\n",
    "\n",
    "pd_flightsPerHouroftheDay = flightsPerHouroftheDay.toPandas()\n",
    "\n",
    "# print(flightsPerHouroftheDay)\n",
    "\n",
    "# plot the number of flights per day of the month\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\"Number of Flights vs Hour of the Day (Global)\",fontsize=15)\n",
    "plt.xlabel(\"Hour of the Day\",fontsize=15)\n",
    "plt.ylabel(\"Number of Flights\",fontsize=15)\n",
    "plt.xticks(np.arange(0,24),fontsize=15)\n",
    "plt.bar(pd_flightsPerHouroftheDay[\"Hour_of_the_Day\"],\n",
    "         pd_flightsPerHouroftheDay[\"count\"]\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Note: A heatmap was used since the bar chart for the monthly aggregate was unclear and it was hard for comparison.</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group the months of the year and then hour of the day within each month and aggregate over the number of flights\n",
    "# sort by month and then by hour\n",
    "# convert the dataframe to a pandas dataframe\n",
    "MonthlyAggFlightsPerHouroftheDay = df.groupBy(df.month,(((df['scheduled_departure_time']/100).cast('int'))%24).alias('Hour_of_the_Day'))\\\n",
    ".count()\\\n",
    ".orderBy([\"month\",\"Hour_of_the_Day\"])\\\n",
    ".toPandas()\n",
    "\n",
    "\n",
    "# construct a color map showing the monthly aggregates of the number of flights for each hour of the day\n",
    "MonthlyAggFlightsPerHouroftheDay_reshape = MonthlyAggFlightsPerHouroftheDay.pivot(\"month\", \"Hour_of_the_Day\", \"count\")\n",
    "figure, axes = plt.subplots(figsize=(14,10))\n",
    "axes = sns.heatmap(MonthlyAggFlightsPerHouroftheDay_reshape,\n",
    "                 cmap='coolwarm',\n",
    "                 linewidths=.5,\n",
    "                 fmt='d')\n",
    "axes.invert_yaxis()\n",
    "axes.set_title(\"Monthly Aggregates of the Number of Flights for each Hour of the Day\", fontsize=15)\n",
    "axes.set_yticklabels(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], rotation=0, fontsize = 15)\n",
    "plt.ylabel('Month',fontsize = 15)\n",
    "plt.xlabel('Hour of the Day',fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION (Hours of the Day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "    \n",
    "The hourly number of flights without regarding the months of the year show a higher number of flights during the day (6h00 to 18h00), with peak hours at 7h00 and 8h00, that then decrease starting from 19h00 in the evening until reaching very low values from 22h00 to 5h00 in the morning.<br>\n",
    "<br>\n",
    "Now, when moving to the monthly statistics of the hourly number of flights, the color map also shows higher flight numbers during the day, and these numbers also decrease by evening until reaching very low values from 22h00 to 5h00 in the morning and the pattern is quite uniform among all the months of the year.<br>\n",
    "<br>\n",
    "This signifies passengers highly prefer day flights over night flights regardless of the month they're in which leads to carriers providing less night flights.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.4\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Which are the **top 20** busiest airports? Compute this in terms of the inbound, outbound and total number of flights.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 20 busiest airports with inbound flights:\n",
      "+-------+------+\n",
      "|Airport| count|\n",
      "+-------+------+\n",
      "|    ORD|283720|\n",
      "|    DFW|260249|\n",
      "|    ATL|221588|\n",
      "|    LAX|152959|\n",
      "|    STL|152800|\n",
      "|    DEN|144099|\n",
      "|    PHX|140979|\n",
      "|    DTW|139435|\n",
      "|    PIT|131962|\n",
      "|    CLT|130321|\n",
      "|    MSP|124758|\n",
      "|    SFO|117245|\n",
      "|    EWR|117223|\n",
      "|    IAH|104409|\n",
      "|    LGA|101498|\n",
      "|    BOS| 99331|\n",
      "|    LAS| 94948|\n",
      "|    PHL| 93312|\n",
      "|    DCA| 87670|\n",
      "|    MCO| 76600|\n",
      "+-------+------+\n",
      "only showing top 20 rows\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "The top 20 busiest airports with outbound flights:\n",
      "+-------+------+\n",
      "|Airport| count|\n",
      "+-------+------+\n",
      "|    ORD|277741|\n",
      "|    DFW|256274|\n",
      "|    ATL|221486|\n",
      "|    LAX|153494|\n",
      "|    STL|151609|\n",
      "|    DEN|141427|\n",
      "|    PHX|139581|\n",
      "|    DTW|136837|\n",
      "|    PIT|130977|\n",
      "|    CLT|129391|\n",
      "|    MSP|123222|\n",
      "|    SFO|118233|\n",
      "|    EWR|116768|\n",
      "|    IAH|104182|\n",
      "|    LGA|101864|\n",
      "|    BOS|100365|\n",
      "|    LAS| 94972|\n",
      "|    PHL| 93585|\n",
      "|    DCA| 88445|\n",
      "|    MCO| 77120|\n",
      "+-------+------+\n",
      "only showing top 20 rows\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "The top 20 busiest airports:\n",
      "+-------+----------+\n",
      "|Airport|sum(count)|\n",
      "+-------+----------+\n",
      "|    ORD|    561461|\n",
      "|    DFW|    516523|\n",
      "|    ATL|    443074|\n",
      "|    LAX|    306453|\n",
      "|    STL|    304409|\n",
      "|    DEN|    285526|\n",
      "|    PHX|    280560|\n",
      "|    DTW|    276272|\n",
      "|    PIT|    262939|\n",
      "|    CLT|    259712|\n",
      "|    MSP|    247980|\n",
      "|    SFO|    235478|\n",
      "|    EWR|    233991|\n",
      "|    IAH|    208591|\n",
      "|    LGA|    203362|\n",
      "|    BOS|    199696|\n",
      "|    LAS|    189920|\n",
      "|    PHL|    186897|\n",
      "|    DCA|    176115|\n",
      "|    MCO|    153720|\n",
      "+-------+----------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# group according to the destination airport and aggregate over the number of flights\n",
    "# cache the dataframe for later use\n",
    "airpots_in_density = df.groupBy(df.dest_airport.alias('Airport'))\\\n",
    ".count()\\\n",
    ".orderBy(desc(\"count\"))\\\n",
    ".cache()\n",
    "\n",
    "# group according to the origin airport and aggregate over the number of flights\n",
    "# cache the dataframe for later use\n",
    "airpots_out_density = df.groupBy(df.src_airport.alias('Airport'))\\\n",
    ".count()\\\n",
    ".orderBy(desc(\"count\"))\\\n",
    ".cache()\n",
    "\n",
    "\n",
    "print(\"The top 20 busiest airports with inbound flights:\")\n",
    "airpots_in_density.show(20)\n",
    "\n",
    "print(\"--------------------------------------------------\\n\\n\")\n",
    "print(\"The top 20 busiest airports with outbound flights:\")\n",
    "airpots_out_density.show(20)\n",
    "\n",
    "print(\"--------------------------------------------------\\n\\n\")\n",
    "print(\"The top 20 busiest airports:\")\n",
    "# perform a union operation to stack the two dataframes vertically\n",
    "# group identical airport names and aggregate by summing the number of flights\n",
    "# sort according to decreasing number of flights\n",
    "# display the top 20 results\n",
    "busiest_airports = airpots_in_density\\\n",
    ".union(airpots_out_density)\\\n",
    ".groupBy('Airport')\\\n",
    ".sum()\\\n",
    ".orderBy(desc('sum(count)'))\\\n",
    ".cache()\n",
    "\n",
    "busiest_airports.show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.5\n",
    "<div class=\"alert alert-info\">\n",
    "Which are the **top 20** busiest carriers? Compute this in terms of number of flights.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+\n",
      "|carrier| count|\n",
      "+-------+------+\n",
      "|     DL|874526|\n",
      "|     US|857906|\n",
      "|     AA|722277|\n",
      "|     UA|638750|\n",
      "|     WN|565426|\n",
      "|     CO|484834|\n",
      "|     NW|482798|\n",
      "|     TW|258205|\n",
      "|     HP|177851|\n",
      "|     AS|117475|\n",
      "+-------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# group according to the carrier and aggregate over the number of flights\n",
    "# sort in the decreasing number of flights\n",
    "# display the top 20 carriers\n",
    "Busiest_carriers = (\n",
    "                    df.groupBy('carrier')\\\n",
    "                    .count()\\\n",
    "                    .orderBy(desc('count'))\\\n",
    "                    .cache()\n",
    "                    )\n",
    "\n",
    "Busiest_carriers.show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4 \n",
    "In this series of questions we focus on the computation of statistics about the percentage of delayed flights.\n",
    "\n",
    "### Question 4.1\n",
    "<div class=\"alert alert-info\">\n",
    "What is the percentage of delayed flights for different hours of the day?  \n",
    "\n",
    "Plot a bar chart and interpret the figure.  \n",
    "\n",
    "\n",
    "<div class=\"label label-success\">IMPORTANT!</div> A flight is considered as delayed if it's actual arrival time is more than 15 minutes later than the scheduled arrival time.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+------+\n",
      "|Hour_of_the_Day| count|\n",
      "+---------------+------+\n",
      "|              0| 13642|\n",
      "|              1| 12825|\n",
      "|              2|  2842|\n",
      "|              3|   564|\n",
      "|              4|   486|\n",
      "|              5| 13357|\n",
      "|              6|257486|\n",
      "|              7|398796|\n",
      "|              8|397060|\n",
      "|              9|341597|\n",
      "|             10|300375|\n",
      "|             11|313577|\n",
      "|             12|356830|\n",
      "|             13|360762|\n",
      "|             14|294214|\n",
      "|             15|313131|\n",
      "|             16|317809|\n",
      "|             17|368695|\n",
      "|             18|338147|\n",
      "|             19|287033|\n",
      "+---------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "flightsPerHouroftheDay.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of delayed flights per hour\n",
    "df_flights_hour_delay = df[df.arrival_delay > 15]\\\n",
    ".groupBy(((df.scheduled_departure_time/100).cast('int')%24).alias('hour'))\\\n",
    ".count()\\\n",
    ".orderBy('hour')\n",
    "\n",
    "# Percentage of delayed flights per hour\n",
    "percentage_hour_delay = (\n",
    "    df_flights_hour_delay.join(flightsPerHouroftheDay, df_flights_hour_delay.hour == flightsPerHouroftheDay['Hour_of_the_Day'], 'inner')\n",
    "    .select(flightsPerHouroftheDay['Hour_of_the_Day'], (df_flights_hour_delay['count']*100/flightsPerHouroftheDay['count']).alias('Delay % per Hour'))\n",
    "    .orderBy('hour')\n",
    "    .collect()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 24 artists>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentage_hour_delay_pd = pd.DataFrame(data=percentage_hour_delay, columns=['hour', 'Delay % per Hour'])\n",
    "\n",
    "# plot the percentage of flights in delay per hour\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\"Percentage of delayed flights per each hour of the day\",fontsize=15)\n",
    "plt.xlabel(\"Hour of the Day\",fontsize=15)\n",
    "plt.ylabel(\"Percentage of delayed flights\",fontsize=15)\n",
    "plt.xticks(np.arange(0,24),fontsize=15)\n",
    "plt.bar(percentage_hour_delay_pd[\"hour\"],\n",
    "         percentage_hour_delay_pd[\"Delay % per Hour\"]\n",
    "        ,color='b')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "    \n",
    "By looking at the plot above, we can notice that the percentage starts to increase at 4:00 AM and reaches its peak between 17:00 and 19:00 (almost one flight out of five), then it decreases again. \n",
    " <br>   \n",
    "Our results before showed that the number of flights at night are low. This plot provide a clearer image about the relation between the number of flights and the percentage of delay. When looking at the above plot and building above what was observed in <b>part 3.3</b> the peak hours in the number of flights have a higher percentage of delayed flights. <br>\n",
    "<br>\n",
    "By looking at the number of flights per day hours the number of flights starts increasing at 6:00 and decreases starting from 17:00. This is coherent with the result above. However, to get a better intuition about delays it would be better to look at actual average values of the delay.\n",
    "<br>\n",
    "\n",
    "Also, by observing the increase in the percentage of delay starting from ~ 4:00 until is reaches it's maximum before 20:00. We assume that the delay could be cumulative among flights,in other words, the delay of a flight may affect the delay of other flights arriving at the same airport in a similar time range. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.2\n",
    "<div class=\"alert alert-info\">\n",
    "You will realize that saying *\"at 4 A.M. there is a very low chance of a flight being delayed\"* is not giving you a full picture of the situation. Indeed, it might be true that there is very little probability for an early flight to be delayed, but if it does, the delay might be huge, like 6 hours!  \n",
    "\n",
    "<ul></ul>\n",
    "\n",
    "Then, the question is: **which hours of the day are characterized by the largest mean delay?** (you can consider the median too)  \n",
    "\n",
    "Plot a Bar chart and explain it.  \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# average delay per hour\n",
    "avg_delay_per_hour = (\n",
    "    df.groupBy((((df.scheduled_departure_time/100)).cast('int')%24).alias('hour'))\n",
    "    .avg('arrival_delay')\n",
    "    .orderBy('hour')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 24 artists>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_delay_per_hour_pd = pd.DataFrame(data=avg_delay_per_hour, columns=['hour', 'avg'])\n",
    "\n",
    "\n",
    "# plot the percentage of flights in delay per hour\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\"Average delay of the flights during the day\",fontsize=15)\n",
    "plt.xlabel(\"Hour of the Day\",fontsize=15)\n",
    "plt.ylabel(\"Average Delay\",fontsize=15)\n",
    "plt.xticks(np.arange(0,24),fontsize=15)\n",
    "plt.bar(avg_delay_per_hour_pd[\"hour\"],\n",
    "         avg_delay_per_hour_pd[\"avg\"]\n",
    "        ,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "By looking at the plot above, we can notice:\n",
    "<br>\n",
    "\n",
    "<b>1- A negative values of delay between 2 AM and 3 AM.</b><br>\n",
    "This means that the flights arrive earlier than the scheduled time. The definition of the delay here is somehow tricky, since early flights are also considered as having negative delay.\n",
    "\n",
    "<b>2- A siginificant increase in the average delay starting from 8 AM until 8 PM when it starts decreasing.</b><br>\n",
    "    The flights in the morning have less delay compared with afternoon and evening. Which match our interpretation in the question before.\n",
    "\n",
    "<b>3- A Correlation between the percentage of delayed fligts and the average value of delays can be seen.</b><br>\n",
    "The information from this plot is coherent with what we've already seen,  knowing that night hours have less flights and the shape of this curve is closer to the one of the previous one. In conclusion, we can notice this correlation.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With data of year 1994, the flight from 3AM to 4AM often depart earlier than in their schedule. The flights in the morning have less delay then in the afternoon and evening.\n",
    "\n",
    "So, an attentive student should notice here that we have somehow a problem with the definition of delay! Next, we will improve how to represent and visualize data to overcome this problem.\n",
    "\n",
    "**NOTE**: the following piece of code assumes you correctly answered the previous questions. The naming convention of the DataFrames we use next should be clear enough for you to produce them by answering the questions above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pdf2 = pd.DataFrame(data=mean_delay_per_hour.collect())\n",
    "pdf_delay_ratio_per_hour =  pd.DataFrame(data=percentage_hour_delay)\n",
    "pdf_mean_delay_per_hour =  pd.DataFrame(data=avg_delay_per_hour)\n",
    "\n",
    "plt.ylabel(\"Ratio of delay\")\n",
    "plt.title('Figure 6: The radio of delay over hours in day')\n",
    "plt.grid(True,which=\"both\",ls=\"-\")\n",
    "bars = plt.bar(pdf_delay_ratio_per_hour[0], pdf_delay_ratio_per_hour[1], align='center', edgecolor = \"black\")\n",
    "for i in range(0, len(bars)):\n",
    "    color = 'red'\n",
    "    if pdf_mean_delay_per_hour[1][i] < 0:\n",
    "        color = 'lightgreen'\n",
    "    elif pdf_mean_delay_per_hour[1][i] < 2:\n",
    "        color = 'green'\n",
    "    elif pdf_mean_delay_per_hour[1][i] < 4:\n",
    "        color = 'yellow'\n",
    "    elif pdf_mean_delay_per_hour[1][i] < 8:\n",
    "        color = 'orange'\n",
    "\n",
    "    bars[i].set_color(color)\n",
    "        \n",
    "patch1 = mpatches.Patch(color='lightgreen', label='Depart earlier')\n",
    "patch2 = mpatches.Patch(color='green', label='delay < 2 minutes')\n",
    "patch3 = mpatches.Patch(color='yellow', label='delay < 4 minutes')\n",
    "patch4 = mpatches.Patch(color='orange', label='delay < 8 minutes')\n",
    "patch5 = mpatches.Patch(color='red', label='delay >= 8 minutes')\n",
    "\n",
    "plt.legend(handles=[patch1, patch2, patch3, patch4, patch5], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.margins(0.05, 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the new figure (Figure 6), we have more information in a single plot. The flights in 3AM to 4AM have very low probability of being delayed, and actually depart earlier than their schedule. In contrast, the flights in the 4PM to 8PM range have higher chances of being delayed: in more than 50% of the cases, the delay is 8 minutes or more.\n",
    "\n",
    "This example shows us that the way representing results are also important.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Plot a bar chart to show the percentage of delayed flights over days in a month. Discuss your findings from the figure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of delayed flights per day of the month\n",
    "Delayed_per_MonthDays = (df[df.arrival_delay > 15]\\\n",
    "                        .groupBy(df.day_of_month)\\\n",
    "                        .count()\n",
    "                        .orderBy('day_of_month')\n",
    "                       )\n",
    "\n",
    "# The percentage of delayed flights over days in a month\n",
    "Percentage_delayed_per_MonthDays = (\n",
    "                     \n",
    "    flightsPerDayoftheMonth.join(Delayed_per_MonthDays,Delayed_per_MonthDays['day_of_month']==flightsPerDayoftheMonth['day_of_month'], 'inner')\n",
    "    .select(flightsPerDayoftheMonth.day_of_month,(Delayed_per_MonthDays['count']/flightsPerDayoftheMonth['count'])*100)\n",
    "    .orderBy(\"day_of_month\")\n",
    "    .collect()\n",
    "\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 31 artists>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Percentage_delayed_per_MonthDays_pd = pd.DataFrame(data=Percentage_delayed_per_MonthDays, columns=['hour', 'Delay % per Days in month'])\n",
    "\n",
    "# plot the percentage of flights in delay per hour\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\" The percentage of delayed flights over days in a month\",fontsize=15)\n",
    "plt.xlabel(\"Month Days\",fontsize=15)\n",
    "plt.ylabel(\"Percentage of delayed flights\",fontsize=15)\n",
    "plt.xticks(np.arange(1,32),fontsize=15)\n",
    "plt.bar(Percentage_delayed_per_MonthDays_pd[\"hour\"],\n",
    "         Percentage_delayed_per_MonthDays_pd[\"Delay % per Days in month\"]\n",
    "        ,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "The percentage of delayed flights oscillates around <b>15%</b> over the days of month. Also, by looking at the oscillation pattern, we can notice a periodical drops and peaks, we expect this related to the days of the week during the month.\n",
    "<br>\n",
    "<br>\n",
    "Knowing that the day of the month is not constant, where the values from the 30 to the 31st is not always counted, we can see that there is less delay in those days of the month. In addition, We can observe an increase in the percenatge delayed flight in the days of the month between 26 until 28th.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.4\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Plot a bar chart to show the percentage of delayed flights over days in a week. Discuss your findings from the figure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of delayed flights per day of the week\n",
    "\n",
    "Delayed_per_WeekDays = (df[df.arrival_delay > 15]\\\n",
    "                        .groupBy(df.day_of_week)\\\n",
    "                        .count()\n",
    "                        .orderBy('day_of_week')\n",
    "                       )\n",
    "\n",
    "\n",
    "# the percentage of delayed flights over days in a week\n",
    "Percentage_delayed_per_WeekDays = (\n",
    "                     \n",
    "    flightsPerDayofTheWeek.join(Delayed_per_WeekDays,Delayed_per_WeekDays['day_of_week']==flightsPerDayofTheWeek['day_of_week'], 'inner')\\\n",
    "    .select(flightsPerDayofTheWeek.day_of_week,(Delayed_per_WeekDays['count']/flightsPerDayofTheWeek['count'])*100).orderBy(\"day_of_week\")\\\n",
    "    .collect()\n",
    "                                )\n",
    "                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Percentage_delayed_per_WeekDays_pd = pd.DataFrame(data=Percentage_delayed_per_WeekDays, columns=['hour', 'Delay % per Week Days'])\n",
    "\n",
    "# plot the percentage of flights in delay per hour\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\" The percentage of delayed flights over days in a week\",fontsize=15)\n",
    "plt.xlabel(\"Week Days\",fontsize=15)\n",
    "plt.ylabel(\"Percentage of delayed flights\",fontsize=15)\n",
    "plt.xticks(np.arange(1,8),['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],fontsize=15, rotation = 20)\n",
    "plt.bar(Percentage_delayed_per_WeekDays_pd[\"hour\"],\n",
    "         Percentage_delayed_per_WeekDays_pd[\"Delay % per Week Days\"]\n",
    "        ,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "As our assumption in the previous question, the percentage of delayed flights is higher in the middle of the week and it shows a decrease at the beginning and at the end. This is the reason behind the periodical oscillation pattern in the previous plot (The percentage of delayed flights over days in a month).  \n",
    "<br>\n",
    "\n",
    "We can also notice that the percentage of delayed flights hits its minimum during the weekend, which is understandable as we already saw in the previous results that there is less flights during this period.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.5\n",
    "<div class=\"alert alert-info\">\n",
    "Plot a bar chart to show the percentage of delayed flights over months in a year. Discuss your findings from the figure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of delayed flights over months in a year\n",
    "Delayed_per_Month = (df[df.arrival_delay > 15]\\\n",
    "                        .groupBy(df.month)\\\n",
    "                        .count()\n",
    "                        .orderBy('month')\n",
    "                       )\n",
    "\n",
    "#  The percentage of delayed flights over months in a year\n",
    "Percentage_delayed_per_Month = (\n",
    "                     \n",
    "    flightsPerMonth.join(Delayed_per_Month,Delayed_per_Month['month']==flightsPerMonth['Month'], 'inner')\\\n",
    "    .select(flightsPerMonth.Month,(Delayed_per_Month['count']/flightsPerMonth['count'])*100).orderBy(\"month\")\\\n",
    "    .collect()\n",
    ")\n",
    "\n",
    "             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Percentage_delayed_per_Month_pd = pd.DataFrame(data=Percentage_delayed_per_Month, columns=['hour', 'Delay % per Month'])\n",
    "\n",
    "# plot the percentage of flights in delay per hour\n",
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "plt.title(\" The percentage of delayed flights over months in a year\",fontsize=15)\n",
    "plt.xlabel(\"Months\",fontsize=15)\n",
    "plt.ylabel(\"Percentage of delayed flights\",fontsize=15)\n",
    "plt.xticks(np.arange(1,13),['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'],fontsize=15)\n",
    "plt.bar(Percentage_delayed_per_Month_pd[\"hour\"],\n",
    "         Percentage_delayed_per_Month_pd[\"Delay % per Month\"]\n",
    "        ,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "The plot above shows that the percentage of delays is high in <b>Decemeber, January and February</b>. Winter season can be a correlation between this 3 months, so we expect that this delay is due to the bad weather condition during this period of the year. Also, <b>July</b> achieves a peak and this is because of the summer vacations where the number of flights start to increase gradually causing airline traffic. However, May and September achieve the lowest percentage of delay.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready now to draw some observations from our data, even if we have only looked at data coming from a year worth of flights:\n",
    "\n",
    "- The probability for a flight to be delayed is low at the beginning or at the very end of a given months\n",
    "- Flights on two first weekdays and on the weekend, are less likely to be delayed\n",
    "- May and September are very good months for travelling, as the probability of delay is low (remember we're working on US data. Do you think this is also true in France?)\n",
    "\n",
    "Putting things together, we can have a global picture of the whole year!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_delay = df.withColumn('is_delay', when(df[\"arrival_delay\"] >= 15, 1).otherwise(0))\n",
    "statistic_day = df_with_delay.groupBy(['year','month', 'day_of_month', 'day_of_week'])\\\n",
    "    .agg((func.sum('is_delay')/func.count('*')).alias('delay_ratio'))\n",
    "\n",
    "# assume that we do statistic on year 1994\n",
    "statistic_day = statistic_day\\\n",
    "    .orderBy('year', 'month', 'day_of_month', 'day_of_week')\n",
    "pdf = pd.DataFrame(data=statistic_day.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+losAvIDLk4jvwjwk/jSAHJive3sfwHKLDLvenYMBZItIOcyfH2Ps70GqBaXUKfw0emq3zfJGr3cNrlMiIiIickAau62ZiKyxPL0NwD34aXbQ+wF8ppRiEpSIiK6biGwGkKvMs2LTzwTft5tLRJIBBCmlHM0GTxYichTm2e4/vknHT4V50qLnb8bxiYiIiMis0XuAKqUmAYCIbAcQbb1xvIjcDuCNGxMeERHdaixDhItg7k02EOaegy/f1KCoSXzfbi7LsPdWMPdOvhvmiXym3NSgWjgRGQVz78tPm9q2mY4fDvM9RuNuxvGJiIiI6CfOTIIUbjNrJmAejqNvpniIiOjWFwTzkFl/mIe3JymlMm5uSOQEvm83lzfMw947APgB5mHS/++mRtSCichnAKIBTLC5L+mNPP4CAM8AWKSU+v5GH5+IiIiILtfoEPhLG4gsBxAB849uBfO9nI4opZ684o5EREREREREREREN1mTCVDAPCESgH6Wl18opT640vZERERERERERERELYFTCVAiIiIiIiIiIiKin6Mm7wFq6f2ZDPNs8GJ5KKVU28b28fT0VD4+PpoFSURERERERET0S3f27NkflVLtb3YczeXAgQO3ubq6rgYQA0B3s+Ohnw0TgKy6uropPXv2/MHRBs5MgvQKgGFKqUPOHtXHxwfTp093dnMiIiIiIiIiImrCvHnzjt/sGJqTq6vr6qCgoG7t27cv1ul0HLJMTjGZTHLhwoXoc+fOrQYw3NE2zmTTz19N8pOIiIiIiIiIiOgaxLRv376UyU+6GjqdTrVv374E5p7DDjnTA3S/iGwG8CGAautCpdT71x8iERERERERERERAEDH5CddC8t502hHT2d6gLYFYAQwEMAwy2OoJtERERERERERERG1QDNnzuwwd+7cwOvdRktffvll682bN7ezvt6wYUO75557Lqi5jjd//vzbunbt2j0iIqL7sGHDOhmNRmls2zVr1viKSM8vvvjCEwD+/e9/t9Hr9dExMTHdsrKy3AHgxx9/dOnbt2+EyWRqrpAdarIHqFJq0o0IhIiIiIiIiIiI6CcnYwGTM6OXnaSrAzpmalde86itrYWbm5vDdfv37/fcv39/m8TExBIAGDduXAmAkus95vnz510CAwPrbZd9//33bm+99Vbg4cOHs7y8vFRCQkLn1atX+/3hD38otN+/uLhY98Ybb9x25513VliX/fWvfw1KS0s7euTIEfelS5e2T0lJOTVnzpzb58yZc1anu7FzXDkzC/waAA26HyulHm2WiFqYefPmoaKioukNnaBqjRA3T23KqjRCWmtUVr0R4tLy4gJ+IW1WZ4S4alNWmzZtNDtfTXVG6DSKS8v2ArR9L2tNtXDTOf5iuVqmSiN0LTAuLcvS8hxTqh4iLpqUBWj8Xqp66DSKraW2v6bXuIafY1qWBWh8XrTQz8WW2v6afl5o/fvCaIR4atRmGv5W0bLNWur3iJbXkZbtBWj72a8qKyGtW2tTlpafFxqWpek13kI/EwGN/w+h5f9tWuh5oeXvu4qKcqxYsVKTsujnTMvkp3PlGQyGoM2bNwd06NChxt/fvzYuLs4IANnZ2e4zZswILSoqcvXw8DCtXr36eFxcXJXtvosXLw5Ys2ZN+9raWgkPD6/eunXr93V1dYiJiel+7NixLHd3d1VUVKS74447Lr227jtq1KhwX1/fuoMHD3reeeedxrFjxxbNnDkztKqqSufh4WFau3bt95GRkTWLFi3qUFVVpYuKivKaNWvW2crKSt3+/fvbvPvuuyfy8vJaTZw4MbywsNDV39+/7t133y2IiIioaayutbW12LJlS7u33367fVlZmcs333xz2H6b+vp6qaio0Lm7u9dXVlbqQkJCah2VNWvWrOBZs2ade+211y71RnVzc1NGo1FXUVGhc3NzU9nZ2e5nz55t9eCDD5Y39T5ozZkTabvNcw8AIwCcaZ5wWibPNm00KcdYUQHPDzUq66EKbeM6oVFZodrFBfyC2kyjOgJASnWKJuVMdZ+qbdtr1F6AtueZsaJCuzZrPbVlxuU+VbOyAGh77nte0KQsADAa22t7/msUm5ZxAS34Gm+BZVnLa7Ft1gK/R7Ru/5b4HQ5Yvsdb4O87AC3ys0fL7xEtryNAu89EQOPPfrRvub87NfxNdqt/JgLN8H+IltpmLfL3XXuNyiFy3u7duz0/+OADv4MHD+bU1tbirrvuirYmQKdMmRL21ltvHb/jjjuqP/300zZJSUmhX3/9dZ7t/uPGjSueNWvWjwDwhz/8ocOyZcsC/vznP/8QHx9ftmXLlnYTJky4mJqa6peQkFBsm/y0Onr0qMfevXvzXF1dUVRUpNu3b1+um5sbPvzwQ+/Zs2eH/Otf/zo6Z86cM9aEJwAsW7bM37r/jBkzQseOHVv45JNPFr7++uv+SUlJHT/++OOj9sfJyspyX7lyZcD27dt9e/bsWT5r1qxzjpKSnTp1qn388cfPderU6U53d3dTv379SkeOHFlqv93evXtbnz59utXvf//7EtsE6J///Oez06ZNC/Pw8DC999573z/11FMdFy1adPrq3hVtODMEPs32tYhsBPBxs0VERERERERERER0g6Wnp3slJCRc9Pb2NgHAwIEDLwJASUmJLiMjw2v06NFdrNvW1NQ0uBfmgQMHWs+dOze4rKzMpaKiwqV///4lADBt2rQLycnJQRMmTLi4fv36gJSUlAJHxx85cmSxq6s5VVdUVOSSmJjYqaCgwENEVG1tbaP33rTKyMhos3PnzqMAkJSUVDR//vwQ+23Wrl3rM2XKlC5PPvnk2e+++y7H19e30ZtxXrhwwWXHjh0+R44cOejv71//4IMPdl6xYoXfY489VmTdpr6+Hs8880zounXrvrff/5577qnMzMzMBYCdO3d6BQUF1Sil8OCDD3Z2dXVVy5cvP9mxY8e6puqlhWsZcB8BIFTrQIiIiIiIiIiIiG4mkYZ5xvr6enh7e9fl5ubmWB/Hjh3Ltt9u2rRpnZYvX34iLy8vx2AwnKmurtYBwMCBAytOnTrlvmPHDq/6+nq5++67qxocBICXl9elZKTBYAju379/WX5+fva2bduO1NTUaHLTzOHDh5cuWLDgxK5du9oNHTq0y6pVq/wam9ho27ZtbUNDQ6s7dOhQ5+7urh566KGLX375pZftNhcvXnTJz8/3GDBgQGRwcPAdmZmZbR5++OGu1omQAMBkMmHhwoW3L1q06Ozzzz/fYeHChWcmTJhQ+Morr9ywyaOabDwRKRORUuu/ALYBMDR/aERERERERERERDfGgAEDynfs2OFTXl4uxcXFul27dvkAgJ+fnykkJKQmNTXVFzAn9L766qsGN3s2Go260NDQ2urqatm0aZOf7boxY8YUTpo0qfP48eN/dCaW0tJSl5CQkBoAWLVqVYB1edu2bevLy8sd5vPi4uIqVq9e7WvZx69Xr14NhrX7+fmZ5syZcyErK+vQK6+8cmrPnj1eERERMTNmzGjQWzQ8PLzm22+/9SorK9OZTCZ8+umn3t26dbsseevv719fXFycefr06YOnT58+GBsbW7F169Yj9913n9G6zfLly/0HDx5c0r59+/rKykqdi4uL0ul0MBqNN2wmpCYPpJTyVkq1tflXbz8snoiIiIiIiIiI6Oesb9++xhEjRhTFxMR0Hzp0aJfevXtfSiBu3Ljx2Jo1awIiIyOjIyIiuqelpfnY7//ss8+e6d27d7d+/frpIyIiLksUTp48ubC0tNR18uTJRfb7OWIwGM7NmzcvpEePHlH19T9Nzj5kyJCyvLy81lFRUdEpKSm+tvusXLnyxLp16wL0en30xo0b/VesWHHySse49957K9etW3ciPz8/6/777y+zXz9gwICKYcOGFd95553dIiMju5tMJpk5c+YFAHj66ac7bNiwoV1T9SgrK9Nt2LDBf/bs2RcAYNasWecffvjhLn/5y1+Cn3rqqR+caQstNHoPUBEJA3BRKVVieX0/gIcAFAB4QynV6CxSRERERERERERE10dXp+1M8Lom7zeZnJx8Ljk5+Zz98qioqJrdu3fn2y9fsmTJpYnCDQbDBYPB4HAmsE8++cR78ODBxQEBAfWO1qelpRXYvn7ggQcqCgoKsqyvly5degYAAgMD67Oysg7Z7V4IAJGRkTX2EzM5w9PTUyUmJpY4Wvfaa6+dee211xpMhv766687nCB93759l80k7+3tbfrPf/5zKabBgweX5+Xl5VxtjNfrSifRFphnfC8RkbsA/B3AIgB3AVgBYErzh0dERERERERERL9MHTNvdgRamDhxYsf09PR227dvb5BApRvjSgnQ1kopazZ3PIBUpdRiEdEB+K75QyMiIiIiIiIiIvp5e+edd04CuOJwdGpeV7oHqO0MUAMAfAIASimT482JiIiIiIiIiIiIWpYr9QD9VES2ADgLwBfApwAgIrcD4P0/iYiIiIiIiIiIqMW7UgL0aQCJAG4H0FcpVWtZHgTgz80dGBEREREREREREdH1ajQBqpRSADY5WJ7RrBERERERERERERERaeRK9wC9RETeutJrIiIiIiIiIiKiW8nMmTM7zJ07N/B6t9HSl19+2Xrz5s3trK83bNjQ7rnnngtqruONHj063M/PLzYiIqK77fLU1FTfrl27dtfpdD2/+OILT0f7ZmZmukdFRUVbH15eXnEvvvjibQCQlJQUrNfro0eMGBFu3f6NN97wW7BgwW3NUY8rDYG3taqJ10RERERERERERJpZdXFVbJWqcjZ31SQP8aib7jM9U6vymkttbS3c3Nwcrtu/f7/n/v372yQmJpYAwLhx40oAlFzvMc+fP+8SGBhYb7/80Ucf/fGpp576YdKkSZ1sl991112VaWlpR6ZOnRreWJmxsbHVubm5OQBQV1eHoKCg2DFjxlwsLCx02bdvn1deXl7O8OHDO+3bt691dHR01fr16wM+++yz/OutiyNO9QBVSh240msiIiIiIiIiIiItaZn8dLY8g8EQFB4eHnPPPffo8/Pz3a3Ls7Oz3fv16xfRvXv3bj179ozMyMjwsN938eLFATExMd0iIyOjBw0a1KWsrExXXFysCw4OvqO6uloAoKio6LLXVqNGjQqfMmVKSJ8+ffSPPfZYSHp6umdcXFxUt27douPi4qIyMzPdq6qqZNGiRR22bdvmGxUVFZ2SkuK7bNky/0ceeSQUAPLy8lrFx8fr9Xp9dHx8vD4/P7/VlepaW1uLDRs2tBswYEDXoUOHdnW0zZAhQ8rbt29fZ7+8R48eVbGxsdVNtafVP/7xj7ahoaHVer2+RqfTqdraWjGZTKiqqtK5ubmpefPmBT3++OM/uLu7K2fLvBpOJUCJiIiIiIiIiIhuZbt37/b84IMP/A4ePJizffv2I5mZmW2s66ZMmRK2YsWKE9nZ2YdeffXVU0lJSaH2+48bN644Kyvr0OHDh3MiIyMrly1bFuDr62uKj48v27JlSzsASE1N9UtISCh2lOg7evSox969e/NSUlJOxcbGVu3bty/30KFDOS+88MLp2bNnh3h4eKg5c+acGTZsWHFubm7O1KlTi233nzFjRujYsWML8/LychITEwuTkpI6OqpnVlaW++OPPx7ctWvXmLS0NN9Zs2ad++abbw5ffws2buPGjX4PP/xwIQD4+vqahg8fXhwdHR0dFhZW7efnV//tt9+2GT9+/MXmOr6mmXQiIiIiIiIiIqKfo/T0dK+EhISL3t7eJgAYOHDgRQAoKSnRZWRkeI0ePbqLdduamhqx3//AgQOt586dG1xWVuZSUVHh0r9//xIAmDZt2oXk5OSgCRMmXFy/fn1ASkpKgaPjjxw5stjV1ZyqKyoqcklMTOxUUFDgISKqtra2wfHsZWRktNm5c+dRAEhKSiqaP39+iP02a9eu9ZkyZUqXJ5988ux3332X4+vra3Kmba5HVVWVfPzxx+2WLFlyyrps4cKF5xcuXHgeABITE8MWLFhwesmSJQEff/xx25iYmMpXXnnlrJYxsAcoERERERERERERAJGGecb6+np4e3vX5ebm5lgfx44dy7bfbtq0aZ2WL19+Ii8vL8dgMJyprq7WAcDAgQNDisnGAAAgAElEQVQrTp065b5jxw6v+vp6ufvuu6scHdvLy+tSMtJgMAT379+/LD8/P3vbtm1HampqNMnhDR8+vHTBggUndu3a1W7o0KFdVq1a5Wc0GptMrl6PrVu3touOjjZ27NixwVD6vXv3tgaAO+64o/q9997z/+ijj47l5OS0PnjwoHvDkq5doz1ARWQJgDSl1F4tD/hzcywnB8awMG0Kq6+E8aEKbcqqqoSxQpuylKkSxtCWFxcAbdusUsPYtGwzDeuYd/Q0gt2DNSlLadn2WrYXoOl7WafqMNV9qiZlaXn+16lazeLSso5Ty8bDqGutSVnnz59DYKB2kxVq+l5CwWhsr0lJWsaVcDRBs2tc08/XlloWtL2WUNdCPxdbaPsfyz8OYweNfkNp+R2udXlattmxcy3ys0fTz9cW+rsHABRMmrW/lueYpr/VVZVmZWl6jZu0i0vT37BAi/280PR7RNPf6hpeR1VV+NOf/qRJURWFhViRmqpJWXRrGzBgQPmjjz4avmDBgrO1tbWya9cun4kTJ17w8/MzhYSE1KSmpvo++uijxSaTCf/5z39ax8fHV9rubzQadaGhobXV1dWyadMmv9tvv73Wum7MmDGFkyZN6jxr1iynejaWlpa6hISE1ADAqlWrAqzL27ZtW19eXu4wGRoXF1exevVq38cff7xo1apVfr169Sq338bPz880Z86cC3PmzLmwd+/e1m+++Wb7F198MXjYsGHFb7755ilH5V6vTZs2+f3ud78rcrTu+eefD16zZs3xmpoaMZlMAgAiohqr47W60hD4CQDuE5H2ADYD2KiUytDy4D8HnaOjoQ5pU5YxtAKeH7ZpekNnynqoAp4nNCortAIp1SmalDXVY6pmdQS0r6dnG43KqtA4Lo3aTD8OeL3odU3Kmuo+Vbvzwn2qZu0FaP9eanr+a3leaFmW5wVtyjK21yyuzt20O18By3mmZT1bYFn6LnooVaBJWUZje22/k1pgWZfK4/eI82Vp2P6dx0Hb31AatT1gaf8W+Jusczdoe423wM8xLb9H9M3wPaLZb4LWU1vmbxUNvys7R0RDbdCkKPN1pOE5plV7Adq/ly3xs0fz7zct66jVZ7+Wf0SjW1rfvn2NI0aMKIqJiekeHBxc3bt370sJxI0bNx6bOnVqWHJy8u11dXUyYsSIIvsE6LPPPnumd+/e3YKDg2u6detmLC8vd7Gumzx5cmFycnLw5MmTHSYC7RkMhnNTpkzptGzZsqB+/fqVWpcPGTKk7K9//evtUVFR0fbJ1JUrV56YOHFi+NKlS4P8/f3r3n333YIrHePee++tvPfee08YjUbZtm1bW0fbDBs2rNPXX3/tXVxc7BoYGHjns88+e+aZZ5758d133/X505/+FFpcXOw6YsSIiG7duhn37NmTX1BQ4DZx4sSwzz///AgAlJWV6fbs2dP2nXfeOW5f9rp163x69OhRER4eXgsAvXr1Ktfr9dFRUVGV9m17va6UAD2llOolIhEAxgBYLyIuADbCnAzN0zIQIiIiIiIiIiIiKw/xqNNyJngP8WgwBNtecnLyueTk5HP2y6Oiomp2796db798yZIlZ6zPDQbDBYPB4PAvOp988on34MGDiwMCAuodrU9LSyuwff3AAw9UFBQUZFlfL1269AwABAYG1mdlZdn/mbkQACIjI2u+/vrrq87XeXp6qsTExBJH67Zt2/a9o+WPPPLIxUceeaTBpEXh4eG11uQnAHh7e5suXrz4naMyJkyYcHHChAmXynjrrbeapQcqcOUEqAIApVQ+gAUAFojInQB+D+AjAF2bKygiIiIiIiIiIvplm+4zPfNmx6CFiRMndkxPT2+3ffv2BglUujGulABtcANUpdR/AfwXwJxmi4iIiIiIiIiIiOgW8c4775wEcPJmx/FLdqUbiva7YVEQERERERERERERNYNGE6BKqQYzRRERERERERERERH9nGg6pTwRERERERERERFRS8IEKBEREREREREREd2ymAAlIiIiIiIiIiKyM3PmzA5z584NvN5ttPTll1+23rx5czvr6w0bNrR77rnngprreKNHjw738/OLjYiI6G67fObMmR1uu+22O6OioqKjoqKibWOyOnLkiFufPn30nTt37t61a9fuCxYsuM26LikpKViv10ePGDEi3LrsjTfe8LPdRktXmgX+EhHxBHAGwCil1CfNEQgREREREREREdEl+QGxqC90KnflFBf/OkT8mKlZec2ktrYWbm5uDtft37/fc//+/W0SExNLAGDcuHElAEqu95jnz593CQwMrLdf/uijj/741FNP/TBp0qRO9utmzJhx/sUXXzzfWJlubm5YvHjxqb59+xqLi4t1cXFx0QkJCaXh4eG1+/bt88rLy8sZPnx4p3379rWOjo6uWr9+fcBnn32Wf711ccTZHqC/A5ANYEpzBEFERERERERERHQZLZOfTpZnMBiCwsPDY+655x59fn6+u3V5dna2e79+/SK6d+/erWfPnpEZGRke9vsuXrw4ICYmpltkZGT0oEGDupSVlemKi4t1wcHBd1RXVwsAFBUVXfbaatSoUeFTpkwJ6dOnj/6xxx4LSU9P94yLi4vq1q1bdFxcXFRmZqZ7VVWVLFq0qMO2bdt8o6KiolNSUnyXLVvm/8gjj4QCQF5eXqv4+Hi9Xq+Pjo+P1+fn57e6Ul1ra2uxYcOGdgMGDOg6dOjQro62GTJkSHn79u3rmmo3R8LCwmr79u1rBABfX19Tly5dKk+cONFKp9Op2tpaMZlMqKqq0rm5ual58+YFPf744z+4u7urazlWU5xNgD4Kc/LzLhHxbY5AiIiIiIiIiIiIbpbdu3d7fvDBB34HDx7M2b59+5HMzMw21nVTpkwJW7FixYns7OxDr7766qmkpKRQ+/3HjRtXnJWVdejw4cM5kZGRlcuWLQvw9fU1xcfHl23ZsqUdAKSmpvolJCQUO0r0HT161GPv3r15KSkpp2JjY6v27duXe+jQoZwXXnjh9OzZs0M8PDzUnDlzzgwbNqw4Nzc3Z+rUqcW2+8+YMSN07NixhXl5eTmJiYmFSUlJHR3VMysry/3xxx8P7tq1a0xaWprvrFmzzn3zzTeHr7a93n777dv0en306NGjwy9cuOBypW0PHz7cKicnx7N///7lvr6+puHDhxdHR0dHh4WFVfv5+dV/++23bcaPH3/xamNwVpMJUBGJAqBTSh0CsBHA+OYKhoiIiIiIiIiI6GZIT0/3SkhIuOjt7W3y8/MzDRw48CIAlJSU6DIyMrxGjx7dJSoqKvqxxx4L++GHHxqMUT9w4EDrnj17Rur1+ui0tDT/7OxsDwCYNm3ahbVr1/oDwPr16wOmTZv2o6Pjjxw5stjV1dxJtaioyCUhIaFLRERE99mzZ3fMy8tr0OPUXkZGRptp06YVAUBSUlLRgQMHvOy3Wbt2rc9dd90V06pVK/Xdd9/lvP/++wUPPvhg+VU0EwDgmWee+eH48eMHDx06lBMUFFT72GOPOUy2Aub2GzlyZJeXX375pJ+fnwkAFi5ceD43NzcnJSXl1OzZszssWLDg9JIlSwISEhI6z549+/arjacpzvQAnQxgjeX5WgCTtA6CiIiIiIiIiIjoZhORBsvq6+vh7e1dl5ubm2N9HDt2LNt+u2nTpnVavnz5iby8vByDwXCmurpaBwADBw6sOHXqlPuOHTu86uvr5e67765ydGwvLy+T9bnBYAju379/WX5+fva2bduO1NTUaDKR+fDhw0sXLFhwYteuXe2GDh3aZdWqVX5Go7FhpZvQsWPHOldXV7i4uOCJJ5648N1337VxtF11dbU8+OCDXUaPHl00ceLEBj089+7d2xoA7rjjjur33nvP/6OPPjqWk5PT+uDBg+4NS7t2V2w8EXEDMArAJgBQSp0AUCgivbQMgoiIiIiIiIiI6GYaMGBA+Y4dO3zKy8uluLhYt2vXLh8A8PPzM4WEhNSkpqb6AoDJZMJXX33V2n5/o9GoCw0Nra2urpZNmzb52a4bM2ZM4aRJkzqPHz/eYe9Pe6WlpS4hISE1ALBq1aoA6/K2bdvWl5eXO8znxcXFVaxevdrXso9fr169GvTs9PPzM82ZM+dCVlbWoVdeeeXUnj17vCIiImJmzJgR4kxcVsePH7/UA3bTpk0+kZGRlfbbmEwmjBkzJkyv11fNmzfP4WRJzz//fHBycvKZmpoaMZlMAgAiohqr47VqqjA3ACOVUhU2y6YAOKdlEERERERERERERDdT3759jSNGjCiKiYnpPnTo0C69e/e+lEDcuHHjsTVr1gRERkZGR0REdE9LS/Ox3//ZZ58907t37279+vXTR0REXNbLc/LkyYWlpaWukydPLnImFoPBcG7evHkhPXr0iKqv/2ly9iFDhpTl5eW1tk6CZLvPypUrT6xbty5Ar9dHb9y40X/FihUnr3SMe++9t3LdunUn8vPzs+6///4yR9sMGzasU9++faO+//5798DAwDtfe+21AAB46qmnQvR6fbRer4/+/PPP277xxhsnAaCgoMCtf//+XQFg165dXh9++KH/nj17vKOioqKjoqKiN2/e3M5a9rp163x69OhRER4eXhsQEFDfq1evcr1eHy0iiI+Pb5BQvR5XnP1KKWUE8J31tYj0UEp9q2UAREREREREREREDbj412k6E7yLf5OzmScnJ59LTk5u0PEvKiqqZvfu3fn2y5csWXLG+txgMFwwGAwXHJX7ySefeA8ePLg4ICCg3tH6tLS0AtvXDzzwQEVBQUGW9fXSpUvPAEBgYGB9VlbWIbvdCwEgMjKy5uuvv85rvHaOeXp6qsTExBJH67Zt2/a9o+Uffvihw+Xh4eG1n3/++REAGDRoULlS6kBjx50wYcLFCRMmXBoW/9Zbb526usidd7Un0WoAPZojECIiIiIiIiIioksifsy82SFoYeLEiR3T09Pbbd++vUEClW6Mq02AXvVNUW8F0k2bcioqmt6GiIiIiIic87Tf05qVNbViqmZlERER2XrnnXdOArjicHRqXqKUcn5jkYeUUh82tV2HDh3U9OnTryuwluKxpEfRxstfk7LOnzmOwA5hmpQFUyWga3C/3WtSp2rhKm5Nb+hUYZWAqzZxAdC0nqisBFprVJaWcWnYZqfPnUZwULAmZWl6XmjZXgBQVQl4aHX+18FVNBpRoWU9NW0zE5q+5bOzRWkXl5bnq5l29VQwQbRqMw3jOn/+HAIDgzQpS9PP65ZaFsDvkZtY1vlzxxEYpNHvHg0/9wFoWk9lqoRo9F5qeo1r+dnfQr9HNP0Oh8a/fTT8vKgz1cJVp01cWn6/afl/Gy3j0vq8UFWVEI0+f1R9JcSlBX6PaPn9puV3koaf/arSiL8uf0OTsm62efPmHVBK3bITU2dmZhbExsY6NUkQkb3MzMyA2NjYcEfrruqbwZnk561mxcpUzcqaN28enE83X5mxogKebdrc0mVdKu9DjWJ7SON6ntCorFDt4tIDUBs0KcrcXi2wjkAznBeeDm/RcvVlGdu3yDb7JZyvgOW91PC8SKlO0aSsqe5TNWv/zt0ApQo0KctobN8iP/ub5XukBcb2SyirM6Dt7x6Nrm9A+98EWn1ePN356RbZZpr/JmiBn9WA5fO6pf7ubInna4S256um37saf49oGVtLrGdL/R7R+jcsEf2yaTqlPBEREREREREREVFLwgQoERERERERERER3bKaTICKyF9FpPuNCIaIiIiIiIiIiKglmDlzZoe5c+cGXu82Wvryyy9bb968uZ319YYNG9o999xzWt1E/DJHjhxx69Onj75z587du3bt2n3BggW3WdedP3/e5Z577okICwuLueeeeyIuXLjg4qiMfv36RXh7e991//33d7VdPnz48E56vT76iSeeuDQxxJ/+9Kfb169f79McdXHmHqC5AN4SEVcAawBsVEqVNEcwREREREREREREAICtAbGoKdRuZrNW/nV4+MdMzcprJrW1tXBzczwB3v79+z3379/fJjExsQQAxo0bVwLguvN058+fdwkMDKy3Xebm5obFixef6tu3r7G4uFgXFxcXnZCQUNqzZ8+qF1544fZf//rXZS+99FL+c889FzR37tyglStXnrYv949//OO5iooKXUpKSnvrsv/85z+tASAvLy+nZ8+ekYWFhS7l5eW6AwcOtHn11VfPXm9dHGmyB6hSarVS6l4AjwAIB/BfEXlPRO5vjoCIiIiIiIiIiIg0TX46WZ7BYAgKDw+Pueeee/T5+fnu1uXZ2dnu/fr1i+jevXu3nj17RmZkZHjY77t48eKAmJiYbpGRkdGDBg3qUlZWpisuLtYFBwffUV1dLQBQVFR02WurUaNGhU+ZMiWkT58++sceeywkPT3dMy4uLqpbt27RcXFxUZmZme5VVVWyaNGiDtu2bfONioqKTklJ8V22bJn/I488EgoAeXl5reLj4/V6vT46Pj5en5+f3+pKda2trcWGDRvaDRgwoOvQoUO72q8PCwur7du3rxEAfH19TV26dKk8ceJEKwD45z//6TN9+vRCAJg+fXrhzp07fR0d47e//W1Z27ZtTbbL3NzcVFVVla6+vh61tbXi6uqqDAZDhwULFjRIoGrFqXuAiogLgCjL40cAmQBmisim5gqMiIiIiIiIiIjoRtm9e7fnBx984Hfw4MGc7du3H8nMzGxjXTdlypSwFStWnMjOzj706quvnkpKSgq133/cuHHFWVlZhw4fPpwTGRlZuWzZsgBfX19TfHx82ZYtW9oBQGpqql9CQkKxu7u7st//6NGjHnv37s1LSUk5FRsbW7Vv377cQ4cO5bzwwgunZ8+eHeLh4aHmzJlzZtiwYcW5ubk5U6dOLbbdf8aMGaFjx44tzMvLy0lMTCxMSkrq6KieWVlZ7o8//nhw165dY9LS0nxnzZp17ptvvjl8pbY5fPhwq5ycHM/+/fuXA0BhYaFrWFhYLWBOlBYVFTmdrO7Ro0dVSEhITffu3aNHjBhRnJ2d7a6Uwr333lvpbBlXq8ngRGQJgOEAPgHwklJqn2VVsohcsXGIiIiIiIiIiIh+DtLT070SEhIuent7mwBg4MCBFwGgpKREl5GR4TV69Ogu1m1ramrEfv8DBw60njt3bnBZWZlLRUWFS//+/UsAYNq0aReSk5ODJkyYcHH9+vUBKSkpBY6OP3LkyGJXV3OqrqioyCUxMbFTQUGBh4io2traBsezl5GR0Wbnzp1HASApKalo/vz5IfbbrF271mfKlCldnnzyybPfffddjq+vr6lhSZcrKSnRjRw5ssvLL7980s/Pr8ntnZGamnrS+nzAgAFdU1NTjxsMhqCDBw96/uY3vymdNWvWj1ocx8qZHqBZAO5USk23SX5a9dYyGCIiIiIiIiIioptFpGGesb6+Ht7e3nW5ubk51sexY8ey7bebNm1ap+XLl5/Iy8vLMRgMZ6qrq3UAMHDgwIpTp06579ixw6u+vl7uvvvuKkfH9vLyupRcNBgMwf379y/Lz8/P3rZt25GamhqnRnE3Zfjw4aULFiw4sWvXrnZDhw7tsmrVKj+j0dhocrW6uloefPDBLqNHjy6aOHHiRetyf3//uuPHj7sBwPHjx938/PzqriWe9evX+8TFxVWUlZXpcnJyWn/00UfHNm7c6F9WVqZJfa2cuQdoKgB3EektIvdZH5Z1nAyJiIiIiIiIiIh+9gYMGFC+Y8cOn/LycikuLtbt2rXLBwD8/PxMISEhNampqb4AYDKZ8NVXX7W2399oNOpCQ0Nrq6urZdOmTX6268aMGVM4adKkzuPHj3eqZ2NpaalLSEhIDQCsWrUqwLq8bdu29eXl5Q7zeXFxcRWrV6/2tezj16tXr3L7bfz8/Exz5sy5kJWVdeiVV145tWfPHq+IiIiYGTNmNOgtajKZMGbMmDC9Xl81b96887brBg0adHHVqlX+lmP5Dx48+KL9/k2prq6W5cuX3zZv3rzztnVSSsH+HqnXq8kEqIhMAfAFgH8BmG/5d56WQRAREREREREREd1Mffv2NY4YMaIoJiam+9ChQ7v07t37UgJx48aNx9asWRMQGRkZHRER0T0tLc3Hfv9nn332TO/evbv169dPHxERcVkvz8mTJxeWlpa6Tp48uciZWAwGw7l58+aF9OjRI6q+/qfJ2YcMGVKWl5fX2joJku0+K1euPLFu3boAvV4fvXHjRv8VK1acbFCwjXvvvbdy3bp1J/Lz87Puv//+Mvv1u3bt8vrwww/99+zZ4x0VFRUdFRUVvXnz5nYAMH/+/LPp6eltw8LCYtLT09vOnz//LAB88cUXnomJiWHWMnr27Bk5YcKEzl999VXbwMDAO9PS0tpa1yUnJ7cfN25cobe3t6lPnz6VAKDX66N79+5dHhAQUG8fz/Vw5galTwG4G8DXSqn7RSQK5kQoERERERERERFR82jlX6fpTPCt/Jscpp2cnHwuOTn5nP3yqKiomt27d+fbL1+yZMkZ63ODwXDBYDBccFTuJ5984j148ODixhJ7aWlpBbavH3jggYqCgoIs6+ulS5eeAYDAwMD6rKysQ3a7FwJAZGRkzddff53XeO0c8/T0VImJiQ1GeQ8aNKhcKXXA0T5BQUH1X331VYNj3Xfffcb77rvvuPX1gQMHGp0/aO7cuT9Yn+t0Omzbtu37q43dWc6cRFVKqSoRgYi4K6VyRSSyuQIiIiIiIiIiIiLCwz9m3uwQtDBx4sSO6enp7bZv394ggUo3hjMJ0FMi4gPgQwC7RKQYwJkm9iEiIiIiIiIiIvrFe+edd04CuOJwdGpeTSZAlVIjLE/niUg6gHYA/tmsURERERERERERERFpoNEEqIj4OVh80PKvFwCnbtpKREREREREREREdLNcqQfoAQAKgAAIBVBsee4D4ASATs0eHREREREREREREdF1aDQBqpTqBAAi8iaAfyilPrK8HgLggRsTHhFR82rTpo1mZVVUVGhWFhFpQ9NrXLOSiIiIiIjoRhKl1JU3EDmglOppt2y/UqpXY/t06NBBTZ8+XaMQbx2P/O53CAwL06awykqgdetbuywAqKsEXFtgbKZKQNfy4jp/5jgCO2h0jmlZx6pKwKNlnhcKJgh0mpR1/txxBAZp0/6qqhKiUZspUyXkVj9fAaC+EnDRJrY6VQtXcdOkLC2vpfPnzyEwMEiTsjS9Llvy90hLje0XUNb548e1+92j4fUNQNPzv07VwVWcmVe0aS22zbT8TaDpZ7V2bQ8Aqr4SolWbtdDP2JZ6vtaZauGq0+h7V+PvES3bTNNztoV+9rfU/3OpeiP+uuQNTcq62ebNm3fgSvmYn7vMzMyC2NjYH292HFYzZ87s4OXlVf/iiy+ev55ttPTll1+2PnnyZKvExMQSANiwYUO77Ozs1i+99NK5G3F8e9OnTw/597//3c7NzU2FhYVVb9y4sSAgIKAeAObMmRO0YcOGAJ1Oh8WLF58YNWpUqf3+L730Uvs333wz8OTJk+5nzpzJvP322+sAYO3atT4LFy4MbteuXd2OHTuOBAUF1WdnZ7sbDIbg7du3H3MUS2ZmZkBsbGy4o3XOfPr+KCLPA1gP85D48QAKnWsGstU5OhpXTjc7z1hRAU+NerW01LK0Lk/zsj7UqKyHtIurMwC1QZOiNI3LWFEBzxManheh2saWUp2iSVlPd3ka6pAmRcEYql1cU92naldW66manq+vF72uSVmAuZ6aXpcanbNanq9attlUj6ktso6/qO+RW7yszoC2v3u0/h7R8PzX7Hsk+mltv8e1vMa1LEvLz2rPC5qUBQBGY3vtvi9b8Gdsiz1fW+Dn2KXyNDrPND3HNPxN1lK/R7T+Pxf9TAUExKKwULu/dvn71+HHHzM1K6+Z1NbWws3N8R+G9u/f77l///421gTouHHjSgCUOFt2YWGhi4+PT72Li4smsQ4aNKh0+fLlp9zc3JCUlBT8l7/8JWjlypWnDxw44PH+++/7HT58OPv48eNu//M//6P/7W9/m+Xqevnb2b9///JRo0aVDBgwINJ2+dKlS4O++eabQ6mpqX5vv/22/5///Ocfnn322Q6LFi06fS1xOtPt6fcA2gP4AMCHAG6zLCMiIiIiIiIiImoeWiY/nSzPYDAEhYeHx9xzzz36/Px8d+vy7Oxs9379+kV07969W8+ePSMzMjI87PddvHhxQExMTLfIyMjoQYMGdSkrK9MVFxfrgoOD76iurhYAKCoquuy11ahRo8KnTJkS0qdPH/1jjz0Wkp6e7hkXFxfVrVu36Li4uKjMzEz3qqoqWbRoUYdt27b5RkVFRaekpPguW7bM/5FHHgkFgLy8vFbx8fF6vV4fHR8fr8/Pz29lH+OuXbu8OnfuHDNz5swOjtZfrZEjR5Zak7Xx8fEVp0+fbgUAW7du9Rk5cmRR69atVVRUVE1YWFj1Z5991uCvGvfee29lZGRkjf1ynU6nqqqqdEajUefm5qb++c9/egUGBtbecccd1dcSZ5MJUKVUkVLqKaVUnOXxlFKKM8ATEREREREREdEtY/fu3Z4ffPCB38GDB3O2b99+JDMz81LCbsqUKWErVqw4kZ2dfejVV189lZSUFGq//7hx44qzsrIOHT58OCcyMrJy2bJlAb6+vqb4+PiyLVu2tAOA1NRUv4SEhGJ3d/cGg2WOHj3qsXfv3ryUlJRTsbGxVfv27cs9dOhQzgsvvHB69uzZIR4eHmrOnDlnhg0bVpybm5szderUYtv9Z8yYETp27NjCvLy8nMTExMKkpKSO9scYM2ZMyddff53r4+NTN3z48K59+/aNSE1N9a2qqhIAKC4u1kVFRUU7ehw4cKBB0tfW2rVrAwYPHlwCAKdPn27VsWPHS4nNDh061Jw8edLphOvzzz9/9oEHHohIT09vO3ny5KKFCxfevmjRorPO7id7kmoAABpRSURBVG+vycy3iOgB/BFAuO32SqkB13pQIiIiIiIiIiKiliQ9Pd0rISHhore3twkABg4ceBEASkpKdBkZGV6jR4/uYt22pqZG7Pc/cOBA67lz5waXlZW5VFRUuPTv378EAKZNm3YhOTk5aMKECRfXr18fkJKSUuDo+CNHjiy2DhEvKipySUxM7FRQUOAhIqq2trbB8exlZGS02blz51EASEpKKpo/f36Io+1uv/32urlz5/4wd+7cHz7++OM206ZNC3/55Zdvz8vLy/H19TXl5ubmNHUsewaDIcjFxUXNmDGjCAAczTkkIk7fIWnEiBGlI0aMKAWAv/3tb/6DBg0q+e9//+vx6quvBvr4+NSnpKSctL5PznCmK/HfAbwJYDWAemcLJiIiIiIiIiIi+jkRaZhnrK+vh7e3d11TicFp06Z12rp165H4+PjKZcuW+X/++efeADBw4MCKJ5980n3Hjh1e9fX1cvfdd1c52t/Ly+tSQs9gMAT379+/bNeuXUcPHz7cyv4emdfrwIEDHm+99VbAzp07fX71q1+VzZgx40fA3AM0Pj4+ytE+GzZsONazZ88Gsf/tb3/z/9e//uWze/fuPJ3OPNg8JCTksh6fZ86caRUSElJ7tXGWlZXpNmzY4P/555/n33fffRE7d+48snr1av+33nrLb9asWU5PmOXMPUDrlFIrlVL7lFIHrI+rDZiIiIiIiIiIiKilGjBgQPmOHTt8ysvLpbi4WLdr1y4fAPDz8zOFhITUpKam+gKAyWTCV1991dp+f6PRqAsNDa2trq6WTZs2+dmuGzNmTOGkSZM6jx8/3qmkXWlpqUtISEgNAKxatSrAurxt27b15eXlDvN5cXFxFatXr/a17OPXq1evcvtt9uzZ4xkbGxs1ZcqU8KioqKqDBw/mbNmy5fiAAQMqAMDaA9TRw1Hyc+vWrW1ff/31oI8++uiIbY/MUaNGXXz//ff9KisrJTc3t1VBQYHHr3/966uekeyFF14IeuKJJ35wd3dXVVVVOhGBTqdTRqPRmZzmJc5svE1EHhOR20XEz/q42oCJiIiIiIiIiIhaqr59+xpHjBhRFBMT033o0KFdevfufSmBuHHjxmNr1qwJiIyMjI6IiOielpbmY7//s88+e6Z3797d+vXrp4+IiLgsWTh58uTC0tJS18mTJzs1r47BYDg3b968kB49ekTV1/80IHvIkCFleXl5ra2TINnus3LlyhPr1q0L0Ov10Rs3bvRfsWLFSftyPT09TWvWrCnIyMjIfeaZZ35s166d08PIHZk5c2ZoRUWFy4ABA/RRUVHRY8eODQWAXr16VT300ENFer2+++DBg/VLliw5bh3e379//64FBQVuALBw4cLbAgMD7zx//nyr2NjY6MTExDBr2QUFBW4ZGRme48ePvwgATz311Pm7776723vvvRfgbDtaOTMEfqLl3z/ZLFMAOl/NgYiIiIiIiIiIiJzm71+n6Uzw/v51TW2SnJx8Ljk5+Zz98qioqJrdu3fn2y9fsmTJGetzg8FwwWAwXHBU7ieffOI9ePDg4oCAAIe3l0xLSyuwff3AAw9UFBQUZFlfL1269AwABAYG1mdlZR2y270QACIjI2u+/vrrvMZrB/To0cPh8PtrdeLEiazG1jXWlp9//vkR6/Pnn3/+h+eff/4HR/uHh4fXpqenX9r20UcfLX700f/f3p0HWXZXBRz/nlmYLQvb2KFHMmGGBBkkBLKoRAFRgiBL0CQEEiYSaEdFqcEyIkppa0lZCEqEBAzDUhHCIotBQopEMAGBkGCKkERBEJIBE2YyYQt0d5Lp7uMf7za2Q/q9x/R53Xd6vp+qqel3332nz/317y59+nfv79zv3Ne6vfTsRJn5sP0JLEmSJEmSJO23O+/8wmKnUOGcc8556FVXXXX4ZZdd9iMFVC2MfmaBXwn8NvCEZtHVwEWZ+WM/uFSSJEmSJEk6mFx88cXfAH7kdnQtnH6GEb8JWAm8sXn9gmbZiweVlCRJkiRJkiRV6KcAemJmPmbW63+NiCUxBFmSJEmSJEmtMT09PR3Lli3LxU5EB5bp6ekA5pzQqZ9Z4KciYvPMi4jYBNznA1slSZIkSZKk/XTznj17Dm+KWVJfpqenY8+ePYcDc07I1M8I0POAqyLia0AAG4EX1qQoSZIkSZIkweTk5It37dr1ll27dv00/Q3ak6Az8vPmycnJOR/X2c8s8B+PiKOBR9ApgH4pM++py1GSJEmSJEkHu+OPP/4O4FmLnYeWnjkLoBHxa3O8tTkiyMwPDignSZIkSZIkSSrRbQTo+4Ebmn/QGf05IwELoJIkSZIkSZJarVsB9NeB5wLHAh8C3p2Z/70gWUmSJEkHoTirJs7YWE0cSXXWrVtXFmtkbKQsliQdDCIzu68QsQ54Np1i6IOAP8nMT3T7zPDwcG7btq0syaVi6xlnMLRxY02wiQlYs2ZpxwK4ewJWtzC3qQlY3r68dt++k6Hh9vWxnJ4gltX1i7x7gijqF5M5yYroZz643nbv2snQETXtP5l7WRErWxersl/ctus2NhyxoSQWQE5NEEX7ZWmfbWmbtXUbS4/70N5z3EEQa/fOnWXXPdXnEaYnoChe5Xmk8vqi8pxU2V6lx2qmicL5MSp/lpV9trXXPS297iz/fSSnIWr6WWn7Fx5jW9v+kxOwomg/2jvOa8+/sCTWYhsdHb0+M09Y7DykA00/R9+7ge8BdwFHAqsHmtEStmnLFrqXm/s3PjbG2qK/ILY11g/jfb0otyOLt7OFeW2C1vaxHffsKIkFMLJ6pCzeyKq6WNs3b29t+7exvx4DnP/t80tiQe3PsjTWmpHSNsu8tSTW+Pj6Vm5jZX+FARz7Ly2KdWpxrBaexzcBeUlJKMZPLT6PrBopbf/KY09VXpvOqj1etLW9yvvF2j0lsUqPsW297jl6O/nFklCtPVZD/fG6rP23tPi6s6WxJB3cuk2C9IvA84CTgI8Bf5eZ/75QiUmSJEmSJEnSfHUbAfpx4EbgU8AqYGtEbJ15MzNfOuDcJEmSJEmSJGleuhVAX7hgWUiSJEmSJEnSAMxZAM3MixcyEUmSJEmSJEmq1td0dhHxm91eS5IkSZIkSVIb9VUABaLHa0mSJEmSJElqnb4KoJl5UbfXkiRJkiRJktRG/Y4A/X8iwgmSJEmSJEmSJLXefhVAgT8vzUKSJEmSJEmSBmDOWeAj4sa53gKGBpOOJEmSJEmSJNWZswBKp8j5VOA7+ywP4DMDy0iSJEmSJEmSinQrgF4GHJKZN+z7RkRcPbCMJEmSJEmSJKnInAXQzHxRl/eeP5h0JEmSJEmSJKnOnJMgRcQhvT7czzqSJEmSJEmStFi6zQL/oYj4m4h4QkSsm1kYEZsi4kURcQXwK4NPUZIkSZIkSZL2T7db4H8pIp4ObANOjogHAJPAfwEfAc7JzF0Lk6YkSZIkSZIk/fi6TYJEZl4OXL5AuUiSpB4ijiqJMzY2VhJHkiRJktouMrM86PDwcG7btq087oFu6xlnMLRxY02wiQlYs2ZpxwKYnoBlLcxt+m5YtromVmFeu3fubGUfm8xJVkTXv7csWrzJ3MuKWFkSq63t39b+etuu29hwxIaSWFDdL+pi5d0TxOqaNkumia5Pr+lfZftXbmPpcZ/i3KYmYHkLY909AVXbWHlOun0nQ8M1x8Tq80hl+5ceL6YmiKK8du/exdDQESWxKvNq6zkcao+xbd3Oyrx279rJ0BE1+3hrj9XF8San97JiWQuvO1t6HqmMlRPjvPaCC0tiLbbR0dHrM/OExc5DOtAUXkmql01btlBVbh4fG2Ptpet6r9hPrFPHWLuuKNZYXawfxvt6UW5HFm/n2j01sVhfltcmqO1jLWwvgPHx9ey4Z0dJrJFVI2V9bNMWyEtKQnX2y8p9vIX99Rggv1gSCujs46X9orDNKvOqirV983bO//b5JbFGVtflVblPwgD6ReV+2dZYleekwmNi1c8RavvZ+JGFx9jC89v2TdvJvLUkVvl5t7C9Wn28qNzOwmuyyvNI5XVnm88jrT3GlkQawO9cLT2/STq41fyJU5IkSZIkSZJaqGcBNCI2R8Sq5usnRcRLI+L+g09NkiRJkiRJkuannxGgHwCmIuLhwFuBhwHvGmhWkiRJkiRJklSgnwLodGZOAs8Bzs/MlwEPGWxakiRJkiRJkjR//RRA90bE84BzgMuaZXXTLUqSJEmSJEnSgPRTAH0h8HPAqzLzloh4GPDOwaYlSZIkSZIkSfO3oo911mTmS2deNEXQ/xhgTpIkSZIkSZJUop8RoDsi4tEzL5rb4V85uJQkSZIkSZIkqUY/I0BPA94fEWcBPw9sBU4ZaFaSJEmSJEmSVKBnATQzvxYRZwKXAt8ATsnMiYFnJkmSJEmSJEnzNGcBNCJuAnLWogcCy4FrI4LMPHbQyUmSJEmSJEnSfHQbAfqMBctCkiRJkiRJkgZgzkmQMnPnzD/gocCTm6/Hu31OkiRJkiRJktqiZyEzIv4MeDnwimbRSuCdg0xKkiRJkiRJkir0M5LzOcCzgDGAzLwdOHSQSUmSJEmSJElShX4KoPdmZtJMiBQR6wabkiRJkiRJkiTV6DYJ0ox/jIiLgPtHxAhwLrBjsGlJHevW1dXbx8bKQrVaFMU5SJpLkiRJ+8nrTknSgSI6gzt7rBTxFOAUOue4KzLzX7qtPzw8nNu2bavJcAnZesYZDG3cWBNsagKWr6mJNTEBa1oYC2B6Apa1L7dkmqiaC6yl7b975866/kpSd4kMkznJiujn7zd9KOxju2/fydBwUZtNTsCKqn2psP3vnoDVRe21aydDR1T1MZjMvayIlUXRpqma76+yv1bGum3XbWw4YkNJrNK2rzzuU5zb1N2wfHVRrMLzeGWsyn288JhYu39T3M/aebzYvXsXQ0NHlMSqzKv0Gqr8eFF4fdHSc2/pvlR43Vl5TqruF6XXZC29Vs/pCaKqzVp6fsvJcV77ugtLYi220dHR6zPzhMXOQzrQ9HuG/zKQmfmxiFgbEYdm5vcHmdhStGnLFnqXm/szPjbG2ktrRkeOnzrG2qKRlpV5QZPb14tyO7J2O3fcUzMQemTNSCvbf9NZkJeUhOr8HNfuqQkGjI+vr2v/VXXtvwnIL5aEKu+vVe0/zvraPlbUXtC0WeXxojBWaX8t+lkes/mY2v7awnMSNP2/jfvSeN2+NH5qcfu39TxStE/CAI4Xle1f1Mc2bdpM5q0lscbH15f2/dJjYvF1ZxuP15Xn3rZeWx8Dtf21+nhReR6pvO4siVT8u82qkdYeXyUd3PqZBX4EeD9wUbNoA3DpIJOSJEmSJEmSpAr93H/yEuBk4C6AzPwK8BODTEqSJEmSJEmSKvRTAL0nM++deRERK6gbbS9JkiRJkiRJA9NPAfQTEfHHwJpmMqT3AR8ebFqSJEmSJEmSNH/9FED/CNgD3ARsAy4HXjnIpCRJkiRJkiSpQj+zwD8JuCQza6aFkyRJkiRJkqQF0k8B9DeAv4+IbwH/1vz7VGZ+Z5CJSZIkSZIkSdJ89SyAZuZWgIgYBk4DLgSG+/msJEmSJEmSJC2mnkXMiDgb+AXg0cCdwAV0RoFKkiRJkiRJUqv1M4rzfOCrwN8DV2XmrQPNSJIkSZIkSZKK9JwFPjMfDJwLrAZeFRHXRcQ7Bp6ZJEmSJEmSJM1TzwJoRBwGHAlsBI4CDgemB5uWJEmSJEmSJM1fP7fAf2rWvwsy838Gm5IkSZIkSZIk1ehnFvhjFyIRSZIkSZIkSarW8xZ4SZIkSZIkSTpQWQCVJEmSJEmStGTNWQCNiFc3/5++cOlIkiRJkiRJUp3IzPt+I+Im4HHAtZn5uB8n6PDwcG7btq0gvaXld849l3UPelBJrNw7TqxcWxNrYpxYUxSrMC+AnBonlrdvO/dO72XlspUlsaYnxlnWwvbPyXFiRVGsnCJieUksaHH7t7S/5vQUsaym/XNyglixpiZWYXtVx6uMVdpfc4plRftSTk8Qy4p+lpXHi8K+D5Dj48TaJb4vVbZ/W2O1+XjR0vNl5jQRNTd7VcYqPSZOjrOsqO2hxcfrlp57W3utUnh+g+LtrDwnFcYq7ftT4yxr4fF17K5v8caL3lYSa7GNjo5en5knLHYe0oGm2yRIHwXuBNZFxF1AADnzf2YetgD5LSlvfNvSOOBKkiRJkiRJB4o5/5Sbmedl5uHARzLzsMw8dPb/C5ijJEmSJEmSJO2XbiNAAcjMZ0fEEHBis+jazNwz2LQkSZIkSZIkaf56PsynmQTpOuB04Azguog4bdCJSZIkSZIkSdJ89RwBCrwSODEz7wCIiPXAx4D3DzIxSZIkSZIkSZqvfqZzXDZT/Gx8q8/PSZIkSZIkSdKiiszsvkLEa4BjgXc3i54L3JiZL+/ymT3AzqokJUmSJEmSxMbMXL/YSUgHmp4FUICI+DXg54EAPpmZ/zToxCRJkiRJkiRpvvoqgEqSJEmSJEnSgchneUqSJEmSJElasiyASpKkniLidRGxfdbrKyLiLbNe/01E/P5+xv7Bj7n+KyLirIgYjYiMiIfPeu9lzbIT9jOXUyNiy6zXV88j1uURcf/9+awkSZKkOj0LoBHxjIiwUCpJ0sHtM8DjAZrrggcDj5r1/uOBTy9QLqcAVzZf3wScOeu904D/nEfsU4EtPdfqQ2Y+PTO/WxFrf0XEisX8/pIkSVIb9FPYPBP4SkT8dUQ8ctAJSZKkVvo0TQGUTuHzZuD7EfGAiFgFPBL4PEBEnBcRn4uIGyPiz2cCRMTZEXFdRNwQERdFxPLZ3yAiHhwR10TEr0bEQyLik826N0fELzTrHAbcLzP3NB+7FHh2894m4HvAnlkxnxcRNzUxXj1r+Q8i4lUR8YWI+GxEDEXE44FnAa9pvu/mZvXTm7y/PCuPR83alhsj4uh9Gywibm226aiI+GJE7IiI/4iIKyNizT7rHhoRt0TEypntbD6/MiI2R8RHI+L6iPi3iPipZp1nRsS1EfH5iPhYRAw1y0cj4s0RcSXwD339dCVJkqQlrGcBNDPPBh4LfBV4e/OLyW9GxKEDz06SJLVCZt4OTEbEkXQKodcA1wI/B5wA3JiZ90bEKcDRwEnAccDxEfGE5o+ozwVOzszjgCngrJn4TfHuI8CfZuZHgOcDVzTrPga4oVn1l4GPz0rtLuAbEfHTwPOA986KOQy8Gnhyk8uJEXFq8/Y64LOZ+Rjgk8BIZn4G+GfgvMw8LjO/2qy7IjNPArYDf9Ys+y3g75r8TgD+p0cTHg1cmJmPAr4L/Po+7ft94GrgV5tFZwIfyMy9wJuB38vM44E/AN7YrPMp4Gcz87HAe4A/nBXyeODZmfn8HnlJkiRJS15ft0Vl5l0R8QFgDZ2L/+cA50XE6zPzDYNMUJIktcbMKNDHA38LbGi+/h6dW+Shc3v6KTSjQYFD6BT/jqVTlPtcREDnmuKOZp2VdIqaL8nMTzTLPge8rRkReWlmzhRAfwV4+z55vYdOwfCpwC8BL2yWnwhcPTNaNCIuAZ5AZ9TovcBlzXrXA0/pst0fnLXeUc3X1wB/EhE/CXwwM7/S5fMAt8zahtlxZnsLnSLmpc02jETEIXTa+H1NuwGsav7/SeC9EfEQ4H7ALbNi/XNmTvTISZIkSToo9PMM0GdGxD8B/0rnF5STMvNpdEZj/MGA85MkSe0x8xzQR9O5Bf6zdEaAzn7+ZwB/1YygPC4zH56Zb22WXzxr+SMyc7T5zCSdouBTZ75RZn6STrHyNuAdEbG1eesk4Lp98vow8ALg65l516zlwdz2ZmY2X0/R/Y/C9+y7Xma+i87t8hPAFRHx5C6fnx1jzu+XmZ8GjoqIJwLLM/NmOtdq353Vbsdl5swjid4AXJCZjwa2AatnhRvrkY8kSZJ00OjnGaCnA6/LzGMz8zWZeQdAZo4D5w40O0mS1CafBp4BfDszpzLz28D96RRBr2nWuQI4txm5SERsiIifoDPC87TmayLigRGxsflM0rmm+KmI+KPm/Y3AHZm5A3gr8LiIeBTwpcycmp1UM9Lx5cCr9sn3WuCJzXM4l9O5Rf4TdPd9oOdjfprnjX4tM19P57b5Y3t9pk//ALybZpRrU9C9JSJOb75vRMRjmnUPp1MgBjin6PtLkiRJS07PW+Azc2uX9z4+13uSJGnJuYnO7O/v2mfZIZl5J0BmXtk87/Oa5pbtHwBnZ+Z/RsQrgSubWeT3Ai8Bdjafm4qIM4EPR8RddEYwnhcRe5sYW+k8N/Oj95VYZr7nPpZ9MyJeAVxFZzTo5Zn5oR7b+B5gR0S8lM6M8nN5LnB2k98u4C96xO3XJcBf0imCzjgLeFPTfiubHL8AjNK5Nf42OqNxH1aUgyRJkrSkxP/d/TXHChE/S+cWq0fSeb7UcmAsMw8bfHqSJEkdEfEvwNbM/OZi5zIoEXEancmLXrDYuUiSJElLRT+TIF1AZ2KB99GZ5XQr8PBBJiVJkrSvzOw2UdEBLyLeADwNePpi5yJJkiQtJf3OAv/fEbG8eebW2yPiMz0/JEmSpL5l5u8tdg6SJEnSUtRPAXQ8Iu4H3BARfw18E1g32LQkSZIkSZIkaf76mQX+Bc16v0tnQoKH0pmEQJIkSZIkSZJareckSAARsR4gM/cMPCNJkiRJkiRJKjLnCNDoGI2IO4EvAV+OiD0R8acLl54kSZIkSZIk7b9ut8BvB04GTszMB2XmA4CfAU6OiJctSHaSJEmSJEmSNA9z3gIfEZ8HnpKZd+6zfD1wZWY+dgHykyRJkiRJkqT91m0E6Mp9i5/ww+eArhxcSpIkSZIkSZJUo1sB9N79fE+SJEmSJEmSWqHbLfBTwNh9vQWszkxHgUqSJEmSJElqtTkLoJIkSZIkSZJ0oOt2C7wkSZIkSZIkHdAsgEqSJEmSJElasiyASpIkSZIkSVqyLIBKkiRJkiRJWrIsgEqSJEmSJElasiyASpIkSZIkSVqy/hezCTdBN+Lo0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))                                                             \n",
    "ax = fig.add_subplot(1,1,1)     \n",
    "plt.xlabel(\"Weeks/Months in year\")\n",
    "plt.ylabel(\"Day of weeks (1:Monday -> 7 :Sunday)\")\n",
    "plt.title('Figure 10: The change of number flights over days in year')\n",
    "  \n",
    "rec_size = 0.3\n",
    "from matplotlib.patches import Rectangle\n",
    "import datetime\n",
    "num_days = len(pdf[0])\n",
    "ax.patch.set_facecolor('gray')\n",
    "ax.set_aspect('equal', 'box')\n",
    "ax.xaxis.set_major_locator(plt.NullLocator())\n",
    "ax.yaxis.set_major_locator(plt.NullLocator())\n",
    "\n",
    "for i in range(0, num_days):\n",
    "    # extract information from the result\n",
    "    year = pdf[0][i]\n",
    "    month = pdf[1][i]\n",
    "    day_of_month = pdf[2][i]\n",
    "    day_of_week = pdf[3][i]\n",
    "    day_of_year= datetime.date(year=year, month=month, day=day_of_month).timetuple()\n",
    "    week_of_year = datetime.date(year=year, month=month, day=day_of_month).isocalendar()[1]\n",
    "    \n",
    "    # dealing with the week of the previous year\n",
    "    if week_of_year == 52 and month == 1:\n",
    "        week_of_year = 0\n",
    "        \n",
    "    # the coordinate of a day in graph\n",
    "    X = week_of_year*rec_size\n",
    "    Y = day_of_week*rec_size\n",
    "    \n",
    "    # use different colors to show the delay ratio\n",
    "    color = 'white'\n",
    "    if pdf[4][i] <= 0.084:\n",
    "        color = 'lightyellow'\n",
    "    elif pdf[4][i] <= 0.117:\n",
    "        color = 'lightgreen'\n",
    "    elif pdf[4][i] <= 0.152:\n",
    "        color = 'gold'\n",
    "    elif pdf[4][i] <= 0.201:\n",
    "        color = 'orange'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    rect = plt.Rectangle((X - rec_size/2.0, Y - rec_size/2.0), rec_size, rec_size,\n",
    "                      alpha=1, facecolor=color, edgecolor='whitesmoke')\n",
    "\n",
    "    ax.add_patch(rect)\n",
    "    \n",
    "    # drawing boders to separate months\n",
    "    if day_of_month <= 7:\n",
    "        rect2 = plt.Rectangle((X -rec_size/2.0, Y - rec_size/2.0), 0.01, rec_size,\n",
    "                      alpha=1, facecolor='black')\n",
    "        ax.add_patch(rect2)\n",
    "    if day_of_month == 1:\n",
    "        rect2 = plt.Rectangle((X - rec_size/2.0, Y - rec_size/2.0), rec_size, 0.01,\n",
    "                      alpha=1, facecolor='black')\n",
    "        ax.add_patch(rect2)\n",
    "ax.autoscale_view()\n",
    "\n",
    "patch1 = mpatches.Patch(color='lightyellow', label='delay ratio < 8.4%')\n",
    "patch2 = mpatches.Patch(color='lightgreen', label='delay ratio < 11.7%')\n",
    "patch3 = mpatches.Patch(color='gold', label='delay ratio < 15.2%')\n",
    "patch4 = mpatches.Patch(color='orange', label='delay ratio < 20.1%')\n",
    "patch5 = mpatches.Patch(color='red', label='delay ratio >= 20.1%')\n",
    "\n",
    "plt.legend(handles=[patch1, patch2, patch3, patch4, patch5], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "As we know, in France, the winter season starts in December untill February, which means we may expect a high % of delayed flights as the previous plot. But in the other periods it may differ depending on the holidays and other aspects. For example, In USA, it shows that in May is the lowest percentage while we expect a higher percentage of flights delay  in France due to the high amount of holidays in this month. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.6\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "What is the delay probability for the top 20 busiest airports?  \n",
    "\n",
    "\n",
    "By drawing the flight volume of each airport and the associated delay probability in a single plot, we can observe the relationship between airports, number of flights and the delay.  \n",
    "\n",
    "<div class=\"label label-success\">HINT </div>  Function `<df_colum>.isin(<list>)` helps checking whether a value in column belongs to a list.\n",
    "<ul></ul>\n",
    "<div class=\"label label-success\">SUGGESTION </div>  You can try with different chart type to have a better visualization.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------+------+\n",
      "|Airport|   Delay Probability|Volume|\n",
      "+-------+--------------------+------+\n",
      "|    OAK| 0.06401264893556949| 88545|\n",
      "|    CMH| 0.08803080281051122| 81551|\n",
      "|    IAH| 0.07834949734168781|208591|\n",
      "|    CVG| 0.06635894453732645|137570|\n",
      "|    EWR| 0.12392784337859149|233991|\n",
      "|    LAS| 0.08044966301600674|189920|\n",
      "|    MCI| 0.08131267575741498|113082|\n",
      "|    BNA| 0.05301219161367762|100807|\n",
      "|    DAL| 0.05743133713121208| 86364|\n",
      "|    CLE| 0.07749343685623554|134463|\n",
      "|    BWI| 0.08165500969073902|142404|\n",
      "|    TPA|  0.0928711411869148|119402|\n",
      "|    PHX| 0.07597661819218705|280560|\n",
      "|    SFO| 0.09587307519173766|235478|\n",
      "|    RDU|  0.0551477547586497| 95090|\n",
      "|    MDW|0.060992312450296016| 90536|\n",
      "|    DTW| 0.05933283141252099|276272|\n",
      "|    HOU| 0.06123663940357015|109183|\n",
      "|    SAN| 0.07545364538700257|120193|\n",
      "|    SLC| 0.06879220075825962|138475|\n",
      "+-------+--------------------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Top 20 busiest airports\n",
    "\n",
    "df_busiest_airports = busiest_airports.limit(20)\n",
    "df_busiest_airports = df_busiest_airports.withColumnRenamed( \"sum(count)\", \"Volume\")\n",
    "\n",
    "\n",
    "# Number of delayed flights in source airports\n",
    "df_no_delayed_flights_src_airports = (\n",
    "    \n",
    "    df[(df.src_airport.isin([x[0] for x in df_busiest_airports.collect()])) & (df.arrival_delay > 15)]\n",
    "    .groupBy(df.src_airport)\n",
    "    .count()\n",
    "    .withColumnRenamed('src_airport','Airport')\n",
    "                            )\n",
    "\n",
    "\n",
    "# Number of delayed flights in destination airports\n",
    "df_no_delayed_flights_dest_airports = (\n",
    "    df[(df.dest_airport.isin([x[0] for x in df_busiest_airports.collect()])) & (df.arrival_delay > 15)]\n",
    "    .groupBy(df.dest_airport)\n",
    "    .count()\n",
    "                            )\n",
    "\n",
    "# Total delayed flights for busiest airports computation\n",
    "total_delayed_flights = df_no_delayed_flights_src_airports\\\n",
    ".union(df_no_delayed_flights_dest_airports)\\\n",
    ".groupBy('Airport')\\\n",
    ".sum('count')\\\n",
    ".join(df_busiest_airports,'Airport','inner')\\\n",
    ".cache()\n",
    "\n",
    "# Delay probability computation\n",
    "total_delayed_flights = (\n",
    "                        total_delayed_flights\n",
    "                        .select('Airport', (total_delayed_flights['sum(count)']/df_busiest_airports.Volume)\n",
    "                        .alias('Delay Probability'),\"Volume\")\n",
    "                        )\n",
    "\n",
    "total_delayed_flights.show()\n",
    "total_delayed_flights = total_delayed_flights.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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apXtvn2/n+tpTx1parTprPlnrzW8qzHulQlprXiELXMrP3Y1KtlVqQ1Z8/MCG1PAZj4idyc6lX6WUflohyywqPwpiJLU9OqGt609N51tK6VLg0vy+xQOBs4A3gPL732D5vtmfykHW4/k6ZwGfjojVyOr4E+CGiBhe1nuiLZ25hrV2XrZ2TF8oSStV6fyo+dxPKS0gu2f2q5EN5vVt4PKIeCil9GjrS0t9jy2BUmW3kn3xHJxSuq/Cq9ovj9Pzvy2jNuaDWny0LN/fyL7gzayw7serrLt5wJi3Sta9M9k9I+X+SPZL/lSyz/nUCnkqOSBWHBHwQGAR2b0UrbmHrOviriVlW5NsIIG7yvJ+NFYcZfNAsn/kzaPoDaKkjrkju7i8rWmtBWsKWSB+Mdk9K+XdfMsNApY0f3nKVatLW6YDHygZbKV5IJJ6BYHNx6P0XB7EyudyrWpuGcy/kD1C1lp+KFkX4tIvjPeQ7YuWUXAj4t3Azqx8vpWaSzbQSqmO1qe8zE1k97ltXeWa0d4gsKN1rKa8t0N7/Y2sm/X9FerW2MZ2V9hmSukdsq7vB5flPYTsR7d/lKW3jByZf94PAO5trbCRja55PdlgWdUGy/lfYFhEfLhkuQ+StW7W0sWwfETL5ufSNneXnAuMiIjS+lc931JKL6eULiS7t3TbKtn+QXaNe1eV82yFHxFTSktSSreSDYqyMctbPmv9PHblNaxUa8d0LtmPJpXOjzeoPgJuszbP9ZQN5nUS2f/HWgYBkvocWwKlClJKj0fEr8hGOPwfsi/EA8kCt61SSsdWWW5mREwDLsi7sb1A9o+m/GHlPye7R+XWiDiHLKDYCNgNuCuldEWF1f+TrNvkRXmZhpO1Ovy7QjkWR8TlZA8EvyL/BbQWa5Pda3JRXtdTgXNb6/KYb+/GiPg/4Mp89LZXyQY8GEQ2gl6pRWS/SP+U7EvJT4E/lfwSezNwXmSP1riHbFS5PbuyvG2YRRZcfpL8y0jzF/iU0vMR8Vey4Pb0/Mtsa24Gvh4RZ5PdW7cz2XHviEvIRqu9IbKh9weRtSS3txWmJimlRyLiz2Tn8tpkLYPfJDuX29OS0GwWsE9E7EN2fjxd4f6sUleSjUo6mGx0zFJTyLom/m9EnEp2f+wksn1xYSvr/BNwTkR8lyyoPpDKrW0d9W3gbxGxjGyAoTfJRm/9OPDfKaUn2rGuKXSsjhWllN6OiKfJRl59hKyVqrVRbctNIvuSfkPeIv4K2f2QHyUbYOT2KsvNAjaK7BEEj5ANRtRI9jDvGyPiErIfqUaTnc8Xld73mjs2It7Olz+ObOCuw6sVNL8H7q9k18tfAuNi+QCmbzRfa1JK/4iIG4HLIuJbLH9Y/F2ptmcEbhcRFwJ/IOvO+XmyQbOaPx/Xkg3Y8pvIHo3xfpZ3028u6/fJWgdvJ9un7yf7P1CpFZCU0oL88/+LvEXu72SBzFbA+JTSp/JWrjPJPkNPkXVVPhn4V8m1sdbPY1dew0pVPaYppWV5HS+MiFfzMuwGfJFsQJm2nkvb/EPqFyJiKrAwpfRwRNxFdg14hOyHx+OAJtr4QUHqs1I3j0Tjy1d3vGh7dNBWR2zL04JsOOyZZC1TLwN3AEe3se2hZF9qmsi665xKhRHyyG58vyTP8xbZAAu/A7ZrpUz7kv0DW0T2Be5jZF8erqlQjr3y5feqcZ8lsi/555LdK/M6cB6wRkmeCZSNYlgybwOye/ley8t3BzC2LE8j8DOyL5Qv5vvoCmBISZ7++f56iexX3z8AO1bYF+0ub5V92siKo9qtT/ZFYX6ed1JZHY7N07escb9+m2yQhiayQVW2pAOjVuZp25MNH/8W2RedT1LDKH90YHTQPG1dsi+SpefyRcCMts6JCvt187z+r1NhlMoKZX5vnm8xWYt8+fzNyb5kv0n2Zf/68mNSYT+vRvYDzLz8nPkF2aMvSs+RlfZDpXXlabdT9tkjO1f/SnbuNpENsPLzSnWoYR+2u45t7NO9ya4bi/PlRlBlFOIq58xIsuB2PtlnfDZZQDq8lW0OJLvOvUTZSLZkrbwPk7XczCV7vMuACvtlHNmAOIvzbX66jXo2H8NKr9vL8g7Jy7cgP2a/B9ZvY/3N++xIsuvXm2T/H75PPipzWR3mkP14cj1ZENWyv8n+V/0tX34x2ef6lOb1tHJ8PkPWmrqI7Fy+B/hmPm9DstFcn8rXOS8vZ+noyjV/HunANazS+dPeY0p2D9/s/Px4CvhGa9ewsnknkj3aZSn5/16yHxwfzo/XArL7KD9Sy2fHl6+++Gq+yEjqY/LWwkOBzVIN94BE9hDxr6SUKj2nS7mIuArYOKVU6V6iPi0fhfARsns2j+np8qhvy1sPLwHWTiuOKKxVlMdU6j3sDir1MRGxNdn9JF8Evl9LAKi2RcRosqHwDwQKMaJcRBxM1mL9MNnQ7MeRtQIc3ZPlkiRJnWMQKPU9F5J1SZtGdj+MusafybqKnp9SuqanC9NNmsjuYXovWTfdh4H/Sil5D40kSaswu4NKkiRJUoH4iAhJkiRJKhCDQEmSJEkqkD5zT2C/fv3SoEGD2s4oSZIkqbAWLlyYUkqFbgzrM0HgoEGDaGpq6uliSJIkSerFImJRT5ehpxU6ApYkSZKkojEIlCRJkqQCMQiUJEmSpALpM/cEVrJkyRLmzp3L4sWLe7oo6iYDBw5k+PDhrLbaaj1dFEmSJKlX6tNB4Ny5c1l77bUZMWIEEdHTxVGdpZR49dVXmTt3LptttllPF0eSJEnqlfp0d9DFixez3nrrGQAWRESw3nrr2fIrSZIktaJPB4GAAWDBeLwlSZKk1vX5ILCn9e/fnzFjxrDddtvxvve9j5///OcsW7as1WUaGxsZNWpUp7fd2NjIoEGDGDNmDNtuuy3HH398m9vubDl233137rvvvpXSp02bxhlnnAHApEmTOPPMMwE49dRTueWWWwA4++yzWbhwYbu2J0mSJKl9ihUEDhsGEV33GjaszU0OGjSIGTNmMHPmTG6++Wb+8pe/8P3vf78bKpvZYostmDFjBg899BCPPvoo11577Qrz33nnnW4pxyc+8QlOOeWUldJPO+009tprL8AgUJIkSeoOxQoCX3yxR9e34YYb8utf/5pzzz2XlBLvvPMOJ510EmPHjmX77bfnwgsvXGmZxsZGPvKRj7DDDjuwww47cPfddwNw1FFHcd1117XkO/LII5k2bVrVbQ8YMICdd96Z2bNnc/vttzN+/HiOOOIIRo8eDcDPf/5zRo0axahRozj77LNbllu6dCnHHHMM22+/PQcddFBLkHbaaacxduxYRo0axcSJE0kptSzzu9/9jp133plRo0Zx7733AjBlyhROOOGElco1YcIErrnmGn75y1/y/PPPM378eMaPH8/kyZP5xje+0ZLvoosu4pvf/GZN+1mSJElSdcUKAnuBzTffnGXLlvHSSy8xefJkBg8ezPTp05k+fToXXXQRTz/99Ar5N9xwQ26++WYeeOABrrzySr761a8CcOyxx3LJJZcA8Prrr3P33XfzsY99rOp2Fy5cyN/+9reWoO/ee+/lRz/6EY8++ij3338/l1xyCffccw///Oc/ueiii3jwwQcBePzxx5k4cSIPPfQQ66yzDueffz4AJ5xwAtOnT+eRRx5h0aJFXH/99S3bampq4u677+b888/nc5/7XE375atf/Srvete7uO2227jttts47LDDmDZtGkuWLAHgkksu4bOf/WxN65IkSZJUnUFgD2huNbvpppu47LLLGDNmDDvuuCOvvvoqTz755Ap5lyxZwnHHHcfo0aM5+OCDefTRRwHYbbfdmD17Ni+99BJXXHEFn/70pxkwYOUnfsyZM4cxY8awyy678PGPf5z99tsPgHHjxrU8RuGuu+7iU5/6FA0NDay11loceOCB3HnnnQBssskm7LLLLgB85jOf4a677gLgtttuY8cdd2T06NHceuutzJw5s2Wbhx9+OAC77rorb7zxBgsWLGj3PmpoaGCPPfbg+uuvZ9asWSxZsqQlgJUkSZLUcX36OYG90VNPPUX//v3ZcMMNSSlxzjnnsM8++6yQp7GxseX9WWedxUYbbcS//vUvli1bxsCBA1vmHXXUUVx++eVMnTqViy++uOL2mu8JLNfQ0NDyvrQrZ7ny0TYjgsWLF/OlL32J++67j0022YRJkyat8FiGSst0xLHHHsuPf/xjRo4caSugJEmS1EVsCexGL7/8MscffzwnnHACEcE+++zDBRdc0NLl8YknnqCpqWmFZV5//XU23nhj+vXrx29/+9sVBnKZMGFCy/172223XYfLteuuu3LttdeycOFCmpqa+NOf/sRHPvIRAJ599ln+8Y9/AHDFFVfw4Q9/uCXgW3/99fnPf/7DNddcs8L6rrzySiBrYRw8eDCDBw+uqRxrr702b775Zsv0jjvuyHPPPcfvf//7ltZFSZIkSZ1jS2CdLVq0iDFjxrBkyRIGDBjAUUcd1TLAybHHHktjYyM77LADKSU22GCDlUbv/NKXvsSnP/1prr76asaPH79CC95GG23ENttswyc/+clOlXGHHXZgwoQJjBs3rqVc73//+2lsbGSbbbbh0ksv5Qtf+AJbbrklX/ziF1lzzTVbuqiOGDGCsWPHrrC+oUOHsvPOO/PGG29UbaGsZOLEiey3335svPHG3HbbbQAccsghzJgxg6FDh3aqjpIkSZIy0VpXwFVJQ0NDKm9Fe+yxx9hmm22WJwwb1rUjhG60Ecyb13Xra6eFCxcyevRoHnjggZpb21Y1+++/P9/4xjfYc889a15mpeMuSZIk5SJiYUqpoe2cfVexuoPOmwcpdd2rBwPAW265hZEjR/KVr3ylTwaACxYsYKuttmLQoEHtCgAlSZIktc7uoKuovfbai2effbani1E3Q4YM4YknnujpYkiSJEl9TrFaAiVJkiSp4AwCJUmSJKlADAIlSZIkqUAMAiVJkiSpQAwC6ywiOPHEE1umzzzzTCZNmtQl654wYcJKD2qvh6uvvpptttmG8ePHr5De2NjIoEGDGDNmTMvr7bffZsqUKZxwwgkA/OpXv+Kyyy5rdf2l+cv9+Mc/7ppKSJIkSQIKFgQOGwYRXfcaNqztba6xxhr88Y9/5JVXXql/BdvhnXfeqTnv5MmTOf/881se4F5qiy22YMaMGS2v1VdffYX5xx9/PEcffXSHy2kQKEmSJHWtQgWBXfmc+FrXN2DAACZOnMhZZ5210rzylry11loLgNtvv53ddtuNQw45hK222opTTjmFyy+/nHHjxjF69GjmzJnTsswtt9zCRz7yEbbaaiuuv/56IAvwTjrpJMaOHcv222/PhRde2LLe8ePHc8QRRzB69OiVynPFFVcwevRoRo0axcknnwzAaaedxl133cXxxx/PSSedVPvOyU2aNIkzzzwTgOnTp7P99tuz0047cdJJJzFq1KiWfM8//zz77rsvW265Jd/+9rcBOOWUU1i0aBFjxozhyCOPpKmpiY9//OO8733vY9SoUVx55ZXtLk+HdebXAknq4zr7o6okqXv5nMBu8OUvf5ntt9++Jbipxb/+9S8ee+wx1l13XTbffHOOPfZY7r33Xn7xi19wzjnncPbZZwNZl8w77riDOXPmMH78eGbPns1ll13G4MGDmT59Om+99Ra77LILe++9NwD33nsvjzzyCJttttkK23v++ec5+eSTuf/++xk6dCh777031157Laeeeiq33norZ555Jh/84AdXKuecOXMYM2YMALvssgvnnXde1Tp99rOf5de//jU777wzp5xyygrzZsyYwYMPPsgaa6zB1ltvzVe+8hXOOOMMzj33XGbMmAHAH/7wB971rndxww03APD666/XvD8lSZIkZQrVEthT1llnHY4++mh++ctf1rzM2LFj2XjjjVljjTXYYostWoK40aNH09jY2JLvkEMOoV+/fmy55ZZsvvnmzJo1i5tuuonLLruMMWPGsOOOO/Lqq6/y5JNPAjBu3LiVAkDIWul23313NthgAwYMGMCRRx7J3//+9zbLWdodtLUAcMGCBbz55pvsvPPOABxxxBErzN9zzz0ZPHgwAwcOZNttt+WZZ55ZaR2jR4/yBNfDAAAgAElEQVTmlltu4eSTT+bOO+9k8ODBbZZPkiRJ0ooMArvJ17/+dSZPnkxTU1NL2oABA1i2bBkAKSXefvvtlnlrrLFGy/t+/fq1TPfr14+lS5e2zIuyfjQRQUqJc845pyU4e/rpp1uCyIaGhorlSyl1soata2v9pfXt37//CnVsttVWW3H//fczevRovvOd73Daaad1eTklSZKkvs4gsJusu+66HHLIIUyePLklbcSIEdx///0AXHfddSxZsqTd67366qtZtmwZc+bM4amnnmLrrbdmn3324YILLmhZ3xNPPLFC8FnJjjvuyB133MErr7zCO++8wxVXXMFuu+3W7vJUM3ToUNZee23++c9/AjB16tSalltttdVa6vH888+z5ppr8pnPfIZvfetbPPDAA11WPkmSJKkovCewG5144omce+65LdPHHXccBxxwAOPGjWPPPfes2krXmq233prddtuNF198kV/96lcMHDiQY489lsbGRnbYYQdSSmywwQZce+21ra5n44035vTTT2f8+PGklPjYxz7GAQcc0O7ytGby5Mkcd9xxNDQ0sPvuu9fUnXPixIlsv/327LDDDhx99NGcdNJJ9OvXj9VWW40LLrigS8snSZIkFUHUuxtgd2loaEjlrV2PPfYY22yzTcv0sGFdO0LoRhvBvHldt76+7j//+U/LCKhnnHEGL7zwAr/4xS+6fDvlx71LdGb4uj7yGZOkajo7wqeXSUndKSIWppTa3/rShxSqJdCArWfdcMMNnH766SxdupRNN92UKVOm9HSRJEmSpMIpVBConnXooYdy6KGH9nQxJEnSKs7WZ6lzHBhGkiRJkgqkzweBfeWeR9XG4y1JkiS1rk8HgQMHDuTVV181MCiIlBKvvvoqAwcO7OmiSJIkSb1WXe8JjIh9gV8A/YHfpJTOKJu/K3A2sD1wWErpmjx9DHABsA7wDvCjlNKV7d3+8OHDmTt3Li+//HLnKqJVxsCBAxk+fHhPF0OSJEnqter2iIiI6A88AXwUmAtMBw5PKT1akmcEWaD3LWBaSRC4FZBSSk9GxLuA+4FtUkoLqm2v0iMipC7jIyIkqSoH6VB385xTZ/iIiPq2BI4DZqeUngKIiKnAAUBLEJhSasznLStdMKX0RMn75yPiJWADoGoQKEmSJElqWz2DwHcDz5VMzwV2bO9KImIcsDowp7V8y5YtY9asWRXnDRs2jCFDhgCwYMEC5rXywMCRI0e2vG9sbGTx4sUV8w0ZMoRhw4YBsGjRIp555pmq69x0000ZNGgQAPPmzWPBgsqx7MCBAxkxYkTLdLX6gHXq9joddtjyOs2fz4ibblpep5J5K9Vp+nSG5O97XZ1K9JnjVMI6Wadm1qk76jSSzup9dVqu7xyn5Vb9OnX+nGvWe+q0slX/OK2sN9RJ9Q0CKzXUt6vxPSI2Bn4LHJNSWlZh/kRgIsBqq63WkTJKkiRJUqHU857AnYBJKaV98unvAKSUTq+QdwpwffM9gXnaOsDtwOkppavb2p73BKquvCdQkqry/ix1N885dYb3BNb3ERHTgS0jYrOIWB04DJhWy4J5/j8Bl9USAEqSJEmSalO3IDCltBQ4AbgReAy4KqU0MyJOi4hPAETE2IiYCxwMXBgRM/PFDwF2BSZExIz8NaZeZZUkSZKkoqhbd9DuZndQ1ZXdQSWpKrvmqbt5zqkz7A5a3+6gkiRJkqRexiBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsQgUJIkSZIKxCBQkiRJkoCI2CQibouIxyJiZkR8LU+fFBH/jogZ+etjJct8JyJmR8TjEbFPSfq+edrsiDilJH2ziLgnIp6MiCsjYvU8fY18enY+f0S96mkQKEmSJEmZpcCJKaVtgA8BX46IbfN5Z6WUxuSvvwDk8w4DtgP2Bc6PiP4R0R84D9gP2BY4vGQ9P8nXtSXwGvD5PP3zwGsppfcCZ+X56sIgUJIkSZKAlNILKaUH8vdvAo8B725lkQOAqSmlt1JKTwOzgXH5a3ZK6amU0tvAVOCAiAhgD+CafPlLgU+WrOvS/P01wJ55/i5nEChJkiRJZfLumO8H7smTToiIhyLi4ogYmqe9G3iuZLG5eVq19PWABSmlpWXpK6wrn/96nr/LGQRKkiRJKpIBEXFfyWtieYaIWAv4A/D1lNIbwAXAFsAY4AXgZ81ZK6w/dSC9tXV1uQH1WKkkSZIk9VJLU0ofrDYzIlYjCwAvTyn9ESCl9GLJ/IuA6/PJucAmJYsPB57P31dKfwUYEhED8ta+0vzN65obEQOAwcD8DtWwDbYESpIkSRKQ34M3GXgspfTzkvSNS7J9Cngkfz8NOCwf2XMzYEvgXmA6sGU+EujqZIPHTEspJeA24KB8+WOA60rWdUz+/iDg1jx/l7MlUJIkSZIyuwBHAQ9HxIw87btko3uOIeue2Qh8ASClNDMirgIeJRtZ9MsppXcAIuIE4EagP3BxSmlmvr6TgakR8UPgQbKgk/zvbyNiNlkL4GH1qmTUKbjsdg0NDampqamni6G+qjMDM/WRz5gkVdPZseu8TKq9POfUGRGxMKXU0NPl6El2B5UkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAhnQ0wWQJEmqp4iOL5tS15VDknoLWwIlSZIkqUAMAiVJkiSpQOwOKtVZZ7ohgV2RJEmS1LVsCZQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAKpaxAYEftGxOMRMTsiTqkwf9eIeCAilkbEQWXzjomIJ/PXMfUspyRJkiQVRd2CwIjoD5wH7AdsCxweEduWZXsWmAD8vmzZdYHvATsC44DvRcTQepVVkiRJkoqini2B44DZKaWnUkpvA1OBA0ozpJQaU0oPAcvKlt0HuDmlND+l9BpwM7BvHcsqSZIkSYUwoI7rfjfwXMn0XLKWvY4u++7WFli2bBmzZs2qOG/YsGEMGTIEgAULFjBv3ryq6xk5cmTL+8bGRhYvXlwx35AhQxg2bBgAixYt4plnnqm6zk033ZRBgwYBMG/ePBYsWFAx38CBAxkxYkTLdLX6gHXq9joddtjyOs2fz4ibblpep5J5K9Vp+nSYU3V2Tcr3mcfJOjWzTtapkp6p00g6q7516nj5Zs2a1YeO03Krfp06f8416z11Wtmqf5xW1hvqpPoGgVEhLXXlshExEZgIsNpqq9VeMkmSJEkqqEip1risnSuO2AmYlFLaJ5/+DkBK6fQKeacA16eUrsmnDwd2Tyl9IZ++ELg9pXRFte01NDSkpqamLq+HBEBU+l2ixkVr/u2jsjp9RCWpy3TiEgnU/zrXmfJ5De6devs5p94tIhamlBp6uhw9qZ73BE4HtoyIzSJideAwYFqNy94I7B0RQ/MBYfbO0yRJkiRJnVC3IDCltBQ4gSx4ewy4KqU0MyJOi4hPAETE2IiYCxwMXBgRM/Nl5wM/IAskpwOn5WmSJEmSpE6oW3fQ7mZ3UNWV3UElqare3jXP7qB9T28/59S72R20zg+LlyRJkiT1LgaBkiRJklQgBoGSJEmSVCAGgZIkSZJUIAaBkiRJklQgBoGSJEmSVCAGgZIkSZJUIAaBkiRJklQgBoGSJEmSVCAGgZIkSZJUIAaBkiRJklQgBoGSJEmSVCADeroAkiRJrYro5ApSlxRDkvoKWwIlSZIkqUAMAiVJkiSpQAwCJUmSJKlADAIlSZIkqUAMAiVJkiSpQAwCJUmSJKlADAIlSZIkqUAMAiVJkiSpQAwCJUmSJKlADAIlSZIkqUAMAiVJkiSpQAwCJUmSJKlABvR0ASRJKoSIzi2fUteUQ5JUeLYESpIkSVKBGARKkiRJUoHYHVQ160xPJnsxSZIkSb2DLYGSJEmSVCAGgZIkSZJUIAaBkiRJklQgBoGSJEmSVCAGgZIkSZJUIAaBkiRJklQgBoGSJEmSVCAGgZIkSZJUIAaBkiRJklQgBoGSJEmSVCAGgZIkSZJUIAaBkiRJkgRExCYRcVtEPBYRMyPia3n6uhFxc0Q8mf8dmqdHRPwyImZHxEMRsUPJuo7J8z8ZEceUpH8gIh7Ol/llRERr26gHg0BJkiRJyiwFTkwpbQN8CPhyRGwLnAL8LaW0JfC3fBpgP2DL/DURuACygA74HrAjMA74XklQd0Get3m5ffP0atvocgaBkiRJkgSklF5IKT2Qv38TeAx4N3AAcGme7VLgk/n7A4DLUuafwJCI2BjYB7g5pTQ/pfQacDOwbz5vnZTSP1JKCbisbF2VttHlBtRrxZIkSZLUCw2IiPtKpn+dUvp1eaaIGAG8H7gH2Cil9AJkgWJEbJhnezfwXMlic/O01tLnVkinlW10OYNASZIkSUWyNKX0wdYyRMRawB+Ar6eU3shv26uYtUJa6kB6t7I7aFeL6NxLkiRJUo+JiNXIAsDLU0p/zJNfzLtykv99KU+fC2xSsvhw4Pk20odXSG9tG13OIFCSJEmSyEb7BCYDj6WUfl4yaxrQPMLnMcB1JelH56OEfgh4Pe/SeSOwd0QMzQeE2Ru4MZ/3ZkR8KN/W0WXrqrSNLhfZ/YirvoaGhtTU1NTTxeh8a14vPh6dqVovrlZtOlH56GQL/yq/7yRl/P9QVZtV6+QGOnMd7sW7vdD68MdJ3SAiFqaUGqrM+zBwJ/AwsCxP/i7ZfYFXAe8BngUOTinNzwO5c8lG+FwIfDaldF++rs/lywL8KKV0SZ7+QWAKMAj4X+ArKaUUEetV2kZX1r2lngaBXawPX5UMAju4qEGgJPD/QysMAtVeffjjpG7QWhBYFHYHlSRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCGdDTBZDUsyI6vmxKXVcOSZIkdQ9bAiVJkiSpQAwCJUmSJKlADAIlSZIkqUAMAiVJkiSpQAwCJUmSJKlADAIlSZIkqUAMAiVJkiSpQAwCJUmSJKlAfFi8JKnviOjc8il1TTkkSerFbAmUJEmSpAKxJVBS/dgqI0mS1OvYEihJkiRJBVLXIDAi9o2IxyNidkScUmH+GhFxZT7/nogYkaevFhGXRsTDEfFYRHynnuWUJEmSpKKoWxAYEf2B84D9gG2BwyNi27JsnwdeSym9FzgL+EmefjCwRkppNPAB4AvNAaKkMhGde0mSJKlQ6nlP4DhgdkrpKYCImAocADxakucAYFL+/hrg3IgIIAENETEAGAS8DbzR2saWLVvGrFmzKs4bNmwYQ4YMAWDBggXMmzev6npGjhzZ8r6xsZHFixdXzDdkyBCGDRsGwKJFi3jmmWeyGYcdtlLeTW+8kUGvvQbAvLFjWbDFFhXXOXD+fEaUTFerD3RznZavqeo2atX76rTcpptuyqBBgwCYN28eCxYsWD6z5LgOnD+fETfdtLxOFY55S52mT4c5VWfXpHyfrVSnVrZf07k3teNla/M4lbxv3HtvFq+7bsV8Q+bMyfYVsGjoUJ7ZZ59sRoXzpdXjVGLgwIGMGDGiZXqVPfdKWKca6pR/HkZOXX5it+vca2X7na5Thc/qsOnTGTInu0gs2GIL5o0dW3X7K3yeet1x6vz/h1brNHZs5WtEBRWve524zs2aNau4n6dc76xT58+5Zr2nTitb9Y/TynpDnVTf7qDvBp4rmZ6bp1XMk1JaCrwOrEcWEDYBLwDPAmemlOaXbyAiJkbEfRFx3zvvvNP1NZAkSZKkPiZSnUbfi4iDgX1SSsfm00cB41JKXynJMzPPMzefnkPWgjgS+BIwARgK3Ans19yqWElDQ0NqamqqS13apQ+PhtiZqvXiatWmE5UPOlf5NvddJ8+5zpSv3mVb9U8cdbvefM715rJ1Ut2r1puvc+oRffjjpG4QEQtTSg09XY6eVM+WwLnAJiXTw4Hnq+XJu34OBuYDRwB/TSktSSm9BPwf8ME6llWSJEmSCqGeQeB0YMuI2CwiVgcOA6aV5ZkGHJO/Pwi4NWVNk88Ce0SmAfgQUL1zsSRJkiSpJnULAvN7/E4AbgQeA65KKc2MiNMi4hN5tsnAehExG/gm0PwYifOAtYBHyILJS1JKD9WrrJIkSZJUFHW7J7C7eU9g/XlPYAcX9Z7AjlvlTxx1u958zvXmsnWS9wSqu/Xhj5O6gfcE1vlh8ZIkSZKk3qWezwlUb9PpB4P7s5kkSZK0qrMlUJIkSZIKxCBQkiRJkgrEIFCSJEmSCsR7AiVJ7eOwfJIkrdJsCZQkSZKkAjEIlCRJkqQCMQiUJEmSpAIxCJQkSZKkAjEIlCRJkqQCcXRQSZLUyVFfHfFVklYltgRKkiRJUoHYEihJkiStKnxWq7qALYGSJEmSVCAGgZIkSZJUIG0GgRFxX0R8OSKGdkeBJEmSJEn1U0tL4GHAu4DpETE1IvaJ6GxnZEmSJElST2gzCEwpzU4p/TewFfB74GLg2Yj4fkSsW+8CSpIkSZK6Tk33BEbE9sDPgJ8CfwAOAt4Abq1f0SRJkiRJXa3NR0RExP3AAmAycEpK6a181j0RsUs9CydJkiRJ6lq1PCfw4JTSU6UJEbFZSunplNKBdSqXJEmSJKkOaukOek2NaZIkSZKkXq5qS2BEjAS2AwZHRGmL3zrAwHoXTJIkSZLU9VrrDro1sD8wBPivkvQ3gePqWShJkiRJUmX5I/uOBDZPKZ0WEe8BhqWU7q1p+ZRSWxvYKaX0j84Xtb4aGhpSU1NTTxcDOvsIxTaOR6d0smxBx8tWz2p1i07su87sN6hh3/Xm49qbPw/quN58XC1bx3mdq8jLUO/U2z9OdVXoyneNiFiYUmro6XJ0RkRcACwD9kgpbRMRQ4GbUkpja1m+te6g304p/Q9wREQcXj4/pfTVjhZakiRJktRhO6aUdoiIBwFSSq9FxOq1Ltxad9DH8r/3daZ0kiRJkqQutSQi+kPW1SEiNiBrGaxJ1SAwpfTn/O+lnS2hJEmSJKnL/BL4E7BhRPwIOAj4f7Uu3Fp30D9D9U70KaVPtKOQkiRJkqQukFK6PCLuB/YEAvhkSumxNhZr0Vp30DM7WzhJkiRJUl28CNxJFtMNiogdUkoP1LJga91B7+iiwkmSJEmSukhE/ACYAMxhee/NBOxRy/KtdQe9KqV0SEQ8zIrdQgNIKaXtO1RiSZIkSVJnHAJskVJ6uyMLt9Yd9Gv53/07smJJkiRJUl08AgwBXurIwq11B30h//tMRAwDxpG1CE5PKc3ryMYkSZIkSZ12OvBgRDwCvNWcWOvgna21BAIQEccCpwK3knUFPSciTkspXdyx8kqSJEmSOuFS4CfAw7Tj+YDN2gwCgZOA96eUXgWIiPWAuwGDQEmSJEnqfq+klH7Z0YVrCQLnAm+WTL8JPNfRDUqSJEmSOuX+iDgdmMaK3UE794iIiPhm/vbfwD0RcR3ZPYEHAPd2uLiSJEmSpM54f/73QyVpnX9EBLB2/ndO/mp2Xc1FkyRJkiR1qZTS+M4s39rooN/vzIolSZIkSV0vIk6tlJ5SOq2W5WsZHXQD4NvAdsDAkg3U1NQoSZIkqQAiOrd8Sl1TjmJoKnk/kOzZ7o/VunAtA8NcDlyZr/h44Bjg5XYUUJIkSZLURVJKPyudjogzyQaJqUm/GvKsl1KaDCxJKd2RUvocK96AKEmSJEnqOWsCm9eauZaWwCX53xci4uPA88DwDhRMkiRJktRJEfEw2WigAP2BDYCa7geE2oLAH0bEYOBE4BxgHeAb7SynJEmSJKlr7F/yfinwYkppaa0LR+ojN2A2NDSkpqamtjPWW2++IbaTZQs6XrZV/jTrxL7rzH6DGvZdbz6uvfnzoI7rzcfVsnWc17mKvAz1Tr3941RXvbnyvblsJSJiYUqpoVs21sUiYt3W5qeU5teynlpGB90c+AWwE7AM+AfwjZTSU7VsQJIkSZLUJe4n6wZaKeJO1HhfYC0Dw/weuAoYBrwLuBq4orYySpIkSdKqIyIujoiXIuKRkrRJEfHviJiRvz5WMu87ETE7Ih6PiH1K0vfN02ZHxCkl6ZtFxD0R8WREXBkRq+fpa+TTs/P5I8rLllLaLKW0ef63/FXzwDC1BIGRUvptSmlp/voddLLfhyRJkiT1TlOAfSukn5VSGpO//gIQEdsCh5E9U31f4PyI6B8R/YHzgP2AbYHD87wAP8nXtSXwGvD5PP3zwGsppfcCZ+X5qoqIT0TEmflr/9bylqsaBEbEunmf09si4pSIGBERm0bEt4Eb2rMRSZIkSVoVpJT+DtR0bx1wADA1pfRWSulpYDYwLn/NTik9lVJ6G5gKHBARAewBXJMvfynwyZJ1XZq/vwbYM8+/kog4A/ga8Gj++lpEnF5rHVu7J7C8v+kXSuYl4Ae1bkRq0ypyI7EkSZIK64SIOBq4DzgxpfQa8G7gnyV55uZpAM+Vpe8IrAcsKBnJszT/u5uXSSktjYjX8/yvVCjLx4AxKaVlABFxKfAg8J1aKlI1CEwpbVbLCiRJkiRpFTIgIu4rmf51SunXbSxzAVkjWHNj2M+Az1F9gJZKPS5bG9CFNuZVMoTlLZaDW8m3klpGB10N+CKwa550O3BhSmlJ1YUkSZIkqXdamlL6YHsWSCm92Pw+Ii4Crs8n5wKblGQdDjyfv6+U/gowJCIG5K2Bpfmb1zU3IgaQBXbVuqWeDjwYEbeRBY+7UmMrINQ2MMwFwAeA8/PXB/I0SZIkSerzImLjkslPAc0jh04DDstH9twM2BK4F5gObJmPBLo62eAx01L2kPbbgIPy5Y8BritZ1zH5+4OAW1OVh7qnlK4APgT8MX/tlFKaWmt92mwJBMamlN5XMn1rRPyr1g1IkiRJ0qoiIq4AdgfWj4i5wPeA3SNiDFn3zEby8VJSSjMj4iqywVmWAl9OKb2Tr+cE4EagP3BxSmlmvomTgakR8UOy+/gm5+mTgd9GxGyyFsDDWinjNLLH9k1LKTW1u45VgsvSDTwAHJxSmpNPbw5ck1Laob0bq6eGhobU1NTu+ne93jzASSfLFp14Mkib1erN+w06Vb7O7Deo/74r9HFVx/Tm42rZOs7rXEVehnqn3v5xqqveXPneXLYSEbEwpdTQLRurk4jYDTgU+DhZy+OVwPUppcW1LF9LS+BJZI+JeIqsv+mmwGc7VlxJkiRJUmeklO4A7sifR7gHcBxwMbBOLcu3GgRGRD9gEVnf1q3JgsBZKaW3OlNoSZIkSVLHRcQg4L/IWgR3YPkzBtvUahCYUloWET9LKe0EPNSpUkqSJEmSOi0iriR77uBfgfOA25ufGViLWrqD3hQRnwb+WG10GkmSJElSt7kEOKJ5EJr2qmVgmDeBBuAdsq6hAaSUUk39TbtLQ0Rtw+L04hvrgV59s26hBxBxwISKVvnjqo7pzcfVsnWc17mKvAz1Tr3941RXvbnyvblsJfrCwDCd1WZLYEpp7e4oiCRJkiSp/mrpDkpEHAh8mOy5GHemlK6ta6kkSZIkSXXRr60MEXE+cDzwMPAIcHxEnFfvgkmSJEmSVhYRf6slrZpaWgJ3A0Y1DwoTEZeSBYSSJEmSpG4SEQOBNYH1I2Io2XgtkD0f8F21rqeWIPBx4D3AM/n0Jvi4CElSH7SKjGkgSSquLwBfJwv47md5EPgG2aMialLL6KB3AGOBe/OkscA/gIUAKaVPtKfU9eLooDXozaOr9eb9Bo6aV8Uqf1zVMb35uPbg5wHaqFpv3m/gda4KL0O9U2//ONVVb658by5bib4wOmhEfCWldE5Hl6+lJfDUjq5ckiRJktS1UkrnRMTOwAhKYrqU0mW1LF/LIyLu6HDpJEmSJEldKiJ++/+3d+9xdpXlocd/j4mSiOKAF0JBE4zQiPVSNYBiL9YawKpoKzXQClqU1qNt1WNPlValWOsFTxGrx+rxhtoSqYrFigIHb1VBg5eKwFgSSCTqCJoELyQi8Jw/3rWTPTt7z0xmZs9aM+v3/Xzyyd5rrb3XM3u9e6317PcGrAS+SZnPHcpMDrOTBEqSJEmSGuWxwBE5Wd++ASadIkKSJEmS1CjfBpZN98WT1gRGxFOBizPzrunuRJIkSZI0MxHxCUqzz3sD10bEV4FfdNZPddDOqTQHXQucGxEfBd6XmddNI15JkiRpfpgno1wuNH7sU/Lm2XiTSaeIAIiI/YCTgOdRMs/3Aedn5k8ned1xwLnAIuDdmfmGnvX7UDovPgb4MfDszNxUrXsE8E7KxId3Aaszc+egfTlFxBQ0eYjtJn9u4NDpA8z746rpafJxdYqI6fM819e8Pg01vczNwND/tCZ/dgs4tqGfSzr7WQBTRMzUlPoEZuZPgI8C64CDgGcCX4+IPx/0mohYRJmw8HjgCOCkiPIM3TEAACAASURBVDiiZ7PTgG2Z+RDgHOCN1WsXAx8C/iwzHwb8NvDLqf9ZapuImf2TJEmS5ouI+GlE/KTn300RcWFEPHiy10+lT+DTgD+hDEH6QeDIzLw5Iu4JXAcMmqTwSGBDZt5Qvc864ATg2q5tTgDOrB5/BHhbRASwBvhWZv4XQGb+eLI4JUmSJKkl/hH4PvCvQFC68C0DvgO8l1KJNtBU+gSeCJyTmV/oXpiZt0XEn0zwuoOBm7qebwGOGrRNZt4REbcC9wUOBzIiLgHuD6zLzDdNFORdixYxeuKJfdctW7+ekY0bAdi+fTtjY2MD32fVqlW7Hm/atImdO/u3QB0ZGWHZsjIgz44dO9i8eXNZsXbtHtsuv+QSlm7bBsDY6tVsX7my73su2bqVFV3PR0dHB8a5bNkyRkZGgL38m9asYecBB/TdbmTjRpatXw/Ajv33Z/Oxx47fYN3AXUzZwL9p7drxx2nlSsZWrx74PqvW7Q5m05o1cOnM41q+fDlLly4FYGxsjO3bt4+Lr2PJ1q2suHT3Dkf7HPOOZevXw8aZx9Ztj7I3wf6nVPZmcFwnLXtdj6dV9vqUlwmPU5clS5awYsWKXc+H8n2azjmij3n5N/U7R3SZsOx1xT3rf1P1feg9R0y17LFt4O6nZHR0dPDf1Oe7ulfnva7HQyl7k12fJjrvzcL1YcK/afXqia9PXfqWvRnENzo6On/PEZNcnyYte1XZbdTftOscsarv9tPR92+qPrtJ7426jCt7wzyXDziuUz7vjY01t+zNwrlkKn/TAnFcZnbnVu+KiCsz86yIOGOyF09lsvhTJlh3+QQv7dfIrrel7qBtFgNPAFYDtwGXR8TXevcXEacDpwPc/W7OdiFJkiSpFe6KiD+ktKYEeFbXukl7R046MExEHE1p8vlQ4B6UQV5+npn7TfK6xwFnZuax1fNXAmTm67u2uaTa5oqqH+AYpebv2ZTs9rnVdq8Cdmbm2YP258AwU9DkjvVN70jsgAl9zeuO9Zq+Jh/XJp9Lmvy5gee5Aeb1aajpZW4GHBhmBhocmwPDTF3V7+9c4HGUpO9K4KXA94DHZOYXJ3r9VKrP3kYZGfR6YCnwfAb3A+y2HjgsIg6NiHtQ2qle1LPNRcCp1eNnAZ+pZr2/BHhERNyzSg5/i/F9CSVJ85SDOEmSNDOZeUNmPi0z75eZ968eb8jMHZMlgDC1PoFk5oaIWJSZdwLvi4gvT+E1d0TEiykJ3SLgvZl5TUScBVyVmRcB7wE+GBEbgK2URJHM3BYR/0hJJJMyWf0npxKrJEmS5oEZ/bLT3FpKaZgi4n9l5psi4p/o80XIzL+YyvtMJQm8rarJ+2ZEvAn4ATCl6tPMvBi4uGfZq7se76QMPNPvtR+iTBMhSZIkSSqzMwBcNZM3mUqfwOXAzcDdKe1M7wP8n8zcMJMdzzb7BE5Bk/tUNL0NuX1l+prXfSo0fQu4zNkncJovbfN5rskscwPN6+vXAo7NPoFzZ9IkcL4wCZyCJl9Em37SWMAXqiYf1/l997WALeAyZxI4zZe2+TzXZJa5geb19WsBx2YSOLmI+AQTtIfOzKdP5X0GNgeNiKsn2cEjprIDSZIkSdKsePNsvMlEfQKfOhs7kCRJkiTNihsz87szfZOBU0Rk5ubOv2rRYdXjmykjeUqSJEmS5s7HOw8i4qPTfZNJ5wmMiBdQZqJ/Z7XokO6dS5IkSZLmRHfHywdP902mMln8i4BjgJ8AZOb1wAOmu0NJ0hQ4o7okSdpTDni8V6YyT+AvMvP2qG4sImLxTHYoSZIkSZqWR0bETyg1gkurx1TPMzP3m8qbTCUJ/HxEnFHt5MnA/wA+MZ2IJUmSJEnTk5mLZuN9ptIc9BXALcDVwJ8CFwN/Oxs7lyRJkiTNrUlrAjPzroj4OPDxzLxlDmKSJEmSJA3JwJrAKM6MiB8Bo8B3IuKWiHj13IUnSZIkSZpNEzUHfQllVNDVmXnfzDwAOAo4JiJeOifRSZIkSZJmVWT2H+gzIr4BPDkzf9Sz/P7ApZn563MQ35TtG5E/n8qGA/7eWTPT4dmHGd8MY4sZDAo76Z9VY2ww3PiaHBs0+7gO/fvaZDP57Bp+nmtymRvq97Xp3wfPc33N69OQZW6geX39WsCxDf24dvYTcVtm7jujnc1zE9UE3r03AQSo+gXefXghSZIkSZKGZaKBYW6f5jpJkqR2aHKtjCQNMFES+MiuyQe7BbBkSPFIkiRJkoZoYBI4WxMRSpIkSZKaYyqTxUuSJEmSFgiTQEmSJElqEZNASZIkSWoRk0BJkiRJahGTQEmSJElqEZNASZIkSWqRieYJlKSFywmeJUkt5OVPYBIoSdK84I2bJGm22BxUkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWmRx3QFI0iARM3t95uzEIUmStJBYEyhJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS3iFBGSJEnSLJrJFEdOb6S5YE2gJEmSJLWISaAkSZIktYhJoCRJkiS1iEmgJEmSJLWISaAkSZIktYhJoCRJkiS1iEmgJEmSJLWISaAkSZIktYhJoCRJkiS1iEmgJEmSJLWISaAkSZIktYhJoCRJkiS1iEmgJEmSJFUi4r0RcXNEfLtr2QERcVlEXF/9v3+1PCLirRGxISK+FRGP7nrNqdX210fEqV3LHxMRV1eveWtExET7GAaTQEmSJEna7f3AcT3LXgFcnpmHAZdXzwGOBw6r/p0OvANKQge8BjgKOBJ4TVdS945q287rjptkH7POJFCSJEmSKpn5BWBrz+ITgPOqx+cBz+ha/oEsrgRGIuIg4FjgsszcmpnbgMuA46p1+2XmFZmZwAd63qvfPmbd4mG9sSQtZKXhxvRlzk4ckiRpThyYmT8AyMwfRMQDquUHAzd1bbelWjbR8i19lk+0j1lnEihJkiSpTRZHxFVdz9+Vme+a5nv1+1k4p7F8TpkESpIkSWqTOzLzsXv5mh9GxEFVDd1BwM3V8i3AA7u2OwT4frX8t3uWf65afkif7Sfax6yzT6AkSZIkTewioDPC56nAv3ctP6UaJfRo4NaqSeclwJqI2L8aEGYNcEm17qcRcXQ1KugpPe/Vbx+zzppASZIkSapExPmUWrz7RcQWyiifbwAuiIjTgO8CJ1abXww8BdgA3AY8DyAzt0bEa4H11XZnZWZnsJkXUkYgXQp8qvrHBPuYdZELZHSCfSPy51PZcNh/b5NHi5hhbDGD5sqT/lk1xgbDja/JsUGLj2uTY4OZxdfw85xlbnqaXOaaHBs0u8w1+brf5HOJZW76Wn2e6+wn4rbM3HdGO5vnbA4qSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS3i6KCSJEk1afrYLZIWJmsCJUmSJKlFTAIlSZIkqUVMAiVJkiSpRUwCJUmSJKlFhpoERsRxEfGdiNgQEa/os36fiPhwtf4rEbGiZ/2DIuJnEfHyYcYpSZIkSW0xtCQwIhYBbweOB44AToqII3o2Ow3YlpkPAc4B3tiz/hzgU8OKUZIkSZLaZphTRBwJbMjMGwAiYh1wAnBt1zYnAGdWjz8CvC0iIjMzIp4B3AD8fCo7u2vRIkZPPLHvumXr1zOycSMA27dvZ2xsbOD7rFq1atfjTZs2sXPnzr7bjYyMsGzZMgB27NjB5s2by4q1a/fYdvkll7B02zYAxlavZvvKlX3fc8nWrazoej46OjowzmXLljEyMgLs5d+0Zg07Dzig73YjGzeybP16AHbsvz+bjz12/AbrBu5iygb+TWvXjj9OK1cytnr1wPdZtW53MJvWrIFLZx7X8uXLWbp0KQBjY2Ns3759XHwdS7ZuZcWlu3c42ueYdyxbvx42zjy2bnuUvQn2P6WyN4PjOmnZ63o8rbI3C2WuY4+y1/W5TbfszVY8fcten+M65bI3Ojr9c8RUz3v9zhFdJix7s/ARDvybqs+k9xwx1bLHtpnFNTo6ypIlS1ixYsW4Zd2xddubsjfTz63f+Xdc2Zvs+jRR2ZuFYzph2Vu9euLrU5e+ZW8G8Y1O9n3q+SymU/Zmovu47lH2Jrk+TVr2qvee8b1RH8uXL2dp9XhaZW8Wrw99/6ZqP5PeG3XpLnsz0X1M+5a9Acd1qmVvGOeSWSt7s3Bcp1L2NNwk8GDgpq7nW4CjBm2TmXdExK3AfSNiB/DXwJOBgU1BI+J04HSAu9/N7o2SJEmz5aEPXdVn6Yopvnop1103i8FImlWRQ5plNCJOBI7NzOdXz58DHJmZf961zTXVNluq5xspNYivBL6amRdExJnAzzLzzRPtb9+InFKV4bBnVW3yrK8zjC2YfmyT/lk1xgbDja/JsUGLj2uTY4OZxdfw85xlbnqaXOaaHBtY5qaryce1ybGBZW66hn5cO/uJuC0z953Rzua5YdYEbgEe2PX8EOD7A7bZEhGLgfsAWyk1hs+KiDcBI8BdEbEzM982xHglSZIkacEbZhK4HjgsIg4FvgesBU7u2eYi4FTgCuBZwGeyVE3+RmeDrppAE0BJkiRJmqGhJYFVH78XA5cAi4D3ZuY1EXEWcFVmXgS8B/hgRGyg1AAO7kU6S5rcWlOSJEmShm1ofQLn2lT7BDa9Dbl9Aqenyce1ybFBi49rk2MD+wQOYJmbPs9z09Pk2MAyN11NPq5Njg3meZnr7Mc+gcOdLF6SJEmS1CwmgZIkSZLUIsMcGEaSVIMmt0qXJEn1syZQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsTJ4htmJpM8O8GzJEmSpMlYEyhJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJLWISKEmSJEktYhIoSZIkSS1iEihJkiRJlYjYFBFXR8Q3I+KqatkBEXFZRFxf/b9/tTwi4q0RsSEivhURj+56n1Or7a+PiFO7lj+mev8N1Wtjrv9Gk0BJkiRJGu+JmfmozHxs9fwVwOWZeRhwefUc4HjgsOrf6cA7oCSNwGuAo4Ajgdd0Esdqm9O7Xnfc8P+c8UwCJUmSJGliJwDnVY/PA57RtfwDWVwJjETEQcCxwGWZuTUztwGXAcdV6/bLzCsyM4EPdL3XnDEJlCRJktQmiyPiqq5/p/esT+DSiPha17oDM/MHANX/D6iWHwzc1PXaLdWyiZZv6bN8Ti2e6x1KkiRJUo3u6Grm2c8xmfn9iHgAcFlEjE6wbb/+fDmN5XPKmkBJkiRJqmTm96v/bwYupPTp+2HVlJPq/5urzbcAD+x6+SHA9ydZfkif5XPKJFCSJEmSgIjYNyLu3XkMrAG+DVwEdEb4PBX49+rxRcAp1SihRwO3Vs1FLwHWRMT+1YAwa4BLqnU/jYijq1FBT+l6rzljc1BJkiRJKg4ELqxmbVgM/Gtmfjoi1gMXRMRpwHeBE6vtLwaeAmwAbgOeB5CZWyPitcD6aruzMnNr9fiFwPuBpcCnqn9zKsqgNEN684jjgHOBRcC7M/MNPev3oYyI8xjgx8CzM3NTRDwZeANwD+B24K8y8zMT7WvfiPz5VGKaYZPbST+uGU7zMZP4jG36hhlfk2ODFh/XJscGlrkBmhwbWOamq8nHtcmxgWVuupp8XJscG8zzMtfZT8RtmbnvjHY2zw2tOWhELALeTpk74wjgpIg4omez04BtmfkQ4BzgjdXyHwFPy8yHU6pbPzisOCVJkiSpTYbZJ/BIYENm3pCZtwPrKPNodOueb+MjwJMiIjLzG50OmcA1wJKq1lCSJEmSNAPD7BPYb26MowZtk5l3RMStwH0pNYEdfwB8IzN/MdHO7lq0iNETT+y7btn69Yxs3Lh30Q8wOlpGiB0ZGWHZsmUA7Nixg82bN5cN1q7d4zXLL7mEpdu2ATC2ejXbV67s+95Ltm6FS2ceW69Vq1bterxpzRp2HnBA3+1GNm5k2frSbHnH/vuz+dhjx2+wbvqxTRYja9eOO07bV65kbPXqge+zat3uYDatWTOjz60T1/Lly1m6dCkAY2NjbN++fVx8HUu2bmXFpbt3ONrnmHcsW78eZlj0ej+zPcreBPufUtmbwXHdvn07Y2NjA9ev6no8rbI3C2WuY4+y1/W5TbfszVY8fcten+M65bI3wxAHfU/Hlb1+54guE5a9WfgIB5a96jPpPUdMteyxbWZxjY6OsmTJElasWDFuWXds3fam7A3juI4re5NcnyYse7NwTDdt2sTOnTv7rhtZvXri61OXvmVvBvGNjo6ybNkyRkZGgD5lr+ezmE7Zm4nu47pH2Zvk+jRp2RvSuQSqslc9nlbZm8XrQ9+yV+1n0nujLt1lbya6P7e+ZW/AcZ1q2RvGcZ21sjcLx3XcfXkfy5cvn/lOFoBhJoFTmQNjwm0i4mGUJqJr+u6gTN54OsDd7+ZAp5IkSZI0maENDBMRjwPOzMxjq+evBMjM13dtc0m1zRURsRgYA+6fmRkRhwCfAZ6XmV+abH8ODGNsM2Hn9elpcmwwzzuvW+b6anJsYJmbriYf1ybHBpa56WrycW1ybDDPy1xnPw4MM9Q+geuBwyLi0Ii4B7CWMo9Gt+75Np4FfKZKAEeATwKvnEoCKEmSJEmamqElgZl5B/BiykSJ1wEXZOY1EXFWRDy92uw9wH0jYgPwMuAV1fIXAw8BXhUR36z+PWBYsUqSJElSWwx1nsC5ZHNQY5sJm6xMT5Njg3neZMUy11eTYwPL3HQ1+bg2OTawzE1Xk49rk2ODeV7mOvuxOehQm4NKkiRJkhrGJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaxCRQkiRJklrEJFCSJEmSWsQkUJIkSZJaZKhJYEQcFxHfiYgNEfGKPuv3iYgPV+u/EhEruta9slr+nYg4dphxSpIkSRJMnsMsBENLAiNiEfB24HjgCOCkiDiiZ7PTgG2Z+RDgHOCN1WuPANYCDwOOA/5P9X6SJEmSNBRTzGHmvWHWBB4JbMjMGzLzdmAdcELPNicA51WPPwI8KSKiWr4uM3+RmTcCG6r3kyRJkqRhmUoOM+8tHuJ7Hwzc1PV8C3DUoG0y846IuBW4b7X8yp7XHjzRzu5atIjRE0/su27Z+vWMbNy4V8EPMjo6CsDIyAjLli0DYMeOHWzevLlssHbtHq9ZfsklLN22DYCx1avZvnJl3/desnUrXDrz2HqtWrVq1+NNa9aw84AD+m43snEjy9avB2DH/vuz+dieVrjrph/bZDGydu2447R95UrGVq8e+D6r1u0OZtOaNTP63DpxLV++nKVLlwIwNjbG9u3bx8XXsWTrVlZcunuHo32Oecey9ethhkWv9zPbo+xNsP8plb0ZHNft27czNjY2cP2qrsfTKnuzUOY69ih7XZ/bdMvebMXTt+z1Oa5TLnszDHHQ93Rc2et3jugyYdmbhY9wYNmrPpPec8RUyx7bZhbX6OgoS5YsYcWKFeOWdcfWbW/K3jCO67iyN8n1acKyNwvHdNOmTezcubPvupHVqye+PnXpW/ZmEN/o6CjLli1jZGQE6FP2ej6L6ZS9meg+rnuUvUmuT5OWvSGdS6Aqe9XjaZW9Wbw+9C171X4mvTfq0l32ZqL7c+tb9gYc16mWvWEc11kre7NwXMfdl/exfPnyyd5iKjnMvBeZOZw3jjgRODYzn189fw5wZGb+edc211TbbKmeb6Rk32cBV2Tmh6rl7wEuzsyP9uzjdOD06umjgR1TCG0xcMdM/rYha3J8xjY9TY4Nmh2fsU1Pk2ODZsdnbNPT5Nig2fEZ2/Q0OTZodnzGBkuBr3c9f1dmvgumlsMsBMOsCdwCPLDr+SHA9wdssyUiFgP3AbZO8bVUB+tdexNURFyVmY/dm9fMpSbHZ2zT0+TYoNnxGdv0NDk2aHZ8xjY9TY4Nmh2fsU1Pk2ODZsdnbJOaUh4y3w2zT+B64LCIODQi7kEZ6OWinm0uAk6tHj8L+EyWqsmLgLXV6KGHAocBXx1irJIkSZI0lRxm3htaTWDVx+/FwCXAIuC9mXlNRJwFXJWZFwHvAT4YERsoNYBrq9deExEXANdSqoRflJl3DitWSZIkSRqUw9Qc1qwbZnNQMvNi4OKeZa/uerwT6DuaS2a+DnjdEMLaq+ajNWhyfMY2PU2ODZodn7FNT5Njg2bHZ2zT0+TYoNnxGdv0NDk2aHZ8xjaJfjnMQjO0gWEkSZIkSc0zzD6BkiRJkqSGMQmUJEmSpBZpbRIYEfvXHYPUBBGxf0RE3XHMVxFx97pjGCQifqXuGAappgXSXoiIYyLi7XXHIc21iHiU16mFJyKWRMT9+yx/QEQsqSOmNlnQF+GIeHdnosee5Q8EPgX82txHtSuGAyZan5lb5yqWfiLi9ydan5kfm6tYekXEw4CV1QizRMQ5lDkmAd6WmV8f+OKaRMQIZZTbYQx2tDdxvBq4IDNHI2If4NPAI4E7IuLkzPx/Ncb2sonWZ+Y/zlUsk6luRp4InAw8DTiw3ogGuhJ4UF07j4gvZuYTqscfzMzndK3+KvDoeiIrIuITwMCO8Zn59DkMp6+IeBSlnP0hcCNQ27m3IyL+CTgjM3/as3wV5Rz8u/VENi6WJcBDKMd3YzUQXe36XPsT2J4NGKAhIv4L+CLwZeBLmbmp3ojGeTdwaER8HfgSJcYrM/Mn9YZV9DmXJPAj4LOZ+aF6oioi4n0MPs9lZp42l/H0eCvlPqT3vPZk4AnAC+c8ohZZ0EkgsDgiPgSckpl3AUTEQymj/fxdrZHB1yhfygAOokxC2fmVK4EH1xRXx9N6Hn+i63lS743IG4DXdz0/FngVcE/g1cAz6ggKdv3A8CrgV4CPA/8KvBZ4DnB+XXF1eTYlHtg9R+f9gcOB84DakkDg3jXue0oi4ijKDfkzgQOAFwF/VWtQE6v7l/N9ux4/rGdd3bEBvLnuAPqJiMMpUyadBPwY+DBlILcn1hrYbmPANyPiVZn5rxFxT+BMyrn3r+sMrKph/gfgT4DNlBZPh1Q3wn+Tmb+sMz7GX/s77lUlYM+vOfH6I+DxlBvw10TEvpRk68vAlzPzK3UFlpmPrcrZkVWMf0GZYmyMkrD+j7piq/Q7lxwA/HFE/FpmvmKuA+ryH32WPQh4CWX6gzo9ITNP712Ymf8SEWfUEVCbLOjRQatf698J7E+5oB5FuZj+WWZ+ss7YukXENzLz1+uOY5CmxRcRV2XmY7ueX5mZR1ePd9U81BTbZ4HPA1cAxwFPAq4BXpqZY3XF1dF9LCPio8ClmfnO6vnXM7PWmpmmiojXUWpivktJ5i+kzHd6aK2BTSIivpuZddYE7ipTveWrCeUtIp5Bubm9uc44ekXEXcB/Aqdl5oZq2Q2ZWfePg7tExKHA2yg/3vwKcAHw95l5W81xnVPF9NJOTWVE7Ee5Sd+RmX9ZZ3yDVK1vTs/M4+qOpSMi7ke5d3oJcGhm1p0wAFAlp0cDxwCnAHdr0nejW0QsAr6WmY+qOxaAiHgwcAbwm8A5wHsy8/Ya47kuMx+6t+s0OxZ0TWDVvOL0iDgX+BywHDgxM6+sNbA9NT0Tb1p842qMOglg5QFzHEuvAzLzzOrxJRHx7x5avgAAE2NJREFUQ2B1Zv6ixpi6/SIifg34IaU548u71t2znpCKiHgTcENm/nPP8pcCyzKzzhqG04HvAO8A/iMzd0ZEI74XVdO8frEEMDLH4fQaiYhnUmpjRrqamQe7m3DX6Y+Bt0fEbexuYvalBkwK/AeUm+/PRsSngXU0o+a0W6fMLaYc3+vqTgArTwUO725emZk/iYgXAqNAI5PAzPxYRPxtnTFUCcuvU2rajgFWAt+jNMW8osbQiIiTq7geBfwCWA98hVKTVPsPrINk5p1N6MpYtYL7G8rxPZtSGXJHvVEBcHNEHJmZX+1eGBGrgVtqiqk1FnQS2HVzFMARwNeBk6uTCZn5FzWGp+n7fkQc1ds0JSKOpjSrrVU16FDnrD8G3LP65bL2vp6UG6CPUJqAnpOZNwJExFOAb9QZGOXmrV8/3XOBb1FvM7NlwBpK87y3VDW+SyNicQMupFdNc91c+Dzw9K7H3c3MvzD34YyXmc8CiIgVlBvMxwN/GhEPAtZn5lNqiutC4MLqvPEM4KXAgRHxDuDCzLy0jrg6qmTluZTmlR+OiIOBcyPi+cALM/PaGsPLfv3rqpvxRvxw009E3Iv6B+v7CXAd8HbgFZ3rQ0O8i5LE/zPwhcz875rjGWfAOA/7U2oqa/1RKSL+DXgspTb8pcCdwH6d5LTm+5K/Ai6IiPdTmkpDifUUyg9hGqKF3hz01InWZ+Z5cxVLr55BMF4GjBv0ou5BMHo6Of8mPTdsdQ6YEBFHUpr1vp+S2AM8htLH7dm9vyjNpYjYxMQdsBvZZAUgIg7MzB/WuP9rMrO339ik6+ZaNeDEUykJ4ROAyzPz5Hqj2lMV59My89/qjqWfiPiDzPxo3XF0VIOaHENJBI8Gbm5QH7zOTeaJlHPc79Qcy7nA3/YZGOZ44B/rbMIVER8HPpaZH+hZ/sfAH9Y92M+AAbD2p/xY8rbM/L9zHNIuEXES8DjK9fROSm3bFcAVmfm9uuKqYltEGcSs82PNrwI/YHd8n6kxPCLiRsb39UxKf97PUppJ1zaATc99Sef/XXHWfV8SEQ+g9K/v/Ah8DeW70Khm+gvRgk4CB2nCzVFEvGai9ZlZ68A1EfFbE63PzM/PVSz9VCeNF7N7sIlrgLfXmcTMRxFxH0rTs5OBh2bmwTXGsh44OTOv71l+GHB+dz/QpoiIewO/X+cPSt2qG6VOjeWxwH92aruapu7+ilUMZ1Bueu9Pae57ZfXvW5l5Z41xLQH+jDK65dWUfjt11zhPSUTsU2fz96pW8mPADnYPwrIaWAo8swHJTO+1v5MsfCEzr64hpL66BmE5hlLre4/MXF5rUF0i4kDgWZSarcb0V9TsiYgPZ+az645jIWtNEjifbo6arBr9cm1mnl1jDC8H1mXmlrpiGKT6FbrTt2h9nR2uB4mIpZRfnU+mDNF/b0qTsy90RtGtKa7jgX8C/p7xzUJeCbwkMy+uMbZTJlrfW+sw1yLiNynH8/coUy8cAzy4IX20+oqImzLzgTXHMAr8jDJ63peBr2TmrXXGBOXmB/glZXCY44HNTRrQJCLeOtH6JnS1iIjfofxIGMA1mXl5zSHNC1UT5KPY3S9wNXATpa/si2uM6xHsrgV8PHAPSi1gpx9v3U3fu2u0HkZJ7q+l/DjduBqtiFhJNQJxZtY2XdpEmvBD4UK34JPApt4czYeLaEc1QtiJlAT6YEqflJdP/KqhxnMO5RfAGykjNV6QmT+uK55uEfFUdl+kHkHpw9BJCr9cd01lRPwLpXnvpZTBJj4DbGjKKJfVoDV/xfhmIWfX/Qt51b94j8WUPm4HZ2Zt/asjYgtl1NJ3AB/PzJ9GxI1NOaaDNOUCXzWz7HxnjwbuBfwX5fv6vppiujozH149Xgx8NRs0cm9PV4u/A8bVbtXc1eL3s5rHNiL2z8xtdcUySPX5/SWlSSOUfnhvbcCPSd+gTB1wFVVyRZmL72d1xgVlNGHGX0s31xzSOBFxDGVKqPdTfsQMyo+spwJ/lJlfqi+6IiIOokwTdTLl/uT1lKbTjamB7taUa8RCtqCTwCbfHEXE7cC3KcNqd88RCNR7EYVdzdyeSTlZHE4ZEv/ZmXlInXF1ROnR/JuUX7JOoNy0nU9JUH860WvnStdIa79NadpVe5OVKHNRBfAB4MOZeVM0bOj5pqvK3h9RBqq5FnhdZn6rxnjOpdTkXk25Cfl34OomHNOIuJrBI5cenpn7zHFIA1XJ1mMo55U/pcbvazRwOo1BonlTCA2clqQJqlYFL6WMBfB1dicLZwPn1pkIVrVtV/cbWKduEXFeZk44zkOdIuJKyqBI3+hZ/ijgnZl5VD2RQUS8gPIj/iGUe84LgH9vyL3woO9nUEbiPmgu42mbhZ4ENvnm6L5UnfyBOygDnXy0Kb9aRsQOSs3p3wJfzMxsarJQJVu/S5lE/lczs+6pDu7H+JqFJcA3KZ3Xa+87Vg2AcTKl7N0MrAIenjUPsx0RF020vgEDOiym9I35n5ShyV+fmd+pM6aOKjF9IuVC/xRgP+A04OI6f8Wv+nMeSGlO1m058P2s5sCrS0Q8nd3N3h5GqXnunhy7liHKo8wT2DluQenPdlv1ODNzvzri6qdpiVaMnwu1UQkq7EoW1mbPpPBRRqhdl+OnPJpzEfGrlClxVlWLrgP+b93nuqaVs14RcW1mHrG36+ZCVelwBfA/O81mm3I/F2Wk7d4BdXatzgYNzrUQLegkEPreHN2HcnP0ySY0cYBdHdlPovwy+NeZ+cGaQyLK3GxrgX0pCfSHgcuacNLoFhEPp8T5bErn+vMz8y01xnM9cCvwUcoAE+ubUs76iYjHUsreicCWzHx8jbHcQkkWzqckWb2147UNRhQRL6I037oceEPTmiJ1i4i7U/qRrQXWZOb9aozlP4AzemtKq3L3msx8Wv9Xzo2I+Bi7m719rSl9eJuYvAzStJvzqp/nSZTpFj5EqbXfJTO/3u91c6XhycLjKIPqvIvdtZS/DryAMgBWbXMsdx3XvpPuNeC4Xgc8vveH/Kq5+Zczc1X/Vw5fT5eeAyk1gc+tu082QJTR3m/KzB9Uz0+lDFa3CTgz659Wa0Fb8Ekg0Blp7SGUi8Jyyheh1pujjqoq/CTgyZR25P87651jaZyIeDAlvrXAYZS+HxdmjXP0VLULa6u47qT0bTs/M2+oK6aOiHglpfbvYOC/qYavBr6RNY42OJmIuBvwl5l5To0xLKJ8D06i9Ff4JOW41j1xd6dm5mbK5LXjfqmk1Mw8opbAekTE/QE6NVgRsTQzd9QYz7cHDTrQ3e+tSaobph/X2SSuaYlVr4j4Kbt/ve/UUkIDaionqVkg659e42uZ+Zi9XTcXIuJTwBsz83M9y3+LMm/g8bUExq4yt57+SWA24LieTkmWX874qaveCLw3M99ZV2zdIuIQdt8/3ZNyP3dGjfF8HfjdzNxajeGxDvhz4FGUEcsdwHGIFnQSWDXf+gfgT4DNlCTwEErH3ddmvfO2/B1lrrHrKIX+09nwIcCrWreTKXMtrawxjrsoc++8pLtDc0T8BqWJ2ca6YusWEYdTmpo9DvgN4JbMnHDqjTo1qRN2ROxDuUidDZyVmf0GZpnLeCYcGr3OmsGqtcNrKFOm3I1yk3QH8E+ZeVZdcVWxbcjMh+zturkSEUdTmpFvBV4LfBC4H+VzPCUzP11TXFvomTu2W9Y8j2yTNb1mISJuA/o1gw7KoHX7znFIuwOI+O/MPHzAuu9k5q/2WzcX5kPteJSB4f4X46euOjszP1FfVBARqynfibHqeec7sZgyMFxtAxFGxH9l5iOrx2+n3CedWT3/ZmY+qq7Y2qC2Ee3myNmU4e8PzWqwkIjYD3gzcBbwkhpjexVwA2Xy00cC/1Du5Yqm1Cx0qxKuV1b/6vQp4JW554hWO4C3UEZsrFVVg3okZajtoynzkNVeUzmJvs1s5jSAkvz9HiUBXAG8ldI8qVb9krwm1BhVXkI1lHtm3gi7yt87IuKlddbuAusj4gXZMwF2RJzG7mlA6vQ24AxKN4HPAMdn5pVVv9nzgVqSQGARZZTS2r+T/cT4eQy/RanpaMqPmP9M6SNOVbPwenbXLLyLMrJ0nR5a8/4nMtGgaj+fsyjmqcz8D8p0M03zTgZ/J+ouj4siYnF1/ngSpT9qx0LPUWq30GsCr6eMQJc9yxcBo5l5WD2RzYsBEzrNffZYRf3NfRrbxCwiLqQkfbdSmoF+idIfoDFNfAepuyYwIs6jTA3xKcoACd+uK5ZeTa0xqmL7BvDkzPxRz/L7A5fW+et5lAmdLwRuZ/zcj/egTNxd92BEu35pjojrMvOhXetqq3mYB81BGzuP4XytWajuS9Zm5r/UGMPNlJZJe6yitAA6cI5D2h1AxJrMvLR6PK7ZexNEmUZo4A21tW0DY/sbyngdP6JMT/LozMyIeAhwXmYeU1dsbbDQs+zs9yt9Zt4ZEXVnv+dQBkwYV8NQndzOoebarMy8d537n8SSCdYtnbMo+nsf8ILeG/KmmCS5r/uzew7l1+bDgb/oqhmv/YcHmltjBHD3fuUtM2+pBompTZZ5MR8fEU9k99yPn8zMz9QYVre7uh739p2s8xrRyBrALkfk7nkM30MZSbopGl2zULVGehGl3/hFwGWUptwvp4wiXVsSSJmjdZC6J2O/LCLOpHxWAdwtIhrR7L3S/fnsMXdmzRr7ncjM10XE5cBBlB8tO+fdu1FqKzVEtZ8Qh+zaiDgle+bdiYg/pkziXacV2Wduscy8KspQ0RqsyU3MVmXmRVU8J2bmv3VWRMQ/1NkBG5qd3Gfm3eqOYQKLu36FPiurUfIyc7S7GXdNJhrRshGjXWbmZyn9eJvmkRHxE6ofQarHVM8n+rFp2J5U476n4pedB5l5RwO+A93OBz4fET+iJPb/CVDVLNxaZ2CVDwLbKC1Fnk9JvO4BnJCZ36wzsGzAFEYTaHKz93GfXUS8pGGfZaO/E9ln1NmscfDBNlnozUEPpvQn2kFJDhJYTanxeGZmfq/G2Bo9YEKTNbmJWUwwUXHTm3hpsCYf14i4k/79dQJYkpm11gZq4ekpc42bx7Bqvt2pWfh5texw4F5Z/1QCu7osVE1AfwQ8qDNuQZ2iwXO1NrnZe6+6rwn9NPk7ofos6JrAKsk7KiJ+hzJaUwCfyszL640MaHZtVqM1vIlZDHjc77nmj6bWGJGZi+rcv9qn6WWu4TUL3bWod0bEjU1IACuPY4K5WmvW2Gbv80HDvxOqyYKuCWyyJtdmafqaXGMkSapXk2tRo9lztQ68fjbh2trT3/6eNGjuTGkQk8Ca9dRmXdOQ2ixNU9cFvt8kyjbNkyQ1XjRvrlabvUuzzCRQkiRJ/eZqvYgyD2RtYyhIGg6TQEmSpJZr8lytkmafSaAkSVLLRcRd7G5y2X1zaL82aQEyCZQkSZKkFmny5MySJEmSpFlmEihJkiRJLWISKEmqVRQ3RkRGxEN61j23Wn6vuuLrFhGnR8Qz6o5DkqSZMAmUJNXtcZTh6AHW9qz7ZLX+NprhdMAkUJI0r5kESpLqdhJlVMKvVI93ycxbMvPKzLxr0IsjYumQ45uTfUiSNFdMAiVJtYmIRcCJVJNSA0dExCO61o9rDhoRK6rnfxQRH4iI7cAnqnWbIuLNEfGqiBiLiJ9FxL9ExH169nloRHw8In4SET+NiE/0aYaaEfGyiHhLRNwCXB0RnwMeA5xarc+IeO7wPh1JkoZjcd0BSJJa7XeAA4F1wBeBt1FqA781yeveDHyMkkDe2bX8JGAD8ALgIOBNwLur7YiIfYDLgV9W29wB/B3w+Yh4eGZu7XqvvwK+ADyH8qPpJuCjwA3Aa6ttNu7l3ytJUu1MAiVJdToJ2A58OjNvj4jLgLURcUZOPJHtlZn5oj7LlwK/l5k/A4iInwMfjIiHZuZ1wPOABwGHZ+YN1TZfoSR2fwq8vuu9xjLz2d1vXr3fLZl55bT+WkmSGsDmoJKkWlS1cs8ELszM26vF51MGiTl6kpd/csDyyzoJYOVjQACrq+dHAl/vJIAAmbkF+BLwhCnuQ5Kkec0kUJJUl+OBEeDiiBiJiBHgc8Av6Bkgpo8fDlh+c/eTzNwB/IzSNJTq/36v/SFwwBT3IUnSvGYSKEmqSyfR+zdgW/XvJmAf4A+rQWMGGdRU9AHdT6pRPe8F/KBa9IPebSoHAlt7lk3UHFWSpHnLJFCSNOeq0T6fSmn++cSefy+jJGVPnMZbP7lnYvnfpyRzV1XPvwI8JiIO7YrlYODxlIFpJnM7sGQacUmS1BgODCNJqsMJwD2BczPzK90rIuJLwN9Qagr/cy/fdwfwyYg4m9L082xKn8Nrq/XvB/4a+FREvJoysuiZwI+Ad07h/UeBYyPiWODHwI2Z+eO9jFGSpFpZEyhJqsNJwPW9CSBAZv4SuIBSi7fPXr7vOuCzwHuAtwCfAk7reu9fAL9LSebeA5wHbAZ+u2d6iEH+Hriuim898LS9jE+SpNrFxCNwS5I0P0TEJuAjmfnyumORJKnJrAmUJEmSpBYxCZQkSZKkFrE5qCRJkiS1iDWBkiRJktQiJoGSJEmS1CImgZIkSZLUIiaBkiRJktQiJoGSJEmS1CImgZIkSZLUIv8ffeYCD8AoRBoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting to Pandas \n",
    "total_delayed_flights_pd = pd.DataFrame(data= total_delayed_flights, columns=['Airport', 'Prob','Volume'])\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (14,10)) # Create matplotlib figure\n",
    "\n",
    "ax = fig.add_subplot(111) # Create matplotlib axes\n",
    "ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.\n",
    "# ax3 = ax.twinx()\n",
    "width = 0.4\n",
    "\n",
    "total_delayed_flights_pd.Prob.plot(kind='bar', color='red', ax=ax, width=width, position=1, label='Delay probability')\n",
    "total_delayed_flights_pd.Volume.plot(kind='bar', color='blue', ax=ax2, width=width, position=0,  label='flight volume')\n",
    "\n",
    "ax.set_xticklabels(total_delayed_flights_pd.Airport)\n",
    "ax.set_title(\" The delay probability and flight volume for the top 20 busiest airports\",fontsize=15)\n",
    "ax.set_xlabel(\"Airport\",fontsize=15)\n",
    "ax2.set_ylabel('Flight volume')\n",
    "ax.set_ylabel('Delay probability')\n",
    "# ax3.plot(Result_Table_pd.Airport, Result_Table_pd.Prob, color='r')\n",
    "\n",
    "dr_patch = mpatches.Patch(color='r', label='Delay Probability')\n",
    "nf_patch = mpatches.Patch(color='b', label='Number of Flights')\n",
    "plt.legend(handles=[dr_patch,nf_patch])\n",
    "\n",
    "ax.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPRETATION:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "\n",
    "In the above figure, we have added the flight volume of each airport and the associated delay probability in a single plot. To calculate the delay probability for each airport in the list, we considered both, source and destination airports delayed flights.\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "From the plot, we can see that there are two airports with high flight volume and small delay probabilty at the same time <b>(DTW, PHX)</b>, and some other airports with less flights volume suffered from high delay probabilty <b>(CMH, TPA)</b>.  Therefore, it is hard to figure out a direct relationship between the airport's flight volume and the delay. This is understandable since the flights delay does not depend only on the airports flights volume but also on group of different aspects.\n",
    "<br>\n",
    "<br>\n",
    "As we said before, the delay probability may vary no matter of the volume of flights at certain airport, and it could be the case some times. We may assume that some airports could be more prepared and ready to face this huge amount of traffic take into consideration not to affect the flights scheduled time while other are not.\n",
    "\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.7\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "What is the percentage of delayed flights which belong to one of the top 20 busiest carriers?  \n",
    "Comment the figure!\n",
    "\n",
    "<ul></ul>\n",
    "<div class=\"label label-success\">SUGGESTION </div>  You can try with different chart type to have a better visualization.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+\n",
      "|carrier| count|\n",
      "+-------+------+\n",
      "|     US|148540|\n",
      "|     DL|148181|\n",
      "|     AA|119390|\n",
      "|     UA|101438|\n",
      "|     CO| 89421|\n",
      "|     WN| 68280|\n",
      "|     NW| 58811|\n",
      "|     TW| 44193|\n",
      "|     HP| 30701|\n",
      "|     AS| 16910|\n",
      "+-------+------+\n",
      "\n",
      "The total number of delayed flights is 825865\n"
     ]
    }
   ],
   "source": [
    "Busiest_20_carriers= Busiest_carriers.limit(20)\n",
    "\n",
    "# Number of delayed flight per carrier:\n",
    "df_Carriers_delay_flights = (\n",
    "                                df[df.arrival_delay > 15]\n",
    "                                .groupBy(df.carrier)\n",
    "                                .count()\n",
    "                                .orderBy(desc('count'))\n",
    "                            )\n",
    "df_Carriers_delay_flights.show()\n",
    "\n",
    "# Total number of delayed flights\n",
    "total_delayed_flights = (\n",
    "    df_Carriers_delay_flights\n",
    "    .agg(func.sum('count').alias('Total'))\n",
    "     .collect()[0].Total\n",
    ")\n",
    "\n",
    "print(\"The total number of delayed flights is\",total_delayed_flights)\n",
    "# Table : Carriers, The percentage of delayed flight per carrier, and delay proportion among carriers             \n",
    "Result_Table = (\n",
    "    Busiest_20_carriers\n",
    "    .join(df_Carriers_delay_flights,df_Carriers_delay_flights.carrier == Busiest_20_carriers.carrier, \"inner\")\n",
    "    .select(Busiest_20_carriers.carrier,Busiest_20_carriers['count'].alias(\"Tot_flights\"), \n",
    "            ((df_Carriers_delay_flights[\"count\"]/Busiest_20_carriers['count'])*100).alias('delay_perc'),\n",
    "            ((df_Carriers_delay_flights[\"count\"]/total_delayed_flights)*100).alias('% Delay per total delay'))\n",
    "    .collect()\n",
    "    \n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting to Pandas \n",
    "Result_Table_pd = pd.DataFrame(data= Result_Table, columns=['Airport',\"Tot_flights\", 'delay_perc','delayed_perc'])\n",
    "\n",
    "\n",
    "# Figure 1\n",
    "fig = plt.figure(figsize = (14,8)) # Create matplotlib figure\n",
    "\n",
    "ax = fig.add_subplot(111) # Create matplotlib axes\n",
    "ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.\n",
    "\n",
    "width = 0.4\n",
    "\n",
    "Result_Table_pd.delay_perc.plot(kind='bar', color='red', ax=ax, width=width, position=1, label='Delay probability')\n",
    "Result_Table_pd.Tot_flights.plot(kind='bar', color='blue', ax=ax2, width=width, position=0,  label='flight volume')\n",
    "\n",
    "ax.set_xticklabels(Result_Table_pd.Airport)\n",
    "ax.set_title(\" The percentage of delayed flights and flight volume for the top 20 busiest carriers\",fontsize=15)\n",
    "ax.set_xlabel(\"Carrier\",fontsize=15)\n",
    "ax2.set_ylabel('Flight volume')\n",
    "ax.set_ylabel('Delay ratio (%)')\n",
    "\n",
    "dr_patch = mpatches.Patch(color='r', label='Delay ratio (%)')\n",
    "nf_patch = mpatches.Patch(color='b', label='Number of Flights')\n",
    "plt.legend(handles=[dr_patch,nf_patch])\n",
    "\n",
    "ax.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "\n",
    "# Figure 2\n",
    "fig = plt.figure(figsize = (14,8)) # Create matplotlib figure\n",
    "\n",
    "ax3 = fig.add_subplot(111) # Create matplotlib axes\n",
    "\n",
    "width = 0.4\n",
    "\n",
    "Result_Table_pd.delayed_perc.plot(kind='bar', color='red', ax=ax3, width=width)\n",
    "\n",
    "ax3.set_xticklabels(Result_Table_pd.Airport)\n",
    "ax3.set_title(\" The delay proportion among carriers \",fontsize=15)\n",
    "ax3.set_xlabel(\"Carrier\",fontsize=15)\n",
    "ax3.set_ylabel('delay proportion (%)')\n",
    "\n",
    "\n",
    "dr_patch = mpatches.Patch(color='r', label='delay proportion (%)')\n",
    "plt.legend(handles=[dr_patch,])\n",
    "\n",
    "ax3.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### INTERPRETATION:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "\n",
    "\n",
    "First intuition, we can see that there are only 10 carriers, which means that the percentage of delayed fights of the 20 top busiest carriers with respect to the total number of flights in delay is 100%. So, we analyzed the percentage of delay for each carrier.\n",
    "<br>\n",
    "<br>\n",
    "In the first figure, we see both the percentage of delay and the flight volume for each carrier in the same plot. In the second plot, we tried to show which carrier has the biggest percenatge of delay compared to other carriers who experienced delay too. \n",
    "<br>\n",
    "<br>\n",
    "In the second figure, it seems like there are some carriers that are more likely to face delays compared to others. However, by looking on the percentage of delay compared with the number of flights in the first figure, we can see that some carriers with a very small number of flights have almost the same delay percentage of a carrier that has one of the highest numbers of flights. As a conclusion, The percentage of delayed flights varies slightly between carriers, which means all carriers are almost equally probable to face delays.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Interesting Queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Study Flight Distance per Carrier and whether Airlines cover Short-haul, Medium-haul, or Long-haul flights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of the next series of queries is to study the flights' length with respect to each carrier in order to identify the type of flights provided by the airlines present in the 1994 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the carriers csv file from hdfs into a SparkSQL DataFrame\n",
    "df_carriers = sqlContext.read.load('/user/kandakji/carriers.csv', \n",
    "    format='com.databricks.spark.csv', \n",
    "    header='true', \n",
    "    inferSchema='true',\n",
    "    nullValue='NA'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a - Distance Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, the categorization is done according to the flight distance statistics. According to Wikipedia:<br> Short-Haul flights have a distance below <b> 932.0568 Miles</b><br>Medium-Haul flights have a distance between <b> 932.0568 Miles</b> and <b>2485.485 Miles</b><br> and Long-Haul flights cover a distance larger than <b>2485.485 Miles</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the dataframe per carrier and aggregate by averaging the flight distance per carrier\n",
    "\n",
    "carrier_mean_dist = df.groupBy('carrier')\\\n",
    ".avg('distance')\\\n",
    ".toPandas()\n",
    "\n",
    "# Group the dataframe per carrier and aggregate by the standard deviation the flight distance per carrier\n",
    "\n",
    "carrier_stddev_dist = df.groupBy('carrier')\\\n",
    ".agg(stddev_pop('distance'))\\\n",
    ".toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The airlines involved in the flights present in the dataset are the following:\n",
      "\n",
      "\n",
      "+-------+--------------------+\n",
      "|carrier|         Description|\n",
      "+-------+--------------------+\n",
      "|     UA|United Air Lines ...|\n",
      "|     AA|American Airlines...|\n",
      "|     NW|Northwest Airline...|\n",
      "|     HP|America West Airl...|\n",
      "|     TW|Trans World Airwa...|\n",
      "|     DL|Delta Air Lines Inc.|\n",
      "|     US|US Airways Inc. (...|\n",
      "|     AS|Alaska Airlines Inc.|\n",
      "|     CO|Continental Air L...|\n",
      "|     WN|Southwest Airline...|\n",
      "+-------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('The airlines involved in the flights present in the dataset are the following:\\n\\n')\n",
    "df.select('carrier').distinct().join(df_carriers, df.carrier ==  df_carriers.Code, 'inner').select('carrier','Description').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2, which = 'minor')\n",
    "plt.title(\"Distance Statistics per Airline for the Year 1994\",fontsize=15)\n",
    "plt.xlabel(\"Airline\",fontsize=15)\n",
    "plt.ylabel(\"Average Distance in Miles\",fontsize=15)\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.minorticks_on()\n",
    "plt.axes().set_yticks(ticks = [932.0568,2485.485], minor = True)\n",
    "plt.annotate(\"Short-Haul\",(-0.8, 50),fontsize=20)\n",
    "plt.annotate(\"Medium-Haul\",(-0.8, 970),fontsize=20)\n",
    "# plt.annotate(\"Long-Haul\",(-0.8, 3000),fontsize=20, rotation = 'vertical')\n",
    "plt.bar(carrier_mean_dist[\"carrier\"],\n",
    "        carrier_mean_dist[\"avg(distance)\"],\n",
    "        yerr = carrier_stddev_dist['stddev_pop(distance)'],\n",
    "        capsize=10,\n",
    "        ecolor = 'r'\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### INTERPRETATION:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "\n",
    "\n",
    "According to the obtained distance means and standard deviations corresponding to each airline, the airlines are distributed as short-haul or medium-haul airlines.<br>\n",
    "<br>\n",
    "United Airlines (UA) and American Airlines (AA) provide both a great deal of medium-haul flights as well as short-haul flights. Airlines like US Airways (US), Alaska Airlines (AS), and Southwest Airline (WN) provide mostly short-haul trips. The other Airlines provide both short-haul flights mainly, but also provide medium-haul flight but at a lower rate. However, there are no airlines in the dataset that provide long-haul flights in general.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b - Duration Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, the categorization is done according to the flight duration statistics. According to Wikipedia:<br> Short-Haul flights have a duration below <b> 3 hours</b><br>Medium-Haul flights have a duration between <b> 3 hours</b> and <b>6 hours</b><br> and Long-Haul flights cover a duration longer than <b>6 hours</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = sqlContext.createDataFrame(cleaned_data_to_columns, airline_data_schema)\\\n",
    "    .select(['carrier','scheduled_elapsed_time'])\\\n",
    "    .cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the dataframe per carrier and aggregate by averaging the flight duration per carrier\n",
    "\n",
    "carrier_mean_dur = df1.groupBy('carrier')\\\n",
    ".avg('scheduled_elapsed_time')\\\n",
    ".select('carrier',(col('avg(scheduled_elapsed_time)')/60).alias('average_duration'))\\\n",
    ".toPandas()\n",
    "\n",
    "# Group the dataframe per carrier and aggregate by the standard deviation the flight distance per carrier\n",
    "\n",
    "carrier_stddev_dur = df1.groupBy('carrier')\\\n",
    ".agg(stddev_pop('scheduled_elapsed_time'))\\\n",
    ".select('carrier',(col('stddev_pop(scheduled_elapsed_time)')/60).alias('stddev_duration'))\\\n",
    ".toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,10))\n",
    "plt.grid(True, axis='y', linestyle='--',alpha = 0.5,linewidth=2, which = 'minor')\n",
    "plt.title(\"Duration Statistics per Airline for the Year 1994\",fontsize=15)\n",
    "plt.xlabel(\"Airline\",fontsize=15)\n",
    "plt.ylabel(\"Average Flight Duration in Hours\",fontsize=15)\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.minorticks_on()\n",
    "plt.axes().set_yticks(ticks = [3,6], minor = True)\n",
    "plt.annotate(\"Short-Haul\",(-0.8, 0.1),fontsize=20)\n",
    "plt.annotate(\"Medium-Haul\",(-0.8, 3.1),fontsize=20)\n",
    "# plt.annotate(\"Long-Haul\",(-0.8, 3000),fontsize=20, rotation = 'vertical')\n",
    "plt.bar(carrier_mean_dur[\"carrier\"],\n",
    "        carrier_mean_dur[\"average_duration\"],\n",
    "        yerr = carrier_stddev_dur['stddev_duration'],\n",
    "        capsize=10,\n",
    "        ecolor = 'r'\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### INTERPRETATION:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"background-color: AliceBlue; border-color: CornflowerBlue \"> \n",
    "\n",
    "\n",
    "According to the obtained duration means and standard deviations corresponding to each airline, all the airlines are mainly operating or providing short-haul flights with some of them providing some medium-haul flights.<br>\n",
    "<br>\n",
    "In this case, United Airlines (UA) and American Airlines (AA) provide some flights that are up to 3.6 hours in duration (medium-haul) but the flights are short-haul in majority. The other Airlines provide short-haul flights mainly. However, there are no airlines in the dataset that provide long-haul flights in general.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Addition (bonus) questions\n",
    "\n",
    "### Additional data\n",
    "In the HDFS file system you have used for running the Notebook, you will notice that there are several other years available (in addition to 1994).\n",
    "\n",
    "There are some other datasets that related to this use case:\n",
    "\n",
    "- Airport IATA Codes to City names and Coordinates mapping: http://stat-computing.org/dataexpo/2009/airports.csv\n",
    "- Carrier codes to Full name mapping: http://stat-computing.org/dataexpo/2009/carriers.csv\n",
    "- Information about individual planes: http://stat-computing.org/dataexpo/2009/plane-data.csv\n",
    "- Weather information: http://www.wunderground.com/weather/api/. You can subscribe for free to the developers' API and obtain (at a limited rate) historical weather information in many different formats. Also, to get an idea of the kind of information is available, you can use this link: http://www.wunderground.com/history/\n",
    "\n",
    "### Question 5.1\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Which airports are present in the dataset only as destination airport?\n",
    "\n",
    "Which airport is present only as source airport in the dataset? \n",
    "\n",
    "</div>\n",
    "\n",
    "### Question 5.2\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Which airports have biggest delay probability ?\n",
    "\n",
    "Which cities have largest delay duration in average ?\n",
    "\n",
    "</div>\n",
    "\n",
    "### Question 5.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Is there any relationship between weather condition and the delay of flights in 1994 ?\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The airports which are present as destination airports only:\n",
      "\n",
      "\n",
      "+------------+\n",
      "|dest_airport|\n",
      "+------------+\n",
      "|         BFI|\n",
      "|         GUM|\n",
      "+------------+\n",
      "\n",
      "The airports which are present as source airports only:\n",
      "\n",
      "\n",
      "+-----------+\n",
      "|src_airport|\n",
      "+-----------+\n",
      "|        SPN|\n",
      "+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The airports which are present as destination airports only:\\n\\n\")\n",
    "df.select(\"dest_airport\").join(df.select(\"src_airport\"), df.dest_airport == df.src_airport, how = 'left_anti').drop_duplicates().show()\n",
    "print(\"The airports which are present as source airports only:\\n\\n\")\n",
    "df.select(\"src_airport\").join(df.select(\"dest_airport\"), df.src_airport == df.dest_airport, how = 'left_anti').drop_duplicates().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 5.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The source airports that have the highest delay probability are:\n",
      "\n",
      "+-----------+-------------------+\n",
      "|src_airport|  delay_Probability|\n",
      "+-----------+-------------------+\n",
      "|        JFK|0.24380740328416364|\n",
      "|        DFW|0.21335367614350265|\n",
      "|        CLT| 0.2089326150968769|\n",
      "|        PIT| 0.2054788245264436|\n",
      "|        PHL|0.20388951220815302|\n",
      "|        ATL|0.19632843610882855|\n",
      "|        BOS| 0.1936730932097843|\n",
      "|        DEN|0.19178091877788542|\n",
      "|        MIA|0.18117234039612445|\n",
      "|        LGA| 0.1801028822744051|\n",
      "|        STL| 0.1768232756630543|\n",
      "|        FLL|0.17089452603471295|\n",
      "|        ORD|0.15830215920587887|\n",
      "|        LAX|0.15795405683609784|\n",
      "|        MCO|0.15595176348547718|\n",
      "|        SEA| 0.1557269012785027|\n",
      "|        DCA|0.15122392447283622|\n",
      "|        MSP|0.14714904805959975|\n",
      "|        MEM|0.12234584423626231|\n",
      "|        MSY|0.11930080116533139|\n",
      "+-----------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Number of flights per source airport\n",
    "df_flights_per_src_airport = df.groupBy(df.src_airport)\\\n",
    ".count()\n",
    "\n",
    "\n",
    "# probability of delayed flights per source airport\n",
    "\n",
    "\n",
    "df_delay_prob_src_airport = df_flights_per_src_airport.join(df_no_delayed_flights_src_airports, df_flights_per_src_airport.src_airport == df_no_delayed_flights_src_airports.Airport, 'inner')\\\n",
    ".select(df_flights_per_src_airport.src_airport, (df_no_delayed_flights_src_airports['count']/df_flights_per_src_airport['count']).alias('delay_Probability'))\\\n",
    ".orderBy('delay_Probability', ascending = False)\\\n",
    ".cache()\n",
    "\n",
    "print(\"The source airports that have the highest delay probability are:\\n\")\n",
    "\n",
    "df_delay_prob_src_airport.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The destination airports that have the highest delay probability are:\n",
      "\n",
      "+------------+-------------------+\n",
      "|dest_airport|  delay_Probability|\n",
      "+------------+-------------------+\n",
      "|         EWR|0.24737466196906752|\n",
      "|         SFO|0.19255405347776025|\n",
      "|         TPA|0.18627269825805043|\n",
      "|         CMH|0.17682701544373014|\n",
      "|         MCI|0.16341437406696524|\n",
      "|         BWI|  0.163298552108641|\n",
      "|         LAS| 0.1609196612882841|\n",
      "|         IAH|0.15652865174458142|\n",
      "|         CLE|0.15487975266803414|\n",
      "|         SAN|0.15195027142952885|\n",
      "|         PHX|0.15119982408727542|\n",
      "|         SLC|0.13730379509649895|\n",
      "|         CVG|0.13272753707473103|\n",
      "|         OAK|0.12814541837173024|\n",
      "|         MDW| 0.1218231556653724|\n",
      "|         HOU|0.12139368520435027|\n",
      "|         DTW|0.11756015347653029|\n",
      "|         DAL|0.11415420023014959|\n",
      "|         RDU|0.11002475766858294|\n",
      "|         BNA|0.10588888008242847|\n",
      "+------------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Number of flights per destination airport\n",
    "df_flights_per_dest_airport = df.groupBy(df.dest_airport)\\\n",
    ".count()\n",
    "\n",
    "# probability of delayed flights per destination airport\n",
    "\n",
    "\n",
    "\n",
    "df_delay_prob_dest_airport = df_flights_per_dest_airport.join(df_no_delayed_flights_dest_airports, df_flights_per_dest_airport.dest_airport == df_no_delayed_flights_dest_airports.dest_airport, 'inner')\\\n",
    ".select(df_flights_per_dest_airport.dest_airport, (df_no_delayed_flights_dest_airports['count']/df_flights_per_dest_airport['count']).alias('delay_Probability'))\\\n",
    ".orderBy('delay_Probability', ascending = False).cache()\n",
    "\n",
    "print(\"The destination airports that have the highest delay probability are:\\n\")\n",
    "df_delay_prob_dest_airport.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------------+\n",
      "|Airport|  Delay Probability|\n",
      "+-------+-------------------+\n",
      "|    EWR|0.24737466196906752|\n",
      "|    JFK|0.24380740328416364|\n",
      "|    DFW|0.21335367614350265|\n",
      "|    CLT| 0.2089326150968769|\n",
      "|    PIT| 0.2054788245264436|\n",
      "|    PHL|0.20388951220815302|\n",
      "|    ATL|0.19632843610882855|\n",
      "|    BOS| 0.1936730932097843|\n",
      "|    SFO|0.19255405347776025|\n",
      "|    DEN|0.19178091877788542|\n",
      "|    TPA|0.18627269825805043|\n",
      "|    MIA|0.18117234039612445|\n",
      "|    LGA| 0.1801028822744051|\n",
      "|    CMH|0.17682701544373014|\n",
      "|    STL| 0.1768232756630543|\n",
      "|    FLL|0.17089452603471295|\n",
      "|    MCI|0.16341437406696524|\n",
      "|    BWI|  0.163298552108641|\n",
      "|    LAS| 0.1609196612882841|\n",
      "|    ORD|0.15830215920587887|\n",
      "+-------+-------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Total delay probability for all airports\n",
    "df_delay_prob_src_airport\\\n",
    ".union(df_delay_prob_dest_airport)\\\n",
    ".groupBy('src_airport')\\\n",
    ".avg('delay_Probability')\\\n",
    ".orderBy('avg(delay_Probability)', ascending = 0)\\\n",
    ".withColumnRenamed('avg(delay_Probability)', 'Delay Probability')\\\n",
    ".withColumnRenamed('src_airport', 'Airport')\\\n",
    ".show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cities = sqlContext.read.load('/user/kandakji/airports.csv', \n",
    "    format='com.databricks.spark.csv', \n",
    "    header='true', \n",
    "    inferSchema='true',\n",
    "    nullValue='NA'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cities that have the largest delay duration on average are:\n",
      "\n",
      "\n",
      "+-----------------+------------------+\n",
      "|             city|avg(arrival_delay)|\n",
      "+-----------------+------------------+\n",
      "|        Nantucket|              15.9|\n",
      "|           Newark|11.807407869413359|\n",
      "|        Aguadilla|11.173722627737225|\n",
      "|      Panama City|10.543461237274864|\n",
      "|           Juneau|10.095909342177999|\n",
      "|      Bakersfield|10.074786324786325|\n",
      "|         New York| 9.989412965556532|\n",
      "|            Sitka|  9.56711915535445|\n",
      "|         Unalaska| 9.528169014084508|\n",
      "|Dallas-Fort Worth|  9.32878026820831|\n",
      "|           Kodiak| 9.127604166666666|\n",
      "|       Petersburg| 8.864820846905538|\n",
      "|         Wrangell| 8.834670947030498|\n",
      "|           Bethel| 8.685785536159601|\n",
      "|       St. Mary's| 8.629032258064516|\n",
      "|         Gunnison| 8.558823529411764|\n",
      "|          Yakutat| 8.274596182085169|\n",
      "|        Charlotte| 8.260802384407231|\n",
      "|         Kotzebue| 8.195842450765864|\n",
      "|     Philadelphia| 8.159339634032557|\n",
      "+-----------------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Average delay of flights per source airport\n",
    "df_flights_per_src_airport = df.groupBy(df.src_airport.alias('iata'))\\\n",
    "                            .avg('arrival_delay')\\\n",
    "                            .join(df_cities,\"iata\",\"inner\")\\\n",
    "                            .orderBy('avg(arrival_delay)', ascending = 0)\\\n",
    "                            .select('city','avg(arrival_delay)')\n",
    "\n",
    "print(\"The cities that have the largest delay duration on average are:\\n\\n\")\n",
    "df_flights_per_src_airport.show()"
   ]
  }
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